[
  {
    "path": ".github/workflows/tests.yml",
    "content": "name: MNIST1D tests\n\non: [push]\n\njobs:\n  build: #building the library locally\n\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [\"3.7\", \"3.8\", \"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n\n    steps:\n      - uses: actions/checkout@v4\n      - name: Set up Python ${{ matrix.python-version }}\n        uses: actions/setup-python@v4\n        with:\n          python-version: ${{ matrix.python-version }}\n      # You can test your matrix by printing the current Python version\n      - name: Display Python version\n        run: python -c \"import sys; print(sys.version)\"\n      - name: Install dev version locally\n        run: python -m pip install '.[dev]'\n      - name: test dev version locally\n        run: python -m pytest .\n      - name: install notebooks\n        run: python -m pip install notebook jupyter\n      - name: run minimal example notebooks\n        run: jupyter nbconvert --execute --to html \"notebooks/mnist1d-pip.ipynb\"\n      \n  install: #installing the library as if one would not want to git-clone it\n\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [\"3.7\", \"3.8\", \"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n\n    steps:\n      - uses: actions/checkout@v4\n      - name: Set up Python ${{ matrix.python-version }}\n        uses: actions/setup-python@v4\n        with:\n          python-version: ${{ matrix.python-version }}\n      - name: Display Python version\n        run: python -c \"import sys; print(sys.version)\"\n      - name: Install dev version locally\n        run: python -m pip install git+https://github.com/greydanus/mnist1d.git@master\n\n"
  },
  {
    "path": ".gitignore",
    "content": "# The MNIST-1D dataset | 2020\n# Sam Greydanus\n\n.DS_Store\n*.egg-info\n.ipynb_checkpoints\n*/.ipynb_checkpoints/*\n.pytest_cache\n*.tar\n*.zip\nnotebooks/*.pkl\nnotebooks/mnist1d\n# *.pkl # allow pickle files\n__pycache__\n.vscode\n*~\n\ndist/*\n"
  },
  {
    "path": "CITATIONS.bib",
    "content": "@article{greydanus2024scaling,\n  title={Scaling Down Deep Learning with MNIST-1D},\n  author={Greydanus, Sam and Kobak, Dmitry},\n  journal={ICML},\n  year={2024}\n}\n"
  },
  {
    "path": "LICENSE",
    "content": "\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "MANIFEST.in",
    "content": "global-exclude notebooks/*\nglobal-exclude .git*\nglobal-exclude static/*\nglobal-exclude *png\nglobal-exclude *pkl\n"
  },
  {
    "path": "README.md",
    "content": "The MNIST-1D Dataset\n=======\n\nICML 2024 | [Blog post](https://greydanus.github.io/2020/12/01/scaling-down/) | [Paper at arXiv](https://arxiv.org/abs/2011.14439) | [Paper at OpenReview](https://openreview.net/forum?id=n9pru4bJU9) | [GitHub](https://github.com/greydanus/mnist1d)\n\nMost machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of separating between models with/without nonlinearity and models with/without spatial inductive biases.\n\n_**Dec 5, 2023**: MNIST-1D is now a core teaching dataset in Simon Prince's [Understanding Deep Learning](https://udlbook.github.io/udlbook/) textbook_\n\nCitation:\n```\n@inproceedings{greydanus2024scaling,\n  title={Scaling down deep learning with {MNIST}-{1D}},\n  author={Greydanus, Sam and Kobak, Dmitry},\n  booktitle={Proceedings of the 41st International Conference on Machine Learning},\n  year={2024}\n}\n```\n\n![overview.png](static/overview.png)\n\nQuickstart and use cases\n--------\n* Getting started\n  * [Quickstart](https://github.com/greydanus/mnist1d/blob/master/notebooks/quickstart.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/quickstart.ipynb))\n  * [Building MNIST-1D](https://github.com/greydanus/mnist1d/blob/master/notebooks/building-mnist1d.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/building-mnist1d.ipynb))\n  * [Pip installation (3 lines)](https://github.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-pip.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-pip.ipynb))\n* Example use cases\n  *  [Quantifying CNN spatial priors](https://github.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-classification.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-classification.ipynb))\n  * [Self-supervised learning](https://github.com/greydanus/mnist1d/blob/master/notebooks/self-supervised-learning.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/self-supervised-learning.ipynb))\n  * [Finding lottery tickets](https://github.com/greydanus/mnist1d/blob/master/notebooks/lottery-tickets.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/lottery-tickets.ipynb))\n  * [Observing deep double descent](https://github.com/greydanus/mnist1d/blob/master/notebooks/deep-double-descent.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/deep-double-descent.ipynb))\n  * [Metalearning a learning rate](https://github.com/greydanus/mnist1d/blob/master/notebooks/metalearn-learn-rate.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/metalearn-learn-rate.ipynb))\n  * [Metalearning an activation function](https://github.com/greydanus/mnist1d/blob/master/notebooks/metalearn-activation-function.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/metalearn-activation-function.ipynb))\n  * [Benchmarking pooling methods](https://github.com/greydanus/mnist1d/blob/master/notebooks/benchmark-pooling.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/benchmark-pooling.ipynb))\n  * [t-SNE visualisations of MNIST-1D and MNIST](https://github.com/greydanus/mnist1d/blob/master/notebooks/tsne-mnist-vs-mnist1d.ipynb) ([Colab](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/tsne-mnist-vs-mnist1d.ipynb))\n* Community contributions\n  * [A from-scratch, Numpy-only MLP with handwritten backprop](https://colab.research.google.com/drive/1E4w9chTkK-rPK-Zl-D0t4Q3FrdpQrHRQ?usp=sharing)\n  * [Simon Prince's _Understanding Deep Learning_](https://udlbook.github.io/udlbook/) textbook uses MNIST1D as a core teaching example\n  * [A matching GoLang implementation](https://github.com/mdcfrancis/gomnist1d) including matching random seeds, etc. by [mdcfrancis](https://github.com/mdcfrancis/)\n  * This dataset is also on [HuggingFace](https://huggingface.co/datasets/christopher/mnist1d) (thanks to [@cakiki](https://github.com/cakiki))\n  * ...send me a Colab link to your experiment and I'll feature it here.\n\n\nInstalling with `pip`\n--------\n\n``` shell\npip install mnist1d\n```\n\nThis allows you to build the default dataset locally:\n\n```python\nfrom mnist1d.data import make_dataset, get_dataset_args\n\ndefaults = get_dataset_args()\ndata = make_dataset(defaults)\nx, y, t = data['x'], data['y'], data['t']\n```\n\nIf you want to play around with this, see [notebooks/mnist1d-pip.ipynb](https://github.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-pip.ipynb).\n\n\nAlternatively, you can always `pip install` via the GitHub repo:\n\n``` shell\npython -m pip install git+https://github.com/greydanus/mnist1d.git@master\n```\n\n\nComparing MNIST and MNIST-1D\n--------\n\n| Dataset\t\t| Logistic regression\t\t| MLP \t| CNN \t| GRU* | Human expert |\n| ------------- \t\t\t| :---------------: | :---------------: | :---------------: | :---------------: | :---------------: |\n| MNIST \t\t\t\t\t    | 94% | 99+% | 99+% | 99+% | 99+% |\n| MNIST-1D \t\t\t\t\t  | 32% | 68%  | 94%  | 91%  | 96%  |\n| MNIST-1D (shuffle**)\t| 32% | 68%  | 56%  | 57%  | ~30% |\n\n*Training the GRU takes at least 10x the walltime of the CNN.\n\n**The term \"shuffle\" refers to shuffling the spatial dimension of the dataset, as in [Zhang et al. (2017)](https://arxiv.org/abs/1611.03530).\n\n\n-----------\n\nAccording to Geoffrey Hinton, the original MNIST dataset is the [Drosophila of machine learning](https://twitter.com/ivanukhov/status/639122460722528257). But we argue that it has a few drawbacks:\n* **Discrimination between models.** The difference between major ML models comes down to a few percentage points.\n* **Dimensionality.** Examples are 784-dimensional vectors so training ML models can take non-trivial compute and memory (think neural architecture search and metalearning).\n* **Hard to hack.** MNIST is not procedurally generated so it's hard to change the noise distribution, the scale/rotation/translation/shear/etc of the digits, or the resolution.\n\n We developed MNIST-1D to address these issues. It is:\n* **Discriminative between models.** There is a broad spread in test accuracy between key ML models.\n* **Low dimensional.** Each MNIST-1D example is a 40-dimensional vector. This means faster training and less memory.\n* **Easy to hack.** There's an API for adjusting max_translation, corr_noise_scale, shear_scale, final_seq_length and more. The code is clean and modular.\n* **Still has some real-world relevance.** Though it's low-dimensional and synthetic, this task is arguably more interesting than [Sklearn's datasets](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets) such as two_moons, two_circles, or gaussian_blobs.\n\nDimensionality reduction\n--------\n\nVisualizing the MNIST and MNIST-1D datasets with t-SNE. The well-defined clusters in the MNIST plot indicate that the majority of the examples are separable via a kNN classifier in pixel space. The MNIST-1D plot, meanwhile, reveals a lack of well-defined clusters which suggests that learning a nonlinear representation of the data is much more important to achieve successful classification.\n\n<img src=\"notebooks/figures/tsne.png\" width=500>\n\nDownloading the dataset\n--------\n\nHere's a minimal example of how to download the frozen dataset. This is arguably worse than installing this repo with `pip` and generating it from scratch. But it does have its uses. It can also be used for double-checking that the procedurally generated dataset exactly matches the one used in the paper and blog post:\n\n```python\nfrom urllib.request import urlopen\nimport pickle\n\nurl = 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'\ndata = pickle.load(urlopen(url))\n\ndata.keys()\n\n>>> dict_keys(['x', 'x_test', 'y', 'y_test', 't', 'templates'])  # these are NumPy arrays\n```\n\n\nConstructing the dataset\n--------\n\nThis is a synthetically-generated dataset which, by default, consists of 4000 training examples and 1000 testing examples (you can change this as you wish). Each example contains a template pattern that resembles a handwritten digit between 0 and 9. These patterns are analogous to the digits in the original [MNIST dataset](http://yann.lecun.com/exdb/mnist/).\n\n**Original MNIST digits**\n\n![mnist1d_black.png](static/mnist.png)\n\n**1D template patterns**\n\n![mnist1d_black.png](static/mnist1d_black_small.png)\n\n**1D templates as lines**\n\n![mnist1d_white.png](static/mnist1d_white_small.png)\n\nIn order to build the synthetic dataset, we pass the templates through a series of random transformations. This includes adding random amounts of padding, translation, correlated noise, iid noise, and scaling. We use these transformations because they are relevant for both 1D signals and 2D images. So even though our dataset is 1D, we can expect some of our findings to hold for 2D (image) data. For example, we can study the advantage of using a translation-invariant model (eg. a CNN) by making a dataset where signals occur at different locations in the sequence. We can do this by using large padding and translation coefficients. Here's an animation of how those transformations are applied.\n\n![mnist1d_tranforms.gif](static/mnist1d_transforms.gif)\n\nUnlike the original MNIST dataset, which consisted of 2D arrays of pixels (each image had 28x28=784 dimensions), this dataset consists of 1D timeseries of length 40. This means each example is ~20x smaller, making the dataset much quicker and easier to iterate over. Another nice thing about this toy dataset is that it does a good job of separating different types of deep learning models, many of which get the same 98-99% test accuracy on MNIST.\n\n\nExample use cases\n--------\n\n### [Quantifying CNN spatial priors](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/mnist1d-classification.ipynb)\nFor a fixed number of training examples, we show that a CNN achieves far better test generalization than a comparable MLP. This highlights the value of the inductive biases that we build into ML models.\n\n<img src=\"notebooks/figures/benchmark.png\" width=500>\n\n### [Finding lottery tickets](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/lottery-tickets.ipynb)\nWe obtain sparse \"lottery ticket\" masks as described by [Frankle & Carbin (2018)](https://arxiv.org/abs/1803.03635). Then we perform some ablation studies and analysis on them to determine exactly what makes these masks special (spoiler: they have spatial priors including local connectivity). One result, which contradicts the original paper, is that lottery ticket masks can be beneficial even under different initial weights. We suspect this effect is present but vanishingly small in the experiments performed by Frankle & Carbin.\n\n![lottery.png](static/lottery.png)\n\n![lottery_summary.png](static/lottery_summary_small.png)\n\n### [Observing deep double descent](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/deep-double-descent.ipynb)\nWe replicate the \"deep double descent\" phenomenon described by [Belkin et al. (2018)](https://arxiv.org/abs/1812.11118) and more recently studied at scale by [Nakkiran et al. (2019)](https://openai.com/blog/deep-double-descent/).\n\n<img src=\"notebooks/figures/double-descent.png\" width=500>\n\n### [Metalearning a learning rate](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/metalearn-learn-rate.ipynb)\nA simple notebook that introduces gradient-based metalearning, also known as \"unrolled optimization.\" In the spirit of [Maclaurin et al (2015)](http://proceedings.mlr.press/v37/maclaurin15.pdf) we use this technique to obtain the optimal learning rate for an MLP.\n\n![metalearn_lr.png](static/metalearn_lr.png)\n\n### [Metalearning an activation function](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/metalearn-activation-function.ipynb)\nThis project uses the same principles as the learning rate example, but tackles a new problem that (to our knowledge) has not been tackled via gradient-based metalearning: how to obtain the perfect nonlinearity for a neural network. We start from an ELU activation function and parameterize the offset with an MLP. We use unrolled optimization to find the offset that leads to lowest training loss, across the last 200 steps, for an MLP classifier trained on MNIST-1D. Interestingly, the result somewhat resembles the Swish activation described by [Ramachandran et al. (2017)](https://arxiv.org/abs/1710.05941); the main difference is a positive regime between -4 and -1.\n\n![metalearn_afunc.png](static/metalearn_afunc.png)\n\n### [Benchmarking pooling methods](https://githubtocolab.com/greydanus/mnist1d/blob/master/notebooks/benchmark-pooling.ipynb)\nWe investigate the relationship between number of training samples and usefulness of pooling methods. We find that pooling is typically very useful in the low-data regime but this advantage diminishes as the amount of training data increases.\n\n![pooling.png](static/pooling.png)\n\n\nDependencies\n--------\n * NumPy\n * SciPy\n * PyTorch\n * (others)\n"
  },
  {
    "path": "mnist1d/.gitignore",
    "content": "py3*/\n"
  },
  {
    "path": "mnist1d/__init__.py",
    "content": "# The MNIST-1D dataset | 2024\n# Sam Greydanus, Peter Steinbach\n\nfrom .data import get_dataset, get_dataset_args, get_templates\nfrom .utils import to_pickle, from_pickle, ObjectView, set_seed, plot_signals\nfrom .transform import transform"
  },
  {
    "path": "mnist1d/data.py",
    "content": "# The MNIST-1D dataset | 2020\n# Sam Greydanus\n\nimport numpy as np\nimport os\nimport requests\nimport mnist1d\nfrom mnist1d.transform import transform\nfrom mnist1d.utils import from_pickle, to_pickle, ObjectView, set_seed\n\ndef get_dataset_args(as_dict=False):\n    \"\"\" Generate dictionary with dataset properties\n\n    Parameters\n    ----------\n    as_dict : bool, optional\n        if true, return the dataset properties as dictionary; if false, return an ObjectView, by default False\n\n    Returns\n    -------\n    _type_\n        _description_\n    \"\"\"\n    arg_dict = {'num_samples': 5000,\n            'train_split': 0.8,\n            'template_len': 12,\n            'padding': [36,60],\n            'scale_coeff': .4, \n            'max_translation': 48,\n            'corr_noise_scale': 0.25,\n            'iid_noise_scale': 2e-2,\n            'shear_scale': 0.75,\n            'shuffle_seq': False,\n            'final_seq_length': 40,\n            'seed': 42,\n            'url': 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'}\n    return arg_dict if as_dict else ObjectView(arg_dict)\n\n\n# basic 1D templates for the 10 digits\ndef get_templates():\n    d0 = np.asarray([5,6,6.5,6.75,7,7,7,7,6.75,6.5,6,5])\n    d1 = np.asarray([5,3,3,3.4,3.8,4.2,4.6,5,5.4,5.8,5,5])\n    d2 = np.asarray([5,6,6.5,6.5,6,5.25,4.75,4,3.5,3.5,4,5])\n    d3 = np.asarray([5,6,6.5,6.5,6,5,5,6,6.5,6.5,6,5])\n    d4 = np.asarray([5,4.4,3.8,3.2,2.6,2.6,5,5,5,5,5,5])\n    d5 = np.asarray([5,3,3,3,3,5,6,6.5,6.5,6,4.5,5])\n    d6 = np.asarray([5,4,3.5,3.25,3,3,3,3,3.25,3.5,4,5])\n    d7 = np.asarray([5,7,7,6.6,6.2,5.8,5.4,5,4.6,4.2,5,5])\n    d8 = np.asarray([5,4,3.5,3.5,4,5,5,4,3.5,3.5,4,5])\n    d9 = np.asarray([5,4,3.5,3.5,4,5,5,5,5,4.7,4.3,5])\n    \n    x = np.stack([d0,d1,d2,d3,d4,d5,d6,d7,d8,d9])\n    x -= x.mean(1,keepdims=True) # whiten\n    x /= x.std(1,keepdims=True)\n    x -= x[:,:1]  # signal starts and ends at 0\n    \n    templates = {'x': x/6., 't': np.linspace(-5, 5, len(d0))/6.,\n                 'y': np.asarray([0,1,2,3,4,5,6,7,8,9])}\n    return templates\n\n\n# make a dataset\ndef make_dataset(args=None, template=None, ):\n    templates = get_templates() if template is None else template\n    args = get_dataset_args() if args is None else args\n    set_seed(args.seed) # reproducibility\n    \n    xs, ys = [], []\n    samples_per_class = args.num_samples // len(templates['y'])\n    for label_ix in range(len(templates['y'])):\n        for example_ix in range(samples_per_class):\n            x = templates['x'][label_ix]\n            t = templates['t']\n            y = templates['y'][label_ix]\n            x, new_t = transform(x, t, args) # new_t transformation is same each time\n            xs.append(x) ; ys.append(y)\n    \n    batch_shuffle = np.random.permutation(len(ys)) # shuffle batch dimension\n    xs = np.stack(xs)[batch_shuffle]\n    ys = np.stack(ys)[batch_shuffle]\n    \n    if args.shuffle_seq: # maybe shuffle the spatial dimension\n        seq_shuffle = np.random.permutation(args.final_seq_length)\n        xs = xs[...,seq_shuffle]\n    \n    new_t = new_t/xs.std()\n    xs = (xs-xs.mean())/xs.std() # center the dataset & set standard deviation to 1\n\n    # train / test split\n    split_ix = int(len(ys)*args.train_split)\n    dataset = {'x': xs[:split_ix], 'x_test': xs[split_ix:],\n               'y': ys[:split_ix], 'y_test': ys[split_ix:],\n               't':new_t, 'templates': templates}\n    return dataset\n\n\n\n# we'll cache the dataset so that it doesn't have to be rebuild every time\n# args must not be a dict\ndef get_dataset(args, path=None, verbose=True, download=True, regenerate=False, **kwargs):\n    if 'args' in kwargs.keys() and kwargs['args'].shuffle_seq:\n        shuffle = \"_shuffle\"\n    else:\n        shuffle = \"\"\n    path = './mnist1d_data{}.pkl'.format(shuffle) if path is None else path\n\n    assert not (download and regenerate), \"You can either download the o.g. MNIST1D dataset or generate your own - but not both\"\n    try:\n        if regenerate:\n            raise ValueError(\"Regenerating dataset\") # yes this is hacky\n        if download:\n            if os.path.exists(path):\n                if verbose:\n                    print(\"File already exists. Skipping download.\")\n            else:\n                print(\"Downloading MNIST1D dataset from {}\".format(args.url))\n                r = requests.get(args.url, allow_redirects=True)\n                open(path, 'wb').write(r.content)\n                print(\"Saving to {}\".format(path))\n        dataset = from_pickle(path)\n        if verbose:\n            print(\"Successfully loaded data from {}\".format(path))\n    except:\n        if verbose:\n            print(\"Did or could not load data from {}. Rebuilding dataset...\".format(path))\n        dataset = make_dataset(args, **kwargs)\n        to_pickle(dataset, path)\n    return dataset"
  },
  {
    "path": "mnist1d/transform.py",
    "content": "# The MNIST-1D dataset | 2024\n# Sam Greydanus, Peter Steinbach\n\nimport numpy as np\nimport scipy\nfrom scipy.interpolate import interp1d\nfrom scipy.ndimage import gaussian_filter\n\n\n# transformations of the templates which will make them harder to classify\ndef pad(x, padding: tuple):\n    \"\"\"pad signal x with random number of zeros. Note, the signal is only padded at indices given by the interval in padding\n\n    Parameters\n    ----------\n    x : _type_\n        signal\n    padding : tuple\n        (low, high) corresponds to (start,end) of padding\n\n    Returns\n    -------\n    _type_\n        a padded signal\n    \"\"\"\n    low, high = padding\n    p = low + int(np.random.rand() * (high - low + 1))\n    if len(x.shape) == 1:\n        return np.concatenate([x, np.zeros((p))])\n    else:\n        padding = np.zeros((x.shape[0], p))\n        return np.concatenate([x, padding], axis=-1)\n\n\ndef shear(x, scale=10):\n    # TODO: add docstring\n    coeff = scale * (np.random.rand() - 0.5)\n    return x - coeff * np.linspace(-0.5, 0.5, len(x))\n\n\ndef translate(x, max_translation):\n    # TODO: add docstring\n    k = np.random.choice(max_translation)\n    return np.concatenate([x[-k:], x[:-k]])\n\n\ndef corr_noise_like(x, scale):\n    # TODO: add docstring\n    noise = scale * np.random.randn(*x.shape)\n    return gaussian_filter(noise, 2)\n\n\ndef iid_noise_like(x, scale):\n    # TODO: add docstring\n    noise = scale * np.random.randn(*x.shape)\n    return noise\n\n\ndef interpolate(x, N):\n    # TODO: add docstring\n    scale = np.linspace(0, 1, len(x))\n    new_scale = np.linspace(0, 1, N)\n    new_x = interp1d(scale, x, axis=0, kind=\"linear\")(new_scale)\n    return new_x\n\n\ndef transform(x, y, args, eps=1e-8):\n    new_x = pad(x + eps, args.padding)  # pad\n    new_x = interpolate(new_x, args.template_len + args.padding[-1])  # dilate\n    new_y = interpolate(y, args.template_len + args.padding[-1])\n    new_x *= 1 + args.scale_coeff * (np.random.rand() - 0.5)  # scale\n    new_x = translate(new_x, args.max_translation)  # translate\n\n    # add noise\n    mask = new_x != 0\n    new_x = mask * new_x + (1 - mask) * corr_noise_like(new_x, args.corr_noise_scale)\n    new_x = new_x + iid_noise_like(new_x, args.iid_noise_scale)\n\n    # shear and interpolate\n    new_x = shear(new_x, args.shear_scale)\n    new_x = interpolate(new_x, args.final_seq_length)  # subsample\n    new_y = interpolate(new_y, args.final_seq_length)\n    return new_x, new_y\n"
  },
  {
    "path": "mnist1d/utils.py",
    "content": "# The MNIST-1D dataset | 2024\n# Sam Greydanus, Peter Steinbach\n\nimport numpy as np\nimport random\nimport pickle\nimport matplotlib.pyplot as plt\nfrom mnist1d.transform import transform\n\ndef set_seed(seed):\n    random.seed(seed)\n    np.random.seed(seed)\n\ndef to_pickle(thing, path): # save something\n    with open(path, 'wb') as handle:\n        pickle.dump(thing, handle, protocol=3)\n\n\ndef from_pickle(path): # load something\n    value = None\n    with open(path, 'rb') as handle:\n        value = pickle.load(handle)\n    return value\n\nclass ObjectView(object):\n    def __init__(self, d): self.__dict__ = d\n\n\ndef plot_signals(xs, t, labels=None, args=None, ratio=2.6, do_transform=False, dark_mode=False, zoom=1):\n    rows, cols = 1, 10\n    fig = plt.figure(figsize=[cols*1.5,rows*1.5*ratio], dpi=60)\n    for r in range(rows):\n        for c in range(cols):\n            ix = r*cols + c\n            x, t = xs[ix], t\n            ax = plt.subplot(rows,cols,ix+1)\n\n            # plot the data\n            if do_transform:\n                assert args is not None, \"Need an args object in order to do transforms\"\n                x, t = transform(x, t, args)  # optionally, transform the signal in some manner\n            if dark_mode:\n                plt.plot(x, t, 'wo', linewidth=6)\n                ax.set_facecolor('k')\n            else:\n                plt.plot(x, t, 'k-', linewidth=2)\n            if labels is not None:\n                plt.title(\"label=\" + str(labels[ix]), fontsize=22)\n\n            plt.xlim(-zoom,zoom) ; plt.ylim(-zoom,zoom)\n            plt.gca().invert_yaxis() ; plt.xticks([], []), plt.yticks([], [])\n    plt.subplots_adjust(wspace=0, hspace=0)\n    plt.tight_layout() ; plt.show()\n    return fig"
  },
  {
    "path": "notebooks/README.md",
    "content": "# Try out the MNIST1D dataset\n\n## Installation instructions\n\n1. clone the repo\n``` shell\ngit clone https://github.com/greydanus/mnist1d.git\n```\n2. install all additional depdendencies\n``` shell\ncd mnist1d\npython -m pip install '.[all]'\n```\n3. run the notebooks, [quickstart.ipynb](quickstart.ipynb) or [mnist1d_tiny.ipynb](mnist1d-pip.ipynb) are a good start.\n"
  },
  {
    "path": "notebooks/benchmark-pooling.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"dKUcDM76bHx3\"\n      },\n      \"source\": [\n        \"# **MNIST-1D**: Benchmarking pooling methods\\n\",\n        \"Sam Greydanus\\n\",\n        \"\\n\",\n        \"In this notebook, I will examine the effect of pooling on model generalization and sample efficiency. It looks like pooling is better than striding (or no pooling) on small datasets, but the discrepancy decreases as the number of training examples increase. I'm not yet sure why this is, but my best hypothesis is that pooling is a mediocre architectural prior which is better than nothing in low-data regimes and then ends up restricting model expression in high-data regimes. Let me know if you have a better explanation.\\n\",\n        \"\\n\",\n        \"This case study is meant to show the convenience and computational savings of working with the low-dimensional MNIST-1D dataset. You can find details at https://github.com/greydanus/mnist1d.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 12,\n      \"metadata\": {\n        \"id\": \"Sg2i1QmhKW5d\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n        \"\\n\",\n        \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n        \"!git clone https://github.com/greydanus/mnist1d\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 2,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"KaQo7QhvXvid\",\n        \"outputId\": \"1cb2675c-776e-4891-db05-1c4649c15d3c\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Using: cuda\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"import numpy as np\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"from scipy.ndimage import gaussian_filter\\n\",\n        \"import torch, os\\n\",\n        \"import torch.nn as nn\\n\",\n        \"import torch.nn.functional as F\\n\",\n        \"\\n\",\n        \"import mnist1d\\n\",\n        \"\\n\",\n        \"# Try attaching to GPU\\n\",\n        \"DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\\n\",\n        \"print('Using:', DEVICE)\\n\",\n        \"\\n\",\n        \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 4,\n      \"metadata\": {\n        \"id\": \"cxzRpVn1wO1h\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# from mnist1d.data import get_dataset, get_dataset_args\\n\",\n        \"# from mnist1d.utils import set_seed, to_pickle\\n\",\n        \"\\n\",\n        \"import sys ; sys.path.append('./mnist1d/notebooks')\\n\",\n        \"from train import get_model_args, train_model\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"CP-Pmxw3wO1h\"\n      },\n      \"source\": [\n        \"## Only run this if you're in Google Colab\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 5,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"JjJoQ1KAwO1i\",\n        \"outputId\": \"39c80efe-59ce-49a1-f656-a26461e7e43a\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Mounted at /content/gdrive\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"if True:\\n\",\n        \"    # Only run this in Colab\\n\",\n        \"    from google.colab import drive\\n\",\n        \"    drive.mount('/content/gdrive')\\n\",\n        \"    project_dir = \\\"/content/gdrive/My Drive/Research/mnist1d/\\\"\\n\",\n        \"else:\\n\",\n        \"    project_dir = './'\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"O2vy0FKjfDwr\"\n      },\n      \"source\": [\n        \"## Systematically generate different pooling settings\\n\",\n        \"We'll need to define a custom model for this. It looks similar to the `ConvBase` model in [mnist1d/models.py](https://github.com/greydanus/mnist1d/blob/master/models.py)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 6,\n      \"metadata\": {\n        \"id\": \"uBx5gNW-mqH_\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"\\n\",\n        \"class ConvPooling(nn.Module):\\n\",\n        \"    def __init__(self, output_size, channels=20, stride=1, pool_fun=None, linear_in=200, seed=0):\\n\",\n        \"        super(ConvPooling, self).__init__()\\n\",\n        \"        _ = torch.manual_seed(seed) # use same initialization each time\\n\",\n        \"        self.conv1 = nn.Conv1d(1, channels, 3, stride=stride, padding=1)\\n\",\n        \"        self.conv2 = nn.Conv1d(channels, channels, 3, stride=stride, padding=1)\\n\",\n        \"        self.linear = nn.Linear(linear_in, output_size)\\n\",\n        \"        max_pool = lambda x: F.max_pool1d(x, 2)\\n\",\n        \"        self.pool_fun = max_pool if pool_fun is None else pool_fun\\n\",\n        \"\\n\",\n        \"    def forward(self, x): # the print statements are for debugging\\n\",\n        \"        x = x.view(-1,1,x.shape[-1])\\n\",\n        \"        h1 = self.pool_fun(self.conv1(x).relu())\\n\",\n        \"        h2 = self.pool_fun(self.conv2(h1).relu())\\n\",\n        \"        h2_flat = h2.view(h2.shape[0], -1) # flatten the conv features\\n\",\n        \"        # print(h2_flat.shape)\\n\",\n        \"        return self.linear(h2_flat) # a linear classifier goes on top\\n\",\n        \"\\n\",\n        \"def get_models(args):\\n\",\n        \"  models = [ConvPooling(args.output_size, stride=1, pool_fun=lambda x: x, linear_in=800),\\n\",\n        \"            ConvPooling(args.output_size, stride=2, pool_fun=lambda x: x),\\n\",\n        \"            ConvPooling(args.output_size, stride=1, pool_fun=lambda x: F.max_pool1d(x, 2)),\\n\",\n        \"            ConvPooling(args.output_size, stride=1, pool_fun=lambda x: F.avg_pool1d(x, 2)),\\n\",\n        \"            ConvPooling(args.output_size, stride=1, pool_fun=lambda x: F.lp_pool1d(x, norm_type=2, kernel_size=2))]\\n\",\n        \"\\n\",\n        \"  for m, n in zip(models, ['no_pool','stride_2','max_pool','avg_pool','l2_pool']):\\n\",\n        \"    m.name = n\\n\",\n        \"  return models\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"XH8nHKh0Ss5M\"\n      },\n      \"source\": [\n        \"## Let's see how pooling affects learning in different data regimes\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 7,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"BD4YKFwiSeah\",\n        \"outputId\": \"f06e465c-7f17-4ada-9a37-efeb8bbd963e\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=1000, test set size=1000\\n\",\n            \"step 1000, dt 3.05s, train_loss 1.452e-03, test_loss 2.785e+00, train_acc 100.0, test_acc 59.9\\n\",\n            \"step 2000, dt 1.60s, train_loss 3.115e-04, test_loss 3.279e+00, train_acc 100.0, test_acc 60.1\\n\",\n            \"step 3000, dt 1.57s, train_loss 1.155e-04, test_loss 3.599e+00, train_acc 100.0, test_acc 59.9\\n\",\n            \"step 4000, dt 1.53s, train_loss 5.209e-05, test_loss 3.858e+00, train_acc 100.0, test_acc 59.6\\n\",\n            \"step 5000, dt 1.56s, train_loss 2.587e-05, test_loss 4.088e+00, train_acc 100.0, test_acc 59.0\\n\",\n            \"step 6000, dt 1.53s, train_loss 1.361e-05, test_loss 4.296e+00, train_acc 100.0, test_acc 59.2\\n\",\n            \"step 1000, dt 1.61s, train_loss 1.222e-02, test_loss 2.728e+00, train_acc 100.0, test_acc 57.4\\n\",\n            \"step 2000, dt 1.49s, train_loss 1.769e-03, test_loss 3.687e+00, train_acc 100.0, test_acc 57.2\\n\",\n            \"step 3000, dt 1.55s, train_loss 5.796e-04, test_loss 4.239e+00, train_acc 100.0, test_acc 57.0\\n\",\n            \"step 4000, dt 1.54s, train_loss 2.459e-04, test_loss 4.657e+00, train_acc 100.0, test_acc 56.9\\n\",\n            \"step 5000, dt 1.51s, train_loss 1.183e-04, test_loss 5.018e+00, train_acc 100.0, test_acc 57.0\\n\",\n            \"step 6000, dt 1.54s, train_loss 6.033e-05, test_loss 5.345e+00, train_acc 100.0, test_acc 57.4\\n\",\n            \"step 1000, dt 1.79s, train_loss 2.974e-03, test_loss 1.021e+00, train_acc 100.0, test_acc 78.2\\n\",\n            \"step 2000, dt 1.74s, train_loss 6.399e-04, test_loss 1.253e+00, train_acc 100.0, test_acc 78.0\\n\",\n            \"step 3000, dt 1.74s, train_loss 2.322e-04, test_loss 1.404e+00, train_acc 100.0, test_acc 78.2\\n\",\n            \"step 4000, dt 1.76s, train_loss 1.042e-04, test_loss 1.523e+00, train_acc 100.0, test_acc 78.4\\n\",\n            \"step 5000, dt 1.74s, train_loss 5.147e-05, test_loss 1.626e+00, train_acc 100.0, test_acc 78.3\\n\",\n            \"step 6000, dt 1.74s, train_loss 2.692e-05, test_loss 1.724e+00, train_acc 100.0, test_acc 78.2\\n\",\n            \"step 1000, dt 1.67s, train_loss 3.266e-02, test_loss 1.082e+00, train_acc 99.9, test_acc 77.8\\n\",\n            \"step 2000, dt 1.69s, train_loss 1.565e-03, test_loss 1.417e+00, train_acc 100.0, test_acc 77.9\\n\",\n            \"step 3000, dt 1.67s, train_loss 5.245e-04, test_loss 1.634e+00, train_acc 100.0, test_acc 77.6\\n\",\n            \"step 4000, dt 1.67s, train_loss 2.236e-04, test_loss 1.808e+00, train_acc 100.0, test_acc 77.7\\n\",\n            \"step 5000, dt 1.66s, train_loss 1.063e-04, test_loss 1.961e+00, train_acc 100.0, test_acc 77.9\\n\",\n            \"step 6000, dt 1.73s, train_loss 5.385e-05, test_loss 2.103e+00, train_acc 100.0, test_acc 78.0\\n\",\n            \"step 1000, dt 2.70s, train_loss 2.322e-03, test_loss 1.150e+00, train_acc 100.0, test_acc 78.1\\n\",\n            \"step 2000, dt 2.49s, train_loss 4.466e-04, test_loss 1.389e+00, train_acc 100.0, test_acc 78.0\\n\",\n            \"step 3000, dt 2.46s, train_loss 1.593e-04, test_loss 1.544e+00, train_acc 100.0, test_acc 77.3\\n\",\n            \"step 4000, dt 2.41s, train_loss 7.082e-05, test_loss 1.665e+00, train_acc 100.0, test_acc 77.0\\n\",\n            \"step 5000, dt 2.46s, train_loss 3.477e-05, test_loss 1.772e+00, train_acc 100.0, test_acc 77.1\\n\",\n            \"step 6000, dt 2.45s, train_loss 1.819e-05, test_loss 1.870e+00, train_acc 100.0, test_acc 77.2\\n\",\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=2000, test set size=1000\\n\",\n            \"step 1000, dt 1.62s, train_loss 6.647e-02, test_loss 1.052e+00, train_acc 96.3, test_acc 78.6\\n\",\n            \"step 2000, dt 1.62s, train_loss 6.551e-04, test_loss 1.108e+00, train_acc 100.0, test_acc 82.1\\n\",\n            \"step 3000, dt 1.56s, train_loss 2.801e-04, test_loss 1.226e+00, train_acc 100.0, test_acc 81.8\\n\",\n            \"step 4000, dt 1.59s, train_loss 1.443e-04, test_loss 1.319e+00, train_acc 100.0, test_acc 81.8\\n\",\n            \"step 5000, dt 1.65s, train_loss 7.967e-05, test_loss 1.407e+00, train_acc 100.0, test_acc 81.9\\n\",\n            \"step 6000, dt 1.60s, train_loss 4.415e-05, test_loss 1.487e+00, train_acc 100.0, test_acc 82.0\\n\",\n            \"step 1000, dt 1.55s, train_loss 1.372e-01, test_loss 9.035e-01, train_acc 90.2, test_acc 75.1\\n\",\n            \"step 2000, dt 1.53s, train_loss 2.271e-02, test_loss 9.773e-01, train_acc 98.2, test_acc 79.0\\n\",\n            \"step 3000, dt 1.53s, train_loss 6.885e-03, test_loss 1.190e+00, train_acc 100.0, test_acc 81.3\\n\",\n            \"step 4000, dt 1.54s, train_loss 2.373e-03, test_loss 1.408e+00, train_acc 100.0, test_acc 81.5\\n\",\n            \"step 5000, dt 1.54s, train_loss 1.016e-03, test_loss 1.599e+00, train_acc 100.0, test_acc 81.4\\n\",\n            \"step 6000, dt 1.53s, train_loss 5.105e-04, test_loss 1.771e+00, train_acc 100.0, test_acc 81.7\\n\",\n            \"step 1000, dt 1.81s, train_loss 1.631e-02, test_loss 5.726e-01, train_acc 99.4, test_acc 87.1\\n\",\n            \"step 2000, dt 1.76s, train_loss 1.546e-03, test_loss 6.931e-01, train_acc 100.0, test_acc 89.3\\n\",\n            \"step 3000, dt 1.78s, train_loss 5.004e-04, test_loss 7.743e-01, train_acc 100.0, test_acc 89.3\\n\",\n            \"step 4000, dt 1.77s, train_loss 2.421e-04, test_loss 8.333e-01, train_acc 100.0, test_acc 89.4\\n\",\n            \"step 5000, dt 1.77s, train_loss 1.294e-04, test_loss 8.888e-01, train_acc 100.0, test_acc 89.1\\n\",\n            \"step 6000, dt 1.76s, train_loss 7.083e-05, test_loss 9.415e-01, train_acc 100.0, test_acc 89.2\\n\",\n            \"step 1000, dt 1.74s, train_loss 8.820e-02, test_loss 5.222e-01, train_acc 96.2, test_acc 85.9\\n\",\n            \"step 2000, dt 1.73s, train_loss 1.877e-02, test_loss 5.738e-01, train_acc 99.2, test_acc 89.5\\n\",\n            \"step 3000, dt 1.70s, train_loss 1.401e-03, test_loss 5.621e-01, train_acc 100.0, test_acc 90.1\\n\",\n            \"step 4000, dt 1.71s, train_loss 6.315e-04, test_loss 6.110e-01, train_acc 100.0, test_acc 89.8\\n\",\n            \"step 5000, dt 1.72s, train_loss 3.401e-04, test_loss 6.533e-01, train_acc 100.0, test_acc 90.0\\n\",\n            \"step 6000, dt 1.74s, train_loss 1.949e-04, test_loss 6.957e-01, train_acc 100.0, test_acc 90.0\\n\",\n            \"step 1000, dt 2.47s, train_loss 6.138e-02, test_loss 4.424e-01, train_acc 98.0, test_acc 88.6\\n\",\n            \"step 2000, dt 2.50s, train_loss 1.116e-02, test_loss 4.875e-01, train_acc 100.0, test_acc 91.0\\n\",\n            \"step 3000, dt 2.45s, train_loss 7.532e-04, test_loss 5.874e-01, train_acc 100.0, test_acc 91.0\\n\",\n            \"step 4000, dt 2.44s, train_loss 3.389e-04, test_loss 6.328e-01, train_acc 100.0, test_acc 90.5\\n\",\n            \"step 5000, dt 2.45s, train_loss 1.825e-04, test_loss 6.732e-01, train_acc 100.0, test_acc 90.2\\n\",\n            \"step 6000, dt 2.45s, train_loss 1.038e-04, test_loss 7.133e-01, train_acc 100.0, test_acc 89.9\\n\",\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=5000, test set size=1000\\n\",\n            \"step 1000, dt 1.60s, train_loss 6.357e-02, test_loss 3.726e-01, train_acc 97.9, test_acc 89.5\\n\",\n            \"step 2000, dt 1.55s, train_loss 6.281e-03, test_loss 4.866e-01, train_acc 99.6, test_acc 89.7\\n\",\n            \"step 3000, dt 1.58s, train_loss 2.777e-02, test_loss 5.479e-01, train_acc 99.2, test_acc 89.7\\n\",\n            \"step 4000, dt 1.58s, train_loss 1.460e-04, test_loss 4.633e-01, train_acc 100.0, test_acc 92.0\\n\",\n            \"step 5000, dt 1.58s, train_loss 9.093e-05, test_loss 4.866e-01, train_acc 100.0, test_acc 92.0\\n\",\n            \"step 6000, dt 1.61s, train_loss 6.229e-05, test_loss 5.048e-01, train_acc 100.0, test_acc 91.9\\n\",\n            \"step 1000, dt 1.62s, train_loss 1.056e-01, test_loss 4.257e-01, train_acc 94.5, test_acc 85.8\\n\",\n            \"step 2000, dt 1.59s, train_loss 1.069e-01, test_loss 5.056e-01, train_acc 96.3, test_acc 88.5\\n\",\n            \"step 3000, dt 1.57s, train_loss 3.146e-02, test_loss 5.121e-01, train_acc 97.8, test_acc 88.4\\n\",\n            \"step 4000, dt 1.59s, train_loss 5.262e-02, test_loss 5.659e-01, train_acc 98.6, test_acc 89.5\\n\",\n            \"step 5000, dt 1.54s, train_loss 4.487e-02, test_loss 6.898e-01, train_acc 98.4, test_acc 88.9\\n\",\n            \"step 6000, dt 1.51s, train_loss 1.630e-03, test_loss 7.076e-01, train_acc 99.6, test_acc 89.9\\n\",\n            \"step 1000, dt 1.74s, train_loss 6.580e-02, test_loss 2.896e-01, train_acc 97.3, test_acc 92.4\\n\",\n            \"step 2000, dt 1.78s, train_loss 7.009e-02, test_loss 4.034e-01, train_acc 96.5, test_acc 91.6\\n\",\n            \"step 3000, dt 1.77s, train_loss 1.755e-02, test_loss 3.655e-01, train_acc 99.7, test_acc 93.7\\n\",\n            \"step 4000, dt 1.74s, train_loss 1.276e-03, test_loss 4.159e-01, train_acc 100.0, test_acc 94.2\\n\",\n            \"step 5000, dt 1.73s, train_loss 4.149e-04, test_loss 4.464e-01, train_acc 100.0, test_acc 94.6\\n\",\n            \"step 6000, dt 1.73s, train_loss 2.434e-04, test_loss 4.736e-01, train_acc 100.0, test_acc 94.5\\n\",\n            \"step 1000, dt 1.69s, train_loss 1.472e-01, test_loss 3.249e-01, train_acc 94.3, test_acc 88.6\\n\",\n            \"step 2000, dt 1.68s, train_loss 5.878e-02, test_loss 2.491e-01, train_acc 97.4, test_acc 93.5\\n\",\n            \"step 3000, dt 1.72s, train_loss 3.734e-02, test_loss 2.645e-01, train_acc 98.1, test_acc 93.3\\n\",\n            \"step 4000, dt 1.75s, train_loss 8.816e-03, test_loss 2.959e-01, train_acc 98.7, test_acc 93.1\\n\",\n            \"step 5000, dt 1.70s, train_loss 2.268e-02, test_loss 3.870e-01, train_acc 98.9, test_acc 93.3\\n\",\n            \"step 6000, dt 1.73s, train_loss 2.471e-02, test_loss 3.406e-01, train_acc 99.2, test_acc 94.0\\n\",\n            \"step 1000, dt 2.49s, train_loss 5.895e-02, test_loss 2.953e-01, train_acc 96.1, test_acc 90.9\\n\",\n            \"step 2000, dt 2.47s, train_loss 2.253e-02, test_loss 2.380e-01, train_acc 99.2, test_acc 94.4\\n\",\n            \"step 3000, dt 2.51s, train_loss 3.262e-03, test_loss 2.879e-01, train_acc 99.9, test_acc 94.0\\n\",\n            \"step 4000, dt 2.48s, train_loss 2.204e-03, test_loss 3.246e-01, train_acc 99.9, test_acc 94.4\\n\",\n            \"step 5000, dt 2.50s, train_loss 1.358e-02, test_loss 4.344e-01, train_acc 99.1, test_acc 94.0\\n\",\n            \"step 6000, dt 2.45s, train_loss 5.408e-04, test_loss 3.288e-01, train_acc 100.0, test_acc 95.3\\n\",\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=10000, test set size=1000\\n\",\n            \"step 1000, dt 1.61s, train_loss 6.490e-02, test_loss 2.128e-01, train_acc 96.6, test_acc 93.7\\n\",\n            \"step 2000, dt 1.59s, train_loss 3.926e-02, test_loss 2.481e-01, train_acc 97.6, test_acc 92.6\\n\",\n            \"step 3000, dt 1.57s, train_loss 2.231e-02, test_loss 3.569e-01, train_acc 96.2, test_acc 92.5\\n\",\n            \"step 4000, dt 1.60s, train_loss 2.069e-02, test_loss 2.970e-01, train_acc 98.7, test_acc 93.8\\n\",\n            \"step 5000, dt 1.62s, train_loss 3.838e-02, test_loss 2.727e-01, train_acc 99.0, test_acc 94.9\\n\",\n            \"step 6000, dt 1.61s, train_loss 5.584e-02, test_loss 3.307e-01, train_acc 98.3, test_acc 94.3\\n\",\n            \"step 1000, dt 1.57s, train_loss 1.529e-01, test_loss 2.215e-01, train_acc 94.1, test_acc 92.4\\n\",\n            \"step 2000, dt 1.56s, train_loss 8.962e-02, test_loss 2.316e-01, train_acc 95.8, test_acc 94.4\\n\",\n            \"step 3000, dt 1.54s, train_loss 1.006e-01, test_loss 2.705e-01, train_acc 95.5, test_acc 92.2\\n\",\n            \"step 4000, dt 1.53s, train_loss 6.084e-02, test_loss 3.377e-01, train_acc 95.5, test_acc 92.7\\n\",\n            \"step 5000, dt 1.53s, train_loss 6.458e-02, test_loss 3.558e-01, train_acc 96.8, test_acc 92.1\\n\",\n            \"step 6000, dt 1.55s, train_loss 3.079e-02, test_loss 4.216e-01, train_acc 96.4, test_acc 91.3\\n\",\n            \"step 1000, dt 1.74s, train_loss 3.803e-02, test_loss 1.283e-01, train_acc 96.1, test_acc 95.3\\n\",\n            \"step 2000, dt 1.77s, train_loss 5.471e-02, test_loss 1.476e-01, train_acc 96.3, test_acc 94.6\\n\",\n            \"step 3000, dt 1.79s, train_loss 2.909e-02, test_loss 1.753e-01, train_acc 96.2, test_acc 94.6\\n\",\n            \"step 4000, dt 1.75s, train_loss 1.382e-02, test_loss 1.087e-01, train_acc 97.9, test_acc 96.5\\n\",\n            \"step 5000, dt 1.73s, train_loss 1.656e-02, test_loss 1.651e-01, train_acc 97.5, test_acc 94.8\\n\",\n            \"step 6000, dt 1.74s, train_loss 9.807e-03, test_loss 1.033e-01, train_acc 98.9, test_acc 97.0\\n\",\n            \"step 1000, dt 1.73s, train_loss 6.617e-02, test_loss 1.560e-01, train_acc 94.0, test_acc 94.1\\n\",\n            \"step 2000, dt 1.75s, train_loss 1.983e-02, test_loss 1.080e-01, train_acc 96.8, test_acc 96.4\\n\",\n            \"step 3000, dt 1.72s, train_loss 2.736e-02, test_loss 1.253e-01, train_acc 97.2, test_acc 96.2\\n\",\n            \"step 4000, dt 1.77s, train_loss 9.317e-03, test_loss 1.427e-01, train_acc 96.8, test_acc 95.2\\n\",\n            \"step 5000, dt 1.71s, train_loss 4.564e-03, test_loss 1.508e-01, train_acc 97.6, test_acc 96.4\\n\",\n            \"step 6000, dt 1.71s, train_loss 2.449e-03, test_loss 1.681e-01, train_acc 97.7, test_acc 96.0\\n\",\n            \"step 1000, dt 2.46s, train_loss 5.464e-02, test_loss 1.262e-01, train_acc 95.9, test_acc 95.5\\n\",\n            \"step 2000, dt 2.48s, train_loss 2.815e-02, test_loss 9.669e-02, train_acc 97.7, test_acc 96.3\\n\",\n            \"step 3000, dt 2.52s, train_loss 5.754e-02, test_loss 1.387e-01, train_acc 97.2, test_acc 95.5\\n\",\n            \"step 4000, dt 2.51s, train_loss 3.447e-02, test_loss 1.724e-01, train_acc 96.1, test_acc 95.4\\n\",\n            \"step 5000, dt 2.47s, train_loss 1.558e-02, test_loss 1.057e-01, train_acc 98.3, test_acc 97.0\\n\",\n            \"step 6000, dt 2.50s, train_loss 5.077e-02, test_loss 1.476e-01, train_acc 98.0, test_acc 96.6\\n\",\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=20000, test set size=1000\\n\",\n            \"step 1000, dt 1.63s, train_loss 1.885e-01, test_loss 1.926e-01, train_acc 94.7, test_acc 93.9\\n\",\n            \"step 2000, dt 1.61s, train_loss 1.854e-01, test_loss 1.803e-01, train_acc 94.7, test_acc 94.6\\n\",\n            \"step 3000, dt 1.68s, train_loss 9.691e-02, test_loss 1.736e-01, train_acc 96.0, test_acc 94.1\\n\",\n            \"step 4000, dt 1.69s, train_loss 4.889e-02, test_loss 1.545e-01, train_acc 96.5, test_acc 95.1\\n\",\n            \"step 5000, dt 1.63s, train_loss 8.644e-02, test_loss 1.541e-01, train_acc 96.5, test_acc 95.3\\n\",\n            \"step 6000, dt 1.62s, train_loss 5.165e-02, test_loss 1.904e-01, train_acc 96.3, test_acc 94.4\\n\",\n            \"step 1000, dt 1.55s, train_loss 4.043e-01, test_loss 2.679e-01, train_acc 91.4, test_acc 91.2\\n\",\n            \"step 2000, dt 1.56s, train_loss 1.870e-01, test_loss 2.070e-01, train_acc 94.1, test_acc 93.4\\n\",\n            \"step 3000, dt 1.56s, train_loss 1.352e-01, test_loss 2.134e-01, train_acc 94.0, test_acc 93.1\\n\",\n            \"step 4000, dt 1.56s, train_loss 1.081e-01, test_loss 2.186e-01, train_acc 94.7, test_acc 93.5\\n\",\n            \"step 5000, dt 1.57s, train_loss 8.404e-02, test_loss 2.497e-01, train_acc 95.1, test_acc 92.8\\n\",\n            \"step 6000, dt 1.61s, train_loss 9.615e-02, test_loss 2.977e-01, train_acc 94.7, test_acc 92.2\\n\",\n            \"step 1000, dt 1.79s, train_loss 1.787e-01, test_loss 1.251e-01, train_acc 96.6, test_acc 96.7\\n\",\n            \"step 2000, dt 1.77s, train_loss 8.813e-02, test_loss 1.334e-01, train_acc 97.3, test_acc 96.0\\n\",\n            \"step 3000, dt 1.79s, train_loss 6.658e-02, test_loss 1.014e-01, train_acc 97.9, test_acc 97.1\\n\",\n            \"step 4000, dt 1.79s, train_loss 2.576e-02, test_loss 9.413e-02, train_acc 98.2, test_acc 97.1\\n\",\n            \"step 5000, dt 1.81s, train_loss 3.511e-02, test_loss 1.047e-01, train_acc 98.0, test_acc 96.4\\n\",\n            \"step 6000, dt 1.84s, train_loss 2.144e-02, test_loss 1.194e-01, train_acc 98.2, test_acc 96.8\\n\",\n            \"step 1000, dt 1.77s, train_loss 1.431e-01, test_loss 1.761e-01, train_acc 94.2, test_acc 94.7\\n\",\n            \"step 2000, dt 1.74s, train_loss 8.928e-02, test_loss 1.217e-01, train_acc 97.2, test_acc 96.3\\n\",\n            \"step 3000, dt 1.73s, train_loss 4.217e-02, test_loss 1.089e-01, train_acc 97.9, test_acc 97.0\\n\",\n            \"step 4000, dt 1.71s, train_loss 1.961e-02, test_loss 1.013e-01, train_acc 98.6, test_acc 96.8\\n\",\n            \"step 5000, dt 1.75s, train_loss 3.450e-02, test_loss 1.004e-01, train_acc 98.4, test_acc 96.7\\n\",\n            \"step 6000, dt 1.74s, train_loss 1.617e-02, test_loss 1.172e-01, train_acc 97.7, test_acc 96.2\\n\",\n            \"step 1000, dt 2.56s, train_loss 1.061e-01, test_loss 1.580e-01, train_acc 95.2, test_acc 95.4\\n\",\n            \"step 2000, dt 2.50s, train_loss 6.005e-02, test_loss 1.012e-01, train_acc 97.7, test_acc 96.8\\n\",\n            \"step 3000, dt 2.50s, train_loss 8.669e-02, test_loss 9.806e-02, train_acc 98.0, test_acc 97.1\\n\",\n            \"step 4000, dt 2.48s, train_loss 4.814e-02, test_loss 7.269e-02, train_acc 98.6, test_acc 98.2\\n\",\n            \"step 5000, dt 2.50s, train_loss 5.166e-02, test_loss 9.254e-02, train_acc 98.3, test_acc 97.4\\n\",\n            \"step 6000, dt 2.56s, train_loss 4.138e-02, test_loss 8.727e-02, train_acc 98.6, test_acc 97.8\\n\",\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"\\n\",\n            \"New dataset, train set size=50000, test set size=1000\\n\",\n            \"step 1000, dt 1.70s, train_loss 1.733e-01, test_loss 1.549e-01, train_acc 96.4, test_acc 95.3\\n\",\n            \"step 2000, dt 1.67s, train_loss 8.866e-02, test_loss 1.494e-01, train_acc 96.6, test_acc 96.5\\n\",\n            \"step 3000, dt 1.66s, train_loss 1.025e-01, test_loss 1.449e-01, train_acc 97.1, test_acc 96.4\\n\",\n            \"step 4000, dt 1.70s, train_loss 1.023e-01, test_loss 1.439e-01, train_acc 97.4, test_acc 96.1\\n\",\n            \"step 5000, dt 1.69s, train_loss 1.172e-01, test_loss 1.432e-01, train_acc 97.4, test_acc 96.6\\n\",\n            \"step 6000, dt 1.70s, train_loss 8.589e-02, test_loss 1.643e-01, train_acc 96.9, test_acc 96.3\\n\",\n            \"step 1000, dt 1.63s, train_loss 2.023e-01, test_loss 2.477e-01, train_acc 92.7, test_acc 93.0\\n\",\n            \"step 2000, dt 1.65s, train_loss 1.273e-01, test_loss 1.865e-01, train_acc 94.7, test_acc 94.4\\n\",\n            \"step 3000, dt 1.64s, train_loss 9.741e-02, test_loss 1.758e-01, train_acc 95.0, test_acc 94.8\\n\",\n            \"step 4000, dt 1.61s, train_loss 6.318e-02, test_loss 1.748e-01, train_acc 95.6, test_acc 94.7\\n\",\n            \"step 5000, dt 1.64s, train_loss 7.016e-02, test_loss 1.626e-01, train_acc 96.1, test_acc 95.4\\n\",\n            \"step 6000, dt 1.63s, train_loss 9.876e-02, test_loss 1.687e-01, train_acc 95.9, test_acc 95.4\\n\",\n            \"step 1000, dt 1.90s, train_loss 8.345e-02, test_loss 1.365e-01, train_acc 96.5, test_acc 95.8\\n\",\n            \"step 2000, dt 1.83s, train_loss 5.780e-02, test_loss 1.120e-01, train_acc 97.2, test_acc 97.1\\n\",\n            \"step 3000, dt 1.82s, train_loss 3.284e-02, test_loss 9.956e-02, train_acc 97.6, test_acc 97.3\\n\",\n            \"step 4000, dt 1.86s, train_loss 1.737e-02, test_loss 7.605e-02, train_acc 98.3, test_acc 97.8\\n\",\n            \"step 5000, dt 1.84s, train_loss 6.650e-02, test_loss 8.646e-02, train_acc 98.2, test_acc 98.1\\n\",\n            \"step 6000, dt 1.83s, train_loss 2.151e-02, test_loss 7.358e-02, train_acc 98.5, test_acc 98.1\\n\",\n            \"step 1000, dt 1.83s, train_loss 1.803e-01, test_loss 2.064e-01, train_acc 93.7, test_acc 93.6\\n\",\n            \"step 2000, dt 1.82s, train_loss 1.939e-01, test_loss 1.279e-01, train_acc 96.9, test_acc 96.1\\n\",\n            \"step 3000, dt 1.79s, train_loss 1.401e-01, test_loss 1.037e-01, train_acc 97.8, test_acc 96.9\\n\",\n            \"step 4000, dt 1.78s, train_loss 1.196e-01, test_loss 9.660e-02, train_acc 98.2, test_acc 97.1\\n\",\n            \"step 5000, dt 1.76s, train_loss 1.295e-01, test_loss 1.037e-01, train_acc 98.0, test_acc 97.4\\n\",\n            \"step 6000, dt 1.79s, train_loss 1.177e-01, test_loss 9.063e-02, train_acc 98.3, test_acc 97.3\\n\",\n            \"step 1000, dt 2.63s, train_loss 1.067e-01, test_loss 1.470e-01, train_acc 96.3, test_acc 95.6\\n\",\n            \"step 2000, dt 2.60s, train_loss 1.235e-01, test_loss 1.275e-01, train_acc 97.2, test_acc 96.6\\n\",\n            \"step 3000, dt 2.58s, train_loss 1.193e-01, test_loss 1.283e-01, train_acc 97.0, test_acc 96.6\\n\",\n            \"step 4000, dt 2.56s, train_loss 8.530e-02, test_loss 1.038e-01, train_acc 97.5, test_acc 97.1\\n\",\n            \"step 5000, dt 2.58s, train_loss 1.378e-01, test_loss 8.621e-02, train_acc 98.0, test_acc 97.7\\n\",\n            \"step 6000, dt 2.60s, train_loss 9.037e-02, test_loss 9.042e-02, train_acc 97.7, test_acc 97.1\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"dataset_sizes = [2000, 3000, 6000, 11000, 21000, 51000]\\n\",\n        \"train_splits = [1-(1000./ds) for ds in dataset_sizes]  # split so that there are always 1000 test examples\\n\",\n        \"regime_results = []\\n\",\n        \"for i, num_samples in enumerate(dataset_sizes):\\n\",\n        \"\\n\",\n        \"  # make dataset\\n\",\n        \"  data_args = mnist1d.get_dataset_args()\\n\",\n        \"  data_args.num_samples = num_samples\\n\",\n        \"  data_args.train_split = train_splits[i]\\n\",\n        \"  dataset = mnist1d.get_dataset(args=data_args, download=False, regenerate=True)\\n\",\n        \"\\n\",\n        \"  # build the models\\n\",\n        \"  model_args = get_model_args()\\n\",\n        \"  model_args.total_steps = 6000\\n\",\n        \"  model_args.eval_every = 100\\n\",\n        \"  model_args.device = DEVICE\\n\",\n        \"  models = get_models(model_args)\\n\",\n        \"\\n\",\n        \"  # train the models, save results\\n\",\n        \"  print('\\\\nNew dataset, train set size={}, test set size={}'\\\\\\n\",\n        \"        .format(dataset['x'].shape[0], dataset['x_test'].shape[0]))\\n\",\n        \"  result = [train_model(dataset, m, model_args) for m in models]\\n\",\n        \"  regime_results.append(result)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"source\": [\n        \"rows, cols = 1, 3\\n\",\n        \"fig = plt.figure(figsize=[cols*3.2,rows*2.4], dpi=120)\\n\",\n        \"\\n\",\n        \"t = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"styles = ['k-', 'k--', 'r-', 'g-', 'b-']\\n\",\n        \"ylims = [[30,85],[80,100],[90,100]]\\n\",\n        \"runs_to_show = [0, 2, 5]\\n\",\n        \"\\n\",\n        \"for r in range(rows):\\n\",\n        \"    for c in range(cols):\\n\",\n        \"        ix = r*cols + c\\n\",\n        \"        ax = plt.subplot(rows,cols,ix+1)\\n\",\n        \"\\n\",\n        \"        results = regime_results[runs_to_show[ix]]\\n\",\n        \"        for j, result in enumerate(results):\\n\",\n        \"          model = result['checkpoints'][-1]\\n\",\n        \"          plt.plot(t, gaussian_filter(result['test_acc'], 1), styles[j], label=model.name)\\n\",\n        \"\\n\",\n        \"        plt.title('{} examples'.format(dataset_sizes[runs_to_show[ix]]-1000), fontsize=10)\\n\",\n        \"        if ix==0:\\n\",\n        \"          plt.ylabel(\\\"Test accuracy\\\", fontsize=9)\\n\",\n        \"          plt.xlabel(\\\"Train step\\\", fontsize=9)\\n\",\n        \"        if ix==0:\\n\",\n        \"          plt.legend(fontsize=8, ncol=2)\\n\",\n        \"        plt.ylim(*ylims[ix])\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"os.makedirs(project_dir + 'figures/', exist_ok=True)\\n\",\n        \"fig.savefig(project_dir + 'figures/pooling.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/pooling.pdf')\"\n      ],\n      \"metadata\": {\n        \"id\": \"rxWZwLWlxMFv\",\n        \"outputId\": \"1e61f48c-73d4-4c40-d31d-a170b9838181\",\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 318\n        }\n      },\n      \"execution_count\": 11,\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 1152x288 with 3 Axes>\"\n            ],\n            \"image/png\": 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bMmIEePXqgYMGCqFixIr7//nt0794dRYoUSXFbxhhzc3NDy5YtsXv3bmg0Gshksq+erz89V2fWNeBL539AnA8fP34MW1vbZJd/fg2YNWsW9u7dizt37mDz5s1Jrh1arRbz58/H4sWL8eLFC73ATb58+ZKUn5ZrgFarRXh4uF45RYsWTVJmsWLFEBMTgw8fPnzxGpAYGPtcYk4RpVKJ6dOn47fffkP+/PlRrVo1NGvWDN27d091HkPG2LcrN57HU1tmctJy/wsArq6umDBhAkaOHInSpUvjzz//TLL+w4cPMW7cOJw6dQoRERFJyvqUk5NTknt2CwuLVN/zA0nP+a6urpBKpV/MM+fj44N79+599To3cOBAbN++HU2bNoWjoyMaNWqEDh06oEmTJimWzb4tHDRi3wQHBwcAgL+/f5Jl/v7+sLa21r09cHBwwOnTp0FEeifYxG0LFCiQ5jKTo9VqYWdnh02bNiW7/PMT7PPnz3UXnfv37ydZf+PGjejZsydatWqFkSNHws7ODjKZDNOmTYOvr2+S9WUyWbL7TWk+EaV4LKmV+BZl5syZ8PDwSHYdU1NTAECHDh1Qq1Yt/PPPPzh27BhmzpyJ6dOnY/fu3WjatGmG68IYy7sKFiyIhIQEREdHw9zc/Kvn68TzOiDO7Z+3rPx029ReAz4tMzlarRZlypTBnDlzUjyGT92+fVt3g33//n107txZb/nUqVPx559/onfv3pg8eTKsra0hlUoxbNiwJAlHgZy9BmzYsCHZ4M+nrVyHDRuG5s2bY8+ePTh69Cj+/PNPTJs2DadOnUL58uUzXBfGWO6W287jqS0zOWm5/0107NgxAICfnx+Cg4P1zplhYWGoU6cOzM3NMWnSJLi6usLQ0BC3bt3C6NGjk5zzs+J8/3kQKjlarRYNGzbEqFGjkl2e+LLZzs4Od+7cwdGjR3H48GEcPnwYa9asQffu3bFu3bqv7oflfhw0Yt8ER0dH2Nra4saNG0mWXbt2Te8E7uHhgZUrV+Lx48d6rXmuXr2qW57WMpPj6uqKEydOoEaNGl99I63VatGzZ0+Ym5tj2LBhmDp1Ktq1a4c2bdro1tm5cyeKFCmC3bt3653Ix48f/8Wy0+vZs2dJAmtPnz4FIEZXS46rqysA8Ta5QYMGX92Hg4MDBg4ciIEDByIwMBAVKlTAX3/9xUEjxtgXPX/+HIaGhrqb8NKlS0Mul+PGjRvo0KGDbr2EhATcuXNHb56HhwdOnz6NiIgIvdF0Pr8GpKXM5Li6uuLu3buoX7/+V2++o6Oj0atXL7i7u8PT0xMzZsxA69atUblyZd06O3fuRL169bBq1Sq9bcPCwmBjY/PF8tMj8SXGp54+fQpjY+MU3yonXgPs7OxSdQ1wdXXFb7/9ht9++w0+Pj7w8PDA7NmzsXHjxoxVnjGW6+W283hqy0xOWu9/ly5diuPHj+Ovv/7CtGnT0L9/f+zdu1e3/MyZMwgODsbu3btRu3Zt3fwXL158tez08vHx0eu58OzZM2i12hTv+QFx3FFRUak6ZoVCgebNm6N58+bQarUYOHAgli1bhj///BNubm6ZcQgsB3FOI/bNaNu2LQ4cOKA3JObJkyfx9OlTtG/fXjevZcuWMDAwwOLFi3XziAhLly6Fo6MjPD0901xmcjp06ACNRoPJkycnWaZWqxEWFqb7fc6cObh06RKWL1+OyZMnw9PTEwMGDEBQUJBuncS3BZ++Hbh69SouX778xXqkl5+fH/755x/d7xEREVi/fj08PDxS7D5QsWJFuLq6YtasWYiKikqy/MOHDwBEPo7Pm9ba2dmhQIECyTYNZoz9NyWeMz519+5d7Nu3D40aNdLl+LGwsECDBg2wceNGREZG6tbdsGEDoqKi9M7X7dq1g0ajwfLly3Xz4uPjsWbNGlStWlXXAigtZSanQ4cOePfuHVasWJFkWWxsLKKjo3W/jx49Gq9fv8a6deswZ84cuLi4oEePHnrnQ5lMluTt8I4dO5J9M54ZLl++rJcr6c2bN9i7dy8aNWqU4tvrxo0bw9zcHFOnTtXrQp0o8d8zJiYmyVDOrq6uMDMz42sAY3nMt3IeT22ZyUnt/S8gAj8jR45E27Zt8fvvv2PWrFnYt28f1q9fr1snuXv+hIQEvWeXzObl5aX3+8KFCwHgiy9yO3TogMuXL+Po0aNJloWFhely5QUHB+stk0qlKFu2LIDkuw6ybw+3NGI5btGiRQgLC9MlTN6/fz/evn0LQCRpS+yf+/vvv2PHjh2oV68ehg4diqioKMycORNlypRBr169dOU5OTlh2LBhmDlzJlQqFSpXrow9e/bg/Pnz2LRpk97NcGrLTE6dOnXQv39/TJs2DXfu3EGjRo1gYGAAHx8f7NixA/Pnz0e7du3w+PFj/Pnnn+jZsyeaN28OAFi7di08PDx0fYABoFmzZti9ezdat26NH374AS9evMDSpUvh7u6e7AUqo4oVK4Y+ffrg+vXryJ8/P1avXo2AgACsWbMmxW2kUilWrlyJpk2bolSpUujVqxccHR3x7t07nD59Gubm5ti/fz8iIyPh5OSEdu3aoVy5cjA1NcWJEydw/fp1zJ49O9OPhTH2berYsSOMjIzg6ekJOzs7PHr0CMuXL4exsTH+/vtvvXX/+usveHp6ok6dOujXrx/evn2L2bNno1GjRnp5E6pWrYr27dtj7NixCAwMhJubG9atW4eXL18macWT2jKT061bN2zfvh0///wzTp8+jRo1akCj0cDb2xvbt2/H0aNHUalSJZw6dQqLFy/G+PHjUaFCBQDAmjVrULduXfz555+YMWMGAHENmDRpEnr16gVPT0/cv38fmzZtyrI8cKVLl0bjxo0xZMgQKJVK3cPKxIkTU9zG3NwcS5YsQbdu3VChQgV06tQJtra2eP36NQ4ePIgaNWpg0aJFePr0KerXr48OHTrA3d0dcrkc//zzDwICAvSSuTLGvn3fynk8LWV+LrX3v4mDERgZGWHJkiUAgP79+2PXrl0YOnQoGjRogAIFCsDT0xNWVlbo0aMHhgwZAolEgg0bNmRKN+KUvHjxAi1atECTJk1w+fJlbNy4EV26dEG5cuVS3GbkyJHYt28fmjVrhp49e6JixYqIjo7G/fv3sXPnTrx8+RI2Njbo27cvQkJC8N1338HJyQmvXr3CwoUL4eHhgZIlS2bZMbFslDODtjH2UeLw78lNicP8Jnrw4AE1atSIjI2NydLSkn788Ud6//59kjI1Gg1NnTqVnJ2dSaFQUKlSpWjjxo3J7j+1ZaZk+fLlVLFiRTIyMiIzMzMqU6YMjRo1ivz8/EitVlPlypXJycmJwsLC9LabP38+AaBt27YRkRjOM7HOSqWSypcvTwcOHKAePXroDZf84sWLZIcyThyGeceOHXrzE4dM/nSoT2dnZ/rhhx/o6NGjVLZsWVIqlVSiRIkk2yaWefr0ab35t2/fpjZt2lC+fPlIqVSSs7MzdejQgU6ePElERPHx8TRy5EgqV64cmZmZkYmJCZUrV44WL16c6u+VMZb3zZ8/n6pUqULW1tYkl8vJwcGBunbtSj4+Psmuf/78efL09CRDQ0OytbWlQYMGUURERJL1YmNjacSIEWRvb09KpZIqV65MR44cyVCZyUlISKDp06dTqVKlSKlUkpWVFVWsWJEmTpxI4eHhFBERQc7OzlShQgVSqVR62w4fPpykUildvnyZiIji4uLot99+IwcHBzIyMqIaNWrQ5cuXqU6dOlSnTh3ddmk51xN9HG75w4cPunkAaNCgQbRx40YqWrSo7prz+bk+sczPr8WnT5+mxo0bk4WFBRkaGpKrqyv17NmTbty4QUSkG2a6RIkSZGJiQhYWFlS1alXavn17qr5Xxti341s6j6elzOR87f438d5+165detu9fv2azM3N6fvvv9fNu3jxIlWrVo2MjIyoQIECNGrUKDp69GiS++46depQqVKlktQl8V7+c4nn90SJ14BHjx5Ru3btyMzMjKysrGjw4MEUGxubpMwePXrozYuMjKSxY8eSm5sbKRQKsrGxIU9PT5o1axYlJCQQEdHOnTupUaNGZGdnRwqFggoVKkT9+/cnf3//1H2xLNeTEGVhSJMxliu5uLigdOnSOHDgQE5XhTHGWDaTSCQYNGgQFi1alNNVYYwxloUmTJiAiRMn4sOHD1mSH4/9N3BOI8YYY5mid+/esLOzQ+nSpXXzQkJC0LBhQxQtWhQNGzbUDQNLRBgyZAjc3NxQtmxZvdwqn7p58ybKlCkDNzc3DBkyJEubbjPGGEu/rLgGMMYYy3kcNGKMMZYpevbsiSNHjujN+/vvv1G/fn34+Pigfv36uvwGhw8fho+PD3x8fLB8+XIMGDAg2TIHDBiAFStW6Nb9vHzGGGO5Q1ZcAxhjjOU8DhoxxhjLFLVr14a1tbXevL1796JHjx4AgB49emDPnj26+d27d4dEIkG1atUQFhYGf39/vW39/f0RERGBatWqQSKRoHv37rrtGWOM5S6ZfQ1gjDGWO+S5oFGNGjVyugqM5XovX77kfEYsWwQEBMDBwQEAYG9vj4CAAADAu3fv9Ia3dXJySjK0+Lt37+Dk5PTFdQAxjKy7uzvc3d3h6uqaFYfBWJ5CRJzPiGWLjFwDAD6/M5ZREyZMABFxPiOWIXkuaJTYV5oxxljuIpFIIJFIMr3cQYMG4dGjR3j06BGUSmWml88YYyzj0nMN4PM7Y4zlvDwXNGKMMZZ75M+fX9flwN/fH3Z2dgAAR0dHvHnzRrfe27dv4ejoqLeto6Mj3r59+8V1GGOM5V4ZuQYwxhjLHThoxBhjLMu0aNEC69atAwCsW7cOLVu21M1fv349iAhXrlyBhYWFrgtDIgcHB5ibm+PKlSsgIqxfv163PWOMsdwvI9cAxhhjuYM8pyvAGGMsb+jcuTPOnDmDoKAgODk5YeLEiRgzZgw6dOiAVatWwdnZGdu3bwcAfP/99zh06BDc3NxgbGyMNWvW6Mrx8PDAnTt3AACLFy9Gz549ERsbi6ZNm6Jp06Y5cWiMMca+IrOuAYwxxnIXCRFRTlciM7m7u+PRo0c5XQ3GGGM5gK8BjDGWN/H5nTHGcgZ3T2OMMcYYY4wxxhhjSXDQiDHGGGOMMcYYY4wlwUEjxhhjjDHGGGOMMZYEB40YY4wxxhhjjDHGWBIcNGKMMcYYY4wxxhhjSXDQiDHGGGOMMcYYY4wlwUEjxhhjjDHGGGOMMZYEB40YY4wxxhhjjDHGWBIcNGKMMcYYY4wxxhhjSXDQiDHGGGOMMcYYY4wlwUEjxhhjjDHGGGOMMZZErgoazZ07F6VKlULp0qXRuXNnxMXF4cWLF6hatSrc3NzQsWNHJCQk5HQ1GWOMMcYYY4wxxvK8XBM0evfuHRYsWIAbN27gwYMH0Gg02Lp1K0aPHo3hw4fj2bNnsLKywqpVq3K6qowxxhhjjDHGGGN5Xq4JGgGAWq1GbGws1Go1YmJi4ODggFOnTqFdu3YAgB49emDPnj05W0nGGGOMMcYYY4yx/4BcEzRydHTEiBEjUKhQITg4OMDCwgIVK1aEpaUl5HI5AMDJyQnv3r1Lsq2Xlxfc3d3h7u6O0NDQ7K46Y4wxxhhjjDHGWJ6Ta4JGoaGh2Lt3L168eAE/Pz9ER0fjyJEjqdp20KBBePToER49egQrK6ssriljjDHGGGOMMcZY3pdrgkYnTpxA4cKFYWtrCwMDA7Rp0wYXL15EWFgY1Go1AODt27dwdHTM4ZpmMiJg9WrA1hZwdwd69QKWLQPu3AH+PW7GGGOMMcYYY4yx7JZrgkaFChXClStXEBMTAyLCyZMn4e7ujnr16mHnzp0AgHXr1qFly5Y5XNNM5OcHNGsGDBwIDB8O/PQTEBMDTJ0KlC8PWFgATZoA8+YB3t4iwMQYY4wxxhhjjDGWDeQ5XYFEVatWRbt27VChQgXI5XKUL18e/fr1ww8//IBOnTph3LhxKF++PPr06ZPTVc04ImDzZuCXXwA3N+D2baBkSf11/P2BK1eAEyeABQtEUMnZGWjcGKhbF6hcGXB1BSSSHDkExhhjjDHGGGOM5W0SorzVfMXd3R2PHj3K6WqkLCYG6NYN2L8fGD8eGD0akH8ldkcEPHsGHD0KHDkCXLoEhIYCVlZApUpiqlgRKF1aBJK+Vh779hEBWm3SKXHZp+slN2k0ovtj4mfiz59OarUISspk+pNcDhgYiOnTnxMnDmSyZMyfPx8rVqwAEeGnn37CsGHD0LFjRzx58gQAEBYWBktLS9y5cyfJti4uLjAzM4NMJoNcLseNGzdS3E+uvwYwxhhLFz6/M8ZYzuDoQnabMAG4fBm4dg3w8PjiqkSE91Hv8ST4CbzDveHt9gxPOmug6lAB9mQC+3AN7P0jYf9sNxz2zUPB97EoGKeAUVF3EUAqWRIoXBjqgoURbVcY0SZ2iImVIDYWuikuTkwqVdKYgVwOKJWAQiE+U5rk8o+xCFFv8ZkYT5DLxaRQiClXIwLi44HoaDHFxIjPuDggIUF8UQkJH3/+NPii0YjAjUwGSKX6gRapVARTpFIxAaKMxH+ExH+QyEggLEx/iooS9YiJEevExIh951bJBZY+/WNI7vtRKgEjI/3JxERMpqYfJ2NjUU7iH5NCIcr8PFBFJL4jtVp8Jk7x8R//6BN/Tvy3/HTSaJIG2rKKRPJxSvxePg/IKRSAoaH+pFTqB+sS1/+0vMTJw0N0d80hDx48wIoVK3Dt2jUoFAo0adIEzZo1w7Zt23Tr/Pbbb7D4Qh1Pnz4NGxub7KguY4wxxhhj7F8cNMpON2/i1Kxb+F+J+4jtk0/3jJj4aWwMmJmJKZze4vz7QwhTBQASwMrIArYmlWBj0hgSUuBuZAIuRKsQFaNFTIwWsRopYGECGJpB9s4C0uemwA4TaFQm0GqVKVZJLlVBKVNDIVVDJtFCDi1k0EBGGqi1UsRr5UjQGiCeDJCAlMtJLbk8CkpFCIwUITBWhsBEGQYzwwhYGEXB0iga1iZxyGcaDwuFDMYyQ5jIDWEqN4SJgTGMpArIpDIQJLoggUwGmChUMFGoYKxQw0ShgqkiAQYS9cfWN4kBncSAS+JnTAwQHg6EhYHCQqEND4M2PAxylRYptpX5NGCRGAz5tAWORKK/z8QpsWXQp59KpXj4TwySGBoC5uaApaWYChUSD/pmZuKPw8jo46dCoR98SfxDSgyefBpESS6IkFjvz4/h02ORyT62Svq8BVJicOXzoMzn8xNbMX06LzG49ml5iQGcTyOaEREi71dU1McpMWD2aQAvpQDaZ8EUrVwGrZEhyNAQZKgUn8p//y0/Cb6QgRwkk4GkAEkkukkjAVQSLVQSLdTQQiUhJECDeIkGcZ9NKoj1Ej/VIGglBAJA+PgJ4N+gFACQfguyeA0QowE02o/fo1oFqD75WfPvco0G0P77MxLL+6jdlN1wbNA67f9hM8njx49RtWpVGBsbAwDq1KmD3bt3Y9SoUQBEgHz79u04depUjtWRMcYYY4wxlhQHjbKJOlaFST/cxF90DD2rS1GihH4jBq1WNGgJCo3Hmac38dTPDwUNa6K4SSEoZcaQxEqhjf63BZAB4GQEGJkChjb/xhuMtJAoo6CWhyJeGowY6UvESAKhkoUhgYIQp/JDrOY9YigAUZIQRMrDECkPh1qmhRpA9Cd1NSQZTKCAgUQGA4kEBhIJTCQSaCFBvFqCyHgttBoDQK0ENEpAK4cMUljKjWElN4al3BRmUiPIoYRcawgZFJCRElArEB1hhKhII0RHmiA2yhQRUWYIjLFHfKgFEvwsoY61gjbGGtBmrEmSVBoPmSIKMoMoSBVRkBpEg+QJ0MoToJXFQyNPgEaWAJJo/j1oAEYE2AMSSGEgUUAhNYCBRAm5TAmlgRwmxjKYGMlhbmIAMxMlbCwN4OQog0shA7gWMkIxF1PY2xnkyt5ZGq0G8Zp4xKnjEKeOg1qr/mSKgUYrvgeZVAYZZJBqpJCRDDKJDDKZDFK5VPwsVUACJTSkgUargYY0UGvVej8n/q7WqkWAhESYREta3c+ffmpJCw1poNKooNKqdJ/x6njEqmMRq4pFrDoWMaoYvZ9jVDFJ5id+xqnjPikvBiqtClrSZvn3bCg3hFKmhIHMAAZSA92nXCqHVCKFRCKBBBK9z5RJ8PEUnYH/DwTUdndGTo47Wbp0afzxxx8IDg6GkZERDh06hEqVKumWnz9/Hvnz50fRokWT3V4ikaBRo0aQSCTo378/+vXrp7fcy8sLXl5eAIDQ0NCsOxDGGGOMMcb+YzholA3evgW61HqP+4EdsGtNBFr1tEx2vUM+h9D/QH8oSihwusUq1HVxT8NepADM/52cU7WFlrSIjI9EeHw4ohOiEa2KRnRCNKISohCtitYFEj6llCthpjCDudIcZkozmCnMYGFoATOF2VcegNOGiBCviUdEXCTC4yIQFheOaFUUIuMjEZkQiagE8RkZG4e4WClioiWIiZEgLkaG+Fg5tPHG0MSJSR1nCE28EaRaQ0i0JpBqlZBoFIBGAQmkkEACfDJpSAWVNh4J2ngkaBIQr41DXEIComLiEPxBi7g3WsTFS6CONgUiHYBIm49BLnkcpJZvILN+CwPrd1DYvINhvvcwsAqA3CIACssgyBTqVAQMvv79aEmbJCij0qig1qqh0v77qVGJAIo2F3dnAyCTyCCXyvWCLUqZEkYGRjCSG+l9mhiYwMjACPmM8sHYwDjJOoZyQxjKDaGQKZIEb1IbuPl8uQQSyKQyXVlyqVz3c+I+FTJFpv4fyEtKliyJ0aNHo1GjRjAxMYGHhwdkMplu+ZYtW9C5c+cUt79w4QIcHR0RGBiIhg0bokSJEqhdu7Zu+aBBgzBo0CAAIucFY4wxxhhjLHNw0CiLHTwI9OiqQbHwt7gz/Q2ce3ZIso5Ko0L/A/2x7u46DK06FJPrTYaJwiTL6yaVSGFhaAELw5zLdZISiUQiHv5NDWFnapvT1UlWYtAtLO49XvlF4fmbOLx+TfB7awD/NwUQ8K4IAl8aI+iSCSJDjHXbmVnFwMI2CiYWcTAyi4exWTyMTONhZJYAhZEKBkq1mBQaGCjVUBipoDRSwdBYBYVxApT//m5goB/YkEqkSVq3GMgMdEGUxCmxJYxcKodcKv+3BZF4gNdoNbqWP4mthz7/JCKxnVSmC/bIpDJdWbpypTK9uiUXiEkM4uRlarXoVRcRkTRdVWLarE97TKpUSfObJ6ZU+jz9UXK5yJPLQda4MWBnl3PfAQD06dNHN/rl77//DicnJwCAWq3G7t27cfPmzRS3dXQU7aTs7OzQunVrXLt2TS9oxBhjjDHGGMsaHDTKQkeOAM2bE0Y7bcWkChtgMOJwknWICAMODsD+p/txvtd5eBb0zIGasvT4NOjmbAnU/kIDh/h4kZ7n7Vvg7VtjvH1rjNDQTwIIIUCQ78ec25+m9omJSb5MmexjKqTEz+RSE6UUVPg8r/Lng6AZGIgyPk+H9Gn6pM8DGJ+mEfpS7ubP0ylptUnTIiXmBU+coqKS/z06Omk6JbX6Y87x5PJufzp9nrbp00HlPh9cLjGA83nX0i+lz0pISPpvZ2ws0lUl5vb+NF2VUvmx7omTRJL8fpNLK5WQIP7ePp2KF8/5oFFgYCDs7Ozw+vVr7N69G1euXAEAnDhxAiVKlNAFkT4XHR0NrVYLMzMzREdH49ixY/jf//6XnVVnjDHGGGPsP4uDRllo9WqgfcXnmPbwJ+DMg2SHIp92YRo23d+E0z1Oo5pTtRyoJcsOSiVQuLCY0kqrFQGIT/NBR0UlDS7FxSWfszqloEJcnAi6BAWJ7ZPLZ53cIGIpBU4+D9wk7iejDAw+Jog3Nf34c+JkZ6efyzoxGJUYDEouL/mnk0SSNP/353nC5XL9AM6nU2JQ6tMgVWIQ6NOAkIWFyG9uYfENjCKYBdq2bYvg4GAYGBjAy8sLlpaWAICtW7cm6Zrm5+eHvn374tChQwgICEDr1iKJt1qtRpcuXdCkSZPsrj5jjDHGGGP/SRKirBxLOvu5u7vj0aNHOV0NREYCdnaELdKuaDWpAvDbb0nW2Xx/M7r90w072u9Am5JtcqCWjGUtIhE8ShzlPi5Ov+VO4s+JAZpPWysZGYmg0H8xwMLSL7dcAxhjjGUuPr8zxljO4JZGWWTfPkCpjUXTYj7A0HVJlp99eRa99vbCrIazOGDE8iyJ5GNeHYvclzqLMcYYY4wxxtgXcNAoi2xdEYHWCbuhnD1VNKH4hHeQN1pva41+FfphWLVhOVNBxhhjjDHGGGOMsS+Q5nQF8qKQEODoOSN0qugDNGigtyw0NhTfb/oetZxrYV6TeXl+5CjGGGOMMcYYY4x9mzholAX+meYNCwrDd0s7JFk27cI0SCQSbG6zWTfMOWOMMcYYY4wxxlhuw93TMptWi63LwtGu2DsYVKqvt+h1+GssuLoAa1uthYnCJIcqyBhjjDHGGGOMMfZ13NIokwWs2IdTkZXQaVKpJMv+d/p/KG1XGh1KJW2BxBhjjDHGGGOMMZabcEujzJSQgJ1/3Ia9aT3UbGevt+h+wH2sv7seJ7qfgFTCsTrGGGOMMcYYY4zlbhy9yExLl2JreBN06KqE7LN0RWNOjkFjt8b4rvB3OVM3xhhjjDHGcsD8+fNRunRplCpVCvPmzQMA3LlzB9WqVYOHhwcqVaqEa9eu5WwlGWOMJYuDRpklPBxvJqzCBXV1dOppqLfozMszOOxzGH/X/zuHKscYY4wxxlj2e/DgAVasWIFr167h7t27OHDgAJ49e4ZRo0Zh/PjxuHPnDiZNmoRRo0bldFUZY4wlg7unZZbly7Fd2w4uzlpUqfIxFkdEGHV8FLqW7Ypy9uVysIKMMcYYY4xlr8ePH6Nq1aowNjYGANSpUwe7d++GRCJBREQEACA8PBwFChTIyWoyxhhLAQeNMsu1a9hqMBOdOkshkXycvfPRTtwNuIsd7XfkXN0YY4wxxhjLAaVLl8Yff/yB4OBgGBkZ4dChQ6hUqRLmzZuHxo0bY8SIEdBqtbh06VKSbb28vODl5QUACA0Nze6qM8YYAyAhIsrpSmQmd3d3PHr0KNv361O4EYq9PIY7d4By/zYoUmlUcF/sjpbFW2JWo1nZXifGGPuvyalrAGOMsZStWrUKixcvhomJCUqVKgWlUgmtVos6deqgbdu22L59O5YvX44TJ06kWAaf3xljeZ1GA8THA/82zMw1OKdRZoiJwT8vy6N4oRiULftx9raH2xAYHYjfa/2ec3VjjDHGGGMsB/Xp0wc3b97EuXPnYGVlhWLFimHdunVo06YNAKB9+/acCJsxljwiIC4OCAsDIiNzujZZQqsFtm8HSpUCSpQA3r/P6Rrp4+5pmeHhQ9yGBzxryvS6pu14tAOtS7SGtZF1ztWNMcYYY4yxHBQYGAg7Ozu8fv0au3fvxpUrV7Bw4UKcPXsWdevWxalTp1C0aNGcriZjLCtoNMCjR8ClS8CVK8DnXU2JgIQEIDoaiIr6OMXEiGBRfLz++sWKAdWqfZzKlAFiY4GAgI9TYCAQHi6CTJGRH8usUQPo2zfXNOUhAg4eBMaNA7y9gYEDxdfUqhVw+jRgZJTTNRQ4aJQZ7t3DI4Pq6F5BqZsVGR+Jo8+Oci4jxhhjjDH2n9a2bVsEBwfDwMAAXl5esLS0xIoVKzB06FCo1WoYGhpi+fLlOV1NxlhmSUgAFiwAjh8XgaKICKBQIaB6dcDFJen6BgaAmRlgagqYmIhPY2MRNTE0BJRK8RkbC1y/LsqcMQN4+RKQSkVTnUQWFoCdHWBpKcpMnExNgcmTxTR8uIjQWFpmz/eRjNu3gUGDxOH07g0cOAA4OYlWRlWqAH36AJs2Qa9Rih61GpBnTziHg0aZQH3nAbw13eHu/nHeIZ9DMJAZoKFrw5yrGGOMMcYYYzns/PnzSebVrFkTN2/ezIHaMMay1JMnQJcuwJs34rNvXxEscnLKnPI9PYGhQ8XP798DDx6IQFH+/CJYZGiY8rYxMcDKlcDMmcD06SJqM2yY2C4bxcUBbdsCJUuKFkaurh+X2dsD+/aJRlFTpgB//plMAdeuAT/+CBw5or9xFuGcRpnA91owErQGKFXq47xdj3ehWbFmMJR/4Y+WMcYYY4wxxhj71hEBq1YBFSqIIMz9+8C8eUD79pkXMPqcvT3QoAFQubJoyfSlgBEgWi8NGQL4+gJz5wI7d4qWT0OHAm/fZk0dkzF/vugtt3lz8jEfDw/RymjCBGDH5x2XTp4EvvsOqF0bcHbOhtpyS6OMI8KjR4CpoQoFCxoAAGJVsTjkcwhrW63N2boxxhhjjLFcJ3Hs4hS7HaSyjIgIIDhYTCEhInVHdLR4mR4dLabGjUXaD8YYyzKhoUC/fqKJzPTpIjAjzcXtUxQK0SesRw8RlZk6FViyRPw+ejTg5pZlu37/HvjrL9HYycIi5fVatRLr9egBFC4MVKoEYPduoHNn8f3OmJGxi0gacNAoo/z88DCqENzLqCGRiKDRUd+j0JIWTd2a5nDlGGMsd5g/fz5WrFgBIsJPP/2EYcOGYcKECVixYgVsbW0BAFOnTsX333+fZNsjR45g6NCh0Gg06Nu3L8aMGZPd1WeMsQzRakUe2LNngTNngHPnxFvmQoXEVLCg+LSx+fgMkPiZkCAeMvz9P07v34tAkUbzcR9yuUjbYWKiP5Upk+2Hyxj7Vmm1IhodEiImCwvgS0nqVSpg40bgf/8TJ6Br14By5bKvvhklkwGdOgEdOoikQn/9BRQvLiI2bdoA338PWFklv62fH3Djhmih5OcnTs5+fkBQkCjvl1+Sbfn055+igVCfPl+v3ujR4trRsiVwa8Rm5B/ZXfRZGz062wJGACAhSnzXkTe4u7vj0aNH2bfDw4fRuVkEjLq1x+q1Ipra7Z9uiE6Ixu6Ou7OvHowxlks9ePAAnTp1wrVr16BQKNCkSRMsXboUGzduhKmpKUaMGJHithqNBsWKFcPx48fh5OSEypUrY8uWLXD/NIncJ7L9GsAYY1/w+DEwbRpw+LB4jnBxAerUAerWFc8hb94Ar19//AwJEdt9encul4tUHQ4OHyd7exFgypfv42Rqmq3PENmOz++MZRGtVgQ4tm4Vw9p/mlQaEPmI+vYVgRBTUzFPpQLWrxdBlg8fRMuXP/7INaOSfYpINICaMkUMqObsrD8VLy7iXIaG/6586pToZnfokGiuWbu2iNpUqCCyV1++LIY4e/VKHG/BgkCBAh8npRJYulRE7adOFUGpf1td3bkjijl6FGiYytTHcbGEGkUDYPnuIY4t9oVsQL8s+65Swi2NMurePTxStEb3MuIPIUGTgP1P9mPR94tyuGKMMZY7PH78GFWrVoXxvzcSderUwe7dqQuqX7t2DW5ubihSpAgAoFOnTti7d2+KQSPGGMsNvL2BSZPEM1jt2sDs2SJYlE3pJxhjeUFCAjBypGie+OOPQM+emZ+wmQgYPFgk0FmwQARArKwAa2vx+eKFCKCMGCHy/nTqJJovzp0rmjsOGSJGIsuXL3PrlUlOngR+/13Een76CShdWsR6Xr0SwfyXL0UDIblcLKtUSYJKleqjeL/6MByohuLBLSjPn4Dy752w8p+IfG7WIog2ZoxIyF2qlGit9LkRI4C//xZd4ObOBWbNAtWug2HDgGbNUhEw8vcXwatTp2B48iR2BihQweQBJvjVx+Qs+J6+hoNGGaS+8wDeCb/qRk47+fwkYlQxaFasWc5WjDHGconSpUvjjz/+QHBwMIyMjHDo0CFUqlQJ+fLlw6JFi7B+/XpUqlQJs2fPhtVnTYDfvXuHggUL6n53cnLC1atX9dbx8vKCl5cXACA0NDTrD4gxxlLw5IkYzXnLFqBmTXHPX7duTteKMfbNeftWJJB+8UIktfHyEi15WrUSuYPq1894ziAi0c1p3TrR9KVmzaTrlCsngkkzZgD//CNGHtu9WwxXP3y4CC7lQlevAmPHii7B3bqJAH7hwsmvGxIC3Lwpphs3ROvQV68AESqp8u/0OyQSQn0XCXo2Blq3Bk6/PYjJa/qhoEVBVLCvgAoOYrI1sRXd+qZNAwYMAMaNA+rVwx77Abj4fj4e/roaOOYClC8vmjf5+ABPn4pPHx9RkUePAEtLoF49YMQIFG7aFOseKtC6tYhVNc3mLDjcPS2DnhRrjhI++/HqleiL3ndfX/hF+uHQj4eyrQ6MMZbbrVq1CosXL4aJiQlKlSoFpVKJsWPHwsbGBhKJBH/++Sf8/f2xevVqve127tyJI0eOYOXKlQCADRs24OrVq1i0KPnWnNx9gTGW3RJ7M8yfL1Ji1KgBTJwo7vXzcnex7Mbnd5arrFkDzJol+osWKfJxSuzrlBGnT4sWPW5uIklzgQIigdmxY8CKFaKvVYkSYr1/80Kmy+TJos/WwYNiBLI8wstLNIBq1UocYnoap2s0oqFXQgIQHy8+nz8HNmwAtm0jxKnjoCqxBS06BsGupA9u+t/Eg8AHUGlVKGheEE3dmqKte1vUc6kHA5kB4m89hHsjJ7S0v4KZBqNxM+QBjhTWwMcaiFACEaZyRJgpEWEsRT6lFfqU6oYuTUfBxMhcr15jxog/gdu3Rewhu+SalkZPnjxBx44ddb8/f/4ckyZNQvfu3dGxY0e8fPkSLi4u2L59e5I30TkmPh6PfJUwNVKjYEE51Fo19njvwfQG03O6Zowxlqv06dMHff7N+Pf777/DyckJ+fPn1y3/6aef0KxZ0haajo6OePPmje73t2/fwtHRMesrzBjL1cLCgCNHxIjOQ4dmfo+N1IiNFflfFywQL4VbtxZvtWvW5GARY3mWWi26Hi1aJD4lEhFN2LVLfIaEiGjFwoVpH2aeSASixo4VLXlmzRKjfAGiC1TTpmLy9xcJmjt0EIEkA4O0H8fcuaIP7a5deSZgpNWK3nwLFogedT17pr8smQwwMhJTIicnwMTtJs6W7IPgW7VQ5PVE7B9rjf79gcvzAMji8SDwAa6+u4p9T/bhh80/wExhhhbFW0BzdjQCNUq8HrkT+QPeIjhWAw/z4vAwc0NhWyeYm9vBXGkOC6UFvIO8MfrOYox4uhA9yvXAz5V+hrutiHxNmSJSKrVvD5w///HPI6vlypZGGo0Gjo6OuHr1Kry8vGBtbY0xY8bg77//RmhoKKZPTzkok61vIe7exRSPHdhffjyu3jLAqRen0GhDI7wf8R42xjbZUwfGGPsGBAYGws7ODq9fv0ajRo1w5coVxMbGwsHBAQAwd+5cXL16FVu3btXbTq1Wo1ixYjh58iQcHR1RuXJlbN68GaVKlUp2P/wmmrG8y9cX2L9fTOfOifyj+fIBajVh9+pwVLJ8JhJUvH4NxMWJV8WJk1YrkpJaW+tnj7awECP+mJml+u774UPxQLJunSj2p5+AQYM4X1FW4/M7y3HBwUDHjqKZx44dwHffJV3nzh3RfczbW3RP+vnn5HPefO7FC5Fb6PRp0ZTkxx+/vP6rV2IM9k6dRIAqLZYvF92mNm4Uw7dnEa024z3oUismRnRDO3FC9J6rXz9zy1dpVJh1aRb+d+Z/aO/eHot/WAxLQ0ucPy9idwULAjt36rf+CY0NxbrzJzDjfw7wv1YdJu2HoEWHUDRxa4JGro1gb2qf4v5iVbHY/nA7ltxYgqvvrsKzoCfqudRDjYI1UFjuibrVLdChgwiQZYdc09LoUydPnoSrqyucnZ2xd+9enDlzBgDQo0cP1K1b94tBo2x17x4eKiuilIeI7u56tAt1XOpwwIgxxj7Ttm1bBAcHw8DAAF5eXrC0tMQvv/yCO3fuQCKRwMXFBcuWLQMA+Pn5oW/fvjh06BDkcjkWLVqExo0bQ6PRoHfv3ikGjBhjec/Ll8C2baI7wO3bErg4xKFFqecY2+4Sascdh8bnOfo/GY6aDdtgGRaih+U+cddubCwe1BInqVSMghMc/HEo6c/fmyoUInj0+bYyGaLIBNsjm2BlSFtcjvVAFaN7mGb1D360OgSTI7HAkTQcVGIzJKlUTJ/uS6n8GMQyNxefFhaiKZWtrfhM/DmvD5fGWG7y8CHQooU4P1y/LrqiJcfDQzQF8fISfYk2bBBBoDJlkl8/Pl60KJoyBahYUZSdmvscZ2cRpWjQQHSH69s3dccxfz7w668icJQFAaO4ONFNd8MGkSapVy9g3jxxassqgYFA8+bA+/diULPMvE0MjwvHilsrsODqAkTER2Bdq3XoUqaLbnmtWiKG2LGjGBVt0yagcWPRIG39civ878/2KFYM2HEpHtWqLIBMmooAIgAjAyP08OiBHh49cNv/NrY82ILTL09j5qWZUGlUcOnUC4sXL0XHXmGoUT4DXRRTKVe2NOrduzcqVKiAwYMHw9LSEmFhYQAAIoKVlZXu90SfJ0H19/fPnoqOHIlyS39G9wmuGP6rFk5znDCu9jgMrDwwe/bPGGNMD7+JZiz7PHwo0nocOCAeFLRa/Umt/jipVOLTwkJ/ZOICBUTjH2OlBsZxITCK+gDjiPd4/lSFrTfccCXQFa4Gr9CJtqKDehPK4D4kTk5AsWJiKloUVLgIFl6uhF/nOGLAAAnmzElFbw2tVvRxCw8HIiOBiAjxGRkp+p3920Lp4RtzeJ1xx4ZrxWEg06JbZW/0qfYIZR2DkwadUiNxG6KPX9SnLaLi4z/WI7FOYWFiSOvAQBH4SiSRiMBRYnDJzEwc+OeBqEGDxHDR3zg+v7Mcs2ePaMbSoIEYZt7MLHXbvXkj/v8dOiRG3KpRQ2Qx9vQEbGxEs5hBg0QQe+ZMoHv3tDfNWbJE9NE9c0aUmxIiEZiaOFE0k/xaS6Y0ungRWLtWNMBSqUR33Tp1gD//FAmod+4EsiK7wNGjojFXvnyiFeq/Ddgz7HX4a8y/Mh8rbq2AqcIUQ6oOQf+K/WFllHyaHJVKjNI2Zw4wbJjIc/f8OTB1auobm6VGnDoON/1u4uKbizh59zF29lkAM2Uq/x4zINcFjRISElCgQAE8fPgQ+fPn1wsaAYCVldUXR8fJzguKumFTmJzejz375TAvdRG11tTCu1/fwcEsk/5aGWOMpQk/VDCWtcLCxMhga9aIF+KVKomX1fnyiRiGVPrxUy4XMYzETxk0CPP5AL8HwfB7Gg2/txr4fVAgLNoAMSo5YmAsJokJbAwi0L7wDXSq8hwVKssgcXEWb9bd3MSb/mScOSO6CZQoIQb5Se8I0Go1sHevSFmS+Bw2cCDQtq0Y6CZHxcSIANKHDx+DSp8GvFSqpIGoZs3Ew+o3js/vLNup1SLqMWOG+Pzf/9Ie1CESXc5OnBCRlWvXRJS9UCERVOrfH/jrr/SPQkYkohJ794qhv5LLo0Qkkv0sXAhs356pQWSVSqR2WrhQdAnr1k0EjBLjan5+QLt2onvxjh1A7dqZs9+HD8V+jx0TXYRnzxY9kFPyJvwN1t1dB/9If0gkEkglUt0Up45DaFwoQmNDERoXirC4MPiG+KKkbUmMqD4CnUp3glKeuqZSu3cDvXuLlFOzZ2deECunpbt7WsWKFdGnTx907tw5UxNTHz58GBUqVNAlSM2fPz/8/f3h4OAAf39/2OVEpsMU+N6JRIJGjlKlgBXPjqCKYxUOGDHGGGMsbyAS/cNu3QJu3cL+IwbocHsMzOSx6Fr4Elb9+ABlShNgaQcojET3rsRJJhO5hZ4+FePQP30KPHsmhqBRKoGiRYFyJcRIQ66uIiDk7AwUtPw3t5AJgOZpqm7duuKZ6YcfxA37iROpbxAAiOfDZcuAv/8GgoLEi/g5c8SoyLmGsfHH74oxlnU+fBAR8Zs3RROW779PXzkSich9lJj/KCFB5D26dg2oWhWoXDlj9ZRIRMTm0SPRR+vXX0V3tRIlxLlUoxH5izZvFi2eMjHZT3CwCNTfvQucPClGjPxcgQIi+D58uNj17NnAL7+kv2dtYCAwfrzoXdeggdh36dLJr6vWqnHY5zCW31qOQz6HUNS6KMrkLwMigpa0ukkpV8LK0AoFzQvCytAKloaWKGFTAnVd6kKSxoq2aSNicpnVsii3SHfQqEyZMhg9ejR+++03tGjRAn369EHDhg3T/MV+bsuWLej8Sf/KFi1aYN26dRgzZgzWrVuHlrmleW1gIB4F2cLUWIOCBWV4fOUxyuXP4PCKjDHGGGM5iUgMybJsmXjACAsDTE3x1r0Rej5aj0F1H2Jq9QNQfHgnEkj8EyDu4uPjP45LHB8vIjCJ3ciKFxcRnWLFxINMoUJZdkddqBBw/LgYwax1azGSdGpyaVy+LFoT+fqKgYv690//i3/G2Dfu2jXRtNDGRgSNUspflB4KBVClipgys8xdu0ReoxEjxDnZwECMNW9kJJJyHz8uushlkvv3RXDEzEy0Oi1c+MvV8/IS8bGffxbn2+XL0xbUj40V6ZimThVJpw8eBJo0SX7deHU8Zl+ejSU3liAwOhBtS7bFqe6nUNu5doZjFamR1wJGQAa7p0VHR2Pbtm1Yt24dzp8/D0dHR3Tv3h29evWCm5tbusorVKgQnj9/DgsLCwBAcHAwOnTogNevX8PZ2Rnbt2+H9Reu4tnWdPXkSUxpeBb7K03A1WtSlFlSBr08euHX6r9m/b4ZY4wli7svsPSKihI3pC9fioYvbm5icnX9+o0tEfDggbgnf/UKePcOePtWfAYGikY11auLqVo1ETdJqYdDfLy4Gb95U7SaCQ0V2yem8ClWTDzHZPp9b2ioyNWxbJl4wGjaVDS1qVwZGhdX1G8oRUKCGLVMnppXjkQ5mqT5xQvRI6t6dZFIO6U6f/ggctWuWSMaFcyalXe6E+Q1fH5n6RIXJxLMHD8uTr7Nm4vmL58jEhmNd+4UzWE6dxb5gj4dc/1b8f69aIJz966IhA8cKFofZZLdu0X6pSZNRB4jU9PUb3v7thguXiYT3dXKlv3y+lotsHWrCObHxoqUTD/9lPI5/UHgA3Td3RX+Uf4Y5TkKPTx68CBVmSDTchr5+vpi3bp1WL9+Pd68eYMaNWqgd+/e6NSpEwyzsQN4tl1Q5s5F5wnFYdT2e6xcpYXxX8bY1WEXfij2Q9bvmzHGWLL4oYKllUolhk+fMEHchHp6iuSVz56JHMmAaDBTrZqYqlcXI6QoFOJl9D//iBvoZ8/E80jRomJ9R0cx2diIGMzly2Ly9/+YDNrI6ONkaCiW3bsn6uTsLPIF2diIsp8+FekvAPGWdfx4oGfPf99oBgeLgI9aLSqmVH6cnJzEK2BHR/1IVUSEiEpduwZcuSKyiVpZAX36iLfVn3R/mjJF5Gi9exdwccmmf5hM8OCByJ/Rpo0YvOjTGJaPj3hgSQwSeXmJxlDfOpVGhaCYIHyI+YAP0R/wIeYDyuUvh5K2JXO6ahnG53eWamFhoqXknj3A4cOiBWSNGsDjxyKgUqWKGAmteXMgIEDkA9q3T5xkPTxE/6levXh0ws/ExQF//CFGQ5s4Ufycnq8oPFxcag4eFOfelL7qCxdEb7t798TnmDEi739ytKTF/CvzMfbkWDRxa4IVzVfA1iTrRxX7r0h397TPyWQyXXMvmUwGIsLAgQMxZswYbNiwAQ0bNsysXeUO9+7hkbQNurmL7OrxmngUy1csp2vFGGOMsVQgEs8TY8aIZ4gxY8QANIk5lolELMbXVwRLrlwRwaURI0SrfwsLkfemWjXx1rN1axEwSk6zZh/LfPMGuHpVtECKjdWfqlUT+VArVhTBos/FxIgA0p49oq7z5xNm1j2Ixpt6iFe9trYfu4fFx4tCP3wQO1YoRMTHxUU0g3r8WASRypQRD1CbNokHqM+GHbt0SQTUNm36tgJGgMhzceiQyHthbS0StO7eLXpx3L8v/r3++AMYMiQVo63lQlrS4n7AfZx9dRZnXp7BxTcXERgdqFsulUiRzygf/m7wd54IGjGWKnfviui/XC5OvqtXiyYxZmai2cr16yJItGkTMG6cWK9OHZEoukWLXJcv7PlzcRqvWjVn63H/vmh8+uHDl7uGpYaFhQjaL1wouqudPSuuad7eIsF14uTrC3TpIhp/FSqUcnlvwt+g596euPr2Kry+90Lv8r2zpRvaf0mGWhrFxMRgx44dWLt2Lc6fPw83Nzf07t0bPXr0QP78+RESEoLBgwfjypUreP78eWbWO0XZ9RZCXaEKTO5dwp79ckiLHkXzLc0R80cM5NJMi8MxxhhLI34TnXcQiVyhhw6JKTRUJDhu2VK09slIzoBr18Rby2vXRKv9ceOSD9IkJzRUbOfnBzRqlDVDCKfG+/3XMb7Xa6wMboUGxd5g5mZHlK2YTOQjLk70mXv+XPTZevFCNK2pUkU0mUphJDJAvKz38BDJS1etyrJDyXRRCVGIUcXAQGoAA5kBzp5SoHULA6hUEpQpI1KVtG0LlCr17TUkUGlUOPD0ADbc24Czr84iJDYErlauqONcB7Wda6OwVWHYGtvC1sQWVoZWkEnzTnINPr+zr9JoxAXCwUFEJRSKL6//4gVgaSlaWeYykZHiJcLcueJdwIABotdcdveW02pFy6KxY0UMbtmy1F8vExERbvrfxI6HO7D/6X7UKFgDcxrPgZnSDNeuiWTar16JckuV+jjVqiXea3zJgacH0O2fbihpUxIbWm+Aq7Vruo+VpSzdEY7evXtj586d0Gq1aNeuHSZNmoRatWrprWNtbY2hQ4di69atGa5orqJWw/dBrG7ktL3+T+Fq7coBI8YYYywDiMSIV9u2iUCRv79oLfLDD+Keft8+cdNsYyNuXlu1Aho3Tl2iY0AM5jV2rBhEpn170djGNaX7S5VKvLG+cEG87vy3glYAGgOiEvIBAPJn9LDTJigI+O032G/YgGU//oghfWpj1EwXlKsk3vwOHiw+dUE1Q0ORiLp48TTthgjo109svmBB5h9GVtnxcAd67OmBWHWs/oL+xdDIrSFW9xoLR/McivRlwMuwl1h5ayVW316N0LhQtC3ZFguaLEAdlzpwMk9miG3G/osWLhTNVf755+sBI+DL2ZtziFYLbNggWr8aGIif7eyArl1FTrmtW1MeLSwj4tXxuP3+Nh4GPsTDD2K6+yQU4dvmgd5VgtdiKfr0lqc60J4YKNr2YBt2Pt6Jl2EvUdWxKjqV7oS1d9ai/LLy2NhmI6pVqQZvb9FjOi2DpGu0Gkw8OxF/nf8LY2qMwcR6E/lZPAul+5t9+PAhZs2ahc6dO8PsCxkiS5UqhdOnT6d3N7mTjw8eqdxgaqJFwYJSPHnwhLumMcYYY+mkUokb4VmzRJP0Jk2A//1PjHD8aZP00aNF+okDB0Tvgg4dRMCoVSugY0fRDSm554TISDGM+pw54q3lhQsivUUSDx+Kt9MXLoj+aNHRIvN0yZIfozCJd8wnTohEP0OGiG4N2THU1vnzIjmrpaVoz1+rFkoBOFhX5EtatEh0kytYULyV7t07/dVavlx8x1euACYmmXgMWYSIMO3CNPx5+k9MrjcZLYu3hEqrgkqjglqrRmhcKMafGQ/3xe74u/7f6F+pP6SSFLKR5yJ33t/B2JNjcfTZUZS0LYkxNcega9musDbiod3YN+b9e6BhQ5HQxsFBJHZL/OzRQ5y4MuLVK9FsdMaMnGsCmgFEhDGbNmPVpKqIeO2Mln2e4LfftChXsCiMDIxw965IN1e5snh5MmBA5rWUDIwORMMNDXEv4B6czJ1Q0roM4i8OQtjGprBz9UPooMqYGhUJ6Z1x6Fa2GwxkKffnjVPHYduDbVh0fRFu+N1Adafq+KXKL2jn3g6FLMQFfVi1YRh6ZChqrq6JcbXHYVztcbCzS31YIiQ2BF13d8XFNxexu8NutCyRS0ZXz8sojylZsmTW72TrVppsOIWqVNESEVGD9Q1o5LGRWb9fxhhjX5Qt1wBGRETBwUSTJxN99x3Rr78S7dhB9O5d2soICyOaNYvIyYnI1JRo+HCily9Tv31EBNGmTUQtWhApFERWVkSdOhF17EjUsCFRxYpEhQsTGRkRFSwo1tVokilIrSaaOpXIwICoQgVxQLt3EwUEpLxztZpo/XqiIkWIzM2JJkwQB5QVNBqiv/4iksmI+vcniolJcdX378W/i6OjOO5Zs1I45i84fZpILidasiRj1c4ucao46vFPDzKaYkQ7H+5McT21Rk1zL88lk79MyHOVJz0IeJCNtUwbjVZDsy7OIoNJBtRiSwu68OoCabXanK5WjuLz+zdMpSKqU4eoUiWiNWvE+XbwYKK2bYnc3IhKl/7iee2rtFqipk2JPD3TfsJLB39/oilTiDp0ILp/P+PlxapiqfXyXwgm76lAxZtUfnpLsvzbkjABJJkgoUrLK9Fhn8Ok0WhpyRIiQ0Nx3UvrNTc5b8PfUvGFxanisorkH+lPV68SeXgQWVgQLV0qvs7gmGD64+QfZDrVlFznu9KCKwtor/deOvvyLN17f49eh72mZ8HPaOyJsWQzw4bMpprRkEND6EnQky/ue8fDHWT1txVVXVGVngY9TVV9b/vfpsLzCpO7l/tXy2eZJ905jbZu3Yo3b95g5MiRSZbNmjULhQoVQocOHTIc1EqrbOnv/Mcf6LziOxg1q4/VqwHnec74X+3/oU+FPlm7X8YYY1/EOS+y3qtXIsfCypVAvnyiZcuDByK5c1SUaBlUtaoYrt7Z+eNUoIAYser6dTFo1/XromFP/vwiAWa/fhlLKxEWJrqvnTgh0vRYW4vyrK1FT7KGDVNI3/P8uRg7+MEDMYxLly5pe32rUokxhydNEl9A165iWLMKFTLnNXBgoCjzyhXR/KdTp69u8izkGQ56H8OWjQrcW98TNarLsW5d8qNMJ9n2mfj3+/HHr3dLIyI8D30OnxAfeNh7wN7UPpUHlXmCY4LRZnsbPA1+in2d9qGyY+WvbvMq7BUGHByAE89P4Pui36OAWQHYm9rrJjOFma6VUuJnPuN8qOdSL1uSq/pF+qHHnh64/OYyFn2/CD3K9eCkruDz+zdt5EiRkPrWraSJpoODRfK0778XCXPSY8sW0Vrpzh3A3T2jtU0WEXD6NLB0qej9VqiQGBzg3DnRCnbcONGdN60CogLQcmMH3Jo2FyXtiuH6RVMoFOL8+iHmA7yDvLHx3kasur0K9VzqYXqD6VCGVETPnuKaOm2aSCYtTaHhJJHo+hUWJnLyhYaKnwEgQhOAsWeHw9bCFIuaz8bW9WZYskRcZubMAew/O6UHxQRh9qXZ2PxgM4JjghGtitZbXtKmJAZXGYxuZbvBTJlyT6RPvYt4h557e+L8q/MYU3MMRtcYDSODpImbIuIjMO/KPPx94W80L94cq1qsgqnCNFX7YBmX7qBRuXLl0LdvX/zyyy9Jli1evBgrVqzA7du3M1zBtMqWC0rbtih3dj66jXHCwKExMJlqgnM9z6GWc62vb8sYYyzL8ENF1nn1Sow0lZhPYdQokRcocdQpjUYEgS5dEkGhFy/ENq9fi7hKIhcX0bw+capePfU5iTIVkXiIGTZMVGTt2i8Pz/I1cXFiNJ61a0X3ttKlRfDoxx/FGMEhIeLhKCRE3LUrlWK0Mxsb8WlqKr7Ed+/EF/fypZiWLhV37tu2pTg8m5a0OPLsCA75HMKRZ0fgG+qLQhaF4GHvgf2XH8Py0EEgpAhWr5KhVauUDyE8XIzgVqiQGB1H/llvgTh1HI75HsO1d9dw3e86bvjdQEhsCKQSKbSkRbF8xVC7UG3Udq6NOi51dF0RsoJGq8E/3v9gzIkxMFGYYH/n/WnaHxFh9+PdOPPyDN5Hv8f7qI9TVEIU5FK5LpG2gdQA4fHhqFmoJuY3mY+y+ctm2XH98/gf9N3fF0Wti2Jjm41ws3bLsn19a/j8/o3avRto3x6qfYcx404jtGuXTIq1CxeAunWBjRtTFRjXExwsuhAPGCDGgc8CFy+K7r7PnokB1n7+WbyIkEjEyF6//CJGBFu+XAzEllr3Au6JwZR2z4X0aUvcviVLMbj/6MMjjD05Fvue7EPn0p0xsfZfOLipMMaNE92uly//mDQ6JEScw/ftA44cEe8zPmVuDhA0iIxRAeqPka4iRYAlS8QgD6mh0qgQHh+O0NhQJGgS4G7rnq4ANxFh0/1NGHFsBEwUJljYdCG+L/o9ACA6IRpe170w/eJ0GBsYY0KdCTw6Wk5IbxMlY2NjOnXqVLLLTp06RSYmJuktOkOyo+mqqmZdUshUdOgQ0R3/O4QJoICoLzRhZ4wxli24+0LWCA0lKl6cqHJloqNHRU+A1NJoiN6+Jbp2jSgwMMuqmDbx8aJfgVJJNGdO5ndnePqUaNw40SdOhKc+ThIJkaWl6F/w6XylUnQ/S/y9QAGi6tWJxowhio1NcVePAh9RrdW1SDFZQY03NKY5l+bQo8BHuq5MN/1uUoXFVUlRbzpJpBr66ScNhYQkLUelImrcmKhECfHv/anohGiac2kOOcxyIMMphlRjVQ0adngYbbq3iXyCfUilUdFNv5s059IcarW1FVlPtyZMALXb3o6ehzz/6tel0qjIJ9iH9j/ZT7MuzqK+e/tSrz29aN2ddfQ67LXeulHxUbTw6kIqPK8wGU4xpAEHBlBEXMRX95EWyXUDexr0lJptbkbSiVIaeGAgBUUHZeo+o+KjqN++fiSdKKU/T/1JCeqETC0/L+Dz+zfI25vIzIzU4ydRp07i9Jc/fwpduqZOJTIzI/LxSds+evYkKlGC1NFx5OtLdOgQ0ebNRAmZ9F8oJkZ0c+7QgejNm+TXCQkh+ukncXx9+lCy59jP7X+yn0z+MqGK/ZeQXK6lCxdSV59zL89RtZXVyGCSAfXb148u3ntDzZuLLsV9+xLVrSsuJZaWRD/+SLR1K9GNG0S+vqJeajXR/YD7ZD/Lnr7f9D1FxcVQdDRRUJC4DuSk0NhQGnJoCEknSqnllpY06+Isyj8zP9nNtKN5l+dRrCrlayHLWukOGtnY2ND69euTXbZu3TqytrZOd6UyIjsuKN5uPxBA9OoV0bYH28jyb8v/fD9zxhjLDfL8Q4VaTRQdne27bNKEqGTJrEvZk60SEohatRJPLnfvZu2+NBqiK1fE9PSpuCtXq8UyrZYoMpLo+XMRUTt4kOj4cfHAFBf31aLjVHE04fQEXbDoS8EZtUZNC64sIKOfGpEi3zuSyTXUoAGRl5cI6BERDRlCZG2t/7wWERdB0y9MJ9sZtmQzw4amnZ9G4XHhXz9srYbOvzpP1VdWJ+VkJY09MTZJYMc/0p8WXFlAnqs8STFZQZgAMpxiSGWXlKUOOzpQ221tyWaGDWECyG2BG/Xb149GHhtJVn9bkc0MG5pwegIFRmV/FPLQ00NUbGExsp5uTYuuLqI41df/rb7mjv8dKrGoBBWcU5DOvTyXCbXMm/L8+T2viYoiKlWKNE2+p549tGRpKU51bdsS5ctHdPv2Z+trNCIZXYUKqToHEhG9WX2MOmILlSocRUqliLcrFCIm37dv2l5wpOSPP4gcHFJ3/TtzhqhYMSJ7e6KdO1Pe/+Z7m0k2UUa9vJaQQqElL6+01Umr1dJe771UYVkFMphkQD/t7UdL1gVSzZriXH7yZPJBs9dhr2nggYGkmKygttvaUrw6Pm07zia3/W+T5ypPsp5uTX+f/5ui4qNyukr/eekOGnXq1ImKFi1KAZ8liQwMDKRixYpRp06dMly59MiOC8puy15kaphAWi3R5LOTqeqKqlm+T8YYY1+X5x8qevcWLVLatSPas0e0mMlMQUHiNe2ECSIr9eTJ9Ot3t8nKJI58Vp9L+TXrtyIhQTyx2NkRPXqU07XREx4XTiExqXg9TeJNc4lFJch2hi1turcp1S+u3oa/pfZbuhC61SeXhgfJzj6BAKJSpcRb6tOnxXreH7xpzPExZD3dmuxn2dPsS7PTddOu1Wppy/0tVHBOQco/Mz+tuLmCVt5cSfXX1SfpRCkVmF2Ahh8ZToeeHqIXoS9Io9Vv8aXRauju+7s09/Jcar65OVVcVpGWXl9KMQkZSJibCeLV8TTr4iyymGZB+Wfmp8lnJ9OH6A9pLker1dK8y/NIMVlB7be3T/W//39Vnj+/55S4OKL580VwO7NotURdupDW2YUG9o4lMzOiq1fFIpWKqEsXMXDBtWufbff+vYi4/PLL1/dx/z79KN9C7jbvae5cceny9RVx+UuXRODor78ydhiPHonxEbZtS/02sbEi0CSXi/cTiYH5RGturyHpRClNPLCEnJyIevRIf3BLq9XSPu99uuBR7z29acPdDXTH/45eQPtl6Evqv78/GUwyoHJLytGuR7uSnG9zI26UkXukO6fR69evUa1aNURGRqJJkyZwcHCAv78/jh49CktLS1y8eBEFMzp0YjpkeX9nIkyRT8D+osNx1dsS3f7pBgkkWN96fdbtkzHGWKrk6ZwXmzaJRJ9z54qh1/ftE3lwOnYU86tUSXuZL16IhAcXL4pM1s+eiTHry5cHbGyw2tsT/XxH4ahlJ9SP2isybY4bJ7J+Jje2fRaIV8dj3d11OPfqHORSORQyBQykBlDIFLAyskI9l3qo5lTti0MAAwDUapHk+vRp4MwZoFSpbKn/l/iG+GL/0/3Y/3Q/zr06B7VWrctFVN6+PDzsPWBpaIknQU/gHeSNx0GP4R3kjdfhr9HToydmNpyJfMb50rzfG3438PvJ33H82Uk0MRmHwgHDUK2iAvHFN2PNnTW4/PYyyuYvi34V+qF3+d7JJiVNi1hVLGZfno1pF6bBSG6E9u7t0al0J9RyrvVNDHufksj4SKy+vRrzrs7D+6j36FGuB4ZXG47iNp8nbNFHRHgR9gK/HP4FZ16ewYImCzhHRyrk6fN7TjlxAhg0SCSfk8mAQ4eA2rUzXu62baCu3TCq82ss3mWPo0eBmjU/LtZoxPDxu3cDhw8Dnp6fbHv6NNCggchv1Llz8uUHBeGeR3d4vDuA06eAOvWSnkd27QI6dADWrxep5dKKSKRZMjISdUzrf8+7d8UxPn0qklU7OwPrTl/Ajgu3UFreAqEvXZA/v0jnZJSxUyyICAeeHsCi64tw2/82PsR8gFwqR7F8xVDIohBOPj+J0nalMb7OeLQo3oLPNSzN0h00AoAPHz5gzpw5OH36NIKDg5EvXz7Ur18fw4cPh42NTWbWM9Wy/IISGYkfzfdB0awR1uy3RZUVVdCyeEv8UfuPrNsnY4yxVMmzDxU+PmI0rsSADSCGP9m1C9iwATh7VmQw/vVXMZzZ5xmME8XGiqFeDh8W09OngKOjyNxZrZoYNqtcOUCpxMWLQL16wOzZIskniETgavhwwM4OWLHiszv9zBWrisXKWysx49IMRCVEoXmx5pBIJEjQJOhGtfKP9MdN/5swMTBBvcL10KhIIzR0bYii1kX1b4rVaqBbN+D4cfFAkpgtNAdotBrMvDQT6+6ug3eQN1wsXdC8WHM0L9YcFoYWuPv+Lu68v4M7AXdw9/1dxKhiUNiqMErYlEBJm5IoYVMClQtURjn7chmuy+kXpzH25Fjc8r8FuVQOYwNj/FjmR/T06InyDuUz4Wj1RSdEi6Df1wJ83xi1Vo093nsw+/JsXHl7BXYmdnCzdoOrlSvcrN1QxKoIQmJD8CDwAR4EPsDDDw8RER8BD3sPbGm7BSVsSuT0IXwT8uz5PSf4+QG//SYS7Pfrh7GGc+Fw+yAGX+8J6cH94uSfXmFhQMmSmFB0E/6+9h0OHBAxoM9ptSJ39aZNInZVrdonC2fNEqMtjBgB/PXXxxEXACAhAWjYEM3vTIKqSg0cOZ7C9Q7iHcvo0cCxYyIAlBbr1omE1w8eAAVdEnD93XV8iPmAoJgg3RSvjkcJmxIobVcape1KJwniazRiFMpx44AEtRpqi6coV8IUdSsUgqureOdjZ5e2eqVGQFQA7gfex/2A+3ga/BTfF/0ezYo142ARS7cMBY1yoyy/oLx8ie8KP0eNQeUxaaElrKZbYWWLlWjn3i7r9skYYyxV8uRDRXy8CM7Y2IhAT3Lj6np7A/PmibvcxDHse/USI3Fdv/5xuntXBH9q1QKaNAGaNhUtbj67kXz9Wgwo1rKlGAFZb3FQkHjY2LBB3FFPmyaGjckk0QnRWHpjKWZdngWVRoVfq/+KQZUHwcIw+X2ExIbg5POTOOZ7DEd9j+JNxBsUMCuAOs51xGRRFsX/nAfJ8RPAqVMiKJZDwuLC0GVXF1x6cwmja4xGi+ItvjjajJa0UGlUUMqzbng5IsKRZ0cQp47D90W/z9J9/RfcC7iHB4EP8CzkGZ6FPINvqC98Q3xhbWSNUnalUNpWPFyWsiuFotZFIZPKcrrK34w8eX7PCUuWiEiKmxuwZAl2va2Kjh0BQ0NC9XxPsTbwBzjuX5p8pCc1Bg7EpX0fUNNvO/btk6BZs5RXJRKDTF66JC5PxsafLDx8GOjeHXB1FcN2uriIDfr1w8Wd/qgZdgA3b4r3KV8qf+hQcbm6eBFwd0/dIQQHAyVKiME1h4yIxA+bf8D51+dhobRAPuN8sDG2gY2xDeRSOR5/eIxnIc9AIDiYOqCkbUnYGtvC0tBSN70JCcTSWwuxse16dC6TQuspxnIxDhql1c2bKF1Jif7TXdFhYATsZ9vj7s93s3T4VcYYY6mTJx8qhg8HtmwRd9T583953aAgMUT7okVAQICYlz+//hj3NWsCZmYpFhEVJVYxNxdvf1PshXbiBNC/v3jru307UL16+o7vXyGxIVh0bREWXF0AuVSOEZ4j8HOln2GqME11GUQEnxAfnH15Fmd9juOs9xG8lUTCLlaG8oWqoIxbDZTJXwZl7MqgpG1JGMoNv15oJvEO8kbLrS0hlUixt9NeFMtXLNv2zVhekCfP79nt1CkRDJo3Dxg0CCHhMri7A336iPcMXbsSnt6NxXJNX7Tb3wNo3Dht5V+5Anh6olW194CdHfbs+fom4eEimPPjj8CMGZ8t9PMTC27fBlatAt6+BY0chbqlAmFfzALTl73E/CvzYaY0g52JHexM7JDfJD8KWRRCYavCAERrn7ZtgTt3REPbQoW+Xqe+fUWQ6cyVMLTa2RTBMcE42f0kClokn3olRhUD7yBv3A+4D+8gb4TEhiAsPgxhcWKKUcVgUt1JaF2y9dd3znIUEeH333/H+vXr4erqihIlSqB48eIoUaIEypcvjwIFCuR0FXNEhoJG27Ztw4oVK/D06VPExcUlWR4YGJihyqVHll9Qjh2DXWMPLNxiC4fq51F3bV1E/x6d4f7+jDGWl82fPx8rVqwAEeGnn37CsGHDMHLkSOzfvx8KhQKurq5Ys2YNLC0tk2zr4uICMzMzyGQyyOVy3LhxI8X95LmHiv37gVatgKNH0/bWNy4OuHxZvEl2ckp1MgatFmjTBrh3D7h2TTRu+qKYGPEad+1aYM4cYPDg1Cd+UKmA27fhl98Yc56sxbKby2BtZI2RniPRu3xvGBsYf72M5ERGAvPnA7Nng8zN8OKPQThfyRZ3PzzQNdcPiA6AVCJFO/d2mFZ/GopYFUnfvlLp4NOD6LK7C2o718amNptgrjTP0v0xlhflufN7douLA8qWFV3Pli0DIAJFV66ImIyhoejJO2UyYcpkLbpJNmL+9gIwb9swdeWrVEClSvAu2BAlD87CxYup78GceKm7ehWoVOmzhRqN6KI2cSJAhCO/HkOzeQ1w8WYIup+vDgOpAWxNbBEQFYDA6EAExwYDAH6v+TsmfzcZUokUMTHADz8ADx8CO3aIHtkpOXtWfEV7Dkdg0qvvEKuOxYluJ+Bg5pC6g2HfLCLCr7/+imXLlmHq1KkICQmBt7c3njx5gqdPn0Kr1WLLli1o06ZNpuzvwYMHUCgUKFbsG3iJlN4M2ps2bSKlUkn9+/cniURCffr0oV69elG+fPnI1dWVJk6cmIH83OmX1SMrqDdtJQk0dOoU0YqbK8h5rnOW7o8xxr519+/fp1KlSlF0dDSpVCqqX78++fj40NGjR0mlUhER0ahRo2jUqFHJbu/s7EwfPqRuZKI8NbrOmzdiXOKxY7Ntl6NHE5mbEz18mMYNV60So7p17iyGWU7JixdES5cStW5NEfnMaHBTkGIcyH28Da2/tIQS1MmMEZxaWi3RunVENjZEBQqI8eRTGLY5MCqQDj49SJ6rPMlgkgH9euRXCo4JTv++U6DRamjquakknSil30/8TmqNOtP3wdh/RZ46v+eE8ePFyJEhYpS+I0eIJBKiCxeSrnr5kpaKWAZRQ+lx0p45m7ryZ84ksrSkPl1iqEaNtFevY0eiMmW+MCjouXOkWbmaPDyIevZWUfWV1ancknIUHheut5pKo6J93vvIfJo5tdzSkiLiIohIDJ45eLAY1WzhwqQjloWGEv36qxgtrXvvGCq7pCyVWVyGAqL0RwpneZNWq6WhQ4eSkZERnTp1KslyjUZDf/31F0mlUlqxYkWG9vX8+XPq3LkzASC5XE6TJ0/W3Q/nVukOGnl4eNCUKVNIrVaTRCKhmzdvEhFRREQEVa1alWbOnJlplUyLrL6gBExbRQDRgwdEI46OoEYbGmXp/hhj7Fu3fft26t27t+73SZMm0fTp0/XW2b17N3Xp0iXZ7f+zQaNOnYiqVhV3utlgzRoimYzo8OF0FnDrFlHhwmL89kePiHx8iHbvJpo4kahdOyI3NyKAyMGBTv7cmJyn2FDh2YXon4WDSONciMjCgmjSJKLw8K/uKonnz4kaNSJSKIgmTyaKSd2Q7FqtlnY+3Emu813J6m8rmnNpjt4wxRnxJvwN1V9Xn0z+MqGt97dmSpmM/ZflqfN7dvP2FufHTZuIiCgigqhQoS+PbP/4kZbkUjX9Y9iJ6MaNL5f/4gWRsTH5zVhPCgXRnj1pr2JAAJG1tTiFp2TrViKlUkuNvfpQobmF6F3EuxTXfRj4kFznu1KZxWXoRegL3fzVq8VX0bMnUWwskUpFtHixeN9QuDDRyo0hVHKRO1VYVoGCooPSfiDsm6PVamnIkCFkbGxMp0+f/uK6y5YtI6lUStOmTSPt55HHrwgKCqLhw4eTQqGgatWq0fnz52nz5s1kZWVFVapUocePH2fgKLJWuoNGJiYmui9VLpfrfcG7d+8mZ2fnDFYtfbL6gnJ/4GICxImt+ebmNPjg4CzdH2OMfesePXpERYsWpaCgIIqOjqZq1arR4MH6585mzZrRhg0bkt3excWFypcvTxUqVKBly5YlWb5o0SIqWbIklSxZkuzt7bPkGLJdRASRkRHRP/9ky+7OnxdvVxcsyGBBISFEP/wggkMAkYmJCHz17Uu0YAFF3bpCgw4OJMkECQ04MIAi4yPFdvHxREuWEDk6iqeGWbO+8Lr5E2o10Zw5RMbGRDVrEqXzhiteHU9zL88l6+nWVGVFlQw/KGx/sJ2s/raiqiuqkk+wT4bKYowJHDRKJ62WqG5dogYNdM1rBg8mcnYmioz88qbDh2mpsGkAxeZzFC8DUir/hx+IPD1pzGgtFS9OpNGkr6obNoiATnKtXRMSiIoW1ZJH6xNk+bclPQz8epPYoOggqre2HtnMsKFzL8/p5l+5IhqkVqwo3nOYmRFNn0508fkNKjS3EFVZUYVCY0PTdxDsm6LVamnw4MFkYmJCZ8+mrlXdzp07SaFQ0K+//kqaVPyxBwUF0V9//UUWFhbk5uZGO3fu1As4vXv3jpo0aUKGhoY0b968VJWZ3dIdNHJwcKAjR44QkXgLvHjxYt2yXbt2kampacZrlw5ZfUE52XohSaAhtZqo+MLitPDqwizdH2OM5QUrV66kChUqUK1atejnn3+moUOH6pZNmTKFWrVqleIbm7dv3xIRUUBAAJUtW/aLF/U881CxaZPoJxYbm+W78vUVb1gHDEjaXD9dNBoRhfL11XtyuPDqArnOd6WCcwrScd/jyW8bG0s0b54IHLm5Ee3dm3yl4uOJ9u0jqlJF3O0vXpz+p5RP+Ef6U9klZcndy53ehr9N8/bhceHU/Z/uJJsoowmnJ5BKk7ubmxMRxcXF0fPnz8nX15fCw8PT/OaUseySZ87v2W3tWtF92EcEsM+fF93Sjh79+qahoUS2tlr6q+QGEdR/8eLjwvh4oh07RDBKLqeIKw/JwoIoIz13tFqipk2JqlcX7wRiY4nOniWaMoWodm0ipXEcGYxx0AsAfU2COoEGHBhABpMMaMzxMbpgkL+/2Fe/fkTv3xOturWKlJOV1HV3V4pOiE7/QbBMp1Kp6MCBA9SqVSsqXbo03bp1K9PKHjFiBJmYmNC5c6n/myIiOnHiBJmamtKPP/5IV65codhk7tfu3r1Lffr0IUNDQ3JycqKFCxdSQgqtx7VaLS1btoxMTEyoTp065OOTu144pTto1KJFC5oxYwYREf3yyy9kb29Py5cvp7Vr11KRIkWoQYMGmVbJtMjqC8qWGgvJ1jCcEtQJJJ8kp2PPjmXp/hhjLK8ZO3YseXl5ERHRmjVrqFq1ahQdnbobtPHjx3+x+3Oeeaho1ky0nc9iQUFEJUuKe/6s7AV3+sVpMphkQL339Kaw2LCvbxASQjR0qEg+Ub8+0b174mni4kUR3cqXTzwEdekicj9lopCYEPJc5Uku81zS1Ero4NOD5DLPhVznu9LlN5cztU6ZJTw8nMaNG0dNmzalsmXLko2NDQHQmxQKBRUoUIDKli1LjRo1op9//plmzJhBO3fupJs3b1J4eroPMpYJ8sz5PTsFBYm3Av/2+VKriUqUSNvlZcUKIhMTLb2t0UEE8y9eJBo1isjWVrSI7dmT6Pp1mj2byN4+4+86Xr0iMjUlcncXrY4kEqLy5bVUp8NtkvStTtsfbE9XuZvvbSaXeS5k9bcVzbgwg2ISRDfmOFUc9dvXj+ST5LTw6kIOnOciz58/p3HjxpGjoyMpFArq2LEjtWvXjkxNTenYsYw/g2/YsIFkMhkdP57Ci6yvuHbtGpUoUYIAkIGBAVWsWJF+/vlnmjdvHtWpU4cAUO3atWnnzp2pzlnk6+tL9erVIyMjI5o1axap1cnnQnz37h3NmzcvxeWZLd1Bo8uXL9PWraKPfmhoKLVo0YJkMhlJJBKqUqUK+fr6Zlol0yKrLyjzSyymUvn86UnQE8IE0MvQl1m6P8YYywsCAkQiyVevXlHx4sUpNDSUDh8+TCVLlqTAwMAUt4uKiqKIiAjdz9WrV6fDX0i6kyceKoKDRV+xf1vzZpWoKKJq1YjKlRNvk7PK06CnZD3dmoYcGpL2jR8/Ft0epFKiggXF00O9eiLxdlgqgk/pFBUfRU02NqH8M/PT3fd3v7iub4gvNd/cnKQTpfTLoV8+drnLRbRaLW3atIns7e3JxcWFfvvtN5ozZw5t3bqVzp8/T76+vvTy5Uu6fv06HT58mDZs2EBz5syhESNGUNu2bal8+fJkYWFBAEgikVDp0qWpX79+tG7dOvLx8eGHLPZV8+bNo1KlSpG7uzvNnTtXN3/BggVUvHhxcnd3p5EjR36xjDxxfs9uvXuLKNG/gwLs2SMCMe/fp74ItZqofHmirh0TROtOQFw4vLx05+GEBCInJ6Jp01IuJ0GVQL2n96ZBMwfRviP7yM/PL8Vzx969YgyIQ4eI/D/EUPd/upNisoLW3F6T+oonI04VR/OvzCfbGbbkONuRFl5dSFVWVCGHWQ504VUyGcFZjoiIiKDevXsTAN05IzG3pUajoVGjRpFcLqf169enex+3bt0iQ0NDmj17dobrGxwcTEePHqUpU6ZQy5YtqUiRItS7d2+6fft2usrTarW0fPlyMjc3pypVqtD9+/eJiCg2Npa2bdtGTZs2JalUSi4uLtkWc5EQEWXWSGzx8fGIj4+HuXnODSWb1cNxjnNcjUuG32H4ofvouLMjon6PglQizbL9McZYXlCrVi0EBwfDwMAAc+bMQf369eHm5ob4+Hjky5cPAFCtWjUsXboUfn5+6Nu3Lw4dOoTnz5+jdevWAAC1Wo0uXbrgjz/+SHE/eWJI5lWrgDFjAD8/wMAgS3ahUgEtWgBPngAXLwIOWTSScGhsKKqtqoYiVkWwv/N+yKXy9BV07Bjw6BHQti1QsGDmVjIFCZoEdP+nO476HsWqFqvgYe8BRzNHKOVKAECsKhZ/X/gb0y9OR2XHyljUdBHK2ZfLlrqlxcOHDzFo0CBcvnwZo0ePxpgxY2BsbJyuskJDQ/Ho0SNcunQJly5dwsWLF/HhwwfY2tqiYsWKqFixIipUqICKFSuiUKFCkEgkmXw0OYOIEBsbi7CwMISFhSE0NFT3c3BwMIKDgxEUFITg4GCEhoZCo9EkKcPU1BT58uVDvnz5YG1tjXz58sHKygpmZmYwNzeHmZkZzMzMYGJiAoVCoZuk0m//HvPBgwfo1KkTrl27BoVCgSZNmmDp0qV48+YN/vrrLxw8eBBKpRKBgYGws7NLsZw8cX7PTvv3Ay1bAqdP68aYr1cPcHEB1qxJW1EXLgC1awMXj0Wjuu0zoGxZ4JP/3xs2AAMHAm/eAJaWSbd/FPgI9ebUQ6BBICCDaNcYCCiDlCgkK4RxXcahe4vuSbZ7EfoCbba3QXBMMHZ22IkqjlXSVvEURMZHYs7lOZh1eRY87D2wvd12OJhl0YWQpcnFixfRrVs3SCQSrFixAvXq1Uv2WrJgwQIMHz4cU6dOxahRo9J0vQkKCkKlSpXg6emJTZs25dpr1du3bzFgwAAcPXoULVq0wMmTJ6FSqdCuXTv07NkTtWvXzrZrRLqCRnFxcbCwsMC2bdvQqlWrLKhW+mX1BaWf+VZEFK+MyvP3YP299bj7890s2xdjjLG0yRMPFQ0aAEWLAkuWZEnxWi3QvTtw/LgIGLm5ZcluoNKo0HRTU7yPeo+LvS/CwtAia3aUhTRaDYYeGYrF1xeDIG6X8pvkR0GLggiICoBKq8KshrPQpUyXXHfTqdVq8ccff2DWrFlo2LAhFixYALdM/scmIvj6+uLSpUu4desWbt68idu3byM6OhrW1tZwdXWFs7MznJ2dUahQITg7O8PGxgYWFhYwNzfXBUtkMlmm1is1oqOj4efnBz8/P/j7++tNHz580AWGEj9VKpXe9oaGhrC0tNQFgmxsbHQBIblcPzhKRIiMjNQFmBKn8PBwREREQK1Wp1hPmUwGpVKpF1xK/O5sbW1hZ2enm2xtbWFhYaELQJmZmcHU1DRHvt9P7dixA0eOHMGqVasAAJMnT4ZSqcSNGzfQr18/NGjQIFXl5Inze3Z5/hyoUAEYPBiYMgUAcO8eUK4ccPs24OGR9iK7dAF8fICrV4FPn1OJRLkNGwKzZ+tvo9KoMOvSLIw7OQ7S11L80/sf1KlcB5dfXcbhe4dx6dUl3A+7j1h5LNzM3NDRoyNaFm+JigUq4pjvMXTZ1QXlHcpja9utsDWxTf/3kQKVRgW5VJ7rzt//RSqVCpMmTcLUqVPRs2dPzJs3D2ZmZl/cZufOnejatSt++uknzJ8/P1UBFLVajSZNmiAoKAiXLl1K90uU7EJE2LJlC3bu3IkWLVqgXbt2MDU1zfZ6pLulUcGCBbFkyRI0a9Yss+uUIVl9QWmtOICCDUogbsAMhMWFYXv77Vm2L8YYY2nzzT9UvH8PODqKN8O1a2d68UTA8OHA6tXA2bNA+fKZvot/90MYcHAAdj3ehWt9r6GwVeGs2VE2SdAk4F3EO7yJeIM34W/wJuINJJBgQOUBMFfmXOvqL/n111+xatUqrF27Fq1atcq2hyKNRgMfHx/cvn0bL168wOvXr/Hq1SvdZ3R0dJJtDA0N9VrXKBQKGBoa6gU/zMzMYGFhoWulkzhZWVkleVDQarUICQlBYGCg3uTv7493797Bz88P4eHhuvVtbGzg4OAABwcHFChQAHZ2drCysoKlpaXu89OfLSwsoFQqM+X7IiLEx8cjMjISkZGRiI6OhkqlQkJCgm6Ki4tDZGQkIiIidJ9hYWEICgrSO76goKBkWzktXLgQgwcPzpT6psfjx4/RsmVLXL58GUZGRqhfvz4qVaqE8+fPo2XLljhy5AgMDQ0xa9YsVK5cWW9bLy8veHl5ARAt3fz9/XPiEL4tsbGApyeQLx9w9Cjwb9CwTx/A1xc4cyZ9xb59CxQvDgwbBtjaigCSjw/w9KloGOvrq98Q9Lb/bfTZ1wcP/R5CdlyGM/POoEqVpC2FtFotWvVvhVN+p1CkaRHcD74Pe1N7BEYHYkT1Efir/l/pb6XKvgnPnj1Dly5d8Pz5c6xYsULXwjw1zp07hx9++AEjR47E//73v6+uP2LECKxZswY3btxA4cLf9r1Jdkr3/8D+/ftjwYIFaNy4MQyyqPl8rqNWI1BlhYoOMhwPfoJahWrldI0YY4zlJTt2APb2QM2aWVL833+LBkxHj2ZdwAgAFlxdgDV31uBU91PffMAIABQyBQpbFf5mjmX27NlYtGgRjhw5gu+++y5b9y2TyVCiRAmUKFEi2eXx8fGIiIjQTeHh4YiNjUVCQoJesCQ2NlYvUBIZGQk/Pz88ePBAr7VOQkJCsvtRKpXInz+/rgWOnZ0dKlWqhBYtWsDR0REFChRAgQIFYG9vD4VCkZVfyRdJJBIYGhrC0NAQtrYZa0mh1WoRExODyMhIREVF6b63nH4wKlmyJEaPHo1GjRrBxMQEHh4ekMlkUKvVCAkJwZUrV3D9+nV06NABz58/1wtwDho0CIMGDQIgXgqwryAS/cSCgkS33n8DRh8+AJs2AVu3pr9oJydg/HgxFSkCFCsmWiy1bw9Ur64fMDr67CiabWmGgtEFYbDWACf3nkw2YAQAUqkUO712omnTpvBf7I/7R+7jQuAFuFi6oIlbk/RXOIcQEbdcSgM/Pz/Uq1cPxYsXx/379+GQxv7ytWvXxoYNG9C2bVuUK1cOLVu2THHdzZs3Y+7cuThy5EiOnxe/NekOGoWFheHBgwdwcXFB/fr1kT9/fr3/IBKJBNOnT8+USuYaoaEIhB3snAzwNPgp+pbvm9M1Yowxlpds3Qp07Kjf9j+TbNkCjBsn4lJ162Z68QAAtVaNKeemYPK5yVjbci1qFKqRNTtiKdq8eTNGjRqFTZs2ZXvAKDWUSiVsbW0zHCABxMNZXFwcPm80nxiI+a89uEmlUpiamuZI14Wv6dOnD/r06QMA+P333+Hk5ARvb2+0adMGEokEVapUgVQqRVBQUKb8beQZJ08Chw8Drq5icnMDChUC5Ck8wq1aJaJD586J5kD/WrYMKFAAaN48Y9UZNQoYOVIvnVES9wLuof2O9igTWQY+S3xw7OgxVK1a9YvlKhQK7Nq1CzVr1sQvPX7BkSNHMq01X1ZTq9W4du0ajhw5giNHjuDu3bvo0KEDxowZg1KlSuV09XK16OhoNG/eHAULFsSBAwdgaGiYrnJatWqFP//8E127dsXVq1eTDTAvXboUgwcPxowZM9CwYcOMVv0/J91Bo127dun+M58/fz7J8jwZNAoORiAcYOoYhff+71HcpnhO14gxxlhe8eoVcOkSMHduphd99SrQqxcwfTrQpk2mFw8A8A3xRdd/uuJJ0BNsa7cN7dzbZc2OWIpOnjyJnj17YtasWejUqVNOVyfLSSQSGBkZ5XQ1WCokJrl+/fo1du/ejStXrkAqleL06dOoV68enj59ioSEBNjY2OR0VXMPf3+R/N/NDdi7F3j5ElCrRcCoZEmRRKhRI9GV2cgIuHlT5DCaOxeoVk1XTEIC4OUlgj2Zkd7qSwEjv0g//LD5B7gr3HFr/i2cOnkKnp6eqSrX0tISBw8eRLVq1dC3b1+sX78+1wZ+Y2NjceDAAezYsQPHjx9HWFgYKlWqhMaNG2PYsGFYvnw5SpcujVatWuH3339P0u0yOQEBAdizZw9sbW3RunXrLD32xEB7Tn6/Go0GXbp0QWhoKK5evZrugFGi//3vf7h9+zZatWqFa9euwfLfjOwajQYjR47EwoULsXTpUvTty40+0iPdQaMXL15kZj2+CXF+IYhACcRavwT8gWL5iuV0lRhjjOUV27aJNv+puLlMizdvgFatRBLT337L1KIBiJvPdXfX4ZfDv6CKYxXcG3APTuZOmb8j9kV37txB69atMWTIEAwfPjynq8OYnrZt2+pG0PTy8oKlpSV69+6N3r17o3Tp0lAoFFi3bl2uDRLkiKFDReuiK1dEoEitBl6/FsmDbtwQ3c8WLhSRoNq1AW9vEWQaOFCvmB07gKgokdMoK0UlRKH5luZwMHbAi0kvMHbMWNRMY1drZ2dnHDx4ELVr10anTp0wbNgwVKtWLVf8XWg0Gpw9exYbN27Erl27oFar0bJlSyxatAgNGzbUG/mvS5cuuHjxIqZNm4YqVaqgfv36aNiwIYoXL47ixYvD1dUVCoUCQUFB2L17N7Zv347Tp0/DxsYGYWFhqFWrFry8vFC8eOY2UCAi7NmzB8OGDUNgYCDs7e1hb2+vy+lWsGBBuLq6okiRInB1ddUFXrLCqFGjcPbsWVy+fDlTWhdKpVJs2LABVatWRZcuXbB//37ExsaiS5cuOHfuHI4cOYL69etnQs3/m9KdCDu3ysokqG9WH0ehPg0xa/9ezPDuh4ARAVmyH8YYY+nzTSfCrlABaNoU+OuvTCsyOlqkRzIzA06cADI7dcu7iHcYfnQ49njvwdT6U/Fr9V8hlXz7Q4R/a968eYMqVaqgXr162LhxY54Ypp2xz33T5/e0OngQaNECuHYNqFgx5fUiI8WoBkePipZJ69YBJia6xURAlSqi4dHChVlXXY1Wg9bbWuNB4APU862Hs4fO4v79++luCXjhwgVMnDgRJ06cQIkSJdC7d290794d+fPnR0REBG7duoUbN27gxo0bePHiBezt7eHk5ISCBQvCyckJRYoUQfXq1TMt2LRhwwaMHTsW/v7+aNSoEbp27YqWLVumqivonTt3sHDhQty5cwdPnz5FVFQUpFIpChUqhDdv3sDa2hpt27ZFx44dUatWLfj6+mLgwIE4f/48Ro0ahd9//z1TWlS+ePECv/wiuv4NHz4c9erVg7+/P96/f68bOfL169fw9fVFaGgoAMDa2hrVqlXDxIkTUalSpQzXIdHSpUt1dcnsQM7Tp09RpUoVdOnSBZcvX0ZERAQOHjyYYp49ljrpDhotXrz4q+sM/CzSnR2y8oJyc8J+VJrYHCP3/YUrQUdxrte5LNkPY4yx9PlmHyqePAFKlBBjIpcpkylFarXipfPdu+K5IzN7fTz+8BgzL83ExnsbUdK2JNa1WgcPe4/M2wFLtbi4ONSuXRtKpRInTpz4ZvKAMJZW3+z5Pa2iooBSpURf4gx2V750CahRQ4xwVrRoJtUvGUMPD8WGexuwotoKdPiuAw4ePIgmTTKexPrly5dYu3Yt1qxZg3fv3sHZ2RnPnz+HVCpFqVKlUKlSJbi5uSEgIABv377F27dv8ebNG7x//x5VqlTBrFmz0tza6VMajQZjx47FnDlz8L///Q/9+/dH/vz5010eEcHPzw9PnjyBj48PXF1dUbduXcg/y1FFRNi2bRuGDx8OIyMjzJ8/H82aNUtXECwhIQGzZ8/G5MmTUaFCBSxZsgRlvnKfERoaCl9fX/j6+mLLli3Yu3cvOnTogClTpqBoBv6QiAj79u1D27ZtsWzZMl2es8x26NAhNGvWDDVq1MA///zD3V4zA6WTRCJJcZJKpSSVStNbdIaULFkyy8o+1GcnKSTx1HFHJ+q9p3eW7Ycxxlj6ZOU1IEtNmEDk7k6k1WZakWPHEpmbEz18mGlF0sXXF6nFlhaECaCaq2vS/if7SaPVZN4OWJpotVrq2bMnOTo6kr+/f05Xh7Es9c2e39Pq11+JChYkiozMcFEdOhD98EMm1CkFH6I/0I+7fiSDSQZ04tkJqlSpErVr1y7T96PRaOjYsWO0cOFCunjxIkVHR39x/ZcvX9KPP/5IEomEWrduTU+ePEnzPiMiIqh58+ZkYWFBR48eTW/VMyQsLIx++eUXkslk5OnpSadOnUr1tmq1mrZs2ULFixcna2trWrVqFWk06bteX7x4kWrWrElyuZwGDBiQ5uvN69evadq0aeTu7k4A6I8//khXPdLi3r17FBcXl+X7+a9Id/tlrVabZAoJCcGWLVtQrly5PPkmIPC9FnaKMHyICYSDWdqGA2Q5j4gQGxuLwMBA+Pr64t69e7hz506SydvbG2/evEFoaChUKlVOV5sx9l+wYwfQqdOXM4ymglotUiNVqwbMmCEGY8usUapHHBuBGqtrQAIJLva+iPO9zqNZsWbcHS0HLV68GJs3b8auXbtgb2+f09VhjGXUrVvAvHkic3UGR8HbvRvYtUukRspsRIRN9zahpFdJ3Hl/B+d6ncPTY0/h7e2NefPmZfr+pFIpGjZsiMGDB8PT0xPGxsZfXN/Z2RkbN27EtWvXEBISglKlSuGXX35BUFBQqvb38uVLeHp64vHjx7hy5QoaNWqUGYeRZhYWFliwYAG8vb1RpEgR1K9fH/Xr18fly5dT3Eaj0WDz5s0oXbo0evXqhaZNm+LJkyfo3bt3ursue3p64ty5c/jnn39w/vx5FCpUCA0bNsSCBQuSzXMcExOD69evY9myZfjuu+/g7OyMlStXon379vDx8cGUKVPSVY+0KFOmDLe8zURZktNo+fLl2Lx5M86cOZOm7cLCwtC3b188ePAAEokEq1evRvHixdGxY0e8fPkSLi4u2L59O6ysrFIsIyubrs6stgtbn5aHZmob9CjXA8Or/3cTTWq1WiQkJCA+Pl43xcXF6T4TEhKSBBUBwMTEBGZmZjA1NdV9GhgYpKsOKpUKHz58QEBAAAICAvD+/XsEBAQgMDAQQUFBCA4ORlBQEIKCghASEoKIiAhoNJo070ehUMDMzAwWFhZ6k5WVFaytrZEvXz7dZGdnB2dnZzg6OiZpasoYy3rfZPeF589FstMMdE0LCwNWrhQ5Kz58AHr2FA8KmZVDc8n1JRhyZAiO/HgE9YtwIsnc4Pz58/juu++wdOnSLGviz1hu8k2e39NCrRYR/8KFxYsEABoNoFIBaRlYSqsFxo8Hpk4FJk4E/vgj7e8jEjQJ2PdkH/wj/eFm7Yai+YrCxdIFcqkcr8Je4eeDP+PUi1MYV2scRtccjdCgUBQvXhzjx4/PdYn4iQgHDhzAyJEjERAQgEmTJuHnn39O9vlDo9HgwIED+Omnn1C6dGns3LkT1tbWOVDr5D18+BDjx4/Hrl27UKtWLbi5ucHW1lY3xcfHY86cOXj9+jV+/vlnjBo1KtNfKGg0Gpw4cQL79+/H/v378fr1a5QqVQp169bFu3fv8ODBA/j6+oKI4OjoiObNm6Nbt26ZmmMqL9NqtVCr1VBkdhLKDMqSoNHx48fRunVrREVFpWm7Hj16oFatWujbty8SEhIQExODqVOnwtraGmPGjMHff/+N0NBQTJ8+PcUysvKCMrLYXjyIc8P9YY0xtf5UdC/XPUv2k5WICOHh4brgSuJnSEgIgoODERISoptiYmIQFxenmz4NEKU1+CKRSJDSn5qJiQmsrKx0QRgrK6sk/1GICNHR0bogUFBQECIiInTLDQwMkD9/ftjb28POzg42NjZ6k7W1NczNzWFmZqabTExMIPts7FEiQnx8PKKiohAdHY2oqChERUUhIiIC4eHhelNoaCiCg4N131twcDBCQ0NBRJDJZHBycoKLiwtcXFxQpEgR3UgERYoUgZ2dHZ84GcsC3+RDxYIFwOzZYjjlVJ4XVCrxQvr8eTGdOiWSXf/yC9CvH5AvX+ZV75jvMXy/6Xssb74cvcv3zryCWbq9ffsWFStWRJs2bbBkyZKcrg5j2eKbPL+nxbx5Itrz+DE0+Qtg40bx66tXQP78gIuLmJydgdKlge+/T3quDw8HunYFzp0DNm4EmjdP3a6joqJgaGgIv2g/LL+5HCtvrUREfAScLZ3xPPQ5EjQJkEvlcLF0gX+kP8rZl8OK5ivgbiuasnbr1g337t3DzZs3c+1LU5VKBS8vL0yYMAGOjo6YP38+GjRoAEAMJrB69WqsXr0a/v7++PnnnzF79ux0v9jOardu3cKmTZsQEBCADx8+4MOHD/g/e3cdFlX2xgH8O8zQ3SAhIqggGIBid3d357p2u666xqprrO26dqyu3YFioa6KChggSkpJzww9wMT5/TFyfyA1M9So5/M88wBzz733HcSJ977nPcnJyRAIBJgwYQIWL15cLdWnhBAEBgbi+vXrePr0KWrXrg1XV1e4uLjAxcWl0hNuz549w507d7B69epv/nPUf//9hz///BOvX79GdnY2OBwOEhISkJaWhrp162L58uWYOXMmNOTJGFehSk8aJSQkYOLEifj8+TMCAwNl3i89PR1NmjRBZGRkkT+C+vXrw8fHB5aWlkhISECHDh0QEhJS6nGq8gVlguVtEFNznBveCheHXUQvx15Vcp4C+fn5SE9PR1ZWFjIzM5kERm5uLkQiEYRCIUQiEUQiEfLy8pCTk4Ps7Gzma1ZWVrGkBpfLLTLlytDQEKampkxixdjYmEnc6OjoQF1dHRoaGtDQ0GC+V1NTg5qaGtTV1ZmvhbcX3M9ms6GiogIWi8UkjQQCQZHHk5GRgbS0NPD5fPD5fPB4vFKnhWlra8PExATGxsZMMsjU1BTm5uYwMDBQiieP/Px8xMbGIioqCtHR0YiKisKnT58QGRmJyMhIJCYmApA+FnNzc+YxFHzV1tYu9vtWVVUFi8WCiopKkRuHwwGbzQabzQaHw2F+14URQiCRSJivBbeCfVRVVcHhcMDhcKChoQEtLS1oampCS0sLWlpa0NDQUIrfK0XJ6pv8UNG1q7QkaM+ecof6+QFLl0pXYM7JAZycgLZtgc6dgQEDKn91tOCUYLQ83BI/uf+ETV1Lv2BDVZ/c3Fy0b98eHA4HDx8+VLqrkRRVVb7J53dZZWUBtrYgv63GFds5WLFCeh1h3jygRw8gJkb6c3S09Ku/vzRB1KGDtF/2gAFARob0KwBcuSJdW0EWERER8BziibxmecixykE9k3qY4TED4xqPg4GGAcQSMWIzYhHGDUMYLwx66noY5TqKmZp87do1DBgwAP/99x9atWpVyb+YypecnIwVK1bg0KFD6Nu3L8RiMby8vFC3bl1MnToV48ePh5mZWU2HSRUSHx+PpUuX4uTJkxg/fjyOHTuGkJAQXLt2DWPHjq30BBmXy8WTJ0+QnJyMlJQUDBo0CE5OTti6dStMTU3Rr1+/Mmc+lSYnJwfJycmws7PD6tWrERQUhF69ejEzcHr27AmxWIxdu3Zh/fr10NLSwtq1azF27NhihQ7VTeGkkampabEPk/n5+cjMzISGhgYuXbqE7t27y3y8N2/eYNq0aXB2dsbbt2/h7u6OnTt3wsrKCmlpaQCkH4ANDQ2Znwvs3bsXe/fuBSDt9p6QkKDIQypXL70nqOeuhp0dWsB3si88rT0rfEyRSITXr1/j2bNniIyMRGxsLHNLSkoqNp7D4UBTU5P5oF/wwV9VVRXa2trQ1taGlpYW833hqVMFSSFzc3OYmZnB1NSUvtmsZtnZ2UwSqeCJKDU1lblKUFDdVTDNryBB+HXiRywWQywWQyQSMd+XVv31dbKJxWJBIpFAKBSWWzGmoqICPT095qarqws9PT3o6+sXu//rm56eHvM3p6WlRZNPVLX45j5UZGRIlzW7fh2Q4TWzdWvpNIXZs4E2bSp3RbSvpWSnwPOQJxpbNMbFYRdp7yIlkJWVhUGDBiE4OBivXr2CpSXtr0j9OL6553d57NqFZ7/dwXyH63j9VgXTp0unlZX2WVgkklYTXboEXL4MxMcD6urSaxAnTwL6+rKdNi4uDh7DPJDcNRm182oj6mwUBroNxLY/t8HOzq7c/f38/NC+fXssWrQIa9askf3xKoHXr19jxYoVMDAwwNSpU9G+fXv6XlUJ3bt3DwMGDEC9evWwa9cuZjU8b29vTJ06FZ8/f0afPn1w6NChSlklLSAgAB06dIBEImFmsGzYsAEdOnTAn3/+ifXr1yMzMxOdO3fGmDFjMGbMmDKPFxERgRcvXuDZs2c4ffo02rRpg6tXr4IQUubfW0ZGBrZu3Yrdu3fD398fdnZ2SE5OrrH+hQonjUoqC9PQ0IC1tTV69OgBYzlr4/38/NCiRQs8ffoUnp6emDt3LvT09LB79+4iSSJDQ0Pw+fxSj1OVLygeam/RbUA6NjZsj7DZYXAwcpD7GIQQPH36FD4+Pnj8+DGePXuG7OxsNGjQAI6OjrCxsSlyMzIyYnr/FFT+UFRlIYRALBZDKBQiNzcXAoEAOTk5zK2gGiwzMxMZGRnMNL2C7wvfMjMzmVtubm6R86irqxfp+2Rubl7sZmpqyiQzNTU1a+g3Qn3rvrkPFRcuSBsQpaaW27SiYOnkDx9kv4KsqFxRLjqf6AyBUIAnE59AW027ak9IlYvH46F3795ITk7G3bt3YW9vX9MhUVS1+uae32UlEuFD7R7wSLmFfoPVsGGDtK2RrCQS4OVLaTXSkCGArL2Ok5OT0XxQc8R2jMWclnOwrec2vH79GrNnz0ZAQACWLl2KpUuXlvqe7NOnT2jRogV69OiBY8eO0YQLVan4fD7zuf/SpUuYMGFCsWobiUSChw8fYv78+TA0NISPj49Cf4cCgQDHjx9n+gOeOXMGI0aMKHF6olAoxMOHD3Hx4kWw2Wz89ddf8PLywvz582FpaQkLCwsYGxtj4cKFqFOnDpo1a4bIyEg0a9YMAwcOxJgxY6CtLft7qpycHGhpaSEkJAQNGjSApaUl3N3d4ebmhsGDB6NRo0ZyP15FVElPI0UkJiaiRYsWiIqKAiBt8PjHH38gPDxcaaan2bJiMeXnD/jNrDv4S/kw0DCQa//Pnz9j2rRpuH37Npo0aYJ27dqhXbt2aNOmDUxNTaskZoqqCSKRCBkZGUWmRRY0Ji/opVX4lpKSgry8PGZ/HR2dEpNLpqamMDIyKnLT19eHuro6My1SWd+0EEKQn58PgUDA3AoSc4VvAoEA+fn5EAqFzNeSqs0IIUyfsMJfS6pAW7JkCRwc5E9yf4u+uQ8V48cDmZnSy8XlGDRIenX52rWqD2vGjRm4FnoNL6e8hJWeVdWfkCpTfHw8unfvDhaLhTt37tAKI+qH9M09v8so5/h5eE50hl1XR1y7rVbRRTRlkpaWhhb9WyC8TTjGuI3B0UFHmfdPhBCcPHkSS5YsgYaGBnbu3Il+/foV2Z/H46F169awsrLCrVu36MwFqlLFx8fDw8MDe/bswaBBg8odn5CQgI8fP6Jjx45ynYcQguPHj+PXX3+FUCjEgwcP4OLiIne8kZGRuH37NhISEpCYmMhMgWzWrBl4PB4MDQ0r/PlEJBIhODgY/v7+CAgIQEBAAObMmYPhw4dX6LiyUrhT2du3b/H582f06lW8r8+tW7dgbW0tV+bLwsICNjY2CAkJQf369XH//n04OzvD2dkZx48fx7Jly3D8+HH0799f0ZArhGTnIBmmYJsGgM1iQ19dxrpPSP8gjx49igULFqBBgwYIDAyEc2WtgUxRSojD4TBJHVmSFYQQZGZmMo38Cm6FE0sfPnxASkoK0/sqPz+/xGMV9Nkq6KVVuK9WSU/YFXkSLynn/nX/qILphHl5eaU2g1dRUWGas2tqajJ9w1RVVYv0Bys8xbDw4yn8feFeVwW9qxRZNZCqBmIxcOsWsHlzuUPDwqQ9KuRclFQhL+JeYL//ftwbd48mjJRAREQEunbtCgsLC9y8eVOhPgoURSkpQjBnIQfp2pY49m/1JIyysrLQcUhHRLSMQB/nPjg88HCR90IsFgtjx45F//79sWbNGgwaNAg9e/bEzp07YW9vj7y8PAwcOBAcDgcXL16kCSOqUuXn52Po0KGwtbVF7969ZdrH0tISlpaWiI+Px7Fjx/DLL7+U+/4+IyMDw4cPx8OHD7F8+XLMnz8furq6CsVsb2+Pn3/+ucRtldUMnMPhoFGjRmjUqBEmTpxYKceU6/yK7jh//ny0bdu2xKTRq1ev8Oeff+L+/ftyHXP37t0YPXo08vPzYW9vj6NHj0IikWDYsGE4fPgwateujXPnzikacoVkRvOQB2uwTLNglGMk8wfNmJgYTJs2DT4+Pvj9998xf/78Gm9kRVHKhsViMf2R6tatW+54QghycnLA5/ORlpZWZGW/gltBJU7hZuAlHacyYv/a10keFRUVpsG5pqYmNDU1oaGhUWTaqbJWSFFV6MULgMuVLoFTjm3bgGbNpE2vq5JYIsbMWzMxrOEwdKrTqWpPRpXr6dOnGDJkCBo3boyLFy/KVdJOUZTyO7k8GMe4ffH4fHKlrnpZGqFQiF4jeiGoSRDaOrbFuRHnwFYp+XOJnp4e/vzzT0ycOBEzZ86Es7MzfvnlF3z48AHh4eHw9fWFvqzNkyhKRvPmzUNERAT8/f3lbssSGxuL33//Hfn5+Vi9enWJYwqq8nV1deHs7Izt27ejQVXP+f8OKJw0CggIwLJly0rc1rJlS+zcuVPuYzZp0gR+fn7F7pc3+VQVksOly7tLTDNgkiJbk63Hjx+jT58+cHV1xdu3b1G/fv2qDJGifhgsFotp9m5tbV3T4VCUYq5fB5o3l66lXIaUFODYMeDECVT5VehDAYfwMfUjroy4UrUn+g59+PAB69evR2pqKrKzs5lbfn4+evTogdmzZ8tcZRwYGIgVK1bg2rVrmDBhAvbv30+v5lPUd+bjR+CnLfbY0PgcWg0ZVS3nnDJ/Cp7VfYYm9k1wY9wNqLHLf15xcXGBj48PTp06hUWLFiE7OxtPnjyBjY1NNURM/UhSUlJw8+ZNnD9/HlZW8lc6e3p64uzZsxg4cCBq1aqFadOmAZD2LMrIyEBCQgKWLFmCZs2aYf369fjzzz8r+yF8txReCkUsFiM7O7vEbQVvkr4nydE5AIBcfS6Mtcq/FMDj8TBq1CgMHz4cjx8/pgkjiqIoqqgbN4C+fcsd9tdfgKUlMHBg1YbDzeFi+YPlWNV+Faz1aDJWVoQQ7N27F25ubkhISECjRo3QpUsXjBo1CvPnz8eiRYvw5s0bNGzYEF26dMHVq1dLnTIaGRmJMWPGoHHjxsjNzYWfnx+OHj1KE0YU9Z0RCIBh/QRoJ36IRYeqp8ph7e61OME5Aac6Trg36R60VLVk3pfFYmHMmDEIDQ1FYGAgmjRpUnWBUj8kQghMTU0RGhqKthUoq+7bty/+/vtvTJ8+HWFhYQCAxo0bw8LCAk2bNkV2djbGjx9fWWH/MBSuNGrWrBkOHDiAgSW8iz1w4AA8PDwqFJiySY7Jgy4ykSFJhrFm2UkjQgimTZsGfX197Nq1i05Hoyjqh7dz504cPHgQhBBMnToV8+bNA4/Hw/DhwxEVFQU7OzucO3euxH4tx48fx++//w4AWLFixffxYh8VBQQFlZs0yskB9uwBfvsN4Cj8ii2b5feXw0zbDPNazKvaE1Wi8pasrWqJiYmYNGkSfHx8sH37dkyfPr3EeGbOnAk/Pz/s3r0bw4YNg6WlJRo2bMj0PCtYxfL58+fw8PDAgwcP0KFDh+p/QBRFVYv58wFenAAP2hyEisdlufZNSUnBu3fv0LFjR6jIuFTa4RuH8Vv0b2ho0RC+P/sqvCJmQSuB71Vubi46deqErKws9O/fHwMGDICbmxttIVCOpKQkbNy4ETt27EB+fj6ePHkCBwcHWFtby/Q5ODk5GT169MDRo0fRuHHjCsczZcoUODg4wMREOjvo0qVLYLPZ0NPTQ61atei/pwIUrjRavXo17t+/D09PT/z111+4dOkS9u7dC09PTzx8+BDr1q2rzDhrXHK8CGaqPHAFXJholT097ciRI7h+/TpOnz5Nlw6nKOqHFxQUhIMHD+Lly5d4+/Ytbty4gfDwcPzxxx/o3LkzwsLC0LlzZ/zxxx/F9uXxeFizZg1evHiBly9fYs2aNeDz+TXwKCrZ9euAjQ3g6lrmsBMnpMspV3XPQ794PxwMOIjdPXfLNF2hugmFQrx48QJHjhzBwoUL0aNHD9jY2MDIyAjLli1DfHx8tcd09epVuLq6Ijk5Ga9fv8ZPP/1U5htRDw8PHD9+HLGxsZg5cyYaNGgAV1dXuLu7o2XLlujYsSMuX76MZ8+e0YQRRX3HwsOB/fuBY3kjYbLiJ7n3nzJlCrp06YJ69ephx44dSE9PL3P8xRcXMfXpVDiqOyJgcYDCCaPvVXR0NH755Rd8+vQJGhoaGDNmDIYOHQovLy94eHhg5cqVAKTLuysiIiICW7ZsAQCEhIRg27ZtuHjxIvz8/L6LhUr4fD66desGX19fANJ+vt27d4ednR10dXXx559/ltlDNC4uDt27dwcA1KtXr9Li6tChA3Mh0sXFBU5OTrCysqIJIwUpfN2yXbt28Pb2xi+//ILZs2eDEAIVFRV4enri7t27FSorU0bJSQRm6hlIzUlFE/MmpY4LDQ3FnDlzsGnTJrlWjysPIQSvX7/GzZs3ER8fDw6Hg2HDhqFt27a4fv063r17BxMTE5iYmMDU1BStWrUCh8Op8auwFEVVrYL/41FRUQgJCYFAIIBAIEBubi6GDRumFI1zP3z4AE9PT2hpSUvh27dvj0uXLuHq1avw+bIc2Pjx49GhQwds2rSpyL537txB165dmdUnunbtitu3b2PkyJHV+hgqXcHUtDKen8Vi4M8/gZ9/Bqryn1FCJJh5ayYGOw9GF/suVXciBQiFQpw4cQLr1q1DdHQ0rK2t0bBhQzRs2BDDhg1Dfn4+duzYgW3btmHs2LFYtGgRnJycqjSm3NxcLFq0CPv27cMvv/yCVatWyTV9zMzMDIsXL67CCKkC0dHRsLGxkbkaQ1FisRh+fn64c+cOfvnlF6iqqmLq1KngcDjMe7MePXrQVgUUAGnCqLHJZ3S2TAS6dZNr3/v37+P69evw8vLCy5cvsWnTJqxcuRITJkzA1KlT4ezsDE6hstSrQVcx9MZQ1EqrhcCdgUp5UaCmiMVi/PTTTzhy5AicnJzQr18/1KlTh1kFa+XKlYiJiQGLxQIhBIMHD0azZs2wbNkymZ5TCCE4ceIEZs2ahXHjxoEQgpiYGBw/fhzR0dFIT0/HoEGDcP78+Up7jkpLS0NCQgK4XC64XC6MjY3Rpk2bSjl2STIzM9GzZ09IJBLcvHkTAODg4ACBQICoqCjcu3cPCxYsQNu2bdG8efNi+79+/Rp9+vSBnZ0drly5QostlBmpBNnZ2SQuLo5kZ2dXxuEqxMnJqUqOO6fJI9Lf/Blx3+9ONv+3ucQxeXl5xN3dnXTv3p2IxeIKnzM7O5s8efKEEELI+/fvCQDSuHFjMmzYMDJw4EBy69YtQggh27dvJy1atCCOjo7EwMCAACB5eXlEIpEQfX19UrduXdK+fXsyZswYcurUKUIIIZGRkeTKlSvkyZMnJCgoiISHhxOBQEAIISQtLY3w+XySlZXFHKe6xcXFkYCAAOLj40OuXbtGLly4QAghJDc3lxw/fpycOHGCnDhxgpw6dYoEBAQwj8nLy4vcv3+fPHnyhLx48YL5m4yNjSUJCQkkIyODiESian881I9LJBKRvLw8IhQKZf6/VPCcGhwcTJ4/f04ePnxICCEkOTmZjBkzhnTo0IE4OjoSPT09smbNGkIIIb/99htRVVUlBgYGxMLCgtSuXZvExcVV1cOSS3BwMHF0dCSpqakkOzubtGjRgsyaNYvo6+szYwqer762ZcsWsm7dOubntWvXki1bthQZs2fPHuLk5EScnJyIhYVFVT2MypORQYiaGiFeXmUOu3RJOiwxsWrDOeh/kGit1yIxaTFy7SeRSEhSUhKJjY2t9JiEQiE5duwYsbe3J7q6umTlypUkJSWlxLFisZhcuXKFtG7dmgAg/fv3r7K//fDwcOLm5kYsLS2Jj49PlZyDUlxKSgoZNGgQ+fjxI8nOziYsFotoaWmRZs2akUmTJjHPpZXFz8+PjBkzhpiYmBAAxN3dnURFRRFCCJkzZw4ZOnQo6dixI6lXrx4ZPXo0IYTQ9yAVUFXv8auTQECIsbGE/K0+m5Djx+XaVyQSkUaNGpFJkyYx9+Xl5ZGTJ0+SZs2aEQCEw+GQevXqkT59+pCxi8YS1ioWMRhqQLhcbmU/FKUgkUgU/j81Y8YMYmhoSLy9vWV6f3b27Fmio6ND+vTpQ3g8Xplj+Xw+GT58OFFVVSVbtmwp8XPh27dvSe/evUl6erpC8X+Ny+USIyMjAoAAIDo6OmTy5MmEEEJevXpF8vPzK+U8hU2ZMoU4ODiQ+Pj4UscUbOPz+SQpKanItuPHj5NRo0Yxn0Ep5cUiRLE1pzMzM5GVlQVLS8ti2xISEqCrqwsdHR3Fs1kKcnZ2RnBwcKUfd2QdX+iq5uLuzIlY2W4lJjWdVGzML7/8gkOHDuHdu3cl/l5klZWVhV27dmHLli0Qi8VITEyEpqYmPn/+LNNKUUKhEKqqqhCLxfD29kZsbCzi4uIQFxcHNzc3zJo1i8l8Z2ZmMvs9ffoUrVq1QsuWLZkSQwBwdHREaGgowsLC4O7uDjU1Nea2dOlSzJgxA3///TczHU9LSwuamprYs2cPDA0NmaUP1dXVkZeXhzp16mDixIl48+YNNm3ahNTUVCQnJyMpKQkHDx5E3759MXDgQFy5cgUAoK2tDUtLS4SFhSEtLQ2uX6ZzkC9LJk6ePBm///47du3ahcWLFxdpwv727Vs0atQIjRo1QmBgIHN/8+bN8eLFC4SEhGDKlCnMlUATExMMGjQIzZo1Q0JCAlRUVGBqaqrQFQCxWAwejwd9fX2oqanh9u3biIiIQEpKClJSUmBkZIR169YhMjISU6dORU6OtNk6m83GrFmzMGLECBw8eBDXr1+HlpYWtLW1oaWlhQ0bNkBXVxfbtm1Dfn4+VFVVweFw4ODggN69eyMsLAwXLlyASCSCWCwGh8PBkCFD0KBBA9y8eRNRUVHMsu86Ojro27cvRCIRs3Ihi8UCi8WChYUFbG1tERsbi48fPzJL2QuFQnTv3h2Ghoa4dOkSUlJSIJFIwOFwYGpqigEDBiAtLQ3e3t5QV1dnlpp3dHRErVq1kJiYiPT0dHA4HLDZbHA4HNSqVQsqKioIDw+HUCiESCRCfn4+dHV1Ua9ePcTHx+P58+fIy8tDfn4+8vPz0a1bN9jZ2eH8+fP4+PEjc7+qqip+//13ZGZm4qeffkJubi5zGzx4MH7++Wf8888/2Lp1K/M7IoTg2rVrqF+/Pnr37o2PHz8y/44uLi64evUqAgMD0fdL/xlCCAghWLt2LSZMmIBly5Zh//79EIvFzDFTU1OZ58HCiwYMHjwYFy5cwOXLlzFq1P9XS2GxWLhw4QJ69eqFXr16wcvLi9lmaWmJ+Ph4ZGZmYvbs2bCxsYGVlRXMzc3h6uoKBwcHiEQisNlspa0sPHz4MP766y9oa2ujYcOGUFdXx7Fjx5CWlsaMMTQ0LDb1bOvWrcjNzcWKFSsAAOvWrYOmpiYWLVpU4nmq6jWgUl26BIwbB6SmAhoapQ7r1g2wtgaOHKmc0758+RKzZ8+Gubk5OnfujE6dOsHC3gJOe52wsOVC/NL2l1L3zc7Ohre3N549e4aIiAhERkYiIiICWVlZAIAePXpg/vz56Nq1a4X+BgkhuHjxIpYvX474+HjMmTMHCxcuhLGMa1E/ffoUCxcuxKdPn3Du3Dm0b99e4Vi+dv78eUyZMgWenp44efIkzMzMKu3YVMW9evUKQ4YMgYGBAS5dugR7e3tERUUhMDAQgYGB8PPzQ+fOnZn3Prq6ugqdJzc3FwEBAWjVqhUePnyIv/76C7169ULPnj1hYWFR4j6EEOTm5kJTUxOTJk0Cl8vFr7/+WuKVd6p038Tzezn++QeYOV2IeJY1dJIj5SojPXjwIBYsWIDQ0NBinzPIlyqWkJAQhIWFISQ0BCc5J0HyCJ7+/FTmlRuVHflSXZ2WloYZM2bgyZMnyMjIwJEjRzBkyBC5jnX69GnUrl0brVq1knmfDx8+YPDgwcjNzcXdu3dRt27dYvERQhAWFoZRo0bh4MGDcHNzK/e4CQkJFfrsWCA6Ohrq6uowMjJiKmAFAgHq1q2LQYMGYc+ePRU+R2HJyckQCASoXbt2uWMXLFiAixcv4tq1a3j37h0GDBgAXV1dOivmG6Hw9LTJkydDX18fBw8eLLZt9erVSE9Px5kzZyoUnDJJztRE3bqZSM1JLbER9pMnT7B582ZcvXq1Qv/pfXx8MHToUKipqWH9+vWYMGECM6VD1qXFVVVVAUiTDz179ixxzLhx4zBu3Djk5+eDz+cjPz+feQP8zz//ICMjAyKRiElAAdIPridPnmQ+nOfn56Np06YApHNQO3bsCIFAgJycHAgEAqbxWWBgIJKSkpjEUcH8XTabDR0dHdjZ2cHMzAzm5ubM8Q4fPoxjx45BR0enSAM1AwMDxMbGlviY5syZgzlz5jDJpILzAdIpLunp6cjKykJ2djbzmHR0dNC1a1ekpqYiNTUVUVFRaNGiBQBpw90jR45AVVUVpqamMDQ0xJs3b8BmszFx4kSoqqoiPz8feXl5aNeuHX7++WdcvnwZv/76K1JSUsDj8SCRSJhk3J49exAREQEzMzOYmpoyb1h1dHTQqlUraGpqgsViQSQSMU++lpaWqFu3LnJycpCdnQ0+n88ksLy9vcHn8yESiSASidCmTRv07t0biYmJuHbtGjgcDlRUVCAWi9GmTRs0aNAA9+7dw507d5Cbm4ucnByoqqqib9++yM7ORsuWLYv9Pnfu3IlLly5h0aJFUFdXh5qaGlRVVXHv3j0YGhri2LFjCAkJAYvFglgsRr169TBgwADExcVh5syZyMvLQ25uLoRCIXbs2IG5c+di48aN2LVrV5FzpaWlQV9fH+7u7sjIyGDu79+/P65cuYKAgABMnToV6urqTBz29vaws7PDq1ev8Pz5cyaRaWBgwPx9aWlpwcjICBoaGlBXV4e9vT0A6RvPiRMnMokrFovFNMubNGlSkURGwYdVS0tL/Pbbb8wLG4vFYt7wjxgxAs2bN2eOx2azmRJbb29vEEKYhrcFx2vRogUuXboEAMxcb3d3dwDAnj17IBKJoKenB319fWh8SSzo6uri2LFjJf79c6q6S3IFTZ48GZMnTwYALF++HNbW1jA3N2feKCUkJJT4IdzKyoqZwgZI575/8/1erl8HunYtM2GUnAzcvy+9VRQhBDt37sSSJUswePBg6OnpYffu3Zg3bx40RmhArY4axP+JcS7hHOzt7VGnTh0YGRkhNTUVN27cwJUrV+Dt7Q0Wi4XWrVvD0dERrVu3hr29Pezt7ZGTk4Ndu3ahV69ecHJywvz58zFq1Cjm71ZWcXFx+Pnnn+Hl5YW5c+di6dKlMDU1lesYrVu3xuPHj7FgwQJ07twZmzZtwoIFCyr0hrTwdLTVq1dj+fLldIELJXP48GH8/PPPGDFiBPbt28e8b6pTpw7q1KmDfv36MWMJIWjdujXs7Ozw+++/y9xKICcnBwcOHMDmzZvBZrMRGRmJjh07omPHjuXuy2KxmNeE6dOnY/369fD09ET//v2xe/duunT5D2TfPmCs5X3oNO8kV8IoIyMDK1aswC+//FLi5wwWi4XatWujdu3a6NatG84GncX+K/sRNCMIjsaOlfkQqh0hBM+ePcOBAwdw584dREVFQVdXF9ra2li1ahXi4+MxfPhw+Pr6olmzZuUe7+rVq+jQoYNC09ydnJyYaYFWVlZ4//49Tp06hfDwcISFhSE8PBy3b99G69at4efnJ9Nrz+vXr9GqVSvcvXtXoalkfD4fI0eOxJ49e+Dg4FBsu6amJk6fPo0uXbrA1dUV06dPl/schYnFYkydOhXDhg1Djx49ZN7v999/x+fPn9GsWTOwWCzUqlULnTt3pgmjb4WiJUrm5ubk8uXLJW67cuUKsbS0VPTQFVJVpasumuHkz163CFaD/Bf9X7Htw4YNI4MHD1bo2BEREeT8+fOEEEKSkpLItm3bSE5OToXipSouPT2dBAUFES8vL3LixAmyZ88eQoh02sSECRPIiBEjyPjx48m0adPIiRMnCCGEBAYGkt27d5MzZ86Q+/fvk3fv3n0T/5YSiYQIBAKSk5NDsrOzSVZWFsnNzWW2VZRYLGbKhzMyMkh8fDyJiYkhkZGRJDQ0lNn2+fNnkpiYSFJSUkhGRkaVlNJSNaOgJDk6OprUr1+f8Pl8smjRIrJx40ZCCCEbN24kixcvLrYfl8sldnZ2hMfjER6PR+zs7Moss1f66QtiMSGmpoQcOlTmsL17CbG0JKSiM1l4PB7p378/0dTUJEePHi2y7d/n/xLWahbpPrU78fDwKFLWrqenR1RUVIixsTGZMGECuXr1arlT0KOjo8miRYuInp4eMTMzI6tWrSqzZL2AWCwm+/btI7q6uqR58+bk3bt3FXnIjBMnThBNTU0yZMgQkpGRIff+cXFxZMWKFcTMzIyYm5uTBw8eVEpcVOXbu3cv2bdvn8yvV35+fqR79+6ExWKRXr16kfv375c5/ubNm8TMzIyYmJiQjRs3Vsp0En9/f+Lp6UkWLVpU4WOVJjAwkJw7d67Kjl/dlP75vRxv3hACEBKo5kbIlSty7bt06VJSu3Ztmd5TZuZlEutt1mTZ3WWKhqo0Hjx4QJydnQmLxSI9evQgp06dKnEqU2BgICGEkKysrFKnMhNCyPnz54mKigrT9qKi7t+/Tzp27EimTZtGNm/eTC5fviz3VECJREJmzJhBTExMSEREhFz7CoVC0rVrV9KwYcNyn5f+/vtvwuFwyKNHj+Q6x9d+/fVXoq+vT968eSP3vhKJhOzbt488e/asQjFQ1U/hpJGGhga5c+dOidtu375NNDQ0FA6qIqrqBcWMnUL+mnCFYDXIh5QPRbbl5eURPT098u+//8p8vCdPnpBJkyYROzs7AoBYWlqShISEyg6boihKKbRp04Y4OTmRRo0akXv37hFCCElNTSWdOnUiDg4OpHPnzswbrVevXjHz8Akh5PDhw6Ru3bqkbt265MiRI2WeR+k/VPj7Sz81fP5c5rC2bQmZO7dip3rx4gWpXbs2cXJyIkFBQUW2CYQCUm93PTL12tQi96elpZHXr1+TixcvEh8fHyIUCuU+b0ZGBtmxYwdxcHAgHA6HjBo1ivj6+hYZI5FICJfLJb6+vqRdu3ZES0uLbNu2rdL7vbx584bY29uTBg0akPPnzzPJ8NJIJBLy6NEjMnToUMJms0m9evXIrl27Kq3nxLcsPDycLF26lISHhxNCpP3G5s+fT9avX0/2799Prnz5EJyXl0eCgoJIXFxcpfR3LI1EIiE7duwgmZmZCh/j8ePHZNKkScwHyMOHD5P9+/eTq1evkkmTJpEJEyYQQggJCgoiu3btIllZWZUSewGRSEQEAgGRSCRk6dKlCn0IK8mlS5eIm5sbAUA6depUI70pq4LSP7+XY/p0QtrUSyJET4+Qcp6LCouIiCBqamrk7NmzMo3/5d4vxOpPK5KZp/j/jZqUnp7OXEwPDAwkK1asIJ8+fZJp3+XLlxNbW1vi5+dHJBIJ8xqWn59Pbt++TdTU1JiLVcokPz+fdOnShTg5OZG0tDSZ95s/fz4xMjJinpfLs2TJEnLjxg1FwyReXl5ERUWFXL16VeFjUN8mhZNGrq6uZM6cOSVumzNnDmnYsKHCQVVEVbygiESEqEBEDi49Q7AaJDkrucj2+/fvEzabXW5TtLy8POYNwT///EMGDhxIdu/eTYKDg7+bF3SKoqiapPQfKjZuJMTFpcwhsbHSvNLz54qf5vbt20RVVZWMGzeuxA+6vz38jZhtMSPcnKprjioWi8nNmzdJjx49CADStGlT0qZNG2Jvb080NDSYqqauXbuSyMjIKouDz+eTSZMmEU1NTWJoaEh+/vln8uLFC+Z1Nzo6mhw/fpxMnDiR2NnZERaLRfr160e8vb2rNOnxLcjPzycXLlwgXbt2JQCIq6srefr0KSFE2pS+T58+xNPTk9StW5c0b96cEEJIWFhYkUasrVu3ZirTk5OTKy0xuGLFCqKlpUVev35dKccjRPqYzMzMiLq6OunTpw/5559/Ku3YZcnOzib9+/cnbDabDBs2jMyZM4f5Pb98+ZKcPn2aHDx4kGzfvp2sW7eO+bucOnUqGTt2LBk+fDgZMGAAOfSlgvHixYtk2bJl5P3799USf3VR+uf3MqSnE6KtTcipplsIGT9ern0HDx5MWrduLdNnhTBuGFFbp0ZOB55WMNKade3aNWJlZUUcHBwUWmApOzubjB49mnkO6ty5MyGEEG9vbwKAzJ07V2k/c/H5fOLp6cks8FOeZ8+eETabXW6lZElSUlLkTrgLBAJiaWlZpdWRlPJSOGl04MABoqKiQhYtWkSCgoIIl8slQUFBZPHixYTNZpMDBw5UZpwyq4oXlOREMQEIObLtKGGtZhGhuOiV1/nz55P27duXeYyQkBDi4eFBHB0dFbpyS1EURZVP6T9UdOxIyPz5ZQ75809CatcmRNH3tQEBAURHR4csWbKkxO0fUz4StXVq5NS7U4qdQAEfP34kv/76K1m9ejU5cOAAuXHjBgkICCBJSUlV9gZ+0aJFpFOnTqROnTpETU2NDBw4kBw+fJhZZa1+/fqkTp06BAAxNjYmgwcPJrt375b5ivaPIDk5mRgYGJAJEyaQ58+fy/RvJRKJSFJSEvnw4QO5ePEiWblyJbPS3KRJk0ijRo2YlWEVdeDAAcJmsyt0xbw0BdU/NeHy5ctk3LhxpF+/fuTmzZuEEEIWL15MzMzMiL29PWncuDFp3bo1E9/48ePJxIkTyfTp08mcOXPI7t27ayTu6qL0z+9l2LuXEFMTMcnlaBPyZfVjWTx69IiwWCzy8uVLmcb3PtWbtD/aXmkTI6VJTk4mw4YNIyoqKmTx4sUVWpFbIpEQX19f4uPjw0xby8jIIB8/flT630tBfJ8/fy6zRUNB4vjrKmJZdenShQwePFju34e/vz9tHfGDUjhpRAgh69atI1paWkRFRYW5aWlp1WjZX1W8oAQ9TSMAIYdO7iWGfxgW2SaRSIiDg0OxJaALbz948CDR0tIi3bp1k6m/A0VRFKUYpf5QkZVFiJoaIV5eZQ5r1oyQpUsVO0VUVBSxsLAgo0aNKrFKRiKRkA7HOpCuJ7oq/ZtnRWzYsIEpm//111/JsmXLyP79+8n169eZKfXHjh0jLBaL1KpViwwcOJC8ePFCqSuKrl27RjZv3kyWL19OZsyYQeZ+mbeYlZVF/vzzT3L+/Hni6+tLPn/+XClVPDExMaR79+7kv/+k/RsrM4ESFxdHxo0bR1gsFhk3bhxJTEyU+xiPHj0ibDab7N+/v9Lior4NSv38XgaJRFpgurTnW0KMjAiR40N37969yfDhw2Uaez3kOmGvYZO3iW8VDbXaFbwOJSUlkc6dOxM/P78ajqjmSSQS4ubmVuLruEQiIRs2bCBDhgyp0Gv4u3fv5PrM/tdff5GoqCiFz0d9+yqUNCJE2v/Ay8uLnDx5knh5eck1D7MqVMULyoMTsYQFMdnrtZE47HIosu3jx48EAPn48WOJ+75//55oaWmRHTt2KPWbUoqiqO+BUn+ouHVLmjQq4wpqeLh0apoiM254PB5xcnIiHTt2LLV3z7HXx4jG7xoknCtb/4Nvyblz54iKigq5du1ameNyc3PJ48ePycqVK4mZmRkxNjZmGrXXtIiICLJv3z4yaNAg8vDhQ0IIIVOmTCEeHh6kS5cuZMiQIWTevHmEEGlyp3HjxsTQ0JCZirFmzRpCCCH79u0j3bt3JxMmTCD79++X6c2+RCIhJ06cIPr6+qRVq1YkLCysyh7nkydPSKNGjchvv/0m9745OTnk9Olvc+oNVTFK/fxehidPCGGxCIlsM5aQqVPL3+GL9PR0oqamxlSdlUUgFJC6O+uS2bdmVyTUavXhwwfSvn17ps8h9X8BAQFEX1+fTJ8+nUkO5eTkkJEjRxJNTc1KaXB/5swZoqKiQrzKuZB17do1mcZR37cKJ42UTVW8oJxZF0pMkEzW319NWhxqUWTbli1biIODQ4nZ3oIkUVld/CmKoqjKo9QfKubNI+RLf4XSrF9PSP368k9NEwgEpF27dsTFxYXw+fwSx6RkpxDjTcbk90e/y3fwb0BAQADR1NQkGzZskGu/nJwccvHiRUIIIQkJCWTx4sUKVb9UVHh4OKlfvz4BQKysrMjEiROJv7+/zPtnZmaS4OBg8vlLg/W7d++SJUuWkBEjRhBLS0vCZrNJRkYGkUgkxMvLi7x584a5FSzCMXXqVKKqqkr++OOPSm9IXhKhUMgkNwcPHkzWrl1L4uLiShzL4/HIP//8Q6uLfnBK/fxehtGjCenZOZcQNpsQOfrPnD59mujq6pbbwJ8QQnY830GMNxkTXk7Z/VWVgUAgIKtWrSJqamqkW7duMjdx/tE8ffqUaGlpkUWLFpHExETi4eFBrK2t5XptKM/ixYvJsmWlr7L34cMHYmhoSH799ddKOyf1beKggv777z+EhoYiNze32Laff/65oodXCslx+TBjpYArzICxpnGRbTdu3EDfvn3BYrGK3P/27VuMGDECT58+hYmJSXWGS1EURSkjb29g/Pgyh5w5A4wYAXz1klImiUSC8ePHIyIiAs+fP4eBgUGJ45bcXQIzbTMsbr1YjqCVn1AoxNChQ9G/f38sW7ZMrn01NTUxaNAgAEBcXByuX7+OAwcOYPPmzZgyZQpUVFQqNVaJRIKEhASEh4fj9u3buH//Pv777z/Y2tpi8uTJ6N27N5ycnIq9pyiPjo4OnJycmJ+7dOmCLl26AAAIIYiIiICuri5iYmIwYMAA5OXlMWN//fVX/P777xg0aBBmzpyJxo0bV86DLQeHwwGHw4FYLIanpycOHDiA1atXo0+fPli8eDHatGmD48eP4/jx43j8+DF0dHQwfPhwiMVisNnsaomRoioqLQ04fx44P+kBEGQCtG8v876XLl1Cnz59oK6uXuY4gVCAP57+gUWtFsFQ07CCEVe9Xr164f379zh27BhGjBgh9/Pdj6JVq1a4cuUKnjx5Ah0dHbi5uWHt2rUwNzevtHNs2rQJLBYLhBAIhUKoqakx20JCQuDq6oru3btj9erVlXZO6hulaLYpMTGRNGzYkLBYLKKiokJYLBbzfcGtJlTFVYiV/d+SDmpPybjL48i4y+OY+3k8Xold6yUSCWnXrh3p06dPpcdCURRFlU5pr0QXLIlWxqooQUHSIcHBsh82IiKCdOrUiejp6ZG3b0vvY/Eo6hFhrWaRR1GP5In6m/H8+fMKNU4tIBQKydatW4mWlhZp3759hRauEIvF5MmTJ2TevHnMVIIVK1YwU8k8PDzIpk2bKn0Z9/Lk5uYSHo/H3Crj91YZxGIxefDgARkxYgQ5evQoIYSQNWvWkDlz5pB79+6RvLy8mg2QqnFK+/xeBm9vQjgcQgRtuxIya5bM++Xk5BBtbW1m6fmyFFQZZeRmVCTUKpWamso083716lW5K05T1WvBggVk5MiRJDk5mSxbtozweDwikUjIo0ePvsv+h5T8FK40WrhwIfT19REbGwsbGxu8ePEC5ubmOHnyJE6cOIGbN29WXmarhiUnA2aaGeDmcFHfuD5z/+3bt6GtrY02bdoUGX/+/Hn4+vri/fv31R0qRVEUpYzu3gVMTYEyqjjOngUaNQIKFYyUSiKRYM+ePfjll1/QrFkzBAQEoG7duiWOzRfnY8bNGRjfZDza1W6n6CNQOoQQbNq0CWPGjEGLFi0q5ZgcDgcLFy7E4MGD8eDBA3A4HKSkpEBXVxcaGhoyHUMkEuG3337Dv//+i+joaLRv3x6tW7cGAEyfPh2jRo2CnZ0dNDU1KyVmeamrq5dbuVATVFRU0LFjR3Ts2JG5b9WqVTUYEUVVXEAA4FJfCI3/7gEbZP979vb2hlgsRo8ePcocJxAKsOnpJixqtQi66roKxxkZGQkNDQ3UqlULkZGREAgE0NbWRq1atYpUn8iLEILTp09j3rx5aNSoEe7duwcPDw+Fj0dVjbFjx6JVq1a4dOkSbG1tMXz4cBgaGqJdu+/nPQNVMQrXXT969AgLFy6EpaUlAOmTgq2tLZYvX44xY8Z8N1PTACCZx4GZTg64Ai6Mtf4/Pe3GjRvo0aNHkSfT3NxcLFq0CAsWLICDg0NNhEtRFEUpm7t3ga5dgVKmOxHy/6lp5QkNDUW7du2wfPlybNmyBQ8ePCg1YQQAfz77E4lZidjSdYui0Sulc+fO4bfffkNcXFylH9vOzg6TJk0CAMyfPx92dnaYPHkyLl26hIyMjCJjMzIy4OXlhaVLlyI2NhYcDgfR0dGYOXMmYmJi8PDhQwwZMgQAYG1tDScnpxpLGFEUVb0CAgA3nVDAygpo1Urm/S5duoQePXpAR0enzHEHAw4iT5yHmc1mKhxjYmIimjZtikePHgGQPue5uLigTp06sLW1xebNm0EIkfu4UVFR6NWrFyZMmICZM2d+VwUF35smTZrgypUr2L9/P4KDg9GkSZOaDolSMgpXGqWlpcHU1BQqKirQ09NDcnIys61Vq1bYtGlTpQSoDJLT1dDUKB+pOalMTyORSAQvLy/s3LmzyFh1dXXs2bOnyJUyiqIo6gcmkUiTRlu3ljrk9WsgLAwYPrz0wxBC8Ndff2HRokVo27YtgoKCYGdnV+apI/mRWPt4Lfb22gsTre+nv55IJMLKlSsxe/bsSqsyKs3u3btx/vx53Lx5E2PHjkWtWrUQFhaGly9fYtasWfD39weHw4GnpydGjhwJGxsbnDx5skpjoijq2+DvD8yX3AaGDSv1osHXhEIhrl27hl27dpU5LleUiz/++wOLWlasymjWrFmoW7cuhg4dCgA4ffo0srKykJWVhStXriAiIgIsFgtJSUkAUG5PHZFIBA6Hg4iICGRlZeHt27dFeq5Ryqlbt241HQKlxBSuNKpTpw4SEhIAAA0bNsSpU6eYbdevX4eRkVHFo1MSyVnaMDMSgZvDZd50P3v2DOnp6ejZsyczLikpCZmZmejXrx90dRV/8qYoiqK+I2/eAKmp0kqjUpw5AzRvDtjbl7w9OzsbY8aMwcKFC7F9+3bcuXOn3IQRIQSzbs2CRy0PTGgyQeHwldHJkycRHx+PpUuXVvm5DA0NMW3aNFy9ehVcLhfXrl0DIP3g1KtXL9y7dw9paWl4/PgxvTpLURQjLQ2IiADcPl2QJo1k5OPjg6ysLPTp06fMcQf9pVVGs5rPUjjGixcv4sqVKzh8+DA4HGktgZaWFszMzGBvb48FCxZg7969AIA9e/YwVZjnz58Hj8crcqysrCxs2LABDg4O4PF46Ny5Mx4/fkwTRhT1HVC40qh3797w9vbGsGHDsGLFCvTv3x/W1tZQVVVFTEzM91VplKsLYzOCtNw0ZnrajRs30LJlyyIro82aNQtCoRBXrlypoUgpiqIopePtDTRsCNSqVeLmtDTg6FGgtPYtoaGhGDx4MDIzM/H06VO4u7vLdNqLHy7ibuRdvJn+Biqsyl0FrKbl5eVh5cqVMDU1rdbzamhoMB+AateuTVeUoSiqVG/eACosCRrrRQPNmsm838WLF9GpUycYGpa+ElquKBd/PP0DC1surFCV0YULF7BkyRI0bdq03LGrV6+Gq6srTp48iUmTJkFFRQVcLheEEGzevBm7du0Cm83GihUrmGl1dGU0ivo+KJw02rhxI/N9z5498ezZM1y+fBkCgQBdu3YtUoHzLcvLA9JFOtCwEIKAMNPTrl+/jvGFlk728fHBxYsX8eLFi5oKlaIoilJG3t5AGWXfv/0G6OsD06YV33b58mVMmDABrVq1wsmTJ2FsbFx8UAmy8rMw9/ZcLGq5CA3NGioaudKaPn16TYdAURRVpoAAwMkgEVqt3WWemiYWi3HlyhWsWbOmzHGHAg5BIBRUqMoIAP7991+IRCKZxrLZbAwbNgzDhg2DUChEWFgYOBwO3r59i3379mHRokWYOXMmtLS0KhQTRVHKR+Gk0dc8PDy+y274KSnSrxyLPEACmGiZIDw8HB8/fkTfvn2ZcUeOHEH//v3RTI4rCRRFUdR3LjsbePoUWLKkxM1BQcDevcCVK8DXC1pt27YNixcvxsqVK7Fq1SqoyPihA5BOWxBJRFjZfmUFglc+ubm5GDVqFDZt2gRHR8eaDoeiKKpU/v6Am8QPkGMFqufPnyM5ORn9+/cvdUxBL6OFLRdCT11Podju3r2LR48eYd26dVBVVZV7f1VVVTg7OwMAGjduXCULElAUpTy+r3r1KsD097bIAwAYaxnj5s2bsLOzY54sCSG4d+8eevXqVUNRUhRFUUrp8WPp1xI+NBACzJ4NdO8OfN264u3bt1i6dCmOHTuG1atXy5UwyhfnY5vvNsz1nAst1e/riu/Bgwfh4+MDMzOzmg6Foig57Ny5Ey4uLmjYsCF27NhRZNuff/4JFouF1NTUmgmuigS8EsEt/YFcSaNLly6hTZs2sLCwKHXM0ddHkSPMwWzP2QrFlZWVhWnTpiE9PZ1OH6MoSiaVVmn0vUr+nA9VALnGudBJ1YEaWw2PHj1Ct27dijzR3rt3r8wneIqiKOoH5O0NtG0LlFCuf/488OyZtNqoMJFIhEmTJqFv374YM2aM3Kc8E3QGablpmOExQ9GolVJOTg42bNiAJUuWQF9fv6bDoShKRkFBQTh48CBevnwJNTU19OjRA3369IGDgwNiY2Ph7e0NW1vbmg6zUmVlASHhbLirvwfc3GTahxCCS5cuYd68eaWOEUlE2Pp8K2Y3n61wldGKFSsgkUiwYcMGhfanKOrHQyuNypEcmQ0zJIOnIWZWTouJiUHdunWZMYQQODs7f1crxlEURVGVwNu7xFXTsrOBhQult69nWW3duhWRkZHYu3ev3FeBJUSCzU83Y5rbNBhqlt5E9Vu0b98+iMVizJpVsR4eFEVVrw8fPsDT0xNaWlrgcDho3749Ll26BACYP38+Nm/e/N1VvLx9CxDCQpMWGoCM078CAgIQHR2NgQMHljrm0odLiM+MV7iXUXx8PPbs2YPdu3fTlZ4pipIZTRqVIyUuF2ZIBpeTzzTBjouLg7W1NTNm4MCB2Lp1a02FSFEURSmjhAQgOLjEpNGGDdLpacuXF70/JCQEq1evxvbt22FpaSn3Kb3CvBDCDcG8FvMUDFp51apVC5s3b2ZW5aEo6tvg4uKCJ0+egMvlIicnB7du3UJsbCyuXr0KKysrNG7cuNR99+7dC2dnZzg7O4PP51dj1BXj7w/UU4+GbifZe51eunQJ7u7uqF27donbCSHY/HQzJjWZBFNtxVaOlEgkWLJkCfp8PSeaoiiqDAonjU6cOAEul1viNh6PhxMnTigclDJJ/iyEGZKRihwYaxkjLy8PSUlJTNIoLy8P9+7dK1J5RFEURVF4/x5gswFX1yJ3h4cDW7dKb4XzHxKJBFOmTEGHDh2KrM4pj83PNmO062jY6NtUJHKlI5FIMHLkSEyYMKGmQ6EoSk5OTk5YunQpunXrhh49eqBJkybIy8vDhg0bsHbt2jL3nTlzJoKDgxEcHFzmEvTKJuCFEG55z2XuZxQVFYUDBw5g+PDhpY55GPUQrxNfY2GrhQrHZW1tjQ0bNsjVJ4+iKErhZ4yJEyciIiKixG2fPn3CxIkTFQ5KmSQnEpippoGby4eJlgni4+MBgEkaPX/+HLm5uejYsWNNhklRFKXUtm/fjoYNG8LFxQUjR45Ebm4u2rZtiyZNmqBJkyaoVasWBgwYUOK+bDabGdevX7/qDbwiQkMBe/tiUxMWLABatgS+/mzw119/4fXr19i/f79CUzV843zxOPoxFrdaXJGolU54eDhcXFyY11+Kor49kydPhr+/Px4/fgxDQ0M0bNgQnz59QuPGjWFnZ4e4uDi4ubkhMTGxpkOtFAHPcuGu8hrw9Cx3bEZGBvr27YtGjRqV2c9o89PNGOo8FPaG9grFdPfuXYwbNw6EEIX2pyjqx6VwI+yynnC4XC709BRrzqZsklNYcNLKQoSACxs9G2ZJyVq1agGQPgE3b94cBgYGNRglRVGU8vr8+TN27dqF4OBgaGpqYtiwYThz5gyePHnCjBk8eHCpSwxramrizZs31RRtJQoLA+rVK3LX58/A9evSBtiF80JRUVFYtmwZ/vjjj1KnJpRn89PN6FOvDxqaNaxI1JWCx+PByMgIoaGhWL16NT58+ICYmBisXbsWM2fOlPk4hBDMnDkTFhYWCk3XoyhKOSQnJ8PMzAwxMTG4dOkSfH19MXfuXGa7nZ0d/Pz8YGJiUoNRVg6BAAiO1oKbcw6gqVnmWLFYjFGjRiEvLw/nz5+Hain9j94mvsWdiDvwm+qncFz79u0Dm83+7vpHURRV9eRKGl29ehVXr15lfl63bh1MTYvOqc3NzcWTJ0/QrJnsc3iVWTJfFe11BXiRk4om5k0QFxcHU1NTaGhoAAAcHR3h4OBQw1FSFEUpN5FIBIFAAFVVVeTk5DCJd0B6lfXBgwc4evRoDUZYBUJDgfr1i9x1+zZgZlb84vO8efPQtGlT/PzzzwqdKiQ1BFc+XsGjCY8UjbbSPHr0CDNmzMD79++hpqYGdXV1jBo1ChKJBMuWLUO/fv1gYyPb9Llz587h4cOHePfuHf2gQ1HfsMGDB4PL5UJVVRV79+79ri+2vnsHiAkbTbuWnwBbsmQJnj59Cl9f3zIX1NnybAs61+kM91ruCsWUkJCAa9euwcvLS6H9KYr6scmVNEpOTkZgYCDzc0RERLEyUjU1NXTr1g0rVqyQOxg7Ozvo6uqCzWaDw+HAz88PPB4Pw4cPR1RUFOzs7HDu3LlqndOcmqkOY1sRuDlcmGiZIC4ursibXdpfgaIoqmxWVlZYtGgRbG1toampiW7duqFbt27M9itXrqBz586lVqjm5ubCw8MDHA4Hy5YtKzaNbe/evdi7dy8AKFej1NBQ4Ktmo7dvA927A4XbSWRkZODmzZu4efOmwn0mtj7bihbWLdDGtk1FIq4wiUSChQsXokmTJmCxWLCzsyuSDJw8eTJMTEzA5XJhZGRUZiIoOzsb8+bNw9KlS9GgQYPqCJ+iqCpSuLK0JFFRUdUTSDUIeCFEHcTBsFvzMscdOnQIO3fuxO3bt1H/qwsMhUWnReNM0BncGn1L4ZiOHj2K2rVro3Pnzgofg6KoH5dcSaOpU6di6tSpAICOHTvir7/+gpOTU6UG9PDhwyKlqX/88Qc6d+7MlO3/8ccf2LRpU6Wesyz8XA0YGbHAFXBhrGWM0LhQpp/R06dPERQUhOnTp1dbPBRFUd8aPp+Pq1ev4tOnTzAwMMDQoUNx8uRJjBkzBgBw+vRpTJkypdT9o6OjYWVlhcjISHTq1Amurq5FFh+YOXMmM+XJ2dm5ah+MrPLzgU+fikxPE4mAu3eBffuKDr137x7U1NTQvn17hU6VkJmAE+9O4OyQszVejfPvv//i/fv3uHz5conbTUxMQAhB9+7d0ahRIxw4cAAcTslvRbS1tXHkyBF06NChCiOmKIqqXAHeqXCHP9Cq+MqZBXx8fDBjxgzs3r0bXbp0KfN42323w8XMBV3tSz9eebS0tLB48WLaAJuiKIUo/Mzx8OHDEhNGaWlpFYmnmKtXrzKryIwfPx5Xrlyp1OOXRSwG0vO1YGiiAm4OF8aaxoiNjWWSRv/88w+uX79ebfFQFEV9i+7du4c6derA1NQUqqqqGDRoEJ49ewYASE1NxcuXL9G7d+9S97eysgIA2Nvbo0OHDnj9+nW1xF0hnz5JX0QKJY18fYGMDKDrV+/7vby80LlzZ6irqyt0qn/e/QMrXSv0q1+zTcLz8vKwfPlyLFiwoMzpZywWC3v37sWVK1cwfPhwCASCYmPCw8ORlpaGnj17QrOcniAURVHKJOA14GaZAOjrl7g9KSkJQ4cOxfTp0zFjxowyj8XN4eJgwEEsab2kQhcF5s2bh59++knh/SmK+rEpnDTat28fNm/ezPz85s0bWFtbw9jYGO7u7kzDaHmwWCx069YN7u7uOHDgAADpE2tB80sLCwskJSUV22/v3r1wdnaGs7NzpU5NKMh/ccwIxETMTE8rSBrdvXsXXb9+909RFEUVYWtrC19fX+Tk5IAQgvv37zMXHS5cuIA+ffowfeK+xufzkZeXB0CaYHr69KnyVBOVJTRU2gD1S8ILALy8gObNgcJ9Xgkh8PLyQs+ePRU+1dn3ZzHSZSRUWDV7BVlNTQ379+/H0qVLyx3r6emJx48f4/nz5zA1NWVe29PT0yEWizFixAgsXvx9rQJHUdT3Ly8PCEwwgZtnyQ2tCSGYPn06rK2tsX379nKPt99/P0y1TDHUeajCMW3YsAHPnz9XeH+KoiiF32Hu3r27SP+JOXPmoFatWjh16hTT7FJe//33HwICAuDl5YW9e/fi8ePHRbazWKwSs+wzZ85EcHAwgoODK7XfEY/35RsLEQDAWMuYSRpFRkYiMjKSJo0oiqLK4enpiSFDhsDNzQ2urq6QSCSYNm0aAODMmTMYOXJkkfF+fn7MdLUPHz7Aw8MDjRs3RseOHbFs2bJvI2kUFgY4OhZpXnT7NtCjR9FhgYGB+Pz5s8JJozBuGAISAjDcZXhFoq0wPp+P9PR09OzZU+bVU11cXBASEoKzZ8/C3NwcKSkpMDExgYuLC7PqGkVR1Lfk/VsRhEQVTfuVXG3577//4tatWzhx4kSpK6UVdvnjZYxvPB6q7PLHliQhIQG//fYbsrKyFNqfoigKkLOnUWExMTFM07aUlBQ8ffoU9+/fR4cOHaCmpoZZs2bJfcyCKQhmZmYYOHAgXr58CXNzcyQkJMDS0hIJCQkwMzNTNGS5FRQtiUzzgBRAj6OHxMRE2NjY4NWrV7C2tq70nk4URVHfozVr1mDNmjXF7vfx8Sl2n4eHBw4dOgQAaNWqVZEFGL4ZoaFFpqYlJgIBAcBffxUdduvWLTg5OcHOzk6h05x9fxZOJk5wNXOtQLAVt3LlSrx7967YxZ7y6OrqMlMTDQ0N8eDBA1y/fh2enp7MewKKoqhvRcDVWFiDA7M+xZtgx8fHY9asWVi9ejVcXct/zuYJePCP98f27uVXJJXm2LFjsLGxoQ2wKYqqEIUrjdTV1ZGfnw9A2t9IS0sLbdu2BQAYGRnJ3dsoOzsbmZmZzPfe3t5wcXFBv379cPz4cQDA8ePH0b9/f0VDlhuPB6ghD9n6QmhwNJCemg5CCKytrTF8+HB8/PixxpuOUhRFUUooNFRaafSFtzdgZAR4eBQdVtGpaWeCzmB4w+E1+loUEhKCv//+W6ZpaWXhcDho27YtNm/ejMGDB1dSdBRFUdXH/2E63HVCAFPTIvcTQjBt2jQ4ODhgyZIlMh3rwacH0FbThqeVp0KxSCQSHDx4EFOnTqUNsCmKqhCFK42aN2+OvXv3wtraGrt27UKPHj3AZrMBAJGRkahVq5Zcx0tKSsLAgQMBACKRCKNGjUKPHj3QrFkzDBs2DIcPH0bt2rVx7tw5RUOWGz8pH4bgg6sugrGmMdOnycLCAtHR0ahdu3a1xUJRFEV9Q0JDgXHjmB+9vIBu3YAvL5MApP17nj59ilWrVil0ivfJ7/E+5T0uuFyoaLQVsmTJErRv3x69evWq0TgoiqJqWsAHLfSun1Ps/uPHj+Pu3bt4/fp1qStGfu1e5D20r91e4alpcXFx0NDQwMSJExXan6IoqoDCSaM///wTffv2haurK2xsbHDkyBFm29mzZ9G6dWu5jmdvb4+3b98Wu9/Y2Bj3799XNMwK4cXnwgg8cDlCpp+RsbExgoOD4enpiaSkpGqdLkdRFEV9A7KygM+fmelpYrG00ujrnqd3796FhoYG2rRpo9Bpzr4/i8bmjdHApEFFI1bY69evcf36dfj7+9PKW4qifmjCPAneptliZYf4IvfHxcVh7ty5WLdunVw9+e5F3sOs5vK3+wCklU22trYICgqiVUYURVWYwkkjZ2dnREREgMvlwsjIqMibxa1bt8LCwqJSAqxJ/MQ8aaWRSm6RldPu3bsHV1dXmjCiKIqiigsPl379kjTy85NOd+7evegwLy8vdO7cGerq6nKfghCCM0FnMLFJzV5Bbtq0KRITE+nrIUVRP7yPNyOQB0e4Df//1GRCCKZMmQJnZ2csXLhQ5mN94n9CBD8CXe3lX3BHIBCgZ8+e2LhxI1q2bCn3/hRFUV9TOGlUwMjICHFxcYiNjUXjxo2hra0tU3O3bwEvSQgj8JBKspnpadbW1vD19UW7du1qOjyKoihKGYWFAYaGgLExAOnUNDc3wNz8/0MIIfDy8sJvv/2m0CneJL5BGC+sxldNS0tLowkjiqIoAKFPEqHPMkOtZv9v4v/x40fcuXMH7969Y9p4yOJe5D1Y6FjA2VT+1UKXLVuGsLAw1Cu0GANFUVRFVKhe8a+//oKVlRVq166Ntm3bIiQkBAAwaNAg7NixozLiq1F8rgSGKhng5qcxSSMbGxtERUXB3t6+psOjKIqilFFBE+wvFbi3bwM9ehQd8vbtWyQkJCjcBPtM0Bk0q9UM9oY191qUnp4OU1NT+Pr61lgMFEVRyiIhPAeW6vwi9wUFBUFfXx8uLi5yHevep3voYt9F7mm/d+7cwe7du3Hs2DEYf7lwQVEUVVEKJ422bNmCBQsWYOrUqXjw4AEIIcy2Dh064OzZs5USYE3i8QAjjRxwc7hFpqctWLAAXbvKXy5KURRF/QBCQ5mpaampwMuXwNe5IS8vLzRs2BC2trZyH54QgnPB5zC8Yc1WGT1+/BgcDgdNmjSp0TgoiqKUQWKsEJZ62UXuCwoKQsOGDeVK/kiIBPcj76NLnS5ynT83NxeTJ0/GvHnz6OcUiqIqlcLT0/bu3Yu1a9diyZIlEIvFRbbVr18foaGhFQ6upvHTVeCmlYvUnFQYaxkjNjYW1tbWGD9+fE2HRlEURSmr0FDgy0pid+8CenpAixZFh9y6dUvhKqOXn18iKi0KwxoOq2ikFfLgwQO0bt0aGhoaNRoHRVGUMkhIUoGFqajIfe/fv5e7yuht4ltwBVx0sZcvaaShoYHz58+jadOmcu1HURRVHoUrjRITE+Hu7l7yQVVUkJubq3BQyoKXyYGRjhBcARcG6gZISEgAm83GrFmzvovHR1EURVWBQpVGXl5A165A4RWW+Xw+nj9/XqGpaa1tWsNG36YyolXYgwcP0Llz5xqNgaIoSlkkpmnA0qpo36KCSiN53I28CycTJ1jpWZU/+Itjx44hISEBLVu2pIl8iqIqncJJIwcHBzx69KjEbY8fP5ZrSUllxc9Rh4GeCNwcLti5bEgkEmRlZeHw4cMKrXZDURRFfee4XOnc5nr1IJEAd+4U72d09+5daGpqok2bNnIfXkIkSjE1jRCCSZMmYcCAATUaB0VRlFLIz0dCrgEs7TWZu/Ly8hAeHi5/P6PIe3JVGR07dgyTJ0/Gq1ev5DoPRVGUrOSanvb48WO4ublBR0cH8+bNw88//ww1NTUMGTIEAJCcnIzDhw9j27ZtOHjwYJUEXJ14Ak1oGYmRJ85Dfno+AOkylra2tnI3pqMoiqJ+AGFh0q+OjggPB5KTgU6dig7x8vJCly5doKamJvfh/4v5D4lZiRjacGglBKs4FouFuXPn1mgMFEVRSiM6GomwgEX9/z+vh4SEQCwWy1VplCvKxZOYJ5jVfJZM42/cuIEpU6Zgz5496Nevn9xhUxRFyUKuSqOOHTsiODgYADBlyhSsX78emzZtYp4Me/Xqhblz52L16tUYNWpU5UdbjQQCIFesBpaxEACQy8uFoaEhEhMTUbt27RqOjqIoilJKoaGApSWgo4O4OEBFBbApNIuMEILbt28rPDXtWsg1tLFtAwsdi0oKWDHbtm37Lha8oCiKqgzisEgkwRyWzobMfUFBQTAxMYGZmZnMx3kW+wxCsRAd7DqUO/bDhw8YNmwYfv31V8yYMUORsCmKomQiV6VR4RXSAGDx4sX46aef8Pz5c6SmpsLIyAgtW7aEvr5+pQZZE/gFK2aaSnsXpSemw9raGvHx8QqtdkNRFEX9AAr1M4qPB8zNi/Yz4nK5SExMRPPmzRU6/KPoR+jj2KcyIq2Qv/76C7NmyXYlnKIo6nuX8i4BErBhUagN0fv37+VeOe1e5D14WntCT12v3LH169fH8ePHmRkfFEVRVUXh1dMK6Orqolu3bpURi1IpSBqJTPLAUeEg9XMqrK2tceLECeTl5dVscBRFUZRy+ippVKtW0c3h4eEAgLp168p96Iy8DAQkBGBL1y0VDrMioqOjERERQZtgUxRFfZH4QfrBwdLy//cFBQXJ3c/obuRd9HLoVeaYuLg4HD16FL/++iuGDq3ZqcoURf0Y5E4a3bp1Cx8/fpRp7Lhx4+QOSFnweNKvecY5MBYZ43PQZ1hbWyM/P5+uSkApDR8fH3Ts2BFCoRAcToVzwNR36tixY1ixYgXi4uJqOpTvX2goMGYMgNKTRubm5tDV1ZX70E9jnoKjwoGnlWdlRKqwBw8ewNTUVO4VgSiKor5XCRE5UFMRwtBQlbnv/fv36PH1Sghl4Al48I/3x7Zu28oct2bNGgQEBGDJkiV0YR6KoqqF3J8y165dK9M4Fov1TSeN+HxAB5lI1xLCWGSMuLg41K9fH1paWnj06JFCq95QFEVR3zFCpI2wy6g0ioiIgIODg0KH94nygaeVJzRVNcsfXIViY2PRvXt3qKgovAArRVHUdyUxVggLPQFYLGnSKCcnB5GRkXIl1x9+eghtNW20sG5R6pj09HScPn2aruRMUVS1kjtp9PDhQ3h4eFRFLEqFlyqBEXhIVRXCWNUY0XHR0NbWhkQigZWVVfkHoCiKon4s8fFATk6RpNHXMxPCw8MVmpoGSPsZdbXvWtEoK2zVqlXFehxSFEX9sAhBQpIKLG2FzF0fPnwAIUSupNG9yHtoX7s9VNmqpY45deoUtLS0MHDgwAqFTFEUJQ+5LxNqampCW1tbptu3jJ+YB0PwwWXnwVjTGPHx8WCz2WCxWDRp9J3p0KED5s6di1GjRkFfXx82NjbYt28fs/3mzZtwd3eHvr4+6tWrh61bt0IikZR7XB8fH7BYLJw6dQr29vYwMDDAwIEDkZyczIxJS0vDtGnTYG1tDRMTE/Ts2RMhISHMdrFYjC1btqBevXrQ19eHh4cHvLy8KvcXQAEA9u7dCxcXF+jp6cHCwgJjx45FamoqQkNDwWazER0dXWR8v379MGfOHABAZmYmJkyYAGNjY1hbW2Pnzp2wtrbGsWPHyj3vsWPHiuxjbGyMSZMmISsrixnz+fNnDBs2DObm5jA3N8fw4cMRHx/PbM/NzcXSpUtRp04dGBoaom3btnjx4kXl/GIo2YWGSpdLs7cHACQklDw9TZFKo6z8LPjF+8m0ok5VSklJQVBQUI3GQFEUpVT4fCTmGcDCis3c9f79e1hYWMDY2Fjmw/hE+6BTnU5ljomKisL06dOhpqamcLgURVHyok1QSsFL+JI0IjnQgi5EIhGEQiFq1apFn6gVIBKJqr2firW1tcx9fo4dO4bLly/j5MmTuHz5MoYNG4auXbuCz+dj4MCBOHnyJAYNGoS3b9+ib9++4HA4mDdvnkzHPn36NPz8/MBisTBmzBiMGTMG3t7eAICxY8ciKysLfn5+0NPTw/Lly9GlSxd8+PABOjo62LFjB3bu3Ilr166hUaNGuHDhAvr37w9fX1+4ubkp+qupESKJCHEZ1fc3YK1nDY6K7E9xFhYWuHTpEhwcHBAbG4thw4Zh9uzZOH36NFq3bo2jR49i9erVAID4+HjcunULAQEBAIC5c+ciODgYgYGBMDAwwJw5c5CUlCTzuRMTE/HmzRuEhIQgLS0NAwYMwPz583Hw4EGIxWL06dMHDRo0QGhoKAghmDZtGvr164cXL16AzWZj8eLF8PHxwd27d2Fra4vdu3czf0fW1tZy/d6oCggNBezsADU1EFJ6TyNFkkbPYp9BhaWCljYtS9xOCJFrhR5FnT9/Hps3b8anT5+q/FwURVHfhMhIJMASlvb/nzosbxNsnoCHj6kf0cqmVZnjNm/eTCs9KYqqdjRpVAp+slA6PU2SBUuhIQCgUaNGmDZtWg1H9m2Ki4tDnTp1qvWcnz59gp2dnUxjBw8ejE6dOjHfGxkZwd/fHw8ePEDv3r0xbNgwAIC7uzsWL16Mv//+W+ak0caNG2FkZAQA2Lp1K5ydnREbGwsOh4MbN27gzZs3sLCwAABs2bIFJ0+exI0bNzBixAgcOHAAixcvZhJEI0aMwOnTp3HgwAH8/fffcvw2al5cRhzq7Ky+v4FPcz/BzsBO5vGDBw9mvq9duzaWLVuGKVOmAACmTJmClStXYtWqVVBRUcHRo0fh5uaGRo0aQSwW49SpU7h8+TJqfckQbNu2DUePHpX53IQQ7Nixg6nSXLt2Lfr374+///4bL1++xNu3b/HgwQPo6+sDAPbt2wdTU1O8evUKzZs3x+HDh3H69GkmGbFw4UKcOHECJ0+exLJly2SOoypt374dhw4dAovFgqurK44ePYqffvoJjx49Yh7XsWPH0KRJk2L7Hj9+HL///jsAYMWKFRg/fnx1hi67QiunpaUBublFk0ZpaWlITU1VKGn0KOoRmlk1g5aqFnNfTEwMLly4gAsXLiAkJARcLhc8Hg+1a9eGqqoqTExMcO7cuRJ/p4q6f/8+OnXqVC0JKoqiqG9CZCQSOTZwsf1/j6H379/LNTXt5eeXUGOroalF01LHnDp1Ci1atFB4ijNFUZSi5EoayTIl53vBS5XAEGmIyk+HZb4N9PT00Lt3b/Tu3bumQ/smWVtbV/uVaXkqLGp9VQ6gra2NzMxMxMbGwtnZucg2BwcHxMTEyHzswsmygu8LkkZA0aW3VVVVUbt2beb4sbGxxd4cODg44MOHDzKfX1lY61nj09zq+xuw1pOvwubSpUvYunUrwsPDkZubC4lEguzsbIjFYgwdOhRz5szB3bt30a1bNxw5cgRLly4FAKSmpiI/Px+1a9dmjqWnpwdDQ0OZz21oaMgkTgDp34lQKERSUhJiY2NhZGRU5HjGxsYwNDRETEwM7O3tIRAISvw7kefvtCp9/vwZu3btQnBwMDQ1NTFs2DCcOXMGgDRROmTIkFL35fF4WLNmDVOt5+7ujn79+sn1+602hZJGBbMHCz+1REREAIBiSaPoR2hfuz3z89q1a/Hbb7/Bzs4OQ4YMwfr16wFIn7tOnToFoVCI06dPo2fPnvD39y/2HKcIiUSChw8fYvfu3RU+FkVR1HcjMhIJ7JawtPz/XUFBQXL1HfKN80VTi6ZQ55Tc3JrH42Hy5Mk4c+YMTRpRFFXtaKVRKfh8Amv1bHAFXAiFQtjY2OD06dNo0KABmjYt/SoAVTIOhyNz1Y8ysbGxYT7oFYiIiICtra3Mx4iKimJKlKOiogBIE1qqqqrM8Ro3bgxAOo0vJiaGOX5lnF9ZcFQ4clX+VKe4uDgMHToUJ0+exMCBA6GhoYHLly9j0KBBIIRAU1MTo0aNwqFDh8DhcJCUlISRI0cCAExMTKCmpobo6GjmqmJGRgb4fL7M5+fz+UhPT2cSR1FRUVBVVYW5uTlsbGzA5/PB5/OZRAmPxwOfz4etrS1MTEygoaGBiIiIIqXwERERaNasWWX9iipMJBJBIBBAVVUVOTk5Micx7ty5g65duzLVel27dsXt27eZ379SCQ0FviyvHB8PcDiAicn/N0dERBRLAMoiR5iDl59fojOnM1atWoW1a9diyJAh6NWrF9zd3YtU/airq6Nfv34AgL59++LAgQMwNzev+GMD8ObNG/D5fHTs2LFSjkdRFPU9IBGRSBCZ4kvRODIzMxETEyNXpZFvnC9aWpc8/RiQVtwaGxujT58+FQ2XoihKbnS93FLw0tgw1MwFT8CDgCeAtbU1Vq9ejefPn9d0aFQ1mjRpEm7evImLFy9CLBbj9evX2LJli1zTFJcvXw4ej4e0tDQsXrwYnTp1gq2tLSwtLdGrVy8sXLgQSUlJEAgEWLp0KdTU1JiKtilTpmDr1q148+YNRCIRzp07h1u3bjHTpqjKkZWVBYlEwiRgwsLCsHHjxiJjpkyZgmvXrmHz5s0YNmwYdHV1AQBsNhujRo3C2rVrkZCQgJycHCxevFiu5chZLBYWLlyI7OxsxMfH47fffsPYsWPBZrPRvHlzuLi4YNasWcjIyEB6ejpmzpyJJk2aoFmzZlBRUcGkSZOwatUqREZGIj8/H9u3b0d4eDhGjx5dqb8nRVlZWWHRokXM372+vj66desGAPj111/RqFEjzJ8/H3l5ecX2/fz5M2xsbJifra2t8fnz5yJj9u7dC2dnZzg7O8uVrKtUIhEQGQk4OgKQJo0sLKR9sQso2s/o7oe7EIlFWDd9HcRiMQghcHZ2hoeHR5nTxNTU1DBr1iyoqKhg7969RZqrK8Le3h5XrlyplKoliqKo70VWWAJyxBpMpVFwcDAAyJw0khAJfON80cK6RYnbCSHYv38/pkyZInOvToqiqMpEk0al4Geyoa+bj8z8TGQkZcDKygoxMTFFpqBQ3z9PT09cuHAB69evh6GhITNNae7cuTIfY/jw4fDw8EDt2rWhoqKCU6dOMdv++ecf2NnZwc3NDdbW1nj//j3u3bvHJCQWLFiAmTNnYsiQITAyMsKmTZtw6dIleHh4VPpj/ZE1aNAAGzduxLhx46Crq4vx48djzJgxRca4ubmhYcOG8Pb2Lpa027lzJ+rVq4eGDRvC0dERzs7OMDIygoaGhkznt7CwgKurK+rVqwcXFxc4OTlhx44dAKRJqRs3biAvLw8ODg5wdHSESCTCtWvXwGZLV2rZunUrunXrho4dO8LMzAwXL17E3bt3iyRbahKfz8fVq1fx6dMnxMfHIzs7GydPnsTGjRvx8eNHvHr1CjweD5s2bVLo+DNnzkRwcDCCg4NrbtpaVJQ0cVRoelpJTbDlnVYQEBCAsSvHQp2rjmcPn2H9+vVy9xNKS0vDtm3bMGzYMAiFwvJ3KAEhBHp6eujfv79C+1MURX2vEiJyAIBJGgUFBcHGRtraQhYhqSFIz0svNWkUGhqKz58/0wuGFEXVHPKdcXJyqpTjGGtmkf2NVhKsBnHr7kYWLVpEAJB3795VyvGp79/Dhw8JACIUCms6FKqa8Xg8wmKxyLNnz8ode/ToUWJlZVUNUdWcc+fOkUmTJjE/Hz9+nMyYMaPImIcPH5LevXsX2/fff/8l06ZNY36eNm0a+ffff0s9V2W9BsjNy4sQNTVCRCJCCCGzZhEyYEDRIW3btiUrV66U67CpqanEZpUNmX9rfoXCCw0NJaampmT8+PFEIpHIvf/9+/dJ/fr1SW5uboXioCiKUlSNPb+XJT+fPFLpQABC8vKkd82fP5/06NFD5kMcCThCLLZalPncnJOTU9FIKYqiFEYrjUogkQD8XE2wjXMBAMkxyVBTUwMAWmlEUVQxMTExePToEcRiMbhcLn7++Wc4OjoqVU+hmmRrawtfX1/k5OSAEIL79+/DyckJCQkJAKRVLFeuXClxeeLu3bvD29ub6evk7e2N7t27V/dDKB+PBxgZAV+qv0qrNJJ1eppQKMSlS5fA0eAgWTUZXRy6VCg8R0dH3Lp1C+fPn8e+ffvk3n///v2oX78+1NVLbtJKURT1Q4qNRYLEDCZGYnz5qICgoKASX89K8zzuOVpatyyxijQ1NRXPnj2TuXKZoiiqKtCkUQkyMwEJUQGMpP01EiMT4enpiTdv3shcakp9/06dOgUdHZ0SbwUra1E/hvz8fMyePRsGBgZwdHREWloarl27Bg6Hgw0bNpT6d/LXX3/VdOjVwtPTE0OGDIGbmxtcXV0hkUgwbdo0jB49Gq6urnB1dUVqaipWrFgBAPDz82PK8I2MjLBy5Uo0a9YMzZo1w6pVq5im2EolMxP4Mq0UKJ40ys7ORkJCgsxJI29vb4wcORJPIp9AKBGijW2bCofo4eGBs2fPyt1INTk5GZcvX5arlxtFUdQP4dMnJLJqwcLy/x+p3r9/L3cT7NKmph0+fBijRo36oVawpihK+dBuaiUo6KMqMRZAW1Ub2XnZqFu3rlwvANT3b/To0eU2GiaEVFM0VE1ycHDAu3fvSty2fPlyLF++vMz9J0yYUAVRKZc1a9ZgzZo1Re578OBBiWM9PDxw6NAh5udJkyZh0qRJVRpfhWVkAIUuKnydNCpYBVHWpNHx48fRr18/+HP90dSiKfTUK+eCRUHC6MOHD2jQoIFM/ZGOHz8OCwsL9PiyMhxFURT1RWQkEvTqw7KW9LmUz+cjPj5e5kqjzLxMBCUHlZo0On/+PMaMGcP0MKQoiqoJtNKoBDye9KvQKAe6HOmV4+PHj2PevHk1FxRFURSlvApVGkkkQEJC8aSRrq4uTE1Nyz0Un8/HtWvXMG7cODyKfoQOdh0qNdTk5GS4ubnh7NmzMo1v2rQpNm3aRD+0UBRFfS0yEgma9rCwkP74/v17AICTk5NMu7+KfwUVlgrcLd2LbUtISIC/v7/c1aEURVGVjSaNSsDnAyoQI8dQAC1oQUdHB2FhYRCJRDUdGkVRFKWMCiWNuFxAKCyaNCroZyRLZc/169ehp6eHjl064nncc7Sv3b5SQzUzM8OSJUswZ84ccLnccsd36dIFI0eOrNQYKIqivguRkUhkWzErp71//x729vbQ1taWaffnsc/R2KIxtNWKj/fy8oKJiQntj0hRVI2jSaMS8HiAASsdfA0ROCIOrK2tER0dDVtb25oOjaIoilJGhZJG8fHSu0pKGsli7NixePnyJd6kvEGeKA9ta7et7GixfPlyGBsbY+HChWWOmz17Nk6ePFnp56co6seyc+dOuLi4oGHDhtixYwcAYPHixWjQoAEaNWqEgQMHIi0trUZjVEhkJBLEpkzSKCgoSL5+Rp990cKq5KlpI0aMwN27d2mVJ0VRNY4mjUrA5wNG4IHHEYElYMHa2hoxMTF05TSKoiiqZIV6GsXHA2pq0sXUCoSHh6Nu3brlHobP5yMrKwt2dnZ4FPUIjS0aw0DDoNLDVVdXx6FDh3Dz5k0kJyeXOCYlJQX79++HoaFhpZ+foqgfR1BQEA4ePIiXL1/i7du3uHHjBsLDw9G1a1cEBQXh3bt3qFevHjZu3FjTocovMhKJOXpFpqfJmjQihJTaBFssFkNDQwNNmjSpxGApiqIUQ5NGJeAlCWFIeOCq5EKSJYGVlRUaN26MevXq1XRoFEVRlDIqVGlU0M+o8Ew0WSuNduzYgc6dOwMAnsU9Qxubiq+aVprWrVsjKioKZmZmJa7MQxtgUxRVGT58+ABPT09oaWmBw+Ggffv2uHTpErp16wYOR7omT4sWLRAXF1fDkcopLQ1CXgZSMjSKTE+TtQl2JD8SqTmpJSaNHj58CFtbW+Tn51dmxBRFUQqhSaMS8JPypJVGJAd5aXmwtbXF/fv30bRp05oOjaIoilJGX01PKzw1LS8vD7GxseUmjSQSCU6cOIEhQ4aAEAL/eH941PKoyqihra2NDx8+oG7dutiwYQMSExMBSK+AHzhwAFOmTKFTIyiKqhAXFxc8efIEXC4XOTk5uHXrFmJjY4uMOXLkCHr27Fls371798LZ2RnOzs7gFyxvrCw+fUIyzAAAFhbS6szk5GSZK42exz2HsaYxHIyKvzbcvHkTjo6OUFNTq9SQKYqiFKF0SSOxWIymTZsyKwV8+vQJnp6ecHBwwPDhw6sl485LEsEQfPDEWcjmZkNLSwv//fdflZ+Xoqia0aFDB6xYsaKmw5DJhAkTMGbMmJoOg/paGUmjT58+gRBSbtLov//+Q0xMDMaMGYOErAQkZSfBvVbxFXUqm5mZGX766SccOnQINjY2GDJkCIKCguDh4YFJkyZV+fkpivq+OTk5YenSpejWrRt69OiBJk2aFElGr1+/HhwOB6NHjy6278yZMxEcHIzg4GDlmyobGYkEbUcAgKUlEBwcDBUVFTRo0ECm3QumppW0QMLNmzfRu3fvSg2XoihKUUqXNNq5c2eRZSqXLl2K+fPnIzw8HIaGhjh8+HCVx8BLFcMIPHCF6chOyUZ0dDTGjx9f5eelvm0NGzbE8ePHS91+6NAh2NnZVV9AlNySk5Mxfvx41KlTBzo6OrCzs8Mvv/yCvLy8mg6NUnZlJI3Cw8OhqakJy4L5C6U4fvw4unTpglq1asE/3h+aHE00MJHtw0dFGBsbY+nSpQgPD8fNmzfBZrNhbW2Nf//9F9bW1lV+foqivn+TJ0+Gv78/Hj9+DENDQ6blw7Fjx3Djxg2cOnVKptUllUpkJBLNG0NTU/r0n5iYCGNjY2hoaMi0e2n9jMLCwhAWFkaTRhRFKQ2lShrFxcXh5s2bmDJlCgBpefyDBw8wZMgQAMD48eNx5cqVKo+DzwMM2Zng5fKRy89FdnY2bYL9g7Kzs8OhQ4dkGvv+/ftqSy5u27YN7u7u0NfXh5mZGfr06YP3799Xy7m/Z1lZWahfvz7u3buHjIwM3Lt3Dzdv3sTSpUtrOjRK2X3VCLtwfqigCbaKStkvuXPmzMH69esBAP4J/mhs0RgcFU6Vhfw1FRUVdOvWDWfPnlW+K/oURX3TChrux8TE4NKlSxg1ahRu376NzZs349q1a9DS0qrhCBUQGYkEAydYWkp72HG5XBgbG8u0a44wB2+T3qKldcsSty9cuFDmiiWKoqiqVn3vRmUwb948bN68GZmZmQCkT74GBgZMkzxra2t8/vy52H579+7F3r17AaBS5jvz0lSgpy1AVn4WIAAyMjJga2tb4eP+6NLS0ootp2ptbQ0Oh4O4uDiIRCLmfg0NDVhYWCA3N5fpsVHAxMQEOjo6SE1NRVZWVpFtBZU8aWlpMDAwqIqHUUx+fn61zznPzc3Fjh074OEh7XeycuVKdOnSBZGRkdDU1KzWWGQlEgHV2ePS2hrgyPkMZ29vj+XLlzM/Ozg4YNKkSTh69KhM+0+YMAE5OTnQ09PDhQsXoKenh1mzZmHJkiXMmOfPn2PJkiUICgqCgYEBhg8fjjVr1kBdXR0A8PnzZ8yfPx+PHj0CIJ06t337dtQqXLpCKZ9yKo1k6WfUuHFj5ueAhAC4W1b91DSKoqjqMHjwYHC5XKiqqmLv3r0wMDDArFmzkJeXh65duwKQNsP++++/azhSOURGIkFzICy/vO3i8XgwKrxsZhn84/0hlojRzKpZsW2Ojo7YunVrZUZKURRVIUpTaXTjxg2YmZnB3V3+N8mVPd+Zn8mBmv6X6Sg50sZ2tNKo4nbs2IE6deoUuRWslNGmTZsi948YMQIA4OvrW2yfCxcuAAAWLVpUbFvhc8ljz549qFu3LnR1dWFubo4JEyagZ8+eiImJwaxZs6Cjo8M0Nly9ejXatGmDlStXolatWsxyqF9XJd25cweurq7Q0dFBp06dijV9FIvF+PPPP+Hk5AR9fX24u7vj/v37MsW7fPlytG3bFpqamtDU1MSKFSuQmJiIjx8/yvW4q1NcHFCnTvXdKitB5e3tLVcT/MuXL6NZs2ZISUnBuXPn8Mcff+DUqVMApFdYu3TpgsGDByMpKQne3t64fv06U8kkFovRp08fsNlshIaGIiQkBIQQ9OvXD2KxuHIeEFX58vOlN11diMVAYqL8SaPBgwdjz549zM/+Cf5ws3SrqogpiqKq1ZMnTxAcHIy3b98yK0SGh4cjNjYWb968wZs3b76thBEAfPqERLYVLCykP8pTaeQb54uGZg2hp65X5P7MzExMnz4dKSkplR0tRVGUwpSm0ujp06e4du0abt26hdzcXGRkZGDu3LlIS0uDSCRiqlGsrKyqPBZetho4NrkAADWxGtatW1duLwqqfPPmzcOECROK3FfQL+O///4rVmkESK86ffr0qcg+JiYmAICtW7di9erVpZ5LVmFhYViyZAlevnwJFxcXZGVlISAgAO3atYOdnR1WrFjBTJks4Ovri27duiEyMrLEpao/ffqEfv36Yffu3Zg4cSJevXqF/v37Q1tbmxmzbt06XLlyBVeuXIGjoyOuXr2Kfv364d27d6hbt67M8QPSxIa2tjbTI0AZWVsDX/1TVvn5KmrdunV4/fo1Xr16JfM+jRo1wvTp0wFI/36nTp2KI0eOYPTo0Th16hTq1avH/H06Ojri999/x+jRo7F9+3a8fPkSb9++xYMHD6Cvrw8A2LdvH0xNTfHq1Su0aFG89wGlBL5Ux0JXFykpgFhcPGk0YMCAUncnhMDHx4dJlidmJSI+M55WGlEURSkrsRiIikKCqQmsv3xE4PF4sieNPvuihVXx1/S7d+/i5MmT2LlzZ2VGS1EUVSFKkzTauHEjNm7cCADw8fHB1q1bcerUKQwdOhQXLlzAiBEjcPz4cfTv379K4xAKgax8dcBImjQy0zVDp06dqvScPwoDA4NSp4yV1mxVQ0Oj1ObRJiYmTAKppHPJisPhgBCC9+/fw9bWFnp6emjXrl2Z+5ibm2PlypWlNm38999/4eLigmnTpgEAWrVqhXHjxuHixYvMmO3bt+PcuXOoX78+AGDgwIFo1aoVTp8+LddKXu/evcNPP/2EHTt2FElKKRsOB/iW+oCvXLkSx44dg4+Pj1zNgAtXvBX8fPnyZQBAbGxssYSgg4MDBAIBUlJSEBsbCyMjoyIVk8bGxjA0NERMTAxNGimrjAzpVz09xMdLvy1IGgmFQkRFRZVZafT582ekpaWhUaNGAKRT09TZ6nA2da7KqCmKoihFff4MCIVIzNGDR6FKo4L3dOXxjfPF2g5ri91/8+ZNdOnSReZm2hRFUdVBaaanlWbTpk3Ytm0bHBwcwOVyMXny5Co9X0FLJIlxLtShDiMDI/Tr1w+pqalVel6q5tSpUwdnzpzB0aNHYWtri2bNmuH06dNl7lO7du0yV/mIi4srMXlQICkpCRkZGRg6dCiTTDMwMMCzZ89K7NtVmpcvX6Jz585Yu3ZtsWooSjGEEMycOROnT5/GkydPZH4DWCAqKqrYzwVJJxsbG0RGRhbZHhERAU1NTZiamsLGxgZ8Pr9IbzYejwc+n0/7qimzQpVG8fGApibwpVAMMTExEIvFZSaNAgMDoa6uDkdH6dLN/vH+aGTeCKps1aqOnKIoilJEZCTAYiGBr8EsfCDr9LT03HTEZ8ajkXmjIvdLJBLcunWLrppGUZTSUcqkUYcOHXDjxg0A0sa0L1++RHh4OM6fP880i60qBZ/VREY5UJeoQ1NTE9evX4eOjk6VnpeqWf3798ft27eRmpqKxYsXY/To0QgNDS11taPyVkGytrYuMXlQwMDAABoaGrhx4wbTIDwtLQ3Z2dnYt2+fTDHfv38f3bp1w5YtWzBr1iyZ9qHKJhKJMGbMGPj4+ODJkyelVrmV5e3btzh06BBEIhFevnyJgwcPYuLEiQCAUaNGISQkBLt370Z+fj4iIiKwcuVKTJkyBSwWC82bN4eLiwtmzZqFjIwMpKenY+bMmWjSpAmaNSveLJNSEl8ljWrVkq6kA0inpqmqqsLGxqbU3dXU1DB27Fhm0Qf/BH86NY2iKEqZRUaCWNsgMZHFJI1knZ4WxgsDADgaOxa5PzY2FiKRCL169ar0cCmKoipCKZNGNYnHk37NN84CR8iBmpoazM3NaZnodywkJAS3bt1CVlYWOBwO00uGzWbDwsICISEhch9z5MiRCAwMZJIHvr6+OHHiBLNdXV0dP/30E5YsWYIPHz6AEAKBQIDHjx8jNDS03ONfvnwZAwcOxKFDh4r1iaIU9/TpU/z777+IiIiAo6MjdHR0mJusBg4cCF9fX5iYmGDw4MFYtGgRxowZA0Baoebt7Y2zZ8/CzEw69bVnz57YvHkzAOnf3I0bN5CXlwcHBwc4OjpCJBLh2rVrYLPZVfKYqUqQmQmoqQFqaiWunGZvb1/mv1/nzp1x8OBB5ueAhADaBJuiKEqZJSYizawe8vJQpBG2LKunhXJDYaplCgMNgyL3165dG0lJSXJNiacoiqoOStPTSFnw+YA6Kw8ZunlgpUgvFdNpId+3/Px8rF+/HiNHjgQhBLa2tjhx4gTq1q2LVatWYc6cOTh48CBsbW3x7t07mY5pb2+Py5cvY8mSJZg3bx6aN2+OGTNm4Pjx48yYrVu3Ys+ePRg6dChiY2OhoaEBNzc3mZZZXbBgAbKzszFhwoQiSaP9+/dj9OjRcv8OfnQ+Pj7M94SQCh1LTU0Nhw4dKrKSXmGtW7fGf//9V+r+NjY2zAqBJTl27FiF4qOqQEYGoCddAaekpFF5K6edO3cOrVq1grW1NVKyUxCbEQv3WrTSiKIoSmllZiJBTbqysqWldGoZn8+XrdKIG4Z6xsUXLvH19UWjRo2gpaVV6eFSFEVVBE0afYXHA4xU0sBTE0OcLYa6urpCU1Sob4erqyuePn1a4rYePXoUq/xZvXp1iau2fT0drVevXsVKjNesWcN8z2azMXfuXMydO1fumL9eUY6iqBqUmQno6gKQJo0KL2JYXtJIKBRizJgxuHbtGqytrRGQEABVFVW4mLlUddQURVGUojIzkci2gooKYGoKpKenQyKRyFZpxAstrR+ndgAAJcxJREFUNjUtMzMTbdq0wZ07d9C5c+eqipqiKEohdHraV/h8wJDwwGXnQZguxNixY4tMK6Io6sfVs2fPIlPWCt8CAwNrOjyqphRKGiUkFK80+nrFvMJCQ0MhFArh6uoKQNrPyNXcFWpstSoNmaIoiqqAjAwksGrBzAxgs6VT0wDIVGkUyg1FPaOilUbPnz+HiooKWrZsWSXhUhRFVQStNPoKjyuBkSQVPFYeBDwBWCwW8vLyqrwBN0UV1rNnTzx58qTEbc+fP2c+YFLVy8vLq8ztdOpY6bZv345Dhw6BxWLB1dUVR48exeTJk+Hn5wdVVVU0b94c+/fvh6pq8RXD2Gw28zdva2uLa9euVXf4Zfuq0qggaSQWixEZGVnuymmGhoao9WUn2gSboijqG5CZiUSJWZF+RkD5SSNCCMK4YcUqjR49eoRmzZrRqWkURSklWmn0FX6SEIbgI1WcCUmWBLNmzWJWcqOo6uLl5YWsrKwSbzRhRH1rPn/+jF27dsHPzw9BQUEQi8U4c+YMRo8ejY8fPyIwMBACgaDUPlCampp48+YN3rx5o3wJI0CaNNLTg0gEJCX9P2kUHx+P/Pz8MiuNAgMD4erqCtaX5dZoE2yKoqhvQEYGEkSmRVZOU1eXrrpclpScFKTnpRfrafT48WO0b9++qqKlKIqqEFpp9BVekhBG4MFPmA4IgIyMDFgWvCJQFEVRChGJRBAIBFBVVUVOTg5q1aqFbt26MdubN2+OuLi4GoywAjIyAF1dJCUBhPw/aZSYmAgATBVRSWbNmoWRI0cCALg5XESlRdFKI4qiKGWXmYlEoSGTNOJyuTA2NmYuAJQmjBsGAHAwKlqBumHDBlgUlC1RFEUpGVpp9BV+qhiG4CNNmAkIpPeV9YafoiiKKpuVlRUWLVoEW1tbWFpaQl9fv0jCSCgU4p9//kGPHj1K3D83NxceHh5o0aIFrly5Umz73r174ezsDGdnZ/D5/Kp6GKX7Mj0tPl76Y8GHiNTUVKirq0NbW7vUXS0tLeHiIm16HZAQAI4KB67mtJqQoihKqWVmIiFHn5mexuPxZO5nZK1nDS3VotPQ2rZtC0dHx1L2oiiKqlk0afQVHg/QU+FBIMmFJqQlprTSiKIoSnF8Ph9Xr17Fp0+fEB8fj+zsbJw8eZLZ/vPPP6Ndu3Zo27ZtiftHR0fDz88P//77L+bNm4eIiIgi22fOnIng4GAEBwfD0NCwSh9LiQoljXR1mfZGSE1NhYmJSalXnjMyMlC/fn1mhcaAhAA0NG0IDY5GdUVOURRFKSIjAwlZOkUqjWRZOS2MFwZHo6LJoW3btmHx4sVVESVFUVSloEmjr/DTWVDTlZYYGWoaYt26dbQJNkVRVAXcu3cPderUgampKVRVVTFo0CA8e/YMALBmzRqkpKRg27Ztpe5vZWUFALC3t0eHDh3w+vXraolbZl96GsXH/7/KCPh/0qg0QUFBCA0NZS5M0CbYFEVR34jMTCRmaBVphC3zymlf9TO6ceMGCCFVESVFUVSloEmjr/AyOFA1kCaNrI2ssWLFihqOiKIqV4cOHejfNVWtbG1t4evri5ycHBBCcP/+fTg5OeHQoUO4c+cOTp8+DRWVkl+O+Hw+8vLyAEiTME+fPoWzs3N1hl++Lz2NCq+cBpSfNAoMDISdnR10v5Qm+Sf4w70WTRpRFEUpNYkEuZn54GepFWmELUvSKIwXViRplJeXh+fPn9Mm2BRFKTWaNCqEEICfrQaWQS4AgJXLwtmzZ2s4KoqiqG+bp6cnhgwZAjc3N7i6ukIikWDatGn46aefkJSUhJYtW6JJkyZYu3YtAMDPzw9TpkwBAHz48AEeHh5o3LgxOnbsiGXLlilf0qjQ9DR5k0aNGjUCAPAFfETyI+nKaRRFUcouOxtJMAeAIpVG5U1PkxAJwrhFp6f5+fkhLy8Pbdq0qbJwKYqiKoqunlZITg6QL+aAGAnAEXPAS+Xhzp07GD58eE2HRlEU9U1bs2YN1qxZU+Q+kUhU4lgPDw8cOnQIANCqVSsEBgZWeXwVUihp9KWnNQAgJSWFmVpXEmNjY9SvXx8A8DrxNdgsNhqbN67qaCmKoqiKyMxEAqQlRl+vnlaWzxmfIRAJilQaZWVlYciQITXTj4+iKEpGtNKokIJFd0TGOWDnsyEUCunKaT+ADh06YPbs2Rg+fDj09PRgbW2NM2fOIDAwEC1btoSuri6aN2+OkJAQAMD58+fh7u4OQ0NDmJiYoF+/fvj06RNzvClTpqBFixbIz88HAISFhcHAwADnz5+XKZZZs2Zh0KBB0NXVhYODA06cOFFkzM2bN+Hu7g59fX3Uq1cPW7duhUQiYbZ/+PABPXv2hImJCaytrTF9+nSkp6dXxq+KoqiSKFhptGbNGsyePRuAtAm2k6kTNFU1qzpaiqIoqiIyMpAIC+jpSqD1ZRE0Ho9XbqVRGC8MKiwV1DGsw9zXvXt3nDt3riqjpSiKqjCaNCqEx5N+FRplgyVgQSAQ0KRRZRGJgKio6r2VUsVQkn/++QczZ85EWloaFi9ejMmTJ2PZsmU4ffo0uFwubGxsMGfOHACArq4ujhw5gtTUVHz8+BGEEIwaNYo51p49eyASiTB//nzk5ORg0KBBmDRpEoYOHSpTLIcPH8bEiRPB5/Oxa9cuTJkyBU+fPgUAvHr1CgMHDsTSpUvB5XJx+vRpbNu2Dbt27QIAZGZmokuXLnB2dkZMTAxevXqFjx8/Yvz48TL/LiiKkoNYDGRnM42wZU0a8fl8XL9+nUku0ybYFEVR34gvlUYW5v+/S5ZKo1BuKOoY1IEaWw2AtNr25MmTyMrKqspoKYqiKoxOTyukoNIoxzALohQRMjIyaNKossTFAXXqlD+uMn36BNjZyTR08ODBaNeuHQBgwoQJmDdvHsaMGQO7L/uPGjWK6bHSo0cPZj8TExOsXbsWbm5uyMzMhK6uLjQ0NHDhwgV4eHjg+fPnMDAwwObNm2UOu1evXujbty/z/cCBA3HkyBG0bt0ahw4dQu/evTFs2DAAgLu7OxYvXoy///4b8+bNw40bN5Cfn49NmzaBw+FAS0sLO3bsgJubGxITE2FRMPmeoqjK8eXNfp66HlJTZU8a+fr6YvDgwcjOzgYA+Mf7Y1bzWVUeLkVRFFVBGRngwwhGJiwAgFAoREZGRrlJozBuGByN/9/PKCAgAGPHjkVycjJ0dHSqNGSKoqiKoEmjQng8QI+dBb6mEKJ0ES6fu4zOnTvXdFjfB2traRKnus8pI8tC62Rra2uXeF9mZiYA4NGjR1i7di2Cg4OZD3wAkJyczKyCZGdnh0GDBuHgwYO4c+cOOBzZ/6vV+Sq5VqdOHQQEBAAAYmNjizUBdnBwQExMDLO9du3aRc7n4OAAAIiJiaFJI4qqbF+eFxJzDQD8P2kkkUjA5XJLTRoFBgbC2dkZqqqqSMtNQxgvDM1qNauOiCmKoqiKyMxEtpohtLWlSSP+l6vO5U1PC+WFop7R//sZPXr0CE5OTjA1Na26WCmKoioBTRoVwucDhirpSGblAgLph21NTdpfolJwODJX/Siz/Px89OnTB6tWrcKVK1egq6uL169fw83NDYQQZtzNmzdx5swZTJ48GT///DP8/f2hr68v0zmioqKK/Wz9JQFmY2ODiIiIItsjIiJga2vLbI+JiYFIJGISRwXjC8ZQFFWJviSNErL1APy/KWpaWhokEkmZSSNXV1cA0n5GbBYbjS1oE2yKoiill5mJbDUDfLnGCC6XCwAyVRp1r9ud+fnx48do3759lYVJURRVWWhPo0J4PMCI8JAMASAA2rZtC7FYXNNhUUokPz8fAoEAhoaG0NXVRXx8PFasWFFkTGRkJMaOHYuDBw/iwIEDcHR0xPjx44sklcpy69Yt3Lx5E2KxGLdv38bly5cxceJEAMCkSZNw8+ZNXLx4EWKxGK9fv8aWLVswbdo0AEDv3r3B4XCwfPlyCAQCJCYmYv78+ejbty+tMqKoqpCRAQBIzNKBri6YpqipqakAIFPSyC/eDw3NGkJLVavq46UoiqIqJiMD2Rx9JmnE+9IUtaxKI5FEhAh+BLNymlgsxpMnT2jSiKKobwJNGhXC5wOGklRwSQ5YuSyw2Wyw2eyaDotSIjo6Ojh06BB+//136OjooGfPnkUaXAsEAgwePBjjx4/H8OHDoaKiglOnTuHNmzfYtGmTTOeYNGkSDh8+DAMDA8ycORN///032rZtCwDw9PTEhQsXsH79ehgaGmLo0KGYM2cO5s6dCwDQ09PD3bt38fbtW1hbW8Pd3R0ODg44fvx45f8yKIqSVhqpqCAzTw16ev+/u7yk0YEDBzBixAgA0qSRh6VHlYdKURRFVYLMTGSr6BWpNNLR0YGamlqpu0SnRUMkETFJIxUVFfj6+qJXr17VETFFUVSF0OlphfBSRDCSpOIdyYG6RJ02wf5B+Pj4FPmZw+EUqwrq0aMHRF9WY5swYQImTJhQZHvhn1+/fl1km5GRUbEpZ2UxMDDAnj17St3er18/9OvXr9TtDRs2xJ07d0rd/vXjpSiqAjIzAV1dZGWzULiPaUpKCnR1daGurl7ibs2bN2e+94v3w+JWi6s6UoqiKKoyZGQgS0UPZoWSRrKsnKbGVoONng1zX4MGDaoySoqiqEpDK40K4ScLYQg+MiCAmlitSCNkiqIoiiqmIGmUhSJJo7JWTrt+/ToGDhwIAODmcPEp7RM8atFKI4qiqG9CZiayWdrMcz6Pxyu/nxEvDA5GDmCrSGcwjBgxAuvWravqSCmKoioFTRoVwkuRQI/FQz5LCEN1Q3Tp0qWmQ6K+Mw0bNoSOjk6Jt/T09JoOj6IoeWVmAnp6ciWNXrx4wfTA8E/wh6qKKhqZN6qOaCmKoqiKysxENtEuMj2t3JXTuKFwNHJkfvbx8aELlFAU9c2g09MK4fMI1FWly2Y2cmyE+fPn13BE1Pfm/fv3ZW6nU8co6huTkcFUGunq/v/uspJG7969K9IE29XcFeqckqexURRFUUomIwPZEs0iSSNZpqc1sWgCAMjOzkZycjKdnkZR1DeDVhoVwktTgaqWtNojLz0PcXFxNRwRRVEUpdQUmJ729cpptAk2RVHUNyQzE9kSjSKrp5VXaRTGC2MqjQo+X1hbW1dpmBRFUZWFJo0K4WeyoWKQCwB4cPNBsYbGFEVRFFWEnEkjsVgMKysrNGnSBMCXpBHtZ0RRFPXtyMhAllBd5kqjXFEuotOimZXTWCwWBgwYAAsLi+qIlqIoqsLo9LQvJBIgLUcNMMwBK48FUb6Irp5GURRFla2gpxEfMDP7/92pqalo2bJlseFsNhv//fcfACApKwmxGbE0aURRFPUtycxEtlCtSKVRWUmjSH4kCAgcjaWVRvXq1cPly5erI1KKoqhKQSuNCnky5QQMrENBcqTLrdOkEUVRFFWmLz2NMjOLVhqlpKSUWGmUkJAAf39/ANIm2OpsdTQ0a1hd0VIURVEVJMnIQk6+qsyNsEO5odBW1YaljnRVZn9/f+biAUVR1LeAJo2+UFEBWusHQWCcA+QAKioqMCt82ZiiKIqivlbG9DRTU9Niwy9fvoxRo0YBkE5Na2zRGGpsteqKlqIoiqogQYYQwP+f88ubnhbGDYOjsSNYLBYA4ODBg9i1a1eVx0lRFFVZaNKoMD4fSRoEEAAnTpwAm82u6YgoiqIoZVZC0kgoFCI9Pb3ESqNPnz7Bzs4OAG2CTVEU9c0hBNmZEgCAtjYgEAggEAjKTBqFckOZfkYAEBsbCxsbmyoPlaIoqrLQpFFhaWlIVhUBAmDgwIE1HQ1FURSl7Ap6GmUVveoMoNSkUZ06dQDQJtgURVHfnOzs/7V3/0FN3nccwN/RKL88DbpGMJkSEhATQKgI7trbig43i9NTI2B/aKc7vdltrjt0s7OFbq16O13RFe9qZ09312pd10Kn1v7QrWstllmpd2g9FWWAcPIzKkICIZ/9gabGCGIF8iS+X3fcwUPyPN938vj5ch+f7xO0ontdWlhY9/2MAPS6PO1s81nEjv6maVRTU8NPTiMiv6KoppHdbkdqaiomT54Mi8WCvLw8AN1/ZKelpcFkMiE7OxsdHR0DM4CWFjSqOzHEMQSbNm0amGMQEd2HXn75ZVgsFsTHx2PRokWw2+19ru0bNmyAyWTCxIkT8cEHHwzyyO/g+j2Nbm4aNTY2Arh906iyshIGgwG1V2tR11rHphER3Re2bNmC+Ph4WCwWFBQUAOhuuGRkZCAmJgYZGRloaWnx7SD74upVXLupaXTjPwnudKXRjZtgA2waEZH/UVTTKCgoCIcPH8aJEyfw1Vdf4eDBgzh69Ch++9vf4plnnsG5c+cQHh6OHTt2DMwAbDY0DO3A0I6hqK2tHZhjEBHdZy5evIitW7fi2LFjKC8vR1dXF/bs2dOn2n7q1Cns2bMHJ0+exMGDB7Fy5Up0dXX5IEUPbrM8rbGxESqVCuHh4V4Pf+GFFzB37lwcqz2GEHUIJj0waZAHTEQ0uMrLy/Haa6+htLQUJ06cwL59+3Du3Dls3LgRM2bMwNmzZzFjxgxs3LjR10O9s1uaRs3NzVCpVNBoNLd9eGtHK+pa6zyWp7377rt45JFHBmGwRET9Q1FNI5VKhRHX/+ru7OxEZ2cnVCoVDh8+DKvVCgBYsmQJioqKBmYALS1oHurAEPsQfnIaEVE/cjqdaG9vh9PpRFtbGyIjI/tU24uLi5GTk4OgoCAYDAaYTCaUlpYO8uh7IAJcvYqO4JHo7PRsGoWHh0OtVns9JTMzE3FxcThWewzJkclQD/F+DBFRIPn666+RlpaG0NBQqNVq/OAHP8A777yD4uJiLFmyBMAA/33fn65cwTWEQaUShIR0X2mk0Wh6vA9q9eVqAMCEURPc277//e/f9oMSiIiUSlFNIwDo6upCUlIStFotMjIyYDQaodFo3H986/V6XLx40eM5hYWFMJvNMJvNcDgc3/7gNhsuD+mAtAmbRkRE/USn0yE3Nxfjx49HZGQkRo0ahSlTptyxtgPdVyndfMPQAZ0D7lZ7O+ByoXXoKACeTaPbLU07c+YMsrOzYbfbeRNsIrpvxMfH49NPP0VTUxPa2tpw4MABVFdX49KlS4iM7P4Y+oiICFy6dMnruT6r7z25fqVRaCigUnVfadTb0rT6a/UAgAfCuptEX3zxBX74wx9CRAZluERE/UElCq1aNpsN8+bNwx//+Ec89dRTOHfuHIDuTxyYNWsWysvLb/u8hx566J7WRLe0tNx2SYE/CqQsAPMoWSBlAZjnZuHh4Thy5Mg9H3/BggV46623oNFosHDhQlitVuTn59+xtv/iF7/AtGnT8MQTTwAAli1bhlmzZrmvULoV54BvBFIWgHmULJCyAMxzs/6YAwBgx44d2LZtG8LCwmCxWBAUFISdO3fCZrN5HKu3+s36/o1AygIwj5IFUhYgsPLca5a7qe+KvS5eo9EgPT0dJSUlsNlscDqdUKvVqKmpgU6n6/F59zqxmc1mnDp16p72oRSBlAVgHiULpCwA8/S3jz/+GAaDwX05/vz583HkyJE+1XadTofq6mr3z5wD+i6QsgDMo2SBlAVgnoGwbNkyLFu2DADw7LPPQq/XY+zYsairq0NkZCTq6uqg1Wp73Qfr+zcCKQvAPEoWSFmAwMozmFkUtTytoaHB/T8O7e3t+OijjzBp0iSkp6fj7bffBgDs2rULc+fO9eEoiYjobowfPx5Hjx5FW1sbRASHDh2C2WzuU22fM2cO9uzZA4fDgQsXLuDs2bNITU0d7AhERHQP6uu7l2lVVVXhnXfewWOPPYY5c+Zg165dAPj3PRGRkinqSqO6ujosWbIEXV1dcLlcyMrKwuzZs2E2m5GTk4N169YhOTnZ/T8VA+Hpp58esH0PtkDKAjCPkgVSFoB5+ltaWhqsVisefPBBqNVqJCcnY/ny5cjMzLxtbX/vvfdw7Ngx/OEPf4DFYkFWVhbMZjPUajUKCwt7vOFof/D1a9WfAikLwDxKFkhZAOYZCAsWLEBTUxOGDRuGwsJCaDQa/O53v0NWVhZ27NiBCRMmYO/evQM6BiW8Dv0lkLIAzKNkgZQFCKw8g5lFsfc0IiIiIiIiIiIi31HU8jQiIiIiIiIiIlIGNo2uO3jwICZOnAiTyYSNGzf6ejg9Wrp0KbRaLeLj493bmpubkZGRgZiYGGRkZLg/WUJE8Ktf/QomkwmJiYk4fvy4+zm7du1CTEwMYmJi3OvJB1t1dTXS09NhNpthsViwZcsWv85jt9uRmpqKyZMnw2KxIC8vDwBw4cIFpKWlwWQyITs7Gx0dHQAAh8OB7OxsmEwmpKWlobKy0r2vDRs2wGQyYeLEifjggw98EQcA0NXVheTkZMyePRuAf2eJiopCQkICkpKSkJLS/VHn/nquAd2fMGm1WhEXF4dJkyahpKTEr/P4GueAwcc5QPl1k3OAMs81gHPA3WB9H3ys78qviazvyjzXAIXWdyFxOp0SHR0tFRUV4nA4JDExUU6ePOnrYd3WJ598Il9++aVYLBb3ttWrV8uGDRtERGTDhg2yZs0aERHZv3+//PjHPxaXyyUlJSWSmpoqIiJNTU1iMBikqalJmpubxWAwSHNz86Bnqa2tlS+//FJERK5cuSIxMTFy8uRJv83jcrnk6tWrIiLS0dEhqampUlJSIgsXLpTdu3eLiMiKFStk27ZtIiJSWFgoK1asEBGR3bt3S1ZWloiInDx5UhITE8Vut8v58+clOjpanE7noOcREdm8ebMsWrRIMjMzRUT8OsuECROkoaHBY5u/nmsiIosXL5bXXntNREQcDoe0tLT4dR5f4hzAOaA/cA5QdhbOAcrOM1BY31nf+wPru7KzsL4PfB42jUTk888/l5kzZ7p/Xr9+vaxfv96HI+rdhQsXPCaU2NhYqa2tFZHuIh0bGysiIsuXL5c333zT63FvvvmmLF++3L391sf5ypw5c+TDDz8MiDzXrl2T5ORkOXr0qIwZM0Y6OztFxPNcmzlzpnz++eciItLZ2SljxowRl8vldf7d/LjBVF1dLdOnT5dDhw5JZmamuFwuv80icvsJxV/PNZvNJlFRUeJyuTy2+2seX+McoIz3nXOAsuom5wDlnmucA/qO9V0Z7ynru7JqIuu7cs81pdZ3Lk8DcPHiRXz3u991/6zX63Hx4kUfjujuXLp0CZGRkQCAiIgIXLp0CUDPuZSYt7KyEmVlZUhLS/PrPF1dXUhKSoJWq0VGRgaMRiM0Gg3UarXX2G4et1qtxqhRo9DU1KSYPL/+9a/xpz/9CUOGdJeJpqYmv80CACqVCjNnzsSUKVOwfft2AP77b+fChQt44IEH8NOf/hTJycn42c9+hmvXrvltHl/z99chEN53zgHKq5ucA5R7rnEO6Dt/zxgI7ynru/JqIuu7cs81pdZ3No0CjEqlgkql8vUw7kpraysWLFiAgoICjBw50uN3/pZn6NCh+Oqrr1BTU4PS0lKcPn3a10P6Vvbt2wetVospU6b4eij95rPPPsPx48fx/vvvo7CwEP/5z388fu9P55rT6cTx48fx85//HGVlZQgLC/O6T4M/5aH+44/vO+cA5eEcoGycA+5P/viesr4rD+u7sim1vrNpBECn06G6utr9c01NDXQ6nQ9HdHfGjh2Luro6AEBdXR20Wi2AnnMpKW9nZycWLFiAxx9/HPPnzwfg33lu0Gg0SE9PR0lJCWw2G5xOp9fYbh630+nE5cuXMWbMGEXkOXLkCN577z1ERUUhJycHhw8fxqpVq/wyyw03jqvVajFv3jyUlpb67bmm1+uh1+uRlpYGALBarTh+/Ljf5vE1f38d/Pl95xygzLrJOUDZ5xrngL7z94z+/J6yviuzJrK+K/tcU2x9/9YL2wJIZ2enGAwGOX/+vPsmeeXl5b4eVo9uXe+cm5vrcWOs1atXi4jIvn37PG6MNXXqVBHpvjFWVFSUNDc3S3Nzs0RFRUlTU9Og53C5XPLkk0/KqlWrPLb7a576+nppaWkREZG2tjZ5+OGH5Z///KdYrVaPG8sVFhaKiMgrr7zicWO5hQsXiohIeXm5x43lDAaDz24sJyLyr3/9y32TPH/N0traKleuXHF//73vfU/ef/99vz3XREQefvhhOX36tIiI5OXlSW5url/n8SXOAZwD+gPnAOVm4Ryg/DwDhfWd9b0/sL4rNwvr++DkYdPouv3790tMTIxER0fLiy++6Ovh9CgnJ0ciIiJErVaLTqeTv/71r9LY2CjTp08Xk8kkM2bMcJ8QLpdLVq5cKdHR0RIfHy///e9/3fvZsWOHGI1GMRqN8vrrr/sky6effioAJCEhQSZPniyTJ0+W/fv3+22eEydOSFJSkiQkJIjFYpEXXnhBREQqKipk6tSpYjQaxWq1it1uFxGR9vZ2sVqtYjQaZerUqVJRUeHe14svvijR0dESGxsrBw4c8EmeG26eUPw1S0VFhSQmJkpiYqKYzWb3v3F/PddERMrKymTKlCmSkJAgc+fOlebmZr/O42ucAwYf5wBl180bOAco71wT4RxwN1jfBx/ru7Jr4g2s78o710SUWd9VIiLf/jolIiIiIiIiIiIKRLynEREREREREREReWHTiIiIiIiIiIiIvLBpREREREREREREXtg0IiIiIiIiIiIiL2waERERERERERGRFzaNiIiIiIiIiIjIC5tGRERERERERETkhU0jIiIiIiIiIiLywqYR3RdUKtUdv/79739/q31XVlZCpVJh3759/Tvo29i7dy927tw54MchIiIiIiIiUomI+HoQRAPt6NGj7u/b29sxffp0rFu3DpmZme7tZrMZI0eOvOt9OxwOlJWVIS4uDhqNpj+G2yOr1YrGxsZv3eAiIiIiIiIi6iu1rwdANBimTZvm/r61tRUAYDQaPbbfrKurC11dXRg+fPgd9x0UFNTjfoiIiIiIiIj8FZenEQF46qmnkJKSgqKiIlgsFgQHB+OLL75AXV0dli5diujoaISEhCA2Nhbr1q1DR0eH+7m3W54WFRWF3NxcvPzyy9Dr9QgPD0dOTg5sNluv46ipqUFWVha0Wi1CQkJgNBrx3HPPucf4j3/8A5988ol7SV1+fr77ucXFxUhJSUFwcDAiIiKwZs0adHZ2un+fn5+P73znOzhy5AgefPBBBAcHIykpCZ999ln/vIhEREREREQUUHilEdF1lZWVWLNmDZ5//nlERETAYDCgsbERo0ePxp///GeEh4fjzJkzyM/PR0NDA1599dVe97d3714kJiZi+/btqKmpwW9+8xs8++yz2LZtW4/PWbx4Mdrb27F9+3ZoNBqcP38ep0+fBgA899xzqKqqgs1mc+9Dr9e7j7Vo0SKsWLEC69evR0VFBdauXQuXy4VNmza599/W1oYnnngCa9euRWRkJDZv3oxZs2bh7NmziIiIuNeXkIiIiIiIiAIIm0ZE1zU1NeHjjz9GUlKSe5ter/doujz00EMICwvD0qVL8Ze//KXX5WvDhg1DUVER1Oruf2anTp3Cnj17em0alZaWYvfu3fjJT34CAHjkkUfcvzMajRg9ejRcLpfHcjgRwerVq7F48WKPfQcFBeHpp5/G2rVrMWbMGADd93N66aWX8NhjjwEA0tPTMX78eBQUFGDjxo19eJWIiIiIiIjofsHlaUTX6XQ6j4YR0N2QKSgogNlsRkhICIYNG4bHH38cDocDVVVVve4vPT3d3TACum+0XV9f77Fk7FZJSUlYu3Ytdu7cecf933DmzBlUVVUhKysLTqfT/TV9+nTY7XaUl5d7PH7evHnu70eMGIGMjAyUlpb26VhERERERER0/2DTiOi6sWPHem0rKChAbm4u5s2bh+LiYpSWlqKwsBAAYLfbe93frZ+kNnz4cIgIHA5Hj8956623kJKSgmeeeQYTJkxAUlISDh061OtxGhsbAQCPPvoohg0b5v4yGAwAgOrqavdjR4wYgZCQEI/na7Va1NXV9XoMIiIiIiIiuv9weRrRdSqVymvb3//+d1itVrz00kvubadOnRqwMeh0OuzcuRMulwulpaXIz8/HnDlzUFVV5V5idqvRo0cDALZv347k5GSv399oHgHdnxzX3t7u0Tiqr69HZGRkPychIiIiIiIif8crjYh60d7ejqCgII9tb7zxxoAfd8iQIZg2bRry8vLQ1taG//3vfwC6r1a69QqniRMnQqfTobKyEikpKV5ftzab3n33Xff3ra2t+Oijj5CamjrgmYiIiIiIiMi/8Eojol5kZGRg69atSEtLg9FoxBtvvIFz584NyLEuX76MH/3oR1i8eDFiY2PhcDiwefNmREREYNKkSQCAuLg4FBcXo6ioCHq9HuPGjcO4ceOwefNmPPnkk7hy5QpmzZqF4cOH4/z58ygqKsLbb7+N0NBQAEBISAh+//vfo7W1FePGjcOmTZvQ0dGBVatWDUgmIiIiIiIi8l9sGhH14vnnn0dDQwPWrVsHAJg/fz62bt3q/nSz/hQcHIyEhARs2bIF1dXVCA0NxbRp0/Dhhx+6l5OtXLkSZWVlWLp0KVpaWpCXl4f8/HxkZ2dj5MiRWL9+PV5//XUMHToU0dHRmD17tscnvIWGhuJvf/sbfvnLX+Lrr79GXFwcDhw4wOVpRERERERE5EUlIuLrQRDRwMvPz8crr7zivnE2ERERERERUW94TyMiIiIiIiIiIvLCphEREREREREREXnh8jQiIiIiIiIiIvLCK42IiIiIiIiIiMgLm0ZEREREREREROSFTSMiIiIiIiIiIvLCphEREREREREREXlh04iIiIiIiIiIiLywaURERERERERERF7YNCIiIiIiIiIiIi9sGhERERERERERkRc2jYiIiIiIiIiIyAubRkRERERERERE5OX/tcSHfMrtrv0AAAAASUVORK5CYII=\\n\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"source\": [\n        \"rows, cols = 2, 3\\n\",\n        \"fig = plt.figure(figsize=[cols*3,rows*2.3], dpi=120)\\n\",\n        \"\\n\",\n        \"t = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"styles = ['k-', 'k--', 'r-', 'g-', 'b-']\\n\",\n        \"ylims = [[30,85],[70,92],[80,100],[90,100],[90,100],[90,100]]\\n\",\n        \"\\n\",\n        \"for r in range(rows):\\n\",\n        \"    for c in range(cols):\\n\",\n        \"        ix = r*cols + c\\n\",\n        \"        ax = plt.subplot(rows,cols,ix+1)\\n\",\n        \"\\n\",\n        \"        results = regime_results[ix]\\n\",\n        \"        for j, result in enumerate(results):\\n\",\n        \"          model = result['checkpoints'][-1]\\n\",\n        \"          plt.plot(t, gaussian_filter(result['test_acc'], 1), styles[j], label=model.name)\\n\",\n        \"\\n\",\n        \"        plt.title('{} examples'.format(dataset_sizes[ix]-1000), fontsize=10)\\n\",\n        \"        if ix==0:\\n\",\n        \"          plt.ylabel(\\\"Test accuracy\\\", fontsize=9)\\n\",\n        \"          plt.xlabel(\\\"Train step\\\", fontsize=9)\\n\",\n        \"        if ix==0:\\n\",\n        \"          plt.legend(fontsize=8, ncol=2)\\n\",\n        \"        plt.ylim(*ylims[ix])\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'pooling_all.png')\\n\",\n        \"fig.savefig(project_dir + 'pooling_all.pdf')\"\n      ],\n      \"metadata\": {\n        \"id\": \"L4n_TyCVxfO9\",\n        \"outputId\": \"6a517928-5cd0-46d8-c3dc-841197a162b1\",\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 582\n        }\n      },\n      \"execution_count\": 10,\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 1080x552 with 6 Axes>\"\n            ],\n            \"image/png\": 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3Lm2++iZeXF1qtlsmTJ9v6xcIqs39etmxZscGNwiMIJ0+ezIABA/jxxx/ZsGEDr7zyCjNmzGDLli20aNHiltuilD8VFFEqXGBgID4+Puzfv7/Ia3v37rU7MWzevDmLFi3ixIkTdncB9+zZY3u9pHUWp27dumzatImOHTveMFpusVh4+OGHcXd3Z/Lkybz99tsMHTqUIUOG2Pb57rvvqFOnDqtXr7a7iHjttdeuW3dpnTlzpsgFy+nTpwG5KkJx6tatC8hId48ePW74HgEBAUyYMIEJEyYQFxdHy5Yt+d///qdOxhWlisnJyWHAgAGcPn2aTZs2FTuCQvXDZU/1w4qilMa5c+dwdHS0BUAbN26MXq9n//79DB8+3LZfXl4eBw8etNvWvHlztm7dSlpamt2qMlf3zyWpszh169bl0KFDdO/evciIuKtlZmYyZswYwsLC6NChA++88w6DBw+mTZs2tn2+++47unXrxuLFi+2OTUlJwdvb+7r1l4Y1gF7Y6dOncXZ2vuboF2v/7Ovre1P9c926dZkyZQpTpkwhIiKC5s2b895777F8+fJba7xSIVROEaVS3Hvvvaxdu9a2VBjA5s2bOX36NMOGDbNtGzRoEAaDgY8//ti2TQjBggULCAwMpEOHDiWuszjDhw/HbDbz5ptvFnnNZDKRkpJiez5nzhx27tzJZ599xptvvkmHDh144oknSEhIsO1jjVwXjlTv2bPHbunKsnTlyhV++OEH2/O0tDS+/PJLmjdvfs2he61ataJu3bq8++67ZGRkFHk9Pj4ekHM9U1NT7V7z9fWlRo0axQ7DVBSl8pjNZu677z527drFqlWraN++/TX3Vf1w2VL9sKIo12P9/7mwQ4cO8dNPP9GrVy9bjg0PDw969OjB8uXLSU9Pt+27bNkyMjIy7PrSoUOHYjab+eyzz2zbcnNzWbJkCW3btrWN4ChJncUZPnw4UVFRLFy4sMhr2dnZZGZm2p6/+OKLXLp0iaVLlzJnzhxq1arF6NGj7foqnU5XZDTHqlWrih31UhZ27dpll6vk8uXLrFmzhl69el1ztEnv3r1xd3fn7bfftpt+aWX9e2ZlZdmmK1nVrVsXNzc31T/fRtRIEaVMffTRR6SkpNgSwf38889ERkYC8NRTT9nmAE6bNo1Vq1bRrVs3nn76aTIyMpg9ezZNmjRhzJgxtvqCgoKYPHkys2fPJj8/nzZt2vDjjz+yfft2vvrqK7uO7GbrLE6XLl0YP348M2bM4ODBg/Tq1QuDwUBERASrVq1i7ty5DB06lBMnTvDKK6/w8MMPM2DAAAC++OILmjdvzoQJE2zrvN99992sXr2awYMH079/f86fP8+CBQsICwsr9sT3VtWvX59HHnmEffv24efnx+eff05sbCxLliy55jFarZZFixbRt29fwsPDGTNmDIGBgURFRbF161bc3d35+eefSU9PJygoiKFDh9KsWTNcXV3ZtGkT+/bt47333ivzz6IoSulNmTKFn376iQEDBpCUlFTkDtWDDz5o+1n1w2VL9cOKolzPfffdh5OTEx06dMDX15fjx4/z2Wef4ezszMyZM+32/d///keHDh3o0qULjz32GJGRkbz33nv06tWLPn362PZr27Ytw4YN46WXXiIuLo7Q0FCWLl3KhQsXiozCuNk6izNq1ChWrlzJ448/ztatW+nYsSNms5mTJ0+ycuVKNmzYQOvWrdmyZQsff/wxr732Gi1btgRgyZIldO3alVdeeYV33nkHkP3z9OnTGTNmDB06dODIkSN89dVX5ZYjqXHjxvTu3ZtJkyZhNBptQf433njjmse4u7vzySefMGrUKFq2bMmIESPw8fHh0qVL/PLLL3Ts2JGPPvqI06dP0717d4YPH05YWBh6vZ4ffviB2NhYRowYUS6fRykHlbLmjfKvZV2WsLhSeD1vIYQ4evSobc1zT09P8cADD4iYmJgidZrNZvH222+LmjVrCgcHBxEeHi6WL19e7PvfbJ3X8tlnn4lWrVoJJycn4ebmJpo0aSJeeOEFceXKFWEymUSbNm1EUFCQ3bKNQggxd+5cu/XfLRaLrc1Go1G0aNFCrF27VowePdpuqTDrUpBXL+NlXYJs1apVdtuty4UVXtO+Zs2aon///mLDhg2iadOmwmg0ioYNGxY59uqlIK3+/vtvMWTIEFG9enVhNBpFzZo1xfDhw8XmzZuFEELk5uaK559/XjRr1ky4ubkJFxcX0axZM/Hxxx/f9O9VUZSK0aVLl2v2wcV95at+WPXDiqJUjLlz54o77rhDeHl5Cb1eLwICAsSDDz4oIiIiit1/+/btokOHDsLR0VH4+PiIiRMnirS0tCL7ZWdni+eee074+/sLo9Eo2rRpI9avX39LdRYnLy9PzJo1S4SHhwuj0SiqVasmWrVqJd544w2Rmpoq0tLSRM2aNUXLli3tljEXQohnnnlGaLVasWvXLiGEXJJ3ypQpIiAgQDg5OYmOHTuKXbt2iS5duoguXbrYjitJPyyEEK+99poARHx8vG0bICZOnCiWL18u6tWrZ/s+uLofvnpJ3sJt6N27t/Dw8BCOjo6ibt264uGHH7Yt8ZuQkCAmTpwoGjZsKFxcXISHh4do27atWLly5U39XpWqQSNEGWSiURSl0tSqVYvGjRuzdu3aym6KoijKf5LqhxVFUaomjUbDxIkT+eijjyq7KUoVpnKKKIqiKIqiKEo5Gzt2LL6+vjRu3Ni2LSkpiZ49e1KvXj169uxpW+pZCMGkSZMIDQ2ladOmdvkQCvvrr79o0qQJoaGhTJo0qUxW3VAURfmvUUERRVEURVEURSlnDz/8MOvXr7fbNnPmTLp3705ERATdu3e35Zb49ddfiYiIICIigs8++4wnnnii2DqfeOIJFi5caNv36voVRVGUG1NBEUVRFEVRFEUpZ507d8bLy8tu25o1axg9ejQAo0eP5scff7Rtf+ihh9BoNLRr146UlBSio6Ptjo2OjiYtLY127dqh0Wh46KGHbMcriqIoN+9ft/pMx44d2bFjR2U3Q1EqzIULFyq7CYpiR/XDyn+N6oeV0oqNjSUgIAAAf39/YmNjAYiKirItpwpyFaioqCjbvtZ9goKCiuxztfnz5zN//nxALtd69uzZcvksilIVqSllys34140Usc7FVBRFUSqH6ocVRVFKTqPRoNFoyrzeiRMncvz4cY4fP47RaCzz+hVFUW53/7qgiKIoiqIoiqLcDvz8/GzTYqKjo/H19QUgMDCQy5cv2/aLjIwkMDDQ7tjAwEAiIyOvu4+iKIpyYyoooiiKoiiKoiiVYODAgSxduhSApUuXMmjQINv2L7/8EiEEu3fvxsPDw27qDEBAQADu7u7s3r0bIQRffvml7XhFURTl5v3rcoooiqIoilK1xMbCxYvg6FhQnJzAywt0uspunaJUjJEjR7Jt2zYSEhIICgrijTfeYOrUqQwfPpzFixdTs2ZNVq5cCUC/fv1Yt24doaGhODs7s2TJEls9zZs35+DBgwB8/PHHPPzww2RnZ9O3b1/69u1bGR9NURTltqYR/7LsM2FhYRw/fryym6EoivKfpfphJToafv9dlm3b4OTJ4vfz9ISuXaFbN7jrLggPh3JIqaAoyj9U/6woilKUGimiKIqiKMotEQKOHYMff4QffoADB8DDA+68Ex59VAY+GjaE3FzIyZElKwsOH4atW2HuXHj6afDxgd69oX9/+VitWmV/MkVRFEVR/u1UUERRFEVRlBJLS4M//4QtW2Qw5OxZaNwY7rkHFiyAli2LTo1xcbF/3rgx3H+//PniRdi8GX79FR57TAZNOnaUAZJu3aBFC9CrsxZFURRFUcqYOr1QFEVRFOWGhJBTYX75RU6LOXBABinat4fHH5fBkNDQ0tdfsyaMHStLXp4MuPzyC3zxBbz4Iri6yiBJ587yPevVgxo1QKtSxiuKoiiKcgtUThFFURSlTKl++N9n/3544QUZDOncWU6H6doV2raVSVPLW1ycDJL88Ydsw+HDYLGA0Qh16kDduvKxVi37oqbfKIo91T8riqIUpUaKKIqiKIpSrDNn4OWXYeVKGDxY5g1p2LDi2+HrC0OGyAIyN8mFC3LKjrWcPy9Hspw/D+npcj8fH5m8tXCpXRsCAtRUHEVRFEVRJHVKoCiKoihKEZ98ApMmydEgO3ZAhw6V3aICRiM0aCDL1YSAlBQZHDl5Eo4elcGc9evh3Dn5uk4nAyPBwbL4+cnAi7UEBMjpOV5eFf7RFEVRFEWpYCoooiiKoiiKnYwMmDYN3nxT5vO4nZbJ1WjktJlq1WSy18Kys+HyZfsSGSkf//pLTtOJi5NJZAGqV4f69WWAJCwMmjWD5s3B37/CP5aiKIqiKOVEBUUURVEURbGzZAkYDHKZ3NspIHIjTk4yyFG//vX3S0uTU4dOny4oK1bAK69Afr4cTdKsGTRqVBA0qVcPQkKKrrijKIqiKErVpoIiiqIoiqLYmM3wwQcwYYIMIvwXubvLUSZXjzTJy5NTcg4dgoMH4dSpgmk5JpMMJHl5gadnQXF3l9N9HBxkMRplclo3N/mau7v8uXp1OW3H318+/zcFoxRFURSlKlNBEUVRFEVRbH78EaKiZFBEsefgAE2byjJqVMF2kwkuXpQJX5OSIDlZ5jVJSYHUVBlMyc2VCWDz8iArS/5sLWlpcl8rJycZHKlRQ5aAAPuf/f3lo5eXCp4oiqIoyq1SQRFFURRFUWzeew8eekhOEVFujl4vlwWuW7f0deTny3wm0dEQEyMfo6PhyhWZNHbHDvlzbKxcjhjkyBRfX5k/pfDolMLFw0M+envL1Xh8fWUwRU3zURRFURSpSgVF3n//fRYtWoRGo6FJkyYsWbKE6OhoRowYQWJiIq1atWLZsmU4ODhUdlMVRVFuO3PnzmXhwoUIIRg3bhyTJ08mKSmJ++67jwsXLlCrVi1WrlxJtWrVihy7dOlS3nrrLQD+7//+j9GjR1d085UKsGuXLIsXV3ZLri3PnMf55PNoNBq0Gi0a5KNeq8eoN2LUGTHqjTjoHNBqtJXd3JtmMEBgoCzXYzZDYmJB8CQ2Vo5GsY5MSUmRI32OHy94bh25IoSsQ6OR03U8PORUHWtxcZEBHp2uoGi1BaNRrI9arSzWffR6ObrFOhXIOjXIGojx8VFTghRFUZSqSyOE9SuyckVFRdGpUyeOHz+Ok5MTw4cPp1+/fqxbt44hQ4YwYsQIHn/8cZo1a8YTTzxxzXrCwsI4fvx4BbZcURSl6jt69CgjRoxg7969ODg40KdPHxYsWMBnn32Gl5cXU6dOZebMmSQnJzNr1iy7Y5OSkmjdujX79+9Ho9HQqlUr/vrrr2KDJ6D64dvZsGFyascvv1R2SwqYLCYORB9gy/ktbL2wlT8v/UlWftZNHeth9CDALQB/V38CXAMIcA2glmct6lSrQ12vutTyrIWj3rGcP0HVYDLJYEp8vP0qO9YpPBkZspjN9sU6KsV6tiiE3GaxFOxjMsl/N2npgpRUM2npgox0DbnZBffedAYTTh4ZODjlYnDKweCUg94pB51DHhqtBa3WAhohH7XyzTRYozjY9tFohfxZb8bBORsHl2yMLlk4uOTg6J5BtYBkXDxy0Gm16LQ6HPWOuDq44ubghpvRDVcHV6o5VsPLyYvqztXxcvLC2eBckX+KSqX6Z0VRlKKq1EgRk8lEdnY2BoOBrKwsAgIC2LJlC19//TUAo0eP5vXXX79uUERRFEUp6sSJE7Rt2xZnZ3ny36VLF1avXs2aNWvYtm0bIPvYrl27FgmKbNiwgZ49e+Ll5QVAz549Wb9+PSNHjqzQz6CUr/PnYfVq2LixsltSYPvF7Qz+djCJ2Yk09m1Mt1rdeLzV47Sq0QqdRodAYBEWLMKCyWIi15RLnjmPXHMuuaZcErMTicmIITo9muiMaE4knODXM79yLvkcueZcNGio4VaDYI9ggt2DCXIPsj0WDqa4OLhU9q/ilun14OcnS0nlmHKIzYiVv8uMaKLTo7mSfoXoDPlo/Tk+Mx7xTyDDqDPio/PDKTcEx7wQDNk10Gb7Ysl1wZLrjCXHBXOuM5Y8IxaLFpNFi/inIApG+AihAaFBCA3CXLCPxawnP9uJ/Exn8jNdyMtyxpIvRxLrnbJw9o3B2TcaB+8otF7nMHv+Tb77KbJdT5KWn2hrJ4Cj3pGNozbSKaTTrf2SFUVRlNtSlQmKBAYG8txzzxESEoKTkxO9evWiVatWeHp6otfLZgYFBREVFVXk2Pnz5zN//nwAkpOTK7TdiqIot4PGjRvz8ssvk5iYiJOTE+vWraN169bExsYSEBAAgL+/P7GxsUWOjYqKIjg42Pa8uL5Y9cO3vw8+kAlEu3Wr7JZIe6P20v/r/oxsPJI373oTX5eyS3JiERai06M5m3yW88nnuZx2mci0SE4lnmLTuU1EpUeRlJ1k29/NwQ0fFx88HT0LitHT/vk/xcfFBx9nH3xdfHF1cEVTAXNGzBYzSdlJxGXGEZ8VT3J2Mul56aTlppGem05GXga55lzMFjNmYcZsMWOymOTP/zw3CzP55nzS89JJz023PSbnJJOSk2L3uwhwC6CGWw1quNUg3CecHnV6UMOtBv6u/rZAkrvRvUI+e2FpaXIloHPnnDl3rg5nz9bh3Dk4uxsiL8oRLVoteFcXuLlbcHbLx8ktFwfnLLR9nSCkQpurKIqiVBFVJiiSnJzMmjVrOH/+PJ6engwbNoz169ff1LETJ05k4sSJgBwWeFuwWOQ4VXf3ym6Joij/AY0aNeLFF1+kV69euLi40Lx5c3RXZVrUaDSlvoi5LfthxSY5WeYRWbCgauR9OBx7mD7L+3Bv2L18cvcnZZ4bRKvREugeSKB7IJ1rdi52n1xTLrGZsbZRJglZCaTkpNhKck4ypxJP2Z6n5qaSlJ1kN7XHqDPi7eyNu9EdN6ObbQqHk94JnVaHTvNP0eqKfEYh5CiYwoELk8VERl6GXcAjNTeVxCz7kQ8eRg+793NzcMOoN9reS6/V2362PjpoHHB3cKemR027Yz0dPQlwDbAFPKryqBl3d2jeXJarmUwQGSmDJvHxGlJSdKSm6khNdSQlxQM/j4puraIoilJVVJmgyKZNm6hduzY+Pj4ADBkyhB07dpCSkoLJZEKv1xMZGUngjTKQ3Q4uXoT774edO6FBA+jUSZaOHSE0tGqckSqK8q/zyCOP8MgjjwAwbdo0goKC8PPzIzo6moCAAKKjo/EtZsmRwMBA2xQbgMjISLp27VpBrVYqwtdfywvK4cMruyVwMuEkPb7sQa+6vVg0YFGlJUs16o2EeIQQ4lGy4QNZ+VnEZ8YTnxVPXGYcCVkJdiMv0vPSyc7PtgU7cs25mE1mikvxptVobYELo86Ii8GFQLdAu6CFh9EDHxc5MsXH2YfqztXRa6vM6V2VoddDrVqyKIqiKEphVeZbMyQkhN27d5OVlYWTkxObN2+mdevWdOvWje+++44RI0awdOlSBg0aVNlNvTXffQfjxkGTJrBpExw+LNfZmzpVppAPDoa774YBA+QYZsf/RgI4RVHKX1xcHL6+vly6dInVq1eze/duzp8/z9KlS5k6deo1+9jevXszbdo027SY3377jRkzZlR085VytHkz9O0Llb2427nkc3T/sjvtg9uzbPAydNrbb91YZ4MzNT1rUtOzZmU3RVEURVGUm1Bl1qpr27YtQ4cOpWXLljRp0gSLxcJjjz3GrFmzmDNnDqGhoSQmJtruct52srJg/Hi47z6YPBm2bIHu3eGZZ2SgJDoaTp+GF16QYzvvuUeul3fPPTB3LuzdC3l5lfwhFEW5nd17772EhYUxYMAA5s+fj6enJ1OnTmXjxo3Uq1ePTZs2MXXqVAD279/Po48+CoCXlxevvPIKbdq0oU2bNrz66qu2pKvK7c9igd9/r/xcIpl5mfRa1otwn3C+HfotBp2hchukKIqiKMp/QpVZkresVMmlxmJiZAAkPR2++gruvPPGx6SnyyUA1q6FP/+EiAg5aqRVK2jXDlq0kJNmGzSQY0KV20dengySZWZCdnbBuobWNQ6LW/+wuGJdB9H6aC2Ft1ssoNPJotcXPBoM8pawwWBfittm3V+vV1O7lJtSJfth5ZoOHpRfKZGRUJkzVJ//7XlWHFvB8QnHcTO6VV5Dykt2tlwTNylJfgdY+2Zrv+ztDb6+qp/9j5o7dy4LFy5ECMG4ceOYPHky9913H6dOnQIgJSUFT09PDh48WOTYWrVq4ebmhk6nQ6/Xs3///mu+j+qfFUVRilJX0xXhuefkCc/Bg3Cdu6vZ+dkcij3EsbhjHIs/xvGs45xsehKHFg4EOLanRo6BGvG51Lj0EyEffkbNyHRqZjlQrVYzMhq0ISOwARl+dcmoXpOMasFk6tzJztGQnY2t5OXZX0NbLLJpRiM4GswYycWoycPRKDA6CPloBAeDQKCxXY8DaBAYdBb0OoFeJ392r6bDJ9ABl+qOaJwcK/fkTgh54pmcDKmpMgiRkVHwmJMjfyGFS36+fVDBbJap6gsHFXQ6+bm0Wlk0GrlfVhZ2v+zUVPne1pKSIt/bZCr7z1r4xLpwO7Xags9R+A+fn1+699Fo7E/kDQZwdgYnJ/no7AwuLuDmBq6u8tHNTQb0jEYZdLEGXrRXDVSzWGS7Cpe8PPl3sv5Oc3IgN7foPhZL0aBRWdJo7ItWWzR4ZDTKz+noKH8fTk5y29X7Wf/9FC6DBsE/+ZQUpUJkZ8v+KCuLbSsdqRfiTmDcCcivJr+n3NxK138LIYP6MTFw6ZJ9SUyU/88W7oe0WjAaOeiZw/thf/L9lTtxe+xJ+z6scJ9zdd9TuLi4FBRXV/lo/X/QGtw1GOT/m1f3PzcrLw8SEiAuDuLjC/r2lBTZ51t/vvp5YqLsv27E2Rnq1IG6deVjeDg0ayYfnZxK12alyjt69CgLFy5k7969ODg40KdPH+6++26+/fZb2z5TpkzBw+Pa2WC3bt2Kt7d3RTRXURTlX0cFRcqZ+P0PPvvKlf/z3E9uLYPddZCDg0xs5+4OwiGFkxn7yBZJODt44+E0AE/H+wlw8iA/T0N0hokzmRaysjXkZGvJy3LAkuMCue7wtyP8XfS99bpMHHTZOGhzcNTl4ajPw6g14WAxYRAmjJZ8DGYTZpOWXLOeXOFALo7k4EiODI+QiyN5whELV8/rtnC92VdaXTYOxss4GuNxMibhYkzC3TEZD6c0PB0zcdLocNbocdYacNI64KQx4CgMOKLHKPRotVoctGZcDLm46nNxNeTiasjBTZ+DmzYTd30WjppcNBYZjDBnZZKZm05GbjrZuRlY0lKxpKVhMeVj0YDBAp45UC0bDE7/nDQ7Ock/gvVivfDICGuAQastGMlROFhivfC2juzQ6QoCA9YLYn9/aNQIqv1zkeHpWXCibj2Jd3QseB/ryb/1HwjYP15dCgdqSnrxYh1pYg0EFQ4wFH5uvXCxPhYOsJjNclt2tgwIWUtmprwoysiQFwyXLslARl5ewWNeni1wIRDkaQXZesgz6sl30Mli0GMy6LA4GhHWUs0B4WAEvYvtIkfodQitBqHRIDQgAKGBfI0gHzN5wiwfMZODiWyRLx/JJxdToX0s5GPGjEXW8c9aDnIw3T9FFHq05ICl0AgfsxlMZjCbIM8MWSb5urnQCCCLudAIoH/+I+C5sBrU9ulXsr+hopREdjZs3y5HIG7cCIcO2V7ayo90JRZaji/YX6eTfZeHR0GQ01qswVbrv/v8fBkoiI2VwQLrxb9OB0FBMldWSAjUrm3fz+r1YLFgzs3mMf1SBuTVYpA5FDT/1Jmba9/fFO6L8/Pt+xzrY0bGzQWeHR0L+mNr8MQa2DQa5We8un5rgP3q35GnZ0Hx8JCPAQEFP3t4yOmwXl4Fjy4uRfvT2FjrerJw9iycOAGrVskhPFqtHBnatKl8rF8f6tWTpVq1W//38V9kNsuAVVKSDFqFhVXaioAnTpygbdu2ODs7A9ClSxdWr17NCy+8AMjvoZUrV7Jly5ZKaZ+iKMq/nQqKlKOc9HyeHBzPl9qPeOslPQ0a2N/IzsuD5FQT3//9G5tP7qexTwda+w3CQetoN5PCaLS/Ee/kJL+39Y7ZZOtiSCeKdHGFPH0SOdpEckxXyMq4RGpmLCm5aaTkp5NiziTBkkWGpuR5SYxaB9x17ng7euLj6Im3sRrexmq46lxw0DqhF07ocUQrjGSl6UlOlCUt2ZHUFCPpqU5kpLkQmeFLRIIHednuWIQMq5htN/Q1gACN+OeZQGN2QOS5IvJcwVxMwlmNCY0xHaHLBX0u6PJk0ebb6kH/z6MO0GvBVYMGPTqNHoMZHIQFJ8BJq8XVUYu3j5mAQAvBwRpqhxgIrWWkTm0dbs4OGHVGHHQOGHQGNJR+6dLrEUJgFmbyzfnkW/Jtj7mmXHLyc8g2ZZNjyiE7P5t8Sz4mi8muaDXaIsssXr0Eo0ajwWQxYbbIlQ+sP5ssJrs6LcKC0AqEUSAcZIhACPloERbbcpF55jzyLfny0ZxPrtlMVj5k5WvJNjmQle9EVr4gMy+frHwLmfm5ZOVnkZWfRXZ+Nln5WXZLSZaK5dov6TQ6DDoDBq0BJ4MTTnonHPWOOBmcMOqMGHQG+XfVOmLQGWy/I+vfuLz+1oWZGtQr1/qV/65j+3/l088nkBp3CYRA4+2DZkggjhMH0sKvOa392vB7r3588l42DB0uL/yTkuRjYiKkpRUEOTMy5M9C2I/ksE798POT0z/8/GRQOCBA7nMDn+z9iBObM/l+wj7wCL71D52XJwMZmZkysGIN6lqLNdBRuOTmFpScHPnlWziA7ewsgxu+vnJUl6+vDHiUdsRJcXx8oHHjotsTE2VS9kOH5OOWLXLt5Lg4+bqHR9HgjLOz/ahB69/qarfzVJ3iRgQWDpxZf87JKfi3Wzhgn5JiX8fWrVBJq2o1btyYl19+mcTERJycnFi3bh2tW7e2vb59+3b8/PyoV6/47wqNRkOvXr3QaDSMHz+exx57zO71+fPnM3/+fABbwmxFURSlgMopUk4uX4ahnWK4fFnw/c9G2vcvOm3mVMIpHlj9ABdTL7JowCIGNSz/lXUswkJWfhYZeRm2YhH2V5QaNLZl/tyN7hj1xnJtU64pl9TcVNJz00nLTbOVbFO2LSiQk2siIxNM2Y7kZzthynEiP8uJ/GwjWoszOosTWosjGrMjGosDWo0G0NouaE1mMznmLHLMWWSbZEnLziIlI5uUjDzSM/NIz7SQnexObrIP5pQAyAgAoQMs4B4J1c6B11nwPA8el/4pl9F4RKHVm0p14Vw4yFBSeq0eg9aAXquXAQ+tDouw2IIdVz8Wp3DA5Oqi1WiLBAY0aOT2Qj9bA0UysGDAqDfibHDG2eCMk14GIVwcXHAxuOBscMbFwcW2zUnvZNvXUe9oq8uglfXptLoi7Shuec7ighgGrQGDzlBpy3n+l1WVfvi/bG/kHmYsH8+PuYfomOpBvTqtEX5+CIO8F5Kem85f0X9x6YQ3fPYX7d4fwqiOPZjQZkKFtjMqLYpG8xvx1l1vMantpAp979teWprMN3bpkv00HutUzatHolx9uvdvOP27+nu38BRP6+hLo7FgKqd11JN1BGfhETyVPD1p8eLFfPzxx7i4uBAeHo7RaOSDDz4A4IknniA0NJQpU6YUe2xUVBSBgYHExcXRs2dPPvzwQzp37lzsvqp/VhRFKUqNFCkHf/wBw+41Uy/5PH9NP05A/6Ir5vx86mfu++4+utTqwtr71+Lv6l8hbdNqtLg6uOLq4Foh73czjHojvnpffF18y/mdrj0X92pmi5nkrHROn8/g3HnB+XNaLl5owKUL4URdMhJ3wkhirANCaEAjcPXMx9UjHzePfFw9TLh55uPkbMboaMboaMHoZMZotODkasLZxYyTS8Gjo5PAyVnI150FjkYNRgc9Rr3BFiBw1DvaipPBCQedQ4kv9i3CgsliQghREPS4ne8SllLhWU+Fc95aR8oXTgWTlCSvLzIy7PfLzra/EWlNaQJFZzhdncfWwaFghL51tP6ECXKGgaLcqr1Re5n26xQ2R/1J/zNa/mwxmY6vvHfNEQ2vvp3K53XS6RhWh0m/TsLVwZWHmj1UYe19ev3T1K9en4ltJlbYe/5ruLvL5OutWlV2S5Qy8Mgjj9hWWJw2bRpBQUEAmEwmVq9ezV9//XXNYwP/yZDs6+vL4MGD2bt37zWDIoqiKEpRKihSxpKToXdveDhkG3MDXsRh6u4i+xyNO8rI70fyTLtneOuut/6TF6ZVnU6rw9vVE+8mnnRoUvw+eXlw5QpcvKghNtaB5GQH26jzpCR5IZ2dBImFUm5kZBSMRs/Kun4bCqcN0evt055YL6ytOT2tj1cvImNNVyIv0rVoNA6FnhfkDL364t1gKFq3o6N9KgDrYjRXpyLJzpaf0fo5r/Xz1flSTSb7nIrF5bfV6wvSodxo8R1roKJwDtbibpZeTauVo88Lp4JxcysYPR8QIH8fxaWCKW6BoKvTtVhTq6SlFYzUv9G/BUW5Gem56fRb0oMOETkcvBxGs4+/h4YNr3vMgZ0eDOgF7/Z6lyD3IMavHU9j38a0DGhZ7u39+dTP/HDyB/aN24dOe+NpNorybxYXF4evry+XLl1i9erV7N4tzx83bdpEw4YNbUGSq2VmZmKxWHBzcyMzM5PffvuNV199tSKbriiKcttTQZEytnYtOOrzmXe6L4Y/txZZLjcpO4lBKwbRO7Q3b971pgqI3MYcHKBWLVlKw2QqWJXXOvogK6sgt+DVF/zFXVgXXpQlO7toztTs7KKBgasXaimcK7Hwsdb6rXXn5NgHHgq7emEHawJhd3cZUHB3l1Pw69aVP7u6FgRZCuddLDzy4upFc6yPV+eYLRw4Krz4TuHFgQqP3CgccDEYCtIGWHPvuriUbZoARako8357E2NKBt/6TsJp0Wz5D/w6TCY5snHRIvn86bZPs+/KPgZ/O5i/HvsLb+fyW8liX9Q+Rv0wiintp1RIAEZRqrp7772XxMREDAYD8+fPx9PTE4AVK1YwcuRIu32vXLnCo48+yrp164iNjWXw4MGAHFVy//3306dPn4puvqIoym1N5RQpY4PvEbj/8TNLB3wPS5favWaymOj3VT+iM6LZ9ciuKjWFRVFKwjoKwmKR110qtqcUVtn98H9RSk4Ktd/25a2TNZi44txNRfb27YM77pCLnvj+M3sxKz+LDos7UN25Ohse3IBeW/b3TvZG7aXXsl4MbjSYRQMWqVEiilKBVP+sKIpSlLofWoYyM2H9OjNDMpfDzJlFXn9x44v8Ff0Xa0asUQER5bam0RRM6VEBEUWpfO9//RRuGfk8OuXrmx7qtHUrhIcXBEQAnA3O/HDfDxyMOchLm14q83buidxDz2U9GdJoCIsHLlYBEUVRFEVRKp0KipSh9b+Y0Zry6PVEXZl4oJBlh5Yxd89cvh36LXWq1amkFiqKoij/NkmpMbx/7mteEZ0xtu1w08ddawXS2tVq88293zBn9xxWHVtVZu3cHbmbXst7MSxsGIsGLlIrQymKoiiKUiWoM5IytPqDi/TTbsDp5Wfttp9MOMljax9jds/Z9KjTo5JapyiKovwbvfvhSLyz4OGXvr3pY/LzYft26Nat+Nd71e3Fy3e+zOQNk8nKv/VMwDsv76TXsl4MDxvOZwM+UwERRVEURVGqDHVWUkZyM/JZu8eHIf1zwcfH7rXXtr1G28C2TG43uXIapyiKovwrxZ8+yLzsbbxa8yEMvje/tPtff8kpn126XHufFzq+gMli4qO9H91SG9dFrKPHlz14oMkDfDrgUxUQURRFURSlSlFnJmVkyytbybE40H9uL7vtR+OOsurYKqZ3m65WmlEURVHK1KwP7yMwz5EHJ35aouO2boWmTcH7OgvMuDq48krnV5jx5wySs5NL1b5lh5YxaMUgnuvwHB/3/1gFRBRFURRFqXLU2UlZyM9n9aIketS9gHstL7uX3vj9De6qfReda3aupMYpiqIot6ucHPj+exgyBFq2lD9b14yLXvct8z1O81qnl9HrHUpU77XyiVztsVaP4enoyeyds0vc9vd3vc/Dax5mTq856saAoiiKoihVlgqKlAHz50v5MbMHQyYF2m0/HHuY745/xxtd36iklimKoijlLTsbli+H55+XQYyysH07PPoo+PvDqFHg6AidOsHIkTKYceAAfLjqeWoLD+4bULJVYvLyYMeOa+cTKcxB58Cb3d7kg90fEJ0efVP1CyF4adNLvLDpBZYPXs5TbZ8qUfsURVEURVEqkgqK3KrcXP589TeS8GLgSPtldl/f9jo96/SkY0jHSmqcoiiKUl6OHoWnn4bAQJgwAb74Ah54AMzmW6v3xx9lro/ISPjwQ4iNha+/hnnz4Ngx8PKC1q0Fn+56nbtDnijxsrYbN8rASOebHMA4svFIQr1CefOPN29q/zf/eJN5e+exduRaRjYZWaK2KYqiKIqiVDQVFLlVn3/O6pS76NzRbJdf9WDMQX44+YMaJaIoSpXx/vvvEx4eTuPGjRk5ciQ5OTnceeedNG/enObNm1OjRg3uueeeYo/V6XS2/QYOHFixDa9isrLgrrugSRPYswfefReuXIFt2+S0lAkTCqa4lFRiIjz+OEydCuvXy1Eibm4Fr9erBz/8AN/fPYuklFZ8Of11Ll4s2XvMmiWDN15eN94XQKfVMaP7DBYeWMiZpDPX3ffHkz/yxu9vsHzwcnqH9i5ZwxRFURRFUSqBvrIbcLsTn37GauM2XhhusNv++rbX6RPah/bB7SupZYqiKAWioqKYN28ex48fx8nJieHDh7NixQq2b99u2+fee+9l0KBBxR7v5OTEwYMHK6i1VdvcuXD8OBw6JJOVWoWHw9q10KMH+PrCmzc3sMLOpElQvTq89tp1dkpOxhT5Gq6PvUPYjgTuugv++EOOWLmRHTvk1JwFC0rWrn71+tEuqB2vbH2Fb+79pth9jsYdZdQPo3i186sMbjS4ZG+gKIqiKIpSSdRIkVthMrH/mBOR6R4Uvrn615W/WHNqjRoloihKlWIymcjOzsZkMpGVlUWNGjVsr6WlpbFly5ZrjhRRpMREmDlTBi0KB0SsOnSA776T+8ybV7K6f/wRvv1WTsMxGq+z4xdfsKmenq4N2vPTGi0BAdC9u5xmcyMzZ8I990BYWMnaptFomNF9BiuOrmD1idWIq4bCJGUnMWjFIHrW6ckrXV4pWeWKoiiKoiiVSAVFbsXZs6w2DeCOZrkEBxdsfv331+lXrx93BN5ReW1TFEUpJDAwkOeee46QkBACAgLw8PCgV6+CJcR//PFHunfvjru7e7HH5+Tk0Lp1a9q1a8ePP/5Y5PX58+cTFhZGWFgYycmlW771djBjhhwF8uij196nXz/4/HN45hn46qt/NprNcOGCnF8TE1PkGOu0mRdegDZtrtMAiwXmz2dzmCM96vbC1RV++UVOsenZU9ZzLUeOyJEsU6fezCctqlNIJ55r/xzDVw2n1Wet+O74d5gtZkwWE/d9dx/OBmeW3rNULburKIqiKMptRZ253Ipjx9ig7cuAewuWQoxMi2Tt6bVM6zStEhumKIpiLzk5mTVr1nD+/HmuXLlCZmYmy5cvt73+zTffMHLktZNiXrx4kf379/P1118zefJkzp49a/f6xIkTOX78OMePH6datWrl9jkq06VLMvHp//4HBkMxOwghAx7btjEqcwHvdfqB0aPMfOn/Ajg5Qe3aMhlJeDisW2d36FNP3cS0GYANG7iQepGzJNG9TncAPDxgwwbQaKB3b0hNLf7QmTPlijNt25b8s1vN7jWbM5PO0CG4Aw+ufpCwj8MYtmoYB6IPsGbEGtyMbjeuRFEURVEUpQpRQZFbYDlyjJM0pFlzjW3b2tNrqeFWQ+USURSlStm0aRO1a9fGx8cHg8HAkCFD2LlzJwAJCQns3buX/v37X/P4wH8SVtSpU4euXbvy999/V0i7q5LXXpPJVYcOveqF06dh4kQZ1bDOZXn3XSa7Leb9LmsYEzeTTx//GyIiZJbWceNgwACYNg1MJn74AVauvIlpMwAffcTmoS3xc/Ej3CfcttnLS64qk5kpV665csX+sHPnYMUKeKlkq/cWq5ZnLT7q9xEXJl9gcMPB7L+yn5VDV1KnWp1br1xRFEVRFKWCqaDILbi8P5ZsiyMNGxZs++nUT9xd7241fFhRlColJCSE3bt3k5WVhRCCzZs306hRIwC+++477r77bhwdHYs9Njk5mdzcXEAGUHbs2EFYSZNS3OaOHoUvv5SjLbRa5KiQrVth4EBo2BD27YP33oPDh2Vk4swZWLuWp7YOYeEiLRPmh/P+z6FyxMjMmbBmDVEfr+H52t8x6kHLjafNgKzz11/Z3NyD7nW6o9Fo7F729ZVJVJ2doV07uXyv1bvvQosWMglsWfF39Wdmj5lcfuaybdSKoiiKoijK7UZdud+Ck0dNGHRmateWzzPzMtlyfgsDGgyo3IYpiqJcpW3btgwdOpSWLVvSpEkTLBYLjz32GAArVqwoMnVm//79PPpP4owTJ07QunVrmjVrRrdu3Zg6dep/LigybZocANKjB5CeLqMOPXqAg4OMROzZA2PGyKEkVwWXxo6F5cvh+efl1Jvjx2HM93dTO+so6xLu4EPdM7x5z183bsTHHyNaNGdz+iG61y4+COHtDZs3Q+vW0LFjQQqTzz+XuUSuiqMoiqIoiqL852nE1Snkb3NhYWEcP368/N/IZGKu44t8Fvg6xy7KOdQ/nvyR+7+/n8QXEnEyOJV/GxRFUaqgCuuHK8iff8Kdd8Jff0HLlsjowtdfw7ZtUOfmp4z8+CPcdx/k5cn6XngB+vXIQ/vIGNi0CXbtunZ9mZkQGMiRWVNoGvMqF56+QE3Pmtd8L7MZpkyBjz+WK+JER8tgjE5Xoo+uKMq/zL+tf1YURSkL+spuwG3rzBlOmevSoFHBGebPp36mR50eKiCiKIryL/LyyzKY0bIlcPYsvP++nEtTgoAIyKVwt2+XM28Kkp06wJIl0L8/9O0LO3fK3CSFZWTA6NFgMLA5zJHQvNDrBkRABj8++ABq1pTBkcWLVUBEURRFUZSyk5sL+/fLmcG1akG1ajc3IjU3V+Y+i42F+HhISCgoqanyPlBGRkGZN+8mphjfIhUUKa1jxzipC6ddCxkAsQgLayPW8r+7/lfJDVMURVHKysGD8McfMlUIAM89B3fcAcOHl6q+O4pbqd3BAb7/Xg4fGThQjhpx+ie4fu6cjKZkZMCWLWw+Ou2aU2eK88wzcO+92C0bryhK1TN37lwWLlyIEIJx48YxefJkXn/9dRYuXIiPjw8Ab7/9Nv369Sty7Pr163n66acxm808+uijTC3tutuKoijXYbHI86FNm2T54w/Izi543c1NBkeCgwtW6bMGSayBkKgoGfywcnGRU3+txdNTbgsJAVdXWby9y/+zqaBIaR07xkntEzzcSP6l90btJS4zjv71rr16g6IoinJ7+fhj6NxZpgph82ZYs0YmVS3r5Bzu7nKZ3vbt4YEHYNUqOT1n+HCZIXXrVvI93dm2dhtLBi0pUdUhIWXbVEVRytbRo0dZuHAhe/fuxcHBgT59+nD33XcD8Mwzz/Dcc89d81iz2czEiRPZuHEjQUFBtGnThoEDB/7n8j4pilJ+hJD3bqZNkwvpNWwIPXvChAlyxTsh4MKFgnL5spzGWzhJh8EgU7EFBhYUf/+Ce0CVTQVFSint0Hmi831o0EA+//nUz7Sp0YYAt4DKbZiiKIpSJlJS4KuvZJJSTCaYPFkmU23VqnzeMDAQfv1VZkjt0UPOtZk0Cd55B/R69l3eSWZeJt1qdSuf91cUpVKcOHGCtm3b4uzsDECXLl1YvXr1TR27d+9eQkNDqfPPdL4RI0awZs0aFRRRFKVMbNsGL74oR85OnChHoBY3+rR5c1luV2r1mVI6dVCOFbIFRU7/zID6atUZRVGUf4svv5RDQQcPBj77DC5elMvHlKfwcJmR9fRpGY2ZMwf08v7F5nObae7fnOrO1a9fh6Iot5XGjRuzfft2EhMTycrKYt26dVy+fBmAjz76iKZNmzJ27FiSk5OLHBsVFUVwoSuUoKAgoqKi7PaZP38+YWFhhIWFFVuHoijK1Q4ckOnO7roL6teHU6fkKcm/dTquCoqURn4+Jy864V8tF09PuJBygSNxRxjYYGBlt0xRFEUpA0LIqTPjxoFDRhK8+iq88ooc61neunaVk24feshu86bzm+hRp0f5v7+iKBWqUaNGvPjii/Tq1Ys+ffrQvHlzdDodTzzxBGfPnuXgwYMEBAQwZcqUUtU/ceJEjh8/zvHjx6lWrVoZt15RlH8LIeRM4V695KBYIWRwZNkymSvk36zKBEVOnTpF8+bNbcXd3Z0PPviApKQkevbsSb169ejZs2fViHCfOcNJcygNG8qJUj+f+plg92Ca+jWt5IYpiqIoZWHrVjlv9rHHgOnTZeavSZMqrT2ZeZnsuryrRElWFUW5fTzyyCP89ddf/PHHH1SrVo369evj5+eHTqdDq9Uybtw49u7dW+S4wMBA26gSgMjISAIDAyuy6YqilAGzWU5Veeopmcds8eKKe2+TSaYya9NGBkS8vGQwZN2623tKTElUmZwiDRo04ODBg4BMGhUYGMjgwYOZOXMm3bt3Z+rUqcycOZOZM2cya9asym3ssWOc0jemQRMjUDB1RlPWifcURVGUSvHxxzBoEAQ7xMInn8jkIkZjpbVn+6XtAHQK6VRpbVAUpfzExcXh6+vLpUuXWL16Nbt37yY6OpqAAJmr7ocffqBx48ZFjmvTpg0RERGcP3+ewMBAVqxYwddff13RzVcUpRTMZnkTZuVKOXM2MRG6dBH0aJvB+Mdcifj+CG83X4n23BmZvTQvTx5kMslHvd5+6Zbq1WVEw93dvjg4gE4n99fpZNFoiEvUsWilOwu+cScuUcfYYel8OzuVujVNsoHnbvKDaLX2dev1cv6xdQma20CVCYoUtnnzZurWrUvNmjVZs2YN27ZtA2D06NF07dq1SgRFThoe5NFGGtJy09h2YRvPtn+2ctukKIqilInISHlysn49MjoSHPxPYpHKs/7Meu6seScuDi6V2g5FUcrHvffeS2JiIgaDgfnz5+Pp6clTTz3FwYMH0Wg01KpVi08//RSAK1eu8Oijj7Ju3Tr0ej0fffQRvXv3xmw2M3bsWMLDwyv50yjKv0diokwpJoRcktZa8vNlbCI/XxYhwM8PgoLko0537TqPHxN8+VkOy1foiE3U06Pued6uu41BNVbgs283bM2gD70Ztn4VZ/f35Mv+STj1CZc3ZwoHN/LzZQMTEiA2Fo4elVni09IKislU5P33cAcf8SQrGY4/MUxgNo+wGJ+vEuCrMvzlOTkVBGaqVQNfXzkN2c9PPnp7F7zu4SEfrZ+xcIDF0fH6v9AyoBGi8GI5VcPYsWNp2bIlTz75JJ6enqSkpAAghKBatWq251bz589n/vz5ACQnJxMdHV2u7TMPG4Hz6mWs+cVARsh3PPzjwyS8kICj3rFc31dRFOV2EBYWxvHjxyu7GaX22muwYgWcOJCNtlYIvP66TLleiRp81IBxLcfxXIdrL82pKIpyI7d7/6woFUEI+OMPWLBALkWbn3/tfQ2GggERWVnyUaeDGgECf89sXMjCxZKOc14KzjlJHE3056/scFpwgNEsZaTbL/g2qAaNGhWUhg2hdm2OnDZy990QEABr1shYQok/SE4O5OVhzjPz0y863p3vxM79DvTqnMPEh9Lp3z1HxhtKGxKwRouso1fMZvkLy8iQQZnUVPmYnCwDNzEx8jE2VgZz0tJkG6/nt9/kGsDlqNRBkVatWvHII48wcuTIMk3alJeXR40aNTh27Bh+fn52QRGAatWqXTevSEV09mfr9yU04lfOnYPXDj5EZn4m3w//vlzfU1EU5XZxO5905+dDSIhcfm6y06fw0ktyyKpLyUdoJGQlcCL+BO2C2mHQlX4I6ZmkM9T7sB7HJhwjzEcts6koSundzv2zopS3zMtJLFpgYsF31Tl9Rkv//hoefxw6dJCBDq1WFo1GBkJ0OiAzUy7Ncvw46X+fIepgPJGnMom8oiVW+JCp9yTLI4BMV1+ynKoTUEPLAwPTadLFS2Yv9fS8bptiYmDAABk/2L5djkQpiaws+OILeP99uHRJ5nB/5hmoUqt25+UVjGzJzS0IrlinCjVoIEeSlKNST59p0qQJL774IlOmTGHgwIE88sgj9OzZ85bzavz666+0bNkSv39CYX5+frY5ldHR0fj6+t5S/bcsP5+T5xxwdDATEqJjzy97eOqOpyq3TYqiKEqZ+PFHeVPj4Ycs0OF9ePzxUgVELMLC8FXD2XphKx5GD/qE9uHu+nfTN7RviZfU/TXiV2p61KSRd6MSt0NRFEVRlGKYzbBnD+zdC3v3IvbuY9TZWeygI+P5H486fU3ICTPMqiEDFw4OcmqH0SgjIhcvwokT8hHAxwe38HAahoXRcEgbCA+XIz78/GQUpZT8/eH332UC1LvvloERN7ebO/aHH+RA17w8mDBB/lzi0SYVwcGhIC9KJSl1UOSLL75g/vz5fPvttyxdupQ+ffoQGBjIQw89xJgxYwgNDS1Vvd988w0jR460PR84cCBLly5l6tSpLF26lEGDBpW2yWUjIoKT5lDq1zcjNILzyeepX71+5bZJURRFuWUWC8yYAaNGgefOdXDuHDz5ZKnq+uyvz9gduZt94/YRkRjBz6d/5qlfnyItN413erzDlA43v7Tmr2d+pW9oX5XMW1EURSkXQtzSdTs5OTIf1+XLcmTCHXeAj0/J6sjNldfG5f5Vl5oKS5bAhx/K7/kGDeCOO/i6w0esvdiT/b/E0dSrH1xpDtHRcOVKwQgGa0lPl9NchgwpmPJSvWQ3PErC2VnetGnfHkaMkFNp9Ne5io+JkavY/PADPP88/N//ler+zn9KmeUUOXv2LEuXLuXLL7/k8uXLdOzYkbFjxzJixAgcHW8u10ZmZiYhISGcO3cOj3+GyCQmJjJ8+HAuXbpEzZo1WblyJV5eXteso9yHBa5axbgHskgb/BAzF1ygzrw6nJ10ljrV6pTfeyqKotxGbtfh2UuXyjspp09D4IPd5DyapUtLXM/FlIs0/qQxb3Z7k8ntJtu2mywm5u6eyxu/v8GFyRfwcrr2d5lVdn42Xu94sXLoSgY0GFDitiiKohR2u/bPSvk4cQImT5ajD0JDoX59WRo0kEuxNm5cfH7LiAj49ltYu1bGFeLj5XatVg6iyM2VgyTuvFOW8HB5Ue7kJC/wHR3h/HnYvVsO1ti9G44fl3k2GzYsiDM0bw49esh6b9mpUzB/vgyIuLjAE0/A+PHg78+VK7KNzz0HL79cBu9VTiIioF07GRj56KOiASQh5GnLs89CzZrw+efQokXltPV2U2arz+h0OttdLJ1OhxCCCRMmMHXqVJYtW0bPm0iO4uLiQmJiot226tWrs3nz5rJq5q07fpxTjoPo2kDDmaQz6LV6QjxCKrtViqIoyi3IypInQs89B4GxB2DbNvhnmfiSEELw2NrHaOrXtMjUSr1Wz6S2k5i/bz7v73qfN+9684b1bbuwDYuwcFftu0rcFkVRFEUpTmoqvPGGHCxx112wbJnMN3HqlAxQfPGFHCTh4SHzadx5pxz98ddfMhhy4IAMXgwdKnNTBAfLUqOGrP/vv2WgZft2mb/iqss7m6AgeZH/8MPQsqXc7+RJGaz55hs5wqFNGxkAaBGeB7Nny/VrDYaCqSxGo5x2ERpaUOrUkR9oxw7YuVM+RkZCq1ZyVbnhw+VxyEDCuHHysBdfrJBff6nVqydHiXTvDnXryuAHyI/27bfw1VcyuPT66zBlym21Ii4mi4m/rvzF2eSzxGbEEpcZR1xmHLGZsbx111s0929eru9/S0GRrKwsVq1axRdffMH27dsJDQ1lwoQJjB49Gj8/P5KSknjyyScZP348587d7ELHVdyxY5zMf4bHG8rkd7U9a6PXVsmVjRVFUZSbNGeOnF78/PPA43PkralmzUpcz5KDS/jj4h8cHH8Qnbbo7TWDzsC0O6cx5bcpPNv+Wao5XT9R+bqIdXSp2UUtxasoivIfFR1dcD1/qyMmLBYZ8HjpJTlY4rvvYODA4qesREXBn3/KwMaKFfLGQe3acN99crBFkybXnupyxx2yTJkigw6pqZCdLW9AZGXJnwMDZbme8+flhX/r1oLxnqt5SyzB69mHZUCj8HSWuDjYuBE++USuamIVGiqjOq+8Ah07ygjOVY1esgQ2b5aBnutNSakqOnWSf8MHH5TTZPbskX+jkBA5gmTlSvmxqzohBKcTT7Pp3CY2ntvI1gtbSctNw8/FDz9XP3xdfPFz8aNB9QY4G5zLvT2l/tOPHTuW7777DovFwtChQ5k+fTp33nmn3T5eXl48/fTTrFix4pYbWlUkHookPsedhg3hq/gzhHrdBv/qFEVRgPfff59Fixah0Who0qQJS5Ys4fHHH+f333+3TVn84osvaN68eZFjly5dyltvvQXA//3f/zF69OiKbHq5iomBmTPhgw/ANeWf2y0//VTieqLSonh2w7NM7zqdBt4NrrnfQ80e4q0/3uKD3R/wRrc3rrmfEIJ1Z9bxZJvS5TVRFEVRbk+nT8t8ED/+KEdugFyW9d57YdgweX1f3LSW6zlwQE4RPXxYBjimTJHTWK4lMFAGQO67Tz7PypLTX0qa80OjkXlKb7DISrFqe6fzQ/DLrLdEMCn/c1YaTvNmdS1jxlyn7Wlpck5PQMANs4peuiRHsrz5ZhVbjeUqGXkZLD24lHxLPgatAUN9A0OebMkXy5owYpiRGTNkvpHbIfVYVn4WXx3+ig/3fsiRuCMEuwfTs05PPr37U+6qfRe+LpWzqEqpgyLHjh3j3XffZeTIkbhdJwVueHg4W7duLe3bVC15eZw6K39l9etDxKkI6nnVq+RGKYqi3FhUVBTz5s3j+PHjODk5MXz4cFvAevbs2QwdOvSaxyYlJfHGG2+wf/9+NBoNrVq1YuDAgWW6HHtlevVVefdrzBhg4luyg+/Tp0R1CCEYv3Y8Dbwb8Gz7Z6+7r4POgWl3TuOFjS/wTPtn8HT0LHa/iKQIziWfo1+9fiVqi6IoinL7yc2V+SDmzpVTIOrVg8GD5VKqISEyQLJqlZz94esrZ4CMGiVHkFzvYjg5WQ6U+OQTuOceOZIgpLiZ/zExcuqotcTEyO3/pJ901mplVGb69IJ5MuVp50459EGno8/6BRzpGsD778tRLq+/LhOJPvFEMflN3d1lMpIbEAIefVTmEnn2+l/blSohK4H+X/fnfPJ5arjVIM+cR74ln/za+SQ+cZm0pqMIbzkXjaZ8l6y9VRdTLvLxvo9ZeGAhAI+2fJSVw1bSoHqDKpFIvtRBkT179tzUfq6urnTp0qW0b1O1RERwylyXoAATrq56ziSdoWedG+dKURRFqQpMJhPZ2dkYDAaysrKocZMnNRs2bKBnz562JNc9e/Zk/fr1diuF3a6OHoXFi2HdOtDNe19mJfv11xLfbllxdAUbz23kwGMHip02c7WHmz/M/7b/jw92f8DrXV8vdp91EeuoU62OWuFMURSlCjOb5RSXwMDS3anPyYFFi2DWLLmoyZNPymktDRva1zdhgiyxsbB6tcwfMW+e3G/UKDmdIiAAkpJkbo7ERPkd99prMk7wyy/FxPuFkN97770nE3m4u0PnzvDYY/JugbUBGo1s3OzZMlrz/PMyCZera6l/b9f12WfyFzF6tBzG6eKCEZg6VS4ru3ix3Pz22/KGxgsvyMSiJTFnjpwedPBgyUfdVJRLqZfovbw3Rp2Rw08cxt/V3+71nZd3MvrH0TT5pAmfD/qcHnV6VFJLr81kMfHa1teYuWMmYT5hzOoxiweaPlAhU2JKRJTSN998I955551iX5s9e7b49ttvS1v1LWnUqFH5Vf7tt+IFh/dFjx4WYTKbhMObDmLd6XXl936Koihl6IMPPhAuLi7C29tb3H///UIIIUaPHi3q168vmjRpIiZPnixycnKKHDd79mzx5ptv2p5Pnz5dzJ49226fjz76SDRq1Eg0atRI+Pv7l+8HKUN9+gjRu7cQ4ptvhNBqhfjqqxLXkZKdIvzf9RevbHmlRMd9su8T4THDQyRnJxf7eq9lvcTEXyaWuD2KoijXUq7nyf8xJpP8ymjUSAgQwt9fiGHDhJg3T4iDB+Xr15KbK8SePUK8/bY8zstLiDffFCIlpWRtOHtWiDfeECI0VLbh6lKtmnw9O7uYg9PShBg5UggHByFee02I/fuv32jrh16yRIgaNWTDP/tMiGLOG0otJ0eIceOEMBiEWLDgurvm5wuxYoUQLVpahLu7RaxeffNv8/vvQuh0QixdeovtLUfH4o6JoDlBosuSLiIl+9r/MDJyM8RT654Smtc1YuIvE0VCZoKwWCwV2NJri06PFt2+6Caqzawmvj/+fZVpV3FKPVJkxowZPProo8W+5uzszIwZMxg+fHipgzVV0vHjnHTpTsOGGqLSo8gz56mcIoqi3BaSk5NZs2YN58+fx9PTk2HDhrF8+XJmzJiBv78/eXl5PPbYY8yaNYtXX321xPVPnDiRiRMnAnLJx6pm1y55YysvT2ZjNxjkKeOff8LBBbvhoYfkHbD77y9x3f+35f9wNjjzUqeXSnTcmOZj+N/2/zFvzzxe7WL/O8/My2TbhW083fbpErdHURRFKT8mk0w8+tZbMifF44/L1VsOHZIJLz/4ACZNkrlAa9WSAy7q1JE/x8bK76O//pLTZerXlzktnngCrpON4Jrq1JFTQF95ReYMycyU00mqVwcvL3BwuMaBBw/K+TcWi5ym0qrVzb2hTieXihk2TM7refZZmDZNbnvsMTmKpLSuXJHL2Zw/D1u3ysQp15CQlcC6iHX8rP2ZiCEbMfw5naFDn+KZZzTMmHH9VVeuXJEf/bHH5Fd/ccwWM8fij7Hr8i72RO3BSe9E9zrd6Var2w0TpJeFXZd30f/r/nSp1YVv7v0GR/21k7+4OLgwr+887ml4D2PWjGH+vvk4G5wJcA2ghlsNAtwCMGgNBdNuzPloNVomtZ1UriNLfr/wOyO+H0GQexAHxh+glmetcnuvslDqoMiZM2do3Lhxsa81atSIiIiIUjeqyrp8mVPmuvRsIFee0Wl01PQs4VgtRVGUSrBp0yZq166Nj48PAEOGDGHnzp08+OCDABiNRsaMGcO7775b5NjAwEC2bdtmex4ZGUnXrl0rotllYu1aeQI0aJCcZpyfL4vJBBP7X6DJlF7yDLYUk4r3X9nPx/s/5pf7f8HJ4FSiY416I9M6TWPalmlMbDOR6s4FE6O3XtiKBg1da3UtcZsURVGU8hEVJRcnu3RJTmV57rmCXJ6tWsHYsQX7HTokr+/Pn5d5P3fulIGKbt1kHKFdu2LyYZSSRnOTcQ0hYMECGYkZNEhOU/EoRS4KFxe5Xu6kSfD11/Dpp/Duu3J933vukcvLJCZCQoJ8tFhkIpTCJT1d/mKs5dQpaNwY9u+/5rI06yLWMePPGey8vBMPowf96vVj4aAFbG6ymUXefZi/8Ht27nJi1UpdsVXk58vzgVq1ZEynMLPFzPLDy1l+ZDl7IveQnpdOsHsw7YPbE5MRw6gfRpFjyqFlQEt61O7B+NbjS3yhn2fOQwiBUW8s9vVzyeeYsX0GSw8tZXSz0Xxy9yc3vcrpXbXv4sTEE5yIP0F0RjRX0q8QnS4fzcKMm4MbBp0Bg9ZAdEY0vZb14oGmDzCn1xx8XHxK9DmuxyIszN4xm5e3vMz4VuOZ03vONT9vVVLqoIizszORkZHFvnb58mWMxqr/4UsqPyGVsxl+NPxnOd6anjVx0F0rBKsoilJ1hISEsHv3brKysnBycmLz5s20bt2a6OhoAgICEELw448/Fhvs7t27N9OmTSM5ORmA3377jRkzZlT0RyiVL76QidReflkmZrOb733hgkzXPmAAvPNOies2W8w8vvZxhjQaQp/QkiVmtRrbYiwf7PmAuvPqMq7lOJ5q+xQhHiGsi1hHt9rdqt6cW0VRlP+o/Hy5EounJ/z+u7yuv5abWW62Urz+ulxube5cGD/+1pcrcXeXQ2XGj4d9+2SQZe5cqFatYMhKzZryfeLj5T5xcXLIjJubHOpSpw60aSPXke3fXw6xuUpabhrPbniWJQeXMK7lOP531//oENzBFjAY0XgE94at5+Fa3Tiw/DMaNwtn+msODB0q86xYvfCCjL0cOFDwNkIIVp9YzStbX+FS6iUebv4w41uNp31QewLdC/6IeeY89kTuYfP5zaw5tYa5e+bySudXmNJhSrHXg0nZSWw+t5lj8cdkiTtGRFIEeq2eLjW70Ltub3qH9qaRdyNOJZ5ixp8z+OrwV7QIaMF3w79jQP0BJU5A6mxwplWNmxv18+QdTzJ+7Xgazm/I7J6zGdN8zC0nPE3JSWH0j6PZfG4zywYvY2ST2yj3XGnn3YwYMULUq1dPxMbG2m2Pi4sT9evXFyNGjLi1iT2lVJ5zJU+0GSVAiMuXhXj+t+dFr2W9yu29FEVRytqrr74qGjRoIMLDw8WDDz4ocnJyRLdu3UTjxo1FeHi4eOCBB0R6eroQQoh9+/aJRx55xHbs4sWLRd26dUXdunXF559/ft33qQpz1i0WIWbOlGlC5s8vZofUVCHCw4W46y45ubsUPtrzkXB921VEpkbeUluz8rLEor8WifD54UL3hk4MXzVcBL4XKObunntL9SqKolytKvTPZWr1aiHWrpUJJsrZlClCeHsLcelSub9V+Zg3TybS+Pnnym5JiWw+t1mEvB8i6sytI7Zf3H7dfZOzk8VDqx4VdHlDOHslC43GIrp0EeLjj4X49FN5TrBpk9zXYrGI3878Jlp/1lo4vOkgnv71aRGTHnNTbTJbzGLRX4uE1ywv0eijRmLr+a1CCCHyTHni51M/i6ErhwqHNx2E+wx30XFxRzHup3Hig10fiN/O/CbWnFwjJqydIELnhQpeR/i/6y80r2tEx8UdxfqI9RWadyPPlCdmbJ8hHN9yFJ0+7yS2nNtS6vc/FHNI1J1bVzT8qKE4FnesjFta/jRC/LPOUgldunSJdu3akZ6eTp8+fQgICCA6OpoNGzbg6enJjh07CA4OLusYzg2FhYVx/Pjxcqn7x5pP82DMbNJzHLh35RACXAOY339+ubyXoijK7ao8++Gb9cIL8mbVV1/JKcp2zGY5bPj0adizR97RKqGYjBgafNSAN7q+weR2k8ukzUIINp3bxPu732fz+c2cmHiCOtXqlEndiqIoUDX65zKzaxd06iTzXPj4yGVIxo6VIw9KKzNTfi/s2CHLP6ttrjaOZFjsh6xv8yo9m8XBiy/KkQ0VJCs/C51Gh0FnQKvRlryCb76Ry9MsWXLtRBoVJCIxgjWn1qDT6GhdozUtAlrg6mC/ik18ZjzH4o+x6tgqPtn/CU+0foJZPWcV2e9afjv7G0+ve4ZLR4Npnvg/zv7ZkthYmW9kwjNpLD+8nAX7F3As/hgPN3uYV7u8WqqUCAlZCby48UWWHFxCr7q9+Dvmb1JyUhjYYCCjm42md93eGHTXTnByLvkcm89tpn71+nSu2bnSlqY9l3yOaZunsfLYStoFtePlO1+mX71+N92eLw99yeNrH+fu+nezeOBi3IylSJBTyUodFAGIj49nzpw5bN26lcTERKpXr0737t155pln8Pb2Lst23rTy7OxnebzNymrj+etCdZp+0pQxzcfwTPtnyuW9FEVRbleVfdKdlianSH/3Hdx7bzE7PP+8XP9w925o0OCG9R2NO8r55PNoNBo0aNBoNCw8sJALKRfYN27fTc/3LYk8c56anqkoSpmr7P65zGRmQosW0KGDzGWxfLns148dk3ktnn0W+vW78fQQIeRStD/9BD//bAuC0Ly5TPTZvj0RCdVo/Xw3pnTczavNf5L7HDggp10+8QRoSxGkuEm7Lu9i2pZpbLuwzbZNr9Vj0BqoXa02/UL7cXf9u+kQ3OHaF9/r18tporNmlSp31