Repository: fastmachinelearning/hls4ml-tutorial Branch: main Commit: b5f5a8e13e24 Files: 28 Total size: 1.5 MB Directory structure: gitextract_otx4oemz/ ├── .github/ │ ├── dependabot.yml │ └── workflows/ │ └── deploy.yml ├── .gitignore ├── .gitlab-ci.yml ├── .pre-commit-config.yaml ├── README.md ├── _config.yml ├── _toc.yml ├── callbacks.py ├── environment.yml ├── nn_utils.py ├── part1_getting_started.ipynb ├── part2_advanced_config.ipynb ├── part3_compression.ipynb ├── part4.1_HG_quantization.ipynb ├── part4_quantization.ipynb ├── part5_bdt.ipynb ├── part6_cnns.ipynb ├── part7a_bitstream.ipynb ├── part7b_deployment.ipynb ├── part7c_validation.ipynb ├── part8_symbolic_regression.ipynb ├── plotting.py ├── pruned_cnn/ │ ├── myproject_prj/ │ │ └── solution1/ │ │ └── syn/ │ │ └── report/ │ │ └── myproject_csynth.rpt │ └── vivado_synth.rpt ├── quantized_pruned_cnn/ │ ├── myproject_prj/ │ │ └── solution1/ │ │ └── syn/ │ │ └── report/ │ │ └── myproject_csynth.rpt │ └── vivado_synth.rpt └── sr/ └── example.pkl ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: # Maintain dependencies for GitHub Actions - package-ecosystem: "github-actions" directory: "/" schedule: interval: "weekly" ================================================ FILE: .github/workflows/deploy.yml ================================================ name: deploy-book # Only run this when the master branch changes on: push: branches: - main pull_request: branches: - main # This job installs dependencies, build the book, and pushes it to `gh-pages` jobs: deploy-book: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 # Install dependencies - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v3 with: miniforge-version: latest use-mamba: true channels: conda-forge activate-environment: hls4ml-tutorial environment-file: environment.yml python-version: 3.10.16 auto-activate-base: false # Check dependencies - name: Check Miniconda shell: bash -l {0} run: | conda info conda list conda config --show-sources conda config --show printenv | sort - name: Build the book shell: bash -l {0} run: | jupyter nbextension enable --py widgetsnbextension jupyter-book build . - name: GitHub Pages action uses: peaceiris/actions-gh-pages@v4.0.0 if: ${{ github.event_name != 'pull_request' }} with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: _build/html force_orphan: true user_name: 'github-actions[bot]' user_email: 'github-actions[bot]@users.noreply.github.com' ================================================ FILE: .gitignore ================================================ .ipynb_checkpoints __pycache__ *~ *.npy _build model_1 model_2 model_3 .DS_Store ================================================ FILE: .gitlab-ci.yml ================================================ image: gcr.io/kaniko-project/executor:debug stages: - build-and-push build-and-push-job: stage: build-and-push script: - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/docker/Dockerfile --destination $CI_REGISTRY_IMAGE/hls4ml-0.8.0:${CI_COMMIT_SHA:0:8} --destination $CI_REGISTRY_IMAGE/hls4ml-0.8.0:latest build-and-push-vivado-job: stage: build-and-push script: - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/docker/Dockerfile.vivado --destination $CI_REGISTRY_IMAGE/hls4ml-0.8.0-vivado-2019.1:${CI_COMMIT_SHA:0:8} --destination $CI_REGISTRY_IMAGE/hls4ml-0.8.0-vivado-2019.1:latest ================================================ FILE: .pre-commit-config.yaml ================================================ exclude: .*\.rpt$ repos: - repo: https://github.com/psf/black-pre-commit-mirror rev: 26.3.1 hooks: - id: black-jupyter language_version: python3 args: ['--line-length=125', '--skip-string-normalization'] - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: check-added-large-files - id: check-case-conflict - id: check-merge-conflict - id: check-symlinks - id: check-yaml - id: debug-statements - id: end-of-file-fixer - id: mixed-line-ending - id: requirements-txt-fixer - id: trailing-whitespace - repo: https://github.com/PyCQA/isort rev: 8.0.1 hooks: - id: isort args: ["--profile", "black", --line-length=125] - repo: https://github.com/asottile/pyupgrade rev: v3.21.2 hooks: - id: pyupgrade args: ["--py36-plus"] - repo: https://github.com/asottile/setup-cfg-fmt rev: v3.2.0 hooks: - id: setup-cfg-fmt - repo: https://github.com/pycqa/flake8 rev: 7.3.0 hooks: - id: flake8 exclude: docs/conf.py additional_dependencies: [flake8-bugbear, flake8-print] args: ['--max-line-length=125', # github viewer width '--extend-ignore=E203,T201'] # E203 is not PEP8 compliant - repo: https://github.com/mgedmin/check-manifest rev: "0.51" hooks: - id: check-manifest stages: [manual] - repo: https://github.com/jmduarte/p-clang-format rev: "v1.0.4" hooks: - id: p-clang-format types_or: [c++, c, cuda] ci: autofix_commit_msg: '[pre-commit.ci] auto fixes from pre-commit hooks' autofix_prs: true # default is true autoupdate_branch: 'main' autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate' autoupdate_schedule: weekly skip: [] submodules: true ================================================ FILE: README.md ================================================ # hls4ml-tutorial: Tutorial notebooks for `hls4ml` [![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://fastmachinelearning.org/hls4ml-tutorial) ![deploy-book](https://github.com/fastmachinelearning/hls4ml-tutorial/actions/workflows/deploy.yml/badge.svg) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastmachinelearning/hls4ml-tutorial) There are several ways to run the tutorial notebooks: ## Online [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastmachinelearning/hls4ml-tutorial/HEAD) ## Conda Running the tutorials requires AMD Vitis HLS to be installed, see [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vitis.html). After the installation, the necessary environmental variables can be set using ``` source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh ``` The Python environment used for the tutorials is specified in the `environment.yml` file. It can be setup like: ```bash conda env create -f environment.yml conda activate hls4ml-tutorial source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh ``` Note that part 7 of the tutorial makes use of the `VivadoAccelator` backend of hls4ml for which no Vitis equivalent is available yet. For this part of the tutorial it is therefore necesary to install and source Vivado HLS version 2019.2 or 2020.1, which can be obtained [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vivado-design-tools/archive.html). ## Companion material We have prepared a set of slides with some introduction and more details on each of the exercises. Please find them [here](https://docs.google.com/presentation/d/1c4LvEc6yMByx2HJs8zUP5oxLtY6ACSizQdKvw5cg5Ck/edit?usp=sharing). ## Notebooks ```{tableofcontents} ``` ================================================ FILE: _config.yml ================================================ # Book settings # Learn more at https://jupyterbook.org/customize/config.html title: hls4ml tutorial author: Fast ML team logo: images/hls4ml_logo.svg favicon: images/hls4ml_logo.svg # Force re-execution of notebooks on each build. # See https://jupyterbook.org/content/execute.html execute: execute_notebooks: force timeout: -1 # Define the name of the latex output file for PDF builds latex: latex_documents: targetname: book.tex # Information about where the book exists on the web repository: url: https://github.com/fastmachinelearning/hls4ml-tutorial # Online location of your book path_to_book: "" # Optional path to your book, relative to the repository root branch: main # Which branch of the repository should be used when creating links (optional) # Add GitHub buttons to your book # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository html: use_issues_button: true use_repository_button: true baseurl: "https://fastmachinlearning.org/hls4ml-tutorial/" # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/ launch_buttons: binderhub_url: "https://mybinder.org" colab_url: "https://colab.research.google.com" ================================================ FILE: _toc.yml ================================================ format: jb-book root: README.md chapters: - file: part1_getting_started.ipynb - file: part2_advanced_config.ipynb - file: part3_compression.ipynb - file: part4_quantization.ipynb - file: part5_bdt.ipynb - file: part6_cnns.ipynb - file: part7a_bitstream.ipynb - file: part7b_deployment.ipynb - file: part7c_validation.ipynb - file: part8_symbolic_regression.ipynb ================================================ FILE: callbacks.py ================================================ ''' Created on 7 Apr 2017 @author: jkiesele ''' import json # loss per epoch from time import time from tensorflow.keras.callbacks import Callback, EarlyStopping, History, ModelCheckpoint, ReduceLROnPlateau, TensorBoard class newline_callbacks_begin(Callback): def __init__(self, outputDir): self.outputDir = outputDir self.loss = [] self.val_loss = [] self.full_logs = [] def on_epoch_end(self, epoch, epoch_logs={}): # noqa: B006 import os lossfile = os.path.join(self.outputDir, 'losses.log') print('\n***callbacks***\nsaving losses to ' + lossfile) self.loss.append(epoch_logs.get('loss')) self.val_loss.append(epoch_logs.get('val_loss')) f = open(lossfile, 'w') for i in range(len(self.loss)): f.write(str(self.loss[i])) f.write(" ") f.write(str(self.val_loss[i])) f.write("\n") f.close() normed = {} for vv in epoch_logs: normed[vv] = float(epoch_logs[vv]) self.full_logs.append(normed) lossfile = os.path.join(self.outputDir, 'full_info.log') with open(lossfile, 'w') as out: out.write(json.dumps(self.full_logs)) class newline_callbacks_end(Callback): def on_epoch_end(self, epoch, epoch_logs={}): # noqa: B006 print('\n***callbacks end***\n') class Losstimer(Callback): def __init__(self, every=5): self.points = [] self.every = every def on_train_begin(self, logs): self.start = time() def on_batch_end(self, batch, logs): if (batch % self.every) != 0: return elapsed = time() - self.start cop = {} for i, j in logs.items(): cop[i] = float(j) cop['elapsed'] = elapsed self.points.append(cop) class all_callbacks: def __init__( self, stop_patience=10, lr_factor=0.5, lr_patience=1, lr_epsilon=0.001, lr_cooldown=4, lr_minimum=1e-5, outputDir='' ): self.nl_begin = newline_callbacks_begin(outputDir) self.nl_end = newline_callbacks_end() self.stopping = EarlyStopping(monitor='val_loss', patience=stop_patience, verbose=1, mode='min') self.reduce_lr = ReduceLROnPlateau( monitor='val_loss', factor=lr_factor, patience=lr_patience, mode='min', verbose=1, epsilon=lr_epsilon, cooldown=lr_cooldown, min_lr=lr_minimum, ) self.modelbestcheck = ModelCheckpoint( outputDir + "/KERAS_check_best_model.h5", monitor='val_loss', verbose=1, save_best_only=True ) self.modelbestcheckweights = ModelCheckpoint( outputDir + "/KERAS_check_best_model_weights.h5", monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, ) self.modelcheckperiod = ModelCheckpoint(outputDir + "/KERAS_check_model_epoch{epoch:02d}.h5", verbose=1, period=10) self.modelcheck = ModelCheckpoint(outputDir + "/KERAS_check_model_last.h5", verbose=1) self.modelcheckweights = ModelCheckpoint( outputDir + "/KERAS_check_model_last_weights.h5", verbose=1, save_weights_only=True ) self.tb = TensorBoard(log_dir=outputDir + '/logs') self.history = History() self.timer = Losstimer() self.callbacks = [ self.nl_begin, self.modelbestcheck, self.modelbestcheckweights, self.modelcheck, self.modelcheckweights, self.modelcheckperiod, self.reduce_lr, self.stopping, self.nl_end, self.tb, self.history, self.timer, ] ================================================ FILE: environment.yml ================================================ name: hls4ml-tutorial channels: - conda-forge dependencies: - python=3.10.16 - notebook==6.4.12 - jupyter_contrib_nbextensions - jupyterhub - jupyter-book - jsonschema-with-format-nongpl - pydot==1.4.2 - graphviz==7.1.0 - scikit-learn==1.2.2 - tensorflow==2.14.0 - tensorflow-datasets==4.8.3 - webcolors - widgetsnbextension==3.6.0 - pip==23.0.1 - pip: - hls4ml[profiling,optimization,sr,HGQ,qkeras]==1.2.0 - conifer==1.5 - pysr==0.16.3 - xgboost==1.7.5 - zstd - tensorflow-model-optimization ================================================ FILE: nn_utils.py ================================================ import json import os import pickle as pkl import random from io import BytesIO from pathlib import Path from typing import Callable import h5py as h5 import numpy as np import tensorflow as tf import zstd from HGQ.bops import trace_minmax from keras.layers import Dense from keras.src.layers.convolutional.base_conv import Conv from keras.src.saving.legacy import hdf5_format from matplotlib import pyplot as plt from tensorflow import keras from tqdm.auto import tqdm class NumpyFloatValuesEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.float32): # type: ignore return float(obj) return json.JSONEncoder.default(self, obj) class SaveTopN(keras.callbacks.Callback): def __init__( self, metric_fn: Callable[[dict], float], n: int, path: str | Path, side: str = 'max', fname_format='epoch={epoch}-metric={metric:.4e}.h5', cond_fn: Callable[[dict], bool] = lambda x: True, ): self.n = n self.metric_fn = metric_fn self.path = Path(path) self.fname_format = fname_format os.makedirs(path, exist_ok=True) self.weight_paths = np.full(n, '/dev/null', dtype=object) if side == 'max': self.best = np.full(n, -np.inf) self.side = np.greater elif side == 'min': self.best = np.full(n, np.inf) self.side = np.less self.cond = cond_fn def on_epoch_end(self, epoch, logs=None): assert isinstance(logs, dict) assert isinstance(self.model, keras.models.Model) logs = logs.copy() logs['epoch'] = epoch if not self.cond(logs): return metric = self.metric_fn(logs) if self.side(metric, self.best[-1]): try: os.remove(self.weight_paths[-1]) except OSError: pass logs['metric'] = metric fname = self.path / self.fname_format.format(**logs) self.best[-1] = metric self.weight_paths[-1] = fname self.model.save_weights(fname) with h5.File(fname, 'r+') as f: log_str = json.dumps(logs, cls=NumpyFloatValuesEncoder) f.attrs['train_log'] = log_str idx = np.argsort(self.best) if self.side == np.greater: idx = idx[::-1] self.best = self.best[idx] self.weight_paths = self.weight_paths[idx] def rename_ckpts(self, dataset, bsz=65536): assert self.weight_paths[0] != '/dev/null', 'No checkpoints to rename' assert isinstance(self.model, keras.models.Model) weight_buf = BytesIO() with h5.File(weight_buf, 'w') as f: hdf5_format.save_weights_to_hdf5_group(f, self.model) weight_buf.seek(0) for i, path in enumerate(tqdm(self.weight_paths, desc='Renaming checkpoints')): if path == '/dev/null': continue self.model.load_weights(path) bops = trace_minmax(self.model, dataset, bsz=bsz, verbose=False) with h5.File(path, 'r+') as f: logs = json.loads(f.attrs['train_log']) # type: ignore logs['bops'] = bops metric = self.metric_fn(logs) logs['metric'] = metric f.attrs['train_log'] = json.dumps(logs, cls=NumpyFloatValuesEncoder) self.best[i] = metric new_fname = self.path / self.fname_format.format(**logs) os.rename(path, new_fname) self.weight_paths[i] = new_fname idx = np.argsort(self.best) self.best = self.best[idx] self.weight_paths = self.weight_paths[idx] with h5.File(weight_buf, 'r') as f: hdf5_format.load_weights_from_hdf5_group_by_name(f, self.model) class PBarCallback(tf.keras.callbacks.Callback): def __init__(self, metric='loss: {loss:.2f}/{val_loss:.2f}'): self.pbar = None self.template = metric def on_epoch_begin(self, epoch, logs=None): if self.pbar is None: self.pbar = tqdm(total=self.params['epochs'], unit='epoch') def on_epoch_end(self, epoch, logs=None): assert isinstance(self.pbar, tqdm) assert isinstance(logs, dict) self.pbar.update(1) string = self.template.format(**logs) if 'bops' in logs: string += f' - BOPs: {logs["bops"]:,.0f}' self.pbar.set_description(string) def on_train_end(self, logs=None): if self.pbar is not None: self.pbar.close() def plot_history(histry: dict, metrics=('loss', 'val_loss'), ylabel='Loss', logy=False): fig, ax = plt.subplots() for metric in metrics: ax.plot(histry[metric], label=metric) ax.set_xlabel('Epoch') ax.set_ylabel(ylabel) if logy: ax.set_yscale('log') ax.legend() return fig, ax def save_model(model: keras.models.Model, path: str): _path = Path(path) model.save(path) if model.history is not None: history = model.history.history else: history = {} with open(_path.with_suffix('.history'), 'wb') as f: f.write(zstd.compress(pkl.dumps(history))) def load_model(path: str, co=None): _path = Path(path) model: keras.Model = keras.models.load_model(path, custom_objects=co) # type: ignore with open(_path.with_suffix('.history'), 'rb') as f: history: dict[str, list] = pkl.loads(zstd.decompress(f.read())) return model, history def save_history(history, path): with open(path, 'wb') as f: f.write(zstd.compress(pkl.dumps(history))) def load_history(path): with open(path, 'rb') as f: history = pkl.loads(zstd.decompress(f.read())) return history def absorb_batchNorm(model_target, model_original): for layer in model_target.layers: if layer.__class__.__name__ == 'Functional': absorb_batchNorm(layer, model_original.get_layer(layer.name)) continue if ( (isinstance(layer, Dense) or isinstance(layer, Conv)) and len(nodes := model_original.get_layer(layer.name)._outbound_nodes) > 0 and isinstance(nodes[0].outbound_layer, keras.layers.BatchNormalization) ): _gamma, _beta, _mu, _var = model_original.get_layer(layer.name)._outbound_nodes[0].outbound_layer.get_weights() _ratio = _gamma / np.sqrt(0.001 + _var) _bias = -_gamma * _mu / np.sqrt(0.001 + _var) + _beta k, *_b = model_original.get_layer(layer.name).get_weights() if _b: b = _b[0] else: b = np.zeros(layer.output_shape[-1]) nk = np.einsum('...c, c-> ...c', k, _ratio, optimize=True) nb = np.einsum('...c, c-> ...c', b, _ratio, optimize=True) + _bias extras = layer.get_weights()[2:] layer.set_weights([nk, nb, *extras]) elif hasattr(layer, 'kernel'): for w in layer.weights: if '_bw' not in w.name: break else: continue weights = layer.get_weights() new_weights = model_original.get_layer(layer.name).get_weights() l = len(new_weights) # noqa: E741 # If l looks like 1 by any chance, change your font. layer.set_weights([*new_weights, *weights[l:]][: len(weights)]) def set_seed(seed): np.random.seed(seed) tf.random.set_seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) tf.config.experimental.enable_op_determinism() def get_best_ckpt(save_path: Path, take_min=False): ckpts = list(save_path.glob('*.h5')) def rank(ckpt: Path): with h5.File(ckpt, 'r') as f: log: dict = f.attrs['train_log'] # type: ignore log = json.loads(log) # type: ignore metric = log['metric'] # type: ignore return metric ckpts = sorted(ckpts, key=rank, reverse=not take_min) ckpt = ckpts[0] return ckpt class PeratoFront(keras.callbacks.Callback): def __init__( self, path: str | Path, fname_format: str, metrics_names: list[str], sides: list[int], cond_fn: Callable[[dict], bool] = lambda x: True, ): self.path = Path(path) self.fname_format = fname_format os.makedirs(path, exist_ok=True) self.paths = [] self.metrics = [] self.metric_names = metrics_names self.sides = np.array(sides) self.cond_fn = cond_fn def on_epoch_end(self, epoch, logs=None): assert isinstance(self.model, keras.models.Model) assert isinstance(logs, dict) logs = logs.copy() logs['epoch'] = epoch if not self.cond_fn(logs): return new_metrics = np.array([logs[metric_name] for metric_name in self.metric_names]) _rm_idx = [] for i, old_metrics in enumerate(self.metrics): _old_metrics = self.sides * old_metrics _new_metrics = self.sides * new_metrics if np.all(_new_metrics <= _old_metrics): return if np.all(_new_metrics >= _old_metrics): _rm_idx.append(i) for i in _rm_idx[::-1]: self.metrics.pop(i) p = self.paths.pop(i) os.remove(p) path = self.path / self.fname_format.format(**logs) self.metrics.append(new_metrics) self.paths.append(path) self.model.save_weights(self.paths[-1]) with h5.File(path, 'r+') as f: log_str = json.dumps(logs, cls=NumpyFloatValuesEncoder) f.attrs['train_log'] = log_str def rename_ckpts(self, dataset, bsz=65536): assert isinstance(self.model, keras.models.Model) weight_buf = BytesIO() with h5.File(weight_buf, 'w') as f: hdf5_format.save_weights_to_hdf5_group(f, self.model) weight_buf.seek(0) for i, path in enumerate(tqdm(self.paths, desc='Renaming checkpoints')): self.model.load_weights(path) bops = trace_minmax(self.model, dataset, bsz=bsz, verbose=False) with h5.File(path, 'r+') as f: logs = json.loads(f.attrs['train_log']) # type: ignore logs['bops'] = bops f.attrs['train_log'] = json.dumps(logs, cls=NumpyFloatValuesEncoder) metrics = np.array([logs[metric_name] for metric_name in self.metric_names]) self.metrics[i] = metrics new_fname = self.path / self.fname_format.format(**logs) os.rename(path, new_fname) self.paths[i] = new_fname with h5.File(weight_buf, 'r') as f: hdf5_format.load_weights_from_hdf5_group_by_name(f, self.model) class BetaScheduler(keras.callbacks.Callback): def __init__(self, beta_fn: Callable[[int], float]): self.beta_fn = beta_fn def on_epoch_begin(self, epoch, logs=None): assert isinstance(self.model, keras.models.Model) beta = self.beta_fn(epoch) for layer in self.model.layers: if hasattr(layer, 'beta'): layer.beta.assign(keras.backend.constant(beta, dtype=keras.backend.floatx())) def on_epoch_end(self, epoch, logs=None): assert isinstance(logs, dict) logs['beta'] = self.beta_fn(epoch) @classmethod def from_config(cls, config): return cls(get_schedule(config.beta, config.train.epochs)) def get_schedule(beta_conf, total_epochs): epochs = [] betas = [] interpolations = [] for block in beta_conf.intervals: epochs.append(block.epochs) betas.append(block.betas) interpolation = block.interpolation assert interpolation in ['linear', 'log'] interpolations.append(interpolation == 'log') epochs = np.array(epochs + [total_epochs]) assert np.all(np.diff(epochs) >= 0) betas = np.array(betas) interpolations = np.array(interpolations) def schedule(epoch): if epoch >= total_epochs: return betas[-1, -1] idx = np.searchsorted(epochs, epoch, side='right') - 1 beta0, beta1 = betas[idx] epoch0, epoch1 = epochs[idx], epochs[idx + 1] if interpolations[idx]: beta = beta0 * (beta1 / beta0) ** ((epoch - epoch0) / (epoch1 - epoch0)) else: beta = beta0 + (beta1 - beta0) * (epoch - epoch0) / (epoch1 - epoch0) return float(beta) return schedule ================================================ FILE: part1_getting_started.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 1: Getting started" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "seed = 0\n", "np.random.seed(seed)\n", "import tensorflow as tf\n", "\n", "tf.random.set_seed(seed)\n", "import os\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetch the jet tagging dataset from Open ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = fetch_openml('hls4ml_lhc_jets_hlf')\n", "X, y = data['data'], data['target']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's print some information about the dataset\n", "Print the feature names and the dataset shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "print(data['feature_names'])\n", "print(X.shape, y.shape)\n", "print(X[:5])\n", "print(y[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you saw above, the `y` target is an array of strings, e.g. \\['g', 'w',...