q0SQnAk7girT6zm+xPfczTuKKFeoRi0Bk4mnASgoXdDmvo1JS4zjmPxx4jLjEOr0RLmE8Z7vd6jV91eJX5fk8XEZ399xqtbX8XN4MnoGjOJdNzAimPf4Gxw5pEWjzCu1bgyufnw56U/+Xjfx9wZcif3Nb4PLyevW66zMpxOPM3MP2ey7PAyGvs2Znyr8YT7hNPAuwE+zj62IEmOKYdjccc4FHuIjec28t3x75jdczZPt3260gI7t+qWgiJVUXl29uMNi0lu25dvtwfg8rYL3w79lgENBpTLeymKotyuKvuk+88/4c47ZQ4316tvKi1ZAuPGyZPEHtfPui6EYP6++UxeP9mWJEwIgUDg6uDK2pFraRvUtpw+haIoStmr7P65zDz5pAxkHDlSkChUCNi7Fz75RA4TDA+XGU3vvVeOJrFKTJRB8c2bZR1nz0LDhjBwIPTqBW3b2r48srJk6qmAAFi37p/4h8Ui3+OFF2RQZvFiCAkp0493OPYw/7fl//j59M8MCxvGk3c8abeCSK4plyNxR/gl4hd2Xt6Jm4MbvUN706duH7rX6U6Ixz/t2bVLftc9/TS8/XaZtvFmZOZl8sDqB1hzag3N/JoxpNEQhjQaQrhPOBqNhvTcdA7GHGT/lf0ciTuCn4sf4b7hhPuE09C7YYkTmBcnJSeFt/54iw/3fkiH4A483upxBjcarG48XMfFlIvM3jmbn0//zKXUSwB4GD2oX70+mfmZnEo4hVmYqeFWg+b+zXmp00t0CulUya2+NSoocrNyc7nXcS0BQzow7QtB4JxAjk84TiOfRmX/XoqiKLexyj7p/vhjmDMHzpy56oXt26F7d5ly/p/lg68l35zPpF8nsfjvxSy4ewFjW4wtvwYriqJUkMrun8vEpk0yeLFxo+zTi3PxohxBsmiRTPT50ENyyuSuXfLRaJTBj4ED5SiK+vWLVCGEnI2zebMcGFJkEHxEhFyK9uhR+PDDMpmWcjrxNK9ve50VR1fQO7Q3/7vrf7QMaHndYxKzEtlwdgO/RPzCxrMbic+Kp161UHpk+tH9+wO0aXMPwfOXoynHES3FicmIYcA3A0jMSuSH+36gmX+zCn3/q1mEpXRTj/7jsvOzOZN0htOJpzmVeApHvSPN/JrRzL8Z3s6VMzOkPNxSUOTbb79l4cKFnD59mpycnCKvx8XF3VLjSqPcOvuYGDoHnOauCY2464UTdP2iK1kvZ1133WhFUZT/oso+6X78cZnk/vvvC22Mj4ewMBg2TEZNriMxK5Fhq4ZxJO4IP9z3w21/90NRFMWqsvvnW5aSAk2ayGVfP/zwxvvHxspA+M8/y+PatZNDP5o3L3aVk8IWLpTx8z/+kIcVy2yWUfhp0+RUmjlzQF/yKZVnks7w5h9vsvzwcjoEd+B/d/2PzjU7l7gei8XM0a/eZ/O3M9jkncbvtbVkkoeXkxfN/ZvT3K857YPbM7jhYHRa3Y0rLKUT8Sfo93U/fJx9+Hnkz/i5+pXbeylKWSh1uOzrr79m9OjRhIaGEhkZycCBA7n77ruxWCy4u7vz5JNPlmU7K19yMgl44xNk5EzSGYI9glVARFEUpQo6dAiaNr1q49q1cm753LnXPfZ04mnaLmpLfFY8+8btUwERRVEqxNy5c2ncuDHh4eF88MEHADz//PM0bNiQpk2bMnjwYFJSUoo9tlatWjRp0oTmzZvTunXrimt0ZXj6aXB2lvkxboafn1yC9tgxWLECJk+WI0RuEBDZt0/O0Jk79zoBEZDTcp5/Xo5e+fpruaTsNf5Odi5fhgULOP/QAB6Z04WGHzXkdOJpNjy4gT8e/qNUARF27ULbsRNNH32ZZxqN5Zf340h9JYsTE08wv9982tRoIxOL/vgwdyy6g12Xd5X8PW7CHxf/oMPnHWjq15Sto7eqgIhyWyh1UGT27Nm88sorzJ8vV1+ZMGECn3/+OefPn8fb2xtnZ+cya2SVkJREPD54BztxJukMoV4Vl3FaURRFuTkWi5xi3uzqUbobNsjh1oZrZ4EHeHbDswR7BLNz7E5qedYqt3YqiqJYHT16lIULF7J3714OHTrE2rVrOXPmDD179uTo0aMcPnyY+vXrM2PGjGvWsXXrVg4ePMj+/fsrsOUVbM0amVB16VIZGCknCQkyDcmIEXLk4U3p0kXmM4mMlFGUiAj719PS5BTOV1+FFi0QISHM//5FGtVex5FT21m7sxY7a71Fjzo9Sp6oMjdXBmY6doRateDkSZg9G6pVQ6fV0dC7ISMaj2Bmj5msf3A9p586TUPvhnT8vCNj1owhNiO2ZO93DUIIFh1YRM9lPXmwyYOsHr4aFweXMqlbUcpbqYMiERERdOzYEZ1Oh06nIy0tDQA3NzdefPFFPvroozJrZFVgjk8iCS98ahhkUKSaCoooiqJUNefOyUUJ7EaKmM1y7nmv62evz87PZvP5zTzT7pnbcjk5RVFuTydOnKBt27Y4Ozuj1+vp0qULq1evplevXuj/mYrRrl07IiMjK7mllez99+Gxx24wdOPWmM0wciR4eclcqiWKT9SpAzt3yse2beGVV+SUzXr1ZDLYLl3gl19IHNSTez7pzHNd8/hgwHz2/N9F+gTciaZnT7liztGjN/+ehw5BmzawbJmcIvTNN1C79nUPqeFWg6+GfMXW0VvZf2U/DT5qwKIDi0rwQYvKzMtk9I+jmfDLBOb0msO8vvPKdXqOopS1UgdF3N3dyc3NBSAwMJATJ07YXhNCkJiYeOutq0KSo7KwoMPbGzVSRFEUpYo6fBjc3OTNMpsDByAp6YZBkW0XtmERFu6qfVe5tlFRFKWwxo0bs337dhITE8nKymLdunVcvnzZbp/PP/+cvn37Fnu8RqOhV69etGrVis8++6zI6/PnzycsLIywsDCSk5PL5TOUu7g4OdLi/vvL9W1efRX275c5qUo1GMXDQwYnxo+XIxQ9PeWUnR07IDWVbd+/RzP3rzknktg/bj+Pt34cTXCwXBnt779lVKZZM3nMPzeci2U2yylEbdrIJLFHj8qpOyXQpVYX/h7/N692eZXH1z7OuzvfLcUHhmNxx2izsA3bL21n5yM7mXjHxNt2WVblv6vkmYD+0aZNGw4fPkzv3r0ZOHAg06dPR6/X4+DgwPTp02lXjlHcypAQKRPJVq8uVFBEURSlijp0SObSs0uyv2GDPMn097/usesi1tG5ZmdcHa5ex1dRFKX8NGrUiBdffJFevXrh4uJC8+bN0RVaQvZ///sfer2eBx54oNjj//zzTwIDA4mLi6Nnz540bNiQzp0LclJMnDiRif+suBUWFla+H6a8rFkjl3/p0KFc32LmTLlKb926t1CRTgfFTHV6Z8c7vLT5JZ5o/QSze84uutxss2by+2rtWnjqKVi1So6OGTasYMhKTAx8+aVcBjgmRj4++GAJh7QU0Gv1PNv+WQLdAnlg9QMIIXi+4/M3dawQgmWHl/HEL0/Qs05PlgxaQjWnaqVqR1nLysri3LlzREZGcvnyZS5fvkxMTAzu7u74+vrair+/P3Xq1MHT07Oym6xUslIHRV566SUuXrwIwPTp07l48SJPPPEEFouFNm3a8Omnn5ZZI6uChBgTAMI5nvS8dBUUURRFqYIOHy4mn8hvv91wlIgQgnVn1vFkm39ZknBFUW4LjzzyCI888ggA06ZNIygoCIAvvviCtWvXsnnz5mvefQ8MDATA19eXwYMHs3fvXrugyL/C6tVyxRld+UzJOHQIHnhAzngp4YCLm/L98e95afNLfD3ka+5rfN/1d777brjrLnjrLRnw+Pxzudzvt9/CL79AUBCMGQPjxkGNGmXSvvsa34dGo+H+7+VInBsFRqLSopi4biK/RPzCrB6zeKbdM1VidIgQgmXLlvHMM8+QlJSEwWAgKCiI4OBg/P39iYqK4vfffycuLo7Y2FjbrAdvb29CQ0MJDQ0lPDycO++8kzZt2uDg4FDJn0ipKKUOirRr1842GsTT05M1a9aQm5tLbm4u7u7uZdbAqiI+1oK7PpNLGWcAqFOtTiW3SFEURbna4cPQu3ehDWlpsGsXvPbadY+LSIrgXPI5+tXrV74NVBRFKUZcXBy+vr5cunSJ1atXs3v3btavX88777zD77//fs0FDDIzM7FYLLi5uZGZmclvv/3Gq6++WsGtL2cpKbB5sxxBUQ6io2Uc4u67b/hVUSonE07y8JqHeb3L6zcOiFg5O8Pbb8OoUTBhAowdC0OGwPr1MmCiLXUGhGsaHj4c4LqBEYuw8On+T3lx04s09G7IX4/9RVO/q5d7qxwXLlxg/Pjx/P7777z66qs88sgj+Pj4oL3G70oIQUpKCmfPnuXMmTO28s033/DSSy/h5ORE+/bt6dKlC506daJ169b/ymvcq1ksFlJTU4mPjycjI6PI687Ozvj4+FCtWrVr/m5vR6UKiuTk5ODh4cG3337LPffcY9tuNBox3mCJq9tVQpIGH6cMziSdoYZbDZVNWVEUpYpJS5OJVu2SrG7ZIlec6XT9pXXXRayjTrU61K9ev3wbqSiKUox7772XxMREDAYD8+fPx9PTkyeffJLc3Fx69uwJyBuSCxYs4MqVKzz66KOsW7eO2NhYBg8eDIDJZOL++++nT58+lflRyt7ateDiAl27lnnVmZkwYIAcfLFkSalnoVxTem46Q74dQtdaXXm588slr6BRI/k9ZjLdcPW0slA4MLLlwhbqe9WnlmctanrWxMPowWvbXuNgzEHeuustnrrjqSqRTNVsNvPRRx/x8ssv06JFCw4ePEjDhg1veJxGo6FatWq0bt26yFLWSUlJbN++nd9//501a9Ywffp0LBYLDRs25I477uCOO+6gYcOG1K5dm+DgYFtC5KomJyeHK1eukJCQQFJSEklJSSQnJxd5tP6ckJBAQkICZrP5hnVrtVq8vLzw9vbG09MTd3d33N3d8fDwwM3NDUdHRxwcHDAajTg4OODo6Gh73bqvl5cX/v7+ODk53fD9ylup/oKOjo74+vpW2X8A5SE+WY+3a47KJ6IoilJFWRP2N2lSaONvv8kT6RsE7NdFrKNfaL8qMfxXUZT/nu3btxfZdubMmWL3rVGjBuvWrQOgTp06HDp0qFzbVulWr5aRizKeymCxyIEYiYmwezeU9XWZEIKxP40lz5zHssHL0GpKeVddo6mQgIjV8PDheDl58dOpnzifcp6tF7ZyIeUC6Xnp9Antw9EJR6vMkvUWi4Xhw4fz22+/8c477/D444+XyegFLy8vBg0axKBBgwCZo+TAgQPs3buXvXv38t5773HhwgUsFgs6nY6goCBq1qyJl5cXnp6eeHh44OnpiZubmy0oYH10dna2BQWsQQJPT88STdWxjnKJjo625U2JjIy0laioKKKiokhKSrId4+DggJeXF15eXlSrVs1WatWqRYsWLfDy8sLHxwdvb2/bo5ubm915kRCCrKws4uPjbQGUhIQEUlJSSEtLIy0tjdTUVGJjY8nLyyMvL4/c3Fzy8vLIzs62vZ6enm73edzd3fHz88Pf3x8vLy/b78bNzQ13d3dGjBhB7RusqnSrSh3VGD9+PPPmzaN3794YKvB/1MqSkGbE2yOPM0lnqOdVr7KboyiKolzl0CG5EqJb4dV0N2yASZOue1xGXga/X/ydZ9o9U74NVBRFUUomM1NOGfn66zKv+qWX5KycnTvBz6/Mq2fOrjn8cvoXdj+6G09Hz7J/g3LUo04PetTpYXsuhCAzPxMXg0uVunnw8ssvs2nTJnbv3k14eHi5vY+zszOdOnWiU6FRp/n5+Vy+fJnz589z/vx5Ll26RHJyMqmpqZw7d8528V84MJCXl0dmZiYZGRkIIezew8PDA29vb1u5evBBfn4+8fHxxMTE2IIOAAaDgcDAQIKCgggKCiI8PJzevXsTGBhIYGAgNWrUwNfXFycnpzL523l4eBAQEHBLdVgsFtLT00lMTCQ2Ntb2mWJiYmwBlpiYGE6fPk1aWhrdu3evukGRlJQUjh49Sq1atejevTt+fn52v2iNRsOsWbPKpJFVQUKmEz61zBxLOsOQRkMquzmKoijKVYokWT1zRs6nuUGS1S3nt6DVaOlaq2u5tk9RFEUpoQ0b5EgJu2RRt+6LL+C992DdOiiPa+lfI37lxU0vsvSepVUm58at0Gg0VW5ltmXLlvHOO++wdu3acg2IXIvBYKBOnTrUqVPyPJMWi4XMzExSU1NJTU0lOTnZNvoiPj6exMTEIlNYdDodnTt3xt/f3zaqwt/fH19f39sut4dWq8XDwwMPD49S/f7KQ6mDIt9//70tf0hxQ/7+bUGR+Bw3wr0FEUkRavqMoihKFXT4MPwz9V767TcIDoYbzC1eF7GOu2rfVXR5REVRFKVyff899OtXpnNbDhyAxx+HOXNuGDMvMYuwMGP7DF7d9iovdXqJB5oWv4yycmt27drFo48+yrvvvkvfvn0ruzklptVqcXNzw83NzbbSlFK5Sh0UOX/+fFm2o2oTgoR8d1x8TKTkpKigiKIot6X333+fRYsWodFoaNKkCUuWLOGRRx5h//79GAwG7rjjDj799NNip0TqdDqa/JOsIyQkhJ9++qmim39dFosMijz3XKGNGzbIu4vXGS4qhGBdxDpe7Phi+TeylE6fPs3LL7/Mhg0baNasGXfccQdt27albdu2hISEVKmhzIqiKGUmN1cmWV2woMyqTEqCe++VC7k89VSZVQtAQlYCo34YxY5LO1hx7wqGhQ8r2zdQALh06RL33HMPo0aNYvLkyZXdHOVf4vYaa1NZ0tOJxwdztVQA6larW8kNUhRFKZmoqCjmzZvH/v37OXr0KGazmRUrVvDAAw9w8uRJjhw5QnZ2NosWLSr2eCcnJw4ePMjBgwerXEAE4MIFyMgotPJMXp7M2H+D24DH4o9xOe0yfetVvTtNsbGxTJgwgbCwMGJjY5k/fz6dO3fm8OHDjBs3jlq1alGzZk0mTpzIhg0byM3NrewmK4qilJ0tWyAnB/r3L5PqLBZ48EG5kM3ChWW70syuy7to8WkLotKi2P/YfhUQKScZGRkMHDiQhg0b8vHHH6ubAkqZKfVIkY8//viG+0yYMKG01Vctyckk4E2WxyH8nP1wM7rd+BhFUZQqxmQykZ2djcFgICsrixo1atCrUNDgjjvuIDIyshJbWHqHDoGrK9jycO3eDVlZ0L37dY9bF7GOht4NqVOtasxpBTnX+H//+x+zZs2iVq1a/Pjjj/Tv39/u5M9isXDq1Ck2b97MTz/9xIABAzAajfTu3Zv+/fvTu3dvatSoUYmfQlEU5RatXg09eoC7e5lU9+ab8OefsH+/DIyUhSvpV5j15yw+3v8xDzV9iA/7fYizwblsKlfsCCEYP348qampbNq0qUSrtSjKjZQ6KPLkk09e8zXridu/JSiSfSWZTGqS5hZFqIeaOqMoyu0nMDCQ5557jpCQEJycnOjVq5ddQCQ/P59ly5Yxd+7cYo/PycmhdevW6PV6pk6dyj333GP3+vz585k/fz4AycnJ5fY5ruXwYbkUry3X2IYN0KYNeHld9zjrUrxVyU8//cRbb73FJ598wujRo9HpdEX20Wq1NGrUiEaNGvHkk0+SlpbGhg0b+Omnn3jxxRcZO3YsTZo0oXfv3vTu3ZtOnTrh6OhYCZ9GURSlFMxm+PFHKKP8hOvXw/Tp8N13UL/+rdcXlRbFrB2z+Oyvzwj1CmXFvSu4N+zeW69YuaYlS5awatUqdu7cibe3d2U3R/mXKfX0GYvFUqQkJSXxzTff0KxZM44fP16W7axUCRczAUhziiTYI7iSW6MoilJyycnJrFmzhvPnz3PlyhUyMzNZvny57fUJEybQuXNn7rzzzmKPv3jxIvv37+frr79m8uTJnD171u71iRMncvz4cY4fP061atXK9bMU59Chq1ae+e23G65WkJqTyo7LO+hXr2oFRd577z1GjRrF2LFjiw2IFMfd3Z1hw4axbNkyYmJi2L9/P/fddx979uyhT58+eHl50adPH+bMmcORI0eKLAWoKIpSpfz5p0wAMnDgLVVjscAPP8D998Pzz8PgwbfWrDxzHpPXT6buvLpsOb+FZYOXcfiJwyogUs6OHTvGk08+yezZs2ndunVlN0f5Fyr1SJHieHp6ct9995Gamsr48ePZtm1biY5PSUnh0Ucf5ejRo2g0Gj7//HMaNGjAfffdx4ULF6hVqxYrV66s8BPuhMvZAGQbL1PTqXqFvrdSIDs7m7i4OOLj420lISHBtg54WloaaWlpZGZmFjlWr9fj5uaGq6urLduzh4cHnp6etlKtWjWqV6+Ot7e3bWUlRfm32LRpE7Vr18bHxweAIUOGsHPnTh588EHeeOMN4uPj+fTTT695fGBgIAB16tSha9eu/P3339StW3XyKx0+DFOm/PMkJwf++gveeee6x2w6twmjzkinkE7l38CbtHfvXv78808W3EJiQa1WS6tWrWjVqhUvv/wyqampbNu2jY0bN7JgwQKmTJmCj48PjRo1on79+tSrV4969epRo0YNTCYT+fn55Ofnk5eXR0ZGBgkJCSQmJtoeMzMzycnJsZX8/Hw8PT3x8fGxFT8/P2rWrGnLe+JUhitHKIryH7B2LXTsCKUcEZCfDytWwIwZcPYsjB8Pb7116816dsOzrDy2kuVDljOk0RC0GpWesbxlZmYyfPhwevXqxaRJkyq7Ocq/VJkGRaxq167N/v37S3zc008/TZ8+ffjuu+/Iy8sjKyuLt99+m+7duzN16lRmzpzJzJkzK3yp3/ioXHSYSNdE4uVU+8YHVGEWi4Xk5GTbye3VJ7jZ2dnk5OSQm5tLbm4ueXl5tp8LnwTn5OSQl5dXZLSQo6Mjbm5uuLu72x6twQZr8fT0LJIYKScnh/j4eLugR0xMDNHR0baSmppq29/BwQEfHx+8vb3x9PTE3d0dd3d3vL29i12NIT8/n/T0dJKTk7l06ZItiJKSkkJycjLp6el2+7u6utpO7mvVqmVbh9xaQkJCbvoOrqJUBSEhIezevZusrCycnJzYvHkzrVu3ZtGiRWzYsIHNmzdfc5375ORknJ2dMRqNJCQksGPHDl544YUK/gTXlpEhT3ptSVbPnQMhoEGD6x7356U/6RjSEaO+6gRB33//ffr06UN4eHiZ1enh4cGgQYMYNGgQIEf9/PHHH5w+fZqIiAhWrFhBREQEGRkZtmN0Oh0GgwFXV1e8vb1tAWPro6OjI46Ojjg5OaHX60lJSSE+Pp6oqCgOHjxITEwMly5dIi8vDwA/Pz+Cg4Nt/aqvry8+Pj4EBAQQEhJCcHAwgYGBKiCtKIq0YQOMGFGiQ4SA8+dlPOX99yE+Hp54Ap55BsoixdKXh75kwf4FbHpoE11rdb31CpWbMmnSJDIyMvj8889VYlWl3JR5UCQ6Opr33nuP2rVLFjxITU3ljz/+4IsvvgDkRa+DgwNr1qyxjTgZPXo0Xbt2rfCgSEKsGW99Ksk5SXg5XX9+ekWyWCxkZWWRmZlJeno6CQkJdiUuLo6YmBi7kpiYiMVisdXh7OyMq6ur7eTWeqJrNBqLFEdHR9zd3W376PV6dDodOp0OrVaLRqMhJyfHFnBITU3l0qVLJCcn2wIwKSkp1xy27ezsbDtRtt5pbN++PQEBAbbi5+eHj48Pbm5uZdoxmkwmUlNTbXdDrSUmJoYLFy6wb98+vv32Wy5duoTZbMbBwYG6detSr1496tevT82aNfH19bUVHx8f3N3dcXBwUB24UiW0bduWoUOH0rJlS/R6PS1atOCxxx7DxcWFmjVr0r59e0COIHn11VfZv38/CxYsYNGiRZw4cYLx48ej1WqxWCxMnTqVsLCwSv5EBY4ckY//rBgMEREyi15AwHWP2xO1hx51epRv40rg0qVLrFq1il9//bVc36dmzZqMGjXKbpsQguzsbPR6PQaDoUz6LYvFYutDL1y4QGRkpC34ffToUeLi4rhy5QoxMTG2Y/z9/WnUqBFNmzalWbNmNG3alPDwcJUPpZJYLBa7YJmVi4tLlb0xYDKZiI2N5cqVK0RHR5OSkkJeXp5dsZ4HFT4fefDBBwkOVlOkq4QrV2THvmTJDXdNSIBff4WtW+ViNRcvgr8/PP64XHL3Bmmlbtrf0X8zfu143un5jgqIVKDly5fz5Zdf8scff+BVVn9MRSlGqYMiPj4+RU6a8vLySE9Px9HRkdWrV5eovvPnz+Pj48OYMWM4dOgQrVq1Yu7cucTGxhLwz4mtv78/sbGxRY4t7wR/8XHg45hGUnbZBkUyMzO5ePEiFy9e5MKFC1y8eJH4+HjS09PtSm5uLvn5+XbDmjMzM8nOzi5Sp5OTk20EhY+PD/7+/rRs2dIWUPD19bW741fRJ5pms7nIqAyQQTBn58rL1q3X620jWepfJwNXfn4+Fy9eJCIiwlYOHz7MunXriIuLIzExsUjQxxpEcnR0tAWQtFqt7WdrYMkaZLIGmAoTQthG45jNZiwWi+0Yg8GAwWDAwcEBJycnXFxccHZ2tgW83N3d8fDwuG7R68tl0JhSxbzxxhu88cYbdttMJlOx+1pHkQB06NCBI9bIQxV0+LBcdca2QEFEBISGXne9xTxzHgeiD/DynS9XTCNvwrx58wgLC6NHj4oP1Gg0mjLvg7VaLTVq1KBGjRp06NDhmvvl5eURFRXF5cuXuXjxIseOHePw4cOsWrWKK1euoNVqqVevHk2aNKFx48Y0adKEJk2aULdu3WuObvqvMplMJCcnk5mZiclkwmw224pGo7H73tFoNCQnJ9tGaMbFxREXF0dkZCSRkZFcvnyZqKioYvsInU5HcHCw3RQp642CevXqUb169XK9ISCEIC4ujuPHj3Ps2DFbiYiIIDY21vY97OzsjJeXF0aj0XazzWAw2AV0rO3s27evCopUFRs2gI8PtGhx3d3MZmjfXqYe6dZN5gy56y5o2LBsl9tNzEpkyMohDGowiGfaPVN2FSvXdeDAAR5//HHefvtt240bRSkvpb4SmjhxYpEvPEdHR4KCgujTpw/Vq5cs94bJZOLAgQN8+OGHtG3blqeffpqZM2fa7aPRaIr9kp04cSITJ04EKJe7lwnJWrydszh3i0GRxMREtm3bxubNm9myZQunTp0CZEAgJCSEWrVq2YIWtWrVsuW+MBqNtgtf60Wws7MzLi4utuLm5oa3t3elBhZuhk6nw9PTs7KbUWoGg4HQ0FBCQ0Pp27dvkddNJhOJiYnExsbapiVZpyTl5OTYBTWsPxcuJpPJbiQPyJM/ayClcLHuX3j+f3Z2NllZWWRlZREfH8/58+dto3aspbiglLOzs20KkrV4eXnZBdisQTZ/f38CAgJwd3dXo2CUKqFIktWICKhX77rHHI49TK45lzsC7yjfxt2ktLQ0Fi5cyNy5c/9z/185ODhQu3btYkeYJiQkcOTIEY4cOcLRo0dZv3497777LhkZGbi5udGiRQtatmxJq1ataNasGfXr17+lKTh5eXm2IEFKSoqtFO4/C5fs7Gy7kpubawtQW0dgOjk52QLV1mI0Gu1GWlqDFIVZg+HW7weLxYLJZCIjI8OuDWlpabbRmIWnmd4so9Fom9Lk6+tLUFAQPXr0ICgoiODg4CIBDiEEycnJtps5Fy5cYMuWLSxcuJDo6GhA5pgLDQ0lKCjIFhirUaMGfn5+uLq64urqiouLC66uruj1etv3pPX3mJGRYff7T05OJioqyhasiYyMJCcnB61WS926dQkPD6dz586MGzeOwMBA2/uV9ahSpYJs2AC9ehVaTqx4v/wCly7B5cvg61s+TTFbzDyw+gFcHVxZPHCx+vdUQf766y969OjBkCFDmGJLGKYo5afUQZHXX3+9DJsBQUFBBAUF0bZtWwCGDh3KzJkz8fPzIzo6moCAAKKjo/Etr17vOhJSDHi5ZZOVn1WqoMjGjRuZOnUqf//9Ny4uLrYv7vbt21O7dm38/PzU3a5/Cb1ej5+fH35+fpXdlGuyjtaxnuSnpKSQlpZml6w2NTWVpKQk4uPjOXHihG06Vnx8vO0OnJOTE/7+/rbP6+fnZ3t+9ZSnf/PQdyGELTBlsVjsRvUIIYoU64VN4WBYcHCwSkR5C44fh06Fc6VGREC7dtc9Zk/kHupUq4OPi0/5Nu4mLV68GCcnJ0aOHFnZTalSvL296datG926dbNtE0Jw/vx5Dhw4YCvLly8nISEBnU5HvXr1CA8PJzw8HG9vb7sbCjqdjtTUVLtpktYcVtYppoVZR9NZc1dZb1a4ubnh5+eHs7OzXQDEaDSSl5dnl6fLWrKyskhKSrJd0FsDHdZ+oDiFRxNqtVr0er0tYXhwcLAtd5eXl5dttKOXl5ct2GA9VqfTFQmyCCGoVq0arq6uZXahl5GRwdmzZ4mIiODMmTNcuXKFK1eu8Pfff3PlyhViY2PJycm5YT0Gg8EuEbqHhwc1atSgffv2DB8+nODgYIKDg6lfvz6Ojo4IIUhNTSUtLc32+aOjo4mMjESj0dhGiFhHgiQmJtpNp3F1daVGWSSdUG6d2QwbN8IHH9xw148+gvvuK7+ACMDr215nd+Ru9o3bh4uDS/m9kWKzf/9+evbsycCBA/n888/VNZJSIUodFDl06BBRUVH061d0KcN169YRFBREU1vWuxvz9/cnODiYU6dO0aBBAzZv3kxYWBhhYWEsXbqUqVOnsnTpUluiuIoUn+GEW7D8Ei9JUCQ3N5dp06bx/vvvM378eD788EPatGmDwWAor6Yqyg1ZR+uUZsSOyWQiLi7Olvw2JiaG2NhYWzl16pQtQW5aWprtODc3N7tku9WrV7fdLbVOLXJwcLBNHbq6lJfCSYKtFyi5ublFLmSsd2atwaOMjAzbyfS1pp+UxJ49e7jjjqoxYuF2lJwsR1rbRETAVTkzrrYnag9tA9uWb8NukslkYu7cuTz55JMq0ehN0Gg0tqTXQ4cOBWSgJCYmxm4qxebNm0lJSbEbUWc2m/H09LRNI/X29qZ27dq2UXDWYs0LVVXzZlRVrq6uNGvWjGbNmmEymdBoNEWmhJpMJlu/mpGRgclkwsnJCZ1OZwskZWZmkpaWRufOnXF2duaTTz4hMjKSlJQULl68SK1atejfvz87duxg8ODBJCUl2QJLX375JaNGjeKFF16wW3a88AhLHx/7YOjYsWNZvHhxxfySlOvbv1/Oh+nV67q7nTwpYyd795ZfU47HH+ftP99m1bBV1Kt+/dGHStm4OiCi+mClopQ6KPLMM89w5513FhsU2bdvH++99x6bN28uUZ0ffvghDzzwAHl5edSpU4clS5ZgsVgYPnw4ixcvpmbNmqxcubK0TS61hCxnfKrJXCU3GxQ5fvw4999/PzExMaxbt44+ffqUZxMVpULo9XrbsOQbycrKsgVPCq92lJiYSFJSEtnZ2aSmptqtdmS9k1n4sTwVnmNvLUaj0W64e/Xq1Yss5+zq6mo3R93BwQG9Xm9XT3HBHesFQuE7uHq9Hu9SLjmoSGlphfKJZGVBZOQNp8/sidrDhNYTyr9xN2H16tXExcXx+OOPV3ZTblsajcY2Oq0ycrL8F6WkpHDw4EG7aS3jx4+nSZMmDBkyhB9++MG2b40aNYiKiiI2NpYGDRrYRu7o9XomTZrE888/z1tvvcUrr7xiO8ZgMHDs2DHq1atnC3BZR41Ygxr16tVj3rx5tmC7u7s7/v7+gDynfPfdd22j+awrIel0Og4fPmzXh7u6ulbgb065rg0bZC6RG4y4nT8f7rgD2rQpv6a8sPEFutfuzuCGg8vvTRSb/fv306NHD+655x4WL16sAiJKhSp1UOTAgQNMnTq12Nfat/9/9u47Lur6D+D469hDpiIKiCCgOTDc5ihHWu7cmqamppnl+FlqpqbmSLPU0rQ0V67Mkebeey8cOAEF2Xuv4z6/P04uEVBAuAP9PHvwgLvveh9eX773/n4+7/dbLFq0qMD79PLyyrWVb0GTK0UtIt0SL9tgAGxMbF64/m+//caYMWNo06YNBw8ezHFHQpJeB2ZmZri5ueHm5qbrUKRXXLakiK+v+vtzkiIxKTHci7pHI6eSMVJkwYIFDBw4UCbHSomYmBhu3ryJv78/YWFhREVFMWrUKBwcHBg8eDChoaGaBKq7uzsTJ04kIiKCP//8UzMyDqBBgwZ4enqyZcsWDhw4QEZGhiZ5unjxYkxNTZk6dSrGxsbY2tpiY2ODh4cH9erVIz4+nrCwMMzNzTExMSEjIwMrKytMTEwICAggMTFRk3g1NDSkcuXKCCFQKpUvHK2akZFBcHAwjo6OGBgYsGjRIu7fv09AQACBgYF4enqydu1aTp8+TadOnbC3t8fR0ZFKlSpppsaMHz+e0aNHo6enh1Kp1Hy4sbS0ZPXq1dmKx1evXh2AoUOH0r17d6ysrLCxscHExEQzwmTLli25xlq+fHn65NG2Na/RkAqFAk9NqyqpxNm3D95777mrxMfD6tWwdGnxhXHY7zB77u/h6vCrso7Ic1y9elVTRy9r9JeZmRk9e/bMd7IxJSWFBQsWMHv2bHr06CETIpJOFDopkpmZSVJSUq7LkpKSNBn5V0Gk0hr9sslYGFlgqP/8i4k9e/bw2Wef8csvvzBixAh5IpUkSSpGQqgvkC0snjxx/776wXMmmV8IuoChniFeFby0EuPz+Pv7c+7cOX799Vddh/JaS0lJISYmhtjYWGrUqEFmZia//fabpiZGcHAwb7/9NpMmTWLz5s18+umnmpEp5cqV0xSwbty4Mffv39cUQQ0PDwfUNSzWrVunqTUihGDatGl4enpqCrgaGhpqag9luX79OmFhYcTExBATE0ObNm1Yt24de/fuzZEM2LNnD+3atWP48OHs27dP83z58uUJCwsjIiJCUwclq0bK8OHDGTNmDEuXLuWHH34gNTWV0NBQhBDcv38fd3d3jh8/jhACV1dX3n77bc3U6LZt25KWlpZrkqVxHjV9TE1N+eCDD3JdljV1SXqNxcTA+fMwe/ZzV1u7FkxNoWfP4gkjU5XJuAPj+NjrY96s8OaLN3gNJScnM3LkSFavXo2xsbGmcHKZMmUICwtj3LhxjBgxgi+++CLP/6+FEPz1119MmDCB1NRUfvrpJ4YOHSpriEg6oRCFHJ/eqlUrjI2N2bt3b45l7dq1IyUlhWPHjr1sfAVWo0YNfHx8imx/qrQMjEwUjPlmNVvKzeThmId5rhsbG0utWrXo3bs3P/74Y5HFIEmSVJoU9Xn4eVJSwMwMzp59Ult17lzYvBkuX85zmxnHZ7Dr3i4ufFKMk9Hz6aeffmLx4sX4+vrKJPoTGRkZnDlzhocPH/Lw4UMCAgJo2bIl/fv3Z/Xq1cycOZOkpCTNdLRDhw7xxhtv0KVLF4KDgzVtyGvWrMl3333H/fv3mTBhgmaaXlJSEl9++SXdu3fnf//7H7/++itpaWma46ekpGBsbEydOnWwtbXVTBls3rw5nTp1Ijk5GYVCodPiyKmpqURERJCUlKTpeFOpUiXKlClDTEwM6enpmjpJoC5mn5aWxoULFzQFtuPi4qhTpw5NmjTB29uby5cvY2RkpBn14eLiItu1a9GiRYtYvnw5Qgg++eQTxowZQ3R0NL179+bhw4e4uLiwefNmbGxyjlhes2YNM2fOBGDy5MkMHDgwz+No8/xcYFu2wMcfQ1QUGBnluooQUL069OgBT15ykVt9bTUj94zk/hf3cbCQBXifdfv2bXr27ElCQgKbN2/WNMnIkpKSwtq1a5k/fz6BgYF89NFHdO2qnoKUVeg5JSWFX375hcuXLzN27FgmTZqEpWbIpyRp30t1n3n33Xdp1KgRAwcOpEKFCoSEhLB27Vq8vb05ePBgUcapM7GP4sikHCqb+BfWExk3bhxmZmZ89913WopOkiTp9ZZVz1dzLZWPdrwlqcjqtm3b6Nat22udEFEqlRw9epTdu3fz008/kZGRQcuWLXFwcMDFxQVnZ2fNaAQvLy8mTJiAubm5ZlRFVrev3r178/jxY00HrayitVkf9I2NjTE2NqZMmTJUqVIFgIEDB9KmTRtsbGywsbHB2toaY2NjFAoF165dyzVeMzOz4v+lvICJiYmmk8qzcvvQDOrWu82bN891WVZxVEk3bt68yfLly7lw4QJGRka8//77dOzYkd9//53WrVszceJEvv/+e77//nvmzp2bbdvo6GimT5/OpUuXUCgU1KtXj86dO+f5PijR9u2DVq3yTIgAHD4MDx5AcZVgSs5I5psj3/BVk69KXEIkMzMTPz8/rl+/zvXr16lQoQKDBg3SaoJ2/fr1DB8+nJYtW7JmzRpsbXN+NjI1NWX48OEMHTqUHTt2MH/+fFavXq2po5Y1ta9NmzasX78+13bskqR14iUcPXpUNG7cWOjp6QmFQiH09fVFkyZNxIkTJ15mty+levXqRbq/u3t9BQjx+fIpovWa1nmut3fvXqFQKMTJkyeL9PgqlUrcuHFD3LlzR/j6+oqAgAARHR1dpMeQJEk3VCqVUCqVug6jyBX1efh57t0TAoQIDHzyxNtvCzF5cp7rq1QqUXZuWfGn95/aCfA5goODhUKhEKdPn9Z1KDpx+/ZtMWrUKGFvby8MDAxEhw4dRGRkpBBCiNTUVB1HJ0nas3nzZjF48GDN4xkzZoi5c+eKqlWriuDgYCGE+nxRtWrVHNtu2LBBDBs2TPN42LBhYsOGDXkeS5vn5wJRqYRwchJiyZLnrta5sxA9ehRfGDOOzRAV5lcQCWkJxXeQAkhPTxe//vqraNiwoTAzMxOAsLCwEE2aNBFly5YVFSpUED/99JNISkoq1jhSU1PF8OHDhb6+vpg3b57IzMws1uNJupWZmSnOnDkj/vzzT3H06FEhhBBKpVIkJyfrNrBi9FKTtlq0aMHZs2dJSEggMDCQ+Ph4Tp8+neediNIo4lEyAKmWEXmOFImLi+OTTz5h9OjRNGvW7KWOJ4Tgxo0bzJkzRzPEzNPTkzfeeAM3NzecnZ358ssvAdi0aRPVqlWjefPm9OjRg//97388evQIAF9fX0JDQ8nIyHipeCRJyj+lUqlpC7l9+3a+//57xowZQ58+ffjiiy8AdZHqrJbE+vr6muelwinoSBG/GD+iUqJKxEiRHTt2YG9vn2f9hVfd3bt3uXLlCtOmTSMkJIRdu3ZRtmxZANmaWHqt1KpVi5MnTxIVFUVycjJ79uwhMDCQsLAwKlasCKhrroSFheXYNigoKNuoIScnJ4KCgrKts2TJEmrUqEGNGjWIiYkp3hdTWLdvqzuHPadb48OH8O+/UFx/NkMTQ5l7ei4zW86kjJFuOxIJIdi6dSu1atVi4sSJtGrVig0bNuDn50dcXBynT5/G39+fsWPHMmfOHFxdXZk/fz7JyclFHktISAgtW7Zkx44dHDt2jK+++krW/dAiIQQ7duxApVIRFxdHr169mD17Nnv27CEyMrLIjzdz5kwcHBxo0qQJX331FePGjQPUf7PNzMxo0qSJzpugFIdCT59JSEggMTGRihUralpXZgkJCdG0rSztIh+nUoYE4hXReSZFxo0bh4mJCbNmzSr0cZKTk1m4cCGrVq3iwYMHeHp68tFHH+Ho6EhERAQZGRmaL4snFQW9vLwYM2YM4eHhhIaGcvfuXU0SpGvXrty4cQMAKysrmjZtyu7du7l//z6jR4/G0NBQ8/Xxxx/Ttm1bNm/ezMmTJzUt6oyNjfn6668xNjbm999/x8DAQPNv7ezsjJeXF3FxcYSEhGiez8zM1FTAv3PnDmFhYZr4lEolY8eOJTQ0lF69ehEXF6cZ5jx27FimTJnC7NmzmTVrFnp6epphdpcvX6Zy5cq0bNmSsLAwTRu9Bg0asHjxYm7fvs2sWbM0LVMtLS354IMP8PT0xM/Pj4yMDCpWrFjkcxWFEKSlpWFiYkJcXBxXr14lOTmZpKQkkpOTady4MdWqVePixYs8fPhQ8/srU6YMb731Fnp6eoSEhGRrTWhsbIyhoSEZGRkkJydrqvMrFApsbGwwMjIiLS1N09a1KIbciydlhbQxfF+lUpGenq75srS0xMjIiEePHhEXF0dKSgqpqano6+vTrFkz4uPj2bFjh6adYkZGBo0aNaJhw4acPn2ac+fOZStKOHz4cCwsLFi6dCnx8fGa4fUuLi707dsXX19fVq1aRWZmJkqlEqVSSb9+/ahfvz4rVqzg7Nmzmja9hoaGLF68GJVKpfmDkNUZokmTJnTv3p09e/bw448/EhsbS0REBEFBQZw/f5769euzcuVKQkNDqVixIhUqVMDd3R0AFxcXfvvtN0xNTTExMcHZ2bnYf++vsif1LSlTBkhMhJCQ5yZFzgedx9bUFndbd+0E+Bzbt2+na9eur93F5ZUrV6hcuTJdunShS5cuug5HknSuevXqTJgwgbZt22Jubo6Xl1eO7htZf38KY+TIkYwcORJQ1xQpkfbtA3d3eDK1LTe//Qa1akFR3Hu9c+cOp06dokWLFpq/z1OPTqWKTRUGeQ167raRkZHcvXuXe/fucf/+fe7fv09aWhojR46kbdu2L309derUKb766isuX77MyJEj+eabb3LtTmZhYcH48eMZOXIky5YtY968eWzcuJGdO3fi6Oj4UjFkOX/+PF27dqVSpUpcunSpyPYr5U96ejojRoxg/fr1nDt3jgoVKmBpacn27duZMWMGCoWC9evX061bt0IfIzo6mnnz5vHmm2/St29fnJ2dmTFjBp06ddIkZUF9/XrixAlWrFhB27Zteeedd1izZk2eUzlLm0InRYYMGYKVlRXLly/PsWzatGnExcWxadOmlwquJIgMVWKnH010ajRutjlbi+7fv5+VK1dy/Pjxl5pnfPToUX7++WfGjh1L9+7dNSdoIM82jW+88QZvvPFGrstOnDhBcHAwUVFRREZGauZjm5iYULNmzWxJlqxCavHx8QQFBWk+fKanp/P1118D8PvvvxMdHU1ycjLJycl07NiRDRs2sG/fvjwr4I8dO5Z9+/ZRtmxZKlSogIeHB2PHjsXc3Jx3331XUwzP0tJSU82+R48e1K5dG5VKhUqlIiMjQ/P6hw0bRkxMjKZYnoODeq6nQqHAxMSEmJgYAgMDiYuLo169enh6evLNN99o3odWVlZUqVKFS5cukZmZydy5czVtDm1tbfHw8KBKlSoEBARw+/Zt0tLSSE5OJiIign79+mFra8uwYcM03QDCwsKoU6cOp0+f5ubNm7Rs2RJTU1PMzMwwNTVl/vz5VKtWjfXr17Nu3TqUSiVJSUmaD+NCCBwdHbN9qP/oo49Yu3YtGzZsYNCgQdl+r4cPH6ZVq1a0a9eOo0ePoq+vj7m5Oe7u7ly+fJng4GDatWun+WOsUCgYMWIEw4YNY+HChcyfP5/U1FRNK0QfHx8qV66Mi4sLAQEBmiRUixYt2LdvH2fPnqVPnz6aD+8mJiZMnTqV9u3bM3/+fA4fPqxpt5iZmcmhQ4dQqVQ0bNiQ1NRUTYKjR48eLFq0iJUrVzJkyJBsr+n48eO8/fbb9O/fn1OnTmmer1q1Knfv3iU6Opqvv/4aQ0NDjIyMMDQ0xNjYmIYNG3Lt2jU2bNiQ7eLjo48+wsLCgl27dhEREaG5iGzQoAF9+/YlLi6Os2fPYmBgoJnXmpiYCKB5XwkhNKM9QJ008vPzy9YVwsnJCVD/v9mkSROsrKwoW7Yszs7OVKtWDYB///031/83bW1t6dGjR67LpILL6jyjp0e+2vGef3yeho4NdV7DIyYmhiNHjmhG/r0uzp8/T9u2bfnqq6+YPHmyrsORpBJjyJAhmr+RkyZNwsnJCXt7e0JCQqhYsSIhISGUz6WrlqOjY7bGBo8fP6ZFixZairoI7d//3Fa8QsDff8OIEfCyp+9t27YxYMAAjIyMiImJwc3Njbod6rLFdgubO21GT5EzUf348WO2bNnC5s2bOXv2LKC+FvDw8MDDw4OEhAQ6dOiAl5cXkyZN4oMPPihwwjsxMZFx48axfPly+vTpw/r16zX1j57H3NyccePGaQqaNmzYkJ07d1KvXr0CHf9Zq1at4tNPP6Vfv378+uuvmnbiknZERkbSvXt37t69y/Hjx/Hy8gJgxYoVgHp08qZNmzSFbo8ePcrbb7+d73bGiYmJLFq0iB9++IGyZcvSqlUrAAYMGJDr+mZmZjRv3pzmzZszYcIEFixYQLly5RBCEBgYWOpv8hW6+0yFChVYtmxZrq3VduzYwYgRIwgODn7Z+AqsqKtqf9/5DNsOWpC5aBB9a/XlyybZL2C9vLxo1qwZixcvLvC+Dx48yLx589i6dSsWFhakpKSUiAJuBZGWlpYtWWJgYICzszPm5uakpqZiaGio017j6enpREREEBwcTEBAAJGRkQwfPpz4+HjatWtHdHS05mvUqFH8+OOP/PTTT0ycOBFjY2NMTU2xs7Nj27ZtVKtWjR9//JHU1FQqVKiAvb09lStXxtPTU1PhPz9/ANPT0zEyMkIIwb179zRJioyMDGxsbHB3dycyMpKAgADNh3eAypUrY25ujo+PD9HR0aSkpJCcnIyenh6dOnUiPj6elStXAmg+wDdq1IimTZty7do1bty4gYmJiWaEUMuWLTEzM+P8+fOkpKRokjVWVla89dZbhISEsH///mwJjg4dOlC7dm3+/vtvTXIpK8asAsPz58/XJFJMTU3x8PCgYcOGBAcHc//+/WwjkVxdXTE3NycqKgpQF+cyNjaW/elLOW12N1i3DiZOVI+6ZssW+OQTiI7O86q58YrGvOf2HtNbTtdKfHn5888/GTVqFOHh4bm2NH0VXbhwgTZt2tCjRw+WL1/+2o2QkaTnCQ8Pp3z58gQEBNC2bVvOnTvHrFmzKFu2rKbQatYd3adFR0dTr149rly5AkDdunW5fPlyrgUwoYR2n0lJAVtbddajY8dcV7l+Hd58U537zkeeIFcqlYpvv/2W2bNnM2PGDL7++mvu3r3Lvn37mPF4BvGh8ag2qLCyssLd3R13d3ecnZ05c+YMp0+fxtnZmV69etGtWzfefPPNHNfs/v7+zJs3j5UrV+Lm5saECRPo27cvRs8pHJvl9OnTDBgwgMzMTNasWcM777xTqNeYmprKsGHD2LJlC2vXri3UTRgfHx9mz57Npk2bWLhwISNHjtT5jYTXUY8ePXjw4AE7d+58YcIhPDwcNzc3qlSpwrfffkuHDh1eOA21devW3L59mylTpjBkyJB8vU9zs3v3bnr27MmcOXP44osvSu/f9sIWIzExMRH79+/Pddm+ffuEiYlJYXf9Uoq6gNS4JmdEO5szwmWhi/jjyh/Zlj1+/FgA4vLly/ne3/3798Xo0aPFG2+8IfT09MSQIUM0heUk3VGpVCI9PV3zsyRJhafNQn5LlgjxxhtPHsyeLUT9+nmum5qRKoy+MxK77+3WTnDP8cEHH4gBAwboOgytuXbtmrCyshIff/yxLNAnSblo1qyZqF69uqhdu7Y4dOiQEEKIyMhI0apVK+Hu7i5at24toqKihBBCXLx4UQwZMkSz7R9//CHc3NyEm5ubWLly5XOPUyILrR44IISBgRAJeRc3/fZbIby8Cn+I2NhY0bFjR2FpaSn+/fffbMs23tgojL4zEj4hPsLb21ts3bpVfP/992Lo0KGidevW4n//+584d+5cvq8Pg4ODxbhx40SZMmWEk5OTmD9/voiLi8t13bS0NPH1118LPT09MXjw4DzXKwiVSiVmz54t9PT0xHfffZevgu4qlUocOXJEtG/fXgCiefPmOm2coSve3t7ijz/+EDt37hRnz54Vvr6+Wi+In3W84OBgkfCc/yeeFRQUJIYMGSJMTEyElZWVOHbsmGZZdHS0+OOPP8R7770n5s+fL4QQ4u7du0VSoFelUolly5YJc3Nz0aJFC+Hn5/fS+4yJiRGLFy8WLVq0ECkpKS+9v/wodFLE09NTjBo1Ktdlo0aNEjVr1ix0UC+jqE/2A6qfFwOcDgvLOZZi++3t2ZatXLlS2NnZvfACz9vbW0yaNEkIob4wbNWqlZg3b564e/dukcYqSZJUEmjzonvOHCEaNnzy4OOPhejbN891zz8+L5iGiEiK0E5weUhMTBQmJibin3/+0Wkc2hQbGyu2b98uEyKSpGMlMiny9ddCNGny3FU8PYWYPr1wuw8ICBDVqlUT1apVE3fu3Mm2LDEtUTj95CS+PvR14Xb+HNHR0WL27NnC3t5eWFlZiYkTJ4o///xTzJkzR4wcOVJ06dJFuLq6Cjs7u2L5e7B161ZhZmYmnJycxLRp00RAQEC25SqVSvj4+IhFixaJevXqCT09PdGjRw9x7ty5Io+lJMvIyBBBQUFCCCG+/fZb4eTkJMzNzQUgAE2iqkqVKsLDw0PUr19ftGnTRixcuFAIIURKSorIyMgoklhiYmLEm2++qUmMFkZcXJxYu3atiIqKEiqVSrRo0UIYGhoKW1tb8cknn4izZ88WSazP8vX1FW+//bZo0qSJUKlUIjQ0VPj6+oro6Ogcf/sDAgLEjRs3xLlz58SJEyeEt7e3EEKIR48eiQEDBghTU1NRvnx5MX78eBETE1Ms8T6r0DVFvvjiCz799FOMjIwYNGiQZr7jmjVrWLJkCUuXLi2qwSw6FRlvRFWrNOLT4nMUWt23bx9t27Z97jChVatW8dlnn9GpUycA3nzzzVeyYq8kSZIuxMc/03nmyZzY3Jx/fB43GzfKmeVep0lb9u3bh56eHm3bttVpHNqiUqk0BbAlSZJyOHoUWrfOc/H9+3DjBmzYUPBdJycn06VLF8qVK8fu3buxsrLKtnzu6bmohIpJzScVfOcvYGNjw9dff83YsWNZt24dCxYsYM2aNTg5OeHk5ISzszPNmjVjwIABudaLKYyYmBgePHiAr68vaWlpPHr0iD/++INly5YxY8YM2rdvT/v27blw4QIHDx4kKCgIFxcXOnfuzF9//YWbW876ia+qlJQUfvnlFxYvXswbb7zBgQMHmDp1KtOmTQMgKSmJiIgITYOL2bNnExsbS2xsLNHR0ZrpU2vXrmXMmDE0aNCAGTNmFHrqU2ZmJn369CEtLY369esX+nVZWlry0UcfafbZr18/TQej4pyuW6VKFY4ePUp4eDgKhYIff/yRH374AVDXOTQ2NiYgIAA7Ozvq1KmjmToP0KFDB3bt2sXdu3cJCwvjzz//pFOnToWe0lMoL5NR+e6774SZmZnQ09PTfJmZmYk5c+YUVdKmwIo6A97A6q6Y3HSrYBriRtgNzfNKpVLY2NiItWvX5rpdcnKyGDx4sDA0NBSLFi2SUzIkSdK5n376SdSoUUPUrFlT9OnTR6SkpAg/Pz/RsGFD4ebmJnr16iXS0tJy3Xb27NnCzc1NVK1aVezbt++5x9HmnciRI4Xo1u3JA3t7If78M891+23tJz7c+qF2AnuODz/8UPTo0UPXYWjN1q1bRe3ateUoEUkqAUrcSJH4eCH09YV4zp3xuXOF8PAQoqCX0iqVSvTq1UtUrlxZhIeH51juH+MvTGaaiD+98/67UVqoVCrx7rvvakY3lCtXTnTq1EkIIcSxY8cEIBwcHIS7u7uwtbUVXbt2FUuXLhUPHjwocZ9RAgICxOXLl8WhQ4fEzp07xfXr14v8GDdv3hQ1a9YUFSpUEHPmzHmpUgYRERFix44dYtCgQUKhUIhhw4YVauTIuHHjhI2Njbh3716hYylJEhMThb+/v7hy5Yo4cuSI2L17t2YqzKNHj0R4eLhISkrS+vSkvBR6pAjA5MmT+eKLLzh79ixRUVGULVuWt956K0cWtjSLTDXHuGwaQLaRIpcuXSImJibPO31paWn4+/tz4sQJGjdurJVYJUmS8hIUFMTPP/+Mj48Ppqam9OrVi02bNrFnzx7Gjh1Lnz59+PTTT/njjz8YMWJEtm19fHzYtGkTt27dIjg4mHfffZd79+6ViIK4mpEi8fEQFvbCdrxfNPxCe8HlIj09nV27drFs2TKdxqFNf//9N1WrVi29xdckSSo+p06Bvj40aZLnKtu2QbduBe86M3v2bHbt2sWZM2ews7PLsfzLA19Sp0Id+nn2K2jUJY5CoWDBggXExcVRq1atbJ/F3n77bW7dusXBgwc5cOAAhw8f5rPPPuPdd9/VYcRqjx8/Zu/evezZs4cFCxbg4uJC+/btuXnzJnp6epiYmODp6cm5c+e4evUqAwYMoHLlyjg4OODg4ED37t3x9PQkLS3thYVFn3bx4kXc3Nw4ceJEnkWJ86tcuXJ07tyZzp07M2DAAPbv34+BgQEZGRn5Hpnx6NEjfvvtN7Zv347Hc65jShNzc3PMzc1xcXHJsawkdqp56SsUKysr3n//ffr168f777//SiVEACIzrDAomwqAjYmN5vn9+/fj5eWFvb19tvXDw8M5ePAg1tbWHDlyRCZEJEkqMZRKpabTUHJyMhUrVuTIkSOa6vQDBw7kn3/+ybHdjh076NOnj6ZjkLu7OxcuXNBy9LlLSHiSFHnwQP1EHhcTUclRPIh+QCPHRtoLLhdHjhzRdHJ6HaSkpPDvv//Ss2dPXYciSVJJdPQoNG4Mpqa5Ln78GM6fVydFCmLHjh1MnTqVtWvX8uabb+Y8rP9Rtt3exs/tfi71nVW2bt3KzZs3qVWrFk2bNs3xWUyhUFCjRg1Gjx7N7t27efToEa1btyYhIYE2bdrw77//IgrXjLTQzp49i5eXF5UqVWLKlCnY2NigVCoBOHDgALGxsWRkZJCUlMTRo0cBKF++PEOGDMHDw4P4+HiOHDlCYGAgoG4jW7NmTSZMmMDJkyc1+3pafHw848ePJykpSXO987IJkWe1bNmS77//HoARI0bwwQcfcPbs2ef+ftPT06lcuTIPHz4sEYmq19VLjRQBOHXqFPfu3SM1NTXHss8+++xld69TaWmQoCqDvl0ypgammBr+d8Lev38/7+XST33GjBkcOXKEGzdulIi7qJIkSQCOjo58+eWXODs7Y2pqStu2balXrx7W1taats9OTk4EBQXl2DYoKChbgje39ZYsWcKSJUsA9ZxmbdGMFLl/X93SMY8LnKuhVzHQM8CrgpfWYsvN5s2bad26NZaaQiivtn379pGZmUn79u11HYokSSXR0aPwnCTxP/+AkxMUpMTCjRs36N+/P1OnTqV79+45litVSsbsH8Mgr0HUdyh87YaSwNvbm/79+zNv3jxq1aqVr22ybuimp6fj5uZGt27daNiwIatWraJq1arFEmdQUBCbNm3i+vXrrFmzBhcXF7p168Yff/xBnTp1so0krFixYrZtTZ8kzBwdHRkzZkyu+58yZQo7d+5k9+7d/PDDD9StW5dLly7h7e2tqa8RFhaGpaUlw4YNw93dvVhe59M+++wzvvnmG5o0aUK9evUYP348vXr1AsDX15ctW7awdetWkpKSOHz4MBUqVCj2mKS8FTopEhYWRuvWrfHx8UGhUGgyYE9nW0t7UiQyQgAKRNkkbA3+u9COiYnR9I9/2v379/ntt9/YunWrTIhIklSixMTEsGPHDvz9/bG2tqZnz57s27evyPY/cuRIRo4cCUCNGjWKbL8vki0p8pwhp9fDrlOtbDWMDfI/vLao+fv7s27dOjZv3qyzGLStfPnyzJgxgzJlyug6FEmSSpq4OLhyBebPz3OVbduga1fI7+y7q1ev0rVrV9577z2mTJmS6zorrqzAP8af/f33FybqEiMuLo4ePXrQqVMnPv/88wJvX7ZsWZYtW8b48eP59NNPqVOnDvfv38fBwaHQMSUlJXHy5ElcXV2pVq0aCxcu5Pfff+fOnTs4Ozvz4YcfkpmZScWKFZk6dWqhj/OsWrVqUatWLSZNmkRERAQPHz4E1H+Dhg4dCoCxsTF9+/bV2k2JunXrsnfvXu7du8eSJUu4fv06vXr1YtKkScyZMwd3d3d69OhBjx49csw8kLSv0EmRcePGYWVlRWBgIJUqVeL8+fPY29uzbt061q5dy+7du4syTp2ICEwFTFGWjcc2/b+kyOHDhzEzM6PJM/Mfv/nmGxo3bqzpNCNJklRSHDp0CFdXV8286m7dunH69GliY2NRKpUYGBjw+PFjHB0dc2zr6OioGaIK5LmeLsTHg4UFcPP5SZEb4TeobV9be4Hl4ttvv6Vu3bp06dJFp3FoU9OmTWnatKmuw5AkqSQ6cQKMjNTTZ3IRGQnHj0MeuY1sVCoV8+fPZ/LkyXzwwQesWrUq1zpGMSkxTD4ymclvT6ZCmdJ7Z14IwZAhQ9DT02PFihUvNQWoSpUq7N+/n1OnTuHg4EB4eDhJSUm4urrmO5bz58+zcuVKNm3aRFpaGvPnz6datWrUqFGDESNGUK9ePRo3bqyV2lJ2dnaaa52KFSsyatSoYj/m81StWpVFixZpHvfr148+ffrg6elZ6qduvUoK/c48fvw448aN0wxxEkLg7OzMpEmT6N+/f6kfJQIQ+TARPTJJtYzLVmR1//79tGzZMluboLi4OC5cuMC8efPkG1ySpBLH2dmZc+fOkZycjBCCw4cPU6NGDVq2bMmWLVsAWLNmTa4f2Dt37qy50PH39+f+/fs0bNhQ2y8hVwUZKaLLpMiNGzdYt24d33///WvzN+Lw4cNFeidQkqRXzNGj6gKreRTI3LkTbGygefPn7yYwMJDWrVszc+ZMVqxYwV9//YW5uXmu6844PgNrE2tGNxr9stHrXJs2bdiyZUuRjHxQKBQ0f/KLXrJkCbVr12bq1Kl4e3vnWg9DCIGPjw/p6emkpqbSrl07/Pz8WLZsGbGxsXzxhbqoedu2bfniiy9o0qSJLLb9RM2aNaldu/Zrcy1QWhT63RkbG4udnR16enpYWloSHh6uWdakSRPOnDlTJAHqUmRAMmWJIkY/RZMUEULkWk/EysqK+/fv89Zbb+kiVEmSpOdq1KgRPXr0oG7dunh6eqJSqRg2bBhz587lp59+wt3dnaioKIYMGQLAzp07NR9oa9asSa9evahRowbvv/8+S5YsKTFTBPOTFFGqlNwKv4VneU/tBveUyZMn07ZtW1q0aKGzGLRt5cqV3Lx5U9dhSJJUUh09Ci1b5rl42zbo0gUMnjOufcuWLdSuXZv09HS8vb0ZMGBAnh8270TeYfHFxfzY9kedTqV8Wenp6SiVSoYPH46nZ9H/Xfv222/5/vvv2blzJ15eXjRs2BAhBMnJyaxdu5YBAwbg6OhIzZo1uXTpEqampvj7+3Po0CE+/PBDTQ0QSSpNCp0UcXV1JSQkBFBfMK9fv16z7N9//y3yar66EBGUjh0RRKuSNEmRO3fuEBgYmC0pcuTIETZs2JDvtkuSJEm6MH36dO7cucPNmzf5888/MTY2pkqVKly4cIEHDx7w999/a1rade7cmRkzZmi2/eabb/D19eXu3bu0a9dOVy8hG6USkpPBUpGgHmedR1LkftR90jLTdDZS5MyZM+zcuZPZs2fr5Pi6kJqaKrvOSJKUt+ho8PaGVq1yXRwfDwcP5t11RqVSMWXKFHr37s3YsWM5fvz4C6d7/G///2jh0oLO1Tq/bPQ69fvvv/PWW28VW8cYPT09Ro4cybVr1/Dz82PmzJkoFAqOHz/O//73P9LS0pg+fTq+vr6aUgLW1tbFEoskaUuha4p06NCBAwcO0KtXLyZPnkyXLl1wcnLC0NCQgIAA5s6dW5Rx6kRkmJJy+jFEp8XiaVUJUE+dqVKliqZqcWZmJqNHj6ZZs2Z8+OGHugxXkiTptZKQoP5uGRug/iGPpMiN8BtYm1jjZOmkpcj+I4Rg4sSJ9OrVi7p162r9+Lqyf/9+0tPT6dixo65DkSSpJDp+HMzMoEGDXBdv26YuN9K6dc5lWS1V9+/fz44dO/J1ntlzfw/7fffj/al3qZ62kJ6ezrx58/jkk0+08jpcXV01yaZ33nmH8PBwOQ1GeiUVOikyZ84czc/t2rXjzJkzbN++nZSUFNq0aVNi7iS+jIhwsDNOIDAlWjNS5NmpM2vXrsXf35+DBw/qKkxJkqTXUny8+rtlhC/Y2YGVVa7rZdUT0cWF8L59+zhz5gw+Pj5aP7YuBQQE0KNHDywsLHQdiiRJJdHRo9CsGeQyyjotDaZNg5EjwcQk+7LHjx/TpUsXoqOjOXPmTL6mj2RkZvC//f9jRP0R1Cqfv7a1JdWGDRuIjY0tVLeZl2VmZqb1Y0qSthQ6KfKs+vXrU78gTcRLgchoPcqZJuH9JCmSkpLC8ePH+fTTTzXr/PTTT4waNUr2lpYkSdIyzUiRkLsvLLKqi3oiKpWKr7/+miFDhlC1alWtH1+XvvjiC51ctEuSVEocPQr9++e6aPFiSEyEr7/O/vzVq1fp0KEDVapU4fz585QvXz5fh1p/Yz0hiSFMbzH9ZaPWqczMTL7//ns+++wzbGxsdB2OJL1Siiwp8iqKjDOgqkUa0U+SIidPniQjI4NWT+Y/pqWlYWVlRbe8JjxKkiRJxSZrpIhFoM8LkyIdq2p/Gsf69eu5e/fuK9GiviBu3rxJQkKCLDwuSVLuwsPh5s1ci6xGR8PMmTB9evbBf6mpqfTq1YsmTZqwfv16Tf2rFxFC8PP5nxnsNZiyZmWL6hXohJ6eHgsXLqROnTq6DkWSXjkyKfIcEQkmlKuURkxKDLamthw4coAmTZpohgMbGxtz6tQpHUcpSZL0eoqPV885Nw64D23a5LpOXGocj+Ieab3IamBgIKNGjeKbb77B0dFRq8cujHPnzrF+/Xrc3d2pWbMmDRs2LHSbx/nz5xMdHc3OnTuLOEpJkl4Jx46BhQXkUmdp1iwoWxaeGpQNwLx584iLi2P58uX5TogAnAo4xbXQa/zd8++XDFq3hBAkJSXx/vvv6zoUSXolyUo5zxGZbIZZ2QwEAltTW/z8/Khevbpm+fHjx/Hz89NhhJIkSa8vTTvemBgoVy7XdW6Gq1vC1rSrqbW4MjMz+eijj6hZsyYTJ07U2nELKzQ0lLZt23L58mV+++033n//fc6fPw+g6TKXXxkZGezcuZPu3bsXR6iSJL0Kjh6Ft9/O0WvX3189dWbOHHXCO4ufnx9z5szhhx9+KPC0kZ8v/EzHqh1xs3Urish1Zu/evbi5uZGUlKTrUCTplSSTInkQAiLTLDCySwXA1tSW4OBgHBwcNOt89tlnbNu2TVchSpIkvdY0SZHY2OcWWa1iUwULY+0V/Jw/fz5Xr15l3bp1GBiU/AGZFSpUYPv27Zw6dQofHx+Sk5Np0aIFZ86coUqVKjx69Cjf+zp69CgJCQl07ly6W15KklSMjh3LderMN9+oB4/06PHfc0IIRo0aRYMGDRgwYECBDhMQF8D229sZ1WjUSwasW0IIZs2aRfv27TE3N9d1OJL0Sip0UmTt2rVERUXluiw6Opq1a9cWOqiSIC4OlMIA/TySIuHh4fj4+NCiRQsdRilJkvT6ypYUsbbOdZ2szjO5EUKQnJxcpDFdvnyZKVOmsGTJElxcXIp030Xt6tWr9OvXj9TUVFq3bq1ps2hkZIShoSGNGzemVq1afPvtt/ne5z///EPr1q1lEUBJknKXmgp37+ZoxXvpEmzcCPPnw9ONwnbu3Mm+fftYsmRJgTuI/XrxV6qVq0Zr11z6+pYix48f5+zZs0yYMEHXoUjSK6vQSZGPP/4YX1/fXJf5+/vz8ccfFzqokiAy8skPdikY6hliZmCWLSly7NgxLC0t8fLy0lmMkiRJr7P4eLC0UEFycp5JkRvhN6hdPmdSJDU1la5du1KrVi2USmWRxJOUlES/fv3o0aMH/fr1K5J9FpcbN27Q5kkdFsNcWmKCuqjf3LlzWbt2LTdu3MjXfn/88Ud+//33IotTkqRXjL+/eji2u7vmKSHgyy+ha1do2vS/VZOSkhg1ahSjR4/OV+vdpyVnJLP8ynJGNRylk3bsRUUIwYQJE/jwww954403dB2OJL2yCp0UEULkuSwqKqrQBdpKiogI9ffMcsnYmtoSExNDWlqapmDesWPHaN68eakYGi1JkvQqio8HS9MnCY1ckiJCCHU7XvvsF9NJSUl06tSJixcvEhwczK5du4oknnHjxpGSksKvv/5aoi/CQ0JCaN26Na1atWLNmjXo6+vnuW6rVq147733mD179gv3m5CQgJGREc7OzkUZriS9FhYsWEDNmjWpVasWffv2JTU1lebNm+Pl5YWXlxcODg588MEHuW6rr6+vWa/ET13z9QVTU6hYUfPU5ctw/Dg8e5qZNWsWSqWSadOmFfgwG25sQCVU9K+de9tfbVCpVPj7+7Nv3z5A/TfpypUrBdqHQqFg6dKlzJ07tzhClCTpiQJ9ot+xYwc7duzQPP7uu++ws7PLtk5qaionT56kwTPD4kqbyLBMzEglySoNW6GeOgNoRopMmTKFuLg4XYYoSZL0WktIAEvjNPWDXJIij+IekZCekG36TGxsLB06dCA0NJRTp04xdepUli5dmueHjfw6ceIEy5cv5+jRo1jnMWqlpFi5ciW2trasX78+X4n93377Das8arY8bdKkSQQFBclaW5JUQEFBQfz888/4+PhgampKr1692LRpEydPntSs0717d7p06ZLr9qamply7dk1L0b6kBw/AzS3bHJmDB6FWLXh6IMS9e/eYP38+69at03R9zK+sNryf1P0EcyPd1OBYvnw5//vf/0hMTMTU1JSwsDCuX7/OO++8w5o1a/I1mjAhIYHo6Gjq5tKlR5KkolWgkSLh4eHcuHFDM4zW19dX8zjr69GjR7Rt25bffvutwMG4uLjg6emJl5cX9evXB9T1Sdq0aYOHhwdt2rQhJiamwPstjMjAFMoSRbRBhqaeiKGhIWXLlkUIQcWKFeUwNkmSJB2KjwdLoxT1g1wSEdfDrmNqYIqbjbrrQHh4OC1btiQ2NpaTJ0/i6urKiBEjOHDgAA8ePHipWH755Rc6d+7M22+//VL70Ybx48eze/fuPKfNPMvZ2RlLS0uOHTuW5yhRlUrFtm3baNWqVVGGKkmvDaVSSUpKCkqlkuTk5GyF/ePj4zly5MhLJ29LBF9fdVLkKYcOwbvvZl/tzz//pFatWvTs2bPAhzj28Bi3Im7xWYPPXibSQouLi2P8+PF8/vnn3L17l4SEBCwsLGjatCkLFixg4MCBbNiw4YX7mTNnDm3atEGlUmkhakl6vRUoKfLJJ59w8eJFLl68yDvvvMPWrVs1j7O+Tp8+zR9//IGrq2uhAjp69CjXrl3j0qVLAHz//fe0bt2a+/fv07p1a77//vtC7begYkNTsCGGaL10TVKkYsWK6OnpsXnzZho1avTcKUSSJElS8YqPB0v9ZNDXh1wq8t8Iu0HN8jXR19MnPj6ed955B319fY4fP675wPHWW29Ru3btQiXys4SEhPDPP/8wYsSIQu9DWx49eoQQAje3grWnDAkJ4b333mPr1q25Lj9//jzBwcF069atKMKUpNeKo6MjX375Jc7OzlSsWBErKyvatm2rWZ5VwDivqempqanUr1+fxo0b888//+RYvmTJEmrUqEGNGjW0dnMxT88kRVJS4PRpaP1MLdRDhw7Rvn37Qk1F/PnCz3Sp1gUXa5eXDLZwLC0tWbt2LdOmTaNq1arZpih+8cUX/PjjjwwYMCDP8ymo6zP+9NNPTJ8+XVMEW5Kk4lPo/8uOHj1K9erVczwfGxv7MvHksGPHDgYOHAjAwIEDcz3ZF4fY8HR1UgR1TZGgoKBs9UTs7OxK9JxxSZKkV118PFjoJapHieRyPr4efl1TZHX//v0EBwdz+PBhypUrp1lHoVAwYsQIVq1aRWpqaqHiWLFiBZUrV+bdZ291lkA9e/Zk6tSpBd7OwcGBkSNHMmnSJDIyMnIs37p1K02aNMl2d1uSpPyJiYlhx44d+Pv7ExwcTFJSEuvWrdMs37hxI3379s1z+0ePHnHp0iU2bNjAmDFjcjRCGDlyJD4+Pvj4+Oi+M9QzSZEzZ0CphHfe+W+VuLg4Lly4UKhzalhiGDvv7uTzhp8XRbQFFh8fT3JyMp06dcLY2DjXdUaPHs2yZcuoV69envv56quvqFevHn369CmuUCVJekqhkyJLly5l3rx5msfXrl3DycmJsmXLUq9ePR4/flzgfSoUCtq2bUu9evU01evDwsKo+KQYU4UKFQgLC8uxXXFkwGPClVgr4ojOSMjRjvfYsWO0zKW/uiRJkqQ98fFgSUK+2vEeP36cZs2a5Vobo1+/fqSlpfH3338XOAalUsnvv//O8OHDS/zdvPPnz3Px4sVCd4ebNGkSYWFhjBgxIkcCqX379oVKtkiSpB4V4erqip2dHYaGhnTr1o0zZ84AEBkZyYULF+jQoUOe22fdtKtSpQotWrTg6tWrWom7wDIz1d1nnkqKHDoEjRrB02VDjh49iomJCW+99VaBD7Hj7g7szOx4p/I7L165GHz33Xe8//77L1xv6NChuLi4cPr0aXr37s1ff/1FYmIiAAEBAezevZuFCxfKG7CSpCWFvoL75Zdfsg3jGzVqFA4ODqxfvx6VSsXEiRMLvM9Tp05x5coV9u7dy5IlSzhx4kS25QqFIteTQ3FkwGNjVFgbpRCdGp0tKRIaGsqdO3do0aJFkRxHkiRJKpz4eLBUxUIuiY5UZSr3ou5lS4rkVe/DwsKCjz76iGXLlhU4ht27dxMREVEq2tAvXryYtm3bUq1atUJtX65cOXbv3s3du3dzJEWyutRIklRwzs7OnDt3juTkZIQQHD58WDMae8uWLXTs2BETE5Nct83qjgjqBMrp06epUaOG1mIvkMBAyMjI1o738OGc9UQOHTrE22+/nedIi+fZfmc7Xap1QV8v765axSU4OJjFixfz2Wf5r2WSnJyMnp4eQ4cOxc7Ojg8++ABzc3MCAwNLfdMKSSpNCt1PNiAgQHNhFRERwenTpzl8+DAtWrTAyMiIzz8v+LC1rEx3+fLl6dq1KxcuXMDe3p6QkBAqVqxISEgI5cuXL2zIBRIbq6CKaRrRKf8lRRo3bkxCQgJ9+/bFy8tLK3FIkiQVhbt379K7d2/NYz8/P2bMmMHZs2e5e/cuoJ7+aG1tnWsXAxcXFywsLNDX18fAwEBT90mX4uPBMjMm15EiPhE+qIQKT3tPIiMjuXnzJu+8k/edwxEjRlC7dm2uX79O7dq181zvWUuXLqVnz57ZpuSUROHh4fz1118v3RmmWbNmnDhxAoVCwT///ENmZib+/v6oVCrGjx9fRNFK0uulUaNG9OjRg7p162JgYECdOnUYNmwYAJs2bcpxo/HSpUssW7aMFStWcPv2bc1ItaybkiU2KeLrq64B9aRtd0wMXLoE8+dnX+3gwYOa118QcalxHPY7zM6+O4si2gKbPXs27u7u2f7WvkibNm1o06YNqampHDhwgP3792NjY1PiRx5K0qum0EkRY2Nj0tPTAfUwNzMzM5o3bw6Ara1tgWuLJCUloVKpsLCwICkpiQMHDjB16lQ6d+7MmjVrmDhxImvWrMmzHVlRi03Qx8Yse1LE0dERDw+PfFWMliRJKkmqVaumSXZkZmbi6OhI165dGTNmjGadcePGPbf16tGjR0vMh38hniRFMqLy7DxTsUxFypmV458D/2BmZvbc+duenp40bdqUpUuXsnTp0nzF4Ofnx/79+zl9+nRhX4bW2NnZcfDgQZo1a/bS+8oasenr68v48eMxMTFh8uTJL71fSXqdTZ8+nenTp+d4/tixYzmeq1+/PitWrACgSZMmmq6QJZ6vL1SuDE86Xx07Bqam0Ljxf6sEBARw79492rRpU+Dd776/G1NDU1q5ar8LVnh4OMuXL+evv/4qVELDxMSEzp0707lz52KITpKkFyl0GrJhw4YsWbKEW7du8fPPP/P+++9rqiv7+fkVuNhaWFgYzZo1480336Rhw4Z06NCB999/n4kTJ3Lw4EE8PDw4dOhQoablFEZMkiFWZTKITonGysiK0NBQHBwc+PHHH3n48KFWYpAkSSoOhw8fxs3NjcqVK2ueE0KwefPm5xbzK0lSUkClAsv0yDyTIk9PnWnSpMkLW9COGDGCdevWkZCQkK8YfvvtNzw9PQs1712bMjIySExM1HTfKSrjxo3j4MGDvPnmm3z44YdFtl9Jkl5RzxRZPXQI3n4bjIz+W+Xw4cOUL1+eWrVqFXj32+9sp2PVjhjpG7145SJWvnx5zp49q7Wbt5IkFa1CJ0V+/PFHbt26haenJ4GBgcyaNUuz7K+//qJp06YF2l+VKlXw9vbG29ubW7du8c033wBQtmxZDh8+zP379zl06BC2traFDblAYpONMbFWolQpUaQqyMzMxNDQkC+//JKoqCitxCBJklQcNm3alCP5cfLkSezt7fHw8Mh1m9wKYT9N2y0f4+PV3y1Tw3NNitwIv4FneU9AnRR53tSZLD169MDExCRb14e8pKWlsXLlSkaMGFHiC+Ft27aNqlWrakZ3FqVWrVpx5syZbAk2SZKkXD14kC0pkls9kYMHD/Luu+8WeLRFSkYKe+7voesbXYsi0gIJDg4mNDSUunXrlvi/B5Ik5a7QSZEaNWrg6+tLREQEDx8+pGrVqppl8+fPZ/6zEwRLmdg0E/St1YWr0mLV3319fbG0tJT1RCRJKrXS09PZuXMnPXv2zPb8i1o+vqgQtrZbPmqSIkkhzx0pEhsby7Vr1/KVFDE2NmbYsGH8+OOPKJXK5667ZcsWUlNT6d+/f2HC16rFixfTuXNnjIy0f/dUkiRJw9dXU2T18WO4exdat/5vsUql4tChQ4VqxXvQ7yAqoeJ99xd3filqGzdupF27dlo/riRJReelq/jY2try+PFjzpw5Q1JSEqCem21nZ/fSwelKRgYkKk1R2KiTIclRyZibm3P16lWaNm1apMOPJUmStGnv3r3UrVsXe3t7zXNKpZJt27Y9tzhcboWwdSkrKWKRmDMpkpSeRHhSOO627pw6dQojI6N8V/EfO3YsoaGhbNy48bnrLV26lP79+2PxdB/JEigoKIhTp04xdOhQXYciSdLrTIhs02cOH4Zy5eDputY3btwgIiKiUEmR7Xe209atLWWMyhRVxPl24sQJGjVqpPXjSpJUdF4qKfLrr7/i6OhI5cqVad68uaaDQbdu3Vi4cGFRxKcTcXHq7yrbNPQUesSExuDg4MCjR49wf6qNmCRJUmmT24iQQ4cO8cYbb+Dk5JTrNklJSZo6G1mFsAsz37soxceDQgHm8TmTIiGJIQA4Wjpy4sQJGjdunGc7y2eVK1eOkSNHMnPmTDIzM3NdZ//+/Zw5c6ZAbRd15eDBg5QrV+65RWYlSZKKXUQEJCZqkiKHDkGrVvD0LJlDhw5RrVo1KlWqVKBdK1VKdt7dqZOpMyqVipMnT+bZ8l2SpNKh0EmRH374gf/973988sknHDlyBCGEZlmLFi3466+/iiRAXchqnKO0TcHGxIbQEHWR1Q0bNuRaGVySJKk0SEpK4uDBg3Tr1i3b87nVGAkODqZ9+/ZA3oWwdSk+HiwsQC8uZ0ve4IRgACqUqZDveiJPGzduHIGBgbn+HUtKSuLTTz/l888/x9PTs9Dxa0tWUXTZ3lGSJJ3y9VV/r1IFIXKvJ1LYqTMnHp0gLjWOTlU7FUGgBXPr1i1iYmI0HTglSSqdCt2Sd8mSJcyYMYPx48fnuJtWrVo17t2799LB6UpWUiTdOglbY1uC7wXj4OCAubk55ubmOo1NkiSpsMzNzXMtFL169eoczzk4OLBnzx7gv0LYJUl8PFhaCnicmGtSxM7MjrTkNC5fvsycOXMKtO/y5cszYsQIvvvuO3r37p1tyuTUqVPJzMzMVly8JMsqfitJkqRTDx5AhQpgbs5tHwgJyV5PJC0tjePHj/Ppp58WeNfbb2/nHZd3KGtWtggDzp/y5cuzfPnyAo9ukSSpZCn0raPQ0NA8h+Pq6emRmppa6KB0LSYG9MgkySIRW1NbgoODsbe3x8rKikuXLuk6PEmSpNdefDxYmj9JyFtZZVsWnBCMg4UDZ86cQU9Pj8aNGxd4/1999RUPHz5ky5YtmucuXbrEwoUL+fXXX0t8LRGAmzdv0qVLl1L991iSpFfEU0VWDx8GV1eoUuW/xWfPniUtLY0WLVoUaLcqoWL7ne06mToDYG9vL2s2SdIroNBJEXd3d44fP57rshMnTpTqO1Ox0ZlYE0uMQQa2prYEBQVhbm5OfHw85cqV03V4kiRJr734eLA0fdIhJpeRIg4WDpw4cYIGDRpgZmZW4P1XqFCB4cOH891336FSqcjIyOCTTz6hR48edOzYsQheQfHbt28fd+/ezXc9FUmSpGLzVJHVQ4eyjxJRP3eIhg0bYvVMkvtFLgVfIighiA/e+KCIAs0/IQRdu3blypUrWj+2JElFq0BJkRMnTpCYmAjAmDFj+P7775k5cyb3798HIDw8nD/++IOffvqJsWPHFn20WhIbkooNMUTrpWlGiujr66NQKHBwcNB1eJIkSa+9hASwNFF3CMstKVKxTMVC1RN52vjx43nw4AHbtm1jwYIFPHz4kEWLFr1E1Np18OBB2rRpo+swJEmSsiVFTp6Eli2zLy7s+Wr77e00dGyIk2XuhcKL04MHD/jnn39KxchBSZKer0BJkZYtW+Lj4wPA0KFDmTVrFnPnzqVmzZoAtG/fntGjRzNt2jQ+/PDDoo9WS2JC07AmlmiRjJWRFZGRkahUKuzt7TEyMtJ1eJIkSa+9+HiwNEpVty4ok70FY3BCMHamdly4cOGlOgI4ODjwySef8PXXXzNt2jTmz59PhQoVXjZ0rUhNTeXEiRMyKSJJUsnw4AG4uZGUpJ6m/iQ/AkBsbCyXLl0qVJHVf+/9S5dqXYow0Pw7ceIEFSpUkJ0pJekVUKBCq093mAH1nOtPP/2Us2fPEhkZia2tLW+99VaBh76VNLERGeqkSGYiLkpDANzc3Ojdu7eOI5MkSZLgSfcZgxR1PZFnOqsEJwSTGp6KSqWiadOmL3WcCRMm8Pvvv9OkSRMGDx78UvvSpsDAQDw8PAo8P1+SJKnIJSSoW/K6uREWpn7q6fzyzZs3UalUNGjQoEC7jU6J5lbELVq4tCi6WAsgqxWvQqHQyfElSSo6he4+k8XCwoK2bdsWRSwlRmyUEhtiuJ0RjyJVfaLr3bs3gwYN0m1gkiRJEqBOirjqJeWYOiOEIDghmNCoUOrWrfvSw5qdnJzYs2cPNWrUKFUXvh4eHly/fl3XYUiSJP3XjtfdnZA76h/t7f9b7O/vj4ODA6ampgXa7bnH5zDWN6ZexdwbPxS3tLQ0Wj9bHEWSpFKpwEmRPXv2cOfOnXytO2DAgAIHVBLERgusDZKITokmMzETGxsbTp48iZOTU6kuICtp17Fjx2jZsiUZGRkYGLx0/lF6Ra1evZrJkyfz+PFjXYdSqsTHg6VhQo6kSEJ6AkkZSdy/cp+Wb7fMfeMCKo0Xvf/88w9vv/02tra2ug5FkqTXna+velSfrS2hoerT9tP1n/38/HB1dS3wbk8HnKa+Q32MDYyLLtYC2Lhxo06OK0lS0SvwJ7UZM2bkaz2FQlFqkyIxsQocTFKJTokmjTQcHBz4+uuv6d27t0yKSJIklQDx8WBpG5drkVWAW2dv8e3Qb3UQme6Fh4fTtWtXzpw5w1tvvaXrcCRJet1lFVlVKAgNhYoVsy/29/enytP9efPpdOBpmlZ6uSmShXX37l2USqWmrqIkSaVbgVvyHj16lISEhBd+xcfHF0e8WhGboIdFmVTSMtNIjkzG0dGRwMBAKlWqpOvQJEmSJJ4kRTJjck2KKFCQFp1G3bp1dROcjh0+fBgrK6sCz8+XJEkqFk+KrAKEhmavJwKFGymSkZnBhaALNHXWTVLkp59+YtKkSTo5tiRJRa/ASRFTU1PMzc3z9VVaxSYaYmCZCkBCeALly5cnIiICJyftt/vSBqVSycOHD7X2pVQq8x1bixYtGD16NB9++CFWVlZUqlSJpUuXapbv3r2bevXqYWVlRdWqVZk/fz4qleqF+z127BgKhYL169dTpUoVrK2t6dq1K+Hh4Zp1YmNjGTZsGE5OTpQrV4527dpx9+5dzfLMzEx++OEHqlatipWVFfXr12fv3r35fm0lhVKl5GHsQ61+KVX5fw8ALFmyhFq1amFpaUmFChX46KOPiIyM5N69e+jr6/Po0aNs63fu3JlRo0YBkJCQwKBBgyhbtixOTk4sWrQIJycnVq9e/cLjrl69Ots2ZcuWZfDgwZrW5ABBQUH06tULe3t77O3t6d27N8HBwZrlqampTJgwAVdXV2xsbGjevDnnz58v0OuXcoqPB8uMKPWQ7KcEJwRjpW+FjZUNjo6OOopOtw4ePEirVq3ktD1JkkqGp9rx5pYU8ff3L3BS5GroVVKUKTSp1KSooiyQrCKrkiS9GuQVUy5iko3Qd00DIOpxFA5uDgCv7EiRx48fF2ouZ2H5+/vj4uKS7/VXr17N9u3bWbduHdu3b6dXr160adOGmJgYunbtyrp16+jWrRve3t506tQJAwMDxowZk699b9y4kUuXLqFQKOjfvz/9+/fnwIEDAHz00UckJiZy6dIlLC0tmTRpEu+++y63b9+mTJkyLFy4kEWLFrFz505q167Nli1b6NKlC+fOnStVd6gfxz/GdZH2/v0B/Ef742Ltku/1K1SowLZt23B3dycwMJBevXrxxRdfsHHjRpo2bcqqVauYNm0aAMHBwezZs4crV64AMHr0aHx8fLhx4wbW1taMGjWKsKzy9/kQGhrKtWvXuHv3LrGxsXzwwQeMHTuW5cuXk5mZSceOHXnjjTe4d+8eQgiGDRtG586dOX/+PPr6+nz11VccO3aMgwcP4uzszC+//KJ5H72qiVZtSEgAy/TIXEeKGGcYU/3N6qWqMGpREUJw8OBBeQdTkqSSw9cXPvwQUCdFPDz+W5SWlkZQUFCBp8+cDjhN1bJVKWdWrigjzZfw8HBu374tkyKS9AqRSZFcxKaZorBWjxSJCIzA430PDh069MredXRycsLf31+rxyuI7t2706pVK83Ptra2XL58mSNHjtChQwd69eoFQL169fjqq69YtmxZvpMic+bM0RQinD9/PjVq1CAwMBADAwN27drFtWvXqPDklsYPP/zAunXr2LVrF3369OH333/nq6++0iRA+vTpw8aNG/n9999ZtmxZgV6jLjlZOuE/Wnv//lnHLIju3btrfq5cuTITJ05k6NChAAwdOpQpU6YwdepU9PT0WLVqFXXr1qV27dpkZmayfv16tm/fjoODOrn5008/sWrVqnwfWwjBwoULNSPgZsyYQZcuXVi2bBkXLlzA29ubI0eOaFqRL126FDs7Oy5evEjDhg35448/2LhxI+7u7gCMGzeOtWvXsm7dOiZOnFig34OklpEBKSlgmRoO1uWzLQtOCEbECWrXrq2j6HTv0KFDlCun/Q8KkiQV3oIFC1ixYgUKhQJPT09WrVrFp59+yvHjxzV/X1avXo2Xl1eObdesWcPMmTMBmDx5MgMHDtRm6M+XlgaBgdlGijRv/t/iR48eIYQo8M253OqJ3Lp1i/Xr17Nz506USiV37txBqVTy1ltv4eTkxMqVK7GxsXnpl3Tq1CnMzc2pU6fOS+9LkqSSoUBJkfxMSyjtUlMhLdMQlU0KVsZWhASF4OrqWiq7D+SXgYFBgUZuaFvWh9ks5ubmJCQkEBgYmKPwrbu7OwEBAfne99N/hLN+zkqKALg9+SMOYGhoSOXKlTX7DwwMzLY86/i3b9/O9/FLAgM9gwKN2tCFbdu2MX/+fB48eEBqaioqlYqkpCQyMzPp2bMno0aN4uDBg7Rt25aVK1cyYcIEACIjI0lPT6dy5cqafVlaWhboosjGxkZzQQrq90lGRgZhYWEEBgZia2ubbX9ly5bFxsaGgIAAqlSpQkpKSq7vk4K8T6XsEhLU3y2Sw8C6arZlwQnBJIUmUbvN65kUEUJQrVo1XYchSVIBBAUF8fPPP+Pj44OpqSm9evVi06ZNgPqGTI8ePfLcNjo6munTp2tGvdarV4/OnTsXyYf/IvHwIahUeU6f8ff3x8jIKMe13vMIITgdeJqZLWdqnps5cyZTpkzBy8uL/v37Y2dnp1nWs2dPVq1aRa9evdizZw+GhoYv9ZLeeecddu3aJacoStIrpMA1RV51sbHq70qbFKyNrYmPj+fSpUt88cUXOo1LyqlSpUr4+vpme87X1xdnZ+d87+Phw4c5fnZyctJMlXp6/0qlkoCAAM3+i+L40os9fvyYnj178sUXXxAQEEB8fDx//vknoL4wMjU15cMPP2TFihUcOXKEsLAw+vbtC0C5cuUwMjLKVnMkPj6emJiYfB8/JiaGuLg4zeOHDx9iaGiIvb09lSpVIiYmJtv+oqOjiYmJwdnZmXLlymFiYiLfJ0Usq463ZVJIjukzATEB6qTIazpSpHv37syZM0fXYUiSVEBKpZKUlBSUSiXJycn5ThLs37+fNm3aaBL0bdq0Yd++fcUcbQH4+oKxMTg6olLlTIr4+flRuXJl9PX1873Lh7EPCU0MJfJqJMOHD0cIQb9+/bh16xZXr15l4sSJDBkyBFDf+Bs/fjy7du3i6tWrjB079qVfUtmyZWnRosVL70eSpJJDJkWekfXZJt0qkTL6ZQD1h7KgoCAdRiXlZvDgwezevZutW7eSmZnJ1atX+eGHHxg2bFi+9zFp0iSio6OJjY3lq6++olWrVjg7O1OxYkXat2/PuHHjCAsLIyUlhQkTJmBkZESHDh0A9bSN+fPnc+3aNZRKJZs3b2bPnj2aaR1S0UhMTESlUmkSDPfv38/xoW/o0KHs3LmTefPm0atXLywsLADQ19fnww8/ZMaMGYSEhJCcnMxXX32Fnl7+T30KhYJx48aRlJREcHAw3377LR999BH6+vo0bNiQWrVq8fnnnxMfH09cXBwjR47Ey8uLBg0aoKenx+DBg5k6dSp+fn6kp6ezYMECHjx4QL9+/Yr09/Q60SRFEoJyTYooEhWvZZvE5ORk9u/fzxtvvKHrUCRJKgBHR0e+/PJLzfWHlZUVbdu2BeCbb76hdu3ajB07lrS0tBzbBgUFZat55+TklOOadcmSJdSoUYMaNWoU6KZAkfD1BVdX0NMjOhqUyuwteQtTZPXQvUMYZxrzzaffaEZiurq65hg9/DQ3Nze2b99Ou3btCvUyskRHR1O9enUePHjwUvuRJKlkkUmRZ2SNFEmzisdYGKNQKIiOjpYFEUugRo0asWXLFmbNmoWNjY1mGsXo0aPzvY/evXtTv359KleujJ6eHuvXr9cs+/PPP3FxcaFu3bo4OTlx69YtDh06pPnA/b///Y+RI0fSo0cPbG1tmTt3Ltu2baN+/fpF/lpfZ2+88QZz5sxhwIABWFhYMHDgQPr3759tnbp161KzZk0OHDiQIym1aNEiqlatSs2aNfHw8KBGjRrY2tpiYmKSr+NXqFABT09PqlatSq1atahevToLFy4E1EmXXbt2kZaWhru7Ox4eHiiVSnbu3Km56zV//nzatm1Ly5YtKV++PFu3buXgwYOvbOFmbchKilgkBmdLigghiEiNwMHSATMzM90Ep0OHDx9GpVJpPkxJklQ6xMTEsGPHDvz9/QkODiYpKYl169YxZ84c7ty5w8WLF4mOjmbu3LmF2v/IkSPx8fHBx8dH+9NqfH3hSU2t0FD1U89OnylIkdX79+/z1c9foR+sz+lTpxk/fny+i2o3b96cDh06EBERwZkzZ/J9zKdt3ryZqKiobNNyJUl6BYhXTPXq1V9q+z17hDAmRXy6oLVoOL+hqFChgqhbt66YN29eEUUolQRHjx4VgMjIyNB1KJKWRUdHC4VCIc6cOfPCdVetWiUcHR21EFXxu3PnjnjzzTc1XxYWFmLBggXi22+/FQ4ODprnd+/enev2e/fuFVWrVhVubm5izpw5zz3Wy56HX2T3biFMTFRCgBDXrmmej0qOEkxDvPvRu8V6/JLqk08+ER06dNB1GJIkFdDmzZvF4MGDNY/XrFkjRowYkW2do0eP5vr/94YNG8SwYcM0j4cNGyY2bNiQ57GK+/ycQ6dOQowaJYQQ4uBBIfT1hcjM/G9x3bp1xdy5c/O9u4SEBFFuSjnx7cFvCx3S7NmzhZWVlfDx8Snwto0bNxZjxowp9LElSSqZ5EiRZ8TEgDWxxOhnoEhV4ODggLW1taZzhCRJpUtAQADHjx8nMzOTqKgoPvvsMzw8PGjQoIGuQ9OqatWqce3aNa5du8bly5cxMzOja9euAIwdO1azrH379jm2zczMZOTIkezduxcfHx82btyIj4+Ptl+CRnw8WJo/Kfz91EiRkIQQAOp51NNBVLpXr169Ak0flCSpZHB2dubcuXMkJycjhODw4cNUr16dkBD1OU0IwT///EOtWrVybPvee+9x4MABTX2rAwcO8N5772n7JeQtMhKeFD0NDQV7e3h6BmtBRoocOHCARGUiUfpRtPYofAOE8ePH8/bbb9O9e3fS09Pzvd3du3c5d+5cyeruI0lSkZBJkWfERmZgQwwxilSUiUocHBw4fPiw5sODVDqsX7+eMmXK5PqV1ZlEej2kp6fzxRdfYG1tjYeHB7GxsezcuRMDAwNmz56d5/vk119/1XXoxebw4cO4ubnle/jvhQsXcHd3p0qVKhgZGdGnTx927NhRzFHmLSEBLM0y1A+eSooExgWCChrXbqybwHRs+PDhdO7cWddhSJJUQI0aNaJHjx7UrVsXT09PVCoVw4YNo1+/fnh6euLp6UlkZCSTJ08G4NKlS5qpora2tkyZMoUGDRrQoEEDpk6diq2trS5fTnaJifBk2vGzRVZjY2OJiYnJV00RHx8f3nvvPXZc2YGBngH1HQo/VVlfX5+VK1cSFhbGokWL8r1dRkYGo0aNyrUtsiRJpZvsJfWM2NA09UgRVTLpcQrs7Oy4efMm1atXL1BlbEm3+vXr98JClkIILUUj6ZK7uzvXr1/PddmkSZOYNGnSc7cfNGhQMUSlW5s2bdJ06AFYvHgxa9eupX79+vz444855pznVsjv/Pnz2dZZsmQJS5YsASj2Qn7x8WBpkgEKheZiG8Db1xsSoI5XnWI9fkm0Zs0ajI2N6dOnj65DkSSpEKZPn8706dOzPXfkyJFc161fvz4rVqzQPB48eDCDBw8u1vgKLTERyqgbF+TWjhfIV1Lkr7/+onbt2gQpgqjnUA9TQ9OXCqtcuXLMnTs3WxfCF6lVq1aBkiiSJJUecqTIM2LD07EmlmhlAsnRyQDUrl2bzMxMHUcmSZL08tLT09m5cyc9e/YEYMSIEfj6+nLt2jUqVqzIuHHjCrVfbRbyi48HC6NUsLTMNg77uv919JL1SlW74zNnzjBo0CBu3br1UvtZsGABd+/eLaKoJEmSikhCgiYpEhKSMyliZWX1wr8ZQgg2bdpEnz59OB14mqaVmhZJaEOHDmXmzJn5WvfUqVNMnDhR3lCTpFeUTIo8IyYyUz19JiOehPAE9PT0sLe3x8jISNehSZIkvbS9e/dSt25d7O3tAbC3t0dfXx89PT0++eQTLly4kGMbR0dHAgMDNY8fP36Mo6Oj1mJ+Vnw8WBqm5GjHey/0Htb61vnuRKALvr6+TJs2TdNWOiEhgZMnTzJmzJhC7zMgIABvb285dUaSpJLnOdNn/Pz8cHV1feE529vbm3v37tG9Z3fOPz5fZEkRAKVSSceOHTl69Ohz1/vtt9+4detWif77IklS4cmkyDNio1VYKeKITYsjJjgGpVIp2/FKkvTK2LhxY7apM1mF/AC2b9+eayG/Bg0acP/+ffz9/UlPT2fTpk06/QAeHw+Wekk5kiJBcUE4lHHQTVD58PjxY5o0acLWrVsxNVUP/X7vvffYuXMnR44c4eDBg4Xa77///ouTk5Oc5y5JUsmiVEJqarbpMxUr/rfY398/X1NnateuzZUrV0gwSyApI4kmlZoUWYgGBgbY29vz+eefk5GRkes6CQkJbNu2TRZYlaRXmEyKPCM2FszMUlAJFRnxGaSmpmabSy9JklRaJSUlcfDgQbp166Z5bvz48Xh6elK7dm2OHj3KggULAAgODtZ0ojEwMGDx4sW89957VK9enV69elGzZk2dvAbISook5EiKRGdEU6V8/roYaFtaWho9e/bEzc2Ny5cvZxsZUrNmTQYOHMgvv/xSqH3fv3+fzp07yzuYkiSVLImJ6u951BTx8/N7YecZIQQpKSnUqVOH04GncbNxw76MfZGG+f333xMcHMzixYtzXb5lyxaMjY3p1KlTkR5XkqSSQxZafUZMvB6GZVLUD1KhV69emJub6zYoSZKkImBubk5UVFS25/78889c13VwcGDPnj2ax+3bt8+1Xa8uxMeDm4jPlhSJi4sjzSgNTxdP3QX2HCkpKbi5uTFv3rxcp2POnz9fM3qkoBYuXIhKpXrZECVJkopWVlLEwoK0NIiOzllTpGPHjs/dxfnz52nTpg2BgYGcDzrPW5XeKvIw7ezsmDlzJl9//TV9+vSh4tPDWYDQ0FAGDx6MsbFxkR9bkqSSQSZFnhGbaIBhWXVSxEBpwAcffICenhxQI0mSVFLEx4OlKjZbUuT6jetgAfWrFb5NY3EJCgrC0dGRdevW5bmOra0tKpWKrVu30rlzZwwNDfO17/v372NgYJCvIeiSJEla9dRIkfBw9Y9ZSRGVSsXDhw9fOFJk06ZN1KlTB2tra7xDvfnY6+NiCXX48OHo6elhY2NDWFgYpqamWFpaAvD1118XyzElSSo5Styn/czMTOrUqaPJHPv7+9OoUSPc3d3p3bs36enpxXr82GQj9KzSUKCggnUFxowZw40bN4r1mJIk5a1FixZMnjxZ12Hky6BBg+jfv7+uw3jlxceDpTI6W1LkrPdZ0Ac3OzfdBZaLixcv4u7unmsB22fFxcUxZMgQli9fnu/9z5w5ky+//PJlQpQkSSoeTyVFQkPVP2YlRUJCQkhLS3tuQjczM5PNmzfTp08fUpWp3Im8w5sV3iyWUA0MDBgxYgQmJibMmjULJycnxowZw4oVK7hy5UqxHFOSpJKjxCVFFi1aRPXq1TWPJ0yYwNixY3nw4AE2Njb88ccfxXZsISA21QRhlYIJJpQrW45ffvmF2NjYYjumpB01a9ZkzZo1eS5fsWIFLi4u2gtIKrDw8HAGDhyIq6srZcqUwcXFha+//pq0tDRdhyZpWUICWKZHZkuKXLx9EQAHi5JTaDU8PJzu3bvTs2dPGjRo8ML1bWxsmDBhAjNmzCAx68PEc2RmZrJ79245z12SpJIpIUH93dyc0FAwN9eUF8Hf3x/guddeJ0+eJCwsjO7du+MT4UOmyORN++JJijzthx9+YPHixZw8eZJPPvmk0EWwJUkqPUpUUuTx48fs3r2boUOHAuriSkeOHKFHjx4ADBw4kH/++afYjp+YCJlCH5VNMkYqIyyetBCThVZLJhcXF1asWJGvdW/duqW1quE//fQT9erVw8rKivLly9OxY0du3bqllWO/yhITE6lWrRqHDh0iPj6eQ4cOsXv3biZMmKDr0CQti48Hy7SIbEmRmwE30UcfW1Nb3QX2lGPHjuHl5UX58uX57bff8l0EdfTo0ejp6bFw4cIXrnv27Fmio6Pp0KHDS0YrSZJUDBIT1ZkQPT1CQnLWE3FwcMDExCTPzZ2cnFi8eDH29vZ4h3rjYOGAnbldsYdtbGzMgAEDuHTpEt7e3owdO7bYjylJkm6VqJoiY8aMYd68eSQ8ySxHRUVhbW2NgYE6TCcnJ4KCgnJst2TJEpYsWQJATExMoY+fNSAkwzoJA6UBxsbGKBQKHBxKzp3H4hIbG5tjRIyTkxMGBgY8fvwYpVKped7ExIQKFSqQmppKaNZ4yCfKlStHmTJliIyMzHGnUxcjMdLT03MtalicUlNTWbhwIfXrq2sbTJkyhXfffRc/P79CF1IsTkolPH6s3WM6OYFBAc8+VapUYdKkSZrH7u7uDB48mFWrVuVr+0GDBpGcnIylpSVbtmzB0tKSzz//nPHjx2vWOXv2LOPHj+fmzZtYW1vTu3dvpk+frimuFhQUxNixYzl+/DigntqzYMGC1+IcUVIIoU6KWBiHgVVtQD033T/Cn7KGZXXegSXrnGNnZ8dHH33EtGnTCvT/vZmZGdOmTWPt2rV88803z309f//9N02aNMHOrvg/JEiSJBVYYmKe7Xj9/PxeWAvJ3d0dd3d3AK6FXtPKKJGnKRQKateurdVjSpKkGyVmpMiuXbsoX7489erVK/C2I0eOxMfHBx8fH2xsbAodQ1ZOIM0qEUWqAkNDQ+zt7bX+oVoXFi5ciKura7avx08+KTdr1izb83369AHg3LlzObbZsmULAF9++WWOZYW1ePFi3NzcsLCwwN7enkGDBtGuXTsCAgL4/PPPKVOmjKY96LRp02jWrBlTpkzBwcEBLy8vIOeokv379+Pp6UmZMmVo1aoVgYGB2Y6ZmZnJjz/+SPXq1bGysqJevXocPnw4X/FOmjSJ5s2bY2pqiqmpKZMnTyY0NJQ7d+4U+ndQnB4/BldX7X4VVRLmwIED1KlTJ9/rb9++nQYNGhAREcHmzZv5/vvvWb9+PQABAQG8++67dO/enbCwMA4cOMC///6rGYmSmZlJx44d0dfX5969e9y9exchBJ07dyYzM7NoXpD0QklJ6sSIZVKIZqTIw4cPSTNKw8naSWdxCSFYt24drq6u3Lt3j5o1azJ37txCJUKHDBnC8ePHX5jgmTJlSp4tJCVJknQuIQGejLp+th2vv7//c4usHjlyhD59+iCEAMA7zFvrSRFJkl4fJWakyOnTp9m5cyd79uwhNTWV+Ph4Ro8eTWxsLEqlUjNiwdHRsdhiyBpkkmIZjwgWNG3alO3btxfb8UqSMWPGMGjQoGzPOTmpP2CcOnUqx0gRgMaNG2vmhGYpV64coG4vOW3atJeO6/79+4wfP54LFy5Qq1YtEhMTuXLlCm+//TYuLi5MnjxZM90qy7lz52jbti1+fn65tqn09/enc+fO/PLLL3z88cdcvHiRLl26ZGu9/N133/HPP//wzz//4OHhwY4dO+jcuTPXr1/Hza1ghRwPHDiAubk5VatWLdwvoZg5OcEz/4xaOebL+u6777h69SoXL17M9za1a9dm+PDhgPr9+8knn7By5Ur69evH+vXrqVq1KmPGjAHAw8ODmTNn0q9fPxYsWMCFCxfw9vbmyJEjWFlZAbB06VLs7Oy4ePEijRs3fvkXJb1QfLz6+9NJkevXr2Nga4BrOd10YFEqlXz++eesWrWKKVOmvHQnGH19fQBWrlzJ7du3+eGHH7Itv3//Pvfu3aNDhw6ac64kSVKJ88xIkacv4f38/GjZsmWem54+fRp/f38UCgVCCLzDvPm0/qfFHbEkSa+pEpMUmTNnDnPmzAHUc7Hnz5/P+vXr6dmzJ1u2bKFPnz6sWbOGLl26FFsMsbFQRpFIrHEqGQkZWFtbvxajRACsra2xfmp+/tOc8vgEa2JikueUmHLlyhXJxbqBgQFCCG7duoWzszOWlpa8/fbbz93G3t6eKVOm5HmXdcOGDdSqVYthw4YB0KRJEwYMGMDWrVs16yxYsIDNmzdTrVo1ALp27UqTJk3YuHFjgTqhXL9+nU8//ZSFCxdmS7qUJAYGUNpqzE6ZMoXVq1dz7NixPN+fuXn2w6qrq6sm8RkYGJgj4eXu7k5KSgoREREEBgZia2ubbTRa2bJlsbGxISAgQCZFtESTFCFOkxTx9vbGoqIFjhbFlzTPixCCvn37cuTIEY4ePUqTJk2KbN+VK1fmk08+oX79+vTu3RtQT8/r2bMnDg4OtG/fXufThSRJkvL0TFLk6cHg/v7+DB48OM9NfXx8NKOAA+MDiU2NlSNFJEkqNiVm+kxe5s6dy08//YS7uztRUVEMGTKk2I4VGwvWxBGjl05abBpr1qxh3LhxxXY86cVcXV3ZtGkTq1atwtnZmQYNGrBx48bnblO5cuXnflB4/Phxrh+Os4SFhREfH0/Pnj01ySJra2vOnDmTa02bvFy4cIHWrVszY8aMHKNZpMIRQjBy5Eg2btzIyZMnNUmr/Hr48GGOx1lJlUqVKuHn55dtua+vL6amptjZ2VGpUiViYmKy1S2Kjo4mJiYGZ2fnwr0gqcD+S4rEZx8pYmOgk84zCoWCDz/8kNOnTxdpQgSgdevWfPfddwwdOpTbt28DMHbsWCIiIlizZo1MiEiSVLLlMX0mLS2NoKCg506f8fHxoUaNGgB4h3pjYmCCR1mPYg9ZkqTXU4lMirRo0YJdu3YB6uKKFy5c4MGDB/z999+agofFISZaYC2iiSaFtNg04uLiqPD0BEhJJ7p06cK+ffuIjIzkq6++ol+/fty7dw89vdzfvnk9n8XJySnXD8dZrK2tMTExYdeuXZoCtLGxsSQlJbF06dJ8xXz48GHatm3LDz/8wOeff56vbaTnUyqV9O/fn2PHjnHy5MlCFe719vZmxYoVKJVKLly4wPLly/n4448B+PDDD7l79y6//PIL6enp+Pr6MmXKFIYOHYpCoaBhw4bUqlWLzz//nPj4eOLi4hg5ciReXl75arcqFY34eNDTE5iRnL2miHEaFS0qPn/jIuTv70+PHj2Ii4uja9euvPHGG8VynIkTJ9KiRQsGDRrEX3/9xe+//87GjRtlcVVJkkq+JyNFhMieFHn06BFCiDynGgohMDIy0hQ59Q7zxrO8JwZ6JWaAuyRJr5gSmRTRldiIdGyIIUqZCKnq7jeyHa9u3b17lz179pCYmIiBgYGmloO+vj4VKlTg7t27Bd5n3759uXHjhubD8blz51i7dq1mubGxMZ9++injx4/n9u3bCCFISUnhxIkT3Lt374X73759O127dmXFihU56rRIhXf69Gk2bNiAr68vHh4elClTRvOVX127duXcuXOUK1eO7t278+WXX9K/f39APcLowIED/PXXX5QvX55WrVrRrl075s2bB6jfc7t27SItLQ13d3c8PDxQKpXs3LlTUwNCKn4JCWBppkQBYGkJQGR0JAkiQSsjRR4/fsy0adNo2LAhcXFxxT5aQ09Pj7Vr17JmzRrs7OxYuHDhC6cQSpIklQhPkiLx8ZCS8l9SxN/fH0NDwzw7tykUCi5evEjbtm0BWWRVkqTiJ1OuT4kNS8eaWK4rEyFFnRQpSL0Cqeilp6cza9Ys+vbtixACZ2dn1q5di5ubG1OnTmXUqFEsX74cZ2dnrl+/nq99VqlShe3btzN+/HjGjBlDw4YNGTFiBGvWrNGsM3/+fBYvXkzPnj0JDAzExMSEunXrMn/+/Bfu/3//+x9JSUkMGjQoW1Lkt99+o1+/fgX+Hbzujh07pvk5qwp9YRkZGbFixYpsnYie1rRpU06dOpXn9pUqVdJ0WMrN6tWrXyo+6cXi48HSNAP0LOFJMioyJRKBKLakiBAChUJBQEAAVapUwc3NjUmTJjFy5Eit1J2ysbHBxsaGN954g1atWhX78SRJkopEQgJUrkxoqPphVktePz8/XFxc8ryhEBISQlJSkqYdr3eoN6MajdJGxJIkvaZkUuQpsVFKrIglITMJUmH48OGaE7KkG56enpw+fTrXZe+//36OkRvTpk3LtevNs9Nl2rdvT/v27bM9N336dM3P+vr6jB49mtGjRxc45mc78kiSVHTi48HCKA1MrQF14jRZLxmgyJIivr6+HDt2jJs3b3Lz5k18fHy4dOkSzs7OnD17lvr168t6HpIkSS/yZKRIaCgoFJA168/f3/+5XbpWr17Ntm3buHjxIknpSTyIfiBHikiSVKxkUuQpMVEqKujHoEKFtbE1y5Yt03VIkiTlU7t27Th58mSuy86ePavlaKTiEh8PloYpYKGeShcVFQUWYKJvgpWx1UvtOzMzE319ffbu3csPP/xArVq1qFOnDh999JGmFbmsHyNJkpRPTyVFypUDQ0P10/7+/vkusnoj/AYCQW372tqIWJKk15RMijwlNhYqmyQAUMagDBs2bODDDz/UbVBSifOiD9+enp5ajkgC2Lt373OXy6ktr4b4eLDQ/6/IalZSpIJ5hZcavbF582amTZvG6dOnGTlypCyQLEmSVi1YsIAVK1agUCjw9PRk1apVDBkyhEuXLmFoaEjDhg357bffMMzKLDxFX19fc+3h7OzMzp07tR1+7p50n3m6yCqop888L8Hs4+NDr169ALgWeg0XaxesTF4u6S1JkvQ8stDqU2Lj9TAokwKAXrpevupHSK+fvXv3kpiYmOuXTIhIUvFKTSVb55mspIiTVeHqPymVSsaNG0ffvn3p378/VlZWcmqMJElaFRQUxM8//8ylS5e4efMmmZmZbNq0iX79+nHnzh1u3LhBSkpKnvWwTE1NuXbtGteuXSs5CRHINlLk6aTIw4cP85w+o1KpuH37NtWrVwfU9US8KnhpIVhJkl5ncqTIU2ISDNAvlwICjFRGsh2vJEmvjLt379K7d2/NYz8/P2bMmEFQUBD//vsvRkZGuLm5sWrVKqyfJBye5uLigoWFBfr6+hgYGHDp0iUtRv+ftDQwykzNlhQxLGuIo6VjIfaVxvvvv8/169fZu3evptOBJEmStimVSlJSUjA0NCQ5ORkHB4ds56SGDRvy+PFjHUZYCLkkRZRKJdHR0djb2+e6SWZmJkuXLqVRo0aAuvNMWzd5bpYkqXjJkSJPiU0yRGGVgpHKCAUKmRSRJOmVUa1aNc2dxMuXL2NmZkbXrl1p06YNN2/e5Pr161StWpU5c+bkuY+jR49y7do1nSVEANLTwUiZfaSIoY1hoYqsGhsb06VLFy5fviwTIpIk6YyjoyNffvklzs7OVKxYESsrq2znpIyMDP7880/ef//9XLdPTU2lfv36NG7cmH/++SfH8iVLllCjRg1q1KhBTExMcb2MnBITwcKCkJD/kiLR0dEAlC1bNtdNDA0NGThwIPb29qiEiuth12WRVUmSip1MijyRmQnxaSaorJLRS9dDqVTKpIgkSa+kw4cP4+bmRuXKlWnbti0GBupBg40bNy7xdyLT08FYmZQtKaIoo8DOzK5A+8nMzCQzM5MxY8bg4uJS9IFKkiTlU0xMDDt27MDf35/g4GCSkpJYt26dZvlnn33G22+/TfPmzXPd/tGjR1y6dIkNGzYwZswYfH19sy0fOXIkPj4++Pj4YGNjU6yvRSM9Xf31zEiRqKgoAGxtbXPdbNOmTcyaNQsAvxg/kjKSeLOCTIpIklS8ZFLkifh49XeldRKKVAX9+/dn5MiRug1KkiSpGGzatIm+ffvmeH7lypW0a9cu120UCgVt27alXr16/P777zmWa+tOZFoaGGVkT4pgDBbGFgXaz549e6hWrRpKpbIYopQkScq/Q4cO4erqip2dHYaGhnTr1o0zZ84AMH36dCIiIvjpp5/y3N7RUT19sEqVKrRo0YKrV69qJe7nSkxUf3+SFKlYUf3wRSNF9u/fz61btwB1PRELIwtcrF2KO1pJkl5zMinyRNY1fJp1EplJmVSrVk3zR0aStKVFixZMnjxZ12FIr7D09HR27txJz549sz0/a9YsDAwM6NevX67bnTp1iitXrrB3716WLFnCiRMnsi3X1p3I9HQwSk/MlhQRBgILo4IlRVatWkW9evU0o2QkSZJ0xdnZmXPnzpGcnIwQgsOHD1O9enVWrFjB/v372bhxI3p6uV+yx8TEkJaWBkBkZCSnT5/WtLPVqSdJkUwzCyIiso8UKVOmDEZGRrlu5uPjQ82aNQF1PZHa9rXRU8iPK5IkFS95lnkiNlb9PcUynoz4DL7++muuX7+u05gkSZKK2t69e6lbt262InerV69m165drF+/Ps/OK1lJ4vLly9O1a1cuXLiglXiflZ4OxukJ2ZIiSn1lgUaKRERE8O+///Lxxx8XU5SSJEn516hRI3r06EHdunXx9PREpVIxbNgwPv30U8LCwnjrrbfw8vJixowZAFy6dImhQ4cCcPv2berXr8+bb75Jy5YtmThxYslIiiQkABCRZolKlT0pktcoESEEPj4+mvi9w7xlPRFJkrRC3iJ7IjYWFKiIN49FJAsCAwMxNTXVdViSJElFauPGjdmmzuzbt4958+Zx/PhxzMzMct0mKSkJlUqFhYUFSUlJHDhwgKlTp2or5GzS0gRGaf8lRSKjIsnQyyjQSJH169dTvnx52rRpU0xRSpIkFcz06dOZPn16tufymt5Xv359TXveJk2acOPGjWKPr8CejBQJTTAHsidF8qonEhkZiZGR0X9JkVBvOnp0LP5YJUl67cmRIk/ExIC1fgJReqmQqn5OFlrVvRYtWvDFF1/Qu3dvLC0tcXJyYtOmTdy4cYO33noLCwsLGjZsyN27dwH4+++/qVevHjY2NpQrV47OnTvj7++v2d/QoUNp3Lgx6enpANy/fx9ra2v+/vvvfMXy+eef061bNywsLHB3d2ft2rXZ1tm9ezf16tXDysqKqlWrMn/+fFQqlWb57du3adeuHeXKlcPJyYnhw4cTFxdXFL8qSXqhpKQkDh48SLdu3TTPff755yQkJNCmTRu8vLz49NNPAQgODqZ9+/YAhIWF0axZM958800aNmxIhw4d8uyCUNzSUzIxIg2srACIjI1EIAo0UqR+/frMnz8ffX394gpTkiTp9ZaYCHp6hMaaYGSkyWMTHR2d50gROzs7IiMjqVq1KrGpsTyKeySLrEqSpBVypMgTsbFgTSxRpEAKmJmZUaZMGV2HpR1KJWiz44STExRgHv+ff/7Jzp072bhxI7/88gtDhgyhRYsWbNy4EQcHB/r27cuoUaPYv38/FhYWrFy5klq1ahETE8PHH3/Mhx9+yNmzZwFYvHgxzZo1Y+zYsfzwww9069aNwYMH56ivkJc//viDzZs3s3nzZg4cOMAHH3yAm5sbTZs25eLFi3Tt2pV169bRrVs3vL296dSpEwYGBowZM4aEhATeffdd+vTpw9atW4mLi6NPnz4MHDgw1xZ6klTUzM3NNZX/szx48CDXdR0cHNizZw+gLt7n7e1d7PHlR3pKJsak/Td9JkH9esoY5f983axZs+IITZIkScqSkKAushqmoEIFyJqZ+bzpM2FhYZiammJpaYl3qDd6Cj1qla+lxaAlSXpdyaTIE7GxYC1iiCAVgwwDWrdunefc+lfO48fg6qq94/n7QwFaYHbv3p23334bgEGDBjFmzBj69++vaaP54YcfaubWPn33uly5csyYMYO6deuSkJCAhYUFJiYmbNmyhfr163P27Fmsra2ZN29evmNp3749nTp10vzctWtXVq5cSdOmTVmxYgUdOnSgV69eANSrV4+vvvqKZcuWMWbMGHbt2kV6ejpz587FwMAAMzMzFi5cSN26dQkNDZUjkyQpH9JSVBiRDtbWqFQqYpLVVbLzO31m6tSplCtXjlGjRhVnmJIkSa+3xEQoU4aQkP+mzoA6KfJ0TaunTZ48mfj4eP766y+8w7zxsPXAzDD3aZ2SJElFSSZFnoiNVmGjiuIBKdiY2rBz505dh6Q9Tk7qRIU2j1cAFbP6uKG+053bcwlPCnodP36cGTNm4OPjQ1JSkmad8PBwLCzUH5pcXFzo1q0by5cvZ//+/QXqPuH6TPLI1dWVK1euABAYGJijuJm7uzsBAQGa5ZUrV852PHd3dwACAgJkUkSS8iE9VaiTIlZWxMXFoTJQT0/Lz/SZtLQ0lixZwty5c4s7TEmSpNdbYiJYWGRrxwvqpEj16tVz3cTHx4e2bdsCcC30GnUq1tFGpJIkSTIpkiUmPB0rYkkkDVs9e/z8/KhSpYquw9IOA4MCjdwoqdLT0+nYsSNTp07ln3/+wcLCgqtXr1K3bl2EEJr1du/ezaZNmxgyZAifffYZly9fxupJfYIXefjwYY7HTk+SPJUqVcLX1zfbcl9fX5ydnTXLAwICUCqVmsRI1vpZ60iS9Hzp6QJjYwUYGKinAj3p6pifkSL//vsvKSkpmtFckiRJUjHJmj4Tmn2kSF41RYQQ3Lp1izFjxgBwNfQqfWr20VKwkiS97mSh1SdiI5SY68UiFIKE8AR++uknXYckFVB6ejopKSnY2NhgYWFBcHAwkydPzraOn58fH330EcuXL+f333/Hw8ODgQMHZkuaPM+ePXvYvXs3mZmZ7Nu3j+3bt2vaeg4ePJjdu3ezdetWMjMzuXr1Kj/88APDhg0DoEOHDhgYGDBp0iRSUlIIDQ1l7NixdOrUSY4SkaR8SksDIzN1UjErKWKsb4yhvuELt121ahXdu3fH0tKyuMOUJEl6vT2ZPhMRAXZ2/z2dV02RkJAQ4uLiqFGjBumZ6dwKvyVHikiSpDUyKfJEbHQmJobquemkyM4zpVGZMmVYsWIFM2fOpEyZMrRr1y5bAdWUlBS6d+/OwIED6d27N3p6eqxfv55r167lezj94aTBqgABAABJREFU4MGD+eOPP7C2tmbkyJEsW7aM5s2bA9CoUSO2bNnCrFmzsLGxoWfPnowaNYrRo0cDYGlpycGDB/H29sbJyYl69erh7u7OmjVriv6XIUmvqPQMBUZl1AmQqKgoDMwN8jV1RgiBk5MTn3zySXGHKEmSJD2ZPvMkN6KRV0teQ0NDZs2ahYeHBz4RPmSoMnjTXnaekSRJO+T0mSdiYqCyYSwAykSlTIqUEMeOHcv22MDAIMeojvfffx+lUgmoC7EOGjQo2/KnH1+9ejXbMltb2xxTYp7H2tqaxYsX57m8c+fOdO7cOc/lNWvWZP/+/Xkuf/b1SpKUXbpSD+My6jkzUVFRmNua52vqjEKh4Lfffivu8CRJkiTQjBRJ8oMn5eBITk4mNTU115EidnZ2TJo0CVDXE6lYpiL2ZXIvyCpJklTU5EiRJ2LjFOiXSQYByTHJMikiSZJUAqUp9TEy/2+kiJm1Wb7a8R4/fpy9e/cWd3iSJEkSaGqKJCX9lxSJjo4GyDUpsnz5cjZv3gzA1ZCrcuqMJElaJZMiT8QmGIBlMnrpegz4aAD16tXTdUiSltWsWZMyZcrk+hUXF6fr8CRJAtIz9TEyUf/pioqKwtjSOF/TZ/744w82btxY3OFJkiRJkOv0maioKCD3pMiff/7JjRs3ALgWdg0vey9tRSpJkiSnz2SJTTZEWCcjUgSdOnXK1vJVej3cunXrucvl1BZJ0q3MTFAJPYyN1Y+joqIwKmOUr+kzt27dylZjSJIkSSpGWdNnnhopEhUVhUKhyLXj3+3btxkxYgQqoeJa6DW+aPiFlgOWJOl1JkeKAOnpkJxuiNIyCZEkWL58eb67kUiSJEnakZam/v70SBEDsxcXWlWpVNy+fZuaNWsWd4iSJEkSQEICmWYWpKZmT4rY2Nigr6+fbdWIiAgiIyOpUaMGD2MfEp8Wj1cFL+3HLEnSa0smRYDYWPX3VIt4SIUzZ86gUCh0GpMkSZKUXXq6+vvTSRGFieKFI0UePnxISkoKNWrUKO4QJUmSJIDERJKMbIDsNUVymzrz8OFDFAoF7u7uXA25ioWRBVVsqmgzWkmSXnNy+gz/JUWSLWIhWbbjlSRJKomykiLGJuqkdVRUFBjxwqSIs7Mzt27dwtXVtbhDlCRJkkCdFDG0BrLXFMktKdKgQQNSU1MxMjLiWug13qzwJnoKed9WkiTtkWccoEoVCGj/KUmWoZAKjo6Oug5JkiRJekZu02dUhqoXTp8xMDCgRo0a6OnJP3mSJElakZBAkr4lkH36jK2tbY5VU1JSMDRUdxW7GnpVFlmVJEnr5BUiYGAAlTL8iDZKwyDDAAcHB12HJEmSJD1DM33GVD0fPSoqiky9zBe25B0+fDjffPNNcYcnSZIkAQgBiYkk6uVMiuQ2UmTcuHH07t0bgGuh12Q7XkmStE4mRbLExhKjn05Z87KsXr1a19FIkiRJz9BMnzHTJzU1leTkZNIV6S+cPnPp0iUsLF7coUaSJEkqAmlpkJlJkkKdsH5RTZFHjx7h6OhIRFIEQQlBssiqJElaJ5MiWeLiiNXPoIxBGVlkVZIkqQTSTJ8x1VfXEwFSVanPnT6T1XlGFlmVJEnSkoQEAJIUZTAwACMj9dN5jRQJCAigcuXKXAu9hoGeATXtZKcwSZK0q0QlRVJTU2nYsCFvvvkmNWvW5NtvvwXA39+fRo0a4e7uTu/evUnPul1YlOLiSNDPIOBuABs2bCj6/UuSJOnQ3bt38fLy0nxZWlqycOFCoqOjadOmDR4eHrRp04aYmJhct1+zZg0eHh54eHiwZs0aLUevppk+Y2agSYokZyY/d6RIVucZ2Y5XkqSSbMGCBdSsWZNatWrRt29fUlNT8339O2fOHNzd3alWrRr79+/XcuS5SExUfxNmmJtD1r3G3GqKCCF49OgRlStX5mroVWrY1cDYwFjbEUuS9JorUUkRY2Njjhw5gre3N9euXWPfvn2cO3eOCRMmMHbsWB48eICNjQ1//PFHkR9bFRtDskEGGQkZsvuMJEmvnGrVqnHt2jWuXbvG5cuXMTMzo2vXrnz//fe0bt2a+/fv07p1a77//vsc20ZHRzN9+nTOnz/PhQsXmD59ep7Jk+KkmT5j/iQpooDEjMTnjhRJTU2lY8eOuLi4aCdISZKkAgoKCuLnn3/m0qVL3Lx5k8zMTDZt2pSv618fHx82bdrErVu32LdvH5999hmZmZk6eBVPeZIUSVKZaqbOQO4jRZRKJR9//DGenp7qeiIVZD0RSZK0r0QlRRQKBWWe9O3KyMggIyMDhULBkSNH6NGjBwADBw7kn3/+KdoDp6WRQDpCAaTIlrySJL3aDh8+jJubG5UrV2bHjh0MHDgQyPv8un//ftq0aYOtrS02Nja0adOGffv2aTlqdVJEgQp9UyOioqKwLmuNSqieO1KkRo0a/Pvvv+jr62sxUkmSpIJRKpWkpKSgVCpJTk6mYsWK+br+3bFjB3369MHY2BhXV1fc3d25cOGClqN/RlZSJNNEkxRRqVS51hQxNDRk0aJFuLu7cy30mqwnIkmSThjoOoBnZWZmUq9ePR48eMDIkSNxc3PD2toaAwN1qE5OTgQFBWXbZsmSJSxZsgQAPz+/Qs0dj7GpQPW/bMAaevbs+dKvQ9tiYmKwsbHRdRiFJuPXLRm/bhU2fhsbG06fPl3g7TZt2kTfvn0BCAsLo2LFioA6IRwWFpZj/aCgICpVqqR5XFznYXjx7+KN6lBzvvrninYVqfhXRT7+6+NCHauova7vw5JCxq9br2v8hT0PP83R0ZEvv/wSZ2dnTE1Nadu2LfXq1Xvh9S+oz8+NGzfWPNbl+Tmb6tXh1zcxMICsw1WrVo1Ro0Y9d7Pfn/xX1F7X92dJIePXrdc1/oKcn0tcUkRfX59r164RGxtL165duXPnzgu3GTlyJCNHjnyp49aoUQMfH5+X2ocuyfh1S8avWzL+/EtPT2fnzp3MmTMnxzKFQlHoQtNFcR6G0v1vWZpjBxm/rsn4dUuX8cfExLBjxw78/f2xtramZ8+eRToaT56fS3fsIOPXNRm/bmkj/hI1feZp1tbWtGzZkrNnzxIbG4tSqQTg8ePHODo66jg6SZKk0mnv3r3UrVsXe3t7AOzt7QkJCQEgJCSE8uXL59jG0dGRwMBAzWN5HpYkSSo6hw4dwtXVFTs7OwwNDenWrRunT5/O1/WvPD9LkiS9vBKVFImIiCA2NhaAlJQUDh48SPXq1WnZsiVbtmwB1B0QunTpUuTHLooMui7J+HVLxq9bMv7827hxo2bqDEDnzp013WTyOr++9957HDhwgJiYGGJiYjhw4ADvvfdescRXmv8tS3PsIOPXNRm/bukyfmdnZ86dO0dycjJCCA4fPkyNGjXydf3buXNnNm3aRFpaGv7+/ty/f5+GDRsWS5yl+d+4NMcOMn5dk/HrljbiVwghRLEfJZ+uX7/OwIEDyczMRKVS0atXL6ZOnYqfnx99+vQhOjqaOnXqsG7dOoyNZbsuSZKkgkhKSsLZ2Rk/Pz+srKwAdTeAXr16ERAQQOXKldm8eTO2trZcunSJZcuWsWLFCgBWrlzJ7NmzAfjmm2/4+OOSUcdDkiTpVfDtt9/y119/YWBgQJ06dVixYgVBQUG5Xv/u3LmTS5cuMWPGDABmzZrFypUrMTAwYOHChbRr107Hr0aSJKl0KVFJEUmSJEmSJEmSJEmSJG0pUdNnJEmSJEmSJEmSJEmStEUmRYB9+/ZRrVo13N3d+f7773UdjsbgwYMpX748tWrV0jwXHR1NmzZt8PDwoE2bNsTExAAghGDUqFG4u7tTu3Ztrly5otlmzZo1eHh44OHhoakdUNwCAwNp2bIlNWrUoGbNmixatKhUxZ+amkrDhg158803qVmzJt9++y0A/v7+NGrUCHd3d3r37k16ejoAaWlp9O7dG3d3dxo1asTDhw81+5ozZw7u7u5Uq1aN/fv3ayX+LJmZmdSpU4eOHTuWuvhdXFzw9PTEy8uL+vXrA6Xn/QMQGxtLjx49eOONN6hevTpnz54tVfFrmzwPFz15Hn6o2Zc8DxeOPA/rNv6SQp6fi548Pz/U7EuenwtHnp+LOH7xmlMqlaJKlSrC19dXpKWlidq1a4tbt27pOiwhhBDHjx8Xly9fFjVr1tQ899VXX4k5c+YIIYSYM2eOGD9+vBBCiN27d4v3339fqFQqcfbsWdGwYUMhhBBRUVHC1dVVREVFiejoaOHq6iqio6OLPfbg4GBx+fJlIYQQ8fHxwsPDQ9y6davUxK9SqURCQoIQQoj09HTRsGFDcfbsWdGzZ0+xceNGIYQQw4cPF7/++qsQQoglS5aI4cOHCyGE2Lhxo+jVq5cQQohbt26J2rVri9TUVOHn5yeqVKkilEplscef5ccffxR9+/YVHTp0EEKIUhV/5cqVRURERLbnSsv7RwghBgwYIJYvXy6EECItLU3ExMSUqvi1SZ6Hi4c8D+v+PCaEPA/L83DpJs/PxUOen3V/fhNCnp/l+fk/r31S5MyZM6Jt27aax7NnzxazZ8/WYUTZ+fv7ZzvZV61aVQQHBwsh1CfUqlWrCiGEGDZsmNiwYUOO9TZs2CCGDRumef7Z9bSlc+fO4sCBA6Uy/qSkJFGnTh1x7tw5UbZsWZGRkSGEyP7eadu2rThz5owQQoiMjAxRtmxZoVKpcryfnl6vuAUGBopWrVqJw4cPiw4dOgiVSlWq4s/tZF9a3j+xsbHCxcVFqFSqUhm/tsnzsHbI87DIsV5xk+dheR4u7eT5WTvk+VnkWK+4yfOzPD8/7bWfPhMUFESlSpU0j52cnAgKCtJhRM8XFhZGxYoVAahQoQJhYWFA3q+jJLy+hw8fcvXqVRo1alSq4s/MzMTLy4vy5cvTpk0b3NzcsLa2xsDAIEcsT8dpYGCAlZUVUVFROo1/zJgxzJs3Dz099f/mUVFRpSp+hUJB27ZtqVevHr///jtQet7//v7+2NnZ8fHHH1OnTh2GDh1KUlJSqYlf20rb6yyN/47yPCzPw4Uhz8Ml4/9fXSptr780/vvK87M8PxeGPD8XbfyvfVKkNFMoFCgUCl2H8VyJiYl0796dhQsXYmlpmW1ZSY9fX1+fa9eu8fjxYy5cuMCdO3d0HVK+7dq1i/Lly1OvXj1dh1Jop06d4sqVK+zdu5clS5Zw4sSJbMtL8vtHqVRy5coVRowYwdWrVzE3N88xD7skxy/lX2n4d5TnYd2Q52HdkudhqTT8+8rzs27I87NulcTz82ufFHF0dCQwMFDz+PHjxzg6Ouowouezt7cnJCQEgJCQEMqXLw/k/Tp0+foyMjLo3r07/fr1o1u3bqUu/izW1ta0bNmSs2fPEhsbi1KpzBHL03EqlUri4uIoW7aszuI/ffo0O3fuxMXFhT59+nDkyBFGjx5dauLPigmgfPnydO3alQsXLpSa94+TkxNOTk40atQIgB49enDlypVSE7+2lbbXWZr+HeV5WJ6HX4Y8D5eM978ulbbXX5r+feX5WZ6fX4Y8Pxdx/IWadPMKycjIEK6ursLPz09TQOrmzZu6Dkvj2bmSX375ZbYCNF999ZUQQohdu3ZlK0DToEEDIYS6AI2Li4uIjo4W0dHRwsXFRURFRRV73CqVSnz00Udi9OjR2Z4vLfGHh4eLmJgYIYQQycnJolmzZuLff/8VPXr0yFaAacmSJUIIIRYvXpytAFPPnj2FEELcvHkzWwEmV1dXrRaQEkKIo0ePagpIlZb4ExMTRXx8vObnt956S+zdu7fUvH+EEKJZs2bizp07Qgghvv32W/Hll1+Wqvi1SZ6Hi4c8D8vz8MuQ52Hdx18SyPNz8ZDnZ3l+fhny/Fz08b/2SREh1BVtPTw8RJX/s3ffYVFcXRjA36UjCIKKCHbs2Hvvxl5i7xpjNPZ8SezdxN5b7C1qjDW22I2994YFu2BBBVQ67LzfHzduRDpS5fyeh0fdnZ25i3B25sy55+bJw19//TW5h2PQrl07Ojo60sTEhM7Ozly2bBlfv37NWrVqMW/evKxdu7bhP17TNPbp04d58uRhkSJFeP78ecN+li9fThcXF7q4uHDFihVJMvbjx48TAIsWLcrixYuzePHi/Pvvv1PN+K9evcoSJUqwaNGidHV15bhx40iS9+/fZ9myZeni4sJWrVoxKCiIJBkYGMhWrVrRxcWFZcuW5f379w37+vXXX5knTx7mz5+fu3fvTpLxf+zjYJ9axn///n0WK1aMxYoVY+HChQ2/l6nl54ckL1++zNKlS7No0aJs1qwZvb29U9X4k5rE4YQncVji8OeQOJz8408pJD4nPInPEp8/h8TnhB+/jiTjV2MihBBCCCGEEEIIkXql+Z4iQgghhBBCCCGESJskKSKEEEIIIYQQQog0SZIiQgghhBBCCCGESJMkKSKEEEIIIYQQQog0SZIiQgghhBBCCCGESJMkKSKEEEIIIYQQQog0SZIiQgghhBBCCCGESJMkKSKEEEIIIYQQQog0SZIiItXR6XQxfh05ciRe+3706BF0Oh127dqVsIOOxMaNG7Fq1apEP44QQgghhBBCiMjpSDK5ByFEXJw5c8bw98DAQNSqVQsjR45Eo0aNDI8XLlwYNjY2cd53cHAwLl++jIIFCyJDhgwJMdwotWrVCq9fv453AkcIIYQQQgghxOcxSe4BCBFXFSpUMPzdz88PAODi4hLu8Y/p9Xro9XqYmZnFuG9zc/Mo9yOEEEIIIYQQ4ssi02fEF6dbt24oU6YMtm3bBldXV1hYWODs2bN4/vw5unfvjjx58sDS0hL58+fHyJEjERISYnhtZNNncuXKhZ9//hmzZs1CtmzZYGdnh3bt2sHX1zfacXh4eKBNmzZwcHCApaUlXFxcMGrUKMMYt2zZgqNHjxqm/IwdO9bw2u3bt6NMmTKwsLCAo6MjBg8ejNDQUMPzY8eORaZMmXDy5EmUKlUKFhYWKFGiBE6cOJEw30QhhBBCCCGESAOkUkR8kR49eoTBgwdj9OjRcHR0RO7cufH69WvY29tj5syZsLOzw927dzF27Fi8evUKixcvjnZ/GzduRLFixbBkyRJ4eHjgxx9/xPDhw/Hbb79F+ZouXbogMDAQS5YsQYYMGfDgwQPcvn0bADBq1Cg8efIEvr6+hn1ky5bNcKz27dujV69emDhxIu7fv49hw4ZB0zRMnz7dsP+AgAB06tQJw4YNQ9asWTFjxgw0aNAA7u7ucHR0/NxvoRBCCCGEEEJ88SQpIr5Ib968wcGDB1GiRAnDY9myZQuXVKhcuTKsrKzQvXt3zJs3L9rpNaampti2bRtMTNSvjJubG/78889okyLnzp3D+vXr0aRJEwBAjRo1DM+5uLjA3t4emqaFm65DEoMGDUKXLl3C7dvc3Bx9+/bFsGHDkDFjRgCqn8qECRPQoUMHAEDNmjWRI0cOzJ49G5MnT47Fd0kIIYQQQggh0jaZPiO+SM7OzuESIoBKOMyePRuFCxeGpaUlTE1N0bFjRwQHB+PJkyfR7q9mzZqGhAigGrl6eXmFm9LyqRIlSmDYsGFYtWpVjPv/4O7du3jy5AnatGmDsLAww1etWrUQFBSEGzduhNv+66+/Nvzd2toadevWxblz52J1LCGEEEIIIYRI6yQpIr5IWbJkifDY7Nmz8fPPP+Prr7/G9u3bce7cOSxYsAAAEBQUFO3+Pl2JxszMDCQRHBwc5Ws2bNiAMmXK4H//+x9y5syJEiVK4NChQ9Ee5/Xr1wCAhg0bwtTU1PCVO3duAMDTp08N21pbW8PS0jLc6x0cHPD8+fNojyGEEEIIIYQQQpHpM+KLpNPpIjy2adMmtGrVChMmTDA85ubmlmhjcHZ2xqpVq6BpGs6dO4exY8eiadOmePLkiWEKzKfs7e0BAEuWLEHJkiUjPP8hOQKolXcCAwPDJUa8vLyQNWvWBH4nQgghhBBCCPFlkkoRkWYEBgbC3Nw83GPr1q1L9OMaGRmhQoUKGDNmDAICAvD48WMAqtrk0wqVAgUKwNnZGY8ePUKZMmUifH2aTPnrr78Mf/fz88OBAwdQrly5RH9PQgghhBBCCPElkEoRkWbUrVsXc+fORfny5eHi4oJ169bh3r17iXKst2/fol69eujSpQvy58+P4OBgzJgxA46OjihUqBAAoGDBgti+fTu2bduGbNmywcnJCU5OTpgxYwY6d+6Md+/eoUGDBjAzM8ODBw+wbds2bN68GenSpQMAWFpaYsSIEfDz84OTkxOmT5+OkJAQDBw4MFHekxBCCCGEEEJ8aSQpItKM0aNH49WrVxg5ciQAoEWLFpg7d65hdZiEZGFhgaJFi2LOnDl4+vQp0qVLhwoVKmD//v2G6S59+vTB5cuX0b17d/j4+GDMmDEYO3Ys2rZtCxsbG0ycOBErVqyAsbEx8uTJg8aNG4dbISddunT4/fff0b9/f9y6dQsFCxbE7t27ZfqMEEIIIYQQQsSSjiSTexBCiLgZO3Ys5s+fb2jMKoQQQgghhBAi7qSniBBCCCGEEEIIIdIkSYoIIYQQQgghhBAiTZLpM0IIIYQQQgghhEiTpFJECCGEEEIIIYQQaZIkRYQQQgghhBBCCJEmSVJECCGEEEIIIYQQaZIkRYQQQgghhBBCCJEmSVJECCGEEEIIIYQQaZIkRYQQQgghhBBCCJEmSVJECCGEEEIIIYQQaZIkRYQQQgghhBBCCJEmSVJECCGEEEIIIYQQaZIkRYRI42rUqIEaNWok9zCEECLNkjgshBApU65cudCtW7fkHoZIZJIUEfHi5+eHMWPGoH79+rC3t4dOp8OqVaui3P7WrVuoX78+rK2tYW9vj86dO+PVq1cRttM0DVOnTkXu3LlhYWGBYsWKYf369Um2TyGESC3Onz+Pfv36wdXVFVZWVsiRIwfatGmDu3fvRrq9xGEhhEgaR44cgU6ni/TrzJkzEbY/deoUqlSpgnTp0sHR0REDBgyAn59fhO2Cg4MxZMgQODk5wdLSEuXLl8eBAwciHUNi7FOIL5VJcg9ApE6vX7/G+PHjkSNHDhQvXhxHjhyJclsPDw9Uq1YNtra2mDhxIvz8/DB9+nRcv34d586dg5mZmWHbESNGYPLkyfjuu+9QtmxZbN++HR06dIBOp0O7du0SdZ9CCJGaTJkyBSdPnkTr1q1RrFgxvHjxAvPnz0epUqVw5swZFClSxLCtxGEhhEh6AwYMQNmyZcM9ljdv3nD/vnLlCmrXro1ChQph5syZ8PDwwPTp0+Hu7o49e/aE27Zbt27YvHkzfvjhB+TLlw+rVq1Cw4YNcfjwYVSpUiVR9ynEF41CxENQUBCfP39Okjx//jwBcOXKlZFu27t3b1paWvLx48eGxw4cOEAAXLx4seExDw8Pmpqasm/fvobHNE1j1apVmS1bNoaFhSXqPtOq6tWrs3r16sk9DCFEHJ08eZLBwcHhHrt79y7Nzc3ZsWPHcI9LHE7ZJA4L8WU5fPgwAXDTpk0xbtugQQNmzZqVb9++NTy2dOlSAuC+ffsMj509e5YAOG3aNMNjgYGBdHFxYcWKFRN9n2lVzpw52bVr1+QehkhkMn1GxIu5uTkcHR1jte2WLVvQuHFj5MiRw/BYnTp1kD9/fmzcuNHw2Pbt2xEaGoo+ffoYHtPpdOjduzc8PDxw+vTpRN1nVG7fvo1WrVrB3t4eFhYWKFOmDHbs2GF43svLC5kzZ0aNGjVA0vD4vXv3YGVlhbZt2xoeO378OFq3bo0cOXLA3Nwc2bNnx//+9z8EBgaGO2a3bt1gbW2NJ0+eoHHjxrC2toazszMWLFgAALh+/Tpq1aoFKysr5MyZE3/88Ue4169atQo6nQ7Hjh1Dr169kDFjRtjY2KBLly7w8fGJ8T0HBwdjzJgxyJs3r2GcgwcPRnBwcLjtDhw4gCpVqiBDhgywtrZGgQIFMHz48Bj3L4T4fJUqVQpXjQEA+fLlg6urK27duhXucYnDEoeFEMnj/fv3CAsLi/S5d+/e4cCBA+jUqRNsbGwMj3fp0gXW1tbhYunmzZthbGyMnj17Gh6zsLDAt99+i9OnT+Pp06eJts/onD17FvXr14etrS3SpUuH6tWr4+TJk4bnb926BUtLS3Tp0iXc606cOAFjY2MMGTLE8Nj27dvRqFEjODk5wdzcHC4uLvjll1+g1+vDvbZGjRooUqQIrl27hurVqyNdunTImzcvNm/eDAA4evQoypcvD0tLSxQoUAAHDx4M9/qxY8dCp9Ph9u3baNOmDWxsbJAxY0YMHDgQQUFBMb5nX19f/PDDD8iePTvMzc2RN29eTJkyBZqmhdvuzz//ROnSpZE+fXrY2NigaNGimDNnToz7F0lPkiIiUXl6esLLywtlypSJ8Fy5cuVw+fJlw78vX74MKysrFCpUKMJ2H55PrH1G5ebNm6hQoQJu3bqFoUOHYsaMGbCyskLz5s3x119/AQAcHBywcOFCHD16FPPmzQOg5s9369YN6dOnx2+//WbY36ZNmxAQEIDevXtj3rx5qFevHubNmxfhgwIA9Ho9GjRogOzZs2Pq1KnIlSsX+vXrh1WrVqF+/fooU6YMpkyZgvTp06NLly54+PBhhH3069cPt27dwtixY9GlSxesW7cOzZs3D3fR8ClN09C0aVNMnz4dTZo0wbx589C8eXPMmjUr3IXFzZs30bhxYwQHB2P8+PGYMWMGmjZtGu6DUAiRtEji5cuXyJQpk+ExicMSh4UQyeObb76BjY0NLCwsULNmTVy4cCHc89evX0dYWFiEWGpmZoYSJUpEiKX58+cPl+gA/oulV65cSbR9RuWff/5BtWrV8O7dO4wZMwYTJ06Er68vatWqhXPnzgEAChUqhF9++QVr1qwxJLP9/f3RrVs3FCxYEOPHjzfsb9WqVbC2tsaPP/6IOXPmoHTp0hg9ejSGDh0a4dg+Pj5o3Lgxypcvj6lTp8Lc3Bzt2rXDhg0b0K5dOzRs2BCTJ0+Gv78/WrVqhffv30fYR5s2bRAUFIRJkyahYcOGmDt3brgEUWQCAgJQvXp1rF27Fl26dMHcuXNRuXJlDBs2DD/++KNhuwMHDqB9+/aws7PDlClTMHnyZNSoUUPic0qVrHUq4osQ3fSZD8/9/vvvEZ4bNGgQATAoKIgk2ahRI+bJkyfCdv7+/gTAoUOHJto+o1K7dm0WLVrUsD9SlX1XqlSJ+fLlC7dt+/btmS5dOt69e5fTpk0jAG7bti3cNgEBARGOMWnSJOp0unAl6F27diUATpw40fCYj48PLS0tqdPp+Oeffxoev337NgFwzJgxhsdWrlxJACxdujRDQkIMj0+dOpUAuH37dsNjn5Ztr1mzhkZGRjx+/Hi4cS5atIgAePLkSZLkrFmzCICvXr2K9HsnhEh6a9asIQAuX77c8JjEYYnDQoikdfLkSbZs2ZLLly/n9u3bOWnSJGbMmJEWFha8dOmSYbtNmzYRAI8dOxZhH61bt6ajo6Ph366urqxVq1aE7W7evEkAXLRoUaLtMzKapjFfvnysV68eNU0zPB4QEMDcuXOzbt26hsf0ej2rVKnCLFmy8PXr1+zbty9NTEx4/vz5cPuMLD736tWL6dKlC/cZUL16dQLgH3/8YXjsQxw2MjLimTNnDI/v27cvwnXKmDFjCIBNmzYNd6w+ffoQAK9evWp47NPpM7/88gutrKx49+7dcK8dOnQojY2N+eTJE5LkwIEDaWNjI1NEUwmpFBGJ6kM5srm5eYTnLCwswm0TGBgY6+0Sep+R8fb2xj///IM2bdrg/fv3eP36NV6/fo03b96gXr16cHd3h6enp2H7+fPnw9bWFq1atcKoUaPQuXNnNGvWLNw+LS0tDX/39/fH69evUalSJZCM9G5pjx49DH/PkCEDChQoACsrK7Rp08bweIECBZAhQwY8ePAgwut79uwJU1NTw7979+4NExMT7N69O8r3vWnTJhQqVAgFCxY0vOfXr1+jVq1aAIDDhw8bxgOoUsdPywWFEEnv9u3b6Nu3LypWrIiuXbsaHpc4LHFYCJG0KlWqhM2bN6N79+5o2rQphg4dijNnzkCn02HYsGGG7WKKpR/Hx4SKz/HZZ2SuXLkCd3d3dOjQAW/evDHEKX9/f9SuXRvHjh0zxCUjIyOsWrUKfn5+aNCgAX777TcMGzYsQjXLx/H5Q8yvWrUqAgICcPv27XDbWltbh2vU/SEOFypUCOXLlzc8/uHvkcXnvn37hvt3//79ASDG+Fy1alXY2dmFi8916tSBXq/HsWPHAKj47O/vLyv5pBKSFBGJ6kNw+3QONADDnL0P21haWsZ6u4TeZ2Tu3bsHkhg1ahQyZ84c7mvMmDEA1Dz2D+zt7TF37lxcu3YNtra2mDt3boR9PnnyBN26dYO9vT2sra2ROXNmVK9eHQDw9u3bcNtaWFggc+bM4R6ztbVFtmzZoNPpIjwe2Rz1fPnyhfu3tbU1smbNikePHkX5vt3d3XHz5s0I7zl//vzh3nPbtm1RuXJl9OjRA1myZEG7du2wceNGOTEXIhm8ePECjRo1gq2trWGO+AcSh8OTOCyESA558+ZFs2bNcPjwYUOPjJhi6cfxMaHic3z2GRl3d3cAQNeuXSPEqmXLliE4ODhcTHVxccHYsWNx/vx5uLq6YtSoURH2efPmTXz99dewtbWFjY0NMmfOjE6dOgGIGJ+jisPZs2eP8BiAWMVnFxcXGBkZxRif9+7dG+E916lTB8B/8blPnz7Inz8/GjRogGzZsqF79+7Yu3dvlPsVyUuW5BWJKmvWrACA58+fR3ju+fPnsLe3N2Sos2bNisOHD4NkuCD34bVOTk6Jts/IfDip/Pnnn1GvXr1It/l0WbV9+/YBUIHXw8PDcBcPUHPT69atC29vbwwZMgQFCxaElZUVPD090a1btwgnsR9f1MTmcUYzPz0uNE1D0aJFMXPmzEif//BhY2lpiWPHjuHw4cP4+++/sXfvXmzYsAG1atXC/v37oxynECJhvX37Fg0aNICvry+OHz8eIa5JHM5geE7isBAiOWXPnh0hISHw9/eHjY1NjLH04/iYNWvWcJVxH28HxD4+x2efkfkQL6dNm4YSJUpEuo21tXW4f+/fvx8A8OzZM7x58ybcog2+vr6oXr06bGxsMH78eLi4uMDCwgKXLl3CkCFDkiQ+f5pkiYymaahbty4GDx4c6fMfktcODg64cuUK9u3bhz179mDPnj1YuXIlunTpgtWrV8d4HJG0JCkiEpWzszMyZ84cobEUAJw7dy5cEC1RogSWLVuGW7duoXDhwobHz549a3g+sfYZmTx58gAATE1NDdnf6OzduxfLli3D4MGDsW7dOnTt2hVnz56FiYn6Nbt+/Tru3r2L1atXh2vol5hlde7u7qhZs6bh335+fnj+/DkaNmwY5WtcXFxw9epV1K5dO8YPByMjI9SuXRu1a9fGzJkzMXHiRIwYMQKHDx+O1fdMCPF5goKC0KRJE9y9excHDx4MF+c+kDgscVgIkTI8ePAAFhYWhmRBkSJFYGJiggsXLoSbkhcSEoIrV66Ee6xEiRI4fPgw3r17F64x6qexNDH2GRkXFxcAgI2NTaxizaJFi3DgwAFMmDABkyZNQq9evbB9+3bD80eOHMGbN2+wdetWVKtWzfB4ZA2sE4q7uzty585t+Pe9e/egaRpy5coV5WtcXFzg5+cXq/dsZmaGJk2aoEmTJtA0DX369MHixYsxatSoCAl9kbxk+oxIdC1btsSuXbvCLet16NAh3L17F61btzY81qxZM5iamoZbJYAkFi1aBGdnZ1SqVClR9/kpBwcH1KhRA4sXL4402/7q1SvD3319fdGjRw+UK1cOEydOxLJly3Dp0iVMnDjRsM2HzPXHmWqSibo015IlSxAaGmr498KFCxEWFoYGDRpE+Zo2bdrA09MTS5cujfBcYGAg/P39Aai5/p/68OEZWSmmECJh6fV6tG3bFqdPn8amTZtQsWLFKLeVOKxIHBZCJIWPY9MHV69exY4dO/DVV1/ByEhdgtna2qJOnTpYu3ZtuNVR1qxZAz8/v3CxtFWrVtDr9ViyZInhseDgYKxcuRLly5c3VJAlxj4jU7p0abi4uGD69Onw8/OL9nvw8OFDDBo0CC1btsTw4cMxffp07NixA7///rthm8jic0hISLjPjoT2YYn1Dz6sXhZTfD59+rShKvFjvr6+huWX37x5E+45IyMjFCtWDIDE55RIKkVEvM2fPx++vr549uwZAGDnzp3w8PAAoBoVfZjDN3z4cGzatAk1a9bEwIED4efnh2nTpqFo0aL45ptvDPvLli0bfvjhB0ybNg2hoaEoW7Ystm3bhuPHj2PdunXhyuESY5+RWbBgAapUqYKiRYviu+++Q548efDy5UucPn0aHh4euHr1KgBg4MCBePPmDQ4ePAhjY2PUr18fPXr0wK+//opmzZqhePHiKFiwIFxcXPDzzz/D09MTNjY22LJlS6RzHBNKSEgIateujTZt2uDOnTv47bffUKVKFTRt2jTK13Tu3BkbN27E999/j8OHD6Ny5crQ6/W4ffs2Nm7ciH379qFMmTIYP348jh07hkaNGiFnzpzw8vLCb7/9hmzZsqFKlSqJ9p6EEMpPP/2EHTt2oEmTJvD29sbatWvDPf9hHjYgcVjisBAiKbVt2xaWlpaoVKkSHBwc4ObmhiVLliBdunSYPHlyuG0nTJiASpUqoXr16ujZsyc8PDwwY8YMfPXVV6hfv75hu/Lly6N169YYNmwYvLy8kDdvXqxevRqPHj3C8uXLE32fnzIyMsKyZcvQoEEDuLq64ptvvoGzszM8PT1x+PBh2NjYYOfOnSCJ7t27w9LSEgsXLgQA9OrVC1u2bMHAgQNRp04dODk5oVKlSrCzs0PXrl0xYMAA6HQ6rFmzJsGmJUbm4cOHaNq0KerXr4/Tp09j7dq16NChA4oXLx7lawYNGoQdO3agcePG6NatG0qXLg1/f39cv34dmzdvxqNHj5ApUyb06NED3t7eqFWrFrJly4bHjx9j3rx5KFGiRIQl6kUKkJRL3YgvS86cOQkg0q+HDx+G2/bGjRv86quvmC5dOmbIkIEdO3bkixcvIuxTr9dz4sSJzJkzJ83MzOjq6sq1a9dGevzE2Gdk7t+/zy5dutDR0ZGmpqZ0dnZm48aNuXnzZpLk9u3bCYAzZswI97p3794xZ86cLF68uGE5Rjc3N9apU4fW1tbMlCkTv/vuO169ejXCUmFdu3allZVVhLFUr16drq6uER7PmTMnGzVqZPj3h6Ugjx49yp49e9LOzo7W1tbs2LEj37x5E2GfHy8FSZIhISGcMmUKXV1daW5uTjs7O5YuXZrjxo3j27dvSZKHDh1is2bN6OTkRDMzMzo5ObF9+/YRligTQiSOD0sSRvX1KYnDEoeFEEljzpw5LFeuHO3t7WliYsKsWbOyU6dOdHd3j3T748ePs1KlSrSwsGDmzJnZt29fvnv3LsJ2gYGB/Pnnn+no6Ehzc3OWLVuWe/fuTbJ9Ruby5cts0aIFM2bMSHNzc+bMmZNt2rThoUOHDN8LANyyZUu41z158oQ2NjZs2LCh4bGTJ0+yQoUKtLS0pJOTEwcPHmxYUvfw4cOG7WIbhz8AwL59+xr+/WFJXjc3N7Zq1Yrp06ennZ0d+/Xrx8DAwAj7/HhJXpJ8//49hw0bxrx589LMzIyZMmVipUqVOH36dMNnzebNm/nVV1/RwcGBZmZmzJEjB3v16sXnz5/H7hsrkpSOTMT0mxAiWaxatQrffPMNzp8/H2G5MyGEEIlP4rAQQqRMY8eOxbhx4/Dq1StkypQpuYcjUoBk6SnSvXt3ODg4oEiRIobHvL29UbduXeTLlw9169Y1lLKSxIABA5A3b14UK1YMly5dSo4hCyHEF0XisBBCpEwSn4UQImklS1KkW7duEdZpnjx5MmrXrg13d3fUrl3bMN9uz549cHd3h7u7O5YsWYLevXsnx5CFEOKLInFYCCFSJonPQgiRtJIlKVKtWjXY29uHe2z79u3o2rUrAKBr167Ytm2b4fEuXbpAp9OhQoUK8PX1jbQDvRBCiNiTOCyEECmTxGchhEhaKWZJ3pcvXyJr1qwAAEdHR7x8+RIA4OnpGW45qGzZssHT0zPcaxcsWIDChQujcOHChjWzhUjLunXrBpIyj13EicRhIRKOxGGRkCQ+C5Fwxo4dC5LST0QYpJikyMd0Oh10Ol2st+/bty/c3Nzg5uYGc3PzRByZEEKkDRKHhRAiZZL4LIQQCSvFJEWyZMliKPd7/vw5HBwcAADOzs54+vSpYTsPDw84OzsnyxiFEOJLJnFYCCFSJonPQgiReFJMUqRp06ZYvXo1AGD16tVo1qyZ4fHff/8dJHHmzBnY2toaygeFEEIkHInDQgiRMkl8FkKIxGOSHAdt3749jhw5gtevXyNbtmwYN24chg4dijZt2mD58uXImTMnNm7cCABo2LAhdu/ejbx58yJdunRYuXJlcgxZCCG+KBKHhRAiZZL4LIQQSUtHksk9iIRUuHBhuLm5JfcwhBAizZI4LIQQKZPEZyGEiCjFTJ8RQgghhBBCCCGESEqSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEmSFBFCCCGEEEIIIUSaJEkRIYQQQgghhBBCpEkpKikyZ84cFClSBK6urpg9ezYA4MqVK6hQoQJKlCiBMmXK4Ny5c8k7SCGE+IJJHBZCiJRJ4rMQQiSOFJMUuXHjBpYuXYpz587h6tWr2LVrF+7du4fBgwdjzJgxuHLlCsaPH4/Bgwcn91CFEOKLJHFYCCFSJonPQgiReEySewAf3Lp1C+XLl0e6dOkAANWrV8fWrVuh0+nw7t07AMDbt2/h5OSUnMMUQogvlsRhIYRImSQ+CyFE4tGRZHIPAlDBvlmzZjh9+jQsLS1Ru3ZtlClTBn369EG9evVAEpqm4dSpU8iZM2e41y5YsAALFiwAAPj4+OD58+fJ8RaEECJVkzgshBApk8RnIYRIPCkmKQIAy5cvx2+//QYrKyu4urrC3NwcmqahevXqaNmyJTZu3IglS5bg4MGDUe6jcOHCcHNzS8JRCyHEl0PisBBCpEwSn4UQXwIS+PtvwNgYaNAguUejpKikyMeGDx+ObNmyYdiwYfD19YVOpwNJ2NraGsoEIyPBXgghEobEYSGESJkkPguRCoWEAF5e4R8jgaAg4P17wM/vvz8DA9VXUJD6EwDKlAEqVgTSp4/5WCQQHKz2lTEjoNMl/PuJh4sXgZ9+Ao4fV0mR48eB8uWTe1QpqKcIAHh5ecHBwQFPnjzB1q1bcebMGcybNw9Hjx5FjRo18M8//yBfvnzJPUwhhPhiSRwWQoiUSeKzEKlMQABw9ixw9Chw7Bhw+rRKckTH0hKwsgLSpVN/t7BQf4aEAGPGAJoGlCgBVKkC5MunkizPn6uvFy+AN2+Ad+/UV1iY2meJEsD48UDjxsmWHPHwAIYPB9auBZo1A27dAiZOBFq1Ai5dAjJnTpZhGaSopEjLli3x5s0bmJqaYsGCBciQIQOWLl2KgQMHIiwsDBYWFliyZElyD1MIIb5YEoeFEElFrwf8/QEbm+QeSeog8VmIVELTgF9+UVf9ej1QqhRQrRrwww9AwYKA0ScLwFpYANbW6sskmsvzgADg/HlVXnHiBLBvH5AlC5A1q0qQVK0KZMoE2NqqahIbG8DUFFi2DGjRQo1j/Hjgq6+SNDly9KiaJlOoEHD4MFC9unp84UKgUiWgfXv1VoyNI3nx3r1A7drqfSSiFDt9Jr6kLFAIIZKXxGEhREwePgRat1Z3CIsXB2rWBGrVUuf0trbJPbovl8RnIRLZmzdAp07AyZPA/PnA11/HbrpLYnv4UCVqfv8dqFAB+PVXoEaNRD+spqlZP0WKAKtWRcwHPXgAlC4N9O6tckjh/PYb0L8/sH+/SowkIqOYNxFCCCGEECJh7NqlToJtbNTdwRYtgCtXVBm1vT0wcmTSjSU0FFi+/L8p+0IIEW/nz6tqDA8P4MIFoEuXlJEQAYDcuYEVK9S8lZw5VRa6Th01pScRbdoEuLmphMenCREAyJMHWLMGmDIF2L79oycWLFAJkRUrEj0hAkhSRAghhBAiTbl/H/j2W9Xs7vffgWvX1HT1qOj16ibjnj3A7NnqJDc0NO7HDQtTc8qbNVN3BffvB+rWBUaNAv75B/D1BdatAyZPBpYuje+7ix1NA9avV+XcP/6okjJCCBEvJLBokerzUb06cOYMkD9/co8qcvnyqUB7/TqQIYOav9K4sUrixGUCSUAAcPeumsYTEBDpJqGhKsndvz+QLVvUu2rcGBgxQuWQ3N2hEiIDBqiESNeucXp78ZWieooIIYQQQojEodcDc+aok9QSJdQNzLVrVZ8+U1Mgb141td3YWH0ZGamFC9zdVW9AMzPAxQV49EhVdPTuDfTsGbsGee7uQK9ewOXL6m5g48YRt7GwANq1U8mRPn3UsWrVStjvAamSO8OHA3fuqJP1IUPU4gxCiDTuzRtg927g9Wv19w9fBQqoYJc9e8TXHDoEDB2qkgxz56rtUshKL6Tq5xEQoApFcuVSPVsBAK6uwObNKiiPHg2ULav6nbRurb6KFFHvQ69X7+3YMdXL5M4dVQnj4/PfgXLmBGbNApo3D/feV6xQny9Dh8Y81jFjVC6pfW0vnPL4CWarVqosSRKRpIgQQgghxBfuxg1VHXL7tqr26NHjv1LmFy9UpcTdu+rOnl6vKin0enUCXaCAOlfOmVP1APT2VlNOFixQU9TbtwfatlX9QKyswh/38mVV+bFpE1C5suohkjt39GP9/nt13t2ypVq4ISFuuJLq2mXsWHXi/e23wN9/A87On79vIUQSefVKBasaNRK+8ebbtyoL+/SpSn5kzKi+7OxU8mDSJKBpU6BvX7Xd5cvqav/gQXXxvnmzCpIpxOXLqhrw2DEVt4OD1eNZs6opK6VLq9Yi5cuXRO4dO6Fzv6sC9ebNKrDnz68y06dOqe+Ni4tqFvvNN+r7ky2b+rKyUnNj2rRR35e5c4ECBRAQAIwbBwweHIukMwnj225YXXAHih3ogTGNL2BSlyKJ/j36mDRaFUIIkaAkDgsRewEBajEAf3/VU6NKlYgd+AMC1AX9uXNA4cKq2jlHjtjdjAwKUufykyYB9eqpbv/RlTHHhV4P7NypeuEdOaIeq1BBTVMvXFglTvbuVQsdDBumqspjewNVr1fXH+7uKolhbx+/MZJqms748Wo/HTqo6ToptbI9sUl8FqnW1atAkyYqaeHsrLKnPXsCDg6fv++QEKBhQ7XvU6ciXsWTao7fggWq1M3ZWW3btCkwYYKqqkghPD1VNeDq1erbNXWqmjHz4oVqavrwIXDvnmp/cuaMKvjInFlVD1pZAebmgHnwO5h73kdu46fo0plwblYGcHbGA58HeBPwBsWyFIO5iXn4A9+6pUrvjh0DBgzAFP++mLU1J+7dN4K19SeDJIFnz9T3et8+9eXhAeTLh53NlqH5zGo4dChJ+sAaSFJECCFEgpI4LETsXLwIdOyokh4uLupc0sFBNR5t1kz1/ti1S52Lk6p/3+3b6iTWyUklR6pXV1UakU1h2bNHnaP6+anK5nbtEq+q299fTS0/dEh9Xb2qKqmHDVN3JOPj3TtVXZIpk0q+RDixjsb796oKftYsNVW+Y0c1Zz2tJkM+kPgsEpWfn5qvkS2bCmpx+aWNzo4dKqPZqJH6pf7jD5WgePZMBcARI1RJW3yQqtLjwAHVdDSmUrYnT4ANG1QArlw5fsdMJPPnq+mA+fMDM2eqVb2iQ/6XeL5xQzWcDg5WXyEhKnHy6BHQuDGRo+Y+LH7bAiEMhJmxGUo4lkA5p3Io51wO9fLWg4OVg9rhli3wGT0LeW7twq+6Uehb5BhYpjRe5cwE38d38faJO94+f4i3DEL2UEuULVwHuvoNVNY+Tx4Aavrkzp2q35WdXeJ/3wBJigghhEhgEoeFiJ5eD0ybpioWWrVSlRZ2dsDLl8DWraqC+ehRwNFR9d5o3FhVJVtZqWktd+6oG2ynTqlKDC8vtU23bupm54sXwP/+B/z1lzq5/OUX1U8vqd/jpxUv8fHokbru8PVVN2Xbtwfq11f9TT7l7a1OpLdsUdUhpqaqonvYMNUvRUh8Fono+XMViK5eVQEAUEEsXz7VUXnwYFWGEBckMH26mqYyapRqPPEhs6vXqzlwU6eq8ocrV2LX4OhTw4YB8+apoBvfDG4yI9W3Z8oU9Va++y5h4q+mAbv3B6LfL1fx+Gwp2GUOwi/jdXCpeRJnPc7i3LNzOONxBm+D3qJOnjroULQDmhdsjkljbfDnej0WTpmLPe5bsDPwCh6a+Rv2awQj2Jpawyf0Hco5l8P/KvwPLQu1hKmxmhIVEPDfMr4bNiRNixZJigghhEhQEoeFiJqHh6pauHJF3ejs2DHyEz4/P5UEielkUK9XU9pXrVJJEBsbdULp6qqmypQqlRjvImkFBqprn/Xr1Z/mlmGwLnwCduaZkF6fE0HvrPHmjQ7Pnqn336wZ0KLIXdS9MAkWtubqosnGJrnfRoog8VkkCjc3lZHNmlVVdeh0qgTh3j3VrGjxYvXc778DxYvHbp+BgUC/fmqllFWrVKlbZIKCgIoVVZnd7t1xywb89hswcKDKptavH/vXpSCapioCV65UCeEGDRJu3ze9bqL1ptYIDAvEohpbcWZbSfzyi/qWTZ2qvtV6TY8jj45g/Y312HJrC/yv1YV+8+8wb94PIa6rUC1nNTTJ3wS1ctdCpnSZYGthCytTK+h0Otx9cxdzzszBqqurYG9pj/7l+qNj0Y5wtnHGlStAuXLAkiUq4Z/YJCkihBAiQUkcFiJyDx+qig9HR3WBnytXwu7f11dVmZibq2RLQtwpTGkuPHBH7eHzYP64MfQm7+CNu8iUSYfy+fOiVfnS6Bh6BabzZgInT6pmJvfvqxdu2JBq7wInJInPIsEdParmytWsqZazSpcu4javXqkeIDt3qmqPIUNU98+onDihuiG/f6/K5ypUiH4M9+6pDPCgQapkIjY2blSlZ8uXJ/hVd1iYapPx5Imq2ojurX6O0FC1Yu3u3WqqZZUqCbNfvxA/LDy/EGOPjkXdPHWxstlK2FmqeSwHD6rFaSpVUp9jH/LNr18DffrpsWWzDqVaHcIPw16jYf76htdFxzvQG0suLsH8c/Ph+d4Tee3zonrO6gg42hs7lpTElctGiV/txy9MoUKFknsIQgiRpkkcFiIid3cye3ayTh3S3z+5R5M6ub9xp9MMJzb5owmDw4JJkndf3+Wk45NYekYBmo8CT+UxI7/7jrxxQ73o7VuyXTvS1JScPZvUtGR8B8lP4rNIUH/8QZqZkQMGkGFh0W+raeS6dWSGDGTZsuTff6vfz4+9f0/270/qdOQ335De3rEfy8aNpJEReehQzNtu2kQaG5PTp8d+/7Hw4AE5ciTp7KxCTvr0ZP36Ed9mQvD3Jxs2JB0cyMuXE2afPoE+/OXoL7SfYk+HaQ6cf3Y+tUhi5p07ZP78ZOHC5P376tuZOTNZpAh5/nz8jx+mD+Pl55c56/QsNv+zOe0mZSTKz+KyA0c/413FjlSKCCGESFASh0VKFBamloN9+VI18Hz7Vv3p76/u4pmZqR4UpqZq5ZS6dRPu2HfvqpuoxYqpm56Wlgm377Tike8jVFtZDa4OrtjWdlv4lQ9u3wYrVUTP7pmx3cEHZ747izx2ef57nlR3gwcMUP+xK1fGfzmbVE7is0gQJPDrr2qN62nTVBOj2DZ+ePZMTYv5+28VmEuVUku9FiyolnYl1ZyJr76K+7gGDFAVIJcvq+k6kfnrL9VsaMIE1eckAXyoCNm/X31+9OgBdO6smmI3bqw+V3btSrjqwJ071Vv9sLpWdA2kg8KC4B/iDyOdEYx0RtD9+//0LvgdvAO9DV8Xnl3AgvMLYGNugyGVh+Dbkt/C0jTqDysfH9Xj9vhxVbEybJha9SaubWOio1HDDa8byGOXB9ZmCdS0NwqSFBFCCJGgJA6LlOL9e1XCvGOHOv/29laLIdjY/PeVLp3qyxEaqrrth4So6fGDB6tzZiOjzxvDrVtqykyZMsDmzQl7wpjqvHypvrm3b6uvW7fUMgeurqqj3oevTxIWT98+RfVV1ZHHLg92tt8Z/kT95UtVWl+sGEI3bUDDDU3g8c4Dp7qfili2ffOmqvs2M1M14JkyJcGbTlkkPovPFhioprZs3676fTRvHv/9nD2rpt8cO6aW4+rUSa0fnj59/PYZHAxUraoC+8GDEeetbN+uuluPGwcMHx6/Y3zi8mW1KE7evKrRaYUK4fND3t7qkDdvqsPHNBMoOg8fqmTInj3qz7Fjo26XdPv1bcw7Ow+rr66Gf6h/5Bv9K4NFBuSwzYH+5fqjS/EuMDOOpJN1JMLC1Io31asDJUvG8c2kMJIUEUIIkaAkDovkpmlq+vrq1eoOXb16qvlmw4axuw7eu1fdAateXZ3zx+b8/OVLlfR49UpVn3z42rNHrZ6yYUPkK6Z86TzfeWLbyWXYemgBnoS8wqLdOtRGHnVXuFAh9U1xc1PrQd6/r259NmumKjsyZsRj38eos6YOslpnxZ6Oe2BlZvXfzv39gRo11N+PHAGsrPA26C0qr6gMBysH7O20N+LJ/Zs3QO3a6u9pMDEi8Vl8lhcvVBLE01Nlm1PilfCjR2pcpqaqj9CHr4AA4JtvVM+R2PYdicGePSrP2qwZsGJF1EnvkBC1EtjatdH3jI1KUJBqbDppkkqwL1igKg8/pVHD3nt7MffsXOy7vw/lncujf7n+KJalGDRq0KiBIEjC1sIW9pb2sDW3hbHRF9iAKo4kKSKEECJBSRwWyW30aGD2bGDNGpUQsbCI+z5u3waaNFEnuTt2AHnyRL7duXNqCcSNG9XiB7lzq2oUKyv1lSePKis2Nf2st5SqhGlhmHd2HjZc+wNnX1yA8zvga9+sCKtYDkue7cS4GuMwvOpwGOk+KcMJCFB3i/v0AXx9cXv5FNS9OQT57PNhe7vtSG/+UXZKrwe+/hq4fh04cwbIksXw1GPfxyi/rDzq562Plc1WGsrFDdJwYkTis4ggNFRVbcS0QtOVK2pd7KxZgW3bop6ekhI8fQocPqzmTF68qMo5/P1Vk9exYxPkEEuWqFA1ZIha9jymqkISmDFDbf/hNbFphv3332q1l/fv1Uylzp0jn6n02Pcx2m1ph4vPLqKNaxv0L9cf5bOVj9+bS4MkKSKEECJBSRwWyWnTJnUXbts2ldT4HN7eqmLk8mU1Zd7cXJ3EGhmp64iNG4Hz51Wbiv79VSXKl7jiS1wEhgai3eZ2OHHvEL67CLR4ao0yg2bBqG07QKfDppub8O2Ob1E5R2Ws+XoNMqWLJCEREIBLP3dEPettqGhdEBuHXoSF2UcrWrx+rTJff/4JnDqlqk4+cd7zPKqvqo6xNcZicOVI+ga8eQPUqaOuVNJQYkTiswCgAtg//6gg9tdfqsFSzZqq7OHrr4HMmdV2Hh5qnddNm9SKTm3bqp48qa0xkl6vfucdHBJkVyNHqgTFb78BPXvG7fV79qhFbypXBv74A7C1jXy7hw+BH35QvUj69gXGjwcyZIh82513dqLrtq4omqUo1n69Ftlts8dtUEKSIkIIIRKWxGGRXC5fVieao0ap6oyEEBYGjBihrh80TX3p9epaukYNdbIayTV5mvQ26C2a/tkUD55ew4Hf/FCwz2jg558jXEDdfXMXrTa2gm+QL+Y1mIcqOaogY7qMhuePPz6Oxusbo4lZUawcdwWm5SoCBQqoSfluboCXl+oZsGePatAYhVVXVqH3371xp98d5LDNEXGDNJgYkficxvn6AkOHqrl+b9+qZqZt2gBOTqoL9Nat6veiRg1VPXLqFJA9u0qWtG4NlC8f+4aqX6AXL9Ry5xcvquVoGzSI337u3lVFN4DqM1KggApDL14AV6+q2YBz5qhZP/PnAyVKRL6fUH0ohh0ahllnZmFYlWEYW2MsTIwSaf3fL5wkRYQQQiQoicMiOXh5qbnWH+6+peHz9mTx0u8l6q+rj6C3b7B/sieyz1wOdOsW5fYBoQEYuGcgVl5ZCT31yG6THaWylkI++3xYcH4BupfsjrkN5sLo3n1VO25iohqyurqq5R0KFlSJkWho1FBhWQXktc+LP1r+EflGb96o0qKZM4GiRT/jO5A6SHxO43r3Vk2TRo9WvUHsPmlGrNerpqdbt6rSuNatgXLlUkRAfftW9Ua9eFH15UjqZPThw0CHDoCzsyqwiWpKZVRC9CHYf38/rM2sUSNXDbx9qxIsx4+rz65r11QRnKmpCnM//KCmykQ1Lefp26dos7kN7nnfw9qv16Je3nqf/R7TMkmKCCESRXBYMC4+v4hTT09BBx2c0jvBKb0TnG2c4ZTeCelMoz+ZFamXxGGR1EJCVIuIwEB1Ph/DtXKa4x3oDUsTy2iXV/wcD30e4qu1X8EOltg94REyde6lastjISgsCDe8buDy88u4/OIyrr28hvp562NE1RERe4HEw6mnp1B5RWWc6n4KFbNX/Oz9pXYSn9OwS5fU1ffevfFb7jaZkGr2zsCBKra7uACnT6vESMuWiXfcl34vce3lNVx9fgNXNjXE+vn50aePDtOnx34VMY0aTj09hXXX1mGj20b4hfghTAvDD+V/wOQ6k2EEU8yZo5L6xYqprwIFYu5Bdc7zHJr92Qwudi7Y0GoDnG2cP/8Np3GSFBFCJIhQfShOPDmBQw8P4fiT4zjneQ5BYUFwzewKU2NTPHv/DF7+XgAAHXRoVrAZ/lfhf6iao2qCnPiKlEPisIgVNzdVQ1yggDrLjefSLHq9Kkg4cAC4cAHIli1hh5naPX3pjrILiiMAoWhpURKdinVGjVrdYZzOKuYXR8M/xB977u3BZrfN2HV3FypkKY2/ZngifZ6Cqh48BTVXabe5HR75PsKpb09FbO6axkh8TuECA9UyJhYWqiQhWzb1Z4YMn1etoWlAlSqAo6OqAkkFSGLv+TuYMSoPjh02w+DBaiqjuTkwcaLqmfrjj2pFlk9X3o2vgw8OYsrJKbj28hq8/L1g/NYF5n+vRcDjwijYfTpWDm+ICtliXlPXN8gXiy8sxqKLi/DY9zFq5KqBjkU7omXhljjneQ4dt3ZEXvu82NBqQ+RT+6Kx6eYmdNnWBW1d22Jx48UwN0nL67wnHEmKCCHi7V3wO+y9txc77uzA3+5/433we5RzLoeqOaqias6qqJS9Euwt7Q3bh+hD8MLvBW69uoWFFxZix50dKJm1JP5X4X9o49om1uuii5RN4nA0NE0tZXjvnvpyd1eN7AYMACrEfKL1xbh7FyhbFggOVl/GxioxUqiQKusuVSpWu9E01eRu82bg0CE1/1r8J+D1c1SdlB/pAkLQ27gC1oVexD5Hf2TxB9q/zoouLcahWNPvYtxPiD4ET94+wQOfB7jvfR//PPoHu913AwAa52+MlvmaofnPy2D23Ev1IIhpFYsk9tj3MQrML4AVzVagQ9EOyT2cZCXxOYXr3Vs1q7CzU58VoaHq8eLFgRMn1NJW8fH770CvXsCtW0CuXAk23E/5+ABPnqjhfo4wLQxfz5iMXSP+B2S9BLvWw1CsiAkKZy6MKjmqoK1rWxw8YIwOHYAiRdSS546On3fMA/cPoMn6Jmjt2hp1c3+Fu/tqYs6vzihRQoexsx5h0cNB2Oy2GS0LtcTE2hORP2P+CPt48vYJZp+ZjaWXlsLW3Bb9yvVDp2KdkM0mfLbe850n2m1pB7dXblj79Vo0yBdzcxKSmHh8IkYfGY0JtSZgSOUhclMxAUlSRIgvREhIvG+0xglJHH9yHPPPzce229tgbmKOBnkboGmBpmiQt0G4Znkxued9D3PPzsWKyytgY26DbiW6oXvJ7shrnzcR34FIbBKHo/D4serqf/mymiScMyeQL58qdTh/XjV6LFs2uUeZ+Pz8VLO+XLnUWrcvXqj1b2/fVo0zjx9X7farVo12N6RqcrpmjaoSSRU5pYAAdVvz8GF10WNv/99Xy5aqV0YC4cuXaD+yIE7b+eH8t2fhUEAlmrw87mDDwdlY474F581eoaTmgK5fDUaHEl2Q2SozwrQwXHlxBccfH8exJ8dw6fkleLzzgEYNpkamyJkhJ8o6lUXrwq1RL289pAvTqf+IHTvU+sRxnWifREb+MxKrr67GnX530vT0TYnPKdhffwGtWqmuztWrq6zv69cqy9CypVreauHCuO/37VtVkde7tyqvSAR37wJz56opLf7+QNeualn0qFZLiU5AaABarO6OA4OnoG5NC0yc9xy337jhptdNuL12w4H7B5A/Y37MazAP2VgZrVoBjx4BixbFfzrN0UdH0WBdA/Qt2xd9809Fjx46nDqlwnX//v/19TjrcRaDDw7GyScnkcM2B+wt7WFvaQ87SzuE6EOw885OFM5cGIMqDULbIm2jvdkXqg/FyH9GYvrp6RhYfiB+rfVrlLHpffB79N3dF5vdNmNti7VoUahF/N6oiJIkRYT4AuzapT5HR41STagSI3HsF+KHddfWYf75+bjpdRNNCjRBz1I9USdPnc8u3fMN8sWqK6uw/PJy3PC6gRq5auDbkt+iZaGWiTYHXiQeicOROHlSJURcXYEFC4C8ef/LYmoa8O23ag3Zw4ejbjP/JSDVko4XL6q5Lp82+dM0oF8/dWb9119Avcgbx5FqidylS4F9+1RVeGILDgvGhWcXcOzxMRx7cgynn55Grgy50LRAUzTJ3wSlnUpHmJoRHBaM1wGvYaQzgsnhozAdNAQmgcGw7PotjP0D1Zq/3t7qjN7DQ5W7lCz5+YN99AgT/1cGE4r44GSHAyhRqFakm93avAirf/8RawqHwSsdUdqpNG6+ugm/ED8UyFgA1XJWQznncshrnxd57PLAOb0zjI0+mhazfz/Qp49KdG3enDT/EfHkF+KHfPPyoU+ZPhhVfVRyDyfZSHxOoTw8VHlFnz7w/emXiMmEI0eAWrWAv/+O+5InP/6o4qmbW4IvpXv4MDBjhhpW2bIqLmfLpj7SAgPV6r116sR+f28C3qDRH41xfd4oZAmojWuXzSMUx3i+88TQQ0Ox7to6dCjaAb9Um4KVc50xcaJaSGfePCDjR/fnNA04e1aN8dmz/8Kut7cKXTQJgEeAOxwy2MDVKRfOntWhRAk1iylfvohjJIljj4/hnvc9eAd6wzvQGz5BPggKC0L7Iu3xlctXcarg2OO+Bz139YSZsRmWNVmGmrlrGp4L08Kw7NIyjDkyBhYmFtjaZitKO0lJZGKQpIgQqdy5c2pp+UaNVMBv2lQF8oT83Lv28hpqrKoBI50RepTqge/LfI9cGXIl3AH+RRLnn53H8kvLsf7Gepgam6J3md7oV64fHK0/sy5SJBmJw59YsQL4/nt1ljh3buQd1PR6oEsXdYV/5IiqB/4SzZihsrenT0ddX01CGzYCt2f8jZPfrsB1s9LInBnIkUMV1+TMqe4Izp0L7N6t4l9kgsOCQRAWJhbxGmqYFoYLzy7g0INDOPTwEE57nEZwWDCKZSmGajmroWK2irj75i523t2Ji88vIqt1VtTJUweBYYF48vYJnrx9ghd+LyLdt52FHZoVbIZWhVqpxLLORP3/792r7hJ/Tu25mxt29KiKr7/yxp9NVqN16S7Rb//iBfTfdMPBJ0dwrFsNlKrRHlUK1UcW6yxRv+b5c3X1s3Gjuvs8YUL8bgknsZWXV6Lfnn642+9umm1MKPE5BdLrVeYgOBjz2x7HgP8ZY9UqFRLC+ekntbTWjRvhr/qjc/OmiidbtgDNmiXosP/5Rw27dWu1UkqFCv/dlPP3V8uiz5+v8qZTpgBWMbQxeuz7GPXW1oPfqU54vXUEzp3ToVixqLc/+eQkBuwdgDuv72BolaGobvoTvu9hCW9vYPFiVYC3ebP6evZMjc/FJXyB3qvQh5h2fC5KZqyEhrlaITBQh9y5gW++Sdq2SO+C32HIgSFYfHExepTqgal1p+LEkxMYfGAwPN55YHjV4RhYfqDcKExEkhQRIhW7dw+oVAlo3BhYvhy4ckV95mXJom46OyfAOV+IPgTllpZDzgw58WfLP5MsIPuH+GPNtTWYeXomHr99jE5FO+HHij/C1cE13HYPHwJOTrHvBJ5UQvQhMDUyjflugZcX4OCQNINKIhKH/xUWBgwerK7e585VZ4Yxbd++vVo+5ejRpF9vMLEdPgzUrauqQDp1ivB0SIg6kd23T7Wl8PEBnOGBEsUJ73TZ8eSJOrElVZHNzp3/LaDgF+KHE09O4OqLq7j68iquvLiCO2/uQKMGCxML2FnYwc7SDnYWdjAzNoOxkTGMdcYwNjKGiZEJTI1MYWZsBlNjU5gZmcErwAtHHh3Bu+B3KJalGOrkroOauWuiSo4qyGCRIcLYPd55YNfdXTj6+CjsLOyQ3SY7cpjYI8e+M8iychOYKxdCx45EWKGCCNPCcMPrBrbc2oL99/fDwsQCTQs0xY9lB6DkoJlqLtDhw/FbHvbhQ9xsXA4VWvrgx6pDMK7uhNi9TtPUz+iIEer2brFiqny/enUgf37g5Uv1zX/+HHj6VPUncHFR2aly5eI+zmSiUUO5peWQNX1W7Gi3I03Ox5f4nAJNnAhMmYLNk9zRtr8D2rZV+cb161XCwSAoSK0eU6iQ2iCmn18Sdyt2xdmAonBr+DPcbung5qY+ag4cUAWL8RUWplo/lSsHLFsW9XaHDqkEg7m5ChsVo1gA6r73fVRfVR1OAfVx7delmDVLh969Yx6HXtNj5ZWVGH14NAhieIXxeLytO2bNNAapZmG2aqWm1Tg5/fc6jRr+vPEn+u7ui+YFm2N50+Upognz0UdH0WNnDzx7/wzBYcH4vsz3GFN9DDJbZU7uoX3xJCkiRCr16pVKiLi4qIuDDzefX7wAWrRQ1djbtn3++erow6Px2/nfcLPPzejvHCYSjRp23tmJ6aen48STE+hUrBMWNFwAa1MbTJ+upgt9ODeP6o5xYtNrehx/chyXn1/GlZdXcOXFFbi9ckN55/LY33l/1PPXHz9WJzeHDkV9ppAKSRyGqstt3171Ctm0Sa0XGxuhoeoM7sIFNeUmERviJamnT1UX1Hbt1MX3J86fV4U0z56p2TWVK6uvHH8vhK5fX5Ug6NQJIS3bw1OXDaam/60yc/zxcXT+qzOevX8GVwdXFM9SHCUcS6BYlmKwNLGET5APfAJ94BPkA98gX4ToQ6DX9NBTj7CwEIQFByDUxBgh+hCEaqEI0YcgvVl61MxdE7Vy14KDVRyTll5ewMyZappUxowqSH37baS3Hd8GvcWuu7uw5toaHHl0BEsaLkSXyXtUtdDhw2q6VRyOG1qlEso2e4mcJWrgrw7b436SHxys/jOOHlVfp06pW75GRirb7uQEZM2qpjV9/33CLfmQhNxeuaHMkjKYWncq+pXrl9zDSXISnxOAr2/CVUadOQNUqYJjI/bhqym1MW4cMGQIMHWqylFu3Qo0afLR9pcvq55MK1ZEmlz+2O1By1F0ehfYZ9TBtZgJChdWbYu2bVOzdc6ciX9P5IUL1Tjd3VVoiM7bt6qS5PffgaFDVVuTj3vgPfZ9jGqrqiGfdQl4zPgLRVyNsGlT3KaC+4f4Y87ZOZhycgqyWGVBX5fZaFm8HrI5h4+7JLHn3h4MPzQcd97cwY8VfsT4muPDTwtMZoGhgVh3fR2q5qiKApkKJPdw0g5+YQoVKpTcQxCpQKg+lJqmJfcw4s3PjyxXjixVinz3LuLzQUFkly5k+vTk69fxP84Fzws0HmfMjTc2xn8nCejE4xPMOzcvc04qwSp1vGllRS5dSvbsSep06j17eSX9uPr+3ZdG44xYeEFhdtjSgVNPTOWO2zuYa3YuNl3flGH6sMhf2KkTWaYMqdcn7YATWZqPw9evky4upKsr6e4e99cHBZE1a5KFC5M+PrF+maaRN2583u98ovD1JYsVI6tUIYODwz3l708OGkQaGZHt2kXx+3vnDjlqFJk7t/pFr1WLnDGDwb/N47DpDWg0VseeM2ry/cHdZEhI5GPQ68kjR8gffiCbNFHBM0sWtT+AzJWL7NCBnD+fvHSJDA2N23v08SF37yb79SMtLcm8eckVK6Iezyc0TePs07NpPM6YA//uz5CWX5MODuS5c7E7/tu3ZMmSnNrGmRkmZeDz98/jNv6ohISQz5+TYVHEsFRq8YXFNP/FnFdfXE3uoSS5NB+fP4ev738nHL/99vn7Cwgg8+Th9abDaWurwsfHp6ZjxpBmZuT+/Z+8bsIE0taWfPIk6n0fOcJOujWsWuAlPz3d9fYm8+UjmzaN3+mHtzeZMSM5ZUrcXrd1K5kpE1mypPqsIslHbzyYY2QtFv1xMJs1D2XOnHH62Ivgtf9r/rTvJ5r9YkbLXy1Zfml5fr/zey46v4jbbm1jtZXVaDzOmD139KTHW4/4H0h8cSQpIr5YQUHkhQvk/N9COHf9dc47O4/dt3VnyUUlafaLGbNMy8JOWztxzdU1fPH+RXIPN9YCAsiGDdU5/PNozntDQtQ2o0bF7zhBoUF0XeDKtpvaxm8HieTIST9aO3gRmW/wxzVLqdfUJ/qJE+oa1N5eXYsklb/v/k3dWB0PPTgU4blbr27RbrId++zqEzEJd+kSqdNx+4TrX9r1RtqOw1u2kFZWZIsWkWcsY8vbmyxUSCUAPkkkfOzNG/LPP8lu3UhHR3V9b2FBfvfdfyed8aHX9AwICTD8fsVbcLB6DwUKhMvWaBq5b5/KHTg5kdu3x2JfmkaePEn27s1bFfOxdH9zZhpqxO1VHUhnZ9LYWF0otG1LrllDvnqlPgR++onMlk1lXmrWJAcOJKdOJdetIw8fJs+eVRc4nTqRefKob6K5OVm0KNm6NTlyJLl2rUp6/PWX+oavWkUuXEh+/73aTqdTVy9Vqqj9xjWp8q+D9w8y45SMrLmyBl91a0OamKgrj+iuXAIDyZo1eb9kLlr+asmlF5fG69hpiaZpbLGhBQvNL0T/EP/kHk6SStPx+XPs2kU6O1PL48ITnRcxVGeq4sznGD+eTzKVpLOTnq1aRcw9ahr5888qz3r06EdPhIaSlSqRRYqQ9+5F3O+zZ7ybsQKNdHoeOBD5od3c1I2z+Jwj/vCDyvsHBYV/3C/Yjw99HvLO6zsM1UceA1+8UHlpc3PSJV8oYRRCgDQz01i0qArHCeGl30tuv72d446MY/M/mzPnrJzEWLD1xta88/pOwhxEfFFk+oxIkfz81Lzx9Onj9rqrV1XF8oULxPUbRFioEXQ2z0C/TMg2oDMqVQZKZCmBEo4l8Oz9M+x/sB8H7h+AT5APSjiWwI8VfkTHYh1TxLzCyPj4qEaqjx6p1TsLxFBVt3ixKm98/BiwtY3bsYYeHIpVV1bhZp+bcVpmN64ePFBVoMeOqUUXqlZVCxh8WG9e09Q2ly+rUs/581V38Zr9NuCHf75DOedyWNlsJbLbZkdIiKpaHzFCVZ5Xq5ZowwYAePl7odjCYuhcrDOmfTUt0m1OPDmBOr/Xwfia4zG48mD1IAnUrYtFXi3Qz60PTp1KVdPyY5Qm47CmqZrgiROBcePUlAmjz4wjjx6pMumGDdUvyUe1xK9eAQMHAhs2ANbWqtld/frqz4sX1VKIJ0+qf//wg9rFh5cHhwXj2ONjcPd2x33v+3jg+wD3ve/jud9zBIUFITgsGKFaKADA0sQShTIXgmtmV7hmdkURhyIon608MqXLFLvvSefOqhvf6dNArlzQ61U5+JQp6nf6229VmXhMlegBoQG4+OwiznqexTnPc9h1dxdq5KqBFc1W/NeE2dtbNSrdtUst7evrqx4vXx7o0EEFDsdYNGx+8QK4dAm4cwe4dUstFXzrFvDuHWBhoSbHf/izUCE1z6dKFTU9yCJ+TV0/9sj3EZr/2Ry+Qb742+Z7uA6coN7D77+HnxQPqB4DHTuCZ06j/ti8CDY1wuGuh9Nkr4y48g70RolFJdAwX0MsarwouYeTZNJkfP4cb96oIPrHH8APP2BD8Ylo19Uc1Vw8sf5hBThtnqtWFourp0+hz18IpTI/QYbc9ti3L/Lw8WHp8Q0bVEjK9CH0vnmjpiNeuKCaj9Svrx4PDQVq10b3W4NwO28jnDxlFOU0lJ07gebN1b5btYrdsG/fVrMZN28GilS9jx47e+Chz0O8CniFgNAAw3YWJhYo4lDEcM6dL2O+j3o72WP7VmOM2jUfVlmfYfP3k+Ca1ybRG5uG6EOiXSJXpG2SFBEJxsvfC2c9zuLmq5u44XUDN1/dxAOfByjvXB4tCrVA84LNo11BhFSxfdEi4M8/1dTmChVUX766ddVFY1TTl9+8UQsaLF5M5Cn5BG+d/sIr2z2oWNYCPat9jSPzOmDfbjNcuqSmQ39Mr+lx4dkFbLm1BfPOzUNRh6KYXX82KmWvlCDfF01Tn6Vz56r38eOPsW8a/rGnT9VnnpGROt//MJ8+OsHBqpFWr17AyJGxP9YZjzOovKIytrbZimYFE7ZbOaDO47dtU825Dh1S0+YbNACuXVNT2P381LgdHVWi6/17Ne+1RAmga1fVtEunAx74PECnrZ1w7eU1jK4+Gj9U+AFmxmZo1Eit9Ll2bYIP3YAkmm9ojse+j3G2x9lolyXe7LYZbTe3xZqv16BD0Q7Anj3Y2WgRvjbahsWLdfj228QbZ3JIc3GYVE1U161TX+EmgH+mc+eAGjVUG/9RahnRLVvUgh9ZsgCzZqlemJEtaHPxIjBnjoqnTZoQAyZexeb7y7Hu+jr4hfght11uuNi5II9dHrjYucDZxhkWJhYwNzZXf5qY45X/K9x8dVN9ed3Erde3EBwWjFJZS6Funrr4yuUrVMpeKfKf/8GD1cTzY8cQWLAkfv8dmD5dzWXv3l0tpJArt4YHPg9w7eU1XH1xFTdf3cT7kPcI1YciTAtDqBaK98Hvcfv1bWjUUChzIZR3Lo+6eeqiXZF2UV/8h4Wp752jI5AnT8L9fySRgNAAdNraCcceH8O+GstRuv9E4P59lfUNCVF9Py5cAK5fB9Knx9o1g/DtpTG49v01mYMeB8cfH0eN1TWwqfUmtCjUIrmHkyTSXHz+HAEBKgNgYQGsWIH3hcujYEGVZL52DXh00x9/BLdE7b9//K/rc2x16IDt553Q5sk0PHmii7YvR0iIWkCmalVgyZKPntDr1V2gadOAX35RnxODB+PhskPI9/4idu7UoVhlT2jUkMU6S6QJgYkT1eJRp08j2pVePmjYUI1nxeanqLaqKvLY5UGnYp2QOV1mZLbKDAcrBxjpjHDD6wauvLiCqy+v4vLzy3jk+wh66sPtq1iWYjjc9TDsLe1j+U0TieXly5dYvnw5smfPDldXVxQsWBDp0kXRD+9Llax1KolAygKTx+67u2kzyYaWv1qy1OJS7Ly1Mycfn8z119ez967edJzuSN1YHSsvr8xZp2fxlf8rw2sDAshFi9QcQ0BVWm/cSB48SA4ZoqZ+63SkjY2aNjJxInn8uKoYDgtTFcz29mROl0A6fd+dTjOcOObwGN73vm84RlCQ6sFRuXK0leh87PuYHbZ0IMaC7Ta34yOfR5/1fTlxgixbVpWz9+6tKrOtrclhw1Rld2zduKGqw6tWVVX1cTF3rvr+xLaSPzgsmAXmFWDnrZ3jdqBY8vRU5fJWVmSPHuSZM+Hn0IaGkhcvknPmqKr1TZtUdWhU1eN6Tc8lF5bQfoo9C80vxH8e/MNt21Rp5ps3ifIWSJJLLiyh+S/mvPEydnMUZp6aSdPxpjxwdy/P5mnHdCZBHD068caXnNJUHNY01RQjXTr1C58Y/vqLNDLiq/l/sm1bNUtk5MiIpcuR8fLz4qA1v9PM4RFhf5clxnbh8n9m8N2OTbHbwSdCwkJ44vEJjjk8hhWXVaTxOGOmm5CObTa14Va3rQwMDVQbzplDzdiEx2acY48eKn5nyKCxa/8nnLJ3JXvt7MWKyyrSeqI1MRa0nmjNSssrsdfOXhx6YChH/TOK446M44RjEzjj1AwevH+Qb4Pexnm8qVmoPpSdt3amzSQbnrh/hBwxQk0Byp6d/Ppr1Vdg3z6+enaPmaZm4vgj45N7yKnS6H9GM8PkDHzs+zi5h5Ik0lR8/lxjxpBZsxpOoH78kcyRQ/V1Cw4mBw7QaKTTc5zJeIYdOR77/R4/Tup0rF3Gh127xu4lBw+qc+EzZyJ5cuNGdVJVsSJpZMTvGjxhmTLk33d202icETEWxFjQfoo9Cy8ozBYbWhj6Dmka2aaNepsXL0Y/hr//ViHo8NlXzDc3H6uuqBrr6WeapvFd0Ds+9n3MK8+v8MjDI2kupqdUb968YZEiRZgtWzY6ODgQAHU6HfPkycMffviBYV/aHO8oSFJEfBZN0zjj1AwajTPi0ANDo2woqdf0PPnkJH/a9xOzzcxGs1/M2G5zO245d4JlymjMmFHNm7x7N/LjvHqlpnH37av69X2Yvp0tG2ltrbF5v5M0G2PN1htb0zfQN9J9PH2q+tb17Rvz+zr15BTLLy1Pi18tOGj/IL4JiNvV9aNH6kMGUL37Hv97rhUSQq5cqeZiWlmpqe2//66myb948V9yICSEfPiQPHZMNRLNkEG1KAgMjNMwSKqkU5YssW+INe3kNNpPsY/ze44NTVONvcqU+bx2C5F55f+K327/lrqxOrbb2IlZHPWcPTthj/HB3dd3mW5COs45MydOr/t538+0GFiQGYxfsltb/wjNz74UaSoO//prFJ3wEtaVwevogBcsYvuYF3Y+i3bbkLAQbru1jc3/bE6T8SZ0muHE/20Ywnol3WhpFMhVum4qiDo7k/PmxS+w/Ms30Jebbm5i642tafmrJdNPTM8WP9ViM8cxtM/4nNDpmaHwedp16E8MT0eMBfPMycOv//yaYw6P4Va3rbzvff/ze5d8ofSant/v/J7pJqTjgfsHIs3qd/2rKwsvKMzgsGgy/iJKofpQ9trZi/feRNKb4QuUpuLz53j8WN3RWr2aJHntmkpI//VX+M02b9JoYxbARiZ7qL96Peb9hoWRJUvyRuMhBFTbo9hq21bdKIz0GvX6dbJIET4avICmpuSCNU9oM8mGg/cP5t3Xd3ns0TFuvLGRc8/MZZklZZh7dm7efnWbpMqPd+qkzkt37Ij82AcPqnPu7j0DWeS3Iiy3tJwkNb4A79+/Z/ny5VmkSBG++fdO4qtXr3js2DEuWLCA9vb2bN++PUPj2SeLJF++fMkePXqwaNGiPHLkSEINPcFJUkTEW1BoELtt60bzX8y59uraWL8uTB/GnXd2ssa074kMD2ie4yqn7l0dp9VgfHxUxvqXSQFsuLAHzX8x56Lzi2Lcx5Ej6kNt1aqYj6HX9Fx/fT1d5rjQdpItJx6bSL9gvxhfd/48aWdHli9Pnj4d+TahoWoM1aqp7Lyqv1dVJFmz/rcgAqASGj/+GPfm/6efnmbXv7rypd9LTp+uEkL+MST0X/q9pM0kG84/Oz9uB4ul9etJU1N1cpFYTj89TdcFrkxfZw5z5fNL8MRDcFgwyy0tx6/WfBXnCzmvh+9pa32Xxi77eex+FD8cX4A0E4fnzFEBZevWRD2Mpqkqt0aVvBlUprI6UR85knz/Ptx2Hm89OGj/IGaempnmv5iz3bqvufe3HxnWpDFpakrNPiNnVtxIY2ONHVsHcWnzXdxt3ZpXM9Xi619+o+YXdYDQNHV39NmzqBdUubjpImtkX08joyAaZ7zLbF/PY9PFfTji0AiuuLSCJx6fkJPoeNA0zbCawoYbG3jyyUmuuLSCQw4MYdP1Takbq+PJJyeTe5gilUgz8flztWmjTuT0emqaqtRt2JCRnlPcuqmnuVEw12fsq4JkdJYuJa2s+H3n96xUKW5D8vBQ54kLFkS9Te/eZNFioXSZk5dN1zeN9DzFL9iPjf9ozIxTMvLUk1Mk1fsaM0ZVgsyd+9+2z56R7dur89JvegSxxJyqLLawWKLcOBNJKzAwkLVr16aLiwufRfFze+XKFWbMmJFt2rRhSCxXU/sgJCSEs2fPpq2tLV1dXdmlSxcaGRlxzJgxKbL6RJIiIl5e+r1kpeWVmHV6Vp71iHur6KNHVeKgTv0ADv37F6abkI7N1jejd0Ds54bcenWLuWfnZoF5BXjl+ZVYv272bDW1IrYrHYaEhXDh+YV0nO5Ix+mOXHxhcZQXw6dOqTLxrl3jlsTw91dJ/m3bVDPzw4fVlJF4VLeTJN283Gg32Y52k+2YY1YOnrp/lZkykbNmRf+673Z8R9cFrlF2Df8cXl5qKbaxYxN81xH4h/iz3ZJhBMjvFqyMekncOArTh7Hd5nZ0mOZAz3eecXqtppHVcz1iCZNr7Lr2e9pMsuF5z/MJMq6UJk3E4ZUr1dnj778n+qE2bVLFKPfuUc0jW7tWVXlkzUqOHEm3WcP5zYxqNB1nwsLTcvG3qa3pXbeKStjY26uAtPu/5WqPHyerV1erU5mZaYYErBHCaGUZxkyZ1AyN/PnVlL+MGdUiKB+2MzdX1V49e6rpizu3hrB5gZvUQc9KGW9x+2qfL22V6WSnaRrHHh5rKIN3nuHMWqtrsfeu3tzitiW5hydSkTQRnz/X0aPh5qqsXq3iXmQLvXww6H8hzGP+lMElykVIWBv4+JCZM9N75AymS6duFMXV9Omqevjly4jPPX367you/UezyG9F+C4o6pLcUH0ov9vxHS1+teC2W9sMj69erW5eDRigkiM2NmSJEuSewz6ssKwCC84vyJd+kRxcpCqhoaFs1qwZnZ2d+fDhw2i3vXr1KjNlysSWLVvGOjFy4MABFi5cmBkyZODcuXMNlSabN29mhgwZWK1aNT59+vRz30aCkqSIiDO9pmfVFVVZclHJeK3xvX69OsHv2/e/xMFNr5ssvKAwc87KGasky1mPs8w4JSOb/NGE74Oj+PCJgqapawR7e/Ly5di/zi/YjxOOTaDVBCtWX1k9Qqnt0aMqg//dd/Fb9z2heL7zZI5ZOdh0fVO+C3rHdpvbMd2EdGw/8BqzZo26Uv7Ss0vUjdVx/73EmQbQrp1auTK6ni4JrVilZzQttZa1Vtfis3cx3L2JgaZp/H7n97SdZBunJNwH6+Z70xyBfDDhD+o1Pbv81YV2k+3ita+U7ouPw1u2qIRDdLfrEkhQEJk7t5peGI6fH2+M7ctmfeyJsWCVXqbc6WpCvQ4qo9G/P/nPPzEuDatpanrilVP+PFB3CncYN+eGb/dx1UqNCxeSS5ao6er79qnrg5s3VZXer7+qKX25nIKpg55NzPbwxPiIy1KLhPXI51GcP/OE+NgXH58/V1gYWbw42aULSZXHcHCI+YaOtzeZwVbPuRnHko0aRYy9T5+q6pPcuTljcgidnKKuuotOSAjp6qqWYf9A09QSu23akBlzeTDj5Mx84P0gxn1pmsbxR8bTaJwRZ56aabiBdPiwSrykT68KIq89c2OeOXlYYlGJON8QEp9P0zQeO3aMU6dOpZ9fzBXrsdlfly5dmDFjRrq5ucXqNdevX2fmzJnZvHlzBkdxIq/X67ljxw5WqVKFOp2O3333Hb28vCJs9+jRI1asWJEZM2bk9u3bP+u9JCRJiog4W3xhMdNNSMeHPg/j9DpNIydNUjdXp0+PWILoF+zHrn91pel4U846PSvKqTD77u2j1QQrfrPtm3hXNISGql4fcU2MkOR97/usuaqmoaeEXtPz4EHVZ7Fv3+RNiPgG+rLYwmKsuKyiofmVpmmceGwidcNsaZHen/PnRxygpmmstrIam65vmijj2rZN/b+fT+LCiE2bSAtLPUvOrknH6Y5084pd8I/MsIPDaPmrJU88jnszTT8/Mlu61xzusNSQCfxQdZJpaqbPGldK9EXH4f37VVZ34sQkOdy0aapSw8cn/OOHHhxi+onpWff3uuGnTgQERF7fHRuaRi5erN5f587Rz7fTNHL+fNLcnMHNWqtSMCFEivdFx+eEsHixusP173SCvn1VH7jYtF6aOpXMZBfKt3Y51TwWTVPN4Vq1Uon0AgUYdvgYc+cmx39GX+QjR1Qhy/jx6lzW0VFV8dk7vqNR17o8+uhonPa38vJKWk+0ZvGFxXn44WGSaqrOy5fqnNt2ki2brW8mCdkk5ufnx8WLF7No0aLU6XTMkCEDq1SpwrdvP28a6uzZs2lpacnzcTwpv3nzJrNkycLMmTOzWbNmnDx5Mo8ePUofHx+uWLGChQoVoqmpKbt37x5jsiUkJITDhw+nkZERe/bsyfdRVVeR9Pb2pj4JLq5SVFJk9uzZdHV1ZeHChTnrozr/uXPnskCBAixcuDAHDRoU7T4k2CeuZ++e0XaSLWecmhGn14WEkN9+q6bCb9wY/bYrL680rGIz4dgE3nh5w5Ag+ePaHzQdb8ohB4bEqQdJZGKbGHn7ljx0SF0DNW+uygirV9dYvMYDmpRaw6y1NtPcQs8ff4zjtciJE6qGvXp1sn59tZpAhw5kr17kqFGqAeKGDSplH4uupEGhQay1uhYLzCvA1/6vIzy/4/YOmtUZT7MMr3jwdvh+FptubqLpeFPefR1Fp9vP4OOjqvwHD07wXccoOFjd4Zk9N5TN1jdjlmlZ4pWAmHpiKk3Hm3KP+554jWPMt0+ZFZ58vyd8d/qQsBAOPzg8Rc3NlTgcjVOnVPZz0KD4Jx7iwMtLlS7P/6TFz/rr62k63pQ/7v0xcRqUnjunllgoWvS/pb4+9uqV6phsYaGWDvtSOwYLkcJIfE5kPj5qnu+kSSRVA3xT04jNVaMSGKgK9UZ1faySy7lzq+xFkyYqoa5p3L5dPfXiRdT7efz4MQNjyML06KHOrTp0IJcu1fjj+vnUjTXisovLYjfYTzx794zfbPuGurE6ttjQgve97/O3c7/ReJwxB+8fLM2wk5C/vz8HDRpEW1tb2tvbc9CgQXzw4AGfPn3K/Pnzs2zZsoamqHF1/vx5mpqacvny5fF6/fPnz7ly5Ur27NnTkKwBQBsbGw4ePJiennGrJDp69Chz5szJvHnz8sxHSytpmsYTJ06wc+fOtLCw4N69e+M13rhIMUmR69ev09XVlf7+/gwNDWXt2rXp7u7Of/75h7Vr12bQv80VXkY2ie4jEuyj5uERc/+nmLTa2IqlF5eOU4WGry9Zpw6ZOXPUjUc/def1HQ4/OJyFFxQmxoJ55+Zlhy0daDTOKM4Jmeh8mhjRNPLBAzWnskcPslAh9XlmbKySIb16qTsBo0er6vQWbd8zY4lTtGo0lm5et2J/4L17SUtLVes4erS6yOrfXx20dWuyRg2ycGF1ixhQE/tv345yd5qmsf3m9nSc7hhtBc+Fh7dpnuENUXMEa62uxcMPDzMwNJC5Zufiz/s+rc9PGD16qN4EAQGJsvsYDR6sViwKCg2OV2Jk0flFNBpnxA03NsTr+I8f6tWKH+USp3ltQpI4HI1r11Q9cY8eSZYE6NuXLFAgfIn1rNOzaDTOiNNPTk/cg79+TTZurOKPqSlZurS68zlrllpTu2hRtVa4ECJJSHxOAv36qbKQf7+XkyaROXPGrUfcypUqd/5s1T5yyBDy/v1wz9epY5iZE0FIWAhr/1qbGAHie9Cukx0r9qzIIZOG8EIUy9QEhQax09ZOtPzVMkH6C13wvMDKyyvTZLwJTcebcvml+F08i/i5desWixQpwuzZs3PlypUM+OTk+cWLFyxatCiLFSsW4+/6p3x9fZknTx527Njxs28sf/D27VseP36cvr6Rr/wZ23F16dKFxsbGHDNmDOfNm8ciRYoQAL/66itu3bo1zk1e4yPFJEU2btzI7t27G/49fvx4Tpkyha1bt+aBAwdivR8J9hGFhqrAbm6upjA0aqSmxMe1t8P229tpPM6Yl55divVrHj1Scx8LFozwuRBrd1/f5bST01hvTT2uu7YufjuJxofEiK2t6l0IqBsFzZuraT4nTkRfRR4SFsKm65sy6/SsdH/jHvMBN29WFxmDB8fu4urdO3VxYmenaiYjsfD8Qlr+asnLzy/HuLslS8h0VmFsuvQ76sbqmGNWDmaemjnKpYw/x4MH6mdu374E33Ws3b2r/k/PnlUrx8Q2MXLx2UXW/b0udWN1XHJhSbyP3678A5bVnaf+4eN47yOpSByOgru7qk9u3Truy0DFk5ubSsbu3Kn+rdf0/HnfzzQdb5oocTBKb96oX+BfflHVITlyqAuH5MpyCpFGSXxOZHv2qLtg/56w6PWq0GPChLjtJiyMLFKE/P77iM/dvKnORyKbtXD71W3mmpCLGAx2ndeV3Vd2Z6FfC9FslJlqsPwD2G5lO55+etpwQevl58XKyyvTaYYTL3jGYW3fGGiaxq1uW2VVqyS2bt06WllZsXHjxtFWgrx584ZlypRhgQIF6OERu96OmqaxTZs2zJcvH9/Fovo8OWzatIl2dnZ0cHDg0KFDeT++F47xlGKSIm5ubsyXLx9fv35Nf39/VqhQgf369WPx4sU5evRolitXjtWqVeO5SJYMmT9/PgsVKsRChQrR0dExGUafcl2/rlYJyJBBLQF7+LCaKm5pqS78f/hBrUnu4RH59XloqLoe2LXPjxl7tWXLCUv499/qNfv2qeoKT8//Eize3v+dPzdurBINNWuqx1Oy0FB1A3TxYnUxEtcEalBoEOuvrc8cs3Lwkc+jqDdctUpd6fxbmhlrYWGqksTUNMJqF/e979NqghXnnJkTq12FhqoilN691So1Pbb34Fa3xFlStF8/slSp5K+ur1lT3eAnwydGjj06Ru8A73AZc/c37my7qS0xFmy6vilvvIz/3fAT+/0JkKe6L/3ct5AkJA5HIjBQlTp99VWSdglu1IisXVv97gSFBrH95va0nmjNA/djf/EjhPhySHxORC9eqLm2P/1keGjvXrXq1vPncd/drl3qVO/2bXV+ffCgmhFdrRpZsWL4bTVN4/yz82k2zozoCE5ZMCXc83pNzxsvb7D8D+Vp+r0pMRbMPjM7B+4ZyNyzc8d70QORcgQEBLBnz540NjbmlClTYtU/w9fXl1WqVGH+/Plj1WNk8eLFNDc35+W4NlJMYoGBgUlSFRKZFJMUIclly5axVKlSrFq1Kr///nsOHDiQrq6u7NevHzVN49mzZ5krV65oS34kA66EhKjEhKmpurn36RQvX1+VAKhSRU0LB9T0lq++UheyzZqp6g5T0/+WYYQujKamGi0sSCsrFfANz0HNff9QaV2mDNmnj7p+T8rVRpJTQEgAa66qyTxz8kT+ATVvniqb+JwVK+bMUfsYPZrUNOo1PauvrM4aq2rEab7nhw/sW3GY8RNXXl4q+fbnn4l3jNhav16Vs77+t9VKcFgwW2xoYVje0vJXS+abm89QMlplRZV4NVT9mF5PlnZ8yg7ptkZfapTCSBz+xKhRZJYsSZrZ/ftv9Wt+5QrpHeDN6iurM+v0rLGqBBNCfLkkPicCvV71dStVKtwJa/Pmqj9qfGiaahdnZKTOi42NVW69SRPVsumDoNAgNv6jMc3Hm9OkggnHR9N9NSgoiJUqVWLxqsU5/fh0VltZjZ23dqZf8OevRiKST1BQECtXrkxnZ2ceP3485hd85O3bt8ybNy/btm0b7e/81atXaWFhwQVJsGJeapaikiIfGzZsGBcsWMB69erxn3/+MTyeJ0+eSJf3+UCCvQrGrVurPhnr1sV8lz40VE0NX7OG/PFHlUTp21etT753L7nt9BVitDH3uodvcqPXq8rq27dVL76tW9WSjbHp0P2l8gv2Y5UVVZh/Xn4+f//R7YU7d9Qn48qVn3+Q7dvVFX6/fpx9ehatJljFaum1j2kaWauW+r9OLGPGqNLTGFYETRIhIWpe8MdL6mmaxpd+L3np2SXuuL2DC88v5Kh/RnHnnZ0JMtdy2cQXtIQ/nyzY8dn7Si5pPg67uaks7x9/JNkh/f3JXLnIAQPU8quF5hei6wJXPvZN+dOvhBBJJ83H5+hcu0beuxe7E5CZM9U51Ud92zw8VCLj4MH4D+HpU9Wg1c0t6puDPXf0ZMZJGWmVw4oDBgyI8dzj+fPndHZ2ZpcuXRKsJ0RiCg0N5dGjR/nzzz9zyJAhfB6fspsvXM+ePeno6BjraTCfunTpEs3Nzblw4cJIn3/w4AHz5MnDli1bpoqfmeSUopIiHxrGPH78mAUKFKCPjw8XLlzIUaNGkSTv3LnDbNmySQY8BpMmqfh+7VrC7K/p+qaJtlTrl+ht0FuWX1qeBecX5LN3/3a2/d//VKPChApIR47wTlYzWo4x4cLzkQfCmFy8qKbPHo3bym2x4uenesR+umpGcpo/XyUKo1n1K8Gc2fGSFrpATsu9IPnnDsWRxOF/aZqqdf7qqyT9PxwyRPUxPXL7Eh2nO7Lmqpr0CfRJsuMLIVIuic+xcPbsfyXMpqaqY36zZuSwYaov28el+ZcuqW0+WYlj3DgyXz518y+xLL6wmKbjTWlfwp4dO3aM9ZKj586do7m5ebjVh1ISPz8/btiwgR07dqSdnR2NjIxYuXJlFixYkJaWlhw0aBBfvXoVq32FhYXx6dOniTzi5LNkyRKampryxInPq0xeuHAhzc3NeelS+J6PV69epaOjI+vUqRPtkrdCSVFJkSpVqrBQoUIsVqwYD/6bng0ODmbHjh3p6urKkiVL8tChQ9Hu44sP9jHYs0eV622I30IZEdx6dYsYi8+eSpDW+Ab6svzS8iwwrwCfvbynmrrEc/mryITpw1hxeiHW7aKjNnFivPfTqRNZtmzCf/DPm6eSIilp1khAgJoyPHNm4h7n4d836WDkxW6Zd1F7GfXdupRK4vC/VqxQcwvv3UuyQ167puawj5h3kVYTrNhpaycGh6WR+YdCiBhJfI6BXk+WK6fKpR88UOXOc+eq8udKldQJcoYMZLt2an53gQJqFcCPkkihoWS2bOS0aYk3zJNPTtJ0vCnrDqvLXLlyxbj87qd+//13Ghsb888//4x1MiUxaZrGM2fO8LvvvmP69OlpbW3NVq1acfXq1YYESFhYGNesWcO8efPS2tqaI0aM4O3btxkaSTXPtWvXOGjQIDo5OREAu3TpEu8laKNz5MgRdujQgX379uXEiRO5atUqHjhwgLdv32ZwIs/9P336NM3MzKKs8IgLTdPYtm1b5s2b19Bf5OjRo7S1tWXbtm0T/b18KVJUUiQhfNHBPgbu7irWDxmScPvssb0HKy6rGPOGIoK3QW9ZcVlF5v81Cz2dbRI0QzDlxBTaTLLhkz8WqRrPefNiftGbN6qzbokSZMmSZKlSfFy0Ic11QfxfE3c+fJgwYwsNVeX/H09VSSkmTlQrDEX5+eDl9Vm9I3w27mdhIzfWdLjO4Ldpdx5Zqo/Dr16prF5clx34DHq9asBXrOpjGo015vCDw6XUVQiR4FJ9fI7OypWqmdnjKKYbvn5Nrl2rkiIZMqh5tZ985m/fTpqZqY+BxOD5zpOO0x3Zdm1bmpqactOmTfHaz+jRo2lsbMx8+fJxxowZEZIG796946FDhzhz5kyuWLGCBw8epLu7u2Hp5oQQGhrK2bNns3DhwgTAmjVrcu3atRGWkf30NStWrGCuXLkIgGZmZixSpAhbt27Nn3/+mcWLFycAli5dmrNnz+aePXtYsGBBOjo68q+//kqQcb9//559+/alTqdjs2bN2Lx5c5YrV47Ozs40NjYmABobG9PFxYUNGjTggAEDYkw2xsXz58/p5OTEb7/9NkGXxv3QX2Tbtm00Nzdnv379UkTSLLXQkSS+IIULF4abm1tyDyPJvX8PVKwIZMsG/P03YGz8+ft84fcCOWfnxJ8t/8TXhb7+/B2mQe+C3qLBEGe8sjXF4Z9vwNnG+bP2F6oPxch/RmLaqWlY1XwVuhTvAvz+O/DNN8Dy5UC3bpG8KBRYvBgYMwawtwd69ABMTQFNA0isW6PH+HsdcDcwB6pVAzp3Blq3Bmxt1UvfvwfevQNMTNTPV0z+/BPo3h148gTIlOmz3m6C8/UFcuQAZs9WY4SPD3D0KPDPP8ChQ8CH2JEtG1C0KJ7krIpJd1ugQK4QdKjxDA4Z9eqXy9QUsLFR36QMGQBbW4QuWYmG/V3gkbEYTt3JBLuMRsn3RpNZqo/D33wDnDsHXL4MmJklySEXLyb6/xAKfe+C+K3DEPQq0ytJjiuESFtSfXyOytu3QIECQN++wKhRMW8fFgaEhADp0oV7uGFDdaq0dm3CDzE4LBg1VteADjpk3p0ZPq98cPToUeh0unjtz9PTE0uXLsXSpUvh7e2Ntm3bwtTUFGfOnMHNmzdhZGSEggUL4v379/D09IRerwcAuLi4YPz48Wjfvn28jx0QEIC2bdvi5MmT6Nu3L7755hvkyZMn1q/XNA0eHh5wc3PDrVu34Obmhvv376NChQro3LkzChUqZNg2KCgI48aNw7Rp09CqVSvMmzcPmTNnjte4Dx06hB49eoAkli1bhjp16oR7Xq/Xw9PTE+7u7nB3d8fdu3fh5uaG/fv3o0mTJpgxYwby5s0br2MDQEhICGrXro3g4GAcO3YMFhYW8d7Xpy5fvoyKFSsiJCQE48aNw8iRI+P9/5smJXNSJsF90RnwKGga2bIlmSePKgZIKMMPDme+ufkYpg9LuJ2mNWfP8p0ZWHl+Keadm5ennpyK964e+TxixWUVmXFKRv599+/wTy5YoObP5smjykanTCEPHSJ37lTzaW1tyRkzIi+ROHqUms6IZ3a/Yd++qu+GiQlpbh5+dSGAnDo1+vYKmqaKUPr1i/fbTHRDhqgu8GETJqumKunSkfXqqe/ZuXPkuXPUli3n0trrmd7YjyVMrtFJ50kThLCZ0Q5uNW7FYJiG+8ZoAHvoljFz+gAm8bLqKVKqjsOHD6ufi2PHkuyQTz1DaGblR9P6w7nzzs4kO64Q8XHw4EEeOCBLQ6dWqTo+R+enn1SZajRVCjF5+FCF/zguAhIr74LesfXG1sw6PSs37tlInU7HCxcuJMi+Q0JCuHnzZjZs2JAtW7bk1KlTeezYMfp/VKEcGhrKx48f8/jx4xwxYgTNzc1ZrVo1Xr16Nc7He/XqFStUqMCcOXPy9kcNahPb+fPnWbRoUdrZ2XHWrFlxmhby8uVLfvfdd9TpdOzTpw/fvXsXp2NfunSJ1apVo6mpKQcPHhyrZXA/9v79e65YsYJly5alg4MDnzx5EqfXx9Zff/3FdevWJcq+v3SSFEnlwsLI779XS+QmVGNVknwf/J52k+3i3cRT/KtrV7JuXb4Lesc2m9oQY8EGaxvwnMe5GF/6sW23ttFush2rrqjKp2+jaDp1+bJKjnzzDVmsmJpWY2SkfkCi6UTPsDC1HvPSpSRV3mT/frUs6LFjalnQ+/dVxamZmUp4hEWRJ9u3Tx3yQdwWw0lSz5+T5mZ6bjZuQy5cGCFR9Pix6q1pYaHySGFh6mvvXrJ9e/V4unQa7TJotEmvp5VlGC3Mwmhupuep+Oe8viipOg5Xr0527pxkh/N6HcIspU/S2PEmTz08n2THFSI+Ll26RAsLC+p0Os6ePTu5hyPiIVXH56jcuqXu5mzdGu9daJo6v3F1Tfje2sceHWOu2bmYe3Zunnt6jsWLF+c333yTsAeJo3v37rFJkyY0NjbmgAED6OPjE6vXPXz4kPnz52fx4sX57NmzxB1kJIKDgzlr1iza2dnRxcWFmzZtinYKyrt37zhmzBhaW1uzYMGCPHz4cLyPrWkaN27cyBw5cjBLliwcMWIEL126FOXx9Xq9odeKtbU1M2TIwP79+/Pu3bvxHoNIPJIUScWCg8m2bdW0yM9sXBzB7NOzmXlqZgaExD/jnua9fq3KLT6aA3np2SU2+aMJMRZsur4pTz45yWfvntE/xN8QVDVN4wPvB/zr1l8ce3gsG61rRKNxRhz1zyiG6uOwvq2/P/niRey2/e47skGDGDc7elT9vDVvHr5FyoMHZK9eKmmSzJ/zMdM09nbextLp71DT//dB9vChKhZJn171Y7tzJ/KX+/qS27aRW7aoP3fsUAmkJLxZkuKl2jjs7q6qf87FLWkZH56e5KBBpJllII1sPbn1YOLcNRIiobx584a5cuVi27Zt+fvvv9PU1JT9+/dnWFRZcpEipdr4HBVNU3cy6tSJdzYjNFT1YjUzU5/pcX69PpTub9wjNMYODA3koP2DaDTOiN/t+I7vgt5x6dKltLKySpaEQmR27dpFFxcXOjg4cN26ddEmGC5fvkxHR0fWrl07zpUSCc3b25s//vgjTU1NWbFiRa5evZr79+/n1atX+fz5c/r7+3POnDnMnDkzs2XLxuXLl0fa1DU+AgICOG3aNJYqVYoAmDt3bv7000/ct28fly9fzv79+7NKlSpMnz69odfKunXrou21IpKfJEVSKT8/9Rng6EjGo/ItWqH6UOaclZPjjoxL2B2nNdOmqRbmkQThcx7nWH9tfWIsDF8m402YcUpGpp+YnhgLWk2wYoVlFdhrZy8eeXgkcce6Z49als7XN8ZNb94kc+QgK1QgT54ku3RRRSmlS6v8T4rv6bR+Pe+bFqCRkcbffiNHjyaLF1fXwjlyqNVp5Bz/86TaODx8OFm0aKItwRsaSl64oHKQZmakU25f6pp/w323DyfK8YRIKGFhYaxXrx6LFClCPz8/kmrlBjs7OzZu3FiWe0xFUm18jsr27eok5OZNkmoGZKNGaibxkCHk4sXkgQNkVCu7vn9PNm5M2tmpFXvjwjfQlzNOzWDOWTmJsaDxOGPmm5uPTf5owp/3/cwivxVhlmlZuOvOLpKqGWaWLFn466+/fsYbTniBgYEcO3YszczMWLduXd77aNU1TdN44sQJtmnThiYmJmzfvn2KWs3k/v37bNeuHbNmzWpokvrhy87OjtOmTUvUZMSDBw84ffp0VqpUiTqdjtmyZWPjxo05cuRIbt68mY+javobRyEhITx79ixDPl5SOpXQNI1HjhzhTz/9ZJjS1a5dO9asWZO9evVKMQlCabSaCnl7A40aAV5ewP79gItLwu7/zxt/ovv27njyvyfIlO7fTpkkIM16Yk/TgHz5VMPGkSOj3MzL3wvegd7wDfLF26C3eBv8FiZGJiiWpRjy2OWBkS6JmnWGhACZMwOLFgHt28e4+bNn6mfwyhWgcmX1FuvVSwU/Im/fAgULAj16oPOjX7B2LVCiBNCsGdC8OVC8eCp4D6lAqozDYWFAzpzAoEHADz8kyC59fYGDB4GzZ4EzZ4CLF4HAQKB8eaBNzwcY7lkEk+pMwP8q/i9BjidEYhk1ahTmzZuH8+fPI1++fIbH79y5g4YNG8LW1hZbtmxB7ty5k3GUIjZSZXyOiqYBrq5AvXq41282Bg0Ctm8H2rZVvdAfPFBfjx8Dej1QpgzQrh3Qpg2QPbs6l2ncWJ0a7N6t+rTG5N69e+g8oDPsG9jjuN9xWJlZoW/ZvmhduDU833vi9uvbhi+n9E6Y/tV0w7n04MGDsWHDBty+fRuWlpaJ/M2Juzt37uD777/HmTNnMHLkSDg5OWHevHm4cuUKGjZsiP79++Orr75Ksc07NU2Dt7c3Xr58idevX6N48eLIkCFDkh0/ODgY5ubmCb7fs2fPomfPnrh27RpKlCiBS5cuJdr/ga+vLx4/fgwvLy+8fPkSVlZWaNCgQbwawmqahu3bt2PKlCk4d+4catSogS1btsDOzg5jxoyBXq/Hzp078eDBAyxfvhxt2rRJhHcUB8mdlUloX1wG/BOvX5NFiqibmYmRWNM0jaUWl2Lfv/uqB54/V+l2ExPVwKpWLbJHD7W26eXLCT+AL8Xu3ep79vx5co8k9jp0IFu1ivXm79+ru96patXQgQPJ3LnJgAD6+UV950h8nlQZh3ftUtVSCbAO4+XLqhokXTrS2lqFzWHD1A3NFy/IV/6vmHNWTnbY0kGW3RUp3rZt22hkZMSdOyNvAuzl5cXq1avTwsKCo0aNCtfcUaQ8qTI+R2XvXvoYZ+RPvd7T1JSsWlWdl3wqNJS8dIkcOlSdygJklSpk9uyq6jW6tmsf8/LyolNVJ+pG64jeYPEuxXnnfhRzbT/x4fdoR3zm5yQhTdO4evVqZsyYkTY2Nvzf//5Hd3f35B5WmrVp0ybqdDq2b9+et2/f5smTJ0mqn6dGjRp9Vo+UD168eGFo+jt+/HjDksRZs2Zl+vTpDZUcx48fj9UUJK9/f6EOHTpEMzMzfvfdd7wTxZz0sLAwLl68mNf+bYx58+bNZDsvkqRIKhIYSFaurHpofrKseoI58vAIdWN1vPfanVy+XDWQKFGC3LxZNeIcNkw1MilRQtV///574gwktWvcWH2fUpPNm9VV3Jc65/HyZdUFNooTe5FwUmUc/vprlQCOp7Aw8o8/VD8agKxWjdywIeKCT6H6UNZeXZvFFxanf4hcPIqU7e7du7SxseHo0aOj3U7TNK5Zs4ZZs2Zl9uzZ+eeff1LTNGqaxtu3b3PhwoVs3bo1y5QpwxEjRvDixYuSEEwmqTI+R+H9Vy2YP70nc+dWpzCx+ZHSNPL0aXWPZODA2J/y+Pv7s2idojQaYcSBuwby+vXrrFSpEq2srDhr1qxoe+vcuHGD1tbWHDt2bOwOlgIEBARIgjMZnT59mpqm8e3bt9y3b1+E5y9fvmyY0vTPP//E6xgBAQGcOHEi06dPz6ZNm5IkfXx8+OrVK+r/nQsfFBREkvT09KSJiQkzZ87MZs2asU+fPoaEzN27dzlr1iy2adOG2bJlo6WlJUNCQqhpGl/EtrchSV9fX1pbW9PJyYnNmzfnxIkTeTkJb8BLUiSV0OvVNbaz8yd3t9+8UcuQubioJiP/+59KZpw5E74TZiy13tiaTZbWUrc2LSxU58nI5q9pGjl7trrIHD48FTSSSEJ37iTemm6Jyc9P/Z9v25bcI0l4ej1ZsaLqECsSXaqLwy9fqsquPXvi9fKHD9VdRysrtdhTdCuBDTs4jPZT7PnAOwUv0STEvzp27MiqVasaTpBj8u7dOw4dOpRmZmYsUaIEnZycCIDZsmVjly5dOHLkSENzwpw5c/J///uf4Q5hbIWFhXH16tVs3Lgxd+/eHZ+3laaluvgcFXd39sRi5nQMZGL3/AwLC+NXrb6i8c/GbPJ7E+o19fug1+u5cOFC2tjYsEyZMrxy5UqE175584YuLi78+uuvY/17JNK2AwcOUKfT8fr16zFuO2DAANrb28e5mmfXrl3MkSMHHRwcuGjRolhVgLx+/ZpLlixhv3792KJFC27evNkwhgIFCrBbt25csmQJb9y4Ee+f9YcPH3L58uXs1asXS5YsyYkTJ8ZrP/EhSZFUYsgQtSqGId4GBpJTp6pKjjx5VFPPH38k69VTzT0BdYberZvqHBWLH07Py0dpMsaIewuZkjVrqpUYYvL332pgLVvGKwnzRerThyxbNpXNK/lX8+aqc2pKsGGD6t5auTJZty7ZrJlaE3f79rjva/Zs0tKSfPQowYcpIkp1cXj6dFVHHccOu5pGrllD2tiolXxj6qd24+UNGo8z5la3+C8bKURSefbsGU1NTblr1644v9bd3Z0//fQTFy9eTHd39whVIQ8fPuTMmTNZsWJFmpmZcdWqVTHuU9M07tq1i0WLFqWlpSWbNm1KIyMjtmjRgk+eyOpNsZXq4nMUdjZdQiOE8eiRxD3X0jSN3/X7jkZ9jVhiXgkGhgZG2MbT05Nff/01TUxMOHLkSMPd9dDQUNapU4dFihSRZsQiVl68eMEsWbJw4MCBsdo+NDSU33zzTawrKj7E4l27dnHYsGEJsorQl1L1J0mRVGDRItVYe+/efx9Yu1adwGfKRM6dG7E+myR9fMg//yTr11fVHHnykOPGqTVV795VDSFIdVb/zz9k48YcUwPMO8iC+o0b4nZBf/26mqRZujTp4fG5bzd1e/NGTUH544/kHkn8/P67SrQld3drNzeVxPjuO/KXX9S0rQED/utvE5cpMGfPql4Ry5Yl3nhFOKkqDmsaWagQOWpUnF7m7a2q90xNycmTY86naJrGWqtrsd6ael/MCYRIXJ6ensm6hOPo0aOZL1++RL27rWka586dS2NjY/7000+RTkH4sPpFtWrVaGxszF69etHT05MkefHiRZYrV47p0qXjlClTDCXb/v7+9PDw4I0bN3jy5Enu2bOHGzdu5PLlyzl37lx6pOFzlVQVn6Pg9eA9HXQvObhB3KqM4mPy1MnUddbReYozX/lH3XNK0zRu2rSJDg4OLFiwIE+cOMEffviB9vb2vH//fqKPU6R+er2edevWZalSpQyJtbh49OhRtBUfBw8eZOnSpQ09P0R4khRJ4XbvVgmRpUv/fWDnTpXkGDGCsa4X9PAgJ01SJ/5qHRn1ZW1NZs1KGhkxpFULZp2UiTNPzYzfQF++VHf0s2RJfdNGEtLkyapSJ7mTCvHl7a2SDgcOJN8YAgJU45zGjSNPzo0apab5HD0a8768vcmcOVX1i1yIJplUFYdPn1bx8EHsprM8fUqOH69CZ8GC5MWLsTvMppubaDrelLdf3f6MwYq04N27dxwwYACNjIxoaWnJxo0bc9GiRXyahJ2hg4KC6ODgwLlz5ybJ8fbv388MGTKwQYMG9P13afjXr19zzpw5LFq0KAGwVatWkTbr0+v1XLx4Me3s7Jg+fXqamZmFW5YTAE1NTZkxY0bmypWLjo6OLFmyZIpaVjQppar4HAlNI5sVu8/ixtcZ5BuxaiMh7du3j2gCWo+35t3Xd2P1mjdv3rBbt27U6XQ0NjbmoUOHEnWM4svh6enJ0qVLx6uxbWhoKPPmzRtphUlwcDAHDRpEIyMjDhgwIFmT7SmZJEVSMH9/MmNGcvDgfx8ICFArZ8SypCpSwcHkkyfq7vm2beru+f373HBjAy1/taR3wGd0cA0OJnv3VhfVCxakvYvQkBDV9GXKlOQeyeepW1f9PyaXPn1IJ6eoVwHRNLJfPzVnIborUk0jmzYlCxdW/VJEkklVcbhHD9VDKRohISpcNmqkctJ586r8Z2xnDPqH+DPHrBwcvH9wzBuLNG379u3Mli0bc+bMye3bt3Pbtm3s0aMHHR0dCYAlS5bksmXLGBiYuBeDq1atYvr06ROktDq27ty5wwIFCrBgwYJs27YtzczMmDVrVg4bNixWFwleXl7csGEDd+/ezdOnT/P27dt88eJFhO/VixcvmDlzZg4dOjSx3kqKlqricySWL9Nopgvm9R6zE/U4T548oUULC5qOMeXJJyfj/PqDBw+m+JVmRMrxoUrucypJ//nnH5qYmLBRo0Zs2LChoVFu7ty56eDgID2YYiBJkRRs4ULS3v6jE+9Ro0hHR/LfuygJqdrKauyxvUfC7GzZMrUyTbduqvdJWrFunZo6k1hLAyWVhQvVz1lyNATbulVddca0xJheT3bqRGbOTN6O4s779Onq/+PmzQQfpoheqonD79+rirl16yJ9WtPUigY5c5Lm5mTHjupHM67nLKP+GcWs07PyXdC7zx6y+DJ5enqyZcuWNDIy4k8//US/TxK5er2eFy5c4KBBg2hra8ssWbJwwoQJ9E6EzxtN01iyZMlYz2lPSL6+vuzQoQObN2/OHTt2xKr5X3zs2LGDRkZGPBqbisMvTKqJz5G4f5+0tgzlDN1Pn6w6ELO49PQIDg5mlu5ZaDTSiIfuSaXHx9atWyfTgSLxcTLD29s7TtMO37x5w8KFCxuW2/0cf/31F3v06MGffvrJ8DO/YcMGvnz58rP3/aWTpEgKpdeT+fOrWTIkVR8QM7MoT94/x7UX14ix4OXnlxNup2fPqqqJ0qXV4vBfOk0jy5Qh+/ZN7pF8vmfP1Oo5J04k7XEfPybt7GLf2yEkhGzSRPXXWblSrbj04a7mqVOqYkmWjE4WqSYOr11L2tpGuibjjRuqgMTUlBw0SLULio/73vdp/os5115d+3ljFV+sCxcuMEuWLCxVqhQvxmI+1tu3bzlz5kxmz56dVlZW7NGjB1esWMErV64wJAGmbh4/fpw6nS5eJdypSa9evZgjRw76+Pgk91CSVKqJz5Fo25asan+D+pZxWz5927ZtNDIyYsuWLXnu3Llot9U0jcWHFSeGg5vObfqc4aZ6er2ef//9N2vVqsULFy4wODiYBQoUIACWKVOGU6dO5aN4NLDXNI0bN27k9OnTuWXLllQfazRNY58+fThjxgySZLVq1WhhYcGiRYuyV69e0Sbk/P39+dVXX7Fw4cKyBHIyk6RICrVzpzoZf/aM6oK7Xj21IkwiTEn5fuf3rLS8UoLvly9eqL4QANmihWrI+qU6flwlEu7Gbs5pilelCvnDD3F/3ePHakniuAoNVcesUkX9PbYCAlTFiLPzf71ynJxUcuXbb+M+DpEgUk0c7tkzwjLNvr5qhqKxsepTHVUhUmw1W9+MVVZUkeaqIlK7du1iunTp2Llz5zj3uAgJCeG6devYpEkTw9K3ZmZmLFOmDJs3b84GDRqwdu3arFq1KitUqMAxY8bE6qS7VatWbNy4cXzfUqrh5+fH/Pnzs2PHjsk9lCSVauLzJ16/Js3MNO5E49j1FPtXaGgoCxUqxBYtWrBhw4YEwBo1anD37t0R4rKmaaw/tz4xHJy+aXpCv4VUZdeuXSxcuDBNTEzYuXNnQ+JC0zReunSJQ4cOZZ48eThhwoQ47VfTNHbv3p1mZmYsVqwYbWxs2PffG4pr165NtCker1694q1btxLls3jy5Mk0MzPjsWPHSJIPHjzgrl27OGPGDObOnZvffPNNpK/z8fFhlSpVmCNHDt79Uq4fUjFJiqRQNWuSXbv++4/Nm9Vdbze3BD+Ob6AvrSZYcd21hK9AMTh1iqxTRyUN2rYlb91KvGMll6+/Vv0rvhRz5qiGsXGZQuPtrXremJiQo0dHvipSZHx9VdIvU6aY1zSNaT9nzqiqkZkzZYnoZJRq4nDx4qoJ9Ue+/lotprV9++fnoPe476HROKOErcITX4zFixfT2NiYI0eOTJAT9efPn3P37t385Zdf2L9/f/78888cMWIEx40bx9GjR9PR0ZE5c+bkli1bojze48ePaWxszAPJ2Ww7CZ0/f54mJib8I7WuGBcPqSY+f2LuXNLR6i1Di5aMU3BesWIFraysDNMHrl27xs6dO9PExITZs2dnw4YN+eOPP3Lp0qVsvbA1MRzsPq57Yr2NVCEgIIDOzs7s06dPtA2eNU1jcHAwQ0JCOG3atBhXTPkQd1asWGGo2NE0zVDhNm3aNGbIkIH37t37rPGHhoayXbt2zJo1q6F30PTp0wmAM2fGc0GJKKxbt45GRkbcsGFDpM+/ePGCz549I8kI/Y0OHTrE4sWLp+nVsFISSYqkQJcuqRveV65QzXl3diaHDEmUY809M5cO0xwYFBr3pZ/i7MgRslo1dQt25EgyHstNpUj376uET0x9MFITDw/1nmI7v1HTyGbN1HIcq1erXh+urmoaVXTu31erIhUoQKby8knxn1QRh9+/j9C/5vFj9dA//3z+7n0DfZltZjb2393/83cmviiapnH48OE0NjbmkiVLkuy4b9++5c8//0wTExPWqVOHbpHcaBkyZAgLFy6cpiqbfv31V9ra2vLx5yTlU5FUEZ8jUaKExiG2C8l/pyjERmBgILNnz85RkUzLffz4MRctWsSBAweyXr16zOqalRgFlu5UOlGXoU4qu3btYr9+/Vi9enXu378/1q/78Lv/6tWrWMeBe/fuMWvWrKxYsSJfvHgR6TYbN25knTp1oq2ICwsLY506dVi8ePHPmkoyePBg2tvbc/Xq1bx2TS3b7Ofnx2XLltHExCRBeneQ//18zYjFz+S5c+fo5OTEs2fP0t3d3ZCki2wZcpE8JCmSAnXuTNau/e8/Bg9WPRMSYfUMTdP4//buO6zpq4sD+DdhygYVVETBrSCCC617gNZtsa4O966z2jraOl73ts7WbbWOWieKW0Rc4B7gApQle2+SnPePVCplJSEhAc7nefpYknvv77Auyfmde2+jLY1owdUFxTdW3kWJjh2Tvml2cCDy85Ot34sXRN9/Lz3y4fFjzTnZRiwmGjyYyMlJc2JSFnmW0KxbR1SpknQjBiLpyTHDh0vfYX7//T/rwP7D21t6vFL37mV/c1qWR5mYh728pD+fn6z1/eknaV5PGb/KI0+NpPq/1qe0bK5YYv/KycmhkSNHkqGhodpOAggICCBXV1cSCoVka2tLHTt2pK+//prmz59P5ubmtGPHDrXEpS4ikYgmTZpU5vc1kFWZmJ//4+PNwpdoIPPx6UREGzZsoMqVK+ce81yUQccG0Wc7PyuzCZHg4GCaO3cuPXr0iIiIvvzyS+rRowcNHz6ctLS0ZKqGSkhIoM6dO9OTJ0/kvn5YWBi1bNmSbGxs6O7du0REdOfOHRo1ahS1adOGhEIhLVu2rNivb0xMDNWqVYtG5JbLy+f+/fskFArJ09OzwOfHjh1L1tbWFKfoRmH/kDd5lJ2dTSNHjiQDAwOqWrUqzZo1q0TXZ8rHSRENExYmXX1w7hxJd/bT1yc6fFgl17oceJm0FmtRSGKISsYvUnQ00dCh0qqR+fMLrhoRi4k8PaVLKwCiVq2kVQWA9HSUESOIjh6VbripDhKJNGlgZER0/756YlAlWZfQ3Lol/aHdvz//c2fO/LvfR40a0o1RFy+WJrd0dKTH76rr+8dUpkzMwytXEjk65n6YnS2dVjYq4ZTH0y9Pk3CxkG6H3C75YKzcyMrKokGDBpGFhUWxmz2qmkQiIR8fH9q5cyf9/PPPNGLECOrSpQt17Ngx38k3rHwpE/Pzf0ydSvRZzffSG1AySk5OpipVqtDatcXvDXI75DZhEehu6N2ShFnqJBIJXb16lfr3709CoZAcHR3Jp4BN8vfu3Vts0i87O5tcXV2pSZMmMiWRCpKenk5Dhw6lL7+UboR7/fp1Gjp0KC1cuJDu3Lkj8zh+fn4KL+GTSCR0v4jX5Onp6bRz584SJb8SEhKoVatW9PzjjUA5Ylu6dClNnjxZ7j2kmOpxUkTDzJsnvVMpFhPRhg3SN5IqetPY73A/GnRskErGltnffxNZWkpPgGjUiKhDB+mmrOPHSz/W1pZWHHy6DOP9e6Lff5e2MzSUJkouXCj92P/3P+k5ncqotddEYWHSZMbtIt7YxcRIEydjizjOOSeH6MkToj17pEkQFxdphcjmzXKHtGrVKlq2bJlSTlhgqlMm5uEBA6TzzD+OHZMWO5X0IIqYtBiyWmNFcy/PLdlArFxJS0ujnj17UrVq1ehZed50nGm8MjE/fyIjQ7p3+q4aPxMtWSJzv0WLFlHNmjUpvYDTxT4lkUjos92f0eC/Bpc01FKXmJhIpqam9MUXX5CXl1exFQv37t2jwYMH5zsNRSKR0MSJE6lKlSolPm5XIpEo7USnDx8+0EMZT7DMyMigRYsWybzsJjs7my5evKhQXMOHD6cGDRpwArmc4aSIBklNlU78v/1G0iqEhg2lG1aqQHBCMAkWCcgr2Esl48slLk5a8bFlC9HChdI3zl9+Ka0gKe4c+uhoogkTpGXwvXsXfFREVhbR27fSxMnWrUSzZknfEM2cKa3wUKRWfts2aZXLqVPy9y1LilpCIxZLq3gcHQs80lTZVq1aRTo6OmRhYUFOTk70+PFjlV+TKUbj52GJRFoWsmdP7kNduhCNLuHeehKJhAYdG0QO2xxKZ58mViYkJSVRx44dqXbt2hVmiQbTXBo/P//H0aNEBpXElAwjmU8xjI6OJiMjI9q5c2exbf/2/5t0lujQ27iSbe5ZWuLi4mjo0KF09epVIpLOL7J6+fIl1a1bl2xsbKhly5bUrl07IpJ+vbS1tenmzZsqiVlRs2fPpho1ahS52etHEyZMoBo1auTu1VGcK1eukFAolDsxcujQIdLW1iY/WZf/szKDkyIaZMsW6QEc6ekkrT4QCotPCihozqU51HRb0/KzmdqTJ0Rdu0orS4YMkZ4E07y5tArl41Gt2tpE9etLz9mcNInos8+kjzdsKF3SIetu13/+Kf3e7Nun2s9JExS1hGbFCiJjY8WO4JXT7t27SSgU0rFjxygyMpIGDhxIOjo6tGTJEq4a0UAaPw+/fy/93X/xgoikB2IBsm9xVJjDzw6T9hJtehgh250tVv4lJiZSq1atqEGDBhQSooalqoz9h8bPz//RsyfRyBZPierVk/km1owZM6hBgwaUk5NTZLtsUTbV/7U+TfecroRIVc/T05OqV69OjRs3lrmC4r9iYmJozZo1tGrVKtr8T8VudnY23StuY3w1yMjIoHbt2pGjo2ORyZ+dO3eSlpaW3Emd77//nqpUqSJT0oVIOp+bmZnR0qVL5boOKxs4KaJBOnaUFjEQkbRSon9/lVwnLTuNzFea02/3f1PJ+GojkRCdPEk0bBjRtGlEq1cTHTok3VAxKEi6jOO/AgOly2AaNZK+K2rRQrrXwH/LB5OSpGd0Tp4sTa4o+UgvjVXYEpo7d6Rfh1I4xvDkyZOkpaVF27dvz31MIpHQoUOHyNzcnJo3b07BwcEqj4PJTuPn4aNHiUxMcpN906cTtWxZsiE/pHwg85Xm9L8b/yt5fKzcmDNnDtnZ2RV6IgNjpU3j5+dPhIRID8K70WSi9OABGSQkJJCenl6hR6R+asu9LWS6wpRi02JLGqrKLViwgAQCAc2aNavYJUHlSWxsLDVs2JC6d+9e4E2w48ePk1AopG3btsk9dnZ2NrVt25Y6duxYbALtIx8fHz4xppzipIiGyMiQbk/h4UFEHz5I33AWsnNySe16sIvMVppRahavhcslkRA9eCDd1KVePWkiwNlZusSmQwfp90NfX7pcpCJUiHyqXTvp1+GjxEQiOzuikSNVfunr16+Tnp5eoVn5Dx8+UMeOHcnBwSHfGlmmPho/D8+aJT31iIjS0ojMzIh27y7hkBdmUeMtjSlHLNsLK1b+hYWFkb6+vkynPjBWWjR+fv7E0qVE9WxzSAJIb8bI4MiRI2RsbFzsRpZJmUlUdXVVWuWzShmhqkxycjIREV24cIGuf3KEfEUSFBRECxcuzLM56sdK95SUFDp37pzCY797947c3d2L3QflxIkTFB0drfB1mOYTgmkEX18gOxto3x7A7t2AjQ3g5qb06xARNvtuxhjnMTDUNVT6+GWWQAA0bw4sXw68fg08fgz06gU8egS0aQN4egIJCcCFC8CIEeqOtnQNHgz89RcgkUgXIk2cCGhrA5s3q/Syz549Q79+/TBx4kTMnz+/wDbVqlXDyZMnkZ6ejpEjR0Iikag0JmXLysrC7du3kZGRoe5QKpa7d6W/1wCOHJE+NHSo4sPFpcfhtwe/YX6H+dAWaishQFYeLF26FA0aNMCQIUPUHQpjZQ4RsHcvMKrpfQhq1ABat5ap39mzZ9GjRw/o6uoW2W7DnQ3Q19bHNJdpyghX6dLT0zFjxgzY29sjIyMDPXr0QOfOndUdllrY2dlh0aJFAIALFy4gIyMD7u7uOHDgAIyMjNCrVy+Fx65duzaOHz8OMzMzpKSkFNjm0aNHGDJkCC5duqTwdZjm46SIhvD2BpycAFMjMfD778CECYBQ+d+eW6G38DTqKSa3mqz0scsNgQBo1gxYuhS4fh1YvRro3h3Q11d3ZOrh7g6EhQH37gH79wN//y19J2lkpNLLLlmyBG3btsX69eshEAgKbWdhYYHTp0/jwoULWLZsmUpjKqnExEScPn0aP/zwA9q1awcTExO0a9cOT548UXdoFUd2NvDwYW5SZPt2aZ7TwEDxITf7bkZVw6oY6lCCzAorVwIDA7Fr1y4sW7YMQhX8LWesvPPxAYKDgW8TNgEDBsj0mlgkEsHT0xN9+/Ytsl1adho2+27Gj+1+hL625r22u3XrFpo1a4a///4bO3fuRKVKldQdkkZ48OAB+vTpA0dHRzx48AAtW7ZU2tjnz59Ho0aNEBUVlftYRkYGVqxYgU6dOuGLL77A8OHDlXY9pnn4L7WG8PYGOnaEtBLhwwdg9GiVXGez72b0btAbdczrqGR8Vg5ZWwPt2gGrVgHffSf9t3lzlV4yKioKp06dwqxZs2R6Q+Hg4IA//vgDixYtwunTp1Uam6IyMjLg7OyMYcOGwc/PD126dMHJkycRHx+PNv+8QWel4OlTIDMTaN0a9+8D9+9Li58UlZqdil/v/Yo5n83hKhGW65dffkHr1q3Ru3dvdYfCWJnk5QW0aCZCzTt/AV98IVOfO3fuICEhAZ9//nmR7fY82gOBQIBRzqOUEKlyvX79Gh07dkTHjh3x/Plz9OjRQ90haYxWrVrh999/R61atXDnzh00adJEaWN369YN1apVwzfffJNbdXzkyBFs3LgRa9euxcGDB4u8QcfKPn4FpwFycoDbt/95Yb59OzBoEFC1qtKvE54cjhMBJ+AxzEPpY7NybvBgYPp04PPPpf+q2L59+2BtbQ1XV1eZ+wwcOBA//fQTvv76a9y7d0+pfyyVYcOGDUhPT0dkZCRMTEzUHU7FdfcuULcuULUqTm2SVmQ3aqT4cL8/+B06WjoY5aR5L66Zejx9+hSHDx+Gl5cXv4hmTEFPngBOJkGAick/dw2L5+HhgbZt26JqEa+hc8Q5WHdnHaa1ngYDHcVKBOPj45GdnQ1DQ0MYGBhAS0tLoXE+9eDBA9SuXRsNGjTA48eP0bRp0xKPWR6NHj0ao1Vw41hPTw9HjhxB8+bN0bVrV1y6dAnffPMN3N3d+TVbBcGVIhrg4UMgLQ3oYBsKnD8PTJqkkuvsuL8DdczrwLWu7G80GQMg3XBh6FBg3z6VLOv6lEQiwc6dOzFu3Di5y84XLlyIbt264fPPP8egQYPQs2dPdOjQAc7OzujSpQuePXumoqiLFhkZiRUrVuB///sf/3FVt3v3ABcXANLy7E6dFB8qS5SFdXfWYVabWaikw+XNpYWIkJ2dXejz2dnZ2LdvH1q0aIEWLVpg6tSp+PPPPxEcHAwiUnl8P/30E9zc3NBRxjdyjLH8nj4FHOO9gH79AB0dmfqcPXsWffr0KbLNsRfHEJMeo/Ay8sDAQFSpUgXVq1eHiYkJtLW1sXjxYgBAWFhYkXNTQXJycrBo0SK4uLjgzz//BABOiKhJ/fr1cejQIdSvXx9paWnQ1tbm12wVCFeKaABvb6BJE6Dqid+k/9O+vdKv8TL2JdbdWYfNn2+GUMC5MCYnS0vg8OFSudT169fx7t07jBol/513oVCIP/74AwsXLsy9i2NkZAQjIyN4eXmhVatWWL16NaZOnVqqd3AXLlwIW1tbldzdYHK6exeYNg3Z2dL8yKxZig914MkBpGWnYVIr1SSyGSAWi7Fr1y48ffoUwcHBCA4Oxrt37yAWi9GuXTv06NEDPXr0QLNmzZCSkoLff/8dmzZtQnJyMsaNGwdzc3PcuXMHhw4dQkJCAmrUqIETJ07A5Z/EmLLduXMHZ8+exYMHD1QyPmMVQVoa8PYtoZnOMeB/U2XqExgYiICAgCL3EyEirL69GuOaj0Nlg8oKxVa3bl3cvXsXxsbGSEtLQ1paGmxsbAAA48ePR0BAAH7++Wd888030CkmmXPz5k1Mnz4dISEh+PPPPzF48GCFYmLK069fP/Tr10/dYTA1EFBp3DYpRU2aNIG/v7+6w5BL375AzZrA9htNgG+/BebOVer42eJstN3dFtWMqsFjmAeX8zKNNmTIEGRlZeHUqVNKHZeIsHPnTsyYMQOdOnXCvn37YGVlpdRrFOT58+do1qwZzp8/X2HWBmvsPBwbK12a6OuLu+JWaNtW+lBlBV4biyQiNNrSCEPsh2BZN83e4Lcs27VrF6ZNm4YePXrAzs4u9z+hUIgrV67g4sWL8Pf3h6WlJTIzM2FoaIgZM2Zg/PjxMDMzyx1HIpHg9evXWLhwIR4+fIhHjx7BSMmbRRMRunbtiqpVq+LYsWNKHZsxZdHY+fkTd+8CbdsCiZWqwzQuCJBho9FNmzZhw4YNCA4OLvR17oW3F9D3cF8ETgtELdNacsd15coVODg4oFq1agU+n5CQgA0bNmDjxo2oWrUq1q1bhwEDBuRrR0QQCASYOXMmYmJisHbt2kLHZIyVDk6KqJlYLH1Bvn1xNIbNsJLWCyq5bG7elXnY/Wg3nk16Bisj1b8JZExRMTExsLa2xqlTp0p0xFpRXr58ia+++gqhoaHYt2+fyq7zUc+ePSEQCODp6anS62gSjZ2Hz5+XbtiXnIy1v+pi717gxQvFhjry/AhGnx6NdzPewdLQUrlxMgDSIykbNGiA8ePH45dffim0XWhoKC5dugQ9PT18+eWX0NPTK7RtcnIyHB0d0bNnT+zYsUOp8R48eBCjR4/G06dP0agkG9UwpkIaOz9/4rffgJU/xCG4zXDg4kWZ+ri6uqJRo0bYvHlzoW267O8CGxMbHBh4QO6YoqOj0aBBAyxevBjTi9lbLS4uDuvXr0edOnUwZswY7Nu3D/7+/nBzc4OnpyeCgoJw8uRJSCQSPp2KMQ3By2fU7PlzICkJ6JDqKS0XcXBQ6vg33t3A6turcWrIKY1NiERHR8PSUvqmYtasWcjKyoJQKESPHj2KXRvKypf9+/ejWrVqKq2oaNSoEe7cuYOff/4Zffr0wcyZM7FixQro6urmaxsSEoLt27ejR48e6Ny5s9zXunjxIi5fvoynT58qIXJWYnfvSk9O0tWFj4/iKxWJCCt9VmJs87GcEFGhzZs3IycnB7OKWeNkY2ODMWPGyDSmiYkJ9u/fj65du6Jfv35KS4qGhYXhu+++w+LFizkhwlgJPXkCNNN6Li0XkUFycjJu3LiBOXPmFNrGN9wXXu+88HSiYn+Pf/jhB9SqVQtTpkwptm3lypWxbNm/FYS6urq4ffs21q1bBzs7O6xatQpExAkRxjQI/zaq2Y0bQJ06kB451qsXoMSlLYmZifjm5DcY13wc+jYs+sx2dQkLC0PTpk1x9OhRANIdvaOjoxEcHIz+/fsr/U4e01xEhN9//x1jx45Vyk7uRdHV1cWqVavg6emJP/74A+3bt0dQUFDu82lpaVi4cCEaNWqEY8eOoWvXrvj666/x4cMHma8hEonw/fffY9y4cbC3t1fFp8Hk9c8mq0QoUVLkRcwLPIl6gmku05QbH8sVHx+PlStX4ueff1b6MpdOnTph1qxZGD16NGJjY0s8HhFhzJgxaNy4cZFvyhhjsnn6WALHJB/gs89kan/x4kXo6emhUxE7Z6+6tQq96vdCUyv5q7F9fHxw4MABbN26Fdra8t9PHj58OHx8fJCQkICAgAC4u7vzUnbGNIxGJUU2bdoEBwcH2NvbY+PGjXmeW7duHQQCgVJewGgSb2+gUzsRcO2aNCmiRJPPTUYlnUpY57ZOqeMqS2ZmJgYOHIj69etj4MCBAKRHsf7111/w8PDA+fPncytF5N3NmwGnTp1CzZo1sW3bNohEInWHU6wbN24gMDCwVDcj7dGjB548eQJjY2M4Ozvj6NGjOHToEBo2bIidO3dix44dePPmDW7duoUXL16gYcOG2Lhxo0xfz99++w3v37/P3ZW+rCi387BEIk2KtGmDV6+AuDjFkyKXAy+jnkU91LOoV+KwRCIRli5dilmzZmH16tU4cOAALl26BH9//1I5KaU4RITg4GCcPn0aS5YswfDhw3Hu3DmVX3flypWwsLDA+PHjVTL+0qVLYWlpiQkTJpT467xjxw7cvHkT+/fvV+gNE2OyKrfz8yckEuDpEwmaSR7lnhRWHA8PD7i5uRW6dO5N3BucDDiJH9v9qFBM9+/fx6hRo9ChQweF+n9kYmJS7OarjDE1IQ3x7Nkzsre3p7S0NMrJyaFu3brRmzdviIgoJCSE3NzcqFatWhQTE1PkOI0bNy6NcJVCIiGqWpVo76ynRDo6RCkpShhTQk8in9DU81NJe4k23Q+/r4RIlU8ikdCIESPI2tqaPnz4UGTbO3fukJ2dHd27d6+Uoiv7MjIyyNbWljp27EjGxsbk4OBAly9fVndYRRo2bBj16dNHLdcWiUS0ZMkSEgqFpKenRwsWLKCU//w+ikQi2rJlC5mampKjoyMFBgYWOt7Zs2dJW1ubtm3bpurQlapcz8MvXxIBRMHBtHMnUY0a0jlYET0P9qTJHpNLHJJIJKKvv/6aLCwsqHfv3tSiRQuytrYmbW1tAkB16tShn376ifz9/Ut8LUUsW7aMTExMCAAZGBiQi4sLDRgwgIRCIW3atEll1w0JCSE9PT36888/VXYNIqLHjx+Tjo4O7d+/X+Ex3rx5QwYGBvTrr78qMTLG8ivX8/MnAgOlU/Wb+p/L1F4kElGVKlVoz549hbaZen4qtfy9JUkUnfSJStSXMab5NCYpcuzYMRo9enTux0uWLKFVq1YREZG7uzs9fvyYateuXeYn+08FBEgn/sBvFxF1716isd7EvaElXkuo8ZbGhEWgFr+1oGPPjykpUuWLjo6mRo0aka+vb7Ft09PTaeTIkaSrq0srVqygrKysUoiwbFu5ciVVqVKFEhMTKTIyksaOHUsCgYD69+9f5Jt5dYmNjSVdXV06c+aMWuMICAigkJCQIttERkaSq6srWVpa0v37+ZOO165dIz09PVq4cKGKolSdcj0PX75MJBQSicX07bdEgwcrNkxmTiZVWlqJTgacLFE4YrGYRo4cSWZmZvTgwYN8zwUGBtLKlSvJ0dGRAJCTkxOtXLmSXr9+XaLryurBgwckFAppw4YN9OrVKxKJRLnP7du3j3R0dGj69Ol5HleW0aNHk7OzM4nFYqWP/V+rVq0iY2Njevv2rdx9RSIRtWvXjrp27VoqsbKKrVzPz584cYLIUCudxGPHy9T+1q1bJBAIKDIyssDnkzKTyGi5Ef3x5A+5Y8nIyKCRI0dSaGio3H0ZY2WLxiRF/P39qX79+hQbG0tpaWnUpk0b+u677+jUqVM0bdo0IqJCJ/stW7ZQ48aNqXHjxlStWrXSDl1hO3YQWVtLSGJXh2j9eoXGSMlKoXFnxhEWgRpvaUxLvJbQ69jSedGsqJycHCIiuV5MSyQS2rt3L1WuXJk6duzIGfsiREZGkrGxcb4qhYcPH1K7du2oVq1a+aog1G3Hjh1kZWWV+7Oh6bKysuibb74hQ0ND8vT0zH387t27ZGhoSDNnziyTP6Pleh4+dIjIyoqIiOrUIVL0xv7VoKuktViLEjMSFQ5FLBbTuHHjyMTERKbE8LNnz2j+/PlUt25dAkAODg70008/0YMHD1TycyYSiahVq1bk7u5eaJtr166Rqakp9e/fn1JTU5V27RcvXpBQKKSLFy8qbcyiiEQi6t69Ozk7O1NGRoZcfVesWEEmJib0/v17FUXH2L/K9fz8iUULJdRGx49o716Z2s+dO5dcXFwKfX7T3U1ktcaKMnMy5Y7l2LFjZGBgQMnJyXL3ZYyVLRqTFCEi2rVrFzVv3pw6dOhAEydOpHHjxlHr1q0pMVH64rM8ZMA/NXw40fA+SdJykZcv5e5/K+QW1dlUh2w32tL14Otl4k1YcHAw1a1blx4/fqxQ/9jYWPLx8SEiIj8/P4qIiFBmeOXC+PHjyd7evsAEQ3JyMtnY2NCMGTPUEFnhunfvTpMmTVJ3GHKRSCQ0b9480tLSoj179tCTJ0/I3Nycxo4dWyZ+FwtTbufh9euJHB0pPFw65T56pNgwcy/Ppc92f6ZwGBKJhKZMmUJGRkZ0+/Ztufs+e/aMlixZQs7OzgSAbGxsaNy4cfT3339TUlKSwnF9auvWrWRkZFTs3VF/f3+ytbWlFi1a5Kt2UYREIqG+fftS165dS/V3KCoqiqpXr04TJ06UqX1aWhpNnDiRhEIhHTx4UMXRMfavcjs/f2KgWypNwHaZXhfHxsZSzZo1afny5QU+L5aIqd6v9Wjh9YUKxdK7d2/65ptvFOrLGCtbNCop8ql58+bRxo0bqWrVqlS7dm2qXbs2aWlpkY2NTZF7UGj6ZP+RREJkbU2048srRHZ2ci1uzxJl0YKrC0i4WEijT42mpEzlvBBWtdTUVGrWrBl17tyZsrOzSzzeF198QSYmJmpbb6+Jnj59SkKhkC5cuFBoGw8PDxIKhTLdoS4NsbGxpKWlRVeuXFF3KArZtm0bCYVCMjY2piFDhqhkOYG6lKt5+McfiVxd6dgxImNjIkW/Tc1/a06Lri9SOIz58+eTgYEBeXt7KzzGR8HBwbR161bq27cvGRgYkLa2NnXs2JE8PDwUHjMiIoJMTExow4YNMrWPjIykXr16EQDq3bs33b17V+Frr1y5kvT09OiRohmrErhx4wZpaWnRoUOHimz38OFDatSoEdWsWZO8vLxKKTrG8itX8/Mn6lgm0zaD74t9XSwSiahnz57k4OBQaLXa+dfnSXuJNkUky38DLTIysky/NmGMyUejkiJRUVFERPT+/Xtq2LAhJSQk5Hm+PGTAPwoKkt6t9P9sDNGUKTL3S8tOo1a/t6Iqq6vQCf8TKoxQuSQSCQ0ePFim76GsRCIRubq6lvpdRU0lkUioe/fu9PnnxW9ONnToUHJ0dFRKcqqkdu/eTZUrVy4zS2cKcubMGZo6dWq52O+m3M7Do0YRffUVTZtG1KOHYkNEp0aTYJGAboXcUqi/t7c3CQQCOnv2rGIBFCEzM5OuXLlCEyZMIKFQSHPnzlXod2ro0KHk7Owsd19fX1/q168fASBXV1e6dUu+r9GxY8dIKBTSsWPq2wtr5cqVZGhoSAEBAfmeE4vFtHbtWtLR0aEvv/yS4uPj1RAhq+jK7fz8j6R/iqdvtf2+2LYLFy4kExOTIvdZ6nmwJw3/e7hCsXh7e5OLiwvvF8RYBaFRSZH27dtT48aNydHRscDMbFmf7D+1fz9RlcoSkujoEp07J3O/Lfe2UOVVlelDStEntmgaPz8/MjU1VXjZTGFevXpFOjo6dOTIEaWOWxZ5eHiQlpYWvXjxoti2kZGRZG5uTitWrCiFyIr2+eef09ixY9UdBvtHuZ2He/UimjWLmjcn+t//FBvi8LPDZLLChHLE8icbUlNTqW7dujRmzBjFLi6Hc+fOkbm5OXXp0qXQzQcLcvHiRRIIBCU66evRo0c0aNAgEggE9OOPP8qUeL1z5w7p6+urfT4Si8XUp0+f3BM+UlJSyMPDg2bMmEGNGjUiQ0ND2rNnDyfhmdqU2/n5Hz4+0qRI8i9rimx37tw5EgqFdOrUqULbvIx5SVgEuhuqePUaY6zi0KikiDJo8mT/qQULiDo3jSHS1ydKS5OpT444h2w32paodFudVHVnbfXq1fTXX3+pZOyyIicnhxo2bEiTJ8t+TOiePXtIT0+v1E6zKEh8fDzp6OgUudyHlT0aOQ+3aEHJSzaQUEh0/bpiQ4w+NZoGHBmgUN+pU6eSjY1N7tp/VQsODqaWLVtS9erV6ebNm8W2T09Pp7p168o1hxTl3LlzZGFhQe3atStyb5KgoCCqWrUqjRkzRiOSDXFxcVSrVi2ytbUlHR0d0tXVpa5du9KKFSsoODhY3eExVmIaOT//Y9uGDKqDt0TXrhXaJigoiMzNzWnu3LlFjvXdue+o9c7WCsXx6tUrOnbsmEbMSYyx0iEEU4uQEKBW1lugSxfAwECmPsdeHEN0WjS+a/2diqNTnoCAALi5uSEhIQHm5uYqucacOXMwaNAgSCQSlYxfFjx8+BCvXr3CTz/9JHOfkSNHol27dpgwYQKISIXRFe7s2bMwMjJC165d1XJ9VoFER+NucmMIhUDr1vJ3JyJcDroM1zqucve9ceMGtmzZgl27dsHU1FT+iyvA1tYWPj4+6N+/Pzp37owzZ84U2X7evHlITU3FsmXLlHL9Xr164fHjxyAiODk5wdPTM1+bxMRE9O7dG46Ojti+fTsEAoFSrl0SFhYWOH36NIYOHYqzZ88iISEBV69exdy5c2Fra6vu8Bgr155ci0MzPAVatSrw+YyMDLi7u6N58+b43//+V+g4SZlJ2PdkH6a1nqZQHNu3b8f69es1Yk5ijJUObXUHUFGFhhLaRfoBU3vJ1J6IsPrWaox1HovKBpVVHJ1y3L9/H0OHDkWTJk1U/kYgOjoaHTt2xIkTJ9CkSROVXksT3bx5E40bN0b16tVl7iMQCPDbb7+hadOmOHDgAEaMGKHCCAt2/Phx9O/fHzo6OqV+bVaBEAFRUfCJqIMWLWTOQ+fxKu4VQpND4VbXTa5+qampGDVqFMaNGwc3N/n6lpSenh62b98OKysrfPXVV7h79y7s7e3ztTt48CC2bNmCS5cuwczMTGnXt7GxgZeXF3766Sf06dMHbm5uyMnJQWJiIhITExEdHY2aNWvi+PHjGjUHODk5wcnJSd1hMFbhPHlM6FktCjAyKvD5OXPmICYmBhcvXoS2duFvYfY93gcjXSN8af+l3DHk5OTg0KFDWLJkidx9GWNlF1eKqEloYDZskp8DvWRLilwMvIgXMS8wq+0sFUdWchKJBN9//z1cXFzQvHlzHDx4EEKhan/UqlatCjs7O3z33Xdqq3pQp5s3b6J9+/Zy96tXrx6+//57LF68GDk5OSqIrHDJycm4ePEi3N3dS/W6rAJKSgKys3HzdTUo8GsCALgceBm2Zraoa15Xrn5z586FRCLB2rVrFbuwEvzyyy/o3r07+vfvj/j4+DzPPX78GOPHj8eqVatUUrGlo6ODVatW4fz586hbty5cXFwwZMgQ/Pjjj9i1axe8vb2VmohhjJVNEgnwLKIymjkX/Hrxzp072LZtG/bt24eqVasWOg4RYYvfFkxsMRG6Wrpyx3HhwgUkJydjyJAhcvdljJVdXCmiBhIJEPZBCzbVxUCdOjL1WemzEsMchqG2WW0VR1cyOTk50NHRgY6ODk6fPo0+ffqUynUFAgF+/fVXODg44OjRoxg6dGipXFcTEBF8fHywYcMGhfrPnDkTmzZtwp9//lmq1SLnzp2Dnp4eXF3lX47AmFyio5EDbdx9bojpsq8wy+NS0CW41XGTq5z6xo0b2LZtGy5fvgxjY2PFLqwEQqEQBw4cQNu2bTF06FCcP38e2traiIuLw8CBA9G/f3/MmqXahHuPHj3Qo0cPlV5DWcRiMaKjo1G9enVkZGQgKSkJ2trasLCwUHmCn7GKKiiQkCauBEdXq3zP5eTkYPz48fj666/RrVu3Isfxj/HH2/i3+LbZtwrF4enpiX79+qlsyTdjTDPxX3c1iIkBskTaqOVgIlP7e2H3cOP9DfzQ7gcVR6a4zMxMjBgxAmPGjAEArFy5stQSIh/Vr18fc+bMwezZs0u96kGdXr58ibi4OHTo0EGh/pUrV8aUKVOwbNkyiMViJUdXuOPHj6Nfv37Q09MrtWuyCioqCi9gj4xMIdq2lb97tjgbXu+84FpXvgTeokWLMHz48GJfxJcGY2NjnD59Gvfv38fcuXORnZ2NYcOGwdjYGLt27eK18/949eoV2rZti0mTJgGQ3jWuXr06qlatii+++KJCViIyVhqeeEbAGMmw7eeY77n169cjIiIC69atK3acG+9voLZpbdiZ2ykUx9atW7Fz506F+jLGyi5OiqhBaKj0XxtH2bLQq26tQu/6veFg6aDCqBQXGxuLbt264erVq5gyZYpaY5k9ezZ2794NLS0ttcZRmm7evAlra2vUrq14FdGsWbMQHh6Oo0ePKjGywqWmpuL8+fMYNGhQqVyPVXBRUQgxbAIDA6CIqutC3Q27i/ScdHS1k315yePHj+Hl5YU5c+bIf0ElkkgkiImJAQA8efIE5ubmWLduHfT19eHl5YXt27fD0NBQrTFqAiLCli1b4OzsDAsLi9w3X927d4e/vz+uXr2KS5cuYdu2bWqOlLHy6cnVGDjqBEBYxzbP40FBQVi8eDHWrl1b5LKZj7zeeaGzbWeFYnj16hVSU1NLbUNsxpjm4KSIGoSGAsaCFJg6FZ/Ffhn7EqdensLc9nNLITL5vXv3Dm3btkV6ejru3bsHFxcXtcZjZmaGHj16VKiTaHx8fNChQ4cS3em1tLTEpEmTsHTp0lKpFvH09ISWllaZKadnZVx0NMING8DaGlDk1+Ry4GW0rNESFpUsZO6zceNGdOnSBc2aNZP/gkqSkZGBwYMHo1+/fiCi3H2XRo4cCSJC+/bt4ezsDIlEguXLlyMoKEhtsarbiRMn8MMPP2DdunXw9PRE3brSvWOMjY3RuHFjdO3aFdu2bYOVVf7SfsZYyT15AjSrGZ9nkiYiTJ48Ga1atcLIkSOLHYOIcOP9DYWSIikpKejRowdWrFghd1/GWNnHSRE1CHmVjlr0HmjYsNi2a26tQVubtmhfS8HdAVWsSpUq6Nu3L7y9vWFtba3ucABIM/01atRAZGSkukMpFTdv3lR46cynZs+ejeDgYPz9999KiKpox48fR58+fVCpUiWVX4sxREUhXK8OFJ2irgRfkeso3sjISBw+fBgzZ85U7IJKEBsbi+7du+PevXvYuXMnBAIBnJ2dMXPmTOzduxfR0dG4du0aDAwM8OHDBxw+fBgNGzbErFmzkJiYqLa4S0NUVBQ8PT2xbNkyDBo0CKmpqRg4cCD8/f0xadKkQhPMI0eOxKBBg5CcnIysrKxSjpqx8u1JRNV8m6weOXIE165dw2+//SbTjZ+XsS8RnRaNTrU7yX39efPmAQDmz58vd1/GWNnHSRE1CH2WBBuEFpsUkZAExwOOY3LLyaUUmexOnz6N/fv3w8jICOvXr1frJoL/Va9ePVSqVAkHDx5UdygqFxYWhnfv3iklKVKtWjWMHz8eS5cuVUqlTVJSEjw9PfHTTz9h5MiR6N+/Pzp27AhHR0ecOHGCT51hpScqCuFCG4WSIhKS4EnkE7Sq0UrmPtu2bUPt2rXRu3dv+S+oBHFxcWjXrh3S0tJw9+5dODjkX3r5aRm6tbU1nj59ir/++gunTp1C/fr18ebNm9IMWenEYjE2btyI4cOHo1evXti6dSsAYPPmzahWrRoGDBiAU6dOwcLCAmlpaRAKhbC1tS12XCKCm5sbfvnlFxV/BoxVHEkhSXifYw1Ht2q5jyUkJGDGjBmYN28eGjVqJNM4N97fQC3TWrA1s5Xr+h83xd61axeMCjkOmDFWvnFSRA1C32bBxiAOMCl6o9XXca+RnJWscVUiL168wPDhw/Hu3Tt1h1IgLS0tjBw5Env27Cn3m+L5+PjAzMwM9vb2Shnvhx9+wKtXr3D69GmF+j9//hzTpk3LXZc/YMAAeHl55Zbud+3aFWPGjMGBAwcwcOBApcTMWLGioxEuqYaaNeXvGpIUggxRBppUbSJT+4yMDGzfvh3Tp09X20klFhYWmDx5slwVfAKBAAMGDIC/vz9WrFiBunXrIjMzE/fu3VNxtMpHRHB3d8fChQthYGAAe3v73IRH79698fjxY6SmpsLPzw+///67XEtiBAIBfvrpJ6xduxZeXl4lipExJvX26nsAQKPe/x55fuTIEWhra+dWcMjC650XOtXuJPdy4vPnz2Ps2LHo3r27XP0YY+UHH8mrBqHhAjS1yi62nW+4LywNLVHLtFYpRCWblJQUuLu7w9XVVaPvlI0cORJLlizBvXv30KZNG3WHozI3b95Eu3btlPbmy9raGmPGjMH//vc/DBgwQK4XFikpKejduzeqVauGQYMGYdOmTWjdujX09fWVEhtjCouKQnhmFfRVoFIkICYAulq6Mp9kcOjQIYhEolI93vqj+Ph4/Prrr5g7dy6mT5+u0Bj6+voYO3YsAODixYv44osvMH/+fPzyyy/Q0dFRZrgqIZFIIBQKMWzYMKxevRoNGjTI83ydOnVKfI0+ffpgwoQJ+Oabb/DkyRNYWMi+10xUVBSGDh2KO3fuoHr16qhRowamTZuGIUOGICMjg5cUsgopKiAeesiEqfW/VRr+/v5o2bKlzK8hiAhe77ywvNtyua+/atUqiEQiufsxxsoPrhRRg5A4I9jYFn86im+4L1rVaKVRRyVOnjwZIpEI+/btU2lcr1+/xvv37xXub2dnhzVr1sj1YvW/AgICMH/+fKSlpSk8hqopaz+RT82dOxfPnz+X+ySa+fPnQyAQ4OrVq1iwYAE6duxYooRIdnY2YmNjFe7PWK6oKISnmii0fMY/xh8NKzeEtrD4ewhEhI0bN2LcuHFqKcGeM2cOjhw5orTx+vfvj+PHj2P79u1o3769xi+p2b9/Pzp06ICsrCwMGTIkX0JEmdauXQszMzOZv96hoaHIzs5G5cqV0axZMxw7dgw///wzPv/8c9SoUQMAMGzYMPTq1QsBAQEqi5sxTRQVlAYr3YQ8G2G/fPlS5mUzgLS6OiotSq79RG7fvo0xY8ZALBZDW5vvEzNWkXFSpJSJRMCHTDPYNC7+BbNvuC9aW7cuhahk9/333+PEiRMwMzNTyfhEhK1bt8LR0RHNmzfHzZs3FR5r9uzZCr0ozszMxMKFC9GsWTOsWLFCY98IJCQk4Pnz52jfXrnLq2rVqoX58+fju+++k3mzWh8fH2zduhW///67Ut4MxsfHo0uXLqhVqxbWrFmDnJycEo/JKq7UqDQkZegplBQJiA1A46qNZWp7+fJlvHz5ElOnTpX/QiV0/fp17N27F7///rtSq7MGDhyIp0+fwszMDHPnauYpaBkZGZg4cSLGjh2LwYMHQ1dXV+XXNDAwwK1btzBp0iSIRCIEBwcX2C4xMRELFixAgwYNcPToUWhra2Pjxo3o168fRo8ejZ9++ik3sb106VKIRCI0bdoUU6dORVxcnMo/D8Y0QVRoNqwM896ACggIkCsp4vXOCzVNaqKOuWzVYJmZmRg9ejREIhG0tIq/UckYK984KVLKIkJEkEALtVpaFtkuS5SFx5GPNSYp4uvri9evX8PJyQmOjo4quUZ8fDy++OILzJ49Gxs3bsTQoUPRvXt3uSsWPvXdd9/J1V8ikaBdu3bYsWMHDhw4AIlEAicnp9yyysePH2Py5MlISEhQOCZluX37NnR1ddGyZUulj71gwQLUqlUL48ePL3bte2ZmJsaOHYsRI0bAzc1NKde/fv06UlNTsWLFCixfvhwTJkxQyrisAsrIQHiqdP8mRStFmlSRbT+RDRs2YNCgQbCxsZH/QiWQmZmJCRMmYOzYsejYsaPSx69RowY8PT2xb98+AICHhweys4tfAloa4uLi0LZtW3h4eOD69euYPn16qVVXmpiYQCAQYOfOnWjRogVu3bqV+5xIJMK8efNQq1YtHDx4ELt27cJXX31V5HgODg64ePEiTp8+jUuXLuG3334DAHh7e+PmzZsaXbWoiWJjY7Ft27ZSOVGNlUxUFGBp/u+ckpKSgvDwcDRuLFtCGpBusirrfiJEhJkzZyIpKQkbNmxQKGbGWDlD5Uzjxo3VHUKRbh4OJYAo4+W7ItvdC7tHWASKTYstpcgKFxUVRTVq1KBp06ap7Bo3b94kGxsbatKkCT179oyIiCQSCa1evZqEQiGtXLmSJBJJkWNIJBI6ePAgjRo1inJycoiIaOLEidS5c+diry8WiykuLo6IiC5fvkzx8fG5z4WHh1OdOnXo4cOHNHDgQBIIBGRnZ0cPHz5U9NNVih9//JE6duyosvGfPXtGurq6tHfv3iLbzZ8/n6ysrHK/fiUREBBAycnJRESUlZVFRESRkZH05s0bIiK6dOlS7vNMc2nUPBwcTFfRhYRCCf0zLchMIpGQ2UozOvr8aLFtX758SQDo7t27CgaquNjYWBo9enSeeUtVEhISqFq1atS1a1dKSEhQ+fWKIhaLSSKR0NKlSykmJkZtceTk5NDEiRNJT0+Pdu7cmft1+fbbb2n37t25c5k8srKyKCMjg4iIRowYQQKBgIRCITk4ONDkyZOJSPr5Z2dnK+3zKC9u3LhBffv2JW1tbbKysiJnZ2fKzMxUd1gaQ6Pm538MNzlDo9u8yP3Y19eXAMg8x0gkEqq+tjrtfLBTpvb79+8nXV1dunnzpiLhMsbKIU6KlLI/Zz+gqogiEouLbLf53maqu6luvscfPHhANWrUoPnz51NaWpqqwswlkUiof//+5OTklPsCTdmOHz9OWlpaNG7cuAI/p6NHj5Kenh5NnDix0BeXISEh1KtXL9LR0SEDAwPaunUrERHdu3ePANDbt2+LjOHnn3+mpk2bkriA74tEIqGhQ4dS7dq1SSAQ0KlTp2jo0KGkr69Pe/bsUeAzVo527drR/PnzVXqN1atXk4mJCb1//77A5x89ekRaWlp0/PjxEl/r5s2bZGZmRsuWLSvw+ezsbGrYsCENHjy4xNdiqqVR8/C9e3QAX1P1akXPuQWJSI4gLAI9i3pWbNuVK1dSo0aNFImwRBR5w11SQUFB1KhRI7K3ty90blBUfHw8vXjxgu7du0dXr16lU6dOUVJSEhERXblyhU6fPk3nzp2jcePG0ffff6/Ua5eERCKhZcuWEQDatGmT0sdPTk4mLy8vWrNmDf30009ERPTw4UPS09MjNzc38vf3V/o1P/p4Q+L9+/d0//598vLyovDwcJVdTxEhISF07do1IiI6cuQIffXVV3Tx4kUSiUS5N0kuXbpEiYmJ6gxTI2jU/ExEJJFQN+FVmjfode5DBw4coGrVqsk8xOvY14RFoDdxb2Rqn5ycTJcvX5Y7VMZY+cVJkVK2qpcXtaj0vNh23578loYdH5bnscjISKpZsyZ169aNatSoQbVr16YzZ86oKlQiIvrjjz9IR0eHnj59qpLxs7KyyNbWlmbPnl1ku5s3b5KFhQWZmprS8OHD6dixY5ScnExisZi2bdtGxsbG1KZNG3r+/DmtXbuWLCwsKC4ujiQSCdnb29OcOXMKHfvUqVMkFArp9OnThbZJTk6mypUrk5GRUe7dyU2bNpG2tjaNGzdOZQmjwmRkZJCuri55enqq9DoikYjatWtH3bp1y5cwysnJIWdnZ3J3dy/xdc6cOUP6+vo0efJkEolEhbbz9vYmgUCg0jcArOQ0ah4+c4ZW6PxMLVvK3/Vq0FUSLhZSZk7xd5k7d+5MM2fOVCBAxYnFYmrXrh399ttvpXpdImnyolOnTuTk5FRsFZ88Vq1aRQAIAAkEAjI2Ns79+/PZZ5+RsbExVapUierUqaORd3nfvXun1K9HUZKTk8nDw4M+//xz0tXVpWXLlim1ckQikdDMmTPp0qVLRETUvXv33O+Nrq5usX+3S8OjR4/oq6++Im1tberZs2eh7XJycsjR0ZEaN25c7E2S8k6j5mcioqgocsBT2vjDv4m2efPmyVTl+9Hv93+nGutqFPu79/TpU/rjjz8UDpUxVn5xUqSUTWlylQZY3yu2XaMtjWjDnQ25H2dlZVG7du2oRYsWlJ6eTsnJyTRr1izS0tKivn37UnBwsEri3bFjB61du1YlYxMRbdu2jUxNTWUq+05ISKBDhw7Rl19+SUZGRqSrq0v169cnAwMD2rRpU+6b6aysLKpfvz5NnTqViKR3HFasWFHgmC9fviRjY2P65Zdfirx2dnY2Va5cmXR1dcnLyyv3cR8fH6pRowbVr1+fDh06VOQbemW6ceMGCYXC3DuoqvTmzRsyMDCg9evX0+3bt2nNmjU0YMAAsrS0JHNzc/rw4UOJxr969SppaWnRokWLZHoz0bFjR/rmm29KdE2mWho1D+/cSd8Z76MBA+TvuvneZqr/a/1i2yUlJZG2tnbum8fS8uuvv5KhoaHSqzVklZmZSQEBAUREdOfOHYWX05w7d46+/PJLEolElJWVRZGRkZSWllZqyYWyTiKR0IEDB6hly5aUmpqqtHGXL19Oenp6uUvC4uLiKCkpiXJycujvv//OrYh5/fo1hYaGKu26slqwYAEBoO7du9OFCxeK/XlJSEggV1dXqly5Mvn5+ZVSlJpHo+ZnIqJ796gqoujw7n8rhQcOHEiTJk2SeYjhfw+n4X8PL7JNZGQk1a5dmwYNGsRzC2MsH06KlLJ+Ft40tfWdItskZiQSFoFuhdwiIukLnnHjxpGVlVW+Fx5Pnjyhzz77jKpUqaLUMmqJRJJbcqoq6enpVKNGDVq6dKncfTMyMujcuXP0yy+/UFBQUL7nPTw8SEtLi54//7cqJzs7mxYtWpRnT4rVq1dTr169Clw286ljx45RpUqVCqxQiImJodmzZ1OlSpWoSZMmdOzYsWLHK6lly5aRk5OTSq/xqa1bt+beIWzSpAmNGzeO9u7dS2FhYSUeOyMjQ67lNxcvXqQGDRqUenUOk51GzcPLltFA8+s0ZYr8XSd7TKb+h/sX2+7kyZNUqVKlUv2ZvHfvHunq6qqlSuS/JBIJtWnThkxMTOjnn3+WeX+hN2/e0MCBA0koFNLUqVMpPT1dxZGWbx/f6P3555+0ePHiEv0d2rNnDwmFQpnm5ilTppCuri7169ePtm/fTu/eFb1nWknExsbSuXPniIjo8ePHcu/tlZOTQ4MHD6amTZtW2DfGGjU/E1HO4b9IADH9s/qJiIgaNWok8zI0iURC1uus6bf7hc+FmZmZ1KZNG2rZsmWpLD1njJU9nBQpZc7aT2jN8KL/iF8JvEJai7UoPVv6AnHLli2kq6tLt27dKrB9QkICaWlp0ZUrV5QW5969e+mzzz5T6Zv7tWvXUtWqVSklJUXpY0skEurZsyd1794994VPSEgI1a1bl+zt7enNmze5j8tSbtylSxcaM2YMERFFR0fTjz/+mK/fhw8faPr06aSnp0fNmjWj6dOn05w5c2jBggW0ePFiWrNmjdLu6Pbs2TO3EkYWoaGhNGTIEHr+/DlFRETQoUOH5LqeRCKhO3fuKGUz1Y/jLV68mDZs2KBQX1Un7GT1+vVrWr58OUkkEoqNjaVt27apPCFWFmjUPDxtGrUye0XLl8vftcu+LjT38txi202YMIF69eqlQHCKkUgk1LZtWxoxYoTGvLHLysqi33//nWxtbcnIyCj3zXRCQkLuXCmRSHIry7y9vQkAtWnTRu2bVpc3p06dImNjY+rfv79CG1OHh4eTnp4ebdu2Tab2YrGYzp49S2PGjCFra2uytbUliURCqamp5Ofnp5SfUZFIRDt27CALCwtycnIqUVVmbGwsPXr0qMQxlVUaNT8T0YeftxJA9OKffVazs7Plqrx7E/eGsAj0KvZVoW12795NZmZmFBERoYyQGWPlECdFSlNsLFVBNB1Zkb+y4VPLvZeT0w5pFcC1a9dIS0uLdu/eXWSfTp06KW09e2hoKJmYmNDq1auVMl5BkpOTqUqVKrR+/XqVXSMgIIC0tbXp1KlTuY/FxcWRm5sb6erq0rfffivTOP7+/gSAHjx4QEREERERZG5uXmiFS2hoKM2cOZP69+9Pn3/+OXXr1o06dOhA9erVIx0dHZo8eXKJSo1TU1PJ0NCQ/v7772LbZmdn09q1a8nQ0JBatWpFz549o6NHj5JQKKQdO3YoHENJxMbG0pQpU0hPT6/IfVyKIhKJaOfOnWop2ZZIJPTXX39R165dCQA5OTlRVFQUXb58mQwMDMjV1VXjNiEsbRo1Dw8ZQjUM4mn/fvm7Wq2xogOPDxTZRiKRUO3atenXX39VMEDFREVFaeQdz+zsbNqzZ0/uG5oxY8aQjo4ONW3alBo0aEAWFhaUk5NDWVlZ9PDhQ41J6pQ3/v7+VK9ePbK3t1doD41Xrwp/g1mUTxNfHh4eBIAcHBxo3bp1FBkZqdCYb968oZYtW5KRkRGtXr1aaVWxb9++LbDStLzTqPmZiB4PWU4AUew/hy1+PMkrJCREpv67HuyiamurFTmX3Lt3j/bt26eMcBlj5RQnRUpR2tU7BBDdulz0C9kBRwbQ+DPjKTw8nKpUqSJTRcCqVauoYcOGJY5RIpFQjx49qE2bNirdH2PJkiVkbW2t8nLzmTNnUp06dfIcx5eTk0NLly4lDw8PmcaYOnUqtW7dOs9jBw8eJB0dndzjg2UhkUjo5MmT5OjoSHp6ejR16lSF7locPXqUjIyMin1DlJSURE5OTmRmZkbbt2/P8/3cvXu3WhIj+/btI11dXapevXruSQGKEIvF5OjoqNJjogtz9uxZ0tfXp9GjR9O9e/fyvBB7/fo1tW7dmiwsLJRyIk9ZpUnzcE6nbiQUiEneQrq49DjCIpBfeNF7D3xMmr5+/brIdsqyevXqUt+7pCQ+fPhAFy5coLVr19KaNWsUfrPN5BcfH0/9+/eX6ZhoiURCx44dox49eij1qOU3b97QTz/9RDY2NqStra1Qgub169fk7u6ulOWan+rduzd169atwiXmNGl+JiK62Pon0hKIcg9lPHnyJBkaGsr8ffn6xNc09PhQFUbIGKsIOClSil4tP04AUXE3t2usq0G/+/1OnTt3JhcXF5mWdzx//lymo2eL89dff5GBgUHu5nmqEBcXRyYmJqXyhjwhIYGqVKlCK1euVKh/amoqmZiY5LvDIJFIqF+/ftSiRQu5l3KIxWI6fvw42dvbk4mJidzVDgMHDqSvvvpKprYPHz6kqKioAp/7mBj5dONYZZNIJLRmzRqaPHkyEUlfIJ89e1Ypy1+OHDlC+vr6Ct99lJdYLCaRSEQSiaTIu4vZ2dm0cOFCmj59eqnEpYk0aR4Oq9+ZACJ5pzSf9z6ERaCUrKKX961fv57q1s1/fLoqeHh4FHtSFmMFCQwMpIkTJ9L169fzLfHz8fGhNm3akK6uLs2ZM0clyxNFIhF5e3uTRCKhmJgY6tGjR5GboyYnJ9OoUaNyKzRV4dWrV6Snp1fhTiPRpPmZiOiA9VyqbvLvPLtixQpq0aKFzP3tNtrRVt+thT6/atUqWq7I+knGWIUiBCs1oU/ioCUQo3r1wtuEJ4cjIiUCj849wqNHj3DkyBHo6OgUO3aTJk1Qq1YtnDt3rkQxfvHFF/Dz80OjRo1KNE5R1qxZgypVqmD06NEqu8ZHZmZmmDVrFg4ePKhQ/z///BPa2toYPHhwnscFAgF27NgBV1dXiEQiucYUCoVwd3fHkydPUK9ePcyfP1/mvklJSTh//jyGDh1aZDsvLy8EBwfD2dkZlpaWBbYZPXo0bty4gQ4dOsgVv6yys7MxcuRILFq0CPXr1wcA1KtXD3369IG2tnaJxx80aBBq1aqF9evXl3is4kgkEkyYMAFTpkyBQCCAnZ1doW11dHSwaNEibNiwAUSE1NRUlcfHChceJf1Zs7aWr19AbABqmdaCka5Rke0uXLiAnj17KhqezF6/fo2vv/4aP/74I/r166fy67HyJSQkBMHBwejWrRtq1aqFOXPmICYmBvfu3UP79u1hZ2eHly9fYvXq1UqZn/9LS0sLHTp0gEAgQFZWFiwtLdG7d2+0aNEC69atQ2xsLADA09MTBw8eRPPmzeHl5QUiUnosHzVo0AALFizAzJkzERcXp7LrsCIQITpGAKvK/76OCggIkPk1aGx6LIITg9HaunWBz4vFYmzevBm6urpKCZcxVo6pOyujbJqWAf/UHqdNZGNU9EaVJ/xPkP4SfRJoCeQuv580aRL16NFDodguX75MM2bMUHkZaVRUFBkYGNCBA0Wv01cmb29vEggEcm84J5FIyMnJib7//vti2ym6AenH2O7dK/6YZiLp8hMzM7Mi11QnJCRQtWrVaOHChTKNKRaL6fvvv5epxFpWycnJ5ObmRpaWlio9+nDfvn15NtNVBZFIRKNGjSITExO6c6fok6P+a+zYsRXy+GCNmYdzcugEBpCxgfx3vmdemEk9/ih6Pk1LSyM9PT06e/asohHKJD09nWrWrEl9+vQptWO/WfkUGRlJv/76K3Xo0IESExNJIpHkOaWtNAUFBdHkyZPJ0dExd/lZp06dyMLCgr766itKTExUeQyZmZnk5OSk8t9hTaIx8zMRUVwczcEq6tE2Kfeh1q1by3wq4YU3F0hniQ5l5mQW+PylS5dIS0ur1CpKGWNlF1eKlKLQUKCWZWaRbbzeeEESJsHE8RPh7u4u1/i9e/eGl5cX0tLS5Or39OlTfPHFF9DW1oZAIJCrr7zOnDkDIyMjDB8+XKXX+VTz5s0hFApx//59ufq9ePECjx8/xoQJE4pst2LFCtSqVQvTp09HYGCgTGNfu3YNEyZMwPjx42FsbIy+ffuCiPD8+XNMnz4d8fHxBfY7cuQI3N3di7zrsWDBAhgaGmLu3LkyxSIQCBAXFwd3d3dERUXJ1EeWMc3NzXHnzh20bNlSKWMW5JtvvsGlS5cgEAggkUiUPr5EIsGYMWNw8uRJXLlyBW3atJGr//Dhw3Hw4EE8ffpU6bExGcTEIBzWsK4mlrtrQGwAmlRtUmSbj3eyu3TpomiExZJIJKhUqRKOHDmCEydOQEtLS2XXYuWflZUVpk6dCm9vb5iamkIgEMDe3l4tsdjZ2WHr1q148uRJbjWhl5cX4uLicPDgQZiamqo8Bj09Pdy/fx99+vRR+bVYAd69QxSsYFVbDwBARHJVityPuI9m1ZpBT1uvwOf37t2LXr16wcrKSmkhM8bKJ06KlJacHITGG8KmVuFfcolEgkNeh2CSaqLQkoAuXbpAIBDgypUr8PPzw6VLl5CVlVVkn7CwMPTq1Qs9e/bEqlWr5L6mvC5fvgxXV9dSfWFvaGgIBwcH3Lt3T65+d+/eRc2aNXNfrBVm9uzZ2LFjB3x8fFC/fn188cUXyMnJQVZWFt69e4cXL17g3r17OHv2LMLCwgBIEynv3r3DhAkTMHPmTMTHx+Po0aPQ09PDhQsX0LhxYxw9ejRP6XBsbCwuX75c5NIZX19fbN++Hdu2bYO+vr5Mn6dAIMD27dtRrVo1DB48GDk5OTL1K8jr169x7NgxGBkZ4ciRI6hTp47CY8lCKBRCIBBg69atcHd3L1HsBUlOTsb79+9x5coVtGrVSu7+Xbp0gZubm1xLpJgSRUUhDDVR00b+ZK9/jD8aV2lcZJsLFy6gY8eOMDQ0VDTCIp06dQpt27ZFeno62rVrJ9NSSsaYfDjRqEbv3iFKyxpWNaU3ej58+ICUlBQ0blz03PuRX4QfWlYv/MaLpaUlJk6cqJRQGWPlGydFSktQEELJGjYNDQpt8uvmXxGnH4cFIxfI/IY27yWCYGlpia+++gqtW7dG3759kZCQAEC6N0ZYWBhiYmJw7do1+Pv7AwCWL18OW1tbHDhwAEKhan8cxGIxrly5Ajc3N5VepyAuLi7w9fWVq4+vry9aty54neqndHV18fXXX+P+/fvw8vJCixYtoKOjAz8/P9jZ2cHBwQFt2rSBu7s7vL29AUjfTF28eBEzZszAokWLMGfOHPzwww+wtrbGkydPMGHCBHzzzTdwd3fPTYz8/fffqFy5Mjp37lxoLM+fP8eIESPk/hrr6+vjxIkT8Pf3x9KlS+Xq+9HTp0/Rvn17HD58WKXrwAvStWtX3Lp1C99++y3EYvmrAgry+vVrmJmZ4dq1a2jRooXC46xYsQLnzp3DnTt3lBIXk0N0NMIFNrCuLV8yITU7FSFJIcVWiqhyP5E9e/Zg0KBB6NevHypVqqSSazDGmFq9f48oHWtYVZMmrl++fAktLS3UrVtXpu73I+6jZY3CkyIbN25Er169lBIqY6x846RIaXn5EiECW9g0KXjTPiLCmr1rAD1gYKuBcg398Q1oaGgoKlWqBB0dHYSEhCAxMRHVqlVDQkICZs+eDRsbG1haWsLV1RVnzpwBAIwZMwZnz55VKAkjr0ePHiE+Ph7du3dX+bX+y8XFRe5KET8/P7mqAwQCATp27IgFCxYAAFq0aIGgoCBER0cjPT0dWVlZucuG/ntnat68ecjJycG6deugr6+PJUuW4NGjR+jduzcEAgGSk5Nx+PBhDB48uMhN8EaPHo09e/bI9Xl+9HGj3ilTpsjd19fXF507d0bXrl1x7NgxlS/D+q/GjRvj8uXLuHDhAsaNG1fipTRr166Fs7MzYmJiSvy5ODs749q1azIl2JiSRUUhXMcW1jXl+x6+in0FAGhctfC7lYGBgXjz5o1KkiJ79+7FuHHjsHXrVixYsKDUf58YY6xUvHuHKLLEx9UtAQEBqFOnDvT0Cl4O86kPKR8QnhKOVtYFv05buHAhAgIClBktY6wc46RIKaGAlwgV2KBW7YK/5Ldv30YEIlBFvwpqmdaSeVyxWIxu3brh4sWL+Pzzz+Hp6YnExEQkJibm3l00NzdHWFgYHj58iAcPHiA1NTV3v4kWLVrA3Ny85J+gDC5fvgwHBwfUqFGjVK73KRcXF0REROQuXylOeno6nj17VqI3spUqVYKdnR2qVq2KSpUqFfnGxtjYGMuXL8fKlSsRHh4OALC3t8eYMWMAAP369cONGzfQo0ePfH1FIhF+++03uLq6lvhNfOvWrWFpaQlPT0/s3btXpqqLzMxMDBgwAAMGDMChQ4fUVuLfrFkzXLx4Ed7e3ggLC0NoaCg8PT1x/vx5nD9/Hj4+PjKdFLRz5078+OOP2Lt3L6pWraqU2Lp06QKxWCzznjNMSaKjES6wlvvkGf8Yf1gZWsGikkWhbS5cuICaNWuiSZOiq0kUcePGDWzZsqXY/YwYY6wsk7wLQXS2GT4ekvfy5UuZl87cj7iPStqVCqzoCwgIwJIlS5Cdna3McBlj5RgnRUpJ0rMQpEoMYWNT8POHDh1CjdY14GLjIteb2n379sHX1xdNmzYFIN24rHHjxvmO5hUKhXB2dkbz5s3VVop96dIluLq6quXajRo1gpGRkczVIo8ePYJEIinRsgl5jRgxAg0bNsSkSZPybZbbtm1b6OjoYNy4cbh06RIAaYXQuXPn4OjoiDlz5qBr166wsCj8TZw8Hj58iO+++w5NmzbF33//XehyGJFIBH19fVy7dg27du1S+9rs1q1b4+XLl6hVqxauXr2KL774Au7u7hg0aBA6duyIu3fvApB+fh/32yEiZGZKN0A+e/YsJk6ciB07duQ7hrmk/ve//6Ffv35KW97DikeRUQgXWSl0HG9RVSLAv0tnlFnFERYWBiLC3r17MWnSJKWNyxhjmig+MAFi0spTKSLPJqvO1Z2hLcxfPbtv3z44OzujWbNmygyXMVaOcVKklIQ8TwaAApMiOTk5OHbsGMzqmBW7hv1TycnJWLBgAebNm5en+qJXr175kiLqlpaWhlu3bqllPxFAulylVatWMu8r4uvri0aNGpXK7vcfCYVC7N69G48fP0azZs3g4+OT+5yXlxemTJmCYcOGoWfPnrh8+TJu3bqF/v37o0uXLnj79i3mzZuntKTEggULEBQUBFdXVwwfPhzjx48HANy5cwfr16/HzJkzMWjQIDg6OiIpKQmNGjVS+Z40svq4vGjkyJHIyMhARkYG0tPTERMTg7Zt20IkEsHNzQ1Vq1aFo6MjjI2NMWPGDABARkYGNm7ciHHjxik9ru+++w7v37/Hn3/+qfSxWcGSw5KRJq6kUKVIUZusZmVl4dq1a/j8889LGOG/Xrx4gaZNm+LEiRO8XIYxViFEhUhvTnxMishTKVLYJqtisRh//PEHRo0apbQ4GWPlX+GbEzDlIUJoUDb0dUSoUiX/l/zSpUtISUlBZYPKqG1aW+Zhly9fDn19fcyaNSvP471798aGDRuQkJBQaktjinPjxo3cPTfUxcXFRebNLuXdT0RZnJ2d8ezZM8yaNQsdO3bEzJkzMWbMGNy9exdbtmxBixYt4O7ujjZt2kAoFOLNmzews7NTSSxWVlbYtGkTZs2aheRkaVLv4sWLOHXqFGrWrAkbGxvMnTsXJiYmKrm+slWuXDn3/4ODg3Hu3DmEh4ejXr16uZVWyq4O+ZSVlRUmTJiAtWvX4uuvv+Y3vqUgPExa4aRIpUj3OoXvfXT9+nXk5OSgW7duJQkvV0hICHr06IFu3bphwIABShmTMcY0WlISolP0IRAQqlSR7p0WHh4uU6UIEeF+xH0McxiW77nQ0FAAwJdffqn0kBlj5RcnRUpDbCxCU81hU0sMgSD/l/zQoUPo1bsXLqZclGs/kWHDhqFHjx75lsO0b98ehoaGuHjxYpHHt5amy5cvo3379jAwKPz0HVVr3bo1Nm/eDLFYXGxFha+vL2bOnFlKkeVlamqK3bt3w93dHePGjcP27dtRr149NG/eHADQrl273LaqSoh8qnbtfxN1ixYtwqJFi1R+TVUzNjZWy+/GtGnTsHHjRty+fTvP95GpRvgHIbSEElhayl7FlCXKwtv4t0VWipw5cwadO3dWSiWZWCxGnz59UL9+fRw8eFDtS9AYY6xUvH+PKFihSmWCtrYAr15JN7iWJSkSkhSCmPSYAjdZtbW1RXh4ON94YIzJRTPq3cu7t28RChvY2OVPiKSmpuL06dPoN6QfMkQZMidFoqOj0axZM3Tp0iXfczo6OnBzc4OHh0eJQ1cWde4n8pGLiwvS0tLw4sWLItvFxcUhMDBQ7aeF9OrVC8+fP8e3336L+fPn8x/4cqB27dp4+vQpPvvsM3WHUiGExemjulkG5MkzvIl/AwlJCl3KSEQ4e/Ys+vXrp5QYIyMjYWxsjCNHjpTKKWCMMaYR3r1DlI5NnuN4q1WrBjMzs2K73o+4DyNdIzSo3CDfcz4+Pvn2ZWOMseJoVFJk06ZNcHBwgL29PTZu3AgAmDNnDho1agRHR0cMHDgQiYmJao1RITExCNGug1p2+V+Znzp1Ctra2mjkIs2M1zYrfvnMjRs3YGtri8jIyELbDB48GCdPnkRSUpLicStJeHg4/P391bafyEc1atRAzZo1i91XxM/PD7q6unB0dCylyApnbm6OHTt28NrYcsTe3h5JSUmIiopSdygFKjfzMBHCk41R0zJHrm4BMQEw1TNFNaNqBT7/+PFjhIWFoW/fvsqIEtbW1rh16xasPi6qZ4yxQpSb+RmQJkVM6sPKSpoUCQgIkOvkmRbVW0AoyPs2JiUlBV26dMndVJ0xxmSlMUmR58+fY+fOnfD19cWTJ0/g4eGBt2/fwtXVFc+fP8fTp0/RoEEDrFixQt2hyi8uDqFadgVusnro0CG4u7sjKjMKxrrGMNUruhxbIpFgxowZGDx4MKpVK/hFOwAMGDAAxsbGOHDgQEmjL7HLly+jSpUqcHJyUncoaN26dbEn0Pj5+cHJyQl6enqlFBWraL788kssXLhQ3WHkU67m4YQEhEuqK7TJapOqTQqtzDp79iwcHR3zLCtTlJ+fH9asWVPo6U6MMfZRuZqfAenymUq18xzHK+vJM34RfmhVI//SGS8vL2hra6N9+/bKjJQxVgFoTFIkICAALi4uMDAwgLa2Njp16oQTJ07Azc0t9zSJNm3aICwsTM2RKiA2FqFUM19SJDo6GpcvX8ZXX32FkKQQ1DKtVewSiWfPnuHx48fF7uugq6uLMWPGYPv27Wp/wX358mV0795dI04ncXFxKTYp4uvrq5ZNVlnFMX78eOzfvx+xsbHqDiWPcjUPR0UhHNawtpVv66w38W8KLMn+6MyZM0pbOrNy5Up4e3vz0jjGWLHK1fwMSCtFtGrIfRzvx01WW9bIf/LMpUuX0KlTJ16KyBiTm/rfpf7DwcEBN2/eRFxcHNLT03H+/PncHaQ/2rNnT4FHIG7duhVNmjRBkyZNkJCQUFohy0wSE4fQHCvU+s92IceOHYOlpSU6d+6cmxQpjo+PD+zs7GBra1ts2/Hjx+Ply5fw9vZWMPKSk0gkuHz5str3E/nIxcUFL168QGpqaoHPExF8fX3Vvp8IK98GDhwIKysr/Pbbb+oOJY9yNQ9HR0uTInUrFd/2E+Ep4ahpUrPg58LD8eDBA6UkRd6+fYuTJ09i9uzZJR6LMVb+lav5GZBWioirwMoKyMnJwdu3b2VaPvM2/i2SspIKTYqoe6k2Y6xs0pikSOPGjfHjjz/Czc0NPXv2hJOTU55d+JctWwZtbW189dVX+fpOmTIF/v7+8Pf315gjaD8VHZaNHNLJVyly6NAhDBs2DFpaWnif9F6mpEifPn2we/duma5bu3Zt9O7dG9u3b1ckbKV4+vQpYmJiNCYp0qJFCwDAgwcPCnw+NDQU0dHRnBRhKqWtrY1p06Zh69atEIlE6g4nV7mah6OiEC6oCeta8p3mEpESAWvjgtfceHh4oFq1arnzSEls2LABLVq0UOsx5YyxsqNczc+AtFIkwwRWVsC7d+8gEonQsGHDYrvdj7gPc31z1DGvk+dxIsK+ffs05tRFxljZojFJEQAYM2YMHjx4AG9vb5ibm6NBA2kJ8759++Dh4YFDhw6VyTLjsHBpzDU/ufkYGBiIu3fv5v7xkrVSpHbt2gWeOFOYSZMm4cSJE2rb1PHy5cto1KgRbAraUEUNjIyMYG9vX+gSGl9fX5iYmOT+7DGmKmPHjsWlS5dyy541RXmZh7PDYxBNVeXaU4SIEJ4cjhrGNQp8/syZM+jbt2+JlwISEaKjozF79uwy8bVkjGmG8jI/IzUVFBeH6JRKsLICYmJiAECmDac/Lp357+cpEAjQtm1b1KhR8PzNGGNF0aikSHR0NAAgJCQEJ06cwPDhw3HhwgWsXr0aZ86cgYGBgZojVEx8rAQ6WmIYG//72MmTJ1G/fn04OzsDkCZFapsWvXFfaGgoXFxccr9OsujRowesra1lri5RNk0sZSxqXxFfX1+0bNlSI/Y/YeWbiYkJHBwcEBERofZ9fz5VXubhD0EZIAjlSoqkZKcgLScN1ib5O6WlpeHq1atKWTojEAjw119/YciQISUeizFWcZSX+Rnv3yMZJsjKFsLKCkhISEClSpVk2uC+sE1WR40ahfXr16siWsZYBaBR7/zc3d3RpEkT9O3bF1u3boWZmRm+++47pKSkwNXVFU5OTpg4caK6w5RbQqIA5gZZ+DSpfe/ePXTo0AECgQBZoixEpkYWWyni4+OD169fo0qVKjJfW0tLCxMmTMBvv/0GsVis6KegkMzMTNy8eVNjls585OLiUuixvLyfCCtN8fHxWL9+PTIzM9UdSq7yMg+Hh0iXJcmTFAlPDgeAAitFLl++DKFQiG7dupUorszMTPz000+Ij48v0TiMsYqnvMzPeP8eUTrSCmIrK+nfQgsLi2K7iSViPPzwMN9+ImKxGKdOnSryVEbGGCuKRtVt37x5M99jb9++VUMkypWYJICZSd59A/z8/DB37lwAQFiydKdwWZIin332mdxVDKNHj8Yvv/wCT09P9OnTR66+JeHr64vs7Gx06NCh1K4pi9atWyMsLAwRERF5yizFYjEePHiA6dOnqzE6VpFYWFhg7dq16g4jj/IyD4dHCGCulwYDA0OZ+0SkREAoEMLS0DLfc2fOnIGrqysqVZJv49b/OnjwILZs2YIff/yxROMwxiqe8jI/4907RFk5AmGApaW0UkSWvU5exb1CWk5avqTI/fv3kZiYiO7du6sqYsZYOadRlSLlkkSCxHQdmJn+Wx4fExOD9+/f5x77GpIUAqFAWOg69o9u3bql0NnrlpaWGDRoUKlvuHrz5k04OTnB1NS0VK9bHHt7exgaGuZbQvPy5UukpqZypQhj5UB4jB6szdLk6hOREoFqRtWgLcx7v0AsFsPDw6PES2eICJs3b8a4ceNg/Ol6SsYYq0jev0eURWOYmQG6utKkiCyVIn7hfrAytMp3QtilS5fg7OwMS8v8CW3GGJMFJ0VULTERiWQKM/N/v9R+fn7Q1dVF06ZNAUiTIjWMa0BHS6fQYcRiMaysrNC5c2eFwpg0aRI8PT0RHBysUH9FeHt7a1yVCCBdUtShQwf88MMPOH/+fO7jvr6+qF69OqzlqbdnjGmk8EQDWFfJkq9PSsGbrPr6+iImJga9e/cuUUwPHjzA06dPMX78+BKNwxhjZdq7d4gyqouP+6rGx8fLVCny4MMDtKjRIt8mq1WrVsXo0aNVESljrILgpIiqxcYiAeYwr/rvsWl+fn5wcnKCrq4uAMh0HK+WlhYuXryItm3bKhRG+/bt4eDggCFDhsDHx0ehMeQhEolw+/ZtjT1u8tChQ+jZsyf69euHXr164eXLl7yfCGPlSHiqGayrS+TqU9hxvGfPnoWLi0uJ16s/efIEbm5uqF+/fonGYYyxMu3dO0Tp1cpNisi6fOZ13Gs0qtwo3+MTJ07Ed999p+woGWMVCCdFVC02Fokwg5mlbu5Dfn5+uUtnANlOnrl16xaeP3+ucBgCgQCnT59G3bp10aFDB/Tp0wdPnjxReLziPHnyBKmpqQot9ykNFhYW2Lx5Mx4/foysrCw0bdoUhw8f5qQIY+WBWIxwsRWs5TyZMSIlosBKEQ8PD/Tt27fEYY0ZMwaenp4lHocxxsq09+8RBas8SRFZls8EJgSirkXdPI/5+fnh0KFDqoiSMVaBcFJE1eLikKhVGWaVpWvUiajApEhxlSI///wz9u/fX6JQ7OzscPjwYTx8+BASiQTOzs746quvVHIKgre3Nxo0aCDTmfPq5ODggCtXruCvv/5Cw4YN0atXL3WHxBgrqYwMhKEmrGvId9RxQctnoqOj8ezZsxKfonXnzh08fPiQj/tmjFVsREBcHKIyTeVaPiOSiPAu8R3qmudNiuzbt4+TIoyxEuNXZ6oWG4tErSowM5N+GBoaiujoaLmSIjk5Obh7967Sqi6cnZ1x/vx5eHl54f79+xgzZgyI5HvzUJybN29q7NKZ/xIIBBgwYADu3bsHJycndYfDGCuptDR8QHXUqCnfn7iCls/cuHEDxsbGaN68eYlC+uGHH7Br164SjcEYY2VeVhYgEiE6pZJcy2fCksMgkojyVYpcuXKFT51hjJUYJ0VULTYWCQJzfJzr/fz8YGRkhIYNGwKQVo4UlxR5/PgxMjIy8Nlnnyk1tI4dO+L48eM4f/489u7dq7RxiQg3b97UyE1WGWPlX05SOjJgALOqhW9e/V8SkuBDyod8lSJeXl7o0KEDtLUVP8H+1atX8PHxwfuwdZEAABo9SURBVNixYxUegzHGyoXUVABAVKJunkqR4pbPBMYHQkuglWe5eWxsLF6/fs2vNxljJcZJEVWLi5OePmMm/dDPzw8tWrSAlpZ049W4jDhkiDKKTIr4+PigYcOGqFq1qtLDa9q0KVasWIHp06cjKChIKWO+fPkSsbGxZaZShDFWvqTFZQIADC30ZO4Tmx6LHElOvqTI9evX0aVLlxLFs3v3bjg7O5e42oQxxsq8lBQAQFS8DqyspDfSZKkUCUwIRC3TWnlOavTz80OlSpW4ypcxVmKcFFExiolFosgoT1Lk08083ye+B4AikyIjRozA0aNHVRbjjBkz0Lp1a3zzzTcQiUQlHs/b2xs1a9ZE7dpFbx7LGGOq8DEpYlRFX+Y+ESkRAABrk3+Xz0RGRiIgIKBESREiwunTp7lKhDHGACA1FWkwQFq6EJaWQEZGBrKzs4tPisTn32S1Z8+eCA4Oho6O7FWBjDFWEE6KqFhmdDKyJTowMwMkEgnu37+fbz8REz0TmOmbFTqGhYUFmjVrprIYhUIh9u3bhxcvXmDVqlUlHu/jfiL/PUeeMcZKQ2p8NgDAsLJ8SRE9LT2Y6//7wvzGjRswNTUt0V1IgUCAR48eYeTIkQqPwRhj5UZKCqIgXTdjZYXczf6LXT6TEJhvk9WMjAyN39CfMVY2cFJExRKipC/Ozc2BN2/eIDk5Wa5NVgMDA1G7dm1ER0erNE4bGxts27YNixYtwv3790s0lre3N6/vZIypTVpiDgDA0Fj2P3HhyeGwNrHOk8y9fv06OnbsmLvcURGPHj2Cvr4+DAwMFB6DMcbKjdRURGlJK/KsrKSbrAKQafnMp0mR7OxsVK1aFdeuXVNdrIyxCoOTIiqWGCtdjmJmJl06U6VKlTzLSopLivj4+CAjI0Ml+4n81/DhwzFo0CB8/fXXyMzMVGiM9+/fIzQ0lPcTYYypTVqSdN41NJS9T0RKRIH7iXTu3FnhOEJDQ9GyZUvcvn1b4TEYY6xcSUlBlH5tGBkBBgb/JkXMPq4zLwAR5Vs+8+jRI6Snp6u0kpoxVnFwUkTFEuMlAABTU8DX1xetWrXKcycyJDkEtUyKToq0b9++1JaibNu2DXFxcdi8ebNC/W/evInKlSujcePGSo6MMcZkk5aYA11kQZ5l5uEp4XmSIhEREXj9+nWJ9hP5448/UKdOHbRr107hMRhjrFxJTUWUrk2ek2dMTEyKPOErNj0WKdkpeSpFbt++jUaNGqFy5cqqjpgxVgFwUkSVJBIkJgtRSU8MPT1ppcinS2eA4itFbt++XaovqM3NzbFw4UIsW7YMcXFxcvf/uHSG9xNhjKlLarIEhsIMufpEpETA2vjfTVa9vLxgbm5eoruQf/31F4YNG8bzIWOMfZSSgmjtGrlJEVlPngGAOuZ1ch+7ffs2PvvsM5WFyRirWDgpokqJiUggU5ibSJCTk4PHjx/nS4q8T3xfaFKEiNCqVatS359jwoQJsLS0xP/+9z+5+968eZP3E2GMqVVaigSGWvItAfzv8hkvLy907NgRQqFifyYDAwPx+PFjuLu7K9SfMcbKpdRURAmq5akUkeXkGUtDSxjrGec+5urqimHDhqkyUsZYBcJJEVWKi0MizGBmDjx//hyZmZl5kiKZokxEpUWhtlnBR9cKBALs27cvzxG+pUFHRwerV6/G1q1b8ebNG5n7RUdH4+XLl5wUYYypVVoawUhbvqRIeEp4nkqR69evl2jpTM2aNXHp0iU4OjoqPAZjjJU7KSmIgmWeShFFTp4ZP348unfvrqooGWMVDCdFVCk2VpoUsRDCz88PNjY2eY4OC0sOA4BCK0VevHiBs2fPlkqo/9W/f3+0bdsW8+bNk7mPj48PDA0N4ezsrMLIGGOsaGmpgKFOtsztc8Q5iE6Lzq0UCQsLw9u3b0u0yaq2tjZcXV156QxjjH0qNRVR4iqwtJR+KOvymU83Wb1+/To2btyowiAZYxUNJ0VUKTYWidpVYWahVeh+IkKBMN+JBx8dP34cK1asKI1I8xEIBFi7di3+/vtv3Lp1S6Y+3t7e+Oyzz4rcLIsxxlQtNU0AQ50cmdtHpkYCQO5c7OXlhcqVK6Np06YKXf/9+/eoXr06QkJCFOrPGGPlVkoKorIt5F4+82mlyPHjx3HlyhVVRskYq2A4KaJKcXFI0KsGc/PCN1m1NraGtrDgJIK/vz+aNGlSGpEWqHXr1hg6dChmz54NIiqybXp6Os6cOYNOnTqVUnSMMVawtAwhjPRkT4qEp4QD+Dcpcv36dXTq1Enh/UROnDgBAwMD2NjYKNSfMcbKrdRURGeZ5qkUkXf5TGkfQsAYK/84KaJKsbFI1KkCI6McPH/+XO6TZwICAtSaFAGA5cuX4+HDhzh+/HiR7ebPn4/s7GxMmTKllCJjjLGCpWUKYagnkrl9REoETPVMYahrCEBaKVKSpTPHjx/HoEGDeOkMY4z9ByWnICVHD6am0o+LWz6Tlp2GyNTI3OUzKSkpePr0KZ88wxhTKk6KqFJsLBKFFsjOjoZYLEaLFi3yPP0+8X2hm6yKRCK8evVK7UkROzs7TJs2DbNmzUJYWFiBba5fv45ff/0Ve/bsgZmZWekGyBhj/5GaqQ1DfbHM7SNSImBtIt1kNSQkBEFBQQpvshoeHo7bt29j0KBBCvVnjLHyLDslC2LSgoGB9OP4+PgiK0WCEoIAILdS5Pnz59DV1c13o5ExxkqCkyKq9M/pMzk5sbCwsMiXMAhJDkEtk4IrRYRCIe7evasRmfDFixfDzs4O3bp1Q2RkZJ7nUlJSMGrUKEyYMAFubm5qipAxxv6VlqUDIwOJzO3Dk8PzLJ2pUqWKwgnpnJwczJgxo9RPDWOMsbIgPUm6tNFQWphXbKVIYEIgDHUMYWkoXW/Ttm1bxMfHw+BjVoUxxpSAkyKqFBuLBJEJsrIiC1xbXtTyGaFQCGdnZ5iYmKg6ymIZGBjg3LlzMDc3R7du3RATE5P73Pfffw+hUIg1a9aoMULGGPtXWo4ODA2K3gfpUxGpEblJER8fH3Ts2FHh/URsbW2xYcMGhfszxlh5lp4qTVgbGAASiaT4pEi89OSZj8sRw8PDoa+vXyqxMsYqDn7VpkqxsUjMMUBaWni+pAgRFZkU2bRpE0aNGlUaUcrE2NgYFy5cgJ6eHtzc3JCQkABPT0/s2rUL+/btg5GRkbpDZIwxAEBatm7uXUhZRKREwNpYunzm9evXsLe3V+i6UVFRGDNmDBITExXqzxhj5V1aijQpYmgorTaWSCRFLp/5dJNVsViMJk2a4MSJE6USK2Os4uCkiApRbBwSM/WRnBySLykSmx6LTFFmoUmR+/fvQyKRvfy7NJiZmeHSpUsQiUTo0aMHxowZg5kzZ6Jjx47qDo0xxnKlivQhT5720+UzgYGBqFOnjkLXPXXqFM6dOwdjY2OF+jPGWHmXni7918BAunQGQLHLZz4mRfz9/ZGcnAwXFxeVx8kYq1g4KaJCqTEZkJAQ8fFB+ZIiIUkhAFBoUkTdx/EWpkqVKrhy5QqSk5NhamqKpUuXqjskxhjLI02sD0Nj2f+8RaRIl89kZmYiPDwcdevWLb5TAY4fP44vvvgCWlpaCvVnjLFyTSxGWqZ0bjYwkG6yChSTFPln+QwA3Lp1C7Vq1ULNmjVVHytjrELRVncA5ZZEgoR46Zr26OhXsLHJuwlpSFIITPVMYapvWkBXCV6+fInGjRuXSqjysrKygp+fH0QiESpVqqTucBhjLI80iT4MTWRLTKRlpyEpKwnWxtYIDg4GAIUqReLi4nD9+nXMmzdP7r6MMVYhpKUhHQbQ0iLo6gqQkJAAoVBY6P55IokI75Pe51aKPHz4kDexZoypBCdFVCUpCYkkneRjY9/mqxQJTQ5FTZOCM90fSwMdHBxUHqaiuDycMaap0shA5qRIREoEAKCGcQ08ffIU+vr6qF69utzXfPnyJRo0aMDLCRljrDApKUiHAQz0JRAItJCQkAAzM7NCN6YOSQqBSCLKrRTp378/b2LNGFMJToqoSmwsEmH2zwdJ+ZIiUalRqGZUrcCuZmZmuHbtmmrjY4yxcohyREiFEYzMdWRqH5ESAQEEqGZUDScDT8LOzk6hF93t2rWDv7+/3P0YY6zCSE1FGgxzN8KOj48vdumMtlA7d6l57969SyNKxlgFxOlWVfknKWJgIAIghrW1dZ6no9Oic89c/6+nT5/i0aNHpRAkY4yVL1mJGZBAC4ZmsiVFwlPCYWloCR0tHQQFBSm8n8jDhw/51BnGGCvKx0oRA+nxugkJCUWePBOUEITaprWhLZTew/3hhx84+cwYUwlOiqhKXBwSdKuhUqUsWFlZQU9PL8/TMekxqGpQtcCuGzduxOrVq0sjSsYYK1fSYqRHGxia68rU/uMmq0DJTp7p1asXzpw5o1BfxhirEP6pFDEwkiZFiq0USfh3k9WsrCysWbMGsbGxpRIqY6xi4aSIqsTGItGgBnR00vItnQGKrhQJCAjQyJNnGGNM06XGZQEAjCrrFdNSKiIlAtYm0ko+RStFPnz4gKioKDg7O8vdlzHGKoyUFKTrmMHQ8N9KEVmP442MjASAfJXXjDGmDJwUUZXYWCTqV4NQmH8/EUCaFKlqmL9ShIg09jhexhjTdGnx0qSIYWV9mdqHp4SjhlENSCQSBAUFKVQp8ujRI+jp6aFRo0Zy92WMsQojNRXpuqYwMJB+WNzymcD4f5Mi4eHhAKDQRtiMMVYcToqoSlwcEnUtIRbHF5gUiUmPKbBSJCIiAsnJyZwUYYwxBaQlZAMADKvIdlz4x0qRyMhIZGZmKlQp8ujRIzRt2hQ6OrLtY8IYYxVSSgrStM1k2miViPIsn4mIiICZmRkMPmZUGGNMifj0GVWJjUWCVmVkZ0flS4pkijKRnJVcYFLE3NwcHh4eqFevXmlFyhhj5cbHpIiBhWyVIh/3FAkMDAQA2Nrayn3N/v37w8XFRe5+jDFWoaSmIl27Vp5KkcKSIjHpMUjNTs2tFHF3d4erq2tpRcoYq2A0qlJk06ZNcHBwgL29PTZu3AhAmkV2dXVF/fr14erqioSEBPUGKavYWCTCHBkZEfmSIjFpMQBQ4EarBgYG6N27N99xZIypRVmfh1MTRTBAGoTaxf95IyKEJ4ejhnENBAUFwdraGpUqyVZh8ikHBwd0795dkXAZY0xmZX1+RkoK0rSMZVo+ExgvTVTbmdvlPmZqaqryEBljFZPGJEWeP3+OnTt3wtfXF0+ePIGHhwfevn2LlStXolu3bnjz5g26deuGlStXqjtU2cTFIUFsjMzMyPxJkXRpUqSgSpElS5bwyTOMMbUoD/NwWrIIhoJ0mdomZCYgS5wFa2NrhU+eSUxMRM+ePREWFiZ3X8YYk1V5mJ+Rmop0gZFMy2eCEoJgZWgFI10jAMDIkSPx3XfflVakjLEKRmOSIgEBAXBxcYGBgQG0tbXRqVMnnDhxAqdPn8aIESMAACNGjMCpU6fUG6isYmMRl6UPIDFfUiQ6LRo6Qh2Y6Jnk63blyhWkpKSUUpCMMfav8jAPpyVLYCjMlKltREoEAORWiiiyn8jjx49x+fLlIjcLZIyxkioP87M0KWIAAwNALBYjOTm50KRIZGokqhv/u6lqeHg4V4owxlRGQESk7iAA6WTfv39/3LlzB5UqVUK3bt3QsmVL/PHHH0hMTAQgLXU2NzfP/fijrVu3YuvWrQCg8OkBxR0Lpuk4fvXi+NWrosZvbm6OW7duKS0Odc/DQNn+Xpbl2AGOX904fvXSlHm4MDw/l0xZjh3g+NWN41ev0pifNSYpAgC7d+/Gtm3bYGhoCHt7e+jp6WHfvn15Jndzc3OVrJds0qQJ/P39lT5uaeH41YvjVy+OX3nUOQ8DmvW1kFdZjh3g+NWN41evshA/z8+KK8uxAxy/unH86lUa8WvM8hkAGDNmDB48eABvb2+Ym5ujQYMGsLKywocPHwAAHz58gKVl/n04GGOMKQfPw4wxppl4fmaMMdXQqKRIdHQ0ACAkJAQnTpzA8OHD0a9fP+zfvx8AsH//fvTv318l154yZYpKxi0tHL96cfzqxfErjzrnYUCzvhbyKsuxAxy/unH86lUW4uf5WXFlOXaA41c3jl+9SiN+jVo+06FDB8TFxUFHRwfr169Ht27dEBcXh8GDByMkJAS1a9fGsWPHeEM7xhhTEZ6HGWNMM/H8zBhjqqFRSRHGGGOMMcYYY4yx0qJRy2cYY4wxxhhjjDHGSgsnRQBcuHABDRs2RL169bBy5Up1h5Nr9OjRsLS0hIODQ+5j8fHxcHV1Rf369eHq6pq7wzgRYdq0aahXrx4cHR3x8OHD3D779+9H/fr1Ub9+/dx1p6oWGhqKLl26oEmTJrC3t8emTZvKVPyZmZlo3bo1mjVrBnt7eyxcuBAAEBwcDBcXF9SrVw9DhgxBdnY2ACArKwtDhgxBvXr14OLignfv3uWOtWLFCtSrVw8NGzbExYsXSyX+j8RiMZydndGnT58yF7+trS2aNm0KJycntGzZEkDZ+fkBgMTERAwaNAiNGjVC48aNcefOnTIVf2njeVj5eB5+lzsWz8OK4XlYvfFrCp6flY/n53e5Y/H8rBien5UcP1VwIpGI6tSpQ4GBgZSVlUWOjo704sULdYdFREQ3btygBw8ekL29fe5jc+bMoRUrVhAR0YoVK+iHH34gIqJz585Rz549SSKR0J07d6h169ZERBQXF0d2dnYUFxdH8fHxZGdnR/Hx8SqPPSIigh48eEBERMnJyVS/fn168eJFmYlfIpFQSkoKERFlZ2dT69at6c6dO/Tll1/S4cOHiYhowoQJtG3bNiIi2rp1K02YMIGIiA4fPkyDBw8mIqIXL16Qo6MjZWZmUlBQENWpU4dEIpHK4/9o3bp1NGzYMOrduzcRUZmKv3bt2hQTE5PnsbLy80NE9O2339LOnTuJiCgrK4sSEhLKVPyliedh1eB5WP3zGBHPwzwPl208P6sGz8/qn9+IeH7m+flfFT4pcvv2bXJzc8v9ePny5bR8+XI1RpRXcHBwnsm+QYMGFBERQUTSCbVBgwZERDR+/Hj6888/87X7888/afz48bmP/7ddaenXrx9dunSpTMaflpZGzs7OdPfuXapcuTLl5OQQUd6fHTc3N7p9+zYREeXk5FDlypVJIpHk+3n6tJ2qhYaGUteuXenq1avUu3dvkkgkZSr+gib7svLzk5iYSLa2tiSRSMpk/KWN5+HSwfMw5WunajwP8zxc1vH8XDp4fqZ87VSN52eenz9V4ZfPhIeHw8bGJvfjmjVrIjw8XI0RFS0qKgrVq1cHAFSrVg1RUVEACv88NOHze/fuHR49egQXF5cyFb9YLIaTkxMsLS3h6uqKunXrwszMDNra2vli+TRObW1tmJqaIi4uTq3xz5gxA6tXr4ZQKP01j4uLK1PxCwQCuLm5oUWLFvj9998BlJ2f/+DgYFStWhWjRo2Cs7Mzxo4di7S0tDITf2kra59nWfw+8jzM87AieB7WjN9fdSprn39Z/P7y/MzzsyJ4flZu/BU+KVKWCQQCCAQCdYdRpNTUVLi7u2Pjxo0wMTHJ85ymx6+lpYXHjx8jLCwMvr6+ePnypbpDkpmHhwcsLS3RokULdYeiMB8fHzx8+BCenp7YunUrvL298zyvyT8/IpEIDx8+xKRJk/Do0SMYGhrmW4etyfEz2ZWF7yPPw+rB87B68TzMysL3l+dn9eD5Wb00cX6u8EkRa2trhIaG5n4cFhYGa2trNUZUNCsrK3z48AEA8OHDB1haWgIo/PNQ5+eXk5MDd3d3fPXVV/jiiy/KXPwfmZmZoUuXLrhz5w4SExMhEonyxfJpnCKRCElJSahcubLa4r916xbOnDkDW1tbDB06FNeuXcP06dPLTPwfYwIAS0tLDBw4EL6+vmXm56dmzZqoWbMmXFxcAACDBg3Cw4cPy0z8pa2sfZ5l6fvI8zDPwyXB87Bm/PyrU1n7/MvS95fnZ56fS4LnZyXHr9Cim3IkJyeH7OzsKCgoKHcDqefPn6s7rFz/XSs5e/bsPBvQzJkzh4iIPDw88mxA06pVKyKSbkBja2tL8fHxFB8fT7a2thQXF6fyuCUSCX3zzTc0ffr0PI+Xlfijo6MpISGBiIjS09Opffv2dPbsWRo0aFCeDZi2bt1KRERbtmzJswHTl19+SUREz58/z7MBk52dXaluIEVEdP369dwNpMpK/KmpqZScnJz7/23btiVPT88y8/NDRNS+fXt6+fIlEREtXLiQZs+eXabiL008D6sGz8M8D5cEz8Pqj18T8PysGjw/8/xcEjw/Kz/+Cp8UIZLuaFu/fn2qU6cOLV26VN3h5Bo6dChVq1aNtLW1ydramnbt2kWxsbHUtWtXqlevHnXr1i33Gy+RSGjy5MlUp04dcnBwID8/v9xxdu/eTXXr1qW6devSnj17SiX2mzdvEgBq2rQpNWvWjJo1a0bnzp0rM/E/efKEnJycqGnTpmRvb0+LFy8mIqLAwEBq1aoV1a1blwYNGkSZmZlERJSRkUGDBg2iunXrUqtWrSgwMDB3rKVLl1KdOnWoQYMGdP78+VKJ/1OfTvZlJf7AwEBydHQkR0dHatKkSe7vZVn5+SEievToEbVo0YKaNm1K/fv3p/j4+DIVf2njeVj5eB7mebgkeB5Wf/yagudn5eP5mefnkuD5WfnxC4iIFKsxYYwxxhhjjDHGGCu7KvyeIowxxhhjjDHGGKuYOCnCGGOMMcYYY4yxComTIowxxhhjjDHGGKuQOCnCGGOMMcYYY4yxComTIowxxhhjjDHGGKuQOCnCGGOMMcYYY4yxComTIowxxhhjjDHGGKuQOCnCGGOMMcYYY4yxComTIowxxhhjjDHGGKuQOCnCGGOMMcYYY4yxComTIowxxhhjjDHGGKuQOCnCGGOMMcYYY4yxCun/hboDZOP7QacAAAAASUVORK5CYII=\\n\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"source\": [],\n      \"metadata\": {\n        \"id\": \"53S1NeW3zJ2M\"\n      },\n      \"execution_count\": null,\n      \"outputs\": []\n    }\n  ],\n  \"metadata\": {\n    \"accelerator\": \"GPU\",\n    \"colab\": {\n      \"provenance\": [],\n      \"gpuType\": \"A100\"\n    },\n    \"kernelspec\": {\n      \"display_name\": \"Python 3\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.9.18\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}"
  },
  {
    "path": "notebooks/building-mnist1d.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Building MNIST-1D\\n\",\n    \"Sam Greydanus\\n\",\n    \"\\n\",\n    \"This notebook shows how to build the MNIST-1D dataset in full detail*.\\n\",\n    \"\\n\",\n    \"_* If you're running this in Colab, it's best to use a GPU runtime._\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n    \"    \\n\",\n    \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n    \"!git clone https://github.com/greydanus/mnist1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using: cpu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import random\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from scipy.ndimage import gaussian_filter\\n\",\n    \"from scipy.interpolate import interp1d\\n\",\n    \"\\n\",\n    \"PROJECT_DIR = './'\\n\",\n    \"\\n\",\n    \"class ObjectView(object):\\n\",\n    \"    def __init__(self, d): self.__dict__ = d\\n\",\n    \"        \\n\",\n    \"# Try attaching to GPU\\n\",\n    \"DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\\n\",\n    \"print('Using:', DEVICE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Templates\\n\",\n    \"These are 1D signals, consisting of 12 points each, that resemble the digits 0-9. They are meant to be analogous to the handwritten digits 0-9 in the original MNIST dataset:\\n\",\n    \"\\n\",\n    \"![mnist digits](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist2d_black.png)\\n\",\n    \"\\n\",\n    \"But unlike the original MNIST dataset, which consisted of 2D arrays of pixels (each image had 28x28=784 dimensions), this dataset consists of 1D timeseries of length 40. This means each example is ~20x smaller, making the dataset much quicker and easier to iterate over.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_templates():\\n\",\n    \"    d0 = np.asarray([5,6,6.5,6.75,7,7,7,7,6.75,6.5,6,5])\\n\",\n    \"    d1 = np.asarray([5,3,3,3.4,3.8,4.2,4.6,5,5.4,5.8,5,5])\\n\",\n    \"    d2 = np.asarray([5,6,6.5,6.5,6,5.25,4.75,4,3.5,3.5,4,5])\\n\",\n    \"    d3 = np.asarray([5,6,6.5,6.5,6,5,5,6,6.5,6.5,6,5])\\n\",\n    \"    d4 = np.asarray([5,4.4,3.8,3.2,2.6,2.6,5,5,5,5,5,5])\\n\",\n    \"    d5 = np.asarray([5,3,3,3,3,5,6,6.5,6.5,6,4.5,5])\\n\",\n    \"    d6 = np.asarray([5,4,3.5,3.25,3,3,3,3,3.25,3.5,4,5])\\n\",\n    \"    d7 = np.asarray([5,7,7,6.6,6.2,5.8,5.4,5,4.6,4.2,5,5])\\n\",\n    \"    d8 = np.asarray([5,4,3.5,3.5,4,5,5,4,3.5,3.5,4,5])\\n\",\n    \"    d9 = np.asarray([5,4,3.5,3.5,4,5,5,5,5,4.7,4.3,5])\\n\",\n    \"    \\n\",\n    \"    x = np.stack([d0,d1,d2,d3,d4,d5,d6,d7,d8,d9])\\n\",\n    \"    x -= x.mean(1,keepdims=True) # whiten\\n\",\n    \"    x /= x.std(1,keepdims=True)\\n\",\n    \"    x -= x[:,:1]  # signal starts and ends at 0\\n\",\n    \"    \\n\",\n    \"    templates = {'x': x/6., 't': np.linspace(-5, 5, len(d0))/6.,\\n\",\n    \"                 'y': np.asarray([0,1,2,3,4,5,6,7,8,9])}\\n\",\n    \"    return templates\\n\",\n    \"\\n\",\n    \"def plot_signals(xs, t, labels=None, args=None, ratio=2.6, do_transform=False, dark_mode=False, zoom=1):\\n\",\n    \"    rows, cols = 1, 10\\n\",\n    \"    fig = plt.figure(figsize=[cols*1.5,rows*1.5*ratio], dpi=60)\\n\",\n    \"    for r in range(rows):\\n\",\n    \"        for c in range(cols):\\n\",\n    \"            ix = r*cols + c\\n\",\n    \"            x, t = xs[ix], t\\n\",\n    \"            ax = plt.subplot(rows,cols,ix+1)\\n\",\n    \"\\n\",\n    \"            # plot the data\\n\",\n    \"            if do_transform:\\n\",\n    \"                assert args is not None, \\\"Need an args object in order to do transforms\\\"\\n\",\n    \"                x, t = transform(x, t, args)  # optionally, transform the signal in some manner\\n\",\n    \"            if dark_mode:\\n\",\n    \"                plt.plot(x, t, 'wo', linewidth=6)\\n\",\n    \"                ax.set_facecolor('k')\\n\",\n    \"            else:\\n\",\n    \"                plt.plot(x, t, 'k-', linewidth=2)\\n\",\n    \"            if labels is not None:\\n\",\n    \"                plt.title(\\\"label=\\\" + str(labels[ix]), fontsize=22)\\n\",\n    \"\\n\",\n    \"            plt.xlim(-zoom,zoom) ; plt.ylim(-zoom,zoom)\\n\",\n    \"            plt.gca().invert_yaxis() ; plt.xticks([], []), plt.yticks([], [])\\n\",\n    \"    plt.subplots_adjust(wspace=0, hspace=0)\\n\",\n    \"    plt.tight_layout() ; plt.show()\\n\",\n    \"    return fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Examples from original MNIST dataset:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"https://raw.githubusercontent.com/greydanus/mnist1d/master/static/mnist.png\\\" width=\\\"800\\\"/>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import Image\\n\",\n    \"from IPython.core.display import HTML \\n\",\n    \"print(\\\"Examples from original MNIST dataset:\\\")\\n\",\n    \"URL = 'https://raw.githubusercontent.com/greydanus/mnist1d/master/static/mnist.png'\\n\",\n    \"Image(url= URL,  width=800)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Templates for the MNIST-1D dataset:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x119.7 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"templates = get_templates()\\n\",\n    \"print(\\\"Templates for the MNIST-1D dataset:\\\")\\n\",\n    \"x = templates['x']\\n\",\n    \"t = templates['t']\\n\",\n    \"y = templates['y']\\n\",\n    \"fig = plot_signals(x, t, labels=y, ratio=1.33, dark_mode=True)\\n\",\n    \"\\n\",\n    \"# fig.savefig(PROJECT_DIR + 'static/mnist1d_black.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Templates for the MNIST-1D dataset:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x119.7 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"templates = get_templates()\\n\",\n    \"print(\\\"Templates for the MNIST-1D dataset:\\\")\\n\",\n    \"x = templates['x']\\n\",\n    \"t = templates['t']\\n\",\n    \"y = templates['y']\\n\",\n    \"fig = plot_signals(x, t, labels=y, ratio=1.33, dark_mode=False)\\n\",\n    \"\\n\",\n    \"# fig.savefig(PROJECT_DIR + 'static/mnist1d_white.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Transformations\\n\",\n    \"In order to build a synthetic dataset, we are going to pass the templates through a series of random transformations. This includes adding random amounts of padding, translation, correlated noise, iid noise, and scaling.\\n\",\n    \"\\n\",\n    \"We use these transformations because they are relevant for both 1D and 2D images. So even though our dataset is 1D, we can expect some of our findings to hold for 2D (image) data. For example, we can study the advantage of using a translation-invariant model by making a dataset where signals occur at different locations in the sequence. We can do this by using large padding and translation coefficients.\\n\",\n    \"\\n\",\n    \"In the following section, we plot the step-by-step transformations of digit templates into dataset examples. Note that you can generate your own synthetic datasets by changing the relevant hyperparameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# transformations of the templates which will make them harder to fit\\n\",\n    \"def pad(x, padding):\\n\",\n    \"    low, high = padding\\n\",\n    \"    p = low + int(np.random.rand()*(high-low+1))\\n\",\n    \"    return np.concatenate([x, np.zeros((p))])\\n\",\n    \"\\n\",\n    \"def shear(x, scale=10):\\n\",\n    \"    coeff = scale*(np.random.rand() - 0.5)\\n\",\n    \"    return x - coeff*np.linspace(-0.5,.5,len(x))\\n\",\n    \"\\n\",\n    \"def translate(x, max_translation):\\n\",\n    \"    k = np.random.choice(max_translation)\\n\",\n    \"    return np.concatenate([x[-k:], x[:-k]])\\n\",\n    \"\\n\",\n    \"def corr_noise_like(x, scale):\\n\",\n    \"    noise = scale * np.random.randn(*x.shape)\\n\",\n    \"    return gaussian_filter(noise, 2)\\n\",\n    \"\\n\",\n    \"def iid_noise_like(x, scale):\\n\",\n    \"    noise = scale * np.random.randn(*x.shape)\\n\",\n    \"    return noise\\n\",\n    \"\\n\",\n    \"def interpolate(x, N):\\n\",\n    \"    scale = np.linspace(0,1,len(x))\\n\",\n    \"    new_scale = np.linspace(0,1,N)\\n\",\n    \"    new_x = interp1d(scale, x, axis=0, kind='linear')(new_scale)\\n\",\n    \"    return new_x\\n\",\n    \"\\n\",\n    \"def transform(x, y, args, eps=1e-8):\\n\",\n    \"    new_x = pad(x+eps, args.padding) # pad\\n\",\n    \"    new_x = interpolate(new_x, args.template_len + args.padding[-1])  # dilate\\n\",\n    \"    new_y = interpolate(y, args.template_len + args.padding[-1])\\n\",\n    \"    new_x *= (1 + args.scale_coeff*(np.random.rand() - 0.5))  # scale\\n\",\n    \"    new_x = translate(new_x, args.max_translation)  #translate\\n\",\n    \"    \\n\",\n    \"    # add noise\\n\",\n    \"    mask = new_x != 0\\n\",\n    \"    new_x = mask*new_x + (1-mask)*corr_noise_like(new_x, args.corr_noise_scale)\\n\",\n    \"    new_x = new_x + iid_noise_like(new_x, args.iid_noise_scale)\\n\",\n    \"    \\n\",\n    \"    # shear and interpolate\\n\",\n    \"    new_x = shear(new_x, args.shear_scale)\\n\",\n    \"    new_x = interpolate(new_x, args.final_seq_length) # subsample\\n\",\n    \"    new_y = interpolate(new_y, args.final_seq_length)\\n\",\n    \"    return new_x, new_y\\n\",\n    \"\\n\",\n    \"def get_dataset_args(as_dict=False):\\n\",\n    \"    arg_dict = {'num_samples': 5000,\\n\",\n    \"            'train_split': 0.8,\\n\",\n    \"            'template_len': 12,\\n\",\n    \"            'padding': [36,60],\\n\",\n    \"            'scale_coeff': .4, \\n\",\n    \"            'max_translation': 48,\\n\",\n    \"            'corr_noise_scale': 0.25,\\n\",\n    \"            'iid_noise_scale': 2e-2,\\n\",\n    \"            'shear_scale': 0.75,\\n\",\n    \"            'shuffle_seq': False,\\n\",\n    \"            'final_seq_length': 40,\\n\",\n    \"            'seed': 42}\\n\",\n    \"    return arg_dict if as_dict else ObjectView(arg_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualize Transformations\\n\",\n    \"We'll apply one at a time so it's clear what each transformation is doing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x72 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def apply_ablations(arg_dict, n=7):\\n\",\n    \"    ablations = [('shear_scale', 0),\\n\",\n    \"                ('iid_noise_scale', 0),\\n\",\n    \"                ('corr_noise_scale', 0),\\n\",\n    \"                 ('max_translation', 1),\\n\",\n    \"                 ('scale_coeff', 0),\\n\",\n    \"                 ('padding', [arg_dict['padding'][-1], arg_dict['padding'][-1]]),\\n\",\n    \"                 ('padding', [0, 0]),]\\n\",\n    \"    num_ablations = min(n, len(ablations))\\n\",\n    \"    for i in range(num_ablations):\\n\",\n    \"        k, v = ablations[i]\\n\",\n    \"        arg_dict[k] = v\\n\",\n    \"    return arg_dict\\n\",\n    \"\\n\",\n    \"templates = get_templates()\\n\",\n    \"for i, n in enumerate(reversed(range(8))):\\n\",\n    \"    np.random.seed(0)\\n\",\n    \"    arg_dict = get_dataset_args(as_dict=True)\\n\",\n    \"    arg_dict = apply_ablations(arg_dict, n=n)\\n\",\n    \"    args = ObjectView(arg_dict)\\n\",\n    \"    do_transform = args.padding[0] != 0\\n\",\n    \"    fig = plot_signals(templates['x'], templates['t'], labels=None if do_transform else templates['y'],\\n\",\n    \"                 args=args, ratio=2.2 if do_transform else 0.8,\\n\",\n    \"                 do_transform=do_transform)\\n\",\n    \"#     fig.savefig(PROJECT_DIR + 'static/transform_{}.png'.format(i))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Construct a  dataset\\n\",\n    \"Now we can construct a dataset by applying random transformations to the template signals.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_dataset(args=None, template=None, ):\\n\",\n    \"    templates = get_templates() if template is None else template\\n\",\n    \"    args = get_dataset_args() if args is None else args\\n\",\n    \"    np.random.seed(args.seed) # reproducibility\\n\",\n    \"    \\n\",\n    \"    xs, ys = [], []\\n\",\n    \"    samples_per_class = args.num_samples // len(templates['y'])\\n\",\n    \"    for label_ix in range(len(templates['y'])):\\n\",\n    \"        for example_ix in range(samples_per_class):\\n\",\n    \"            x = templates['x'][label_ix]\\n\",\n    \"            t = templates['t']\\n\",\n    \"            y = templates['y'][label_ix]\\n\",\n    \"            x, new_t = transform(x, t, args) # new_t transformation is same each time\\n\",\n    \"            xs.append(x) ; ys.append(y)\\n\",\n    \"    \\n\",\n    \"    batch_shuffle = np.random.permutation(len(ys)) # shuffle batch dimension\\n\",\n    \"    xs = np.stack(xs)[batch_shuffle]\\n\",\n    \"    ys = np.stack(ys)[batch_shuffle]\\n\",\n    \"    \\n\",\n    \"    if args.shuffle_seq: # maybe shuffle the spatial dimension\\n\",\n    \"        seq_shuffle = np.random.permutation(args.final_seq_length)\\n\",\n    \"        xs = xs[...,seq_shuffle]\\n\",\n    \"    \\n\",\n    \"    new_t = new_t/xs.std()\\n\",\n    \"    xs = (xs-xs.mean())/xs.std() # center the dataset & set standard deviation to 1\\n\",\n    \"\\n\",\n    \"    # train / test split\\n\",\n    \"    split_ix = int(len(ys)*args.train_split)\\n\",\n    \"    dataset = {'x': xs[:split_ix], 'x_test': xs[split_ix:],\\n\",\n    \"               'y': ys[:split_ix], 'y_test': ys[split_ix:],\\n\",\n    \"               't':new_t, 'templates': templates}\\n\",\n    \"    return dataset\\n\",\n    \"\\n\",\n    \"def set_seed(seed):\\n\",\n    \"    random.seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    torch.manual_seed(seed)\\n\",\n    \"    torch.cuda.manual_seed_all(seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Examples in training set: 4000\\n\",\n      \"Examples in test set: 1000\\n\",\n      \"Length of each example: 40\\n\",\n      \"Number of classes: 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = get_dataset_args()\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"args.shuffle_seq = False\\n\",\n    \"data = make_dataset(args=args)  # make the dataset\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"args.shuffle_seq = True\\n\",\n    \"data_shuff = make_dataset(args=args)  # make the dataset, shuffling the spatial dimension\\n\",\n    \"\\n\",\n    \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n    \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n    \"print(\\\"Length of each example: {}\\\".format(data['x'].shape[-1]))\\n\",\n    \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## You can also download the original dataset from the GitHub repo\\n\",\n    \"The .pkl file hosted in the repo will never change. The procedural generation code used here should never either, but it's more susceptible to change (if we are unlucky, the libraries used to synthesize the dataset may change slightly, etc.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"File already exists. Skipping download.\\n\",\n      \"Successfully loaded data from ./mnist1d_data.pkl\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import sys ; sys.path.append('..')  # useful if you're running locally\\n\",\n    \"import mnist1d\\n\",\n    \"\\n\",\n    \"args = mnist1d.get_dataset_args()\\n\",\n    \"data = mnist1d.get_dataset(args, path='./mnist1d_data.pkl', download=True) # This is the default setting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Get a strong human baseline\\n\",\n    \"This takes time and care. It can be oddly engaging, especially when you realize that you might end up losing to an ML model. I tested myself on five quizzes of a hundred examples each.\\n\",\n    \"\\n\",\n    \"### Quiz: step 1\\n\",\n    \"Run the following cell to set up the quiz. It will initialize the relevant variables and set ``ix`` to the test set example from which you will start.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from mnist1d.utils import from_pickle, to_pickle  # we'll need these utilities to load/save results\\n\",\n    \"\\n\",\n    \"# ix = 0\\n\",\n    \"ix = 0\\n\",\n    \"ix = ix - 10 # the first thing we're going to do is add 10\\n\",\n    \"datas, guesses, trues = [], [], []\\n\",\n    \"quiz_tag = 'q1' # name of the quiz. Set this manually\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Quiz: steps 2 & 3\\n\",\n    \"Run the next cell to plot 10 test set examples. Enter your guess in the next cell and run it to compare your response to the ground truth.\\n\",\n    \"\\n\",\n    \"Once you have understood your mistakes, run the first cell again in order to plot the next 10 examples. Repeat this process until ``len(guesses)=100``.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x243 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ix = ix + 10 # advance the test set pointer\\n\",\n    \"xs = data['x_test'][ix:ix+10]\\n\",\n    \"t = data['t']\\n\",\n    \"_ = plot_signals(xs, t, args=args, ratio=2.7, zoom=6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"guess: [2 6 3 9 4 3 1 9 5 2]\\n\",\n      \"truth: [2 6 3 9 4 3 1 9 5 2]\\n\",\n      \"10 correct\\n\",\n      \"len(guesses) = 10/100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# guess = [0,0,0,0,0,0,0,0,0,0]\\n\",\n    \"guess = [2,6,3,9,4,3,1,9,5,2]\\n\",\n    \"\\n\",\n    \"true = data['y_test'][ix:ix+10]\\n\",\n    \"print('guess:', np.asarray(guess))\\n\",\n    \"print('truth:', true)\\n\",\n    \"print(np.sum(np.asarray(guess)==true), 'correct')\\n\",\n    \"datas += list(xs) ; guesses += list(guess) ; trues += list(true)\\n\",\n    \"print('len(guesses) = {}/100'.format(len(guesses)))\\n\",\n    \"# path = './static/human_{}.pkl'.format(quiz_tag)\\n\",\n    \"# to_pickle([datas, guesses, trues], path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Quiz: step 4\\n\",\n    \"Visualize human performance stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"q1 96.0\\n\",\n      \"q2 96.0\\n\",\n      \"q3 96.0\\n\",\n      \"q4 97.0\\n\",\n      \"q5 94.0\\n\",\n      \"Bootstrap accuracy estimate: 95.8 +/- 0.44\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tags = ['q1', 'q2', 'q3', 'q4', 'q5']\\n\",\n    \"\\n\",\n    \"datas, guesses, trues = [], [], []\\n\",\n    \"accs = []\\n\",\n    \"for tag in tags:\\n\",\n    \"    [d, g, t] = from_pickle('./mnist1d/static/human_{}.pkl'.format(tag))\\n\",\n    \"    acc = 100.*np.sum(np.asarray(g)==np.asarray(t))/len(g)\\n\",\n    \"    print(tag, acc)\\n\",\n    \"    accs.append(acc)\\n\",\n    \"    datas += d ; guesses += g ; trues += t\\n\",\n    \"accs, datas, guesses, trues = [np.array(v) for v in [accs, datas, guesses, trues]]\\n\",\n    \"    \\n\",\n    \"print(\\\"Bootstrap accuracy estimate:\\\", 100.*np.sum(guesses==trues)/len(guesses),\\\\\\n\",\n    \"     '+/- {:.2f}'.format(np.std(accs)/np.sqrt(len(accs))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 525x375 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(3.5, 2.5), dpi=150)\\n\",\n    \"err_ixs = np.where(guesses!=trues)\\n\",\n    \"err_classes = trues[err_ixs]\\n\",\n    \"\\n\",\n    \"labels = [i for i in range(10)]\\n\",\n    \"err_rate = [100. * np.sum(err_classes == i)/np.sum(trues==i) for i in labels]\\n\",\n    \"x_pos = [i for i, _ in enumerate(labels)]\\n\",\n    \"\\n\",\n    \"plt.bar(x_pos, err_rate, color='green')\\n\",\n    \"plt.xlabel(\\\"Label\\\")\\n\",\n    \"plt.ylabel(\\\"% misclassified\\\")\\n\",\n    \"plt.title(\\\"Human mistakes by class label\\\")\\n\",\n    \"\\n\",\n    \"plt.xticks(x_pos, labels)\\n\",\n    \"\\n\",\n    \"plt.tight_layout() ; plt.show()\\n\",\n    \"# fig.savefig(PROJECT_DIR + 'static/classwise_human_errors.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Run ML model benchmarks\\n\",\n    \"We'll use some boilerplate models and training code that is included in the ``mnist1d`` repo.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append('./mnist1d/notebooks/')\\n\",\n    \"sys.path.append('./mnist1d/mnist1d/')\\n\",\n    \"\\n\",\n    \"from models import ConvBase, MLPBase, LinearBase, GRUBase\\n\",\n    \"from train import get_model_args, train_model\\n\",\n    \"from utils import set_seed\\n\",\n    \"\\n\",\n    \"DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 2.19s, train_loss 4.485e-02, test_loss 3.860e-01, train_acc 96.9, test_acc 88.8\\n\",\n      \"step 2000, dt 2.20s, train_loss 1.061e-01, test_loss 4.487e-01, train_acc 96.8, test_acc 90.5\\n\",\n      \"step 3000, dt 2.24s, train_loss 3.154e-04, test_loss 2.930e-01, train_acc 100.0, test_acc 94.5\\n\",\n      \"step 4000, dt 2.30s, train_loss 9.826e-05, test_loss 3.197e-01, train_acc 100.0, test_acc 94.5\\n\",\n      \"step 5000, dt 2.31s, train_loss 5.446e-05, test_loss 3.398e-01, train_acc 100.0, test_acc 94.4\\n\",\n      \"step 6000, dt 2.32s, train_loss 3.322e-05, test_loss 3.517e-01, train_acc 100.0, test_acc 94.2\\n\",\n      \"\\n\",\n      \"Initialized GRUBase model with 5134 parameters\\n\",\n      \"step 1000, dt 14.38s, train_loss 1.374e-01, test_loss 4.551e-01, train_acc 93.5, test_acc 85.2\\n\",\n      \"step 2000, dt 14.21s, train_loss 7.173e-02, test_loss 4.236e-01, train_acc 96.0, test_acc 87.2\\n\",\n      \"step 3000, dt 14.23s, train_loss 6.557e-02, test_loss 6.397e-01, train_acc 96.2, test_acc 85.4\\n\",\n      \"step 4000, dt 14.37s, train_loss 3.309e-02, test_loss 4.872e-01, train_acc 98.8, test_acc 88.6\\n\",\n      \"step 5000, dt 14.37s, train_loss 2.953e-03, test_loss 5.327e-01, train_acc 100.0, test_acc 90.5\\n\",\n      \"step 6000, dt 14.18s, train_loss 1.856e-03, test_loss 5.819e-01, train_acc 100.0, test_acc 90.8\\n\",\n      \"\\n\",\n      \"Initialized MLPBase model with 15210 parameters\\n\",\n      \"step 1000, dt 0.85s, train_loss 1.848e-01, test_loss 1.671e+00, train_acc 93.5, test_acc 62.8\\n\",\n      \"step 2000, dt 0.93s, train_loss 5.415e-02, test_loss 2.860e+00, train_acc 97.9, test_acc 63.8\\n\",\n      \"step 3000, dt 0.94s, train_loss 8.269e-04, test_loss 3.302e+00, train_acc 100.0, test_acc 64.3\\n\",\n      \"step 4000, dt 0.98s, train_loss 2.364e-04, test_loss 3.352e+00, train_acc 100.0, test_acc 64.2\\n\",\n      \"step 5000, dt 0.99s, train_loss 1.396e-04, test_loss 3.417e+00, train_acc 100.0, test_acc 64.1\\n\",\n      \"step 6000, dt 1.04s, train_loss 9.143e-05, test_loss 3.486e+00, train_acc 100.0, test_acc 64.2\\n\",\n      \"\\n\",\n      \"Initialized LinearBase model with 410 parameters\\n\",\n      \"step 1000, dt 0.33s, train_loss 1.534e+00, test_loss 1.663e+00, train_acc 37.0, test_acc 32.2\\n\",\n      \"step 2000, dt 0.30s, train_loss 1.532e+00, test_loss 1.667e+00, train_acc 37.2, test_acc 31.7\\n\",\n      \"step 3000, dt 0.30s, train_loss 1.532e+00, test_loss 1.670e+00, train_acc 37.1, test_acc 31.6\\n\",\n      \"step 4000, dt 0.32s, train_loss 1.533e+00, test_loss 1.671e+00, train_acc 37.2, test_acc 31.4\\n\",\n      \"step 5000, dt 0.31s, train_loss 1.534e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.3\\n\",\n      \"step 6000, dt 0.31s, train_loss 1.534e+00, test_loss 1.673e+00, train_acc 37.1, test_acc 31.5\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Note: if you run on a GPU, the walltimes will be _much_ lower\\n\",\n    \"args = get_model_args()\\n\",\n    \"args.total_steps = 6000\\n\",\n    \"args.device = DEVICE\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"results_cnn = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = GRUBase(input_size=1, output_size=args.output_size)\\n\",\n    \"results_gru = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = MLPBase(args.input_size, args.output_size)\\n\",\n    \"results_mlp = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = LinearBase(args.input_size, args.output_size)\\n\",\n    \"results_lin = train_model(data, model, args)\\n\",\n    \"print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 2.52s, train_loss 5.595e-01, test_loss 1.413e+00, train_acc 75.3, test_acc 55.6\\n\",\n      \"step 2000, dt 2.56s, train_loss 3.532e-01, test_loss 2.134e+00, train_acc 80.7, test_acc 58.0\\n\",\n      \"step 3000, dt 2.53s, train_loss 2.891e-01, test_loss 2.651e+00, train_acc 89.1, test_acc 57.4\\n\",\n      \"step 4000, dt 2.62s, train_loss 2.351e-01, test_loss 3.078e+00, train_acc 91.7, test_acc 57.2\\n\",\n      \"step 5000, dt 2.49s, train_loss 2.467e-01, test_loss 3.983e+00, train_acc 91.3, test_acc 56.4\\n\",\n      \"step 6000, dt 2.59s, train_loss 1.868e-01, test_loss 4.687e+00, train_acc 95.4, test_acc 57.9\\n\",\n      \"\\n\",\n      \"Initialized GRUBase model with 5134 parameters\\n\",\n      \"step 1000, dt 15.66s, train_loss 8.114e-01, test_loss 1.111e+00, train_acc 72.2, test_acc 53.0\\n\",\n      \"step 2000, dt 15.23s, train_loss 5.320e-01, test_loss 1.315e+00, train_acc 79.8, test_acc 53.5\\n\",\n      \"step 3000, dt 14.75s, train_loss 5.019e-01, test_loss 1.684e+00, train_acc 79.3, test_acc 53.2\\n\",\n      \"step 4000, dt 15.17s, train_loss 3.934e-01, test_loss 1.786e+00, train_acc 84.3, test_acc 53.4\\n\",\n      \"step 5000, dt 15.35s, train_loss 3.797e-01, test_loss 2.088e+00, train_acc 87.2, test_acc 51.7\\n\",\n      \"step 6000, dt 14.61s, train_loss 3.792e-01, test_loss 2.534e+00, train_acc 85.5, test_acc 50.8\\n\",\n      \"\\n\",\n      \"Initialized MLPBase model with 15210 parameters\\n\",\n      \"step 1000, dt 0.91s, train_loss 1.896e-01, test_loss 1.966e+00, train_acc 90.4, test_acc 60.2\\n\",\n      \"step 2000, dt 0.97s, train_loss 1.416e-02, test_loss 2.657e+00, train_acc 98.9, test_acc 63.8\\n\",\n      \"step 3000, dt 0.94s, train_loss 1.131e-02, test_loss 3.068e+00, train_acc 99.8, test_acc 63.8\\n\",\n      \"step 4000, dt 0.98s, train_loss 2.639e-04, test_loss 3.141e+00, train_acc 100.0, test_acc 64.6\\n\",\n      \"step 5000, dt 1.01s, train_loss 1.397e-04, test_loss 3.270e+00, train_acc 100.0, test_acc 64.1\\n\",\n      \"step 6000, dt 1.00s, train_loss 8.528e-05, test_loss 3.387e+00, train_acc 100.0, test_acc 63.7\\n\",\n      \"\\n\",\n      \"Initialized LinearBase model with 410 parameters\\n\",\n      \"step 1000, dt 0.32s, train_loss 1.534e+00, test_loss 1.662e+00, train_acc 36.9, test_acc 32.4\\n\",\n      \"step 2000, dt 0.33s, train_loss 1.532e+00, test_loss 1.667e+00, train_acc 37.1, test_acc 31.7\\n\",\n      \"step 3000, dt 0.32s, train_loss 1.533e+00, test_loss 1.670e+00, train_acc 37.2, test_acc 31.4\\n\",\n      \"step 4000, dt 0.31s, train_loss 1.533e+00, test_loss 1.671e+00, train_acc 37.3, test_acc 31.3\\n\",\n      \"step 5000, dt 0.31s, train_loss 1.534e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.4\\n\",\n      \"step 6000, dt 0.31s, train_loss 1.535e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Note: if you run on a GPU, the walltimes will be _much_ lower\\n\",\n    \"args = get_model_args()\\n\",\n    \"args.total_steps = 6000\\n\",\n    \"args.device = DEVICE\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"results_cnn_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = GRUBase(input_size=1, output_size=args.output_size)\\n\",\n    \"results_gru_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = MLPBase(args.input_size, args.output_size)\\n\",\n    \"results_mlp_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = LinearBase(args.input_size, args.output_size)\\n\",\n    \"results_lin_shuff = train_model(data_shuff, model, args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Use early-stopping to select a good CNN model\\n\",\n    \"We'll run a per-class error rate analysis on it, like we did for human labels.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 2.18s, train_loss 1.854e-01, test_loss 4.479e-01, train_acc 95.5, test_acc 87.1\\n\",\n      \"step 2000, dt 2.28s, train_loss 5.832e-02, test_loss 6.452e-01, train_acc 96.5, test_acc 88.3\\n\",\n      \"step 3000, dt 2.29s, train_loss 4.614e-04, test_loss 4.465e-01, train_acc 100.0, test_acc 92.2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = get_model_args()\\n\",\n    \"args.total_steps = 3000  # approx early stopping based on CNN training in earlier cells\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"_ = train_model(data, model, args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy:  90.4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"preds = model(torch.Tensor(data['x_test'])).cpu().detach().numpy()\\n\",\n    \"guesses = preds.argmax(-1)[:500]\\n\",\n    \"trues = data['y_test'][:500]\\n\",\n    \"print(\\\"Accuracy: \\\", 100.*np.sum(guesses==trues)/len(guesses))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 525x375 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"err_ixs = np.where(guesses!=trues)\\n\",\n    \"err_classes = trues[err_ixs]\\n\",\n    \"\\n\",\n    \"labels = [i for i in range(10)]\\n\",\n    \"err_rate = [100. * np.sum(err_classes == i)/np.sum(trues==i) for i in labels]\\n\",\n    \"x_pos = [i for i, _ in enumerate(labels)]\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(3.5, 2.5), dpi=150)\\n\",\n    \"plt.bar(x_pos, err_rate, color='green')\\n\",\n    \"plt.xlabel(\\\"Label\\\")\\n\",\n    \"plt.ylabel(\\\"% misclassified\\\")\\n\",\n    \"plt.title(\\\"CNN mistakes by class label\\\")\\n\",\n    \"\\n\",\n    \"plt.xticks(x_pos, labels)\\n\",\n    \"\\n\",\n    \"plt.tight_layout() ; plt.show()\\n\",\n    \"# fig.savefig(PROJECT_DIR + 'static/classwise_cnn_errors.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Takeaways\\n\",\n    \"An experienced human can classify these examples at almost 96% accuracy. The CNN can do so at 93% accuracy. Both the human and the CNN struggle primarily with classifying 2s and 7s, and to a lesser degree 4s. The human had a harder time classifying 9s whereas the CNN had a harder time classifying 1s. Both had zero errors classifying 3s and 6s.\\n\",\n    \"\\n\",\n    \"Classification errors were fairly evenly balanced across classes, which is a good sign. If only one or two classes were responsible for most of the mistakes, that would have been a sign that those classes were too difficult compared to the others.\\n\",\n    \"\\n\",\n    \"It's interesting that a human can outperform a CNN on this simple task. Part of the issue is that the CNN is only given 4000 training examples -- with more examples it might be able to match or exceed the human baseline. Even though the data is low dimensional, the classification objective is quite difficult and spatial/relational priors matter _a lot_. It may be that the architecture of the CNN prevents it from learning all of the tricks that humans are capable of using (eg, using relational reasoning about two signals to determine how they work together to form the digit signal).\\n\",\n    \"\\n\",\n    \"It's worth noting that CNNs outperform human experts on most large-scale image classification tasks like ImageNet and CIFAR-100. But here is a tiny benchmark where humans are still competitive -- this is a nice quality, as it suggests that the key to performing well on the dataset does not rest on [shortcut learning](https://arxiv.org/abs/2004.07780) such as memorizing specific numbers or patterns to machine precision. A high-performing ML model, we can hope, would have to solve the problem using strategies that would be intuitive to a human.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plot all benchmarks\\n\",\n    \"The goal here is to give a global overview of all the benchmarks we just ran. The large gap in performance between logistic regression and the MLP suggests that nonlinear features are important. The large gap between the MLP and the CNN suggests that spatial inductive biases are important. The strong human performance shows that one can attain good performance on this dataset without using shortcut learning or memorization (if that were the case, then we'd expect the CNN to do better than the human).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 615x450 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(4.1, 3), dpi=150)\\n\",\n    \"args.total_steps = 6000\\n\",\n    \"\\n\",\n    \"plt.subplot(1,1,1)\\n\",\n    \"t = range(0, args.total_steps+1, args.eval_every)\\n\",\n    \"plt.plot(t, results_lin['test_acc'], 'r-', label='logistic')\\n\",\n    \"plt.plot(t, results_lin_shuff['test_acc'], 'r--', label='shuffle')\\n\",\n    \"\\n\",\n    \"plt.plot(t, results_mlp['test_acc'], 'g-', label='mlp')\\n\",\n    \"plt.plot(t, results_mlp_shuff['test_acc'], 'g--', label='shuffle')\\n\",\n    \"\\n\",\n    \"plt.plot(t, results_cnn['test_acc'], 'b-', label='cnn')\\n\",\n    \"plt.plot(t, results_cnn_shuff['test_acc'], 'b--', label='shuffle')\\n\",\n    \"\\n\",\n    \"plt.plot(t, results_gru['test_acc'], 'c-', label='gru')\\n\",\n    \"plt.plot(t, results_gru_shuff['test_acc'], 'c--', label='shuffle')\\n\",\n    \"\\n\",\n    \"plt.plot(t, [95.8]*len(t), 'k-', label='human')\\n\",\n    \"\\n\",\n    \"plt.title('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n    \"plt.legend(fontsize=6, ncol=5, loc='lower right')\\n\",\n    \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9)\\n\",\n    \"plt.ylim(0,105)\\n\",\n    \"plt.tight_layout() ; plt.show()\\n\",\n    \"# fig.savefig(PROJECT_DIR + 'static/benchmarks.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Conclusions\\n\",\n    \"We believe this dataset is ideal for performing small-scale, science of deep learning experiments.\\n\",\n    \"\\n\",\n    \"Try it out, do something interesting, and share it with us via a Colab. We wil feature your work in the README.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/deep-double-descent.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"456b6e7a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# **MNIST-1D**: Observing deep double descent\\n\",\n    \"\\n\",\n    \"This notebook investigates double descent as described in section 8.4 of the [\\\"Understanding Deep Learning\\\"](https://udlbook.github.io/udlbook/) textbook.\\n\",\n    \"\\n\",\n    \"The deep double descent phenomenon was [originally described here](https://arxiv.org/abs/1812.11118) and later extended to modern architectures and large datasets in an [OpenAI research project](https://openai.com/blog/deep-double-descent/).\\n\",\n    \"\\n\",\n    \"This case study is meant to show the convenience and computational savings of working with the low-dimensional MNIST-1D dataset. You can find more details at https://github.com/greydanus/mnist1d.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"1f6c3dd5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.utils.data import TensorDataset, DataLoader\\n\",\n    \"\\n\",\n    \"# Figure style (loading from the web to make running in a Colab easier)\\n\",\n    \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"850b5643\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4000, 40)\\n\",\n      \"(1000, 40)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load MNIST-1D\\n\",\n    \"# (loading from the web to make running in a Colab easier)\\n\",\n    \"\\n\",\n    \"from urllib.request import urlopen\\n\",\n    \"import pickle\\n\",\n    \"\\n\",\n    \"url = 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'\\n\",\n    \"data = pickle.load(urlopen(url))\\n\",\n    \"\\n\",\n    \"print(data['x'].shape)\\n\",\n    \"print(data['x_test'].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"e05b4dd2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4000, 40)\\n\",\n      \"(12000, 40)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The experiment works with the default frozen dataset as well,\\n\",\n    \"# but having a larger test set makes the test performance curve smoother.\\n\",\n    \"# So here we generate MNIST-1D with 4000 + 12000 samples.\\n\",\n    \"# Requires installed mnist1d package (pip install mnist1d)\\n\",\n    \"\\n\",\n    \"# If running in Google Colab, you can uncomment this line:\\n\",\n    \"# !pip install mnist1d\\n\",\n    \"\\n\",\n    \"from mnist1d.data import make_dataset, get_dataset_args\\n\",\n    \"\\n\",\n    \"args = get_dataset_args()\\n\",\n    \"args.num_samples = 16_000\\n\",\n    \"args.train_split = 0.25\\n\",\n    \"\\n\",\n    \"data = make_dataset(args)\\n\",\n    \"\\n\",\n    \"print(data['x'].shape)\\n\",\n    \"print(data['x_test'].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"73de6de1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Add 15% noise to training labels\\n\",\n    \"\\n\",\n    \"import copy\\n\",\n    \"data_with_label_noise = copy.deepcopy(data)\\n\",\n    \"\\n\",\n    \"for i in range(len(data['y'])):\\n\",\n    \"    if np.random.random_sample() < 0.15:\\n\",\n    \"        data_with_label_noise['y'][i] = np.random.randint(0, 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"06fd8ec2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Define MLP architecture with one hidden layer\\n\",\n    \"\\n\",\n    \"def get_model(n_hidden):\\n\",\n    \"    return nn.Sequential(\\n\",\n    \"        nn.Linear(40, n_hidden),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Linear(n_hidden, n_hidden),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Linear(n_hidden, 10)\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"163a0f7b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def fit_model(model, data, n_epoch=500):\\n\",\n    \"    loss_function = torch.nn.CrossEntropyLoss()\\n\",\n    \"    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\\n\",\n    \"\\n\",\n    \"    x_train = torch.tensor(data[\\\"x\\\"].astype(\\\"float32\\\"))\\n\",\n    \"    y_train = torch.tensor(data[\\\"y\\\"].transpose().astype(\\\"long\\\"))\\n\",\n    \"    x_test = torch.tensor(data[\\\"x_test\\\"].astype(\\\"float32\\\"))\\n\",\n    \"    y_test = torch.tensor(data[\\\"y_test\\\"].astype(\\\"long\\\"))\\n\",\n    \"\\n\",\n    \"    data_loader = DataLoader(\\n\",\n    \"        TensorDataset(x_train, y_train), batch_size=100, shuffle=True\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    # Train the model\\n\",\n    \"    for epoch in range(n_epoch):\\n\",\n    \"        for i, batch in enumerate(data_loader):\\n\",\n    \"            x_batch, y_batch = batch\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            pred = model(x_batch)\\n\",\n    \"            loss = loss_function(pred, y_batch)\\n\",\n    \"            loss.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"\\n\",\n    \"    # Evaluate the model\\n\",\n    \"    pred_train = model(x_train)\\n\",\n    \"    pred_test = model(x_test)\\n\",\n    \"    _, predicted_train_class = torch.max(pred_train.data, 1)\\n\",\n    \"    _, predicted_test_class = torch.max(pred_test.data, 1)\\n\",\n    \"    errors_train = 100 * (predicted_train_class != y_train).float().mean()\\n\",\n    \"    errors_test = 100 * (predicted_test_class != y_test).float().mean()\\n\",\n    \"    losses_train = loss_function(pred_train, y_train).item()\\n\",\n    \"    losses_test = loss_function(pred_test, y_test).item()\\n\",\n    \"    print(\\n\",\n    \"        f\\\"Epoch {epoch + 1:5d}, train loss {losses_train:.6f}, train error {errors_train:3.2f}, \\\"\\n\",\n    \"        f\\\"test loss {losses_test:.6f}, test error {errors_test:3.2f}\\\"\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    return errors_train, errors_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"db433354\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training model with   2 hidden variables\\n\",\n      \"Epoch   500, train loss 1.626742, train error 67.15, test loss 1.676046, test error 68.53\\n\",\n      \"Training model with   3 hidden variables\\n\",\n      \"Epoch   500, train loss 1.592728, train error 63.88, test loss 1.663565, test error 66.83\\n\",\n      \"Training model with   4 hidden variables\\n\",\n      \"Epoch   500, train loss 1.356913, train error 54.78, test loss 1.447518, test error 58.27\\n\",\n      \"Training model with   5 hidden variables\\n\",\n      \"Epoch   500, train loss 1.435448, train error 57.23, test loss 1.537323, test error 61.37\\n\",\n      \"Training model with   7 hidden variables\\n\",\n      \"Epoch   500, train loss 1.199873, train error 48.03, test loss 1.375124, test error 54.20\\n\",\n      \"Training model with   9 hidden variables\\n\",\n      \"Epoch   500, train loss 1.040277, train error 41.28, test loss 1.307993, test error 49.33\\n\",\n      \"Training model with  12 hidden variables\\n\",\n      \"Epoch   500, train loss 0.979797, train error 38.40, test loss 1.356770, test error 50.08\\n\",\n      \"Training model with  16 hidden variables\\n\",\n      \"Epoch   500, train loss 0.733982, train error 28.80, test loss 1.392089, test error 44.55\\n\",\n      \"Training model with  21 hidden variables\\n\",\n      \"Epoch   500, train loss 0.519828, train error 19.95, test loss 1.603583, test error 42.63\\n\",\n      \"Training model with  27 hidden variables\\n\",\n      \"Epoch   500, train loss 0.310109, train error 12.05, test loss 2.306223, test error 41.02\\n\",\n      \"Training model with  36 hidden variables\\n\",\n      \"Epoch   500, train loss 0.060272, train error 1.35, test loss 4.160654, test error 41.66\\n\",\n      \"Training model with  47 hidden variables\\n\",\n      \"Epoch   500, train loss 0.004774, train error 0.00, test loss 4.415065, test error 41.01\\n\",\n      \"Training model with  61 hidden variables\\n\",\n      \"Epoch   500, train loss 0.002923, train error 0.00, test loss 3.775428, test error 41.24\\n\",\n      \"Training model with  80 hidden variables\\n\",\n      \"Epoch   500, train loss 0.002362, train error 0.00, test loss 3.404342, test error 39.59\\n\",\n      \"Training model with 104 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001791, train error 0.00, test loss 3.069527, test error 38.61\\n\",\n      \"Training model with 135 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001580, train error 0.00, test loss 2.834522, test error 38.58\\n\",\n      \"Training model with 177 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001416, train error 0.00, test loss 2.715816, test error 38.09\\n\",\n      \"Training model with 230 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001280, train error 0.00, test loss 2.414671, test error 37.10\\n\",\n      \"Training model with 300 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001193, train error 0.00, test loss 2.338388, test error 37.25\\n\",\n      \"CPU times: user 44min 26s, sys: 21.3 s, total: 44min 47s\\n\",\n      \"Wall time: 11min 12s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"\\n\",\n    \"# Without label noise\\n\",\n    \"\\n\",\n    \"hidden_variables = np.unique(np.geomspace(2, 300, 20, dtype=int))\\n\",\n    \"\\n\",\n    \"errors_train_raw = np.zeros_like(hidden_variables)\\n\",\n    \"errors_test_raw = np.zeros_like(hidden_variables)\\n\",\n    \"\\n\",\n    \"for i, size in enumerate(hidden_variables):\\n\",\n    \"    print(f'Training model with {size:3d} hidden variables')\\n\",\n    \"    \\n\",\n    \"    model = get_model(size)\\n\",\n    \"    errors_train, errors_test = fit_model(model, data)\\n\",\n    \"    errors_train_raw[i] = errors_train\\n\",\n    \"    errors_test_raw[i] = errors_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"c68f67e6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training model with   2 hidden variables\\n\",\n      \"Epoch   500, train loss 1.918945, train error 71.68, test loss 1.757478, test error 69.59\\n\",\n      \"Training model with   3 hidden variables\\n\",\n      \"Epoch   500, train loss 1.833644, train error 68.65, test loss 1.714114, test error 67.50\\n\",\n      \"Training model with   4 hidden variables\\n\",\n      \"Epoch   500, train loss 1.767615, train error 62.75, test loss 1.653679, test error 62.55\\n\",\n      \"Training model with   5 hidden variables\\n\",\n      \"Epoch   500, train loss 1.623392, train error 57.95, test loss 1.466705, test error 57.45\\n\",\n      \"Training model with   7 hidden variables\\n\",\n      \"Epoch   500, train loss 1.545471, train error 54.45, test loss 1.419071, test error 54.80\\n\",\n      \"Training model with   9 hidden variables\\n\",\n      \"Epoch   500, train loss 1.429673, train error 47.45, test loss 1.344300, test error 48.97\\n\",\n      \"Training model with  12 hidden variables\\n\",\n      \"Epoch   500, train loss 1.373848, train error 46.47, test loss 1.371256, test error 50.28\\n\",\n      \"Training model with  16 hidden variables\\n\",\n      \"Epoch   500, train loss 1.204145, train error 39.70, test loss 1.317827, test error 47.24\\n\",\n      \"Training model with  21 hidden variables\\n\",\n      \"Epoch   500, train loss 1.094465, train error 38.08, test loss 1.528045, test error 50.84\\n\",\n      \"Training model with  27 hidden variables\\n\",\n      \"Epoch   500, train loss 0.895353, train error 29.55, test loss 1.663771, test error 50.69\\n\",\n      \"Training model with  36 hidden variables\\n\",\n      \"Epoch   500, train loss 0.570960, train error 19.02, test loss 2.504689, test error 52.80\\n\",\n      \"Training model with  47 hidden variables\\n\",\n      \"Epoch   500, train loss 0.163747, train error 5.47, test loss 5.697664, test error 54.05\\n\",\n      \"Training model with  61 hidden variables\\n\",\n      \"Epoch   500, train loss 0.006272, train error 0.00, test loss 5.786091, test error 54.02\\n\",\n      \"Training model with  80 hidden variables\\n\",\n      \"Epoch   500, train loss 0.003608, train error 0.00, test loss 4.720016, test error 52.08\\n\",\n      \"Training model with 104 hidden variables\\n\",\n      \"Epoch   500, train loss 0.002570, train error 0.00, test loss 3.918840, test error 50.23\\n\",\n      \"Training model with 135 hidden variables\\n\",\n      \"Epoch   500, train loss 0.002111, train error 0.00, test loss 3.494201, test error 48.39\\n\",\n      \"Training model with 177 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001841, train error 0.00, test loss 3.120997, test error 47.88\\n\",\n      \"Training model with 230 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001669, train error 0.00, test loss 2.887975, test error 46.54\\n\",\n      \"Training model with 300 hidden variables\\n\",\n      \"Epoch   500, train loss 0.001547, train error 0.00, test loss 2.629015, test error 45.47\\n\",\n      \"CPU times: user 50min 21s, sys: 28 s, total: 50min 49s\\n\",\n      \"Wall time: 12min 43s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"\\n\",\n    \"# With label noise\\n\",\n    \"\\n\",\n    \"hidden_variables = np.unique(np.geomspace(2, 300, 20, dtype=int))\\n\",\n    \"\\n\",\n    \"errors_train_noisy = np.zeros_like(hidden_variables)\\n\",\n    \"errors_test_noisy = np.zeros_like(hidden_variables)\\n\",\n    \"\\n\",\n    \"for i, size in enumerate(hidden_variables):\\n\",\n    \"    print(f'Training model with {size:3d} hidden variables')\\n\",\n    \"        \\n\",\n    \"    model = get_model(size)\\n\",\n    \"    errors_train, errors_test = fit_model(model, data_with_label_noise)\\n\",\n    \"    errors_train_noisy[i] = errors_train\\n\",\n    \"    errors_test_noisy[i] = errors_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"16d398cb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Define CNN architecture\\n\",\n    \"\\n\",\n    \"def get_model_cnn(channels):\\n\",\n    \"    return nn.Sequential(\\n\",\n    \"        nn.Conv1d(1, channels, 5, stride=2, padding=1),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Conv1d(channels, channels, 3, stride=2, padding=1),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Conv1d(channels, channels, 3, stride=2, padding=1),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Flatten(),\\n\",\n    \"        nn.Linear(channels * 5, 10),\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"bdff5718\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training model with   2 channels\\n\",\n      \"Epoch   500, train loss 1.842762, train error 63.90, test loss 1.634232, test error 61.20\\n\",\n      \"Training model with   4 channels\\n\",\n      \"Epoch   500, train loss 1.353123, train error 40.40, test loss 1.020193, test error 35.78\\n\",\n      \"Training model with   6 channels\\n\",\n      \"Epoch   500, train loss 1.090348, train error 29.15, test loss 0.787633, test error 23.87\\n\",\n      \"Training model with   8 channels\\n\",\n      \"Epoch   500, train loss 0.930178, train error 24.90, test loss 0.691253, test error 21.95\\n\",\n      \"Training model with  10 channels\\n\",\n      \"Epoch   500, train loss 0.737799, train error 19.55, test loss 0.623556, test error 19.64\\n\",\n      \"Training model with  12 channels\\n\",\n      \"Epoch   500, train loss 0.697752, train error 19.82, test loss 0.756008, test error 23.70\\n\",\n      \"Training model with  14 channels\\n\",\n      \"Epoch   500, train loss 0.589387, train error 17.98, test loss 0.951923, test error 27.74\\n\",\n      \"Training model with  16 channels\\n\",\n      \"Epoch   500, train loss 0.413599, train error 12.70, test loss 0.985720, test error 27.29\\n\",\n      \"Training model with  18 channels\\n\",\n      \"Epoch   500, train loss 0.313949, train error 10.88, test loss 1.676921, test error 33.80\\n\",\n      \"Training model with  20 channels\\n\",\n      \"Epoch   500, train loss 0.175648, train error 6.15, test loss 2.137947, test error 32.48\\n\",\n      \"Training model with  22 channels\\n\",\n      \"Epoch   500, train loss 0.170042, train error 5.50, test loss 2.724582, test error 31.34\\n\",\n      \"Training model with  24 channels\\n\",\n      \"Epoch   500, train loss 0.001942, train error 0.00, test loss 3.098534, test error 32.03\\n\",\n      \"Training model with  26 channels\\n\",\n      \"Epoch   500, train loss 0.001800, train error 0.00, test loss 2.675985, test error 29.98\\n\",\n      \"Training model with  28 channels\\n\",\n      \"Epoch   500, train loss 0.001214, train error 0.00, test loss 2.998804, test error 32.46\\n\",\n      \"Training model with  30 channels\\n\",\n      \"Epoch   500, train loss 0.001273, train error 0.00, test loss 2.608783, test error 29.82\\n\",\n      \"Training model with  35 channels\\n\",\n      \"Epoch   500, train loss 0.000907, train error 0.00, test loss 2.564325, test error 30.17\\n\",\n      \"Training model with  40 channels\\n\",\n      \"Epoch   500, train loss 0.000744, train error 0.00, test loss 2.492599, test error 30.48\\n\",\n      \"Training model with  45 channels\\n\",\n      \"Epoch   500, train loss 0.000644, train error 0.00, test loss 2.462661, test error 30.23\\n\",\n      \"Training model with  50 channels\\n\",\n      \"Epoch   500, train loss 0.000627, train error 0.00, test loss 2.165869, test error 28.67\\n\",\n      \"Training model with  55 channels\\n\",\n      \"Epoch   500, train loss 0.000602, train error 0.00, test loss 2.106489, test error 28.17\\n\",\n      \"Training model with  60 channels\\n\",\n      \"Epoch   500, train loss 0.000550, train error 0.00, test loss 2.259036, test error 30.04\\n\",\n      \"Training model with  65 channels\\n\",\n      \"Epoch   500, train loss 0.000540, train error 0.00, test loss 1.948158, test error 28.11\\n\",\n      \"Training model with  70 channels\\n\",\n      \"Epoch   500, train loss 0.000505, train error 0.00, test loss 2.132110, test error 28.83\\n\",\n      \"Training model with  75 channels\\n\",\n      \"Epoch   500, train loss 0.000484, train error 0.00, test loss 2.046781, test error 29.44\\n\",\n      \"Training model with  80 channels\\n\",\n      \"Epoch   500, train loss 0.000473, train error 0.00, test loss 1.883680, test error 28.07\\n\",\n      \"Training model with  85 channels\\n\",\n      \"Epoch   500, train loss 0.000479, train error 0.00, test loss 1.867926, test error 28.12\\n\",\n      \"Training model with  90 channels\\n\",\n      \"Epoch   500, train loss 0.000445, train error 0.00, test loss 1.961712, test error 28.84\\n\",\n      \"Training model with  95 channels\\n\",\n      \"Epoch   500, train loss 0.000442, train error 0.00, test loss 1.795554, test error 27.38\\n\",\n      \"Training model with 100 channels\\n\",\n      \"Epoch   500, train loss 0.000423, train error 0.00, test loss 1.774604, test error 27.66\\n\",\n      \"CPU times: user 3h 8min 15s, sys: 1min 19s, total: 3h 9min 34s\\n\",\n      \"Wall time: 47min 28s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"\\n\",\n    \"# With label noise\\n\",\n    \"# Need to reshape the data arrays to make them compatible with the conv1d layers\\n\",\n    \"\\n\",\n    \"ddata = data_with_label_noise.copy()\\n\",\n    \"ddata['x'] = data['x'][:, np.newaxis, :]\\n\",\n    \"ddata['x_test'] = data['x_test'][:, np.newaxis, :]\\n\",\n    \"\\n\",\n    \"channels = np.concatenate((np.arange(2, 30, 2), np.arange(30, 101, 5)))\\n\",\n    \"\\n\",\n    \"errors_train_cnn = np.zeros_like(channels)\\n\",\n    \"errors_test_cnn = np.zeros_like(channels)\\n\",\n    \"\\n\",\n    \"for i, size in enumerate(channels):\\n\",\n    \"    print(f'Training model with {size:3d} channels')\\n\",\n    \"    \\n\",\n    \"    model = get_model_cnn(size)\\n\",\n    \"    errors_train, errors_test = fit_model(model, ddata, n_epoch=500)\\n\",\n    \"    errors_train_cnn[i] = errors_train\\n\",\n    \"    errors_test_cnn[i] = errors_test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"2f35a111\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 406.25x250 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axs = plt.subplots(ncols=3, figsize=(3.25, 2), dpi=125)\\n\",\n    \"\\n\",\n    \"axs[0].plot(hidden_variables, errors_test_raw, '-', label='Test')\\n\",\n    \"axs[0].plot(hidden_variables, errors_train_raw, '--', label='Train', zorder=0)\\n\",\n    \"\\n\",\n    \"axs[0].set_ylim(0, 70)\\n\",\n    \"axs[0].set_xlim(0, 300)\\n\",\n    \"axs[1].set_xticks([0, 100, 200, 300])\\n\",\n    \"axs[0].set_xlabel('Hidden layer size')\\n\",\n    \"axs[0].set_ylabel('Classification error (%)')\\n\",\n    \"axs[0].legend()\\n\",\n    \"axs[0].set_title('MLP\\\\nNo label noise')\\n\",\n    \"\\n\",\n    \"axs[0].spines.left.set_position(('outward', 3))\\n\",\n    \"axs[0].spines.bottom.set_position(('outward', 3))\\n\",\n    \"\\n\",\n    \"axs[1].plot(hidden_variables, errors_test_noisy, '-', label='Test')\\n\",\n    \"axs[1].plot(hidden_variables, errors_train_noisy, '--', label='Train', zorder=0)\\n\",\n    \"\\n\",\n    \"axs[1].set_ylim(0, 70)\\n\",\n    \"axs[1].set_xlim(0, 300)\\n\",\n    \"axs[1].set_xticks([0, 100, 200, 300])\\n\",\n    \"axs[1].set_xlabel('Hidden layer size')\\n\",\n    \"axs[1].set_yticklabels([])\\n\",\n    \"axs[1].set_title('MLP\\\\nLabel noise')\\n\",\n    \"axs[1].plot([0, 300], [15, 15], ':', zorder=-1, color='#aaaaaa')\\n\",\n    \"\\n\",\n    \"axs[1].spines.left.set_position(('outward', 3))\\n\",\n    \"axs[1].spines.bottom.set_position(('outward', 3))\\n\",\n    \"\\n\",\n    \"axs[2].plot(channels, errors_test_cnn, '-', label='Test')\\n\",\n    \"axs[2].plot(channels, errors_train_cnn, '--', label='Train', zorder=0)\\n\",\n    \"\\n\",\n    \"axs[2].set_ylim(0, 70)\\n\",\n    \"axs[2].set_xlim(0, 100)\\n\",\n    \"axs[2].set_yticklabels([])\\n\",\n    \"axs[2].set_xlabel('Num. of channels')\\n\",\n    \"axs[2].set_title('CNN\\\\nLabel noise')\\n\",\n    \"axs[2].plot([0, 100], [15, 15], ':', zorder=-1, color='#aaaaaa')\\n\",\n    \"\\n\",\n    \"axs[2].spines.left.set_position(('outward', 3))\\n\",\n    \"axs[2].spines.bottom.set_position(('outward', 3))\\n\",\n    \"\\n\",\n    \"fig.text(  0, .94, 'a', fontsize=8, weight='bold')\\n\",\n    \"fig.text(.37, .94, 'b', fontsize=8, weight='bold')\\n\",\n    \"fig.text(.67, .94, 'c', fontsize=8, weight='bold')\\n\",\n    \"\\n\",\n    \"fig.savefig('figures/double-descent.png', dpi=300)\\n\",\n    \"fig.savefig('figures/double-descent.pdf')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/lottery-tickets.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"dKUcDM76bHx3\"\n      },\n      \"source\": [\n        \"# **MNIST-1D**: Finding & analyzing lottery tickets\\n\",\n        \"Sam Greydanus\\n\",\n        \"\\n\",\n        \"In this notebook we'll show how to find a sparsely-masked MLP which can train and generalize better than its fully-connected counterpart. This phenomenon was first described by [Frankle & Carbin (2018)](https://arxiv.org/abs/1803.03635), who referred to these sparse masks as \\\"winning lottery tickets.\\\" We'll also analyze these lottery tickets and show that they have learned spatial priors such as local connectivity.\\n\",\n        \"\\n\",\n        \"This case study is meant to demonstrate the convenience and computational savings of working with the MNIST-1D dataset. You can find more details at https://github.com/greydanus/mnist1d.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 21,\n      \"metadata\": {\n        \"id\": \"Sg2i1QmhKW5d\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n        \"\\n\",\n        \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n        \"!git clone https://github.com/greydanus/mnist1d\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 22,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"KaQo7QhvXvid\",\n        \"outputId\": \"bd69f1a3-680f-4638-c07a-0cd64df8ed24\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Using: cuda\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"import numpy as np\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import copy\\n\",\n        \"\\n\",\n        \"import torch, os\\n\",\n        \"import torch.nn as nn\\n\",\n        \"import torch.nn.functional as F\\n\",\n        \"import torch.optim as optim\\n\",\n        \"\\n\",\n        \"# Try attaching to GPU\\n\",\n        \"DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\\n\",\n        \"print('Using:', DEVICE)\\n\",\n        \"\\n\",\n        \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"P2LCuLJjrqf6\"\n      },\n      \"source\": [\n        \"## Only run this if you're in Google Colab\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 23,\n      \"metadata\": {\n        \"id\": \"k28zaUnjrqf6\",\n        \"outputId\": \"577c1519-5b0f-4dd6-dd21-9b9bed441890\",\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        }\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\\\"/content/gdrive\\\", force_remount=True).\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"if True:\\n\",\n        \"    # Only run this in Colab\\n\",\n        \"    from google.colab import drive\\n\",\n        \"    drive.mount('/content/gdrive')\\n\",\n        \"    project_dir = \\\"/content/gdrive/My Drive/Research/mnist1d/\\\"\\n\",\n        \"else:\\n\",\n        \"    project_dir = './'\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"nre26wEOfZsM\"\n      },\n      \"source\": [\n        \"## Get the MNIST1D dataset\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 28,\n      \"metadata\": {\n        \"id\": \"NOn2afSRrqf7\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"from mnist1d.data import get_dataset, get_dataset_args\\n\",\n        \"from mnist1d.utils import set_seed, to_pickle, from_pickle\\n\",\n        \"\\n\",\n        \"import sys ; sys.path.append('./mnist1d/notebooks')\\n\",\n        \"from train import get_model_args, train_model\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 14,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"I-vm_gh5xTJs\",\n        \"outputId\": \"73b73308-0052-424f-be4e-628e75eef117\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Downloading MNIST1D dataset from https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl\\n\",\n            \"Saving to ./mnist1d_data.pkl\\n\",\n            \"Successfully loaded data from ./mnist1d_data.pkl\\n\",\n            \"Examples in training set: 4000\\n\",\n            \"Examples in test set: 1000\\n\",\n            \"Length of each input: 40\\n\",\n            \"Number of classes: 10\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"args = get_dataset_args()\\n\",\n        \"data = get_dataset(args=args)  # by default, this will download a pre-made dataset from the GitHub repo\\n\",\n        \"\\n\",\n        \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n        \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n        \"print(\\\"Length of each input: {}\\\".format(data['x'].shape[-1]))\\n\",\n        \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"O2vy0FKjfDwr\"\n      },\n      \"source\": [\n        \"## Make an MLP that can be masked\\n\",\n        \"These parameter-wise binary masks are how we will represent sparsity in this project. There's not a great PyTorch API for this yet, so here's a temporary solution.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 15,\n      \"metadata\": {\n        \"id\": \"uBx5gNW-mqH_\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"class SparseLinear(torch.nn.Module):\\n\",\n        \"  def __init__(self, x_size, y_size):\\n\",\n        \"    super(SparseLinear, self).__init__()\\n\",\n        \"    self.linear = torch.nn.Linear(x_size, y_size)\\n\",\n        \"    param_vec = torch.cat([p.flatten() for p in self.parameters()])\\n\",\n        \"    self.mask = torch.ones_like(param_vec).to(DEVICE)\\n\",\n        \"\\n\",\n        \"  def forward(self, x, apply_mask=True):\\n\",\n        \"    if apply_mask:\\n\",\n        \"      self.apply_mask()\\n\",\n        \"    return self.linear(x)\\n\",\n        \"\\n\",\n        \"  def update_mask(self, new_mask):\\n\",\n        \"    self.mask = new_mask\\n\",\n        \"    self.apply_mask()\\n\",\n        \"\\n\",\n        \"  def apply_mask(self):\\n\",\n        \"    self.vec2param(self.param2vec())\\n\",\n        \"\\n\",\n        \"  def param2vec(self):\\n\",\n        \"    vec = torch.cat([p.flatten() for p in self.parameters()])\\n\",\n        \"    return self.mask * vec\\n\",\n        \"\\n\",\n        \"  def vec2param(self, vec):\\n\",\n        \"    pointer = 0\\n\",\n        \"    for param in self.parameters():\\n\",\n        \"      param_len = np.cumprod(param.shape)[-1]\\n\",\n        \"      new_param = vec[pointer:pointer+param_len].reshape(param.shape)\\n\",\n        \"      param.data = new_param.data\\n\",\n        \"      pointer += param_len\\n\",\n        \"\\n\",\n        \"class SparseMLP(torch.nn.Module):\\n\",\n        \"  def __init__(self, input_size, output_size, hidden_size=100):\\n\",\n        \"    super(SparseMLP, self).__init__()\\n\",\n        \"    self.linear1 = SparseLinear(input_size, hidden_size)\\n\",\n        \"    self.linear2 = SparseLinear(hidden_size, hidden_size)\\n\",\n        \"    self.linear3 = SparseLinear(hidden_size, output_size)\\n\",\n        \"    self.layers = [self.linear1, self.linear2, self.linear3]\\n\",\n        \"\\n\",\n        \"  def forward(self, x):\\n\",\n        \"    h = torch.relu(self.linear1(x))\\n\",\n        \"    h = h + torch.relu(self.linear2(h))\\n\",\n        \"    h = self.linear3(h)\\n\",\n        \"    return h\\n\",\n        \"\\n\",\n        \"  def get_layer_masks(self):\\n\",\n        \"    return [l.mask for l in self.layers]\\n\",\n        \"\\n\",\n        \"  def set_layer_masks(self, new_masks):\\n\",\n        \"    for i, l in enumerate(self.layers):\\n\",\n        \"      l.update_mask(new_masks[i])\\n\",\n        \"\\n\",\n        \"  def get_layer_vecs(self):\\n\",\n        \"    return [l.param2vec() for l in self.layers]\\n\",\n        \"\\n\",\n        \"  def set_layer_vecs(self, vecs):\\n\",\n        \"    for i, l in enumerate(self.layers):\\n\",\n        \"      l.vec2param(vecs[i])\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"2hwmH2vIbHin\"\n      },\n      \"source\": [\n        \"## Updating masks\\n\",\n        \"Here's a helper function for updating masks.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 16,\n      \"metadata\": {\n        \"id\": \"Md2F9WDgYSqT\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# find a mask, given some heuristic and desired sparsity\\n\",\n        \"def get_mask(scores, percent_sparse):\\n\",\n        \"  # scores: per-weight scores for determining which weights to drop\\n\",\n        \"  # percent_sparse: how much to sparsify the model\\n\",\n        \"  num_to_drop = int(percent_sparse * len(scores))\\n\",\n        \"  ixs_to_drop = torch.sort(scores)[1][:num_to_drop] # sort from low score to high, select k with lowest score\\n\",\n        \"  mask = torch.ones_like(scores)\\n\",\n        \"  mask[ixs_to_drop] = 0\\n\",\n        \"  return mask\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"z0McGMV-a3Xo\"\n      },\n      \"source\": [\n        \"## The prune-and-retrain cycle\\n\",\n        \"This is the core method for finding a lottery ticket. We train a model for a fixed number of epochs, prune it, and then re-train and re-prune. We repeat this cycle until we achieve the desired level of sparsity.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 17,\n      \"metadata\": {\n        \"id\": \"5idcbyA3Ylz_\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None,\\n\",\n        \"                        prune_print_every=None, **kwargs):\\n\",\n        \"  if prune_print_every is None:\\n\",\n        \"    prune_print_every = np.inf\\n\",\n        \"\\n\",\n        \"  if criteria_fn is None:\\n\",\n        \"    print(\\\"Using default magnitude-based pruning\\\")\\n\",\n        \"    criteria_fn = lambda init_params, final_params: final_params.abs()\\n\",\n        \"\\n\",\n        \"  init_params = model.get_layer_vecs()\\n\",\n        \"  stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\\n\",\n        \"  models = []\\n\",\n        \"  for i, percent_sparse in enumerate(sparsity_schedule):\\n\",\n        \"\\n\",\n        \"    # layer-wise pruning, where pruning heuristic is determined by criteria_fn\\n\",\n        \"    final_params = model.get_layer_vecs()\\n\",\n        \"    scores = [criteria_fn(ip, fp) for ip, fp in zip(init_params, final_params)]\\n\",\n        \"    masks = [get_mask(s, percent_sparse) for s in scores]\\n\",\n        \"\\n\",\n        \"    # update model with mask and init parameters\\n\",\n        \"    model.set_layer_vecs(init_params)\\n\",\n        \"    model.set_layer_masks(masks)\\n\",\n        \"\\n\",\n        \"    # training process\\n\",\n        \"    results = train_model(dataset, model, args)\\n\",\n        \"    model = results['checkpoints'][-1]\\n\",\n        \"\\n\",\n        \"    # store stats\\n\",\n        \"    stats['train_losses'].append(results['train_losses'])\\n\",\n        \"    stats['test_losses'].append(results['test_losses'])\\n\",\n        \"    stats['train_accs'].append(results['train_acc'])\\n\",\n        \"    stats['test_accs'].append(results['test_acc'])\\n\",\n        \"\\n\",\n        \"    # print progress\\n\",\n        \"    if (i+1) % prune_print_every == 0:\\n\",\n        \"      print('\\\\tretrain #{}, sparsity {:.2f}, final_train_loss {:.3e}, max_acc {:.1f}, last_acc {:.1f}, mean_acc {:.1f}'\\n\",\n        \"            .format(i+1, percent_sparse, results['train_losses'][-1], np.max(results['test_acc']),\\n\",\n        \"            results['test_acc'][-1], np.mean(results['test_acc']) ))\\n\",\n        \"      models.append(copy.deepcopy(model))\\n\",\n        \"\\n\",\n        \"  stats = {k: np.stack(v) for k, v in stats.items()}\\n\",\n        \"  return models, stats\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"m4lokvdD4DKI\"\n      },\n      \"source\": [\n        \"## Choose hyperparameters\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 18,\n      \"metadata\": {\n        \"id\": \"OUe7-b-7Yl2c\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# train settings\\n\",\n        \"model_args = get_model_args()\\n\",\n        \"model_args.total_steps = 1501\\n\",\n        \"model_args.hidden_size = 500\\n\",\n        \"model_args.print_every = 5000 # print never\\n\",\n        \"model_args.eval_every = 100\\n\",\n        \"model_args.learning_rate = 2e-2\\n\",\n        \"model_args.device = DEVICE\\n\",\n        \"\\n\",\n        \"# sparsity settings\\n\",\n        \"num_retrains = 100\\n\",\n        \"sparsity_schedule = np.linspace(0,1.,num_retrains) #1-np.cumprod(np.ones(num_retrains)*tau)/tau # tau = .97\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"hVgDM5rI4J65\"\n      },\n      \"source\": [\n        \"## Find a lottery ticket and a random ticket (takes ~1 hour)\\n\",\n        \"You can also cache results as shown in subsequent cells. Then you can load them anytime to perform analysis.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 18,\n      \"metadata\": {\n        \"id\": \"7S98UZVwrqf-\"\n      },\n      \"outputs\": [],\n      \"source\": []\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 19,\n      \"metadata\": {\n        \"id\": \"OZG3nMVarqf-\",\n        \"outputId\": \"872f067b-5add-4deb-a71f-afe0b7c964bd\",\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        }\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"############  Trial 0  ############\\n\",\n            \"   Random pruning\\n\",\n            \"\\tretrain #1, sparsity 0.00, final_train_loss 3.049e-01, max_acc 63.8, last_acc 60.6, mean_acc 57.5\\n\",\n            \"\\tretrain #2, sparsity 0.01, final_train_loss 1.643e-01, max_acc 63.0, last_acc 62.7, mean_acc 56.5\\n\",\n            \"\\tretrain #3, sparsity 0.02, final_train_loss 3.007e-01, max_acc 64.6, last_acc 61.9, mean_acc 56.6\\n\",\n            \"\\tretrain #4, sparsity 0.03, final_train_loss 3.816e-01, max_acc 66.3, last_acc 65.1, mean_acc 57.8\\n\",\n            \"\\tretrain #5, sparsity 0.04, final_train_loss 1.357e-01, max_acc 65.2, last_acc 63.4, mean_acc 57.7\\n\",\n            \"\\tretrain #6, sparsity 0.05, final_train_loss 1.819e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.2\\n\",\n            \"\\tretrain #7, sparsity 0.06, final_train_loss 2.058e-01, max_acc 64.7, last_acc 63.5, mean_acc 56.7\\n\",\n            \"\\tretrain #8, sparsity 0.07, final_train_loss 1.882e-01, max_acc 63.5, last_acc 62.6, mean_acc 57.3\\n\",\n            \"\\tretrain #9, sparsity 0.08, final_train_loss 1.787e-01, max_acc 66.2, last_acc 66.2, mean_acc 58.1\\n\",\n            \"\\tretrain #10, sparsity 0.09, final_train_loss 2.929e-01, max_acc 65.2, last_acc 61.5, mean_acc 57.2\\n\",\n            \"\\tretrain #11, sparsity 0.10, final_train_loss 1.403e-01, max_acc 64.4, last_acc 64.1, mean_acc 57.6\\n\",\n            \"\\tretrain #12, sparsity 0.11, final_train_loss 4.808e-01, max_acc 66.5, last_acc 66.5, mean_acc 58.4\\n\",\n            \"\\tretrain #13, sparsity 0.12, final_train_loss 1.503e-02, max_acc 65.6, last_acc 65.6, mean_acc 58.2\\n\",\n            \"\\tretrain #14, sparsity 0.13, final_train_loss 3.335e-01, max_acc 65.3, last_acc 64.5, mean_acc 57.8\\n\",\n            \"\\tretrain #15, sparsity 0.14, final_train_loss 3.036e-01, max_acc 64.8, last_acc 63.3, mean_acc 57.7\\n\",\n            \"\\tretrain #16, sparsity 0.15, final_train_loss 1.258e-01, max_acc 64.1, last_acc 64.0, mean_acc 57.7\\n\",\n            \"\\tretrain #17, sparsity 0.16, final_train_loss 1.454e-01, max_acc 66.7, last_acc 66.7, mean_acc 58.4\\n\",\n            \"\\tretrain #18, sparsity 0.17, final_train_loss 1.902e-01, max_acc 65.9, last_acc 65.9, mean_acc 57.6\\n\",\n            \"\\tretrain #19, sparsity 0.18, final_train_loss 1.574e-01, max_acc 65.2, last_acc 65.2, mean_acc 57.5\\n\",\n            \"\\tretrain #20, sparsity 0.19, final_train_loss 1.899e-01, max_acc 66.4, last_acc 64.4, mean_acc 57.5\\n\",\n            \"\\tretrain #21, sparsity 0.20, final_train_loss 1.964e-01, max_acc 64.0, last_acc 61.9, mean_acc 56.5\\n\",\n            \"\\tretrain #22, sparsity 0.21, final_train_loss 2.644e-01, max_acc 65.3, last_acc 64.1, mean_acc 58.1\\n\",\n            \"\\tretrain #23, sparsity 0.22, final_train_loss 1.974e-01, max_acc 64.9, last_acc 63.3, mean_acc 57.9\\n\",\n            \"\\tretrain #24, sparsity 0.23, final_train_loss 2.788e-01, max_acc 67.4, last_acc 66.7, mean_acc 58.3\\n\",\n            \"\\tretrain #25, sparsity 0.24, final_train_loss 4.549e-01, max_acc 66.0, last_acc 64.8, mean_acc 58.8\\n\",\n            \"\\tretrain #26, sparsity 0.25, final_train_loss 4.360e-01, max_acc 66.6, last_acc 66.6, mean_acc 58.6\\n\",\n            \"\\tretrain #27, sparsity 0.26, final_train_loss 2.478e-01, max_acc 63.0, last_acc 63.0, mean_acc 56.6\\n\",\n            \"\\tretrain #28, sparsity 0.27, final_train_loss 1.934e-01, max_acc 64.9, last_acc 61.3, mean_acc 56.8\\n\",\n            \"\\tretrain #29, sparsity 0.28, final_train_loss 3.084e-01, max_acc 64.2, last_acc 62.8, mean_acc 57.0\\n\",\n            \"\\tretrain #30, sparsity 0.29, final_train_loss 1.207e-01, max_acc 65.8, last_acc 65.8, mean_acc 57.9\\n\",\n            \"\\tretrain #31, sparsity 0.30, final_train_loss 2.014e-01, max_acc 66.7, last_acc 63.7, mean_acc 57.9\\n\",\n            \"\\tretrain #32, sparsity 0.31, final_train_loss 8.057e-02, max_acc 65.6, last_acc 61.3, mean_acc 57.3\\n\",\n            \"\\tretrain #33, sparsity 0.32, final_train_loss 8.852e-02, max_acc 68.1, last_acc 68.1, mean_acc 58.7\\n\",\n            \"\\tretrain #34, sparsity 0.33, final_train_loss 1.510e-01, max_acc 65.0, last_acc 63.2, mean_acc 57.4\\n\",\n            \"\\tretrain #35, sparsity 0.34, final_train_loss 2.996e-01, max_acc 64.2, last_acc 64.2, mean_acc 57.9\\n\",\n            \"\\tretrain #36, sparsity 0.35, final_train_loss 8.030e-02, max_acc 64.9, last_acc 62.2, mean_acc 57.5\\n\",\n            \"\\tretrain #37, sparsity 0.36, final_train_loss 1.635e-01, max_acc 66.3, last_acc 65.2, mean_acc 58.8\\n\",\n            \"\\tretrain #38, sparsity 0.37, final_train_loss 6.735e-02, max_acc 63.7, last_acc 63.7, mean_acc 57.8\\n\",\n            \"\\tretrain #39, sparsity 0.38, final_train_loss 3.698e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.4\\n\",\n            \"\\tretrain #40, sparsity 0.39, final_train_loss 2.767e-01, max_acc 64.8, last_acc 63.3, mean_acc 56.8\\n\",\n            \"\\tretrain #41, sparsity 0.40, final_train_loss 1.180e-01, max_acc 65.5, last_acc 62.3, mean_acc 58.1\\n\",\n            \"\\tretrain #42, sparsity 0.41, final_train_loss 3.294e-01, max_acc 64.4, last_acc 64.0, mean_acc 57.6\\n\",\n            \"\\tretrain #43, sparsity 0.42, final_train_loss 2.655e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.9\\n\",\n            \"\\tretrain #44, sparsity 0.43, final_train_loss 4.925e-02, max_acc 66.4, last_acc 66.4, mean_acc 57.6\\n\",\n            \"\\tretrain #45, sparsity 0.44, final_train_loss 1.300e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.1\\n\",\n            \"\\tretrain #46, sparsity 0.45, final_train_loss 1.975e-01, max_acc 64.3, last_acc 64.0, mean_acc 57.9\\n\",\n            \"\\tretrain #47, sparsity 0.46, final_train_loss 1.274e-01, max_acc 66.8, last_acc 66.8, mean_acc 58.3\\n\",\n            \"\\tretrain #48, sparsity 0.47, final_train_loss 1.977e-01, max_acc 65.8, last_acc 64.7, mean_acc 58.0\\n\",\n            \"\\tretrain #49, sparsity 0.48, final_train_loss 2.444e-01, max_acc 65.1, last_acc 61.1, mean_acc 57.4\\n\",\n            \"\\tretrain #50, sparsity 0.49, final_train_loss 2.323e-01, max_acc 65.5, last_acc 63.6, mean_acc 57.9\\n\",\n            \"\\tretrain #51, sparsity 0.51, final_train_loss 4.207e-01, max_acc 64.5, last_acc 63.8, mean_acc 57.7\\n\",\n            \"\\tretrain #52, sparsity 0.52, final_train_loss 6.677e-02, max_acc 66.4, last_acc 66.4, mean_acc 58.3\\n\",\n            \"\\tretrain #53, sparsity 0.53, final_train_loss 1.237e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.7\\n\",\n            \"\\tretrain #54, sparsity 0.54, final_train_loss 2.749e-01, max_acc 65.1, last_acc 65.1, mean_acc 57.2\\n\",\n            \"\\tretrain #55, sparsity 0.55, final_train_loss 1.711e-01, max_acc 65.3, last_acc 65.3, mean_acc 58.4\\n\",\n            \"\\tretrain #56, sparsity 0.56, final_train_loss 3.145e-01, max_acc 65.4, last_acc 62.8, mean_acc 57.9\\n\",\n            \"\\tretrain #57, sparsity 0.57, final_train_loss 3.244e-01, max_acc 64.5, last_acc 62.8, mean_acc 58.0\\n\",\n            \"\\tretrain #58, sparsity 0.58, final_train_loss 2.251e-01, max_acc 65.3, last_acc 65.3, mean_acc 58.2\\n\",\n            \"\\tretrain #59, sparsity 0.59, final_train_loss 3.221e-01, max_acc 65.6, last_acc 61.4, mean_acc 57.4\\n\",\n            \"\\tretrain #60, sparsity 0.60, final_train_loss 2.820e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.9\\n\",\n            \"\\tretrain #61, sparsity 0.61, final_train_loss 1.327e-01, max_acc 66.5, last_acc 65.8, mean_acc 58.7\\n\",\n            \"\\tretrain #62, sparsity 0.62, final_train_loss 2.563e-01, max_acc 66.8, last_acc 64.2, mean_acc 58.7\\n\",\n            \"\\tretrain #63, sparsity 0.63, final_train_loss 2.175e-01, max_acc 66.8, last_acc 62.6, mean_acc 58.7\\n\",\n            \"\\tretrain #64, sparsity 0.64, final_train_loss 1.689e-01, max_acc 67.0, last_acc 63.4, mean_acc 59.1\\n\",\n            \"\\tretrain #65, sparsity 0.65, final_train_loss 1.267e-01, max_acc 66.8, last_acc 66.8, mean_acc 59.2\\n\",\n            \"\\tretrain #66, sparsity 0.66, final_train_loss 4.512e-02, max_acc 66.6, last_acc 66.0, mean_acc 58.9\\n\",\n            \"\\tretrain #67, sparsity 0.67, final_train_loss 4.977e-01, max_acc 68.1, last_acc 65.3, mean_acc 60.7\\n\",\n            \"\\tretrain #68, sparsity 0.68, final_train_loss 2.331e-01, max_acc 66.4, last_acc 63.7, mean_acc 60.2\\n\",\n            \"\\tretrain #69, sparsity 0.69, final_train_loss 6.705e-01, max_acc 66.0, last_acc 65.8, mean_acc 58.9\\n\",\n            \"\\tretrain #70, sparsity 0.70, final_train_loss 3.464e-01, max_acc 67.6, last_acc 65.2, mean_acc 59.6\\n\",\n            \"\\tretrain #71, sparsity 0.71, final_train_loss 5.212e-02, max_acc 66.7, last_acc 66.7, mean_acc 59.6\\n\",\n            \"\\tretrain #72, sparsity 0.72, final_train_loss 8.926e-02, max_acc 69.6, last_acc 69.6, mean_acc 60.5\\n\",\n            \"\\tretrain #73, sparsity 0.73, final_train_loss 2.125e-01, max_acc 67.4, last_acc 65.4, mean_acc 59.9\\n\",\n            \"\\tretrain #74, sparsity 0.74, final_train_loss 1.469e-01, max_acc 67.7, last_acc 66.2, mean_acc 60.1\\n\",\n            \"\\tretrain #75, sparsity 0.75, final_train_loss 3.047e-01, max_acc 68.9, last_acc 68.9, mean_acc 61.0\\n\",\n            \"\\tretrain #76, sparsity 0.76, final_train_loss 8.189e-02, max_acc 70.8, last_acc 68.8, mean_acc 61.0\\n\",\n            \"\\tretrain #77, sparsity 0.77, final_train_loss 1.042e-01, max_acc 67.8, last_acc 67.8, mean_acc 60.0\\n\",\n            \"\\tretrain #78, sparsity 0.78, final_train_loss 2.325e-01, max_acc 68.2, last_acc 67.5, mean_acc 61.2\\n\",\n            \"\\tretrain #79, sparsity 0.79, final_train_loss 4.025e-02, max_acc 69.2, last_acc 69.0, mean_acc 60.0\\n\",\n            \"\\tretrain #80, sparsity 0.80, final_train_loss 2.265e-02, max_acc 70.4, last_acc 68.6, mean_acc 61.6\\n\",\n            \"\\tretrain #81, sparsity 0.81, final_train_loss 3.038e-01, max_acc 67.5, last_acc 64.3, mean_acc 59.6\\n\",\n            \"\\tretrain #82, sparsity 0.82, final_train_loss 1.315e-01, max_acc 66.6, last_acc 64.3, mean_acc 59.0\\n\",\n            \"\\tretrain #83, sparsity 0.83, final_train_loss 1.024e-01, max_acc 66.5, last_acc 64.5, mean_acc 58.6\\n\",\n            \"\\tretrain #84, sparsity 0.84, final_train_loss 1.428e-01, max_acc 68.0, last_acc 67.7, mean_acc 59.9\\n\",\n            \"\\tretrain #85, sparsity 0.85, final_train_loss 1.841e-01, max_acc 66.4, last_acc 63.6, mean_acc 59.4\\n\",\n            \"\\tretrain #86, sparsity 0.86, final_train_loss 1.311e-01, max_acc 69.2, last_acc 64.9, mean_acc 60.4\\n\",\n            \"\\tretrain #87, sparsity 0.87, final_train_loss 9.295e-02, max_acc 65.4, last_acc 65.4, mean_acc 57.0\\n\",\n            \"\\tretrain #88, sparsity 0.88, final_train_loss 2.912e-02, max_acc 66.3, last_acc 65.1, mean_acc 58.8\\n\",\n            \"\\tretrain #89, sparsity 0.89, final_train_loss 1.244e-01, max_acc 66.1, last_acc 63.3, mean_acc 58.2\\n\",\n            \"\\tretrain #90, sparsity 0.90, final_train_loss 9.934e-02, max_acc 64.6, last_acc 60.5, mean_acc 57.5\\n\",\n            \"\\tretrain #91, sparsity 0.91, final_train_loss 8.553e-02, max_acc 62.3, last_acc 62.3, mean_acc 55.5\\n\",\n            \"\\tretrain #92, sparsity 0.92, final_train_loss 1.895e-01, max_acc 61.1, last_acc 60.6, mean_acc 55.2\\n\",\n            \"\\tretrain #93, sparsity 0.93, final_train_loss 2.010e-01, max_acc 62.1, last_acc 59.4, mean_acc 55.1\\n\",\n            \"\\tretrain #94, sparsity 0.94, final_train_loss 4.363e-01, max_acc 58.0, last_acc 55.5, mean_acc 51.5\\n\",\n            \"\\tretrain #95, sparsity 0.95, final_train_loss 4.079e-01, max_acc 58.0, last_acc 55.9, mean_acc 50.4\\n\",\n            \"\\tretrain #96, sparsity 0.96, final_train_loss 6.772e-01, max_acc 51.5, last_acc 51.5, mean_acc 45.7\\n\",\n            \"\\tretrain #97, sparsity 0.97, final_train_loss 1.144e+00, max_acc 41.2, last_acc 41.2, mean_acc 37.1\\n\",\n            \"\\tretrain #98, sparsity 0.98, final_train_loss 1.639e+00, max_acc 35.0, last_acc 34.6, mean_acc 32.0\\n\",\n            \"\\tretrain #99, sparsity 0.99, final_train_loss 2.161e+00, max_acc 16.9, last_acc 16.7, mean_acc 16.1\\n\",\n            \"\\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\\n\",\n            \"   Magnitude pruning\\n\",\n            \"\\tretrain #1, sparsity 0.00, final_train_loss 2.098e-01, max_acc 66.4, last_acc 66.4, mean_acc 57.5\\n\",\n            \"\\tretrain #2, sparsity 0.01, final_train_loss 2.680e-01, max_acc 65.6, last_acc 62.0, mean_acc 57.4\\n\",\n            \"\\tretrain #3, sparsity 0.02, final_train_loss 1.899e-01, max_acc 66.6, last_acc 66.6, mean_acc 57.9\\n\",\n            \"\\tretrain #4, sparsity 0.03, final_train_loss 1.663e-01, max_acc 65.4, last_acc 65.0, mean_acc 57.6\\n\",\n            \"\\tretrain #5, sparsity 0.04, final_train_loss 8.916e-02, max_acc 67.0, last_acc 64.8, mean_acc 57.9\\n\",\n            \"\\tretrain #6, sparsity 0.05, final_train_loss 1.819e-01, max_acc 65.2, last_acc 64.5, mean_acc 57.4\\n\",\n            \"\\tretrain #7, sparsity 0.06, final_train_loss 6.880e-02, max_acc 65.2, last_acc 63.7, mean_acc 58.7\\n\",\n            \"\\tretrain #8, sparsity 0.07, final_train_loss 3.691e-01, max_acc 64.6, last_acc 62.2, mean_acc 57.5\\n\",\n            \"\\tretrain #9, sparsity 0.08, final_train_loss 1.075e-01, max_acc 65.1, last_acc 65.1, mean_acc 58.1\\n\",\n            \"\\tretrain #10, sparsity 0.09, final_train_loss 3.350e-01, max_acc 64.8, last_acc 64.4, mean_acc 58.0\\n\",\n            \"\\tretrain #11, sparsity 0.10, final_train_loss 3.674e-01, max_acc 67.1, last_acc 66.0, mean_acc 58.6\\n\",\n            \"\\tretrain #12, sparsity 0.11, final_train_loss 5.202e-02, max_acc 66.4, last_acc 66.4, mean_acc 57.7\\n\",\n            \"\\tretrain #13, sparsity 0.12, final_train_loss 2.351e-01, max_acc 65.2, last_acc 62.5, mean_acc 57.9\\n\",\n            \"\\tretrain #14, sparsity 0.13, final_train_loss 1.356e-01, max_acc 65.2, last_acc 64.9, mean_acc 58.4\\n\",\n            \"\\tretrain #15, sparsity 0.14, final_train_loss 1.443e-01, max_acc 65.5, last_acc 62.9, mean_acc 58.1\\n\",\n            \"\\tretrain #16, sparsity 0.15, final_train_loss 1.327e-01, max_acc 67.1, last_acc 64.6, mean_acc 59.1\\n\",\n            \"\\tretrain #17, sparsity 0.16, final_train_loss 1.914e-02, max_acc 66.0, last_acc 65.8, mean_acc 58.4\\n\",\n            \"\\tretrain #18, sparsity 0.17, final_train_loss 8.779e-02, max_acc 67.6, last_acc 65.7, mean_acc 59.2\\n\",\n            \"\\tretrain #19, sparsity 0.18, final_train_loss 2.390e-01, max_acc 66.9, last_acc 65.8, mean_acc 59.8\\n\",\n            \"\\tretrain #20, sparsity 0.19, final_train_loss 1.094e-01, max_acc 64.5, last_acc 64.4, mean_acc 58.5\\n\",\n            \"\\tretrain #21, sparsity 0.20, final_train_loss 2.105e-01, max_acc 65.8, last_acc 64.9, mean_acc 59.0\\n\",\n            \"\\tretrain #22, sparsity 0.21, final_train_loss 1.279e-01, max_acc 65.6, last_acc 65.2, mean_acc 58.7\\n\",\n            \"\\tretrain #23, sparsity 0.22, final_train_loss 1.527e-01, max_acc 66.7, last_acc 63.9, mean_acc 59.5\\n\",\n            \"\\tretrain #24, sparsity 0.23, final_train_loss 2.863e-01, max_acc 66.0, last_acc 65.5, mean_acc 59.3\\n\",\n            \"\\tretrain #25, sparsity 0.24, final_train_loss 2.645e-01, max_acc 65.9, last_acc 65.0, mean_acc 59.5\\n\",\n            \"\\tretrain #26, sparsity 0.25, final_train_loss 2.483e-02, max_acc 66.5, last_acc 66.5, mean_acc 59.8\\n\",\n            \"\\tretrain #27, sparsity 0.26, final_train_loss 8.681e-02, max_acc 67.8, last_acc 67.8, mean_acc 60.2\\n\",\n            \"\\tretrain #28, sparsity 0.27, final_train_loss 6.083e-02, max_acc 67.5, last_acc 67.5, mean_acc 59.6\\n\",\n            \"\\tretrain #29, sparsity 0.28, final_train_loss 8.720e-02, max_acc 65.9, last_acc 65.2, mean_acc 59.0\\n\",\n            \"\\tretrain #30, sparsity 0.29, final_train_loss 1.702e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.8\\n\",\n            \"\\tretrain #31, sparsity 0.30, final_train_loss 4.470e-02, max_acc 66.1, last_acc 64.4, mean_acc 59.4\\n\",\n            \"\\tretrain #32, sparsity 0.31, final_train_loss 1.289e-01, max_acc 67.2, last_acc 63.9, mean_acc 59.3\\n\",\n            \"\\tretrain #33, sparsity 0.32, final_train_loss 4.072e-01, max_acc 67.8, last_acc 66.1, mean_acc 60.2\\n\",\n            \"\\tretrain #34, sparsity 0.33, final_train_loss 5.773e-02, max_acc 67.5, last_acc 67.5, mean_acc 59.7\\n\",\n            \"\\tretrain #35, sparsity 0.34, final_train_loss 1.195e-01, max_acc 67.7, last_acc 65.9, mean_acc 60.4\\n\",\n            \"\\tretrain #36, sparsity 0.35, final_train_loss 4.007e-02, max_acc 66.1, last_acc 65.7, mean_acc 59.9\\n\",\n            \"\\tretrain #37, sparsity 0.36, final_train_loss 1.940e-01, max_acc 67.5, last_acc 66.5, mean_acc 60.3\\n\",\n            \"\\tretrain #38, sparsity 0.37, final_train_loss 8.476e-02, max_acc 66.8, last_acc 66.8, mean_acc 60.2\\n\",\n            \"\\tretrain #39, sparsity 0.38, final_train_loss 1.641e-01, max_acc 66.9, last_acc 66.9, mean_acc 60.3\\n\",\n            \"\\tretrain #40, sparsity 0.39, final_train_loss 7.798e-03, max_acc 67.6, last_acc 67.6, mean_acc 60.8\\n\",\n            \"\\tretrain #41, sparsity 0.40, final_train_loss 5.746e-02, max_acc 66.0, last_acc 66.0, mean_acc 60.1\\n\",\n            \"\\tretrain #42, sparsity 0.41, final_train_loss 4.911e-01, max_acc 66.2, last_acc 63.8, mean_acc 60.2\\n\",\n            \"\\tretrain #43, sparsity 0.42, final_train_loss 9.151e-02, max_acc 67.5, last_acc 66.3, mean_acc 60.8\\n\",\n            \"\\tretrain #44, sparsity 0.43, final_train_loss 3.245e-02, max_acc 68.2, last_acc 67.1, mean_acc 61.7\\n\",\n            \"\\tretrain #45, sparsity 0.44, final_train_loss 2.915e-01, max_acc 66.8, last_acc 65.7, mean_acc 60.9\\n\",\n            \"\\tretrain #46, sparsity 0.45, final_train_loss 1.463e-01, max_acc 68.2, last_acc 66.5, mean_acc 61.4\\n\",\n            \"\\tretrain #47, sparsity 0.46, final_train_loss 4.686e-02, max_acc 69.1, last_acc 69.1, mean_acc 62.1\\n\",\n            \"\\tretrain #48, sparsity 0.47, final_train_loss 2.505e-02, max_acc 67.9, last_acc 65.9, mean_acc 61.0\\n\",\n            \"\\tretrain #49, sparsity 0.48, final_train_loss 1.867e-02, max_acc 68.0, last_acc 68.0, mean_acc 61.7\\n\",\n            \"\\tretrain #50, sparsity 0.49, final_train_loss 1.702e-01, max_acc 68.6, last_acc 68.6, mean_acc 62.0\\n\",\n            \"\\tretrain #51, sparsity 0.51, final_train_loss 4.023e-01, max_acc 67.8, last_acc 67.4, mean_acc 62.1\\n\",\n            \"\\tretrain #52, sparsity 0.52, final_train_loss 5.574e-01, max_acc 68.5, last_acc 63.8, mean_acc 61.8\\n\",\n            \"\\tretrain #53, sparsity 0.53, final_train_loss 1.979e-02, max_acc 68.5, last_acc 68.5, mean_acc 62.0\\n\",\n            \"\\tretrain #54, sparsity 0.54, final_train_loss 1.828e-01, max_acc 68.1, last_acc 66.5, mean_acc 61.8\\n\",\n            \"\\tretrain #55, sparsity 0.55, final_train_loss 2.017e-01, max_acc 69.3, last_acc 66.6, mean_acc 62.4\\n\",\n            \"\\tretrain #56, sparsity 0.56, final_train_loss 1.099e-01, max_acc 69.7, last_acc 67.0, mean_acc 63.3\\n\",\n            \"\\tretrain #57, sparsity 0.57, final_train_loss 2.292e-01, max_acc 68.5, last_acc 65.0, mean_acc 62.1\\n\",\n            \"\\tretrain #58, sparsity 0.58, final_train_loss 7.869e-02, max_acc 68.3, last_acc 66.7, mean_acc 62.2\\n\",\n            \"\\tretrain #59, sparsity 0.59, final_train_loss 3.369e-01, max_acc 71.8, last_acc 63.7, mean_acc 63.1\\n\",\n            \"\\tretrain #60, sparsity 0.60, final_train_loss 2.457e-01, max_acc 69.3, last_acc 68.0, mean_acc 63.6\\n\",\n            \"\\tretrain #61, sparsity 0.61, final_train_loss 1.809e-01, max_acc 71.4, last_acc 71.4, mean_acc 63.9\\n\",\n            \"\\tretrain #62, sparsity 0.62, final_train_loss 5.555e-02, max_acc 68.4, last_acc 67.5, mean_acc 62.1\\n\",\n            \"\\tretrain #63, sparsity 0.63, final_train_loss 6.513e-02, max_acc 70.7, last_acc 67.8, mean_acc 64.2\\n\",\n            \"\\tretrain #64, sparsity 0.64, final_train_loss 1.546e-01, max_acc 69.9, last_acc 67.0, mean_acc 63.8\\n\",\n            \"\\tretrain #65, sparsity 0.65, final_train_loss 2.609e-02, max_acc 69.7, last_acc 67.9, mean_acc 63.7\\n\",\n            \"\\tretrain #66, sparsity 0.66, final_train_loss 7.977e-02, max_acc 70.8, last_acc 69.3, mean_acc 64.3\\n\",\n            \"\\tretrain #67, sparsity 0.67, final_train_loss 6.476e-02, max_acc 71.4, last_acc 68.6, mean_acc 64.3\\n\",\n            \"\\tretrain #68, sparsity 0.68, final_train_loss 2.149e-02, max_acc 69.8, last_acc 69.8, mean_acc 64.4\\n\",\n            \"\\tretrain #69, sparsity 0.69, final_train_loss 2.586e-01, max_acc 72.6, last_acc 69.8, mean_acc 65.4\\n\",\n            \"\\tretrain #70, sparsity 0.70, final_train_loss 4.413e-03, max_acc 70.7, last_acc 70.2, mean_acc 65.1\\n\",\n            \"\\tretrain #71, sparsity 0.71, final_train_loss 8.177e-02, max_acc 71.6, last_acc 69.2, mean_acc 65.0\\n\",\n            \"\\tretrain #72, sparsity 0.72, final_train_loss 1.059e-01, max_acc 71.6, last_acc 70.6, mean_acc 65.1\\n\",\n            \"\\tretrain #73, sparsity 0.73, final_train_loss 1.178e-01, max_acc 72.8, last_acc 70.5, mean_acc 65.1\\n\",\n            \"\\tretrain #74, sparsity 0.74, final_train_loss 2.022e-01, max_acc 70.9, last_acc 68.0, mean_acc 64.8\\n\",\n            \"\\tretrain #75, sparsity 0.75, final_train_loss 2.201e-01, max_acc 71.5, last_acc 71.5, mean_acc 66.1\\n\",\n            \"\\tretrain #76, sparsity 0.76, final_train_loss 1.076e-03, max_acc 72.9, last_acc 72.8, mean_acc 66.3\\n\",\n            \"\\tretrain #77, sparsity 0.77, final_train_loss 2.370e-02, max_acc 72.5, last_acc 71.2, mean_acc 66.2\\n\",\n            \"\\tretrain #78, sparsity 0.78, final_train_loss 2.687e-01, max_acc 72.8, last_acc 71.5, mean_acc 66.1\\n\",\n            \"\\tretrain #79, sparsity 0.79, final_train_loss 3.283e-01, max_acc 74.7, last_acc 70.4, mean_acc 67.0\\n\",\n            \"\\tretrain #80, sparsity 0.80, final_train_loss 1.361e-01, max_acc 72.9, last_acc 72.9, mean_acc 66.7\\n\",\n            \"\\tretrain #81, sparsity 0.81, final_train_loss 8.892e-02, max_acc 72.1, last_acc 71.2, mean_acc 66.1\\n\",\n            \"\\tretrain #82, sparsity 0.82, final_train_loss 1.099e-02, max_acc 74.1, last_acc 72.2, mean_acc 67.6\\n\",\n            \"\\tretrain #83, sparsity 0.83, final_train_loss 7.655e-02, max_acc 73.2, last_acc 70.6, mean_acc 67.0\\n\",\n            \"\\tretrain #84, sparsity 0.84, final_train_loss 2.787e-02, max_acc 73.2, last_acc 73.2, mean_acc 66.8\\n\",\n            \"\\tretrain #85, sparsity 0.85, final_train_loss 1.591e-01, max_acc 74.2, last_acc 70.4, mean_acc 66.5\\n\",\n            \"\\tretrain #86, sparsity 0.86, final_train_loss 3.671e-02, max_acc 72.5, last_acc 72.0, mean_acc 66.5\\n\",\n            \"\\tretrain #87, sparsity 0.87, final_train_loss 1.610e-02, max_acc 73.2, last_acc 72.6, mean_acc 67.3\\n\",\n            \"\\tretrain #88, sparsity 0.88, final_train_loss 5.061e-02, max_acc 73.7, last_acc 73.7, mean_acc 67.4\\n\",\n            \"\\tretrain #89, sparsity 0.89, final_train_loss 8.562e-03, max_acc 73.5, last_acc 73.1, mean_acc 67.8\\n\",\n            \"\\tretrain #90, sparsity 0.90, final_train_loss 1.127e-03, max_acc 74.3, last_acc 74.3, mean_acc 67.7\\n\",\n            \"\\tretrain #91, sparsity 0.91, final_train_loss 1.294e-03, max_acc 75.0, last_acc 75.0, mean_acc 67.4\\n\",\n            \"\\tretrain #92, sparsity 0.92, final_train_loss 2.673e-03, max_acc 76.1, last_acc 75.6, mean_acc 68.4\\n\",\n            \"\\tretrain #93, sparsity 0.93, final_train_loss 4.614e-02, max_acc 73.2, last_acc 71.7, mean_acc 67.0\\n\",\n            \"\\tretrain #94, sparsity 0.94, final_train_loss 4.515e-02, max_acc 72.1, last_acc 70.4, mean_acc 66.2\\n\",\n            \"\\tretrain #95, sparsity 0.95, final_train_loss 1.088e-01, max_acc 71.8, last_acc 69.2, mean_acc 65.4\\n\",\n            \"\\tretrain #96, sparsity 0.96, final_train_loss 1.432e-01, max_acc 70.4, last_acc 69.3, mean_acc 64.4\\n\",\n            \"\\tretrain #97, sparsity 0.97, final_train_loss 1.624e-01, max_acc 69.0, last_acc 68.1, mean_acc 62.5\\n\",\n            \"\\tretrain #98, sparsity 0.98, final_train_loss 5.247e-01, max_acc 63.9, last_acc 63.0, mean_acc 58.0\\n\",\n            \"\\tretrain #99, sparsity 0.99, final_train_loss 1.332e+00, max_acc 41.9, last_acc 41.9, mean_acc 37.1\\n\",\n            \"\\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"   Random pruning\\n\",\n            \"\\tretrain #1, sparsity 0.00, final_train_loss 2.207e-01, max_acc 65.2, last_acc 61.4, mean_acc 57.7\\n\",\n            \"\\tretrain #2, sparsity 0.01, final_train_loss 3.362e-01, max_acc 63.6, last_acc 63.3, mean_acc 57.4\\n\",\n            \"\\tretrain #3, sparsity 0.02, final_train_loss 8.536e-02, max_acc 66.5, last_acc 64.5, mean_acc 58.2\\n\",\n            \"\\tretrain #4, sparsity 0.03, final_train_loss 2.506e-01, max_acc 65.8, last_acc 64.1, mean_acc 57.8\\n\",\n            \"\\tretrain #5, sparsity 0.04, final_train_loss 2.158e-01, max_acc 66.9, last_acc 66.9, mean_acc 58.9\\n\",\n            \"\\tretrain #6, sparsity 0.05, final_train_loss 1.427e-01, max_acc 65.0, last_acc 64.1, mean_acc 58.0\\n\",\n            \"\\tretrain #7, sparsity 0.06, final_train_loss 3.646e-01, max_acc 66.4, last_acc 62.8, mean_acc 57.4\\n\",\n            \"\\tretrain #8, sparsity 0.07, final_train_loss 3.427e-01, max_acc 63.9, last_acc 61.7, mean_acc 57.2\\n\",\n            \"\\tretrain #9, sparsity 0.08, final_train_loss 4.462e-01, max_acc 66.0, last_acc 66.0, mean_acc 58.3\\n\",\n            \"\\tretrain #10, sparsity 0.09, final_train_loss 3.153e-01, max_acc 65.7, last_acc 62.0, mean_acc 57.5\\n\",\n            \"\\tretrain #11, sparsity 0.10, final_train_loss 1.032e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.3\\n\",\n            \"\\tretrain #12, sparsity 0.11, final_train_loss 3.200e-01, max_acc 66.3, last_acc 64.0, mean_acc 58.0\\n\",\n            \"\\tretrain #13, sparsity 0.12, final_train_loss 3.028e-01, max_acc 66.8, last_acc 62.8, mean_acc 57.2\\n\",\n            \"\\tretrain #14, sparsity 0.13, final_train_loss 2.299e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.6\\n\",\n            \"\\tretrain #15, sparsity 0.14, final_train_loss 1.602e-01, max_acc 64.0, last_acc 64.0, mean_acc 57.1\\n\",\n            \"\\tretrain #16, sparsity 0.15, final_train_loss 1.575e-01, max_acc 66.4, last_acc 65.0, mean_acc 58.4\\n\",\n            \"\\tretrain #17, sparsity 0.16, final_train_loss 2.326e-01, max_acc 65.5, last_acc 64.1, mean_acc 57.8\\n\",\n            \"\\tretrain #18, sparsity 0.17, final_train_loss 1.836e-01, max_acc 64.3, last_acc 64.3, mean_acc 57.3\\n\",\n            \"\\tretrain #19, sparsity 0.18, final_train_loss 3.852e-01, max_acc 64.9, last_acc 61.8, mean_acc 57.9\\n\",\n            \"\\tretrain #20, sparsity 0.19, final_train_loss 1.805e-01, max_acc 66.1, last_acc 64.8, mean_acc 58.6\\n\",\n            \"\\tretrain #21, sparsity 0.20, final_train_loss 1.983e-01, max_acc 64.8, last_acc 64.0, mean_acc 58.2\\n\",\n            \"\\tretrain #22, sparsity 0.21, final_train_loss 3.935e-01, max_acc 65.9, last_acc 63.5, mean_acc 57.9\\n\",\n            \"\\tretrain #23, sparsity 0.22, final_train_loss 1.159e-01, max_acc 66.6, last_acc 65.4, mean_acc 59.0\\n\",\n            \"\\tretrain #24, sparsity 0.23, final_train_loss 3.676e-02, max_acc 65.5, last_acc 63.8, mean_acc 58.1\\n\",\n            \"\\tretrain #25, sparsity 0.24, final_train_loss 4.002e-01, max_acc 64.6, last_acc 64.4, mean_acc 58.0\\n\",\n            \"\\tretrain #26, sparsity 0.25, final_train_loss 1.545e-01, max_acc 64.1, last_acc 64.1, mean_acc 56.9\\n\",\n            \"\\tretrain #27, sparsity 0.26, final_train_loss 2.444e-01, max_acc 65.8, last_acc 63.8, mean_acc 57.9\\n\",\n            \"\\tretrain #28, sparsity 0.27, final_train_loss 4.631e-02, max_acc 65.6, last_acc 65.6, mean_acc 58.0\\n\",\n            \"\\tretrain #29, sparsity 0.28, final_train_loss 1.937e-01, max_acc 66.5, last_acc 65.8, mean_acc 58.2\\n\",\n            \"\\tretrain #30, sparsity 0.29, final_train_loss 1.904e-01, max_acc 63.3, last_acc 61.5, mean_acc 57.0\\n\",\n            \"\\tretrain #31, sparsity 0.30, final_train_loss 1.601e-01, max_acc 65.9, last_acc 64.0, mean_acc 58.6\\n\",\n            \"\\tretrain #32, sparsity 0.31, final_train_loss 6.696e-02, max_acc 65.2, last_acc 64.3, mean_acc 57.9\\n\",\n            \"\\tretrain #33, sparsity 0.32, final_train_loss 4.213e-01, max_acc 65.1, last_acc 61.9, mean_acc 57.6\\n\",\n            \"\\tretrain #34, sparsity 0.33, final_train_loss 2.514e-01, max_acc 65.3, last_acc 64.2, mean_acc 57.1\\n\",\n            \"\\tretrain #35, sparsity 0.34, final_train_loss 3.237e-01, max_acc 66.1, last_acc 64.1, mean_acc 58.4\\n\",\n            \"\\tretrain #36, sparsity 0.35, final_train_loss 1.561e-01, max_acc 66.3, last_acc 65.8, mean_acc 58.4\\n\",\n            \"\\tretrain #37, sparsity 0.36, final_train_loss 1.816e-01, max_acc 64.6, last_acc 63.3, mean_acc 57.8\\n\",\n            \"\\tretrain #38, sparsity 0.37, final_train_loss 4.493e-01, max_acc 64.9, last_acc 64.3, mean_acc 58.2\\n\",\n            \"\\tretrain #39, sparsity 0.38, final_train_loss 1.117e-01, max_acc 65.4, last_acc 64.5, mean_acc 58.4\\n\",\n            \"\\tretrain #40, sparsity 0.39, final_train_loss 1.525e-01, max_acc 66.4, last_acc 64.3, mean_acc 58.3\\n\",\n            \"\\tretrain #41, sparsity 0.40, final_train_loss 1.725e-01, max_acc 66.4, last_acc 64.8, mean_acc 58.3\\n\",\n            \"\\tretrain #42, sparsity 0.41, final_train_loss 1.022e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.9\\n\",\n            \"\\tretrain #43, sparsity 0.42, final_train_loss 2.723e-01, max_acc 64.8, last_acc 61.5, mean_acc 56.7\\n\",\n            \"\\tretrain #44, sparsity 0.43, final_train_loss 2.613e-01, max_acc 63.4, last_acc 63.4, mean_acc 56.6\\n\",\n            \"\\tretrain #45, sparsity 0.44, final_train_loss 2.547e-01, max_acc 65.0, last_acc 65.0, mean_acc 57.1\\n\",\n            \"\\tretrain #46, sparsity 0.45, final_train_loss 6.731e-02, max_acc 65.4, last_acc 65.4, mean_acc 57.1\\n\",\n            \"\\tretrain #47, sparsity 0.46, final_train_loss 1.824e-01, max_acc 66.2, last_acc 64.4, mean_acc 57.9\\n\",\n            \"\\tretrain #48, sparsity 0.47, final_train_loss 2.740e-01, max_acc 64.7, last_acc 64.7, mean_acc 57.4\\n\",\n            \"\\tretrain #49, sparsity 0.48, final_train_loss 1.234e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.8\\n\",\n            \"\\tretrain #50, sparsity 0.49, final_train_loss 1.978e-01, max_acc 64.2, last_acc 62.1, mean_acc 56.9\\n\",\n            \"\\tretrain #51, sparsity 0.51, final_train_loss 3.184e-01, max_acc 64.6, last_acc 61.7, mean_acc 57.5\\n\",\n            \"\\tretrain #52, sparsity 0.52, final_train_loss 3.193e-01, max_acc 65.3, last_acc 61.4, mean_acc 57.5\\n\",\n            \"\\tretrain #53, sparsity 0.53, final_train_loss 2.327e-01, max_acc 64.6, last_acc 63.9, mean_acc 57.4\\n\",\n            \"\\tretrain #54, sparsity 0.54, final_train_loss 1.270e-01, max_acc 66.8, last_acc 64.4, mean_acc 58.4\\n\",\n            \"\\tretrain #55, sparsity 0.55, final_train_loss 2.003e-01, max_acc 66.0, last_acc 65.6, mean_acc 58.9\\n\",\n            \"\\tretrain #56, sparsity 0.56, final_train_loss 2.780e-01, max_acc 65.1, last_acc 65.1, mean_acc 58.7\\n\",\n            \"\\tretrain #57, sparsity 0.57, final_train_loss 2.825e-01, max_acc 63.9, last_acc 61.5, mean_acc 56.7\\n\",\n            \"\\tretrain #58, sparsity 0.58, final_train_loss 2.648e-01, max_acc 64.6, last_acc 64.6, mean_acc 58.3\\n\",\n            \"\\tretrain #59, sparsity 0.59, final_train_loss 7.645e-02, max_acc 67.3, last_acc 66.5, mean_acc 59.0\\n\",\n            \"\\tretrain #60, sparsity 0.60, final_train_loss 7.587e-02, max_acc 65.2, last_acc 64.5, mean_acc 58.9\\n\",\n            \"\\tretrain #61, sparsity 0.61, final_train_loss 2.544e-01, max_acc 65.9, last_acc 65.5, mean_acc 58.9\\n\",\n            \"\\tretrain #62, sparsity 0.62, final_train_loss 6.965e-02, max_acc 65.0, last_acc 65.0, mean_acc 57.9\\n\",\n            \"\\tretrain #63, sparsity 0.63, final_train_loss 1.750e-01, max_acc 66.9, last_acc 63.1, mean_acc 59.4\\n\",\n            \"\\tretrain #64, sparsity 0.64, final_train_loss 1.237e-01, max_acc 66.5, last_acc 64.4, mean_acc 58.0\\n\",\n            \"\\tretrain #65, sparsity 0.65, final_train_loss 5.669e-01, max_acc 65.7, last_acc 64.6, mean_acc 59.4\\n\",\n            \"\\tretrain #66, sparsity 0.66, final_train_loss 5.150e-01, max_acc 66.4, last_acc 63.9, mean_acc 59.1\\n\",\n            \"\\tretrain #67, sparsity 0.67, final_train_loss 3.314e-02, max_acc 66.1, last_acc 65.8, mean_acc 59.3\\n\",\n            \"\\tretrain #68, sparsity 0.68, final_train_loss 1.040e-01, max_acc 70.0, last_acc 70.0, mean_acc 60.8\\n\",\n            \"\\tretrain #69, sparsity 0.69, final_train_loss 2.082e-01, max_acc 67.3, last_acc 67.2, mean_acc 59.9\\n\",\n            \"\\tretrain #70, sparsity 0.70, final_train_loss 2.170e-01, max_acc 67.3, last_acc 66.3, mean_acc 59.1\\n\",\n            \"\\tretrain #71, sparsity 0.71, final_train_loss 1.172e-02, max_acc 67.2, last_acc 67.2, mean_acc 60.0\\n\",\n            \"\\tretrain #72, sparsity 0.72, final_train_loss 1.133e-01, max_acc 67.3, last_acc 67.3, mean_acc 59.8\\n\",\n            \"\\tretrain #73, sparsity 0.73, final_train_loss 3.219e-01, max_acc 68.5, last_acc 64.4, mean_acc 59.6\\n\",\n            \"\\tretrain #74, sparsity 0.74, final_train_loss 7.892e-02, max_acc 69.0, last_acc 69.0, mean_acc 60.9\\n\",\n            \"\\tretrain #75, sparsity 0.75, final_train_loss 1.364e-01, max_acc 67.8, last_acc 67.5, mean_acc 60.0\\n\",\n            \"\\tretrain #76, sparsity 0.76, final_train_loss 1.902e-01, max_acc 67.6, last_acc 64.9, mean_acc 60.7\\n\",\n            \"\\tretrain #77, sparsity 0.77, final_train_loss 5.066e-02, max_acc 66.1, last_acc 66.1, mean_acc 58.8\\n\",\n            \"\\tretrain #78, sparsity 0.78, final_train_loss 2.923e-02, max_acc 68.0, last_acc 67.5, mean_acc 60.3\\n\",\n            \"\\tretrain #79, sparsity 0.79, final_train_loss 1.753e-02, max_acc 67.4, last_acc 67.4, mean_acc 58.9\\n\",\n            \"\\tretrain #80, sparsity 0.80, final_train_loss 2.361e-01, max_acc 69.7, last_acc 66.3, mean_acc 61.0\\n\",\n            \"\\tretrain #81, sparsity 0.81, final_train_loss 2.193e-01, max_acc 69.0, last_acc 65.4, mean_acc 60.6\\n\",\n            \"\\tretrain #82, sparsity 0.82, final_train_loss 2.304e-01, max_acc 69.6, last_acc 67.2, mean_acc 61.8\\n\",\n            \"\\tretrain #83, sparsity 0.83, final_train_loss 4.335e-02, max_acc 69.4, last_acc 69.4, mean_acc 60.7\\n\",\n            \"\\tretrain #84, sparsity 0.84, final_train_loss 1.548e-01, max_acc 70.0, last_acc 68.0, mean_acc 61.2\\n\",\n            \"\\tretrain #85, sparsity 0.85, final_train_loss 9.804e-03, max_acc 68.1, last_acc 67.4, mean_acc 61.0\\n\",\n            \"\\tretrain #86, sparsity 0.86, final_train_loss 7.915e-02, max_acc 66.7, last_acc 66.0, mean_acc 59.5\\n\",\n            \"\\tretrain #87, sparsity 0.87, final_train_loss 6.169e-02, max_acc 66.3, last_acc 66.1, mean_acc 59.8\\n\",\n            \"\\tretrain #88, sparsity 0.88, final_train_loss 1.341e-01, max_acc 65.2, last_acc 62.6, mean_acc 57.7\\n\",\n            \"\\tretrain #89, sparsity 0.89, final_train_loss 1.145e-01, max_acc 66.2, last_acc 63.9, mean_acc 58.2\\n\",\n            \"\\tretrain #90, sparsity 0.90, final_train_loss 1.098e-01, max_acc 65.7, last_acc 65.7, mean_acc 57.1\\n\",\n            \"\\tretrain #91, sparsity 0.91, final_train_loss 1.535e-01, max_acc 62.1, last_acc 60.6, mean_acc 54.9\\n\",\n            \"\\tretrain #92, sparsity 0.92, final_train_loss 1.370e-01, max_acc 62.4, last_acc 61.8, mean_acc 56.1\\n\",\n            \"\\tretrain #93, sparsity 0.93, final_train_loss 1.365e-01, max_acc 61.9, last_acc 61.9, mean_acc 53.8\\n\",\n            \"\\tretrain #94, sparsity 0.94, final_train_loss 2.537e-01, max_acc 60.4, last_acc 60.4, mean_acc 52.8\\n\",\n            \"\\tretrain #95, sparsity 0.95, final_train_loss 5.880e-01, max_acc 56.1, last_acc 54.6, mean_acc 49.3\\n\",\n            \"\\tretrain #96, sparsity 0.96, final_train_loss 6.913e-01, max_acc 50.3, last_acc 49.9, mean_acc 43.7\\n\",\n            \"\\tretrain #97, sparsity 0.97, final_train_loss 1.221e+00, max_acc 43.9, last_acc 43.7, mean_acc 38.7\\n\",\n            \"\\tretrain #98, sparsity 0.98, final_train_loss 1.651e+00, max_acc 27.4, last_acc 27.2, mean_acc 25.4\\n\",\n            \"\\tretrain #99, sparsity 0.99, final_train_loss 2.140e+00, max_acc 17.1, last_acc 15.4, mean_acc 15.9\\n\",\n            \"\\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\\n\",\n            \"   Magnitude pruning\\n\",\n            \"\\tretrain #1, sparsity 0.00, final_train_loss 2.163e-01, max_acc 65.4, last_acc 64.8, mean_acc 58.4\\n\",\n            \"\\tretrain #2, sparsity 0.01, final_train_loss 2.496e-01, max_acc 66.3, last_acc 64.5, mean_acc 57.7\\n\",\n            \"\\tretrain #3, sparsity 0.02, final_train_loss 2.256e-01, max_acc 64.8, last_acc 64.3, mean_acc 58.1\\n\",\n            \"\\tretrain #4, sparsity 0.03, final_train_loss 1.605e-01, max_acc 66.1, last_acc 63.6, mean_acc 58.8\\n\",\n            \"\\tretrain #5, sparsity 0.04, final_train_loss 1.577e-01, max_acc 65.9, last_acc 65.9, mean_acc 58.1\\n\",\n            \"\\tretrain #6, sparsity 0.05, final_train_loss 2.365e-01, max_acc 64.5, last_acc 61.9, mean_acc 57.5\\n\",\n            \"\\tretrain #7, sparsity 0.06, final_train_loss 4.548e-01, max_acc 64.6, last_acc 64.5, mean_acc 58.3\\n\",\n            \"\\tretrain #8, sparsity 0.07, final_train_loss 2.571e-01, max_acc 66.1, last_acc 65.1, mean_acc 58.2\\n\",\n            \"\\tretrain #9, sparsity 0.08, final_train_loss 2.494e-01, max_acc 65.7, last_acc 63.3, mean_acc 58.8\\n\",\n            \"\\tretrain #10, sparsity 0.09, final_train_loss 2.295e-01, max_acc 67.3, last_acc 65.9, mean_acc 58.5\\n\",\n            \"\\tretrain #11, sparsity 0.10, final_train_loss 2.025e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.4\\n\",\n            \"\\tretrain #12, sparsity 0.11, final_train_loss 1.314e-01, max_acc 68.3, last_acc 63.1, mean_acc 58.4\\n\",\n            \"\\tretrain #13, sparsity 0.12, final_train_loss 1.235e-01, max_acc 66.7, last_acc 66.7, mean_acc 58.8\\n\",\n            \"\\tretrain #14, sparsity 0.13, final_train_loss 2.829e-01, max_acc 65.3, last_acc 65.2, mean_acc 59.0\\n\",\n            \"\\tretrain #15, sparsity 0.14, final_train_loss 3.520e-01, max_acc 66.4, last_acc 63.1, mean_acc 59.0\\n\",\n            \"\\tretrain #16, sparsity 0.15, final_train_loss 9.407e-02, max_acc 66.0, last_acc 64.9, mean_acc 59.1\\n\",\n            \"\\tretrain #17, sparsity 0.16, final_train_loss 5.955e-01, max_acc 64.8, last_acc 63.8, mean_acc 59.0\\n\",\n            \"\\tretrain #18, sparsity 0.17, final_train_loss 9.409e-02, max_acc 67.5, last_acc 65.0, mean_acc 59.5\\n\",\n            \"\\tretrain #19, sparsity 0.18, final_train_loss 8.431e-02, max_acc 65.9, last_acc 63.1, mean_acc 59.5\\n\",\n            \"\\tretrain #20, sparsity 0.19, final_train_loss 1.912e-01, max_acc 68.5, last_acc 67.3, mean_acc 59.4\\n\",\n            \"\\tretrain #21, sparsity 0.20, final_train_loss 1.168e-01, max_acc 67.8, last_acc 64.9, mean_acc 60.2\\n\",\n            \"\\tretrain #22, sparsity 0.21, final_train_loss 1.616e-01, max_acc 68.0, last_acc 66.8, mean_acc 59.9\\n\",\n            \"\\tretrain #23, sparsity 0.22, final_train_loss 2.757e-01, max_acc 65.8, last_acc 64.4, mean_acc 59.5\\n\",\n            \"\\tretrain #24, sparsity 0.23, final_train_loss 1.645e-01, max_acc 66.3, last_acc 65.5, mean_acc 59.1\\n\",\n            \"\\tretrain #25, sparsity 0.24, final_train_loss 2.350e-01, max_acc 65.8, last_acc 63.4, mean_acc 58.3\\n\",\n            \"\\tretrain #26, sparsity 0.25, final_train_loss 1.111e-01, max_acc 65.6, last_acc 65.6, mean_acc 59.0\\n\",\n            \"\\tretrain #27, sparsity 0.26, final_train_loss 7.742e-02, max_acc 67.4, last_acc 65.2, mean_acc 59.7\\n\",\n            \"\\tretrain #28, sparsity 0.27, final_train_loss 1.275e-01, max_acc 67.4, last_acc 64.7, mean_acc 60.3\\n\",\n            \"\\tretrain #29, sparsity 0.28, final_train_loss 3.118e-02, max_acc 66.6, last_acc 65.2, mean_acc 60.4\\n\",\n            \"\\tretrain #30, sparsity 0.29, final_train_loss 1.879e-01, max_acc 67.1, last_acc 66.0, mean_acc 59.4\\n\",\n            \"\\tretrain #31, sparsity 0.30, final_train_loss 2.051e-01, max_acc 67.1, last_acc 67.1, mean_acc 59.9\\n\",\n            \"\\tretrain #32, sparsity 0.31, final_train_loss 1.074e-01, max_acc 67.2, last_acc 67.2, mean_acc 59.4\\n\",\n            \"\\tretrain #33, sparsity 0.32, final_train_loss 1.168e-01, max_acc 68.9, last_acc 68.7, mean_acc 60.8\\n\",\n            \"\\tretrain #34, sparsity 0.33, final_train_loss 1.290e-01, max_acc 67.2, last_acc 62.6, mean_acc 60.2\\n\",\n            \"\\tretrain #35, sparsity 0.34, final_train_loss 8.810e-02, max_acc 66.4, last_acc 65.2, mean_acc 59.5\\n\",\n            \"\\tretrain #36, sparsity 0.35, final_train_loss 2.669e-01, max_acc 67.5, last_acc 67.5, mean_acc 59.9\\n\",\n            \"\\tretrain #37, sparsity 0.36, final_train_loss 1.822e-01, max_acc 67.1, last_acc 65.3, mean_acc 61.1\\n\",\n            \"\\tretrain #38, sparsity 0.37, final_train_loss 1.708e-01, max_acc 67.9, last_acc 67.7, mean_acc 60.5\\n\",\n            \"\\tretrain #39, sparsity 0.38, final_train_loss 6.793e-02, max_acc 69.7, last_acc 67.6, mean_acc 61.8\\n\",\n            \"\\tretrain #40, sparsity 0.39, final_train_loss 7.624e-02, max_acc 67.0, last_acc 66.5, mean_acc 60.1\\n\",\n            \"\\tretrain #41, sparsity 0.40, final_train_loss 1.223e-01, max_acc 68.4, last_acc 65.4, mean_acc 60.4\\n\",\n            \"\\tretrain #42, sparsity 0.41, final_train_loss 2.133e-01, max_acc 66.3, last_acc 65.7, mean_acc 60.2\\n\",\n            \"\\tretrain #43, sparsity 0.42, final_train_loss 1.590e-01, max_acc 68.1, last_acc 66.3, mean_acc 61.5\\n\",\n            \"\\tretrain #44, sparsity 0.43, final_train_loss 2.161e-01, max_acc 69.1, last_acc 68.3, mean_acc 61.7\\n\",\n            \"\\tretrain #45, sparsity 0.44, final_train_loss 1.438e-01, max_acc 69.2, last_acc 69.2, mean_acc 61.7\\n\",\n            \"\\tretrain #46, sparsity 0.45, final_train_loss 3.218e-01, max_acc 68.6, last_acc 66.4, mean_acc 61.0\\n\",\n            \"\\tretrain #47, sparsity 0.46, final_train_loss 1.055e-01, max_acc 69.7, last_acc 68.3, mean_acc 62.4\\n\",\n            \"\\tretrain #48, sparsity 0.47, final_train_loss 6.646e-03, max_acc 70.3, last_acc 69.6, mean_acc 62.4\\n\",\n            \"\\tretrain #49, sparsity 0.48, final_train_loss 3.413e-01, max_acc 69.2, last_acc 68.8, mean_acc 62.1\\n\",\n            \"\\tretrain #50, sparsity 0.49, final_train_loss 2.401e-01, max_acc 70.4, last_acc 69.1, mean_acc 62.2\\n\",\n            \"\\tretrain #51, sparsity 0.51, final_train_loss 2.569e-01, max_acc 68.0, last_acc 65.0, mean_acc 61.3\\n\",\n            \"\\tretrain #52, sparsity 0.52, final_train_loss 2.675e-01, max_acc 70.0, last_acc 65.5, mean_acc 61.8\\n\",\n            \"\\tretrain #53, sparsity 0.53, final_train_loss 1.676e-01, max_acc 68.4, last_acc 65.4, mean_acc 61.8\\n\",\n            \"\\tretrain #54, sparsity 0.54, final_train_loss 2.891e-01, max_acc 68.7, last_acc 67.1, mean_acc 62.5\\n\",\n            \"\\tretrain #55, sparsity 0.55, final_train_loss 5.487e-01, max_acc 70.8, last_acc 65.2, mean_acc 63.2\\n\",\n            \"\\tretrain #56, sparsity 0.56, final_train_loss 1.458e-01, max_acc 67.3, last_acc 66.9, mean_acc 62.1\\n\",\n            \"\\tretrain #57, sparsity 0.57, final_train_loss 8.602e-02, max_acc 69.9, last_acc 68.7, mean_acc 63.1\\n\",\n            \"\\tretrain #58, sparsity 0.58, final_train_loss 2.292e-01, max_acc 69.8, last_acc 68.2, mean_acc 62.6\\n\",\n            \"\\tretrain #59, sparsity 0.59, final_train_loss 8.172e-04, max_acc 70.9, last_acc 70.7, mean_acc 63.2\\n\",\n            \"\\tretrain #60, sparsity 0.60, final_train_loss 3.560e-01, max_acc 69.7, last_acc 64.6, mean_acc 63.1\\n\",\n            \"\\tretrain #61, sparsity 0.61, final_train_loss 1.309e-01, max_acc 69.2, last_acc 67.9, mean_acc 63.0\\n\",\n            \"\\tretrain #62, sparsity 0.62, final_train_loss 8.264e-02, max_acc 70.5, last_acc 69.7, mean_acc 64.0\\n\",\n            \"\\tretrain #63, sparsity 0.63, final_train_loss 1.658e-01, max_acc 71.0, last_acc 68.3, mean_acc 63.9\\n\",\n            \"\\tretrain #64, sparsity 0.64, final_train_loss 3.615e-02, max_acc 71.1, last_acc 67.1, mean_acc 63.4\\n\",\n            \"\\tretrain #65, sparsity 0.65, final_train_loss 2.848e-01, max_acc 69.2, last_acc 66.0, mean_acc 63.1\\n\",\n            \"\\tretrain #66, sparsity 0.66, final_train_loss 4.801e-02, max_acc 70.6, last_acc 67.9, mean_acc 64.3\\n\",\n            \"\\tretrain #67, sparsity 0.67, final_train_loss 1.565e-01, max_acc 70.5, last_acc 70.1, mean_acc 63.8\\n\",\n            \"\\tretrain #68, sparsity 0.68, final_train_loss 2.377e-02, max_acc 72.1, last_acc 71.6, mean_acc 65.7\\n\",\n            \"\\tretrain #69, sparsity 0.69, final_train_loss 9.818e-02, max_acc 70.6, last_acc 67.4, mean_acc 64.1\\n\",\n            \"\\tretrain #70, sparsity 0.70, final_train_loss 1.072e-01, max_acc 71.9, last_acc 68.4, mean_acc 63.8\\n\",\n            \"\\tretrain #71, sparsity 0.71, final_train_loss 1.351e-01, max_acc 70.9, last_acc 70.4, mean_acc 64.8\\n\",\n            \"\\tretrain #72, sparsity 0.72, final_train_loss 9.054e-02, max_acc 70.9, last_acc 70.9, mean_acc 64.7\\n\",\n            \"\\tretrain #73, sparsity 0.73, final_train_loss 3.488e-02, max_acc 71.1, last_acc 69.1, mean_acc 65.0\\n\",\n            \"\\tretrain #74, sparsity 0.74, final_train_loss 2.987e-02, max_acc 71.5, last_acc 70.5, mean_acc 64.4\\n\",\n            \"\\tretrain #75, sparsity 0.75, final_train_loss 1.610e-01, max_acc 71.8, last_acc 68.6, mean_acc 64.8\\n\",\n            \"\\tretrain #76, sparsity 0.76, final_train_loss 1.373e-01, max_acc 72.1, last_acc 69.9, mean_acc 66.2\\n\",\n            \"\\tretrain #77, sparsity 0.77, final_train_loss 8.389e-02, max_acc 71.7, last_acc 70.8, mean_acc 64.8\\n\",\n            \"\\tretrain #78, sparsity 0.78, final_train_loss 1.139e-01, max_acc 71.3, last_acc 71.3, mean_acc 65.3\\n\",\n            \"\\tretrain #79, sparsity 0.79, final_train_loss 1.330e-01, max_acc 72.8, last_acc 71.5, mean_acc 67.1\\n\",\n            \"\\tretrain #80, sparsity 0.80, final_train_loss 2.172e-02, max_acc 72.1, last_acc 72.1, mean_acc 66.4\\n\",\n            \"\\tretrain #81, sparsity 0.81, final_train_loss 1.449e-01, max_acc 73.4, last_acc 70.5, mean_acc 65.9\\n\",\n            \"\\tretrain #82, sparsity 0.82, final_train_loss 3.688e-03, max_acc 73.1, last_acc 72.4, mean_acc 66.7\\n\",\n            \"\\tretrain #83, sparsity 0.83, final_train_loss 3.161e-01, max_acc 72.6, last_acc 68.6, mean_acc 65.6\\n\",\n            \"\\tretrain #84, sparsity 0.84, final_train_loss 9.952e-02, max_acc 72.1, last_acc 72.0, mean_acc 66.1\\n\",\n            \"\\tretrain #85, sparsity 0.85, final_train_loss 2.680e-01, max_acc 73.4, last_acc 71.0, mean_acc 66.8\\n\",\n            \"\\tretrain #86, sparsity 0.86, final_train_loss 5.618e-03, max_acc 74.8, last_acc 74.1, mean_acc 67.1\\n\",\n            \"\\tretrain #87, sparsity 0.87, final_train_loss 1.564e-01, max_acc 72.9, last_acc 68.7, mean_acc 66.0\\n\",\n            \"\\tretrain #88, sparsity 0.88, final_train_loss 1.238e-02, max_acc 74.5, last_acc 74.5, mean_acc 67.0\\n\",\n            \"\\tretrain #89, sparsity 0.89, final_train_loss 1.012e-01, max_acc 74.7, last_acc 71.9, mean_acc 68.0\\n\",\n            \"\\tretrain #90, sparsity 0.90, final_train_loss 2.759e-02, max_acc 74.1, last_acc 74.1, mean_acc 67.1\\n\",\n            \"\\tretrain #91, sparsity 0.91, final_train_loss 4.520e-02, max_acc 72.9, last_acc 72.4, mean_acc 66.4\\n\",\n            \"\\tretrain #92, sparsity 0.92, final_train_loss 1.914e-02, max_acc 74.4, last_acc 74.4, mean_acc 66.3\\n\",\n            \"\\tretrain #93, sparsity 0.93, final_train_loss 9.094e-02, max_acc 73.7, last_acc 70.0, mean_acc 66.6\\n\",\n            \"\\tretrain #94, sparsity 0.94, final_train_loss 1.071e-01, max_acc 74.3, last_acc 70.3, mean_acc 67.1\\n\",\n            \"\\tretrain #95, sparsity 0.95, final_train_loss 7.500e-02, max_acc 70.8, last_acc 70.5, mean_acc 64.9\\n\",\n            \"\\tretrain #96, sparsity 0.96, final_train_loss 7.664e-02, max_acc 71.5, last_acc 71.5, mean_acc 65.6\\n\",\n            \"\\tretrain #97, sparsity 0.97, final_train_loss 2.434e-01, max_acc 68.3, last_acc 67.3, mean_acc 61.7\\n\",\n            \"\\tretrain #98, sparsity 0.98, final_train_loss 4.330e-01, max_acc 65.0, last_acc 63.8, mean_acc 59.3\\n\",\n            \"\\tretrain #99, sparsity 0.99, final_train_loss 1.397e+00, max_acc 42.4, last_acc 41.5, mean_acc 37.7\\n\",\n            \"\\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"num_trials = 2\\n\",\n        \"trials = {'rand_models': [], 'rand_stats': [], 'lott_models': [], 'lott_stats': []}\\n\",\n        \"for t in range(num_trials):\\n\",\n        \"  print(\\\"############  Trial {}  ############\\\".format(t))\\n\",\n        \"  print(\\\"   Random pruning\\\")\\n\",\n        \"  set_seed(model_args.seed+t)\\n\",\n        \"  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"\\n\",\n        \"  def criteria_fn(init_params, final_params):\\n\",\n        \"    mask = (final_params == 0).int()  # if params are already set to zero, keep them set to zero\\n\",\n        \"    return torch.rand(final_params.shape).to(DEVICE) #* mask\\n\",\n        \"  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\\n\",\n        \"                                      criteria_fn=criteria_fn, prune_print_every=1)\\n\",\n        \"  trials['rand_models'].append(models)\\n\",\n        \"  trials['rand_stats'].append(stats)\\n\",\n        \"\\n\",\n        \"  print(\\\"   Magnitude pruning\\\")\\n\",\n        \"  set_seed(model_args.seed+t)\\n\",\n        \"  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"\\n\",\n        \"  criteria_fn = lambda init_params, final_params: final_params.abs()\\n\",\n        \"  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\\n\",\n        \"                  criteria_fn=criteria_fn, prune_print_every=1)\\n\",\n        \"  trials['lott_models'].append(models)\\n\",\n        \"  trials['lott_stats'].append(stats)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 20,\n      \"metadata\": {\n        \"id\": \"AgKoAhiluzTt\"\n      },\n      \"outputs\": [],\n      \"source\": []\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"uVy2yEsA4UMT\"\n      },\n      \"source\": [\n        \"## Plot results\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 25,\n      \"metadata\": {\n        \"id\": \"Wy6JFxutxVfQ\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"to_pickle(trials, path=project_dir + 'lottery.pkl')  # cache results because they take awhile (~1hr) to compute\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 29,\n      \"metadata\": {\n        \"id\": \"ImA4Y9wnyx8k\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# trials = from_pickle(path=project_dir + 'lottery.pkl')   # optionally load precomputed results from your Drive\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 32,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 559\n        },\n        \"id\": \"LrqTjYn9NySW\",\n        \"outputId\": \"3dc00e06-30bb-4a1c-b290-da0c3edb29c3\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 1600x600 with 2 Axes>\"\n            ],\n            \"image/png\": 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POw8SJEzF37lzcd999ePDBBzFixAjMnTsX2dnZqK6uxv79+/H111/joYcewpgxY+BwOHDyySdj1KhRmDp1KoYMGQKr1YoVK1agrKwMd911V3MN4tGjR2PgwIF48803ER0djaysLJhMJtx6661Ba1kHExYWhqVLl2LOnDk4++yzcckllyA7Oxvr16/HmjVrMHz4cDzyyCPN+7/66qt47rnncNppp2H48OFITEzE7t278dFHHyE1NRXXXnstAGDVqlX4zW9+0/yc0tLScPDgQXzwwQeIiYnBzTff3OXXnoiIiIh+fDiG93frrbfi1ltvxbHHHouLL74YTU1NWLVqFQzDwKRJk7Bt2za//W+66SZ8+eWXePvttzFy5Eicd955SExMRGFhIVauXIkXX3wRF1xwAQDgmWeewc6dO/HHP/4RH330EU4//XQYhoG8vDx8+umnKC8vb85aufPOO7Fw4UKceOKJuOSSS+D1evHxxx/j+OOP7/TrfyS/484cq3LjjTfiuuuuA8CsFSLqIwwiIqI2HDp0yABgzJkzp839vvjiCwOAsXjxYr/t2dnZRnZ2dsDbzJgxw2j5r6ihocG48847jZycHCM6OtoYOXKk8eCDDxput9sAYMyYMcNv/6uvvtoAYBw6dKhDz6e0tNSYN2+e0a9fPyMsLMwAYLz88st++6xatco499xzjfT0dCMyMtLIyMgwTjzxROPBBx80CgsLDcMwDLfbbTz66KPG7NmzjaysLCMqKsoYMGCAcdpppxmvv/664fV6/e5zw4YNxowZM4yEhAQDQIeOOdhrahiGsX37duPiiy82+vXrZ0RGRhrZ2dnGr371K6OysrLV4y5atMgYP368kZycbJjNZmPkyJHGLbfcYhQUFDTvt3v3buNXv/qVceyxxxppaWlGdHS0MWzYMOPqq682du3a1aHXloiIiIhCA8fwvTeG93q9xj//+U9j3LhxRkxMjJGRkWFcd911RkVFRcDXTt3mhRdeME444QQjLi7OiI2NNUaOHGnceOONzceuWCwW47777jNGjx5tREdHG0lJScbkyZONP/zhD4bb7fbb9x//+IcxcuRIIzIy0hgyZEjzPoF+J8GOTens77izx2oYhmGz2Yzo6GjDbDYbtbW1wV9kIqIQYTIMwziq0RwiIiIiIiIiIiIiH5s2bcLxxx+PK6+8Eq+88kpvHw4RUbvYc4WIiIiIiIiIiIh61WOPPQZASqUREfUFDK4QEREREREFsG/fPlx22WXIyspCbGwsRo8ejQceeAB2u91vv3Xr1uGUU05BbGwsMjIycNttt8FqtfbSURMREfUdhYWFeOSRR3DllVfi7bffxpw5c3DiiSf29mEREXUIy4IRERERERG1cPjwYUycOBFJSUm48cYbkZqaivXr12Pp0qU477zz8P777wMAtm7dihNPPBFjxozBwoULUVRUhMcffxyzZs3Cxx9/3MvPgoiIKLStWbMGs2bNQnx8PGbNmoXnn38eGRkZvX1YREQdEtHbB0BERERERBRqXn31VdTV1WHt2rUYN24cAGDhwoXwer145ZVXUFtbi5SUFPzud79DSkoK1qxZg8TERABATk4ObrjhBnz66aeYPXt2bz4NIiKikDZz5kxw3TcR9VUsC0ZERERERNRCfX09AGDAgAF+2zMzMxEWFoaoqCjU19dj1apVWLBgQXNgBQCuuuoqxMfH4+233z6qx0xEREREREcPgytEREREREQtzJw5EwBw3XXXYevWrTh8+DDeeustPPvss7jtttsQFxeHHTt2oKmpCccdd5zfbaOiojB58mRs2bKlF46ciIiIiIiOBpYFO0pKS0tRWloKh8MBq9WKU089FbGxsb19WEREREREFMDcuXPx4IMP4uGHH8YHH3zQvP3//u//8NBDDwGQMT4g2SwtZWZm4uuvvw56/2p+4ItzBSIiIiKivoPBlaPkueeew5IlS5p/3rx5M6ZMmdKLR0RERERERG3JycnBaaedhosuughpaWn48MMP8fDDDyMjIwO33HILHA4HACA6OrrVbWNiYpqvD6Tl/MAX5wpERERERKGPwZWjZNGiRTjvvPOQm5uLBQsW9PbhEBERERFRG958800sXLgQeXl5yMrKAgD8/Oc/h9frxT333IP58+fDbDYDAFwuV6vbO53O5usDUfMDX5wrEBERERH1HQyuHCWZmZkBywUQEREREVHoeeaZZ3Dsscc2B1aU8847D0uXLsWWLVuax/cty3upbQMHDgx6/5wfEBERERH1bWxoT0RERERE1EJ5eTk8Hk+r7Y2NjQCApqYmjB8/HhEREdi0aZPfPm63G1u3bsXkyZOPxqESEREREVEvYHCFiIiIiIiohVGjRmHLli3Iy8vz2/7GG28gLCwMEydORFJSEs4880y89tpraGhoaN7n1VdfhdVqxSWXXHK0D5uIiIiIiI4SlgUjIiIiIiJq4Te/+Q0+/vhjnHrqqbjllluQlpaGFStW4OOPP8b111/fXPLrj3/8I0466STMmDEDCxcuRFFREf7yl79g9uzZmDt3bi8/CyIiIiIi6inMXCEiIiIiImrhtNNOw7p16zB16lQ888wzuP3223HgwAH88Y9/xLPPPtu835QpU7B69WqYzWbccccdeP7553Hddddh+fLlvXj0RERERETU05i5QkREREREFMC0adPw0UcftbvfKaecgm+++eYoHBEREREREYUKZq4QERERERERERERERF1AoMrREREREREREREREREncDgChERERERERERERERUScwuEJERERERERERERERNQJDK4QERERERERERERERF1AoMrREREREREREREREREncDgChERERERERERERERUScwuEJERERERERERERERNQJDK4QERERERERERERERF1AoMrREREREREREREREREncDgCtERyMnJQU5OTm8fBvWy/Px8mEwmXHPNNb19KER8PxIREYUIzhUI4NiMQgvfj0REPYPBFSKiXjRz5kyYTKaA13EATERERET008W5AhERUWhjcIWIiIiIiIiIiIiIiKgTGFwhIiIiIiIiIiIiIiLqBAZXKGStWbMGJpMJ999/P9atW4fZs2cjOTm5OS36pZdewvnnn4+cnBzExMQgNTUVc+bMwRdffNHmfW3atAlnnXUWEhISkJSUhAsvvBD5+fkBj+H999/H8ccfD7PZjAEDBuCGG25AbW1t0GOuqqrC7bffjqFDhyI6Ohr9+/fHvHnzsHPnzlb7XnPNNTCZTDh48CAef/xxjBo1CmazGWPHjsWbb74JAHC73fi///u/5uc4ceJEfPzxx0fwatLRVFBQgOuuuw6DBg1CVFQUsrKycN1116GwsNBvP5PJhC+//LL5e/V1zTXXYOnSpRg6dCgAYNmyZX7Xr1mzpvk+DMPASy+9hJNPPhmJiYmIjY3Fcccdh5deeqnVcd1///3Nt1+6dCmmTJmC2NhYzJw5Ey+88AJMJhP+/Oc/B3xOn3/+OUwmExYtWtRNrxIdKY/Hg0cffRQjRoxATEwMRowYgT/96U/wer0B96+oqMAdd9yBESNGIDo6Gv369cNFF10U8HNJ1Yi3Wq341a9+hYEDByI6OhoTJ07E8uXLW+1vsVjwhz/8AWPHjkV8fDwSExMxYsQIXH311SgoKPDbtzPvVSIiovZwrsC5Ql/FuQL1JM4ViIiOrojePgCi9qxbtw4PP/wwZs2ahYULFzYPOm+++WZMmjQJZ555JtLT01FcXIz33nsPZ555Jt59912cf/75re7ru+++w5///GfMmjULixYtwpYtW/Dee+9hx44d2LlzJ2JiYpr3feWVV3D11VcjMTERV155JZKTk7FixQqceeaZcLvdiIqK8rvvyspKnHjiiThw4ABmzpyJyy67DIcOHcLy5cvx4YcfYuXKlTjllFNaHdOvf/1rbNy4Eeeeey7Cw8Px5ptv4vLLL0dKSgqefvpp7N69G+eccw6cTidef/11nH/++cjNzcXw4cO7+ZWm7pCXl4dTTjkFlZWVOPfcczFu3Djs3LkTL730Ev73v/9h7dq1GDVqFABg8eLFWLp0KQoKCrB48eLm+5g8eTJycnLwq1/9Ck899RQmTZqECy64oPl61SDVMAxcccUVeOONNzBy5EhcfvnliIqKwqpVq3Dddddh9+7dePzxx1sd42OPPYYvvvgC559/PmbPno3w8HDMnz8fd955J1588UXcfffdrW7zr3/9CwBwww03dOOrRUdi4cKFeOmllzB06FDcfPPNcDqdeOKJJ7Bu3bpW+6rPo6KiIsyePRsXXHABKioq8J///AcrV67EZ599hunTp/vdprGxEbNnz0ZtbS0uuugi2O12vPnmm5g3bx4++eQTzJ49G4C8/+bMmYONGzfi5JNPxty5cxEWFoaCggJ88MEHuPLKK5Gdnd2875G8V4mIiNrDuQLnCn0J5wrU0zhXICI6ygw6qjZv3mwAMDZv3nzkd+L1GobVGvpfXm+XXqsvvvjCAGAAMF566aVW1x88eLDVtpKSEmPgwIHGyJEjg97Xm2++6XfdlVdeaQAw3njjjeZtFovFSExMNOLi4oy9e/c2b3e73cZpp51mADCys7P97ufaa681ABj33nuv3/YPP/zQAGCMGDHC8Hg8zduvvvpqA4AxatQoo6Kionn7xo0bDQBGcnKyccoppxhWq7X5urfeessAYNx6662BXrKQ8BN5exqGYRiHDh0yABhXX31187ZZs2YZAIznnnvOb99//OMfBgDj9NNP99s+Y8YMI9hHcaD79/X8888bAIxrr73WcLvdzdtdLpdx7rnnGgCMTZs2NW9fvHixAcCIi4sztm/f3ur+brrpJgOAsWbNGr/t1dXVRnR0tDF58uSAxxFKrFZr0C+Hw9Hhfe12+xHva7PZAu7XHdRn2aRJk/zus6ioyOjXr1+r98tJJ51khIeHG5988onf/ezdu9dISEgwJkyY4Lc9OzvbAGCcf/75hsvlat6+evVqA4AxZ86c5m3bt283ABgXXHBBq+N0Op1GQ0ND88+dfa8SEf1UdXmu8BMaiHGuwLlCiL9FOVcIQZwrcK5ARNTdGFw5yroluGK1GgYQ+l9dHCCogcGUKVM6dbtbb73VAGDk5+e3uq/TTjst6OP8+te/bt62bNmyoBOTr7/+utWEyeVyGTExMUZaWpphs9la3eass84yABhfffVV8zY1YVq2bFmr/YcNG2YAML788ku/7U1NTUZkZGTA5xEqfiJvT8MwWk9oCgoKDADG2LFjDW+LGZnH4zFGjx5tADAKCwubt3dlwjRx4kQjLi6u1YDdMPRg9s4772zepiZMd9xxR8D727ZtmwHAWLBggd/2J5980gBg/OMf/wh4u1CiTowE+jr77LP99o2NjQ2674wZM/z2VZORQF/HHXec375q0tHyqzuoEzP/+c9/Wl334IMP+r1fvv/+ewOA8Ytf/CLgff361782ABg7duxodeyBTkhlZ2cbqampzT+r99j8+fPbPe7OvleJiH6qujxX+AkNxDhX4FwhxN+inCuEIM4VOFcgIupuLAtGIe/4448PuP3gwYP405/+hM8//xzFxcVwuVx+15eUlDSnmSpTp05tdT9ZWVkAgLq6uuZt27ZtAwCceuqprfY/8cQTERHh/6ezZ88eOJ1OzJo1C7Gxsa1uM2vWLKxatQpbt25tdZ+TJ09utX9mZiYOHjzY6rrw8HD0798fJSUlrW5DvW/r1q0AgBkzZjTX+1bCwsJw2mmnYc+ePdi6dSsGDx7cpcey2+3YsWMHBg4ciEcffbTV9Y2NjQDkvdnStGnTAt7nxIkTccIJJ2D58uV4+umnkZycDAB48cUXERsbiyuuuKJLx0xd19ZnU8ttGzZsAACUl5fj/vvvb7W/em/s2bMH48ePb96enJzcXMPbV1ZWFtavX9/885gxYzBx4kS88cYbKCoqwgUXXICZM2di8uTJCAvTLd268l4lIiJqD+cKGucKoY1zBeppnCsQER19DK70RbGxgNXa20fRvgAThyMxYMCAVtv279+PadOmob6+HrNmzcK5556LxMREhIWFYc2aNfjyyy9bTaAAIDExsdU2NfnxeDzN2ywWCwCgf//+rfYPDw9HWlqa37b6+vqgxwrIBMh3v44eU7Dr1AAjFP3E3p5+uvI+6Kza2loYhoHi4mIsWbIk6H42m63VtmDHBwCLFi3Ctddei9deew233HILNm7ciB07duDqq69GUlJSl4+7p1nbePOFh4f7/VxRURF0X98BP4CgjWwD7bt7924YhtHGUR45i8WCsLAw9OvXr9V1LX+vNTU1AIAPP/wQH374YdD7bPkeCfZ7joiI8GuEGRERgc8//xz3338//vOf/+DOO+8EAKSnp+OWW27B//3f/yE8PLxL71UiIuqkn+BAjHOF1tdxrtB1nCsExrmC4FyhNc4ViOinisGVvshkAuLievsojpqWq3oA4K9//Stqa2vx6quvYsGCBX7X3Xjjjfjyyy+79JhqwBBoQOXxeFBdXY1BgwY1b1MTm/Ly8oD3V1ZW5rffj9lP7O3p52i+D9R9TJ06FZs2berUbQP9TSmXXnop7rjjDrzwwgu45ZZb8MILLwDoO80p4zrx5uupfQOtSO0uSUlJ8Hq9qKqqQnp6ut91Ld936j3y9NNP45ZbbumR40lLS8PTTz+Nv/3tb9izZw8+//xzPP3001i8eDEiIyNx7733dum9SkREnfQTHIhxrtC3/ATfos04V+h9nCtonCsQEXWPsPZ3IQo9Bw4cAACcf/75ftsNw8A333zT5fufNGkSAODrr79udd369evR1NTkt2306NGIiYnBd999B7vd3uo2a9asARA4rZ9+PNTv96uvvmq1GskwDHz11Vd++wF6hZTvasiOXJeQkIAxY8YgNzfXr0xFV5nNZlx11VXYtm0bvvjiC7z11lsYM2YMTj755G57DDpybX02tdw2ffp0APBLz+8pJpMJY8aMwc0334xVq1YBAD744AMAPfdeJSIiCoZzBQpFnCtQT+NcgYjo6GNwhfokVR957dq1ftsfeeQR7Ny5s8v3f/755yMxMREvvfQS8vLymrc3Njbi97//fav9o6KiMH/+fFRVVeFPf/qT33WffPIJVq5ciREjRnDQ+SM3ZMgQzJo1C7t27cJLL73kd93zzz+P3NxcnH766X41lFNTUwEAhw8fbnV/KSkpMJlMAa8DgNtuuw12ux033HBDwDTpQ4cOtZmiHsyiRYsAAAsWLEBDQ0OfWYn2U3DllVcCAB544AG/33lxcTGeeuopv32nTZuG6dOn44033sBbb73V6r68Xm+XVu7m5+cHfH+pVXExMTHN23rqvUpERBQI5woUijhXoJ7GuQIR0dHHsmDUJ9144414+eWXcdFFF2HevHlIS0vDhg0b8P333+Occ85ps2ZoRyQlJeFvf/sbrrnmGhx//PG47LLLkJSUhBUrVsBsNjfXw/X16KOP4ssvv8RDDz2EdevWYfr06cjPz8c777yD2NhYvPzyy63qrdKPz7PPPotTTjkFN9xwA/73v/9h7Nix2LVrFz744AOkp6fj2Wef9dv/9NNPx/Lly3HRRRfhZz/7GWJiYjBp0iSce+65iI+Px/HHH4+vvvoKV155JUaOHImwsDBceeWVyM7OxqJFi7BhwwYsW7YM33zzDc4880wMHDgQ5eXl2LNnDzZu3IjXX38dOTk5nXoOY8eOxamnnoqvv/4a0dHRuOqqq7rxFaKumDVrFq699lq8/PLLmDBhAi688EK4XC689dZbOOGEE7BixQq//d944w3MmjULl112GZ588klMmTIFZrMZhYWFWL9+PSorK+F0Oo/oWLZu3Yqf//znmDZtGsaOHYuMjAwUFxfjvffeQ1hYGO64447mfXvqvUpERBQI5woUqjhXoJ7EuQIRUS8w6KjavHmzAcDYvHlzbx9KyPviiy8MAMbixYuDXn/yyScbCQkJRnJysnH22WcbmzdvNhYvXmwAML744osO3dehQ4cMAMbVV1/d6rr//ve/xtSpU43o6Gijf//+xvXXX2/U1NQY2dnZRnZ2dqv9Kysrjdtuu83Izs42IiMjjX79+hkXX3yxsWPHjlb7Xn311QYA49ChQ62umzFjhhHszzPYY9PRF+y9k5+fb1x77bVGZmamERERYWRmZhrXXnutkZ+f3+o+GhsbjbvvvtsYMmSIERER0er+9u7da5x99tlGcnKyYTKZWr23DcMw3nrrLePMM880UlJSjMjISGPQoEHGzJkzjb/85S9GZWVl836B/jaCeeGFFwwAxmWXXdaZl4SOgqamJuNPf/qTMWzYMCMqKsoYNmyY8fDDDxv79+8P+H6sqakxfv/73xvjx483zGazER8fb4wcOdK4/PLLjXfffddv37Y+X1p+Lh0+fNj47W9/a5xwwglG//79jaioKGPIkCHGz3/+c2P9+vUB76Oj71Uiop8qzhU6jnMFzhVCHecK1Bs4VyAiOrpMhtGi2Cf1qO+//x5Tp07F5s2bMWXKlN4+HCKigG655Rb84x//wGeffYbTTz+9tw+HiIjoJ4FzBSLqCzhXICIiEsw7JiIiP5WVlVi2bBmOOeYYzJo1q7cPh4iIiIiIQgTnCkRERBp7rhAREQDgww8/xPfff4/ly5fDarXi/vvvh8lk6u3DIiIiIiKiXsa5AhERUWsMrhAREQDgnXfewbJlyzBw4EA8/PDDuOyyy3r7kIiIiIiIKARwrkBERNQagytERAQAWLp0KZYuXdrbh0FERERERCGGcwUiIqLW2HOFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwRUiIiIiIiIiIiIiIqJOYHCFiIiIiIiIiIiIiIioExhcISIiIiIiIiIiIiIi6gQGV4iIiIiIiIiIiIiIiDqBwZUOqqysxDnnnIO4uDgcc8wx+Oyzz3r7kIiIiIiIiIiIiIiIqBdE9PYB9BU333wzMjIyUFlZidWrV2PevHnYt28fUlNTe/vQiIiIiIiIiIiIiIjoKGLmSgdYrVa89957WLJkCWJjY3HeeedhwoQJeP/993v70IiIiIiIiIiIiIiI6Cg7asGVP/7xjzCZTBg/fnyPPo7VasXixYsxd+5cpKamwmQyYenSpUH3d7lcuOeeezBw4ECYzWZMnz4dq1at8ttn3759iI+PR1ZWVvO2CRMmYNeuXT31NIiIiIiIiIiIiIiIKEQdleBKUVERHn74YcTFxfX4Y1VVVeGBBx5Abm4uJk2a1O7+11xzDZ544glcccUVeOqppxAeHo6zzz4ba9eubd7HarUiMTHR73aJiYmwWq3dfvxERERERERERERERBTajkpw5a677sIJJ5yA4447rt19a2tr8e677wa9/o033oDNZgt6fWZmJkpLS1FQUIDHHnuszcf69ttv8eabb+JPf/oTHnvsMSxcuBCff/45srOzcffddzfvFx8fj/r6er/b1tfXIz4+vt3nQ0REREREREREREREPy49Hlz56quvsHz5cjz55JMd2v+ZZ57BvHnz8N///rfVdS+++CIuv/xyLFu2LOjto6OjkZGR0aHHWr58OcLDw7Fw4cLmbTExMbjuuuuwfv16HD58GAAwcuRIWK1WFBcXN++3c+dOjBs3rkOPQ0REREREREREREREPx49GlzxeDy49dZbcf3112PChAkdus0999yDc845B/Pnz8dnn33WvP3dd9/FokWLsGDBAtx0003dcnxbtmzBqFGjWpX8mjZtGgBg69atACRz5fzzz8fixYvhcDiwYsUKbN++Heeff363HAcREREREREREREREfUdET155//85z9RUFCA1atXd/g2EREReOuttzB37lxccMEFWL16NaxWKy6//HL87Gc/w8svvwyTydQtx1daWorMzMxW29W2kpKS5m3PPPMMrr76aqSlpSErKwtvvfUWUlNTO/VYpaWlyM3N7fqBExERERERERERERFRr+mx4Ep1dTX+8Ic/4L777kN6enqnbhsTE4MPPvgAs2bNwtlnnw23243p06fj7bffRkRE9x2yw+FAdHR0wMdX1yvp6en46KOPjvixnnvuOSxZsuSIb09ERERERERERERERKGhx8qC/f73v0dqaipuvfXWI7p9YmIiHn/8cdTU1MBqteLJJ5+E2Wzu1mM0m81wuVyttjudzubru8uiRYuwefNmvPbaa912n0REREREREREREREdPT1SObKvn378Pzzz+PJJ5/0K63ldDrR2NiI/Px8JCYmtllW6+DBg7jiiiswevRoOBwOXHTRRfjmm28ClvE6UpmZmX5N6pXS0lIAwMCBA7v1sbrz2ImIiIiIiIiIiIiIqHf0SOZKcXExvF4vbrvtNgwdOrT5a+PGjcjLy8PQoUPxwAMPBL19aWkpzjrrLERGRmLVqlVYtWoVbDYbZs+ejZqamm47zsmTJyMvLw/19fV+2zdu3Nh8PRERERERERERERERka8eyVwZP348/vvf/7ba/vvf/x4NDQ146qmnMHz48IC3ra2txZw5c2C1WrF27VpkZWUBAFauXImZM2finHPOwerVqxEXF9fl47z44ovx+OOP4/nnn8ddd90FAHC5XHj55Zcxffp0DB48uMuPQUREREREREREREREPy49Elzp168fLrjgglbbn3zySQAIeJ3yzDPP4PDhw1izZg1GjhzZvH3y5MlYsWIFZs+ejWXLluGXv/xl0Pv4+9//jrq6uuaSZP/73/9QVFQEALj11luRlJQEAJg+fTouueQS3HvvvaioqMCIESOwbNky5Ofn48UXX+zksyYiIiIiIiIiIiIiop8Ck2EYxtF6sJkzZ6Kqqgo7d+4Muk9TUxP27t2LcePGBbx++/btmDBhAkwmU9D7yMnJQUFBQcDrDh06hJycnOafnU4n7rvvPrz22muora3FxIkT8eCDD2LOnDkde1Kd9P3332Pq1KnYvHkzpkyZ0iOPQUREREREfQ/nCkREREREfcdRDa4QJ0xERERERBQY5wpERERERH1HjzS0JyIiIiIiIiIiIiIi+rFicIWIiIiIiIiIiIiIiKgTGFwhIiIiIiIiIiIiIiLqBAZXiIiIiIiIiIiIiIiIOoHBFSIiIiIiIiIiIiIiok5gcIWIiIiIiIiIiIiIiKgTGFwhIiIiIiIiIiIiIiLqBAZXiIiIiIiIiIiIiIiIOoHBFSIiIiIiIiIiIiKiXtbUBDQ09PZRUEdF9PYBEBERERERERERERH9lBkGsHUrYLUC2dnA0KG9fUTUHmauEBERERERERERERH1oupqCazY7UBtbW8fDXUEgytERERERERERERERL2ouFiCKw0NgNvd20dDHcHgChERERERERERERFRL1HZKtXV0nelsbG3j4g6gsEVIiIiIiKiAK655hqYTKagX8XFxQCAmTNnBrx+7ty5vfwMiIiIiKgvKCmRoEpdnVx6PPJFoY0N7YmIiIiIiAJYtGgRzjzzTL9thmHgxhtvRE5ODgYNGtS8PSsrC3/605/89h04cOBROU4iIiIi6rs8HqCsTLJWDEOCK4Bkr4SH9+6xUdsYXCEiIiIiIgrgxBNPxIknnui3be3atbDb7bjiiiv8ticlJWHBggVH8/CIiIiI6EegokICKdXV8rNvcCUmpveOi9rHsmBEREREREQd9Prrr8NkMuHyyy9vdV1TUxOsVmsvHBURERER9VXFxUB9vTSxT0zUwRU2tQ99DK4QERERERF1QGNjI95++22cdNJJyMnJ8bsuLy8PcXFxSEhIQEZGBu677z40shMpEREREbWhvh6wWoGqKiA2VsqCbdkipcI4lAx9LAtGRERERETUAStXrkR1dXWrkmDDhw/HrFmzMGHCBNhsNixfvhwPPfQQ8vLy8NZbbwW8r9LSUpSWlvpty83N7bFjJyIiIqLQU1wMuFxAQwMwZAjwxBPAN98AAwcCo0b19tFRexhcISIiIiIi6oDXX38dkZGRmDdvnt/2F1980e/nK6+8EgsXLsS//vUv3HHHHTjhhBNa3ddzzz2HJUuW9OjxEhEREVHoamwEKislayU8HEhIALZuleuKilgWrC9gWTAiIiIiIqJ2WK1WvP/++5gzZw7S0tLa3f/OO+8EAKxevTrg9YsWLcLmzZv9vl577bVuPWYiIiIiCl2lpdJfpaYGSE0FDh4EbDa5rraWZcH6AmauEBERERERteO9996D3W5vVRIsmMGDBwMAampqAl6fmZmJzMzMbjs+IiIiIuo7DAMoKQHq6qS/Sr9+wNdf6+vr6hhc6QuYuUJERERERNSOf//734iPj8d5553Xof0PHjwIAEhPT+/JwyIiIiKiPqimBnA6pSxYQgIQHS2N7BWLhWXB+gIGV4iIiIiIiNpQWVmJ1atX48ILL0RsbKzfdfX19XC5XH7bDMPAQw89BACYM2fOUTtOIiIiIuobSkoAux1wOCRrpakJ2L5dX2+xMHOlL2BZMCIiIiIioja89dZbaGpqClgS7Pvvv8f8+fMxf/58jBgxAg6HA//973/xzTffYOHChZgyZUovHDERERERhSqnE6iulkb2UVFAYiKwa5dsVxoamLnSFzC4QkRERERE1IZ///vf6N+/P84888xW12VnZ+PUU0/Ff//7X5SVlSEsLAxjxozBP//5TyxcuLAXjpaIiIiIQllhofRZqa0FMjIAk0mXBBsxAti/H6ivB7xe2S88vHePl4JjcIWIiIiIiKgN69evD3rd0KFD8fbbbx/FoyEiIiKivqqgQEqClZbKz6mpcqmCKxMnSnDF5ZKvxkYGV0IZe64QEREREREREREREfWgoiLg0CEJrFRVAYMGAZGRUv5r507ZZ9QoHUxhU/vQx+AKEREREREREREREVEPKSmRjJTycvkaOFAa2QPSb6WxEUhJAfr3BxISZHtdHZvahzoGV4iIiIiIiIiIiIiIAvB6JdDh9R7Z7cvLgbw8oLJSslYyMiSIoqiSYGPHAhER0uAekMwVBldCG3uuEBEREREREREREREFsHu3lPFKTwfGjevcbSsrgT17gOpqoLhY7iMjw38fFVwZNQowm3VwpbaWZcFCHTNXiIiIiIiIiIiIiIhaMAygpgZwOCTA4vF0/LbV1RKYqa4GDh+WMmCDBvnv43AAubnyfU6OBFeSk+XnmhpmroQ6BleIiIiIiIiIiIiIiFpwuaQcWG2tBFoaGjp2O5tNeqnU1QGFhUBqauvACgDs2CEBm4wMyVgxm4GkJLmOmSuhj8EVIiIiIiIiIiIiIqIWHA65tFgkCGKxdOx2paXAwYPAtm2SiTJ4MGAytd7v++/lcsIEuTSbpbE9wIb2fQF7rhARERERERERERERteBwSMaKyyXZKPX1HbtdQQFw001AeDjw0kuBAysAsHWrXI4eLfvExOiyYGxoH/qYuUJERERERERERERE1ILDIV+FhRJYsVgk2NKWpibgu+8Ap1MCMk8+Gfg2DQ3Avn3y/fDhElgxmaSEGCCPxbJgoY3BFSIiIiIiIiIiIiKiFhwO4J13gL/+FfjgAwmc2O1t38Zi0UETAFi/Hvjss9b7bdsm/VwGD5bAitks21XmSn09M1dCHYMrREREREREREREREQtOBzSOwUAdu6UDJT2SoPV1QF79sj3/fvL5dNPAzU1/vupkmDHHitZLiq4ohra19dL8MXj6eqzoJ7C4AoRERERERERERERkQ/DkOCKCooUFsrP7TW1r6sD8vLk++uvB7KyJFDyxBP+5cG2bJHLceNku9kMxMXpzBWbTQIrLA0WuhhcISIiIiIiIiIiIiLy4XZL5khtrfxsswGHDrWdudLUBBQVAVVV8vPEicD8+UBEBPDNN8Dq1bK9tlZnxIwaJZdmM5CSAiQmys+GAVitLA0WyhhcISIiIiIiIiIiIiLy4XDIZV2d3rZrl/RcCRbw8O23kpkJpKcDgwYBF10k255+Gqiu1iXBhg0DoqLkKzxcslYiIoDYWP3YzFwJXQyuEBEREREREREREdGPnsXSuvdJMA6HBDZsNr1NlfsKVhqsrg7IzZXvjzkGCAuTUl9nnAGMHAk0NEh5MFUSbMoUeRzVbyUhQYIrCQn6cZi5EroYXCEiIiIiIiIiIiKiHzWbTYIa27dL9kh7HA6gvNx/W36+BFyClQazWHRwRZX7iouThvX33COBk3XrgJUr5brJk3VwJTISiI6WS1UarLqawZVQxuAKEREREREREREREf2oWSzSx8Tp7Fj2im9wJeyHs+iHD0tgJVDmiscjmSkHDsjPo0ZJYCU+Xq4bOBC46iq5zu2W+xw7Vvq0mM2yHyDBlaQk+b62lmXBQhmDK0RERERERERERET0o+Z0ShaI3S6N4ttjtwMVFfJ9To5klbjdwP79EkTxev33t1hku7rNqFFARoYEWEwmecz586U8mLo+PFy+j4kJHlxh5kroYnCFiIiIiIiIiIiIiH7UXC4JjjgcEugwjLb3dziAqir5vl8/YPBg+T43VwIrLQM0vv1W0tOloX1ysmSoxMbK/hERwO9/D0ydCixYII8RHi6BG9/gSnKyvk8GV0IXgytEREQU2gxDlv80NfX2kRAREREREVEf5Rtc8Xjksq19vV4dXElLA4YPl+/375fbtywNVlcH7Nol348YIQGV+HgJqMTH62DMkCHA448DJ58sxxATI9sDBVcsFpYFC2UMrhAREVFoKy4GNm8GNm6UvGwiIiIiIiKiTlJlwVRQpa3SYGqf2lq5TEvTDeoLC+V63+CK6reSlyc/H3OMLgeWlKT7rrQM6Khm9iq7BZDgSmqqfG+xMHMllDG4QkRERKGtslJGlPX1wPbtXLZDREREREREnWIYOnPF45HL9oIrhqGDK/36AePGyfelpUBNjUxRFYtF9j94UH4ePVoHS5KT9fc2m76N1yvHZDbL9SaTbI+MBFJS5Pv6ek6BQxmDK0RERHT0lJYCu3e3nX/tyzBkxGu1yihVBVhYIoyIiIiIiIg6yO2W6aXKAlF9V4JxOGRfFUDp10/KeakMlLw8XWIMkOCKxaKb2Y8c6R9cCQ/XfVcUp1MuzWZdEgzwLwtWXy/HzSlwaGJwhYiIiI6O8nJg714JsKjlPO1xOmXkqvK3Dx6UEeuuXbLMh4iIiIiIiKgdLpdcqiyQjgRXXC4dXElLk94oQ4bIz3v3yqW6XvVbMQzJOklLk6AJIIGT8HD/vivqMQC5X9/gSkSEDq40NPgHhSi0MLhCREREPc9ul6U9NTXSEdA3f7otauTpcMjosrEROHQIqK7Wo1kiIiIiIiKiNqjgim/mitsdvOSWwyFfDQ3yc2qqlO0aNkx+zs/XfVc8Hpni5ubKdcOG6T4qgH/flaYmnbGimtmHhbXOXElK0sfrdLI0WKhicIWIiIh6ltcrpcBsNqCoSAItqthte6xWGU02Ncnyn6FD5X4KCyUTpqMZMERERERERPSTpYoieDwSvGivqb3DIWv6VMEE1QPlmGPksrBQpraqPahhAPv2yXWjRkkze1/JyXqb7xpC3+wWJTJStkdEyM9sah+6GFwhIiKinrV/v4w28/NlZKpGsWoJUFus1ta50jk5knNdXCwj2uLiHjpwIiIiIiIi+jHwXd+XmKgb2wcKrqjrKivl5/h4CXjExABjx8q2ykr5stmkOENTk0xPAf9m9krLviuGoTNXYmJ0IAWQxzKZ5DgBmf4yuBKaGFwhIiKinlNRAZSUSMaKywUMGCAj1aamzgVXwsOBqCjZlpQEDBqkR7P79smSIiIiIiIiIqIAVBtPQJfcCtZ3Ra3vq6qSS9X/JDkZyMiQEmGGocuAlZbKlLSsTH4eM6Z1cCUhQaa1cXESkHG7Ze1hy2b2gARWIiLkNoBU12ZZsNDE4AoRERH1DIdD+qLU1MjX4MEyCgUkf7qt7oGAjHxdLr2cx2TS16WnA/37S9aKyoohIiIiIiIiCsA3cyU+XqaXnQmuREZKsCMmRqa2gFSpbmqSIElurgRcEhKAfv1alwXz7bvS2CjZKEDg4Aogj6cyV2pqmLkSqhhcISIiou7n9QK7duk+KykpEliJipLlOr6dAYOx2eTStxCtr8xMub/6etnXMLr/eRAREREREVGf55u54nJJkMThkHV/Ho//vqrZfX29/JyaKpkk8fHSfH74cNleWKinrXv3yuXQoRJIaZm5AujgCiCBm4gICaIEC66oDJu6OmauhCoGV4iIiKj7qT4rBQUyKszKku0mkwRKOtLU3mqVUa7LFTi4ou7L5ZJgjsvVM8+FiIiIiIiIQkpVFbB+PXDoUPv7er0SWHG7gccfBxYskCBJsKb2arrqG1zxDYKopvaHD8ttm5r0cQwfLtPUsABn3VXfFbNZjkdNc1tmuQDyeKocWW0tM1dCFYMrRERE1L18+6w4ndKAPjxcX282d6ypvdUqt1e3CSQqSgdV1H0SERERERHRj1phoQRBCgokuNEWNWWsrpbK0qpytcMhgZeWwRWHwz+4kpYmwQ4VGBk9Wtb61dZKvxWrVaa/ADBqVOCsFUBKhoWF6SCN2SzZKzExrff1Da6woX3oYnCFiIiIuk9TkzSYr62V0WpWVuvASGxsx5raq2b2QODRJgBER8t9GQaDK0RERERERD8RTqcOirQ3FVRr9kpK9DbV88Tl6nhwBZAASWqqtAAFdJtR1cx+7NjAmSiABFZ8S4OZzbKvb3tRxTe4Ul/PsmChisEVIiIi6j75+TJyLS6WUWNamr7O6wWWL5euf4AsMwoWXPF6pXitamYfKKca0EEXl0vuj4iIiIiIiH7UvF7/nijtTQVV5kppqd5WUSGXLZvau91Sndrt1tPVtDTJMAEkMGI2A9nZ8nNhoTSz93hkHeHgwcEzVwAJmPgGVwL1WwEkuJKaKt9bLMxcCVUMrhAREVH3sNkkqFJWJiPLQYP8r//qK+Af/wAeeUSCJXZ76yVCit2us1GClQQDpCwYIKNlZq4QERERERH1qpbN4XuCykRxOiUIoprKt7V/YyNQWam3lZZKIQS7XW5vGLJdTSt9M1f699eZK/HxUh5s6FD5ubxc3++QIcGb2Suq70pcnDx+W8GVlBT5vr5ejq+98md09DG4QkRERN1j/34ZiVZVAQMG6MCH8umncllVpQvcBmtqb7XK6NHpbD+4EhYm98HgChERERERUa+pqgLWrgW+/14HK3qCCq40NnasiIHLJftWVeltJSW6HajXq+9DTSstFj1V9Q2uJCTI5ahRcnnokExvAWDYMLlsK7ii+q706yeBmLaCK6osmMqgYWmw0MPgChEREXVdZaX0WSkullGgKkCr1NYCGzfqn/fubbupvdUqI0evt+3gCiDLfZxOub+eHMETERERERFRUCUlOuNDZX30BKdTpn5ut3zfkeCK2+0fXCkulqmmCtT49m9xuyW4Ash6vthYXRYsMlKmoMccIxko9fXAN9/IdSNGyHVq30DCwiRo4pvBEkhkpFTaBuT5eTwsDRaKGFwhIiKirvF4gAMHZPTZ0CDlwFr2SPnsMwmUqE59O3e23dS+I83slagoGS2rTBciIiIiIiI6qgxDpoQ1NTL1C1YBujuoMl/q+/bW2anyYb7BlaoqmbZ6PP5N7VVwRU1TVYBDZa4Akm2SkKArYavMlTFjggdLfI0cKbcdOzZ4e9HISCAxUU+hfTNpKHQwuEJERERdc/iwLKUpLpZRphp9+lq1Si5//nO53LNHLzFqK7gSGek/ivXldgOvvQZs2KBHmX2pNJjdDhQUMCBERERERER9ns0mgYqGBpniBJrmdReViaLKgql2nW3trypTA3r9Xl2dXPo2tVfVq1XmTbDgitksPVaUmBj5ua2SYIrZLGXF0tKC7xMZ6Z/ZUlfHzJVQxOAKERERHTmnEygslLJgbjeQldV6n0OHgLw8GRleeaUs0WlqkmyXQMEVp1Oub6uZfWEh8MtfAi++KF9Wq3+h3L5g1y55bXJze/tIiIiIiIiIusRi0VOyYGvouovTCWzdCtx7L/DBB7It2FSwsVGCPkVF8nN6ug6KlJdLCS+HQx+vCq7YbPKz6nviW+orIUGCHzk5etugQTJ97UhwpSPU46keLzU1DK6EIgZXiIiI6MgdOCAj2/JyGaUGKuGlslamT5dlP9Omyc/79umca5dL7++7ZCjQ/X3yCbBokTw2ICPlsjK5j76SuaLKodXVMb+biIiIiIj6vPp6XZ7L4ZBgh9fbM4/ldALbt8tUUF0GC66oqWZxsVxmZQEDB8r3vk3t1RStqcm/TFhKily2zFwBpO+KkpMjJbw6UhasI0wmCbAkJsrPtbWcNoYiBleIiIjoyNTWSsZKaakUis3IaL2PxwOsXi3fz54tl8cfL5e7d+sRsG9BXqtVRrSNjf6ZK3Y78PDDwKOPymj62GMllxqQY3C7+05wpb4eWLgQuOcemX3U1vb2ERERERERER0xiwXIzwf+7/+Al16SaU5P9F3xeiX4UVkpP5eW6myZQFRwpaxMLgcO1L1Siosl00RNI9V9ut3+wZWwMCnEoMTESOBj6FC9HnDYMLnsrswVQPddAZi5EqoYXCEiIqLOMwxg/34ZcdbWygjVd7SpbN0qI9T4eODEE2Xb5MkySqyslNzslk3tbTY9ulXBlX37JFtl1SoZ2V53HfDYY8DEiXJ9aamMmvtKWbDcXMm8ycuTrB/VAZGIiIiIiKiPcblk/du6dTIl+/ZbCYL0RGkwFSxRzendbt0GNBCnU46lokJ+DpS50tgo01IVXHG59LGnpgZuAxofL1kqp5wiwZrjjweiooK3DD0SkZG65wt7roQmBleIiIio88rLJbCilvqoXOmWPv1ULk8/XUaagIxeJ02S7/fsaV2QVzWzDwsDoqMloHLzzRKISU8HnnwSWLBAgjlqeVBZmYyaXa6eyz3vTgcP6u/372fmChERERER9QiXC9i8Gdi5s+emShaLXOblyaXdLlksHc1cOXhQyns5ne3vq/bxXZ+2f7/ukdKSyyVBCRWMGTRIZ66o4Io6ZlW12uvVze7T0vz7rSgJCXLbc84B7roL6Neve7NWAAmuqJ4vdXUsCxaKGFwhIiKizrNYZOTpcACZmVIQtiWHA/jqK/n+rLP8r1OlwVoGV1Qje9VvxWqVYEpjI3DyycALLwATJuj7UcGV4mIZNRtGx0bkva2gQH+fn++fd05ERERERNRNiopkLVdVlQ4wdDeLRaZj+fl6265dHctcqa8HCgsla+Tw4fb3dzpbV1bOz5eK1L6tPH339w2uqKAIIGv0wsLkSxVPUAEM3+BKsMyV6GhdwCEmpmeCKypzxWJh5kooYnCFiIiIOs9u10GMYCPItWtln4EDgXHjZFttrYycp0yRn/fv10twXC693MjhkBHvu+/KYw0bBjzwgC44q2Rny0jYatU53H2h74rvrKG4WGYCzF4hIiIiIqJuVlurm833VDXi+nqp1OwbvNmzR6Z37WXL1NQAf/kLcNllEghqj9Opn4+i1q4FKg3mcgHV1fo6t1uOKypKlwtTTe3V/k1N8hiAZKQEC64AOlATHd19zeyVyEgpSwbI8TQ2SmCJQgeDK0RERNR5Kl86IiJwrxVAlwSbPVsyWxobZdRbVyej2AEDZNS6fbvsZ7XKl9erAzf/+Y9cLlggQZSWYmJ0TndhoQQpQr3vitcrARWlqkqeN/uuEBERERFRN2ps1NOshoaeWc/l8cj979jhv/3AAQkEBCvXpZSVAZ98ItOidevafzyXS8p5+SoulmlWoKmg06nXtqWmSjAkOhro31/ftmVwRWXAhIVJWa5AZcFiY+V6s1kCNWFhPZO5oipw19fL69nU1L2PQV3D4AoRERF1TlOTBFacTgluBFJZCXz/vXyvSoKpUfWgQTLqnTxZft65Uze1t1p1YOXzz2Xb4MHAaacFPx5VGqy0VI4r1DNX7HadZQPo4IrFIjMTIiIiIiKibqBKW6ngisvV/WvR1En/PXvkZ9UsvqBApjdtlQbzeIDvvtMBg7Ky9ktfOZ2yH6AzRSor5Tm2fG6GIVNEtbYtPV2CIZGR8j2ggysuly4tpu4nMTF4k3qTSbJXzGadvdKTmSvqdWRpsNDC4AoRERF1ju+SHtWkvqXPPpOlQxMm6NG1yr1OS5PRpwqK+PZdUcEVtxt47z25/vLLg2fHAMDw4XJZUiLH1BeCK+Xl+ueqKpmReL26EyQREREREVEX1dXJFEmVwvJ6uz9h3mKR4MjBg/LzjBkSFHA6gUOH2g6uWCzSm0UpK2s/+ON06unU4MGSLeL1SqZMy9u63RJgUZku/fq1Dq74NrVXU1G1LjAxUbJWAgVXAB1ciY6W/YJNj4+Ub0N7q1UHiyh0MLhCREREneNbrDZQ5oph+JcEU6xWXZg2IwMYMkSCJpWVetRts0lwZNMmmQlkZABnntn28QwdKpelpTIaDvWyYC0zV1TWitvNvitERERERNRt6up0cMPrlelWd085LBaZ0hQWys+TJgFZWfL9rl1yXTC1tbpKNCBBk7bKiBmGBIvUdCo9HcjMlO/37289FVRFEVSmS0qKTGF9M0KKi/W01uHwbwWakCBT1kBlwQCZ3qpG9t1dEgzwb2jf1CTPj5kroYXBFSIiIuoch0NGdB6PLNFp6cABCZZERgIzZ8o2j0duFxcn+dNJSTKyVVknmzZJcMHrlSyO1atl+/z5wUeygCy7Ulkt5eUy2nS52u6aaLcDGzdK2bLeyBRRxwnoEXh1tcx62HeFiIiIiIi6gcrAsFpl2hERIVOOurrua4puGDJ9U1kjERHAmDG6SMHevbqtZiA1Nf6ZK6Wlba+Vc7nkMauq5OcBA3Rw5dAh3Yzed39AZ7qozJWoKN3LpKRE905paJBjVQEpFdgIlrmSkCDT24QEvY6wO0VGSvBGZcTU1TG4EmoYXCEiIqLOja5VAAMIHFxZuVIuTzpJjzB9gwkZGTJyHTAAGDlStm/dqm//1VeyhKlfP2Du3ODH4fFIZ8LYWBlxNjXpToVtlQYrKpKgSmkpsGULkJd3dLsCHjokl7GxQE6OfF9ZqTN31GtLRERERER0hHz7rcTHSwCgoUGmUfX13fMYNpvcnwqQDB4sj3PMMfLz/v3Bm9o3NkqFaN9jaS9zRWWiVFfL5YABQHa2fK8yZ3yDM06nHJ8KxqSn68yVtDTZVlIiARWzWR+LulQludoKrmRlSRaMytbpTupxExLksrqaZcFCDYMrHVRZWYlzzjkHcXFxOOaYY/DZZ5/19iERERF1jwMHgK+/1l3+2qNypYHWRWUbGoBVq+T7OXP0dptNMkzMZhmhZmfLpWpqn5enS4KpkmKXXtp20VqLRUbqmZkSsAF04KKt5U7V1XLbffsk0HL4MPDtt0BFRQeefBcZhh71p6TofjQqc8UwWBqsozweyXjauJEBKSIiIiKiFmprZXrV1CQn5xMS9M/dlTBvsUhgYt8++XnYMMnkGDtWfi4slGF7oNJgdXXAzp36doBMiVR/lEBUcEUdf//++rZFRXLpOxV0uWSqpQI2gwZJlorqZRIeLkGeqioJ1CQkyNRSFThQ2S1tFVMYMQI49tieKQumHlcFV2pqmLkSahhc6aCbb74ZGRkZqKysxGOPPYZ58+ahhqU7iIiorzMMGYW63Trroz12u4xqo6JkZOrr2WdlJDpkCHD88Xq71SolwQDJrR4wQJYMTZkiI0W3W8p0rVwpI8akJOCcc9o+jpoaWYKVkqJzwYuKdAmyQFRmSH29HH9NjSyXqqgAdu8GduzQI/ae4Nt9MTVVRveAzHzUcXN80TFVVfK+stmOTmCMiIiIiKgPqavTQY3YWF1UoKGh+9ZzWSwyhSkokJ9Hj5bLkSNluuVyyVq+QE3ta2uBbdvk++nTdQChrCx4YQGXS4ILKisnI0MXQ7BYJJDSMrii1rYlJekyX1FRElgZMEB+Li6WogzDhsl9qimZ6ssSLHOlp5lM8tiJifJzXR0zV0INgysdYLVa8d5772HJkiWIjY3FeeedhwkTJuD999/v7UMjIiLqGlW0tr5eTvy3Vx7L7ZYggNvduiTY5s3Axx/LCPCuu/QyG8OQEW5cnNwmJkb2GTJEAiPjxsl+GzYA77wj38+bJ1kuwTQ2ykwhJUVGm0OGyPaSEnlOwYIrNTV66VR6uoz+Y2OB/HzJeiktBb77TgdAuptvM/t+/fwzV8LC5PdQW9t9RZB/zFRBZbebMwwiIiIiIh9Op0yJVEmw8HAJKMTESKCjoaF7KiNbLDJ9UUUQxo+XaU1UlJQIA6RkWLDgispcGTdOBzrKyoIXInA6dUAHkOlUv346wyQvr3VZMJXRovqtAHqq6htc8aXKjqn77a3gCiDHqoJCzFwJPT0WXNm1axcuueQSDBs2DLGxsejXrx9OO+00/O9//+uphwQggZDFixdj7ty5SE1NhclkwtKlS4Pu73K5cM8992DgwIEwm82YPn06VqlyJj/Yt28f4uPjkeVTPG/ChAnY5dtxiYiIfhqCnPS2WKSebFvVqEKSytJQec+B8rV9qSfodMrIXHE4gL/8Rb4//3xgwgT/67xeCa6oUSEgS4JiYqQ3CyDlwEpKJNhx/vltH0ddnQRoVBHcESPksrRUTroH+0VUV8tzNAxZGhUVBQwdKr1P7HbJYqmslMu2+rYcKbtdB27S03XmSnm5HI/VqgNH1Da1bK2piTMMIiIiIiIfqmm9Cq4oasrRHdWInU4ZkufmynQvPl7WvKWny/XDh8vl3r2SbO47lXa5JIjiG5RRlZ7bC66Ulsr3MTHymDExes1ayzm5y9U6uBIeLlPJqCh9rL6lyLxenbmSlibBDZOp869Pd4mM1NNoi4VTn1DTY8GVgoICNDQ04Oqrr8ZTTz2F++67DwBw3nnn4fnnn++ph0VVVRUeeOAB5ObmYtKkSe3uf8011+CJJ57AFVdcgaeeegrh4eE4++yzsXbt2uZ9rFYrElX+1Q8SExNh5YkPIqKfli++AP7wBykf5cPjkWpShw/rWrN9hgogNDTIKLK9/20Oh4yK3W7/figvvyyj3P79gRtu8L+NzSaj0dhY/+BKWJiMvk87Ta5XS6fmzNElxIKpqZHc6PBwGW2OGiXb6+okgBIoMNLUJKPRhga9bEtJTpYslsREXSYtP7/tYzgSNpsOrvhmrtTVyfFYrfKGYt+V9rlcOpOKMwwiIiIiomZ1dTIl8nj8gyvx8TKEdjq7PuVQTd9zc+UyO1u32IyL829q7/X6N6r3zVoZNEhuo4IrJSVtB1fUdEpllZhMOkvm0CE9ZVXTBBWMUcEV32wUFVzxzVypr5fbAlIWrK1+K0dDy+AKk/ZDS4+9Pc4++2ycffbZfttuueUWTJ06FU888QQWLlwY8Ha1tbX44osv8POf/zzg9W+88QbOO+88xAU56ZKZmYnS0lJkZGRg06ZNON633nsL3377Ld5880089thjuOuuuwAAV111FcaPH4+7774b69atAwDEx8ejXn1i/KC+vh7xvp9ORET043fDDVIwtl8/yXT4oWNdWZlugteb6cJHxOmU0ZnXq/PG22K3y/6GoYMTubnAf/4j3//61607+Vmtsi0szD+4AkivlPR0CY7s3Stlwy66qP1jdjgkhzs+Xu6zoUFGybW1wMGDksni8UjwRVHLt+rrddFaX+HhMirfvVt+qZGR8nN3/r+323V/kAEDZBZhNsvzUbMNq1WCR6rUGQWmMlcAzjCIiIiIiHzU1sq0Qq1xU7qz74rFIlMztSZtxAh5vIQE+VJN7Q8flnVuDQ368Wtrga1b5fvx42VapEp0lZb6B2IUw5ApgJpOqX4ogEzPAemvYhj+a+3KyuSyf3+Z4qWl6bm7CrT4BldU1kpcnEx5e3uO73ucKnPFMHo3m4a0o9pzJTw8HIMHD0ad6joUwDPPPIN58+bhv//9b6vrXnzxRVx++eVYtmxZ0NtHR0cjQ4U627F8+XKEh4f7BXpiYmJw3XXXYf369Tj8Q2PfkSNHwmq1otjnL23nzp0Yp2rEExHRj5/TKctgADn5/sOIyzBkIKaaBaoWEH2GCq4AHQuuOBwyogUkENLYCDz2mARnzjxTOhG2ZLPJyDQ8vHVGispeOess+XnmTF0qK5i6OrmvxEQZgScl+eeCq99Ty+yV6mr9fFW3xJbCw2XJlNr34MG2j6WzbDYZyQNy7CaTXqJVXi4ZNQ0NMmpWy6UoMKeTZcGIiIiIiFpQUzarVaZfYT5nf8PDJcBhtcp+XamEbLHI2jHfZvZhYRLMSUiQsmDR0TL9OnDAf6pZWyvVHwBg4kQJeLRXFkytCVQtLNPSZDoVF6eb2hcX65afatqqgjGq20NSki6AoAI0JSW6bJnqt5KYKFkroRRcUb1ruqNfDnWPHg+u2Gw2VFVV4cCBA/jrX/+Kjz/+GGeccUbQ/e+55x6cc845mD9/Pj777LPm7e+++y4WLVqEBQsW4KabbuqWY9uyZQtGjRrVquTXtGnTAABbfwihxsfH4/zzz8fixYvhcDiwYsUKbN++Hee3Vw/eR2lpKb7//nvkqlw5IiLqW3btkpEcIMGVH4qy1tbKwK2qSrcv6VN9V1oGV2w2/TwDUaNUk0lGeW+8IcGMpCTgllta76+ajqt+K4GW12RmApdeCvz1r1ISrL0Rfk2N3FdYmCw/UtkwvsuV1LG2vF1DgxxDW9koaWkS5Cgtldu0sSikU9xu/w6EamlWZqZcFhXJLKS+Xkb23fW4P0Zer7yOjY0sC0ZERERE5KO2VvdbCbSmTAVXutJ3xeORqWNJiUxxTCZpSh8fL9O0hASZLqpk/J07dWDAbpfHP3BAfh4/XqZgalpUUSHT1JbTUjXfVmvV0tPlMRITJZATFqZ7stjt8n1Dgw7qDBkiQRWzWYI+UVF6iupw6NdCBVcSEiS4EgplwVQQSBVW6lMLOn/kejy4cueddyI9PR0jRozAXXfdhQsvvBB///vfg+4fERGBt956CyeccAIuuOACbNy4EZ999hkuv/xy/OxnP8PLL78MUzflPZWWliJT/eX6UNtKfLoZPfPMMygpKUFaWhp+/etf46233kKqb/5ZO5577jlMnToVCxYs6PqBExHR0bdli/5+377mE/dFRfKtzSZxBLVKps9wOPyDK209AZVf7XLJaLSgAHj1Vbnu1ltbl/wCdD63Kt8VSHi4FOidNEkyUA4f9u922PL+3G5ZupOSIscRHS23U03ti4sloOMbpFG/IItFjsW3XFhLYWGybEotxequ7BWbTS+bSkiQUT2gM3WKimRm4HbLF/uuBKeWobndOsDCTB8iIiIiItTVyTRGNZlvKTFRB0eOdMqh1oPt2iU/9+8v1bPV48XHS9BCTdH27vUP6OzdK1O2hARg2DCZGqm+KQ0NMpVrOS1VUwAV/EhPl6lgfLzcj1q7tnevnv6prJqEBAlQxMXJcUVHS9AiLKx1U3tVFsy3xWdv8g2uqEAR15aFjh4Prtx+++1YtWoVli1bhp/97GfweDxwtxNei4mJwQcffIBRo0bh7LPPxgUXXIDp06fj7bffRkQ3hgsdDgeio6MDPr66XklPT8dHH30Eu92OvLw8nHnmmZ16rEWLFmHz5s147bXXunbQRETUO1RBWEDKN1kssNsM1NTolTOADOD6THDF69Un8gG9FChYaTCnU0bDTqeM8B5/XEbEJ5wAnH564NtYrRL4CA8PHlwBJMAQFyfLiXz7krRUWyuPHR8vI3glKUny0AFZqtQyx726Ws8gAvVbaSklRY67pERmDir3PBgVYWtvH1XwNzlZlkBFRen89JISPRupr9ejempNzaxUWTD1PRERERHRT1xdnQQoVImulsxmmZ5ZrTrLJZCyMt2OsuU6JotFhuEq+yQnR6ZPKlMmLEymNqqp/YEDMv202+Ux1drFMWP09CwtTR9veXnrebXTKY+pEvwHDJDpVEKCPJ6aVh04oDNXVFED1cxeTbdUcAXQGTOqG0QoBlfUVNrhkGkPM1dCR48HV0aPHo0zzzwTV111FVasWAGr1Ypzzz0XRrC/3B8kJibi8ccfR01NDaxWK5588kmY1QrPbmI2m+FSk3Mfzh9OLnXn42VmZmLKlCkYM2ZMt90nEREdRbt3+/+8bx+K99nR2CiDQ7XapU8FV1Qwxe2WE/1ery7OG4gKVrjdwDffyDKl2FjgjjuCd9NT/VZUZ8NgwsIkOBIXJy9mWZk+PkWVykpOllGuetEBGW2OGCHPw+2W7BffX0RNjV4q1ZHgiskko2yrVQIdhw4FnnUYhmS2fPst8N13MssIxm6XwA8gwRvVg0Yt0Sot1dsaGvSMgFrzDa6omR6DK0RERET0E6cS/a1WnT3SkqqSXF+vG823VFAA7NmjL9evlwIOaqposchjqcwQ32b2Ssum9h6PPGZdnZQJA6TfirpNdLReP6cqRPhSFa1VcCUjQ2eumExSDAGQ6ZmqZl1UJNvS0yX4o1qA+gZX1GOq4ErLniuhUBYsIUH/LuvqOPUJJUe1oT0AXHzxxfjuu++Ql5fX5n4HDx7EFVdcgdGjRyM7OxsXXXQRStUJiW6SmZkZ8D7VtoGqMS4REZEqDZWcDADw5O5F2X4rqqt1T/LwcBnw9cngigo4tNXUXuWWq+AKAFxzjX8Gia+mJhnRxsXJaLCtUlyAHMPgwRLUiIqSZUa+AY2GBrnP1FRZ1uQ7yk1Kkp/V/+79+3UwqKlJRv/19XK/AbJWA0pKkmNXRXtV1onidgPbtsmMoqREHk+NyAPxvQ+V1x0Xp4+5okJmHAkJXS+C3Jvq62WpWTsLabpE9fJRvVcALt8iIiIiop+8ujoZIttsba9tS0yU6YnH03rKkZ8va8uefx64/nrg449lmpOfD2zaBGzeLEP+hgadGTJ2rKyXU8ELQJf8iomRIXtenkytGhslYAP4B1eionRpr5KS1oUBnE55XDXNy8iQ26gMHVWCrLBQnldDg56eZWb6t970Da74Pibgn7kSFhYamSsqEwiQqRbbc4aOox5cUaW2LG2s7CwtLcVZZ52FyMhIrFq1CqtWrYLNZsPs2bNR040lMiZPnoy8vDzUq25AP9i4cWPz9URERHC79ZKXH8pfubbvRVNFNaqrdRJCdLSc83U6O9j+we2WJTvd1dOjs1SPFbdbRsGRkW0HV1S/FbdbH/MppwS//470W2lp6FDZX5UH8y3HVVsrL7LZrEfASmysBFdUU/tDh3TJKJXr3tDQsawVX5mZurthfr7+xdbWysyiokICORUVaK4Rp8pUtWSzyUgYkOAQoDN1IiLkvisq5Pl7PPK4bWXChCKnU0ro5ebK0rae4nLpoArLghERERFRNzAMWTu1fn3wKVGoq62VaZRh6JPxLhfwq18B99+v1yOpgIYqDaYcOiTTnjfekK+SEuDFF2X6t2uXXF9UJEPwgwdl+B8VBYwc2TpTRq2vUxklu3ZJcOTwYZmaRURI2TDf4Ipvia5AmStqrVpMjEwx1bq5hARdgkwFcJqadOEA1ebSN7hiMskUuF8//ZiAzlxR6+FCIbgC6NeppESmnl5v7x0TaT0WXKkIUCu9sbERr7zyCsxmM8aqvLAWamtrMWfOHFitVqxevRpZWVkYOXIkVq5cicOHD+Occ86Brb2a5h108cUXw+Px4Pnnn2/e5nK58PLLL2P69OkYrMp0EBHRT9vu3XKyOyamOZgQfiAPddUeNDbKYOy99yROopJBfNt9BFVYKCfbCwt7Z+mJyqsGZCRrNsuBNzUFLkelcqsLC2Wffv1kuVAwNpuMBKOiOh5cCQuTUbEKOpSW6miVxSKRrIgIPdJVTCZdGgzQ+el2u4w81XPtbHAlPl5uo46juFjue/t2GXXv3Sv3m5Agv0OPx78Jj9LUJPupYJEqaRYfL89ZjehLSnSje6ez/T4uoaamRl6D4mI96u8JKsineDwMrhARERFRl9TUSKDBZtNZDH2JqqJstUpQ44eW0ti6VaYvX34JLF4sw+aoKPmqr5dplscjwZKCAuDzzyWgAsh0rrpaslcGDpQh+KFDMkVW2SdZWTIdapkpExcnUx01RVP7b94sl8OGyXRIBUiio3VwpaxMB4kU3+BKcrJMAaOi5Of4eCmCEBMjQQc1HVRr2wYP1r1m1GOp56fWvbXMXElJ0fv0JlWwQU1ly8sDZxxR7+ixqnGLFi1CfX09TjvtNAwaNAhlZWX497//jT179uAvf/kL4lWosIVnnnkGhw8fxpo1azBy5Mjm7ZMnT8aKFSswe/ZsLFu2DL/85S+DPvbf//531NXVoeSHv4r//e9/KPphxfGtt96KpB9O8EyfPh2XXHIJ7r33XlRUVGDEiBFYtmwZ8vPz8aL6FCHqo5qa5J9GsDYIRNQJqpn9oEHAqFEAgOjacjSUWBE/xYvi4jA89ZQM7B59VHa12/WqmKCqq2VElJQklz+UHDtqAgVX1EhSNaL3pTJX8vPl5/Hj2/6QUf1WgM4FNZKSZPTr9cpov7BQgg9erwRV0tNllB7odmq5kso0cjjkOdXX++eBd0ZmpgRRqqrkPgxDRrRlZTKDyM6W10UVIS4vbx10Usuu1DIoFVxRHRszMuT+iouBqVPl9+FwyGtoGH3nw1zNRisr5bXZuxc4/vjuL1Tsm7kCsKsjEREREXVZfb0MKxsa+mbmitWqe6j4ZpGo5vEAsGED8Mc/AvfdJ1O0+nqZbuzYIYGZrVuBJ56Qk/fHHQfMnw/cfTfw9dfAlCnABRfopvQqcJGTI8GKlsEV1YPlmGOADz/UxQ927JDLceP8bxMVpadRlZVyXA6HTJncbpkOqrX8aq2db+ZKeLgEgA4elBJkmZny/AApkOBbskxNdSMj9TS8vl4eVy2UVI/R2z1XVIaNeq3q6mQqX1WlA0PUe3rs7XHppZfixRdfxLPPPovq6mokJCRg6tSpePTRR3HeeecFvd0999yDCy64AOPGjWt13SmnnIINGzZgwoQJbT72448/jgIVogTw7rvv4t133wUALFiwoDm4AgCvvPIK7rvvPrz66quora3FxIkTsWLFCpx22mmdfcpEIaO6WlbQx8bKP8O+ck6OKGRt3y6XgwcDcXFwZmQjpqwACUW5CDt1IlaulVGa2y0DnMbGDvRdsdtl1FZfL6PA2loZ8R1NKlgC6OCKyp+2WnU2BSAjWafTP7jS1v9jVeh30CD5MFJLijoqJ0dezMGDpexWcbGMhn0L8bbkm7lSVaVHxy6XfK+yRDrLbJaRdXm5zEAOH5bXJyNDjsVkkhF3VJT8HhMS5DF9e7vY7fK6qgyl9HQZIYeHy23VLEItl1JZRF6vnlG0x+GQoFJEhBxnYuLRXWblu1QOkNcpLk6CTmPGdO9jtQyuNDUxc4WIiIiIukQ1ae+La5wAnUivpmHK99/L5dy5wOrVksFiNgMLF8q0STWJ378f+POf5faDB0uWS3w8cOGFwPLlwDPPyPq6ESNkeqL6rYwc2bqZvZKQILcBZHrg229l8mT/NXjR0XpapIoB2O3yWGraqraroIJv5gog694OHgQOHNBTw/h42d93nV1EhEzFoqJkmpuSIlM5FfiJjtbBmN7OXFHHoIJANpu8V6uru/4ezc+XqfLIkbqAAnVOjwVXLrvsMlx22WWdvl1ERETAwIoyceLEdu8jX5306YCYmBg89thjeOyxxzp8G6JQV14u/7BstiNrMUBELezaJZc5ObBHJaNh4FjElBVgYPV22E1n4/vv9RKYqioZ+LUbXFFFUq1WGSk1NMhIuL2m791JZa5ERcmITC3fCdR3RS3fcTj0kqO2givqBVBltTorPBwYPVoeLz1dgiTJyTLKDVZiLCFBAkJqCdb+/bKvmmGoxvFHIiNDRtt79sixDR/eevaQmirHqZZU+ZYXVdkcXq8EePr31zOBlsWFARnZqhJiNlvHgit5efIPQC1tAuR2KtCSnNyx+zlSNpv887Fa9QyoqEher/R0/2BdV3i98r5V9QzcbpYFIyIiIqIuMQyZQthsuiqxy9U6mT+UqSRyQE9VGhpkWgRIc/qTTpLeK598IlOrGTPkedvtwF//KtWQExOBBx/Uw/fLLpMyYLt3Aw88APzznzKlUdNC1cw+0FQjIUHWzam1Y99+q/ug+DazB/zX0anG9WpaqapWq0IA/fr5T3vCw+Xxhw4FvvhCggaqmIFqcdmyiIFqat/YKMGo2lq9rjIxUW6j1tH1tshIPQ12uyW4MmCAvE4drcDdUl2dbitqNkuAhTrvqDe0J6Ke53Tq86Id6vtARG1To8bhw1Hc2B91QycDAAZVbYOnqgbbtuldKyvlb7BDwRWrVQdYDOPoNi9vatJ9QNRJ/uhoGRWrpVq+1BM6eFBnUgwbFvz+bTZd6PdIR3tJSVLANzNT7iclRWeKBBIWJqNzlQG0f7+8rur17UqkOSpKlj5lZPh3XfSVkqJ7w6gcecVu16N7lVGiZgJRUTrw4xtcUQGDjvRdUe+fykoJBu7eLYWGCwrkd7Z3r8xk1DH0hLo6nbGUkqJnKBaLBH66K/ihlq253Xp5FcuCEREREVEXqKmZCq6obX2Fmg5YrRIMUEGhbdvkuiFDJHvj1FOB3/5WplTvvw+sXCnJ80uXyhQiMhL4zW8kIDJ2rAzpU1KAq66StWSHD0sQJi9PXi81ZWvZzF5JTJTtOTny83/+I5cZGbL2y3dapQII6tgLC/VUSAW8VBXr9HS9RlBJSGiu4t08FQL0OrZgwRWvV++jMldUcCUUAiuAf+aKCjo1NgZu99lRZWUytbJaee6wKxhcIfoRcjrlg7GxMXBPaiLqhMbG5nznpiFDUVYfi6LBJwEA+pXvRu6+SL+/s5oanbni23zPj8cjJ6IbGuRn1Zz7aHakUwftG1xR2SsOh25srzgcctxq2dPYsW1n2ahsC9Vo/kip4rjDh8vItn//tvdPTtb53yooVl8vz9G3TNeRiIuT4E6wvPDoaHnOqjSWb1DEbtdLtJKT5T7U6x4ZKTMSQGY2hqFnFL7R8rbY7f4lxJKS5H1VUiIznx075P2ljqEnqKVyhiEzl9RUOY7Dh+X48vK653FUcEVlroSHsywYEREREXWJxaKH06pSckfWOIUK1ZRe9VtRVEmwY4/V2846C7jjDvl+5UrgpZck2wMArr4aOPlkKa+VmChBh8hI+XnBAlnPtnq1ZK8AErSJjQ289gyQtVAREXqKptqZjh4tUx7fqZXJJFMqlTFTVOSfuaIyNgCZFrac3iUkyP0CkuGyb598P3CgXvfnSwVXAJ0xc+iQXCYmym1CoSQYIK9hSop8r14Ti+XIgytNTVJsobpaXlc1xaLOY3CF6EfG45EPRvXF6DNRF+3eLX9MkZEoi85GY0QM9vebDgMmxNRXYPsu+VeqVrRUVcnAz+ttY4CiVvj75vBarbofx9GgPhxcLv9+KCpfG2gdHHA69WizrTKdhqGb2UdGdq0UVXi4FOnNyJAAS3sN6X37rqilSg0NXQvwdEZKioxym5p09orqV1NWpveJiGiduWIyye+jpka2BcsiCkQF6ux2ea6DBsmyrQkT5PWIjNR1DnoiCKH6rTQ0yHNTGSVZWXLd4cOSVaM6UHZFy+BKZCTLghERERFRl1gs/gvknM6+FVwpL5dhssPhP/VRwQzf4AoAnHsu8MtfyveqEsOcOcDFF8vts7NlW0SEBB7S0qRwwbx5sj03Vy5zciRoESy4Ash1qgWjen3Hjw98m6govZ6utFT/TlRwRa1HHDCgdVvP+Hg5dpXhoQJLQ4YEnkZGR+v7UMEVdXzx8f5Ttt4WGamDKxaLHJ/F0vHpYksVFTKFqq1lEYCuYnCF6EfGdzG6y8XMFaIuU0VXMzNRY41GgysKjth+sPWX0lNbc2W5zGmnyW4VFfrcb9DSYCq9xeWS1f0xMRJcsVr9s0V6ksqr9nhaB1dUdMg3Y8LhkO0d6bei7luNbrsqLk4ez7eHSTCJibpcmcqYcLvbHu37KizUHRY7o6xMXjM1kq+rkxmOYegZgeqhkpwsI3Xfnitmsx4tl5ToLCKViujxtP34DQ369+YbzAoL031v1Ki7J8rPqZ5BVqv/zEVl5VgsMnLPy+v6yN3lksfyeuX+IyJkRqDez0REREREnaTWIYWFyVA8UBvKUOX1ylSjtlaOX1VDrqnRa+MmT259u0suAa67Tp7vlCnSkyU2VgIhvuW2srJ0WaqTTwamTtXXHXOMXLYXXGnZXvvYYwPfxrepfWmp7n3jdMqUQp3jyswMnLkC6GmjmpdnZwcPrqjgScv2kCpzJZTKgqWmyvdqjabVKq/PkWSvlJXJ/agsrcZGeR9R5zG4QvQj4xtcYeYKUTfYuVMuBw+GwxUGlzsMYeEmWAePgR1mbC+TJS7nniu7VVXJ353X20ZwpbpaRjKAjPLi449+3xW19AdoHVxR1/vOJux26Qdisehm88Gok/hm89HLGFEiImTkHhYmx1FYKDOD9jJePB5g2TLg2muBm24C1q3r+GM+9xwwfz6wfLmMehMTZWbjcuklcIAe9argihrJqwCBGtGrviuqRBvQ/nKkhgb9OIEyhVwuec+53T2TIVVbK6+h3d56lpSSIs+5qEiez969XXssVUYPkNdOlQUDmL1CRERE1MsMQyrSbtzYgT6UIcLplCGmqmzsWym5J08422zdE8CpqZHhcG2tTL9U9WaVtTJ8ePBp2bx5wB//CNx8s0xHRo7UU0IlNlZO7Keny+PceqtkeiQmyrRQNZMPJjFRAh5xcfJzXJxkvATLXFHBFZX0roooqMIAqq1ny8wV36b2vlSl6Zaio3XD+mDBlVDKXPENriQmyt9afX3ngys2m9xO9a9RUyuWBjsyDK5Qn+PxtNHH4EeoqEhSGTt6vlUtXFZpfS4Xo89tOlpZAtR37doFADCGDIEzIh4ulwzCGkZMxjc4GW4jEunJjZg0SQZ5Xq/ETlTflVbUyLC+Xk74h4fLqFJFRI9W3xXf4Irvkh9ViNZ3qZZayqICTaNGtS5Y68tqlVFtePjRD64Akkc+aJB8v3OnvL5hbQx5iouB226TLo7qA/Phh3WQoy2rVwNvvinff/21XKakyIjV7ZYZgOo2qEavqakyivfNXDGZZLYCSOYKoLOIVJm1YAxDdyFUPUh8HTggAaMXXpD9eiKAp/rMAIEDWVlZ8hwPH5Y/kK70fnG5dBAlMlKXBQMYXCEiIiLqZRaLDPesVjmf0Reo4bHNJkNZlUCuktB76jE3bQI2b+5aU3JATzlcLp0MDwQvCebr8GE5UZ+dLdMoFdhoKStLpnhxcTLk/te/gHvvlalNsGb2SkKCf1P7ESNkehYsuDJwoHxfXS3DfNVPRgVX1Fq1QC014+MlmOT7c0JC8MwVQLfD9N1HNbQPpeCKmlp7PPL+NJvltWlo6FxgpKxMpvhq3aSaQrE02JFhcIX6FKtVFhNv3PjTOH/i8cg5sbo6ID+/Y7dxOPQHorpkabAgSkuBtWt12SeiQA4cAAC4h4yAERff3KKkYfyJ+BynAwCmjGyAyaTP51dWthFcqa7WJbdUvrZaRnM0+66oDwuTyX/EGB4uo0xVvNV3RqHKZbVVEkw9h+RkHTg62lr2XUlLC7yfYQAffwzccIP01omLkxnC+PHy3O+7r+30v337gMce0z/v2SMfuImJMluoqZE3g9Uqbwj1u1VBFN+eK4Au9KuCOmazHKPL1faSNptNp0oFWjL25pvyuz5wQGa43V1+zuuVkbnVKs+lrEyXQFMiImS5Wn29/A3s33/kqZUtM1dUWTCAMwIiIiKiXqZO8tbX62T9UFdfrysbx8XpNU5Az5UGO3BAT5327Tvy6rZNTTK8rq2VYbHv9GvLFrkMFlyprZUT8ypwMmpU8MdJSZF90tP1uq6YGHmt2pvyqd4m06fLz6eeKvcVqORWdLQOrqhq2mqNmppiqABSy8wVQI5FlSoDdLAoWOaKup/GRj2fB0IzuBIVpaeS//63THsbGmQ61tEAndcr07WaGjkVkJamp1LMXDkyDK5Qn1JRocvPtzxv82Pku2C5ow2qfBejqw9GBleCKC3Vq8lZP40Ccbubm6I7skYCMTFwu2UQZh07DZ/hDADA8YNKUHq4qXlQWV0tf3cBgys1NboEmAquqAbgqu/K0Ygeqw+LQCNS1dRenbBXfx/79sllsOBKU5O8XvHxMuobMqTtJUw9JSlJ911ZvVry3F9+GdiwQS9Ls1iA++8H/vxneX4TJ0pmx+zZsj01VQoUP/544HRJi0WCL263zBL69ZPnv3u3BJWSk2W2omY7DQ266bwaEftmrgA6uKIyV3yziNr6J6DeTw5H6+BKRQXwxRf65y1bdP54d7FY5L3S0CC//2uukfoC8+bJa/n221IbIiZGRu/FxfK+ys09slRUlbkSGSnvL5YFIyIiIgoZbrcEChoaZAjbF1riWSx6uK3Kgnm9ulRYd6uqksf8y1+ARx6R4Xxh4ZHfl8ej17ep6VdFhayrCguTqU5LHo8My5OS5GvkyLb7i6jFhKocV2mpDMPba2avJCQA550HLFkCzJ0b/Da+ZcHq6/2nUeo8oFo7FyhzJSFByoCpwgUDB+qiCi1FRsp+kZGBgyuh1nMFAC67TC6XL5ffr2p72dHgSnW1Pg2WlCRTf69X97ahzmNwhfoU1U/ZZtOR6x8zFRRpaJABSkcWGvsGV1RDKsYNgnA69UryvrKkho6uvDz5AwoPhy0tG97I6ObgSn1EGjZDOvmdGvMtyg+7ms+ZqxU2rf5uVU5zfb2MGj/9FLjiCglaxMfrUWN72Suqb8aR1kh0u+XDoa3giu9SLbtdRmtlZbJt/PjA93v4sNzvkCGynGjIkCM7vq6KjgZmzpTlSU4n8N13wCuvSFbKBRfIa/6LXwBffSUj5uuvB554Qo/i09IkKBAeDnz+OfCf//jfv8cjs4LychmB//73wKRJct22bXKZkqLTlwxDN7ePiNBLrXx7rgB6iZbKXFFLpdQ/vmBUHrjX27pA8rvv+s9oc3Pln0N3ZkjV1cl9Op2SWqpUVgJffgk8+6yUXTvnHODRRyXoVFAgfwsdTctU1PtWBVcMQ79+Hg+DK0RERES9TA3V1DA41KfaTU16QavZLEP/xx+X7e0Nw4+EYcgaru3bZQ3Uhg1SXfjw4SM7d1Nertfn+ZYEU1kro0YFLolVUiLHkpUl68Ra9hwJJCNDht5paXJbNfXoaHAlLk6CFm1lu0RHS5BIDfVVNWGPR1fQVsGVQFPZ+HjZrtatZWUFzlrxfTwVXFHTMVWAoWWRh96kjuPYY4GTTpLX5qmnZJvFIlOyjpwzLCuT97TTKesJ1f2q1gLUeQyuUJ/icMj5o4aGrp1X7CtU5opKQ+3IP1rf4Aog3zNzJQB1gs73TUXUkjpR3r8/HN5ouMNkaUx0NLB1exi8CMco7EVOxbeA3dEcS6iq0n93fn+3tbXy3quvlxHeK6/IqPb552UU2NjoXz4qkKYmKc67das0ZDqSPHV1cCpS1JLZrJeuqJxvVT4vK0tGuy1VVsqobsgQeW5jxvRO1ooydarkSv/619JvZPZsKUsFyGteUyM//+MfEmxpuZRpwgTgxhvl+3/+07984HPPyWwlJgZ48EH53bUMrsTHS3BEzQBUNkpKil4ipZZBhYfLV2am/NzQoGehquCzem8EEqyZvc0GrFgh3194oVzm5cn+3dl3pbZW3icejxSNBiSI8te/AgsXAqecIs/b45EeRrm5crxlZbJErzPH4lv3Mi9Plr+tXSvbOCMgIiIi6nUulw5MeDyhH1xpaNAVQ6KigBdflGDHwYP+bSi7S3m5PNaHH+ptq1fL67V/f+fuy+WSoXhtrRy7bxChrX4rVqtkMGRkyHRj5MiOPZ6asvTrJ9MZs1m2tVzfFYgqs5WUFLzfCqDbUapgj1p35rs+LD1dZ5y0pIpCqGydyZMDB5cUFVzxePR0LCmp9Tq43uY7dbz4Ynl9ioqAlStlOmUY8jttiyqxVl0tzyshwT+4wsyVIxMiyU1EHaPOg9ts8s+6oUFX1fkxUivf1Qecw9H2ioCmJvlSi9FVf2xmrgTge3LZZgv9ER/1jh+a2SMrCw5XGFweGXlERemVQGfgMyQU7ALG12P4cBkBlpfrv1u73efvtqZGR0Crq/XoZ9MmnQfu03fF6w3Qh10taSookNGf1SoBjezstpu2+1IfCi07HipqdKxmE42NOrgQqCSYwyHBg379ZCQ6enTgoM3RlJSkOzNmZEiJKkD+ceTmyuVJJ7U9E7joIumj8tlnksny/PMyS3nnHbn+t7+VnHNAB1d279YfwikpMtMZOFDy8gHdfbHlMivVQTExUT6PSkrk+5gY/w6bLV/XtprZf/ih3GbIEODcc4EPPpDjOXRIlnt5PIHz4ztD/TNWJcGsVnneU6fKfU+erI/znXcki+XQIckgKiuTP47cXOC44zqWc6/+sNR70mqVv5+sLDkWZq4QERER9Sp1HgLoG1Nti0WGkerEszr22lo9dVNJ013l9Uridk2NJNcr27bJ9qgomSIGaxnZUmWlbn/om3liGMH7rXi9clI+NlZuM3Ro56ZugwbJ7VUPlvaa2StqTtyvn+wfLOChKv+mp0vWispccbv1tCg9PXDWiu9j3XQTcPLJwPHHtx9cUfc1apRMqbOz9TQpVMqCqSyaiAg55rvukunoypUSHBs6VBZ5qoydQMrLdSP79HT/zBwGV44cM1eoz2hslPNAbrecrPRNCfyxUouVVaWTgP0bfKjzpZs2yYJtp1M+HJm5EoBvcMVulxN0Xm/vHhMdXSp7qS25uXKZnQ2HKba5/3tUlCSNAMDp+BzJpbmIDmtsPs9eXa2Do35/t6r3hskErF8v29Ro7d//ltHpD8WJDx9w4+uvZXF+M7dbRrJVVTJLycuT0WZ+vmQMdHTm4nTKqMrrDTwqVaM2p1OfuFevRcuCvR6PPH5MjAQRBg3q+GygJyUlyescG+ufy5+QAEybBpxxRvtLrEwm4M47pX9Lba2MXh9/XK674gpgxgy97+DBMsNobJQACyA/q9GrCqQlJQXujBgVpXPsAZ3pYjbrwtWBls0Fa2bf1CQlwQDJWrFYgJwc+fn772XG1R0Ze2qZlNWqg5Enn9w6aGMy6XJyeXlA//6ytK6wUJ5DR5fpqc/uxkZdpk7VCWVZMCIiIqJe51sa2W7vG8EVNV1QLSYBWRulzrF0V2mw0lK5z7VrZT2d2SzTA7sd+PZbea327ev4qYnycrmNx+O/Zq6kRK6LiGhd0bmyUobUgwfLWi7fPiMdERMjAZL0dJl+dKQkGCBTnfh42V/1MwlEzbf799fHaxjyvlLnADMy2g4IxcfLV1aW/jkY3+DKgAHAm29KAQM1TQ+VzBVAT9M9Hmn7OXeuvDZvvinv15qatt87paXyfvd4pCQYIMGk8HAWAegKBleoz1D/1NQfu9X64++70rI5fXvBFXXOacUKYN06Oc/FzJUgVM01lbni9fZMpzoKTV6vLOVZt04vhQnkhxO+RnYOHOHxcLlk8FVTo1tFzMSXMNurkeIqQ3qyGzEx8taqqmrxd2uzyYb6ermTr76S7bffLiOajRtl5PjDCfRNX9mxf78MjJtrpxYWynu2okJGQ/37ywnmvXvltlu2AAcOtN810vfDJdiSH9XUvrFRLg8elO0tM1eKi2Wf7GwZJQ8f3vZjHy2xsTLyjIvrWi6/2Qw88IDcz4ED8jucPh249lr//Uym1qXBYmPld11aqnPYVQHhlq+7GimrJWe+wRUgeFN7VcegZTP7L7+UGVVKigQ7AGDKFLnctUveVN3Rd6W2VtefVMvjTj018L45OfJer6uTYFN2thxHcbG8j1WHyra4XPL+9nr1364aDHBGQERERNTrXC693sVm09OJUKR6wthsMhzfuVNfV1am2xp2R2kwj0cSvWtqpM8KAJxwglTQBWQKUVws88fDh9u/P7tdpgK1tTJliInR16mSYGPG+K8nc7nkeanAyDHHHFkl58GD5X6jozvWq0UZNw4YMUKOqy2+PVNU8QeLRZ/zGjCg/cyV8HC9rq2tQEx0tA6kNDbqkmOhHlzxeoGbb5bXv7ISeO21theh19XJ32FNje5L43u/zFw5cgyu/BSpHMCOnMQIIb5VbAD5B6gi9D9WLYMr7Q1IVE1TdZ6pvFwveOZi3hZUWpD6XvXBoJ+GujpdyujAgcCd31TpLQDunFEwoqKbgyvqHPKIEQZiMmWJ0MDqHQh3OZqb2qu+K83BlZoanX1w4ICM4Pv1A372M8miAID//Q9oaoKr3oUb74rHLbfIoVoskA+BkhIJrHi9UhIsM1OPiPPy5LO9oECyWNrqZuf74RJspKmCK4CUxmpqktGp6vKnXseaGlnuFBcHjB3b8dJkPc1kkuONi5PXvSspfIMGAb/7nTy3wYOlgX2gpVYtgyuAjPxVUA2Q4Ep4ePDMlZbFhdXvJ1g3zYYG/RmmgiuGAbz1lnx//vnyu4uIkDJogCyHU83LuqquTt7TBQUyko+LC1zYWQVD1Cxpxw55zllZ8h6qq5MgYXsjepUS5nbLHxkgl2FhLAtGRERE1Ms8HvnyzVwBQneqbbPJ8ap+KyoRG9BrnVRp+q4qKpJhe1GRni6cdZZ8AVLx1mKRU3UFBe1PX8rLdU+blpWeVZWFlsPyoiKZcmRkyDC8rYyOtiQmSlXfqVMDt+MMxmyWx/UNBAUSHS3HCMgUw+WS5wvIbRMT2w6YqGb0ycntZ9ZER+tgipoiq+lTWFjoTG8B+d2paajHI7+/u+6Sn7/4QqrYqClSSypYaLXqrBXf+21slCkbp1OdF0JvETpqKipkNfauXd3b0LaHqQXUqoqNWqzbHeeGQpGqWKQ+3P1O0gahzr2pSi/Fxfr2obpSpNf4nlxWq75DdcRH3a+6Wn7/KhNEncj2lZ/fXMLLnjEMiImBy+VfEmzKFBMsg2TZzYAy6f+gmuCpQaDDIW8xVFfLSMYw9FKlM86Q0dqCBTL627gRKC7Gtl3hqKiJgNMpj1VX98PxuN0y2k5P1yfnzWYpDpuZKSOpvXvluNVsIBDVwEo1Ug/EtxzVjh2ybfRovbTJ7ZYlVcnJUspq5Ej/zIlQoIIrQNdnRCedJPnWL7wQfBbi23dFjUpTUyUXX32AB8tcUf1SWmauhIXppvY22w9vJh+qbBugl6Vt2yYBlKgo6W2iSoaNHy/3ZbfLGKC+vmvlEBsb5fEbGnQptBNPbB04Mgx5r1itepnanj369UlKkuvtdnn/tkUthfRdIKK6pnIlAREREVGvUlPsxka9wt7pDN2ptsWiK+xWVMixqulDXZ1sD5ZA3hmNjTLcra6W7Bi7XQIE06ZJ1eX0dHntCgokiOB0ynq8tlRUyDEahn+AI1i/ldpaGbYPGiRTA1Ux+EjFxXW8JFhnRUXp5vIqc8W3hWVUVNuZKxERkiGTliYVntuigjQqwADI1CLQerjepjJXAB3A9C0P9pe/yPvs4EF5r/k+n8pK2RYe7v9+ef99aYup3uMsBNB5DK78FKmTym63Dv32Aap/CCDnYtRT+LGWBlPPVX2wqXOcbX3QOZ3+FY4OH9arHToUXDGMtle7/5j4vqFMJp1TSz8NVVUykm5slBHG4cOt3/sqlzo9HQ6YYURFw+32z1w59ligOltKLaWW7gRqapoHgdXVOqHAaf2h70ZDg/whb9woO6mMlSFDgJkz5fvPP8em3TpIsWULUFfmlKUm5eXyflUFaBWTSTICRo6U93VDQ/AlK4ahV/+3NSL1LUelllb5lgQ7fFhGZoMHy/GopUWhJClJjtFs7p7lZm11TrTb5fqkJHl9VfAAkBGwCgakpLTdc6Vl5gogAREVpfONsvs2s4+O1oEylbUyd64u4qzKpKngxpYt8ubsyueeWt3Q0KDfI4FKgpWUyGwuO1tqAQD+PVYGD5YgUlmZfxfRQAIFV9QxqCZlP+aUViIiIqIQpqbYjY0SPABkGB6q63rr6/UwW/VbmTIFzdUIamp0cKXlGqfOKCzUJblU6bHp02X4bjLJ+iRAFtaFhcnwubIy+Pkuddy1ta1LPBUWyvaoKCksAMjwuLhYpgZJSbI2L9gau1AQFaV7wahEeTW9VYUA2spcAeT2kyZ1LHNFPaYKRng8oR9c8Z3y3HijvC5lZcDf/iaBuR07gG++kV4+O3fK6Y7aWpmOqmyc/ftl/82b9XpKlgbrPAZXfspqa3VnqD7A4dDnW1S/3/r6Dja1b2yUHfvIcwX8+637/txW9orDofv7AnKuyWKRD9F2K+I0NMiJuEceCX5S9sdEBRkjI+XEq90uXz+V4FI36srC915htcqIQY3wKyoCZ6+onPCBA+FwhcFtklFXba0EMcPDZZVR+TAZCSeV5AJNTc2DwMpKPTCxl1p0Qd+8PPlMGjJEltMoV14pl1u2NA9sAEkIaNhfjib7D2WQ0tP1iKql2Fg5Ea/qJgY6Se1y6X5DbQVXoqNlpG+z6ddi8mQ0vwi+y55GjQp+P70pIUFGjl3tu9Ie9Xvdv1/PYHxLg/m+31JS5M0TqOeKb+Csuto/I0V9iPs+D99m9ioYVlAgmVEmE3DJJbpHicoqOu44udy1S7Z3ZaZbWyvHVVAgb/ioKOD44/33KS+X67KyZMSv3iuHDul9IiLkOhXoaWtpowquVFf7b29o0J/fzF4hIiIi6hW+ZZViYuTLZtPlt0KNamZvMulE7EmTZE0QoLMmulJl2OWSqWZlpQzv1UK900+XIbrZLN8DUtYpLk4HFPbulXM8LU9lqRLwgUo8qSoL48frKUdVlUwbsrJkOqnOqYWq6Gg97TUMeQ3U8F8937amsp2hpmEqc8XjkceMiAg+7e4tvsGV6mqZhuXmytRq3jzZvmIF8MEH8n7Oz5d9SkpkitXYqF8/rxd48kl9PkedHmZwpfMYXPkpq6vTQYc+QAVX1ILfuDg5l2K3t/PHr3Iit23zP5kT4nz7rcfEyKWqXtXWbXyDK4CcBO5Ivxb8+9/Ap59K2HrHjg4HoqqqJBq+d28fil2pFCB1cjkuTq9qD9V85RBkGPJntXZtH2vhVF2te5+kpwfPXsnLk8shQ+AwYuBqlH+ZubmyefRoGQyXDZwCrykcUbY6xFvLMCRTZhRlZXpwZi+p0wG9b7+VOzjjDP/ugUOHyqp/w0DePv3vOT/fgLO8DpZDNRIo8HqBJ54APvoo8B9dUpLMEgyj9QloQH8YtBdcMZlkpL9/v3zQRkVJf5eWy55Gjgy9UacSFiYBlrg4eb49cdLdMOT1UEvGVMDMN7iicthVgWAgcOYKIEEGVYtApSLGxOjPLd8MnEDN7N95Ry5PPllmTyoirwJvJ5wgPx88KOOArtTWVEG27dvl52nT/Dtm1tTIc8jIkIycYcOA4cPlvaWa2ivx8bohfbDPYd96mS0/dHybsDG4QkRERNQrVMUNj0emCHFxMhw1jNArFOFy6cq70dE6o2TSJFkHB8gwXk2fjjQRvqBAD18LCuRx+/WTdWspKXr9UVaWDHe3b5fXrbBQHn/PHil8UFIi1xuG3FddnW4z6StQSbCGBhluR0bKtDPUqYrJqpdMWZmetqjAUHuZKx1lMsl9RUbq9y8QmpkrERE646iuTt5LCQlSCOCCC3R5sLfe0ucRi4vl1MahQ/K+UtPGTz7x7zFUWyunQ1gWrPMYXPkpczj8CxeGMHVOSTWTBuQDRLUvaDM+pJqVuFx9qoaYb7/1hAQdaAmWueJ2yz/aluebSkrkunZXWaiRRGWl5BB2MOhWWCjntEpK2q8JGjJ804KionRviaYmBlc6wWqVt4nVKv+s+0zST3W1Htmnp8vSjZbZK16vLls0dCgcYXFwuWTgpc6ZT5FqYLBFpqAuXU6oZ1TtRE5/GX3X1gIuuweuGhvsZT9kklit+kS0Kgnm64fsld12WSplMhnwek3YuS8adcU/3PaGG6Tx/WOPyVfL0U9iou7KGCgLreX7H5DjCjRbMJv1iGvYMBnNqVH9oEEyK1BlrEKVb9+V9hpXHYmqKvn/MmiQjGBVUV+Vew3o/7OpqToQFajnCiDXqzoEqu+Kb4m2lsEV32b2NTUSJAf00iUVGIuIkCV4Q4fKe6SxUUrfqUBcZ6movdWq3yO+JcEsFvkHkZYmwZVhw2SWGhcHDBwo+6gAJqBH+W3VjfCtk6leU9X/Ri3F8t2PqBtcc801MJlMQb+KfbIe161bh1NOOQWxsbHIyMjAbbfdBmtPZs0RERGFGNUKD9CLYlXmR6hNtdXx2Gw6qyQhQQ9bATmp39QkX50NrhiGnNAuKdHZJ5s3y3XHHy9TBxVcMZt1gvlnn8mwPSpKbr93r6xX2rtXgix5eTLcra2VYb1veS+vV89XVXDF65VjT0iQxwy1NpmBqPN+aqoZFqaH+Gpbd2WuqMeLjPQvxR+oknNvU9PC0aOlisaoURJYycmRad7NN8t7qrJS1mKOGiX7jRihp4KATLeee06+983Samxk5sqRYHDlp66uTucHhjC1UsA3uBIfLx98Dkc7MRN1IrG+Xi+Z6CibrddWwPr2W1fnjtrKQFFPUwVX1D+asrIOZq74Rka+/BIoKmr3GA1DXqKaGtm9qKjtHtoho+XJZd8Tr6G2nCaEqUXz+fnykhYW9vYRdYBaFW+xyMgyKkp6lbTMXnE45IkBMHKGwoGY5ipyaiXQlCk/9FNpDIdlkJSCSsjfgX6uYsTFyHKXqu3FcOYehL3hh9nE7t1yozFjdBFZXyNH4vDk/4cyZMIEL6aNlhH/zk0OpL/2V2mmbrfLqCgsDPj4Y+D22/2iqoY5Ft6wCHmOtbWt8+/Vh4thyPO3WoGrrgIuvFCCNb4ZfmazbjB+zDHyB19dLd0FzWb/smahKilJdzzs7pOcHo98yKamyuthNsv3CQnyOqvggW+/FTVCD5a54htcUSdt1dItp9P/OTQ0+JcOe+89eS+PGSN1AAC5Xo3C09Lk2CZNkp+3bNFZXJ2lSm0ePCjHGR6ui0VbrfL3k5QkS/CysvQMNTpaj+p9gyuRkfI81edwoHGJbzMy1TNOPRe11Apg5gp1q0WLFuHVV1/1+3rllVcQGxuLsWPHYtAPn+Vbt27FGWecAbvdjieeeALXX389nn/+eVxyySW9/AyIiIiOHt9k8chIfSLfbg+94IrFooNBal3dxIkyzVInnNX5jZbD8PZ4vVLxoKBAhspVVXLaYdMmuf6002ThXnKybi5+6qmybccOOccyYoR8RUTI/ezZI0GWkhI5HoejdUmwgwfldTabZfoG6NNg8fH+jcxDmZoaqWlRQ4M+79e/v0w9urN4QnS0fkx1uigUgytJSfKeGD5cAixTp8r7ZupU+XnQIODWW2Xfd9+VtZlhYfK7V60/ATmtUF8v07Jf/EK2Mbhy5Bhc+amrq5P/JCGe0aE+3Fwu/YEXFycfEqrvStCYiepOZrXKf7iOflKUlgLffSf//XphSb7qNw3IB6HJJNuCLbxWr5GqsqJWKZSW6qyfNmNohw/r77/5Rndua4Nv7dHqajl/uG9fH6g0p1Z6NzbKG0o1gg7FEV8IU4vmVYpzUdGR16E9aqqrde8TlT8dHd06e+Xw4eY3snvICHgjY+Byyc1ra+UmY8fqjxPrSPmDizm4C+FWC9IT5IWoKXbAVVAO+65D8hmkRtOBslZ+sG7yzQCAUcjDSYMkYlW+tRyJhbtgREfLcpR//Qt49FE5UZ6bKx3sdu6E0x2G9buTsKFqBFxVP5ygbvkH6Ru5jYqSory1tfL38NFHMrq6+24pXxYbq4Mt48fLL9lsluVCOTkSoAp1qgyXb/m/7qKWoGVmys9ms7zmKrChlo2pLIvk5OCZK+Hh8k8tMlLnuvtGq1VTezUDVEvQ7HZ5QzY1Ae+/L/vOmyf/NAxDN7NXM4a0NCnfBUjGidfbub4rqpxoWZkcz9atsv3YY+X92NSkc86zsyV4OXy4vn10tJSSA/yDK4DujeP1Bp69qj+4mhp5XiaTDq6o5+DxMLhC3erEE0/EggUL/L6GDh0Ku92OK664onm/3/3ud0hJScGaNWtw44034qGHHsLf//53fPLJJ/hUZZQRERH9yLUMrqipts0WelNt1W8FkMAFIENLp1MHLcrL9dq7jk4lGhtlGlBWJsPiyko56a1aHmZlSeAjIUGmBtHRMo0YPFivXfviC7mMj5eh9KhRMh0oLJTpX1GRvK4tm7WrfisTJ+ppR0OD7Gs26zJboa5lO8qyMr1erX//7s1aAXTmCqBPg3V3AKc7mEzy/hk5UqagqsWoMmAAMHOmVII2DDll0PK03u7dwIcfyve3367XfKpTAgyudB6DKz91Ktwd4g0THA75J+Ry6cWqJpMuDdbY2MY/OtWsRX2itJvC8YOqKrlTh6N1o+ujQJUFCw+Xr+honYESKJDkcMg/fXUeVS0gLi3VAZk2T3z7nsQ7dEj+e7WTvaJec4dD/ukUF8sAZdeutl/mPXuA2bMlWt4rpaR8a66p/8qxsTpTqaPvkZ841fMIkHPHLlcfaGtUXa27KaqT7kDr7BV1wjglBY6oJCBGgitqRdOECfLWaf5YmTgdABCVtxPxW7/GgFiZOdTYouEMM6MxqR8ao+Ml+hgWBsyapR9brdS32wG3G99axwAAJmMrLv3yJgDABkxH9bDjUPfkMuDii+VD4bjjgH/+U/LWa2qAO+5A/dsfw+YMgyMiHpW1EfJeb1kaTH0mAvIkVG76iSfKEqqwMAks33OPBG1qa2XbiBFy28GD5cM3K6tbfiU9Ti2ZUwWfu6uTpssl/zv799cdF1WGyBj5HbYKriQl6SVQvv12FFW+S+W6b9umP/B9m9qrrqCqmX1sLLB6tcxYBwzQ5blUVD02Vs+8UlN10/nCQnl/BOu7ovr2HDokS+jWr5fg+7Ztsr2uTrYD+jGrq+Uxc3IkkDN6tP9zjY7WTe0DBVccDrl9oNm3yyW/PzUQyMjQgS21SET1bSHqQa+//jpMJhMuv/xyAEB9fT1WrVqFBQsWINHnf8tVV12F+Ph4vP322711qEREREeVqrZtMsmUxWSSoegPU52QmWqr5G2bTYbmqsrt2LEyRK2ulqGpWofkcOhhalscDglwVFXJ3LGhQbID0tOl3BcgFRBaZpEkJ8s2tUh29Wr/+42Nlfs55hj53uWSSrthLc7q+q57UqxWXQ2lr2SumEy6yAQg0xF1/mnAgO7rt6IEC66EWuZKe9LT5bh/8Qv5XZeUAC++qK/3eKSJvWHIObmJE/VrbLPJ+5VTqc5jcIV0aTCPB263nE/vibL0XaHOBa5cKZHVVatke3y8Pk8aNFvC6ZQTQkVF+kRUR6iTVzU1EjXoYuk0t1v+0e3c2f5dGYbOXFHn/qOjdZP7QEEStRhdnSObMEH+6TY16RhJ0OCKzaZfQFW65bPPdIHRNm6m6o8OGiTnqgsK5J/3jh2tb+rxSKWYa66R3+G//63Pyx1VLVfuA/rEK8DSYB2gFs07HPISmkwSyCsvD+GXT2Vx1NfLCWzfYrMts1dUwGHQIDhcYTCio+F2yyoPQA9W1fO3j5+OpmPGweR2Y/J7SzAqUU7+Vjclw5XQD0hJgWfdBrnRlCl6KVRNjYzeDxyQy927m1uyTMZWTG3cgDhYUYcU/Oe8ZahJyPZ/TgMHAn//OzBjBtDUhP4vPoJhbz6MRlM07E1RMhNQ2TqKev+rbnhqedM55wBLlgCvvSYBHLNZZ7SNGCGvW3q6vG6jRgUODoQq374r3TWjKymR0Xb//hJwSkuTN0NYmM7M2LlTPvjUAgbf4EogKrgydqy8/gUFkkEEyFI1lXVnteqmY6rs1/Llst/Pf64LL/s2s1cnfBMT5QM7I0Pu67vvAvdd8Xjk+HfskJnhoUMSjMnPl+VyO3bIe7agQN4LJ5+sgzEq53zUqNYzvuhomRWaTDL28P3n7VuiMVA2jfrHqIJVWVk6EKWCK01NzFyhHtXY2Ii3334bJ510EnJycgAAO3bsQFNTE45TxdJ/EBUVhcmTJ2OLqifZQmlpKb7//nu/r9zc3J5+CkRERD3G5ZKhmO9w1zeBPFSyV1SJa9Wm0maT4+zXT69N8l3Do87FtJW9YrHoogD79smwdcQImQJUVEiJJpNJsgoiI/2zSFTflWOPlaH8wYOBFy6azbKGadw4neyu1NXpQglTp8qlasUZH6+T2fuKqCiZsgDyegIyJVJVn7tTdLRMW1QlZpWx0teCK5GRcqph0CDg0ktlmyoPBgAffCCvZXw8sGiRbIuP18E3VfnmSFpy/pQxuPJTFxnZXJO/vqAWmzbJH5rvYtlQoCqhqPnmxx/LpWr0rmIgAeXmygnDl15qu66WL1XryunUS/LLyrr0HA4flnNOVVW6dFcwLpduYO8bXFHxgEDnBp1O3VYGkPN9qqx9YaEMEIKeU1Srh+PjgbPOku/Xr5eTVG08b5tNB2zMZqkAExmp63zu2iXPwzDkbr79FlixQpqwAXJusjPVaLqNOrn8zjvyH8VikZGGSo8KlRFfCLPZdMWhuDgZ9KhKcr7te0KK6j9iscgJ5pbBAd/slR/6rSArC47GCDQakTAMnbmiKhE1t7OIj4f3kT8Do0Yhwm3H3N1/AQBU1EXC5Q6D4TUQ8cUPy49USTD1h5GUJCfTR42CN2cY9tZIYdn+Y/uhdsIMjM6oAwB8vycOddYAeclmM7B4MWwLFsEwmTB88zsYsuYV2COT5Lk2Nur3tNcr73314VJeLtHXsDD9pDIzpfTY228DN90ky1lmz5aRZkaGjqT2JUlJ8jqFh7dfLNntlg+ntj4HrFZ5bTMzZYQ/ZIiu3xgTI+8lNYvcv7915kqw2YAKrpjNwNlny7Y335RLVYLN5dLLitQsb98+ec/GxOjbAbpkmG/NAJNJRtxTpsjPW7bIe8T3f6PLJasBKirkD3rXLvlgr6iQ/wuJifKc1fMaO1Zmd/X18vr16yc/q0weX9HR8jqoAspqpqSeY1hY8LoRKutQBasGD9bBldpaeX/3Rlmw8nLg6691PQn6UVu5ciWqq6v9SoKVlpYCADLVWRgfmZmZKAnSkO65557D1KlT/b4WLFjQMwdORETUw7xevc7F96S0mmqrcxahwGLRx6SCGBMn6vmt2axLUlVU+CeQB1JVJefRVGAlLEzWGan1fJ9/LpfDh8swOixMV6kG9PcDBugKwyrTpaM++khe+2OO0eXF1PEmJPSdrBUlKkoHuNQCZVVluScyV9Rjer06uBJqZcE6YsAAed9NmiSFKVR5sNJSncVy3XX+/XpUEEsVCGBpsM5hcOWnLjkZcLtRUdKErV83oKZGzp+4XMGrhPQGh0POI/0wd8W2bXJ8MTHyT7uhQf45BswIUUvNy8t1PmdHHhDwTwc5fLhLEafKSl3DsL1ze749e9U5uJgY+TlY8o3TqV+f2FhdmxPQ0eegmSuqYXVGBnDSSXpbaalk7QR53larXF1crM/fDRumM1RqauTl37xZzjmVlQH/+Ie+fZBzDT1PBc6++05WXa9Zo0c9oVgMNoCqKlk43luvoVrp43DIS9evn7xXS0p0MlzIqa6WPy6Xy38kq/hmrxQUyLacHDhMsXC5TbBa9efisGFyqYIrUeZwROUMBP70J1hyJmK0R/LKKypMMGBCdGEeIooL5ANLlU+qq5M/zIwMedwRI1CZOgpF1XJCOnH+ObD/7iGMHSu779gTAevhWjS5A3zQmUw4dPp12H3ubwEAA3d8IsEVu10HjAD9IeB2y/NVWSujR+vlKkp8vPTuWLJEPkyysuTJqqhtX6J+36r8X1sOHZI3sFou1jIv2jDkQ081r8/JkQ+/2FgJXJjNchvfvisqGKBmA8GWQEVG6uvOO0/ud+tW+QBVgQqHQz58fZvZq6K5c+f6/x5VyTDAvyBzy74rhqHf3DabvC+qqyUwZLfLDHDsWEmJHDFCAmypqTpSftppcllZKY8XG6uL97akloUNHiw/+5YGU3UjbDb5O235T0sthVRBncGDZclfWJj8c2xokBn90cxl93jkdaqvl39y7a2eoD7v9ddfR2RkJObNm9e8zfHD32J0gJl+TExM8/UtLVq0CP+fvfMOk6ys8v/nVo5d3dU5T+zJgRyVFQEVUVRAMK+6q666uu7qGn+Lcc0RVIwYQFHRBUUMSB7CDMwQJvaEzjlVV851f3+ceutWdZ5hBhms7/P0U9VVt25473vf97zne8737Ny5s+TvpptuOjEnXkYZZZRRRhknGMoES6dLndLFycnPlaV2KDS73srmzWJme72G6i+IiafK9861lEgkZB9TU2IWOp2SyF4cT6WIklNOEXPd5ytN8HY45HcejxHzdvfdS3dBZbOSlQDwqlcZn0cici8cjpOPXLHbjeL1CpWVslw6EZkrYCzF1DFPtswVkKWexSLLpEsvlX48NCSK39GokH6veEXpb2aSK2VpsKNDmVz5R8SRI/CZz4hTxuOhZ7qSfXt1JvpjHDmULWjsPVeco0oiq6vLIE9yOZF9B5n4wuF5avKq4tQKR44sLXNFzZiK5Rgbk/Y6xkaJRGTCnZ6W18V8e8qfpOqtgzHYq7orxVBSYSrJpLZW2kL5roaGFtE3VSlB9fXiOK2vFwfVtm3yoznSglQmzOc+B1/5iuFjs9lkF9Go8FHj43JeBw9KtMaBA4YRofyDzypUVPPAgBHd/MADRjS5clw+Qxm4E4V0WgirPXvkvh469PepW1McNO90ik+0uVk+D4XkUTsmLjKVkk5zvOpiFGNyUk5O02YTCQoqe6WrS/5fsULIlaRBXjY1yTWn03KaygimvR38fgbf/FE8yyRHO5B00br3Tmp3/ll+fO65xupibEx+6HQWqhQ+FlqLrmtUVEDN+euoPH8jp58qfbFzogY9GCL4ZPesVUkqrTEZtLB35SvImixUjh/GHJ4mk9VkWzV2qUEgmTSK2YORxTAT2ayMoT6f/K1adXKG7zidcr1K/m++zhmJSBstWyb3MxqVQWt83PhNICDbNDfL/lQ4lckkxIAqPK9WRY88Ygz6i60GVOaK2lZlOd1yi1j4aoyKRuVclXzWo3nJuVe/2thXMfvpdJbet6oq2LpVnoWRERmsg0EZ63ftksnq0CG5/6tWCZEy85yDQUPU+fzzZUCIRIRpXahapprM8nJKs+quKL1PmL36VuRKceaK2WyEXakibM9m5sroqIxbvb0yAHZ1PbdSf8s4rohEItx+++285CUvobpIi8OZJz+Tc4T5JRKJwvcz0djYyKmnnlryt07VbCqjjDLKKKOMkwxqGsxkSk1Hs1lMZGXC/r2X2tmsmLvRqJjDe/bI52vXynczyRUV0KjM3WLousSlxmJiUvt8srQrJgR6eoR0MZtlieDxzG0qq7orGzZIe42MGLHCi2H7djFLKypKy3uGw0aM1clGrthss0mhqiqjJvHxPpamGWTKySoLBrIsrauTJZLdbsh/KVfIBz5Q2j/BqLui3AblzJWjQ5lc+UeDrsN73wvbtqH//g/s6/fSk2piaAj6RuxUmsNomvhMnivkSrHqSTEeeEBevV5DKWQWB1DMOIBEISvN+jxU6moJVLW1bFYcRSp1pq/vmK5hfFyOofx2SyFXVC2TG2+EP/+5lFyZyQ8pGTEVzFtZKZeqJoKhobmDgAtQjdvUZGjngzjZotE5C9vHYgaBAkKwKHLL4xGflyoncfiwnJ+6Z2edZfjCFPHzrEE1gspMAHEQBoOGjI8qKPIcw9iYSKsNDcnp9/QY0lzPNlRfBiOgXpW1GBqSczrqrBpdlyj/vXtnO1yfKcJheQiCQemgM60JBbtdLkhFn69cSRxnoXY5GD7h4ut3uxFLr6ICZ2stvRe/g2qz1JJov+9nLNuRL2asnOUq66C+XgaxvHWtSr2oY7gbvJz72lZMms5kxM6weyXTGY84cHt7C4PX2LSNbE5jNFPNYJMUK6/e94AhDRaLyZ8a/1SuvjqgEuWdCeWsbmyUh1blxp+M8Pnk3qv8/7kwPi4WfEWFhPysWyf3ZnBQ+mQkIiyb2tfKlaXycoosg9lF7T0eI8VvsZorIPdWCeU++KCcg6q7ks0aD/9DD8n7s84yamZBKftZnLWijtPSYmQhPfaY9Pndu2XgPnRIzmP16rmlvUBIo1xO2qCpSYwGi0UmoObm+WvyqMlMpX/NfNaLJRqLIyaUpF0sZhgoKoJAObkjkWdXFkzXZX4MBg05uWjUCPkq43mH2267jVgsViIJBoYcmJIHK8bw8DBNTU3PyvmVUUYZZZRRxt8T82WugFHUXtf//jU6R0fF1J6aMjJYXC6jGLiqTaIczgMDYtrO5csZGpLYq74+cWq3tc02g1Vx+k2bjEyM+cgVp1P+VBm3mYXt58Ntt8nry15mmNvZbCGmGrf7+Gd7nGjY7dKPiuWrlNl/vK9F02SfMzNXTsa4QpC+q2ILly2Dyy6Tz1/xCiERZ0JlrkxMGFlaZSwdZXLlHw2aBp/8pLx99BHiT+ynO1rH2LSNJneQds8UHo/4CZLJv/+kB4YDU+lgKvWRnTtLWfhweI6i9vG4EW4OBjmS90Ynk+IfevTRGb8tLiZSWyuz29iYHOQY9NLGx8Vv1dUlpxSPLxyYr9TIenqkvsy3vy2DuyquNdOZrk61uGZysUbo9LQRbD0nVOOq4sBKGmzvXrE8AoFZVkQ0apSlALm+b37T+N/vF7+XxSIGRjQqRbTMZrj8cvHFgRgji8mkPVNks0V8mmosxQqBfPnww2JFxePy/3MlXxnpp7t3S9TK2JgkGgUChu/0uJEryeSS0mBUPe3icg4Kzc1yXlNT0j+OKqtGWbaBgFHb4XhhclI6QiQytyRYMZQlUVFByltN1iLF7JW/tFgSTCUTFBJh2ttxVDmxVLjwVckUe4jVWFMxMg4PuTPOku1GRw2nd5FDXBWaW7FCnh2LBZqaNZavEAt913Aj09V5od5QSJjLdJqRKRuhmJlszsTIugsBqNl9HzGHX8atbFbaQDHRIMRzIAB2O/q69SRSc5gE6iIdDsPiOlnh8xnSXXMNOsqZX1srHfvss2UQa201CtTn25umJhnkii19MASaQR4Gp9PIYlC1QRbLXFGVFNNp6QhnnSUP3a9/LftTY5iuy4P24IPy/xVXlO5LDQxzkSsgK5MzhIjjiSekj4yNyXzg9UrGykKhWuq4L3iB/HZqyshBX6ivWK1yD9SDpCQ7FVRm10yJRrVaHx6W9nA4jDZVr0oWLJs9MdlvMxEISDuPjxtZRYGAtOHfOySzjBOCm2++GY/Hwytf+cqSzzdu3IjFYuFxVUU2j1QqxZNPPsnWrVufxbMso4wyyiijjL8PlIx5NjvbjHS7DT/I33upPTholKZUcaSbNhlEhHK0+/1yHSq+Jx6X92o5FYuJYsP4uCwv2tpmx/AND8Nvfyvvt24VM9timVtIQS1TPR6DXLnzzlKX1lwYGJBYKU0TZWEFteSZL1PmuQ6VTaJMfTDIleOduQKyvFDLNLNZ7tN88WLPdVRUyPVUVckS6T3vga9/Hd73vrm3V8s3VcqgLAt2dCiTK/+ASGw9m9GzLwdgxe++QixlZnlthDqL0PY+T5ZIRPwTz4XsFeXrVokGF14owbbZrPjDVe3fcFgmj5JBIB4vDZ9XDvU8yzA8LH6qQEAc1oXfxmLiaFOzal2dEWl+lNkrKmD8Rz+C666T8h6wcGKEIldUJkokUvCBkkrJqRX7jZSvTQXbq4iH6mpjIurqWiCgV7VRc7M4Fs85R3YQiYi3d6a8Wv78VeHy+nrxB957r1GoDeT4K1bIgP6Tn8hn55wjnyklHRXpcaLQ2SlpkH/5S/7+qsh9dc0qx/SBB8TxqqR0/t4WXx7Dw2IojYyIv663V4yjtjY51Zl1qI8Z4+PCND722KJhCsXF7FU5BwWXS+63eraO6nEZH4dUikz/sBzgeNYuUJJgsHgxdtU3GhuJJ03gcJBMGh+rYP9EwqgxrvzBVFfj9DvR/FXUe+Qhf6BVIpyHz7ycuO6QhotEZFxxOgsPqa4bad8dHUbXrKgwync8PVJPpHY5GX+dOPyzWSJ7eoiEckyFrLjsWaa3XACAr+dJEglddhyJyICuBhcQ8hRg0yb2DVXy6L4KDg/OyFJQRWVgfim1kwVK2NjpnHsAHh83xGmbm2Xs7+gQYeS6Onnf3CwDnsNhFLUqhsqKstlK666ArM7M5tJ885kozkNX5OI118jrn/9skGOZjLzfvl1e29uNFZhCLCbnaTbP3ef9fiNjad8+WZUNDsocoOrIzIe775axAoRcmZqScbW6WtpnoRAvTZPJrLLSmKCKU1Nn6kaoyU6NS2ouamkxNCbVKkutkuHZyV7p7zfSOJubpZ3V4Ddjzizj5Mf4+Dh/+9vfePWrX41rxuTn8/m46KKLuOmmmwgXRSb9/Oc/JxKJcNVVVz3bp1tGGWWUUUYZxwWZjKzplhJjWhyrN1fmChiqtn8vqLjRiQlZw6n116ZNYnqqJY+SnlJOZxWnBrKdrot6sIrnramZHc+Uy8GXvyy/27BBjuHxiBk8l9Pe4ZA/j0fM+1NOkSXFddctfE2q1sqZZxpBrOo8bTbD9D7ZoIiOYnJFSbWdiCwcu710OXYySoIpaJosy6qq5H04LOTefEs8laU1NSXLqHLmytGhTK78A2JswsSBl7yfqMNP1XQPlxz6Nr5aW2GG8CEzXbFM/98TStdSqXutWWNkrxRLg6m1bMmkH4uVynOMjMhsn/dGj4/LxN7bKxPs/v2gZ/PFRB54AG66Sb6orJTRe2xMRpujSLVQkmA7dsj/Bw7IRLwQuZJMlpIrID4cpQgDpVkoyl+qSIqKCpkMKioMY0BlmczKXsnlSotJuN0ye6mI5r17xZIYGSlxVkUiBrlyzjnw5jfL+298Y3a/2blTMi+sVlFFqqgwJv2BgTlLuhw3fPKTQmz95jf581KNpTqUKki7c6e0haZJ/3gOpG319ws5NDYm/SYaFZ/n8uWG0TdXJtNRQ9eFfQsG5SAqk2keKGNSlXOYicZG8YeOj8v9XdLErOswPs6+vTm2Hayj60jOSMV6plBpeKGQUXtjLmQyYpn+4Afyf0sL8ZRY1YmEEdWkyJV4XJ5JVYNbwbm6BVwumqrkYd3tO58fv/1Bjlz+Adnf2JicQ2WlZEXkLWtVrgFEUUrdY7fbKN/R2Qm6v5rgqtMKNVBGgk7SfcOEwuCvSENdHaO1G9F0HesTj4mVGAzK9St5NCjUy8idchrjQRvxpImhCZvUaVFQN9lsnl8e6mSBIj6U/F8xVOZFTY0MVMWrEp9PiItVq2RAbWwUx36BUZtxDJC2Kq67AmLZqlXmQpkrIOegVqZbtkjudipl6AIkEnIv1ST4mtfMXqEp9nO+GkNer9TasViMCIPm5oUlvVIpCXn67GdlPjj3XHkgJiaknWy2+QvZF8NuN6TJYLbup7pHxboRqt8qllNJgoGxygqFDDLmRJMrxVl2NptMbI2N0kaTkyWyfWU8P/CrX/2KTCYzSxJM4XOf+xxTU1NccMEF3HDDDXziE5/gve99L5dccgkvfelLn+WzLaOMMsooo4zjg+5uWaPs3r24aVOc1fHww/D97xummYr5mZmc/GxjcNAos+r3G8oBHR3y6vUaREQxuTI+LuaoUhBXJQt7e8WcVsGjxfj97yVB3G6Hd7xDtlPBiPNB1V0B+Nd/ld888ohRc3gmEglRO4HSQvZgkEWadnKSKyo7pViZurZW2mShOLBncrznC7kCQpioOLuFfG6RiHGtkYj8lcmVo0OZXPkHRE7XSNq93LtJ8sFW3vcjXNGxgg69NRLA7Tb8rH+Peg7FiMeNuroVFTJAXCCB2Tz2mJyf1ysT/aza63198oVyFE1PywaxWCHYNBiUTXp75au+g3mtpd/8Bh5/HP76V/l9ba04UlKpUkmpRTAxIQXSVHTG6KgMVItlrhTX7AU5pM1mDHLFJIlKT1XEUkWFDI5erzHJq1OeVWpAFXZXVa8cDnGSveAF8v2ePXIRmUxJ/ZpiWbAVK+ANbxCDJByW6AylhKPr8OMfy/sXvUjun91uKMIMDp7Y8iYqEqWrK98GSpdNMVcXXCBpIOm0RIE7nUa60dE6xhIJOWBxPZdjRCAg5zw6Kt3Y5xP/qjKKlLLOXDV4jhrj49ImIyNCtI2MLEguzSxmPxM2mzwuStlrSRxJKEQqkmJsMEMobqWvK8tkd+j4SPtMTsrJhkJzS4LpOtx/P7ztbeI4VvJGr3wl8ZydVNbM1JRcs8UiPt1cTv5X9VZMRbOprakGk9NOc5M8BGNTFkKOBrK6mch0Rh7UurpSax0pzZFKycdtbYZRrWmiDAXSh0MhCKaccMop5CoqGfV1EIjZ0SbGqbLFsFp0jqy4CADX4w/INYdCxkmnUmJl5WuBRDcIkTo0IXVbxgJ5yyqTkW2dTsMqP5mhaTI4ejyzc50nJqQf1NTIPZkZaqdpcuPPPlsIibmyVsAQ6p2PXFFW60I1V8CQBVPHVrVX7rhDzjsel1XW1JQM9JdcUrqfXM4gxhQ5Nld7NDXB+vXy/2OPLbyCGByUmm2//7389k1vgk9/2rC+a2uNWjSLQa1cFLnS2Vn6vdtt1JZRq2+VsqkCJtRvwchcmZ42xu1jzWXv7ZVxfF4dzTz6+42Jt7ZW2sTplFX6yIic7zHWaSvjuYmbb76Zuro6Lrroojm/P/XUU/nb3/6G0+nkAx/4AN///vd5+9vfzq233vosn2kZZZRRRhllHD9MTxvJxIut24vJlR/8AH75S5FgB8NUUuVt560HewKRSMjScHxcTNFAQExNh0OWZ6qAuipFq0RMwHCFJBLy++5uWe/GYpJlMpcc2Pe+J+//9V/FvFXrxsXIFUVEVVUZsaDXXTd3m919t9yfpibJXFFQ/jGPR/5OxtohyuehsipAlmonqnZMMbmiZMFOZrhcslT0+w03WDGyWVmuHD4sz4Ly7TzrdZGfByiTK//AONx0ASOrz8eUzbDm9i+D2SRe03AYnztTCAA9nso8x4J4XKL2QZz3mibR+62tMnE/+qhMUirVLRAwHPuFMHC/35jBjhyBeJzxccNv4/PJhDQ6Cj0HUwRH44YDZ/t2Yx/mfNT52NiSrAEVXPzII8Zno6MLF7VXOqXFNR7AyFzJZkuSbwrHSSYNcsXnk0nB5TKCiAcHjQm2BPv3y6uq3maxyA7OOktm/pEROZHpadmJrpPJyPEUYbNsmfzsYx+T4+7YAX/4g9F8+/fLRPVP/yS+TZNJUlyhhK85IVDZBv39+WtPJOSfTMZwbisi6cEHpdHUzTmakJp0WpzVg4NiaT0DrbN4XHx7waAYZfX14mwvntw1zchkUtJ5x4z+fkP2bnJSXlVa0hxQm8L8yQxVVXJOsdgSm3F8nNB0Dl/XLmIDUwQnUnT2OkiPHIcBaHJS7qliaIvx9NPiMP7kJ6UdKitFkPQjH4HWVuKai2TSSO5S90GRnIVi9kXQTBqOtjraWmUgGp4U6y8YNTPaHUM356vyNTeXsDKqtnxrq3xc7KNes0b8/rmcRD9NTwMOB1NtW0k7vEy5W/E505hHBtHSKQY6pO6K78B2chab9HfVr5XMXzwOFRUEG9aSy0E4biYcMzMayFur6iYrq+z5AJ+vtKYHSKNOTBiZJcVO+5mwWheXlVNF7bNZYZFVyJXPZ6y65lsRWCxy84szV0DGqKYmeZh27ZJ7c+ed8t1llxn6dApLvXfV1ZJ6CHDXXXDVVfIsPPZY6aBy//2ir3jokFzHF74gZKTZLG2n9AuWkrUCi2euqFQwFQEBRkqnYmuLM1eKyZVnIgumas4MDMjYMN8+VGrp+Li0QXHtnYaGY0jdK+NkwCOPPMLo6CjmBUIlzz//fB566CHi8ThjY2Ncf/31eJ8v42cZZZRRRhn/cMhmZT03Pb20Wp/JpJhPKikcYNs243uP59iW2scLQ0NiYk9Pi/moslY2biytt+L3i7lqtxuSVErxNZEQ8zQWE19GXd3s9WAuB1/6kmy7ebPUnI1ExCy32+dWf1BQkmEej/zmjW8Uf8DoqAirFEPX4fbb5f0rX1ka8Fdcb+VkzFoBg+BStWyqqow2PBFQ9WSVW+xkz1wB6Tter1xTsYsqEpH4tmBQ2rey0ljSqDixMpaOMrnyjwxN4+mXfIiM3YWv5ymaOu8zpMH06YJM//FS5jlWxOPCpIKRqqlphj/8/vsNR2QwWEQyKBoWZJRob5f3R45ALMbEhEzoui7+oIaGfMD+RJKBB4tkkZ54QmZFsxlqa8mMB+juM7PrL+OLts34uEysefUdQCbhsbH5lcUUZ6N0QBX6+41JJJmcLQsWCBgDYGWlwfKrNhselt/M4oRUxHBxAWKHQ6yNtWuNNhsfL1Qqj0bl/JTB1NQk19neLumuAN/9rhggN94o/192mfgbKyrk/Natk88nJowSAscbk5PGOQ4Nyb3W4wkj5aatTTqP0pnbsUPucyolJ7RUabBcTjJ8QiFxEiYSi1edmwfZrOwqEpEA6mJpt5mw2+We6/oziPwJBOQ6VYF1m00aa3p6Tl1AlQY9VzH7Yjgc0rRLIlfykmDR/b1suf3TvPKB/2JwzEosmObg48/Q6s5m5RqDQaNAE0gDf+IT8P73C5PlcEgk/k03wcteJts6HLPIlWJJMHWdcwXqO5v9tLaIczqStEEywcSUmfhElICzSR7QGY5oNU4sW2bsW6GiQjR6QRYBqm73SMBOrGkVCYsH/6oquSGDg8Sq2whWtmHKpEg/tU+uR92IVAoOHpT3p5xCJGnNF7PXmApbCEYtUmsmFjPEhufJRlCaxScNfD5pC7vdOHHlkK+tldXTM5U/83iMm5fJiBai3S6dx2qVB2OhMCirtbTmCsh9UGFrd98tEQcHD8q+Zub/g9w7xcAu5NitqhJC5a1vlQE8k5FJ9b//G17/evjZz+Bb3xLCJRqVlecPfmCExaVS8mzV1Bgpa0vBzMyVoaHSSVGF6xUPIGq1rrIO29qM7dVx1YA/b4GxBZDNylw3PS1GRygkg/FczPXQkOx/asoIvFCw2aQ9xsakfdR8U0YZZZRRRhlllHGSQclBR6NGUfeFoJbRxWvThx82xAhcLnmfTD775IpSQ1eiBtXVxvpr0yY5Z6/XKDjvcolJqszMvj4xX1VgY1+ffD/XWv33v5d9OxxiVivVB6938cLyxXVXolExm9/7XvnuV78qTYzet0/cDzYbzFQgDYeNeKaTsZi9gjKt/+M/4N3vlntwIjNXQI5hNj8/yJW6OrmWykpZ5uRy4qc7fFjacc0ag0xU5MroqBHUXcbSUCZX/sGRqKin++X/DsCK+36EPTQOsRj2iUEcg0cIPn6I0CN7ST3wqKSI7Nw55yyYSp2YtDGlaakSUDqa8kUJBga44IUSFb5jh0xW1dUy+aiCYiQSRlpETY0h49LbSyKWIzyVJhg0/Mn19TKB9XbnsB7eb5xEOg1PPIGuwxBNbO+pp6szxdCBIPt3ZxZUDpmYEP+KSgJQ/raurtmqNArKEMkniRSgZMFUuyjDJpeT/9Wler2GMg3IZVutcqyBgTkyVxS5MlMk1OczIpp37y6R0yqWBPP55NyOHJHzfc1rpFBWIiET4MGDct2XXGKQYNXV4hdzOOQ3Y2OLK7AcC/bsMd4nEjAxliURyxmyXcqLvXq1WEVK1guOIuUCcXQGAhLxrPT2FWu0GHRd7sGOHRAOc+CAHLa7W4y69vb51ZiKa/AcszSYSulRBdabm436JEeOzHIsxmKG4pDNJn7WXbtm71bVIYlGjcyqeREKQTJJ9um9mHJZXIkA9VP76e/LMd4bY3ToGUiDKQsiFBKGQtPkHv3P/4hwrckkYT433yyR+EqOCMBmI46TZNLIIismV2w2o4THTDicGt5lNVR6pA8kAzFik3HiGStDNAkjOcPBrvrr6tWzo4uKi9qruk0TE3kCMWzF0t6Ety6fqmY24wgM09Uu2Sv6Qw/LDoozAFTG2mmnEYmbhUwBghEL2SyMTNlKi9nP4aAfGpJkrccf//uk9R8TVB8orrsyPi7X53SWZkMcKzwesU5NJmnD//xPWWEpYmcxK10N4JlM6STw0pfKPiYmDI2B884rFSFWUAWBTKaFyRWrVazoV75SBuzvfAde/Wq5htFRYcf/7/9k22uuEdm8YgJlYsIII1P6CUuBap+KCmN/c9VdiURkHI3FjMgJRXrPJQumtleSdkeD3l5pN5UHrzIQZ0qWqRXJ1JSsOuYilOrrpS1GR2Vy/nvrq5ZRRhlllFFGGWUcAyIRY+03M8BzJnI5McPS6dKYmVCooEZckkD+bJMro6Ni4k1MyFrLYjEyV1atktdi8kP5iZT6qwpuU2ra8bgRq1mMmXJganmtSlguJYtE1V1R2ULnnSfCIpkMfPObxhJBZa1ceOFs9evieitzKWOfLLDZDHEBVR7zRGaugBHrdrLLgoFBrlVVSf/fv1+egaYm8RXW1RlEouqbKpatLA22dJTJlTIYPO8qxttO4RfJK3jnL/+Jf7t+PfHBSXzxEYL9QfSRUSb2jIg3fWzMSCPJIxQS6adHHjmqOu8MDMhvFgrwT0SzpMYCDA7I7HHxg//Due87nYrH72Z1Q7jgD9+xwyjiPjUlvrJUMG7svK7OKPIxMMD4tJVcIllSgkHTZHLMJVNYDoszJWcVNiPx4GM83unl4EgFo9RzoNPEkT4r8d6xQgD4TKiA3u3bxf9SX284ZpVvf66IbyUrr05d+W2GhsRgsVpLDRvltFYDoEpbVZNBZaWhUXn48BxOUMX8NDeTcFYVpMXw+UTHC8Sbm0zKBU1OEgnlCt2gvt7wU05MiHHx4Q/LAK0SH1StZa9XXqurZZBXfM6JqruieBKFge4MkbjZ0DNTN6Q4FerRR2dHTC8EJbba2yuNq6qFZTKlum7zYWJCbvbUFH0PdDM+LtEo6bSc3kKF2ux2OYxK114KcrmiCIRIRM51dNQosK5qJgwOyk5V/nMe4bBRzH7PHvjFL6S29VxRDcX+6wWbcnwcPZXG3GdkjG2Kbic4mWVqWuPQztCxpaXqulybYnd8PmmAL35RMtKcTvj2t+EDHyiV9QmFwOUirVvI2FykUsbzqIaRYt5hzswVJ9gbKqmplIaZDoA1HmJCq2Ey6iBZWyo9lUgYQ+vatbP3abdLqQ8QcjYel1eVmFNVY0FbvUoesuZm7JYsh+rPB8D6+MNyMxQrG4sVDpY75VSicROxpAmrJYcOTEcsjAZs6NGYXIjJNCt3PZuVKUFJBCje5jkPxYZ5PNKIwaC81tZK2x2PlYdaNTqdRtajIksslsVDrdQKAkofLLtdiA8wMjRULZaZWKyYfTH8fmNwrqqC970Pbr1VdB63bJFB/nOfE1mw4hVGLifsnt8vnzc1LXycYqiVi81mkNwzJ1O3W65D1UtSko7qnItZTRVVAHJP1cp+qYjFZN+jo/K79nbpF3198llxHS21Mh8flzHTbBYR7I99zJiYLRbpUxMT8typCJEyyiijjDLKKKOMkwiq1iYsXuuzWJl1pl9ISYOppPhYTPb9jKStjxKDg2JSplLiM+npEbPRbhdz1+kUE66YXFGZH8VJ0kqJpKFhdsJ7sRzYli1Ggnk4bJjkS8kiKa67EomImf6+98m57NoF994ra8D77pPtZyayp1Jyv7xecU+ciOLvzxaK66DAic1csdkMKbLnS+YKSF/1eIxlZkeHfLZqlfRTtZRSfVMp9JSlwZaOMrnyD45Y0sRvHmhgY2Abb+Fn7E6t4cBIFU8ccOHLBchGEkTG40wMJMShMDpqODmQyUNF2hdLoS+GXA4++lG44YY5g+Nlh/v2EX/gMbofHSOb06gyTXPO9m9giwaoefJutFi0UNheSYP5/UYw6UhfkUe0vt5w4AwNMR6wEJ5IkcuVRg7YrDrtVWH8oxLVPXTOFQDo27czOmWls89JX7IetzWJNR5mYF+IwGOHGe2ebWWo+siPPy7/b9hgqAANDc1fEE7Vm1ZkySmnyMSq0ljtdqPgfTpt+HJU21fllYFMJrlsm80Ixu7qkt+WtHe+KEm6voWdA/U8+WTen+7zSXhAY6OcbG+vzOCZDNGhYIGTaWiQNqyulgiOTEY++3dJiMLlgiuukGutqBB/mMrgUeRKf/+JCexVtXoUerpyQq4owkD1CTCkwR55RGbRWEwaeKGQ/JERaZehIbHM2tvFwZjNGgVTFkNfH4TDTHZO0HUox8iRaEH3cmYZhZlQ3ycSS2s/VaPooYfyzvC+vtkFmUE6ajIpnbi3t8RJqQzsXE54JZBuUVxXCIBgENdYN5mBEZKx7PzO97wkWGQkgnvccGA2Dj1OlS3K4LCZ2GiokK2xZKTTwv6Mjcl1KCfz978P99wjD8mnP21I3ynkcnKRFRXEc3Zwuwt6umDwcaqYvc02t3HndILJbKK+UabZwYiPalecAH4yFX6Gp0rDbQ4flntiMomxM5c/fOtWw1+/f7/cOlUby+9HrmnZMslcqbAx7F1N0l2FORqWm6Vp0hZdXYUHNVrVio5GPGnG48zidWWZCltJxHWCIW3eYvZDQ9JFVJD/SRWYX1x3RRWzqqhYuNbK0UCRGqqoPRhaCEvNXJmLXAFZOakHf+1aoxh9MbJZ6SSKRJoZTjcTdXUGGaCIA7sdLr4YvvENuOUWOPfc2b+bnpZj1dTIb49mlVMcFqYmqLnIlVxOrmV6Ws5L6SDMzDDSNCOyQD0UR0OuKDnHsTF5jrxeGc+np2UcVyQ6yJypjJ7qavja1+B3v5NB8De/Mfap6pgND1PQIS2jjDLKKKOMMso4iRCJGHa+qvU535pMOWKL1bWVyf3gg4YPQgXg6frSVbifKYJBuZaJCTHV3W4jm2bDBqPeitlslFdUMWZWqxFDFAqJv6WhobTIusLtt5fKgZlMRhK21yvHXkrWxcy6KyDn8PrXy/vvfEdiodJpWRKsWVP6++dDvRWFYlUW5ec6UZkrilhRSfbPF3Klulrarq1NVDJqa+G002RJpRRHHA4j3lQFSZfJlaXjeZDkVMaxYHgyzR/21PHon5qIJS2ATo11kMZ0L7s5lSenWjn3QgdWm4tRqwOrr4p2bwrL+KhB87e20tNjIhJx0tcn/tixseicCiUAJpMJZ57aP3AAbropBuicf774MAqB48kk2mOP4crliPekOLwtAbRySu4BCv67wU7ifT2cdVYFv/qVxqOPugp+joGBOCMjOUy5EP6hITSQL/JimO5QiNjwNNOuNCZHHF3PGemtqSTO1CDWyABRoPOct9L48G9xTg0x/FQPqfpWWhqTuH1pQiNd9HTV4DbbyYbGqH5FI5aV7WAykUgk6OvLMjYGe/fKrpctMwyKkREXyaRGJALJZJJMkQNtakombeWXb2110dqqcegQdHUlaWnJFAqKT0wY9S8kScJJVZUJmw3S6RQuV5pUStr3kUfEP6RqlldXOzGZTDA0RAr408R6/vntbi58cZT3vQ8qT8vLJ23ZgmN4GPOePdDRQSoUYmJsmO5ucaTV1IhhVFkp5zA46KC93cxLXgJmc5qamlQhccBqlUFbkUotLXbAwsAATE6mqa+fP+/QbrdjyTscM5kMyQVGepvNhtVqzavMZNC0JLoOh4/EGFobpU55yhsasGUyWC0WWL+erN9PYmpKZGCam4X9GR4uyO5YrVZseQdidnKSxJNPyk0YHBRrx2aTxrBYsI6OYquqglCInMdDfK4c6kAAxsZId42w/4iPYGWWoaFp/OtlV3P9xGy2YLPZ8+ejk0zGVELRLLLOYrFgz1seuq7T3y/bJhKw74kkmxN94ijMZDB7PBS4HKeTqNstxIrTKSlA+VzpsTEIBs2AoyiJLcrvfw+nn6ZDKJjXeUtgiiZITfqJdmUJNQizGJ15kqEQTE8z3BegZtIgV7zDh2gyH2Ey7OTQ3hTZRj9Op6lAUGqahqsomyIWi6ErSz8UkkEmEoGBAbRwGNfy5WLt/upXxIHc+99vWNLFCIdx53Lg8xF3VZOMJujqypHNGjWDQiFp69pacBdFzycSCbJ5J3o2m0+IaMoBGXrDNvz1FkbiFgK2Orq7E9TUZAucxYMPymtdnXQhuTb5Uo0RDocQL0qKa906IX0cDhdOp2ybQidrhqwLEskYR5afS/ueP8IDD8Cll+KcnMSUd2KnTjmFsek0sUScYFSj3pbCZdfpG3MQtIcYyjqozJMrqVSKdN5Znc3KIzI6KguiYBDCYQcgIVHF284Fh8NRKAidTqdJ5fONEwlJzdd1IZKkiKTx3BdvOxeWPEZYLNjMZnnuMxkydXUkc7nSVKsiqPFErj1LYgHCtTBGuN1krVYSwaDsM52WQTCTkb9otGQ8yeVyxhihqoAmk4WVkdViwWa1gs9H7sorid90k8h0zXEulkQCO4DTie7xEFsgLdBisWB3OqG5GT2dJjYyIuSbqlFWBLPZjEMRKLkcUaVVqaIUio5jNptxFLHDs557VSgql8NUX48TCrJg0eJnMpmUwS1/r0z9/bJtnlyJJRLGc+/3C9s3MQGRCFooRHG+VckYUYyJCbThYVwDA3DnnfDXvxJvayP3P/8j++zrk/NNJuW4ExO4x8dlFXLjjcTvvJNCvMItt4gGpsqAqqzEPTEhD3ZXF4m1awtjxFyYbzyZCy6XC00rHSOOx7ZOZ942YPFneb5t3XNpJZZRRhlllFFGGScVVK3NYsUMpWAwV0F2ZaarADCQ+MX77xfzrLNT1i8ejyH0MDX17EhWDQ6K6RkOG2X77r1XXjdtknOpqJBzUXFJ6hrtdkOFt79fitPPheFhieMDqUOrCJnxcUNFV8UClUA1bJHtXFx3RSmYmEzwutfBX/8qn/3iF7LtXOUXIxH5vVIyOZlhtxtxZ4rsOFGZK+p4KiPp+SALBtL/Vq2S/ltbK0u94vg7l0uuVfXPfEx1WRbsaKCX8axi586dOqDv3Lnz73YOb7sqpAML/F2qr/X06fd+/Qn95x/bq9ssznm33bLlAv3mm3X961/X9Vtu0fXKypp5tz399NML5/CTn+g6tM+77fr2dl3/1rf0nqv/W3+76cc6rJ93W5OpXQdd/9zndP3ee3W9vf30ebet0TRdB33n27+tf+sjg/q6dRfMu60LTf/Wv3fq3W0v0HXQX1CzfMF26/zJw7r+6KO6PjWlv+Y1Vy64bUVFRP/tb3X98cd1/S1vecuC2374w2P6hRfqOuj6+vXvXuTedetXXqnrP/yhrl999QcX3HbPnj26HgrpOujXLrhP9B2g6x6Prn/96/r/Xv2GBbd9z3vu1e+8U+7F+953/YLbXn31HTro+pln6vqHP3zjgtv++te/LvSfX//61wtue+ONN+qZjK5v2qTrcMeC217/vvfJyd57r37veectuO2Xrr1W1yMRXQ+F9B3f//6C2177kpfo+l/+ousHDuh79uxZcNs3nPZy/U/v+J3+zTdu17/0zr8tuO3ll79bna7+f/83tuC2b3nTmwptFolEFtz2yhUrdL2xUdc/8xldv/feBbfdsuVS/brrdN1mk34Jrnm3XV1/iv7zj+3V77sjrGezul5Ts8AYITvTI9Vtug56k7Ni3m3Xr19fMq6tXz//GNFeV6frn/ykruef/9MXOIcar1fXv/1tXb/3Xr378Ql98+b5xwi73aUfPmycw6WXXrpgu937xe36jz/Vp99wg65fcMHCY0QoFCnsd7Ex4oc/HCv0icsvX3iM6P7yl3V92TJdB/2D55yz4LY/fcPX9Mxd9+j60JB+7bXXLrjtT36yo3C+X/rSlxZuh3vvLWx7/fULjxF33HFHYdsbbzyOY8SHP6zrP/6xrl93nX7H5z634LbXX399Yb/3LvJsfOlLX5IN9+3Td3ztawtue+211xb2u9gY8cGrry6MU92/+MWC2777kkt0/Zvf1PV77tHHdu9ecNu3vOUtcgLptB7528Jjz5UXXFA4B/2mmxbc9tJLLy15Pl2u+ceIC5YvVwOJrt9xh17j88277ekul2z3b/+m6/feq7fX18+77frly5c+RtTW6vqWLYXzOH2Ba6upqBCj54ordB30CxbY1uVw6Pp3vqPrP/qRrt97r37pxRcv2G7FuPLKhceISGTpY8TY2Fhh23e/e5Exoru7sO0HP7gEOyKP4jGijGeG58JaoYwyyiijjDKCQTH7brhBTJ/rrpP/x8fn3n5wUNfvuUe2Vf6Lt71N11/0Inn/+tfL7//6V9nmt7/V9cceO/HXkUjo+n336frPfy7XcPfduv7d78o5WSxyfd/4hnze12f8LpeT3/3sZ7r+xjfK9qefbpjDM/9e8hLZZssW2de99+r63/4mZvnNN+v6/ffrejI54+RiMTnIvffqejhc8tX+/br+pz9JW912m3GcL37RMJ0rKsTlMPNcvv1tXb/pJjlmNnv82/TZxMSEXNP11+v6D34g7+PxE3e8PXt0/a675Dih0Ik7znMJ4bBc71e/avSt227T9YMH/95ndvKgLAv2D4h4cvGCswcircT6xvG5Fy4knc2Kksctt0iw61Jle558cpENdB3uuYe2X3+FJ3KbF9xUEfwPPCCvC6bu5aOlzX1dZBPpBZloXdPwV2QIrD9PjhObWvA8hp4cI9QbgKeeIjW5cH5rKDR3psFcaG42FFCWUtPG5zuK9MV8UZIu86qFt3M45OCjoyTDC9PXVuusMh3zIp9MVJBcOp7IJy0cHTYv3NcYGYHHHoOdOxfXQdI0ucljY4Yk0DxI6xa6My3YEmGaqo6jvtLEhFGbYTF0d0u4zbXXSvHtBZDNwsSIZEV53DlMCwwpZj1DNJhF7+snHFxcVDfkbmB82RkAmDLHKVQik5GaEbouhbtV8eu5oOuFkKW4vXJBHWBVF32p0Ct8VK+vIx6fu0ZNMRZTcirGUUV7eb2SeQhzF0IvQtZsYyJkW7xmBxIJdlSybX9vWK1y82pqTkxIkhK1PRFYrGh8JiPhVkfTQS2WUpnEhRAMLl0DdCkwmYy+OKOm2yyoLAo1KS7U6Y6mQ05NSUqYxQLvfe/Cmox5+4Tf/lb+V+l086GhQSb9Yl2NMsooo4wyyiijjOc4IhEKycYu1+K1PpNJw1RTktB+P5wvZSB58EHZn9Uqpqoyj0609NDQkJElU10tpqdScn3Ri4xlgZKZV9A0MQntdmP5qBRqZ2JkBO66S96/853GWk5JxdfWikk4a3kwOWk07Ix1e12dHN/pLP3qzDMNRfNXvGL2PlMp+fN4JBvnaNaVz0Wo6ysuXXkiM1dUOcm8GME/BFSmjqqNDIYMeBlLg6brJ5U75KTHrl27OO2009i5cyenqurEzzK2/WaYB+4cwRSYZIVvEq8jPwNqGlgs/Of31nMgs4nvt3+WVe+/jJ0HNaor0jTXpDin9jCmsRG6/afSa17BkXAjv/61k4cegrPOgmuvjbJ589yph8WyYC9+Mdxzj8iCgaRPXnpp/ndPPon2P/+D6557SGLDq0VI62l+/PYHcTdX8k9fuhR3Yort7/oRyQtewp7+Kv77v114PCJ7nsnE2bc3y9rtN/JPt76PXF0dpu9+FyoqyH73e1T87lYObHgN91z8eVa+pIVc8SPQ38e6b7yT2v0P0vXif2XyjCtwRic467o3EjOZ2Xbtn8naS0fYiaCFoRGNTZ4J6ixTnLY5zc7YMnr0Fj5xXQOdnVJz5IUvFH3D178epqddfPSjGpdcAlu3JrHqMRgeJuOt4uE9FTz1lNSk0TT4859dPPigxmc/C+vXJ/nf/81w4IAU1l62TCbOnh54//thctLJf/yHiTPPhMbGFGeckSYeFz7gmmvEgPnSl+ClL4UNG5yYbr4Z/c1v5gzLDnZmDO3+d71LzvPU5QHYvRvHpz6Feft2uPxyDm98GbeNnMqHfrSeigr4zGfkurZsEaItEHDQ22tm2TLweNJMT6c4ckS2aWuTVGCFHTvsXHihBbsd/vCHNFu3pnB1PiFWy8SEOD4rK8Fqxd7WhmXZMrDblyQL1tVlzZfTyHDDDUne9S7Q0Nl5/r/Tse0ncOGF8MEPYrNaRR4IyKZSJK68UvKF/+u/jMp1eVgdDmz5QhvZcJiErsuNmKNCnHVkBFssBuvXk+voID7zodi3j+nOEZ5+MMiT4bVY3BVsMu3FVuclWeGBdevn3G+xLJiu63R1xQiHRWd18yadyr6npe26u7G0tGCvroaWFrLty/nbPQn6+2Hy8BQ16WGaY4c4c2sK+00/xrxtGw6leQVEr7kG3vAGuakuF7S3M2JqovOQxv4DVvZ1+fjxn5o4vSPIi9Z38eXbOqhzR/nRP9+PuboKXG7QdQKHJhkLVbHpbDerz6mlektRBb9QCJ58kmRnD/23P07HfT9mrOlsJs97Jaf+5mMEnRU8/MbryFVVcyjRimnFclatNtHRAU1NRbJgsRixxx5DD4eFUQsGpe80Noos2H/9F65IROpGfPrTxDMZcnOxJvE4HDqEe/16aG9nV2YzBw7E+dzncuzbJ8/FK18p6bTJpKTWvuAF7gL3MFPG5+mnRenoLW+R/3/7WzdVVVIvxWpN0NKS5YwzxKBfu1aM8v/5HznGqafOLePz4IMyVuq6jBHt7dDRYWybSiXJZjNwsJOhzjDhoTCvOvRlavc9ANdcg3PVKkyf/SysXEnyO9/l/qfc9I/ZCMUsrG2TnP9oQuPIkxHWLYP2jZVseeuppDIZ0uk0/f1SJ0t1C12XtcDatQ7OPdeM03lssmC9vXDRRUZdqV//WhZiFRUnQBYMsB05gnVwEKxWMs3NJJU+wFzbHossWCBAdtcuEk8/Lasam00ubtMmeVjr6+eXBZuels5z4ID8tqnJkAUDclNTxPMSWtTWysqraKywHDyIvaYGWlvRzz+f2Fz6gmrbYunAXI7Yww8LcXLwoGgZFGkXmM1mkQ7s7ASPh6iqgjhHIftFZcFAyP2eHkzd3Thvvx0efhje/W6il11mbDM9LfNBfuIwXXEFzkwGfv5zaGkh1t2NPjUlq8ZHH5WOc9pp8I53oLW14br44kLbzJIFi8dFrPrjH0ebmsKlJrTNm4mHw+S+/W34y19k2/POg8suk855zz2477hDPv+3fyN++eXGePKxj0n0SH5+AXA7HNKeZjOJtjaya9bMowlRlgUrQ/BcWCuUUUYZZZRRRmenBNAqxezBQZEoXrlydo0Ptb1aJ1x3nfz2C18Q8/dVrxLi5cYbxYcxMiIm58aNsg5StViPN3I5MREHB+Vv/XoxL1/3OvnuhhtkXVdfL7E7M8sMPv20xCDu2QOf+IR8duedswvZf/ObcNttYoZ+5SvGsfftk0C4tjYhRWb+joMHpdFMJrGrV64sfKXrYh4PDsoyYuNGw+RPJmH7djjnnNmBtZOTsl7dtEl2OYfa70mFVEraoatLyKb2djHNTyTCYTnW86XmylLw6KOiCv8f/yHP5//7fyKBd9ppf+8zOznwPFGQK+No0LK+gtP7J9jXtQxHQx1OW76Cel5Ub+sqcd7v7K3h9O4nqKs8m1jSgc0aJ+1pwToSZnLYRlDTybichZoivb1gt7vR9cUZXglOdRUKdG3bBv/0T/kA0Pvvh3vuQdc0/vSCz5N+wIrbBsvW1XIk0USmdjXu/u3Ye4cw5TROPdVFVZXoAj7xBJx5ppPG2hSuiQBuIFbZKE6T1lamm8QK8Ix3U+nOYMcEzqIIVd1E/fAhnEC2diWEw8SxE6tuwTU5QFPvbiY2vai0PWshlnDSjwenz0Vv12FipizhZJauLqmbsGaNsMBOp0ysyl8EkE7bqRzuhIkJIr2TONNbGB2VKmotLaUF6QcH7VRU2AsFvIoDmFV0iNcrTLvHY8PttuF2C/u+fLkE5vb2yu9MJuDgQe7gMnZmzsBqyXHOuSYeeECcv9ksmCodOF1H4JRTZPbeu5f0hivpG6gA3DQ26Ph8Gk6nGCQ2mxgegYAw3WvXWkmlrIVzaGkp7Rvr1sm5JJMQjVoho+HOZqUv6rpkfQQC4ogymWSUr6vD0taGZZFOpvqlx2Oho8OCwyFRN9GhLG4QS2OGdWO22XCffz786U/SSV/2MrEC02kjBCT/3uzx4G5unj9sor5ejKVQCNPoqGyrEIuRDUXpP2jmiaEVfOKO01jRlOA7b+jFFArjrPJDLLFoZoGmafh87sLkr/V24w4FxUPv9cJrXiMOxFyO2EAQe249uYwNeyxNNGzF6rATHwjj37ZN2vfrXxfv/U9+gvuWW6SjvvWthXCf3FgQbdSMOeigp1vSJdZVDHJRfTc3ODYwFq1jX+ZUzqwqytxaZmVsV4zYSIjQwSxtW/2GgO3wMFgsxCIm6oIjOIFw9TLCK08hbXfji4eoj08S8lSyqgEOR0zkcm6SySKd33gcnnoKVywmD5V4+sWKTaXgU5+SQWb9erEQzGacc5BWgDxELpeE+dTUEO8GcObrGcliwumkUGPI5SrVG3bMiHavqpLn3u+XaKOBAXlfUwPDww6sVjmkxWJkb23ZIre9+Nm22+0FB/jy5cLnHTkiHNqFF5ZuK8SbHeqb8E2OER7WiXWch3vfA7Bjh1E18tRTyebs2KwudBxUesCZH1eclixjZhPTGQ8VGS/JtAm73YbZbGNyUppc08SwDQTk2sxmiWIT7tFWIA0Wg9VqxWyW+kiqzlQyKbzCGWeUJpVYrdYCybEYLBZLgWiZE83NcuIWC5bly7EslKlQBLPZvDTHsdst21ZWSp+0WGTwczql88zYh8lkKt1vvtZNIbRPIZvFNDSEu65OPh8dlRvS1CQdTo2dTid4vWgz97sANJMJ9+bNsGuX7G96Wla6qh113Vj8tbbibmgQ1nwJmPMcqqpk9WezScd++GEhN4uv12yWa9R1eVgyGTmf/ArclUrJA5XJyP5AnneTSQbFdLqwCnXNFAf/6U+FAInHZX9f/nIhC8Xp9cJHPiLjxnXXwUMPycp2yxZQxMo//zO89rWUzCLvepf83XuvRCioBXJDA3R340gmZV5ra1s0A2nmeLIQiseI47nt0TzLR7NtGWWUUUYZZTxXkEqJqTDf8uAfGcVJt5WVRt2S+TJX1FIZjEyL6mpZL512mjhvt20TcsXrlfVPNCrm4IkiV8bH5bwmJuQabDYJyM3lxMXR3CzLfq/XMCULFxCP47Q3YrebcLtleRkMypKzmFyamoI//lHev+ENpZ9nMrK2q62dg1gBacxEQmz+GQEtmia/jUZlOR4MGpk1druRvTITkYgcy2w++eutgDSNpsmr1Xpis1YUvN4Tf4znGpxO6VdVVfJsDg+Xa64cDU7yBLEyjglut0TFNzeLt7u5WRwpdXVQV8emM8UBcQ8XsvK2r+BzpkikzCRTGmMxDwdCTUQnE4yN5IhMJpmelt1KoevFpa5UcDnA1VfL6333yWStJ5IFr3j6nBfyoOMSAJY1JkjbPUScdcSbxVlhHzhCNhTFbIYXvED2o6TBqt1JvMF+AEKuRnSzVPIa968FwB/sptKTKc1z03WskyM4p8XLGfY1S9QwMLXqLPndvm2zrkfToKUuSTxlYUKvpifVRC4YZucBF+m0Rk2N+H5UdLuKHBgeztc1nogXPK+JiQh0dzPYI6OYIlXUqxSOlkEvlZK5OJ02skk1TSZ9m6100vH7C/XI6eoyaiCnO4/wcT4HwIWnB7n4Yvm8s1MMjomAWU78DJFp4vBhkmkTXYPimGmoSeeLTsvkXVMjg3FTk5zf2JgkJ+SbcZYaU0WFkRzS1wexkZA40SIR2dmaNYbltXevdJz+fknF2bt3QZ20zk55ra+XdmltkYjlgfG8U0ml/QwOGg5nMKyUbduMXGCvV06+sVGcYqtWiTNwoZnd5TJyeFXOs0J/P4d7LEQn4/xs50Z0NI4MOfnx9o3iiI1EjBziRaD8b8nRaWI9YyJV89RTcv5PPy2d4+BBwgNB9M5O4t0j1FXEyYUjTFKN9xc3yA5e8hLx2r/lLZK1YzLBn/8MX/ua3NwjRwjv6yc+OAWxGIfyBOC6NTlsy5u56Axh9/74SOlNdnismCtcRMeihAJ5HUGQaxsfh+lpQgkbrrFuAGK1beiVVQRaRKLNP7gbUincpgSmWIRo1CiQSDIp1xoMitPXbJY+o3Sy/vhHsUSrq+F//3dhqR+Q/Xi9oGmkK6pJp+WjyUn5esUKeS7icbm1LtfCadbKQFF9XI17yiiempKuvWOH/F9VJd8tpMJVUSFRSGp/8/IHPh8OpwY2O0N1p6CbzPLwq0HytNOIxGUVGU+acNqLIuRTKfyuBNNpD1m7QS4NDkqTj47Kuao0eVV//VgVj0ZG4PHHSz974IGlK9odE6qrJXzszDMX7xfHAjUIO50UdOBmVmJc6Lcg28/MGhgakmenpUUc9uvWSUfs7ZWVYSAg27lcx7YqqKiQ/aoVrmK8QG58JCKTmNs9d8ji0cBuN9pCTYxqfFCw2WSbSESuEeTczGbpdPG4PDB2u0FYTU8bUozzZV185Svw7/8uv1+9WogVn0+OE4kYlVtf+UoZA/1+SRG9/Xb5/Jpr4M1vnr3fNWskUkTX4Qc/MD73+eSeDA2JkTQ2dhQNVUYZZZRRRhllHA/kcrKO6O+XpeTDD8vfjh3G1L8UhEJiFy8m83syI5czTCIVwW+1ylpgMVmwbJaCj0it/5U0mFqKuFxizoVCYr4uJMX8TDA2ZkiP1dTIuas4mauuEjeA2SznUyBXEgnYvRsOHcIVGimY5ipWcqY02K23ynWvXw9bt8pnKk60slLMVOXPmYVYTE4um53Tk60CWD2epa+NIhFZBpjNzw+SQNOMJYHVCkuMESrjKOFySdsqX8XoqHTJstbV0lAmV/5RYTaL08fvl1lixQqZDdra2Lw2iUnLcYgOgv0hVh34A5qmE4xaGJmyEbZU0TdkwWnNsH+34bjI5cQvslhdkF27ZFuPR/wWFotMUH19EB4WcgEgWd3MwQEhela1ppjytmO2msisFnkQ/9RhpkZkAir2hyeTYCeJPyrkSsTXzHTEQsbiYKROvJKu5DS+5FgpuZJO4+kTYidW2UTW4pCTtFiYXC65cNX7t805urgdOWp8KYYnbaRcPmLRHLsPS2jCli0yIXg80uTLl8tvRkbEWIn2jItlFgiQ6BoiF4kx3CXWnVKqcToNFZH+fgpZGMoIUc7Pqiq5tRZL6aTj90vUPUjzKuPxph0d7GYzbnOCf74yxsaNxrkNDeVl9SsqpI/knW22/sP0TYr3t94bx+Eojd5XCSG1tXJeiYTsoqJitk/Rbjd8eH19EBsNl4qEqlSf9etlh1NTklvb1yee5ccfl1SZYnIEaZeuLnnf0JAnV5rE2dYXyc8Wy5aJVTwxIY55xQqeeqo46SYnCzVpjhl+v3jn0+mSsPypgxMMd8V5qLuJzuEKTMiN/NUDjTw2mk9tSqWMdKQFYLcDqRTJrkFpvz/9yfjyppvk4jWN0L4B4iNB9EgMX24avz2Gt+dpKrqeImezS4aKwmWXwWc/Kzvfvh2uvx7d5yNSu5x43TIydY30B6QPrFurg93Oy88WBuKhPT4CYcPjr2ngqnETzTpIDU0QHw1JW4TD8vwFgwSpwDN6BIB0fSvYbEytOF2a8MhjoGlo0QiubIhoRCeZhEQoJfI7oZDcP4tFosSV9ZtIyPWDOEEXK0ySSsmD4fOB10tClwdI+XP9fvlKDRkOx+KlSJxOOS2VgKTIFYtwvUxMSNd4+GH5XPmXl0qu3H+/8du5Dm53yzgfydiJrN4qn4fDcgKbNxOJm0mlNbI5E0570YommaTKlSBnthLMuBkZEXu/v18ei3RaDH3VDvmfHBO5ouvSLo89Jv8rhalHHpEFyQk15lyuExv6pMYwxTwpcmWxY1osBZnOklW7Cu1raJCG37pVBlCVzpROy5hmNhuk8LFgxQp59hsa5HixmEzsIyPymdcrY/IzzZN3OIxQUTXZ9fXN9m643XIO6mGcWYTM65XzVeGA09MGqTJXuNXkpGSl5HKiZ/rOd8pkd/iw8dfZKelTExNyrd/7nqFp+epXwzveMX/mydvfLte0fXtpgbnGRrm2YFCImvJKpYwyyiijjDKeFQwOig9i2zZ5PXhQ1sR9ffIaDEpM2lKitMfGRC3j4EHDNHk+IhoVU0VlpoOYW4mEIewwE0oMRZlyZrOxBDvvPAlKO3RITEpNk3VNOCym8hKWvccEFeNozseM/ulPcm2trWIGhsOGyVwgV9TFx+M4U0FMJjHfVb3YYnIlHDZib97wBsM8VMv5+npZR85plueVY0gk5P0cjVpRIe1fVSXXsYBaK2AE33o8hTKizwsUkyvlROkTg5nkyvi4PAbl7JWl4XnyqJVx3OD14nHm6GiQ0PB7eRGr7rwOvyVMMCqOodG0n0TaRJtrkod2iHNF+Vg6O425aD7s2iWvra0yWSgNv7vvhvG+eIFciflb6JwURmH1FieTETuVlRBbJrVBaoOHmQhK+MTWrTLhBIMSdD89kaEqLLNesKKF4UkbExEH2coaghXimHEPHjRSOAASCbz94kwP166QzxwOcDgI1q8ha3VgD47hHj5Ez4id4cnSUb3Rn8Jk0hmM+JhOe+jsE8Zh7ep0IS2zsdGoGTw2BolYjuhAoBCGkLR6SA2MMjYpEeVtTcbsqfxJ/f0yoRTzQqq2sIoMmTnpVFYa6iT9/TJ3B4Pw2YF/BuDKdXtoaLfT0WEcZ+dOMUaSDp+MslLAhMqeJ+mfFu9vgyeMw54rUbhxu8VB2tBgpFerxI+ZMJuNCJCBAYiNRw2HWbGH2WqVxlufz7gKhUS7rLdXfrhzp0SX5EmWWMxwZDc15VVnWqUtD+mryFod6LV1sn19vRH5nc3KxmefLT/+4x/FcT84KI0cCh0dfe/3i4U1NSVMUy5HpmeAzh4bobE4t+8VSZ1r+CX/YrkRgM//eSuBaU2s0iVEN1tNWcxjwyTCacyPPiSsmM0mbZXJwBe/CE1NhJ11BAejbHvUTGwiQl1VmrMe/SYAwxe+Eb2mtL4M55wjEdsVFXDwIPq1nwSzmVjGxuiUdK6m6iSVHiGtVjYlWNsWJZvT+MtjVSW7crt1Yq5aiMcJDYaNNk2nyYUi5IIRzJkUKYuLdE0jaBpTa88BwNu/F4uWgUgEjy1NdEIsxtDDe6QTHz4sbbxyZWkax223Sbs3NIi822JQFn1FBVRXFxYFynhWpKj63OlcXP5QEQ+qj//xj9JNQZ6HVEq64IED8tmKFYbROB9cLiGTV6yQfv7xj8O3vz23oW2t9mF2O0ikzUx0nGN8sX49OJ2EY2biSTEDZpIrdpcFtzNHIOEiFpNzVIpGKmsFDBJ3IYmAhaB8908/Lf9fcYXse2pKFl8naqH1rECRKyAdR93YxUiJ4vAsdWMVC6WY9uXLZWDftEn+6uoka6Kx0Rg7j5VcsdlkoqqpkZvR3y/jo8cj4+WyZYuTlUuB6jyqimhTk1ynKhSvoMgVNagXkysqQ8hmM1Ikk0mDcJ/rwXjwQRnrm5rgk58UAet160r/Vq6U8xsYEJI9kZDslp/9TDJeFpL0ammBl79c3n//+8Z84fVKGw4Py/UUZwWVUUYZZZRRRhknBOGw2JQqmLOzU+zxw4dleReNSlBeKCSfL1DujIkJWYJOTkq2hcrOeD5CFbOPx41ASrvd8EHMtPt13VDSVst5v99w7iuzFYTkAjHd4nH5zYnIWFdEQzQq5mQ2a5iZV15pkEder3xfCE5NJgtqGs6sXIzNZgTMFZNqt90m+1ixwnAhgKyZvF4jVnTeE1THm4+xQo5bWSnm52J9bnRUztXrnbfE30kJm02W+uXMlRMHp1OeV6W6oXyMZXJlaSiTK2WUIq/vfupKIVfusl2GPTjGaU/+iGjCRChqZjTspKE2x/hIhv5ROxazzplnys+PHDFkc+aDcqIpkkFJUd13HwztDRSKD0zUrKV7QpxEbWtcpNMyQdtP3QCANzJCNpYgPBbHbJYyEx6PpPi+53+qGZwSp9aIYxnjQRvDkzYiOScTfnFou4cOlo4UiQTefOZKuG6lMbrY7eQyOcZXnc2tXMG7f3gab/3iOt72pTUc6DOEM81mWJvdy6U/eS2mJx5jNOTEZNJprwzj8ciu6uuN6PTJSQgOholHc+TGRAQ0Ud9O2uRgbEqcb23aQOEci8kVh8Pw8edyYuhBKblSPOmYzWLMmExiXExMwFe+lKUru4w6Rrn64kmqG620thpGz549su/xVN6JtmULAKYD+5mOi2e1oSqJPRlmppT9smVyjsrPZzYbDPhMqPYYGswRnco7xRQbNRNms4z2imSJRMTr29MjzvqdO2FsjEhE/gXxczkcsLJFLMGDdBCtbScWTMsFer1yEpmM8SOVCvXnP8O//Au88Y3w2tdKRa+XvAQuuUTEWheD2WzUFchkYHiYQzsCxEbCPNFfzVN9QkJ8iC/zrcy7afNOEYjY+NJ9Z6BPBcTgmkNnLxiEa6+VAOlw5xD2bJTk0CS1T90lG5x6qmSi+P0wNETu698g5qnjD32b+e2uFVz7+zNY3n8//uluYq5qui54K1OhOfSl1q+H66+HqipM/X3UPPFX4kkTfWPSuda2l1rVl50j2St3Plpdwj+5HTmyNidxu49g95Q4KkdHYXqacNKGKyBO02n/ciK6m2RKI1XXSqS6HU3X8Y/sh1QKlzlBNhAmcaCb4EhcFW+SdKlih3U0Cr/8pbx/y1uWFmEfChmdtaaGeNxIBIBScsVmM6KfFoLyq7/gBdLFQiEhf+++W37rcBiJUyDZZYv5wzVNHoEvftHoprfeCu99r3GuBfh82B0aSbObkebTjc/zBZIjcTPxlBmLOYfNUnTDkkmw2/F704RTNlIpMa5mZq2AtIOSCFhMEnIuDAzIn/Izn366oUL44IOGJNtJCbfb6CxgpBUuJYTMajUyV5SEXjwuE4HXazB2YEicrVxpMPgOxzzCzktEc7Ocf0tLoW4TbW0ypsy7QjxKFJMr6bTUMAHJOFNFiEDOQ9dl8oNSckU9hHa7tLViPINBWUHPtUi97z557eiQdlq9evZfS4s89OvXy8p0elo8M8mkeFMWW2W85S2y7/37DQ8CyP1JJGQfvb0nTgOjjDLKKKOMMsoAjMDP3l4xD5xOmebXrJH4io4O+b6rS6b7ffvmjqObnBQ/w9SUkWgbiz1/E1HDYTFZVCk/ENNG8Q4zyRXlm8hkjDXBzOBKJQ324IPyqtY94fCJsflDIeNc3W4poTc8LKTOJZeUJkGX1FtRKTiJBI5cDA0dh6NUzhykD9x6q7x//esNEz8Uku/q6+VY89Y9UY2YSMwrCwayH7NZ9rUQCRWLSR9XSe4q0+b5AGXql8mVEwfl01P9Rj2TxUHdZcyPMrlSxmx4vZyyTjI6/mZ+CTqw5qEf44mO0D3swGnLUd9o4qH94i1ftypVkF7v6ZHXhaTB9uyR11WrhIV/7Wtlsujrg/HH8zvw+XjKtJVM1oTLqeN0ygThdkPd5kZyPpn9GgP7mRiUp33rVvEDNzbC0LiVFybv4j4uoN+2kqzFRjCkEUy5mK6W4iOukS70eKIgI1+SuVK3ojC6hHU3t+zq4LSe33EVt/J4QFJAEikzH/vhCkbyUfyWaJDzbnkf9eN7mdw9JG3THMIaDxXSMlX9BZdLJvq+/TH0WJxoRIfqahIZC0FvC9NxmTFavQHxvGYyJeSKmlCSSZmDVQSD328oysxMl2xoMHxi+/fDDd8VS/Bj2udxNVezYo0Nl4uCNNiRI2LoTITt0vinnopuMtM9XQlApTNBpT2ONh2YRa5YreLf8/sNndD5nMYFmbShHDldIxmILe61VpT6unViHUejQrJMT0NXF+GQXhLkbLdDR4sYL52sIdawksh43BBYtdtlP1NT4vQ6+2w491xptKqq2cU1MhmRiZnlzZ4Dfn8hhGf88V5Gx00M9GbZ91icLGYu4i7qmq04SXBd5SewWnI82t3A/21vpODVLsLTTwvf88ADsgh49OEcjsgkpqkJag8/Khu94AWwYQO86U1gMmG69x4att/GU73y3Owf9fPYX6Q2w6Nn/Dsh3Uv/+Dx1J1pbJZ0AaH7wFnI56B4WC3tdW6k3/UVbp3HYsvSPO9jdZaR1uBxZQCfmriWUsEl2DUAwSMjixzUkJElP5VZe/8MLecsX1jEQqWSqbas0YZcU5HBnJK87NhEn1DksD9GqVbM7+623ilXb2mqwtwshm5XOrh5Sj4d4XJ4v5fQvJlfUAmOxbqr845WV8P73y4IinRbFtZ/9TBYck5PGuLlu3eL7BHmWfD5RJ/roRwvJRbzjHXDPPUUbut04nBoJq4ek2U124xbpx+edRzKlkc6aiCVmSILlctLvHA4qayxoZnNBB3lm1oqCkghQme1LRTgsj+wjj8j/jY0yJ5x7rvz/0EOGP/2khLqZqsMcTR67Wj2ANO7IiDF5dHTMzpwwmYTBO/NMIQc2b160YPqCMJnk2fJ4pAO3tckEvHbtM9tvMWw2ozplKgUXXSQTeTIpk7mC0ynbKsKlrc3QnFBtrNpVsfjB4PwdUmnQrV0rbbpixey/U04Rpq+9XeaG9evlva6LsbJvnxgz3d1GLZriUFe/X4S8AX74Q+M7VQ11ZETuqxoLyyijjDLKKKOMEwIVMKVK1rW1SdyE02msmVesEJOhu1sCn2aWgAsEhFiZnhaSpqsLbrlFTJbnq+OxuARdsSyYylCZGUyrTK502kggnkmuqDq5e/ZIm1osYhqFQkIMHE3dm6UgGDR4C7cbfvMb+fwVr5D1TDhsJEDPIlfSaUgm0dBxmpIlckmq3s4f/yjn3tQkJfcUxsbEZPd4FolJisVKWalcbs7UKVVKsapK2qhYfKUYIyNGQfK2tuePJBgYtT41rUyunCjY7dJnVAxfMPj8HuOON55Hj1sZx4LpaXEeDgyIodDdDUcmK2lrSGEx5RiJ+3iy5RWY0wle8tSXqalMs6Ipgeb18FC3PHWb20OslmQQhodlwJ+PXFFRISD+H69X/ERKGuy+HQZd+lS/zF4trRrRqExmViv4mxxoyyXdoS28l+BEujCZt7eLRM6mpkkC+LmEv/Jg3zKmk2IRBBN2Yk0reIrNfPXgK3jjp1dzzTUywV72ziZOD97Ny7iTa/e/jp89soqv/6aF137hNL73yGaG4lXUMM7H+Rw3f+BxVjTFCYStfOT7y4lEYf3PP4pzUjz6d+UukrZpHIdMGg+RQsSCCgYG6OsHgkGimgc8HhIpE70T0gZVzgQVgT4hDo4coa05XzOkb35ypbLS8MfNnHT8fjEcAb77XZgIWGijlzf7bqepPovbb8dsNqLG+/oK9cZJOX3g9xNrWsVeJHOosSKGIx2BUAiXY3b0bVOToS86bwE3DKf1+LgGqZQEcCzFwwwy+tfUGF7pfJGXkc7pQl3n9va8qlmzdMpRGhis2kA0kJKboWkGE1RZaUjPfO5zkv3wu9+J5XT33XDXXVIB77TTpOG/8Y3Fw5XyxZbTo1Mc7HMQHI7hfWobd0yJTNPFWyd4+mUfBuDSwR/yrxeJLN4ND2/myKFsQbA1l4Obb4YPfECMfpNJjrtrrw17KkTHwN1ouSyplWuNwjenn16Qp2n77Tc4Mmh0ik8kP8FkbQeHNl/J2LSV6YiFSDif+61CsaJ5mbZ/+idyFhu+gX3UjzzJoQF5ntbPyFxxOXK8+NRpAO541LCmzSZw2nJE0zai3gayE3kpvEiEkLUa1+AhAH6buZxwwsZowMb7v7OGx2peCoD/4KPgcmKOhXGY00R6J4nETGSXrZztrA6FDMv5n//ZkJBThYnmgloB+HyF/GmVoq7IFVXMXqWVOxxL85MXG4LXXgtXXy2f33ijKAYND4vt7nTKc7KUrq/qF1mtQob+4AeScRaLwWc+I7W6UylA07BXe0iZZUyZetdH5cCrVhGOS6ZSPGnCaSt6ftVgarNh9jjx+WTxo7JW5oqAUjWg4Oikwfr7ZQxT2YyrVklbvfjF8mh3d8si4aQ16BQpq9golcu+FBRnvPT2yvuGBhlYlfzVXHA4ZIKZyXgfC/x+eR7a2uTZWLfu+K5mlEfDZpPOpWnwvvfJtT70kMG6aZo8HGpQb20tlY9UqaE2myFXpmpdzcxcSadFDwSEMJlLr1LB45Gw1nPOEUOlqUkMl40bZeKqrpYF8OioZNHt3l1KuF99tdyrvj7JglRoaJDnbHLSkKMso4wyyiijjDJOCBIJw7ydz4xRdVGjUZm2h4eN4KfpaZniAwH5LJEQ2/uOO6T24bFkbj/XkVfEIh6XNlMmqTJp55IDXgq5Ul8vJlUuZ9SNVHVXcrnjLw0WChn3p6dHSB2LBV71KrnGUEh8FZo2I7tEyXTlFzhOPVZQoLXbhQfp64Nf/Uo2f93rjDZSy2elPL6QqUksZiyilM06jzSYyoIxmw2TeOauQiHZTimIPCtQNWNOMOrr5R7V1MyviFLGM4OmGQrUVqs8I8PDZVmwpaJMrvyDY3q64OM0ZKZsDkaCDtY3i9j9b9Z8HIDWJ/7A2tgTWC06U3EH+0ZkVOtoCtNeF8fplLng0KH5jYy+PmPSXLnS0La85BL57K9dwtLozS3s65XI95YWOa+qKvFJmBw2tLxHvna6E3M6wcS44eCu8mb48Ytv5ip+TRobv3iwlR//vpZ9++D232u8Ydt72MpTfD38rwxN2AtO6mjCwl428mdexh8OrObGu1r5/cM1JNJmVtSE+NCle9hf90I+yyfYMnUvX/jXLmp8KXpHnXzhq1Y8nY+TtTrY98oPczcvBmCroxOnOYU5OFWYsJ1OQwpreMJKcjqOZcfD5L76NTLJDH2j+ayVhvwNGRqCSIQ2q3h5h4bEj2QyGeSKmmCLyZWZjl+Px6i7ogKAP8kn0f3VLGtIFEIb1q4V/1Q2KxE6ug4TWcneCLRsKpArDVVJHKkQFi2LLTw7j1fTJHj6vPMWJlc6OuR1OmwmHkwSTVmXTq4omEwS8h6Po4cjHHw8VLjmysp8xpArSr1Japg8ZT2dyHSmQHywbJn8vrVVLJbe3rlJExVe84EPSEM//riQLosgW1XDvk4z8ZiOtv0R9uyBGG7WefqwnnsG2qpVhBtXY8mleavpp5y9Pkg6a+Yzv9tAMpFj+vAEH/lghh/+UAzPi0+b4to3CQmz67APp9fKhk6RKZs+9ULD+VpXBy96EZE1p7Ers4lkxkKFM0ULA/SyjM+2/5Dqap1g1EIqEGXg4bwQcWenhGwdOgSHD6OPjTO2StIJ6p74C9NRKxZzjlXNs8OLLs0Xtr//qUoicUPazeXIEU2Y0St8hMxVMhiYTAS1SrxDEh52+6SEMzntWabCVt627W3ssJyNLTyJJzwMySTu0BCxOOhNzYT1OfrJr34lA9CKFXDBBeLs1DSxDOaz2INBgy3JW8Cq5E0kIj9vb5fPdN0IPl8KVNA9yDP1rnfBf/6ndNm//U0WZyBdT9OWTq6AGOyxmNzmr39dEpU0TbjAm2+WbRy1XrJmGymzg2jcKBweiZvJZCGdnV3MHpDnwuWiulrs5eHhvCzjHIvShSQC5kMiIeTt+LhBuKukiC1bjAy6hx46iaXBNE1ukgr3s1iOPXOluVn2oxjyZwtr18oxN248MSsZu12uU0XrLV8uItgA111n9EdVUVGlgYbDhj6AWj3OJFfmkgXbu1c6lMkkRMlSSCiLRQyRM8+UzJpVq+Q8W1vl/aZNQsLU18t5KuLH7RZJSYDvfEfmC5D7WFkppEwyaRD6ZZRRRhlllFHGcYcKmIKFzTCVZRAIyHq7p0eUHHbvFrOip0em8B/9yPAlDww8P8mVaFTMsuJi9iAmm6aJ+bKQLJgK/JyLWFDZK0oarKLCCGA7nuSKkidT16AUvS+8UJzHqpSqiq8sUSRXmSuZDGQyOIkXovpVkOwPfiABjzU1hi8LZP1ot4tJ2ta2SMJ3LGbYuirYZh5Pdl2dnKMKfJuJ4WEja6W9/VnKWonHYft22LHjhC/Y7HZRtt648fgl0ZcxG2rpqjK5BgdP4kDHZxllcuUfEF6vGBZbtojze8MG8Z+sXi0+lOZWEzm7i00rxFJ4ZHwVI6dJ9PuKO74ln+2tQEdjdW2ACkcaT2a64PPp7Jw/c2XnTnlV6iZKHv3qq8Fs1umMt9FJB+H6lRzsF6dHa6tMIlZrUdR0PlXGO9aF35NicjhlSJcnk1SGermFa3hf1U8B+OP9Ht7zHvjrX6E/4MFOglfzOz51xdPc8bMAd96e5q7zP8lfuZiv1n+Rt5x3mJedNclLL87ytXcf5odvf4RL13QRXy9pHf7926itTPP5f+3GZUnyUGAD7+IGDlx9LX9t/RcieKljlJf13YAnG8QUDOB1yYTpcsHyZXKyoxNmvL27qfnd9zHd8Qfa7vkpgxN5cqU+JZGymQxMTFBnmsBm00mnjZTPREIGO1X02es1anrPZTxu3Wq8X+Ya5U38HFtLHdaiegter+HYPHxY7uV4soJsTmNs2ZkFcqWuOoNdS+LWYrOkqxSU4stCqK8Ht0uO3zdgJjs8CjfcALffLtbsUr21FRXgcJAYmqKvR/ZXW2sEjjsyYdbkpHL43uQqIglLqcBqR4d0yLY2scQWynRoboY3v1nef+c7Yp3Ng1Ra48nJVibCdkIPPsEFD3+e63kPAC+4wIzLnsVuh9EzXwlA2xO38+HX9eH3puidquDTN7byrx+r4bEnLNitWT70ygN89EWPcobjaSymHGMRF6aug7jjk8Qr6phYdrq0hQqtqamh80Xv5K+2ywC4KPlH/pePAvD93UKYmHMZxrvDjOp1pJZ1SFusWSN/69YRW7mJgRe+DoDRHiFUVjYlsFlnE1Dr2mIsb4yTSps48umbqOoUqTK3M0sybSabhVCt1IZItK5GD4VxRsbZzUY6gw1YzDm+/8kh1i5LEIrbuDj3Vx7hbPx9T4LHg9ueJeFrIGuxE4zMqMszNWVYzm97mxh8mYwMIn6/pErMVX0xFJJBxmyGysqCmpBy+jc2ipERjUp/crmOjlwpzjQDyZT74helu6nIruXLZd9LKZNhsxkyiUpH2myWS/7AB2Sbe+6Rz+1+N5hMJG1eYlOJQo2HSNxMPCnt57IXRc4nk3KA/Mmo8h5+f2mZj2KoKLa5FlrzQaXT9/cbykhq3PH7DWmwbdtOct9zcVH7o8lcUZpyYIg1r1plfPZswWIxitufCChyBaRDgNQrqamRVaKqnVRcb0XTjHormiaTiNIIUPqTodDcsmAPPCCvzc3ScY8mw0eFNS5bZkQOnHmmZPS0txsDRbHU1+WXy8Qbi8FHPgJ33imfNzbKon1yUq5tnijFMsooo4wyyijDQCIhStBjY0v/TTwu8Q3bty/ulK2qEhNhbEyWt/39ss7u7hYT489/Nko+gtizz0dyZa5i9iDtpzLWVdCZQjJpmHLKN7EQubJrl7Sd0ymmYCgkpMHxSuhVy/NIRM73/vvlf6XaOjoqa6liVZECVM0VgEQCpx4rrOeamuT10bwa92tfa/hd4nG59vp6MUvr6hY4QdXAiqlTx5vHJrTZxAxViuPF/S4alTVlQ00aZ2SchvHd8NRTRycpcCwYHTUifTs7T6w9m8tJcGZxXcYyjjuUYr6KqRseLpMrS0WZXPkHhN8vxMqaNaKKsXmzsMBnninkitMJmtvFmmXyFD152MuRS9+DrmlUHdqBfWqIh3aLZ/H0llGIx3EmA4W6K4cPG5mUM/Hkk/KqtCfVJNXRAaetF6ftb7iKHu9GuofFY6f8ohUVBhnD2rUAeKd6qfGlycaShQgJkkkck4OY0Pmv5bfxX6/tw2TSMZvlWj/2vjA91afxO67gcv+DOE1JnFqCrYF7uZi/8YrVnfzzJUP891sn+PDHzJyyMY3msEMqxeRa8fhV738Icjm28CS3cDUmstzI2/ja5FvYvl+cOy/hL6zv/RN1E/vxOZOYJsQKdDphea3MhuNTFrZu+3ahfdr+9iPGhsWiaKuXotIT1gYOd5tIRDO01IqjSNVdSaVkTlbXrogzJWU/E8phCfDhuhuxkMW1vL5EZNTtlnYCMSSnp2E66SSYsBNu38i+PLmy1t6Fw6bjSkyJg+gYLSGnExrr85Jnw2YafvsdqZnxjW+IRMzLXw5veAP8v/8HP/mJIekyF2priYzHGRwVp7EqvG03pXGM9LAGIVcOB2tJRX9KmgABAABJREFU6VZSFpeRA2yxiJPM6xUWb2RkYWv56qvFoRYIiL7THEikTDxxyMNU3MHwlJ1L7v4It2SvYpw66iuTrGmLUeXNYLPkmDzzZeRMFuqGn6YpeoiPvF6ciQ8frGUiaKOtKsx3r7ibS1v3iPZrvY/17cJiHn5UIkWObLicGG6ZFZuboa6OdHUDYXMVt1e8CYAX5+7iDdzMurpJYkkzP/9rPdXpYabiLjLVdQya28SJu2qVkJirVhG21xBZsYUh/wYe06Uw+rr2udtG0+DyLb0A3Jh4HS33/hwAd96BH4mbCcZtUF9PiAo8+ayVH9vfDcBZa4K0tJr4yiem2bQ8TCjn5WLuYu+ToknlXiFFfKIJM6HYDEfzL34hBuratdLZg0Ej26ilRTpbd7dhvILc42xWBhi/H0ymgo2r5AAUcRyNStNq2tGRK8p3XGyYnH66lJVQhPGmTYaveClQ46GyyxUuvFCONzAg5293msDtImH1EkuYCsx3JG4mljBh0vRSkixfzF6d/Pi4ZMXs3Dl/BFQxebQUGz6TEf/z+LhEBII8q3V1MnZZLJJeD8Kvjo2dxHW/PZ5SWbCjyVwxm+WvpUXG6AVXaCcpiskVRYQ4nfAeIaD5xS/Ec1FMrqRS0tm8XnkQLBZDf0+lfoVCs2XBcjkje2TVqlIC61igMpMaGmTucLtlxa2qiYLcxy9+UbTusln48pfhxz+Wz/1+Q/euXHuljDLKKKOMMhaErous09CQECzF5vx8UDXCr79eaqQoX8RCqK2Vv8FBmaa7umS6HxiA//s/2WbLFnkdHn5+kivhsJgnudzswC+bzchYL16DqGQPMDIr5iJX2tvFnEunhfACMelCITlewafzDFFcfu/ee2Xfp5wiJmAkYmT/u90zzlPXSy8mmcSlRwtqtsq/AGKGXnaZ8f/4uJiXVVVyjQtmjyh2Si0QFyFXIB+U6pZjFLJXkklG9k3iGO+janAv7cmDmPp7pXPu3XtiF1GBgHSW/n65nmLm8XhjZEQWjgcOnMSyBs99KNUNRa6MjJRlwZaKMrnyDwhNkwG/qUkmFEVcuFzyuaaBq9pJY20KhzXDdMxGZ2I50yulMIrnsXvZeUgIhM3LQ9hSESzZFKvbZGLoFrWiOQ2N3bvldfny0qLINhu8dIM4T37Na9mROZ101oTLJedYUTFD6z8f4mybHseZmsZriTExkf8ulcI5IaHO4YomVjXH+dRHE3z1q/DOd8JFF5swN8ms6Bo6JM7YkmL2K40KZGDQt0CweQMZuwtbeJKqg9vZ+OP/4hWZ2/li3VcB+PGfGrnjYRmJzq47glnPsuGJn1NJsMCyu1zQ7pZMj/GwA1t0ilhNK4l1p2BOJwkNifOzrS5BNgsDiRqiGQfDXQlaa8SC6e83okaUWorZLIOhxTK/nuz69RJd8bKXwTW5XwBgam0p0fD3eIwI8p4emTNzukZv2E/IVMkgEr5+Vvhu7JUOXMl8iMkx5vHa7dBUIyN2qCeAfXJIGumss4xo6aEhCWH/6U/h3e82UqBmoqqKSMZB36CQK4114tC36wlsA910II78wQkHOJ0iW1UssOrzicWnRFJ7ew3tvPFxic4YHhZre3pa9J1AdJhU4Yg8InEzuw56mI5YmHyyn5ff9X6ciQBfNkvWyKVnT2EyaVR60tRWpnHWVzDacT4ANY/ewRlrw7zpxaLff8m6Pm5463aWb3DJw9PcDFVVnLpGHrKHA+vImG0caL+EmKVCooj8fmhvJ5xxknJVsm9KpHNexL2Mn/pS3nGV3K87HqkhGsqSrfQzlfTQ517Lo1MdPBFZzd7kKg7rKxnWGklg58kVr2E7ZwGwviVcuNaZCmrvGvwEdhI8xVa6DmawRKex23Qs5hyxpJAiug7BqAVH30GymLglK1JAl5wRAJsNd52bL76zmzObBoji4bVD3+CxJy3YbTpmky77iZqNY4+Nwe9/L+/f/nYZyIJBuaeKDVm2TE62p8c46WICJt/fVNKSyphQdYFUsLzVuvSAd4fDKC0x0zBZtkyIi3e/W4ogHo0ans9nFOIsHmvdbqOG1bZt+YD+KjdJzUFMd6JPB0lnNBIpE/GUSIIVCB1l4Cu5JbOZP/5RSl984xvw8Y/PveCxWmX8mUt/eS6oOjMTE0Ybr1xpZOOAkP1tbTK07Nhh+KpPOqh6IHb70ddcASFWHA5DP/H5BtXXoHQxecEFwkCm0yIPVkyuFNdbKdbbtNuNuSwUmi0LNj0t9ZfAyFQ8XtA0YWG9XvkbGjLGGJtNHp43CcHNz38On/+8jNHZrHTukZHF63eVUUYZZZRRxj8whofFBBgYMCSrFoMKQlSZ4ktQcwaM2qEjI4ZMzpe/LN+99KVG9sXoqJzH820KV+QDzF7zKDlgKL0Hc5Er8yU+z5QG8/mMwtnHSxpM1VtJJKRsKhhZK2Njch1KuqsE6bTcUHUxiQTOrKTyzMxGueIKg3zK5kulVleLub9ozRPVeImEnIyuzy1pW4SamoLQAoHBKPr+A0R2dRLunqQhN4xjaoiGoV1CcvT0SMdXkWzHG9msNHI4LMTQwIA8ECeK+AiFpM0iEclgKeOEQD3vqp8rFeMyFkeZXCmjBMoP5Kqyk8LBpjbJp9x10M3YqS8DYPf2GKm0iUZ/kuo6M049CpkMHXXTgPhAMpm5pcEOSOIAq1bNkG/PZnlT2/1YSLObzdx1UIqStLTIBGU2zwjabW4uOFVqxvZR44gQi+XnqFQKx6Q4pQ+zmlwOTj/LzMqV+RTKrINogxQfcQ8fgVQK23AP9tAEuqYRaeoQJ1hxYYM8W6FncwQ6zgZg443/hXNqkLi/mbPfdxZXv0gyUyIJKxo6vk0yUzceelAkjYJBiMVwZiPUO0LYTWnSWDnCSg687tMcueZjpE02etJCXrTVJYkmzKCZqGx0Eg+laa2KFNpYFVNTadE1NXLvrFawmbMywc0Q5LTZpKj2f39Ixz2dL7zb1lYSkuJwSOC/zSYGiYrICWY9HJqUDJdmBtgwcg9mrxsXMbFcR0aOKXvFZoOmSiGNYkPTAORe+jL4whekMPltt8HXviaRzKecItb0tdcK8TETJhNhZx0D48LcNVWEpU2yCejpKZArI0EHWbuTKO5Slg+EXFHSL9msGCZ9fUKojI0ZERqDg2J55wvG87WvFYyh6YiFJw55mA6ZcP3hV1x1yxVUBbq51fVmjmSX4XVlOG1NCLcji80KdZUpKj0Zxs96BQCNO/8IuSxvu2ycOz7/NB99xxTO1hp5QIsEYU9dLf3hHi7kSMfLCOo+ci4PSYevwACEXfXsnm4lnTNT6UoR/Y9P0HnNtWxdFeX8DVPkdI0f7dxKVY2ZUVsL/UMWenuFJO3qkrIrUwk3saSZAy0XsYtTATgvdhe6Dt3DDvZ0uwnH5Lz8+7exZvdvuQKR5/qq/gFqd98DgDtfdyWT1YglTISiZpwDh7iXFzGSqcVrT3H2xrB0CpcLpz3HZ95wgEutfyWBk4/9fB2PHfDicWaJxE2ksybiyfw0dtNN0v6bNwu7oASWfT65nyo1ZNkyGZxUpHgoJM+6ppGr9LNvnzxfIyPGJsuXi92bzYo/Nr/5kqAeLZVpNhO5nPh5i/ncpaCiwpARm7m4PF84usKCxeF3kUibybk8JCYiRGLSZvGkCWexJFhxtc+8ZbVrl/H1I4/Av/zL3Nym3S6Gl2qn+aDrMjRNT8v4pez9meSKwyGqS+o6TlppMHVBxUXXlwL1rFdVSZ9dil7cyQhVJdVkKl1MquL2FouENarOrMgVFUmgsi5VmJVKKVMdLJs1OuTEhGGEbNhwdJJgS0F1tRy/qWm2d0DTRLfvgx+Ua73rLsnG1DRZhCodiTLKKKOMMsooYxbSaVmbTE4ay9ulkCuJRKkNef/9S4vC1jRZIq9fL2bY5z4n5seaNXDRRZKwChJ7l80+K/W8nzUUF7NXidTFsNvlfmSzpZkrShZMkQwwf7m+F75QXh96SNZdxYnHx8M3r1Sfo1EpIxqLiV/prLPknEMh8S05HHMkhitPchG5YjdnMGVT2O0S8KuSl1/9auNnwaCs6/x+oz7KgojFpLEyGWO9MJekbREsFjE3/X7IDo8THgozcjCEc3KAynA/y9zjaLU1snhNJsVfMTh4YgiPYNDoLDabPJjBoKiMLCWt7GihmLKxMTlO2W4+IVDLI5WhNT5uPNdlLIwyuVJGCTRNgi5dLkhZ3WxZLSHRT3S6GN/8YnJmC38JnAnAeZuCJMxunNYM9mSQjbUjWK06iQT0HkoRmciLceZTHpNJQ2ZnVtBoJMLyyB4u4m8A/N+jkqbS0iKTR23tDPUOp1MsHaApsI8KUxSbVZfslUQCe0CKvyeqm+loTeCptBZql09Om4m1iqyYe7QL4nG8B8WDGPW3kquolIZQJ+h0Gk6xRIKpdeLxM6fiZG0O9rz9a2TcPt5x2RAvXCMh7+vaokxXraS//Vw0PYfnjl/KiDQygnl0CG98jLW6ZMrcsfy9TC/bynjFKh44/QMkcWAnQaM7SDRhxmLOUVFrJ2Oy0eyQ9Jz+7jQ+nxgiisRSkSFWcw57b74Y+dNPz2K51q6F05ZNYg3lHT+NjbMIhqqqQlkburvzc5fbzeFRsXw2sJem4ccxWc24HDlxIk1OioW0e7d4ppeouamlkrRVSz8bi0qbx17yGmMDn09IlSuvFMJl40aZXD/2sTkn1Yi9msGg7KfZFcBuyaIlhVxZg0iKjUdcxEweIpbKOU5IE4vZ5ZLXjRvFYb91K2zahL5uPb2VW9gXauHQYY3+V/07WV8V9PaS+NmvGZmy8fQRN/GBCTZ+9928YNvnMWfTjK97IZ/2fwOAV5w+QjoYpzI9jn1yiIqhA/hGDxLoOIu4oxJneAy/qlXimD+Vd3NlH24iTFLD39reRjIj6UsxlxEmFK5o5rHDcp1bm8aYalhHNOcEXeedZzyB2ZRj+5FahjM1uGvdRCJis/T1ieO7sxN2H3bSN+ZkMltJEgdVTHHuk98mnjQRjFowaTqjARumVJzVt34egDedtheAX3ENI4/25q8lSyxhQtchELESiZvxDh/k50hE94vWDGGz6IYl73Jh89q5fu31XMlvyOTM3Py3elyOLLGEuZD9wtCQUcugOGvFbJaHpKZG9rlhA4UiIuPjhiSPz0fSWckTe6wMD0ufHxoyMliWLzeyQ46m3grImKPKQRRHfei6LCbGxuT0NK0kgWxRKF+9qrtSjPPOk+8OHcrXZ3KZSVo94PEQi2tEJpNks5BMm3DNVczeZpN+FIN9MkzxqU8J7zg5Kf7hG24ofcRVJh2ULrRmQskMTEzI7VF1bVatKuF0AHhNfhjYseMklte1WktrgRxNzZXTT5dxZ1ZI3fMIKs3Sap09Z7S2ivwiGJ1KkSvqAZgZBFGcuTJzcfzUU/Jbi0W8I8ebXAFDW7WyUjrtTCmGl79c5jGXS7RJvvxl8fokEhLJUEYZZZRRRhllzEJ3tzFVKtWmpWauFMfjRSJiVy4VNhv88IeS+OrxwBvfKOuA1avFdk+nxaf8fJIGUz7/eHzu2J75ai2qzBWldmUylQpEFKOjQ4iOdBq+9S3Z1uMR822pmfALQak+q0BRkNg7k0nWXjabId01K2BOkRuKIEgmpdaMniiYmp/7nKgPKPMexB3i8ci+SxRX5kMsZiyelN9ppqTtHKivB6c9h8OUZKg/QySYo6HVinNVM/Vn5msA+nyy3p2clDXxgQPHX9spEDCkepuapGEGBk6MPJiuG+SK6iQqq72M4wqlYq1qC6nSymVpsMVRJlfKmIVCbROXi7XLxTnxVHcFSYePsY4XcAciLHnm2hBZLLh8NjypAH5ngmV14gDZf+8I0e17JOJ0+3bYuZO9u3MkEvLAtrXNJlfMPUe4it8AkM5I11y1SibwWWmVReRKxdghrOYc1Z4EgQBMHhzHkkmio1HXUUFNrcaZZ2lYrULUTE9DqF10r9zjPaDreA8JuRKuzUuCud2GSGZx6HkyydS689A1+e7ANZ8i2iRyLSYTfOziHbz3ov28/8oB0hXVHN76WgC0J58UR8rICIyOsuo3n2eDvgeA+2uvIpmW/d23/K0AdHCQFXf/gGjChNuRw2HXoaKCOvs0IHOJSdNL6hsrrVDL6AC2aEA84/G44b3Mw2QC71C+bonHU1oTIA+325AG6+3NcxguF93D0hYd5iNYMkl8fU/jaKySUVeF/Pf2igX68MPizCqWR5kL09MsbxTDoosVTHWcTbh62dzb2mzwmc/IaD80JJG/RSN9Kq0RSDgIJcRh11Ydwx6ZlAm4u5vldGPWcqSyZobCHiLmebzkDoc4NVetMoq7r10LGzZw2LOVrlE3XcFqDvdZOTBdz4HLPiSnd8vP6N05gf3he3jht66ibeARslYHnVd9nF+e+XX2DfiwmrO8qHEfTE9TlZukzhFCs9vwmqJooSC9GyUTpuGx38/fZnm0Pf5bXogUaH4svJaU2UkWMzG7UUMnlHHxVI9c55amMQ4c0OjsdzHRF6XFHeBVZwuD8IPf1tDWJpe5aZPwSevXSxMsW2GirR1Cabn/Z/AYlX27sXXtR0OnripFJG6i7c/fxzk1RKKyAfdVL+fi9RIq9qXeq7FGArgcWXK6SFINjtsgk8Y8McJvuQKAizeNGPcZCsXAQ8s28zk+DsC+HhcWc46crmEe6MV24/fg/e8XC/qMM4yCQcGgWLwmk8E8qtVQba2R628yETJVsnOwgclJsQXDYTl0IiE+35YWo+CiSsVeKlThx2JZMJWxMTUlRn1zs4yJR5O5oohwt7s0DR+MRB0QaTCHA9J2L1mbk5juJDIeJ54yARrOmeSK0vhyuXjqKWnW6mqZF77yFXjlK2XTX/0K3vtew64tJo8WWhBNTck+YzFZ7ORyRr2VYk4bRL6wqkq2ffrphUmb5zQ8HmP1tdTMFTCc9M9nFJMrc1ntb3yjETqlafLsplLSpj5f6TxtMslzrWmlot3ptHhTlHRjS0shO+64w+eT8UYVrC/olRbhjDPEk6AE3e+805CePBGRfmWUUUYZZZRxEiMclmXfyEiJv3tJdmEiYSgIKZPhnnuWfuxHHxWbF+Ad7xDzo7lZzBa19h4aen6RKyouc2YxewVluhWTIMVlSlRb+P2G0tVMaBr8+79LOz7+uGQU+Xxy7FzumSdaqKSKWMxYq3R0GLXXa2sXIEFSKUMWzOWS/7NZXFocu12+Ou20UsXeVErO3e8Xk3RJAXOxmLF4euopaYhFZMFAjmHR01R6MiTi4PSa8bV4WdaWKyWKamqkUfv65FgHDhxf/bpAoFSqt6VFbp4qVnQ8s2Xicdm3eujHxsTGfqYsXBlzwumUcQ7kNmcyZWmwpaBMrpQxCxUV+aLCXicNVSk89jTRpJVDg07+0vI2pqjGrwVY0SCDmavaiVcP4R09TIdfHAmHDuaIHhlBP3RYQk0iEXY9IINvU1PBd2cgHIauLl7FbVg0YwbessXQwyyB3V4gV0yDAzRWJah2xEDPkekWuaukp5qmJhObN+YKxcfUJD9YtRHdZMaSimEPjhXVW1khByyeEVUxE4cDUimS3hr2/vOX2fPWrzB+yiXGdqkUdj3JFRdMUl0jej25jZuJrjtNDnrTTTLz3nMPVU/dx9p8cfXhoIt4UmbCnim50LUcoPXen+MY6sZlz2KzSDW5Wr9MiJNBK9EByYlWsmBVVcCUZKTYx/plshseFqfNDHkwDoo8FrW1RnsWweORZBEQbiaVgljGRv+4ONfrK4QMqTvyKFpTo2j6eL1ynMOHpXhaX58QLZ2d8v98mJ5mZYVcRBcr6Drraim8PR8qK+F//1e8sLt3w1e/WjAUInEzg+NyLR57Cp8Wwh4YkQiHwUFspGn0iuhuz4iDmNk7f403JSHV1iazS0MD/YlaBqOV9Id9HJ6uoXPYx94jTu7yXUlv2/mYMik2feNtnPXrD+JIBgm1rufxD/6S4XOu4Jb7hSF8yanj0NCEZ2U9lpXt1J3SDO3tmOpq8GanGTpVyMvq3fdhiYfnOTnQsmmaHr6VFyPiwXv7K8DhIIWNmCYe6mRSLv1Qn7TJ8uY0WjhEjT3MQE+GqKuGN186gdej09Wt8ec/G/tXyVqqrIG/3kbnaCUAG+slhGHZw7/E7czhc2eonjxI2/03AXDoio+Qtbt486tCmMlwJ5fSe8+RfJaETiRuIp4y4xjq5vf6K4jiockXYUN7xCjire6BxUKwfTMrzL00MUg6ayJy13au+c0VvPBrr6L6jz8T46qqCv7t3+R3qZQYXz6f9Mvi/t3UJI7PfIH70VwtT3ZVMKVXcvCg2LSrVxtRGm1tQmAWB8sfDQkCRjmIXE7ux8GDsiBZuVIewbVrJeD9aOHzGePoQtJgdjvgdpPMmInZKglPJInHNUDHbp1BrhQVs1eSYKtXS18Ih+EDHxB+s6JCruMd75Ah3uEw1gML2blKVQ+MoKbVqw1+t5hccbvhnHPk/bZtJ3HN7+ZmozLgiXDon8xQZJPNNvdi0uEQFg9kPFYETHG9FTCCIIpTy9SiLpWS96reinrYTtS9WL5cnqOaGkl/m8ursHIlfOIT8n7fPkNXRE3oZZRRRhlllFEGui6Z2PG4TJUq4HKp2Q3xuOFcVzUJH354ab8dH5cSaSDBRe3tYrerOEwlJ9XX9/wiV1SWeTY7d+aK2SykSHHmiipTUiwNX12dV0DYPbeCU3MzvOEN8v7b35b96roc/5nWXVHlObJZI850zRoxs8xmObfm5nmku5S+GRjBUakUTj1WWCbNdDQHArJG9PlKC94vCJW5ks1KKswvfmFkgywAkwlqK9P4vRnI5Wjwp3DZs9RVzWFHq9Sc3l5p1OOls6zYpHDYkOq12aRRp6bkhh88ePyChtQDFo9L5wsEpNOVs1dOCFwu6cfqmZyYKJMrS0GZXCljFhSv4K4wEze72bpC6q48ccDFnfELAbhM/z3uvv1YzTksPjeelkq8riyrGsRr1jVRQc5kIZa1y+AaifDUY/JEtks5ldIA3nAYenrwE+CMZvGguVw6HR1iRM1K19Q0mSEBhodp8oSwJKNUe1KsNOVn0Go/a5anMLnEa9fQIGOxzwcT6UpitSK14h46ZJArretlFCnO8QSDXAFIpZjYfCETm19cuk0kUhDgjNircTb7MddUkXvNVfL5k0+Kp/NrX5PTWy8z71jARjwmv+8flUZpq41jymW44P5P4XZkMJvBZslhrvZR5RRio393AHK5ghPY74hhCU6iTU5I5orfX2h7jhwpjVQ4dIhCo6j2LILHIwpKmiZGSDQqQcADE3niok7aovJgPq/a65XJe8MG8ZRWVRUIM3p6ZESez2k0Pc2GQ7dhJkMCJ7vrLiKWXESktL1d6q6YTPDXv8LNN8stiJvpG5NzrPEksEWmcORiQu5kMuRsdlqqZHLum/SgW6xLDngYG5NmHB6G3T0V1NdlWV0bYHlVgNa6JLtf+TEyFjuu2CS6ZqL34n/hiff/hHjdMvZ32XikpxFN03nNJREi7nqqOupwrl+O94JTpZNXVuKzJ8g2NDNevQZzJkXtE3+Z93xqnr4He3Cc811PALB3pJqs1U7CVkEsLvczHIY9eyCb1aj2ZbD6PfhscZqTR3C7NXqSjbjaanjTm2X7H/94gcWG08mBAXku2rZUArB8/534s2PYzDkuuf/jmHIZxjdfyOTGCwBoqU1xZet2AK5