\\] etc.\n", "We need to make this a \"One Hot\" encoding for the training.\n", "Then, split the dataset into training and validation sets" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "le = LabelEncoder()\n", "y = le.fit_transform(y)\n", "y = to_categorical(y, 5)\n", "X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "print(y[:5])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "X_train_val = scaler.fit_transform(X_train_val)\n", "X_test = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.save('X_train_val.npy', X_train_val)\n", "np.save('X_test.npy', X_test)\n", "np.save('y_train_val.npy', y_train_val)\n", "np.save('y_test.npy', y_test)\n", "np.save('classes.npy', le.classes_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now construct a model\n", "We'll use 3 hidden layers with 64, then 32, then 32 neurons. Each layer will use `relu` activation.\n", "Add an output layer with 5 neurons (one for each class), then finish with Softmax activation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Activation, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.regularizers import l1\n", "from callbacks import all_callbacks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(64, input_shape=(16,), name='fc1', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu1'))\n", "model.add(Dense(32, name='fc2', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu2'))\n", "model.add(Dense(32, name='fc3', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu3'))\n", "model.add(Dense(5, name='output', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='softmax', name='softmax'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model\n", "We'll use Adam optimizer with categorical crossentropy loss.\n", "The callbacks will decay the learning rate and save the model into a directory 'model_1'\n", "The model isn't very complex, so this should just take a few minutes even on the CPU.\n", "If you've restarted the notebook kernel after training once, set `train = False` to load the trained model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True\n", "if train:\n", " adam = Adam(lr=0.0001)\n", " model.compile(optimizer=adam, loss=['categorical_crossentropy'], metrics=['accuracy'])\n", " callbacks = all_callbacks(\n", " stop_patience=1000,\n", " lr_factor=0.5,\n", " lr_patience=10,\n", " lr_epsilon=0.000001,\n", " lr_cooldown=2,\n", " lr_minimum=0.0000001,\n", " outputDir='model_1',\n", " )\n", " model.fit(\n", " X_train_val,\n", " y_train_val,\n", " batch_size=1024,\n", " epochs=10,\n", " validation_split=0.25,\n", " shuffle=True,\n", " callbacks=callbacks.callbacks,\n", " )\n", "else:\n", " from tensorflow.keras.models import load_model\n", "\n", " model = load_model('model_1/KERAS_check_best_model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check performance\n", "Check the accuracy and make a ROC curve" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import plotting\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import accuracy_score\n", "\n", "y_keras = model.predict(X_test)\n", "print(\"Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n", "plt.figure(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_keras, le.classes_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert the model to FPGA firmware with hls4ml\n", "Now we will go through the steps to convert the model we trained to a low-latency optimized FPGA firmware with hls4ml.\n", "First, we will evaluate its classification performance to make sure we haven't lost accuracy using the fixed-point data types. \n", "Then we will synthesize the model with Vitis HLS and check the metrics of latency and FPGA resource usage.\n", "\n", "### Make an hls4ml config & model\n", "The hls4ml Neural Network inference library is controlled through a configuration dictionary.\n", "In this example we'll use the most simple variation, later exercises will look at more advanced configuration." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "\n", "config = hls4ml.utils.config_from_keras_model(model, granularity='model', backend='Vitis')\n", "print(\"-----------------------------------\")\n", "print(\"Configuration\")\n", "plotting.print_dict(config)\n", "print(\"-----------------------------------\")\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, backend='Vitis', output_dir='model_1/hls4ml_prj', part='xcu250-figd2104-2L-e'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualise what we created. The model architecture is shown, annotated with the shape and data types" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compile, predict\n", "Now we need to check that this model performance is still good. We compile the hls_model, and then use `hls_model.predict` to execute the FPGA firmware with bit-accurate emulation on the CPU." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_model.compile()\n", "X_test = np.ascontiguousarray(X_test)\n", "y_hls = hls_model.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare\n", "That was easy! Now let's see how the performance compares to Keras:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Keras Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n", "print(\"hls4ml Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_keras, le.classes_)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls, le.classes_, linestyle='--')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['keras', 'hls4ml'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthesize\n", "Now we'll actually use Vitis HLS to synthesize the model. We can run the build using a method of our `hls_model` object.\n", "After running this step, we can integrate the generated IP into a workflow to compile for a specific FPGA board.\n", "In this case, we'll just review the reports that Vitis HLS generates, checking the latency and resource usage.\n", "\n", "**This can take several minutes.**\n", "\n", "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n", "\n", "`tail -f model_1/hls4ml_prj/vitis_hls.log`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "hls_model.build(csim=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check the reports\n", "Print out the reports generated by Vitis HLS. Pay attention to the Latency and the 'Utilization Estimates' sections" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_1/hls4ml_prj/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "Since `ReuseFactor = 1` we expect each multiplication used in the inference of our neural network to use 1 DSP. Is this what we see? (Note that the Softmax layer should use 5 DSPs, or 1 per class)\n", "Calculate how many multiplications are performed for the inference of this network...\n", "(We'll discuss the outcome)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part2_advanced_config.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 2: Advanced Configuration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "from sklearn.metrics import accuracy_score\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import plotting\n", "import os\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_val = np.load('X_train_val.npy')\n", "X_test = np.ascontiguousarray(np.load('X_test.npy'))\n", "y_train_val = np.load('y_train_val.npy')\n", "y_test = np.load('y_test.npy', allow_pickle=True)\n", "classes = np.load('classes.npy', allow_pickle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the model\n", "Load the model trained in 'part1_getting_started'. **Make sure you've run through that walkthrough first!**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.models import load_model\n", "\n", "model = load_model('model_1/KERAS_check_best_model.h5')\n", "y_keras = model.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make an hls4ml config & model\n", "\n", "When the parameter `granularity` is set to `'name'` in the `config_from_keras_model` function, hls4ml automatically chooses the fixed-point precision for the ouput variables and accumulators for each layer. The accumulators are internal variables used for accumulating values during matrix multiplications. \n", "\n", "This precision choice is **conservative**. It avoids overflow and truncation based solely on input bitwidths, without considering the actual input values. Once again, this approach can be overly conservative, especially when post-training quantization is employed or if the initial input bitwidth settings are relatively loose. In such cases, it is advisable to manually edit the configuration to explicitly set specific widths, potentially iteratively after profiling the data.\n", "\n", "In this notebook, we'll create a configuration with the finer granularity (`'name'`). When we print the config dictionary, you'll notice that an entry is created for each named Layer of the model and the types are set to `auto`. For example, for the first layer we have:\n", "```\n", "LayerName:\n", " ...\n", " fc1:\n", " Trace: False\n", " Precision:\n", " weight: auto\n", " bias: auto\n", " result: auto\n", " accum: auto\n", " ReuseFactor: 1\n", " ...\n", "```\n", "\n", "In Part 1, all the parameters were set to the same default model precision. In this notebook instead, because of the `granularity='name'` and thus `'auto'` precision selection:\n", "- `weight` and `bias` are set to the default model precision;\n", "- `result` and `accum` are set to conservative bit-widths that avoid overflow and truncation.\n", "\n", "Later on, you will see that you can use this configuration as a template to start modifying things." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "\n", "config = hls4ml.utils.config_from_keras_model(model, granularity='name', backend='Vitis')\n", "print(\"-----------------------------------\")\n", "plotting.print_dict(config)\n", "print(\"-----------------------------------\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Profiling\n", "As you can see, we can choose the precision of _everything_ in our Neural Network. This is a powerful way to tune the performance, but it's also complicated. The tools in `hls4ml.model.profiling` can help you choose the right precision for your model. (That said, training your model with quantization built in can get around this problem, and that is introduced in Part 4. So, don't go too far down the rabbit hole of tuning your data types without first trying out quantization aware training with QKeras.)\n", "\n", "The first thing to try is to numerically profile your model. This method plots the distribution of the weights (and biases) as a box and whisker plot. The grey boxes show the values which can be represented with the data types used in the `hls_model`. Generally, you need the box to overlap completely with the whisker 'to the right' (large values) otherwise you'll get saturation & wrap-around issues. It can be okay for the box not to overlap completely 'to the left' (small values), but finding how small you can go is a matter of trial-and-error.