/9DR5POw5Ygm5v7a+Q/yMNwNwSUcvms1aOjDkPe05j49g60ZexL0AHN42TMPY0+Q0MxMbXkjmfz4Ft9xiVJ4PBg2drbkqKa5eDZWVdDvXsz/SykTCw5F+e6FuuNMphAHILlMp+VP1VkxHOXOqchAgj4TZLMfx+yVBakmp43NAEeFW6+xFnSoUuXt3PsjHbCZh9RBx1BBLmIj3juLUEsa1zCxmb7EUyJWODvFjR6SWI+efLxIJa9bImuDuuylZbMy3wMxkZLETCsn2u3fL58uWGdk9JfKPSPYKiOLgSVu70O8XrTaVVVWGAcXiziULpnD++TJvfuYzRr0VVY9GoVh/TxkxilxRGSRK4271ajnujKCC4wa3Wx7q+np5YJS+4Exs2CAPViwmC9BQqCwNVkYZZZTxPMdS5azKEIyOFmLksNuF3FDZ0kp+aiEU11w54wyZnpNJsSsXww9/KMdevVpUPU0m+X1Tk5ggyn4fHHx+3dNweP5i9gp2u7StUrFSfEA6bQRR+XzyudMpbTRXQOPrXiftOTEhGUIqmEuVzjtW5EvdFsra2WwSVzc5KX3IajUi82ehWBJARdTF4zj1aGE9Nxe54vOJebokciWVMtIBBgeFYNF1cTgsQVq93pfAZtWpdUbweXXa6xNz1wO1WMRvonS/80HPzxgqOzwcLvWb+f3y/8CA3IDjJQ8WjRqFfhob5WEcH5cBoqxXddzhcslzq2omDQ6WyZWloEyulDELyrfmckHc5GFrh3jKdnW62bZf8l8v53Zanv6T1NvQNLzLqnGuaWPtRvGM9QYqoKGBaN1y2dnUFHs75bvly2ekSuZy4rVOJMBs5qVnSZZFR4eGxbLABLV+vbxOTeGMTlBrD9FSm6QmJgy2o75Sjp0nRZQyi98Pcd1BqG4VAP5927BFpsiZzERb18kkNLPIucsFNhvxlJmRUTjY7+SpI276xuxGUGosJtuZTEQ0L542PyZ/Je4LThdrDqRwQShEprEFz6VSyW10ykq8ZwRGRhgalkfSe95mshY7rYPbWb5PaknYrTmSJhet1WLt9A+aYWKiwFn4spNYE2KN2FrqhOxwOsVZoyZUBRXC0dg4Z0iK221kd4JR3DsYFqe4qVnChhzd+w0LCgxdn+ZmuT9tbWLdBAJC6Mw0FvKWcc2Dt9GOCOL2TbgXzlxROOMMKXgM8KMfwf33E46Z6R2Ve1dblcWaiWNPRwqecr2ugdZKMSiGAq5CDbbFMD0tmbRTU/CTn8BXflTFjXe14Xbq+EwRqn0ZPCsbOfi6TzK19lyefO8P6b70PehmK7oO3/uDWG+XnDaJp8mHVldL5cpq6lb7jNAnmw1flQlrMsrB9a8CoGHH3NJgFT1Ps+KP18t1nr+GCmeKeNrKYMhD0uYtGMShkGQYA6zfoJHCjq/Whga0r3Oi2530JOq5/HIj0OQ735m7DcI5F/3j0rbL1roItmzAnEuzbvevaXrkt9QPP0XK6ubgqz5c8rvXvyqGnQSPxE9h5xNSdyWaJ1fCXePcjZCUF6/pM6JeFFTWmNPJZPtpBXLlb7ZL2XnRf/PTt9/Pnn/5JsFTLyz9XTBoFASfi1wxmZhs2EBv2M9Qwk9fvBa/XwLalXNfkSsrVhhkgdt9dPVWFBRxAOJLXb1aIqZOO+3o6qzMRHG5iZmERn29kCK6LpIGFgskqxoIaxXQ0kI8Y8UZGDKeXxVyZreD08nUVGmkl89nyHmBLExeLgp27N9v+Knj8fkXmIGAUffQajUS6FasKC1mX4zXvU72PT4u08S8mWbPdRwtI/ePBLvdIFfm85KccooMUmoRNzMIwmSSTlRMrgSD0mkjERnEjxyRz9eulYdmzlXoccKyZYYXaHx87oWf2Qxnny3v1QQTDh+fRW8ZZZRRRhnPOSSTopa9Y8dsUYEyZiOTEVtUqQ+1tBjTfTIpNuFCDj9VpqG4hqJakt9998LH7uuDv0kpWN71LrFtm5rk2Mvz7g2VRTMyIt8fT8WlvxdmFrOfGfSkYLcbpk0sNje5ohKM29rkexUQWgybzVjO33qrmG6hkJyH8t8fLVIpcStFIsa9X71azCxNM9Rb5y2DWEyuKBs1mcSZjaJpcs7F/U4loFRVyXpprmyfWVCLpWLdOpCTXAK54nMkcdnSNHnDuD3a3FkrCl6v2KPDw/JA7Nv3zKuTBwLSSTKZ2UHJra2l8mDPNA0JjHorIDZ8dbUwZen08cvGKaMAFcempA8HB8sc1lJQXu2XMScqKmTcytpdbFgh2oa7uqsYmrRjM2e4hL+y7MCfcFnT2Cw57DYdTYMtK6OYNJ1wzMLwpIVIwgJ+P7mpaQ4P5Ot1dMwYg6NRw5NZX8+7XjnEp98zyoc+JBPUTJ6jgKYmw9N55AhraiZZ2xymJZPfl98vk2HRDhob5dqsDjPjlVKxve5JyQ6I+lvJ+ark5PJOFyXjMxBws6/XTed0PWPjJqyWHPVVaYIRCwf6XYRCugz4bjdJzUFas4kk/MZWTJUVop1jMslEZjKR/pd/o0kfwqzlSGbMjOTqoKWF4ZCEhzSucPHUue8EYPXvv4o5HsZhy5FImWhtkjCO/n5gdJSxUbHkKsxRrMFJ8PmwN1XLNTQ1SfsGg9LGyjPZ0yOvLS1zeouVT3vtWvl/YMCYt/zeNFpNLdHqVjQ9Jxk5cyEelxCUJ5+UEXmu4maBABw4gK3/CMuR+zY4YSORMs3rRE2mihxil18OV0i9Dv73f9H376c/n7lSX5NBs1qwh8YL16s11NPiFy/06LStkNG6EGIxyf4IBuFPf5JEGYC7dvp5fKS5xLoaO/WlPP3ObxNccUrhs0f3VfBUbyVWc5a3XTpKIO0p+AZVOjmVlWCx4GtwQjTK4JZLyWlmfL27cY12F/ZlTsZY9bsvccq3/hnn5ABJXy3D51/FKc1irXYNuUhYPAWbMBw2bs+a9WbMXhfeRg9ta114qmy0n1FLJGpichL+8z+ly/zxj3NrEe/vlueoqTKGzxzh4JmSx71ixy2suONbADx09n8y7SxNw6hYUcs7PL8E4MY/1OG0Z0llTKQzcFfXKnKYObW6hyZfVJ7XmTUp8nVXhta/mOZLtwDwePYUDp3zJoL2etIZCMWKLH+Vj15ZKZ14Do99NgsHe+2Emtcy5l1J47pKWltL/d9qSFq2TB4hFVx/LOSKMlBaWmR/jY3iK36mgfOKO3a7517UqewVVXclgRPWrEGv8JGobMDps8mqY3zcMFjz5MoTkhBFe7s0pcsl11DMpa5bJ6+dnQYvs1AkYSAgh0mlZDzJ5eQZqKqan1yprpa67iBRhs9GYfvpaZE+fjaOVQbGwhUWXlAmEvJ8q3orM8kRpb9XXNQ+k5E+3tMjHc9uF6/IiZZnczhk/q2rk4FFre51XR6QiQmRaGiTDFr27JGOl06Xs1fKKKOMMp6nmJyUaSmRKKtALgU9PTJlDg0ZSr+w9Dp/yaRMv+m0kATNzSI5DvDYYws773/yE7FTzz1XzAuPR1wLK1eWKiCBHKO4HMTJjHjcCKZaiCRwOKQfK7OmmFxR7aoyOZxO8e2rezETZ50la5ZcTlTUk0nZ97GW7FASZLGYISm8erWYXtXVYnK2ti6wg2JZMKu1kKbjNEu2iSL3FAIB6RNe71GoEaiFWzJpBP+AUZxyEfJDS6fY1Bpkde00W1ZGFo8XamyUm9bTI4u54mMeCxTjmVdtKYGqhj41Jba4Wig+E0Qi0ilUlnpNjbTR5KTc5GdKFpVRAnVLVQWBkZFy5spSUCZXypgTilzB6aDWn6PKnSKny6h9akcEu9OEJzpGy9AOPE5jMKurTNFSK0/evl6PRKj7/UxGbAzmJaXWdOizitkXQqQbGqipM/Hql6dYsUImwnlRVNSe7m4sZp0GbRTLiNRcKaS8FJErdXWGmshgtUi02KLTAITrVslgnbfcQiFRk+rqglDSToUny8qWJBtrRljemKTRn2RNaxSHNUdXt4m+gIes003EUgnkJeGrzVIZfs0auCRfn+WlL8Ve6cKaTVBXKW01kKxlKuUmkhTHcmtdkh2b/pWQfxm28CRr//AV7JYMybRGa4NYJX3DFlKxNIFpuS8VyXGsHjtabQ1WS34C83rlRIaGDOsUjNd83Ym54HYbKjb79hmO56aaJA63mUBr/sudO2f/OBKBD30Ifv1r+OlPJTxocFCsqmJLaXoafvtbAJqrpS2GJu3oaMSTpcNTOqPx1GE3j+zz8cQhj0Gy/Nu/iVWWSrH2W+9mYkCcxA3VaaisxB41QvBNTQ0saxTrb3zKTDy+MLmSSkkN5HBYLvMXv5DPVTLEN/+2kVQkNa/BkM3C9/4gYU1XntlHpTdLLGunslLat/Ac5MNoLP4KPNYkZpeD7naR1qp/7A8AVB14mDO+eCUtD/4STdcZOf0yHvvQr0nbPZzaLI67zkEPyYxkhcRi0txKAa69HXzLqjA57TR3uFl/QS0VdU4aG2W7lSsN3duvfW22f+/AQdnvutYwpFJ0LnspMVc19tAElkSEUOt69m5+HaHo7BCnN517GC8h9k03sfOQPF+RmIn/C0nWykvW5/VSZ2augPRfqxVsduxb1lFflSKTNdE9LP02ljATLD5mSGQM55UEQ4iTWAwGhsx4qu2zsuPSaSOISGWueDyG0tjRQpEHdXXSzuvWHb9EhooK6UdqcVMMRa7s2iV9MZkEbDYSLavQvT6cLdXSRsGgECwWS2EVpB7r9eul+TVN2qCYXFm+XK4rGpX2UgutXM7gaoqhAvM1zchaWbfOsJPn83dfKGqUbNtm/O5EortbFmDHUyq4jAWg0mVhYXJFdR63u1QSTEHp7ykGNBg09CqUBp2a856N2jft7XJtDQ0y7x05IiRKZ6ewi4kEnHmmPHdjYzIQT00ZXpoyyiijjDKeVwiFKKw9lLlaxtyIRo2lYyZTKuGUL4NKLrcwuZJIGCUZ6uuNgPeODvntfffN/bsjR+BeSZbn8svFfm5uFptbrRnU9A5GRsbJVHdlPl93cTH7hcgVVdhd1Vos5iOKl2IqdqahQUy4+eJH3vteua/79onqQih07AkPoZCRfNIrwhg0NclaqLZW7uGCAW7qxxaLnHSeTbFbc5iS8RJyT9flPKuqZAmlnNGLQjVaLmcs1sFIaVsseyWZxGlK0lIZweaaI71o3z6JClU32mQSuzSdFj/Q0NCxpwbF4/JwhcNik+/cCfffX7pNdbUsHJVX/pk8HNmsHDMel3uh0of8fnn4yoFJxx2qmdV4VyZXloYyuVLGnKiokAnC4dCImT2cstKwAM/fHGJo/UUAtO+7E6/LIFe8riyrW8TKOdjvJBI3g83GE5Pt5HQNtyNDtT0ym1xRGQ1NTZgcNjaeYuXssxcpHF1MrvT1yeQUDhuDq7J4isgVi0UcnNXVMN6wqWR34dZ1BQ9iIiHEvtstvMj6jSZa20y0teRYUzXG2WsCnLU+TH1VmpXNCVqck0xnK+gc9DKZ8RUi1Ssr8+e5fj3813/BF74Al1+OaeVyHKtbaWkQK2RUa6BzXEQNa9wxLIkICRzsfuXHAKh95Pecf+sHsKaiNPhlZOsPeBjvl/c2c1Yi6+v92Kx6afRCcz67YmpKLIxIxAjJbm2dNzWouKj94cOGU7OhNovdY2G6MR+2rgozKExPSxrE3r2Gw+y222SCV9W8lfXV21uYjCvXyOg9GrCBrhOL5C3mYJD44BRPPJJg/GCAvr0hxkcy7DzoJRjJFz//f/+PzKZTsCRjjE/KsNZcnQCvF4ddLyHvVi2XY09NaQsqsGSz4o9TARc33iins24dfP3rUFmRYWDKxS93dcybJ/nnx/z0jjqpsCd5/YtGCGQr0MwmfL6irBWFmhpwOvH5wJ0Nsm/ta+SUH7+DtTd/gi3few+OwDBxfxNPvfM7HHjDZ8i4KyEW45QWCX87PGAvpFJPTsqtyeVk1x4P+Bqc+M7owH7WVio3NLNihUyaFRXyCL3+9cIFRqPw6U+XOpZVLeh17fJ8h+JWDp9yFQC6ycTBqz6B1wOh6Ox6Odkzz+WDfAWAn/+pFrMpx4GnU+zV12MjyXmnJeU+qvoLxVCDgNOJloizdZXcsN1dHqyWHJG4mXDMbCwSgkF5cK3WOS3cUEj8miMjYovNFbk0MCD33+2WsSIel9Nwu+dPj18IZrPwf+eeu0ik1DFApZ9r2my7tb1dAuPTabGxk8k8CZMyQ0MDzhVS64fm5pIaFLrTVXis16wxrtnrlWOo4CCz2Sh9tW8fJZFcMxe7KmVe2eFPPy2fr15tlMuYK3MF4C1vke8PH352Ij0TCRnGcrljj5or4yiw1MyVSMRIoSouZq+gMleKyRXVWffskdf2dkP39ERDhUZWVxseitpaWLUKNm2Sh2flSkPi9PBhmaczGWH3yiijjDLKeF5B1bKIxcSeKgdwzI/Dh40i9vX1pcuD4roXC5ErxcXsm5uNJe/558vrfNJgP/mJvL7whfIbVcS+o8NImrXZDFfD1JQsBU8GckXXxSR68EFZks90mIYlhm7eYvYKqi0TCUMWTJlwih9Q8XEgrw0N0lZzZfjU1cGbpQwnf/iDuHOSyWNTSg0G5V7kcoYSQVWV+OJttkXWYrpuXIw6+XzmioaOU0tgs8kmui7rymxW9l1TcxTrREWujI+XdhxFeCxGrhQ3+Ex9s2AQ/vu/4UtfMoKLwMiqVoVoFPN0tJieNjT3olH46Efhk5+crVDi8Rg+kmfimVcP+UzGr7ZW2iAQkAf9+aDL9xyBkl9UY1y5tM3SUCZXypgTShnL5YKYycspa2RQ09A5Z0OII2svA6B+99/wWIwQZa8rS0eLzJhHhpwk0ybSGY0nJ2UWa61NoE1NljrRwmFj5lMSGUtxfBSTKyMjhmSIcko0NRkh0UVoaJCP0ivXkjUZk1G4fSM4HGTNNrq7ZfJtb5csyg0b4LzzNbasS9FcGcWRjeKw5diyKkpHY5h6yyRrV6SwOUzEcg48HhmUCvJnfr94Ec86Syyzpiaca9tZtkEcx6NjJo4EhFxpqYoSGxNLIrr+DMbe8XF0i5WGffdw9W9fS7suE+Fg0MvooFjldd4Y2aparDYNm2VGxKvTKRbF8LC00bZtMhGp8Ip5yBVVOqWysjS6p7ZOw+GxkmhfKxfZ329InoyPw3/8h0RgVFVJAY9rrpHvfv1ruP12mSC7uuRc/u//5Fza2qhZI9c/MWUmfbiH6O4uOHiQ4L5Bdm1PMzWS5OCAk6lpM51PJZmaNvHkYQ8D43Zwuxn96Nc5cPobGENImlft/H9YtAwWf4VBuNXX09Ki4bDnyOWEUFC15IqRSIjzd3pauuatt8q2FRXwgQ9IF3vFRdLvb965lsHB2e0XT5q48U+StfLG0w/gqbIynfEUiMtZ5IrfD2YzvnoHjlSY/hUvJOn0YQ+O0/D4H9E1jf4XvoHH//s3BNaeY/wuGqWlIUttZYpMRqOrS5p0dNSQBFu5Uo5ZUQF17c6C87G1VQzB9nb5vq8PPvYxsYUOHJBSNiC2SoFcWZEkndVIxTL0n/86QuvPRnv3u9E6VlPhzhBPmUllSnOTE9XNvL35T9QwTv+km6eOeHh0l6yMLrXfg9uRNazRmeRKUd0VUim2rpBc7ycOe3A7ssSSZrI5TYjcbFasXJ9P9jNDA1bXZSERi0lXVWPBTCgubtkyw5471norCpp2bMTMYqiokMfQ6Zx7gakWkDt3GuuFWEyu21yfd/R6vRLR7/WCxcLQuJXRUTnf5cuN81ZZO8ULHSUNpuquqFqDM88lEJBxJBKR7To75XNVbwXmJ1dUtg8IYXgioz2L2ygSmVsfuozjDLtdOpnJNP9iUomA5/vonJEXTqfsQ1VfDAYNz5WKClyxwtj22UBLi9gzikhpa5OBpaMDTj1VzkPVXdm92/AklCPwyiijjDKeV1C2kSJWoDQbuAwDU1NiNw4Oit945ppJ2Y1KinY+FJezaG2VpYHJJEmjmiYkw0wJ2M5OWSqbTPDSl5YWsS82PVRZNU0TU0PVXXmuIxKRdezYmLTN9u2y7lHm0lKK2YOR1a4IrsXIlfZ2WXPa7cy5bga48krZLhyG3/zGUH06Gqi1RjQq6/h4XI7p90s/qqmZf70BGPX/Mhnj5JXNmEziJF6StRMIGMvUJRWyV1BRZzMJDkWuLObJTiblBOZaYP70p8YgMzMItrpaHoSRETl5paF2NAgE5PyzWfjLX4xs69/8pnQ7q1XaUcmfHSuiUWMfxX4rp1MWp2Nj8l1Za/G4wuWScQ9kPayCNMuYH2VypYw5YTYbskVxs5uzN4TwuxO8aM0Qfm+GnoaziLlrsMZC+PY/Wvid055jTZvMyD0jMvhFE2b2jEg1pGVVIbRQEAv5GVzXZVBXs+zy5fK6FHJFaaeDYdGMjck+LRaxeFRIdBFUKYaqVg+BSvl9zmwhumwDusdLb6/MA8uXy3i9aZPsylLllQHdZCqxnpocU5zROkJds5VV6200t2hUVIgjtkT6p71dhF63boXTT8fVVsOy5XJuo6NGe7U3pomEslhzSWxWMF36MgKfuY5kRQ01U4d47S1XYDFlSWbM7BuXyPwafw4sFixmHbttjlGvsVEmwPFx8ZqDTK6SnjRn8yoDUkmzqTm6rsGMw23GXuk0HFW7dokz6P3v///svXd8ZFd5Pv5Mr5rRjHpb7a62eIvbutsY04wNAfIlBhICBAL8IPQklJgSWoIDhNAMgUBoCd0GEkoAAzbFxt1e765319u0RVr1GU3vc39/PHp17oymSqP1lvt8PvqMNLpz595zzj3nPe/zvs9LI6GrC/j0p2nB3HgjcMMN7JdvfAP4xS+Yinr4MPDjhYLtN96IdT20jmejNmTsPiS712K6ezses1+GcN9WHLScB1NPDzZd6ILLlsOhAwVMhe04NO7CvmNuRDIu/PKK9/MaMYWLHv82Lvz3v6FVVihwLPT0wOW1YLCfbSQGtziLNY1ZCw8+yKY6coSRPffcw2F08820g9rbgadeZ8aW4QRyBQs+89ORJYvN7b/rwlzUhl5/Cn960TGkizak4EJgoazPEt+e2Qx0dMDf5waKRbiseezfzsyQRO8IHnnbN3D4he9AwaF7NhbSZE1eD3YsEKAHDvAaMxml2LZxoypRUK6Udd557Ot16/i5XI7BLgDw3e+y6ObEBB9Tm1XDhqEMEpoHyOVg6/Bh9r2fAm66CR3+PHzuAgBNZa+IJA+A9I6n4D24BQDw43s6cc/4WgDACwceKDVgy8kVYLHuCgBcPEjy9MCYGyYTkEyzPk80YWFHahpvtrNzybN/4gQN9hMneLolBNfCGPjDH/j7unUc91ZraUD86QQhcj2eyhFzIg328MOqwGNJ4I/XS8dvIMDfOzsXx822bZwi9F3jcJSSG3pyRb/ZLd9ghkIqimxsjF3e1cXrdjprF80EOA8D9D/r6z62GrKnyuU45kX62MAqQhhOq7X6ZjKdZkd4vZyAK4lL67NDAA7UXI6vYmNIqtWpyFwB+ADt2MEBLOlr27fTy+Pz8bmTh/TAAT6c4lWqpK1nwIABAwbOSMRitC8kYF3igQwsxfw8zYFYjMRGuZSuqNhK1kQ16DNX1q5VMZcuF7fjwNI6k1/7Gl+f+cxFs7jE5aC/BrtdFXw+cWJlmSszMwzsW46vuxmIXTs2Rtv95EkGEt53H+1rKWZvs9Uo+A7VlrKvkBjXQkGRKyIkIIm8LhedtfF45fu02RijCQB//CPw6KPNkyuxGPcaiYSK/VyzhiaX09mAgoDYodms2piInZrJwKUlF/c7UtY2EOC1S2xPXRQKarMuA1RsV2mYRjJX9PtnwfHjyr8CsIikHiYTH6pIZHnZK5rGDhb5j1//Wv3vzjtLM6/1WekrJVdEB7Dcb9XdzbaMRFZ3g3gOwu1Wz4sk1tcbluc6DHLFQFUs1l2x2uD02vCDd96Pf3zWvUChgGTOhhPbbgQA2H//65LPXbyBYThzUTuiCQviKQv2HWOIwIh/FgF3Rq12Mqnn81y4+vvre9kEZrNylITD/JFoz2CQ56tCHPT1Ae1ddoQ6NgAAEh3D0FweTKZ8iEbJg3i99IFYROVI0nnKQ8QjETi9Vly4NYeNO9rQ28tDK0YvBAKLjiGXi4GsgMit01k0PFRAMu+AJ0fLxOfOw37hVjz899/CVO+FcGfC2FCkRtfDkxSg7QzQ+2e3aXAUEozS1RcPs9tpHc7MqMr0coGVnNlg01ksynkq6B2ywWkvwuPTaQL94hfAW9/K9u/vBz77WTayFE179rMZJlQoAJ/5DMNkfvxjHu92A894BtYv3FM8Y0fY1Yc5zxrsLWzGrHcYh/Nr4Fzfj4dnh/Gj+3qxfh3QjRmcnLbi6KQDJ2ftmJ6349gU+zsYAHIOL9oO7yQjIp2+IBM2NMy2npjgJcXj7NJHH2VG6+QkOajRURWE8cpXsslMJgYjt/c68P+eMgObpYgHRzvxu8eU5z0cs+I7d9Jz/9qr98Le5sB83Aazx8nskQpOfXZkJxx+J5wuE7zFGH532dvx0Fv/Cw+9/TuIDZ+/9HgJ13K7cfFFjBo5eJD2i75W3YYNJAb8/qWZGlYrnegeD+8rFAIuuYQawwCV7O65h7+PbADsVg0JeGAvpmG3AX4PidIOXw4WC+B1FVl3RaJxjh0DslnMXHQ93oAvYAjHMROxYy7rQydmcNl50UVd24LJgqMnLNi5c6HW0YLE2WLok9WKHsc8+jszKBZNGJ1wLtbniSatNKycTlXorqypjh7lI5BK0Vio5J/91reoVGc2A9dfT3tOIpxOR3LFZOJ84/GURo0JNm+mvZ5Oc2xLlF8JuWezKami3t5FcuWSSzg1S/KQfFelovajoypwqXyzWyxyoxyLcbwJv3v++cpOrhlFBuDpT+frnj2rq5ikL8gZiRjSYAD7bVWlS2RSsturW+1Sb8XtrlxvBVACwTLBJhIc7Hv2KJ2/gYGFtK2l8oWrBvG+VMqWaW/nxCtZu6OjfFgKBSN7xYABAwbOIsRiyqcK0E4yMlcqQ5ZvoLpEuNS9EKKqEtJpte2VZdbl4vvPZNnHEmmwxx/nFtVsBl76UiWN5fNVdk1I9grAGA6pUb4cPPEE7VvJ7F4tzM2pcef30yWzbx9NjsOHae/VK2YvkD6Q5OJslv2maUptWALUzGb6Pfx+VQ62UltddBHwrGfxf9/9roqTaRQi01UuCSeqsnVrZwoJkM8rH4nNtsjmuYvxxawdiekNBJSPoCHI4E6nlXqLSA3IDdS66WJRBTCWD8wvf5mfFyfT448vDVwKBHhvUuuvGZY3keD3xuN0EORydJht3842+5//UccKuZLNroxckWL2wNKB6fWyc0VebbmFegwsgctFHs6yIA4yO2tIg9WDQa4YqIo2XaJGwupfjPTMxtLIF8yYuojkCu65pyQnd7Ari54An7y9x9wIRa04slB8emPHHDz5iMrB1UuC9fY2X2i2v5+rpLZQV0OcEZLyWIVckYkisvFSAEB47cWYj1swFXWjr48L/9atZfO3x8PGcLuV51DENn2spTGwxYcrrwQuu0xpFFaD260MvXhcGX9DvTkk7e3w5CJwmTOw2zS47AVk/d341cu/jie2/Rk2g5bX3iO8vy0Td+Km/3kFnv4v12Pj//d04HWvA/7mbxj+IVaakCmihymV5apYAlIDQYraS7O6/HbYnWa42+0qc2XXLs64w8MkVqSAr8lE7+vatbRSt2zhIvzBDwJf/So/e/nlwPQ0Oh0J+Fw0JMa0QRTWbcCkbQjHU90IDPux+4gXX/mmA//5s348MrcG/e1JrHVMIJqw4uC4C5msCcen2R5tPS788Q3/jWzXgFoFursBrxfOgAtrhjn1iaN9YoLF86anSU6Mj3Po/Nd/8eOXXw4897ns6qEh2jG+dgu6u834f5czIvrzPxpAIs3z/tcdPUhlLNg0mMDT140CTifCWQ/8AQvM5hrkSkcHYDajvdsOT34eRVgw2XshNGuV0KFEYqHYux07LqdxNTbGpn/sMQ7P7m5yjbVIHa+XNlgwyNOFQsAb38juDYeBL36Rx23ZwjCleNEFjykJFIvwebij8boKsFuLaHPlEU9ZUAzP82RmMxCPIx3sR3bNRnwAH1r83r/Ad5EdXA/k88ibbDgy3YZolMMnl+MjvW8fMJP0cOPkcgGpFC5eqLuy95gHJpOGRNrC+juRCB/eCjUZJKNnclJpJ5fjrruUFNpb36rqz3i9irM5HeFX0/OSCD6TSdnre/ZwDBeLVaZZiwWFAklGgBscTWM3Op38nra2Uhu5q4s8VrHITZnNtjRzRfYJsRg/L0FUQsw0Qq78yZ/w9fjx1Y3q0++pRD/7XJYGO3mSWU8PPriKNdblwbLZqm8m43G1BleqtwKonXwwqDabc3Oq3oqETZ6qrJVGIIztJZfwdfduNnQ4zMnKyL83YMCAgbMCsViphFUiYWSuVINkT1gsVWMAS+r8VZMGi0SUDTc4qMyEdJr1VKxW7jVEDli2ps95jorjcLuXqAwvwm5X2+uJCZWZ1CwkCSEUKpWNazVyOY7DaJTtt2YNbXGfj3vffftUTZRGTCV9H4iSllx7IKBKaUofdnXR7JFysNWCpf78z/m6axfboxl/uSRkAAyqA/h9Lhf3c3UJECmmUk5cLAwclykNU5G1blMpjg2bbRmSYACDaU6e5O+yWZOIplpebH0kmD5zZedOpWn3vvexE3I5pe0tkErl8/OVpclqIRzmpm56mhtnAHjZy4CXvIS//+QnqgOk41uRuSITQqV0qu5uFSlrZK+0DG43HwGZC6emjKL29WCQKwaqwufT113xLuZ1pub5VKVHtiLXPcBJ+d57Fz/HuitcNPYfc2N0wolQzAYTNGwcSMKdmlOphPpi9n19XKWbcXw4naVF7WWBkoplVcgVu52HzD7/1fjJsz+LB658C45HA2jvsKCnh07lJcGxErbtdisrKJnkq9+/WDPDZqvvKASUPqekE4vEZtdaDzS3Bx5HHv4crQ6LBXDai7C5bLjj6f+C7k3tAICsxgXm/JO/xND4fXBGFrQmpebErl0kWT76UX5Bd7dicfr7qzupFuDxMPJdfF/9/QtN6naTXFmzRv1z40ZmpXR00Dk0O6ucXD09DFN/xzvYuBJGD1AmxeWCc/Mw+roYHj0W9eHYMfqWenvZzF/4grqur/5yAJq/He35WWzsjULTTDgw5saJaV5Llz+H7MBaTH7kK5RiA8geeL1wdbdhcJBvTU9zrRaC5YknuP6PjADf/CYNzZ4e4J3v5P+DQTat6Pa62u24/tJ59PvimI3a8fWf92Jsxo6f/JFZE3/z7FGYTUDK7EHG4kYgUDl7ZBEWCxAIwN/jhMuchTmXQSJdI8I6meR4NJnQNezB0BDtwd27lYNcLwlWob77Inp6Fr8e4TC77f3vZ3+LU3XLFqDgdCOlueCx5+CxpGGz0vlnMjF7xefJo5gvIh7K8nmRXQyA6YuuxyvxDWy1HYQFebwGX0GiZz2yBTMOzfiR1uwYGWGfb97MZAq3GxifsuLxiSBOxANIJQqLdVd2HvLC4ywikTYjHclwwff7F0kqgcjKnjjB+6pEfO7dy8cEAG66iZk7EoHm8dR9VJ5U+HwcjzZb5Q3ZU5/KV5EbEI64Eg4d4vTsdivuVKQPgkEVQVhJGmzvXtXdOkU4hEL8XXSP9+7l+xs3KlmBetO+Prh/z57VU0zS76kAtpcoC56LmJtTZNqqRdiazYskcUVyJZ+nreD18phaC6zLxUlLQhOnplQKn6xXpxO5Itdz9dX8+5FH+F4oxEYXbQ0DBgwYMHBGIxqlXWkycTmTOhWGAmQpxA+bTtfOnpCsCaAyoZHP02esaWq/3d6u9jUOB9U6AWav7NzJJdhqBV7+cp5TJK1qkSuyp5ByD8shRuQ+RLJrtYJ6wmG2hwQ7AbyHNWu473K5VD3SuhkeKK21CKiEBkBJZEnmimDDBtUfk5NLM6OLRfobJNDut79tLoM8GlXXIPG7Q0Ps92pZUCUQHw9Q6sh3Okmu2AtAJrN4T4EAzdJqY6Qikkne3OgoO6SvT2WaSGGmWpkrlciVYlFFQz7vefSPiQ+kXBpMLtxmo52sT2eqh3CYg/z3v+fAHRlRsrf9/eyAO+7gsRaLqqe4XK98LqdSoqpNCOL3mpmhv8tgAFoC2S6J/2hy0pAFqweDXDFQFcJWut1AQnMtpjKk5rOwmAqw203IXvsMHqzLqW1zF7Bhoaj9oZMuHF7IWun05+Du8sKTWfC0TU5y9RPHh1SXbsbxUV7UXtjqOpkrANcxV68fJ7c9G2PZHjgCbgwN0clcVY9TyBWAC18kogrUCEvSICRFVhz9cjvO3gDMNgtcHW74UlOLnm23owCnvYiCZkbbhSMl5ypsPR+/vv5jeORt30D4mz9jSuZ//zd1jQAWG3vFK4Cf/1xlDUkYRw1IpqU08WIBcI8H7qCTA+Q1r+Ei/slPqijc+XlVVKGri6kfQ0NcgD/2MeUlPe88pgiNjMDRF8TAeloqY2M8xfAwv/+DH+TpLruMXbrvmAf3nVwDmM1wJeawaTAJvzePuSgjJLr8WdisgC3YBvzbv/E7n/IUwG6Hs7d9sX9nZtiFBw6QPOnq4iX93/+x7obNBnzgA7wWq5VN1tdHp7PNBvi7HUhrDrz1up0AgB/+oQu3fGsYhaIJV2yJ4OL+KcBiQSjjhtXrqC4Xp0dXF/xddpisFriLMSRSlcmVfDKLmYgNKVsbO8piwY4d/N+ePaqY/YYN3Ey0t1eP/gJ4f52dtLUky2B4GHjLW9QxW7cCSY0Tg8eeg99caogFfXm4HBrshRSiKbuqlbKwc5y58FmwooA/5K7AHmzHVu8JRC1BHJxpR9Fiw8YNGgIBBnFLWYK1aylb1j1oR7ToxRPTAWzwMEPt0LgLWpGZK0gkEC14OGB0kmC5HKeYUIj3NDCwVBFocpIBPtksa0u/4Q18P5FQxeJPR0kwgWyA3O7Km7rzz+cxiQRt4nXrqqsiSd1DyVoBFLkSCPBzXm/1ovb6KDbZ7ErbA5yiczmeKxBQZbEaIaSl7sqePYojbjXK91Tz85yCz9Us83RayfOtaqFWh4MNLuyWHmNjC+xtR/111uUqLZA0Pa10IYaGTj9yBeDkvHUrH4hsltebTCrW34ABAwYMnNHI5ZRkqttNO0rsNSN7pRRiX6ZSNbfxcDiUelIl+0QvCdbfTztTV8JxiTSYZK0873kqsE/MhWqOc1E0BxhTWO1a6kEItkymNNum1ZCslFxuKXnicnF/cN55tOsbzVwBSrO+pf8kKE1iZwSyFxZS6sQJBjMePszgq127uC8Xm/+3v+V1N5LIm0rRjEom+Vyl07zG7u7GsuQXb6YauZLJwGHXYMmmFv04fn+TWSuAKrwkqTVbt7Jh9FnXtbzY0uB6cuXOOxkl6nYDr3oV35PCQuIU0MNs5oVLjb9GsleKRQ7Q6WlVoPRlL6ONbrEwOhEAbr9dRWZKVvpyWWSZKKUzK8FkYkfccQcfnnNdz7lFsNvZraJ8MjFhkCv1YJArBqpCr+WfL1qQdVMTJpkxw63RcrBcv0CuPPDAovfMaS/ivCFOhKMTLhw5yYlwqDsDeL3wOBbyXqem+BlZWIaGaodUV4JYAgA9pOKIkFmgho5PRwdg7/IjOOyFJejDuov8aG9XZUQqQiLxLRZFrkhaQJPkipRv0RM5a9YAybwd7g4XTO1++G3JxchVt7MIh50LVU+wNFU0dPWf4Mj5f4ro2gtIKEgbvOc9wL//O73T6TQLykth3zVralutUCosN95IQkHIDXu7G1bHQq721VcDb397aTjIzAytNoeD7JHNBmzaRK/5yAilw17xCuDv/o7e7uFhOHdsRd8Ap6RIhIad389sgqkpGq/vfz/wwhfyK776ywEU2wNANApLIYs13RnMRWmU9HawfZz2IvuqrW2xZo61w79IFsViyoDdtInfsXs38KUv8b03vYndGo+zuTweXr7JtJDF0uNCQTNjy5oknrZ5AkXNhH3HPDCbNLz++RNAOg3N4cR83Ir2XhfM5trZIwCAjg643YDN54K3GEUibVliUKYyZhw4ZMZ4tA0HZoKImelIvPhi/n/XLpXivmkTb73u90Ip87lcypn8nOcw+em1r2VXJTQ3LDYTXF7LUnKlLQcTNPgQIdEhFdCLRSCbRSbYj8jw+QgijPPwBCK9m3DwhAMWk4YNazIIdNlw8cW83s5OBtxcfjkJloERJ7ZuyMLlt0OLJzDUzoyl0SNF5DNFZCMpRG0LGSvBIJJJBgQ9/DCN7fFxGvrlJEk8Drz73XzMRkaAf/xHRTwkEouJQac1uSI8skRClo8Xi0UFxu/dWzu6Suqt7NhRurew2VTpG69XFWYFuCcASK7o9Zel/mA8zuNdLpXkeOWV/J9MQY1seK66iq+7dp0acqWjQwVLrdZGVza5pytSKfanbFZXDUKuAKVhjOEwGa7BQT6M5RVly+Fy8YGQBzafVzXeRkaatzFOBdrbeU2iwblzJx/aUIjeGsPzZsCAAQNnNCTARBz2bjeJgUzGmOLLkUiotqmXuQKoWoLlSKdLa254PPyx22kKpNO0K10umgm7d/N/L3sZPyOB8jZbdXeC3c5zA8pHvZzMlXSa91wocA+srznTKmiaKq9hNle3u5uRQZbjxG+ey6nxLAog5eQKQJ+Cw0FXRSSirknUITZsoBw3QCWGaLSx50SOSSSUS2jtWrWXazhzRTJDymXBFvazLpBcaW+nubYsciWdVj6wbdvYANJo9YpbSHH3QkHpMf/nf/J/L32pOo9kruzdW3mzIXrg09P8Tn3kXCWIzvMvfsF7GBpS8ggAnQYeDzdp993H9yRwSurENItEgp/NZGr7rX79a+Db36bfyyBXWga3WxGhBrlSHwa5YqAmFovaA0g6WCQ+pbngykVhtWhwbl7LFTKf50S7gB2bODlPhuzYf5wnWNubgsNpgiXoVzox8bjKoxWnyUoyV+RcMgvUmIRNJqC3z4Tg9gGsu6oPHp9lcW2rCvFKulwq7VAqhddKC6gCfVYIQAd+IgG4B4OwOG1wd3kW78ntKMBh0wBo6PKXLrg+d2FRnslhKxPG37IFuPVWeo1l9Zeq7HXIFTFCLrqIil5dXQuRH10LfeTxLLUiRSi3q4sDSB8a4/WSAbjyShb12LaNP+vXw+E0LUb/zM2xOb/3PTpjbTZmrxSLdLa7XMxa+MOxYVo14TDiKTPm43TODXRmVVuIpo3Xy/6zWBZTwwFe3ubN7ItQCPjwh/k9z3oWE38ko8XrZTSP2FmdnYCr3QG7HYhqXrzpmp1wO5gXfcPlIazrYeRxwtyGHOwIdNsWbZiasNmA9nb4e5zwWtIopHNIZ9WgjCUtOHQIsMQj2DKShdddxGjIh2SSTWsyKYm5vj52cz1JMIHUtwsGaezm8/zsn/+52mzEC054nEXAbocfpcUvLBag3ZWBrzCPrKMNqYxZjTHJXrno+sXjj7dtg9OUxcaeKILtGi6+xLxkI+V2Uz7qqht8sAe98K/xIx5cg4s2cNztO8SCfPGkCWFTEGOZLjz8mBUPPECCaWxMJcfJBkhQKLC/jx6lI/2WW9T0I+SAEAqnmz+2HDJXa1rlDZnYvn/4Q/XaGdksN5cAeVAxoETzWkhFn4/nkEd/0ybOm7OzfE/TVN0VUTWKxThdSKDTU5+qohLt9sqFQsshG62DB1evoF4mo+57QekR8/Ock1pdcyQSUVGCDz7Y2nO3ArIXEtmSVSdX9NrM8jo2xsk6EOBEUG+ddbmUviHAVKxiUVUbBU6/h1mIoEtZAw733cfrFZmGXbuMqscGDBgwcAZDX0ZBX/rLqLuyFLVqV+uhJ1cq2Sf6gubDw7RBpd0XFJ7gdKpSFwDwghdwf5fNqmL2tQKS9JkrK6mXIkEsgPJftzqoRwrOiyRYTX9Hg7BYaL/rEymkLuLClntR/lcPh4M+j+5u+v+3bKE9vGYN/SKDg3y/u5vPzd13N+Yvj0QUUSViJoODahytOHMFoDTYArkSDKos/IYhqeCplMoWkSg10VKTNKhq0EeCWa3AD39IhrCrC3jRi9Rxw8O0oTMZYP/+peeRQrAiRVsve2V+ntf+q1/x75e+tFQKweUCnv98/n7bbXzVS/4uR64rHlefqzYhaJq6piNHVF0YAyuG263mOEMWrD4McsVATfh8KuIgYW5DTrMi5/DCnY/Ca1+wAm64ga9f+ALw/e8DmoaR/jT8nhyKmgn37KHjYEN/Cl5XgQtHJsPJUsRQvV5O7lJprlHoyZVQSDHu/f2VV/MyDAxw0ggEKENUh2tQjhu3m6u3hEE0mbWiP53UNZDLzucBT6cLvh4XTN1di2FNbmeRt2Qrwm7V4HNzUfU487BZNFgtRZigwWqpkDdrMgHPeAYzV17+cuCv/ooWRp0btlp5iMul1jWHA3D7rCr0KpUq9TrOzChrVK95pkdPDwmWq69e9PrrDdTpacqDShDGW97Cbj56lM1/3XV8/2u/7EfBHwBiMYxPcDprc+cX28ZhyqpMHa930eHmdKpLE8NbHO2hEL/r7/6O/3M4FEmhr7sRDAJmiwn+ThsiOQ86XQm85y+O4Jk7Qnjd804uNlgo54Xd54DH00Rky4I0mNtRANIpJNK8t9mIFYdHzfDEp7BhOAv3QBBrt7rhbLPj8GE+pxs2qNM0KgkmkPp24pcsl/vXNCCZtsDjs8DRZoMzH1+SJtGBOXgdOZjdTkSLHlV8bqE9Zi581uKx8f5NGOkIozOQx0Ub4rD7qo9Hq92M4OUb4Lt4AwrBbmxZsEN3zfXD0eVD0upDHF4cmO/G2BizVkQ+ymplW+htZE1jAtWDD3I8fOQjKuENUMa5x3N6Z60I/H4+diZT5Y3dJZfw/7OzwM9+Vvkce/bQXu/ooD0uAVGAeg0E1DQo/l59AqFkTMlmVyQI8nmWxJqbY5uefz7P33CaPrjRktqMTzyxVKe5FchmleFotbJd5+d5ra2WBvv857kXOnKEWT+rcT8rgV6mIpNZvQKrAEozV6QDTpxQgQDd3aUPaDXIxkvWZNlMSr0Vi6XJXfApgEiVXXIJHwjJ1nG7OTgiERIs9SIKDRgwYMDAaQmptwLQ5pE6FMkkp/ZWB2+cyRByZf9+FfBTCVIsvVrwh55cWbNGbVsBLrUSiPSsZ6n3/vIv+bucr1Yxe4Df39XFaykU6Hws3xY3AqlVCKiaKK0mV6SeSzzeZH2QOhA54EKB1y7kij7utNIedHiYnMLmzQzivOoq4NpraQpdcAFNwmuu4bG/+U1j5Eo0qmxV2Y/09SlVhmqSyCWQjYDVShtUIFFmmQzcSCzK+zWdtZLJcIAcPaqKUUq9FbFdw+H6smDy/2QS+Na3+PtrX8sO0TT62A4cYBArULnuinyn1coNycxMbWM/HGbB+miUA/9Zz1p6zJ/9GRt6505+/0rJFX0aVzW/1b59yt8zOcnBaNQsbAlcLhWcOj1tkCv1YJArBmpCFl+3G0imTEi56Flz23JoK8zznzfdxFAPTSPB8pnPwOdIY9MQJ8J0livZpqEUPM6CKkobCqmi5vp6K/qFrB5EL1KfHeFyqRD8OmEZDgd9/Fdd1aADVV/UHlBhGboaD83A7VbrKaB8Rx4P4N/YrcRhZ2boaC8U4MzFkT4ZwpBvnp9xJ5CdjcBmKcJh12o3n8MBPO1pip2oU3NFrkW/li1K1vt8/Kc+VF7EYjs7eWC9dAndxZpMiiebmwP+6Z9oe1x/PfVvJyZ4zLp1jHr3eoFjU07ceXgtYLVi/Cg9k13+HGxWDbZ0DJaD+2lFDg9zPCz0k55ckYzcr3yFdofLBXzoQ7QbUika5F5vKQkGLGRptAO+bgdyFidSWQuu2TCF973iONq9BSCdRhEmRLJuBHodsFia4OA6O9HeVoC5zQN3IY54yoLxWTvGxkzoTJ/AusEses4L4srrHOi4aAgjIzRCDx9W8qoADdZGJcEEvb3KqVzuTJYNgyfogD9gYd+Xaah2FKZh9rjg9QJRdx/fXNjFaBowWhjGwZEbkHW0wXrpRehxxXD+2jhJwTpOz2AQcAccsPUEMXwFz31kwo2Crx1Rdy9OzLfh8fF2jI7SNu7vp005MrJ0qD/2GPDjH3NMvfe9S+UAxbZ0u88McsXnU/VhKtnFdrsKZvrUp1iGqRx6STCTSe0t5PMAp1aZBvXB9FJ35eBBPhuy2Q2HVbr/Aw/wmKuvVpxcM+SK2aw0mHfvVmpPrYTsqaQGowR8tVoarFAguSLYt08lXp4ukGldMlfS6VV0ADkcaiObzXIRiEYpOSDpa41AHvTyNXlggIPtdMtaEQgLLg/SvfdysXO5FMHy2GOrnD5kwIABAwZWA7EYp289vy918opFgzsXSFD/1BTw5S8DN98MfOIT1X2yIkUrMmJ6pNPK3zo0RFvTYlmob+pUEraXXQa89a3AP/+zCi5rpJi9fL/FohIOTp5cXo26dFplrjidjK+IxZZfpqIS9DGojRSrbxTSB3L94tP2etUeotL2TgL6+vtp/uiPEffOs5/Nvx95hDZ4rfbI5/k8JRLcswi5MjTEPm9IEkzT1EagXGrCZFpMeepzRxFsLzYc91MCGRxPPMHXzZvZUKGQslEl86Ja9oU+Euw73+FNb9yoyI5olOfIZpWjqRq5Io0dCvH4atkr+QVZ/x/8gH//xV+wjST6TFi1ri76mgDWXpGaKyJr0AxERiKVUgVAKkG/qc1mVTSfgRXD7VbkyszM6ilHnC0wyBUDNSFa/m4314KkPQCL3Qx7mwNt6QVPk8UC/O3fsgq0yQT87/+i/WPvxqZeledssxSxti9NckW0ZebnyWgDDCtYruOjvHBJMFi3mL0eJlOTqbF6csXvLw2FaRIuFw0ccbr39CiJHN/wQoh4dzcQi8F+9ACsxw7DEZlCJq1hqIezW1ewgEIsBVtoGnZzndBnEXKVBaquRlVp8T/hqxbJFQmVF0NhZkZJsgwMNEeUgR+xWrlOz82RE/m7v6MxFQrR8e/1stmf8xx+5ut39CHv78D4NC24Tl8WtvlpOKZP8ELPO48L/ZYtixay08lzA8xsuOce2iYA8K538XsmJ/nq8fCjlcZIZyfg7XLBYjMjkveU6jGl04hpbShoZgT6XejsbDBiZqGhvb1eWNrc8JiTmA8DM9MaBvLHMNhbwJoLg9h2mRu2C7di+/km+P0kf0wmZeSZTMpJ3gy54vGwmYJB3o7+lhIJns8ddMDfsWAx6w/IZuHKRuDudMMXsCChuVGwOQGnE4V0FkdP2jA9b8POl34M937kN+je4MfWzmmYnY2Nx2BQOfaFaAOAk/Z1KAyvR6xzHTq6Ldi8mbZqV1f1U/7613y98cZSSQD9vbrdvKwzgVzxepWGcrVN3V//NfCnf0pb9WMfUxnUAilmv2MHX/P5peSKw8Hv8Pn4PZJtIT7hvXvVRks2iyIJdvfdPEYkwSS5sJnpU65t1y6V8t9KSKa93K/XyzEQibRWGux736PtL9i9WxG9pwvSaRJu//M/akO+ar592VXbbPyy8fGFwlZ+PswNrFUAVPZr+aQnmSunM7nicin28I9/5MATYfLDh7lZ3rmz9ULsBgwYMGBg1aCvW6YP9NEn/z8Z0mC53OmnnJNMsj1271b21s9+RiXpSjafkCTA0qVxdlY5+oeGSmv8OZ1K9tRkYj3PSy5Rn5V6K0Btp7zYihLPIXU+mrWVZIzY7TQHolFe3+xsc+ephnye54xESlVYWwGRWJP9gLR5W5sy3ZbzfX19NP96enjuP/6xdgZ5JKJ88cIruN30sUhdyroQEiCfr2x3OhxAOg2nvYiLz0th69ZlyKvJIBfN6m3bePHHj6uTSSNWSxOQzJXpaeCnP+V7b3iD+nw0ykb3etVmec+e6inywaDKXpmerjyAIxHgzjvp62lrA57+dKaGPf44r310VOmSv/jFfL3zTjW55XLNkyvCnNYqZp/NAnfdxd/F73TwIDdt5UVIDTQNt1spt+TzqosNVIZBrhioC72WfyjthqvNCni98ObnFX1pMgEveQnwgQ8AdjssD9yHZ+/6xOI5+jqzsJgBj2vBUgoGubBI5kpf3/IdH3ppMGChUn3j5ErTaGvTMSC+ZUuCAep2//Zv+eP1qsXf5zcpLaoFVsM9GIRzXT+yHX3YuJELxpqBPBAMwpaJwz51orKlrGn05I2O8vp7exu+bin+J9E+8h58vgVPu1tVHwyFlEe7r6/p9tBLZzmdzCBxuehncziU8er3A1dcQQ7n5KwDv9w/jLEow3A6LWHYs3E4hrrpmOrtZaEWXWiJ00lfG8C083/5F/5+000Mtpia4v12ddEgrxa11NEBmDwe+Nx5RApeZTQsZPOECn5GSPmdTacNm7q74OtywOvMwxyPYB1G0d1RwKZLfRi5wAPTdhYIslqZPu33MzhlwwZmrzzjGSSrGpUE06OnRyVl6bNq43F2t9njht+nKU0Dwfw8YDIh2OeAb5CNFtW8yFldODTbjlhEw7reNLo7itiwJoeNA0mY8rmGn1ebjcPO72dTix9y717g/Itt2Hq+Bf399ROy8nlV++OZz6x8TCLB8Wg2N2iQP8kQ0snjKQ1oKj/mrW9lJpimAR/9KO1egASIcN2ywdTvLfR7jGBQtYlkrwi5cuAAj5XoskKBbTk9zefK6WSUYDrN4WMyNZ65AigVyscfb72POZdT9RaFVDKbOebm59kercgy1zTg05/m71IebN8+lSFyuiCVAr74Re5ZhHhbdXLFbucGzmrlBNbX1/waq6++KBDPyulKrgiDe9llHHRHj3Lhs1g4sdvtpQTL6TRQDBgwYMBAVYhvMZkstXfk91Tq1JMrExMMLPvDHyiP+8QT3CbGl6r9nlJIIIcoel5wAfd6R44Ar3/90qAgCeYpzxbRNPpXAe6DuruV31WvyFBtKU0maS7YbLW3J1arKlsBqDrkzcioFgq0OyVZwu+nLdpKabBQSMmNtTJrBWAfCKlRLCrnq7SfqGk1C6+X1yo1I2tJgxWLHCOJBMeDkFzr1qnE6Ib2GuLXymYrF4MUJglYvkGcTPIiJaJq61YOHL0OtTRiNXJFIsHuu483f9VVLLwKsDOiUZ7L6aQN7fPxumWjVw6LRdVeyWa5yTp0iJNCOMzrnZtT8mNPfSozXKJRfm7rVnaYdNDmzXx4CwXg5z9X99IsuSIPUi1y5d57ObD9fmr8A2zbbNZICWwBJNNPfEmtInzPVhjkioG60Be1z2YBd3cbLH4vXPbCUvryuuuoOdPejqtCSth/TTcXIin4vSjRJPm6AwPLrxpdXrikycyVpiGe9s5OXvMyJcEAftxq5eWPjNDAdruVHu+iXNqWLcDICNzDXXCs6QYGBvCsl/fjlluAl7ySDiPbYA8c+QQXQ33OXi5Hp8z0NHNv163jeTdtaugaxYnqcikDxW6H8jxLWpNECHR00DvfaKSxDg4H12KTCXjzm5ldEolwbRSnuQQzWyxK5ui/ftWLoxHmcnf6s7Cv6YOjp51ZK+efv4RdcDpVstPcHNfubdtouIsijWQR6ZOiKl1vW8AKX7sZKZMb2XiWbZDLoZDXEC14EOixw+4wLaaaN4zOTvjbivC227DRM4Ggv4jzr/Gh/zwfjQddqIzdznoUbW20Z179amYoNCsJJujpUQlI4bDaaAnhYG1zMQvN5Sr1cIfDgN+PjkAR9u4AXC5gLu3FwWk/8kULNnaEEPTlsX1dAgNdWRUh1MTz2tHBMWkyqSG8c2dz9/fII7QHA4FSGTVBNssfMexbUfTxVEAS6YDqNr/ZzGyw5zyH9vhHPgL87ndsw2KRpKOMGZHHks2jQFQXnU5FroiedTrNzaDYz7JRF+f8lVeqzbA+irBRPO1p/JyU7GqlTJVMm+VqAO3tSharFRtdcWaYTMDb3873jh/no7QaUmfLRTqtsmukiOEpyVwBOKA8nlLdzEbhcpWS+729nAyt1tOXXHE4eN09PcqeufdevgrBYrWWEizL0a42YMCAAQOnFLGY8oXqlyB98r9eZvVU4MQJbuGPH6ctdfgwCZaHHqKN8uij9MOePMl92KmqCZdIsK0k9vJP/5TyYBdfTJvklltKZcKqFbXPZKhMANDFoLcz3W4VNFiJXGm0mL3A4VCBgaEQz9kMuSL3Ipkrst+ORPjTChkeqX+Yy7WeXBFbPh5XSRlmM98XcmW56OtT0mAPP0y3USW7f2yM3z82xvaTLCeRBAMaDJSTxq6WueJ0cnDk8ysjV+bmlMG/aRMngGBQ6cuJxFalzpcosFxOebqvukr9Xzra7+dAKhToXAGqS4MB3GCbzWzkkyf5ED7xBD9z772sq3zsGNvgxhvpT9q6VRUw9vt5HzJZSPbKL36hUrOatVvjccU+VoucFEmwSy5RwdZjY0oGxcCKIFKWMm8YCUG1cYa4jAw8mdBr+QOAq8cHr0eDye+rHMa7dSvw+c9jTV8ebWAozoWWPXA7CqVOSp9PLR6i0dQKcqWjo36oyUrgcqkKdhJKvwK43bxUWYtKCmhbLLQot2wBLrsM7qsuhHMdWYZC0YSrrgLsQR8wMABbuwf2kSGe6NAhWnfxOBfGTIYpDT09fN22rXJERpXblf4vCfw1mRTzls2SvAkE2CbVCtnXgdNJx+8HPkA7QRJuRAps/XoGSAj5dNVV5Lamw3YcOEkLuGvICVuXH85Lty+NXtZ9T1+fkuny+/mdNhudiFYrh9HgYH2OqLMT8HU5YLJbEUnbFws0zKcc0Gw2tPc60NW1jKgdpxP9a+3wdTnQ3VnEjuvaEFznX0Ks6O9JCJZ169gVZvPyyBWbjfcfDNKeicV4W/n8giRU0AqTy6k0DaTuTioFBALw97lhcTvg8wHxggsWC7BxKIV2cxQXb4yjw78w2MVobJJcsVh4HYODbNdjx5orNi7Zw099amWpNpmWzpRi9gKfj00pykp65HIc2/v3M6LrHe9gFkixyPpG3/wmjxPZLUAVtC/fGLW3c2zp665YLOQyAQYMiS0cj/Mcf/wj/yflnlIpFQnT4FQEgJ+RLJk9e1pbr1C/p9JfU1sb73d+nvuYlRiVxSLwmc/w94sv5l7A5+N37t7NPjpdoFcGEMJs1cgVYfFsNk5aXi8HVDODQyB6mzKnrF2rfj9dyRWAD5bXqyLv5KEB2A4jIxyIhw6RYNm790m5TAMGDBg4nSFZqKcLpN4KUOof1Cf/iz/0VEDW8nCYP2NjJFJ27WK2x4kTFDoQwuXRRynreu+9tFOOH1+99o3Hac9L7OX559Pu/9d/BV75SrbZz35GBSRRNZB7KlNmXiRXJFZDIL/rkxD0aLSYvcBuV9vNUIjX0gy5IteQyyl7u71dyVytNFJc03hdsZiSD66Fffs4FhqFzcbzSr0TQO0TbLa65TRrorubpUT6+tg+d9+9NK43leK+Y2aGvw8NqQSN/n72s9Xa4DZTnP+Vaq4A6maaZdD0SCZpv2kaL1DYqPZ2lamtl9Iqhz4STDa/en9HJKI2ynK9W7fytVY0osXCjXU8zglg/34SK/v2sUH/4z943P/7f7RTBwYY0Xn11ewg0Y6STfRVV/GYWIys7XIzV2oVsw+FgPvv5++XX64iYmdneR1GmkVL4HYrd+epJNvPRBjkioG6kChxWYzdAQfaul2VizII+vuR/MS/4+meB2BGAa/Z+Ras/cUXS60xSYfs6FAV5BoosL4E5bJgPT2q6NhqYeNG3v/mzcvLddVBuBqA65rTWcbXuFyLxT+kBoTVWhrpYnI5YT1vAxxdPl6b2UwL+dAhnnDTJrbzRRc1TXxI3zudFZTbRIcI4Ezb1bVQdXx5DiyHQzn2JYMkk+Ha7PPRyJLACr+fQ+8Vryg9R/dGP6ybN8Dhr97/YmRt2MBz/eM/8tIzGa7TPT28llpZK4KODsDi88DrMyOSVsUmwnkvvB4N9nZP05JgAnt/Jy67pIhLntkOz0A7dxk1Crd4PDzER75tMcNgOejpYTc6HNx8iQ0pRNdi7aFikfc8P89ra2uDubcbweCChFeXHRvWZNHRacIlXcfhdelk68RobIJcEVtRSA/hVRvNXhHjHFD19vTIZkk+dHRwnKwgMe2UQ+YNSSYDaP8fP047fnqaz9fMDB/Xd76TtQ8LhaWSYMUi3xd/tx7y/LW1sT1lYyikh8gIZzLcH8zPkyS12ynnJzrfzRSz10MCsHbtohOgVai2pxJpMDEoV0Lo7N8P/N//8fcXvpDPk7TbY489OfIglaBppTVgZNO63L1kQ3A4OKj6+pQk5nLgcnEuks8PDqoN5nJsjFMFqbuijzDUhzPbbFy0TCZ6lSIRo8C9AQMGDOgwN0e5qwceOD2mR5FikgLpdju3Zg8+yP/r7bVTtfZLRno8Tlv//PO5tPT38xql/IMEre/fT3vg+HFKLx06pGSXWo14nLadptEUkAAxiwV41atIsgQC9P3+67+qWqDlwR+plLIPh4ZKt6Vut6r5V4lcSaWU7dtItoPDwbYD2LapFM/bKAGVTqs66mJ7+v20k+PxlWdMSzZQNKrqM1bDsWPAW94CvO1tFCJpJGvGZGJfSV8AKgFjUe1imbBaOUYlMOuuu5YmIxw8yDacnFRxr1LMvq+PZlXDew29rnI9cmU5E0w+z++Q4JitW+l4aG/nINeTK/l8bXJFHCVAKbkSjSrpdtlbb97M1927axdaCgQ4IWzbxklhcJDn+tnPyFb6fCxkf8klwKWXcuCLjro8MMJ+WSzUWwdWj1z5zW/4oK1fzwddfFyzs2yHeNzI8m4BJDEJ4PpwqgIBzkQY5IqBupAIZXHsOxxA2/CC57S8KIMO3m43/ubmAH669Z24GDvR/eP/BN73PhVSLSufSF85nU1U/NbB5eKi1NnJhUSKaawmudLVxbDjFnheJXNFfjeZqkfLi3Goj7ZZzFx1OmHfsZ0WzYYNPKinh9Gu3d1cBJcZhu/xVMhcAVSovJV1eOByNcZIVIG0g83GtXRigrfjcvGW5H9+P3+yWTrIxabw+4G2Ad5jvUgZpxN473s5JCVaf2qqNGulkaBprxdwBt3weYuIF90oJDPIxjKIa14EfEW4OtzLT24aGKDhMjhYl1gR+P20eTZsUJkEy4GQC8Eg7aRIRPksF8kVcVRKCJzotXV3L2YYrV1nQu+QDRdtycKObKmlns3yeGEVm7g2n4+bkW3b+J7ITtXDQw9xCuroUDVb9Bgb4+X099MobyRq7XSBcFReL7vkyBFuimMxPiPbtnFIAarpb76Z9XkAzvUikyZRKdU2RlJ3xWRaWnfliSf4Go3SppUs9Msv55ARO3e55IrUydm9u7WF9US2Q9OW7qna2zknZTLL3+gWCqxhkk5zbF19NceZBJQdOMBraCR7JZlsjVRENaTTpc4TIbolUW1V0NXFB87vVwU4lwOZly66iAvWeecp2a3TWeNPyKC1a1U0oxRFEkhmTzTKAXU66cgZMGDAwJOMEydo44XDDLpZ1YCABpBKKQUhqV/6rnfR9pqZKa2TdyrJlVSKS4jXS1vQ6+XSsnYtbRIhXERSS2pnj46qguGthkjy7t7NvyvZ6JdcAtx6K39/7DG2oZAkekIjnS4lV/S2pqgxuFyK2NBD+gpoPHNFT64kk0trwNSC1CaXcwFKumx+nj8rcWaGQqr+Yb37+dKXlO/9xz8G3vjGxoKYurq4BxDfhMTMrlQWDOD+Rez+Bx9Ukl8Ax2QopPZuvb0co7kcx3RHB/u54dqZItMgF18Os1lpG0tHNwMZFFIQaGSEg15IFXmVCaGSoS+bqEhENbhEcWazfLiFXJE9tsjjplLqu2tBiJLOTnagSG+9/vUsZF8+kCRbvL29VBpMNpXT00pvr9G0h2KR7SX1VioFM8t1XXUV+0U2uTMzbD9NM6TBWgDpWsAgV+rhNN5lGjidIAkKi7qVwx0qhXF+vuLiYrNqCARNSP35q7HvpR+CZrMzp/gNb2AIzOgoD+zpWVmhWYuFE//NN9MKEK31leShnkK4XHRgSganzVY9uFb0efXRNvriyw6fgwtZZ6fKo123jpGwy6iBIvD7lWO9hCiQPzweWlYeD5ovLqJQXtNY02gPdHeXfq+oxlgsXHdf9Sq+PzysbrOer97p5H0FAiqYQrJW7PbmEnw6h1zwewuA3YHoXA7zMQtMDjvau+3o6VvBNGux0FrdvLkpeRyPh5uJlRi0ZjPbIhBgP0QiPK+owS12gN2uKjgGg4vpMl1d9Glu3Qps3eGExbews9HvdEVgGGiaXHE4+NNs5opIgl133VKuKhymLTY4yPG+nHIPTzZEqW+h9A/WrFGSuO3t6vkQe91iAd7zHj5Db3+72oDo9xaVpg6RnfN4lpIrx4+X2rOSsS1FKVMpNY8tZ9q/4QZ+99TUymW69NAHrJU/bq2QBjt2DLj9dv7+nOeo+pGy9zh+nGNwerp2xOOxY8B//zfbdbUCssrJlWiU11YsrmIt9bVrycDt2LG8QAuBzCUvfSnwsY9xwnA6T++sFUARQF4v2wGgxnV5lGF7Owfg/LxBrhgwYMDAAnI52qqhELMrIpEnn2AR+yiV4vQ+NaXW0tHR0jp5p5JcqScRpSdchoa4pdy+ne8nEqtzrdJPEqCzZUtl3/LAgAqs+v3vuXRms0qhGGB7iizY4ODSLYYoMkjGiB6NFrMXOBzcq5rNXK6np0vvpx7S6VKVZIHfzzFcLK7MPzw3p+of1gr2e/RRqpFKbcb2dmaiv/71S+M8qkFUqsQRu1JZMDnX9u0ksHI57uMSCaWCPj+v9m4Wixo/69YpLqRhcqXWRkAgEa6i3NAMpChNeYCxbLDb2lSDzcxUz1wpFlWkl9QaBjhgZKMuaV9OJ88jbGWzhUo//3ne58aNwPOfX52h6+oqTW+Q+5P7lkmj0TaTaK5qxewPHeIAtVoZ8Cz6fCYT22h+ngPFIFdWDLvdIFcahUGuGGgIPp9anMxmwN1uV47UbLaqBeFz5+H35DF1+QuQ+cSt9CSNjZEEkWKt/f3L97IJXC6uohs2cAZwOE7vCFUd5LZlba2VXGIyqQySTEY5UMX5abeDi8wFF9CjeumldFitULqst5fNu3FjmVKLeLgDAQ6SZdZaEdhsyl8PcLg4naUldQByR5JRFYnQ2fqJTwAvf7n6bD1iQS/HJtHiIkk2NNSc1H9Hlxn2NidcHjMiSSvCKQd8ATMsPg+6uxs/z+kGIZrEKPV4lJN50bgSTQOJctHdcG/vgkqf36dSIPShXMskV0TLV+TPpP5evYyCbJZyEcBSSbB8nlOTZEVt3Li8cg9PNoQIHRkhJ9ffz3u58koSLRLEpN9MWizUsn7uc9V7Yjjpn0c9vF6+39bGTVuxyCWhp4fzUjTKfsnnSRpIjSSAdrLDUSo32Qy6u9WcsGfP0voyy4U+YK38nhcU7xYjCMfGSLLMzfEnFOKPbGDLkcsB3/seCQu3m20dDPKxufRSHjM9zXGcz1ffCyST3PT+zd9ws7sa0aOAyhzUQxwVqyq1IumbK4Gk2NrtqkNXamOcKkjdlSuv5IA7eZLeIz1sNv4vHObDJJvY0wWh0OqmVRkwYMBABczO0hYRh7SUp9q5s3V2QrOIRjlNFwq0d0Q2FeB6L3XyhLBYTuBGLMalohEZqnic03MkAjz+OPCjHwE//OHSn1/8YulaL3tQyZxtdaBFPK7qZwBcsvfuraw+Ljb8735XquYgx46N8TpNJtrA5WaFkCtA6X00W8weYB/a7Sq2cGampmtkCfTkij6Yye+nCZNILL90RD7PcRWNqi17JRSLwBe+wN9f8AL+fPnLrOWZSrE24yc/Wd8vLtfpW9j2mUwrz1wBuJd5+tP5+1130cwYHeW1jY8vyFAv+E+EXFmzRiU8LItcqRaUKpkrQPPMbTLJSSCdZsP4fKpWsGSJ6CuHVyNX9BrFeu1xiYSU+in6673wQv5dq6h9OR58kA+ZyQS89a1KJaYSuruXSoO5XKpjJC2/UXJFJu10unJwlD5rRXx/drtSlRHZjXC4thSagboQ1RiAc4lBrlTHmeF9NvCkQ+b5jg4lBYPubuVhq1JNek1PBu3ePAa7MnBesInFsC66iKuheEKFtV8puaL3qq+mJFiLIeuFJO/Uk5CSOhgSbSPkihSOA8A/pH9aAJOJHI1kW5ago4MOIZEhWyGk7orVyiCIShFHIs0vUj3ZLFPFJY1ar/taDVJMG1BR2d3dpVmljaK9HbD63PAHWHclVXQi0K6hrcd9RvjzqkGyIEQ7t4T8k3AkGcDCeFSSypMdisdTOXPFYmkqs8piUXye1aok437zm9qfEw3uri4V9SY4eZKvg4P8vwT8nGno7WUXSMbKFVcoiTt5juz2+oaR2KG1Uvqlrk6xqGxgyV45cYJtLLLCl1yipqNMRl3LcsgVQAVg7d6tCp+uFPUC1vTzzeHDJHZ27+bPrl38eeghRv7t3UtyQjbtx48Dt93G36+5htOmTJcDAyq469AhPiKVEhKKRX6XjPPdu0tLcrQS6bR6JgQTExwXp4OOfV24XKVehDOJXHG5eK3XX8/3vvOdpR63QEB5yU6z7JXnXTSGgCOJr77k50/2pRgwYOAcwuws189CgQFhDofKYHnssSeHYNEXs3e7VcA6AIyPaYvvJ5PLW19zOZJHBw4oQYhakKyZe+6hBNStt1b++djHGHRz552ly4/ejG919ko8TsInl+O2YXCQNmglF4PU4BA7KJ8vbT9xsHd3qz2MHpKZYrGUkjfyezNSUmIjy/YnFGqu3nkmU6qSLPB4aItKNtZy/MNSXycWq+1f+PWvqRbl8bDf5X7+7d9Y29RkAn7yE+BNb6ptc0tf+XxqW9cKIZHeXkWoPfgg+3d8nP76QqE0tlNqSA4MKMWPhs0/0WgTZqgSnE7FwjX7wCaT3CwAjBIzm2nPiYqLBM8AJFeqyYJVKmYvRXr8flXAVwrm6smVenVXBNks8NnP8vfrrmP6UKWHSdDWVlkaTBRlpqY4GBslV6RYkBTp1KNQUJshKR4qD6K0RyymmPbVikQ7R6AnV1YqU3i2wyBXDDQEl0vN+4slNTo7lZdTJq8yeF0FXLI5jg0DC9ZKeztTDF70Iv4tJ2xq5atygXpNqTOIXJEgWylX0Qi5oo+2EXKlFZEhy8KGDfSi7tjRkmwhCTzo6+N9Sgmdcog8v8mkJMSkLRox5JxOXq7Vqgp9LydrBeA1dAy64A+YocEMi8sOn7uAnvXL9ByfRujtVXaa3V6WWSXFmAAaXFKopRw2G49zu7lzkblCyJVlPK9S88NiUQb3N76h0vErQSTBnva00qEajdJGFUNcyJozERYLE9cuvJCbSv3eQD9F1gssz+V4LpOpOu8lWsYOh7JbhVzZt4+vEnQvG2GAQ0DG03Kzg665hq+7djVWo6QRCLlisVSeyqTM2P79ilTZs4c/jz/On0OHSCxJvZv77qN81913U3LBZGJWvcWiNuIuV6mkWjhcOWDt6FGeTxwa4+OtI5bKoZcFk2jM2VnuiZ5sDfuGoLcJJE3qTCFXAE5u11zDaz94EHj44dLj/H4O0kZ05E4lkkmExhKYRzvCUQsSYSODxYABA6sPCeSORJR/cmSEU+jhw3QI7dy5egEJlSCBJ6mUiiM6fEDVGxg/ykVeyBXJ+m0G8/P0K4ZCtIXqLQXhMK9p/37+PTLCjIDyn74+rvn/9E+UjNVnkxQKtAVaTa4kEkqxaN06FcBTKTlTHyQly6PUGM/nq9dbEch7+qwXgJ+XRPtGM1fE1JCgrPn5xuudi685l1u6jzeZaBKsRBosFFJBQdXuJ5MB/vM/+fvLXlaqUGGxAK9+Nck2kQm75Zbq3yeZK21tZaoaK4TdzmCxgQElDZZMMk63t7dUiUJIxt5eVVunIaXZ8qjVapA9q9RdaQbJpIo6W7OGtpzNxgdOokv15Eq1zJVK5Io8kH6/qt0itnChwChZYUf1KXTV8L3vqSL2f/7nHJC1yBWAD4FeP0p/faFQc0Xt9cXsyzNXHniAk1l7u9JWlgdRXwBJUuwMabAVwSBXGodBrhhoGFu2AE95ii6iW7xDgQAn7UYtVouFoQ+f+hTw939fKi+0XAjbbzar1MAzCF6vctzVM+YkO8NsVqnmrdA0XTbMZg6KFl2Ay8W1PxikHVDN+Srcnl6qB2hcI1cfxQ+QPFxO1oqgY40HLkcRdpcF/nbA7HGhu/8M1JUqQ08P21naZQm5Ijp1wsBWg8/HjhVR5FyOvy+TXOnoUKneF1/MgJp0Gvjc5yofn04zowAolQQrFGg7er0cc7IZPxsh80YjmSv5vHr2qm2MhEsTfr1QUMXZ9+2j4//wYX7n1VfzfZmz5DlfLp7zHL4eOVJZMkIwPc0aJY0EaYksWLU5x2KhvITUgeru5jzU2cm26OjgMTMzJFn27OEm7/hx4Lvf5TnOPx/YtIlTphA4UnMdUORKsVhKFM7P83+/+pV6r1y2q5XQZ89In87MNO4weNKhJ1dkfjkTyBV93RWzWWn1fec7pcdZLJyMw2EO2irZw6cc//3fiGkL4b5r1yFTOPPXQAMGDJz+CIWUJJjfr2o5j4xwCThyRGWwnCqCJZHgNSUSyt45ckRFvIxP8HePR9Uza5awiEToP5yeLvW3VkKxSFsiFlMZNP/f/we8//1Lf77+ddbis9sZGPLa1wJf/KIK2Gl13RVppz17+Pf69fSbtrerGt3lEFv+7rv5msnwuHRayZgODVVe+iWjoRK50kwxe0DZyOXkSipV3/YUiW+9SrIefj//l0wuTxosFGJ/15LGuu022nc9PcBNN1U+5rLLKBtmNpMbOHas+vcBKusGaF3wp14a7He/I4HmcpUqDRw5QpPI51OJwA2LeOTz7IxaGwFA2ZbNGsTFIgeFpFUNDioG0eNRkvay0RYyohzCxpXLgkWjSslFordE7gRoru7KxATwzW/y9xe8gBsffTpSNXR3s+28XnV9krmyHHIlnVYbVz1EEuyZz1QPmRyjL2pvMrFd5uZaV6DzHISeXDFqrtSGQa4YaApLMiQlncXlan5zf9FFnAAdDhUmslwIoy2s/xmUuQLQ+O/vpxOrXnSFvkaLbBCe1MyVFqO/nwZRb68KPqgESYRob+f6K/ZNo9yaDBHJlOnoUDUploPggAsmqwW+djMCHRYE+pxnRZ84HCpLKBAos6u8Xj67YkzVimjx+dRuRgSbgWWToU6nkilLpykFa7EAf/iDIlH0uP9+Htfbq7IEAEb65fPs+/Z2ZQOerRAeul7mSj6v+rqaLS3qg8Ggcmps3Mh+CIdZixtgUpsYZXNzihStFwBVC+edx+WnWORGr9L+ZnSU/zt8eKnEVTnyeZ4rl+Pep9rxTifvuaeHP729pT/r1pHo27iRz02hQKJFstevv57DXc9DlmeuFArcC0hGTj5PsioWY7CWYHq6cjHWVkBqv5jNSkngjCNXRGOjFTbGqYTUXQGAP/kTdsIjj6gNuSAQUB6c00Ua7N//HTHQI+VyabA7jW2GAQMGVh+zs8q01AcBWSx01DudtAUikaVT6WohFlPxRG73gtN/Qs2JE9PWxWATYHlF7SMRZqLI2lwrkzcapZ1z4gTby2ymvVIJdjvlob7+dQbHFAoMZH/Na5TNJfX2WgGRRTt4kH9v2cI+kwz1StkrkhH9+OPcB0oiQSpVSq5UCuQxm5UShBAcch3NFLMHaF5YLMquC4cVGVTPXhI/czVyRe5/fp72czPtHY+rDCOJ1yhHKAR8+9v8/bWvrW0m9fYyewQA7rhj6f8lgwpgm4tM90rL6AmCQdXnDzxAO31wsPT88myvX69qBDUcyCWdUS9zRaSsm03lDoc5KUjK+caN3BtLZofshSVdXGqFlDN05TVXentV2pvoZcskqA80aqbuyuc+x+8RZZJGN216abB4nNcp9yeZOI0Ua8rneVylYvaxmNro33BDqaae3a4kdsbHeT3RKI95sgpvnQWQeC7AqLlSD8aux8DKEAhwIgsG+bQ1IwhaKHCyk2IjK4FYplIQ5gwjV9xuFp+WLM56xwJcK2VNt1rPnmh7r5cRMlu21DfIurqUjJpE9DSaxSO2kd2uslZqkTn1YLUC7T0OdHRb4PVbzgpJMMGWLeRCl2zCJN1KKpnXkoXz+ZQ0j+ioAiuS8evoUJFlHR3AS17C3z/72aVRbnpJMH3knaSUO518Bs92CJlYLCo53EoQeSyg9marp4f/lyAlh4NkMQD89Kd8vfZavhaL3HgFg3xexN5eDvROgd27VQkvwbFj/BkfbyzCUoZjLgf867/SqfD448u7NtnQAWyT3/+e5+3vBy6/nG1ULruwdSvvKRLhXkKiDRMJ6kcnk9xMzsyofkml1DGtRKGgJDXa25lBCPC7JbvntK9Xrg+4aIWNcSoh4ZYWC6/7mc/k++XZK21tfJBCIW5aaz3QpwI7dwK7diEKLsqe9T1njV1iwICB0xcimRSJcNosj1QXgsVm414hHj819Y1jMa7nmsYp/ehRQNNM8HtysFmKyBdMmJ7m9blcKlCs0WuTEgtiA1STFBWEw/yf2DYbNyrHs2SOzMzQdjp0iNff1wd85COUgurvZ/t95Su8F329vZUiHmdATDLJZXv7dlX2ohFpsD17lFR2LKZsmEp1OwUeD9tdpLmWU8xeIHLWAPug0Xrn4meu5s83mejUlOzwZuJYpU5LIlFdcvwb36AtuXkz8Ixn1D/nDTfw9Y47lo5TkS8zm9nmrfZNmEzcww0Osr0+/GFK1n3jGyTlNE2RK8PDSoqvqWL2QH1yBeCNpdPNGcQzM4rU6OjgRUqAopwTUOSKFIXXP9ASBVYuCybFpvx+brL0qfGS+ZHJKAmt3burM3X33EPywmIB/uIv+DBYLI05qQDej14abDmZK/LgVCpmf9ddPM/69dTxzmTURtXnU+TKyZO8dpnwDWmwFUG6PxZrPPnoXIRBrhhYGUwmNYlqWnMTlyxGrSg0KxXQxRo6w8iVZiABuE6nirSR6JBzDZ2dKvs0Hi8NVG4EEhUVDHItXm7WimBoux9uvw0eD9C1wV//A2cIzGY+4kvax2ZjgY8NG2jk1ILHo5yFQq5Ih62AXJFyLtEoiy729DCI+xvfUMelUqxVAaiUck0rTSlfu3ap/XY2QmqdALUjT0RuUOoSVYPUSwwEaHDlcioLo1jkEvGUp/Bv0Qbv6OCzW2/vUg+XX87XXbuY8SE4cYKb9ImJxrMtZDkKh1U200c+0jhxEY1yzH3gA4zsfM5zgJe+FHjXu4Bf/ILHXHst55ryWjiAypwDeM3RqMpYmZ7mPUkNzI0bVQDZyZOtJ1dSKSU5pr+uZFIt8ad99oqeXDlTitkL9HVX4nEOJIAsnYTjAhxEgYCKbixnGE81br0VGoDYArni7m5b8TNuwIABA/UgU6BeEqwcFgvNUHFmn4raYdGo+h6XS5U52NCbQH+Ai6gEsevrrjQqWyaZMfIdIo1WbSkIhbikHDrEvzdv5pLyxBO0Lw4epE2RyfA8R46owJSrrgK+9jVmDReLPL5YbJ00WCLBBE2A9nhnJ20ln499mkpVduiJNNjDDys7bmpKtauUda2E8hqmyylmL3A4SskV8SHXI58kYKVY5Bh+4IGln/H7VVaOkEaNYGICuPNO2rSVyKKjR1UQ1BveoPzxmlbd737VVTzX7Czl4vSQIEfZL66GqsbAAPC3f8sscYBj9+tfB173OgbY3XMP39eTag33ZaOZKwAHiUwmjTywxWJpo61dS0O+q6tUP01f10QYRf1mTQZ5JKIGbHe3KjbldpeSIPqI3EyGGwi3u1QbUI+ZGeDWW/n7i1/Mzvb5VBRdI5B7kqg7fc2VTKZxckXTKmeuiCTYDTewvfRpX36/SmdKpZSjLBJZnq6egUWI/F6xqJKmDCyFQa4YWDn6+jihB4O0aBoNuZFFqVVRpW43LeczMHOlWYg0J6B0hc/FCNG2Nt63pCqKLdRo9wcCqib7SrJWBMEtPbjmr0Zw2V9tgcV5jniVxJCpx0yZTGxsj4eGYjKpjKFlPq+S/ezz0U50OoG3vY3/u+02tZG9917acv39tCsBlW08MMDLkkCXsx0iCwbUDrbK5di29fYXJhMJrfZ2/h4Ol8quXXCB2ifMzdHWdjpb87w9+9l83bdP3YvUeZmc5HJkMikd7lpyu2Lr6+uYTEyoPUYtpFIkUb7+dfq/jxzh9VitDEy75hpKFj/zmXyvUmkil0tlTh07xmuVwrOhEImpAwf4/wsuUJv4Y8da7yTSF7MPBvmIi79/fJzXdtqTKyIPcCZmrujrriQSZLeuvJINL8V7BIEAPTPx+JMrDRaLAT/8IdJwIg86CtxeY4thwMCZinS6dZJPq43ZWWXTldQFLIP4QzVt9RViCgWuk8mkSkQUX+b67jgGFsiVsTEaJkL8iCxoI5BshnRayZDqJUX1yOc5Tet9qt3dynYeGGA9OKkLt3EjbePRUeU7tttVUMvhw8uTMauGeFwFkGzezO+SunZtbap0QjlEJurgQbUEHj2qpG1rZaRLXRCrVQXhNFvMXiDXa7Eooi+Vqj/O0mllv/7gB8A//ANJgs9/XvWjz0ez4OTJxn3E09MM+LnlFuCjH2XQz+c/TwJLkly/9CU+4095ilKLymaZBfT445X3CHa7ClITH7dAgm/E5l8NcsXlou3/+c8DH/wg8MY30sZ2OtkuMkZ6enhsU9kzIrclF18LXq/aXNTS4hOEQjy3pI1t3lzKyAEc5Pp6KUKu6DtCfpfvlEznSEQpROjJFanjIuSKxaLS/nfu5ADYv5/MqTBUU1MczC98ISdLn6/xrBVgqTSYFEnNZlU6Vb1Ma6m3Iml/gokJ6hKazcCznqXaROR/pcaMZP9MTfHz0ajSyTOwLMj+HSitCWqgFEalSQMrh8fDVSyXowdoaqoxz1kmo4RKW+H4GB7mNfT21pYnOgugj7aR9f9czFwBaIOIhKm0QaOG1Lp1NAIlqaIVsAar5F4bUEX7AKUNCyybXJEAH9lMJhKMqnrqU+nk/uQn6RwXSbCnP11FsMmGx+3mlNEqTeDTHVKWSuzcStDXc2xkXunpoZPf51tKrjz1qXxNpdg/w8OqVtJKccUVHE6JBDf6IyPcVE9Pczz09qpox2KRr9WGmshByOa8u5sbtV/+ks6EanIJhQLwT//E6DmfD3j5y+kLHxzk91ss/N59+zhXeb2Vg79cLspb/PKXJFGe+1y2pd/Puc1qVXIHl17K/cnjjzPqtNVOonRa1Zzp6FByF/PzShrsVET9rhgitt0qG+NUQjJSAHbwX/4l0+/uuAP4679WG13ZSIbD3NBWivI7FfjGN4D5eUR9G4EF58a5kAlowMDZiOlp+q+cTq5/p/OWStO4VkcivM5aTnGRgEqnV38Ni8dVvRWZCyXgZ31XFMg5AXRj/EQRgGVxiWqGsIhE1H10dqpgDL+/tDA7oJaTsTH2r8lECeZNm/i+200bRgLVjx/n9Y+OkowZGaHtIk74w4dbW9Q+FlPF7HfsoN3h9/P1yBFe1/x8afFygH9v387PPvoo4xAku2NgoLZPWLYjTif7qVhsvpi9QGI5AgE1HhslVyQxQQJoUing9tuBH/6QNvRLXkK3yugo2/vIEeWvrgTJOvrVr9R7J0/ynLffzvvevp21KC0W+tQFojSlaWxvUazS44YbgB//mDUuEwnVjkKuiH2/WpLlQ0P8roEB2tkvfjHt0p07eU99fey/pjOQslnVGZJNMjlJw1cKwQskui8U4qCpl+0yM8OHUNLGLr+cnytng6XQKcCHolAozVwRckC827LRyWZVmpdebkB0ih0OVdT9wguZInX77QzY0avOmEzUKX7Tm1RGiNPZfJHM7m4O/rExTkYdHXwwJiYYISY+wGoQcgUotWml8OT27bwmKTwp1ymb1u5u9s34OKXQZmaUwk4rovvOQdhsHGLpNOc3TTt3fCfN4DQ2lwycUVi3Ti0IMzON6U/qnQCtcHx0dNDbNjy88nOd5hB/CqDW8nMxcwXgkLPbaTuI07hRoslkohFoSJecIvh8KpRJ05Tu1AqYwY4OPg9WqwryefObOR727mUGy/33832JtgLUxtNsrq5FfDZCT8pWm6Yl+dBma+zZcLtVrcNUin0yMMA+EHIlFFqoS9TeOrvW5VIBWHv2kFiZmeEmUorNi8wwsLQOjx6yp5L9ylVXAS97GX//1KcqR+loGuv73Hsvv+eWW7jJu+IK3r/FwmXuxAmOM7+/ctYKwDbcupW/HzrEdkomuUG2WFQ2y9AQzy3BbidPKidOq5BKKXKlq4vXPjDAv8+oovZ6JqvhiqanCYJBtVGMRrmx37aNg/T220uPDQSU5t6TEU6WywH/9V8AgNjlrA/jcmmntUPWgAED1TE5yXUznW6d83y1EIlwCopEaIfUmnf0ElCrTa5IGdJUisuPpqmMkQ1dEQz6mA4yfoKLt8PBtb5RwkLqVycSyokdCKjvLU9kDId53/v38+/+fq7vbW3MXLj8ckp+STb3li30T65bx+s/coS2xtattAsjETr79ZkXy0U2Sxtnfp5tsGMHr03KNIoPOpGoLGcr2SuPPcZxqy9mX8u94HKp+iAiCyZ7yWZjFCRwSXzQsRjPl8/XDpaXWi8mk7K7Xvc64JJLSJL89rfMzvjHf2QbTUzQ/qqVLDE2Rnv40CGe973vBW6+GbjxRo6RRELti17wgtLMfSFLJNGhErZs4WcyGeB3v1Pvl5Mrq5G5Iudva+P4FALL4aDt/da38j4BJf3dMDIZNcBk83PzzTzp979feqzIskomRq0OEUmw++/ngAgEuHmpVHjS4VDMmaSwl8uCyfkAbiqiUcUsS9aLHkKuaBrPJXVXpqbYabJZ+4d/YPrU5z7HTpYgSLnXZqCXBpufV5sWsVHrZZAIuSKpZYIHH+TrZZfxVdpGMr5lwMlm6+RJ3oMUHzKkwZYNm01xgbLuGlgKY+tjoDUQnZfubk7w9VIkJZzH4Sit/mugIejrk+mlOs9F+P2c8IUkkSFl4DSEsBiy21lBMXtBMKgKXsqGtKsLePWr+fsXv0gDYGiotCyMbCDM5ua1lc9kCAlrt1ffEIvBJMpKjaC3l31gsdDo+uxnga9+lXa+FOEMBvn/lRSyL4dEUT72GG3m8XH2v9jxTqfai9QiBET3WrTKBweBv/orOhvicRIn5YqX3/0uo/dMJuB971OFVQEeOz5OR0Y2u7RuZTlcLkow2+1cGqNR7mVMJjo3pGbQtm08Vu5vclJF4rYK6bRawqWvZPN9RpErQ0PcYG3YcObZGIGAYuRkYvvLv+TrT35SGg4bCCjx+ydDGuz++4GHHgIAzF9Fr4bbbSzCBgyciRCnfTisipyfzpid5RqbTNaWBANKJaBWWxZMnOsATd7ZWb5nNmtY449ioI3z+vgE50ohEpJJ3k+9NT2RoM2izxwIBFTGwdRUadBFOMzvl+yZkRF+LhCoHEBuNpNI6eqi7ex2k2ApFGgXAayJDaycgIvH1bnWrlWOc0Fnp+rbWtJgo6O0u4RcGRysHVchbe50KhvQ7V7enkCKp0umjL4kRq2xJuRUsajIiec9D/jEJ4Avf5kSWFYr2+ezn2X/hUIkTyqpsAtRJZJd27bRjrvgAvrOb7+dklovexm/R/ZKgKrfU49cMZlUYXu9NJhcvyR1mEyrF/g5NMTvcbmWxpSkUkoJq6m4Gn3mis1GQ/jYMf79hS/Q4Nejo0NpwOk1hcshBIwQAxdcwAaqRq44nerCywOWRbpMUtF6e0uZ5UqpWuXFhbZsAV75SuDP/gz4+MeB//kf4EMfUuybHJfN8rzt7bWzTCpBUocCAT4AQnbIxFSPcczllhazz+VUzRohV+Q84k8QUkw2SWNjvH+bjRNjOFxfksxARRjkSmMwyBUDrcPwMBeF3l4uJLXChKViXiBAK8bwhjcF8U1LYGu9otNnM0ReVNLHz9UMnjMCkmIkRmMLyBVJU5WCj2KDvvCFqr4KUCoJlsvxOMm4OJcirPXkSjXDSJ+50ii5Iry6qBkFAmrfILrgUqC0lXOVSO4++ihlszo7VZYFoO5XpJGrIZvlj2zS1qzhdb7vfRyijz1WGrj2m99Qrxpg9vy11/J3kSjZu5cbzb4+OiK6uxmoVm1+Ek32tWv59/79XFI3b2a7LviusX07x63sG4QMaqWjKBZTm2TJMpKi9iILlsk0Xl7tSYPTSe/Q4OCTfSXNw2Lhhtbn44OaSlHvZHiY3o+f/EQdKx6EcJj/W22voR6FAvCVr3Dgb96MUNtaAGdeopABAwaIZFKVcToTJCBFggmoT64Aqu5KvYyCRhCLUYrowQcZAPHHPwJ3381o/ulpTttSBlRIjTXdadiRxcBC5srEpHlxLRWZU31gejVEo6XOcIA2W1sbt+Ai3QKorIx4XGXPnHce7btamdtmM53zHR0M8nC5+HnJsj1wgHZTK8iVxx7j72In6a+rs5P2mMej7kkPkQYDWNBcZMH0Rc2rQe93BniPzUqCAcq2k8QBSTiQZ6kScjkuodms8pX7/er7N2wA3v1u4NvfZrJBscjxNjnJ/hwfX3rO0VGO67vv5t9PfSrbTa5BSLPXvhZ4+9tLiSSRRpPaCrUIvmc/m2N71y7FK+jJldWWLO/q4jV2dXH86e37VIr/M5mWKQtmsfDD4siXm/j0pynNKhASZG6utp6fTAaie3fVVYw2q9Q4dnvpgzkzs1QWLJdT+m1dXYpZ9ngqBxLppT0yGb6+6lXAW95CkqLSdcRiqlZqs5Jggq4uNSlLOpPUnqk1+cpglY4UPP4477W9XW3wZdMv/gWzmfcqmyR5SNrbSa40MrkaqAhRnwAMcqUWziGXkoFVh81GD0xnJye5aiz+7CwXC8k9Hhk5tdd5FkAiZKR+17lOKIiR5fUabXHaw+draeYKQLtPDGixbS0W4O//XhEq5ZJgAO3Qc0kSDFDRXI1krjRS0F4gcgiBAD+vd8jMznKqt9tbL3X7jGeooqxf+MLS/pT5QCJbq0HIFbG5xR8/MMD9B0Af8hNPcHP7sY/xvRe9CLjpJv4ej9PZMDbG/cSWLbTv16+n5Eat/YlIU8h+QaQ7ABYgTafp4Bgc5D1t2MD/zc213gF29CidNvq6mqK2OTurNrFnRPbKmYyODk5skg5mNgMvfSn/d9ttpTsb0YPJ509t9sqJE8DPfsbfb7gBcwk+cGdaopABAwYI0VJPJlWttNMVsZgiEdraGqudKPU1gJXf2xNPcKt79CgD3OXn+HH+zMzQzjSZdMXs+9NAoYAubxI2SxG5vGkxSKK9nc72WKy+wmMkwj7StFIyW2SfMhm1FIRCPO7kSTrmTSZVQqIeIWU2k7jo6KAt43Aou+DQodbUXdEXs7/0Utp0+phLsR/9flWGohySvXLffaWyYPViN/X+6OUWsweWqhGJLVmr7or4l7NZRUxUigXp6mLdFYAlJ1IpHn/8eKkZEI+zfw8cUGTetdfSjEgm6VeuhXic/e12837y+epBNF1dlG8DFN8g99DWtvqS5SYT2yoQ4HfJMwQo1XmTqclAD0lfks4UcuXFL1bF3T/2sVIttI4ONlw2W9nvVSiwYQ4eVMWOrryyusyW6APKZmZubqksmJ5ckcFaq+i8263SiBpllCMRDpxq2TCNQC8NJpv0uTneQ63riEZVFKS+AyXz55JLVFRkJsP+EhYb4N/yII2Ps9/8fpXqpx8sBhpGeeaKkQBUGQa5YqC1kDCRvj5OjuUWRTRKq6eriz+bNrWmsvE5Bn0q82ppmp5JCAQ44TudrZUcMrAKkDxuKY7TAnIlEFA1q/VTznnnsdj4+96nMgIA2lby3Jxr5AqgyJV8vvJmK59n9zQjCwZwU+nx8DNi96dS3NRJMfdWt3cwCPzbv/G84+PAO99ZOgakrI9EblaD7IvyeX5GXxvlOc9h9F+hAHz4w9S+zuW4mX/DG3hMIqH0rTdtYpzBwAA1oNeubczp43KRkAGAffvU+/fcw9cLL1S85ObN3LMUi1xSW+UAy2ZVoFcgoGQWBwb4mk6rDfTp7HQ7KyC6216v8l4985kc9OGwEk0HlB0VDnMD38oiPNVQLFJjZGaGA/OZz0S4wJ2XkbliwMCZCYkCLxZP/8yV2VmVGdBI1grAdTabVRL8y0U4zO8dH+fP9DTfi0Zp84iijWTSLspx9bEQh8UM9AWZGiBrrtPJz8h5amUOSDF7k4n2iARk+P20ecJhTs3FokpqPHiQx/T10cYRtZx6sFhIxgSDjIdcv57vzc+TWIrFVrbkHDtG4sdkYoZGuXyqyUQbsr2d3xOLLT2HkCuHDilSad26+t/t8fBerFZFsqwkc0VsR/Gj15Kgk/7N5RQZo8+81mP7dvZtJEKbT+oiiWoVwDGWSrHQvKbx/nt7aUIIYVoL8bjyw8vWrJYPXKTB7rhD1QoH+H2SKLGaNU37+nj+zk6VKVQsqto5IqHeEHI5VZPEauXvQq7s2MFinjfeyC/4539W9pffzwE0N8dOL/c4iySYHL9uHT9TbcKSgST/LydXJHNFUp1EDcJqrVxvBVAbB4ejMQ3hQoGDwefjQFhuXWS9NJgYhaEQr78euSKDVf/d5fVWAFXMHlAPsM2mNi2JBB8aj0dJg0n2jIGmYMiCNQaDXDHQWpjNXDgCAU6I+pzVVIpWgM/H8OWhIZW2Z6BpSFF7qTNyLsNsBi6+mEUZl5u9auAUwedjh4kx2AJypa2Np2prW7rJu+Ya+iP1kGL2cjnnGkROEKhsHAnBADS3Mers5B4jGFT1tWdnlXRbq7NWANrOO3YwmMzjYcReOcEiwVrpdGUyqVDgPcty1d9fSoaYTJRP6OxUBeS3bwfe8x61cZueZrtu2sTN9cUXU3qhmbnZ7VZ1W44cUTrc997L97ZuVZvFzk5FJJ840ToHWDqtgu86OhQBaberQLupqfqZQAZaAEnH9PvZ2LLpv/56/v8Xv1DHWq08LhzmYJ+fX/3rm5wE/vd/+fuVVwI+H+Y1TqhG5ooBA2cmxGkPKPmsRgqWaxpVW+69d+WZDI1idlZ9VzPkCrDyuitjYyorYXiY5MPWrQyQ2LyZtoBIaQE6cqVXLdaDQUZ86LfKUmarWKweYC32jEiCfehDDPS4916lKCn+w5kZLgfxeGm9lWaDXSwW2j1WK2MjRSp0166VEVXFIrMxAJ6zq6syuSGiGC5XdWkwqQUj2TyS4VsL4vMVH/JytyUWS2lNwZkZVUMvlaqcASI2aT6vMpUqZa6kUrQJpQ75Qw/xc9PTtEkl6CUc5t+SBXT11bwmkZ2tNd5FYk4SDMR2reWLf8pTeO6TJynrJkFV4seWhILVgsVCe72zk98zO6t89noF6oYgH8zlePFjY2oDs20bDe93vIMyCPk88P73M4290gOnx/Q07TeRBLv4Yn6mml6ZNLxeRqu85oqefHA6VWpXNVbQZlMyJ41M5jJQ/P7lZ60IgkF2hDhnxEatRq5I0S99FKR8TthhPbkimStAqe61x6MWhfFxDpD2dpWaaUiDNQ2DXGkMBrlioPWQ8OX+floEUjxqdJRP5vCwqpBnYNkwMleW4lyqnXHGQqrIi6e2BeSK2ExtbdzA1NoMSPSWx8OvPheJSX3Nv0p2tkgOA83NLWYzp3apry21Azs6ahdzXyk6O7mpfutbuV/Zvx+4+ebSvUcmw76vlL0ibSAODnEa6OHzUfta1C//+Z9V22SzNDRl83/xxY07evRwuRhz4PVyyTx0iPcSCnG+Hx5WG0Z9lp44eVpRAyWVUsXsOzqU9rnZrGIhZmfPoKL2Zzo6OpQHTLyIN7JoPO67T0UvAnzwkkkO9tXeOEpUpzB/N94IWCyYz9CTuNxARwMGDDx5kPJO4ihvRj4rFqNfMZmsXAui1UgmlwYlC0ZHqzuSxeZbieRZMklntjjQ6wkwZLOqDsj67hhyBRMyeTP627mIiowVoOynaLS6NJiQC4kE1+KdO/n3f/0Xp+ZgkN8Zj7Mtcjn2z+goj9u4kTZEs3aKONd8PrWF37tX+UOXg0RCkQGVslYEUlNbyKdKmTJS+w4gSdGIX9jppH0jmQ7LyVoRiJSqZBULsQJUHo/6OpGSbVMpc2V6mm3/tKfx7/vv5/VKDbwjR0iciZzcgQM87ulPV0FNXm9tciVNtTp4vbx+i4XfWSvBwOVSGUO33cZ7lqwXkVhbbQwM8Ls6Omj2JCIsdONyLaPeCqCiyyRrZds2NWlYLIyquvpqHv/ud/MB6OjgQxaNlkqDiSTY3JzqlCuu4ANUjXWS79KTEeLFLhb5uxjpor8mhYJqMVniNJKorVqIRJTEwUojVj0enqu9ne1XKHCwVxtYySSP0UdBAqrw5IYNpdckmSvyIAP8W1hgQC1Ifj/bLx43pMGWAT25Mj9vkCvVYLgiDbQeJlNpWMzEBC26YpHWmIjRG0XsVwQJ/JdIHgMGzgiYzTQCZTfaAnIFUMlyZnNlyQCByF243edm1gqgMt6AyuRKobD8YpS9vTy/JC4Wi7SDe3paW8hej44OVXj1TW/i0vP44yRYUqlSmeFa5IrsiarVP9+xA/jud1l7Re+UmJvjniEQ4EZ2uSSvRBdKfZP9+1kgV75bZCu83tL6NRMTjUk+NIJ0mlGIAPtN9m16eRPZ0BvkyimAMJMej/JerV1LNrFQAH7zG3WsbERrRQW2CvPzwI9/zGtYu5YhzZ2diMbMi5diBH0YMHBmQZz2ySSnHfHDNUquFIuqFshqY3aW0080Wroe33kn8OpX80dkivSQunMSILAcOauxMaXM09VVfzt77BjbxucpwGdPY99UB/ZNdaDTw4bSk1EOh5IGi8Uq2yyRiCoPsXu3en//fvqEhWwKh5XTfGpKfc/27bzm5QSBSI1DyQo5dIjX2Apy5YILlD+0HGYzv9vvV6pF5XjGM9Tvg4MVnOvij9BFouhltpdbzF4g/miJHYtE1J6j0vWm08pBKXZXuf0pxe67umjj9vfzM3v28NonJ0moJBLs39FRjovubrpcurt5XR4Pj6nmV4/HVVsIwdWIipRIg4mtKjLNpyrw0+Hg/qKrCyiks5i87yjs46OwZJLLI1ckQ/iRR/j3xReXHme1Ah/4AI3ydJq/i3EeCpVK4s/NscF37uSxbjcHea0HTwqyCzMoMgCFwlImTjqqEekul0t1SD37MBZTUVUrle4XGXCXS93T5GT1gj7CnEoUpKCSJJi0i8NR6kuw25VeHKDYa5kYIxFDGmwZMDJXGoNBrhhYHQSDnJD7+pR+yLp1tFrOP78xAXoDNdHRQSOrt7e0PoABA6c9JAS/v79l1ncgoNTGapErolF9LpMrEuBjtVY2jmRvATSvl+z3086V6Eu/n128mgqQQmz4/ZwP/+mfOA5272aQGaDs+EpOH9lnSDDY0FD17woGS0miYpFOHnl/JdJnsjcaGeGrnly5+GIVESiFe4XsmJpSkg4rhZ5c6elR3+l0qk2/ZK6I08DAKkIiE30+5b0EVPZKuTQYwMG+2rueZFIVsr/2Ws7nXV2LzlmRLTVgoCHEYpW1fgycUkSj3LJlsyo4WOSn6kGKy9erb9YqzM4qGVi9+su//Rt/n5lhvb1KfkSXS0kyNUsESeD4zAzXx0YCu0WOa/1ABomECUXNBKvLAr+TFzc+VsrwtLerwu2VsldENQdQPkfxgX7rW7yuQIB+WXHsHznC//f18UdfY6QZBINKIsxspo/y2LHlkyvHjqlru+CC2tkGXV3KR1xpuujoUHVWhobKYreiUeCJJ1gkRoycBXg8KmhlpZkrdrvyIcs1VqtdJJkrqZQ6tpxcmZlRcrs+n/It/+pXtHfn5jiWQyG+Su2dyy9X6lPt7aruSrVnU+qtmM08r9Q/r6cidcEFpbVOhVg6lZLlQ0MLGWSWOPKZIlxIAVNTyytmr2l8OCQdrJxcAfhl//zPdN7PzpLRDQaVx1k2E8J6iSTYli0qi6MWHA41sYjEqxR4l/MCqi6f3V6fXBEGUe61GlIpfpfPp5iylUBf70VPrlS7Dik2JVGQAH+XzJXyeivA0ihju53XXZ65IjIX8/OGNNgyYLGotTYaNciVajDIFQOrh5ERTnbBIJ2pPh+tMWPH3RJI4eStW1cvItyAgVVBMMi06k2bWnZKiZRua6sdnZVMcloym89dckUvS1tp45TPc06RgpTNwGSiU142WMEg+2QlG9ZG0NmpCpN2dwMf/zjHxM6dwDe/yWMymeqZK3onRrXMlUqQoLLOTtrxK+EKnU41rwOUfhgd5Vg97zz1f3E+CAk0O6skQFYKvSyYbJglolO+TzJXNK2x2piNYmaGZJI4WgyAHS7SYPrw12c8gw/o4cMMHQY4UCyW0k34auEPf+CG1eGg9siC90ccbF6vYeoZaBDxOKOEH31UaScZeFKgzzoRP1gz5IqoEjZap2W5yGTo3IlEuDaJE/hDH+I1nHcep8z9+2kLlGenOJ3NSZ7pMT7OKXZujm3UiO9R1rT1/Smk0xrs1iKcTjM6XWzskydLg7j10mDl6jWStRGPs50fe4zvf+ADvJZHHqFSUTCoMnv05MrICG0jv395AhJCQnR3K1vp0UfZ7ssJBP/d73ivfX3AhRfWPjYY5DInhd2lXzWN7bRvH/CiFwFPfSrwkpeU3d/cHC8wHl/CzHR1qYzulQTqOxy0ncX+lRokUpunHOm0ykyR+9P7yEVVSuoZOhwsb2Y2c2wnEuyL8XEm5Xi9ygd97bVKfaq9Xe17qtmJUr/HYlF1yJ1OXmOt7C6zWZWBA0rJlVOVvSolPbq9lJRyZeZhS8zDgSYyeLPZ0jSiSIQNIIV8yuFyAX/2Z/z9ttt44yYTO31qiucKhbhJeOIJHidRUvU2RA6HIgbEqNIXgZdJIRBQG4N6TJLLpfTe6hWTl0jFVhSxdTjU4NUXbgSqkysyKesLVoXDvNft29Wx8vlKmSuA2sToyVRDGmxFkGSgeLy1+7+zCQa5YmD10NZG62toiCv7li2r72EzYMDAOYtAgJuLWtGIsoEwm5vU4z2LIDZoLXLFZms+a0UgEmBdXatXyL4cnZ2lm+6tW4F3vYv/k+z+anVChJiQDW6lmivVMDPDZc3hqKyV3QwkS0T2crKnuuACVZRUv4eSCM1QaGX68XrMzanML+k3yVyR75uZWb5jqhaOHeN5jx9vTf2YswYdHSpcVwZFWxtwzTX8vTx7ZbU9mwDrvQDc6Pb2Lj6A4rjRK1AYMFATMzN0dkxN0QMsHkkDjWNmBrjnHqXrvwwUi5z7xWErEkeN1NeSrFAhV4Ams1eaDIGVYG69JNh//AfrHft8wIc/TKLFYmFQuQRYCFwupbLTzBpWLNJPFwrx89UkrMqxSGz0JpFOm+BwAlabCW22NKyWInJ5U0kQtQSiSyF6ffuLak4iwe4uFkmYXHSRcnJ/+9sqKCIc5mf05Iq+1vNyIEEzkmUrgfnLyV7RS4LVs6H0WcpSHygeZzuMj/O6brgBuOUWlrYogV5nrYxh6Ozk8VdcsXy7F1BqROKTnpxURe3j8VKSolhU/nyZ8soDe2Zn+ZnOTpVJPDhI+xZg9srAAG9LJMKSSbbPBReoADIh0kQarBzpNM0Gr1cdK2u4ptV/PEUaTL4LUPXTTxWGhgC3OQ2vLQNnNgqvPdtc8Sc9uSID+vzzaw+I5z2PD9qRI2Q5pbB9Lkf2q1ikJNXx4zz+8stV6nkt6MmVZFJdm+g0it5hIKAauZHMFTl3LXIlEuE1ig5fKyBZM8JcCqlRfh2FAgeoREFKO0l63sUXl/ZHNqtkGCqRKyLrMjamHj69NJjUhDbQMGRYFouGqVYNBrliYHWxeTOtrwsvVHSnAQMGDKwCpO6KxVI5OktsUylaudzaGGc6rFaVmVLugy0UaDRZLMt3jno83NT19akIx9WGKCf5/bTX02ngkkv4v4kJbm5FfqEcmYza+0ipsEaQSPB8nZ0cTytxVgjcbp5Pv1xedRW/R2qfyH5DCsrGYnTCrJTo0DQSHIByvjgcqthrfz833dmskitulba+6PpLQOmpkJU5YxAMsuF9vlLvlUiD/frXyikg5Io8yKsFSW/q7l6UBAPUeJAapgbOLYRC9IM0lYAikaoTE5zI9u41JoBmcfQoFwLx/C8D8bhKjhMCXyLX8/na/rhEQkkOSVZjw2vD+DiJIfGyN4BoFEhHsygkM/B4gLvvBn74Q/7v5ps5HV10EfC2t/G9r36VGRICCYhOp5vL+Jya4vo3M0M/YSM2kqYpWbCRviTSaQ1OB2BzmFDIFdHXQSNMX9Qe4Pmj0aXSYJGI6g+puS2F3F/6Ui4V99zDrNeODlVeQM6/bRttiJVkbgvfL4EgBw/ympolV+bnVc2Y88+v7x8GSrOUjx5l4qZk/A4O8ufyy8v8wvk8nw9Jr9IXO1mABPavBBKkL/bb1BTPKxKq+mlNnqdsVqkT6cklkZwNBGjfDg9zTLS1Kdv2V7+izbptG/tCYh4uuIDPrvSxZPYLuVKeiSK2o8ej/N+NqkjJdUtCQXu7yno/lQEWgQDgNSfR7Y7BZcnCk5njmtKoHSQpd4CaiypJgunR1gY85zn8/bbb+GBkMpxU5ub4+vjjbPC+PnZiIxsFyfIQYz8UUnqN+bya471edpIUcK8FmfRqkSvCpEukYqsK+opOrDyUQkCVpz5IZFd5MftK9VYAtocYmuWyYIDSo47H1eSklwbTE1UGGoLLpbpGEpAMlOIcdS0ZOGWwWBgGLKmABgwYMLBKkGnG661cd0Xv+DtXJcEEEpWay5VutCRjYKUp/Vu3ss61ZF2cCnR1KdJsfp7jQKSsJieVBEN5oFI2q8iVoaHGpTJmZ9lGPt/Ks1YEInGiz5659FL1P33yZ2enyr4aG1N7r+WivJi93a42105naY3NmZnGIpobhRQWlohkw7eqg8XCzaDPV8oQXnqp8p7dfz/fE3IFWN3sFYk87O5WgvBQ48GouXJuYnRURck3FBCqaaUFgI8f5++7d69eROlyKpifzsjn6R2dnmbbHT26rNNEo8oBLOSKvqB1LfJeykGlUirSveE5/MQJOvskBbMBxGYzSO46CJw4gdhEHB/7GN9/yUsYjCB4/vOBm27i7//yLyqxx2ajndCo5JlgbIzTbTbbeNZKOMx1zWzWMNSdRjYDOF0m2B1m5IpmDHSwgcuD7Nvb2ZaRSKl6TSTCa85klCTYU57C1zVrKIkFsPZKRwf9iyKh1dOj6mmsREhCFJAuvJCvs7PsxmbJlQMHVJ/o1X5qQcpMSGH7wUFg40YuRTt2MKZzSbKB1FiIx9UYa4WOahnEZpaxMTmpyJXyr5TnSk+u6DNX5uf5HHV309Zzu7nMer1sK4+H42LnTnW/99zDV8na0e9z/H5+tlBY6tOOx1Xgjvj+hZAxmRqT/3nLWzj2rrlG+flPqQ1QKGBNIAafJQmHrYj21CQbsFLRokoQA9pkUg9WPXIF4ARjMtEGm5tjo4nDPhJRA3zLFv6vUXJFNheAyu7MZPgqafbt7TTOGykuI9Jc+km9HHNzinnVF9JZKURjTjYQc3OlMmcCYZPTaXVPqZTKJLr88tLjMxn10OkzV4Td83iUY0A/weqlwRodHwYAqEBGQGVRGiiFQa4YMGDAgIGzAg6HykpJJJZKCyUSijQ418kVCXbStFIflj4AfiXyCE4nMytOpRKkSIO1takMCImsPHasulxJNquiOmsVs9dD9jdSZ6VV2Tnl5MratSqS0OUqlbJzOFRgluwbVpK9kk6rhIRK5Aqgvk/qrrSKXBGng+jIt+q8Zw2ESTOb1eC2WJQOjEiD2WzqgV7NapPiDervVw8e1PgzyJUzH/v2Ub+/0WdRshXEj1kpwGEJ4nElBeLzcfyOjvLD+/a1fueeSjG8+6GHWpfZ1SpNxuVCPNrxOCfwaHRZ0bj6eivi13K5VE2yeuSK3l/XMPEuWQQSAtuAd75YBOLjEaSSgLWYxS2fsCEe51r/2tcuPf4Nb2DAcyYDvPe9bBqR4Eyl+PWNyFCGQmyDmRm2T6PFsiVrZaCvCJPJBBSLcDhNsDkt0DQT+gM0SMpqrC9mGkhWqmQcSKLXwYOc4vv7VRYrALzsZXy96y6es6eHjxSgJMEaUSaqBauVj2tPjwoseeSR5sgVTWOCgPhRJfumHsQ/3d1Nf3VvL7NWLrmkhl0fDqvq8ZmMKlzTYpQHzEsxepGgKydXNI3vy/DXB+lMT/N+nE5llwaDKtZCgm5++Uu+7tnDseJ2kxMQG1/Q3s7/mUxLbz2RUOaF2OwiQ1tPRUqwaROl+Px+9b2nVBo0lUJ3IIfzOmaweSiJDluUA7JRaTCJvJqaUvrRGzfW/9zAgJJovf12EghSjDEcVuTKjh18bYRckYaTY2dnlfRCLKY6MBhUG99GIOlIhcLS4AVN4/XKIBNJrVbA7VZa0YBabCqRK/oIHYDpefk8J7ryKLZsVhWv1w82k0lpW0samX4cGNJgy4aeXIlEjKL2lWCQKwYMGDBg4KxBIKA2B+UbCH2mcSsknM5kSGAUUBrgLjam1Xrm1UxwOpVmtMiAbdnC/+nJlXKnz3LIlbk5JUnc19c6iTnZ/D7lKfx585t5vSI3oSdX7Ha1iZ+cVNJay0U6TRUFgPtDu11l2sveReqwhELKgdYK/6dE4haLfDUyV8rQ0aE8H3oPloid33cfN4lWq9rtrFbmil5subu7xKNlyIKdHUgk6GOKRpfKFVWDOOFFtqisZnRlRCIq5aGtjYWd8nlmX8zOLjsLoyqkrsv4eGsEwxMJSpY8+KCKJl6AZB004phcEaJRVWdJKp2LN73J0yQSnGbKSfVMpj65Is++ydTEHB4Ksf/n5/mhBhi5RAIoxpNIRPP4+QMdePygAx4P8P73Vw4IsVj4v+FhDqn3vY9fJZJncs56GBvjPcbjjWetALpi9mvyyORMQKEApxOwOmg09PhSi+cvR3s7m0SkwSRoKJFQwdzXXluabbtxIzMXikXgu9/lexKIPzLSnPRpLUjdlQ0b+PeuXc0FRoTDVAAESIw1U95hZIR9MDTEYPaBgToZx1JvRdCsHlyDkDVP9iGapkiz8lIvkoQAqKAWyVyJRnmJsrzKfkXWVZ9PJVX84Q9s87vv5t9bt/L48j72+1Wdcv11SMKGjAuxZfUlOpopXJ3L0QwR//Ypw8LA63XOo2+tAya3i+xWLFaf9cvl+MDkcooMufDCxhnIF7+Yr3fcwQbUNKZyHT/OsWezscPc7sYaRQaSnlwRYlCMdLdbRWM1Sq7I8UBlYiOf54PY2dnazpPr6+xUUX0TE5WvIZFQWTYA8MADfBU2USDMpD4KTA+RSpNsGT25IqlvhjRY07DZVMCfQa5UhkGuGDBgwICBswZS389uL91ASFStx7M0outchL4NqpErp3Rj1CJ0dqr9iBS2Bxg9KtGDeqeP7Kdkv9IIuaJptMUDAbaTEA6tgJAZ7e3Aq1/NaEyptwJUz1wRsmMl5EoqpfYfQq7o9ywul9r8z86qANBWOA/1ah2tzIg5ayDSD34/G0d2NGvX0jNVKLD2yqmQBctmlSM5GCyZKKTfjIL2ZzYkkr+eU10P6XuZhxqKYpcIVk3j5OZwcExL/ZBjx1or2zEzA7zmNdSwaYVXYHJSTVgLk6em8ZIffJBBtw89tMoOCNGJAvjQTU5yQtVXSK+DdFr1tRD8gPJv1Vpb9MXsxXcn5Epd4l3SQSQyoIFBE4sBWjyBR57w4P8eZnT1O9+p1sJK8HqBj3yEzuP9+1ng3uVS2QP1/OyJBC91ZqZxZR/BYr2VoRzSKcBiKsDqsMBuZyN3t3HhqxRg7/eXSoMJFxmNqrIQlTI+JHvll78kz3boEP/evLn5668GCXAXOa/9+/naaPbK5KQiiLZsaa68g89H20jupybk+YzF1JdUKGrfClgsKhBleJjvHTzI98q/Usq+JBLqfQnMn55Wmcp6iVhAkVpr1tD2TKeB3/6WJAtATsDlWtrHHg/Ng3JyRX7X11sBlG/a4WjQlCgUgFgMuWxxUSGgUYndliCZVPXmpCB8LMb+rxchIDeoJ1cakQQTnH8+B2M2yyziYJCOe3n4N2zghqHRB0/P0gFKRiubVUxcZ6cqithoGp1ovQFLDfdQSBV2bKUkGKCeO5dLkR3l5IroNksUpAyeavVWRNNaHwWmh91emrlSqaiVPIB63UUDNWGzle6xDXJlKQxyxYABAwYMnDVob6dNVl53RQpKut2GJBhAX61sBPXGUS7H9051McpWobOT1y/SYOvX0xiMxVTwl95xn82qICqgMXJFtLA7O7lPqBQ0tVzIXkkcVFIkWCSL9YSXw6GInbm5lQdj6jNXRBFAf296eYrpaRXN2AoipJxcMTJXKqCjQ6Xl6Sc3KWz/i18osfN8fvV2PXpnro5ckbEKqILDBs5sNEPYio88m1V+8rrO9UiED7/FoiabtjZ6GWdm6PDZv791jtDduznBnDjRoG5ZHYjHOxyGNhfC9Mk8HnyQEfkTE/StSQH0VYFeJ8pmY7vF47y30dGG0wrFIS7yQAJxFKdS1bMU9cXsAeXPlCzEqpCMFflycYDXkWuLzWWRihfw/T9yMfp/187huuvq3+PAgCpw/4tfqADqRsa41DQTKdBmnMaL5MpAGumUBqetAFgsnDbNZnS5uYCePLn01u129kc4zOY5eZKvBw6wvTs6VHauHuefz3p3uRzwz//M++zuVvZCK8gVr5fXd/HFbI/paV5fI49VLkd7TDJXtm5dxUzHcFgxaH4/jSlhA1slDaiDw8FHUUiRfftU3RV9XTzxJcvc0NWlpOricfaX3hctCAYViSlqVN/4BpPy7HbgootoQ5bvcyRYX+qu6OvAOJ00HfTkCqBUpCQrsSpSKWD/fmiHDiM/Ogablj31+wfR+APYOO3tnMBmZ9nItRgimajS6cbJlWKRjOjMDBtXsld+9CMa0Bs2qAxCYQ8bffCsVl67kCuhkEp10mv3yprZTOaKxcIBqp+cczmuY6IH3EwaWSOQ9d3hUPc0MaHIMKB0ERKyaHyck4rFsrQ/pD8djtqZK5JmWM5eC9s4P8/2rSQNlk4zwKMVWa5nCYzMlfowyBUDBgwYMHDWwGrl5sHrVZFhAO01k8kgVwSykbXZSvcchYJy4J+J5IrXq/Yw8Tj7XGSTjx/nmNA77rNZFTxtMjVWmH52Vn1PqwrZC0ym0uhfcVK53aVOL4D9IwFmUgt4JTJdyaTS/pYo4PLMFYnGFHKlWFw5uSJ7RtkXy9+GDHIZOjr4cLrdpZpLz3gG3z9yhBtBQEU5rgZkkyphsAsTht5XpQ88NHDmIpNRilP1oJdQlyDimnODeBvLI1UBOkSCQZIg8ThD3FuhP6hnOSScvwFMTgKPPw488QQfsclJYP5EDKlIFsXQPGaOp/DQXjf23hvB5CT9c1I6JhZT82rLodeJcru58LnddFw1EZEbiajaI+V+OsnwqJalqC9m/4UvAO9+t5qGava/SLLonWoNpJFEJxI4MW1HOOmA2VTE65831ljRFFBqs62Na/i+fXyvHrkiNTFmZ+nja8bvmMvR7gCA9X0pZNIanNYCYLXCZAKsdhPanUlYzFpJYXM9/H62cT6vaq/s3s3/XXMNHenj48yQ0Le3ZK+ILJlIgrUqc9tkUrKoQto8/HCVzJXZWf5zwTE8Pc0xJ5JZ5Yo/ADg29u0juboSYyAc5uAsFJThJmlVq1R3RU+u7N9fWtRexpqUgBFFIskKnp7mOdrbGcxSvo4GAiqA7MILFbEFkKQKBKonM0jdFf11iC/bZFpaI1GvIlV1DYhEOPhiMeSOc96xHjsER7YF5HUz0C9AEqXU0aEc5+VFjfSQmzt8mOfw+ylRWQszMxzXIjF53XVct8JhFjyyWFRqltRbKWevasHhUMyasKuA6mwpZt+MhnM1rbf5eQ6AQICbitUw3oSpE3JFFkXps2hUpcPLdUrWyvbtSwe09FmjmSvl/W8ysQ0rSYMlEpx77r+fY0KCMgwYmSsNwCBXDBgwYMDAWYVKdVdEssJsNuqtAMppbrcvzVyR4PczURYMKJUGi0ZVUfuxsaWSUxLEDHAfUy96UmoXd3bS1pd9QishG1oJSpb3yskVk0mRHXNzvK9CoTl9bD1E4cZkUvsrfXtI5orFwr2qyOSvlFyRZ1SfuQIY0mBLIAW/fb7SCO+2NnoNAW7qAaWBtxoQcsXnK0lxk7Gqr9lg4MyGPJONPIt635b4yWvWXZF/6iNV9Rgc5EAaG2tdjQR9XZQG5MaKRTpH9++nk/rwYRIn+/cDO38fxf273fj9rnY8Pt6OifEiDjyWxJEjfCxGRnj54TBvdVWy8UQnKplUC0RfH/+ORFizpgFSSl9HuLwr9H64SiSEFLOPRFjbo1CgX6ou8R4KqToCXq9KL66hK1UoAMmZBA4c4wSzJhiH06417Piy28lFA1RRtNnqD62TJ2kXzc0pKaxGceIE10qPh/Jf6TTgtBVgsvIkNpsJxZyGvg5efyVpMH2UMKC4RoCSYCIZlsuxu4Vnuuyy0nrcIyOKf2sVJItCvmfnTl7fEq7r0CEaDAcPAsUiJicpmScZNRWDVE6eJEk4NkbnZoME2hJIvRVhHJrRg1sGyjNXTp5UGSuFAr9S01QQiQTFDwyUZkfZ7ZVrilutXHp9Pt7OhReq/23fzj5ua6tcB1CSOdxuXkcupx4/n2/p2Ha7S6UBl2BmhizyQuH4fCIDzMzAno7BfmT/KrLKFZBK8WbsdnXznZ2qsHyl1DCBFLM/eJB/S/pPLcRiqiDO5CQb78/+jP+77TY+DLkcH5KRkeoZFtXgcChiIBxW1y6Eud/PYxqVBAP4/WLY6+3DuTnei9XaekkwgWxshDCS9VdPrsgCU06ulEuCyedstuoGZ3nNlWh06dpSLg0WjXJyffBBTt4nTjC9Tl9n8ByHvqC9qDgYKIVBrhgwYMCAgbMKgQANAKdTSRSI/6YZedqzGaKHXJ65ks8rcuVMzFwBVC1GCfAX2Yzjx5fWCclmVWRpI1koMzMqcqfVWSsCfbSg1MKUjKxyDA+zH/XyEsvxFxQKqh1EOkL2YfrrsljUfm9mRhW1XwnEGZPN8jukbwxpsDJIqLDfr2oUCEQa7K67lCTYau16RJZCvHQLE4Y4/1wuI2vlbEE2u3SoVUMyqRxwIhNVs/6CMA4SUV4OiYTQS7asFHq2pybzw3vfuZM+ucOHgfvuI7Hy+OMkEfbuzOLQYeB4yIsDoSAzBBJJjAxlsXEjHZyBAL9GCpK3HNGoisIXw6atjb9PTrJT6nyxOHxFHkgcrNPTPLXTyalEEmTKIcXsReIJYJvVlXcMhdQAEYdeMllz0MTjgJZI4sgYIz/Wdyz0YRNE8g038PXuu5UsWD5fmZ8R5Z/Dh7neNVPIHlCSYOvXA/lMAVq+AIe9iDYPHaU2pxm5nIaBTn55pdIQNpuSBgPIM8RifO+ii9jNXi9ViPJ5RdCYTCp7BeD/W02uSBbFBRfwb/FDltxHNstGnpkBCgXET0YRi/HZAmifVVQ1CoXY4UeO8Pe9e5vPXksk+P3SYJIaLB2/SpkrVmtpjTrJ5BLJL5lX9Xbb4CB/l+yogYHqRJ7UXQFUXIXFwswVj6d6dr7Xy+O8XpUFBahybuVwu5VCVcnzIQXbJWtjchLweJAbWAvYbLBOjcERW0gPO3KkNVmHtSDkSCZTGhEkBYZEE7haHSphuoRckUyTapBF0evl3JXNsh2e9zxOmEeOAF/9Ko/dvJnHNfvg6YmI+Xn1vtyDyII1KgkGqPHvdCqCURbujg6V+bgakHovkvon9yHp+fE4r0UeoFyODCxQmVyRYvZAdXLFYuH9ShRcLWmw2VngkUc4eR0/zvlGsp6SydLAjHMYRuZKfRjkigEDBgwYOKvg99Mv09amorOy2doRXecaJHjJbl9Krkgw0JlaM8HnU7IK0SiwaRPfP358aZZENqs2vrIRLocELT3xBF+FvKkUVdgKyB7EZCotZl8u2QBwb6APBJMgrGaRSql6KyK7XL5fkb/FDxYO87sikZVlzCcSKiMon2d753IGuVIRHR2qWrzeCXnJJRyY0SgdGo1qOS0HQq5ISPXChCr7/9Xamxs49ZAI63oEqgy3TEYpfMncUBX6SNVq1awdDnr1V5KSJ5AIZv33V0E8ThWj2Vn62774ReCWW+ijXbcO6A+k4HekYE0nkLZ6YG3zYL13CpsGEmgrzC+eJxDgfBaJrFIQt9Q/EqeZoK+PE2gD2SuxmPKxCT9z7Bgd8+9/vzptJfksfTF7kdkCFLlSddwIKx+L8Usl8qUOuRKdL6IQT+L4FBejkeB801l6553HrIJMhiRZuVyTHlNT9Me97W3AP/0T8LrXAV/6UuOJFCLJtX49kE4UgGIRTpcZfk8eJmiw2YBcRsNAJ6+/UuYKwKlWCDAhJa66Sq2dvb1cEgYGaKPIc/eUpwAveAHw3OfyfyZTa2VxbTbaJZdcwr9FEu/oUR1vIf0ZCgG5HCYPREvqhku2RQmkHo8wd6OjZB7272/OUS8R/4mEMqBWuai9qBEBKqNHSDb5Sn1wj5Ar0nei/lkreEcyqLxe1te5/nrgz/9c2b7V+ljqrng8KmvGblf2cjmkX5xOnY1XKPCGZmf5gIRCvKDeXuRgBbo6YQu0wT61QL4cO0ZH9XIzjxqBTDTp9NL0864uvh+LVS9sn82yY44e5d8XXVT/+4TQdrnYeJOTbLDnPIfHiOzkeectj9V0OJjWBfCB1rTSDIpAgMc0a3BJOpKmqUEgD/JqZa3I95rNauO0MB8gk2Hby3Mqi9Djj/OBaW8nM1yObFb1dTVyBSB5It9ZPsGKNJhoQ4+Oco6JxfgAbtvGa47FVO2mcxx6ciUaNciVSjBcTAYMGDBg4KyCBLx6vSrNHqgd0XUuQrS3xXcFqIL2Z2rWCkB7WYKwNE1JKOTzys8kezG9LFh5Mft8nvulvXtJzNhstPF7euicWS3ySSL/pQ/cbn53Jckyh0PVR5Gi9sshV/TF7KuRK6K2IN8XjarArmpOoUYgxey/+EXgne9U0suGLFgFBIPKQzY/rzZ7Fgs9LACj71azoL14idvbSyYK2fMb5MrZhUbmFL1vS4JpxfFb0e8tLPBCrZBE1oZYssKEKuNLot9Xgmy2NAK4CvMzM8OA2XCYxMqJE8BDD/F/v/0t15UuaxgDgRTWemawaaSI9SMm+OyZJRGu5QXJGyn23TAyGdU5UjRBIIXnJifZCUKIVoBk1qTTyq91772cQh56SAUYV8pSFImjSIQ+KcHEBL+2KkEeCilZprY2VRAvkVCVvisgNplAKmPGeJgO8uFAtHraSRWYTMCzn83f77mnVK6pHGNjwNe+pobesWPAd74DvPWtVAC65RaOiWpfv1jvZF0B6YwJ5mIedjvgcRVht2mwOczI5U0Y6GBDVSsLIY5vUcgCSJxMTKiu7u6m3ePzKTkyiwX4u7+jv1eyEFqdud3RQT+k2AX33cdxsn//gpJRNLqoi1WMxDA1mkQopIJapNZ3CUTuLhZT4+L4cY5jYSoagUSAaJrKjhMDV5iOFjtMHQ5+hdlMUg1QdVfkGZLnSOr5AOw/SeILBmvL8nq9yh+eTgM33ww8/elq7a21z5G9EcCukYSeSp+RflmUBsxmyYpJwZxYjBe+kI2Qy5ths1DrzbGmm5Pp0aPsN5HJWg1I9p7e4S6QNOzZWd5wpUk4k6Ghn89zQJdvBsqRSKisCIuFpIRoB950k5qLzWbqti1Hj1pPrugLLsn19/byu5t9oPWp8akUn5FAgOdqNjWvGcjgFNZQCpJlMuyXYpGDrFwS7NJLK0dEigScZKiUQ2wHm021Y6WNSjCoxnUqxb7fsoVtYbHwmoQIWs7m6iyDfigbmSuVYZArBgwYMGDgrEMgoDYQ09O0r2pFdJ2LEG1ogLalpqmC9mcyuQLQLhaJY33dlWPHeK/6zBUJZpP6JYUC39u7lxtfv5+fX7+ehVu3bVN62qsB/YZW/q6kmgOwn1pFrohjp6OjMrliMvE9yfAJhVQdyJMnlxeYKJHPk5Ns81SKjqG6kjLnKqxWTm6BAHc1ekeBSFlIClMutzqRdiIzFAiUDBLxKRvkytmFRohOPVntdNLfI/NQxUQEXRHzmNmPh55ow8MH2jAft5YepydXVjoh5HJ1yZWjRxkwKxkrNhtr2gruu2+htvb8vPLGe73KyROL8Tp1RFAgwLdzuRZnr+gLwVd66Hp7eS3z81z4qtQb0CcQiZ9OHPgAlQbFuSp+aoEEHD/2GO+vp4dtlslwTUinq3xtKMSTFQrKKHO71UCqkr0Sm0oiEjNjJsZFMlc04/AJG2Lh5gqeP/vZXM/27WNfV1o3w2H68X/1K/79mteQ/H/mM+nUjkb5vw99CHjlK5XMmB7CA4ysySGdM8NhygJWK9yOAhy2ImwOC/JFM/rbObarBdZbrfQBHjrEpnM4qDaUTrObfT5KQolfWFSbAKWYJMFFrZZslCwKsbFGR8mDRKMLBIquoE9oMotcLI1DTxQQj/NzGzdWGL5SjyedpjG3di3H8dgYf4SZqQXJfolGlU6XQCrM1y0M1Dz0Pl2pib5vH/tMOIBQiENflBGllrhcWjV7TyAKoT6fCviXzDOns3btQKm7Is0h5JzVuvRYKWfhcCwQiJL+LQUMBwZKNlW5ggk2Kx8Ce28HjeZYjAM3HG6s35aDZFJtYvL5pXN7Zyffy2Y5se/fD+zaRQf+PfdwjEhK2I4d9R+SeFylaJ53HhspGOQE39urtNrWrqVjfzmspmxgpKNCIWWkOxxqwCwnc0VS46enORA7OvicVRoErYKwjlKoCVApjrJu6uUt77uPr5dfvvRchQL7uVoxe6A0c0W0jCuRK243J6H160mqdHQoMud73wP+4R+Af/93PmiGNBgAxcFJAI2BUhjkigEDBgwYOOsgGwiPhzaY2J9GMXsFyVwB6BjJL/gnrNYzt5i9QPrf7+eeSjb+x4+XRuDq5bCEXDl+nDZ0d7ciUoaGuOfasWN1g7sAtQdphFxxOFQgWCjE+5HC9s1A3w6yx6q0Z3G5VFDf+Dj3SbOzy3ccikPriSfUe7Ozq0+unDjBPfVKMm6eNPT3Ky/K3Jx6X/ThJBRdZB9aDSFXysJrJXOl2l7XwJkJyT6pNZTEt1Usqhq7IsdZMUFEHF3ZLCYyQWRyZmSyJsxGyhYe0ahcjcwViZZdwNGj/JmYoA+wvZ3z7p138v92O+9zz8MZJSMjkcsAnYxCGOicMO3t9GOFw3x0WsZ3ShuK57wcXi9ZgBrZK5J1IoHYDgebRE+u/PrXyjEswcUC8YlJgfWrr1brqF4KqQTipIpGS728+kFTgVzJ5YDUXBKHjtpQ1ExwO/LY3hdCPlPE4VHzomxnI+3b1aW46IceUrKUekjWSqFAO+CSSyhh9b73AT/6EfDpT1OKqbOTa98//iPw7nerNWV+Xk3P6/qzyGTNcFpyJFecRZIrTrphenw0SGrV3B4aUr7Vyy5jE7a1sZvXruX7mzfT1h0cZL+Gw4oDXK3MbcmikMLqhw+zPaemgOPHNEQnk4uNO3FSQzJtxoE9zEwaHlZ1QEog9VYA3qTfzwaYm+MDOjpaPc1HEIuplKRyTVUhV4CWS4OJ3Waz0c/ucPD25+cXylzE4wjdsw/ZoycXl9KeHlXv0GZrzA8vPmqrlfNKsci+r9fHIo0sNmW1eisCt5umRnE+itx8Qk1ig4NLFvt8XkeuWIu8mI0bORgPHuSgqDbAVwIpZl8oMFXr1a8uDTyRzIyZGRqYhw8zrezoUU72x4+TbAHqS4Jpmqq3YrdzMunqYmcXCjz/a1/L81x//fJZTRlI0qGhUGl6uaw9tZi0StCnxkvWoz4FfjUh9V6k7sr4uMpcSSaVvOWRI/yxWiuTK5LZWCkKTGC1cqDbbMo2rmbwl/dRsQh8/vNMpwdIDkajBrmyAOGqhCg2UIpVpCgNGDBgwICBJwdtbaoIuNiPejLBANtDn7kiv1utZ347mc20p+Nx7ufE+VCeuXLsGPdDIssrzsChIe6X+voq7iFXHZK5LwoWleqtAEtlweS+ksnqn6kEvSyYyBNX2rM4ncp5NjHBPZJovJ84wWtpZg8pqhyjo+q9mRnut6SOw2qMxWPH+B3HjtXWNj8tIalFHR3cLOZypRtICWMF+L9WNmA+rzaYZeSKONGNzJWzAydPAo89lsD69fQbzcyUOuEsFgucC5NEMgnMzycW5ZFMJj5fc3Ncg/v7zXDpJtHE5CQwN4diKoNjaQdOzmfhsBXRFs5ioDMDl85hlASg6T3FugnGZDLBrRtwyWQSWhXvumluDm6dcyQVCqEYiQB2OwoFqoJMTJCD6OsD1qzx4NOfpp9l06YUuruLuPtu4He/jmPz0+NANEwvQyYFl2OBAZ+dRTY8h4JZA9qVl9Ph4Hnb2oBw2LPoW0qn0yjUYMLdbjdMC/ebyWSQlwgIgAubMNFmM9yapo7NZpEvFJDzBhDeM4aCFkGwcBiOhQ50uVwwm81IJoFUKotQKAebjevHsWP0S9rtkgHhwtSUGW1tQC6XxfR0btG5Mj3NPn78cf591VVOJBIWHDoEHDmSRSqVW6wpsYiFrB9nJALLgkMrl88jK4MmFOK96YqaORwOxGJWIJHAweN2AAn0BZPweBPwOE8i7i1iJt+BgwdNcLkc6O21LkyHeWSzlTW7nv504OGH7XjoIRte9jIgGs0jHs/AZOJ4vv9+lbXyqlcBbW12hEK2hczNAjZtSmPTJhIs3/se8IMf8DMPPwy85CU2bN/OebevrwAUIogk0ujMR1A025HJWpAvaCjAgXwxh4AzBbNZQyajYXw8tdi+5bj3XhsAO3bsAJLJIoaHU4uSoUIO9ffTD+jxWDE25ljwAWvI5ZKwWitntlqtVjgWnjlN05Cskc2hf+4BIJlMwOkkWWU2k5SSuiJOLYv9EQcuMadQMDkwPpfHyUQCo0diAIoYGVH1mczmhTkimwUSCSRmZnhCycJ0u+kkP34c5mIRLoDrT1dX5ed+bAyIxWCKROCWiJhUCsloFJq+vs/09CLTUD6fpFIpFGuQAR4dCyLHFgqKiMzlKCX7+OPA0aMerF0LZMZmkJsPI5qMYnLMA8CKvj6un2YzP+v1qvNWmyOE8GxrcyMcNi30YwZWa75q9rLb7YbZbFqozZJBsZhHschmLP+MzBFuN2DWUshMnkDEPQtPLMwFQSsAGRqcDpsDZjMl7uy2FNKZJLK5JHJ5UGPxox9le3/5y3COjMCyUNsjm80iV4O1dzqdsCwwb7lcDtlqtZXm5uBIJmGdmQGmppADkP3Rj4AXv1gd09a2mPnksFphtVgAkwl5sxmZTEbVW9m6tYQRtttssC1kdOQLBWQk3UiM80SCkVgnTsDu98M2PQ1s3YrCu9+N9OiocvCXNbDNZoN9wTYrFApIlwcPSOFBnw+2qSnYZ2eBmRkUAaRkIZbJqsp5i8UiUuXstqYtpktZ83k4gkHA6YTm8yFZI+19JXNEQn9eTWPEwfg4cPw4LOEwnNI+bjcS6TTws5/x2MsvVxJ+WJgjHI5FciVRLCqyqwxmsxkuu30x7S8JQDtxomLUlslkgluuN5tF8pZboP3ud/JPTmZPPAG43TCtXw+3LtptOXNEI8fWsw2aObamHbGMYyXBKJFwYXqaAQL1nmWZT8qP9bRap/J0gGbglOLhhx/WAGgPP/zwk30pBgwYMHBWY/duTfvJTzTtU5/StJ/+VNMef/zJvqLTC3NzmnbXXZr2+c9r2re+pWk//jHb6pe/1LQjR57sq1s5pqbU/X3uc5pGk1DTvvAFTfvtbzWtUNC0j36U7/X389jvfU/TPv1pTfv1rzVtdPTJu/Y9e9gfX/4yryuRqHxcLKZpt97KewgENO0zn9G0O+/UtImJ5r7vj3/UNIuF5/nEJ/idmczS406c0LQ77tA0m43HfvObvMb/+A9+Zna2ue89cEDT/u//NO3881X/bNjAPrrrLk2bn69/julpTZuc1LRisbHvTKd57h/9iK+FQnPXfFrg8GEO0k9/WtO++13eyJ13qo758If5XijU2u9NJDRtaIjf8bGPadqhQ4v/evvb+fazn83ny8Dy8WTvFSIRTfP5NA1A1Z/nPve5i8fff7+mORzuqsded9116uSFgtbp91c99tLNmzl2F36GOzqqHrt169aS6966dWvVY4f7+zXN4VicaC71eKoe6/d3aj/4gXqc1qy5ruqxTrtTu+tTj/LnHT/Vrlh3ac1227tXXe+LXvSimsfG4/HFY1/5ylfWPHb6Rz/SCr+5Swv/5A/ay5/1ZzWPHV1Y3E6e1LSXvOQdNY8F9mh/+qe0DV7xig/UPPbWWx/Q3vAGtllv78drHnvXm96kaT/4gabddZf2ube+teaxP/3pT7Wj+1Parz7xqLa555M1j33Tm76vffrTXPff977v1zzWZvuaBmjaO9+pabfc8tOax77xjZ/TPvUprhuf+tRdddrs45rVyna46KIHah57w3mv0n7097/XBrrSGrCnznnfoVksXKM/+tHRmsc+//lv1G69lf12663TNY995StfuTjO4vF4zWNf9KIXlTxztY7dtvl67TefeEQ7+N6vacc/97+a0+qoeuziHDExoWl33aV11ng+Lx0a4tj53e80LRLRhoeHqx67taeHRu1vfqNpGzdqW02mqscODw+X3Null1Z/ljs7O0uOve666nME4Nae/3z2xQ8/ul+7YuOVNdtNb8vUmyO+/c2I9qn3zmhf/FhYu+GGOnPE9LSmabTvn//8N9Y8VuaIsTFNe8lzX1/z2K++63btrk89qt361ie0m576hprHPvCtby3e28c/XmeOuOuuxWM/97nP1Tz2p3//95r213+taYD2tZrPELTvv/vdmvarX2naXXdp3/9A7Tnta//wD4tr0U9vuaXmsZ9785tpk33nO9pdN99c89iPf/zji/f2wAO154gPAJr2+tdr2p/+qbanzr294x3vWDzv6GjtOeKNT3nK4mZnevrJmSOee8UVaqP2zW9qbqez6rHXXXghj/3+9zXt05+ubUdceqmmPfywpt1+u6Z99KPacI1r2Do8zPP+9KeatmOHtrXGscNDQ6syR7jd7pJjn/vc59ZsNz1aakcszBGapmlvfGPtOQIY1T7zGe6h3vGO2nbEnj17Fs/7Ad0zdzbCkAUzYMCAAQNnJQIBBruJ8oRRb6UUEqhjt6vgQIDtdabLggEM5BeJ30JBZXg88QSgaQxaFF1y+V84zHFisagaiE8G3G5GJkrmf7XMGYeDkaoAA4IXlHaaVro4cYJtJPctQXnlkGwnvYRxVxeD5xKJ6prx1SDF7KUfAEaPSxBfPWmwn/yEkaGf/7wqvFwP5bL+K1UbelLQ18dOam9X2jMmk8pekVz9VsuCZTIqc6Wzs2rmSrNKFQZOPzSa0SWBsLXkmEqCKutUdS8Wy1LfWlUkolgsrTxeRzvxttv4+Kxd20QWYFsbUKx93tnZ5dWnqoe9x9y4Z48fOw95EUnUFqaQKUMUBOvh7rvZfDUCXgEwSFiKeNfpZsLnQypjxp7R+tGr0YkEkmkL5pO1M/E6OymdXyzWXxM2b+brH/5Q/1JFKrMRZRivV7WV1CirCrMJuayGgY7KGTbl2L69MfvEZlO14Z6s4GAtX8TEpAlj814cT3aixhShEA7Xr4NitdJoiEapSVdr8jGbaczs2UNpqtWoQ9YA9u8HbJYiUvFC3TmimSnPGxkHQnPwRE7ClG9svW9vr1wjvBLcWgKmBjRaNQ3IF8ww17v2aLR0Hm4VcjmVfl0PktHQalgsnICmp1teywezs0qStVWQ9PiFTKInDSIH6fE0tiA1mtJus7GfHY76Az4UoqTcI4/UfgBXQ9buDIZR1H4pDFkwAwYMGDBwViIQoD3V1cVXo95KKfTa0NksbVqzubpj/UyDOCBEbn5khHuvQ4f4//l55dTv76eTPZWiGklb25Mrb+RysQ+8Xu43qtn6ogZlt7MPw+HK+vG1kM0qKeIFhYCqMsZC8vT0sC3Hxqj/brdTNsjjIWFSryArwM24yLZJKQSRCIlGeV319qff/jaP/d3vgJtuaqweTiLB/ZEQUOn0GShl5XJxgovHlVyS18vBMDmpvKbVZDSWi1hMNVwZuSJklcdzdswf5zoGBoB9++K44QbKIq1dC5x/vvq/yLUIsfLJT04v1rny+Tgsx8dZr2LzZp1jIxLB0W9+E4Xde3Fv8gKMpTsxG7XDbNKwbW0S560pZTv3fupT0CYm6FXevLlELspUNjE++OCD1WXBbr+dN7KA3weDKP7ud0j4+vDww0oycuNGDvFXvpLHXX898MIX/hxAEX/3piyeGLXjry/fgxc/MwT420u/xOPBh298BwqB9gUv/9bFyXt8XJWBmJ3lbfz3f/83vv71r1ftA71E0X/8x3/g85//PP84cYIL2eOPQxsYxCNzw0ikvQhF7YinLHjqhe/HNee/Fy57AR5bFtrJCcxZe2Dv8KH/6rWIRFzYuZN9d9NNH8Fll30Q27fzUl/5Sl7fRz7CJn/pS12Ym6ME/vOf/x684Q3vxI4dvJ+DB1nofWoKeMc7gPXrnYtzeSLxNnzlK2/EeeepehzIZKiddfw4nMUiYLXiiz/qxxd/8kH82bX/H/7zVffAPjchg2bR6edwOPDADyaQiBUQT78KwKvw4VcdwdPWHcUO5z4OzIWxUSg4sGcPiYVM5oX4r/+KV5XZ2rvXjne8g8pF73rXDdizJw6LhSUT7rkHuOIK4JOfBC64AHjkETumpiiXt3Xrtfi//6sewZDP23DbbaznctNNO/DnV+/D+P4IttsPYtNVnejv0zCfsGLXYS8O7swgm9Ew0JkBsAUvelEcr3516fkOHwbe9jagWLTh8sv5fA0Pr8GJE/HFutbliESsOHCAa3pXVyd27oxjw4bKx1p1jma32414jegMS1mBFDl2506Oh9e/ns/RJz4B9MZHMTOah99mg9fixL/91R04OlrEJ+66DC5nEbf/wIzt26WGtJkTSTgMxGI4+oEPcBxUMH7MJhONj9FRwG7H3m99C9oFF6jiLbOzwN69wP79MElU1S9/CQB4EIDW1we89700eNeupfyQ07lkPvn9HXegeN99Slv4mmsqFIghfv7zny9K/uzaRZWp48dpk7zmNXx+zIUsUmkzPvy8mzEaa8eHf3wRTs46cPPNJDw2bVLkpKDWHBE9MY/9P59GIDwFb1sbPvCq9+D2//1SxWMBNZ/4fMDf/d0n8frXfxzDw0rqVQ+RcXSPH8RrnvU6XBe8CfD5sHbz0sgJh82BXIFt96bh8/GltgBssTA3GS97GXV2P/QhAIDT76eNMjyMt73tbXjjG99Y9Xr10lKve93r8Crd/L2IBV1Cx8GDwG9/CwB42eAgXjw2RqP/s59dMoYcOgPlhevXI26xcGL+yEeAiy8uOdaus3FuuPxyxP/1XzlgBweBq64qObddinbNzeHa4WHEv/1tNu7iBKhg0513x44dlZ+5xx4DPvtZ2L73PTr/p6exBUD8Fa8AXvpSSpiVTW76865Zs6byefftA44epTRaIAA4neh0OGo+962YIxCPk7z4n/8BvvQlYGAAlg9/WBn6bjemr7ySbPcLXgD8zd+UnEdkpZDJAA4Hjn7nO5ykK0TzmM1mPoAL7bF382Zo+/bxnx0d3PQNDgIDAzB1dwNvfjP7LhDAgx/6ELSREc4vP/0p8ItfcBF497thEsZ6Ab///e9rSn3poZ8j6uEHP/hBTakvPZZtR9Q59pOf/CQ+/vGPLznm+HHgr/4KeOghF6JRkisf+chH8MEPfrDqefWysO95z3vwzne+s+qxZzoMcsWAAQMGDJyVkAhqIVfORmnPlUAyVOx22ry5nArmOhsyVwAG+E9MkCyRDI8jR7iP0pMrAwP822LhxvPJzFoB1Fhta6ufceVw8HrHxlTR1HhcZaLUg77eihA11cgVeb+vj86UsTHuLbu66GzLZvneeec19r1S6wDg/rtQoPNqaqqxovZSeHlqqvFak8kkz53N0pdzRmauAOyE8IIDQ4pbSDGH1SJXpIiwxcLBWYVcMTJXznww4t6z6CwXne1yiF9E0zw4fhz4l3/hxvsv/kJlDpQMw0gEHk3DZK4djrYg0lEf/B4N6awFJhOjnwH14Lvb2ngiq5XjrsZC7q7FkpYxzq6F1IqJqAdWK+eBgQHylrfdxrlncBC48krlGNixMYUnRj144PgI/ipoWRr9bLHA7g8AmRxgsQP54uIE3tvLeVkI5Z6eUudhPTgcjkXNe+Tz7BCHA9PWPhQ1L0JHogjZe+D3FtEV0NDm0mCzmgA4AIsNA9kITjoCmDhiRixmRibDe81m7QgE7PB46POcnWUzX3wx//+0pwH/93/08115pR2aZofbTYJ6aoo/ZjP9jIcOsa/b24H5eTvGx+3YvFnXZdEoF5FsFujsRLEI/HZnOwA7fvfYAE6mBrHNNstFJZ9f/GAmA2Tnk5iaMiGR88Bk0rBlLdAZdMOTt/OCdUbeeefRwd3TY8XcnBWdnZWzPy++mP0wNQXcfbcVIyNW3HsviRWTiUTBBRdI3SBeB0kyCzo6ahuUr341FkgSC8bHHPA57PDYnejscMPjKsJkMsPlcMHt0pDLZjDQlQFgxtSUp+RaczngU59ie195JbNyenuBQMCMwcHq1+DxKDLPYjFhYMDTkA1sMpma0sGXY/v7+cxccAFJpbv/UMRbr8sjZUrgeKID63IW5KydmE8WAHiwfiAFt9uFzk5dcEM8zrERi8FT8o8KWLuWxsORI3Bv3EhjbusCmXnyJMeDyUTnczq96Hh3m800eGZmaLi4XFUnN9f8PA2i8XGep8ZGQu88bG/n8ig18aQ23eR4AV1ZM+waoGUsmI20AzCjrw+LpSjKbc+qc4SmwR3di/GJI/jLH/w1Yj0bEf7of8BjsVQ34BZgsQBXXulAIuFAIFAjUH9qCvbkPJzzMfi8LkTaeuFyVDaYcukirrz/M7ji4c/DpGlkid73Pg7C7m4yRwcOAHfdxf8ND8Nuty/WB6kHm81WQhwsIhxmHxUKZMgB2N78Ztje/36ykgcPViQ3BNavfAXWQoEE29VX17wGay5HkqGzkw9heRTRhg20xbq7YZmchKejg8fVeZ4sFkvlZ669XWWVhELA3BzMADxDQxy3XV01nxGz2Vz5vN3dtBF1WSvNPPfLnSPgdPK6hc0Lhdh3s7O0JTMZeO67j//7kz+pnrKfzQJeL89bawDrMpTcz30uF7hwmH00N6c2EIL+fuDjH4d7YIBzRC4HXHIJyZWpKSXxoNtcuZooyNnMsc3YBsu2I5Z5rN+vkuQlc8XrbfxZbua5PxNhyIIZMGDAgIGzFhs20LexYUPjafDnEkTmSWxG8RWdLXaPz8d9TUeHkv46fpxOkmhUyVgNDdHO9/s5Tp5scqWtjdfU2cnXWnA4VDB3LMa9QzrNPWUjOHFC+cyDwdrkimQ1iWSQtF8wyP8t1BNtyKcvwWySSbRxo9pzNUKuFArcOwNYWjS5BhIJdd5cbnUUKk4JJHOko4OsWj6vohglnKzV+foyUNrauKHVTRTiuzbIlbMDQkbPznI+yWQqy0Ilk3wW83kGiwPAz3+uFEficSUZt6jHmEhgOulFvOBCrmBGsC2HTM6EVMaCWLKMEZYxls3WZ1trQVgi3WRZnI9icpJzv8nEeSyVYnFygEXP/f6F+TCVwqUjZHD3TnZgPl1lkLe1KX1G+U6ojK5QiHP0injPSARIJKCZLTga8sH5yD142Zeuw0t/9gqs980i2JaHzcoMnmIRCGkBuApxjHTHsS4wj2yW8phSfFx8X7t28XXjRuXXeuYz+froozy2WGQbxWIkIQAmjogj3+1WY+fw4bJpKBRSA8bnw2TIjscO00E5F7Xj4RNdVF2R9MUFROf5pQdPsM27/DkE7XF4rAuTd9nYCAa5RvX3cxweP15ZzcVsBm64gb//+udZhB49hq99mYP8aU8DnvpU5T/t7uZ06/U2TuQL0qkinOYsYDbD7WK/OGy8IJvDjHxWMldUFqngm99kQIjfD7zwhXx1u4F16+p/74YNPG7duiUB7i1Hezvtxyuv5N+//S1QKAJrPHPIWxw4fNIFs92Go/NMId/Yl4BJK5b6T0Mhjo1Eon76azRK4yibZQdPTy861yX7BQDPc/fdHHd9fWpA33svB2Y+X1lHVdOwODnI2GpQ6sluV3EHhQIJMQA4frSIfFZDPqdhNmRFNmeG2azB6+UcYzI1EQQ2Pg5TIo6RX30RtlwKwbFd8Odm1RpdBw4Hn5OqxEo+Dxw+DNN8GK58DI4eP7IFS1VVpHV3fR1XPvQ5mDQNsWe8APj3f+fNaBptlGuv5YF338321M2NK0IyyTEQjfLHbAYuukg92LfdVv2zu3cDv/89G+FZz1JyqtUg48Tt5oAvh+j0Cmlnt69MMkGiAwEaubKQDgzwmptw1pegr4+kyuDgqd3sWCylWsYyDpJJtuldd/GZXLeOi1A1LGSuLD401aCXf7v6auCHPwR+/GPqCL/73cArXgFcdx0znK69Frj1VrZtNMpNSG8v2WKA7R8KcTy3auyeobDZ1PA3ZMGWwnA1GTBgwICBsxZdXZQtElvOQCkcDuW7SqXUhvBsIVcA7iN8PsqEiLNvbIz7d3HKd3dzfxYI8Od0cA6PjNCur3ctdrsa34kEDd2xMfoFJidrf/bECbaBkBT1yBWA/5PMeCFXLBbun0Mh7snLHUSVINd6/Dj/3rRJnXduTpEr1STSd+9WWSfhsJJtrgfJXAF4/BmbuWI201Es2SrhsPo9GmVHtDpzRQaUOAx0kaRCrrhcp8fzY2BlEAJ1dlY9L5V8i8mkeoZkeMzMUBbH7Va+r3QafKBzOWTnkwijHeGYBRZzEe/6jxF87DtrMB+3IJ4qc+DpyZWVPKziENGx1bMH5pDPc74JBDiP/fSnfHx6e+mj8/loRzhS8+gPpjHoj0GDCfftreI0c7v5bMZi/M6FmzGZ+B2SVLZs+fxkkpNdIoHpQgeSWSv6//A9AEDn6IO46NbXwD6vTv6J7w/hpn+7Gg8c6wHicfgLIZy3WUN/P6eMQkFNG0KuiD8JYNB3Rwef70cf5XvRKC/j/vv59yWX8FwDA2wvCbYWX3cqhRLJJ1gsgNuNRw96cWxKLTaPHPJhKt+hingtsHmxyTiyOWBsmhPLQEcKl9/6Sgze/HKOiQpjY2SEPNfwcGl2Zjme/Wy+7t5vwx9+ncEfH7DCbNLwqpdlsXatOs7nK1VjbMahlEkW4TDnYHVY4LBzQbNYAKtFg9VuQjYLDC6QKydPqjXv4EHgW9/i7695DR+Fvj46tir5dsthsSwmCaw6fD6O8R07yGfMR8x4ZL8HdmSxbigPt6OI4Z409s8w5HnLmgRchXipb1QkLjWtdsrunXcCL34x8JnP8OYiEXbw0aM0SpJJjjMpurggCYbrrwduvJG/33OP0h6tRK6IESKETy7XMLnicCifbjaryJXDoxYgn0cia8NElCxKX2cOuRzHVsPkSjbLe92/H+27fr/4dmDfPWyHVtSFOHqUD+7Jk3AHnXC2uwCYkM1Xdmb37LsLAHDkT96CxJv+QWUktLfTTnj603ngE0/w/XrGaaOQBUhIpaEhdsCLXsS///jHysUANQ34whf4+xVXcPIaG6td8yORUGOqGmmyZg3vXR66lZIrQn5MTPCapeih2738emQ2GyX3NmxoXU2zRiHElJCnJ04ocuWOO/jeDTdUv65cju1Qb6MC8BjRupYJu62NGW7PfjZTCz/4QeA//xP48Ie5EGazXLja2riQCVFWLHJ8iP7yOQybTQ1rg1xZCoNcaRAzMzP4kz/5E3g8HmzevBm/+c1vnuxLMmDAgAEDBlYEp1P5rtJp2qAm0+rUenyy0NPD++rpUSTE448rAkCC66xW2vtPdtZKsxDpCYDBVoODtP1DITpnqvkD5ucZETs1pfa5XV1sq1oBcS4XFp1OExMMUMzluN8rFPi9J0/W39+XF7PXkyvT0/Rr1FK2evBB9bum0aFbr9ZMNstrFT/cGU2uABzQVisdUXNzpZkrq0GuTE3xtQK5IuPMyFw5OyCFuCMR/mha5edLT1bqSdX77+dYkM9EIlg80cy0hoLdhfm4FZMhO45NuRCO2XD4pAsaTEikddkrFgt/MpnlP6zFoiJXOjoWK9TPHZ5HLMbHRPwq3/8+D7vxRl6/JIe5knNw5uLY1stq8H/cQ+evOZuCJaObZEU6KBbj9y6m7dAxXyiowNhlIRoFNA1aIomjiU5kTs5h8BhTSLLeALwTB7Hjs6+Ee2oUDx/w4uf305n9w72beU25HMypBLq76V/btk3N96KSUlpbR/lFH3yQ08r0NE8lEvZDQ0qV0OtV5IrM7ckk1JwUjS5mvj30RFvJrT24vw0nUp3Q4guDZiH7IDaRQCplwsl5XuiFlj3wThyEORLmYlNhIjeb6dT2eLg+zsyoZAY9BtxhbBpM4v9n773jJLmq6/FT1V1VnePkuDnvahVXEhJKSCggkkEyQuCfCcIyDjLBGBuQjQEbAzYIGwv7S7YRSYDJQgGBhLSrtFrtrjaHmdnJoadzrvr9cfv2q+7pnunZnd3VjOp8PvOZmQ4VXr33qt49955jGBI+cd9KAMC1503ihuZnYes7WhFsbWmhuKAkNZ68XMwWkMtLcMi5ctUKQ1N0qKqEvC6j1ZuCLBvIZGgqz+eBf/5n2v0VV9D90e+vvP8CoIG5fz8NuMnJxg7qNMBmo2vv9xMpCQAPPU33CW/QjhUdGcgycGKK2IP1XTE482JsoFiksRKP00NpvZvIiRNk6KLrwMMP08NLRwd1yqkpKoVlQzefjy78c8/Rd6+7jg6utZUmphdfFGVY1Rgepo7LVSvZbMPkCsffNY12w+TKgWMKZD2PWEbFeIraoTOcQSZVhNNJ17YRKVccPUr9/bvfhWR60LK98DyNhVM1Pk8kaDIfGQEKBbh6mqApNA6yuZmhQzmXRnCYJoOx866HateFcV5zM12Hri764es2Pl67FHK+SKfp2jB7upLGMHp6qIzKMEQpohm/+Q1NYA4HcMMNRIYYhtCyrAUu82NZ1FpQVRqsZIx0aospTRP6S8y4hkLUURadUWAJrNfNjP6xY+L+vHcvTdyvelX97/MzrarOXbnDi1tFaayvGQYRK7JM144rtPlhaHhYyAO8jMFLDoAum0WuVMIiVxrEe9/7XrS1tWF8fByf+cxncMstt2BqrvJBCxYsWLBg4SUMlgVjKAr9nOlkptMJRaH1XSgkgvcHDoiKieZmWgBz5nIjpugvJWiaePYfGaHzDIVEdc7evTOJjmyWXo/FaL3AgSIOis1VudLaKtY/3/8+eUGOjVEbjo/T+meuxMREgr7D6xSzLNjwsAjY1lMC4ngJY3h4bnKFYyNLhlxxuSiaxbryvOA+XbJgHLTx+2ewsCZP0iVV+cZ47rnn8NrXvhahUAgulwubNm3CPffcU37/yiuvhCRJM36u50zpRQafT3SniQmR5F0NThy22SqrA7ZvpzhUsUjvl8mVdBqjUQfiRTeKuozdR4X8z8CYg5TDkqUII5d42O10ANls/VK22WDONg0EypGBzFgck5M0h3o8lDg7MUHDafNm6uZ2O+C3J+EyktCyMWzsJrmqpw94UYincMFn/xAXffJ1sKVNAVqvl4I5VfI3LDnPBRwNxmorEY0CmQxGIhrSNi982x+AbOiILt+K5/7qf5Bq7oUjMoKNX7gD//4doQX19PFmUr3J58ttwUm9AB0m3xPN5Aog5vo9e+irkQjw1FN0X2ltpXm/q4umBLMs2NCQSd6RyxpTKcDnQyYn4/mSJNj5eAYA8MJRDxKGG1MRqcxCGQYQH0sjGSuUs/2vGbtPHBzfcLLZGTc6j4cUZlpa6O/+/qoYWzIJ9PfjinXEdOWKVEn19g3PoDnVR9n7O3aUs8Y5UcPnm1tBiJFN0BysITsjHqradShOGwxDglQsoC1MBzc4CHzrW0IO7M//nNrQ662hSBSLCbOcPXsaP7C5cBLEvN9P7Xz++fT/4/tCyBSV8kPmvn5qgCZ3Ci1qFK7stPhyNErXLx6vH7jO5SjDPJ0WHfdrX6MLHAoRm8e6n8UiHcxDD9F2N2+m6gRZpgoWAHj2WRGcN98rMxlqR/aCABozgTO1gyTRacTjwNq19P/YlIJcWkes4MREhvp+WzAHI5WB0zm3Elq5nUZGiFB74gl67RWvoN9HjlB/aKR0eDYcOkTnOj4OtLbC6bFBsQM22UAmPzN06O3fC1kvIOluQSbYDlUpZbu4XPTDgWr2NHniCRqI89FzrYVika5LNiseOFetEu9z9cqvflUhM4hcDvjv/6a/b7yRHvrZv2NiovY9hjNzPB5RplUPra10ro1o980GTROdiREK0Y1ksZqIMrnCpBHfdH5XqsC68ELxXi3wwqDRyhWAbuKNPAvzIqK3l9p382Zqe144MkOfTC584tIigrlyxSJXZsIiVxpAIpHAj3/8Y/zDP/wDXC4XXvva12Lz5s34v//7v7N9aBYsWLBgwcJJg3XxeZ1qty8dM3sz2tooqMUyvocPC6UArlQJBumZfrFV7Zg9UDgburOTXj9+nNYC7GsCUJxh715aHxw/Tn2A438cFJttzcJS0q95DfDXf03rzIMHgTvuoMznXI7W/7WUGBiFAsUvDhwQxytJIkk4EhHZ8vUCkNVelENDtdU9zEgmaZtmWbAaMbnFhfZ2ClywBAJAgYSFNrTn8iCABkvVRGEmV5Za5cqvf/1rXHLJJRgbG8NHP/pRfOELX8BrXvManKjq5F1dXfjWt75V8fPXf/3XZ+moTx1MNLPvSjV5mcvRWGYPK5PPK/bsEeO5bKERjSIdSSOW1RDJe6ApRTz5opD/0Q8eQu+PPg/fP/0N8Pa3kzzIrbcC3/wm7cwwTo4N5UkJoL5bigzI8WlEo6Lq7r5SzP71r6eh5POVfAkmxuFS8tDycXR1FBHy5pHJ2TD0/SfgGu+HFp9A+MXHxf44BZ0NZ0wR/VBItNVJVa/EYtDjSfRFfIhknVi998cAgJELb0Ym1IGdf/FVxHo24Yvpd+J4JICQI4VVnSnouoSHDvYIubKqACLPp729M5Vs1qwh8iSfBx57jL762GP03saNdA91OulznPQryxSfHRwszQ1TUxU+GBNRBbsOUzT5A/gs3EggkbbjxbEmDEx7y50mnQYKsRRiER0jcQooXjb9M3FwIyNinqvRN7q66JL39NA8PzBQKpKYyGFwxwnsPyhhuTYIxUY3gRsumsTrr4rSxdm3j7Z/4ADw/PNwOXR4PLQ9jsnPhUySrr1DysLlrgzIaqoBRaU5O5/T0dlCUapHHxVyYH/5l4LkdDhqBOAnJujCHD5M13XPnlP3BNi9mwLgrOvWIAIBug2t6smh2Z9DOmfHk4Mkw2fLJLDvOJ1IbzgBNZ+AqxgX125qStyQ65ErX/4yBf59PqpekWUiv/bupQvtclEm/NQUved0Ckkw9uAAhBbc3r0iKG9+eChVbGB6mh4KVZX6VoNsKBNwHg+dkqIAy3ppvI1F7MhDxVSa2qLZlwZSqcbIFcOg808mgf/7P7ruvb3AH/8xvc/eM/F4JZkwH0xNiQc4VQVaWuBy0NhQ7XrNyhX/sV10bl3nAZIErZCk9mxqomsQCtGgufpq+sK+ffQsUU+rr1GYq4r4XsyVKwBp1K1YQdfuZ6Y548c/pmvc1ETEFKfhNzdXkM8V4P7h8TSmybcQ4AWaWSIvFKIOtZgrV2RZPFyMjtJrrAjEY7MWDIP6J7dLo5UrdvvclSvRKI2djg6afzZuFBV0XCo4Oir6wcu4esVMrnAelwWB00auPP300/izP/szbNy4EW63Gz09Pbjllltw8ODB07VLAESE3H333bj++usRCoUgSRK+/vWv1/18NpvFhz70IXR0dMDpdGLbtm148MEHKz5z6NAheDwedHFqKIDNmzdj7969p+s0LFiwYMGChdMODqKbn0GXYtZ5MEjneu659H9/PxELQElPX6Pn+8UmCQZUyoIlEvRjs9Gam9ecQ0Oi6ODIEYoZHDsmDOoBap9gkPrAbASTuc9s2UJyxeecQ+vcz30O+M53hIxyvSTaWmb2rDXP68jR0foe1tksZfUCotpmeHhucsXsD8HJ8Ly9RYvmZjoZszsta67p+uwa4vNBPi8u6Czkitu9+AjK2RCLxfD2t78dN910E5544gn81V/9Fd797nfjn//5n/Ev//IvFZ/1+/24/fbbK36u5oDSIgTPh0yuVMcWzZVgrEi0fDnNPbpOHh1OJ8UCk9N5FBIZjA0VUVSdiKYUTMUVTETFDSfXN4wVj30D3p2PiSg4QEHQWQLoc8JcueL3w/BRZCA3ScH+YJDIgqEhmn8uvpj6sMtVSqIdH4crG4EsAZpXw7mr6XtP7BbR0Kbdj4j9SRIF4djl3ZShHQgIT9x5J24XCkAyieEhA2nZhcLBI2iaOoSiomFsK2Xj5z0h/OotX8c/SncDAP41eyf+IER+CA8cWk5zA2uTmVDLb8V8OuwD/rvf0ddZlpFVlrq76bcsU3vyvHzoEJCO5akt4vEyO7/rsAvHRig4dg0exuUgtub3B8KYzjoRnyST6vhYGigU0DesoqjL8EhJ9KIPeX+pKofLY4CaNwtJAtato3mpu5tiaLt3FXHs98OIThXhio1gXU8ab79uBBuXJXHn64bhWt5KX3K56GHh4EG6YKOjaG2lPiLLjcXXsskiFJsOm1GYEQ/VFB2KViJXsnrZ1P7HP6bxc8UVJMnGXb5mwvrkZFkqDsePl05wd22pq0aQTNI2Uykag/OoFON7t0dO4fy1tP+HD3Sj6YVHcPmHL8exXdTnVrSnIaXTcGq6aEQz+VaLXHn8cTKkBiiro71dlFR99at0QZYto9/s9XH4MBFEqkqNyejupuCpYZCxfbEoHh4Mgx4mIhG6CFwpwDqlDT4sBIOCLEkkgPWrS1VJkw5sWJbCdJrmvbAjBbWQKsuqzYqhIWqjQ4eoPQDynlm2TBzjoUP0+2SrVxIJmmcSCRrEsgyXRvOwQ9WRrVG54j/2PABgonsrJBhQpsdpAg0GReZMKETsa1MTPUv87nfCvOlkwd+Nx2tXrkgStQ8A/OhHtN9olMrCADI0ZxZd01BmuGpNzIkEtfFsfisLDVWlczDvj2/Ki5VcYUKEbxCpVHluhdsNXHZZ/e9OTNB16O4WC5bZwJmDijI7A5DL0Tzv91P7rlwpJjOHgwg6gBZTNSpSX26Q5UpyZSHU/ZYSThu58ulPfxr3338/rrnmGnzhC1/AHXfcgd/97nc477zzsGfPntO1W0xMTODjH/849u3bh3POOWfOz/9//9//h3/913/FW9/6VnzhC1+AzWbDjTfeiMf5pgUibHxVxmo+nw+JuVbxFixYsGDBwksY1eSKoixNckWS6Fl+/Xp6ts/nhRlvS4sgFWarRn+pgqWHOR7Ba0ynk5I5p6YoTnDgAMUZBgfpJ5OhNTmvI0Mhsb6cDfy+qgpJ7c99jpInOZH04x+neOjRo7VjM4kExS2OHaP/V6+mNVYgINaOY2N0jLXIlRMnRNIjK00wuTJbLMjsD+HznVq89mzBMKhNyufJhkLhsBB7BwQRslDVK+YAdRW5ousiztGwbvwiwbe//W2Mjo7ik5/8JGRZRjKZhD5LqVOhUFgy6wNOLo1EaIxkMpVcXSolKsFYqr67m8gJQEiDJZOAkUginrJhdExCVPfCgITdRylS3Axifncr52Pf+bfj0B98CMVPfxb4xjdo8o7HqT+fbOVKPi+CIcEgMs4AAKAYiZWlvziv7uabqauz8kvIR4SGKzcNaBo0p4zNy+n6/gyvQbydyiHD+34POWc6tkBAeK6Mj5cbTlFojExMCP6zYcRi0HWg/4SMiO7Hqj2koDCx6UoUnSIg/cVfrETKcOJi7x683fgmPrT7dihyEUfHfTg86KQTrAoO1fJbMYM5wt27Ka6bTNK1vfxy+s3VKl4v/c+xsyNHgNRonOarkt+KrgPPHaJ19QbshS+k4CoQAXRwv4Gs3YWBExKQzyN2bBKZnIQT41QOt9V4DhKAqbf9Be2ATYZ1vW7f0DSSZgoEgPY2A53FfqxrmcQG5RB6m1Lo3eLH37x1AA99bhduvHhKfGn5crpJplJ0wlNTaGmh8wwEGiNXMokCNBtFn9zeyrCLpuhQ7AZgsyGf0dEZEjc7v5+qVgAaX5z0UBFTTaXoJxqlRvd46KYajRJbNpdOZi2MjVFf5UDiPDK0VZWOzy2nsKVnGgCw40gYyrNPwgCwe6wVALC2l4gKl56g7bOfSSxWeQ9jjIwAn/40/X3rrTTJDA8Dl1xCjfLcc8Dzz9PgWruWgqNdXaJq5bLLZjIXnCH/9NOVpvZTU3Q8k5M0CXD2OvetBsmAQIAOzekskSsr6MFj31AANs2GoUnqzwF7HE4QgTir0lM+T9d2cpKkzjIZGmTXXkvtxaRCfz9NLmNjJ3fvT6fF90oLBJuN+qqm6shUV67oOnzHqXIl0nsO9fVIhEgUu11MBD4f/X/RRfT/9u3Uv07F2D6VonY5cYLuDcGg8PJgXH01vTYxQSVh3/oWXZCVK+lY2COru5smr5YWaoPqe3gySX2IyZgzAUmiPm0mEdrbhf7iYgRXnXBG2OSkqFq56qr6C9BMhsjF5ma6DuvWNfagqaqzV64wKW2zUXljU5PQWQZoAHM11OSk6Bsvc2sIXqexCqMFgdNGrrzvfe9DX18f7rnnHrzrXe/CRz7yETz22GMoFAr453/+57rfi0Qi+CFnJtTAfffdh+QsDwvt7e0YHh5GX18fPvOZz8x6jE899RS+853v4J/+6Z/wmc98BnfccQceeeQR9Pb2VpTxezwexKoyfGKxGDwNiWNasGDBggULL00oCq0V+HmWk3yWItra6Nw4CYnXyM3NtHZpahKqSosJLMHEgdChIfFeOEznxpUkrJjR30+/f/ITIYUTDtO2GpExlmX6LBMVNhup+Hz+8/TQPTFBhEs8Xnk8jGSS1kqs5MBeouEw8QSAqFypFcvgrOlgUEi9cWXObLFX3i8HPU5Faehs4cABIrBefNH0Yns7dW6Tn0S5lGChyJVsVgTawuGKicIch3C7F+c4qoeHHnoIPp8Pg4ODWLt2LTweD3w+H+68805kqjrOwYMH4Xa74fV60dbWho9+9KPIL2LNhOrKFaByPJrJSpa46uoS5MpTT4mE6kI0icFhGam0hEjBC7ejiCefJ6b2LnweAHDEWI5HL/sIBi/7QyQ2XETBDvOEkM+f3Eo+kxFkQiCAuEIBOHsyinCgiGhUkO1XXEHH6/fTUFJ0OkFNT0PWFDhUHddNfx9uJDCILtx/01eRCbbBlksjeHC72KeiULCOZcFM2dAPPAD8yZ+Qx8u8TicaxdCIjEzawFjchfWHf0pNc+HN5Y9sf9GLx3cHYJMN/OHbFRxaeQOCmMaVPjKpeuDgMiFXVmJ20mlKeAdqV64AdCmWL6evfPGL9NrWrTTlrF4t4lx+f6XvyvHjgB6JIhfLUDv4fIgm7Xi+JAl2BX6LzBvegq1rqWMdHPZiOO3H+DiQycmIH3OI5CUAAOBjSURBVJ9EKl7EUJSCiVvwAibXXQrjsstpomFyYQ4DraamUnfKDaDHPYUVxcPY2DyGV1zrwnnrUljVmUZbqMZc6fcLibdIBJpqIBCg827E4zybLsJhy0GWDDhcpYmxWATyeaiKQdZVKpDPGegMi+O/6y4RU02nxX25IgA/MUHbisfpOJcvF9JYsRiwa9f8KwPGxmis8OCezeC7Bvx+wIMk2t1xLAtGUSjKePjochzBSkzpAdhlHSuXFahqNh2lBxEmTtmE3oxCAfjHf6T31q0D3vEOut7hMF0EZv2++lXaBpuNF4siaGuWBGNcfTWN0eFhuply1czQkDCy52wbh0MQeA22J1c3eTwlcqWXYlcHxoIYTXmRL8hQbDrcSMKp6VAy8dnlNNnEvq9P+FPcfLPIrlm/nl4bHBT+Ricju5XJVJqGl+DUdDhUHUVdQsFEsKtDx6Ck48jbnbCtWYlAcVI8zLW1iTJWSaIOzRVEe/fSnDgycnI+WoDQ5uOHTLMkWPkAVeANb6C/v/51KgsDgDvvpAvDhEk4TDcvn4+ut7l6JZ+n/bjd1LfO5AOO2fwdoGc9h2PxPmRJEvVX1jIeHhb9udY4Bah/9PfTtWxvp+82Ks2mqjTOi8XamQyRCI1p9llZt67yfYdDzDX8+Xh8Xh5MSxHmQvl53iKWPE7byLz00kuhVrGPq1evxsaNG7Fv37663/vSl76EW265BT/60Y9mvPeVr3wFt912G77xjW/U/b6maWhjlnwO/OAHP4DNZsMdd9xRfs3hcOCd73wnnnzySQwMDJSPO5FIYNBUYrlnzx5s3Lixof1YsGDBggULL1WYTe2XauUKQOcZCgEbNojXJIkS1jRNxPAWG2w2Wr/y8VevpzkJ6ytfAT78YeBd7wI+8hGqLvnyl4UcTHd3Yx6RkkSf4coVMzZvBu65h9Z9x47R+vnYsZkV+YkErV05/t/RQdt1u0VQbmJCrF+q197PP0+/e3pEAJi3VS//Jp8X8Tc+fn59sZAruRzFIiIRar+yZ4PHQ4v+UEgEpzizbqGC++bKlaamCnKF84/MMnNLBYcOHUKhUMDrXvc6vPrVr8b999+Pd7zjHbj33nvxx6x1D2DlypX4u7/7O9x333345je/iW3btuETn/gEbr/99lm3Pzw8jOeee67iZ7Z10pkAx214ThkbEySKeXyZyRWOb3V3A5s2UYx3elrMR8mJNCZGCsgXJcSLLiRSQH/EBxVZ3Lx6H+w2HfmCjKEpFboOxFKloFxvL/1mb42TGazJZJlcyfhbkSiRK+5cBB5nsSx1tWoVxVBY1SscRvkEpWIBLrcET3Ear3zin3Adfg0AeHRgFSY2U4C3afdvKvcbDAoJrlL1yjPPiPjejh3zKy4oTkXRPwBMJTV0ntgBR3oaWW8TImu2AQCyOQlf/CFN+G+6Yhx+j47BlZcDAG6TiUV/6GAPCpE4BZpKA3fvXmFOP9t9kOOiPNdec43wEmf4/TQHsAfwwIABxONIjZX0Kt1uTMXs2L2fru8rpcfgvvEKeF93DfyYRsJw48SLceTzFE9LxHSkYwWMTVPkeQtewIkr3waXx0adDRB9Y45g1wr1BK7oPoptnr1Y4x1G85Z22F1zTFjcGVj/JB5HSwtNt3b77IUdhgFkUjo0OQ+nUoCklubMD3wAuO02aFnyAVJUGfmcga2rEtiwTseb3wxceaXYDt+z2EakjMlJUa7p99MHli+nB5rDh4mE2LWr8TETj1Mb8klFo/UNvuvA7ynCoSdhK+Zw8UrKePhB4nrsAPXRnnACXpcOl1+h/eVylGWRSgkTejO++lUiP9xu4GMfo88Ui8I76aqr6F60e7fIugCoMiIWo3vV+efPPFCPRxjBP/wwnTdrmU5O0jb5XsqsRyNsWgksncO+K+3+FBxqEam8gicOUiZMWzADvQA4kYanGK2/sViMJtLhYZIxi8dp0N14I+0kHKZJFyASplik+Y61VucDLlE0jAp9T5dWhKbQtlgarKgD+V2U5RHv2QifV8Iq2zGaRBVFBNAZwSD5oPh8tI8nn6TGOdkqAL4BcXyuFrkCEAmladQexSKwbRsxw0zmOZ30wxrBzc3U96tveh7PmZMEY1STK+wttJjhctF5SJK4p3d1kVRfLYyN0bXu6aF5gDPkGgFXrgC1q1empui6ut1U9Vataetw0Gu8MDHPuSfju5LNLgmTErP/Fye2WSCcUdrTMAyMjo6iqamp7mc+9KEP4aabbsJb3vIWPMwZBwB++MMf4j3veQ9uv/123HnnnQtyPDt37sSaNWtmSH5dVCpZfL60evd4PHjd616Hu+++G+l0Gj/72c/wwgsv4HWve92CHIcFCxYsWLBwtqBplZ4rS7VyBaCkJ7NiaChEwSRVPXMelacDnNAFzCRXXnwR+MxngO9/n+INvB7o6KBg2bveBXz0oxQoU5S5yRWAPqNpFJAzrxMKBYoDcDXJr35F6yb2RwFoTZJMAvv30/+scuB00m8Oyo2MiDW+OTZULIqqjWXLRDBwaoqOpV6w0uwPYe7zJxuvPRsYG6M2P3GCruORI6b27+igQAEv/tm0eqEqVxIJwaJUVa5wn+JruJSQSCSQSqXw9re/Hffccw/e+MY34p577sF73vMefOc738GhUsr/V77yFdx999144xvfiLe97W34v//7P7z73e/G9773PWzfvr3u9r/85S/j/PPPr/iZi5A53eA5gGNjU1Miobm6ciWToZg5x7d6eug+csEF9P+zzwJ2qYDkNA206aIXsMk49jh94RrpEey45H1o9lNHHotoSGVlxFOlUgizmezJDtaxsXJgZVjtRbYkC+bLT0IqFsoJ7tdcQzE1j4fOyUyuIJeDy2XgnN9+Ee70JK7z0jV9Yq8P45uvAgA07fktpKIpgKOqtLFIBMjnMXUkgk99SsQ7jx+fR2GBYWDwWA6ZaBajaR+2HPsxNcsFN8KwUUDo2w+3YmhSQ5M/h9uvHUEsaUdmOWUTvDn6FfjdeUynNDx1tInasTRwmWCvJwnGuPJKMb7tdqpQMlscACIeza+PjgLZjIH0ZKqstXbwhAuHx2me2roxB3vIB7m3Bxf56KaQeOEoJpJODA0BuiEhGStidJom7GX+CCJrtpHJNveN4eG5+4ZhAH19kCYnqEP39DRgcmE6KQ7+T02huVlIg/E0WwvZLIB8AQ4pB5eSp0ZLpSg7YGICjt1EBiiqhHxWh1PT8R+fSeFP/1RsQ9dpOw4HxSTL82suR501GhU3ZIA67ooV1PeOHhUVLI0I44+N0Q0lHheG7ryfBuG3JSABcEspbF1O33sUV+JHoOqBDe4+aKoOV1Cjm3WxSL/j8TL5VsZTT4nS2g9+kB4W4nFqfJdL+KHcdBN95mtfExeDJcFe9ar68kGcKc9B/iNHhBRaOCwamyfEeZArAPUP7mLpaB5r2+n++eBOioO1hWnOcxYT8ORnCdQePkzkz+goSVsBJGsWCNB93+kUJWdDQ3S+nJ0yn7Ryfjj7wAfoodH07OBy6NAUyvzP5mRSUxpxoOXETgBAavU52NI8DHsxS4RWKDSTBGD9XTY/fOYZas+TNbbnyhX+/ooV1FaHD1f2d79fXGtZBt7zHlFu6/MJ8kKW6abHx8nVK4mEqIA40wsFTRMVVIpCY2BW/bhFAJdrJlF13XW1Hx7TaVoMtLSIypL56M7ydQNmkhq5HF3bYJDGUC0PF2azzUS+rtM4mS+5Eo3SImz79kVu9FjJPUejC2ftuBRwRsmV//3f/8Xg4CBuvfXWup+x2+347ne/i4svvhivf/3rsWPHDjz88MO47bbbcMMNN+BrX/sapAVauQ0PD6OdoxEm8GtDJi2LL33pSxgaGkI4HMb73vc+fPe730WoWtdxjn29FLLRLFiwYMGCBTM4i58TdpZa5rkZ4XBlAKm5Wfh8LOagsKYJaWt+dEmlqIrkL/+SgvFeL/Da1wJ//ufkC/u//wv8/d8Db30rKUpoWo3M2DooeRIDqDSFP3SI4ls33kivPf00Hc/wsIjLp1K0NmEZmjVr6DVeLy5fTr/Hx0Ucw5yQHI/T2hmgREWWQ8vnKTY0G7nC/hDxuFAJmk+81pypfzYwNkak1PAwBbPTaRNxxXX6/GzKckQLlSXHAQx2rDZNFBx7W+wJlbXgLA2It7zlLRWv33bbbQCAJ598su533//+9wMgabF6eM973oNnn3224ud//ud/TvWwTwns7xAKifmA5cZ5fBWLNG7Y55njDFwpt40S1bFjB+AuxpBKSUAigUjRh2bbFB7fS/30vNVxFMMtaC1JMo1NK0hnbYJcYbaVy2dORoqjNCkaTieGC82YkilY5c5FMDZcLJMLV1whkpnL5uG5XHkMBSaOoHfHd+k8bzoHkmTg0AkXDocuQs4dgJKKwn/0ucp9h0JAsYhiNI5PfM6BSETE5wYGZvjK10VhOoGBYTsmJwC5kEH30d8CEJJgg+Mq7nuEyvj+7PWDKBQl6IYEeVk3CpoL7mIMVy7vAwA8cGAZTYKxGFAs1vRbYVJ7cpIqSPbto2vMyeHnnUfx3OrnBUWheaCzk9rPMCQcPKYgFS8Cfj+yOQk7DzhhQMZa7EfgOtKQ8ziLWH0eRWuen14G2+Bx6OksDAOYnNAxnqfAl3LpBdBUA3abIW4W3Dfm0oRk7x2fb24zZDPY0CweByIRKApd1mBwdkI/mwVQJFkwl9OgubOkigEA9heeIwPwUuUKgBlkON9vmFwpY7IkvxSNzpTSstsp0CzLRBgkEhX7rQnDEJJgDz9MpvE7d9LxzCNA78zHoMoFuKUknB47zg8egQEZ9+NNAIALbDuhKTqcIacI5gPUth6PeBDL5QCWkX/960XZVDwutCd9Pgq0XnstNdD+/USUTE9TABMQ3iq1cOGFNBgTCZqoJifpu2xkb25Pm4361zzIlWCQvuZ06IhHi1jfNg0AOHiCLmRLMA+bQ4GaT8Ijp2rPbWNjNE4HB4kFZS+YG2+k42pupo4RCIhMk/Fx4Q0xH2P7bJb2NzhI9/tnnim/5dKKJCFsJ9+VgTENiZQN3ePPAgCaLl0DbXq0ZLrjnlm1AtB7DoeoGNqzh/rW5OT8k0ByOWEuzufY1CRMIMplvSW85S00X/zRH9HvaFRk2ZjNFjs6aBJraiISlsk/7ptnym+FwZU0AHUoTVv8D1ouF7Wxud2vvXbm51gOjBc43d3zrxyarXIlEhEseb2STSZW+Tmkv79CJnJelWFDQ9Q3ubJsEUNRxKXgpYYFwhkjV/bv34/3vve9uOSSS/BHf/RHs37W4XDgJz/5CdasWYMbb7wRr3/967Ft2zZ873vfg726XOsUkE6nodUQuHSUBlLadJNrbm7GL37xC6RSKRw8eBCvetWr5rUvzkw729loFixYsGDBghksC8bJPUuZXJFlqjxnKanOTjrvxSoJxtC0Sgnjp54iafIf/Yie/a++GvibvwHe/W5K/OJnfEYuJxJfG61c4X6SzdI64+BB+n/NGlEFMzZGah2s6c+y6oAws1+1ivbP68WODlEVMzREv83HGomI765dSw/4HAc5cWKmDykjmaRjzWaBD30I+NM/FYUdjZAr09PUrjt2nJ1Kl0yGru0HP0jeNsmkIK6iUdAFkSSxYGUZm4WqXGFyhfVwalSuLPY1fy10lOQgWqsmiZbSJBKZJXuxu5TtODWL7El7ezvOO++8ip/1rJ9/FsHJ8Cw2wInQ1YSnqQAC4bAgSZlc2b8fyI9PIzmdQyYrIWX3o+fRb2GnvhUyivBcugU+dxEdTRRFnoopSGVkpHM2FIrSzMqVfH7+aZKlQEbeE0IOKiYNGiNqahq/+S1lYW/ZIogkVtoBUDKMKQC6jubv/jskw8CBNTcjuWYrNpR8FJ7YF8TkpisBAE0vPFK5b1UF3G58+8Fm7DzogaYauOMOGj65HJEWjWDsSBzZHDA2ZcO54w9BLhYQ71qPZPsqGAZwzw+7kC/IuGBtDK88J4rphB1OtQjNISPRRf3pZu+jAIAnj7cjNpEFDAP5iWi5EpCT31Mpqg7cv59i8uk0xRZ7eqjSsaMDuOUWoZRSDZ+Ppon2dgo8HTpuR7pAHjRTcQX7nqZJ+jL7dtgvJbUIt1PH1vMpLPEYLse5e76FqUmD5trSzWWFdAyJi66BSytdfyZXuHJlNjKZg+bJpCBLGgWbmsdi5Xm1tZX6uqrWVzXKZACbnocCk1m5ieSQdu6EqhhQVAm5olz2YqneBkB9syJhfXJSVH7UCjiywVwuR58dHJx93MRiotLht7+lm+OTT9LNZXy88SBiLIaAPQG3moduV/Em9acVb78y9xAUO+Dyle4h8bgIYJuvy6FDNLH4/eSPAYjr5/EIw8BgkF5nX42vfpUM34tFehjhPlILNpvQX/vlL+n3xIQwsjeDTeaYTW4AXm/JAkbLI5G2YX1rZUcJ+/JweO1AOg2PozAzE17XiRyLRun6cIndlVdSsLm1VVTxAKJc7OhROt6JCer3jWoPZjKVGj9cJQPyXAHI2H4iqmAqrmC1exCeyX4YkgTHhhX08NXURJ21XgJyMEg3B4eDrv3zzwuScD7gG9HgIF0TTqV3uYiMmJysjPi2tVHfePvb6f94XBjjmKtR7Hb6bDhMxzU6KiZAj2embNTphqYRk93WBlx6Kb222B+0+EbLpNG554rsMDO47Xt6aDDNNpbrwUyuVN8bpqaEp1a9RaCq0vu872PHaCLm6r56C45qGAbtb3qavrdYyuXroJpcWQJKZwuGM0KujIyM4KabboLf7y/7nMwFn8+Hz372s5iamkIikcDnP//5cvbYQsHpdCJbIwWRDSoXcn+cmXa2s9EsWLBgwYIFMzSt0mtlKcuCAfQMf9559PfGjcJ3eDFDVUXG+IkTRB6MjtK5fvazlLTX0kJrw9Wraa1x+LBYc2ez4vo3Qq44nbTesNlovXDkCL22ejWtqVesEKbWzz5LxxSPU0V9Mknxnv5+ep+PmwNHTmelh3W1EsfevbQuURSKu3LlEUAxtnS6tm8lV52Mj9N6aHqaYhm5nDC2nw0TE/S5s5V0NjZGbcleuyMjIr528CDJ51RISCw0uTIyQr85c9M0UXBwcbGv+Wvh/JJe/2BVBjBXtzdzgKAGjpbKimb7zEsVTKAyuTI9TeOOEy95TJqVZ1g5A6BuyPKA+w8rMBIpDGVCaIu8iGdfoGDHlrYxONx2eF0FdJXIlbFpBaksLU/jKZvIGJ2eFgNvHoGJWAzo209MUMoZRv+Iimk7jRElMY2Hf0f9+Jpr6LN83hXkSi4HbN8O5ch+FBwePHXFB5HNy3jFJio7eXKvH+Ml35Xm3b+ZMQHtmu7F17cTwfHu146gvVUvz3tHjzYWt471RRAZz+PwRADP75bxMfwDbjXuw7s/uwY3/s1mPLXfB8Wm4y//4AQMA4gl7fB7ClBsOpLLSMv+ovgjWNmRRr4o45H9HUA6jYO7UsjlKFDS2wtA1zE6kIMtl8bK3gI2byYSu6eH3n/jG8l/+K/+qn61p99P7djZSkHoIyccSCkUxJqMKdhVMrPfujJenk48ziJWdmTgdeSQghsjYzbY9+9GMpJFvp+I3a5ADIrTTpJgQKUsGPeJen0jEqHJ0jAalwMzw+er0NoPh+n+FwxScKnWNJtNFkhKqVAQc6O5gmRgAJ7kKFSHjKIuQ8/NJIcyGVE4USZXSvJkiEbpzXoTL/s1jI/TAZoUOWZgbIw+88wzImB44ADdZLjcsxFEo/BLMbg0HZJiw22Je2EDBbmbMI7zpsmXyKnp9OAVj1dKNDH27qXfGzeKhxMue2V/sXCYfhcKVMnhctHDyNe+Rp+vNshmszf20zAMUWb71FMimGvOqGc4HKJvNVi9Ikkl3xUlg0JRxgr/ZMX7QW8BTp8KGTpcxfhMcoWZzcFBIpuGhugcX/1quv8yu8kB4LVr6f8XX6SJm2XdGq1eyWQqKz6eeKLcsR2qDlkyoKk6dENCRziLnnGq0pOWLRMdNRCgTJ96kwP7wnGZ3M6ds5d/1QOb8R0/Tv8vX07H4HZXZgTU+24+T8cRCMw0h+/qovYNBgXZdDb8VgAxhj/2MargAhb/gxYf/2WX0cN7raT7VIqeOdvahBxY9XVqBJx0ZLdXkm28IGDvptlivg4H3WtkWWRpMbHdqDRYPE59LhajMbUEZMGYk4zFLHLFjNNOrkSjUdxwww2Ynp7Gr371q3IG2Fw4evQo3vrWt2LdunXo7e3FH/zBH2D4ZDUZ66C9vb3mNvm1Ro+10X29VLLRLFiwYMGCBQYH0/nZcilXrgD0nP6hD5Gp+2teU1u9YLFB00iKmdcekgT8wR9Qot7mzRRTYImfUIgSHB0OikPws76q0k8j6xfuM5pG2w4EiFBpbqYktNZWUeW/cyetK6amKJA4PU1rXl73dnTQukdV6dg0rZJcMfsUG4Yws29vp+/4/eLzw8P0mVqxj2SS1t4sow2ImBPLhc2GRIK2m8+fnDLRqWJsjGIdjBdeoOs5MGBSfTGXGvCKZ6FWPRx0qUGucMx7sa/5a+GWW24BQJ4qZvy///f/YLfbceWVVyIWi81I1jIMA5/4xCcAAK+uDvItAjDJwLwQ+64ANA54LBSLYkyZyRVAEKw7D7igFNOIw4trH7+77L+weYsEm6zD7dDR00obH5lSkc3byAc+aaOgFvdpZmQbIFciEZornntGR3yISJBJuQmxtB2h5QEAwKF0Jw71qbDZgFe+Uigs2WymZGaWRfr5zwEAx66/E3qoCdmcjEs2Urb1Mwe8+Pi+N+OQuh5adAzegRfLxxFN2PCJ766Cbki4bn0/Ll49BTUbLyfC9vU1cDrpNMYHc3j3v2/F5397Hv5k+tP4R3wMvxw8B4cHXcjkbFBsOv7sDYPoas4hnrJBNyQEPAU0+fMorlkHAGgZ3Y1rziMm9IEDy4BEAi/spgl/87I4pL17gBdeQGJvH/xTxxAY2Y/Vyws491zg8svJF3z1apIGm+0+4ffTPWhZG02Ux8fdyKg+FIvA0PEs9mbIkHjdNZ1QSybZbgfJDq3tpcb4Da7ChXu/DttzT+N4hrKavb1haIohKle49DSXE+RvrcY0DJqkEgm62ZxM8iQ7k5dKtWw2igs2N1N/OXZsZkFDJlmAQyVyxekuNViVPFfw8NOwa/ReIVu7coXvt2VyJRIhkoE77Gyapi0ttM1IhPZdK/PAMGgQT09XSEEhkyG/lny+8sZZDyUi0o8oZKeG5sIIepIHcB0eBABsww744ydgT8fpGnq9tI+pKfEAwuByKrPJdSJR0tkq+SO0tQmpqUIBePOb6XOpFF3na64R32U90aEhulj79tFNtFikflQoAL/4RaWZgBmaJhi0eTwABIOA25YB9CI0OYcmn7hP+N0FOL02uBw6pGSVzFA2S/PdxATt71e/otcvv5yC/z6f6BBsWreB/JWwfz8NQlmmtm3UcTqTqSTgkslyf5AkIsQ0RUezP4eWYB6946W+smmT8KlRlNpVCAyeWPnm8MILdL0arQBgpFKVxNGyZXQNubokHKY+W6vKKBajtnG7a1fYOJ10z+GbH2efnQ1jRi4pVxT6W9POfPXMQsNmo/M4/3xaiJmNMAGab/r7RaYVV66cDMxZg+a5dWpKZNbN1l8BOg6XS2Rxmb14GiVXuJKKH5ysypUli9NKrmQyGdx88804ePAgfvazn2EDT/pzYHh4GNdeey0URcGDDz6IBx98EMlkEtddd92spfXzxdatW3Hw4EHEqgRvd+zYUX7fggULFixYWMrghbvLRc+8J5MctNiweTP5nLpccz9XLwawZ+Nll1Hw64tfBP7sz2hNMDVF19Tvp3PdupXWjStX0jr06FF65lfVxmNO/DkmQnp7iSTZvJnWfX4/KT+4XBSvGR+nNXsmQ+talsJpbaU+x0F5NrdnwqvaZiGdFvJjnNDu81VKjQMzkyCLRSHJP2lKHh0ZEfGSudY6LOVtJnvOFFIpig09+6x4bccOanNW8ujrA9Iwlf0sZOWKrgs2jFdUdQztlxrOPfdcvOMd78C3v/1t3HrrrfjSl76EW265Bffddx8++MEPoqOjA8899xyWLVuG973vffjSl76Ez33uc7j88svx3e9+F3fccQfO41K5RYTqypXx8ZnkCvNJnKfW1UWLbI4PbruAMkWfPujDiqY4rpj6IfThUTyOywAA63qS8LqKkCSgt4U2Pp1QkM7KSGVtiKdLQaTeXvpdSyewCvk8xYN37SrFUQ8VEB2iCUEKBrBhowzvMspM/w7+EABw0UXUnYtFYcdRvg9ms8D999MJr1yJiaveBE0l34He1iwu3zyNoi7h/t+3YV1uN96M76H/8eMA6FD/+b4eTERVdDelcddlzyIdzcGZmkBPDzVSX9/cifD6yBie3e9CJKlBlXK4Er/B24M/xZ2vHcSn3nUU3/zwPvzi0y/gta+gyW06aYdDLcKhGmgO5CGvp4z2pskDuGT1BGTZwP7REPr7DOw+QoN2c+sYMDqK9MAEiqMT8Ez1I6Cm0OOaKMdpG4XTSe25unkaANAf8UJ3ujEaUXHskaPQYcMK23F0XCAyGxyqDptsYPNyulYPS69C59QeXL39U3gBpFfW0SWRChJXrthsom+w7FWtvsHSU+zrcTJgzwXOFADdQ1mtJpudaWuSSRShKTocUhY2rdSXT5yg36UbmO/gM1BUatxcVq9Jrjid1P7lqtLJSXqDS45mg8NBn+GbabUXBSAqHAYGKitGAPLFiEYb810pxVPchSjsLhXdU7sAAHeFv4X2UBZvc/4AABAc2Uee1MxkRqMzPXD4OMyxI7MvSzBIgXE2wIlGSRqMg7AXXyzaRtep3T0eCuRu2EAXr6ODXruM5iP89rf0/VpklcMhZNvmaWpvy2fhtmWRyKpY3039W7Xr8HuKcGoGPEFlpszQsWOikmTXLqrQcLlI45U09yp35HJR1oym0Rjo76dzS6Vou41kyqfTYjJnlRmTNJjbUUTAU0Bncw49LRl4j9L1xfr11DZOJ13T2YL/TF5dcgn9PTVFFVInU7mSzQpyxVwCbbMRMcIVXtXgfiTLtauUeHtOJ7U1k1hnq3IFoLbiIP9SAJOixWJlRUkiQf2hWBSkCs/xJwMmV8yVK7qOsvkZ95XZwBqpPOYGB2m7bN7YCLMwOSkM1pZI5YpFrtTGaQuhFItF3HrrrXjyySfx/e9/H5dccklD34tEInj1q1+NRCKBhx56CF1dXVi9ejUeeOABDAwM4KabbkJyvhNwHbzpTW9CsVjEf/3Xf5Vfy2az+NrXvoZt27aVtZItWLBgwYKFpQp+9nS5ln7VCiMcJi/TCy5oTAbrpQ5ef912G6kHmJM9WbacZYVtNiJBWlspKMQxDVVtvC048ay9nX6WL6+s2rfZKCjLyYm7dtF6hpOLzf4sqZToe5xEyGupsTGKL2QyIlH3yBF6b9kykbDJhcZTU7RuqX5M5FhIdeXK0JBYFMxGrrD00W9+Q+ujM02ujI2JJFaWh2E5sOZmir9kMsDBIY8gVxIJIWl0qsjlBIPC2ZsmcsVsaL8Uydl7770Xf//3f48dO3bgrrvuws6dO/Fv//Zv+NSnPgUA6O3txeWXX44f/ehHeP/734+PfexjyGQyuPfee3Hvvfee5aM/OTgcdC3NVWS6LsZXKiXGDCc7e70UEy2poWFdawQ+VwHJrIJjk15sfPge/ASvhQ4bVnWm4NIM+FwU8Ah4i/C76e/xaTtSGVmY2vOEwKVsdQZrOg089xyNl6NHaXykkzpaQZnbtpAfsmqH4XIj5/Di27gNgJAE47FVjrexKdOhQ/T/W94Cl0uCQ9VR1CUUdeAf/vg4/uU9R3DB2hh02PADvBlvfOYj+LMvrMK//aAL21/0Q7HruPuP++D0q0hPJOGUMljWREHU48fnjtWm+idw6CgFLK+TH8JvcDU++PpDuOWqcVyyMYbulizspabSdSCasCPgKcBuMxDwFODubUbOE4RNz6N1ah+2raMgzy/39WLPUQocbvEdB2IxJBKAJAMuPY6AHJuXmTlDkgCfmsGa1igkyUAip2Iq5cCJcQ17XiBi5PyOEfg8esV33I4izltN8lNP4lKk4YA7OYa9oBvaina67m6HKRvd7LtSz9Q+EhFadiebAc2dIx4v6+PZ7ZS0z34009OCu8jlAD1fhEPV4ZJLkkmGIRiYm28GALj2PUvPXXLJ1N4UpdJ1OiU2s5ck0DYmJ2nSleXGzqelhTYUjdKNpFqHjomXJ56gdlq+nIL4AGVCRCLCIH02lMpgpWIBvpAdbWMvAADWr8zim3+7D+e30UQRGNkv2nTVKmo8s+fB2Bj1O1kWUldmXxaXix5AZJnOLRikc8rnyUytqYm0UKvPr7OTvr96NZXaLltG+771VtpWX199+TN+yKrWKZ0DHg9gz6fhsWeQyClY30vfbQvlIEsGHKoOT1irlBliDdWREWrT+++njd10Ex2v3S6y6BlOJ/3wXPnii3TM85EyM3uuXHgh/TZJgy1ry6AtlMeK9jRWhKOVD3JA4w+RwSA9uPC1feIJoTfZKJjd55tPa6vQq125ko6FZb3M/b1YpH7s84k2q4VAgPpKczNdRLf77Ggmy7IwxdS0KuOlRQwew4Doo+PjVF3mcFCfCgSICD2Vh0q+ZubKFSbbQyGaK+aqBOJMEyZXjh6lRRVXEM5VGZbJUJ/jB+UlRq40yi+9XHDa6sre//734yc/+QluvvlmTE1NzfAaqWfs/qUvfQkDAwN49NFHsZrFekFVJD/72c9w3XXX4Rvf+Ab+9E//tO6+//3f/x3T09NlPeSf/vSnOFHKFPnzP/9z+Eu9Ydu2bXjzm9+MD3/4wxgbG8OqVavwjW98A8ePH59R/m/BggULFiwsRcgyPV9OTIgs5ZcDlkoCGCDWKHZ75UMuywN3ddE6hde9skxrFrtdyBHPh1wBaO0zPEzVMLX6jd8PXHcd8MgjwGOPAW99K62dQiERfF2+nNY4brdIYnU6Kz2sMxlaG3PVy+HD9N6qVfRZc6ULx4CqyRWzt4w5cffECRGTmY1cSSaBr3wF+OlPaV3EVTNnCmNj1IYAtVlLC8UjHn+cKpSiUYrZqT4No3I7WmWZFn2Tk6TVpOuntkDN5UQGKGf7mbZnJle4Ly4lKIqCu+++G3fffXfN95cvX47vfe97Z/ioTi94LuCxxZ5DmYwgV9hHmJPxORmVbRQ8UxO4aJWBh15owd4nYlDScXxfexuQBS5cGwdgwOcuQpYMqHYdLYEcokk7phPku5LNy8jlJagcMORSsxqDNRYDdu+mfR89Sl2+txcISFl4c0QQ5D2hMjO8XbsCRzKr4LDncemlCvr7hcJSmVxhYpLL3drb4XKQNA4AZHIyPE4dF66L48J1cRw/ruPRe3bj28ZbsPe4B3uPU5XEn71hECs7MsjFwyjmsnAWEuh1jgHwoq9vjph1MonEZBaHBmhgXVjcjrQjgIn1l9f8eLUkmCwZ8DoLiHZvRHjf4/D178V1F27Dky/68ePdq5DJ2+BQi1i9LQTYw0gMO9CsD+GVX38njL0XAivvpkm6Ab9UM/x6BCFPHs3uFMYSbhw84URX9gh2xKgSYf0Fbvhc0xXfcTuKWNGRgd9dQDSp4DH7VegpHEUGTqiKjq7mLOw2oywlBkDcLLhv1GK+WRIMOPnKFYCCrcwyTk0BnZ1wuylxf88eiu8OD4v7EgoFOJx5uJQ8oHppEGUy1JavfjVw772wjw3BPT0I2e4XsmCGAUhSOf5WEVNlHc9YjI6nkXnd7abzHh2l+XtiQmRr6zrdmCMR4Omn6bVzzqGbnKJQ3z90iG484+Ozt18sVr7Z+psUeId3AwDGWzcjm5cx3rweq449CO/gfgCvpe/UCnBz1crKleI9vn5eb2WVS1sbZZJ7vXQO119PP4xcjs67pYUmqLVrK8/BMOj755xDGqZPPlmZncIwB4LnQa5IEhBQkpi2pzEKOy7dEscDz2Zw+eZpaIoOWQY8TQ5gAEJmaHKS+vHkJMmBlarmcPHF9ADFWTJmcIbK8uVEeuzdC7ziFXTNdJ22UV0dVA0zubJtG1UQRCIkDXbppXA5dGxeUXqYeuEAVQKEQvQTjzde/hwM0kPnBRfQwGEpumSytiRbNQyD2mdiQiR9hEKiGqWtTbRhJEI/nLnD5JnPV1sSzIyuLlFRdDaqVhgsB6ZpS2fh4nSKMZVO03w6NUXzUkcH9ZENG04944/JKXPlytSUmHcakS7g0kFOuj96VHhZxWI0v8ymLz01RX2W57B8no6lUFi0Em9W5Up9nLb8sudLotg//elP8ba3vW3GTz186EMfwuOPP45zqvX3AFx22WXYvn077rzzzln3/dnPfhYf/ehH8Z//+Z8AgB/+8If46Ec/io9+9KOIVGnjffOb38Rdd92Fb33rW/iLv/gL5PN5/OxnP8MrX/nKeZ6xBQsWLFiwsDixcSNJo6xcebaPxMLJoJ6scCRCr3k8lYmhAC362ai4s3P+ktLhMGXt1iPkAgFaO7MP8PHjtI8TJyhBFBAkhcslEnDdboqX2WwiLgLQ+uvECbH2X71arDN5zROJiOCvGSzPXShUSoqPjFB7zZIMD4COf88e+ru/n7ZzphYT7PXCca+1aylmAgC//z2t+To7KQYQSWs4MuqB4S0FKJgQOdWDzWZFECMYnJHByWoHHs/SJFdejmByhb2aAOpO2SzFqnVdxBlzOYoReL00L6gqMDVAzO62bpJt+d3oGsTgxSN5IgU2Lk/Apemw24CWYB6KHWgNEpkxEVOQzlLwMJ6yVVau1KhOmJggf5VIhOLAskzkbzAISMUC1AT13Zy3RK4oCr6j3woAuLrnMGSZ5he/n+arcjwnmyVigU2FWlrg0orQFAOAgWyuchm9bJmMz63/CvrQiztXPoAmfw43XzKBmy8hciYtuwFVhTM5gS53BLJsIJ2mGGZdjI4iETdwaIQmyK14HgdW34zxZFWQrVgEMhlMj2bgSE/BMTmE5tE9wK5dsO/dhXQH+ZyEhnbj4vUxeJwFZPLUxhuXpWCzkxRSIm3DhiM/hZKJQ9nxOM0dJyHJ7ctNwJZJojtAAaVDJ1zwbH8Qz4Ek8jZvkeBxVmaqe5xU6bGqk4LXP175ATzjpP7SHsrBpenCb4VhrlypNZFzyWMiQRf2VCYon08Eqk1t0tRE96z2dvpIXx/NiZJegCrl4VbzNEDYM6ijgwbLOvLCCRx+GooqIZctkUal+Zp5IqfTRK5MTgoD8PkEfFtaaIPxuDgOgAZNoUAVKgMDND7OP5/GHFck7NlDJzSb74qu07ZTKUBV4ffq8JW8hwabz0E2J2O8iYg1x8DB2Y+1nt8KXz8zScCyTaGQyCYx48QJul+1ttKNspockiT6/pVX0v9mY7Pqz2maqFyprv6ph3weAWcWLqQgKXa4NB1f/5v9eOU5UTg1Imnd/lJ2C5Mr0SgRPn19lEEhScAtt9BDlaqKUl0z+GGIE5O5cgVorNqGJ3SuVGtrA1jO0iQNVgY/EG3aRP3RZqM+3kiGDmfSXHQR/T80ND/fFc644Syd1lbaN5vOyzI9XLJMmbl6JRajdlHV+pJgjJYWGtTh8ExDsTMJTRNyeEuFXOEyZ1Wlvh6JiMVITw+RnQslpcC6yUxoRKM0X3B101zgPs3PIf39QqZtaor61Gzja3KS+jZL53HSxiKuXrHIlfo4beTKo48+CsMw6v7Ug91ux8ZaGQMlbNmyBdJsxm0Ajh8/Xne/yzjDpQSHw4HPfOYzGB4eRiaTwVNPPbUojSctWLBgwYKFk8VSemZ/OUKSxPqBH3JZVjgYnF1WeMUK8mHZsqWxdUaj8PtpvcvSYI88QmumSETEaNraaN1os4mERbebfpi0OXGC1iSxmEhoDQTofe6zTNLE4/S5TKZSxpn9UlhantfhXNE/F7kyNSUIIT72MyUNNjZG+9pfUlLZsAG44gq63qyi4PfTz+C4inRWRs5TysjkjPuFJFfYtNYEjom43S8facGlDrudfjRNjMVotFK9JZsVXayjg2IlmkZdJDIQRzGdw4VtA5AlA3uKG/Bl6U+Q123oas7A69ThcxdhtxloCeQgSUBbmIINYxEFuYKMQhHku8Jrt0hkhi7f4CDF+SYnSTLQ6aT4Yrkf5vNQEhQIz7tpgitKdtyfugEAcHPns4hGaQ71eqvIYj5Bw6BJKhiEy0HZ5qpdRyY/cxk9vvkqtGMEn87+Fb7/9y/ifbecKFs4ZHIybD4X1v3uy7j6by9Gr4caj1XHamJsDBODWfRHiFw5FzsxeN5rMDKlIl+QRLscOwZ94ARigwkE9AjsKCDYplH2tdcLvYUkTVrHdkM3JFx97nR5F5tX0ABOZ2UUdQk9hx8BAEi5HE0w85UGy+fhzU9BSiWxvIkyxY8MOnDk6SkUYUeHexorOjIzii7cTmrbDcuIHf9d+gL86twPAwA6PDFoU8NwTfTThT58mMgAnvzHx2mi58ArIxajiT6ROLWqFYCCbHY7bZMZxhJ6e6nv9PbSR8bHAU3OQyoW4FILNGeyJBj7Q5x7LgAgcIh8V/K50vZKNy+uDGNFMgB0LZjNbiTLn8EySKOjFb4x5RvM44/T/xs2UCA5GBTkxr59dL5stlQLySS1RzIJuN3wRY7DnkmgYHfghGctsnkZkVYik5QTx2eXq5zLb6U6C6StTWifmonAaJTaqrOTHhaYiKuGw0FG8ZJEXifmDIzqz7FGaaOB0VQKQU8ecjFPqnIlqcNMToZTpSo4xW7QuXFVRTRK7c1yYNdfT4H+cJgmqVr9mBnwEmGHEyfEQ00j5EomQ/vn82prE0bjJmmwMszkSjYrJtxGyBVZpmvY1UX90jCojzUq+8/ncuwY/eY5wO0WEV9+KGppEWXPAJ2jzyeOYTZwBtK5555dQzm/n26sbMC+FMAP70x0rVlDixT2Q5oj1jsvqCpNylzNDdD81tra2H64T3d00N+FAs3lwSD1q2KxvjQYS/3FYsJvqBEt4pc4JEncfmIxi1wxYwkqI1uwYMGCBQsWLLy8wGsvVhVhWeFgcG5Z4UBgboWE+YIJDJZuf+KJctI4AFpH2e0iaMRrRo+HPsPV+myzMDIi/FY6O0XiGG+LkzTZ18W8TmcJIyZGOjuJVDJvf7Z1zgsviDja2SBXduygY/R6SQ6NpWgAql4BKMGyAAWprE2QKxxoOlXfFbPnCpsIm8AxoaUqC/ZyRbWpfSQiYm/FInULjruzHLnDAYR8BRjxBKaGMvC7CtgSJt2wj4Nk1S7eEINuSPC5Cgh58+UM7q5m2vjwJHWiZLSI2P4hUVbCnhXFIoxcHkeOEDExNkbkZyBA47pCMadYhFKqXMl7aVw8v1fBWDGMECZxhfc5RKeNcjJ1BbliLp0LUdULH6tDnVm5AgCTm66AIcnwntgHbaoySJtNFXDDY3+Lnud/ClkvYnPuWQB07DWHaIkp3rVPhW7ICGMCwVYN2qbVAAwMT6o0xicnAb8f8VAviq0d8K9rQ3hDK+Te7rJngL2bLlAwcgy5aBKvvlAEobeUpH4SaRtcqQkEB3aLY3jhBdq+iUiYE5OTsEk6PIVprOqmiXVgwMATCTKm37w2D597pr8CV7JsWUZkz74BD/b0UQSnw5eAw1aS2JJlunlEIvS300kBL9Z5NAe+uTIjnT51coUZOL65csC29Nb69RRwWr68pD4nk/zLDHKFs+BL5Erw8FN0785VVq5kMiKu53JBkBuxGN0E5isp09oqSiH7++kcJiaoDz1LfRHnn09BfJZtAqjslLMc6lWvsPxUOg24XJAPUjlWtH0dskUVibQN+VAL8u4AJL0oAuPVMHscMbnDup1eL/1UnzfLZAUCdL0Ng471xAm6IH4/3TjrSdtxNQzfVH/72/qf477VqDRYKgW3owilmIHHCyQzNmRzEoq6BKemi+otr5fOMZcjxnj7duovgQA9RHFVRj35IdZ2DYeFH8uhQ3TOjUiZZTJirvP56Hg6O2lCTCaFdBdA15kJsE2b6JjnQ64ANJ+63YJo3L+/8coVZvh5PHV2CuLTTJj09Ai/FCYR83k6v0Bg8RjEdXeTUeJ5581bnvElC/ZMCoeJwAqF6PyqvYQWAuxZA9A85fNRX2lEEgygNueqOX7QOXaM+pBhVJptVYNJ+GhUlMXyIm0RV64AQg0hnZ6XUuKSxyKZVSxYsGDBggULFizUA1euABRLMssKV0uCnSkEAlQV09REa/cnnhCeKatXl+MwFXEiJlt4zc1KQNmsiLl0d1dWW6mqOMeREVq3MLnC8Z5MRiSt9fSIZPjxceEvWauwWtcpvsiYmqov7b/Q4CqcHTvo/3XrBAnFcS8mVxQFgM2GvG5D1lcqU2JC5FTJlWRSBBJZ98kEbgu32yJXlhKqyZXJSUFCcpdiMpOTQBUFUBIR+J05TI4VAa8X17koKz5hlMzTlydgt+lwOXSEfAVoig4JBnqaaeNDkypgFJE+MYl4TKedcEC6vx/FIrB3Zw79/RQ/HRqi/ff21oiXpVJQk9N0zEGaJB76LU02b8b34UxOIhnX4ffTfFJRwZnNktwUUC790xQdNtmAJueRnU7TBMWBZZCvS3TFVgBA0+5Hy5uyZRK46nt/ilUHfgFdtsGwK9icIa2/vuN67aTt0VFkEgUcOkEs8rnYifS1N8OhAR3hHKYG00gMxygw1dyMqKsDWlcznOuWoeWazZSNHwwCDgfcAQXpYAckGHD17cf63hResSmKDb1JbFwmyJW1Q49Agmki3LePbigsjdYIJiaAVAp+JYUNK6mjDEz78CCuBQBsXZuCz1WY8TW7zYBWSKK7cBRhVxq6IWPXIHW+7hUKbN0dcK3pIvZi2TIKnCaTYjJnuSszU84G4cCpkysATcDpNLVJldS4zSYM7nt7AactDwV5KKpEg4PNibgvb9wIKAq06DhC6RPI5yXqR6XBlU7TGLTZSjFrJrni8ZPzgPD7aUCPjtL1PH6cgtTbt1Pwz+2mACe3U3e3kMLZu7e2NJhh0E29v5/eM0pVGPv2AQASvUSQJJOAphpIdJZMzOuVax08SG0bDIpApvn61SqvZXmfYJDaLpmkcywU6EGiND7qguWJLrmE/q9HrjgctH2WhmsEJd3EoDMDj5dIlak4Pag5taIgV7jNjx+nY//Zz+j/W26hSbW7m67fbA9znN3A42HvXvqfH3BmM4w3kystLdQesgxcdhm9ZpYGGxgQ8lqrVwtyhduxEQSD9HmuJjp8eH6VK0xC8fFyVZO5soMJnJYW2vbwMO3T7Z5bEuylBDYCO5vVMwsNSaJKkECA+vT555vK8xYYXLkCCJ8gJt0aBT8MMSFz9Cj97/EIs0c2HzRjcpLGXi5H5Aov0vL5RU+uhELieWs2xciXGyxyxYIFCxYsWLBgYZGDK1cAoYLAssILXZXSKAIBOq4LL6T/H36YYieAWFO73ZXrYfaO5JjO2JiIlTExw7GDWuTK1FSl7wrHQMxy4mZyhX2Q6yWSpdOVcaB8XiRBnm6MjdH+dpcSydesEevBiy+m9enhw3QO7DFflBVBrrBkwanW7I+OUgPJck3PFW5rs0ephcWPanJlbEx0Jx6TzD20tNC1lyTAHhlHkz2KTE5GQg3hdcn7ytts8ufQFMjDWwoqhrx5iskpBlrDeSh2HYWijNR4Cqm0hHxLF3mDlHwG8n1DePagF6MDORw7RmO6q0vEYathj4xTpjyAfFMbcjngscdJCuQ2fBtGNAboRfh8NeKv2awIODY3A7EYpKFBOIePQhvpQ3Y8Cj2TFfJDJYxvvoa+svthAIAam8DWf383uvufQEFx4PCbPgxceSU2gLwljh8uzIzVGgYwPo7ESALHB2i5vlV6AfZXvwrL29IIF8fgykzihNEJIxiC3tqOqKcTgRUh2EJ+BDtKrupOJ6Cq8Gh5xDtJMsg3sBeSBHzincfwH3cdgqoY5LebtmHFcZIEM8wZutls49JgrEcZjcLvLqCrW4JTLaAAO57D+XQeq5IzK1cMAxgZgWfsKByagVXdlZPx8otbgQ0b4L7yQjKdYh+CVEpM5gMDtB3unMUiBeYTCSE/c6rgm1UsVtOLxukkzsTvB5rdSXiVjNhvdeWKppWrMzpGnkVel8sDjDkWh4Puc5IEugbxOLXxfCTBGJJEN8polNpoYIDaj31Gtm6l4KGqUn/3+YQ01/79RMgkEuLmVyjQzWlggH6Gh2n7TmfZSCi5bCPUdAQYGYEjPoFMT8nHpR65wn4rGzYIyZ5EgrapKPW1S9vaqD+oKh3H2Bgdi8MhfEhmg8MBXHop7fPAgdrSYHwdG5HZYqRSQCaDgDMLt9cGWTIwEVVgt+lQ7KJaC4pCAfRUikzss1lqg3Xr6NycTsrwn03GyOmk8+CHpxdfFFJmwOwPLZlMJVnB58qEk1kajCXB1q6loDWTK41WrQCCCFpbItuOHaP+1IhUEvuzMLPf0kIPRlx+yJAkQUo5HDRmvV56jjlbD8UWBFatIvJu06bTa+xuzjxjDeJGq1YYpftoReUKQP0okaiscjVjcrJkwCWJsnxg7nL5RQBNE7fD+SqHLmVY5IoFCxYsWLBgwcIih3n9wAmkgQCtOxdSvng+4OTaV76Sfj/1FLBrF/3N1ScOR2WciP1lOV7GnijFovA9WbmS1mJ8vqoq1ASqTe35tzlO2tMjyJ2hIREzqLXWSSRmxoEGB08/uVKKreL4cRHzWLtWkCttbeIcODZmtwN5WUPaV2qMaJQCFqdaucJBDC5NqSJXOM5kVa4sLVSTK9wNMhn6sdtFMn44XLr2iQR6A1F481NQnTYkIjlcMvlTtIOClZduiiGbt8HnLsDrLEBVqErCoepwKDpaAkQETo0XkXKEAIcDcUkwH9HjU5iclnHwgIFkkmTAKqS8qqBGaNDnHV4YLg+2bweSSQltziguw+OQ41F4HEXY7TW2Yw6Y2GyUrTo9DZfPDkdnGGhrR669lwKFpqzric1XAgD8R5+H7/gunPuF/w/ewf1IOUPYfvOnoJ9/IaTX3FQmV/r6JSQjVWO0ZBCVGE3i+DAFLVf05OFu9aCjOABvcgTdK1RkHAFMaJ2Iu1pRLAo/qnIFTymIaZMNZLtX0rUa2o1ilcpXJicDuRzaj1IpnHTrrfTG4CCRCBMTjZl4RyI0WUej8LU6IckSNipiAm3y5dDTkoFDNR0AlyWOjMDdGYBzWRtWrxDkSygEhNpUyA4VDl/pRhcICOMslkoaHibmjyfn6WmhkTmXV8G//RvwxjdWmr3XAutRJhK03RrEdTAIbF6XR3dzFqtCUyIAzQPIbI5dkgZrG3gWhiGhkCVyhYl+h6M05+fzFKSLRmmgNRrI1vXK68bkOHsEDA4K9v6ii6ix2cTb5SIzNoAqUSYnhZRYOg3s3Ek3qSNH6L3ubvpuPl++acZ7NsKTmwZkGWp6GoUVJaKDNT6rwXJTZv9d9luR5fqkEmufsrG9ptFDARubzwVNozlm1Sr6v1b1Crf5fMiVdBrI5RBw5SApdrgdRRR1qdz/y+QKQO2XydCDkixTf3S56DyWLZvbGNHlogmbH5727aM2aUTKLJ0W/ZMJNoC2VS0NVm1mbxj0+flWVoRC4joPDtKzSiPVK+k0BbeLRTpnrkSoVc3V2ir6AkDzAJd0Wzj7OJ2kCkNV6f7Nfk022/zlx/hhiMmVo0fpt99PYzUSoTnVLJ+ZSNDYi0ap37G8GCCqyRYx2EIGoFOcrTDu5QSLXLFgwYIFCxYsWFjk0DSxTuEEPbN3ydkA+66sWUPxL7Nke1ubyMitjpe43USgALQ+mZigtXcuR+fU21sZZ1AUUblSTa6wgkSxKLLsq2XBWHWk1lonGhVxIF67Dw6e/qSzaJSO58kn6f/eXor92GwUS3A6KbYBCGkwux0oSHakvaXGYKfJU61c4aALLxDrkCsu15lZK1s4M3A4KG7Ac8j4uEguzmRo7HKMlqvk7NMT6PTGYM8mEW5T4Dq8CxKA9zq/iqAzgys7DwOGDp+riLBfSEM5VB2qYqAlQINwIuNBweVDLg/E4MOwQgFpbWIQh/o0SIU81qyZO4G/bGbvDQJ2Ox6mYhLcsPIgZBhQktPwewoVWZhlmI2aurooi3zjRriWt0LrDANNTciiVFrAPgAAsqEOxLvWQzJ0nPvFd8I5NYhEsAvfff23kW3thjukAVu2oLc5BQk64mkFR7ZXGeKW9BBjYxnsS5Fhc+slK6BOjUAaGcbqjSqcbX40rQpiuNCMiQm6BzidVRU4XAqoqjB6ltF2xnYjna0MASTSNvSceAL2fAYFXxC46iraYD5PVQu5XEV1Tl1MTFDnyGahNXnRNLgLFyUfKb+9eWVV1UosRpUChQKwejU8vWGoPW1Ye4nILG9vF+dWThQIBMS5caCMyxB5ci4RVMjlZpcEGx8nGaZIBPjqV+c+R49HtEWVNBgj5M5iTXcaLjlD8+XgIAWh3e7K6osSudLU9wxg6MhniFxhfogrV8qSYLFY45Jgu3cDt98O/PEfiwwBWaYOEolQuzz6KF3j1lYyMLfZqME5s3/dOvqb+0E8ThkJzz1HpNvBg9T3V60SUkvHjtHnvV7Irc1w2zKA0wmHrQCjdxl95siRmdE4w6isXAHoWubzInhezyeD+wEfd1cXtTWbnc8Fh4Ou09at9L9ZBothtwsPk0ayK0yapC6fHZpqwOuic3aqJC9YQTLmcsAXv0h/X389tWdPD01MZkKuHlwuOr7ubhobLPXF5YazkSuZTCW5Ikk0oRcKwJVX0uvcJtV+K8D8K1cAup69vcIk/OjRuX1XCgWal7mMubtbJHzUMqjn6pVgkI7R51tckmAWTh1MaChKiakPzb+KkStXShW0GBmh8WSzUT+emhJ6zAwmoxMJ8aBis9GPufx3kUJRxK0oGrVM7RkWuWLBggULFixYsLDIwZI8HPdmuemFkJk/FXCC8Xnnideamug4ORZQLX3sdtNDO8eg+vsFwdHePtMbwSwLxuRKPi/k17NZinFwHKy7m46L/ShHRuqvdfbsoe1oGnmKAkTS5HK0ljpd4IodrvRZt47ahaWqbTbRprt20fqNyBUF2aCJXDnVyhXOVAZogWjuZCWYyZWl4rdqQcTKwmH62zCon5Vi52UbDq+3FFuzF+BMT0GOTKItkEGoTUXHIGU7//HyR/HD9z2OgByDOzEGm6Qj5BWrcYeqQ7XraHFRcG0kR6UwKcmDE+kwjhpUpuWOjSBgi2J1R2LuKildhxovkSueEKaT9jJZed1GKrlxZKbhd2RrV7+Y5bB6eigovXEjXJefD2XDGthawsioPjGBmQKu41uuBgBIehHxrvV44G3/i4ynBTYZ8DQ5gEAA+quuw3KQvMiB502m07pO+41EMPDUMFJww4UUVmzx0OTT1gbfiiZ0bG1B++YmyDLFvDkpt0LxhidKTYPS0w5DkuBLDEOfrCQFEmkb1pwg5snYsInOacUKevPQIZog59L+MIxKGRSnEyu//084B7vKHzlvdVz4rbAptdtNZXlNTXBv2wS0taG1TSonCXd0mEgGht8vyhz5hMfHK2WrIhERrJ3tRvjAAyLj+Le/FYHbevD5aF5Np2tKgwEQc24+T/MlV8RwySZj3TromgNaahpNsWPI53Qgn0emxMnYUIQ7Nkxlm8kk7XcuRlHXgfvuA+66i/pLXx/w3vcC//d/dI3CYSIj+vrIbwUgE6+mJmpLztL2emdKg0Wj4mbKGp9r1lS27/799HvtWgTsCbi1AiSXE6pdh9IWFlJVXJLJGBujPmazCbko8/WrJwnG4IqLri469tWrGzctZ9m4zZvpOwcP1pYGcziECdxcDwCZjNAb1TQEPIVypYpTK8LtKIquUCwC99xD+wyHyS+ptZU6/bp1jZUfczWG0ynKWvnBaa5qm0xGEMk88FSV+vEVV9D/TzxBn2F5u40bRUbKyZArnKzBBNi+fXNXrrCvBVcOtLcLv5V646K9nY6vu/vs6uRaODtgIsXtpp+TyTjjvs3eToCQBgsGxfhighKgeyFnbpn7Jpva1zN6XCSwyJXasMgVCxYsWLBgwYKFRQ5zcpYsz+19eqYQCNCxbdsmXlu1itbs7LdSHTfweEQCLUDxhpI/Ljo6RBYzQ1FE8vL0tIitJZNlyfNyhn04LGKhHH+oTng24/nnxX5ZfYa3dboSz1gSbHKyLF2PVatEe/GCpreX2og9iYlcUZELlhaP8bgw0zxZZLOCleIFoolcKRZFO7jdVuXKUgLHE8yqKtPTglzhWHtXV+lzqQicthwwOYmOZSoUVULv6FMAgGjHeuj+IOKedngRgzI+BK+jklxRilm0qNMAgMGIE0pXK1KSG3m7A/tSy5BVvZChY72+D7Z8du64RKEAJUl9N+cJ4cvf9iKfp5jr8h4KprsyU1D17Ey/lWKRfiYn6f/2dgo+NzfD5VfK7ZJVSyWCilIRGBw9/0bk3AFMbHglnn/vfyOitsKhJ8n/xANg2TLor74R60ET2/CLUygcKAVCS1mwhYlpjBykYOIa3wgC6REKDLW1AStWYPkrOuBwiGTaQEDEzcvQNHrB4YDXWUCqhSY91/G95Y8YBpBIyeg9+hsAgH3rJpqA2atiYICiJ3O51sbjNNdEozRX/PzncPYdwAZNSECduzohKlcmJoTpeFMTcMEFcLX5ylYxr3wlzXfnnFODXGEJJI+HJh2fj07k+HHBricSwq+j3sSk68AvfkF/M8P2ta/Nfp7McsfjdStXKsgVu32m3wpDUZBbdw4AoGtyF/LZErkSScExPQLs3Qv34EHgO98B3vQm4L//m8iuep0/GgX+9m+B//ovOrerryYvkXwe+PzngY9/nNqno4PIlaNH6Vwuu4zOizsTQEFon0+USO7ZQxPA+DgRUE4nESvVLCeTK+vWwS/FSNrMZ6PrKucqSTszuCJi5Uox+cTjdFw229zkis8ndPHa2uYXROcyvUBAkEm1pME0rTEPE6DS8M3hQNBbgFOjihWnpgtJsFwO+Md/BH7+c/r/zW8WD3Dd3Y1nyHBpl9nUngmwTKY+ucKVLTy+ebJXVeH90txM89vXv07v9fZSe+dyIht/vuQKZ2Pwg9jBg3NXrnDFFZOVra3UPjwP1ALvw+ulz9WqcLGwdMGLo5YW6iMnU7nECw5VFQsBJvi8XtpuJEL3bs7sisXoh8lqhqIIo8dFzEiYi8UsckXAIlcsWLBgwYIFCxYWOTi+wQ+8svzSIFeYCOjqErE6Xku7XLWl8Jn84KDt6KiIEXR10TrGHGiTZRETikQoHsBexiVVjnIMzKwSUm1qX02W5POC1OnqEsfDMYjT5bsSiQgj+2SS1mbLllFcwOeja8zBRpYs//3vS+QK7Mh5QzAkiRZvvLGTRS4nGo9XUiZyhRPzAKHSY2FpwG6nH1UV8YipKeqTTAACggTVklNwFWJAsQhXRwBBexzhURpAQy3nIJlXoWtO+HuDCGEKUt/xcsWAQylCGhtFZ5gGVf+4C66ghljRhYOjAaR1FakwBaZdI0fxyHYXbr7ZwIMPznIChQKUOPXdJ/SL8atHaJL8y78EcgFiU7RMDEo+OVNpKZsVPheAYHUh4iwOB5CRS9qGLldF8DIb6sATH38Ye979BRQdbqSzMpzFBFSvBkUj9tu5YTmWhSmYOLZnDKmhaQpej44C6TTSR4ZwYIqOc3lnDp6gUiZW0NMDRaE/WenE6awhJW8ytXcjiXgvTRj+E4JcyeRkhEdfhCs+hqLqgLSlVKLH1QMDA4JVmy34OTFBbcYd5CtfAQAEbrgUva0ZbFkRR2c4C6+zQKTK2Jgoi1q1CrDZysUoDgdw8cXEEfT00JRTXeFYLos0DEFaHDtGcxZXlLBfRz3s3EnVHW438E//RBPYE0+Iib8WZFlIg2WztbPtuf8ANIDYnKiGvFPxHJIG65l8DvkcAF1H5vAJOOJjsE2OQfvtr4H/+A/a5osvAu97H/Ce9wAPPVRZPbF7N/CudwE7dtA+P/AB4CMfAT7xCeDOOynI/OijwB13UPtwgHr1amp/80AHqFPZ7VTN4XBQBK2vjypOwmEiQWqVKnJGwNq18EsxQNMQ8hUAVYULKeFrUk2usCQY39S4VI41ThshGTZvpiqcdevm/qwZ/AClqvR9oLY0GFeuAHP7rqRS9CBSoHMPeAqQJMDrKsChGkSuJBLAhz5ERI7dTpPT2rXU6T0e8ZDSCHisOxziYeXFF0sscCmzvhYpV/KFKc91ra1lKUHkcvQ3V6/88pf0mwk3NrPntpkP2OR7zRr6/+hROhazb0U1pqaoH3JVUUdHfb8VMzo6gEsuIRk+6yHl5QWuwnM6aVyczPVXVUFccuULV65IEhG/7Dc2Pk791DBEooEZiiLuDYtYGsxcucIKxBYscsWCBQsWLFiwYGHRgytWWFY4GHxpmItzUMzjAd7wBuC1ryU5fw7a1lJyUEuexUyEjI2JOBDHGqq9XTs7aY1TKAgfyfFxWt9kMiIB3Rzb4m2NjtYmV5JJoRCzfLkgcFhK+XSRK5OTdPwsCbZhg5B85sWM10ttwDGOp6hAAAVJAWQbdE+pYcfHF65ypQa5wm/x9WxEvcTC4gEnXXLMdXqaEsPb24XXe0sLIOcyUAppOJMT1DEdDvRMPAdZLyDhasEJdRViKRuU9iY4wy6E17dQ4Pv4cUDX4YjSIOxaQYG6WEImyX/FCVXRsbY7jUyYMkbdY8fwvw+3IpmU8LnPiRjHDOTzUJMRFGDD3/T9CQDghhsodhtxUIBEgoGW/NDMfsulOYYhogil4CEnaWsakM3LNBjdbpoUzMZNpSBOriChmC3AKefgCWtl7whXZxCtm4g86ZvwIDUapwlncpKCM0/twPPYCgBY0ZqEtydIE6mJIW5ro83NqnhTuh6SBOR6KZDZPLynbHmRSNuw8thDdNqrN4kgNge5h4aEY+1s1SsTEyJAe999QDIJY80aRK54HT71rqP42Nv74HHpFI8fHaW25UocE3PidgsCi/3b+TQqEAjQi2Z2nW8Uw8OigmU2M3uuFrj2Wgr6X3st/T9X9YrPRzcIXa8tDWbWjZytcgUo+650ju1EPpVDMZZAbmgSztE+eKYHIH31K3QemzcDr3gFNcihQ8AnPwncdhvw3e8C//M/JAM2MUH7+NKXgJtuoglZkoBbbiHZqdZWup4sEwZQWWkwSIPaPBB8PmESz2TFyAgF/6vlzRjpNI1pAFi/Hlo+AafPDr+7AMVhg1pMiyyLalN7rlxZv15sq1gU1QaN3FxstpPTQmViQFWpVKqeNJim0TXP5RojV3g+cDjgUHU4VB1N/jyRLKlRIlOef5768Sc+QWxpc7OQyptvINjppGPs7aX/2awuk6HjrmUsZy7t5YcMr5e2wxV87LvC5MxCkCsA7Yevd18fHWM9aTAmiAYG6DOSRHOhqjZWjaJplm7pyxXnngtceKEYF/OFJImHIc5a48oVgObPQoGeaUZH6R7O5Gr1IodJS2BRm9qbyZXpaYtcYVjkigULFixYsGDBwhKAqtIa3eN5aVStMDjBuL0deMc7hFQ+UF8m2+MRFS6DgyKIz4oiZlkwgM6bVUMiEYrLcFUF+7oCtBbnqhaOUZo9V8yJnYmESK5ds0YkrLGvy+kiV1Ip2jYnAK9bJwKLZnLF6RRerakUJVvrko3iIf5SlHVy8tTkB7JZETwMBunimeQ3WAJ9RuDTwpIAxxNYMWl4mJI/W1tFvLi5GdCy5O/jykbKEf7AsZ0AgMm2DZguuBFNKvB2+yGtWoFgl5sGeDwOHD0KbWIQ8PvhDdgRClLm8tQUsOlcFat68lC9GpJ+CqAfPaHg6BBNANks8A//UGcsFotQ4lP4d/wZ9ie64fMaeM976K3pop8kvQA05Wp4K+RyIuDI/d4UPOQYZrEI5J1eIclTIzCYzspANgunUqDqk9JEJTeH0bSVzmm/sQbSr35Jk46uA+PjcDy/HTtBgfdV7UloYc+M4LwkUcx99Wpgy5Y6sVg+WABYSRNo6+gLSGXow4m0DSv7SRJM3rhenCcbY+fzNElGo/V9V44fp0loepqqNB56CJAkSHfdBZ/HgNtRhMtRJL+VXI6209JCnasqO9/jEYeQTou/q+f8csDd5RIlO1whMj0tqmxmlLyUEI0Cjz9Of990E/1++9spAPv001QJUg9cMZNICLbdDHPlyhzkirJuJQoOD7R8Ev7h/cieIFJPaw9i+XP3080rEADuvpukwf7zP+lGGgwS2XXvvVQlpOtEDn35y1RVUo0NG6gUiGXCYlRVgssvr9ThZEgSjWWvVxBtO3bUuBAmHDpEx9HURN8rFrF6WQFhfwGru9LULubKFb7h5nIik4H3FY9Th3a755YEO1Vwdgp7h7CxfbU0GHfGuTxMAFE2C5THX0sgB49Th3/iCDwfvJMCtMEgSbY1N1NfaWujbJG5qjFqweWifblconplYGD2aptMhiZ2oDSZayKbA6Brs349KrQT2YCOyRW7fYYXW0NgbxybjdpraKg+ucLVAFzG3NYm+sXJtJWFlw94HjmV7B++j/Li6tgxMX+VEhgQidB9hb3HaplKKoogLRd55Qqv3yxyRcAiVyxYsGDBggULFpYAOAZhs2Gmh8BZRCBAaxJFoVhUKiViAPWqa9xuER9ikoTlb1ge3QxVFfE1Jj8AeuAvFkXsoKeH4oBHjgiOgJUmDKOyyGP/flrn22wUD2pupv3m82XlntMC9gvmjPwVKyiWZ453sBes0yniQM8+C8BmQ1GyI+8z6ThxQ5wMzBI7wSA1mmmByqTXbPE2C4sX1eQKx64NQ8SLw2EDWiYKxONwqsVywEva/QIAINW5GrAryEkafD4Jvg4vlPM208SwfDmQTEJ2qNDaglBbgmhppcHNRQisE5UKEBHxw9HLAADnb8kjHKaE5y9+scbB5/OYjEj4GD4OAHjXuykGZxhANKsh5w4AAPzFyZnfzWaFOW04LCRBSuD5CwCyDn9NaTBGOivDlktD9agUZzFVgK3fSJnUw+hA5vdPC1Jg506MpryYQDNkScfWTYVK8xsT7HaKo9ZN2Gd/EkmC1tMK3abAmZ0GRoYoNjQ2hqaxF2FIErRNa0SAWZYFAz06SpNkMjlz4jtyhCbVoSE6/u99j15/zWuA9evh9xThcRbhUA3yWxkZETeprq4ZNwG22OC5TtPocGYknZt9V7i0amhIyAqZ/Tpq4de/pnlx7VoR8O/ooPImAPjqV+s0KGjCUxTax/Q06TLu2kUDJJWieZPn3FRK3MQ44G2Cotkwvep8AEBL4igyrb1ASwuWHf8tAo//nPrW+99P5+h00vm87W3kw/LBDxI55XCQDNiHPzz7ZOzzUYXEn/4ptd/VV1Mgn+XZqhEKiRJJWSYywGzaXA2T3wqPhVCrgq2rEmhplahdli2jbZnJuoMHKfObK2gAIenGsjunGw6H8ENgGazf/KbyM9wpZ/MwYXDliqKU++CytgzOye7A1i++E9LYKPWH//gPusnHYsLEnrNI5gvTWC/36aNH6Zx0vT65whU6TK6YfSKqpcGCQRon/MB0Mmb2DParYB+LffvqSw9GIqJyBaB+wiblJ0PsWLAwH5grV2SZxuuk6dkhGBQVnrpO7/t8MwkdHldsar9IYRna14ZFrliwYMGCBQsWLCwBrFpFa/XNm19a6gf8AO7xUJxe14U5ez243RRHNCd9tbfXMDYuwUyuRKMi/sf+KxwP6u6mmI3TSbEcltHp66Pf5kSynTvFftnrhGN4g4Onh1zRdTqGXbvo7/Z22qfbXZmcyYFUs+/KU0+VilRkFXlfKRrO7MfJSoOZZcFCoRlBDKtyZWmD4wlM1o6NCRueVIpiDH57Epqch5KKwh4ssX6FQtm3ItG5jvqupsLrLY25QIBKLZhg6e6Gw69B7Wwu74tjaHC7AU1DKtSFPOz4Xva1AIA/eHUCH/kIHcMvf4mZ/iuFAu4efS/i8GFTyxhuuomCHMkkUIQdBU8AACDHojNP3EyucMDRFCSpIFd0VWSLZzIzqhgyWRlOgxhlj1eqYEF61nvQ7KWJZHBIJomgqSkYTz1VrlrpCcbR0uMQ2ofzBQfbNQ1eNYdEJ0mDefv2IpMTRvap5RsheUtlIzzRMcM9MkLBIrM0mGFQ9cHAAJEKY2N0/AMDNFm9610AgI5wFgFPASFvHi1alG4CbW3UZmYTrBK4eczeNnXnF7+fvsCT/9QUkR0ABWnrMU6GISTBbryx8r23vY3mueefB557rs6OISpHXnyRzrmvj0iCp54SIvR2u2AkW1trZhNIEpBYRz4f7YPPImNoaI4fwbr7P0kfuOEGkgMD6Hw4+KyqdOxf/Srw058KGbC5IElkmn7PPcDrXkeNW121wgiF6PPt7aIvPPFE/W1XkyuKQj92e+W5szwPl4ay38qGDbQ/lofyeqkDnAn2XtOoTXWdqntkmY7PLA3GFWzZLD0A1PIwAejaczms6bzlE/0I3v2XkGJRaqMvfpHaloOsTie1+ck+wLlcM03t+eGmXrVNOi2yT7iPMnkoy+LZ4TWvoetx/fW0D7On0MmSK0wWMpl04EDtyhX2j4vH5++3YsHCQoCzm1RVzJfV0mC6Tg/FuRyNq3q6xwB9ZhGTK3a75blSCxa5YsGCBQsWLFiwsATgdJLCQiPy02cSqioSDDnu6HTWlwQDRMKqOebT0UHbqhVnURQh2xWNCpIknabkskKB1v+8GOAALqu0cCCXv2cYQhWmu5u+6/WK4xkaonXRbN6rJwMmbJ5/nn5v3kzxjer2UhR6zemkOJXDQec5MAAUbBqy/tIJMjFysisfc9Z1OCwWhiVwUYtVubI0wQUM7OGk6xRn56qS1lYA8QQ0Iw2XkRRs5cGDNJg8HiidzWhqAjwBBTabyTObCZZQCPD74Vi3DKrTVlbdqKhcsdmQCXfg5/LNGEcLgs4M2oJprFlDSk4A8K//aiJkADy3y4bvZ18LGUV88PoXytVu0SigaDJ0jyntshq5nNASbG6eETxkqw9VLc0ZPp9gAKoCmOlkEQ5bHrLLAVdbZSaruzOA3g4am/uwHsZjjwOHDkHavx/P4TwAwOrOFLx+k6/IfMHHpWlw6kkkSqb2gRN7kUjbsOL4wwCAwpbz6di4GkTTRPZ7X5+QwWIvmgMHiDjo76fXnE7gBz+gz7/nPeUJS1UMnL82gXNWJSGPDNF2w2GauEwSgwz23GqIXGHfFa9XTO5Hjgi/jnoM/t69dE4OB3DNNZXvtbRQIBkg4qJWAD2Xo6qGT30K2L6d+tDRo8CePfR7fJyOQVFER67lt1JCej1VrnQMPYN8PI3rf3kXbLk0iitXC7KHvX1yuUqyvEqqsSHkcjSvh8PietQC9wWzN8Zs5IpZyzKdFhexqUncO6qlwQDht8KZAskktbvHc2aqVgBRuQJQO9eTBtM04WFST9aH54BstnLueOghem3jRpqw+GGNb/wOR30Zu0ZgIlLLDyvsgVOv2sasm8rkitmDhgPAvb3AT34C3HEH/c990Pz5+UKShDQYQNJwtSpXEgl6hpmeFn1mxQra70vtgdfC0gSXT9psogLx0UfFoobnyqkp4T1Wa5HDcwyTr4sU5oJCi1wRsMgVCxYsWLBgwYIFC6cVnGAMiDXKbOQKB9PMKiodHSJBvBqqWumJUijQj7nwortbLABY158DucPD9Hle62QyQtp7xQpaSDQ3i31wLGKhq1d4exxrWrtWJKNWJ2h6PPSe3Q6cR3FY7NkDFKAg6y+dWDQqDHhPBmNj9H1eSVVVrnCSuFW5sjTBMTOz1PjgoCAxOjso4K5lonA6DDGomZlcuxbt4Tw8HqC53V6OP5QRCAAXXwxccgkczd6KCrQyUVKOsjvxdeXdAIArmvciOqWjrw+4/XaKg2Yy5L/Cikxf+Bat/O/Ef2LlRhH8i0YBf1BGviQLhlhsZgA9mxUVGq2tNckVbp9sFqK0TVUrMq+LOpCN5+FUCnAHFEihykCxq8mF3g4yPn8RG2A8+2x58D/tIhme1csL8Cxvnn8AnWGqHJByWRRWkTl508huJCYz6B7cDgCwn7dZVOi4XPTDAfVjx2gb09PUXnv20KTZ30/BpJ4ekgPLZChw/OpXzzyORIIC+u3tdE1ZCqgGeH5mrqduvDkQIJbL5RLbO3qU9sPnUQtctXLllbU3/ta30o737qVKFDOeegp45zuB//f/qI/84hdEJqxdSzcIXadBEo3SpM2VK7OQK3rvcmRdQSiFNK79/h0Ij+9HzhWA7Y/eRjeejg4iIXnw1JNOagSFAh0fs6bVRvbVCIepf7OJ+fPP1/ajiUZFVcHatUL/Uyn5DKkq7TObrTS1N4zKyhVA6HE6nWcueM6VKwBNImziXi0NpmniflrvASCVovPKZisrdrbTWMPNN1dmJHCFiyzPou/XANj/RNOo30gSjc9kUlTbVMNsaM8PWWZypd6zA79+KpUrQCVxd/w4Td7V+4xEKIi9ezedi9stfF+syhULZwLmMXEuVZXil78kaUazdC77cHk8tSvQJEnIDy7iyhVAPKtlMiIH6+UOi1yxYMGCBQsWLFiwcFoRCNDaxG4XZMFssmAcVzGrxvT2ijhaNVgKGRBkSiZDPyyL3N1NaxkO2Hm9lUbduZwgVxIJQa6w32owKMgVjkWcDnKFE8FtNjpmt1u0mxlsag+Itd7Ro0BBVpD2lRojRkbjJ02usDRSSZrJIldeXjDHE7jay0yutAYzgGHAkYnC1WwyjH2B/FawciWamww41SL8rY7y+KmA3Q7Y7eXEcV6wDw2Vum1pMojpHvwyR1UGr3P+GiFnGtksde+/+zuaY44cIb/v738f6B/R0IJR/CM+ikKQNppO0zb9IRtsgdIEVEsw3EyusB6hCZpGh8VJ7HC7xeTEgVUA2VzJzN5nh9tlzAgUu1xA73Jqsz3yFsjRacoOB7BT3woAWNWVhXNVfSKiIbDhbj4PaQ0FtlvH96L14OOwFfNIN3fD1eoTgWCXS0zALhe1TzwuiKjxcQqERiIkP2QYlMULAH/xFzNNsQDqOC4XtQH7btRByWanguup+0FFqZQGO36cJnC+JtVIJMSxspF9NcJh4A1voL+5emV0FPjYx4APfYgIE7+fBsbkJEngOZ10DKtWUeB33Tq6YcxiZs/QHBKmVpI0WMcwSZH13/gnJJlnt1PQOxAQgfN6pt9zYWqKpLsSCcpcsNvrS4IxQiH6XG8vkSbFInDXXcB991WWbnLVSnc3tUuxSG1ivlFp2szKlbExccNbu5ZeN1e9zPagsJDgCUgqecNcfrmQBhscrPxcLkfnV893JZ2mMWMYYkxNTIiqi4sumvl5Pt9TqVwBxFiXJEE4jo9Tu/OEyWBTOva+Ybk+JsL4etVCLkf9QpZPnVxZt46Od3qa5pRq8nBqil7j+8qmTTT+2GTcgoXTDfMcdv75wN/+LfX7nTtJAvPZZ2mOVlUaz7y4qAVFESTiQpe/n0EEAoI/4ink5Q6LXLFgwYIFCxYsWLBwWsExRY+HYgcu19yy4h4PxZYAWq9wbKpWoM0clJ2cpHV6Ol2ZlNnTU5lI6vOJgPHoaCW5cuwYrfElidb9LP3OweGJCYpJLHRVfzotklvXraPzYr+V6uRiNrXXNKHGFI0CBUlFylc6UCZXTqZmP58XGXlsPDsLufJS8vmxsDAo8R5QVaEcdOKEiVxxJ2HLZ2BDEc6WUhBU16myAQB6eiC7HDhvfRpbL1LL47kWOB7Y1ER/67rJ7sDlwiMHOpAzVJyD57Et+zu0elKQoCMWo+98+MP00R//GPj61+nvz+ID8LgMGBoF/6JR6qcevw1qUx1yhY2amZXt6KgZPOQYZi4H6JCFbJOulwOS6awE5LJw+FR4fPKMwKnNBixbQxPSXvs59GKxiDFXD4YyNKg3n2s/teAlH2wpK9/R04KC5oZSSOP8nf8NAEieexmkfE4EmBVFGMtw+eDwME16sRhNkLEYTdCBAPDAA3TeGzcCa9bM3P/0NE1uHR3UBsyE1wGrkvFcPyt5GwjQF8x6colE/aD8I4/QxN3bK2SoauEP/5Da4+BB4JOfBP7oj4DHHqNg8pveBNx9N8naATOlo2w2cc0aIVcUHdHVF5T/P3LJW1FcvY46Ng8INkf2eOZPrmSzxDz299P3162jm9/69XMHp30+mgR8PqrYufpqutb/9V8UYGRZPfZb4aoVQEi21SNXRkaAHTvo75UrRZsx2cCamGcC3A6cVR4ICGkwJuPMn8vl6pMrqZR4OOBz4gqodetmSp2l04JNPFVzdvaGAITvyvBwpVaqeb+6Lu7zLAvG3jJzVa7wfk7lGnH/4Ae4at+VYpH6WDQq7iubN1t+KxbOLMwPQ7kccO21wJe/TKXtkQhVsHzzm5SNtXHj7BV35nG1iKtXOEkNEI9LL3dY5IoFCxYsWLBgwYKF0wpVpTgLS1nNJgnGcLtJ7mr5cuCKK0Sspcr2o7x9Jj7icfpcIkG8Ahdf9PRU+su63SJpN1ryWeZ1zjPP0O+WFoqDeL0U8+BE0Kkp4Vm5kEinKQEOEHG7ajN7Bi9qzObasRiQh4KMt1m8UK3R3yjMmmrM5FQFflhamgtbLCw9cIyNCbzBQaF01OxMQsvFAFWFM1DqAH191DGYjXQ4oPicCASlWdWHzBJkPC7NvisP7KTg2x/hGwhHj0JTDbiVfFmO4qKLgLe8hf7O54HzOoZxO/4HOU+wzPxNT5di1DYZrtbSAKoWDM/naSBy9nRLS11ypWxqz9JgbFJTCrqmYwVocgGyywlPe+1g/9qtFJgczLUgASJfftP5NgBAazCLZefX8cOYD5zO8jl47RnEu0mGp3WcJMiMbZfQeZsZDTab5gDt0aP0/vHjFPxcsYImJl0XMls33CC8HeJxavDJSWLJfD66AaxcOafxutstpBCZ66kLv7/SlP3ECTqmeuQKH+tcBvCBAJEoAPDww3SRt2wB/vu/gT/5E/rupZfS+489VtubpVgUDOEs5Ipq1zG1+UpkNB/6ui5F3wV/AFewJM/E5JbNJgi8TKayAqEeuOJm/346/uXL6Xp2dNCA4YD2bGBJSL4J3nUX8P7306SwYwdlbe/eXd/Mnr+nKIJc8XrFDbtUqVUmuopFul85ncJ87UzAXKbH88HVV9PvBx4Q15c/V8/DBKDXuUyWOy+TSBdfXPlZroJxOk+9agUQrC9ABCJAkzY/3JiPOZMROqrMbPOkxiSNYdROzjCTK6dC/jqdFLRm5n3fvsrKlelpOoa9e+lvh4NIQctvxcKZBldK5XLUJ3t6gC99ie4lhkHkyl//tXgwrgcmcIFFTa4oiljLRaPCfublDItcsWDBggULFixYsHDaEQiI+GOj5IrPB7zvfcCNN4q4X61YC+vy8xqfY3uGIRQ9enpoPcOJmbJMsQQOGh87RrEGwwB27aLXWOGEj5dlyqam6LOng1w5coT+Xr5cxDdrtZfdTu+bktKRywGxjB05bwiGJFGQMRI5ucqVeZAr5kCzhaUFJldY5aKvT8SLg54sHHmqEnCqpZW1yW+lnMHfQNCQ+08t35X+STf297thk3Tchm8jFOsDdB1eRx6JhFDWeMc7SCLP7TLwsQt+AQlA3kOyRtksjVm/v2SREqxTuWKWBNO0usyhOYaZzYLGCGtYlTKvM/E8nJoOOBxwt9ee9FaslBDwUts90XULDJcLT3ivBwCsWZaHt2MBZJHYnMlmg0NPIdW7ofxW3uWDa9MK+sfsHs+/V5TeO3iQAvMtLZSdy+TFrl3UIZxO6iT799NnjxwhImZggIK37e10E+AJdxawopfX24AFBfuu8HHGYqI8shqHDtGxKQpw3XVzHgduuYWOOxymKo3Pf572wxP/FVdQu46M0A2kGmzmxR4YdaCpBnKBFnzjjt/j5zd9CbJehLvDTw1gZtbN5mVzVa8Ui3Suw8N0XdaupWu3aRMRGfOZsEMhUZ6YSACveQ0FFbu7qYzzrrtEVkC1mT33Ew5MshSV2XcFEH4r3LZMrpwpmCcgDnxedRWNiYEBIUlls9E1z2ZrkyuGIcpmuRwvnxcZG9u2VX5+oc/XNNbR0UGv9fcL77VqcmV4mP5mTzVuB574gdoBYCZXFOXUy1bNpvaHDlX27UiE9sXtt2kTjflaRnQWLJxOmMcEzxGaBnzgA6RN6nSSJ9W73y2yumrBTOAuYlN7RRFDMBqtnV/wcoNFrliwYMGCBQsWLFg47WDrAk2bXY6YwXEGDmCa437VsNnoh7fLiaAOB8WCJIniDLmS8o3XS8fh8wk1mT6K1yKToeRJQMTrOD7U3U1xvGKREoIXklzRdUry5vL6UEhkcNcjo1hRg1V8AGAqqsCwKTA8pYMeHz+5ypVcTsiF8AqqilzhqgGW8bGw9FBNroyPU191akU4i0motgK0oEvE1zgIyf4JDZIrLPFv9nfhypUHfkMBjQtXTiIsR2AvZuGIDMOnZaHrIhZntwOf+Qxw//9msMFGpkk5bwhQFExPC6+npiaIPl1duZLNCi3BUEgcWBXYC8nhoLlAt6uC7Sx5G6TjBTh9dmiqAaUlOGMbAI2dZb3EDj26/k5MfvDT2JemjPNV62wLE182yzLlciisWV9+a3r9pXDZsuJ9/iwHhlnm69gxmmTZkJ7xs5/R73PPJbmnVavo2m/YQPI955xDFR9uN1WtNABZphh9S0sDX2HfleZmQdzUqyjgqpXLLmssMOv1At/4Bpn4XHutYPZTKVHRwaTAY4/N/D6zg52ds3rMaApdf5tmhyMXBxQF7maXqFphsM+Lqs5tas9al2vX0nZ6e6lapZGbbzVCITF4mFFfuRK4917gmmtE4N5mo+tvNrM3V0KYJbVYGozBlSvptJClOpPkCpfFsh8CQOfA1SvcdwA6tkyGzqO6giidFmb2HIjds4faJBicKZuXyVC7cYbIqcI81plcGRwUBtrV5ApnnzQ30zFwH68VSGawdKKmnbpkIVBpan/sGE3ozJhHItTnmLTfuFE8cJwpyTgLFoBKyb3qMfGqV9F8uGwZ9dl/+Zf6firmirBFXrnCxWMWuUKwyBULFixYsGDBggULpx1eL6moXHxxY7LirLDD62dVnX0tbQ4ARyKUCMkxErORNm8nFKpUJ+E42OQkcPgw/b1mDR0rxw88HrGPoSFR6bIQyGQo0RsQRpEsWV8vLufxiDbhRc5UhKRIdH8p0DgxcfKyYHOQKxzUdrtry7VZWPzgGJvPV8kxdDVloSczcPhUuDymDspBsJUr6Ys2W8NBw2oip7+fiMxfP0gBv6vOmcK0hwLO3pGDcMpZ2O2VKhw2G6DJeShJqrrKe0KAzYbpaerGNluJvDGnXJpZ0lxOkCtNTcLcuQqczN/bS2N3ZAQ0WEssZ246hWImD4dXgcdvqxuEdLuBnuXETPUlmjCR9eLQJI3d1Zscs/uNNAozYZLJQF6/tvxW5sLLIOWyItuddyhJ9L3OTjrIQoGkwcyIRgWpcOmlVOHR3U2B89Wr6YeJlgsumJc5ORdZzBlf5wx2j0dM5jyRmpHJAA89RH/XM7KvBTY5N4PJA0kCLryQXpuNXJlFEgwQ5IqCPBz5GOwhHzSPMrPahfus2z07uWIYNHcHg3ScTHydbIWBplH7+nzCrB2gbf/d31HmtqZRVYYkVZrZM6p19LhSAaDj5GvH/iPsL3Mmwewum9EDoq/89reizc1G70xW5PNUBXLgAP2fzYoxz5JgF100cy7h8wUW5nzNY93ppGum60S2VUuZpdOicqW5uXKC57JZm23m8wP/r6oLR66sW0d/j47SMabT1IbJJFXDjY/THMUmeJYkmIUzjbmquXp6gE98gsbRzp3AT39aezv8HJ3PL/rKFR6GsZhFrgAWuWLBggULFixYsGDhDMFunzWBtwKSRDEks9fKbIFGs5zQ6Cj95sz37m6xFuIKmFCI/mYfFU7gPHZMxBvWr6+sGjH7QQwN0WJiodZGqZSICfI+5vJsNZva8+ciEQCqikKg5NUwOUnBrnpZdPVgrlzhFVQdcsWSBVu6YJJTUSp9yDtClLmt+Z0kfQUQwzA2Rp2yu1sE3hpkCDh2YZYF27mT+EGPW8fGNTlMeamqo32afCR8PlFBVUahADVOfTfnCSFdsCOdphiuz1eKP/JO4vHKDWSzQtKjublu8FCWiTNgu4+xMSAu+8qmt+kx2qbTr8LdVp9UcLmAZctpUjwe8aMvFkD/BLXX2nWz+9Q0DJ4kVJXabFkY0d7NyATbob7iIiFhxAfE4GoDJgc4cMx48EEKEPX0iAqOtWspcL58Ob3e0UFtvSAsUR0EApW+K/v308+DDwJf/Srw8Y+TT0oySZ8599xT2x+TKwBlC8gyyVvxjYPB5ArrSdaBLAN2mwElFYVDNahqpaNj5s1SUeim6HZT8LmeyH0sRmRYOEwEYSM6nHOBsxGAyvEiSURA/PjHwD/+40wzewZ7a9hslab2AFUjcEc3S4otRCXHfGAOnjKBtH499eVcjvoTf44fKAYHqVrviSeoz504Qdc9nxd9ZPt2+l0tCQbQ2HM6BZl5qmApRvaG4HYeG6Nj5soa3jfPdWxmb24LQGzHjNNBrgSDNI4Ng7JbEgl6mDEM0X7r11M/dDorb0YWLJwJMOlrlg6sRmcnyYIBVMlSSx7MPMcs8soVSxasEha5YsGCBQsWLFiwYOElCfZR4TX/bPE5RRHxUl7PMLnS00NrGJZLdzppLS9JQnZmeJhiVSwdHwrR9szxITO5wsntCyUNlk6LxPC2NjofVZ2bXAGoXTjpNRYDCpKCQqCU9cwEyXyrV8yVK8GSpFEVucLnbhnaL12YfZ7N/tetbgqwqgETucJVK6tX00rb4RDBvgb3xZUrskyx8O98h967+vICCoYdsSCRK56xI/CpaXi9QqGnjHweSolcyXuCiMTtZe+i8jlwprxhiMEMUL9ndra1ddZj93hIOrClhf7un3CjABvg8SCdkWCzA6pbhaetfka6zSZ8p/smXDiW74auS/D5jLJS0oKATWKKRfidOWQ/+++Y/I/70NKp0DmzGZU5wMvfYXLl4EHxnmEIqaSLLhKT9UIEW+cL9l1hEuNXvwLuvBP41KeAb30L+M1vSPcRIJP6Rhn+WjAbrgN08Vk/srp6pcHKFaBUbZWOwRlyUiUYSzpVoxHflclJOj6nU9ywThXhMM3/Ho/QrjSDWdh0utLMnsE3b676aGoSNzcm5jhbgeX1TtXLY77QNHGP4wmFySOA+rth0CTFUmgjI/Sg0d9PputHjhD51NZGE87ICPU9WabqLTNYh5SlE0+lX5rBpGixKIzih4dpX7xPbmv2l2prq02u1Aok8//mst5TgcNB2+Jj3b+f+vbUFPUnNsHbsIEezDRNPJNYsHCmYC6jr0WKFItEDK5eTWWXmUxteTC7neYVlupbpKg2tLfIFYtcsWDBggULFixYsPAShcdTGa+bSxaMkxnrVa6YSRq7nRYGrEaRSFCcgeOHPT3iMwyHQ8Rk2XvidJArLS0iaXe2pGOzqT1/PhYD8rKKHJMrbEp/MuTK9DT9HQ4LiRATzJUrjUi9WVh8MMfYzHYNLWoUileDTbHBpZUy6NlvZfNmYXA0j+xzjrGxhQYgyM5X32hDMmNDunUZAEAZPI6gLQ6vgzLMK1SSCgUoCa5cCWI6bi/H38vkit8vjs2cXWqWBWtrmzN42NVFcb6eHkA3JAwkKbM6nbfD6bEDADxdgVm3wcnlw5MaXpygA1y9WlqQgoMyzJ4X2SxamnR0thuQYAgJI86gN39HVWtXruzbR6V2mgacd97cZXanE2yAc955Iis4HCa/l5tuIqLlk58E/ud/gDe+8dT2Za7MAKjdNm+mv0+FXElPQ5UK0Jq9cHcF6+ssBgK0T7u9NrmSy9FNIBxe2CA0j5fmZtpvPV+bVGqmmT1Q6QWSzVI/u+IK+v8Vr6D3OOh/ps3sGfV8Rq69lialI0foAYHP5cQJIlQOHyZChT1VNmyguUOSRNXFpk0zZfE4sOp0LmyVjrmUlAnHwcFKKbNcjtqayZWOjkpyxW4Xnjm1yBVFqdRtPVV4vWIiPHBAVK4cOULtLEnUhiwhtyAlfRYszAOcgFCLcDQMWnCk0zR23v52mk/qyYMpCm1jkcuC8W0wlbLIFcAiVyxYsGDBggULFiy8RMHJnD4frWfs9vqfVVWR7MuxUbMqi5lc4XhAMFjpg3zkCHDoEP3NSZTVlStM4ExN0dpoIckVTq5ubxeSaHN5mbCpPQdi43GgKKvIBUoHyuSK2bS70QOKRunvYLAme2KOM852bSwsXtjtZaWr8jgBgLAzAc1PA2lG5co8zewZZtUNc8J9dzewep0NOWjIdlGpmTQ4iJAzDXtiGi5Xpe8KCgWoCer3MSWMXNGGYJC6cXk8mQcN93OAJoqJCfq7AXJFkoigdbtpnokWvZjMeZAuKnB67ZCdGpz+2Qfx2rUUS9YNCb/ZT+zt6tXzsiiZG9WeFwz2l9C0maWB7CvC5X3HjonvspH9tm1isjpbPgjsu9LbSxJgP/gB/Xz+8+QHcsst5AnDGpC10GhkKJkU1VihEHWoc86h9/buFdV+nHkPzDSmr0Y+D216FO6wA7KqwL2yrf5nzb4rtciVqSm6aQaDNIgWMgjd2SluxjxGqlHLzB4Qpu1mv5K//EvgJz8RBADfTM8muSLLM31GfD7gla+kv3/+czqP5mYaO8EgDdYNG6h9qscQ+61cfPHM/fH5OhwLe77msc59vr+fzqlYFJ4mgKhCam+fWX7KZFMuV5l9n8uJiXShKtXMvitHj9JzSz4P/P739NrKlXQjsNkqTfQsWDhTkGUaI7UIx9FReo7o6RH+VGZ5sGrJSPZ2KhZJwnERQlHEc7+uW+QKYJErFixYsGDBggULFl6i4HiDzze3ZL+iiIDsxAQlhPF6pqeH/ud1ERdghMjruhz7OnpUkCtr1lCMx8wpmGXBpqaEhPlCYGRE8CCtrRQ7ayQRvJpcicWAvKQg7S2l/nPgeD6VK7pOjWgYFJwLheYkV860gouFMweOsQV9wuMh6ClC8yiQYMCh6tTPmB3kYPxJkCtAZRUaAFx3XamvORwwli2HIclAJgPf5DHYY5Nl35Xy4j4vDO3H5RaoKs0lFTL9ZsFwMzNjJlfmkAVjaBoRJH4/EOpyYnDCgZzdTX4rLe4549vMCwDAnn0UrVi9eoFtSlwuYZ5jzpblv9mMygxmoVtbhTH2kSMU1P/Nb+i9Sy+la8wEx9kC+65o2vz8pQyDzmnPnhrmPTVg9gThDtXeTmy8YYhgMDP7weDcgfOhIbR401DbQnB3+OHvnOXzmibIh2Sy8lzZyD4QOD1B6NZW6j/hMN2sqoOCHLyvNrNn8PUpFOhzbKbGSKfpf5vt7JArTC5w4NMMlgZ7+GE6zs5OGvSdnfXnuGyWMteB2n4r5vNdyMoVs1FdUxNds1RKZISkUsLcnm/i1Z4rQG0PGj4v/uzpIFfYs6ZYBJ55hl7btImeQwKBhauWsWBhvuAxUSwKz6tYjB7g29roHsieTK9/PbBlC421z3ymcq42V78sUmkwttACqCkscsUiVyxYsGDBggULFiy8RKEoFHT0eETwsR7YE0KWKXazezc97Hs8tG4vFkVciuH10j6WLaP/Dx0SMbENG2bGh8xETCRCa6aFqOrXdZIZBygW53LRcTYSq/R46LjCJf968lxRkfG2mF4ozK9yJZkUTA8HxKrIlXxerA0tcmVpg+MJnf4EPM4C1rdOQXK7oKlErMhGEbj/fvpwb68wDJLleQUNzbFNlgWTJEGu2N0aFL8bxTC9Kb24F0E5Bo+SKSdkA4CUSUFJU6B8TOkqS4KZZc1muLECNBCjUcGYtrQ0HDxsaqIYe2evAsWtAm43HG4bPO1zl5+4XDPnt1WrFs6CAcBMWSYGSzSp6kxyhZloh0NUFxw4ADzyCE18PT2izK7W988kmFBwu6s04uZAMkmkis0m9CRnA1dm2GyiQzkconqFpcEalQSLx4FIBMHVTbj0vAwueF3n3NedJboMo1KeK5GgSTkcpkD0Qvvf2Gx0vflmU129Us/MnlElTTcDZuLqbFWuALVlf7ZuJSIllQIefbSx7T3/PJ1nc7MohTXjdJ2v2d9G18XkMjRE4zadpt+Dg+Lz5moXhvl6mduDK1d4jl8IeL3Ut5xOel45fpzkwFgr9bzzxGcsWDhbMJeTs2dKXx8lH7S1ieoWXad+/Nd/LeTBfvITsR0zgbtIpcEkySJXqmGRKxYsWLBgwYIFCxZesli+nJI+55KO5wRQjpc+/TT97ukRcYHq5GxJEqoeACVJ6jqt4Ts7a8eHOjtpP8UixeLS6VNfVGQyFEsAaH3GajyNeC7wMXISNRvaJ93N4oVcbn6VK2ZJG69X6K+bYFZSsmTBljaYXHFKGXzlz5/HZ1/7KHTNCU3REe7fCbznPWQcDgBXXSVMqYF5kSucbK2q5BGuKLS5lpZSTDtEumFSZ0n/7+hRhNxZuLMR2Gyi8ECJkC6gLtmQdIQRDFIcvIIANBsqRaM08LNZoSnIDGd1wHEWrFpFw6V3oweSpsLhluFum5tccbsryRVNA849t+HdNgbWXKsmV9hQG6hNjnAbsObiwYPAL35Bf193HbXb2fRbYXg8dE25oqNRJBLUMZqb6W/ORq6FfJ5+mDyw2ah9HA5hyr5zJ22nEXJF1+lzHg8QCsG+diUkbQ4dSIDa2umk/ZuJpMlJUS12uoLQHR3UzsEg7c9882O/gWoze4Z5PNUKKDLZwJPAmQb7iNSqXJEk4MYb6e+f/7yx7ZklwWqVr/E8yWT0QkHT6DwcDtpH2dRpmMY+V64wucIkYa3KFb7v85zBQWNVXVjyjsuKOdPl4EEiKg2DMlpWr64kNC1YOBtwOMQ4Safpwd1moxt4UxPNjzwusllaMLA82Je/LMrpFUVIci7SyhXAIleqYZErFixYsGDBggULFhY9OAbASbWsJsF+K0ApQFylKBEKkQQYIBLWe3po0VCL3HC7xfp+aIhiDfP1iq9GOk12BgDFxJisaCQRnE3tOZaWTgOpnB0pT4ltKRbJmH4+B5lIiCAzBxGryBUubGEvHMtfdumirA6TzcJezEE3bHDlpnHBj/8Oqz79bpJV8nqBu+4Cbr9dBOxPImhYliALAj/8IfDhD4sEfVdIIxJ11Qr68IEDCOkTkKYj8HhK6l6GASVGPgJZZwBOlwyns0oSDJgpC5bP00TBBs/h8Lwzs202YP16wN0TxrLLuiCvWwtPYG7W0WYTMUWAVNXM/jYLAkkSREA2KyIhtcyozHC56DtMEvz+91RmZ7eLoLHLdfbJFZYl83go+NuoXmMiUUkOzSYNVqsyg9unuZluHIUC8OSTlYZf9TA2Rv2uq4v236iMVyAg2p2JpEKB5nnut3wjXGg4nXQDZM8RM8vOVT1A7UoMrvix22ubpLOk2NmoWmHwvFXrfnn99TQf7N0rbtj1YBiCXKklCZbP0zVbaL8VQPQNHutcNTM8THNzLkf9nAO9zc3UZ6pv4mYTLG4P/r3Q5Aow09R++3b6e8sWmhBbWqwSWQtnFw4HzV+yTOQkjy+WteMqTkkSBPLrX0+VjZkM8K//Sq/x83Q+v2grV4BKcmU+apxLFRa5YsGCBQsWLFiwYGHRg2O4THxw7IPJFUWhhUA1YREMUtKZOa6wohS7rRXzcDhEoHZkhH6fqu9KKiWOt62N1mdeb+OEhddLsTRer01OArrDBcNTCgByEK9RJBIiyFyncoXJFUv+fOnD4QBkyYBdzyGfzKJr76/wR/ddj85nf0ofuPFG4JvfBF73Ovo/n6/t4dHgvngs2+30k83S4t3tluBrcQhz6YMHoY0NwC2n4bMlkUoBhYwws086wgiEJChKDbKiWhaMde6YVGxuPqngIccHA+1OuPxKQ9VnQKVq0IKb2TNY+scwKiVJWPavhq9SuZKAyRUmH17xCgowsZfL2TKzNyMQEP4vjUiDFYuCXGHm3UwWVCOVog6pqoJ5d7tFP7noIvr9+OOCXKlnZp/JUOljSwvtd82axid8viZcpcNeK1yK2dq6wJpyVejsFCQI3yeASjP7WmOnnjQdcPbN7BmaRm2r6zOrmEIh8hgCRPVWPQwMUPaFopCkVTU4oOp0LqzfCsPrFfMv98ETJ0S7J5PiAaalpXaFHl+veuTKQt/8zb4ru3YJrdRt22h/liSYhbMN8xxWLNJ90eMBNm4UGVHVFaKyDHzwg/T3M8/QPbRaWmyRgm8zZgualzMscsWCBQsWLFiwYMHCogdXT1SrRnR3z56crWkUMzEnDa9ZI7xMqmFWyBkbo7jWQpAr1bJg8wmuchyFvzM1BUBVYQRLEeWJiflXrkxS9n9ZbqeOLNjZtFmwcGbgcADI5aBJWVz5o7uw9cl7oeXiiHetR+xf7qXAAQfXeTDM08zevC9z3AGoLBjwtTmpPKS1lTK/d+xAyJGCt0iESny6AKVErqQcIQRDEpqba8StzaVp5soV9t2Yh99KNTo7qajjggsaj3Gfc47Y3erVpyfeOsPzolikNtS0+gPZ5RKlRGZtxptuEsSE3X6aDnieCIWowctlTHOAOxYH830++l49fRNzZQZPtpypLMvA+efTa089RYFsoL4s2IkTNKe2ttJn5tt+TCTpOo25yUkiC+320x+EZmOwpiYK0qfTc5vZA5WByeps7XSaxiSTRmcLZimsWvdMNrb/9a9nv6dy1cU559QmIdJp4c9wOs7XLB3XUvJfm5gQcp+AIMZaW2vPdWYTLA4A8znXI9BOBV4vsHYt/d3XR/2ppYXM7F2uxnRSLVg4nTD7MjU301zIFStApd+ReY7r7KyU1jTPMYuYXLEqVyphkSsWLFiwYMGCBQsWFj0kidYrHEdgcOWKpgllnGqEQpWZ47XM7BmaJmJXU1MUkz1VcmVwkGJ6kkRJpooyvzgCK3dxfHtyEoCqohgMixcarVzJZCjgyuSK3y8a14TpafptVa4sfTgcADIZdEy8gNbRF1Cwa/j9q+7Gs3/1LajnrK/8MAcUNO2kyZXq2GYySa/bbICvw0sf4ED2rl0IFcehJiJwqDri0zrUBAUQ894QVKd9piQYo7pyxUyu1As4Ngi2PWgUXi8laDscVBRyWtRvmChhyRK+VrNVGZkD4qxd1tpKmbpUTiTmiLMNt5uO0+eb2z8FoM+wriJ78BSL9T1balVmcLs5HHRjaG2lds1mhQF8NaamaN9dXfR9s+FOo/D7RZb06CjtLxymm8CZYLxZykxRKEg/l5k9IHxANG0mMXG2zewZXLkC1CZPLryQgqqxGFUo1cNskmBApdfR6SAmeW7jPsIPRkND4ryYXGlrq125wtfLXLmSzYo5ZKHJFY+HxoLZwG3LFurTVtWKhZcC2NwxFCKypKeH5gMGV4CyJJ8ZrD988CBtw2azZMGWGCxyxYIFCxYsWLBgwcKSgKpWkis2G61/mFxhCfFqhELkcwBQfGfZsvrkhsMhqlympmjbp0quvPgi/ebYGDA/coVjUvzd6WkAioJisLTom5gQ5plzgQOLzJ7wRq3KlZct7HbAnk+jafIAAGC47Vwcu+gWyDYZmlK1omaZKVk+aXKlWuafY9qSBHjbS4FXluc5cAD+yHHIegFeOYH4dBG2GJErRiAIh9tW3w7ETK7kcpWyYO3tCx88nAVuN/C3fwt85zsieXvB4XRWNi4Hf2arXDGTK5s309+33iomPSZXXioIhSi4bxhzG9vH4yKQ39ND52K31656qVeZYSZXMhlixhhs/m5GoUBseiBAkzwbdc8Xfr8YY9EoXVOv98wFoVtb6Z7Q1EQakfH47Gb2DK6e4qopBpMrsnx2GXtmdyWpNrliswE33EB/1zO2T6WAF16gvy++uPZn0mkx2Z0OcoWl49xuOh5+wBkcFMFcTqDo7KxNrgCCDGPdn1xOkE8LPT8yIW+u9rrsMmrzugy5BQtnGC0tNP82N1dmZTFYfjOfryT4mVw5QM9RFffhReoGb5ErlbDIFQsWLFiwYMGCBQtLAopSKe/V2UkP/Lo+e/zQ76dqFYBiELI8e+XKQpIruk6JbICQBDMnzzYCTiRlX4npaUC3q8gHS0xTJFLpszAbEgkKejF7wuRK1QFZnisvLzj0FILjhwAAE8E10BQdTk2fSVaeYka2WXWD49npNG3K7QZszpLW/9q1ZWkweeezCCoJ+AoR5DNFFCao70pBP1o7Zglcmw3ts9lKQ/tTkAU7Gfh8FFNsb6+vJHXKqCYCZjOjYnAmrqZR1v73vw+84Q00T7Ds0EvBb4URCols+9nM6YtFCjp7PHTu7e00ifp8tX1XmKhxuyuZb5uN2oEzlS+/XLxX60KyiXhnJwXnTtZ4nokgHmOhEP1vzqI+nbDZ6IYVNlVHzmZmz+AbHCCC/By0Z7+Vs1kFxcdWz9QeIHJFkoDnngP+4z+A55+vJIqefZb+7+ys7bljGHTuTqcglE4HfD5qz2JRVEcNDVE/zedFAkV7++zkirmSx0yu1PvOqcBsah8IUIViODy/ByILFk4n1q4l0nTTptpzlcslnh3M1SucNcEP/Ioikp7mI9v7EoJFrlTCPvdHLFiwYMGCBQsWLFh46aPa87SnR6xZNK0+ESDLFC+JRun7syXdm2XBIhEK/J4KuZLJVJrZn4y0OPtncpwrFgMKsoKcv1kcKEALubmCFIkEHRQHJpmxqSMLZlWuvDzg0FMITBC5Mqm2Q7MV4NRqrKYzGcpmV9XaBulz7cdErmQyYmxVjIumJurkW7cCDzwA7NqF4LZxjOs+yAUH5ChVrhS8IXS1z0KucJlbPE4/mQxVefF7Z5BcAchK5rSCJU1UlSY79nwAZh/IHBAvFITvSjIpqifOpoxTNYLBUomTl/pIZ2ftzzFZ4vFQEJe1GP1+wZqbg8epFLWb3T6Teedgmq5TAM3vp/atJlfYG6Wzkz7PQeSTgSTRfiIRkgULh0+/kX01OjvJOyYYpDabzcyeYfb94ZvzS8XMHqicgOolI7S1AVddBTzyCPCDH9CPxwNcdBFwySXAk0/S5+pVrWQyFFA93efLviuAGAfDw7T/kRE6Brudrl89oqT6euVytN356h42Cq+XqlUefJB+ezyVGTMWLLwUMNscV00g872VK1eGh0W1Id+Hqu83iwQWuVIJq3LFggULFixYsGDBwpKAqlJ8jOMJPT2VFhCzxQ/DYeDaaynB0+utnzwrSUSu2Gy0mBgdFYm3J4N0WpAr7e3zN7NnzCBXJAVZbxO9wExIIweZSNCCL5Gg/zkrvUrexiwLdhIxdAuLCboORzEJ3wR11El3Dxyxcbi0Kk+LQoF+TtLMHqB4naqKypVkUkj/l8dFUxN1Og5eHjiAUKIPsqHDU5yGK03kii3khzswS+fkoJ2ukxzYxIQYI83NZ5xcOSNgIiCXq5Qmmk9AnH88HkGwvFTA3ik+HwWs6s158bggApgwCodF5US1NJjZE6R6gna7RfvlcsCNN9Lf7AvEmJ6mG0dTE91oTjWYxv4q7H1ypn0pnE5qM66Wmc3M3vwdWabj5axuJldOl7n7fGA2cZ/tfvk3fwP8/d8D111H7Z9IENnyyU/Sb2B2vxWA2uJ0SIIx/H5RWWX2XEmliBQDqO+bSdZqOBw0pmSZrlOxSG1zukpWvV7gla8EPvEJ4PbbaV8nW91lwcLZgMtF4848xwE0t3ElG5vam72MFiEscqUSL6EnIQsWLFiwYMGCBQsWTh6sYMOxsu7uSv/V2eIB4XDJMNs3t8qNyyXiSazycrLVK6kUcPw4/d3TI45hvtA0cUxUuaIi4yu9EI3SymcuDwLWYJqYECulUEho0JvA5IrbbSl2LHmkUnDmo3AkqKpjqnMzlFQUznxVALoRg/QGwEo0hQLFLNlvpTwufD7qk+vXE0FSKMC19xk4Cgl4HXk4S+SKt8M7O/PHpuAAsaRsZs9yOi8l0mChYM6qzeUE6zybFJNZ5iSTEcTrS81vhREKiSB9Lf8UgM6BP8MTPt8EPJ5KaTDDEMY/tTQbeRKUZbrhvPOdwPe+RzJqZkSj1LckaWGy8Tl43tlJQemzQUx0dgqSoJHMAB5vmibmC5bIOkmfpgUFs7ss2VMPigJccQXw4Q8D998PfPGLwG23kWEbQDfjc86p/d10Wsjxnc5rxnMYS985nTSp9vdT5QofJ1D/Jm6u5OFxr6qnj3jm9ujqEtVYZ1MmzoKF+aLWHMcwm9qrqvAyWqSm9kyusPzyyx1L8InRggULFixYsGDBwssRrHpzxRXAxo2k0JHNijjAbPFep5MklFeuJJJjNmia8FfleOzJro2OHRPZ+eyNeTKVKw5HpcpRQbch7SsF8GIxCoywPFg9MPnCgRcOqtYIULNqmNu9KNUMLMwHySTco1S1knA1A60tkFxOOMf7Kw1b02mhUXcKQVKzzH88Tt3QbjeNX0mi7H+/XwQwX3gBIWMCXlcRrgwZNQd7fbMTJJomGJtoVPitNDUtzaoVoLIKBaDznCsLnb1VbDaaUBMJkdH+UvJbYYRCInBdy3eFSWSPh/opz29uN50rVyJw385kKHJU7bfCqPaysdlmep9ks/Se30/bWAhG2uulvupyAStWnPr2TgahkPCsUZS5yQKulNK0Slkw7oNnu3IFEAQa+yHMBZuNHh7e/W7ga18jYu0rX6l/jZlMAk4vmcTmcW43kSr8gNHfTxUsAPVTJgZrgY9T08TzgaadvvmRiZvWVppfLEkwC4sNDocot61eGLDvyoED4r7DpvaLEFblSiUscsWCBQsWLFiwYMHCkgDHMl7xCuDv/o7iWCxlzFYDsyEcFtUjs8HhEAosrCR0spUre/fSb44Vu91z778WNE2QK4kEnXcyWNJZz+eJBYpGZw8WJRL0PhtudnZSgGMWcsXjsciVJY9kEo4B6hPToRVwqAbQ0gKXnBGlWwAFEjSNgqcLRK4AtKkZUn1MgHB1wL59CMf74DJSUPMpAIDa0TT7jhRFkAOxGEmDARRwXKqdmnX8eJKZSy8RqAywZrMUZGX5rJNhgk83PB46R6+XJqrqqA9n4Hs8osyREQ4LAoWrXsyeILXO10yu1LsRRKOivZrm6JeNQpIoqH/ZZTPP40xi2TLR5nMdB0vQORzUlwxDkCtM4p1tmBMKZqteqYfm5tnHBcvx2e2nn8TlhwpAZI0MDop5u6Vl9rmOiRdVpWslSXP76pwq2JOotfXsVzJZsDBfcJk83y/Nz9zVlSsAzTGLvHLFIlcIFrliwYIFCxYsWLBgYUmA4yF2OyVqGoYgV5zOhVOX0DSgo4P+jkROjVzZt49+n4rfCh9TKERxEMMg3+Sc5hOZwIOD1Ci1MrkZbGZ/9Cj9v3y50I6uAiexmlWGLCxRJJNQ+8jMPtq0CqpPg82pQu1uJXaRg9WZjAi6nSK5Yu5yFWb2jGCQ+iZLgxWLCB55GoF4PwDAsNnnDvQqipC1ikYFudLaurQrV4DKoGmj5IrDQQM/mxVVHC+FYHg1JIkmQ5+PIj6pVOX7iQSdt6bNrLwJh4WnBJMrqZRg6GtN0OxrYSYMqhGN0nfZc2UpoaWFzNy3bZvhzVUTLhe1sa4LQv+lYGbPMEu/nayZWj0UChRMPd1+Kwz241FV8dAyPEwPCADNnXPdwM1sN/8+nfNjdzdw+eU0t1uwsBhhltI0V6WsXk33p9FR8RCdzy/ayhV+TrPIFYJFrliwYMGCBQsWLFhYEuB1P5MrHBdhcmWhoGlCrWJq6uTJFV0HDh+mv9va6BhPxm+Fj4lVQACKnRRkFUYoRC+MjdEKiM3tayGRoBNhcmXFirqVKxyvtGTBXgZIJiH39wEAEu2r4Ah74FrRRhnabjfJzLBuuMNBHaKRIGsdsHIQG9srSo1xIctEnpikweQ9u7FFplIwKeCfu1TNvOFoVGj8LWVyxVxlwQN3LnLFZvv/27vzMLnqOt/jn+pae63uTneTbrKSkEASwhowLAKPsow6MjJhiTIwDiPKqnPH0evcgYCQcUSFAVwZHRglOkiAq97HBVFgVAKCARQTSFiyr72lt3R6O/ePL78+VdVLeqmqrq5+v56nn1N1uqr6dHK66vzO53y/P/+Es3tTdZPZ56rKSntDDYUGzrsy2HwrTjxuzykrs+clzrciDZ1+u5NpfX0Dqx26u+1EmpvjZxzzEeUs17tvJBJb07lWlbkUriSmu+kOVxLnpcrG7+ve34qL/VZ1e/b4F1nU1h7+A7ywMLvhipSboS0wUolXHSUGJ8XF/qT2mzf7k9oTruQFwhUAAADkBXegHw5buOLOY4yk881oxGLJ4cqhQ3b+bbQdRDo7bc4VSZo9e3xddty5DneusKFBUiQir2KarThwYPh5VzzPTgDu3Gm/VEGBbdQQ4Upi5QoT2ucxF5ps3y5J6qidr2hpRIWza+xk8axZtuNv326PTcMV2YlzKLu/20FDRzffxMkn2/0NG6QtW+y2u2J7OImVK4ltwfI5XHH9EaPRkYcr7jGJ/zGJLdVykQuVy8qSq/V6evz5VkpLBwYCBQX23Hjc9mcXOLtQZKgAobjY//dJTdpduBOP51/Vylgknng8cMCfLC1XwhVXpRQMpj9ccfNSxWLZqVwJh+3fu7jYL21tbvbbgtXVHf69LrVyxc2ZA2Bw7sA4GBx+3hU3t1NX16RMJwhXkhGuAAAAIC8UFNi5r1DIH6+4zjfprlxxc640N1te0dkpvfTS6CpY2tv9c8Hz5tm2jvX8UjjsnxeULB9ROKzeae9MxNLUZCcKDxwYfBTU2WkjpFdftftz5vh94Qc5Se1+z7HOEYNJor09KZQ7WDtX0bKIiooDdpKgsND+GFxFVBpOGiaew3dzjA+ak0ybZjv9woW2Db290nPP2ffKyw+f+oVCg7cFq6nJ33BF8k9uu5OmI6k4SKw2cG9SYy2zywY350ppqb1ZuZPkifOtDBUOTZvmByl791rwfLiejcXFfqu11KuQW1rs+6EQ4Yrk9+iMRPxAVsqdcCXxDWgsc64Mx823Eghk7/d1864ktgZzxwBjqVxx2w9gcO6ChUhk4OdB6qT27rNpElavEK4kI1wBAABA3nDnCl3lymguzh6pcNjOkYVCNqDYv9/mp2xslNavH77zVqLNm20bg0HLMgZM2j1Kbt4V6Z1tiETUNX+R/8Pa2myDU9vkSP5Jx9dft+XChbYxI5hzZRwdoJDr2tv7y6t64tMUKi9RuDhq50OLimxenupqu+1OmI4zXHGBqKtcGfIcvqs8icelpUtt3WabG0YVFYevXHHtnyT7g3HzEFRX53e44oKS0ZT0JVYblJTYV67/4VdW+oGIe39z861EIkOHK5WVti+XlfnPG2oyeyex3Vrilcq9vfZ+G3+nTd1YSxPziQtT3P7k0tNcqYZw25HYBi9VW5vfanM0Ojv93z9bk7XH4/Yzg0Gbz8QpK/Or2IYTi9lzQyE/XAEwNPd5UFg4fOVKYrgyCSe1d4dYnmdjrqmOcAUAAAB5w4UrfX12kai74DKdlSuBgL2euwi5qMhOCG/ebAHLK69YW/PDcUUiNe90WBrvebdo1C66lt4pNIhE1HH8u2zFW2/Zyp6ewVuDtbXZVbpuEpgFC/yTp4NUALjKlaIiKlfyWnu79OabkiRvxkxFiiMKhgv8ueJnzPDbg7mAJQ1JpiuqOOw8RFVV9odz0knJ60cSrgQCfuXKtm32txEI2NXc+bxTJ1aujPT/qrDQ3uTC4eGrPnJJZaW9hxUV+YGym28l8f8+lZuLx33f/e4jDVcSyxfdhO2uJRhX/PsThLmT9Lk034rkVyANVbnS3m6fk7t22Yf+SFuHeZ4/L5ULO7LB7cdFRX7JreQfLIwkXJH8IIZwBRiem6csGh1YkTJ/vr2/1Ncn9xOexJUrUvqL/CYjwhUAAADkjXDYP+Dv6PDnpj3cedbRikZtagbJKleOPtrOXbz5pt1/7TXLMzxv6Nf4s829rbo6e73xdtmJxfzzJS0tUm8gpM6qmbahfX1WXtPWNnhpTVubXbm/a5fdnzfPD1dS/vE6O/2BFOFKnmtvtx1ZUnjeLJ16Sp9OOy0hbwu80x6sqMi/KjoNV2THYnZO8HDntFVVZQ86+mi/5Y00snBF8v/oduywZXl5bp3ozYTSUvujjcVGnugmTugeieT2ZPZOWZlfndTaaifBOzv9dmHDVd5UVfkhjGtjNdx+EQza42IxO0nm3vgPHLB10SgtwRz3b5p45UOu/c25A4fU4KS3V9q61f4eFi60z9U33hjZiVE3r0K2f19XGVRcbFdyOG5/PFz7xMRwhcoVYGTcPFy9vcnJQ2Ghf6y0bZt9VnR3T8pwJfHYP93TU01GhCsAAADIG6lTCESj6a1aSXxdN6n9zp02yDjqKDtfsWOHrdu61QKUoTqHbNpkSzfOSkflijt30tIi9fQVqEsR6cQT/R/Y2vpO8pKyUe3t0h//aAO92lr/BKw04CR1YuFLNi/AxQRInBjoqKNUVFU08FxccbFVOpWVSbNnp2WHOPJI27eqqw8TOsZidqIysTWYNLIJ7d3jElVV5f/Jw/Jyu3p2zhw/IT4cN8+C6zs4GSpXAgEL2UpL7f1u/35bP5LKm2nTbD8uLbX9eySTSxUV2b9TX5+dLPM8C1ficftQmgz/ZtniWtMFg/ahnWvhSjRq29XXl/xZuX273Z8zx36H+fNtP9u8+fATrrnvFxZmryWYE4/bv7E7aJHsYMFV6QzHzbtWWGi38/39EUiHxFaaQ8278k7LVXV3T+q2YBKVKxLhCgAAAPJIODwwXEnnfCtOLOZfKL97ty0DAeuSdOSRdh7v7bdtPuQNGwZWsLgLXiUrEnHnLsYjNVzp7pa6gzHp5JNt5caNFq64k36Om6DGldIcd5ytG6JyJXHu8mCQcCVvdXXZl6vqmD9/6D+m6dOl00+3OVjSIB63l1u8eAQPrqqyJ5xwgr9upJUriVdyu/tT4eThjBn2f3W4E6uOa6NVUmJpV7pLATOlstIvr6uvtzfJcPjwQUdRkd+T7nCT2TvuSmXJTqS3t9uJ+HjctmOk/9ZTgQuicm0ye8dVrkj+JdmNjfbhN2OGbf9JJ9n7zNFH22PfeMOfjCxVT4995oZC9pXt39fNu1JW5qfVNTUjn+fGhdhSZq5WAfJNYrgy1Lwr77RcVVfXpKxcSTwMYM4VwhUAAADkkWxWrrhwZe/e5O9VV1sVi7vov6HBqv8TtbdbZYtk56zTMc9x4pwrra022OkKRO2kczhsJ4e2brWBXGL5iZu0+bXXbJkYrhQUDEhPXLjCOZY819Fh+4bbV2bPzkxSOV5VVbaPzptnV5QHgxYcjCQASJyDQLKQaCqEK2Nx7LFWoTSixCtHuMnpS0stVD7cfCuJpk2zx420fVpxsV8JcOiQnUwPh+1vhpZgyVyrKjdhWa69r7jKFclvJ7djh+1PFRV2crSszD5bKyvtQzwWs5Olra32PHcRw9tv24ULTU3qn6xqIipXgkH7dz76aFu3YMHIw5WSEn9OLT74gcNzfy+DzbviwpVNm+wxVK7khWEajQIAAACTSyRiYxWXB7j5jNMtGvXPy+7b569vbJT+8AfpxRell16yi1z/1//yz+e5C6Y3bLDBSDhs54HHO9+K2ybXtae317ZlZmVUKiqxE8+vvWZfp52WPO9KW5udQHItCo47zk4QuXYgKQhXpoj2dn+fqKjwT7DlmpISf5KWj33M9tm6uuHn1HDKymxHdi17RnM191STmChPFtGoncguK7M3rtJSuz2ScrsZM6wEMRgcWOE0GPe3EY3aibK2NtsnE9upwbgTj/G4/f8EAhO9Rclc5UogYCdG9+yx+0ceaR/81dX2uHBYOv546dVX7bFvv21zVFVUvNObs8feX+rq7MM/HLZ9KdsfniUlFmKVlEgrVkjnnGNtFEf6Xjd7ti3d7wBgeO7zIBYbGJzMm2d/j42NdhGLq1zp65tUFY7BoG2u64Q51RGuAAAAIG+4cX847I9RMnEeIxbzw5WGBumb37RAxVX5O/v3S3ffLd1yiwUqp5xiAdAf/2jfnz7dzm+ko3LFnQ8qKbHzevX1UldVzH7gokUWrLhJ7Vtb/XSnvd2urO3uthOPM2faBgaDg55IcR3FcvE8O9Korc3vXTdzpt8HLhdVVdnJzKIim0ckFBrZCdto1E7wJoYrVK7kl8pKv/pqJPOtOLGY9K532e2R7EvuDbGw0N4kXUuwioqRBX1TiZvDprjYr+bIJS50CIetNLWvzyo9SkutSiVRMGgXJGzYYPvJ1q32/19ZaV+FhfYZXFNjH/gT0QKtoMCfPygWs+AwGh1dWzB3tT2Aw3Nl9NFo8sVMkv09zZljQezu3fYe2N1tx1sLFkzE1o6JK2wnXDGTJxYDAAAADsN18kicdzVTbcHctA6eJz38sB+sHH20tHKldOONNvBYv94CltZWf/4VN72Jm8w+HZUroZD9PHfusKFB6i4ssxHQKafYyjfesNRF8gd8bW3SK6/Y7cWLkyfeHeREc+KcK8hj7e02+JfsREAup2lVVfbH7yZdHunV1eFwcosowpX8U1lp+4YLOUZzMj8QGHlVRTBo+04sZsFKMGgn0mkJNlAoZBUfRx/tV0XkEvceEInY/2VdnQUTixYNHjAXFNhn5/Tp9vssWWIBxqxZFrwsX26hzETOLeOqhNz2BoNU6QGZ5OaW6uqy95FELkSpr7eqlR07pF27rEpukkjsGsycK1SuAAAAII8khiuRiJ07yMTF9m5e2ve+V3rhBcsuTjnFn+O2r0/avNlClocekp55xkKPT37SOods3Giv46aISFenDdcabMcO6zjQF46qJ1qs0IIFNodAQ4MlPIsXW0pSVeVXrkjWKqS52X654uJBTwy6cCWXz7UjDdykQZL1rsv2PAGjEY/7Qclo/qBCoYHhClUG+cXtE7W1djYoHUn2UIqL/RPWZWUWzLiJsJAscXL1XOP+DyMR23+qq62Vz3DhSCAgHXOMfQD39dlnZy610HLvka53qkSQDGRS4qT2XV3JV3otXCj9/Od2AcuKFTYxY3GxVZcXF6ennD3DCFeSceQIAACAvOF6AIfDdt4gkwFALCZ99KPSBRfYRbiJdu2yNst/+Zd2jvrxx6Uf/cjOu1x5pY2fJLtwN53n3hLnXWlstGV3ebVCHS02IfVvf2vJTmurtcrp6LAr6l5/3R583HHW0iQet3/IQTaOcGUK6Oy0/WLHDrs/f35u/4e7k9gtLX67u5FIrFwJBv1SMuSPggL7f9261aoJMtnTvrjYP4EWj1t4QHXA5FNQYCFEUZFdFVFVZfOtHE4gYG0Jc5ELsoqL/R4+7JtA5rjKFcmOqVLDFckGAxUVNlDYscMe8+c/SyefnFvh7CAIV5LRFgwAAAB5JbHVcSbnjY1G7Wd5XvLAoqXFKv3r6uxC+KuvtgBGkh58UPrJT6Tt2+3+ggXpvXg3FvOLTdw0A11lVTYCOuEEW/H66xaudHRYArNjh90Ph+3kY1eXnVByfc9SJM654iqFkGfa261dnEvScr0tmGQ7fmHh6HbMcNj/A6yszO3qHIzdnDnSWWdJRx2V2Z/j9r1g0K48piXY5OVCleLi/JhvJBy2/bO42H9/5AMcyJyiIvssCIWs9VeiefPse01NNjnjkUfa8cuWLXbstXGjDS5yWGK4ktr1bCoiXAEAAEBeiUT87heZPB8cjfrZQ1eXLXt6LDgpLbVOIkceaRnFpz4lnXmmjZXuvtsGIrGYdapJZ/V/NGo/V/LPi3dHS+yHnXqqjYQaGvwJYnbulF5+2W4vWGAn1d1cAe6FUrS02DKxAw7yTOJ8KxUVtj/kevBQWWknRAsLbe6DkUisXKmupk1OPstkxYrj/kaqq+19lHBl8jr6aKv2POWUnL+CfMTcvCvuACkbfxPAVOUGILGYVa4kikSs3apkFzwVFNhFAL29FrA0NPhtWXMU4Uoy3k0BAACQV8JhO78aDGa2ciUW88+5uC4bO3ZYu/WZM+2c9Pz5Ng/LtGnSzTfbbXcx2owZ1kUknXPcJoYrBw7Yz+rqkp3kO+IIu1pOkl591Sau7+z0J7M/7jhLZMrLbdQ0xIlBwpUpoL3dJg2SbGd25WC5rKDAqrNOP33k5WChkP0hSva3QbiC8Sgqsjf16mq7nevVXhhaOGyfmfkSrEgWrsRi9sWHN5BZhYX2eRCNDqxckfyKONeWNxKxgKWtzSa237rVQpYcRbiSjHAFAAAAeaW42M4dpDu4SOUqVwIBCzAaGy2bmDHDzqkdc4x9LxaTTjzRztOsXm0Xw0oWvCQOTtK1TW6alJYWq6Tp7pYFJZGITVgvSa+9ZgM4yZ/M/thj/ZZg5eVDnlRyT0vsLoI8094uvf223Z49e3KdJHaTNY9EQYF07rmWfH7iE4QrGJ9g0FqPlZXZGzyQS+Jxe38sK+O9Dsi0QMACFle5ktrma8ECW7pJGCUrZa+tlfbutSukNm60PsM5mF4QriTL8cuPAAAAgNGZOdPGMKWlmb040712OGznoltbrVqlosLGTIk/OxSywpBoVLrrLpt35d3vHnn3opGKxQaGK11dspMq4bC0fLm0dq21BWtosJHRnj02CKyttQHgMC3BJPtdJXsYF7/mIc+z+Xi2brX7Rx2V+y3BxiMWk5YtS558FhirmTPtC8g1hYUWljc2SrNmTfTWAPmvqMgOlD3PrnRKvCIpsXLF8/wLQ444wo7Btm2zx7/6qn0vHrcBRmWlHYCP5kKSDCBcSUa4AgAAgLwSDvvdrzLJnYeNRKxixc0Hf8QRNpF9qoICq2ZxXTkkPwhJl2jUxl2ShSotLe9UrgQCVr1y7LE2OGtqsrlWamvtwTNm2APj8WFbgkk25pNoC5a3Dh603nY7dtj9efMmV+XKaLk+ghI7NID8NneuP9cDgMxKvGijszM5XJk71668ammxSpXEq61mzZLeeMOCl2jUrhZrbvYvigqH7Th9/vz0lr+PAuFKMsIVAAAAYAzceVg3Vpo1y8ZRRx89/PPmzLFQpbfXum+lUzBoYzDXhWD//ncqVyQbiO3eLS1eLP32t9YObM8e+96CBX5LsHh82H5fLlwpKsr9aTgwBu3t1vutudnuz52b3+HKtGlWdjZMKzwAAIBRKSqy4+mCgoHzrkQiVhm8aZOFKInhSjBog4mWFjs+aWmx9mCSXdlUWmqvV1zszxuXZYQryRgOAQAAAGNQUGDnYsNh66JVWmqVKSMJHEpLM7dd0agVp+zebRe5dXe/842KChsJLVtm4cprr/kbctRRfjIzTEswKTlcmaAL5pBJ7e12xaRk+0xJSX6HK3PmWKmZq14BAAAYL3fsFI1aVXCqhQstXPnjH6Wzz07+XkGBPweiZGGKC1r27LHj9dbWTG79sAhXkjGhPQAAADBGsZg//+SMGXYueqLFYn5rsPr6hMqVggL7xumn24iovl7assW+N2uWP9ntCMOV4mLClbyUGK7MmGFpYb63yyoqmvD+5QAAII8khiuplSuStHSpLR97TPra1xKuhhpENGoV6HPm2P2DB/1JECdAQYF9SYQrEuEKAAAAMGZHHGEX9ldUWPFHLohG/blcXOWK573zzaoq+3KT0nie3S8psXDlMC3B+vr8sRxtwfJUe7v09tt2e/bs/K5aAQAAyIRQyI6pXa/eVOeeK116qd1eu1a66SYrOx9OQYH/eu3tdmA+AahcSUa4AgAAAIzRjBnS8uXSCSf4V3BNNHdxm2Tz1ksJF8NNm2ZX6J90kv+EBQtG1RLMjeNoC5aH+vrsasitW+3+UUcRrgAAAIyFm9S+t1fq6Un+XjAoXXutdMcddgz+2mvSxz4m/eY3w79mLGbHap43eLuxLCBcSZYjQ0AAAABgcopGc6ujUGK44uYk7w9XQiErsznjDP8Js2ZJZWU2UjpMuNLYaMtAwMZ2hCt5pqPDBus7d9r9+fOt/xsAAABGp6jIb606VBuvM86Q7r9fWrTIHnPLLdJXvzp0m7DCQr8Spq0t/ds8AoQryQhXAAAAgDwSi/ltwVy40j/vimTJy6JFNol3MCjNnWsTZpaVHXZuDfd6sVjywAp5oq/PBuruP3ruXCpXAAAAxqKszA6ai4ul7dtTDsgTTJ8u3XOPdNlldv/RR6Ubb/SPxxK5SpiurgmbdyUQIFxJRLgCAAAA5JHEOVcGVK5IFq4UFEj/+39Ln/ykDehKSy1sOQz3eoWFtsylih2kyZYttqyosLl4CFcAAABG74gj7Hhqzhw7aN6yZeh5UkIh6ROfkFavtuPy11+3CpZU7iD84MEJq1xJDFcmaNqXnEK4AgAAAOSRaFSqrLTbLS3+xW39IhG7ku6oo6SZM/2WYK6X2DAOHLAl59vzmAtXZszwJ04FAADA6AQC0uLFFpbMnWuByI4dwz/n9NOlL33JnvurX0nr1yd/PxKxZCNHwhUqVwhXAAAAgLxSUGDFKJJNoXHw4CBtm6urrSohEPBbgo3gJLoLVzjfnsdcuDJrlqVolCcBAACMTTgsLVlix90zZ9oEhvv3D/+chQuliy6y2/fcM/BAvrDQDvC7uoaemyWDCFeSEa4AAAAAeaaqysZyko3fBrR4rqqyUZELVg4zkb2T2hYMeciFK0cdRYkSAADAeJWUSMccY6Xl1dXSzp2Hrzq5+mprKbZtm7R2bfL3JnhSe8KVZIQrAAAAQJ4pLLTcRLJwZcBFbYWFNrlmXZ2VuowwXGlqsiXn3PPY1q22nD/f9hEAAACMT02NVQXX1VnY8vbbQ09wL9ljPv5xu/3d70p79/rfi8WkQ4dswpMJmNSecCUZ4QoAAACQZxInta+vH2LsNm+eVa3Mnj3iPl+ucqWoyObdRJ7xPJtM9X3vs3CFFA0AACA95s61A/Q5cyydePvt4dOJ88+Xli61KpWvfc1f70rIOzupXMkBhCsAAABAnkkMVxoahghXKiul006zgd4INTbacoRTtGCyCQSk97xHWrnSghXCFQAAgPQIBKRFi/wJ7g8dkjZtsknuGxsHHrAHAtKnPmVV5r/5jfTcc7Y+GrXlwYNUruQAwhUAAAAgz6SGK+ma69K1BSNcyXNu0M7kOgAAAOkTCknHHWdtv9z8di0tNrfKhg3Sq69aRcu+fZZczJ0rrVhhz73vPgtkgkE7VuvstHDF87L6KxCuJCNcAQAAAPJMLOaHK01NNvBJx+AnMVxx59+Rh1yoUsBwEQAAIK2KiixgmT7d2vMuWiQtWeK3DevtlfbssYoWSbrqKqmqStq1S/rBD2xdLGaVK319tswiwpVkHC0DAAAAeSaxcsUFIumoXmlttWVZmRQOj//1kGMOHbIlyRkAAEDmlJdLp5winXmmzasyf76FKzNn2u3p022yw95eC2Ouv96e9/3vSzt32oUwLlTJ8rwrhCvJCFcAAACAPBON2pQqknTggC0HnXdllFpabFleTriSl8rLbcQcDlurCgAAAGROKGQH7XPmWMhyxhlSba0dk3mef/B99tkWxnR3S/fea+FKb68d4E/AvCuEKz7CFQAAACDPBAJ2wZtkF71JUkfH+F83sXIlFBr/6yHHhMPSiSdKxx5rV04CAAAgewIBqaZGikQsQHEH8oGAdNNN1rL197/3D8o7O7NeuSL5nWMJVwhXAAAAgLxUV2fL1lapsVHassXaMo9Vb69/8VxpKeFK3iork444wgbxAAAAyC5XIl5ebgffLsGYOdM/wK+vt4Tj4EEqVyYY4QoAAACQh2prbczledKmTTb22r597K/X1mYXx0nMuQIAAABkRCAgVVf7rcFclYrkl6bv2ePPu9LZKfX0ZHUTE8MVz8vqj845hCsAAABAHioqkuJxu11fb1/btvlzlo9Wfb0tAwGpuJjKFQAAACAjqqttEsXCQqmpyV9/xBG23LtXisX8K5+yXL1C5YqPcAUAAADIQ9GoNG2a3W5rswvcDh2S3n57bK+3b58ti4utIoZwBQAAAMiAxNZgra1+ipFaudLZaX1/szzvCpUrPsIVAAAAIA/FYn644sZju3fbWCyxu8BIucqV4mJb0hYMAAAAyIBAQKqqsnClr88/eHeVKy5ckezqqQmqXOnrI1whXAEAAADyUDTqj79efdUudGtosNbMb7wx+tdraLBlSYktme8cAAAAyJDE1mDNzbbOVa64tmCSHdxTuTJhCFcAAACAPBSNSh/4gN1+7jkLR6JRaedO6cABv83XSLnKFReuAAAAAMiQigorFY/HpZYWKxNx4Yo7kI9ELFxpb89qykG44iNcAQAAAPJQJCItWCCdfrrd/9a3pLo6u7DtwAHpzTdHNwllY6MtS0vTv625bP369frgBz+oyspKFRUVacmSJbr33nuTHvPss8/qzDPPVFFRkaZPn66bbrpJbVm+ghAAAAB5xLUGq6jwW4NVVtrEh729duVUYaGFK729/uT2WUC44iNcAQAAAPJQIGCVKpdfbmOwl1+WNmywcGTnThuHbd8+8tdzbcFKSy24mQqeeOIJLV++XPv27dPNN9+se+65Rx/4wAe0Y8eO/se8/PLLes973qOOjg7ddddd+vu//3vdf//9uuSSSyZwywEAADDppbYGCwalmhr7XuKk9lJWW4MRrvhCE70BAAAAADIjGpWOPFI66yzpqaeseuVrX5M2b5b277dOA7W19rjDceFKWZk/f2Y+a2lp0ZVXXqn3v//9Wrt2rQoKBr8u7Z//+Z9VUVGhp59+WmVlZZKkOXPm6GMf+5ieeOIJnX/++dncbAAAAOSL8nK7Sioet1ZgfX02qeKuXRauzJol9fRI3d3WGqy6OiubRbjio3IFAAAAyFPRqFWZvPe9NlfK1q3S00/buGvvXrvQ7a23RvZabh7NqRKufP/739fevXu1evVqFRQUqL29XX19fUmPaWlp0S9/+UtdccUV/cGKJF155ZUqKSnRD3/4w2xvNgAAAPJFQYG1Bisv91uDJU5q7w7KOzupXJkghCsAAABAnopGrTqlqEi69FJb98ADdvFbIGAXwLmL4A7nwAFbxuNToy3Yk08+qbKyMu3cuVMLFy5USUmJysrKdO2116rznfYLf/rTn9TT06NTTjkl6bmRSEQnnHCCXnrppYnYdAAAAOSL6mopFrOv5marXJGsciUSsQDGTWqfJYnhykjGEfmMtmAAAABAnioutjFXJCKddpr0s59Ju3dLjz0mvfvdNg7zPKmry8Zrw2lttaXrTpDvNm/erJ6eHl100UW6+uqr9YUvfEFPP/207rvvPjU3N+sHP/iBdu/eLUmqra0d8Pza2lr95je/GfL1d+/e3f98Z+PGjen9JQAAADC5VVTYwXd5ufX1TQxXAgE7iO/s9Ce2d8lHBlG54psCwyIAAABgaqqutrZf06dL27ZJV1whfelL0n//t3TKKXahmzS6cCUenxrhSltbmzo6OvSJT3xC9957ryTp4osvVldXl771rW/p85//vA4ePChJig4yaU0sFuv//mC+9a1v6bbbbsvMxgMAACA/FBRI06bZwfiePdajV7K2YJIdxLtjzvZ2//sZROWKj7ZgAAAAQJ4KBqU5c+yCt1hMmj9fWrjQxl//9//a3JeShSvD6emRWlrsdlmZtRrLd4Xv9LBeuXJl0voPf/jDkqR169b1P+bQoUMDnt/Z2dn//cF8/OMf1x/+8Iekr4ceeihdmw8AAIB8UVNj86u49mCShSt9fba+s9NKSLI07wrhim8KXHMGAAAATF2uaqW2Vnr7benKK6X/83+kX/3KqleWLDl8uHLokD9WKyubGpUrdXV1+vOf/6wjXOuFd9TU1EiSmpqaNG/ePEka0N7Lraurqxvy9WtrawdtJwYAAAAkqaiwRKO83IKUggK7+qmhwcIVz7P1WZp3xVW/E65QuQIAAADktYICae5ca+dVVCRVVUnLl9tA6P/9P6teOVy4sn+/f7u0dGqEKyeffLIkaefOnUnrd+3aJUmqrq7WkiVLFAqF9OKLLyY9pqurSy+//LJOOOGErGwrAAAA8lhBgR3Ex+N2v6rKlnv3WrgiWWk6lStZR7gCAAAA5LmaGpvcvrZW6uiQPvxhG6P96U9W1XK4cKW+3paFhTaYmgptwS699FJJ0ne+852k9d/+9rcVCoV0zjnnKB6P673vfa8eeughtbpJaSR973vfU1tbmy655JKsbjMAAADy1LRpdjAeCtltyeZgcQfnWaxcIVzxTYFrzgAAAICpLRCw6pX2dqmkxLoIzJolbdki7dw58sqV4mJbToXKlRNPPFF/93d/p//8z/9UT0+Pzj77bD399NN65JFH9LnPfa6/5dfq1at1+umn6+yzz9Y111yjHTt26Ctf+YrOP/98XXjhhRP8WwAAACAvuAqVcDg5XHHfO3jQDvI7O/15WTKEcMU3BYZFAAAAAKqqbL6U2lpp82a7LVmr5pFWrpSU2HIqhCuS9M1vflOzZs3SAw88oMcff1yzZ8/W3XffrU996lP9jznppJP05JNP6rOf/az+4R/+QaWlpbr66qv1hS98YeI2HAAAAPklErFlKCRVVtrtvXttGY1KLS12+9ChrIUrfX2EK1NkWAQAAABg7lwbd8XjNgaTRhauNDba0lWuTBXhcFirVq3SqlWrhn3cmWeeqd/97ndZ2ioAAABMOa4vbzhsE9tLfuVKMGhVK5JNqJhhVK74CFcAAACAKaKiwr4OHvSrUJqaDh+uNDTYsrQ0s9sHAAAAYBAFBRashEJ2QC/5lSvhsCUdnnf4A/s0IFzxMaE9AAAAMIXMnWttmWtq7H5Tkw2K3MVug3GVK6WlU6clGAAAAJBTIhELUtwVT3v3WqDiDtB7eqhcyTLCFQAAAGAKKSuz+Vdmz7b7zc22HO4iNxeulJX5c2kCAAAAyKJIxIKUeNwqWbq67EopwpUJQ7gCAAAATDFz5/qVKwcO2HKk4UqG58cEAAAAMBhXuRIKSdOm2bo9e/xwpbeXtmBZRrgCAAAATDHFxdKcOXZ7JOGKq26Jx21MBwAAACDLXOWKJFVX2zIxXOnuzkrlSjhsS8IVwhUAAABgSjrySFt2dEidncOHK62ttozHmXMFAAAAmBCuckXyw5W9e62UJBDIWlswwhUf4QoAAAAwBVVX+0FJff3IwxU3mAIAAACQRZGIH6QktgWT7MC+pycrbcEIV3yEKwAAAMAUFItJFRV2e//+ocdhPT1SS4vdLi+ncgUAAACYEK4/bzgsVVba7b17benCle5uyfMyuhmEKz7CFQAAAGAKikb9MdlwlStdXX7lSmkp4QoAAAAwIVy4Egr5B/KplSueZ8sMcuFKFn5UziNcAQAAAKagSMQfkzU0DN2eubHRvyKttJS2YAAAAMCESKxcicft9t69lnK4cEXK+LwrieOBLEzxktMIVwAAAIApKDFcaWwcunJl/37/8dEolSsAAADAhAiFbL6VUEgqK7N1nZ3SgQNZDVcSxwNZmOIlpxGuAAAAAFPQYOHKYO2Z6+ttWVxsSypXAAAAgAkQCNhBvDsgr6qy5Z49yeFKhhOPxHCFyhUAAAAAU05BgVRdbbebm4fumZwarlC5AgAAAEyQSMQPUmpqbN3evbaut9cO6rPYFow5VwAAAABMSW481txsy8EucmtosGVJiS0LGEEAAAAAEyOxcuWII2zpKlckSzuYcyVrGBoBAAAAU5Qbjx04YMvBwpXGRluWlmZnmwAAAAAMwVWuSH4Zemq4kuG2YMGgf8EVlSsAAAAApqTaWlu2tFgHgcOFK4FA9rYNAAAAQIrEypVp02zp2oJJWalcKSiwgEWicoVwBQAAAJiiXLjS1SW1tQ0erjQ12bK0VCoszN62AQAAAEiRWLniwpUsV64QrvgIVwAAAIApqrxcKiqy2/v2DT/nSlkZ4QoAAAAwoSIRP92oqLB1e/bYukAg65UrtAUDAAAAMCVFIlJlpd2urx88XHGT3ZeVJU9eCQAAACDLIhFbhkJ+uHLwoNTaagfrhCtZRbgCAAAATFEjCVdaW20ZjxOuAAAAABPKhSvhsFWqJFavBIN+uOJ5GduExHCltzdjP2ZSIFwBAAAApqhIxB+PNTQMDFc8zya7lyxcca2cAQAAAEyAxMqVnh7piCPsvpt3pafHDuIzWFJCuOIjXAEAAACmqMTKlcbGgeFKb69fuVJeTrgCAAAATKhg0L7CYatQmT7d1u/d67cFkzLaGoxwxUe4AgAAAExR4bA0bZrdbmoa2EGgq8uvXGHOFQAAACAHRCJ+kOLCFdcWzIUqg/X7TRPCFR/hCgAAADBFBQJSdbXdbmqyZeI4rLXVH5+VlVG5AgAAAEy4SMQOzHt7pZoaW+fagrm0g8qVrCBcAQAAAKYw16b5wAFbJoYr+/bZMhiUCgupXAEAAAAmnKtckfwrpVxbsN5eK0WnciUrCFcAAACAKcx1EhgsXKmvt2VxsVW5ULkCAAAATDBXuSL5PX5dWzDJqlaoXMkKwhUAAABgCquttWVrq9TXN3S4IhGuAAAAABMusXKlstKW7e3SoUN2u7eXcCVLCFcAAACAKWz6dKtK6euTGhuTw5WGBlsSrgAAAAA5IrFyJRiU4nG77Q7ee3oy3has4J1UgXAFAAAAwJRVXOyPx/btSx6HNTbasrTUloFAdrcNAAAAQIpIxJbhsAUpbhLF/fttSVuwrCFcAQAAAKawSMTvJlBfP3y4AgAAAGCCuXAlFLIQxU2iuG+fJR+0BcsawhUAAABgCksNVxLHYYnhSiyW/W0DAAAAkCK1csWFK3v3+oFLhtuCuXClry9jP2ZSIFwBAAAAprBIRKqosNupc64khiuFhdnfNgAAAAApUitXXFuwvXst9ejpsfWel5EfT+WKj3AFAAAAmMISK1dSw5WmJlvG43ZhHAAAAIAJFgjYwXlq5cqePbbOJR4Zag0WCBCuOIQrAAAAwBQWDEpVVXa7qcnGZ26Q1Npqy7IyuzAOAAAAQA6IRAbOuZLYFkzK6LwrhCuGcAUAAACY4mpqbNncbEvXRaClxe6XlxOuAAAAADkjErEqFc/zr5RqabED+Z4eu0/lSsYRrgAAAABTnGvTfO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         },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"def average_over(trials, trial_name, key):\\n\",\n        \"  ys = [trials[trial_name][i][key] for i in range(len(trials[trial_name]))]\\n\",\n        \"  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\\n\",\n        \"\\n\",\n        \"x = sparsity_schedule\\n\",\n        \"rand_color, lott_color = 'r', 'b'\\n\",\n        \"fig = plt.figure(figsize=[8,3], dpi=200)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,1)\\n\",\n        \"y, y_err = average_over(trials, 'rand_stats', 'test_losses')\\n\",\n        \"y, y_err = y[:,-1], y_err[:,-1]\\n\",\n        \"plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\\n\",\n        \"y, y_err = average_over(trials, 'lott_stats', 'test_losses')\\n\",\n        \"y, y_err = y[:,-1], y_err[:,-1]\\n\",\n        \"plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\\n\",\n        \"plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\\n\",\n        \"plt.xlabel('Sparsity') ; plt.title('Final test loss')\\n\",\n        \"plt.yscale('log')\\n\",\n        \"plt.ylim(None, 5e0)\\n\",\n        \"plt.legend(fontsize=7, ncol=3, loc='upper left')\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,2)\\n\",\n        \"y, y_err = average_over(trials, 'rand_stats', 'test_accs')\\n\",\n        \"y, y_err = y[:,-1], y_err[:,-1]\\n\",\n        \"plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\\n\",\n        \"y, y_err = average_over(trials, 'lott_stats', 'test_accs')\\n\",\n        \"y, y_err = y[:,-1], y_err[:,-1]\\n\",\n        \"plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\\n\",\n        \"plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\\n\",\n        \"plt.xlabel('Sparsity') ; plt.title('Final test accuracy')\\n\",\n        \"plt.ylim(55, 80) #plt.ylim(70, 85)\\n\",\n        \"plt.legend(fontsize=7, ncol=3, loc='upper left')\\n\",\n        \"\\n\",\n        \"# plt.subplot(1,3,3)\\n\",\n        \"# y, y_err = average_over(trials, 'rand_stats', 'test_accs')\\n\",\n        \"# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\\n\",\n        \"# plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\\n\",\n        \"# y, y_err = average_over(trials, 'lott_stats', 'test_accs')\\n\",\n        \"# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\\n\",\n        \"# plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\\n\",\n        \"# plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\\n\",\n        \"# plt.xlabel('Sparsity') ; plt.title('Max test accuracy')\\n\",\n        \"# plt.ylim(55, 80) #plt.ylim(55, 75)\\n\",\n        \"# plt.legend(fontsize=7, ncol=3, loc='upper left')\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"\\n\",\n        \"os.makedirs(project_dir + 'figures/', exist_ok=True)\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery.pdf')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"XvVxUbWNRirk\"\n      },\n      \"source\": [\n        \"## Qualitative analysis of masks\\n\",\n        \"What do the masks look like? Looking at the first layer in particular is interesting because we'd like to see whether the lottery ticket has learned any biases towards local connectivity, which would indicate a spatial prior.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 33,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 905\n        },\n        \"id\": \"CA8k18eGRhqy\",\n        \"outputId\": \"234d2f65-a55b-4383-e10b-c8e76da431fb\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stderr\",\n          \"text\": [\n            \"/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\\n\",\n            \"  return _methods._mean(a, axis=axis, dtype=dtype,\\n\",\n            \"/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\\n\",\n            \"  ret = ret.dtype.type(ret / rcount)\\n\"\n          ]\n        },\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 3000x1800 with 4 Axes>\"\n            ],\n            \"image/png\": 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         },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"# Get the masks for the weights of the first layer weights\\n\",\n        \"retrain_step = 90\\n\",\n        \"lott_model = trials['lott_models'][0][retrain_step]\\n\",\n        \"lott_mask1 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\\n\",\n        \"lott_model = trials['lott_models'][1][retrain_step]\\n\",\n        \"lott_mask2 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\\n\",\n        \"\\n\",\n        \"retrain_step = 91\\n\",\n        \"rand_model = trials['rand_models'][0][retrain_step]\\n\",\n        \"rand_mask1 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\\n\",\n        \"rand_model = trials['rand_models'][1][retrain_step]\\n\",\n        \"rand_mask2 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\\n\",\n        \"\\n\",\n        \"# Sort masks along hidden layer axis\\n\",\n        \"def sort_by_adjacency(mask):\\n\",\n        \"  mask = 1. * mask\\n\",\n        \"  def score_fn(m):\\n\",\n        \"    adjacency = np.sum(m[:-1]*m[1:])\\n\",\n        \"    first_nonzero = np.where(m==1)[0][0] if np.sum(m) > 0 else 41\\n\",\n        \"    num_nonzero = np.sum(m)\\n\",\n        \"    center_of_mass = np.mean(np.where(m==1)[0])\\n\",\n        \"    return adjacency + 0.001 * num_nonzero #- 0.001 * center_of_mass # + 0.001 * num_nonzero #- 0.001 * first_nonzero\\n\",\n        \"  scores = [-score_fn(1-mask[:,i]) for i in range(mask.shape[1])]\\n\",\n        \"  sorted_ixs = np.argsort(scores)\\n\",\n        \"  return mask[:,sorted_ixs].copy()\\n\",\n        \"\\n\",\n        \"sort_fn = sort_by_adjacency\\n\",\n        \"\\n\",\n        \"# Plot masks\\n\",\n        \"fig = plt.figure(figsize=[10,6], dpi=300)\\n\",\n        \"plt.subplot(4,1,1)\\n\",\n        \"plt.imshow(sort_fn(rand_mask1), cmap='gray')\\n\",\n        \"plt.title('Random ticket #1 ({:.0f} % sparse)'.format(rand_mask1.sum()/(40*5)), loc='left')\\n\",\n        \"plt.xlabel('Hidden layer axis') ; plt.ylabel(\\\"Input axis\\\")\\n\",\n        \"plt.subplot(4,1,2)\\n\",\n        \"plt.imshow(sort_fn(rand_mask2), cmap='gray')\\n\",\n        \"plt.title('Random ticket #2 ({:.0f} % sparse)'.format(rand_mask2.sum()/(40*5)), loc='left')\\n\",\n        \"plt.xlabel('Hidden layer axis') ; plt.ylabel(\\\"Input axis\\\")\\n\",\n        \"plt.subplot(4,1,3)\\n\",\n        \"plt.imshow(sort_fn(lott_mask1), cmap='gray')\\n\",\n        \"plt.title('Lottery ticket #1 ({:.0f} % sparse)'.format(lott_mask1.sum()/(40*5)), loc='left')\\n\",\n        \"plt.xlabel('Hidden layer axis') ; plt.ylabel(\\\"Input axis\\\")\\n\",\n        \"plt.subplot(4,1,4)\\n\",\n        \"plt.imshow(sort_fn(lott_mask2), cmap='gray')\\n\",\n        \"plt.title('Lottery ticket #2 ({:.0f} % sparse)'.format(lott_mask2.sum()/(40*5)), loc='left')\\n\",\n        \"plt.xlabel('Hidden layer axis') ; plt.ylabel(\\\"Input axis\\\")\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + \\\"figures/lottery_mask_vis.png\\\")\\n\",\n        \"fig.savefig(project_dir + \\\"figures/lottery_mask_vis.pdf\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"-7ZaXZ9zv3UL\"\n      },\n      \"source\": [\n        \"## Let's make a new dataset (from the same distribution) to see how the ticket transfers\\n\",\n        \"Nuance: in order to properly evaluate a lottery ticket, we need to train it on a new dataset from the same distribution, and evaluate it on a new test set from the same distribution. This isn't done in other papers because it's hard to duplicate, eg CIFAR-10. But we can do it here.\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 34,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"HugIsM2_iMqm\",\n        \"outputId\": \"b794237a-491c-49b0-81ca-910ce231153a\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"Examples in training set: 4000\\n\",\n            \"Examples in test set: 1000\\n\",\n            \"Length of each input: 40\\n\",\n            \"Number of classes: 10\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"args = get_dataset_args()\\n\",\n        \"args.seed = args.seed + 1  # new manual seed -> new dataset from same dataset\\n\",\n        \"data = get_dataset(args=args, download=False, regenerate=True)\\n\",\n        \"\\n\",\n        \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n        \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n        \"print(\\\"Length of each input: {}\\\".format(data['x'].shape[-1]))\\n\",\n        \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 35,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"Hr39T9Dmwzh4\",\n        \"outputId\": \"013104f1-71e6-4a15-8059-9eccb2fb3667\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"\\n\",\n            \"############  Trial 0  ############\\n\",\n            \"step 1000, dt 2.13s, train_loss 1.240e-03, test_loss 2.061e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 2000, dt 2.11s, train_loss 3.471e-04, test_loss 2.366e+00, train_acc 100.0, test_acc 63.9\\n\",\n            \"step 3000, dt 2.11s, train_loss 1.445e-04, test_loss 2.573e+00, train_acc 100.0, test_acc 64.1\\n\",\n            \"step 4000, dt 2.30s, train_loss 6.962e-05, test_loss 2.752e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 5000, dt 2.59s, train_loss 3.619e-05, test_loss 2.913e+00, train_acc 100.0, test_acc 64.4\\n\",\n            \"step 6000, dt 2.45s, train_loss 1.959e-05, test_loss 3.065e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 1000, dt 2.49s, train_loss 4.270e-03, test_loss 2.168e+00, train_acc 100.0, test_acc 65.2\\n\",\n            \"step 2000, dt 2.10s, train_loss 7.413e-04, test_loss 2.697e+00, train_acc 100.0, test_acc 66.0\\n\",\n            \"step 3000, dt 2.10s, train_loss 2.565e-04, test_loss 3.024e+00, train_acc 100.0, test_acc 65.7\\n\",\n            \"step 4000, dt 2.56s, train_loss 1.127e-04, test_loss 3.285e+00, train_acc 100.0, test_acc 65.3\\n\",\n            \"step 5000, dt 2.42s, train_loss 5.497e-05, test_loss 3.514e+00, train_acc 100.0, test_acc 65.0\\n\",\n            \"step 6000, dt 2.19s, train_loss 2.843e-05, test_loss 3.723e+00, train_acc 100.0, test_acc 64.9\\n\",\n            \"step 1000, dt 2.20s, train_loss 4.512e-03, test_loss 2.093e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 2000, dt 3.68s, train_loss 8.575e-04, test_loss 2.572e+00, train_acc 100.0, test_acc 62.7\\n\",\n            \"step 3000, dt 5.50s, train_loss 3.075e-04, test_loss 2.885e+00, train_acc 100.0, test_acc 62.3\\n\",\n            \"step 4000, dt 2.55s, train_loss 1.374e-04, test_loss 3.135e+00, train_acc 100.0, test_acc 62.1\\n\",\n            \"step 5000, dt 2.16s, train_loss 6.770e-05, test_loss 3.355e+00, train_acc 100.0, test_acc 61.8\\n\",\n            \"step 6000, dt 2.11s, train_loss 3.529e-05, test_loss 3.560e+00, train_acc 100.0, test_acc 61.5\\n\",\n            \"\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"step 1000, dt 2.12s, train_loss 1.915e-03, test_loss 1.847e+00, train_acc 100.0, test_acc 64.9\\n\",\n            \"step 2000, dt 2.43s, train_loss 4.859e-04, test_loss 2.125e+00, train_acc 100.0, test_acc 64.9\\n\",\n            \"step 3000, dt 2.60s, train_loss 1.959e-04, test_loss 2.317e+00, train_acc 100.0, test_acc 64.9\\n\",\n            \"step 4000, dt 2.08s, train_loss 9.348e-05, test_loss 2.475e+00, train_acc 100.0, test_acc 64.4\\n\",\n            \"step 5000, dt 2.09s, train_loss 4.838e-05, test_loss 2.616e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 6000, dt 2.11s, train_loss 2.624e-05, test_loss 2.749e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 1000, dt 2.07s, train_loss 2.831e-03, test_loss 1.931e+00, train_acc 100.0, test_acc 67.4\\n\",\n            \"step 2000, dt 2.39s, train_loss 5.438e-04, test_loss 2.335e+00, train_acc 100.0, test_acc 67.4\\n\",\n            \"step 3000, dt 2.49s, train_loss 1.919e-04, test_loss 2.599e+00, train_acc 100.0, test_acc 66.7\\n\",\n            \"step 4000, dt 2.11s, train_loss 8.503e-05, test_loss 2.804e+00, train_acc 100.0, test_acc 66.9\\n\",\n            \"step 5000, dt 2.09s, train_loss 4.208e-05, test_loss 2.982e+00, train_acc 100.0, test_acc 66.7\\n\",\n            \"step 6000, dt 2.10s, train_loss 2.203e-05, test_loss 3.147e+00, train_acc 100.0, test_acc 66.8\\n\",\n            \"step 1000, dt 2.10s, train_loss 5.974e-03, test_loss 2.531e+00, train_acc 100.0, test_acc 58.5\\n\",\n            \"step 2000, dt 2.42s, train_loss 1.162e-03, test_loss 3.096e+00, train_acc 100.0, test_acc 58.3\\n\",\n            \"step 3000, dt 2.78s, train_loss 4.151e-04, test_loss 3.466e+00, train_acc 100.0, test_acc 58.9\\n\",\n            \"step 4000, dt 2.10s, train_loss 1.835e-04, test_loss 3.758e+00, train_acc 100.0, test_acc 58.9\\n\",\n            \"step 5000, dt 2.11s, train_loss 9.036e-05, test_loss 4.015e+00, train_acc 100.0, test_acc 58.7\\n\",\n            \"step 6000, dt 2.13s, train_loss 4.693e-05, test_loss 4.252e+00, train_acc 100.0, test_acc 58.4\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"retrain_step = 91\\n\",\n        \"\\n\",\n        \"model_args = get_model_args()\\n\",\n        \"model_args.hidden_size = 500\\n\",\n        \"model_args.eval_every = 100\\n\",\n        \"model_args.learning_rate = 2e-2\\n\",\n        \"model_args.device = DEVICE\\n\",\n        \"model_args.total_steps = 6000\\n\",\n        \"model_args.print_every = 1000\\n\",\n        \"model_args.batch_size = 500 # higher batch size since we want to show overfitting\\n\",\n        \"\\n\",\n        \"results = {'dense': [], 'lott': [], 'rand': []}\\n\",\n        \"for t in range(len(trials['rand_stats'])):\\n\",\n        \"  print(\\\"\\\\n############  Trial {}  ############\\\".format(t))\\n\",\n        \"  set_seed(model_args.seed + t)\\n\",\n        \"  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\\\\n\",\n        \"                          hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\\n\",\n        \"  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\\n\",\n        \"\\n\",\n        \"  rand_model = copy.deepcopy(dense_model)\\n\",\n        \"  rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  lott_model = copy.deepcopy(dense_model)\\n\",\n        \"  lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  dense = train_model(data, dense_model, model_args) ; results['dense'].append(dense)\\n\",\n        \"  lott = train_model(data, lott_model, model_args)   ; results['lott'].append(lott)\\n\",\n        \"  rand = train_model(data, rand_model, model_args)   ; results['rand'].append(rand)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 36,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 422\n        },\n        \"id\": \"mV4WcbmKLdDu\",\n        \"outputId\": \"387173db-226d-4ceb-c53c-412b25dda0a6\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 520x390 with 1 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"def average_over_results(results, name):\\n\",\n        \"  ys = [results[name][t]['test_acc'] for t in range(len(results[name]))]\\n\",\n        \"  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\\n\",\n        \"\\n\",\n        \"fig = plt.figure(figsize=(4, 3), dpi=130)\\n\",\n        \"plt.subplot(1,1,1)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"y, y_err = average_over_results(results, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"\\n\",\n        \"plt.title('Lottery ticket ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=7, ncol=3)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_asymptotes.png')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"DuzZ6YNzcOrN\"\n      },\n      \"source\": [\n        \"## Remove spatial priors and see how lottery ticket fares\\n\",\n        \"First, we do this by shuffling the entire sequence.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 37,\n      \"metadata\": {\n        \"id\": \"LYyFs2PMcXxM\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"data_shuff = {}\\n\",\n        \"np.random.seed(0)\\n\",\n        \"shuffle_ixs = np.random.permutation(40) #np.array(range(32)) #\\n\",\n        \"for k in data.keys():\\n\",\n        \"  if k in ['x', 'x_test', 'steps']:\\n\",\n        \"    data_shuff[k] = data[k][...,shuffle_ixs].copy() # shuffle sequence\\n\",\n        \"  else:\\n\",\n        \"    data_shuff[k] = data[k].copy()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 38,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"zi1BcrH61OjV\",\n        \"outputId\": \"53c07096-0a97-4012-f0f3-a9f23c589a3e\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"\\n\",\n            \"############  Trial 0  ############\\n\",\n            \"step 1000, dt 2.38s, train_loss 1.026e-03, test_loss 2.220e+00, train_acc 100.0, test_acc 62.0\\n\",\n            \"step 2000, dt 4.73s, train_loss 2.889e-04, test_loss 2.539e+00, train_acc 100.0, test_acc 62.5\\n\",\n            \"step 3000, dt 3.96s, train_loss 1.204e-04, test_loss 2.755e+00, train_acc 100.0, test_acc 62.8\\n\",\n            \"step 4000, dt 2.64s, train_loss 5.798e-05, test_loss 2.935e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 5000, dt 2.13s, train_loss 3.012e-05, test_loss 3.100e+00, train_acc 100.0, test_acc 62.9\\n\",\n            \"step 6000, dt 2.09s, train_loss 1.635e-05, test_loss 3.258e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 1000, dt 4.69s, train_loss 6.246e-03, test_loss 2.304e+00, train_acc 100.0, test_acc 63.9\\n\",\n            \"step 2000, dt 3.86s, train_loss 1.050e-03, test_loss 2.967e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 3000, dt 2.36s, train_loss 3.544e-04, test_loss 3.381e+00, train_acc 100.0, test_acc 63.5\\n\",\n            \"step 4000, dt 2.44s, train_loss 1.532e-04, test_loss 3.698e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 5000, dt 2.10s, train_loss 7.416e-05, test_loss 3.975e+00, train_acc 100.0, test_acc 62.9\\n\",\n            \"step 6000, dt 2.24s, train_loss 3.817e-05, test_loss 4.232e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 1000, dt 2.93s, train_loss 4.180e-03, test_loss 2.455e+00, train_acc 100.0, test_acc 61.8\\n\",\n            \"step 2000, dt 3.27s, train_loss 8.040e-04, test_loss 3.021e+00, train_acc 100.0, test_acc 62.5\\n\",\n            \"step 3000, dt 2.11s, train_loss 2.879e-04, test_loss 3.369e+00, train_acc 100.0, test_acc 62.1\\n\",\n            \"step 4000, dt 2.12s, train_loss 1.281e-04, test_loss 3.643e+00, train_acc 100.0, test_acc 61.9\\n\",\n            \"step 5000, dt 2.12s, train_loss 6.355e-05, test_loss 3.881e+00, train_acc 100.0, test_acc 62.0\\n\",\n            \"step 6000, dt 2.54s, train_loss 3.321e-05, test_loss 4.102e+00, train_acc 100.0, test_acc 61.9\\n\",\n            \"\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"step 1000, dt 2.48s, train_loss 1.180e-03, test_loss 2.192e+00, train_acc 100.0, test_acc 64.8\\n\",\n            \"step 2000, dt 2.14s, train_loss 3.177e-04, test_loss 2.537e+00, train_acc 100.0, test_acc 64.6\\n\",\n            \"step 3000, dt 2.09s, train_loss 1.286e-04, test_loss 2.784e+00, train_acc 100.0, test_acc 64.7\\n\",\n            \"step 4000, dt 2.12s, train_loss 6.140e-05, test_loss 2.988e+00, train_acc 100.0, test_acc 64.5\\n\",\n            \"step 5000, dt 2.10s, train_loss 3.186e-05, test_loss 3.166e+00, train_acc 100.0, test_acc 64.4\\n\",\n            \"step 6000, dt 2.51s, train_loss 1.718e-05, test_loss 3.335e+00, train_acc 100.0, test_acc 64.4\\n\",\n            \"step 1000, dt 2.41s, train_loss 5.973e-03, test_loss 2.463e+00, train_acc 100.0, test_acc 63.3\\n\",\n            \"step 2000, dt 2.10s, train_loss 9.788e-04, test_loss 3.133e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 3000, dt 2.08s, train_loss 3.328e-04, test_loss 3.546e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 4000, dt 2.10s, train_loss 1.454e-04, test_loss 3.860e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 5000, dt 2.17s, train_loss 7.071e-05, test_loss 4.131e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 6000, dt 2.57s, train_loss 3.626e-05, test_loss 4.380e+00, train_acc 100.0, test_acc 62.6\\n\",\n            \"step 1000, dt 2.41s, train_loss 4.246e-03, test_loss 2.450e+00, train_acc 100.0, test_acc 58.9\\n\",\n            \"step 2000, dt 2.16s, train_loss 8.269e-04, test_loss 2.975e+00, train_acc 100.0, test_acc 58.9\\n\",\n            \"step 3000, dt 2.14s, train_loss 2.962e-04, test_loss 3.309e+00, train_acc 100.0, test_acc 59.2\\n\",\n            \"step 4000, dt 2.07s, train_loss 1.311e-04, test_loss 3.572e+00, train_acc 100.0, test_acc 59.7\\n\",\n            \"step 5000, dt 2.08s, train_loss 6.450e-05, test_loss 3.803e+00, train_acc 100.0, test_acc 59.6\\n\",\n            \"step 6000, dt 2.56s, train_loss 3.344e-05, test_loss 4.020e+00, train_acc 100.0, test_acc 59.5\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"results_shuff = {'dense': [], 'lott': [], 'rand': []}\\n\",\n        \"for t in range(len(trials['rand_stats'])):\\n\",\n        \"  print(\\\"\\\\n############  Trial {}  ############\\\".format(t))\\n\",\n        \"  set_seed(model_args.seed + t)\\n\",\n        \"  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\\\\n\",\n        \"                          hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\\n\",\n        \"  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\\n\",\n        \"\\n\",\n        \"  rand_model = copy.deepcopy(dense_model)\\n\",\n        \"  rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  lott_model = copy.deepcopy(dense_model)\\n\",\n        \"  lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  dense = train_model(data_shuff, dense_model, model_args) ; results_shuff['dense'].append(dense)\\n\",\n        \"  lott = train_model(data_shuff, lott_model, model_args)   ; results_shuff['lott'].append(lott)\\n\",\n        \"  rand = train_model(data_shuff, rand_model, model_args)   ; results_shuff['rand'].append(rand)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 39,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 422\n        },\n        \"id\": \"fuZli6M8YorY\",\n        \"outputId\": \"8204f7a7-2da1-4a3f-bef7-f9d7f0695b62\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 975x390 with 2 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(7.5, 3), dpi=130)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,1)\\n\",\n        \"plt.title('LT on baseline examples')\\n\",\n        \"y, y_err = average_over_results(results, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,2)\\n\",\n        \"plt.title('LT on spatially shuffled examples')\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_asymptotes_shuffled.png')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"WEcny3zHkHBU\"\n      },\n      \"source\": [\n        \"## Looks like the lottery ticket has some spatial priors, but we need to run more tests to be sure:\\n\",\n        \"There may be a correlation between the spatial priors of the data and how important particular input indices are. If that is the case, then the lottery ticket procedure may have disproportionately pruned weights corresponding to inputs that didn't contribute much to classification. If this is the case, then the \\\"spatial priors\\\" of the lottery ticket are not particularly significant and won't transfer well to other datasets.\\n\",\n        \"\\n\",\n        \"But maybe the lottery ticket does have some spatial priors that will transfer well. In order to test whether that is the case, we can simply flip the non-shuffled dataset upside down. This will break the correlation between particular indices that matter more than others while still leaving spatial priors intact in the data. Think about it this way: a CNN will still do well if you flip all your images upside down. So our model's performance on flipped data is a good hint at how well its spatial priors will transfer. If this doesn't hurt performance much, then the lottery ticket represents some sort of reasonably good spatial prior.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 40,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"l1D-huHK55gF\",\n        \"outputId\": \"f556c5bc-e56c-47db-9a1d-1a68e3abd4f3\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"execute_result\",\n          \"data\": {\n            \"text/plain\": [\n              \"array([ 0.79027827,  0.81387517,  0.96495485,  0.72122595, -0.03240748,\\n\",\n              \"       -0.28947403,  0.38705837,  0.73723783,  0.38279205, -0.16746251,\\n\",\n              \"       -0.21399577,  0.025955  ,  0.00291882,  0.28282577, -1.25675551,\\n\",\n              \"       -2.06009933, -1.71629861, -0.30305327,  0.02960669, -1.49053473,\\n\",\n              \"       -2.16724905, -1.83906903, -0.26118889,  0.36413035, -0.17891161,\\n\",\n              \"       -0.27629091, -0.13228634,  0.01394688, -0.47465012, -0.57515785,\\n\",\n              \"       -0.37445353,  0.05180054,  0.32990678,  0.05195449, -0.91084646,\\n\",\n              \"       -0.94110542, -0.34693864, -0.3011403 , -0.95394325, -1.39942009])\"\n            ]\n          },\n          \"metadata\": {},\n          \"execution_count\": 40\n        }\n      ],\n      \"source\": [\n        \"data['x_test'][0]\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 41,\n      \"metadata\": {\n        \"id\": \"KQLbj83smpxo\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"data_flip = {}\\n\",\n        \"for k in data.keys():\\n\",\n        \"  if k in ['x', 'x_test', 'steps']:\\n\",\n        \"    data_flip[k] = data[k][...,::-1].copy() # flip sequence upside-down\\n\",\n        \"  else:\\n\",\n        \"    data_flip[k] = data[k].copy()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 42,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"4WZzYfTk57Y0\",\n        \"outputId\": \"5883b071-5b02-403b-eea1-6d96d097d340\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"execute_result\",\n          \"data\": {\n            \"text/plain\": [\n              \"array([-1.39942009, -0.95394325, -0.3011403 , -0.34693864, -0.94110542,\\n\",\n              \"       -0.91084646,  0.05195449,  0.32990678,  0.05180054, -0.37445353,\\n\",\n              \"       -0.57515785, -0.47465012,  0.01394688, -0.13228634, -0.27629091,\\n\",\n              \"       -0.17891161,  0.36413035, -0.26118889, -1.83906903, -2.16724905,\\n\",\n              \"       -1.49053473,  0.02960669, -0.30305327, -1.71629861, -2.06009933,\\n\",\n              \"       -1.25675551,  0.28282577,  0.00291882,  0.025955  , -0.21399577,\\n\",\n              \"       -0.16746251,  0.38279205,  0.73723783,  0.38705837, -0.28947403,\\n\",\n              \"       -0.03240748,  0.72122595,  0.96495485,  0.81387517,  0.79027827])\"\n            ]\n          },\n          \"metadata\": {},\n          \"execution_count\": 42\n        }\n      ],\n      \"source\": [\n        \"data_flip['x_test'][0]\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 43,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"XhioZOATWY_D\",\n        \"outputId\": \"f1fb330a-bf11-4674-e246-e057cdd1b22d\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"\\n\",\n            \"############  Trial 0  ############\\n\",\n            \"step 1000, dt 2.12s, train_loss 2.058e-03, test_loss 1.848e+00, train_acc 100.0, test_acc 63.8\\n\",\n            \"step 2000, dt 2.12s, train_loss 5.547e-04, test_loss 2.112e+00, train_acc 100.0, test_acc 63.6\\n\",\n            \"step 3000, dt 2.12s, train_loss 2.285e-04, test_loss 2.296e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 4000, dt 2.09s, train_loss 1.108e-04, test_loss 2.446e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 5000, dt 2.28s, train_loss 5.790e-05, test_loss 2.581e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 6000, dt 2.69s, train_loss 3.170e-05, test_loss 2.708e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 1000, dt 2.12s, train_loss 4.957e-03, test_loss 2.464e+00, train_acc 100.0, test_acc 62.3\\n\",\n            \"step 2000, dt 2.49s, train_loss 8.296e-04, test_loss 3.146e+00, train_acc 100.0, test_acc 62.1\\n\",\n            \"step 3000, dt 2.90s, train_loss 2.855e-04, test_loss 3.552e+00, train_acc 100.0, test_acc 62.2\\n\",\n            \"step 4000, dt 2.11s, train_loss 1.243e-04, test_loss 3.868e+00, train_acc 100.0, test_acc 62.3\\n\",\n            \"step 5000, dt 2.59s, train_loss 6.059e-05, test_loss 4.137e+00, train_acc 100.0, test_acc 62.2\\n\",\n            \"step 6000, dt 2.36s, train_loss 3.133e-05, test_loss 4.387e+00, train_acc 100.0, test_acc 61.8\\n\",\n            \"step 1000, dt 2.10s, train_loss 5.640e-03, test_loss 2.355e+00, train_acc 100.0, test_acc 61.2\\n\",\n            \"step 2000, dt 2.05s, train_loss 1.047e-03, test_loss 2.922e+00, train_acc 100.0, test_acc 61.5\\n\",\n            \"step 3000, dt 2.06s, train_loss 3.650e-04, test_loss 3.274e+00, train_acc 100.0, test_acc 60.9\\n\",\n            \"step 4000, dt 2.09s, train_loss 1.605e-04, test_loss 3.552e+00, train_acc 100.0, test_acc 61.1\\n\",\n            \"step 5000, dt 2.54s, train_loss 7.831e-05, test_loss 3.796e+00, train_acc 100.0, test_acc 61.0\\n\",\n            \"step 6000, dt 2.46s, train_loss 4.053e-05, test_loss 4.017e+00, train_acc 100.0, test_acc 60.9\\n\",\n            \"\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"step 1000, dt 2.10s, train_loss 1.874e-03, test_loss 1.812e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 2000, dt 2.10s, train_loss 5.455e-04, test_loss 2.040e+00, train_acc 100.0, test_acc 62.5\\n\",\n            \"step 3000, dt 2.08s, train_loss 2.332e-04, test_loss 2.199e+00, train_acc 100.0, test_acc 62.7\\n\",\n            \"step 4000, dt 2.08s, train_loss 1.145e-04, test_loss 2.336e+00, train_acc 100.0, test_acc 62.8\\n\",\n            \"step 5000, dt 2.62s, train_loss 6.067e-05, test_loss 2.461e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 6000, dt 2.45s, train_loss 3.349e-05, test_loss 2.580e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 1000, dt 2.45s, train_loss 3.344e-03, test_loss 2.244e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 2000, dt 2.10s, train_loss 6.178e-04, test_loss 2.775e+00, train_acc 100.0, test_acc 63.5\\n\",\n            \"step 3000, dt 2.10s, train_loss 2.206e-04, test_loss 3.099e+00, train_acc 100.0, test_acc 63.8\\n\",\n            \"step 4000, dt 2.75s, train_loss 9.788e-05, test_loss 3.353e+00, train_acc 100.0, test_acc 63.9\\n\",\n            \"step 5000, dt 2.68s, train_loss 4.829e-05, test_loss 3.577e+00, train_acc 100.0, test_acc 64.1\\n\",\n            \"step 6000, dt 2.09s, train_loss 2.518e-05, test_loss 3.785e+00, train_acc 100.0, test_acc 63.8\\n\",\n            \"step 1000, dt 2.13s, train_loss 4.186e-03, test_loss 2.534e+00, train_acc 100.0, test_acc 59.2\\n\",\n            \"step 2000, dt 2.08s, train_loss 8.259e-04, test_loss 3.073e+00, train_acc 100.0, test_acc 59.4\\n\",\n            \"step 3000, dt 2.08s, train_loss 2.980e-04, test_loss 3.413e+00, train_acc 100.0, test_acc 58.4\\n\",\n            \"step 4000, dt 2.20s, train_loss 1.329e-04, test_loss 3.683e+00, train_acc 100.0, test_acc 58.7\\n\",\n            \"step 5000, dt 2.66s, train_loss 6.548e-05, test_loss 3.921e+00, train_acc 100.0, test_acc 59.3\\n\",\n            \"step 6000, dt 2.13s, train_loss 3.394e-05, test_loss 4.143e+00, train_acc 100.0, test_acc 59.6\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"results_flip = {'dense': [], 'lott': [], 'rand': []}\\n\",\n        \"for t in range(len(trials['rand_stats'])):\\n\",\n        \"  print(\\\"\\\\n############  Trial {}  ############\\\".format(t))\\n\",\n        \"  set_seed(model_args.seed + t)\\n\",\n        \"  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\\\\n\",\n        \"                          hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\\n\",\n        \"  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\\n\",\n        \"\\n\",\n        \"  rand_model = copy.deepcopy(dense_model)\\n\",\n        \"  rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  lott_model = copy.deepcopy(dense_model)\\n\",\n        \"  lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  dense = train_model(data_flip, dense_model, model_args) ; results_flip['dense'].append(dense)\\n\",\n        \"  lott = train_model(data_flip, lott_model, model_args)   ; results_flip['lott'].append(lott)\\n\",\n        \"  rand = train_model(data_flip, rand_model, model_args)   ; results_flip['rand'].append(rand)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 44,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 422\n        },\n        \"id\": \"oHJP9rKCYsPO\",\n        \"outputId\": \"25924188-39c4-41bd-daeb-2659628df5e4\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 975x390 with 2 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(7.5, 3), dpi=130)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,1)\\n\",\n        \"plt.title('LT on spatially shuffled examples')\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,2)\\n\",\n        \"plt.title('LT on spatially flipped examples')\\n\",\n        \"y, y_err = average_over_results(results_flip, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_flip, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_flip, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_asymptotes_flipped.png')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"ZNYf4pgm63YM\"\n      },\n      \"source\": [\n        \"## This has become a monster notebook.\\n\",\n        \"Instead of responsibly dividing it into two notebooks, let's save a checkpoint and continue on. Now we can start from here without having to recompute everything from scratch.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 45,\n      \"metadata\": {\n        \"id\": \"flw5BuLq28hk\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"things = [trials, model_args, retrain_step, results, results_shuff, results_flip]\\n\",\n        \"to_pickle(things, path=project_dir + 'lottery_analysis.pkl')\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 46,\n      \"metadata\": {\n        \"id\": \"m6z7ja7w22eA\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"things = from_pickle(path=project_dir + 'lottery_analysis.pkl')\\n\",\n        \"[trials, model_args, retrain_step, results, results_shuff, results_flip] = things\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"HeKNrtfX8uZ6\"\n      },\n      \"source\": [\n        \"## Our lottery ticket generalizes surprisingly well to a flipped version of the dataset. What happens if we shuffle _chunks_ of the sequence\\n\",\n        \"If the lottery ticket has learned good spatial priors, then we would expect its performance to decrease only partially when we shuffle chunks of the sequence, since some of the spatial information is still available.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 47,\n      \"metadata\": {\n        \"id\": \"dkW-FFb68wRs\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"data_chunks = {}\\n\",\n        \"for k in data.keys():\\n\",\n        \"  if k in ['x', 'x_test', 't']:\\n\",\n        \"    data_chunks[k] = np.concatenate([data[k][...,20:30],\\n\",\n        \"                                     data[k][...,30:40],\\n\",\n        \"                                     data[k][...,:10],\\n\",\n        \"                                     data[k][...,10:20]],\\n\",\n        \"                                     axis=-1).copy() # exhange first and second halves\\n\",\n        \"  else:\\n\",\n        \"    data_chunks[k] = data[k].copy()\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 48,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"1Z3H-k-fXTty\",\n        \"outputId\": \"b4badfca-8a69-4e2d-ea38-82307fa295c0\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"\\n\",\n            \"############  Trial 0  ############\\n\",\n            \"step 1000, dt 2.11s, train_loss 7.306e-04, test_loss 1.987e+00, train_acc 100.0, test_acc 65.8\\n\",\n            \"step 2000, dt 3.06s, train_loss 1.904e-04, test_loss 2.261e+00, train_acc 100.0, test_acc 65.6\\n\",\n            \"step 3000, dt 6.64s, train_loss 7.772e-05, test_loss 2.442e+00, train_acc 100.0, test_acc 65.3\\n\",\n            \"step 4000, dt 3.71s, train_loss 3.719e-05, test_loss 2.596e+00, train_acc 100.0, test_acc 65.3\\n\",\n            \"step 5000, dt 2.28s, train_loss 1.899e-05, test_loss 2.733e+00, train_acc 100.0, test_acc 65.4\\n\",\n            \"step 6000, dt 2.50s, train_loss 1.017e-05, test_loss 2.859e+00, train_acc 100.0, test_acc 65.3\\n\",\n            \"step 1000, dt 2.51s, train_loss 6.653e-03, test_loss 2.594e+00, train_acc 100.0, test_acc 60.8\\n\",\n            \"step 2000, dt 2.19s, train_loss 1.126e-03, test_loss 3.315e+00, train_acc 100.0, test_acc 60.9\\n\",\n            \"step 3000, dt 2.89s, train_loss 3.901e-04, test_loss 3.751e+00, train_acc 100.0, test_acc 61.4\\n\",\n            \"step 4000, dt 4.32s, train_loss 1.694e-04, test_loss 4.089e+00, train_acc 100.0, test_acc 61.4\\n\",\n            \"step 5000, dt 4.50s, train_loss 8.211e-05, test_loss 4.376e+00, train_acc 100.0, test_acc 61.4\\n\",\n            \"step 6000, dt 2.28s, train_loss 4.241e-05, test_loss 4.637e+00, train_acc 100.0, test_acc 61.3\\n\",\n            \"step 1000, dt 2.77s, train_loss 4.013e-03, test_loss 2.463e+00, train_acc 100.0, test_acc 59.6\\n\",\n            \"step 2000, dt 3.43s, train_loss 8.205e-04, test_loss 3.009e+00, train_acc 100.0, test_acc 59.4\\n\",\n            \"step 3000, dt 5.78s, train_loss 2.991e-04, test_loss 3.371e+00, train_acc 100.0, test_acc 59.5\\n\",\n            \"step 4000, dt 4.58s, train_loss 1.345e-04, test_loss 3.661e+00, train_acc 100.0, test_acc 59.7\\n\",\n            \"step 5000, dt 4.22s, train_loss 6.639e-05, test_loss 3.917e+00, train_acc 100.0, test_acc 59.6\\n\",\n            \"step 6000, dt 2.51s, train_loss 3.482e-05, test_loss 4.155e+00, train_acc 100.0, test_acc 59.4\\n\",\n            \"\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"step 1000, dt 2.51s, train_loss 2.273e-03, test_loss 1.851e+00, train_acc 100.0, test_acc 63.4\\n\",\n            \"step 2000, dt 2.13s, train_loss 6.219e-04, test_loss 2.101e+00, train_acc 100.0, test_acc 63.3\\n\",\n            \"step 3000, dt 2.10s, train_loss 2.555e-04, test_loss 2.273e+00, train_acc 100.0, test_acc 62.9\\n\",\n            \"step 4000, dt 2.10s, train_loss 1.230e-04, test_loss 2.419e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 5000, dt 2.15s, train_loss 6.432e-05, test_loss 2.552e+00, train_acc 100.0, test_acc 63.3\\n\",\n            \"step 6000, dt 2.52s, train_loss 3.497e-05, test_loss 2.675e+00, train_acc 100.0, test_acc 62.9\\n\",\n            \"step 1000, dt 2.50s, train_loss 5.340e-03, test_loss 2.263e+00, train_acc 100.0, test_acc 64.0\\n\",\n            \"step 2000, dt 2.08s, train_loss 9.568e-04, test_loss 2.852e+00, train_acc 100.0, test_acc 64.1\\n\",\n            \"step 3000, dt 2.06s, train_loss 3.413e-04, test_loss 3.234e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 4000, dt 2.06s, train_loss 1.518e-04, test_loss 3.538e+00, train_acc 100.0, test_acc 63.2\\n\",\n            \"step 5000, dt 2.14s, train_loss 7.474e-05, test_loss 3.811e+00, train_acc 100.0, test_acc 62.8\\n\",\n            \"step 6000, dt 2.57s, train_loss 3.874e-05, test_loss 4.062e+00, train_acc 100.0, test_acc 62.4\\n\",\n            \"step 1000, dt 2.42s, train_loss 4.071e-03, test_loss 2.451e+00, train_acc 100.0, test_acc 58.9\\n\",\n            \"step 2000, dt 2.11s, train_loss 8.358e-04, test_loss 2.953e+00, train_acc 100.0, test_acc 59.0\\n\",\n            \"step 3000, dt 2.09s, train_loss 3.024e-04, test_loss 3.289e+00, train_acc 100.0, test_acc 58.4\\n\",\n            \"step 4000, dt 2.11s, train_loss 1.355e-04, test_loss 3.558e+00, train_acc 100.0, test_acc 58.5\\n\",\n            \"step 5000, dt 2.10s, train_loss 6.709e-05, test_loss 3.789e+00, train_acc 100.0, test_acc 58.6\\n\",\n            \"step 6000, dt 2.62s, train_loss 3.518e-05, test_loss 4.005e+00, train_acc 100.0, test_acc 59.0\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"results_chunks = {'dense': [], 'lott': [], 'rand': []}\\n\",\n        \"for t in range(len(trials['rand_stats'])):\\n\",\n        \"  print(\\\"\\\\n############  Trial {}  ############\\\".format(t))\\n\",\n        \"  set_seed(model_args.seed + t)\\n\",\n        \"  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\\\\n\",\n        \"                          hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\\n\",\n        \"  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\\n\",\n        \"\\n\",\n        \"  rand_model = copy.deepcopy(dense_model)\\n\",\n        \"  rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  lott_model = copy.deepcopy(dense_model)\\n\",\n        \"  lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  dense = train_model(data_chunks, dense_model, model_args) ; results_chunks['dense'].append(dense)\\n\",\n        \"  lott = train_model(data_chunks, lott_model, model_args)   ; results_chunks['lott'].append(lott)\\n\",\n        \"  rand = train_model(data_chunks, rand_model, model_args)   ; results_chunks['rand'].append(rand)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 49,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 422\n        },\n        \"id\": \"XCyIMdQaYxPF\",\n        \"outputId\": \"a851f3fa-3238-4b5f-84dd-9eafc2896fb3\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 975x390 with 2 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(7.5, 3), dpi=130)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,1)\\n\",\n        \"plt.title('LT on spatially shuffled examples')\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,2)\\n\",\n        \"plt.title('LT on spatially chunked examples')\\n\",\n        \"y, y_err = average_over_results(results_chunks, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_chunks, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_chunks, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_asymptotes_chunks.png')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"IxL3o0QpY1aB\"\n      },\n      \"source\": [\n        \"## Rebuild models\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 50,\n      \"metadata\": {\n        \"id\": \"T0ndBUSlYGSX\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"set_seed(model_args.seed)\\n\",\n        \"dense_model = SparseMLP(model_args.input_size, model_args.output_size,\\\\\\n\",\n        \"                        hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"_rand_model = copy.deepcopy(trials['rand_models'][0][retrain_step])\\n\",\n        \"_lott_model = copy.deepcopy(trials['lott_models'][0][retrain_step])\\n\",\n        \"\\n\",\n        \"rand_model = copy.deepcopy(dense_model)\\n\",\n        \"rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"lott_model = copy.deepcopy(dense_model)\\n\",\n        \"lott_model.set_layer_masks(_lott_model.get_layer_masks())\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"prwCrfGMmhqG\"\n      },\n      \"source\": [\n        \"## Looks like our lottery ticket has some decent spatial priors\\n\",\n        \"One thing this implies is that the lottery ticket may have more local connectivity\\n\",\n        \"## We can test for this by measuring the probability of nonzero parameters being adjacent to one another\\n\",\n        \"This will tell us whether local connections are more frequent in lottery tickets compared to random tickets. Let's do this.\\n\",\n        \"\\n\",\n        \"### 1. Get masks indicating nonzero weights of first fully-connected layer\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 51,\n      \"metadata\": {\n        \"id\": \"e_KnW4D-ldpo\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"rand_model = copy.deepcopy(dense_model)\\n\",\n        \"rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"lott_model = copy.deepcopy(dense_model)\\n\",\n        \"lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"w1_rand = rand_model.linear1.linear.weight.cpu().detach().numpy()\\n\",\n        \"w1_lott = lott_model.linear1.linear.weight.cpu().detach().numpy()\\n\",\n        \"\\n\",\n        \"w1_rand_nonzero = (w1_rand!=0).astype(np.float32)\\n\",\n        \"w1_lott_nonzero = (w1_lott!=0).astype(np.float32)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"wimJAObnPgmh\"\n      },\n      \"source\": [\n        \"### 2. Count the number of times that nonzero parameters are adjacent to one another\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 52,\n      \"metadata\": {\n        \"id\": \"qFySsiEPHxgl\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"w1_rand_2adjacent, w1_lott_2adjacent = [], []\\n\",\n        \"w1_rand_3adjacent, w1_lott_3adjacent = [], []\\n\",\n        \"w1_rand_4adjacent, w1_lott_4adjacent = [], []\\n\",\n        \"input_dim = w1_rand.shape[1] * 1.\\n\",\n        \"for k in range(w1_rand.shape[0]):\\n\",\n        \"  w1_rand_2adjacent += [np.sum(w1_rand_nonzero[k,:-1] * w1_rand_nonzero[k,1:]) / input_dim]\\n\",\n        \"  w1_lott_2adjacent += [np.sum(w1_lott_nonzero[k,:-1] * w1_lott_nonzero[k,1:]) / input_dim]\\n\",\n        \"\\n\",\n        \"  w1_rand_3adjacent += [np.sum(w1_rand_nonzero[k,:-2] * w1_rand_nonzero[k,1:-1] * w1_rand_nonzero[k,2:]) / input_dim]\\n\",\n        \"  w1_lott_3adjacent += [np.sum(w1_lott_nonzero[k,:-2] * w1_lott_nonzero[k,1:-1] * w1_lott_nonzero[k,2:]) / input_dim]\\n\",\n        \"\\n\",\n        \"  w1_rand_4adjacent += [np.sum(w1_rand_nonzero[k,:-3] * w1_rand_nonzero[k,1:-2] * \\\\\\n\",\n        \"                               w1_rand_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]\\n\",\n        \"  w1_lott_4adjacent += [np.sum(w1_lott_nonzero[k,:-3] * w1_lott_nonzero[k,1:-2] * \\\\\\n\",\n        \"                              w1_lott_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"6v3g661oPmXk\"\n      },\n      \"source\": [\n        \"### 3. Calculate basic statistics for these adjacencies\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 53,\n      \"metadata\": {\n        \"id\": \"C2Q4VavTJ2vY\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"ticket_names = ['random', 'lottery']\\n\",\n        \"sqrtN = np.sqrt(len(w1_rand_2adjacent))\\n\",\n        \"\\n\",\n        \"p_2adjacent = [np.mean(w1_rand_2adjacent), np.mean(w1_lott_2adjacent)]\\n\",\n        \"p_2adjacent_err = [np.std(w1_rand_2adjacent)/sqrtN, np.std(w1_lott_2adjacent)/sqrtN]\\n\",\n        \"\\n\",\n        \"p_3adjacent = [np.mean(w1_rand_3adjacent), np.mean(w1_lott_3adjacent)]\\n\",\n        \"p_3adjacent_err = [np.std(w1_rand_3adjacent)/sqrtN, np.std(w1_lott_3adjacent)/sqrtN]\\n\",\n        \"\\n\",\n        \"p_4adjacent = [np.mean(w1_rand_4adjacent), np.mean(w1_lott_4adjacent)]\\n\",\n        \"p_4adjacent_err = [np.std(w1_rand_4adjacent)/sqrtN, np.std(w1_lott_4adjacent)/sqrtN]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"3tjV5fJVPrLF\"\n      },\n      \"source\": [\n        \"### 4. Plot the empirical statistics\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 54,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 328\n        },\n        \"id\": \"QAsv0pzCdG7L\",\n        \"outputId\": \"88f425f0-07ed-466b-9232-12c1765a9797\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 850x300 with 3 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=[8.5,3], dpi=100)\\n\",\n        \"\\n\",\n        \"ax = plt.subplot(1,3,1)\\n\",\n        \"x = np.arange(len(ticket_names))\\n\",\n        \"ax.set_xticks(x)\\n\",\n        \"ax.set_xticklabels(ticket_names)\\n\",\n        \"barlist = plt.bar(x, p_2adjacent, yerr=p_2adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\\n\",\n        \"barlist[0].set_color('red')\\n\",\n        \"plt.title(\\\"p(two adjacent weights)\\\")\\n\",\n        \"\\n\",\n        \"ax = plt.subplot(1,3,2)\\n\",\n        \"x = np.arange(len(ticket_names))\\n\",\n        \"ax.set_xticks(x)\\n\",\n        \"ax.set_xticklabels(ticket_names)\\n\",\n        \"barlist = plt.bar(x, p_3adjacent, yerr=p_3adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\\n\",\n        \"barlist[0].set_color('red')\\n\",\n        \"plt.title(\\\"p(three adjacent weights)\\\")\\n\",\n        \"\\n\",\n        \"ax = plt.subplot(1,3,3)\\n\",\n        \"x = np.arange(len(ticket_names))\\n\",\n        \"ax.set_xticks(x)\\n\",\n        \"ax.set_xticklabels(ticket_names)\\n\",\n        \"barlist = plt.bar(x, p_4adjacent, yerr=p_4adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\\n\",\n        \"barlist[0].set_color('red')\\n\",\n        \"plt.title(\\\"p(four adjacent weights)\\\")\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_adjacency.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_adjacency.pdf')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"pypkWV9Zkv26\"\n      },\n      \"source\": [\n        \"## One more sanity check: keep sparsity pattern but use different initialization\\n\",\n        \"Based on what we're seeing above, the sparsity pattern itself represents some sort of inductive bias for local connectivity. If this is true, then we should be able to start from a different (scratch) initialization and still see a jump in performance. Let's try it.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 55,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"uKVimjBFaXXs\",\n        \"outputId\": \"1e1284bf-e110-4c3d-c690-7820245cebc4\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"\\n\",\n            \"############  Trial 0  ############\\n\",\n            \"step 1000, dt 2.16s, train_loss 1.894e-03, test_loss 1.885e+00, train_acc 100.0, test_acc 63.7\\n\",\n            \"step 2000, dt 2.14s, train_loss 5.030e-04, test_loss 2.173e+00, train_acc 100.0, test_acc 63.7\\n\",\n            \"step 3000, dt 2.11s, train_loss 2.060e-04, test_loss 2.362e+00, train_acc 100.0, test_acc 63.2\\n\",\n            \"step 4000, dt 2.12s, train_loss 9.894e-05, test_loss 2.518e+00, train_acc 100.0, test_acc 63.1\\n\",\n            \"step 5000, dt 2.67s, train_loss 5.124e-05, test_loss 2.658e+00, train_acc 100.0, test_acc 63.0\\n\",\n            \"step 6000, dt 2.30s, train_loss 2.772e-05, test_loss 2.792e+00, train_acc 100.0, test_acc 63.3\\n\",\n            \"step 1000, dt 2.10s, train_loss 3.732e-03, test_loss 2.108e+00, train_acc 100.0, test_acc 64.4\\n\",\n            \"step 2000, dt 2.12s, train_loss 6.494e-04, test_loss 2.648e+00, train_acc 100.0, test_acc 64.1\\n\",\n            \"step 3000, dt 2.15s, train_loss 2.236e-04, test_loss 2.985e+00, train_acc 100.0, test_acc 64.0\\n\",\n            \"step 4000, dt 2.20s, train_loss 9.737e-05, test_loss 3.251e+00, train_acc 100.0, test_acc 63.8\\n\",\n            \"step 5000, dt 2.68s, train_loss 4.741e-05, test_loss 3.478e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 6000, dt 2.26s, train_loss 2.450e-05, test_loss 3.686e+00, train_acc 100.0, test_acc 64.7\\n\",\n            \"step 1000, dt 2.11s, train_loss 4.827e-03, test_loss 2.364e+00, train_acc 100.0, test_acc 61.4\\n\",\n            \"step 2000, dt 2.09s, train_loss 8.880e-04, test_loss 2.919e+00, train_acc 100.0, test_acc 61.2\\n\",\n            \"step 3000, dt 2.10s, train_loss 3.137e-04, test_loss 3.274e+00, train_acc 100.0, test_acc 61.1\\n\",\n            \"step 4000, dt 2.20s, train_loss 1.377e-04, test_loss 3.553e+00, train_acc 100.0, test_acc 61.0\\n\",\n            \"step 5000, dt 2.70s, train_loss 6.715e-05, test_loss 3.797e+00, train_acc 100.0, test_acc 60.8\\n\",\n            \"step 6000, dt 2.19s, train_loss 3.493e-05, test_loss 4.020e+00, train_acc 100.0, test_acc 60.8\\n\",\n            \"\\n\",\n            \"############  Trial 1  ############\\n\",\n            \"step 1000, dt 2.18s, train_loss 6.785e-04, test_loss 2.296e+00, train_acc 100.0, test_acc 63.3\\n\",\n            \"step 2000, dt 2.10s, train_loss 1.693e-04, test_loss 2.644e+00, train_acc 100.0, test_acc 63.7\\n\",\n            \"step 3000, dt 2.11s, train_loss 6.550e-05, test_loss 2.879e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 4000, dt 2.48s, train_loss 3.064e-05, test_loss 3.076e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 5000, dt 4.33s, train_loss 1.563e-05, test_loss 3.256e+00, train_acc 100.0, test_acc 64.3\\n\",\n            \"step 6000, dt 2.43s, train_loss 8.339e-06, test_loss 3.427e+00, train_acc 100.0, test_acc 64.2\\n\",\n            \"step 1000, dt 2.14s, train_loss 3.437e-03, test_loss 1.917e+00, train_acc 100.0, test_acc 65.9\\n\",\n            \"step 2000, dt 2.09s, train_loss 6.429e-04, test_loss 2.340e+00, train_acc 100.0, test_acc 66.4\\n\",\n            \"step 3000, dt 2.12s, train_loss 2.280e-04, test_loss 2.604e+00, train_acc 100.0, test_acc 66.2\\n\",\n            \"step 4000, dt 2.08s, train_loss 1.008e-04, test_loss 2.818e+00, train_acc 100.0, test_acc 66.2\\n\",\n            \"step 5000, dt 2.57s, train_loss 4.977e-05, test_loss 3.008e+00, train_acc 100.0, test_acc 66.5\\n\",\n            \"step 6000, dt 2.38s, train_loss 2.579e-05, test_loss 3.185e+00, train_acc 100.0, test_acc 66.3\\n\",\n            \"step 1000, dt 2.11s, train_loss 4.176e-03, test_loss 2.460e+00, train_acc 100.0, test_acc 60.0\\n\",\n            \"step 2000, dt 2.08s, train_loss 8.480e-04, test_loss 2.974e+00, train_acc 100.0, test_acc 60.2\\n\",\n            \"step 3000, dt 2.08s, train_loss 3.081e-04, test_loss 3.310e+00, train_acc 100.0, test_acc 60.5\\n\",\n            \"step 4000, dt 2.11s, train_loss 1.384e-04, test_loss 3.581e+00, train_acc 100.0, test_acc 60.5\\n\",\n            \"step 5000, dt 2.61s, train_loss 6.869e-05, test_loss 3.818e+00, train_acc 100.0, test_acc 60.2\\n\",\n            \"step 6000, dt 2.33s, train_loss 3.587e-05, test_loss 4.039e+00, train_acc 100.0, test_acc 60.1\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"results_seed = {'dense': [], 'lott': [], 'rand': []}\\n\",\n        \"for t in range(len(trials['rand_stats'])):\\n\",\n        \"  print(\\\"\\\\n############  Trial {}  ############\\\".format(t))\\n\",\n        \"  set_seed(model_args.seed + t + 1)\\n\",\n        \"  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\\\\n\",\n        \"                          hidden_size=model_args.hidden_size).to(DEVICE)\\n\",\n        \"  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\\n\",\n        \"  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\\n\",\n        \"\\n\",\n        \"  rand_model = copy.deepcopy(dense_model)\\n\",\n        \"  rand_model.set_layer_masks(_rand_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  lott_model = copy.deepcopy(dense_model)\\n\",\n        \"  lott_model.set_layer_masks(_lott_model.get_layer_masks())\\n\",\n        \"\\n\",\n        \"  dense = train_model(data, dense_model, model_args) ; results_seed['dense'].append(dense)\\n\",\n        \"  lott = train_model(data, lott_model, model_args)   ; results_seed['lott'].append(lott)\\n\",\n        \"  rand = train_model(data, rand_model, model_args)   ; results_seed['rand'].append(rand)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 56,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 458\n        },\n        \"id\": \"Q6aftRykasYD\",\n        \"outputId\": \"0008766a-bfdc-47cf-a587-3bf77e01ee17\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stderr\",\n          \"text\": [\n            \"<ipython-input-56-fba28d50d039>:28: UserWarning: The figure layout has changed to tight\\n\",\n            \"  plt.tight_layout() ; plt.show()\\n\"\n          ]\n        },\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 975x390 with 2 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(7.5, 3), dpi=130)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,1)\\n\",\n        \"plt.title('LT on baseline examples')\\n\",\n        \"y, y_err = average_over_results(results, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,2,2)\\n\",\n        \"plt.title('LT with different initializations')\\n\",\n        \"y, y_err = average_over_results(results_seed, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_seed, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_seed, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_asymptotes_reinit.png')\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 57,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 640\n        },\n        \"id\": \"R1mZVHMdbG9w\",\n        \"outputId\": \"3039b71f-06bf-489d-939d-35782c382d91\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 750x600 with 1 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(7.5/2, 3), dpi=200)\\n\",\n        \"x = range(0, model_args.total_steps+1, model_args.eval_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,1,1)\\n\",\n        \"plt.title('Lottery ticket analysis ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\\n\",\n        \"y, y_err = average_over_results(results_seed, 'dense')\\n\",\n        \"plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_seed, 'rand')\\n\",\n        \"plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results, 'lott')\\n\",\n        \"plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_shuff, 'lott')\\n\",\n        \"plt.plot(x, y, 'g-', label='lottery (shuffle spatial dim. of data)') ; plt.fill_between(x, y-y_err, y+y_err, color='g', alpha=0.15)\\n\",\n        \"y, y_err = average_over_results(results_seed, 'lott')\\n\",\n        \"plt.plot(x, y, 'c-', label='lottery (reinit weights, keep mask)') ; plt.fill_between(x, y-y_err, y+y_err, color='c', alpha=0.15)\\n\",\n        \"plt.ylim(50,76)\\n\",\n        \"plt.ylabel('Test accuracy') ; plt.xlabel(\\\"Train step\\\")\\n\",\n        \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=2, loc='lower right')\\n\",\n        \"\\n\",\n        \"plt.show()\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_summary.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/lottery_summary.pdf')\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"source\": [],\n      \"metadata\": {\n        \"id\": \"B7QpF0J213tI\"\n      },\n      \"execution_count\": null,\n      \"outputs\": []\n    }\n  ],\n  \"metadata\": {\n    \"accelerator\": \"GPU\",\n    \"colab\": {\n      \"machine_shape\": \"hm\",\n      \"provenance\": []\n    },\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.9.18\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}"
  },
  {
    "path": "notebooks/metalearn-activation-function.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"dKUcDM76bHx3\"\n      },\n      \"source\": [\n        \"# Metalearning an activation function\\n\",\n        \"Sam Greydanus\\n\",\n        \"\\n\",\n        \"In this notebook, we will show how to metalearn the activation function of an MLP. Then we will try scaling this activation function to larger models and datasets.\\n\",\n        \"\\n\",\n        \"This case study is meant to demonstrate the convenience and computational savings of working with the MNIST-1D dataset. You can find more details at https://github.com/greydanus/mnist1d.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 20,\n      \"metadata\": {\n        \"id\": \"Sg2i1QmhKW5d\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n        \"\\n\",\n        \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n        \"!git clone https://github.com/greydanus/mnist1d\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 14,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"KaQo7QhvXvid\",\n        \"outputId\": \"93d37162-762f-430f-82e7-737698d1c719\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Using: cuda\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"import numpy as np\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import matplotlib.pylab as pl\\n\",\n        \"import time, copy, os\\n\",\n        \"import torch\\n\",\n        \"import torch.nn.functional as F\\n\",\n        \"\\n\",\n        \"import mnist1d\\n\",\n        \"\\n\",\n        \"# Try attaching to GPU -- Use \\\"Change Runtime Type to change to GPUT\\\"\\n\",\n        \"DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\\n\",\n        \"print('Using:', DEVICE)\\n\",\n        \"\\n\",\n        \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"CdLcAx_6cXkQ\"\n      },\n      \"source\": [\n        \"## Only run this if you're in Google Colab\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 3,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"ZUy1K891cXHv\",\n        \"outputId\": \"ae8390a6-ce2b-4f99-93c0-72f806d1947b\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Mounted at /content/gdrive\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"if True:\\n\",\n        \"    from google.colab import drive\\n\",\n        \"    drive.mount('/content/gdrive')\\n\",\n        \"    project_dir = \\\"/content/gdrive/My Drive/Research/metalearn_afunc/\\\"\\n\",\n        \"else:\\n\",\n        \"    project_dir = './'\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"nre26wEOfZsM\"\n      },\n      \"source\": [\n        \"## Visualizing the MNIST-1D dataset\\n\",\n        \"The dataset is procedurally generated from a set of 1D template sequences that look like this:\\n\",\n        \"![MNIST-1D Templates](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist1d_black.png)\\n\",\n        \"\\n\",\n        \"These templates were chosen as 1D timeseries analogues to the [MNIST dataset](http://yann.lecun.com/exdb/mnist/):\\n\",\n        \"![MNIST-2D Templates](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist2d_black.png)\\n\",\n        \"\\n\",\n        \"It's also possible to plot the 1D templates as lines:\\n\",\n        \"\\n\",\n        \"![MNIST-1D Templates (lines)](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist1d_templates.png)\\n\",\n        \"\\n\",\n        \"In order to generate a challenging synthetic dataset, we applied a series of transformations to these templates, including linear transformations (up/down), adding correlated noise, and subsampling. Here are some class-wise examples:\\n\",\n        \"\\n\",\n        \"![MNIST-1D Templates (lines)](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist1d_transform.png)\\n\",\n        \"\\n\",\n        \"The nice thing about this toy dataset is that it does a good job of separating different types of deep learning models, many of which get the same 98-99% test accuracy on MNIST.\\n\",\n        \"\\n\",\n        \"![MNIST-1D Benchmarks](https://raw.githubusercontent.com/greydanus/greydanus.github.io/master/files/mnist1d_benchmarks.png)\\n\",\n        \"\\n\",\n        \"## Loading the MNIST-1D dataset\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 4,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"I-vm_gh5xTJs\",\n        \"outputId\": \"f1c925de-b971-4b7d-c9c4-0d4afc7c1f2c\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\\n\",\n            \"Examples in training set: 8000\\n\",\n            \"Examples in test set: 2000\\n\",\n            \"Length of each input: 40\\n\",\n            \"Number of classes: 10\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"args = mnist1d.get_dataset_args()\\n\",\n        \"args.num_samples = 10000\\n\",\n        \"data = mnist1d.get_dataset(args=args, download=False, regenerate=True)  # need to regenerate, since baseline dataset only has 4000/1000 samples\\n\",\n        \"\\n\",\n        \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n        \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n        \"print(\\\"Length of each input: {}\\\".format(data['x'].shape[-1]))\\n\",\n        \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"XH8nHKh0Ss5M\"\n      },\n      \"source\": [\n        \"## First we need a functional representation of an MLP\\n\",\n        \"\\n\",\n        \"This is an important first step when doing gradient-based metalearning. The entire inner training loop needs to be end-to-end differentiable and ordinary PyTorch modules and optimizers were not designed with this in mind. People have come up with various ways to get around the issue. My personal favorite is to make PyTorch look more functional and avoid working with objects at all.\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 5,\n      \"metadata\": {\n        \"id\": \"HIL9UcFzrVxX\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# returns a tuple (state, function) for a vanilla MLP model\\n\",\n        \"def get_functional_mlp(args):\\n\",\n        \"  D, H, O = args.input_size, args.hidden_size, args.output_size\\n\",\n        \"  linear1, linear2 = torch.nn.Linear(D,H), torch.nn.Linear(H,O) # use PyTorch to get initializations\\n\",\n        \"  params = [linear1.weight, linear1.bias, linear2.weight, linear2.bias]\\n\",\n        \"  params = [torch.Tensor(p.reshape(-1)) for p in params]\\n\",\n        \"  param_vec = torch.cat(params).to(args.device)  # flattened vector of parameters, maybe on GPU\\n\",\n        \"\\n\",\n        \"  def forward_fn(x, param_vec, afunc): # afunc is a differentiable, elementwise nonlinearity\\n\",\n        \"    pointer = 0\\n\",\n        \"    W1 = param_vec[pointer:pointer+D*H].reshape(H,D)\\n\",\n        \"    pointer += D*H\\n\",\n        \"    b1 = param_vec[pointer:pointer+H].reshape(1,-1)\\n\",\n        \"    pointer += H\\n\",\n        \"    W2 = param_vec[pointer:pointer+H*O].reshape(O,H)\\n\",\n        \"    pointer += O*H\\n\",\n        \"    b2 = param_vec[pointer:pointer+O].reshape(1,-1)\\n\",\n        \"\\n\",\n        \"    h = afunc((W1 @ x.t()).t() + b1) #+ noise_post\\n\",\n        \"    h = h + .1*(2*torch.rand(*b1.shape).to(b1.device)-1)  # smooth out gradients w/noise THIS IS IMPORTANT\\n\",\n        \"    logits = (W2 @ h.t()).t() + b2\\n\",\n        \"    return logits\\n\",\n        \"  return param_vec, forward_fn # separates the state from the function\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"MhvV_VbWRgUC\"\n      },\n      \"source\": [\n        \"## Inner optimization loop\\n\",\n        \"In the inner loop, we'll train an MLP to classify MNIST-1D images.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 6,\n      \"metadata\": {\n        \"id\": \"_gOv4HC9xPkt\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# accuracy of the model\\n\",\n        \"def accuracy(model, inputs, targets):\\n\",\n        \"  preds = model(inputs).argmax(-1).cpu().numpy()\\n\",\n        \"  targets = targets.cpu().numpy().astype(np.float32)\\n\",\n        \"  return 100*sum(preds==targets)/len(targets)\\n\",\n        \"\\n\",\n        \"def inner_optimization(model, args, data, afunc):\\n\",\n        \"  torch.manual_seed(args.seed)\\n\",\n        \"  (param_vec, fwd_fn) = model\\n\",\n        \"  x_train, x_test = torch.Tensor(data['x']), torch.Tensor(data['x_test'])\\n\",\n        \"  y_train, y_test = torch.LongTensor(data['y']), torch.LongTensor(data['y_test'])\\n\",\n        \"  x_train, x_test, y_train, y_test = [v.to(args.device) for v in [x_train, x_test, y_train, y_test]]\\n\",\n        \"\\n\",\n        \"  criterion = torch.nn.CrossEntropyLoss()\\n\",\n        \"  results = {'train_losses': [], 'test_acc': []}\\n\",\n        \"  avg_sq_grad = torch.ones_like(param_vec) # for RMSprop\\n\",\n        \"\\n\",\n        \"  for step in range(args.total_steps):\\n\",\n        \"    bix = (args.batch_ix + (step*args.batch_size))%len(x_train) # batch index\\n\",\n        \"    x, y = x_train[bix:bix+args.batch_size], y_train[bix:bix+args.batch_size] # data\\n\",\n        \"    loss = criterion(fwd_fn(x, param_vec, afunc), y) # forward pass\\n\",\n        \"\\n\",\n        \"    grads = torch.autograd.grad(loss, param_vec, retain_graph=True, create_graph=True)[0] # get gradients\\n\",\n        \"    avg_sq_grad = avg_sq_grad * args.mu + grads**2 * (1 - args.mu) # RMSProp update line 1\\n\",\n        \"    param_vec = param_vec - args.learning_rate * grads/(torch.sqrt(avg_sq_grad) + 1e-8) # RMSProp update line 2\\n\",\n        \"\\n\",\n        \"    results['train_losses'].append(loss)\\n\",\n        \"    if step % args.test_every:\\n\",\n        \"      k = 500  # use first 500 test examples (speeds up inner loop by a factor of 5x)\\n\",\n        \"      test_acc = accuracy(lambda x: fwd_fn(x, param_vec, afunc), x_test[:k], y_test[:k])\\n\",\n        \"      results['test_acc'].append(test_acc)\\n\",\n        \"\\n\",\n        \"  results['train_losses'] = torch.stack(results['train_losses'])\\n\",\n        \"  results['test_acc'] = np.asarray(results['test_acc'])\\n\",\n        \"  return results\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"KNOjj17WRlOm\"\n      },\n      \"source\": [\n        \"## Outer optimization loop\\n\",\n        \"The outer (meta) optimization objective is to optimize the activation function of the inner model. We do this by taking the gradient of the average loss over training with respect to a second (meta) MLP which parameterizes the activation function. This approach is called **gradient-based metalearning** since we are computing an exact gradient by backpropagating through the entire unrolled optimization loop. A good paper on this approach is [Maclaurin et al., 2015](https://arxiv.org/abs/1502.03492): _\\\"Gradient-based Hyperparameter Optimization through Reversible Learning.\\\"_\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 7,\n      \"metadata\": {\n        \"id\": \"dhgt1UnzxPhs\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# parameterize the activation function. It starts out being perfectly linear\\n\",\n        \"base_activation = torch.nn.ELU()\\n\",\n        \"\\n\",\n        \"def mlp_afunc(model, x):\\n\",\n        \"  (param_vec, fwd_fn) = model\\n\",\n        \"  x_hat = 0.2 * fwd_fn(x.reshape(-1, 1), param_vec, torch.tanh)\\n\",\n        \"  x_hat = x_hat.reshape(*x.shape)\\n\",\n        \"  return base_activation(x) + x_hat  # our learned function looks leaky relu-like @ init\\n\",\n        \"\\n\",\n        \"def outer_optimization(inner_args, outer_args):\\n\",\n        \"  torch.manual_seed(outer_args.seed)\\n\",\n        \"  torch.cuda.manual_seed_all(outer_args.seed)\\n\",\n        \"  (outer_param_vec, outer_fwd_fn) = outer_model = get_functional_mlp(outer_args)\\n\",\n        \"\\n\",\n        \"  t0 = time.time()\\n\",\n        \"  results = {'inner_test_accs':[], 'train_losses':[], 'inner_afunc':[], 'grad_norm':[]}\\n\",\n        \"  momentum = torch.zeros_like(outer_param_vec) # outer loop uses SGD + momentum\\n\",\n        \"  for step in range(outer_args.total_steps):\\n\",\n        \"\\n\",\n        \"    # run inner optimization problem\\n\",\n        \"    inner_afunc = lambda x: mlp_afunc(outer_model, x) # parameterize afunc\\n\",\n        \"    inner_model = get_functional_mlp(inner_args) # randomly initialize inner mlp\\n\",\n        \"    inner_results = inner_optimization(inner_model, inner_args, data, inner_afunc)\\n\",\n        \"\\n\",\n        \"    # run outer optimization problem\\n\",\n        \"    (outer_param_vec, outer_fwd_fn) = outer_model # unwrap outer model\\n\",\n        \"    outer_loss = inner_results['train_losses'][-200:].mean() # meta loss = mean inner loss\\n\",\n        \"    outer_grads = torch.autograd.grad(outer_loss, outer_param_vec)[0] # meta-gradients\\n\",\n        \"    outer_grads = outer_grads.clamp(-1e-1, 1e-1)  # for stability\\n\",\n        \"\\n\",\n        \"    momentum = outer_args.mu * momentum + outer_grads # SGD w/momentum line 1\\n\",\n        \"    outer_param_vec = outer_param_vec - outer_args.learning_rate * momentum # line 2\\n\",\n        \"    outer_model = (outer_param_vec, outer_fwd_fn) # wrap up outer model\\n\",\n        \"\\n\",\n        \"    # boring bookkeeping\\n\",\n        \"    results['train_losses'].append(outer_loss.item())\\n\",\n        \"    if step % outer_args.print_every == 0: # print out training progress\\n\",\n        \"      t1 = time.time()\\n\",\n        \"      grad_norm = outer_grads @ outer_grads # to debug when things inevitably blow up\\n\",\n        \"      results['grad_norm'].append(grad_norm.item())\\n\",\n        \"      print('step: {}, dt: {:.1f}s, meta_grad_norm: {:.1e}, mean_inner_loss: {:.3e}, final_inner_loss: {:.3e}, test_acc: {:.1f}'\\n\",\n        \"          .format(step, t1-t0, grad_norm.item(),\\n\",\n        \"                  results['train_losses'][-1],\\n\",\n        \"                  inner_results['train_losses'][-1],\\n\",\n        \"                  inner_results['test_acc'][-1]))\\n\",\n        \"      t0 = t1\\n\",\n        \"\\n\",\n        \"    # sample the activation function over x=[-5,5]\\n\",\n        \"    x_fn = torch.linspace(-5,5,steps=100).to(outer_args.device)\\n\",\n        \"    x_fn, y_fn = x_fn.cpu().numpy(), inner_afunc(x_fn).detach().cpu().numpy()\\n\",\n        \"    results['inner_afunc'].append([x_fn, y_fn])\\n\",\n        \"    results['inner_test_accs'].append(inner_results['test_acc'])\\n\",\n        \"\\n\",\n        \"  return results\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"7wtLSP9wRo3P\"\n      },\n      \"source\": [\n        \"## Hyperparameter settings\\n\",\n        \"Since we have two optimization processes, we'll need _two_ sets of hyperparameters. One of the hardest parts of metalearning is keeping the code organized.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 8,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"ikgPIRHJdbyN\",\n        \"outputId\": \"1b7438bd-a55a-462b-b4b1-c44c622523d7\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"step: 0, dt: 2.6s, meta_grad_norm: 5.2e-02, mean_inner_loss: 1.352e+00, final_inner_loss: 1.353e+00, test_acc: 48.6\\n\",\n            \"step: 1, dt: 1.6s, meta_grad_norm: 8.3e-02, mean_inner_loss: 1.314e+00, final_inner_loss: 1.284e+00, test_acc: 50.6\\n\",\n            \"step: 2, dt: 1.6s, meta_grad_norm: 9.5e-02, mean_inner_loss: 1.284e+00, final_inner_loss: 1.247e+00, test_acc: 52.4\\n\",\n            \"step: 3, dt: 1.6s, meta_grad_norm: 1.2e-01, mean_inner_loss: 1.252e+00, final_inner_loss: 1.208e+00, test_acc: 55.4\\n\",\n            \"step: 4, dt: 1.6s, meta_grad_norm: 1.5e-01, mean_inner_loss: 1.212e+00, final_inner_loss: 1.156e+00, test_acc: 58.2\\n\",\n            \"step: 5, dt: 1.6s, meta_grad_norm: 1.2e-01, mean_inner_loss: 1.170e+00, final_inner_loss: 1.089e+00, test_acc: 59.2\\n\",\n            \"step: 6, dt: 1.6s, meta_grad_norm: 1.3e-01, mean_inner_loss: 1.132e+00, final_inner_loss: 1.050e+00, test_acc: 59.4\\n\",\n            \"step: 7, dt: 1.6s, meta_grad_norm: 1.3e-01, mean_inner_loss: 1.093e+00, final_inner_loss: 1.023e+00, test_acc: 61.0\\n\",\n            \"step: 8, dt: 1.6s, meta_grad_norm: 6.6e-02, mean_inner_loss: 1.058e+00, final_inner_loss: 9.984e-01, test_acc: 61.8\\n\",\n            \"step: 9, dt: 1.6s, meta_grad_norm: 3.4e-02, mean_inner_loss: 1.043e+00, final_inner_loss: 9.887e-01, test_acc: 61.4\\n\",\n            \"step: 10, dt: 1.6s, meta_grad_norm: 2.7e-02, mean_inner_loss: 1.033e+00, final_inner_loss: 1.007e+00, test_acc: 59.4\\n\",\n            \"step: 11, dt: 1.6s, meta_grad_norm: 9.0e-02, mean_inner_loss: 1.022e+00, final_inner_loss: 1.031e+00, test_acc: 61.8\\n\",\n            \"step: 12, dt: 1.6s, meta_grad_norm: 1.2e-02, mean_inner_loss: 9.986e-01, final_inner_loss: 1.040e+00, test_acc: 63.8\\n\",\n            \"step: 13, dt: 1.6s, meta_grad_norm: 3.9e-01, mean_inner_loss: 9.936e-01, final_inner_loss: 1.025e+00, test_acc: 63.6\\n\",\n            \"step: 14, dt: 1.6s, meta_grad_norm: 2.3e-02, mean_inner_loss: 9.943e-01, final_inner_loss: 9.233e-01, test_acc: 63.0\\n\",\n            \"step: 15, dt: 1.6s, meta_grad_norm: 4.4e-03, mean_inner_loss: 9.917e-01, final_inner_loss: 9.322e-01, test_acc: 63.8\\n\",\n            \"step: 16, dt: 1.6s, meta_grad_norm: 3.4e-03, mean_inner_loss: 9.907e-01, final_inner_loss: 9.282e-01, test_acc: 63.4\\n\",\n            \"step: 17, dt: 1.6s, meta_grad_norm: 3.7e-03, mean_inner_loss: 9.896e-01, final_inner_loss: 9.261e-01, test_acc: 63.8\\n\",\n            \"step: 18, dt: 1.6s, meta_grad_norm: 3.2e-03, mean_inner_loss: 9.885e-01, final_inner_loss: 9.252e-01, test_acc: 64.6\\n\",\n            \"step: 19, dt: 1.6s, meta_grad_norm: 3.4e-03, mean_inner_loss: 9.876e-01, final_inner_loss: 9.249e-01, test_acc: 64.4\\n\",\n            \"step: 20, dt: 1.6s, meta_grad_norm: 4.0e-03, mean_inner_loss: 9.867e-01, final_inner_loss: 9.244e-01, test_acc: 64.0\\n\",\n            \"step: 21, dt: 1.6s, meta_grad_norm: 3.5e-03, mean_inner_loss: 9.857e-01, final_inner_loss: 9.228e-01, test_acc: 64.0\\n\",\n            \"step: 22, dt: 1.6s, meta_grad_norm: 3.2e-03, mean_inner_loss: 9.847e-01, final_inner_loss: 9.233e-01, test_acc: 64.4\\n\",\n            \"step: 23, dt: 1.6s, meta_grad_norm: 3.2e-03, mean_inner_loss: 9.838e-01, final_inner_loss: 9.213e-01, test_acc: 64.8\\n\",\n            \"step: 24, dt: 1.6s, meta_grad_norm: 3.9e-03, mean_inner_loss: 9.829e-01, final_inner_loss: 9.197e-01, test_acc: 64.6\\n\",\n            \"step: 25, dt: 1.7s, 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67.0\\n\",\n            \"step: 255, dt: 1.6s, meta_grad_norm: 4.4e-03, mean_inner_loss: 8.919e-01, final_inner_loss: 7.965e-01, test_acc: 66.8\\n\",\n            \"step: 256, dt: 1.6s, meta_grad_norm: 6.6e-03, mean_inner_loss: 8.914e-01, final_inner_loss: 8.050e-01, test_acc: 66.8\\n\",\n            \"step: 257, dt: 1.6s, meta_grad_norm: 1.3e-02, mean_inner_loss: 8.914e-01, final_inner_loss: 7.898e-01, test_acc: 67.6\\n\",\n            \"step: 258, dt: 1.6s, meta_grad_norm: 1.4e-03, mean_inner_loss: 8.907e-01, final_inner_loss: 8.155e-01, test_acc: 67.4\\n\",\n            \"step: 259, dt: 1.6s, meta_grad_norm: 1.5e-03, mean_inner_loss: 8.903e-01, final_inner_loss: 8.180e-01, test_acc: 67.0\\n\",\n            \"step: 260, dt: 1.6s, meta_grad_norm: 2.2e-03, mean_inner_loss: 8.899e-01, final_inner_loss: 8.200e-01, test_acc: 66.6\\n\",\n            \"step: 261, dt: 1.6s, meta_grad_norm: 5.2e-03, mean_inner_loss: 8.890e-01, final_inner_loss: 8.169e-01, test_acc: 66.4\\n\",\n            \"step: 262, dt: 1.6s, meta_grad_norm: 2.1e-02, mean_inner_loss: 8.892e-01, final_inner_loss: 7.925e-01, test_acc: 66.4\\n\",\n            \"step: 263, dt: 1.6s, meta_grad_norm: 1.3e-02, mean_inner_loss: 8.891e-01, final_inner_loss: 7.973e-01, test_acc: 67.0\\n\",\n            \"step: 264, dt: 1.6s, meta_grad_norm: 2.7e-01, mean_inner_loss: 8.898e-01, final_inner_loss: 8.011e-01, test_acc: 66.0\\n\",\n            \"step: 265, dt: 1.6s, meta_grad_norm: 3.3e-02, mean_inner_loss: 8.949e-01, final_inner_loss: 7.741e-01, test_acc: 68.0\\n\",\n            \"step: 266, dt: 1.6s, meta_grad_norm: 3.0e-02, mean_inner_loss: 8.887e-01, final_inner_loss: 7.920e-01, test_acc: 67.0\\n\",\n            \"step: 267, dt: 1.6s, meta_grad_norm: 2.5e-01, mean_inner_loss: 8.915e-01, final_inner_loss: 7.910e-01, test_acc: 66.8\\n\",\n            \"step: 268, dt: 1.6s, meta_grad_norm: 1.3e-02, mean_inner_loss: 8.870e-01, final_inner_loss: 8.346e-01, test_acc: 67.0\\n\",\n            \"step: 269, dt: 1.6s, meta_grad_norm: 1.8e-02, mean_inner_loss: 8.851e-01, final_inner_loss: 8.326e-01, test_acc: 64.8\\n\",\n            \"step: 270, dt: 1.6s, meta_grad_norm: 1.1e-01, mean_inner_loss: 8.833e-01, final_inner_loss: 8.325e-01, test_acc: 67.8\\n\",\n            \"step: 271, dt: 1.6s, meta_grad_norm: 3.2e-02, mean_inner_loss: 8.945e-01, final_inner_loss: 7.881e-01, test_acc: 66.8\\n\",\n            \"step: 272, dt: 1.6s, meta_grad_norm: 7.7e-02, mean_inner_loss: 8.887e-01, final_inner_loss: 8.283e-01, test_acc: 68.0\\n\",\n            \"step: 273, dt: 1.6s, meta_grad_norm: 6.5e-02, mean_inner_loss: 8.870e-01, final_inner_loss: 7.649e-01, test_acc: 68.2\\n\",\n            \"step: 274, dt: 1.6s, meta_grad_norm: 3.6e-03, mean_inner_loss: 8.847e-01, final_inner_loss: 8.211e-01, test_acc: 66.6\\n\",\n            \"step: 275, dt: 1.6s, meta_grad_norm: 2.1e-02, mean_inner_loss: 8.841e-01, final_inner_loss: 8.369e-01, test_acc: 68.6\\n\",\n            \"step: 276, dt: 1.6s, meta_grad_norm: 4.1e-01, mean_inner_loss: 8.845e-01, final_inner_loss: 8.581e-01, test_acc: 66.8\\n\",\n            \"step: 277, dt: 1.7s, meta_grad_norm: 4.0e-02, mean_inner_loss: 8.932e-01, final_inner_loss: 7.801e-01, test_acc: 66.4\\n\",\n            \"step: 278, dt: 1.6s, meta_grad_norm: 1.7e-01, mean_inner_loss: 8.895e-01, final_inner_loss: 7.612e-01, test_acc: 66.0\\n\",\n            \"step: 279, dt: 1.6s, meta_grad_norm: 4.8e-03, mean_inner_loss: 8.854e-01, final_inner_loss: 8.227e-01, test_acc: 68.6\\n\",\n            \"step: 280, dt: 1.6s, meta_grad_norm: 3.4e-03, mean_inner_loss: 8.848e-01, final_inner_loss: 8.276e-01, test_acc: 66.6\\n\",\n            \"step: 281, dt: 1.6s, meta_grad_norm: 2.0e-02, mean_inner_loss: 8.839e-01, final_inner_loss: 8.169e-01, test_acc: 66.2\\n\",\n            \"step: 282, dt: 1.6s, meta_grad_norm: 2.0e-02, mean_inner_loss: 8.812e-01, final_inner_loss: 8.460e-01, test_acc: 67.6\\n\",\n            \"step: 283, dt: 1.6s, meta_grad_norm: 1.1e-01, mean_inner_loss: 8.848e-01, final_inner_loss: 8.146e-01, test_acc: 67.0\\n\",\n            \"step: 284, dt: 1.6s, meta_grad_norm: 7.3e-03, mean_inner_loss: 8.854e-01, final_inner_loss: 7.600e-01, test_acc: 67.2\\n\",\n            \"step: 285, dt: 1.6s, meta_grad_norm: 7.9e-02, mean_inner_loss: 8.852e-01, final_inner_loss: 7.465e-01, test_acc: 65.8\\n\",\n            \"step: 286, dt: 1.6s, meta_grad_norm: 7.8e-03, mean_inner_loss: 8.838e-01, final_inner_loss: 8.443e-01, test_acc: 67.6\\n\",\n            \"step: 287, dt: 1.6s, meta_grad_norm: 2.2e-03, mean_inner_loss: 8.787e-01, final_inner_loss: 8.271e-01, test_acc: 68.0\\n\",\n            \"step: 288, dt: 1.6s, meta_grad_norm: 4.0e-02, mean_inner_loss: 8.787e-01, final_inner_loss: 8.367e-01, test_acc: 67.2\\n\",\n            \"step: 289, dt: 1.6s, meta_grad_norm: 2.4e-02, mean_inner_loss: 8.852e-01, final_inner_loss: 7.939e-01, test_acc: 68.4\\n\",\n            \"step: 290, dt: 1.6s, meta_grad_norm: 5.7e-02, mean_inner_loss: 8.845e-01, final_inner_loss: 7.842e-01, test_acc: 67.8\\n\",\n            \"step: 291, dt: 1.6s, meta_grad_norm: 7.6e-03, mean_inner_loss: 8.881e-01, final_inner_loss: 7.712e-01, test_acc: 65.6\\n\",\n            \"step: 292, dt: 1.6s, meta_grad_norm: 2.3e-01, mean_inner_loss: 8.861e-01, final_inner_loss: 8.038e-01, test_acc: 65.8\\n\",\n            \"step: 293, dt: 1.6s, meta_grad_norm: 1.7e-02, mean_inner_loss: 8.877e-01, final_inner_loss: 8.483e-01, test_acc: 67.8\\n\",\n            \"step: 294, dt: 1.6s, meta_grad_norm: 7.0e-03, mean_inner_loss: 8.854e-01, final_inner_loss: 8.809e-01, test_acc: 66.4\\n\",\n            \"step: 295, dt: 1.6s, meta_grad_norm: 3.6e-02, mean_inner_loss: 8.854e-01, final_inner_loss: 8.309e-01, test_acc: 67.2\\n\",\n            \"step: 296, dt: 1.6s, meta_grad_norm: 3.1e-03, mean_inner_loss: 8.871e-01, final_inner_loss: 8.192e-01, test_acc: 69.4\\n\",\n            \"step: 297, dt: 1.6s, meta_grad_norm: 2.1e-01, mean_inner_loss: 8.846e-01, final_inner_loss: 8.207e-01, test_acc: 68.0\\n\",\n            \"step: 298, dt: 1.6s, meta_grad_norm: 1.2e-02, mean_inner_loss: 8.853e-01, final_inner_loss: 7.617e-01, test_acc: 65.8\\n\",\n            \"step: 299, dt: 1.6s, meta_grad_norm: 2.4e-02, mean_inner_loss: 8.840e-01, final_inner_loss: 7.789e-01, test_acc: 67.2\\n\",\n            \"step: 300, dt: 1.6s, meta_grad_norm: 1.9e-03, mean_inner_loss: 8.842e-01, final_inner_loss: 7.386e-01, test_acc: 68.4\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"class ObjectView(object):\\n\",\n        \"    def __init__(self, d): self.__dict__ = d\\n\",\n        \"\\n\",\n        \"def get_inner_args(as_dict=False):\\n\",\n        \"  arg_dict = {'input_size': 40,\\n\",\n        \"              'hidden_size': 128,\\n\",\n        \"              'output_size': 10,\\n\",\n        \"              'batch_size': 100,\\n\",\n        \"              'total_steps': 301,\\n\",\n        \"              'test_every': 400,\\n\",\n        \"              'learning_rate': 1e-2,\\n\",\n        \"              'seed': 0,\\n\",\n        \"              'device': DEVICE,\\n\",\n        \"              'mu': 0.4,  # RMSprop momentum term\\n\",\n        \"              'batch_ix': 0}\\n\",\n        \"  return arg_dict if as_dict else ObjectView(arg_dict)\\n\",\n        \"\\n\",\n        \"def get_outer_args(as_dict=False):\\n\",\n        \"  arg_dict = {'input_size': 1,\\n\",\n        \"              'hidden_size': 100,\\n\",\n        \"              'output_size': 1,\\n\",\n        \"              'batch_size': 1,\\n\",\n        \"              'learning_rate': 3e-1,\\n\",\n        \"              'mu': 0.0,  # SGD momentum term\\n\",\n        \"              'total_steps': 301,\\n\",\n        \"              'print_every': 1,\\n\",\n        \"              'device': DEVICE,\\n\",\n        \"              'seed': 0}\\n\",\n        \"  return arg_dict if as_dict else ObjectView(arg_dict)\\n\",\n        \"\\n\",\n        \"inner_args, outer_args = get_inner_args(), get_outer_args()\\n\",\n        \"mnist1d.set_seed(outer_args.seed)\\n\",\n        \"results = outer_optimization(inner_args, outer_args)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"-la6G6e6Rr_y\"\n      },\n      \"source\": [\n        \"## Evaluate some baseline activation functions\\n\",\n        \"We'll just pass them through the inner training loop once\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 9,\n      \"metadata\": {\n        \"id\": \"navUnaFAdSwY\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"afuncs = [torch.relu, lambda x: x*torch.sigmoid(x), torch.nn.ELU(), \\\\\\n\",\n        \"          torch.nn.Softplus(), torch.tanh, torch.sigmoid]\\n\",\n        \"afunc_names = ['relu','swish', 'elu', 'softplus', 'tanh', 'sigmoid']\\n\",\n        \"afunc_colors = ['yellow', 'orange', 'red', 'purple', 'blue', 'green']\\n\",\n        \"\\n\",\n        \"baseline_results = []\\n\",\n        \"results['baselines'] = [] # save these baselines to overall results\\n\",\n        \"for i, f in enumerate(afuncs):\\n\",\n        \"  torch.manual_seed(inner_args.seed)\\n\",\n        \"  inner_model = get_functional_mlp(inner_args)\\n\",\n        \"  r = inner_optimization(inner_model, inner_args, data, afunc=f)\\n\",\n        \"  baseline_results.append(r)\\n\",\n        \"  results['baselines'] += [(afunc_names[i],  r['test_acc'][-1], afunc_colors[i])]\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 10,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"UQNukytOvReM\",\n        \"outputId\": \"790e61f8-6c14-4ae5-d193-32e968325ed6\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"0.936288898965449\\n\",\n            \"['relu', 'swish', 'elu', 'softplus', 'tanh', 'sigmoid']\\n\"\n          ]\n        },\n        {\n          \"output_type\": \"execute_result\",\n          \"data\": {\n            \"text/plain\": [\n              \"[1.3369015, 1.3675405, 1.4736847, 1.6467019, 1.5242596, 1.7532207]\"\n            ]\n          },\n          \"metadata\": {},\n          \"execution_count\": 10\n        }\n      ],\n      \"source\": [\n        \"print(np.mean(results['train_losses']))\\n\",\n        \"print(afunc_names)\\n\",\n        \"[r['train_losses'].detach().cpu().numpy().mean() for r in baseline_results]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"3xt7VR5654II\"\n      },\n      \"source\": [\n        \"## Loading and saving results\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 11,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"PmkXwfI-3xBs\",\n        \"outputId\": \"fb947530-90ce-44cd-e1ec-7c91ec4e3069\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"saved: steps301_seed0_lr3e-01_elu_init\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"# saving\\n\",\n        \"run_tag = 'steps{:.0f}_seed{:.0f}_lr{:.0e}_elu_init' \\\\\\n\",\n        \"        .format(outer_args.total_steps, outer_args.seed,\\n\",\n        \"                outer_args.learning_rate)\\n\",\n        \"mnist1d.to_pickle(results, path=project_dir + run_tag)\\n\",\n        \"print('saved: {}'.format(run_tag))\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 12,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"MNSZGKZi4ypO\",\n        \"outputId\": \"c27165f0-3101-4267-d13a-55c03b565ca6\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stdout\",\n          \"text\": [\n            \"loaded: steps301_seed0_lr3e-01_elu_init\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"# loading\\n\",\n        \"run_tag = run_tag# 'steps100_seed0_lr1e+00_linear_init'\\n\",\n        \"results = mnist1d.from_pickle(path=project_dir + run_tag)\\n\",\n        \"print('loaded: {}'.format(run_tag))\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"ECD7ywvmRvks\"\n      },\n      \"source\": [\n        \"## Examine our results\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"source\": [\n        \"fig = plt.figure(figsize=(10.3, 3), dpi=200)\\n\",\n        \"t = range(0,outer_args.total_steps, outer_args.print_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,1)\\n\",\n        \"plt.plot(results['train_losses'], 'k-', label='Final training loss')\\n\",\n        \"plt.title('Outer objective (last 200 inner losses)', fontsize=10)\\n\",\n        \"plt.xlabel(\\\"Outer training step\\\")\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,2)\\n\",\n        \"meta_test_acc = [r[-1] for r in results['inner_test_accs']]\\n\",\n        \"for (name, acc, color) in results['baselines']:\\n\",\n        \"  plt.plot(t, [acc]*len(t), '-', color=color, label=name, linewidth=2)\\n\",\n        \"plt.plot(t, meta_test_acc, 'k-', label='learned')\\n\",\n        \"plt.title('MNIST-1D test accuracy', fontsize=10) ; plt.xlabel(\\\"Outer training step\\\")\\n\",\n        \"plt.ylim(None, 80)\\n\",\n        \"plt.legend(fontsize=6, ncol=4)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,3)\\n\",\n        \"plt.plot([0, 0], [-6,6], '-', color='gray', linewidth=1)\\n\",\n        \"plt.plot([-6, 6], [0,0], '-', color='gray', linewidth=1)\\n\",\n        \"N = len(results['inner_afunc'])\\n\",\n        \"colors = pl.cm.viridis(np.linspace(0,1,N))\\n\",\n        \"for i, (x, y) in enumerate( results['inner_afunc']):\\n\",\n        \"  if i%(N//50)==0 or i==len(results['inner_afunc']):\\n\",\n        \"    l, m = 0,100 #30,70\\n\",\n        \"    if i%(N//10)==0 or i==len(results['inner_afunc']):\\n\",\n        \"      label = 'step {}'.format(i*outer_args.print_every)\\n\",\n        \"    else:\\n\",\n        \"      label=None\\n\",\n        \"    plt.plot(x[l:m], y[l:m], 'k-', color=colors[i], label=label)\\n\",\n        \"plt.legend(fontsize=6,ncol=3) ; plt.title(\\\"Learned activation function\\\", fontsize=10)\\n\",\n        \"plt.xlim(-5,5) ; plt.ylim(-1.5,4)\\n\",\n        \"plt.xlabel(\\\"x\\\") ; plt.ylabel(\\\"y\\\")\\n\",\n        \"plt.tight_layout() ; plt.show()\\n\",\n        \"\\n\",\n        \"os.makedirs(project_dir + 'figures/', exist_ok=True)\\n\",\n        \"fig.savefig(project_dir + 'figures/metalearn_afunc.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/metalearn_afunc.pdf')\"\n      ],\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 531\n        },\n        \"id\": \"PgUKhcTMuJo7\",\n        \"outputId\": \"0c7ba983-14d7-4314-a53b-1eae9535d048\"\n      },\n      \"execution_count\": 15,\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stderr\",\n          \"text\": [\n            \"<ipython-input-15-9846b40a9842>:30: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \\\"k-\\\" (-> color='k'). The keyword argument will take precedence.\\n\",\n            \"  plt.plot(x[l:m], y[l:m], 'k-', color=colors[i], label=label)\\n\",\n            \"<ipython-input-15-9846b40a9842>:34: UserWarning: The figure layout has changed to tight\\n\",\n            \"  plt.tight_layout() ; plt.show()\\n\"\n          ]\n        },\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 2060x600 with 3 Axes>\"\n            ],\n            \"image/png\": 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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"9bYqX-1z9PuY\"\n      },\n      \"source\": [\n        \"## Approximate the learned activation\\n\",\n        \"Now let's approximate our learned activation as a piecewise polynomial. We could use `np.polyfit` to find a high-order polynomial approximation, but it turns out that linear (polynomial order 1) approximations work just fine.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 16,\n      \"metadata\": {\n        \"id\": \"MD8skl9LYZcr\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"def polyfunc(coeffs):\\n\",\n        \"  # designed to work on scalars, np arrays, and Tensors (you can backprop through it too)\\n\",\n        \"  def f(x):\\n\",\n        \"    r = 0.*x\\n\",\n        \"    for i, c in enumerate(coeffs[::-1]):\\n\",\n        \"      r += c * x**i\\n\",\n        \"    return r\\n\",\n        \"  return f\\n\",\n        \"\\n\",\n        \"def get_approx_afun():\\n\",\n        \"  # designed to work on scalars, np arrays, and Tensors\\n\",\n        \"  def f(x):\\n\",\n        \"    seg0, seg1, seg2, seg3, seg4, seg5 = [.15, .9], [-.25, 0.1], [-1.2, -.85], [-.5, -.75], [0.2, -.85], [1.2, -1.4]\\n\",\n        \"    y = (x < -2) * polyfunc(seg0)(x) + \\\\\\n\",\n        \"        (x > -2) * (x < -1) * polyfunc(seg1)(x) + \\\\\\n\",\n        \"        (x > -1) * (x < -0.25) * polyfunc(seg2)(x) + \\\\\\n\",\n        \"        (x > -0.25) * (x < 0.1) * polyfunc(seg3)(x) + \\\\\\n\",\n        \"        (x > 0.1) * (x < .6) * polyfunc(seg4)(x) + \\\\\\n\",\n        \"        (x > .6) * polyfunc(seg5)(x)\\n\",\n        \"    return y\\n\",\n        \"  return f\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 17,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 390\n        },\n        \"id\": \"PXsKzaWoV9Pc\",\n        \"outputId\": \"ee07241c-6402-45ae-f433-3cd1a2cb9127\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 360x360 with 1 Axes>\"\n            ],\n            \"image/png\": \"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\\n\"\n          },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"# plot a comparison\\n\",\n        \"fig = pl.figure(figsize=[3,3],dpi=120)\\n\",\n        \"\\n\",\n        \"approx_afunc = get_approx_afun()\\n\",\n        \"x, y = results['inner_afunc'][-1]\\n\",\n        \"plt.plot(x[::3], y[::3], 'y-', label='mlp')\\n\",\n        \"x_ = np.linspace(-5,5,100)\\n\",\n        \"plt.plot(x_, approx_afunc(x_), 'k-', label='approx_fit', linewidth=1)\\n\",\n        \"plt.legend() ; plt.xlim(-5,5) ; plt.ylim(-2,4) ; plt.show()\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"4PCYGpPY-_MS\"\n      },\n      \"source\": [\n        \"## Evaluate the fitted piecewise activation function\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 18,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"qdNdknxnarWX\",\n        \"outputId\": \"7a120fa9-0e92-431a-91c2-7abf8f171520\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"execute_result\",\n          \"data\": {\n            \"text/plain\": [\n              \"66.4\"\n            ]\n          },\n          \"metadata\": {},\n          \"execution_count\": 18\n        }\n      ],\n      \"source\": [\n        \"torch.manual_seed(inner_args.seed)\\n\",\n        \"inner_model = get_functional_mlp(inner_args)\\n\",\n        \"poly_results = inner_optimization(inner_model, inner_args, data, afunc=approx_afunc)\\n\",\n        \"results['baselines'] += [('approx_fit', poly_results['test_acc'][-1], 'grey')]\\n\",\n        \"poly_results['test_acc'][-1]\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"IJNq5WPZ_I4r\"\n      },\n      \"source\": [\n        \"## Add it to our results\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 19,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 504\n        },\n        \"id\": \"nTixN_-q6aA-\",\n        \"outputId\": \"b691fe13-f20c-4e27-eab7-f8556075308c\"\n      },\n      \"outputs\": [\n        {\n          \"output_type\": \"stream\",\n          \"name\": \"stderr\",\n          \"text\": [\n            \"<ipython-input-19-0e7201f42988>:31: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \\\"k-\\\" (-> color='k'). The keyword argument will take precedence.\\n\",\n            \"  plt.plot(x[l:m], y[l:m], 'k-', color=colors[i], label=label)\\n\",\n            \"<ipython-input-19-0e7201f42988>:36: UserWarning: The figure layout has changed to tight\\n\",\n            \"  plt.tight_layout() ; plt.show()\\n\"\n          ]\n        },\n        {\n          \"output_type\": \"display_data\",\n          \"data\": {\n            \"text/plain\": [\n              \"<Figure size 1980x540 with 3 Axes>\"\n            ],\n            \"image/png\": 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         },\n          \"metadata\": {}\n        }\n      ],\n      \"source\": [\n        \"fig = plt.figure(figsize=(11, 3), dpi=180)\\n\",\n        \"t = range(0,outer_args.total_steps, outer_args.print_every)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,1)\\n\",\n        \"plt.plot(results['train_losses'], 'k-', label='Final training loss')\\n\",\n        \"plt.title('Outer objective (mean inner loss)', fontsize=10)\\n\",\n        \"plt.xlabel(\\\"Outer training step\\\")\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,2)\\n\",\n        \"meta_test_acc = [r[-1] for r in results['inner_test_accs']]\\n\",\n        \"plt.plot(t, meta_test_acc, 'k-', label='learned')\\n\",\n        \"for (name, acc, color) in results['baselines']:\\n\",\n        \"  line = '--' if name == 'approx_fit' else '-'\\n\",\n        \"  plt.plot(t, [acc]*len(t), line, color=color, label=name, linewidth=2)\\n\",\n        \"plt.title('Test accuracy', fontsize=10) ; plt.xlabel(\\\"Outer training step\\\")\\n\",\n        \"plt.ylim(None, 80)\\n\",\n        \"plt.legend(fontsize=6, ncol=4)\\n\",\n        \"\\n\",\n        \"plt.subplot(1,3,3)\\n\",\n        \"plt.plot([0, 0], [-6,6], '-', color='gray', linewidth=1)\\n\",\n        \"plt.plot([-6, 6], [0,0], '-', color='gray', linewidth=1)\\n\",\n        \"N = len(results['inner_afunc'])\\n\",\n        \"colors = pl.cm.viridis(np.linspace(0,1,N))\\n\",\n        \"for i, (x, y) in enumerate( results['inner_afunc']):\\n\",\n        \"  if i%(N//50)==0 or i==len(results['inner_afunc']):\\n\",\n        \"    l, m = 0,100 #30,70\\n\",\n        \"    if i%(N//10)==0 or i==len(results['inner_afunc']):\\n\",\n        \"      label = 'step {}'.format(i*outer_args.print_every)\\n\",\n        \"    else:\\n\",\n        \"      label=None\\n\",\n        \"    plt.plot(x[l:m], y[l:m], 'k-', color=colors[i], label=label)\\n\",\n        \"plt.plot(x[l:m], approx_afunc(x[l:m]), '--', color='grey', label='approx_fit'.format(i))\\n\",\n        \"plt.legend(fontsize=6,ncol=3) ; plt.title(\\\"Learned activation function\\\", fontsize=10)\\n\",\n        \"plt.xlim(-5,5) ; plt.ylim(-1.5,4)\\n\",\n        \"plt.xlabel(\\\"x\\\") ; plt.ylabel(\\\"y\\\")\\n\",\n        \"plt.tight_layout() ; plt.show()\\n\",\n        \"\\n\",\n        \"fig.savefig(project_dir + 'figures/metalearn_afunc_approx.png')\\n\",\n        \"fig.savefig(project_dir + 'figures/metalearn_afunc_approx.pdf')\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"esjT7chRndSr\"\n      },\n      \"source\": [\n        \"## Does it transfer?\\n\",\n        \"\\n\",\n        \"It transfers reasonably well, but it's not SOTA.\\n\",\n        \"\\n\",\n        \"When we transfer it to convolutional models trained on MNIST and CIFAR10, it does well in terms of mean training loss during the first epoch (which is roughly speaking the objective it was trained on in this notebook). However, it's a bit worse than ReLU, Swish, and Elu for training these larger models to convergence. If we were to perform metalearning on a longer inner training loop or run the same code on larger models & datasets, we could, in theory, do at least as well as the best hand-designed activation function — and probably a decent bit better. We leave this to future work.\\n\",\n        \"\\n\",\n        \"* Transferring to a CNN trained on MNIST (email me if you'd like access)\\n\",\n        \"* Transferring to a CNN trained on CIFAR10  (email me if you'd like access)\"\n      ]\n    }\n  ],\n  \"metadata\": {\n    \"accelerator\": \"GPU\",\n    \"colab\": {\n      \"provenance\": [],\n      \"gpuType\": \"A100\"\n    },\n    \"kernelspec\": {\n      \"display_name\": \"Python 3\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.9.18\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}"
  },
  {
    "path": "notebooks/metalearn-learn-rate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"dKUcDM76bHx3\"\n   },\n   \"source\": [\n    \"# **MNIST-1D**: Metalearning a learning rate\\n\",\n    \"Sam Greydanus | 2020\\n\",\n    \"\\n\",\n    \"In this notebook, we will show how to metalearn a learning rate for an MLP trained on the MNIST-1D datset.\\n\",\n    \"\\n\",\n    \"This case study is meant to show the convenience and computational savings of working with the low-dimensional MNIST-1D dataset.  You can find more details at https://github.com/greydanus/mnist1d.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"id\": \"Sg2i1QmhKW5d\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n    \"\\n\",\n    \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n    \"!git clone https://github.com/greydanus/mnist1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"KaQo7QhvXvid\",\n    \"outputId\": \"8a11019f-6383-4058-b072-664524e4aceb\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using: cuda\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import time, os\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"\\n\",\n    \"import mnist1d\\n\",\n    \"project_dir = './'\\n\",\n    \"\\n\",\n    \"# Try attaching to GPU\\n\",\n    \"DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\\n\",\n    \"print('Using:', DEVICE)\\n\",\n    \"\\n\",\n    \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"2GF-lQ8ttd55\"\n   },\n   \"source\": [\n    \"## Only run this if you're in Google Colab\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"KG9uD9x7td55\",\n    \"outputId\": \"8cf9f2a6-636a-470f-ae8b-e7020b263cc3\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Mounted at /content/gdrive\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if True:\\n\",\n    \"    # Only run this in Colab\\n\",\n    \"    from google.colab import drive\\n\",\n    \"    drive.mount('/content/gdrive')\\n\",\n    \"    project_dir = \\\"/content/gdrive/My Drive/Research/mnist1d/\\\"\\n\",\n    \"else:\\n\",\n    \"    project_dir = './'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"nre26wEOfZsM\"\n   },\n   \"source\": [\n    \"## Get the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"I-vm_gh5xTJs\",\n    \"outputId\": \"dd241318-df6f-4f12-b3db-f796eb826318\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading MNIST1D dataset from https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl\\n\",\n      \"Saving to ./mnist1d_data.pkl\\n\",\n      \"Successfully loaded data from ./mnist1d_data.pkl\\n\",\n      \"Examples in training set: 4000\\n\",\n      \"Examples in test set: 1000\\n\",\n      \"Length of each input: 40\\n\",\n      \"Number of classes: 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = mnist1d.get_dataset_args()\\n\",\n    \"data = mnist1d.get_dataset(args=args)  # by default, this will download a pre-made dataset from the GitHub repo\\n\",\n    \"\\n\",\n    \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n    \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n    \"print(\\\"Length of each input: {}\\\".format(data['x'].shape[-1]))\\n\",\n    \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"XH8nHKh0Ss5M\"\n   },\n   \"source\": [\n    \"## First we need a functional representation of an MLP\\n\",\n    \"\\n\",\n    \"This is an important first step when doing gradient-based metalearning. The entire inner training loop needs to be end-to-end differentiable and ordinary PyTorch modules and optimizers were not designed with this in mind. People have come up with various ways to get around the issue. My personal favorite is to make PyTorch look more functional and avoid working with objects altogether.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"id\": \"HIL9UcFzrVxX\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# returns a tuple (state, function) for a vanilla MLP model\\n\",\n    \"def get_functional_mlp(args):\\n\",\n    \"  D, H, O = args.input_size, args.hidden_size, args.output_size\\n\",\n    \"  linear1, linear2 = torch.nn.Linear(D,H), torch.nn.Linear(H,O)\\n\",\n    \"  params = [linear1.weight, linear1.bias, linear2.weight, linear2.bias]\\n\",\n    \"  params = [torch.Tensor(p.reshape(-1)) for p in params]\\n\",\n    \"  param_vec = torch.cat(params)\\n\",\n    \"\\n\",\n    \"  def forward_fn(x, param_vec):\\n\",\n    \"    pointer = 0\\n\",\n    \"    W1 = param_vec[pointer:pointer+D*H].reshape(H,D)\\n\",\n    \"    pointer += D*H\\n\",\n    \"    b1 = param_vec[pointer:pointer+H].reshape(1,-1)\\n\",\n    \"    pointer += H\\n\",\n    \"    W2 = param_vec[pointer:pointer+H*O].reshape(O,H)\\n\",\n    \"    pointer += O*H\\n\",\n    \"    b2 = param_vec[pointer:pointer+O].reshape(1,-1)\\n\",\n    \"\\n\",\n    \"    h = torch.tanh((W1 @ x.t()).t() + b1)\\n\",\n    \"    logits = (W2 @ h.t()).t() + b2\\n\",\n    \"    return logits\\n\",\n    \"  return param_vec, forward_fn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"oRXVDX1dsJ7q\"\n   },\n   \"source\": [\n    \"## Inner optimization loop\\n\",\n    \"In the inner loop, we'll train an MLP to classify MNIST-1D images.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"id\": \"_gOv4HC9xPkt\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def inner_optimization(learning_rate, model, args, data):\\n\",\n    \"  torch.manual_seed(args.seed)\\n\",\n    \"  (param_vec, fwd_fn) = model\\n\",\n    \"  x_train, x_test = torch.Tensor(data['x']), torch.Tensor(data['x_test'])\\n\",\n    \"  y_train, y_test = torch.LongTensor(data['y']), torch.LongTensor(data['y_test'])\\n\",\n    \"\\n\",\n    \"  criterion = torch.nn.CrossEntropyLoss()\\n\",\n    \"  results = {'train_losses': []}\\n\",\n    \"  for step in range(args.total_steps):\\n\",\n    \"    bix = (args.batch_ix + (step*args.batch_size))%len(x_train) # batch index\\n\",\n    \"    x, y = x_train[bix:bix+args.batch_size], y_train[bix:bix+args.batch_size]\\n\",\n    \"    loss = criterion(fwd_fn(x,param_vec), y) # forward pass\\n\",\n    \"\\n\",\n    \"    grads = torch.autograd.grad(loss, param_vec, retain_graph=True, create_graph=True)[0]\\n\",\n    \"    param_vec = param_vec - learning_rate * grads # SGD update\\n\",\n    \"    results['train_losses'].append(loss)\\n\",\n    \"\\n\",\n    \"  results['train_losses'] = torch.stack(results['train_losses'])\\n\",\n    \"  return results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"7Gq8t_RjsRtw\"\n   },\n   \"source\": [\n    \"## Outer optimization loop\\n\",\n    \"The outer (meta) optimization objective is to choose the optimal learning rate for the inner model. We can do this by taking the gradient of the average loss over training with respect to the learning rate. This approach is called **gradient-based metalearning** since we are computing an exact gradient by backpropagating through the entire unrolled optimization loop. A good paper on this approach is [Maclaurin et al., 2015](https://arxiv.org/abs/1502.03492): _\\\"Gradient-based Hyperparameter Optimization through Reversible Learning.\\\"_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"id\": \"dhgt1UnzxPhs\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def outer_optimization(inner_args, outer_args):\\n\",\n    \"  torch.manual_seed(outer_args.seed)\\n\",\n    \"\\n\",\n    \"  inner_model = get_functional_mlp(inner_args)\\n\",\n    \"  np_inner_lr = np.asarray([inner_args.initial_lr])\\n\",\n    \"  inner_lr = Variable( torch.FloatTensor(np_inner_lr), requires_grad=True)\\n\",\n    \"\\n\",\n    \"  results = {'all_losses':[], 'train_losses':[], 'inner_lr':[]}\\n\",\n    \"  t0 = time.time()\\n\",\n    \"  for step in range(outer_args.total_steps):\\n\",\n    \"\\n\",\n    \"    # run inner optimization problem\\n\",\n    \"    inner_results = inner_optimization(inner_lr, inner_model, inner_args, data)\\n\",\n    \"\\n\",\n    \"    # run outer optimization problem\\n\",\n    \"    outer_loss = inner_results['train_losses'].mean()\\n\",\n    \"    outer_grads = torch.autograd.grad(outer_loss, inner_lr)[0]\\n\",\n    \"    inner_lr = inner_lr - outer_args.learning_rate * outer_grads\\n\",\n    \"\\n\",\n    \"    # boring bookkeeping\\n\",\n    \"    results['train_losses'].append(outer_loss.item())\\n\",\n    \"    results['inner_lr'].append(inner_lr.item())\\n\",\n    \"    results['all_losses'].append(inner_results['train_losses'].detach().cpu().numpy())\\n\",\n    \"    if step > 0 and step % outer_args.print_every == 0: # print out training progress\\n\",\n    \"      t1 = time.time()\\n\",\n    \"      print(\\\"step: {}, dt: {:.3e}s, mean_inner_loss: {:.3e}, final_inner_loss: {:.3e}, inner_lr: {:.3e}\\\"\\n\",\n    \"          .format(step, t1-t0, results['train_losses'][-1], inner_results['train_losses'][-1], results['inner_lr'][-1]))\\n\",\n    \"      t0 = t1\\n\",\n    \"\\n\",\n    \"  return results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"rHqYpbnKtUrw\"\n   },\n   \"source\": [\n    \"## Hyperparameter settings\\n\",\n    \"Since we have two optimization processes, we'll need _two_ sets of hyperparameters. One of the hardest parts of metalearning is keeping the code organized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"id\": \"7kFq6KkD8WwC\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class ObjectView(object):\\n\",\n    \"    def __init__(self, d): self.__dict__ = d\\n\",\n    \"\\n\",\n    \"def get_inner_args(as_dict=False):\\n\",\n    \"  arg_dict = {'input_size': 40,\\n\",\n    \"              'hidden_size': 50,\\n\",\n    \"              'output_size': 10,\\n\",\n    \"              'batch_size': 50,\\n\",\n    \"              'total_steps': 200,\\n\",\n    \"              'initial_lr': 1e-2,\\n\",\n    \"              'seed': 0,\\n\",\n    \"              'batch_ix': 0}\\n\",\n    \"  return arg_dict if as_dict else ObjectView(arg_dict)\\n\",\n    \"\\n\",\n    \"def get_outer_args(as_dict=False):\\n\",\n    \"  arg_dict = {'batch_size': 1,\\n\",\n    \"              'learning_rate': 3e-1,\\n\",\n    \"              'total_steps': 50,\\n\",\n    \"              'print_every': 3,\\n\",\n    \"              'seed': 0}\\n\",\n    \"  return arg_dict if as_dict else ObjectView(arg_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"AHBRr_Wytnoz\"\n   },\n   \"source\": [\n    \"## Use metalearning to find an optimal learning rate\\n\",\n    \"Let's initialize the learning rate at a very small value and then at a very large value. If both optimization procedures converge to the same final learning rate, then we can trust that they probably found a global or close-to-global minima (FYI, metalearning loss landscapes are notorious for having strange shapes; see Figure 2 of [this paper](https://arxiv.org/abs/1810.10180) for example).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"n_svXMj9td58\",\n    \"outputId\": \"11e04d23-5803-45b1-e2ce-500068d75699\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"step: 3, dt: 1.374e+00s, mean_inner_loss: 1.808e+00, final_inner_loss: 1.588e+00, inner_lr: 1.259e+00\\n\",\n      \"step: 6, dt: 9.249e-01s, mean_inner_loss: 1.762e+00, final_inner_loss: 1.508e+00, inner_lr: 1.044e+00\\n\",\n      \"step: 9, dt: 8.981e-01s, mean_inner_loss: 1.737e+00, final_inner_loss: 1.500e+00, inner_lr: 9.111e-01\\n\",\n      \"step: 12, dt: 9.289e-01s, mean_inner_loss: 1.726e+00, final_inner_loss: 1.491e+00, inner_lr: 8.209e-01\\n\",\n      \"step: 15, dt: 8.998e-01s, mean_inner_loss: 1.721e+00, final_inner_loss: 1.484e+00, inner_lr: 7.606e-01\\n\",\n      \"step: 18, dt: 9.100e-01s, mean_inner_loss: 1.718e+00, final_inner_loss: 1.485e+00, inner_lr: 7.205e-01\\n\",\n      \"step: 21, dt: 8.877e-01s, mean_inner_loss: 1.717e+00, final_inner_loss: 1.488e+00, inner_lr: 6.931e-01\\n\",\n      \"step: 24, dt: 8.972e-01s, mean_inner_loss: 1.717e+00, final_inner_loss: 1.492e+00, inner_lr: 6.735e-01\\n\",\n      \"step: 27, dt: 9.018e-01s, mean_inner_loss: 1.717e+00, final_inner_loss: 1.495e+00, inner_lr: 6.590e-01\\n\",\n      \"step: 30, dt: 9.136e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.498e+00, inner_lr: 6.482e-01\\n\",\n      \"step: 33, dt: 9.373e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.500e+00, inner_lr: 6.404e-01\\n\",\n      \"step: 36, dt: 9.114e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.502e+00, inner_lr: 6.348e-01\\n\",\n      \"step: 39, dt: 8.986e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.503e+00, inner_lr: 6.307e-01\\n\",\n      \"step: 42, dt: 8.770e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.504e+00, inner_lr: 6.278e-01\\n\",\n      \"step: 45, dt: 8.949e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.504e+00, inner_lr: 6.258e-01\\n\",\n      \"step: 48, dt: 8.944e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.505e+00, inner_lr: 6.243e-01\\n\",\n      \"\\n\",\n      \"step: 3, dt: 1.129e+00s, mean_inner_loss: 1.722e+00, final_inner_loss: 1.555e+00, inner_lr: 4.909e-01\\n\",\n      \"step: 6, dt: 8.726e-01s, mean_inner_loss: 1.718e+00, final_inner_loss: 1.535e+00, inner_lr: 5.407e-01\\n\",\n      \"step: 9, dt: 9.030e-01s, mean_inner_loss: 1.717e+00, final_inner_loss: 1.524e+00, inner_lr: 5.689e-01\\n\",\n      \"step: 12, dt: 8.804e-01s, mean_inner_loss: 1.717e+00, final_inner_loss: 1.518e+00, inner_lr: 5.857e-01\\n\",\n      \"step: 15, dt: 8.981e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.514e+00, inner_lr: 5.965e-01\\n\",\n      \"step: 18, dt: 9.036e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.511e+00, inner_lr: 6.038e-01\\n\",\n      \"step: 21, dt: 8.900e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.510e+00, inner_lr: 6.088e-01\\n\",\n      \"step: 24, dt: 9.341e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.509e+00, inner_lr: 6.124e-01\\n\",\n      \"step: 27, dt: 8.809e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.508e+00, inner_lr: 6.149e-01\\n\",\n      \"step: 30, dt: 1.050e+00s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.507e+00, inner_lr: 6.166e-01\\n\",\n      \"step: 33, dt: 8.727e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.507e+00, inner_lr: 6.179e-01\\n\",\n      \"step: 36, dt: 8.998e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.506e+00, inner_lr: 6.187e-01\\n\",\n      \"step: 39, dt: 8.840e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.506e+00, inner_lr: 6.193e-01\\n\",\n      \"step: 42, dt: 9.072e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.506e+00, inner_lr: 6.198e-01\\n\",\n      \"step: 45, dt: 8.898e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.506e+00, inner_lr: 6.201e-01\\n\",\n      \"step: 48, dt: 8.878e-01s, mean_inner_loss: 1.716e+00, final_inner_loss: 1.506e+00, inner_lr: 6.203e-01\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"inner_args, outer_args = get_inner_args(), get_outer_args()\\n\",\n    \"inner_args.initial_lr = 1.5\\n\",\n    \"results_trial1 = outer_optimization(inner_args, outer_args)\\n\",\n    \"\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"inner_args, outer_args = get_inner_args(), get_outer_args()\\n\",\n    \"inner_args.initial_lr = 1.5e-1\\n\",\n    \"results_trial2 = outer_optimization(inner_args, outer_args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"zjbZYRK4uFBN\"\n   },\n   \"source\": [\n    \"## Examine our results\\n\",\n    \"Sure enough, both metalearning runs converged to an optimal learning rate of ~0.62. This is the optimal learning rate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 422\n    },\n    \"id\": \"5KwqWEumtd59\",\n    \"outputId\": \"0c6a00fe-3251-4889-e642-8a9332ad33c3\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1980x550 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(9, 2.5), dpi=220)\\n\",\n    \"\\n\",\n    \"plt.subplot(1,3,1)\\n\",\n    \"plt.plot(results_trial1['train_losses'], 'r-', label='Trial 1: init_lr=1.5')\\n\",\n    \"plt.plot(results_trial2['train_losses'], 'b-', label='Trial 2: init_lr=0.15')\\n\",\n    \"plt.title('Outer objective (mean inner loss)', fontsize=10)\\n\",\n    \"plt.xlabel(\\\"Outer training step\\\") ; plt.legend(fontsize=7)\\n\",\n    \"plt.ylim(None, 1.75)\\n\",\n    \"\\n\",\n    \"plt.subplot(1,3,2)\\n\",\n    \"plt.plot([r[-1] for r in results_trial1['all_losses']], 'r-', label='Trial 1: init_lr=1.5')\\n\",\n    \"plt.plot([r[-1] for r in results_trial2['all_losses']], 'b-', label='Trial 2: init_lr=0.15')\\n\",\n    \"plt.title('Final MLP training loss', fontsize=10)\\n\",\n    \"plt.xlabel(\\\"Outer training step\\\") ; plt.legend(fontsize=7)\\n\",\n    \"\\n\",\n    \"plt.subplot(1,3,3)\\n\",\n    \"plt.plot(results_trial1['inner_lr'], 'r.-', label='Trial 1: init_lr=1.5')\\n\",\n    \"plt.plot(results_trial2['inner_lr'], 'b.-', label='Trial 2: init_lr=0.15')\\n\",\n    \"plt.title('Metalearned learning rate', fontsize=10)\\n\",\n    \"plt.xlabel(\\\"Outer training step\\\") ; plt.legend(fontsize=7)\\n\",\n    \"\\n\",\n    \"# plt.tight_layout() ;\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"os.makedirs(project_dir + 'figures/', exist_ok=True)\\n\",\n    \"fig.savefig(project_dir + 'figures/metalearn_lr.png')\\n\",\n    \"fig.savefig(project_dir + 'figures/metalearn_lr.pdf')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"id\": \"MjzywntL07YT\"\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"gpuType\": \"A100\",\n   \"provenance\": []\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/mnist1d-classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5134e153\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Benchmarking classification models on MNIST-1D\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"c4679237\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Run this cell if you are in Colab \\n\",\n    \"# (otherwise make sure to do python -m pip install mnist1d first)\\n\",\n    \"\\n\",\n    \"#!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n    \"    \\n\",\n    \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n    \"# !git clone https://github.com/greydanus/mnist1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"a60112cb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.append('./mnist1d/notebooks/')\\n\",\n    \"sys.path.append('./mnist1d/mnist1d/')\\n\",\n    \"\\n\",\n    \"from models import ConvBase, MLPBase, LinearBase, GRUBase\\n\",\n    \"from train import get_model_args, train_model\\n\",\n    \"from mnist1d.utils import set_seed\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"\\n\",\n    \"DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"75018495\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load MNIST-1D\\n\",\n    \"# (loading from the web to make running in a Colab easier)\\n\",\n    \"\\n\",\n    \"from urllib.request import urlopen\\n\",\n    \"import pickle\\n\",\n    \"\\n\",\n    \"url = 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'\\n\",\n    \"data = pickle.load(urlopen(url))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"72a7f0de\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Run the benchmarks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"5b8a0c33\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 1.83s, train_loss 1.158e-01, test_loss 3.517e-01, train_acc 96.5, test_acc 87.9\\n\",\n      \"step 2000, dt 1.79s, train_loss 2.384e-02, test_loss 4.325e-01, train_acc 99.0, test_acc 90.8\\n\",\n      \"step 3000, dt 1.84s, train_loss 3.321e-02, test_loss 4.907e-01, train_acc 98.6, test_acc 90.6\\n\",\n      \"step 4000, dt 1.92s, train_loss 6.015e-04, test_loss 4.824e-01, train_acc 100.0, test_acc 92.2\\n\",\n      \"step 5000, dt 2.06s, train_loss 2.361e-04, test_loss 5.045e-01, train_acc 100.0, test_acc 91.9\\n\",\n      \"step 6000, dt 1.96s, train_loss 1.257e-04, test_loss 5.231e-01, train_acc 100.0, test_acc 92.3\\n\",\n      \"\\n\",\n      \"Initialized GRUBase model with 5134 parameters\\n\",\n      \"step 1000, dt 22.64s, train_loss 1.753e-01, test_loss 4.227e-01, train_acc 92.3, test_acc 86.1\\n\",\n      \"step 2000, dt 26.65s, train_loss 1.234e-01, test_loss 5.126e-01, train_acc 93.8, test_acc 86.5\\n\",\n      \"step 3000, dt 25.23s, train_loss 6.646e-02, test_loss 4.873e-01, train_acc 98.8, test_acc 89.2\\n\",\n      \"step 4000, dt 27.12s, train_loss 2.809e-02, test_loss 5.349e-01, train_acc 98.9, test_acc 90.3\\n\",\n      \"step 5000, dt 27.44s, train_loss 1.306e-02, test_loss 5.751e-01, train_acc 99.8, test_acc 89.9\\n\",\n      \"step 6000, dt 27.66s, train_loss 4.650e-03, test_loss 6.483e-01, train_acc 98.2, test_acc 89.1\\n\",\n      \"\\n\",\n      \"Initialized MLPBase model with 15210 parameters\\n\",\n      \"step 1000, dt 1.37s, train_loss 3.303e-01, test_loss 2.090e+00, train_acc 91.2, test_acc 60.9\\n\",\n      \"step 2000, dt 1.50s, train_loss 4.399e-02, test_loss 2.830e+00, train_acc 98.5, test_acc 64.7\\n\",\n      \"step 3000, dt 1.57s, train_loss 4.762e-04, test_loss 2.989e+00, train_acc 100.0, test_acc 65.6\\n\",\n      \"step 4000, dt 1.40s, train_loss 2.352e-04, test_loss 3.098e+00, train_acc 100.0, test_acc 65.2\\n\",\n      \"step 5000, dt 2.03s, train_loss 1.353e-04, test_loss 3.195e+00, train_acc 100.0, test_acc 65.7\\n\",\n      \"step 6000, dt 1.51s, train_loss 8.083e-05, test_loss 3.294e+00, train_acc 100.0, test_acc 65.1\\n\",\n      \"\\n\",\n      \"Initialized LinearBase model with 410 parameters\\n\",\n      \"step 1000, dt 0.77s, train_loss 1.539e+00, test_loss 1.663e+00, train_acc 37.2, test_acc 32.1\\n\",\n      \"step 2000, dt 0.58s, train_loss 1.535e+00, test_loss 1.667e+00, train_acc 37.0, test_acc 32.0\\n\",\n      \"step 3000, dt 0.56s, train_loss 1.534e+00, test_loss 1.670e+00, train_acc 37.1, test_acc 31.6\\n\",\n      \"step 4000, dt 0.71s, train_loss 1.535e+00, test_loss 1.671e+00, train_acc 37.3, test_acc 31.3\\n\",\n      \"step 5000, dt 0.60s, train_loss 1.535e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.5\\n\",\n      \"step 6000, dt 0.58s, train_loss 1.535e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.5\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Note: if you run on a GPU, the walltimes will be _much_ lower\\n\",\n    \"\\n\",\n    \"args = get_model_args()\\n\",\n    \"args.total_steps = 6000\\n\",\n    \"args.device = DEVICE\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"results_cnn = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = GRUBase(input_size=1, output_size=args.output_size)\\n\",\n    \"results_gru = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = MLPBase(args.input_size, args.output_size)\\n\",\n    \"results_mlp = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = LinearBase(args.input_size, args.output_size)\\n\",\n    \"results_lin = train_model(data, model, args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"46b41980\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Shuffle the pixels\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\\n\",\n    \"shuffled_order = np.random.permutation(data['x'].shape[1])\\n\",\n    \"\\n\",\n    \"data_shuff = data.copy()\\n\",\n    \"data_shuff['x'] = data_shuff['x'][:, shuffled_order]\\n\",\n    \"data_shuff['x_test'] = data_shuff['x_test'][:, shuffled_order]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"1671e0af\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 1.80s, train_loss 5.503e-01, test_loss 1.630e+00, train_acc 73.0, test_acc 53.3\\n\",\n      \"step 2000, dt 1.83s, train_loss 2.484e-01, test_loss 2.053e+00, train_acc 83.4, test_acc 56.6\\n\",\n      \"step 3000, dt 2.02s, train_loss 4.203e-01, test_loss 2.405e+00, train_acc 83.3, test_acc 54.7\\n\",\n      \"step 4000, dt 2.01s, train_loss 2.079e-01, test_loss 3.117e+00, train_acc 90.7, test_acc 57.7\\n\",\n      \"step 5000, dt 1.95s, train_loss 1.932e-01, test_loss 3.580e+00, train_acc 90.4, test_acc 58.3\\n\",\n      \"step 6000, dt 2.01s, train_loss 1.257e-01, test_loss 4.385e+00, train_acc 92.0, test_acc 58.7\\n\",\n      \"\\n\",\n      \"Initialized GRUBase model with 5134 parameters\\n\",\n      \"step 1000, dt 26.13s, train_loss 7.978e-01, test_loss 1.129e+00, train_acc 72.8, test_acc 56.8\\n\",\n      \"step 2000, dt 33.21s, train_loss 6.272e-01, test_loss 1.417e+00, train_acc 80.9, test_acc 56.2\\n\",\n      \"step 3000, dt 29.63s, train_loss 5.342e-01, test_loss 1.577e+00, train_acc 84.4, test_acc 55.8\\n\",\n      \"step 4000, dt 31.62s, train_loss 5.860e-01, test_loss 1.956e+00, train_acc 82.8, test_acc 53.8\\n\",\n      \"step 5000, dt 34.38s, train_loss 4.179e-01, test_loss 2.077e+00, train_acc 87.8, test_acc 54.4\\n\",\n      \"step 6000, dt 29.49s, train_loss 3.777e-01, test_loss 2.377e+00, train_acc 87.1, test_acc 53.3\\n\",\n      \"\\n\",\n      \"Initialized MLPBase model with 15210 parameters\\n\",\n      \"step 1000, dt 1.36s, train_loss 2.199e-01, test_loss 1.870e+00, train_acc 89.5, test_acc 61.1\\n\",\n      \"step 2000, dt 1.45s, train_loss 7.136e-03, test_loss 2.435e+00, train_acc 99.9, test_acc 65.5\\n\",\n      \"step 3000, dt 1.48s, train_loss 7.508e-04, test_loss 2.566e+00, train_acc 100.0, test_acc 66.5\\n\",\n      \"step 4000, dt 1.47s, train_loss 3.654e-04, test_loss 2.728e+00, train_acc 100.0, test_acc 66.1\\n\",\n      \"step 5000, dt 2.15s, train_loss 2.058e-04, test_loss 2.878e+00, train_acc 100.0, test_acc 66.0\\n\",\n      \"step 6000, dt 1.98s, train_loss 1.162e-04, test_loss 3.023e+00, train_acc 100.0, test_acc 66.0\\n\",\n      \"\\n\",\n      \"Initialized LinearBase model with 410 parameters\\n\",\n      \"step 1000, dt 0.76s, train_loss 1.537e+00, test_loss 1.662e+00, train_acc 36.9, test_acc 32.3\\n\",\n      \"step 2000, dt 0.59s, train_loss 1.534e+00, test_loss 1.667e+00, train_acc 37.0, test_acc 31.6\\n\",\n      \"step 3000, dt 0.86s, train_loss 1.534e+00, test_loss 1.670e+00, train_acc 37.2, test_acc 31.4\\n\",\n      \"step 4000, dt 0.64s, train_loss 1.534e+00, test_loss 1.671e+00, train_acc 37.2, test_acc 31.3\\n\",\n      \"step 5000, dt 0.74s, train_loss 1.535e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.5\\n\",\n      \"step 6000, dt 0.57s, train_loss 1.535e+00, test_loss 1.672e+00, train_acc 37.2, test_acc 31.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"results_cnn_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = GRUBase(input_size=1, output_size=args.output_size)\\n\",\n    \"results_gru_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = MLPBase(args.input_size, args.output_size)\\n\",\n    \"results_mlp_shuff = train_model(data_shuff, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = LinearBase(args.input_size, args.output_size)\\n\",\n    \"results_lin_shuff = train_model(data_shuff, model, args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8f5781f8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Construct the figure\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"id\": \"c3d48048\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 438.75x303.75 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pylab as plt\\n\",\n    \"\\n\",\n    \"# Figure style (loading from the web to make running in a Colab easier)\\n\",\n    \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\\n\",\n    \"\\n\",\n    \"fig, axs = plt.subplots(ncols=2, figsize=(3.25, 2.25))\\n\",\n    \"\\n\",\n    \"steps = 6000\\n\",\n    \"eval_every = 250\\n\",\n    \"t = range(0, steps + 1, eval_every)\\n\",\n    \"\\n\",\n    \"axs[0].plot(t, results_lin['train_acc'], '-', color='tab:red', label='Logistic')\\n\",\n    \"axs[0].plot(t, results_lin_shuff['train_acc'], '--', color='tab:red')\\n\",\n    \"\\n\",\n    \"axs[0].plot(t, results_mlp['train_acc'], '-', color='tab:green', label='MLP', clip_on=False)\\n\",\n    \"axs[0].plot(t, results_mlp_shuff['train_acc'], '--', color='tab:green', clip_on=False)\\n\",\n    \"\\n\",\n    \"axs[0].plot(t, results_cnn['train_acc'], '-', color='tab:blue', label='CNN', clip_on=False)\\n\",\n    \"axs[0].plot(t, results_cnn_shuff['train_acc'], '--', color='tab:blue', clip_on=False)\\n\",\n    \"\\n\",\n    \"axs[0].plot(t, results_gru['train_acc'], '-', color='tab:orange', label='GRU', clip_on=False)\\n\",\n    \"axs[0].plot(t, results_gru_shuff['train_acc'], '--', color='tab:orange', clip_on=False)\\n\",\n    \"\\n\",\n    \"axs[0].plot(t, [-1] * len(t), 'k--', label='After shuffling')\\n\",\n    \"\\n\",\n    \"###\\n\",\n    \"\\n\",\n    \"axs[1].plot(t, results_lin['test_acc'], '-', color='tab:red', label='logistic')\\n\",\n    \"axs[1].plot(t, results_lin_shuff['test_acc'], '--', color='tab:red')\\n\",\n    \"\\n\",\n    \"axs[1].plot(t, results_mlp['test_acc'], '-', color='tab:green', label='mlp')\\n\",\n    \"axs[1].plot(t, results_mlp_shuff['test_acc'], '--', color='tab:green')\\n\",\n    \"\\n\",\n    \"axs[1].plot(t, results_cnn['test_acc'], '-', color='tab:blue', label='cnn')\\n\",\n    \"axs[1].plot(t, results_cnn_shuff['test_acc'], '--', color='tab:blue')\\n\",\n    \"\\n\",\n    \"axs[1].plot(t, results_gru['test_acc'], '-', color='tab:orange', label='gru')\\n\",\n    \"axs[1].plot(t, results_gru_shuff['test_acc'], '--', color='tab:orange')\\n\",\n    \"\\n\",\n    \"axs[1].plot(t, [95.8] * len(t), 'k-')\\n\",\n    \"axs[1].text(0, 91, 'Human')\\n\",\n    \"\\n\",\n    \"axs[0].set_title('Training accuracy')\\n\",\n    \"axs[1].set_title('Test accuracy')\\n\",\n    \"axs[1].set_xlabel(\\\"Training step\\\")\\n\",\n    \"axs[0].set_xlabel(\\\"Training step\\\")\\n\",\n    \"axs[0].set_ylabel(\\\"Accuracy (%)\\\")\\n\",\n    \"axs[0].legend()\\n\",\n    \"axs[0].set_ylim(0, 100)\\n\",\n    \"axs[1].set_ylim(0, 100)\\n\",\n    \"\\n\",\n    \"fig.text(0, .95, 'a', fontsize=8, weight='bold')\\n\",\n    \"fig.text(.5, .95, 'b', fontsize=8, weight='bold')\\n\",\n    \"\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"fig.savefig('figures/benchmark.png', dpi=300)\\n\",\n    \"fig.savefig('figures/benchmark.pdf')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/mnist1d-pip.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"733c5564\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# !pip install mnist1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"87fa18b4-7d57-46e5-aa42-694ac83ef4e6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((4000, 40), (4000,), (40,))\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from mnist1d.data import make_dataset, get_dataset_args\\n\",\n    \"\\n\",\n    \"default_args = get_dataset_args()\\n\",\n    \"data = make_dataset(default_args)\\n\",\n    \"x,y,t = data['x'], data['y'], data['t']\\n\",\n    \"x.shape, y.shape, t.shape\\n\",\n    \"# >>> ((4000, 40), (4000,), (40,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"dae1e6ec\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"((8000, 40), (8000,), (40,))\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"default_args.num_samples = 10000\\n\",\n    \"data = make_dataset(default_args)\\n\",\n    \"x,y,t = data['x'], data['y'], data['t']\\n\",\n    \"x.shape, y.shape, t.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"64f2699a-9642-4b5e-92a0-a13f0460b23e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"assert x.shape[0] == y.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8c0adc3c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/models.py",
    "content": "# The MNIST-1D dataset | 2020\n# Sam Greydanus\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass LinearBase(nn.Module):\n  def __init__(self, input_size, output_size):\n    super(LinearBase, self).__init__()\n    self.linear = nn.Linear(input_size, output_size)\n    print(\"Initialized LinearBase model with {} parameters\".format(self.count_params()))\n\n  def count_params(self):\n    return sum([p.view(-1).shape[0] for p in self.parameters()])\n\n  def forward(self, x):\n    return self.linear(x)\n\nclass MLPBase(nn.Module):\n  def __init__(self, input_size, output_size, hidden_size=100):\n    super(MLPBase, self).__init__()\n    self.linear1 = nn.Linear(input_size, hidden_size)\n    self.linear2 = nn.Linear(hidden_size, hidden_size)\n    self.linear3 = nn.Linear(hidden_size, output_size)\n    print(\"Initialized MLPBase model with {} parameters\".format(self.count_params()))\n\n  def count_params(self):\n    return sum([p.view(-1).shape[0] for p in self.parameters()])\n\n  def forward(self, x):\n    h = self.linear1(x).relu()\n    h = h + self.linear2(h).relu()\n    return self.linear3(h)\n\nclass ConvBase(nn.Module):\n    def __init__(self, output_size, channels=25, linear_in=125):\n        super(ConvBase, self).__init__()\n        self.conv1 = nn.Conv1d(1, channels, 5, stride=2, padding=1)\n        self.conv2 = nn.Conv1d(channels, channels, 3, stride=2, padding=1)\n        self.conv3 = nn.Conv1d(channels, channels, 3, stride=2, padding=1)\n        self.linear = nn.Linear(linear_in, output_size) # flattened channels -> 10 (assumes input has dim 50)\n        print(\"Initialized ConvBase model with {} parameters\".format(self.count_params()))\n\n    def count_params(self):\n        return sum([p.view(-1).shape[0] for p in self.parameters()])\n\n    def forward(self, x, verbose=False): # the print statements are for debugging\n        x = x.view(-1,1,x.shape[-1])\n        h1 = self.conv1(x).relu()\n        h2 = self.conv2(h1).relu()\n        h3 = self.conv3(h2).relu()\n        h3 = h3.view(h3.shape[0], -1) # flatten the conv features\n        return self.linear(h3) # a linear classifier goes on top\n\nclass GRUBase(torch.nn.Module):\n  def __init__(self, input_size, output_size, hidden_size=6, time_steps=40, bidirectional=True):\n    super(GRUBase, self).__init__()\n    self.gru = torch.nn.GRU(input_size=input_size, hidden_size=hidden_size,\n                            batch_first=True, bidirectional=bidirectional)\n    flat_size = 2*hidden_size*time_steps if bidirectional else hidden_size*time_steps\n    self.linear = torch.nn.Linear(flat_size, output_size)\n    self.hidden_size = hidden_size\n    self.bidirectional = bidirectional\n    print(\"Initialized GRUBase model with {} parameters\".format(self.count_params()))\n\n  def count_params(self):\n    return sum([p.view(-1).shape[0] for p in self.parameters()])\n\n  def forward(self, x, h0=None): # assumes seq has [batch, time]\n    x = x.view(*x.shape[:2], 1) # add a spatial dimension\n    k = 2 if self.bidirectional else 1\n    h0 = 0*torch.randn(k, x.shape[0], self.hidden_size) if h0 is None else h0\n    h0 = h0.to(x.device) # GPU support\n\n    output, hn = self.gru(x, h0)\n    output = output.reshape(output.shape[0],-1) # [batch, time*hidden_size]\n    return self.linear(output)"
  },
  {
    "path": "notebooks/mpl_style.txt",
    "content": "#### * LINES *\nlines.linewidth: .75\nlines.markersize: 4\n\n#### * FONT *\nfont.size: 6\n\n#### * AXES *\naxes.titlesize: 7    \naxes.labelsize: 6    \naxes.titlepad: 3\naxes.linewidth: .5\naxes.spines.right: False\naxes.spines.top: False\naxes.unicode_minus: True   \n\n#### * TICKS *\nxtick.major.width: .5\nytick.major.width: .5\nxtick.minor.width: .5\nytick.minor.width: .5\nxtick.major.size: 2\nytick.major.size: 2\nxtick.minor.size: 1\nytick.minor.size: 1\nxtick.major.pad: 1.5\nytick.major.pad: 1.5\nxtick.top: False\nytick.right: False\nxtick.labelsize: 6\nytick.labelsize: 6\n\n#### * LEGEND *\nlegend.fancybox: False\nlegend.frameon: False\nlegend.fontsize: 6\n\n#### * FIGURE *\nfigure.constrained_layout.use: True\nfigure.dpi: 135      \nfigure.facecolor: white    \nfigure.titlesize: 8  \n\n#### * IMAGES *\nimage.cmap: tab10             \n\n#### * SCATTER PLOTS *\nscatter.marker: .               \n\n#### * SAVING FIGURES *\nsavefig.dpi: 200           \nsavefig.facecolor: white\n"
  },
  {
    "path": "notebooks/quickstart.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Quickstart for MNIST-1D\\n\",\n    \"Sam Greydanus\\n\",\n    \"\\n\",\n    \"This notebook shows how to build the MNIST-1D dataset and train some baselines*.\\n\",\n    \"\\n\",\n    \"_* If you're running this in Colab, it's best to use a GPU runtime._\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\\n\",\n    \"    \\n\",\n    \"# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\\n\",\n    \"!git clone https://github.com/greydanus/mnist1d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using: cpu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import random\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('./mnist1d/mnist1d') ; sys.path.append('./mnist1d/notebooks')\\n\",\n    \"import mnist1d\\n\",\n    \"\\n\",\n    \"from data import get_templates, get_dataset_args, get_dataset\\n\",\n    \"from train import get_model_args, train_model\\n\",\n    \"from models import ConvBase, GRUBase, MLPBase, LinearBase\\n\",\n    \"from utils import set_seed, plot_signals, ObjectView, from_pickle\\n\",\n    \"\\n\",\n    \"# Try attaching to GPU\\n\",\n    \"DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\\n\",\n    \"print('Using:', DEVICE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 1: Visualize templates\\n\",\n    \"To construct MNIST-1D, we start with ten 1D signals, each of which resembles a digit in the range 0-9.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Templates for the MNIST-1D dataset:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x119.7 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"templates = get_templates()\\n\",\n    \"print(\\\"Templates for the MNIST-1D dataset:\\\")\\n\",\n    \"x = templates['x']\\n\",\n    \"t = templates['t']\\n\",\n    \"y = templates['y']\\n\",\n    \"fig = plot_signals(x, t, labels=y, ratio=1.33, dark_mode=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x119.7 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plot_signals(x, t, labels=y, ratio=1.33, dark_mode=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 2: Transform\\n\",\n    \"\\n\",\n    \"Now we are going to transform these signals in such a way that they become difficult to classify. Nonlinear representations and good spatial priors will become very important in order to classify them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'num_samples': 5000,\\n\",\n       \" 'train_split': 0.8,\\n\",\n       \" 'template_len': 12,\\n\",\n       \" 'padding': [36, 60],\\n\",\n       \" 'scale_coeff': 0.4,\\n\",\n       \" 'max_translation': 48,\\n\",\n       \" 'corr_noise_scale': 0.25,\\n\",\n       \" 'iid_noise_scale': 0.02,\\n\",\n       \" 'shear_scale': 0.75,\\n\",\n       \" 'shuffle_seq': False,\\n\",\n       \" 'final_seq_length': 40,\\n\",\n       \" 'seed': 42,\\n\",\n       \" 'url': 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'}\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"get_dataset_args(as_dict=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arg_dict = get_dataset_args(as_dict=True)\\n\",\n    \"\\n\",\n    \"arg_dict['padding'] = [36, 60]\\n\",\n    \"arg_dict['max_translation'] = 48\\n\",\n    \"arg_dict['scale_coeff'] = 0\\n\",\n    \"arg_dict['corr_noise_scale'] = 0\\n\",\n    \"arg_dict['iid_noise_scale'] = 0\\n\",\n    \"arg_dict['shear_scale'] = 0\\n\",\n    \"args = ObjectView(arg_dict)\\n\",\n    \"\\n\",\n    \"x = templates['x']\\n\",\n    \"t = templates['t']\\n\",\n    \"y = templates['y']\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"fig = plot_signals(x, t, labels=y, args=args, ratio=2.2, do_transform=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x198 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"args = get_dataset_args()\\n\",\n    \"\\n\",\n    \"x = templates['x']\\n\",\n    \"t = templates['t']\\n\",\n    \"y = templates['y']\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"fig = plot_signals(x, t, labels=y, args=args, ratio=2.2, do_transform=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 3: Make a dataset\\n\",\n    \"Next we are going to construct a classification dataset, analogous to MNIST but much smaller, using these signals. Note that the dataset gets saved as a small pickle file, so you don't have to build it from scratch every time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Did or could not load data from ./mnist1d/mnist1d_data_custom.pkl. Rebuilding dataset...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = get_dataset_args()\\n\",\n    \"data = get_dataset(args, path='./mnist1d/mnist1d_data_custom.pkl', download=False, regenerate=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Successfully loaded data from ./mnist1d/mnist1d_data_custom.pkl\\n\",\n      \"Examples in training set: 4000\\n\",\n      \"Examples in test set: 1000\\n\",\n      \"Length of each example: 40\\n\",\n      \"Number of classes: 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = get_dataset_args()\\n\",\n    \"data = get_dataset(args, path='./mnist1d/mnist1d_data_custom.pkl', download=False, regenerate=False)\\n\",\n    \"\\n\",\n    \"print(\\\"Examples in training set: {}\\\".format(len(data['y'])))\\n\",\n    \"print(\\\"Examples in test set: {}\\\".format(len(data['y_test'])))\\n\",\n    \"print(\\\"Length of each example: {}\\\".format(data['x'].shape[-1]))\\n\",\n    \"print(\\\"Number of classes: {}\\\".format(len(data['templates']['y'])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 3b: Download the baseline dataset\\n\",\n    \"It's always possible that the open-source libraries that we used to synthesize this dataset will change. Even if they change slightly, the dataset one obtains from running the code above may change slightly, making it hard for you and I to compare results.\\n\",\n    \"\\n\",\n    \"If you want to use MNIST-1D as a baseline, we suggest that you load it using the pickle file provided in the repo. The dataset contained in that file will serve as the one-and-only, original MNIST1D dataset. The link is https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl. The `mnist1d.get_dataset` function loads this file when `download` is set to `True`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading MNIST1D dataset from https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl\\n\",\n      \"Saving to ./mnist1d_data.pkl\\n\",\n      \"Successfully loaded data from ./mnist1d_data.pkl\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"args = get_dataset_args()\\n\",\n    \"data = get_dataset(args, path='./mnist1d_data.pkl', download=True) # This is the default setting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 4: Run baselines*\\n\",\n    \"\\n\",\n    \"We'll see that:\\n\",\n    \"* Nonlinear classifiers do better than linear classifiers.\\n\",\n    \"* Classifiers with transformation invariance and local connectivity (CNNs) do better than ones without these properties (MLPs).\\n\",\n    \"\\n\",\n    \"An additional note:\\n\",\n    \"* Humans do just a bit better than the other baselines (human performance is 95.8% on 500 test set examples). This suggests that shortcut learning and memorization don't help much.\\n\",\n    \"\\n\",\n    \"_*You may see slightly different numbers than the ones from the default run. This is due to the fact that you are using a GPU instead of CPU: PyTorch's random seeding function ``torch.cuda.manual_seed_all(seed)`` causes this difference. Your results will be reproducible between GPUs._\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'input_size': 40,\\n\",\n       \" 'output_size': 10,\\n\",\n       \" 'hidden_size': 256,\\n\",\n       \" 'learning_rate': 0.01,\\n\",\n       \" 'weight_decay': 0,\\n\",\n       \" 'batch_size': 100,\\n\",\n       \" 'total_steps': 8000,\\n\",\n       \" 'print_every': 1000,\\n\",\n       \" 'eval_every': 250,\\n\",\n       \" 'checkpoint_every': 1000,\\n\",\n       \" 'device': 'cpu',\\n\",\n       \" 'seed': 42}\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"get_model_args(as_dict=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized ConvBase model with 5210 parameters\\n\",\n      \"step 1000, dt 2.30s, train_loss 9.187e-02, test_loss 3.890e-01, train_acc 96.4, test_acc 90.3\\n\",\n      \"step 2000, dt 2.41s, train_loss 2.179e-02, test_loss 4.225e-01, train_acc 97.9, test_acc 90.4\\n\",\n      \"step 3000, dt 2.27s, train_loss 4.038e-03, test_loss 5.780e-01, train_acc 98.8, test_acc 89.8\\n\",\n      \"step 4000, dt 2.51s, train_loss 2.747e-02, test_loss 6.497e-01, train_acc 99.2, test_acc 91.5\\n\",\n      \"step 5000, dt 2.53s, train_loss 6.466e-05, test_loss 5.027e-01, train_acc 100.0, test_acc 92.3\\n\",\n      \"step 6000, dt 2.39s, train_loss 4.284e-05, test_loss 5.180e-01, train_acc 100.0, test_acc 92.3\\n\",\n      \"\\n\",\n      \"Initialized GRUBase model with 5134 parameters\\n\",\n      \"step 1000, dt 14.23s, train_loss 1.437e-01, test_loss 3.321e-01, train_acc 95.6, test_acc 89.9\\n\",\n      \"step 2000, dt 14.19s, train_loss 3.515e-02, test_loss 3.512e-01, train_acc 98.0, test_acc 91.4\\n\",\n      \"step 3000, dt 13.95s, train_loss 4.744e-02, test_loss 3.700e-01, train_acc 98.0, test_acc 90.4\\n\",\n      \"step 4000, dt 14.74s, train_loss 3.942e-03, test_loss 4.863e-01, train_acc 99.7, test_acc 90.8\\n\",\n      \"step 5000, dt 15.84s, train_loss 3.685e-03, test_loss 4.673e-01, train_acc 99.8, test_acc 92.0\\n\",\n      \"step 6000, dt 15.49s, train_loss 1.403e-03, test_loss 5.329e-01, train_acc 100.0, test_acc 92.1\\n\",\n      \"\\n\",\n      \"Initialized MLPBase model with 15210 parameters\\n\",\n      \"step 1000, dt 1.01s, train_loss 1.246e-01, test_loss 1.902e+00, train_acc 90.6, test_acc 59.6\\n\",\n      \"step 2000, dt 1.05s, train_loss 9.828e-02, test_loss 2.691e+00, train_acc 95.5, test_acc 62.1\\n\",\n      \"step 3000, dt 1.07s, train_loss 5.002e-04, test_loss 2.821e+00, train_acc 100.0, test_acc 63.5\\n\",\n      \"step 4000, dt 1.05s, train_loss 2.388e-04, test_loss 2.911e+00, train_acc 100.0, test_acc 64.2\\n\",\n      \"step 5000, dt 1.07s, train_loss 1.388e-04, test_loss 3.004e+00, train_acc 100.0, test_acc 64.1\\n\",\n      \"step 6000, dt 1.07s, train_loss 8.646e-05, test_loss 3.099e+00, train_acc 100.0, test_acc 64.6\\n\",\n      \"\\n\",\n      \"Initialized LinearBase model with 410 parameters\\n\",\n      \"step 1000, dt 0.33s, train_loss 1.537e+00, test_loss 1.664e+00, train_acc 36.9, test_acc 31.9\\n\",\n      \"step 2000, dt 0.35s, train_loss 1.533e+00, test_loss 1.668e+00, train_acc 37.0, test_acc 31.9\\n\",\n      \"step 3000, dt 0.35s, train_loss 1.533e+00, test_loss 1.670e+00, train_acc 37.2, test_acc 31.5\\n\",\n      \"step 4000, dt 0.32s, train_loss 1.533e+00, test_loss 1.671e+00, train_acc 37.2, test_acc 31.4\\n\",\n      \"step 5000, dt 0.31s, train_loss 1.534e+00, test_loss 1.672e+00, train_acc 37.3, test_acc 31.6\\n\",\n      \"step 6000, dt 0.31s, train_loss 1.534e+00, test_loss 1.673e+00, train_acc 37.1, test_acc 31.5\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Note: if you run on a GPU, the walltimes will be _much_ lower\\n\",\n    \"args = get_model_args()\\n\",\n    \"args.total_steps = 6000\\n\",\n    \"args.device = DEVICE\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = ConvBase(output_size=args.output_size)\\n\",\n    \"results_cnn = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = GRUBase(input_size=1, output_size=args.output_size)\\n\",\n    \"results_gru = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = MLPBase(args.input_size, args.output_size)\\n\",\n    \"results_mlp = train_model(data, model, args)\\n\",\n    \"print()\\n\",\n    \"\\n\",\n    \"set_seed(args.seed)\\n\",\n    \"model = LinearBase(args.input_size, args.output_size)\\n\",\n    \"results_lin = train_model(data, model, args)\\n\",\n    \"print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 615x450 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(4.1, 3), dpi=150)\\n\",\n    \"\\n\",\n    \"plt.subplot(1,1,1)\\n\",\n    \"t = range(0, args.total_steps+1, args.eval_every)\\n\",\n    \"plt.plot(t, results_lin['test_acc'], 'r-', label='logistic')\\n\",\n    \"plt.plot(t, results_mlp['test_acc'], 'g-', label='mlp')\\n\",\n    \"plt.plot(t, results_cnn['test_acc'], 'b-', label='cnn')\\n\",\n    \"plt.plot(t, results_gru['test_acc'], 'c-', label='gru')\\n\",\n    \"\\n\",\n    \"plt.plot(t, [95.8]*len(t), 'k-', label='human')\\n\",\n    \"\\n\",\n    \"plt.title('Test accuracy') ; plt.xlabel(\\\"Train step\\\") ; plt.legend(fontsize=6.5, ncol=5, loc='lower right')\\n\",\n    \"plt.xticks(fontsize=9) ; plt.yticks(fontsize=9)\\n\",\n    \"plt.ylim(0,105)\\n\",\n    \"plt.tight_layout() ; plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 5: Conclusions\\n\",\n    \"We believe this dataset is ideal for performing small-scale, \\\"science of deep learning\\\" experiments.\\n\",\n    \"\\n\",\n    \"Try it out, do something interesting, and share it with us via a Colab. We would like to feature your work in our README.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/self-supervised-learning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"4puOgIezTQsS\"\n   },\n   \"source\": [\n    \"# Self-supervised contrastive learning on MNIST-1D\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"yBMoRDRbQxMu\",\n    \"outputId\": \"9a68827f-b967-4de9-976b-45498dfba831\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using: cpu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import pylab as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"from sklearn.manifold import TSNE\\n\",\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"\\n\",\n    \"# Try attaching to GPU\\n\",\n    \"DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\\n\",\n    \"print('Using:', DEVICE)\\n\",\n    \"\\n\",\n    \"# Set the seed\\n\",\n    \"import random\\n\",\n    \"def set_seed(seed):\\n\",\n    \"    random.seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    torch.manual_seed(seed)\\n\",\n    \"    torch.cuda.manual_seed_all(seed)\\n\",\n    \"\\n\",\n    \"# Figure style (loading from the web to make running in a Colab easier)\\n\",\n    \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"id\": \"xQomfrzTXj8U\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4000, 40)\\n\",\n      \"(1000, 40)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load MNIST-1D\\n\",\n    \"# (loading from the web to make running in a Colab easier)\\n\",\n    \"\\n\",\n    \"from urllib.request import urlopen\\n\",\n    \"import pickle\\n\",\n    \"\\n\",\n    \"url = 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'\\n\",\n    \"data = pickle.load(urlopen(url))\\n\",\n    \"\\n\",\n    \"print(data['x'].shape)\\n\",\n    \"print(data['x_test'].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kNN accuracy: 59.6%\\n\",\n      \"Linear accuracy: 32.8%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Classification accuracy in \\\"pixel space\\\"\\n\",\n    \"\\n\",\n    \"knn = KNeighborsClassifier(n_neighbors=10)\\n\",\n    \"knn.fit(data['x'], data['y'])\\n\",\n    \"knn_acc = knn.score(data['x_test'], data['y_test']) * 100\\n\",\n    \"print(f'kNN accuracy: {knn_acc:.1f}%')\\n\",\n    \"\\n\",\n    \"lin = make_pipeline(StandardScaler(), LogisticRegression(tol=1e-2, max_iter=1000))\\n\",\n    \"lin.fit(data['x'], data['y'])\\n\",\n    \"lin_acc = lin.score(data['x_test'], data['y_test']) * 100\\n\",\n    \"print(f'Linear accuracy: {lin_acc:.1f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 628\n    },\n    \"id\": \"jrxWvKpFRMV3\",\n    \"outputId\": \"377c9e9a-255d-4bbe-b822-dddbe6e0e8b1\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Khx4XOSC2uw0gqxs1Gc4MxTYhEIp5cFUxkThV74rs2Bt0VW8gVH55g4VuHuOPbc8QXVA/mkEcVRdUyCqplWimrtRHYmlqYNgYdAG1Gjqet9pEcUBs5eZWN5FaMLcN7lq8d5zIryCitx9PWVKtU4f6ID+yOK+LFv+L5/nT2kF6XKpWKjw+ncc8PkQS7WPDn+QJe+TtBiOgMArIWBWczK7Sux2kj2MVyxBnJ+kpSUS21TXLm9HEO2sSdzulRXY5g5OiAtNK+eanV4gPaL8i2ReYR9up+pr6yj6Bnd3PbN+fG5MJDG5KLahnnYolY3PtLto0gZ/W8pYzh+gNdk1pSh5+DOZI+zAvAokAH5vjZ8cbupE49c/6KLuCBn6NYMd6JV66YQHppPdd+eopTY6BPSX84n6vOm9ZGWa0Nf0fzMSvKkVVej5kWEYmLaUuTHWvRnFm+asn3fQnFeGuZ4hfsZEFKcZ3WhkKTXMGzf8SyNTKPF/6MY8W7R3lme6zOnVMtCiVPb4vljd3J3LvIj633zOH5NSF8fSKLd/enCobOAIkvqKZJrmS2r/bZF6BeRyUX1QoRtUEgo7WxaqCT9pkWAE6WxrjbmBCVU6WDUfUPwcjRAanFdRgbaO+lDnG1JKGgpteHo0ql4vk/43jst2iWhzjx0Y1TeX5NCMlFtVzy3jE+P5o+GMMfVaQU17YbLdriY6/ODxfEB3RHaknfRAfaEIlEPL1qHBll9Ty5JYb6Jjmbj2fy8C9RrJ7owqbrp3DTLC92PDiPie5W3PL1WQ4mdY36jHXCsypxtDDCvQ+RNFNDKR42Y1OUo00GWZuIREfszI0IcrLg9BgRH2gj1MMKYwMx1Y0tWqlagtq5VNckJ1/LVJfdcUWU1TXz+z2ziX9pJU+uDOav8wWs//KMzgwNuULJXd+F83tEHq9fPZEnVwYjFou4bZ4Pjy4LZNOBVF79O1EwdAZAZpk66qlNJkxHgvt4/Qh0T3ZZPRbGUmxMta/HaSPQyUKvZKSlA9k5MjKSnJwcADw9PZk6deqgDGqkk1xc2ycv9XhXK2pkcs5kVjCrB+/FuaxKvjuVzdOrgrlrgW/7C/fGmZ5sPpHJRDfrwRj+qEHWoiCrvJ7b5vn0ab+2/PDkMbiYGwpUKhXpJXUsH+fYr/0nulvxv7UTeeGveA6nlFJR38xtc334z+px7fecuZGUb26dwf0/RnIoqZQlwT03lBtrhGdXEuZt0+dFe6CTxZisV9OmoWV3zPK1ZX9iySCPSL9pq8s5kVaOp5a/W3CrQlZyUS3uNr3v88PpbGZ427bvd/dCP8a7WnHTV2c4kVaudX+P7sgsqyeluJaaxhZWT3LB1FDK1yeyOJRcylcbw1g6rvMz5eFlAZgbS3llZwLmRlIeXR44oPOPVbLL67E3N8TCuG8L7OD2+uYaPHqRLBfomazyBnzs++7UAfBzMOPvmEIdjKp/9MvIiYiIYP369QB4eakbo2VlZQHw008/MW3atMEZ3Qgluahv0YMFgfZM9bTm/h8j+fOBud0+4L8+kYm3nSl3zvftdPEZG0i4b5H/gMc92kgrqUOpos+RHGgVHxCMHJ1QVCOjrkneZ09dR66b7sk0Lxte/CuBpeMcuXVuV0PW2EDCpxumIe7Hg3q4KK9ror5JgUlralR/XjK90disIL6gmstDXfu8b6CTOb+F5w36mPSd7PIGrfoJaSLUw5pvT2VT3dCCVT88oyOVWT52nEgr1zpdzd3GBAtjKedzq7oYEBeTWFjDuaxKNt0wpdPnc/3t8Hc055uTmQMycs5mVnD956doy3z6PTyPl68czzv7Urhplme347t9ng8lNTI+OZLONdPchcV2P8gqb9D6mumIq5UxlsZSEgprWDHeWQcjGztkl9f3SUmyI/6O5hRUy6hvkmNmNKA4yqDQr3S1u+66iw8//JCkpCT27NnDnj17SE5O5sMPP+TOO+8c7DGOKOQKJaklde267dpgJJXw2YYwjA0k3PFtuMY+ILkVDeyJL+KWOd59qi8Zy7TJkfY1r7Rtn+Si2hGbdqBSqfQibK/p90ttrXXy74Oymib8HS344Y6ZGg2cNgwk4j7X/QwXdU1yFr91mAVvHmL6f/dzx7fhOlEyi86rokWhIsy774t2tShHExX1Y6dpo6xFQVGNDO9+RnLanCxjrSP7ivHOuFmbEKylk0ksFjHP356jKaU9bidrUfDe/hTszQ1ZedFiViQSccscbw4klZBdXt/vsX9yOA1vezNOP72UX++aRXxBNZd9cBxLEylPrAzucd8HlwZgaWzA//2T2O/zj2Wyyvq3wBaJRIR6WBORrV99WkYiakOzf8+7NudlRmnn+6+xWTEsPe76ZeTU1dWxbNmyLp8vX76c+vr+P1hGA1nlDTTLlX2OHjhYGPHFzWGkl9bx8SF1bY1coeTnszlEZFfw7ckszAylXBPmoYthj0pSimtxsTLuk857G4FOFtTI5JTU9r2IdbgNo5zyBtZ/eYa5rx9kT3zRsIyhqqGZu78PZ9ZrB9gd1zl0fTazAnMjab/Tf0Yrf0TlU9sk56Mbp/J/V03kbGYFl394nLSSwc1vjsiuxMRA0i5f3xfaHAZjqS4np1UZTdu0q4vxd1SnLus6/VXWouCLoxnE5umHqmCQswUnnlqCnbmR1vssCHQgJr+6WyM6PKuCSzcd42BSCU+uDNbY4+mqKW6YG0n59mR2v8adUlzLoeRS7l7gi7OVMTN97fj2thk4WRrzf1dNxLKXNCpzIylPXBLErrgiTqaX9WsMYxWVSkVWeX2/F9gzvG2JzK5ErhCaq/eXWlkLZXVN/Y7k+Dm0qnCWXnjepZXUsvitw9z/Y+SgjLEv9MvIcXR0ZPPmzSgUF+SLFQoFX375Jfb2A8uDHem0RQ+09V51JMTVktvn+fL50Qyyy+t5aUcCT2+LZe0np/jyeCbrwjww14Pw38U0yRXsjS/io0NpPP9nHJ8dSR82NaHYvGr+PJ8PqGuj+hPFgQve14R+6O7f+V0EHxxI7dd5B0pSUQ2XvHeUzLJ6pnha8/S2WEr7YagNhKicSlZvOs7J9HJ87c2554dIHvvtfLvxtzehiMXBjhhIRqfuSVx+Nbf3UfFQpVLx45kcFgc5snqSCzfO9OSvB+chFol44KfITkpy2nAqvZwtEZrTysKzKpjiad2v39/XwQwDiYj4Pkrej2SyWpWG+pNCA+pIva+92aD2QlOpVJzOKOfpbTG8sjOB709lcen7x/jvP4k89tt5FCNUYWpBoAMqFRxL7RzNKa6R8div57nm01OYGkrY8eA81nXj8DMzkrJ+phffn87qV2PCz49m4GBhxJVT3No/C/O25fiTS3pNo2vjmmnuTPaw5p7vI/RKTlffqWxooVYmx8u+f/damLct9c0KQUp6ALT3BOunoWltaoi9uWG7cy4uv5prPztNrayFQ8mlQy6n369Vxrfffssvv/yCra0twcHBBAcHY2try6+//sq333472GMcVJRK9cuhP5Z+Vll9rxOUXFSDjakBDhbae6868uASf2zMDLjxizN8fzqbFy4L4ec7Z/Hw0gDuW+zXr2MONmV1TRxLLaVG1kJqcS1XfnSSu76P4ONDaRxPLeOdfSlc//lpTqQNrRerRaHkgZ8jeey3aHLKG0guqu2XsQngY2eGuZG0z17RuPxq9icW42qtvWrVYLIzuhADiYg9jy7gs5umoVKpeHpbzJBFl7ZF5nHdZ6exMzfkn4fm89OdM3l+TQjbIvOJzKlsLeat45Lxo1MIoLqhhXt/jOBAUglXf3xSawnrqNwqEgtruHGGZ/tnPvZmvHfdZFKKa/nsSM/KiflVjexPKCa1uJY39yRx45en+ffv0eyK7RxFUypVRGRXEubVv/oSYwMJE92sOKdnXa11SXZ5A4ZSMc6Wxv0+Rk89YJRKFZV9SP8rqpaxetNxrv/8NEdTytgdV8Rzf8ZjaWLAG2snkVpSx7bIkVk35WZtQoCjOUc6pKzlVzVy2QfHOZhcwitXjOeP++a2iw10x6PLAwh1t+bu78P71By4uEbGn+fzuXWuN0ZS7RpRakIsFvHNrdMJcrbgpi/PjEqFx+qGFj4/ms7itw6z4aszgxI9ySpvcyj0b4E92cMaqVg06gzL38JzOZ9bNSTnajNy+hvJAXU0J72kHpVKxcO/ROFsacyuhxdgYiDh926cb7qiX2EBX19f9u7dS1lZWSd1taGI4vwdU8j3p7P49KZpWJv2rWdBUbWMf/8ezfG0Mp5eFczdC7s3GpRKFY9viaGxRc7CQAdOppfzV3QBKpVaLWfleGcmulsxwc2q08MwsVV0oL8Fw2ZGUp65dBwP/3KeW+d6t9cbzPbrm2a8LpC1KPjqeCYfH0qjvlmBRCxCIhLhbmPC9vvmMNnDGpFIhFKp4tJNx/jwYFqfG3oNhN/Cc8kub8DcSMrruxMprJb1O5IjFouY4GZJTF5Vn/b79Eg6btYmXD6570Xdg8GxtDLm+NljaWyApbEB/71qIvf9GMmOmMJ+FZprS25FA5sOpPJ7RB5XTnbl9bWT2rtVb5zjzTcns/jqeCah7tYYSsQsDHTQ2ViGC5VKxb+3RFNV38JfD8zlv38ncvPmM+x5ZAG+Dj2LLPx4OgdXK2MWB3dWnAv1sOa2uT5sOpDGygkuGsUaVCoVd38fTly+ehEtFsEjSwOJL6jmiS0xjHOxxLvVM5pWWkeNTM40b9t+/53TfWzZEp6HSqXS6jn35bEM/oouoLS2iameNny0fmSpcGZXqBtaDqQWcpyLJYeTS1EqVe3HKamV8dKOBE6klVErk/PznbPam+l1R0mNjBu/OE19s5xvb5vBfH97xGIRdU1yzAwliEQi9iUW897+VC4LddW6Y7w+sTDQgT/OF6BUqqhrlnPr12cxkIjZ8eA8nLQ0NNV1rtO48uMTrPvsJBtmebFyggvmRlKsTAwwMdT8u/x0JgeJWMT6GV4D/jusTQ35/vaZ3PNDBA//cp7djyzAbZicX4ONXKHkhi9Ok1Jcy8JABw4ll/DOvpRe65UATqaV8e7+FCa5W7M02LFTw8m2Oqr+LrBNDCVMcLMiPKuyx1rNkURuRQNPbY3B1dqE/Y8t1Pk9nVVej7mRtM89wTri72jOmcwK4gtqSC+t56uNYXjambJ6kgtbwnN5eGnAkNXKDihfxN7enqlTpzJ16tQhMXBUKhXv7k/hdEYF9/4QSbNce89BXmUDq94/SlJRLTN8bPniWAaNzYput98SmcfWyDzSS+p5cmssJ9LKeWFNCG9cMwmFUsUrfyey9pNT3PjFmU6pAeroQd9z3TtyeagrOx+cx7OrQwZ0nMHmvf2pvLMvhbXT3NnxwDxevCyEh5cFsPOheUzxvCBHKxaLuG+xP6cyyoesCFDWomDTgVRWT3Lh3kV+/BOrrkXpj7JaG6Hu1kTnVWsdBckqq+ef2ELunO8zLKlY1Q0txOZVdVIVunSiC0uDHfm/vxOpb+p70Z+sRcHT22K467twNh/PpLhG1un70tomnt4Wy6K3DrM/sZgXLgvh3esmd3oQS8TqYuDdcUX8ci6Xuf52fZYHHQnsiS9mX0Ixb1wziUnu1nxz6wxMDCT8eCanx/3qm+TsjCnguumeGh/8j60IxMnKiEd/PU+TvOsza098EXH5NXxwwxR+vGMm/zw8n4eXBfDmulCszQy49ZtzHElRpwk8/2ccBhIRUzyt+/13zvSxpby+mfTS3usvT2eU8+rfidiZGXLlFDdWTtCt6tHuuEJu/+bcoAojZA+gCLeNIKeuPTw+OpjG0eRSbpzhyQRXSx799Tw1su6FJmQtCm788gx1TWqDaGGgQ7vBZG4kbX/+Pn5JEAXVjfzUy3WnrywIdKCsrokPD6Wx/oszFFbJ2HzLdK0NnDbszI344faZzPGzZ9PBNJa9c4RZrx1g9usHqNXwO7colPxyLocrQt0GTQXP2EDCe9dNxtRQwmO/jtw0wov55VwuCYU1/HzXLL66ZTqPLgvk48PpvLYrkQd+iuT2b87xyeH0Lim79U1yHt8SQ1GNjJ0xBdz45ZlOQhNZZQ3Ymhn2q462jRk+tpzLqhj22tiI7EpWvHuEG784zTPbY3u8t3vim5NZmBhIKKhq5JuTWYM7SA1kt8rlD0TZ08/BnKyyerZG5mFtasD8ALVT87rpHhRUyzg+hFk+Iyop/kxmBWklddy7yI9zWRU8/2ec1vv+b3cyErGIXQ/P539rJ1FR38xPZzW/BCrrm3ntn0RWT3Jhz6MLCH92GcefXMwtc324NsyD3++ZQ/xLl/DpTVOJyK7k6xOZgPoGzqlo6HeKVBsikYgJblZ6pQqlUqn463w+14Z58PIVE5jobsWG2d7cv9gfU8OuAcHVE13wsTfjo0NpQzK+r09kUVbXzL+WB3LzbC+sTAwQi/reUKwjk9ytKa1touiihX13fHY0HWtTQ66b7tn7xjrgVEYZShXMuyh69tyaECrqm/mwj3PRJFdwzw8RbI3Mp6yuif/7J5EV7x5lT3wRRdUy3t2XwuK3DrMzpoB/rwji+JNLuHWuj8aH47XTPTAzlJJZVs8lo1Te83ByCT72Zqya6AKovYprp7mzJSIPWUv3DpVjqaU0yZWsCXXR+L2poZRN108hsbCG1/5J6vSdQqninX0pzPK1Zc0kF+b627c7WaxMDPhq43QsTQzYuPksi946TFZZA5/fHNZr8XRPTPOyRSSi13qHhmY5T2yJYYqnNV9unM6TK4O5TEfRRIVSxeu7krjnh0gOJpfwn+2xg7bIySjtv5xqGxcU1tSLvlpZC1si8lg/y4snVgaz6YYpVDY088Kf8d0eY2tkHmkldXx96/QeI4OBThZcOsGF709nD/tCrz/M8LHF1FDCO/tSMJCI+OqW6f12VnnZmfHudZM5+8xSvr1tBptumEJNYwt/ROV32XZ/QjHFNU3cNGvgUZyOWJsa8va6yZzJrOD1XYl9rq8bChqbFdz1XThv7kmipJf3XXVDC2/vTeaKya5Mb40I37/YnyXBjmw+nkleZSP1zXLe25/Cpe8fb6+RBXh3XwqldU18d9tMTj21lFm+tjy9LZa6VgfcQPpRtRHmZUNJbVO7YMhwYWEsZYaPLZbGBmyPzOfVnQl9PkatrIVfz+WyfpYX10335KODaZTX6bbGtr8S3h3xdzRHrlTx89kcVk1waRcICfOywdfBjN/O5Q7GULViRBk5P5zOxsvOlMdXBPH0peP45VyuVvm2EdmV7Igu4LHlQThYGOFjb8YVk9349Ei6xsXH67uSaFGoeH6NOpJib27UJURobCBh5QQXNszy4q29yWSX17crDg0keqCvROVWUVAtY/VEzQuxi5GIRdy7yI+DSSXtYgyDiVyhRNnqFUsoqOHdfSlsmOWFr4M5FsYGPLosgIWBDgMK7U5ytwIgOlddl6NUqvjtXC7z3zjYRTEsq6ye38PzuG2ud7epELrmeFoZ7jYmXV4S3vZm3LXAly+PZWhdDN8sV3LfD5GcTCvn8w3T2HbfXM48s5QwLxvu/j6Cuf87yKdH0rlqihuH/72Iexf59aiJb24k5drpHohFaF28O5JQqVQcSy1jrn/ntNL1Mz2pbmzhn9jum6PtSyjB196sXZVGE1M8bXhqVTDfnMxi8/FMmuVKVCoVP53JJqW4jn+tCNJoXAY6WfDHfXP49KZpPLosgL2PLWBxUP+asLZhZWJAsLNlj3nvDc1y/rM9jqIaGW9eE6pzh83XJzL57Gg6j18SxDvXhrIrrojtGhayfaVG1kJ+VeOAn+nuNiaYG0lJahUy2R6VT2OLgvUz1Q4RLzszXrxsPNuj8vlWg7dWoVTxxdEMloc4Md7Vqtfz3TjTk8yyek5njLzaBGMDCdvvm8vxJxez7b65vabwaYO1qSELAx24PNSVJcFOfHeqqwH4w5lsQt2tmOje++/bV+YF2PP4JUF8eTyT1ZuO9TkNWte8uSeZw8mlbD6exdz/HWR/Qvc1RO8dSEHWouSpVRdS08RiEV/cHEb8Syv54/65/HLXbKJfWMHKCc48/Mt5nt4Ww4t/xbP5RCYPLfHHx94MsVjU7nB+fZdabnswFtjTWusNz2UNr5R0oJMFr145kU83TOOZS4P5LTyvU62ZNvx6LpfGFgUb53jz2PJAlCoVT2+Lbc9iCs+qIC5/cNUUswfB0GxzLstalFzRIXVfJBKxdqo7+xKLNUZTdcGIMXJKa5vYE1/EjTM8EYtFXDfdA2MDcScvgSZUKhWv/p1AkJMF14a5t39+/2J/yuuaurxQonOr+DU8l8eWB2oVHn9iZRC2poZs3HyWzSfUx+pvHYg+83dMIbZmhszy1f6Fc8VkV2xMDfi5m4jZxSiUKt7dl0J8Qc83rVKpYuPXZ1n41iF2xhTw4M+R+NibdXro3jLXh69vnaH1WDXhbmOCrZkhMXlVNDYruPazUzyxNYb6JgVv7E7ulHrw5t5kbMwMuW3e8OUBH08tY56/vcbF7n2L/XCxMuHxLTG9pkzIFUoe/fU8R1NL+XTDVBa1LortzI34cmMYb60L5fk1IZx9ZhmvXDlBa4nYf60I5Pd7ZvdblEOfyaloIL+qsUsUzd/Rgpk+tt2mrCmUKg4ll7AspHfD7/Z5Pqyb5s7LOxNY8MYhFr55mOf+jGfleOd2j6omRCIRKyc488CSgAFFcDoy08e2UyQnt6KBS98/xtK3D/Of7bEsffsIf8cU8tLl4wcUTdWGxmYFnx5JZ+1Ud+5f7M+Vk91YPcmFF/6MJ69yYN7cpFZFtJB+yG13RCQSEeRsQVKxuvfWtyezWDrOqVOzyHVh7tw824uXdsSz76JF5u64IrLKG7h3kXbiM7N97fC0NdX62atvBDlbdNsUe6BsmO1FakkdZzpcv8dSSzmRVj7oUZyO3L/Yn7/un4dKBY//HqOz8/SVs5kVfH0yk0eXB3Lq6SWEuFrx2VHNQie1shZ+OZvLbfO8cbHqXF8kEYs6SXq3peo9sNifg0klHEwq4ZLxzty14MI17GVnxuOXBPHD6Rx2xhQMygLbztyIYGcLvjmZ2WMEfShZP9OLWb62PLU1RuvFvUql4vvT2aycoO435WBhxFvrQjmYVMK9P0Tw4l/xXPPpKW744vSAekKpVCoicyr55kQmyUW1FNc0DdjQdLEyxtRQgrOlMTMuejddHupKs1zJ7rihaW8xIowclUrFpgOpiESidtlIcyMpy8Y58cf5/B5D8mcyK4jKqeKpVcFIO9RJ+Duac/0MTz44mNYusatSqXhpRzz+juZsmK3dw87C2IAvN07H086MHdEFeNuZ6kWX18FEqVSxK7aQlROcO/2GvWEklXD1VHe2R+Vr9bB57Z9E3j+QylNbY9ujNJrYEpnHibRybEwNeeCnKPIqG9l0w5RBL8gTiURMcrciJq+ajw+nEZ1Xxfe3z+CrjWFklNWzI7oAUEsm/x1TyGPLAzWm7g0FuRUNZJU3dNvl29RQyhvXTCIqp4rNxzO7PY5KpeI/2+PYFVfIe9dNYUlw58W3SCTimmnubJzj3ee8dVNDKdO8Bu6V1UeOp5UhFsFs366//40zPYnIruSMBln1qJxKKuqbWaZFdEskEvHmulD+eWg+8wLsCfO24cc7ZvLxMBTyT/e2Jb+qkbzKBtJL67j2s1PUyFqY4mnDkZRSxrtasvfRBdwwQ/epmz+eyaayoYUHFvsD6t/pv1dOwNRIwr9/j+7xWdIbSUU1SMSiQTHUgpwtOJdZwYM/R5FeWs/G2d6dvheJRLxw2XiWBDvy4M+R/Gd7LEdSSjmUVMIHB1OZ4WPLVE/tVPHEYhHXz/Bgd1xRn5TbxgLz/e3xtjPly2MZhGdV8PquJG7efJYZ3rY6S6dsY6K7FQ8s8Se5uHbABrgmYvKquO/HCK3rlWUtCp7YEs0kd2vunO+DtakhN8/y4lxWJRmlXftz/RVdgEyu0Pq+FotF/PuSIM48s4yjTyzmk5umdeltdMscby6d6Mxjv0VT1dCCTz/lozvyv7WTSCmu45lBTFsdCGKxiDfWhlJe38xHh3pWymwjqaiW7PIG1k69IGW+aqILn22YxrG0Mn46k8PjlwRhZWLAvT9EklhYw8O/RLH07cOs+/Rkj6UCeZXq+sw7vg1nwZuHuPrjk7y4I4FL3jsKMGBDUyQSsXScExs1NK/3sDUlzMuGv1rXT7pmRBg5Hx5M4/vT2TxxSRC2ZhcUH66c7EZ6aX2PPRsOJBZjZ2bIAg1qTv9aHohIBG/uUee5/xVdQGROFc+tCelT4XiIqyXf3TaDPY8s4NMN0/rwl40M2lLV1miZqtaRG2Z4UN3Y0qPVrlSq+P5UFl8ez+SyUFdi86vZ1c32Fa31UmsmufDn/XP5amMYX24M01mKYKi7NZE5lXx2JIPb5/kyP8CBKZ42LAx0YNPBVE6ll/PM9jj8Hc1ZN8299wMOkOIaGf/9O4FrPjnJ1R+fILmoFlmLgpd2JGAoETPHr3sBkFm+dmycrU6vfPaPWH48k91FjOC7U9n8Gp7L/101kdWT+j7fY5XjqWVMdLfWaPhdOtGFKZ7WPPzL+S751PsSi7ExNWBqH4QAQlwteWtdKO9cO5m5repaQ810H/Vi+5J3j7L07SNqadB7ZvPWulCOP7mELzdOb1d00yXqKE4GV0x27XQ+a1ND3rgmlNMZFWw+0b1R3xuJhTX42psNigNltq8dlQ1qwYZ7F/l1SW0EtTd80w1T2DDLi/2JxWzcfJZbvzlHRlk9jywL6NP5rpnmjlKlYusIlZPWFWKxiA2zvdmfWMI1n57ii2MZPLoskJ/unDkkanQLAx0Qi+BQct9Sl7Rld1wRn/YiOd/Gp0fSyats5M1rJrU7MC+dqFah0yT1+/PZHBYGOgxqlE0sFvH2usnttcwDrX8DtSrlq1dOYFtkPh8fTtcLQ8fTzpQ75/uw+XimVr1i9iUUY2Ig6fJOXzrOiT/um8s/D8/n/sX+fLJ+Gmkldax6/xin0suZ5WuHSCTi3X0pGgVYZC0K7vg2nB3RBTTJFczzd+Db22YQ/cIKXr96IteGuTPJ3XrAf+8HN0zpNvJ8xWRXTqSVUVKrXb3zQND7kMOv53J4e18KDy3x5475vp2+WxDogLWpAX9E5TPBTXMe7cGkEhYGOWjMCbczN+KRZYG8+ncCycV1JBfVsDTYsd/ytqOxFgfUvU/szAz7lRvt72hBmJcNP5/NaW+uplCqeG9/CoXVMmplLYRnVVJe38x1YR68vnYilfXNvL03mUvGO3WJHL25Jwl5a71Um7dAl4R6WNHQrMDFypgHl/i3f/7wsgCu/vgkN3xxGm87U96+NrRPUa7+8twfcZxKL2euvz2pJbVc+dEJAp3MSSys5cMbp3RyAmjiiZXB1DcrOJlezk9ncvj5bA6bN07H0dKYiOxKXv07gZtmeXL9EHjgRwsKpYqT6eXcNEvzb2YgEfPhjVNZvekYj/4WzTe3TG83TPYnFLM42HFIrp3BxNHCmBcuC6GqoQVXa2NWhDhj08u1pwt+PJNNRX0TDy7pagAsDHRg42wv3tidzCXjnTulhmlLYmEt4waYqtbGZaGurJ7o0qtRamoo5T+rQ3h61ThSSmoxM5TiYNG1LrQ3HC2MWTnBmc3HM9kw22tAfV9GGxtnezHF0xoTAwkOFkbYa5lyOxhYmxoy1dOGw0klbBjk9Dh1RMaXDw6msmqCMwE9pM7nlDfw8eF0bpvn0ynF3sRQwmWhrmyNyONfywPbn02xedXE5dfw6U2D78g1MZTw5c1hfHcqm/Gug3O/XRvmQWZZPW/uSSa/qpGXLh8/7A2o713kz2/heby2K5GP1/f8O+5PLGZBoL3G+z6kw2800d2Kj9ZPJaeigRtneGJiKKGkVsas/zvA3zEFbLgoYvzCn/FklNWz7d45XdbN188Ymnf/6kmuvLQjgR3Rhdyu4xR/vX6zltTKeHVnIldNcePR5YFdvjeUilk90YW/ogs0ykFnldWTXlrP0uDuF8I3z/biqslu+NiZcu9Cf964ZtKg/g0jneIaGb9H5LFhtle/F2LXz/BsV8YDOJJSwgcH00gqqqG6sYW109z5/vYZvHb1REQiEY9fEkRGWT3fnsrudJzK+ma2RuZz90JfHAfQmK8vTPGwwcrEgBcvH98pDXGqpw2vXz2RzbeEcfBfi4YkDSujtI59icU8uSqYTzdM488H5rFknCOpJXVsvmU6K7RQLTMzkqrzev+1iD/vn0dxTRNXfnSC6z47xYavzhDiasVza/RLulzfiS+oprqxhXn+3TtH3KxNeOfaUI6mlPLZ0QwAdkQXkF5ar7WYh75x61wfHl0eyHXTPYfFwKlvkvPJ4XSumuLebYrL4yuDkYhF/WpAp1Cq1C0BXAbPedWXqJtYLCLY2RIPW9N+RxgeXBJAQbWM38O7/v0DSeMb6UglYqZ62jDOxXJIDZw2Fgc7ciK9TCc1I48sC8TNWl1/2Z2Uukql4uWd8diYGvDQ0q4Oguume1BS28ThDtGmn87m4GBhxNJxAxMu6Q5HS2P+fUnQoBoiT64M5tUrJ/DruVxu+vIMRdW6jxz0hLmRlMdXBPFPbBER2d2LghRVy4jJq2Z5iHZKpMtDnLh9nk+76JGjhTFz/e27iK/sjivi1/BcXrgspNvAwFBga6YWAvn5bE63TWQzSuvaSwIGgl4bOa//k4RYLOLZ1eO61ezeOMeb6sYW/vNH19zLg0klSMUi5gd2n8JjIBHzznWTee/6KTy8LEDrIuqxwudHMzCUiLl1Tv+t7TWTXLA1M2xPG/nlbC6BTubseGAev9w1m2cuHcf8gAs9H0I9rNkwy4v/+yeRQ0kl7cfZGpmHUqni2ukeA/uj+oCNmSHnn1+uUfb4+hmeLAl2GrJ0oa+OZ2Jjasjaqeq0OHMjKR/dOJXI55Z3W4vTExPdrdh+3xyCXSyxNjXgrgW+fLUxTPD49pGDSSWYGkqY6mXd43ZLgp24Y54Pb+9NZndcEc/+Eccl451YEqybRcNo55uTWVQ3tvCwhkVaG+ZGUi6d6MLWiLxuF/XppXUaxThyKhpobFEMWiRnOAhytmD1RBc+OpTW3mOpurGF/2yP5ZFfzw/v4MYwi4MckbUoOa2hTm+gmBhKeOOaUJKKapj/v4O8uSepi2z1R4fS2J9YwrOrQzDXUEMc6m5FqLsV/9udRJNcQVJRDVsiclk/03PYoyF95aZZXnx/2wzSS+tZ9f5RjqXqJk1QW9ZOc8ff0Zz39qd2u83+xGLEIgb0brhqihuROVWdFIh/PJPNZA9rbtSDTI0HlviTVlLHz61y0kqlWgDh9V1JLHn7MEvePsJTW2MG7AjQ26v1TEY526LyefySoB4Nj0AnC165Qp17+ctF2tuHkkvadcoF+k5ZXRM/nslW950ZQHM0YwMJG2Z5sTUij8TCGg4klXD9dM8em029cFkICwLsuf+nSKJzq1Cp1Jrry8Y54WgxNFGcNgbSFGuwKK9rYktEHhtmeXWRqB5IHrm7jSmbb5nOZxvCeGRZ4LB4NUcK+VWNXaTDQe0dWxLsqJVx+PjKIIJdLLjnhwgMJGL+76qJenF9jTSqG1v47Eg668I88OylSPbaMHfyqxo5pWFBGZtXzdK3j/D+/pQu3yW2yj2PG2Bz5+HmoaUBFFbL+Ndv0fzfP4ksffsI2yLzmeRupRe1CmORcS4WOFkadXLiDSYzfGw59sQSbpzpyWdHMnh62wUn8PensnhrbwoPLPbvVmhBJBLx36smklFWz0cH03hiSwzuNqbcs1A7dT99Y46/Pbsenk+QswX3/RDZqSnvUCMRi3hwiT/HUsuIzNEsc70voZgwL9te0897YsV4Z4wNxPzRqkBc1NqE85pp7nrxzpniacNVU9x4Z28yqcW13Pjlaa7++CQ/n81hsrs1n6yfypn/LBtwnZxeGjlROZXc/UMEoR7WWql4XDvdg2vD3Hn+z7j2BmjldU2czigXvKQD4ItjGYgQDUrO5M2tanV3fBuORCTiqiluPW4vba1j8Hc058YvTrPpQBrppfXcOHP4PRDDwbcns1CB1qp/AoPPd6eyeOTX852a5WWU1pFUVMuqCdqlnBlJJWy6fgoBjua8tW6SEDnuJ9+cyELWouxUJ9cdM3xs8bIz5bfwrg3o3mgVndl8IouqhmbkCiWv7UrkUHIJSYU12Jga4GQ5sucoyNmCjbO9CM9Sq0BO97Zh32MLuGO+r14sdsYiIpGIZeOc+Du2UGOq/WDgYGHEf1aH8NrVE9kSkcdzf8Zx2zfneO7PeG6e7cW/VnQtAejIBDcrbp/nw6aDacTkVfO/tZOGRJhBVzhYGPHpTdMwMZTw798Gpro4UNZMcsXX3oxNB7pGc7LK6jmRVsYlEwbWNNvcSMqKEGd+PZdLXZOcP87nYyAWs0aPBIWeWBmErEXJiveOklZSz+cbphH+7DLeuW4yq1oFMAaK3hk5x1PLWP/lGbzszPjmlulaN5F7+YoJXDPNnef+iOPKj08y938HkYhFrNAyp1GgMwVVjXxzIouNc7wHZSFmZ27ENdPUHtVLJmhXpGxmJOXHO2YyxdOGd/en4G5j0qUPyVigtLaJL49nsn6mpxBpGUbuXeiHoUTMux3SDHbFFWEkFbMoSHuxEl8Hc/Y9trC9/5BA39k4x4uP10/F1dqk121FIhHrprmzO66IqoYLNQon08o4llrGc2tCaFEo+eJYBi/vTOCzIxnc9s05fjmXS7Cz5agwBF66YgKnn1nKiaeW8MlN03TWg0ZAe26f50N5fTO/nNNtL6N1YR48uiyQH07nkFFaxxvXTOLFy8ZrdV0/uiyQYGcL7l7oOyhNWYcba1ND3lwXyqmMcr44ltHl+/CsCv63O6nXXnIDRSIWcf9ifw4nl3ZpLfBWa8+9G2YMPC3/oaX+VNQ389TWGLZH5rN0nCPWpkNfP9kdLlYmPLtmHJdOcGHXw/NZMd550NMh9crIic2r5q7vw5niac1Pd8zsUzGrsYGE166exLvXhWIgFnHfIn8O/mtRr6kMApp5e28KpoYS7ls8eOHpO+f7YmEsZWMfohEWxgZsvmU6dy3w5T+XjhsWudzh5oODqYhFIo0KUgJDh7WpIfct9ue38Nx2EY3dcUUsCnIYdb2x9B1rU0OtGqi2sS7MAwOJmAd/jqJFoUTWouB/u5MY72rJrXO8uWmWF58dyeC7U9k8vyaE2+f6UFLb1EnFSEBgMPF1MGfNJFc+O5LRXi+lKx5a6s/OB+ex/7GFXBvmofV71MRQwj8PzefpVeN0Or6hZGGgA3fM8+G1XUl8cCC1PY0vt6KBu76P4GxmBXKldn2GBsIVk12Z7NG5tUB0bhU7Ywp5ZFnAoPTc83e04LWrJ7IzppDk4tpeM2iGg/Uzvfho/VSdNQnXmzdzXmUDt317Dl8HMz7fENbvRcNVU9y5aoru+5WMZhIKatgWlcdzq0MGtZ7J296M2Bcv6fN+hlIxz1w6eh6yfSG7vJ6fzuTwyLKAAeXnCgwOt8zx5tuTWTy9LYa1U92Jza/WuQSmwMBxsjTmo/VTue2bczz0cxTJRbXkVDTw3W0zEItF3LPQj9/Dc7lqihu3tc7nknGOBPUgwSsgMFAeWOzPJdFH2RKRx/qZuktFFolE/VbTGo2Oxf+sHoepoYS396UQnVfFpRNd+PRIOqaGEj7bMG1IxHekEjEfrZ/Kmk3HePiX89www5PPjqbja2/GdWGDJ6505RQ3zudWsT+xeExmD4hUw1h5aG1tTVVVFQDXfnaK/MpGtt83Z8jkgQUu0HEu7vwunOSiWvY/trBLd2IB3dNxLv71WzRHU0s58viiQfHsCPSNjnPRxr6EYp7eFkNZXTPGBmLO/meZIG4yBGiai77yw+lsnv0jjlAPa/63diLBHUQFGpsVXUQ9BDQzGHMhoObu78NJKKzhyL8X98ugEOai/2yJyOOLoxkkF9dibiRl671zBtTvsD9zcSSllFu+PotKpa4beu86dZPnwaZZrhyT6zm9MXIyy+qRK5Q9Nq8S0B0d56K4RkZhtYzJHtbDOqaxSse5KK9rIrOsnjDvkZ8PPRLp7qWlUqkoqJbRLFd226NFYHAZrMVcanEtvg7mWtd7CnRFWFgPHhmldciVqk4NOfuCMBcDp7C6EblC1a+GwR3p71ykFNdiaawWORkNNYD6hN64hoWFgv7gZGmMkxBN0wvszI0EBS49RCQS4aZF0buA/iE40gT0CV8H8+EewpjHxWp4n+X9NXAFemdYIzlisRhLS6GwU1cYGRlRXFys1bbCXOgWYS70B2Eu9AdhLvQHYS70B2Eu9AdhLvSHvsxFG8Nq5AgICAgICAgICAgICAw2Y68KSUBAQEBAQEBAQEBgVCMYOQICAgICAgICAgICowrByBEQEBAQEBAQEBAQGFUIRo6AgICAgICAgICAwKhCMHIEBAQEBAQEBAQEBEYVgpEjICAgICAgICAgIDCqEIwcAQEBAQEBAQEBAYFRhWDkCAgICAgICAgICAiMKgQjR0BAQEBAQEBAQEBgVDGsRo6Tk9Nwnn5UUVnfPKD9hbnQH4S50B+EuRg8amUtqFSqfu8vzIX+IMyF/iDMhf5w8VzUyFqGaSQCbQyrkdPU1NSv/XLKG/joUBqv7Uoc0EtztKBQqrjy4xO8sTup38fo71wMNy/vSOD+HyOHexiDylDPxe64QqobB/9h/Ht4LivfO0pOecOgH3uoGKn3hT7yxJYYbv3mXL/3H865UKlUrP/yNNd/forU4tphG4e+0J+5OJZaypzXDhCbV62DEY1dRtozSqlUkV5aR62shdyKBh777Txhr+4jLn/kXxcd5+JQUgmTX9pLUbVsGEckMOLS1T49ks6CNw/xzr4UPjuSQVx+zXAPadjZl1BMdnkDS8c5DvdQhhS5QsnWyDz+ji0ksVC4DvpDVlk99/wQyXcnswb92AeTSkgqquW6z0+RVVY/6McXGDkolCpOpJUx3tVyuIfSL3bFFXEirZyM0nou3XSMLRF5wz2kEUVkTiV3fRdBQbWMT4+mD/dwBPrAR4fSOJFWNijHkiuUPPBzJEvfPsLEF/ey4M1DHEkuxcLYgLu+C6e0dmQZbD0x3s0SpYpB++0E+seIMnIisit4c08yN8/2IuLZZdiaGbI1UnjZfHEsg6me1kzzsh3uoQwpEdmVVDe2YCQV88WxjOEezohkX0IxAEdTS7XeJ76gmrSSul63SyisYeV4Z4ykYm7efFaIuo5hYvKqqJHJmefvMGTnVCgH53qTK5S8tSeZef72HH1iMfMDHHj/QIpwPWtJXmUDt359jmAXC/61PJDdcUXkVzUO97AEtKCsrom39ybz9t7kAR9LqVTxxJYYdscV8fIV43l7XSivXjmBI08s5oc7ZtIkV3LPDxE0y5WDMPLhx9HCmCAnC06kC0bOcDJijJwaWQsP/XyeCa6WPLcmBGtTQy4PdWVHdAEtitFxU/SHiOwKIrIruWuB73APZcg5kFSCrZkhDy0NYEd0gc7CwspBWizpI/sSipGKRUTmVGmdP/zgz1E8vS2mx21qZS1klzewJNiRFy8fT05FAynFvRtGAqOT46llmBpKmOplrfNzxRdUc+vXZxn/wm4qBlirCLAlIo+MsnoevyQIYwMJN8/2IreikRgh7Uor3tyTjFQsYvPG6dwy1xsTA4lOIscCg8/e+GKUKojMqSJzgNH4TQdT2X4+n7fWhXLzbG/WTnNn/UwvzI2kuFmb8OmGaUTlVI4qh+UcfztOpJUJDpFhZMQYOR8eTKOyoZn3r5+CgUQ97KumuFFe38yxPnihRxufH83Ay86U5SHOwz0UWhRK/oou4LMj6Xx2JB25jo3P/QnFLA5y5KZZXhhKxHx9MnPQz1FW18Tkl/fyT2zhoB9bG06ll/P96Wytt1epVFqH/CvqmwnPrmDjHG8UShUn08p73SerrJ6M0noisiupauh+AZlUpK5bCHG1ZLq3LVKxiJOCR2tEsz+hmHNZFchaFH3e91haGTN9bDGSSnQwsgucy6pgzQfHicypQtaipGAQIgafHc1g5XhnQj2sAZjrb4+NqQE7YwoGfOzRTkxeFX+eL+CRZQHYmBliYWzAujB3fjqbQ12TfLiHJ9ALu+IKmeRuhamhhO0DyJpJKqrhw4Np3L3Aj6unumvcZrq3LbfM8WHTgVSyy3s3qFoUSha+eYi5rx/k7u/D2RNfpHfGxFw/e4prmkgvFdK1h4sRYeTIFUq2R+Vz9VQ3vO3N2j+f5G6Fr4MZ2yLzh3F0w8e3J7PYE1/MfYv8kIhFwz0cPjiYxkM/R/Hu/hRe25XEz+dy+32siOzKHhfrGaV1ZJTVs2ycI1YmBlw+2U0nhsjJ9HJqZHKe+yNuwAp2/eFoaimv7EzQunj/cHIpM/5vPwcSi3vd9kCi2kt353xfPGxNtEpZO5hUglgEShUcSel++4SCGiRiEf6O5pgZSZnsYc2p9N6NKAH9JC6/mju+C2fdp6eY9OJeDiWVaL1vfZOcqJxK5gXoPlXtQGIJdmaG/Hb3bABqBiiokVfZQGZZPWtCXdo/M5CIWTnBmb9jCkd1lLc/qFQqDiYV8/jv0fx8NodX/07E196M62d4tm9z21wfmuVK1n58kuQiQcRBX6msb+ZkejlXTXFj1QQXtkXlU1HfzB3fnuOTw9rXVckVSp7YEoOnrSmPLAvocdvHVgRia2bIs3/E9WqwnMuqILu8gZk+thRUybj7+wiu+vgkKXokDDLT1xaJWCTU5QwjI8LIOZleTmltE1dNcev0uUgk4uopbuxLKKZ+FHuFSmubuOHz00TlVLZ/tjuukBd3xLNxthfXhnkM4+jUlNTK+PJYBrfN9SHplVVcMdmV9/alUNsPCUVZi4INX53hzu/Cu01FPJBYgqFEzPxA9cJpurcNuRWNlNcNbuHiqfRynCyNaJYreWVnwqAeWxvuX+yPtYkBr/6t3bnDsytQqeCRX86TUdpzetj+xGImuVvhbGXMggAHjqaU9vpiOZRcwgwfWwKdzHtc6CYW1uDvYI6xgdpzP8fPjtMZ5YNWJyGgHTF5VXw+CIXeWyLysDMzZPt9c5jgZsn/didpvcA/k1lOi0LF/AD7AY+jN85lVRDmZYuduSHAgFUDz2RUADDTx67T52smuVJQLSMqt1LTbmOSwupGbvrqDLd9E87J9HKe2R7L2cwKnlwV3J59AeBha8rWe+fQolRy+YfHOZtZobMxxeZV9ykSLnCBfYnFKJQqVk5wZu1UN/IqG1nx7lH2J5bw1fEMrTM1fg3PJSavmv9dM6n9fdAd5kZSXr5iAsdSy9jai/N6f0IJ9uZGvLUulL8emMs3t06nurGFO78L15sooYWxAaHuVoKRM4yMCCPnj6h8PG1Nmepp0+W7lRNcaJIrOZY6Oi8ilUrFc3/EcSqjnNd3qSWi8yobeOTX81wS4szzl41HJBr+KM6mA6lIRCIeWOIPwL9XBFHbJOfTI31fYJ1ML6OhWcH53Co+OJimcZvd8UXM8rPD3EgKwJTWa+N8blX//oBuOJ1RzuIgR55ZPY5tUflDHo0wN5Ly1Kpg9iYUc1yLazw2v4YpntbYmRty9/cR3Rr/jc0KjqaUsXycWtd/QaADeZWNPeZd1zfJOZNRwdJgJxYHO3IkpbRboyWhsIaQDkpas/zsqJHJSSgQVPCGkq+OZ7LpgOZ7SBPFNTLu+i68k7OgWa7kz/P5XD7ZlSmeNvx7RRBJRbUc0DKaczSlDCdLIwIczfs8/r4ga1EQk1dFmLcNViYGwMCNnNMZ5fg7muNgYdTp85k+ttibG7IjenjSWPWNhmY5t38TTmpxHZ/eNJXjTy4m/D/L+P2e2awI6drHZYKbFTsfnEeQswXP/RGnk9RmpVLF41uieWVHgs5Tp0cju+OKmOJpjYuVCbN87fCwNcHEUMxb60Ipq2vmVIZ278K/YwqZ62/HdG/thJGWhzixZpILL++Ip7jmQp1tTF4Vd34XTkJBDSqViv2JxSwNdkQsFiESiVgU5MiXG8MorpHx0l/x/fqbdcE8f3tOZZQL1+AwofdGTkOznN3xRVw5xU3jYt7PwQwfezP2a5GeE5VTqXc5m72xI6aQ3fFFXBbqypnMCs5mVvDaP0mYG0l5Y90kvUhTyyyr5+ezudyzyA9bM7UH1cPWlFvnevPlscw+R1f2JZTgZm3CA4v9+fBgKhHZnb2lmWXqmpArJ7u2f+ZtZ4q1qcGgGjlF1TIyy+qZ7WfH9dM98LIz5ffw/qfg9ZcrJ7sxxdOaN3tRuFGpVMTnVzPb147PNoSRW9nAyzs0R4D+ji2ksUXB6knqNJw5fnZIxaIenQXH08poVihZHOzI4iBHKhtaNP7ecoWSpKJaQlwuGDlTPW0wlIo5lTE6nRH6iEql4mR6OXVNcq1fsB8fSmNvQjHboy54UQ8ll1DZ0MLa1lz62X52TPW05sODqVo9TyOyK5nta6dzZ0xMXjUtChXTvW0xkIgxNZT0ychJK6nroux0OrOc2b52XbaVSsRcFurK9qj8ftUojSaUShX/+i2ajLI6Nt8ynZUTXBCJRNiZGzHd27bbeTc1lPLS5eNJLq7lp7M5gz6uv2MLSSqqpVmhHHDR/EhHpVIRkV2ptUiTUqniZHoZy1qdYGKxiG33zmXXwwtYO9UNdxsT/jrfe01arayFs5kVLAnuW8PSly4fj4FEzNPbYtmXUMzLOxK48qMT7E8s5pntsaQU15FT0cCyiwxoPwdznl8znt8j8lj36UmWvHWY//6dMKxppXP87amVyUkQ2lwMC3pv5OyNL6ahWdFpQdsRkUjE0mBHDiWV9JgKE5FdwVUfn2yXzB0J1MpaeOHPOFaEOPHedZPxczDjiS3R/B1byBMrg7E0NhjuIQLw3aksLI2l3DbXp9Pnl4e60iRXktWHZpBKpTqne3mIEw8vC2CciyUv7YjvtJjaGpGHmaGElRMuiC2IRCJC3a0H1chpW5C3LdAuD3VlT3wRjc1Du6gRi0WsCHEmtbi2x0VlYbWM8vpmJrpZEeRswbOrQ/g1PFdjrdKv53KY6WOLr4Pau25hbMBEdytO9+CdO5RUgqetKX4OZkzzssHCWKoxZS2jrJ5mubJTJMfYQEKYlw0nhzgS9sKfcWw6kDqk59QXUkvq2uvatEnfKK1t4pdzuRhIRPwVfWEBszUij2Bni/YeN6LWiG10XnWvEXS5QklKcW2na0FXnMuqwNRQ0j5OKxMDrY2ckloZq94/2knZKa+ygdyKRmZpMHIAbprlRXVjCzuix7YAwXenstgVV8Tb6yYzwc2qT/tO8bTh6qluvL03ZVBrHuUKJe/uT2Fi63gSx3jtz+8Reaz95CSXfXCciOze0wPzKhuRtSgZ52LR/pmDhRHmRlJEIhGXhbqyO76IJvmFd2FJjaxLP7RjqWXIlSqWBPeth5+duREvXzGBg0kl3PldOD+dzebhpYFs3jid87lVPLElGiOpmHn+XVNgb5jhwQOL/bE2NWSyhzVfHMvk+b96r/HRFZM9rJGKRV2ctQJDg94bOYeSSxjnYtm+GNPEshAnyuubOd9DfvSfrV6Hwz0USw+UPfFFbI8avL49J9LKqGxo4alVwUjE6oVFVnkDoe5WXNONQslQo05lKeCKyW6YGHbOt7UxVUd1elLhupi4gmqKa5pYNs4JA4mYJ1cGE5NXzd5W41SpVLEtMo9LJ7pgaijttO9kD7WR05PXJr20jhf+jCMmr6rXsZxKL8fXwQxHS2MArpjsSn2zgoN9KLoeLJytjGhoVlDbw2I1trVjdNtCY/1MT5aHOPHU1phOUrppJbWcy6rk+hmda7lm+thxJrNC48tAbXyWsCTYEZFIhIFEzGxfO4359G0paeNcOi9s5/ipt+/4YtQlcoWSP84XDNn59I2OeeA1jb0bOV8ez0AiFvHs6hBi8qrJKK0jt6KBg0klrJ3q3skjvzjIkQlulrx/oOdoTlZ5A01yJUHOujdywrMqmOJpjbS1/qMvRs72yHxaFOpnS9vf016P46s5zcbPwZy5/nb8MMZrPtZOc+f96ye3R4X7ylMrg2lsVvB7xOBFybdH5ZNRWs8rV07A3tyIpDHsRa+RtfDG7iTm+NkhFolY+8mpXtcpbcX7AY4WGr+/PNSVWpmcI8nq9VRFfTNXfXySyz48TnqHWtCDSSX42quzbfrK6kku7Hp4PieeWkL8Syt5eFkAi4MdWR7iRHReNfP87busOUDthPn3JUF8cXMY71w3mVevnMAPp3N44Kcociu0d7gOFsYGEsa7WQlGzjCh90ZObF41Uzyte9wmzEudg70/UfPiU65Q8k9sIRKxSKvi6r4ia1Hw9LZY7v4+gqe2xg44D7yNIylleNqatht4l01y5c75Pry5LhSxHqSpARxMKqaivpl1YV2NLmtTdaSpskH732N/QjEWRlJm+KgXFvMD7Jnhbcs7e1NQKFWcyiinoFrG2mldzzfZ05pamZzMHuQnX/gznm9PZXP5hyfYuPlsjwvgUxmdU1X8HS0Y52LJn+d7Loi894cIPjqkfR2ENji1GlrFPfQCisuvxtrUAHcbE0D9sH9+TQg1MnknY+SXs7lYGktZNaHzomSWry0V9c2kamj0GZtfTUltU3v6AkCQs0WnF1obiYU1OFsat6cutrEoyJGGZkX74lHXROdVU93YwsLAvnkRRwsn0soxa10E9NYDqbqhhR9OZbN+pifrwtwxNZTwR1Q+//otGgcLI66d3tkgFolEPLw0kIjsyh6jOW3qWeOcNS+WBgulUkV4diVhHRoiW2pp5KhUKn4Lz8XZ0pj00nriW4300xnlBDiaY29u1O2+G2Z5EZ1XrZXTZLRiYWzAFZPdet+wGxwtjQnztunxOvr8aLrWrSIamuW8tTeZFSFOTPawZpyLRbuk/Vhk0/5U6prkvHHNJP56YC5XTXHjiS0xnOkhap9SUouJgQQ3axON3wc7WxDgaM7ru5M4mV7G/T9GUt8sx9bMkDu/C6dG1oJSqeJwcgmL+xjF6cg4F0vcrE06peU/u3ocJgYSLp2onVF90ywv3rk2lNMZ5Sx9+4jWabaDyTRPGyIHYOS8tiuRvfFFgziisYNeGzm1shYyyuqZ1EsIXCoRszjIgf3dpKKdzqigrK6ZO+b5kFfZ2Kf0KW149e8Etkbm8eiyQJoVyk7pC3KFkpi8Kg4mFfdJ8UOlUnE0pZSFgRdkV6USMf9ZHUKgk24XDH3h9/A8QlwsGe/adY5MDCQYSsV9iuTsSyxhQZADhlL1pdnmlUkuruXu78N5/s84PGxNmKGhiHGyuzUA53OqNB77WGopx9PK2HTDFN5eF8qRlFL+iNJssLSlqsz265yqcsVkVw4nl3a7eKqVtbA/sbhXFZm+4txq5BTVdG/kxOZXM8HVqpPH3d3GBFszQ+IL1FGeZrmSbVH5XD3VvcsYw7zVcpeaUtYOJHY2PgH8Hc0pr2/ukmaSXFxLkIZF7XhXS5wtjbWqnxsMjqSUYmEk7dVJMhqRK5ScyShnSatR2puU8u74QuqbFdw+zxdTQykrQpz4+HA6Z7MqeGtdaHshf0eWjXNkopsV7+5P6XbRkFRUg62ZYZfC/cEmpaSWWpm8U3GztpGcyJwq0lu9/jamBmyPyqeyvpnDKaXdpqq1sWycE06WRnx3amxHcwbK/AAHzmZq7sEka1Hw1t4Udmop8vDZkQwq6pt5+tJxgHpBrs+RnLzKBp01sk4oqOGbk1ncu9AfdxtTpBIxr6+dyBRPG+76PoLCas19pNKK6whwMu/WmSoSiXj3usmYGEi48YsznM2q4KMbp/LVxjBKaprY8OUZvjiWQVldc59T1XrDy86M088s5eqp2hvWV09158gTi9k4x4u39qZ0K2ikK6Z52VBQLetX366U4lo+O5LB50dHT5PUoUSvjZy4fPWDaaJ773m+y0OcSS2pI62kq8dmR3QBbtYm3LfYvz2aM5icTCvnmmnuPLwsgPkBDu3F6b+czSH0pb1c/uEJbvsmnKkv7+PBn6O0KgJOL60jv6qxk5Gjb5TUyjicUqoxigPqB6G1iQFVWkZyCqsbSSysYelFD8UZPrbcMMOTwmoZ7jam/OfScRofvjZmhnjbmWqsy1EqVfxvdxKhHtZcNsmFtdPcWRrsyGdHMzSmtx1JKUUsUjfz6shloa40K5TdNgI8kVZGi6LvOci94WzVauR08zJUqVTE5Vd3yYkXiUSMd7VsT2WLzKmkor6ZKzTUuJkbSZngZqUx0nKx8QnqdB2gSzQntbiOQKeu6aUikYhlIY7sTygeEk/akZRS5vrbd5KvHSvE5ldT2yRnVWvdWm+L/YNJJYS2yokDXDHZDblSxe3zfJirIe8d1PP5yLIAonKqONqNFz6xsJZgZwudiw4cSirFUCJmcgeD1srEQKs+OVsi1FGcJcGOrJ7kwo7oAu77MZJmuZLb5/n0uK9UImbjHG/+iMofllSY0cL8AHua5ErOZXV99kTmVNIsV1KphbOssLqRz46mc/Ns7/YUqWBnSwqqZVRr8R6KyK5k4ZuHhrRx8UeH0rnm05OD/kyskbVw348R+DqYcfdC3/bPjaQSNl0/herGFsKzNEcXUkpq8e9FDXGCmxV/PTCPN66ZxAc3TGGuvz3+jhZ8cXMYcqWK13apBZK0VVXrC1YmBn1+ppgbSfnP6hAeXhrAO/tS+qX82l+melkD6mu5r3xzMguAiJyeewcKaEav3/6x+VUYSsVaRS6WjnPE3EjaSRUI1J7rXXGFrAl1wcrEgKme1oNq5FTWN5NRVt8ub31tmDvRedX8eCabZ/+IY1GwI7/cNYt9jy7goaX+7Igu4G8NheDldU2dlE+OpJRhIBF1iSToEz+czkEiEvWYqmBjakhVo3aRnLb83gUaDLvXrp7I3w/N59vbZrByQvdh6imeNhxLLe3iEfz5XA5x+TU8tTK4/eF4zyI/Mkrr2achsnAkuZRQD2tsLkq5crM2YVGQA9+ezNL4UjqUVIq3nWm/cpB7wtRQioWxtJOkZkeKa5ooq2tuL7TtyAQ3K+Lyq1GpVJzJqMDcSKpxO4BZPraczijv9LflV6mNz2XjOhtuvg7qvzGtQ3pbXZOc/KpGArq5Z5eNc6KgWtaeEqQrKuqbicmrYmGQ/joJdMmJtDJMDCTtTpKe0tWa5UqOp5axKOjC/C4KcuDb22bw5MrgHs+zJNgRH3sz/onR7GVPLq7RGNUbKCU1svYorFKp4tdzOVwywbldUh60i+TIWhTsiC7k6qluSMQirpzsRkltE+eyKvjkpqmdmk93x82zvTE3lvKxlg0St0Tkcen7xyjpISrbEblCyfensykb5B5g+kSIiyV2ZoYaU9ZOt4qVaOMs++xIBsYGEh5acqHpZHBr8XxSUc/PnCa5gie3xpBT0cBd30W0R791iVKplkJeHuI0qI4AlUrFE7/HUFrbxCc3TesStXe0MEIsQqPhqFSqSCup02rdJRGLuDbMo1Pq2Gw/O3Y+OI+t985h8y3TOznG9IFHlgVw7yI/Xt+VxAdDJErjYmWCm7VJn+tyqhta2BaZx/Wt6cIHkwY/C2KkKQ73Ff26+i4iJq+aEBdLrTyxxgYSLp3ozB9RBZ088yfTy6iRyblsktpzPT/AgVMZ5V2kQvtLW9SgLSVmeYgT1qYG/Gd7HBPdrXj32snM8rUjwMmCB5YEMD/Ang8PprWPsVbWwmO/nWfaq/sJenYXC988xJ/n8zmSUkqYly1mRtJuzjy8lNY28eWxDG6a5dWl9qIjVqYGWtfkHE4uJdTdqscc+N64ba4PBVUyXtpxQSf/UFIJz/8Zz7pp7p2MxjAvG6Z6WvPpkfRON3qzXMmJtLJuo2i3zfUhpbiOE2md07pUKhWHkks6LRYHE2dL427T1S6IDnQt8J7gakVZXTMltU2czignzNumvTj7Ymb52lFe39zJcDmYWIxELGLxRX+XqaEUN2uTTpGc1NaC1e5ekLP97DAzlOg8Ze1YaikqlWaDeSywM6aQhYEOmBlJMTYQ9yg8cC6rgvpmRafoo0gkYmGgQ68LFJFIxFx/O05qkAavlbWQW9HIOB2IDvwekcdjv50nLr+a0xnlZJU3cMNFdUPaGDnnsiqoa5K3L9KmedmwepILb1wziTl+2jUvNTeScud8X34PzyWvsudoTnhWBU9viyGhsIZ//R6tlbTtr+G5PPdHHLd+fY6GZv1ocjjYiMUi5vrbazRy2hQZtYnknEovZ9k4J6xML6RX+juaIxGLSC7uuS7n40Pp6nYId87Cw9aUW74+161TabA4n1dFaW0TK0Kce9+4D/wVXcDu+CL+d82k9oh7R8RiETamhp0EadpoU1bTFI3XFpFIxDQvm07pzfqCSCTiiUuCeGiJP2/vSxkyQ2eqV9/rcn4Nz0GuUPHIskAme1gPujrwoaQSJr+8j5xBLuHQJ/TayInNr2aSFqlqbVw1xZ38qkbOdCiy3pdQjJu1Sbus6IJABxqaFVp1Wf7oUFqv20XlVGJlYoCPndrjZySVcP10TxwtjPhk/bQui4QHlwSQWlLH7vgiDiQWs+r9Y+yOK+I/l47jpSsmEOBozsO/nFfX4wyzF1qlUnWbWvfBwVTEHZp/doeNqYFWaQItCiXH08pYOEADYaK7Fc9fFsLPZ3N5Y3cS7+xL4b4fI1kQYM//XT2x07YikYi7FvgSlVPVScM+IruS+mZFt8bK/AB7/B3N+fpEZqfP4wtqKKltGvRUtTacrYwpqtbszY0vqMbCWIqnrWmX79qiNpHZlUTmVHbp3t6RMG8bxCI6ST3vTShmmpcN1qZdjVk/R/NOBlFqsfr/u2v8aCSVsDDIQedGzpGUUgIczbstnB3NJBbWkFRUy1WtOeuWxgY9RnIOJpVgb27YbXSvN+b42ZNb0dglXatNoSnYZfAjOXfO98XPwZwnt8bw/elsvO1Mu9TPWJkYUCOT9+ipPJJcir25UXtPJ5FIxEc3TuXqPqpX3jzbC3NjKa/sTKC2m9+6sLqRe36IYKKbFZ/eNJVjqWVsvugZcjG1shbe2ZvCDG9b0krquO/HSF7dmcCStw9r1Rx4JDE/wJ7EwhpKai8YFvVNcs7nVmFrZtirs6y6oYWUklqme3duGm4kleDnYEZioWYjp7qhhc+PpvPx4TTuWuDLLF87vr11Oi0KJf9rbcCtK/bGF2NtatBlzANBqVTx4cE05gfYs2aS5tYboBYG0hQdSy3pWVltNCASiXhsRRD3LPTjvQOpGsscBptpntbEF9Ro3YJCpVLxw+kcVk5wxtnKmBUhzhxLLRtUR8f3p7OpbmzhhWGU2NY1emvkVDe0kF3e0KcX70wfW9ysTdrlEdu64i4b59geCp7kZoWnrSm/9tLU8VhqKW/uSWZLL7KWUblVTPaw7lQj8sQlQRx9YnF7fntHZvjYMsPHlkd+Pc/t34bjamXC3w/N584FvmyY5cUXN4fx/vWTCXW3YrWW6iG64j9/xHHd56e7XPxZZfX8dCaHexb69hjFAbA2MdTKAxeeVUldk5xFg2DYrZ/pybpp7nx8OJ1vT2axINCej9ZP1RgRXBzsiIWRlN1xF5RLDqeUYGNq0O21JxKJuG2uDweSSjo1mTucXIKJgURn3isnS+NuPYvxBTWEuFhqTHnwsDXBwljKj2dyaJIru5XEBbVS0lx/e7ZGqu+h/KpGTqSVdXst+jmYkV564TdIKa7Fzdqkxwjk4iBH4vJrNHoRBwOVSsXJtHLmBWjniR9pJBXVsP7L0902ev0jKh8rE4P2e8myl9qUQ8klLAx07LdiY5txceqiHkhJRbWIRLpZLBlK1QXUCYU17Ior4voZnl3Gb2VigEKp6lHw5UhKKQsC7QesVmlhbMCzq0M4lFzK4rcO87eG9L3n/ogHRHx60zRWTnDhljnevLE7ucdowUeH0qltkvPOdaF8cMMUjqaUsiUyj4r65l7fTSON+QHq6/VgB5XUc1kVyJUqLhnvRFVDc48LscicSlQqtYDKxQQ7W3IstZTrPz/FxBf3cP3np3hqawzXfnqKma/t543dyVw2yZWHl6rT3BwtjXlseSDbovK71FFkldUPWq+0vQlFLAl27Day3h92xxeRWlLHA4t7dkDammmO5KQU1/WorDaaeGRZAM6WxvzfP7o1ZkGdSi9Xqkgo1C4NMrGwlpyKhvYmzMtDnGiSKzmaMjjOjZJaGUdSSpkfYM+h5FL2jFL1Nr01ctrSbya1KmZpg1gs4qopbvwTW0StrIXY/NaeKx264orFIm6c6cnuuMJui7haFEpeal1AZPcQxlMqVZzPqeqi3iQWi3pU13pqVTCT3a359KZp/Hr3rE71G6LWGpc/H5iHhwav/FCRUFDDz2dziMiu5MhFNUyfHknH2tSA23opygWwNtNOeKDNsAjtw3x3h0gk4s11ocS+uILoF1bw2YawLj112jCSSlgyzpFdHYycI8mlLAh06CRbeTFXT3XD0ljKr+cuLDQOJpUw199+0JXV2ugpXS2hoEajwh2of48JrlYcTyvD1FDSq+PgxhmexORVE5dfzc9ncjA2kLRHBS7G39Gc3MqG9hqolBLNogMdmdJav9Z2jw82uRWNFNXIeoxY6RNNcgUL3jjE3NcPcuvXZ4nqoTj1gwOprN50nLj8GjafyOyyrUKp4s/zBaye5IKRVH0dWhpLqZFpXuhnl9eTUVo/oOijrZkhIS6WXYq1kwpr8bEz09jLYjCY5mXLhlleGBuI2xcCHWlThOsuZS2/qpHUkrpBE3e5Zpo7Bx5bSIirFU9sie60ED6QWMz+xGKeWzOuve/WI8sCQKSu0dFERmkdm09kcsc8H9xtTFkW4sSxJ5dw9pllXDfdgyMppT02wB5pOFupxR/eP5BKfatheiq9HDszQ2b42CLvxWA9l1WBrZkhvhrqqKZ4WpNX2YhSCRtne2NmKCUqpwo7c0MeXBLA8SeX8M51kzs9u2+c4UmwswUv/hVPfZOc+iY5T22NYdFbh5nz+gHe2pPcJUtBpVJx8+azzH39IFd9fIJDyd33VEsrqSOjtH5QU9VUKhUfHExjho8tM3tRBrQx1eyATC2u7VFZbTRhbCDhiZVBHEwq6dRXTBe0ZVnkVWqnsLY3oQgzQ0l7ir2/ozlBThY8sz2WL45maFQi7At/RhUgFsH7109hQaADL+1I0JnjcTjRiZFTUDDwDtAx+VWYGKjDzH3h+hkeKJQqXt2ZyL7WnisXL3bWTXNHhIjfuonmfH8qm7SSOub625HTg2JOemkdtU3y9kWbtkz1tOG3e2azcoKzzlWH+sube5JwtzEh2NmiUxfwyvpm/jifz/qZXt0aDh2xNjHUSkJaG8Oir1gYd5W91cSqCc6ktSrzZZfXk1RU2+vCx9hAworxzuyKK0SlUpFb0UBkThUrxjv1uN9AcLIypuwigQpQz0l+VWN7SqYm2hQKp3nZ9FrjtizECQcLI745mcUv53K4cooblt38ln4O5qhUtEe0Uotrey1Y9bU3w8xQQqyOeoucbVVoGswUEF0SnlVJTkUDc/zsyK9qZOPmsyQV1SBrUfDbudz2tKTPjqTz9r4U7pjvw6mnlxDoZM7LOxM6ebfPZJRTVCPjqikXjNKeIjlt6kpz/QdmEM7xs+NkemfBipj8ap2IDnTkxcvGc+TxxRolqi17MXKOppQiEl2IIAwGHramPL9mHPXNCg60FgnLWhS8uCOeWb62XB56IX3I2tSQVROc+fVcbpfaHLlCyaO/ReNkacR9HTzybtYmGErFLAlypLKhpb0B9om0slGh7vb8mhDK65r56FAaxTUy9iYUM9vPrkNj6e4dZuFZlUzzstH4Tt0wy4vwZ5fx2z2z+fclQXx1y3T2PLqAT26axv2L/TVmXUglYp6/LITY/GrGv7CHmf93gO1R+Ty9Kpg1k1z58ngGK98/2klyf29CMUdTSpnnb09js4JXLro/O7I3oQgjqZgFgYMXcT6QWEJiYU0n4YXu6C6Sk1pS16uy2mjiskmuhLpb8fyfcb32ExsI1qYGGBuIKdRSLnxvfDGLghw7Gd5f3zqdpcGOvLYrsd0R3x9UKhVbIvJYEuyIrZkhr14xAVmLgtu+OdfuYBgt6MTImTVr1oCPEZ1bxXhXyz6Hcd1tTHnm0mB+Dc/l+9PZLAzqWjxrZ27EpROd+elMThdPWEmtjHf3p7BumjurJ7pSVCPr1mKOau3HMnkQog/6xJmMcg4ll/Kv5UHcvdCXE2nlxLV63X8Nz0WhVLF+pqdWx7IxNaC+WdGj0EN5XRNJRbWDutjoCwsCHTA2ELMzppB//RaNs6UxS8f1bqxcOtGZ7PIGEgpr+CMqHyOpuF2yVxc4WxqjUtElAtlWTzReg+hAG20GUG99PwAMJGKuDXNnS0QeZXXNbJjl1e22bS/DtJI6amQtFFbLulVWa0MsFjHezYqYPN1Ecs5lVuDvaI7dAAQshpIjKaXYmxvyv7WT+P3uObhYmXDzV2dZ9s4Rntgaw01fneHyD4/z2q4k7l7gy9OrxmFqKOW5NSFE5VSxNVKtMlYja+H13Ul42JowrYPjpaeanJSSWpwsjTTWW/WFOf52lNQ2tacuppXUEZ1b1al5rC4Qi0XtjXIvprdIzpHkUia5W/eacttX/B0tGO9qyZ/n1c6+jw+nU1gl4+UrJnRZgF833YOcioYuvak+OpROTF4V71w7uZNiXBvTvGywNJZyKKmUxMIaNnx1hlXvH9OYJjeS8LY3464FvnxxLIPl7xyhurGFW+f6tBs53aU+N8kVnM+r6taxIZWI+yVoM8fPnt0PL+C1qydy82wv/n5oHncv9OOVKyew79GFuFqbcMMXp/nymLoVwbv71PVTr6+dyFOrgskorSdKQ0sDgGMpZczytdPKWagtwS4WPH5JkFZOC2tTwy5GY7NcSWpJ746q0YRYLOKNa0IpqWniru/Ce2wQPhBEIhGuViYUatErJ7dCva5YHtL5+elqbcKb60K5caYnpwYgcx5fUENycW17BNzTzpTNt0wnuaiWe3+M1KrNSUcUShW/ncvVS0dLv42cv/76q9v/ZLKBKZKoVCoisiuZ1k9P7PqZXszzt6eqoaXLRdLGTbO8yK9q5LZvzrE9Kq/dkHl5RwISsYinVgXjbWeKSkW3ijlRuZX4OZh1UnIZDXxxLJNgZwsuD3VlzSRXXKyMeXNPMmV1TXx/KptLJ7q0p1z0hnXrb9OTjHSbrOJwed5NDaUsCnTkw4NpROZU8t71kzU2P7yYuf72WBhJ2RVbxPaofFaMd9Y6etQfumsIGl9QjaFUrFFFp43p3rZYGku1Tku6fronIpF6Tsa5dG882ZkZYm1qQFpJXbvogDaqPJPcrLpNV1OpVF1krPvC2awKnfRm0BWHk0tYEOCAWCzCytSA726fgYWxlABHc/Y8soCP10+lsVnBDTM8eWrVBUnn+QEOrJrgzONbonlmeywbvjpLZlk9H9wwtVOqiaWJtFt1tbTiukGpmZne2ki2LbX157M5WJkYsHrS8NUVtt3DmqJYLYqeFRQHypWT3TicXEJUTiWfHk7n9nk+GheOs33t8LYz5ecOaa9/xxSy6WAq9yz06/Y6lkrELAh04GBSCS/+FY+nrSlz/e24/6dIPj48tI0OB5v7FvsR4GjB4mBH9j26gGleNh2MHM0Ga1x+Nc1ypcZ6nIES5GzBDTM8eWJlMP4d7hUPW1N+vWsWt87x4dW/E7nzu3CSimp5dHkgIpGI+QEOOFka8Xt413TEZrmSyJxKrZxOfcHdxpT7F/trlSFia2bQJZJzPrcKWYtSL1XRdEmQswWf3xxGZHYVj/2mnephf3CxNqZAi0jOvoRipBoUTduY7GFDVnlDnxqtd2R/YjHmRtJO4kpTPG348EZ13d+hZO3brJTUytjw1Rme2BrDpiFSqusL/XYhXHXVVSxcuFDjQqS2dmBKFdnlDZTVNXfyRvYFsVjEm+smselAareexGleNrxy5QR+D8/l0V+j+fBgGmunubMzppA3rpmEnbkRnnZqwyerrKHTw62NhIKaLs0XRwPvXT+Z4hoZYrEIMWqD7/EtMcx+7QAtChUfzpmi9bHaPMTVDS04Wmg2jCJyKrE3N9SoDDZUrJrozO74Ih5ZFqD1i8dIKmFZiBPfnsqiVibnuTUhOh2jk5XaE1lcfbGRU0Ows0WPaWiu1ibEvHiJ1ufysDXlhTUhhHpY97idSCTCz8Gc0xnlSFsX1tqkOkx0t+LL45mU1Mq6XBeHU0q59etzfHTj1F4XyR8dSiO/qhEbUwPWTfPA1EhCZlk9Dy3tuehWXyioaiSluI77O6QkOVkac+Bfi9r/HeRs0akPRUc23TCFb05k8e7+FCRiEd/fPpPJF81ZT5Gc1JK6QVEDtDA24JLxTry/P4UlwY5sjczj6qluOqtP04aeIjltzVLn60icYk2oC/+3K5GNm8/iYGHEw8s0pw+JRCKune7Bu/tSePEvQ0wNJXx8OJ1LJzrz6LLAHs+xOMiRnTHRAGy+JYzFQY68viuJN/ckE+xswZJg3UbRdIWpoZR/Hp7f6TNrs1ZnWTeLunNZlRhJxUzopi5RV0glYp5bMw4zIwkfHExjtq9dew2FRCziqinu/Hg6mxcuC+l0L8TkVfUqAqNrbEwNaWxR0NisaK+bO5FWhoWRlEmjcF3TG7P97HjnulB2RhfSrFBiLB78Z5eLlUmv/ZpAnco4y9euWwf6ZA/1/ETnVffLUXM2s4Iwb5suWU5Lgh0JdDJne1RetwGCjjQ2K7jqo5PIWhTM8bPjcEopKpVKr8ow+m3k+Pv7s3nzZry9vbt85+Hh0XWHPtDm2Z/m1X/PvouVCa9dPanb70UiERtmebFhlhfxBdU8tTWWN3YnM8vXlnXT3NuPYSARka0hBKdUqkgtqeMSHaYnDRfmRlLMO0QFrpjsxmxfO748nkl9H2uQevPAgVrauLtc6qFizSRXrEwM+pwyt2qCM9uj8rEzM9S5mpedmRESsUhDJKdGJ1GwW+b2LiwBcHmoKy/tiOdMZgUetiZapV+0CYrE5VezJLizkfN7a63cr+G5PRo5VQ3NvLknGTdrE2pkLfx5voB7FvoBMGOEiA601YXM8+/ftWMgEXPnAl+umOJKs1yJu01XR0F3/WIamxXkVjYMWmrKq1dOZNX7R7nmk5NUNbRw4wztUlp1haFUjImBROPffiq9HGMD8aAInWjCxcqEmT62nM6o4K11oT3eExtne1NcLWNnTAFldc3cu8iPx1cE9Vr4vSjIAZEIFgY6tBs0T6wMJqmolod/Oc+OB+Zp1cx0JGBhJEUqFlHZTWH0ucwKQj2sh6XxpEgk4l8rgghxsezi9LxmmhufHklnT3xRp6bZZzIrMDHoXQRGl3RMATQxVCupnUgrY6av3aCqvY0k1kxyZfVEF52tRVytjDmU1L0YBUBOeQNnMyt4+YoJ3W7ja2+OuZGU6NyqPhs5bVHEh5d2daKIRGrD/N19KVQ3tPSapfTT2RwKqxvZ++gCCqpk3Lz5LPF65vzv95W8YcMGSko0T9Ydd9zR7wEBhGdX4mtvNmQ59eNdrdh+3xzeuTaU96+f0n6BS8QiPGxMySmv77JPflUjDc0KgsZI7qqjpTHPXDqO/141sfeNO9CWrtZTLnV0XvWADNrBQCIWsSjIsc/CBwsCHbAwlnLFZDetmtYOBIlYhKOFUScjp7FZQUZpHSFD7MHsyMY53kQ8u5y314Xyf1peH162plgYS4nN6+zVqqhvZl9CMUFOFhxLLSW/h/zltka83942g50PzqNWJufFv+JxszYZMfKnR1JKmeRmNeBnnaOFsUYDB9QF+A3Nii6CFemldahUEDCApn8dsTUz5N1rJ1PR0Mx0b5tea7OGgu4MvNMZ5YR52ep0UfzIskAeWRbAivE9O8LMjKS8dMUEzjyzjLPPLOXJlcFaKVvZmRvx2U3TeOOaC848iVjE+9dPxtLYgDf26F4Wd6gQiURYd9NYWq5QcjazgtmDnPrVV1ZNdOmiiOrvaEGohzXbo/I7fX4ms0IrERhdYmPWuc6prrUn0bwBipCMdHTpbHWxNqG8vrlHZbRvT2VhZiTtJB5zMWKxiEnuVkR3U+/VEzF5PackXjnFlRalkp2xPQuIyVoUfHoknctDXfF3tGCGjy0mBhIO96Ao2BNHUkr5798JPb7z+0O/77Bnn32WGTNmaPzuhRde6PeAACKyK4Z80SuViLl6qnuXIlZPO1ONkZzkop47uwuoaU8Z6SaSE19QQ7NcOexGTn8xNpCw44F5/PuSnlNLBgsnS+NO6WqJRTUoVfSorDYU2JgZsnaau9aRMLFYxEQ3K2Lzqzp9/uf5fJQq+Gj9VAwlYrZ2I68LEJlThaWxFF97M7zszNh0wxQUKtWIUVVrUSg5njrwBri90aaMV3uRjHRb0z//Hmq5+socf3s+u2kar13dN2eIrrA0kXYxcprlSsKzKtvTinTFLF87Hukl5awjErFI61rHNlaMd+6S7mltasiNMz3Zn1jSbWPSkYh1N5LHcQU11DbJdT6f/eWySS6cSCtrvw7lCiURWRXDXvfSJrhRWa8e19nMcuRKFXP7GVUW6B2XVhW/om7qcuqa5Px2Lpfrp3v02GsOINTDmui8qj7XrvYWRXSxMmGunz3bI/M1ft/Gr+dyKatram8Ib2wgYa6/HYf7UM/TRmROJXd9F85XxzNZ9OYhXh/EJrx6F5OsbmghpbiOMD1ZqHjbmWnslZNcXIup4dhomDUQjA0kmBhIuo3kRGZXYigR61V4s69425sNqkJOT1zcKyexsAaRCMY5D6+R0x8mundVWPs9PI9FgQ74O5pz6UQXfgvvKq/bRlROJZM9bdq93gsDHfj6luk8unxoDM6BEpNXRW2TnAU6TnO0NFFfmxcX4KcW12FvbtTu0R0sVox31ljDOByoIzmdjbvY/CoaWxTMGsZ6CF1zeag6fbFjk+ORjk03kZxT6eUYScVd+tXpCysnONOiULE/QS0pHl9QQ32zgpnDbeS0pqtVtL6bj6eW42hhNKbko4ca19b1YkG15mjF1og86pvl3Dzbu9djhbpbUVbX3OfIx5nMCqZ69ZzaedUUN8KzK8kq65rFBOoMkk8Op7N6okunZ/3CIEcicyr7JIiQXV7PHd+GM87FkjPPLOOWOd58eiS9X1EqTeidkdPWXVhfPPuetqbkVTZ0kZpOKa4lwMliTDTMGig2pgZU9dCnY6K7VXvjQoGecbYyprjmgoR0WkkdHjamOmu4qEumeNhQUtvU3kTyVHo5CYU1rAtT18StC3Mnr7KRYxqatCmVKs7nVjH1ooXNoiBHvOx0U4ewO66IJ7fEDFofgeOp5ZgbSXsVdxgobZGci8UHUrVo3DrS0ZSudiq9HFNDSZ8aTY80PGxNCfOyaZexHg2oJY+7Lp5OZZQzzctGb98h7jamhLpbsStOLe99JrMcQ6lY5/d9b1gYSxGLaK9zOpFWxjx/e70qGh9ttEVyCqu6RnKUShXfnMxiRYizVo3g264fTa0YzmVV8NOZnC6ft0URe2uUvXKCM6aGErZFaY7mfHAwlfL6pi4OxUWBDihVanU4bSira2Lj5rOYG0n5cmMYDhZGPLkyGE9bUz46NDgqkXpn5IRnV2BtaoCvvX68fL3sTGlRqCi4yFpOLqolaJQvEAYLq25eTiqVioicSsL0xKAdCbjbmJBf2dheX5FeWt/nhrn6wvIQJ6Z52fDElhjyqxp57LfzTPW0bldEnO1rR7CzBZ8fTe+yb3ppHbWyvjfiHQh1TXL+jM7n0k3H+PN8Pt+fyuL9/al8diSdvfF995ifSCtjlq+tzvPyLdullDsbZ2kldQSMcq+tpQYj53RGBWHeuv/dh5srprhxMr2MkpqBtXTQF9SRnM7vkRaFkvCs4a/H6Y1LJ7pwNKWM9NI6vj+dzXRvm2FVHgR1yrBNawpgdWMLycW1gy5pLdAZC2MDLIykFGqI5BxOKSGzrJ5b53prdSxnS2McLYy6RDwamxU8+FMUz2yP5WhK59Sxtihib6mSZkZSLp3owtaIvC6ZFGkltXxxLIO7F/h1aVvhYWvKDB9bntwaw5NbYsi5KAtKpVLx8C9RrHzvKL+dy+X2b85RI5Pz7W0z2vtYSSVi7lnox96EYlKKB6bUDHpo5JzOqCDMy1ZvIiRedmqLOqdDXU6LQklGab1Qj6MlNqYGGjtVJxXVUlrbNOy5ySOJcS6WNCuUZLaGkdNL6nrsj6PPSMQi3rxmEmV1Tax87yj1TXLev35Ku7KPSCRqb0Ybe5G3ajga8V4zzZ2/H5qPpbEBD/9ynhf+iufbU1m8fyCVu76P4NdzXT1n3VHfJCcyp3JI8t8tjVvT1TpEcmQtCrLL6/Ef5c8wKxODTml6zXIl4dkVozpVrY3VE10Qi0T8FT06ojk2pobt9SNtxORV0dCsYI6eF8uvmuBCs0LJlR+eQNai1FqgRdfYmBlSWd9MUmtD6ZBhru0cC3TXK+frE1mEuFhqvR4SiURM97FlT3xRp0yjz49mUFbXxAQ3S57cGtPpuX80pRQjqbhLmwFNrJ3qTn5VI6czLzQqliuU/Gd7HM5Wxu21OBfz/e0zeObSceyOL2LBm4e44qMT7Gh9Bv1wJoc/zxdgYijhia0xpBTXsfmW6fhcpAK5dpobjhZGfHK4q4Ozr+iVkVPd2ML53CoWBOpP4Zu7jSkiEWR1UFjLLq+nWaEkyHl0LxAGC2sNHjiAXbGFmBtJhULHPhDces0lFtbQ0Cwnv6pxROdQ+zqY8+TKYGplcv63dlKXMP2aSa64WZvw6ZHOD7vInEr8Hc2HvBGvn4M52++bw/EnF5P0yioin1tO/EuXsG6aO8/9Ea91HvGZ1iLf/kpH94W2BrUdF/sZpfUoVYz6SM7F6Wp7E4qQtSh11gRUn7A1M2RRkCNbIvL63VhXn9CUrjZSUg897UyZ6GaFWCzih9tn6iyltq/YmhpS0dBCUlEtErFoRL9LRgouViZdhAdSi2s5llrGrXO9+5QueNtcb7LKG9jTmklQXCPj0yPpbJjtxSfrp1Hd2MJ/dyYC6ijK9qh8loc4aRVFnOlji7uNCVtaxX+a5Aoe+CmKc1kVvHrlxG6PYSSVcMd8X048tYR3rwvFwkjKgz9Hcff34by6M4Hrp3uw/b657H5kPtvum6PR4DKSSrhrgS+HkkuoG2B6uF4ZOSfTylAoVXr1AjI2kOBiadxJfCC5SN3ZfazIRw8U9cupswdOpVLxd2whS8c5DnvYfiRhZ26Eg4URyUW1ZJSqDW+/Ef5iunWuD2eeWcoqDQ0vDSRi7pzvw664wk5FkFE5VUwZppx2qUSMu41pe+GmSCTilSsnEORswV3fh2sloTmURb5t/WI6evQSWz23Y8XIUalUqFQqvjiWyQxvW8YPo+T6ULJ+pidJRbXtta4jGRtTA+qbFTTLL0ihn8ooHzGph5/cNJW/H5qnV85Ra1MDKuubSSyswc/BTHgXDwGu1sZdyh++PpmFnZkhl4W69ulY07xsmeFjy8eH05C1KPj379EYSsU8vDQAD1tTHr8kiF/Dc0ksrOF8bhUZZfWsnequ1bHFYhFrp7qzK7aI9/encuMXZziYVMLH66dqtUY3N5Jy1RR3vr99Bm+tC+V4ahluNiY8f5m6aXqwsyXjXLqPHK6f6cXRJxZj3ovKXK9/x4D2HmSOppbiZWeqN16ONsa7WbU3KAW16IC1qQEOFkPTx2ekY23SNV0ttaSO9NJ6Vk3ouaO9QFeCnS1IKqolvVRtbI/UdLWOXCzd3pFrp3tgYnChCLK4RkZKSS3TvfUn5cjYQMJnG6bhYWPKLV+fa8017l6+d6iLfC1NpJ1qck6klxHoZD5kvciGCysTAxRKFfXNCiKyK4nOreL2+do1uR0NLAh0wM3ahB9Pa59Kqa9Yt6qBtUVzmuQKtRT4CKkjcbcx7baX1XBha6auyUksqu1xwSkweLhYmVDYIZJT3yRnW2Qe62d69svIvHeRH3H5NVz2wXHOZFbw3vWT2++V9TO9cLcx4e29KWyNzMPBwoj5fVDzvGaaO1KJiG9PZdHYrGDzLdNZ2cc1m0gk4ppp7hx+fDFb75mjtRKtiaGkXTRnIOiNkaNSqTiSXMqCPnacHwrm+dtzPreqfdGSVFRDoJOFoEKiJTamhlQ1dk4z+Ce2EFNDCYuC9G++9Z1xLpYkFdaQXlqPjalBe7+D0YqpoZSl45z4O6YAlUrFjugCpGIRy0OchntonXC1NuH3e2az6YYpnM2s4Lavz9HQ3DXUfjSllOTiWubpWDq6I5bGF9K2VCoVx1LLtO5pNJJp69OVVVbPF8cy8LIzbRe2GAtIxCJumOHBztjCPsm66iM27Y2l1ddxdG41TXLlmKiv0hU2ZoaU1TWRXFRD8AhsQzAScbEyprqxpf3dcCy1DFmLkit6aP7ZE4sCHRjnYkleZSNf3zKdxR36rrVFdfYnFrMlIo8rJ7u217xqg4etKTEvrCDyueX88/D8Ab2zHCwGv12BNuiNkZNeWkdBtYwFepSq1sa8AHsUShVnMipoaJZzLLVMUCHpA1amBshalJ26/O6KLWJJsJCq1h+CnS0oqJYRmV05KqI42rB6kgvppfUkF9fyV3QBCwMdhuWB2RsikYjLQ135+tbpxBfUsOGrs7zwZxxPb4thZ0wBh5NLuOv7cOYH2LN60tBFMS1NDNqdNImFasEPfXzWDjZtfSnWfHCcPfHF3D7PB4meiNoMFdeGeaBUqtpz60cqbfd7W33nqXS1BHt3TQ0FesfW1JDimiZkLUrGuehPGt1opr1XTquM9MGkYnzszfr9LheJRHy1MYx/Hp6vsb75qilu+DqYIWtRcrWWqWoXH38kMzQdDLXgSEoZUrFIL7sW+9qb4WJlzPHUUuqb5DQ0K7iqn1b3WMSmPc2gBWcrCVE5lSQX1/Lo8oBhHtnIpC2n+1RGOeum9f2hNRJZGOiAmaGEDw6mEZNXzaYbpgz3kHokzNuWLzeG8dwfcRxPK0MF/Hw2F4BZvrZ8viFsSPt6WBpL24UHjqaqFXaGuxnhUDDOxZLD/15EQmENRdUyrg3zGO4hDTmOlsZcMt6Zb09lccsc7z55cvUJ69ZITltE6nRGOdO9bUbs36MPWHcQbhHS1YYGj9aUxbj8anztzTiYVMoVk/tWi3Mxrj00pZdKxLx65QT2JRSPyTnWGyPnaEopYd42Ay4y0gUikYh5/vYcTysjq7yByR7WXSTvBLqn7UFaXt+Es5Uxmw6k4mtvxvIQ52Ee2cjE39EciViEQqkaM5EcYwMJy0Oc+ON8AaaGEpaNc+x9p2Fmrr89B/+9qP3f6aV1nMmo4IrJrkPevNXKxIDcSnWx67HUUmb42I6ZKKq3vRneY/x5ffdCXy7/8AQ7Ywq5coobKpWKJrlyRF0D1iZtkZwWZC0KInIq+ddFzQgF+kZbqrOtmSGOQo3xkOBpZ8ocPzs+PJSGp50pZXVNLA3W7ftsjp89c/zGpoqt3rhAnloVzBMrg4d7GN0yL8Ce9NJ6jqWWcvVUIYrTF/wczLEwlvLaP0lEZFdyKLmU+xf7j7m0kcHCSCppbwDq5zh2Fm+rJ6m9XStCnLQuXtQn/BzMuXGmJ2bD4MixbO0X09As51xmpV7WPgrojknu1szzt+eTw+mtKkwx3PNDxIiSljaUijE3klLZ0ExUThXNcqVeZn6MJNpSAIOdhRrjoeRfKwJJK6njmW2xWBhJCdMjEZ3Rht4YOeNcLJk6hN3L+0qbFSwWiVgzaWChxbGGrZkhn6yfxqmMcm7+6gxedqYDDs+OddqKRMdKJAdgfoA98/ztuXmO93APZcRhaWxASW0T//07kWaFckzU4wh05r5FfiQX17Lq/WP8FZ3P6okuI25ha21qQEZpPXvii7Awko4ZKXBd0ZZKPhbTmIaTaV62LAx0IKmolgVBDu3tCAQGH+GX1RIHCyMmulmxJNhx1KtZ6YJ5Afa8fMV46psVPLDYX8ijHiCT3K2wMJbqnSSpLjE2kPDDHTP12hmirzhZGlHd2MJf0QXcNMuTQKexYxwLqJntZ8dUT2sq6pv57raZrBuB9Umu1urmhN+czGK2n52QDTBA7M0NMZSICR2mnmNjmceWByISqTMTBHSHSDWM8Wpra2uqqqqG6/R9prS2CUOpuF2WdDQxVHORW9GAu43JiPMgDiXazEWzXElJrWxMGTnDwUh7RnVHk1xBZlk9/g7mI9bBMFrmYjipamhGoVQNuD/ScM1FVUMzmWX1NMmVBDlZ6KXC4lAz0LnILq/Hw8YUsWAwDpi+zkVGaR3edmbCb69DhtXIEYvFWFoKYVJdYWRkRHFxsVbbCnOhW4S50B+EudAfhLnQH4S50B+EudAfhLnQH/oyF20Mq5EjICAgICAgICAgICAw2IzMvAUBAQEBAQEBAQEBAYFuEIwcAQEBAQEBAQEBAYFRhWDkCAgICAgICAgICAiMKgQjR0BAQEBAQEBAQEBgVCEYOQICAgICAgICAgICowrByBEQEBAQEBAQEBAQGFUIRo6AgICAgICAgICAwKhCMHIEBAQEBAQEBAQEBEYVgpEjICAgICAgICAgIDCqEIwcAQEBAQEBAQEBAYFRxbAaOU5OToNyHJVKNSjHGcsM1lwIDBxhLvQHYS70B2Eu9AdhLvQHYS70B2EuBo/GZgUtCuWAjzOsRk5TU1O/9z2WWsr0/+4n8NldzH39IM3ygf8YvVFY3UhlfbPOzzMcDGQuBNSG9o1fnObZP2JRKgdmdAtz0ZmU4loWvHGIc1kVQ35ubeZiZ0wBV318guf+iONgUvGQOl2e/SOWp7fFDNn5hpOhvC9UKhVXfHSCua8f5L4fI4jNqx6yc48E+jIXCQU1TP/vfrLL63U4orHLUL8vHvo5itd2JQ7pOUcKwrt78PgtPJepr+yjSa4Y0HFGbLra2cwKZC0KbpzhSUG1jKwheIDesvkc9/0YqfPzCIw8UkvqOJlezg+nc3h5Z4LeRhdlLQru+zGCnTEFwz0UrahrknPPDxHkVDTw67nc4R5OF/48n89DP0ehUsHBpBJu+yacx7fEIGsZ2INZG6obWvgtPI8DiSU6P9dYI7eikejcKoKdLTidUcGXxzOGe0gjlo8OpVFa28S+hOLhHorAAFEqVRxILOanMzlD8owTGLucSi/H39EcI6lkQMcZsUZOYbUMH3szHlziD0BSUa1Oz5ddXk9ycS2nMsqJyqnU6bkELvDwL1F8ezJruIfRK7tiizAxkPDUqmC+OZnF+wdSh3tIGkkuquWf2CIe+CmKB36KpLFZf19UKpWKp7fFUlglY+V4Z/bGFw1JxFZbjqWW8uiv57ks1JWt987h+JOLeWPtJP46X8B1n5/W+SLgr+h8muVKSmqbKKmR6fRcY41TGWUAvLkulFUTnIkRIjn9IrOsnn/iCjGUiDmWWjbcwxEYIBll9dQ3K6iVyQXnyihArlASl69/zzalUsXpzHJm+9oN+Fgj1sgpqpbhbGmMnbkR9uaGpOjYyDmQWIJULMLN2oRPj6Tr9FwCFziQWMKmA6l67zXaFVfI4mAH7lnox1OrgpnmZTPcQ9JITkUDAM+uHsfOmEK9juicSi9nR3QBr1w5gfsX+1Mjk3Mqo3y4h9XOpgOpTHS35p1rJyMRixCJRFw73YMf75xJdG4Vf53X7W/7e0Qefg5mAMQX1Oj0XGON0xkVBDtbYGtmSKi7NZll9VQ3tgz3sEYcnx/NwMbUkHsW+nIms1zvn+MCPdO2IPa0NWV7VP4wj0ZgoPwdW8hlHx4ns0y/UkmTimqpamhh1pg2cmpkOFsZAxDoZEFysY6NnKRiZvjYct9iP/bEF5NWotvzCUCNrIW6Jjnl9c38occP1KyyepKKalk5wQWAexb6MT/AYZhHpZmcigbMDCXcPs8HZ0tj0krrhntI3fLl8UwCHM1ZO9WNCW6WuNuYsCu2sNf9lEoVh5JLyNDh3xaXX825rErumOeDRCzq9N10b1sWBDrw9cksnaUtJhfVEpNXzSPLAjE3kuqlN26kolKpOJVe3v6CneRhBTCsdTnppXWcz60iVcfvucGkpFbG1og8bpnjzYrxzshalENeV3c4uYT7f4okr7JhSM87WonNr8bVypibZ3txOLmEilFaozxWiC+oQaVSO2n1iVMZ5RhIRIR5D9xZPHKNnOrORk6KDh/+NbIWzmRUsHScE2unumNvbsTmE1k6O5+AmsIqdQqOg4URXx7P1Ns6l93xRRhKxSwJdhzuofRKbkUDHramiEQi/BzNSC8ZfA9OVUPzgA2MtJI6dY3LPB9EInWU5NKJLuyJL0Leg+LK4eQSLt10jFu/PseNX5yhpFY3aVybT2TibGnMygnOGr+/ZY4XiYU1nMvSTWrrlohcrEwMWB7iRIirJbGCkTNoZJU3UFQjY7af2sjxdzDHxEBCdF7VsIwntbiWZe8c4cqPTrD83aN8fzp7WMbRV/6MKkCFig2zvAhxscTOzHDIU9Z+C8/l75hCVr1/TCsHiUDPxOZXM8HNistDXVGqVHqdCdCGSqVie1QenxxO53RGeY8pz43Nih7fL6ON5NYMqN1xRcM8ks6cSi8n1N0aU0PpgI81Io2c2lYPv0urkRPkbEFORQMNzXKdnO9oSilypYpl4xwxNpCwPMSJs5lDr/Q01iiobgTgX8sDSSup40hKqc7OVVwj46Gfo9gZU9Dnh9yuuCIWBNhjbjTwG1LX5FQ04GlrCqgXb+k6iHa8viuJqz852af7sUWhZFtkHvf/GMnuuEI2n8jE1syQq6a4tW+zaoIzlQ0tnOnm3kssrOGu7yIwM5Ly4Y1TaFEouf/HyEGv4ymtbWJndCEbZnthINH8CF0U6IiXnWm/68myy+vJq2zo1rA/m1nBoiAHjA0kTHSzGnPpai06zCU/lV6OSASzfNRGjlQiZoKbJTHDZOT8ck5t0P71wFwuGe/Ehwf1K303r7KBn8/mdPl8R0wBCwMdsDEzRCwWMT/AnqM6fIZrIjK7irVT3QnzsuHhX8/rdQ2ivqNUqojPr2aSuxWOlsbM8rXT+7qc3IoG1n95hkd/jea9/Slc//lp/v17dLfbbz6RyaK3Do8ZQye5qBY3axNi8qp1Hu1UKFUotFCeVShVnMksb3cyDZQRaeQUVau9s86WJoA6kqNSqb2/2tCXIl2VSsWu2CL8HMzwslPnv09ytyK9tI66Jt0YVQJq2iI5V011w9fBjB3RuvPEfXI4nZ0xBTzwUxTL3jlCsZbXSF2TnJi8KhYF6X8UByC38oKR4+doTk5Fw4AlGjuiUqk4mlJKVUML2yK1SzHMrWhgyduHeey3aOIKqrnnh0h+OpPDTTM9MTa4oKwy2cMa2268wU1yBY/+eh5PO1N+vGMmaya58tH6qUTlVPHfvxMG5W9rkiv4LTyXG744jVgMN8zw7HZbsVjEhlle7I4vorRWe1nRyJxKbvn6LAvfPMy8/x0i9KW9Gj3QuZWN7c+jCf/P3lmGt3FmbfgW2JZlkpkxpjjkMDO0TZmZGbe01O1XWGq3W2amdEspc8OMDseOmZlllGxJ8/2QpViWZJRj2Zn7T69KsjTRaN55zznPeU64N2WN7aeVdOSVTblc/tZuTlQ4PrjbnV9HSqg3PkoX82OTI1QjYj6g1en55mApF04NZ3KEiodXJVHdrGVtmvM4De7KreOv3xyzSJgU1LZytFTNuVPCzI8tSgwks7K532vrUClvbKeyScPS5ED+eEYyHbpTL5cbS5hMByaGG+Wbk8J9zJUAR+HIexHAnf87QF5NCx/cOJPjT57BHYvHsS6j0u7n/HC4nMkRPsjtJK/GEuq2TiqbNNy6MBYXmWRYqzntHXoueXMXM/65nj99dYSMXpJyGeVNNGt0DjEdgFEa5FR0BTmhZrmaJ0C/LrjqJg3znt7E94f73oBlVjZxxdt7+PlYBZfNiDQ/PincB0Gg1xMlMnQq1O0EeLrhJpcxMcyH/Nrh6bGoa9Hy+f5i7loSzzd3zaOqScv/+ikJOVraiCDAtCjnNBroTqfeQHmjhij/riAn0BO9QaC4znEZnMK6NsrVGvw9XPlgZ0G/Zga9uz2fFo2OH+9ZwJaHl/DpLbO5clYUN86PtXidRCJhVowf+wqszQde3JBDbnULL1yWag6M5sT58/fzJzqsP+qpXzL501dHifB153+3zMHPw7XX169MCUZvEPpdAcipaubyt3ZTUt/GMxdP5q1rpxMT4ME72y3ti1u0OupbO8zB6sQw46Yjvfz0kazdujCWCF8lt3yUNqAgsj/ss+HqMyVSRYVaM2zyR3v8nl5FQ1snV8w0BtQJwV6snhTK61vyhrQh/D290mGB0nmpYaiULnzcrWr505FyFC5SVow/ORxxYUIgEglsOHFqrKQPFTcCxrU5OcQLfw9XduaKDm+DxVQ5ndQV5CQGe1HZpEHd5hhDju05NUx6Yp3DJHDVTRqOlzXxpzOSWZoUhItMyupJxt6wAzZkxJmVTWRVNXNet8B8LGPqY58R48e8cQH8OkxBjiAIPNwV2JwzOYztObVc9/4+mjS2fzfbcmpwlUmZ5iDzplEZ5JgrOV1BjpfChXCVe7/6ck5UNqMzCLzXS4/Hjpxarnh7N2e+uJ1ydTvvXT+D2xePMz+fGOyFq0w6YvKF04WyxnbCVMZzHBfoMWwOIB909VfdOD+GaVG+nJ8axuf7S/o1bfdQcSPuLjJzoO1saHV69hfWIwgCFY0a9AaBSJNcLch4zP2tgPaHnbm1SCXwrwsnkVfTyrac3uUprVodXx8s47KZkUyK8EEikTAvPoCnLpqEr40gYnacH0dL1RZSuBatjvd2FHDbojgmRfhYvP6q2VGsSHHMFOqbF8Ty2/0L+fDGWf1yz4v0VaJ0lfXL3t5gMNplh6nc+fm+hVw2M5IzJoRw5awoDpU0WmzkS7oc8iJ9jZXsuK6ekeNlp0/SxUvhwrvXz0Cr03PnJwcc1q/XoTNQ1aRlXJDl9Tyl63d1tOTUBpJf7C9mapSKpBAv82P3LounQq1hze6TiZj2Dv2AhhC/v6OAn446pjKucJFx+cxIvjpQSnPXxuXHo+UsHx+MRzcJb6CXGzOj/U6Z/v9gcQMh3grCVO5IpcZ1ZWeeGOQMlqOlasJV7vh7ugGYf5OZlY5Zd17fnIfeIPDAF4cdEozu6HqPBQkB5scmhPngq3Rhmw01wA+Hy/Fyk48aVcZQyapsQiox7gPOmhjCgaIGaluGnjBam1Zi8Zt4fUsePx+t4NlLp/CPCyay9o65tGg7eXG99ZiNTr2B/+0p4oyJIRYqjqEwOoOcJg0qpYvFl5AY7NmvzYRpQ3e0VM2hkkar5zWdem75eD9N7Tqeu3QK6x9YzPLxlpskV7mU8aFeYrPvMFPRqDFX62IDPGhs63S4JKdZ08nHuwu5YmaUefG+enY01c1aNvYj43i4pNEpy9sGg8D3h8tY/txWLn1zN7vz6sz20ZG+xiAnyMsNTze5Q/tydubWMjlCxRkTgkkO8eK9HQW9vv7bQ2W0dui4ZnZ0v95/dqw/OoPAwaJG82ObM6vp0Bksqq3DQaSfkuQQ736/XiqVGJ0f+7EufZFWQlpRA/+8YKLFurZ8vPGG2/23aDqPpoqcTCohJcz7tHNYi/RT8vi5E0grajB/J0OlrtV4kw/sWgtMRPkpUSldhpTYMhiEAW0iyhrb2Zlbx+U9ftfJId5cPTuKZ9dldTk7NjHv6Y2c+dI2fj5a0Wewo27vJK2ogaVJjnOAvHZONO2der4+UMoPR8rJrmrh3MnWGfEzJ4awO6/OYdn/tWklPPFDOpuzqq0qWweLG5gWrTL///xx/qSXN9EwgHuIIAh8vq9Y7OXBWMmZGH5y/YsP8kQmlTjE2fZYqZrd+XX85+LJzIr147aP04Y8j3B7Ti1JwV4EeyvMj8mkEubHB7C9R/JNEAR+PFru0M21s5NV1UxMgAcKFxkzY/0Ahryn/TKthD9+dZT/++44YDTtem1zLjfOjzFLVyN8ldy9JJ6Pdhda3Rt/PlpBuVrD7YvihnQc3XGunVk/qeiakdOdxJD+OazlVjcTH+RJuMrdZlPw/sJ6NJ0GnrlkMhdPj7D7g58U4TOilqKnAxXqdkJ9urLVAcbMaoGDJWvv7SigvVPPLQtPSqMmRfgwOcKH/+21bqbtjiAIHCpuJDVK5dBjGgqCIPDbcaOb0B8+P0x8kCf+Hq5szKw2bwQjuioARoc1T4dVcvQGgV15dcyP90cikXDj/Bi259TadVoTBIE1u4tYmhRkri71RVKIF94KOXu7SdZ+O15JcogXMQEeDvl3OJLxoV59Zjq1Oj1P/5rJBalhVtK6IC8FqZEqi2nxJfVtuMqkBHudXAOT+rn+jTXmxxuztI4ygjFVzAK9LIMciUTCpHAfDg9yzTcYBO757CCLntnc70Bnc2Y1Ugk2Hfz+uno8/h5u3Pf5Ia55dx9BXgoCPN24+9ODPLc+q9f33Z5Tg94gsCzZMRVOMG5cVowP5p8/n+C+zw6xID6ApcnWQdSZE0PQGQTWO0Cyll3VzCPfHmNtWgk3frCf+z8/bH5Oq9OTXtZkISOeHx+AIDCgWVsnKpr5yzfH+NSGscLphCAIZFU1WyR5FC4yYvyVDunLeWd7PuEqd85PDeOta2eQEOzFde/vG/QeSxAEtufUsrBbFcfEooRA0subqOt2HR4qaaSkvv20kaqBsb0juasaF+vvgdJVNqQWjCMljTz63XHigzzZX9hAermarw+UounUc+tCy6Dl1kVxRPi684+fTvbLCoLAW9vymTfO39z35QhGZZBTqW43Z/hNJAV7UdWkpbGt9yxNbnULScFeXDc3ml+OVViZEOzIqcXfw5WU0N4ztpPDVeTXttrVFYoMDUEQqFBrzHK12K6hh/k1jpOsVTdreHtbPtfOiSHC13KTffXsKLbn1FJUZ//zyhrbqW3RMjXSefpxfjxawR2fHESldOHz2+bw4Y2zWJocxOYsY5AT4q2wCNzHBXqQ56DvNL1cjbq907zxPG9KON4Kud1gcXNWNVlVzVw7t39VHDBm4mbF+rE337ip1XTq2ZxVzVldM4qcjaRgL/JrWnvtnzhc3Ii6vZMbevQgmViZEsyO3FqzRK+kvo0IX6MEx0SsvwfF9W19ZvF35NSy4vmt3LHmAGt2D98cn1OFn4crCUGeDmsoNwU5AT2CHICpkSqOlDQO6jt7+rdMfjlWSafewCf97PfbklVDaqQKldJatunpJuc/F0/maKkab4WcT26Zzae3zuGm+bF8vKvILBuzxabMasYFepgrgY7ivuUJzI7z493rZrDm5lm4ya0ThGEqd6ZEqvhtiHM5uss70x5dyb3L4tmSVWOWGB8va6JDb2BqtyAn0k9JtL/SLGPqD6bE0FcHSod0vKOd+tYO1O2dVjLO5BDvIQc5x8vU/Hysghvnx+Aik+LpJuejm2YR7a/kqnf3cPuaNJ74IZ03tuTx1YFS1mdUcaCovtfrMLOymdoWrYVUzYTpMdPv4HiZmke+OUaApxvzHOTo5ewIgkBWZTOJwcYgRyqVMD7Ue1B9nR06Ax/sLOD6D/aRFOzFt3fNI9DLjQ93FrJmdxGrUkIIU7lb/I3CRcaDKxPZkVvL4S5F1Y7cWqNDqgOrODBKg5wKtYYQH8svzXSyestKC4JATnUL44I8uXxmJIIAv/RwLtqWU8uChACLDYQtTNr/dBs6+Batjr35dRwvU/d6sxGxT31rB1qdwXxxeLrJCfJy61dfjrHC0kBaHxufFzfkIJNKuHdZvNVzZ00KRSKBnbn2s36mxtapTlTJ+WhXIbNi/fji9rnmYYbLkoPIr2llZ26tuVndxLguG2lHbHZ35NaicJGas6furjIunRHJ2rQSK7lHcV0bD355hHnj/Fk8QGOA2bH+HC5pRNOpZ2t2DW0des6aZHtezUiTFOKNziD0Oo9od34dnm5yJobZTqysSglGqzOwLdt4Uy5paCeix3mM9lei1Rmo6qMx/r0d+bRpddS2aPm/79P54Yjzz7noi5mxfg6v5AR4WgcWUyJVqNs7KRqgUcc3B0t5e1s+j6xO5pLpkazZXdSnBbRWp2dXXm2v/QELEgL4+KZZfHH7XHPl6eaFsbR16vliv21TAYNBYGtWzbDM9JoY7sP/bpnDipRgJBL798+zJoawLad2SO6k/9tXzIGiBv594STcXWUsTAikvVNPZoVxw32ouAHXLuvv7swbFzCgfg9T/9uJiqbTTg7aHVMibFygZbXcNIh9MPcPvUHg1U05XPj6TmL8lVw+86Qs08fdhU9uns35qWE0a3Rsza7h1U05PLz2CLd+nMbFb+zm1U25dt97R04trjIps2Otg5YwlTvxQZ68uCGHK9/ew/mv7URvEHjv+hlOJzsfLiqbNDRpdOZKDsCEMO8BjyKobtKw+uXt/P2nDJYlBfH+DTPxUrhw1awovjpYSn5tK9fZSWKePSmUKD8lr2/OpVWr4/Hv05kY7s3iRMcOUh+VZ7SyyVquFtd18fUW5NS1dtDY1klCkCcqpStTIlXsyju5ia1u1nCiookF8dbRf08Sgjxxk0s5VtZo9dw/fszg8rf3cM4rO1jx/FbRanoQnHTQOxnMxgZ49FnJ2VdQz/LntnLh67u4/v19djPoeTUtfLG/hLuWxNtscPdWuJAQ5NmrLvhwSSNhPgoLze9Ikl6u5kBRg9WisiAhALlUwrEytZUsLD7Ik7YOvfn7Hix6g8DatFIWJQRaVIqumRNNk0bHj902061aHbetScPTTc6rV03rM6HQk9lxfnToDXy8u5C1aaXEBXqQEOScxg+mm0hWlf2bx+68OmbF+tm9wY4L9CQ2wMPcl2OcdWSZ5DHZSRfW2t+AVzdp2JZTy+2Lx/HVnfPM8qLRXo2eFeNHYV2bQ5zPapq1+Li72KxCTIlUAZgzj/2hvcMoRTxjQjC3Lozj5gWx1LV28O2h3t09DxQ20Nah7/OGvygx0EJaF65yZ/WkUD7YWWhz1seR0kbqWjtYOoKDi1dPDKVTb+CDPvr17KHp1PPShmwunBpurhpPjvBBLpVwoMgY7G7NriE1SmV1HufE+VFU19ZvR77i+jai/ZX4ebie1tUcU9+mSTZuIinEi2aNjvJB3D++OVjKs+uyuXF+LD/ftxAvhYvF8yqlK/+8YBKf3jqHzQ8vIf3vZ5L+5Bns+etyrpwVxVvb8i0kZ93ZllPDjBhf3F1ttxvcujCWIC83fNxdeGhVIj/dt8B8fY9FBEGgsa0Dg0GgVavj+8PG+7GpOADGIKeorq3f94PGtg6ufW8f6vZOfrxnAc9fnmpei66aHYVMIiE+yNPuvBu5TMrti+NYl1HFHZ8coFzdzouXp/aaIBkMoy7I0XTqaWzrtJKrKV3lRPi6k9NLkJNTZXzO5Co1b5w/e/LrzAOKTBme/ljOymVSUsK8OdJDM9qi1fHj0XJumBfDxzfNoq6lY9ADAU9nyhuNg0BNcjUwukj1Vcl5e1s+AvD4uSm0dujtZni/P1yOu4uMG+fH2H2vaVG+Ns0pTBwuca5+nE/2FBPo5caqFMuqhrfChRkxxuqKrUoOMGTzgd/TKymobeX2xZal5tgADxYmBPDujnwa2zrQdOq5fc0BiuraePvaGX3aMNsiJdSbuAAP/v1LJhtOVLF6YqjDF0ZH4evhSrC3mznD3BNNp55DxY29zgSQSCQsTgxka3YNBoNASX2b2TzChOm89iav/O5wGVIJZt354+em0Kzp5B8/ZvDp3mL++3smFV0DeEcTpqbZ/QVDa1QGqGnRWvXjmAjwdCPC131AQc4Huwqoa+3gz2cmI+m66S9LDuLd7fm9Zr+3ZNfg7+FqtusdCLcsiKWssd2mJeymzGo83eTMiPYb8Ps6iih/JbcujOOljTmDqo58c7CM2pYO7l560vFU4SJjQrgPB7qkn7vz6lhlw1VxSoQKwGZy0hbF9W3EBXhwQWo43x0uc/gcl9FCXnUL4Sp3q6DBnMQZhMPapsxqpkT48Mjq8f1u9vdwkxPio+DBlYkYBIHXt+RZvaakvo2dubWcMcF+df/ymVF8cftc3rx2OnctibeZ1BhLfLCzkNS/ryfx0V9J/fs6nv41k9mxfubkGBid5wBO9KOa06LVceOH+6ls0rDm5llWPTTB3gqeOG8CT543odd788XTIgj0cmN7Ti1PnDuB+CAvu68dLKMuyOlpH92dhCDPXoOc3JoWpBLjxguMpesmjc7cbLU9p5bEYE+b722LGdG+7M231Ib+dKSc9k49Ny+IZVFiIJfOiOTtbfkW0XFGeRN3rDnAJ3uKHGLZNxYpb2xHJpUQ1K25Oi7Ag4K6Vrt9B4IgcLikkTMmhHDDvBiCvNzsTmTenFnNwoSAXhfXqVEqcqtbbDoBtXXoOFamdpr5OE2aTr47VMaVMyNxlVtf1iZ5SqRVBUCJTCoZUq+TIAi8uTWPmTG+TLexeXpgZSIVag1nv7yDWz9OY19BPe9cN4MUO/KsvpDLpGx8aDH7/racr+6Yy91LreWGzkRyiLdd58eDRQ106A19TndenBRIdbOWHbm1aHUGq2DV3VVGiLeCIjsuY4Ig8PWBMpYlB5krl5F+Su5dlsDaA6X87btjfLCzkJXPb+PTPgw3nI1wlTvhKneH9OXUtmitnNW6MyVS1e8gp6G1gze25HHFzEjiAk9mwG+YF0NeTSsHiuwHZVuyqlmUGDjgKqfpGBcmBPCPnzIsKhaHSxp5Z3s+Z08KtblGnEoeXJlIXKAHD315pE/pXncMBoF3t+ezYnyQ1YZoepQvB4sa2JJVjc4gWCV7wLjeeSvkHOmnFXhJvXF48iXTI2hs62R79ulpQZ1X02LVjwPG5Iq7i4ysyoElyTr1Bnbk1A5amhTo5cbNC2JZs7uIskbLxMzHuwvxcJVz8fSIQb33WGRrdg0Twrx59Ozx/PWs8Wx8aDFf3D4XWbf1JSHYE7lU0qdkrUnTyXXv7SWnyjhk1Z7j6DVzos2VVnsoXGT884KJ3Lcs3kKu6EhGXZDTcxBodxKCvcjtxWEot6qZKD+leWM7NUqFm1zKrrxaNJ16tmXXsiC+/xfd/PgAalu0ZFedvMC/SCth/rgAsyzonmXxtHXo+LBrFotWp+f+Lw6xK6+Wx74/zvLntg7I0vJ0oUKtIdjLzeIijA3woENnoNxOtrm0wWgEkBqpQiKRsDQpiE2Z1VYZ0+omDcfK1H1KNkwBzGEbtrE7cmrp0BlY4kAb1qHw05EKNDo9V8yKsvn8qpQQ3F1kVhkXF5mUUB+FWXs+GHbn1XG0VM0d3WZJdWdalC8/37sQPw9XduXV8epVU202hA4EicQYAM+I8bMrSXAWkkPs20jvzq/DWyFnfB9GJ3Ni/XGVS81N67bc6KL9lXYrOcfLjIPuLplueSO5Y/E4vr5zLkceX8XOPy9jxfggHvn22KhzjpwV68fO3Fq259RwcAjWszXN9is5YDQfyChvokNnf4ZWermaez87xMoXtqLTC/xhRYLF8/PjAwj2duMbO5K10oY2sqtahrS2/PeSKegNAn/4/BB6g0BudTM3f7ifpBBvnjhvwqDf11EoXGQ8f1kqeTUt/Oe3zH7/3foTVeTXtnLbIuu1Znq0L2WN7Xy8u4jkEC+bxgoSiYTJEap+WYEbDAKlDe1E+ikZH+pFgKcrab0EpmOZvJpWq34cMNnkew64knO4pJFmrY7FQ/iN37ooDjcXqYVSpkWr4/P9JVw+MxLPbjOaTmf0BoEDRQ2sSgnhhvmx3LQg1qzg6I6bXEZCsFevQU6n3sD17+8jp6qFj2+e5ZAk7xkTQnhwVdKwqTFGXZBT2WTc4NqqtsQHelKu1tjtgcmtaTFL1cC40M6I8WVnXh1vbc2noa2DK2b1P5qcFeuHi0xidunIqWrmUHEjl3WLSMNV7lwxM4rXt+SyNq2EVzflklfTyqe3zuGHexagbu+0uYk+HTB0NR7utWHpWa7WENrDkSOuD4c1k7TMZASwNDmI4vo2K/ewLVlGj/y+NhHjAj3xcpPb7MvZeKKaaH+lzcViJFiXUcmMaF8rFxMTMQEepD95hoUG10SUn5KShsEFOTtyarn/i8Mkh3ixtJcm6Sh/JV/fOY8tDy9hVS8ygrFIcqhxMrgt58fdeXXMjvO3COZt4e4qY06cv3lavK0gJ8bfw25PztZso0yp529eJpUwPdoPb4ULvh6uPHXRZKQSY+/GaGLuOH9yqlu49r19XPT6rkG7d9U0awnoo5LToTf0agv+1tZ8dubWctG0CNbeMdeiGg3G7/z81HB+OlJuU/707cEy3ORSliQOvm8mxEfBS1dMZXd+HVOeXMeK57ehdJPx3vUznCYpMDHchz+dmcQHOwvZeKKKDp2BjSeqzJLk8sZ2Hv/+OJsyjb95TaeeFzfkkBqpYmaM9ebKNBPnQFFDr2vMpAgfjpWp+2yWr2rW0KE3Vk0lEgmpkb5Dnt0yGtF06ilpaLN7r0sM9rJI9PbkqwOlzH96E29tzTPvzbZm1eCtkJvlg4PBW+HCeVPC+PZQmbn/7OsDpbRqdVw/L2bQ7zvWOFHRRItWx6zYviWqRvMB+wmuX45VcKi4kXeun+E0Kpa+GFSom5eXxy233EJRUREXXHAB//73v1EojAv5rFmz2Ldvn0MPsjvljRo8XGVWTWoA8V1T5/OqW2w2keVWt3DhVMsS5rxxAbzStdG+aX6MzU2gPZSucqZF+bIrt5abF8Tyv73F+Li7WGmB/3xWMi1aHX/86igA9yyNZ2K4DwaDgIerjPQyda8bxLHKVweMjYdgdNyZFuWLQRAYF+hJUV2rlSQn0s8orSqobWWRjTL34WJLI4AFCQG4yqRszqy2CG43ZVYzKdzHavPRE6lUQmqUioNdLmomDAaBjZnVnDclzCl6QZo1nezKreOPZyT1+jp70pcoP6VVb1l3aluMg1F35BpdwFalBNOhN/DrsQq+O1zOrFg/Xrw8tU9pjatc2u95OGOJSeEqwPi7u2jayfWnulnDkdJG/nrW+H69z+LEQLZlGzcHPu7W61+Uv5KfjpYjCILV7zK/tpW4QA9c+nAPcneVERPgwYkKx0wxP1VcNDWc8SHeeCrk/P3HdB797jizYv0H3PPVVyVnYpgPMqmkawiwyuZr0svVnDkxhEdW2z+vF00L5+1t+Ww6Uc1Zk07anxsMAl+klXD2pFB8lNbneCAsSAjgjaunkV7eREKwFwviAwbVAzec3LIgjp25dTz45RHc5FKqu+R106N9OV6mRmcQ+Gx/CZ/dOpuvDpSSV93C2jvm2lx3Q32MssWyxnab/TgmpkT48MaWPMrVGsLtJIXA6AAJJ4fuTo1S8eqmXHR6w2njwgVQWNeKIGA3yEkI9uSHI+XoDYLNZM3GE1U0aTp55vcs3ttRwKe3zmZrdg0LEwKH/D1eMj2C/+0tZntOLXPi/Hl3Rz4rU4JPy/uMPfYW1OMik/TLBXZCmDffHTL2nvXsUxIEgbe35TM3zt/s3DoaGNQv7O677+aiiy5i7dq11NTUsGLFClpajJF8Z+fwOvXkVtvWhsJJQwFbfTlNmk6qmrQWm10wZgA1nQZUShf+sCJxwMczPz6APfl1FNW18uneYq6fF2PV5+HpJueFy1N58fJULp4Wwb3LjT0EUqmECWE+fU6Z/e14JbP+tYHNWbb7S0YjLVodz/yexVkTQ/jvJcZ5D8+vz+aFDdnc8nEaR0vVhPta3oBcZFKi/JR2h0seKmmwmIvg6SZndpwfGzNPDp7r0BnYkVvbb3ehqVG+HC5usOgDOlLaSG2LlhUpzhGYbs2uoUNvYGUvN/beiPRTUlLfZjezee17+/jz18fIr2lhW3YNN364n9vXHOBwSSOPrE7ms1vn2K0giRjXpcWJgby6OddscgLwr59PoHSVc35q/wbQmfTr9uabxPh70Nqhp7bFumJUUNtKjH//hqWmhHqTMcqCHLlMyqQIH2IDPHjqoslodQYe/yF9QO/RqtXR2qHvNchxd5WRFOzF4R6JDxNtHTrya1uZ0Ee/WXKIN+NDva0kazvzailtaHeYPv3MiaE8tCqJ86aEOV2AA8Z74HOXTSHKT8mixEC+u3s+z1w8GYWLlBvmxbD7r8uYFO7DNe/u47N9JTx+XkqvLlgzY3yJ8HXv9fuf1BWcHu2jt8o0I8dk8jEtytdoU+2A4ZejCZP9/bgg2+tHQpAXWp3BruT5aKmaS6dHsvmhJXgp5Fzx9h6OlakdYhWcGqliXKAHXx0o5bl1WVSptTy0qvdk3+nG/oJ6Jkeo+mXuMCHMB51BINtGj9XuvDrSyx0/x2a4GVQlp6qqinvvvReANWvW8OSTT7Jy5UrWrVs37Jnt7B5Td7vjrXAhxFtBTrX1IpRXbemsZmJyuA/TolTcsXjcoDSc8+MDeH59Nnd+chClm4xbF9oe6AdwwdRwLpgabvHYxHAffk+3dsEx8fz6bF7emIObXMpz67JYkhjoFNWDofLa5lyaNJ08sno8kX5KLp1hvKkLgkBBbSsHixttWnnH+CttNlebJlyfPclyKORZE0P523fHOF6mZmK4D3vy62jR6vo9J2JqlIqXN+rYV1hvzl5sPFGNl0LOzJiRcyjqzrr0KhKDPYkJ6N8mtieRfkpatDoa2jqtNkImPf8jq5O5bdE4BEEgs7IZF5mEcYGeY+K3eCq4b3kCF7+xi5+OlnN+ajg7cmr5/nA5T180Cf9e5FHdGRfoQbjK3arCaSK6K/gprm+12qgX1rayqJ/ziMaHerMps9puZtbZCfFR8H/npPCnr45y/dxoZvTzOjWZwPQW5IBRsravwPb8rBMVzQjCSaei3rhoajjP/J5JVZPGXH3+fF8JcYEe/ZKWjBUCPN348d4F5v9PjVRZSL7fvW4GV76zh6lRvlxlp+fQxKPnpNCq1fW6LoX5KAjwdOVomdqiitaTkvo2Ajxd8ejaF0yO8EEqMcqiHTmR3VloaO1ApXSx+u7yalrwUsjtGnIkBJ9MLve8B9W2aClrbGdKpA9R/ko+u3UOV7yzh9qWDptqjIEikUi4ZHokz63LQi8IPLwqaUBqnLGOIAjsL6w376/6Ynyo8bvLqFCbZ0GaeHt7PglBnk7Th9xfBlXJaW+3bPx+/PHHOfvss1m1apW5ojMcGDdcLSQG2++DiA/yJNeGPtTUlxHXo3lOLpPyzV3zB90nMCXCB083ORkVTdy1ZJxNGV1vTAz3pqyx3ab5QFZlMy9vzOHOJeN485rpHC9rYkt2zaCOcyh8c7CU+z47ZHPuwmDYV1DPe9sLuGVBrFVZWSKREBfoySXTI2z2XUX5Kc0ygu6cqGimQ28gtUeW79IZEcT6e/DPnzNo1ep44sd0kkO8mNzPm9SMaF/CfBRc8fYeLn1zF0/8kM63h8pYkhTUp/TH0RgMAjtyann29yye+CGdZ37LJL+mhc1Z1TadhPqLadNsKxNX3thOp14wSxUkEuNk5PggLzHAGQDTo31ZmBDAyxtz+HBnAX/55ijTolRc1s+bDxi/+1evmsqDK21nKk1BTs++HHVbJw1tnWZXyb5ICfOmrUPfqx11d9o79A4ZJutILp4WQYy/kne3938WiznI6SPonBLhQ35tq81BzxnlaqQSLIbs2ePSGRG4yWW8udVog1vTrGVdRiVXzIwUr61u+Hq48usfFvLURZP6/F4CPN0sbHFt0V/zgeL6NiK6WbV7uMlJCvG225fTqtWNWovpL9NKmPmvDby80XrAZl5NS68JrTAfd5SuMrJtmD6ZvmOTFXqQt4K1t8/lk5tn99vFti8unBqOQRCYFO7D7aOsyjDc5NW0Utfawex+Jk28FC5E+yutzAeqmzRsyarhpgWxo25tGtQuLSEhgU2bNlk89uijj3LmmWeSm2t/Cu1QKa5vQ6szkNjLDSQ+yJNcG3Km/JoWAr3c8B5gENIXcpmUeeP8CfFWcN3cmAH/veniP26j2SutqB6JBO5aMo4lSYFMCvfhtU25p3RDYTAIPL8+mx+OlPPaZmtP+oFytLSRmz7cT2qkinuXJfT9Bz2I8vegtKHdQvYDxgnXcqnEpnvYX1ePZ09+PVe8vYeyhnZeuXJqv61ZvRQubHp4CS9cPgWFi4xt2TVoOvVcPC287z92IJuzqln+/FaueW8vn+0rZntODWv2FLHsua00a3SsmjA4qRqcDHKKbQQ5hV0b3b42DiJ9c/+KBPJqWvnHzyfw83DlmUsmD9gieGqUr1U12oSXwoUAT1er4KSg6//7W+lL6XJ6O2Fntk93apq1LPjPJv79y4l+vfepQiaVcNOCWH7PqOx3sGayWw7w6l3WNTlChSBgU2acXt7EuEDPfklDVEpXbpgXw6d7i6lq0vDw2iMoXeVcPE20vu2JozdWk8J9OFqqtjuOAExDdy2TcFOjVBZSxSZNJw+vPcL8pzcx4fHfSXr0N2b8cwOf7RsdNuyCIPDC+mz+9NVRwlTuvLYl1+p6ya1u6dVgRyo1zn+yNYj9SIkaL4XcQirr7+k2ZHfN7oT4KHj/hpm8fe2M06pXqj/sKzDuIadF998kYEKYt3msigmTsYQtww9nZ1BytTVr1thcdB5//HEuvfTSQR3Ic+uykEok3L8iwe6CZsoU9FaOTAj25KPdhWg69RY3mryaFuIGKefpi39dOMnq8/pLXKAnChcpx8uarIaQHixqJCnYy1wdunvpOO745CD7CuqZfYoav0wa8YUJAby8KYcFCQFMH8AF06k38OHOQt7dkY9OL9Ci1ZEU4sV7NwzO5SfKT0mH3kBVk8aiD+RYmZqkEC+b52DF+CDmxvmzO7+Of14wkYQBlrMVLjIunBphZVpxqtiVV8vtaw4wNVLFvy6cyNw4fyQSCa1aHV+mlVBU1zaooYEmfJUueLjK7AQ5bUgl1vN1RAbO9Gg/tv9pKYFeboNaK/pDlJ+Swh6VzoJa4w0qtp+BapCXG/4erpyoaOLsyfblPABP/XKCutYO3t1RwDmTw2z2S5Q3tuMql/bqWjYcXDI9gufWZfP+jgKePH9in6+vadYilYC/R+/HmRhsXLOPlKiZN85ys5Ze3tRnP053bl4Qywc7C7j0zd0U17fx/g0z+i1fFBk8qVEqmjfqyKtpsXs/KK5vtzq/UyNVfLq3mIbWDpo0ndz8URqVag2XzYgkKcQTvcGYkHr8+3RmRPsO+F5zqvnqQCkvbczh/hUJ3LIwjhXPbeXxH9L54IaZSCQSShvayKho4po50b2+T0KQF1lV1n18x8rURpnfMMtel5yGxk39Ia2onqRgL5tGNfZICfVma1YeBoNgPm/5tS3IpBKi/EZfsnNQYa9KpcLHx/amKiUlZXAHIpHw0sYc7v3skN3hYNmVzXi6yQnrpcw5LtATQcBsQ2kiv6bVrmHBUAn0chu0m4dMKiEl1Nvm5OdDxZaN9KtSQogL9OD9nf2XYAyVz/eXEBvgwXvXz2RCmDf3fHqQPDuN/z0pbWjj7Je38+9fT7AgPpBr50Zz77J4Prpx1oBlfSbsVR2K6tosBu51RyKR8N9LJ/OvCydy9ezeNd3OxtHSRm79KI1pUSo+umkW88YFmJMAHm5ybpwfyxN9TBXuC4lEYjYf6ElRbSthKvcxPxH6VBHZbU7XcBDj72Fdyaltw1fp0m+3LpMksS/zgd15dXxzqIwnzk0hKdiLv3xzjM5uklaDQeCDnQUsfXYL57+6kwo7862GC6WrnGvmRPFlWqnNgb49qWnW4ufh1mcfklwmZWKYj5XcqVNvIKuyuV/9OCZ8PVy5YX4MxfVt3LVkHMuSB1+RFek/06N9kUhgn50Bsm0dOmpbtDYqOcb78coXtrLsua1odXq+uWsej52bwuUzo7hqdhQvXzGVcF93Hlp7xGES7+Egt7qZx75P58Kp4fxheQKebnIeOzeFLVk1/J5uNOv56kApbnIp5/SR7EgINlZyulfGBEHgaKl9F0KR4edwceOAqjhg7Cds7dBb9D7nVbcQ5acc8SHCg8FpjviBlYm8fOVU1mVUcdf/Dtp8TXZ1CwnBvTc7m8qiRd2ymTq9wbgJHqZKzlCZFO5jJVdraO0gv7aVad1s/6RSCTfMi2F9RtWQhjf2l/rWDtalV3L5zEhc5VJev3oa7i4yLnljF7vz6qwkY91p69Bx68cHaNHo+P7u+Tx32RTuX5HIPcsSzBPXB4OpomAryInuJdCM8FVy9ezoUacn/eZgGXGBnrxz3Yxh3Rzbm5VTWNd/Vy6RkScmwIOC2lYLSWthbWu/+3FMpIR592ojXanW8Mi3x5gS4cO1c2P490WTyKxs4raP09iRU8u3h0q56I1dPPljBuenhqE3CFz//r5+BRuO5KrZ0bR36vvlTFnT0rt9dHeMPR2Wa3ZeTQsdesOAKjkAdy+N5/nLpvDgyoG7e4oMDm+FC+NDvNlfYDvIMe0feiYvxwV6cPfScVw0LYLHzknh+7sXWClL3F1l/PeSyRwrMzqGmmho7TD33gqCwHs7Crjrfwf47++Z/Hik3GH/tv7I2TWdeu759BAhPgr+ccFE833xrIkhLEwI4B8/GXtY16aVsnpSaJ9JyYQgTzSdBkobTiYyytUaals6mBIx9kwahgODQeCONQfYY2Nu4GAw7SGn9uJGaIuUrvWr+7wce8NgRwNOE+QAnDcljOcuncKmzGq22Wiwz6lqJjGo9/JvsLcbChepuZcAoLShnQ69wWkGN/ZkQrgPRXVtFhuAQyXG5saeUfhF0yLwcJWbJ58PJ98cLEUQMGvEI3yVfHXnPKL9PbjynT2Mf+w3Ln1zFzk9Gg4NBoE/rj1KQW0Lb183w6GZHKWrnABPNwvzgVat7azbWOCxc1L4362zB1356i+Rfkq7crWYgLH3vY5VYgM8aNLoqO9mZFJQ2zpg573xoV5UqDU2DVEOlzRy3qs7aNXq+O+lU5BJJUyL8uXZS6ZQWNfGNe/t5YEvjuAqk/LRTbN45pIpfHTTLCrVGu75zHYCa7gIV7mTHOLFpsx+BDnNHf0OcqZE+lDW2G7u4wFILzMGhSkDDHKUrnIumhYh9hOcYmbG+LK/0LaJgKknoaeBhEQi4Y9nJPPI6vFcPy/Gri33jBg/HlqZyOtb8nh9Sy4bT1Sx7LktzHt6E8/+nsXtaw7wj58yKGto54v9pby+Zej9rmC8Nq94ew91LdpeX/fED+nk17byypVTLVxlJRIJT5w3gepmDbd8lEZZY3u/zFFMgV53Z1uTRbdYyekf2dXN/JZeyVO/nHBI3/Vh83D0gVVygrzcCPB0tejLya9psauUcXYG1ZMznJwzOZR3dxTw9K+ZLIgPMGsCO/UG8mpa+rTCk0gkVpKN/C5NurMGOSbni205NZw7xTgz42BRIyqli1X1ydNNzqUzIvlsXzF/WJGA0nX4TuHW7BrmjvO3uPH7ebjy+W1z2J1XR15NC5/uLea8V3fy8BlJJAZ7UtWk5a2teeRUt/DKlVOHxWoz2t9yQ26qQNibHzKakUolDjfLsEWUn5LyRg2deoPZNU5vECiua+MKB83sEBl+TBWbgtpW/D3dEASBwtrWXocj2sK0MflgZwEPdps7cbC4gave2UNisBdvXzvDwiHp4ukRXDg1nAPFDajcXSz6EZJCvPjgxplIR6CSunx8EGt2F/U5xLGmRdvvbKVpUvvR0kaWjzd+t+nlTYSr3FEpnW8ejYg1M2P9+Gh3EWWN7VZDQY+XqwlXuQ9JdXD30njaOvQ881sWAIsSA0kI8uTt7fm4SCW8cfU0s4W1PYn+QJFLJeRUt3DZW7v55JbZhPpY91J+faCUz/eX8K8LJ9q8P48L9OTmBXG8uTWPGH9lv5y5wlXuKFykZFe1mK+Ho2VqAjxdCXWQi9pYZ3eesYJzpFTNzty6IZszHCpuwMfdeg/ZFya5sslhra1DR7laI1ZyHIVEIuGvZyWTUdHE90dODkorqmulUy/0ah9tItpfaWGjml/TiqtcajVc0lmI9vcgOcSL346fnJdzsLiBqZEqm/Kq6+dF06zVsWb38FVzdHoDB4sabC5wChcZS5ODuGVhHD/eu4CzJobwj58yuPa9fTy89gghPgq+vH2uOWBzNFE9qg4maUH0GAxyThVRfkr0BoGKRo35sQq1sQIqOquNHkxBTn5XT2JdawfNWt2AKznjAj35w/IEXt6Uy1cHSo3vWdPCzR/uZ3yoN1/cNtemBaxUKmFmjJ/Nhuvp0X4Dzio6gmXJwTRpdBwosp21N1Hb3H+5WrS/Eh93F450k6ztL6wntR9TxUWcg1ld85PSbPTlpJc1MTF8YBW5nhirPkn85axkHjsnhQ9vmMn/nZPC9j8tZcNDiy1m9DhKijwx3Icvb59Lq1bPpW/upkWrs3i+Uq3hb98d44LUsF5nDt27LJ7YAA+unxfTL4m3yWGteyUno7yJCWE+o04iPlLsya9jWpSK5BAvXts8dJfiQyWNpEaqBmX6MCHMx9yTmW8ev+KcRYK+cLogB2BOnD8rxgfx718yzWVXk4VdUj/cSmICLCs5eTUtxPp7OPVgu9WTQtmcVY2mU4/eIHCkpNHuhiDa34OrZ0fx/Ppss22jIAgObXLMrGymtUPf5yA9Dzc5z1+eyoFHV7D54SVs++NS1tw8e1iH2fVski+ua8NVLiXYS8wYDRZTr1P3vhxT8BgrytVGDR5ucoK93czGK4Vd/x1oTw4YLa8vnhbBn78+ypL/bubC13fhq3TlvetnDsoZcaRIjVTh5+Haq2RNpzdQ06ztc0aOCYlEwpRIlbmnQ93eSXq5mrmnyPVSZOgEeSuI9leyr0dfjsEgkF6uZuIADCTsIZFIuGPxOG5aEGvebAZ7K2xWWBxFfJAna26eRWlDO+szLAeNf5lWAsCT50/sNfjwcJOz6aHF3Djf/nDzniQGe1nYzmdUNA1Yunm6YjAI7C2oZ+44f+5eGs/u/Dq29KOPsLf3O1zcyNRBJl1SwrypadZS3awxG005qxKqL5wyyAH4xwUT6dQb+ONXR7sa4EpQKV36lWmL8fegXK0xl4DzalqthoA6G2dNDKGtQ8/W7Bp+PV5Ba4eemb0EGH89azzB3goe+vIwa9NKOOeVHXy4q9Bhx7O/sB4XmcQsy+gLf083YgM8TolkLMpPSV1rhzlLVVzfRqSv+7DbVI5lTEPvuveyFda1IpFgMRBPxPmJDfCgoCv7ZqroDLSSA8YN2lMXTeLPZyaxLDmYi6dF8PHNs+z2ITgrMqmEJYmBvQY5+wsb6NAbBrQpWJYUyL7CetRtnewvqMcgwNxxYpAzmpgZ48f+HpWcwrpWWjv0TBzFDfMJwV5MiVTxw+GThgYGg8AX+0tYPSm0X5bCA63ATI1UkVXZRKtWR3WzhppmrXnmlkjvZFY209jWydy4AFZPCmV6tC83frifJ35It6rG9YfcmhaatbpBV85N7oO/H68kv6YVldJl1K37Jpw2yAn1cec/F09mU2Y1i57ZzO78Ov7RR/bBhEm2ZJI0GZumnDvISQj2YlygBx/tKuSRb46xKiWYOXH2gxwPNznPXTaFo2Vq/vjVUfw8XB26oKQVNjAhzMcpM7ZmG+muSkNRfZsoqRoiChcZySFepHVrxC2sbSXMx31YXd1EHE9sgKe5kpNR3kSoj8KiuXgguMql3LZoHI+dm8Jj56aM2oB32fggcqpb7Nrfr8+oIsDTldTI/m8KVk4IQW8Q2JRVxZ78OgK93JzWwVPENrNi/MiuarFo1D/e1YvgiErOSHLelDC259SazUN25tVS1tjOFTOHZ4zC1ChfDAIcKW00V3TESk7/2JNfh4tMwvRoX2RSCZ/fNoc/nZHMZ/uKOe/VHeYZkf3lULHxPp46QGc1E+Eqd1alBPPejoI+h8E6O04b5ACcMSGEWxfGEujlxnd3z+93j4dJmlFY24q6vZPalo5RcZLOmhjKrrw6PNzkPHPJ5D4Dupkxfvzvltmsf2ARa26ezbx4x0wRFgSB/YX1TjvdtuesnOK61jHprHaqmR8fwM7cWrOzi+isNjqJC/CgoK4Vg0FgW3YN8x20LoxmliYFEejlxn+7msC7IwgC609Usjw5eECS5nCVOxPDvVmXXsXu/DrzkF6R0cPCROO1sS3npJtrepmaYG+3fvdnOStnTwpFLwj8lm6UrH2+v4S4AI9hu68nh3ihdJVxqLiRjPIm3F1kp/X4gf2F9Sx/bgvLntvCOa9st+i57snu/DpSI1XmpLKLTMqdS8bx070LkEoknP/qzn7Z4JvYml0z4CGgPbl1YRyFdW2sz6ga1ckbpw5yAP52dgq/3b+I5JD+ZwSCvRS4yaUU1bVxsCui7ell74xcMDWcCF93Xrpiar8deuaNC3D4VOXShnaqm7V99uOMFEFebrjJpZTUt6E3CJQ2tItBjgOYH+9PdbPW3OeVX9MiVshGIbEBHnToDOwpqCO/tpUlSYEjfUgjjoebnD+fmcxv6ZXszK21eC6rqpmS+nZWDtCBDowDmjdnVZNR0cQcsR9n1BHq405KqDcbT5zcQB53UD/OSBPio2BWjB/fHirjxyPl5pl3wxWIy2VSpkSoOFDUQEZFE8mhXk7dBz3c+CpdWJgQyKKEQLwVLtzxyQFu/TiNN7fm8cX+YqqajCY/6eVq9uTX2Vw/EoK9+P7u+cyI8eXhL4+YK44V6nab9v4A1U0a1qVXccn0iCEd//RoX6ZEqozjV4Kcv0hgD6ezkHYEUqmEaH8lBXWtHChqYFygx4AHtI0E8UGebP/T0hHPBpo0yjMGOCn3VCGVSsxzXcob29EZBNFZzQHMivVHLpWwM7cWTaeBvJpW7l8hDigcbcR2SXM/2FmIVAIL48UgB+CiqeF8sqeIJ39M5+f7Fpqt0tenV+HuIhuUZeuqCcHmgY9iP87oZPn4ID7cVUin3oBcKuF4WRPXz4sZ6cNyCOdOCePR746zr6CeWTF+XD7M4wCmRav4dG8xvh6up70JR3yQF0+cNwEwVou/O1zGyxtz2Z1XR4tWh1RiHAR/pFRNlJ/SblDi4Sbn2UunsOL5rTz5YwYXTg3nnk8PEu7rzg/3LLCSk3++vwSZVDLkIEcikXDrwlju+fTQqFBC2WNMBjlgNB84UNhAXk0LfzkrecQDh/7iDMe5v7CBuEAP/PvpNDQSJHUN+FuUaNzAiUHO0PF0k5MaqWJHbh2HSxoJ81Fw5sSQkT4skQES6atEJpWw4UQV06N88VEO/5yl0YBUKuHJ8yZwwes7eW9HAXcsHgfA+hNVLEwIGFTvWVKwF1F+SrQ6PTHiGjQqWZYcxCubckkrbCDER4G6vZOJoyAp2h8umhZOk6aTxYmBTDgF1anp0b68tjmPhrZObl7Qf2e2sY5EIuHCqRFcONUYeDS2dfD94XI2Zlbz9/MncMXMKFzl9oVVwd4K/u/sFP709VF+PFrOrBg/DhU38p/fMnn83Anm1+n0Bj7bV8y5U8KGNOPJxFkTQ3nxcoHFiaM3UTZ2g5wAD9ZlVOEql3LxtKFFtKcbixMDmTrIhrVTxYMrEznrxe088UM6IDqAOYr58QG8sz2fDp2BP52ZZM52i4weXOVSIn3dKaxrE6VqPZgSqeL6uTE8vz6blSnB7Cuo52ipmpsuH9yGTCKR8NCqRNo69E6RoBIZOFMiVPh7uPLb8QoyKprw83Dt1dl0NKF0lXPXkvhT9nlTuxl3iM5q9lEpXbl+XsyAKoaXzoggrageX6Urfzwjifd3FvDvXzJZmBDAsmSj1HbDiSoq1BqumRPtkOOUSSVcMDXcIe81UozZIMeU2V89McQhEe3pxGjI3o8L9OS+5fE8uy6bEG+F6ADmIBYkBPDSxhyUrjIuHyYXHpHhJzbAoyvICRrpQ3E6/nhGEhtOVHHjB/spaWjjylmRnJ86+MHF56eO7k3A6Y5UKmFpchAf7S7CRSbhk5tni3uGQeLr4UpcoAeFta0D6qMW6RuJRMIzl0wx//8tC+LYnlPLzR+lcdWsKFRKF97ZXsC0KBVTRrH9uaMZs2la09DQqx0U0Yo4H7cvHkdyiBcJwaNXL+pspEaq8FbIuXxm5JCcWURGluRQb0J9FGI21QYebnKevmgyxfVtXJAazj8vmCRWYU5zzpxgTOz984KJzD7Ne0mGyuxYfxKDvZxy/MRYQiqV8N71M/nrWcl8e6iMd7YVcNP8WD66aZa4nnVDIpj8YkcAlUpFY2PjsLy3IAgU1LYSN4obpk4lw3kuhpPGtg50BoEAJ+4fGigjfS5K6tsI8nbDTS7epEb6XAyWtg4dzRodwd6KkT4Uh+Hoc1Fc10a4r/tp7QA1WEbrddEbpQ1to1L27GznokWro02rI2gMrT39ZaTORUOrcR802m3Ph4MxK1eTSCRigHMa0F+rbZH+EynacY96lK5ylK5jdnl3CFGiUYBIN0ZjgOOMeLrJBz18WGRwiPJK+4xoJUcqleLtLcophgs3Nzeqqqr69VrxXAwv4rlwHsRz4TyI58J5EM+F8yCeC+dBPBfOw0DOhYkRDXJEREREREREREREREQczZg1HhAREREREREREREROT0RgxwRERERERERERERkTGFGOSIiIiIiIiIiIiIiIwpxCBHRERERERERERERGRMIQY5IiIiIiIiIiIiIiJjCjHIEREREREREREREREZU4hBjoiIiIiIiIiIiIjImEIMckRERERERERERERExhRikCMiIiIiIiIiIiIiMqYY0SAnODh4JD9epBviuXAexHPhPIjnwnkQz4XzIJ4L50E8F86DeC6cjxENcrRarcPe60BRPQv+s4mG1g6HvefphCPPxalgV24tu/Jq7T7/v71FnP/aTvQG4RQelWMYbefCWRAEgcve2s1bW/Mc9p69nYtDxQ386asj6PQGh33eUHlrax53rDkw0ocxLIzUdXHd+/v4cGeB1eN//zGDJ35IH4EjGnmcbY169vcs7v/80EgfxojgiHPRqTew+L+b+f5wmQOO6PTF2a4LR1HbomXeUxs5UtI40ocyYMaMXO1QcSOlDe38cKR8pA9F5BTwwoZsHvve/gZjf0E9R0oa+eVYxSk8KpGR5EBRA/sK6ll7oPSUfN6XaSV8mVbKl2mn5vP6w76CerZm12DoEdxrdXrKGttH6KhGL/WtHWzLrmFvQb3Vc/sK6/j2UNmoTKSMNfbk1/HL8Uo0nfqRPpRRSVZlM0V1bTz9a6bd7/Du/x1k4TObuO+zQxwsbjjFRygykvx6vJJytYYdufYTy87KmAlyTDfwr07RBkdkZClv1JBb3UJRXavN54vr2wB4bXMugiBuQk4HPt1XjFQCudUtFNTa/l04kj359Ugl8Pz6LJo1ncP+ef2htKGd9k7rgObzfSUsfXYLudXNI3Rko5N9XcFNUV2b1XOVai3q9k6Ol6lP9WGJ9KCovo0OncF8vkQGhuk3XNWk4ZM9RTZfsz2nBpW7Kztzax1aLRdxfn4+aiweZJQ39fKailNy3x0ooybIqWvRklZofwErb2zHVS7lWJmazEr7J0Jk9KM3CFQ2aQDYcKLa5muK69uZFqUis7KZzVm2XyMydlC3dfLz0QpuXRiHq0zK+ozKYf28SrWGgtpWHlqVRJNGxxtbRv6mLwgCJQ3GzXh2lWUwU9pg3AT+6aujYuVhAJwMclotkiUdOgN1rUZpyvacmhE5NhEjbR06aprFczEUjpapGRfowYVTI3h9Sx4tWp3F8+r2Tpo0Om5aEMPy8UGUNohV4dOFmmYt+wrq8XF3Ib3cdkJHEAQeWnuY69/fh7rdORJ+JkZNkPPWtnwueXM3r27KsZmZL2ts56yJIaiULqx1IvmIiOOpbtagNwi4yaVsyqyyer6tQ0dti5YrZkYxJcLHKTagjqKhtaNXXazBIJyW2fpvD5WiMwjcOD+WueP8WZ9h/btwJLvzjWX7S6dHcOP8GN7bUTDi1ZyGtk7aOoxSk+yqFovnals6UCldOFjcyEe7Ckfg6EYn+wrr8HST09qhp65bv2d1swZBAHcXGdtzRp+EYyxhqtqHq9ztnoucqmY+31d8Kg9rVHGsVM2kcB/uX5GAur2Tbw9Z9uaUdiVPIn2VRPgqKam3rmyKjE1+SzcmDG9bFEdhXZvN+1x1sxZNp4Hi+jYe+vKIlVx6JBk1QU5pQxserjKeXZfNv34+YfV8eaOGGH8Pzp8SxneHyuh0ombgwXKouIFFz2xm44nh3bCNNsq7pDjnTgljb349TT0uupJ64/NR/krOSw3nUHHjmMlef7CzgGve22tXgvfmtjxWvrDNnNkczRTXtfHW1rw+b6iCIPDZvhKWJQcR4qNgZUowB4oaqGsZvu9gT149cYEeBHkruHJmFFqdgR0jvNk1fU+ucik5PSo5tS1aZkT7ccXMSJ5dl+VUZgnOSpOmk4zyJs6dEgZgIY2t6qoknzUphIPFDbT2yHyLnDqKu6SEV86KJLOymequc2OirLGdq9/dy9++Oz5m7gOORKvTk1nZxKQIFZF+SiJ93cmrtkySmO6pkX5KIv3cadLonC5jLzI8/HK0gtmx/ixKCATgRIV1EtUk571veQIbTlTxRVrJKT3G3hg1QU55o4ZVE0K4bVEcH+8ustjktXXoqG/tINzXnVUTQqhr7XBKbeBAyKlq5sYP91Op1nD/F4fNC7kIlDUab2LXzolGZxDYlm0pUTBl9qL8lISrFOgMwrBueE8lJQ3tNGt0NoOY0oY2Xt6YgyBAhXr0ygk0nXr+9u0xlj23had+zWT1S9t7NRQ5XNJIVlUzV86KBGBlSjAGATZmDp9McXd+HXPj/AGICfAgLsBjWD7vtc25bOmn3NIkIZkb509Oj01KTbOWQC83liYH0dahp2aMXA/DyYHCBgwCXDojAoDC2pNrcKXa+P1dOj2STr3A3oK6ETlGEeN67yqXcvF043nq3hzdpOnkpg/2U9fagd4gjInkj6PJrmyhUy8wOcIHMAYypsqNidIG43cc6OlGhK/S/JjI2Ka+tYO9BXWsnhxKYogncqnEpmTNtOe6bVEcE8O9OVDkPMYUoybIqVC3E+qjYGK4Dx16g0UWobxr0xuucifKz3gBlo1izai6rZPr3t+Hv4crvz+wCB93F+783wHROaaL8sZ23F1kTI7wIT7Ik009+nKK69twlUkJ9lYQ6uMOQIVaY+utRh2mhvK8Gusg/u8/ZiCTSABj5n608vqWPL7YX8IDKxPZ9ZdlzI8P4L7PDvF7uu0+my/2lxDirTBnmoK9FUwK9xk2GVFZYzvF9W3M6QpyAJYlB7Elq9qhZfq6Fi3PrcvigS8O9+t8ljS04e4iY06cP7nVLRbHUtvSQaCnKyHeCsDYUyTSO3sL6vH3cGVqpAo/D1eKulUUK5s0uMgkzI71I9jbjW3ZomRtpCiqayPS151QH3cSgz0tKqof7CikqL6V/1w8GUB0GLTB0bJGpBJICfUG6JKjWX5PJfVtRKjckUolRJqDHPG7HOsU1rViEGBqpAo3uYz4IE/SbZgPFNe1EuDpiqebnDAfd6dKso6KIKdTb6C6WUuoj4JgLzcAqppO3vRNC1e4yp1gbwUSCZSO4sXsq4OlVDdr+fDGWcQGePD61dPIrGy263pyulHe2E6YSoFEImFGtC8nKi3LpyX1bUT4uiOTSgj1MW7qnOmiGwqmf0fPSuX+wnrWZVTx2LkpAKM2Y5lX08KbW/K4eWEsdy+NJ0zlzhvXTGNKpMpmL0mrVsePR8q5dEYEctnJ5Wx6tC+HS4Ynm7SrK1NsEeSMD6K2pYOjDnTaWpdRhQDoDAJP/pjR5+tLG4y/+8RgT9o79eZNiN4gUN+qJcDLjZCu66GqafQHOQOV3NU0awfktLivoI5ZsX5IJBKi/ZVWcrUgLwVSqYSZMX4cLW0c0LGIOI6i+jai/T0AmBXrx+Fu56KgtoXkEG+WJhkTIGPlPuBIjpWqiQ/yxMNNDkCErzulDW0W10pJQzsRXQnkIC83XGVSsS/nNMAk/QzuSo5NCPOx6bBWVN9GZNfvI0zlbi48OAOjIsipbtYiCBDq427+srvfpE1VmxAfBa5yKcFeCnPfxmhDEAQ+3VvEyvHB5h/N5AgVZ00M4cNdhaKmGGPlLkxlrNBE+ikprbdckIu7XXD+nm7IpZIxUckxGARzBr6g1lKOdKKiCZlUwiXTI/FWyKltcd6huDXNWp5fl8Uzv2Xy0oYctufU0NjWQUl9G49+e5xALzf+sDzB/HqJRMI1s6PYlVdHfo3lv/vnoxW0dui5dHqkxeOpkSpK6tsdXtEyGAQ+2FnI9GhfArsSLgAzY/zwcpOzyYH9c78cq2BmtB+PnZPCj0fK2dCHmUJJfXtXkOMFnHRYq2/twCBAgKcbAZ5uyKSSUV/J+XJ/CRe9sQutrn/V7Te35jHzXxs4++UdrE0r6TPY0RsEjpc3kRqpAiDaT2lhI12p1pgDxnBf91H/fQ6FrMpm/v5jxohZ9RfXtZoVHNF+HpQ1tJuPpayxnXBfd/w8XHGTS6lwos2Xs3C0VM2kcJX5/yP9lLR26GloO6mWKak3VssApFIJ4b7uYiXnNKCqSYtMKsHfwxWACWHe5FQ306GzTDAV17cR3XUNhqvcKW9sd5rRHaMiyKnoClhCVQqCvI0bi+pumeryxnYCvdxQuMgA401ntMrV9hc2kFfTypWzoywev3F+LKUN7WwQTQgob2wnvCvIifB1p1mro6n9ZONvcX2b+aYnk0oI9laMiU1IbYuWTr2ATCohv4dcrbZZi5+HKzKphAAvN6eu5Ly0MZs3t+bz3aEy3t2ez7Xv7SP17+tZ+Mxm9hTU8Y8LJqB0lVv8zTmTw/BSyPlsXzG1LVoe+