\n", "\n", "Providing data, in this case just using the first 1000 examples for speed, will show the same distributions captured at the output of each layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from hls4ml.model.profiling import numerical, get_ymodel_keras\n", "\n", "for layer in config['LayerName'].keys():\n", " config['LayerName'][layer]['Trace'] = True\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, output_dir='model_1/hls4ml_prj_2', part='xcu250-figd2104-2L-e'\n", ")\n", "numerical(model=model, hls_model=hls_model, X=X_test[:1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customize\n", "Let's just try setting the precision of the first layer weights to something more narrow than 16 bits. Using fewer bits can save resources in the FPGA. After inspecting the profiling plot above, let's try 8 bits with 2 integer bit.\n", "\n", "**NOTE** Using `auto` precision can lead to undesired side effects. In case of this model, the bit width used for the output of the last fully connected layer is larger than can be reasonably represented with the look-up table in the softmax implementation. We therefore need to restrict it by hand to achieve proper results. \n", "\n", "Then create a new `HLSModel`, and display the profiling with the new config. This time, just display the weight profile by not providing any data '`X`'. Then create the `HLSModel` and display the architecture. Notice the box around the weights of the first layer reflects the different precision." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "config['LayerName']['fc1']['Precision']['weight'] = 'ap_fixed<8,2>'\n", "config['LayerName']['output']['Precision']['result'] = 'fixed<16,6,RND,SAT>'\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, output_dir='model_1/hls4ml_prj_2', part='xcu250-figd2104-2L-e'\n", ")\n", "numerical(model=model, hls_model=hls_model)\n", "hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trace\n", "When we start using customised precision throughout the model, it can be useful to collect the output from each layer to find out when things have gone wrong. We enable this trace collection by setting `Trace = True` for each layer whose output we want to collect." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for layer in config['LayerName'].keys():\n", " config['LayerName'][layer]['Trace'] = True\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, backend='Vitis', output_dir='model_1/hls4ml_prj_2', part='xcu250-figd2104-2L-e'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compile, trace, predict\n", "Now we need to check that this model performance is still good after reducing the precision. We compile the `hls_model`, and now use the `hls_model.trace` method to collect the model output, and also the output for all the layers we enabled tracing for. This returns a dictionary with keys corresponding to the layer names of the model. Stored at that key is the array of values output by that layer, sampled from the provided data.\n", "A helper function `get_ymodel_keras` will return the same dictionary for the Keras model.\n", "\n", "We'll just run the `trace` for the first 1000 examples, since it takes a bit longer and uses more memory than just running `predict`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_model.compile()\n", "hls4ml_pred, hls4ml_trace = hls_model.trace(X_test[:1000])\n", "keras_trace = get_ymodel_keras(model, X_test[:1000])\n", "y_hls = hls_model.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspect\n", "Now we can print out, make plots, or do any other more detailed analysis on the output of each layer to make sure we haven't made the performance worse. And if we have, we can quickly find out where. Let's just print the output of the first layer, for the first sample, for both the Keras and hls4ml models." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Keras layer 'fc1', first sample:\")\n", "print(keras_trace['fc1'][0])\n", "print(\"hls4ml layer 'fc1', first sample:\")\n", "print(hls4ml_trace['fc1'][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare\n", "Let's see if we lost performance by using 8 bits for the weights of the first layer by inspecting the accuracy and ROC curve." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Keras Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n", "print(\"hls4ml Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_keras, classes)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls, classes, linestyle='--')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['keras', 'hls4ml'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Profiling & Trace Summary\n", "We lost a small amount of accuracy compared to when we used `ap_fixed<16,6>`, but in many cases this difference will be small enough to be worth the resource saving. You can choose how aggressive to go with quantization, but it's always sensible to make the profiling plots even with the default configuration. Layer-level `trace` is very useful for finding when you reduced the bitwidth too far, or when the default configuration is no good for your model.\n", "\n", "With this 'post training quantization', around 8-bits width generally seems to be the limit to how low you can go before suffering significant performance loss. In Part 4, we'll look at using 'training aware quantization' with QKeras to go much lower without losing much performance.\n", "\n", "## ReuseFactor\n", "Now let's look at the other configuration parameter: `ReuseFactor`.\n", "Recall that `ReuseFactor` is our mechanism for tuning the parallelism:" ] }, { "attachments": { "reuse.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![reuse.png](attachment:reuse.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now let's make a new configuration for this model, and set the `ReuseFactor` to `2` for every layer:\n", "we'll compile the model, then evaulate its performance. (Note, by creating a new config with `granularity=Model`, we're implicitly resetting the precision to `ap_fixed<16,6>` throughout.) Changing the `ReuseFactor` should not change the classification results, but let's just verify that by inspecting the accuracy and ROC curve again!\n", "Then we'll build the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "config = hls4ml.utils.config_from_keras_model(model, granularity='Model', backend='Vitis')\n", "print(\"-----------------------------------\")\n", "print(config)\n", "print(\"-----------------------------------\")\n", "# Set the ReuseFactor to 2 throughout\n", "config['Model']['ReuseFactor'] = 2\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, backend='vitis', output_dir='model_1/hls4ml_prj_2', part='xcu250-figd2104-2L-e'\n", ")\n", "hls_model.compile()\n", "y_hls = hls_model.predict(X_test)\n", "print(\"Keras Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n", "print(\"hls4ml Accuracy: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "plt.figure(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_keras, classes)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls, classes, linestyle='--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now build the model\n", "\n", "**This can take several minutes.**\n", "\n", "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n", "\n", "`tail -f model_1/hls4ml_prj_2/vitis_hls.log`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_model.build(csim=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now print the report, compare this to the report from Exercise 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_1/hls4ml_prj_2')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_1/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "- Recall the outcome of the exercise of part 1 where we estimated how many DSPs our network should use.\n", "How does this change now we've used `ReuseFactor = 2` for the network? Does the expectation match the report this time?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part3_compression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 3: Compression" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "seed = 0\n", "np.random.seed(seed)\n", "import tensorflow as tf\n", "\n", "tf.random.set_seed(seed)\n", "import os\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetch the jet tagging dataset from Open ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_val = np.load('X_train_val.npy')\n", "X_test = np.load('X_test.npy')\n", "y_train_val = np.load('y_train_val.npy')\n", "y_test = np.load('y_test.npy')\n", "classes = np.load('classes.npy', allow_pickle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now construct a model\n", "We'll use the same architecture as in part 1: 3 hidden layers with 64, then 32, then 32 neurons. Each layer will use `relu` activation.\n", "Add an output layer with 5 neurons (one for each class), then finish with Softmax activation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Activation, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.regularizers import l1\n", "from callbacks import all_callbacks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(64, input_shape=(16,), name='fc1', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu1'))\n", "model.add(Dense(32, name='fc2', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu2'))\n", "model.add(Dense(32, name='fc3', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='relu', name='relu3'))\n", "model.add(Dense(5, name='output', kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001)))\n", "model.add(Activation(activation='softmax', name='softmax'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train sparse\n", "This time we'll use the Tensorflow model optimization sparsity to train a sparse model (forcing many weights to '0'). In this instance, the target sparsity is 75%" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\n", "from tensorflow_model_optimization.sparsity.keras import strip_pruning\n", "\n", "pruning_params = {\"pruning_schedule\": pruning_schedule.ConstantSparsity(0.75, begin_step=2000, frequency=100)}\n", "model = prune.prune_low_magnitude(model, **pruning_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model\n", "We'll use the same settings as the model for part 1: Adam optimizer with categorical crossentropy loss.\n", "The callbacks will decay the learning rate and save the model into a directory 'model_2'\n", "The model isn't very complex, so this should just take a few minutes even on the CPU.\n", "If you've restarted the notebook kernel after training once, set `train = False` to load the trained model rather than training again." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True\n", "if train:\n", " adam = Adam(lr=0.0001)\n", " model.compile(optimizer=adam, loss=['categorical_crossentropy'], metrics=['accuracy'])\n", " callbacks = all_callbacks(\n", " stop_patience=1000,\n", " lr_factor=0.5,\n", " lr_patience=10,\n", " lr_epsilon=0.000001,\n", " lr_cooldown=2,\n", " lr_minimum=0.0000001,\n", " outputDir='model_2',\n", " )\n", " callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep())\n", " model.fit(\n", " X_train_val,\n", " y_train_val,\n", " batch_size=1024,\n", " epochs=10,\n", " validation_split=0.25,\n", " shuffle=True,\n", " callbacks=callbacks.callbacks,\n", " )\n", " # Save the model again but with the pruning 'stripped' to use the regular layer types\n", " model = strip_pruning(model)\n", " model.save('model_2/KERAS_check_best_model.h5')\n", "else:\n", " from tensorflow.keras.models import load_model\n", "\n", " model = load_model('model_2/KERAS_check_best_model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check sparsity\n", "Make a quick check that the model was indeed trained sparse. We'll just make a histogram of the weights of the 1st layer, and hopefully observe a large peak in the bin containing '0'. Note logarithmic y axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "w = model.layers[0].weights[0].numpy()\n", "h, b = np.histogram(w, bins=100)\n", "plt.figure(figsize=(7, 7))\n", "plt.bar(b[:-1], h, width=b[1] - b[0])\n", "plt.semilogy()\n", "print('% of zeros = {}'.format(np.sum(w == 0) / np.size(w)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check performance\n", "How does this 75% sparse model compare against the unpruned model? Let's report the accuracy and make a ROC curve. The pruned model is shown with solid lines, the unpruned model from part 1 is shown with dashed lines.\n", "**Make sure you've trained the model from part 1**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import plotting\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import accuracy_score\n", "from tensorflow.keras.models import load_model\n", "\n", "model_ref = load_model('model_1/KERAS_check_best_model.h5')\n", "\n", "y_ref = model_ref.predict(X_test)\n", "y_prune = model.predict(X_test)\n", "\n", "print(\"Accuracy unpruned: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_ref, axis=1))))\n", "print(\"Accuracy pruned: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_prune, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_ref, classes)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_prune, classes, linestyle='--')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['unpruned', 'pruned'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert the model to FPGA firmware with hls4ml\n", "Let's use the default configuration: `ap_fixed<16,6>` precision everywhere and `ReuseFactor=1`, so we can compare with the part 1 model. We need to use `strip_pruning` to change the layer types back to their originals.\n", "\n", "**The synthesis will take a while**\n", "\n", "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n", "\n", "`tail -f model_2/hls4ml_prj/vitis_hls.log`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "\n", "config = hls4ml.utils.config_from_keras_model(model, granularity='model', backend='Vitis')\n", "print(config)\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, backend='Vitis', output_dir='model_2/hls4ml_prj', part='xcu250-figd2104-2L-e'\n", ")\n", "hls_model.compile()\n", "hls_model.build(csim=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check the reports\n", "Print out the reports generated by Vitis HLS. Pay attention to the Utilization Estimates' section in particular this time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_2/hls4ml_prj/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the report for the model trained in part 1. Remember these models have the same architecture, but the model in this section was trained using the sparsity API from tensorflow_model_optimization. Notice how the resource usage had dramatically reduced (particularly the DSPs). When Vitis HLS notices an operation like `y = 0 * x` it can avoid placing a DSP for that operation. The impact of this is biggest when `ReuseFactor = 1`, but still applies at higher reuse as well. **Note you need to have trained and synthesized the model from part 1**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_1/hls4ml_prj')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part4.1_HG_quantization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 4: HG Quantization" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import keras\n", "from keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "seed = 0\n", "np.random.seed(seed)\n", "import tensorflow as tf\n", "\n", "tf.random.set_seed(seed)\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetch the jet tagging dataset from Open ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# If you haven't finished part 1 already, uncomment the following lines to download, process, and save the dataset\n", "\n", "# le = LabelEncoder()\n", "# y = le.fit_transform(y)\n", "# y = to_categorical(y, 5)\n", "# X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "# # print(y[:5])\n", "# scaler = StandardScaler()\n", "# X_train_val = scaler.fit_transform(X_train_val)\n", "# X_test = scaler.transform(X_test)\n", "# np.save('X_train_val.npy', X_train_val)\n", "# np.save('X_test.npy', X_test)\n", "# np.save('y_train_val.npy', y_train_val)\n", "# np.save('y_test.npy', y_test)\n", "# np.save('classes.npy', le.classes_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_val = np.load('X_train_val.npy')\n", "X_test = np.load('X_test.npy')\n", "y_train_val = np.load('y_train_val.npy')\n", "y_test = np.load('y_test.npy')\n", "classes = np.load('classes.npy', allow_pickle=True)\n", "\n", "# Convert everything to tf.Tensor to avoid casting\n", "with tf.device('/cpu:0'): # type: ignore\n", " _X_train_val = tf.convert_to_tensor(X_train_val, dtype=tf.float32)\n", " # We don't make explicit y categorical tensor:\n", " # Use SparseCategoricalCrossentropy loss instead.\n", " _y_train_val = tf.convert_to_tensor(np.argmax(y_train_val, axis=1), dtype=tf.int32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Construct a model\n", "This time we're going to use HGQ layers.\n", "\n", "HGQ is \"High Granularity Quantization\" for heterogeneous quantization at arbitrary granularity, up to per-weight and per-activation level.\n", "\n", "https://github.com/calad0i/HGQ\n", "\n", "Depending on the specific task, HGQ can achieve more than 10x resource savings comparing to QKeras. (For example, on this dataset and requiring an accuracy of around 0.72~0.74)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.optimizers import Adam\n", "from keras.losses import SparseCategoricalCrossentropy\n", "from HGQ.layers import HQuantize, HDense, HActivation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For any layer that needs to be quantized (i.e., layers that perform the actual computation), add a `H` in front of the layer name. For example, `HDense`, `HConv2D`, `HActivation`, etc.\n", "\n", "HGQ requires the input number to be quantized. To achieve it, you can simply add a `HQuantizer` layer at the beginning of the model. You may refer to https://calad0i.github.io/HGQ/ for full documentation.\n", "\n", "As all quantization bitwidths are learnt, you don't need to specify them. Instead, for each `H-` layer, you need to specify the `beta` parameter that controls the trade-off between accuracy and resource savings. The higher the `beta`, the more aggressive the quantization will be." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta = 3e-6\n", "# The bigger the beta, the smaller the models is, at the cost of accuracy.\n", "\n", "model = Sequential(\n", " [\n", " HQuantize(beta=beta),\n", " HDense(64, activation='relu', beta=beta),\n", " HDense(32, activation='relu', beta=beta),\n", " HDense(32, activation='relu', beta=beta),\n", " HDense(5, beta=beta),\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train sparse\n", "\n", "No need to do anything. Unstructured sparsity comes for free with HGQ." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This is a empty code cell, you don't need to put anything here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model\n", "We'll use the same settings as the model for part 1: Adam optimizer with categorical crossentropy loss.\n", "\n", "However, we can skip the softmax layer in the model by adding `from_logits=True` to the loss function. `Softmax` is expensive in hardware, so we want to avoid it if possible.\n", "\n", "For any HGQ model, it's essential to use `ResetMinMax` callback to reset the quantization ranges after each epoch. This is because the ranges are calculated based on the data seen so far, and we want to make sure they are recalculated after each epoch.\n", "\n", "It is recommended to use the `FreeBOPs` callback to monitor the number of (effective) bits operations in the model. This is a good proxy for ressource usage in FPGA (BOPs ~ 55*DSPs+LUTs) for **post place&route resource**. Notice that CSynth tends to overestimate at least by a factor of 2." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from HGQ import ResetMinMax, FreeBOPs\n", "from keras.callbacks import LearningRateScheduler\n", "from keras.experimental import CosineDecay\n", "from nn_utils import PBarCallback\n", "\n", "_sched = CosineDecay(2e-2, 200)\n", "sched = LearningRateScheduler(_sched)\n", "pbar = PBarCallback(metric='loss: {loss:.3f}/{val_loss:.3f} - acc: {accuracy:.3f}/{val_accuracy:.3f}')\n", "\n", "callbacks = [ResetMinMax(), FreeBOPs(), pbar, sched]\n", "\n", "# ResetMinMax: necessary callback for all HGQ models\n", "# FreeBOPs: recommended callback\n", "# pbar: progress bar callback, useful when the number of epochs is high\n", "# sched: learning rate scheduler. Cosine decay in this case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notice\n", "\n", "- Due to the stochasticness of surrogate gradient on the individual bitwidth, it is recommended to train the model with a large batchsize over more epochs.\n", "\n", "- HGQ is jit-compiled for many parts. The first epoch will take longer to compile.\n", "\n", "- We train for 200 epochs here, which takes ~1min on a 3070-maxq GPU, similar to the time taken part 4.\n", "\n", "- Parameters used in this tutorial are not optimized for the best performance. Please refer to [HGQ-demos](https://github.com/calad0i/HGQ-demos) for more advanced examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True\n", "if train:\n", " opt = Adam(learning_rate=0)\n", " loss = SparseCategoricalCrossentropy(from_logits=True)\n", " model.compile(optimizer=opt, loss=loss, metrics=['accuracy'])\n", "\n", " model.fit(\n", " _X_train_val,\n", " _y_train_val,\n", " batch_size=16384,\n", " epochs=200,\n", " validation_split=0.25,\n", " shuffle=True,\n", " callbacks=callbacks,\n", " verbose=0, # type: ignore\n", " )\n", " model.save('model_3.1/model.h5')\n", "else:\n", " from keras.models import load_model\n", "\n", " # No need to use custom_objects as the custom layers are already registered\n", " model: keras.Model = load_model('model_3.1/model.h5') # type: ignore" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare for conversion\n", "\n", "HGQ model cannot be converted to hls4ml model directly, and we need to convert it to a proxy model first. The proxy model also serves as a bit-accurate emulator of the hls4ml model that takes numerical overflow into account.\n", "\n", "To convert to a proxy model, we need to set appropriate ranges of the model internal variables. This is done by using the `trace_minmax` function. You can add a scaler factor `cover_range` to the ranges to make sure the model more robust to numerical overflow. `trace_minmax` also resturns the exact (effective) BOPs of the model (the number provided during training is approximated).\n", "\n", "If you keep all parameters the same and everything goes correctly, total BOPs of the model should be around 6500. This means, after running place&route (or vsynth), the model should take around 6500 LUTs, which means DSPs*55+LUTs used should be around 6500." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from HGQ import trace_minmax, to_proxy_model\n", "\n", "trace_minmax(model, X_train_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that the model is indeed sparse without explicit pruning or `l1` regularization." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for layer in model.layers:\n", " if layer._has_kernel:\n", " k = layer.fused_qkernel.numpy()\n", " print(f'{layer.name}: {np.mean(k==0):.2%} sparsity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, convert the model to a proxy model using the `to_proxy_model` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "proxy = to_proxy_model(model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "import plotting\n", "\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " proxy, output_dir='model_3.1/hls4ml_prj', part='xcu250-figd2104-2L-e', backend='Vitis'\n", ")\n", "hls_model.compile()\n", "\n", "X_test = np.ascontiguousarray(X_test)\n", "y_keras = model.predict(X_test, batch_size=16384, verbose=0)\n", "y_proxy = proxy.predict(X_test, batch_size=16384, verbose=0)\n", "y_hls = hls_model.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check bit-accuracy\n", "If you are unlucky, `y_keras` and `y_hls` will not fully match due to numerical overflow (for a few entries). However, `y_keras` and `y_proxy` should match perfectly. (Sometime mismatch could also happen - only due to machine precision limit.\n", "\n", "For newer nvidia GPUs, TF32 is enabled by default (fp32 with reduced mantissa bits), which could cause this issue). This will make this issue more prevalent." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.mean(y_keras == y_hls), np.mean(y_proxy == y_hls)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The plotting script assumes 0-1 range for the predictions.\n", "y_keras_softmax = tf.nn.softmax(y_keras).numpy()\n", "y_hls_softmax = tf.nn.softmax(y_hls).numpy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from sklearn.metrics import accuracy_score\n", "from keras.models import load_model\n", "\n", "model_ref = load_model('model_1/KERAS_check_best_model.h5')\n", "y_ref = model_ref.predict(X_test, batch_size=1024, verbose=0)\n", "\n", "print(\"Accuracy baseline: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_ref, axis=1))))\n", "print(\"Accuracy pruned, quantized: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))\n", "print(\"Accuracy hls4ml: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_ref, classes)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_keras_softmax, classes, linestyle='--')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls_softmax, classes, linestyle=':')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--'), Line2D([0], [0], ls=':')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['baseline', 'pruned, quantized', 'hls4ml'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthesize\n", "Now let's synthesize this quantized, pruned model.\n", "\n", "**The synthesis will take a while**\n", "\n", "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n", "\n", "`tail -f model_3.1/hls4ml_prj/vitis_hls.log`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_model.build(csim=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check the reports\n", "Print out the reports generated by Vitis HLS. Pay attention to the Utilization Estimates' section in particular this time.\n", "\n", "## Notice\n", "We strip away the softmax layer compare to part 4, which takes 3~5 cycles to compute. The overall latency could be comparable." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_3.1/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the report for the model trained in part 4. You should notice that the resource usage is significantly lower than the model trained in part 4.\n", "\n", "**Note you need to have trained and synthesized the model from part 4**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_3/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NB\n", "Note as well that the Vitis HLS `csynth` resource estimates tend to _overestimate_ on chip resource usage. Running the subsequent stages of FPGA compilation reveals the more realistic resource usage, You can run the next step, 'logic synthesis' with `hls_model.build(synth=True, vsynth=True)`, but we skipped it in this tutorial in the interest of time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part4_quantization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 4: Quantization" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "seed = 0\n", "np.random.seed(seed)\n", "import tensorflow as tf\n", "\n", "tf.random.set_seed(seed)\n", "import os\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetch the jet tagging dataset from Open ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_val = np.load('X_train_val.npy')\n", "X_test = np.load('X_test.npy')\n", "y_train_val = np.load('y_train_val.npy')\n", "y_test = np.load('y_test.npy')\n", "classes = np.load('classes.npy', allow_pickle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Construct a model\n", "This time we're going to use QKeras layers.\n", "QKeras is \"Quantized Keras\" for deep heterogeneous quantization of ML models.\n", "\n", "https://github.com/google/qkeras\n", "\n", "It is maintained by Google and we recently added support for QKeras model to hls4ml." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.regularizers import l1\n", "from callbacks import all_callbacks\n", "from tensorflow.keras.layers import Activation\n", "from qkeras.qlayers import QDense, QActivation\n", "from qkeras.quantizers import quantized_bits, quantized_relu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're using `QDense` layer instead of `Dense`, and `QActivation` instead of `Activation`. We're also specifying `kernel_quantizer = quantized_bits(6,0,0)`. This will use 6-bits (of which 0 are integer) for the weights. We also use the same quantization for the biases, and `quantized_relu(6)` for 6-bit ReLU activations." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = Sequential()\n", "model.add(\n", " QDense(\n", " 64,\n", " input_shape=(16,),\n", " name='fc1',\n", " kernel_quantizer=quantized_bits(6, 0, alpha=1),\n", " bias_quantizer=quantized_bits(6, 0, alpha=1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " )\n", ")\n", "model.add(QActivation(activation=quantized_relu(6), name='relu1'))\n", "model.add(\n", " QDense(\n", " 32,\n", " name='fc2',\n", " kernel_quantizer=quantized_bits(6, 0, alpha=1),\n", " bias_quantizer=quantized_bits(6, 0, alpha=1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " )\n", ")\n", "model.add(QActivation(activation=quantized_relu(6), name='relu2'))\n", "model.add(\n", " QDense(\n", " 32,\n", " name='fc3',\n", " kernel_quantizer=quantized_bits(6, 0, alpha=1),\n", " bias_quantizer=quantized_bits(6, 0, alpha=1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " )\n", ")\n", "model.add(QActivation(activation=quantized_relu(6), name='relu3'))\n", "model.add(\n", " QDense(\n", " 5,\n", " name='output',\n", " kernel_quantizer=quantized_bits(6, 0, alpha=1),\n", " bias_quantizer=quantized_bits(6, 0, alpha=1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " )\n", ")\n", "model.add(Activation(activation='softmax', name='softmax'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train sparse\n", "Let's train with model sparsity again, since QKeras layers are prunable." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\n", "from tensorflow_model_optimization.sparsity.keras import strip_pruning\n", "\n", "pruning_params = {\"pruning_schedule\": pruning_schedule.ConstantSparsity(0.75, begin_step=2000, frequency=100)}\n", "model = prune.prune_low_magnitude(model, **pruning_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model\n", "We'll use the same settings as the model for part 1: Adam optimizer with categorical crossentropy loss.\n", "The callbacks will decay the learning rate and save the model into a directory 'model_2'\n", "The model isn't very complex, so this should just take a few minutes even on the CPU.\n", "If you've restarted the notebook kernel after training once, set `train = False` to load the trained model rather than training again." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True\n", "if train:\n", " adam = Adam(lr=0.0001)\n", " model.compile(optimizer=adam, loss=['categorical_crossentropy'], metrics=['accuracy'])\n", " callbacks = all_callbacks(\n", " stop_patience=1000,\n", " lr_factor=0.5,\n", " lr_patience=10,\n", " lr_epsilon=0.000001,\n", " lr_cooldown=2,\n", " lr_minimum=0.0000001,\n", " outputDir='model_3',\n", " )\n", " callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep())\n", " model.fit(\n", " X_train_val,\n", " y_train_val,\n", " batch_size=1024,\n", " epochs=30,\n", " validation_split=0.25,\n", " shuffle=True,\n", " callbacks=callbacks.callbacks,\n", " )\n", " # Save the model again but with the pruning 'stripped' to use the regular layer types\n", " model = strip_pruning(model)\n", " model.save('model_3/KERAS_check_best_model.h5')\n", "else:\n", " from tensorflow.keras.models import load_model\n", " from qkeras.utils import _add_supported_quantized_objects\n", "\n", " co = {}\n", " _add_supported_quantized_objects(co)\n", " model = load_model('model_3/KERAS_check_best_model.h5', custom_objects=co)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check performance\n", "How does this model which was trained using 6-bits, and 75% sparsity model compare against the original model? Let's report the accuracy and make a ROC curve. The quantized, pruned model is shown with solid lines, the unpruned model from part 1 is shown with dashed lines.\n", "\n", "\n", "We should also check that hls4ml can respect the choice to use 6-bits throughout the model, and match the accuracy. We'll generate a configuration from this Quantized model, and plot its performance as the dotted line.\n", "The generated configuration is printed out. You'll notice that it uses 7 bits for the type, but we specified 6!? That's just because QKeras doesn't count the sign-bit when we specify the number of bits, so the type that actually gets used needs 1 more.\n", "\n", "We also use the `OutputRoundingSaturationMode` optimizer pass of `hls4ml` to set the Activation layers to round, rather than truncate, the cast. This is important for getting good model accuracy when using small bit precision activations. And we'll set a different data type for the tables used in the Softmax, just for a bit of extra performance.\n", "\n", "\n", "**Make sure you've trained the model from part 1**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "import plotting\n", "\n", "config = hls4ml.utils.config_from_keras_model(model, granularity='name', backend='Vitis')\n", "config['LayerName']['softmax']['exp_table_t'] = 'ap_fixed<18,8>'\n", "config['LayerName']['softmax']['inv_table_t'] = 'ap_fixed<18,4>'\n", "print(\"-----------------------------------\")\n", "plotting.print_dict(config)\n", "print(\"-----------------------------------\")\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, backend='Vitis', output_dir='model_3/hls4ml_prj', part='xcu250-figd2104-2L-e'\n", ")\n", "hls_model.compile()\n", "\n", "y_qkeras = model.predict(np.ascontiguousarray(X_test))\n", "y_hls = hls_model.predict(np.ascontiguousarray(X_test))\n", "np.save('model_3/y_qkeras.npy', y_qkeras)\n", "np.save('model_3/y_hls.npy', y_hls)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from sklearn.metrics import accuracy_score\n", "from tensorflow.keras.models import load_model\n", "\n", "model_ref = load_model('model_1/KERAS_check_best_model.h5')\n", "y_ref = model_ref.predict(X_test)\n", "\n", "print(\"Accuracy baseline: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_ref, axis=1))))\n", "print(\"Accuracy pruned, quantized: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_qkeras, axis=1))))\n", "print(\"Accuracy hls4ml: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_ref, classes)\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_qkeras, classes, linestyle='--')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls, classes, linestyle=':')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--'), Line2D([0], [0], ls=':')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['baseline', 'pruned, quantized', 'hls4ml'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthesize\n", "Now let's synthesize this quantized, pruned model.