OIwN36wjxc3ZDM/3p8of6XF602b08PFjQ499l+PV5JR0cRDKxMtHneRSVmUGMgmB9mVN7Z1sDuvjrMmhXDJ9AhSI1V83Eclt7TBGNyHq9xxd5GR3eWyZwr0TAFOkJcblU3O+/voD0khXqSXN/HihpxeXycIAi+sz+bpXzO5YmYk/p6u/PGro/x2vHeL8bKGdjp0BhKCPQGI9vewqORUNmnM0r9QbwVVzdrTNgFVWNfK+zsLeH9n4Sn/bL1BoLSh3bzeR/i6o9UZzD1nZQ3tRKjckUgkhKncRblaD9o79GRXNTMp3Nv8mCmYMVVqBMH4HZsSh3Cy2iMyMgiCwMHiBv76zbFhVfhUNWkI9HRDKjXK4CeEedOpF6xGFBTXtRHVVU0NVSnQ6gzUtzpHolXe90tGnvKuDWqYjztKVzlebnLLSk5juzmzD1gsZmWN7Zzz8naUrnJiApQ8f1mquRrkjHy2r5gIX3cWxgdYPD4tSsWUCB8+2FnAGRNCRujonINydbv5O4gwLcgNbfgofTAYBErq27h8xsnMfoiPYkxUcky/6ckRPlZytZqWDnO2JdDTeYOcuhYta9NKuWNxHA+uSsJgEMiqauZERRO+Hq7E+HsQG+Bh9XfurjIunhbBl2ml/HKskrYOHamRKiL8lNy9NN7q9dH+SnyVLhwuaWRFSrBDjl1vEHh+fRbzxvkzr8f1CbA0OYiH1x6huklD0BDXmPUZVegMAmdODEEikXDmxBCeX59Ne4ced1eZ1etNG5EIX6NuPiHYk5wuG+mTQY7x9xHsrRj1crUpkSruXRbPyxtzWJ4cxIwYP5uv21tQz0sbc3hgRSL3LY9HIpGw6oWt/JZeyVmTQu2+f26N8SYeH2isikX7K2lo60Td3omPuwtVTRomhhkbtUN83NF3mZsM9byPRs6YEML1c6N5+tcTzIzxZXKE6pR9dnljOzqDQHRXksO0ES+pb8dP6Uplk4bwrntEqI9ClKv1YEtWNTqDwIKEk+tZRLeemymRKmpbOmjv1Jt7cUyvOehEzeWnG/d8doifj1agcJHyzUGj2c5w7GurmrQEe59ULCR39W1lVzUzMdy4/rVoddS1dpgTDaa9eHmjBn9PN0aaUVPJUbhIUSldAAjydrOwiSxrNGZrTJjKZQBphfU0tHWyakIwu/Lq2GhneKQzUN/awc/HKrhyVpQ5cjYhkUi4cX4se/LreWF9NhtPVFlZJ58OtGp1NLZ1EqYyXtCRPZxealq0aHUGi6xTqM/YqOSYArUF8QEU17dZ2KTXtmjN8qlALzendc/6eLcx63TdvBjAKH0YH+rNRdMiWJoUZDPAMXH17CjU7Z14usn54Z4FfHDjLL68fS7zxlkHHBKJhCmRKg73MlNooHx7qIy8mlYePiPJ5vOLujYKjjA8+O14JamRKrNxxtKkIDp0BvN8np6YfvemDUpCkJc522YOcrp+HyFjpLJ599J4JoX7cOOH+zn/tZ088u0xqz6dDRlVBHi6cu8yY4ADsColhE2Z1VaSi+7kVrfgJpeaN8imno/iOqM01ihX6/o+zX1/o/87HSx/XT2ehCAv7v3sEO0dp84gx+TqZApyTEmv0oY2Kps0GATMVf9QH+fqFXAGfj5WQWKwJ/FBXubHAjxdcXeRmQcLm/4b6XdyjxXpZ923I3Jq2J5Tw89HK3ji3BR2/WU5bnIpr2zqvaI9WKqbLRN2Pu4ueLnJLdY6k/Ov6RoM67pnlTtJQmF0BDlqDaE+7uabVJCXwkKuVtbQbt70glGuVtWkoVNvILOymUAvNx4/dwJJwV5OZW3Xk5c35iCXSrhsRqTN51dPCmXF+CDe3pbPzR+lMeOfG7h9TRp5PfoUxjKmTJzpxhXg6YarXGrWB3e3jzYR6qOgosk5LrihUN7YjqtcyvRoX3RdFSsTtS1aArqyJgGebtSOYCWnvLGdvfmWlrqCINCi1bFmTxEXT48wH+tASAj24vu75/PNXfMsglh7pEaqOFLS6DDHMxeZhCtnRTItytfm80HeCpJDvIY8db2kvo2t2TWcM/lkpSEx2JMwHwVbsmy/t3mOhSnICfYkr8bosFbb3IGbXIpXV2NxiM/or+SAUSL4+jXTuXxGJNF+Sj7dW8zv6ZZy3s1Z1SxODLJIGq2aEEyzRseefPu2z7nVLcQGeCDr+ruYrht4UX0r6vZOtDqDOXMaOoxBzmjZRCpcZLx8ZSplDe28uz3/lH2uqU/KFNz7uLvg6SantKHdLFk3BarhKrGS0x1Np55NmdWs7lHRlEgkRPi6m+8vpv/2rOT07NsRGX70BoF//5JJaqSK6+fF4Ofhyu2Lx/H5vpJhGTNS1aSxqOQABPdIGhfXG1Ulpj1XoJexD9pZ+uJHSZDTbr6RAAR7u5lv0nqDYCxJW1RyFBgEY3NoZkUTySHGLMX0aF8OFjtnkJNT1cyaPUXctWScRUNzd1zlUt69fibpT57BxocW88CKRI6XNXHbx2mnNHs2kpgycaaSqFQqIUJ1sgnSdKF3zzqF+Bgbgwe72c2pauZ4mXrEs9/ljRrCfBSMCzT2CXSXrNW1dJjlSKe6ktOpN5iz4rnVLVzw2k4uf3sPt36cxtcHSrnqnT2Me+QXJj7+Ow1tHdyyIHbQnzUlUmV2AeqL1EgVzVod+bWOSQKcnxrOUxdN7vU1CxMC2JFbN6TA6s2teXgq5Fwx62RfnkQiYXFSEJsyq21ufE2VTFMmOzHYE02ngZKGNnMAbEoSBXsrqGzSWLyPur2TF9Zn97uRfzAcL1M73JEpXOXOo+ek8PKVU5kR7ct7O05usIvr2siraWVZcpDF30wK9yHEW8G6DPt9ObnVLcQHeZr/38/DaI9aVGesEADmnpyTZg6OvamXNrQx698b+XBngUPfd7iID/LimjnRvLE1z2YQ3aEz8PPRil4raAOlqL6VEG+FuR/XtEEvbWgzy3tN94pQlTu1LR3iKIYutmTV0NahtwpywCj7K+m6p5Y2tOPpJjcracC6b0fEmlatjtc25zp0MP23h8o4UdHEI6vHm9fzG+bFoFK6DEs1p6pJS7CXpQwu1EdhXgPBmGhQuEgJ6tq3mvqgnaWyPUqCHI1ZtgEmTblxE1fVpEFvECx6csJVXbNyGtvJqmxmfJeOcEaMLwW1rU43Q0QQBP7x8wlCvBXcsjCuz9dLpRLGBXpy55JxvH/DTEoa2vnnz31bzI4FTNmBkG5Bb4Sf0rzYFte34efhipfi5IIc6qOgUy9QN4hGuPd2FLDyhW2c88oO5jy1kbe35Q3xXzB4yhvbCfVxJ0zljqtcahHkdK/kBHq60azRDfvN/GhpI3d+coCpf1/P1L+v4w+fH+KKt/fgqZDzrwsncrikkYfWHqGtQ89fzxrPfy6exDd3ziMu0LPvN3cAJvOBQw42H+iNhQmB1LZoyay0ngrdHyrVGtamlXLT/Fg8ewRzS5MCKW1otzkjqedGJCHI5LDWQk2L1ixVAwjxcaOtQ0+z9qRZx2f7inlpYw7fH7Y/dHUodOoNPPDFYf741ZFheX+AmxfEcrC40ZzI2pRZhUwqseg3AONGeNWEYNZnVNkMRgVBIK+m1SLIkUgkjAvy5EBRgznZYVqDZFIJwV5uVDi4OvbLsQpqmrU88WMGz6/LGhVVnftXJOAik/Ls71kWj+v0Bu7/4hB3f3qQ93Y4LmgzNjxbVnUjfJXmSo6XQo53173AtEcY6WSVs/Dr8QrigzzNbozd6W4sYLKmN22q4WTvk+iwZp/9hfU8vz6b69/fR2Pb0JvwO/UGXlifzYrxwcyKPdmD6OEm5/KZkfyeXjlgW/3e0HTqUbd3WvX69OzpNBk9df99hDuRyceoCHKMlsEnv+jALvcoQRBOzsjx7W48YHxtRnkT5WoNSV0X8fQo4w/DmRrm2jv0PP5DOtuya3hk9XhzRqq/JIV48chZyfxvbzHr+7CYdWZK6tv65U5U3thOgKerxfcU0c3O8niZ2mJzAic3IwO9uX24s4B//JTBDfNi+PrOeVw9O4qnf81kd97ITDevUBsNNmRSCTH+SvNmt61DR1uH3tzkZ6oEDrf5wJ++OsrRUjU3LYjl5gWxHCtTE+nnzhe3zeXq2dFsfngJWx5ewnd3z+fWRXFcPjOKqXakXsOBSulKbICHQ/ty+mJWrB+ucumAJGs6vYHL39rNXf87wKPfHcdNLuX6rp6l7syPD8BFJmGLDQe3nhuRcJU7SlcZ2VXN1DRrCeyq8sHJmQem60EQBL5MKwHgo12Fw7KZ/nBnIbk1LTyyerzD39vEqgkhRPq5mzfRm7NqmBHti4+7i/VrU0KoatLyy/EKq+dqWzpQt3darSOXTI9gc1Y1ewvqASxu/iHD0Pf3e3oVS5MCeXBlIi9vyuW69/dRWGsd4DoTKqUr969I4KuDpeZrQG8Q+FOXo11qpIo3t+ZZDPMeClmVzST0OE8mqVVZNxdOgLCu+4Cz9AqMJOr2TjaeqGb1RNsmRpFdgaLRyKfdSh7s72HZtyNizZKkID6+aRbp5U2sfGEbi/+7mWn/WE9u9eASYN8fLqessZ37VyRYPbc0KYgmjY5DDrzXVXcVEoJ6yNVCexg5dXezNRGmUphdkUcapw9ytDo9tS1aq0pOh95AY1unOdvQfTHzUrjgrZCzuWszkBxqDHIi/dwJ8HRzir4cQRDYeKKKs1/ezuf7S3j07PGsnjQ417Tr58Vw1sQQh2QLRoLf0ytZ/N/N/PuXE32+tqzbjBwTpqyTTm9gX0E9c7sNaQQGNRD0eJmaJ340BjiPn5vC9GhfnjxvAjNi/Lj3s4O9bmgyK5to69DZfX6wlHUL9mMDPMyzcmqbjee9u1wNGNaKZXljO5mVzTy0KpEHVyby4KokNj20hG/vmm/+fE83OTG9GAmcCm6cH2OR9RpuFC4yZsX4Dch84EhpI3sL6jle1sSGE1XcMD/G5sbcw03OnDh/mzbyJmc1E1KphIQgT7Krmqlt6bDogTKtpabf8MHiRvJrWrl2TjTp5U2kOXh9rG7S8OKGbK6cFTWszlsyqYQb58Xyy7EK7v7fQXbn17G0h1TNxJw4PxYlBnLPp4d46pcTFpKS3GrjdTWuR8Xx4mnheCtc+GBnASqli0WiJdTHsbNyqps0HCxu4IwJIdy3PIF3rptBXnULq17cRlphvcM+Zzi4bm4Mc+P8+cPnhymqa+WeTw/y7eEynrtsCm9cM432Tr1DKuJtHToK6lpJCfO2eDzST0lZYzslXYG/idCu+8bpPitHpzdwz6cHkQCX2un/jfRzp0NnIL+2lYyKJvMMFBPdZYEi9pkfH8B3d89naVIgy5OD6dQbWHugdMDvozcIvL4llyVJgWZXs+6kRqrwVshtJsAGS1Wz5SBQE8Heiq5xFsY1s6iujSg/y/t8qBMNBHVIkKPT6Th06BBqteOmfZuoUhs3apY9OV0DQZs1HC1VE+qjsNoUhPsq2ZNfh0wqMWfkJBIJM6J9RzzIKaht5fzXdnLzR2n4erjy070LuGVhnEW5byBIJBJev3qa3QXrVKHTG9iUWcW69Mp+Z89359Vx72eHCPRy48NdhWT1IfMxSrYsLzpTE+SO3FqatTrmjrMMcoK8FEbN/ADkJB/tKiTEW8Hfzj6pfZXLpLx65VSkEgm3r0mzKQdr0eq48YP9/N936f3+rP5gCvZNAV5coKd5Vk5Ntzko3f87nJWczVnVSCSwODFw2D7DEVw3N4bzU8NP6WcuTAhgX2E9rdr+Bbpbs2vxcpOz8aHF7PrLMotBqD1ZlRLMvoJ6qxkEZQ2WWWswGjVkV7VYSBnhZC+J6Xr46kAJwd5uPHZuClF+Sj7cVdiv4+4v//z5BC5yKX9cZduVzpFcNzeax85J4UhpI516AyvG2w5y5DIpH94wkz+ekcQ72/MtZk3k1rQglWDl9Kd0lXP17Cg0nQbzd2gipIdOHeBwSSNP/5rJTR/u5/7PDw2oQrYuowoJmO3PV6YEs/7BxUT4ujtU7jUcyKQSXrpiKnKphJXPb2NjZjWvXzWNC6dGEOrjzvVzo3l/RyFbsqqHNFsos7IZQcAsRzcR4etOp17gaIna4prwdJPjrZA7TUP0SKA3CDzxYzo7c2t59eppdg1cTEYO9312CE2nnuvmxli9JiHY85RKgUcrsQEePHPJFB47N4XVE0P56UjFgHs216VXkl/TanNcAhjXs4WJgXaNaQaDSZLWM8gJ9VEgCMZZlVqdnrLGdmICelZy3Klu1ji0H2mwDCrI2bRpE/7+/gQEBLB161bmzZvHZZddRlxcHFu3bnXoAZqy76Hd5GqmBqeqJi1phQ3MjPGzChDCVcaFLi7AAzf5yYzb9Ghfjpaph7XBtjeaNJ3c/NF+Gts6+eimWXx1x1ybmtiBMtgAyZG8uCGHmz5M47Y1B7jgtZ282kcjXF2Llts+TmNqpIp1Dxhv4I99f7zXzUBxfZvZztWEqQlybVopbnIpU6NUFs+bBiD2txGuobWDH46Uc9XsKFxklpdIkLeCt6+bQWZlM3/86qjVsT77exb1rR3ct9z2YjRYTFliU4AXF+BBdbOWJk2nuWJjqqD4d1V0aluGr7K3ObOa1EiVU/jgOxtnTQzFYBB4d3v/NqPbc2qYO84fF5mUMJU7cpn9ZXllSggGAYtqjkm2212yC0bzgbyaFupbOyzMTNxdZXgr5FSpNbR36PnxSAUXTYvARSblurnR/Ha80mEuVL8dr+SHI+U8ctZ4fD1c+/6DISKXSblxfixbHl7C1oeXWljj9kQqlXD30niWJQfzxf4S87WcV91CpJ/SpnT4urkxyKUSi55AOCnhEASBwtpWrn1vLxe8tpO1aSU0tnXw3eHyAclcf0+vZEaMn0Vw6uEm58qZUazPqHLaOVgmAr3ceO3qacQHebLmplkWM4nuWhJPlJ+SGz7Yz/ynN3G0tHFQn3GiogmJBLOxkAlT9aZZq7O6JsJU7ua5e6cTmk49H+4sYNlzW/hkTzGPnzuh1wSVyUkto6KJR89Osep7AuM6l17eRP5p5O46VM5LDaOssZ1DJQNLtL+5NY9ZMX7MtDMPDGBJYiDp5U1UNzvm913VpMVFJsFXaVlA6C53LqhtRW8QrKS9YT5G8y9ncPEcVJDzyCOPsHHjRr766isuueQS/vnPf5KTk8NPP/3Eo48+6tADrDBv7k4uViaNYEFNC+nlambGWOv8w7uCouQeWZ5p0b506AwcK3V81akvDAaBBz4/THWTlvdvmMHixECnCE4cQVFdK29vy+fWhbHsfWQ59yyN59l12byxxb4s4cNdhXToDbx61TR83F14/NwU9hbU89NRa508GKsZ5ep2sx+7CVPWaX1GFdOjfS2CWhMD0cx/mVaCQRC4YpbtylhqpIpnLpnMj0fKWfXCNv7+YwYbMqrYmVvLR7sLuX9FolUgNlTMvWddmUnTopJX3UJdVzDj17WJdJFJ8VW6DNtGSNOpZ2duHcuSbGfJT3ei/JVcOzeaN+24THVH3dbJkZJGFvazIhbio2BqlIrfj590Bqtt6UCrM5gNV0wkBHvRoTOgNwhWlt2mysMPR8po0eq4dHoEYJSvKORSPnDA9Pr61g4e/e4YS5MCuXRGxJDfbyDIZVKbGzNbXD4zkszKZo6VGe8JeTUtxNsxxwjxUfDwGUlcODXc6vEOnYGGtk7+/csJsiqbefHyVPY+spyv75xHfJAn7/fTJU3d3snuvDqbQ58vmhaORAJfHxy45OVUMzPGj1/+sJDZPeTDvh6u/Hb/Qr6+cy4+7i786aujg8r4ZpQ3EevvgdLV0qAjopvVcc9rItRHcVpVcjr1BtbsLmTRM5v5+08ZpIR68/Wdc232/HXHR+mCn4crS5ICudLOfXD5+CDcXWR279ci1syJ8yfA040fBmDwkl6u5kipmhvnx/T6usVJxnvIVgdVc6qbNAR5Kaz2qKHdepxNypukHon67gNBR5pBBTkdHR2kpqayZMkSvL29WbVqFQBz586lpcWxUX25uh2PrsyjCaWrHC+FnN/SKzEIMNOG5t6UwemZ5Zkc4YO3Qs6GIQwF7dQbBrUof32wlI2Z1Tx/2ZReM4yjkX/8lIGvhwv3r0gk2FvBQ6sSuXPJOP7zW6ZNDXmzppOPdhVyxcxIc5Z5WXIwCxMCeH59tk2XkJL6dgQBYnoEEEYjAikdeoNVP46J/t7c9AaBT/YWcebEUIJ6WCd25/zUcN69bgaTwn346Wg5t3ycxtXv7iUp2ItbFg7eItkeJh25SVc+zhTk1BjdAn2VLhZVJ6ON9PAsMHvy62jv1NvtdxCBPyxPwEUm4bl1Wb2+bldeLQbh5CDR/nDGhBC259bS0iWHM/clWlVyLAf8dcdk8fnWtnyWJQeZHe983F24clYUn+4tHtKwYUEQePS7Y3ToDDx10WSnTuYsSQokwNONL9NKaNJ0cqKiyXx92eKOxeOsJJAm+VphXSvbc2q5enY0F0wNRy6Tdg1yjmFjZrWFI6I9TNI5Wz2a/p5urJoQYlF5Go1IJBKmR/vxzCWTyapq7nfVszsnKpoY36MfB4y/YdN+wVYl53SZlbMuvZJVL2zj/75PZ1asH+sfXMwb10xnenT/ehS/vH0Or101ze61q3SVsyIlmB+OlI/q3+KpRCaVcM7kUH4+VtFvJ7S1aaX4e7iyfHxwr68L8lIwIcx7UJK19g493xwstZDR2ZqRA8ZkqqtMSmWThpyqFvw9XK0UHaYgxxmutUEFOQbDyZNz/vnnWzyn1ztWBpZR3kRkD3s6MErW9hbU46WQk2gjYDBlcMaHWj7nIpNyxoQQfj42uAtTpzdw6Zu7ufOTAwP+21+OVTAj2pdVNjJ0o5ldubVsOFHNI6vHm2eYSCQS/rgqiWh/pc2s8Kd7i2nr0HPrIkvL7IdXJVFQ28o3B8us/qaoznLolAljE6TxsZ79OCZCvN371ZOzLr2Skvp2rp8b3edrV6QE83xXtnbjQ4t5+qJJvHnNdCuJmyMob2zHWyE32wp7K1wI9nYjt9rYc9FzkQn0cjMbEjiaTZnVBHu7McHGBkPEiErpyn3LE1h7oLTX5txtObVE+SkHVPk7Y0IIHTqDucm0Z5XPRJiPwvx7CegxeyvEW8GOnFrya1q5vcc1eNOCWDSdej7bW9zvY+rJVwdK+eVYJX8/f6KVtMvZcJFJuXh6ON8fLueyN3ej7TRw3pSwAb2H6d/43aEy2jv1LO/RC3TR1Ah83F34qI9+pxatjne253PpjEgL9UJ3rpwZRUFtq9nlbTQzJVLF9XNjeGljNk/9coLn12f3q+JuMAhkVjaTEmp7DTLdD6yuCZU7ZQ3tw9Ir8N2hskE7ZzmaH4+Uc9uaAwR7u/HTvQt49appVkYafREf5NXnTLJzJ4eSW91CenkTn+8r5mexqtMn56WGUdtilMTbQ93WicEgoOnU8+2hMi6cGo6rvO99xfLxwWzNrhnw+IhvDpXy4JdHLKrNVU1aq34cMO63gn3cqFS3k13VTEKw9e/KWyHHw1XmFDbSg9qNJScn09TUBMDzzz9vfrywsBBPT8fNwKhu1vB7eiUXTLVuHA72NjY/zYj2tZhmbWJqlIqZMb42p5OfPTmUkvp2szxhIHy8u4jDJY1sOFHNkQHY9bVodezMrWNlSu/R+Gjk64NlRPsrrTYGUqmE6+bG8Ft6pUUVRavT8+6OAs5PDbeQFoDxprcqJZiXNuZY9U0V1rXhIpNYuauBUYft7iKz695ktDTUWGVPqps1fLavGJ3egCAIvLwpl1kxfszoRfvaE4nEOLfoillRw+YmllfTYvXe8UGe5iCnZ6Y+wHPgA0FrmrV93vwPFTfw+b4Szk8Nd+rsvDNwfmo4goBdoxNBENiWXcPCAVRxwNjImhjsyYYuy/iyhnbc5FKr34BEctJ0xZZcrUNvIDVSZeU+F6Zy57wpYby/s2BQgxsLa1t54od0LkgNs7l2OyOXzYikWaOjoa2DL++Ya9PBqDeMsg745mAZoT4KqwSAu6uMq2ZF8WVXj449PtpVSItGx91Lx9l9zbxx/oT6KPixl03SaOKhVYlMi/Llp6MVvLMtn3s+PdhnY3ZRfRttHfpeghzjLLGe18SihEDaO/W8tdWx887yalp44MvD/PPnvh1Chxu9QeCFDdksTAjgs1vnDPi3PBAWJwXipZBzyZu7+Ms3x/i/748PyUzidGBqpIrVk0J4/Pt0i2GqOr2Bj3cXcsFrO5ny93Vc9tZu1uwuQt3eyWUz+2cqtXpSCC1a3YDcPQE2ZFQhlcAzv2eRXWUM1KuaNTaDHDAmySqbtORUt9jsKZdIJIT7ulNSP0qDnM8//xxvb+vFJSQkhJ9//nnIB2Xi073FSCUSLrfhGmb68m1J1cB4o157xzxUSutm1/nxAfi4u/DzMdtZB3tlxAp1O8+ty+LS6RFE+Sl5bXNuf/8pbMuuoUNvGHNBTofOwPqMSs6aGGpz03vpjAgUcilrurkX/Z5ubJy9dZFtWdeDqxIpV7ezNs1Sd15U10qknxKZjaD2zAkhXDcv2m62Y0KYDx16g8WQxvUZVZz54nb++s0xHvshnfUZVZyoaOK+XtytRorj5U1MCLO8WcUHGhvLa5s7rDaxgZ5u/baQPlHRxO1r0pj5rw2c/fJ2DhTZzhDXNGu585ODjA/14sGViYP7h5xGBHq5Ea5y50iJ7WRKaUM7ZY3tLIgfWJADRm23aSaCyXTA1vWXGOyJq0xqIfeFk5WHOxbbdnW8bXEcVU3aQfV+/O27Y/h6uPL3CyYO+G9HinGBnrx73Qy+u3u+lVtXfzBuqN1o0epYlhxk8zu9YV4MOr3Amt3GtbCssZ0XN2SbXfga2zrMVZyeyZ/uSKUSzpwYwu/plWNiQ+mlcOHTW+ew8y/LePf6GaQVNfDx7kKbrzWpL05UGJOs9s7VhDAfxod6W52HSRE+3Lwglpc35po3c47gzS15CAJsyaoZ8VlGPx0tJ7+mlftXJAx7IspNLuOm+bHMjPHjiXNTqG/t4FDxyI/ocGYkEglPXTgZL4Wc+z4/RE5VM5mVTVz21m4e/yEdX6ULj549nuL6Nv71ywlSI1X9NqdKCvYiLsCDX+zsbW3R1qFjZ14d9yxLIELlzoNfHkbTqae6SWs1I8dEiI87RXWtFNW1kmDn2BKCvciqbOr3cQwXDtXVKBQKfH0dM+yvQ2fgf3uLuSA13KYrj8lhrTe3CXsYJWvB/Hy0wkqy1tDaweL/buGJHywtgAVB4Ikf0lG4yPjb2eO5c8k41mVU9Wl5bGJdeiXxQZ6nbNr7qWJ3fh1NGp3dGT/eChcumR7BZ/uKae8wVma+2F/M1CgVySG2b1DJId4sTQoyDyg0UVTXZtWPY+KKWVH89Sz7gwanRPogk0rM09C3Ztdw68dpTI7w4c9nJvPp3mIeXnuEaVEq5sfblryNFG0dOvJqWpgYbvl9xQd5UlTXSrm63SrICegamNsXrVodl7yxi+NlTTyyOtko3XljN2t7fPfFdW3c+OE+OvUG3rhm+oCH1p6uTI7w4Ygd9yjTb3G6DeOUvpgU7kNRXRuNbR027aNNnDsljCtnRVptdlaMD+bBlYmsTLF93SaHeHPmhBBe3ZQ7oGpOs8bYNH/7ojjzpPnRwoqUYLsSsf5gashdYUc7H+St4KJp4Xy4q5AWrY57Pj3IixtyuOLtPewvrOei13dhMAi9VnFMrJ4USm1LB/udfGbOQJkfH8CVsyL5z29ZPPDFYeb8eyOXvLGLT/cW89SvJ5j6j/Wc8cI2vj5Qip+Hq82eAYB7lsXzxW1zbD730Kokwn3d+ePaIw4JEssa2/n2UBn3LYvHWyG3SOidavQGgZc25rAwIaDfvTdD5YGViay5eTbXzIlGpXQZUr/z6YKP0oUXr5jKsVI1K1/YxpkvbqeqScvnt87hgxtnccvCOH75w0IumR7BQ6v6n1CUSCSsnhTKhoyqfjsI78ippUNn4NzJoTx32RRyqlq44u09tGh1BNvpSw7xduN4mRqDYG06YCIl1JvMyuYB22U7GqcdBvrr8QpqmrV2XUBiAjzwcpMzaZCl2NWTQiltaOerA5bNVo/9kE51s4YPdxXy5f6TG70fj1bwe3oVj52bgkrpykXTwgnxVvDod8f6tOzr1BvYlFk95qo4AL8eqyBc5d7rebhhfiwtGh0vbMimqK6Vnbl1XDkzqtf3vWhaOEdL1eR0y7YV1bVaOav1F6WrnPGhXmbp0M9Hy4nxV/LBDTO5c8k4bl8UR5NGx33Lhz/7NVBOVDQhCDCxRyVnXJAnBsFYEQj0sq7ktHXo+5zVkl7eRGuHnreunc5ti8bx/d3zOWtiCE//mmlubP/lWAVnv7ydhtZO3r9hpk25oIhtpkSqSC9X25QBHihqIMpP2avBRW/vC3CsTE1Zo+Ug0O4sTAjkyfOtKyrB3gruW55gsypq4r7lCZQ1tg+ompNW2IBBMFaaTjdCvBW4u8js9gUC3LYojvq2Di57czdHShp58rwJVKjbufTN3QjAt3fP77WKY2J6lC+BXm781s1lb6zw19XjCfFRcLxMzZkTQ3CVS3nk22N8sruIcyaH4u4qY2NmNeNDveyu1TKpxG4iRuEi4z8XT+ZIqZpP99oOSOpatFz73l7uWHOAf/6UwWubc/lif7F5TezOO9vycXeRccuiOC6dEcnatJIBDYNu1eocVpHrXsU51chlUpYmBbHRxrDi0YLeIJB3iiyxZ8X6sePPy/j01tm8cfU0fr3f0okwwNONZy+dwsKEgc2iO2tSCM1aHTv6KVnbeKKaaH8l8UGeTI3y5X+3zDYbpNiVq/m4Y/rJJtroyQFjP3xbh57i+pEdGNt7V9kI8vm+EmZE+1pNMzZxyfQIlo8PGnRGeX58AAsTAvjjV0d5f2chl06PQC6T8OORcp69dAp78ut49LvjKFxlTItS8dj3xzlzQoi578RNLuO/l07mD58fZtUL23ju0ilW7hd6g0BWZTObs6pp0ujGXJCj0xv4Pb2SS6ZH9BoYxAZ48MDKRJ5dl8WJiiY8XGWcPTnU7uvBmA31Usj55lAZfz4zmU69gdKGdruVnP4wLcqXzVnVCILA5qwazpl8UmL35zOTuXxmpFNW2o6XNSGTSkjq4RTY3Zvev0e101RmrmrS9PpvOlraiKtMai6Hy2VSHlk9nuXPbeXtbflMDPPmnk8PsjIlmGcunoKPcnRl50eayRE+aDoNZFc1W8kNDxQ1MK3HTKf+Mi7QE6WrjKOlasoa2jmnj+tpMKSEnazmXDwtol+Nr3sK6vD3cLWam3A6cNG0cGbE+PZ6T4oL9OSMlBB+S6/knqXxXD8vxly1vmVhrE15tS2kUglnTgjh1+MVPHZOis2+1NGKt8KFzQ8vsXisukmD0s1ovGIwCKzLqOxXMGiPWbF+XDwtgv/+nsVZk0KtKuGf7i1mb34906JVpFeoaWzrpFmj44OdheZEj94g8NrmXD7eXchdS+LxVrhw7Zxo3ttRwHeHjHPW+sPLG3PYml3Dr39YOOQE25KkIJ69dMopq+L0ZPn4IL49VEZxXZuVhXtGeRP/21tElJ8Sd1cZewvqKW1o58wJISxODCS9XE1mZTOhPgpiAzxQKY2VuqGc5/6gNwi0aHVszqzm5Y05NGt17Pzzsn6td0MlxEfhcGOWlFBvYvyV/HKssk9HNoNBYGNmNedNCTP/9mbE+PH1nXN5d3sBUyJtJ69NbpKBXm521yyTuaM7mgABAABJREFUlPRERdOw9Sr3B6cMcsob29lTUMe/L5xk9zUuMumgMqDd//7jm2axO6+ON7bm8e9fTqAzCKwYH8TF08I5Z3IopQ1t3PfZISQSULm78I8LJlosQgsTAvn9/kU8+OVh7vvsEJseXkKwt4KS+jbe2Z7PL8cqzAMZZ8X6kWqnKX60sregnoa2TotBb/a4fVEcmzKr2Z5Ty5Wzovp0bVG4yDh7UijfHSrj4VVJlDe2ozMI/Z59YYvp0b58vLuILdk11DRrWdptzotUKnHKAAfgeJmahCBPq81ToKcb3go5TRqd1U3a9G/JrW7pI8hRMz7M22JBj/RTcs2caN7Zlo+AwLLkYF67alqvQypFbDMp3AeJxPg9dw9yWrU6TlQ0cUU/G0p7IpNKmBjuw/acGptDDx3FfcsTWP3ydtbsKeLmBX1bo+/Jr2d2nPVw5tOBMyf2L9D86+pk4oM8+UNXtj3KX8nDZyQN+PPOmhTCmj1FHCppGLFN7akiqFtG2diTNPSg/q+rk1mXUclTv2Ty3GVTzI/rDQKf7SvmnMmhPH95qvnxjPImbv5oP+e/tpMpESpK6tvIrm7mzsXjzOcyJsCDRYmBfLqvqF9BjqZTzxdpJVzgICMXH3ejPHykWJQYiFwqYcOJKm7qtl506g088MVhytXtdOgMdOgNTAr3IchLwQvrs/nPb5mA0RGytqWDjq7Kt0QCb10zfdgcaZ9bl8Urm072Vi9ODOQPKxJOSYAzXEgkEs6aFMone4rQ6ibanBto4lBJA7UtWlb0cIOMD/Li6Ysn2/07U2Bmr4oDxkDIV+nCiYqmfu0RhwunDHK+P1yOi1TK6mH+YiQSCfPiA5gXH0CzppO0ogamR/sikRhL3Z/dOofMymZ+PVbBnDh/K0kQGCPZl6+YytLntvDULyd49JwUrnp3D61aPedNCWNVSjApYd79ztA5A5VqDVIpfQaRPx4pJ8xH0a/gTS6T8sJlqTy09jA39THUysRF0yL4fH8Je/Lr0HXVRodayQF4YX027i4yK1cpZ+V4eZNNhxyTe9bB4kYri2CTfXB2VXOvN4ijpY02y+H3LovnqwMljAvy4pUrp4oBziDxUrgwLtCTo6WNXDnr5KbnSEkjBsE4nHiwTA73MVt+Dle2MyXMm8tnRPLi+mzOndL77KgWrY7jZWounjY6HNVGimh/j0EFNT2ZFeOHt0LO1qyaMR/kDAcBnm788YwkHvs+nWvnRpPaJQHdnFlNuVrD1XMsxwikhHnz3d3z+ftPGbRodMQHefLYuSnM72EcctWsKO745ABHSxvtun2a+PloBY1tnVwzp39VH2fHW+HC7Dg/fjlWwY3zY8yB2/s7Csiubua7u+YzKdwHrc6Au6tx893Q2sGR0kYmhvsQ4OmG3iBQoW5H3d7JP386wT9/PsHipMBeN+v2EASBoro2dufXodMbSAj2YkqECndXGTlVzby+JY/LZkQwPz6AcYGew+pEdyo5d3IYb2zJY2tWjd37v94g8K+fTxDjr7Rr4GUPU5CT0Mu8R4lEwvhQbzIqRtZW3emCHEEQ+PZQKcvHB+HjfuqkMV4KF4vMPpw8SX257fh6uPLwqiQe/e44x8rUtGr1fHfX/CFVHUaSR787ztHSRl6/eppdK2WtTs8vxyq4cnZUv6USUf5K1t4xr9/HMSPalxh/JS9tyGH1pBBkUondBuv+EOHrTqCXG0dL1awYgtTxVKLV6cmpauZyOxPjzUGODfvghGBPsqrs64vVbZ0U1rVx11Lrhd3Xw5V1DyxGpXQZFd+TMzM5wofDPRzWDhQ14OEqs9u02a/3jVSZddFDuS764k9nJvHr8Qqe/jWT5y9Ltfu6tMJ69AaB2bGnXz/OSCCXSUmN8jW77IkMnKtmRfHRrkKe/vUEn906B4lEwid7ixgf6m1TShrsreC1q6b1+p7LxwcR5OXGZ/uK+wxyPt5TxLxx/mNqOPi1c6K545OD/HKssmtcRxsvbsjhujnR5l5CU4ADxnvNkm57L5nUOPcuwhcePy+F1S9t58Odhdy+uG9DDhOCILDxRDXPrssis7IZiQSkEgl6g0CUn5IPbpzJP38+QYi3gr+fP3HM3ePGh3oRH+TJj0cr7AY5a3YXcrC4kU9vnT3g2X5BXm6olC59JunGh3qPeN+g06VnMyqayK5qGTXzFUxcOSuKCWHelDS0885100dtgAPwzwsmEuHrzhVv7+GzfbYHAm7NqqFJo+P8KcN3nqRSCf+6cBL7Cut5dXMe4Sr3IZWRJRKJ+ca1pEdA66xkV7agMwh2M0wJQV5IJNZzUMDoepLdi/vf8XLjxnuKnRtxiI9izC3+I0FqpIrsqmazuyAYndVSo1RDqpBNiTD+JuRSid0GUUfg7+nGw2ck8c3BMs54YRvnv7rD5uyfPfn1+Hm4knAa9uOMFKmRKmNVcAxYSY8EcpmUP52ZzJ78erZk1fB7eiVbs2u4Zk7UoOVjLjIpl82I5PvD5TRrOu2+7mhpI0dKGrm2R8VotHPGhBCWJQfxxI/pHClp5Op39+LtLuehQVQvk0O8uXJWFK9syu2XWygYA5w7PznILR+n4aWQ8+Y10zn8f6tIf/IMvrlrHjKphPNe2cHW7Br+clbymLzHSSQSzp0cxoaMKpsmGCX1bTzzexZXzIxk3riBjzBwkUnZ+edlnNtHL+j4UG/KGo1VuZHC6YKc7w6VoVJaV1WcHZlUwgc3zOTbu+aNeulAiI+Cz2+by3mpYVYDq0x8f6SchCBPxocObwZqfnwAN82PpbZFO2hnte7M6Do3S5IG5lgyUhwvVyOR2J8HcdnMSN65dobNhTox2Iv82ha7Az6PlDbi7iJjXODINQWeDkyJUKE3CBzuyrgbDAIHixuZbmNQ8UCI8lPi4+5CiI+iV5c0R3D17Gj+dGYSM2N9adHq+MvXR61+V7vzapkV4zemmuCdnamRKpo0OgrrRnY2y2hmVUow06N9uefTg9y+5gAL4gO4aOrQ+lqumBVJe6ee7w/bH9j6vz3FBHu7sWKMGRJJJBKePG8CLRod57+2E5lUwpe3zx20pfyDKxONgyq7+nb64tfjlfyWXskzF0/my9vncubEEHy6FAnTonz55s55TI5QsSA+YFgMW5yFc6eE0t6pt7L07tAZuPezQ3grXPjravtjN/rCw03eZyLAtD/MrLA/L6emWcuBooZhS9Q4VZAjCAK/Hq/kjJSQUdn4FeStsHJQGq24yqX8/fyJeLu7mJsCTbRodWzIqOL81LBT0mD8pzOTmBTuw9QhbgoBrpwdxUc3zRp2xxZHcbxMTVyAh12jBh93F7s3yaQQLzr1gt3hdMdK1UwM9xb7bYaZieE+qJQubM2uAYy23er2TrtS0P4ikUiYGqUaUp9af5FJJdy1JJ5/XjCJ5y9LJbemxTzUEmB7Tg1HStWcZWdelsjwMLmrmnd4gJK1j3cX8p/fMq3mxJ2OSCQS/u+cFEJ8FDxzyWQ+vmmWhZxqMET4KlkQH8APR2wHOc2aTn44Us7lMyIHLBUaDUT6KXnyvAmsTAnm6zvnET2ENcrf042HViWx9kCp3UHVJjSdev718wkWJQZy6Qzbrq++Hq58dtsc1tw8a0wbpMQFejIx3JsfDpdZPP7Uryc4Vqbm1aumDntLSHyQJ3KphIxegpwHvzzMxW/sYvGzm3l1U06/h5j3F6e6ujIrmyltaGfVhLGV2RiteLrJeXBlIj8drTAPLgT47XglWp2B84ZRqtYdhYuM7++ez4Mr+z8Uyx6ebnIWJ46OKg7AHYvH8cwl9l1OesNkC51VZRzItTW7xmKw49FSNZPCVY44TJFekEklLE4MZEuWMaP2y/EKvBRyh8ySefqiyfxnkL+PwTIlUsWl0yN4YUM2Nc1adHoD//gpg9RIFedODjulx3K64+/pRpSfckBBjrqtk+fWZVPXoh3Tm7yBkBqpYuNDS7hshvXg3MFyxoQQ0grrbW7afjpagUan59IZg3NXHA1cNjOSd66bgZ+NYe4D5erZUaSEevN/36Wjs6NMAHh9cy5VTRoeO2d8n+fxdPjtXzEzig0nqlmXbuyL+epAKR/sLOQvZyYPOcnWH9zkMuKDPNmbbzs4PVHRxPacWm5fHMf0KF9e3pTLvKc28c+fMhx2DE4V5KzPqELpKrNyKxEZOS6bEUFisCf/+vkEgiAgCAIf7y5kdqzfKe07Ol0lMJF+ykHLHwM8XfFVupBd2cyPR8u5/v19PLsuC4A9+XWUNbaTOsg5LSIDY2lSEJmVzZQ3tvPrsQpWpgQ7pFod4qMYVtMBe/zxjGRkUglnvriNB788QnZVC4+fO7bmtYwWUiNVdoOcSrWGXXm15FQ1mxMcr2/NRdOp5wEHJI1E7LMqJRgB2JBhPRzz8/0lLIgPINJvdCgKRhq5TMo/LphARkUTaw9YDyc+WNzAlW/v4eVNudy0IHZMGTkMhatmRbE8OYiH1h7h1U05/PGrI1w0LZxbFvY9DsBRXD0nmt/SK22uUe9syyfY242HVibx4hVT2f2XZdw4P4Z3dxSwPafGIZ/vVEHOuoxKFiUEjslGsNGKXCblr6vHc6Cogd/TKzlQ1MDRUnW/ZmaIjCwSiYTEYC9OVDbz+uY8vNzkvLM9n5+PVnDfZ4eYEe3LWRNFedGpYFFiIBIJvLElj8K6Ns4ewbkBjiDQy42f71vI3HH+/HCknIumhjtETioycFIjVZyoaELTqbd4vKyxnTNf2sZV7+xl5QvbWPrsFtbsKeKDnYXcvCCWUJ9THxyfTgR5K5gW5cvv6ZbuUicqmjhS0sjlg5yRdboyPdqPMyeE8MrGHLQ642+9U2/gP79lcvEbu6ht0fLG1dP4y5nJI3ykzoNUKuG5y6bgrXDh2XXZXDo9gv9eMuWUVrGunBlJQpAn//gpw0IeW6Fu54cj5dw4P9ac8PP3dOMvZyUzOcKH//yW6ZA+HacJcsob2zle1iRK1ZyQJYmBzBvnz39+y+KtbflE+yv7nKQr4hwkhXixObOarKpmXr9mGqmRKu7+9CA6g8ArV00dk3pwZ8TPw5WpkSo+2VuEp5ucBQmjv1odrnLn1aumsf6BRfz7IvuDm0WGl9QoFZ16gfTyk7r3Dp2Bu/53EIVcxg/3zGfNzbNICPbk/747joerjDuW9N+OV2TwnDEhmJ25dWaXtVatjufWZeOrdGHlGDMcOBU8sDKRiiYNX+wvoULdziVv7ubtbfk8sCKR3+5fxFmTQsVqcg9USlfev2EmT5ybwtMXTR52k5qeyGVS/na2MVH+7SFjf1Cn3sBTv2TiJpdazI8DY3L2z2cmc7ysiV+OVwz984f8Dg5iw4kqZFIJy5JHl6va6YBEIuGR1eM555UdFNS28vi5Kaf8QhEZHInBXugMAlOjjG4ykb5K7vjkAI+sHi9mck8xS5OCOFjcyIrxQYMabOesJAxh1o/I0EkJ9cZFJuHZ37O4YX4MeoPANwfLSC9T88Xtc82zWhYmBLI9pwalq2zQTlciA+OMCSH8+5dMPtpVSKiPOy9tzKG6WcPTF00eU2vAqSIpxItzJofx8sZcXt6Yi0wKX94+Z9Q72g43SSFeJIWM3Dq9JCmIVSnBPPjlEfYXNlBU18regnqeunCSTfOD+fEBLEwI4NnfszhjQsiQkrFOE+SsS69iVowfKuXQm9REHM/EcB8umhrOxszqMd0sOdYwzde5d1k8EomEmAAPfrt/0Qgf1enJ8vHBPLc+m3PE5nwRB6JwkfHURZN5c2set685ABirbE9dNInpPYb1LUwYPaYrY4Fofw8mhHnz7LpswCgt/ODGmYwLFGdJDZb7VyTw67EKJkf48OY10wkaxhlhIo7j9aun8eGuQp5fn41cKuGjG2f1qmj40xnJrMuoRG8QGEoHi0QYQQ9JlUpFY2MjAIW1rbRodXaHHooML93PhT10egP1bR0EeYmLynDSn3MxEPJrWogTb6qDYjjORWyAx2nh7ONoHH0uxhqCIJBX04LCRTbsFvniueg/1U0aqpu1RPi64+Pu4vBr/3Q8FyX1bQR7K5xu1MjpeC4GSk2zFkEQTllwOqJBjlQqxdvb9pBDkaHj5uZGVZW1s4stxHMxvIjnwnkQz4XzIJ4L50E8F86DeC6cB/FcOA8DORcmRjTIERERERERERERERERcTTOVesTEREREREREREREREZImKQIyIiIiIiIiIiIiIyphCDHBERERERERERERGRMYUY5IiIiIiIiIiIiIiIjCnEIEdEREREREREREREZEwhBjkiIiIiIiIiIiIiImMKMcgRERERERERERERERlTiEGOiIiIiIiIiIiIiMiYQgxyRERERERERERERETGFGKQIyIiIiIiIiIiIiIyphjRICc4OHgkP16kG+K5cB7Ec+E8iOfCeRDPhfMgngvH0aLVoTcIg/578Vw4D+K5cD5GNMjRarUj+fEOp65Fy7O/Z7Hi+a3Me2ojb23No1nTOdKH1S/G2rlwFCX1bcz81wb25tedss88FeeirUPHO9vyKW1os/m8Tm/g/Fd38MmeomE/FmdmIOdic2Y185/eRFuHbhiP6PTldF+jWrU6lj27had/zUSnN4zosZzu58KRvLQhmzNe3DbovxfPRf956MsjnPvKDg6XNA7L+/fnXFQ3a1j67BbWZ1QNyzGIWCLK1RzIv34+wXs7CpgQ5s3sOH+e+T2LS97YjSAMPksjMrI8ty6LmmYtH+0uHOlDcRi51c1c8NpO/vXLCS58fRdHSxutXvPd4XKOlKrZnXfqgrvRjMEg8J/fMilrbCe3umWkD0dkDHKkpJH82lbe3JrHde/vo2mUJNBEemdbdi2Twn1G+jAcRpOmk/LG9pE+DCsyypv4+mApFep2Lnx9J29tzRuR49iSVUNBbSsPfnmYorrWETmG0wkxyHEgewvquXxmJC9dMZUXLk/lxctTyapqprTB+S54kb45Xqbmu8PlpIR6sz6jirqW0Z8xq1RruOC1XXTqBdbcPIsQbwWXv7WHA0UN5tfo9AZe2ZQDIG7Y+8nv6ZVkVjYDkF8j3rhEHM+BogY8XGV8eONM9hXU882B0pE+JJEhUqFuJ6uqmUWJASN9KA7jXz+dYPXL26lQO9e+55VNOYSr3Nn+p2VcmBrOm1vzhiQTHCw7c2uJC/RApXThjk8OounUn/JjOJ1w2iBHEARatTo69YZRUQkpa2ynrLGdmTF+5sfmxPkDkFZUP1KHJTJIBEHgqV9PEBvgwXs3zEAQ4NtDZSN9WENmzZ5CDILAt3fNY2FCIF/cPoe4QA8e/e64ecH/9lAZRXVtnJ8aRn5ty4hLY5wdg0HgpY05zI71w9/Dlbya4QsMN56oYktW9bC9v4jzcqC4gdQoFUuSgojyU5JfKwbTo53t2bUALEwIHOEjcRw782ppbOvk/s8Pj0gQYYvMyiZ+PV7JXUvH4e4q47KZkTS0ddpUMQwnBoPAztxaVowP5o2rp5NT1cya3ae3JHy4cdog58kfM5jw+O8k/O1XLn5jl9NHu2mFxkBmRoyv+bFALzdi/JWkFTbY+zMRJyW9vImduXU8tCqRUB93liUH8WVayagIuO2h6dTz6d5iLpkegUrpCoDSVc4T503gREUTX6aVkF/TwvPrs1meHMSFU8Pp1AsU19vu2xEBvUHghQ3ZZFY2c/+KROICPYatklPVpOHuTw9ywwf7efLHdDp0YvBpQm8QyKlqHunDGDYMBoFDxY1MjzLeX2IDPCgQg5xRz9acGiaGexPg6TbSh+IQyhrbKW1o5+rZUewrrOf1zbkjfUgAvLoplzAfBZdOjwRgerQvnm5ytmTVnNLjyKpqpralg/nxAUwM9+GsSaF8tLvQKhjMKG+iVSv2djoCpwxyNJ16vj5QytmTQnni3BSOlzXx958yRvqwemV/YT1RfkqCvRUWj8+I8RODnFHIxhPVKF1lrEwxuqVcMSuS7KoWDvWjYbGkvs1hmmSDQaC6WeOQ9/rhcDkNbZ1cNzfG4vGZMX6cOyWM//yWyQWv7UThIuPxcyeQEOwFQI4oWbNJXYuWK9/Zw6ubc7lnaTxzx/kzLtBz2Co5L27IwVUm5aGViazZXcSK57fy7vZ8p+jNaNZ08tQvJ9iec3LTkFPVTFWTY367ffH+jgJWvrCNH46U9/na0WgMkV/bgrq9k2nRxiAnJsCDQlHPP6rRGwR25NSyaAxVcUwGPfevSOSWBbG8sjmXmuaRlXkX1bXyy7EKblsUh6vcuOV1kUmZH+/P1uxTG+TsyKnFVSZlVpfi56b5MZQ2tFuYEDS0dnDB6zt55Ntjp/TYxipOGeRszqymWavj3uXx3DA/lr+dPZ5P9xbz89GKQb2fuq3TZga+qknTr3LqX785ys0f7mdbdo3dTH5aYYOFVM3EjGhfsqubUbeN/EbkVKNu6+T/vjvOuvTKUSd52pRZxcKEANzkMgAWJQQS7O3G2jT7OvhOvYG3t+Wx6oVtPLsuyyHH8ddvjnHjB/uH/D6CIPD+zgIWJQYSH+Rp9fxfzkqmU2dgcoSK7+6aT5S/kjAfBUpXmdiXY4f3dhSQXqbmk5tn8/AZSQDEBRoz7L2tK5pOPfd9doiXN+ZQXNe/KlleTQtfppVw55J47l2ewHd3z2dShA9P/ZrJLR+lDfnf0qrV8f6OgkFdpweKGlj98nbe2pbPHWsOkFPVzP7Ces55ZQfXvrd32CtOgiDw+f5iXOVSHl57hIPFtpNK27JruPa9vaQ89juH7LzGWTH1zE2NOhnklDW0i9W8UcyR0kbU7Z0sThxLQU494wI9CPRy484l8Ugl8NGuwmH7vJ+PVvDF/mK259TYVfu8sz0fH3cXLpsZafH44sQgjpQ2Ut/aMWzH15MdubVMj/bF3dW4r5ga5UtqpIr3dxaYX/P94TI6dAa+P1zOgRFqdRAEgS1Z1RicRG44FJwyyPnucBnJIV4kh3gDcN3caM6YEMzDa48M2O0pp6qZ1H+s45xXdvDF/mLzScutbmb+05u47eO0XjN7LVoda9NK2V9Yz3Xv7yPp0d+Y99RG3thy0plD3dZJVlUzM7tJ1UzMiPFDELB74x3LfJFWzJo9Rdy25gCL/7uF3OrRISepadZypFTNsuQg82NymZSLpkXw45Fy2jssF9OC2laeX5/Nyue38tSvmVw6I4InzpvgkGOZHedHennTkJs4fztubIy/cX6MzefDVe7s+styPr5pFj5KFwAkEgnxQZ5ikGOHzVk1LBsfzPz4k03D4wI90eoMvVbyjpaq+eFIOa9symHRfzfz1C8neg2KBEHg6V8zCfB05YZ5MQBMDPfhtaum8dIVqewrqOfIEC1RP91bzN9/ymDnANfXZk0n17+/D1+lKz/es4AQHwU3fbSfmz7cT1yg8bcz3C5GB4sbyatp5eUrpjIhzJvbPk6jtodJyLFSNde9v4+6lg5cZJJeLWQNBoHvD5dx9svbufyt3by7PX/EXZAOFDWQGOyJj7vx2oz198AgIEpJRzHbsmvwdJObq3Njgb0FdcyKNfYi+3m4cvmMSD7eXUjLAKVXJfVtfLavmCd+SLe6lk0cL1Nz96cH+fPXx7j2vX1c/e5e1O2WyeTaFi1r00q5bm4MSle5xXOLkwIRBCyqz8OJVqdnb0EdCxIsTSZuWhBrsYavPVDK4sRAkkO8eOKHjBEJNPYV1HPDB/v55fjgCgvOhNMFOeq2TjZn1nB+arj5MYlEwguXp5IaqeLGD/cN6EeZ1pUB8/Nw5c9fH+Od7fkAvLIpFy+FnJ15tVz59h4a7ETz+wvr0RkE1t4xj09vnc2fzkxiapQv//09k+NlasAYwAiCMaDpybhAD3yVLoM2HxjpUu9gEQSBL9NKOXNCCN/+P3tnGd3GuXXhPWJbZJSZmWPHDjNzQ21TbsrcW2bm9n6lW25TTtqmSZOGmclx4piZmWRLsmxLsqT5fshSLEuyZVvGzLPWXetWkUevPZqZ95yzzz4PTQOLQcMjW66M+t4qADje1dg9N0xk9Pr1E70hV6pxIPvqhZ9TI8Oyz05j0+kSxPk4YMdD0/HmddEQcJg2WcucMBEIAjiWN/Bmc2lHJ17dlY05Ya6Y00vWUGjPBI1GGL0W7Dp6ghySJHE4p94kyBwJ6qQK5NbKTP6ega66KllvkrX0SglYDBpSXlqApxeF4rvTJbjnlxSLM7W2Xa7C4Zx6vLA0wpAB1LMkyh2eQg5+6pYJ7C8kSeKPlAoAwLHc/s1u2JlWgzaVGl/dkoAYbyG+vz0RkrZOeDnY4c/7puDuGQH437Eim0v4sqqleGdvDjo1WmxNqYS7gIOFkW749taJUKm1eLuHvPlIbj14bAb+fWQ6Aly4vUow7/w5BY//mQZHexZYDBo+OJCH2R+dwOJPTuHCMM7L6k5qhQQTu22G/V3sAQBlVF/OmOVAVh1mhbqASR9127ABUS9ToEzcjimBV/dB98wMRJtKgz8vVlh9nJMFjZj90XG8uCMTWy5W4Im/0sxu9L88XgQvBztkvr4Iv989GQX1rbj5+wtGLqg/ny0DQegS5T3xcrBDiIiHk8PUl3O5vAWKTi1mBBsHOUuj3REs4uHJrWm4WNqM7BoZbprkg9dWRiGzWmqVBNfW6JNAf16sHPbPtjWj7ural1ULlUaLVRM8jV63ZzHw451JSPRzwm2bLuKGb89jT0ZNn1FuVrUUAc5c/Hb3ZNw+1Q+fHCnAifwG7E6vwWPzQ/DnfVNR2CA3BD89OV8shguPhVA3HqYFueCemYH45MYJ8Hfh4tV/s6BSa7E7owZOXBaCXLkmP08QBCb6OSHFTF9OubgNt/xwAYs/OYVln51GTo3M8G9nCnXBV9I7R3Asb+wNjUqrlKCoQY4bkrwR7+uI/90Uj+JGOd7fnzfSS+uT43kNiPESQtSjvyrQlYdEP0eDZK2lTYX7frsEP2d7XHhxPj7bEI8JPg42XYsTl4UEX0ccy+1fkFMhbseTW9Pw58UKvLk7B21KNd5eHQ2CIPr+4W4Eu+l6TPqbTVKptciokuBAVi2O5zXgeF4Dtl2uwpEBDkAjSRLv7M3Fvb9ews9DKH/ojT0ZNXjhH51O+mSB7nzM6hHk+DjagUknUNyL+UBalQRRngI42LPwyLwQbLojERdLm/HoH1dMKjoljXK8tisbqyd4YnW8l8mxGHQabp/mj72ZtQPuf7lY2oySxjYEuXJxLL/BanMNkiSxJbkCc0Jd4e2o23QHuvJw6MlZ2P7gNAjtmHhiYShEAjbe22fb6z6nRoYfz5bhlu+TsSejBusneoNOIyAScPDisgjsTKsx0tufLGjE9GBnMOk0hIj4KKo3H+RotSROFzbiyYWh+P2eyfjt7slIfWUh/ndTPJRqDb4/Zf45MZS0tKlQ1CA3SNUAwFNoBxaDds2aD4x1GU1hfSvy6lqxMtaz7zePEZJLdYncSQFXgxwfJ3ssj/HAD6dLrUpwtqvUePGfTCT5O+HKKwvxza0JOF3YZLI/K6hvxf6sOjw4Jwh8DhMzQlzwx71TUCtVYMN3F9AgU+BAVh2+PlmMWyb7wdmCscPSaHfsyawdlkrtyYJGOHFZJjORmHQavr4lATUSBe76OQVOXBbmhbthapAzwt35OFfc1OexD2TVYkuy9YFkX2RUS0GnEThT1GS1pHq0MmqCnKqWdrzwTwZe2ZmFmSEu8HKwM3mPHYuOH+9Mwic3xkGrJfHIlitY9eWZXqfRZ9fIENX1pXpmcRgc7Fi4+5dLcOaxcdMkX0zwccCKWA/8m2Y+YDpX3ISpQS5Gm0MWg4a3rotGaoUEMz88hn9Sq7Fxmr/FDWSivyPSKyXo7KF3//1COTIqpUgKcIRaq8XtPyYjq1qKp7am49ZNyWhpV8FTyMGOK8MfyQ+Wvy9XQcRnG5oqo72EeHZxOH4+V2Y0k2W0oVJrcbqwyUiq1p3rE71xrliMDw/k4dZNyWhVqPHdbYng26hyY4554SKcKWqyuoIhU3Tirl9ScDCrDi/syMT21Co8tSjMsBHtD8GuPLSrNKixUi4nU3TihX8yEP36Qaz64iwe+D0VG39OwcafU/D03+m459dLA9oofnK4AD+cKYUzlzViFsoqtRZ/XKzAifwGnMhvRKy3EK5844cng06DnzMXJX1UcuK8HQz/PS/cDf+7OR4nCxrx0UFdL5dao8W/adXY+HMKXHhsvLU62uLxNiT5gEGjDVj7/sfFCng72uGZxeGobO6wuuqSVilBbq0MN082zpJ6CO3AZeukIfYsBu6fHYSjefWotKG06oYkH/x+92QUN8rRptJg/UTvq/+W6INJAU54aUcmOlQatLSpkF4lwexQ3TUdLOKhoKHVbDDXqlSDJAE/56vXCp/DxMo4TyyIcENurczkZ4aaU13Khe6ySBqNgJ+TPUqvUfOBB36/jAd/vzxmg53dGbXgsuiYa+E5MxY5X9wEXyd7eAiN926PzAtGfasCf1hRzfn0SCEa5Uq8tzYGDva6zf5d0wPw0cF8g8ICAL46XgQ3Advouo/2EuKv+6ZA2tGJNV+dw6N/pGJBhAjPLw23+Hn3zQ6Coz0Tr+/KBkmSqGxux79pQzMq4mR+I2aGuJioJQAgxI2Pd9ZEQ65UY/UEL4NBQpg73yrjn29OluClnZm4WGqbHp6MKgnWxnuBy6Ljr0u2C55GglET5Dy1NR37s+rw5KJQfHPrRIvvYzFoWBPvjW0PTsMf906BVgvc8dNFs1kCtUaL3FoZoj11vT18DhNvrY6GRkvi/lmB4DB10o818d6olnQYMhF6JO0qZNfIMD3I2eTY04NdcNMkX/g42mPHQ9Pw6PwQi2uO9RZCqdaisFv2UKMl8W9aDVZN8MTbq2Ow+Z4pEHCYWPG/M9ibWYP31sZg/+MzcUOSD47m1o8JmZeeDpUGu9NqsDbBG4xupfi7ZwTATcDGjiujd4jdF8eLIFeqsSjKzey/L4/1hBOXhd8ulMOOScd3t02Er3P/g4f+sCDCDUq11qqMjqRdhYc3p6JeqsCOh6cj+YX52HRHoqGXo7/oHdaskaxdLG3Goo9P4d+0Gjw8Jxhb75+Kiy/Ox5nn5uL0s3OR8+Zi3DcrEO/sy8X2fgwyzKiS4PNjRfjPghA8Mi8Yl8tbLEq7hpI18V6Y5O+E13dl40xhk0XpX5Ar12Kg0CRXoqqlA/G+Dkavzwt3wzOLw/DNyWJMf/8Y4t86jMf/TIO3ox1+uKP3INrBnoVbp/jim5PF+Culfw8kSbsK+7LqcNMkX8wMcQGLTrNaGrkluQIeQg7mhvXeOL0m3gv2TLpVm5z+MDXIGXsfm4lf7poEf5erVXQajcC7a6JRK1Vg05kSnC5qAknCMHAxxI0HSXsnxGYkyrIuTb/AzvTvHekpQI1UAUn78DUqA7rNUagbzyTx5+/C7VOudrm8ZdTITW1FRpUEh3LqsT+rDpvODFymOVKQJIk96TVYGOlm2IOMdRSdGuzJqMWSaHeTfwt14+O6OE98eby41/7n/LpWbDpTikfmBhtkvwDw3NIwTA92wT2/XMIPp0vw8OZU7EyrwQOzg0z+fiFufGy9fypoNJ2xwP9uSuhVDshjM/Dy8kgcz2/EM9sysOiTU/jwQL7N91t1UgXy6loxp5d75doEb/x0ZxKeWHh1Lxki4qGoXt5rdV2l1iKnRgYGjcCTW9NQIW7HBwfy8PaenAGNvGhpU6GyuQNTAp2xaoIX/r5UNWzGUZ0a3T7Hln1SjL7fMjx8sC4WzjxWvzLiU4Oc8fLyCNz8QzKqWtoRLOIb/XtJUxuUai2iPK+WBxdGuuHQE7MQ3O0imhzgBC8HO+y4UoWp3QKaCyVikCQwLcj8NOL31sZYtc7orkpSVrUUkV0B1/liMRpalVjTJUFx5bPx2z2T8cWxItw13d+wuVwe44FPjxTiRH4DlkR7WPV5I83RvHq0KtVGWRZAt/lYHuOJnWnVeH1llFEANJIo1Rqw6DTsyajF50cL8Z8FIUbfme7w2AxcfHE+6DSi39KvgaLf4BzNa8D8CPPBV26tDM9vz9CVmQkCm+5MQmjXd2h+D9ldf/BxtAOLTkNRgxxzwixnHRtaFXjw98vwdbbH3xumwsfJfOD3/JJwNLUq8ez2DDhymZgXbv736c6mM6XwFHLwyNxgVLZ04I3dOThb1DTs1wNBEHhzdRSWf34GGi2J2Rb+HoGuPItBnL65tHslR8+Ds4Ngz6SjVqoAl83ArFBXq+WPzy+NgKxDjee266oXd04PsOrnjuY2QKXWYl2CN7hsBqYEOeNYXgPumxXU6891qDTYnVGD+2cF9Xkd89gMrE3wxl8plXh8QYjBsdAWuAs5cBeafr+DRXzcMtkXX58oRlKAE4JFPEMlM6TrOVFYLzeZT6JvXBaaCXIiPHT37pxamcVngq3RakmcKGjE9T3upYBuVs6eXvT6DTIFbt+UDGceG4efnGXTv/twUdXSDhce22gz+92pEng52GF+hAgfHMjDpAAnxNlYJjyUZNfIUNLUhpeWR4z0UmzGgaw6tCrUuCHRx+y//2dBKHZnnMSv58vxwGzz95YvjxdBxGfj/tmBRq+zGXT8cEciXtqRibf35sKZy8KH62OxPsH0mgB0wf/xp+ZY/YxeEeuBv1IqsT21CrdN8cPTi8NsFnxqtCToNAKnuqSzfQ197VnZCxbx0apUo06mMKmQ6cmrk0Gl0eKj9bF49d9szProONgMGpRqLbwc7bDRymeBnoyuXvM4HyGCRDz8cbEC50vEQzqwliRJfH2yGN+cKIZMoQadRuDIk7MR4GLaAtJfRscuE7ov5kAkP/oHV2WLqZxGbwwQ1RVY6Al14xuVDGk0AqvjPbEvs84ogj9XLIaXgx18nMx/uaxFwGEiwIWLjGqJ4bV/rlTBx8nOqJnUy8EO762NMQQ4gC4zEerGw97MukGtYTjZn1WHcHe+WavilXEeaG5T4Vw/XZx6klYpsWpaMUmSUKotZ2X2ZdYi+rWDiHj1AJ74Kw3LYzzw2DzLVTlAJ0kargAH0G2uF0SIcCi7zuzvoujU4NE/rqBVocY7q2Nw4pk5NrMlZdBpCBLxkFtr2RmPJEk883cGSADf3jbRYoAD6K61D9bHYnaoKx78PbXP8nqdVIG9GbW4Y5o/GHQaAly48HO2H/YhbnrC3QW4Z0YA3AUciwFIkCsPDa1Ks/Nr0islENoxjeRQegiCwJ3TA/DCsgg8Nj+kX/1ddBqB99fFYEOSDz44kG91pSu1ogX+zvaGQGFemCtSylpMXIp6cqFEDEWnFktjTDO35rhtqh/EbSrsH8b72OPzQ0AjCJzIbzS6Hvxd7EEjYNbtsbcgJ8iVByad6PVa6E61pAP7MmsHZfOcUS1Fc5vKbIIhwIWLGqnCYtb5gwP5oBEEqlra8fPZsgGvYaQQy5VY+ulpPPD7ZUNGurK5Hfsya3HXDN1oiWARD+/tz7X6mFcqWvDKzqwRHenwT2o1BBzGkG4ah5u/Uiox0c/R7DMf0O3vrp/oja+OF5ntIysXt2FPRg3umRloNhhn0mn4YF0sft6YhGNPz8ENiT5mZV96+vOMJggCX92agCNPzrapaVBGlQSLPjmJ3FoZThY0IsZL2O+hr6Fuur9noYUeQkD3TKHTCCyP9cCnGybgkbnBOPv8PNw2xQ/v7su1ap/UncwqCbgsOgJceIj1EoLPZgxpi0G7So1HtlzBhwfysWqCJ/66bwoc7Vn45HCBTY4/aoKcgeLhwAGNAKrMBDnZNTJ4OdjBkcvq8zhr4nXOWTuv6PSYcqUaR3LqMS3I2SYb2hgvITKrdEFXu0qNg1l1WDPBy6pjL4vxGDOSNUWnBsfzGsyWrQFggo8DvB3tsHsQjiEdKg3u/fUSHt6S2ueco/f252HiW0fwx8UKk9Jtk1yJl3ZkYnKAM55YEIonFobio+tje715jhQ3T/ZDk1yFf9NM/24fHshHhbgdX96SgJsn+w6o96Y3Jvg4IK3S8k1uy8UKnCxoxIfrYiHi9101YtJp+PLmBMR6C3HHjxfx/PYMixbrv10oA4tBw4YkX8Nrc0JdcSLf8syqngykZN8bzy8Nx9GnZoNu4XuiNyApNWM+kFYlRZyPw5AEyQRB4NH5IVCoNWa/J+ZIrZAgoVtD+4JIN2i0JA5m9R6MnMhvgLuAgzA3fq/v0xPqxsfkACebS9Z6w5nHxkNzgwHAKMhhM+jwdzbvsNZbkMNi6EwLrO3LeX57Bh7anIqZHx7D1pSBuRQdz2sAj81AopnxBP7Ouu9ZuZnG4LRKCbanVuHpxWHYMMkX/ztWZNGKd7Ty5fFidHRqcCK/Ef+m1YAkSXxxrAhcNgM3JvmAzaBjQ5IPUsparBqIWy9T4N5fL+O3C+VY89XZXvvmhooLJWL8fK4Ut07xM/RdjDXOF4uxJ6PGELyXi9twvkSMGy1UcfQ8tyQcjlwW7v45xUTy+d2pEgjsmNiQZPkYBEFgTpjI7LU5WAQcJoJczQdoA8XRngUmnYZ1X5/D8fyGASUefZ3swaLTeu3LSauUItSND3sWA4uj3PH04jC48Nh4aXkEQt34eHJrer+egelVUkR7CUGnEaDRCEzwdcCVCkm/124tb+zKwdG8evzvpni8vToGkwOd8ei8YOzOqEFe3eB7IMfmVdYNJp0GD6Edqsw0tWZVS02qOJYIFvGwItYDr+/ORmaVFM9ty4CkoxP3zQrs+4etINZbiNy6VqjUWhzMrkObSoPrzLglmWNFrAfaVRpDADaaOV3YhHaVxmKQQxAEVsR64qCFqoQ1bE4uR2OrEpXNHTjai+Xt2aImfHeqBH7O9njhn0ys+eocXv03Cz+dLUVWtRSv/ZsNLQl8fEMc7p8dhIfnBpt46Y8Wwtz5mBXqik2nS41uWCcLGvHj2VI8uSjUIKexNfG+DihubLOY/Tye14jJAU5YENm39EyPHYuOTXcmYeN0f5zIb8Tar87hQA9P/jalGluSK7B+ordhdg+gs9WukymQX997Rj2rWoqHNl+22WBWPQRBGBrrzWHJRpokSaRXSjDB27wU0hZ4OdhhdqgrtiSbBvU9aVOqkV8nM+oP8na0x+QAJ2xLtdwzRZIkjuc3Ym64a7+CtXfWROPrXvoth4K7ZwTgi5vjTWxbg0U8s9nR3oIcQCdZ6+6CaYn0SglOFzbhiQWhiPIU4oUdmVZtxHtyoqARM4LN2wzrpRw9M+NtSp1DVagbD7dM9sWTC0NBAPjsSGG/P3+kqGppx+8XyvHQ3GAsjXbHG7uzccdPKfjrUiUenRcMXtf1NydMBI2WxNnC3vsVVWotHtqcCoIA/rh3CkgAa746Z1B7WEuTXInln5/G439e6ZKzW77G2lVqo8Rkg0yBR7ZcwUQ/RzyxMLRfnzuaeHtvDh7ZcgXT3j+Ghzen4rE/02DPomNZbO/yYUcuCz/emYQmuRJ3/pSCg9l1qJcpsC+zFn9frsKd0/x7va+ONXyc7LHtwWmYGuiMdpVmQCYTDDoNga5cFPbyrEuvkmCCj+kzhcOk4/ml4ShqkPdrTmNGlQSx3Z5R8T4OSKuUDInJh6RdhZ1p1XhoTjBWxl11GtwwyQeeQjv836HBV3PGfJADAN6OdiaVHK2WRE6NzGJvhTk+XB+LIFcebvj2PPZm1uL9dbFG0rHBEO0lhEqtRUF9K36/UIFJAU5WZw6CRXysiPXAe/vz0NA6MJvY4WJ/Vi0CXLi9ZnhXxnlAplDjdEHfjfS10g60dRsk1q5S4+sTxVib4IVYb6HRpODuSDs68fTf6Zjk74Rdj8zA97cnwsGeiTNFTXh3X26XwUMtXl0RaWIVPVq5b2Yg8utbcarrgZ5XJ8Mjm1MxI9gF9860TTBujvgu2VSahbJ3c5sSnmbcEPtCwGHi2SXhOPv8PCyIcMMz2zKMrDzf25+LdpUG98ww/t2mBDqDz2Hgoc2pRpPrO1Qa/JtWjZd3ZuK6L85gxf/OIL1SCj+nwet6+4PQjgkXHtskyKlq6YC0oxMxZvpxbMlNk3yRUytDZh8buPQqCbQkjKyJAWDdRG9cLG226IZW0tSGiub2Xnu0zBEs4sPJiqq6LWExaFgR62lSnQ1x41ms5Niz6BablSM8+ChqkJs4Zfbki+NF8HWyx8Nzg/DS8ghotCQu9FOiK5YrkVElwdxw8xlgNwEbXBbd6HumUmvxwO+XUSZuw3+vjwODToMLj417Zgbir0uVo/75oefjwwXgcRi4d2YA3rguChotiexqKb6/PdGoX8zfhYsAF66R85Y5fjxbirRKCb68OQFTg5yx46Fp8Ha0w62bkq2uzJEkiRf+yURlcztSK1qw4bsLeGO3+ebuBpkCCz8+hYlvHcbDW1LxyJZULPr0FAASX9zcezP8SECSJH67UI6vTxRjV3qN0TO3J2K5CitiPbA4yg3iNiVYdAJPLAg1BJ69EeTKw/e3J0LW0Yn7f7uMye8exUObU+HnZI87pvrb8DcaHfDYDHx3eyL2Pz7TqDWhP4S4WXZYkyk6UdwoN9vjCej6yd0EbGxPtS5BXi9ToF6mRGy348X7OkLa0YmSPkxODmbX4XI/50Fuu1wFjZY0qeCxGXQ8Pj8ExQ3yQZsMjYuw2dvRHoU99NWVLe1oVaoR7WV9dtuexcAPdyRi/dfnsTzWA6vibOdhH+UpAEEAf1+qxOXyFnx+U3y/fv71VVFY8PFJvL4rG1/dMrzZUGtRqbU4klOPW6b49ZrhjfQQwN/ZHvuyanvN/u+4UoXnt2fChcfGxzfEIdJTgM+OFELS0YnH54fgSoUE//krDbm1MpMqxseH8iHr6MT/3R8HOo3Awkg3LOz6LEWnBqkVLWhsVdr0HA8104N1vvlv78nB5RgP/H2pEl6Odvjq1gSL0ilbEOTKA5/NwJWKFrMld3GbymSj3B/oNAL/d30clv/vNB7anIovb05AZUs7fr9QgVdWRJq419mx6Nj2wDQ89Xca1n19DhEeArjw2Egtb0GrUo0AFy4iPQW4c7o/VsR6jsiGIsiVi5IecrUaiS4R49tLz5ItmBcugojPxh8XK40eVj25UiGBPYuOcHfjhMSyGA+89m82tqdW4T8LTDPOx/MawKQTRpbGY40QER9NciVa2lRGcmZJeyccepHDRHoKoNJoUdwoR7i7+WdLXp0Mh3Pq8d7aGF0m1oULLwc7nC5swqIo63qYAOBMlyucpd4NgiAQ5SU00ty/+m8WzheLsenOJKNzf/tUP3x7qhg/nS3Dc0ssW+qOBjadKcU/qdV487oo8DlM8DlM7H1sJnhshlnp+exQV+zPqgVJkmafO1otic3J5VgR62GY4eJgz8Lvd0/GTd9fwOovz8KVz4angx2+uDneouRWP5T3i5vjsSzaAz+dK8Nbe3LgaM+CSMDGF8eKECzi4YVl4Xh2WwaUai3umhGAo7kNoNGADUm+WD/RG26jMKm26Uwp3t6bC3sWHe0qDR6bH4InzVSbSJKEuE2JCT4OuGeAibXJgc44+tRsFDbIkVsrwwQfB/g62Q9rn+twQqcRg1JZ6AaWNpj9fmdVSUGSsGi8QacRWD3BC39crMBrKyP7NB/RVza7V3L0vaFXKlos9lxVSzp0lu4kMDPEBa+tjDQxAuuJ7rqswKIoN7OJ5nUTvbE2wWvQBlU2e/p/9dVXtjpUvzFXydGX8C2dFEt4CO1w6tm5eHGZbZ1P+BwmAl24+O1COVx4LCzpx8MOAFx4bLy6IhL7MusGPFBxqEkuFUOmUPf5uxEEgaUxHjiSU2+xKfeTwwV44q90LIh0g5uAjQ3fX8DEt47ghzOluHtGAPycuVgW4wERn20y7b1epsAfKZW4Z2ag2SZ4DpOOaUEuuM7KnqjRAkEQeG1lFDhMOjZfKDfMjbJVo6Ql+tLlNstVcOYNLkMvtGfiq1sSUC3pwNz/O4H7f7uMSQFO2GjB+jrMnY8dD03HayujEOUpAAng5im+OPrUbBx/eg6+vDkBa+K9RyxjGujKM6nk1LfqeiLcBP1rPu0vTDoNq+I8cbiP+0RqeQtivYUmDxEem4El0e74J7XabJb6RH4jkvydrMrcjlb0z4WeGVJpR6dZ+2g9kV2ble7Z/y+PF+Hdfbnd/rsY7gIO1ibo5MgEQWBGsAvOFPVdue7OmcImBLlye62SxnkLkV6p25h0qDTYnlqFx+aHmCQjHLksbEjyxe/nywckmxsutiRX4K09ObhregBum3J1/pKPk73F3to5Ya6olyktGkKcKxajsrnDqK8P0P1Nttw7BY/ND8HyGA/k17Xi7T3mTQzaVWq8sTsH103wNFQG754RgMfmBeOTIwV44Z9MRHkKUFDfiiWfnkZBfSs23ZGIpxaFYd/jM7Hn0Zl4fml4v/cjw8GViha8vz8PG6f7I/uNxVgW425xj9GqVKNTQ/a7gb4nBEHorKUneMHPmTumnsPDTYiIB5lCjYZW0566tCoJ7Jh0hPTyvVqT4AWZQm3VQPGsahn4HIZRIs6Ry0KgCxdXupxBzbEluRwcJh0frotFubgd139zHtk1xkqCNqXa6N5zrliM0qY23NpjzpoeOo2wiQPvgJ5Su3btMnntzTffhLe3ztJv1apVg1tVP/FxskdzmwptSrVB09lg2FD0P2syVFnxGC8hihvbsCHJd0BNh2vivfDnxUp8eDAPc8NFQ5q9745YroS4TWWwJLbEuWIxHO2ZJhN9zbE02h1fnyjGueIms7KXH8+U4qZJvnh3jW6u0ZaLFVB0arA4yh1+XQ23uoZ0H/xwphSvrYwynPtvT5aARafhrn5aJ44FpgY5Y/ejM4b9c+N9HPDL+XJotaSR9Eep1qBVqYazDWRIsd4OOPf8POy8UoNjefV4bWVUryYQTDoNdwxw/s9QE+TKxfauUrz+Om2QKcBi0IakcbYnUV4C/HCmFJJ2FRzsTc8NSZK4Uimx2Oi7NsELO65U41J5C5L8r04wlyvVuFjajKcXj92eAgAGqXBJo9xoQruso7PX8+Ngz4KHkIOMKinWxHujqKEVHx8ugEZLYnmMB7hsBvZk1OD1lVFGWdOZoS7461IlKpvbe3Uf1EOSJM4UNWFxHwmjOB8HfH+6FPUyRZeMjrQ4yPiemQH49XwZtiRXWLTxHSlUai3e25+Ln86W4aZJvnhlRYTVG98pgc7gMGk4nt9gGNHQnT8uViDAhYspgU4m/+bEZeHhLnOKQFcuntueifUTvTGrR5BY1dIBuVKNmyYZB0pPLAyFu9AOoW48JPo7Qa5U47tTJUj0cxwTttYyRSce/eMKIjwEeH5peJeTpxue3JqOWmmHiW2xWK4zDBhu2em1TEg3h7We+9nMKimivQS9BgPh7gJEegiwPbUaS2N675vKqpEi0kNgcu1N8HVAqgWHNaVagz8vVmJNvBduSPLBgkg33PpDMm7+Phmb75lsGKFy508XkV4pxYJIEZh0Gg5l1yNYxDMa2zIUDChMWr16NT744AN88sknhv9JpVJ88skn+PTTT228xL7xdtRdiN2rOY2tSvA5jFE1bCve1xF0GoGbJvv2/WYzEASBZ5eEoaBejl3pw2NC0KZUY8N3F7D+63N9ursll4gxKcDJKneyGC8hvBzscMCMi1ObUo1WpRqJfo4gCF00f/tUf9w3K8gQ4OhZN9Eb7SoN9ncdp0muxJaL5bh9qp9RszrF4NDrcntOWG/uGqjozLVNdcKexcDNk33xwx1JVm0GRytBIh5UGi2qWq72tdTLFHATsIclaxns2vsQ13JxO5rbVEbOat2ZFuQCFx7bZA7L6YJGqDRaLLAwr2msYMeiw0PIMWnal/YR5AC64bybL1TgUlkz3t+fBzc+GwEuXHx4MA9fHi+CK4+NG3sEj9ODXEAQsLqaU9zYhlqpok9JoF6Ln14pwYUSMQQchkVpjKeDHZbGeOCfXkwlRgKZohM3fncev50vx8vLI/Dumuh+XSP6yvyBrDqTyqNYrsShnDpsSPLp85jXT/RBkr8jXt6ZZfKsa5DpkqYivvF9jiAI3DzZF4ldiQAem4EnF4aaBEmjldd3ZaO5TYX/3RRvCMrnhIlAI3QztHrS3Kb7Owy2ck9hPX7OXDDphElLBqDb8/o7991zun6iN47nN1jss9STUyMzBCXdSfB1REF9K+RmerX2Z9ZB3KbC7V09VU5cFrbcOxnuAg5e+CfTYLiTUtaCFXEeKGqQI6taigdmB+H3uycP+fNwQEHOpk2bAAAff/wxjh8/juPHj8Pd3R3Hjx/HsWPHbLpAa9BvhrpvKBpblXDlD60spL9smOSD/Y/PNJlc3R8S/Z0wL1yEjw8XDGr+gjWQJInn/8lERXM7ZAo19vdwv+pOu0qNjCopJgdYF5UTBIGl0e44mF1nMk1XX4UTWSHr8XPmYlKAE7Zd1lm0fnK4AAR0UgIK23FVlysxet2Q2aMeekYEuZg6rNXLlHCzwmLbJp8v0j34LAU56V19HBO6Oat1h04jsDzGHXsz64xs2o/kNiDQlWs0kXysEuDCNWmmtSbIeWl5BKK9BNj4UwqO5Dbg6cVheHpRGM4WibHjSjXuNzOJ3ZHLQoyXEGf6cAHTc6awEXQaYbb60B1vRzs4cVnIqJLiQokYkwKce63wzw8XoaBejlqp6ciFkUCp1uD+Xy+jqF6Ov+6fgntmBg5o03PzJF9kVktxvsTY3EFvWb7OzDDVntBoBF5aHomK5naTrLXesGGsmNT0xNy8uH2ZtfgntRqvroiEf7ehi05cFhJ8Hc06lzbJbZvUougbJp2GQBce8utMgxxd4qzv7+QNST46E4RTJRbf09KmQrWkw6wjcbyvA7QkkGZGsv7r+TJMDnBCWLfeTgd7Fl5cHoHMaimO5TXgl3Nl8BBy8OG6WBx6YjaOPjUHjy8IMTvI2dYMKMjZuHEjtmzZgmeffRZvvvkmNBrNiGoq3fhsMGiEUZTa2Ko0ybqMNGwGvU/JlzU8tSgUlc0d+DdtaKs5Wy5WYHd6DT5YF4tEP0f81cush9RyCdRaEpP7eCh3Z2mMB1raO00GQjbIdA8Ua6WG6xO8caGkGV+dKMLm5Ao8vTgMzoPUDFMY48hlIcCFazIvR2yo5FBBTne8HO3AYtBQ3HB1E93QqrAqcLcF9iwGvBzsLLryVDa3g89h9KqtXxnniSa5EsldG0eNlsSxvPoxX8XRE+DCRdkAghwOk47vbk+E0J6JaC8BVk/wwrIYd8R6C+HCY+HmSeYr9fq+nJ5JHXOcKRIj3sehzwHZBEEg1luI5FIx0iulfQZFM0N0FSX9BPaRhCRJPLstA5fKm/HtbRMx0c/6Z0dP5oWLEOrGw9cnig2v1csU+PpEMW5M8rG6hyTaUwAOk2biTNjQqoQ9iz7m+tBKm9qw5NNTCHvlAOLeOITThbrzXidV4MUdmVgQ4WZSdQSA+RFuOFssRrvKOHOvr9xTcrXhJdyDj9weQY5ao0WTXGlVjyePzcAd0/yx9VIlGs309gC6uZIAzFZywt0FcOWzcaRH4NvYqkRqhQTrzSQRZoW4IN7XAR8cyMOejFrcOsXPJj02/WXAn+jn54dDhw6By+Vi5syZUCpHbtAYg06DhwPHSK7W0KqA6zBlTYebKE8h4ryFOJbXdyPZQOnUaPHFsSKsiPXA6ngv3JjkgwslzSabAj3JpTqphCXHIXPE+zhAaMfEhR7ZN32DtrVB6rJYD9gx6fjwQD6Wx3jgrun+Vq+BwnoiPQUmzb1X5QtUUNkdOo1AoAsXJU1Xg4wGmdKqYam2IljEs1jJqZZ09FlRTvB1hIeQg90ZugpuakULWto7x1WQUy5uN6pUWRPkADojmP2Pz8Qf904BjUaAIAj8vHEStj84DXYs8xLpeeEiSDs6LU4P12hJHM6px8HsOlwoEWNGiHXudbHeDkgpa4FKo8WUwN4r6c48NqI9hThlhX3/UHMktwH/ptXg/bWxmDZIpz4ajcCDc4JwurDJMHT7vX25YDJoeGphmNXHYdBpiPAQmAY5stGXNLWG/x7KR3ObCs8uDkOUpxDPbsuAtL0TT/yVBgaNhvfXxZhNUC+IEEGl1uJskfGzWSzXtQGM1UGmY5UIDwHy62RG96omuQpa0vpk8MZp/qARhMWxG1k1UrAZOjfIntBpBJZEuWN/Vq3RvJzkUt33w9z1SxAE/rMgFAX1coBAr4Neh5JBfVMJgsBTTz2F77//Hq+88oqt1jQgfBztUdlTrjaON14zQ1xxtqjJ6EtvSw5m16FWqsD9XTMJlsd6gMdmYOsl89Wc5JJmTApw6pcZAo1GIMHXAZd7DKrqb4M2j83A9YneCHfnW7xpUwyeCHc+8utajXTvYrkKLAYNXAsbu2uZQFeuUSXHWmmBregtyKlq6YC3Y+89TzQagRWxHtifVYtOjc4e3tGeiQQLErexRqArFyqN1mDtrdWSkCk6re7l09sb63Hiskx6BrsT7+sIZy7Louvd1yeKcO+vl3D/b5chV6otGgj0RD8IkN9LP053ZoXqKkpD9eywBo2WxH8P5iPJ39HgQjdYVsR6wsvBDk9sTcML/2RgZ1oNnl4UZtGVzRIxXkKTIaH1rYphTVDYgtxaGfZm1OLR+SG4Z2YgPrlhAqQdnVjxxWmcLxHj4xviLFa4gkU8eAo5ON9jtpO4TTVoZzWK/hPhIYCiU4uybj2xdf1UvDhyWbhpki9+OVeGIjP9Pdk1ulEclqotS2PcUS9T4ko3NceFEjF8newtJsxmhbhgerAzNiT5jFgi1CbheFRUFB544AFbHGrA9LSRbmhVDps0ZCSYEeICmUJtNCPBlvx0tgyJfo6I6fJLt2cxsDLOE1svVZloexWdGqRVSqzux+nORD9HpFVIjCQcDa3Kfjdov74yCnsfm9mnvINi4IS5CyBXqo2uM3GbCs5cFhVYmiGom420XKlGm0oz5PbR3QkW8VAt6TCRnAD6IKfv3sBVcV6QtHdi7VfnsONKNeaGi0ZEcjAUBHT1Ten7clqVapAkhsz9jk4jMC9chMO59SYN8qVNbfj8WBE2TvdH8ovzcea5ub3OOOqO/n2TrUwyzQpxhbSj09CXNRLsSq9Gfn0rnl0SbrN7B5NOw0frY+Eh5OBMUROmBjqbuKFZQ7SXEGXidkg7rtrdNsrG3n7ik8MF8HKww42Jugy6r7M9XlwWgcrmDtw/K7BXcwSdDNLBJNgTy1WUVG0EiOjqd8nrpqSo7wpy+tPXou+DufuXS2jpkh7qya6Wmu3H0TPJ3wnOXBb2ZV41i7pQ0tyrRJYgCGy+ZwrevC7a6jXamvHxtEJXJaerJ6dNqUa7SjOuKzkJvo6wZ9Fx2spG1v6QXinB5fIW3NWjef+OaX5okiuxO93YgOBKhQQqjbZf/Th6Evwc0abSIL/e+OLtb9aMRiOGzVL7WkU/NDKvmzbYFjNyxitBrjyI21SQtKsMD6ThruQAMBlKqtWSqJZYF+TEeAvx7W0T4cpno7lNhesm2CbrPhrwdrQDg0agtCsQlXVtanubkzNYFka6oVzcbtQrRZIkXvwnE648Np5eFAY3AafPKlt3XHhszAsXWX1uEvwcwWMzcDJ/ZPpyOjVafHy4APPDRUb25LZgWrALfrt7Mk4/Ow9/3DdlQM8E/QiE7G4b/IYxVMnJqZHhzd05OJRTj8fmBxtJy26Z7Iu/H5iKZxb3LeGL8RYiu0ZqJE9q7kpqUQwvrnw2nLkso/lcDTIFaET/+mGFdkxsuiMJkvZOPPD7ZbR1uaXJlWqUitvM9uPoYdBpWBTljv2ZusG7ja1KFDXI+5TIjjTjJsjxdrKDTKGGtL2zX+5cYxUWg4apgc5Wu/X0h5/OlsLLwQ6LIo219+HuAswMccEPp0uMMpGHc+rhxGUZBuX1hzhvB9AIGLnZ6K12KUYX3o524LEZyK+7eqMVtynhRDntmEU/3yC3trVbkDOMlRxX/cBLY2lCk1wJlVprVZADAIuj3PHjnUkoeHupyZDJsQyTToOvk73BRlqfuR/KOUYzQ1zBYdKMJGv7s+pwvkSMt1ZfnfXVX368Mwkr4zytei+TTsO0IGecKhyZIOdSWQsqmzvw0NzRNatHT4iIBzbD2HxgrChDtl+uwrLPT+OfK1W4d2YA1iYYN4QTBIEkfyerqrFRngK0qTRGDoRNciWV1BoBCIJAhIfAKMipkyngymf3u7Ie4MLF17cmILNaivXfnEdmlRSfHi4ASaLXSg4ALItxR41UgeTSZkM/zmQqyBke9HMhChtaDe4Ro81C2tbMCHFBakWLWe/ywXD3jEC8vTra7MVzz8xA5NW1GhoSNVoSezJqsCzGfUAyFi5bpyPv3ozb0Dq8DdoU1kEQBMLcjV1exG0quFCZPbOEuvHBZtCQUSUxzNkYTjMURy4LLjyWSV9OZZfcsD/VAgBWzb8aa/i7cFEq1ikAhiPIsWPRMSPY1RDkaLUkPj9aiKmBzpgXPnyGDo/ND8FbIyQhOVvUBKEdExN8zM9oGmkYdBrCu5kPyLuUIWPBeGBuuAjf3DoRF19cgJeWR4I5CGmpvqLVXbKmq+SM/r/DeCTCg28U5NTLlANWBkwLcsHfD0yFtF2FlV+cwW8XyrFxuj+iPXsf5D4l0BkRHgI8+scV7Eit7rUfZ7QwfoIcEQ8EARTUy6962o/zjfLMEFeotSQu9GgOHCwx3kLMtdD0OivEBWFufHx3Wue3nlwqRkOrEqviBi5jmejnaGQ+0DAG9c/XCuFd5gN6KI22ZZh0GqK9hEivkqChVQEOkwYBZ3gtaINcTc0H9PPErK3kjGcCXLgo7XLAG44gBwCWx7ojrVKCf9OqcTi3Hnl1rXh0XvCQfmZPor2EvUpThpLTRU2YFtT7PJ+RJsZLYNjc60cajIX9hBOXhSXR7jZxP3PmseEp5BiCPZIkdUEOVckZEcLdBaiRKiBt192nBmtkE+UpxL+PzMDbq6Nx7vl5eG1lVJ+JLCadhl82JoHNoOFoXkOflvWjgXET5Nix6PBzskdBva6Sw6ARcBjih9VIE+TKhY+THfb1MqTT1hAEgYfmBuFUQSN2XKnC7vQaeAg5SPQbeFZuop8jKps70CBToE2phlypHrahiRT9I9ydj5JGuWEieHObihoE2gtx3g5Ir5Qasm7DbdAQLOKZzMqplnSAy6IP+WZ+LBDgwkVVSweUas2wBTnXxXlhZZwnnvk7A+/szcVEP0dMDRrdkg9bIW3vRGaVBNMHaRk91MR0mQ/IFJ2ol41/+bslor2EhiBH1qGGWktSSa0RQu+cmNslF7eFrN+Vz8atU/z65XwmEnDw292TEebGt1oiO5KMmyAH0MlD8uta0dCqhCufPS7lFd0hCAJr471xIKvO0EA2HKyK88TKOE+8vCMLezJqsTLOc1B/6wRfXYB0ubzlmuinGsuEewigJYGiBl2gI1eq4ULJFywS5yNEtaQD2TXSEQncozyFKG1qM3KK0ttHU454QKALFyQJVHS5aXFZ9EFJfKyBRiPw0fpYxHgLUdHcjkfnBY/pc9Gh0uB4XgNe+zcLd/2cgoPZdUbN6t05XyKGltQNRh3N6B3r0islBmXItZh4i/ESIqdGBq2WRFPXTDTKQnpkCBbxwKQTBslanVQxYt/JABcuDj4xCzNDRn+P5rgKcsLc+YZKznjvx9GzLsEb7SoN9mUObzXnnTXRcOKx0KpQY9Ugo3lvRzudJ3+JeERcqCisJ9TtqsMaNf26b+K6NkspZS0jErhPCnAESRobe1hrH30tEOCqm2tT3Ci3ehCoLeAw6dh0RyK+viVhzJs5/JlSgY0/p+Bgdj3qZQrc/9tlrPryjFnr8jNFjfB2tIOfc//6wYabMDc+hHZMJJc0o7FVCRaDBoHd8EpNRwPR3kKD8xZ1vx9ZWAwaQkR8ZFRJ0aHSQKZQw60f9tHXKuMqyAl140PcpkJurWxMNAnaAl9ne0wOcMK2y1XD+rkCDhPf356IZxaH9enI0RcEQWBGiAvOFDYZgpxr5fyNNYR2THg52CGvVnb1oUfJ1Szi52wPoR0TGi05Ipr+IFcenLgsJJc2G16rbmmHFxXkAADcBRy4Czg4WySGtKNzSO2je+Jgz8LSGI8xXcUBgJVxnjjwn5k4/8I87Hl0Bn7amISsahkOZteZvPdskRgzgl1G/e9MoxGYFOCE8yXiLiOc/s1tGy/oG9GzqqUQy3WVHKonZ+SYEeKCUwWN/R4Eei0z7oIcQDe59Vqp5ADA+oneSC5tRkWXS9BwEe4uwMNzbSO1mBHiipKmNqRVSsBi0Kh+gVHMBF8HnCxoRFPXQ4+Sq1mGIAjE+TgAGF776O6fn+TviJQyXZBDkiRVyekGQRBYECnCkdx6SNuHr5IznnDhsRHuLgBBECAIAnPDRIj3dcCOKzVG7ytrakNpU9uo78fRMzXQGemVEpQ1tV2zSTdXPhteDna4UNIMcVdSy9GeCnJGijlhrhC3qXA4R5dAcKeCnD4ZV0FOgAsXjK7ekPE8CLQny2I8YM+iY3vq8FZzbMn0rsbbXWk1cBNcm1mzscLaeC8UNshxomuYIFXJ6Z0J3rps6Ehl3ZL8nZBRJYGiU4MmuQpKtbbf9tHjmYWR7qjtmv1ABTm2YU28F84UNhqcyQDghzMlENoxMc+Cc+doY0qgM9RaEicLGseEs9pQsWqCJ3an16CiuR0O9swh71mjsEyinxN4bAb+TKkEMDKJs7HGuPq2shg0BHZprF2voQiXy2bgvbUxWB7rMdJLGTDOPDaivQQQt6mu6QfKWGB2qCtc+Wz8mVIBNoMGLos+0ksa1eibmEcqGzw5wBmdGhJXKiQG++jRPttgOJkSqNs4NMmVVJBjI1bEeoJGENiVrqvmNLQqsPVSFe6c5j/ggafDTbg7Hw72TCjV2mvaCOemJF+0qdTYmlJJ9eOMMCwGDTOCXVDS2AY2pXixinEV5ABXJWvXUiUHAK6b4GX43ccqM4J1DbhUdmJ0w6DTsC7BG4pOLZy5LKrq1gczQlzwnwUhSBiEzfpgiPDgg8uiI6WsGdk1OmceSq52FTaDbmj+pzYNtsGJy8KcMFfsuFINkiSx6UwpGDQCd07zH+mlWQ2NRmBygG4OyLXc++DrbI9ZIa5oae+kpMmjgLnh+n3S8I8kGIuMuyAnrGujfy1nXsYqM0N0Wm2qkjP6uT7RGwAlVbMGDpOO/ywIBYc5MhUvBp2GBD9H/Hq+DK/+m4UZwS5URrYHCyPdAFBBji1Zl+CN7BoZZnxwHL+eK8dNk3zhOMa+d1MDdTLqa6nH1xy3TPYFQDmrjQbmhOnknlQy2DrGRt24H8R4C0EjKDnGWGSinyP4bAZ8nah+gdFOkCsPk/yd4MilNoVjgRnBLjhT1IRH5gbj8fkhVAawB3PDRGAzaJQlqw1ZEu2OH25PxOnCRhQ3tuG+WYEjvaR+MyPEBQSBa/6ZNC9cBC8HO8qVcRTgJuAgzscBfs7ckV7KmIAgSdL81K5hwMHBARKJxKbHJEkS5eJ2+LtQX4D+MBTnYiBUSzrgwmOBzbh2+zxGy7noixa92844zu6NlXPRF50aLeqkCviM4c3aUJ+LqpZ2iPgcsBjjTuBgc8bLdWENpU1t8HcevcNzh+tcNMgUsGczwBsjPVUjwXCdC7FcCQad6smxhnH3bSUIggpwxjBUBW7sMJ6Dm/EGk04b0wHOcEA5zlGYI4DaTwAARNdwX9Jow/ka6zkfDCNayaHRaBAIBjdIksIybDYb9fX1Vr2XOhdDC3UuRg/UuRg9UOdi9ECdi9EDdS5GD9S5GD3051zoGdEgh4KCgoKCgoKCgoKCwtZQ4mMKCgoKCgoKCgoKinEFFeRQUFBQUFBQUFBQUIwrqCCHgoKCgoKCgoKCgmJcQQU5FBQUFBQUFBQUFBTjCirIoaCgoKCgoKCgoKAYV1BBDgUFBQUFBQUFBQXFuIIKcigoKCgoKCgoKCgoxhVUkENBQUFBQUFBQUFBMa6gghwKCgoKCgoKCgoKinHFiAY5bm5uI/nxFN2gzsXogToXowfqXIweqHMxehjouSBJ0sYroaCui9FDz3PRplRT3/kRZkSDHKVSOWKfLVN0Yvr7x3A8v2HE1jCaGMlzYY7rvzmH386XjfQyRgRbnItLZc2Y/O4RNLaOrvM61hjMuTiSU4+7f06BWD6wY6jUWqz64gw2nSkd8BrGE7a8R4nlSkx+9wgO59Tb7JjXEv09Fxotie9OFSPhrcP4K6ViiFZlPYpODS6XN4/0MmyCuXOx9VIlEt46jBu+PY9vThZDrdGOwMquPbqfi+P5DYh+/SDqZdQzeCS5ZuVqR3LqUS3pwLcni4fsMwrrW7H9ctWQHX+80tymQkpZCzYnj/zDcCxCkiQ+OJCHepkSWdXSkV7ONYdKrcWLOzJxz6+XcDSvAb9fGNj3+OdzpciokmJXeo2NV0ixL7MW9TIlXv03C21K9UgvZ9yh0ZI4V9SE9/fn4eWdmVj71Vm8uy8Pvs5cPLc9E18cKxzRDPcHB/Kw/pvzA05AjGZ2pdfgue0ZiPdxAI/NwPv787A9ldqHDDdBLjyQJKhn8AhzzQY5+zLrwGLQcKGkGfl1rTY/vkzRibt+ScELOzKh0VLlyv6Q2XVTyKtrRXGjfIRX039e+CcTv45gFepMURNSyloAAEUNY+/vN9b5+3Il/rhYgddXRuLGRB/8dqEcSrWmX8doaFXg86NFcBdwkFElQXObaohWe22yK70G4e58iNtU+Pxo4UgvZ1yRXinB9PeP4eYfkrHtchUulbWAw6Rjy72TsfOhaXh0XjD+e6gAP50tG5H1FTfK8dv5cpAkkF0jG5E1DBXH8xrwxF9pWBHrie9uT8SPdyZhYaQb/nesCJ1UNWdY8XGyA5/DQFYNFeSMJNdkkNOq6MSpwkY8MjcYzlwWfrtQZtPjkySJl3dkobK5Ayq1FjWSDpsef6yiVGvw1YkiyPvInGZVS2HHpIPDpGFfRu0wrc42lIvb8GdKBUYqSUmSJP7vUAGivQQId+ejsMH2ATxF72y7XIUZwS64c3oA7poRgCa5EnvS+/c9/u/BfBAE8P3tiSBJXeBKYRuqJR1IKWvBXdMD8MDsIGw6U4oi6jqxCa2KTjzyRyqcuCz8/cBUXHxxPg78Zxb+un8qpgW5gCAIPLUoDLdP9cN7+3ORVikZ9jW+uzcXIj4bHCZt3AU5Ye583DbFDx/fEAc6jQAAPD4/BFUtHZSqZJghCALRnkJkVffvO9ah0uBMYRNaFZ1DtLJri2syyDmW1wCVWovVE7xwY5IPdqRW2/QLtSejFrvSa/DovGAAGJPViKHgq+PF+PBAPpJLxL2+L7NKimgvAeaHu2Fv5ugOckiSxNN/pxskRT+eKYWAw8T1id4jsp6UshakVUrwxIJQhLjxqUrOMFPUIMeVCgnWT9Sd/zB3PmYEu2DTmVKr5TkqtRY7r9Tg3pmBiPEWIsiVi1MFjUO57GuKPek1YNFpWBztjofmBMGOScfufgah1pBVLcXm5HKbH3c088rOLIjlKnx5SwKS/J1A69po9+Sl5REIc+fj4c2pkLQPX5XyfLEYR/Ma8NzScIS7C5A9zrLsng52eH1VFJj0q1u7aC8hFnVVczpU/asoUwyOaC/rv2NaLYmntqZjwpuHcOumZLy+K2eIV3dtcE0GOXszahHtJYCvsz1umeKHNpUGh7Ktb0CtkXT02tD9V0olJvk74fH5IWDSCRQ3ttli2WOaogY5vj6h63+S9RFQZlZLEe0lxLIYD+TVtY7qLOvx/AZsu1yFp/9Ox+nCRmy9VIVbp/jCnsWwyfGLGlpxutD6DW5+fStoBDAzxBXBrjwUNsgpd5dhZNvlKvDZDCyOcje8tnG6P3JqZVZnrQvqW6HSaDE5wAkAMCvUFacLG0f0PKrUWryyMwvl4rF/L9uVXoPZYa4Q2jHBYdIxOdAJ54t7T7wMhF/OleHlnVmobG63+bFHI8fy6rEzrQZvrIpCgAu31/eyGXR8eXMCWhWduPfXS1B0Xt18tynVeGRL6pAE9pfKmsHnMLAy1hPRXgLkjLNKjiWeWBiKRrkSyz4/jcvlLSO9nGuGaC8haqUKNFnR+3WhVIztqVW4e4auwrzjShUK60fv3mescM0FOR0qDU4WNGJZjAcAwMvBDk5cFiqseBC1q9T46GAe5nx0Aqu+OINqMzI0uVKN5FIxFkW5gUGnwc+Zi5JrvJJDkiRe3JEJkYANOo2AtN1ykNPSpkK1pAMxXkLMDXeFHZOO/Zl1w7ha6yFJEl8cK0KMlxDBrjzc+VMKNFoSd0z1t9ln/HKuHC/vzLL6/VUt7XATcMBi0BDixkOrQk05rNkItUaL0ibLm3yNlsSOK1VYEecJDpNueH1WqCv4HAaO5lrn5JhRJQVB6B6QADArxBX1MiXyR/CBt/NKNX67UI7/HioYsTXYgqqWdmTXyLAi1sPw2rQgF1ypbEG7yrYGBNk1MpAksOXitWGg8ufFSkR4CAxVzL7wc+Zi051JyKiS4pEtqVCqNVBrtHhkSyr2ZNTi48O2/67VSDvg5WAHGo1AlKcQpeK2PuXT44EIDwH2PDoDPDYD6785h+e2ZaBephiSz+pQaXp9xl9LRHnq7uHWyCK3plTCx8kOTy8Kw38WhMBdwMF/D+UP9RLHPWMuyFF0anA8vwG/XyjH7gG4DlU0t0Op1iLB19HwmpuA0+sFn1srwys7szDt/WP47lQJ7pjmBxpB4LYfkk0i9DOFjejUkJgXLgIABLpwUXKNV3JSKyS4WNqMV1dEQsBhQNph+aGiNx2I8RLCnsVAvK8D0qskw7TS/pFc2ozUCgkemReMr29NgD2LjnUTvSAScGz2GfG+DigXt1uVCQKAqpYOeDvaAQBCRDwAQCElWbMJ7+7Lw83fX7BoJPJXSiXqZUqTTR6TTsOsUFccy7MuyMmsliDYlQcuW1cNnBzoBBadNmKSNY2WxDenisFnM7A3o6bXQG+0k1erCxS73/+nBTujU0MazDpsgUqtRWFDKwQcBv5Kqey38cRYo6VNheP5DVgb7wWCMC9RM0eSvxO+vDkBx/MbkfT2Eaz75jxOFjTipkk+SKuUWKy0ZFVL8cfFCuzLrLX63ggANRIFPIS6+3OUpwAkqXu+XwuEuvHxz0PT8NKyCOzPqsWcj04gtcJ23/k6qQKv/ZuFSe8cwfyPT1AyfQABLlzYs+h9OqxJOzqxP6sON0z0AY1GgMOk4/EFITiYXT8ifWvjiTEX5Hx9ohgbf0rByzuz8NifV/pt/1nXFcy4d9uIugvYhte709ymwgv/ZGDZ56exP6sO6xO8cfiJ2XhpeSR+u3sSpB2dePrvdKOfOZrbgAAXLgJddRvMQFceSpqsu9hJkkRmlXTcyYsulzeDxaAZJCK9ydUyq6WwZ9ENf79Ij6GRFJAkiTrp4DJZXx4vQoiIh4URbvBz5uLUM3Px5nXRNlqhDv1mLK1CYtX7dUGOPQBdppROI0ZNyVul1g76bz6SLI91R61UgVNm5IPbLlfhpZ2ZWJvghQRfB5N/nxcmQk6tDLXSvk1I0iuliPEWGv7bnsXABB8HpJZLBrP8AXMouw4ljW346tYEONqz8PWJohFZhy0oaGiFHZMOLwc7w2uhIj6cuSycK7aduUNhQys6NSSeWRyG5jbVqK1G24o9GTXQaElcN8Gz3z+7ININ+x6biVum+KFDpcb7a2PxxqpoOHNZ+NPMTJ1OjRb3/noJL/yTiYc2p+L6b85DpbbOOaxW2gHPrnMf6sYHnUYg+xqy+GXSabhnZiBOPTsXfs72eGtPjs32G0/8lYadaTW4abIvBHZM3PJ98jUj1bQEnUYg0qPvvpxd6TXo1Gixvlsv77oEbwSLeHhlZ9aon3NEkiTu+SUFB7JGXw/1mAtyzhQ1YX64CFvvnwqS1OnX+4O+YuMu7BbkCDlmN1+3/5iM3em1eGlZBM49Pw8vr4iEf5fWONCVh6cXh+FEfqPhQtZqSRzPb8TcMJHhGIGuXNTLlFaVxE8UNGLlF2eszvgOFb+eL8NnR2xnq3qprAVx3kKwGXQI7JiQdlgOcrKqpYj0EBicYaK8BKiRKtBiYwvdrZcqMfPDYwPedGdVS3G6sAkPzgkyNNc6cllGDZ+2wM/ZHk5cltUZt+pulRwWgwZ/Z3sUjXBGTa3RYvvlKsz7vxOY9eHxMasJT/B1RJArF1tTKo1eP1PYhGe2pWNtvDc+Wh9nNpM9J8wVBAEcz+u9GqPo1KCgvhWxXkKj1yM9BciuHf7NGEmS+PpkMZL8HTEzxBV3zwzAP6nVo7pPrjeK6uUIceMZNcTTaASmBDkb+nKk7Z3QDtL2Xy9PWTXBC5MDnPDbhfFtQPDPlWrMCHEdcBU7zJ2P55aE49ATs3FDkg9YDBrWJ3pjR2q1SbP8gaw61EoV+Oehadj+4DSUidvwy7kyqz6nRqIwBDkcJh0hIl6fUqLGViUOZNXiy+NFYzpJ0x0HexaeXxqOKxUSHMwefABe1CDH+RIxXl0RiReXRWDzPZPBoBN44PfLNljt2Cbaq2+Hta0plZgV6goP4dXkC4NOwwfrYpBVI8V3p0uGepmDIq1SgiO5DTjYj972vrBV8D2mghy5Uo30Sglmhboi0lMAAP2ecVMvVRgaTvW4CTho6NG30KZUI6tahpeWR+CemYFgMUz/VCtiPWDHpOPvLmvGzGopmuRKzI+4GuQEueqCImv6cvQWjz9becMeKuqkCnx7qrhPgwBrIEkSqRUtSPDTVSSEdkzIegtyaqSGXgQAiPTQ/X9bSgpIksQPp0vRqSFxOHdgF+X3p0vgKeRgZVz/M5f9gSAIxPs44IoVlRxFpwZNcqVRljpYxENh/cgEOWK5Ep8dKcTMD4/jqb/TEe7OR4QHHw/+fnnI9OBDCUEQuDHJB0dy642GCP5+oRwhIh4+XB9rCM574sxjY4KPA47l9f59y62VQa0lEePtYPR6pKcAlc0dvSYIhoKUshZkVElx/6wgAMBtU/wg4rOx7LMzeH1XNl7flY0ln54akHR4JChoaEVwl4yzO9ODXJBZLcWXx4uQ9O4R3PlzyqAcN3NqZPBxsoPQjok18V64XN4y7OduuChp1DkKro33sulxb0ryRatSbeJQ99PZUiT5OyLB1xET/RyxIckXnx8t7FO2JlN0Qq5Uw9PhaiAW6SnoNcgpbpRj5ofH8MDvqfj4cAHWfX0OZWNYrtmd2aGumBrojA8P5A+6UvDHxQoI7ZhY3tXr5iG0w4vLIpBdIxs3f6+BEuUpQEVzu8U+paKGVmRWS832sk30c8LGaQH49HDhqE4s/Zumu//bUnWzM60aK/93ZtC9kmMqyEkpa4ZaS2JakDN4bAZ8nOyQ188gp06mgJuAbfSau4CD5jaVkW5aXyEKd+dbPBafw8SKWA/8fakSGi2Jvy9XgsdmIMnfyfCeQBfdA7WvvpxWRScO59Qj3J2P04VNI/qFvmWKHxSdGvxjA1/9iuZ2NMlVmNglu+qtkqPVkqhu6UCg61VnnkBXLlgMGnJsGOScLRKjsEEOR3smjuT0P8ipamnHnoxa3DUjwOaVG3Po+5L6ehBVteikUHq5GgCEiPjDro1Wa7T46Wwp5vz3BL4+WYTpwS749+Hp+OGOJHx7WyJIAA9tTh2Tssw18d4gSWDHlWoAuqz/sbwGrE3wthjg6JkXJsLZIrGRk1RPMqulBolDd6K6kjrD3T/w24Vy+DjZGXoM+RwmDj05Gw/MCcLWS5U4kFUHpVqLDw7kjfphg1otiaIGOULdTO/p04KcQZLARwfzsSjSDZfLmnHDtxfQ0DqwYDynRoaorgRNor/u3nfFhv0Po4mzRU3gsuhYFOVm0+P6u3Bx0yQfvL03F9+f0mWy0yolSK2QYOP0AMP7nl4UChDA//XRpF0r0Z3L7tnyKE8hChtacbm82ezP/HKuDHZMOk49Mxcnnp4DJp3A+m/Oj4uNO0EQeGFZOEqa2gaVgVd0arDtchXWJXgbJY9nhriAQSNGXJky0sR2JazSLPQW78mohT2Ljvnh5q+fZxaHwU3Ixnv78oZohYOjU6PF7vQaONgzUdQo7/X51h+2plSBzaAN2ql2TAU554qa4MJjGzJxYW6C/ldyZEq49Sipu3VJ1xpkVzNBeXWtIAiYfSB2Z8MkH9RKFbjnlxT8fqECD84JMqr6OHJZcOKy+qzk7O/aLHxxcwIc7Zn45dzIyRu8HOywMNINv54vH7RsQy9NmthVyRFwmJApzEfmko5OaEnAhXc1CGXSaQhz49s0Q/DzuVIEi3h4YHYQzheL++2us+lMKexZdGyY5GuzNfVGgq8j2lUaFPRRkdG7/enlagAQ4sZDk1xlc7lfb/zf4QK8sTsHS6PdcfrZefjv9XGI83EAoJOGvr4yCpfLW1AmHnt6bVc+G/PCRdhysQJKtQb7s2rRqdVilRUVvTlhInR0anptJM2okiLUjQ87Ft3o9RARH0w6MayWt3qZzi2T/YzkXTw2A08uDEXGa4tw/oV5+HxDPKpaOrArbXRXc6paOqDo1BoMObrj52yPx+eH4KeNSfji5gRsfWAqqlraDZvr/qDVksiplRnUBoEuPAjtmEgdQZnmjitV+PNixZAkFm6b6o8Tz8y1mW1+d95ZHYP7ZgXinX25mPd/J/DQ75fh5WCHRZFXN4TOPDYenReMv1Iqe5Wv64dye3YLcpZGu8PH0R7rvj6P2zYlG0njWhWd2H65Chsm+cLX2R4+TvbY+sBUsBk0vLE72+a/60gQ6+0ALwc7XLIQ5FnDvsxaSDs6cfNkH6PX+RwmJgc6XfNBToiIBwGHgctlpn9jkiSxJ6MW8yPcTO75euxYdDw6NwRH8xqMklwaLYmHt6Tiib/STK7rgvrWYUuqnClqgrhNhUfmBkOjJfvdQmKOCnE7zpeIcUOST99v7oOxFeQUizEtyNmgeQ935yO/vrVfN+56mcLIdAC4akLQ3Xwgr1YGPyd7g8ORJfQ6/eP5jXhmcRgenhts8p5AFy6K+8j87EitxiR/JwSLeNgwyRfbU6uGdUhaT+6Y6o+SpjacHWQz7qXyFgS4cOHcFbj0JlfTS4CcuSyj1yM9BDar5JSL23A0rwF3TvPHoih3qDRanMy33rVKrlTjr5RK3DrFD7w+vhu2ItbHATQCffblVLW0gyAAj25yjIiuioAtXXR6gyRJ7EqrwYYkH3y4Pg6ufLbJe5ICdAFvZh8Nv2qNFicLGkfdALtH5gWjsrkdb+3JwY4r1Zgc4GTQ+feGv4uuwtab+UBmldSkHwfQ9VeFiPjDOqF966VKEASBGxLNP2gYdBoIgkCMtxBzwlzx5Ykii85zo4HCruq4ucQVQRB4YmGooZ8yylOIKYHOfX5HzVHZ0g65Um2ovtFoBCb6OeLSCAU5qRUtePrvDDz/TyYe3pJqExlyT8xd57aARiPw4rIIfLZhAmYEuyDezxGvrowEo0cF/fap/vAQ2uH9/Zaz3TXSDhAE4Ca8ulZPBzscfnI2Pr4hDqcLm3Ao52p/yrbLVejo1ODWKX6G10R8Dp5dEobj+Y39ml82mpng4zAoB69tl6u69i6m19XcMBGSS8U2Hbbenewa6ai3AKfRCCT6O5m9/gvq5ShqkBtZ2ptjdbwXPIQcfNU1axAA/nesEHszarHjSjV+OlsGQFdV++/BfCz77DTu/CllWAwLdl6phr+zPW7sCkhskYjbdrkS9iw6lsf0/nexhjET5LS0qZBTK8O0IGfDa2HufDS3qdDYDwtJnVzNQpDTrakwr64V4e7GkhFzEASB99fF4vOb4s0GOIBOctWbXK1a0oELpWKsSdBpmm+f6gc6QeCeXy6hXaWGVksivVIyZDcKc0wNckaIiIcPDuQhs8r4Qa9Ua6y2kU0tbzGyaxXYMSzK1ZrkuqDOmWf8wIz0FKCowboyaEpZM97cnYMvjhWadUvaeaUGdkw61iZ4IcCFiyBXLo70oy/ncE4d2lUabLBBhsFaeGwGQt34ffblVLV0QMRng824mhEKEfHg42TXr2G3gyG7RoZqSQcWR7tbfI+Iz4GHkIPMXqzBs2ukWPPVOdzx40Us+ewUzhTazvlqsMR6O+CVFZH4/UIFkkubscbKXgQemwF7Fh11UvP3K62WRHGjHGEWJLJRnqbBfquic0h6PTRaEluSK7A8xgNOPZIO5nh0XjBKGttwIGv0uogV1MtNnNV6I8ZLiOxqWb+r2fqHvL6SA+gq2WmVfUtObY1cqcZ//kxDpIcAn944ASfzG3HvL5fGnFT0ugleePO6aHx5c4LRoF09HCYdzywOw7G8BosuebUSBVx4xvdHQOeAtTbBG3E+DgYZqlZL4pdzZVgc5W7yfVkV54kJPg54Z2/uqA7qrWWCjwOya2RmHerkSjU2fHfeohFRc5sKyaXNWBFnfjM6P8INnRpySO7f0vZOXPfFWdz1U4rV7nojxUQ/R1ypkJhIevdk1IDHZmB2qGuvP89i0HDfrEDszajBueImbE2pxGdHC/HovGDcPtUP7+3PxXv7cjHrw+P45mQxVsR6QNrRiYtmqke2hCRJHMmpx8o4T/A5TPg72w86Ia3Rkth2uQorYj36LDJYw5gJcpJLxSBJ3eA2Pfp+GWsla2qNFk1ypUGepsfBngkWg2ZohiZJEnl1rRY3Gz1J8nfqVa4S6MpDaZPc4sPy6xNF4LEZhgGlHkI7/LQxCdk1Mtz8fTIWfHIS1315FknvHMHjf14ZsE68PxAEgTevi4a0oxMrvziDp/9ONzwYX92ZjUWfnOyz10Om6ER+fatBkw7oKjlypdrsw17cptv8ufB6VHI8BVB36el740KJGLf+kIxd6dX4+kQx7v75kkkV4Fh+A2YEuxikFQsj3XEsr8HqfoKdV2owwccBfs69T/S2NdbMC+puH62HIAgsjnTH4dz6YXkgH8yuA4/NMEpGmCPaS4iMKvNZ8j8vVmDVF2fR0anB5zfFw5XHxq2bknGuaPQEOrdN8cPqCZ7gsuhYEm1dtokgiF5nckk7OqHWkhaz4pGeAhTWtxp6B88VNWHuf09gw3cXBi0r7cmW5HJUSzpw+1S/vt8MXYNsnLcQO9OqbboOW1LYZTpA66N3Sk+0lwCtSrVVg6K7k1MrgxOXZaQY0EtO+9tDOlhe35WNxlYlPt0wAavjvfDZhngklzZbPZh2LLEqzhPRXgJ8etj8hrxG0gFPoWX3tzUTPHG6sAmNrUrsuFKNMnE77pzmb/I+giDwyooI5NW14tfzZYbX1RrtmAseASDOxwEqtRZ5dcabU42WxON/XMHF0mZ8cqQA/5q5to90PVcWRZpPagW4cBHowsXRIZCsnS5qhFqrMzZ6aUfmqP7bJ/k7oaNTY1TlIEkSezNqsTDSzaiXyRIbknzhaM/Czd8n49ntGZgV4or/LAjFi8siECLi48ezpZgZ4op9j8/ExzdMgCufPeTJzYZWJdpUGkPVOspTOOhKztmiJtRIFRYVBP1lzAQ5l8tb4C7gwMfpalYlwIULFp1mdZDTKFeCJGEiVyMIAu6CqzbSdTIFpB2diPCwLsjpC39nLhSdWhMHN0Ann/rzYiUemB0EoR3T8HqivxN+uCMRRQ1yBLpwsemORDw0Jxgn8hvxzt5cm6yrL6YGOePYU3Pw8vIIbLtchX2ZdShqkOPvy5XQksAbu3v32E+rkIAkjQfvCTi637HVTF+OWK4CnUYY3qNHH8z2dvFkVUtxzy+XEOfjgNPPzsO/j0xHR6cGJ/Kv3lyb5EpkVEkMTdQAMC9cBGlHZ5/DuvQ/f6aoCasHMAtisPg5c1Ej6ej1713d0m7Uj6NncbQ7mttUSBnirA6gC3LmhotMsqU9ifUSIqtaarQ5J0kSnx0pxPP/ZGJ9gjf2PjYDq+I8sfX+qfAQcnBoACYRQwVBEPj4hgk48cxco+u2L9wEbItJCnFX31RPuaaeKE8h1FoShfVyfH2iGLduSoYLj43cWhn2ZurmE2TXSLE/s3ZQD/waSQfe35+H6yd6I77btdsXCyPdcKawyWaNp7amsMs+2lr0Lo/9laxVSzrg62RvZCUe5yMEnUYMm2wU0PXhbLtchVdXRiKoa+7Y/AgREv0c8eHBvHFRhegOjUbg9qn+uFjWbNbquabbjBxzrOhKVP52oRzv7MvF0mh3TA40n6yZ6OeEWyb74r19eciqliKzSoqp7x/DhwfH3oT6GC/dd7OnZO2jg/k4lt+Ab29LxIpYDzy3PcPkOXkouw4TfByMRnL0ZF64CEdz621+XziR34ggVy7eXRODvy9XGapwo5FYbyFYdJrRM7igXo6SpjYs7UX10B07Fh1/3DcFv9w1Cceemo0f70wCvWtw6NYHpiL5xQX4vxviEOrGB41GYGGkGw7n1A9p8KdPAPk66ZK+kZ4C5Nb2v/rdnX2ZtfB1sjf0cQ+WMRPklDa1IVjEM3pwMOg0BIt4VmfH9De+nkGO/jV9T47+eNbI1axBr8UvF5tKvD4+XAAHexY2Tvc3+bfpwS7IfH0RfrgjCfMj3PDY/BA8tSgUu9Jrhq0BmUmn4e4ZAZgb5oo392Tjnb05EPE5+O/1sThV0IjDvWw8UytawGczjBp99RtCc7pwsVwJJy7LJNOqL4NuT62yqL/98ngRRHw2Nt2RCDsWHcEiPsLc+NiTeXU41Yn8RpAkMLdbkBPnIwSLQcPF0r4DgH1dm8flscMf5LgJ2GhXaXrVH1d1m5HTnQRfR7jwWDaZh9AbpU1tKKiXY7EVLksx3kK0qTQo6SZ7TO7KGD46Lxjvr4sxBEo0GoFpQS42HdZoC2g0ot+9CLpKjnm5WnNXkOPEMx/k6JMuT25NwwcH8nDvzEDseXQGZgS74JMjBciukeKm7y7gwc2peHhLqkXL0t4gSRIv78yCHYuBl5dH9utn54W7oaNTg/Ml4n5/7lCjd1YLMdM3YAkRnwMRn42sPgb59UQsV5lUo+1ZDER6CHCpbGiDnGN59diVXoPkEjFe2pGFlXGeRtJagiDw/NJwFNTLR/WmcKAsjHADnUaYvdfVShVGzmo9ceGxMTvUFZ8fLYRKrcVrK6N6/axXVkQi0JWL+3+7jA3f6QaSfnuy2KqE2WjCjkVHmBvfaOB0Y6sS358uwaPzQrAw0g0fro+Fj6O9UYJVrlTjVGGTWflgdzZM8oWkoxPbU3VurXl1MryxOxsNPSraF0ubcf035/Dw5lR8c7K4V3WFVkviZEEjZoeKcEOSDyb4OAybJHsgcJh0xHgLjWbEHcmtB5tBw8yQ3qVq3Ql142N2qCsCXXlGbp48NsNEVrwo0g3Vko4h7eMs7zIP8nXW7XEjPQRoU2lQPsAhsCRJ4lRBY9dcOesq7n1hkyCHJEmUlZVBoxm6DF5pU5shWOhOuDsf+XWtaFep+3SQ0stEelpIAzqHNf2/59XqpmL7Opl+3kDQH6e8h5tUQX0r/k2rwWPzgy060/Q80RuSfOHtaIf/9mGXaUsIgsAbq6Ihae/E8fxG/GdBCFZP8MK0IGe8tTfHYoYmtUKCCb4ORkGLPsgx10fQ1KaymMV+bVWUzkv+63OoajG9gLJrZJgV6gp+tyrQshgPHMttMEjWjuc1IMpTYNSTxWbQEe/jYFWQs/NKNaYHuwxZk21vuPF1a7a0QVZ0atDQqoSXg+l3lt6V1TmUXY8LJWJ8f6pkSPq79mXWgkWnYU63YbiWiOnKknffEJwqaISDPRNPLAg1+d5PD3ZGQb28T6nmtstVeOyPK7jpuwt4/M8r+CulYlRN3XYTmB88DHQ33jD//eJzmPBztkdRgxxvrY7GC8siwKDT8OSiUJQ0tmHtV+d0zddrY3C6oAnXf3uu37bOh3PqcSyvAW9eFwWhvfUVKkAXhHkKOTg6wNlTQ0lVSwc6OjUI7UclB9B9T/u7aRW3Kc2ew4l+jrhc3jIkmVWtlsR7+3Jx18+X8NgfV3Djdxcg4rPx7ppok2sp0d8JCyJE2HSmdFRLfAaCI5eFKYFOJr1hWi2JWonCaEaOOVZ39dc9tSi01+oEoNu4fnlLAiTtKkzwdcCJp+cgwIWLl3Zkjrkq2QRfY/MB/dwrvVzVnsXQuZGWiA3uWSfzG6FSa/tMagWLeFgY4YbvT5WgTanGw5tT8dPZMiz4+CQ2J5ejsVWJM4VNuP3HZLQq1KjqqiT3pjzIrZOhsVWJOWG6AGFKoDMuljXbXLZrSxL9HZFSdvX6P5pbjxnBLhZd1QbL1K5RK4ey6yBt7xz0vBlzVIjb4MxlGUyY9H2IPZPw0o5OfHeqGMfze5ctFjXIUSNVYFY/Ar++GFCQ8/bbbxv+f0FBAQICApCYmAgvLy9cvmz7CbcaLYnK5g4EuJg+oMLc+cisliLy1YNIfOcItiRXWDxOvUwJOo0waWwHAHcBu1slR4ZQd77V2u2+sGcx4CZgo6xHJedobgPYDJrBlcIaWAwanlwYimN5DRa9/YcCX2d7vLQ8AtOCnLF+ondX4BOFWokC35mxWdVqSaRVtJjIXQT6Sk6HObma0sg+ujtzw0TY/uA0NLepTBx0ZIpOVDS3GzX6AsDyWHd0dGpwPF/Xc3OqoNFIqqZncoATLpY19/pg2nmlGqkVEqxLMB3YNRzoJ4lb2uTXmLGP7s7iKHdUSzqw4bsLeGdfLv53rKjXzxPLlfjhdImJ6YQl0iol+PxoIVbHe1rlOufMY8PLwc6oL+dssRhTA53NXnfTg3W9ePqp9OY4XyzG03+no6RJDqEdE0UNcjz/TyY+ODB65gvoBg8rzG4u9XI1x16Cizevi8Zvd0/Gbd0cnxJ8HbE4yg0uPDZ+uWsSNkzyxW/3TEZBvRx/plRavTa1RosPD+ZjcoCT1RKK7hAEgXkRIhzLbRh1m2e9RW6Mt6lzXW9EdU0r78/vI5ar4GymGjcj2AXVko4BN+bqm3x7VsFJksQz2zLw7akSvLA0HBdemI+vb0nAb3dPNkr6dOfN66Lx571TbJYtHU0siXJHcqnYaGCvuE0FlUbbpwvi8hgPfH97Im6f6m/VZwW58nD2+Xn49a7JcOSy8M6aGKRXSfH9KJ9Q35MJ3g4oaWozVH93XKnG7FBXo+fx8lidCcmv58ug1ZL461IlQt14CHTtO3Fw/+wglInbcdP3F1DZ3IEt90zG7DARXtqRhaR3juD2H5OR6OeEHQ9Nx9/3TwWTTiC31rJC50R+IzhMGiYF6GYSTgl0QnObCoV99O2OJIl+TmiSK1HUIEeTXIkrlRLMj7DtbKnusBl0zAlzxefHihD35iHM+OC4xQTbQKlobjdUcQBAxGfDhccysq/enFyOGe8fw7v78vpstThZ0AgmncDUPnp6+8OAgpx//vnH8P9ffvllvPbaa2hqasKPP/6IZ555xmaL01Mj6YBKo0WAmUrOmngvPD4/BB+ui8W6BC+8uCMTH1uoctTJFBDx2WaH9ullJCRJIq+2FRFWmg5Yi58T16SSc7FUjARfxz77F3qyKs4LLjw2DucMb/Po7VP9seXeKQb7zhA3Pu6Y5o8vjxeZVFdKmuSQKdRI8HUwer23So6lzYGeCA8BFke5m2QJcvVuRj0GKOola7+eL8OnRwrQqlQbSdX0TApwRqtCbbG361xRE57Zlo7VEzxx3Qj04wBXq48NFio55mbkdGdWiCs+viEOOx+ejgdmB+GXc2UWb3jv78/D1PeP4e29ubjp+wtGJXZz1Eo7cO+vlxDuzseb10Vb+yshxkuIzGoJAN33IbNKgmnBLmbf6ybgIMiVi7MWzAfaVWo8tz0D8b4O+PfhGfjmtonY+9hMpL68EM8tCbd6TUONm4CNTg2JFjNSsuY2FRzsmSb2uN2ZHepqCPi687+bEnD0qdmG7PMEHwesjPPEZ0cK0Walxer21CoUNcjxwrKIAW9+54e7oUaq6HWDYgvy61oNVqmxrx/Eo39cwfFempvPFYsR6saDiN97dr4nMV5CSDs6DYN2+4IkyS65mmmyZlaoK4R2zAHPEzpfLMY9v17CI1uuGGWsTxQ0YntqFd5bG4P7ZwfBXcjB0hgP+PSiRPB0sOt3pW6ssDjKHSRg5Jqpt2336KM6o6969zXYtzsO9izD+6cEOuOeGQF4f39erwnX0caErud0SlkzihpakVktNXGN5DDpuGmSD/5JrcZLOzNxqqARj80Pser4E/0ckeTviIwqKZ5YGIppwS74303xOP3sXHxyYxxeXBaBH7qk5iwGDcEifq+Dj0/mN2JqoLOhYT/R3wl0GoHk0tEnldUzLcgZIj4b7+zLxfG8BpCkrkduKHlyYSieXRKGj2+IA40AntueYdMEVHlzu5HiiSAILIx0x670Gqg1Wkg7OvHWnhzMCHHBy8sjUNQg71VZcbKgEYl+TjZxVdMzaLlafn4+Nm7cCABYtmwZmpttX13Q2xX7m3G0Egk4eGJhKG5I8sEH62Lx0JwgfH6syOwfsl6qMGTEe+Im4ECl1uJobgPy61ttGkkCumFz3Ss5Gi2JS+UtSOrKRPQHOo1ArHf/ZRRDweMLQsDnME0i9NRyCQAg3se4ksPn6L68ZoOcNpVFqY6eMHc+ysRtRq5pObUyMGiE2abitQleuFDSjK9PFCPOW4i4runD3Yn3dQCdRuCimRtkfl0r7v/9MhL9nPDh+rgRy3zq7YctOXNVd23CLGUqaV02qRN8HPDg7CCwGTR8dtTUhahW2oFvThbjhkRvnHxmDkLdeLjjx4tItzBHIa1SgvVfnweNAL67PdEqlxg9Md66LLlao0VyiRhaEpjey3U3PdgFZ4vEJjdpkiTx7r5c1MkU+Gh9rNEGxZHL6nWzN9y4mbGr1yOWKy3KNfuCxaCZ/O2fWRQGaYfKqqyyolODTw4XYlmMOyZ0DW4dCFODnMFh0oZcsna2qAm/nC9DgCsXN0/2Q26tDBt/TjE7vJMkSZzvqhL2l2gvXeLE3L22TqowuR5blWqoNFqzyRoWg4ZlMR7YlV4zIFnNVyeK4cpn41RBI748rqvEqjVavLs3F4l+jsNqaz+aEQk4SPB1xP5ukjXDIFAr7cMHw0vLI3DTJF+8uCPTIPsa7QS78hDmxscTf6XhrT254LMZWBhpWmW4dYoflGot/rhYiVdXRGJFP/pTX10RhftmBeK+WYGG13yc7LEm3hv3zAw0un9FeFgOchSdGlyuaDHqZeGxGYj2FCC5ZHD7T5Ik+y3xtRYum4E3r4vCifxG/PdQPmK8hCbjTGxNoCsPD80JxtoEb7y7JgYnCxrxVz+q+31R2dwOvx7P1xsSvdHQqsSpwkZsv1yFTg2JV1dG4vpEH9BphMXhsIpODS6WNmNWH3ba/WVAQY5EIsHu3buxa9cudHaals5tTWlTG+g0os/NCkEQBk2tucxbfasC7mb6cQAYMqCv/puFEBGvXxevNfi7cFEhbjf8ffLqZGhVqDF5AEEOoHP+yayWjrgsRMBh4vml4difVYcL3RqOUytaEOTKNckWMuk0cFl0s8YDTXJlr5UcQNeDpSWvDvYDdPrPYBHPbEXsvlmBSH1lIQrfWYZ/H5lhNkPHZTMQ7SVESo+m4HqZAht/uggPIQff3DYRLMbI+XQQBAERn22xJ6dGqoATl2VVkCG0Z+KBOUHYeqnSpHp1JKceNAJ4amEY/Jy5+OWuSfAQcvDx4QKj9yk6NfjyeBGu/+YcnLgs/H3/tH7fsGeFuKKjU4OfzpbhXLEYnkIOAlwsW3NPC9LJfbpb+ja2KnHXzyn4/UIFnl0cZnYg3WhCb3pSb0Z2aE2Q3x98ne1x2xR/fHOy2MQeticfHsiHuE2JpxeFDeozOUw65oSKjDaYQ8FNk3yR+spCfHlzAp5fGo7DT8zC/bMC8c6+XPx4ptTovRXN7aiWdGBqkPkqYW+4Czhw4bHMOqw99XcaXvgn0+i1pla9Db7583jdBE/UShX9nl+RVinBmaImvLw8Ag/NCcInRwrwwYE8/O9YEQob5Hhp+cCrb+OR5TEeOFPYZJCsVTS3g0EjLJ4XW0IQBN5ZHY0FESK8szfXYPk+mqHRCGy9fyri/RxxsqARS2PczT5LPIR2+M/8ELy8PAJ3zQjo12fEeAvx4rIIq6pkEe4CFNbLzY6aqJMqoNGSCBIZJzWnBDp3jRoZ+J7ol3NluP6b80PmELkk2gOLo9xQL1MOeRWnJ4ui3LE23gvv7M21yYBtuVKNJrnKZF8+wccBISIe/kqpxO8XyrEwwg0eQjsI7ZiY6OdoMci5UCKGUq3tc2ZQfxnQrs3X1xcff/wxPvnkE7i5uaGqSueaIRaLwWTavgRe2tQGb0c7MHuRcejRl6PNTRWvkyrMOqsBVzcfNVIFnloU2q9ytTX4OdujVak2OCillDaDQSMQ30POZS16GUVls3UyiqFkbbwXojwFeHdfriFDeaVCYmQd3R2BHdOkkqNUa9CqUJu4EvUktEtG2N1RL7tGhihP81p7giDgxGX1eT4nBzghubTZcIOskXTgzp9SoCFJ/LxxUr9sgocKkYBjdnMMAHXSjj6lGN3ZOC0Afs72uG1TstFg18O5DUj0d4JjV0WBz2Hi5sm+OFPUZPjuHs6px5yPTuDjwwW4bYo/tj041UiXay0x3kLcNsUP/z2UjwNZdZgW7NLrRm1qoDMYNMKwgVZ0arD6y7NIr5Liu9sm4p6ZgRZ/drSgN63o6SwE6ORq1gze7A9PLw6Fr5M9HtqcatGZ73RhI348W4pnFodZpa/vi+WxHsiplaGkjzlag8GORTd6Huhdw+6aHoA39+QYacLPF4tBEDrdfn8hCAJRnkJk9ZDIarQkrlRIDE3Yegw24BbuY5P8neAu4ODffkrWvjpeBF8neyyP8cCTC0Nx2xQ//Ha+HJ8dLcSqOM9+WX1fC6yM8wQJYE+GzhFz55UaTAt2sflz3RI0GoFnl4SjvlWBrTbMnA8lQnsmfrwjEe+uicETC0Mtvu/R+SFDfq+N8BBApdEauW/qqena2/WceTQ50AlNclWf8/sskVLWjLf35iLWW9gvRUJ/efO6aEwNdMbqCdYNkbYlD80NQqtS3acBgDVUdLVf9JwZSBAEbkzywcHsepQ0teG2brPW5oeLcL5EbNYE4VheA1z5bJuNbtEzoCDnxIkTOH78uOF/3t66Zmwej4f9+/fbdIEAUCZuMytVMwefwwSfzUCtGTlIvUxpUa4m6qrwxHgJ+7REHAj69Zd1fTEuljUj2kto0VWtL2IGOMNhKKDRCLy0LAIZVVLszqiBTNGJgoZWJFjwORfaMSHrEeQ0G2aE9J5pE3CY8HKwM1QgVGotChtaTUwH+svUQGc0yZW486cUfHqkAIs+OYUmuRI/3TlpWCQO1uAm4JjdHAN926P2xI5Fxx/3TgGXzcBN311AZXM7WhWdOF/chIU9miGXx3hAS5LYn1ULSbsKT/6VhkBXLg49MQuvrozsd09Zd55bGg4Xns70Y3pw73IioT0Tq+I88eOZUijVGvxxsQK10g5svX8KFg3BNTsUcJh0ONgzzVbk+upJGwj2LAa+uiUBdVIFHt2Sipwa4yb6gvpWPP13OqYGOuOeGbbZuMwLF4HDpGFfN/v24YAgCLy4LBzBIh7e3Zdr+D3PFYsR5SmAg/3A/rbRXgJk96iaFzfK0a7SoFrSYZT17cshj0YjsGqCJ/Zl1lotiykXt+FQTj3unx0IBp0GBp2GN66LRspLC/DD7Yl4qx99cNcKrnw2ZgS7YMeValwub0FOrQx3WDnY1laEuvGxLNoDX50oHhPVHEA3luPmyb79epYMBfqNrjnJWq1E9wz06PFcTvR3Ao3AgOapNcgUeGhzKmK8hf22zu8vbgIO/rhvCvx7US0MFcEiPsLd+diTMXgZ5dUZOaYJztXxXmDQCAS6co0Gg88LF0Gl1uJskXFrQKdGiz0ZtVge42HzirRN9TdsNhuurrYtNQFAWVNbrzKWnng4cAwaXD1ypRpypdpiJYfNoOOhOUF4x4zlpi3QZ7rLxW0gSRIXS1sGLFUDdA3MLjz2qAhyAGBasAvmhrni5Z1ZmPXhcZAkLA5zEnBMKzliee8Z0O7obcMBnWytU0OamA70lzlhrnh3TQzEbUp8eqQQi6PccfiJWYMOnmyJWy9yNV2Q0z+5mJuAgy33TgadRuDeXy/hYHY9OjUkFvTQYosEHEwJcMautBp8f7oEKo0Wn944wTBgcDDw2Ax8uD4W3o52Vs0LuH92EBpaldiaUolvT5ZgRaznqJeo9aT7TK7uiHuxUB8MwSI+Pr4hDpfKW7Ds89OY898TuOeXFNy2KRmLPjkFGkHg/26Is5mbJJfNwLxwEfZkDG+QA+g2aS8sDUdKWQsOdQ3CO1csxrQBSNX0RHsKIW5TGZ0zfY8aScKo17JJrgJB9O6QtzjKHdKOzj4NPfT8m1YDDpNmkvm1Y9GxINJt3BoIDJa1CV5Iq5Tg3X258Ha0s8ra3tY8Oj8YtVIF/rw4Nqo5owVnHhsiPtusE2GttAN8DsPExVPAYWJ1vBc+PJCP/zuU3y8b70+OFECt0eKrWxJGVJY+HKyI9cDR3IZeZ+7p0c+tMfe3rGhuA5tBg8jMSA0XHhsvLIvAKysijfbTwSIevB3tcCzPOBA9VdCI5jYV1ibYvro16s9mp0aLypYO+PdDDuMutDNp7K2V9O2u8uyScMSaaUy3BQIOE05cFsrE7ShtakOTXIkk/4EHOQRBIMZLMCrMB/S8ujIKs0JdcfsUP/y8MQmhbuY3nwI7JmQK4wusSd67lr07Ye58g1xN77Q22GCEIAjcPNkXex6diaw3FuP/bogbcOZ3qNA5AJraD5MkiVpJBzz6mAFhDg+hHb69bSJKm9rw4o5MBIt4ZhMKqyZ44mJZM348U4bbp/pZrIgOhOnBLjjz3Dyrz/28cBHe3JODOpkCj8wLttk6hguRmYqcVkuipd32cjU9S6I9cOnlBfjutomYFeIKpVqLNqUaH6yLwYln5ti8Wrk8xhN5da0oGgFL13nhIkwJdMLru7Jx26aLaJIrB2Q6oCfaMNPp6oYro0pqkLCWNF4NcsRyFRztWb065E3wcYCDPRMn8huNXm9TqnEou85oQ0GSJHamVWNhpLtNHYeuBRZGusGeRUdqhQS3TvEbNqlad8LdBVg/0Rtv783BkQFUGK5lIjwEyDPj0lgjVcDTQqXpv+vj8NTCUHxxvAgfHrRudIBKrcW+zDpcn+gz4hWs4WBFrCeUaq1V5jBplRLc/uNFI6dCPRVdzmqWkmO6IfLGiQWCILAo0r0roXq1kr3jSjWCXLkGhZItGfVBTlVLBzRasl+lPU8hBzU9gpz8Lu10iIWN93Dg52yPCnEbfj5XBiadGFSQA+gteEfefEBPgAsXX96cgCcXhfWaNROa6cnRV3Ks2eSFufPRJFeiSa5Edo0M3o52Nu2ZsWbOy0ggErChVGtNZgy1KtVoU2n6XcnRE+0lxPvrYqBSa7HAgm//kih30AkCBAE8MDtoQJ9jKx6YHYRODYml0e4WA+nRjLmKnLSjExotaXaGl61gM+hYFOWOt1brZu3889B03JjkOyi5oSXmhrvCjknH3hGo5hAEgVdXREFoxwSTTuDhuUFmbbetRX9/6Z5QyqiSYHaoK7gsulFPm24QaO/3MDqNwKwQV5zopotPKWvGss9P477fLhuc0wBdYFXS2IbVI2RdP5axZzGwJNodLAYNNySOnPPcu2tiMDtUhAc3X8Z7+3LxwYE8m/REjHfCLTis9ZbQo9EIPDo/BPfNDMTv58vNurj25GxxE6QdnVgR6zHoNY8F/F10wcTu9L7vzee65tJdMmOUUi5uNytV64s18V5oblPhdKEuySNTdOJwTj3WxHsNiYpq1Ac5ZV0PkH7J1YR2JsYDebWtcOGxRmRavR5/Zy6O5jbg1/PleH5pxKBlBtGjyHygPwjsGGZ7cjhMGuytmP4b0SVNO5hdh79SKg1Tj8c7bhYGgho0yoPIQq2J98bvd0+2WBlx5LJw94wAPLckfEg34taQ5O+IN1ZF4aXlESO6joHiLjSVqxka1oeokjPc2LMYmB3qiqN5I5O9jvQU4MB/ZuGnjZPwzOLwQUlQCILQ9eXU6IIclVqL3NpWxHoLEejKM2p0travak6YK/LqWlEr7cDx/Abc8O15ONgxsSHJB58eKTA4Ve5Mq4ajPdPmtqrXCi8sjcAf904esgqpNbAYNHx5SzyWRntg2+UqbL5Qjif/ShsyB6/xQqSHAA2tSqOhroB1/ad3TveHUq3FXyl9zyrak14LXyf7IakijFZWxnngZEFDrzNrAOBcsW4u3SUz0tqeg0CtJdpLgCBXLnZc0fUF7c+shVKtxXVDZMQw6oOc0qY2MGgEvPohp/Bw4EDS3mlkk5dXJ0O4+8j2V+gd1hZHueGu6f6DPp5eWjda+nKsxZzxQFObEs5ctlWRfIALF0w6gdd3ZcOZxxpVwx6HEn2Q07MKUGPloLu+mBHi0msV64VlEbhjmv+gPsMWEASBO6b5w9tx9My/6Q8iAQdNcqWRParBeGOEA0hbMjfcFRlVUoMUdSwT7Sk03Gfz6mRQabSI83FAoCvXSK7WKFdaJbvUBy37M+vw8o4sTAlwxvYHp+Ht1dGY6OeIR7Zcwbv7cvFvWjWWx3pY5SxKYYorn42JfoNTTNgCNoOOz2+Kx+VXFmLnw9Mh6ejEP6nVI72sUY2+z7ans2G1pMPEWa0nHkI7rIj1wE9ny3o1+FCqNTiUU4flsbZveB/N3DTJF472LLy2K9uiEkjRqcGlshZ4Odghq1pqFJR3arSobukYUCWHIAisiffCoew65Ne14sMD+ZgT5jpk8+xG/Z2zsEEOX2f7XjXOPTFnI51b24pw95GVtswMccWsUFebDZV0E+ia81IrrGtgHS3ojQe6X1y6KeHWZduYdBqCXHnQaEl8euME8DnXRuOtvsGv5wBCff+Z+yCDHIrhwV3AAUnqmtT16LOVI5lxtjWzQ3WS1VMFjX28c/QT5SVEvUyJhlYF0isloBFAlKcAAS5clDTKDfcysZVBjguPjVhvId4/kIfGViXeWRNtcE77/KZ4RHsJsDejFm1KDW5M9B3qX49iGAl05WF+uAibzpQMaCjstUKQKw98NsPIDl6uVKNVoTZxVjPHPTMDUStV9OryeLqgCa0K9TUjVdPD5zDxyopIHMtrwGELvWJXKiRQqrV4YI5OHp5RdTWZnl/XCrV24IZP103wglKtxfqvz4HNoOGj9XEDOo41jPogJ6WsGRP7OQNAX8rU20hLOzpRLelA+CAduAbLRD9H/HqX7WauEASByYHORkM4xwJCOybUWhIdPaxX+5PFvntGAN5aHY3EQfY1jSW4bAb4bIbJrJxaSQdceKwh6a2gsD1uAtNgVS9X682Va6zhLuQg3J2P4/ljP8iJ7jI2yayS4nyJGKFufNizGAh05UGmuDr/rD8OeXPCdHaqD80NMppP5CG0w88bJ+Hs8/OQ8+ZixHhfOzKaa4W7ZwSiuLENJwvH/rUxVNBoBCb4Ohi5EOoNpPqq5AA6Of8kf6dene3+TKlEoAt30O6sY5EVsR6YGeKCV/7NwsHsOpOA+3xxEwQcBq6f6A0Ok4ZL5Vf7ctKrdIme6AFK/Hyc7JHk7wiVRotvb0sc0jaSUR3kNMmVKGqQY3I/nXE8u5rS9DbServhka7kDAVTA52RUyuDpF3V95tHCYKuIK97U2B/7XOvT/TBLZOHd+7BaMBVwEaDzFSjTFVxxg7ejvag0wjsuHJVrtLcpoKjPbNfFeuxwNxwEU4XmrcgHUv4O3PBZdHx0OZU7MusM8xlCuzqFS1pakOnRgtJe6fVyZobEr1x5zR/PDjHspHHtSShuZaYEuiESA8BfjpbNtJLGdVM9HNEWoXEsAHXG0pZ6wa5Ms4DyaVis5LZy+XNOJJbjwfmBF2T1xlBEHh3TQy8HOxw/2+XsfSz02hsvfp3OlcsxuRAZ3CYdMR5OyC1W7CZUSlFsIg3KMfHj2+YgO0PThvyJM6ofqJeLNVFjv2dJ2PPYkBoxzRUcvLqZKDTCASLBj/XY7QxNcgZJAkkl5q6X4xW9JWs7i5huobd8dOPMFS48TkmcrX+DgKlGFmcuCy8sDQcP58rw/bLVQB0lczxJFXTMyfUFZL2TqR1zZUZq9BoBDZM8sX8CBH+fmAqnlgQAuCqIU5JoxwtbdbP+gJ0we7rq6KoCuw1CEEQWD/RG+eKmiBT9O0Adq2S4OuIVqUahV1W9PpKjrVJvcXR7iChMynqDkmS+GB/PkJEPKxL8LbpmscSPk722P7gNPx13xQ0tCrw7LZ0kCSJVoXunq0f5Jno74jL5S0GWW56lWTQ41Z8nOwHXAnqD6MmyPnmZDG+P1Vi9NqFEjG8HOwG1JDkIeQYgpzc2lYEunDBYY6/h4m/sz3cBZwxJVkT2Omif30lR63RolHet/UqhU7qZBrkdAzadIBieLl7RgDWxHvhhR2ZyKmRdVUyx1+Qn+DnCD6HgeN5Y98y95UVkfjqlolI8ncyZH65bAbcBRyUNLUZeqys7S2kuLZZEOEGtZbEyXEg5xwqJvg6gCBg6DuukSrgzGVZvZcT8TmY5O9k0pdzIr8RF8ua8czisBGZnzSa0Lc9vL8uFsfzG/H+/jys+/oc6DQC88J1fZUT/RzR0t6J4sY2dKg0KGyQI26MyGhHTZBTK+nA58cKjSRMySXNmBw4sJ4LXZCji/rz6mQG2+HxBkEQmBrkjPPFYyfIcbDTbQL05+dgdj1Uau2Az/W1hJuAg8qWDoMzF0mSVCVnDEIQBN5bGwNPIQfv7stFc5t11sNjDSadhoURbtieWgWV2rLL0Vgm0JWL/LpWgyRmPAarFLbH19keoW48q4YyXqsIOEyEiviGvpyBDL1eHuuB88Vig7lLfl0rnvo7HZP8nbAw0vxcuGuRxVHuuGmSD749VQKNlsTOh6fDz1lXqZ7o5wQ2g4Z/06qRXSOFRksOupIzXIyaIOfBOcFQqrXYdKYUgE6jnl/fiikBA5tU7eFgh1qJAlotify6VoR7jL9+HD1TA52RV9dqaH4d7bgJ2IjzccB3p3TuMj+eLUWin+OYuWhGkkVRbmiSK/G/Y7qBgbIONdpVGkMfGsXYgcOk4+nFYThT1ITUipZxKVcDgLtnBqBWqsDu9JqRXsqQsDDSDScLGg3D7cZjsEoxNMyPcMPx/EYjO3kKYxL8HAyVnIEk9JZ0Sda+O12CfZm1uPn7C3DlsfH1rQnXZC9Ob7y6IgofrovFrkdmGBUGhF0zvH45V4azRWIw6cSY2VOPmiDHXcjBzZN88dOZUkjbO6/24wwwu+8p5KBG2oFzxWK0qzSIGOEZOUPJ1C7dZPIYkawRBIHnl4Qju0aGt/fm4nJ5C+6aETDSyxoTTPRzwkNzgvC/Y4W4VNaMWlmXRllABTljkWXRHojxEkLRqR23cs0oTyFmhbri21PF49Iy95bJfvBzssemM6VgM2i9zpqioOjOgggRpB2dZoctUuhI8HVESWMbWtpUqJH2PSOnJyI+BzOCXfDtyRI8tDkVzjwWNt87meoBNoMdi44bknzMGgrcMzMQbSoNvj5ZhAgPwZjpJRw1QQ4APDgnCEqNFrf/mIy39uTAXcAZ0LAhAHAX2qFVocbGny8i3tcBU/rp0DaW8Ha0g5eD3Zi6UU4NcsacMFf8eLYUXg52WESVja3mPwtCEePtgPt+u4xPDxcCsN5thmJ0QaMRhmG24/mh+8CsQBTUy3GiYOz35vSExaDh+aUR0JK6+TdUdpjCWib4OMKZy6Ika70wKcAJBAHc+XMKqls6rJqR05Mf7kjEmefm4vSzc7Hn0ZlWzbKiMMbHyR7XxXlC0alF7BjpxwFGWZDjJuDgyYWhoNN0fSbvro0e8AMjyFWnJVw9wQt/3jcFdqyxEXUOBIIgsO3BqXhpWcRIL6VfPLckHDQCuHOa/7izzh1KmHQavrk1AfPDRThV2AgOkwaRgLppj1VmhLjg85visTLOc6SXMmRMDXJGjJcQ3/UwlxkvLI5yw+QAJ0o2StEv6DQCc8NFOJo7/oJ/W+HnzMUvGyeBAKBUaw2Ohv2BzaDD29EePk72YDGovcZAeXBOEGgEkOg3dvqnCbL72PlhxsHBARKJZMiOXyFuh4+THZVZs4KhPheWKGmUw8+Ze807nHSnP+dCrlSjWa6Cr/PAKp4UvTNS18V4JKtaCgd7JrwdB/ZdHe3nQqbohKJTAxF//Ac6o/1cjCWKGlrRqSER7s4f0F7lWjkXJEmiuLENgS5c0EbpfuFaOBdlTW3wcbIfM3u2EQ1yaDQaBILx2ysz0rDZbNTXW1cGp87F0EKdi9EDdS5GD9S5GD1Q52L0QJ2L0QN1LkYP/TkXekY0yKGgoKCgoKCgoKCgoLA1lDiRgoKCgoKCgoKCgmJcQQU5FBQUFBQUFBQUFBTjCirIoaCgoKCgoKCgoKAYV1BBDgUFBQUFBQUFBQXFuIIKcigoKCgoKCgoKCgoxhVUkENBQUFBQUFBQUFBMa6gghwKCgoKCgoKCgoKinEFFeRQUFBQUFBQUFBQUIwrqCCHgoKCgoKCgoKCgmJcQQU5FBQUFBQUFBQUFBTjihENctzc3Eby4ym6QZ0LU0iSHJHP7X4uSJKEWK4ckXVQUNfFaII6F7ajVdEJRadmwD9PnQvb0arohFqjHfDP9zwXLW2qwS6JYoBQ18XoY0SDHKXSePOm1mhx76+XMOmdI3jhnwxcLm8eoZVde/Q8F9c6RQ1yzProOE4XNg77Z3c/Fy/vzMLtP16EVjsyAde1DnVd9J+SRjne3J2Dae8dxY3fnkdBfatNjkudC9tAkiSe/ycTN39/YcCJHOpc2I7PjxZiyWenbXIu/k2rxtT3j1KJsRFivF8XGi2JrZcqcc8vlxDz2kH8cLpkpJfUJ6NGrkaSJF7fnY2jufWYGeKKUwVNWP/NeXx5vIja4FEMOx8dzENlcwee+TsD0o7OEVvHilhPZNfIsDOtesTWQEFhLRotiVt+SMaOK1WYHSZCnUyBZZ+dxlNb0/FvWjUaW8f3JmAs8PflKuzNqMXtU/1BEMRIL+eaRqslsSejFkn+TjY5FzOCXaAlgd8vVNhgdRQUV6lsbseG787j2W0ZaGlXYXKgM97em4u9GbUjvbReYYz0AvRsOlOK3y9U4K3ronDbVH+oNVr83+ECfHQwH7m1Mny+IR40GnVDphh6UitacDC7Ho/NC8amM6V4Y3c2Xl4eCWlHJ/yd7Yd1YzA1yBnzw0X478F8LIvxAIdJH7bPpqDoLxdLm1ErVWDr/VMxKcAJik4NvjlZjF3pNdieWoVpQc7Ycu+UQX+OSq0FjQAY9FGTpxsTlDTK8fqubKyN98LqeK+RXs645FhePbKqZQCApdHuCHHjW3zvpfIW1EoVWBnnYZPPduaxsTbeC79dKMP9swOp5wWFTciqluKm7y6Ax2Fgyz2TMS3YBVotiUf/uIIntqbB18keMd7CkV6mWUbNE6KiuR13TQ/AbVP9AegeXs8tCcdnGyZgT0YtPj9WOLILpLgmIEkS7+/PQ7CIh8fmh+DlFZH4J7UaCW8dxtz/nsCWi8OfIXt+aTjqZAr8fK5s2D+bgqI/7MmogbuAg0Q/RwAAh0nHfxaE4thTc3D+hXl4bWWUTT7nr5QKzPrwOL48XoQmSppjFZ0aLf7zVxpceGy8cZ1tzgOFMYpODR7dcgXfnizG1yeKccsPyb1Kx3an10DEZ2NygLPN1nDXjAA0yVXYlV5js2NS9J+WNhV+OF2CGknHSC/FhC3JFYh/8xCe+Tsdl8p6bwupbG7Hxp9T4OtsjwOPz8K0YBcAAI1G4P9uiIObgI1fzpdZ/PnGViVSK1pwrqgJhfWtIEkSJEkit1aGnBrZkPc+j5pKzhuromDud71ugheKG+T49EghYryEmB9BNXaNVVraVFj06SksiHDDs4vD4MhlDfln5te1Iq9OhtmhrnCw7/vzjuQ24GJpM769bSIYdBo2JPnAhceGUq3BXymV+P5UCTYk+YLeR1VRqyVtVnkMceNjXYI3fjxTivtnBVISE4pRiVqjxf6sOqyJ9zL73fcQ2sHDRsm+BD9HTAl0xmdHCvHT2VKcfGYuuOyBP87UGi02fHcBj84PwexQV9sscpTxv6OFyKqW4u8HpoHPYY70csYl54vFaFNpsO+xmXCwZ2LpZ6fxzLYMbLoj0eS+rdZosS+zFqsmePb5POkPoW58zA51xabTpbh+oveoe16kVUrw/ekSPLkwFEGuvJFezpCRXiXBBwfy8PbeXEwJdMK3tyVCaDfy111yiRiv/puFRH9HnCsW4+/LVfhswwRcN8G0slvUIMf9v10Ci07DT3cmQWhvvH4Ok45l0R7461Il1BqtUWX9RH4Dvj9dgvPFYnTvOHHh6fZhTXKdQUagKxe3T/HDHdOGRj47aio5BEFY3BT+Z0Eo5oS54rntmYNyIaEYWYoa5WhsVWLb5UrM/b8TyK+zTUOyOWqlHXj0jytY8tkpPP5nGpLeOYKntqb3+v1RqjV4e28OpgY6Y1GkLpgmCAILI92wItYTj88PQZm4HYdz6i0eI7+uFXf+dBFfnyy26e+zMNINDa1KlIvbbXpcipFDqyXHlRPSuWIxmttUWBFrG+lNb0R5CvHxjROw7/EZaJKrsD+rblDHS6uU4FJ5C/5JrbLRCkcHzW0qXC5vxu70GnxxvAgPzw3GxK4qG4XtOZRTB29HO0R48OHpYIcP1sXiWF4DPjtaaJKxPl8ihrhNhZVxnjZfx/2zApFf3zrqejlJksTru7KxN6MWyz47jd96qQCMdeaEiXDppYX4cF0sLpY2j4rKWrWkAw9tTkWCryN+vWsyTj87FyvjPPHM3xlI6arokCSJkkY5PjqYh6WfnYJSrcXPG5MgEnDMHnNxtDsk7Z24WHq1IlTZ3I77f7sMWYcab14XjT2PzsDRp2bjp41JWDfRG+sSvPHrXZOw6Y5ERHgI8PruHDzyxxW0q9Q2/51HTSWnN2g0Ao/ND8Har87hQkkzZoS4jPSSKAaAvmy797GZuH3TRXx/ugT/vT7O5p+jUutc+molCryxKgpzw0TYlV6D/x7KR6gbD/fPDjL7c5vOlKKyuR3f3jbRbEZhop8j4n0d8P3pEiyJdjf6N6Vag7f35GJzcjk8HexwY6KPTX+nRH8nAEBKWTP8Xbg2PTbF8FLSKMcr/2YhrUKCNpUGD80JwrNLwkd6WYNmT0YNvB3tMMHHYdg+M1jEx6QAJ/x9qRLrJ3oP+DjH8xsAAKcLm2xahR0OpB2deGRLKoJceXh6cRh4XRWtwvpW3PjdBTR3BdJx3kI8Nj9kJJc6rtFoSRzOqceqOC/D82NJtDsemRuMT48UIrdWhntnBqKiuR3JJc04nFsPHyc7xA/B9TIt2AVLotzx9p5czA0TWaViGA6O5TUgrVKCL29OwNG8erzybzYWRLrBQ2g30ksbEoT2TNyQ5INd6TXYlVaN26b4jdhatFoST29NB4NO4MtbEsBi6GocH62PRa2kA7f+kAyRgA1FpxaNrUow6QTumRmIx+aFwI5lubdrgrcDRHw2DmbXGaRs7+/PA5/DwJZ7JxtVjYNceZgbJjL6+fkRblgSVYNntqXjhm/P46c7J8GVz7bZ7z0mghwAiPdxgJeDHXan1/QryCFJElUtHfBxsh/C1VFYQ61UATsmHSEiHm6a5IuvTxbhlRWRNi/h/t+hfOTVtmLbg9MMG66H5wajsVWJ/ztcgPkRbggWGZfJ62UKfHGsCLdO8UO4u8DscQmCwH0zA/Hg5lTc+dNFEAAm+Dhibrgr3tydg/QqCZ5bEo47p/uDzbBtw6cTl4VgEQ8pZc243sYBFMXwcbqwEQ9vToUjl4WH5gZDLFfhqxPF8HGyB5/DwJfHi/Hw3CCsiLV9dncokbZ3Yl9mHW6b6jfs8pj1E73x7LYMVIjb4es8sPv8ifxGeAo5qJEqkFUjRay3g20XOUQoOjW495dLyKmVIaWsGQez63D7VH8Ei3h4cUcmnLks/HRnElgMGgJduWBSRg1DRlplC5rkKiyKMpbUP704DBEeAjy3PQMHs3UqAF8ne6yJ98JNk3yH7Hp5bVUkFvzfSby1Jxer4z3R3KbCyljPYQvgayQdaGlXIcpTp1HVakn836ECTPRzxLIYd0zwdcA/qdW4UiGBR8z4DHL0rJrgiWe3ZaCqpR3ejiOzF918sQLnS8T4aWOSURDBYdLxwx2J+PV8OdqUahAEgYl+jpgc6ASBFbJWGo3Aoig3HMyux2sro5BS1oy9mbX4cF2s1bLYlXGe8HfmYuPPKVj/zTn8etck+DnbJpk7ZoIcgiCwMs4TW5LL8dbqaEMU2hOtlkRmtRQt7SqUNrVhc3IFihrkeHZJGB6aEzzMq6boTq2kAx4OHBAEgRuTfPD5sULsSK3CndMDbPYZZ4ua8O2pEjyzOMwko/zskjAcy2vAs9vSse2BaUY3+3f25oLNoOHJhaG9Hn9RlDvWJngZrHC/PlmET44UwNGeic33TMGkACeb/S49SfJ3QnKJeMiObytIkoSWhE115uOB7Zer8Oz2DEwJdMKXNyfAwZ4FkiTRplTjhX8yAQAO9ky8szcXCyLcxpQz0s/nyqBSa3FHl3HMcLI8xgOv78rGtsuVeHJRWL9/vkGmQHaNDO+uicHbe3NwMr9xTAQ5JEniib/SkFYlwW93TYK7kIO39+bi0yMFUKq1CHDhYvM9ky3KTChsy6HsejjaMw2mG91ZHuuBpABH1EoU8HfhDktvhofQDk8uCsNbe3KwvUuG6elghyT/oXtGATqDi5/OluKTw4UgQeLQf2bD19keuzNqkFMrw5Z7J4MgCHgKORDx2UirlGBZzNBLXEeSJdHueHlnFnan1+LB/2fvLMOjOrc2fO+ZTNzdXUlCCAnuXkpbqFOhdmrUvef01Pv11N3dWwq0tNAWirsmhAgQd/eJTiYj349J0oTYxIV9/+FiMrPnTfbM3u9a61nPmt+1kmQoyats4KW/znJVtHunSgqAtanhgKq8F4S68P3RXP77WyK7zpYS5mbZ58p6uLsVv6ydwZovjnPVJ0fYfM9snAbh2jVmghyAiyNc+HhfBgfSylgY7IiiWdOhjJZX2cAjG+I51qINFARYFOxEtJcNr25LwdJYxvUjWC483ymUK3BtKUs7WxmzMNiRH47l9tpw1qhUsyE2j0AnC6b7du9Co1Rp+O+mRKZ423BnF5I0U0MD/m9VGDd8eZwdZ0tYFqqTnB3JqGBzfCEvXRbea1lfKhF486pJbf+va1JxMK2cie5WuFoPbTZqircNPx3Ppay2aVDLuYPN2zvT+O1UAb+snYm9+ehd53Dy1aEsnttyhssmu/HK5RPbMuqCIPB/l4bhZGlEpJcN7tYmLHt7Pz8cy+Vfswcv+B9K6ppUfHkoiyuj3XG2Gv4NtZmRAReGu7A+Jp+rp3ri1sfv4d5U3cDfJROc2J1cwr7UMu4dBFlXkbyR7PIGZvh1fc3KKq/n0/0Z3DbHF99zGrDPbeLtisMZFWxNKuatqyOY1nJd/OyGaJQqDSnFtXjbm4oGA8PIjrMlLAx26va8OVoY42gxvN+Pm2d6E+JsgbmxAZe8f4i8yoYhDXJUag13fBfLnpRSrp/mxY4zJTz5exLPXxLKk5uSWBbqxEw/nRJHEAQmeVgTl1s1ZOsZLVgay1gY5MjvpwqGNMipVTRzMK2cs0U1OFoac900T5RqDff8FIe1iYwnL5owJO87zdcWe3MjNp8qZGmoM/ctCuhXxdDLzoyf75jOxe8dYu33say7fUa3BQ19GVNBzgQXS3ztzXhrZyr/++ssWeX1RHvbEulhTUZZPYczyrEykfHJmigmuFhiZSrD0liGVqtFKhF46vckLIwNunSRGG2U1ijGXQauSN5ISDsp2HXTPLnpqxO8vzudG2d5d1ka/fVkPv/7K5nyuiYMpRI+WRPFguDOmQiArw9nkVvZwAfXTe62ijAnwJ4p3ja8uyuNpROcUGm0PLM5iQh3q3710ZgbGXTqzxkqWm9OsTmVXBDWdeYrPq+au388ydb754zIBkfTMhG5SK7gru9P8v2t0wZ8kRrr7E0p5bktZ7hppjdPXzSh08VfJpV0qEBcGunOh3vSuXqKR1t/xWjm+6M51DWpukwsDBe3z/Vlb0oZS9/cx9r5fliZyKhtUlHd0IxSpeGyyW7dVmf2pugyjw4WRswLdOCZzaeRNzR3chLqKx/sSef3U4XEP7200znXarU8+Vsih9Ir+OVkAfcvCuDOeX5IJQJbE4t4aH08z68M7VaaqtVqeXtnKhNcLFl1zv3M0EAyKmdWVNQ18eD6eK6d6tHt9WusUlHXRGZZPfcuHF1qEYlEaOuTsDQ2oKBq6OyMtVotT/2exL7UMj6/IZpFIU7MDrDnju9iueLjI1ibyXj18o49uJGeNryzK5VmtWbcSylXTnJl7Q8nSS+t6ySX7y8x2ZU0KNXMDXSgrknFlR8fIbm4FktjA2oUKpIK5BhIBU4XyPn5jhl6yc/6g0wqYfuDczGRSXvs39EHFysTPrp+Mtd8epRnt5zmf5eGD+h4Y+pTJQgCl0e5c6awBj8Hc564MAQjAwm/xhXQoFRx8yxvtj0wl2WhznjYmradUEEQeH5lGBdPdOWh9fHs7MEda6TRarW8/ncKU/+3i/Un8kZ6OYNKUbUCl3ZZ1rkBDlwV7c7bu9KY8b9dbEvqODm3tEbB478kEOFuxbYH5jDL3447vovl5a3JfH4gk8R8edtzy+uaeG9XOldP8WzTAHeFIAjcvyiQ04U1/JFQxF0tF53nV4aN+mZjdxsTnC2NOZ7VfeZrX2oZ+VWNZJXXD+PK/uFUfjVFcgX3LfTnZG4VL/55ZkTWMVpQqTW8+OdZorxseObizgFOVzywOIAaRTPrRmAmU1+pb1Lx+YFMVk1yG9G+x0AnC3Y+NJcLw114fXsqT28+zYd7MtiaVKSz6X3/EDd8eRx5Q3OH1yma1RxIK2+TcMwLdESjhYPp5QNe0+nCGmoVKvK72FjuPFvKofQK3rwqgjXTvXh9ewq3fnOC/allPPDzKSyMDXjslwR+78Yd63BGBSeyq7h/ccCoswjujo2x+exPLePO70/y2MZ4FM3qkV7SoHEqrxqASI/R61znbmPa5WexN749ks0TmxJ5/e8U0ku7d0T9+nA2Px3P48VVYW2jPpaFOrNkghM1jc18cO3kTomDSE9rFM2aIXVaHS20VlvPFNUM2jH//WsiN3x5nHd3pXH/T3HkVjaw6a6ZxD+zlJcuC2d9TJ5ORrYiZMhdFW3NDAcc4LQyxduWZy4J5UhGBfLG5t5f0AOjP014DnfO8+P6aV5tX5Zb5/jq9Tppy+CiBqWKu348ya9rZxLmNrqyXWqNlv9uSmTdiTz8Hc154Y8zzA6wH3IZ1HCgaFZTUa/EtZ2cRSIRePWKCB5YHMjTvyfxwM+nWG9t0pZx/fpwNgYSCW9cFYG1qSEfr4nisY0JbIzNo0ahQqXWcN+iAKK8bHhlWzII8MjSnntqAGb52zHZ05p7f4rDWCbho+ujiBhGR6j+IggCU3xs26weuyIhvxqAvMrGEekr2JpYhI2pjPsWBaAFPtmfyZMXTRj3Wbru+Dkmj7TSOjbdNVPvzaiHrSnRXrYcyajQ+/o2UGoUzXywJx2pIGBnbsRV0e56VQK/PpxNdUMz9y0a+Qy2takhr10ZwYuXhiOTCm1/b5Vawx8JRfz71wQ+3JfOf5aHtL3mm8PZ1Dep2qr7nnamOFoYkVQoZ8UArLDVGi3JRbqN2+lCeQdDhCaVmhf/PMN0X1sujXTjssnuzPSz4/51p9iTUkakpzXf/2saT2xK5KH18fg7mndK3LRWcVqt7kc7Wq2uwnthuDMz/ex5fssZzIwMBm047EiQV9mAi5UxBlIJcbnV2JjK8Oqn8cVw4GZjQkEfB1NqtVpe3ZaCsUyKUqVmfUwef9w7u0uVyfdHc1gx0YXVUz07PP7u6kiK5I2dJJkA4W5WSASIy6sedfuxwcbWzBAbUxkZpXWDcrzC6kbSS+uY5mPLmztSEQT4bE00kZ66YOaaqZ44WhhxtqiGm2Z6D8p7DifXT/PkisnuAw6cxtzOQyoR+i0jkEklvH/tZNytTXh5a7Jer6lRNHPtZ0f58mBWv96zL/xwLId1J/J4+bJwNt45AyOZlH//mjjkE2GHg2K5AqBDJacVV2sT3rtmMgGOFtz2bQwF1Y3UN6n44VguV0/xaOuTMTKQ8s7qSGKeXMLp55axdr4f7+xKY80XxwH4/IZo7PToAREEgf9cGEK4mxU/3z6jrTdnLBDtZcOZohoalV1nQRNaqlv5VcM/T0er1fJXYjHLQp0xkEqY6WePUqUhrWRwLupjjcLqRt7akcrFEa5tNx59meJtQ0xOFRrN8Hz3vzyYxRcHstgUV8DLW89ywdsHONRLNaO6QcnH+zK4eorHoDnhDAaGBpIOAaWBVMKqSDdunOnNt4dz2kxD5A26wO7qKR4d5CPedmbkDnAeVVZ5PY0tlYrThR0zt2/uSCWnsoGnLwptW+eiECd+vWsm103z5LMbojEzMuD1KyOwMpGx6WTHak5ycQ0nsqu4a4HfmKninMytIqOsnquiPbh+uhcPLQ3kq0PZHM4YeMWsPXtTSoelAtqgVLHkrX183rIviMurItLTZlSfD3cbkz7fF8pqm6hrUvH8ylC2PzgPjVbLXT+cRKnqOG8us6yOjLJ6VnRhIGBiKO0ywAFdP12QsyWncqv7tK6xip+DORllg3M/PJCm6yX84LrJfHx9FO9fM5nF5yQ9FoU4cc/CsVPtbY8gCINSGRpzQc5AMZZJeWhpIAfTyzncxU1c0azmaGYFWeX1NKnU3PFtLIczKrqVDQwWFXVNvP53CpdPdmf1VE+sTQ158dIw9qeWcSBtcG8EI0GhXJdBcu2mMdnEUMpnN0QjEQSWv72fxzYmUKto5pZunNdkUgmPLgvmt7tm8fXNU9hyz+y2crA+TPG2Zcu9s8dEBac9kZ7WqFscBM+lpEZBacsGLm8EgpzEAjkF1Y0sb7nRhbpZtjxePexrGUlUag1v70xl4Rt70WrhsWV9d/yK9rZF3thMej9viOV1TZwu7PwZOZxezgd70jskThTNar49ksNVUzw48p9F7HxoHm7WJlz3+THu/SmOvMquP0uf7M+kuaWaOha4c66u5+WjvbpBvR/uTUep1vDA4o7VXy87U7IrBib3bP3bh7hYdjgPm+Ly+WRfJo8sDWKCa0er+kAnC168NLzNrEMmlbAs1Jm/Eos6BLubTxViYWTA4pCxUcUBWH8iHxcrY+YEOABw2xxforxseHRDQicJYX9JK6ll7fcneWJTYrfyJ0WzmjOFA5cLxefJUTRr2Bibj1qjJT5PPqzzofqDm7UJhdWKPiVOMltkzz72ZjhbGfPBtZOJy6vmuS2nO1xDdp0txVAqYW6gQ5/XNcnDmri88W8+AK1BzuBIyfenlhPmZom9uREXhDkPqPI8njnvghyAC8NcCHW15JW/U9q+qE0qNQ+si2PS89tZ/elRFry+l+n/20VsThUXTXQhqbCG+qbBn8bayuvbU9Bq4fHl/2yIlk5wwtpU1mGS7FilqLr7Sk4rzlbG/HHvbGb52/NnYhEXhDn3OvciwsOa+UGOYzJT0R+CnS0xMpBwqoubQnyLLtzf0bxf2uuB8kdCEVYmMma2uElZGsvwsTfrMiAbz3x5KIt3d6Vx7VQvdj88v1+9KpGe1kgEepQm9sQLf5zhqo+PUKPQbSCr6pXc+1Mc135+jNf+TmHn2dK25/56soDKemWbm5uXnRk/3T6dFy8N43B6OQvf2MuiN/Zy45fH2ZpYhFar5fujOXx+IJMbZ3oPis3ncGBjZsgts7z5/mgOy97az2cHMvnXbJ9O6/eyMyW3omFAFfQzhTU4WRoxN8C+TYOfVCDn8V8SWTnJlbv0dFhaEe5CoVxBXMt3W6vVsjm+kAvCnMeMxXh9k4o/Egq5Isq9zRBGKhF448oIahqbuej9A5xscdjSarVU1StJKpD3qWenUanm7h9P4mJtjJuNCS/+dbbTc1ott1d9cIgm1cD6gVrXm15ax29xBdQ1qYj0tB7QMYcadxsTlGoNZXVNer8ms6weQdAFOaDrK3n2klB+OJbLhy3JAtA5y033s+uXUUqkpzWZZfVtQ2vHM74OZmSV1w24Qq/WaDmYXs7cgL4HlecbY64nZzCQSAQeXRbETV+d4MO9Gdw134+nfzvNn4lF3LcwgLmBDhRUN7I3pZTFIU6425jyR0IRJ3Or2jJRg0lCfjXrTuTx5IoJHSwmBUEgytOG2Jyxn+UokjdiYWzQ60XQztyID6+bzJGMCoKcLYZpdWMHQwMJ4W5WxHVR3k8skGNtKmOGrx1HBjBPJ6+ygd/iCtibWka0lw0PLgnEWCalrkmFoVTSpVtaXZOKdcdzuWyye4f+m3A3KxILBq/RcrQjb2zmgz0ZXBnlwdMX99+u08JYRoiLJSeyKrluWt9s7+uaVPx9uhhFs4b1J/K4dY4vT28+zd7kUp0UNjafN3eksqjFpfDzA5ksDnHCr52kRCoRuG6aF5dEuLIhJp/sinqSCuSs/eFki+ylkWumevY6V2q0cetcX1JL6rA2lXH9DC+uiu48y8HLzozaJhWV9Uq95K9dcaaohlBXKya4WvLJ/kzK65r4YE86LlbGvHL5RL2TMtN9bbE1M+TPhCKivGw4mVtNflUjl0waO8NiD6WXU69Ud3I19bY3Y8u9s7lvXRxXfHQYC2MZTSo1imadFOqu+X48dkGwXu/x+vYUcioa+O3uWWSU1XHPj3HsSy1jXrvKwk/H89iaVAzoehYH4nB1MqeKaC8bMsrqeHlbMoLAqFcFtA6hzK9q1DsxkVlWh6uVSYeAes10L4qqG3nt7xSsTWVcGOZCTHYlz13Sv/6q2S3ub7uTS/s8W2Ws4edgjqJZQ6G8cUBDQRPyq5E3Nvercna+cV4GOQDzAh24Y65vS1azhLjcal6+LLytaS7Cw7ptQJVao8XC2IATWZWDHuRoNFqe3XwafwdzbpjReTMz2cuG93en6zU3YTTTfkZObwjCP7aXIp2J9LRmS3xRp8fj8+WEu1nhYWvChlhdJrqvFS6tVsuVHx9B3tjMFB9bvjyUxe7kUtxtTDiYXs68QEc+uyGq03HXHc+lXqnuNNsl3M2KbaeLzwuLUIDP9mfS2Kzm/sUDl3BN8bZlRz+cILcmFqFo1hDlZcPXh7OZ7GXDlvhC/m9VGKuneuJpa8q1nx9jc3whMTmVZJbX89JlXdt0WhjLuKXlnGq1WvamlvHpvkzuWxjAVVP6brk+0lgay/h4TVSPz2ltHs+pbOhXkKPVajldWMO1U/9xetx9tpTtZ0p44sKQPlVgDKQSLghzZmtSEU+uCGHzqQLszY2Y0Qdp7khzLKsSRwsj/Bw6921525ux8c6Z/HwiF3ljM4YGEpwsjdmaWMzvpwp5ZGlQr46EimZdQ/xNM70JcbEk2NmCLzyz+M8vCbx2ZQQzfO3Ym1rK83+cZk6APQfSysmpqO93kKPVajmZW8W10zyZ4GrJt0dyCHA0HzJ73sGidX5UflWD3k5bWeX1+HZx3h5dFkRVg5L/bkrix2O5aLS0Oar1FVdrEya6W/H36eI+BzmKZjVG5/TgjWb8Wj5zGWX1AwpyDqSVY2YoZXIfez3PRwZl15Gdnc2vv/5KWlraYBxuWGhtPn/l8nAS8+XcOMOrkytIK1KJwBRvW473UzrSE7+dKuBkbjXPXBza5SZwsqcNjc1qkse4xWJRdSMu1mND1jLaifS0obhGQZH8H0maVqslMb+aCHdr3G1MUTRrKK/re/m/pKaJ4hoFb14Vwbe3TOX3u2djaSKjurGZK6M92Hm2hL9PF3d4TbNaw5cHs7gw3KWTNCvMzQqlSkNqydj+/ALsOlvC81vOUNeNbLWkRsEXB7O4cYbXoDgiRnvbUFDdSGEfHZE2xRUwxduGJy4MIb+qkdu/jcHPwYzVLUHJDD87pvnY6twMT+Tz1EUT9OpnEwSBBUGO/HT79DEZ4OiLl61uU9df84HiGgWV9UpCXS3xsTfDRCbl1b+TMZRK+pWpXhHuQpFcwYr3DrIxNp+LJrqMqYTX8axKpvnadbsRNTSQsGaGN/csDOD2uX6snOTGDTO8KKhuJPacQZGltQri86qpVfzTx/P36WJqFaq2mUKCIPD21ZNwaekrm/7SLm75OoYgZ0s+vG4yhlIJ2QMwlsgqr6eqoZkoLxsujdRVp0a7VA3A2lSGmaG0Tw5rmeX1+Np3DnIEQeB/l4bz0mXhpJfWEeZmOaBr3rJQZ/anltGg1K8lYH1MHld/coQJT2/j+6M5/X7f4cbDxgSZVBiww9qh9HKm+9qd9zPo9KFff6FVq1ZRWqrTdP/5559MmzaNjz/+mHnz5rFu3bpBXeBQc/UUT078dzHP9lJqneJtS1xudSdXkb6QXlrLfzcltunkaxTNvLw1maUTdEOzuiLCwwqpRBjzkrUiuQIXPSs5Ij3T2uDaXrKWX9VIVUMz4e5WeLTJEvp+I09vufgGOOkyThNcLfll7Uw23TWLF1eFMS/QgWc3/7PR12q1/Hwij0K5gjvmdrY7bjUfSBoHfTnFNQp+OJbDojf28ldix0paZb2SG788jqmhlLXzB8dOOdpLN/w1pg/f/SJ5I0cyK7g00p3JntZEeFhTXqfkiQtD2jbGgiDw5IoJLAx25Le7Z3Wqvp3vWJnKsDaV9dt8oLWxPdRVd+0OdrGgvE7JqkhXrEz6nu2f7mvHw0sCCXO1ZH6QIzeOITvYGkUzpwvlTPOx7dPrpvna4Whh1MHwJ6+ygaVv7WflB4cIf3Y796+LQ6PRsiEmn8me1h0qM152Zmy4YwYvXhrGohBH1t0+nd/umomFsQwPWxNyBmAscbLluhvpYcMkD2sui3TrNJB1NCIIAm4tUlN9UKo05FY2dOuMJggC10z1ZMeD8/joup6ro72xLNSJJpWG/allvT5319kSHtuYgEQQCHez4stD2cPmQjlQDKQSvO3MBuSwptFoSSqQM3mI596MF/olV8vKysLRUafnfvnll9m/fz9BQUEUFRWxfPlyVq9ePaiLHGpszAx7fc5UHxuaVBoSC+T9Hqr0/dFcfjiWS2KBnP9bFcYjG+Kpa1Lx5IrutfumhgZMcLEkNqdqTN3czqWwurFLe0mRvuNiZYyTpRFxuVVtksrWBvUId2uMWrI7eVWNfbYuTi+txUAidGkJLAgCL6wMY8lb+7jio8NEetpwplBOfL6cJROcupxzYGksw9fejIR8OVdP6etv2jMajZYnNiWyKtKN6cMg37lumhdz/B14ZnMSd/1wkpWTXPnvhSFkVzTw7ObTlNQo+PG26djqcT3RB2crY9ysTYjLreKSCP16MDafKkQmkbAi3AVBEHj24gnsTSljYUv/TSvh7lZ8edMgn5BxhJetab8rOacLa7AwMsDdRpfUCXW1JC63muun9623qhWpRODeMeJgdy6x2VVotPQ5yJFKBC6OcOXXk/k8c3EoKrWW27+LxczQgI+vjyI+r5qXtiYjk0o4lFHe5VR0SUtf2bl425kNaFhybE4Vvg5mbfuGN6+e1O9jDTdu1iYU6Bnk5FU1oNZou5Srtac3cyB98He0wNfBjL9Pl3BBWPf7BEWzmme3nGaGrx0/3jaNI5kVXPvZMQ5nVHSbKB5tDNRGOquinnqlmtBz3BlFuqZfQU5j4z9fkqamJoKCdI5gLi4uaDT9r3SMZsLddJvHE9mV/Q5yDqaXE+1lQ2pJLZe8fwgPWxN+WTuz14tElJdNv7T5Q8WW+EK2nynhvWsi9Xp+fZOKGoWqR2c1Ef0RBIFID5sOlZwfj+US7WWDs5UxWq0WM0Npvyo5aaV1eNmZdts/42lnygfXTubXuHxicyqxNzfi0zVRPeqxw9yshqSSs/1MCetO5JFb2TAsQQ7ofv8vb5rCxth8nttyht9PFQJgZ2bI97dOI8RlcG88IS4WfZozFJtTRaSnddsssUhPmz4HuiK6SkB/KzkpxbUEOVu09ZJcHe2Jo4Vxp4Ge5wNHsyqwNTPsV//LykmufHEwi8c3JpBdUU9mWR2/tAzxnu5rh0qj5bW/UzCWSbioD/a53vZmA7qfxuVWjdleCHcbU71NaTLL/rGPHg4uCHXmu6M5KFWabmVYH+5Jp6hawZc3TkEQBGb42uHnYMZ3R7PHTJDj62DGxtj+K3Na76XjfXjqYNEvudqKFSu45557qKurY8mSJXzzzTeoVCq2bNnSVuEZbxgaSIj0tO63nXOxXEF6aR3XT/di3e0zuHmWN5vvnq3XpijS05qC6kZKahT9eu/B5ExhDY9siGdLfCEVelpRtvYUdDcjR6TvTPGxJT6/mtSSWpIK5MTkVHHTLG9AFwS525j2y0Y6vbSu1w3J4glOfHhdFNsfnMePt01naahzmzVsV0R52XC6sKZtIOxgoNFoeWdXGuZGBhzOqBiQ/KSvCILAldEebL1/Di9fFs7vd8/iyH8WDckmNsDJgpQ+9DOll9W1SQ1F+o+XnSm53cwH6o3UkloCnP5xhgx3txozs4QGm+NZlUz1tu1XY3i4mxVRXjYcTC/HQKIb5N1+Y3fXfD9um+PDXfP9sehD07+3nSn5VQ39kp7XNalIKakds0GOW8tAUH3s0TPL6jCWSfQ2DBooF010pVahYmtSZ1MdgFN51Xy8L5N/zfFp+34JgsCa6V7sOFPSoUe1PUqVhhu/PM6t38R06PmprFfyyrZk3t89vL3kfg7mlNY2tbUt9JXThTW4WBm3zdMS6Zl+BTmvvvoqxsbGuLm58cMPP3DzzTdjbGzMxx9/zBdffDHYaxw1TPWx40R2Jep+6D8PtgweneVvzwRXS565OFQvmRzQVjk6OIxDQZtUar4/msNLW8+yJ7mU/KoGzhTWsPaHWKxbssRni/TbfLWaJviLm69B49qpnrjbmPLw+ni+OJiFs6Uxy0Kd237uYWvS7RDHnsgo6z3I6SurIt0wMpDw9eHsQTvm9jMlnC2q4Z3Vk7AwMmDdibxBO7a+eNiasnqqJxEe1kPWABroZE5ZbRPVDb2bSChVGnIqGvDvRkMvoj9edmaU1ym7NZnoDqVKQ1Z5PYHitY4GpYrEfDnTfPsmVWtFEAR+WTuT4/9dzPo7Z7DknGnugiDw3xUT+hxAetmZodH2r2fxdIEcrRYmuo/NLLq7jQmKZo1eM2myyuvxtjPr1d1usJjgasksfzs+2pvRKQgrkjdy27cxhLhY8OA5w3svi3LHRCbl/nWnKD8n8arVanlmcxKH0ss5lF7Omi+OszellKd/T2LOK7v5eF8Gb+5I7de9sr+0Oaz103wgqUB+XlaF+0u/7swymYzXX3+d4uJitmzZQmxsLCUlJfz55594efVPdzwWmOZjS61CRXJx3+d+HEovJ9jZAgeLvkff7jamTPWx5Zsj2QMaUKcvG2PzWfDaXp76PYkNMfnc/PUJZr+yhwvfPUBlnZIfbp2GkYGEM0X6SZCSCuQ4WRp1mAEkMjBMDKW8fmUEpwvlbIor4Prpnh0kZu42pnprr1upblBSXqcc9CDHykTGVVM8+PFYjl4DdbVabY8bEK1WV8WZ6mPLwmBHLpmkm+XSrB5/UtkAR13GMlUPyVpORT1qjRZ/R3G+1EBps5HuY4Uwp6IelUZLoJN4Dv5MKEKl0Q6blFRfvFv6DXP60XOVWCDHUCoZs+e31UY6T497Q2ZZfYfZWcPB2nn+JBfXsjflHwMCRbOa27+NRSoIfHpDdCcLdktjGZ/dGE16aR0Xv3ewTW2j0Wj5eF8mPx3P47mVoXz3r6mkltRy01cn2JZUzLXTPNn3yALMjAz46lD2sP2OAY7mCIL+SeL2aLU604EwN7EfR18GNCfHxMSE8PCu5yuMRyI9rTGQCBzPquxTJK3V6qbTrtSzebgrbp3tw+3fxRKTU8UU764zY/2Zi9IVaaW1RHhY8+XiAIKcLEgrrSO/qgFTQwN8HcxwtDAm2NmC04X6BXtJhXLCxMzDoBPlZcNd8/355nB2J/tzdxsT8qsb0Wi0emfiWp3V/B0G/wZ+yywfvjmczfqYPG6epXPzUqo0xOXqPs+tazxdKOeFP85wNLOSj6+f3GUTalZ5PWeLavj4+qg2h58fjuWy62zPTatjEX9HcySCTgI1tZfm7dZm1sEOUs9HWoOc3IqGPl3rW4PR810yWFqr4P/+PMuF4c6D3qc2UFytjZFJhX71XCUVyAlythiz1r2tVYTkopo2l86u0Gq1ZJTV9XrNGWxm+dsR7mbFR3szWNBilvL+7nTOFtXwy9qZ3Q4xnelnz5Z7Z3PXDye56pMjXDbZjdyKBmJyqvjXbJ82A4rtD86ltKaJcDertnvONVM9+fFYLg8uCeiT7LG/mBkZ4GtvRlJh3/tU8yobqVGoCBf7cfRmbH5TRwhTQwMmultxLLNvfTmpJXWU1TYxawCNcYtCnPC2M+XzA5kdHt+WVMzjGxNY+tY+Pt2f2c2r+8bjy4L56Poogp0tEQSBQCcLFgY7Md3Xrq0aM8HVss0qtSd0mYcaQsUv5ZDwyLIgjjyxqJM+193GFKVKQ0mt/n0wrUGOn+PgN5p62JpyQZgznx/IoqKuCa1Wy+O/JHD1p0dZ8d5BvjuSzY1fHuei9w5SLFcwwcWSV/9OQdVFdeZgejlSicBMf12GOMzNinA3K74/mjvo6x5pjGVSvOzM9JozlF5ah7mRAU6WolZ7oDiYG2FqKCWzjy5cqSW1WJvKcDjP9fLP/H4aiQDPXRI20kvphIFUgoeNKdn9cFhLKqwZ0w3flsYy/BzMiM+v7vF5hXIFFfXKYa8YCILA2vl+HM+u5K0dqZwprOHjfRncOc+PiB6CMtBVqX5dO5PnLgllx+kSKuuVfHvLVJ666B/3WhcrEyI8rDsk/m6c6U1js5qfh1HyHN5PM57WwGgsfwaHGzHI6SNTfew4nl3ZJ9nYofRyZFKBqd1UYPRBKhG4eZYP28+UcDK3Cq1Wyyvbkrnz+1hic6uIcLcetIyZPpn/CS6WZJTVoWhW9/i8/KpG5I3NhIl2h0OGuVHngmxrlu5Aqv59XOmldbhZm2BqOKACb7c8tCSQBqWKqz89ysvbktkUV8C9C/0xMpDw1O+nKa9r4sVV4Wx/cB4vXRZOZlk9G2PzOx3nQFo5Ee5WHSaMr5nhxcH08rZAbTwR4Giud5Dj52A2ZqZ/j2YEQSDKy4Y9yaV9el1aaW2LHOX8OQfNak3b/VCp0vB/f5xha1Ixz14S2i959nDgZWfa54Gg9U0qMsrqxnwWfdI5zpxdkdgSBE10tx7y9ZzL8jBn7lsUwDu70rji48N42plyz0L9Zo9JJQI3zvTm5NNL2PHQPOYGOvT6GjdrE5aHOfPVoWyaVD3vZwaLMDcrkotq+2x+kVggx97cCMdR+r0ajYhBTh+Z5mNLZb2yTz7nx7MqCXezwqyLzWhfuCLKHS9bUy778DCL3tjHR3szePyCYHY8OJfXrozQ6ws9WExwtUSj1dml9sTplsxD+Bht1ByrOFsZE+Vlw1/dONV0RVppXZucYSjwd7Rg/R0zqGls5pN9mdwxz5eHlwax6a6ZHHtiEX/cO5trp3liaCAhwsOa5WHOvL0zrUMgrVJrOJpRweyAjp/1SyJcsTaVjanp1/oS6KSfjXR62dCev/ONiya6EJNT1eYOqQ9pJXUdnNXOBzafKiTiue1c9/lRLnn/IF8fzubxC4L1nu00Enjbm/W53+pMUQ1aLWM/yPG0JrWktsf+yPh8OfbmhriMgCOqIAg8tCSQD66djJ25Ia9dMbFTH05vyKSSHh0/z+XehQEUyhv56djwqAHC3KxQqjV6Ja/acyq3mnA3y/MqiTJQxCCnj0R52yAR4KiekjWtVsuJ7EqmDIK21czIgO0PzuOtqyNwtDTitSsmsna+34h84HVSNnrty0kskGNnZohzN1pakaFjeZgzh9LLkTfoZ1WZXlpHwBBvkgOcLPilRVLw2LJgQHdTc7I07vQ5fnhpECW1Cn45+U81Jz5fTm2TijnnSD+NZVKujvbgl9j8tpt3k0pNemntmK/uBDiZU1Gv7OQc1B6NRktGab3YjzOILAt1RiYV+CtRv0RBm7PaeXYOwtysuGW2D8YGUiyMDdi4duaI3Zf0xdvOjLyqxj5l7hPz5cikAoHOY/v8TnK3RqOlR7lUQn41E92tR/QcrpjowoHHFhLlNfR9QUHOFqya5Mb7e9L1MscZKK2DPPsiWduaWMSRzAqWj7O+06FGDHL6iKWxjAmulhxIK+v9yUBGWT0V9co+T3zuDkMDCZdGurPu9hlcGe0xKMfsD2ZGBvjYmfXqsNbajzOab3jjleXhLjSrtew82/vgu8LqRgqqG5kwDE3CHram3DjTu9dMm7+jOfMDHfjuSE6bHOZgWjlmhtIum2avn+5FnVLFRe8dZMZLuwh5ahuL39zP2ztTh+LXGDZanZx6yvoVyhtpbFaL9tGDiLWpIXMCHNiSoF+Qk32eOqsFOVvwwOJAvrhpChvunNljQ/toYbKnDWqNtk/9tUmFcgKdLDAy6FtVYbQR7GKBkYGEU3nVXf5co9GSkC8fszbZ/eXBxYFUNzTz1aGsIX8vC2MZvvZmJOoZ5BTLFfz710QWhzhyZbT7EK9ufCEGOf3gskh3dpwp0ctb/UR2JYLAsGQjhpuQXswH2uwOxX6cEcHNWtdkqU8m+q/EImRSgcUhTr0+dzi5YaY3ycW1bbagh9LLme5r18EuuxUPW1MeWxbMZE8bVkW68fzKMH64dVqHxtOxiK+DGVKJ0KNkLaNlOrlYyRlcLproQnxeNbl69G+0BqHnm1xtLBLmZomTpRG79EgAtZJUIB/zUjXQSbnC3Ky6DXKyK+qpVajOuyDH086Ua6Z6DltvTqibFUl6OtT+d1MiMqmEVy6fKCaM+4gY5PSDq6d4YGEs6+R01hUnsioJcrLAymTorQmHmwkulpwtqu3WfKCkpomKeuW4uDGMVVaEO3MgrbzX6cp/JhYxy98eK9PR9TmdF+CAl50p3x7N4fdTBcTmVjG7B5fCtfP9eOOqCB6/IJjrp3sxy9++W9vRsYKRgbTXrF96aR0yqYCnrekwrmz8s2SCE4YGEjbHF/T63LSSOqxNZdib6zfkWWTkEASBRSFO7DxbqpeJUFltE+mldePG1WqShzXx3QQ5rdeZkTAdGGnuXxzAb3fPGpZqXbibJWeLanqd71Zao2B3SikPLgnA7jx3bewPYpDTD8yMDLhhhhc/x+RR0YNOHuB4duWwe80PF0smONHYrGb7ma6zYftSdc5EkZ42w7kskXYsD3NBqdb0mLEsrG4kLreaC8NHn9ZXIhFYM92LPxOKuH/dKZaHObN6imfvLxxnLAh2ZNfZki4ttQFSimvwtjPDoIsKl0j/sTCWsSLche+P5vbohNSkUrMlvpBIj5HtYxDRn8UhjhRUN5LSS/O3WqPl/nVxWJsasnTC6Kp095cID2sK5QpKazqPGIjPk+NmbdJpLMH5gL25ER7DlCgKc7NCqdL0at709+liBOCCUOdhWdd4Q7wj9pMbZ3qj1cI3R7p3cyqsbiS/qrHb4Z1jnUAnCyI9rfn5RNeOJL+cLGC6ry3OI+DQIqLDw9aUCHcr/uyhr6BVqrZswui8iF4Z5cFkT2ueXxnKe9dEYmI4tjXx/WFZqDNVDc1tsr32NKs17Dxb2mOFS6T/3DbHl+IaBZvjC7t9zpcHs8muqOfRFjMNkdHPTD97jGUSdp3t2Sb8rR2pHMms4J3Vk3Ac41XhViZ7WgOwJ6Xz756QXy2qL4aBie7W2JjKeHh9PMXy7ufZbU0qZpqPnVjF6SdikNNP7M2NuDLanW+PZNOg7NqNo3VDMl4rOQBXR3twKL2iU39SXmUDx7MquSxSbJIbaS4Md2F/aveStdEqVWvFylTGr3fN4oYZ3udtljzSwxonSyO2nS7u9LODaeVU1iu5NNJtBFY2/pngasmcAHs+3Z/RpbSpSN7Ie7vTWDPdiwli/+GYwVgmZba/Q4/GLIn5ct7fk84DiwKZEzB8IxqGGncbUxYGO/LBnowOcqnKeiWJBXImtQRBIkOHuZEB626fQVWDkss/OkxBF1b1FXVNHM2sYHn46ExAjgXEIGcA3DbHl5rGZtZ3MSm3oLqRl7aeJcLDesz3BPTERRGumBpK2RDT8W/w+6kCjAwk4pdzFHBhuE6ytrMLWeEXB7OIy60WN8ijHIlE4IJQZ7YlFaPRdNxob4orwNfeTMy+DiF3zvMjtaSOX04WdPj7a7Vanvn9NMYyKQ8tCRrBFYr0hyUTHDmVV01Mdtcua2/uSMHLzpS7F/gN88qGnoeWBJJb2cAv7QYuf7IvA4kgcPlkMTk5HAQ560YqNKs1PP1bUqef7zhTgkarq+SL9A8xyBkAXnZmLA934bMDWR2yIRV1Tdz45XGkgsAn10eN4AqHHnMjAy6a6ML6mPw2fa9Wq+XXkwUsDXXGwnh0VgfOJ1ola+e6rH13NIcX/jjDLbN8RvXgPhEdy8KcKa1tIi6vqu2xuiYV288UsyrS7bytcg0HM/3smOpjyyMb4pn6v528uysNtUbLT8fz2H6mhOcuCR21lVCR7lkV6cZkTxtu/y6W3Aqd+uCN7SkUVDcSm1PFnpQy7l8UMC573cLcrFgW6sR7u9NpUqkpq23imyPZ3DDTCwcLURo1XHjYmvLkRRPYlVzKzjMlVNYreXRDPGu/j+XT/ZlEe9mM60T5UGMw0gsY69w514+L3z/Ix3szsDEz5EBaGXtSyjCRSdl454zzoh/l9rm+7E4u5cJ3D/LYBUEcz6oks7x+zFv3jidWTHTh9b9T+TOhCEsTAz7dn8mBtHKun+7JUxeFiBvkMcBUb1tszQx54Y+z3DzLm1BXS/Ykl6Fo1rBykhikDiWCIPDDrdOIya5iS0Ihb+5I5UhGBXF5VVwZ5c7FYpJgTGJkIOXTNVGs+vAQy97eT2OzGgOJwDeHs3GxMsHPwYyVk8ZvlfuBxYGsePcAK98/hKu1CVJB4I65469qNdq5eKILPx3L5ZnNpwFd8irERWdFv2aG10gubcwjaPXxTxwirK2tqa6uHqm3HzTWfHGMA2nlSAQIcbHk4ghXVk1yG1MBzkDPRUmNgnt/jON4diXOlsasnurBvQsDeh34KNKZofheFMsVXPbhIQpbGhy97Ux5aGkQF090EQOcHhht16i/Txfz3u40kgr+ma8Q7WXDxrUzR3BVw8NoOhe7k0t4YN0p7MyN+OPe2ZgZnV/5wtF0LgaD9NJa3t6ZxsURrkR72fDvXxPZcaaE96+N5KKJozuAHei5OJpZwZs7UjmeVcl9C/15aKkou+wvAzkXqSW1XPjOAYJdLPjouqhhc3kb74hBziBQ3aAkv6oRf0dzjGVj0/lpMM6FSq0hq7weXwdzMbgZAEP1vdBqtZTWNlEkVxDqatnlQE2RjozWa1RORT2F1QpkUgE/B3NszMb/bJbRdi4q6poQBAHb8+Bvfy6j7VwMNlqtlszyevwcRv9w3cE6F2kltfjYizb0A2Gg5yKvsgFHS6NhmdNzvnB+pZ+GCGtTQ6xNz78b3bkYSCXitO9RjCAIOFkai/recYCXnRledmYjvYzzGtHSdfwiCMKYCHAGE/HePfKI1ZvBZ0QrORKJBEtL0XJzqDAyMqKkpHt7zPaI52JoEc/F6EE8F6MH8VyMHsRzMXoQz8XoQTwXo4e+nItWRjTIERERERERERERERERGWxE8aWIiIiIiIiIiIiIyLhCDHJERERERERERERERMYVYpAjIiIiIiIiIiIiIjKuEIMcEREREREREREREZFxhRjkiIiIiIiIiIiIiIiMK8QgR0RERERERERERERkXCEGOSIiIiIiIiIiIiIi4woxyBERERERERERERERGVeIQY6IiIiIiIiIiIiIyLhiRIMcJyenkXx7kXaI52L0IJ6L0YN4LkYP4+FcaLXakV7CoDAezsV4QTwXowfxXIw+RjTIaWpqGsm374BWq2Xl+wd55vckVGrNSC9n2BnJc6FoVjPvtT38Eps/YmsYTQzFuSirbWLa/3by0M+nSC2pHfTjj1eG+3ux/J0DrD+RN6zvOVYYTfeL/rAtqZi5r+0ht6JhpJcyYAbjXCia1cx5dTfHMisGYUXnL2P9e5FUIGfmS7sokjeO9FIGzFCdizVfHOOlv84OybHHO6JcrYXyOiXx+XK+OZLDLd/EUKtoHuklnTfsTy0jp6KBl7cl06BUjfRyxiXZFfWU1DSx40wJy97eT1KBfKSXJHIOzWoNZ4tqeH17Copm9UgvR2SQSSqQk1fZyI1fHaeyXjnSyxlxiuUK8iob2RxfONJLERlB0kprKZQr+HR/5kgvZVSi1WqJya7ik/2Z7EkpHenljDnEIKeF7Ip6AB6/IJjjWRV8fiBrhFd0/vBHQhHOlsZU1Sv56lD2SC9nXFJRp9tU/XX/HAwkAseyKkd4RSLnIm/UJVZKa5v46XjuCK9GZLAprVXgYmVMdYOS276NQaMZH9K1/lLZoLsm7U0pGzcyPpG+U6fQJTZ/Op5Led3YrkoNBVUNzTQ2q7E0NuDRDQni36iPiEFOC1nluiDnhhleTPWx41Re9cgu6DyhUalm59kSrp7iweqpHny8N4MqMcs56FS1bCicrYzxtTcnTZSsjTpqWoIcN2sTPtqbIVZzxhlltU0EO1vwyuUTic2pIqnw/K6mtl7nC6obySirH+HViIwUNQoVRgYSBAS+OCgml8+loEon43vl8omoNBre3JE6wisaW4hBTgvZ5fU4WhhhZmTARDcrEgvkYnZpGNiTUkqDUs1FE124b1EASrWGn06IWezBprJeiYWxATKphAAnc9JK60Z6SSLn0FrJeeyCIMrqmvj1ZMEIr0hkMCmtbcLRwpj5QY6YGxmw8+z5LT1plewZSAT2ijKc85ZahQp7cyOumerJd0dyxFaBcyio1gU5k71suGKyO1sTi87LvvH+IgY5LWSV1+NtbwZAmJsVlfVKCuWKEV7V+OfPhCKCnCwIcLLA0cKY6b52HEwrH+lljTsq65XYmRkCEOhkQWpJrRjEjzJag5xIDxtCnC05mVs1wisSGUxKa5twtDTC0EDCvEAHdp4pGekljShVDUqMZRKm+tiyL7VspJcjMkLUKpqxMDbg2mme1DWpOJYpSqnbU1DdiEwq4GBuxPJwZ6oamjkuys315rwNcrRaLd8dzWnLJmWV1+Njpwtywt2tAEjMrx6p5Z0XNKnU7Eou4aKJLm2PzfSzIyanSpTqDDKV9UpsWoKcAEdzahUqSmpEbe9ooqZFm25lIiPI2UKUFI4j1BotFXVNOFoYAbB4giNnimrasrTnI5X1zdiaGjIv0IFjWZU0KsVr/vlIrUKFuZEBfg5m2JsbcTxb3MC3p6CqERcrEyQSgUgPG5wsjdiaVDzSyxoznLdBzrGsSp76LYl1J3LRarXkVDTg46ALclytjLEzMyRRdKDqlsGoAqSV1KFo1jDdz67tsZl+9ihVGjGL3UJycQ1/nx74Ba19JSfAyQJAtJIeZcgbmxEEsDA2aKm21Z33zenjhYq6JjRacLAwBmBBkCNSicDus+dvNaeqJfEyP8gRpUrDkUyxgn8+0lrJEQSBaT62oinOORRUN+BmbQKARCKwLNSZv08Xi/cGPTlvg5wfj+n6Pg6ll1NS00RjsxrvlkqOIAiEuVmRkH9+Bjm/xRXwzeHsbn/+7q40Lnj7wICrLWcKawAIdrZoe2yCqyVWJjIOp4uzE0D3OX3i10SaB6jBraxXYmOqC3K87UyRSYUx2ZdTWa8ctzK7msZmzI0MkEgEAp3MaWxWn9eZ/vFEaa2uaurQUsmxNjUkysuGHedxX05lgxJbM0MCncxxsDDiSIZ4zT8fqWtSYWEsA2Cqjy1JBXLqm8RREq0UVitwszFp+/8FYc6U1jYRlycmgvXhvAxyKuuVbEsqxs3ahBPZVZwt0m22fVp6cgDC3axIOk/NB2JyKvnsQNee9VviC3lzRyopJbWsG6DN7ZmiGrzsTNsucABSicB0X1sOZ4hZPYCroj2oqFeyO3lgm6HKeiW25rogx0AqGVUOa7kVDVz/+bFeG05LaxTMfHnXuJ2rUdPYjJWJ7rsQ2FJtSykeHedIZGCU1ur6O1vlagBLQpw4klFOzXnaaF1VrwtyBEFguq8dR8VejPOSWoUKC2MDQBfkqDVaUcnRjoLqRlyt/wlypnrbYmtmyF+JomRNH87LIOfXk/lotFr+d1k4SpWGDbG6CeNedqZtzwl3t6KqoZn8qvMvkzrD1578qkbyKjtO5k4qkPPoxngumujCxRGufLSvs81tX0qoZwprCHG27PT4TD974vPl1InZHEJdLQlxsWRDTN6AjlNZr8S2pZIDEOBkPmrkaruSSziYXs6JXrTYm+IKUDRrxm2TsrxdkONmbYKpoZTU0tFxjkQGRmlNx0oO6DKyzWot287TzUplwz/V5em+tpwulLeZb4icP+iCHN11L8jJAktjA70a67VaLRe9d4BHN8SPib1CRlkda744xrFM/SuWDUoVlfVK3NsFOQZSCReGO/P7qULRZU0PzrsgR6vV8uPxXJaGOjHH3x47M0P+Pl2Cm7UJxjJp2/PC3XTmA+fjZPjpvrYAHGn3ZWxWa3hkQzzedma8dkUE9y70p7S2iQ2x+W3P2Ribz/SXdlFS07srnVar5WxRDRNcOwc5s/ztUGu0nBC1uQiCwFXR7uxJKaNUj79rVzQq1TQ2q7E1+yfICXSyIK2kblRUKltnUsVkd5+902q1bGz5rI1XZxl5YzOWLTd7iUQgwMmCVLGSMy4oq23CykTW4R7jYWvKVB9bNsWdn1bhrZUcgOm+dmi0ECM2nZ931LT05IDuujdVz76c0tomkgpq2BCbz4XvHBg1yoSuqKhr4uavTnA4o4LrvzjGhpg8ve69hS1y5fZyNYArojwor2vigOhE2yvnXZCzMTafzLJ6rp/uhUQiMMvfHrVGi7e9aYfnuVgZ42BhxKa4glHZ4FXdoByyddmZGxHsbMHRdhrpzw5kklpSyyuXT8TEUEqgkwUXhrnw3q400kvrSC+t5anfkvSe1p5f1Uhtk4oJLp2DHD8HcxwtjNifNj4z9n1l1SQ3pILAr/3cDLVOFm8f5AQ4mlPbNDoc1lqDnNic7oOchHw5aaV1XDbZjfyqxnHZq1Kj+KeSAxDkZE5qydjrmxLpjG5GjlGnxy+NdONoVkXbZuZ8Qa3RUt3Y3Ob46Guvc9Y62ocst8jYR6PRtvTkGLQ9NtXHllN51b32/Ga3DHD/8LrJqDVa3tg+OodkNqnU3PptDHVNKv5+YA6XRLjx6MYEZr+yh8c2xrdJWbuioFr3MzfrjkFOhLsV/o7mbYk/ke45r4Kc8romXvzrLCsmujDTzx6A2QG6f1tNB1oRBIEnV4Sw/UzJqJswW9ekYs6re/hwb/qQvcd0XzsOZ1S0OM/V887ONG6c6U2Eh3Xbc/69PBhzIwNWfXCIW7+JwdnKmBUTXfjpeG6vjfKnW0wHQrqo5AiCwPwgB/amDDzIKatt4pN9Gby5I5VfT47NC4KNmSGLJzjyWz+DnNbJ4h2CnJaej+TimoEvcABU1SvJqWggwNGc+Pzqbj83G2PzsTc34tFlQQBtJX/1KExA9Jf2cjXQVdvSy+pEScI4oLRWgaNl5yDnwjAXZBIJv58an31m3SFvbEarpU1Cq+vLEZ21zjfqlSq0WjoEOdN87FCqNG3Jr+7IrtAFOfODHLh5ljc7zpb0W+0wlGw/XUJcbjWfrInC39GC16+cyBc3RrM4xJHtZ0q4/6dT3d7HClraJZytjDs8LggCV0S5s+NMCdUtSUyRrjmvgpznt5xBo9HyzMUT2h6b0xLktDcdaGXlJDceXhLI+3vS2TKKmp23JRVTq1Dxyf7MIdMwz/Czo7hGQUZZHQ+tj8fOzJCHlwZ1eI6HrSm/3TOL6b52FMoVvLs6kjvm+lJS08SuXqxRzxTVYGUiw/WcL28rC4IcySqvJ6slW9MftFotD/58ite3p/D90RweWh8/Zvs5ZvrZk1pS269ZEhVdBDk+9mY4WhixY4QHEp5qmUV1y2wfFM2aNhOQ9jSp1GyOL+TSSFdcrEzwdTDjWGYllfVK5r66h1e2JQ/zqocGeWMzlib/3OwDnSxQqjTknNMbJzL20FVyOl/rrExlLApxZFNc/qiQjg4XrfPpbMz+Ceqn+9qRVCA/b40YzkdqW2aDWRj98zkIdbXEzFDa61DQ7IoGnCyNMDU04Iood6QSgfUD7F0dCnacKSHIyYIp3ro2AEEQWBTixHMrw3h3dSRHMiv4qJuEdUF1Aw4WRh1krq1cGumGSqMZVXvT0ch5E+TE5lSyOb6QJy4M6XCzcbEy4ZM1UVwR5d7l6+5Z6E+Ul82o0k3/FldAoJM5Tc0avjqUNSTvMd3HDkGAO78/SVxuFW9dPQlzI4NOz7M0lvHZDVGc+O9iwt2tmOhuTYS7Fd8dzenx+GeLapjgYokgCF3+fHaAPQYSYUCuYntSSjmYXs67qyOJfXIxkz2teX7L6QHbMY8EE1wt0WghpR+6464qOVKJwMpJrvyRUESTauSG8MXnVWNhbMDKSa4YSIQuJWt7ksuQNzZz2WTdd3Sajx3Hsyt58c+zFNco+GhvxrhwXKtpVHWq5ACjWmsuoh+lNU0dTAfac2mkG6kldZzMrR7eRY0gVV1IaFv7csRezPOHVsOA9pUcA6mEKT62vUoXcyrq8WpR4FibGrIi3IWfjufpXd1v7fMcqKlPTyhVGvYkl7JkglOXP58b6MAd83x5a2cae7rY6xRUNXaSqrXiZGnMLH/7cXHvG0rOmyDnwz0Z+NqbcWW0R6efLQt1xrqd81R7BEHXCJeQXz0qMm0lNQoOZZRz40xvVk/14IuDWUNSzbEylRHqakl6aR0PLQlkmq9dt88VBKHD5uy66V4cSq8gp6L7KsyZwhpCuujHacXCWMYUb1v2pvQvyGlWa3jxz7NM9bblgjBnBEHg2UtCySyv57sjPQdgo5FgZwsE4Z/ZQn2hol6JTCp0ClIvjXRH3tjMnuSRq26dyqsmwt0aU0MDQt2siOkiyPktroBgZ4u2z8s0H1uyyuv55WQ+z14SyoqJLjy+MWFM2y1rNNpOPTlOlkZYGhuQUiz25YxltFotZd305AAsCnHC09aUT/dnDPPKRo7WSk57x0c/BzMcLMS+nPOJ1rEB7cdIgC7gPZlb1WMCLqu8AZ92bQbXTvOkoLpRr17e8rombv0mhkc2xPPcljNDlvg8llVBbZOq2yAH4OElQcz2t+fWb2Pa5je2cu6MnHNZMsGJk7nVyBvE6md3nBdBztmiGnYll3LHPF+kkq4rBz0R4W5FeZ1y0JqdNRotmWX927hsPlWIgURgRbgLa+f70dSs4Z2daYOyrnNZPcWTyyLduGu+f59ed2G4C0YGEv5IKOry5/KGZgqqGwlxsejy560sDHbkWGZlvwaDrTuRR0ZZPU9eFNJWLZrobs2VUe68vTN1RKsX/cHU0ABvO7Mu5Vy90X4eRXsmuFoS7GzBpriR6VXSarXE51UzqaXPK8rThpPnBDnyxmZ2J5eycpJb22PTWtz/orxsuG6qJ69ePhEnSyNe3np22NY+2NQ26bTplu2CHEEQCHa2JKVkZPumRAaGvLEZpVrTbSVHKhG4bY4P28+U9Pu+MNZorS63Ty62zss5MgxBTq2iecSlukNJXG4VF7y9n8/2Z/ZL4jxc1Cg6V3JAF+Q0qTTE53XtbtvaK+zVzjAq2ssGf0dzvSoz//k1kdjcKu5b6E9dk6pHZ8+BsONMCU6WRm1uvV1haCDh8xujuSranSc2JfKfXxOoa1JxNLOCs0U1uPcQ5MwPdESt0XIgfWzK8IeDfgU5SUlJg72OIeWjvRm4WBlzaWTXkrTeaG22T8gfHDvpTw9ksvCNfbz2d3KXDmk1imYeXh/P2ztTOZpZ0VZB0mi0/BpXwIIgR6xNDXGxMuGhpYF8eSiLPf2sePTE9dO9ePPqSUj6GBiaGxmwMNixW61oax/GpHYmBl2xINgRpVrDwfS+2SQ2qzV8vDeDFRNdmOje8T2umepJjUI1Jq3BJ7hY9ivIqaj/Zx7FuVwa6cbu5NIRaV7MrWygqqG57fsV7W1DkVzRIZmwLakIpVrDykmubY+5WJnw3CWhvN3y2TQzMuCuBf7sSSkbcSOF/lLTUo1tH+QABDlbkFw0ditUIjrzE6DLnpxWrojywMbUkM8P9i4/1mi0vPDHmTEtY6xsUGJhZIChQcctyAxfO04X1gz59WhDTD63fRvDoT7eW8YCWq2WZ7ecoaRGwUtbzzLn1T1siS8cFUqUc2ntybE8p5IT1tKX011Vr6y2iQalukMlRxAELp/szs6zpT1WNqoblOxNKeWeBf7cvzgQG1MZe1MHf/+k1WrZeaaExSFOve6hZFIJ/7s0nOdXhrIproD5r+1h9adH8Xcy56aZ3t2+ztPOFD8Hs0ExaRqv9CvImThxIpGRkXzwwQfI5aN7s5hb0cAfCYXcOse30wVVX5wtdXbS8S2b84FQ36Tik30Z+Dua88GeDNb+ENupQvTZ/kw2xxfw+YEsVn96lIfXx6NUaXhm82nOFtVw3XSvtufePseXOQH2PLI+flQ5i1wc4UpycS3pXQwzPJlThYWxAX4O5j0ew8/BDHcbEw700Up6S3whBdWNrJ3n1+lnoa5WGMsknBiizM1QEuJiwdmimj5bh1fVK7Ez7zrIWTnJDZVGy9+nh38gYesNbFK7IAfgeNY/N7ZNcQVM87HtMPEZ4MaZ3njY/pPFWznJFSdLIz7dlznEq+47rcYJDcruK5KtklOrc4KcYBcLsivqR3U2VqRnSluDnC7c1VoxMZSyZroXG2Pz26oc3bHjbAlfHMzi8wND0485HFTVK9vso9sz088OrZYhd1mLa3HuenVb8qjc/A+ELQlFxOdV8+41kex6eD6TPKy496c41n5/sldb5uGmVa5mZtSxsb63vpzsCp0Zi9c5rrirIl1pVmv4M7FrFQnojJtUGi0XTXRFKhGYG+jAviEIEk4X1lAoV/QoVWuPIAjcMMObv+6bwyQPG/6zPJiNd87Exar7Sg7oTJr2ppT1aV+wJ6WUxzbGj8n+5L7Sr13/hAkTePzxx9m0aRNubm7ceOONHDx4cFAWtPNMCVt7+ID2lc8PZmJhLGP1lM69OPoiCAIR7tbE92JpqA/fHc2hVqHiq5um8MG1kzmSUcH81/bwn18TqWvSTbf98mAWa6Z7E//MUl69YiKb4wtZ8PpevjuawwsrQ5kX6NB2PIlE4I2rIhAE+HgUbfAWBDliaihlS3zncxnXIlHqLbshCAJRXjbdlqy7QqPR8vG+DOYGOhDWRYnY0EDCJA/rMTl0LsTFknqlmryqvrltVfZQyXG2MmaCiyUH04dfB/9HQhFTvG3aZDyOFsb4OZhxpGU+U7FcwbGsSlZFuvV0GACMDKT8a7YPm1sC3NGCUqXh7h/iuO+nOFa+f6jb7HtbJeecjGaws85wIq2LZMH5zPdHc5j/2h6u/uQIr/+dMqo3qq1zMLrryWnliih3lCpNjz0pWq3u+gawNWlkTUMGQkW7QaDt8bIzxcXKuO0acC5/JBTy1o5U/kgobNsg94f4vGqCnS2Iz5ezLWn4EzxDhaJZzStbk5kf5MCcAAd87M347IZo3ro6gm2ni0fd71qrUGFqKMVA2nkrOt3XjticrvtyWmfkdJ5vaMJMP7seJdib4wuZ4m3bZsu8IMiR5OLaQZ9VZWgg4epoD2b4dd/P3BW+DuZ8fmM0d8zz06u9Yn6QI+V1TW1jOUA3RHRfalmX4wd+PZnPrd/EsD4mf1QZag0V/QpyDA0NWb16NTt37iQ+Ph53d3dWr15NSEgIb7zxRr8XE59XzdofYnlkQ/ygNFJV1itZH5PHDTO8MOvCGawvRLhbkVTQMYteo2ju0zoblCo+3Z/JZZPd8LA1ZcVEFw79eyEPLA7k91MFXPPpUV7ZmoxGC2vn6z7gV0V78NXNU1CqNTx78QTWzPDudFxHC2PW3T6d/1wYPKDfcTAxMZSyOMSJPxI6lsk1Gi1xuVVEetrodZxJHtacLarROwO182wJqSV1XVZxWpnibUtMTtWoHPLaExNaZgr1VbJW2aDErosNRSuz/O05klE+rBvF8romDqWXc3GEa4fHp/vacbTFOnRbUhFSQWB5mLNex7xmqicmMumoMZZQqjTc+9NJ9qWW8vRFE1BrtVzy/qEu+y5abXPPreQEObfMMxIlax3wdzRnQbAjZkYGvL8nvc+S1uGktKYJE5m0S3fK9rjbmOBiZdxjlfl4ViVxudU8sDiAGoWKA6mj9/duVndtCQ//9AmeiyAIzPC16zLQq6pX8siGeD7al8E9P8Zxz49x/VpXRV0TuZUN3D7Xl5l+dry2PWXczNvaHF9IkbyR/ywPaXtMEAQujXRnortVt32yrbTa8l/9yRG2JRUPeaa/VtHcqR+nlWk+tt325WRX1ONoobOPPpdLI905kV1FbkXnZGBpjYIjmRVc0u6+MzfQAUFg0MdLBDpZ8MoVEzEy6Gz/PJhM8bHB1FDa1rKg0WhZ+8NJbvzyOHNe3cND609x3edHWfrWPua8upuH1sezMsKV+UEOfLgnvU9z2EprFTz086khaY8YKgZsPODn58eLL75Ibm4ur7zyCvv37+/XcaoblNz1w0n8HMxpVmv57mj2QJfGt0ey0Wh10paBMtHDmromFZnl/2xQHlx3ilu/PaHX67VaLc9vOYO8sZm7F/zTyG9hLOPuBf6su306hdWN/ByTxw0zvTo0qc4JcOD4E4u4aZZPt8f3d7RA1kU2ZCS5OMKVjLL6DhmGzPI6ahUqJnta63WMSR7WqDRaThf2Xs0prG7kiU1JTPW2ZXpLc3pXRHvbUt3QTMYYa/J1tjTG2lTWZ4e1ym6kIa3M9LOjvE7ZL3vq/tJarV0e5tLh8Rl+duRWNlBQ3chfScXM8LPr1vnwXCyMZSwNdWb7CEjvQPcdzyqvR63RuWld+9lRdieX8sG1k7lltg+/3T0LlUbTpX5a3taT0/GmbW5kgIetCWfHaK/RUDHd145nLg7lixujCXa24NP9o6eKfS5FcgUuVsbd2uW3IggCU7xtOdFNlVmj0fLh3gz8HMy4b2EAvg5mo9Y+9mBaORe+c4Dl7xzo0iGzsqG52+rydD87kotrqahr6vD4j8dz0Wjg4GMLeGFVGPtSy8jrxwyp+HY9oWvn+5FZVt8v18rRyBWT3fll7cy25Eh7VoS7sD+1rNs5RFqtlv/8mkB1gxKFSsOd38cy8dntrPniGCdzh0beXadQdXJWayXMTSctj8np/H3IrqjvNMC9lQvCnDGRSbusUvyVWITknMSZrZkhEe7WXVo4jwWMDKTMC3Tgx2O5lNU2sSWhkPi8ap65eELbfD0zQwOm+dhxYZgL/7s0nNevjOD+RQFkVzT0Gvi2kllWx+UfHWZzfCE3f3WC//vjDErV6Je79WtX3FXGVyKRcMkll/D777/3ayGPbkygRtHMp2uiuTTSja8PZ/dJP3quY1mDUsU3h7O5Isode/OeZQL6EOGukz61ZhUalWoOpJdzIrtKrwvtG9tTWXcijxdWhnXSkYLO+evXu2ZywwyvLqsQvd0gRyPzAh2wNzdk3Yl/bBFbZ0H0ZjrQSoiLJTKpwKlzsjlarbbD57C+ScW/volBJhV4/9rIHv9ekz2tkQiMub4cQRCY4GLJmT5k9dUaLdW9VHKm+tgikwocTBu+rPDm+EJm+dt3cpya5qMr7f8RX8iJ7MpOQVBvLJngSGZ5/YgEsBti81nw+l4mPbedZW/vJ6u8nh9unc7SUN0N1dJYRqirVZezgOSNzRjLJF1m/YKdLce0PfZQIggCt83x5UBaeb9MObqiWa3hh2M5zHp5N0/+ljjgLH9hdSMu1t2bDrRnircNpwvlbfNDWvn9VAFL397PvtQy7lnoj0QicPFEV3acKemx12sk2J9axvVfHMPc2IBgZwve2J7aac+gq+R0vbmd2SLvOdpuGGSTSs3Xh7O5ZJIrjpbGrJrkiolMysbYvjtDnsqtxspEho+9GdFeumtfd4HlWEMiEbpVSVwY7oJSrWFnN65yv5ws4O/TJbx4aTi/3z2LLffM5p6F/hRWN3Lvj3FD8jmrVai6reTIpBIi3K07OW4CZJc3dJKqtWJuZMDSUCd+O1XQ4XOn1WrZEJvPbH977M7ZE14Y7szu5NIBDR8fSZ68aAIqjYY7vovh1W0pLAhy4OZZPrxxVQR/3DuHT2+I5oVVYfznwhCunebZ9jmZE2DP+3pUc07mVnH5R4cxkEjY9fA8/r08mK8PZ/PUb6PfhKxfQU5sbOxgr4MbZnjxzupJeNqZcvs8XyrqlX26gH28P4OFb+zj430ZNKnUrP3+JPVKNbfN8R2U9VmbGuJlZ9o2x+NIZjlKlQaJQK/ZtF9P5vP+nnQeWRrItdM8u32el50Zz68M0ztzPdoxNJBweZQ7v8f903Qdl1uFr4OZ3r+jsUzKBBdLTp3TD3X7d7FMen4H//r6BLd/G8PsV3aTXV7PZzdE42jZ84bCwlhGsLPlmO3LOV0o11taJm9sRqOlx0qOqaEBkZ42HD5HB69oVvco6curbCC1H9WfwupGTmRXcfFE104/c7AwIsDRnPd3pyMAS0P1a9psZU6AA4YGkm5v5EPJ90dziPS05pbZPiwOcWTzvbOZ6tOxohjtZUNMTmWn83fuIND2BDvrDCdGc9/JSHJxhM504rMDA6/mKJrVrHz/EP/dlIS3vSk/HMvl/nVxA5Lt6Co5PTcPtxLtbYtGq7tOtpJX2cD9607hbGnMT7dNb3MJvWSSK43NanaeHV0Z6PUxefg7mvPLnTN54sIQEgvk/H264/exO+MBAHcbXV9O+7/BH/FFlNU28a/ZOjWDhbGMFRNd2Bib3+cgNC6vmggPawRBwMRQSpib1bgJcnrCw9aUCA9r/jwnc59X2cBTvyXxxKZEVk5ybZMQh7tbcfcCfz69IZqy2ibe3ZXer/etblB2e6+t6aGSAzozmticqk6S9/aDQLtiVaQbWeX1xLdzxD2RXcXpwhpumOHV6fnXT/fC1syQ1/5O1udXGnW4WZvw8fVRJBbIKa5R8MSFIb2/CHhwSSAZZXV8sKfjjK66JhW/xRVwIK2MzfGFXPvZUbzszNh45wy87My4c54fL6wK4+eYPH4+kdvN0UcH/QpyJJLBl0XNCXBgYbBuQ+PnYM7iECd+OKbfH6+irokP92TgbWfKy1uTueDtAxzJqODTNVH42Hf/RegrS1p6TOqbVOxJLsPVyphloc4drJLTS2v5z6+JPPTzKVRqDfVNKt2aQp07yN/nunYAAKyjSURBVNTOF1ZP8aS2SdV2YY3LrWaynv04rUzy6Gj6cCi9nB1nSlgQ5EC9UkVVg5LVUz3ZuHZGl2YDXTHF24YTXZTBz0Wr1XI4o5xfYvP58Vhu2xC7kWKajy1FcgXppV1XKuQNzby5I5X1MXkcyajg91O6kn1X+vf2zPKz51hmRYfN3Ip3D/DiX13PntFqtdzy9QmWvrWfB9bFdWj2TyqQ8+DPp1j4+l4mv7CD6z4/yndH/+mT+SOhEJlUYFlo1702033tqG1SMcXbts9VWDMjA2b52bHz7PAGOUkFchLy5dw+x5cHlwTy6hURXU6qjva2oaSmifyqjk2u8sbmHoIcS6oamtusiHuioq5pULKRJTUK1nxxjCVv7mPRG3tHdSXJ0EDCLbN8+P1UYb8y++15d1caaaW1/LJ2Bj/cOp13V0eyLamYaz492i9pFOiC+nPdAbsjyMkCC2ODDlXmVrnvq1dM7NDE7OdgToS7Vae5IBqNTnb0wZ70YQ+M65tU7DxbwiURrkgkAnMC7JnqY8ubO1LassVKlYbaJlWHQaDnEuJi2UE++93RHGb523UYIH1VtAcF1Y0cztC/At02m8v9n/vE1BaJ4PmQRFgR7syBtPK2XuKkAjmL39zHloRC7pznx0uXhXd6jZ+DOXfO8+XzA5n9Smr997ckrvrkSJfGTT315IBuFlpVQzMZZf9c0/5ILKJeqe6UQGrPHH977MwM+a2dZO3Lg1l425myIMix0/NNDQ14aEkgfyUWD5k0b6iJ9rbl4+ujeOnScAKcep5B2MpkTxvWzvPj3d1pxOZU0ahUsyEmj4Wv7+WBn0+x5ovj3PdTHLP87PnptukdKmCrp3hwRZQ7T/1+mvUxeZ2qz6OF0dXE0Y6lE5w4W1RDeV3vN/b3dusu5hvunMmjy4IoqVHw0fWTmd/Fh3kg3DDDm7omFb+ezGdPSinzghy5pMUqOT6vmsc2xrP4zf1sP13Mb6cKeHlrMp/uz6SqQcl/Lgwek5KzgeJjb8Z0X1t+OJbL5vhCUkpqidSzH6eVSZ7W5FY2UFHXhFar5ZVtyYS7WfHmVZNYd/sMNtw5k8cvCCbUVb8AB2CKjy15lY3kVHS/IZQ3NnPXDye59rNjPLwhnic2JbL8nf0cy6ygtEbB3pTSYf9izw6wx9BAwq5u9MMbT+bz7q40HtuYwDWfHeW5LWewNTPE175nu+5Z/nbUK9UktOjVC6sbySir59sj2V02cO5NLSOttI4bZ3hxIK2cyz48RFltE9UNSm79JoYT2ZXM8LPj2qmeqNRanvotqU1O+vupQuYHOWJl2vWmfrqvbiN3YXjfpGqtLJ7gRGxOVSdN/1Dy0/Fc7M2NWNyLXehkL12Af65kTd7Y3MlZrZXglqG5yb0EGqkltax49yDL39nfp43fuWi1Wv79SwKnC2uY7mtHZb2SH4+NDjOH7rhltg8rJ7nyyIZ43tye0i/nrbNFNXy6P5O18/yI8tJtoC6OcOW7f02joLqR5e8c6DBA8q0dqfzfH2d6dDhTNKupqFfiaqWfXE0iEYj2suFEOwvllOJaLIwNcOniGFdP8eRgenmHAOyjfRn8dDyP1/5O4dnNp4fVYGXn2RIUzZq2xm5BEHj8giDSSuu4+esTVNYr2RCrC8p6Srzoqpe6z7uiWU1igZylEzomRaZ42+Bjb9Yno5Gs8npqFComtbsHTfG2pbxO2WZLPJ65aKIrEgnc/eNJymqbuOfHk/jYm3Hw8YU8tCSwy0Z+gLsW+ONibcxbO1L79H55lQ1sTSxCJpXw2MaETj0ctQoVFj0YcrQmRGNbEpJKlYY3tqcwN9CBKd7dBzkGUgkXR7iyJb6QZrWGvMoGtp8p5uZZPt26ul4R5U6Aozkv/XV2zAa8i0KcuKqPTsIPLA4k1NWSm746TsRz23l0YwKRntbsengeOx+ax7rbp/PJmihMDDtKqQVB4IWVYczwteOxjQlEvbCDL/SY8zXcjNogZ0abLrdna9vs8nq+P5rDnfP8cLAw4u4F/iQ8s5RFIX2TueiDp50pi4KdeGdXGvlVjSwIcmBBsCMWRgZc+ckRfj9VyLMXT+DQvxfy7+XBfH4wiw/3pnPDDO8eS6vjnWumenIqr5r7fopjkod1txn87ohoGegZn1/Nb6cKSMiX8+/lwX0eUtqe+UGOGBlIum26q6xXcvF7BzmQVs47qydx5vllHP73QjxsTLn606NM/d8ubvrqBE8PsybV1NCAmX527OqmUrE/tYxpPrYkPruUvx+YS9xTS4h9cnGbXWZ3RHhYY2Yo5UBLX05rNstEJuWNHSmdnv/5gUwmuFjy7CWh/HHfbFRqLXf/cJL//JpIvVLFutun8+Kl4TyyLIhvbpmKpbEBPx3PJb20jtOFNR3cbc5lfpAD10/37DAAtC8sCnZCo4Xdw9RIWt+k4vdThVwZ7d6r+YejhTGetqadmmlrFN1XcrztzDAykHQ56PRMYQ2f7s/g/d1pXPHRYSxNDJjkYc0tX5/o1oa3OxTNarRaLetj8tiTUsZLl4XzwqowLo10Z3N84ahuMpVJJbxxZQT3LQrg3d3pRL2wkzu+i9G7qqXRaHliUyKetqbcdU7FfYafHdvun8tUH1vu+fEkp/KqWR+Txzu70vjykG6WWbG86xllRS2P61vJAV0CJi6vqq2qmlpSS5CTRZdJsosjXDA2kLKhpYJ1PKuSN7ancMc8X55fGco3R3J4YlPisG3aNp8qZKK7Fd7tFBRRXrZ8ffNUEvLlTP/fLv67KYnlYc7MbTcK4VyCXSwpr2tqs8ZVa7RMdO+YxBIEgTvm+rL9TEnbJrg34lp6QiPaDYmOakk8nBji2TyjAVdrEz6/YQonsitZ+PpeSmub+OC6yb06/xnLpNww3ZsdZ0r0Sjy38sXBLMyNDPjmlqmkl9XxwZ6Okrfapp4rOdamhgQ4mrclhdadyCWnooHHlgX1+t6rIt2oqFfy4Z4Mntl8GjNDAy6P6n4gvIFUwhMXhhDuZo3yPJgf04qhgYR3V0eyIMiRR5cFse2BOXyyJho/B3P8Hc2Z7mvXpcU36Fx0v7llKvsfXcDlUe688MeZTud4pBm1QY67jSledqad+gTO5d3daVibGvKvOf84j3V3QgaDm2Z6U16nRCYVmOlvj7FMyhXR7njamrLprlncNMsHY5mU2+b4cnGEK5bGMu5deP7J1NqzItyFly8LZ8eDc9l016w+S5B87M2wMpFx1w8nefDneOYFOjDL335AazI3MmBRiGMHqWF7PjuQSWmtgs33zGLlJDdMDQ1wtTZh3e3TefWKibx3TSSPLA3k17iCYS9vLwrRVSrOHRqoaFZzLKuCuYEOWBjLCHK2wMbMUK8KokwqYaa/PftbbDRP5lTjamXMw0uD+P1UYQd3u9OFcg6lV3DbXB8EQcDFyoT3r51MbG4VW5OK+b9VYbjb/NMUaiyTctlkdzbG5rMxNh/TFnvx7jAzMuD/VoX3uzetdfbPcEwzVzSreeq3JOqaVHrP4or2siEmu3Mlp7sgRyoRCHax7KAvb+U/mxJ5dVsKn+zLZJKnDRvumMmXN01hkoc1t35zgqSC3l0J8yobuOfHkwQ/tY2I57bz1O+nuSzSrS0ZcdlkN6oamgfdYnWwEQSBh5YEcuCxBTy8NJCkghqWv7Ofrw9l9VrN+Pt0MXG51Ty3MhRjWWfzBytTGR9cO5kgZwtu/eYET/2WxGWRbmy4cwYFVY3c9NXxLntDilpknK56Gg+ArrKgaNaQ0HK+k4trunTLAl1vysURLmyIyWN3cgm3fxdDpKcNjywN4oYZ3rx8WTjrTuiqOkNNdYOS/WllXSYw5gU6sOWe2Vwa6ca626fz0fVRXf6dWwlu+X1TimtJyK/GQCJ0kKq1cmW0B8HOFjz/x1m9KlaHMyrwdzTvILuxMTMk0Mmc4+dBXw7o1ACf3xiNVCrw0mXhvQ7lbuXSyW4IAmw6qd9sFXlDM+tj8rhuuhfTfe24ZZY3H+3L6GBgUNtLTw7ogtCYnCrSS+t4d1cal0S46iVLj3C3ws/BjLd2pnIqr5rHlgf3GswtCHbk6YsnDLnt82jD296Md6+J5La5vgQ7d/6e9YannSkvrgrjngX+vPZ3Ct8dyR78RfaTURvkgM5l5XAPG5XcigZ+P1XIHXN9uy2zDjaz/O0IdNJFt61fmKcvmsDOh+a1zTEB3Q333dWT2Pvo/HFjJNBfDKQSVk/11Fsnei6CIHD/ogCujPLgkzVRfLImalDW1So1TC2pRanSsC+1DLVGS2W9km8PZ7Nmuhe+59wADKQSror24OIIV+6c50ewswXPDbMkZGGwIxot7E3tWKk4kV2JolnTYVhsX5gb6MCpvGrkDc3E5lYx2cuGa6Z64mpl3EES8vWhbJwsjVgR/s9mZoafHa9fOZEHFgewclLn4Z3XTvOkqqGZzw9ksnSCU6fS92AT7mbVJncZbIrkjXywJ50P96Zz1SdH+COhiFcvn6h3tTbK24aUktoOkip5YzOW3QQ5AFO9bTie1bFvoLRWQXxeNS9eGkbic8v49papWJnKMDU04PMbp+DjYMZNXx3vUm7Yyqm8aha9uY+jmRX8e3kwt87x5dqpnjxzSWjbc0JdLQlwNO9xwN5owsPWlDvm+fH3g3O5NNKdZ7ec4eavT3Tb06TWaHljRyozfO2YE9D9d8fEUMpnN0RjIJHg62DOi5eGE+Vly0fXTya5uJaNsXmdXtPaq6av8QDoqgymhlIOpZejaFaTXdHQbZADOslakVzBLV/HEO5mxadrotoqiqunevLYBUF8uDdjyDcef58uplmtZcXErmWmnnamvHLFxDY5ak/42JthKJVwtqiGhHw5wS4WXQZFUonAkysmEJ9XzZaEng2AtFotB9LKmBPQOUE2xdt2TBrR9Jc5AQ6cfHJJl9fq7rA3N2JxiBPrTuTqVRn88XguzWoNN7WM8Lgy2gOlStNWYdZqtS0W0j3v3aK8bMgsq2fl+wexNJbx7+X6zQMUBIGfbp/O3kfmE/vkYtZM72w4IDJ4CILAI8uCuHyyO69vTx2UWZeDwSgPcuzJrmjodoL5R/sysDQ26NGxbLARBIHv/jWNN6+a1OGx7p7bW5ZCRD9ume3DC6vCWBbq3GMGsC/MD3LE3MiAX07mc/ePuuFZt3x9gnd2pqLWarl9bvcDRUEX8DxzcSjx+fJhnVfhZm1CiIslu85xVdqfWoadmSETush46sO8AAc0WtiVXMKZQjmTPW0wNJAwy9++Teah1WrZm6rL1hoadLx8XBrpzgOLA7s8dqCTBdFeNqg02j7dWPtLiIsFGWV1gz4Rvlmt4bZvY3hnZxof7cmgTqFi/Z0z+qSDnuJti1YLe9rNy6lpVPUY5EzzsaOstqOpwN5k3eu7aqQ1NzLgy5umYGIo5fbvYrrdlHxzOBtHCyP2PrqAO+f5cd+iAJ69JLRDVUkQBC6d7MbOs6Wj5salD+ZGBrx0WThf3BhNYoGc5e/s73IQ6+b4AtJL63h4adef3fY4WRrz94Nz+XXtzLZAPcrLlgvDnXl9eyr15/ToFckVWJnI+jSM2tBAwgxfOw6mlZNRVodaoyWohwTRZE9rLot049FlQXx989RO9rhr5/lxVbQ7r25LGVLjlG1JxUR52fQpoOsOmVSCv6O5rt81v5qJ7eRl5zI7wJ75QQ68uyutx813WmkdpbVNzO0ikJ3qY0t2RcOoNtgYbPoj9756igcZZfVd2uC3R6XW8P3RHC6a6IpTi9tpgKM5LlbGbRVhRbMGlUbb6x5puq8dUonADD97frtnVp+kn44Wxnjbm52X/dAjxaPLglA0q/lwX99ka60ubv/5NYHvjmR3KwHuK6M6yGnN+HSlLS+SN7IxNo9/zfbp0w1kMHCyNO4030Nk7GEsk7I01IlP9mWyN6WU+xb6czKnim+O5HD9NC+9zvEMPzsiPa35M1G/gVqDxaJgR/allHXYxO9PLWduoEO/e5U87UzxsTfjw70ZNKu1bVr1SE8bUkt1lYe8ykbKapuI7qHpszvWzvdjkoc1s7vIpA42IS6WqDTabl3o+svHezM4U1jDT7dPJ/G5Zex+ZL7eM59aCXA0Z06APf/78yy1imaUKg01PcjVQBcYCQIca9c3sCu5hAh3q24t0x0tjHlyxQSSi2vJ7KI3pUbRzF+JRVwV7dGrjGPVJDc0Gi2vb/9H9lRao+D7oznc/m0Md/9wktf/TiG/avQ1by8KcWLb/XMwlkl5dGNCB1lZk0rN2zvTmB/koPdn2spE1qkS+fgFwVQ3KPnknKGkfXFWa8/sAHtO5la1zQjpqZIjCAJvXj2Juxf4I+3iuy8IAo8uC0al0fLR3qHRy9comjmUXsEFfey37IlgFwtisivJLKtvm1PXHTfP8ul1870/tQyZVGBaF4Oil0xwwtfejPvXxXWYz1fXpBr0frTC6o6GN4fTy/lKD0nlaGBOgAOuVsa9Ot/uSi6loLqxg12zIAjMD3Jgb0oZWq22rZLdWyXHw9aUw/9eyKdroro1ZxEZPThbGXPLbB++OpRNYTcFinNJKpDzyPp4ahTNnMyp5tktZ5j+0i4u+/BQn+ZldsWoDnIcLIwIcrLoUrK2/kQ+MqmENTO8h39hIuOG1VM8sTQ24KPronhoaRCb7p7JVdHurJ3fcxWnPXMCHDiaUTGgWRp95YIwZ2qbVBxO1yUAiuUKUkpqmRs4sABiXqAD6aV1GBlI2jTwkZ7WaLWQkC9va5hvDYD6wqIQJ367e1avzfmDQauueDAla2eLanh3dxq3zfHt1+/fiiAIvLgqnOpGJS/8cYabvjqORqvt0XXQylQ32+lYixFLk0rNgbTyNtv97pjlb4+BRGBfSud+mi3xhSjVmh6bcVtxtTbhyRUhfHc0h++P5vDp/gxmv7qHp39PoryuiZIaBV8fzua2b2M7bQjrm1Qczijn15P53RpmDDWOlsa8evlEYnOq+OrQPw5AH+3NIL+qkceW6SeB6Q4vOzOunuLBj8dyOgRRhXKF3s5q7ZkT4IBKo+WbIzk4WRoNWPLsYGHELbO9+eZIDkVy/TYefWFPcilKtabPpjI9EeJs2eZ41lMlB2C2vz1u1iasO9FZMtjKgbRyorxsupS2mxoa8O41kWSW1fPs5tMcSi/no70ZzH11D/f9FDdos0DkDc1c8dFhVn1wiMLqRqoblDy0Pp6tScWM/hBHJw+8caY3W+ILu1XYAHx7JJuJ7ladEkDzAh3JrWxoc7kDenRXa8XJ0nhARkMiw8ud8/wwNZTq5cZXo2jm7h9P4udozsY7Z/L3g3OJ+e9iXr1iIhEe1gNW7ozqIAd0N+l9qWWdJrLuSSlllr99j9lPEZHemOpjy6mnl7ZZ//o7WvDqFRGdJB89MTfAntomVZdzAIaKUFdLPG1N2ypIfyUWIRFgtn//+nFaae3niXC3bpOjBTpZYGooJS63ipicKnzszfpsHjHcWJnKcLM24WxRZ0ey/vJXYhGetqY8uKR3WVNveNqZ8uDiQNbH5JOYL+ebW6b2Oj9qmo8tx1r6co5lVtKgVLMopGebfHMjA6K9bbo0DVgfk9+2OdSHG2d6c3W0B0/+lsT//krm2qmexDy5hF/vmsXGtTP5/tZppBTX8PG+joPlbvzyONd+doyH1sfrPftsKJjpb8/10z157e8Udp0tIb20lg/3ZHDrbJ8O/ZT95aKJrpTXKTmV9081obC6EZc+mA604udghouVMemldQT1oxG4K26f64eJTMqb2/tmA6wPf58uZoKLJZ52XU+h7w+t1StjmYQAx56b46USgSuj3fkjoZCaLuzDm1Q6U5aeeq7C3Kx4fHkw607kcd3nx3jt72QWBjsy1duW9TED70fTarX8+9cEahUqZFIJ9/4U1+ZG+dbVk7qswo1Grp3miYmhlC8OdG0XnF5ay6H0Cm6Y4d1JJjbL306XdEkta1fJEfdw4w0rExl3z/fnl5P5pJd2nWg8W1TD/evimP/aXsprm/jwusltAY2NmSFXRXvwzMWhXb62L4z6IOeiCBcq6pUdXNYq65XE51czP2hgGzoREeifNrk9ER7WWBgZsD9t6N28WhEEgQvDXdh+upgGpYrPDmSyYqLrgGWU03xtMTSQEOX9z4ZbKhGIcLcmLrea2OyqAVUxhpMQF4subZf7y8NLg/h17axB6wn712wfHlwcyMa1M/VyC5zuqxsEm1fZyJb4QpwsjQjVY3M+L9CRo5kVHcr+KS2zva6K1r+XSBAEnl8Vyh3zfPnx1mk8e0loh1knkzysuW2OL+/tTmv7u8fnVROTU8Url4eT+OxSvrxpit7vNxT8e3kIUV42/OubGC7/6AjOVsbd9pH1lWgvG6xNZWw/ratWabVaivopVxMEoa1BPshJP/er3rAykfHg4gA2xOb3aOjTVxTNavYkl3FB2OBVceCf+VBhrlZ6OaZeGe1Bk0rD5lOd+yNjs6tQNGu6NB1ozy2zvNl6/xx2PjSPxGeX8fqVEdw8y5vEAjlnCgd2LfnpeB5bk4p58bJw3rsmkrh2bpT6JhpGAxbGMq6f7sW6E7lUN3Tu8Xp/dzo2pjIu6sKAwsJYRrS3DXtTyqhtreT0IlcTGZusmeGFk6Uxr//ddVLlsY0JHM+q5MpodzaunYmP/dCMWRn1QU6khzUetib8duof28IDaWVotQz6sE8Rkf4gk0qY7mfHwbThtdhdEe5CjULFoxsSKJIruKsPErvuMDU04Ofbp3PHXN8Oj0d6WnM8q5LU0lqmeI+NICfY2ZKzRbWDOiOkuwGm/cFAKuH+xQE99lu0Z6qPrkfxpq+OsyE2nzXTvfRqqJ0f5ECTStNh5tjXh7OwNTNkSS/DS8/FyEDKf5aHMLOboOzBJYF42Jry2MYEVGoN3x3NwdnSmMsnu4+KjK25kQE/3DqNt6+ehIuVMa9cPnHQnP4MpBIWBju2DQytaVRRr1Tj2s9G/NktVYfBquSAbqB1lJcNj/+a0MHKdyDsTy2jsVk96EGOg7kRTpZGbQN0e8PN2oS5AQ58eyS7k533zzF5OFgY9TowWhB0VtX+juZtvb6LQpywNTNkfUz3UrjeOFtUw3NbTnNllDuXRLgyzdeOly+byH0L/YfFiGWwuXmWt05OebjjINatiUX8dqqQR5cFd5sMWhDkyL7UMu78PhYQg5zxirFMygOLA9h2uriTyuVMYQ2JBXKeumgC/1ke0qU9/GAx6oMcQRBYGeHG30nFbZnIvSllBDqZj6nsh8j4Zm6Avc5+uXH43KfC3CzxsDXhz8QiFgU7DtqFItLTplMPQKSnDbVNKrRa2qbBj3ZCXCyprFdS2o118FjD1syQEBdLSmubeGf1JO5ZGKDX64KdLXC0MGqTrJXWKvgltoCbZnoPWlWqFWOZlFcvn0higZw3dqSyJb6Qa6d5Dunssr4iCAKrIt3Y9sDctqHTg8XSCc5klteTXlpHobx1Rk7/7lPzgxxYHOLE7AHOBGuPRCLwyuUTKalpGrTZOZYmMlZP8ehVUtZXBEHg59tn9GnO3L0L/UktqeOX2H/kZQn51fx+qpD7FnZtzNAbhgYSLo1047dTBV26NWaV15NSXEujUk1eZQN7kkupaDcws65Jxd0/nMTT1pTnVv4jv7lqigcPLe19qOVoxNHCmGumePDp/gxKa3QuWKU1Cp7YlMiiYEeumdp9hfiGGd68dXUEt8zy4d6F/h2qwSLji8snu+PnYMZ/fk3soCTYEJuHjamsV7n1YDB67jw9sCrSlXqlml1nS9FotOxPLROrOCKjijkt9suDKQPpjVbJGsDdQzxwtrWB1MZUhp/D0JSVB5uQFrnLYPbljDSf3RDFrofn9Sn7KwgC8wId2HGmBHlDM18fykYqETo4Hw0m0d623DjDm4/2ZqDWaPUekjoemBtoj5GBhO1nituchVz6YTwAYGks4/Mbo3Hu5+u7w9/RnAcXB6JSawelyjnd146XL584JDa93vZmfaoARnvbsjzMmde3p1DfpEKr1fK/v87ia2/G6qn9HzVxVbQH1Q3NvLw1ue1vptVq+XBvOove2Muyt/cT8vQ25ry6h5u/PsFlHx2mtFZBo1LNgz+fokiu4MPrJg/bPL/h4IHFgcgMJLy8LRl5QzO3fxeLIAi9fhZMDKVcGunOI8uCeHhpkGjvPI4xkEp4Z3Uk6WV1/HdTElqtliaVmt/iClgV6TYsQ1fHxDfO39GCCS6WfHEwk7JaBRX1Sub3c+ChiMhQ4GVnSpCTBY9uTCCrop5bZvkMepa8K26f48sEF8tem9YHioOFER62JgQ5WYyZm5KXnRkmMilni2rHTVLE3aZ/jd3XTfdiW1IxK947gLyxmdVTPYZ0SPGjy4LYk1LKZE+bbi2uxyOmhgbM9rfn60PZhLpaIggMepAyGNw5z3fMfI/7yr+XB7P4zX08tjEBSxMDjmZW8vH1UQNydQxytuCpiybwwh9nqGlUMcnTmu2nizmQVs4d83yZH+hIXmUD1qYy7MwNueO7k9zwxXEMpAJpJXW8szqy38OwRys2ZoY8vDSIp35LIi63moq6Jr6+Zao4XkOkA2FuVvzfqjAe25iAkUyCnZkhVQ3NXBk1PMkvQTuYgvU+Ym1tTXV1tV7P/TOhiMc2xlOvVGNhbEDsk0s6DSMU6T99ORciXVNR18RbO1P58Vgul0a688ZVEf06zmg9Fydzq7A2keHrMLiylKFk5QeH8LI15d1rIvv1+tF6LvpDTkU9d/1wktSSWvY8Mr/fAZO+NCrVGEiFQbMMHyvnIqOsjle2JrM7uRQ3GxP2PbpgpJc06Iz2c/Ha38l8tj8LJysj5gU68MLKsEEJ6tbH5PHvXxIAXc/fvQv9WR7eucH+bFENV31yBHMjAz5dE014L3N+BsJIngu1RsvF7x2kpEbBt/+a2mvP03hntH8vRpI3d6Ty3ZFsqhqaiXC34vd7Zg/L+46ZIAdAo9GSW9mARBAG1apSRPxyDiapJbUYSIR+BwPiuRg8TuVVY2Ui67dzy3g7F00qNaU1TXjYjr3r51g7F5X1SppUalz6aTwwmhkL50Kr1Q5JtaqstglTQ2mvQ8iL5QpMDKVDPuZipM+FvKEZtVYr9tYw8uditKPVaskoq8faVDZsYyhGNMiRSCRYWg6dq8L5jpGRESUl+g3fE8/F0CKei9GDeC5GD+K5GD2I52L0IJ6L0YN4LkYPfTkXrYxokCMiIiIiIiIiIiIiIjLYiE0tIiIiIiIiIiIiIiLjCjHIEREREREREREREREZV4hBjoiIiIiIiIiIiIjIuEIMckRERERERERERERExhVikCMiIiIiIiIiIiIiMq4QgxwRERERERERERERkXGFGOSIiIiIiIiIiIiIiIwrxCBHRERERERERERERGRcIQY5IiIiIiIiIiIiIiLjCjHIEREREREREREREREZV4xokOPk5DSSbz+uqKpXDuj14rkYPYjnYvQgnovBQ6XWUN3Q/+uUeC5GD+K5GD2I52L0IJ6L0ceIBjlNTU0j+fbjBo1Gy6oPD/HqtuR+H0M8Fx05mlnBrJd3E59XPezvfb6ci7jcKma9vJvSWsVIL6VbzpdzMdScKaxh1YeHeODnU/0+xmg5Fw+si+PWb05Q36Qa6aWMGEN5Lsrrmlj61j6i/28n13x6lONZlUP2XuOB0fK9aOVQejmXf3SYyS/s4IqPDo/0coaV0XYuhhutVst7u9K45P2DhD69jWc3nx7pJYlytfHA7uRScioaWBDsONJLGRekltRy+7cxFFQ38un+zJFezrglpbiWgupGvj2cM9JLERlCvj+awyXvH0TRrOHehQEjvZwBUVWvZEtCETvPlnLtZ0epHGAFXURHk0qNVqtFpdZwz48nKa9TcmW0O5nldXx3VLw+jCX+78+zyBubmRtgT0xOFemltSO9JJFhYkNsPm/sSMXdxoSloc58fTibw+nlI7omMchpQaPR8vmBTHIq6of8vSrqmvj+aA4qtWZQjvfV4SzC3CyJ9rIZlOOdz8gbm7npy+M4WRpz30J/tp0upkjeONLLGpdUtGwQvzuaQ4Py/M2Kj2c0Gi1v70xlaagTf943m6gxfo3acbYEtUbLJ2uiyK1s4PFfEgb9PXIq6lnzxTHOFNYM+rFHI6kltUS/sJNFb+zj5q9PcDyrkveuieTxC4JZEe7KiaxKtFrtSC9TRA+Si2s4W1TDw0sC+d9l4RjLJGxLKh7pZYkMA+mltTzz+2kum+zGh9dF8caVEUR72fD4rwkjWvUeVUGOVqvliU2J/OfXwb9x9Ma7u9P4vz/P8trfKUP+Xk9vPs2TvyXx8IZ41JqBXbyTi2s4lF7BLbN8EARhkFZ4/nI0s4JCuYIPr5vMv+b4YiiV8OOx3JFe1rikql6JhZEBNYpmNsbmj/RyRIaAhAI55XVKrp/mhZGBdFCPrWhWD+rx9OHvpGIiPa1ZFurM2vl+7Espo24Qb+AajZbHNiZwIK2cW74+Me4TLHVNKu78PhYHSyMiPKxJyJfzxIUhzPK3B2CKtw3FNQryq8b332G88FtcIRbGBiwIdsTU0ID5gY5sOy0GOeMdrVbLAz+fwtnKmBdWhgEgkQi8csVESmqahmVf3R2jKsj5dH8mPx7L5afjecPaC7HjTAlv70zDz8GM7adLhlSCcDSzgj8Tirg4wpXN8YU8ujEeeUNzv4/39aFs7M2NWDHRZRBXef6SVCDH2lSGv6M5ViYyLpvsxk/Hc2lSDf+GarxT2aDE19GcC0Kd+fxA1oAD/vGKolnN1Z8c4cJ3DvDmjlRKa4a/h0mt0fLdkWxe+OMM/92USF5lg16v251cirmRAdHetoO6ni8OZrHw9b1ohvEzU6to5kBaOcvDnAFYMsEZpVrD3pTSQXuPH4/nciyrklcvnwjAzV+dGLdVzrLaJh5ZH09RtYKPr4/irasnEf/MUm6d49v2nNbPTUyO2Jcz2tFotGw+VcCKcBeMZbqExgVhziQV1Oh9vRAZm8TmVJFUUMPTF0/AzMig7XE/B3MeWhLIN0eyickeme/wqAlyDqSV8cq2ZG6e5Y23nSlv70zt8PPfTxXw0PpTg162Vqk1PLz+FIuCHfnxtumotVo2xRX06RjPbzlD9P/t5MGfT7E/tazH93p282kmulvxztWTeOXyiWw+Vci0l3by302JKFV9k6+p1Bo2xxdyzVSPQc+Snq8kFsgJd7Nqq4rdONOb8jolf8QXjfDKxh9V9UpsTWXcNNOb3MoGTozQRXA0o9VqefK3JOLyqvGwNeHzA5nc82PcsK/jxT/P8vTm0+w6W8KGmHzWx+Tp9brdySXMDbTH0GBwbzXBzhYUyhWcyq/u9zFKaxV9up/sTi5FqdZwQaguoeRjb0agkznbT5f0ew0d1lOj4OWtyVwR5c5VUzz47IZokotr2XV28IKo0UCjUs2t35xg2v92suNsCa9cMZFAJ4sun+tgYYSPvRknsquGeZUifeV4diWFcgWrIt3aHlsQ7IhMKvC3WM0Z1/x8Ig9XK2PmBjh0+tmts30Ic7XisV8SRqT6PmqCnE/3ZzLDz47/XhjCfYsC2JNSxslc3YVN3tDM07+f5teTBRxIG9wmpoLqRmoUKtbM8MLJ0phFwY78fCJX75tfZb2S74/l4OtgRmKBnBu+PM77u9PQarUkF9ew7ngun+3P5MnfEln85j6Si2t59pJQJBKBq6I9OPj4Qm6f48sPx3L5/VTfgqvUkjoalGpm+Nn151fvkUbl+Ve50Gq1JBXICXOzanss0MmCWf52fH04W9SFDzKV9UpszYyI9rbF1syQ3cnjazM3GHx/LJeNsfn836owPlkTzatXTOR4diWnhrHS/c3hbL48lMWTKyaw99EFzA9y4HBGRa+vK6lRkFRQw4KgwTdEmepji7WprN+bp/K6Jma/vEfv+0mzWsPG2HwmuFjiaWfa9viyUGf2JJf2OUHVFT8dz0Ol0fDfC0MACHe3wtXKuO0+OF7YHF/AzrOlPH3RBI49sYhLIlx7fH60lw0nRIe1UYtGo2VbUjFP/ZaEq5UxU9tVba1MZMzyt2er2JczbqlrUvFnYhFXRLkjlXRumTCQSnjl8onkVjTw1jnFi+Fg1AQ5n90QzYfXRmEglXBJhCu+9mY8u/k08oZmPtyXjqJZja+9GR/tzdDreJX1Su79Ka5XvXROha6M6mVnBsDqqR6kltQRp+cmYl1LQPT+tZFsf2Audy/w4/XtqUz93y4uePsA//41kTd2pHAgrZzpvnZ8c8tUJnv+03zrbGXMQ0uDmBNgz5eH+raRPpVXjSBAeLtN+WDw6IZ47vohdsDHqW9S8daOVCrqxoatYnGNgvI6Zae/580zfUgskBObM742GyNNZYMSWzMZUonAgiBHdp4dnIz4UJFcXMO+1LJhlUi9szONSyPduCraA4ALQp1xszbhswND7/qn1Wr5bH8mz245zY0zvLhlljcAs/ztic+r7vXauie5FEGA+UMQ5MikEhYFO/F3UnGHa2ZivpyrPznSa8YwuagWpVpDVnnvRjPFcgXXfnaUQ+nl3DHPt8PPlk5wprZJxZHM3oO+ntBotKyPyePCcBdszAzbHo/0suFkbvWAjj3a+OFYLnMC7Llplg/25ka9Pn+Kjy1ppXUDngUnMvjUKpq57vNj3Pl9LFYmMt6/bjKScza6i0KciMutokbRf1m+yOiivknFHd/F8Nn+TDbFFdCgVHNlyz2qKya4WnL/ogA+2ZfJzjPDe58fNUGOsUyKlakM0EV+r14xkazyei796BBfHcrmX7N9eGBJIEcyKziQVsZ9P8Wx6I293QYFcblVbIkv7LW3J6eiHqlEwM3aBIC5AQ44Whix6WTvVRWVWsP3R3JYEe6Co4UxEonAo8uCeWf1JBaHOPLpmijOPn8ByS8sZ9+jC3j58onMC+xczgO4ZbYPZ4tqOJqpf8YqPq+aAEdzLIxler9GH6K8bNiTUjZg68edZ0t4Z1caN3x5HHnj6L/AJebLgc5B48JgR7zsTPnqUHaHx9UarVjdGQBV9c1tG7rFIY5kltXrtekcKX6JzefGL48z59U9vLk9hWOZFUPaq1Ve10R5XVMHa3gDqYR/zfZha2LRkOrcG5VqHtmQwIt/neVfs3x4+uLQNgnnTD87VBptr9n1XcmlRLhb42DR+0a2P1wQ5kx2RQOpJXVtj72zK41jWZWkl9b18Eqdoxfoqk29cft3MWRXNPDjbdNZOcmtw8/C3CxxtTIesBznUEY5BdWNXH3ORiHSw5ozhfIRkXkMBQn51STky7lumqfer5nSUhkQk0wji1arbUvwqNQa8qsauOazoyQWyPnq5ilsXDuzQwK3lZl+dmi0cLwPexuRnnnt72Re3ZY8YvuP57acZk9yGf/bepanfktilr8dHramPb7m7gX+LAhy4MH1p8itGL4erVET5JxLtLctv6ydSVOzBjNDKXfO9+PCMGe87Ey54cvjbI4vJKOsvttsYkWdLuuTUdbzzS67ogFXa+M2zbiBVMIFYc5sP1Pca8Z259kSCuUKbprl0+HxlZPceOmyiSwNdcbEUL9emXkBDvg6mPHFwSy9ng+6Sk6Eu7Xez9eXVZFu2JkZ9mktXXEsqxJ7cyPyKhu46avjo/5GnVQgx8pEhruNSYfHJRKBG2Z4s+10MZ/uz+DrQ1nc+k0MIU9v44K3D/Dp/gxq22Wpfj2ZT1KBfLiXP6ZoUqmpa1Jh1xLkzAl0wFAqYdcoruY8cWEI6++YwTQfW748lM3Vnx5l3qt7KaweGuen1GLdRjzYuWO/wlVTPDAzMuDzIarmxGRXsvyd/WyJL+TVKyby5EUTOsgQ/B3NcbAw4nBG91IvlVrD0YwK5naT1BkM5gTYY2oobbOozS6vZ1ey7vPT23U/rbQ1yOm5ytykUpNUIOe+hf5M9+0sCxYEgXlBjhzRQ74Huo3i3pRS3t6ZSkG7z83PJ/LwsTdjqk9Hg4bJXjY0q7WcLhwf15Mfj+XiYGHEohD9J8N725lib27E0QFWy0QGxg1fHsf3ib/wf+Iv/P+7ldmv7KGwWsG626f3KEn1tTfD0cJowNVOER1ltU18si+TD/dm8NLW4Q90/kgoZH1MPs9eEsrvd89icYgj9yzoff6ZRCLw1tWTsDKRcdNXxztc/4aSURvkgK4f4q/75/DHfXOwNJZhIJXwn+UhzPSz4/mVoQCU13Vdwi6v1928MnrJ6OVU1OPdIlVr5YJQZ0pqmnptat0Qk89EdysmeVjr9wv1gEQicPMsH3aeLWHKizuZ9fJuXt2WTHk3Uq+6JhWppbVM8hz4e5+LsUzKmhle/HKyoNv314cTWZUsDHbgy5umEJdbPSRypEPp5Ty35TRvbE8ZcKbvXNOB9lwV7Y6nrSmv/53Ks1vOUFqr4J4F/vjYm/Ha3ync+X0sao2up+fxXxL45aRoidwTVfW6oNDGVBfkmBsZMM3XdlRL1gRBYKqPLW9ePYlTTy/hl7UzAbjz+1gUzbrN8HdHcwbtppNSUotMKuBj3/H6ZG5kwK2zdX18aSWDO2gvMV/OVZ8cwdJExp/3zW6TybVHEARm+tlxKL37TUtSYQ21TSpmDkG/YCvGMinzgxz4M7GQRqWarw5lYWUiw8LYoNeKYFpL9ae0tudKTlZ5PRot+Dt23RgPup6RrPL6bmW5hdWNvLMzjf/74wyrPjzMTV+d4IM96Sx4bS+PbojnnZ1pbD9dwlXRHp2uPaGulhhKJZzMqe5xnWOBWkUzv58qZPUUD2RS/bcegiAwP8iBnWdL+vzdkjc2c7ZocOYNDUbf1VilvknF4YwKLot0478rQnjx0jDevzaSv+6b06GHtSsEQWCGn53eiQCRnvktrgBBgLvm+/Hpfl2wMxxklNXx/JYzPLYxgWWhTlwz1YOJ7tZ8fuMUvfvCrU0N+faWqSia1Vz50eFek1GDwagOckDXuNYqJQOdROGHW6e3ZbzKaru+sbRWctJ7+SPmVDTgZdexzNbW1NpDs5xKreF4ViULgwdPb35llDuPXxDMNVM9mRvowFeHspn9yu4us3iJ+XK0WgYlwOqKNdO9AN208v5QWa8krbSOKd62RHvb4mtv1qXz3C+x+dz+bQyzXt7NQ+tPkd1HudKzm0/zS2w+3xzO5uavjvfbblWr1ZJYUNPtBdvCWMaeR+aT+uJyMv53IZvvmc19iwL4eE0Un984hcMZFby6LZn718Xha2/O4xcE92sdY4mByPVabdpt2/UfLA5x4kR21ZjQ3htIJUR52fDxmiiSi2q58N0DXPTeQZ76LYlcPWRk+gxHSymuxc/BvMsN4R3zfHG2MubZLacHNZP39eFsnCyN+WXtTAK6cbwCnQTlTFFNt+fqSEYFRgYSIocgCdOea6d6kVVez9K397EhNp9rp3ri72hOZln31xGtVtsmVyuW9xzktAZDAU7m3T5ncsuA07guemcUzWpu+foEn+zPYOfZEswMpXx10xRin1rC2vl+nMiu5OvDWVibyrg8yq3T640MpIS5WY4L84HdyaU0Nqu5Isq9z6+9IFQnTUzpY1D/6IZ4rvrkCM0DHLxdUqMg8vnt7BlEu/DRQLNaw1UfH+lV1h+fV41ao+WW2T7cPMuH66Z5cdFEV5ytjPV6nxm+dpwtrqG6YfRf2weL1JJa7l8XN6iSZq1W17u3OMSJxy4IZu18P17fnjLkCojSGgUr3j3ALyd119jXrozo91xGXwdzNqydibFMymUfHmbPEBsOjfogpztaGxa7qzS0ZtUySru/2Wk0WnIqG/Cy7ZgpNZBKWBLixLbTxd1uIM4U6TKV03wGL1NpLJOydr4fDy0J5KXLwjn074WYG8nYENO5KnAqrxpjmYSgHjYiA8HO3IgLw5z7Pa241Q649e8zN9CBfallHf6eaSW1PLwhnpIaBQuCHdifWsaiN/fprW9PLaklrbSOV6+IYMu9s6ltUvGLHr1UXVFSo+t/0MfE4VwHkXmBDty7wJ9P9meSX9XIe9dGts0JGM88siGe67841q/5Nl0FOReEOSOVCHx+UD8ZVkJ+Ndd/fmxENx6TPKx56bJw1Bot9y3Slex76wcpliuY/MKOXuU3KSW13VrrGsukPH3RBA6lVwyac1FVvZItCYVcO9Wz10z7TD/dsMbuXNaOZFYQ7W0z5Nb2swPs+eu+ObhYmqDVwg0zvPG1NyezvPtzUFrbRI1CxQQXy157ctJK67AxlbXJKrvC284UWzNDYrsIRF7ZlkxGWR3r75jB3kcX8ONt01kQ7IilsYwHlwSy99EFxD29lOP/XYyjRdcbxsmeNpzMrRrz/X/bz5QQ7GzRZvLTF2afI03Uh9icKrafKaFWoRqwG+HvpwqoV6r5+bh+1uljhWK5guPZlfyRUNjj82JyqjAzlHaSzurLDD87tFr61HM8HtgcX8jPJwbvM3Mqr5q00rq2CvsjS4OYF+jAAz+fGtJ+1mNZlSiaNfx532yevGgClgPsA3ezNuGXtTOJ8LDm5q9P8M7OtCG7vo3ZIMfG1BCJ0EOQU69EEHSOWd317RTXKFCqNJ0qOaDbcOVUNJBc3HXm6FhmJYZDnKm0NTPkgjAntiYVdeoPis+rJtzNCoM+lP37ipedWbeVst44kVWJk6URHra6Kty8QAdKapo6NAlvSyrGRCZl3e0z+L9V4ex/bAETXCz1vij8mVCEmaFOsuJlZ8biECe+OpTVL/eruJYNykT3/jnV3b84kOum6TIc3W1MxxPldU1sji/kUHoFXx3qe+9WZUPnIMfJ0pibZ3rzxcGsDpvPInkjd34Xyx3fxfDs5tP876+zPLw+nlUfHOJQRjkf7kkf+C80AC6Pcmffowt4YFEAxjIJab0EOYfSy2lSaThT2L2MRqPRklpcS1APm4olE5yYE2DPe7sH5/ffGJuPRqPl6qndu+S04mFrSqCTOVviO2+OlCoNJ7Iq2wKhoSbAyYJ1t0/n2H8X4WxljK+DGVll9d3eNFurM3MC7KlRqHq0y08rqSXA0aLHrKUgCLpA5By57IG0Mr46lM1jy4J7lfT0xGQvG0pqmijqperUrNZQJG9ENcCqxUBQa7RsiMnj7h9OdvgON6nU7EspY8kE/Xtx2mMsk7Ig2FHvIEer1fLK1mT8Hc2xNDbocX6dPvx6sgBDAwm7k0u7NdF5dVsy3xzOHlVDjTPL6rj4vYPdmpS0yjWP9WIiEptTRaSnTb/3G562prhaGZ9XfVWBThasCHfhwz0Zg9aPvD4mDydLI+YE6K6tUonAO1dHYmtmyAPr4nrd+xzOKOfuH092+bOe3DJjc6pwtTLG3aZnc4G+YGNmyFc3TeHehf68tTOV5/84MySBzpgNcqQSATtzo2434eV1yrYqR2Y3krVz7aPbMzvAHnMjA7Ymdj0E8mhmBZEe1kOesb8w3IWSmqYOWUKtVjtkpgPtcbAworJB2a9S//HsSqZ427ZtDqb52mJoIGFf6j9Z961JxcwPcmgzZzA1NGB5uDOH0sv1mtPzZ2IRi0Kc2s7BLbN8yCyrZ19a329ohzLKcbcx6dUhpDukEoEX/7+9swxs8u7a+HXHvWms7q5QWooWHzCGDZi7j71zd5dnz+SZszEfU4aMbciGu0sF6u5tarEmaeT9EKFpkmqq3L9PkKTJndz2P+dc5zpXJvU482Gw2HmhHnPe24fjfbiJVDZr+n1RsS1uFyf74Z1/8vusrW1R60GlEE4ZodWzIsCgUvDBrkIAlmbyVWuO4kxFCxTtBhwoaMSOnDqcqWjBI/Oi8faKZJwsa3F7jg8lFAqBCCmvx0qOrQG3O1lbdWs71Hpjt5VagiBwzcQg5NYqBqxtNpnM+PF4ORYk+rqtKHRlVWogdufV26tyNrKqWtHeYXTZqD9YUDodS+ESLtR6Ixrc3BsKrL1ONslzd305hQ2qbqVqNiaECJFZ1epwrfz9VBWifXi4Y3pYN3/ZM2kh3qBSCHx/pMzta2786jiin9+OKW/twUceCnr7SmWzBgs/OIAnNmRhb34DrvvyGBqsgc6xkmaodIZ+BzmARbKWV6d0KWn+/VQl1h4oti/ytufU4URZM55aGItpkRIccDMPqaa1HU9vzOq23ya3VoG8OiWeXhgLvdHkUsaeXdWGz/YV46U/z2PlmiMobxoZLpG/naxEdnUb3tyW6/L5BqvxRk51m4N5TmdMJjPOVLTYZZn9gSAITB7ivhyz2YwGpXZILf+78tDcKNQrtR6p5tS2tWPjmWpcMzHYIdj04tDxxvIkZFa1Yaub9aqNtQdKsDWr1inoOl3eggmv7XS7nj5d3oLUUJHL5wYClULgsfkxeHFxPL49XIb7fznrcdOmURvkABbJWndyNdtNzN0CwHYhCnaxsGXSqJif4IM/ztU4LQSNJjNOlDZj0hDcxCeFiSHhMbA16+LBm1+vRJ1CO+iLCCmfCbMZTouYnlDpDDhfo8CkTk5BHAYN6aEi7Ldm1CqaNLhQq8DCRF+Hv50X5wOdwYTDRd0P6SuoV6KoQYVFSX72xyaHixDvJ8CHuwrd9uboDSbku6jOHSlqwrQhyjx7mjZNB57ZlI2a1nbc+PXxXk2j35vfgIz/7sWv/bz4bjpTjZnRUry9MhlSPhNvb8/r0983qfXw5tCdZioIOQysnhWJ305WYPa7+7D440OgUQlsXD0Vv9w9GXsen4UDT87G3sdn4YG5UVgyzh98Jg3rXUg6h4NIWS+CnOKegxzbMdpdJQew2Juz6VT8ndn9za07zGYz3v03H+VNGnsvXm9YnhIAk9nSCNuZI8VN4DKo/a6KDpRwqSUocXfdL2xQIkzCRYDVRdGdw5reYEKZXI0oWc9BTmqwN7QdJocm99PlLZgaIXE6xvuKTMDCo5dF44sDJS7165XNGhwqkuPWqaEYFyS0V6WHmi8OFKNZrcfG1VPx1wPTodIacO2Xx1DZrMHOC3XwFbAGNNNtdqwMDCoFmzsdb0aTGa/8dR5PbMjCm9vycOcPp/D+zgLc//MZzIqRYl6cDBlRUmRVtbrsHztcJMevJyuR2Y3J0Oaz1fBi03HD5GCkh4rwh4uh3d8eLoWfFws/3jEJtW3tboOKocRkMuPPzBoECNnYnlPn0g3RlggwmS2SNFcUNCih1BqQNoAgBwAmh4mRX68cUF9Os1qPbw6VYtWaI1i55gju/P4kXv7zPL49XIq1B4rx6l8XcPcPp7DwgwOIf/EfpL+xG7d/f3LYpJ5RPnwsTvbHZ/uKnAILuUqHpzZk9Tox+eGuQrDpVJdJk+lREmRESfDOP/luA/ZGpc4+/Ljrurm4UQW9wWR3neyMRm/AhVoFUgdRtXT79DC8syoZx4qbsPjjQ7h27VG3QXdfGeVBDgONSucTxmQyo1mtR7iECwmP6XbRUdakgY+A6dbmeUVKICqaNU6uXbnWfpzJ4Z6PbLtCpRBYkODrIFn7J6ceHAYV06MGd1Fum2/RV8lalrVJMa1L5D8zWoqTpS3Q6A3Ycb4WDCrFybghSsZDkIhtt4J1x9+ZNXapmg2CIPD84jjk1Slw/ZfHnYIzs9mMx37PxIIPDuCt7bl2WUFtWztK5GpMjRy6zLMneWt7Ltr1Bux4aAaWjQ/AkxuyutWgy1U6PPF7FgBLBrSvFDUokV3dhitTAsBl0nBZvI+9kbu3tKj1dme1rtw2LRSPL4jBnFgZbpoSgt/vmeK2wsZmULFkvD82nqkaVpmOjShrkOPuplrZrEF1azt8BMxus7359UpwGFQH0xVXcBg0zI2T4a8s52RMb9AbTHjo13P4bF8xHp8f3afEiYzPwuwYKX4/7RhgHixsxMQwUZ8ctDxJiJgDgoBbjXphvQpRPnz4WCtW7vpyypvUMJjM3Row2EgOFIJGIeyStdq2dlS3tiN1gAtDG6tnRmBGtBSPrj/ntL22yuADc6IwPVKMCzWKIV/UteuN2HK2BlelBSE1xBsRUh5+vmsydB0mLPnkELZm1eKyeJ9+NysDFlfBqycG4sPdhVizrxhnKlpw3dpj+O5IGV5YHI+1N6XiRGkzPt5TiNWzIvDVzWkgCAIZURKYzZZqfVcarYu9E27kWkaTGVvOVWNxsh+YNCqWpfjjaEmTwz5oUGjxV1YNbp4SiulREtw2LQx78xqHfTbcibJm1LZp8c6qZCQHeuHVvy44XSMblFpI+UyIuAy3v8Hp8hYQBAYszU8L9ba/X3+oatFg5n/34o1tueCzaAjyZkNnMGF/QSPe2JqL93cWYFduPVQ6A1KChXhoXhSevjwW+/Ib+22g5AkemhsFuUrvMMi+urUdV39+FOtPV+K6L4/hkz2F3VacShpV+P10FVbPioAX23U/zNOXx6KyRYPP9xe7DHT+zKyxr3m6uhLb1niu5tecc7Oe8zRXpQXh6DNz8cn1KThfrcAd35/yiMxvVAc5Ur7rSo5C2wGDyQwxj4kIKdet+UBFs7rbJsgpEWL4ClhOzezHSprAoFJcDr4aDK6wStZOWJv5d5yvw+xY2aBL5aS87oOcbdm1uPXbE0431HJrhrqr9e3sWBk6TCZct/YYfjtZielREqdBpgRBYG6sD3bnNrg96U+WNeOLAyVYOt7f6TeYGiHBz3dNRlmT2mnbvjhQgr8ya7BknD/WHijB7d+dhN5gwhGrFe5Q9RB4kuMlTfj1ZCUeXxCDUAkX/1mRhDAJFx/uKnD5erPZjKc3ZsNgMuGphbE4U9HaZ0e7TWeqwbcGNwDg58VCbZvW6TjYl9+AF7fkuHyPZo3eYbJ7Z1h0Ku6bFYkXFsfjqYWxkAm6l09dkxaERqUO+/IHprv3BJEyHlQ6g9vqgK2Ks2JCICpb2t0e4wVW04HeVAEWJ/ujqEHVZ+cpAPjmcCm2Ztfiw2vH4/45Pc866MqqVItcziYx2J5di5NlLVgxoe8OWp6CRbcEh64c1mzOatEyPoQcOhhUitsgx9Zb1ZtKDptBRby/AKetDmunyiwLOdvCbqBQKATev3octB0mrO9SfT1a3IQ4PwFEXAbi/ARoUuv73UvZX7Zl10KpM+DqtIv7PVLGw18PTEecrwAtmg4sSPDt5h16x6tLE3HPjHC8vSMPKz47gia1Dt/dlo47podhfoIvtj2YgT/um4YnFsTaJT1BIo5bd0/b7+RugV/cqEK9QmdXHCxK9AODSsGnnfoAfzxeASqFwHXWXrYl4/zdytqGki3nquHnxcLkcDGeWhiLvDqlU8WqQaGDj4CJ9FCR24rC6bIWxPjwBzx0PEzChZjLcFsxsnGhRoGZ7+zFxi7Jk/f/LQCdRsHhp+bg29vS8cG1KVh3xyTsfXwW8l+/HLmvLsSBJy3mHm+tSMa9MyNw78wIXDsxCG9syx22QdORMotkdc2+YpTK1Thf04ar1hyBtsOIHQ/NwF0zwvHuvwVuFRhmsxnv/JMPMZeBW6aEuv2cBH8vXDsxGO/vLEDKq/9iyceHMO0/e7ByzRFUNmuw+WwV4vwEAOBkd29bR5e5CHLOlLeAMwDTib7AoFGwONkfX986EZmVrVj94+kBW7f3O8ipq6uDSmW5CVRVVWHjxo3Iy+ubZGWgSN305NiiVDGPgQgZz61soUyuQagL0wEbVAqBZSn+2JpV4xBR7i9oxLggryFz0JoULkakjIe3d+ShTK5Gbq3CIzeMnuiukpNZ2YpHfjuHffmNTg1r1S3tkPCYTr9PpIyHH25Ph8kMFDeqHaRmnZkbJ0ODUoezlY6OQmqdAcdLmnDHdyeRFOCFFxcnuPz7CcHeeP/qcciqasMZ66LjaHET3t6Rh3tmhOPj61Lw9S1pOFQkxyd7i3C4WI4YH/6gTWYfLPQGE577IwfjAr1ws/XiR6NS8MCcSOzNb3QpW9mb34BdufV4Y3kSbpkaAg6Dik1ne+9IV93ajh+OlmN5SoB9//p5WTJqLRrHzOW+/EasO1busqGxWaXv1rGqLyQHeiExQIDP9hUNuwNVpHVB7KrsD1iy7rG+fIwPEkJvMKHeTT9Ifp2y186Js2Kk4DFpLk0AukOtM2DtgRKsmhCIZeOd7Yt7w5xYGXwFLNz/8xmcqWjBC1vOY3aMFEuSXZ/bQ0W4lOdyUdNodVaL8uGBIAjIBEy3vTuF9SoIWLReXxemRkiwL68Bap0Bp8tbECBkw8+r+0pcX5DwLA3HuzpJ1sxmM44UyzHFWoGLty5iLnhoNkxv+e1UJdLDRHapoA0Rl4F1d6Rjw71TMM0DlXIKhcAzi+Lw0XUpeP/qcfjn4RmY2WngbLCYg3EuxirMiJa6nOtku7edKW9xaRhgG1hom6XnzWXg8fkx+OFoOY4Uy3G2ogXfHS7FlSmBEFor0wFCNtJDRdiS2T+nT0+gMxixNasWS8f5g0Ih7MdFdavj9aZBqYOMz0J6mAhZVW0uZd7nKls9UpEkCAKpId44Vebe5MBsNuO1vy+grk2Lx37PxItbcqDRG5BT3YbN56rx0Nwol7bVVArhtkr4/OJ4SHhMPLUha9juDw/NjYKEx8A9605hxWdHIGDT8fvqqYjx5eOZy+OQESXBL26k4+tPVWJ7Th2eWhjb43D5N5Yn4q/7p+OuGeEW44NkP9S1abHkk0PIqVbgrgyL1K1rccBeyWl2vmaeKm/B+CDhoJpcdSU9TITPb0qFGYDBNAxBzhdffIHIyEhER0fjzz//RHp6Or744gvMnj0b33333YA2qC/YenK6Hri2KFXCYyJSykNZk9qpTGs2m1He1H0lB7BI1hRaA/ZYbyzna9pwsFDeL6///kKlEHh5SQLOVrTi4d/OgUGlYHYnmdZgwaJTwWfR7CV9Gw1KLe5ed8q+yO0aBFW3ttv17l3JiJLiz/un4e8HpmNFiuuF1aQwMfgsGlauOYrI57Yj+vntiH5uOxJe+gfXrD0GXy8WvrolrdsTfma0ZfG14XQlzGYz/rMjD0kBXnjSOr9mTqwP7pkRjs/2FmHXhfpeD7MaDsxmM/bmNWDj6SqHY/3LgyUoaVThjSuTHGytl47zR5iEi//tciyBm81mS5YnWIhFSb7gMGhYmOiLTWeqetWcaTab8eymbLAZVDw2P9r+uL/QctOp6TLBuEGphdlsacrtSks3lZy+QhCW5sUzFa3YlTu8cyxCxFzQKIRLiazZbMbR4iZMDhfb+wBdyQMMRhNKGtW9angHLOfpknF+WLPPoknvzQweAPj+aBkU7R24f05kr17vCgaNgp/vmgQAWPHZEbTrDXjjyqQByZI8QbiE69KMwtYHZXPU9BGwuqnkKBHl072zWmdumBQMtd6AjWeqcKq82WNVnM7Mi/NBZmWr3SyhRK5GvUJnH7oaIuaCTacit9azQ2K7o6RRhROlzbh2omtXPhqVgrROJjSeYOk4f6yYENjrhVeMLx/Vre1OM0salTpIeEwodQaXQ0NrWttBEJbjxMbt08OQGuKNh389h+u+PIYwKQ9PLIhx+LtlKf44UtzUo0W5DU/OUgGAzWeqodAa7MkLIYcOFp2CWqdrtA4yPhOTwkUwmMxOQ2fNZjOqWtudVBn9JS3UG5lVbW6/7978BhwtacKH147Hy0vi8fPxCsz471488ts5hIg4uC49uM+fyWPS8PryRJwoa3bZTzUUcJk0vLQ0AQX1Klye6IvN901zkCJflRaEzMpWp+HOOdVteGHLeaxKDcSKCT0noigUAkmBXnh4XjTeu3ocnl0Uhz/+bxrCJFyIuAwsSvIDn0VzkqvZgp7yLvcjk8mMM+UtHpPd9oXZMTJ8e+tEcBi0Ab1Pv/76k08+QWFhIRQKBSZOnIijR48iISEBVVVVWLRoEW699dYBbVRvkfAZ0BlMUOkMDqXUJmsvhphrqeR0GM2oaNY4ZJlaNB1Q6409umnF+PKREizEO//kY3qUBJ/tLUaAkI0rU4ZWjjE9SoKFCb4WqVqMdMCl494i5TtXy347UQml1oA1N6bilm9OoEGpc/htq1vaEdhNLwFBEN1aqjJoFPx29xTk1yvQprFIDwmCgBebDn8hC+ODhD0e+FQKgZWpAfj+SDlmRkuRWdmKb2+d6BAMPDQvCrty61FQr8K0yJEpVdub34D3/s1HTrXlBlzYoMJTC2OQU63AR7sLcfOUUKffkkal4KG5UXj4t3NIe2MXpkaI8dDcKJTK1cipVmDdHen2BcfKCYHYdKYap8pb7EYdNvQGE46VNGFPXgPMZjPMsFQx196Uas9aArBnq2vbtA7bYjtusqpanYLIZrUeIjc9Of1hVrQU6WEivPNPHubEyuz7WdthhFpngJg3NFU6OpWCUAnXZZBT1dJuNwyxXXcqmjVOBiYVzRrojaZe9YLYeGVpIgKEbHy0pwh78urx3W3pCO1mYaKyVXFSA/vtKGgjXMrD5vum4cU/z2N+vA/8e+gjGgrCpVysO9YOvcEEBu3iQriqxbLIs1mh+giYbheiRQ2qPg1bDhJxMC/OB18dLEV1azuuSevZiruvzI6VgSCAPbkNuDY9GEeKm0AhgHRrfyiVQiDGlz+klZxjJc0QsGi4PHF4q3fd4SOwnP8NCp3D8d6o0mFenAwbz1ThZFmz07W0prUdUh7T4RiiUgi8syoZiz8+hIwoKT66NsUp4bYo0Q8vbTmPR9efQ7QPH0vH+SOli7x9/clKfH2oFDVt7QgQsrHj4Rke+a71Ci3e2JaLZeP9Ee9vqeAQBAE/L7aTDXmjUgsZX4Y4XwEYVAry6hQOvb5Naj30BlOvh372RGqICHqDCTnVCqeFs8Fowpvb8pAeKsKCBF8QBIFZMTJ8tLsQWzJr8NkNExz2Q1+YFSPD/HgfvLktD3PjfAY856U/LEjwxeGn58Dfi+UU8M+P94GARcPvp6vw7KI4AJaE+l0/nEK4hIvXliX2O0kg5TPx+z1ToNIZwKJTXRp2NSp1IAhLkGM2m+2fVd6sgUJrGPShzu7wRGKkX0cMjUaDn58fYmJiIBKJkJBgkQ0FBgaCSh26IYhSnuXE67oIl6t0oBAWpyZbxtR2c7NR22b5v38vTt53rxqHRqUOd353CttyanHvzPB+n2wD4fnFceAxaVjupgIyGLiSBJY3axAu5SLZekPoSyWnt8T7C3BlSiBunRaGOzPCccf0MKxKDcTUCEmvI/urUoOg0hnw2PpMJAd6OZgUABYHvfevHo8Z0VJ7JnSkcba8BUwaFd/fno6nL4/F5/uLMee9/VjyySH4CFgOFZXOLE8JwM93TcI1E4NwrrIViz46iOf+yMHEUG9M7xTQTQ4XQ8Rl2CuVNtQ6AxZ9dBA3f3MCO3LqcLBQjp+PV+DKlADM7yKVlPGZoBBAXZvjOWY7LrrqwM1mM1o0eocZOQOFIAg8tTAGBfUqvL7VInfYmlWLue/tx4tbznvsc3pDpJTnclZOkbWyEOvLB49Jg4THcOmwZvvbyF70gthg0Ci4f04Utj2YATOAlWuOYP3JSvxvZwG+PuQ4O6pdb8T//XQGGp0R/ze7/1WcznhzGfj4uhQsGSYL9a6EirkwmsyoanH8fatb28Fn0ezNuzI+y26j2xmz2YxSuRrh0r5lsG+dFoqKZg2MJjNSQzzfqCvlMzE+SGivWB4tliMpUOiwaIv3F7isSgwW108KxsGn5vQopRlObLboXe3CG5WWoCcxwMtlX05Nq9Zl0B4u5eH4s3Ox9qZUl9/bm8vAfbMioGg34O+sWtzx/SkHd7fMylY8uzkbPl4s3DMjHA/0ox/OFWazGc9tzgaDSsFLSxzl3H5eLIdqu8FoQpNaD6mABQrFIt3sGvDXWYMiT8kuEwMEYNIoLiVraw+WoKhBhWeviLMvbkMlXLx/zXicf2XBgCX6LyyOh1LbgQ92Fg7ofQZCgJDtcuHOolOxbHwANp2pRpumA7+eqMDKNUfgxab3qFrpDTQqxZ6YlPAYaHKq5FhGrqh0BgfDJtvxEizyTCVvOOhXJYdCoeD8+fNoaWmBRqPBgQMHMGPGDJw/fx6mAern+oKEb9lpcpUe4Z3Wr3KVZQFFpRDwswYxdV0yGLaT2aeHpmYAiJDy8M6qZKz+6QykfCauGoQMXW8I9Obg1PPzwBzCAEvKZzotAiqbNQjy5kDIoYNOJRyCHIPRhDqFtkdXqKEgVMJFepgIJ0qb8eCcKJcXl8QAL/xwe/owbF3veGBuFB65LBoEQWBmtBQCFh3bc2rx8LwoLEz07Xai/NQICaZGSPDgnCh8sLsA3x8pw+PzYxx+ByqFwJRwsZO96H+256GyWYNf756MSWEWqYnBaHKohNmgUSmQ8VmoccoS6kCnEsisdJSrKXUGdBjNHg1yAEuWcPWsCHx9qBTfHi4DYNHiP3KZZxYQvSXKh4efj1c4PV4mV4NGIRBoTQAEiTgug5yiBhW4DGqvEjBdiZTxsHH1VNzx/Sk8uTELAhYNCq0Bta3teO6KOLRoOrD6x9M4V9mKtTenDriKM1KxLUxr27QOVeaqFo3DtcmdXK1RqYPOYEJQH4ffTQkXI8aHj5rW9h7tv/vLvDgffLynEL+fqsT+/Ebc1KUZOc5PgF9PVEDbYRyyvlF3jk8jBV/7OuDivUrbYYRSa4CUz0R6mMguB+58faxubXd7L+tJTfHo/Bg8Oj8GdW1aXPb+fry+NRfvXT0Oap0BD/9mqfB8eXNqt9fwvrI1uxa7chvwyfUpTtdXPy+2gwumXKWH2WxJUgGAr4CFui73etsi1yZJHihMGhXjAoU4Vd6Cezo9nlPdhvf/LcBdGWEuq6eeOI6DRBzcNysSH+0uxA2TgxEh7X0SaSi4Ki0Q646VY/xr/8JsBq5I9sM7q5IHLNfqipjrWMnRGYxoa+/A4mQ/yxyqJo1d+WBbN3uqkjcc9OvXe+211zB9+nQQBIH169fjpZdeQm1tLWpqavDVV195ehvdIrHuiK6ltyaVzv4ci06FN4fuVKa1/b83QQ4AXJ7kh7dXJsFHwBqyG4crhvqzpXwmzneZzF7V0o5xQUIQBGGp9HT6/esUWhhN5hER5ADAA3Mi8VdmDebGyXp+8Qikqw3v9ZOCcf2kvumS2Qwqnrk8Dk8tiHXp1jU1Uozn/6hFm6YDXhw6DhfJse5YOZ6/Is7BUrg7/bufkOWg91brDFDrjciIkuBgoRzyTuekLaPpqZ6czjy1MBb3zAjHtuw6+AtZmBktHfL+kEgZD01qvcUmu9N3LJOrESTi2H/HEBHHpZtNcYMKETJev7dbwmNi471TIFfp4SNg4utDpXh9ay7y65U4WdYMGoWC729PH9JhnUONuz6xqpZ2h6ndPgIm1HojVDoDeMyLt8NKawWor0EgQRB4bXkiyuRqlwkBTzAvzgfv/JOPJzZkYV6cDHdmOM7NiPcTwGS2mFe4asK/FBFxGKBTCYeA1pack/KZ8OYw8MX+ElS1tDvs85rWdowb4LwnXy8Wnrw8Fi/8kQMek4oTZS2oaW3H1genezTAAYDpkRK8vCQeV7gw9fEXsuxz6oCLVS1bkOPjxUJ9l3VSnUILCnHRadUTpIZ647vDZbjp6+PgMKiIkPLwz/k6RMp4eLxLb5OnuSsjHL+eqMCbW3Px9a0TB/Wz+kpSgBeeWBADDoOKjCgpIqTcQbl3SfgMB1MWW1UnNcQbPx2vQEWz2i4lrFNowWfSHK6No41+bfmiRYvQ3NwMs9kMCoWC2bNn49y5cwgKCoJMNnSLSW8OAxTCVZCjh5h3cXHh68VGncLxZlffpoWEx+iT7OyaiX1vehvtdO3J6TCaUNvWjiBrNrrr89VWWeBA5WqeIiNKioyowTdpGA24syOeFmGZI3G0pAmzY6V4amMW0kNFuH1a7ye1+3uxHSo5tmNiXpwPDhbKkVXVijmxFsvp5k49c4OBkMPocyDoSWxOTKVNaocgp7TJ0c0xWMSxD2frTGGDqle2xd1Bo1Ls2bc7M8Kh1Brw0/EK3J0RjpumhI46J8G+wmFYJGldk1vVLe2Y0cmNy5bkqldoweuU2a1stlzH+lPpSg8TOfW3eZJoHx6eXBiDBH8vB2cxG7G+fBCEZZ4bGeRYoFAIyPgsBzdDm6uetJMTaEWzxr7PjSYz6tpcy9X6yg3pwfgrswa/n67C1AgJnrk8FpEyz1f6hBwGbnVz3fbzYkOu0kFnMIJJo9oVGjaLfl8By8kkpqZVCx8By6POWqtSA1HV0g5dhxGKdgPWn6qCRm/Apvumejzo6wqbQcVTl8fioV/P4UBBo8O1YLghCMJj8uHu6FrJsf07QsqDN4eOMvnFxFtdm3ZUV3GAfgY5gGWH2KJMKpWK1NRUj21Ub6FSCIhd9Iw0qXUOGlLbHI/O1Cm0va7iXMpIeUyodAZo9AZwGDTUtLbDZAYCrTcCpyCndWQFOSQ9EyLmIEDIxpFiS8WlqqUdX92S1qdJ7X5eLGRXX7xB2qp7aaHe4LNoyKxscwpyBqOSMxKwOXdVNGkcZmmVydUOFcVgMRdNar1DFcFkMqO4UYXLkzxrEf/IZdF45DLX/VtjFct1/2Jyy2SyOEUFeneWq1mCvXqF1kG+UtGsgaBT785IgiAI3DfL/WKIy6QhVMxFZlUbrh25StwhRyZgOlQqbPctGZ8JgXU/V3aSjzYqdTCYzB4JcigUAj/daXEhHK4huX7W6mZ9mw7BYo5DkAfY5GpaB8leXVu7xxe5EVIePr4uxeExo8k8aJXPriwd54/vj5Thsd8zsfamVCdDiLGOhM9Es0Zv/81t54GEz0SwmOsgoa4dA0HOqB4GCsClU4RzJYeF2lZnuZrfKN95Q4Et4ytXWham9gynt5sgp8XS2Dsc7iUk/YMgCEyLFONAQSM+21uEyxN9Eesr6NN7+HqxUNemtTe4244JHwELyYFeyOpkPmC7uQ5WJWe4EXIYELBoDnaceoMJVS0aBytWVzbStQotNHojIkeYXnw00jW5JVfpoDeYHIIcWxa7a89mZaeM/mhkaoTlfB7uuVEjCV8By2FIb6PK4igl4jLAolMh4zPtMkUAqLEGyJ6SXtOplGELcADY1zu279Wo1MGbQ7erWXy8WNAbTGjtNO+spk0Lfw/OenLHUAU4gOV+t+bGVPh7sXDN2mP4O6tv88VGOxIuA2azZYwDcLGSI+ExECLioLzpopStXqGF7ygvBoyBIIeBRqWz57ekk4bUT+CY0QMsO4+s5PSMfSCoyrIIsN0EbAsFKc9xmF53jZokI5dpkRKUNWlQ06bFg3P73qjvL2RDb3XrASw3UCqFgDeHgXGBQmRWtdkXXMUNKgQI2cPa2zbYhHTJiFW1aGAyX5SyWV5jWUR3vqnY5iT0xVmNxDV+QrZDcquq1dE+GgAELDokPAZKGh2H4FW2aPpsOjCSmBMrQ3VrOwrqXQ/CvhTxETjK1RqVOoi5DLsUK9Cb7eDCerHpfmzczy5a/Vu+V4NSa3edA2BfzNZ16lsaC3IlV/gIWPjtninIiJTgxS3nneYojmUkfMde9kalDgIWDUwaFaFijkNyjqzkjACkfMfGd73BBIXW4JAl9hOyodAaHIbkkZWc3mEPcqyBTGWzBjL+RQ2zlM9Es1pnnxZd3UUOQjI6sM2xWZjgizi/vlVxgItZwtpOWUKx1eEwwd8LzWq9PRgubFD1etDlaCVYzHGYHl1mDWQ6V3JkfCb4TJrDTJ2iBhUYVIq9ykPSf/y9WPasNXBxjEDXJEykjIfCBschfJXN7QgWj959MDVCAiaNgt159cO9KSMGHwHLSa7WORkaJOI4yNVqWtvBpFHgzRkbqgQBiwYug2qvbjYodZAJLn7/rkGOydqTNFbXSSw6FffNjkCzWo+TZS3DvTlDhm1tbDMckKv09sDHJqFWajugN5ggV+nIIGe4kfKYkHeqJNibmjtXcrwcT161zgCl1kBWcnqBmGuZgdJoPSEqu7jPSPlMmMyWPijAIlcjKzmjDxmfhXevGocXl8T36+87W/YC1iyh9QYa62dpsLUNKCzyQGP9SMdS9r+4YCqVa8CgUhyywgRBINqXj7xOtq7FjSqESbgebfS9VPHzYkOpNUBlTW5Vt7SDy6BC2GXRGinjOQSaXc1VRiNsBhVTI8TY22X+1aVMZyc9wBLkdDbgCPRmo9KhkqN1O9dkNEIQhEN1s6HL97ddr22BYLNGD73RNGYqWa5ICfKGlM/EP+frhntThgynSo5KZ+/LsiXhihvVdvc9Uq42zNh6cmxSGNuO69qTA1zUXduCndEeoQ4FVAoBEZfpUMnpfPPvXOkxm80eGQRKMjysSg3s9w1NwmOCRiHsNtKNyosXzlAxFyw6Bbm1Cqh0BlS3tiNqEJyFRhIh1sbedr0RgM0+mu2kPY/24aOg7mKQU1ivIqVqHsLWaG07JqtaNAj05jgtWqNkfJQ1aaA3WCQrXc1VRitz4nxwurwFrRq903MNSq3dJOZSwbdL/1WjynGRH+TNQaNSB22H5Zytbm0fcwv8zmYcjQpHuZpt3IZtfWQLhsbyOolCIbAgwQc7cuocBiaPZfhMGhhUCuSqi9JyW+AT58cHhbDMLRoLM3KAMRDkBIs50BlM9nkTth0j4TqXYW1ZZlumYqyWYT1NZ3OBqhbHhlzbRbJRqUOjyjJAL0A4uhcHJH2HSiHgI7jY6N15AUGlEIjxFSCvVolia8Y8cqzL1awTom19OWVNagepmo1YXz5K5GroDEaYzWYUWmfkkAwcfy/H6mJVi+sETKSMB6PJbO+N6mquMlqZEyuDyQyH2SgA0K434q4fTuOu709dUsYENpOJBusiXt6lkmG7r9lkjTWt7R4bgjlS8Pdio6bV4qDWqNLZZ+TY6Dwc1yb1HArjgeHk8kQ/1Cm0yOxkjjOWIQgCEh7DXhCQd6rkcBg0RMp4yK5qu1gMICs5w8vkcDGoFAIHrBfyQ0VyyPhMh74QLpMGAYtmz+jZdh4pV+sdtiBHozdArtI73PxtmuZGpW7EzcghGVr8vFj2WTldpSDxfnzk1ipQaAtyxvhCvqupQKlcjRCxc5AT7cOH0WRGSaMa+fVKtLV3YEKwcCg3dczi26VPzF2/oE06aTs2u5qrjFYChGwk+Avw+tZc/HS8HB1GE0wmMx5dfw55tQq8cWXimJFi9Qa7XbjSusjvVG0GLu5v2/6vGYuVHKGlklMqV6PDaHboyQEuumQCloQxlUKM+Zla6WEiCDl07LiEJGtiHhNNnYwHOu/jxAAvZFsrOQwqBaJR7oI66oMcLzYdKUFCu13m7rx6zI2TOc348BeyUWsrw7ZpwWPSwCdtjnuFlMdEvUJrz3AFii5e+NkMKvhMGhpVOhRanXzIpulLEz8hG9UtGphMZshVeocFRJyfACVyNXKq2+ArYI15i3EfAQsMKgUVzRroDEbUtLYj1E0lB7BMpz9c1AQ6lRjUQZKXEiw6FSIuw565tsjVnBetUj4TAhbNfv2qbNbAR8AcE+5/n9+YiklhIjy3OQcJL/6D6W/vwfacOvzvmvGX3HwQPosOLoOKujYdFO0G6I0mh8WdnxcbFMJSydHoDWjRdIy9IMeLhRZNBx789Sz8vVjIiHQchmmZlWNZ/Na0tcOHzxxSe+fhgE6lYEG8L344Uo53/8l3Ke8ca1gqOXpoO4xQag2QdGrvSArwQkG9EuVNGvh4MUd9IqTfw0BHEjOipfh8fzHO1yhQ2dxuHzrYmc4ZCot99NjOTniSlGAhNp6pwlvbcgE4BzFSPhMNCh1OljZjQrBw1Ef+JP0jLcQb27JrUdiggtFktstDAEuQYzSZsSOnbsw7qwEWiV6giI3yJg2KGlQwmYEwF5Ucby4DMj4T+fVK5NcpkRLsDQ5jTFyWRwS2HoQmtR7aDtdSWoIgLOYDjZYgp6J5dNtHdyZIxMEn10/A6lltOFbSjIomNSaEeGNRkt9wb9qwYJNj2UYidA5yGDQKfAUsVDVr7KYhY02qZbORzqtV4rd7psCriwmH5fexOPKNVftoVzy7KA48Fg1fHizBb6cqceip2WDSRn+Swx1iHhOF9Uq7ZK3zeZAc6AWDyYy9+Q1j4vgf9ZUcAJgZLYVGb8TbO/LApFEwPVLi9JrOg+HGgvf3UHLDpGDcMzMce/MbQaMQ9gulDQmfieJGFQ4WynFFsv8wbSXJcLM42bJw+uZQKQDHC2eMtWJRp9COeamajRARB+XNGnx9sBQSHgMTQoQuXxfjy0dOdRuOlzS5vHaR9B8/LzZq27Q4UtwEwL1MMkrGt88o6uogORZI8PfCHdPD8MqyRCwbHzDcmzNs2IIcm519156UQBEHlS0abDhdBT6L5vacHa3Y+gKfXBiD1BDnSp6vFwvNaj10BiNqW7XwG2OVLHd4ceh4YXE8Dj45G28sTxzTAQ5gM+zS280HOlupx/t52SuaPmNgnTwmgpzEAC94c+g4WCjHtEgJ2AznA9RXwEadVZttmeJ6aZy8noAgCDy9MBaPz4/G0nH+TuVrKZ+JQ0VyGExmLEryHaatJBluxDwmZkZLsflsNQA4yNUELLpdKjTWndVshIi5OFfRgj/OVWP1rEi3FZoYHz4OF8mh1hsxLVI8xFs5tvEXslDd2o5P9xRhaoTYHmx3JVLGQ4lcjRa1HqWNqlFtH03iHh+BRXp9pMgS9Ep5jou4IG8O8uuUWH+qEtdODBpzVdUgEQcHn5yNuzLCXT5vN2lq1aKiWQP/MbDI7QsyAQvzE8b+GkbCY6BeocWbVnVO5yCHzaDak0FjwZxrTAQ5VAqB6VEWbencOJnL19i0qNoOo7UMS8rV+gJBELh/ThTev2a803NSHhNms0Wu1LXKQ3JpsTwlAHrr9OiuDau2IaOXglwNsMg6FVoDZHwWbpgU7PZ10b58mMwAj0lDcqBw6DbwEsDXi2U3dbh/TqTb10X68KA3mHD9V8dhNgPLUi7dasdYxseLhcyqNnyytwh3Tg9zkmsFerNR3KiGSmfATZNDh2cjB5kgkbONug2bGdOzm7NRr9Ribpyz9J9k9JMWKkKkjAcqQeC2aaFODmpJAUIAo99ZDRgjPTkAMC9Ohh05tZjroh8HuOi0c6CgEY0qHXzJxbjHsC1mr0i+NHXeJBe5LM4HPCYNJrMZXKbj5SXOT4CdF+rH/CBQG6ESi+Tp/+ZEdtvEbjMfmBQmAp0cAupRbJry1BBvTAl3XyWzHZMF9Up8d9tEREgvjWP0UiNYxIHRZMaTC2OwemaE0/M2meLcWB8Ei8eWZLE32NZJR4qb8MSCGEzu5pwhGb2MDxJix8Mz3D6fFCDAxjOjf0YOMIaCnKXj/JEeJnK7U2yWrnevOw3AdRMwSf8IFnFApxK4PJEMci512Awqrkjyw7nKVqfnrkoNhJBNh5BzaRhTTIuU4I0rE3F1WlC3r4uS8cGkUTAzRtrt60j6js3R7oE5kd26BPl7sZEW4o2rJwYhI4rcD2OVq1KDkBYi6la2CAC3Twsdwq0aOXhz6OAzaZgaKcZ9s5yDQJJLg9QQi8NnyBgI9AnzME4DEwqFaG1tHbLPy6lug95ogpBNR5iEO+qt8TzJQPaFwWhCTav2ksx8DQZDfV54Go3eAKXWMCbmUA3Vvqho0sBfyAKNrOS4pT/7wmw2o1SuRjhZmfEoo/0a1R0ljapRdbx4el+UN6nh58UGg0Zei/rKWDovSuWuB1iPNoY1yKFQKBAIBMP18WMeJpOJ+vr6Xr2W3BeDC7kvRg7kvhg5kPti5EDui5EDuS9GDuS+GDn0ZV/YGNYgh4SEhISEhISEpH/4+Pj0eeFHQnKpQAY5JCQkJCQkJCQkJCRjClJ0SUJCQkJCQkJCQkIypiCDHBISEhISEhISEhKSMQUZ5JCQkJCQkJCQkJCQjCnIIIeEhISEhISEhISEZExBBjkkJCQkJCQkJCQkJGMKMsghISEhISEhISEhIRlTkEEOCQkJCQkJCQkJCcmYggxySEhISEhISEhISEjGFGSQQ0JCQkJCQkJCQkIyphjWIMfHx2c4P56kE+S+GDmQ+2LkQO6LkUPXfdGm6RimLSEhz4uRA7kvRg7u9oXZbB7iLSGxMaxBjk6n69PrDUYTntucjYUfHEC73jhIW3Vp0td9calgMpnx9aFSXLf2GBJe3IFdF+oH/TM9uS8qmjSY/OZu1LS2e+w9LyXGynmx5Vw1Jry2E09tyMKpsubh3px+0XlffLavCHPf3w+jiVw8DAeePC/a2jsw4797caCg0WPveSnR3b44UiRH4kv/4MrPDuODXQXoMJqGcMsuPVztiz/OVmPiG7tQ16Ydhi0iGfFytROlzXh7Rx62nKvGXT+cwq8nK1HYoMJXB0v6/F56gwkljSqcLm/p1c2RjL5JtuXU4rW/L4BGJeAvZOP9nQWj6rg4XtqEOoUWWVVtw70pJMPItuxa0CgEDhY2YtXnR7H6x9OjOvBNCxFBrtLhROnoDNhILvLT8XJUNGvIfelhVDoDntiQhWARBz58Fj7YVYjtOXXDvVmXFL+cqMAj689BrtKjpFE13JtzSTJigpya1nZoOxyrM/UKLe5ZdwrfHi7FQ7+ew7GSZnx1cxpumhyCNfuL0aDsfWRcKlcj/c1dmPPefqxccwSvb73Q7eubVDrM/98BfLCroF/fh2T0YzSZ8eGuQkyNEGPdHZPw/OJ4XKhVYP8oyjheqFUAAMqb1MO8JSRdMZnM+P5IGc5Vtg765xwvbcZVaYE49NQcvHvVOJwobcaCDw6gslkzqJ89WKSFeEPGZ2Jbdm2Pr1XrDEOwRSRd0XYYseF0VbcJRZ3BiG8PlwEAislF4IDRdhjRotbDaDLjrW25aFLr8NkNE/D5TamI9xNgey/OFxLPsOtCPZ7ZlI2l4/wBAI2qsaEKGG2MmCDnsfWZmPPuPmw+WwWTyQyTyYzH1meCSiFw8Mk5OPncPBx+eg5mx8rw0Nwo0CgE/rez9wHIx3sKQaNQ8MPt6Xjssmh8e7gMW85Vu3ytzmDEPetOo7BBhc/2FaN6FGc8SfrP31k1KGxQ4ZHLogEAM6IkSPAX4LN9xcO8Zb0n1xrklJFBzoAxm81OiZiB8MneIrz053ks//Qwrl17FBVNgxNw5NYp0KrpwJRwCSgUAqtSA7Hr0ZngMmh4ZlP2qKpM2qBQCCxK8sP2nLpuF9E/HivH+Ff/xZmKliHcOhIA2JpVi8d/z8RfmTVuX/PH2Wo0KnWYGOpNBjkeYFduPVJe24mIZ7fhp+MVeHJBLEIlXADAoiRf7M1vgEZPBv1DQUa0BG9emYT/XT0eTBoFjUoyyBkORkyQ89ryRMT7e+GR3zIx4fWdmPf+fhwqkuOdVeMg5TMh5TMh4jIAAN5cBu6ZGYH1p6qg1PbcfFomV2PLuRrcOzMcM6KluH9OJBYn++GpjVkoqFc6vf75zTnIqmrDt7dOBJdB7VMwRTI2MJrM+HB3ITKiJJgYKgIAEASB1bMicKK0ecj6GhqVOhwvaerX35rNZlyosQY58tGZsR9JrNlfjKs+Pwq5BzJyhwrl+N+uAqyeFYGPrktBeZMGj6w/B9Mg9JgcLW4CnUogNcTb/pg3l4HXlyfiUJEcv5+q8vhnDgWLkvwgV+lw0s25uL+gES/9eR4mM/D5KEpMjBX25jcAsPRPuTquTSYz1h4owewYKRYk+KJMroGB7BkZEBOCvfHxdSl4fXki3r96HG6dGmp/bmGiH7QdJuzL750S4a/MGvxvZwE+3Vs0aiu+wwmTRsX1k4JBoRCQ8plkkDNMjJggJ1LGw1e3pGH9PVNw69RQTAwV4a0VSZgdK3P5+gUJvjCazDhe0vNi89O9RRCy6bh+UjAAy2L1v6uS4efFxtMbsxwuwIcK5fj9dBVeXpqA2bEyPDAnChvPVCGvTuGZL0oyKjhUJEdJoxoPzIlyePzyRD+ES7n4cHfhkGzHf7bn4Z4fT6NB0femxZo2LRRaA8IkXLKS4wEyIqWoaW3HyjVHBiT/U+kMeOjXs5gcJsbj82OwdJw/3rtqHE6Xt2DdsXIPbrGFYyVNSAnyBptBdXh8XrwPlo7zx2tbL4zKptjuJGu5tQrc/9MZTI0Q44Ur4rAzt57UxA8hBqMJBwvlSA8VoaBehV25zoYt2dVtKG5U49ZpYYiQ8aA3mlDVQqomBoK/kI0l4/xx4+QQrJgQCAqFsD8XKeMh2ofXq76cZrUeD/92Dt8cLsV7/+bjuyNlg7jVYx8pn4kGMsgZFkZMkGMjPUyEh+dF4+1VybguPdjt6yKkXPgImDhcLHf5vLbDiPf+zcet357AprPVuHtGODgMmv15DoOGN5Yn4kxFK345WQEA6DCa8PJf5zEhWIhrJwYBAG6YHAw/AQvfHCr14LckASw3wod/PYtP9xaNODvYLeeqESLmYGKot8PjVAqBh+ZG4WChfEiqOc8sigWFIPolK7JVcS5P9EVtm9ajUqtLkaRAL2xcPRVmM7D4o0N4a3tuv4KDY8VNaFLr8eqyBFCti5CpkRJcOzEIb+/I82jW1Gjtx5kcIXb5/MtLE8CkUfDUxqxRJ1ujUAhcnuiLbdl1DhKc4kYVbvr6OAK82fj0hgm4Nj0Y3hwGviKv4UPGucpWtLV34PEFMUgN8cane4ucjq/deQ3gMWmYEi5GpJQHgOzLGWwuT/TDntz6Hu8F23NqYTabsfuxmRgXJESLWj9EWzg2kfJGRiVn54V6zP/ffuwcApfYkcKIC3J6C0EQmBYhwZEi11Ke309X4ZO9RTCbgftmReCWTmVbG1MjJVgxIQD/2Z6HHTm1+Hh3IYobVXh5aYI9A8KkUTE3zgdHivsnGSJxz9nKVvxxrgbv/ZuPqf/ZjaMj5DfWdhjxT04dlo7zB0EQTs8vTvZHpIyH/w2BKYWEx8SbVyZid15Dn2VFubUK8Jk0TI+SAADKB6nn41IiVMLFpvum4qq0IKw7Wo657+3rs5zwUJEcvgIWImU8h8efWRQHIZuOa9ceQ1GDs4y2P5yvaYNSa8CUcNdBjojLwJtXJmF/QSN+OVHpkc8cSm6aEgqFtgPv/mM5F8ub1Ljxq+MQsOhYd8ckCFh0sOhU3DwlBBtOV5GL6CFiX34j+CwaJgQLcd+sCGRWtTm5p+3Jq8eMaAkYNAr8hWwwaRRy/wwyCxJ8odYbcbwHJ7u/MmswOVwMGZ8Fbw4DLRoyyBkII0Wu9t2RUpTJNbjrh1N48JezgyKPHmmM2iAHsAQp+fVKlwfPpjNVmB4pwfe3p+Ox+TFg0aku3gF4blEcvNh03PvjGXy0pwjXpAUhOVDo8JopEWJUtbSTulQPsz+/EQIWDQefmoMgEQef7i0a7k0CAOzJa4Bab8Sy8f4un7dVcw4XNWHdsfJBnz2wMNEPy8dbZEVNfegHuVCjQJyfAGHWxlNSsuYZJDwmXlwSjyNPz0FCgBdu+fZEn2Z8HC6SY1qkxCmA9mLTsf7eKWDRKVj1+VF7JW4gHCtpAoNGQUqw0O1r5if4YuWEQLy+9QKqWgZ2jTOazLhu7TFct/YYjhTLB706FCnj4eF5Ufj2SCnWHSvHyjVHQKdS8OOdkyDlM+2vu2VKKPy8WFj+6WF7rwiJZzGZzGi1Lob3FTRgRpQUNCoFs2NkCBCy8dupi0F0vUKLnGoF5sRahidSKQTCpTwUN3R/jdJ2GMfMrJc/zlbji/1D2ysW48sHm07F+Rr3IwXqFVocL23GEqsrmJBDR/MIU1qMNmR81rC7qzUotTha3IQXlsTjjSsT8WdmzSVxLRzVQc60SEt28kgXyVqpXI2zFa1YMSGgx/cQ85g48MRsHHl6Dn67ezJeWpLg9JrJ1izo0X42gJO4Zl9BAzKipAgQsnH79DBrH8zwZ/K2nKtGnJ8AkTK+29dckeSHBQk+eOGPHGS8vRdf7C9GW/vg3QieuyIeZjPwXh9MMHLrFIjz48OHzwKLTiFtpD2MkMPA97elIy1EhJu/OYFp/9mD5zZnQ29wvwirV2hR2KBChrW61pVAbw423DsVPCYNXx3q+yywrmRWtSHRX+A2yWPjxSXx4DJpeHHL+QEFJj8dL8fRkiY0KLW4/svjuPXbk4OeHLo7IxyJ/l544Y8c+HqxsHH1VPgL2Q6v8eYy8Of/TUdKsDdu/+4k9uQ5yzWU2o5+ZTY/31+MB345e8kmwfLqFHj5z/OY8p/dGP/qTlzzxVHkVCswM0YKwCIrvCotENuya6GwGgXtzWsAQQCzrK8BLBL0IjfX/3qFFq/9fQETXtuJF7ec9/h32J1bj0UfHuzTWIqBoNYZ8PJf5/HlwaGVUFIpBGL9+DjfTQJla1YtqASBhQm+AABvDsMevJL0DymfiWa1vt8B+tmKFvx0vBzfHCrtd5V/a1YtCILAokRfXJ8ejHGBXvhi/8DvMSOdUR3k+HmxES7h2iVrthvU5jNV4DCoWGA9SXuCQrEMepwULnZqzgUsko5YXz6ODZGcStthxJcHStCuH7s9FA1KrcONcEmyPwQsGn46XjHs27U3r9FtFccGhULgi5vSsPXB6ZgSIcY7/+Rjylu78dHuwkHJNEr5TDwwJxK/nqiw20J3h1LbgfImDeL9BaBQCISIuCglHdY8DptBxde3puGT61MwN06GX05U4PNusrOHiywJmamRruVjgGVBfkWSH/blN/Y4tPh8TRs2nq5ya0yRXdWGpACvHr+HF5uOl5bEY09eQ78HBspVOrzzTz5WpQZi5yMz8cn1KcivU+Ky/+3Hz4N4XtOoFHx47XjcMzMcv949xaGC0xkvDh3f3joR0yMleHJDNpo79Rnk1Skw6c3dmPf+fnx/pAw6Q++uvQ0KLd7fWYAdObWY9/5+vL+zACrrXJ7q1naPyQ5HMvl1SvydVYv58b54dVkCOowmcBlUhwDmqrQg6Awmu5307rwGjA8SQsK7uK8ipDwUNahcBtn3/ngav52sRLiUi23ZtR69xp6rbMX//XwGF2oV2Hja9VgJT/PT8XK0ajogV+mGXMaU4C/otkr8Z2YNpkdJ4G1zs+XQyZ6cAWK7Jrlz5tToDThV1oxfTlQ4XJcAi1HNTV+fwHObc/D61gtY/umRfkn7/8yswfRICcQ8JgiCwN0zInCirHnM2+uP6iAHsEjJtufUYva7+xD/0g78Z3seNp2txuWJfg5GAwNlcrgYR0uaBl1+YTab8ezmbLyxLRf/Xhi704kPFlgWe7OiLTdCNoOKValB2HC6ql8N8mazecA3vurWdlz7xTHwWTSsSOm5CggACf5e+N8143HoqTm4dmIwPthVgBWfHXFpTT5Qbp0WiiARx2GQ7d9ZNZj5zl6n2Qe2m1icnwAAECLmkJWcQYJJo2Jxsj9eXZaI26eF4ZM9RShqcJ2RPlQkR4wPHzI+q9v3nB0rQ7Na3+Og0Pf+LcBjv2ci/c3deGZTtsNzbZoOVDRrkNiLIAewVCdnx0jx8p/n7Rn3vvDfHXkAgKcvjwWFQmBxsj92PjoDS8f549nN2fhgV8GgXT/DpTw8c3kceMzur/lUisVZU2cw4vk/LGYeSm0HVv94Bn5eLET58PDyX+fx7KacXn3u2gMloFMI7HlsFm6eEoI1+4ow6529uPGr45j+9h7Me/8AXv3rwpg2/ViU5Ifjz87Fa8sTcfOUUGy6bxqyX17gcIwHCNmYHinBbycrsetCPQ4VyjG3i3NqhIyHtvYOp0UeABQ3qLB6VgReXZaItvYOp/6e/lLZrMEd351EjA8f8+Jk+P105aDf47UdRqw9UIoJVglpb5JWniTB3wulcrU9GO/MkWI5zlW2YlVqoP0xby4DCq2BtPceALYgx1VAq+0wYup/9mDV50fxzKZsfHXQsbqy8XQVNHoDDj45G5kvzUeSVSL99aHSXlceK5s1OFvRah9MCgALE30RLOJg7Riv5oz6IGd5SoClChMmwnXpwfj2cCmqWtp7JVXrC5PDxaht0zo1bxfWK/H63xfwo4esX388Vo5NZ6rBpFF6bA4czewraEScnwAywcUb4Q2Tg9HW3oE/uxke54qa1nYs//QwFnxwwClTYjZbdOJlcjXOVLRgW3YttmbVOmVqj5U0YdWaI9AZTPj93ikO29UbfL1YeHFJPH6/dypUOgPWHfW8FTCTRsVTC2NxuKgJR4ubYDKZ8f7OApQ3abDlnONvdqhIDj6TZg9ywiRclMn7F+S0avRuF2l6gwmv/X0B7/yT16/3Hms8Oj8aMgETz27KdpI+mc1mHCq09OP0RGqINwQsGvbmda+ZblBqsSDBB/fOjMAvJyoc5J45Vt19UmDvghyCIOyLyA939c0i3Wgy46/MWtw+LcwhO89n0fH2ymQ8PC8KH+wqxCt/XejmXYYGPy82Xl2WgG3ZdVjwwQHc8NVxNCi0+OKmNHxxUxpeX56IjWeqsDevAa0aPR745Sxe+/uCUza7SaXDT8crcNMUS/LhuSviseexWZgRLYXOYMTryxPx5MIYrDtWhqu/ONpjVa4njCYzntyQiZu+Po6bvzmBnGr3fRVDCZ1KsbsE2qBQnA1brpkYhKyqNtz5wykEidhYMSHQ4fkIqaV3sLjR8Tql7TBCoTVAymNifKAQMj4T/5wfeALQYDTh4d/OgUGj4OtbJ+LGySEoaVQPemb7t5OVaFLr8PbKZLDoFFwY8iDHck/oGlyZTGa8uS0XSQFeWJToZ3/cm2Op6LiTYw+VK2NBvRJXf3F0VI7z6C7IqWrRoFXTgf+uTMaiJF/szr14zTeZzPj+SBkui/dBkIgDPouOb2+biEWJvnhj6wVMenO3PbnUHX9n1YJBo2B+go/9MSqFwJ0ZYfjnQh1qxvDAe8+VOoaJiaEi7Hh4hv3/d2WE40RpM6a6sUztL5PDRSAIS5n9likhOFrShE/3FuGYdU5PhJSLGyeHuPzbtvYObDlXjeIGFXQGE26aEoIEf+eFR6lcjVf/voDrJwXDaDTj2DD2AJlMZpc3Kk9gmaHQ6GQRHiHlYXqkBD8cLcNVqYEunc26cqCgEY/8dg50KgUdRhNu+/Ykfrl7MrgMKg4XNeH9nfk4U9Hq9Hf+XizcMDkEbDoV5ypb8WdmDcYFCbHmhglOev6+kBrijW0PZsCMwbnwL0zwRYwPHx/uLsBt08JQ0qhGkIiNH46W49qJQfbfbF9+IzKiJaBTLXmMEDEXNVYb6a79GWsPFKO4QQ2dwYjVsyIR43uxF0mjN+DyDw9iQYIvXl7q2K/WqNRh9Y+ncaq8BQQBLB8fgCgf931MlwIcBg1vrUjCTV+fwIe7C/HIZdH253ZeqEeDUue2H6czdCoFM6Kl2J3XgMcXxLh9XZNKj4woKR6cE4VfT1bg+yNleGVZIgDLHBIWnWK35+0NQSIO/m92JD7cXYhrJgYhupf7s6xJjfYOo0uDA4Ig8PC8aHhzGDCMEDef5eMDwKRRsetCPc5UtOC9q8fZ3e6uTw/G1qxaPL0pCyw6Fc0qPYxmM9afqsTKCYGYHC6G2WzGhtNVMMOMOzPC7O8bJOLg/avHO3zW+EAhrv/qOLZm1zpkUvtKqVyN9aeqMClMhFK5Gu/8k4/vb0/v9/sNNQsTfPHa8kSMDxQiMUDgdH0Pl1y0kU4PE9kfb7IGlxI+AxQKgfkJPvj3fD1eWZrQq3tEZxqUWmzPrsO8eB/8fqoSZypa8MtdkyHhMZERJYWvgIXfT1UhNUTU85u5Yd3RMsT4Chy+Q2d25zVgeqQEUT58xPjwh7ySE+3DB5VC4Hx1m33QNQBsyaxGTrUCv9w12eHeL+TQAQAtmg6Iec5S0Be3nEeJXIVvbp0IJq373r/+klnZilu+PYFWTQe2Z9ch1lcwKJ8zWEh4lkDRVZBTYe3lmxIhBotBxYPW/r4gEQcHi+Qokavx+pWJ9tez6FR8cG0Knl8cj492F+KLAyW4dmIwgsUct5+/J68e0yLE4LPoDo8vGxeA1/6+gG3ZtbgzI7zX30elM+CDnQWQq3Rg0Ci4fXrYiN0no76S0xV/IRvLUwL6fPHrCSGHgaQAL7z29wVEPb8dN319Am3tBnxwzXg8Mi8aNa1atxmNn46X48Ut53GwUI79BY244qNDeOCXs06Zvc1nq8GgUvDCFfGYFC5CSaO61+VIZT/kJe747448PLFh8OZmfHGgBG3tHbgiyc/puVumhiKnWuEyMOlMRZMGd35/Ejd/cwKRMh7+emA6vr89HaVyNaa+tRuxL+zAjV8fh85gwn9XJeOH29Px1/3TcfK5edj+UAZSgr3x3r/5eGt7Lo6XNuGNKxOxyUXDcn9gM6gelUp2hkIh8NC8KBwracZLW84jJViIl5ckILdWYc9ANii1yK5uw6zoi3KQUInlAljRpTm6Wa3Hm9vycLy0CbtyG/DtYcdG2G8OlaK2TYt9LlxYntmUjRK5GuvuSIeMz8THe0aGO95wkxElxYNzo/Dh7kLsss4jqGrR4IkNWZgdI8XMaGkP72BhbpwMubUKVDRpsL+g0Wkmj9lsRpNKDwmPCTaDiuvTg/H76Sp7xjW7ug1xfgLQqH27zN89IxwBQjZe6oMJgU0eGe/v/kZ3y9RQ3DE9zO3zQwlBEFiU5If3rxmPfU/MxsJOmWuCIPD2ymQotQbQKAS23D8N+56YhZUTAvHv+Trc++NprP7J0sPx/BXxDpUrV0yNlGBqhBif7ikakGVrfp1FAvvxdSl4YkEM9hc0DvkCeSDQqBTcNDkESYFeLu/PbAYVQg7daSFo+7+UZ6muL0jwRZ1Ci6yqvleyPttbjJf+PI/pb+/Bh7sLcd+sCLuxEJVCYGVqAP7OqnWS//YWtc6A1/7OxRvbct2+pkyuRpTV1CbOTzDk+5BFpyJKxnMwH9AZjHj3nwLMi/PBlC4JYlslx5X5gMFowpZz1Thc1IRXXVRp9QYT1h0tw5vbcvvde5Rfp8QNXx1HkDcHKcFCnO1BwjsSYdIsx7argaAVTRrQKAT8vFiYGS0FjULYh+d+c6gUMT58lyMAJDwmnloYCy82HZ/sdV95b1Hrcbq8BXPifJye8+LQMSNKau+V6w0VTRqs+Owwfj5RgaqWduwvaMQd350asX1bo76SM5R8dUsaMivbUNvWjmARBzOjpSAIAn9m1qC9w4hWTYe9Wa8zpY1qJPgLsPXBDOgNJnx3pBRvbsvDLVNCkGbNpJjNZvydVYN58T5gM6iYZD2oT5Q2Y3Fy99k/bYcRK9ccwaIkPzw8L7rb1/aGMAkXn+0rRowvD3fPiBjw+3Umt1aBD3YV4K6McJe9AnNiZQj0ZuP7I2VIDfF28Q4W2cbt35+EUtuB/10zDsvGBYBCISDlM/HzXZPwz/k6eHMYiJTx7PuoM1I+E5/eMGFQq1WDia2ak1+vxCvLEjArxvKbrTtajtQQEQ5Y+51mdmr8DRRagpya1naH7LzNMvh/14zHX5m1+DOzGkaTGVQKAblKh8/3lyBcwkWJXG3PLgGWm9fhIjn+b3YEMqKkWD0zAq/8fQEPzo1ymv9yKfLw3ChkV7XiwV/PYnaMDMWNKnAYVLx39fheH3Mzo2UgCGDe+/uhN5pww6RgvHFlkv15RbsBeqPJniW8aUoIvjhQgvUnK3HXjHDkVLf1OqDqDItOxYuL43HnD6fwwpYcvLQkwV4RdMeFWgUkPGaPvUajhSARB7senQkhh25PWLy8NAEvLYlHZXM7qFQCAX1IiNw/OxLXf3Ucu3LrMb+Xhjhdya9XQsihQ8pnYtn4ALz3bwHWHijB/64Z36/3G4mIuAynnhx7kGOV/EwKE4PPouGTvUV4bH40Ynz4vUpqdhgtxgdXpwUiJdgbJY0qp/vl1WlBWLOvGL+cqOxXQH6kuAl6owmZla3IrVXY5cI29AYTqlo0CLMmneL9Bfjd2ofakwOiJ4n3FzgEOZvPVKO6tR3f3DrR6bW2IMdVr1RmVRsUWgOuTAnAT8crMC5IiKvTLIPUMytbcf8vZ1DZ3A4ug4pfT1Tg5aUJTjLF7mjTdODudafg68XCT3dNwlcHS/H9kbJRee92NxC0orkdAd5s0KgUeLEpSA8TYXduA8Q8JvYXNOKDa8a7Pb65TBrunhGOd/7Jx/2zo1xWcw4UNsJktqytXLFknD8e/u0cKpo03VaDAMv+WLHmMFh0KjbdNxWxvgJUNmuw+ONDeGT9OXx+Yyqa1XrI+Mw+J9cGi5GxFaMEGZ+Fy+J9cPOUUMyKkdkPvACh5cZe7UbXWN6sQYj14GHQKLh9WhiEHDp25l60Mc2tVaKkUW0PaAKEbASJ2Dhe0nNfzn+256FUrsZl8c6Ren+4Ki0Id2WE4a3tedjYTyMAVxiMJjy2PhPBIg4evcx1MEalELhpcgi2Zde6dYzaml2LogYVPr1+Aq5MCXS42CUHCvHEgljcmRHusI9cMdoukjYoFAIvLonH8vH+uCzOB1QKgRsmhWBrdi1OlzdjX34D4v0E8OnUVyTiub5RVbVYjtlAbw4ui/eBXKXHuUpLReij3Zbs0NqbU0EQF53BACCzqhXtHUZMtfaXXJseDCmPOWJmHQ03FAqBD65NwfXpwahta0eTWo9Prk+ByEUSxB0iLgP/NysSN0wORryfwCkLaJu7ILVWEvy82LgiyQ+f7y9Gbq0C5U2aXjmruWJevA9eXZaAX05U4pZvTvRYKc6tVXRbxRmN+AvZThVZgiAQLOb0KcABLFKUCcFCfLi7sN/X0/w6hX1Bz6BRcMf0MPyZWTPg2UYjCRHHfZAjtl7DGDQKHp4XjaPFTVj4wUG8sdV91aQzhwrlaFLrccOkEFyXHoznroh3Ct5DxFwsGx+Az/cX92s/7ctvQICQDRGXgV9PODsKVrVoYDJbPgewVHKMJjMK64d2dEKCvxcKG5TQG0wwGE1Ys78Yl8X7OEiVbdjkaq0uZuUcLGwEj0nDf1clY2GCL9bsu+gsudbaQL/twQzsf3I2JoWL8cSGLJeGB64wmsx46LezaFbrsfamVAhYdKQECdHW3oHSUWii424gaEWzBsGii8HFvDgfHCtpwnObs7EgwadHp9ebJodY3DH/zHH52+7ObUCMD9/tNWtevA+YNAr+yuq5mvPD0TIo2g1Yf88UuzzNItEdh335jYh9YQem/mfPiOi9tEEGOR7AJnFy17xV0aRBsIhr/z+NSsGcGJlDg9nfWTXgs2iYEX1Rrz8pTIzjpa77ci7UKHCushXbsmvx3ZEyPLEgxmWfT395+vI4zI2V4bHfM5H8yr945a+BzyY4XtqMC7UKvLY8sdus1TUTg0CjEi4nsBtNZny0uxAZURJ7FexSZFqkBB9cm2IP1G6dGorkQCFu+/Yk9uc3Oti3AgCXQQWDRnER5GjApFEg4TEwMdQbQg4d/16oR16dAj8dr8DqWRGIlPGR4C/AoU5BzuEiOXhMGpKti2gWnYpbp4Via1YtOVPBihebjucXx2PTfdNw8rl5/dL5P74gBi8tSUC0D89p39lMNiSdLJNfWBwPBo2C6788BqD3pgOuuHlKKH64PR3nKlt7vGldqFEg3m9sBTmehCAIPH15HIoaVLj56xP9mqmVX6dEbKdF6LXpQeAwqINicjJceHMZaNE4BzkiLsMhILljehhOPT8Py8b7OyQLu+OPc9UIk3CR3MM5cf+cSDSpdPjFRZDSHWazGfvyGzEnVoaVEwKw+Wy1U6BkMy6yDWi27c+hd1gToMNoxpFiObbl1KG8SYP7ZrlWbbDoVLDpVKf9Alh6YqdEiEGnUrAw0RelcjUalTqYzWacLmvBZXG+iPcXQMJj4vH5MTCazDhd3rOxg9lsxvN/5NgrGeHWvsLxQUIAwNke5OwjESmf6XIgaGeFBGAJcgwmM9h0Kv6zIrnHKiWXScPryxNxtKQJiz486GCcYTCasL+gEXPiXFdxAIDHpGFOrAx/nqvB2YoWtwZF7Xojvj1ShpWpAU6y/rlxPlh3RzreXpmEGyYF45cTFSNmbtiAgpxz587hjz/+wN9//42SkrFtQ9cdMj4LVArhMsjRdhhRp9DaKzk25sb5oKhBhVK52ipVs8wZ6Ny4NylMhIJ6ldOU+8pmDZZ+cgjLPz2M+346gynhYtw5vfdNY72Bap0D8+f907ByQgC+O1Lm1uO967a9808erv/ymNPr9+Q1QMJjYnJY96YQQg4DixL9sOFMpZOG3VbFeWhuVN+/1BiGzaDi61vS4OvFglJnwOwupWmCICDhMiBXOVdyAr3ZIAjCEnzHyrDzfD1e+CMHQd5su2RjeqQUR6yOboBFljEpTORQkr4yJQAdJhP+zqod5G976SHiMp2uA/Ygp1NPiJTPxJc3p6G9w9hn0wFXTIuU4Pkr4rHhdBX+deNo1ajUoUGpQ5zfpW060RPpYSKsu2MScusUuOGrY33qz9HoDShv1iCmU3Mvn0XHipQA/H66qtdzfUY6YldyNZXWXq3sDItOxZRwMcqbNFD3UB1Q6wz493w9lo/vuV83QsrDknH+WLOvb9WcwgYVqlvbMStGimsmBkOhNWBbtuO1sKxJDTrV0n8BWPZhsIgz5A5ryYFeCJdwceu3J/HspmxMjRAjJdi1PBywzsrpUslp03TgXGUrZliNVNJCLX9/urwZ1a3tqFNo7Y8BQJSMByGHjuO9MFR69998/HKiAm8sT8LcTr0k3lwGQsUcu9pgNCFzUckxm81OlZxgMQf3zYrAZzdMcNn+4IpFSX7Y9mAGvDl0XLv2mL0X9GxlK9raO9xK1WwsGx+A/HolrvzsCGa/tw+HCuVOr1l/qhItGr3bFoaMKMtx/8yiOIucdIT06PYryMnKykJSUhJmzpyJlStX4plnnkFaWhquuuoqKBSjpxHSU1ApBHwFLNS0OcurbI3eXYOcGdES0KkEdufWY3tOHSqaNVg8zrERf2qkBAQB/HrSsaLx+f5icJk0bLh3CtbelIq1N6cOivSKSiGQHCjEI/OiYTZbpkJ3x/GSJsx8Zy++O1yGMxUt+Hi3YzPc3rwGzIqR9mpbV6UGorK5HSfKHOV6aw8UY1qk+JKu4rhDyGFg3R2T8NryRKS6uGGJeAw0qx0vspYg5+KxOT/eByVyNU6WteDlpQn2itv0SAma1Xrk1img0RtwtqLFLlWz4edlmYWx6UzVIHy7Sxsxj4GmLgGqXKkDlUJAyHZ0zEkM8MKaG1Lx0Nxoj+iir0sPwqwYKZ7ZlO0UaAEXs9AJY0yuNhikh4nw6rIE5FQr0Oym4mk2m52aeC1DMuEkJ7p+Ugia1Xr8c7531YyRjrebnhx3A15jrdXD/G7mkpU3qfHyn+fR3mHE8pTeuds9MCcSDUodNvbhWrY3rwEMGgVTIsSIlPEwLtDL3kBuo0yuRpA3x+G8jPPjD3mQw2HQsOPhGXh7ZRKifHjdOjgClntL1wr9kWI5TGZghrXvL0DIhp8XCyfLWuzVmrROfbUUCoH0UFGPM47OVLTg073FeOyyaFw/Kdjp+ZRgb6dKjrbDOGRW1v1FymeiQeloUNWo0qG9w+gQ5ADAkwtj+7zGCZfy8Ns9UzAjSop7fjyNB345i3vWnYaYy0CKtQLmjgUJPtjxcAb+fmA6kgO88Nb2XIckjN5gwtoDJViU6GevQrqDx6Th7hkR2HCmakTM5uvXHfDee+/FmjVr0NbWhs2bN2P27Nmoq6tDTEwM7r//fk9v46ggQMh22ZNjK0/bNLg2+Cw6JoeL8c2hUjz4y1nMi/PBjChHiVGAkI3bpobhw92F9uGCdW1a/H6qCrdPC0NaqAjzE3ydbAE9jUzAQkqwEP/2cCP97WQlgkUcHH9uHv5vViR+Ol6BUmvps0yuRolc3WNGwcbkcDEChGxsOH3xJpNfp0ROtQI3THJt1U0C+AhYuGlyiMtAUsxlOi2Uq1o0CPS+WHqeES0Fi07BwgRfzIq5uK/SQr3BoFHwT04dTpW1oMNodmnTvmJCAM5UtNr3O4lnkPAYUOoMDhl7uUoPMZfhcl/PjpVhtRv5SV8hCAL/XZkMbYcRXx4sdXr+Qq0CLDoFYRLScKI3BFmTCg0Kx4BRrTPgpS05mP72XqS8thM7ci5WAfKszmrRPo6/cYwvH6kh3vj5eN8kayN1QWjryXFYCHYT5ET78EAQQF6t6yBn/alKzHxnH3acr8Njl0U73YfdESnjY16cD746WOqw2OswmvDVwRIUugiq9uY3YHK42N7HlRLsjewus4zKmjQI7bJIzIiS2mcEDSUMGgXXTAzG5vumYUI3VRwA8ObSneRqBwobESLm2H9TgiCQFirCybJmnCprQbCI4zRvLj1MhMyq1m4rZLsu1MOLTXd7/RofJERenRLtest7FNYrMeWt3Xj+j94N8HVFTnUbtg6yAkHKZ0LbYXLom7FJuroGOf2FRadizY0TcHVaILKrWrF8fAB+vXtyj8kugiAQ6ytAYoAXnr48DudrFA7zCt/fWYA6hRb/NzuyV9tx85QQCNl03LPuNIoaPD8YvS/0K8jRaDSYPn06AGDp0qU4evQoGAwGXn/9dRw7dsyjGzha8BeyXMrVypvUYFAp8HUxXPKyeB/UtGmxIMEXa26c4DRQDQAeXxANXwELT2zIRGWzBp/uLQKDRsGtU0MH42u4ZX68Lw4Wyd3KArQdRuy8UI/Fyf7gMWm4IyMMIi4D7/6TD8AiVaNRCEzvxYwQwJL1WTkhANuya+2fuelsFfgsWq8DJRJHxFyGfeYEYFnodK3kcBg0bLh3Kt65Ktnhb1l0KpaO88dHe4rw2O+ZEHMZiHExQ2VBgi84DCo2k9UcjyLiWhZ5nbPcTWpdj/bFnkImYOGaicH46Xi5U3Nrbq0CMb4Cl9cvEmdsDnRdxwOcKG3G90fLMS1SjKkRYjy9KRv1VvOV/DolAoRslwmt69KDcaykGc9uzkba6zvxzKasHqVw7+8swGPrMz30jTyHiMuAzmBCe6dFcKPKfZDDYdAQJua6HBBpNpvx9cFSzIiW4vizc/FAHyXO98wMR6lcbe/5MRhNePjXc3h9ay4Wf3wI646V24OxogYVjpc2Y2En57ykAC9UNrc7VEDKmtROqo4bJ4fgrRWO19uRhjeH4SRXy61VOlUIJoZ643yNAoeK5A5VHBuTw8XoMJq7Hbi6N78RM6KlbhfmKcFCGE1mbM2uRVWLBrd8cwIUgsBPxyvw8/G+9VEBFtndHd+fxCPrz/WrV6632CzQO0vWbEqfIA8FOYBlxtpbK5Kx74nZeHFJfJ9n102JEGNOrAzv/JOPymYNTpQ244sDxXhwTlSvzWW4TBq+uy0d2g4jFn98qNvhvafKml3K4zxFv4IcOp2OvDzLlNWjR4+Cy72YhaBSh84GcSThL2S7CXI0CBSxXS4Ark4LwsfXpeDDa8e7tWjlMCzOJecqW5Hx371Yd6wcN08JgRdncKs3XZmf4AO9wTLE0xUHC+VQ6gy4ItkiueMwaHj0smhsza7Fh7sKsTuvHhNDRRD0oeq0MjUQGr0Rf5yz2BpvOVuDxcn+Q2q1OZboas/aoumARm90qOQAFrmTq8XUO6uS8cE140EhLMeDqwoCh0HDFUl++PZwmUemkveGtvYOHChoHNPVI5uzVOdKXKNSb398KLhtWig0eqOTa9T5GgXiyX6cXiMTWBbsXSs5Va3tIAjg9eVJ+Oi6FNAoBJ7YYAlYupoOdGZxsh/EXAb+zqzBpDAxfjlRide2XnBbrdEZjPj5eAXYjJHnO2RzH7Qd52az2VLJ6SaYj/Xju6zkXKhVIL9eiRsnBfdrbllaiDfGBwnxxf5inK1owf0/n8WO83V476pxWD4+AC/8kYOPdlv6DtYeKIaYy8CKCQH2v7eZfuRUWwKwDqMJVS3tCO1lNWkk4c1hOEkoa9vanRrQJ4aKYDSZUSpXIzXUOciJ8xOAz6S5lazVtWmRW6vA7Bj31vexvgJE+/Dw+O+ZyPjvXpjMwJ8PTMe1E4Pw0p853QZQrnj17wtQag3QG0yDes+ynfd1nVxjK5ra4cWmw4s9tOu5nnjm8lgotB3I+O9e3PbtCYwLFOL/ZvdNGZAU6IWtD2YgNcQbb2zNdUq8nK9pw41fHceqz4/i5m+Ou+35HCj9mpPz2muvYfr06ZBIJGhqasKGDRsAAHV1dcjIyPDoBo4W/IVsNCh10BtMYNAu3jzKmzVuL2osOhVLejEBe3K4GDsfmYHKlnZ0GEx2DexQEiHlIULKxb/n6x0G59nYmlWDcCnX4UZ8dVoQqlra8b9dBQCA5xbF9ekzQ8RcLEryxSt/XkBFswZ1Cq3DTYSkb4h5js3rNuvZrkGOOwiCwPKUACwd54/u8sTPXxGPZrUe96w7jftnR/ao9+4vBqMJd/1wCvsKGmFbz40L9MILi+PHXM+W2Lb467TQkKt0CO9BH+1JgkQcLLIGsLdMDQWdSkFOdRuKGlR4YE7vZAwkluu+gEVzquTUtLbDh88Cg0aBhMfE2yuTccf3p3DVF0dR3KjC9enO/Qm299v56ExwGFSw6FSkHS7FK39dQIiIg1unOc962ZFThya1HjdOHnmyX1ujdYtGjyARByqdAdoOEyR898F8rK8AXxaWwGw2O5gKbD5TDW8O3UF22xcIgsA9M8Kx+qczuPKzI+AyqPjw2vFYnOyPlamBEPEY+GhPIWJ8edh8thoPz4t2SMBFSHlg06nIrm7D9CgJqlvaYTSZneRqo4GuxgMdRhMalDq7gYKNaB8++CwalFoD0ly4SVIpBNJCvd0GOfvyG0AQ6HaNw6BRsP2hGThb0YIDBY1YOt4fAUI2XlmWgLMVrXj/3wL8eOekXn2vPXn12HimCm9cmYjNZ6qtc5SCevW3fcUWENa2dgpyupgOjBSifPg4+sxc/J1Zg/0FjXj68th+9XdymTQ8OCcK16w9hgOFjZgVI4PZbMYvJyrx8p/nEShi46PrUvBXZg0e+OUsfr5rUr9cSLujX6mchQsXoqioCD/99BOKi4sxc+ZMAICvry/Wrl3r0Q0cLQQI2TCbYZcX2ChvUnvkII6U8TE7Rob5Cb7DVsmYF+eDAy4qOTap2hVJfg43GQqFwOMLYvD97emYHC6yV3n6wvtXj8f0KAm+2F+CIBHbZQmcpHeIuQyo9Ua7HrrzjJy+QKEQ3UqTvDh0fHlzGh6cG4VP9hZhX36D29cOhL+yarA3vxHPLYrDPw/PwIfXjoe2w4QnNmShw2galM8cLsTWTHbnIFWu0jnYRw8Fd2eEo7q13W6GsmZ/MQKEbCxK6vu5fSnjI2A5zT2qaW2Hv/DiotFmy9qi0aNV0+E0WLIzIi7Dfl+4bVoYlo33x1eHSl3K1n46VoG0EG/7nIuRhKjL4En7IFCe+yGzsb58KLUGh55Yg9GELZmWyn/npGNfWZDgi3evGof190zB2RfnOwzmfmReNOL8+Fj90xkwaVSnoJFKIRDvL0B2dSsA2Ge7hPYwcHEkYjMesFUHLTbRFrOZzlApBFJDvCFg0RDlZih0epgYZypaXDoC7s1vQHKgsEcZriVYEuHR+TGIlFkSq0waFXdmhOFQkdzew9wdLWo9ntqYjemRElyfHoxl4/1xuEjulHzwFDwmDV5susNxWjlCgxzAsr3XpgdjzY2pve5lc0V6mAjRPjz8eKwCZrMZL/15Hs9uzsaKCQHY9mAGlo7zx8fXpSDBX4AHfj4LYx9cJ3tDv89+oVCI1NRUCAQj70I5HNii9K4X2uqWdicN7mglMcALcpXeyQbxcJEcar3R7UJnZrQUv949xam03RtsjXQ3Tg7Go5dF92qyNYlrRF2qAZ1n5HgaCoXAw3OjkB4mwvN/5ECp7cDbO/Kw+OODPQ6W7A0Gowkf7S7CzGgp7swIR4wvH8vGB+Cdq5JRKlc7GFaMBbgMKpid5hyZzWZLkDOEcjXAIkFYOSEQb2y9gH/P12F7di3uyghzK7clcY1MwHRKiFmCHMdrZEaUFDsemoGvbk7DwkRf9Jbr0oNR1dKOY13sevPrlDhR1jwiqziA89Bie5DTTTBvC/46S9YOFcnRqNThygFW/ikUAqtSA5EeJnIKlhg0Cj64JgUsGtUiIXchOUoK8LKbD5TL1aBRiD4Pkh0JeHPpMJjM9n682jbLOsfXyzn4XD0zAi8sjnfrojotUgxth8lpXo7eYMKhQnm3UrWeWDLOH94cOn485tqI47H1mVjx2WGUNKrwwpYcaDuM+O8qyyyaRdYk7WAaEHRta6ho1iB4jKwP3UEQlmHle/Lq8cSGLPxwtByvLkvAf1Ym2xMzLDoVzyyKQ02bFkeLLdeswnolvjpYMmB7fPLO5CFsGbjOB3BNqxYGk3nMBDk2KVp+naP+ObdWAT6T5lYzPlCYNCpeX56EK1MCB+X9LxVs/RvNKluQc3FGzmBAoRB488okNCh0mP3uPnyxvxj5dUp81mkqdn/Zcq4GpXI1Hrks2uHx5EAhFiX54sNd/Z8sPxIhCALiTnOOLBU505AZD3Tm1WUJCBCycc+Pp+HFpuPqiYMj7xjLyPiuKjlalwtgBo2CefE+fQokJ4WJECLmYP0px/EDv5yogIjLwOVJvQ+YhhIugwoG9WIwbxue2F2QEyBkg8ekOZgPbM+uQ7CI06N17kCJlPFw+Ok5eHy+a0luZ/OBsibL0EdP2LoPNUJrha3VKlmrtY7L6CpXA4BJ4WJc1Y3kK8HfC94cOg52aTY/VtIEtd6I2f2UFwKWxfI1E4Ox4XSVk0FKTnUbNp6pQmG9Cgs/PIi/s2rx6rIEe2JBzGMiI0ri4CrmaTq78NpmKI7USo4nuXJCAJg0KjacrsJzi+Jw85RQp9ekhXgjSMTGprOWBOW7/+bjm0PObp59ZfSdbSMUPosOPovmEOSUN1vK0wMp9Y0kQiVcMKgUp5kEJXI1wqRcssoywhFbHbqarLNyujqrDQaRMh4enR8NgMAPt0/C3TPC8fXBUlQ0OU9DPlHajCUfH8Lp8u7nKGg7jPhoTyFmx0jtE7A78+hlMWhQavHt4TLPfIkRgpjHtM85kiudB4EOFVwmDZ/eMAEsGhV3ZoT3q6n7UkcmYDoYDxiMJtQptP2qdruCIAhclRqI7Tl1dscos9mMf8/X4YokP4eh0yMJgiAc7IoblTrQXMyC6gyFQiDGl49cayXHbDbjUJEcM6OlQ3JPErmxcQcumg9sz6nDzgv1iBjgcN7hwptzsVcKsBgEMGgUuzqgL1ApBKZGSpwctTadqUKImINk62/WX26YFAyN3oCfulRzPthVgCARGweenI2VEwJw0+QQLB/vWOm7dmIwEvwFMAyS3DlAyLIHORfHi4z9IEfAouOFxfF4ZWkC7prhenA9QRC4MiUQO3LqcKykCf+cr8dD86IGfK0igxwP0nVWTlmTBgTR+8bukQ6dSkGEjIf8LnadJY3qHgdEkQw/XR26us7IGSzunRmBE8/OxfQoCVbPioQXh443t+U6vCavToE7vj+J/Dolbv76BE6VuQ903v0nHzWt7XhyYazL5yNlPNw4OQQf7i5A2RhyXOs8EFSuGr4gB7A0ex97di7u89AsnksNGZ9l7Wuw6M/rlToYTWaPBTmAxZ1SbzTZM9OFDSrUtGkxawByoKFAxGXaKzkWSSazxwHSKUFCHC1pgsFoQkWzBtWt7ZgW6TzHa6ixmQ88sykbAPDcFX0z3xkpeFvdXG3mAzWtWvh5sfodRM6IkiCnps3eY6jQdmDH+TqsmhA44MA0SMTBdenBeHtHHrZnW6RnmZWt2JXbgAfmRMGby8BbK5Lx2vJEp89amOiL15cnDVq1zSZXM5vNKLTOj4mSXRrOlNdPCsYtPYw+uTIlABq9Eff+eBphEi5WThi4eocMcjyIv5CN6k7OGUX1SgR5c0Zs1qw/xPjwHORqZrMZJY0qMsgZBXA69XW4mpEzmNgWKTwmDU8tjMWO83V2Q4Ka1nbc8s0J+Huxse+JWYjzE+D6L48j7fWdSHn1Xzy3Odte+TlSJMdXh0rx6GUx3TZiP7kwFmIuE09u6HlmyGhBxGVAru4S5HTjOjXYeLHpZPW2n8j4TOiNJrv8x6YA8GS/hp8XGzOipPjluKXhd19+AxhUCqa4GOI7khBx6Q49Od1J1WwsSvZDs1qPYyXNOFzUBIKwuJION1QKgUnhlsbrjaunjtr7pE2uZrORrlO0u5z911umR0lhNgOHrf0X27JqoTOYsCLVM5L0V5YmYGGiLx789Szu+O4kbvvuJELEHKxIGV531gBvNrQdJjSr9SisV0HIoQ95X+VIJkzCRUqwEK2aDjw8L8ojwSYZ5HiQIG+2Q+Y4p0aBxICxZcwQ4ytAQb3KvnBs0XRAoTUgfJSW4S8lbH0dTWqLeYRGb0SQaOirjCsnBCA9TIQXtuSgRa3H3etOgQCB729Ph7+Qje9uT8dD86Jw4+QQXJUWhK3ZtZjxzl6Me+Vf3P79SUwM9cbdbkreNnhMGt5emYwTZc344WjZ0HyxQUbSWa6m0oMgLrpRkYwufKwLxHqrk9NgBDmAZfL4hVoFTpe3YF9+IyaFi0a8vNAyk8US/PU2yEkJEiJAyMbW7BocLpYj0d/LvjAfbj6/MRXbH5rhskl/tCBg0UClEHa5Wm3bwKSVAUI2wqVcHLK6tW44XYWpEWKPHf80qsUUYkVKIJQ6A1akBODLm9OGvR/K9pvVtGpR1KBClIxHJoq6cM+MCFyR5IclnZwMB8LIvtqNMpIDhfj+aDnkKh28OQxcqFFgTuzYmh8R68tHe4cRlS0ahIi5KJVbrBqHcl4HSf+xzco5W9kKABgXKBzybSAIAm9emYjLPzyIBR8cQFt7B36/d4p9EcBj0vB/sy+eNw/NjcK27FrUK7TQdphw05SQbi2sbUyPkuDGycFODaijFTHXUa7mzWEM+02bpH/I+BcHgsb6Wlw5uQwqBGzP3pJnxcgQ6M3G5/uLcbKsGU+5kXiOJMRcBnJrLZLoRpUOCX4992hY3LF8seF0laUfKW3kmNSMheHVBEE4zMqpbdUOuFKWESnB31m1eG5zNk6Vt+B/14zzxKbaYdAoeHtVskffc6AE2l14NShsUHp8JsxYYGGib5+cJHuCvEN6kFTrDJezFa0oaVShvcOIpICBNdGNNGKsDmp5VslaSaPV+58MckYFIi4DzWo9zpS3QMpnDlu/WKSMj9WzItGg1OE/K5OQ3E2wxWXScFVaEO6fE4XHF8TYs+C94bVlibh/TpQHtnj4EXEZ0OiNaNcbh8U+msRz2Kaf2xzWbPbRns7qUikEbpocgl25Degwmkd8Pw5gGQjaoumA0WRGRZOm1xWQK5L90aLpQLNaj2kRkkHeyksPEZeB+jYtDEYTGpRal85qfWHpeH+w6FScKG3GnFgZFiaM/VlbEh4TDCoF5U0alMrVbmcJkXgOspLjQULEHIi4DJypaIFKZ8l4JI6xIMfPiwU+i4b8OiUWJPiiVK6GjM8Ej0keSqMBMZeBYrkabeUtSA32HtZS+cNzo7B0nD8iB/FCP5akADaTgSa1DnKlfthMB0gGDodBA59Js8/KqW5xnpHjKa5OC8L7Owsg4TFHhbuXiGsZPHm2ogUKraHXPUTjAr0QIGSjUanDxFAyQ+5p0kJF2J/fiAalDiYXg0D7SmqICIefnuOhrRsdUCgE/IQsHC1pQofRjCifkX8+jnbISo4HIQgCE4KFOF3eguwqBQKE7H5ZLI5kCIJAjA/fbiNdKied1UYTYp4lG5dV3WavPA4XFAoxqAHOWMPmjlev0OF0RQv5241ypAKmfdhlTavn7KO74s1l4LH50Vg9K2JUBP0iLgMms2UWFp9J6/V1iiAI3D0jHNdMDAKbMfolYiONOTEyVLe240CBpY9moJWcSxV/L7Z9SO+l4qw2nJDpdw8zIcQbH+0uhMFoQoL/2DIdsBHjy7efpKVyNVKChcO7QSS9RsRlos6aPZ4wzEEOSd+wJUw2n61Co1KHFR6w1yQZPnz4LHslp6a1HUvHe6bR1hV3zxg9Vt82M42/smqQES3p0xDUnixqSfrPtEgJmDQKfj5RAQCj2khhOPEXsnG0pAl8Jg0+ArIaP9iQlRwPMyHYG9oOE85UtI65fhwbk8PFKG5U42BhI1nJGWXYqgEMKmXMOf+NdWzDXH8/VYUoGQ/jBjg0j2R4kQmYaFDqoNB2QKkzwF9ILhoBS+UJAFo1HZgVLRvmrSGxwWZQMSVCjKyqNjCoFIjHmEplqAiw9sFG+pDOakMBGeR4mORAL7vzU+IYXYRckeSHCcFCPLY+EzqDCWESUjYzWrDdmJICvcbU/KZLATaDCg6DCp3BhFWpAx+aRzK8yPhMNCi1dvto/wH2OIwVOku8Z44Co4RLibmxlqDTdwCDQC91AqzJjMhR0B83FiCDHA/DYdAQbx1SmOg/NoMcCoXAa8sT7QMJyUrO6MG2gBjufhyS/iHmMUClELhywvAOtSMZOD4CFuratHhsfSaoFIJ0qLTibZWrxfsJ+uSkSDL4zO4U5JD0jwChZQA3aTowNJBBziAwKUyEIBG7V0PMRisJ/l64eUooWHQKgkWc4d4ckl7iL2SDQgCTw0n3odFIgJCNObEyyPjkImO0EyzioMNoBpNGwbrb08kFvRUGjQIpn4l58T7DvSkkXQj05iDeT4AwMRmQ95dQCQcEYVlDkQw+hNlsNg/XhwuFQrS2tg7Xxw8aGr0BinbDqMp29GdfGIwmVLe2I4S84HmUwT4vyuRqhIg5pNygF4y0a5RcpQODRoGARR/uTRlyRtq+GCgmkxklcjUipNxRdy4O9r6obm2HhMcgJbW9YKjPi0v5GtQTvd0X5D146CDd1QYBDoMGDmPs/7Q0KoUMcEYhpCxm9ELOxhk7kBbq7gkYJDttkoFDXoMGDnkPHjqGtZJDoVAgEJAOT4MFk8lEfX19r15L7ovBhdwXIwdyX4wcyH0xciD3xciB3BcjB3JfjBz6si9sDGuQQ0JCQkJCQkJCQkJC4mlI4wESEhISEhISEhISkjEFGeSQkJCQkJCQkJCQkIwpyCCHhISEhISEhISEhGRMQQY5JCQkJCQkJCQkJCRjCjLIISEhISEhISEhISEZU5BBDgkJCQkJCQkJCQnJmIIMckhISEhISEhISEhIxhRkkENCQkJCQkJCQkJCMqYggxwSEhISEhISEhISkjEFGeSQkJCQkJCQkJCQkIwpyCCHhISEhISEhISEhGRM8f8CVBY8+uUUfAAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 810x540 with 80 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Show digit examples\\n\",\n    \"\\n\",\n    \"fig, axs = plt.subplots(nrows=10, ncols=8, figsize=(6, 4), layout='constrained')\\n\",\n    \"\\n\",\n    \"for y in range(10):\\n\",\n    \"    for i in range(8):\\n\",\n    \"        axs[y, i].plot(data['x'][data['y'] == y][i])\\n\",\n    \"        axs[y, i].set_xticks([])\\n\",\n    \"        axs[y, i].set_yticks([])\\n\",\n    \"\\n\",\n    \"    axs[y, 0].set_ylabel(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"iXu9DmRjTtCZ\",\n    \"outputId\": \"7592f157-b65f-49a2-fa29-d9d9771a5a92\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Offsets: -0.00 +- 0.30\\n\",\n      \"Slopes:   0.00 +- 0.04\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Look at the distribution of means and slopes in the data\\n\",\n    \"# to adjust the strength of data augmentations\\n\",\n    \"\\n\",\n    \"offsets = data['x'].mean(axis=1)\\n\",\n    \"print(f'Offsets: {offsets.mean():.2f} +- {offsets.std():.2f}')\\n\",\n    \"\\n\",\n    \"x = data['x'] - data['x'].mean(axis=1, keepdims=True)\\n\",\n    \"t = np.arange(x.shape[1]) - (x.shape[1] - 1) / 2\\n\",\n    \"slopes = (x * t).sum(axis=1) / (t ** 2).sum()\\n\",\n    \"\\n\",\n    \"print(f'Slopes:   {slopes.mean():.2f} +- {slopes.std():.