\n", "\n", "**The synthesis will take a while**\n", "\n", "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n", "\n", "`tail -f model_3/hls4ml_prj/vitis_hls.log`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_model.build(csim=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check the reports\n", "Print out the reports generated by Vitis HLS. Pay attention to the Utilization Estimates' section in particular this time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_3/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the report for the model trained in part 1. Now, compared to the model from part 1, this model has been trained with low-precision quantization, and 75% pruning. You should be able to see that we have saved a lot of resource compared to where we started in part 1. At the same time, referring to the ROC curve above, the model performance is pretty much identical even with this drastic compression!\n", "\n", "**Note you need to have trained and synthesized the model from part 1**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_1/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the report for the model trained in part 3. Both these models were trained with 75% sparsity, but the new model uses 6-bit precision as well. You can see how Vitis HLS has moved multiplication operations from DSPs into LUTs, reducing the \"critical\" resource usage.\n", "\n", "**Note you need to have trained and synthesized the model from part 3**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.report.read_vivado_report('model_2/hls4ml_prj')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NB\n", "Note as well that the Vitis HLS resource estimates tend to _overestimate_ LUTs, while generally estimating the DSPs correctly. Running the subsequent stages of FPGA compilation reveals the more realistic resource usage, You can run the next step, 'logic synthesis' with `hls_model.build(synth=True, vsynth=True)`, but we skipped it in this tutorial in the interest of time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part5_bdt.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\"conifer\"\n", "\n", "In this notebook we will take the first steps with training a BDT with `xgboost`, then translating it to HLS code for FPGA with `conifer`\n", "\n", "Key concepts:\n", "- model training\n", "- model evaluation\n", "- `conifer` configuration and conversion\n", "- model emulation\n", "- model synthesis\n", "- accelerator creation\n", "\n", "For some use cases, the Forest Processing Unit might be an easier entry point as no FPGA synthesis is required for supported boards. Read more about the FPU here: https://ssummers.web.cern.ch/conifer/fpu.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import xgboost as xgb\n", "import matplotlib.pyplot as plt\n", "import plotting\n", "import numpy as np\n", "from scipy.special import softmax\n", "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", "import conifer\n", "import json\n", "import os\n", "import sys\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']\n", "\n", "# enable more output from conifer\n", "import logging\n", "\n", "logging.basicConfig(stream=sys.stdout, level=logging.WARNING)\n", "logger = logging.getLogger('conifer')\n", "logger.setLevel('DEBUG')\n", "\n", "# create a random seed at we use to make the results repeatable\n", "seed = int('hls4ml-tutorial'.encode('utf-8').hex(), 16) % 2**31\n", "\n", "print(f'Using conifer version {conifer.__version__}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load dataset\n", "\n", "Load the jet tagging dataset.\n", "\n", "**Note**: you need to run part1 first." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_val = np.load('X_train_val.npy')\n", "X_test = np.load('X_test.npy')\n", "y_train_val_one_hot = np.load('y_train_val.npy')\n", "y_test_one_hot = np.load('y_test.npy')\n", "classes = np.load('classes.npy', allow_pickle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to transform the test labels from the one-hot encoded values to labels" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "le = LabelEncoder().fit(classes)\n", "ohe = OneHotEncoder().fit(le.transform(classes).reshape(-1, 1))\n", "y_train_val = ohe.inverse_transform(y_train_val_one_hot.astype(int))\n", "y_test = ohe.inverse_transform(y_test_one_hot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a BDT\n", "We'll use `xgboost`'s `XGBClassifier` with:\n", "\n", "| Parameter | Explanation |\n", "| --- | --- |\n", "| `n_estimators=25` | 25 trees |\n", "| `max_depth=5` | maximum tree depth of 5 |" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "clf = xgb.XGBClassifier(n_estimators=25, max_depth=5, learning_rate=1.0, random_state=seed).fit(X_train_val, y_train_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Validate performance\n", "Now we check whether the trained model is any good. We'll plot the ROC curve." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "from tensorflow.keras.models import load_model\n", "\n", "# load the KERAS model from part 1\n", "model_ref = load_model('model_1/KERAS_check_best_model.h5')\n", "y_ref = model_ref.predict(X_test)\n", "\n", "# compute predictions of the xgboost model\n", "y_xgb = clf.predict_proba(X_test)\n", "print(f'Accuracy baseline: {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_ref, axis=1)):.5f}')\n", "print(f'Accuracy xgboost: {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_xgb, axis=1)):.5f}')\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test_one_hot, y_ref, classes, linestyle='--')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test_one_hot, y_xgb, classes, linestyle='-')\n", "\n", "# add a legend\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [\n", " Line2D([0], [0], ls='--'),\n", " Line2D([0], [0], ls='-'),\n", "]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['part1 Keras', 'xgboost'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"conifer\"\n", "\n", "Now we'll convert this model to FPGA firmware with `conifer`. We first need to create a configuration in the form of a dictionary. The quickest way to get started is to create a default configuration from the intended target backend (`xilinxhls` for us). Each backend may have different configuration options, so getting the configuration this way helps enumerate the possible options.\n", "\n", "We will print the configuration, modify it, and print it again. The modifications are:\n", "- set the `OutputDirectory` to something descriptive\n", "- set the `XilinxPart` to the part number of the FPGA on the Alveo U50" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cfg = conifer.backends.xilinxhls.auto_config()\n", "\n", "# print the config\n", "print('Default Configuration\\n' + '-' * 50)\n", "plotting.print_dict(cfg)\n", "print('-' * 50)\n", "\n", "# modify the config\n", "cfg['OutputDir'] = 'model_5/'\n", "cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'\n", "\n", "# print the config again\n", "print('Modified Configuration\\n' + '-' * 50)\n", "plotting.print_dict(cfg)\n", "print('-' * 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert and write\n", "Convert the `xgboost` model to a `conifer` one, and print the `help` to see what methods it implements.\n", "Then `write` the model, creating the specified output directory and writing all the HLS files to it. We also save the `xgboost` model itself.\n", "\n", "#### Other converters:\n", "`conifer` has converters for several popular BDT training libraries. Each one is used like: `conifer.converters.convert_from_(model, config)`\n", "The converters are:\n", "- `sklearn`\n", "- `xgboost`\n", "- `ydf`\n", "- `tmva`\n", "- `onnx` (exposing `catboost` and `lightGBM`)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# convert the model to the conifer representation\n", "conifer_model = conifer.converters.convert_from_xgboost(clf, cfg)\n", "# print the help to see the API on the conifer_model\n", "help(conifer_model)\n", "# write the project (writing HLS project to disk)\n", "conifer_model.write()\n", "# save the conifer model - we can load this again later\n", "clf.save_model('model_5/xgboost_model.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore\n", "Browse the files in the newly created project directory to take a look at the HLS code.\n", "\n", "The output of `!tree model_5` is:\n", "\n", "```\n", "model_5/\n", "├── bridge.cpp\n", "├── build_hls.tcl\n", "├── firmware\n", "│ ├── BDT.cpp\n", "│ ├── BDT.h\n", "│ ├── my_prj.cpp\n", "│ ├── my_prj.h\n", "│ └── parameters.h\n", "├── hls_parameters.tcl\n", "├── my_prj.json\n", "├── my_prj_test.cpp\n", "├── tb_data\n", "└── vivado_synth.tcl\n", "```\n", "\n", "- files under `firmware` are the HLS implementation of the model\n", "- `my_prj.json` is the saved converted `conifer` model that can be loaded again without the original `xgboost` model\n", "- `tcl` scripts are used for synthesizing the project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Emulate\n", "Before starting the lengthy FPGA build process, we should validate that our conversion was successful and that the choice of precision was suitable with a bit-accurate emulation. To do this we need to run the HLS C++ code on the CPU with some test data first. This is like the HLS C Simulation step, but rather than writing a C++ testbench and invoking `vitis_hls` to run `csim`, `conifer` implements Python bindings for the HLS, just like `hls4ml`.\n", "\n", "We first need to compile (which uses the C++ compiler), then we can make predictions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "conifer_model.compile()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_hls = conifer_model.decision_function(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare\n", "\n", "Now we check whether the emulated predictions are good. To do this we'll plot the ROC curve again with the HLS predictions overlaid." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_hls_proba = softmax(y_hls) # compute class probabilities from the raw predictions\n", "\n", "print(f'Accuracy baseline: {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_ref, axis=1)):.5f}')\n", "print(f'Accuracy xgboost: {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_xgb, axis=1)):.5f}')\n", "print(f'Accuracy conifer: {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_hls_proba, axis=1)):.5f}')\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test_one_hot, y_ref, classes, linestyle='--')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test_one_hot, y_xgb, classes, linestyle=':')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test_one_hot, y_hls_proba, classes, linestyle='-')\n", "\n", "# add a legend\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [\n", " Line2D([0], [0], ls='--'),\n", " Line2D([0], [0], ls=':'),\n", " Line2D([0], [0], ls='-'),\n", "]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['part1 Keras', 'xgboost', 'conifer'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build\n", "Now we'll run the Vitis HLS and Vivado synthesis. HLS C Synthesis compiles our C++ to RTL, performing scheduling and resource mapping. Vivado synthesis synthesizes the RTL from the previous step into a netlist, and produces a more realistic resource estimation. The latency can't change during Vivado synthesis, it's fixed in the RTL description.