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"id\": \"mrBlREVkTPX0\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define data augmentations (such that it works both with numpy and pytorch)\\n\",\n    \"\\n\",\n    \"def augment(x, offsets_std=0.3, slopes_std=0.04, maxshift=40, mode='numpy'):\\n\",\n    \"    lib = torch if mode=='pytorch' else np\\n\",\n    \"    n = x.shape[0]\\n\",\n    \"\\n\",\n    \"    if mode=='pytorch':\\n\",\n    \"        x_new = x.detach().clone()\\n\",\n    \"    else:\\n\",\n    \"        x_new = x.copy()\\n\",\n    \"\\n\",\n    \"    # Subtract offsets\\n\",\n    \"    x_new = x_new - x_new.mean(axis=1, keepdims=True)\\n\",\n    \"\\n\",\n    \"    # De-trend\\n\",\n    \"    if mode=='pytorch':\\n\",\n    \"        t = torch.arange(x.shape[1], device=x.device) - (x.shape[1] - 1) / 2\\n\",\n    \"    else:\\n\",\n    \"        t = np.arange(x.shape[1]) - (x.shape[1] - 1) / 2\\n\",\n    \"    slopes = (x * t).sum(axis=1) / (t ** 2).sum()\\n\",\n    \"    x_new -= slopes[:, None] * t\\n\",\n    \"\\n\",\n    \"    # Circular shift\\n\",\n    \"    if maxshift > 0:\\n\",\n    \"        shifts = np.random.randint(0, maxshift, size=(n,))\\n\",\n    \"        for i in range(x.shape[0]):\\n\",\n    \"            x_new[i] = lib.roll(x_new[i], shifts[i])\\n\",\n    \"\\n\",\n    \"    # Generate random slopes and offsets\\n\",\n    \"    if mode=='pytorch':\\n\",\n    \"        slopes = torch.normal(0, slopes_std, size=(n, 1), device=x.device)\\n\",\n    \"        offsets = torch.normal(0, offsets_std, size=(n, 1), device=x.device)\\n\",\n    \"    else:\\n\",\n    \"        slopes = np.random.normal(0, slopes_std, size=(n, 1))\\n\",\n    \"        offsets = np.random.normal(0, offsets_std, size=(n, 1))\\n\",\n    \"    x_new += slopes * t\\n\",\n    \"    x_new += offsets\\n\",\n    \"    \\n\",\n    \"    return x_new\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Jwn0b5CBJ+AlBBm2GcDUujfxvz2iMatt0gyY/vJrVlxbzu922YTYFKpSIoMYe+LnemFN9B0D5kmZEEOT/J6ZDUuuVqzVzApDWEPqAKgZZt4dmTQkp24zSIv3ZXh0nJKWLgDyfo8uUR3D86yN+nK0WGLiyBgjQY/tmdO3pMBGSidq+JUalUvLvdl7krL1OqULHXJ4F3tvvSwdaI3s6mDTvYtbWw8wWRhtdUJPqA0wCR0nXxT/BaU3eatVIpFLH+GgBLejZ7KuBNAndBfioM/uCOt3Zejwc1TdRGfCYM3joW1IbaGrhbGXA1SjJybuegfxLaGnIGu9fQ9DwlUERtPYQQUM/WpmipyzlTkyNLXVP8BsMkI+eRJC8jAX0KUbd0rXEbMwNtYuV2UnpUUxB+HMoKRegboer1xPILTFl6gcDEHF4c7ML/ZnYhMCGHJ/66eEuRxXUERJ97IFMAotLyW3SfgKj0fJzM9JBnRgAysQCsgU52xiiUKkI13EGlgMTr922cjxzhx8DcnTilKZFp+fR3rSOiYdMVFCUY5YQ0bd3BfSA1r5icojLaWNYjMhN+DMxcceo8lPisQqLS6zC8ZTLRUyLs6INb56drKlKHzN1g/1sNLnZXqVS8td0XhVLF7090ZoCrOWsuRN1KgfH/F9pPFQbh7ehbiOj6fTByAhJy2HI1lndHt2XXon4cf2swzw505rMJnnWmMFYh4pToISJXE/81BUqFUCCz6Qz9XhV1jXtegX8X1uwMKimArXNFxKT3iyK7YcscYWQ2N17/gGN/sHC7463d1xMY4WGFXqdJYNsdjnxW52+wm6MJXtHNK1jREjngn8hgN0v0tNSr3+DGHtDQA+chAGhrqNGztSmnaxIfAJFyHndFSErfJyQjp4WQEiX6SBjZ3nnjViZTxxH9vPsvw/fQE3FS1HOYuVBQUsbTa64SmpzHn7O7cvSNQbw8zJUJnWzY9GxvYjML+OVIsNivzQhQlEDkmeYcfYO4FJHO9L8uMPink/xzPqq5h1MjkWnCyCEjAgxthXR3DbRtZYCmmpyL+a1ATQviHrGUNZXq/tTqqVTCIdBmGOfChMeuzqhGq04gk9+1Z78lEZYicvddLetRc5EWBpZt6e1shlwGZ0PrkfLjOlKkFCX73+NImxE1DRj7I8R7iVq6BrD2QjSnQ1L5fkpHJna25YXBLsRlFnIpMkP89rJiwNKj5gM49hNRiyY2Enddj8dYV4On+7dGLpdhYaDFB2M96ObYgChOaogwJBz6wLhfq2QQNCrpYUKQpVUncW0m/QmT/4KgfSKCdPt3VZgJ6x8Xxvb0tTDqa5i1BTrNgrO/CgGY2vDZAhtnwLan4NLyxv0sKTdEfVG3J+94K7+4jODkXOF0kclEtC/Zr846re5OJkSlF5CaW8fneoSIyyzAPz6HMR2sa97oxh5wG1nluTzQ1YKrUZkUlNQQlWwzTDghI++fAIZk5LQQsuNDALBy8qx1uyIjF6xKYh9cT19LJeIktB6EQqnilU3XCU7KYeWTPRjboRVqlbzP7W2NWNjfma1Xyvt+WHqAkQME7W2+sTeAmPQCZq24RE5hKb1am7LkRDiFJS2z91JUWoHoM5ERAWbOtW6rpa6Gh40h3gkFwmMZ9wD2L1KpxALhbhob/vc8fGUBP7nD9gWibqYpSPIV4hxthnEmNI12NoaY6WvVvo+WPpi7CyWeB5zwlDzkMnAy161747QQMHfDSEeDTvbGnA2rxzVx6gfqOg9uyloFrQeKiMvRT0VNRz1IzS3m2wM3mNnTgeGeQsiid2sz7Ex02OYVK+ShSwvAuGZpZizcoTgHchIa41NUi0KpYrdPAmM7tEJT/S6XUCoV7H4JdM2FQlxdap73QqKP+Ne6Uo+mTk8IYydwJ5z5SbymKBXzz4rhIuV37k7wnCjek8mg78siTSzgv5rPdX4x/PesmH+youHA2+C3vfE+i9caIUXs8dgdbwUl5aBSgWdFr67WA0SNpt/WWg/Zvdww9ZLqcm7iF5cNQK/WNTiwCjKEI8Z1ZJWXB7iZU6JQCqdEdZg6i4yM+5iyJhk5LYSy1FBSVcaYmtTuCVK3dEOHIkoypU69jUZWjFhIOw/mSGAyR28k89uMLnS2N6528yf7OaGlLuev0+Fi8m8/WaiM1OXhagFciEhDqVKx+dnefD+lI5kFJaxtgf1LCksUJOUU0dpcVyz6Tesu5u1sZ4RPbJZIU4j3avpBNiZKJRx4RywQ/hok6sPqS8J1IZrR7Skh0xl+HJb2g8jTjT/OkEOgoYfSoT/nw9PrTlWrwLYrxHs3/njuM6EpeTia6dXYG+cmhVmQnwJmIv24fxtzzoeno6hLqlZDRxgID0Od36hvQN8KVgwTKUZ1sPJsJDJkvD3K/eZrcrmMKV3tOOCXREFqeW2OsWPNB6mI8qTeuIeB187lyAySc4qZ2MmmYTsqym45J0OPQOwl8R1VKMQ1FYk+YGQPerctWNs/DgPehONfwY+u8H1rMf8Y2sKCg+DYp+r2Vp7it3lpWfVO1gt/isasfV6ChUdh4TFhJO15rUoTyKJSBTu94/l8TwAf/udHbn2boZYVi750nWdVG9UPSMhBTS7D3bpSlNWpn4jq1+IUtjPRwdJAS0pZq0RIch4G2upYGdbgwEot7y1k1a7Ky+5WBpjra3EhPL3mgzv1v69OSMnIaSFoZEeSUoN8dGUM7cWPKjXqAU5naGlU9A5oPYitV2PxbGXI6PY1h2mNdDSY39eJjZdiRIi7wzQoyhbhfYCcROEVa4FcisjA3coAY11NnMz1mNLVlmWnwslrYf1LotJFjZOTqQ5kRgoPUB10sjcmJqOAXPNOkBMvrsODwv434fJyGPGl8EBumQ0+m+u37/EvhXds7I8iReP5s+L72jBdeGQbk5CD4DIE3+QiMvJLGORaQ1Hq7dh0EQvPB7B2rTJhKXm4WNSjHic9TPxrLtKP+7UxJ7eoDN+4rLr3dR0hFsDN3NjynjGwgmeOi4jOnleFF74GsgtKWX8xmtm9HDDVqxrVmNrNjsJSBdd9fcULtfUbMmkNapqiWWgTsdsnnlZG2vRwqmdqWsoN2LUIfmgNS/uKBf/xL4Uzxm1Uk43zJok+IlWtOoZ8BGN/gu5PUdbnZXj2FMzfLQya6uj5nFC6LE8HVihVvL/Dj7eXbkN55BNUPZ6BkV8J559MBo/9Txhxax6D3a+gvL6JVzdc4bUt1zkSmMx/3vE8v96LkrJ61G4l+0NRVrVRHIDAhBxcLPSqyh3bdheyxbXImstkMro7mXBFEh+4SUhKLm5WBjWvR1ODANnN+a0CmUxGRzsjAhNqkcG37gipwVBa1HgDroWH3sgpVSjZ7ZNAUWnLTMmpwLAglpxa5KMraNXagzKVnOzYRl68PGoEH4Cl/UVedMRJsO5AUpkeJ4NTmFFLp+oKnu7fGpkMtl6NBav2YNFWNBItzIQ142Hfm03/Ge6CS5EZ9Gp96+H8tnMsS8s+5fjBHc04qjuJLJePdtHOEbK01RUa30ZF5M2fcvGOB0VKOjUYrq6CkV9Dv1dgxgZwHyekXusylqPOCuN66Eci3x5EL6E520Vaz7YnG2ZYKEpF7Ux1ns+8FBEhcxvFyeAU9DTV6F7fhZ5tV1ApIdG3/mNpgYSl5NVPdKBCHMa8DQBdHUzQ0VC7WcdUKy5DxXf1IKZc3o6mHkxaAp3nwP63a7z+/5yPoqRMyTMD73Rm2Jvq0tHOiJTYUFDXBr1aDGs1dbHwui2So1Kp6l4DlORDdjxkRtfo+S8pU7LfL4nHOtnUT0RDqRT1KWHHoNt80atmaV+R9jns46arw6l8/tqMHLkcej5DyYD36HGmC3/cqEEKvgL3MSI9+/SPKBVK3v3Xl61XopiX9gtxZca8mjap6mfSMYZZm4UKacxF5Duf592IJ/l3aBZn3x3K8rnduRyZwQvrvfjtaAi/HA7mXFhatUaPKuE6KuQEKB2rFcwJTMy5lapWgV25mmMdkf0+Lub4xmWRmS/1IAQIScrFzaqWeS4lSNQwa+jc8ZZnK0MCE3NqFjWy7iDqcpow2lqZh97I2eOTwCubrvHnibDmHkrNqFRYlcVTZlyzelQF1iaGxGFFWUrIfRjYQ0pWDPz3HKQEiALLiBPgPJh/r8WhriZnUmfbOg9hoqdJH2czoSQikwlvZfBB2DpP9BTo89J9+CANIy6zgPisQnq2NhOKO1vmYrF7Nt3UwvC4/hWlZS0nmhOZlo+ephpmxeXy0fVIV3My08NQW52zaTpiIRR7qYlH2Uj47xAqNd0XiL/lctHYLjsGfGvPJ+f0j8LIbvc4IBZhEal5ROZA3oQVIi9+31s1p2tkRMCOZ+H0T3D5b1jcHf4eAqd+uHPbihQq15GcDE6lbxvz+tckWLUXPaXuQ11OUamCv09HMHP5RVE310jkFJWSkluMa72MnBCRqqVtBICmupxezqY1y6tWxtQZtIyEx7wxyU2Gk9+J+q3DH4H3hsY9fm2M/RHM2oj58bYanTKFktXnI5nWzRarHVNFBPL6RiHrX46HtSGy7FiRdlWXYWDR9mYkJy2vmC/3BtL/+xO0/fgg3b48wqIN1yhT3LaIjr4A39rBr57we0dYNkA4Hm5T5roQkU52YSnjOtTQO+R2ok6Le3DKChHhWHhcqA26jYbWg+p3jIaQFiYK/jOjxN+xl0SNUk1GTjmhKblkFpTyy9EQTgbX0mdIrgYjPoPQQ2xb9wfbveLY0cWXDopAQnp8ye4b2VyPzaq6j1U7mLKC/YN28VjxV2iZ2tDt/ItwZQX9Xc35aVonrkZnsuF8BNfOH2HOigv0+Pooe3yq1lX5XTlFqLIV45Zdo+93xzlfyWFQqlASlJRLOxujqufWtxTpjXU4DIa1tUSpgpMhjddjKaeolOuxWWQV3LvhdDwo+aboSVNTUqYkMi2/dnGV1Bs1CoB4tDIkI7+ElJqEHCpS3JL8qr5emFklrbGxuCcj59q1a+zcuZOdO3dy7VrLLCo9e/4cZ7Ve5dLpA0S00K62OWnx6FGEpmWbOreVy2Uka9qhldWCjbaWjKIUtj8tmlI9cwKUZVCQjtJJpKqNbmeNka5GvQ41wNWCazGZItWrwxQhQR11Fqauqlbesrm5XF4M2LO1qfDu3dgNY34kdtx6XFXR+BxY2cwjvEVUWj6OZnrIMirko53q3EculzHc04r/vBNQuo0RBsL9UBy7F1QqCNgBbceCZqVi9ladRFHnmZ+FQVodGREiCtnrOWEYAe/t8GXoz6cY8tNJeq1I5LTru+CzEc79fuf+ijJh4AQfhDO/CMlfCw/osRBOfnNnulzIQWjVmQy5KT5xWQxxt6z/51TXEn2Omlhh7UZiDoN+PMG3B27gG5fFezt8G00mvWKRUa9ITnroHakc/VzMuRaTWXdqjkwGNp0az8hRqeDoZ/Bbezj7m4gcBu6qvYC8sdHUFUpdihJY1g/WToR8kbeflFNEVkEpox1UordJ6g2h+vVLOzj8MRRm4W5tgEFRAsraUtUqsGwrPqNKxQ8Hg9h8OYYBruZ8M7kDU7vZsc8vkd23LaDxXi9qUWZuFvO3oY2QdvavWjR/0D8JGyNtOtrdtpiuiWvrhNHqWN60Ut8CntonztMUUZzsWNHSYHEPWD8F/hkrzu/Qu9bdKtKLejiZ8urm67WnG7V7nGjzgQyN/In9bvvoFPgD9FjI4LEzcDDVrdahXFSq4Ot9NzBz64XNK0eh+9Mi2+HqKiZ2ssHnvT5ccVnFej4k0OYbFlhH8PImbz7bHcDZ0DSWnAhDluSDwqoTaxb0xNZYhxc3XiM2Q0hgR6TmU1KmxNPG8I5zY9ejTiPHxlgHj1aGHLvReEaOV3Qmk5aco/MXR+j9zTFuJNbyndZCRGoez6z1YuGaK/clIykqPZ8ypapqbdPtpAYLZ0I1eLQS+93+G/KOySQ0ORe0DUVaaWUjJ+4q/NkXlg+q4txoDO7KyPHy8qJt27bMmjWLpUuXsnTpUmbOnIm7uzteXi2n4DcqLZ92Sf9hJ0vjO40VfLbTu0X2BUmJFmE7Q1v3OrYU5Oo7Y1oY3ZRDejhRKkURZPxV4Vmz6QxzdkDPZ/GWtyc6vYDp3etOVatggKs5pQoVlyLSxYOk1wsiB9l1eJN9hHvhcmQGzhZ6WBhoQfR5kbvedR7O3UfhrdUDW+9fULUQ8YSo9HxaVyirGdnVKh9dmTm9HUnILuKSxRShAnZjdxOP9B5J9hde//JITBUGvAUZ4TWrE3mvF8Z6+b5hKXns9I7nuUHOrH+6F5O62DL/ujs7dKYIlSv/21ISz/9PPFxmboT3Y+HtcJFaMvYnCj2modi5iKJDn4nakMBdQtDAbTRnQlNRqai5SVxNdJ0Hjn0btk8DUChVvLPdFz0tdY68MYhfZnTmZHAqW67ENsrxw5KFkeNSr0hOmIhcVKKDnRGlChUhyfV4iNt0EYISNVHffHalEva9IaR/+74CbwTCsyfgNT+R0ng/MW8Dr1yHycsh6pwQywDiMwsBcFSVX6fZ2+FVH+gyR0QXT35HW2sDWpFKjlY9IigWHlCSS2lmDIcDk5nd25HvpnRkVi8H3h/rwSA3CxafCLslAlFaJOaJDtNEOlb7KTB7q2hceHHpzSioQqniSGASo9pb168XTkGGkNntMudOg6ap0tRchsDLXkIJLTtORI9evHgzolgTgYk52JvqsGxON0z1NBn7vzO8uMHrphFRmb1+icyIm4ahWhmeMRtFHeDYn1BXk/P8IBcOBybf8Rv/53wUidmFfDjWA5lcLuqAus4ThuSv7YTYStRZGPUtOjq6vJr4Ln/3SGT9xWjmrLzE74f88VSLo23X/gxys2D5vO7oaarz/HovikoVBCQINbA70tVAGDlJflBaWOt3MKytJadCUim9Pcp3l/RqbcquRf34/YnO6Gurs2jjNfLvovb1p8PBGOloEJdZyB/Ha+iRmB1fa91RQ6i4dq41pasVZIhnaw1GjqOZHrqaagSWG3WlCiXfHQji8aXneX69l+h5Zd3hVkT3xl5YPUYIYxTniua4jchdGTnPPvssixcvJigoiEOHDnHo0CGCg4NZvHgxzzzzTKMO8F7490oEk9XOonAahJMqng5Ra2tXfaiG2IwC3t3uy36/pitizo0TRk6r1u3q2FKgMm2DhTL1gS/ivS/kJIrFYOwVUdx9fT1M+OPWYsvKE8b+yInwHAy11RvUrbqNpT7Whtq3UlDGfAdd5zbBh6g/mfklfH8wiG8P3ODv0xEUl93y/Ih6nHKFnZgLIm2i3HgoGfQRrZRJRB75676NtaRMSVBS9d6tyLQCYeSkR9RLdKCCLvbGtLMxZGmQrvCeXlrWWMNtGvx3iNSkNsNuvpRdWMq8VZf51t8QhdtYsUi9PbSvKBPpRu2nCIlmYPHxUMz0tXh9uBv9Xc35enIHtj3Xh4/ypuKlNxC2P1WeCrRJePZPfCMa/Tn1p0gBmwIK2Ho1luVnIugbOJmVpaOQX1iM6gdnkWZk6QHd5nMiKAU3K31sjO/Mx66V7gugx9P3+IXVzNoLUfjFZ/Pd4x1xsdBnVDtrHu9qy5d7A6tdrDWUsNQ8Whlpo19Tc7wKlAphnN4WyanwMtfqJa+gVWfIS6oqnqFSicjA6nHwTStRs1UXRz4WaVfjfhE1ILoN6OPSFKhrQqcZQrmrPP0xIVssPi2LokCuXi4z6wSjvxHqXFFncLfSx06WRgL1MKzL02hC/K6ICNFtIjKvDHMlIjWffRXP9NDDIqWrw7Sqx+n1vEivLI8CeEVnkpZXwuh2tfQOqYzfNlCWiv4y9wmFUoV3chkM+wQWXYI+i0QUtQ4CE3LwsDbEVE+Tg68N4MtJ7fGKzmTasgs3M2CKShV8sSeQlzZ607VDOzTm/QtP7Yf+r9802qZ0s8XSQIv/Hbu1GM/IL2HJ8TBm9HDA1ao8OiCXC4fg3J0iYm1kC08fgj4vCmU215GMiPie62935/ibgzg02xw1VRkymy4AmOppsnROV0JT8nhp4zV8YrOwMdLG5DbRCkDU5SjLbklp18AwD0tyi8q4EtU4gh+6mup0sjdmYmdb/pzdlYSsQj7e1TDBKO+YTPb7JfH2KHeeH+TCX6ci7nxmKhWwbjKsGHEzOnovhCTnYayrgUVNrQFSy3sEWlZv5FQo3FVErl7e6M3fZyKY3NmW8NR8ToWkCvGBJD/hhDn6mYg0Pn0UPCcJx0IjCjfdlZGTl5fH8OF3eqtHjBhBfn7LWHiXKZSkeO3CVJaL2qivoPcLvKLxH16+tf/QK7PsVDgf/rqEub5zid/zdZNFgcpSgknEHGPj+klJ6tiISTw7rukUZJqMikLIi0vhyKeiGPVu+oLUhwoRgF2LYOVw8bAf/6vwrN3G6dBU+rUxR12t/reETCZjgKs5Z+rT5O8+seJsBCvORLDPN5HvDgbx/g4/VCoV3jGZRKblC9EBlUoYOZUkQnv0HsQZ9T7oXf1TLKAbQFGpov4yoOWcDU1j9O+nGf3bGVaerZqHm1tUSlpecXmPnPAGGTkymYy5vR05HZJKiseTYoES13Kiy1WoSFXzGH9zIVJYomDhmit4RWWw6lwkExPmUmRgL4yTyr0/wo6IRXC3+QCEp+ax2yeBFwa5VFEX6u5kys/TuzAjfSFH2nwkvH07nxeL5XaTxMIX+PFQMO/v8OOd7b58sz+Ioe3scJ/3O2NUf7BW7ymSp++DhUfxy9HjZEgqgxuSqnYfSMou4ufDIczobi/SMcv59LF2GGhr8PZ2H+FBvAfqLTqQFS3Sssxdq7xsqK2Bo5nuTa9zrZQv5qqkrMVcEL1VZDLoMB1O/1C7Al96uJhnh3zYpMblXdFmhEirKsknPrMQA211tLPKo19qldKFnfpBcgBmJfEYyAoJL6nHM9LECdS1iQn2wtpQm852xlXe7uZoQr82Zvx+NETMW35bwbLdTUWxm78T9zGinuPiUkCkqpnra9ZfbMN7vfichvWs32kE/r0Wx+NLz/PLkZC65crLUalU3EjMuWmEa6mrMbe3IzsX9UNHU40nll/kjS3XGfDDCdZdjOL9MW35Y2ZX5E5974jMaqmr8foIN/b6JvKfdxylCiXvbPdBoVLx+oiq9wMymYg8PfYbzN8jvPsVr4//DcqK0D/xEc4W+rQuDgVkYnFcTkc7Y/6c1ZWTwamsvRhdfaoaiOOqadbZILqTnTFmepocb8SUtQrcrAz4YmJ7dlyLZ69v+TxeUgBLesPvnWHV6GrH9+OhYFws9JjWzY6XhrbBwVSXp1ZfISgph1KFkq1XYgk89DekBYseUgffveexhibn4mZZm7LaDapTVqtMhfhAWEouBwOS+GJiO36e3ol2NoasOBshrklJLnivFam9A94UDtd+r4iUy4Cd9/w5KrgrI8fS0pJVq1ahUNzyEisUClasWIG5eT37JjQhKpWKHw8HM6L4KAWmntCqI7LB76EmA82Q+vWf8I3LQvfIu6xV+wp3zVQmF+/COzK5ScarnR1GsmY9co3LMXMUEZ+0qAdIqUilEpP+Lx7w10CRa+3/r0jFWTtJFMU2hKhzEFNLYXlZCWyZK5qXPXdaeAkWHrtV3F2JjPwS/OKzGejWwPQboL+rOeGp+SRk1R4Kvx8UlSrYeCmGmT0dOPvuUL6f0pEd1+J5ZfN1Zv59kfa2hgzzsBTpUQXp4HDrASWXy1D1fQUrRSIRpzfW+5wJWYWM/+Msfb89zrJT4XXmDOcVl/HOdh/mrLyEsY4G07vbsWrvKbw2fn4zMlkRLnc21xGFiPVQVqvMhM42GGir811ka1SGdnD5/kWnqkVRKupebvdORZ0RBcIdpt586bUt3vjGZbP6qZ7sfXkA+ejyEu+jQgWbnhAPxtJCIRRg1V5E44C/ToVjpq/FrF53ziNjOrTixWEePOPvybru2+DNEHg7TKRsaujgG5fF6nORvD3KnaAvR+P98Qh+nt6JQW4W/LBgFD/nj6b3umzG/3GGxxafxVRXk1k96z9fNTUqlYqPdvqjpS7nvTFVvYtGOhr8MLUjFyMyWHOP/aCCEnNwq/BE18ZNZTXXO95qZ2NIQH0iOSZOoG1c1cg5/4cwAubthsnLoOMM2P3yLQn82zn9k4jc9FlU9/nuN64jhCEYeYb4rEJsjXXK8/xvS9l27Aeobgpw+OUZ131suRoqc1fKEm8wur11tSpo74xqS1J2EbMWH0IZcphCjylcjszg2bVX8fz0oIi2ydVEvVvgLlTp4RwKSGKEp3WV5tA1khoiFNQ6PVH3to3IlK52vDykDX8cD2XuykskZtf9XIrPKiSnqOyOVK9WRjpsebY39qa6BCTkML5jK3a/1J/nBrnU+h080cOexzrZ8MEOf55b58XJ4FT+90QXLA3ql3IMiMjOyK/Ad4tYJyReF/eTVlUnw3BPKxbP6oJcJqOLQw0GsLqWqHGMvVjrKeVyGUPaWnIsqPGNHIBp3ewY7mHJZ7sDhBhBeqgwGGy7iRYUm2dXWQeFp+ZxPjydl4a2QV1NjraGGusX9sJQW4NpSy8w7OdTfPjvNQwu/kSG7VAh7uG3DYL23dM4Q5Jza05Vg1qV1SrwaGVIVFo+f52KwERXgyld7ZDJZDzdvzXnwtIJlZeLbB39TPQSqxDhsOki0kTP/6/RGt7flZGzZs0aNm/ejKmpKW3btqVt27aYmpqyZcsW1qypWQv/fqBSqfhm/w32nb7EUHVfdHs9Kd7QNiTTvBue+ZfrXJCqVCp2/reZeepHUAz9BPmT+zCX5eB/svYc5oC4NPZcCa23B6UC08Jo8g3q7612tLfnhtIew+vLay5KbgnEXBRRlO1Pw99DYdciVPa9yJqylaCnAoidf5mip44LieANU+tfcOa9XkRo1oyHkBo6g5/4Wng+p68VE5x9j1tykoiwfkUY/mxYGioVd2fktBFGfUuI5uy+nkBmQSnz+jgBor/Ei4Nd2OOTwGA3S7Y+1wcDbQ1Rj4MM7HtW2b/f4DFcl7dHXs8JJiwllylLz1NQXMbYDq344WAQk/88T041UZ2UnCI2XY5h7O9n2O2TwJeT2rP9uT587+zDMd336RbyC4WrJkJRNieDUzHQVqeDXjYoihsUyQGRJvDxOE92+KSw0/ZNkZLVnJz+ETbNgHO/VX398nKxaG09GBAPtUMByXw5sT09W5vibm3AJ+M9ORqvztU+f4qF03/Pwdb5opZn7I8gk5FfXMZe30Rm9nSo2iOiEq8Pd2VObwc+3hXIxsBiKqaoolIF7+/ww9XSgGcGOKOtoVYl5aOboynn3x/GN5M7YKGvxRcT23Ho9YEiytZC2OeXyNEbyXw6oV216SoD3SyY1cuB7w4EEZd5d2lrKTlFJGQX0amGBsFVSAsFNS2hBHYb7WyMuJGYU3dUSSYTNYOJ18XfqSEQvF+oNsrl4v0Jf4g0jw3ThBFdmfRwsUDs96qQcW5pWLQFQzsIO0JcZiG2RtpiwXd7nr+pM+hbiyaQwMXM+n2WDF1nHBTRNfY762RvzK4XuvNW0WKUZaUMP2TG9L8uEJyci76WBktOlhfPd5kLhrbk/D2BkqwEJnauZwNQ/+2iXs5tdP22byTU5DLeGOnOP0/1JCQ5l5G/nma7V1ytWSgV6ZMe1dSzWBpq8+8LfTn0+kA+faxdtdvcjkwm49vHO2BjrM2J4BR+mdGZ4Z5WDf8wXeeJdMVdLwsJ7ladq91sdPtWnHxrMAsH1KJO23oQRJyuM0thZk97Fg5o3eA1XH2QyWR8Oak9RaVKvt5345YC3qivYc6/QlZ5+1M3nWE7rsWhr6XO6Ha3IoE2xjpse6EPvZzNcLXU5/iQaOxlqTwbP5rlOb25ptGNgs0L+P7XH1l3OlCkJO9cVG2j8pIyJQUlZcRnFbLbJ4ElJ8IISMgmKr2gdmdOalCNymoVeNoYolTBNq84nqj0XBrf0QZLAy3+8i4EHVORbdPj6ao1an0WCQdB7OX6fbF1UEdycfU4Oztz+PBh0tLSiImJAcDBwaFFRHG2Xo1l55lrHDX5Gbm6FXScfvM9vXZj6JnyJbsDo5nWt/p8QoATQclMSF1Ojqknhv1fB7mcZF03HKL/paDkeXQ17/zaCksUJK6ez+AyL9afmI5O2+HoBGxGtywbw3Ff0qNr12rPVVZciLUiiahqvH41oaulwT67N3gr4XWKLvyNdr/n673vfaEgQ6g2hR0hX9cehb41ck09drj8wncBthR4lwEiNKsml7FkyP8YfblcbeXx5TUfNyVIFKVdXAKdZ4sozZbZQg2ncoOw9HC4sETkCjsPvuMwKpWK9/71ZZtXHMvmdOV0SCouFnrCm9hAzPS16GRvzJHAZGb0aD7vtkqlYvX5KAa6WVRJqXlrpDsj21nT0dbolkcz5oJQutIxrnIMNbmM/O6L6Hz5BbxP/UeXwdUUw5cTmJDDnJWXMNXTZO2CntgY6zCvswFvrzvNc2s1+GdBD5RKOB6UwsbL0ZwLS0cmgz7OZqx+qgcuRjL4byH4/4tG+2m84OvMb6mLYc1jnC/+mIGuFmhEn0EYY70a/H1M72FPUk4Rrx+BPAdj5jawOXmjEeclPOrGjnDqR/CcLAqws2KFx23UtzeV0Q76J6GtIWdcx1sPtcHuFnS2N+YLLxW7Jy1Ftv1JUbfwxMabqSIH/JMoKFEwpWvN0ucymYwvJogH7Af/+bHkRBjtbQ05H5ZOfkkZ257vW6MctL6WOjN7OjCzBUVvKkjKLuKz3QEM97DksY41pwW9P6Ytu7zj2XQ5hrdH3Tb3F+eCVu0RmgpZ3C71MXJSbwivs/xOg9PTxpD8EgVR6fk4lzcVzS8uY93FaNLzitHRVOeFQS7oaKoJr6b3euFwuLBYyKJ3mnnrYOpaMGurkAzeMltEDex6igiJ33YRxakmct0ikMmEQEvoERLKxjPSSR2iM++M5MhkImXN/18Uck38srXILSoVzpoaUCpVHM62Z5p8L7JWNSxxSgpoc/RZXJRXudj9Z951GImFvhY9W5uy9kIUX+4NJDItn9bmhlwZtArHXVPZb/IzFsXGkNFOpI6mBolFuN5t6x6VSnjU246rqph4HxnkZsHh1wfx6e4A3trmQ2FJGXPLnV+3E5iYg4G2OnYmDX/+1YS+ljobFvYmJqOgSvpog5DJYOISWDFcfNc2L9S4qb1pHd9zm2Fw5ifRL8eh5udJN0dTujk2Xd1aKyMd3h3tzse7AnhJKxBHdW0hNS+TwdTVQnXw1PcoB3/If9fiGdvBWswFlTDU1mDF/O7id7b4eUrcxpOR6M63B4MZ7vwx7xT8wruZX5F27H+o1AqRgUhlm7JSZHFEn+NwRCE/+2gQrLS7eVxNdTk/HhL1Nq5W+pCbJAQNcuKEkRlzUcxJSb7Q7alaP2dbawPRDxaYXSm7QFNdzpzejiw5Eca3Lu3RiL9SdU6D8hotB7jyd63Xqr7clZFTgbm5eYswbCrIKijhz/1X2G34A0ZqJfDkftFttxxdz1Fw8lNS/I5CLUbO+b3/8JE8HNX4HTcXIGrd5zHg1MccvOLLuH53Gix7d25kmuIsSRa9mZO2DrVra8iRG1OGGlq7RnPw2vP0mPIGZibGVfZLjrqBrUyFrm0NHYZrYMbUJ/j3t12MO/YFdJwEBvUshrwP5B/7AbXwM7xX+iK7M/qizBDfoZGOBvP7OtDR1ggzfS3yi8vY4R3PKyeTODb0U+zPvCM6QLefcutgATtF3UKij/B8aOrDoHdh8PvCO7F1HmyZA22Gi4aINl3g0IdiQdD/9WrH9/eZCLZ5xeFqqc+bW33QVJczqUvdvXFqYlwHa346FEJOUSmGtTx8m5ILEencSMzhndE9xAsqFYQcQp4TR2eZHKxngbw8XSD6gsg3r4ZeI2cQ4fUjZce/ZXqQNV9Mak9b66reO9+4LOauvIytsQ7rF/YSHcqzYmm3bxL7ZVEcievB09/N4EKBLQqlik72xnwzuQMjPK2w0NMQfYm2fSRS0R7/G7WO09Es8+b1GDP+THwHh5Jj9O27SNSd2HS5cxFRT14e2oa0vGJyiu5z/5/CLLE4VZSIf608Rc75soGw9zWh6Oe1GtR1oPOtCX6/XyKD3SzRq1TYLpPJeHOkG3NXXuYQvRn9+N/CA1ZJwW+7Vyw9W5viaFa7l1sul/Hj1I5M6mzLXt8EAhNzmN/Xicc62dQuF9oC8Q6L5+TxAyyJskFPS50vJ7WvVfHKQFuDCZ1t2HY1jteHu4naO5UKrqyAg++LiEd5fVJ1+MRlYaqnWb+FYELNzRfbldcNBCTk3DRy/ncslJVnI7Ex1iExu5DsghI+n9he/PbP/iqi3BEnxbx3u8qghg6Z41dSdPgLLKKPo+69HpVMjWJDJ0qGfodhC4niBCXlIJfJMNHVxFxfU1wr15Hg9Q+ainDaqpfPMebVKIw69gX/fynTt0VVICckObfWhejvx0LZk+DETC0FRJ8VxkZlcpNE6mdKELJZm+nTpmp98RM9HPjjeBjLTobTy9mUz/dkMtr6e74r+koYk5VJCYRxP1d9LcFb1L6N/r6+X0+TYKqnyR8zu2Corc5newJxsdCnb5s759Ib5U0066UY1wCsjbSxNmpAilp1aBmI5sjb5osmuXeLXQ/QMoTwY42ycL4XZvdyZMvVWPwDfHAwcbr1vbceINYsZ37BV68/CdlFTOlqV/OB4r0gPQzN0d/xn20/isoUWBlqg3IQRSd+4Orp4yR1eosnnXPg36chOUDU7gAjgWGaahwZth9MnOhsb4yxrgYH/BPxis6kR+Z+WPcKUB7RMnYU92HsJRF9selc62fU1VTH1VIfZ3N97EyqGqAzetjz+7FQjpk+wejOT9zhbEWuBj0WCGGVUd8K2fV74J6MnJbGL0dCeEGxESuNdJh//M5cfou25GhaYZZwilLFi2hUU2Qem1HAtNx1pFr1xqLSTWXeezYlpz8n6/w/cJuRE5GUQdeAb4g3aI/tiwdQpoVSkhaBodswFCUFBK99hdFxv5Px2ypOO81nwJNf3fxhp0f7YwtYtu7QoM9qb6rLoX4fUXR+IoUHvsJk+uIG7d9k5Kehfm01G1Sj6DXxBd5va0laXjHJOUX0dja7IwrWx8WMyLQ8nrjchhOu49Dc+7rw3BvZicjN9gWieZTrSHDqL/6tyAXV0Bb9BgJ2wLEvYPlgIR+aegMe/7vaNI0d1+L49kAQzwxozSvDXJm4+BwRafkMdL37G2lM+1Z8sz+IYzeSmdyllkmpiVCpVPx6JAR3KwMGVXwOn02i1wQAMgg/AdP+Ebnt2THCO1oN6upqOE79Cucts3DKusiC1YXsfWWAMGQQ+dtPrr6Co5kuaxf0xFhXGDj8M06kTo76hv7nljI4/wNO9PsTx26jxQK6JB+u/g2X/hLnt/AQKjrW7QERxn7muh2xZu2YqDiPZ5sP4cgp6F2z964uZDIZn09o1+gP71opLYLNs8TDQENP9ASYvF44W8b/Ihas3zuKBXbnmTelXaPT8wlIyOHZajq+929jzgBXc97e5ovtM6PoUKlHR2xGARcjMvhhasc79qsOmUxGf1dz+ru2HOfU3aBx8Q9eTFiJzaDtDO/XG7OalIAq8UQPBzZdjuV4UAojPSxFPcv19WDbXXh5zd2E8lc1+MRm08nOqO7fUmmhmH+6zqv2bUsDbSwMtAhIyOGxTjak5BSx5kIUTw9ozftjPFhxJoKv9t1guKcVAxz6gEErce8MelfIAlfCKzqTd7b7EJ6aDwxETT6Ioa21ic4qIyS5lJ5XTdnSVXV/f//V4BeXzWOLz97821hXg/Y2Rvw4oRfWGno8XnaM1qruIJPfIbsNgGN/ANTNHJGlQlBSzUbOHp8Efj8WyqtDB0KAvZA9r2zkpIcLb3lZsehXY9vtjmPoaKrxVF8nfj4SwparsQz3sOTdqYOR6c4QRdHJAeK6+G+Hq6th2KfiPr/5gbcLR4TLkLv7whqZzya0Izw1jxc3XmPNUz3vSLkMTMxhWNu7SCe7X5i3gRfO3dsx1DSg9UARkRjyQeOM6y6Ry2V8NM6TktVxJBpbUSXJYNA7ELwfy+Ov42T8HT1qE7nw2Qx6luA8BCM1dYzQqDgB2sPe43jmGE4FpDJ7/ONoFGXB9U34df+GqWdteL6nGa8Fz2Z04T4Y8OXNQ07uYsdkXX/Y/JqIDPd8VjiMjexEtEmlEveAYd3rnH+e6om+9p0mhpWhNiM8rPghTJdREwZR7ezUZR6c+BaurYGBb9V5rtq4p2agLYnzYWl4XTrFDPkx5IPevTPsDSCTUew0lL4qby7VICV9zccbd3kcOn2erZonqGtKqsMYhuTt4Xp01fqLS1u+o7UsEdNpv4NcjtzSHU3PMaCuiZquMZ7PryVzwXlizAYwMHoxAef339y3OOkGeSodWtk4Nvgzzx3Wlf80xqJ3Ywvk16OT9n0g/egvKJQqNAe8wsyeDlgZatPOxoihba2qTfPT1lBj6exu5BSX8TnPgYauiMyUFsLhD0WDtqePiPoDz4k3DZwTwSlCDlwuF4XbL10V9TdGtiJ1rVwO9D/vOOatusyWKzH8djSEN7b6MKmzLe+N8cBAW4O/53fnyb5O9HExu+vPbG+qS0c7I/b5JqFSqfhsdwD/ecfd9fEaysmQVK5EZfLWKPdbKWmXl4tJ/ZMMmL5G9GtYPVYoa3V8AtzH1Xg8tbZjwaYrXxrtoqCkjFc3e6NQqigsUfDs2qtoq8tZ9WQPYeDkJMKax4SB8+Re6LMInVcuo+HYi5HXX8U9/l+hoPdrezjyifCiPXUAXrxw08ABGOhmjoG2OiuzujNAzQ/LhBNC1vU2L2tDafIFXuVcd0UZ/PesUMmZtxvej4HX/W+qNuE6QohgDHxL/H/fV27uesA/CU11OcM87lxsyGQylszuipO5HnNXXaoiIbrtaiw6GmqMrW8H9ocEjykfoGlowYzkXzGrTja2GjraGeHRypDNV2JFMfP19ULFaeFRSjynUvLfIi6ePnDHfkqlCp+4rPrV4yQHCLnaWjydQnxAKKwtPhGGhlzO8wOFQ25Bv9b0am3K29t8yZQZw5tBsOCgWPxUKvRdciKM6X9dQE9Lnd9mdGb3S/34ZLwn2Uod3O0seH24G5cjMzgZ3Py1ghVSsmsX9GTxrC7M6+PE5cgM9gXnkOr5FHPUjmKffVU0CKyuH5aFO+iao2bqhJOZXo1NFS+Ep/PmVh/GdWzFq8PdhOc/7NitDRSlwqMtV4NnjlVr4FQwv58T8/o4svnZ3qyY30M4eWQyMHYQUXCbztDzOZEGVFnhLvayWJi1n1JVJa4Z0VCT8+fsbtib6DJt2QU2XopBpVKhUqnYcCma2IxCOtjWs7npg0ybYUISvKBxJKLvhd7OZrhrZ3AmVY+CkkqZBupaJAz+BcuiKL6xPFataAYgfsv+/4q1j1r1sYo5vR1Jzinm2I1k6LEQ1cKjvB/ZkU6trXlt8gBknWaC97qq/YPSwmDbk+L5NGEx2HYFY/tba+GKe0Bet+lgY6xTY2bL7N4ORKTmczGihmuhZwbtJgsnQgPVXm/noTBy1l2MZt6qS/ygvwmVqbPQt68B087jcJCnsvngsWqL8fJuHEWJDH2PO0OjViPfwEaWwfWDt8QVzvmFMC5jHXFOU9Bx7H7HPhWYOLSj46L1RModkZ26FcZWSw8jXt0OeQOkiyvQUldDp98LqJQqsk+3gH4gBRnoXV/FTvXRTB3Ypd672Zvq8sFYDzb45eHddzEkB4rmUGFHYcRnVR58RaUKPt7pz1OrrzB/9WW8ojPFG+qawgia8y/MWA8yGTuuxfHGVh9i0vN5918/fjsayitD2/DL9E431WFcLPT5bEK7Ggu268vYDq04HZLKp7sD+Od8FHnFjSsIUVBSVq1ghlKp4qdDwXRxMGa4R7msb5yXSJno+ax4oHtOFFGE2IuiCH/S0honRkBMZEM/RCv5Ojs6XMI7LJaBP5xgxK+nCEvJ46+53THX1xKa/OsmiUly/m4wLS/81NSFmZtEBG7Pq6LZV/spokndlBUi7H2b8aGlrsaodtbsVfQWnp2D7wl1qVoWIs1KXqpIlfzGBjbOEClPv7aDwN2irqyGSBmtOgm5zBnrbn1fwAG/RAa5WdTYg8VQW4O1C3piZaDN3JWXiU7P53JkBktPhTOjh33dvVseMtR0DJGN/VGkPvptq9c+MpmMmT3tORecQNnRL4W8b/enQCbjg7JnCFA64nr8eUozqjZajkzPJ7eorH5GToI3yNSE8l0NtLMx5HpMFp/u8mfT5RgWDnC+KZggl8v4aVonCksVvLjhWrWNCU+HpPLjoWCe6uvE9uf7MqmLLR3tjJnf14mtz/Xhj5ldeGVYG7o6GPP9waB7ls6uizOhqTy/zotfj4RU22MkPDUPGyNtBrpZML6jDW+McKOLgzHnwtLwc5iNAjkmUftrbC6ITCYi9gPepKeTKadD0u54dgcn5fLsuqt0cTDm52mdxOLQZShkRt5qknj2V5H2PHm5WKjVgqG2Bl9MbE9v51qcX8b24D5W1A6oVCINeN3jYt4b9kmtx7/fmOppsu35PkzuYssH//nR97vjzFt1mQ//87+phvbQ4zIMVEoxZzQ3SgWWimRCS81571+/m79npVLFq6dV/Ks+jj4Ja0WWRHWEHYXCjCo157fT0c6YDrZGrDoXhVKp4lJkBv7xOTw30Fk4/3osFKln/v/e2unCYpEBM3VV7WuEe6SfizmOZrr8cz6y5o36vAi9nhVOo3vggTdyfjsawsc7/Vhmf5R2JX7IR30jFrw1oOYyhFJNIwalbGCvb9UGnwqlCvOUCyTreVSp5alA3a4LCcbd6Ra/nsSsAsoUSpL3fImGXIn941/VOVa5mhoZ3V+lXYkPQZeEGo5hfiRZuk4N+9CVeKxvJ3bLBqHutaLOjr5NTfL2N1EolRgMfb3BRsOM7vb0cDLh1TNqlIz/QywW7Huxs6QX7+/w482tPjyx/AK9vz3G5isxfDTOA89Whjy79mq1jf4O+ifx1jYfJnex5dibgzn77hD2vNSfN0a6N4l3f2z7VpQolKy9EM2HYz2Y27vhkbmaiM8qZOLicwz+8eRNL1wFB/yTCEjI4e1RlT7XlRUiAuZWqe6m+wJ4KxRGfVMvLwwuw6DTTJx9f+a67st8aHmefi7m/D2vu0iXUpSKvPa8FJi3687UUC0DeHIfvHxNdFkf91OVRX11TO5iSxpG5Nv2g5x4sUippni72fDdKmQ+t8yBP3sJ6d4eC4Uct89mcB8tusm3m9Sgw4al5OETl824OqIxJnqarHu6JzoaasxZeYnn1l2li70J74+tub7woabtOGg7XhiY9WyMPKmLLQt0zqCeE03xoI8AOByQxHbfdI51+pVilZy81dMoyMvi453+7PVN4HpMFsAd/VaqJfG6WKzXUnA+0tMae1NdzoWn09vZjAX9naq8b2+qy5JZXbkclcEXewKrvFeqUPLl3kC6Ohjz4TiPGsUiZDIZ745uS1BSLrt84use9z1QqlCSkV/C6nORTP/rwh3zcXhqHi639Rfq18acy5EZhOZpsV45SrxYXfZFBfY9wNiBoR6WxGQUlKfoCcoUSl7d7I21oTbL53W/9exxHiRS4MJPiHv11PdCna4xazJ6LBTF3L93gtWjRdR29vaq6WstBG0NNb6f2pGNz/RiaFtLUnOL+WZyB76b0rHG39FDhYmjkCu+R4nlRiEnHpmyjOF9e7HbJ4Ffj4ZSVKrg7zMRXInKxOHxL5Bp6onsh+q4tk7Ur9WgOFfBq8NcuRyZwS9HQlhxJhJncz2GVPQ4s3AD5yEi60OlEhEun81irdDEtXxyuYwXB7twKCCZE8FVJbtzikq5EJ7OijADvs4aUX10twG0aPdfSZmSlWcjCUrKYWhbS4a2tayiqrL6XCRLjt5gv8MWPJP3wcC3ReF6bWjpozH0A6YdfJen9+1lhOfTNydF/9gMeqr8yG9dc9d6o2GvY/PvbD5dvZIyQ3s+Ld5HepeXaFXPhl+dRz1J1NVfURz/FlWPEbQqiyXJZES99q0OPS11Mjo+g47PMQqvrkenzzN3fax7IdNrB1YRO/jL6BUW9unc4P3lciE7Oeb3M3wT257PZm7Gt8ye19b54Gyuh4G2OlaG2szr7cio9ta0szFiYmdbJi05x/S/LvDrjM43vW4Z+SV88J8fQ9wt+XGqiNrYmehiV79eq3eFg5kuT/Swx8VCn2eqqau4WwISsnlq9RXU5TLGdLDmg//88IvP5pvJ7VGqhJHfx9mMvi7lNRb56cIzM/DtOz0x+g1o4CiTiV4cg99D/eR3jPX5ibGzeoJbee3H0c9E4eOT+2rsfIy6ZoN63PRrY87pt4dgEJMF8WfuOVWt0SkrhpI88UBwGy28tQ0Q/CgqVfDP+ShmdLevInW86XIMRjoaNcrdVsbSUJv1T/diyrLzGGhrsGxuN7TUW5AheL8Z+SX80Q28NwivXx0Yykt4Q+s/9uT35fBZFc7mIfxzPopBbha8+XgPvsn8ljfjXmbvkrdYnzWZdRejcbHQw9FMt/pu6reTcL3OotxO9sbsf3VArdv0dzXn08c8+WRXACHJubw81JWujsZsvRJLaEoeuxb1q9NZ08vZjIFuFvx1KoJJnW2bLHVzaFsrhra1IruwlG5fHuFwYDJP97/l0AhPzWfQbfL8/dqY8cuREA74JaIymMLzalfuaCxZHf3bmKOpJudEUMpNFcnNV2IJSsrl3xf6YqRTKT1Gx0REgk98LRwRdj1EY9TGxHmwSI2Wq4v5oO24WvuHtAT6upjfel48avRYKBpmek4U/zUX5fLRvbt143UNdX49GsL/jon+WjN7OtCnnTMUfSwyIbovEKIEFYQegeB9Ip2sjnt6uKcVb45w4+cjIQB8Nal91RS4vi/B+ilw8jvxu1WW3bfGwdO727PTO4EPd/ix86V+bLgYw26fBCLThANDU01O21YGlCqU1dbP15cWa+QEJ+XyyiZvQlNycbbQZ9f1BMz0NPnnqZ60tzXkn/NRfLHHnz2t1uCRdgom/gldZtd9YIAeT1N8aQUvpK/gs129+HZKR2QyGcE+5+kky0OvU82Gkl67seQccubz7I8gG3I1zGg15p16fy41NTVSur9Nz8uvEPLjUNwoQsu6Fg9WPZgwbBCrvcdglyCjDhOvUSgr99xZGgoLuzQ7Cfne1zgn68Lkpz+oX7O0amhjacDbo9z5Zn8QvZ278euRENrZGLJrUT+hhnQbFgZabH62N69u9mbm3xd5ZoAzLw1tw1f7AiktU/LN4x3ueix3w3dT6lf83RC2XonFXF+L1U/1wMpQm+5Opny80x/PVgYY6mgQmpLHN49XEq24/BegqrHwucGYOAkZz/xUIQIx+hvISxZh7RFfgGOfxjlPOQ5mumAwGdLDwHNCox77nuk6l+KOs+7aqPjtaCjLToVzPSaLpXO6IpPJKCpViC7lXW3rHf10MNPl4KsDUJfLMdJtGXn/zYapM3hMgAt/iMVAXSkW5xejUZyN+vCP2bM/4WY903tj2iKTyZg6fhybFu/g8cID7HrmC7b7ZbD2QjQTKqfzhBwWnnqH3lWPXVoIKTeg6/xG+WhzeztiaaDFb0dDmbPyVuPjqd3s6pc6BzzZ15EF/1zlWkxmk0rjglDP7O1sxpHApJtGTnGZgpiMAlwsqnqGO9oZo6ephk9cNn1drGBhYJ0LNhBOvV7OphwLSuaZgc5kF5Ty8+FgJna2oZtjNV4sz0miT9XYn8Tvo7EjwzKZSMGVeDDo9RxEnoZdL4mU0gY2mm40MstTYo0deWWYDk7muhSVKrA20qFfRX1wl7nCebNtPiw4LAQYivNg7+uiYWaXOfU61UtD2xCdUcDZ0LQ71draDBeG/4mvhXJtu8n3Tam3op/SqN9O0++744DI5nhhkAvtbA1xtTRolAhjizVy3tvhS3GZguPj83Eqvk6yaXeeO63GzL8v0tvZjKM3ktnosJf2KcdE/mBl2eG6UNNAa9x3dF8/hX3ef7PE9FXm9HakOPgYxTIttJxqWbjJ5Rg+dwAiz0BJLgY2Xe7owlsXPcfO56wCOlx9H2Rg6tCuQfvfTisjHZQjvsa4kupSU6FSqfhr1XJsY/fiOHcJnZ1tifp7NmZKBTrT/sTS6N68WAv7O3M2LJ0XN3gBsLMGA6cCe1Ndtj7Xhz9PhrP4RBhbrsSSXVjKV5PaCznFB5yPxntSUqa8KSs8t7cjgQk5fLnvBhb6WgxwNb+lwFKYCReXCg17g0ZUy5GriXts9VihSAViMuzzcu373S2aujD806Y59j3w29EQDvglcej1gQ3eNyAhm7/PRNCrtSkHA5LYeT2eyV3sOOCfSFZBaZVeAvWhPmpijwz9XhHNhgN3ikLcmshNhnO/Q4+FjBnYl93OWbQ216uSHeBubUD25Hcw3DOcjmn76DBhIR1sjehYkaqWFQtb5wqBlJe9RC+a+GtQlCUkalUKIf3cCMhkMka3b8VIT2uuRmcSk1FAZn4JU7vVX8FxkJulkHq/GNPkRg7ACE8rvtgbSFZBCca6msSkF6BQqnCxqPqM1FCT08vZjONBKdgY69TLwKlgWFtLvtx3g8z8Er7cF0hhqYJ3R9cQTe6zSPzXzApzEi0EmQwm/Ql/DYRNM4VYTkMyHBqLzCjRH0dTFxkwsXM1LSzkaqK2deUIWD9ZyEsHHxAOx3m76v2blslEnV9RqaJ6R9rAt0V6uNc/tdazNwVO5np8Nak9F8LTeWWYa5M0mW6RRo5/fDbhMXEccN2N7ZG9oKGHVWk+Owxted/qY3aFlnGw/Qnahm0WNQYNMXAqaDMcej7LJ5f/5qWjJnQ/3J31mhfJtOqGdV05gAbW0HHa3X24cvo/Np8bLl24cHkPw9tW3yi0ITRmilRtbD9znRlxX2Muz8F3wxTOW3anT64Xp3v+yeD2Dev1Ux1yuYyfp3Vi4uKzPNbJ5tbiohbU1eS8MsyVKd3s+PlQMCUKJbNaYOPCu0FDTX5HqPaT8Z5cjcogNCWP/82stKC6sET0ZqmhP9A9oW0klMFK8kQ3d3WtR27h0MZSn+DkXKLT8+vsSVNBZFo+MRkF/HQoGCczXdY+3ZM3tvjwya4AYtILORSQRE8nU9pYPlh9aloUtt2EZ/Pcb+JZUNPv8uQ3YuEw8G2AGueWnt26Q+g4uLgUWfenmdbd/tabxz4XBk5Zsegm3nWeUBcsyRPyxzK1KqqBjYFcLqNna9O7aqqoJpcxq5cDvx8N5aNxHk1uHA/3tOLT3QEcD0rh8a52hKfmAdxRkwPQ10UYOQ1twjy0rRWf7Qlk8p/niM4o4NvJHYShVB2P2BwlUQ90jEUT3TXjxb07f+8992JpMJlRIkuiLvTMRV+11WNEBMfYUfRluosIVI2ZAjIZjPtVqH02Q2RrWnf7qnNsI9NijByV6pae/4ZL0fyisxqbZH+RKtNpJiR4I9/9Mt9lvc1Xzl3QCDsNQz8WXpq7ZfR3kJ/KH4F/opRroK4ohJ4/171fI+Hh2REPz8ZPcbpXsgpKSM8vucP75h+fjd7R99DVUJE7fjXOuxahnxLKBbunGTxuVqOd38JAi1PvDGlwHqatsQ6/zOjcaONokShK0SnOYNWTPbganXErRaMgAy4uE1GcetaHNRi52s2+Lo8iA90sUJfLOHojpUrNQU2suxDFx7sCANDRUGPt0z3RUlfjq0nteXmTNyvPRpBTVMaSWffu5HjkGfiW6H/it+1OxSGlEq6uhGtrRd2EXj3k4nu/CP+MhRu7bwlJxF0Vxx/3i5A3P/aFiB6ZtBYqbUc/A+sOLa4mY3p3e347GsI2rzieH9S0ixhbYx3a2xpyJDC53MjJR19LHUuDO42rfuWNKW3r02C1Eg5murhZ6RObUcjyud0Z4dmCe7xItEws24oGzf+MFz3e5v4n2k/cLzKj6m9QmLaGV31EvUxTCQLI5c2XutfEtBgj57sDQSiUKp4Z6Mw5b3++VruMbOjXt/IO7brDgoPItsxFI+6KkAn2eOzeTipXQzb5L2TmbsjVNMGx35151o8gb23zJSQ5l90v9RO9UIDE7EI2r/qNr+QXKRm3DN0uj5Ok04oIr930fuLLOo7YcO6l0OyhpThXyBXHXcF+yIfYV24OeHEpKEuh/2vNNryHHUNtDVEPcCO5TiPnYkQ6n+8JZGZPe14Y1AZLQ62bnjQTPU3WLxQKT4UlCnQ0H2HhgMbCebDo/XT4Y9HHRKs8MpYeLop3o86IHPfeL9bveI59hfLQzheEYa9tBP89J5rYdp0v0tK8N4g6nNnbhGPBc6KIpLYwLAy0GNehFavORvJkX6d7lsuvixEe1vx1OpyCkjLCUvJwsdCrVvSgrbUBP0ztyJh6CG7czrI53ZDLZE2S3iLxiGDpIYRz1k2GVaOEoWPuen/OnRkl+vbUF3UtQEpRvhtazErSSFeDtRejGfTjCSapjiFT0xAdVyujbQRzd4oGafdq4FSgriU64A58SxRRS+FtvuhZRtuCq7y6+ToKpYrswlI+WrGT9xVLKWwzDs0u4rpYt+1Dx9nfImtCPXWJcgozYe0k0eeh/RQ4+ml5j5oiKMqGS3+Jxdd9Khp8VBnW1orLkRnkFJVW+75KpeJcWBovbrhGVwcTPp/QHgcz3RoXlpKB04iM+lrcJ8e/EipnZ36GpX3FgmLOvzBxcflioR7IZPDEBrDvCRunw4phIk1z2mohbqCuBQsOwfNnbkVO9czBsGX2G3lpqCtpecVsuBTT5Oea1MWGUoWSv05FCPloi+prVmUyGdO721epiaovzhb6koEjce9YtoWnD4n7ec0EMX80JWUlop67IK1+6WoS90yLWZ2+6JTEhDkWfHy+lAWJp5F5Tq62Vw1y+SOdMnM/sPFbylK1Q0wN02DM74UkpmWxXf1bNIws0Zzyp2QI3m/Sw8t70iTDvN1g100UWG98QshhGtmJztv9Xm3ukT70DPcQhdWnglN5rJMNZQolPxwKZtPlGJzNhcf6emwW7W0NWTK766PRf6KlYNpa3AOnf4BLywCZUFMa+nGDxWEAkRoycwsceBuMHMSxK/dgq0/aWwuhjaU+kzrbsvRkGDN72qOr2XSPfkczPeb3ceKv0+EAjGonOV4kWjDGDiKK82df2P8OTPm7cY9flAPX1oh+TTEXxLNaywhspDTl+0GLMXI49QN2UWdY7TIMSlOE3KNE8zDpT9TWTGBTyi/8J5vBKONjmBQlIZt5VBTtSdw/os6K5pPaRvD0kVsN89oMhzHfwb43hYe5y+z7m1P8iOJgpourpT67rsejq6nGijORXIpMZ05vR7ILS8nIL2HZnG6M9LSq2o9A4v4w6B3RD0XHRCxe7lVlUEMbJvzROGNrZl4e5sounwS+2BPIhE42uFkbYN5EQgQvD3Nlh3c8Gfkld8hHS0i0OIwdYPS3sPsl8BjfOD10VCpR03fgXdGnyaEPDHgDWg8WvbTUHnH5//tEyzFyZm2Fi0vg9M+ieNOuR3OP6NFFUw9mb0N71Whmpi2HNiNg4J/QquWJJDz0hB4RdQAz1t/pOe7+NMReEc0/+73WLMN7FBnVzprFJ8I4eiMFMz1N1i7oRX/XR7S5XktDTQPcRjb3KFokrc31eH6QM3+dimDzlVg62hmx+6X+TXIuIx0N3hrpzgf/+eFubdgk55CQaFS6zIGgvbDvLXAdee8CIkF7Yes8cBkmFNFM6xarkWh8ZCqVStVcJzc2NiYrK6vqi/lp4l89adFwP6n2WhRmQmGWdHPeZ6pcC6VC/KdeQ7d1pQJyE0XKmkSjU919UVSq4EZiDpaG2lgZaNXax0mi8ah2jpJoMCVlSiLT8ikoKaOLQzUp4fWgPtdCpVIRkZZfY02OROMg3ReNSG4yZMcKoau7oMq1UJRB2FFwGyWl+DcjLSeSU4Fk3LQcdEyqr4uSuH/I1Wrv0i1Xkwyc+4y2htpdLw4lJJobTXU57tZN35dJJpNJBo7Eg4WBVeM10lZTB/fRjXMsibumWSM5crkcQ0MplN1UaGlpkZycXK9tpWvRtEjXouUgXYuWg3QtWg7StWg5SNei5SBdi5ZDQ65FBc1q5EhISEhISEhISEhISDQ2UjK5hISEhISEhISEhMRDhWTkSEhISEhISEhISEg8VEhGjoSEhISEhISEhITEQ4Vk5EhISEhISEhISEhIPFRIRo6EhISEhISEhISExEOFZORISEhISEhISEhISDxUSEaOhISEhISEhISEhMRDhWTkSEhISEhISEhISEg8VEhGjoSEhISEhISEhITEQ0WzGjlWVlbNefqHitTcYlQq1V3vL12LloN0LVoOla9FYYmCvOKyZhzNo410X7QcpGvRcpCuxb2jUqnuaf1UgXQtWh7NauQUFxc3fKd4L9gyF37vDIt7QGlRo4/rQSO/uIypy87zw6Hguz7GXV2LFsT3B4OYu/JSo0xUzc2Dfi0ancJM+NEV/ugGO56DnIT7durK1+KP46EM//kUB/wSH4rf2YPGA3tfXPpL/H6PfAJZMc09mkahPtfCLy6bXt8cJSVHekY3JQ/sfdFIBCRkM3vFRRYfD0WhbNi8vOZ8FN2/Oor7Rwdp/+khpv91gW/237jr+b3Rr8U/4+HqqsY95iPGg5WuluANaydByg1oPQDSQiBwZ3OPqtn5at8NknOKmNrNrrmH0iwUlynYeCmGM6FpnAhOae7hSDQ24cchPwXse0HIQTj8UbMMY3p3e9ysDXhhwzUWbbwmGToSdVOYBSe+AX1LuLIKlg2A4tzmHtV9YePlaJJzirkYmdHcQ5F4yPCJzWLT5Rg+3unPhMXniEzN56fDIcxfdZm0vPoZGiqViuWnI3A21+Od0e48P8gFYx0NAhKykclkTfwJ6oFSCTEX4Pqm5h7JA416cw+g3qQGw7rHwbQ1zN8D2kaQEQmX/4ZOTzT36JqNYzeS2XQ5hi8mtsPFQr+5hwMIo2OPTyLZhaUYaKvzeBdb1NWazp4+GZxKdmEpdiY6/Hw4hMFulsjlLWCSkmgcwo6BuRtM+lPc7/vfhgFvglW7+zoMJ3M91jzVg21ecbyz3Zd9fomM72hzX8cg8YBxYQmUFsKsraAogf91Af8d0G1+c4+sSSkqVbDXNxGAa9GZTOhU932SklPE53sCmd/XiZ6tTZt6iBIPKFuvxvLOdl8AzPW1eHGwCy8NbcPFiAxe2+zNrL8vsvnZPpjqadZ6nGsxmcRnFfL9lI70dzW/H0NvGIUZoCyDuCuQnw56Zs09ogeSByeSc+hD0DaEOf8JAweg5zMQfxXirzXv2JoJhVLFh//5M8DVnLm9HZt7ODf58WAwb23z4fuDQbyz3Ze/z0Q26fl2esfjbmXA91M6EpCQw8GApCY9n8R9RKUSRo7LMPF313lgZCe8482ATCZjend7BrpZ8MPBYIrLFM0yDomWg398Nnt9Eygqve23kJ8GF/+EHk+Dka1w0DkPAu91zTPQ+8ixGynkFpXh2coQ75jMOrcPTspl8p/n2eeXyJd7A6UoqUS17PdL5L1/fZnWzY6gL0dz9aPhvDnSHS11NQa5WbD52T6k5BYzf9VlcotK79g/IjWPyLR8AHZfT8BcX4s+Li3HeMgvLrv1289NLH9VBeHHmm1MDzoPhpGTHAhhR2Dg21WtWfdxYGADV1Y039iaEa/oTJJyinh5qGvLCK8ivCMrz0Xy1kg3Qr4aw5N9nfjtaAjR6fl3dbxVZyNrTUHLLizl2I0UnnHJpp/Siz7OZiw9GX63w6+VUyGp5FQzcUo0IckBkJcEbYaLv9W1xDwQtLdZnRvvj2lLbGYB6y5E13sflUqFT2wWygbmjbcYsmJECoXETUrKlDy/3ouXNnrT4+ujrDpbyaFzZSWolND/jVuvdZkrPLMpQfd/sPeRHdfi6GBrxLTudgQk5NxpAFYiIauQacvOo6elxteT2+MXn83p0LT7OFqJBwH/+Gxe3ezN6PbWfDelI9oaands425twNoFPYlMy69i6KhUKtZdjGb072eY/Oc5wlJyyyPxrVBrIVkfSqWK/t8fZ+vVWPFCbrL4V8cEQg4138AecB4MI+fCYtC3hg7Tqr6upg7dnwK/7SL3+RHjUEASZnqadHM0ae6hACJN7Z3tvnhYG/LcIBcA3hzphomuJh/t9G+wdy4lt4iv9gXywnovAhKyq93mgF8iJQolExJ+ha3zmNlWjl98Npn5Jff8eSoTmpzL/FWXeX3zdcnL2FgUZMCFP0FRi2JZ2FFQ1wanfrde6zxLpK/texOUzRNJ8WhlyLRudvxxPIysgvr91g4FJDFxyTk2XKq/YdRiSAsTYi+h0sO2Mtu8YonLLGTp7K4MdLXgu4NBZBeUO0LCj4HLUNC3uLVD2/EiE+Ehjuak5RVzMiSVx7va0tXBhDKlCr/46udvlUrFezv80NJQY8uzfZjV04H2toYsPh56n0ct0ZIpLi1j3ca1LNf5k1/7FNVqmHS0M2bNgh6EJOcxb9Vltl6NZdbfl/h4pz+TO9tioqvJ43+eJy2vhMfqkUZ5v0jPLyGzoJR9fuWZKHnl/3acIZ6DzfSse9Bp+UZOTiL4boVezwkv7u10mgmKYrix5463avMePeioVCoOBSQxwtNK3PABOyErtlnHtPFSDBGpefwwtSMa5TU4BtoafDzekzOhaTU+6Gpi9/UE5DIZ9ia6PLfO6w7DRaVSsflKLBMcStFM8oKyIoYmrQTgUiMXux4NTOJp9YMEBQWw+lxUox77keXsL3Dofbjyd83bhB0Fx76goXPrNTUNGP8bJFyDy8ubfJg18eZId0oVSn4/VveCTKFU8cuREACWngx/8NLcrqwAlULUQdaX65sg8kzTjakpCD8Bfw2sl+e0uEzB4uNhjO1gzZgOrfh4vCdlCiU7r8dDcZ5QAm09sOpOGtrQYTr4bH5olUEPBySjVKl4rJMNHq0M0VKXcy1apKwplSrOhqbxyS5/tl6NZfW5KE6HpPLt5A6Y6Gkik8l4aUgbrkRlcjwouZk/iURjUFBSRlTaXWRyJAfCmgmwchRZv/bm+/yPGCjzRmvj4xC0r9ZduzmasvbpnoQl5/HOdl9xr87qwvdTO7L6yR6oyWXYmejQ1cH47j5UE5BcrkJ4MTyd/OIyyE0CLSPwnAhFWSIC3BLx39GiVSNbvpFzdRWoaYqITXUY24NDX/DbdvOly5EZzF15iQ6fHcK/gQvrB4UbibnEZRYysp2V+IH991zti8UmpkyhZOXZSMa0b0V7W6Mq73V3EpGmxOyGPdR3Xo9nsLsFK+f3ILeojA93+lV5/1pMJtdjs3jJyhfUtGDw++gFbqG/QTKXItPv7QPdRqTfWT5WX8sO49/534Hr3EjMadTjP3KUFoL3etA0EPU1udXUURXnQczFW6lqlXHqB13nw7Eva59gvdbU+UC8W6wMtXlhkAvrLkQTnppX67Z7fRMISc7jy0ntScgu4l+v+CYZU5NQkg/XN4r/z6vnwjNwF+x8HnY8I671g8CBd2HdJEj0qdeCYsuVWJJyinh1mBsA1kbaDG1ryabLMahiLoii4duNHIAeC6EgDXweTtWkk8EpdLY3xlxfC011OR1sjbgWk0lofAobv3uG51eeYL9fEu9s9+WLvYFM6mzDcM9b/UVGelrTs7Up7609RvJPvVGcW9yMn0biXvnxUDBj/3em3qpngJj3V4+GnHjydVpxJdeMvR4/ofZ2qKjP3DIHAv6r9RBdHUw48fZgzr83lB3DchmfshwuLMEp5yr/Pd+bVU/2aDFp/iB6HQKUKJScDUsTz0QDK7DrKaK/oYfv/uBhR2HXIlHjeo9UyWTJiIDtT8HGGS12nm/5Rk74MXAdIfISa6LDVIg8DblJbLocw/S/LpCYXYSpniZLToSJbQozYfNsyI67P+O+jVKFks92BzDoxxNkF1at68jML2G/XyJHApNrNMrS84q5HpvFiaAU0vKKORSQhJ6mGn1dzEX/BS0DoTjVTBwMSCIus5CFA1rf8Z6JrlA5yWhAClloci7+8TlM6mKLg5kuH43zYL9fEmcr5WqvOBOJo5kurimHwG0k9H8dmbE972lt42JE40Vy0vOKaZVyhjI1bSzLEvlZ62/+PtU0dT+NSWxGwf09oVJRf+90wH/inpy9DeRqcPjjO7eJuQDKUnAeUv0xRnwhPONnf6v+/YIMITcdcap+Y7oLnhnojKWBFt/su1HjNmUKJb8eCWGQmwVzezsyqp0VS06EUVL2gNS3+G2D4mwwaFU/Iyc1GHa+CI79xPZXRHSVmIsiUtISyY6DS8tgwFtg0Vb8dupg69VYRnhY4W5tcPO1GT0cCErKJdX3COhZiGPdjmVbkbZ27rfaUzUbg+gLwmi7T5SUKTkXlsZgN8ubr3V1NOFiRAb/rPiDOSXb2DEsmysfDuP8e0P5YWpHvpzUvsox5HIZ62e5s9f4Z6zyblB27CvIu4+tAaS6s7vjzM+w+5UqL5WUKdnpHU9BiYJl5bWyKpWqWlGAm8RdFa1CLDxg4VH+afURb6peY/Ckp0BTF6avBY/H4L/nRVuRWjDX18LGQB12vwSXV8DRz2DtBJzW9sAtomWljKbkimenrbEOx2+kiHQ1fStRluE04N6i4se/Fk7FZP97GmN2QSmDfzrJ1ivlWUO+W0FdB9LD4dAHDTpWSk5R1TXKlZXi+jQyLdvIKcqBhOvg1L/27TwngVyNpPMb+Wx3ANO62XH4tYG8NNSVgwFJhKXkwtXVoli5Gbxn2QWlzF15iXUXo0nIKmRleXFqWl4xr2+5Tq9vj/Hihms8s/Yq4/84y79etwwxlUrFqrOR9PrmGJOWnOOpf67Q65tjrDgTweC2lmjHXxQLxmGf3FKdu8+oVCr+Ph1BTydTujhUMkZLi6AwC011OYba6qQ3wJOzwzseAy11hrsagUrFlK52dHEw5rM9AZQqlMSkF3AoIInXO6mQJftD+ykinbHns3gUXCEyKa3WWomk7CJ+ORxMSHLdPStOBKcyRO5NWethyCYuZpjyHGoBW+tdi9Ec7LgWx4AfTlQxCpucva/B8sFQVo/v5coK4eV27ANDPwa/rZAaUnWbyNOgaw6WHtUfQ8cYOs8WNXnVeZHO/yFkewe8ced7jYS2hhqvj3DjWFDKTdWe2zkdmkpUegGvDncF4JVhriRkF/LeDl/KFC18QaVSicVBm+Fg07X6iFtllErY/jQY2sDMzdDxCZGWeHUVrHlMSCq3RKIviH97PgO6ZsIAr4XsglICEnIY7G4p0tKizgEwxN0CSwMtSsJOiYVJTZ7iAW9AZlSd3uh7Ii8VNkyDFSNENLMkHy4urTa1u7G4GpVBfomCwe636pC6OhiTXVjKY2oXAXArDkAmk2FjrMP07vYYaGtUPYhSgea22VgqU9nfZRklCkje+2WTjbkKUefgOwdxbSTqT2khnPsdrq2BRN+bLx8PSiGzoJThHpasuxhNbEYBizZeo/c3x6pvEluQAVvnC0fA3P9Ax4S9vokMcbdEX6u844maOkxaBhbusGlW3XNS+HHIT4X5u+HDZHjqILgMEanSNTnImoGUnGKMdDQY1c6a48EpqHKThWMJxFyScE3cww0l3kvsC+JZeQ/s80skOr2Aj3b64x+XJdJuPR6DkV+JOf7a2nod50ZiDmN+P8P8VZdRKZVw5FPY94a4HnVdzwbSso2c2EsiD9xpQO3b6ZmhcB5G1qWNOJjq8sXE9sjlMqZ1s8NCX4tlx4NvKbA14QRfEyvPReITm83aBT2Z3cuRVWcjSc0t5sX11zgRnMKrw1w5884QLn0wjLEdrPl8TwDJOUVkF5byyubrfLE3kLk9rNnzUn+OvzmIj8d50NnBmAWddEXxtXVHsdhrJi6Ep+MTl80zA53FCwnXRZHy19bwowuEHsFcX4u0vPoZBUqlit3XExjT3grt1SNg0xPIVQo+n9CO8NQ8Zi6/yMK1V9DXUmes7Bxo6oPrKLFz64GoKUvpKgvhcg11OSqVire3+/C/42GM/PU0T62+TEFJzR7VS7436CyPQNtzNHSYSrHjYObL9lcxRlsSCqWKxcdFBPPnI8H3RyghN0nUYKTeqDttMv6amHh7LBR/d5whUlJDDlbdLuqMaPpbW0pB13kiyhC4u+rr+Wmi03yPhWBg3fDP0wDGdmiFlrqc/X6J1b5/yD8ZOxMdutgbA9DOxogfp3Zi1/UEnl9/rXGN5UQfOPsrhB6pc6EOCCOmtuaUIQch2Q96PisaWtblUQ89LLYf97OQ/B/0DhRlw97XhTNqxvoGfZzGJiW3iMXHQ++836PPgamz+K3omNT53V2KTEelgj7OpiJqtf0pUJSiriZnfhdjWhUEk2fTp+YD2HYTEcozP9c/cpCfBusmi2tbTm5Rac339+kfABU4DxbpPb+2h4Pvwb63mixacTIkFTM9TTpUSlke7G7Jp8Nt6aX0Fl7fmIu1H8R7PcSch2lrGDl+Brt0p2AatJGSlMaPnl+OzODxP89RWKIQ38mh96Ek96bRKlFPAneJ+1zHVMw/AIoy9lwJoa21AT9N64Smmpyx/zvDkcBkFCoVKyorEYL4/nc8AyV5MG0NaOoSnprHjcQcxndqVXVbTV14YpNYH26cLlKbKyjMFBGBiro6n01CqMamC8jlwrE2aSn0ew2Ofgpb58Gq0bBpJmQ3XxpxSm4xlgZaDPOwJDW3mNKsBJGuBsLRrywTa+J68L9jocxZUb7tlVVChbjjE6J+5h7WAzuvx9Pd0YQ2lvosXrcRMiNFn8qez0DnObD7ZRE1quUc/vHZzPz7IiogIi2ftP1fiah2/9fFsz5o712PrzpatpETdUZ4ci3c69z0pNZg2ipDWT5aFx1NIS2oraHGMwOcKfbfBTnx0HuRWATU4KVZdyHq7grk6uBCeBr92pjTr405Lwx2oVShZNKSc3jFZLJsTjcWDWmDvakuVobafDmxPRpqcl5Y78WoX09z7EYy64YW8WngODqkH8DZQp8n+7Vmwzhduh2aIjwUExeLlJ9GQqVSkVdcvzSKolIFH+3yp6OdEcPaWoqUpT2vgFwdxv8qutTvfhl7neJ6p6t5x4omXbPtUiElQCy09r9FR1sjPhnviY6mGg6munw+3g1N343gMUFMegBWHVBpGzNSO7jGlLXtXnGcCU1jyayu/DClI6dD0/jnfFS12xaXKdCMLNeodx0JgFaf52gvj+L6hcMtUmntgH8iEWn5vDjYBe+YLE6GpDbp+fb6JuC38xdxzT0nwqnva0/3ubJCeKjcx4q/tfSFI6NysXdRNiT6kG/Tt/bv2NwVHPrc6UE684v4t//rd/ehGoCeljpD21rebH5YGYVSxdEbyYxqZ42sJE8sMLPjmNrNjmVzunEmNJWeXx9j0cZrXKtHP5FaUalg10viAb9hKvzeCUKP3nqvcmpUdpzoPfa/zvB9axE1u52yYpGC4Nhf/PYNrG8p/tTE2V9FxKfCMWXaGkZ/B6O+gceXi/TCxiDhOpz6sUGezYP+SYz69TQ/HQ7hfNhtNXvR54XABZQbObWnq12ISMfaUBunohuQGiTS8oL3AzDXJh41mYqNKU61D2jQO8Ip4LOx7sErFfDvQuGV3joP4q+RmltMv++OM3/1FfKKyyhTKNnrmyBSQNLDhWe132vwxEbo/aLwuE5cIq5h3OW6z3kXnAhKYZCbRZVmzNoaajxlFoBMWQr9XoGUwJqNyMIsOPYFtHscXIagrian64wPyVTpU7ZihFBjbKTc/6JSBe/+68u1mCwhiuO/XawPNPSEE0ai/nitEbXRg96FwJ0QepSyvwbxXtQCpnexwlhXkxeGuKBUqlj9ZE/m93Fi/cXoKg6eiLNbIOwoN/r8CCai798+30R0NNQY2tbyznMa2cKsLUL5cftTEHNJRO//6CbmoW1PQexlCNovFuKVnWUyGQz/TMi7p4aI1NL4a7CsPwQfvPNc94GU3CIsDbXo4WSKnqYceX6yUBUGsPQU81LU2TqPU1SqYNW5SM6GpREeHSN+192fgk4zIDtGfCd3QXxWIZcjM5jW3Y4/Z3dlaPFx0mQmhOp1E9/nxMUw+APhXDnzc7XHUKlUvLnVB2tDbQ68OoBWWsUYei+DXs+L6+HYr9EDES3cyDkrLNg6isNUKhXfR7mSq2ZM64iq6WgzeznwpNoh4g06wuB3Qa4BN+60FEOTc/l4VwCLK2p4Gkp6uMgR3fNalZcLSxRcj82it7Po4GxlqM3sXo7EZxXy4VgPejtXbURlpq/Fl5Pacy0mCxdLPY7NtWKA1+vCij/yqfBYpIXBP+NEus6zJ6BVp7sbcw28vd2XFzfc6kESlpJbo9LN78dCiUkv4PspHcWD7cpK8aCY8Ie4sSYvg+Jcni/8m/T8+qWr7fFJxFxfkw4Zh8XkM/Yn8FoNl5bxVL/WrHu6Fyvm92CyznXITRBehArkcmRO/RmkFcTJkJQ7VKySsov4cm8gUzoYM44zTHdXZ0YPe5adDL8l/VqJMyFp9FV5UWje4VZEwG0URXq2DM3dXWO0qLlQqVQsORFOb2dT3h7lTic7I349EtJkxlhWQQmf7LiGTdgmCj2nwZgfxGL65HfV75CfJkLm3Z8WKmkVuI0SNTgVUvDR50GlZOI+OfNWXa7d+dB1HkSfFfcgiIfaxT/FgkqvETtZ56fVuDgb17EVNxJziLhNgMArOpP0/BJGtbMWhtiVv4WhA4zwtOL0O0N4fYQb/vHZPP7neeauvHTXPaWIOgNJvjBzCyy6IqIFG6aKlI5f28G3diKicvY3WNJLeMydBoBtV7Fwvl057eJS4RAa852Yg/WtoCAdFDXk08dchNiL0P+1qnN2z2egz6I65/EGEX8VTn0H/+sqPkcdBCfl8vx6LzrYGQOQVbkuMj8N0oLFAxbqFcm5EJ5OHxczZNfXi4VIq87CqFCpMAzaSo6GJb97K2qP0jn2FU6Bo5+L1GyouYfSqR8g4qSoRzB3g40zWHfwDAqlCq+oDKYuPc/IX0/z0kZvvtl/Q9Rp6lmI711NHUZ9DRP+J9RI9SyEGmcjE59VSGhKHoMqpardJGCHKKCuaANReaFVlCN+k+f/EJkJJfkw8lZ6mqeTDX84/cFFOsLhD2HTE40SifrzZDixGQWoy2UExqQI48p1FLQdJxk5DSE1RETeus0Xc7GOCWyYQnF2CvayVKZqCxGPFwa54PXxCPq7mvN0/9aUKVU3lUoTswuJPrGaIJUD4w7q8euREPziRJPdoR6W6GqqV39umy4w7R9RWL9qpPjdtx4Eiy6LlNk1E0TKcofpd+4rk8HwT2HRRZixDp4/K463741mKaIXkRxtNNXlDHHQQF1VemvNIZeL+akeRs4B/0SyCkrRUpcTfWKlWDt2nQdOA8W97393KWu7rsejqSZndPtWOJloMUXrCsfVBzFp6UW8ojPE9zn4XdEL7PLyatPWT4akEpycy0fjPLEy1OYDi3PIFcXQt7yWy+MxUXtUj5rI+tJyjZz61uMAFyMyCEkvIctjpsgRLLpVvK+fEUA3WTB/FgynTMNAhO5v7L7jGNvKU48O+ic1XHr66ipY3EOEbL1WQ9Kt4q5rMZmUKlRVjJm3Rrmx6snuPNXPqdrDje3QitNvD2H93Ha02jtX3KxPHxYLjNM/wrb5Im/8yX1g7NCwsdaDwe4WnA5J5URwCoUlChb8c5Vn13oRk161kN0/PpvlpyN4cUgbPFoZiuZVx78UYUvH8lQNYwcY+SV98o5gkB1SzdmqolCq2O+XyLh2FsgDd0K7yWKR1P1psRiovBC7tBzseohFWmVaD8KpKIiMjHTe+9fv5gI/Ki2f6X9dQEtDja90N4vQ+C9t+TTjfTTK8lh2+s50iAO+sQxS8xepahXI1dDs/Qzj1S6x6UTLehieCknlRmIOi4a0QSaT8cZId3zjsvntaP37Tmy6HCMmrXrw1+kIRijOYibLYbN8rJiU+78m7onqJM2vrUGhUjLqjHPVyJ7rSJF6UNHZOfIMeZoWxMptCE/JY+Rvp2uOdHhOFPVoG6bB+cXC4+02SjQNbUwuLIHvnYSBcOKbKiH5oW0t0da4M2XtZi8re0Mx8Rs5QMgBYYhRrtA22IXjbw7m9yc6E56Sx8I1V4WEaEM5/we5Bi58GNCKBA17mL1d1H5kxYiFW+/nxRx19FNRw/bqdeF9e2KTEC7Z9ISYc1Uqkf53+kfo9iRYdxDH1y9PnagpZe3c72DWRhTVNzU9FoqFjEMvoRp0+qdaNz8XloaGmozlc7uhpS6vanzElNfjOJTPWTomVfqurbsQxZWoW/dDRn4JQUm59HfUBb9/ofNMMZ6Ik3DgHQjai2rE55QqqTFCfJORX0FxDpz+gSOByXT+4vCdDqV4LxEdHfiW+K3P2opCqcDOdzHPDXJh6/N9yC0qw9pImyld7VAP3itSPoZ/hqqy9DqIiL/HY+IZ2Fgpa3FXKd7zNq9v9kZLXc5A19uMnIIM8d20f1z8PnTNbn3ngbvEs/PE13D8K7EAG/gmGNlVOUSXTl1ZkPMMGRPXiWOd+63K+/96xbH6XP3lzcNScll6MoynB7TGo5UhFn7LIScBRnwunAPJ/vUTUSkrEfON1z/1PvdDh/daMf96ThQZFSO/hk6z+MRmOb6anTH0/gtUKmQy2c0GnpaG2kzvbsffZyJ4e5sPr646ST+VF60GzOe5QS78fiyUxxafJSQ5j8c6tqr9/G4j4YXz8MwJ+CABpq0W2T/Ty6P7Tv2FCm9d6FuIOXPBoaotC+4TKTkiXQ1goI24N4t1KkWwnAaIaFMd0esNF2Po2dqUxzrZYB5zEJXLUPFcVlMX6ynvDcKZENswSepd3gkM87DESEcDknxQK85i/LQF2JnoVl1f9HxWRLarSTv761Q47WwM6dfGDEoLGZm7g3/L+hNaZCg2aDtOrANuT12/B1qukRNzsX71OMDGyzE4muliO2wRlBWK2oAKrq2jVNuMrfmdORmcCp4TRF5jzq3FSKlCyY5rcfRrY0ZecRlHbzRQn//SctH07Y0bYiFwaal4Xakgwvcsj2lfxyP3/E3vgK6mOkPbWtUqX+hgpossaB9kxwovg00X8SA99xukhcL0NSKS0wSM69CK7o4mfL3vBj8cCiIpuwh9bXV+O1bVSPn+YBAOprosGiIaf3JpmejwPeLzqgcsr5fRK6g73/VKVAYpucU8YREN+SnQfqp4Y/hnYvGx7w2xCEvyE96jns/deZDWA5CpFCzpX8J/3vG8sdWHr/cFMmXpeWQy2D1JCx2fdTD4fXjsf2glXGax/QlWn4usInNZVKog58YJ9ClA5jamyinkXechl8txidyAT2xWnZ/rfrH+YjTuVgb0byMiGIPcLHhtuCu/Hwu9KXhRGwlZhby/w4/pf11k5dnIWiNAKTlFrD0Xxtt6+wkz6MHvvmqi1qHX8+Jhd9tCBEUZqisrOSrvR3CedtVFiWlrMHeHkHKZzKjTXKEdg9wsOfLGIGyNdfirJkU7TT2YvwdMnISn18QJpqxo1BROQHgqJywWkdNT34si23LEPV01ZU2lUnE4MInhHlaohR8RUZEpfwvBhQPvVnHGqMllTOxsy+qnehKXWch7O/waFn1LDYbQw/ySN4KNV+IY+vNJ/j4bLQRJXjgLY38U99DrAfCav/DqVyhW6pmJ6E9pASwfJFLYts4VzoOhlVTvKvLDq0tZK84T9TjdFzT+914TZi5iITPwbeFcOfd7jZteicqgg60R2hpqGOtqkFU5aht9XuSsmziJv3VNheGhKCUlt4jP9gTy5d7Am5tfihCpboMUF0T9Ruc5YgGvVW7I9noBo56zmNHdnpVnIm9Kw1aLsQP0exXVhSWobZ1FP+VVXtxw7ZaTQaUSvxULd5EKBGBgxQG9iUxSO8PTXfRoZ2PE2XeHsPGZ3rzcx4RP5CtIthqIsv10nlh+kZG/nmKPT8KtKJHnRJG+3UjRiuSEaLS8lqObdIU1C3pioqdZdYOwY8Kb7PGY8PY69BHP9+ubRASxVUd46Qp8kAhvhwuFu9sY4m6JXAb7ijpA35eFQVS+SFOpVPxxPJTt9ayRLFMoeXObL62MdHh1mCsDLAsZlrZOONMsPYSRoywTz5i6OPOTUB/c86q4Tk2tltcSiTwtnvEVhkHnmTB5KZeSVPjZzxHR5ahKymBe/8CyAbzdVdRNX4/NwiPrOBooMOoxk3dHt+XKh8PZ8WJf/nmqByM961FTaekh5qvKxomVJyw8CpP/qv9nkcvrZxA1MiqVitTcYizKjZzupmLOCMyt9Hmc+gu10VrSzYKTcrkancnsXg5Ma6tFe0UwMRaDbm0w6D2Ruhe0H1aPqXfEJDItn+Dk3FvNU6POgZoWuq178fSA1pwJTSMspTyLoVVHUaZQoapZjm9cFhcjMnh2oLNY+3qvR7M4kzWyiRzwL3+mGNmJ+89nc+21og2g5Ro55fU4KdqOtfYkSc8r5pB/Ek/0cEBuYi8swcvLhZeqtAj8tqLeZSYu1iZsvhIL7uNEkXOljtNClrmE90Z70M7GkJ3e5YvxggyRl14Du30SOHTBW+RVd5gmjI4ez4DvNrGgWf84c33n8wc/oLb5CfjRFfa/U38Pmt92EakwF6pMDHpHyCqO/7XRU9QqI5PJ+Hi8J2Epeaw+F8VLQ9vw0pA27PSOF0p1iILNM6FpvDbcFS11NWHAef0jutHfniJU/rdGcUbN6Rjl7PVNwMpQi7Zph8UCwL6neEPbEMb9JHLSN8+CbU8Kg9Jz4p0HsWgLehb0Uwvg3dFtORGcwsGAJLo4GPPvc71odfZDkeM64E2xcO2ziN4pW7FSpNy69oioyBDFeUr1be6MFumZIeuxkIXqB1l9uGny2xtKfFYhx4NSmN3boYoB/eowVxb0a82XewMJSKi9b9R+v0TU5TKmd7fny72BvLnNp0ap41+PhjJZfg7zomj0Rn9CTmGpkJbUNhT1b9fWVi3kvLEbWU48S/KH0cfZjH/OR5FTWUrUbZRYKJ/+CVWSPwfyXRnd3ho9LXXm9XHkSGAy8Vk1pBG06gRzd8BzZ+DJvSIy0diYOEHXuaK2pNMsOPjBrRQ5YEInW4KSctlXbuicD08nNqO8l9XFpSKlyb4XjP1Z9En5XxcRHYo4Jf5LDsDdWMl3UzqwxyeBnw43QDTiwhLy1U3YXtqXQ68NZHp3e77ef4MNl6KrbqehU/1D3MoTXr4mVIusOwpltHm7xYK/gtoiOTEXxcKwJrnvpmTIh9DnJZGqknZnurFKpeJyZAY9WovPYqKrSWblSE5FPU7FPVNh/BVmscs7AYVShW9c9k1nxsWIdGyNdTCL2An2vcG8jTC0+78uoljlqVavDndFJoPvDgTVOvzSfm/wj94C7GWpLJX/wFSzKBb8c5XE7EIh0Rp3BUZ/ezO982JEOh/F9UIuV0fv+mpAzNkolThd+BhdWRm/6yxir38SlyIz0NFQ4+VN3sxZeUlECB37iwLxwJ13933fxiXNXsTKbFjidPaO9GsAIk4IB0ZFdMaht5AJ3vOKKIaetVXcW3K5eFZU4/wz0dOku5MpRwOTYegnYv4+Jpxp4an5RKUXkF5Z2ObKChFhqYZlp8Lxjcvip2md0NVUZ2bGMnJV2uT2KY/8WncQ9YV1GYEJ10XtQf/XRd3Z5eVw4C6ixxGnRE1LS5CuLsqBv4fBlrkibajS/FNUqrjzWVBaBMkBYmFaicz8EuIyC9HyGCXSK098KyLKvttESn9GJEabxvN5tyKOvDGIz5wCkDn1F3U2gIWBFl0dTBjsblmlvqvBWLe/ecyWTFZBKSUKJZaGombRSUuss84mVUrTq0ddzuYrMZjoajC6vTU9Si4jl6lYn+F5a92lZwbjfxFKc8rSejcYvVzed7CvS/n9HX0O7LqDhjYTOtlgoqvB+ouVnjU9FooU8pRbrRWWngzH1liHcR1aid/Zqe+RtZuMi0dnFp8IY96qy/x+NJSLJo9B5ClUP7YRgikl99YKo+UaOeHHwXkwr23x4eVNNWuhH/BPokypZFr38gm09yLICBde5KC9UJSNrMscnuhhz4ngFJLK9MRC/OLSm2G/rVfjaGttQHtbQyZ3seVkcCrZCeHwR1eRC10NMekFvL3NB/8zu8QLzoPFv92fEpP0soGoYi/zctmrbOq3H164AN2fhMt/gf+/dX/+/DTxHVREMkAsOBZdhC5Nr6TWyd6Y2b0c6GRvzPODXJjT2xFLA20+3xNISm4RPx8Oxt3KgMc6llv2vltFsW51kRV1LUo1DDBVZVdd1N6GUqnioH8S49uZIwvaI4pPKz/w2o4TaWvZcWDVTtT7qGveeSCZ7GYh+wv9bLn+yUjOvDOUFfO6Y37xG1EzNO7nWzUh/V9Hpm3IDyY7q3gD9/vEMUbDC/V2E6t98MoHvom6mpxOkSvuezTnSlQGc1Zc4sUNXvx8OJjCEgWbL8egraHG5C5VJ3WZTMbbo9yRyeB6HePc55fIAFdzvn28A+cdl/O4/yL+XvwN2TlVjaNzYWn8ezmcd3V2gPs4WrUbyOj21qw6FyUm1F7PCSWlimiOohTV8a/wlrfHzK03P0/vRFGpgnUXKk2MHhPEb+jMLySZdOWUqivD2oqF9ZRudmhrqLHh4m2L9ttp1bHqwrypGPO9eGDsePZmjcqodlaMamfFezt8OR2SyqKN1+juaMJAs2yIPCW+E5kMLNxg0SVoM0IU/6+dIP5b2he+c2Bi/K+8PcKFJSfCeWXz9brTZwsyUPpuZXXJUOb0d8fNyoDPJ7RjVi8HPtkVwJHA5Pql4KppCC/sjHXgPqbKbz6vuIyF/5Z/99VJfEaeFPneNcl9NyUymTB0tIxEuvBtRKTlk55fQk8n8bsw0tG4VZOTlyo8zRWiA3DTyFEVpLPdK47hHpZYGWqx/mI00en57PCOZ2QbPWRRZ8GjUmregDfgiQ035xVzfS3eHOnOv9fiak3//OdiAl+kDyVjzjEwac0n1hdRKlWsORkgUgvdx4lMAcRC8/0dfjjZ2yPvOlss5ksKhDDBrkUQuIuzbT9gR7iK7w8EMcjNgl0v9Wf1Uz3wjsniydWXyStDjLuRIjkTOtthPfot9KIO3ykDr1KJ3kgulYxfhz5igWXTVUQU61mrNcLDigvh6eQp5MI5FX0O8tM4Vp55kZ5ffMspcG1dtT2ZgpJy+P1YKM8McKZna1OIuYh98lG+K51FQEb5ODS0wap9jd9PqUIpPNn/Pg1mriIjoPcLwtC5ukqk4N3cuEhEnXa9JBb4t3vOlQo4+L6oK2sJjSkPvieEIZIDYM14EWkrKaCwRMGY38/Q6fPDPLn6MocDyueA5ADh3LjNCRiQIBzTHexMYNin4pn7W0f471noOF2kypq6wMoR8PcwZNHnxOuPIkoFpWd+Y6L8LLbqwriR5ydTJNPhTHSllEm5XPwuU6t3mqhUKg4HJDOmQyu01NWQB+8nQb8df18vpOtXR/j2QKVebjfTRutQOiznUmQGba0NMNbVFL/Z6As3axi1NdSY0cOB7V5xtwSrPCeK418WSqte0Zkc8E9i0ZA2qKvJRQS0KAeGf8aXE9vz+nA3SsoUrDwbwRNX3RhU/Asr5NOIzCimWK7VwC+0Ki3TyMlJgGR/Ikz6cD48neTsmnNjr0Rl4GljiLl++Rfh2Kc8nP2lyJu37Q6WHkzuYoe2upxV5yJFkVNRFlxbR3peMSeCU5je3R6ZTMZjnWyQq0op2TxPFJ9W00hNpVLx8S5/isuUtMm7gsrS81Yqh565kHNWKfEbspo9Zb3o2K698JSO/ArcRgsPVEW+r1Ipehj897xQC4o4WZ4TvxNQiRzKZuKrSe3574W+aKrL0dZQ49PHPLkalUm/745zKTKD10e4Ci+LSiVS1dqMEF7NaijTNsNMllOrjHRgonj/ceMwkcpT3Wcf/ws8f0akqZQ/+Kul5zMimrZ9gViEqlTiez//h2giWVEzBCLyMOQDeuUfR5ksoh25RaVkBp3GRJWNrLpoEYCeOf9n76zDozyzPnyPxN3d3SAkuFtxKdBSWlrqW6Eu2+7Wtt26bb2l3kILbaEtlOLuDiEhxN3ddeT745nIJJOQQEKAb+7r2mtL5p2ZZ+x9n3PO7/yObPRDLJXvZP3etpPFv37aw5d7L9LAood8tjuFpMJqymub+XJfGgs+O8jqY9nMj3TVnjvRWA173sJEWY23nRmJBV2XgHPK6zidVcGcQa5Qlo5r4R6iLatYXvEO8g8jhDVkfTnVDc38c+1ZnnU4jHlDIUx+HoBlo7zJKqtjf0qJqGqOfkhsws7/BSe/R1KWygv1S3h0aiCu1iYsHOLONwfS2zbgHsPgqRT4VzaPm7xGgK8fVqbitVgaG7Aoyp01x7N73zPXHxhbwoIvxfwBjcmCRCLhrUWDsDCSs+zbY9iYGvLVsqEYpG4X1eOQeW33t/aEhSvgyQR46KQwCrhjk3gvj3/N8pLX+PjGELaeK+C+lSfFxqorTq9ErWhig2waD0z0a13Ly/PCGOVrx70/niD4hS1c9/7ebqvi3fHj4Qx2JFXQYGiju5KTvk/I8AZqo2ZoKmQYZ37u1EtxPL0MiQSGeokgR8jVNOehmJ9BItOuCJuI49KyckgsrGbJME9uHu7Jhhhh+W1lYsBTgUWiodn/um6XtXSEJyEuljy65gxf7UsT1Zl2lNY08tGuZG4a6sEIPweIvgPDpL+5J8oc+1Mfoa4rhemvth7/4c5kcso1Ri+jlotr1CfDhCvU2TVw/ecETLmThmYV+ZX1/HuWCDonBTnyw13Dic+r4unfYoTb3Z2bL+qt1oVB1C3CCfXwJ9o3FCcKc5j2FT7XKCH7vHm1mG3WQ6aGOtGkVLE/qVhI39RqSPibneeLkEslNCvVVDcqxPcz/4zoYVVpnyt+O5GDlYkBT1wXKP6QuBm1qR0bpeOJzWmXyHGLFkFOy4Yu6yjknaFg2wec+e8Y+H6WMDG64Zu21zD8HxA4U9joJm0VScpvrhOmClmH4fd7hI339pegVuPud/ZX4SB63SsDH+Sc/wvO/CS+Gw+dgIVfi8r6D3P5ausxcsrruHusD5X1zfxj5Un+uTaGxqzj4vfjpD3QNTa3EmMDKX4OZiKgfjJBJBbHPCYc/szsRTVh1jtg6SK+E11dZ6919ryJ45HX+NDwM6J+GyG+E9WFNBo7cDq7XNibt2DtKVoYdJBQUE1uRT3XhTiJBH7abpyHL2Ll3cOZGOjAir1plLf0wUokQlXQQ0vqY+llIikAIrBtrATvMa233zrSk7omRdtYDbkRRN0OMWtQ11fw+qbzBDqZs3ioO5SliSLD6IfBxgsbM0MemOjHmn+M4ux/ppP82kzeuGc+Jzzu4NbKB5Be4u/iygxyUoTt6XupojpT3ajocmNzIqO89eLVypSXhMyrLFXISwArUwNuHenFqiOZlBt7iJkNhz5my9ls1Gp1qw+7k6UxXzitx6YqHqXHaHGS7sDmuAL2JhWzdLg7oyWxVLh06Bua+RY8HsuOWl8sjOUEO1u23XbdKyKI2/e26OX5YoyQX+UcF5ryH+eL/538QVQjWoKnAUAikWiVimdGuHDo2ck8NCmApSM8hWMUCDlCUbxobO4KU3vsJJXdDgTdn1yCkVxKUNkusPa6NEme12i44TvRwPblJJFFOvA/0Zcw5tHOx0cuRW1iy+1Ge/nleDYP/nSK6ZIjKE0dxcmgC6RjHkYpN8Mn6TuqGprJKK5haeKjTEh56+LXfgGKqhrYm1TM8kn+rP7HSNY9MJqaRgUlNY0sHeGlffCpH2HP67DteYKcLEjoJsjZFJuPoUzK1FAncXGTyjFZvp/Pw9ewUTkS9aGPYcMjvL0lkbLaRm6TbkESdr0I4IERPrYEOJq3la3HPSlsotfejXrXq2yTjccpaCSRmnkxS0d6UlbbxKnMdoYC5g6U1Ss5ll7G9HBtLfayUV6U1Tax/szAzTLQwmuU6JPY/16rBbO1qSEf3xLFCB9bvrtjmOhPSNkhJDpG5p0fw8JZJAYcAsVFY/zTot8uaQtz98xic/RJTqdk8+L6ON3SNZWS5iNfsUk5nEUTh2HZLsA1kEn55o6h/HDXcN5cGIFUImHR54dYsTeV5/+M5aYVh7n7++O8ujG+28CxtlHBV/vSAKiR23buyakrEwMAfcb3/j3sS4beKSqBHYxljmWUEeRkIQJmRRMLan5BUlMoNsknfxCbsPYSW00l50h8CvbmhkwIcuDm4Z4oVGpSi2r4bGkUZlm7wcrjguMN5DIpHy6JxN/RnLe3JjD9f/uobOfs9r8dSajV8OQ0zeNo5p3d2/wTy9jISffbxPwehJ3ul/vSeGCiP0HOFqInaelvYnNo6wuLV0Lkzfg6mDPUy4ZbR3qJ4zQM97Hl2ZnBbDlXQHatrG831QYmYpMfs6ZtAw/i2iCVa22IkErFdbmXFVcfezP8Hc35+VgWanMn8BhBc9yfnMgsY0qIaNAurWkSwQUA6k6Vk/3JxYwPcGhtgCd9HxLvcQS7WHM2t0OQU5Yq7Ii/myGcu76cgP2hl5ECy5se4dOQH4WioAWJRGzgjSzF7JaVC0Rv193b4OGToh9u2F1C1vapxqho92viHNk+6TYQlKbChkdEb030HeIzGnQj3PE3yqIEDI99xv0T/HhqehC/PzCa1xaEsyEmj7PH9ojqbcsIBw1xuZWEuliKrD2IpNCwu4WbWYuCwtBMyJpuWiVcYgdomPmAkrgF9r1NfOByRjZ8jNJ/Gmx7HkpTkFu70qxUaxvuWHnoNvQBdsQXYmIgY5SfnahiKhqQBs9iXIADj00VQf3J9tdajxEikL/A8O68inpyyuvbgpzMg+I37T6s9Rh3G1OuC3Xi+0MZbdK4YXeDop7zW1ZwMrOcf80KEY5xGx4RiaQuxjsYyKSM9rNnxW1D2fHEBAxklxam9EuQk5eXd2kPkLydWrsI/k5TMk/T6FSqY8ZKXkU9uRX1DPPucLKUGQhbwbGPt1lWAneP80GpUouG57GPQVUOY3ct4mXHvTgaai48Z1YzuWIdbytuYY/xZOEr3s7NoqKuiZc2nGNSkANPDFbgIKkk2Xxo5+c3sSE2p4JB7lbI2mtKHYJEqX3/e7DlGbB0E03TD52AZ7NEA3BJspBQtFv7lYKNmSGPTg3gtQURQgeubBbldtco8O26siK1cMCOqm5n5exPLmaUtyXypE3iwn2pF+GQOcJpxcJZ/PeS1V3PTZEbIRl0EwtkB1hzOIUjqcXcYHoGWdg8ccLvCmNLFIOWME+yj62n09m+YxPh0gy8R8y9tLV3w59ncpFJJa2/jXA3KzY+PJZVd48gvN0QPtRqOPGdyLCeXslU4wQSC6q77PP4+2w+4wPthXtK0hYhKzGxZviwkTzTcDsZgx5HnbiJbSfO8VJUAwaVGaLpWoNEImHpCE92ni8kr6JeNKAv+gbcolE11fFK3SKemBbYenyYqxVWJgYcTC3RWse+pGJUapgaoj0bIcDJgqkhTnyyO6X7ysblZNxT4n36/b7WCkK0lw2/3DcKb3szISXKOAj+U3v+mKHzRW+R70T8Yv/HAeuXOX38II/9cqZzT1XSVgyqs/nDcA7LRnl3eigjuYwJgQ4sGe7JugdHM8bfnjc2J7A3qRgbU0Nh43oogy+6MnUAVh7JpLpBgZ2ZIRUyW+Gi2J6MA4BaWLcOJI4h4rM4oS1ZO55R1naNyNjHjMIvebrydbEBL0sVGcf2aIKcpPQs5ke6YSCTCrvTWSF8sCSSQW5WYiCn/9QenaMCnSz4/s7h7HpyIjWNCv44JbKdSYXV/Hw0i+WT/FsbjjF3gJA5mMb9RK2hHY/lTuF4RhlrT+bwyJrTTA9z4uHJ7arlAdfBjNeFTK6ddO63+0fx8rwwOnL9EDdMDWSsPpZ1wXX3mug7hFlQTDvjn9Tdwjq6j3rknpoWyP7kEjbE5EHoPGQZ+zBX17B4qOgzK61pFJ+NVNPLUNs2I6ygsoGkwhrGBWoC2voKUfHxGU+EmyVxuUJOfTqrnHq3kWJejlMY6tv/IvPGrbzq+D/Gqb/F9qEdeE9YyjvbkvnjdAezAzM74fr30Em4/yA8eKRNymXlLtQcj5wWRkK/LhMGEFNe6pP35qKpKYZVi0SQMf9Tre90k3MUiWpPAowqWD5JfO/Eed6L6WHOOFbHi9fSgbi8Sq2BsHp0UJoq5M4B09jjcgfVho7IZ2lmzKVsx9TWDUtjuZazI9aeIpGjw2FtR0IR4wLsRQB/7neR+HAIBsDLzhR7cyOOt5fNeo4ERYPYa3ZDy/O3yH3JPCj2e4ZmWsfdNcaH9JJa9rbM5bNyp95vJuYx3zHOz5aJ/jZC4pl9VKgYdCX9OtAy8/JS6JcgZ+TIkRd/Z2Uz6rTdrK8JxdfejNtHewPorACc0ESlQ71tOj+OpavI2rf7IBwtjFkyzEM0PNuEUrZgDYmNdiyt+gY+HSnsZ/96BAbdRNOwB/g2UfR7qNvpjF/ZGE99k5JXF0RgW3CQRgw4qgzu9PRqtZrY3CrtTWcLU18WE3efSIBb17bJPKRSCJoh+m7mfXJ1aFQPfyqqXXPe7zYYMLB0xE5STUkXQU59k5ITGeXcaJ8hJBih1/fN+kLni/d4xhsQPKv7Y6Nuw1RZxTyj0/w+JAaj+kJhtXsBLEbdjZWkjowDa3BMWEmloRNGIRd4rotErVZr+gSctFyMrE0NGRvQwfAh8xCUJgt3GffhzMp4A+rLKazq/FvKLqsjJqeSWREuwikr44AwAgCGeFjjZGnEmsaRqFVq5koOMF+6X/RgtPSiaVgY7Y6RXMaalk2UgTENS37jRtkHDA4fRJhr2+9BJpUw2s+OAx0GM+5JLCLY2QIXq842no9NDSC7rJ7fT/XMSanfkcmFDKM6DxL/7nx75kFQNvYuyAFwDBY9Zw8cxtLMlI0mLzEz8XliPrudXz56ltKseDj1I8o/lxOr8mHMxFkXvCCYG8n58rZoYl4SPWpf3BbND3cN556xPny2O5XUDjN+ACrrm/lqXxoLo9wIcragBGthD9qe9L3CGrvFnWwgGXqXcF3MFxfugsoGssvqW00HSN1Nk8yMMFUS/HqHWHPH4MzQFJXMCHlTBdeFtlXS7x7rI34fJUki+RXQvVStIx62pkwJceKno1mo1Wr+uzEeNxuTzqMEht0LEhkNk/5LeZOMG784zFO/xTAt1IkPlwzpUWZTIpHodO+0MDbg+iFu/Hoiu0tDkYvGwknIsU/9KBIsiiZxHmnfj3OJzAh3YUaYMy//Fc8W1TCkagXLbM+3bqhLa+pFJUfj6Eltm7Ryf7LYfI3ROE+2zOLCZwKD3KxJL6kl8uVtLPjsEIM/TOIG27UsKHuQUaubmbCylFX5Lrxy02h87M14aloQsyLEOjrtTQxNRXXWOVy3FbGFM9zym5j/NuNN8VsfKBRN8PONQtZ82+8iyG7Ha3/Hk9lkwTD7prbqlwY3UxXuyuxOQU5lfTOZpXWE6YMcnajVag7GZ9D4082oTW1gwQqKqpuF6YCtj6iIAhILZ3zszbRHd7SYxnSo5hRVNRCTXSFUGNWFYgRA1O2tAatEImGYtw0nMtpVclwihYxaV19OdSGsuxfK0jiaXoa3nalYn1qtPTi5HcN9bAl1sRQtIUBlXTPP543Gk3y+cFyH5Mf5ojVj0Ted9g39yUUHORs2bOjyfw0NPfCX74LmjCNIGqvZ1BDOJ7dE4WQpMlwluoKcjDI8bE1wsuz5FO37JvjRrFRz/8qTrC4L4EHlU9Tcc0h8ubY9J5xV5n7EI1MDyZAIudxTn//K9Z8e5JW/4vn9VC7/mhWMm7UJkvR9JBmGEV/SuZm+qLqRkppG3dkMY0thftCVFM3YSpTze6FX7lOOfSX6mVpI3y80lB2pyBZWusPu0ZnNaY/U3BEHaRVlXfTkHMsoo0mpYnTDQbB07+xmdjlwCgPXKN6x20BE/LvCxELHj7kTDkGU2kYxo3odMzgsMpqyLoaX9ZL1Z3J5f1ubZDI2t5KkwhpuiHbv5l4aTn4nZH9+k2Hexxg3V7DD6GlKj/3S6dCt5wowkEmYEuIk+sKUTa0bBalUwsxwF35PauaQNIq7TA9gkrhemGJ0eJ2WxgZcP8SVH49ktv5mvzlawJkaax6/LqDT8472tyc2p6JVwqNSqdmXXKJ7oCCianVdqBMf70rp+03axeIUKuQtuoZSpuwACxfhjHMxOATCvTuRD7uTaW7NzLLNY2HZl9h9Owo2PMyuxmCeNfwXS0d6XfixEBc7KxMDrb89OjUABwsjnvsjVsv9UK1W8+y6szQqVDw8OQB7cyMKlFbaQY5aLb4vvgPYj9Oe0OtFdfzQR0CbW2CrK1DqbvKcJvGO4iahK4+6XWdypk5miaO8nihPHQm05O2iH+Mi5HlLR3iSXFTD65vOsz+5hOdmhXbaPOI9Bp5OwWX0Eo78ewqbHx3Hr/eN4uObexbgXIhbR3pRUtPElnMFFz64t0TfIQarZh+FM6ugubb73smL4JX5YSiUKu7/q5gUg0AeNN+HjYnmPcw9LTLdkTeLf9e2VYn3J5cQ4mKJo4Vmv5C+V3xX7Py4LtSJZaO8eGV+OL/dP4qnpwfhYGGEj50Zcwa58NWyoZx64brWoFcikfCfuWEoVWpe+/t8++WRVlzTvW04iO/c8HuFGclAcvQL4RJ385pWWWQLv5/K4YfDmXh5+WCp6GycESLJQIYKdYdr/zmN7E9fyelAaSp8PpbMFYtRrV6KsjSDJZUPcbJYomUfzfinRALRIRA3GxNy2juKWmmCnA59OTsTipBIxLw2Tv0gVBRRy7SOGepty9mcijZpsoGx2Le1zKxqz543IPZXWH0LsWk5bVK17KOi101H4kIikXDXWGEn/faWBBZ+fpBtdf402IdhduZbkVC48XsxxuUyctE7sQULFjBhwgSd0pfq6ov3tz60dQ2D1Wbcf8tNhLpatn4gJdWdN8fHM8oZ1rEf5wK4Wpvw9e1Duev74xxKLWVysCOWbkFCMpa8XWxWDIyxNYCfH5lBw5f23ObVwIcY8OPhDMb623PLcE9xcc89SanNHBILO7/elibGq/KHXlcmhuuFXi/sF9feJTJibkNFc3gLe98SmSpN43m3mDlgQxVlNbotgA8kF+NgZoB19jaxeR6oDVPUbUg2Pi4uzNe90uO7WYy+m4iNy1Egx2r03X22nLyKBj7ZncLcwa4EOFnw+Z5UHC2MGB+oOwhopa5MaL4nPisuqI7BqB84zLkPbmPiwUcgwE9r0O6WuAJG+7WTqtn4tFmXAzPCnfn+UAYrpWNZofqf+GMXlcbHpwayKbaAlzac4/7xfvxvexJ3jvHB37GzZGWMnx0qtZg9Mi3MmbO5lZTVNjEx0FHHIwsenRLAnI8P8FdMHot6EuxdDobcBhsfF4F/e4vmlB3gP+XSvs+GZjDzLaSANVBcUsSG338ipc4MI98xvKVxnrtYTA3lvLognDu/O85Dq0/x/uJIjA1krDySyea4Aj6+eQgetqY4WBiRo7CERk0/i0SiqRamiAGAHdgeX4iLlbHuanZ/ITeEUcth2wuoJz/PL8ezmRLiKIxpqgug6BxVw25jRZoHD9wwHevwmTofpkRpRpBlM4ZyHUFFyg7RQ3EREqzxAQ542Jrw1f50RvnaMT2si0SXpl/FwtiAEBcD3cdcJCEulkR72bDqSGar5LXP8JssNmJ/PyUa6iNv7WQvfKk4Whqz8u4RNDQr8VO/g2Tl9XDgHWyMh+CevgYMzcVwYam8Va6mUqk5mFKinRxqZ5ZhY2bIK/Pbmuc7SeC7WMe/Zobw7z9isTI1YJi3LRvP5rEptgBDuZQbot1ZPskfN2vtak5OeR2pxbUEOpnjbGnc7by8XlOUIBz55n504X7emiLhHht9u/Z1HSiubuT5P+OYH+lKiGsAHOxsUuHdmEiTWka9RSDtf+GxuZUYyaUEOF5YjnTN03KeVCnhzwdpri6iuqaWUfJsEke9Q2W8Hy/8GYeJoQzXlu+Jqa2YZSY3wr0wgZjsdhJlSzdAAhXaLqObYvMZ4mGNvYlUyHXDF3XqeRvqZUOzUtjhtwYtHiMgZjWFlfV8oxkXYlmTKaqxkbeiPvc7DzW8S/UEjQQ4Zo1I2nUxv3LuYBfe3pLA1/vTGR/oIK4ljltFgDNAPVcXHeT4+/vz7bff4u3t3ek2D4+LG6Z0Ir0U94KdlLiOZ2yQ+IEaG8gwN5JTUqudGalqaCahoIpbR3r2+nnG+Nvz5bKhPLDqJEuGadYqkYjJue3wsjMD11AiDQv57ubhVDU0YyiTipNSWTrUl6EOjibjSC0NzUqtjUZsbiWWxnI8bbUb8q4KxjwqdNWbnhKVrcZqsend9jzctUW8VxXZ4phJz/VsKKmZPXKU1FXptlLdn1zCYo8KJBnFnT6Hy8rgm4VcK+q2XlVjDActRLntXyh9pyDvQ7OIO8d4s/JwBm9tSWDZKG82xxXw7o2DL5zRjVsnrD3b9czIbDx43+4VRpUvxijrSGuQU1TVwMmsct5YECFOysnbIex6rY35MG9b7M0NKbaagLruBySmdl1W7xwtjXlxTihP/hbD4dRSApws+OcM3Q3aPvZmuFoZczClhGlhzuxJLMLMUEa0l44MuoZwNyuivWxYfyUFOeELRW/amZ9homZoY2mqCAB6kgToBQ72jtz9jy56yy6SSUGOfLY0isd/OcO8Tw5gbCDjXF4VS0d4tg6Ac7AwIrnBHKRNQlJqagtHPhNWsAFtv1m1Ws1725L4ZHcKwc4WbH50XN9u5LohNqeSF48GsNbQgrJt75NYOJ1nZ2rkQGl7AGj2mgj70yh2nYq1QWcVQHltEwXNJviY6ag6KxpF5nPisxe1PqlUwq0jvHh7ayIvzg29bO9LR56eHoRC2cMZTL1BKoMht4pMcOj8XllE94bBGvMSmCgy33vfZrXUj+CCFDHAVm4ksuGaICc+v4rS2ibGBWiSQzVFwixn9MOXtI4lwzxIKKhiw5k8vjuYgZOlEa8tCKe0ponvDqbz15k8Xrk+jOsj3ZBIJDQqlNz2zTHSS0RPxbRQJ75cNvQCz9IL6stFM/mKcUIW5NPNMPWdL4NEqj3wV8Onu1OQSiS8OCcUSbLGTau5Xkt+51ITz3m1F6b1aqzaeSsdSxeDd+V9UHW8qilOFAnisY+LBEv2Ef5p/ArnbYfw54OjCDM04JWAMhavEJWUyNbvNKLKArhZm1BQ1YBCqRLvp9xQBBnt5GoZJbXsTy7hzYURQg5WnSeqhB0IdbXExEDGicx2Tmm+E+HQR/y17ke+THLnfH4VP1h8jtTMAWa9w15JNNNPP051/SZQ3Ann/hB7oy4GPhvJZWx6dByGcqmWCc5ActHfwttuu42iIh1WosA99+gewtUdzUoV361bj580H5+J2o2g9uaGnSo5p7MqUKt7lnHRxYRAB2Jemsa0FoewrrAPEuV3hBSnNZDReOhb+Y1ApaaTnj0ut5JwN6sBu4hdEgbGwtox86AoZ49/UmiHs4+I2UMg5p8YmgupWk8wFTpoZXXn70xqcQ0JBdXMNEsUGlGPS+jpulQMTGDMI20DAXuKoSmye7ZjNO/9Pl2OsYGMp6YHseN8EU/8eoahXjYs7DAHRyexa0WvQYeAK9DFilSpt1az4dZzBUhASDEKYoV7Vod+A5lUwie3RPHGjUORzP9UDGbt5ru9MMqNiUEO1DYq+GhJpBgYqwOJRMJof3sOpoq+nD2JxYzxt9edQW/H7AgXDqWUtFliDjTGVmJTd2aV2Air1SLoMXMAvykDvboeMSvChZ/vHYm9uRG+9mY8NyuEF+a0yezszY3IadZk42oKhRVowt9iRkg7ydcL6+P4ZHcKsyKcSSio5nBaacen6jfWn8nldEEzvzANq4Q1hFo0tFU9U3eBYyhm9uL3U16ne2bX/pQSKtTmOBnoqDrnHBfNupfgJHf3WB92PDGBEBfLCx/cT4z0tevcw9dnD/6g6DdZ+FWXm6E+ZcKz4DMOR8r5xP1dEfSAcMzTBDl7k4oxkkvb+nc1bohdZaR7ilQq4ZX54Zx4fir7/zmJvU9PYukILx6ZEsCepyYxIciBx3+J4Zl1Z1EoVXy5N42ssjq+u3MYT1wXyLb4wou2dddFiV0U64atJqHJAeUP8zjx3ZM0NupoHyhLh9M/iWC9w/DurNI6fjqayb3jfLEzNwJzzR6p/XwslRKbgoMcUwVT1E6aV9ek4EBKCZNDuq7E/79BpRCJoHV3w7bn2Gc1l211QXy6NApjQxEADPexZfYg4ezr2CJXa4ebtQlKlZqCqnafobWHllzt52NZWBjLmRfpKkY2uA3VmYA0kEkZ4mmt3ZfjN5l656GMzfiYOeGOSNN2I43/A/WEZ8DQlE/ygtlvOhWLg68Le/GGCjHAtxvszY2umAAHLiHIef755xk+fLjO2156qfduId8cSGdwxQ4URlbI/LU3BXbmRpR2qOQcSSvF2tQAf4eLL4n2SN/sECQu5ooOGtvcU2Dpjo+PmEuRXNghyLna3UUCrhPDOB1CxFyhwOnigrDhEbF5O7VSbG6Me3ihNhMbDXU7jXQLvx7PxsJYTkj9aVE+NbwKq18gGkj7YRDl9ZFuhLlaUlbbxCvzwy88AboiWwSkETd0uinY2YJTzR6oC2Jb/7Y5roDhPrbigpayXQzx9Brb6b4jfe2EJW3wrAs2DkokEr64NZrtj08gwKl7Wc9Yf3tSimq44fNDxORUMDHowhfIWREuKFRqtsXr7iv4cl9q28C6y8WI+8RGYM1SOL0SkreKzV5PfyNXANFeNvx870g+WDKEu8b6aFWnHSyMKG4RplTlwuHPxGsbfHPrMUfTSll1JIsX5oTyyc1R+Nqb8d3BjMu2/gMpJUR5WvNx3XU0qKS8bvu3cLdsGUrpOwkbU2HY0TorpwN7EotQGdtg3FzR+cb0fWLoqPPF29vLZVJ87M0ufODVirGlyCRfrp5SmRxu/Z0XvH/mMBFtfzdzaO3J2Z1QxBh/+7bvc+ousA/UlpZeAhKJBA9bU63fi5WpAR/fPIQ3F0aw7lQud35/nE92p3DHaG8mBTly3wRf7M0NtSfFXyJH0kp5emsRL9u+xQ772xiS8Q2pb43h1d8OsvZkTls/xrnfNbNMbuv0GP/bkYSViQH3jPMRfzDXnI/bz8fKPISsoYwtymEUVbdtwA8kl9CoUDEttO/UDFcr5eYBbB36FcWL/+Ks9108UHg9bywahF+HPeuzM4KxMJIT4NR5L+tuKypnueUd+nI0lZyGZiW/nchmUZQ7puVJkLG/1bhAF0O9bTmeUUZty9BOiYTPDZYRLM3mA7vf+dr4Q/Yrw9lkMIXssjpOZJZTNuZ5UCMkqI5hwlDjKuKKqSfmlNZwk8kJMV2+wxR7e3PDTsYDh1JKGOVrd+EN36XiECz0hKUdhjvmngS3KGzMDHGwMNLqyymqbqCwqvHyatH7g0XfwH17xclQIhFuT6HzRERvYNztj6kTmiBH1qCd1W1SqFh7MocbBjsiyz4MvgNsQ3sFIpVK+GxpFN/cPoxQ1x5smOPWiYpY8JxONwU5WxCr9EJSlgYNVVTWNXM0vYwZLRXN5O1C4qBDxtNbjA1keNpdOGCdO9iV/8wNxd7ciHBXKy1Hq65wtjJmqJcNG8/md7ptT2IRr29K4IlfYzoNX+xX3KKETXn6PjEQMGSukP1dIziYG1Gkthb/WLUIjn8F0Xe2WoGqVGr++3c8oS6W3DHaG6lUwh1jvNlxvpCDKSW88Gccb27WPa27LyiqbiChoJpbRnjx7xvGskq+kMFFf0JxkjDhqC0C/8mtxgsV9Z0rOWq1mn1JJVjbOQqL4Y6k7xPGAH1kLKKnj5AZYG1hJubktGDmCLXFYhZXVnnrLB3UakjZ2XvHw4tAIpGwZLgnny+N4mh6GdamBjw2VfQ6GsllLBnmyR+nc6lu0F1V7C1TQ5w49txUVt8/lukPfUTW/N/xIZ8JCa/w1G9nmPHBPmHxG/e7SFx26CsrrWlk/Zlc7p/gh5mR5jtuobk2tJ+Pdf4v1OZOnJcHU9TOrXN7fCHedqadNvL/HzmZWc59K08y7Mdq5iVMZeHIYJ09cB62ppx84TomB3e+7rX0c+W0D3LaVXI2xeZTXtfM0hGeoopjat/tNeeGKHcam1Ws0IwMOJNdwUfJ9mQ5TkZ+9DPkjgGs8X2dlzYms+poJoZyKZOiw2HKC8IefvBNl/CODAxXTJDzanQdVs2Foum8A/bmRlonr8r6ZmJzKxnt30/l9va0DHtrPxRU2Qz5Ma0NlUFOFiS0KznHXSvuIlKpdjbOyh3mfghPJcPDp3tXtTC1RY0E48ZSlO0cnHaeL6S0tonbPUuguQ58JvbZ8q8lvOzMmBTcTYWjKEFcuOorIG6t6JHQ0SsV6mLJOZXGiavwHIfTxOcxMUizqcs+dsEp7n2NTCrhjjE+fHFbNH89PLbNZeYCzB7kwqHUUq3ZSzWNCp77I46hXjYYyaW8tP5cfy1bNwFT4ZY1YsL7rPcu73P3Mw4WRtRgSsywd4Rt9pLVWnr+dadyiMut4oU5oa2zwRZFuWNuJGfp10fZEJOHhXH/BQcHU0TWfqy/PXMHu/LAM+8isXAWgxk3Pi6c1HwnY2wgw9hAqrOSk1xUQ0lNI05OzsKlqz1NtUKuNtBDT/XoxM7MkBKtIEfI1fYkFqFSa5ynQExsrym4rDLSaWHOrF8+hlV3j8CinZTn5hGeNDQr+f1U3ww4NjaQCZMNDT5RkzFZ9CnjlEc4MjUdBwsj/vvd71AYR7575zEH2+ILUYOQPrVgag8SWdt8LJUKzv+FJHg2DpYmrSMJlCo1uxKKmBridHXK9PuYKSGOHHx2Mitui+Y/c0N5fk5Il8d2Jc+2MDbA0lhObkeHteoCUDSx6kimGMJtpRKmAFHLuq2getqZcscYb77cn8aprHLuW3mCUBdLnG98DyKXIlm6ln8vGE5dk4IVe9OYGuIopGdD74LZ74uk1lXGFRPkELdWaD+9O8tk7MyNtCo5R9JKUamFM1O/Y+Yg+jOK22Ugi86Dor41yBnqbcORtDLqmkQJ8Ex2JRbGcrx6kMW+KpEbiaFnvUEqo8nIBjuqKG+3uVh9PJtB7lZ4Vx0Xk6IvYEWtpwPFifDZaPhsBKy9E94NFH01Xcz3sTM3wsg1DCVSKIjlYEoJbtYm4ruatltkawL6P8PZF8wMd0GlVvPNgTRAVBJe+escpbWNvHvjYF6cG8q2+EK2Xm7Zmt9kWPbnhd2NrjJszQyRSuCs7TTRixc8q7WioVar+XBnMtNCncTEbQ1mRnLeuWEQr8wP49Czk1sHCvYFLefbFvYnlxDgaI6zlahCSgxNRRBWng4jHhAJGk3vkI2pIRU6enIOp5Yik0pwc3WHphrtaeBZh4XOXh/kXJHYmRtRXtfUZoOukavtTCgi1MWybe5Wyg6QG4uK3GUkxMWyk3TXzdqEKSFOrDyS2eWQ5ksmbAFE34Hz4f/y66QqPhmUTh3GTP7LSAxVbcem2HyGedm22WyD+M2YO7ZVcvJOi+b2kLk4WRi3ytXOZJdTWtvUo0r8/wckEglu1iZMD3PmjjE+XfalXgh3G1NtuZq1F6AmOSWBU1kV3DrSSwQ4inoRjFyA5ZP8MTEQ87cAvrljKIYOvnD9Z2DuiJu1CU9NE8n9eYM1/b9SGQy7+6qSXrdw5dTcTe3Em6ijUdHB3JCy2iaUKjUyqYRDKSW4WhlfHl2zRCKmu2YcbPtb7klAAq6RAMwZ5MoHO5LZlVDErHAX/jydy8QgR302owNKE3vsaqsoq23C3tyIuNxK9icX8/qCCIjdKwJcvQykdxz+FOpK4MYfhBNe/HrhGhSk2xoXYFyIOyn73fHPi+FAaiBj/e3FdzV5h3DK6jAv4UrF2cqY5RP9+WR3Co3NKpKLatibVMyr14fjbW+Gl50pa0/msCO+kOkXMhjRc0FkUgm2ZkY6Z4AkFlaTU17Pf+d31mvPCHfp87V8eyCdHw5nsPWx8RgbyFCr1RxILhHDOtszeIlIRtkHaBllWJkY6DQeOJRawiB3K4wtNAFUQ0VbT0L6PpHVdug6I6tn4LAzN0SpUlNZ3ywGJZs5QFMNxxJzuHlMO3fHlB3gNUb3oM4B4LGpAdQ0KC584KUw402ozEWyegnBRpYow+cyudGLp3+LwcvWlMEe1lTUNXE4tZR/z9Lx/TZ3bJuPdX4DGFuD9zgcLGNbjQe2xxdhbWrQrTOmnt4jZuV0Hgi699gp7M3dxbXtu1+F/LIHPWZWJgY8OzOYNzYn8M3tw3QO3b5jtDf+juaMvRxqqX7myqnkTPo3TPinzpvszI1QqWmtABxMLWV0y8bschA8S0zRrtX0k+SeEL06Gj2rv6M5oS6W/BWTx8HUErLK6sQsHT1aSMzssZVUcTKzHJVKzYvr4/C1N2NRsIl4Ty/jFNxrAqVCuFuF3yB0uHZ+MO4JWPR1txfwKcFOxKm8qEw/SXpJLWMC7DU69R1aVsBXA09ND+LZmcF8fSCdM9kVfHvHUJHZos384O0bBg3wKq8dHCyMKNYxmHl3gnCvGul7GarriDEA2WV1/HAoA4CUohqKqhsZ19ExTCIRA1U7XCtsTA2prNeWq6lUao6ml4nBoS3uinUayVqLtbrPOJ3DQ/UMPLZmope31aRI0wdq2FjG5BBNdaGxRkx4vwz9OD0lzNWKEb52/bufMTCBW34RM6QaK5FFLuHdGwcT6GTBP1aeIKOklu3xhShUamaE60gImTu3ydWSt4t+HpkBjhZtSY9j6aWM9rPTW0f3MW7WJh2MB8TYhIyU89w0zB3DhlKReA+e3ePHvGmYJyeem9pl37hUKmF8oEP/97xfBq6Kb2OLxrSkppHCqgZSimoY4395LqYABM0S5gNJW4QuO+HvTpKFeZGu7E4s5st9afg6mDHSt+9dtq52jK2dCDRv4MX1cfz7j1hOZVXw8rxwDOPXik1E2IKBXuLVRdYhUcXp5QThMFdLsgz8MK9KRo5CyD5LU4Uc4So0frh/gh8/3zOCTY+O69S8aWYk11dU+xAHCyOKdQxm3pNYxEhfO0wML4NlMMJAY8lwTz7ZlUJ2WR3P/RGHuZGcET0MsqxNDSiv1a7knC+ooqKumVG+9m39hvUau9WMA6JCOviWvnwZevqQln1Ca/+uxhrZz6yeQS2buYz9oGq+ooKcy4ZUBtNfgyeTwH8qJoYyvlwWjVwqZeaH+/l0dwqRHtZtQynbY+Ekrg8NleJ34DkKAEcLY4qqGmhSqIjLq2KIh76K09e425iQV9HQJsM0NKPBwAYHVRFLhnkKR1TUvU5Q/n8JRq+KV2lnrsnQ1DRxKFU0l472u4xlNEtXIXlI+Ft4y9dXCLvYdsyOcKFJoWJ/cgm3DPfUb6x0IDFzIMCsgTH+9qw5ns2sCGcxq+HMT+IHat5NY72ezsRvEBk2d91W7l0hlUow84rCEAXTHCqFdXTmQUACngM4o+gSGO1v32myuJ6+x97csFMlp6qhmROZ5UwKcrisa3l8aiBqYPoH+4jLq+Sb24dibtQzuau1qWEnd7XDqaUYyCRCbtNSyWkJco58BnYB/z83x1cJdq2VnJYgR3wfJ7hJ2jLSKTtE47Z9wEAs8cqgXa+gi5UJmx8bx7zBrmSU1jFnUBfS0pZKTu5JQA0e4prjaGFEbZOSE5llNClURHpa9//6/5/hbmNCk1Kldd7NVtsz1qIQD1tTSNoKzoPEPlVPJ66KIKd9JWfn+SKCnS1wsrx0i9teETwbUnfCoY+FNaydn9bNHramRHlaYyiTsjDqCpnCfqVh5oCktpgvbo3m6elB/GdeGOSfFY3yQ5YO9OquTNRq7ebnFjQON4TMuSj5jP+gUSjVEubbamY0ZB0Gx9DeD0HV8/8KBwsjSjr05BxILmlz6LvMa3lsagBSiYTv7hjW4yoOiEpOR3e1w6mlDPG0EdWo1iCnTFQ5Ezd3Gnqq58rC2tQQiUTYIAOk14s9wnDHdv0uKTuFMYg+CdmKpbEBb90wiJ1PTuDOMT66DzJ3FKqBrCNgaCHk+oCjpdibbY8vRCaVEO56lTvKXoG4WQsDqxYb6eLqRlbWjya6/qDoFU/dJeSDenRyVZyxLY3lGMqk5FU0sCexeGAGTQXPEZOuK7NgzKM6D3l2ZgivLghv1Qbr6YCZPdSXYSxVs3ySv3BwOfOzMJ0I0P9IdbL7NfjpBmjoMBU757iQD4T0TqrWwsgwX84ZDWZs437xh8xD4DX6Eher51rHwVxo8Ns7Qe1OKMLX3gzvARhwec84X06+MLVXAQ6AtYmBlrtaQ7OSY+lljGp5HAMTMRQ3aSts/bewY2839FTPlYdMKsHW1LC1krMzpZoatQmB5pqgvDRVOO3pq3E68XMwb7V+74SFs5DsJ24C9+hWg6gWF7Zt5woJdra4bHLV/0+422gGgmpspPcmFfOTcirNDmHwy63QWAWBMwZyiVc0V0WQI5FIsDM3ZENMHjWNCqYNhFOSfaCQK3iOAvehOg8Z7mPL4qF9M0H5mkQjH6BOY+DQVAexv0LE4k4DYPVo8B4Luafg+9naE6dP/SCG3XldnA2qqaGcQdPvxKzgGOSchIpM8BrVR4vWc63iYGFEk1JFlcYNqqFZye7EIiZcZqlaey7GmtXG1JD6ZmXrBPgNZ/KoblQwd3A7uY7rEEjYKBqtRz8ChtfoSIBrCFszw9aenF0JRdTKbTBq1JhHpO4S816uwr7DAcdcs+cqiNWSRztpKjm5FfVEelgPwMKufaxNDTA1lJFTLhzWdicWEexqg8Gc90Sl2dReOADr0clVEeSAkKydz6/CzdqEsJ5Mfe9rJBJY+puw6tVzcbQGOaKviiOfikbGYfcM3JqudHwnwh0boTpfBDpNtVCRBWc1TjmXYrkdMg8kUtjyjPi3p76So6d7HDTS4RZHpZWHMymrbRITt68irEzFQMbK+mbUajXfHkxnfKAD/o7t5pjctRleKoeXyoRroZ4rHjtzQ0prG6luaOZYehmYi4GggOjHcR8GxnpJVa9pP/PLoy3IsTIxaB1kqQ9y+geJRMgAN5zJo6FZyb6kYiYFOYqk5JjH9DLaC3DVvDMt5gPTwgZwmq6tzzU34O+yYqHJkp77UzQx7v+fGF5l33cDAq9JXCPh9r9EcLPlWTj4IRiai7lSl4KprQiico6DjQ9Y9v08Ez3XFg4WbUFOVUMzn+5J4YZod+3g4CrAxlRcTyrqmjmSVkZCQTV3jvEe2EXpuWTszI04l1fFu1sTUajUWNi6iCBH0Qjp+/VStYvFvN2+p52SRSKRtCY+huhNB/qNf80KJrGwmkfXnKa6QcGkYE3C+LqXYfxTA7u4K5yrZvJii/nAtFD9UL+rFhsvGPck7Hsb4taCVA4Tnh3oVV0dOIbAtFdh01NCcjH+6dY5TZdE+EJhqKHvx9HTA1qCnNXHsjA2kFLXpOTRqYEDvKreY62p5JzJLmdTbAG+9mZMCBg4yZ2evmGcvz1H00r54XAmYa6WmNo4C+fIE99Bcy34Tx7oJV6dyI3EAFBzx07mNI6WRlQ1NONrbz4wa/t/wBBPG5aO8GTVkSysTQ2I1Ft195irJshxtzHB3tyIYd76D/eqZsqLogqx82WxaTe7jPOOrnaG3SPcgTIPdrIwv2iCZ8OWf0PAdX3zeHquaaxMDLhtpBcbz+ZRXtfMPWN9rkrrbgdzIyQSeGZdLACvXh9+TQy++//OkuGeLBnuSZNCJQzUDhyB0hQhybX2BJfIgV7i1Yutj873L9DRAicLY/3vp595enowW88VMs7fvmuDCD2dkKjb2+RcZqytramoqOjRsfVNSirrm3G2uszW0f9P6M1n0SeUpoKtr97KUwfdfhbKZqgt6VtpWXM9yI31n4UOLvvv4ipBqVKTWFCNv6N5qya/v+nrzyKhoIraRiUWxnL8Hcz1m7RecNX8LprqoPAcmDuAhes1aXBz2T6L6kJhvtFBQdCsVKFWc9nOA1cy/f1ZFFY1YGIow9LYoN+e41pjQIMcqVSKpeUAmAj8P8HIyIjCwsIeHav/LPoX/Wdx5aD/LK4c9J/FlYP+s7hy0H8WVw76z+LKoTefRQsDGuTo0aNHjx49evTo0aNHT1+jry/q0aNHjx49evTo0aPnmkIf5OjRo0ePHj169OjRo+eaQh/k6NGjR48ePXr06NGj55pCH+To0aNHjx49evTo0aPnmkIf5OjRo0ePHj169OjRo+eaQh/k6NGjR48ePXr06NGj55pCH+To0aNHjx49evTo0aPnmkIf5OjRo0ePHj169OjRo+eaQh/k6NGjR48ePXr06NGj55pCH+To0aNHjx49evTo0aPnmmJAgxwnJ6eBfHo97dB/FlcO+s+iD1E0Qn3FRd9d/1lcOQz4Z6FWi//pGfjPQk8r+s/iykH/WVx5DGiQ09jYePF3jt8Ar7vDV5Nh16v6i88lckmfhR7UajWLVxzmiV/O0NCsvKTH0n8WXaBUwJHPobqgZ8dnH4MvxsHfT170U3b8LLr6bOuaFLy0Po7o/27nyV9jOJlZftHPqUc3A/67+HkxvOoE74WI7+FAoVLBthfgm2nwYSTE/X7Zl3DJn4VK1TcL6SPi86qIfGUbj645zfGMsoFeTq8Y8N9FD1l/Jpfr3t9Lk+LK+uz7kqvls7hY6poUjH1rF/uTiwd6KT3m6pWrZR4EuSEYW8O+d6Aic6BXpOf/MaezKziWXsYfZ3K57ZujVNQ1DfSSrj12vARbnoXNz1z42F2viU2ggTGMeaRPnv7DHclc/+lBVCrthEpacQ0zP9zP6uPZTAlx5HBqCYs+P8S7WxM7HavnKqWuDFJ2QOg8sHSBUysHbi3pe+DQR2DmAKa2sPmf0FA5cOvpLaWp8KYHZB0Z6JW0si+5mLpGJaeyylm84jBxub1/P5/45Qwbz+b1w+quDf6KySe5qIbdiUUDvRQ9F8n5/CpyyutZdeTq2W9fvUFOSTK4DYUFX4h/Zx8f2PXo+X/N76dycLAw4qd7RpBUWMMrG+MHeknXFmd+hsOfgPc4iP8Tck52fezZ32Df2zD+KbhnF7gM7pMlDPW2IaGgWusirVareWF9HAqlmk2PjOXtGwaz/5nJPHldIJ/sTuHh1adpVl67mcv/N6TvBbUKJj8PUbdDUTzUD1C17tjXYB8EN62CG7+HxhrY+/bArOViOPQRNNUINcYVwtmcCiLcrdj5xERMDWTsOF/Yq/srlCrWx+Tx3rYkfWJDB81KFUfSSgH47UT2AK9Gz8USl1sFwO6EYirrmgd4NT3j6g5y7APA3BGsvSDn2ECvSE8/sO5kDqezrmzpT6NCyV8x+Vwf6cpoP3vWPTCa52eHDvSyrh3K0uGvx2DQErjtT7ALEFUdXRLV4iT461EIWwiTngOZvM+WMdrPjkHuVnyxN7X1b7sTiziYUsoLc0Lwd7QAQCaV8PCUAD66eQh/x+az4cwAZHfLMyBtz+V/3muVlJ1g6wc23uA1GlBD1tHLv46KLEjaDMPvBYkErD1h7ONw9AsoSmg7TqW6MiXc1YVwZjUYmELqzoFeTSsx2ZUMcrfCUC5lbIA9uxN7J8cpqGpAqVKTXlLL3qtIynO5OJNdQU2jgrmDXdmdWExRdcNAL0nPRZCQXcROk2eIUsexOS5/oJfTI67OIKepDiqzRJAD4DECsi98wSmqaqCq4eqIPvUIXt90nid/jUFxBWfDd54vorK+mUXR7gD4O5pja2Y4wKu6eFYezuBQaslAL6ON3a+DsSXMfk8ELVNfgoz9nTdJajX88Q+wcIa5H4pNYB8ikUh4YIIfxzPKOZFRRrNSxWt/n2e4ty3Tw5w7HT9vsCvjAx34Ym/q5c/ubn4GfrlN9DHpuSSOp5fSkLiDRq+J4g92/kIqlnXo8i/mxLciQBh0U9vfxjwCVh6w9k5orBaBxGcj4IMI2PY8VFxBmfOjn4NUBlNfhuIEqMy5vM9fVwYxv4CybR9QUtNIbkU9g92tAZgU5MjZnApKanreX5FdVg+AjakB3x3M6MsVXxPsTyrGwljOC3NCkErgz9O5A70kPRdBY84Z/NTZPGR1kD/P9O9n2KhQcjan4pIf5+oMcso0mVS7liBnOBTEQVNtl3fJLqtjxBs7GfSfbQx5ZdtFaW71XF5qGhWU1jaRVlLLzl1bRXb6CmTdyRzCXC0JdrYc6KVcMrWNCl7ccI5bvz7Kir2pqAc6G1wQB7G/wfh/gpG5+FvwHHAME7Kd9uQch7zTMP01ERT1A9PCnPG1N+PJ32KY+v5eUotreX5OCJIuAqr7J/iSXFTDroT+1aH/b3sSz/0RK/5RlQ/J26CxCvJj+vV5r3UUShX//WE9xnX5LD9mzY+HM0Tw7DkSMg9f5sU0wakfYfAS7e+3gYmQrpVnwLp7YOX1wlHQd4I4/pelV0ajf2M1HP9WyP3CFwISUSFrxx+ncyio7Mcs/7EvRSLkm2mi6gutG6nBHtYATAxyRK2GfUk9r8hkl9cB8MiUAPYlFZNSVNOny77a2Z9Swmg/OxwtjJkS7MRvJy5zcKvnkmlUKLEujwNgRNNRTqcVkFdR3y/P1axU8dDPp1n69VEq6y+tMHF1Bjkl4uSEfaD4f/dhoFZC7qku73I+vwq1Gv4zN5QmhYrd/bzp0HPpZJeJC4e7jQkBh55GtfbufpNgnMoqJ/q/23nwp5PsSui5Hru+Scn+5BLmR7r2y7ouN/Ga38nUECfe2JzA94cyLv8ilArR5J2yE7a/ANYeEH1H2+0SCUQtExv59k5rJ74DS3cImNZvS5NJJTwzMxhrU0PG+Nvz7R1DGaTJAOtilK8dgz2stSRufU1hVQOf701l/Zk8lCo1xPwMUgOQG0PGvn573v8PnMqqILr5FCqJHJXnWNad0mQvPUeLgLq5fy7yOsk8AHWlELm0823O4aI/NWmL+E0sWw/zP4XFK0WgG/9n/62rpwHU+Y3QWAkj7gMze3CN1KrGJhZU8/gvMXy1P61/1glC8eEQIvqpvp4KjdWcya7E0liOt50pAM5WxoS4WPZKspZTXo+dmSE3D/fE2tSAX/V9J61U1jcTk13B2AAHAO5wTOTzivspLC0d4JXp6Q1JBTWESdJQGFljoKxjkuws2+N717umC4VSRXU7hZVSpeaJX2PYnVDE+4sjsTIxuKTHv0qDnBQwthInSgCncFHC76YvJ62kFmMDKctGeTPE04aTF9PncewrOPfnxa1ZT69pCXJevz4MN1U+0twTwlWvj1Gr1bz+93mMDWSkFNVw1/cn2HquZzbFp7PKaVKqGO1n3+frGghicyqRSyV8dPMQRvvZsT/5MsvW8mPg6ymwahGsWgipu2DS88JJsT2DFgvZy5mfxb/ry+Hc7yL4kcr6dYnTw5xZv3wMry+IYHJw93MRJBIJ94/35URmOfF5Vf2yni/3pdGkUFHTqCCpoFI4f4XMFTLe9P398pz/X9iTWMRkgzgkniMYF+7DudxK6puU4DUKVM2Qc+LiH7wwHrY+J2Y59YSETWDhCq5DdN8eOh+W/Ax3bQEnTU+g7wTwnSTGLCj7Saodsxre9IIvJ8Lau2DvO5Ch4zwdt04kJG19xL/9pkDqnlZJ5U9HhWPTjvOF/VNBVqmEYUnoPLh5jQi4Mg5yNqeCwR7WWtXYSUEO7EsqFkmDHpBTVoe7jQnGBjLG+NtzNG1gNvDpJbV8sTeVp3+L4R8/nmht9h9IDqeWoFLDOH9xjQwv342/NI/SQz8N8Mr09Ia4vEoiJGmog+eAfSBLzE4Sk11xSY9Z26jghi8OM/+Tg60tCZ/tTuHvs3n876ZIrgu99LlDV2mQkySqOC0nJZkc3KLFXIwuSCuuwcfeHKlUQpSXDacyy3unk6/MEfa16x+CGn1j4eUgq6wOQ5mUMY5NGEkUKJDBgf/1+fPsPF/EicxyXpkfxtbHxjPUy4ZPdqX06EJ7JK0US2M5IS5Xv1QNIC63kkAnC4wNZER6WBOTXXH5JGs5J+DLSSI7fvtf8PApePCoCGg6YmorZGunV4rqXovOPuq2y7PWXjA+0AGpBE5n972BRmlNIz8dzWTZKC8kEsg5swPK08X74DNO2PT21+b2SqMfXufuxGIi5NlIPEYw1MsWhUot5E1OEWBoDlkXKVmrzBWB/OFP4OT3Fz5erYbEzRA0s/tes+DZ4BCk/bcpLwqJ96kfLm6tF8JlEIx8EByChTHCwQ/h+1naDoi1pZC2G8IXtf3Nf6oINHJPUNuo4PdTuQQ7W5BZWtfncq+GZiX7jxyExkrOy4LFe2ThgjptNzHZFQxyt9I6fmKQo6hA9LAnILu8DndbUQka7m1LXF4VtY2Xvx8uqbCaD3YkcS6virSSWpZ8eYTlP52iUXFps9suhaPpZbhaGeNlZwpqNWZ5opfNIeHHK9McQ49OkrLy8ZfmYeARDWELGKU4Rnz2xVdymhQq7l91kvP54ru6Oa6AmkYFXx9I55YRnswd3DfqmKszyClNbuvHacF9mNDkd/GjSSuuxdfBDOrLeSBmIWFNMaQU9+JEeuRzkJuIC8yeN3p8t2t58FV/k63JjskqMgD4RTZXyJj6sM9AqVLz9tYEhnnbMDnYEYlEwvLJ/sTmVrKvB1WMI2llDPexQybt2yb3/qKyrpmHV59m0eeHmPnhfsa8uYvIV7a12iLH5VW2XvAHuVtTWttEbj/pbjux61Ww84P794PPePHfjsFdb+qilkFZmqj47HldbAAtrzzZoJmRHH9Hc2Jzet4H+MfpHO5beYKRr+9k/qcH+WJvqk5t8ncHM5Ag4dEpAQQ5WWCS/JeQ7HmPF/9rru1WxttKU60YMPntTPh4qO5M/BVIcXUjq45kokrYAm96QuKWPnvsgsoGkvLLsVKUgaUrwS4WmBjIOJFZLhJrHsMvrrLcUAk/3Si+1wHTxZy3xgtciwrOQlUOBM3q/fO5RQmjgi3/hvR28sW+2mA6R8DEZ4Rc7p4d8EyGMEI4+kXbMec3gEoJode3/c19GJjYQMLfbIjJo6ZRwQdLIjGSS9lxvm/l5M//Gcdff68H4ObNzVTWK8B3Iork3ZTXNbeaDrQw2MMKQ5mUUz0c6ptTXo+HjQhyhnnbolSpOZ1V0e196poUfW4vPyXYkfiXZ7Dp0XFsfWw8ry0I5+/YfPYlDZyRzNmcSiI9NZWy8nQkVTnsMZmGQ21yjwyj9FwZNOTEIEUNLpEQej1Gqno8yo70umdGqVKzJS6fG784xJG0Ur5aNpRRvnas2JfKz0czqWlUcN94vz5b99UX5KjVQq5m3yHI8Rgu9MpluvW8aSW1+NmbQeZhTGqyeVa+mpM9nWzcUAknf4Do24Vd58nvoTjxgnf7dHcKk97do3d0u0iyy+tFdqw8HYA3a+egsnCF419f4J49Z83xLJIKa3hmRnCrXGFioANhrpZ8uiul2/vWNyk5k13BSF/bPluPLpQqNSv2pjL/kwNM/98+Hlh1kobm3mfm1Go1//rjLLsTivC0NSXSw4o5g12wNjFg5eFM6poUpBTVEO4mgpzBHuL/Y7Iv0qSjplh7U9UdWUdEpnfCMyA36tl9fCaA32RR+Rl0E8x48+LWeRmIcLPmbA+DnNXHsnj8lxgKqxqZPcgFB3ND3t+WxAt/xnU69lBqCZNDHLEzNyLKywbTqlTR6yCVClmTgWnP+nJOfg+HPxUSYCML+HkxZB6CoytE0PPdLCGtqruypsGvOpLJ83/GcWTXn9BcJ5rs437vk8fek1iEvaQKCSqwcMFAJiXSw5qTLRtfr9FiPltvK0g7/yuGVy/9DWa+JaSWRz7v/j4Jm8DQnD3NQWw8m8ehlJLeKRHmfiiukT8vgR0vw/dz4Jdbe7funiKTw7B74NwfbT1zcevAe6wYpNr+uODZqOPXs+pwBuMDHQh2tmSsv32v59S00lgjDBjaBXBJhdX8fiqHOz2LabD2p0JtzvGMMvCdiEFZIg6Ut5oOtGAklxHmZqk7UDnzs9bvoFGhpKCqAXcbEwCCnC2wMJJzTMf+ori6kTc2n2fq+3sJe2krU9/f2ysXtwshl0mRahJuMqmEW4Z74mlr2qs+075EoVRxLq+yrW9RI589E/wYeTiKFgA9VyQJBVXklNdR36TkeEYZFqWxKCVycAoDxxCaTZ2Ilib1KnlXWdfMki8Pc/+qU8ikEr6/czjjAx24b4IvcblVvL89ifmRrnhoqqJ9wdUX5FTliexkxyDHLVr8v46sZUVdE2W1Tfg6aOQFUgMGS9Ooj/vrws+nbIbDn4GiHkY+IMrylq4i+9YN9U1KvtqfRm5FPR/tSO7pq9PTjqyyOjxtTaA8g2ZTR6oxpdxxZJ9VcoqrG3lrcwILhrgx1LstUJFIJCyf5M+xjLJuNact/Tgjfe20b+hD297cinpu/uoIb2xOwNXahGhvG3YlFPHUbzG9lpH9eiKbTbEFvLYgnP/dFMkbCwfxr5khLBvlzd6kYvYnC+10hCbIcbY0xtHC6OJtHLc9Dz/ME9W3C7HnDTHgMGxBzx9fKoXb/hA9CLPeEQYFVyiD3K1IKqy+YHB6ILmEF/6MY8kwD/54cDQvzAnl69uH8fBkf7bHF1LX1PbdUqvVpBTVEKiZzxPtaYOLIpd6S03Pg9xQuIBdqC9HrRZ9PEEz4aaVcPsGIT36bqawonYMERn3I5/D2V97/qLP/CwkVv1IfH4VFsZylPmxZNmMEr1I6+4RDnOXyJ7EYsY6aQIYzeZ8qLcNp7I0UmevseJa1JvzUVGCsIEe96TYLNj6QPSdQuLV3XDRxL+pcB3PHT+e5aGfT3PL10d5b/uFE22tGJjAzatFAHx0hTCl8JvU8/v3lqhlIJWL15qyEzIOaEvVWgi9HklFJhTEcvMw8fudEuLEqazy3m/+1WpYczN8OBg+HARHvwTgna2JuFiZENR8HiPvkdibG3E0vRR8JwJwg00KTpbGnR5uiIdN5xltNUXw5wNw8IPWP+VVNKBW07oxk0klRHvbcKJDkLNibyrj3t7FysOZDPGw5j9zw6hpUHDvjycuKmnVEyQSCZODHdl5vqjL68WJjLJ+S8QmFdbQ0KxqkwNm7AenCHy9ffixeTLq+PXQ0D+9inounlNZ5cz4YD9j39pNyItbuPGLw0RI02myCxFJSIkEuXsUkbL0Hks6i6obuOnLwyQWVLPy7uH8/uAYxmj6tCYEOhDsbEGjQsWDE/uuigNXWJBT1dDMD4cy2BLXTdN3R2e1FswdwcoTcjtPQk8tFtbSvg5mIsgJmkmG2SAm5H6l2xlm63PwzXT4dCS87gZ736Q5/CawcgcDY3GyTtnZravMH6dzqahrZuEQN74/lKG3lOwlarWa7LI6PG1NoSwdqa0vEglkyT2F9ecFHH0q65spqureivTVv+ORSCQ8Nzuk023XhTphaijr1kb0SFopFrr6cVYtgJ2vdPvcPeXDHUlkltay6u4RfH5rNK8viODtGwax8Ww+H+3svtLUnpzyOv6zIZ6FUW7Mj3TTum2exhnu7S0JyKUSgpzFplkikTDI3ZozF9Nc2NwACX+LzdS6e7ufh5GwSQyunPDPfjcNGCgi3K1QqNScz+/6gt6oUPLw6lOM8LXlv9eHazVCzxnsSn2zkr2nz0P8etj5XyrjtlDVoMDfUVhrR7sa4SIpI13VbmaP12jR69Td7yXvFBSfhyGafiYjC7h1HYx9Au7bKwKfJT+JylBPZ8MUxov+xTW3iH6pfiI+r4rF0e5EGeWyodiJknH/FU6bqbsu+bGPZ5Qx1qlJ/MNCBDlRXjZU1DWTVlIjZGByY7GB7ynbXwBLN5Esa2HcEyKJdnqV7vuUpkJBLOsbInGzNuHE81O5c4w33xxIp/AC5zgtjCzgzk3wr2y4da2otvQXprail27/e0JO6j5UYxvdAZ8JNMrMmWNwjAlBwn1rSoiwcN7VW8namZ9F5XjyC2KW0Y6XOJlexPb4Qp6e4IS0JBGJx3BG+NpyLL0MlZkTKXgw00x3sBjlZU1eZYO2pXXLGIMzq1sreDka+2gPTSUHhGTtdFZFqxxt9bEs3ticwI3RHhx4ZjLv3DiY20d78+WyoZzLq+LJ32L6bZbWlBBHiqobW6fVt+dkZhk3fHGYye/u5fdTOX3ef9mSIAt3sxJBaPp+8BlHhJsVe1WDkaiaIf9Mnz6nnktnT0IRU4wS+GGxL28ujODP5WOYY1+IidfQ1mMkblFESDOIybpwdT+lqJpFnx+ipKaJX+4bxTiN017rY0kk/Pf6cF6YHdo6VBtFU8+k1hfgigly3tycwMjXd/LShnM8uuZ0q7NWJ0qSQSIDG5/Ot7lFQW5nt5s0Te+Nj5UE8s6A12iyBj+BnyqDqrMbtA/OPyuaQY3MhYPOlBd40vQ1lle2a2j2nQD1ZVDYWT4CYoP+7cF0pgQ78tqCCJwsjXn17/ievA16NBRXN9KoUIkgpzwdmZ0PXramnGt2ExsCTZ9OR9KKa7hv5QmGvbqD2R8f6FLzfDyjjPVn8nh2ZjD25p3lUQYyKcN9bDmU2rU7zZH0Mkb42Gr34xQnigutfVCX9+sNz80OZcuj4xkb0ObeNj/SjYcn+/O/HUn8fbZnGeuPd6ZgZCDlP/PCOt1mb27ExEAHUotrW00HWhjsbkVcbmWPXYZaSd0JTdVwyxqR+fntTt2ynoJYkXkPmNa7Ks5VRqiLJTKphNhu5nMdSi2lvK6Zf80MwUCmfWr2sTfjXod4xm+dAb8ug4MfYLL5MWQoW4McL8R34Uxdu8qiU4SoNnTxewHE5trcSTSCt2BiLYauugxu+5vXKDEb5kIbIbVamLRYuUPEjfDHfSLQXf/QhWVZvaCyrpncinqibJswU5RzXuXJwQKp6BHpOCi2lzQqlJTWNuEhrwIkYOYIQJSnDRIJQrImNxJ9JT3pyylKEJXN5G3ifTVoVzmwdBXOaMe+FH0rHTnxLSpDS97JCuCusT7Ymxvx6JQADGVSPrgYlcDlSiSMeRQ8RsLCr+GubcIRtSNyQw7Jh7PA8DimmvOOk6Ux0V42/HU2r+fPVVsC256DsIUw/ikY9xQ017F1xw587c2Ya6c5T3oMZ6SPMAY4klbKPkUYQXWndH6nh3jaACKr3UpLkFNb1FqhbhkE6mrdFuQM97GlvllJXG4lW+LyeV5TnX1lfpjWoOhoLxveu3EwDuZG9FcL/nAfW8wMZezUIVl7f3sSvg5mDPWy4YlfY/jvxvO9fvxmparL62xMTiW+DmZYGhtAaQrUFID3OLxsTckz9KZZatQnG1k9fUtF/A6+kbzChL9GsyTmTiK334ykNFlUgltwicRMXUtJdkK3wfHh1FIWfHYIA5mU3x8Y3TkpXFsCZekM87blrrGafX19Ofy0CH6c332FuwdcMUGOT1MST0VJ2P74eCxNDPjvxi6CgpzjQj7R0VIWRLYo/6yIANuRVlKLo4URFqWxwvbTcyRe0dNIUrlRe/I37cc48Y24oC1ZDXP+R3HEP1hX5sO2xLI2W0iPkSAz1NlvUNOo4MfDmaQU1XD3WB9MDGU8PNmfPYnFFFX345Cza4ys1hk5puLCYuNDkLMFR6o1GYCiBJ33e3dbIiczK1g60pPi6sYug5R1J3NwszbhpqEdJE715aJBN+8Mo/3sOJlVrlNKUFnfzOms8s5StRPfCWlP6Pxevd6usDIxwMas83f98amBzAx35snfzrAroZC3tiQw44N9zPpwPzetOMz725NaKzAZJbWsPZXD/RP8xMWmhYJYkSVWqVgQJao7LVK1FgZ7WFPbpCS1NyYdIPT49oGib+aGb0XyoaPEsyoPfr5JSHZu+Pbaq+JUF8Kfy+HrqRh/FsXn5t/QkLC9yyBh27kC3KxNCHPV4dS361Weq36Vo4pAqv5xEu7ahlFdAdNkJ/G2FzIZiWZI8p6Sdp9hi5VwYRfn06Y6iF0Lg28WPRLd4TlabO666HtsJXETpO8VQ1mv/xxGLRcV+IJYKEvv/r694Fy+CBgHGWQBUGMTxMGUEo018W7dAUMPKaoSUil7dZlQCWjeGysTAwIdLTie0dKXM0b0k3X3XDG/wGcj4MT3MOJ+3bKt4fcJZ7KkDsYJTXVweiXHrGciMTTjJo2ky9rUkAcm+vPrieze/zYvF3Z+cOffqCNuoKC6ieMZZeyIL9RKmBRUNvBT9RCcFblQeK7179cPceNgSknPK1U7Xwa1qq0vz3UIaqkcZeZhlo70QpZ1UARZ9kGM8LVDqVLz3vYkTkkjMKor0PmddrUyxsnSSFuyVp4hHscxtLXyll1eh5OlkVZyKMJNGBfc+f1x7l91inEB9p2qsy3MHezKf+aF9Zt5jZFcxrgAh04DiY+klXIwpZQnrgvki9uieX52CN8eTOe7gz37jR5NK+WR1acZ8sp2ln59VOdG92xORZupQ8Z+QAJeo5FKJQS72pBp4K9TfaNn4KhqaMaz9CD1hjYw+z2RrLJ0heH3QvDctgM1AY97XSIFXfxOaxsVPLz6FMHOFvzxwBg87Tr02hTEwedjxHDeFiv9iizx77wYoSIwsbmk13PFBDk3lX/JXQn3ElB7kn/PCmZbfCF7EjuUq9VqkTXzGqP7QdyiQdkIRee0/pxWXNMmVTMwA6cIPG1N2SMbhW3eLiGtAaENPfub0BNrgqiWJlM3axPe3KKJWA1NwX24uJAjfuz/+PEEMz7YR+TL23hpwznG+tszyk9sgCcGiSzg4W6qAlcUu16DjU8M6BJaJkh7mjWLwMPWhyBnSw6WmKA2MBPymg6o1WqOpJVxQ7Q7L84JxdPWlA1nOmcDm5UqtpwrYPYgl9YmzVZ2/AeOfApfTuSGvHeRKBp0OuxsPVdAs1LNjPB20qCmOjGIMXKpdqa2H5BKJby3eDB+Dubc9f0JfjiUQairJYM9rLEwNuC7A+lc/+lBHlh1ktc2ncfG1JBlo7zaHqCmCFaMh4+j4E1Pppkk4WZtolUxAlq11L3yw2+uF70YYQuFg5TXaNGDsO8dyDpCWnEN76zbj/qH+WJjcssvQkpzLdFcD6uXiA2/jQ/4TyVaHc8/Mp/SaeWrVKnZHl/I9DDnzhuh7GOw7x2qhj/OXU1PsiXPCNyjyTUN4R6jnRjJNZur0lQaZWYcK5a1bTisPMDIEoq6CHLS9kBjlQhyLoTnSPH/md1I1soz4e8nhTte8BwRuE5/Tcje7tsLs96+8PP0kPi8KgxlUlwbUkFugndABAdTSlH7TRaV9kvo3Wu5aFspSlqlai0M9bZpMx/wHiPev4LYrh8scZNwJHo6GWa+xcHUUv71e6x29ttjuDjm6Art+8b+Bg2VvFI4mluGe2Ju1BaI3jnGG2dLY/659mzrjIkjaaUkF1Zf7MvuU+Lzqlj42UHCXtrKyDd2cuMXh7nnxxNagzK3ny/kgDoClYm9OPdqvrdzIlyQSSWsP5N74SeqyhPysdGPgIVmroahKYVmQURLk1gU5SY+g4DpIJXi72COjakBJzPLMfIeLo7PO93pYSUSCVGeNpxqbz6gSbgx5FYRkNYUazmrtWBsIGNhlBsRblZ8c/tQvr19WKfq7OVkcogjZ3Mq+e5gOlmldcTlVvLO1kSCnS2YFS6+3/eM8+WO0d68sjFeJAu6oFmp4o3N57npyyPE5VYyZ5ALx9LL2BCjfa1taFaSWFDd1o+Td1r0+plYAyIQPKHw0fne94bqhmau//QgX+9PEzLVPkyk/H/kSGop4yVnUPhMhqF3weIf4IZvRN+rWbukrrkjCgs3IqRpXe4PfjicQXldM+/eOBgr03YJ1upCkST4bpbYT9cWtc2g/PtJaKyGe7a39s1dCldMkMNNK4U0YtUirpfsZ7i3La9sjG89eQPiBFOVKy4sunAZDBJpp8xAanGtxnTgCHgMA5kciURCjss0jJR1wtUJ4OwvQgrVbrr6ycwyHCyMeHVBOKezKtjWMuHVdwJkHkKtaOLZ32NJLKwm0sOa52aHsOOJCay8e3jrZsXZyhh/R3MOXO7BihdLQ2Xbe9KRPmyq746s0nqsTQ2wrNNcEG18CHKyoLxeicI2QGclJ7mohrLaJkb62iKRSJg72IVt5wo6VWIOpZZSUdfM7AjtzQvZx4XL1NT/wLT/YpO4hjuN9+msBv0Vk0e0l42oNLVw7g/x3rX7/vQnpoZyvrtzGG8ujODwv6bw/uJI3lgYwde3D+XUi9fx3o2DOZJWyvb4Qh6c6IepYbtMfUGsCDDmfACGZhgmb+LAM5M6edNbmxriZWfarcyqEyk7oKlGW3424RlwjYLVSyhZeRdzYh6gsboElm0QmaJrCZVKNCcXxQsHrUVfwez32Dx5M38qx6De/lKnWVsnM8spqWlieliH4WcqJWx6GpzCsZz+PNFetq0Sxb+M5hCtim1zeixNpd7Cm4p6BaW1mmq2RCKyzoXaiZ9Wis6BoUXn2Sq6MLUV0+K7mg1TlQc/zgOZAVz/RffzXPqA+PwqAp3NkRXHg1MoowOcyK2oJ9MsQrjKXYJkraWCYNZU3CnIGeZtS3pJLcXVjeA2FKQG3Qd+uafAcxQYmLA7oYg7vz/O6mNZrG+fgJFIYMR9InHWcv1Sq+H4VxQ5jiW+0YGbh3tqPayxgYz3Fw/mVFY5H+1K4fM9qSz58gjTPtjHQz+funzW7zrIKq1j2bfHqGpQsHySP1/cGsXfj4xlaogTn+5OaQ3wtscXMsjLGen8jyBle6tzpo2ZIZOCHPn9VA+CnKMrhLJi2N2tf1Kq1Oyt82W0YSrWdVlQnCBmCCESRMN9hNFMdFiw6JHqQjI1xNOa2NzKtlEQ5Rlg4y3cHNVqiP+zddRBR95cNIiVd49gSohT52TaZWZmuDMTgxx49e/zjH9nN3M+PsCZ7AqemRGstbYX5oQS5GTRZTUnpaiGRZ8f4uv96Tw1LZDtT0zgzUWDmBriyBubErSMUeLzq1Co1G3OanlntOSvEe5WHKr3gspskXS7SF7+K54z2RWs27wV9aobRBWgpOf9qnq0iY2PI1Cai3nYzAseK3MbQpQ8gzM6HFirGppZsTeNxUPd8bIzE3+sKRbqjfcCYf1ycI+Ge3cLE5djKyDrqEbS+5+eXZN6wJUT5JjYwK2/Q9gCJBsf4+VxJqQV1/LriXYNyy3a564qOYZm4oLebgiZQqkis7QWPztjkRH1HN16m4PvEDLULqjO/SnmRBz7UmR72rk0ncgsZ6iXDRMDHYj2suHbA5ofv894aKrh3PHdpJfU8vqCCN5cNIg7x/jg72jeKRs71t+egykll2+w4qXgEChO5h0nce99W2T+a/uuIpVZWqtTDpZVVieyYy1ZGRvv1ob4UjM/nZWcI2mlyKSSVqe0eYPdqG5UsCdRe0O56Ww+7jYm2gPglAr4+3FwCodRD8Poh5EEz+ZOg20cTtE+AZfUCBncvI7Dqk79IL4XHZ3/+hFHC2OWDPfEysRA6+8GMimLot3Z8cQE3lgYwa0jvbTvWHhONE0Pua01YNclpQAIcbYkIb+H2WG1WvRdOEeIGTctyAxg8Y/UBV6PZWU85pJG7uN5Gm36xkmlqKrhyplJde53EfDO/1RIaDUM8rDm1eZbUSiVsP1FrbtsPVeAnZmhlssfAKd+FI25M98GmZwZYc4cSi2hqqGZn2uiqZNbt9mwlqYgsfcHILmwnYTJKbRTJSe7rI6X1seRnxKDyj6g5wGJ1yjdG/rGali5UJwzlm0AK7fOx/Qx8XlVhLpYCsmDUzgjfe2QSuBAejV4j4OUXSJIvAj3poLKBgzlUgzqCsHCWeu2od5CPnEio0xkId2iu+7LqSmGyixwi+ZQagn/WHmCMX52jAuw59PdKdq9bhE3il6+Tf8UgXLMaiiIZa1sJiEulnjbm3V6+BG+djw0yZ+Pdibz1pYE7p/gx8vzwjiSVsrjv5zp9eu+FFYdyWTYaztYvOIwN391BFNDGT/fO4Llk/yZEe5CmKsVj00NIKe8nj9O5XI0rZTDqSViqnnwbHEu2vaCMJYBFgxxI6GguluzDhqrhUQ4apmWrGV/cjF76n2xVhSLeT0yI/Cf0nr7KI3MeEKQgzDUyNMd5ER52tCkEDbIgCbI8RJW687hkH2UnPK6PrW87Q8sjA34/s7hHH9uKl/cGs3a+0dx/LmpTAp21DpOJpVwQ7Q7exKLKe3gbvfriWxmf7Sfirpmfr1vFA9NDmiV2D03O5TS2ka+2JPaevzZ7ArkUomQ3yoaoei8VpAT6mLJWbWv+MdF9uVsis1n7ckcXp0fxgfmq8hW29NsaAE/zL2wrPZaoL4c6iv69jFTdqJCgsRv8gUPlbgOIUySTmx25z3htwfSqW9S8tBkzX4ofR98MVbsw+d+CE8kCHdUE2sY8Q+R3PnjPlHti7ixz17OlRPkgNgIzfkfmNoTcuJFZkc488GOpLbsQMZB8QaY2Xf9GG7RWpWcnPJ6mpVqIgzyhKzAc0TrbZFeNmxSDkOdsFlMn67MgQlPt97eoGkcjPayQSKRsGCIG8cyykRvjVs0GJiReWILXnamrSfNrhjjb09eZQPpJbUX995cTuwDRZa/tO2ERW0JHPhAzHfY8HCfDJI7nVXOpHf3MPfNPyl6dwQpqx7ll8PJ7E4oIqWoWmM6kCEmi5vZ421niqFcSoa0xWFNOzg6klZKhJuVkHSUpRN07n98a7EC+Y4XWo9plapFuGhv6g+8LzZLs99v600Y8QBOzTlY5+2lpt306s2x+ajVama1rwTVlYkfb5gOB6EBxM7ciJuHe2Io7/BTL4oXmRKZXGSZC+O6PFkGOVuQUFDVswA9cZPY8E16vvNtVm58ZbGc+cp3yFl2hH3VzvxyPLvzcb2gplHBqxvjGfXmLt7crLtPq88pTRWWyx16/wDxuzj8idhkR9ygdVO4qxVOrh58bbRMyBo1F/ZmpYotcQVcF+qkrctXq4XEL2xha/V6epgzzUo1G2PyyapWkeW5AGJ/FZLbslTMnAORStAedOwYKtbc3Kabfm9bIj8eyaQ0I5aN+VZdG710xHO0mFvVMv+kZZ0bHhHnhlvXiR6rfqZRoSSlqIYIJ2PR7+MUjpWJARHu1kJq4z9FVJxed4O3vEXQ2QsKqxpwsjRCUp3facCsm7UJLlbG7fpyRovAT5eDnWbz3OA4mGfWnSXSw5ovbovm8esCSS+pZWP75nqZgZDz5Z4QLmwbH0cZvpiPc/yYGe7c+bE1PDIlgEVR7ry+IIJnZwazbJQ3r8wP51h6uz7SfmbtyRye/zOOSA9rrEwMcLMxYeXdw3G00JbthrtZMTXEkbe2JHDrN0cZ4mnDkuGapOKMN0RAufYuaG5gcogjViYGvLctqS0YVDRqn/dP/iCqxqPa3OqUKjX/255Epf0QzTHfCdlLO0nskuGerL1/FG7WJsKwKD9Gp0oh3M0KI7mUo+ll4vdTlScqOQDuw1FmHaOkpqnV/ONKx9bMkBnhzgz1ttUyQGjPvEhXVGo1G9uZ2qQW1/DcH7FMD3Nm86PjiPbS7pPwsTfjrjE+rNiXRk55HQqlip+OZjHU20b0KhWdF/3Q7RrXfezNyJe50ii36DLI1IVSpeaZtWcZ+9Yulv98iutCnVhqeoSgpjg+MPgHd6leQCU3ErOg2u9hrkV+vV0MFu6j5HluRT3hdceotInQlqZ1hWskxup6qnMTOrkD/n4ql+uHuOJmolEj/DBXXBvuPyDULu1nZgXNFkOsy9Nh0nN92p97UUFOamoqkyZNwtfXlyeeeIKGhraL5/Dhwy9tRUYWMPtdSN/LfzzPUlbb1FY9yTzQdRWnBbdocdFrEJmXFg/vYEWCkLK1zNNBTHTfpByOrKlSbHBv/V3r9rM5lTQr1a3Z1RnhzkhAWFzLDGjyGIVz8QGWDPPULkef/Q1WLhBBgaYSMcJXuHB1p3W9YmhxBmux6wY49DGghlnvQuLfOvsKekOTQsUz684S7GjKp8afYVKdiWfySqI2z2fXyteQ5x7D19ZAfOltfIQvu0xKgKM5sY0uoveqnfa2pR+n1Qhg3ztw+DMiDbKYWvErKYlCM78jvpDK+mZmD2r3A8s6Iua0jH1cKwjGazSN9mHcLt3Cgk8P8s7WBH46msnPx7IZ7WePg0U7V7b0feL96c/ZE31Joch+A2KThloEaToIdragqkHRZXNhK8pmUaHwHgeB0zvd3KRQ8dPRTOYNdmWUnx1zBrny8a6Ui54RUVrTyMwP97HySCZhrpb8djJbSy7RL+SehK+nwoaH4LORnWcAZR8TGvORD3S6q1Qq4Z8zgnm3ZCQKuZBTqdVqXlwfR2FVQ2tTeStF54U8t12/jKedKcHOFny5T1y8VYNvEee6M6ugrhS5YyCetqaktresdwoTtsolQtaWWVrLhpg8np8ZSKhBPplSd577M65nQazXKPH/u18XgxeVzWJmyLnfYe5H4rkuA8mFNShUaqJMC8Vrcxbf5bH+dhxILuHt3HBinBfSPP4Z4dz3xwO96tEpqGrE3VwqMqUdKjkSiYRh3racyNRYp3qNFj1A7c+XLeSeAmMrPjytpKCygTcWDsJILiPK04ax/vZ8vCtFe3PgOxFCrxeBsp0/uwL+TX2zqtsgx0Am5b3Fg7llRJucbUaYM/6O5nyyu/9lO1viCvjn2hgWRbmz4tZovlo2lF/vG9UmUenAI1MCKK9rYs4gV1bePRyLFjMUIwu48XvxPd36L4yK49jo/SuDkz/ms7VbUB/7Gt4LFo5LikaR6NrzBupBN/H+8QZeXB9HQ7OSlYcziMmp5NHrx4O1J6gUrVK1FowNZG1VU9coMUi2pLOVtDjORkiWK7MBdbsgZxiyykzsqOzsGHUV42hhzLgAB34/LaSCarWal/+Kx9HCmLcWDcKsXV9Ye5ZP9sfcSM5bWxL56WgWyUU1/GumZjxDi020c0Tr8XKZlGBnS9INg3RWchoVSj7amdwpAbMvuZhfTmQzPtCBz6Ya85nlj0g2Pg7Bc7jrjns5XmbM81ZvojYwEbO+UnYIpc61Rl2ZsK/POaYxdbh0jiTnM1p6DqPgaT27g4tIJPg3J5HWLoFf26ggq6yOKA8rYSF/6keY9irc8bfuKr9MDhOfEXPOQuZ2vv0SuKggZ/ny5SxcuJDffvuN4uJipk6dSk2NuKg2N/fBUKmgmRC2AIcDL/LoYDUf70rhfMI54bqgyWgmFVaz5liWds8OaDIFahG0AEfTy/C2M8Wy5LTIaLbL5liZGFBvF842x7vFALyWC7iGE5llGBtIW92O7M2NGOVn16qJP2w4imhpEosD20Wd1YWicao0RWwEvhgHVflYGhsw2N2KA1dDkGPuCEZWbRft2lIhiRl2t3DYiFwqJBVJWy/6KT7bk0JqcS3f++0hsPYkBkt+pPmunfh5uvGK4UrWGb3M47ELxHPYtEmt/B3NOVqrKbG3k6y178dBrRYN1VHLMH1wN03I2fPnt6QW1/Ds77GM8bdrcxFrqBQWxq5RMOnf2ouUSDAa8yDjZbFMsC1n1ZEsnvsjjvP5Vdw4tEMfSdoecfFruQBeySgVoo/DUeO8ZR8IpnZdSm5aZIIJBReQrB38QHzvp/1Xp/zpt5PZFFU3cvtobwAemxpAaU0jPx/N6vZhaxsVPP1bDLd9c5TbvjnK32fzUShVPLz6NDUNCjY/Oo4PlwyhukHBxphLHwLZJen7xHBTGy8hyTJ3gp8WC0fHFo5+DtZeEDhD50OMD7BnuK8jMWp/qpMP8enuFFYfy+bl+WGtlrWtpO4S/R4dehCnhzmTUSou/G6BQ0RiZq/Guc7OD39Hc+25XI6ajYbGYe2LvWlYmRhwS5AEqbKRMSPHsC+pmD9O96D/wcpd6KVjVsMnQ+HdANEwPuJ+GNR3EoMLEZ8nJEx+Ck2mVhNczY5wxcnKmK3pzSzKuoHXKqaJplk7f1h9i9ak+u4orGwgwExz0e7QkwMwzNuGc3lV1DYqhGmARNpphpBaraY2/SgF5qF8tT+d+yf4aWX8H5zoR0pRDQdTO1wTpr8u+tkW/8imBGHB29tKgVQq4aFJ/uxPLuk80FKztr6Sd/o6mHHLCE/eWhTRo96TQe7WHP33VN5fPLjNNKMF10iY9poYIrpiPB4lB7jfaCsPxy9BsulJUXXOPkrzr3fTuPpWFGaOPFl9Mx/tTGbNsWwWrzjMu9uSuGmoByN87YQTKhKxp+gKV03FpwvJ1Gg/e46nl9FcopE/tZzjPYYBMNwgFV8dUsKrmYVRbsRkV3A6q5xNsQXsSyrmudkhmBh2nWG3NDbgqelB/BWTx9tbElgY5cZgD2txY36M+A12MJgJdbXilMJHJI86JFn2JZXw/vYk5nx8gN3tnOFWH80i0Mmc12a4M/PIbRikbBFzp+Z/QribFe/dGMnPCUr+ZfkGjYbWQqXzpiesmCD2ZqdXif4gXSMNBgKVUqzr1I+9W1PKTlArqTB0FvOo+oCic/uxkNRjGqr7+tUJMzuUVp5ESNO1zAeSNdefkbU7IfuoGEQ8+uHuKzRRy+CmVX3ey3lRQU5hYSEPP/ww0dHRrFy5kuuuu47rrruO6urqLnX9vWbOB2DuyPLCl4hyULHrt88AqHYewSe7kpnz0QGe/T2WZd8e056M7BAsJi1rZtgcSy8TTYbZx8RMgw5EetryftP1omTdgZMZ5Qx2t9ZyRZkd4cqxjDL+isnjn3GeKJFhl9Vuqvf2F8SHdO9ueCJeRKga/f1Yf3sOpZZ28pQ/kVFG9H+3s/Czg7yx+Tz1Tf0z/bjHSCSiL0fT0Kw8+iVqtQpGPypun/2+yDiuWQpx63peKlUqID+GHacS+XrXOX5z/w3HUx/A+KcxDpmGmdcQpPdsR/LvXLh7O9LQeSID06665udgztEyE9SGFlrmA1r9OKUpIgPuOxFjcxvqPcYzpHY/cz8+gKWJnE9ujmr7np5eBdX5sOhrIRfpSPgikBnxfGA2MS9NI+31WST8d0angZqk7+0TJ5B+pUWOUZoCyqY2e2GJRGweumgo97Izw9hASmIXQc7hpDxOfnoH7HqVovB72jYN7TiRUcbLmmGk4ZoA08/BnLmDXflib2q31ZyXNpzjr7N5mBrKqG9SsvznU8z8cD+H00r56OYh+DqY42Nvxhh/O346mtnz96O3FCeK13b7X6KPadmfYOsLGx8XUqXSVIjfIBrIuziZSyQSnpkZzKEmf5RZR3lvWwJ3jPZm6Qivzgen7RaOZobaG6hpGnMCBwsj0YsVuVTMnwCw9cPP0ZzkonaflYmNaK4uOkdBZQPrTuZw1xgfTCpFgBAVPZJZEc688Gccsz/az/xPD3Z2tmzP2MfhgcPCHjxqGdy9o8269zJhaSJnfqQrxuk7hCuZZgZLqKslO56YwM4nJ/LszGC+P5TBztQaMcy0vqzHm4HC6gZ8DTW9IDqCnKHetihVamHTbmwlqqIdepU+251CQ+YJ1hY4Eu5mxfJJ/lq3j/Kzw9fejNXHOgT5Vm5w4/c0Wfmw43whM8N1OO71gDmDXPCxN+PF9ee0fl8qlZrn/ozjgVUn+6RHNNDJglevj0DeC/cwBwujrl/T8Hth6suw4Et4LBaDf6aw3u8V5je+wkqfN4gb+Q6ypI2oStOZXfAPNibV8tnSKNbcN5L8ygaM5FL+NUvTDzjiPrjuZZG46woTa7D161IyNcrPjvpmJXnp50Uwa6WpuNr4UC2zZrJZZq9e+9XAtFBnLIzkLPjsEMt/PsUoX7tuq4ktLB7qQYiLJWrgmRntejLzY7RnbmkIdbVkb627+G1WaJ+79yYV4WplzGAPa+78/jh/ns6lqKqBnQlFLBnmiSR+vajA3bsLprzQ2pM1e5ALby8axK4cKaF5z/OK2xdkDXte9Mqm7BQN719OEPKpCwwVvyxkHxOGGxsehk+Hi0HiiVs690R3QJW4mSQ8ebl2oUiy9sG8IeOcA9RLzXVew7tC5hpJtGFW6+BXgKSCakxowPPU20KK1oP+nv7ion6Z9fXari0vvfQSs2fPZtq0aa0VnUvGxBqW/Iy0poCfK25juXIVB1URRLwbw/vbk7h9tBdfLxtKQkE18z4+QFmLm5DcSMitCs5SWtNISlENY91kUJYqMm4dGOJpTVJhtcjItUOtVnMqq7yT9nR6mBNSiYSHV5/G28NDbHZa9N7p+4VD25QXRd+Qmb3479hfIeMgU0OdqG5QCMma5uKiVKl5cf05rEwNcLEy4bsDGXywQ4fs4XJjH9haySk8to5tiiFkNGiaKw2MRcQdNEPopz8cBDv/q6X516K2lPp1D6J82x9WjGfqhuEcM17OkNK/RUN1xwqKgYn4rOa8D89mi4ngGvwczKluUKKwC9Cq5JzMLCfM1VL046TtEQNjNRlwqyELiZYm4ywp56tlQ7XnzsSsEXKWrvoIDExE87hmAyOVSrTmIQDCNrcsTWz6rkQUjbDjZXjdVfS1tVist8jVQEhuck8J6+MOyKQSAp0sOgc5ajWc/wu3X2cwqGgDLyruYviJSdzzw3EOp5aSXVZHdlkdW+LyuW/lSSLcrXhjYYTWQzw82Z/imkbWdNzoaVh/Jpe1J3P4z9wwVtw2lN/uH8V7Nw6mpKaRZ2cEa01OXjrCi5icSuJ64wTXG4bfC8vWt2Uj5UbiO5p7Av56BL6eIqRNQ27t9mEiPaxZsmAR1pJadixz5aW5oZ0Pam4Qn5UO+WOoiyXuNib4O2iy++ELRWO1qT2YWOPvYE5hVSNVDe2ygo6hUBjP37H5qFGzbJS3cJySG4O1J6/MD+eGaHcGuVthIJVwx3fHeeWv+K6HwNr7w8IVcN0rIqPdz05qHZkR7sKHC4PEpiVkjs5j7hrjw/hAB55ee5YyQxeR7T32FVR03wemVqspqGzAQ675HukIcgKdLLAwlnMsvZ1kLfOQVsInOekcdpJqlt2wkD+Xj+l03pBIJNw83JNt5wqFU1sHNsflU92gYM4g10639QS5TMr/booksbCaZ9edRa1Wo1Sp+ee6s6w+lsWUEKe+S0r2JRIJjH0MBt8kEk9G5sy79REiRkzmhT/jmLPTgY/sXiBlylc8eNM8Nj48llkRLkR52rDtsfFseHgs1qaac7z7UDGU9EK4RXW5SRyk6fMszU4SlcyWZJhEwjlJIFFSHQNZa4qFTCppq5Cv6urf62+a68X17Y/7hTlDLxzMTAxl/LF8DF8tG8pHNw/hi9uiu/+uqFQQtw5Zwl98e8dQ1vxjJE6Wmn4sZbNQ1rhEdrpbqIslZ5Ua84F2clK1Ws3epGImBjvy3bIhfOq+g5/WrePlv+KRSSUsjHKDs7+C91id7pyLh3mw75+TeGXBYHZVODN+byALC+9k/YS/aXoqQwyozToMp3/s8XvSbyRtBmNruGenuCaf+A5W3wTfThd94rpQKlAlb2e7IpJt0jGUyJ1h8zM9rlTrIq+inrCmGCodh194Zlp7XIcQpE7nbHbbcycUVPO0+VakdaVC2TGA9OKVtBEQEMCuXbuYPLktOnv++edRKpW8/PLLfbY4HIJgyU9I0veRYT+eY4VOvGdnzmAP69by/XrnMcz6aD8vbTjHxzdrok/nCCiIbW0MHWmgKTPrquR4WKNSi/6blrk2AJmldZTXNRPVQUJiZ27EtFAniqsb+eaOYcjOLRT6/KwjsO5u8RztLYSjbhe2xBsfI+LW3/GyNaFp+6vw+58QdRvrjRcSn1/Fr/eNYriPLR/sSOLjXSnMj3QjVNdQwMuFfSDE/U5JbjquDcm8r5jKWz8c548Hxgi/c7kh3PiDGLoX/ycc+kjIeZb8pJU1y02NxWDNTRg2VfCjciL7VIOY66VksXcdkshbLqzhl2rH4X6OIqtdbuqDY0nbxSW1uIZgZ837lbZHVH9aJmwHzQKJlC3TqzF0bveeFsZDwVkxw6U7vEaLzZFK1Wk9QOu8pCsyyMmPgT8fFP0dJjaw723x3pjaa2c3vUaLxtDck+LC0YEgJwvi8jTzQPa/Ly7ctSXQVE2BOpSjUd/y4px5RMbk8d62JG7+6ojW/f0czFhxW3QneYq/owVzBrny+d5U5ke6tQagCqWKX0/k8Pqm88yKcG7tV5FIJCyKdmdhlFuni+51oU7Ymxvx64ns1mpRn9OxQuMzHgYtgdMrRbB8/ee6J7t3wCFkLGwAv4ZzIInufED2EWFnryMDJpFIePfGwRi1mEmY2Ag3mlqxiQlwEkFYalFNmwTOKRTO/kqCcRX+jhbiN1ycKLKbUhn25jJeni+CXpVKzed7U3lnayLjAu2ZFNRNFnwgSd0l3qP2A+raIZVKePeGQYx/Zzdf70/jnxMfEcOe974pnO+6oLK+mUaFCkdJubAmNrXtdIxMKiHay0a7L+foF0JSbeOFUqVGln8aJGDpO6LT/VtYFO3OO1sT+e1kNg9ObKv0qNVqvtyXxihfu0vq94j0sObNhRE88WsM6SW1lNc1k1Nexzs3DOaG6KvHul0ikfDKvHAsjA2wMjHgH+NmIZVKiOhwnI2ZIRc1OtA1SszpUDSK5EU75DIpw31sUeSng6t3698bFUoONPjwqGq9qJLL5GJT+uuyzgMuvccJiauu60d/UJwo+lHqSoVMLGYNbHkW/rFX2/WyG/wdRF24WgABAABJREFUzXsmk8yPEXP1ck+AVI7LvbtxcR+kvRZlo85KTrCzBQUSWxoMbTHOj2kdop1RWkd2WT3jAxyQZR9hdsm3zJbD9oRo7IL+hXVjvpBXd/M7NjaQsXSEF0uGebLtXAHfHczg0TVnWB/syLd33CjmHO14GULm6fyNXzYSt4hrh/tQMUpFrRb7ybV3wZcTYeKz4D9VWwqffQR5UxWH5cP598wIHlp/D6uKP0b+5QRY8rNW71NPOZGczUxJCg0h3SfpOuEaiZG6gcb8BJoUYzGUS0kqrOZB9R6IvFkMBR5ALuoXt3LlSqKjO1+YX3rpJeLi4i55UVr4ToApL+A9eAKPTwtmUbS71g/Pw9aUF+eE8ldMHlviNHp85wgoSuBEWhHOlsY4VMaIjYCdf6eHD3a2wEgu1Sq1AZzOFgFSpKd1p/t8cksUv90/SlQNgmcLedwPc8UF8aZV2hshqQzmfwaN1UhWjOdbo/eYVvIDSq+xqE98x3W757AkzLTVt/+BiX542Zry7z9iW7Oo2WV13PbNUd7ektBzF6RLxT4QFPUkbf4EgNtuvZOS6kbu/uF4W9ZRKoOAqTD/E7h9ozAJ+HwMrH8IDn9K7vd3YvbjdBqalewZt5qw2z/iifvuY/G9/0Y6/bWLalL2tjNDIoFcmbuQB6nVqFRqUotq8XMwF/rW9P3a0jEzO/Aag2HSRu0HO7tG9B510T/RitdoaKjofqCi86CeuZFcLpQKUfb+cpJovL1nh3AvStsjJIYd33unCDEoN+uIzocLcrZgXsnXwgIy57jQuI97nIw5v7C48TncwsYil0lZGOXO7qcmsvHhsXx7x1C+vC2a/f+cxI4nJmBvbqTzsR+fGkBDs4qFnx8ioaCK305kM/PD/fz7j1gmBjnwxsJBnQIaXVlFA5mUGeFObDtX2MnppV+Z/S7c8hvc8mv3zo/tMbEWM2eyj+q+PXUXmNiCc+eNAcBIXzvtHp65H4qLGyKgBLT7cpwioDqfvLwcQjQ9VhQnCHlvB6RSCQ9M8MPSWM7p9oMQrzQSNopzejfzFBwtjVk6wosfD2dSoTIRCY0zP3c7R6PFYMNOVSoqc11ksId62XA6q0L0hXpq+jk1ks/U4hrClAk0mjhpuwh1oMXtas2xbK3v7OG0Us7lVfGP8b5d3renLIxy5z9zQ3GxMmFikAPf3jHsqgpwWpBKJTwzI5j7J/j1/dwZ96EiyZN3RufNo/3ssGzIQWHVJitNLqzhhCoAA1WDSPpkHICvpgjnwQVfwvLj8FgsLPxKNIYf+axv19wdJ74FJLD8GDx8Ep5MFPuTuLW6j1c2wy+3il7bnM79MV2ScQC+nSkc7m75Tfwe/3xQu3LVUqFxGdTp7mZGcnzszMky8teq5OxNLEIulTDa304kEY2sqJr2AcMM03m++Ak48D9RhQ6Zd8ElyqQSZka48Ov9o3j3xsHsSijiUGqJaIRXNsGuV3v2WnuLWq01zkQnZWnC8CKo3R5EIhH94f/YI6pfm56GDweLftDCc0LCH7OGSoklln4jWDzUgwKbodwhf5s6iSl8O0Nc53tJ8bm9GEiUWARPufDB7dFU6ILVqSQUCIlvTkEB9spCTU/cwHJRQY61tTVWVrqzlaGhOqQX/cwN0e5MCnLguT/iyK+sF047ykbyU2MY7mOLJEfTj6PjYiUcPiyI7+DFfzqrAk9bU50bM5lU0rbJMrUFvykgNxGD/zo48QBiPfftB5dB+FYe5bGmB9kW8R7/cvwUC+p4OqitjGwkl/HagghicipY8uVh9iQWsXjFYRIKqvnhUAbj39nN/uTizs/R19gHAhCc8yu5JkEMDg7i69uHkVZSy8wP9/NXTB7JhdVtblaeI4Q2NngWZB9Fve15atKPc8pyCnaP7uP6qRMYG2BPtJfNJV2gjA1keNiYkqRwguZaqM6noKqB+mal2NzlnYHGys79MeELxYWmZTK5Silc8MKuF/K77vAYIQJZXfNBlM2imnWl9eOc+l5UXMY9CfftE3KMsIXCbag8o3OQI5OL5EAXDlTBThYslu6kKmgxPHxKBEzjnuSQMhSJREJEu5lDhnIp4W5WTA52YlqYMx62pt1KHXwdzPnjwdGo1WpmfLCfp9eexdnKmN8fHM0nt0R1mgHUHdPDnCmoaujd8NJLxcgCAqf1XrLlMVy3o51aDck7xHeqp5lfmbxVRmNhbICzpbG2jbQms2dQck4YSajVwp3KXneAIJVKGOxh3eUk6wFHqYDEzSLJdIH3/b7xvjQpVXx3MAOG3SOC+bNrujy+oFIEOVaKEp1StRaivWypa1IKQw5zR7HB05wjTmeVM1Yai8Rv4gVfym2jvMgqq2NjbJtpxtf70/F3NGdCoEM39+w5d4zx4YvbonllfjgTr9TK3EDiEimu4Vk6zvHAKF9b3CkiW9323p3Pr+Ksyhe1RAbfTIXvZ4OFk5AdDb5J9LVae8KgxTDsXpF0ahnc25+olEJCH7agLQFg4SQcL8//pfs+5/4Qt8Wtha8nC4nbhZrgU3fDqhtE/8Y9O8U58PrPRDJw3zttx8WsFkkWE901thBXS2KV3uLarQmu9iWXEOVpg6WxAaTtBZ9xWI6+E+vlOzGUIGzBg2aBce+qnAuHuBHiYsm7WxNRWzjDmMdEFf4ShpF2ScJG8V5mdZHIAkjailoq5/k4Z97ZmkBcbiVVDc2ib9vCCW5dC/9ME0FzRZZIMr7pBadX8mvzWCYEO2Mgk/Lp0ijKDN0Ylv80KYZBqH+6UfSH9gKz3IPUyKzbDIl6iqktKmsvBknTOZ1VQVltE/a1miTSZXLb7I5roltOIpHw5qJBGMik3PX9CWpthJuQYUk8I7ythNbWvWtr61BXy1a3nhZOZ1UwREcVRycLvoAHD7W5GOnC3AFu/QPJU0nEO8zgmXVn+SXNkDozd+yKtH8Eo/zsWHX3CHLL67nju+OYGMrY8NAYjj43FU9b01Z3t37FxhulRI4tVZiGC2ea4T62bH50HP6OZjy8+jTX/W8f0f/dwae7U4RTj7Un9dPfZ9XQtUSqf+YFl68Y/cj3mNk4XeDJeoefgxln6jQX/5JkUjWbOT9Hc9GsbWDaWZoYuVSUezc/K06ksWuhOg8GL7nwExqaiYtg5oHOtyVvF02T4VfQfBxFowhwwq6Hyc+1yS9kchj9iPhvXScf18g2q88OhJjXYCepJtFqjJAqaojJriDA0bzNBvYiEYHOGP4zN5Ttj49n5d0jOklFe8IIHzssjOVsiy+48MEDjccI0ffWUUd9/i/RNzVo8UU/tL+jOSntB4La+aOSGROgSifYxVLM+2iq7rYKEulhTUxOxZU5wDjzoKiudiFVa4+jpTE3D/Pg24PpVCk11ffYtV1mq4uqRKXauLFYd9JKQ6SHNXKpRAwFBc0A0p2gUpGWmkSgNBfDwKkXXN8wb1smBDrw7tZEmhQqDqeWsiuhiHvG+vR9xUKPbuSGWr2XHQmxaMRc0sDR8rZN9fn8amxtbJDc8K0wSlq2Hu7aprtyd93LwlBizVJR6VGrhYRs9xuX1Eehk4z9UFPYeaBiyFxRvS3u0POrVgu5uc8EeDJJWMHHrRWT6Ru76LE+vxF+XiyMUZb+BkYadY1btDAm2f+eCFoyDoj1tOur7UioiyX7atyhrgSq8mhUKDmcWioGtTZUCemfz3hxsK0v3LVZyLtGP9Trt0YqlfDUtEBOZVWwO7EIht4lzCROfCcOaK7v/P5cLEGzRI/Nlme6NjhI3Ey103BWnang6/3pzPn4AIP+s42A5zZz/8qTImlvYiOC5uVHYdY7cN3L7Jz4O68rbmG8JgkS5mrFhofG8MScaK6veIyDkihUv98rEpo9oLCqgdDGM1Q4j7qo/kqpaySjTbP56WgmCQVVBEuzUUuk3V5fLhfXRJAD4GRpzDd3DCWztJYbfkyiADvCpFlMMUkWpVSPzv04LYS6WpFaXNPqQFPfpOR8fhVDWuwPL4Sprc7mt05IpWBqy9xBrlQ1KHhqWhCmgRN1epyP8bdny+PjeWFOKL/eNwoXKxPMjeSM9bfvbDnaH8jk5MtEw6vNoLYZA06Wxvx8z0h2PjmBn+8dweKh7ry3LZHRb+5kynt7GPH6Dl5YH8coP0e+un1o5yb9PsDPwZwj5ZaABEpTSCmqQS6ViOGh+TGarFyHQWdyI5j+hghUNj4O6x+E4Dk9L6fqaCwG4MxPIvOho6lywDi9Umxix/+z821DboNRD+mW6LkMFtkiHRddu2qRgTzTrP09P5NdQWRPfycXwMbMkDvG+LT2lFwMhnIpk4Md2XausE/W1K94aHo12s/aaa6Hbc+B76QLyyi7wd/RXLuSI5NTZRlAqDSTYGcLsdkBnXK1Fga7W1NR10xm6WWSyPaGgljhGNfOebE77h3vS3WDQsw4i7hBSGvzTut+6KoGrEwMkFXng0XXTf8mhjLCXC05kamxaA5fBFU5kHkQw8x94m89rPA+MyOY7PI6Xv07nn+sPMEIH1sWRl19krKrGq/RIuveYcg0gLRIKADW5lq1ysjj8zXzccKuh6F3is+6K1WAoRnc/IsYSP7DXPj9H2K6+/534YNBYtxEXzl9xf4GVp6djZb8pwqJV0KHak76XvF7Gv2ISIRF3y7WmnlIzI/r9PhrRd9RwDS4eQ0YmmrfPuGfYnP754Ow6zVxfQy9vsvlhrtZcbJZM+Mp/wwnM8qpb1YyLsBec81Vave7WrmLwKqHv/2OTA52ZIinNR/tTBES84gbRa9eUx3F3y2lasV0GuouMC6hJ0hlQvGQd1pUszpSUwSZBzlpNAILYzmnX7yOVXeP4MMlkTw3K4TjGWVMfW8v9/54guf+iGV9XAkNkXeSHXQnKxJNCXK2wsXKpPXh5DIpd4/1Yd1Dk3jH5FFKFSY0bnhS7Fma6qCy6xEBO08lEi7JwDLkIl3QXIfgp0wntbCST3alECLNRm3rJ4ybBphrJsgBEc1+cssQ6poUVFkFsdSjGOcDL4gNqPe4Lu8X6mKJSk2re1RcXiUKlbrz3Io+4u5xPny+NIoHJ/qB93iRza3unHm2NDbg7rE+2MsbRHNacwNj/O3JLqvv996c+iYlcU3ONMitOp1MpFIJfg7mjPaz5+X54fz18FgWDHFjXIADt43yYteTE/nituheyYx6g5+jOemVSlTWnlCaQmpxDV52psLquzix68bKwOlCWnjyO7GBvPH7nsuBvMdCbbH2wL/aEtG8GHnLZXeX6pKWKk7o/DaL6PYYGMP013T3jrQEarokawVnqZOYsim7Tb5Z06ggqaiaSI/++Z1cLNNCnUkuqiGtuIss5JWCnZ/4Pm5+pu0CdOhj8d8z3ryk71SgkwVZZXVadvRZhn5EyLJwtDASmwcjK5EZ7YKWGRcxHfoVrwhGPwQPnejx79fdxpQoT2sR5PhOFNnRuHU6jy2oaiDMvFbo5XX9htoR7WXLyZYgx2sMWHnQfHo1gTXHKLMI7t66uB2hrpYsiHTjx8OZuFqZ8OWyoRjKr6nL85WP5yghddbVe5kfg1Juyqlae45nlNHQrCQ+r4rQ3phCOASK3tX6Cji/QTTNP35OXD8K4vrGlEDRCPF/QcSizucPQzMR6LSXrCkVYmi5Yyj4t+vFCJgqDJTO/KTtmlpXJpKEIXOE8ZCuoE5uJGRrxQlC/jfhmW5fW7SXDfkSRxrllpAfw5G0UiyN5YS5WokAzNy5TysCEomEu8b4cCa7QvSQjLgfagpRfjUFm7zdfG39KEYmvZtL1SU+40UFbefLnatiu14FA1O+rhzGWH97TA3ljA2wZ36kG/eO92XnkxO4ZYQn9U1KjqaX8eiaMwx7dQcT3tnN+bwqHuxgSd9CkLMF39w3hXeld2GUvgPVxsepfTecxg+iaCzq3IvYrFSRceAXpBI1lqEXrjzrxCUSmbKe+W41HEotJdIwB6lz+IXvdxm45s6ik4Od2Pv0JAIHjcYk76iYCTLvo26HEAU7WyCRwDmNZO10VjmGcmm/TTE2NZQzM8JF9Cn4aIKvDB1SKEUjrLsX3gkQloJ7Xmekr2huP9TP1ZxjGWV80jyP4invdz/ACRFcPjc7lP/MC+Pp6cH49PNgND+NdW6tubcIclpMB5TNwiq8iz4DJBLxXZj2qghwdM3F6QrPkWBkCd/PgePfiOa/FsnLoJsu+TX1GbFrxYygCTqqOBfCPlDo0rsIcmptQjidU0VOuQiwz+ZUoFbDYI9+cjK7SCYEOWAol7JibxqVdVfIwDddSCSwYIX4Hq69C36/T2R0R9zXYwekrghyNket1jYfiFV44kMuEkWD6GcJmNqtVaiDhRFu1iZiFkwPaGhWXl5pW8cM8gWYGe7C/uRiqpoRSYBzf+jMnhdWNjBTfkzIWIJmd36gdgz1tiG/soHcinqxkRu0GMn59YyRxqL0mdir9f1zRjA3Rrvz/V3D+i1BpKcb3IeJ0QO6JGv5MUhdBmFvYcKm2Hw+2plMXZOSmREXnh2jhUOg6JFcflRYzVs4w6y3hStpX5CyQwRq4Tfovj14jqgsbHxcJFc+iBAS77FPdA6Kom+H+nLtoOjQR6JZf+bb3dsMuw4REr2AaRc0BzA3khPuZk26gTAfOJxWynAfO2RSiaYfZ3yfJxGnhTlhbWrAL8ezRc+09zhkxfH8R30/S269r2+t1a97RSRET37X9rf8s3DqR2pHPsHBfJgY1Ln3ztrUkOdmh7LqnhHseGICO56YwB1jvHlhTiiH/z2FeYO7rjLbmxsx48b72KkcgvTkd+yu96dUZUbeD3d3Ouf9fSaTW5t+pcpzardJr25xjQTgvoAqJKjwVWddEf04cA0GOa20RJGjluu0LmyPmZEcH3sz4vNFs/LprArCXS0vTybN0lUMIkvf1/m25O1ixs64J0XD7JHPsW3MIdTFkkOppf26rAPJxRSYBeM2YlG/Ps/F0OIeVWzk2dqT4+doLjKvKkX3WR8rdzF5tzcBDghb4PtEAyR/PwFveYtMTMC0HmdrLwunfhRVy4s5wcjk4nejqy8n/yyWPtEYyCRsjhVVxzPZFZgYyAi6BHlZf2BuJOf+CX6sO5XDyDd28r/tV8Dcqa4wdxDuS9lHIXWnqOBMvXQb/hbJX2Jhm+ziYI0rMlRidkfROaEZvwCRPTQfyCmvY/zbu3nuzz521+xDZoQ706xUs+t8kUZalgtpuzodV1DVwNjGA2JzdQHHxKGaOWqtfTmDliBvrsFWUoNtxPRerc/Zyph3bhysJUHRcxkxMhebtS6CHInLYGaEO/Pn6VxW7EvjwUn+bWMLeoOli7YdMPTdJt55kAhAujr/B88G/+vEazz/F/hPhru3w6AbOx/rGCLk3Ce/F/+uKYajK8S8sG561VoZ/bCQlfWgQjXSx5ajDR6o8k5zJrtCjPOoKRbnKd++H81gJJexcIg7f5zOpaFZyfaA57mj6WkGzXkQV+s+/v3Z+gqJ7OFPReJarYat/wZbH7ZZCMvsCYEX3kP4O5rz5LSg/2PvrMObOt8/fJ8kdXdXaCnQUtzdhg3Yhs1g7r79pt+5G3NhG9sYY8gEtuHuToEWqVJ3d03O74+3lhoFUijs3NfFFZocS46872OfhzuH+Qll3/MwprsLh/t9xGzNZ9jMX8aZ/m/hV3qCk6s/ql9GlmUStv6Atyob68mvXvx3NLMD1xACc7Zyd08NZroycFaMnI6l6wQY/SKMfqFdi/dwE+ID5VVaDsfndViqWov4jWixLoczf4NDgPDKj39dXEhbXmFoFwf2x+V2qNd0T0wOw7o6dsrCV3sLY2zNjYiX3ZALEskvro3k1CnXdFSxm70/zPpRSIOOfw26jG6zoPKykx0l+qv0nX/x23Dr3TySU14ABYmYeIYyIsCJtRHpVGt1bDqVQYinTafs9v3UhED2PjeWe0b44WjVsnR1p8F/FDx0AB47AYMfaF5PdhFYmxrhZmNKdK2RU1JZw65CJ2Qk2P2R8Fg3Tk9phd5etpxKKxLCIq1QWlnDvb8co7iiht8OJbEr+jKoP14EXvbmhHjYsD4iHXyGC8/9v09CRYMSnyzL1BSk4VsWIdSpzoOztSle9mYNKWtOgZwzCqQKY9S+Qzvqqyh0FN5DhAx447G1PF8UcLuFMjnYjaKKGgKcLXmklXShK4qtl4gEt2Y0mVoLxa6HD8FTZ0TKXAtN0uvpd4eoYz31J6x/BpCEIpmBGezvwJFKb1Qlmdhp8xjsb9+gdNdC3zZDMHeAFwVl1Tzy23HuW5uLRfAUZvc3XB1ctVbHhjrFxGGPQ3G6EJvY9JKY7133DjtiCglytcLV5jwKrxfJ8zP6sfLFBYwIcGLctJvZZzON7uHvs/7HtziVUsDzK48wu2w5Od6TWpT4viAGPYAUu4X/+dY2aVciOR2MiSWMfq7dKQ093K05m17MF9tjKKqo5uaB3h18gI3wHSGiEI2VMGoqRUpJz5nigWViCeNehbP/MsX6HNnFlfp9MAxIdnElkRnFDO/azp4flxlJEjVBh4vskWQd3lKmiO5kR4mUsjZkXw2CU6CIEM79te0B4nJzfKmIOHU/v+JUq7iFimux0cSPzFrvvGsvpoa4cTK5gCdWnuBUWhFPTQi8tGPuQFxtTHl6YjduH+xz/oWvNM7dGxSKDESgi1V9nWF0ZjGlmFFp7QuZEaLIuhVJ18aEetlSVaPjeFJ+i5+nF5bz0LIwEnJK+f2BIQz2t+eFP8MprmieJlhYXn3F66QmBbuyKzqb0mqdSBUsyxV9KGo5k17E4Mp9QhkoaFq7tjnIz4Fd0dnIskxZVQ2vlc/hcMCT55emV+h8+AwTymS5jWoX0sPFq1soA/3suW2wN5/O6/3fqJnqORNMbUU6bdR6GPdy+3uBXQD9fe0Ik8VYMsY0mu6u1pB4QAh/2HbM87ubqxV9vG3ZejaTe4b78fm8PgZNU9sRmcWDy8JE/0aXnqIOeMOzcPAruO4dtAGT2BOT3aGS7pIk1TuqJUmi3wPfEeU6jSlJH1Lz3RhejpyOm6oAh6mvXPrOQmaDhTPs+gCMrYR8eifgP3CXto+e7jaUV2v5Zlcc94zwb1+nX0MRMFH0bjj+a8N7cduFxGttB2AAQm8GlxBCoz9Ho0JIIHYAdfU+wwM6p5EDMH+ID/+kiLQ1fykdfydL0VTLMbDziABcLqorhHrKieUQMufSFE1qc2v1ojnp4aAyAqcgxvdwwVitYl14Oi9O6V5fI6bQ+ejmalUfyTmdVoQkgca91lvXbXK7thHqZUOAsyVPrTpJVlFDAXJljZb3NkQy6sOdnEgu4Iub+xDsYcP7N/Uiv6yaed8dZGdUFjklleyNyeH5P8MZ/M42XlwdYfDveSFMDXGjSqvj14OJQvhh8vsQvlLcO8A/x1OZaXRQCMK0s7nv1BA3EnPLOJVaxIG4XHbX9MBp3KMd+TUUOgrf4eJZF7O54b30k6A2AaduqFUSb80Mubg0tasRIzO4ezPctwteSIXBD3bIbqxMjXD08CdO58b1VlFiYp50QDTF7MDx/L0be/HD/P68NLWHqAEyIBN6uDC6mxMv/31a1IaOeEYo9014E4Y8zD8nU8kvq2Zy8AXWdV0CpmYWhDy4hLyJn+Ph6orJ8EdRPbAHyRBRF40JDLwPasqFYEsnmYcpRk4tdSopbtamPDr2MoehTa1FzmbYLw0NuM78LdKjXBopVKhUMPYl1CmHeMI3keVNumQbij0xOXRxsujUueEzenswdmAo5bIxIabZolC3lQ7u1yQl2SLt6Jvh8LYLvOMm+gz0vf3StusUJAb05Ea9mzIiRCG8xhgbMyNuGeTNgiE+3DXM99L2pdChBLpYkV5YQWF5NZtPZ9DHy7bByGmnPLWJRs1Pdw6gSqvjzp+PcCQhj9isEuYsOsjivee4Z7gfu58dw/geoheWj4MFS+4aiEYlccdPR+j/1lZuW3yI7ZFZ3DvSn0/m9u6gb9s+fB0tuG2QD59sjRYKlX1uEx7ItU+gSz2BW9hH9CEKVf872r3N4QGO2Job8W94GjujsnG3MSXQ5TI6yRQMh6k1+A4Tqpl1pJ8UnvgLreO8VnDqJpxfBkijbYvB/g7s1QXTu/okVBZDRrhIH+xAurla1T+7DI0kSbx9QwhllTW8s/6saGPyfBIMe4xqrY5Pt8YwpptTvYrl5cR+6AKcHtqA0fiXDJtW1v8uIVPeeN56hTl/9dJ/BCcrE27q68nMPu6YG1+Bn6X/nRC2RKSodR0PkethwN3NreHASeDRnzvKl/FRzgvsjc1hZKATFdVaTDQqg4Rbb+rryfjunaiYvhVevj6YtNMehJplCw9JTkzzBmjXGtpqOPy96F+grRJRwAF3iRoLc4fzimycF7WRkAc9/qtQ3AFR59NosHlteufItVVomzpBiP2xOeyLzeH1GcEQPF+kczp0afd2PO3M+emOAdz6wyFmf3sAAFdrU1bcN4R+Ps1T3gb62bPm4WHsjsmhsLyaIFcr/B0tOk3t1v9N6sbmMxm8tOYUS+4cgHT955AVifzzVO7QlpDY+xl8es5s9/aM1ComB7vx78k0NGqJUd2cDavOpHB5CZwselVVFIr03/STDSqoCh3GiABHlu4NZkHFFlG7IutEWu1VjIetGc9OCuLVf04zZ4BX/fPyj2MpJOaW8eXNfa/wERoYCwe4fU2nSVUDJZKjx8dzRGH1FcG9jyj63vcpLJ4ANRUtSxNLEoz9H5Z5p7jf7hhLDyaSUVjB7G8PsGj3OYMcypAuDkwK7uC6FgNgaqTGp8cgRkgnIfO0+M2utUhOYYr4B8LAWTVfKLP0mAFPnIK5S4X3pN8CYZwYgoH3ifqw2K1CkjrvnPB4K1xVdHW2RJJg4ZZoVJLEtBA3oQTY59YL3lawhw2HXxrHP48M49O5vVn32PAWDZw6JEliVKAT00PdCXSx6jQGDghRhtenB7M7Ops/jqWIus15v1KEBT+o5uA1/X8XvM3rQ91IL6wgOa+8RTlYhauIwOuESmfsVtEXJjf20p1HCudleFdH/vfIfUK6fc9CYWA6db/Sh3XJ3DbYhy5OFny8WQgjlVXV8MW2GCb1dCXEs3O1XzAIPkPAxuNKH0U9SiSnM9H/Lvj3MSEveffm1ntl+I+GoGk8HbOISZEeTK+VeB3yH6yPUI95Dr7+G/66V7zh2HkL4S+K7W/B6TVCRjwnSsj/zlsmpEA7Cq9B4BICB76E/EQRLbrKPWr/RcyM1XjbmxOTVcL47i7YWVxauomJRk0vT1t6edoa5gCvIJOCXZke6s7r/55hsL8DJZX2zKn+nHkDvC9KUXKQnwNOViYUlFUxrJMKtii0E3s/4SyLXA/hq0QTzS4X2Qleod1IkoS3uzu494XUoxBwnWEapF5h1CqJpyZ04+HfwtgXm8OvBxPJK6vimeuusblKJ0UxcjoToTeL154zhRejNSQJZnyFZtEovtF+ziKzx/jfOHfsTTIA28twoJ0Ie38Y+z+RXqAx61RhUoMw6T2hbrPzXRG+v/H7jjVwQFxfA+8VBjcYrlGdwmUn0MWKxNwybujTeTxrnYU3ZwZz9NPd3PvLUVLzy/GwM+e+ke1P42uMWiW6qCfklLarh4VCJydwksiqQIJ5v11740pnxn+UMHJ8OrYe53IyOdiV7m7W3L/0GCWVNXx9a1+6Oneu/nLXKle/mXwtoTEWaUdtGTh1mNmimvsLgeoMFhY9jf3qm+Hk8o4/xs7I4AfBo79Q9FCpr/TRGBYzW5j8nuijsuCflpu2dQQhs8HMXry6hlyefSoYnBAPG2zMjBh3FdTYXW5szIxYOLc3UZnF9PKyYdUDQ3C6hJ5KD47uwvuzLrHXhELnoPt08Tr2fxB0/qa5Cgak6wTx6jfyyh6HAVGpJJ6ZGEhJZQ1PTwhkSkjnLwe4VpDkjuwoeR5sbW0pKCi4Uru/NihIgtJsMSG1dBah9Yvgqj8XFYVQXd6+TsydnE5zLgqSwNyx3b2mrkU6zbm4SCqqteSVVhm+i/cVoKPORWJuKe62Zhh1orqhzs7Vfl+0i4IksPHqNFK4rXFNnoucWHDshM1Wz8P5zkVyXhmedmaKMMllRImrX+3YeiuhdBDRr/ZEwBTaj3JdXfWYGqmvCQOnI/FxuDjHkMI1jvL8u3JchQZOe/Cy/+86DK8UVzSSo1KpsLb+jzTVugKYmJiQmZnZrmWVc9GxKOei86Cci86Dci46D8q56Dwo56LzoJyLzsOFnIs6rqiRo6CgoKCgoKCgoKCgYGiUJGQFBQUFBQUFBQUFhWsKxchRUFBQUFBQUFBQULimUIwcBQUFBQUFBQUFBYVrCsXIUVBQUFBQUFBQUFC4plCMHAUFBQUFBQUFBQWFawrFyFFQUFBQUFBQUFBQuKZQjBwFBQUFBQUFBQUFhWsKxchRUFBQUFBQUFBQULimUIwcBQUFBQUFBQUFBYVriitq5Li4uFzJ3Ss0QjkXnQflXHQelHPReVDORedBORedB+VcdB6Uc9H5uKJGTmVlpd7f+2JzuP6LvSz48TA6nWyQfciyzK7obGq0ulaX2RebQ2lljUH2d7XS9Fxcs8Rthy/6QXXFlT6SVjHouciLh4+7Q0GS4bb5H+KauS9Or4ZvhoOuyXOwvAAOfA01nf97drZz8do/p3nl71NX+jCuCJ3tXDTl533xzP/xMJz5G36/40ofTodyxc/Fxhfa/o0LU+DgtxC9GUqyL9thXQmu+Lm4Uui0sGgkhC290kfSjE6TrvbUyhPc+sMhKmu07IrO5qf9CQbZ7onkAhb8eJiX/z6FLDc3nEoqa7ht8SG+3BFrkP0pdHJSjkJuLCTtb32Z6vLLdzwdTfJhKE6DpINX+kgUriTndkFmBORE678fswU2vQCr5l8Vhk5n4nhSPmuOp6I1kENOwXAcTcxnd3Q2FZFbhYFfnHGlD+naJWaz+I1bG2MOfw8bn4PfZsPXg6CFeZjCVU7cDkg/CVEbrvSRNKPTGDlDujjw2bzebHx8JLcN9uaDjZHEZpVc8nbPZZcCsPxwMp9ti2n2eUJOKbIMfx5LaTPao9C5Ka2sIa+06vwL1kU0Yre1/HluHLznDclHDHdwV5LsSPGaEX5lj0PhypIdJV6Tm0xECpNAZSTuh9/vaB7pUSA8pYCsouaR3+ziSooqaghPKbj8B6XQJmkFwlFVnJUg3kjYe+UO5lqmskSMmUiw/a2WlylIAs+BMO0TKMsV/xSuLY7/Il5TDrduxO7+ELa8ctmN3E5j5Mzu78WM3h6oVBIvTO6Oi7UpL62OuOTtJuaW4mhpzGPjAvh0awwRyQVwfJm4OYG4bPGaVVzJ7phrO5R6LfPO+rMs+PHw+RcsTBavcdtb/jwjHLRVcOgbwx3claRucpvRyr1UXgBbXoWC5Mt2SAqXGVmG7LPi/0mHGr0ti1QSh64w40uIWg+ZERSWVV+hA+18yLLMXT8fbRbpl2WZnBLhVNkbk3MlDk2hDdILhVEqF6SIN1owcsqqaiDvHGx4XqTbKFw4WWcAGYY+Cgl7RMS4KYXJYOcDLiHibyV1+tqiNAci14P3ECjNhvyElpcLXwX7PhPGzmWk0xg5jbEw0fB/13XjUHwep1ILL3wDNQ0e/YTcMnwcLLhnhB8A6fER8PdD9ZPY+JxSbMyMCHC2ZNWRFIMcv8LlJyarhIjUwhY9rnXUaHViMm/hJB7ORenNF8qLF69n/r42UhyyzwISZJxq7kHJjYMfxsO+T+HsP1fi6BQuB6XZUJ4vrvvaSM6emGz6vrmFmvwksPGELuMASDt3mt5vbuaDjZEtpvf+10gvrCCnpJKE3DK994sqaqjS6jBSS+xRjJxORbVWR2ZRBRqVhEVlBkiqZkbOol1xDHxrM7rVD4q5QEHiFTraq5yMcPH7jn5eGDH7P2++TGEK2HiBrVft34pD7ZoifCUgw5SPxN8pLWTBaKuFQ8EpCHa8DSeWX7bD65RGDsCkYFdcrU35aV/Cha2YfBje9aj/ERPzyvCxN8fa1AhLEw3VmbUpa8eWgE5LfE4p/k4WzOnvxdazmeSWKHnpVyN16QmtTTjWHE9l6LtbkQtToPct4s2Wojn58WDrDSoNHPu5g472MlFdLrwqfiOgLEffaKsqg8UTQdaChbMSyTEkRemw7hkoy7vSRyKoS1nsfasYaEqyORKfR35ZNZU5iWLyYeEIJtYUpUYiy/D1zjie/zPCYAIwVysRtU62lDx9Iye7WIwTo7s5E5aUT0kHCNfklFQy6dPdfL4thopqJdLQXjKLKtDJMDnADAu5jGrf0ZAbU//8i8wo4qPNUczUbkZVl755LTi0LhOFZdWcTiskNqsYbVo4OAaCsQV0Gd2QOVBHTZX4bW29xDijNlbGmmuN479C4CRwDQZbHzEHb0p+Auhq4Lp3hEPt6OLLdnid1sgxUqu4fYgP/55MI+dCDI/UMJFutOYBOLKYxNxSfBwsAHCzMUXKjxPLFSZDzBbic0rxc7Tghr4eyMCGU8rD7mpDq5PJqE1PaC3l8GRKAbqSHCRtJbj3AddeENdCXU5ePLiFQsgsOPqT8EBcreTGgqyDkNni78Ypa/nxwvC5/nNwDlK8a4YkbAkc+V4U83fU9RO7FWK2tm/Z7CiQ1NDnNvF38iFis0sAGaPiVBHJkSSw90fKO4eJRsWbM4NZeTSZg/H/7fz5ukyClPxyPYGBujHpxj4e1OhkDsYZ/nfaHZ1NZEYxn22LYcInu1i4OYojCZ3EcO7E1KWqzeoqARDtMkV8kLCXqhodT686ia9RIc9pVpDvNV58VtxCVP+/Qm6cXi3ej3vj26yHvv/Xo0z9fC/jF+4m9ewhcK1NQ7PxhqI00DYy+ItSAFl8plKBtYeI7ChcG1SXi6yYgInib6+Boi4H2HQ6oyETq874deoGHv1aT2nrADqtkQNw80BvkGDZwQvI4cyPF9Zkvzth3VNYlafg62gOgJutGWbFCSJk5hKCfHQx8dml+Dta4GhpgpedmUHEDhQuL5lFFdToZPydLNgbk9Oi9zkptwwPSRhAso0XdBkrFEGa5mLnxYO9v7h+SjIg6cDl+AodQ92DpdsUMLLQFx+oy4u28xWpBMrAYzjO/gvOPcW1s/4Zwxda1lTCX/cJtaLw38+/fNZZcU07dAVLV0g+SExmCdaUYawrE+cfwN4fs+IEPGzNmNvfC5UEcf/x52FEaiHGahVVtSlQddRFcgb7O+BmY8qeDqjnPBCXSxcnCzY8PoIQDxt+2p/A7G8P8PtRxSHRFnVR/f524nV3mR/Y+UHCXv45mcbptCK+7J+FlVTO4V5vgNrkvxnJyU+A5TfDF30hci0AxRXVvLH2DM/9Gd5quuq57FJm9/NkQjcHXCrONRg5tl4iM6A4rWHhuqiNjWfDMopD7dqhMFW81qUieg6AjFMUFxXwxIoTfF4n9pUTLeYgVu5izlGaDZXFl+UQO7WRY29hzKx+nny6LZrHVxznXHY7Bty8eHDoIgrhAH8pA297YeS425hiW54M9l1gwF0QswWbqjT8HC0B8HawILlJWoJC5ye1dlCbN8CL3NIqzqQXNVsmIbeU3tbipjpZbAXeg6E8T39yX10BRaliQHQKEu911sm/LENVadvLZJ0VdRgWjuDSEzIb9fTITxQpeVZuYgDqrN+zM3N8GXw/Tt9Qzo0Tv/PIp2Hy+yLl0dDKTmf+EQpFfiNh9X2ifqwtsqOEB02SwHsQuqSDxOeU0stK3A/Vlu5iOYcu2JYn425rhrFGhaedOfE5ZZ0n7e4yI8syESmFjAhwBCCp0diQU1KJRiVhY2ZEf197wi+mdvQ8HDiXy9AujgS6WPH1rf04/vIERgY6sXhvvFIv1QapBeVYGKsxLxfRma1pGvAdDgl7SMgpxdnKhEDzYnJkG1IqzcHK9b8TySnLg2Vz4KNu8FmoyHxRGdV71iMzxDPhWGI+m043N/wqa7RkFVfSx9uOEQ4FmFDVKJJTO9FtnI5WN67UTYJtvBXhgctJSXbH/t51BqtNIyNH1nJo7zbKq7WEJRWIZ1VODDh2FdE8e1Efn5EYxZm05nM1Q9OpjRyAV6/vwavTerA/LpdJn+7hy+0xVLcl9ZyfICap1h4AuEm5+Nanq5nhUpMKDv71KTwjVBH4OYrPve3NSFSMnKuO1Hxh5Mzs7YGJRtWsLkerk0nOK2eyVzWVGPPTiVIR7QN9r1JBIiCLm9DEEkxshNHTGdn0khikqtq4XrMjG4w11xD9dLWCJHGPqDXiAVWW0/a2FPTJioR1T0HqUdF7qY6z/4q884CJ0O8uEUE58r1h9310sVCyufVP6DpBqENp26gJyY4E5+7i/16DkdJOoNZVMrurmChHVtiKz+y7YK0rwN9KbMvX0QJd2gn4KECkbv7HSC+sILe0iikhbgB6DrCckkocLU1QqSTcbUzJKjJsLWdyXhkp+eUM6eJQ/55GreKOoT5EZhRzLDHfoPu7lkgrKMfd1gypKJVSYwfOZlUgu4VCbhzZhSW4WJsiFWeQq3IQQjVWbi2L0FyL7P8c4neLutTpX8IjR8DaHUoyATibXoRaJTHQz573NkRSVaM/16pLC3e3NSVQTgCg1L6H+LAlYYHCZDCzFzU7dcsokZyOJ/kILLkePg6Eb4Z1XKSyzoitnW/jGgIaM1JP7cbSRENOSSUp+eWQEwWO3cQydr4A/L1jPzd+s4/jSR37LOs8Rs6WV2Dr683eNtGouWOYH7v/bwwLhvrw8ZZobv7uYLObDxB5pfkJYpJqZEqZkT1+RnnYmhsB4GkF7uRQaeMHJlaUmzrjI2XVp7N525uTnFf2ny+2vSKkHBXRhYsgtaAcSxMNTlYmDPJ3aJY6klFUQZVWhzs5lJm5sf5UBpV13uvGXo46ZTV7f/Fq3UkHv3M74eBXIuQbtb715eo8+CAePrlx9dLpFCQKWU9oSCXorAZdZ6O6Av68RzzYTW0hulEDtLP/ilRIEyvhtRpwD5xda7jrKPO0SIPrf7cwUEc/J9JDYja1vHxpjjBg64xd70FIumpCpHOMcK5EK0sczBLPRxy6ABBkLO4fPwdzRmctFQWjW16FkizDfIerhDrRgYF+9jhYGOsZOdnFlThaGQPgbG1KVnFF8+hKZXHLuefxu+GrwW02Xz1wTtT4DPZ30Ht/VKAznnZm/HJAUQNrjfSCCtxszaAwhSpzd8qqtJSZuQIylfmpuFibQnEGxUYOIgXRyvXaSlfLiYGd78MvM+CPu2DHO1BRJLz6hxbBwHth/KvQ93YwtQZLl/p7+2x6Ef6OFrwyrQeJeWWsPq4f4a/LmvCwNcOrMo502Z7UqloDxtRGOAYbR3IKkhvGFxAOtfL8hnFIwfBUFMKq28U5ve5doX63+eWO2VdhCpg7grGYQ6M2osK2K2ZF8Tw1IRCAsMS82kiO+BtLV5EimneOimoddy852r4srYuk8xg5IG7A8patOjNjNS9N7cGyuwcRlpTPd7vjmi9UnA7aShHJAXLUznQ1KUSSRAGin0rcyDnGwuOQbeROoHE25sYaALztLais0ZFV/B9XWDvwtfAOpxy9PI2bzu0SUsaf9YJvh0PihdXBpOSX42FrhiRJhHraEJ2pn+uZmCvSuuyrM5GtPanWymRVGosJauMHct45Ebqv80pYu3e+NIaKQljzsPDku4VCRCs1GTWVDZKNIIQWkMUkGYRxZ+st/m/TggfuKqeqRtdxneg3vySiI7MWQ8AEiNoo3i9MFZGd7tMblu19i4jsGEqp78hiMaj0qN2He19w7UXZ/u85lthCWlmdslq9sduLapUpw4xjsavOJF/tyOEkcb+UWQmj108lvLqhZtmMrDmAbsSzItVt00vodDJf7YglIec8qZLXAKdSC7E1N8LTzgwve/Mm6WpVOFqaAOBibUK1Via/aX+hXe8Lb2pTUsOEtHvqsVb3fTAulyBXK+wtjPXeV6skbhvsw4ZT6fV1QQr6pBaU42FrCkWpyLXP8ixJpBxKRWm4WJtAcTrlps5kFlWKSE5ne85fLCdXwJf9RVsAlZF4Ju3/An6cJJ5bkgqGPaG/jqVzfSTnTHox3d2sCfawoYuTJadS9dOJ0grqIjlmOBRGcEbnQ0p+owwAWy/RYLiOwkbjTN3ncE2NNZ2Ora+LecItK2HwA8KgjVgF8XsMv6/CJkYscK7GAV9NDrcO9sbD1oyYc3FQWQROtUaOSoVs54tFWQqPjOmKjZkRz/3Zcc3KO4+RM/gh0FXDkR/aXGxoV0fuHu7H59tim4sE5Nd54oWRkyY74KlqGPjddaIgLkUl0g+ScMVf3eD1r6vdqRvM3vj3DC+vOUVS7n8sjae6TEyefxgHS2+46C7oNVodm09ntK2OV5YHqx8QNTI3/iAewitvEyot7TrWCnLy8vCwMwOEhymnpEpPcjWx9vyZl6cj2YkHbkZRRfMHcn68iG6o1OJvK/fOF93Y9KJwBMz8GnrNEypbpS0oO+XGiiLQOiPHpYdQ2KoTHyhIakjZs6k16q4Rac8arY4ZX+3j1X9OnX/hCyVsqXhGXfe2UOkLnCQmrPkJYjKhMYVukxuWN7ODkFloDaHUlxMLYb/AgLtBIybYSBL0vwvTpJ088e3f/HaoSf51dqS4pxwCxN9qIxJMgxhuEotUmEy5hTtHE/KQZZm0SnMKZXM8tOLeG5LxK3lYkdzjPpjwOkSs4uTu1Xy4KYpHlx8XfaeuYSJSCwnxsEGSJLybGDnZxZU41Rs5pgB6wgSAaLxakCQif42pi4i1MumQZZn9cbl6qWqNmdPfCwmJv8KUOrqWSCsox91GRHI09mJSnaKzB8C4NB1Xa1MoTkdr7kJmcaNIzrVQ59RlLMz6Ef4vFm77A+7eBHdvhooC0c9k8INg0eS6qo3kaHUyURlFdHezBsRYWhe5qSM1vxxbcyMsdMWYpB1mj65Xfbo4IBxmTWty6pxodZ/DNTPWXBI6Lez+qPVG3RdD4gGRzjzmpfq0MPouEM6wDc8Z/hpvwcg5WWxNV+M8TDRq+vrYkZ9YOw7XRXKAcgsvPMmiv68ds/p5Eple3GF1hp3HyLFyhdB5cPBbIUvXBk9OCMTFxoQXVze5OOrSjWpPbny1Lc66BiPGoSKFMtmExEobAKKrHHHTptWfeG8HYeQk5pZSWlnDkgMJLDuUyOiPdrDyyH+oWG7kM/B0FNywCM7tuGhN84+3RHPf0mMMemcbd/x0mKziFhp1rnsKqkrgxu+g12y45XdREP/7neefFKafhC8H8Ezak3jaiAmHp504h40fzgm5pbjZmKIqSsbUQUzsMworaosgm0Ry6lLVoHOkq1WVNhS2x24VmvQTXmd9qhm3HvRElnVw+q9mqxVG7UKHiirHnuINIzNh8KSdEF6eioIGD5uRmRAouEbEB34/lsLZ9CL+OJZCYVPv+qWQckxcr6G3wMD7xHtdx4vrdce7cOhbGPUcmNvrrRbpPRd1aSYb/7qEuhZZho3PCa/rsMf1t+94HWWyCY/b7uPF1RH8sOdcw4fZUeJ5aGRa/9YxXTd6aCOhIAkje2/yy6qJyy4hrbCCeNkVu8oUKEjCNeFvfqyZzLlCGfrMB59heO55jgAbmVNphfywN/7CvkPEH3Dqz4v/DS4zp9OKCPYQY4WXvRnJjSZzOSWVOFrVGjlW4rfNaGzk1FSJ5xM0v69qveYktGzknMspJaOogiH+LRs59hbGjAx0ZF3ENRJ9MCAllTUUVdTgZmMCRamYO4pnXHKZKbLGDJvqTNwsVFCWi2ztJmqprNyguvSyqT11KJbOEHxTQw0MiFTle7fDyGdh6GMtrOMCJZkk5JZSUa2ju5sVAJ52ZvpRGhoZkLHbkGQt4RZDSGlsCDWuudHpxLVv28jIsfYAJH3n4n+VzFOw/U34fiwc+AoS94t054tN5dNphZqnWygMeqDhfZVajEtZp/UVVg1BEyO2RqsjutIW2+ps0NbQ19sWTV4MsqTSm1vlGLnhLWXSxckSb3tziitrmkfCDUTnMXJA3IBluXD4+zYtTnNjDQ+P7srh+DyKKxr9MPnx4oY1tqC0sobYSlusq7PqIxFGhedIkVxJK6pAq5MJL7XHRFcu8tZlGcsVN/Kb6QfYn15CeEImWp3MqvuHMMjPgVVHr40JYLtRa4TR2ftWEf5sb2Sllv2xOXy7K477R/nzyrQeRKQU8vjyE/opRJln4PRq0SCqbsJt5SI8USlHhGe8Nc7+C4uvQ5YkuuniGFYjmrrVRXRSGk1IknLL6GYrQ0Uhpk4+GGtUwsix9dYPm+fF16c6AiJdrTRbTFiuFDvegS/6iRTCfx4H3xHQ/24OxOWyL1NNhdeIZilrZVU1RO3+k6O6AM4WNLrF3XtD+omGOqS6SA5cMwprZVU1LNwSzdAuDlRrZf4wpLc7fpdQqZv2iYigAJjZitTB8BXg3KNe1bExf6Y5cErniyb8N1YduUAPZmoYRK6Hoz8KI/e6t/UnMMBvJ/PYoBrFTdJ27hzozIebosgvrb1msxsVfCJEOLaW+mGhLYL0E9i6+SNJcDg+n/TCchJkVyxKEmDra2Bqw3ImEp9TCioVkYPexaKmkO/d1nDHUF8Wbom+MMn9PR+L+qSrgLKqGrKLKxuJ0piTXVxJeZUWWZbJKWmI5Dhbi9esxkZO1mmROg1QkKC/8TojJ+VI8ygP8PfxVMyN1Qzr6tjq8U3t5UZ4SmGnUgPdF5vDsPe2sz+u5YbMl4P02gm3j2kZaKvQ2HnjaGlMelEFNZZuuEl5eBiLFCxjWw9KKmsoN3USK19LdTlNsXKFsS+JGpymWDpDeR6RqeK89aiL5NiZkZpfrudhTysUog5ErQeXYNT2PnpjbX07AlmG0izRs7Cxp19jLIzKa2CsuWRSwwBJzLM2vQg/TRZZLHs+urjtnVwhDKdJ74n5W2O6jAETazi95lKPugGdTqRDNjq/GUUVJOucUMk1UJxOX287fEmj0sq7IfsASMYFTykbD2vjZhlUhqZzGTmOAaIJ45aXhXLUrg+gvKDFRbs6C9nnxMapZI0mqeeyS0mXHVDrqkThLUDuObKMPEgvqOBEcj5x2tqHW368+Be/C291LmPiP0az90MsjNX08bZjfA8XwlMKKK/6D3adnviWuDjXtb/fR0FZFU+sPMEAX3uevS6IBUN9+WxeHw7G5zbopoOYGJraNDSrrCXXsT9HrcZSfnBxy6lyZXnw98PgN5K8+TvZr+3BkKRFoNPiZiO8qo1D6Am5ZYTWyuVKtt64Wps2SldLEfvQ1ojJv30jI8fKHZBFv5wrRY+ZwhO3+SUheT39c1CpiKst1ItynAjJh+pTYHQ6mWeXH6RX9Ul2aPvopxu49Ray0nX9cxrnStt4dto86dSCclYdTSa9sO0IL8APe+IpKKvi3RtDmNDdhWWHEg0XBh/xFNy5US8qAkDQVEAS50ZtpPeRLMtsPpNJjPsMxqpPsvCvXYSnFLRvf9UVonh4xc0iguQ3UlwPjSiv0rL6eCq5wXcjleXxpHMYOllmVV0vlZyYhlxoxH1xqLorMhLoajB18CHI1ZqjCXmkFlSQpfFAlREOp/5EGvsSdvaOwsgBvjqp41uj+fgm/M5zXVNwszHlyZUnWhaBaUpunGga172FGpVOSN2AWzcAe9W+JueXUVheTbVWro/kmBqpsTU3EvUddaQcFWmCSOjykyiraqR+V5IlpFZrKprV5eh0Mn+GpTI1xA0LkyYTlUaM7+6CsUbVqaI5yw4lklpQzoIfD/P3iSuT5lv3vPNU1abw2njiZmNGemEFZaauuEl5uKtE3a+5g5ic5Ui1kddroC7nWGI+D/8WRmXNBcxVLF0ASExMxMHCGCerhqyI0iotBY087Kn55XjbGolGxIGT8LQz109Xs/US13VpdqMeOY0iOXXLKOlqkBYmaiWnfwEPHYQH90Pv20S2xoU6VqvKRFQoaBr4DG32sVZlTIbbWLIPr+TeJUcu7PpojbIc4chpZOSkFVSQItfOqwuS6O5mTaAqjQwj/WsgqsoBY0mLqiTtP2bkAMz8Fm77C3yGiXzFT0Pg4DfNFvOt9bDFNy6AzY+vn6R+sT2GUjNRe1PvNciNpcjcm7TCcjafzqTQtPbk5MULyT3gG7/P2W8yDIfMffTxtkOtkhjkZ0+1Viasg6XuOiXm9qLfR9Q6kW7SDn4/mkJ+WRWfzO2NWiU83sMDHHl0bACfb48hIqVQGBbhv4tJW6NJY1hSPtO+2MvHOQMxK02hpqW89T0fi1S26z8jtUzFwppZWBfHwunVmBqpcbIyIbVA3DCyLJOUW0qAae25s/FqMHJsvISnqSRTdGbWVTdPV4Mrm7LmNQDmLoXHTsA92+qPr87I2aur7VEQvxuAXTHZlEbtxFSqZp+qr366gVuoqNOJ3iiKUq1cGz6z8e50Rk5WcQU3f3eQYe9t59k/whnz0U4+3Rrd6qQ6p6SSRbviuHWQDz4OFtw22Idz2aX1SlUGoamBA0Lp7KGDHK3x5/bFh/TqwaIzS0jMLcNu0K1Iag0LLA7y/Z52pnnFbBYFm7evgbu3wJylDRGkWjacSqe4ooYJI4dBt8lYhy1iSk8Xlh5MRFteKJTXGuVCx2QVU4w51Q61tVo2XgzwteNwQh5pBeUUW/iIe8K5B/SZj7+jBQm5pcTnlLI+Ih3rkQ+A/2hM/32IL69350x6ER9viTr/d4lcKxR1Aia077tfYeqcZz61KcxetWmwSbll9TWGjpYNogAuVqb6NTmpx8C5J1UWrvyxbR+jPtxJaWWtoVOSKdIcTaybpawdOJdLakE5s/rp57k3xcrUiFGBTqwLv7hnU2lljUFz4Esqa9h2NovHxgUwKdiNp1ceY1/s5Y/opNdKHDvWpanbeOJmY0p6YTmFxs64Sbk46ESdro2zmHil6ezEstdAJCe/tIp14enkllzAJLnWyMlOF5PSOqEmD1v9rAhZlkktKKevfAYqC6HbFDxszZpEcmodZwXJDeNJY2ca1EZ7OtdYc0VIPS7qOkFI/Lv0hEH316qmrruwbR38Wqw3vrlCMcCba8/wUnQXnKpSSY06wg/tHYPaov78NhgwaQXlpMq1EeiCJIzVEsHqJKJk/WvgZImt+E9+ArbmRliZakjK7Rgxm85n5Kg10HUc3PANPH5CqAhtfB6i9SVSHSyMsTLR6Kv81EZytp7JZPOZTG6eUGvRFqaIPMeSDCqt/UgvrGDLmUwGdPcTGu5550TqgJ0vDs4e7KoKwqcqhiEewivb3c0aK1MNh+L/m03xCL5JeGDXP9P6QFCYAhmnIO8cf4alMKabc/1Dso7HxnbFxcqU3w4nisG9OE2EamvJLKrgth8O4Wxlwp233EaizpmUbYv095OfAIe/gyGPgLUbqfnlHJWDqPQZLSJ/Op0omKx98OaUVFFapcVXnSc8q9buuNqYkllYoa/0knlG/N+ha8O+rGplposvLFWvQ7DzEeIBiK7UmUWVqCQ4kGMijjl+FwDx2aWMVx9Htvak1CZQfwByDRG/QdRG8d3rBBagNpKTetEiE4amvErLvUuOEpNVzAezerHn2THcOsiHL7bH8sX2mBbX+WpHLACPjBXncGgXB/wcLVh2sIPzvzXGlNp05YmVJ9gTk8PeRn2aNp/OwMxIzeDgrkhB07jZeDcbItLq+020yak/hLHRZQx4DRSpcY3Q6mS+3xPPID97ujhZilS5vDge9YwhJb+cY8cOiwUbpavFZpVgolGh8R0i3rDxZICvPSn55YQl5lNi110IVFz3Dqg1+DpYEJ9TykebonCwMObWwX5ww3cgSYQc+j+eGufPd7vP6X3nFjm7FvxHC2ntq4DkvDKMNar6ehs3G1M0Konk/LJ69U1nq4b0C2drE71Ijpx6jFNSV04WW+NUk0leaRXLD9eKEFQUiFRY7yHNGsX+fjQZb3tzBvrp13W1xLRebkSkFtarR14Ib607y7zvDl7weq2x9UwmlTU6burrwacDCjhjcjd7l3/QkDZ5mUgrKMfR0gSjknThyLFwrjVyKshVOeIu5WJRlQ0qDQ4uQnAlo0IDxpad4zl/idSlTl6Q8p6lMwBFOWn19TgAXrWp33UOw7zSKiprdPQo3icMI/c+eNqZkVNS2eDYqTNoCpPEuKoxA/MmtWX/lUhOSTbs/lAIxjSlqkxEtt376r/v1gs8+l9YX7LqcmHk9LlNNNxswtGEPJYcSKD/uFnIJtY87x3JF9tjLj3VtYVIXWpBObKJNbKprciMKUrDRi7keI1v/TKyLHMgT2RikZ/QorCLITGIkVNTU8Px48cpLDRw12drd7j+C6EYsuYhKM6s/0iSJHwdLYive8CX50NFAZXW3rz6z2mGdXXguoEh4kFXlFpfBKp16sa57BLO5ZQysYeriPzkxwsjx3MAXvbmbK0IQo3MKJNoQMh2DvS155AhPcKG5tRfkB3dMduWJJj6iZgU/3mPmAw3ZveH8ElP+HYYfN6HnlnruLFvc0+kRq3ixr4e/HsynZoTK4TXx2tw/ecLN0ejUUksuWsgE4PdOe4wFbe0LVSVNIqg7XhHeECHiQLK1IJyjNUqjEY9LRpOxW4VucS1aQtJeeL6cNWmiZtRbYSrTV0kp87rlCR6z1i560dyzB2E/O+VFh9owrls8Z2GBzhxKrUI2XdkfSQnNb+McZqTSIET8bS30E8lMDYXE96q4ha8a54iklV6+XqhRGYUcesPB5m76AB3/3yEhVui2Xgqg42n0nl0eRiRGcV8P78/c/p74WVvzsvTenDHUF9+2BPfTMQiOa+MZQeTuHekf720r0ol8d6NIfzfdd1a2r1BeW9DpOidYmnCljMNz6nNZzIZGeiIqZEa+tyGbVki0zWHWXowoe0NVhQJx07wTa0u8meYEFh4emLt9/MeAu596XLuV3q6WxMWdki87xhQv05cdgn+TpaoAsaDsRXYejPAV0yoz+WUIjn3gOcShGEF+DlZkJJfzrqIdJ4YH4iZsVrUzd3wLcTv4kHdCgb7OfDYiuPNlJjqKc6AlMPQfVrb37kDqarRseJwEkv2J/D3idS2G0ojIjledmaoaiPRGrUKDzszYrNKyKn1ktddZyAU1uquSbk8Hyknml+SHTFz9meUSznX93Lj+z3nqCysdRJZuoDvcEg+XN8vp6iimo2nM5jVz7Pem94W47q7YKxW6V1v7aG4opq/T6TWn3dDsDY8jV6eNvg4WKA+uQwjtcRz2kVELLoTua1GtQYmOa9M1GUWJIv5g0qFm60Z6QUVpMkOOEmFSAVJYOmKtZkxpkaqju+VE/EHxGzpmG03oS7V7ILaYNQaOZry7HqhDRDXt7FGVe8oSyuowF9KwyfxL5GBoVLV18Cm1d37Fo7CsMlPELWiriHNos/Y+YnUwFbahVwzZITD7o/hy37w9VDY91nDNZYRIbIqPPSNnKziCmr63iGclrkttElpiZPLRQr/kOb1oJU1Wp77M5xgdxvuHR2E1G0yw6r2Ymms4Y21Z9r/XWRZXMeNy0cKU0R03ryhdlA04jVFsvUWhm76CQD2lTbMB3NKqsiqUFNh6lQvFuZtb65femJALsrI2b59Ow4ODjg6OrJr1y6GDh3KnDlz8Pf3Z9euXQY+QhXM/AaQYe2Teh/5Olo0RHJqf6x/k0XawBszgpFUavGgK0wRzfLM7NF49kMng4lGxchAR3HDZZ4RBVueA/G2NydediVDtiOwLKx+XwP97DmeXKCXitIpqCwRfVP+uFNc7AZk65lM7l96lLt+PsJH+/LQzVwkfqcv+jZoscduhe1vw8D74a5NhDtM4QPjRYyjZS/hrH6e1FSWIp/5W6ipqcQleDa9iFXHknlsXAC25iINJGjSAxjJ1YSv/06sXJAsbrThT9R7hFPyxU2l8hsh0rH2f45no0hOQo64cWzKk+qjNC7W4hrRmdqBkYXwOMVtF8Z04weySiUGv04mI12XqjaztzuF5dXkOA8Rg0p+ImSexlXOhoCJeNg1SSUAIT4ALRs5cNk8bDVaHU+vOklCThmOViZU1Gj5eV88D/x6jAd+DWN3dA6fzO1NH287vfUeHtMVjUrii22xZBZV8No/p3nm95M8tuI4lqYa7hnhr7f8IH+H+tTWjmJvTA5LDybyf9d1Y3qoO9sihWhJSn4ZEamFwpkC4D8GgqbxnuZbjh3c1fazJGq9yG1vxcgprazho01RTA52bfD6SxL0vgUpcR/3DbBHmxVFtZmTXgQoLruULk4Woo7o6UgwscTVxhQvezFZcbc11StO9nMQv52/owVz+jdyXHQdD+NeRbXvE74PCsNEo+KBX462/J0i14kIYrcp5/8xO4gvt8fw/F8RvLH2DI+vOHHeNK+kvDJ8HPSvm7FBzvxzMo347FKM1BI2Zg31Vy7WJvXpapu3iL5JfYeMJ7hHCKqCRB4Y3YXMokp2hdXKqVo6iyidtrL++fLW2jPIMtx0nlS1OixNNPT0sOZ4ckG7lq9jzYk0yqu1zBvodf6F20FhWTW7orO5vpe7iFRFbUQa9hgnQ19heOE64v98xSD7aQ/nckrp4mghJPRrm9u62ZhSXq3lbFntdZ0aBlauSJJUOxZ0YK+c6nIxb1n31GWJkjtYXEQkR2NCpZE1zlIBIwOc6t9WqSS9dLT03Dy+Mvoc2cIJxv4PaJ7ShiSJsWT/l2Iif93bzffXdRwgC0GVa5mu44SU9+yfwcEftr0JnwQLFbW0MKHK6RJcv3hFtZaxH+1i3n43tCZ2sP7/RK1wU0qyRap/8hFxTR34SrQtaCGKs2jXORJyy3jvphA0ahUEz0KdF8e7gyrZciZTv9yjLRL2wp93w+93NKi9FqaI9hOqBjNCGDlmYn5RkATpJ6k0siai1Lp+bKibv+hsfeubJXs7mHeYiMpFGTkvvvgi27Zt448//mDWrFm89dZbxMTEsHbtWv73v/8Z+hjFRHPIIxC3Te9B4edgTkKd9ZcsUjO+OK5ldn8vkb4BDWof0ZshYAJutmLgGhHgKJqA2vtDZoTo6u3Zv3ZgkzhjEopx8v76fQ3yd6CqRsfJCxxQOpSaKvjxOiHLev1nMM5wg0l4SgEP/RZGYm4Z1VodX+6I5Y1Id+THjou80QNfCXGIP+4WBdGT3kXrOYj7C+/grM1IjFbf26Inwt/JkoddTmNUUwqhN9e//876s3jZmXP7kAbFr6BuQYRZjMDv7DfoyotEmpqRGfSdX79MSn658CZJklDnS9hDiDqBjKIKqrU6IjOKcLIywSj/XP2g52ptSrVWJq+8WoTOE/dDbky991oPq87XEDQuuwRHS5N69aUwVa1M9LmdTMv8mkKNI/iNqpcA1cu9d+stXpsaOfVpBpfHyPl5fwKn04r4bF5vvrqlL8vuGcyJVyZy4IWxHHlpPCdfnciUELdm69lbGHP/KH+WH05izEc7WXMilZjMYvJKq3hhchCWbRRrdwTZxZU8sfIEg/zsuXOYHxN6uJBTUsWJ5HwWbo7GxsyI8T1EzjsqFdywCNnOn4W699h0uI0+PhF/iLSFxkIYwKojyTy+4jh3/nyE/LIqnp8cpL9ewASQdUy1iCTYOINzNEyYZVkmNquk4dloYln/WZ1Xv2mKaTdXK8yM1LwwpbsYJBsz/EkYeB+W219kp/ET/Jk7k/1f3kNN46JWnVYU0noPEV7eK0BESiFf7YzjkTFdiX17MsEe1mw81bbXPimvrL4gto57R/hTUa3lx33xOFqa6EVbXKxNyS6uRKuTyYs+QLlkxrwpE8R9VZpNkL2GcUHO7DpWZ+S4EFUmxqKD4WdYczyVVUdTeOX6Hs3OQVuEetq2X8gCcQ0sO5jImG7O9XL7l8qm0xlUa2Wm9nITY3RVMfS8gV4zn+JPy3n4nvkabcz2ZutlF1cy/8fDF3T8bSHLMnFZJXRxtoSc6PpaNDcb8XseyK6NvKWfqK9HdLEyFVGPDojk1Gh16M6uFXV1BUmiHUMHY6xRYW9hfMGNYnOxpYdVBXZNms961jnKSnMI2PcMXaQ0VHOW1DtC3G3rUtqaiA+U5UC/O0SabVNsPIXwxpk1F3SMVyUmltDzBpj7KzwTDa7B8O8TkHRQ1OA0qvE8nVZESWUNsfk6Hix/AG3cDla8dRsrDtemW2eeFkI0H3WFv+6BxePh1xuFQT/kkWa7ziqq4Ntdcdw+2Iee7rURui5jwcqN0WWbMdGoWN9e4ZKI38HERmS8bHtDvNdCjxzRiNdMKLfWGjnlDsGAVH+NxGWXIElg7NxVCOMgIjnpRRWGEURowkUZOVVVVfTu3ZvRo0djbW3NxIkTARgyZAglJRep8X0+nIKEZ7ORV93X0YK80ioKC/Jg94dEOV1HWrV5fU4+ICzN5EOiYV/gdfUqOfXe1bpJhMYUXIJxtjLBRKOi0GWwMH5qGy0Gu1tjYazuXHU5x5eKyMqCf8UDpR0pDu0ht6SSB5YeI9DFkjUPD2Pp3YN49foe/Lw/ga8P5sKEN+CxMOENtvWGmxaDSs3e2BzSS2qomv6tSCnbu7DF7c9T7+CQLogkSeREH0/KZ09MDs9c1w0TjVpvWdXEN7DUlZD21wsQtgT63C4U2WpJyC3Ft87j2mMGWHvSP3UZOln0wtkTk8MIfxsoSAT7WiOntqeO6JXjJaJRSMLT3hRr906XrhaXJbzxLtamOFmZEJajEmkBO96mT/Vxtge8CMbmeNiaUVqlpbC8kcy6W6h4tfXV36i5g8hLb2+IvAWKK6rr+9IUllXzxIrjjPt4Jz/viyclv4w1YSl8/O9R3tsQycIt0dwyyJv+jVJmVCoJNxsznKxMRFpUK9w13I8QTxtm9fNk5zOj+fuR4ez6vzHM7m8Yz3R70elknlx5Ap0s8/nNfVCrJAb42mFrbsRHm6L563gqz1zXTc/jj4klJrevxFkqJOfAry1vuKJIDCbBN+q9XVmj5c21ZziRXEBVjY5Xr+/ZLNqAnS84BKA5t41Q0yyOlDjUR7vzSqsoLK8Wk8Am1Bk57k0m2A6WJoS/NpEJdYZaYyRJyJWOeQmTntNIC7iFsYV/sf7b54jJLOb3o8nkbP5AeC1HP9/mb9lRVNXoeOb3k3R1suTRcV2RJIlJPV3ZGZ3VqlpmXRSuqZHjbmvGjX08KSyv1ktVA2Hk6GThybQsiqHIKkCk99rVOm0Kk3lkbFfkukagFk58uK8AgF82H+LJVSeYGuLGLQObOB/OQ6iXDcl55eS20XC5Wqtjf2wOO6Oy2BmdTWRG8QXvpy2GdHHgg5t6iWvn9BrReNa5h0gpv+lNDmq7U/373cIDXUvdvbM7Opv3NkQa5DgyiioordLS1d5YPO9r0zTrFDdjKmrHjZoK8VwHnOoicFauBnVmybLMrG8PcG7LIuGscAyEY5fQJ+sCcLI0abknXStUVGtJqrIiwKK5J93bWs2I9J/hs9645+znfZNHUXn0rv+8TuhHT+DGIUD0XRv3aus77TET4na0qp57TWJuD9M+FQ7VM2ua1eOEpxSgUUlsfWoUfoNnsMH9UebJG+i18UbkpTfCtyNEtsb1n8ETp0TdZNJB8OjXoqLaJ1ujUaskHh/XkK4sWoPcjNGZv5gQYNU+I6emEs78Df0WwLiXYd+nsPfTWiOnYcyVZZnU/EaRnMIUSA1Dqp1z1EX7YrNK8LIzR+PZT9QlVYlnrSzTPPPEAFyU21PXKJoyY8YMvc+02g5K56orCM+NrS8Yr0tDqdjxEdaVxTxWNp25A7z0PWE2nuLhJamhyzjczcxYdf8Q+vnUpsHU9UVx7wMaY1TAwjm96W3pB7+8B4l7occMNGoVg/0d2B4pFGSuONXloham5w1CgcuAvLT6FGXVWlbe2k/UEgB3DvMjraCcT7ZEM6O3O552njDjK731Vh5Jwt/Jgj7+bjDkYdjxNox6Xr8ZWE4MTnnH+FT9CBXbYvh4Tig/7InHy96MqS147vuE9mbVxhuYG/MrIIkoUi1anUxSbhnzBtRuX20EA+7Ceef7WDCT48kFRGYU82RfNUTV1F9DrrWevcyiCoJtvUDWifPftBM0iMGwiczrlSYuu4QBtSlKvTxsOJVaCH6j4MCX/KkdjtZfOB3qPLUp+eX1KYB49INBDzaPWkmS+A3SwrhQ/gpL4dOtMSTllSFJ0MfLlrSCCkoqaxjsb8/ra8/w2r9nmK/exBtGS8jBjrFmQXQf0MgI1un0wt5tYW6sYfVDwy74OA3NVzti2Rubw093Dqjveq9Rqxgb5MxfYan0dLdueTJp602BY38CMg8QmVFEkGuT3hXxu0R9VOAkvbf3ROdQXFnDqgeG1Hclb5GACRDxOzYV+aRqxrJodxzv3tiLuNpari5OzdP3JvV0JTqzmKBGhcd1GDWN4DRGpYZR/weALxD9mynTo79j8RcpROj8mGm0iJxe9+LoN7L1bXQge2Kyicos5s8Hh2KSGwVO3ZgU7MpHm6PZFZ3NpGDXZuukF5ZTrZWFkaOtEalGyNBrLg+OCuX3Y8l6ympA/fnfdDqD/mRg5FyrmlQXIc1PpE9gNxKdqikosCbiXAHbkmrQmWl4YpAVVtVevDSte7tqcRoT6mkLQHhKIWOCnBs+KEgGYwu+O5rP1zvj9GSA3W1M9Ze9RLzszYXzsLoCojbA4AfqHW4D/J140udVQlPuQLvjHdTXfwLAN7vi2Bubw80DvdlyOIKSL5/F8rpXIGD8RR9HXJa4vrubZIlnuoMYp12sTZEkKJLNqVKbY6wt04vknE4trI3YZ4jaAwM4C8NTCslMjsPf5CgVwz7CVK6ELa+IfVg1v+YMibO1yQVFcg6cy6VQZ0OIuklNdfwenjn3MFYVyTDwHv6XeR3J1c2fHZ52Zvq1n2P/J6K8TZoi69FjhmiLELUeuk8Xzlrvwa0vfxmpqNbWz3sMjntv0ajz4NfN6nHCUwrp5mqFo6UJL0zpDvLrxG1wIOrAJtxKK7Eb84Kou6mL/gx5GIJniWdwk2s2KqOYlUeSeX5yULPoHH1ug70LucsunBvP+pCYW9rcWdaY2G1CLCVktnCmluXB1loDttEYVVRRQ2mVVsy/zbxFdlRpFua+fZEOUm8Ix2SWEOBsKeYishbST+Bj3xsQEfT6TAMDcVGRnKCgIIqKREOthQsbJioJCQlYWhr2AOux8xV53bkNShV+Dha4k4NjxA8c97iFc9X2PDS6SV6itYgW4D2kPjd9oJ99vbRxfSTHs3/9KlN7ueHhHyRCbtGb69+fFurGieSCztGA7cgPQop0zEsG3/Rzk4NYdFu/+qhXHY+PD8TcWM33u881Wye7uJLNpzO5ZaC3GKgH3COaFu7/XH/BsF/AxIbuY2/jz7AU1keks+FUOncO9Ws4J42QJAmzcc+QKjtQ6D9VL30nraCcKq2uvmEfAD1vQKWtZJwqjJVHRJh3kE1tgaODqNdwtjJBkmiQkQYRxm0JK7eGwa+9bHkVThi2PqqOGq2OhNzS+gdBsIcNESmFyME3Ue4+hDeq59cXg3rVN0ZtdL1qjGHyey2nDnn0Ff09zvddt74Gn/eFrwajXf8c76+LwM3GlA9u6sVbM4OxtzCht5ctG58YwQ8LBrDtqVF8Mbcnr9ptBr9ROA6/g4FmqVj9NBr+fRy+GQ7vuMGxn1veX34CfDtc9EZqz/FdBvbEZLNwazQPje7CmG76E8apIW6oJHhjRs8Wr2kA29ApDFaf5c8DLYiFxGwWabS16ZV1rA1Pw9/JgiDX8yiUdR0PpdlIuhq69ujLX2GpVFRr63Oh/R2bP6PtLIx59fqezSKpF0rgvPfJDLmf+eb7+dT4a9I0Hkw9NZo9MdkG7zPWntSGwwl5OFoa07fyCHwzBBZPoKs2ni5OFmw63XJ6UlJj+ehNL4iIecxW+HkqvicX8ti4ACY3cci41Kpa/XsyDT8pExuPWjEIaw8xbhUkAjDCVUeGzoaHl4XRzdUGycqVQPNS3p/VC2tT/R5L7cHXwQJrUw0nG6d8yTIsmUb1V0P5feM2xnRzZs3Dw9j61Eg+mNWLL27p2+p1eUmc2yFS1Zr0crp/2lC+rpmBdGwJZEexOzqbhVuieXB0F96a0ZMvLH7EMuekaIiou/hrJC67BLVKwq26tmVEbbqasUZVG3mTqDSvNTCsxPlzqVXFk61chXR6qWFkr1ceTeZm0/1UoWG9brBIzZZUInWzg3GyNCG7jcheU3ZEZlFm5IB5Va2wUkWRSKlaMg3Z1JbrK9+mcMw7nMg3ahbpBQh0tuJ4ckFDWrSpdUP7hdaw9RIRrgNfw9dDRNp95ul2H7OhSSso5+11Z5j82R4mfbq7w/ZTo9XBmBdh4H3NahRPphTQq9ZpAYAk4T/5Mb6weZaXrN6Akf/XvIWBlUuLY/mnW6NxszFj/hDf5gfh0AW8hxKas7Z9vbYifheCRXUiEte9DXN+EYIDng0O9jrxifpITi1GHn1xsTIlpbax7Km0Qnp62Ih6JLUJpBzFzdYUtUrqkLn1RRk5K1aswNq6uSfR1dWVdesuUN+7vWiMhdHRKJ3GzsKYx03XUqUy4bnM8UwJcWt+E9ZNYgMntrxdSxdh2YbMaf5ZyCwRpqsWJ298dxdMNCr+OXmFpSYLU0SvmN636CknGQo/RwsG+TePaliaaFgw1JcVR5Lre0XU8WdYCipJalBVM7UWYgRhvzSo4tVUCXGEXrOZO7QbXZ0teXT5cSxMNMwZ0Hqq0aQ+ASwwXsiH5vrCE3VFc3pGjr0/uPZihvFh9sXm0t3NGtvyZFHkV6umZqRW4WBhItLV6m7G1owca3dRHFzWzjTF/ETY/0VDA1oDk5wvvMx13vhQLxuKKmpIMOnG4dFLKcSyPpLpaGmCsVrV/hCwRz+hrtaW0EJ1BRz6Tvwunv2RjvzAx1Vv8vpET+YM8OLWQT78sKA/397erz6S5O9kyfXsRV2SDpPehfGvieZn/e+Cs/8K4zNomjB41j8L52qVZWRZCGssv0VMPmK3ww/jRB5yVqMUl/xE+HkaLOxZ20T4ww41hNIKynls+XGGdnFoUDZrxNggZw69OJ5+Pq17MjXdJmFMDRknN+kX68uyUGIK0H9eVVRr2XImk2khbuf39vsMAyPx2/fqPZDKGh0H4nKJyyrBw9aszVTAS0alwuWmDzB67hzctRnr+9ZhZ2PN7YsP0/PVjTy+4rhBdrPsUCIzvtxHcUV1m8sdTcinv4890r5PhWe/ogi+G80DnglsPZvZYs+lOilT33O/iTrASe/Bk6ch9BaI+J0nxgUwp0lqpKjRgYSUVGykUtSOtQaq2gisPeuNHAcK0Fo4U1RRwxPjA5CsXISz6iJRqSR6edrq14rmxEB+AlXlxfxp/Drv982nt5ctXZ2tmNPfqyGLwdCc/Vc8f1166r0d5GqN3djHSJPtSFz5DA8vCxP3zoRA1Md/Zoj2CN/WTIOcKOJ3/nLR/XviskvwcTDHKD9GKAc2ipjUpaxpLWudnrWfudaKEmRa9RQTrlZSrC+Esqoawk8c5R7NBo6ZD+fXE4UiqtF9ujByOthJ42RlQlZR+4wcWZbZEZWFrbMnUkmWGKO/HwsnV8Ck94ib/hdnZR8W7zlHTFZJi9kWk0NcScwtIzzlAtV1e84UJQE2nqJcIGbzeVfpKJ5edZIVR5IJdLHkoTFd0ekMf47CUwro8comZvwQzneWD5BR0+BsKqqo5lx2KaGeNnrrSJLEzQO92Xw6s90piEm5ZWw6ncG9I/xaj0j1uQ110j7m+FawIaKNWrTyfBGdDZmtHy3qMQPdM7F6fc8ajBzThuwdY0uw96+v7UrJL6egrJpgd2sxp3frBanHMFKrcLc17RCFNYP2yTE1NcXOroMeoCDSjRpFcijJ4gZ28ItuMjGFUstWq1svoWLTxLtUjySJtCu3Xs0/6zVPeKYiheFmZWrE2CBn/r2SRk51Bay8Xcg0tpXz2kHcOcwPlSTx496GZlKyLLPicBITe7pg3zg0OvhBkSZ48Gvx98nlomFVvzsxUqv439TuaHUytwzybrNg3FijYmyfbqw7k6cn/RqfU4paJTWLONFzJiM4gQXlQkEvN06kJaob9uFqU2vkBEwUDbS8h7S889rc7bqJf0W1lqUHEtgZlYW2pQfhvs+E8lu/O1v9PpdCXJbwxtdFcurSVU4k59enDLjWDugqldSywlprePQTr22l58XvgupSYazM+JL3Hd6ijyaeoB33tT5463QihzdgYsMEyNhCRJSePSe8Qjf9ABPfFhHKX6YLBb+vBsGvNwmJ91t/hyciYNaPou/AN0Nh+c2w73P4brSI9vSaLc7jjreEMk0HqRl9t/sckiTx+bw+rUYfnRr1UGkRp25UW3kyqOaYft+fzFMivbZJ08xd0dmUVmmZ2sv9/AdoZAq+I8DYiq5dAnCzMWV7ZBZx2SUt1uN0CGoNeA/CzsWbfx4ZzpqHh/H6jOBmUa+LpZ+PHcl5ZTy6/Dg1Wh1andysJ0tFtZbwlAIm2yVD4j7hQX1wH/gMZWbiu1BRxIO/HmNnVJbexDoxrwx3KzVGO94QDrBB94tUyh4zRB56dvMakjrHia9UO2FoLBhRpzQEUJKJr48fL0wOEnWhlpdeCxLqZcPJlMKG7xC3DVnSMLHsbSrtAzFeNhNWLWgu/W9ItDViMhQ0rcV0r7tG92Cd4z345OzmRvPjfHlLXzQ5kbDpJXShtxIX+iy75T6w812+3HZxNTr1oho5scL51+g46owctV2tE662B9qIACdszY14c1+pqBs7+I2oc2iL47/Cppfgr/vhh/HwYVdY94wYm4Edh8JYxJtoLB0oGv0GYUkFxGYVi2spP14I3XQgTlYiktMeY/GPYykk55XTxc9fPNfDloiakTvXweAH8bQXzrQvd8QyIsCRsS2kOQ7r6oidudGFO38H3ge3r4Y71gnxossks92UY4n5HDiXyzs3hPDZvD7M6e9VLx1vSN7fGImLjQlOlsZ8tCmaoe9tY8GPh0kvLOdUrYGoF8mp5aZ+nqgkicWN5lxNaXyuf9wXj6WJpu0a1Z43gIkNC0x3EZFayLnsFmrpZRn+eVQ4iHvfrPdRaWUNIz/cwaJdDUGH1IJyVFJt6q6pDZjagmsvUKnqRZBOp4nvWS9V7tG/fr7hY2/RIb1yOl8z0LZoauQc/AZZUvNN2ViCPazp623bfB0rV3joQEMB6IXgFCiKw8JX1r91fag7kRnFxGQWX/j2LpWKQlj7hJBmnPNLvb795cTewph5A71YeiCRolov6v64XBJyy5rXH5jbC0GEI4trG2N9JAZBVyGbOLqbM0vvHsgT4wI5H5ODXckvq+bQuYaISnxOKV52Zs1rBnrMxJhqxqmOC0nMvLhmqT+u1rW9ckythSS1qhWPR52RU5jCqdRCrv9iLy//fZo7fjrCsPe2cyCuUe+k4gwxAA56QE+5ypDEZotmjnURSwdLE7zszTiZXEhqQRlOViZ63hvPCzFyrD1EZLMtIydynYiIuQSTmFvKohRvjvVfCEn74fRfzZevqYI9H4mBc/iTzT+vQ5Jg6COiR8vDh2HuMtEFOvO0kJB3DRET5+Cb4JHDMPFN8XtveVkYTvftFBGiG76FKR/Bke+FsWNgZFlmy5lMruvpioPleQyZtpAkjLpdx3SL03y1I5alBxLE+zGbhQPDZ7je4uvC0+nqbEmgSzuvqxFPw/hXkVQqxgQ5sz0yi9jskhbrcToaY42K3l623D7Yh5l9PAyyzSBXa764pQ+7o7O5bfEhhry7jSHvbSMnfDN8HATveKL6OJD7WM2YzCXCydFjBmhMYMaXaKqLWd11HQm5pdzx02FWHW1QFUzKK2OsVSpUlQihkzr8Rghvf5PG1HW4WJvgI9VGZRr322pi5FjYu3P/qC5iImXlqtf/7WII9bQlr7Sq/j4vO7uFCFU3zJz9sHtwM0z/QkjArr7/PFu6BJL2Q3meaBrdAiqVxKw7nuCM1TBerfkCm9xwWHkr2HqjmvI+H84OZeg9H+OnymTintmUrHrwgkVQ4rLrjJzoZhkOdQprpva1E7/aSI69hTEvTunOuvB0djrMEzUTax6qz95oRtZZ+Pth5NOr0WZHi+ySnjeIVNvF46lZeQejts/EWKPG+M5/GNO3JzZmRqw8kizqJm284MSyC/peF4qTlQlVNTqKKtruT5RaUM4b/55heqg73brWpvnvfA+6jKt3eDlbiSa4AP+b2qPFKLKRWsWUEDfWhqeh1clU1ejOG2EFxL3YZaxwIHSdIIzL9goRlGTrpzaWZInxoI2eTOVVWn45kMBzf4Tz1KoT/LDnHFqdzFc7YvF3tGhRzdNQ7I3JYV9sLs9P6s4PCwZw5KXxvD4jmNNphbz4VwQnUwoxNVK1+Hy3tzDmgdFdWLTrXDOhgD0x2Ty58gT93trKuI93svVMJquOJnPbYB8s2lIaNTaH0Ll0Tf0HexOZ34+lNF/m0CIRnZ3+eTMVta1nM0nJL+e9jZHsiBJiKqkF5bhamzbMx/xGQuB1gKgPTskv51RqEfYWxvVOBzz7C8dRcSZe9uYX1dj4fFxlRk4XEfavqRJpB0cWc8btBgqwYsEQ3wsu2mwXofNE4VXtQDQ2yBkLY7V4aJ2PrLMQvurS9p8VKbzbv98JHwWKMPLUjw0uNnAh3DvCn/JqLb8dSkKWZT7fFkM3FysGt5DixtBHhJrNrzeI5lCjntP7eESAU7vSZ3p72eJuY8r6Uw03eXxOacs9UBy6kG4WwPVGh+jvaycMY/smRo6NaX1fizax9gRTG4rijzLr2/1IEqx7bDh/PjgEFxtTnvn9JGVVtQ/WA18Jr8egjptMHInPI9TTVi+C0NvLjuPJBaTmlzeTn/WwNWu9SWNTJEkMbqm14gNNPYE6HURvhKApUBvNszTR0H/CbFEHsuU14c0sSBJpilteha8GiAauA+5pPVrWGFNrcOomGkfOWQIvpoi0hsYYmYmiy/t2wHOJMP8f/bzkgfcKSc1D3wnHgAE5m15MakE5E1tSG7tQAiZiXZnBM711vPLPaWEwx2wB/1F6udfVWh07IrOY1NO1/c8470HidwDGdnMmtaBceGwNXNR5JRkb5MJr03uSkFPGxJ4uWKur0Kx9RNRbjHyGGLsRPKH5E+vk7TD00QZHhp0v0oTX6Zqymq1lszlnejtRO1fUe0OTcssYqj4jUv4aKyAZWwhDpxWPs4u1KV3VmchmdmDWKKPB3k9EF6orxGTMstG1Y+UKJZcmXRzqZQvA1zvj+HRjBCTs5YDUm49nh6IxMhKS+xNeh4Q99X0pDE7kOvG9PPq3uoiDlRk9HvoNlZWzkL8tzRHSurU9zzRe/SifuZhYVRd0UetF77d2NhItrqgms6iSLo7mIl2viZHTw91apLIFjoNuU/UUOmf382Swvz0v/RNJxZQvxBxj32d66xdVVDPu4518881CyjChR/bbdIl/hie1j1Ix4T24ezM6rZaUmHCWaceTdsNfYOOJqZGa6aHurDmRRo2MqM05vUak4XYQdVHk7DbSmyprtDz7x0nMjNW8MaNnwzVZliPulVrUKokgNysWDPWlWxu1gNND3cksquS3w0lM+nQ3cxadJxrWlIAJogi9JZnt6grY8Lz4t+1NEbn/qCt83A3+fgQWT4SPAsTfbzrCGw7i3+6PAOGY+uVAAsPf385r/5wmIrWQuKwS3l5/lhu+3sf2yCweGN2lY+rUavf/4aZIQjxsmFwrdGJjbsTtg314fXowO6Ky+Xl/PD3dbZrL9NfyxLgAJge78tSqE/x6MJFtZzO595ej3L5YSLDf1NcDS1Mj7vnlKNVaHQuG+p7/wPrdgVSey7M+Mfx5LEXUC9WRuB82/0+M201UPgH+PpFGiIcNw7s68vjy48RmlZBWUKFfLjJ3qXAgI5yt2cWVHE3Mo6e7dcM4Vp89cpQgVyvisksNXrt59Rk5sk48qI8vhepS1MMeZpCfPdeHtiON42IIvklM/vZ9BjqhunH7EF9+2BvPqp8/peZEQ5TnWGJ+QzFrfiIsuR7+uhfSw+uXaXczUVmGXR/A14NE6k1uDIx4RqTs9FvQbHFDXxht4W5rxozeHizeG8/OqGwOxefx5ISAlkO81u4i1JkRIaI4LaUFtgNJkpgU7MamUxn1aWIJuaX69TiNMOt/C+NVYZhkRYgaphYiOemF7TByVCpw70vG2X0Yq1X88eBQerpZ0093mqUOP+NZEs5n22KgKE0YowPualtV5hKo1uo4eC63vj9OHb29bDmbVkR8blm96EAddWHi9hCWlM+GfHdKE47w2KJ1VHzSR4gM1JF6DEoykbtN5r0NkSw5kMj9I/1Fv6mJb4uUvh/GibqYfx4VEVCXYLh/tzDMO8IJYWbbsirb4IegphyOLTHo7racycTcWM2QLi0Y9BeK30gwtuQhxxP42Juz/eBRIXffRFXtRHIBxZU1jAlyamVDbTO0qwPGGvEbXUtGDsD8Ib4cfHEcb80M4Ru39ZhV5pE3+WsY/gQfGj/Es24/wHXvilShxvS/G2Z+izT+NUptA5lU/AcnkgvIK60iLruEkOpwofakaaJMFDARkg606HEe5GfPUPsipMZRHBARpKpikQpUU6Fv5Fi6iLz36vZL/jbFxdqUHm7WrDySRPj+jZhLldx66x31xk/9MWjM4OTKVrdz0ciyMHK6TTm/QqKZrTBs7P3hxu+aGSNmvWdRPf1r7ip7DNJP8uFbz/DNzvNHdM7VKgcGWZWL39pRPztgdj9Ptj89WpzTm3/TexZJksTbN4SQWVTBokhTkWa9Z2F9g3GATacyiMsuZbbZMVKdRvHi9D48PzmIdRHpzPvuIG8eN+W6yveYXPkOIQsWEhocUr/ujX09yC6uZG9sjqijrS7t0B4xzlbCQZLVisJaRIrISDh0Lo8PZvUSypt116RrCPiP1lt+9UPDeGVajzb3OcDXHjcbU15ec4qckkrOphe1nALVGvZ+omYuZguyLHM2vYj9cTlsPp3BkZXvoDu0CG3URjj6o3BiTP9CzM0S9oi6j+lfiOtq6sdiLOoxA3a9T2lGLI/8dpxX/j7NqEAntj09mvWPj+DvR4bz692DyCiswNPOjBsMFGFuSn5pFQ8tC+NkSiHPTurWbI40JcSV0d2cyCyqpFeTepzGqFQSH88JJcTDhv+tOcXdS45yIrmAT+f2ZutTo3hpag/+enAor13fg1eu71mv9tgmLj3BcyBTqzeSVVzJ7phaifesSFg+T4gKTGzezDWvtIrd0dnM7OPBFzf3wdHShBu+2sfh+NwWhSmgQen1SEJ+Q6oaCEExcwdIOUqoly1anVyf0mYoLm/nvEulsYz0sZ+h2xR69QxhZc8217o0LBxh2OOi0D/5IEx6n+cm9WdYySZGnH4VbYKKcxXmRJj25ZnfT6LVyfx1Z096b5krBhUbb9j5Hrq5y3hpTQTrIzLY9vSoZn0W9NBpRcg8fIUwbIY/2Wb6U3FFNXMXHWRqLzceHtO8621H8MAof/4MS+GR38Lo6W7NdT3bkMUc/pTozjv20hrFTglx5cd98RyOz6vPyW/NyLEd+RBE/Ax/3FUrJ6pv5LhYm1JcUUNJZc15G0iWOoXiFLeYO4b6YG0kwU+TIekA1mpjfjDfyYg9HjyYvxFbjYn4rh3EyeQCSqu0DA/Qn2D39rKhSqsjPKWAQX76BpannTnFFTUUllfr92tpQlFFNfO+O8gUMw8mU8ELGU+g0uXC3k9E+oLfCIhah9bEhkf2mLLhbBzPTQrigVG1EzrnIOEBjFovCrVD5+l5Sy87Nh4ijeTQt2LSor5w5aqW2Ho2k5EBToaRGDU2h5DZqI4vZWK3Gbge/xTZyBwp+Ca9xXZFZWNtqqmvv7pQzI01DPF3YFd0Nl2cL3+6WoeQfETU2XQZK3pynPyNvukrWSjPo+a0xDMeMscS8rlreDAMae6JRKWqzzM3N3di4Op7+WjnDqLxxkxVg2dxOPR7rvl6ARNhw7PC49zzBr2P7h/VBWLywa6JkePUTdRI7flY/N04zbhW5YuSzIaUap0WTq8WhkPqUZj3m5h8tsG6x0R6o7T1EBx3wNKnSUTFxEqkkp1cDqOeNazDIf2ESDnpPq19y7v0hEdbT4md0dudkymTOBh3jEcLV7BwWw1FGRVY97lJdHZvRExmMZU1unrlQD9q644c9I0nSZJQt/GVuzhZsmCoL9/uimPuY4/jGv47bHwBblkBwLqIdKa5F+OYF4fj1FcI6OkLCKXWR387zoaIdEI8bXjvppBmgiO9vWzxd7Tgr7BURt/cR6SinlzR3PA2EA2RHH0jR5Zlvt9zjvc3RtHVSfTAq59wmtmLqOWo55pdG21KyNeiUkk8OjaAk8kFPDkhkJEf7GDb2Sz8z+NUqRM+2HY2i5stBtH17AZmJe7mVLo4nw4UssvkO1boxnLU7WUWzu2tv4HJ77e84cpbkeP3cPLnJ9lR9jCf39yH6U0c4cO6OrL9mdFUVmsbvmPqMUg7Dn3v0KvhvRhOJBdw/9KjlFVp+Wxeb0YENHdSSZLEmzOCmfL5HoZ1abtZsrmxht8fGEpZVQ05xVXN+smpVRJ3DPNrYwstMOAerFbfxwN2Q1l1xJWxLuWwbJaoFZy3rLmaG7A+Ih2tLHN9LzdszY1Z/dAwnv79JFvPZjZzstbhWfu+VicT7N5oXlCXPZJ0kO6j/4exWsWJ5AK9/nmXytVl5Fh7ipzoE8tE3u2kdy/Pfse9IgbTtU/C4vFI7n0YkX6SnK43kRgfi9+GB/ig8m0mh4aSlpOLvOIWZKNUpLu3CMnbfx7hy2V/sOKMORqVxJL9CS2qMtUTtUEYONO/hL63t74cIux8/9JjJOWVMSrw4jy9F0OAixUTeriw5UwmT00IbDuNxt4PHr7A8HUL9PW2w8XahDXHU3G2NkEn06qRg5GpaJa18tbaY9A3cgJdROg9Mr3ovDfU+jx3ZkvF3B2sEl7cpAMw42vwGYrlt8P5xXQhttFn0E56H3UHRXEA9sXmYmGsblac2NPdBo1KokYnN0tXq3u4JOWWEdKGp2h/bC5VNTqemD8HFr+Oq6aEpYHf0TPiPXr+8QCa0Nlw4Ev+qhnGsZQSPpvXmxm9m3i/Jrwu/nUWhjwi5C9PrxGiBJdIemE5EamF3NGeVID20v8uOPYTs8yOYKfbSq7/TBxN9ZUrd0VnMyLAqdVUhvZwUz9PckoqcbqUOqLORPoJ2PluQ78GtTFS6M2Uqx/klz3x7IjMoriyhoF+578f1T1nUrr2edxjlvJl9T38NlGLtLtCRNqaYu8nogS7PxaNdZtGbfLOtazUOPBeWDVf/F8vXa32/3VGTlYk/P2QmGy5BENVqSiGn/l1m99BkqQGZT7/MS1HVELnQcQqES00ZE+SjAgxSfY1TC8kSZJ49fqeUPYl8leDebb0N0qirdHFb+b7kOWUGDsyvKsjW85k8uO+eNRyDc+a/cOXZjlYxnUVUs1Nz0s7eGxsAH+FpfDB9lQWTnxLdJWP3Ua+2wj2xuSwons4FJnpiYL09bZj73Nj2hz/JEnixr4efLE9lqKKaqy7Xy/ksssL6ttaGBJrUw3GGpWekVNYXs3Tq8RE9K5hfjw3uUnjbZVKpP9eArcM8uaWQaIud5C/PVvPZnLvyObnIaOwgkW74ygsr+ZMWhGRGcV42JqRUOLHMk0uY80P8+SC+fg4WOC+9yXMIjUYDXuJvzakMneAV4vKr80wsSKm52MMPfw/fh7/MANbyfSxNNE0ODllWaRYl2QZRDjI2cqEEA9bXp/Rs9m43Bgve3OOvzyh3c93c2MN3g4Gmrr3mgOxW/i/U5/zQVQmurSNqEws4LY/Ws1I+edEGkO7OOBcGy2yMTfiu9v7seFUhigPaAE32wZjKdijiTJz8E2w+n5MYjfSw92W442VIg3A1ZWuplIJj3zkWhEh8W9F9rcj8B0ODx6A2UuE3GGveTje/B29Hv8DY1NLNlm/zaddwlhi8RXdtdF84fImlfaBVPWcQ7aRO31iPuODmT24ZaA3S/YnUFLZRq7xsZ+FKkULBk5ZVQ3JeWVEpBSy6mgyd/98lCMJeXx3ez/9MOBl4IXJQTw9IbBFxZWOQKUSCnp/hKWw+bSokfJtq4lV0FQReteYNfRLqvvIzarea9CYwrJqdkQ2KKfllFTyRZS4KW3ywoV31cJJTBjs/ZAmv08v3RlidB78UDbaUF+1RfbF5jDY36GZZ83USF3fHLLpw7S7mzVqlcSJ5Pw2t707JhtPOzN8PD1gzEtI85Zxy4038pPTc1Cahbz/C36omULJ2LfZ/eyY5gZOZ8S9t/CgR28wyOa2nslEJWHY692tF3gOIODo6zhJRaw30e+dkFNSSURq4SU7MKaHurPusREdU7d4JRh4rxCpuH21UMd8Ogpu+IbHJnbngVH+9PK0Yd6Adsola4yR+85npmoft4TaMFR1VkgQu/VuefkZX4tar29HwNm1De+XF0BZbjOHCiDqQGoVvfQiOZa1EfDidMiOhkUjhVz9HeuFEtzA++HUn+2TsE8+LDqIN4kE1uM/WkSOThq4h1ff+fDUmeapfZeKuT3So0fYN+s4Y8veJb9CR8CRV/h7fzgnfnyUnoefY0n3oxx2eY875dWijurID0IMoAUP9PmwMTfiqQmB/HU8lVP2E0RkY9sbbD6dTo1OJrRop2hFYaw/5rTnnrqhryeVNTo2RKSLbchaiNt+wcfYHiRJwtmqoSFodGYxM77cy6FzuXx7Wz9eub7HJffDOh/ju7twNDGfwkZNaOt45e9T/H40hXPZpXjZm7P07oHsfW4MP776BBU+Y3hK+xPj/C3oWnQQ84ilSCOf5sYRfQjxsOGVv0/rKay2hizLvJTQmwS1DwOiPmif0mbcdpH+NvZ/rQsRXQDutmb8sKB/mwZOHZfiwLokatWFdZ6DeEGzjExjL7h3h16fm8bsjcnhcEJes/FfpZKY2sut1TQ5E40aF2sTrEw1oslyY3rNrQ0iPMVgNxUnkgoaPmuvEEUbXF1GDjSkHfW9vd0d0g2GWiOKoO/aCDd8A2oNRtbOWN63HsuuQ1CtfwqLlD0cGfQ5n8e6MO3zvcxfEsarZXMZrjrN7LOPcn9/a0qrtCw/lNTyPgqSIXarUCVrhCzLLD2QQL83tzLigx1c/+Venv0jnLTCcr64uS9Du7Yd6uwI/J0seXRcwGWdON093A83G1MWbonCuJHKWItIEtzwnUg5aHKtmGjUdHe35mStdGNZVQ3P/H6Sge9s5c6fj7Bot8gD/2hTFPkqe7RWHsK7Grm2Nve89iHY+1aY8CbberzFx9vjicwo6pDvXVpZQ1hSfrN6nDpCvYSB2zRcbGGiobubFUcTWzdyZFlmd3Q2IwOdxLkc9Sx0GYtGreK5W6dyu/wmc4w+Y9C9n3HXmOCO6wbdEcz5BW5abJBNWZkaMXeAV/MO0pdK/7uQKouINQthRZK+l2tPbZ70yMsYpb1qMDITg2Of2+q9jtamRjw1sRsfzArlvZt6tftatRx6L2ZqLW9nPyYyBXyGtJ6u4jUAHtwr0iw2vdAwgcqvreFoKYqg1gjDrKkogYWjkNkvzhTPFkkFD+wF32Hi877zRefwE7+d/0sc+lZM8GsVjZqhUosUu7NrL6npZosYnX8id1GY2jAq2If54/txKOh5xkpH2WX0GPeY7mCqcy4jEj7HTlWO5p4t2L8YCfOWCzWoi2TeQG+87c35ZGsMjH8V0k+QeWgVrzntxCj7NPRsIfWxHXjYmjHIz17ILNv7i2hgKyp9hsCp1siJzSph5lf70KhV/P3IMCYFt5FWbkDGdXdGq5PZGZ2l9/7J5AI2n8nk5WndWfPwML6f358RAWLcMTHSYDr9YyFI8fsC0R/NfzQMfgi1SuLNmcFEZxXrta9ojQNxuRxJKiJ7+BtIqcfg5HnuH1mGbW8Iw7YVhcBrFo0JRrcu51fX57i9+iV0Zi1HyrKKKnhi5XEG+9tzU10/xAvA296cYHeb5vNFSYLrP4OqEuZnf0xxQTbZefkic+qboZcsHnR1pauByLWVVB2Wz3pROHQRk6mss1BVxgjPfqwJLeT//ggnIqWQb+94GEk1Cv64C/dVU5kb/A3f7znHjD7u9UWC9RxfKgaMkIb0mmqtjnt/OcrOqGzm9vdiUogr1qYaujpZYWNumFqDqwVTIzUvTunOQ8vCCHC2OL8iipVLQ0pIE0I9bdgVLSaRa46n8WdYCk+ODyS9sJxPtkTjbGXKyqPJvDSlO+q0fkIpryxH/yEoSTDsMeZX1bAqZS8zvtzH/SP9eXB0V4M2XTyckEeNTmZ4QMtGzhB/R/44ltK8ZxDQz9uObZFZLawliM8pJSW/vMVogZe9OQsfn4+VqUYUqF5tGDB9cGYfD4NJIOtRWzuU6f0AZ3YVkV5YXi95uysqmyBXq/reRwodhI0H0h3rhGxq5Nq25c5B1JuNeFr0dEo+JIyivHPis9ZSpYY9ISSpGw/yKrWI7JRkCCeK92D9+ktrNxGRPvqjENNozbFXlAZn/xEy6m15obtNEX3LUo+B18C2v2Mn4onxgSAHwMYUJG0V6lHPobZyEYINauOG3yVoStsbOg9GahWPjQvgmd9PcnLcMPzdh7Eg9RNspFKR/tpjxkVve0IPF97fGCnqQAOvE4arTmuQqEFTnCxFr5zFe89hbqxh9UNDsTK9fHMFTztzglyt2Ho2S8/r/9HmKPwcLVqfJDt0gRFPiVTUruNFKwGNSLGtk6H/ZGs0k4Pd8HZoPtaBmIy/te4sQa5W9Bs1ArJnCgGd7te3XCcqy3DsJ5ECe/uajhHI6eyY2uA/4T5ivz/E/rjcZvOMGq2Ox2obObfWH+58vDa9J5rWnl+23jDtE1z/fpTdJvsx/tEByjNh4ltgYt3yOu3k6ovkDLwXbl7R0L+kM+HcHTyFJF6whw3/PjKM/S+MEwVnXcbAPVuhLIfnTf9AJ8O8b/eTFXUQItfDieWiduD4r8Jb1Cgvf/PpTHZGZfP1rX15f1YvxnRzpp+P/X/OwKljcrArIwIc9dWDLoJQT1sSc8vIL61i0+kM+vvY8di4AF69vide9uY88/tJ/BwtRJNZj37CwDG2ajFX39xYw5pHhrFgqC/f7Irjf2tOXdKxNWV9eDrOViYEtNLMcUqIK3ueHduiiEI/X3tS8stblczeHZ2NRiUxtBXFMC9786vTwLlaMDKD+3fTfeQsVBL1qZgV1Vq2R2YxqpsSxbkseA+G2T/Bi2nty8n3HS5S0Or6qOWdAxOb1g1rlQosWziXli5CjTPpoJAPb8qAe0Svr3NtpDcd/VFM9s/n/PMeLJr0Ra1ve7nOiCSJBsLTFjY4roxMDZ7RMbO3O36OFjz3Zzh3p0zBQqqgcvBjYsJ1CRPgMUHOVGtl9sfmCAXFstyGnmSt9eW5SJytTYjLKmH18VRuHeR9WQ2cOqaECDXUup6Ce2Ky2ROTw5MTAttOzxr+lGj6PLd54fv/XdcNO3NjXlwd0WKz0/2xOUz5fC9ZxRW8c2OIUDOb+JaQ7N71QfN9FaaIptJrn4TgWWKe9h9liL8Dfo4WLD+sn2UkyzIvrT7FkYR8Pp3bp74W50Lp6W7Tpgw5veYgPRbGVmkImWo3uH8XDLrvko3Oq8/IsXZvPRzfydCoVfqKVg5dYNSzWEcs4Z+pWl4rfxfn5dfBipthzQMiRFuUKmSIG7HkQAJ9vW07tFnV1YQkSfx850A+nHVxctR11BlJe2Nz2B+XU68QZ2qkZuGc3jhYGPP69J5CfrdOzz1wYr1nqSnWpka8OKU7m54YyePjAlpc5mKIzSrhz7AU7hru12pqoCRJ9ao6TamrSziaIFLWknLL0OkaBojdMTn09ba7IgOhQgP2FsaM6ebMskOJyLLMhlPpFFXUMLvfhacGKFwCaqP2DawqNYTMEkpoZXlw5m/RQPpCB2UrN9EEtqZCNIxsiu8IcOoOB78Vf9dUCYn26M3i78wzcPg7kdveOBWute8WMAGiNl7YMf6H0KhVPDauK5EZxZj4DKDyqVhMJr15yZMtf0cLvO3N2RGVDV6DhEG86334cRL8PNVARy9wsjQlrbACrU7m1kEt11d0NPeO8MfdVvSSi8ks5tHlx+nvY8e0881jNMairqyFuiorUyPenBHM3tgckfrXiA0R6cz/8TBdnCxY/9gI+nrX3gu2XqKv2pEfRMPQOhIPwKJRkBYmjKqbfrjUr3xVI0kSNw/0YtPpDD3578+2xbDyaDLv3BDcaiaJwY7BxoO1Ps/xstWbImhgAK4+I+dqZ/DD4BiI+99zGK4+zTO6R/g89G94IRX+Lw6eiWmYUANn04s4HJ/XvuZO/yHUKumSa4H8HS2wMtHw+bYYqrWyngx2by9bjrw0vkH20b2P8LiG3nL+7TpZthpKvxg+3hyFk5XJRat6edia4WZjyrHEfLadzWTkhzt47d/TyLJMdGYx++NyGBl4+Wu6FJozf6gv0ZklHDiXy/JDyQz0taercxveL4UrS+g8qCiA70ZB7rnWJW3bwsoFKotEKo1baPPPJUnIoMduEeIEh74RzXaXz4Ud78IvM4SwyrhX2re/bpMh+2xDep1CM2b29mDZPYNYvGAAFtaGSXmVJInR3ZzYGZWFrNJAt0kQu5WsMh2rVJOR21Mc307qHF5TQ9wu2vN+qZgZq/lwdijhqYVc/+Ve7MyN+W5+/5b76V0A43u4MKabEx9uiqKqRvxmf59I5ZHlx5nY04Wldw9q/p2HPAwqI9hXW691cqVINXXoCg/sa+iH+B9n7gBvPO3MuPPnI2QXV/LuhrN8ujWGx8cFMHfA5TGWB/jacyQhj4ScUoNsTzFyLjcaY6EGFHAdqvt24j58Pl8eLSepREWJxpZVZytZczyVPTHZ5JZU8suBRBwtTZgcrERxDI1KJRHiaUNMVgk93a2b1bPoPYxNLOGZaAgYf1mP8WRyARtOZfDE+MBLKvjv52PHnphsXlwdgaedGb8cSOSlNaeYu+gA3vbm3Dzwynj7FPQZ0dURf0cL3ll/lsMJedw8yOtKH5JCW7j0FFLPRWmiw3cjB1W7qVNY8x3Ren1Grzmiad6210XaTd8FQn5813sitfn2Ne2vP+s6HlQaJZrTBpIkMayrY30TXUMxppsz6YUVRGUWw9SF/DF6KwNTHueg5QS0zbOvLpq61gEX3DfFwAzwteeBUV2wNTNmyZ0DsTeQaMuzk4JILSjnt0OJbIhI58mVJ5jWy43P5/Vp+ZyZ24vUp6OLYcc7sPo+UQu54J+WU0j/o9iYGfHznQMprqhh7Ec7+W73OZ6fHMQT4w2XmXI+5g/xwcHCmOf/CtfLOLlYrj7hgWsBz/5w6yoA7h9Vw2+Hk3ly1QlS88vJaFI3IUnw6JiuBn/YKghCvWzZH5fLpLaamV5Blh5MxN/J4pJTlvr52LE2PB1zYzWbnhjJqqPJfLE9lmAPa365a5DBBh+FS0PIpPvw2r9nsDbVKM6Nq4EbvoXKYvAZenHrW9U+e5p0mtfDyEwYNbs/FD1pxr8mUtO6ThBR5lbEVVrE1EbUE0WthyEPXdwxK1wUg/0dMNGo+HZnHCpJ4q/jWdw+2IfXp/e85AhHY4Z3dWTTEyPbroG4TDw3SbSaMKRMcnc3a2b29mDhlmjKq7Vc19OVj2eHtr2PIY8IYZFd7wsRj4lvX36F3qsAX0cLfljQnxf/iuDJCYFtN3rvAKxMjXjnxhDu+OkIvx1O4rbBPpe0PcXIucJYmGh4ZmIgz/8VwYgAR1bcNxh7S2OyiiqJSC0gNquEu4ZfWW/MtcwAXzu+lWBySOc0ct69MYT0gopLHiAG+QlRgRcmB+Flb85TEwIJ9bRlkL+9UovTybipnycfb45mVj+vq0uu+7+Ka8ilrW/vB0gtNxFtzIB74NgS0XC3LmrTbdLF7XP4kwYvdlc4P2bGakYEOLLmRBo+DuY8PzmI+0f6G7wNg0oldQoDp46O6APz1IRA1kWkM6SLI5/O633+fZjbC6ni8nxxLynpaa3S19uOjU8YprnvxTC6mzM39fXk/Q2RTO/tjvUlzFEkuSWJisuEra0tBQUFV2r3nQZZlonLLqGLk+UVa9b3Xz0XsiyTkFuGn2MbTUUvMx11LmKzSujiZHHtNIS8DFyp+yIlvwxHSxPFyGnENfuMkmVRH1PXA64ttDWt9++5jFyz5+IyUFBWRX5ZNb4O5gZ5Fv+Xz0VqQTlOliadJtPlv3wuDE1hWTWRGUUM8m9Z9bW9XFEjR6VSYW19aRrYCq1jYmJCZmZmu5ZVzkXHopyLzoNyLjoPyrnoPCjnovOgnIvOg3IuOg8Xci7quKJGjoKCgoKCgoKCgoKCgqHpHDE+BQUFBQUFBQUFBQUFA6EYOQoKCgoKCgoKCgoK1xSKkaOgoKCgoKCgoKCgcE2hGDkKCgoKCgoKCgoKCtcUipGjoKCgoKCgoKCgoHBNoRg5CgoKCgoKCgoKCgrXFIqRo6CgoKCgoKCgoKBwTaEYOQoKCgoKCgoKCgoK1xSKkaOgoKCgoKCgoKCgcE2hGDkKCgoKCgoKCgoKCtcUV9TIcXFxuZK7V2iEci46D8q56Dwo58KAlOWBTnvRq1/ouZBl+aL3pdA2nfK++I+e7055Lv6jKOei83FFjZzKysorufvLR0UhRK6H8FUQu+3itiHLcHIlfNQNwn837PFxmc9FSRYs7AlrnxK/jYIeV9t98erfp3h61cmGNzrBZEOnk9HpLv04rrZz0an551H4be5Fr974XESkFPLzvvhWl111JJlh720nNqvkoven0Dot3hc1lZCw7/IfDMCG52H5zaDTXZn9X0GutWdUfE4p/d/aSnhKwZU+lAvmfOdClmUiUgr5ZEs0r/97mrfXnSGrqOK868xddIDn/ghHa4Ax7b+Gkq7Wkei0cGwJfN4XVtwMf90Lv94IeecubDsVRbBqPqy+D5Bh43PCK3q1EvYLlGTCyeXw5QBID2+2SI32vzdYXTVknIKcWABSC8pZdiiJ3THZDZ/veBuWTL9CBweFZdVM+mw372+KvGLHoNCE7CiIXAvdJhtkc8cS83hr3VlKK2uaffbboSTe/XMvU8tW89bfx5WIzuXi6E/w81Qozb2g1WRZ5svtMaQWlJ9/4cT94lpqTG4cHF4E0RvgxK8XtG+FzsfOqCxySip5afWpa2pSn5JfxqRP93D9l3v5aV88u6KzWX44mUeXH2/ze+6KzuZQfB4rjybzf7+fvKZ+k8uBYuR0JPs/h38fA7+R8PBheDoajK0gbGnr68gyJB2Cra9D1EZhEP04CeK2w02L4b6dwmO27Y0OO+z80ioiM4o6ZuM6LRz7GXreAA8fAjM7+Pth0DZMVqq1OsYv3MVtPxwiu/ja8lJdbRSWV3PnT4d57o9wCkor4cBX8N0oWDQCzvzD4j3x1OhksosrySutguIM2P+FmIxoqy/78dZodTyyPIzozBL+Cks1SDRHoRE6HWx788IdNXs/BQtn6H2rQQ5jcBcHanQyRxPz699LyS/j/34/yaurj/OX/de8pF7K4MRFrI/IaN9Ga6oMcmz/WWI2ATLkJ1zQamfSi/hoczQbItLbXrAgGZbeCMvn6Z+rPR+DuQMETYPNL0NJduvbaEJWUQVfbIuhqkZxqnUWDsfn4WRlQkRqIb8eTLzSh2MQarQ6nlhxgsLyan68oz/HXp7A9qdH881tfTkUn8d3u1t/ni7eG093N2sWzgll9YlU3ttw9jIe+dWPYuR0JKf+hB4zYPZP4NQNrFwgZBacWNbyBLAkS0wgf5wIh76F5XPh8z4ipeuuTWJda3cY/bwwFGK2dMhhP/tnOM/+0Ty6YhBitkBhMgy4G2y94frPISNceOJq2XAqg4TcMiJSC5n82R6OJ+W3sUGFjiK3pJKbvzvI0cR81kWks+Wj22HTi9D/bugyFlbdjubIIsZ0cwIgOrMY9n4CNRWgq77wibAB+GBTFPtic3hwdBeyiys5cRWmPHRqojfAno9g98ftX6cgGSJWwZCHwMjUIIcRaCsx3DyJA3EianDwXC5jP9rF1jMZrPX/C9+Ks8g9b+Q+zVr+/fcPyqqaR3z0KMmGj7vBv0/8J1OeLpnKEkjYK/6f33oaYUvsiMwCIDmvrO0Ft7wMGhPIi4cjP4j38uLh5AoY+hhMXSichFtfa7ZqXmkV+2Nzmr3/3sZIPt4SzY9tpD4qXD5kWeZwfB439vVgdj9PPtoUdU04Or/ZGcexpHwWzg1lbJALRmox9R4R4MQ9w/34eHMUZ9KaO5YjM4rYE5PDPcP9uLGvJ0+ND2Tx3nhOpylp/u1FMXI6ivxEyIgQ3qXG9FsgUrWiNzVf5+DXIg3o5hXwQoqI2lz3Dty7DVyDG5Yb9AD4Dodls2D9s1B1nsHhApka4kZ4SiGJuaUG3S4AR38E557gNUj87T0I+t0B29+GwhQAftoXz0BfezY9MRJnKxOeXnWy83naKkvgu9Fwek3H7mffZ/DXfR27jxaQZZk7fjpCZlEFK+4bzLa7fLhJ3swX0i1kj3gT5izlpMuNPCP9yrujzFGrJFISY0XKSt/5YiNZl9fjlFVUwY9743lsXABPTQjE2lTDljOZl/UYOg37v4CvBosISlG6YeqkZFlsT1IJB055QfvWO/AVGFkI49hAqA59w/fyGxyJE+f3530JBNirORTyN93S1iBN/Rjpxu+pcunDS1WfsWx329diza4PqK4ohmM/wbon2zZ0yvKguu08+v8c8btBWyWujYIL875vrzNy8ttIV4vfDadXw8S3RDRw1/sQvwfWPAhmttD/LuFEHPygWK6RE1GWZR5bfpxbFx/Sm0ieyy5hzfFUPGzN+GxrDGntSZdT6FDisksoKy3i5pKlPD/OkyqtjhWHk670YV0URxPymPLZHoa/v51PtkZz/8guDO3i2Gy5/5vUDQ87MxZuiW722eI98ThbmXB9qDsA943yx9fBgpfXnNLLUtgdnc3680VCr0YMMG4pRk5HEbUBJDUETNB/3603uIZA2BL99ytLhAHQ5zaRt65Sg3sfGPIwWLnqL6s2gvl/w4Q3xaD8y3SD1uiM7+GCiUbF2nAD3zSFKRCzGQbcBZLUaIevgUoDR3/kRHIBx5MKuGOYL642pnwwqxfxuaX8ciDBsMdyqex6D9KOw+HvOnhHkhi0Ky9vAfWh+DwiUgv5eE4oPd1tcIlcCiZW/MYkXl5zij+Op3Fz0jQqTOxx3f8qQfYSIWGvgMYUJrwBpjaQfWE1MaWVNRxJyOOjTVHc+8vRCy48XXkkGZVKYsEQX4zUKsYGOXdeI8dQhkdL5MaJlDJJgu1vwcIgeN8XVi24tBTCpAOQchgmvQfaSghfef51aqogfAX0vR1MrS9+303xH42ZXIY6PYyU/DJOR0byi+4FjE//AdM+Ec4ktQbT2d/jpiqAvQsprmj03bU15K15nur4/ZCfiHT0R76sms7LuvtElHz7m833eeg7UV/5gR+se9pw3+VycvBbIQBRbuDoeMxmEZl37nFB6Wq5JZUcTy7AykTTeiQnNw7+fkSMh71vhbH/EwbVkmlQlAozvwUTS7Fs13FQXQopR+tXXxeRzt7YHCyNNXzQqE7v820x2FuY8NdDQzE3VvPWujMX882vDsrzYcsrIp24E3MoPo/x6jB8T3+Fw8nvmNbLneWHk67KOhQbMyNCvWyZGuLGq9f35KkJgS0uZ6JR89DoLmw9m6kXockorGDNiVQWDPXFWKOqX/aNGcGEJRXwW63xl5xXxgO/HuOR38LY10K08qrl5Er4edolO5QUI6ejiFonoi1mdvrvSxL0XQCxW/UfOCeWQWWx8ES1B5Uahj0Gd6yHnBj4aQoUpRnk0C1NNIzp5mx4I+fUX8KYCZ6l/76ZHQReB2fXsmR/Au42pkzsIaQYgz1smNvfi8+2xpBT0knC1pln4MDX4BIMiftEOk5HEXidGNDjd3XcPlrgt0NJeNubMzLASVyXYUtR9Z3P8zP6s/F0Bs/8fpIpfbtiOf0DiN3KLxWP4VNyHG5cJM6nU1C7jJzkvDI+2BjJiA+20/PVTcz+9gBLDiQQmVHE3EUH2R7ZPiNFq5NZfjiJab3csLMwBmBiT1dis0qIz+mAiOSlkHIUPukhngGGRpZh/f+BhRPcvQWeOitq+QbcDWf+hoPfXPy2930GDl1hwL0QOElE7c5nqJ3bKSZYIbPaXu5Cce+L1siSIUTw2j9nuE3agF1NFty7XXj163DsSlnf+5kv/8tf2xqUv9LP7sP+xDeol0yl8pebKNSZUdjnfg7bXc93mltg70JRE1lHWR5sfgns/cFvFCTuPf8xVhRBYaoBv/QlotOJ7xX2C3wzTJwbQyDLIg05YCLY+V6QkbMrOhtZhpv6eZKcX9ZcJCJxP/wwXkSIbloMKhVYu4n/z/gKHjkGgRMblnfvK+pea5+XJZU1vLn2DGO6OfHWDcHsjMpmf2wOm09n8M/JNJ4ZbIHLya94YYIf6yMyiM0qvvTfozOy7Q1x/y6bJa7LTsrh+DymWtZGNA58yfzelqQVVrAzKuvKHthFEOBixbs3hvDClO56hkpL3NDHEw9bM77cHlv/3g97zmGiUXPbYB+9ZYcHODKrnyev/XOajacyeHF1BJYmGvr72PPo8uNXf0RSpxMpp6vvE+UZl4hi5HQEZXlCSjNoasuf97wBkODMP+JvnRYOfk1l1ykcK7ahovoCekl4DYC7NkJFgUh3MxDTQt04m15kWAnWU3+KyJaZbfPPuk+DnCjOhh/ltiE+aNQNl+Yz13UDRF7rFUeWhRfXzgduXw1qYzj1R8ftzzEQbH1aTm9si+pyiPjjouoLcksq2Xgqg5sHeqNSSXBiOVQVw8D7mB7qzq2DvFkwxIf3b+qFqudM6DoeYxUskN9EDpwkNuIUBFmtGzmyLPPVjlhGfbiDJfsTGNbFkfdvCmH1Q0M5/vIENj4+ksH+9tyz5ChL2xHF2x6ZRVphhd6AMDLQCWO1ii1nOpH3UpZhw3Mg60RkxNCcXg1x22DSu8K7bekkDIxxrwhDZ+e7F2eUF6ZC9EYY8oiYaPa7E7LPQtLBttc79YcwDNx6X9TXaRW1BpX/CMYYnWbr2QxuNDmMqsd0/bTeWqwnPk+VkTVuR96lsFxEczLCt1MuG7NINxNNXiyL1XN4elpfvp/fn69rZhBhPhB59f0i7RiE/L9OCzO+hNCbxUS+NRWx5MPCA/mBnzBmF40SaX75V7iIOu24SJWe/oV4pmx/2zDRxKwzUJQCAdfVGjnt/57bIrPo4WbNQD97Kqp1ZNc5sorSYPWDwnnnGAj3bAOHLg0rBk0RWQ8aY/0NqjXgNwLO7aSwvJpHfgsjv6ya16b35Ppe7oR42DD/x8Pct/QYIzwk5px5BLa9wczYlzBVadkTcw15wutIO96QRpyXACtvuyKiMOdDlmUOnctjkHxKpPnLMr3OLaa7mzXLDl2dKWvtxVij4sHRXdhwKoMTyQUUlFXx2+Ekbh3sjY2ZUbPl370xhLFBzjy47Bh7YnJ4+4YQvrq1L0ZqiRdXR1yBb2BAdr0vanvHvQI3fnfJdZyKkXOplOWJyWTj/jeR60DWti6XauEoFNdOrwYg5/DvkJ/Azaf6c9M3B+jzxhYe/i2Moop2PoicuwsP5rhXL/HLNDA2yBlzYzVrww0THSI3DtJPQPBNLX/edTw1KhPGSoe5sY+n3keOliZM7+3OxlMZV14ONjsSkvaLG9DSWURaIjrQyJEksY+YLRc2Idn4Avx5NyTsueBd/nEsBRmZ2f09Rb3XgS+g2xSw80GSJN6+IYTXZwSjVkni+OYtZ9+UrRyq8CSrrkjUKQhyY1scTKtqdDzzezgfbori3hH+HHppPO/d1Iu5A7zp422HRq3CwkTD9/P7c8sgb17++zRv/HuGjMKKVs//0oOJ9HCzpo+Xbf17liYahnV16DSRnOS8MhYufBtSj4KVm5h8nI/yApHa1kh9sFVy40ThfLcp0P365p+PfRmMLWHtk1B1gb9J3HbxWldj2HUc2PmJyEBrVJeLZ2HwLP30VAMh+Y8hhGiGqk7jpM2sdR61gIkVNWP+x0QOcmiviJ4Zpx4k2iiIwfcsZLr5LwTPfAYrUyO8Hcx5bUYIt+fdTYVkBqsfEMbN8aWkOY/g2U2ZvH/KQmw3Lazl/YX9IurRJr4FN3wH1h5CVv2zXrDi1ivXRypqPZjYCCPtjrWi9tMQ5yV+j0hT9R0ujKfClHZNoqu1OnZHZzM2yBlve3MAkvPKheH81WAhcjHpXVjwL1g4tP94/EcjJx/hli+3cCwhn29u7YuPgwUqlcRbM4OZEuLG8gXB/Gz8AaqKfJj0PppzW/nOejH7Y9qvzHZVoNPBumfAMQCmfAxzfhZRruiN5131cpOcV46mOAm7qjThmBn8INKRH7gn1IQdUVnnF6a4ypnd35NuLlbMXXSAh38Lo0Ync/cwvxaXNVKr+PKWvszs7cEdQ32Z0MMFJysT7h7ux/7Y3AtzlHcmijOFKvGQR2DE0wZ5PnUaIye9sJwl+xO45fuDvLm2k+fGHv4efpgAX/SDD7uIyeSvN4rJw8FvxWuXsSJHuTV63iA8uYWpyLve56AczG2zZ/Png0N4aHQXNp7K4M9jKe0/Jmt3UatjIMyNNYzr7mK4lLVTf4HGTKS5tISxBceN+nKT2XFcbZpb7uN7uJBaUM7Z9CucThC5VnyPgOvE3yGzIfOUSGHrKAKug+I0IWTRrmNcL2q1JJWoDbsAqrU6lh1KYmJPVxwtTWDnOyKtsi0DWmNMV3cxCYnOrD0/zkGtKqx9vCWKv0+k8uGsXrwwpTuWJpqWN6tW8eaMYF6d5M+/+08w+N1t9H5jC/+c1De898XmsDs6m7uH+yE1eSguur0/797Y6wJ+gfOTVlDO7ujayVA7J6yyLPPBHzuZV7SYFOdR0Od2YeS0tb4sw/djRU3NW04iOhCzteV1KkvEBNrcHmZ+3fLgYGYLUz+GczvEs+vE8nYdOyBSm1xCRGQIRLrs6BdELUbSoZbXid4EVSWtOzYuFf8xqNHxpvFSZDN7kUbWCnaD51Mo2UD4SmRtDT6l4RQ6D6SPtx3rnpvO5F4NaREz/p+9s46O4mrj8LMbd3clSpSQQHDXAi1QSgul1Ggpder+1d2NGpSWKlpaoLhLgECAQJC4u7usfH/cjWyyMUhKWvY5pydldmZ2dmfn3vva7w1zJqK/N4/X3SscGn8+CHnneCM7gqPJxaxJNqBaYgxZ7Rg5VQWifmTo/TDgFpj/KzyVKL6vi5uviuogIMYC30linpDqdM9w6Igh98FDJ0DfWERylPImEZmO2H+pgIpaGVOCHHGzEkZO3cUdsGqWGD8ejhHfYetoTQckF1TyyjkbJEoZAxTn+ePBEUwIaO5CP8DNks/mD2RY3u9I8s7DgrUwdAnM/obRtfsIS/m2Sz3alEol2+Ny+04KdXucXSucKte9J75H7wnCwZJ25GpfWRu2nM1htI5qHvUcDcMfAomEGcr9mOrrdqsud0tsDs+uj+XeVSfYHtd+JF8mV1BT3zcMAgNdHf54cDg3hrtyOLGImyJcsTdvP4qhryvl41vCeOWGoKZtQ/rZUC9XcDqjtMP3Kq6q75u1TgfeFyUNo3qu5rHPGDmPrT7Na5vPU1BRx/eHU5oXS92kul7G8oPJfLEngZ+OplEn6+EfsLxBFPMqZOAzUQweS8/Cde+L/jfbnhFFtvM6WUAEXA8SKco/FmNXk8xJz8XcGO5KhIc1D0/wZaSPLZvO9FAU5TKZHuJEYn5l+/ciJ1bk+bazUKuqk/HXmWzxIJ1bD/5TmwtEW5FZUs3vFQPwbojXOEEO87LBWF+HXReuciH5xS3Ci60vJmV8pwjv6Nk1vfeeniNBz7hrKWtZMfDXQ+K3GbZAeG+74Tn+KSqNgWU7edotXnhoo76E0U+DneaiyaZLtDFGX0fKpVzVb8Wuv/jbqi4nLruM5QdTeGi8D3MHuamfpKYE9r8Pq2aKQvm33ZG848Fd+4YSbXA/u0P3EulpxeOrTzflaNfJ5Lz05znC3S2ZPdClzXXpJ/wNGdFd/vwdkn6MrD9f4frPDnD798dI/vUJ+CigSypyh7ev5uWs+9BHzu9WS8AlXHzejuoXSlKgOAlGPSlUFmvL4Jc58OP16r1A0o+KIuzSNLjl57Z1gC0JvEH07HKJgI1LxD3uDIVCGDneY9W3h9wk7rOmIn0QqWr2QWLB2hvY+qI0c8abDCQB13fs5NHRJdNlKgPL93Dx9CFMqcbMX7NR1BitPCQP5rD1jXDmN6r1rNmrCGPd/cO4Y4QXsQpvlFknNb9XVYGoiWqJoQVEqlQSO0vx6w1KUiE/rseasaohkYCl6lm28hR/u6CwtuFUJr72pgS7mGNhrIe/YTGRxx4Gj+EiFdjYuluXEZVUxMwvD7Mjz5JqAzteDynEx17DfCOrFyI/oXPFcwAQchOZoQ/zoGQtaVGdR+Y/3hnPfT+d5Lblx5pSIPscDbXi2fSbCt7jxDaJRHy/fczIUSqVrI5O50arJCHMZGIjxjH/6zCIW8P8SDd+O57RpeyWk2nFPPRbDNGpxaQUVvHY6tPt1qk8s/4soa9u57blx3oua+UKMNbX5e0bQ9j88Ehemh7Y7eODnM0xNdDlWHLHQlRf70/iuQ1neXVTXNeyYxQK4RTrzSb0xSlC9GX4I91+9juizxg5b8wK5sQLE9n08EgczAz5bHfCZZ3n010JvPX3Bb47mMLLf57jrpXR6qo6V0ryflH/Mv1DuO5diLxXRGyGLIbFe2GBSt1HzxCFQtn+extbg9dYJKmHOCwPImTENLWXrx/gTEx66VUN0Y71t8NEX4ctmqI5BZfEYnTn/0Sfm1aU1zZwx/fHeeS3U0QdOyLy94NubPe9/jydzUHpIJQSHWFItMJQT4fRvnZX18gpyxLe95a1VnqGwmA9t7730lD0DIWXOnZ123oKhUIYmydWws9z4LtxYGgJM5eJ6yxN67KUc3FVPZm7vuRTvS/x2H2fWDjb9YcRj3Z6rK6OFC87k2aD2MxJGH8t6nLkCiXPbTiLp40x94/1Vj9B/HZYNkw09pPqigXhyEeFuMYNn8PIx/GO/46vPfYy0teW+3+O4f3tF3l103lSC6t4fVawqB9qiaxe1E+d+L5Ln78zzh3bicupj/lK7yO+tt+IV/xyZHK58DwXtV8vFnviEMOPLqHQ2IfP/Fayv9C8uUalo5S1lAOARHg0h94P9x2A+atFDcQ3o2Hb8yKi/P0U0SB4wTreOCFl0kf72dtRoa6NN9z8EzgNEMX0ndVt5Z2D6kLwGqe+XaoD454XKZGti9hrSsQ97WnBgZZIJEi8xor/D25/bGnEbvht2EnKqNz+JvVKHXwjxra7r6OFIY9N8uOe7BlUWwfyM9cxMdgNezNDQlwsOCnvhyLzhOZnvrKgOeLVEmNr8TylX4UF5qWt4rnymdi779OYvdCJ+EBpdT27zuczJ8K1Kfr6kt6v1EhNYO4PoG/SrbfdEZfLHd8fJ9DJnK1Lx2DsPwGdpHZSfC9ugsrcZqNThd31L7NXGY7b3kdh44MiDVnD8cv2JfLZnkQWDHEnq6SG11eso7aiFxd/l8vxb4X63MRX1Le7DxNzdl0PZ0VknYQLmy9rHjyaXExqURWhDWfUI7ID5kNhPPd6l1LbIGf18Y7rCWvq5Ty5NpZgZwu2Lx3N+iXDMdLT4bVNbTMtEvMr2HAqkylBjtTJ5Dz066k+I8Mc7GKBkb5Ot4/T1ZES4WHFsZR26gUBhULJpjPZ9LM1YVVUGq/8FccXexJ4f/tFKuvaSYvOjBZOsfWL2p0vHlt9mm3nuvn9KRTCcDr+HayYLMbIropvdZE+Y+T42JthZaKPoZ4O94/1ZsvZHBK6Gc3JK6/lhyOp3DfGmzMvT+bnRUOIzSxj/ndHL8tYWH8yk5WHU0gqqGy2duM2UGnsxsoUy7a9WxxD1CSjv9ibSOirO5j55WHe+vsCy/YlsqNl6FSVxrFS/xaGe6unDkwOckBfV8qWq/jQGerpMCHAgS1nc9St/bIs+Gm26DJtZIXs9GqyWnhKKmobWLj8GBdzK3A0NyTn1DYxwXqP0/Augo2nshgc4I3EY3i7KVYTAx2IzSwjt6wDScED78Nvt3b7s3aJS3+LFLDGVLVGQuZAaboYCHqLUU9AXTksGyoiiad+EcV5n4fDN6NQbn6MhORk6mYsE556MwfwGitS6y61NRobUSiU/HgklS/3JvLjzyt5XrmCmtCFQrVo2gdw86oup4v4O5pxsTGSI5GIBrgtIjl/n80hNrOMt28MxUC3xQAeuwZ+vVkUGD90HG5bLxbPo56A0U+KgtmJL8Pop9DZ9ybf+h5nSpADvx5L59dj6dwx3JMgZ4u2FxT3h1jMDHugS9ffGRn9F/G96xsMVp5javlavjdYyM3Kd1DqGcFPs9osGqrqZCxedYKojcuolJhic99feHh4cSm3gnpjB1VdTjspTyCMHKcBzZEZiUREQ+87ANb9IG6DuM+zvoIlh9hb58vyQynUyRTctTKa25YfY0NMpmZHi1QqokM5ZzqXgk7eCzoGwgPcmoAbxLh34AP17ef/FFHv0JubNhVV1l12hL5dBswTv3OPkZ3uah8wijypA4Prj5Ok74epaceS1guHeeDmYMuo0ld5q2I6tw8TohbBLhbEKrzRqSkSz31LlEqUVfkkVhmx8VQWB+IL1MdO96FXJ5Jz/i+V2qdl776PnqH4XXciPrA5NocGhYJZYaroa/J+RjYc4WfTO7stNV5SVc/T62MZ6WvLqkWRWBjriShNUWJzg9KWHPsW3IaKZ6sFBnp6/O72P/YYThYpXusXtWn1kJhfwXvbLnHfaC/emBXM71PkvFPwAA2fhAvJ26tdM9pIWZZo2jvwNrAPQKlUEpddJnqreAwXwicZ7aSZtqa+qvMaq4RdyFZcB6sXkPvJWIqS24lyAnsu5hHyynaWH0xuSg38PTqdsZYF6NcWqhs53uPBxA675I1MD3Vi5eEUGjpIJ/xgxyWySmr48OYB6OpIsTDW48UZAWyLy2V3KwfpZ7sTsTM14IO5A1i9eBjXBTvy2OrTnOkk1auvM8TLmpj0knZ7C55MLyGnrJY3ZwezdKIvP0al8fX+ZJYfTOHuH6I1p+8l7BBzQNIe4YhsRXxeBX+cyuJ/f8ZR1Z6h1Jpdr8Jr1kKg5e8nwWsMLNrRbrbP5dJnjJyW3DLYDXszA97ddlGt4VFnfLY7AX1dKUtGCy/xcB9bfl88lMKKeqZ8coAfDqd03Pn62LfCosw9R02djOf+OMurm84z4cP9PPL7aeQNdcji/uKn8oG8uvkC1316gJNpmj04coWSX4+lE+lpjZO5IZvPZLNsbxKLfzrJ8oMiJ1secgu36b6Pc+gENTUxAHNDPcb52/HXac0h1HqZgvyKWtKLqns1r/KGADOGFv1B3p5m6dmSv56nsrqat23fZq/OcIqO/sr493c3KbF9eyCZC7kV/HLPEOZFumGZF4XcaSAYmGl8j/SiahLyK5kW4iRC66mHNMpcjvO3QyqB3e3JChenwP73xKK+q/Ur3eHiFnAf3jaX3XM0mNiL/Ofewm0wPHhMGMZRy+DPB4Q6kvNAqm5ewzjdn5hU/Qa79McLhSEAPSMxSXRQl/PdgSSStnzEkH238XD2s+RZD8Jo5sdg6yOilLa+Xb7EMDdLzmeXNw+S9uoy0imFVVga6xHZr0UouiQVNj8uatRu/7PjOrZxL8DQB9Df9QKfeMcQ89IkDj0zjhemBbTdV6mEo18KgQ/HkC5/ho64LsSJu+95GMl9B2Duj4y4621OlRqyLuBTUTDZaqG/7mQmuy/ksND0JGYDb8TO0oxgZ3Pq5Qqx2HcOh+zTGt/rru+PUXZ+N2f0QsmvaGXUW7jCXX/DExdFelrYrZTUyHlmXSwjfGzY++RYPp0XRkWdjMfXnGHqJwc1TzyeI8F/upCXlXVQW5C0F6X7ULZeLG07fkokMPJxEc3JON68/cxqcX4LV6rrZXy2O4Ex7+/jxY3nOviGLwOvMeJ3o6O5rqv1tWa6iihsmd3gTnfX05Hyyg1BFFU30N/RjEEewti0MzMgx1SVStLaSK2vRCKr5Yvj5SxdfZrbvz/O3K+jOJWu6kvjPlwsviu7IInbUAP73m2/5qmrFCaK6FHovCs7T1ex9Og0krM+JpORPrai/lKhgG3PkmUSyC81I7r9du9tv4RMruSdG0OanSde48Hau20fs5wzkHFUjG0aCPd155GyedQuPgKBM2Hv22rNtlceTsXGRJ/HJvkhqcgh6PAjlFgP4FC9j5C83f58t6+fs+uE7LuiB1LrFXKxhlk2VCh/jhXX8/fZXKZ/dogHfomh2tJXRPvTuqDuWFsGX0TC+z6iV5GmerKLW1D8eguHG/x5zfQlaktzqfn5tnZPeSGngqo6GW9sucCUTw7wvz/PsfVcLo/bRInr8mzhsNDRE8Il59Zx7wg3sstq+bUdpbWM4mp+OJLKQ+N98HNoXmvMCnNhpI8tT6w90yQRnpBXwabYbB4Y642hng5SqYSPbg7D39GM+3462WfqdC6HIf1sqG1QtNtnbtOZbOzNDBjSz4alE/049+oUzr4ymVV3RxKbWcq9q060NSQTdghFw5GPiTrdTHUjdnNsDkZ6OpRU1/PdwWQx/669E97xgPe8hUri0a+gSqVemH8BDn8iSjtu+h4eOAZzljenu/YgfdLIMdTT4aUZgey6kM+Lf57rNGewqk7GhphMVkdnsGSMt/DkqAh2sWDH46O5YYAzr2w6T9irO5n3bRQ/H02jrLqFdyJ+B2x9Sgw2X48g/4+nqZcpWH//cN6cHcyW2GxW/LgC3YYKEu0msfHBERjp63DfTzEaFxAHEwrILa/l6an+fL0wgiPPTeDsK5NZNLIfb2y5wJd7E3no9zMcqnThhgGatcCvH+DMeQ0yzmU1DYz7YB+Rb+5m9Pt7mfPVEdKKelBBKu4Pocjyy1wm/D2GN/RW4njwOcg8wdGTJzBP/JMVylkcLjRkv8E4HCQlTDJO5P3tFymtrmfl4VQWDHFngJslM4IdGMx5kk3D2327Q4mFSCQwwsdG5IwrGoQMbitsTA0Id7ciNWYPfDtWpMvVNjfPYtcrwuttbAsnf2xzvBr1Ve0XcWsi/ahYyGmSBdfRFYv0uD9ALuvQ03RFGFnBDZ/BC9nwfDY8kwpzV/JOvAt5dXq4WBqxtXW4uP80kUagoV/H6YxSUnZ9w2t6PzLI1xXF5Ddxvm/9ZQtYDPa0RqZQNhc9OoaKtEaVkldxVT3WJi2iQnIZbFgsvMszPulcSUUiEdGHQYtgy+NI1i/CNW8vumj4vtMOiwXN0Acv67N0iI03BM3C39GM2QNdePtYPfVDHhL1S0VJIl3pwib+iMngLvcCjGtzkaiitoHOwksdl10mitOzT7cJ/6cVVZGZcBoLRSmfJTsz8cP9rDmR0e442CBX8Pia09Q0yHn/pgHoSCXMDHPhzwdHsOWRkeRX1IqJRxMjlwpRi5wzml9vqIX0KJLNB3P/LzGsPJzadp/AmWJBeVCltFaSJhbVA+ahVCq58/toPtudwE0Rrixb0P448E/gPPoOZEopVqHtCKC0Yri3LS/NCOSNWcFqohYOrl4US23Es9USlfFiY+/M2Vcms/LOwZTVNDB72REe+e0UOZZhYr+uRHN2vy4WFN9PFmmJl9vIMeZHkToaOPPyju8iSqVSeI9b9Moprqpvs9+mM9mcSi/lpgiVimbeWcg/z0X/B8gur+vW+Hk6o5Tfo9NZOtFXvUhbKoXB9wjHVOPYp1DA1mfBwl1EIDUw2s+OepmCPRfzhdhKVQEcEw6+suoGNsRkcesQdwz1dGDd3SDRwfbu39kV8iHvyRfA0WXUntnQ/H4tKUxsK6ufvF8o+FUXAT2gdHfmN+EVD7heLBzNnZArlHy08xI+9qbsi8/n5m+P0eA6pGsS9nveENcWdqtQZFt/r/qcmbgLxZo72CMfwJ8BH/DSE09QGPk0ropsctLiNZ7SMO8Ufxi9weZbHejvZM6+SwXY69YSnL8JIu5srndtZMA8qC4iuPIo8wa78fbWCxpbW3y1PwkzQ13uHqmuSCaRSPh8/kBsTQ24fcVxlh9M5u4fo7E3M2BeZLNTzUhfh+W3D+LtOSGXlSrWVwh1tcBIT4djKW0d8DK5gr/P5jA91EmooyJUSCUSCUO8bPh24SAOJRayOrpFWmB5jkhv9J0M414ECzc154FSqWRLbDaTgxy4fZgn3+xPpvTUH2I9NGC+aF2gow/bXxDrtuJksVYzdxG17MFzeq9ukz5q5ADMCHXmzdnB/Hosnbe3tt9v43RGKYPe2MXja84Q7mHFXSM82+xjbqjHO3NC2fnYaJ6e6k9AQxynN33J528/wflLl0Tu/vbnRK7qcxkw6kncLn7PNLMkwt0tWTBE9ARxSNtEGo4svW0OYW6WfHlrOGU19aw4lNLmPdeeyMTLzoRw9+YiYIlEwgvTArh+gDPvb7/E+ZxyXrk+kAgPzYXC4/vbY6ArbdO1/ev9SRRW1vHZ/IF8Nn8gBRV1TPv0IH+e7qHGcykHhcykUoFkyBLe8P6VSwpXLny/hJSNb1GjY8q9j73C5odH8cqDi8DCnccdT7M9Lo+lq0/TIFdw/xgRTfNRpGApqWJzefsRgcOJhYS4WGBprC8WkLZ+6o34WvCk8WZeyFuKQlYnvAk/zBDF9qd/hfMbRTfssPkiBaqFB04NhUIM1r/MgY0PaA7F15YLGebTvwpj6Oc5okg1/HbN5wy5SUyIx7/hjS+/Y9kfbY20HkXfBAxM2Xo2h5+PpfHYJF9uinBl78V8dfnI/tNBzwSiv2vaJFco2XYuhzd+2sr/dH9CHnwz3LYO/REPILmCrvT9Hc0w0dfhRKpqcHUdLJSWVCpUhZV12JoYNB+QuFOkTMz8sutpNBKJSKOb+KqI1v1+qxBaaDnxKpXNjSt9J7d/rh7gsYl+VNQ28GHNDKr0bSj6dibKT0Nh9W1Mzf2aW01OiiifyjtpZqiHl60J57LKwWWg6D9UlKh2zoMJhYyQxqGU6vLe44sZ62/P0+tiefi3U20WgAqFkqfWnuFAQiGf3BKGs6WR2utBzhYsHOrJtweSyS/XkObpNACkemod4tUouACyWr5NFQpV609mtjW2pDqibit+K1zYJCZAXUMIuIG9l/I5nlrM17dF8MoNQUKx7yri5BOG4vGL+A9tp4eZBhaN7McgT/VC2FAXC07J+6HMalVTpfJU6lk4Ymaox7j+9mx9dBRvzg7mcGIh169KR2nu3PkCM+2I6Hs2/kURrcuLE3V33UVWLxa+oXPbLh57iEu5Fby6KY6R7+4l5JXtJDRYoyxN462/LxD++k4e+e1UU4rxnot5PLb6NDNCnZjRqGiXuBt0DdH1HoNCSbeaGX66Kx4fO1PuGO7Z9sWwW0HXQBQzA5xYIYzvGz5tNwU3wMmcAa4W/HIsTcxFEXeKHkfVxfwenU6DXCF6cdVViHs47nkkZo68MSuY404L2CGPoGHDAyR/txDecYPvrxO/ifSjYoG3bKhw7FzaRmXUShS/3Qoew0TKqbQHlmOht8Dd24XCoirj4M/TWSQVVPHunFBWLx7GuaxyzusFiWe+owhu1kkRFRr7jJDynvmlSONrdECmRaH8/TaiCeIL2xd5a+4gJBIJ3oOFAyHt5A6Np7UvimaA4jzBW2/iy1EyDjw9joOTMpDK6trUSQFijHIMgZM/8NKMQBzMDVm6+pRaOlZOWQ3rTmSyaEQ/jWqdVib6/Hh3JAolvPn3BYKcLPjx7khhrLa8NnNDxvnbd/QN93n0mupy2ho5UclFFFbWc307jvXRfnZMD3Hi8z0JzeuIxqbVPpOEQzdsgUhHVjmYL+VVkFRQxfQQJx4e74OBjgLZjlfAc5T43Yx7Hm7fCI+cEnPFdxOEwTz+pSvugdMVuhDjv3osGOJBVZ2Mt/6+SICTGbNb9U8B2Hk+F0M9KVsfHYWnbcfFir4OZvjq5sPuJ0AP5EioX7sR+k8UHtibvgcDM5Rjn+XC4b94XfIVkvq7wcCUuQbHQOcIGZH/w81GvI+HjQkLhnjwzf4kFgxxx0Y1gRdX1bPjfC5PTvZvI2krwqIDuG+0F4FO5m2LpVtgrK/LCB9bdl/IayrUziuvZeXhFO4c7tkUARrrb8dLG8+h2xODJMAM9d4Xj9Q2cGx3JZOiFxOgk4Bs5HPomlo2fiAIuYl+0csJtb+VfZeElG+TVy31IDKJPisz7LmntgEzQ/UogUKh5HBSIfNbeFTwmwqnfhae/lZpKGFlu9kmH4zB2J8YZ5UPP90oiu0BXAaJB7A4GY58Lh7EsPlqxzfIFVRsfQ3rS1tERCDmR9HIbvTTIldZqiMWyZseEQs2hSpK5xopRCXayxd1HSyMs+3P8yqQYHoLMKGLX3j3yS2r5Z2tF9h4Opvx/e25a0Q/kgoq+XR3AgcTCpkUqJJNNbKCiDsg+nsY+ThFMgPmfXuU5Pwy/jL7HH1TK3Smv98j16SrIyXcw4roNFVqjkOwUIXLOAb9RrWN5JRnARL19ISuIJWKCMSIR+HUT/DXw8IAbUxBObdehNcbO6T3Im7Wxtwa6c43UWmk6sznbd3vOGA/G10DI5ZkLkeZpi8aZ0qbJ9MgFwvOZpXBpIFiQ3aMmnrdwYQC7jKJR+IwCBtrGz6bb8OEAHseX3OGOlkMn9wShlzVOO+XY2nsu1TAJ7eEqUnltuTh8T6sPZnBBzsu8d5N6nUI6BqIBURWO0ZOoTDAtueZc9cIT1YeTiUmvbStY2bAPGFYrlalqQTfhELfjA93nCHc3ZIJAX1n4aBvofl76g7BrhaclnkwNncXOkplcxSySkRy9CyaP6+ujpQFQzzwsjVl/ndHKfMcjGVLI6cwQYxXk18XKmx1lbDxfqHAN+IxMQb2/1PUYI19tnu9I+K3CudL+B1X/Jnb40JOOZvOZDMp0JGiyjq+jlXwoV4RC49ez/1mUj6Mn8uIs8PRlUqpkykY39+ej28Ja/Ikk7QHPEbgai8MyYziGjxsOhceqG2QcySpiMcm+aGno+E5N7IUv8uDH4pnLC1KSLd7j+/wvAuGevD0uliSCirxHvUEnFiBLH4Hq6LsmR7qhIO5IRSqFEBVqnJG+jqsvX84KRmrqFk1CcOsI9QNWojB+TXw7TgRDXEMFhH/Ax9A7GpMgTMKbw47vMo96NF1oewO0NETdV8qGuQKPtmVwDh/u6Zn1t/BjL01vgyQ14kU036jmo9vqIVvx4iooUIO9gF8UTOVS7+dwsLQiRfsQjHa9y7om6H8ZS4JOt48LHucdQuGNhkMVvYupOm4o0w9CDzU9hJriyjVscbSRpXGFDYfSdJeCJoFFm0VMpFIxBi65QlMqrP45JYwbvo6iumfHeTxSX542Jiw/FAyBrpSbtdk7KpwsTTir4dHUC9T4GrVOwZ/X2GYtw1f7EmkTiZXq389nlKMlbGeWj+51jw2yZfJHx/g56Np3DPKS8ylzuHNYiph80VT6bg/IOJOtsTmYGqgy2g/Owz1dPifSwy2WWkoJ65UX/9aecCdfwt1UCsP0X7jH6DPRnIauXeUFzcMcOa5DWc5n922TuNSbgWBzuadGjhNJO8VxeNPJnK/w29c0g8Si6Lw25sKEZOL63ik5l4sZYWwYpLw5Pz5IATdiNt1j6ud7qHxPgC8vbW5fmj5wWQUSpgdruGBRVjawS4WHRo4jUwIsCcmvaQp7P/p7gT0daRq6lTmhnp8Om8g00OduvYddBNzQz0mTb9FNALUN0N3aCtvS9gCJHXlfBiUQqCTOfeN8Wp+LeUAMudBlMt02Xq2bbrF+ZxySqsbGOlj27zRfxrUFEPm8Tb7G9Tkk6HvzZ74InG/HogSzeIeOgl3bxOLSVtflB7DUTZ68FqwdvWPWJ/4mIohjwtj7tbVYiH34wz4OFik3Bz+VDzAs76GpeeE2s/CDR0XxEokcN9BNozeyuT697Cd+mwn3+rlcTylmLt/iGb4O7vZfSGfd+eEsOKOQejpSPF3MKOfrUnblLVhD0JDFZxcyVt/XyS3vIao0K0EyS6ge+NXPVqMPMjDmpi0ElEnpqMrjA+VIENRZT02pi2m8qpCIV4h1WnnbJ0gkYjndvA9sO1ZYRiXpApFtYDre68/SyuentqfbxZG8M7zz7Nl6mHuyJjOPWkTOWgzF4m8vs1gHuxszoWccmSGVqIGqYXCmkyu4EhSEaGKC+DZXJ8wM8yFZQvC2Xcpn6CXtxP6yg7uXXWC7NIaPp0XxiwN8tmNWJno89hEP9acyOTx1afbpte6DmqbdqVCWRhPmcQcbw93XpgWgIO5Aes09e/SNYAlh2DJYSH/O+19tsXlEpddzpNT2jp7/u2EuFgQp/REp65MXS65Mh+FUoKRZVtDaoCbBTpSCecNQkV64KWtImL823zhbNn5P7HjjhfFInPW181OnuA5UBgvlO66Q8wqoeTn1LO9oloyPdSJY89P5O0bQ/jqtggcBs7gJ/kkqnxmYOU9mDeUX7DL7QeemeTFOzeGsGxBeLNRUlcpohw+E3CxNEIigbt/iMbvxa08uz62w9qIo8lF1MkUjPHToGTXyOQ3Yeo7oqmuuRNMebPTz3N9qDPmhrr8cjRdLLrNnDgVfYicshruHaWa2ypVc5lZ85wrkUjwcndF8cBxxss+41PdO+Ge3cIx5hoBt22Aofcje+QMjzuvYjgr+SNiFR/sz2Hu10e61J+nu2w8lUV6cTWPT/Jv2jbM24Z12bYoLVzbKk+e+kmkGA++BwYv4tzIz/lgdzJJ+ZVsi8vnlbIZYl7+8XryjLy4sWwpr900BHcbdaOh0DYS9/KTGuuFDeqLKdd3gNv/ElGii1ugLKPj1OKQucJhduonBrpbsXbJMKxN9Ln/lximfXaQDTFZLBnrjYVRx6nW9maG/3kDB2Ckjy01DXJi0krVtifkVeLnYNbheOxjb8asgS58tS+JyuoaSNqrnhFh6S5qIU/9LFLVzuYwKdBBGLkKBdNKfmGzfAjn8Gl7cgsXsWa7c0uvOyAb6bFIzrJly3jggZ5RMWqJRCLhnTkhXMqt4JHfT7Fj6Wg14+BibgWTAx27fsLk/U1WqZubBw+efZLDd+iJKICKvRfzSZe6UnfrBoyPfAi7XhbezplftPGi2Zoa8Ny0AF7ceI7S6gZcLA35MSqNB8d5Y2925aG4Cf0deEF5jr0X8wlyMWd1dAZPTvYXqV3/NHNWCE9law1zWx/oNxrfjLX8/eiu5u3yBkg7guGIRxmmtGH1iQxuHqzeG+VQYiEGulJ1z7BbpIhAJO5SV3SS1SGpKcbG1YMfLubzmlKJxNQeTJu9pjK5gi1nc0jMH8wTNZ+SlXIJl35igL+YXULExQ+Jxo/jBvN5EISk6mNxIvXg1M+w9y1REzTobpHiAc09IDpDz5B1SVJsPAdg5ezV+f5doLiqnuzSGgoq6lgfk8nm2Bz8Hcx45YYgZg5wUas/k0gkTA125BdVf6gmD46FK4TMpf7QFxSVVfJL/1Ls43+F6R8JZaoeZLCnFR/vkjU5H3AdLNJFlEqKquqxaRnJ0dRP5HKY8jaUZwtHBBJhOE3/uGe6uXcBEwNdpgSJMei2oR5Ep5bw15lsdK97G6yeE2kvLQhxsaBOpiCxoJL+zuFqRk5sVhnK2nKMDUvBRj3Fc0qQIxvuH8GFnHL0daX42JsS5GzeJQPirhGemBrq8vKfcZzNKmPTwyObUzVcBokUs6pCMLFVO6404zyJckceGueDro6UWQNd+PVYOi9fH9gm1QN9Y+GtVvHFnoMM87JhuLf6Of8L2JoaUGTqB/WItElVwWxDRT7lmGJr1tbpZqyvS6CTOWtloxnuewJWLxR1WeXZEHkfHP8GjKxFI9+p76r3pvIeL6I859Z3XUijIldESa5778o/cAe0jKLoSCU8fdMoKq8fJtKGlEo48zv9Ni6h3+hbRSSjJakHxXjrPQFDPR0+nz+QrJIaKutkfHMgmVPppSy/YxBu1m0XpvvjC7A3M6C/o2ZBG0D8JocsFv91ESN9HW6KcGPdyQzuHd0PE4v+1Kaf5v6xDxHsolJxbKyPMm1rzDpam3H7cC9WHk7lzhFjsV9yWIxFquf0q8M5bEzRZeVdwxnjZ8fscFfO55S3ER+6UuQKJV/tT2Kcvx0hrs3qk8O8bfjhSColkfdiffhVKHlFeNblDcLBFzgTJryEUqnktW+O4mtvyqaHR5KYX8l1n9ay1G4AJob6TMlcwtwRAUwNbrv+MvUbi0veOi5cOkdAgPrv1bihlHpza3FvRj8ljJuihDZqd2oYmgsV05ifYMwzhLtb8fviocRmltEgV2BnZtAc/VMqRZF7SaoYjwKu77h3WF+guliILvTQwj/YqJgbDM9wKMGLYd42kHsO8i+QmmfJIJ/O18tLJ/ixOTaHtX+s5676CjXVYEBERNcvIu3SKZILqnhmqqqmJuMohtXZrNe5l4BzOWq/uyZ0DYB/Lm35sr7Rv/76q81/r732WtP/9zTG+rq8fH0gifmVRJ+7JDwNQGWdjMySmo4HuZYoFGJQ7TcagEAnc7LKailzGKaWr7w/voDBntYY+4wSuYQPnRTRgna0+28b6sG3CyOISipk1dE0Xr4+kKem9EwhlaOFIUHO5uy6kMeLf5zD3dpYY93RP4KeYfvqV4PuFh77nBY9czKOi47n/UZzy2A3TqaVNKmbNHI4sZDBntbqCyapjkgPay3JrJpY+vXzJqu0RmPx4df7k3j099NcsBpDPbps/OULsktrUCqV7F79Of7SDHa7PsS6mOzm2gKpVHjNZ38FS2Ph+s/EwrmbFFfVcyylmOtCumF0d8L72y8x4/ND3PVDNFFJRbxzYwh/PzqK24d5qhk4jcwKc6G8VtbG214/9GEaair5Qf89QpO/Falegxf12HU2EuZuiY5UwolG1UG3IVBTjKIwkZLq+qaUTkDjovqy0NWH+b+JQtuRS0XkTVOfkn8AiUTCu3NC+fHuSIZ627YxcIAmueuzmSrxgZxYkZoJHIwvxEdfle6n4VkLcbXg5sFuzBroQrCLRZcjJBKJhJsHubHmvmEkFlTy2/EWCkWNDRE1RHNk+ZdIl7gwQhVpvSnclYpaWaf9JM5nl3M+p1xzrcR/BFsXbyokZmpjXl1pLkVKc2zNNDuhwt0tic6ohJt/FIZL5nHhPJv6thjzDn0kJHRb1yboGnS/H9fZtSDRUYtofrUviWfWxarX7fUCTXUREolIb7EPEunDrUncLQqQ7YQjakaoM/eN8eaJyf5sfGAEJdX1fLRTcwH7/vgCxvjZ9UqU8PZhHiiVMPLdvazJtCREJ51HxrfwTFfmCUnddhbP94/xRlcq4ftDqWJ+aXGNx1KKGd/fvikCNcDNUj1du4fYEZdLckEVD4xT96gP7WeDRAK7jKYIxdOjKuXU2NUioqLqNr8vvoDjqcU8NcUfHakEf0czFgzxZGrp04wvehp/dxee16RsCXgOEp7/7NM727xmJi9FbtRCoVTfuGMDp5GIu0QELe4PQIxpA9wsGeRprZ7eeOxrUWN96W+Ryvz7gr4j7a2Jijz4KBC+GSV6i2m61qhlTZ+7K+jseonPeJcpJxeLfmrfjIYN9/BtxQOMp/NWF+42xjw20Y/qCzuQ6VuIeaol/aeDsS0Nu97ESE+nOZp6dh2YOWHdfwzbzuV2KBqWXlTNidTirjUjvQIuy8iZNWsW7777Lh9//HHTf2VlZXz88cd88sknPXyJgmEm2WwweYfBG4bBl5Hw+wJSLwoPqH9XjZy8s6JJnZfQYW9UOjqf05wGp1QqOZlW0jSpAyJS0YknYHKQI1seGcW6JcO4a0S/DvftLhMCHNh6LpcTaSW8ekNQWw9qX8B/uvDKn2xRHBv7O5g5g+tgpgY7Ymaoy5oTzYvv6noZx1OKGe5j0/Z8roMg65S6Qo3KyOnv54e+rlQo4LTifE45kf2sWb54AjKviUyQH2L8h/sY+toWZpX+QIHrJIaNnUZKYRUxjbKuLTF3hog7aJDqs/FUVvvNsTSw83wucoWyyavfEywa2Y8NDwzn4NPjiHpuAvMi3Ztz2TXg72jGtBDHpnzcRt6Khsj6r7h0yyHRR2fiqz12jS0x1tcl2Nmc6FTVd+sqpHprkqOQK5TqNTk9ZeQ0Yt9fNL5rmWN+FTDS1+lw8WVhrIebtRFx2eWi7kJWIwr8gUOJBYx3VAkEdCSnfZmEuFpwwwBnlu1Lal7o2ngLL2Jr8QGFAvOqdJQ2vujriqnC18GM4d42fHsgucPJaUNMJpbGeozrf3WMzX+CAGeRsqbMbVamk5XnU6i0aFdgIdzDisySGvKrlUJQYMlh0chUqiOKxfvPEH81eXSDG/txtVM/1Zozq8FvSlPkvUGu4JsDSaw+kdHzTbI7I3CmUDBtLQaTtFsYexqelUBnc26KcGXXhbw2PT8yiqtJLqhijH/v/L48bU049Ox4XpgWQJV1IJbKMgxqCpp3qMgRUZx2nnErE31G+NgSk9Z2jsmvqBXS2b2IUqlk2b4kIj2tGdxKNMPCWI8gZ3MOptcK52TMKiEjv+cN8JvK/gonXvjjLM+si2Wgu2VzfSfw2CQ/lFI9pFIpXywYqLkWCjCwcCBDzxPd9MNq26vrZVhSLhRQu4tLuEib2vVKk2JnGxJ3CxnvIUvgsXPiGUs7LLb3Vc6uFdFMPWPRL27XK+qv554TRtvaO4VCmbyTNUlDLSTtJcNhPFb1OSijv4MxT5N209+kKewZd/pxIWzSCfeO6sdkg/McUgRT0/ot9Yxg0qv4Fu7iPtdUsSaVy4T4U9BsJoe4kFxYRYIGRzQIheD53x3lpq+juPmbKI4lt9+89Eq5LCNnxYoVAHz00Ufs3buXvXv34ujoyN69e9mzZ0+PXmAjkq1P46tXyKuyOyif/BFkxRD8x0S26j9LUMwrsG6RKEL/eY6QZNSkGpJyQEjZuQ0BwNvOFH0dqZqRU1BRR3W9HG+77nVcBjEwRnhYd75jN5moKtqdHurE6I7yj68muvoihBm7RoRe66shbiMMuAWkOhjq6TAzzJkNMZlNClF/n82lTqZgqiajwCVcKE8VtvDiVQjvsZGVK0P6WXMosbDNYXnldTipJhDj8Jvpr0zmxaH6fO20CWdJCXYz3xT9GcwNNdcWICaIZ9efZenq09y18niXmlspFEr+PJ1NhIeVKEztIXzsTQl3t8LN2rhpodkZj07wI7e8ljUqGcitZ3P44Ugqj10Xin9AiPCa9mIqV7iHVfPkbmIDNj7I0kSvD/WanB5KV/sXEuJiwbmsMlEvAZB9CrlKfnuQRblonmuuWQHnSnlkgi9FlXX8fFRVSyKRiGhOq0hOWW4KBtRh6xmktv2BsT5czK1g7yXNvV5kcgUbT2dzwwBn9aav/zECncyIlbmjyG6O5Cgr8ynCvH0jR6W2GZNeIsbMFul92PrCvF9EeqkmPEeLhfWZXzu/uNyzwqk3oLk3ztHkIkqrG3j2uv6czSrjrpXRve5FbSJolqgLTGyRzpwRLURiOlBAvC7YiYpaGUeS1Mf6/fEFSCWo13L2MBZGetw9sh+PLlBFwlr2XqvIE414OyBQVXvX+jvOK6/rkVT2jlhxKIWzWWXcP65tJBmENHpUUiHKyPtEjfKJleAQRM6QF7lz5XEOJBQwzNuG928aoOassTbR57fFQ1m3ZHinn6HEbghe1bFqvQ6Lq+qxoRyp2WWO+1PfEfNGo1x9S2pKYcO9QtVrsqr2qv8MkY67+9W2kt4g6nG700qiNzjzu2gwvmiHUBw7/EmzIiDA/neFbPPEV4Ti4p7XOz5f2iFoqEJv3LNMrHuf3ZN3wdhnOafw4s6GZ5A5hYs1cqvm1a3RrS3BR57Izrog7v4huo3DN9NjFicUfiwq/0qst1P2CYGN4JsY42eHsb4Om2PbRvyVSiXPbzhLWU0Db98YQlWdnPnfHeWrfUm9Mh5dlpFz11138euvv/L000/z2muvIZfLe7ewNOskpEchmfQKvzGFn+rGwMMnWevxMtl6HuimH4KyTGHAyFWSmS3y3JtI3i8MHD0hs6qvK8XXwVRN0CC1SHiauqLu8k8R4mLB6zODeO2GoM53vppELgYksPMlUUxYVw4Dbm16ed5gdwor69l4Skhdr45OZ0g/a7zsNCiWNaXQtPBaVuSKxZ+xDQPdrTiTUaphAqltNjL8poKeMbelvcTA7N+RTHkD7ETo/cZwFzadydGYtvHBjkusj8lk8Wgv4rLLueuHaAor1Y3mBrmCV/6KY8WhFIqr6nl09WmOJBX1ifQcf0czpoc48cmuBO75MZon155hUqADi0b2bISxPYKdLcgqraG0WtUjwzUSvWwRIrdpKSF9DRs5Qc4WxGWXI9c3E7U3WTHklNXQIFfiIikQC93LFWToBG87U2aGufD1/qRmSepGI6fF83T+nJD+7h8coXb8CB8bQl0tWLY3SeP5DyYUUlhZx43h7SzW/yMEOJkTp/BApzKnSTpap6aQQqWFujHfAlcrI+zNDDipwcOviaZnCIQIwYD5IiVEkzy+Uil6NW1/QXjlDS3VDIit53JxtjDkvtFe/HbvUO4b4/3PCULY+YNdf/WUtb1viN++pt5jKoJdzHG1MmLbOXXRmp3n8whzs/xnalOt+gkZ/twWqdiVuWDWccQ+wMmcClVKfSO1DXLKahpwMO+dmgSlUsm72y7yxpYL3DXCk7HtOEWHedlQWFlPfLUpPJ0ETyfDbetZeUkXU31dtj06mk/nDcTHvu3cHORs0UZoQBN6rgNwk+STntvsDCkpLcVIUo+++WWqLdp4CyGdI5+JhpItOfihiPDMWtYs2CGRwMSXxb07v1F9f3mDaEHwyxzRiiK3hxsVd4WWzgiJRKQKDlwoGmSfWCkESi78JbaPfEyox17opCQkfjuYOeHoH4mjjSW7s8QznpBfgamRIbpzvhH9vDprXJu8FwlKZs9dSGxmKbctP6a2DtpxvoBX5XdhWp0O340XjXOtPMElHEM9HWaEOvHrsfQ2a6y1JzLZcjaHN2cHMz/Snb8eGsG9o7x4d9tFnlwb27SmSyqo5LPdCVfc7P6yq5w8PDzYsWMHJiYmjBo1irq6DvTWr5SoZWDhhmnYHKY1FlZLDVhbP4w1nq/Awydg0Xa49Xch8yvVa9tJXFUET78xapsDncxFYz4VqYUiDOrRhYf4n0IikbBwmKd6PUNfxNwJJvxPVcD/pvCgtCieDXaxYHKgA+9tv0RsZinRqSXc0kqIoAkjK9HnpGVqRkUOmDqCVMoAVwvKa2VNRimIAT6/vA57M9X3pG8imovmx4kBYlizesusgS5U1snYd6lFCgJCdOLLvUk8NcWf56cFsPLOwZzPLmfEO3t4ceNZ8sprUSqV/O/POH46KnpBDHpjJ9vP5fLZ/IHtNnb9p3lisj+etibUy5XcGO7KB608cr1JkEurNFDXCIxKEzCghbqaXCZSR401pCpeA4S4WFDTICe5oFJELbNPkVEsFkPWDbm9kqrWklsGu1FYWc+lXJU3z3Uw1JZCQXNPstykM8jQwdFDvb5QIpHwwFhvTqSVcFxDL4Z1MZl425kwQFPR6X8INytjUnRVnnLVAli/rohKXat2I1gSiYQIDyti0ks7Pf/2uFwGvr6TB3+JoahxcTFwoXAeaapv2fumWLjEbRR9KMIWqIp8RRH69nO5TA12QiKREOJqoZaG9I8QOFNcV301pB6C5H0w7rkOjXmJRMLUIEd2nM9rWuycSi9hf3wB8wb37jPShFQqIm6tIzmmHRs5mtLhCyrEfeytSM7ak5mi7mpqf/43I7DdMT+ynzXmhrqsikoVvxGJhOp6Gb8fT2fuIDdMNPSa6S523mEA5MTHNG2rLBbGqrEG9cEuM+pJsPSAFZOb++mVpIpanGEPtY2E9hstBIa2PdfkjABED6DCeBH1qcgRctZlmrM7LpuO+hCBiOIYWYm0UhCGzoyPRQrr5qWwfKKI4oQtEK/7TBTRz+J2mjsrleIZ85sCEgmjfG3Ze7EAuUJJQn4lfg6mSGx9YcobIk3x8GftX1vSXrD1Y1BoKD/fM4S0oiqmfnKQ3RfyyCiuZnNsNhb9IpDctkFkHWSdFE4Y1W9u8Wgviqrq1DJmZHIFn+yKZ1qIIzPDhCKoro6U56YF8N6cUNbHZLLuZCa1DXIe/CWGDTGZ1Fxh/eAVSTlIJBKeeOIJvvvuO1566aUrupB2KcsSFnjkYtDRZclYb3LLa/kpKo1LuRX4O7aS9dU1APuAtpGc/AsiXN5CQx7EQJSYX9lUv5BaVIWDuQHG+n26hVDfZfAi4RUuSWnTowbgxemBlNU0cNfKaMwMdbkuuAPZa5dBbSM5Ku9ZqKslALGZpU0vl1Q3UC9XqOc7j38Rpn8oule3wNfeFG87E7UC6nqZgtc3nyfS05oHVBLdQ7xsOPD0OJaM8WbTmRzGfbCPB3+N4bfj6bw2M4h9T47lgbE+/HzPkD5j4AD0szVh/f3DWXV3JK/PCtYoUtBbNKWBNkZIHUKQoMBfmolVo+e1phhQXrORnEaVpnPZKvGBvDiyCksBMK7O7nUjR4gWQGymysHjOVLkhF/YDIj0y4b8eEoNXUTvjVZMDnTEy9aEFYfUJ9uSqnp2xuUxJ8L1Pycb3RqpVIK+gx/1En0hPiCrw1BWQZ1Bx4Z7hIcVZzPLSCsSDrXUwire3npBzeOZmF/BE2vOEOZmycGEAiZ/fEA44Gx9wH24kPptyfHv4MD7Yrx7PA5eyFOTSz6eUkxRVT3TelAUpdsEzwFZLXwxCDYtFWIEgbM7PWxqsCPFVfUcTxFFyu9tu4S3nQk3ttOioVdwDGll5OR2mq7mbGGIuaGuWqZIfoWot7Mz6x2H5Y0DXVh1dyT3j+04SmdioMvi0V6sjs4gXeUo3Hgqm4o6GbcP8+iRa7HxFGICFRnNEbDaUtHY3NTqCowcA1O4Z6dYy/12i+hD9NutInI5cqnmY67/VPz2/lQ1jq4qhH3vwMDbYPhDcM8uIaz054Oa09ouh9TD8NnAtg73RuQyUY8TPKfJGQGI8XbOcli0U9SrTXmruXmt1xiRXthejVHBRVG35ycass4e6EpueS17LuaTmFeJj72qfn3QIhj+iMi6iV7R9jxKpVBm9Ba9/ga6W7Ft6Wj6O5qx6McTjHpvLzHppaJtifc4WLBWRANHPdl0Ch97MyYHOvDtgeQmefQ9F/PJLqttlmJvwc2D3ZgV5syrm87zxNozJBdU8cWt4Rqbu3aHHtGrCwoKYsmSJT1xKnWqi2HTo6J7tqrTfH9Hc+ZGuPHRznjKaho0K6s5D4Sc0+rbSlLEXxt1pZFAJ3NkCiUJeaJAKq2ouk+lqv3rkOqIzshBsyH4pjYvu9sYs3iUF0VV9cwKc8FIv4OUHNdBkHe+OTWjRYqAnZkBLpZGnMlojsLlqTq6q9XEWHsJzf9WA75EImF6iBO7L+Q1LS5WRaWSUlTF/64PbJOH/NgkP/Y/NZa5Ea5sj8vjzuGeLBjigZu1MU9O8SeyX8/XYv1b0dOR4ufYIg3UIRCACIOsZtGEKlUE7Ro1cqxN9HGxNOJsZrmQtFc0UJ95BhsTfXTK0sDSs1ff39RAFx8702Yngb6x8BKqUiES8itxkWWKBrcakEol3DnCkx3nhVevkY2ns5Arldz0H09Va8Tf2YokiYdIK1F5ieWdFFXfFOGKg4UB9646QUJeBQuWH+Ob/cl8d0AYjOW1DSz+6ST25gasujuSnY+PoV6uYO1JUWNH+EJRTK1q1EpWDGx9RhSRNy4y9AzVxry/z+Zgb2bQVBN0VbDzh8X7wWOE8LxPfLlLkrnh7lY4mhvyzPpY3t9+iajkIp6c7N/jkssd4hgivOd1FdBQA3VlnUZyJBJJU11OI/nlwrPfk3WbLdHVkXa5bveuEf2wMNLjk93xpBRWsfxQMmP97Lrec7ATJAam5Ok4IS1oTiurKxepa4aWV9gc2MgK5q8WUQ8bb/E7uu4doRanCQtXoWIYv1Wkpn07FlA2Oz+NreGGL0R08YSGRf/lYOsnMhV+vF4YPK1J3CVU+ga0dQYDopXGrash8IbmbUZWIuqe1E7te/w2sV5WZSyFu1sS5GzOysMpJBdW4tuYfiiRwKTXxJix5XH4/jq4+LdI96uvEuIHFdlqjXMdzA1ZdXckK+4YxMo7B7PzsdHMa5mJY2zdpnn7kjHepBdXN9Xm/HQ0jWAXc8LaaUb66g3BmBrosiU2h+en9W+WbL8C+m4z0MyT8NVwIUM8S71h4eOT/ZpSxzUqqzmHCZnpuhbKDiWpoGvUJo82QBVSPpclFsupRVV49qFUtX8l9gFCxredJpMPjPPmxnCXzmtEXCJAKRcLCFB5z5ojP6GuFpxpEcnJbTRyupgKMC3Uiap6OQfiC0RTxd0JzBvs1u6DZWmsz6szgzn54kRevj6wS+9xrRLoZN6cpmFgRrG+M6F6Gc07XONGDoh6g3PZZWIBJdXFKD8Gf0sF1Jb1eiQHhNLamcxmJwEBN4i0q+IUEvIr8JLmYOzUvhT+nHBXTA10+fFIKiDSRX8/nsH4/vbY99Iirq8R4GTOgfr+KBN3CQ8qIDXteAFnaazPd7cPIrOkhqmfHkSpVDYp3uWU1bD099Pkl9fx7cIIzAz1cDA3ZGKAQ3NdSuBMMLGHNQuhNEMUEdsHiKJsDd77qjoZG09ncf0A5y41oO5VHINhznfwQm5zik4nSKUSfrh7MJ62Jizbl0Soq4XG3iy9imMIoIS8uOYeOWYdZCGoCHSyUEtXyyuvRUcqUe8XdpUwMdDlgXE+/HEqiwkf7qO0uoFHJ2p2alwuJaY+WFYmNv1bXikcAZKeGPelUrFIn7NcNCLurPlzwPWiLw9K8du7dY16qwG/yRBxp4iINtS0d5auY2oHd24Gh2D4+Ub1SCCIaKxdQHP9cVfxniBEtGT1bV/LPCGMIFVLFIlEwu3DPDiSVESDXImvQ4saK4kEpn0Ic3+Ehmr4fT687QYfBYjavlFPCMdXC6RSCRMCHBjX3x7fTpqKgogAjfW347kNZ1lxKIWDCYUsHOrRofLo1wsjeGqKf4/VN/ddI+fQRyJ94v7D6pYswqJ8aLwP1ib6eGqKujgPBJTqhYLFKaIoqtWXa26oh7+DWVMoPLWwShvJ6WWM9XX56Oawzj1GDsGiF0FjylpFjpqROsDNkrjssqZQaL7KyLHvYlGnv4MZXrYm/HQ0jVu+jcLUQJcnJvt3epylsf5/PhXnSglytiAxv7IpSpau740/LTrDN+ZG96SE9L+MYGcLzmeXo9A1AreheJREEWKqMjr+ASNngKsl8XkVzWlSflOEeMuFTaRl5+EkKcbIqf3nwcRAl1sGubH6RAZVdTLOZJZxKa+CWwZ1sXnuf4AAJzPWyEcjqStv6h6v20kaE4iMhE/nDSTI2Zyf7xnCazOD0NeVMuvLw+y9lM+n88KaU0uAKUEOJBVUiT5j+iaif1tlnminUJwMs79RT3lpwR8qKfyeSkPqEXS6l4LS39GcVXdH8tdDI/hmYcQ/P/7aB4lnI+uk+N6h03Q1EL+PzJIaymqEXHd+RR12pgZX39hUsWCIO7PCXHhpRiCHnxnfrof9clHYBeApT6O8Ua68soBaDNrtOdjrjH8R7vpbpLB7DGv7+uQ34Z7dTeJUV4yhBdy2XtTV/PUIKFRjbWW+iLqEL+y+0qnPBNF/MONY29cKE4RKYwtuGOCChZFIOfa1bxUUkEqF8uHifXDfQeEoGbgQHjgq6qt7oDnpsgXhDPe24fXN5zE31OWGAR2nmYa5WfLgOJ8ee8b7rpFTcFGEytrpNv/AWG8OPD1Oc88Q+0AxILXMhSxJbepK3Zph3jZEJRdRUFlHVb2cfj0UrtVyhejqC4M1LUqkrNWWtYnk1DYoiFelGuaV12FprNflPkISiYRpIU4cTChEoYA19w1rV/pVS/cIdBZpoI0NWxMkHnjKUprVu6oKhUCI4X+7OL0jgl0tqKyTkVJUBT4TCKyPJUhP5SX+B4ycUFcL5Aql6NcDoqu41ziIXcOk2MdQIEHiMqjDc9wx3JOqOhm3Lj/Ga5visDczYGwv9S7pi/g7mpOkdKHQKgzOrQPA0LJrUYZJgQ789dBIvOxMsTTW57GJvuSV1/HkZH8mBKgvoEf72WGoJ2V7nGqB7RAEd2wW/UYmvaYuRd0CpVLJD0dSmdDf/j/hvAt1tcTJoocWoN1Bz1DMRelRTa0MOktXg2bxgYuqaE5eeV2XnXD/BIZ6Onx8Sxh3jejXcer4ZWLqHoqtpJykFFEuoFNbRIWOZY+/T49hYNrumvOy0TcWNUHZMXDsG7EtdrX4G3pL98/nPFCkrSW1qsuRy4TDw0bdyDHS12HeYDdsTfXbV/WTSMApFIYsFrV8tj6a97sMjPV1+WZhBA+P9+H5aQG98jvriL5p5MjqxM2ya9+LKJFI2i9I0jUQhk5L8YGSFLDWnB41zNuGnLJaDsQL73JfUla75uk3Sui+l2eLf7eI5ISoiqcbU9byymu7nKrWyPwh7swKc2bNkmG4WWvve0/RWCvXqFx4Xu6GsaKyWb2mqkBEca7hiFiIKi3yTEYpdZ7jMaSeiLIdwvjrQirMlRLgZI6uVMLZFimfBN4AeWexr0lmhecHTfVU7eFmbczn88OxNNLjXHY5C4d6/LO1ElcZUwNdPGyMOWw2DZQKKpWGWFpenuF++zBP/npoRJPoSUuM9XUZ42enLqXsEAhLY2HYA+2e83BiEYn5ldw5/J+Rj/9P4z4U0o8KZTVVK4PO8LE3RVcqaUpZy6+obVb/vAZw8BkIQEGSWIvp15VQo2d5Fa/oKuE5QqTC7XldqLxFrxDKr5eTySDVESlrjcpyjZSmiaairSI5INRWNz886qploOjqSHlisj/zIv8hRcQW9M3ZqDABlAqRZ3y5tBQfkMtE7nI7kZyh/WyQSOC34yKn+r/g8frP4DlKRHASd4p/t1j8mRnq4WVr0lQ8nVde220vmYulEZ/MG4iL5VXwDv6HMTPUw8PGuEl84GStqhA9T9WLoNHIuYaxNTXAx96UI0lFZBp4k6+0xLngkPAk9kCaQGcY6ung72jWrLAGEHQjinEvcn3D2yj6jevSeaaHOvHj3ZFceG0qD43vOQ/gv4UgZ3N+rgxHoWdCkbL9RqCdIZVKCHW1bHchMiXIkbNZZWSVtqgX6GTR8sORVHzsTRnhc21Ktfco7sPFuJV2WDRl7cIzaqCrg499swhLQUXdNVOvBmDg4E8DutRli3HfqKGEOv1rVKRn0msQOEvIv5ekCIWzy6X/NCi4oC4lXZgg/tq0HYP1daXqqrPXEH3TyGns1WDXftFrpzgPFDe9thzKMkQBu5Vmb5aFsR6BTuacTCvB1tTgiiXrtPQgbpEi9TB2jfh3K+GIge5WxKSVAiIVwPEamkD6OkHOQnxArlASV2NBvY5Jc8O1qsJrWnSgkZE+thxKKCSjpIYDilAkKP+RVLVGQl0t1cQ70DcmK+RBMmSWGhsBdoSOVHJN1qpNCXIkOrueZI9biFV6YdtOI9ArZUJ/B/R0JPytoYu4JnLKathzMa/DQl8t3cAtUvxN2CmMnC4y0N2SmHTR/DWv/NqK5KCjR76+O4bFYk1nKi9FZniNGjmGFjD7K3j8PDybIaSXLxefSSLif/Hv5m1FCaKG+R+cP/4N9FEj55IIBV+Jp9d1MKAUxVklqWJbO5EcEB2AAfrZalOW+hR6RuJeZscIY8dIXQI1sp81l/IqKK6qF+lqWiOnzzDQzYozGWUkF1QiV0qptPRvjuRUF4p6gmuckT625JbXsu9SAfsVoWLjP2rkWJBcWNVcGAxNdVTedt0zcq5VJgU6YKyvwxOlc3i44ZFeq+uzMNZjjJ89G09ndWn/NdGZQsxg4D/YT+a/jLG1UMOS1bRxtnVEhIc1SQVV5JXXUlLd0GuNQPsqNdb+2FUnU1RZh4WiDOU1HsEHRP3jlR7fbzRc3NK8rTBBtMzooLnutUgfNXIuiMHkSrAPEKlNibtVPXIkHS4ehnkLI0ebqtYH8Rwl/po5tknPGNpP3LeopCIKK+vaL6zT8o8zW9Wsb9m+JADqbQJapatpIzlDvW3QkUrYEJNJvMlg0eitA2dMTzPMywalEg7GN3cCTyqoRF9Xqq1R6yLG+rpMDnRokuPurUaPALMHuhCXXU5CXkWH+8kVSlZHpzMj1LlJWUlLD9DYTLwbkZzBnsIx11hPda3NUfa+gwmQpLHmyEWsJRXomGrH/R6h/3TIONqsVFqUqLEe51qnjxo5lzoUHegSEokozkrcJSI55s5CIaUdBvezRkcq0Sqr9UX6NRo5bYux3ayNcLIwZMvZbBTK3muypqX72JoaMCPUiT9VnmeJSzgUJQnxgarCa74mB0Th+kA3S8prZVjYOMCCdRB+5z/2/p62JvjYm7LrQl7TtsT8SrxsTTQrV2rRSGO0xNRAt8vqjpfDhAB7TA10243m7Dyfx67zeU2dxedfhULf/zQew8XfbkRy3K2NsTU1YMtZkWZ4rUVyzMNmYSBpIPfoGkwlteiaaY2cHsFfiJ0QrxIg0CAfraUvGTl1laLJlqxeLISupB6nEZ8JIk8xeX+79TiNmBvq8du9Q7ltSB/qJaBF4DpYdPHVMLFIJBKG9LNm9wXRSVlr5PQt7hjuiUKlGq0XPFPcx+gVUFeujeSoGOkrjD03K2MxZpn8s0XiEwMc2HMxv6nfVFJBpTZVrZuM9LHF1lS/1+pxGjHU0+G6YEc2nspG0fhgAQqFktc2nefeVSe4Z9UJFv90An8HM8LdLXv1eq45GiM53VA/lEgkDPa0Ijq1GLj2IjnYeFNq7s8smViMG1l2PQqmpQPMnUQj0dg1QpypKr+NfLSWvmTk/DYP1t4JhfFCJMC+B4wcr7Ei/SPndJdSQCL7WWNhrA3t9zl0DWDoA9B/hsaXh3jZUCcTCzStkdO3GOBmyQA3S6QSsLCyhaDZEL1cvKg1cgCxQAYRlbwaTAq0p6ymgRNpojg6Mb8S726KDlzr6OpIuWeUF2P8ev83PWugC1mlNRxNKQKEgbN09Wm+P5zCi9MD+OuhEdw9oh8vTA/QCg70NJbucMvPYhzrBhEeViiVIJWAzTXYi81owI0MlCYCYGLV9SiYlk4Ycj+k7IeYVeLf2khOG/qOjNjoJ2HVTNjyuPh3T0RyjK2FpZsZDdaeV34+LVePiS+3+9KQfkKtRSqh1z2pWrrPU5P9+etMlkh/Cr8dzvwqXtCmqwHCEIzsZ90kfvJPE+Zmha2pPrvO5+Frb0pJdQPedtq03e6yZEzb/ja9wVAvG/o7mvHU2lj+fGgEq6LS+OtMNp/cEtaUNhfqavmPXMs1ScD13T5ksKeYo2xMDa7JNFCD0Nlw8G0ATK21Rk6PETwHDn0Ee94Q/9YgH32t03ciOV5jhbc+4xgYWfecl9dnovjbSbqaln8v/WxNsDMzwNbU4JpqRPhvYaSvLe/dNED8w30o2Krq7bRGDgB6OlLW3DeMIVfJyNGRShjf355Nsdnc8u1RdKQSBmgXyX0WHamE724fRE2DnJu/juKz3Qk8OsFXq6LWhwl0NsdIT+faS1VrxM6fOis/AK3wQE8ilcL4F0FWK9bMRpZX+4r6HH1rRTjhZaGq5hjSc53Q/a8DiQ44BPXM+bT0OSQSCaN8bPGw0apB9XkkEhh0l5ADN7G/2lejRcXUYEfyyuuwNNJjw/3D8dQKsPRp3KyN+WpBOOnF1UwKdODRCdo0lb6Mno6Uwf2scb+GFQsNwuaCoSXoa8eWHsV/GrgM0q5x20GiVCqVne/WO1haWlJaWqq+sboYFHLoSWu/ulikrmlpF4334l9EeW0DdQ2KXpVv/af4t9+LTlEohKy7zT+T3nMl/OfvhQqlUtkkONBX6ziulXvRHTKKq3EwN0Rf95/1V2rvRfcpqapHIgFL455Nqf7X3At5A5Rng9V/V9zpqt2L3lg3/0foOzU5jfSGMaI1cP7zmBvqgVZz4N+BVPqvMHCuJSQSCT72Zlf7MrR0E20vo38PVibXeL2ojt5/2sC5qmjXuO1yVSM5UqkUc/Mr7PyqpV0MDAzIy8vrfEe096K30d6LvoP2XvQdtPei76C9F30H7b3oO2jvRd+hO/eikatq5GjRokWLFi1atGjRokVLT9O3hAe0aNGiRYsWLVq0aNGi5QrRGjlatGjRokWLFi1atGj5T6E1crRo0aJFixYtWrRo0fKfQmvkaNGiRYsWLVq0aNGi5T+F1sjRokWLFi1atGjRokXLfwqtkaNFixYtWrRo0aJFi5b/FFojR4sWLVq0aNGiRYsWLf8ptEaOFi1atGjRokWLFi1a/lNojRwtWrRo0aJFixYtWrT8p7iqRo6Dg8PVfHstLdDei76D9l70HbT3ogdQKnvkNL12L3ro+q4ltM9F30F7L/oO2nvR97iqRk5dXd3VfPurzproDMZ9sI/S6vqrfSn//nux92349ZarfRU9wlW9Fw21sGw4nPn96l1DH+Jf/1z0AlV1su4dsPYOWHMHyLt5XCt65V5UF8OXkRD3R8+f+z/MtfpclNU0MOq9PRxOLLzal9JEj9yLmhL4dABc2nrl57qG6cnnQimXkfHOYC5seLvHznktok1Xu0psic3hmQ2xpBRWcSSp6Gpfzr+fi1sgfhvknLnaV9KnKK9t4P3tF6ltkHftgJM/QH4cJO/v1evS8u8kraiKwW/u4qMdl7p2QMoBOP8nnN8IO17o1Wu7LI59A4Xx8PfTUFvWpUNq6uXI5IpevjAtfZGNp7LIKK7hUB8ycjRxNrOMWV8epry2oWsHHPkCSlLFPKqlT3Dx4FrcauNxPPsVirrqq305/1q0Rs5V4HRGKUtXn2JasBOuVkYcSeragFle28DMLw/z5+msXr7Cfxl1lWJhDnD8u955D4VcRIvK/l3f/Vf7kvhybxLHU4o737mhBg59JP4//3zvXpiWfyVv/32ROpmCz/cmciC+oOOdlUrY/To4hsCUt+HY13Ds23/mQrtCXYW4Jv/p4v/3vdvpIUqlkgXLj3LzN1E0aA2dawqlUskvx9IAOJ9dfpWvpmPe3XaR0xmlHEvuwrhfWQBHvwJdQ0g70vsXp6VLKI9+Qyb2WCnLSNjVS+uaawCtkXMV+GpfIq5Wxnx8SxjDvW2I6mIk5/tDKZzJKOWpdbGcy+qa1/GaIDsGlAqxWDm7VqSg9DSX/ob978CRz3v+3L1EQUUdPxxOBSA+r6LzA058D1UFMHAhFFwShp2WPkFsZil55bVX9RqOJhexLS6Xt2YHM8zLhsdWn+ZgQgElVfVsO5fLkp9Osjk2u/mAhB2QeRzGvwRD74fB9/Qt4/nkD8K4mfoWjHxMGDz5Fzs85HRGKTHp4r8PtncxmqXlP8HJtBLi8yoJdDLnfE7XjZySqnoquhpR6QGiU4s5lFiIRALHU7qwtjj8ifg74WUoToKKvF69vt4kJr2E/Ks8TvYEaRdjCKw9RUbII0TpDsHi1Dfa+fgy0Ro57aBUKnnw1xhWHErp9rHV9bJ2H7TMkmp2ns/jjmEe6OtKGe5tS1JBVacLmNLqelYcTOGWQW742Jly308nySmrAUCuULLpTPa1a/hkRoOeMUx7DxQyOPVzz7/HkS/E39jVIPt35KMv25eIVALu1sZcyu3EyFEq4fBnEHIzBM4EWY1IX+gr1JRe7Su4aiTkVXDT11E8vub0VbsGhULJ65vPE+RsztwINz6ZF4ahng4LVxxn4Os7WfLzSY6lFPHk2jPit6ZUwt43wTUSfCeDRALXvQczPr5qn0ENeYN4pkNuAitPGPEIGFnC6V86POzno+k4WRjyxCQ/vjmQzN6L+T1+aUqlktMZpSi1ggh9il+OpeNiacTi0V4UVNRRUNH5PLAjLpexH+xj5Lt7WXEohXpZ70f/Pt4Zj6+9KdcFO3Ksswh+bTlEL4ehSyDwBrEtveeiOUqlkq/3J3HXyuNdrz1WyCEtChTd+66USiV3rYxm/ndHKav554zK3iBn56eUYM7AaXdTPfgBHGVZ5BzfcLUv61/JtWvk1FXC6oWQEa3x5f3xBWyJzeGNLefZ31lahgqFQsnaExmMeX8fQ97ezQO/nORCK4/PT0fTMNbXZU6EKwDDvG0A4SXtiG8PJFMvV/DEZD++WRhBTYOcMe/tY+nvp5j08X4e/u0U287lduk6/zUoFLDhPjjX9uEur21ozjfOPAHO4WDhCgHXd7pQ6TYZ0ZBxFEY/DTXF/4rizLzyWn45ms6iUV5EeFh1HsmpKoTKXPCbAvYBYltf8LpXF8OmR+FdT9jzxtW+mn+cBrmCx9eIOrPDiUWkFFZdlev4+1wOcdnlvDg9EKlUgr2ZIfufGsu2paP4YO4ANj00kkPPjMfF0ogHfjlJzaVdoj5uzNPCwAGQ6jT//9UmN1b83sPvEP/WMwLvCZC4u91DSqvr2RybzfxIdx4c50NkP2s+2hnf45d2Mq2EWV8e5q8z2Z3v/F8iJxa+HSvqpK425Tnw041NUY2qOhlbzuYwP9KNYBcLgDZze2u+2JPA4p9OEu5uyeRAB97Ycp5FP0b3qvF6LLmII0lFLJ3oxzAvG85llVHZkVBI+lGQ1QrnloUrWLiLlDW5DNbeCV9EwtejRF1dN2mQK3huw1ne2XqRqOQiFiw/1rmhU1sGv94MK6fC3092S/kwtaiaspoGkgureOCXk11LJ00/Bqd/6/J7/BNUV1UQUriNeJcbMTQyYcS4GcTjQfHxNVf70v4Zakog+3SPne7aNXKOLoMLf8Efi0UtQiuW7Usi0MmcEd62LP39FFmlbfdppLZBzq/H0pn8yQGeWhfLIA8rXpweyJmMMmYvO0xSQSUgClZXR2dwU4QrZoZ6ADiYG+JlZ8KRxPaNnIzialYeTuWO4Z7YmxviZm3MnifG8OhEX06kleBmZcz6+4fx5BT/K/xS+hinVkHs75C8t81Lj/52ivt/PikGwcxocB0kXvCZBAUXezZlLepzsPSAsc+CU1jvRIp6mBOpJRjLy1gwxB0/BzPi8ypRKDqYMEpFrjlWHmDuAgbmkH/hn7lYTdSUwv734bOBcHY9BMyAA+/D0a+v3jVdAZfrWfxybyJx2WWsvHMwZoa6/HY8vYevrHPkCiWf7kpguLdNk1MGQFdHSn9Hc26KcCXE1QITA12WLYggq7SGnC3vgEMw+Ez8x6+3S2TFgEQHnAc2b/OZKGr7yjUbF+tOZiJTKLllsBtSqYSbB7lxNquM7A7mhsvheKoYuz7dnYC8o2f2v0BhIqQcFM/1iklQmg5bn4aoZVf3ug5+CEm7Ie0QANmlNdTLFIR7WNHP1gRDPWmHKWs19XK+2JvIgiHurLhjMO/PHcA3t0VwMKGQ345ntNm3pr5nUpEsjPW4bag71wU7EtnPBoWyMc2ugpHv7iHwf9sIeWU7sZml4oC0w2BsA3aqtYPHcBFFOf6tUBz0GCZSwXe+3O10qS/2JLL2ZCbv3BjCuiXDySyp4a4fOjDyqoth+UTIOA6D74UTK2DHi102dBo/0ye3hHE0uZhv9id1ftChj2Djkj6lrnj+8CZMJLU4jbwNAEN9XbIsI7ArPdX1kyiV4nv8+ynY0j1j8aqz4yX4YcYVq3E2cm0aOVWFcPhT8J0CpRmwT12i70RqMcdTinlwnA+fzgtDRyph2d7ENqdRKpX8eTqLse/v4/k/zuJhbczqxUP56rYIFg33YE9kNJ8YfMPS309TUy/n1U1xlFY3cPswD7XzDPOy4UiyZvEBmVzBo7+fwtJYjwfH+jRttzTW58FxPhx6Zjw/3h1JhId1D3wxV5/8ilrhganMh53/ExtbGSwNcgXHUoo5nFhEduolUUfiOli86DZE/M3UHKHrNuXZcGETDH1AeKIH3iYmv39YgCCzpLrLtVsAdanHOGFwP/YNWfg5mFLTICezpIPFWKORY+kpvO32Ab0ayWl3olMq4cRK+CQUDrwHQbPgoWi4+ScYsgS2PQOph3vtunqa6noZz22IJey1Hd2OtMrkClYcTOH2YZ6M8LFlTrgra09kdF0pr4fYHJtNQn4lj03y63Rff0cznhlQi1fFCaojH+y9yE3ibjF5Xy5ZJ8EhEPSNm7d5j28+twb+OJXFxAB7HMwNAZjQ3x6pBHZd6Nkahpi0UuzNDEguqGLTfzGao1RC7Fr4bjx8EQE/zhDPdeBMWHoWhj4I25+7es6ksiyI+VH8v8rRk69KTbM3M0RHKsHf0bxD8YEDCQXUNii4c7gnUql4BiYHOTIn3JW3/r6g5jR9Z+sFZi873COKff0dzXljVghSqQRfe1OsjPU4llzESxvPIZHAYxP9sDDSY9lelQGQdlgYNo3PqcdwyDsnUk3D74DrP4XpH0JJSrczGLbH5TIj1Il5ke4Eu1jw1uwQTqWXkl7cjlLYufVQlASLdsL0D2DS6xD1hbjGLnAmowwXSyNmhrkwzt++cwU8pVKMA3omsPEBEUnsJTKKq/lg+yVmfXmYzJKOldIa4jaRLXHEvf+g5kt1G4K9ooDaoi46uXa/JpwG59ZD9Hf/CscsINInz62H+opmMakr5No0cg58IH7gM7+A0U+K3Ozcs00vf7UvCS9bE6YGO2JjasAwb1uNoenXN1/g0d9PE+Rszq7Hx7DizsEM8bKB+ipYdycGB95iqmwvBTlpjH5/L2tPZvLGrGC87EzVzjPc25aM4hqNP/7P9yRyKqOUT24Jw8JYr+e/iz7E+exyRr+3l1+PpcN2ldys5ygRvmy1X7XK83Xm6E6xsdHIsfEGI2vIONYzF9UoqRl6s/gbchNIdcWD+A/y7YFkFv90osueXWn+WXQlCiSpB/FzMAPgUkcpayVpYrA3VhnL9gG9EslJKqjkvp9OEPHGrrbPVF0lrJoJm5dC/2nwyGkxyZo7iUl4yttgYgcJ2zt+k4YaEfU58rl4Fq8S+RW1zPjsEBtisvB3MOPFjWcprup6T6zYrDIq6mRMC3EC4NYh7pRUN7A9rgvGUkkaJO+7zCtvRiZX8NnuBEb62DLYs2uOlHm1a8lU2vJb5aDOd75cqovF5J13mRNh1klwiVDfZmonIjuJu9rsXtsg52JuBcO9bZu2WZnoM9jTmp3ne87IUSqVnEovYU6EK6P97Pjs3xDNkTcIx2FxSrtRMDXiNsCGe0Qd5dwf4MHj8GQC3Pgt6JvAlDchdJ6Q9b4adYGHPhbpi04DWhg5ombW3twAgEAn8w7T1bafy8XLzgQfe/W5/n8zAjHW1+HR305RXS/jWHIRP0alcWO4C7o6PbQcUzmQpFIJgz2tWRWVxrGUYl69IYh7R3tx32gvtp/PJTWnALJPgcfI5mM9hgNK0DWAia+IbW5DwGWQMDi6SE5ZDRdzKxjrb9e0bf+9UWQAAQAASURBVISPDRIJ7TvrEnaK97LvL/497EEwtOjyOBabWUqoq0glHOBqwbms8o6zF8oyhYN0xkdg7QV/PtCl9+kuMekljPtgL4mH13NH3jtsOdr+mFVbV49/6UGyHcerOYgcgsYCkBW7r/M3rC1HeewbzrnczD12vxJtNhHl9udFCmZf5+xaMX9LpCIS1QP8+4yctCNiIbRiCqy/p/thuOpiEQYd/hCY2sPIx8VgG78NEJP6wYRCbhnsho7KA+PvYEp8XqXwPudfhAPvozyzmr9OZ3L7MA9W3DlYfTDb/jzE74ApbwHwWlg5SiX8tCiS24Z6tLmkwf2sAJFi1JL0omo+35PAg2N9hPF0lfn9eDpzvz5CRnuemCugrKaB+385SW2DgoLiYjERjnwM7PpDtfqgGJ1ajL6OlGkhjlQmRaG0dAczVadhiUQMlOldNHIqcmHLEyI8+s2YtsoyFzaBx4jmxb+RFXiOhPhOFto9TEphFRW1sq6ppAF6ZSqPT1oULpZGmOjrdHxsaZpIVWscWO0DoSixx0QWzmaW8djq00z++ACnM0qxNNbjrpXRTeIZgLjnKfth/mqY/TVYuKifRCoVxmzmyfbfKPcsfDtOyAHvfFk0uIte0SOfobt8dyCZ/Io6Nj88kh/uiqRepuB/f57r8vFHEgsx1tchzM0SAD8HMwa6W7LxVCdRRHkD/DYPfpkr5GGvgI93xZNcWMUTkzuP4gAQvx2jxC3sd7yTlUcze2+BHngDGNte3r2tLRO9cVobOSBS1pL3tkmVuJRbgVyhJNjFXG375CBHopKKKKtpQK5QUie7sihbWlE1RVX1hLtb8egEH5ILqzqX6r7axPwI73vDZ2Hieesoyl1TAlufAf9pcMcmCJotUqVM7Zv3kUiEiIyRJWx8sNsF6FdERZ74PEMfFPOIKppdUFGHoZ4UMwNdAAKdzEgqqNQYVa2XKdh1IY+pQY5IWkUyLYz1+HJBOHHZ5dz9QzRPr48lzM2SRSO9eub6L20TKb71Yo6O7GdNZZ2M8f3tGd9fzJE3RbhhaaTHnp2bhFCP54jm4218wGuciN40znkSiTA40qM6HntbsP9SARIJjPZtNnIsjfUJcDTXXH/cUCN6avlOat4m1RFzbxci9zK5gnPZZYSojJxQN0sq62QkF1a2f1B2jPjrORIiF0PuORFJ6GFWbD/Bn4av8rX0XWZLD3RYM3zu6A6sJRXYDrpRbbuvjx+ZSluqE7sQ1TrzG8qGGh5KHUVxjZzFBTdRp9SBLY/37bQ1pRJOrhS/AYegHsvG+fcZObtfFyFNfWNh9RV0U8YzLw7k9RCgUhLR1ReDrEo6NL24mnq5An9H4f2mJI1JZet4X/E+8o9DYNkQOPABkj8W83r9e0zxMlA/f1kmnPoFxjwlBgYLdyabJnP8+QlqXsCW2JsZ4mFjzIk09bSsjaez0NeVsmSsd/c+Yw+x71I+36vU5Uqq6nnz7wucTCth5peHiU7tWZnmp9aeobiyHicLQywLT4nB12eiGGhbpaudSC0hxNWCBUM8sKvLpNSk1ffjPkR4auWd1EHUlsPPN8HZdcJjVHBJyCg3Ul0MqYeg/wz14/ymigG/PcWv3HNdbizYVdKKxKR1Iq2kkz0FZtWqvO/0KJG64GDWsZFTkibqjhqxDxT3oDDhci8ZgKzSGu5ddYLrvzjE0eQinpriz74nx/HLPSKt8K6V0c0Fohc2iYWF/9T2T+gSIbyPmvJ1k/fBisni/+/dAw+fEDValT2vgNUZZdUN/HosnQVD3PF1MMPRwpCXrw9ic2wOhxK61hfrUGIhkf2s0ddtHqbH+9tzLKW4Y5Wmo8uao3Atf8/dZH98Acv2JfHoBF8Gult1fkBNCfz1CHiNJWDag2SW1PRolEMNXQMIv12oHXZ3YZKtym1vz8ipLYO/Hha/JVXfrbNZZUglEODUysgJdECmqlma/PF+Rr+394qMkph08XwPdLck3N0KB3ODLgvfXDW8x8PcH+HWNaLOqaPUmJ3/Ewvaae93nMpoaAE3fC5qYqI0yPZXFbaJ8PcIubFifRA6V0Szi1OgoYb88jrszQybjJZAZ3MUSrioQbXyaHIR5bUypgY7anyLwZ7WfH/nYE5nlJJTWsv7N4U2OVSvGFtfkVqmEgqYFOhAgJM5/5sR2LSLkb4OC4d5Upd4ALmBBdgHNR8vkcDtG4Xx2ZKAG4QK4a83izGlk3qJfZcKCHWxwMZUfX001MuGqOSitunKqYeFoqfvZPXtniMh60ST0dYeiQWV1DYoGOBqCUCoShziTEYH83DWSTB1EDWoLuGAssebiZ/NLCMwbRX+knS4dS15TuMZUbO3XbXTyjN/UoIFHmHj1Lbr60pJMQrBouBEx2+oUFB/5Cu2yyNYMnMsGx4YwagB/Xmx4S7RBuPgBz310Xqe7FPCSRlxp1Dl7KFsnH+XkZMXJ+QNJ7wscvSlepC0p3vnKLgoQmG2vs3b7Ps3GUuJ+cLy97E3Fcpen4bid/YjrCUV5DiOE+/7dAq7g95hhDSOYUcWqxfkHflcRIYG3yP+7TEc0o405eW2xyAPa7VIjlKpZOPpLCYFOmKq8h79k5RW17N09Wle23yeX46l8eXeRGRyJVseGYWXrQl3ft8NSchOyCiuZsf5PF6cEYCPvSnOpSfA0FIMvsY2QtFMNSgqlUpOpBUzyNOKYV42OOuWEV+tnhKA2xAxYLZIQWyDvAHW3C4mhDs2wbxfxMR2ciXIVJ8rfjso5dB/uvqxvpPF9iQNufulGfDTLNj67GV/H61pkCuacrhPdMG4VCiU2MmyqdW1EBGa8mz8Hcw6lpEuTReRnEaaFNYuP2Vtf3wBEz/cz+mMUj6cO4ADT49jyRhvjPR1cLIw4v25oVzMrRC57bVlwkgJuL7jk7oOgoYqKGh1XZe2wS83i0jPvbvBKVSkIMz+SghG/MP8fCyNermCu0b0a9p2Y7gLfg6mrDiU3OnxNfVyYtJKGdHKMTLS15bqennTYrgNpRmw7x0x/oTeLKLWsu4/pwUVdTy++jTDvGx4eLxv5wcAbHse6ivhhs8J97Am3N2Sr/Yl9p6a1KC7oKFaGDqtOBBfwPgP9/Hz0bS20aTGPHy7/m3P6TIITB0hfisgEQpPx74lLrsMbztTjPXVx2I3a2P6O5rx/eEUTA318LQx4fbvj/PZ7hbOgfjtYqzpQkQiJr0EDxtjbE0NkEgkjPK142BCHzdyrL1E7ZzfFLE4jlmluUg9/6J4bfxLQsmrM3wmwLCHYNcr6imEFbmwbBisvaunPkGLc6tSesychaMHJRRcIr+iDnuz5gV7f0dzpBKYvewwwS9vV1PC2x6Xi7OFISGqhbYmhnnbsOa+YSy/YxC+qnTiHsHGW6SfxawCwMPGhK2PjsLT1kRttzuGeTBU5yL7arx5/s+4zjMEdHThjs3gPQ42PyaiRQc+0Cjw0yBXcDixkDH+9m1eG+ZtQ155HalFrYyWhB3iO3cIUt/uMUIYnZ149WNVxkyj8p2ViT7u1sbNAguayIoRqqwSCdgFiGaojdGdHmL57lhu192NJHwh+E3GaugCgqWpHDnaNiJTWy/Dp3gfaXajkei0XfNVOw7GpT4ZZUdOnaQ96Jel8If+DGYNFJkQL04PYJtiKHtsbxMKpRc29djn61HiNojovO8UcIsUqapXmIkA/zYjJ3q5yMkPvAEMTMF9aJuFplKpZPeFPJ7/4yzzvz3adiGef0EMyrotPAx2/UX6gkJOYkElRno6OOuUC2WvEUtRPpnAXbzKJudHxXvrG7OqIoKPbV9DmhMjmsqBuCEnf4TIe4UnCoQ6SV5cp16nQZ5WlOelUr/6LijLJC67nOSCKmYOcL7CL+3y+GRXAvUyBdNDnXj5zzh+jErl3tFeBDiZs2xBOA1yJT9FpfXIezU2Lpsc6IilsT5eVafF4CaVivoahQzqxIOdWlRNYWU9kZ7WSKUSnHXKOF9hpH5C54GibqajnM64P0Rays2rxIIYhKJLZR5cVA0CFzcLNTVLN/VjrfuJ30zrlLW6SvhtPugYwKRXL/v7aE12aQ1yhRIvW5M2KY2ayC+vxZ1c8jxUxll6FL4OpiQXVGmW1VQooCwDLN2bt5nYisXeFQz63x1IxtvehF2Pj2FOhCt6rXLOB3lYoyOVcDqjVORjy+vbRM0UCiU/HU1jxDt7eHrdGfLNggCJkA1vRC6DP+6DfqOEN1lffUL/p2WLaxvkrDycwuyBLjhaGLa4DAl3Du/H3ksFnUpBn0grpl6uYLiPeppqqKsl5oa6baJBSqVSFC4f+kg4Wca/CEPuF7/ny1AOemVTHDKFkk9UwiudkhYFZ34Vefyq39HDE3w5k1nGvt6KRFi6i6jqwQ/hzwdFimJ1MckFlTz4awy19XJe3HiOG5cdpqRlLVRWDDiHiXSY1ujowiOn4KkkuHsbRN4HW5/CPHlr0wKqNa/eEMQHcwew4f7h/HbvUOZHuvPN/qTmQvIzvwnPugaVyNbEpJUS0SJqNspX9FHrSN2zTxFxJ5RnahZvOL9RGJcRd3b9fBNfFelTa+8W47lcBuvuFrUUyfu6VgPUHcpzREqynmGzEZx/gfyK2qZ6HAATA11+WjSE124Iop+tCSsOCsdFbYOcredymRLcNlWtNaGuloz2s+twn8si/HbhDO4gCm9joCBMmoShz2i2xOYw+eMDTPv0YMeOMEs3mLNcRMk9hsH+9+D3W9ukQJ1MK6GiTqZWj9NIpKd127ocpVLUWfpOUhurq+pkvBotpUpiwi+rfyG5oP3UszOZpXjZmmBh1Fy3HOpqwenMdiI5CoWQKW6M5urogmOoGBt6iPi8CmziV2MiqUVn+EMA6AdMo1ZqhE7c+jbOn2N7N+FKPnZD5mk8n7nfSHRQkH+h/ZS1uqPfclHpTtDQaRjqifHN3tyQJyf7sShzKnnOE+CPJW3rc6oKRW16Q6t+jXnnYdPSHjE2OqXgEjgN4EJ+NRsLVanqmVdel/PvMXJqy+DMavEANxoo3uNEmLPFjfnrTDaLfjzBgfgCopKL2qZLFFxq68GzCwB5HZSkkphfibe9CdJG42no/UiNLUW6j2oAqJPJOZZShG3wOAi9RShZZJ0UCy2JRChxNeIxAlB2WiMy2NOKF3R/Qv/CBtj0KBtjMrE01uudQbATLuVW8NPRNB4c58PHN4cR7mGFlbE+i0eLvGF7c0PmRLiw8khqj0hfHkooJNjZAisTfewNZHg3xIswNYCxasJX1eU0pslFeFiBXIaJrJSkGhOKKlvUjjQWjWYcbf9NM6NF+N1nQvM2p1BwGyrkTM//KSbq1qlqjfhNEd6nRo9lXSWsu0t0jJ7/m3qO+RXS6PWaE+FKVmlNp5K1uTnpmEjqkHiMEAvB9KP4O5pRL1eQqmlxXZEjDIyW6Wog0sbO/3VZOfElVfVEJRcxJ9xVbeJpiZG+Dv0dzTiTUSq8S44hwoBUUVhZx63Lj/LSxnMEOJmxPS6PMZ+dpMbSV6QwNJJ9CmpLYdQTYnFyFaltkPPQr6coqW5g8ei2aaazB7pgaazHj0dSOzzP4cQirE1EDntLdKQShnvbcrCFclBqYRXXf3GI6784jCJpHwTM4Hiugo/P6lPlPBzlse7Jbu88n8eW2BxemhGIvVkXvk+FQqhjOYTAoLubNo/1s2OAqwWf7ErovWjOmKfB1k9EbQ9/guKzcP5a/jo2xnpsfXQ0vy8eSnJBFa9ualHsmxWjLh3dGn3j5p4+U99B4TKIweU72jVyhnjZcFOEKzpSCVKphJlhzlTVy7mQo2qKmqZqrnj82w4/SmWdjIu55Qz0aDZyRvqISN6hvh7NacQtUsynjY6/llzYBH6Tu/eM6ujCTd8L8ZEVk+DjQJEqfPMq4cg6u07zcfHbRSrymjuaUg67REWOiCiAqAkyc4b88+RX1GHXKvVqhI8tC4d5sni0F2cyy4jPq+Cv09kUV9WzYIh723P/UwTeAAYWTdEcjVz6G4m8nhFT53Ps+QksWxBORV0Db2zpgqKmS4QQibjpe3EvUg+qvbw/vgBLY72m1LGWWBjrEeTcqi6nKEl47Vulqn20M55forNINArFp+ZMh41NYzPLhOhA+lE49AnseoVxFnlcyC7XnNpblCAUvFxajAMu4c2prD2BvIGHjbajDJjZnCWhb0yJ+xTG1O3jXGsDLGYl+TqOuIRP03g63+DBlCuNKL5wQPP7Veajl7SL9Yqx3NZKwff2YZ5MCHBkTvatyKV6IjraiKxOGKs7XhBrGHmDSA/c9y58M1pkt5xceZlfQjcojAdbP976+wJP7C5HaWTTI+ID/x4jJ3aNSEGKaBGi9p4gtqVHNW367Xg6gzysOPj0OEJdLdjTuiN1wYXmVJxGGjXi8y+QlF+Jj50pJO4Ulr2ZyKv1dzDlUp7wJJxMK6G2QSEmoEmvCx3578aLiXb212DSwvtq4yOiT510EfauOMl0neNcsJkMibtQnP6FaSFOavn4/xQf7LiEs6Uhi0b2Q19Xyi/3DGH70tFqaXP3jvJiVO1elB/6CzluDb2GuoJcoeRwUiEjfcVkHiC/iB6yFkaO6rusFhGM6JRi/BxMsTTWh6oCJCjJV1pypnVY2nOkUEbb+mxbMQHQrK4EIgqXeVykl1h5Qth8zRfuN1VE5058Lwomvxsv6nfm/tAcGeoh0ouq0FEtnqDzupyybNGg0MqtP7gPg7QoQl0s0ZVKOKCpHqRlj5yWBN0ovLKX4U3ZeSEPuULZbl56IwPcLDmfniciOY11cgjRjZu+OkJCXiU/3DWY5XcMZv9TY7Ew0uOC1E+9ADZ5n/AQu/SimlcL9l7MZ/GqE20kXyvrZNzx/XEOJBTw1YLwNspKIAy7eYPdWXsio7mZbWuUSqTnNzDDvV5jmutIX1vOZpZSWl3PtnO5zPj8EKXVDdQWpSMtSeai4QAWrjjG53sSeCE1DEl2DBX5XYu6ltU08NLGc4zytWVOuEvnBwCc/lnksl/3jlp0RCKRsHSiH2cySnsvmuM8EO74C+47QMODJzgijWBp3desG3ACC2M9hnrZ8Pz0ADaezmbPxTyRzleRrfnZ14RUSpHtYAZIEgh26lpaUZibJXo6EtHvpjhZRNN8p4iFd1H7vTsu5JSjUMJAldAEgI2pAcEu5hzsYh3XVUciEZGa+G3q3t+iJCFN3OIZ7zJGlnDfQZj3q3AaTv9QLOR9J8PZdhok7nldODRL00TKYRcL5qnIbZrzgSaVyYLyOuzNNRtnkwIdsDDSY9PROFYeTmGUry0+9j2YgtZd9IxEqurpX9tPVT39m8hSsO+PoZ4O00KceHKyPwcTCjnZxbpP/KeJlPL976ltPp5SzNB+Nu1GgIf2E3U5jcpniuT9KCVSFJ6jmvaJzSxl5eEUHp3gy4CR0xkoTSQ9r/1nIDG/kuFmefDDdJGue+QLxhStpl6u0BydaozYOIc3b3MeKH4vVV1v1dARfmWHsZLlozPyUbXttsNuw0Oaz6F9fzdtO5uQzNDaw5QGzBcZLBqwNTcmTjcIw8xDGl+Xn/4dORIagm5qUwsllUr4cG4YGFnzjc58kaWUfkw4YTY/Lr6Psc+JMeqn2fBpKOx/B4beL6TdT//Su6IFDbVQmk6BoRsHEwqRK6DUdmCPiA/8e4ycpD2i1qJl6pBjqMjhU0Vd0ouqOZpczNxBrkgkEsb3t+dAfEGzJV9ZICICrSM5Fq6gb4Yy/wJJBVX42hmK92vhWfB3NCcpv7Ip39TCSE949swchGEz7gV4JEb8IFoikagWmh0YOfIGJNueIcmgP6/qLeWs1SSWylay0LP0Cr6wy+fNWcF8MT+8KdyppyPFykRfbR8vGyNeMP6D8jolyl2vovxySPuF+B0Ql11GaXUDo1QeS5/qM5QqTVA25uYaqRReVJGcS3kVzR7VSiGlW61vx+n0Vu89+mkYsVQUwa6YpP6AyuqFQdpygGsk6EbhJXw4hktzdqI0b2eh5xopoiR/Pwk/Xi9qdO7dIyI8PUxqUTXOloa4WhnjZm3UaV1OXZ5YSJk5+YrfXt45LKTVjPS1ZUushvSOUpUSm2Ur76PnSDCxvyy57K1ncxjobomThVGH+4W5WTK+dL2os1HV4xRX1TPn6yPIlUrW3T+csarcbktjfcb42bGnykPU1jXmJifvFQpBus2/UblCSVpRFfvjC7o+aXeRvZfy2XE+j5+PqhsOyw8mcyqjlFV3RzI5qH3j7tZId6rq5RyM1zBpy2UoNz3K0xXv8nje81DXdoIe5WuLQgmPrT7Nkp9PMszbhi2PjOKTIcIJc/sefQZ5WnHqf5O5/sbbUCglrP79h077cMgVSpb+foqqOhlvzQ7pNN0GEKIOu14V416jY6IFY/3tCHW14Ku97S/ur4SSqnq2ns3hbGYZL+0u4o7SRWQE3Itt1JsQtxGAeYPdGO5twwt/nKP27EYRAfAa2+X3uKTrj52kjCCT0i7tb6inQ6irJdEpxc09PmZ8LFKYo5e3e1xjhNbNylht+yhfOw4lFvZ9KelGAlTjYUvp3wt/iTTelupZ3UFXX9RGzl3ZHC0MvVmM463rBnPOiO1T34ZFu4Sjcc/rgEjrPJpcxP0/n2T8h/vIK2+VnlORA2ZOzf+2D0CRf56KOhl2ZuoLx0YMj3/JToOneOLUFJ4qeolFQ69OirkaA+ZBdaHmbIaKXLFmClugtnlGqDNediZ8urv9NDc1pFIY/YSI5KSL96ltkHM2s0xkWrTDxEAHCirqOJwkxr+0U7uJk7sz7ZszrInOYHNsNs+sP4uvvZnIHvEciT4y9DJbZMOkH2syRqrrZdQ31DMx/jXhmHwmBYJmYVWXgVRCWwcoiDRsq37NCnLQvB7I6aFojv90uHuHSI1tgZ7veMoMnHBL+LmpSXTyru/QkSjxmnRfh6cscBiJW7UGYSOlkproVeyWD+SG4ZqdrBbGenxySxgfFA2nxMwf1iyED/zg9M9UTnqfVQbzkN3wpXAA+06BB6Nh8uvieStJ7XgNe6UUJ4NSwdYcc8wNdTHQlRKvFyCMr84EpDrh32HkKJXiIXIfqr5dKhUpaxc2Q9QyErZ8irVeA9NDxSAzob8DVfVyjjeGOQuEglobI0ciATt/anPOU1knI0InSfyIWgzI/g4i3edUeilrT2Qyxs+u2VPRf7pImzBox3vjMVyEQTVFO5RKIalZGM/p4Bc4mlrK7TlzkJs5E7BpNux9W3hKfrtVhBhTD/W6DKC9uSEDWngTmyhJEwsHpRLO/4l9QzYfWjzH1No3aSjNQXbwY0Dkoi756SQFFZ3LDx9MKMRQT0qEpxgUXctOEq3oT2W9akHWGMmpEfcwr7wWp8Y6B1WExs7Zo23urYEpjH8BbvxGeGdKUppfy1cp7LloMHKkUgicyb5CM6Z8coAd7alD6ejCw6dE87pFO+G+A20jhD1EWlE1njaizmSwhzXRndTlSEpSKJOYi0WV80BACfkXmB7iREx6adv8/pI0IfTQWEfWiFRHFBLHbexWt+vy2gYOJRZyXSdRHIARylM8qbuGjIB7m76/PRfzKaio49d7htKvVcHsaD87dpW7ic+UHSPSBDOOi7x9FfUyBTd+dYQx7+/jju+Ps/xg54X+3aExffCjnfFNfW9qG+T8fDSNOeEuDO1E7t3N2ghTA11Si1qlDiqVIoJ46mc+lc3GtC5PFPm2et49bExwtzZm76UC7h/rzTe3RWBhpMcA2VlKjD2JDAlk+e2DsTDSY8KgICptgnEuOMSLG881L5TLs0VKUeN9LU7hwlcLGJL0Gb+OyMHNUvOCrs31/vmgiGRPfVfjLhKJhEUj+3E8tbjDviKXw6n0EqZ/dpD7f4nh+i8O8Xt0Bm/MCsZt7nsiYvDHfVCUhEQi4Z0bQymsrKMkeo34rbRc3HTCoVpPAEwLTnf5mEGeVpxIK0aZdlh4uy1cIHyhcLq0M2nnl9dhoCvF3Ei96HiUry2l1Q2czbp8xcaiyroeE4rpFAsXkULYsgbpwiaRGtzeHNkJ+y7lN8/jjfhNBQPztmpuMT+J7Am/KWKcHvucuJbUQ6yPyWLet0e5kFNOSVU9z6yPVU+lbBPJCURanoUp1WrCA00UJsDOlzBw8OPdhnmM0jnHmNhnrnhhdsU4h4u5U0PPJ2JVKnghN6lt1pFKeGS8LwfiCzjVnrBJawJngY2vUHREOC3r5Yqm+VwTQ/pZ42Nvyk9RQhTEMDeadJMQrIz1eXp9LA/9eoqM4mremRMi6jgdB1Ck70JY0WZxgrw4+H6ykCw/8D6VF3bzlO4arMrOww1fiEiWjS86xUn0dzRn+cHkto6ukjRh/LbExgf0zSCrh4wcqVQovbbZroMk8l6mSI6y7chJTiTnEZq7gVTbsehaOLXdvwXmwVPRRUFB7E71F7JPYVqWwBGzKWqR4NYM8rRmaogzS6vvRu4QAmG3UnvLOm496cv//ozj+aRAlM9lwawvKTP24O2/LxDxcx31Js4iMthbFAnD+sd4XW4e5EawiwV/KEYJx7FEQ+1kN/h3GDmFCWKR6z6s7WuBs6AkFeWulxmb/B67DJ/GNFn0vAlyNsfezIDdF1UL1YKL4gtrqazWiF1/5HnCI+RXHiUWfS3SX/wcRerJQ7/GUNsg59nrNCjztIdrpCie1yRPeOQzoYB03Xv4h49BIoHFUyOxevSw0G7f/45IB6spFp3gf5je3Cjzn+avh2DtHbDtOdEwzWMk7y1dxGMLZvGDfArKqK+QlWTwxJozbIvLVc+Db8cwO5RQSGQ/Gwx0daChBuvSsxxV9Ke0WjVJ6JuAjj5UFyFXKCmoqGuuE1BFcjzdPTiTUao577/xHrYsKMw6KRT2nAZovCaFQsn724Xa3l+nOyhs1dEV0Q+3yLbF7j1IWlEV7tbCuxvgZE5SQWWHNQ6GlWkU66u8iY0qRuXZTA5yRE9Hwt+xrYoOG3vkaCL4RqjKh7+fEil5+9/v+GLTj1K74nr+0HmOGW6dTPS5Z3He/RBRhLLR5p6mzYcSCujvaIabtXGbQ0b62JKIK9UGdqLgPPUQKBrUPPNf7E3kXFYZn84LY9+TY/lsfgf1F5dBelEVM0KdUChR+50UVtZzdws1tfaQSCR42Bi3FR9Ij4JLW8gc8yEfy+aSMfwNIZN/5vc253h1ZhBf3xbOM1P7N6e0pR7CKmAcX9wajpF+88RgHnwdEw0usO5EKneuPE5pxnkhj7zpUdjyOFnZWeR9fQOO+Qe5zfgoIUceFRLKnRm2x78TdWk3fC5qJtpharAjtqb6rOohoRKA9SczufmbKGzNDNi2dBTr7x/G+vuHMT/SXSwuZn8txvDdQgDE3caYu4P1cCqPpa7/zI5P3oqofH2KdB26lToR6WlNYWU9smRVR3kQ9X115aLgWQO55bU4mBu2iaAN8rDG1lSf1dEZ3bruRhrkCuZ+E8V1nx7stNt6j+E1DpL2inG/NF2MuZ0pJ7bDwYQC7v4hmpu/iWLhimPNSmB6hsLLHPWlEPwBdsamUXfqd1Erq6OqBQy6URiau19n/YkMIvtZs+eJsbw7J5R9lwr45Zgqki2XibGudboa8IDuXzgYangeTv8KBuaYL/iB7JAlnB76MZL4baJO92oilQpp79YCEEqlEMLwm6LR0L9+gDPWJvodSr+nFFY1R8CkOuJcqvn1RGoJ+rpSgp1bOMyqi5siPSDGv4VDPdh1IY91e4/hpMynf+Rkfls8lOMvTODUS5M49b9JzbL1UinJHnMZ2RBFQ1kuHP5MRNtCb4Z972D/x80s0d1EUfDdQhABhMpcTQmfzXTH1FCXm74+wqqo1OZrqipoWzcrlYqoS0/W5bSD+fC7kUv1qD3yLak/LsFdko/r9Kc7PS5sQDjpSjtKz25T2159/CcKlBb0Gzqz0wj801P6c6TWnQ/t3uR80BMsjrIgMb+S+8Z4seZEJu9su8Srm+IY/f5efjiSSr0cDplMEgI2hz4W84aq5UqPURhPg44xSbVmLBjqwUA3S/Zl64JDYLvpe12lR4wcpVJJamoqcvmVF6FrpDHk2tjVviUBM+B/RRxbcJFJde8htfGB1bdBaTpSqYRx/vbsvpBPVFIRZ08fpcbMA4VUv+157PtjWJaEsVSGVfouMUC0kPGzMzXA2kSf/Io63roxBGfLjtNw1HAMEaH61kVUaVGib8DwRyDyXoJdLDj90mTuH+stBvCpb8FTyfBMmlD6eTpZqBcd/bJ3rWpNpEWJ2pOAG+DYV6KfwMjHkEgkTA12QjnycSoVepz84UnisktYONSDzbE57DqfJyb1D3zh0la1U9bUyzmZVsLIRgWpzBNIFQ0cUwRQ0uh1lEhEylp1MUVVdSiU4NCoclORB0ZWBHvYU1bT0FaWEkQ6oYWbuhpX1ilRHNuOYbItLpe47HJG+dqy+2IeVXUd9wToTRQKJenFzZEcBwtD6mWKZiNQA1a1WVSZqFLPjG2EkViejYWRHqN97dh8Nge5Qkl8XoXIi27dI6clrpHitZhVIhK5/x0oiNe876FP4PspVBVkYK9Xg/Pa6ZDRzsLw/J+wYjISc1d+cH6RU5kVTZ/3UGIho3w195SyMNYjxM2ab62eFL/HzY+JXgeqxci5rDKW7U1kyRgvZoa54Glr0kbV7UpokCvILKlhsKc1Syf68tvxdJ7bEMuKQymM9bfrshysp60Jaa0jOUe/AmsvLtmJPkEGEQtEysO+t9v0pRjnb8/U4BaGRVmmiFb2G0UbfCaiL6tgzXRd6jNOo1g+mYxyOasMb4OTP6D7zXCM6os4OOJHTJ5LgFlfifHlr4c1e6TlDSLCvO1ZCL9DjMEdYKCrw/xIdzaeympKz7hSvOxMuDXSnbVLhtHf0ZwID2siPFos2vRNYNzz4nem+g3eZxtLvVKHNRVdr5krqarnXFYZ5bZh3TJyIjyscKYQvYqMZiPHORx0jdoUajeSV16Lo4a6D31dKbc2fn8dPPft8cvRNFIKq5AplCxccbxLEfYrxnucqH0qTBBCLgbmbaX4u0BGcTUP/3aKoV42fDovjPTiauZ+HcW5xqjWhJdFhGzTI9RuXErphscxkFWQ7zO3+SRSqfgtZBxFmnaQ60OdkEolTA5yZG6EK29uuUB6UbVY+CoV6ulqTmGkeC/kXp0t+K8epT72KeTCARE0G4m+CZ/OG8jgqbfDiEeFyERF7mV+eT2EzyRRB9VSgS5pt2hw2ipVrREdqYRAJ/N2Vdaq6mTM/foIs788TGGj2I99oFDnrC3jZFoJA1wt1GuJ/7gPvp+qZnDNDnfBUE+Hg3u2AOAVLgSA7M0MsTLRbzNmN4TMR4mE6p1vwbl1olZk+ofwxCUOX7eDkXWf0DDxjeYDVFEaH2kufzwwgutDnflkV0Jzs96qQqEg2hrnsB6XkdaIkRUlvjdxq2wjN0n2UD/1Awz7De30MEsTA+KMBmOdc6DZcSyXwfmN/K0YxqwIz07P4WlrwoIhHizbl8S0zw4SlVTIsgXhPHddAEvGePPNgWQ2nspi9kAX9j01lvlD3PkwfxBKeb2odzr/F/w8R3Ot8+VSmEiOnhtBzhb0szVhoLsVueW16s3CL5PLmv3feKP5xxQfH0+/fv0YNGgQLi4unDzZxQK/7pB+TCxK20sxkOoQlVREgYE75rd8jWjqFAvA+AB70ourmf/dUaoz49hXYsOo9/aSUdxqQWzXH11FPd8bfYqkKAEGL1J7WSKRMKG/PbcP82BGaDdzbnX1xcPTepKM/k7IWU9slhu2MG6lRGVi02xsSXVEnUnoPCHr18ONqzrkwHsiBWHuD+K/yMVqymR3Twxjrcl8hpRtI874fl6reYOp/XR4aeNZZNtfFBPI+nvVJonoVCGTO9JHpSCXdhiFninnlZ6UtJzMjW2guoj8cjGo2jVFcvLA1LEpte5MRqnma3eJEJ5ERGF1RfIxSiyDNXarliuUfLjjEsO8bHhtZjC1DaJ79dUiv6KOOpkCdxsR1WhM1cspq9W4f4NcgaMiF7mlp9ggkYhJW9X/YXqoE2cyShn85i4mf3yA7w4mi0hO63ochPPiYFIRj5p9wPKh21Hes0uca9uzbSNzSiWKEys5oD+aebofoVy0S0SHfrtFqLc0UlMiDJM1twtHwqId+Hq4c1oVibuYW0FhZT0jW3TKbs1oXzuW5/RDPuwRsZDyGgsSCeezy3nw1xi87Ex4ZEIXe7t0k+zSGmQKJR42xiwa2Y83ZgWz/mQWl/IqWDSy8yhOI/1sTEgpbDEGlWYIyfLI+8gpr0MqQaTHjHpC3J/zGzs+YaqqGNWjbV0MLhFgaEF41q/8avg2MlNnfgv+jvO+97HX/SFsdGvRu/UXZk8eL7yAYbfCrGXC0Pl2rDh3YaLwzO99G74ZI8aDEY/CtK41l7t1iDv1cgXrTmZ2af/OGOhuxaszg0UEuD3CFojU5B0vQnkO1imbiTeN5PMjBTy7PpZJH+3nnh+j+fZAUrvG1774fOQKJVZ+I8Sc0lpitR0sjfW5wUoVuWo0cnT1wX0IDUkH2Hgqq80x+eV1ajLFLVkw1IMGuYI1J7oXzSmraeDT3QnMHujC6sVDKa9pYMz7e7n7h2h+bYxg9AaeI0Xt07n1Qplp8KK26bAdoFQq2XMxj9u/P46Jvi5f3BrOzDAX/nxwBG7WRtz63VEx3kulMONTGP4wdXGbuUG5n8PKUFYmtDIW+0+nxMyPh3U3MKVFvdxL1wdiYaTHCxvPomzqkdPCyJFK2e/1BJMaPkSilEPMj82vJe0V409rg2H4w6KH36FPuvx5ewXv8eJvo3FRXy2KzD1GgP917R7m72imsckpwIpDKZTVNFDTIOeBn2NEvbODaDSqzDvPybQSdWdD8n4R7bVwhfWLhEMNMDfUY2aYCxGSS9QYO4sUxw5wc3VnmyISi3M/Con8RhlyE1sypU5kKu2xMW1xz21UypZFiejpSHlgnDfFVfUiQqVUivWISds5pqT/fMpnfNvrJQEAjpMeRaojpXbIwxgNXdT5ASpqPcZiI8tDXiBSvJRphzBuKKbAc3qb2un2eGZqf75ZGMG6JcM48uyEprrXp6f4s/HBERx7fiKv3BCEk4URswe6EFdrw57pB+C5TJGaL6+H3+Z12qS1yxTGk6x0xlcl1jPQ3RKAU61rrS+DyzJyNmzY0PT/L774Ii+//DKFhYV8//33PPXUU1d8UW1Ij9Kc29iCUxmlhLlZIrV0E/KJeSJVakJ/e96aHcIfDwwn0jSfkLCh5JXXtl24qup0hspPwvQPNBbRvj93AK/NDL68z+A6WBg5jQ9PdbHIUx64sHvhOIkErv9EPKDRKy7vWrpLRrQQYhj9dHOdRquO1Xo6Usbd/j+We3yIdNgDSNKP8YnkYwbUHEU37aDIlTVzEFKFqoLxw0mFWJvo099R5f1OO4zcbSgKpOr548bWUFNMfoVYYDRFcirzwMwBW1MDXK2MRL8VTbgOgpwzJOYUM++LXRiXJfJ+nCmj3tvbVFPRyP74fJIKqnh8sh/9bE0IcbFg05kczedth70X8zvU9O8OjXUbHiojp9HT26ZgVkVufgG2knL07VrIF5s7N3nzJgc5MqG/PTcOdOG6YEe+2nMRZXmWKNhsxb2rTrJwxXFOFenxxp5cnt+cjHzS68IbeOlvtX1LUk8jLU3ll5ohfHPHEByc3eH6z4RgRLwqtJ51Ej4fJKTgJ78pGusamBLubkVxVT1RSUUcSixAX0dKpGf7NROj/eyorJNx0vtB8fxE3Mmvx9KZ9eVhDHSlfHVbRMeL3ysg7f/snXV4FOfXhu+VuLt7QgRCcII7FJdSoFhLS426y6/u3n5taam3tFCgQCnu7hoIJCQhIe7ustn9/ng3m2w8kECge19Xr5TN7GR2Z3fmPec85znqaqGHjQkSiYT5oR6sXzKQl8cHaOx+W4OHjTHZxRUU11QJT/0kbt495pJWUI69mSFymRRce4PnEDjyZfM33oSjIglh5tDwdzK5WPBEbkJm4Yr9o9t54c7BfHhnd0bc9x7yl5Mw6jJC+zk95sLi3ULW+dtE+Ka3GHJ7bKlYsNy7FUa/oWX20BxOFkaMDXK4sVbIMjmMfVdMzv48AFLPYhs6h/yyKo7H5dDT3ZKicgWf7ohm+tIjxDbynd0dmUmQkzmWfgOFLDL9Qqv//GDTZNIldtryJ8/BqBKP89zq0w0qKhlFQq7WGA7mhtzRzZHlx+PbZEDw7f4rlFZW89xYf7ztTNnw6CAeGeZDcbmiofNoe2JgJu55hz4V1ZG6YxVaIK+kkgU/n+S+305jbaLPb4v6Yq1evFka6/Pn/f3xsDHhru+P8cfxBFQSCdEhL9Kr5Ct+HHqUXb2+46+TidrjDSQS/jSYTag0Evvc2kSsuaEe70zrxqGYbI6FXRQP1j1fiERTmakbkoCJ4p5d8z0MWyH6Udz6ab8AY2vo/5Bw3qw/j+RGYmonHNRq+nIOfizuA5O+aHZ2WICjGSn5ZQ3cH3OKK/jhYBxz+7nz3fzenE3M47Nd6pEcEik5V8PIKamkT43pgFIplCpOIfDAPtA3hb/v1bx/j47w4Q6LBAy8B7X4UpwtjVitUvdI97lPK2DOLq7E3FCuXT0yMBNz3nKuqF+TOT3dLfnrZCJUlghX3kaCnB8jZAxfU4HiBph8SOy6IHs+BsPx77a8cR1ce46jSiUj7YzoUco7tYYUlQ0hoa039TDSlzGuqyN9PK21DDWkUgk93Cy13stAJ3MCHM1YE1EqJKCWbnD3KlElPL60TcfeKCoVqpwrhJfb42MnghwnC0PszQyaXtO1gevWcURFRbFokbB1njBhArm5LU9kbxPFWWL2SGP9OGqUShVhiXlCwymRiMxCpghy5DIpc/u709NagaQ0B9cuPfC1N23QBKsydyEVO044L9Sa9dBuuPYV2fRCdQbvwhpR7u4xt+370jMS+uaorW1qCL9mDn0K1j6iP6MZ/BzNWbxoMQajX4HZf2CYfpql8i8IU/pw2mqCsAAtShPla6WSo1dyGOBjI3oKFJWQdAq51yBkUon28D5jIVfL0FRyauRq6eJChpCHbAlP41JqI825Lr2huoL/fb8af2UcMomKu6ZMIbekko1h2hnVf8NS8bY10VyoJ4c4cSA6s9Uym/Kqap77+zw/H77a8satIFG9qK7pyanJ9KbXBDlKpciWHf0atr2E5JDIrJs516lk1AlyTA3k/HxvX16dFMQ707phTx4SlVJI+uqQkFPC7sgMXp0YyIHnh/Pe9G6sPpXIx4n+4DVMVOWidwJisNuaP7+nDAPumXsvPWoaHx27Calm2F9qg42XxI3lsVMw8DFNcD/c347eHlY89/d5tl9Mp6+XlVZPSX1CXC2wMzNg1Zl0mPoN8SYhvPbvRSaFOLHxscGaC2VHkJhdyBz5ftzPfKiRkHVzseChYT6tcyNTU2OoEJ9dIpxrzvwqMsKG5qQXlGsNEWXwU8ItKnZv0zvMTxBBTlP0uR+6jIeFGxpWxJsKVFz7wIP7Ye7fsGADLDkBL8bDvDW12vc28OGd3fn5nkYkxx2J3xjhennX73DHRzgOuJsLb4xl//Mj+HhmCKsfGsC2p4TEb9rSI1oN15UKJQeishgdaC9s4WX6bZKseekXkFBto0nOAOA5BH1lGcGSq1xOr70HqVQqcd6bCHIAFg3yJCm3jAPRrQtOiisUrDieyD0DPTXyajdrYx4f5ceahwfww4JW2mhfK94jRC9qzwWtnhuWnFfKzGVHCU8p0GSa60tALY31WfPQAO7s5cJrGy4S/OZOxn5xEAczAxYP9WHhQE/yS6v4t861vaCsiq/TAsgz8RaL/TqMCXJgYrATe09fQIWkwbFm1vSBBk4R37P08NrKa4+5jQcMAx4V8/z2f1D7mEp1QyoEWviOFteN1QtEL8uQZ2pHZjRBgHo2V3S9as63+2NRqlQ8NtKPUG8bFg3yZOWJRMrRB2tv8uPDAOhVE+RE/ANpYWLMhqkdTPxcSMHUsn1XYyVOpTFIW0hgg5DR5Vj3ZrXba+I11CG7uAJb00YqoLZ+moZ2gLv7uXPkSg7JyeoKa70gR6lUseFcCmMCHUSC6UZg1LRBQ1OE+Lpzkq6Yh/0AxZkYxmxhj2QAw/wbSXC1E9N7urD3cibLDsTy+a5oMsy7Qrc74fRvrV+DluSI5H59ijORVBQSWeWgGbsgkUjo6W7ZegOMZrimM5mfn8+mTZvYuHEjVVXai792H/qWpLYNdGv6ixCXXUJhuUJT4sI+SExqrUuNDtqlN0FO5kTUC3LickoZXP4FBYM6qKm/pp+opppz7g9hUV0va9RqAiaKkmvdXpOOID1cZOIHP934hPCm8BwMEz9DKpGwyvJBXlwfTrmlL0z/HqK2UrbnAy6mFjDIR539TgsDRRkSj0FYGulpy9XUPTmZhRVYGevVZunVlRyAF+4IwMZEnzu/O8qOS/W00E4hKJHRSxrLJ057wdiWnr0HMqyLHWvP1spnSisV7LyUwZQezpoF68TuzlRVq9jXyqznlgtp5JRUcs9Az9a/V80Qn1OCvZkBxvpCsmggl2Ftok96jVxtz1uwfArseQeit+Ec+QsVKj2sPYJrd2LmJGQV9bA1NWBJL3FziCrTHjq581IGejIJs/u6IZFImNffg3sGerL6dDKVM/8AjwGo/prD8qXvcvePxxkpOQ0+oxgY6Kr9R0LmCrnC+b/EvJ3RbzaQJshlUr6c3YPCcgVnE/Nr5YtNIJdJeWioNxvCUojLKuabfVewNNLj3WndNLbnHULcAcYcuosP5T8gO/Y17HjlmnflqQ5y0lLiYfk00SQ/9DkAUgvKah0EQcwDs/GDi+sb7EdDYZoIZpvCawjMXdW4Dr05pDIxwNFnBNgHaPUpthULI71G5/50ONbe0HUahD4McoMGnxEfO1P+eXQQnjYmPL06jNJKEbyevJpLcYWC0UEOYsHq2L1Nw+lsVblkqKw4XccNscqxB2UqfUKlkVqJtsIyBRUKZZNyNYBe7lb42JnwbyNmKIpqZYN77z9nkympVLAgtPF+u/Y6F1XVyoYmGiBm2Vj7wKAnWrWfsspqZi07RnmVknWPDGBcV8cmEwdG+jI+mNGdnxb24aGh3nx6VwjrlgzEUE+Gt50pI/zt+HRnFCtPJBKTUcQ7myOorJagGviEsLYu0JZNvjQ+ALOqbCoMrGsNC9QIsxsD8B4meosiN8K+90Vlop6sXYOxtXB1O/u7+HsVRfDbJFjbAQnU5gicLCoX+YnieIY82+JT/BxMkUogsk6Qo1SqWH82mbv7uWuSjLP6uFFUrhAOpPZBSDIu4W1noqm6Eb5OGP94DxP/9h0lkpLn/xL/TjohrMbru+Y2gZedKesVgxrIHnOKK7ExbSRRY+OjNZdqUncnzAzk7D2jNkSqF+Qcv5pDakE5M1o7H+wmoS+Xstv7JVQVhSh+GIWxIp8in0kdOlNxWk8X9GVSvtgVzfcHYnlw+Wkqe94r5uhF72jdTlbPh38ebvh4tmhhiFU541NntlxPdysuJBdQ1cLog5a4pnfF3d2dzz//nC+++AIHBweSk8UFIycnBz29xqebXzPxh8QXoxE5TQ010Z7GOs+hq6j+1LVsjtklMp3WXgQ5mxOdUaz15h2KzkIilTHAp3n712vGwkVMT046JeQ+GRdFw+S14j5ALP4vb26/Y2yMw1+AuYtwq2krve9F8lICC+fMJT6nlB8Pxokm5aEvYHTkY4ZLztVKfBKOgNwQnHtiaazXqFxNS86hUql7ckSQ42JpxPolA+nrac2bGy9pH4e+CfEyDx6WrEMvbreQ+8n1ubOXKxdTCjUZ1d2RmZRVVTMlpHax6GJphJ+9KQdbKbNZfiyeAd42dGllA3pL2JkZMCpQO7PoaG4o5GopZ4U735Dn4JVUePI8Xw88xCjpTxhb1nmOubOoeikbXiwmuIvH7tuQpmXRujMinYE+tpgZ1n6fZ/Z2Jb+0in3xZVTPWcUO+XDmZ33KP70v4KeIwSi4Efek4LtEpnPjE+KG18QcITdrY96Z1hWpBEYENB/kAMzr74G1iQGv/XuRf86l8NAwb00g2GFkXKJYqcfb9l+IGRwnv4cT37fuufUyWDYm+lgaQI+DD4hFyMINmgxyekG59nwhiURUxNTSi0YpTNXuJdDRJiyM9PhidgipBeV8vF045u2OzMDezKDWKcq1r6a3T4tTPwsHzHoYlKZTrG/HqTpzrS6ml3Fa2YWBskgup9UuIjM0UtymKzkSiYTJIc7sisjQSLGqlSr+OBZPr3d28dK6cE2go1KpWH4sgVEB9o26FLYnfxxLYNyXB/l2/xXtWUz2gaKK1ki/X2OsPJlIemE5y+/v1+phmqODHHh8lB8ze7tqfWfenR5MX09rXvknnDFfHGTHpXSeGdMF6x5TAIlYD9TBzdqYINMSMlQNM+uZRepeKbmBuH6d+V0s1Ie92HyfUf+HxX3638dgxSxhoHRpvfb8oI7GuQe8lgUPHYBhz4vX0AKGejI8bU2IqlNpjEwvJK+0ihH+tfcVPwczQtwsWXsmmQxjX+xKY5nbV60IqFaIXr46fbtIZcIR7dJ6tYnNRyJ5Yx/UqpfibWfSaDCdU1KBjUkjr8vGVwQ56vuesb6cCcFOXI5VjxSoF+SsP5uCi6URfZuRSncWHrtzNP+TPYukMJlklS09B4zu0L/nYG7I+TfGcvmdO1j1YCgRaYW8edZY3JdOt6JtoiRHtJ1cPdCwrzEnBhUSEnHUyPIBZvRyYftTQ5FfZzLmmoKc/fv3s2/fPs1/rq4ie2tqasq2bdtaeHYbUFQIWVfXac1qSM8l5eNtZ4KlsTqad+gqdMA1c3GUSriySzPcM9DJnEqFkris2i/M4SvZ9HSz1FrUtTtufeHCalg5W1z86gwbbTMyuWgevLy540rgObHCNnDg463W3jdA34QgZ3PuGeDJN/uuCMOH4S8TbdaPL/SX4a6XL7aLPyIWEXJ9rIz1GzceKCirlaqV5YnmN9PaEq2xvpy7+riRVlCuJREpr6rmZKUXFtW5QhKktjIdFWiPuaGcdepm6I1hKQS7WOBdT+40xM+OwzHZLVYpzyXmcT65oN2qOACLBnnxwQxtNyhHC0OyCoqF+5VdgLjZqjPsKQVVWFjVC9TNncV7VdpwkrN+cQoqPROcHRxZ8PMJDsdkk1VUwemEPMZ21S5/BzmZ4+9gxj9nU9gYnsGSokXke4yj56UPRe+GXyMBjKmd+Jwrq4TDUTPf4+k9XTnz6hiNXKI5jPRlPDzMmyNXcrA00mN+E9nqdqXfgzys/xGVzn3FAqbP/aKaUzNMtSmyouBjb6HnVyORSJhtdgG74iiY/aeoNiAWp2kF5dqVHBBZydwmBmqWF0JlkUhG6LhmfO3NeHZMF347Gs/Erw6x4kQCowIdaisern2Ei1R916xj34iZQ3VRKqEoHUNrN61KzomruZymK/1kUVxJq/0+1vTYNRfkgBjYWFpZzd7LmZRXVTP7+2O89u8lQtwsWX06iQ+3Xdb8nZjMYhYM8Lym96ItzOnnxvz+HnyyI4o7lx1r1NClJcqrqvnhYCyTQ5zbRW7qYmnEd/N7s/WJISyd24uTr4wWZiQmNkK+XC/IAQgwKSGu3KyBg11WUXmt2U3gZGEzbeXRsqxdKoWpS4WTV1oYLNwoHPZ2vHpjZOaa42h7dTvQ0VwrCD8Wm4O+TNpgyOfM3q4cisnih8uGmEtKWdhVvX5KOw8VBULaXJeQu8UMwnWLRSVn/EetPj4vWxMyiyooatAr1FQlxxeqK0S1Qc2IADuUxeqEpXHtfbKssppt4WnM6OVyc6rNbcTG1IC5c+/hsaon+FT+IP2921ihvwbkMqlaRmbF65O7svJkEpdc7hLGFrktyPNj9wAqUJSLYKcuWdHk6zviYG2p1Utrb2aIl61Jm2TgjdGu9S0DAwPs7FrOwjaG4uphqq/s034wcpOYD9PrnmafezYhj55udb58NUMZ1eYDpIUJaZd6uGegk1hE1cgFqqqVHI/LZXATtrXthvsAMYW41z3igie7zoAqYKKYFFsTzLU3x78VmtFeC697V0+P8cPcSI+3NkWQVlTBC9VLkMr1YO396iGnhzRmD5bG+rUW0iAqVtWVFBYV1C4CitXGEfXkfsEuIrN2sc7gvIspBWyr7kOxQ1+440PN44Z6MiaHOLPubAofbrvMgegspvZoKPkZ4mdLZlEF0RlNmwmoVCp+PnwVF0sjoeHvQFxNVSxI+0BYgU75RisATc2vVwUAUUGERiVrFKYgsXBl+f2h9PG04uE/z/DtflExGBOkHeRIJBJm9HJhz+UMPt0RzTB/B6zn/y60913GiwVEYwx7UfxX4/bTDK11hwFRzeniYMozY7t0fBUHUEpkJOSVCTtviURI7/RM4Og3zT8x4yKggi3PiuBczYzqHUTpBWiZquSVVlGhUGr35IC4YZdkNZx0DbUWsc3J1XS0isVDvLm7nzv+jmY8M8Zfex6aq3rmVl2JcF6CuAbnXtVWDpTmQHUlNs6eXEot0BhMnLyaS4rjCAxU5XhkHxDuVKCRnzo0I1cD8LU3JcjJnE3nU3lvSyQXkgtYsbg/f9zfn5fHB/D9wTgmfnWI5/4+j6eNMUPaYIZxrRjry3l9chCrHgjlUkrBNQ3f/ftMMhmFFTw2wrfljdtAkLM5E7s7aff4+Y0VGeW6ro+AgySfNKUlOyNqg1hFtZKcksraQaC+o4UiZNwHrUv82fjA/HWwaCt4DoJx70FGeKOzrzoT/o5mRKUXaRJ7R2OFUUf9Xskp3Z3Rk0nZnSs+Z/o56rXI1f3CSKX+2A+HIGFEcHmzmBtVt9LTAjXJx/i6rpQI4wGbxnpyaoZ91qmAD/K1xU5SSIXcXOv87biUTkllNdN73jqJooE+toyd+QDjpi2sHUx/g5jf352e7pZ8mtpdVDN3vNJ8sj1mp9oh2bZhb2nyKaJlfh3WS9sphoGqVCqi1r5N+cqFKHLia39x5jfRi+PQdDmzuEJBdEZRbT8OiDfdwr22Lydml9DPqs0LrE30cTQ31AQ5YUn5FFcompzN0W70uV9McJ30+bVXRuriPUJcSC5tuP591aeqHC78DT3nt8ugSzNDPV6dGMjuyAwGfLCXsFx9IkI/F1H9qZ9Fhqe/0GtaGetpz4FRZ1wqC7NqbzY12VRT7SDHw9oYMwM54cm1pfaziXkck/ZE/4GdYKhdJVgwwANLYz02hqXgbm3MlEaCnP7e1ujJJBxqQrJWrVTx1qYINl9I4+Fh3u3btFicJZyzashL4Im4h+hfdRymLRPuW3VILSjDxbLeArlm8duY009BMli4YKQv47v5vXGxNOLXI/H0dLOsHbpah2k9XahWqkjJL+O5cf5intPCDTD7j6Zfg3OPFqs414KRvowdTw1lXv8bUMVBmD1UKpQaEwgMzaHfYjFDqCS76SfmXhXzUSpLYOdr4rGsKPzLzrGiWtsRp2YugHP9c6i5YTdSzakxM9EFOdeNTCrhgxnBfD6rB48M98HCqE4iytJDSFzqmg9cPaD+H5Wo2NWgTih4evmiVEFYYj7VShWnrubi3KU3xdbdmC45QFy2SJxkFlVgZihvVbA+OcSZXZEZ/HE8gZfGBzBIHcg8NMyHD2YEE+Ao3JBenhB4Q7PS/b1tmB8q5m9kNuH+2BjVShXL9scyvptjq+dMXRd+Y6CyuEFGWb80A6m5E1vDa6+TmUUVqFTU3nf0TYRxSsCE1v89z0HgrB5I7DEQgqYKG/6aJGwnJMDRjKIKBSn5ZVRVKzkRl6P5nNXFwliPu3q70jWoOyo9Y43hE3EHxHqrsXVOr3tEcmjce206Jh87sQ65kqXdK5RbUoFtY5UcK08x/D27NsgxM9QjyLycHGplhhWKar7aG0M/L+sGKo7OzvSerowPvvEyZYlEwt193dl3tZTMEZ8KE6yjXze+sbJaOPx1GSd6O2PrFDMqSyEtjKOVflr9OO1JpwhyJBIJiUM/o6BaTsqPs1FUlIkPZvyhWj/0JriQnI9ShXaQA1oOa8TsFLM06uhRA53MNOYDh2KyMTOQE+Jabx/tjVxflMrbC31jccEMW9H+5e+oLaLcHHIN7m9NMCXEmT/v789PC/uw4dFB9Bs5DR47Dc9GiT4ZI0tAZPPz6vfkAKrS3EYqOdrVBqlUQlcXc8LrVHLOJuQT7GLRaGNegKM5e58dztGXR7Hn2eGNLuyN9eX09rDiUEzDhWx5VTWP/3WW34/F8/qkoPaXh+x+Q9hu1vjRb3sBY2Ux0yvepjxoptamKpWK1PyyhoNqzRwBSe1iuC4FKcISGGGn+uuivnjZmjCnX+M6egdzQyYEOzGrjytd6061vgZJRHtwvaXstlBjH11jGgBA/0cAFZxY1vQT8+JFkDLqDWE4suN/cPQrKvQsWV3aW0t+kZYvFoeO9atxajlb40GOrpJzQ5BI1KMA6lRyYveBlXo+UmZk7ePqhIKbuw9mhnJOxecSmVZIUYWCUC9rpD3nMlR6gbir4nxmFDZtH12fSd2dqFaqGOFvx6JBnlq/u7ufO5/NCuGne/pqzYO5UTw5yg+5VMKnO6Na3ljNheR8UvLLWDDgxiQrcOohgtW6kjVFBZRm4+LuzaGYbE1P6NFYISkMdm39jJ8WmfK1cLP8c6a4/nZCaiTDUelFXEguoKSymoFN9Cu/Nz2Ybxf0RWIXIAK3qnIhRasxHKhPn/vgmYhm+6wbw9JYHycLQyLryOjyy6pQqmi8J0emJ/5GHYc1AF+TcpKrTDV9bT8dukp8dglvTG5db5AOwcTuTpgayPk1LxhCHxWKnKPfNJRvp5yBsjzO6PclyrSvqGTWDBJNOQNKBbtLvTVBbHvTKYIcgPH9g4kb9g3OZTHkf94Xfp8k5t0ETWv2eXsjM7E01sO/fgbIoav4wsXsEm9kvf6XIGdzIlILxcDDmCwG+NjcONvA9qTXPUIn3py97LUQtlIEZPYBLW/bSiQSCYP9bBkd5CBmGkklYOsrgrU6WDao5Iggx0JVqD0jR89YVOjqEexioZGrqVQqzibm0at+ENxGhvjZceJqjpbevKCsioW/nGR3RCbf3N2L+9owDLLVDH5GyJRO/yJ0ztHbSQp5mssq9wazcvJKqyivUuJUP8iR6YmbelFjlZwkMK91RHO2NGLvs8OY1cet4bZqvpnbi49nhlzXy7oVSVDPLHKv28htaifknCd/0JKiaZEXD9ae0HcxDH8FTv4I5/4kz38WFehrgieAtMJyJJI6meMajK2FbLMx84GiNCEr1TNq+Dsd7YtLb0g9JxqrlUpRyQmaIiShmXUcPdWVHKm5E308rFh7JpmH/jiDkZ6Mnu5WGPeag0oixSBiHSDkai1J1WpwszZm3SMD+Hpur7YH+REbxYDODsLKRJ8nRvmx5nQyr/97kczCcr7cHc3YLw4Iu/RGOBidjYm+jD51h0h2JFIp+I7RDnLUSbNu/v4oVSo2XRDXyh2X0glxtWgoAb4eDC1g3t/i/9fed+NtpVuBq5URZoZyfjwUx6bzqRjry+jeUhLYa4j4bP3zoOi9qN+PU4NEoklotpUgJ7FuqyG7WEgOG+3JAdG6UDf5ADjrFZOlNON4XA5JuaV8vTeGewZ6aiftdLSIiYGcySHO/H06maqRb4h+tV2vw5fBYlxEzec6ZicqA3OeOqLHOxHqpHSN+UbiMar1TLmscr+95Wo1DB45ka2BH3KozJsq/8kwfVmDBXBdFNVK/j2fyqTuTg0DFPsgsThcMVNoQAO1nZ8CnczJKank5fXhnEvMZ2RAx/ZRdBjuoWDr37Dx9XooTBVB07XM8GkHrIz1Ka5QaPTqGImbnyXFtQ2gRWpntUZu8t1cLEgvFOYDKfllZBZV0Mu97X70dRniZ0t5lZIvd8eQVVTBv2EpTFt6hMi0Qpbf34+J3TuoZGzrC93nCJe7ve8KGWb3OUCtjr+G1HwhdWogVwP1rJx6QU5FMZTnayo5NdzI6sitRHxOKY7mhg1tqgc/LeY8Hf6i8SfmXhXZfqkUhr8Ij56AQU9iMPRJJBL4/mCcxrY4vaAMezMD9BpLuDRlPlCYojMduFG49oWqEsiKFL1WpTlCJWAfqN0bWZgmEgtyfYb725NVXEE/L2vWPDRA9DWY2HDeqD9d0sVwyYyiilZXcgB6e1hjatCGPjSVCg58AmsWiIV1U5/VduC+QV68MiGANaeT6Pf+Hpbuu0Jqfjlf723cHfBAdCYDfGw71AK3AV3GQnZUbWVULX+2cnBnaBc7/j6dRGmlgoPRWYztiIqYhYsYqJ10vE225DcKqVTC/83pQVR6Eb8djaefl3XL52fk68KiOmKjSLo4dm9++2sgyFmM/6jpFaoJchqVqwE4dBMDfOsEksZVeZTpWfPGxkuM/eIg5oZ6PDOmmRljOprk7n5uZBdXsONyDsz6HZ6/AiNfhRPfwaHPIOJfOPEDhW4jSCpUcDhDD4VtIMSobacTj5FjFUI1sg4Lcjq+W7eNBI+ay8gwF+RuPZkc0Lz84khsDllFFUzv6drwl76jhNQtcLKYM1Fv4RakNh9YeyaZF+7w565mMtedGokEet8jIuiijMYnnreVC6tBKhfDnm4CVsZCB59fVinkY4YWKCUyrCVFdSo56U3OGKrJOF1MKaCoXCwee3lcX5DTzdmC+aHu/Hw4jmUHxI1xsK8t3y/o3W520U0y7AVxTmJ2wsTPcbQSfy+9sPEgp4FcDdQ20vWMB2rkaxaNfH90NOC+QZ6M79bIZ87cGQYsEaX6vg+IidA1KCrE+2xdp8pn7QVj3sYK+HCGktf/vSQatu/pQ1p+eUOpWg02vo2bjBSm6qRqNwqXXoBEuEGWF4DMQPQeXNkjbug11DknCwd4cHc/9waLxES3KfSJfhGyLpNZWN6kHKgBZXmwah6EPtIgeadBUSGu4VKZCMC3Pit6x4Y+D9VVQlpSUQQjX2v3XjmpVMKDQ30Y19WRreHpTOruxPaL6Xy4/TJPjvLDvY5NbEFpFWFJ+bw1tVu7HkOL+I4W5y5yo0hSXD0g/m3jw6w+xSxZcZYfDsZRoVAyrms73FMbw3+8SH4c+0bLfKSzMDLAgR1PDeXjHVFMCG5FoCeTiwWu93DRfyht/6A10Mmc3JJKMgorcLQwJKdYyAoblauBsDguLxC9p+rrsqQkCxfXIRgXynhomDd39XHrWFfd25hgFwsG+9ry1qYIBvrYYm1iLa4xVWWw9x2xkf8E/rZ+HD1ZFlXVKqIcJ9H10mcw8AlIOkmMwzysTfTbZDrUFjpVJQeEg0aQkzmbL2gvyLKLK7TmDYAYduZpY9y4FMnICib/n7iYNXIR97Qx4flx/qx7ZCBLhvvecHeKdqX7HGHhG7aiffZXmgtdZ1zTNN72oMYKXCNZk0io1LPASlIsLKSryuHqoVoXvXrUmA/sjszko22X6eZi3qYsaWNIpRLenRbMkZdG8vbUrvz76CD+XNy/4wMcEIvivvcLfXGPeZgbyTHUkzZayZFJJY32FWHmVNu7UUPNQDxdkNMq7M0NCamZxVWfQU+CgakYEliXvARAVdu3UY/Zfd3Z/PhglCoVi347RVRGEU5NfVatfSAnrqG8RRfk3DgMzIQUevuLsP990UiuZySuRQVJws4bREJB7WookUgazYIbB46lTKVP4rG/ySyqwKG+RLEptj4v5opteLSh/j03DrY8Bx95wVc94NRP8Mc0IT+e/H9iETrmLRj1eqOW8u2Jh40Jjwz3wc3amHmh7lgY6WmcG2s4fCUbpQqG+V2bK+s1Y2AmEqERG8X3Kewv4VZqYMaoQHusjPX4eu8VvG1NOizDjFQGoUuE01hLFrw3CXtzQz69K4SRAW0I9DwHNzkP7XqpSU5HpAk5ek5xBXKpRNsgpC6O6uA5PVz8rFZAaQ4Dgv3Z/tRQnhrdBZfGkoI6WoVEIuHjmd2pqKrmpXUXasdsjHxNDJ+d+DnMWcmW2EqGdbHD29aElao7xFpmzUKoLOZghS8Bjh23jup0QQ7ApBAn9kVlaTXkfrz9MnctO8Za9UyTkgoFOy5lMK2nyzXJa6RSCY+O8G160XIrYWIDY98Dj0Hts7+x7wip4E3CSh3k5JXUmg+UyC3xk2cKH/ULq8Wsgn4PNfr8GvOBlScSUShVLJvffmYP9maGLBzgeeM/N+M/hkeOgZ4hEokER3PDhpWcgnIczQ0bD9gbk6vVBDm6BfL1Y2gBQ18QQwKz6zS65qkXL9aNBzkghur9uqgfBWVVXEotbGgfXYONjzADqe/kppOr3VimfSf+m/MXTFcPg61JuNRU2grTwLx5Cevo7p5cMOhFYdhGqpWq1iViLm2A8L9hzDvC6Wv9Q7WmM1lR8P0w0RfRb7GQC215VvQkLPxX28RnyLMw6ct2r+I0hbG+nMVDvFh7JllTcQY4GJ2Fl62JVnXnhhE4BVLPillwubEaebaBXKZxkRzb1bFj5bs95oqAq7VDhf/juFsbY6Iv05gP5JRUYm2i37SLoKUHGJirbfwRI0lQgckt2p7QCXG2NOK96cHsjMjgub8viJ4piQSGvwR97ye7pJKwpHxGBTowyNeW/bGFqMa+B/kJqKRy1mU40s+r4/rxOmeQE+xMpULJ7kjRDKhSqTgQnYWFkR4vrD3Px9sv88Dy05RV3Vq+5h1K/wfbt+R9E/syauRqdQeCnrIYywQOC1nI0a/F4MlmTBH6edlgoi/j10V9cbW6CTfQ9kYi0epPc7QwbGA8IJzVmlgomTuLBXJFnVk/BcnCt17XsN4+9L5H2J0fW1r7WF68kA2ZN18t87I1Ydn83ujJJHg2teCz8RE/6/blVJYK+ZLZjbcR/c/i1F0sTgMm1MqD7dTXopom5zqVnKaQy6R4DbqLbqoY7MnDwcJQZJoTTwijkXpzXCgvgM1Pi5lUAx+H6d9B4lFYPV+YkqycJSS8T5yFMW/DnBWw5AQ8clQzg0yLG3yNnx/qgUSCxqJZpVJxMCaLoR09uqEp/O8Q380tz4j+Tu8Rml/d3c8dY30Zk0M6+HtlYAq9F0HCYWFkoaNZpFIJgXXMB5qckVODRFLblwOiTxtEv5yOdmNyiDOvTAjgQHQmE746xPtba80e9l3ORKWCUQH2DPK1JSW/jETboeA7hlKHPmRXyP57QY67jTEhrhZsDBPymqiMIjIKK/h8VggTgp347kAsZVXVfDgjGA+bjrGd03HzqJWr1VZy1hnMIMIgRGjRc2Jg0BPN7uOxEb4cfWnUbeuY4mhu2KhcrdF+HKhdBNd1WCtM0UnV2hM9I+Ggdv6v2mpL7lVhFytruf0x1NuGvc8O5+7+jdt3Y60Ocuo6rNWcT1017uaibyKyxpmRoh+hvKDFSg6Afe+pqJAwVn4W74KT8IkP/DJWBDN73tbe+OQPoo9m4qdi8eY9HGb+Kub2fD9U/M27V2nLjO0DWnUcNwJzQz1CvW3YGSGSl2FJ+aQVlDPiZpn+GFkJB7CyPAi+S+s72sXBjItvjrsx949hL8KDBzqkh+V2pMZ8AIRcrUnTgRocgyFdXcnRBTkdxoNDfTj60iieGdOFHw7GseZUErkllaw6lUR3VwvszQ0Z4G2DVAKHY3Ng9p+s9f8cPZmEnm4d1xrRab9VM3u7sj86i8ScUg5EZWEglzLI15Zv5vYi4q07+GfJoCZneei4tdGXSzHRl5GrDnJUKhWx2WWs83hNzDpy7tWiNE9fLsXC+PZtJnSwaCzIKW/a6tTWT/Rt1XXhK0jSBTntTd/F4uepn8TPvKvNStXq42ZtLCSZjWFgKobfas1jqZmRo6to33TsgyAtrFYW2prA09QOiVt/3rQ/hOW/9wgn0Pt2iN6ZY9+IoYogKrDHvoWe87S/s91miFljQ1+AuX/XVvs6KWO7OnI6PpfckkrWnE7CwdyAwY0MmbxhdJ0mfjbiJHrDBqnqG9+0OWO3IkFO5sTnlFBcoSCnpBKblhrWHbuJ63B5YW3yyeQmfuZuY/TlUh4f6cvM3q78b0M4oz7bT3R6EU+M9APE8NhgV0v2RmaCniHHksrp7mopHCc7iE4b5Mzo5YqZgZzfjsZzIDqL/t42GuvWjnxDdHQOQtwsWXs6mUqFkm0X04nLLmF43x7w4H6RrfyP2xwHOZmTWlCukX5UVSvJLCpv3D4axMJo9Fti4XRhjXisQFfJaXdM7SBkjpiFU15Yax/dXngNFefw70WQn6QbBNqZCJoKicdq59C0IFfTEDABeW60cG67e5UYCzD4GXAfCBsegeQzcPpnUakZ9FTD5xtZwsj/gVvf9nolHcaYQAeUKth8IZVN59OY2dv15s6n6zFP3FMcut68Y9DRJoKczVGp4HR8LjnFFc3L1UBUckDMsSrOBKme6KHU0SFIJBLem96Nwb62DPK1ZfezwxgdVGtcMbuPG3suZ3IuMY9T8bn09ezY+VidzkK6BhMDOXf3c2fFiUQqFNW8NL5xJy0dtyevTAhk8jeH+fFQHOvOJjPQx4YhfnaArswMMCXEmW3h6Tz/93m6OJhhqCdFqWrCPrqGgY8L7f7Gx4XEpSBZVwHoCAY9BeHrYP0DkJ8A1gvab9/TvhOOXvveh59Gg99o0DcDQ/P2+xs6ro3us+D4Ujj4ifh3a2ViPRcIy9XQJbV9d1KZMH/5fRL8NFJUYbvPaVNVsDPiaGFId1cLPtkeRXGFotmhwzcEqQyce97cY9DRJro4mGFmKOfeX08BzQwCrcEuECQy4bBWkiWkav/xJGlHYyCX8euifo3+blYfV34+HMcza86TU1JJ/w7sx4FOXMkBWDjQk7KqaqqqVQzrolvc/pfo5mLB3H7ufLIjirisEl64o2mTgf8iEomET+7qjoO5Iff+epKl+0QzerNBjkQCU76GnvPh8haorqhtmNbRflh7wYzvIXq7mPzdnpUcmRz6LIKHD4k+kHN/dpqei/88UhmMfReUVaBnIlydWoOxtXAiqh+oWnnA4+dgzkoR4Ax/qf2P+SYwNsiBogoFod7Wup5aHW3GUE/GoRdG8PXdPblngAfjWhrWqmcItl3gzO8Qf0hU23XcNOQyKS/eEcDV7BIx5tGzY0eVdOogx8XSiPHdHPGwMcbHTncx/K/x3Fh/rIz1GN/NkR63g9V3O2NmqMeP9/Shi4MZ684koyeT4GLVglOavjFM/AyevgTPRoPfmBtzsP81AibC8FfE/9v5t//+zRzhnk1i3oB15+7D+E/hPVw4P1p7tU+2WCYXn6Xp34mg5zagZlF6t66nVsc1Ymmsz+QQZ96a2q11c4zGvCW+j8mnhBGMjpvKmCAH+nla093FAvMOHsQqUanqT5a7cVhaWpKfn9/sNsUVCorLFU3PjtDRLrTmXNwMMovKsTDSa7oZ+zbkWs5FUXkVWUUVeHfU4Lr/KNf1vVCpxMwcuy7tekxaVBSBSvmf0Jh31mtUAyqKoaLwtu6Tut5zEZtVjLetScfOoPmPcMt8LzoDeQliLpFxx0ikdOei9RSUVVGhqG58eHk7clODHKlUirm5TkveURgYGJCRkdGqbXXnomPRnYvOg+5cdB5056LzoDsXnQfdueg86M5F56Et56KGmxrk6NChQ4cOHTp06NChQ0d706l7cnTo0KFDhw4dOnTo0KGjreiCHB06dOjQoUOHDh06dNxW6IIcHTp06NChQ4cOHTp03FboghwdOnTo0KFDhw4dOnTcVuiCHB06dOjQoUOHDh06dNxW6IIcHTp06NChQ4cOHTp03FboghwdOnTo0KFDhw4dOnTcVuiCHB06dOjQoUOHDh06dNxW6IIcHTp06NChQ4cOHTp03FboghwdOnTo0KFDhw4dOnTcVtzUIMfBweFm/vlbEpVK1SH71Z2LzoPuXHQe6p6L0koFpZWKm3g0/21034vOg+5cdB5056LzoDsXnY+bGuRUVFTczD9/y5GcV8qwT/az6mRiu+/7hp6L6ir452GI2t7ipg8uP82jK85SWF51Aw6sCTIvg1LZ4marTiZyITn/uv9cu5yLrCj41B+yoq9/X/9h6p6LpfuuMOLT/fx9OgmlsmOSDTqapjXfi72XM+j1zi4+3xVN0c28ZtzmNHculEoVl1ILyC2pbNW+SisVDP9kH4djstvr8P5TNPu9yI6BzwLETx0dzvXcu1UqFRP+7xAvrr1AcYUumdZe6ORqtwhV1Uqe+OscyXmlvP7vJcKTC272IV07efFw/i/4azbs+6DZAOJYXA5bwtOY9s0R4rKKb9wx1hB/BL7tD0e+bHazqPQiXlofzuN/naNS0XJA1OGkh0NxOux+A4DiCgVLVpwhKbf0Jh/YrcvM3m70cLPk+bUXWPDLiXarqhaWV/H70Xj+OBbP/qhMqqo7wefnFqS8qpo3Nl7CxEDGsv2xjPrsANnFFVCcebMP7T/F1otpTPzqML3e2cXgj/aSmNP8NWd3ZCbxOaWcb0OC6ExCLmcT867zSP8DpJ2HojQ4+OnNPhIdLVBYriAirZDVp5OY8H+HuJJ5E9Y7tyG6IOcW4Ytd0YQl5bP8vv742puyZOUZCkpv0UxlUZr4GXI3HPgQzq9sdLPSSgVF5QoeHuZDhULJO5sjbuBBqtn/gfh56HMozmpys6/2xGBtok9ibinLj8XfmGNrjpr3OGorxB/hclohW8PTeftmvIe3CV62Jny/oA8fz+zOkSs5130TKq+q5pu9MQz+cC9vb47gjY2XuPfXU7yw9kI7HfF/i58PXyU1v5yf7+nLjqeHkldayZnty+FTP/htEiQcu9mH+J9g28V0fO1N+b85PVBUq3jt34vaCQFlNZz4HirE92fLhVQA0gvKm95pnecrqpUsWXGWWcuOseFcSoe8htuGgiTxM3wN5MTe3GPR0Sx56srnW1O6UqlQ8t1+3flqD3RBzi3AlcxivjsQy5OjujDYz5bv5vcis7CClR0gW7shFGWIn+M/Ao/BcGFNo5tlForS70AfGxYN8uRQTDb5pa2TQFwP1TVSpPjDEH8IJqizYDUBTz2i0ovYEp7GM2O6MLOXK1/vvXJDjrNZCtPA0h0cgmHXa2SoFxC7IjI4FNN0sKajZSZ3d8ZALmVnRMY172N/VCZjvzjIl7tjmNLDmUMvjCDq3fG8M7Ur/5xLYcuFtHY84tufjMJylu67wrz+7nRxMMPL1oTxAVYEX/oUlVMIlGTBr3dA4vGbfai3NZUKJQeispjQzZGpPVx4Y3IQB6Kz2HYxvXajuP2w7QW4soui8ir2RYnrUXphE0GOSgW/jocVs6Asn0Mx2WQUVtDbw4qnVoex8sQteh+8EeQngaUHGFnD4c9v9tHoaIY89Zqht4cVM3u7svNSOuVV1Tf5qG59dEHOLcDPh+OwMdHnoWHeAHjYmNDDzVK7XH/pH8i+cpOOsI0UpYHcCAzModsMEUg0IinJUN/0HMwNmdTdmWqVih2X0hts1yLFmbDnbfjrbog70ORmZxPzWPz7afz+t5VHV5yldNd7YB8Efe6HIc/Amd9g3WLYsEQrK/Z/e6JxsjDkrj6uPDfOn0qFkq/23ORzUZQG5q4w6nVIOYMq6QR6Mgm93C15a1PELSGJUqlU7IrI6HTN/kb6Mob42bLrGoOcA9FZ3PvrKRwtDNn65BDenRaMs6URejIp80M9mORvStb6F7jy5UQuvT+EU1t/aedXcHtRWqngweWnMdKT8fToLprHnzTbi4Myg4h+H8IjR8Vi73Tb38sFP5/gp0Nx7XnItxeRm+HnsbByDunrX6KsooIxQY4A3NHNkRH+dry16VJtj07EBvGzMI3dkRlUKpT087JuupKTdh4Sj0HsHvhpNPuPHcfX3pS/HghlZm9X3t58idT8so5/nbciBclg6wcDH4fzq3TVnJtBtQJO/1qb3G2CmiDHykSfySHOFFUoOBCtS0heL7ogp5OTXVzBurMpLAj1xFBPpnm8p7sV5xLzhQygvAA2PCr6XG4FitLBzBEkEgiaCkgg4t8Gm2UUiUqOg7kBjhaG9PW0ZtP5Nma4w9fCl8Fw7FvIuQLLp4iMYLW21G/zhVRmfHuUK5lFPDzMB8nVAxinHCUqYAlIpRC6BIJnisxYxEY4/AUAF5Lz2RqezqMjfDGQy3AwN+T9Gd2Y1tP5mt6adqPmPfYeDnIjTDJOYm9myOuTu3Ils5g9kZ2/T+HE1VweWH6aR/482zmCspJsiNoGe97mGelqopPSySxqRmLTCPmllTz/93kGeNuw6oFQujiYaf1eEn+I/8tdwmx2kJZfgqSqjJCTz6NIPCk2UCpbZYLxX0FRreSxleeIzijm53v7YmWiL35RlIF35DI2ysbw2xUTkMqg1wJxnSlrfS9HXFYxh2KyWXe2jizq5I9wcV07v5JbGENzUTWursQ14geeMdlBNxdzACQSCW9P7UalQsmcH46RWVAsgiKAwhQ2n08jxNWC/l7WTVdyzv8Fxjbw4AGqFZXMiX+VWT0dkUolvDYpCGN9OR9su3yDXmwHUJIDlSUds++CJLBwhb6LwcwJNj6uu37caBKPwean4KuecOCTJt//3BKxJrE21sff0Qw/e1M2nU+9gQd6e6ILcjo5fxxLQALMD3XXerynuyXZxRUk55UJuZeiDHrfe1OOsc0UpYkLLoCJLXgPa3TRkFlYjr5cioWRHgCTuztxNDZbNBO3hrwE2PQU+I6Gpy/BkhMw/QeI2QFnf9f6O69uuMiYIAf2PDucF8Z24Svbf4iR+fHcRXfhpKVnCDN+gPt3QP+H4NI/qCqKeX9rJN62Jszu66bZ3/SernR3tbzWd6d9KEoV77FcH1z7YJ93DntzA0JcLXCyMOTE1Zybe3ytYOelDEwN5By+ks3L68M7zD691Rz5Ev6aA2d+IzDhTzbr/4+zx/e3+umllQr+989Fyiqr+XRWCFKppPaXSiUc/AR+n4LMyg3pkqMMen0fqnu3EKF0p2rlfNj1OnziAxsfa/eXdiuiUql4c9Ml9kdl8s3cnvRwsxS/qCyFVXcjkemT3usZtoSniUx/j3lQXQkX/m7136iRWUWmFZJWUAaKClEV3v9RB7yiWxSvoXDnT6jmr+Nv2QQeVK5BUqdi4GZtzKoHB5BbUsX73/4IZbmUSk05eOY8+6IymdTdGQdzQ7KLKxqatlRXQfjfEHwXOHZjW5e3CZQkcLd0BwAWRno8N9afTedTOXk190a+6vbj4Mfw3cCO2XdBsghyDExh8v9BwhGWffYKkWmFHfP3dDSkVO0a2HU67HsXIhsmdEH05BjIpRjpi2T25BBn9kRmdjolw62GLsjpxJRXVfPH8QTu7O2KjamB1u96qm/o5xLzhATDbyxYujWyl05ITZWhhq4zRLajQLuJNLOoAnszAyQSsRgcHywCo41hLWc3KqqqUPyzRFzcp34DJjaiIhMyG7rNhP0fQkURKpWKl9eHI5VIeH96MDKpBC6uQ5p+noqRbxCeWsymC/X+Xo+5UFnM5b1/cjwulxfu8EdP1om+SiqVeI/N1YGkeygeJeE4muojkUjo52XdeRcE6kBGpVKxMyKdSd2d+GBGMGvPJLP38k2uPvV7CJ44B8/HInnoADJ9A+44Mls0tV/e0uTTMgvLmfvjcULe2smW8DTentYVF0uj2g2U1fD3Qtj7Lgx+Gu7dgoG9L1KphK4eDvzi/DblFRWoTv0MDl0hbCVkRt6AF9y5+eVIPH8eT+StKV0ZFaieT6FUwj8PQsYlmLua6UN6YmogZ+JXh9idLEPlO0YkOFoZMG+7mEY/T2skEth3OUvIXSsKITtKuETq0HAptZC3Su5EYWwvKgZ1quX+jmaseSiUqXqnyJback6vF26yfF6dGMQ9Az1xsjBEpaJhZTRmF5TmQMgcAH6Kt+Og6XjMjnws+g6B2X3dCHIy57m/z9+a7pEpZ8EppP33W5YvPqsW6gSp7yiyfGexsPgXSpbPEeeoGTMdHe1EaQ4gEUGmY3CTPci5pZVY11SigUndnSirqr4lVBedmU60MtNRn3/OpZBbUsn9g70a/M7e3BAXSyOyIw9CZoToG7lVqFvJAQicJHp0/poNyWc0D2cUluNgbij+Ua3ANmErOy3eR7r9RV7fEN7o7JySCgVL913h8w9eQZ54mKwRn4KRlfZGo14TN4CjX7M1PJ09lzN5d1o37MwMRKZ279vgN45ugyYzwt+OT3ZEUaGo0wBo44PKfSBVp5fTy92ScV0dtfd/sysO5fmgKK99j91DMVUV01VfLAr6eloTmVZ4c2cPNUbGJfi0C1zZQ2RaEcl5ZYzt6sBdvV2xNdXnTMJNtoy1dANrbyGztPNn7+BVvKh4kNz8fFg1F/IbNkCrVCpe3XCRyLRCXh4fyK6nhzK9p6v2Rmd+g8hNMPMXGP2GkFbV4a5RoYws+5DDUw7CvLXivB74b1cSdkdk8O6WCO4b5MWCAZ61v9jzlpBDzfgRXPto+p66uViwePlpHoroBhkX+faTl7n3lxPsjshoskKYmFPKxZRC5oW6E+Jqyb6oTJGFNXMGiQyid96YF3uLcCw2B4XcGNm0ryHpBKycDRVFmt976+UyQnkC236zGNS7B176Bdw32At9uVRznc+oL1k7vxLsAsGpByUVCsJTCkjr+xLI9GDd/VCWh0wq4bv5vVCqVMxcdpTT8bmUVbaxYTs9HFbPh1KR/FGpVC1aX7cL1VWQfgGce7X/vguSxU+L2uvNyS7PsLk6lPLifDi7HK7sav+/q0Ob0lwwsgSZHLrPgZidQqJYj7ySSqyMa4McbztTvGxNOBXfSROStwi6IKeTolKp+PnwVUYG2ONjZ9roNj3cLfGOXy0yNb6jbvARXiM1VYa6lRwjK7hnk8jC/jQKzggpmQhy1BWslbPg73vxMVdyr3wHemd/ZsFPJ1DU69V4bcNFftodxuOSNeyQDWXBQfOG5V4rT+j3IKqjX7Ns+0mG+NkyQV0l4sT34uYw+k0AXhofSHJeGevPaleZTlhOoHv1JT4MyURSkFwb2CgqhZzozG/t8GZdI+oMpybIce1HtUpCt2qR/e/vZY1SRcOgoSxfVBXU5JVUagd3Hc2F1VCSCWsXcerMKYz1ZQz0sUUikRDsYkF4SueaDTVvsD+VwfMYnv441RK5CFTqsTU8nZ0RGbw5pSv3DfbCr14PDkUZsPstCJkL3e5s9O8M9rXF1t6Zv8MLhWxyyDNwaQNk/HftwPXlUqb1cOF/EwNrHwxbKSSFo9+AoCmah21NDfh9UT9+vbcvg8bP5YLdZJaUfsfD6W+w9c/P+eK7byiIOiiqY3U+/9svpaEvkzIywJ6RAfYcv5KB6vJWCL4T3EOF7FWHhrDkfLo6m6PvNxLmrRGBzvdD4edx8EU30RtZUSSqMubOQlKr7k9wshBBTlpd84HqKriyR3wvJBLOJuZRrVTRvYs3zPod0i8Kw4PYfXhUxrJhuinD5Rd59vt/CHx9O93e2EH3N3ew4OcTzR94RRGsWSi+v0e/BuD3o/EM/WQfW8M7wOUw8zKkhqn/P1IkpFx6t//fqQly6ig84kv0eEu6hHdtPiRfaolKZ0TQ8ZTmip4yEH29KiVcWt9gs9wS7UoOQBcHU2IydPNyrodbKsipVCgpq6xusLC9HTkQncWVzOJGqzg1jLFIYXD5Aap639cg+9tpqSgU/UNm9aofbn3hoQPQfRZsfwlyYsksrMDezFDcEGL3wJSvkSw5Bn0X86psOX5pm/hz+2FNgJFdXMGmC6n86HscU0rxnvUh8Tkl3PfbKZYfiycitY4OedCTqBSVhBZs56XxAeKxkhwxNK3nAnAIAoTMYlgXO/44lqDJ+BZXKHjukhclUjO67LoXvuwmbuYR/8LyqXB+NUjlHfP+pYbB8e+a36ZmRo76PS6RGBOp8sCrNBwAX3tTrIz1OFVXsqaohK96CAceNVOXHuGHAzfIVUqlEoYOAZPA2IYR555krJ+pxmwj2NWSC8kFN78vpw76cimf3RXCnKHdOaToSuqx1Vq/T8wp5Y2NFxkVYM+UkEaMKKoV4rMukcDYd5r8OxKJhIE+NrVuir0WikWiekH2X2RoFzu+mNkN2a5X4X1X+KoXbHpSZEoHPdVge6lUwogAe+4Z7Ev3R/+Emb/QXx7N5/rLeCbzVSz+mgzfhorvcdx+VCoVm86nMcTPFjNDPUYG2BOsuIikLBcCpwp58NVDHdcwfgtyPimfkJpeRN/RsGircKe0cIXAyXDXb/BMhJBmmTuDUqHpV7A20UdfJtV2WEu/AFWl4DkIgFNXczE3lOPvYCb6gBbvFj1Wf0yD74dgu3IMH5W+wQ6bL/hoRjceHeHLhGAnDsVkNz3TSqWCzc+IxFvgZDj5A2GXr/DulkgM5FK+3ntFXHPK8mHt/ZDbDtfDbS/A3/eKv516FpCAc4/r3299CpJAItVSTaTml+FiZcRzY/2JVjiQGnex/f/utVDdyVQF7UlpjrDwhlozoEYka/mlVbXGKWr87M2I0Q0FvS5umSDnm70xdHl1G4Gvb6fXO7v4Zm8MJRW3b0PWz4evEuBoxkAfG+1fhK8V2Z/KUu6Ifp1olRvhrnNvzkFeC0VqC+j6QQ4ICcKET0XW499HySosFTKGUz+BqaMYHgpwx4dIvIbwqd4y7j01mdLvRkJpLqtPJWEnKaB36gro+wB+/l35+u5eZBdX8tamCCZ9fYhV6tlCBTJrdtOPh4z309VRnV0/8CGoqmHE/7QOa0GoBxFphZpF5vcHYsksl5F333FYvBdm/iqOfc1CyLoMC/+FnvPb/71TqWDLM2Jh3NzNtki7kpNRWM4ppT8O+ecAsWju61mvLycrUrhOZYrqQGZhOYm5pUTfqAtsxkXIuwo9F5A25jvclUnMsbmq+XV3FwsKyqpIyu1cVrFSqYSXxwdQ5T8Z58LzfLH+AEeuZPPH8QTG/99B9GVS3p3eTdNXBojzeOxb+L8QkdEb954w4GiGXh5WJOeVkVlYDnIDCJwCVw928KtrZ07+KP5rD8ryRXX3+LfQcx74jYH+Dwvde933uim63Ynk+Vh4OYUVoZuZWPUhGRN/B5k+LJ9K2LpPCE8pYJ7a8CXIyZzphmcp1LMTWfcu46C6ollL+v8SNSY4Pd0tax90CoE5K2Dmz3DHB6LxuuZzbu4ifhaKCrlEIsHBwkA7yEk8Ls6HWsp1Mj6XPp7WtYYddl1gyXFhD37/Lrh/N0z9FsPiJGa75fPIcB9enxyknmnVxOiBmF1iUOaET2Hi56iU1YSteZdgVwuWze9NZFoheyMzhDvWxbWa861SqVhxIqH5AaaNoVSKHpy8qyKISzkDtl3AwKzl57aVgiQhrZTpaR5KzS/D2dKIUYH2VJp7UZgSRcrNtt8OWwkfuMKxpaBSUVBWxcfbLxObdZss7svqVHIAus+G5JMN7uG5pZVYG+tpPebnYEp2cYVmUKiOtnNLBDkxGUV8uTuGO3u58tldIUwOcebL3TGM+uwA8dnXlkmrUFTz+9H46/qCX0wp4HxS/jU/vymOXMnmUEw29w320l4cFaYKHfJ3A+Gn0RgUJ/N89WMcSyhqemedjXoL8AYYmgujgMRjPKb8E1fDMhHY9b639mIt04P5/1Cx5CzvGT1HVWY0FT+Oo/jIj6w3/gCJVA5DnwNgTJADu58ZxqW3xnFXbzdeWh/OoyvOMvjjvfxRPQbbqlSI3Suysqd+hsFPgZmD1iGNCLDHxdKIP44lsDU8je8PxHHPQA9cXV3BtbeY9bN4Dyz4Bx46qMk8tjuxe8VNUSIVemqEOcW5xHqys6I0MLQAfWMAMgorOK30x7AkWWPu0M/LmgvJBbXDxmrkE/kJABpp2A1r5I3cBPpm4D2c3+LMKVCZ0EsvQfPrYFcLAC6k5N+Y42kDEomEMdPvQ4mMvDPrmffTCV7bcJFx3RzZ9tRQnCyMtJ8QvhZ2vCwkTw/sbVVA3Mtd9JVpqjnuoVCYLCzNOzm5JZWcjoyjatfbZMWda5+dnlgGyadg7hoxVHj8R6IapmfY+n1IJGBgyvSRA8gw8uWjOE9YvIfyoJl4XvyKO4PMGRkgrgXSohQmqw5wxHCoMDCxCxAy4Zi29+WEJeVz32+naocOg6YXpL1pUPnsIAvymvtgSGtdJc3Vlc3CWlMXJ3Mj0ur25CQeA+eeoGdIpULJucR8+npaa+9Hz0iYcbj1E2qA4LvE/LWIjQAY68sZ4mfHzktNzCg5t1xUm3rMBVN7wp1mclf1Vr4fY8BwfztC3Cy5tO07MYdOKtdcH385Es///rnIP+dSGt9vU2RHQ6X6fh3xL6ScI1bfnxfXXmjbflpDfpJWPw5Aan45zpZGSCQSevfsjTtpPPXXWY5cyWZbeFrHOnllRkL8Ee3HcuNg6/Mi6N3xCqyax4ebw/l2fyx3fHmQT3ZcFu6mtzKlOdpBTpdx4mfSSa3N8koqG1RyfO1Fq8KV2yXguwl0viCnokhrjoFKpeK1fy/iZGnIe9O7cWdvV96bHsyeZ4dhoCdl3k8n2hyoZBVVMPfHE7yx8RJTvzmsPVSzleSXVnLvryd55Z/wNj+3PjEZRQz6cC+rTiaSlFvKYyvPEuptzYyeLtobpqib8gc9BSVZSMa+h3e3vqw4ntA55oi0huYqOTV4DydnwP94SL6F0UfmgbKqoT22VIqBvQ8LH3iWp40/ID83ixcVyzC2cxfBhrH2zdBQT8aHdwbz0FBv9kVlMrO3Kx8+/bC4wW1/Ef6YLhaOAxra88qkEuaFurPpQhpLVpxlVKA9z471195IIgGfkR3ncKdSCYthx+5CrnRuBVRX8cWuaGZ8d1S7SbYoXSuIzCwq57RSPSQxWVxY+3lZU1mtJKwmSE9VLz7VzfMXU4S0Lznv+rN8WUUVXKzTT6NSqRpOco7YCF3GUqbSY9XpZHLMA9HPrL3xO5gb4mBuQHhy5+rL0WBig9RrEK/7XGHPs8PY/cxQPp/VQ2N/rqGyRFhBdxkvMtyt1OK7WhlhZ2bA2cR88YB7qPiZeLz9XkMHce+vJzm14nUUleW8WzipfXY65Fl4cL+o4Fwnxvpylgz3ZUNYCu9vj+LZzEmYUsrbjupKmUoFW55FITPiK8U08ZhEAh4DRDa+jWwNT2Pv5czaTHXcAWG4oV7wx2UVM+v7Y+yuGTarUonelIq2JbNS8suY+NVh5vxwTFhoF2fBz6Ph2/5cCTt0zcNsG+N8Uj4WRnp42Bi37gmmDsK8oU6Q42hhSEZNZUSlgsQTms95eEoBFQol/bysGttbLXJ96HKHVn/c2K4O6CUfp2z1YlFJzI3jyJVsFi/bSfXlbeT6zgCJBJVKxdv548g3cMZ+1QQkh79gqdlyHij8lij7Cahc+0JePGcScvlgq+hvTMprYxIo5bT42eUOYY2dGcGxco+276cpyvJr+0Fr7KPrkJpfpnF2NHLsggnlJCTGM++nEzyy4ixrzyS3/De2vwI/joKdr4n+opa49A8s7S/koL9NrL1mVStQrX9I9OQ+uE+Md4jaQvy53bw0PoDFQ7xZui+WPTfbVfN6Kc0B4zqfWyMrcX/OrO2pVCpV5JU27MnxsTNFIoHojA5MZFfego6EbaBzBTnVCvhtIsqloXy8eheTvz7M7O+Pczwulzcnd9UahulhY8KKxf1RqVQs+PlEqxb5+aWV/HQojklfHyI+u4Rl83vjamXMnB+O829Y2zIyH2y9THZxJZFphRS3JJsrzYW4/U3+el9UJin5Zby0PpwJXx3CWF/O0rm9kNe3JU45AyZ2Yor98zHQ/0EeGupNakF5q2yVOwVFaaBv2mJ5Ptr3fp6pfFhUHwIn19oh18PN2pjPHp3Dqw5Lec52GWaLN4Frn0a3lUgkvDwhkItvjuONyV1xsTIWQ9Jyrojm1gX/iMxgI8zu44aNiT4PDvVm6dxeWp/FG0L8YZHZHPaCCPhKMim5uJUVJxJRqWDlyTrOXoXa7nUZheUU6NmiMnOGZHGTDXIyx0hPVms+kBYmftYEOakimMgurmi7U5Ga3JJKXlp3gUEf7WXS14e599eTrDmdxLSlR+j9zi7iahZ52TFCLhc4hQ1hKRSUVWHt16/2mNQEu4i+nE5L0DTkSUfxubIc3ybMQjj0uehDGPdem3YtkUjo7W5Ve77MHMHKS3wmbhTXUAG4mFJAenI8D+jvoqrPA7w9//qDEkBUc2182mdfwNz+7gz0sWXz+VQulVmS6jMbkzPLICsKzv0J0ds52/UlovJltWYc1j7i2tHGPrGaxIImYI8/JBI5yadQVCt5Zs15ziXmsXj5ad7eFIHi7Er4cwaqZYO5dGK3doJA2fh382JKAdOWHiGvtJK4rBIe+r81FH07AmVeAgWV4PHPVDK2vo+qnao6YckFhLhZaisPmkMqE5/hwtr7rqOFYa3xQG6cMCFxE0HOqfhcDORSgl0sW9534GRh8Z0VBcAYp3K+1/8cSexu2PYiqm/6839//E1Azm5QVjNpvxNnEvIITyngdJaUy5M2iN7QPW/hknWA8053MT1xJifzzUmOi2Tx76fp5mLB0C52ba90J58GG19xDc9PBFU12/OctGV+18OV3aI37coeIVerk3QrLK+iqEKBs6W62qn+/myZ78yeZ4fhaWNMbEvyZJVKDGctyxPfi98mNOoqCQin0i3Piv4jC1eY9Qc4haDasISjl+I49OksVEmnSB7+ORhaUOY/nSJMmG4ZxwNDvHlhnD9OFoYcuZLdDm/MTaQ0T7uSA6ISXCdALCyvQqlCy10NRHLW3dq4Y8wHqqtg89PwoRtse7FNQ5JvJTpXkHPsa1RpF8gpLmdGxFN0t1FhaijnwaHetXMQ6uBqZczXc3sRl1XC4Ra+CMfjchj44V4+3HaZ3h5W/PvYIO7o5siqB0MZ19WRJ1eFtVgazS+tJDy5gH/DUlh9Oom7+7mhVNFQLlSfTU/CHzOazMSdTcint4cVX8wOwcXSiGXzezeYiwOIC6RLHy3NeTcXC4b42fL9wdhbo6xb31mtCTKLylmvHErxfYeEzr4ZrEz0+eGRCXy0ZE6rbrJaQxh73wuLtsP0ZaLXoQlsTA048cooXpkQqP38G0XYSrGo8p8ITj3AMZisA99ToahmXFcH1pxOql18FdUPcipwMDdE4tpHE+TIZVK6u1qIz66iUtg3O3YXF7ryQi6mFNDVWUwtT8lvw41cpRI24Ikn+P5ALP+GpfLYCF8+nxVCTEYxL6y9gJ5MiqWxPi+svSAkO6d+AkMLVL6j+e1IPCP87bD07gvFGbVOcUB3VwsuphR03s95zwXCyn3Hy7DiroYSpLj9wiwgdMk1LdB7eViqM9rq8+weKhysroXKEohugztYQQp85Amf+MLvre8HWnUqkReNNiDT08d81HNY1NOcdxYM9WT8ubg/R18exf7nR+Ax7Q2xCFjaT7gl+o1F1m0GSlUdCaeND5QXtElqpqhWaoIbjVtgTYU+NYzvD8ZxPjmfP+/vz/8mBLLz2BkqNj+PwmcMaRWG+G+9izXfviaSeiln4DN/2P6yVqBVWF7Fvb+ews7UgA2PDmLbY6H8IP+UnOJKxha+St/M/3HQ+k7meJYikV7/EkClUnE+KZ8eaklpqzFz0q7kmBuSWVQuvt812X63/gCcvJpLDzdL9OWtOF7f0WIkQcRGKMvHavP9VMuNWWy6lPVjDhGrcuYL6Zc8bX0MvIZh4+TJM2vC+P1oAram+gzt6i4k009HIHn6EqEPf8tbM/txPM8cs7Jk7ujmyPcLeuNta9L2SneK+h7uPQL0zVBJ9ThZ5kJPtxYqVK2l253g2ld8JorStSo5afkigHSukc9aewNgV5GMj50pPnamxLUk/y9MFT0mo9+ER0+I93nV3MYNOHa/KZxSx38srO+DplA1ZSnVeYn4rRnOgLL9vG/4JPfvN6SwvIpH/grjpDKASRZxyKQSteGKLUdjb+EgR1Eh5In1gxz7QJHYU5Or7rmpX8kB8LM3bdo44xrQGGn8eac4P8GzhPz96z6QfaXR5+yLymTiV4f441i8SIwknxaVvI2Pi0CpE89t6zxBTvYVlPs+YLlyHF+5fIa3YRHvyb7nl3v78sqEwCaf1svdEg8bYzadb7qSEZFayAPq7MvRl0by7TxRwQFxc/tqTg+eG9uFpfti+WZfw5OsUqlYcyqJIR/tY/I3h3lyVRi93C15e2o3zAzlnI5vJshJOAqRG0VDeyMLEpVKxdnEPHq5WzK9pyvbnxqq6T/QQlkt+iYakbc8MsyH6Ixi9kffAmXdojRhItACGYXlGOpJMXUJFP0lLSCRSMQgz7YilQnZSSuCo1ZnKdsblUoskP3Gin4AiYSqHvfgnnuURUFSnhnjT25JJdvV09lFkFP7HmcUluNgZigqXGlhGiebXh5WnE3MR5V5SbgUBU0FIC/tCmkF5YzvJvbR6mb/uAPCoe2nkfDLWIZeeo3Zzhk8ofyTGamfsefpwex7bjhrHxnIJzO7czohj5UHLsDZPyjstoDH1kYTlVHEvYO8at2GamR0iCCnqELB1ZxO6mgl14eJn8Ldq0S/yA/Dhc1tWb64mfx5J7j3F9W4a6CXuxWVCiWXalwC3UNFcFqW3/adHf1GNO7XmUtFcRbseQe+G9zQ2OLI/wEqIZWsKBQugkf+r9kqRlllNRHnjjNdtQfJ4KcbSEg7NWaOcP9OuHu1sLef/See6urc1eyaIMdX/MxpfGHQGDGZxZRVVWNnZiCCHJVKE+Tkx57ky93RPDDEm/7eNjwwxIt/PdZQqpQzOu5uhuW+winbaSzMW8q5/5uD6vcpYpF5/Fsx3FjN/+2Oobiiih8W9sbB3BCbiN9xqkrC4O7fmTZyMO/c2YuRT/yAfMb37fJWxeeUUlBWRYh6QHWrMXduIFerqlaRU1IpKpS2/mBiw5mEPPZHZTK6kURno+gbg99oMV3+Iw/IvExY6FccToVnNsbzP/kzOMkLkWVcQNZzLl/M7kFGYTnrziYzvadL7WBnCxeNa+ldfdx4dMZILCjmgwkeOJgb4mZtTHJeqXZvVXNUlgrbd9c+ones61RyrHpQiR492quSI5FQOeY9UclCRZVZbZCTqpb1O9cMItY3EYFmrrCR9rI14WpLQU66Wp7vGAym9jBnBdVZ0UR8O5fv90fXypKryiBsBfR/SPwnkVBaqeCB7aV8WnUXlvIqZPNWM/2eZ7iaXcLIT4Vhi0fvcRhnntNIqAb62BCdUUxWUUX7vD83kLySSg6eF9XEasN6QaxdgKiAVYjgJa9UBDmWjSSBfO3NiMlsQa6mqGiVQ92GcymM+mAr5b/fKdaT89fB9O/gsdOiH3r1fM0xgehdf3VDOIt+PUVpZTWrN24i9YOeYtRH2Aqxj8hNIqnXyOyfzkCnCXKqNz9NptKCtRaLeOWe6UgHq0uuLZw4iUTClBBndl7KaKDzV6lU7LucyT2/nsTFyogfF/bBvma4ZL19PDbSj/sHe/Hd/lgtxxSVSsUHyzfyybqDjAy0Z/2SgWx+fDArHwhFTyalt4cVpxOayOQplaKZziFY6DATjjbYJLWgnMyiCk1jcZNkx4iMgEvDoWEDfGzo62lFXFYnXfxBbUNjUUarKjma6sPNCiw6E9kxYqaE9zDNQ5sYQonKkEdMD+DvaEZfTyv+OJYgguHijNrGXhBW3OYGIsOnKBdOZohFc25JJbkxJwGJkHkAyXFqmUeQI1IJJLdWL35imdDZz1sHk76kZ8kh3sx4Ak7/Aqd/wfDCcrxsTQAY6GvLvP7uJO/+jqrKcu44GsiJqzl8fGd3hvrZCimWoYWWZC3YRQS7x+M658VUg/94oTHXN4Flg8RCa9MTwlVn3jrx+DXQzcUCPZmEszWSNfcBgEoEVC2wKyKDBT+fEAsypVJITQBO/iB+Jp0Uc0yOLYW8eDj4We2Ti9Lh7O/CvWzU68LFKnSJ6C1aMVNjZlGfrRdSeVb5K9XmbmL7Ww2n7uB/h7ArlhvgZG6IgVzK1Wz1IqCmGpfb+lkjYUn5SCQwq48rEamFKLJioLyAIutukHqOPu5WPDOmiwh+tj6HTdpBCsd8iqGFHR/P7s2Ax37hYtCz9CvcQZZpACw5CsNfFs6Q4WuJySji96PxPDLMVyTyirNg/0fQcz5OgQN4bKQfs/u6i+tqO1RxoNZ0oHtrTQdqMHdpEOQApOeXQsIRcA+ltFLBc3+fp6uzBfcO8mz9vse8DeM+gOnfw0MHGDNmPFfeG0/0u+NZ+fJCpFO/EQnDwEn42pvyv4lB6MulzOrTdE+l3EZUPsgT5gPu1sZUVasaDjBtirQwkeysSVRO/Jxlrh/ibm2MbWPKjWtgT2QG/X4vYH31YAC2JtTKqlPyy5BIat9nQC25VAc5diak5Jc17JesS/oFYexg6SH+7dyDL0yfJSDvACZ7X+XO745wOj5XmCqUF0CvezRP/XZfLMdic+i/4C30Xk5A4jeGrs4WvDYpkIKySr6+uxe+/e4QCTf1NW2gr6iA3IrVnDMJeby3VhgtLFodx8oTibVGIPbqxH22uNfmlYh1blOVnIzCCgrKmlkLb3gEvu4tzCZUKjj9K/w1FzY+AWF/aTb780gMb5R9iCo9nLRJy3n5vC1Br2/nzpUJ/OHxHqq8ePj3USjNpaRCweLfT7P6VBLvjPdkz6AI/jV6i8JK+NblQ1TPRsHDh4SVe0URrLuvSfnszaRdhnnEx8dz9uxZgoOD8fPzu6Z9fC1bxMmqBD6dNwgjfZnQ4laVigWZc89mnzs5xJmv915h3+VMujpb8MuRq1QolFzNLuZ4XC69Paz4bl6vhk3A9XhipB/rzybz8Y7LfD6rBwBn9//LM3GLec7UAH3fd8E1ROvm0MfDim/3x6KoVtb20BRlCGvYq4dEJnrhv3Dih0aDnJoFS8+WgpyahsVGghyJRMKqBwdcWyXjeji3QkiNFu9p9oa5OyKDh/48w6bHBhNUlNZkz0xdNNUHHXD1gAgePGpd2/bElmJgNIqJl1fBhDeYH+rBk6vCiIqLxV+l1Aok0wvLRXXQKUDsJ/k0OPfU6MAL405hY9tFZKalcvJTr2Ci3xc/e1OcLIxIaq0kIztamC/4jaa8qpox62S8PVDGqDvuFBasu9+GgMka97rXxvuijNpLvOV4Hg4ZzPSeLpgZ1vmOOoXUur4hJIMjA+z5+fBV7u7r3irZ4N7LGRjIZQzybd6iud2x9ha2thfXgZ6x0Ma79W+dvXETGOrJCHK24HxNL4dtFzF/IfFYsw34KpWK/9sTzcWUQg7GZDFCfhEKEsFvLKpL64nq9gwBu58EWz9YsAHO/YFq7zuc83oQb79ALI9+LZylQh8RO5TJRU+Rx0DY9BR8OwB63C2CLr8xoG+CSqUi6sAq7pRdgvEr2uZ61kmRSiV42pjUVnIMzEQDfRsqOeeT8vG1M2WAty1L98WSdfkoTsAHmQN5X36R32Y4YCCXwtbnxLV1wqf49JvF9sG1++g263WeXRpAhtyBPw3MYNiLomfv1E+8gzuOFoY8NEy9IN//PqCCUW+02/tQHx87U54c5YedWRsX6jWVHJUKJBLNQFD5ud8gNw7FxC95a2MEKfllbH58cG2FpTVYe8MA7cBaq8e1251aw3cXhHowrYez9vWnPjUL+7x4cOqOm7WoiCTlltZWR5oj5QzIDMChm/qADDiZUk6PtlbAmsHbzpQZPV3p1vVrtm35kvdOKhkzQoGxvpzU/DIczAy130cbb2FpDXjZmKBSyzEbDC2uIf2CqOKo7/dKpYpf80Lo5/cs82M/Rd/Mhvt+k3Lc6ReM3QcKm2+gWqli3dlkpvVwYYS/vdYuFwzwZGZvN7HuU9qJ5Fb8YfAehpOFEd62JhyLzWFqD5cGh9OZGRlgT8jdvrAegnw9eeWfcA5EZ/L5rB6Y2KmNizIvg0tvctWVnPo9OSBspAGuZBbT26ORdWJZvqimKEVPO279hKmFaz9hVX72dyjJJMHrbh7PeI3B8giekLzE1pUlGMrLmdffnaS8Ut46Afq+zzMr4h2I3Ei6zItHq4z4xtkQi4MXRPDZexFRjo/z8brLOF/IZFpPFzFcfebP8OdMYewR+rDm0CoVSi6nF6JUic/XzZArX1MqZ9q0aWRmCmnUli1b6N+/P8uWLWPYsGGsWrWqhWc3jlNAH6ZNnYl/zcwS557ixprYsua8i4MZAY5mLN1/hSlLD7PpfCqJCXH4lF1i2ZyurH14QKMVnPpYGOvx9JgurD+bwuGYbCpjDxJ04EGuGASh13WSWKj9Pklk1tX08bSmtLKayDR1OTHtPPwwTOgVi1KFdtV7uFgQpJwRZdw6nEvMx9nCUDu70hgpZ8Qi1KjxYKhDAxxFZcN+ovSLYmZL6llN82hxhYJT8blaJhCp+WU8t/Y81UoV4cl5DZy/miKzsAI78/bJbt3yxO0X2T9D0SOjVKo4HpdDSpd5Qh99aT3juznhYG7A9qPihlXzHqtUItPoYG4gZBwOXTV9ObamBrhbGwsXM+eeQpph4UpFdjxdnS2QSiW4Whm1rpKjqITcq2LhDSTklJKissEoaJxY4I59V9wY/31U9IJcXI/h8vEYl2fgN/VFFg7wbLjAcOrRwHzgkeE+xGWVsLMFZ6jSSgX/+yec+347zbrWOAZ1BAam0Pse6H6XkJa1Q1UywMGMmBqnHYlE3NBaqOScS8rnYkohRnoyMSfq7HIqLby5L38RlQoV0j+no8qMhElfgokN9L2fKrkJl/9+gw/ef42K4z9S2H1RQ7lZwEShyw++EyI3w9/3wNJQuLKH8/98yuOFn5HnMFBsd5sgJD119PE2vm0KcsKS8glxs6Sbi/guF189SZbckSgz0WBvkHkeLm/WBDj0e6DR/fTuE8qR+GJRRZBIRJUw8RhXoiN5enQXYYxSmisSUQMeA1O7a3/RLRDsasHTY7q0/YnmzmIwtLrh2c7UAHd5Lh5nP+Ky4xQmbZKw5kwSr00MpEtTi+52pNkAB0RAKzfU2Ei7qSXvia01H0g+LaqDcrGQLa+qJiK1sP1MBxCfz9cnB9HF25vAuz8gp6ya347GA+Je7GRZb51h7SOkqSoVXnaiwtxsX06aOshRk5hbSkllNYq+D8Kwl5hVspKf5B9jnHaCdN9Zmu2OxmaTVlDOzD6uje1VBDiglo8PEkGOmgE+Nhy5BSs5UqkEO5l4L1+aMYhv5vbkUEw2S1acpUrPTFQy1X05eSWVGOvLGjU08rGrCXKakKxFbRUByMJ/RSUlYiNM/RYW7xIzpAY+Abtex/L34fSRRlF110oeXPQAs/u4sfPpobw6KYjvF/ThjSldeTE6gC+C1vCB5AEiq13w93LDwtZZJEkeOw2Tv2RqH28mdnfitQ0XORWfK6pTvqOFguGCGIgdm1XM/b+dotubO5jyzRGmLT1CyNs7eXp12A0f6H1NlZyrV69iby+i8Q8//JCDBw/i7+9PWloa48ePZ86cOW3e5+y+7toP6BuLTG7SCa3IsCkmhzjzyY4oZrsV8H7lh8gK1DM2tppC7HgY806TDl11mdvPnW1nYoj4/QlC5ds5p/TDYv4aJO6OwoN/09Pw3SChve+1kBBXS+RSCacTcgmuDIO/7hYyhgf2akmG8BgoPogpZ8CzNi13NjGPno1F5/VJOSMaFm8kFcXiZnv8O2GDGDRVzIQxshbyGyMrKEqjMiOC/ztRwh/HEigsV2BvZsD0Xi64WBrxz7kUDORSbE0NSEpNFQP0WiNXKyqnm0sbG1lvR5TVwn2p34Oah6Izi8gpqcS/Wz8oGgr7P0A/YiPb5FGYxSaABLAQsovCMgUVCqUYqgqiilZngOEAFz3sY2LBeREAKkt35AlJdPMV772rlXHLemBQ3yirNZm7GntcjcuYia1YtG1YAld2icfcB8D89eLG3xjOPeDoV8J8QP3d7etpTR8PK747EMu4rg5IJBKqlSpe//cipoZy5vXz4FxSHp/siCKrqII3JgdxzwDPlo//FsHPwZR/wlJqK8cOXTUzk5rij2MJuFgace9AT37YfhKV4WaW68/jSpURyS7j6ZK6kUMWUxjiKmQ0VXITlqsmsli+krnsY58klHfP9+PH/sV413eNM7aGSV+I/8+MFE2of86gB3DY7A4G37usXYK7zoKnrQkbzuXXPmDtrVVtbI6SCgXRGUXMD/XA0lgfd2tjVMlnOF7hyexx/eCAg6j8xx0Q340mAhyA8d0cef3fi2y5kMZ9g70gcDKKTU9zl9FJJnZXz126sEZkd3stvPYX3JHUnZVjbI1cWcG/bquozDBkTsIkXJwk/LNkULtWOq4LqVRUc/LiATAxkGNtot/6Snf6BbEYVHMptQCFUtWyiuMa8bQ1YVYfN74/EMfcfu6aGTla2PgIxUxRGg5mThjpyZruyynLFwFenSAnMk30BwY6mUPAy2DjS9+NjwmXtP12/OxbSJCzOWvPJONhY0yf1qx1PAcL04LKUtA3ZpCvLStOiPEabtattCjvLJTmABIwtGRSdzmmBnLu//00L68P5xO7ACRqh7Xc0spGqzggPmeuVkZEpTdhPnDpH7E29BoqbPUrizSmEkgkMOZtVJWlVJ1Zxw+e/8fzQeMIgQY9dAtCPYhKL+Sr44n095rO/Xe/jVUT7R3vTevGnd8d5a5lx+jmYs4QPztGGg2hb9TLfLRqFz+FV2FvZshzY7vQy90KuUzKkSvZfLIjilBv64br/Q7kmoKcsrLaL3VFRQX+/qL05uTkhLI9B4259RfazlZw70BPnC0MmHrhEaQKKdz5s1joxe4R+sSfRsHc1Vpf0MaQV+SzUvkCSv1kvlfOIK/3o/zPXb0o9x0Njx4XEo0tz4H7AIxs/ejmYsG5q5ksOv2EWLDNWyuyuHVx7C6GHcYf0QQ55VXVXEotYFL3FoKvqjLRYNxjXqvei3Zj12tiARU8C2x9RfP0xbXid3IjocX8cSTnz57guwsKFg7wZHSgA5svpLLmVBIFZVUY6cn4+d6+fLc/luJ0oT+tWYA3RaVCSUpeGe7WrZAA3O6khQlts/dwzUNHr+Qgl0rEDUP+nHDSqa7A2G8IH54dil+vYdxlYk9kSgHbLgp3stogp6/okSnNBWNrZsgOIVVVU+YzHiMgW+aIreIkw/xF5tfN2oi9l1sxT0OtLa6p5MRmFmNmINeWsATPhKBpYohlVTnYBzS/z5qgPnavmGqv5uFhPixefpptF9OZEOzEN3uvsPJkIsZ6Mr4/IBrmR/jb8duivvjad3wG+Ebia29KpUJJUl6Z6G+yD4KSLCjJrp0mX4fs4gq2XEjjydF+3NnblYSd36JSKvipsD8/LumFj6EbcX9X8GTSZP5VLyJWnUzky6KRTO8lx6bfbAKseqP68QSzvj/O7/f1patzE8kH+0C4dytb/vycv6LhvUWPgNG19R91VrxtTUgvLKe0UkiAsPEVkkSlssUel/CUApQqNIv2Hs7GeMRcYbvBfB7u6QIxPeHsH6I6O29ts/uyMtFniJ8tmy6kct9gL8rlZhxVhTDH+KTIBqtUYl6K//hWJfduCjVBzonvhKnK3nexyouH2cs57DkGYz3ZzXGybA6r2iAHxAiD5NZUcsoLRSKozvrjXGI++nIpQU7mHXCggidH+bExLIWPtkeRkl/W0ODAWt1XlhOL1NwZT1sTrjbV26vu5cSxNikVmVaIpbEejjX3l+53IXHqjjIvG4ut1cxcdpRnxnRhx6V0Hh3u27oeW69hIiEctx8CJjDA2wa5VMKOS+ksHuLdptd/0ynNBSNLIfEFhvvb8+GMYJ5fe4G5Po70KhIJx7yShjNy6tLN2YLwxgZhl+aK++OYt8W/Te2AelVbiYQLIa8x7fBwfh7Qr9nDfXNyV+7o6kSot3XDESZ1sDTWZ+fTw9h7OZNVJxNZeyaZFUW2nDaQYRK7mQeHPsKjI3zFNTJ2H0TvpUdFIf4uVfyz6RwDnO/D3cW5yf23J9cU5EycOJHHHnuMDz/8kDFjxvD7778zb948tm3bpqnwtAtu/YVzTCNDrepjYiBnutllkfWe9QcETRG/cO8vMlkrZsEvd4hI17aJviGlEtY/iKQkB9kjh3nA2g9Z/S+lvglM/hKSjgtr6Hs209fTCtmZX0EZL/52/QAHxIfcvb9oqFRzKbWQqmoVvVrKbmREiIxcC71J7U7sPtE4OOlz8e9BT4sFanmhWFCZOYKtH5VpkXR3HcWbU7oCMNjPlg/v7I5KpUKhVKEnk7I7IgNJmNoXvoXFbWxWMQqlCn/Hjrv43yoc272O3hID9F371j4Wl0MPN0tMDOTCjGCJ6PUyBEpV4bx1NoV3w3ZSXKFAXy5luL8dQWo7aFzVF7mIf6H3vYSkrWW3sjfmhWYMsINT+aYMlmYTpO5hcbUyJq+0iuIKBaYGzVwusqNFEK+WycVmFeNtb9rwpiaTCw1va7DyEMHd8e/ERHL1vkYG2DMywJ7HVp5lfqgHfx5P4JFhPiwZ4cu28DTcrY3p723T/L5vUWq08jEZRSLIsVN/lzIjwWtIg+03qCeyz+nrhrWJPmNss0nItefukX3VLo4W2N+3kqoP9vDrkXim9nDm//ZcYVQPX2xm3QWAE7DqoVAW/nySu5Yd46s5PRkd1LjT1eXMYp68HMRDw7zxsLm9AhxAI+mJzy4V3ykbX00mHIvmewYuJItFbY0ke6h5JgYSBV16DRPWyE49IHq7WETWyfg3xeQQZ55Zc56EnBJOxeexv3IA3/C1kFOX5ggpzNh3rvs1dxgWbtBjPkRsEkYY9l3hgX3g2I0mpkzdfKw8xaJSjZuVUevkahmXxM86QU5YUj5BTuats8W+RhwtDHl6TBfe3RKJRALO9WXx1t4g1ROuaV5D8LI1brqSkx4utrWrvX9HpBUR6GiufZ2388fCzp817lW8uzlC87dn9G5+DafBoatw1gtfAwETsDLRZ0SAPWvPJHP/YK9by4yoLFcoX+pwVx83EnJK+eugCb30xHoqt6QKq2aCnGBXC77Ze4VqpUq7NeHyZrE2DJrW7GHsuJSOuZEBQ/yal63KZVIG+7Wuf1UmlTAmyIEx6ntBQVkV8nV/81j5JRin/owknhDmNMY2YGLHyNI0RktzKP/pGxiwGLrOEBJQc2cRDHYA1/Tt+vjjjzE0NMTFxYUVK1awaNEiDA0NWbZsGT///HP7HZ3aI79VU72rq2DXG2IRp3aJ0mDhCvdtFz0+NdOAG+PgJ0JKM+N7sPNHTyZtPJOkbyIkGglH4NCnhDrLWFS9hhK/qU1Lb0BI1pJOiv4FxE1PLpW0nMlJPw8StTTlRlGULprW3AfUPiaVgqU7OHbTSM5Udv6YFsXS17NhoCaRSDRNjr72pjiUx6I0d23REvpyuiiBBzjeXln4a8HA0IT1VQOIzRPOKtXqfpyBPo0v4pcM92FkgD0PDPHmz/v7c+GNsfy2qB/mNXpzW1/oNlM4Y4WtwLDgClsMJ/LZTiHv2pdhiDnFSCvFOXCzEtW0FvtysqJF8kB9A4rLLsHHth0WuQMeh4xwrbksUqmEHxb0ZuEAT5YfS6CX2pHK1EDOXY6Z9Ldvx2pyJ8PZwhATfRkxNXMTbP2EmURW45PHL6YUEOhsrpm71c8kgyobf5aMqJ3TY2ogZ24/d345cpWpS4+gL5Pw3Fh/rf3Ymxmy9pGBhHrb8MAfp3l6dRhnE/O09NXVShUvrQvHzdqYx0demwFNZ8dTHbhpFoI1NtKtcFiLSC0kwNFMc00cYZ6MEinDh6kDmhrXrSHPtkriNybIAVMDOaM/P8CbGy9R6D4K9EyEvfc/D4sgwmdk217gjUQqg2lL4YU4ePiwcCR07Hazj6p5LD2E9a9aseJmbUxSa3oW08PF99SudhxGRGqhpjerI7l3oCdBTuaoVDSUq+kZCjMjdfLVy9akaYv+9HAR4MhrF+ORaYVCqtYI5oZ6fDwzhOX39eP96cG4tMacAdQ9ZrMgaptIqAIze7tyOb2o1j6/BSoVSs4l5vHbkau1ttY3g9KchjNygGfGdMHaMwSA+Kiz5JVWYt1MU36IqyVlVdUN5+VEbhYmXS0kWE5czSXU27pt5h1txMJID2nXaaJHtCAZijNFn6ZTD3gqHB45gvSFWNYM2MByxShUp3+BH0fAdwPg8yA4+GmDnvX24JpesZ6eHp9++inp6els2rSJM2fOkJGRwZYtW/Dw8Gi/ozN3EovqpJON/16lEq5lnwXCu/aQeUmU7Rq7QRiaCzeVC2ugWqH9u6pyoSXf/z4MeU6U+FvCdzT0XQz73mPk5sHYUMgh1webf47HYNFoqZ79EZ5SgL+jWaPNZlqknQcbv2u2nr0magJL99BmNys08cZTmdSi1tbPwRR/SRLFFv7NbgdwOb0IezODZjMb/xWCZrzER/qP8tuReEDcGIvKFYQ2EeS4WRuzdF4vnhztx2A/28Y/WxM+EY5f/z4KNn7Mm7OQs4l5zPnhGElKdaYnPwkAV7UGOrmlWTnZ0aB2jFGpVMRmFuNj3w75WN9R4sZ6bKnWw/LMcN4c48Kf9/fnh4V9kBelwOoFYkbPuvuv/+92UiQSCb72prXmA3IDoavPjGh0+6iMYvwdas+DUX4MXbr1bXCze2CoN/P6u/PDgt4cfGFEo9p3UwM5Py7sw8vjAzh5NZcZ3x7l2b/Pa4aT/nrkKmFJ+XwwI7jla9otiq2pPmYGcuJrFoJWnoCkVeYDkWlFWgktm+JopLa+GJqoH/MdJeZWqOdVtYSZoR6bHh/MKxMCGdvVgSfuCIH5a4XRg7mzsJaW3gLnQSYXFY5mhjF3Gqw8hZSqSMiA3ayMySisaN52GUQ/jp2/xmWwRD3vq0npZzsil0n5YEYw9mYGjQckHoNEkKNU4mVrSlZRBUXljdgVp4ZpJXELyqpIyS+rVQk0wdAudtzdr409GMF3iXEHkZsAUb23MdHn79NJLT61qLyKCV8dYvq3R3lzUwRPrQ67eQOkmwhypFIJT8yZRDVStuzcQVZRRfOVHHV/8vnk/NoHldVineYzotlDKKus5kJyPv28boC6wX+8KCb8cgd8GyqKD7OW1363JRIG9O3P+1Vz2Tp6jxhHcO9W6Dkf9r0Py4Y0OZLgWrkuC2kjIyOCg5vvcblu3PoLbabaZlJDZamQi4WvEc4yrn1Fmd+9f9P76jEXTv8sys1+Y4SWOuGo2H9BkmiM7ru49cc28TPo+wCSc3/ww9lSErLMuaO57Z17il6WhMPg3p/w5ILGLQHrk3ah+QpRO7P3cgZup3fhZ+4qrG+bIbLaiVBJKX1tm59n5GtnRqU0iTTDAbSUu4pKL6p12fuPY6gnY15/D34+fJVnxnTht6Px6MulLc9Vag5ja5i6FFbcCf0for+PLU+O6sIXu6Pp7x8ICYhspWM3HM0NkUslzWcrVSohkVFLRDMKKyiprMbHrh2CcolEzFjZ9ISwOnXpJdzZVs4GQ3MGhz4K4XHiu2xkCX3uEz1HsftavPjfqvjam2mqnYAIAjMbVnIU1UpiM4uZWSMTKckR/Tt1ssk12Joa8N70lq/lMqmEB4f6cP9gb9acTuL1fy8Sn12CXCrlZHwu8/q7E3qbSgVBBJlediZEpauDTD1DcY3Mab6SU15VzZWsYub2r7PYSw+vtRMGEZC0QqZWFy9bE7xsveo8MlAoBnR0DDVS27x4sHDBvSYJlFeGb3NJnfTwBr0sKhV0bSFAaC9C3Cw5+b8mPlueg+Dw55AViZet6JOIzy7VHkpeki2SyDU28tQ1HeiAe7WVh1CRXFgNPeehJ5MytYcL688l88rEQAzk9YL37CuQEY6qNJePLjmTli/hj/uFNHvBzyfZdjGdiS31PncEpbm1M3HqYWJmSZmlH3Y5l0isGox1E8YDIJx/PW2MCU8uqJ3llBkBFQUtJqLPJeZRVa2iv9cNGMZsbC3mU2VGiLaN7rMbVJncrI0JdDJna0wZE/upZfieg4QT6Z8z4bcJYgCzZfuYE3SaYaBN0nO+aGq+vFn78W0vQORGMexrxg/Ciaa5AAeEHMDGD86vhJ2viozv1QPCxen+nWIfbdV72gfAuPdI8l/EqaaGgtYg1xfHGH+Y0koFsVnFLTuIVSvEB8bxxgU5uyIyUcQfQ+nWwvsJHCsUmX/r0qvNbmehKsBOUkCUqvmgCUSQo5Oq1bJggAdV1UomfX2Y9eeSeW1S0PVnyv1GwxPnNEH9YyN9eXKUH/eNHyh013nifMqkElysjDRuaY1SmAJVJUJHTa2zmk99J65rpftsMVD3j2lwfjWsWywCmK4z4MBHog9v5P+ExeWEz0R5fM9bIvi6DfFzMOVKZnHtpHX7INF/Ue/1xueUUFmtxL/GfrdG0mbXcjW1JWRSCXf3c2flA6Ek5JRSrqhm6dxevD21k8uN2oEhfrbsu5xZm7238YWsqGafE5Mhzpcm662sFr2WLRjh6Ohk1FTu1N8lzayc5pJA1VWiZ67Oub6UWohMKrkh1tgt4hYqpHTxRzTDmuOy613va+TCdQZSR6YVIpdKmg/urofus8TfLRAjAO7q40p+aRWbz6dpb5dwVFQN/r4XyZZnGBH7Ee/PCGaInx1D/Ozo72XN13tjbk41pzSnofV+HYy8+jHcVFSnLFtQrnR3teRC3UpO4nFx3lpw3T1xNRczA3mTssJ2p/+Dom997LtNXt/GBjmwPypTqwJ6oMCet+0+Ib+kjOJl48hJiWuXw+n8QY7XMCHz2ve+RgdL3AE494ewhQ5pg121RCKG1l36B459A2Pfg8fPwMxfrrupv6+nNXFZJWQXVzS/oedgSDxBZHIOSlVtGbJJsqNF2dYp5LqOry3MCbGkizKOGMOWFyw704ypRtbiTb6m8fJMefOOGgWlVaQVlOtMB+rgYG7IlBBnsooq+ObuXiwIbSdJqLW3JqiXSSU8PaYLvo4WIhkQu0+z2WBfW3ZFZDR9k8iOFj9rnNWyipFJJbjbtJPdp54h3LtJBFH/PChML2b+Ii6kL8TBk+dh8NOikiOVwug3hCS0lc6Mtxp+9qZUqB0IAZFoKcuDYm0XvBrL0S6O6kVIVqTo7bO9hpkmTdDX05qT/xvNxscGM7G7040fSHwTmBLiQlGFgv1RYlacmOtxSNM/0Bg1WW9N8ib3qkgM6IKcWwsDU5EUjdsPiB4XqQQSc5oJcrKjxegErSCnAD97084h6zQwFeuf+ENYGetha6rfsPfl6kER4NXJrkemFeJrb9qwqtJedJ0hBu4e+QoQNtUjA+z5fFd07eI4Nw5WzQOX3mwcc4BXqu5nlOwcU51rxx48McqPy+lF7Ips2iV0T2QGX+2J0Zrx1y6U5jYqV9Pg0hu7sqvM62HNgBYq4N1dLYhMK6JSoT7GxGNC4dOY0VUdTl7NpY+nVae6No/t6kBJZTXHYnMAOBWfywPLT7M3w4RH9N6hqKyc/B8m8sRPOyitVLSwt+bp/EGORAIjXhHVjEvrxYdm05PCYKDvNWjvu88BY1sRIA18rN0Os6+niNZPx7dQzfEYDFUlpEUeRy6VtCzLSjsvft7Am2F3riCTqFiT0bwbSl5JJZFZFZSYujfZ+KwhMwKFRM6RvOZlVlHqXgNdJUeb96YHs+fZYTem5B40VdzE1UP6JnV3JqOwgtMJeY1vnxUtdLjWQjZzMaUADxvj9r35GVnBgn9g8DMwd03tUFwjS5DVa9j0GSm+Z0f+r/3+fifCT22LrZlfVCM/y4zU2i4qowgrYz3s1KYDZF4GKy9NX0B70ZlunjcCf0cxfHrj+VTxQPBMkYi6vKXJ50SkFeJubVw7cDIjXPx0uP0rX7cdPqOEAqRagZ5MSoCjedPXRhBSNWhQyWmpl+WG4jkIEo4iAfp722gWnxquHhAJ5zqEJeV37Cw7I0sY8Cic+VVTzXl5fABpBWX8fjReJApW3AUGZqzx/YAnNqVQHTwHlYk9HP1as5uBPjb087LmqVVhrDyR2GAY5T/nknlg+Wk+3xXNwp9PkldS2T7Hr6gUM2uMmpGJufRGgor3+le1WBHr7mpJZbVSSGVVKkg4pm0M1QgVimrOJuZ1OrfRICdzXK2M+Hb/FX48GMfi30/T3cWC7U8N5a/nZ2N4/2ac9ct4MesVjJWtHLbbBJ0/yAHxBfQeLuRlH3uJD/yUr6+tqdLCBZ6LgUFPtOshuloZ4WhuyKn4Zi52IHoK5EaQcKR1pgPpF8DCvdmSZ3sjSTxOhdyUP+KMyWmiMqVSqVh3Vlx4ZPaBrajkXKTIxIu43ApNo3JjRKUXIpXQcSXwWxQjfdmNG4QWOBmUVRC1HYB+XtbYmRmw+UJq49tnR4mqkEyPaqWKPZGZDO/SjlbyNRiYiipNUxbwden/EKSeheQz7X8cNxlXKyMM9aS1Dms2PkJiWC/REJ1eRBcHs1rL1azLTerDdbSNKT2c2R2ZKRq0rTxF0i18TZPbR6QWartopl8UybZWDEbW0cnwGSlml6WeBYR88ciV7KYr3enhYO6quYdXKpREZxTdENOBVuMxGEqzISuKAd42XEotoKBM3WebnyQqJnWkatnFFURnFHd8/13oI8Ik5+AngLDQn93XjZP7NqD8YQRUV3IkdBkvbEtjfqg7H8zqi6T/Q6KXp1DI2iQSCT/f04eJ3Z145Z9wFv5yksi0QnJLKvlsZxTPrDnP1B4uLL+vH5HphUz/9kjLRhKtoUyd8G6ukmMfJNaDKS3fp7q5mCOVqM0H8hOhKLXFfpzw5AIqFEr63Yh+nDYgkUhYPNiLpNwyPtp+GRdLI366p49mPWzl3hWj+zfh0nPsdRtu3RpBDsCUb0Sj//Qf4KEDLQ8SbI4WhrZdCxKJhL5e1hyKyWqQKdBCbgBufXHIPdWyVA1uuOkAIHos3EJRImXN6WQU9Uq4YUn5TPr6MO9uiWR0oD3GLkG1wyCbIiMChW0gShXENTVsDOGs5mVr0jnK+P9VLN2EZE0t95JJJUwMdmJreHptH0gNKpUw8lDP8TmTkEdOSSXjujY+R+WG4T8BzF3g1I839zg6AKlaBx9d0/wu0xOBXz2HteiMegYeWZfbpR9HB0zu7kylQsmOS2oJTPdZovpZ1FASo1KpiEyrl7lPDxd2ybfSzA8dArd+YiaYel7OYD9bcksqiUhrRK6oqBTb1bmHx2QWUVWtumGmA63CPVRU4yP+ZYCPDUqVkDkBtf04dSo5J+LE70K9O3jxbGghEtLn/hTubsCLPsks4z3CKl3YErqSh7YVMSrAnrendBMjP/reDzJ92PqcxpLYzFCPT+8K4fsFvYnPKWHGV7sZ/cEmvj8QxwNDvPnsrhCGdrHj30cHsWS4b/usP0pbEeTI5EL+2Iogx1hfToCjOWvPJFMVL2bjNVfJqVaqWHkyESM9WevWmjeYewd5cfyVUUS/O54tTwzGsr7xgmMw3PHBdTtE3jpBjqWbaJIOmX1j58W0gbt6uxKdUcy+Gq12E1S6DSKwKoLuji3IRpRKUcm5gaYDAEz9BoP5qxkVaM9H2y/j9+o2xn5xgC0X0vg3LIVZ3x9DpYIVi/vz48I+SByDhWtTbhONYkolZF3GzD0EuVTCibicxrejxnSgE138/6sETYXYPZo+g0ndncguruDE1XrnLv2CcBpS297uuJSOjYk+fTxvcuZIJoc+i+DieuEMdJvR292KI7HZtQkV+yDNIgCEm1d8TkltY3Mzzmo62o6btTG9PaxYU2NpGzQNkIh+z3ok55VRVKHQbvzNuKiTqt2qyPTAayhc2QMIqbq+XMrhK41cZ3a9JnpyBj+teaim36VTydUMzYUz5dGv8DYswcHcgKOx6tdz9YD4rJrUDok8HpeDm7URrlY3QF3Q7yFx3fptIhz5CsvN91PlNph3rd7l0Q2J2Jsb8MWcHrUzDY2sxPylmF3iOUXpml2N6+rIngX2nLR8lYPGL3L8AVdemRCoea6HjQmz+rRyaGlLlKrvlc0FOSASiilnW7XLt6Z25VJqAWcObkFl7QOmjSsmSioUPPTHaf45l8LLEwI6dD7O9SKVSjp0wGvnfeW3IEP8bAlxs+SrPVeareZEW4/EiAqGFW5sfoeJx6CiUEjcbjQyOR/M6M5384Rjkq2pAY+uPMuTq8IY19WR9UsGMsjXVnw4fUaCzAAimng9uXFQVYqhazADfGzYGp7e6GaVCiWXdc5qnYPAKWIeRLSQrPVyt8LJwpBN9Z1tIjaCgTl4D0elUrHjUjqjAx06R59Gr3tApYSzy2/2kbQ7owIdyCis4GKKOnvsO1oEnGrt+pXMYpQqais5NVK266mA69DinoGenLyaS1hSPpjaietg2J8NXO4aLGpLc0W1/EYnr3S0Hz4jIOU0lOVhqCejn6c1h2PqBTmXNsCJZWJ2n1s/zcMXkvNxtzauHdDcWRj2IkikSA58yICavpzCVCFb9h6utemxuBxCb8TcFRAy5UVbRbVp12tgH4jx/JWsemQY70ztyu91h13X0HU63LtFSO2+Gyjm7ZTmwulf0f/1DsxMTDE1t8J6zTShWLi4Hg58LAbp/thOowdM7aH/I2DWgqrBpbe4HhSmNb8dIqD+eUAuPXO3EWnatFTt7U0RHLmSww8L+rBwgGcbD/z2QhfktCMSiYTHR/gSlpTfeFZHzdKLMjZLh+N84Ruh7W0MpVLYXDsE37Sp1dYm+owPdmJBqAcrHwhlxeL+fHRnMF/N6aFdzjU0F4PsmnKzitoqegbc+jMh2IlTCblkFpY32OxIbDbFFQqG+3dAP4eOtmHtJRz91OdUqpasbb+YVutAo1JBxAYxAExuQERaIcl5ZYzrdpOlajWY2ovZWB0wRflm09/bGhN9GbtrHIO6jBN2ourm92i1gUcXtUkBGReFs5pNK/qZdLSKCd0ccbUy4oeD6hk5/R8SMjS18xYIqdr6s8nYmhrgbKGu3Gsa0XWVnFsW31EigaLuWxziZ8vJ+NzaXo7KEtjyrJDNhi7RPO3k1VxWnUy6+XLexjCxhSHPwJnfmWSTRlR6AZXrHhYS+zqVqKyiCq5kFjOgiYHUHYKhOdy9GqYtg3lrwcAUfbmUBQM8m+5VdesLDx8SFsur58MnPrD5KdFbtHiXGEJp5ghrFsLaRcKoRiKDgEm1Tr7Xg50/jP9QSO6aw6W3+BnVtHGJhuidDD33NLFmfZmfOIH80oYmCTnFFfwTlsKS4T6MCeqEn7MbjC7IaWdGBdoT5GTO13san4C9OyKDbRfTMRjzPySVpRp7xAZcXCcaG8e922mmVg/ytWV2X/fGS4tBU8Xx5ic2/F3EBnFTMLJkbJADEoSsqT5bL6ThZm1EN5dOVMb/LxM0TZT8K8SCeUoPZ/JKqzhSE8BnRopJ74FiCOg/Z1Mw0Zcx0Me2iR3eBKZ8JWbo3GYYyGUM7WLHnsvqIMfYWtjTqyeEn0vMx8nCEAtjdYbz4jphddzOzmr/ZeQyKQ8M8WbbxXSuZpeIappDNzj8hWabNaeT2BmRwRuTg8R1U6USzk/Gtu1q5a3jBmPtLc73jlegIJnBfrZUKpScqnFXPf2LcKcc+66m7yolv4xH/jxDT3dLnh/XSSuq/R8G2y6MOjqfP/Q+QD/hAEz9poFUDbjxQ39lcjECpC0mTGaOMHe1GDkw/mN4+hLMWSECD1M7eGCfmK/2fBy8lAgL1sPQ5zqkb7tJLN2h20zY9iJE72x6O0Ul/LsEPAdjc99qSqtlLDvQsEXgr5NiDaY1ePg/jC7IaWckEgmPj/TlZHxug96TkgoFb2y8xCBfG8YN6COGJh1b2rBMWV4ghhl2uaNBmbjT0uUOUa2pL1nLSxBNdUHTALAxNSDU24Yt4dqvWTTxpjMh2KlD9Zk62kDXaWK+Q/QOQMx08rAxrrXOjdgAeibgO4rtF9P4+chV7h3kqTONuEGMCnTgYkoh6QXqqmjgZEg4Qml+BhvCUphUYzeeFQ1JJ6DXwpt3sLcps/q4YWmkx2sbLhKVUQyDnhI9DClniMsq5s2NEdzZy5XJIer5YKd/hiu7RPBd3/pcx63F9O9FlWPtfQTG/8E2w1cp2fwKqvJCURXoPls4HyIqes+uCUNfLuXbeb3Rl3fSpZeeETy4H8nwl+kji2G70USUvmO1Njkel4OHjTHOlkY35xjbikQC3e4Uw94t6vXbyPWFaYuJzc1LJkskMO078B4BaxY07Qh6ebPoqxzzNg7WFiwa5MWvR64Sm1XMzkvpHIrJolKh5I/jCUwJccamZnTAf5xO+k27tRnX1RE/e1O+3ltbzYnPLmH2D8fILq7gvWnBYiE/+BnhAnLgo9onl2TD75NFoDP23Ztw9NeIkaWQ1dWXrEX8K4If//GahyYEO3Hyai5ZRbX21EdisyksVzApuPlhoTpuINbeom9A3UwtkUiY3N2ZnZcyqLxyQNzIg+/kTGo5T60OY2yQA8+O0bl33ShG+NshkVBbzQmYCColF/euoqhcwbz+6qGx5/4AAwsRBOloV4z0Zbw9tRuX0wsZ9+VBloR5oLRwp2rLi3z8y1/Ymxvw5pQgsXFWNOx4FXouEOdKx61NzVDi5FNId72Onb0jY/P/pvTLfqLpfOhzmk0PX8nmeFwub07pip1ZJ1986hnCsOc5M+sUD+fNZf25FM2vsosr2HwhjaF+djfxAG9D5PowaznY+MKWpxuXy535VbiYquctPTzUBwO5lFGfHeDBP86w4OeTjPxsPxmFFdw70PPGHn8nRhfkdABSqYTHRvpy+Eo2J6/msvxYPJO+PkxBWRWrHxqAp63a99vYGoY8LRqjs2Mg+wr8Ol40D9+zqXXzQDoTXadD8km48HftY3WkajWM6+qIvlzK06vDKKsUGmadVK2T0nUaXNkNFWImy+QQZ3wrLyNdNQeFc2++MXiQOT8cw9/RnC9n96x1uNHR4diYGtDb3YrvD8RxJiEPzJ1RufbF+tLvzPEsFteZ6io4v0oMrNS7RTKvtxiTQ5w58tJIPp7Znf1Xcnm7ehGF6XEsK3uO7eYfYFaVI9zt/poN5s7CFlXH7YHHQHhgLzx9Edsl2/jV40Moy+OUxTh6fxvHA8tPU1Kh4NOd0QS7WDD2FuqRGBTkyYRgJz7YGqmZmfP2pghUKhVPjLrF1ia3AvrGMP4jMQA+bIX277JjhJV370WahyyM9fh6bi9emxTEnmeH8duivtibGTAmyKFjh7TeYkhUzQ516VgsLS3Jz8+/WX++Q6lWqhj9+QESc0upVqqY0cuFNyZ3xcKonkShqgy+6iWyQjmxQic692+wu7F67XY5F9UK2PAIXFwrHGXKC+Hgx6JZsMfdWpsejslm8XIxK8jN2pgtF9K4d5AnL4/XWdx2qu9FTix83QuGPAd+YyF8DdWnfiFc6cm8yv9RJjHigaHePD26y20pU+tU56IRojOKeHp1GBFphUwMdmKc4UX6n38Ne0m+aGg1MBON8A/suzkuje1IZz8XABdTClj02ylKysrZNKYQn5NvCFmamaOY0L54t0bCdCtzK5yLm0FxhYJ5X28jv0qfwQHOrD+bgq2ZPkm5Zfy2qG+HmOp05LlIzS9j1GcHcLI0ZHSgAz8cjOOjO4OZ3VfX79EY7XIu1twDCUchZI5IgJvaCxvqzAh45rIIhnS0Gl2Q04Hsi8pk7elkHhnu03xkffYP2PiY0I1O+lI4idxg2u1cKKvh38fg/EoxMC1ggnhNjXwxj8fl8PCfZ7Ax0Wd0oAOPjvTtfLaaN4FO9734fXLtQDh9U/J6PcZR+9mUKvXo6mzRueY9tDOd7lw0gqJayW9H41l9KomYzGI8LeTsGZuJ7Oo+0e9n4QIzfrzlB0/eCucChPtUfmklfg5mwsL2rzliVsrCjeDR9PC+W4lb5VzcDKqVKqQSIe89k5DLol9P4e9oxpqHBnRIv2lHn4tziXl8vfcKey9nEuptzV8PhOr6ZpugXc5FXgIs7Sdk/j3uFi0MUduEe+OYt9rlOP9L6IKczoBKJVyqbHxv2kKkXc+FUgk5MWDlJbSmzW6q0kmc6tHpvhcqlZgxkB8Plp6iSfM/Qqc7Fy2Qkl+GTCLB0eL2c1G71c6FhqoyKM4EK4+bfSTtxi17Lm4C2f/P3lmHN3W+f/hOUnd3gQqlWHF3hyEDxgZzmLu7/vadMhe2MYWNDdmGuwx3SoUadXf3tEnO74+33tSg0MJyX9cu1uTknDc5yTmPfp5SJQZ68qsWwLtW5yI5rxxrU33MdYHIFum0c5GfILI3tQFvVRXI9a6t6tsNgl5XL0AHwrG53vpvWkMuFxrx7dpU5+B0e2Qy4dj8h5yb6xXX60Xx6L+EvvEN5eDo6Bh2N4jKlYetrkzqmmHTs/HfbQSLdbRMl2Zy5HI5FhY3bqlLV2NoaEhWVla7ttWdi6uL7lx0H3TnovugOxfdB9256D7ozkX3QXcuug8dORe1dKmTo0OHDh06dOjQoUOHDh2dja7AT4cOHTp06NChQ4cOHTcUOidHhw4dOnTo0KFDhw4dNxQ6J0eHDh06dOjQoUOHDh03FDonR4cOHTp06NChQ4cOHTcUOidHhw4dOnTo0KFDhw4dNxQ6J0eHDh06dOjQoUOHDh03FDonR4cOHTp06NChQ4cOHTcUOidHhw4dOnTo0KFDhw4dNxQ6J0eHDh06dOjQoUOHDh03FF3q5Dg6Onbl4XU0QHcuug+6c9F90J2L7oPuXDRAkrr08Lpz0X3QnYvug+5cdD+61MlRKpVdefgr5oPdkSz94TR5pdf3+4Ducy5+PZFAdFbJ5e8g7B/Y+dzVMQIkCYL+gE96wdkfO3//NXSXc9Hd0WgkZn5xlL8DU6/aMbrqXCz67iQ/H0/okmN3V3S/ixo0avh6MBz/vMuW0N3OxWf7LvHU+qCuXkaX0N3ORVcwf+UJVp/o+uvlNTsXWx6FbU9cm2Nd5+jK1a6Ao9G5nIrPY9F3J0nKK+vq5Vz3ZBdX8n/bI3h2YzBqzWU6KWd/gnM1/3UmahX8sRi2PgpGlrD7JUg83rnH6O6U5oj/ugkRGcVEZZaw7mxyi9v8eSaZPWEZ13BVV44kSVxMLeKzfZfIKam5aaqqhHGrQ0dZLuTHw4G3r2qw5XoiKKWQHaEZFJRVdfVSdFxjJEkiLK2IT/ZF118vb3TSAuHCbxB/WOvT20LSeeGvkMu3o24gdE7OZaLRSCTklrJ0uDsqjcQLf4V29ZKue45ECwM6LK2Yv86ndHwHVWWQeg7MnGDf65BzqfMWF7MPYvfDop/hkZPgPgL+uheK0zvvGN2dDXfCp36w7nbIjuzq1XA8NheAwKQC0gsrmj1fUaVmxd4oTsfnX+ulXRFKlYYqtYayKjVfHIgWD65dCNuf6tqF6bgqRGYUM37FISLSi9v3gpIap91zDOx6HmIOXL3FXSfkl1Wh1kjsj8jq6qXouMaUKlWoNRKlShWf114vb3TKaoKNu14QAbAmHI7K5q/AVFYeir3GC+t+6JycyySjuJLKag2T/By4Z1QPglMLqVJpunpZ1zWHo3Po52rB3AAXVuy9RFFFdcd2kHQKNNVw21qwcO3cdO6FNeDYH/otAoU+LF4tLi6dnTHqzuTHCecuPUhksrqY4zG5BLhZoq+QsTO0ebZma3AaheXV3D3KswtWd/kU13zvh3has/5cCjFZJZCfAEFrITOsi1eno7MJSysiOb+cB347T257Sp9LMsW/N38nrkkXVl/V9V0PKErS6SnLYNd1lrXVceUUV6oAGN7ThvVnk7mUeQXl7tcDahWU58OQeyEvFk5/22yTvLIq9BUyvjgQzcm43Gu/xm6Ezsm5TOJzSgHwsjdjgJslVSrNlfWS/MdRqTUci85hYi8HXpnVm/IqFWtOJnZsJ/GHwNQe3IbChBch5UznZFqKUkUmZ8g9IJOJx8wdoee4/0TJ2rMbg5n4wV4oyyHIejoMXS7S5V1YPlVZreZsYj4z+zkz3teeHRcbGzeSJLH6ZCITetnjZW/WRau8PIorhZPz5BRfrE0M+PNsMlQWAhIc/L8uXZuOzie7RImRvpyKajWPrA1EpW4jWFabyTF3hj7zRSanqvzqL7SbIkkSDypXs9roM07E5lBU3sHgmI7rmtrz/fQUX+zNDVlzKrFrF3S1qcgHJPCeAn0XQuiGZpvklSmZO8CFoZ42vPzPRaQuFirpSnROzmWSkFuGQi7Dw8aEgenr+Ub/K8ITdVGkyyU4pZDiShUT/OxxsTJmrI89x2I62P+RcAR6ThCOiPcU8Vjcv1e+uKC1oDCE/osbP95jLKRdEGVyNzCHL+XQx1y8xx2JMnAfDlWlkB3RZWs6n1hAlUrDOF875gQ4E5JSSEp+vaF3JiGfqMwS7h3To8vWeLnUZjDtzQwZ5GFFdHqh+Lx7jBPOdsKxrl2gjk4lq7gSN2sTvrhtIOcSC+rKMFukJBNM7EDPAPzngKqic65z1ymlShX25OMppdFDk8qBSF3J2n+J2qCQvbkhU/0dORqdc2Mb9bWlaqb24D1Z3Ieb9Mrml1bhYGHEA+O9SM4vJ64mKP9fROfkdID715zjw91RAMTnlOFhY4KBnhzDmJ3MUZxm6MmHb3iD92px+FIOFkZ6DHK3AmC0ty1ByYWUV6nat4OyPMi8CF4TxN9m9uAcALFXWK+uUcOF36HvAjC2avyc5xhRHpdy9sqOcTXRaODSnsvOuihVavLLqljoIzJYp3KNKHcIAJlcZMq6iGOxOdiYGtDH2YKp/o4Y6MnZHlqftVt9IpGedqZM8LXvsjVeLsUV4jtvYaxHbydz0rNqjLahy8DeH87/0oWr09HZZBVX4mhhyDhfO5wtjTgYmd36C0ozRRYHwL432PpA1I6rv9BuSn5ZFTaIKopl1qHsuqgLNv6XqA0KWRrrM76XPakFFSTk3sB2WEMnp+c48f+J9YEvSZLILavC1tSAUd626MllHIn+75as6ZycdhKRXsyByGy2h6QjSRJxOaX0tDMVT+ZGc8l0KK5lEbDlka5d6HXKkegcxvnao6cQX8nRPraoNBLnEgvat4PEo+Jfr4n1j3lPgbhDdQZ+RlEFq47E8ebWMKrbKgmp2+9xKE6FwXc1f86xHxhZQdKJ9u3rSkgPhpD1HX9d2nlYdxsE/3lZh80uFj0CzjLRvJ+qtiYoUwWOfbvUuTsek8tob1vkchnmRvpM83fk78BUJEkiu7iS/ZFZ3DHCA7lc1mVrvFxqI5MWxvr4OZmjqaj5DRhbg8dIyArvwtXp6GyyipU4mhshk8mY4u/Agcis1iPRJZmiXBZE1rr3HLi0G9T/zTKtvLIqbGVFAEyXneV4bC6V1TolwuuGikI48ZXoNbkMap0cC2N9RtcY9Uej21cFotZI7bcFugtlNQ6LqR1YeYB1j0ZOTlmVmiqVBlszA8wM9Rjiad3uz+NGROfktJPfTiUik0FaYQXxuWXE55ThZWcKFQVQlkNWj5tZqZqPFLNfJ/XaQcqUKi6mFTHax7busV4O5tiaGrS/aS7hmPixW3nUP+YzVfQypF1ge0g6oz/8l0/3RfP76SR+OBrfvv2GbxIiBu4jmz8nl4Pn6Kvfl6NRC+d52xNQWdSx19Y2KR9ZoVWFpS0yiioBsNPkIembIBlZciYhXwgQdFEmJzG3jPD0Yib6OdQ9dstQN+JzyriQXMhfgakoZDIWDXbrkvVdKcUV1chlYGYgMjkW1JThGVkK5zIvFlT/EanU/wBZxZU4WhoBMNXfkYyiSsJbU1oryQBzp/q//eeK69y1CLZ0Q/KLK7CiDKXrSOzKonFUZ3A6Pq+rl6WjvcQfgv1vQNjfl/Xy4opqDPTkGOkrMDfSZ7CHNUdj2mc3rNgbxZD/7efHo/EoVdeJ3VaaDXJ9cT8AUcbcoIS5dm6jjakBAON72XMmIe8/6/jrnJx2UFhexZbgNJaP6YlCLmN/RBbpRRWioTlXSPQ5ePUnSOONrLoccv8jMoadRG29aG8ni7rH5HIZo7xtORXXzptVTpQoT2uI+3AwMEcTe4AvDkQz2tuWc69NZfmYnnx5MKZOPKJF1NUQsVWUqslb+Kn0GCua8K9m42/YP6LuVl0F0Xs79tqymtKXomQIXtvhQ2cWCyfHsjobmYULw3rYcjYhTzg5BYnignuNWXs6CXMjPWb3rzf0xvva42hhyF/nU9hwLoWZ/ZywrrnIX28UVVRjbqSPXC7D09YUW0WNPLaRFTj4g6SG3JguXaOOzkGjkcguUeJobgjAKG9bTA0UrZeslTQoVwNwGSwCMWH/XOXVdk9KC7OQyyTkA29HUhiy2OQChy9du8j1W1vDONFWH5WOlqnNVB/9+LICxMUV1VgY6df9Pb6XHafi8trltAQlFaKvkNcNdr8uennKckSpWq0IUs/xkBcDxaJMM69mVpSdmbimTOhlT2W1hvPtrYrpZC6mFnEuMb/LPludk9MONpxLQaWWeGi8F4M9rDh86jSu5OBlbyq+XEAPvwAi8RIvSA/uusVeh8RkCWfDx6GxCtZobzvC0orap5aTGw12vRo/ptAHrwmUhO0hLqeMxyf5Ymmiz3PTe+FgbsjLm9pQHYk/LC7A/Ra1vI3nGOF8pJ5re42Xg7oaDr0PPtPAeSCEb+nY68tyRZNyn/lw9BOoruzQy7OKKkXfWUUmWLgwvKcNQcmFKJ2HiA2ucclaRZWavwJTWTzEHRMDvbrHFX/dzWseEWw8n0JyfjlLhrtf03V1JsWVKiyMxXvTV8jxtay5WRtZgUMf8f9dKPqgo/PIq5nv4mghMjmGegrG97JvuXlerRKBhYaZHLkcBtwGYZv/kz2hFUXCIdS390bmPZl5Bhc4fOnaBF9ySpSsOZXE0xuCKSzXDSK9LCoKAZnIUIdv7vDLiyqqsTSuvxdM6OVARbW6XUZ9bE4pt4/w4Ie7hnIhufCaOseXTVmOKFWrpUdtX46oKMkrFd/D2kxOH2cLbE0NONpRIadOoLC8ijt/PsPi708x7fOj7AvPvOZr0Dk5bVBYXsVPxxOY3d8ZBwsjxvvY8GH5//GR3g/CycmNBjMnjMyscXR0Jk/fWcwR0dFuYnNKcTA3xNJYv9Hjo71t0UhwOqGNbE55vvjhN3VyAHymYp4XwlhnNSO9bAAwMdDjlVn+nE3Ib111JOwfsO4JLoNa3sapv0gbx+5vfY2XS9BaKEiAya9D35uFkIKyBFLOwcF32i5bKs0WUZ/xL0BxGsQd7NDhM4srcbIwQlacDhauDO9pg1Kl4WKpFZg6XPOSte2h6RRVVHPnyAZliXlxELmNaeW70EjQw9aEUV62Le+km1NcUd3ot+BjXlOrbmQBJjZi2K3OybkhyKrJlDrUODkAU/wduZhWREZR8wG3IjMrNc7kAAy8A6pKIHL7VVxt90RV4+RgYge9ZuBREUFhXhaJ16D5PDilEBCG9js7dL/Jy6KyECzdREbi6CdCLKcDFFeqGl0v+7rUGPVt9KHkl1WRX1aFj4MZU/wd6O9qyXdH4i7nHVxbynLFPb0WC2chPpJwBID8ssblanK5jHG+dhzpAgfum39jqVJp+GrpIBzMDXnszwvtr87pJHROThu8sTWcyio1L8/qDcBsw2B6yLMYJr+EvWFN2YidLwCDPKy4UO2JMuVCVy75uiMmqxRfx+azTDxtTXCxNOJ4W/W1eTVTfWvOQ0MiLceBBM97xiGT1Tehj/W1Qyaj5WhPdSVE7hBZHFkrzetyhdgmZH19z0voX5AR2vqa24uqEgbeCS4DwX8eqJVw4kv4YxEc+xTW3wHVWoyhWspyhNKcYz8wd4HEjtXt1zo51Dg5/V0tMdZXcCaxQJTqRW6/7IbRjiJJEr+fSmKcr13j2Tcx+wAwzjjDA0MteXpqr0bn+nqjqEn5hYdpNWWSEWpZTbTSwR+ydAbVjUCtk+NoYVj32JTeDi0OuK3rsTNzavy4nY/oGwzqeEnq9Y6mTm3KDnymIENigl7YNcnmBCUXYGNqwNtz+7LpQhpH/sMN3pdNRaHIUo99BnIiRfl3ByiqqMaigZNTa9S3lZWpDXB625shk8l4eII3ZxPyCUzqmrKudlOWA2YOjR/zmiRk5CWJ3NIqzAz1MNJX1D092d+RS1klJOVdu0xvcl45a04l8sB4L+YFuLB62XAGe1jz8NpA9oVnsjU4jaDkq/9Z65ycVtgeks72kHTentcXFytjALxiVpOFNQYyFbLk08LJsfUB4KkpvsQofJAyQimt6FhZ0H+Z2OwSfLQMbJTJZEzwc+DQpezWy8pqe6Bsmzs5a0LLCZH3ZkBJ49kilsb6+Dmac76lC1rgr2I2yYDb2n4Dg+8RF57o3VCYIgQCOsvYGPkI3LxS/L+tt8gcHf1YOCyLfoaEo7Buacu1zLVRH5kMeoxppMLSHrKKKnG20BPGlYUL+go5I71s2ByUhmrEIyLLdBklBpdDcEohF9OKuHOkZ+MnoveCnR9IGl7zTeXmQa7XZD1Xi+LKxk6Oi6GSIkzq5wA59oXsyC5anY7OJKtGvdDBvD6TY21qwIReDmwOSmv+glonx9yp+XMDbxe/74Kkq7HUbousPA8NcqE+aOUBdn4sMI/k0DWIXAclFzLI3Yqlw93p7WTOX+dTrvoxbzgqC8V4Bs+xoG9Sl5FoL00z3wAT/Ry4lFVCemHLAcC47NqB7kIld2Y/J3rYmvB9Z2VzssLhz9tg/1sQtgk6qyelabkaQK+ZolIjK4z8sipszRr3o07u7YCBnpxdF69dudhHe6OwMjHgofGijcNAT86qu4Zga2rAg78H8tT6YBZ+d5IVe6KuqsKdzslpgfIqFW9vC2daH0cWDq4xmtIuIEs+xTnfZynXt4HYg5AfX1cm5WBhxOwZszBCyZfrd3bh6q8fKqvVJOeX4+NorvX5yb0dSC2oIDa7lbKy3Ghh9Bs2dpTKq1TsCM2gwHMG8oQj9Q2ONQzxtOZ8Yn7z/SlLRNo8YAnYaymBa4rLQCF6cOE32Pc6GJjCpFfbft3lMPQ+4VTf+Tf0vwWW/CHUac793GgzSZKEykpZtigrA5F5ybzY7HNojcziSnyMSgBJNDcDT0/tRWx2KWtT7IVk97GOlxi0RmJuGV8ciObJdUHc/uPpunP/26kkXK2MmdK7QRRLWSJUpQbfBU4D4NKuTltHV1FcUd+TA2CnV0GxZEpUppgFgoO/EJKobEWBS8d1QVZxJbamBhjoNb4VLxjkSnh6MTFZJY1fUJIByJpHckEIpOgZQ9DvV2/B3RD9yjzKFRYiqw7gM5XhqiDOxOdQUXX1FKXUGomQ1ELmm4UjK81mop8DJ2Jz0Wi6QfN6UZq4F10PDm9lkSj51jMAj1EddnKaZr5BKIrJZLSazYnNLsXVyriut1Mhl/HgeG8qq4UE8xWjqhT/hayDv5dBZidVdzQtVwNxb9c3gUt7yCtV1pWq1WJmqMeEXvbsDrs2M6QCkwrYGZrBs9N6YWpYfy+zMjFgx5Nj2fv0eILfnMYzU3vx/ZE4Hv498KoJE+icnBb47VQShRXVvH6Tf33py/HPwdKdOUsexcRvMoSuF8MgG5RJ9eg3GoDiuHPXjyThZZJdUsn2kPS2N2yFhNwyNBL4OjTP5ACM8bHFQE/Ov1GtlB40KBlsyO6LmZQqVfiOvx00qmbKZEN7WJOYV05OSZO+ltPfiQvvxFcaPVyl0rAnLJNnNwbzx5kklCo1gUn53Lf6HOFOC0S/TMQWmPZ/zQeHdhZDl8Hj50UNM4DvNFHO9u//6qO8iKzH0PcOoCrJro/69BgHSJB0ql2HEjNnlHjqF4oHLFwACHC3YuFgVz4/EEPJ8KeFsl0nDCPML6vi/jXnmPTpYX46lkBqQTmx2aW8+HcI2SWV7AzN4PYRHnWzlAAhDqGuAt8Z0PsmEXi4zuWViysbRyZNNGWUyc24VOfk1IgP5ER1weq6N9VqDcdicurKwLo72SWVjfpxapni74C5oR5bgptkc0oyhYGj0G/2GowsRDbn7I/C+f+PYKjMp8LAuv4B36mYVOfhpU5s1wiCyzWuorNKkKrKmBP+HPxzH+N8bCkor25d/vtaoFHDpgfg5Nfw7Ug49W2nBqE6nYrC+vul1wRIPtMhgZwiLZkcG1MDBrhZtVqyGJdTincTu2PpcHd+v29Es6DDZeE6BO7eCg/VzPDrDIezqgyqy5o7OfpGomQteo+YG2Vq2OylN/V3JjS1qL4ioAOUKVW8sSWsXa+VJIn3d0Xi52jOrUObCwCZGOjh52SOlYkBT07x5fPbBnIwKpvjV0mhUOfkaKFUqWLVkTgWDXbF07Zm4GfIBojcBhNeAoWe+DGW1zRQNTSwTWxQmrnTm3giM27cG40kSTy7IYQn1gU1aySTJInkvPb9kGJqovQtOTkmBnqM9LLlYKtOjhZlNeCvwBSG97DB3ctPiAc0acod6imECBrV4OYniJvD0GVgXV8WFZ9TyvgVh3h4bSDnEvN5bXMYI94/yKLvTnEyLo/7g3og6RmD61AIuL1d7/2yadpvMu0dEcXcW589Ss4vR1+qRq+qmGrjmiZ8Gy/RsNzOuT75ZVVUqTW4KQrFAxb1ZWAvzexNtVrDZ9H2Ivp28ut2Lz+3VMnKQ7FsPJ9SV5Or1kg8tT6I80kFvHdzf86+NoVNj47hiyUDuZBcyD2/CPW6JcOaXDSj94r5SHa+4DdLlBh2sCSvu9E0MimrLEQytCAqs8Z4svcDZDrxAS3sj8jirp/PMuL9g8z4/GjdzIjuSlaxslE/Ti1G+gpm9nNiS1B648xA0xk5TRnzlPgNNMns3qhIkoSJqpBqwwZCIx6jkfSMmWca0WJwLCi5gIkfH8L/jT0E/N++y3KKg5ILGSBPQC6pIPEYwwt3YqQv51hsF/flnPhCZLdv/V1UI+x9BdYt6VAG/5pSWSh6ckCID6iVkNp+1c6mQaFaJvnZcyI2t8WsTGxOKd41pWq1XJVeTlMHMdemKPXK91U3CNS++XN+MyEtEHVxFrZaxidM9nfAQCFnT1jHS9Z+OpbA76eT+Opg26ML9oRlEphUwCuze6NoxzDueQEu9He15JO9l65KNqfbOzl7wjJYsafliGW1WtPpzVRrTiZSUqniick1zktuLOx4BvouhEF3ise8Jop/FYZg2djw0nMbxBB5DCHXoKmqq9gRmsHx2FxsTQ1YsTeq0Zdzw7kUxn98iJAa5ZnWiM0uxdpEH1uz5jf6Wib72ROYVKBdSlpdLea1NHFykvPKOR2fzy1DazIevW8SmZYGAzHdrI1xtDCsL1lLOQs/TRW13eNfrNsuu6SSu385i7GBgh1PjOXYi5PZ+/R45ge4sOKWARx5YSIVclM+c/oQbl3T8kydq4WpLUx5S6jB1TSk55VWYScTRvG+xJpzI5OJtHY7nYDaQaD2Uq74npvY1D3naGHE3aN68FdgGsoBd4mbUn5Cu/b74e4oPtl3iRf/DmXBtyd5+PdAPtwdybGYXD6/bSC3j/CoKyEY7W3HkmHuRGYUMyfAufH3RFUzN8h3hnhvTgPAwk0IRlynSJIk5j40vGlXFqFnYk1kRo2TY2AqHLtrJD6QVVzJpgupvL0tXLviVzciIbcMUwMFXy4ZSGJeGWtPJ3f1klols6gSR/PmmRwQJWtphRWN1SVLMlt3cqw9RR/hqZWtC5J0M07G5lKm7LiASXmVGiuK0Rg3cHL0jZD1HMdMw4scitLez/nFAWGsPT3VF5VG4tcTiR0+dlByAdMtkkWJYN8F6B98k2keMo5Fd+HMnLQLYuTA6CegzzyY8zksXQ/Jp2HVBCjrhkNSa4UHQFzDjSxFr2k7UKrUVFZrGpX31jLRz4GyKrXWkvTKajWpBRXNxlZcFeRysHTV7uQUJndsNlBDkY2m+E4HJPxLTzfryQGwMNJnnK8dOy92rGQtp0TJqqNxOFoYsiU4jewWAgIR6cW8uvkiL/wdyjhfOyb00uKIaUEmk/Hc9F6EpBaxP6IF6fwr4LKssbCwsM5eh1YkSeKTfdF8eziOxOMbhKPR4IKl0Ug8vT6YyZ8eIaGT5CJLlSp+PBbP4qFuuNuYiDTv5gdFDfTcL+uj6FYeIjJu61NfC1yDov9CBsjjMbn4W6esCbiqjVktkZxXrtVRKams5n87IpjS24FPFgcQlFxY9+UsU6r4ZJ8QAvj1RNtGb2x2Cb4O2vtxapnc2xG1RtKu856fIErRmpSrrT6ZiImBgpv610it9hgn6mMb1MXKZDKGetqgijkAmx6C1XPApic88K9QJEOUqC379RxKlYbflg+nn6uYMuznZM7/ze/HrUPdcbAw4q25ffg61p79aVrKSK4FA28HE1s4/wsAeWVKfE2FkbMmtLT+wtSBvpza6KaVKkeUqjWJci0d7k6pUsWO6sGgZwQXxcRqSZJajMjEZpew6UIqr832J/z/ZvDZrQGcScjjx2MJPD7Jh0l+zXsNXpntz7wAFx6b5NP4idANoueoNvAgk4l5QA0V3w7+T5QfXieUVanRSDSOTFYWYWxpS1J+OaW1hqBj32uSyQlPL2LcikM8uzGE304l8tOx9jmyXUVyXjketqbMH+jKwsGu/H46sVtP+s4uqcTRUruTM9LLFi97U3453uAzL23DyQEY+6wwhrqx0lp0VgnqmgzVoahsbv/pDFuDO176nF9WhS3FYNbE6POZhkd5GCVF+Vxq0tcUm13KkegcHp3ow0MTvFkyzIM/TidRUtmOeWwNCEopZKRBnKgSmP0JSHC/YheBSQWUV10bxclmyGTgNxsmv1H/mN8scU8rThPXzHYgSRJvbwvnoz1RrY9ZuFI0GlEaXluuJleIe3V8+/pyiirEOdOWyRngaomNqQGHtSjexeeUIUlCWe2aYOkORU1EKdIC4YsBsOGu9g8Tr3NytDgQ5k5ILoMYXn2uWU9OLdP7OhKcUkhBWftnOn11MAaFTMa6B0air5Dz68nEZttEZRZz87cnOBCRxR0jPfjs1oEdyopN6GXPsB7WfLY/utN72i7LyRkwYACDBg1i5cqVFBUVdeqCGhKUUkhsdinG+nIURz4QBlzSybrnv/o3hp0XMzBQyPntUAhE7bpiBYu1p5MoqVTx6MQagypii/gyzv1C1Dw3ZNxzMOy+5jvpu4CzNnOZn/lVh+UQtZGcV87gd/bzf9vD624MTcktVXZ6qu+jPVE8srZ5Q9h3h+Moqqjm7Xl9mehnz/CeNny4J4rE3DJ+OBpPUUUVt4/wYOfFjBa9/lpiskrx0SIf3RAPWxO87U21697XKqs1yOSEpxex5lQiD473qm96cx4oUsZNhlcuMj7P20VvoEk5KyQs79neKEqyNzyT8PRivr9ziHB6W+Dmga4McLPsOnUdPUNh7IesB2UpeaVVeBqJC2e6yoJDtbXJtX05sW3Py8msOXcmldmNStVq8bQ1ZayPHb9fyBc30osbQZL47kgcUz47orUE5LP90TiYG3HnSE9MDfVYONiN/c9O4JPFATw9tXlfFYgb2FdLBzW+IWnUokfOdwY4D6h/vO8CKM8V2aqyXDj1Tfct09BC7U27UWSyohBLazskCS7Vlqw59hPO6lWcIl1r6LhZGXP2tSncPaoHmy6kdo9ew5D1sGZes/efUlCOu7VQwlw+pie5pVVsbdrX0k2oVmvILa3SWq4GQgr3/rFeHIjMrhdeKclsPiOnKXY+otKgE/rkrgZ7wjKY/vlRnt4QTFF5Na9tvghAWmHHewXyyqqwkRWjb9bE6POdilxSM0k/rFnJ2pqTidiYGjBvoOgxXD62B+XVatafbf+1O69USWx2CV7KSHAbKu4ZvtPwKztPlVrDmYTm2QO1Rrr6wUqXQXDb7+J+0BA7H/CZChf/atdutgSnsfpkImtOJjLl0yPc9fOZuplAnYqyGJDqMzkAPScIu6mtvjJJwvSvpTym2IKFYfNMjlwuY4yPndYRFLWO2zXJ5IDooW2YyZEkobpm4SKkn9fMgcA1cGm3qE5piVonx0RLJgeo8pzIMFkEdi04OSNr5sed0ya41AS1RuKnY/H8eTaZhyd642Vvxq1D3Vl7Oqk+2CZJVB1fybe/rcPd2pjDL0zklVn+2Ju3XJmjDZHN8SMqs4Tdl1FO1xqX5eT06dOHl156ic2bN+Pq6so999zD8ePtq/PvCH+dT8XRwpCvx2lwr05ArW8qZoMgoj9fHIjhySm+PDHOhXlhT8L6pY2coI5SUaXmp2PxLBjkKgxadbVo6PaaVF+e1pBBd2p3coDkEW8TpXFH/c9Dl72eWj4/EI1Gklh9MpFH/whsltavUmlY9N1JVuy9dMXHakhwSiHpRZUkNeivKSyvYs3JRO4a6Ym7jQkymYw35/ShpFLF1M+O8N2ROO4e1YMXZ/ihkMtYe6blcpFqtYbEvDKt8tHkxYGyPoI0xNOakNTC5tvlRoO+aV1TvFoj8ermMDxtTXhkonf9dvpGwhhuUus7In87QRof9kzaCZNeAX3jRs//eSaZwR5WDPG0pjVkMhmT/Bw4FZfXqTeyVUfieGp9O4fLDlkmBgKG/U1eWRVuBuLzc3R25Wjthd7WW/QNXWghyyhJ8Pd98PtC/II/5E3jv1Gknav7fJuyZLg7wSmFJLvOEeci8yJbgtKIzynjyR/3UhpxoG7bsLQidl3M5MkpvkLDX6OG1PPYmehxyxC3xoICbRGxBfLjYPzzjR93GyqiZuGbRF+CJMGwB9q/3y6muNbJMWqcybG1tUcug4jaPj/HvqKWvfjKhD9aY1tIOucSC3hzbh8czI24bZg7BeXVV6WkoMMYWggVpoyQRg+nFJTXBSN8Hc2Z6GfPT8cSWg4AqVWwdhH8NE1EVHPbrjnvLGoFT1oqVwNYONgVW1MDfj6eIO5HZTltZ3IAeo4TDdyq9kdsrwUFZVW8viUMP0dzdoamM+3zIxSVV/C26SaUOR3PEuaXlGNNKQaWjo2fqKmyuMUikkMNnJyiimr+uZDKAwGGGP37Jux4FrfAFczrb88vJxLa7cAfj83FTZaDsTIP3IeLB70mYpQXjp95JT8fa7yvC8kFTPrkMI+s7cIZev1ugfQL4t7aCuWR+/h55zGm93Ek8PVpfLo4gLSCCm5eeYIfj8a3eZiCsirC04vaF3StLBT/NhTq8ZoAkrrtmW7F6Zgm/8sL+hvpe/IprdmQsT62RGQUk98kcxGbXYqlsb7W3pWrQlMnJ2a/CMTNWgH37oTSHNj+pOid2vV8y/spyxHXPn3t14xC20HYyEpx1Wi/L3jYmOBoYcjZpk54dQXEHKgLGsXnlLLw2xO8uzOSJcPcuX9cTwDuG9uTiio1n+4TtqYU/CcGB17ls7KXWe93FBO9y+9pGully2hvW744EN1iMP9yuCwnx8DAgCVLlnDgwAFCQkJwc3NjyZIl+Pv78+mnn17eSpooIlVUqdkeks6iwW5MLt9FtsyWX80fhriDqNOC+GB3JMN72vD0xB48kPk2vUmi2MBB1CK395BNjNE/zyaTX1ZVXxZz4TchET31rQ6/nQE9HPlRdROK/Jg61StJkjosaRmZUcyW4DRemOHHd3cM5vClHCZ8fJjfTyXWGdN/nEkiOb+cuQO0G6KXQ16pkrQajflT8fV1vL8cT6BaI/FgjfY5QD9XS468MJEnp/gy0N2KJyb7YGViwIJBbvx5JqnFcpG49Fw+kH/HEIMmN7fyfFE/vOuFuocC3K2IyS6tjyDUkhsjolQyGRVVat7bGUlISiHv3dwfQ73GZYS4DYeUc/V/FyZjmnqMY2Yz2aKlVCI+p5RT8XksHe7R2kdVx/hedpQoVe3qRWovRvoKdoRmkF3SjsZYm57gPQXO/UxeSSXOeiWgb8owX3dONpQ2HXKvMBC13exSz0HY36AswTv3IPM4LKQp/WZqPeT0Pk7Ymhrwa5Y3GFlRen4d0VmlPDrRm9tLfsZk4y119dWbLqRhZ2bI4to+qcMfwE9T4OvBQhGqVgFIpRR/735JOFwH/yf6bGqflyQ49pnIStUaGLXIZND3ZlGydvYHGLi0rvSwO9DWjb/Oyaktv6iuBLUSfVMbetqZ1vflOPUT/2Z1bulwcWU13x+J453tEfxvRyRT/R2YWFNC6O9sQYC7FSmHV8OmB+G7MeLGWENSXhn/RmWxJShN6/d1f0RWfUbxSvGZCsY2jcpvVGoN6YWVdZkcgAfHexGTXco7OyK0f/bFqaJXT88QUs40Eu/QhlKlZk9YBmFpV17BkFk3CLRlJ8dIX8GdIz3ZdCGVgsxE8aB5y9f5uvfoORZUFZCuJUCi0YhetuA/L3fpl83b28MxUhWxxekXfpmhT26pktW9TnGv+m88sg91eH8lBTnIZRIm1o7Nn/SdztDqQIJTCuocjp2hGVRWq7mn+i8RBEk+Bcc/53mPWHJKlHxWU2rdFkejc5llVZP5cRsm/q0JhH42rIgzCXk8uS6I4JRC/rcjgsXfn6KyWs2ByCwiukp9zW+WuJZf/JsypYq1p5Oa90Gd+wmTDYv5u/oJPrbbgbFUzqIhbux7ZjzzB7rw47H4ZnZTQ5QqNXf9coabvjrOqA/+5YW/Qlh1JI5QbQFKEP040DiTY9dLZCvbkpKuUZf8pHox5skH4fD7zTYZ7S0yHg1V9iRJ4mRcLn6O5tduaLSlmyitrq4Uwb0Db6FyGcaGkv68E2TMow6riX84QYgIBa5uWQG1LFd7P04NGebivuBcql2uWiaTMbynLWebZnL2vgZ/LKIwfD9bg9OY980JCsqr2fjQKN5bUG9LuduY8OJMP349kcihs0Eod7zIDvVIoryWYX/+M9jzipajtp9npvUiJruUHaGdF7y74g5pb29v3nvvPZKTk/noo484erR9DWNNyfl9OaXfTobgdVBdwe6wDEqVKhb3t0QevolCv9v4MD2AMmMXsna+T3RWKS/M8EN+6kv0Ew6z2fcDvlTOQ7q0q81IBYgSsCHvHuDD3aJpPja7lG8PxTIvwIWedqbCsz2yAvrcLFLAHcTb3oxY/ZoSqjQRvdl0IY2RHxykuAO1v5/svYSrlTFLR3gws58z+5+ZwBgfW97YGs4Dv50nu7iSrw7GsHCQG31cLNreYTsJTRU3cRtTgzr1tKKKan49mcjtwz2ayZ6aGOjx5BRfNj40CisTER25Z7QnuaVVjSJpDak8/RO3KI7S78xLjSOOZ38UGYnQDcLJBALcrJAkmhkXyvSLRKrdeHdHBJM/PcxvpxJ5blovRnnb0gz34cKoKaopXwleB3pGmA1ZzOFLORSVV5NZVMmT64I4fCmbdWeTMTfSY047nccANyvMDfXqsyadwNwAF2TAthonTK2RtAsw1DLkHsgMxbQ0QQgPmNkz3teusbRpv4UiGnRhTfPXX/hNGFHL9/CU81pe8twIT5yHfou0Hs5AT84tQ934Kzib6t7zkYf/jUKm4f4xHszSD0YjyVD98yBUFHD4UjaT/OzRV8ghOwqOfwEBS8WMoV0vwNbHRDRu493CwYnZLxozA3+FDXfAya/EQS/tFsb9uOe0fwZ9F4oStfJcGPV4+z7oa8DZhHwGvL2P5/8KqQsgNKW4UhgddTXmtZFOI0v8nS3qnRyrHmBgJkrWtHA6Po/vDse1apA0JTqrhJu/OcFn+6M5dCmbXo5mvDW3b6NtnvWI55H8j6hKDxcGysmvSMor4+n1QUz85DDLV5/n6Q3BLPnhdCMDSpIkVuyJYuO5Tirn1DMQ38mLf9f1X2UUVaLWSI3KSkd72/HW3D78eiKR17aENY/U18q6zv4Epr4NMfsgQ7uB8P2ROIa/d5CH117gtlWnuNBeYZnk06I/pomTlV3n5LRe2nHnSE+q1BouXKw511bNZVlBZMZnf3Wcbw/HinuWvgkkNamwSDwhZIX/vLXdpUudxZ4wMeX8T+eNGF/azMQzDxG2uJxhiT8AYFihPUOYmFvGLd+dZOWh2GbPVRaJ1xhaaJkb5DsNk6pcemkSic4UWe3zSfkMd5AwidwIY56ER0+B+0hcI3/m6am+/HAsvplaaFMkSeJYTA5TzZLA0qM+s2blDrY+9K24wNdLB3EgMpubV57gr/MpPDLBm8MvTMTJwogfj7WdDbkqGJqB32wqLqxn1hdHeX1LGOvO1lRaaDQQuhFp5/OsV0/houttWJ7/Bj7tDTufQ68si2VjepJdomx19swHu6KIyijhg4X9mdbHkYtpRXx+IJpF353U7uhoy+TIZMJhjD8MiODFH2eSmveR5FxCI9Pje/VcqgfeAxd+b5bNcbcxwcPGhBOx9ed0b3gm5xILeKBBoPaqUzvyoTgNZcoFyI7g/uSpvLw5jAORWewJy+RQbJG4XzkPhB1Pa8/CluXUz73TQrbKlDiNMzZ5wS1uM7ynDWFpRfUB44SjcP5nqiQFxzd8ylPrgxnrY8eOJ8cyvKdNs9c/MM6Lqb3tke94khKVgqJJH9Lvns+Eg3Z2VbtFI7QxrIcN43zt+HRfdOt2Tge4LCdHW0RMLpczb948tm7deln7W182iIisctjyMBUrx/PFtlOM9bGjZ8J6qK6g18xHmTfYk4/KbsIlfR+PuicxzF4Dx7+EYfcxcPKt/KkcjcrAEs583+Yx39kRQbVaw/dH4nhuYwiLvz+JpYk+L8/yFxuc+1l43pNf7/D7ATFYysqlF6Vyc5EiBraHplNUUc2+8PaVe/xwNI6DUdk8O61XnSftYWvCl0sG8eu9wzgdn8fUz45QVqXmuentGFrZAUJSCzE30mPBIFdOxechSRI/H09AWa3hoQntuzj0drKgt5M527TN0lGW4nPpB0Lk/ijyY+Hkl3WPc+Y7GLBEqJzVlCf6OZljqCdvlCWR1NXIcqLYnmnNvogsBrpbse+Z8TwxRXtvR13UP/WsuKgHr4U+85k5xI8qtYadFzN4ZkMwO0LTuffXc/xyIpGFg1wxNlBo318T9BRyRnnbclybQMJlYmNqwKTeDvxzQThmr2wKZdj7B1o2YHtOAGT4lIdgQxGY2jOkhzVG+vJ64QYDUxhwKwT90fhCqiyF8M0w8HZicyuIyy7FqYWm6IbcOcKT8ioVBwwmY1KZzZ0OidjkXcCguoiXNI+iriihdNNTxOeWMam3g/jsdzwtFGfmfA63/gY3fyfmTn0ZIGqUl66DJy/A/fvhhTgYfA8c/0w4L0c/FtFTbSWkIAw8G2/wu0nr/KSuIL2wgkf/CMTZyoiDkVlM+uQwkclZIoIW92/ddkVNy9Uqa5z6GifnUmaJyMjJ5WJejpZMTmhqIct+PcdHe6K4+5ezzUo1tBGbXcrNK08gl8vY9eQ4Dj0/kT8fGNm4D626gnFxH3OWvizSfEj56Och4QjLv/iH47F5vDWnD8dfmsT2x8eSUVjJK5su1t0rQlOLiMku5ZYhbpf5CTZHGnCruEbXGEMpBcLA8WjSO7dsTE/eW9CPdWeTGfvRIVYeiq3PLhfWGHlW7tB/sSh1PP5Zs2PtC8/kw91RzOzrxJbHxtDLyZx7fjnbcoQahOrjH4vhlxnCgd/yaKN6+7TCSgwUcuxaUZYEsDc3xM/RnMzkmlI6S+1OzqYLqURmFLP+bAqSQl9c75qW/Bx6T/x7z3a4c1Orx+1M8kqVvLb5Ii+7heGRsRdmfghmDphsux+sPMg274OZMquZbfFvVBZzvzlOeHoxn+2Pbhbkqi6uCaBpa8T2HIOkb8IkRTAXa14XklLIMqPDIGlg2P1iu9GPQ+o5HvbKZbCHNc9tDG5eMdCA6KxSskuU+Ksvgfuwxk/WGOcz+zrxx/0jWLN8OOdfn8bzM/wwMdBj2ZgebA9JJ72FIMfVJt1jDsbF8UzVD2WguxVHQ6JFUGlFD9j0AAU9ZvNq9TL0Z70nrr/DHxCBhL/uJcDVHD9HczZo6TvVaCT+PJPM6pOJvDSzN0uHe/C/m/ux5+nxhLw1nV6O5jy5Lqj556otkwPic8yOgJIstgSn89rmMJ5YF9S4jCkniiJTT9QyPfRHPiAcpvDm3+kxPnZ1mZzKajXv7oxknK8dU/1bdhY6ndrfbFEq4Wf/RSXJGTVpDmdemcLRFyfham1MWkGFEF6Y+6Uo/d7xNNsuJHGy4fyYspxWMzl5ZVVc0PhilHm+xW1G9LRBI9WMzijOgG1PkGjcl5/1lzJbL5C/7/LhuzsHNxuwWotMJuMrv4tMkIdyadi73DG5Jgkw6jFwHymudVcwp+utuX0oKK/iobXnO6X/87KcnMDAK2+mb4hMJuO+B5/m2x5fMb/6PSoKMlij9z4/O2+BA2+LmSVW7rwzvx9HTGdxRtObp8u/hv1vAhKMf5HeTuYYm1pwzm6+MN5amQZ++FI2ByKzeH9Bf16/yZ9NQWl42Jjw98OjhVGnLBU3uoClV2QkDfS0JkTjhSZV9NGcrIkQtacR9vsjcby/K4qHxnuxYFDzpu9JvR34bfkIAB4e74WLlXGzba6E0NQi+rtaMtrblpwSJSfj8vjhaBx3jfLE2bL9x5ob4MLBqOzmyjVnV2GkKmaj66sw4hE48jGEbhTR+spi0R8z+gnRYFyQiL5CTj9Xy0Z9OYFB5zGgmtnTpnH0xUl8d+cQvFpTS7F0E1mKlHNiCnFhMgy6ExcrY4b3tOHdnRGcis/j12XD+XLJQIZ4WrNsTM8OfW7jetkTnFJYZ6x2BosGuxKZUczn+6PZeD6V4T1sWLE3isWrTjUvfzS2QuPYn4GacCzVhWDqgKGeguE9bRs3YA65V2Q6IrfVPXRu169QVcq9Qb2Y+tkRiiuqmdmv7fp/dxsTpvg78nGEJYmSM0v1jwkREFN7ir3nstr4LsxituIkL2Ksrx1EbhVlInM+r++BGrgUFv0kLvK3rYVeM+oPIJPBxJdFGdu620XQYPyLzecFNdx+2S5Y0Haw41pQUaXmod8D0ZPLWXv/CI68OAlDhQz5tseFMMLvC2D9HVBRQHFFNfoKGUb6NZfmWifH2Io+zhaUV6lJqh3I5tQPssLrjlOt1nA+MZ/lq8/h42DGD3cNISKjmPkrj7epPrnxfAr6CjlbHhvTvBlXVSXWcfRj5MVpWC36ksT8chYfd6RMMuQJm7McfmEi947piZu1Cf3dLHl/YT+2haTX9eT9HZiKnZlhu2VF2+KfwFRu26FCsvEWzjHUDapzs24uEHLHCE/2PDWeSX72fHkghgXfnhSjBwqThIFsYCoGbI5+EsK3QE592VJWcSUv/RPKJD97PlzUn4HuVqxeNpyedqbctuq09tIKtUr0+GSGwYIfYP5KIcyxdlFdH1VqQTnOVkbI2zFLYlgPG8pzEsHQsrkADqJM6KuDsbhZG5OcX05IapEoWUs5U+9YSZJYT79FYh7JNSrVkSSJ1zaHYaPO5cGSb8F/Hox4GO7aAr3nwMIfqbLsiQN5FDaI3lZWq3lyXbAwxl+chJedKS9vCm0U3FGX1s4N0WL46Rki6zmBmYahXEwrpLiymuScIsYXbhEOrVmNkes3G6x7onfmWz5dHEB2iZI/Trc8uPFodA5WiirMCyNFCXRDvCYJFa38eEZ62TKhl32jwZJLR3hgpK/gk72X6ua3FFVUX52mfi2syfHhNP15o2IFz/RM4cXsl9DEHYHhD8Ed//BPj7fQ09Ojj7OFUJCd+jbc8guknEYWso5bh7nzb1R2o3LU4zG5TP/iKK9uvsjcABfuG9v4nmmop+CrpYPIKlby5tYmQZkG17dG9JwAgCb+MCsPxeJtb8qJuFw+39+gnDDnErnGXlgY6SO39xWf/dkfm2VMx/jYkpRXTmpBOV8ciCGjqJI35vS5dqVqUCfcIxWlUBRzilQDLx6a0q+uIsbNyqReeMNlIMz9EilkHcabl7H8p6MsX32OLzfspCz+LOeLW67YyS+rIlyvN/KcqPrPFsT/xx+BqnJ8rPW52/g43tsXwmf+SGW5PF35AMr+tyOXwdDCPa1/NoXJmBx6CwKWMnbO3fWPyxVw87eiv+hTf6FWe2lPhz8qHwdzfrhrKIFJBbz4d+gVC2pdlpMjvwpzQEwM9Pjx7qH0GTKeVT0+o4dePobnvoMpb8JNIrJmZqjHz8tGkDflMwyqCiH4D3FTMrNHLpcx2tuW38tHiYmwLQgQVKk0vLM9gmE9rJk/0IX7R7lxftBuNsyiXnbvzPfiSzHhRa37aC8LBrkSqOpJdUogx6JzqFJpuGeUJydic+uaTkHcnLeHpNdLal7K5sPdUTw0wYuXZ/Vu8Qs3vKcNZ1+byjPTOjeLI0kSoamFDHCzYlhPG+QyeGJdEMb6Cp6c3DGnb16AC1UqDXsbZq8qi5FOfMVmaRIOnv4w6VXRSL3pATjykbgBWfcQkTZDCzj5DQAD3CwJSan/4Z4/I+a99B04qv0Lch8GIX+KaEPfBeA5BhDqaOVVah4Y15MJveyZP9CVjQ+NooedaRs7bMx4Xzs0Epxqx6Tt9jKptwOWxvp8eTCGyb0d+P2+4ax/YCQR6cW8tuVis4tAuctIRsgjMVXl1938x/vacT4pv17a1Kk/eIyG099C7fDWoN8I0euPnWdvvlwykHOvT2Wcb/uM0ntH9yA+t5y/VOPoVXBYCAP0msmM/m78lNsfgHscYkR0KGyTmIfgPbnxTvotgueiGjs4tVi4CMMo+aR4re+01hdk7qTVGLzWVFarefD388Rkl7DqriE4mBthYaTPO1Y78cvdBwt/ggWrRGneuZ/rBtvV/ebrIp0ikwPUl6w59oW8WKiu4M8zyQx4ex+3fH8KM0M9frl3GNP7OrHtsbHoyeWsW/kWZd9O1lrKq9FIbA9JZ3Z/Z8waqhRJkjAaPvQQ/x37FEY9Rq/+w/j9vhGklukRbj2F+RzCTL/xNWrBIDfuGObCN9tPExoRyfbgVBYOdu2YuEQruFgZczapgGjH2aJfq6KQlPwK7MwMReZVrRLBjAa/DT8nc1bcEsCmR0dTUlnNnK+Pk5kcDVb1Q38ZfJf4rv2+ADJCkCSJ5/8KQSGX8/HigLrzYmmsz7oHRjLRz57H/wzim3/rBQtySpRk7/tElBLeugYCbhMiNXf8JZzSlSMh+E/SCipwtTIW0dRD77c6w2RYTxtMKzKoNm8e8AIxmyy9qILv7xyCjakB20PSoccYMRi0VpyhKBWUReJ7c5XILVWy6kgcT64LIi6nFEmS+PxADHvD0/nTfjVyPUMR3JDJwMIZlvwBroPRs3LFWZZPeoM5TIeisilVqnh7miv2R1/l+yGphKcX89rmMPJKlewNzyQ1JQkNMpH114bXBHqrY4lMzSU0pYgZ8nMYK3Ng5CP128gVMPJRiNpBj+o45gW4iKqFFqLIR2NyuNMxCZlG1Tyb3GMsyOTi99yU8nwspFKen96LLcFpzP36OK9uvsioDw7y+J8XOl06tykqtYZ/grLY1/9TZPa9mHD2IdxkOWwL+B4mvwa+UwlMKaW/q2UjxwyfKeJeue8NFvY2Ri6D308JJzAyo5gHfjuPpbE+Gx4cyddLB2l12r3tzXj1Jn82XUirv36ByL7o1wQYGmLhDPa9SQ3cRUJuGR8vDuDpKb345lCsmHsjSZATRYa+Z70S5bD7ISO4rj2gllE1imJ3/3KW74/E8cRkH3o5tj62otMxNANjazKTY/GsjETuPrTR067Wxo1LmAffzVrP9xinuMhZu/9hn3GYhVHPUaRnw/L4iS32rOSWKkky7gdIkNogm/Pvu/DbPPioB/LP/XlH+pZ8pRzmfsnpOQcJrnBg4uC+4D9X9AQ1dSwkSQR/TnwFf90r3s/MD5ovwNYb7tsnSkFVlaLMPHpvhz+uUd62/DxZw6TELygpubL+x241DFRfIeeDhQN4ZdmtyO4/AMv3iLr7Bka+j4MZsyeMgdkfi7KUUY/VPTfGx449WeZoTB1brAvceD6F+Nwy3prbV9ywTn2NXeTvGG1YAunBwgA7+gkMvlsY2ldAL0dzqh0HYlhdxIXQYHramfLEFF9kMhk7Q9MJTMrn1lWnGLfiEE+sC+LLgzEoVWr+b1s4w3va8PJMLQ5OeX6jL6CRvqLTIxLpRZXkllYR4GaJhZE+/VwtyS+r4umpvbA06cAcGEnCvSyMwR5WjUvWQtaBspgvq+bS18VC/GAePARPh4mo0YyakgpDMxh0h8jwVFcw0N2KtMIKckqUJOaWoc64SIWBLTJzLU2nLeExSpQ8jXgIFv1SN7hz0RBXPrs1gOdn+LV/X1rwtDXF3caYY53Yl2Oop2DxEDfszAz4cFF/ZDIZI7xs+d/8fmy6kMaGJn0OebbDcJIVYFocV1fGMaGXPdVqqbEy1qhHhVRnylkiDv7GMHk0veY8zSeLA5g/0FUooLWT0d62+DqYcUB/IjJ1lZjJ0HsO0/wdKZBbE6rpyQz9ENHrFntARHM7ytinReR02jvXLAp9OVRUqfn9dBJrTyfx8NpAziTk8+PdQwlwtxIbnFnFgqLfWKlZRHXfRWIquccISD5NUUV1M2U1AIwscbQwxNpEv4GT01+U3WRH8NPxeAZ5WLHhwZHseXp8nYSnh60J28Yl86r0I1J2BNJPU5oFgM4l5pNRVMm8gAa9Z6oq2HiXUPrpf4twxpasg8lvAjDQ3Ypzr01l+IInkRWlNiq5o6IQjn7Cu3G3cEb/QQZsHMk36ndYNLAN6eMOMMrbljE+tryVHICkVkLohhpltZrM4P434OepsO2JZpKs/Vwt2fHEWAZ7WJMSH0lstU19oEDfWNx3jK3gl5nEbXiFotgzvDu/b7OyMlNDPb69YzCPTPTmk33R7L6YQU6JkqdW/oXl6U+I7nlHY2EM78nw2BlhNG55hP6Zm/C0kAtD4MhH8P3YFoNzw3vY4CbLJU+v8bXu36gslq8+x7s7IpkzwIV+rpbM6ufEjtB0NM4186sSa/pyarN+nezkHIzM4tbvTzHh40OMfP8gn+y7xKn4POZ/c4In1wfz1cEYfvM/j33OaVGWqiXrYmTrgSMFZBbUZxy3h6azwC4N739mwbmf8D75Mu9NtWdbSDpjPvqXh34PpL91tZgPJm/hWuU8ED1UkBXB2cR8xupHieyfU//G2w2+WzS8b3mEh8a6k12iZPOF5hUXReXVnE3IZ4ZRuBg8bN/kfmFsJTJD//5P2BMg7teBq8VMlD9v494xPdn86BgUchl7wjJZNqYHfz88ul0ZvSvhSHQOuaVK5g33gzv+gYF38qnzJ6xNsqxZpsSF5AIGe1g1f/GM90FdhfX5L7lnVA++/jeWd7ZH8PDaQNxtjPlt+XBGeGnpg23AbUPdcbQwbDxrq6KweRanBqnnBIxSjjPOx5bBHtY8MdkHVytjUbpdmgWVhSQrPOr7F3vNFBmTmllxtdiaGdLP1YKMwkq+uG0gT0/t3KBwu7F0Iyf2PF7yTFz7jWv0lKtVTblaDdkllfwvtif/DFqDhbkFHynfxd2oEueHtzK6rw8v/h3KpczGJWElldXsDctE38lfBIdTa0SWVErRfzfgNtF+EbCUTSP/ZlHZS4Q6zmdrTBWuVsYEuFnC0OWiDzrsn/odS5Ioq/7rHhGMKc0Wv+OWAgvOA0SC4N5dQghpw13116AOMD7jV+abRmBhdmXBym7l5DTCzhc8Rrb8/KA74cHDwhCuYYy3HZIkI8NmuFYnp7JazddCLezhAAD0BUlEQVT/xnDTAGcx1DE/XogLDL4bbL1g9U3w9zIRSZ7+bqe8jaGjpwKQE3WSKb0dsDMzZKyPHV8ejOGW709RWqni41sG8MhEb746GMNjf1wgpaCCd+b3be685MXBZ/7wz/31ww6vAqE1qfMBNUbZrH7ODHCz5PYRTVTGyvNbl+xOOAo/T+MBzyxOxOaSW6oU/RhnVpHlPIVUyZ6+rg2+wFbuIprf8CY46G4RfYzcToCbWM+ZhDw+3neJfnqpGLg2uVm1xdDlsGy3qAlvkJE01FOwcLBbc0W2y+CTWwJ4eIJ32xt2gFdm+3P4hUk4NJCbvXWYO7cMceOtbeGN5hElWwQAINdU15Vk+DqaM6KnDT8ei6836PxmC0f+8PuMjHyfUJNRGAdoFxhoC5lMxgcL+/PEwknIvCaIpmevCVia6DPax45/NYPwLDwD0Xuguhz853T8IMbWokfHe9JlrfFaseFcMm9sCeP1LWGcic/nu9sHMq76lEjdH/0Ydr9Ilv8yPq5aWN9j4DEaUs5QUq7EvNEg0EIRGTYQSkD9XC0JSi4Uzzn2ASA/Poj4nDKWDPdghJetcE7LckUWZtuTmO95mnzfRYyv/Jxicx/4bb4oJ6xhW0g6ThZGjZtML+0UCnWLfob538CAxdB7NijqMz0GenJxjXYZLPpN8uJEFHXlCDj8IbLes8masYp3NPcxWhGB36XOHcr67DQ/TueZkOE4Ec79TEpeGe7WJqKv7PS3ohQq+E9Yt7RZQ7KViQG/3DsMP8MC9qUb8UvDifdWHrB8L6q+t+AUtYZthm8w4/SdotSrCTKZjBdn+DG9jyPP/xXCnT+d4THlT5Tq23Bz5GRWHWmSOTO1E4GcwffwaPm3PJb2ksj4LF4tymlXz9E6W83J0ggPvXwSVfWGRWW1mgd/CyS9sIJnpvXivQVCWWlugAtZxUrOpZYJJ6tWpSorDAzMG2euOgFDPQUOFoZM9Xfk/emOBM1I5ET/XczqAdtD0vlyeBHjkr8VJVG+U7Xuw8zBE32ZmsIcEQwrVao4GpnGB5Xvgqkt3H8Q5ApuL/ieYy9NYvmYnrwxpw9zfQyRt9KjgFN/JGT4E88/gamM0I9H5ja0+Xb6RsJwy47E79J3TOntwA9H45tJ2f5wLA4J8C87Cz6TtQdbFnwvItprF4ly+1XjYPtTwrlMOQPJZwhwt2LXU+MIfH0qL8zo3a7exyvl78BUfB3MhDFrags3r6Tv4LGcTyogs6iStMIKskuUDPbQYrxauIi2gaA/eG26Jy/P6s2vJxPIL6ti1V1D6+fRaUNVBUWpGOjJuXd0T7aFpJFZVHO/qixs3o9TwwW9QThIuTw/RNyn5XIZM/s5sS88E3VWJABxuNYHhRR6Yih2+OZG4ycAvr19CPueGc/NtaX/1ZVCqGDVeFEqfA0oN3bGt/iMWGqTXi5Xa2MKyqvrKi1Wn0hEIZMxe+pU8d2f8zncuQmZnQ8fLx6As6URN688wTf/xtT1F76/K5L88ipevamvGKWQVNOPF7NPBHZHPyEyLDPfZ9rEifR2suCeX86y62IGs/s7CXuzx1jhLO59TbQNSJIIwJxeCdPfg9cz4Zmw9t2D9QxEJtt5gBAW6kjZWXoQxB5ANu7ZRnba5dB9nZzLwMPWBHcbY87SF7IuCiO8AWtPJ5FTouSZqb3EB77jWREFmvG+iGw4DxTlcYtXixrtTmDsoH5ky2zpI8UytY+Iwi3vrebF6u95fqoXWx8fw+Kh7jw/3Y/xvew5EJnN3aM86e2kxXvd/6aIzIVvgk33tz406goISS3CzswAl5oL7yMTvdn62Bj05TJxcQChjvXjJPh1lkhjaqNGcGGCIhS5TNTRE3cQ8uM4bLUIG1MDnFqRTwXAvpdoZrvwG562Jlga6/PU+mD2hWcy1CgdRa2UbnvRMwTP0Vc1EzDCy7bVwaGXg0Iua1xKVMNrs/1RyGWNjLTsalMiNTWNjg0MgIcmeBGWVlyvHiRXiH6o+MOgUZEz8aMr+lyG9rARSnQzPhD9NTX9NveO9qTMYwqK6lI48H9g6wP2vS/7OF3FoahsvjzQ9hyVzUFpTPSzJ/792Vx8ezpT5BdEVmTdbaJsYORjWC38BAM9Rf28As9RoCzGvDgGC6MG57myUETlai70o7xtOZ+UL0ppDM3Bugc5sYHIZDDOp4Gxd/Ad2P2i6H0a/iBWt61CYW7PN66fCPnljXdB2CaqVEJwY26AM4qGkeTQjSLa3f+W1t+sTAa3bxBrWX0T/DpblAo+eQHmfY3jqCXc9uj/UTzsKTi6AhKOtfPTbpshntZM8rPn04JxkHsJ+/xAhuvHw9bHRdnFbWvF2hKPi2xJdWWj1ys0VVhU5+LSoxef7L1U19MDgKEZP1g9xdCqVaTOWo2sshB+mCDkzJuMO5DJZHx6awCOFkY4FwYyRgrCZt67LB3rz0d7oojJatKEK5NRNm0Fh9QDcSu+IJTd+i4Q8zLsesGuF+vl0muRJFzIJazUsu6h6KwSVBqJ9xb045GJ3nXG3rAeNjhaGApZfJ+pQnygqlxkchz7XLHR0JSxFf/yTcbtvHFxBrcenozZodcxCP+LFflPEzQ5gvnhT4kM+rT/a3EfelbC+CzPFWVQByKyGKkJxkhVBPO+FkbbtHcg7B/sUvbx4sze3DfCGVl2eIuDEQEwNEOy9aW/PIG8wkI8VYliTpg2XAfDuGfh2Gc8O0giPreMP87U9+bklir59UQiTw5UoFeUKD5brcc0F/aEqb3IKth4wR1/iz5B656iD6+Gzq7COBGby8JvTzQrtcsvq+JAZBa3DHFrdMzpfZ0w0JPz47F4LtQETwa3NBNuyDJQFiEL38zDE7zZ8OAo/rx/pFCjbY1tT8A3wyA/gdtHeGCgkLP6ZKJ4roVMjiRJfBpthxo5A1T1CpKz+zuRV1ZF0qULINcjRuVYn8kB4eRUl0FEYwEsYRs2uCdvfRS2PS6coaiaOTVXkficUnYmKTCWVaExMAfbxmX/bjU91WkFFULE4Wwytw51w9rUQDhvQ5eL7ydgbqTPpkfGcNswdz4/EMPIDw7y6B+BrDubwksze4sS+z43iyBz5HahIuvYr1H20txInzXLh2NnZkhxpYrZ/Wuy7DIZzPpI3Hd2vyQCRIc/gAkvC4GOjqJvLNoRsiMgvgMS8cc+E8Gmtu4/7eCGcnJAZHM25tWofyXW31DLq1R8dziOBYPcRGPtpV3iQ5+1QlyUzOxh2c5m5XFXikIuQ+kwkGEGiQytuXhMKNnBUsVBHuuRIeR0a7b78raBPD3Vl2e19dgkHhcTrGd+ICKBEdvg52lao4tXSpVKwwgv20YXQ1l+vHBo3nOCr4fCT1NBYSgu9FsehezI5juqqQU3ST7CzH7O/HEmGen09+DYj72lXvRxtmjfRX7wXZB4DFlBAgsGuTLe1469Dw3AtDKzednBfwxrUwOWDvfgj9NJddLkeWVKAhFR/oZykxN7OeDrYMb3DQe6DbqDDLO+vC09xOiBHXQYW8KxD/S+qe7Pyb0dee3+28VaChKEAdqNy81aIjKzmO+PxKEqyYF9b4g5MStHNDJI43JKCUktYsEgV+RymehBCf5TKKE9HQaPB8KM9zDU12OQu1X95GnXoSBT4F4S3PimXVkERvWG7WhvOyqrNQTXZnOcBqCXGcwAV0txQwQRwIk9IGrUHz8Hsz5ErqfPZD8H9kUXIC1eI7Icmx7gVGgkheXVzAto0OtRni96Cvrf2r4PxswB7t4KCgPRK7Vsl7hB1eDnZI7VzDdEqeHRFR35yNvkpVm92VrSiwJDN16s/o6l4Q+KKoD5K8V3zHcaLP1TGPob724cGCpMASSmjR6BuZEeb2wNq8tyxmaX8vXBWG4d4YXbiAXw8AkY+wwc/xx+mARZEY3WYW6kz98Pj2KV83Zw7I+s3y28MMMPZ0tjPtrTfEhzWnE1j1U/xcUZG4TsO4jI56yPIO28KOltSHk+BpKS4BKzOmnV2nkrfk0CYgq5jAWD3NgRkk6F5yRQK8X9IyuMIoteRGV28pwWG28YeIf4fOZ9A89dgkdPITO1w/rku6Iy4o6/mg1abkRNY7aqUJSIbQ9J5x6zcyIY4lhzXRp4pyj523CnOJc/TRElOYPvbmmvAMhdBjJUP5l+sgTkqMFtSMsbj3seTGzpm7iGpcPdeX9XJHE5Iivw7aE4ZMC9jnEgU9Q1x2vFzF7IU7+YINQjfac16v0hv+ODT9uDnZkhF5IL2dNkavyak4nIZDIWDm6sbmhjasCTk3349UQCf5xOwtXKuOW5TbbeQrDi/K+A6Anu72apfdtaUgOFMIi6GnY8jaWRHrcN8+CPM0nie9xCJud8UgEnU6sotfZHVpuRAAa5W+NoYUhuQijYeFOgpPH10sZL9NkG/9HymgpTRLZn+nviWoUEMR3vG2kvuaVKbvvhNHl64l4sdx3cLNBQK5aSWlhBYl4ZheXVdfPJtGFpos/b8/qy56lx3DbUndDUIsb52nHPqB5ig8F3g+8MEfCJ2StEtJpgY2rAHw+M4NPFAQysLaUGUd0x/nnRu5wWKOzNiS9f/gfgNUnc/05+0/a2IALokdthzFPNe7UugxvPyfGx41S+GSpzt0ZRwyOXcsgrq+KRiV6i1Gv/W0J9poExdrVw7z+OAHkCeqoyYYBE7RBPhDWWO7Q2NeDpqb0wbyrdp1KKIXXOAUJaue8C8eNUloroYpMa1Cvlzbl9WHn74PoHwrcIg64oTfTL9BwnJIjv319fZrH+juYldBkh4gKWHsS9AeYYFEQjizsAwx8kPKNE9OO0hz43i5kgF37j7Xl9+XXZcLw0ieK5q9hEe71w/7ieVKrUdc2geaVVRBiJkjXM6/sg5HIZD4734mh0Tp2BJBmYcYfsfegzr91S2ZeFXF4vFtB77tU7zlVkoJsVFdVqSne8KiTmja3FQLq8+vkdmy+kYWaox/Q+Nap05fmi8TJgqSjHrBlcC8JIOJdYIBqODc3AOQDvitD6QaDQLNLZz8UCc0O9OqVGtfsoPJSXmOrdIJqaEyV6oppEmif7O5CUV058QZUwpjUqoo9voo+zBf0alo1GbAGNqmNRNCt3eDIYbvtdexZcoSeMvSXrmj93BfR2suC+cd58UzoBb3kGWb3ugOV7GzmGeE8W64rZB+d+qn+8UPxeTB29+b95fTl8KYdP90VTXqXiyXVBOFoY8uLMmoyjvpGoZ3/gIGiqxZyZhupFgE3KfgwzA8XwaLkcI30Fz07rxYHIrHpntoa0ggqUGGDlN77xG/KaAH3mw4G3GiuEFgmVujSNXd2+IjOK8bQ10ZrhvW2YOyVKFTszrYWiZOQ2pLxYvg435OaVJzjTYMDzFeM2BKa8IbIgg+8SBr6lmzgPt62FW1aLDHprmDqgRoGsWIxZCIxJZpTqLPS7pT4gIpeLjMjcr8SwRJUS7j8ghB1aw2UgXlISw+WXkBQG9U6TNvSNYMSDELqRNybY4GBuxON/BvHw74H8diqR5WN7YpZyREjYt9BLUodM1rxXaODtIqh66L3GqnetiE50BD8nc4b3tKm7FwCUKVWsPpnIrUPd6nr1GvLgeG96OZpzJiGfQdr6cRoydLlwwluYJdUISRJ2i52fsBPiD0PwHzw43otqtYZvj8S2mMlZdSSeHrYmmPtNFAGKmuCDXC5jZl8n9PKikez9RA+jcRN7aeAdolSrpXmJgb+CnrH4rpo7gesQMXvtKvFvVDY5JUpunVIjkKSlXNLJ0giZDFILKurkzvu5tuFAIsrQX5ntz/GXJvP7fSPq+7pkMqF0pmckPrsB2gNWDuZGLGqS3QNg9FNw8/fw+FnRQnAlQUmZTPTOxx1sFhxqhCQJufLVs0XQY+Cdl3/MBtxwTs5EP3sM9eREmwxq1JdzMCobLztTfBzMIeg3yIuB6deogbnvQmSqSjHcMjtCzE9w7Ce8VW0DnxpSUQC/LxSZklkr6iMAHiPh4ePiB73jGSGbfTXIixOZGu/J8OhJ8WWd8znM+UwYEobmMPcLyI8T9ca1VBaJnqfhDwISgzWhvGq2kwKFDd/kDSG7RFnfiN0Whmbi5nD+l3r99axwkOuJ8o7/OM6Wxtw80JVfTyRQpdKQW1pFhMV4MQvDzqfRtvMHuuJqZcznB4QU597wTOJzyrhliPbZG53K0PvE9/Uyhut2B/q5WWImq8A0drv4HSxeI56oKcvUaCQ2B6Uxs59TvcMY9g9Iaq03mWE9bCiqqCYmu6Z+3HM0faojsGhotDbJ5Ogp5IzwsuFUjZEaZRSAPmpmWCTWvyb2gMiq9Bjb6HhjfewwUMj5NzIbzJ1Q2vfHJecod43ybHyTC/1LBDIs2jcEt462yqDMHRv1UHYWT03xZZ/5QiYpP0U940PtBnWvGeIacuQjcU2FGidHBpZuzOznxJOTfVh5OJZxHx0iJruEr5YOau5AuAyC2zeKfex6of5xZSnsfQV6jGvkXN48yJXeTuas2BPVaDephRXIZWjvxZj6f2IeRm0wDGqyTlBt5sqJGuXGiIxiIfWrhZ52pgzvacPG86ngOxUpdCMySUOWsQ8DXK1Yvvpc+4eZXi4GJiJrq2juhDVDLqfEwB6jigwORWUzUTqPnqYS+jfpEZQrRObrmTB49HT7riXOA9GTqrnL+LhQZmzL4Rp6H8j1MAn6mc9vCyAuu5Sk/HKend6Lx8Y4iQoRnyltH1cbhmaiJD7sH/h5ulAsXDlCKF9doVRuLXeN9OR8UkFdIGvd2WRKlSoeGq+9T9RAT85HiwYgl6F1+GMj/G4SZXgNgwUtEbEVUk6LwKj/HDGked8bOJkpWD6mJ7+eSERVXtAsk3MxtYgDkVk8MN4LeY+xUJpZNxQcYFY/J3poktmXY0VOibJxJgdEkMDATGTQm6JSQuAacT2uva72miWEU5qUs3YWZxPy6eVoho1Lzb1YS7mkgZ4cR3Mj0goquJhahLOlkVaHtEOY2sGdfwv1TrOWs0Ja0TMQYx1aEhfoKP0Xi0qO0ytb3ubM9/DPfaI94YGDIuDQCdxwTo65kT5T+ziyrcgHci9BcQZqjcShqGym+DsIdadDHwjv1LWVtHVnYu0JvtPh3C9C7lTfVNRhVxbWDbJDkiA3VjyvqamnrSwSNe5ZF+Guzc2FGPSNYM4XwuPd9rjwgjsTdTVselA0KS74Tjg02nAfAcY2oqm8ltpJ7P5zwdYH2flfmFh9jK8rb+LTQ0k8OtGbGX3bnsFSx6ia+tnA1eLvjBDh4LR1w/qPsGS4B7mlVQSnFJJXpsTGzEiUFjTBQE/OU1N82R+RRWBSPh/ujmKMj1Cquuq4DRHRpasgQX8tsDDS517LYPTVFcJgNrUVTdw1kqX7I7NIK6xgYcO5VsF/igCBefPv+hBPa0wNFHz9bwySJFHmNBx78nGTNZRbb+zkAIz0siUouYCKKjW7s20owByfsqD6DWIPiL6zJhkVU0M9RnrbciBS7P+c/jDGKy4yv38DmfCCJCHT3d5StW6AiYEeKxYPwsNXNOS2yOTXhSFz9BPxd2GyyHTqGSKTyXh2uh9/3j8SKxN9Xr+pDwNqhE6aYdNTCJeEbhAGkySJHqjSbHE9buAwKuQyHpnozbnEgkY9P2kFFThaGNWVKzfbv+sQUZJcS1EqKAzw9/XhWEwuGo1EZEZJnay4Nm4b6s7ZxHyOSgHI1KKP6MW7F/LLsmH0cjLnzQbled2BSmMnzJTZ7AnL5A7TM8IYtGlh8LSeYfucJxCNz8hwVqVpFx1oiokNDLoLzv/MECd9Qt+ezu6nxvHoRB+Mzq8S0rj9F7f7fTVj2P1C/KYiX9gi9n7C8ekkZvR1ws7MkN9PJ5GSX86Px+KZF+DSap9ogLsV+56ZwG3D2gh26RnAsAdEOWVJZsvbqVVCYc5rYr3TP+ox8Z6TTvLwRG9MDRQoS/IbZXJKlSqeWHeB3k7mLBrsVmPzyOqb6IHhRbuxkZVyqMIXe3PDOkGiOgzNRPVHyPrmvW0RW8V8uOEP1D/mN0uI4bSgyHulnE3IF86j6xDRX9bC+INaGemLaUXtyuK0C6f+QjSmq9EzFN/70L+EKI42wjeLErslf2i9X14u16e10QY3D3RlY6EfkkwBUTtqDL8qJvd2hLhDYkr2uOeu7aKG3QfZ4XDmOxEF8hgpah8vbhQqSF/0h2+GiAbZ2onbB/5PZH2W7W4Wla1DLod5X4kyNoVB56755NciSr3wx2aGVuM1KIQT11APPSNErMe+tzDyEo+BiQ3l/e/ij/tH8OLM3o0bndvC2lOUz5xaKVRRgv/UPk/lP0qAmyXmRnocj8khv6wKW9OWvwsLB7vS086U5avPk5Rfziuz/K/tYLTrmIWyw4ToDRCGKIhm0PQLnIzN5cl1QYzztauXUk04Kn4/WuqhQTgd/7u5HztCM1h7OoknT4hzNt2wQUpfS836aG87qtUSH+2J4odjiaRaDEaeVCPRWVUzI6yFpuhZ/Zw4k5DPg7+dZ1WGN+ZUYJJ5rn6D87+IKGif+R39aLqUUd62rFk+vPU5PBYuQmHo7A8iQ12QJK4rTfZz8LmJ3DO6R+sHHHSnMHS3Pyl6Fc/+ABNfaZY5BTHnSl8hY18D+fbUgnIxI6cl/OeJ6HJt5rooBSxcGdvLgdjsUs4l5lOqVLWYyQGY3d8Zc0M9HjtliQoF1eYeuDs7Ymaox5rlw/np7mHd6nevNnfBgTzOR8UxWBXcKU3HgAjO2bYcRdfKqMeEQ3zwf/VS+mW5Yk7IkGX1v//LxWMkPH4eXkoQpZS9ZnRaVYmBnpylw91ZdzaZcSsOUVyh4tGJbat9+jiYtU9ddPgD4t7eQEChGaHrRRnv5Dfr35fLYBHNj96DhZE+T0zywVBVQlKZcFbF0NiLZJco+eb2weJzN7ERJemJNU5OYTLyPa9A/8V8+MKTHH9pshgw3ZSBt0NxKiQ2cFw0GnH+PMc2LnN37AuWHqJPu5PJLKokOb+c4T1tha02+O4W+0xcrYxJyS8nPL2YAZ3l5HQnhi4HpLqerkYoS0T/j8+UTq+uuiGdnAm97NGY2JJgPgTC/uFgZBYWRnoM7WEtlDRsvEQj1LXEZ6r4IVUUiKZfmUykby/+JWZRuI+AJX+KxsRDH4gmrfM/w6TX2u47kStEpqXPZcweaQmNRnwZByxpXcq7ll7TReastqEyI0R8xnoGdUMf5aMf58MlIxjt3YoaTmuMeQpKMkTWasCtMPmNy9vPDYieQs4oL1uOx+aSV1qFrVnLGS49hZynp/pSVFHNgoGunRc1utHJjcGr4iJrKsdSUaWmslpNsMab6rQQHlpziiGe1vxw11DhvMcfhj9vE9LQ/i33IC0c7MbCQa68sTWcY+mQ7z4dh3MrxIDIolQR5DBpXELS28kcaxN9Vp9MZKKfPb1H3yRmclQWiwZzdVWLTs5tQ935+JYBhKYWcbLSE7WRTX1woroCLvwmnLJuMEj1qjDmKTBzgi2PCBGMBgIJHUImE8GfW3+D4nRRZz9Ku/qQhZE+I71s2RdeH/lOK6zA1boVJ6fPPCEYUHtuilLA0o0xNQp6Px4T5Tv+rfQ1Ghso+HhxAO8uGY3Cazz6nvWytRZG+tdEtrgjKCxdcZHlMZHzKCT15c3SagmXgeLf1kQHGmLtKTJ/Z1fV9/Ye/UTMpbrCIeF1KPRbro64QpaP6ckjE7359o7BnHx5Mr6dOfzSxEYYrOd/baZgC4iSsMMfitK2hp+3XC7shEu7QZK4e4gtejINvwUXUlJZzYq9l9ganM7/5vcT4lC1eI4RmZzyfNj8iMjUzP649TV6jBJB5IYla2F/i6qYSa803lYmE73ZEVua9dldKWdr+ueG92ijDBBwszYmLK2IUqWKfm0JOlyPmNmLCoFzPzZv00g6KfpAWxPzuEzame+9vjDQkzO7vzN/hg3l9eJvCS0MZ6KfF/oyCaJ3i5v4tY5gyRUw/H4xTKnXdPHYsPvEUKth99WXzvlMEyd832tC0nrEw9d2nbWknBbNrgFL2re99xShOBOzTwzazAgRjhsIg2vWClECcCU49hX10kii3K+lAXD/Ucb62vF/2yOQy8DOrPWs3twBLpQqVczq13kDGm94gtai1jdnV+Uwbk8vYuP5FBKDjdloWMWDfkrum+2B8f4XIeeS6E/zHC0CF22UVL5zcz/Kq9QsHuqGjftQ+G4UbHpAOPQG5qI8pAFyuYx7R/ckr0zJG3P6oJ9nJvp+Yg+IWnlL9xYluuVyGYuHujM3wIXk/HIUJ6aL3+yM90SfQEV+TR/dDYqhmSiZXDMXkMT19nKRyUTGq/dc8fm3Uj41rY8jb28Lp6CsCmtTA9IKKuomsWvFxkuUmkRuExmNwhRwFANJ+7pYcCAyGwsjvTqZ/5aY2a+m7MP/d6D7ZG20YWTngSkF3GJ0HslpKDJL17Zf1F58pom+VusOZGBGPSb6Zjc/JF6XfFKor3W0v6ELsDY14KWZ2q8BncKox+DMqpoMZhPlrTOrRIDm9g3NX9drFgSthdxo9PRrFMUqDJn5xTHSCit4caYfi4Y0VoCjxxjhbH7RX5TyL/2z7V4RuVzYece/ELaCnqEon/Odob0qZtRjIot9/HOY+na7P4a2OJuQh4eNSbsCCq7Wxqhq5jL1v1EDjyMfhuC1ojStoWBI/BERfGo6XLcTuCEzOQBLhrmzuXIQVZKC3nkHRT9Oyhkoz7smimpaGfU4PBFY/wO1dBM33Ia9QXoGYgCfx2ghg9reuuPOJnSDUOVpqUyuKcZWwqiL3iNmMuRGCzU4EBGrEQ+JRtQrZc5nQvhA5+A0Y6yPHWqNRLVawqaVcjUQxu4dIzzb3E5HA4YuR7NgFRqFMb+fTuKvwFTmzJgFMjlP9C7GZP+LYr6MkaUwhpZuaNe8LTNDPb6/awhT/B1FtGv+SlHeWVkspJmtmtfJPzXVl3fm9xM9HfZ+ogxk04MiuFArn9wKRvoKejmai4bg3GjYeA+c+lbIfdrf4GIePccJowYuP5PTELm8TanTqf6OaCShtKRUqckuUbaeyQHwny+kvCsKhdFoKYy/cb6ih6qPSzsl+EFkDK6C6ENnYmYvBoIOVwch68yqBBAG1SMnOhbclCtqegj1RP/rzI9g/Attv+6/gLkTDLlXlH8VZ9Q/HrFNKAMOf0B7BYr3JDF64tJuUYoLLBjdj8ziSj5a1J9HJzYv96THOLD3F6I1TwXXVYa0ScASUFXAnpfFMNbClJYdGCt3GPkInP5O/NY6ibp+nHZQW77qbGmEXSuVGNc1Tv1Fr/CRjxoPaE44Ih6/CsmHG9bJGeBmxe6X51HgPJ4HrINEk3vUTjH8szbDcK2RK+puVK1i5wPLd0NHB112Fiql8LT739IxZ8J3uijT+WaoSOvXOjk6rgk97UzrIrutlavpuEysPdHvcxP+zuZsDU7H296MpeP8hUTqmVUi6jtrhWicnPjS5avD9JohyqCW79Ha49EMmUz89oytxEBJrw6k/HvPEbNNEo+JnsEbOYvTkMlviNK1FpqAOxsXK2P6u1qyLyKTjEKh4tRqTw5Av4WihOPT3qJZ2lI4u+NqehBaEx24HtGzEvdGOZrOLVW7Eux84elQuPMfIS2tpwsK1THxZfF5HHhL/B29T6hj9b5JCHNow8BUGLNRO0XAGZg5tDehb03ntmEtBBxMbOCx0zB7Rcca0q17iHEbEVuFoNPYp8UMt5YY9yzom8C/77X/GK1QUFZFdFZpu50ct5qgxw2bxall1grhSB6sGQ5cmgNZYVqFkjqDG7JcrRYHcyMYfbso/TjwKkTtEulSXRagdWL2idrUAW3MH2jKoDtBWSycJCOL61Yq+HpFJpMx1teOjedTWxUe0HFlBLhbEZJaxJtz+ohMiutgMXzOfWT7yzvboqON/zd9AtLHHc+WymRiXoT/HNHc6zerY6+/XtE3gmnvXNNDzuznxKf7LlFQM8yzdgBgi9h6C5nk8E2i16omqz7E0xpPWxPG+lxmb2N3pVay3Kn/lTf267j6mNiIvqWdzwl1ssjtomx90c+t21j9F8PmB+GPGtUvIytMtcx66hQWr27/tkaWMOElIZqgUl6xcuv5JCHRPqLdmRxxPbjhnRwHfzFXa9/rQoyivEZtrSPBuQ5wQzs5gDAW0i4IFbPyvM5tzr9RCd0IDn07nkmqvejp6DIm+jnwd2Bqt2sqvpG4a6QnXnamjO9VI73sPkLIlc7++Nr3+tXS2jT59mBsLRwdHVeN+8f1RE8u48dj8RjqydvO5IBwdMa/0KhMykhfwZEXJl3FlXYRZg6iB63vwq5eiY72MmQZnF8NsQdFhH7YA22PCBhwq3BiI7YK0Q7TbuSsD7tfZLM7YczBlN4O7Hl6HB6tSHc3xNhAwfd3DmZEz2swzqGrGfmoEFXZXFM5YOvbOaXDWpBJXSiUb2VlRWFh4bU5mLpaNAQ79u06Q6Qb0+hcFKUKDfz2zBTQ0elcye9CkiQS88rpadd2L4iOtmnXuVBXC/WrlmZ66OgUrun94ipSWa0mu1iJh20n9Ch2EVftXOQniJLuNnqcdNTT5b+L8nxRVnkdCDJcbbr8XFxPqKogJ1L05li5t6+V4zLoUidHLpdjYXFj1RV3JwwNDcnKymp7Q3Tn4mqjOxfdB9256D7ozkX3QXcuug+6c9F90J2L7kNHzkUtXerk6NChQ4cOHTp06NChQ0dnc8Oqq+nQoUOHDh06dOjQoeO/ic7J0aFDhw4dOnTo0KFDxw2FzsnRoUOHDh06dOjQoUPHDYXOydGhQ4cOHTp06NChQ8cNhc7J0aFDhw4dOnTo0KFDxw2FzsnRoUOHDh06dOjQoUPHDYXOydGhQ4cOHTp06NChQ8cNhc7J0aFDhw4dOnTo0KFDxw2FzsnRoUOHDh06dOjQoUPHDYXOydGhQ4cOHTp06NChQ8cNRZc6OY6Oju3aTpKkq7wSHe09FzquPrpz0X1o81zork3XDN3vovtwzc6F7vfVJs3ORVlu1yxEh+4a1YlIkkROifKK99OlTo5S2fobUGsknv8rhNtWnUat0V3sriZtnQuADeeSmf/N8f/euSjJvKaHa8+5uBFJzitn+HsHiMos7uql1NHiuVBXw87n4bM+EHvw2i7qP0prv4vziflM/vQw960+x/dH4todGKtSaZj48SGORud01jL/E1zVa5RKCX8vh8/6wrsOcPq7q3esjpB8WvzuWyInGvITrt16amh0Li7+DV8MgOL0a74OHf/de3dnI0kSH+6JYvZXxyiqaOU31w66bbmaRiPxwt8hbLqQytnEfDaeT7mmxy+vUvHMhmB2Xcy4psftzpxLLCAktYhDUdldvZRrR/Cf8KkfhGzo6pXc8FzKKiG7RMl7OyO7eiktU10Bqefht5vh/C9gZg9rF8GB/xPGmY4u4e/AVArLqykor+LD3VHE5ZS163VZxZUk5pXzz4XUq7xCHe1mzysQsQ363gyO/SBkXVevCNKD4ZcZcOob7c+X58OPk+CrgfDNMIj791qurh6fKaDQh8MfdM3xdejoBL75N5ZVR+K5b2xPLI31r2hf3dLJ0WgkXt18kc1Bafw5OoOP3U/y6b5LlFTWe3QHI7N46PfzVKk0nX78ymo19685z+agNN7fFXnVMxeSJJGcV35Vj9EZpOSLNa45lXjF+7ouskHR+2Dr46BnJG5ukkRFlZoVe6JIzG2fEaWj/eSWCifhWEwuR7pjZP3wh/C+K/w0BbIj4O4t8MAhmPgKnPgSvhsDiSe6epX/OSRJ4t+obOYFuPD9XUMAiMxoXzYws7gSgENR2VSrO/9eoqOd5MZC8hk4swrO/wwz3ocZ78HwByEjBIrSunZ9gavFv6e+FYGOppz9UWR55n8L+iaw8znQqNu3b40GijspmGpsDeOeg6C1kB3VOfvUoeMasulCKp/uj+aJyT48PMH7ivfX7ZycWgdn/blkdvQ9zMjA51ic8w3OlXF8828sAHmlSl74O5S94VmsP5fc6Wt4en0wgUkFvDjTj9SCCvZHXN1ypZ+PJzD+40N8tu9St+4/Si2owNnSiGMxucRml172fvLLqhjx/gF+OX7tU/vtpjQb/roHvCfDop8hM5SKuBMsW32Wbw/H8cWB6K5e4Q1HbokScyM9BnlY8cE1CC50mB7jYNZHcN9+eCYceo4HuQImvgQPHgZDc1gzR2R6dFwzwtOLyS5RMrm3Aw7mRtiZGbTfySkSTk5xpYpzCfkdO/CplXDg7fYbszq0E3cIvhkKv0yH3S9C/8Uw/AHxnO90kMkhenfXrU9ZChf/Av95UJYDwX80fr6qDM58D4PuEP/N/gTy4yFiS+v7zYuDXS/A531g7cLOW+/wB8HCVXw3rweqKyFsE+Rc0vVg/ceprFazYs8lZvR15NlpvTpln93OyVmx9xJbzsVytOfv9I39ASa8DJYefO6wm1VH4/loTxRvbgtHrZGY0Muerw7GUl6lQqORyC+ranXfgUn5bToRldVq9kVk8vx0Px6d6MNAdyt+OZ7YofewLzyTR/8IZN3Z5DYbpyIzilmx5xJ9nC346t9Y/rcjEqWq+900q9UaMooquG9sT6xN9PntCrI5J+NyyS2t4n87I9gfkdV5i+xMUs5AdTnc9Cn4zUay7knw3x8SmlrE7P5O7LqY2eb3TUfHyC1VYm9uyCuz/InKLOHf7lYW2WOMML7ch4OBSePnnAfAffvAsS9sfazV0rW4nFIWfnuCrw7GUKZUXeVF3+BUFBJ9ajsmBgpGeNkA4O9sQUQ7nZys4koMFHLszQ3ZH9n4WpSYW0ZpS+enulJk9o5/Dpsfar1XQ0fLFKfDP/dDj7Hw8HGRGV2wCmQy8bypLXiMgkuX6eRUlUNB4pWtMewfqCqF6f8D/7kia6tu8L248BtUFsLoJ8Tf7sNEQOTY5y0b7ZVF8PvNwrjvfZMInnSWga9vBJNfF45h0qmOvVaS4MRXwuG4Vpz6Bv5eBiuHw5cBUNoNs/g6rgl/nkkmu6SSF2b4Iau9Blwh3crJSckvZ8fx8xy2/QiPnMMigj7pFZjwIj75h1kxWs33R+LYGZrBu9McWRGQRWF5FW9vC+fmb08w+sODFFdqv9nE55Sy6LtTbZbBJOaVoZGgn6slAPeN7cnZxHwupha16z0oVWre2hbOqbg8Xt18kQkfH9La11NZrSYsrYin1wfjYWvCpkdH88acPvxyIoHRH/zLJ3svXZVSvMslvbACjQTeDmYsHurO1uDLb2w8HZ+Hm7UxU3o78uS6IGKzSzpxpZ1EehCY2IKVB8jlZPvfw7CKE3xzkwP/N68fGknir2vcJ3ajk1taRS/jYob1sMbXwYwtQV1cotJRFPqiXCUvFv59FzIvilr+BiTnlXPHj2dILajg639jGL/iEJ/vjyajSEsJjI62CVnHwrDHmNNDwlBPAUAfZ4t2Z3KyiitxtDRkqr8D+yOy6oJg2cWVzP7qGKuOxGl/Ycw+UBbD+BeFobrrhQ4vPSqzmPvXnCO7pmTuP0dhCmy8W2RDF/0MTv3BdbD4uyF+syDhKCgv4z6x7Qn4YaJwSrUhSSJr3xqBq0VG37oHjHsWCpPhyEfiuYJE4ej2XQA2XvWvGfcsZF0U5c5/3Cqyfg3Z+TyU5YnAyE2fiqxwJxl1gMiG2fvDwXc65jwlHoP9b4h1t/S6rHAoSOqcdVaVw+lvYcBtsHQ9FKVA5NbO2beO64KDkVl8tCeKoOQCvj0cy80DXfFxMO+0/XcrJ+ebg5f4Xv8zHGQFsGwX9L9FPBGwFGy8uDX/B369eyDPTfZiTvizOG6/k6f7VbLxfCoZRZVUVmtIL9RuLKTVPB6e3vrNLyZLlGH5OpoBMKufE86WRu3uQ9lwLoXM4krWPziKM69OYZyvHY/+cYH715xj2mdH8HplJ16v7KT3G3uY8/VxUgrK+eK2gRjpK7hvbE/2Pj2eWf2d+PZw7BVlSzqblHzx+blbm+DrYEZRRTXlVZcXhT4Vl8dob1u+XDIQQ305my50Q2M2PQhcBtXdeE6Yz6QSA0bnb8be3JAZ/Zz482wymu5WUnUdY1oQzvfZdyFLOMLNg1w5EJnVYtCi2+I8AMY+Aye/gu/Hwg8TIDuKymo1/wSmsvTH0+jrydj6+Bj+fW4iU/0dWXU0jjEf/ntNxFUORGSxfPU59oRldL9ywMugPEcYW0vMguse6+NiQVaxkrzStoUgMouVOJobMdXfkdSCCqIyhSH95cEYyqvUXMpswbC++Bc49IXJr5ER8Bjq4PXCYOsAPx5N4EBkNvetOX/Z19LrEo0a9rwKXw8WAYHFa8C8Feldv9mgrup4M3/KWQj7GyoKIGav9m1iD8LnfYXDpY2CJEi/AIPuFH+7DBI9eEdXwK4X4ZdZomdz6tuNX+c1CTzHimxKeS7sfRVOfi0yfie+hIsbYfbHYHvlPQdakStENif5JMR1QP3x+OdgZAWpZyF6j/Zttj4mygo7gwtrxPmZ8BL4zULpNorykM2kFnT/HuV2kxcnFO+yu7GgThdx+FI2D68N5Kdj8Sz49iQF5dU8OcW3U4/RbZyc+JxSVCEb6Ucc8oU/guuQ+icVejBrBSSdZGLoSzyh+R1ZehCY2PGQfAs/3DWEDQ+OBOprrJuSVSxueFEt3bRqiM0uxdpEH1tTAwD0FHIWDXZj98UMKqpaLyOrrFaz8lAscwa44OdkjoO5Ed/dMYQXZ/qRXljJIA8r3pjTh7fn9eX9Bf3Z+NAoTr0ypS5rBODnZM67N/dn/kBXVh2Np7K6C0vXUgMhfAsAKTUXHTdrY+zNDQHILelAuVZFIQDZJZXE5ZQx0ssWU0M9xnjbcSymm+n6S5KIwDsPrHvoQpaKvQZTMQr5DarKuGOEB0l55RyP7WZrv44xLq0xNI6sYF6AC0qVhj1h11a+uzWOROfwxYFo/jqfQnRWK9eRia/A3dvgjn8AKIgPZNyKQzz3VwgeNib8ef9InC2Ncbcx4aNbBnDm1anMDXDh9S1hhKW1L2N8OWwPSeehtYFcTCvi4bUXmP3lsevWuA5LK+LD3VGcCY0AoF/R4brn/J0tAIhsI6AFkFVUyTTFeca4G2Jlos+zG0M4FZfH+nMpmBvqEa9NYKSyGKL3Qv9FAHya1g+FuqJDxmSpUsWuixnM6udETHYJT60Pbl5KXZwOG+4UGcFujCRJHesljdwGp1fCmKfgqVDwHNX69rbeYN9bOJbtpFxZTem2F6iy7Y3GsV/L6pi50cKBurRL+/N5MeLfBvcCJr4syujPrgIDU1i2W2T8GyKTwbKd8EIc3H8QRj0O+14XDtX+N2HIMhh4e7vfz2XR+yZwGQwH/9e+bE56kHAkZ30kSgQPvqO936wgSQisqNu4duTFiSxYuZZeN1WVyKCd+Ar6LgRbbw5fyua9eB8MUk4y56NthKQUtudddn8SjkBhEux+SddzVEO1WsOuixk8vDaQ0d52BL05nW/vGMzK2wfRw860U4/VbZyclftCeUlvPepes8FrQvMNfKfB4tWiNvf0Spj0Kkx8Gf2obUx3KMLFyhg5GrJaSP1nl4jHo9ooY4jNLsXHwUzUAypLQK1iwWBXyqpEr05rrK/pwXmqgScql8t4dKIPu54ax4pbAlg2pid3j+rB7SM8GN7TpkV5vMcm+ZBbqmT92c4XVmg3p74W9eZluaTkl+NgboiRvqLOyckpbWeZRVYErPCCIx9zOl5c8EZ62QIwzteOsPSiZlHX5zaGMObDf+n75h4e+O18ixm6q0JhMlTki6hdDaGpRUS4LxG11CHrGeVli5e9KX+c6aS0vQ4MKmtKSZNO4F4cxPAeNh0qWZMkie0h6cTlXL4oRmtEpBez5mQiL/wdyqJvT7ZcTipXiGuY71QwtScu/BwlldXse2Y86x4cibtN434eS2N9Plo0AG97Mx75I5C3t4Uz5dPD7AhtpSS0uqLlEhwtHIvJ4an1Qczu78zJlyfz5wMjiMkuYcO567Pk8lxiPhvOJeOqKEQj18cg7UzdPCsvO1PG6kcy5K+hbQ5GrC5K56H0NzC6tJU/7h9BXqmSpT+ext7MkIcnepOUV4aqqepa1A5QK6HfIiRJYn+uNYkyV4jc3u717wrNoKJazauz/VlxSwD7I7K4kFxYv4FGDZseFPv887bOU9+6Chy6lM1NXx1nT1hm+zLbp78D9xEi02Bk0b6DDLkXonZBUdtS30UV1fz45duY5QSzPGMBqwqHI8Xs025sF9dcX6J2aN9ZfgLIFM2dmEmvwB1/w/K9YOna8mJkMvHf9HdFz47XRHjoGMz9onPL01o69tS3ICMYDrzV9vbHPwdLD+i3CKa8JRQkm4onVJWLe2NViVC9a41/34XtT4kRDBvvhtC/IOaAkNx/1x4+8YXSTFHahxBgirOdhJ5MwwKTUDZfb+XKLZF2AfSMhbPT0vesiwhKLmDx9yc5E593zY65NTiNke8f5NE/LjDQ3Yrv7hyMmaEes/s7M7Ofc6cfr9s4OQ/rbcdOVoJixrstb9RnnqjbHPMUjH0WBt0FZo6w/SmMfp1KiNED5OUXaH1pdk0mJz63rNXsSEx2iagHjDkAn/rD/jfwtjcjwM2yzR/dtpB0Jvo54ONg1vYbbgMfBzPmDnDhuyNxnI7PIyqz+NqXRhUmg6oSzv1EakFFnXFW5+S0dxpt/CGQ1HDoXfROf42nrQkuVsYAjPW1E72OcfU/sricUv65kMrQHtbcP86LoOQCpn12hINNGoNbIyK9mE/2XmJHaHqdg9tuMoLFvzVOjlKlJjKjGBevfqI+/Mz3yCSJO0d4ciQihbzQPSJqpeOyqVJpMFMVUGlgI6K2R1dw8yBXTsXn1ZWatkZGUQX3/HqOJ9YF8dLfoVdljY9M9Cbozen888goSpQqLiRrv9Y0wr43VRkRTPF3pJdjy3XGRvoKvrtjMMUVKnZezCCvrKpl4YWkk/D1EKFIFXeoXWvfE5aJm7UJX9w2EH2FnNHedszq78xPxxKuS+nk20d4EPj6NHqZlCLve7Mw6GqcDD2FnJcNN2FcXdiqISZJEjalNZH64jT6uliy+bExjPKy5e15fejjYkG1WiK1oMH3LyMEjn0KbsPBugcZRZUUVVSzSzUM6dJuEaFuB38FpjDSywZ3GxNm93PC0li/8fXt+GeiP2L+SuHwrLutw+Vw1worEwNMDRU8vDaQhd+dbL36IDVQiLqMfLRjBxl4uygLO/dz8+ckifQtbxL/7WIundnNrpXP81T5N2T2uJmJs27l16Ih4v4Tvqn5a2udnMQT2p2g/ASwdBP9dk3xnSaEEdpDraOz8AdR0nqt8JoIU/9PlMid+Krl7U6thIitMPZp8V49R4F1T1Hy15DiBjZQ4tGW96dWiczmkGUis50XD5vuhz8WCTnwGR+IoPXDx8GxL7HZJRyLyWX++KHgNowlZsHsCE1vHmC4HkkPFj1bXhNh72sdCk61SVX5Ze9PkiTe3RnJheRClvx4mo/2RF31z1uSJD7eewlfRzO2PjaGdQ+MxMRA76oes9s4Ob5WcuSjH227RtV3Gkx7B+RyoSIy7llIPgXVFZhTQVW+9sh6YWEBvxp+gp0mr0X5Y5VaQ0JuGTdV74U/F4syuQu/g7KUBYNcORaT26Jhn19WRVBKIZN7O3TofbfGE5N9KKlUseSH08z84hjTPj/CxvMp187ZKUwGhQGc/YGs/ALcrYVjYmtqiFzWAScn8QS4j4QxTzE741uWONRfKN2sTfCyM+V4TL0gxI6QDIz1FXywsD/PTOvFgWcn4OtozreHW3EkJKmRotUn+y7x7eFYHv8ziFlfdLAsJz0ITB3AwgWAqIwSqtUSAW6WMPIRUeLw8zTuinmKQIOHsd10m6gv/2UmpJxr/3F01JFXpsSeIqqN7WHc8xB/mLkOOVga6/PO9vBG5TDFldU8uyGYRd+d5KavjjHkf/sZ9cG/RKQXsWxMD84nFXA+sYNywB1goLs1Vib6HG3HLJ8CM2+cqxKZF+DS5rY97Ew59cpkzrwyhSm9HbX3g5z7GVbfJL6blu5CoenoJ23uOyytiIHuVijk9dHjh8Z7kVZYcV0OPDbUUyCXIbI3jv2EOldETcNy4gn6qcLF/+fGtLiPoopqvDWJ4o8a483Vyph1D45kZj9nvO1EsCo+t+Z+cfo7+GGSiOzP+QwQwRSAnaphyJTFIlrbBqGphZxLLGDxEHdAOGWT/Ow5UOvkZEfBoQ9gzNOiF2TpOuFc1b6/bsZgD2s2PjSKX+8dRnBKIdtCWslAnl4psgW953TsIEaWMHCp6OFoaNRp1OT8+RAuwV9imBWI3+4lLC1dTVr/R3G6+1fuGOFJoZ4tqVbDtZesFaeLe5Okhpj9zZ8vSACbnh1ba3dj7NMii7T/jWZCKAAc/0L0DI18DIYur3/c2lPYAA0pqsn8WrpDwrGWj5l6TlQ9BCwRNtojx0Vp4vJ98OgpGPWoMPwd+wKw5mQS1ib64jrpPxff0nNUlhZy6hpmGK4GxSVFIiPmOhhmfiS+b4daCeR3BLUKVs+G78dAScdVao9E5xCYVMDK2wfzzNRerDoSx31rzjeaR9kaZUpVh0eeRGWWkFpQwYPjvQhwt+o0BbXW6DZODjPeExGHjjL8QVH3etvvAKgKtV9gjQouMUl2gXGKiy0q7yTll6NSqxkV/Yn4Ad5/UEhHXtzI3AAXZNDiBfxIdDaSBJM60cnxdTTn1MtT2P/MeH5dNgwPGxNe/DuU309fgxKpqjIxE2DkI1CeR0DenrpMjkIuw8bUsEUnJ6Oogs/2XRJRAY1GND/2GEPOiFfIkSyYqmg8R2Scrx1no9OQkk4hnVlFVNBRpvZxrPPwrUwMWDTEjaDkAgpakm3e8wq86wD/s0e5/h6ORufw2k192PXkOPLLq9gS1AE1uPQgcBlYV04QklqIQi6jr4ulkAad+jZYe6JvaMIpxyXcrbcC1fzvRHnjuiXiQqajQ+SWVGEvK0IycxCTzo2sMI/dyv/N68ve8Cy2hwpDPL+sitt/PM3+iCzcrY3p72rJHSM9+WRxAPuemcDrN/Whp50p37ekitUJKOQyxvi0r5fsXJkjnvJsJnq1kt1VKYUCVPwRTAz0kMtl+DmZEZNd2lwc4PjnotZ+2W64dycXXW6h+vDHdT1v2qhWa4jMLKGfa+PSoAFuVoz2tuX7I/Hdej5XiyiLoboMzJ1FXX/iMREpPfwBBaZexEvOqFuRws0srqS3vMZo01IO5mptjIGenPicmr6c418ICeGHjwklMOqHjoZLPagyc2/VEckurmTZr2eZ980JXCyNmNXfqe65Kf6ORGeVioHLZ1eJoY4TX6lZyGCw9YGU0x34cK4tMpmMSb0dGOdrx68nErV/n4ozRI/niAdFALGjDH8QyvPE5yNJUJJF8a+3YBuzke8snsLsxUiipqwmbd4GXBd9AHI5xgYKRnnZckA9CNICm/eRFKeDxwhwGqC9lCg/QWQ0rhOySyr5+mBM889/ylviO9U0m1WYIkrZRj4mbLCGRqele3NBhtqhrANuheTTLUunx+4Xjqnr0PrHrD3FZ93EsE3OK+efC6ksHe6Bkb4C/G5CrqlinkXsFam4djVHo3N4/LPfhAPtMggceouZaie/Edl4EL+JIx+L6/+ZH1rs2VGpNc3P6dkfhK1Sni+CXWXtdwglSeKz/dEEuFkyo68jT07x5ed7h3E+MZ9bvjvV5niM7JJKRr5/sO6+3JC7fj7D6hPaZyDuj8jCxEDBaG+7dq/1Suk+Tg5cXo2qTAamdmAubhiyUu19M/Iy8fgQo4wWxQdis0txl+WIJtL+t4qsks8UOPcLtqYGTPRzYMO5ZK0X8ENROfR2Mse1pgyrs7A00cfX0ZxJfg78umw4U/0dr00dfe3FzXsKap8ZzFXtxd26vpfA3tyQHC3qRbmlSu746Qxf/RtLbE4p5EQJ9RTP0STmV3BUMwD3vMZT4Sd4WfB75ZPIfp2JbPeLfFf2LG8U/1+j+uvJvR3QSHA0Rkv0XFUFIetEdHDYAxhGbSGAS8wNcKaPiwUTetnz26kWbrxNqRUdaNCPE5JShK+DGcYGCvF9G/sM3PIL3L4ep/nvcLTUjRej+7Ct/zeUq2QkfH8rG07/98rXMosqCU+/vMb53FIldrIiFOaOolyi900QsZV5A5yZ3seRN7eG8eS6IOZ+fZzUggr+fGAkXywZxIeLBvDstF7cMsQNG1MDFHIZD4334kBkduviAFfI+BZ6yRoiSRJb0iyQI2FY2Mr3IeGouFklHq97yM/JgiqVhsS8Bo3vpdkikuo/HxT6aJDxQuY05JoqCP6zxd1HZ5VQpdI0Ejip5ZGJ3kRmFLcefe+u1PTgYO4Eg++GyW/AmVWQeIyCIU8So3GlLK1lRaOsYiX+sppIdUnzm7VCLqOHrQlxOWXCkCjNFN9LPcO6bSIyimuu+TIynSa2Wj649nQSZxLyeX9Bfw48N6FRmcYEP3v05DKOhsZAyHrRg6JvVP9i95GQfKY9n0qXsnxMTyIzijmjbbBq+CZx/Rx4x+Xt3N5PSCPvf5Oiz0dQ/NkQVMlnecfkFW5/+A0sTQ3pPW4BroNnNnrZRD97jueZC2OzuEFPj0YtnBwLV3FeYw82zhJJkpCIvo4yOcHJhXy6P5q1TQOhCn2hUhe5vbEhHbtfDFsd/3xz+8vKA4qaZnJSxWgF3+kiwJAepH0hMfvAe0qrzuzByCxmfXmM8R8fQiGXcedIT/GErTdYuLLIJp69YZldK750BQzysGKcSQrVKIjQiKwtY54Rolr/PAA/T4fP/OHYJ6BvArtfEJLnWkpeF31/ipf/aSBAUpQGh94T14l7d4pr4R+3tLukdXdYJqGpRTw7vX4ezSQ/B/5+ZDSZxZU8tT6oVfXNn44lUKJUcSmzccJAo5E4E5/P4RaqHPZHZDHe1144s9eI7uXkXAmG5igVphhWNE/bSZKEUYWob+9vkEZUpvZMTmx2KQP0a272Dr3Fv0PvE3r3qee5c6QH0VmlnG1yAVepNRyJzunULE5L3DLEjYiM4mbGZGpBeeeWsdWmqa09KbAJwEOWjZt1vQNnb948k1OqVHH3z2fJKBQ3itySKkg6Ico73EeQWlDOEXUARgXRjRyYUbIQ3OU5vGf+Bi/22s3r0qPYF4aIko0aXK2M8XM055C2PoWEI2IY2/jnYfr/yFQ48ZLlQRzMhZFw9yhPojJLOJ/Ujh6KggSxr0aiA4UEuFlp3byfqyUPjvfiTEI+T+5IZ3nZo3iUh9Mn4ou2j3U1yY0VQ/ZyY6/ZIZ/ZEMyClScJTOp4qVhOqRJ7WSGGVjXR7T43Q0EissxQ3lvQn16O5qQVVjC8pw0bHxpFf7fmBnstCwa74mBuyKf7rt5Au7G+9kgSrarrBacUcqLYXvyRHdXyzmob1vPj6x7q7ST6dxqVrKVdEP+6DgZEhjGq3Jz9mqFI534SWVMt1Cq2aXNyxvnaM6W3A+/virzqg0klSepcg6XWMbFwEWIP45+Hh47A1LfxGHcHKQo35HnRLb48u6AEb1kakoldi9lXLzsz4nNKxWwQAIc+jZ6PyChmgp89MhkkmgUII7oFOeKIjBIC3Ky4fYQHJgoJIneIa1xxOhZG+gzvaYMm6A+R2WtYNgQiAp4TKQJG3ZgJvezxsjPlp2MJ5JUqG5cJh/0jgmZG1vx+OonXNl/k4d8DSc7rQK/Rwh8pWfwXoUVGhBoN48KcPbz09PNYmmgX8AFhvCVqan6HDee7lGYLx8fCRUg+V5dBdnj98yWZoKq4rjI50/s6sXS4O+/ujCSmaZDHf664xmQ1eI+xB4XRbWLTfGeW7qLkrLKBrVGcKpxCl8FI+iZo4rX05RRnCEVA32kAnE/M57E/LvD+rkjWnExkS1Aar2wK5b4157Ey1ufLJQM5/uLkuj5dZDLoOYF+ymBKlCqOdzf11XZibqTPPT0KSFL04M41oWIeoEIPFnwvnE5jG5jzOTwbKUamzP1SBGuPf95oP2VKFaGphWw4n8LW4DSRjdz+pOhRm/IWOPYRIhjZEbDlkRbvA7XklCh5fUsYk/zsGe/bOKPi72zx/+yddXhT9/fHXzdJ3d29paWU4u463LYBgxlT5u7bb+4bU7axjTFlMAYMZ7i7S1uou7s3Te7vj0/SNhUopcj45vU8e1gjNzfJzb2fc877vI/4PuLy+HTTOYPEsH59WVBeUxdEpzfsV0Rcx2s02mZHtWQWV3I6vZhR4Rewi78C3DhBDlBl7oJ1TV6TRtriSjVOslh4+dcmE51Z2mxWPy6njN42uSKqttO5qYSMFj/qYz8zOMQFX0dLfj9omN04nlpEcaW6vh8n7Qjs+LA+09iODA9zxcHShL+P1gcJGq3M9O/28976dvRhL0oWGR5bL3JlO+ykCnxs6w8XF+umQc7mqCyiMkv46e5egM59LXkveHQBMxvSCio5adodkCBuS93zLGLXUWkbwGZNd/46VUhlp5lIXWcJn/4GFpZDw1zYeT63aYbhzApxIfLoSmpRNd9VjaZX5d66SddDOrji42jBr/tbIfNL0lWZvMV7KKuuJS63jC4+9i0+5eVxHdn74nBOvTGaX958AuXEz+g86s6Lv9aVoroUlswSlqs/joCEi/cJXC5JeeXkJZ5khOlpHvj1qJDdXAJ5pVU4U4zKVhfkBA4FMzuIWoWLjRl/PdiP5Q/157MZXS/YwA+iX+PlcR3592w265opp7cHXvYWBLlYXVCytiU6G9ncHtnaTSxQm0OrgZh14v8L6qs9rjZm2FmYGAY5GcfEDAvd0EF9wP9L7Sikgnhh8NEMp9OL8XeyxNa8+YXgaxPCKSxX89W2KxcQx2SVcPvCgzzfnqYQDSs5etw6wcCnUJmYYuIWhrU6v0UpX3X2OUwlDVLwCJHYaCYLGuBiJWykc6JAoQLnDnX3lVXXkpxfQaSXHU5WZkSrdAFQavMVF/e0DXxZ8ADM7wufhsHS2cJg4KsesPUt7nY5x6CiVag7jG/q2OXTV7ftS+v523gmk8NXsD+tMQqFxN0D/NkanUmPd7bQ/e3N7IvPE7Kv9KMQcTPvrY/m/1ad4VBiAZujs9l8CYYySBI/pPtxn+YlQh9ewshenUWF/QL4O1th4qirEhQ1uAboA1tbL1ElAshtEBQX6iQ3/6FKDojfs5e9BY8vOUF1bYOkQuAwMLWuT6rU1kDCDgge2ex2clW6xWjDoL04TQQ/KlNiTcKIOdKMbbr+2q7b7s/7ktgVm8u6U5m8seYsTy49wYpj6bw5qROL7+/D5K5eTYPUwCGYFZ4nwraiefXGfwTT7JN4d+qPo5Ups388KAJ65xB44gTMWgI959QHmD3uFj14hxYYnItiskqQZRGAvLLyNGXLHxNW35Png6UjNbVaPo+x4U+vl4Qbnn5QbTPIssyLy08hyzIf3hzZbE/M0FBXnh7ZgW92xBP5xibGfL6LXu9uIeTVDTy55Dgf/3sOjVamT4BjE1Mg/Xyj3NJqcrMzxLGjC7q2RGWjVEjt2rfeGm6oIEdj5Y6bVEhOo8V3dkk1blIRANbqPLTl+c1KrWJzSglXZYgTnkL30ShVYihp9BoUWjWz+/iy8UymwQJ/S1Q2dhYmdPOxFyeOFffDjvfEAKiNL1/cTz5uC3zZrVUXMFOVgsldvVh1IqPOwnZ/fD4ZxVWM7uR+kWe3jq3R2URHnxEnf6UJ6bVCy++hrI/Om6vkJOdXYG9pQt9ARyxMlOSWVAntqV9/ANIKK7F2dBOZaP2JsLYGYtZjETmVDU8O4dXxHXl8RLBwMavIExdGHcNDXSmsUHOioX9+bbVYJHaaigz8uDuBNYphYGYtpCsI2cld/fxZdypDZEIuRMIOoc+2EhmOgwn5yDL08ne46Odma24ipq73uBt8el308VcEWRbD2orT4O71onfg92nNN5w2pKoY/roLkve36WWXHk7hS9Nv+Eb6kG6qBJ5ZdhF70UaUFhdiLqmF4QOAyhTCxomTdhv6RSZ39WRkR1f+b9WZi+qL28qgEBd2nc9tsYK6NTqHoaGuSC5hLVdyUg+K4zxwqHAg0r1XSZIIdbdpVMk5Kn47ugvT1pgcwtxt2K8Np9wuuO54b8zp9JJmqzh6/J2teGBwIAv3JLS7VXtURgnPLjvJuC92k1pQybjO7WgRWpopFm1mzQe9XsFdAChMjWr2fpM83e3Bo+q314hAZytyS6upyTgNTiHiuNShH0cQ7mmLu50Z8VXW4OAvjHAaUZZwiNdqvkC2cgX/AaIh+4Ed8Mw5IbXb9xWjTzyOv5TFae+ZTXfWOURkfi+hL2fD6Uzm/n6MexYdvuSkQ5upLuP2vC+Is7qfzd320MPTgscWH6fkyF+gMmdZeSQL9yTyyriObH56CKFuNi32yDZHSZWaRfuSuK23b53LZ2voG+pNPvZoGxoT6aVrtl5gYS+cWvMaVH8LRJAj2/u1+nUuxNmM4jqjiiuJpamKz2d25VxWCQt21leHMTEXSdvo1eLv1IOi51h//DeguFLNrGW6ILC4YZCTXheAx6udUJSmN006puwXg3KtXdFqZfbF5zOrjy97XxxO/LvjOP3GaI69Noq7+vu33HgeMBiA212TW2Xwcl1SXQp55zH368nv9/bBVKVg1o8HLvxb7PeYkMaerJcfn80oQamQ+OXOrryq+h3rqMVUjpkHoWNILahg+oL9fLUtjk/SOjG/djLanR+3OFtrwa4Etsbk8P60zrjamjf7GBAjTBbe1ZMHhwTSzdeeGT19eHx4CLtj8/jzUAq39falq499k0qOcKKUuVW5A4cfesDnEfC+F2x/jx3ncunh54CDlWmzr3mluKGCHIWtB25SYZOBoNklVbhSiNpG6CLDFKnEZBqWcmVZJj6nHD9NCrh0NNxwp2liERi/jVt7+iBJEot11Zzskip+O5DM1G5eqJQKOPKTODneqbNjPPgdLL+35Qa9/Hj4+x4R8S6e3ip50S09vCkor6mb27PsaCp+TpatWoi3hh3nckmKj6bCygtZltmTJXS1qor6k42LjRl5ZTUGFbGUggp8HS2RJAkXGzPUufFQli2cj4C0ogoheQseKaoLGrWQmlUXQ/hkLEyV3DcoED8nK5G5NLc3GNLW3c8BG3MVn285z6oTQnZYHbMJqouRO03l3XXR/LI/mftGdEHqdZ9Y9B0ThhRzBgQwIdKTp/86ycYzmUL6s2xOXbUnt7SaeZti0CbsEAtOHbvO5+JhZ94utuBXBb0V6OSvxGLq9hVi4XWhOQm1NWLoYNQ/sPODS35JtUZL3JFNdJSSkMzt+UQxn7i07CbV0iNJBdz3y5Fmg4KaYl1W3rpBlid8spBXZBy75H2SJIl3pnSmRqPlrTVnL/6ENjA2wp2c0mr2xjet5qQVVhCTVcqIjq7g2rHlSk70WrB0hi6zxO+ggRwp1M2Gc3rJiSyLY9ZTSNVE/1MJd/f3RyFJnPS5Q0x1TzM09VBrtERnltD5AkEOwP2DA5GQ2rU358Xlpxj35W52ns/lhTFhbH56MGMi2icRAwhZjE3L2+vSVTQ9x0cdbfZ+2+JzFCodwT1Ct72m7z3QRfzu1RlnhCykAdGZJSgk6OBmg5uNuRg47duvae9MWQ4mf9/JedmL3El/wPhPRZO3ZzeRxR37IbycifaxE9zKx+yoDG66s5IkZsu0si/neEohTy49wciOrlibq3hy6Ykrb8dbkADf9kdxcjHKjuMIOfc9v1Y+yizWk3/gd/Ype/LCmgRm9vLh3oGiOhLmYdOifLw5ftufTJVawwODAy9p1yZ28SBZ60xOaoMgpiQDFCZgpZOyOXdoUsmptXAm8v39HG2N1PkCaLQyD/52lIf/OHpV3FEjve25d2AAX2+PE3JLPR0niqpk0l6RaLRwFCY7jfj7aBoJ1bbUygrizuuSAbKsq+R4AxBf44CbnM/pxgOMC5PAWRzDUZklFJTXMChYfMYKhYSNuQlWZhcxnrD1BKcQBqiiSMqvILlhb+J/hcyTgAye3XC3M2fxfaIaO/WbvYaJ2oY4B4v+sH1f16lYzqQVMcYxC9e/xjNDu46P5TuYczKMhXsSuenzXeSUVLH0gb7sfXE4lf2fJVHrSvnyx5oMcv1xdwIfbIjhgcGBF51Ho1BIjOjoxqPDQ3h/WiTP3hTKEyND2PHcUN6b2pmnRnbAy8GCrJIqA+VUWkEF88x+5GOT74l3HAIz/oDIGbDzQ1wythHheeHr0JXghgpyTB28cJMKmwwEzSmtxk0qhIDByJKSUCmVlEbRdH55DdVqNY4VifWlaz0eXcAxCM6uwNHKlFm9ffl6eyz74/P5aOM5TJQKMQC0skgsErvcJhbKw14WDeoxa+GHYfDzBFg0HtY+Dfu/gUM/CFmRhQM8clD8+/vUlpv5dER42TEg2Il310WTUVTJxjNZ3NLdu93s+F4aF0agKo+9eVZ8tvk86xN1B3FZvazAxcaMGo2WkspacVLb+yWp+eUGs3RMC3UXDJ0TUVphJd4OliJzVF0COz6AU0tFg6NHF8OdUKpE1unchrqbTJQKnh0dSkJuOU8sOcGkz7dx4q93SJA96bogkx/3JPLahHAeGhokPvuut8HqR2HPZygVEvOmd2FURzfeXbIN7eIZohH2+6GURW/hzp8OsWn7dhQVeRS59697zd2xeQwOcbkqVoetorYGTv8tqi6/32LoCpVyUAQzfR4SA91AZJ6HvyYqVPHbmm5PlmH1Y8jJ+zlkP048rkFvSGvYFpPDtJo1VNsGwF1rsKnO4gn5d/IbVVD2xuWzJTqb8zlNDQFkvQVmwyAnaISQIW55o03VHHc7cz69tQv3DLwycpPeAY4EOluxpBkjkG0xOSgVEkM6uIjzSWFyUzmULEPMGlGx0i0KGn72oe42JOXr5noVJokhfF496rYvSTAq3A1vB0u2mg4Xi7Stbxm8RGx2GTW12osGOXYWJgwLc7mk4asX4mRqEUsOp/LMqA7sfWE4Dw4JElXO9qQ0UzirtYCLiyt5kiOFKc0Huc4V8eRYBNVZxTdXyQlysUJCi1lBTLP9OEEu1pibKHGzMxfXHd++kH3GsI/hwDdINaU8VPsMQZ4tuAopVSicArDy62o4FLQhvn10DmEXtnhVa7Q89udxwtxt+Oq27nw2oyvHUgpZsOvSfteXzO5PxZDauXvEdW/uXpRe3Xha+zMB2hRO2g3nrckRvD0lou582tHdlvPZZa0KwDRamV/3JzGlq1d9/0Yr6e7rQJGpJ+VZDQxAStLB1qNeteESalDJ0eYnEKt2prS6luXHLj6E9EJsjsomrbCSpPwKtp9rYf5VO/PUqA64WJvx8srT9Qmn0LHiHPLbVDjxBwQNF/1sDdBqZX7bn8SoTl4UKJ05cuqUOAdVFooeJVsvSqrUJNfa4yCVsT+6kTlBYZJIrCGun2YqBT3bkoANHIJXwSEUkvzfrObY+8Kot+vOGz6Olqx8eABe9hbM/H4/i/YmNt/c3/9xIZX8dTIsv58nom9jftlTUFOBdM8mBtz5BkeTC3l7bRSTu3qy4YnB9PR3xNxEyRM3RTDf8mGsco8jH/oBgCq1hvc3RPPOumjuGRDAS2PDxHe057NLvq7amJswq48vdpYmeDtYoJUxKCrYJ61nmrSdH2wf4XPb56DjBBg/D23gCJ6v/pJwm6sfrLY5yMnKyqKsTGQI0tLSWL58OTExF2iuvQqYO3rjShHZxYaLieySKjykQkycApCcQ+hmlkFqoeFj0gor8ZZyUWqrRea1IZIkFo0x60BdycvjOtLd14EHfj3C8mNpPD2qgyjB7Z8v3FmGv1r/3E5TxABTG0/hSmLpKNyUNr8GG54XJ46Zi4WjyO3LxWTc74eJScEXGPL0/tRIiirUzPh+PzUaLdN6eF/ux1eHpamKIJMCzlbY8+W2OGYM7QZIhkGOtZAK5OdliQnGm1+juCAbX32QY22GaVm60LHbeKDRymQUVYpKjlcP6P2g+JGdXiYy9s0FEaFjhTtbg4XfXf392fvicI68PIz9oUvpqYzjTJdXmDMwgIV39azLEKI0gUlfi6FzW9+CkgxUSgVvjQ9kvuITKmtlmLsXjVtnLJbeilPhCT7pUUANKmZshPyyalILKkjIK2dQh6tnd3hR1j4lKoP58ZB1ChaNgZxoEZz8PUcEi6PeIiG3jPt+OcLu2Fzx+Xr1gM2vN10gRf0Dp5aw0vs57s66hQrJEo792urdqdVoWbZlL6OVRzEb8DC4hVPQ6U7GKQ81SSRk6ZIPBxOa9ghIFboLv3WDpkSVqRigl7irvm8FxHs49quYe5EXK/6WZfG4pXfA2ZV1Dx3dyZ3IFkwjLhdJkpjRy4dNZ7OauKxtjc6hh58D9pamusqwLOYrNaQ4VRh8BI+q67NpHOTIsghU6qpZOtOBHedy6Opjj5O1Gf7OViTkV8GwV0RlNGFH3Tb0pgOdLhLkAEzp6kVMVmnz83kukS+3xuLnZMlDQ4MwVV2hXFpp1gUrOQBlNoGoCuKaXUz4qBMpsQ0FM1swsWq2kmNvaUqEZTEqTWXdTA89sdlldNAZRLjZmIvj26cvIBtKjxN3c86yB5Yufhd1Ferm48CJ1KLmFz8+faG2kpKE+mpdWTOzKtaeyiCtsJJ3poh+lb6BTkyM9GTtFepPA4TE5vTf0OteIa0DYd4z4zekJ07C5Pk89OAT3N7XDxNl/fEQ5mHT1EWwBfbE5ZFdUs3M3j6XvHuSJOHoHYJNVYPh0MXpQqqmxzlUqDB07lZ5qeeIrnamT4AjG89ktRiIRWWUXHQO28/7EunibUcnT1sW7U265P1vC5amKt6ZEsGBhIL6QeYmFkLG3GmqGBHR4aYmz9sZm0tSfgV3D/DHyi0Am6pMoVxpMCMnq7iKDFkMQj0X22Ddp64SvyNdkLM3Lo9eugX4JRMwBEVJKmM8q9h5/j9oPmDvCwMeNxgk62JjxpIH+jG5ixdvroli8vw97IvLM/wN+/YRiUmVGdqCBA7WBrM5cp5IHvj0on+QM4vv78vyh/rz/rRIg34mE6WC0RNuZWntUKSNL5D19U28+elnLNtzhmdGdeC1CR2RAFY/LpKHzQ3AbSVe9pZ0kpKwXzwWtrwJ5fmMS/uc0xa9SQ+axRm9OZZCQfLgT9GgYGT0axc1Rmhv2nT1WbBgAcHBwXTo0IHVq1fTu3dvFixYwLBhw/j555/beRdbj8LWAxNJQ0mBYcN/YVERtlK5uCC6htNRmWY4xRohL+kg6bI1LmFNNx4xTehXYzdjqlKw4I4euNiYEeZuw+w+OpOCsyvEyaNx02jIKJj9F0z/RczzeewI/F8+vF4Iz56vv3g6BsBDe8XC7tivcPTnFt+rr5Mlz48JJbWgkgFBzu1rXV1diqq6kF7dunJ7X1+eGh0uelQaVXKUaHBc/6BYcAOU5dQHOTZmWFXq7DkVSnJKq1BrZFHJUShg3Efw6GEY8AT0mdv8fgSPEHKC5feLPpN1z4gq2N/34rx4NE7JG1De8iOTpt3OkyM7MKJjI9cOSYIhz4tt6Cx2XU9+S0dFKq+ZvwzuEXzj+RFRWj9+sF1IRNl+tF69yaxQ8O66aHbH5gnH6ODrJMhJ2gMnfoexH4vhavf8C7IWvukrsj4At/6MRmHCM8tOsuNcDncsPMQjfx6neujrQqf7cbD4LAuToKoENrxIjsdQnj7fiR4h3qxQ90N95LdWT27/Zkc8w/L+QDaxEpUzwMorHBepmPRcwxOovsLa2J0QwLQyHy1KIZ9oSNh4CBgCm14RrkDZZ+Gnm4TV5soH4Oue8LYLfOgPv0yE8/+KE+5VOpFO6+6NLFO/iEC44eyPz2dkR11VSr/oa2AsAIjgFIRcysJB/NcgyNEbLMRklQipmo1n3aI+KrOEbj4iOxrobEVSfgV0nCSC3B31jadnM4rxdbTEzqJl9yk9w8JcsTFX8c/F+tYuwqm0IrbG5PDosGAh4b1SlGZdsJIDYOXZEV9tGs8uO2ng7FZTVog7+dQ4hYnzhK1Hiw5rfa1115NGQU5yQQV+uvOdu50ZRRVqquyDxPeo752pLoOM4+zTdKSjh+Gcoubo7mcvzE6aGVi9NMOZXNmOM78+zahPttLznS1EvP4v9/5yhOIKkbyQz/6DeuP/8Z3zMjpL9dLnXgGOnMsquXIOesd+FfKYHnOa3mfvKxqqm7ET1n8mUTr5uCzLHEjI56Hfj/JXowrp8qNp+DtZ0t23bbLskNBOuEpF/HNI9zssyair4m2Jyub9w1rhtlYQz8YzWZgUJ2PnGcKLY8MoKK/hQDPJGVmWmb5gP19fwLQjOrOEAwkFzBkQwJwBAeyJy2uXREJrGBbmyuhwN95bH0OJfsijiblw+Lp/G0Tc0uQ5v+xLIszdhj4Bjli5BBBiViiSJXpHVDsvMooqyZDFdbEoM6H+uCpOBWRw8KdKreFQUgEDQ9p4/dTJ3Kc6JLE/Pq+uB/m/joWpkg9vieTvuf3QamHWjweZseAAJxtK2AY/C7cvJ3r8Cp6umYt1lykGlvK9/B3p4df87+CmTu785fEMj9c8QklOKu9Xvc0xk/t4LOkRpJIM0QKgH1pc0qhCqdVAzPoLV3gSdsDxPwg48yUrTP8PqSIP9n4BX3bFQlvG1sDn6eRlR2pBJcWV4piLq7DkGfVD2Gfvh31fXvqHdhm06Qr09ddfExsby/bt27n99tvZvHkzmzZt4vDhw8ybN6+997H16C54NQWGF+lq/YBQWw9wC8dPk0xaQdNKTrgqXSzW7JrJFLl2FGXH038BIsO39vGBLJvbT1zIc89DfpyQnlwOShPo/6jIvh+Yf0HTgrv6+XPfwACeHt2hxce0CZ199IAe3XlnSmcUCklk2BsFOdOUu7HL2gfjPgbAVSoyCHLsa7LEBQ7qgsqGNtQ4BcGot+o0vk0wt4PhrwgTgawzwsQgZb84kTqFwPRfRVB5ISwchA75+O8ia3HgW1IDZ7Ai240DCfl8tyeZXZ3exqI8HZJ2Yx46nOfHhLHieDo/7E4g0tteZOOvNbU1IsDz6iGypSCC4nu3iIrVg7vhydNg78tPexI5nlLEr/f25uNbItl8Npt5ca7CXrfHXWKy9zf94c+ZyFXF3J11C8PD3Fh4Vy82W4zFpCrPoBeqMTmlVRxPKeTfs1mkbP+J2cqtqIa9WNcAbuEqqhIlmYaLet/cnWwzfZojCTkGmatajRYrdT5VZo710hE9kgRjPhAL2m/7i//KcmHORng2DmYvFz0OvR+AWX+JamhhYvPSvCuAi40Zo8LdWHwopW4R/dOeRGo0Wkbqg25LJ1EtyG8kF8qJEpVbe3/xt2OQQZBjZ2GCh505sTllIsjRVXEqampJK6wkxE30i/g7WZJaUEGtjKiQpuyrW5BEZ5YS3orFNYC5iZKxEe6sPpHR5r6B7JIq3lwThZ+TJVO7eV38CW1Fli8qVwNwCYgkQJnDptOp3PLdPvbG5VFcqeaDP0Rl0NlPJ0Gz8YDS5oOczqo0UeFscF2oqKklt7QaPydxvnPTNfHmlKpFxUXv0ph6AGQNa0uCWhXkdPWxR5LgWIphD8iyI6m8uPo8/wS8QT9lNC9YrWV6T29eHd+Rw0kFTPx6D5tOpaBZMZcRVZsYVblRDC/V0c3HHq0Mp9LaNsfqgmg1cHihUC3YXJo9rLO1Gc7WZsRklqDRyty16DAzvz/A0eRCnl9+ipdXnqa6VkNplZp/z2Yx7TJk2VZuQQDsOHhEuEKVZKC29uStNVHc9+sRdhaKBMvbP//Dc7/vxkEqpX+vXnT1scfL3oK1p5oeHwXlNZRV17I1umUJ2i/7knC1MWNcZw8mdvHA2dqUBY2GFV9oJsnl8tqEcMqq1Xy2uUElWZLEtaTR+XbZkVR2nMvlvkGB4nO298FdzhXX7+J0MQ7C2p3M4ioyZfF5ucp5HEzUDaLU9bji4M/hpAJqarVtTxJaOoJLR7pL0ZTXaC67L+p6o6e/I2sfG8iCO3pQVFnD1G/28uaasyTmldddH/VWzOGerTuHg6hafjGrJ+NmPYbTc0fhoX0w8UvR971wFGx4URhDgMEoD0AEMEtuqx9X0Ji8OJFQXfUwpns/4R9pOD93WQL3/Its78sHmtuxdg+qM7rRG20k5ZVzUNENuc/DsO3tlrd/BWhTkKNSqfDw8CA0NBRHR0c6dRIfmLe3N0rl1Rvy0wRbccHTNsrIaYt1ZXobD3ANx1xbQW2BoY40rbCCSLNMpIbOao3pchuc2wjlonRqaarCRm/Lem4dKM2ExrU96P+YCDb0TijNoFBIvDohvM2ZrRbRz8hp6Cpj7SrmCuiwNVcRpkynzNxdOAUBLtQHOa42Zrhps9HYigBGby3o5XCJFaeBTwkThwd3wsP7xX/3boJbF0H4pNZto/sdYuG77G6orcZzwkvYmKl48LejSJLEzPGjRQ8PQNBwZvX2pZuvPYl55QxpaxaqvdBq4PwmYUqRHyt89RtqqG3cxPvziASFkoTcMj7ZdI47+/nRP8iZW3v6MHdoEAt3JxIjBYig8tHDEDEVkveyw30OCbXOvD+tM6YqBf0GDuec1ofKU/802ZW4g+vZ9tndxH48gtwfbibmz5d4T/UDmohbod8jdY+TdMdNdW6SwfO9KqIIVGThUhErbHl1FJTX4EQxtRYtfNZu4SKAu3u9CGzn7ga/fmDtAiEjRdA3/BUhvfAfKORhh39o80d+qdw/OJC0wkru+fkw/xxP59PN53l4aFBd0zqSJORoTSo5MULSoz/fOAbWV0V1BLlYk5hTKqSJugbh+Bzx2YXozDD8na2o1cpiIRI6VkhEo9cgyzLRmSWXdIGc1t2b9KJKnv375CVn/X8/kMzwT3aQkFvGO1MirmwVpyIftOqLytVwDkEha1gxw50qtZbZPx6k97tbKMoQmffQsEjxOFtPw/62BgTJKcRLvgaS2tQCkbTxdbQC6oOcrJIq8R2k7BMLgqS9aMwdOKP2aFWQY2NuQgdXG441WNBlFlfywvJTTOvmzb13zkEa9AwjsxfxfKdS7hsUyJpHB2JvacLPS/5EpankHfu3UXS/XRwzOsLcbTA3UXA89QosFM9vFEMjez/Ypqd39LAhJquUjWey2HU+l09v7cKBl0bw9uRO/HU4lclf72Xe5vNU12ovL3DWnZdcNNmM+2wHmuJ0PjtYxq/7k3hpbBjrXr6FGpU1fto0FgwVx76FRxiSJDEh0oONZ7OajKfI1PUjnMsubWKnq+dwUgGjwt0wVSkwUyl5YkQIK46ns+RQCrUaLR9ujOHeXw5fMUMCH0dLHh0WzC/7koTpTgscTS7klZVnmNbdi5u76z5ne19sNYXkFBSK5KKNByhVZBZXYW1tg2zpRIhZcb2VfmGSGD1h58OBhHwcLE1anWRpFr/+OOYdwcZcxf5mDF7+6ygUEjd1cmfd44N49qZQFh9MYdgnO+j+9ma+2hrL6bRifBwtWlWJb4i3gyVjIjxwsrEQFeged8F9m0WyrSRNVPIkZdMgR59ky27enY0zy8WIlecT4bU8fnF4nJRSLfj0Imf2Vn5Wj8TbwZJgV2tMVYq6eY4JeeX4O1khjXpDFAuW3VW3jr7StOkqpFAoOHv2LHv27KGiooJdu8RAqLNnz6K9yno7A3R6flW5YVZFWa6rQOiCHACXqgQDHW1aYSUhUnrTfpyGdJkJyKJZvjEx64XZgKnV5byDerx6gN8A2PdVm5quL4uilLpemjqs3Q3m/kiSRLAqjwITDzCzQa20wF1RhIeduNi72JjhJeVRbilOlmkFldhZmLQ4q+OK4j9YXOASd0LPOZg7ejM+0oPiSjUPDA7EydoM+j8hyvdePVAoJN6d0hl7S5N2s+VuM6sfh8W3is9+6vdNDRoaoNXKvLjiNM7WZrwwpl5y+fDQILwdLHhl5RnickopwRImzyf7nsM8mDSIewcG1C3Sbuvlyy66I8VvNXBnyT61Bb/1t9OpZA8+rk4M8FLymNk6VJ6dUU7+yrCnys4bLQqDmRTVtRrs1aJ5tJfynEFfjn4QqGzp0vLnYK2z3g2fLOxeW0KSoPd9QramzypeYbr7OrDo7l6cSC3SOVq58ezoRuYlTkFNDR1yogyb2R0DmzzG39mSmtxYIZX16AoIq3uAEFdROQtwFuecxLxykf0MGAJRq0grrKS0urZVi2s9fQOd+PDmzqw/ncnEr/eQ1CAYNdz3aPjrTtjwAqQe4lhyAa+tOsNNndzZ/uxQBoVc4LtsD0obJK4uhINY2IaaF7P5qcH8dm9vbu/rx8v9zEUfjs4qHlvPZo0HADxrEjmr9jJYhOrdnvSVHHfd7ye7pAoipwvZ5cFvIWkPOY49kVHQ0ePC8530dPO1N6jk7I7NQyvDy+PCRFV96EuiqnRcOEf6O1ux6pEBfNmzgAqVPXffPBnJo4tOkioWGSqlgkhve463ZGpwOVi7CbMT755tenqYu7CR/m5nPL39Hbm5hzcKhcQd/fxZ8XB/FJLEor1J9A10rDO2aRN23iApeH+4LZNCTFCiwcs3mC1PD+HBIUEolQpM3cK4M6Sa/tmLwa1znZvh+EgPiirUTaS2DS3XtzUzqFqjlUktqKz7jQLc3tePmb18ePWfM0z7dh8LdsbTo70TlY24f3AgIzu6Mff3Y8zfHtekj0ut0fLwH0cJ97Tlvamd66tluuqlsiwdTVG9s1pmUSXuduZItl50si6trxAWJonHKE1IyqsgxNVGHLNtxa8/UmESN/lo2Z+Q3/bt6KhSa6isaceBxO2EiVLBw0ODOfjyCH66uyfjIz2Yt+U8vx1IppNHOzmS2XmLQOfB3SIpautlaA8O9dfsrDNNny/LcOZvCB0nrjMKJd4OFnXBvT6R7e1ggYlSQbiHbV31LTGvTPwGVGaiZaO6TPTPtlIWfzm0Kch5++23GThwIJMmTWLx4sW8/vrrhIWF0a9fP1555ZX23sfWozKj0sQes8p6+1pZljGvykEjmQjpkp0PsqTER8o18PhOK6jAQ51qMOytCdau0GGMsCVueJIoy4G0w5cvVWtM/8dEw/FPN4mm8cZR95WiKEU3I6eBhrpRJQfAR5FLllIEAaUqRwLMy+uyt27mtThKZRSZiPuFs1o79g1dCgqFyGSYWIrKEMJSelS4W71RgUJR51wFojx8/LVRF5wvclXo8wDcu1lUsCJvveBDlxxO5VBiAe9N62xg0WluouSdKZ05nlLIyHm7iHxjE3csPMhzW4qwNFXx4JCgusfaWZpQ6jsU89qSOjtiTV48livv5owiFPNnTuL76Gqs5m5G8VIqins3iWbWhihNKDV1xay8/njNKdE5HALDLRLr5Q26+5ylYpS27RRQRs4Q81MOft8+22sFA4JFM+jsPr58PrNr0wt74yqNVgO55wz7/xwDhYNaAxvpAGdrHIr1vTui6hCbUyaGheoaTr3sLTBRSiLIAREIphwgLj5W/HkJlRyAGb18Wf3oQDRamRnf7zfsDylOg61vw3eDIP24yOwtHMWeJR8T5m7LR7dEXh15Z3ODQJtD31henIokSQwKceG1CeE4qzNFAKRfzNl4im02sl1Fq8GuMpVYrTt55fXmEikFFZgqFXXJAXtLE0xVChHkmFiIIX8nFkPGMaLNInG0Mq0za7kY3X0diM8tp6hCLAD2xeUR5m4jkjEgzsth44WkVLe/kiThnL0Hy7CRdPF1rE+GNJiX0d3XgeMphc0Owr4svHvC2A+aN49pBR09bMksruJ0ejFzhxpaQ0d627P60QF8MK0zr00Ib2ELrURpArZeWJan8fYwEVTMHtUP/wYBCC6hwlo5cZfoF9W9p3APW5QKqUmvVGZxFUqFRFcf+7rhvIb3V1Kj0eLvVP8akiTx1uQIevo7kFFUxe/39uGxESGXFwxcBDOVku9u78HcIUF8/O+5Jg5vJ1OLyC6p5rUJHQ1NAnRyc1+y0WTH1PUbZxZX4WFnAXbeeJBPpj7Ya+CsllpYgbfjZV7zffsBMN4uiROpRRcNUPbF5TWbmFlyKIXxX+4m4vV/6fT6RoZ/uoMnlxxnyaEUTqQWEZ1ZQlxOKXE5ZS0ndq4C9pamDA9z450pnfnjvj4Euli17/BMM5t6y3w776ZrykJdkJPdjCNl9llhnqN3bkUoc/TraH1Lgo+DSEQMDnFmT1wetRotSXkV9b8zB38R6KQdEr21jc+57Uybgpxx48ZRUFBAXl4eI0eOZMuWLfzxxx/ExcUxffr09t7HS6LGwhVHbQGlOqlFUYUaJ7mAagtXccJSqtBYe+At5dY5rMmyTHVRBqZy9cWnG3e7Q8y8aKgp1Nscdxjbvm+mwxiY+IXw8T+6COb3EbbTV7paVpJelwGtQ9+To79AyjLummxStSJjm4cDPqb18w7ctOIkmqsU1bW6GTnXiv5PwOPH6xZFoe42/HBnzwv69V8XttEeXcCnt8ECIrO4kqeXnmDEpzs4oMtunUkv5v310Uzr5iVsixsxMMSZXc8PY/H9fXhnSgT5ZTXsOp/LY8NDmlTXArsNp0S2pOT0OtCoKVw0nQKtBepbfsHOusGCQGVm4BzTkEorLxxqMuvkHVklVXhIIgvahRgOxufXLbbOZZfiKhVj6dhOgyLNbKDvXCFZu0Q77Muhq489707tjHVzx5RjkBj6qbcWLkgETXXTSo7+Ph2BzlaEkUStlVtdv0NsdmldPw6ILL2Po2W9Q1XYBJAUyNFrsDVX4WnX8tC3lujgZsPSB/phbabis+++ZcO8+4n/oD981kk0jvZ9SNjePx1Dhn1PupXv5r2pV1ii1pC6Ss5FghyVmTh3NXcx1y3GACF1ljVNEjmUZKDQqkmR3cgsqne7TM4XCzilbmEqSRJutmb14wt63Q+aGtDWclAbToCzVavPJ30CRa/D1mjRu7Y3Pr9pX0PYeOGOpZ+LVJolrKuDR4i/XcKE4Upm/VDebr725JXVNDHdudaEuYsgPNTNhmGhTRd0KqWCmb196dQeMzbs/cR3nycSAE1Mgpw7iCSDna9Bv6dKqcDDzrwuW60no7gSN11f3r74PANzCxDHCYiKbENMVQp+v7cPe14YRv+rZGyjUEi8MCaUQBcrVh43lPTvicvD2kxFl8ZOlLokwacm32JScA7CpwDifXvamYOdN46aHLJKqoT7XGGSsP0HUgsq6ha8bcbOC+z96KKNQq2ROZLcjBuYVgu7Pub84S3cvvAgE7/aw7aY+v7h4ymFvLzyNE7WZrw6viNvTY6gX6AT57PLeGnlaabM38vYL3Yzct4uRs7byfQFbRuI3d70D3Jm2zNDmd7r0t0EW0WzQU6S+Df7bFMF0Zm/wcyu/hyDSLBlFFWh1cmlbcxU2FqI69+QUBdKq2rZF59PVkkVgQ2TCf4DRS/xqaWw4oGL2uJfDheZyNQykiTVnbSVSiU9evS4yDOuDgo7L9wKCzmWXMjQUFfSiypxkwrRWNVfDJWOfvgU59Wd7PPLa3CpzQYlhn0ozRE8Uki3Dn4L3j+KL+fQD2I+wiU2XV4USYIed4v/Kgth02uw/lkhJevZjItNe3HLIlA3mulh7So08JWFolRZWYilXE5crThBZ2ns8FbVN7Xa14hMazrO9EBE+SMbu59dTZSqiy+I/gP8ezaLJ5Ycx8JEiZeDBbf9cIDuvg4cTS7Ez8nygtlObwdLvB0s6R/kzOw+viTmlRvIKPQM6+jF7n8i6RvzLyUWDjiWxbOy4w/c36n1BheynS9e+SfJLKrC18mSrOIqwqQCat27YZt1HFV1GulFYm5SdEYx90rFKNrz9zPgSVFx3fx/MOP39ttuW3HSVcsKEsQQSP1w0Iby2IY20jqTgQBnK1RSIsV2YTjpHhabU9ZkMRjgZFVfybFygoBBBKavprPH4DYH6+525qwYWY7dyncoLnPgFMEsUT7MvQ88jrubBzW1Wr7aGosmL5gnTVZi6nmZC5pLoSJfDAtuXEVsjpYu5qENklI2+lk5GXW9neJxIuBMkt3JLK6ki489ICo5fo2kU+625mSV6Ko9th4i4xm3lf2lrgS7t/6z8XOyok+AI38eSqGztx25pdUMaLwQ9u0nlAnn1gnLWb3Rhr4nVGUqjq3M+r6cbrp9P5ZSeHmyr3Ym2NUaX0dLnhoVcuUTSw5+ELcV4reLz9CqUVCln5HX/9EmbnDeDhZNAsTMoio87C0YFurKx/+eY398PsMaZN6T8suRJHHubYxKqaC9R0ddDEmSmNzFi+92xlNRU4ulqXiPe+Py6Bvo2DRJYWKObOuFVFzMjt7fMSx8ErIsk1VchbudBai8sK7KRivLZBVX4l2YBBE3U1ZdS2GFun2OM7/+OGQewcFyIvvi85tKYXd+ADs/xBtzJru8S6FDJPf+coQHBgcyt5PM6sUrudPRnFemRGDiaLi+K6qoIaWggppaLWqNWNSbqq6D5ObVwM4bknYb3laULGTRmSeElE1XyRNSteXCxElVX5H2drCgRqMlt6yatMIKvBws6n7DXbztsTFX8cu+JADDiikIN1aliQhyasrg5oXCZKqduaGGgQJYO3vjrSpmU5SI5DdHZeOpKMLCqT5jI9n74q/Mrzth6WfkAE0rGI1RqoQt8ellYi7Avq9EBm30O1fk/dRh4QCTv4bAYXByyZV9LUlq2lukDxD0Dms67ea5akdkWSa5xgYHuV5mY1KahgaJ5BoHwxk5RtpMlVrDG6vP0sPPgR3PDeOfhwfw9MgOyLLMB9M6s/mpIWJeUyuQJIlAF+tmFxV2liakOA3EqTQG8z0f8Lc0gumTplzSvpo5+xtUS/Pz87CRKlF2ngZAT+kcJ1NFUJyamYkJtU0XHJeDmTWMfB2i1wjr7WuNoy7I0UvWcqJFVkw/iBJE8sDc3mCejre9OZ0USaSaiQCzSq0hpaDCoJIDEOJmY2hLO+AJvKrjeb38PTGgsS1oarHb/Sb49sPutUSCn1jDGuVI7v4zljdWn2XM57v4Zkc8nt3GiCq4vqpwNRj4lLDfbw2NgxxNrbiAN67kQFPzAV1VLVPhRnqDSk5KQQV+TobnSFdbc7KKG3zW4z5GnrOBxIJKApyaJhMuxKw+vhxJLmTR3iRUConeAY2s1ZUqUemPXisWIHFbRQ9Jw0SOR6SB+YCrrTle9hZXpi/nMjBVKdj1/LCLTmFvF+z9oCxLNGPP+qupyVDAEDGjpPudTZ7q7WDZNMgprsTDzpyOHja42JhxINGwbyQ5vwJPO4u2zYm5Qkzq6kmlWsNm3RqpvLqW4ylF9A9qvqIkzV7GbJN5HFUICWRJZS0VNRo87UUlR6mpxJ4ycrIzxGLVwZ9UnXutT3tc8/36I+VEMdzPhP3xjfpyzv4DOz9kj9tsYrVefFL9FgtvMufx4SHs2rsHk4XDeL3qI94ofwuTr7rC0tsh5UDd0+0tTYn0tqenvyP9gpzoF+RED79Gv7UbFTtvUQHW98VUFgqlQdh48XdDyVpBgmhj6DjBYBP64D2tsLJ+2LsOlVLBoBBntumkkc0lVOl8C8z8Q8hDfxojHODamRsuyJFsPPBSFrE5KhuNVmb1yQz8TUtQNSxL2/viJeXW/RDTCivwkXLRmtqIRcbF6HmP0L2veRJ2fCCkG21surxkOt8qbEn12smrhX5Ioz7I0ZU1T5c78uPuRDI1tlirG5yAilLIl5zIrqglu6TBjBwjbebX/Ulkl1TxxsRO2FmYoFIqeGxECCseHsDM3r7tOnTRIVJkucs0Kkr6v2wwcKw12LgH4SSVkpEjHFQq88XJS/LsBo6BDDKP40RqIVVqDaV5OumEdTsGOQCRM0VW6uB37bvdtmDpKIIavXwuJ0pk2hsGmZIEbhEGFxdVWQaOUhnR+AMQn1uGLNebDugJ97Qlp7Sa3FJRSSj2HMT9NU8RVHYU/pzZtgbPYz+LCfA3vQuShIedBT/d3Yu8sho2R2UT4WXH8of6c/uUieK96WcvXC0aZBQviJ2PcP/SU5IO2tpG7pFuokLeuBG3IAFsPHGws63rO9BoZXHNaJSl9neyJDGvQQXc3I4CCz9Kq2rxa+4CfwHGRLjjYGnCn4dS6Opj37ysNmy8cOxbdjdErYIOow3v9+gq+r4aBLkDgp1YfTKDgvIr3/B7XRJxsxgQfftyMG+mV83UUswoaaZCKCo5jeRqRVV42ovsdaibTZ3zoZ6kvPI6c4rrhQBnKyK97Vh9Qpx3DyUWUKuVW55n49YJM0fvuveeoQvk9T05AJ5SPqUZOglgwyCnPSo5/oNAUvJG1sMMyfyJskJdP170Glj5IKnuo7gjeSwnhvyI0t4X5aIxPOVylNXO31Bu5so/QzcJd84xH9TPWdPPVKupuPBr38jY+QByvXW+fk3pPwhMbUTyXo8+8eZqqBTRz2f8ZV8SZzNKmiSyh3RwQZbB2kyFs3ULCdjQsXDPRtGP+uNIMY6lHbnhghxsPbCqLaSwtJxf9yeRmFeGo1xgmOGy88FOW0xugag8pBVW4q/MQ2rYiHohJAkmfSUWLtZuYtL41aLjRGFVfeZvw9urr/BwMf0CVK9ZL0xGNrFkYJcw3l0fTS72mKhL6y+oxakUmLiTW1pdl31prbuQkaYUV6qZvz2eW3v4EOJ25T/HQd078UPtON5VPsysoS07urWEibPQZZdmicpFbZEuk27rCT596aOK5URqEXE5ZThRJO5r7yBHoRANjjcvbN/ttgVJAqcG7mk5Mc07Obp1Msyg6XoqDlUJXba+8VlvH61Hb9ManSn64mIyS9ih7UbKTT+JSta6p1vv0lhbLTKk298TSZVGhhyHXh7B3heH8+Vt3ejqYy+qCv4DRDbuesTORwx+1De46h2EGlZyFEpwCobcGMPnFiaCYwCedhZ1dsGZxZWoNXITuVqIqw15ZdV1hgGAGNIKl1zJMVMpuaWHWEC22LMRNFwsRpL2wNAXYNCzhve7R4o+o+youpueHhWKulbLO+ui+J/EORjGvN8mWYy3gyWFFWrKdP2+Gq1MdklVnaNooIsVCbmGxgTJ+U0rftcDk7p4svN8Lnll1eyJy8PFxqzJOaUhDatYWbrfgYedeV3PToBpEdV5unObgz+phZWYKKU6Y47LwjEA5mxA9u3PXOVqlF904cDHU5GX3kGaU39uSrmdW3r4cufwbnDPBuFy+89cTCrzcHtgBVOG9hGyqz4PwqNH4JafxKy1xdN1Q6Qnwc6PxBw+ddXF9ubGQT+fsFg3V1J/XnQMENehhg5rebGgMm8yQ9Le0gRvBws2RWXhamPG6HBDyflgXX/wRXsSPbrAfVtF4uHncc0bH7SRGzDI8UJCppNFIR9ujMHHUoOqtsLQalSnM9QWiuxeWmEFQSb5SA0vehfD3A7u3w73bbkiOsKWX9cWQscIqZweWYbvh4lFyZXCzFYMLtS7GhUmIdn78dGtXRkU4kwe9uL2OjlbCqUWnuSWVvPPiXR6+jkYKzltJK2wgkf+OEaVWsNTo9p58GsLeNlbkND9ZYZOnlOn274kdL8xdX4SAEp9tsjWE3z74FWTRGJ6JqfTiwmW0pElxcX74dqCvW/rM/5XGscgIVerrRYzj1oKcgoThcUmQNYpKpU2HC4U55jz2aU4W5s1kSUGOFthbqIgShfkRGWWoFJIePYYB+PnCbvhHe9ffDZB1mmYp5tjYO8HI99s8pBmXaACBguHyZpr50zUInbeonLTqApdpzfX4xImgs+GFCSIIMe+3io1RRe4NM7QB+sWiQ0duPROTX7Ol37um93HDxtzVZOFQx2mVvDoIXjqDAx+TlQhGuLWCZAgq958wN3OnBfGhrHiWDq7zude8j79L6OXXundpPLKqqnVyqKigZhplazr7wBh6Z9cUI7/dVbJARHkmKkUjPl8N2tOZjAgyOmCi1BvB4s66XFGcaUoOtuai3WVpCDMogSpMElUdC0cSC2owNO+3pjjsvHtg83MH9k4chNHXSbTvXwPf2hHMzj5HnoGe/LeNJ3ttZmN6MEc+zHMWgrOIYbbUShFNe/h/TB3r5ivpjQRg3MXjYUPfMWwy2s5CuVqURfk6BKQhUkikLF2a5psyzsvkkCN5J2SJLHj2aFEvzWGjU8ObpKQ8bCzINzDlg6tSczaeohZeFau8OMoEXi2w/WkzcYD1y0+fUChYo5rPE8muXBrJwXEYFjJ0V3cbKszKasW08O9pFywH3Bpr2XldPHHXAk63yq0pdlnxcGYtFssmnz7XrnXlCSdjXSDnhwHP0xVCn64syeJZ2pg1Uei0uPgD0WpVNtFEpdRRll1LW9Ojrhy+3YDs/hgCu+si8LKTMU3s7vj3ganrLby/rTObX+yjScalEg6mZBpRRYVSlssTSzAqwcSMiGaeFad8GSWeSqSY4emi7QbDcdAMVF6x/sga8UcrMa46X4nOdHg0wsyT1Js35H0jCqq1BrOZ5c1m3FVKiTC3G3rJkwfTCgg3NMWM5VS2KcXxMPOD8V/Xj3g9hVN5w3VVMDf9wg3xztX1VuNtoaAISKQSNkvzFma4+jP4t8ed7d+u+1Bw4u5rae4mFu7Nz3eXMNFQ7osi/OdLENBEoRPwbPSnH26YYTJLUhxglyskSRhDNHTX+j6k/PLcbIybdN8MH9nK069PvrCGdCGPV2NMbMWi7yM4wY3z+rty6oT6by3PppBIc7Xh4vkfwBv3fedWlBBqLtN3YwcT3txTg5ysUajlUkpqCDY1Zqc0mqq1NrrspLjamvOxicH88GGGNadzjQwS2gOHwdLskuqqa7VkFlUhbO1mU4erQAbD7pqEvAuSAQnf5Ak0gorL99ZrRGSJDF1UDcY9D1o5nOrrKBrdhkd3GwwaWiYoFCI0QsX3pg4v7lHCKtwjVqYdCTvFa6FLQ2Ev5EwtxVBqV6iW5gsEluSJNaVRxeJa4KpJeTHNQ0YdVzMUfPnOb1aL6W3doE568U1cueH4prx8IHmpaWt5Mb7Ji3swbcfg2XRBDvN5qyw0nRvsGCz9UJGwkvKI62wgoyCMpw0OVcmk3wlCB4Flk4i+wBwZJGwbQwYemVf19rNQK6ml3uYmyjpGKyrMJRmCclaeQ5aWx9KqmpRSBITOl+FptIbEBmZyV292PL0EEZcS3e6S0WposzcHdOSVIor1VjX5FBpoUs0uIQhq8yJVCRwIKGASGVK3QyYGxonnY30ns/EQMfmggjXMEASemitBtKOoHHrgizDluhsdpzLoV9Q88mVcE9bojJLqFJr2BWby4iwBsfLqLeEhfqUb4Xmec3jTeVr/74sfte3/HRpAQ6IqpSVK8Ruaf5+WYa9Xxg0/V419BKLhhfz5qr2rmFQXVxvT12eBzWl4BiIh50FOaXV1NRqSc6vwM3WrEkzuYWpGI4Xm11fyUnMr7isnozLDkACBgtTggbftUIh8emtXVk0p5cxwLkE3GzMUCmkut6UzDrZlqjkBLqIYCZeJ1nTW7o3to++XvBxtGT+7O4ceGkEk7pcIFiGul6LjKIqMourDG3pbb0YVL4JB3UOjH4X0PU5X+6MnAuhNMFMpSTCy659elGVJuDdAwY8DqPfvvzt/VdoaMqiS1wDYr0sa+v7cvLOg1PzQc7FcLU1v7TZaRb2MPZDeOSQCEAvI8CBGzHIAQgdi0PuYbY91h3vxBWiscmygWOGyrRuVs4Tf56gMj8NlVx7cWe16wUTcxj+Kpz+SzTfRa8R2dErnX2w0c3K0WqE00bDoNDKGSSFuF/3o1E6iIrZ0FDXVrt+GTFkdh8/3p/WGTuLS88EX2vMnf3xIIfvd8XjKhegsdIFukoTJPfO9LdIRUUt3upE4QR1o6N3WAsYDIOeaf4xplai4pN9VmjEK/Iwj5gIwIvLT+Npb8EDgwObfWq4hy0JuWVsi8mhokbDqMYyJ8dA6DoLJn4uGtUPfS+SEkl74c9ZInN307vg1oahi5IkzFjOLBfuZY3JiRbSr44TL33bl4ulo5DaNpRlNHeud9HJB3N09t46+2ghVzNHliG7pIrUggr8HJvPzoe42hCbU98fmZxf3tQ69WrSYawwWmjgsgbg62RZtzg30jpUSgUe9uZ1vSmZxVWYKCWcdNc2d1tzLE2VJOSK4CZZF+T4Xkd23c3hbmd+0WBXH+Qk5ZcTk1ViqCgIGU2i4yCmaj9E9h+ILMukFlQY5en/BRoGOQ0GueLRRZwzk/ZARYGw7G+hknPFcAoSfVSXyY0Z5HQYg6RVE3h2vnAH6nZ7k4coHf0Y4lZFmIcNtwbr9JeNNdrXM93vEtnvZbp5OV1nX/nXtHYTi6LSTDEzp3HjrpWLCHJ0zbvmrmIxNqXbhbNERm5MzFwC6GhRxMI9iXhIBUh2DY4Dz+5ESAkESxmoZPX/RiXHo4uY3zPtR/F7aQm9HjrqH7DxwDFsIBYmSsqqa3lvaucW7WjDPW3RyvD1tji87C1aNvrofIsYarzhefg0VDR65sbAxC+h131tf39dZkJ5jpDkNSZmLZhY1s9xuZpIkriY6+1JG17MG+IYCErT+iBHP5TVQfTkgBi6uzs2l05ezWcXQ1yt63pyZFkWs6iupVwpYBCYWsO5jdduH24gvO3rG/AziypxtzOv61FTKCQCXawaVHJExa9NPY3XGfrj/5UVp4nJKmVm7wZrpSHPcXLQAhKrbSmpEvNxyms019UsJiMtoA9ytFrDxLXKTLQ/JO6qH5x7tYOcduK//+trDqcgMbl439diYR40oslDJDsfwrWJfDGzG5yIhhT+O3I1EIukcR8LO8RO04SW8Urj2R0O/wgbXhB/N14o6Ht2olaDYxCRnbvybk0aN3X67w/hNNIGXDrirf4Lc3Ux7mYFKBy96+/z7IbToQUMUOjK4e6X0f/zX0FlCqOaNvI3wS0C9s8XfTThU5AUSrr72ePraNl0KGQDwtxtkCRhOnB3f/8LZ2cnfAYdJ4GmRpio+PW/cODVGrx6iGrVqaUQ0qgvJ3o1hIxq3fDOK4G9j7iYV5cJyWBzQY5SJa4b+kGtBQlg4QgW9nggJnK/vvosMvDw0OBmXybY1ZrM4ipKq9SoNXKb7KPbFZUZBA2Dc+uFA5uRy8LbwYLoLNH3lllc1aQaFuhsXRfkJOeXX5f9OG3B3ESJq40ZGcVVfDajS5NhxPogKKOoss54oV1m5Bi5sth5Cxlvaaa4FjQ8LwYMgp0fQ47OgMCp+XPe9c6NWckB6HATIEPkjCaTiwFRtSnSzU4oTBY9LlfTJa098O0LMxcLS8yrQddZwqY0Zq34u3Hly9pdfJbn1kPEzZiolMzu42fYFGjkf4fOtyDJGp523IeDVIaVc4Mkgmc3AJ5yOgB2voZy0v913DqJ3pCybCEBA367pw/vTb1wIGhpqqobuNaiI5cepYmYq9JxgriYXW6AA6JiEjlDnB+q6/tSKEgUrm0dJ13+a7QVfcYyapX4u6XKYUOHtcJEUd0BbM1VWJupyCmt5tnRobjYNO/Yp7d3j88tr+vJuKaVHIDQcWKCeUnGtd2PG4CGVsoZxZWGvSkI84GE3HJqNVpOphYT5HJjBDkAcwYE8OmtXZjazbvJfV4O9UGO3oXNWMn5D+AYIAa4zu8t/m4o4w0YArWVcOJPsPEUznX/QW7c1WenaUIe0czkYkBk9sqyhS96Ucp/S6rWkLDxhs5xVxJJghGvCVvZsAlNg0JrN+H0VlMGEdOuzj4ZuX6xcUfqMIbbasXCUuXQYCCvcwiYWGFdEve/0Y9zKbh1Ev9au9U5JioUUquaxMM9bLE1V9Er4BoFjZHTQV1RH0yACHqUphAyuuXnXWnsfERj7fb3IHR8y8YKrh2FdE+W6+yjQRgAeDsIO9TZfVq+VugXtbHZpZdlH92uhIwW/ZLnjZK1y8XbwYKiCjWlVWoyi6rwsDesVgS5WlFcqeb73QmkF1Uyq/d/SB1yER4aGsTNPZoGOCBMGRSSLsgpqMTCRFnXq2TkOiZsokiU95krpMouYfX3eXQVc7jSDon5Uv9Rbky5GoBXd3gpreUMpT6oKU4TF7//klTtWjPwyeZvt3YVjhwuHZufAWLkf48ed2Fybp34f9sGQY5CCZ5dhWXn/0I/zqVg7yckZOGTL7nC8sSIEGb08rl21VPHAGEhvekVEaBJCjjwrejFuUyXnMvCzhuqS0QC5vblLT/OtaN4zIk/xCDW0LF1d315WzdszU0uaJlqY26Ch505p9KKSS6owNnarE320e2KlbOwK9/6lgjeetzdPpW7/0H0DfivrzpLVkkVwS6Gib5AZ/H355tjGRXuRmdvu6u+j9cClVKBu605UZmlnEgtIsj1IsMfjVwfKFUiUR42vvn7/AeI5Ijz1ZnPdyW4cSs5cOETuT6oOfGH8AD/rzirXc/oK0oRN1/b/TBy/RA8sj64aTiQF+oka8ZKTiMUCrh7nXBQvERC3GwYFHIV+vMuxNTvwcIBfr8Zfh4PChWM++Ta7pN+Vk6XWTqb7hbQZzJXPQK+/aDvw3V3dXCzadWcqmBXa347kMzhxALemXKdzAe7+UcxemDd0zCvo3DTO/j9td6r/xz6WTkrjqfz+PBgpnTzMrhfTHaHGo2Wp0b+dxeGbcHT3oI/D6WQXlhxUWmtkf8I/oPEv220j74euLGDnAth7ysWWXvmCdnafzhSvW6w9xOZW6NUzYgehRJ63iMCnMaZfJ/eIClFWdyIIe6dRTXnv4iVE8z+G6qKhUxtznohD76WeHSFiFvEhPML4eAvAjS/AXDbn20ySugf5EyAsxV/P9SPMRHXiemKjTvc/APcu1n0TVUVQ/Kea71X/zk8bM25q58fC+/qydOjQ1EqDKsVFqZKApytGN/Zg3DPa1i5vAb4O1thZ2HC4vv7Eultf613x0h7oHfDvNS5adcRkiw3ngh39bC3t6eoqOhavbyguhTy44UOXvnfm0XSXrTLd6HVQlFSXbOukbZxXfwu2hOtVkiFLOwNb9f3PTgFXZPdag033HdxNSnNEkFCOwVrV+27KMkURjSqtvUUyLJ8w0t1jL+LlsksrsTOwuSqWUdfL99FcYWaqloNbrYXr3beqFwv30W7kh8v1nT/0XPaNQ1yFAoFtrb/W9mOq4mZmRnZ2dmteqzxu7iyGL+L6wfjd3H9YPwurh+M38X1g/G7uH4wfhfXD5fyXei5pkGOESNGjFzPuLm5XfJJ1YgRI0aMGDFy7TEGOUaMGDFixIgRI0aMGLmh+N81HjBixIgRI0aMGDFixMgNiTHIMWLEiBEjRowYMWLEyA2FMcgxYsSIESNGjBgxYsTIDYUxyDFixIgRI0aMGDFixMgNhTHIMWLEiBEjRowYMWLEyA2FMcgxYsSIESNGjBgxYsTIDYUxyDFixIgRI0aMGDFixMgNhTHIMWLEiBEjRowYMWLEyA2FMcgxYsSIESNGjBgxYsTIDcU1DXLc3Nyu5cvfWJTlgiy3+enG7+L6wfhdXD80/C4qazSUVddew73538b4u7h+MH4X1w/G7+L6wfhdXH9c0yCnurr6Wr78jYO6Cn4aDVvfavMmrvvv4q87YfEMKM+71nvSMrU1kHse4rZA5sk2b+aafheyDN8PhZNLr90+XEc0/C6+2hbLqHk7+fds1jXco/9drvtz1LWkKBW2vAGfhsGuT674yxm/i8tg/fPwx/TLSko2xPhdXBi1Rsu9Px/mgw0xV/y1WvVdaLUQuxk06kvadnZJFVO/2csPuxLauHf/mxjlau1JaTZkn736r7vnMyhKgcjpV/+1rwY15RCzDs5vhO8GQcbxa71HTdFq4MfhML8X/H4zHF54rfeobRQmis83Yfu13pPrjhm9fAh2tebB344y97ejdVWdHedyeHLJcXJKqq7xHhr5n+Xn8XDoR7Byhj2fQ0XBtd4jI82hroQTf0Dsv3ByybXem/8JPtgQw9aYHP4+mopW2z6B5WWx+TX44xZI2NnqpyTllXPzt/s4nlLEkeR2/G0XJsOqRyBpb/tt8zrDGOS0F7U18NtU+HGUCDiuFnlxsGce9HsEXDu2yyaLK9TU1GrbZVvtQtph0NbCbUvA3BY2vnyt96gpMesg6zRM+RYeOwZjP2rf7WefhZVz4Zv+8EUXWPPklTkxpRysfz0jBvg5WfHrPb35YmZXdsfmcsu3+/hmRxz3/HyYNacymTx/L2czii/7dUqr1Dz+53E+2BBDXpkxS2vkIlQUQFEyTJgHs/8GTQ0c/O5a79WNhaYWjiyCysLL207sZqgpA68esOkVYzB6hVl3KpOFexIZFe5GXlkNZzNKru0O7fsK9n8t/r8kvdVPe/TPYygkiT4BjuSWtuM14fAPcPx3+Hkc/DxBJJRvMIxBTnux93PIjQYTc1j7VLuVoi/KxhfAyhUGP99um3xjzVlCX9tA3/e28v766FY/T5blK9OzkLwfzOwgZDT0fgBSD1x/srX988G7N3SdBU5B4jhoL9RVsPR2SNwFXt0gaDjEbYVfJkD6McPHyrLIzrSV1APi39xz4sJuxABJkpjc1Ytlc/tTVKHmo43nuK23LzueHYq9pSnTvtnH66vOkF5U2abtF5TXMOuHg2yNzuaXfUkM/HAbfx9Na+d3UU9cThkzv9/Pj7sTKKqouWKvY+QKUqCTrzgFgY079LgbDnwHlUXXcq9uHGQZNjwHa5+8/ATb2RXg0hFm/A611bDt7XbZRSNNySmp4sUVpxjTyZ2vbuuGhYmSnedzDB6j0cqoNVcpoZsfD5tehX6Pgrk9lOVc9CkA1bUaojNLuW9QAJHeduS2V+JLliFqFXSdDbf+Asn74PCP7bPt6whjkNMaSjKhuqzl+3NiYNfH4uCd+AXEbaH6+J9XYb8yRP/H0BfAzLrdNntnPz/emRJBpLcdP+1NpLiyBe1oTQVknan7c29cPt3f3kxcTmm77QsAyXvBtw8olBA6DmStkK5dL6QdEcFBv0euzPZ3fyqqg7P/hsnzYcJn8NhRcbFc9aioIuo5/ht8EQkn2nj8pR4CW2/QVNcvnow0IdzTltWPDeDHO3vyzpQIfBwt+XtuPx4YHMjK4+kM+2QHa09lXNI2tVqZOxYeJK2wgj8f6MveF4czoqMbL688TdQVykAuPZzCsZQi3t8QQ5/3tvLFlliq1Jor8lpGrhD5ceJfxyDx74AnoLYSDn1/7fbpRuLAt3DkJ5FcOrm4vtp9qdSUw/l/IeJmsPWEPnPh1LJL7s0w0jpeX30WlULi3akRmMtVDA2wZOf53Lr7ZVnm8T+P0+/9raw6kY58pRPTWafEvwOeEMmIstb1diblVaDRygS7WONqY05uaXX77GvGcbGu6DQNOk2BLjNh75c3XDXnugly/j6axvIrmLFsE3mxsPx++CwcFo4SPTfN8e9LYOsFQ1+CjhM5aT2IotWvUlJ5hTOj5zeCpIDQ8e262W6+Dszu48f/TQxHrZHZGt3C+z7xB3w3sK7J/nx2KTW1Wj7bEtt+O1NbI+Rqfv3F37Ye4NWDmrNrrp/M8/75YO8LYRPaf9u550XPVf/HwC28/naVKUz+SlQP934ubtNqxUlKZQ5rHhcVsEuhsghyoqHHXeLvHKNk7UK42pgzMtwNSZIAsDJT8czoUPa+OJxR4W489udxftyd0OpM4fHUQs5mlPD5zG5EetvjaGXKJ7d0wd/Jkkf/PEZ5O1dJZVlm/ekspnXzYv+Lw7mjrx//bNtD8XsdUH/VWxh9HPgOiq+z8/L/OLml1WyNzqaiRnc85MeBpTNY2Iu/7bxEf+bhhTfOArqyUFS0rzYJO+Hfl6HvwyLJ5NYZ1j8rejAvlXMbQF0BEdPE36FjoaYUUg607z4bYeOZLDacyeL1iZ1wkgvh6968UPMVx1KK6pK2fx9NY93pTHwcLXliyQke/fP4lQ108mLB3A6sXMDaFUpbF+TE5YgEe7CrNS42ZlSptZS2x7UgapWoKAUOEX8Pekb8zo4satv28uJEcnXHhwbJ72vNdRPk7Dyfy6J9idd6N+pRV8GisUIiNORFIY/6eVzTC37aUYjfBsNeBlNLVp/M4POCvriRz2+r/728fUg5AP88Ikqcp5aJRWxDzm0QEikrp8t7nRbwdrCki7cd60+38GMsSgZkWP8cyDKZxUKis+5UZrv0JgCQeQJqq8C3f/1tYeMhbhsT520iPvcCFbarQVUJRK+BnveCUtX+29/zmcj6NSdH9Oohqkc7PxIBTewmyI+FmX+AZzdYOhuKW6/7Je0wIEPHSWDhCNlR7fY2/pewMTfhq5nduLu/P++si6bH25t5eukJiisuvOBcczITZ2szBgY7191mYark61ndySiq5KE/jlFS1X6L1tPpxaQXVTK2sweutua8OiGcvwZlYS8Xs7UiCG11qVjgfRYhMtlGrilqjZap3+yl17tbuPeXI3yuTyblx4NTsOGDe90vMsUxa6/+jrY36iphOHMZ7qFtojwfVjwAvv1g1NtCSTDuY5GR3zPv0rd3cgm4RwpZIYhztKWTOG8baTcKymt4bdUZhnRwYXK4Hfw5E0rS8M3fi6RVsy8uj6S8ct5cE8WUrp6sfHgAH90SybpTmfxz4hKul5dK3nlw7gCSBNburZarxeWUYWOuwsXGDBcbM4DL78uRZYj6R6yllCbiNqcgkRzZ+4UwyLgIBiYOMevh237wz1yhPPllggh6rgOumyCnf5ATZzNKLp6dzzwJ+75iW0w29/1y5MpF3lH/QHku3LmKsn7PsK7HQvIKi8j+bhJyw3Le7k/AIQA6TSO7pIrX/jmDbehQNJKKgtP/cia9DYt9dZVw6PrpJkjaBWdWwIr74GiDhUZNucgyhY657Ld6IcZEeLArNrf5XpuSDJEJSD0Ip5aSUVxFN197vB0s+Gzz+fbZgeS9ojLh2a3upqqgsZhSQ0TVUW77/gAJ1zLQid8GWjV0nHhltj/hM7h9OZhaNn//8NfAqzv8dQfseB88ukDQCJjxByhU8M9DTYPjlkg9KL5P5w7g1glyjEFOW1EoJP5vQjgrH+7PHf382ByVzQvLT7V4vtJoZdadzmR8Z3eUCsngvg5uNnx7ew+OJhVw8zf7SC2oaJd93HAmC1tzFf0C65MkLhk7qPQeyCPFd/CR+zx4Lg663wFrn4bTf7fL67YHfx9NY8aC/a06/18zR6XKQqFzr22wINFqYPP/wSehsPrxS3KKTM4v53hKEU+N7MDMXj4sPpgigt78uKZBjmdX8O4lqjmyLLT2x367er2i7cnRRVCcCkm7r/xrybKQH8dugZUPiATbtO/rE1h+/WDgU7DtXbGwaw6ttuk5N+0IxG2GPg/W36ZQinN13JYr817+B5FlmReWn6KmVssH0yKQVj8q2gnGfIhCXc5Y+1SeWXaSoZ/swMZcxZuTIwCY3tOHMZ3ceXttNIXlV0ghog9yAGzcWi1Xi8stI9jVGkmS6oKc4qxEOPg9rH5MyClbe43Xk3UKCpMgfIrh7QOfgvIcYaR0Acqra+nz/lYe/O0IxceWi/VH8Eh4IRmejgYLB/jjZjj6i0jUn1l+afvXjlxXQY4sw4GEC7iNaDWw8iHY9CofLd/LluhszmdfZIGrroITiy+91H3oB/AfhNY5lBkL9vPIplLetXsDu4oUTn13D7JWK0py59bDoKdBqeLttVGYKCX+7+ZeSD59GG0WxZtrmpH8qCuFLKglMo6JE9/4T+HxE/B0FAQMhjMr6x+TsEP0TXQYC4hF0pVgbIQ7NbVatsU0k3UoyYDgEUKm9e/LhGauwd/BlKdGdmBLdA5vrD5Lde1l6vuT94uLtcq07qYjZS7Eaz34v+BELE2VvLD81OW9xuVwfqM4cemzc+2NiTk4h7R8v8oMpv8GSlNR9er3mC5T5AKTv4HEnXBgvuFzWsrSpBwAnz6gUIBreJ3DWmnVdea29x9BkiS6+Trw3E1hvDetMxvPZvHnodRmH3swMZ/c0momdvFs9v5hoa6seHgAZdW1vLbq8qUAsiyz4XQmo8LdMVXpLgPl+ZB2CPsuE3lqZAjf7Yxne0otTPhC9BGsfLDlhd1VxtZcxcHEgjopR0vsjs2l17tbiM68Bq5Km14VaoAP/UXSascHIqu890sIGCT6M34Y0WppR3yuSK5N7+XN4yNCqFJrWHwgWfTONXf+6XW/CAwWz4B1z8DqR4Vd7LWQfbWVmgrYPU/IfLLPXvl+gfMb4ccRYoGWsAMmfw32PoaPGf4adLgJVtwvFtANKU6Dr3vAh37w2zSI1lXStr1DkaU/3xb2NHx8yGiRTCpq/rxg5NJYcjiVzVHZvDe1Mx5J/8DZlTDpS+h1H5jZ8bh/Orf28OajWyJZ9cgA7CxM6p77xqROIjhqp3k6VWoNv+xLolajC3rzYuuv5dZuov2hFUmHuJwygl1Ez7WrjRkSWjr8ewdsfFFcsze+CH9e4gzBmPVgagOBQw1vdwkFz+4XtTffHJVNbmk15+MTMVv9IIXeI4R5gYW9UBbN/huqS4Vs/tx6WPPUNXMSvG6CHF9HS7zsLdgff4Ev6vhvdX0CHSuPY6KU2BN3gcfLsviQ/3mI6k1v8PmW8xS0JkrPOA7pR6D3/eyOy+NsRgmL5vTisyfu4FCn1+hSsJGszwaLkpytN0TO5FxWKetOZ/LEiBCcrM1QBA+nB1GcTMoxnJ+REw3fDxO9LC3ZUerdsTrfKrI9ICLu5L31fUHnNoCDvzgogSnz9/LNjvYvD/o7WxHuYcuG05lN7yxJF1Kq8Z+CV08eL53HSykPMq2zI6+O78jvB5KZ/t1+KmvaGOhkR0H8VpEhaMC+hHx2KnrjnrWdBwb6cDS58MplXy6EViOkBh1uuvqv3RAbN5i1VOjGO02pvz1kJPR+UMg89EF1xnH4KFDocRuiroT0o8LgAYQdeWES1JQzY8EBPt18rn33+b+YUb4MJnbxZHpPb95ae7ZZKeeak5l42JnT3dehxW2EutvwwOBAdsfmkX+ZDjsxWaUk5VcwNsK9/sa4zcLUo8MYHhoazOAOLjy59ASpRVUw9TuxIPvrTjh/7eU1gzu4YGGiZOOZlrOh5dW1vLj8NPnlNXx/BQfoffLvOaZ+s5flR9PqkjpydRnqUys47jyBop6PCwv8/fPFouS2JXDzj/DkKXH+3PF+q14nMa8cCxMlbjbmeNpbMKmLJ6v3HheWxM0FOZ2miF6dhO3C2n7S13B6mZCU/Fc4/CNU5IuKtqy5cOVLlkW16ugvbX+9Qz+Ae2d44iQ8F0dJwBg++fecoZW7QgnTfhDf3bK7RCAGIknw21RRueszVyzyls6GJbMhYTvvlE/h483xhr//4BGAJH57NxgJuWVXNbmg0cp8tDGGyV09Ge+rFkNXI24REiylCgIHE1J6iDcnRzC9pw+utuai0nr0F9Bqcbcz59nRHYjJKmkX85V98Xm8seYss348SE5GoujHctIHOe4iSV11AaVPVTHa08spy00h2FUEOXYWJgxSRWNdlgh3rIBHD8PMP4UZxsdB8Fln2PnxxXcubovoxWmQPK6jy0yhULmAnG7ViXS6+tizdkAsCuC5qjmG23IKgidOwXMJ8PABcf7b2c5jNVrJdRPkSJJE/yAn9sXnN3u/trIE9ea3SfMaS7zsyT0eiXT3dWDvhYKc/fPh1FK0gcMwOfwde7au4fXVF2mmlmVR/rPxhNDxLNqbSKibDUM7uAAwePoT7PJ9iOgiJWlBM2H2MlCZ8sXW83jYmjO9ly7rEzQMlaaSHorz7NUHbieXigCnLFt86S1Z/RYli3KfuV39bR0niQx9zBrRB3J+o6jiSBIp+RWcTi/G26EFSdNlMj7Sg+3ncgwla1qtcJ2z9QIbdzS3LeVe9XO4VsYjpR3mvkGB/HpPb06mFbPvQoFrS8iyyD7aeRuW+IH9CfnkeI1GqipitGUcWhl2xea2sKFGVBVfekN+S6QdFhdgXTXtmuLeGca8X6+v1TPqTfEdrXtGfGcbXhAn261vG1pE65tiw3SyO7dOgExZ6hmiMks4mtQgINeoReYxL7b5zGrG8ZalTeX5wszj4+C6/rajyQXc98thsor/QxnmNvDGpE4EOFszZ9Fh0grrJWepBRWsP53JhEgPFI2kao0ZH+mhMwxoJunQgNSCCpYfTWsxqbP9XA7mJgoGhtT3/3BugziO7LxQKiQ+n9EVK1MlD/9xjCqtAm79WThMLb0d4q/tsFhzEyVDQ13YeFYX5OTEwLK74fgf4vwIfLrpPLll1dzW25c1JzPqegYBYbKxbM6FK+qtoLRKzcI9iWQXV/HMspP0eW8rr6w8zQ/ff4GJpoKXc0bSe1dXPvP4WEg5nouvlxirzGDI86JvprEVfDMk5JYR4GxVd4zcPzgQmzLdNaSxXE2//Vl/wf3bhLV99ztg9DsiwXEpvXqXSkmmSKy0xoL+Qo/RaqjY+QUFwVPE9U9lIdwfm6M4HX6dLKpV655uW2WkIEEk1XrdBw7+VKlsuf+XI3y9PY731zfK7pvbwi2LoCARNjwv5t/8PE5k0+9YCcNfgXs3weh34dwGSuxCWV7dE0crM15fdbZeZmnpKJQKsTeOZC0pr5yXf1zF358/xbqf3mnzdqrUGn7em8iivYmsO5V5UQOXE6lFFFaomdXTU8yUM7OB8Z/UPyBwmFDJ6CsKBYmweKZIhC+eDuV53NHPnxUPD8DcRNnm/dYzPMyNP+7tQ2JeOa//pFPhNJSrAZRlU1yp5s9DKYaJq39fgY9DUCy/h8dYWhfkSJLEXabbKTD3gQCdYUDYOHjkgEhiePeE7e+QFXOg2b6df46n0/uVv9CmHeX9WC9OpRU13fGIm8W/LUjM8suq2RWbx9RIZ6xOLCLTbxJbUrScTG20LTNrUdWxcYMBj4uExTVwbL1ughyA/sFOxOaUkVPaYLGTcQKWzKZ8Xne0lcXMTBjDabPudKo6yoAgJw4m5Bse/MVpInPyVU/kTa+SHX4Pz5m+yiltIAtsFrLlZALbm5NegTAZ+GE4nFoKfR8ivqCKHedymTPAv85BCaD/Xe/xjef73HJ+FAXWwZxILWL96SweHhaMmUr34/DoChYOTLaJYd/5bNj0mtD4dpwA926u39fmKEwGez9AyEoOJxXw5vYcMux7UH58udBh1lSIkzGwKSoLE6XE0FCXNnzqF2dCpAdVai1bohq4rFXkiV4UWyGvySmtYrumC2qVtciOAP2CnHC0MuVochsGqJ1cAin7YNwnYGJRd3NZdS2n0orxCB8Atl44p/5LqJtNy99pQ7KjYMEQWDRGZCoul3MbRA+LT5/L39aVwsRCfIbJe2HZnaLvZujLwqDg9LL6x51eJvp5XHQnYd1g2YzzRwCR+a/rbVh2N3weAV/3FL+1hlQWwZ+zhCymse165imY31vIdGQNbHyRo8kF3LnwEFuic3j4j6M3tCzO0lTFz3N6YaJUcPeiw+yJzSMmq4TpC/ZjY65izoCAlp+ccRyi1+IqFzAgyIlVJy5sT71wTyLPLDtJr3e38Oyyk016V/bH59PTz7H+Yl5bI34TDQJ2RytTvrm9BzFZJXy08ZxOGvmr6EtYenudq+K1YkyEO2czSkSP0uEfhQHIqofhy66cOBfPon2JPDEihJfGhWFuouTnvUn1T975oZhZcpnN7KtPZlBVq+Gvuf3Y9NRgbu3hzeaobLoWbKDQqTt/vXw7s/v68sXWWE5llDTNnHa5DRwDYds7F61uJuaWMdQ6pa7Hp6OHLcNddZlyx8Dmn+TdQwSueiKng9KUrF0/Nr/AaQ+2vCGaj9NaCEj0lOXAp6FwcEGzd8cf2oBlTR6/1wwXyRvPbqK3paXXzD4Dt/wEplaw78tL3+8jP4GZLXS+FY1W5qmlJzieUsTN3b1Zfiyt6eflHiESS8d/ExPsTa3gzlV16gokCfo/Cg/t5SOHNwlxs+XT6V04klxo2OAepnNHvR6r25paETBegkvfod//j/fS7uR5k7+4q2ZJm/qmS6rU3LnwEG+tjeKdddE8svgYa05e+Jy341wONuYqeibMh5T9MG2BSBbrCRouKtWJu8RvaNndIpE87Qeh3PlpDEptTZOeyMuhf7Az6x4biFdtKhpJCY66c7y1CHKW7TjMwA+28dKK09y96LBwTFRXiYGhnW8hI+BmRiqPEuysm7tXlsMQ7UH22k4Qx5ceW0/ofge/e75MsuxO7B/PML1Rz6Isy8zfHsdMp1gUkkysTV9m/XCQQ4mNZGRWzkI9c3KJSFikHzU4NtefyUKWZaaaHITyHLxuegovewu+332BAKbfoyKgXzIbEi/QW1dVDGuegB9HinaIduC6CnL6BYqs4v6G1Zy1T6HNOMnymt78GvI5K1++jUlTZ6MozWCESzHlNRrDCHLji5Cyn0S7Xryivof+x4ax4mQ2WcM+xakmnUfcz/HKytNN7Vgri4RuWtbC7L+p7fMI3+2Ix8HShCndvAweqlIq+HxmVypqaunxzmamzN+Ll70F03s20O4qlBAwhKnqDbwdPQZ531cw8k3xg3IMAIUKuSilecelomRw8KNKrWHqN/u49bv9rDmZyYK8zlhl7BOmCBO/AGeRvdsclU3fQCdszU2abqstVBQY6MT9nKzo4m3H6oYnGf20Xlvx2WQUVaFFQZVHLxGcILIO3X0dWg5ytFoxdLJx05wsiyFpYRMgZJTBXYcTC9BoZfoFO4tm/+i1DAt1ZOf53Jb7kmoqxKThH0eIhZpXD/FDuhx9d221yMCGjL4yrmrtSchICJ8sFoEhN4m5SiE3wc4PxMWrokBkIjtPr3+OmQ3YelOWJjLdZdW1pOqrD+nHRLZn8PNCClPQwBVx44tChllb1dQ16OACEXQ9chDGfADRa/hh4QLCPGxZNKcXp9KKee8Shs/+F3GzNefnOb0or67l9oUHGfP5bixMlPz1YD887S2af1LyfvhxlJC+zOvIe5pPOZ6cd0EDgrTCSrr52vP48BD+PprGjnP1lc6aWi1HkgrpF9TAlTH1IFSXNJFedvWx59nRofy0N5Ed53JEj9j038Q57PdbhKSxlRxMyOd4ymVOjG/AsDBXTJQS/57JEL/FHnfDwweQKwrYvPxHIjzteGBwILbmJszQNepnl1SJqs/BBSKoP7cesk63eR+WHEplcIgL3g6WdHCz4ZXx4ex/uAO95DM49L8LG3MTXhnXER9HC77e1oycWGkCw18VFYQ/bm3586ws5P6cd3g+9WGRpdYtOoa7lJIuO3G+oJWWshYOED4F1Yk/eHNV2993i2SfFUlCED0tFyJqlUiWbXwJkvY2uTt116+kal34J093/fXpJarnzS2aC+IheJQ4L/V+EI792mr3KkAsLI//IYJOUyu+3BrLxrNZfD6zKx/c3JkgFyveWhPVdMHe8x5xLrttKdy3FTwim2y61DaYZXEwqYsnQzq4MCzUhe93NThnDnwSbltsuGi91mjU8PstyO+6wecR1LZyCGpxeTWDCleQ4Dqas52ewZESSiouTVpbUVPLbd8fIDqzhD/v70vcu2Pp4m3HhgtIUwF2nMvlEY9zKPd9IX5TAYMNH+AYIEyi/n0Zvu4ljtVbfxaB/52rxTF04NtL2tfW4GprzgjnYlJlN9SIpFJyjQ0A+09Gc3MPbxbe1ZO4nDIeXXyc2oL6doVDjhNwlMrwLjkhbjv+OzIKNpiMaPa1Np0r5HfruxmkPIN3wX6DnvU9cXnE5pQx2ykWXML4Yu5EOnrYMOuHA9z782FD6W+XGaLHd16YSPwfWVh31+oT6QwIcsL2xPcQMASVZ2fuHRjAhtOZpOS3cE0ys4bb/hTnu18miKR/Y5L2wjf94NRf4jy4aKyY43OZXFdBjrudOYEuVuyO1cmbMo5DxjF2+j/BWzWzGTt+Gi42ZigCB4FCRVjFEazNVPV9OUl7IHoNlUPfYEbqzWQEz2TtE0M5+PIIxgwbBi4ducstnoziKtadaiT3OP8vaGpgxm8sLghl0Mc7WHY0jfsGBTZbuvR2sOTPB/ryyriOfHlbN1Y+3L++gVdPn7kUeg1jnvpmUm/dKE5mkgQKJbXWnmzce5he721pqq/XVXLOZZVyIrWID6Z15uDLI3jhqeeoxpRt1hMg8lbx0PIaDicVMDrcrR2+AR2rHhWuXA2Y2MWTXedz693v9FG2rpKjl4Io/AdA6uG6AZU9/Bw4mVZkWG3TakQ59tNQkdk//IPh6+fHiSCq2x1Ndm1/Qj7O1qaEuFoLCUN5DhMd0iisUHOyucxk8n74ootwNIq4Ge7bAlMXiN6mTa8Jr/rCJPH/X3YXGYS/7hTOR9vegbJmZHA1FbBklviees656Md5XTDmAxEUjtHp/4e9LN732idFWVpbW1+m1mPrSU1ROoEuVgBiIGV1KZRmiKzYgCfAxEoYe4Bosj35J4z9EFw71fX9ZJdUse1sOpxbB52millHkTNIte3Oq4qf+OXOLgwLdeWlcR35eV/ShSWoNwAhbjbsfWE4ax8byPvTOvPX3AsEOEWpwrnGqzs8dgzGfYJ31lY+Nv2Bp5ccY+o3e/l6W9O5VFkllQS7WPP4iGC6+dozb/P5ugXaqbQiKtUa+jZwVSN5n2hEbeBiqOf+QYEMCHbi2WWnRG+CuS3MXi5cDxfPuLCuXEdqQQVzfz/KvPZyXQRszU3oH+RM/PGdUJopzgeuHUm0jKR/9W4+n9kVE6U4J987MABTlYJR83aQ/deTyHZecNcakaTZ/Slarcy766KYt/k8++LzWuXIdia9mNPpxdzWu0Fyq6Yc5ba3kFRm4lhHJMXmDgliU1Q257KaGZQccbOokGWdhm8HNm0gVleiWTCUftrjxAfMEhWoXUKGE6jIIlXyZInO0EKt0V40c17aaRbOmmzu9778BURD8suqSV/+ErK9j8gEJ+y88BPOrgS/AeDTW2TVS+qvyfvOpdO9fDenHEaQkF8hei69ewnnp6JmZN5FqfUGAX3mivlx65+FPZ+L5M6FSD8KC0eK47jXvew8n8uX22J5bFgw4zp7YKJU8Or4cI4kF7L0cCMZnCRB34eEBLGFIGVzVDbVtVomdRHB2oRIT6IzS0TAfZ0iH/wOOW4L30ozWa4ZiObobyIRfBGO7VmPh1SAzaAHUToGoJRkCvOaD07OZhRz2/cH2NfofL/zXC5nM0pYPl6mz7YZSEtm86LNBvafz2xxVlhOaRVn0gu5q+ALkXgc8FTzOzjsZXG8hYwWi27vHuJ2j0iRJNn1cavn11wKnc2zOa/xYGt0NoeTChj//SmqMeXZ/na8MakTIzq6MX92N3acy2HHgcPiSQ5+7K8KIF9yRHlurfj8Dy7grP1QEivMm32d1IIK1B0movXszscm35O087e6JPKivUmEulrimrMHgkdiY27Cr/f04cWxYeSUVjP396P1196Ok2H8PHFeCp8i5O3l+WSXVHE4qZDHHQ+K89XAJwGY0csHKzMVfxxqoQUDRHL5/h3Q/3ERTDb4vRO9VvSz2fnAQ/uE1FOrhUXjWnXcXYjrJ8g5/y/ErGNSF09WHEsT1ZzDC5Gt3XkvPoBR4W71PSdmNuDdG2XCdvoGOokvRqsRGSGPLszL6U5xpZq3J0fQ0cMWVxvdARE8Apu0nXT2tG7awxG9Gjy6kq9y49V/TtPJ05aVD/fn4aEtu2Z18rTjvkGBTOriKZrYGuPXD6vZv7BQnsT2YtHgK8syiw+mcLTYGmVpOjW1WpIaRr+11WKB7+BHii5TOzLcDaVCwtLRk2UD1jG38La6eRlbY3LQyuIx7UaXGcJisEGGc0KkJxpZrs+mlGSApKwru2YVV2GqUmAZPFBM29ZJWXr4OVCl1ho2IKYeEuXY0LHgN1BITRpemJN2AxL49jXYrcLyGladSGdgsLOQD/r2BSsXwop2YGOmYkdzkrUtr4vBW48cFk45ZjbC4WToiyI78WmoCIKOLBLzEBwCRN9I+jExCPH7oUIyqSf3vPgxJu2BWUvqh5Re79h6wozfG8xo6AqTvhLDuza8ILJeth4GT5FtPTApz2J0uDuOVqZEZZbUT1d3ChHZmYipIsgpTBYStZCboPudouk5dhPUVPDi8lMs/OMPUeHpOEk8X5L4UJqDNzlYnxd65Tn9/Yn0tuODDTHXzvb3KqFQSER42XFbb1+crc1afuCKB4Rz3vTfxHfX+36kyfOZptjFQwUfoC3O5N+zTYf1ZhZV4WFvgSRJPD2qA6fTi9msk5vuj8/H0lRJpHeDnr+UfeLir2ia0FEoJD69tSu1Wi0vrzgtFtF6s4vidPj7ngv2VlTU1HL/r0ewNFXx+Yyurf6MWsOkLp745W5DbWoPfgNYczKDRcXd6CedJciyfhHpaW/BpqcG84THGdzy9rPD/0khVRnwJJz9h50blvLD7kQW7Ulk1g8H63s3tVqy5o+l4oNQNAuGiXllQEZRJaeWvcs3Ft8xqmyVSBTs+0pkIqNWw03vGfRU3tLDGzdbM+Zvb8EcJnyy6O+sKRVWsw0pSERZlMSj6scpG/4+DHoWtr8Dy+agzDiG0jmYFcfTWHUinT7vbeX+X49csHF6Q3EA8VoPhpS1r1Per3+vwCtnJzk9nhaLyPQjIinSDFlpicjJ+4S5zq0/CwXFhvpZYDvXL8ZWqiDipnsAOJFWJIIcaCpZU1dBeQ65Cp1c28pJmLBErRKJqr/vbdlRLmEH/DCCGnUtrzh8zPQVBTy2+BgDgpx5YmSHuocNDXXhlh7e/N/qs5c8FmL5sTS6+tjj6yTWL4N1/b07z7eyj/RqU5KBZtv7LK4dTlLHBzgT9iRKTTWlB36+6FPl08vJVzjh0mkYFo5izVOS17zs6O8jqZxNSGbWjwd5acWpunP+4aRCHrbeQcjG2SLxXF1C38T5TJK3t/iZ7TqfRxcpAYvqPDE4W9HC0jZyujjexn/SRCXCsFfF+W/Lmxd9n5eKdWkSJdYBfL4lljmLDhPmbouJnTueqvp10fAwN4aFuhJ7/owI0u18iM2t4KztQBEEbHkdqks4EvQouaVNj2eNViatsAIfJysUtywkzzKQm6JfgkVjSEk8z7aYHJ6MqEIqz60zc7IwVXLfoEBWPzqAjh62fLFVlzBTqqDXveK8NPZDsb7e+iZbo3NwlorpcX6eSM4EDQfEIOyxEe6sO5V54SSLQiFsqpUmYt0HlBz+E/mvO8j3HEzO1KWi4uYYCHPWi+9SP+S4jVw/Qc6xX2H5/TwSXkUPPwdeXrwb7ellnHGfSmx+NXf19zd8fIfREPsv83Lu4bH05yl+2x+yTvG56l4W7UvhoaFB+Dg2asQPHgnludzsVcieuLx6eVNNOcRthY4T+fdsNjLw3tTOdPN1MOjFaQs25iZ09bFnS3Q2++LzeHTxcV5eeRqFgy9D3MSBmlHUoCG2OA2QwcGf1MIKLE2VOFnV67iH9IigRiOxLVos6DdHZdHZyw4PuxYywW2hw1gxDPL4H3U3uduZ09vfkb+PpomTUUk62LjXLYoyiqrwsDNH8uouMrw6yVqktx0qhWQoWYvbLBYA4+cJ++2886JnBN1Mi6S9IrPS4OCWZZkXV5yiokbDszfpNM8KJYSORXl+A4NCnNjaOMhJOypkOIOfq5P21THwKXhgp1j437wQnomGKfPh5h9gzjp4aA88vE/oSBeOhp8niD6Eb/qK7+j25U1c3/5zdL9TZLNMrZqtSJWZOuOgLaCrjz3hHraikpOnOwnqrTC73QElafDTGDCxFC5OkiROjuoKYvasZPu5XG5SHKLMzFVkc4CckirWZjuR6TpILA5lGYVC4sUxYZxOL2b9mQs31reEWqNtEiD9Z/t8CpPE72jkG/WNqgBdb4PJ3zBcdZq/1Y/Qq8hwpkGVWkN+eQ2ediLxMjDYmeleBViuvIvaZffhePI7evs71FU50NSK6qtfvxZ3xd3OnLcmR7ApKru+n8AtXPRAxG8TM73itjQrJXpnXTRJ+eV8f2cPnC4U0LWBqV09mWJ2lI213TmQXMzzf5+itsNEJGSRuGqAk6Kce0u+I8p2IA8e8RBVle53UOvehWGHH2Sj46eceLwDL4wJ47cDyew4l8PeVQtwz93HyrJOHMmoQr1iLq9+9RPvfvIRs4oWMNgyGeW/L4tAb+tb4OAHD+0VC4QGmKmUPDosmNUnM3hl5enmrfVtdEmGxjKrUvFbSJLdCHCxgmGvCDlOfiyUZeHTqT9FFWqeWHKCUDcbdsXmMWfR4Raz3mvPZLHB/jYs/Hu37UNvhjPpxdTE7qBcNmOTcrCwptXW1vVnNuRcVinfffsZMgqR9LBxh9Fvi+8rbgsxWSV0LthMqU0wPqG9sDFXcSKlSDzOzlc3tFhQUqXm639Exei9vaX1ioERr8H/FYiMsKba4DkG7PsaXMN52+tb1hR442Jjxqhwdz6f2dWgN0OSJN6eHEGQizVzfz/a7Dy/3bG5bD+XYxBg7ovPY29cPnMG+Nfd5mJjRoSX7bUNcmRZ9EY07LWRZciJQbv6cco1CrZ4PsCHN0fy+JQh/Cv3QXNggVjstkBxWQVdS3eQ4TUGFEpsnYTKo6Kw+cqIInoVJ8znsqzjbpYeSuZwkugNKYrdz/O13yN1ux3u3QJ3r0Xy7csUixMtuinuOJfDDLuzoqfKt+Xz2AWxchK/rbxz7WuzrlM/+IZ0JSarlCBXaxbN6YXCxr3eLVfHlG5eKItTUFt7klWm4UxGCUV+Y4V64ujPMPxVzFwCyC+vEdbUDcgqqUKtkfF1tATHQM4MW8QdNS+hKUzB4beRvGP+B2OOzRUDaBt9RpIk8cSIYA4lFhi2i4D43Q19EY79SuCuJ/nJZgEKZKEOacCESE/SCis50diAoDGWjkIWeuQnoveuwXzdo6yp7UPv2DsZ8eXBesmbvU8T06m2cP0EOVO/Awc/TJbOZsGQWp7Q/opWXcN9ZzrR08/BYGAdIOaBTF2ATegwQj3s2G8/gUdM32ZPTQiz+/gyd0gzFRjffmBiyTDlKYoq1PVWjnFbRfWh4yTWn86kl59j85WZNjIoxJndsXnM+uEgu87n8sXMrvSKjMS0LB1zEwXpDYMcvSbb3p/Uggp8HS0NAi0fR0s6edqy8UwWx1IK2RaTw5iGNrDtgcpUZDxO/1UnOwO4b1Agx1IKxVDD4vQ6qRoIuZqHnbmu56Vn3cXN3ERJJy87wyAndpMYgqZUQeAwZAd/dv35EeH/t5GgV9ZRGbtTVHgasORwKv+ezeb9aZ0NXeRCboLCJCZ6V3I2o8TQoevAN6L8GTah6XuUJFHN6DgROt8iKjyNsfeFe/4VQZKFvdCHDn8FHjsC/gObPv6/SIeb4IWkOmlNQ9Jq7XGTCunmY0e4p62o5OTFCltaS0fxIJ8+aB2D0ZZl87b5Mzy9TifncAlFdgkj78AfRHraMMnsGFu0vZB1x/IO3QXeYsiTkBtTNxCvf7Azgzu48PG/50jILbukptX8smpGfLqTEfN28sfBZH7em8iEr3Y3P6vqcsg8KeSWp/82LLnrKU6Hza+LSfSXQ8x6UJg0b1HebTY8fpxM5/48XvsLtdX11WC9DMZdF+RIksQzvufppT5KcuxpZpf8yO3WDbLhWSdBXQ6+F65KToz0YHxnD/5v1dn6fqAOo8VMBG2t6GlcOddgISTLMv+eyeKufv508rRrYcttR5Ebhbsmk42aXtz2wwH8nCx5deZQJP+BQg7VkM2vgbqKgDu/wcfBksf/PM72hFJedPicp7VPEKLMQrnkNh7s40L/ICeeX3IY3xOfEmPTj0FP/c7hAT+SaR7MM0XvMs9sAergm7B+5gS8lCasUl/NERK4FuZa3d7Xj7cnd+KvI6nc8eOhpse2pSOypOTg6WgmfrWnvvqtk89oLN1E36VCIc5Jc/fAS2m4D72fJ0aE8MmtXVh8fx9+ntOLk2lFvLOuaX9bYXkNe+PysOpzp5CbtgOyLPPOuih6WmSQZuLP3vgi4SJl7d60L0erZfWxJMYr97NHG86+LN1n0OU2cX1e/xzS0juYoDyIee87USgVdPWx57h+4eQcYuBK+n//nOHIKaEaOFVmy+qGhhwKpei7MrMTzeaNKUiAuC1UdLuHv49nc0c/P+bP6s6n07s0W121MFXy3e1CJfLyytMG39+PuxO4Y+Eh5iw6TPe3N/PZ5vNotDIfbjxHJ09bJkYazr8a0sGFPbF51Gq0qDXay59gf6mcXSF6I/Z9Jf6uqYDvh8A3fZDjd/Bq9V08ObEvkiThYGVKRuhd2FdnUB3V8pDIU7tX4SiV4tpfmNHYOov3rC5uGphkFFXSrWwXGpUVvRK/5XfzT9h6Jp2y6lo8Cw5QqzQXhjl6o47QcXTXnOJATHKTBEF1rYZd53MZrjguLLkbO4xeCr3uE4GVSfut//Sqlq7de/HmpE78Oqc3NuYmQmHSaCDoqHA3ApS5ZEqufLE1FnOVgiGjpoheOs9u0GcuLtZmyDJNnDP1wYGvLrk/PMyV3drOPGT1GafVXsxSbEKKuBke2NHs+xsd7k6Yuw1fbG1GUtznQWr7P4FtWTwRNafgpvfF/jegv85oam3jVpDm6PsQVBYQvOkuEk2CCZ/7G+ueHIqtuQmPLD52+fMVG3D9BDlmNiKrrC7HcelEJin2cD7kPhY9PpklD/RtWlFRqqDLTKRJX+D28DrGPPEt819+nL8f6s+bkyOatwA0MQf/Qfjk78PaBFL3LRNzC86uAOdQCiz92Z+Qz7jO7Rs0PDBY2Clvemowh18dyeSuXmDvg1SRh7+dwrCSU5QMSGDvQ2pBZbO20GMj3NlxPoe5vx0l3NOOewdewJWprXSdLeyRz2+su2lUuBsf3hzJ38fSiI+PRbapP3FnFFfhqa8m+fUX7iY6LWgPXweO6YOc0iwhg9OXihUKUgJm0LdqDw/2sGGsRwUW1bkGQYQsy3y1NZYJkR5MaHSxIHAIKEwYJJ1AkqgfWlqcLgwaej9wecYAppYw5DlR8XlwFwx6xsDt7YagGYkSwPlya6ykatzMagj3sCWzuIrq7BjDRZwkscjpaR6sfoJtlcGsOJZelzyIch3PwJq9LKl9EntNPkvKu3FaJ/XYHpNDpLcd9uEjwD3SwA3pxTFh5JRUM/zTnQz6aHudZjsup5SJX+1h5fGmroRqjZZHFh+jtEpNkIsVr6w8w1tro3C1MWd4mGuTx7eZmgrRP3DoB1h+r2jMXHiTmCxfWyN0/X/cAns/h/l9YP1zYq5Mc71dFyNmnfgdmLcQHFg6ktr1aeylckpP/FN3c0aRCHIa9vm4yQVUO4Yyuux1Nmu6MyTpi3opUfJ+IYnTVdlaQpIk3p4Sga25CZPn72WPvncyeIS4cE76SjSd//NwXaATn1tOfnkNfQIdL/39t4bYTWBiyZhJM/FztGTBHT2wMlOJSmLSnvrKY9wWOP47jHwdC2c/vrytG+lFlcxZdJi/j2cQOuIulLf/DUUpKFY9xCcT/bhDXo2XlE/wrE/wdbLk0dGd8J27DAdTDWY2zpjcvEAEHKaWIgt8kaq/JEnc0c+ftydHcCipgKJGpjNFVRoKsOXgmRjSCit4ZPExUY0pzaRCYYWni1PTjZrZgCTx1KgO3NLDWzeKwZk5A/xZczKjyYyyjWez0Moy4zp7NN1WG9kWk8OBhAJ6W2ZR7RjG/oR8NDKimqMPcqqKYfPryO958NyhgfRSnOes/UieXHJC9KRKEoz/FLkwGefCE/zl/TImAx4DoJuPPSdTi0SF1taz3vQG4fo4wU+8x4DADizYFW8YPCqU4jeU1Iyj0+GFYGbLspr+1Gi0zO7jd9H36udkxTtTIlh/OosVx9JRa7R8syOOd9ZFc9/AANY9PpDbegs3vcnz93AytYgXxoQ1sYYf0sGV4ko1h5IKePiPY8z64UCTzPwVo7oU/n0FWaESvRHqKjj4HXL2Wf7wfYse6h9QdbmVLj72dU8ZfdNEorW+ZOz5vdlNyrKM8uRishTuuIWJZInSwp4aVNSWNpWR747JZJDiFOoe98Iti+jPCXKitnM8pZBeUgxV7r0Mg5Ww8ahkNT3VR9l93rCHZ8e5XCyrsnGrOA8dxlzeZ6NQtix1ayv7vwEHf0x9enJXf3/sLHXvy8a9SdXW3ERJuEUhx0rt+etIKg8NDcbOxlLYks/8ExRKXG1FAJ7TKDBOKRBGSvogx9XWnC7edmxK1rK267conk+ACfNE8rYZFAqJx0eEcCChgJdWnBJOb3qUJuz2fYRx1e9x/v44kWRrhEqpYIxOsnYxuXmVXSDbpd6UqhwIeHglwV4uhLnbMn92d2KySrjvlyO88Pcpnl564oLbaQ2X9W2eOHGCf/75h7Vr15KQ0A7+1w7+MGcDzFqG4oUkwm//mHBPW1TKdjzogkeiSDvEevNXGR/1rJBZnF0JHSey6WwWGq3M2Ha8AICwjh3cwYUObjb1wZedaJLsbF1qGOQUJgvZgsqMFF0lpzFjItypUosT4vd39GgXT/cmeEQK69F9Xxo0Fk/v6cNHN0eiLMtgd7ZpneQvs6iyLnOMTx/xnAKRye7h50BGcZV4n7qMfUOp13LNEGRJwWPqRTzsl4FWljhnHlF3f2JeORnFVSI4bIyZDfj2xTplG1197NkWoyv/HvsFlGZCkvU/THGFmv9bdYakvEt3kjtRpDv2SrMI97QFoDb7fJNM9apCP6wiJ/Hvk4NxtTHjpz1JlFfXcs/5fnzh9g6Wtk7IjoEkmHdm+dE01Botu2PzGBrqKhY3A54Qmda/74XSbMI9bTn4ygi+v6MHnvYW3P3zYRbuSWTm9weIyynjmb9ONpEtvLsumsNJhcyf3Z0f7+rF7ueHceDlEfx0dy9GdGzHfrXt7wq54oO74OloMZvA1FLMIfqmr3AcK04TTkuDnxWzsRbfCp+EwNqnWh4A3JjyfCFV09vLtoCNb2eOakNQnaxffOhNQDzsGmTrSjOwdfVl3vQubPF9ElV1kWiyBZGQ8Ozequylo5Upqx4dQKibDXf+dJB/jusWnJIkfmtTvxOBzpY3ADicVIAkQQ+/KxTkpBwArx5M7BHIjueG4eckTDKInCGuJ3/dKc6pKx8ScyV6ChlZJ087Dr8ykj0vDGPnc0N5cEiQkN9N/RZi1uL5XRiPsQS6zkblUX8uwt4X7t8uZFAN7WkvAX9nsY+5jQxnjiQVkqWxZXqYGUsf7EdGUSX/t+oslGaRi2OdAUhrmNbdm7LqWv49a/g72RyVTU8/B9zaUangZG3GPX08sS5LwNo3kuJKtahCBQ6BnCj4vDN8FgEHviUn7E6eUz9AzIDPmHL3U1SpNfUT5t06cWrieoZUfYLv8PvqFpvdfB0orlSTmF+uC3LqqzXpRZX4KvLBwpE5wyI4n13G9nONFtUBg4RcraGbprpSOFV1vY1Fh7IZHe7WsvlHIyZ39WJSF09eX32WQR9u56ON57hvYACvjO9IJ087XpsQzjezuxOXU8aAYCcGNZxFpaObrz02Zioe+PUoO8/n8vyYsPZd51yIHR+grShgTvXTyOW5cOAbtHs+Z0ntUD7P7MQdg8N5a3Ing6f4OVuTZNsDu9wjzUpSTx3dR9/K3RRE3lcf7EsSxZK96AFpRNqZ3dhJFViGj4HwKVSbORFRtp8lBxLpqTyPZcggwyc4BSE7hzLN8qSh/TZi9sssh2hAEg571xNZZ4ThzsCnmyZbrd2bNTlw02QRV+OEk5Upd+vbNDy71fXMuuiqjI2rfykFFbjYmGFhWr8evL2vH30CHHl1YoQwjLkIYyPceXtyJ1YcS2fiV3tIzq//zWyJzsbL3oJQz5bP5RMiPcgqqWLNqQzickpbDHaWHUnloaqHKbxnL6YO9Ynrrj72vDulM6kFFZzNLCa3rLpNFuQNaVOK+9SpU8yePZuUlBTKysoIDw8nPT2dESNGsHDhQmxtL/5htohLaL3P/JUgZBRsfBFLczNmVLzOrb0DiD99gOiYXqRWJbT7BaBFdEFOmHkRywobZJp19tG1Gi0ZRZX4ODY98Qa72vDyuDAGBDtf2X0d9RYsvRMWDBbNejrnpVt7eKNZX8TiHFNW/X2K96d1JresGg/9RULf/1KQAM4hRHiJ4+FcdimesZvEDCFdqVOWZVbF1eDh/QK3nXmHTuabOCf5sfxsGa/4i83sjctDIdFyNjhkNGx7hzH93uKznWlUqTWYR68RxgaX2bR2vROVUcK60xkUVaixszDhiZEh9bOagCWHU/h1fzKrT2bw7ewehrbBF6C0Ss2+HBMwAUoyCPQPwUwFZiWJ4FSfxZFlmcTcckZ1dMNUpeCu/v58sSUWhQSFlbVMmX4POD2GBExYE8VvB5LYE5dHWXUtw/RznSJuFoNIN/+fsPa8ey22HpGM7uTO4A4uPPbncd5eG0Womw3rHu/Na/+c4fE/j7Pgzh4MC3Vl7akMft6XxGsTwukfJBYUTfrx2oPUQ2LA8PBXwTVM3Nb9DvFf5ilhfJJ6UDSQe/cU/w1+XvwOYtaISdRnVoiFspkNjHq73t2nMbH/imbs0HEX3CU3W3M+0Qzj44zvhZW3tpaqjCSszVRCEqGnJAP8BzK5q5dIFmw/Kxy67HxEoNDt9lZ/DM7WZvx2b2+e/uskLyw/RQc3m7ogmC4zhaxzx/vQ+RYOJ8qEutlgZ2EiXk+rERbm7YFWKz7v3vc3vc/cVjgD/TgSvh0gFhhTvzPI0lqYKvE2bXSchE8WdrLluWDtiqI5CZ/j5VXO9VKovNJqOrjVy2SzSqowwY6OUjHubja8MbETL644za12Z9HW2hHg3PogJ8jFmm6+9iw/llY3AkGjlTmcWMA97Vz57+pjT1cTEzhZi3dYL0wPVLAvPo+IvpPFsaCu0CWc7uDbXaVst8jg/eEjUCkVPDmyA2+tjeL2vn508bHnz0RLbO0c6e1ff67XVxROpBQRZOspbKdrqympVVBaVYurnAf2PvQPciLCy5a5vx/D0lTJgGBn5s/qLkxVtLUimNcn16JWQ1UR8woHkZRfwfvTmlo/X4i3p0SQuugQAU5W3DMwgAgvw2rruM4edPWxx9pc1Wxfr4lSwZBQF7bF5PDz3b3oH9w0ELoiZJ9FPvAtSyxmsqO8K7vMejNk65toJFN+Ut7KtmeGGJ43GiD79sMxaiXq/CRMnA2PIfWWt8lVOBM27jGD20tVDqiqDCsvGq2MTdoOqpQ2mHv3AoUCZehNjDy5jVVRB7EyqwL/AU1eXwobz8ADP/B4VDolVZ2xNTehuFLN1ugcXnA/Dc69RUX1emL3p8LBscttTe+zdoWqIlFJ0yeYKgtRqUsxdQnklaEdDQIWPS42LQU5lU2S4rf29OHWhqNNLoK+2tw7wIn7fz3C9AX7+eO+vjhYmrAtJodR4W4X7FPvE+CEu605Tyw5AcC07l7Mm97V4DFqjZYFuxIY1smXIK+mqqnpvXyY3qv1+3wx2pQ6mDt3Lt9++y3FxcWsXLmSYcOGkZWVRWhoKI8++mi77dwVwTEAnjhB0ex/OagJ5YWDZmSF3IZk6UhqYSW39vS+OvthJ17H36SwaSXH3o/M4ipqtXKzlRyABwYHXRGNuwFBw+HBnWJBtnA0HPxeZHEqC1FqqxneuyvLj6XxzrooZJm6RmdsvUGhqpuf4mlvgVIhkZZXAvE7DFxNYnPKSM6v+P/2zju8zfJs3+erLdkatmVbtuMVx44z7AzI3oRASAglBAIBUqANmzJaSku/TlZL+ysdlK8UWjaUsPkYYc8AITuBJAYncRLvES95SB7S749Hku14xpFi2TznceTAKNbK++rVcz/3dV8X9tnfh9N/i+Kqo9o+jVd2lgTa95/tP8qkZFvvOUCZS6DdzTJzPq5WDzt2bhM7iP3sgvsJpv4ThKzqWJlIsKlubOGW9TtZfv+nPPPlEbYequHhTw9y+0sdWnGv18v6rYXMz4plbLyZtf/5knf2dN85+vjbyoBbn58Pv6mkqN13fjnL0KhVzLE3o/G4O1KbgaONLTjdbWIgGrh4egoqFTy/rYir54/u2FkHblmSyU2LM5maEsVF05LJHWUTf+HvAtywTQwbPrdWdDycZRg+v49/rkzlj+fn8t+rZhJvMXD/xVOYlRHDVU9s5d+fHuT2F79i6QQHP+g03BsSDn0qNP49zTIk5MLlr8NtByBjUcftKpUo+ufeAjdsFgVR4hTx/p5eJXKieiLvDdFdsfbQveyEPVLPBs9MWtUmkfj+j1M57evbunZxQBQ5nWbomP9TOOUyYbPbVHXcLoEatYp7V+WSbo/g2qe3Udfc6fyZcxPEjIHXbmbboUqmp0cLWcan94ksnmBR9Y1YJCTP7PnvHTmw/M9ikX3OP7q+/74YvUDM6aXPD0kGVmAn9phOTkW9i3p1NKomsfN94bRkHrx0KtGeakq9NnKSju96v2rqKDburwp8v+SV1eN0t4njEWwq9gKgS8zh1NQoPtt/VBiaLPw5FTN+weOGS3gqz8vru0tZlpMQ6FqsnZVKZlwkv/m/Pfz9/Xxe2VnMOZOTusi7oiN0pESbhNTVl8uGszTwvqJay8GajKIo3Ld6MjctzmTOGDsbvioV52XsODFH2Gkup3b7i3ztzeDRb7TcflY2M49TTmk1ann5ujncd+HkbgWOn0Sbsc/survPzeGDnyw8eQWOxwOv/5hG0yh+V72EXy4fx1+axXfkI21ncu78U3stcAAScsR1rWTX+11u37flA051fUHp5JtQ6bped1y6aAzuroPsu4tqmeXZTuOoeYHPl2bcMtKUMi5Sf0i7ou1ZOpu9HEObk8mevWz4Ssx9bPiqFIOngdTaLScuVQs2VflCJTTn5u4hwCDkaiBs0f34ZrJvXHV6z8oVhKTNbNB0u34cqW4iNUibe2MdZp67ehaReg0r7t/IqXe/R4XTzfcm930NVasUXr1hDi9eO5ubFmfy0vZivirq6kb42q4SimqauW7hmF4eJbgM6gre1NTE3LliZuKcc87hzjvvRKfTcdddd5GVldXPvcMAWwpjgAcunsqEREtAPuDxeLtpZ0OG1gARcSRSSb2rDaerVVxgag5B5hmB4MWQ7EgfDzEZYhDv7dthw0/FbthsUcjOmpzLlWoLD38qipmAw5taI3ara8TtWrWKRJuBppJ94K6DtI5W9Lt7yzFoVcwdYwftzWBNxqzNofKx/XyaX8X8rFg+P1DV3V2vM7HZYE1mVOVGkmxnU7P9FTFj0I/72bflTu7dkMfG/VW8esMcsh0n0IH04fV6uerJbUxJjuLPqyed8OP1xh2v7eH9vAp+u2ICa6anoNOoeGFbEbc+v4uM2EiuXzSGbYdrOFjZyK99HY4bntnODc/s4NErpjHH98W6/UgNlz2ymeW5CWLX08c7e8pIdsRBk1k4uwCTDL6LcSe5WoFPBuffZY6K0LF2Zirv7avodhEzG7TccFrPQ9mA2IVb/QQ8tEjYdB89CO46NCXbWX1RR1ieXqPmoe+fws3P7uSuN/YxKsrIvefnnrATYr/M+wnMvL73wVZF6X1+BsTGxhl3iZ+bqoVU9snzhFVmVKd5gIMfwzcbhKtaP6hVChFmK9vt5zOj5UsYvQhbwSYS4jp1gF31oriwdPrSVGvh7L+IQmDH04NyJDLq1Pzz0lNYcf9G7n0rj3tW5oi/0OjFYz+2nLNaX2BC2m9EF0dRCdlGsDiyCVBESGRvTLlUOHgNQKpxsrAYNejUKqoaug4Ol9e7SdHHQIOQ+SqKwtKJCfBuI8lTlmA8zsXwitxE7nh9Ly/vKOb6RcI5SaNSmJJiC9Zb6fTivxbyG1M0szNi+MeH+/n9m/uod7Xx0vYi2jxe2j1eVIoovvxo1Sp+vWI8a/+zWeSmTE/h+kXdTYOSo41Chul3oKsvobhJnOOm5lJIFZ2YrHgzWfFmDlY28MbuUrYUVIt4hfR5HUVOSxPGIx+xWX8+H924sG8L9xBiNWmxEqQA797wesUMrD0TvnoeCjfxC+1vmDduFOvmjWZzwRy+t+8OivRj+GhW3zNJ4zMzOOhNxL1/IyxeF7i95YPfU6gkkrP8mm73aTXaiWjuupGTl7+fNapDtOXc2nHj6IW0q7RcqP6QptipmHuae02cCpZR/LBtB//aPp+VU0bx/LYiboj7CqXOLezIw4nWZtGJn9o97w8IxG/gLO+Yk/GbakSl9fnQsWZ9907O0UYW+uzJg4HDamD91bN48KMDpMdGsDg7vmMkoQ/iLQbiLQZyfQHy976Vx1PrZgDC+OTet/JYODaWnFEh3qT3MahOjlarJS9P6Gi/+OILIiI6dmvV6hDMh4SI5bkJgQIHOHkFjh9bMvZ2sXAsqXWJgcDmaohKDbgXJfdgPHDS0RrEouW8fwubzxd98hBLIj9bms0M385goq3TByAqPdDJATEM11J1SPxPJ7nHO3vLmZcZK9qyigI55zNxbBbjEyzc/eY+thyqpt7VFpAh9YiiwLgVKLvXsyq9haSyD/CmL+hxYVPX3Mqf3s7j3Ac+Y+lfPyGvzEmUScc9b+YN+p+nM/kVDRysbGRJMHOLjn2Ociev7irhx0uyuGx2WiCE9vxTRnHdwgz+9PY3PPDhfp7dUkii1cC8zFh0GhV/XzOFaelRXPnE1kDWwwMf7MeoVfPG7tKAlt3d1s5H31RyxgSH0AH7dMNZ6jJa0YCt48vQX+SkderY/GLZON66eV6PrfZ+ickQsqLS3ZC9DM7+q0il3/ZYl1/Ta9TcvyyWzxx/4eXcL7Eagr/j3iPBct0xRcOlL4mF/78XCwtnEJscz18uOiszr+3rEQI4LAaes60TnaJJF2HwNpMW0amT6LMg7rGTceoP4Mr3B10EpNsjuHZhBuu3FAbOBQDS5nIg4zJ+rn2W0w79BbY+IjIPgiknKfwS4if0XVhCWBU4IIqXmEidCFbtRLnTRZsxVnS9/Dp0jwecZRij++7o9YTVpOXMCQ7WbynE4/Gy5VA1E5OsmHQh+KyU7xXHAliem8i4BAuv7y7lg7xyrp4/mq3/czoH7lnG3juWdhloB5iXGcu/v38qH966kDu+N7HHbkK8xSCcM/3ncH0JxbXNqBUPmoaSgPzbT7o9gjizni8O+roIWUt94eI7KN/1Fnqvm/jpq4aswDlpfP0i/Gse3JsGb95GXuyZvNmYze3LxgFw4+JMdjOGK+Zn9dnFAXHNLTDlYqvscGYszNvCpObNlEy8BrWm+/09plis7bVdbjMUbQRAk9VpE1JvhrS5aBQPkVkLen4BKhVMPI+5rZ+xo6CC0/78EdsO13C+5hPRdbUFT+IUFBJyYc0zvRsV+Ts5/usziOu/1gQRfRcr8WZDFyfZelcrNU2tvSp/Bos9Us8vzx7PJTNSB1TgdEarVnHrGWPZuL+Kd/aUdYkBudu/IXYSGNTV7s4772Tu3LnY7XaOHj3KCy+8AEBZWRnz5s3r596SANZRmH32isW1TYzFN6BnS+XIN92HyIac3AvEDvALVwAKRDrQqFU8eOkpfH7gKDZTp5Zs9Ogu9qEp0SZU+YUiQNTnyuZ0tbKrsJbfn9f1hFcUhfsunMQ5//iMG57ZjkGrYmqqre/XtuBnsO911pX+lkhPHlWjLqeny8Qf38rjhW1FnJYdx4XTclg5JYl395bzo//u4ONvK1lwgjshG74qw6RTs3Bs8HZUjuWv7+XjsBhYM727S8qtZ4h5tj+9LXbPblycGch7MGjVPLT2VC548AuueWobfzw/l/fzKrh3VQ4vbS/mV698zbu3LGDTwaM0uNs4c0I8lDgCg77J3iKOeOPJ6CThKahqJN6iF45WPhRF6TIXdNxkLxO2vP55iaIt8PYvRDp6rK9TXL4XzVOrSGptgi+3gDNPOOm1t0DcuI4vkHDGlgzr3oNnL4bHlgsZoLMUdJFiBm6AVqhxFgMV/nA43yIww9hJFuYf1DYPUK51nFwxJ43HPj/Efe9+y/1rpgRufyzySsZoGrlsx0NCLjTruuA+8ZFNgTC64UZPO7FldS4wx0GNWxi3GG1i08vTOujzee3MVF7bVcLH+ZVsLqjhvKnHXywNiIq9MPE8QBQYL1/XfaYCQN2Lk2N/YdYJVoMI/TZGgcYoipz6ZsaZ3Sgt7oD824+iKMzKiOnI/Ji4Cj76A3x4DyXVWtzEs2je/ON8k8MMj0d0UJNnwtilNBXu5od7z+Cy2WlkxEYCMDHJyoc/WTjgxXHLqJkk5L9Fu7MCtTmOo+/8P/REkXtWD3NxgCoylhjqaHK3YtKL65m29iD1KiuWY8Kn1WPPgoMfoqT1IZ3NuQD9539nRcRequMW8+iKKKKf2wHzHxrQ6w8rIuJEREBdJ7fQWjGu0J9TY6LNSH5FR9Cuf1PcHzgbLizLcTAtLYqrntzG2Hgz35Q7uX/NFJIGaPIRDAbVyVm6dCn79+/n6aef5sCBAyxYICpvh8PBQw8Nw5NtqLAmo28oRlGguNYFFb5cg9ixFFY3kxwVhlbFE84V4ZlT1wZ0plEROpbnHuNIF50uPrA+K9nkaBPGphK8lsSADrewWmiqx8RFdnuabIeFXy0fR1VDC9PTY/pfNBttcN6/MNeJxf1HnNrtV0pqm3luayHXLxrDPy89hTXTUzBo1Zydm8CUFBv3vLEvkFnk9XpxHjOnMhA2fF3KorFxoXG8w280UMr1i8b0+BwqlcJtS7O5e+VEUqJNXHjMAF+EXsODl55CfXMrlz2ymUSrgZVTRnH3yhwq6t2c84+N/PPjAyTZjIxPsIiFsa+TE+8+wn5PQpd/l4LKxi5dnKDReSD8rHvFIu+57wuHpOJt8OhSccyv/RzOf1RYCT+2DJ48F+4bD89cBJ/9DbY9LiySwxVzvJjlmXuzMCGYsBIufQEiBi5NirfoA7t6LoNYLKZqO+mg/UXOMYuKYGHSabhxcSav7SoJdAgb3G28s6+cPdk3ig7wqod7zqIaLM5yIYdN6WUeJ8yxR+q7dXIqnG40Vr9O37fh5d/lNQ/u2E1LiyIrPpJ7N+RR1eDuMtAfNJprhK1z3IT+f3eQOCwGKp1u2gI20iUU1zQzMdK30LN238WfOTqGfWX1IrhTrRWBhvnvkF31LiWOxYFF94jlmzehch+b069lW/Ll3Ob9Ec06Ozcu7ioZTrNHDFjFEjterPWKdn2Is+IwE46+y97kNRhNPS+utdZ49EorR6urA7cZm4qp0/VQtOdcIOb50vrYKHfkgD2LP4/L57ErppNZ+rrYFBrXQxZeuKNSiZnLusKO22oOdZUu90KSzdBllttf5ARrJidYKIrCU+tm8LeLJmMzabl8dhorJoVms603Bt23ttlsnHJK37kKkn6wpaA4S0iI1IoT1unTNUfYOVL9DWlhVpUHmHheYNeuV6LSxa56fQnYkkWoqaecNvOogAq5yDd3NKqXYu7SmamU1LkGPiibOhvljLt55+NPePcIHKvQ/dfHBzBo1d3mexRF4ddnj2fNw5uY84cPyHaYqXC6qW5sYUKihZVTklg60dFjZlFnDlU1klfm5PpFoRmoa/d4+fWrX5McbWR1P44pl8xI7TX3ISXGxN8umsIPHt/CtYvGoNOoGBMXyXPXzOKeN/axuaCadXPTxYyL2SEG7lubia7dzQ7PSjLqXQFpw6GjjaHR+HdGb4bVTwqnrPWXQtE2IWtb+5LY2bWeJ74Ym6pEp/DAB7DzabFz29oEOashdZAp2CcDrREW/WLQd3dYDIEA0DKiSQMSlE5W1fUlYLCJQfAQcdG0ZB7dWMAt63fy0nWz+et7+dQ2tYrPQkwIZtMKN4n/Js8I/mOfBOyROhGu68Pd1k51YwumKF8x01Au5ij8FrOD7OQoisLaman86lURiHtq2uBsr/ukXJgOED8++I/tw2E14vEKs4YEX1ZOcW0zKwy14hd6kCrNGh2D1wtfFlRz5gQH5FxA3Tt/wNpYQPrc1SF7rWGB1wuf/InKqMmsfkcDiHDuu86dKJwOB8mE8bmUvRJFxCe/w7nRiIKWsct7D5U1+s7nuspikhPEBozVXYYrpoeFrilaOLr2haJAzgUon94nssq2PSo2XkN4bQsp1mThQOin5nAXY6beSLQZqWpoEU6yWjVHqpvQa1QB57VwQq9Rd7h6DgEnScgu6ZGoNPC0MdlWR3FNM7R/HdA1F9U09eivP2zwz93UFASKHI9SRb1hMn5VfmFNM1q1Qry5Z62noij8bGn28T3vrOvY03A6X2wsoK3dE3DxKa938d8thVyzIKPHi/yUlCi+vP103t5bxuf7q1gyPp4Eq5EP8ir4w4Y87npjH+MSLNy9ciJTU3peKGz4ugydRsWiYIZPduLxzw+x9XANz6ybEZjDGSyLsuP48vbFXS6Kk5NtrL96JjsKa8n0d9csvk7OoY2o29187JnEnDoXY+LMeDxeCqoaWTnlJFy8HBPFXNgr1wjnnbUvd53FiIwVf0BI2mb6hmDbW0WxPYKJsxiod7XR3NJOSaNCtNeE3dPJtrW+uKvpQAjQqlU8uPYUzvvfz7nskc3sLKzlx0uyurjrBZWGCojO6DXYLtyxR+qpcnacl37pmtnuO07+kEB/Jydy8PLLc6ck8YcNeYyKMnWVFAeL8j1ic8EeuugHhy8qobTOJYqc6oOU1DaT5qgW9tSm7t+VqTEmHBYDmw4eFUWOSs2DxitZ2voCk8aPYKlazWGR51W6k/uMv2Lh2Dh+vCSLqgY3C7NO7LvJqNfwSMzVpFZ+RBROKkZdy0pH7+emJUYUOY01JcBUXK3txHkqaLZM7fU+/TJxlXh/b/5UBBEvCJIl/VBgSxGmHSBUL3WFXWZeeyPJtzFcUtvM6NhIDh8VmYohN98ZhsgiZyhJEpKqWbr9vFqbBE17IHc1je42qhpaht5Z7UTwu4NUF0D6fFKiTbQpVVSq4wNFTlFNE0k2Y9ANH+aOsfP39/PZXVwXKEie+fIIakXp02bYatKy+tTkLl2Si2ekUNfUykffVvDwpwe57JHNPHvVzG723cU+Kdz8zFgi9cH9WHm9XnYV1fHHt/O4ZEZK0CxH43rIWFIUpWsRZ3aAtx12/RdvRDz7XCkBaVRZvQt3m+e48jtOiMlrhPY+cfLApU9q7YBnW4Yr/gVgeb2LkjoXMd5oRrd2CuCrLwmZVK0zWfFm/r5mMj98fCuj7RFcOX906J5s+pUwbV2/2vVwxR6p52ijCLpTFCXQiYu2xwn7/UCRUya6lSdgeGE2aPnl2eMxhWq+c8olQjYYLFOOHvAPPZf7zAe8BZ9S4XSTmFAlJD89pNT753I25lfh9Xqpa27l4ZJ04pY/wqReZoOGPXtegRfXgcHCvim/4r9fZLP+gowOu/4gcNX1P6PB9ROMOjX6fjbbLL6i3V0rQrqLqxtIUao5Ep02+BcQkyE2uaLSxOzvcMaaLJw0QUjV2lu6hW33hD+4tqTWxejYSAqqGkO3oTTMOUkRu5IeiYgB+1hy2vfQUFMudu3iczh81DdENpyLHK1RzHNUHwTAqm7BrtRT5OlYoBdWN4ekkJuSYiNCp2ZjvtjN9nq9vLa7hCXj4we1k2k1afne5CSeXjeT5CgT3//PZt7eU0Zbuwenq5VnNx9h6V8/oamljZtP7/8CdTw8+lkB8/74Iec+8BnxFkPAFeek4R9W3/c6SubpWAzaQJFzrH30SSF9XnBnO0YA/kDgsnoXZXXNVKli0DaVd/yCs2TgGTEnyGnZ8Tzxg+n857JpJ2Y+MRCGaYEDYDfraW33BvKFyutFJyfeahIDyY2dipxBzuN0Zs30lNDJRXQRwkkqhMRE6NCqFUrrXOKa1FCO4vUQ21Lc4zyOn7NzE8ivaGDj/ire31dBm8crujojldTZsOA2uGkXvyqZzaTkqKDnImnVKqIidBi06n47B9pIOx4U2urF+VxZehit0k5E3AkG0macNvwLHBAyy+ZqMWta9a24rVMOXW/4B/eLa8VaMb+igaz47rPNEtnJGXpSZpKe9xH2hhzQAfETyCsWWu1sxzBfzEWnB7JylPpiAPa3ROM3jiyqaQrJPIdWrWLG6Bg++baSGxdnklfm5GBlIz8/XunbMViNWp744XTWPb6Vq5/cRkyEDqerjZZ2D2dNdHDPyhyiIoIrBzFq1SzIimXR2Dhmj4kJjf1rX/hnAdrdMGYxjgIDZfUdRY6ihJ+jy3eNzp2cXUV1ZOliO8wGQPycffIGc+dlhs5ZcKQQCAR1urGZdJTXu9CqFaJNOiG7bPAVqc6y4eEUGGJUKoU4s2/2LD0RxdtOulJKVMUmWHR7r/c7LTuOCYkW/vZePlEROiaNsgZ2wUckkXG0z/spD396kK2Ha/jfS6YOrYRJraFOsQQ6kw1lYj0Qldg9C+k7iV9uW1sogqG1pj6Ldj8GrZqYCB3FtS5qm1qodLrJlEVOj8giZ6hJnY11++PMUX2FV6VDsWeyd0s+iVZDaPTTJ5OodCjbLX72DdftbRIyL6/XS1FNc8icNs6ZlMjN63eyMb+KLw5WYdZrWBAEW2d7pJ6Xr5vNrqI6Xt1ZTKLVyJLx8V3yloLJRdNTuCgkjzxA/IFligpGL8Jh3d+lk5NkM4Z+x17SJxajBr1GxSffVvHu3nJumjC643PX6oKmoyetkyMZGLFmcW2vbHCTGW+mvN5NnNkgpLuR8dDQyV0t7iR3b8MUh9UgOjm+c3mt+l1U7W7hSNgLiqJw4+JMrn5yGyoFbj0zdHND4cC35U5++vwudhXVsXZmalh0rZzqKDQuoapwHz0EgN7e/9zJdwJ/QVNXKDo5MWN6lF72RKLNSHFNM/srGgDIjBvmm+IhQhY5Q40vaXy1+iPqzRlY1Vr2ldUzLiG8AuwGRXSaCA/1eqH2CF4UdtaJYqCuuZUGd1uvzmonyjmTEnly02F+/erXtHu9LJkQH7TFuKIoTE62MfmYULsRiUYngsmi0sAUjcOiZ0+J6DTmVzQwOlbuHg01iqLgsBp4cXsRiVYDY7Oy4UClMF1w+u2jZZETTvhDKKsahPlAeb2LOIvPBCQirmMY2VkGGYuG4iWGHQ6rr4tsETKlCzUfCxOSftLhzxgfz7gEC/tK68Ni0R9KapvE9+pzV88KukxtsDRro9C7fHlFtUdoUoyYDLYhfU1hgyUJUMQmcOU3EDvwIjzJZqSktpn8igYUhUD2kaQrciZnqLGlgCWJGMVJgTodr9fLvlIn4xNHQpEzGtz1Yie59ghN+lgKne2429oDGTmhMldQqRTu+N4EDh1t5PDRJlbkykXeoMm5QAx5I6xcy+tdtLZ72HaomlNTQ2BJKzlu/A6F1y7MQGtLArxigRziIFDJ4LAatWjVClU+V7XyeleHy2RknMjJ8bQL2doJOKuNJBwWX8p7RCztqDHSdxfHj6Io/O6cCVwxJ23ELwSnp0fzzi0LwqbAAWgx2DG2Ckt7fWMxNVrHsJ6nCyoanZi583dyjsOhMNFmpKSumfzyBpKjTOEVHB9GyCJnqFGUQDdniyuR8nqRzzIiOjnxE8V/D3wIdYW0WZLxeqG4prnfjJxgMCHRyhVz0ok165kTJEey7yRLfw+ThGjOYTFQ1dDCtsM1NLa0Mzsjpp87S04Go6KMOCwGVk9L7nBSc5Z2CgKVRU44oSgKMREdgaDl9a6AgxiRcWKGoa5IOBvKmRwAEnydHK+iUK3yXXcGUOSAWPz/ZkXowkrDCXWQ3UpPFLU5FnN7NVUNbsyuMpqMoXd6HFbYkqFoq9gQHoCzmp+kKCOltS6+LXd2RD5IuiGLnHDAF1T4cW0cWw6JZOARUeTEjoXUubDpAag9giZa6HB3F9VRWOMLr4oMbXjV/ywbx/s/WXDCuTISgcMqjterO4sxatVBtSaVDJ6fn5XN+qtnCkmmv2tTXwy1h0Eb0TVTSBIW2M26TkWOu0OuFhkHnlZ4apUIcU2SodsgXARb2jzkVzRQ0BZNhW2ysJSXhDUpmbkkU8H/fbaTmHYRCC7phDUZjnwhfj4uuZqBlnYPWw9XM0aaDvSKXPmFA9krqMtaxTbPGJ7+8jAmnZrU4Wwf3ZnZN0DJDijeTkRcOlNTbDy75QhFNc2MijKG3PlFpVKwGEZ2TsrJxGERnbfXd5cyLT1aFo9hQpzF0JGTYIoBtU50cXath9ELpTwkDImN1FPpdNPgbqPB3dYhV4vwBTa6auHyN0QOjIQEX6frkY0F/LJtHdrz/zXEr0gyECKnXgCKirpNT5BEFeqo4RngGzJsyeBpE4G60QN3nfO7BLpaPdJ0oA/kCiUcMMdjvug/aAyRbDpYTbbDHPSAzCEj80zxwfW2gy2Fi2eksulgNRv3Vw3vsNPvKH5JjdPVxqzRUqoWlqhUQuK08xk4mg8zrxnqVyTpAXuknqqGFip8luz+vCMScsUc3OVvgmPiEL7C8ML/7/PS9mIcYyYRNerEIgEkJ4kIO3Upp3OR502MSgvGuLShfkXhhd9hLTpdzOgMkKROVugyI6d3ZJETJqhUCqf4hrhHhOmAH5UKZl0nfrYmszwnAbNBw8HKRpKjZJEz3IgyaQPdm1lyHid8MScKh674iZA2b6hfjaQH7GYxk7P9SC0A8X65msEKq/4Nsf2HAn6X8Bc5Le0ezglR9IAkNETP/QEJipDi2xJkRk4X/Fk5x2E6ABAdocOgFd/FI91Q40SQRU4YMS1NOKKMiHmczky+FM64C9LmYtSpWTVVaHJDaTogCQ2KouCwGDDrNUwcScX4SMNvPjDjailVC1PskXrK6l3c9sIu5mXapR17P+g0KuyROnQaFWdMiB/qlyM5DpQxp9OkFzLMyPjRQ/xqwgx/J+c4NzUURSHRZiTJZiRCL9NgekMWOWHETJ/8Z9JIG+bWGmD2j0AjdiovnpEifd2HMWn2CGaPiUGjlpePsCV6NJjsQvYkCUtSo014vfDDuek8evm0sHPFCkdSYyJYMi5ezlkON1RqTDMux6u3oPhnziQCWwroLTBq2nHfdbQ9kglys7FPFK/X6x2qJ7fZbNTW1g7V04clBVWNpNsjTvrznuxjcaiqkZRo08iZPQoi4f65qG5sQa0oWE0jf6ER7seiV1oawVU3oqyjh+2x6AWv10thdTMpMcNPtjtUx6LS6cagVWGWRU6AYfO5aG8Vjo/9hLcOZwZ9LNwNoIs47q57TWMLigI208Bneb5ryB5XmDEUBc5QkPYdeZ8jkegIeUENe3QR4o8kbFEUZVgWOENJrDm0kQOSEKLWjugC54TQD07VEiW/i/tlSDs5KpUKi0W22kKFXq+nvLx8QL8rj0VokccifJDHInyQxyJ8kMcifJDHInyQxyJ8OJ5j4WdIixyJRCKRSCQSiUQiCTZyclgikUgkEolEIpGMKGSRI5FIJBKJRCKRSEYUssiRSCQSiUQikUgkIwpZ5EgkEolEIpFIJJIRhSxyJBKJRCKRSCQSyYhCFjkSiUQikUgkEolkRCGLHIlEIpFIJBKJRDKikEWORCKRSCQSiUQiGVHIIkcikUgkEolEIpGMKGSRI5FIJBKJRCKRSEYUssiRSCQSiUQikUgkI4r/D5uJomGwN//GAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"<Figure size 810x540 with 80 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot twice augmented data\\n\",\n    \"\\n\",\n    \"fig, axs = plt.subplots(nrows=10, ncols=8, figsize=(6, 4), layout='constrained')\\n\",\n    \"\\n\",\n    \"set_seed(42)\\n\",\n    \"\\n\",\n    \"aug_data_1 = augment(data['x'], maxshift=10)\\n\",\n    \"aug_data_2 = augment(data['x'], maxshift=10)\\n\",\n    \"\\n\",\n    \"for y in range(10):\\n\",\n    \"    for i in range(8):\\n\",\n    \"        axs[y, i].plot(aug_data_1[data['y'] == y][i])\\n\",\n    \"        axs[y, i].plot(aug_data_2[data['y'] == y][i])\\n\",\n    \"        axs[y, i].set_xticks([])\\n\",\n    \"        axs[y, i].set_yticks([])\\n\",\n    \"\\n\",\n    \"    axs[y, 0].set_ylabel(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"id\": \"F8gCXM32iZSY\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define model\\n\",\n    \"\\n\",\n    \"class SimCLR(torch.nn.Module):\\n\",\n    \"    def __init__(self, output_dim=16, channels=25, hidden_dim=125, verbose=True):\\n\",\n    \"        super(SimCLR, self).__init__()\\n\",\n    \"        self.conv1 = torch.nn.Conv1d(1, channels, 5, stride=2, padding=1)\\n\",\n    \"        self.conv2 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"        self.conv3 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"        self.linear1 = torch.nn.Linear(channels * 5, hidden_dim)   # 125D\\n\",\n    \"        self.linear2 = torch.nn.Linear(hidden_dim, output_dim)\\n\",\n    \"\\n\",\n    \"        if verbose:\\n\",\n    \"            print(\\\"Initialized CNN with {} parameters\\\".format(self.count_params()))\\n\",\n    \"\\n\",\n    \"    def count_params(self):\\n\",\n    \"        return sum([p.view(-1).shape[0] for p in self.parameters()])\\n\",\n    \"\\n\",\n    \"    def forward(self, x, projector=True, all_layers=False):\\n\",\n    \"        x = x.view(-1, 1, x.shape[-1])\\n\",\n    \"        h1 = self.conv1(x).relu()\\n\",\n    \"        h2 = self.conv2(h1).relu()\\n\",\n    \"        h3 = self.conv3(h2).relu()\\n\",\n    \"        h3 = h3.view(h3.shape[0], -1) # flatten the conv features (125D)\\n\",\n    \"\\n\",\n    \"        if not projector:\\n\",\n    \"            return h3\\n\",\n    \"\\n\",\n    \"        p1 = self.linear1(h3).relu()\\n\",\n    \"        p2 = self.linear2(p1)\\n\",\n    \"        \\n\",\n    \"        if all_layers:\\n\",\n    \"            return [h1.view(h1.shape[0], -1), h2.view(h1.shape[0], -1), h3, p1, p2]\\n\",\n    \"        \\n\",\n    \"        return p2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"id\": \"hfbYR90cn5uX\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Set up contrastive training\\n\",\n    \"\\n\",\n    \"def contrastive_train(dataset, model, lr=0.01, n_epochs=100, batch_size=100,\\n\",\n    \"                      tau=0.5, print_every_epoch=10, maxshift=10):\\n\",\n    \"\\n\",\n    \"    optimizer = torch.optim.Adam(model.parameters(), lr)\\n\",\n    \"\\n\",\n    \"    x = torch.Tensor(dataset['x']).to(DEVICE)\\n\",\n    \"    x_test = torch.Tensor(dataset['x_test']).to(DEVICE)\\n\",\n    \"    model = model.to(DEVICE)\\n\",\n    \"\\n\",\n    \"    knn_acc = np.nan\\n\",\n    \"    lin_acc = np.nan\\n\",\n    \"        \\n\",\n    \"    t0 = time.time()\\n\",\n    \"    for epoch in range(n_epochs):\\n\",\n    \"        acc_loss = 0\\n\",\n    \"\\n\",\n    \"        shuffle = np.random.permutation(len(x))\\n\",\n    \"\\n\",\n    \"        for batch in range(len(x) // batch_size):\\n\",\n    \"            batch_ind = np.arange(batch * batch_size, (batch + 1) * batch_size)\\n\",\n    \"                \\n\",\n    \"            # Augmenting the batch twice\\n\",\n    \"            x_augm1 = augment(x[shuffle][batch_ind], mode='pytorch', maxshift=maxshift)\\n\",\n    \"            x_augm2 = augment(x[shuffle][batch_ind], mode='pytorch', maxshift=maxshift)\\n\",\n    \"\\n\",\n    \"            # Pass both views through the model\\n\",\n    \"            out1 = model(x_augm1)\\n\",\n    \"            out2 = model(x_augm2)\\n\",\n    \"\\n\",\n    \"            # Note: this uses half-batch repulsion instead of full-batch\\n\",\n    \"            o1 = torch.nn.functional.normalize(out1)\\n\",\n    \"            o2 = torch.nn.functional.normalize(out2)\\n\",\n    \"            scores = torch.matmul(o1, torch.t(o2))\\n\",\n    \"            sims = torch.exp(scores / tau)\\n\",\n    \"\\n\",\n    \"            # InfoNCE loss function\\n\",\n    \"            loss = torch.mean(-torch.log(torch.diag(sims)) + torch.log(torch.sum(sims, axis=1)))\\n\",\n    \"\\n\",\n    \"            acc_loss += loss.item()\\n\",\n    \"\\n\",\n    \"            loss.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"\\n\",\n    \"        # Evaluate and print\\n\",\n    \"        if (epoch + 1) % print_every_epoch == 0:\\n\",\n    \"            t1 = time.time()\\n\",\n    \"            mean_loss = acc_loss / (len(x) // batch_size)\\n\",\n    \"            mean_time = (t1 - t0) / print_every_epoch\\n\",\n    \"\\n\",\n    \"            # Compute test set classification accuracy\\n\",\n    \"            model.eval()\\n\",\n    \"            with torch.no_grad():\\n\",\n    \"                knn = KNeighborsClassifier(n_neighbors=10, metric='cosine')\\n\",\n    \"                knn.fit(model(x).detach().numpy(), dataset['y'])\\n\",\n    \"                knn_acc = knn.score(model(x_test).detach().numpy(), dataset['y_test']) * 100\\n\",\n    \"\\n\",\n    \"                lin = make_pipeline(StandardScaler(), LogisticRegression(tol=1e-2, max_iter=1000))\\n\",\n    \"                lin.fit(model(x, projector=False).detach().numpy(), dataset['y'])\\n\",\n    \"                lin_acc = lin.score(model(x_test, projector=False).detach().numpy(), dataset['y_test']) * 100\\n\",\n    \"            model.train()\\n\",\n    \"\\n\",\n    \"            print(f'Epoch {epoch + 1:3.0f}, loss per batch {mean_loss:.2f}, val kNN acc Z: {knn_acc:4.1f}, val lin acc H: {lin_acc:4.1f}, time per epoch {mean_time:.1f}s')\\n\",\n    \"            t0 = t1\\n\",\n    \"\\n\",\n    \"    return acc_loss / (len(x) // batch_size), knn_acc, lin_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"fhlyga_W10q5\",\n    \"outputId\": \"678acbf3-94fa-49b1-f445-014c34328cb2\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized CNN with 21716 parameters\\n\",\n      \"Epoch  10, loss per batch 3.10, val kNN acc Z: 59.9, val lin acc H: 74.3, time per epoch 0.3s\\n\",\n      \"Epoch  20, loss per batch 3.02, val kNN acc Z: 66.2, val lin acc H: 75.9, time per epoch 0.3s\\n\",\n      \"Epoch  30, loss per batch 2.99, val kNN acc Z: 68.4, val lin acc H: 78.9, time per epoch 0.3s\\n\",\n      \"Epoch  40, loss per batch 2.97, val kNN acc Z: 71.7, val lin acc H: 79.0, time per epoch 0.3s\\n\",\n      \"Epoch  50, loss per batch 2.95, val kNN acc Z: 71.4, val lin acc H: 81.1, time per epoch 0.3s\\n\",\n      \"Epoch  60, loss per batch 2.94, val kNN acc Z: 72.2, val lin acc H: 81.8, time per epoch 0.3s\\n\",\n      \"Epoch  70, loss per batch 2.92, val kNN acc Z: 73.5, val lin acc H: 82.4, time per epoch 0.4s\\n\",\n      \"Epoch  80, loss per batch 2.92, val kNN acc Z: 74.7, val lin acc H: 82.2, time per epoch 0.3s\\n\",\n      \"Epoch  90, loss per batch 2.91, val kNN acc Z: 74.5, val lin acc H: 82.7, time per epoch 0.4s\\n\",\n      \"Epoch 100, loss per batch 2.91, val kNN acc Z: 74.7, val lin acc H: 82.7, time per epoch 0.4s\\n\",\n      \"Epoch 110, loss per batch 2.90, val kNN acc Z: 73.7, val lin acc H: 82.8, time per epoch 0.4s\\n\",\n      \"Epoch 120, loss per batch 2.90, val kNN acc Z: 74.6, val lin acc H: 82.8, time per epoch 0.4s\\n\",\n      \"Epoch 130, loss per batch 2.89, val kNN acc Z: 72.9, val lin acc H: 83.2, time per epoch 0.3s\\n\",\n      \"Epoch 140, loss per batch 2.89, val kNN acc Z: 75.0, val lin acc H: 82.3, time per epoch 0.3s\\n\",\n      \"Epoch 150, loss per batch 2.89, val kNN acc Z: 72.8, val lin acc H: 82.0, time per epoch 0.3s\\n\",\n      \"Epoch 160, loss per batch 2.89, val kNN acc Z: 73.1, val lin acc H: 83.5, time per epoch 0.3s\\n\",\n      \"Epoch 170, loss per batch 2.89, val kNN acc Z: 73.9, val lin acc H: 81.9, time per epoch 0.4s\\n\",\n      \"Epoch 180, loss per batch 2.88, val kNN acc Z: 71.7, val lin acc H: 81.7, time per epoch 0.3s\\n\",\n      \"Epoch 190, loss per batch 2.89, val kNN acc Z: 71.7, val lin acc H: 81.9, time per epoch 0.3s\\n\",\n      \"Epoch 200, loss per batch 2.88, val kNN acc Z: 74.1, val lin acc H: 82.1, time per epoch 0.3s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Train the model and pass the entire dataset through it (before/after)\\n\",\n    \"\\n\",\n    \"set_seed(42)\\n\",\n    \"\\n\",\n    \"model = SimCLR(output_dim=16)\\n\",\n    \"\\n\",\n    \"Z0 = model(torch.Tensor(data['x']).to(DEVICE)).detach().numpy()\\n\",\n    \"\\n\",\n    \"contrastive_train(data, model, n_epochs=200, tau=0.5, batch_size=100, maxshift=10)\\n\",\n    \"\\n\",\n    \"Z = model(torch.Tensor(data['x']).to(DEVICE)).detach().numpy()\\n\",\n    \"H = model(torch.Tensor(data['x']).to(DEVICE), projector=False).detach().numpy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"id\": \"yg0vVHhNWf5G\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Visualise using t-SNE\\n\",\n    \"\\n\",\n    \"T  = TSNE(metric='cosine', n_jobs=-1, square_distances=True).fit_transform(Z)\\n\",\n    \"T0 = TSNE(metric='cosine', n_jobs=-1, square_distances=True).fit_transform(Z0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 428\n    },\n    \"id\": \"qr50IfU3W0CG\",\n    \"outputId\": \"a6879414-3c4a-444f-acad-73da8cda1e6b\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 540x270 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot the visualisation\\n\",\n    \"\\n\",\n    \"fig, axs = plt.subplots(ncols=2, figsize=(4, 2), layout='constrained')\\n\",\n    \"\\n\",\n    \"axs[0].scatter(T0[:,0], T0[:,1], c=data['y'], s=2, cmap='tab10')\\n\",\n    \"axs[0].set_aspect('equal', 'datalim')\\n\",\n    \"axs[0].set_title('Before training')\\n\",\n    \"axs[0].axis('off')\\n\",\n    \"\\n\",\n    \"axs[1].scatter(T[:,0], T[:,1], c=data['y'], s=2, cmap='tab10')\\n\",\n    \"axs[1].set_aspect('equal', 'datalim')\\n\",\n    \"axs[1].set_title('After training')\\n\",\n    \"axs[1].axis('off')\\n\",\n    \"\\n\",\n    \"for y in range(10):\\n\",\n    \"    mu = np.median(T[data['y'] == y], axis=0)\\n\",\n    \"    axs[1].text(mu[0], mu[1], y, fontsize=10, ha='center', va='center')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Layer 0: linear accuracy 32.8\\n\",\n      \"Layer 1: linear accuracy 75.1\\n\",\n      \"Layer 2: linear accuracy 80.9\\n\",\n      \"Layer 3: linear accuracy 82.1\\n\",\n      \"Layer 4: linear accuracy 76.1\\n\",\n      \"Layer 5: linear accuracy 75.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluate every layer\\n\",\n    \"\\n\",\n    \"lin = make_pipeline(StandardScaler(), LogisticRegression(tol=1e-2, max_iter=1000))\\n\",\n    \"\\n\",\n    \"L_train = model(torch.Tensor(data['x']), all_layers=True)\\n\",\n    \"L_train = [l.detach().numpy() for l in L_train]\\n\",\n    \"L_train = [data['x']] + L_train\\n\",\n    \"\\n\",\n    \"L_test = model(torch.Tensor(data['x_test']), all_layers=True)\\n\",\n    \"L_test = [l.detach().numpy() for l in L_test]\\n\",\n    \"L_test = [data['x_test']] + L_test\\n\",\n    \"\\n\",\n    \"acc = np.zeros(len(L_train))\\n\",\n    \"\\n\",\n    \"for i in range(len(L_train)):\\n\",\n    \"    lin.fit(L_train[i], data['y'])\\n\",\n    \"    acc[i] = lin.score(L_test[i], data['y_test']) * 100\\n\",\n    \"    print(f'Layer {i}: linear accuracy {acc[i]:.1f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Model without a projector (only convolutional layers)\\n\",\n    \"\\n\",\n    \"class SimCLR_noprojector(torch.nn.Module):\\n\",\n    \"    def __init__(self, output_dim=16, channels=25, hidden_dim=125, verbose=True):\\n\",\n    \"        super(SimCLR_noprojector, self).__init__()\\n\",\n    \"        self.conv1 = torch.nn.Conv1d(1, channels, 5, stride=2, padding=1)\\n\",\n    \"        self.conv2 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"        self.conv3 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"\\n\",\n    \"        if verbose:\\n\",\n    \"            print(\\\"Initialized CNN with {} parameters\\\".format(self.count_params()))\\n\",\n    \"\\n\",\n    \"    def count_params(self):\\n\",\n    \"        return sum([p.view(-1).shape[0] for p in self.parameters()])\\n\",\n    \"\\n\",\n    \"    def forward(self, x, projector=True, all_layers=False):\\n\",\n    \"        x = x.view(-1, 1, x.shape[-1])\\n\",\n    \"        h1 = self.conv1(x).relu()\\n\",\n    \"        h2 = self.conv2(h1).relu()\\n\",\n    \"        h3 = self.conv3(h2).relu()\\n\",\n    \"        h3 = h3.view(h3.shape[0], -1) # flatten the conv features (125D)\\n\",\n    \"        \\n\",\n    \"        if all_layers:\\n\",\n    \"            return [h1.view(h1.shape[0], -1), h2.view(h1.shape[0], -1), h3]\\n\",\n    \"        \\n\",\n    \"        return h3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized CNN with 3950 parameters\\n\",\n      \"Epoch  10, loss per batch 3.58, val kNN acc Z: 40.3, val lin acc H: 40.8, time per epoch 0.3s\\n\",\n      \"Epoch  20, loss per batch 3.51, val kNN acc Z: 45.3, val lin acc H: 46.5, time per epoch 0.3s\\n\",\n      \"Epoch  30, loss per batch 3.46, val kNN acc Z: 54.1, val lin acc H: 53.3, time per epoch 0.3s\\n\",\n      \"Epoch  40, loss per batch 3.44, val kNN acc Z: 51.4, val lin acc H: 51.0, time per epoch 0.3s\\n\",\n      \"Epoch  50, loss per batch 3.43, val kNN acc Z: 51.6, val lin acc H: 53.5, time per epoch 0.3s\\n\",\n      \"Epoch  60, loss per batch 3.45, val kNN acc Z: 49.1, val lin acc H: 53.0, time per epoch 0.3s\\n\",\n      \"Epoch  70, loss per batch 3.44, val kNN acc Z: 55.1, val lin acc H: 51.7, time per epoch 0.3s\\n\",\n      \"Epoch  80, loss per batch 3.43, val kNN acc Z: 48.0, val lin acc H: 48.8, time per epoch 0.3s\\n\",\n      \"Epoch  90, loss per batch 3.42, val kNN acc Z: 47.3, val lin acc H: 48.5, time per epoch 0.3s\\n\",\n      \"Epoch 100, loss per batch 3.43, val kNN acc Z: 50.1, val lin acc H: 49.9, time per epoch 0.3s\\n\",\n      \"Epoch 110, loss per batch 3.41, val kNN acc Z: 49.3, val lin acc H: 49.3, time per epoch 0.3s\\n\",\n      \"Epoch 120, loss per batch 3.43, val kNN acc Z: 51.5, val lin acc H: 47.7, time per epoch 0.3s\\n\",\n      \"Epoch 130, loss per batch 3.40, val kNN acc Z: 47.8, val lin acc H: 49.5, time per epoch 0.3s\\n\",\n      \"Epoch 140, loss per batch 3.41, val kNN acc Z: 48.9, val lin acc H: 50.0, time per epoch 0.3s\\n\",\n      \"Epoch 150, loss per batch 3.40, val kNN acc Z: 45.7, val lin acc H: 52.3, time per epoch 0.3s\\n\",\n      \"Epoch 160, loss per batch 3.40, val kNN acc Z: 48.2, val lin acc H: 51.8, time per epoch 0.3s\\n\",\n      \"Epoch 170, loss per batch 3.42, val kNN acc Z: 50.0, val lin acc H: 53.6, time per epoch 0.3s\\n\",\n      \"Epoch 180, loss per batch 3.39, val kNN acc Z: 52.1, val lin acc H: 51.7, time per epoch 0.3s\\n\",\n      \"Epoch 190, loss per batch 3.39, val kNN acc Z: 49.8, val lin acc H: 51.5, time per epoch 0.3s\\n\",\n      \"Epoch 200, loss per batch 3.39, val kNN acc Z: 48.7, val lin acc H: 53.4, time per epoch 0.3s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Train\\n\",\n    \"\\n\",\n    \"set_seed(42)\\n\",\n    \"model_noprojector = SimCLR_noprojector()\\n\",\n    \"contrastive_train(data, model_noprojector, n_epochs=200, tau=0.5, batch_size=100, maxshift=10);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Layer 0: linear accuracy 32.8\\n\",\n      \"Layer 1: linear accuracy 82.2\\n\",\n      \"Layer 2: linear accuracy 79.5\\n\",\n      \"Layer 3: linear accuracy 53.4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluate \\n\",\n    \"\\n\",\n    \"lin = make_pipeline(StandardScaler(), LogisticRegression(tol=1e-2, max_iter=1000))\\n\",\n    \"\\n\",\n    \"L_train = model_noprojector(torch.Tensor(data['x']), all_layers=True)\\n\",\n    \"L_train = [l.detach().numpy() for l in L_train]\\n\",\n    \"L_train = [data['x']] + L_train\\n\",\n    \"\\n\",\n    \"L_test = model_noprojector(torch.Tensor(data['x_test']), all_layers=True)\\n\",\n    \"L_test = [l.detach().numpy() for l in L_test]\\n\",\n    \"L_test = [data['x_test']] + L_test\\n\",\n    \"\\n\",\n    \"acc_noprojector = np.zeros(len(L_train))\\n\",\n    \"\\n\",\n    \"for i in range(len(L_train)):\\n\",\n    \"    lin.fit(L_train[i], data['y'])\\n\",\n    \"    acc_noprojector[i] = lin.score(L_test[i], data['y_test']) * 100\\n\",\n    \"    print(f'Layer {i}: linear accuracy {acc_noprojector[i]:.1f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Model with a deper projector\\n\",\n    \"\\n\",\n    \"class SimCLR_deepprojector(torch.nn.Module):\\n\",\n    \"    def __init__(self, output_dim=16, channels=25, hidden_dim=125, verbose=True):\\n\",\n    \"        super(SimCLR_deepprojector, self).__init__()\\n\",\n    \"        self.conv1 = torch.nn.Conv1d(1, channels, 5, stride=2, padding=1)\\n\",\n    \"        self.conv2 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"        self.conv3 = torch.nn.Conv1d(channels, channels, 3, stride=2, padding=1)\\n\",\n    \"        self.linear1 = torch.nn.Linear(channels * 5, hidden_dim)\\n\",\n    \"        self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)\\n\",\n    \"        self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim)\\n\",\n    \"        self.linear4 = torch.nn.Linear(hidden_dim, output_dim)\\n\",\n    \"\\n\",\n    \"        if verbose:\\n\",\n    \"            print(\\\"Initialized CNN with {} parameters\\\".format(self.count_params()))\\n\",\n    \"\\n\",\n    \"    def count_params(self):\\n\",\n    \"        return sum([p.view(-1).shape[0] for p in self.parameters()])\\n\",\n    \"\\n\",\n    \"    def forward(self, x, projector=True, all_layers=False):\\n\",\n    \"        x = x.view(-1, 1, x.shape[-1])\\n\",\n    \"        h1 = self.conv1(x).relu()\\n\",\n    \"        h2 = self.conv2(h1).relu()\\n\",\n    \"        h3 = self.conv3(h2).relu()\\n\",\n    \"        h3 = h3.view(h3.shape[0], -1) # flatten the conv features (125D)\\n\",\n    \"\\n\",\n    \"        if not projector:\\n\",\n    \"            return h3\\n\",\n    \"\\n\",\n    \"        p1 = self.linear1(h3).relu()\\n\",\n    \"        p2 = self.linear2(p1).relu()\\n\",\n    \"        p3 = self.linear3(p2).relu()\\n\",\n    \"        p4 = self.linear4(p3)\\n\",\n    \"        \\n\",\n    \"        if all_layers:\\n\",\n    \"            return [h1.view(h1.shape[0], -1), h2.view(h1.shape[0], -1), h3, p1, p2, p3, p4]\\n\",\n    \"        \\n\",\n    \"        return p4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialized CNN with 53216 parameters\\n\",\n      \"Epoch  10, loss per batch 3.27, val kNN acc Z: 28.6, val lin acc H: 67.1, time per epoch 0.3s\\n\",\n      \"Epoch  20, loss per batch 3.07, val kNN acc Z: 43.3, val lin acc H: 71.1, time per epoch 0.4s\\n\",\n      \"Epoch  30, loss per batch 3.00, val kNN acc Z: 48.8, val lin acc H: 75.8, time per epoch 0.4s\\n\",\n      \"Epoch  40, loss per batch 2.97, val kNN acc Z: 51.3, val lin acc H: 75.4, time per epoch 0.4s\\n\",\n      \"Epoch  50, loss per batch 2.94, val kNN acc Z: 53.6, val lin acc H: 77.1, time per epoch 0.4s\\n\",\n      \"Epoch  60, loss per batch 2.93, val kNN acc Z: 54.8, val lin acc H: 77.1, time per epoch 0.4s\\n\",\n      \"Epoch  70, loss per batch 2.92, val kNN acc Z: 55.8, val lin acc H: 77.1, time per epoch 0.4s\\n\",\n      \"Epoch  80, loss per batch 2.91, val kNN acc Z: 54.3, val lin acc H: 76.5, time per epoch 0.4s\\n\",\n      \"Epoch  90, loss per batch 2.90, val kNN acc Z: 56.4, val lin acc H: 77.6, time per epoch 0.4s\\n\",\n      \"Epoch 100, loss per batch 2.89, val kNN acc Z: 56.7, val lin acc H: 77.9, time per epoch 0.4s\\n\",\n      \"Epoch 110, loss per batch 2.89, val kNN acc Z: 57.6, val lin acc H: 77.8, time per epoch 0.5s\\n\",\n      \"Epoch 120, loss per batch 2.89, val kNN acc Z: 57.8, val lin acc H: 77.8, time per epoch 0.4s\\n\",\n      \"Epoch 130, loss per batch 2.89, val kNN acc Z: 58.6, val lin acc H: 78.4, time per epoch 0.5s\\n\",\n      \"Epoch 140, loss per batch 2.88, val kNN acc Z: 59.8, val lin acc H: 78.3, time per epoch 0.4s\\n\",\n      \"Epoch 150, loss per batch 2.88, val kNN acc Z: 59.9, val lin acc H: 79.0, time per epoch 0.4s\\n\",\n      \"Epoch 160, loss per batch 2.88, val kNN acc Z: 60.7, val lin acc H: 78.5, time per epoch 0.4s\\n\",\n      \"Epoch 170, loss per batch 2.88, val kNN acc Z: 61.3, val lin acc H: 78.8, time per epoch 0.4s\\n\",\n      \"Epoch 180, loss per batch 2.87, val kNN acc Z: 59.6, val lin acc H: 80.0, time per epoch 0.4s\\n\",\n      \"Epoch 190, loss per batch 2.86, val kNN acc Z: 59.8, val lin acc H: 79.7, time per epoch 0.4s\\n\",\n      \"Epoch 200, loss per batch 2.87, val kNN acc Z: 61.3, val lin acc H: 79.5, time per epoch 0.4s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Train\\n\",\n    \"\\n\",\n    \"set_seed(42)\\n\",\n    \"model_deepprojector = SimCLR_deepprojector()\\n\",\n    \"contrastive_train(data, model_deepprojector, n_epochs=200, tau=0.5, batch_size=100, maxshift=10);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Layer 0: linear accuracy 32.8\\n\",\n      \"Layer 1: linear accuracy 75.9\\n\",\n      \"Layer 2: linear accuracy 82.4\\n\",\n      \"Layer 3: linear accuracy 79.5\\n\",\n      \"Layer 4: linear accuracy 77.3\\n\",\n      \"Layer 5: linear accuracy 72.8\\n\",\n      \"Layer 6: linear accuracy 65.0\\n\",\n      \"Layer 7: linear accuracy 61.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluate \\n\",\n    \"\\n\",\n    \"lin = make_pipeline(StandardScaler(), LogisticRegression(tol=1e-2, max_iter=1000))\\n\",\n    \"\\n\",\n    \"L_train = model_deepprojector(torch.Tensor(data['x']), all_layers=True)\\n\",\n    \"L_train = [l.detach().numpy() for l in L_train]\\n\",\n    \"L_train = [data['x']] + L_train\\n\",\n    \"\\n\",\n    \"L_test = model_deepprojector(torch.Tensor(data['x_test']), all_layers=True)\\n\",\n    \"L_test = [l.detach().numpy() for l in L_test]\\n\",\n    \"L_test = [data['x_test']] + L_test\\n\",\n    \"\\n\",\n    \"acc_deepprojector = np.zeros(len(L_train))\\n\",\n    \"\\n\",\n    \"for i in range(len(L_train)):\\n\",\n    \"    lin.fit(L_train[i], data['y'])\\n\",\n    \"    acc_deepprojector[i] = lin.score(L_test[i], data['y_test']) * 100\\n\",\n    \"    print(f'Layer {i}: linear accuracy {acc_deepprojector[i]:.1f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 405x270 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot, for all three models\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(3, 2), layout='constrained')\\n\",\n    \"\\n\",\n    \"plt.plot(acc, 'k.-', label='With projector')\\n\",\n    \"plt.plot(acc_noprojector, 'k--', label='Without projector')\\n\",\n    \"plt.plot(acc_deepprojector, 'k:', label='Deep projector')\\n\",\n    \"plt.xlabel('Layer')\\n\",\n    \"plt.ylabel('Linear accuracy')\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 438.75x202.5 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the final figure\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(6.5/2, 1.5), layout=None)\\n\",\n    \"\\n\",\n    \"axs[0] = plt.axes([0, 0, .55, 1])\\n\",\n    \"axs[1] = plt.axes([.7, .18, .28, .78])\\n\",\n    \"\\n\",\n    \"axs[0].scatter(T[:,0], T[:,1], c=data['y'], s=2, cmap='tab10')\\n\",\n    \"axs[0].set_aspect('equal', 'datalim')\\n\",\n    \"axs[0].axis('off')\\n\",\n    \"\\n\",\n    \"for y in range(10):\\n\",\n    \"    mu = np.median(T[data['y'] == y], axis=0)\\n\",\n    \"    axs[0].text(mu[0], mu[1], y, fontsize=9, ha='center', va='center')\\n\",\n    \"    \\n\",\n    \"axs[1].plot(acc, 'k.-', label='Default')\\n\",\n    \"axs[1].plot(acc_noprojector, 'k--', markersize=2, label='No proj.')\\n\",\n    \"axs[1].plot(acc_deepprojector, 'k-.', markersize=2, label='Deep proj.')\\n\",\n    \"axs[1].set_xlabel('Layer')\\n\",\n    \"axs[1].set_ylabel('Linear accuracy')\\n\",\n    \"axs[1].set_xticks([0,1,2,3,4,5,6,7])\\n\",\n    \"axs[1].set_ylim([30, 90])\\n\",\n    \"axs[1].legend()\\n\",\n    \"\\n\",\n    \"fig.text(0, .94, 'a', fontsize=8, weight='bold')\\n\",\n    \"fig.text(.6, .94, 'b', fontsize=8, weight='bold')\\n\",\n    \"\\n\",\n    \"# Ignore annoying savefig warnings (not sure why I am getting them)\\n\",\n    \"import warnings\\n\",\n    \"warnings.filterwarnings('ignore')\\n\",\n    \"\\n\",\n    \"# fig.savefig('figures/ssl.png', dpi=300)\\n\",\n    \"# fig.savefig('figures/ssl.pdf')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"provenance\": []\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/train.py",
    "content": "# The MNIST-1D dataset | 2024\n# Peter Steinbach\n\nimport time, copy\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom mnist1d.utils import ObjectView\n\ndef set_seed(seed):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n\ndef get_model_args(as_dict=False):\n  arg_dict = {'input_size': 40,\n          'output_size': 10,\n          'hidden_size': 256,\n          'learning_rate': 1e-2,\n          'weight_decay': 0,\n          'batch_size': 100,\n          'total_steps': 8000,\n          'print_every': 1000,\n          'eval_every': 250,\n          'checkpoint_every': 1000,\n          'device': 'cpu',\n          'seed': 42}\n  return arg_dict if as_dict else ObjectView(arg_dict)\n\n\ndef accuracy(model, inputs, targets):\n  preds = model(inputs).argmax(-1).cpu().numpy()\n  targets = targets.cpu().numpy().astype(np.float32)\n  return 100*sum(preds==targets)/len(targets)\n\n\ndef train_model(dataset, model, args):\n  criterion = nn.CrossEntropyLoss()\n  optimizer = optim.Adam(model.parameters(), args.learning_rate, weight_decay=args.weight_decay)\n\n  x_train, x_test = torch.Tensor(dataset['x']), torch.Tensor(dataset['x_test'])\n  y_train, y_test = torch.LongTensor(dataset['y']), torch.LongTensor(dataset['y_test'])\n\n  model = model.to(args.device)\n  x_train, x_test, y_train, y_test = [v.to(args.device) for v in [x_train, x_test, y_train, y_test]]\n\n  results = {'checkpoints':[], 'train_losses':[], 'test_losses':[],'train_acc':[],'test_acc':[]}\n  t0 = time.time()\n  for step in range(args.total_steps+1):\n      bix = (step*args.batch_size)%len(x_train) # batch index\n      x, y = x_train[bix:bix+args.batch_size], y_train[bix:bix+args.batch_size]\n      \n      loss = criterion(model(x), y)\n      results['train_losses'].append(loss.item())\n      loss.backward() ; optimizer.step() ; optimizer.zero_grad()\n\n      if args.eval_every > 0 and step % args.eval_every == 0: # evaluate the model\n          test_loss = criterion(model(x_test), y_test)\n          results['test_losses'].append(test_loss.item())\n          results['train_acc'].append(accuracy(model, x_train, y_train))\n          results['test_acc'].append(accuracy(model, x_test, y_test))\n\n      if step > 0 and step % args.print_every == 0: # print out training progress\n          t1 = time.time()\n          print(\"step {}, dt {:.2f}s, train_loss {:.3e}, test_loss {:.3e}, train_acc {:.1f}, test_acc {:.1f}\"\n              .format(step, t1-t0, loss.item(), results['test_losses'][-1], \\\n                      results['train_acc'][-1], results['test_acc'][-1]))\n          t0 = t1\n\n      if args.checkpoint_every > 0 and step % args.checkpoint_every == 0: # save model checkpoints\n          model.step = step\n          results['checkpoints'].append( copy.deepcopy(model) )\n  return results"
  },
  {
    "path": "notebooks/tsne-mnist-vs-mnist1d.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1b68388b\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualising MNIST and MNIST-1D using t-SNE in pixel space\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"d1b5e249\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Run this if using Colab\\n\",\n    \"\\n\",\n    \"# !pip install opentsne\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"cb7cd1a1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import pylab as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"from openTSNE import TSNE\\n\",\n    \"\\n\",\n    \"# Figure style (loading from the web to make running in a Colab easier)\\n\",\n    \"plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"6a581ea9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(70000, 784)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load MNIST\\n\",\n    \"\\n\",\n    \"from sklearn.datasets import fetch_openml\\n\",\n    \"\\n\",\n    \"mnist = fetch_openml('mnist_784')\\n\",\n    \"X_2d = mnist.data.values\\n\",\n    \"y_2d = mnist.target.values.astype('int')\\n\",\n    \"\\n\",\n    \"print(X_2d.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"19cb8c08\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"TSNE(early_exaggeration=12, n_jobs=-1, verbose=True)\\n\",\n      \"--------------------------------------------------------------------------------\\n\",\n      \"===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\\n\",\n      \"   --> Time elapsed: 49.61 seconds\\n\",\n      \"===> Calculating affinity matrix...\\n\",\n      \"   --> Time elapsed: 3.65 seconds\\n\",\n      \"===> Calculating PCA-based initialization...\\n\",\n      \"   --> Time elapsed: 2.43 seconds\\n\",\n      \"===> Running optimization with exaggeration=12.00, lr=5833.33 for 250 iterations...\\n\",\n      \"Iteration   50, KL divergence 5.9916, 50 iterations in 4.9270 sec\\n\",\n      \"Iteration  100, KL divergence 5.8277, 50 iterations in 4.7073 sec\\n\",\n      \"Iteration  150, KL divergence 5.7787, 50 iterations in 4.6496 sec\\n\",\n      \"Iteration  200, KL divergence 5.7614, 50 iterations in 5.4664 sec\\n\",\n      \"Iteration  250, KL divergence 5.7526, 50 iterations in 5.2084 sec\\n\",\n      \"   --> Time elapsed: 24.96 seconds\\n\",\n      \"===> Running optimization with exaggeration=1.00, lr=70000.00 for 500 iterations...\\n\",\n      \"Iteration   50, KL divergence 3.5778, 50 iterations in 4.7098 sec\\n\",\n      \"Iteration  100, KL divergence 3.3168, 50 iterations in 6.3937 sec\\n\",\n      \"Iteration  150, KL divergence 3.1843, 50 iterations in 7.8588 sec\\n\",\n      \"Iteration  200, KL divergence 3.0980, 50 iterations in 8.6795 sec\\n\",\n      \"Iteration  250, KL divergence 3.0353, 50 iterations in 9.1850 sec\\n\",\n      \"Iteration  300, KL divergence 2.9869, 50 iterations in 10.3418 sec\\n\",\n      \"Iteration  350, KL divergence 2.9478, 50 iterations in 11.7389 sec\\n\",\n      \"Iteration  400, KL divergence 2.9154, 50 iterations in 14.3060 sec\\n\",\n      \"Iteration  450, KL divergence 2.8876, 50 iterations in 13.1413 sec\\n\",\n      \"Iteration  500, KL divergence 2.8642, 50 iterations in 15.8153 sec\\n\",\n      \"   --> Time elapsed: 102.18 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z_2d = TSNE(n_jobs=-1, verbose=True).fit(X_2d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"a5c82226\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(5000, 40)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load MNIST-1D\\n\",\n    \"# (loading from the web to make running in a Colab easier)\\n\",\n    \"\\n\",\n    \"from urllib.request import urlopen\\n\",\n    \"import pickle\\n\",\n    \"\\n\",\n    \"url = 'https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl'\\n\",\n    \"mnist1d = pickle.load(urlopen(url))\\n\",\n    \"\\n\",\n    \"X_1d = np.concatenate((mnist1d['x'], mnist1d['x_test']))\\n\",\n    \"y_1d = np.concatenate((mnist1d['y'], mnist1d['y_test']))\\n\",\n    \"\\n\",\n    \"print(X_1d.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"e200a213\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"TSNE(early_exaggeration=12, n_jobs=-1, verbose=True)\\n\",\n      \"--------------------------------------------------------------------------------\\n\",\n      \"===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\\n\",\n      \"   --> Time elapsed: 2.43 seconds\\n\",\n      \"===> Calculating affinity matrix...\\n\",\n      \"   --> Time elapsed: 0.29 seconds\\n\",\n      \"===> Calculating PCA-based initialization...\\n\",\n      \"   --> Time elapsed: 0.02 seconds\\n\",\n      \"===> Running optimization with exaggeration=12.00, lr=416.67 for 250 iterations...\\n\",\n      \"Iteration   50, KL divergence 4.8808, 50 iterations in 2.0153 sec\\n\",\n      \"Iteration  100, KL divergence 4.8335, 50 iterations in 1.6186 sec\\n\",\n      \"Iteration  150, KL divergence 4.8343, 50 iterations in 1.9780 sec\\n\",\n      \"Iteration  200, KL divergence 4.8343, 50 iterations in 1.3721 sec\\n\",\n      \"Iteration  250, KL divergence 4.8344, 50 iterations in 1.1735 sec\\n\",\n      \"   --> Time elapsed: 8.17 seconds\\n\",\n      \"===> Running optimization with exaggeration=1.00, lr=5000.00 for 500 iterations...\\n\",\n      \"Iteration   50, KL divergence 2.9251, 50 iterations in 1.0916 sec\\n\",\n      \"Iteration  100, KL divergence 2.7425, 50 iterations in 0.9804 sec\\n\",\n      \"Iteration  150, KL divergence 2.6568, 50 iterations in 1.2437 sec\\n\",\n      \"Iteration  200, KL divergence 2.6090, 50 iterations in 1.5527 sec\\n\",\n      \"Iteration  250, KL divergence 2.5808, 50 iterations in 1.7763 sec\\n\",\n      \"Iteration  300, KL divergence 2.5634, 50 iterations in 1.7452 sec\\n\",\n      \"Iteration  350, KL divergence 2.5512, 50 iterations in 2.0312 sec\\n\",\n      \"Iteration  400, KL divergence 2.5415, 50 iterations in 1.4767 sec\\n\",\n      \"Iteration  450, KL divergence 2.5338, 50 iterations in 1.1345 sec\\n\",\n      \"Iteration  500, KL divergence 2.5276, 50 iterations in 1.0452 sec\\n\",\n      \"   --> Time elapsed: 14.08 seconds\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z_1d = TSNE(n_jobs=-1, verbose=True).fit(X_1d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"94b9fca8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 438.75x229.5 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Define 10 nice colors\\n\",\n    \"col = np.array(['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99',\\n\",\n    \"                '#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a'])\\n\",\n    \"\\n\",\n    \"fig, axs = plt.subplots(ncols=2, figsize=(6.5/2, 1.7))#, layout=None)\\n\",\n    \"\\n\",\n    \"axs[0].axis('equal')\\n\",\n    \"axs[0].scatter(Z_2d[:,0], Z_2d[:,1], c=col[y_2d], s=1, \\n\",\n    \"               edgecolors='none', rasterized=True)\\n\",\n    \"axs[0].set_title('MNIST')\\n\",\n    \"axs[0].axis('off')\\n\",\n    \"    \\n\",\n    \"for k in range(10):\\n\",\n    \"    mu = np.median(Z_2d[y_2d == k, :], axis=0)\\n\",\n    \"    axs[0].text(mu[0], mu[1], k, ha='center', va='center', fontsize=7)\\n\",\n    \"    \\n\",\n    \"axs[1].axis('equal')\\n\",\n    \"axs[1].scatter(Z_1d[:,0], Z_1d[:,1], c=col[y_1d], s=5, \\n\",\n    \"               edgecolors='none', rasterized=True)\\n\",\n    \"axs[1].set_title('MNIST-1D')\\n\",\n    \"axs[1].axis('off')\\n\",\n    \"\\n\",\n    \"fig.text(0, .93, 'a', fontsize=8, weight='bold')\\n\",\n    \"fig.text(.5, .93, 'b', fontsize=8, weight='bold')\\n\",\n    \"    \\n\",\n    \"fig.savefig('figures/tsne.png', dpi=300)\\n\",\n    \"fig.savefig('figures/tsne.pdf')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pypl_notes.md",
    "content": "# SJG Notes for PYPL uploads:\n\n\n* `git tag v0.0.2`\n* `git push origin v0.0.2`\n* (commit)\n* `python3 -m build`\n* Upload w/`twine` (Test)\n  * `python3 -m twine upload --repository testpypi dist/*`\n  * `python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps mnist1d`\n  * View on `https://test.pypi.org/project/mnist1d/`\n* Upload w/`twine` (Main)\n  * `python3 -m twine upload dist/*`\n  * `python3 -m pip install mnist1d`\n  * View on `https://pypi.org/project/mnist1d/`"
  },
  {
    "path": "pyproject.toml",
    "content": "[build-system]\nrequires = [\"setuptools\", \"setuptools-scm\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"mnist1d\"\nauthors = [\n    {name = \"Sam Greydanus\", email = \"greydanus@users.noreply.github.com\"},\n    {name = \"Peter Steinbach\", email = \"psteinb@users.noreply.github.com\"},\n    {name = \"Karim\", email = \"karim-53@users.noreply.github.com\"},\n    {name = \"Dmitry Kobak\", email= \"dmitry.kobak@uni-tuebingen.de\"},\n    {name = \"Jakob Jordan\", email= \"jakobjordan@posteo.de\"}\n]\ndescription = \"A 1D analogue of the MNIST dataset for measuring spatial biases and answering Science of Deep Learning questions\"\nreadme = \"README.md\"\nrequires-python = \">=3.7\"\nkeywords = [\"dataset\", \"mnist\", \"machine learning\", \"deep learning\", \"1D\"]\nlicense = {text = \"Apache-2.0\"}\nclassifiers = [\n    \"License :: OSI Approved :: Apache Software License\",\n    \"Programming Language :: Python\",\n    \"Programming Language :: Python :: 3\",\n    \"Programming Language :: Python :: 3.7\",\n    \"Programming Language :: Python :: 3.8\",\n    \"Programming Language :: Python :: 3.9\",\n    \"Programming Language :: Python :: 3.10\",\n    \"Programming Language :: Python :: 3.11\",\n    \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n    \"Intended Audience :: Science/Research\"\n]\ndependencies = [\n    \"requests\",\n    \"numpy\",\n    \"matplotlib\",\n    \"scipy\"\n]\ndynamic = [\"version\"]\n\n[project.optional-dependencies]\ndev = [\"pytest\"]\nall = [\"torch\",\"notebook\"]\n\n[tool.setuptools]\npackages = [\"mnist1d\"]\n\n[tool.setuptools_scm]\nversion_scheme = \"post-release\"\nlocal_scheme = \"no-local-version\"\n\n# [tool.setuptools.packages.find]\n# where = [\"mnist1d\"]\n\n# [tool.setuptools.exclude-package-data]\n# mnist1d = [\"notebooks/*\", \"static/*\", \"*pkl\"]\n\n[project.urls]\nHomepage = \"https://github.com/greydanus/mnist1d\"\n\n# [project.scripts]\n# my-script = \"my_package.module:function\"\n"
  },
  {
    "path": "tests/test_data.py",
    "content": "# The MNIST-1D dataset | 2024\n# Peter Steinbach\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom mnist1d.data import get_templates, make_dataset, get_dataset, get_dataset_args\nimport numpy as np\nimport pytest\n\n@pytest.fixture\ndef tmpfile():\n    value = NamedTemporaryFile(delete=True)\n    print(value, value.file)\n    yield value\n    value.close()\n\ndef test_get_templates():\n    obs = get_templates()\n    assert isinstance(obs, dict)\n    assert 'x' in obs.keys()\n    assert 'y' in obs.keys()\n    assert 't' in obs.keys()\n\n    x,y,t = obs['x'], obs['y'], obs['t']\n    assert isinstance(y,np.ndarray)\n    assert (y == np.asarray(list(range(10)))).all()\n\n    assert x.shape == (10,12)\n    assert y.shape == (10,)\n\ndef test_make_dataset():\n\n    obs = make_dataset()\n    assert isinstance(obs, dict)\n    assert 'x' in obs.keys()\n    assert 'y' in obs.keys()\n    assert 't' in obs.keys()\n    assert 'x_test' in obs.keys()\n    assert 'y_test' in obs.keys()\n    assert 'templates' in obs.keys()\n\n    x,y,t = obs['x'], obs['y'], obs['t']\n    x_test,y_test = obs['x_test'], obs['y_test']\n\n    assert x.shape == (4000,40)\n    assert y.shape == (4000,)\n\n    assert x_test.shape == (1000,40)\n    assert y_test.shape == (1000,)\n\ndef test_get_dataset_args():\n    defaults = get_dataset_args(as_dict=True)\n    assert 'seed' in defaults.keys()\n    assert defaults['seed'] == 42\n\ndef test_get_dataset(tmpfile):\n\n    defaults = get_dataset_args(as_dict=False)\n    tmp = str(tmpfile)\n\n    obs = get_dataset(args=defaults,path=tmp)\n    assert isinstance(obs, dict)\n    assert 'x' in obs.keys()\n    assert 'y' in obs.keys()\n    assert 't' in obs.keys()\n    assert 'x_test' in obs.keys()\n    assert 'y_test' in obs.keys()\n    assert 'templates' in obs.keys()\n"
  },
  {
    "path": "tests/test_transform.py",
    "content": "# The MNIST-1D dataset | 2024\n# Peter Steinbach\n\nfrom mnist1d.data import get_dataset, get_dataset_args\nfrom mnist1d.utils import set_seed\nfrom mnist1d.transform import pad\n\nfrom tempfile import NamedTemporaryFile\nimport pytest\n\n@pytest.fixture\ndef tmpfile():\n    value = NamedTemporaryFile(delete=True)\n    print(value, value.file)\n    yield value\n    value.close()\n\ndef test_padding(tmpfile):\n    set_seed(13)\n    \n    defaults = get_dataset_args()\n    data = get_dataset(args=defaults,download=False)\n    x = data['x']\n    obs = pad(x[0,...],(36,44))\n    assert x.shape != obs.shape\n    assert obs.shape == (82,)\n\n    obs = pad(x,(36,44))\n    assert x.shape != obs.shape\n    assert obs.shape == (4000,78)\n\n"
  },
  {
    "path": "tests/test_utils.py",
    "content": "# The MNIST-1D dataset | 2024\n# Peter Steinbach\n\nfrom mnist1d.data import get_dataset, get_dataset_args\nfrom mnist1d.utils import set_seed, to_pickle, from_pickle, ObjectView\nimport numpy as np\n\nfrom tempfile import mkstemp\nimport pytest\nimport os\n\n@pytest.fixture\ndef tmpfile():\n    handle, path = mkstemp()\n    print(\"temporarily using\",path)\n    yield path\n    os.unlink(path)\n\ndef test_seed_is_fixed():\n\n    set_seed(42)\n    exp = np.random.randn(8)\n    set_seed(42)\n    obs = np.random.randn(8)\n\n    assert np.allclose(exp,obs)    \n\ndef test_pickle_roundtrip(tmpfile):\n\n    exp = dict(a=3,b=11.01,c=\"d\",d=\"foobar\")\n    _ = to_pickle(exp,tmpfile)\n    obs = from_pickle(tmpfile)\n\n    assert type(exp) == type(obs)\n    for k,v in exp.items():\n        assert v == obs[k]\n\ndef test_object_view():\n    #TODO: IIRC there are better ways to do this\n    exp = dict(a=3,b=11.01,c=\"d\",d=\"foobar\")\n    obs = ObjectView(exp)\n\n    assert type(obs) != type(exp)\n    assert obs.a == exp['a']"
  }
]