\n", "\n", "After the build completes we can also browse the new log files and reports that are generated.\n", "\n", "**Warning**: this step might take around 10 minutes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "conifer_model.build(synth=True, vsynth=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Report\n", "If the synthesis completed successfuly, we can extract the key metrics from the reports and print them out.\n", "The section `\"vsynth\"` contains the report from the Vivado RTL synthesis, which is usually lower, and more realistic than the HLS report." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "report = conifer_model.read_report()\n", "plotting.print_dict(report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deployment with `pynq`\n", "\n", "There are two main ways to deploy a BDT to an accelerator card with `conifer`:\n", "- build a static accelerator with Xilinx HLS backend\n", "- use the dynamic accelerator Forest Processing Unit (FPU)\n", "\n", "Getting started with the FPU is straightforward. For a supported board, you will need only the converted model JSON, and a bitfile that can be downloaded from the conifer website. Read more about the FPU here: https://ssummers.web.cern.ch/conifer/fpu.html\n", "\n", "However, without a physical device there's not much to show, so in this section we'll see how to deploy the model that we already trained as a static accelerator to a `pynq-z2` board.\n", "We'll use the `AcceleratorConfig` part of the configuration that we previously left undefined." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pynq_model_cfg = conifer.backends.xilinxhls.auto_config()\n", "pynq_model_cfg['OutputDir'] = 'model_5_pynq' # choose a new project directory\n", "pynq_model_cfg['ProjectName'] = 'conifer_jettag'\n", "pynq_model_cfg['AcceleratorConfig'] = {\n", " 'Board': 'pynq-z2', # choose a pynq-z2 board\n", " 'InterfaceType': 'float', # floating point for the data I/O (this is default)\n", "}\n", "\n", "# print the config\n", "print('Modified Configuration\\n' + '-' * 50)\n", "print(json.dumps(pynq_model_cfg, indent=2))\n", "print('-' * 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supported boards\n", "\n", "Here we print the list of supported boards, so you can see what else works \"out of the box\". It's relatively easy to add other Zynq SoC or Alveo boards, for example to add an Alveo U50 card targeting `xilinx_u50_gen3x16_xdma_5_202210_1` platform:\n", "\n", "```\n", "u50 = conifer.backends.boards.AlveoConfig.default_config()\n", "u50['xilinx_part'] = 'xcu50-fsvh2104-2-e'\n", "u50['platform'] = 'xilinx_u50_gen3x16_xdma_5_202210_1'\n", "u50['name'] = 'xilinx_u50_gen3x16_xdma_5_202210_1'\n", "u50 = conifer.backends.boards.AlveoConfig(u50)\n", "conifer.backends.boards.register_board_config(u50.name, u50)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This is the full list of supported boards:\n", "print(f'Supported boards: {conifer.backends.boards.get_available_boards()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the model\n", "\n", "We load the JSON for the conifer model we previously used, applying the new configuration just defined. We'll see that the FPGA part specified by the board overrides the `XilinxPart` specified in the default." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pynq_model = conifer.model.load_model('model_5/my_prj.json', new_config=pynq_model_cfg)\n", "pynq_model.write()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the model\n", "\n", "Now we run `build` again, running HLS Synthesis, Logic Synthesis and Place & Route, finally producing a bitfile and an archive of files that we'll need to run inference on the pynq-z2 board. \n", "\n", "**Warning**: this step might take around 20 minutes to complete.\n", "\n", "The floorplan of the bitfile should like something like this, where the individual tree modules are highlighted in different colours:\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pynq_model.build(synth=True, bitfile=True, package=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inference on pynq-z2\n", "\n", "Running inference on the `pynq-z2` would look like this:\n", "- download the `model_5/conifer_jettag.zip` archive from this notebook\n", "- upload `conifer_jettag.zip` to the pynq-z2 device and unzip it\n", "- start a jupyter notebook on the `pynq-z2` and run the following code:\n", "\n", "```\n", "import conifer\n", "accelerator = conifer.backends.xilinxhls.runtime.ZynqDriver('conifer_jettag.bit', batch_size=1)\n", "X = ... # load some data \n", "y_pynq = accelerator.decision_function(X)\n", "```\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part6_cnns.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "# Part 6: Convolutional Neural Networks in hls4ml\n", "\n", "In this notebook you will learn how to train a pruned and quantized convolutional neural network (CNN) and deploy it using hls4ml. For this exercise, we will use the Street View House Numbers (SVHN) Dataset (http://ufldl.stanford.edu/housenumbers/).\n", "\n", "The SVHN dataset consists of real-world images of house numbers extracted from Google Street View images. The format is similar to that of the MNIST dataset, but is a much more challenging real-world problem, as illustrated by the examples shown below.\n", "\n", "All the images are in RGB format and have been cropped to 32x32 pixels. \n", "Unlike MNIST, more than one digit can be present in the same image and in these cases, the center digit is used to assign a label to the image.\n", "Each image can belong to one of 10 classes, corresponding to digits 0 through 9.\n", "\n", "![alt text](images/test.png \"SVHN examples from the test dataset\")\n", "\n", "The SVHN dataset consists of 73,257 images for training (and 531,131 extra samples that are easier to classify and can be used as additional training data) and 26,032 images for testing." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Start with the neccessary imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import time\n", "import tensorflow.compat.v2 as tf\n", "import tensorflow_datasets as tfds\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Fetch the SVHN dataset using Tensorflow Dataset\n", "\n", "In this part we will fetch the trainining, validation and test dataset using Tensorflow Datasets (https://www.tensorflow.org/datasets). We will not use the 'extra' training in order to save time, but you could fetch it by adding `split='train[:90%]+extra'`. We will use the first 90% of the training data for training and the last 10% for validation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ds_train, info = tfds.load('svhn_cropped', split='train[:90%]', with_info=True, as_supervised=True)\n", "ds_test = tfds.load('svhn_cropped', split='test', shuffle_files=True, as_supervised=True)\n", "ds_val = tfds.load('svhn_cropped', split='train[-10%:]', shuffle_files=True, as_supervised=True)\n", "\n", "assert isinstance(ds_train, tf.data.Dataset)\n", "train_size = int(info.splits['train'].num_examples)\n", "input_shape = info.features['image'].shape\n", "n_classes = info.features['label'].num_classes\n", "\n", "print('Training on {} samples of input shape {}, belonging to {} classes'.format(train_size, input_shape, n_classes))\n", "fig = tfds.show_examples(ds_train, info)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "We'll use TensorFlow Dataset to prepare our datasets. We'll fetch the training dataset as tuples, and the test dataset as numpy arrays" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def preprocess(image, label, nclasses=10):\n", " image = tf.cast(image, tf.float32) / 255.0\n", " label = tf.one_hot(tf.squeeze(label), nclasses)\n", " return image, label" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch_size = 1024\n", "\n", "train_data = ds_train.map(preprocess, n_classes) # Get dataset as image and one-hot encoded labels, divided by max RGB\n", "train_data = train_data.shuffle(4096).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", "for example in train_data.take(1):\n", " break\n", "print(\"X train batch shape = {}, Y train batch shape = {} \".format(example[0].shape, example[1].shape))\n", "\n", "val_data = ds_val.map(preprocess, n_classes)\n", "val_data = val_data.batch(batch_size)\n", "val_data = val_data.prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", "# For testing, we get the full dataset in memory as it's rather small.\n", "# We fetch it as numpy arrays to have access to labels and images separately\n", "X_test, Y_test = tfds.as_numpy(tfds.load('svhn_cropped', split='test', batch_size=-1, as_supervised=True))\n", "X_test, Y_test = preprocess(X_test, Y_test, nclasses=n_classes)\n", "print(\"X test batch shape = {}, Y test batch shape = {} \".format(X_test.shape, Y_test.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Defining the model\n", "\n", "We then need to define a model. For the lowest possible latency, each layer should have a maximum number of trainable parameters of 4096. This is due to fixed limits in the Vivado compiler, beyond which maximally unrolled (=parallel) compilation will fail. This will allow us to use `strategy = 'latency'` in the hls4ml part, rather than `strategy = 'resource'`, in turn resulting in lower latency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", "from tensorflow.keras.layers import BatchNormalization\n", "from tensorflow.keras.layers import Conv2D\n", "from tensorflow.keras.regularizers import l1\n", "from tensorflow.keras.layers import MaxPooling2D\n", "from tensorflow.keras.layers import Activation\n", "from tensorflow.keras.layers import Flatten\n", "from tensorflow.keras.layers import Dense\n", "\n", "from tensorflow.keras.models import Model\n", "\n", "filters_per_conv_layer = [16, 16, 24]\n", "neurons_per_dense_layer = [42, 64]\n", "\n", "x = x_in = Input(input_shape)\n", "\n", "for i, f in enumerate(filters_per_conv_layer):\n", " print(('Adding convolutional block {} with N={} filters').format(i, f))\n", " x = Conv2D(\n", " int(f),\n", " kernel_size=(3, 3),\n", " strides=(1, 1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " use_bias=False,\n", " name='conv_{}'.format(i),\n", " )(x)\n", " x = BatchNormalization(name='bn_conv_{}'.format(i))(x)\n", " x = Activation('relu', name='conv_act_%i' % i)(x)\n", " x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)\n", "x = Flatten()(x)\n", "\n", "for i, n in enumerate(neurons_per_dense_layer):\n", " print(('Adding dense block {} with N={} neurons').format(i, n))\n", " x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)\n", " x = BatchNormalization(name='bn_dense_{}'.format(i))(x)\n", " x = Activation('relu', name='dense_act_%i' % i)(x)\n", "x = Dense(int(n_classes), name='output_dense')(x)\n", "x_out = Activation('softmax', name='output_softmax')(x)\n", "\n", "model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "Lets check if this model can be implemented completely unrolled (=parallel)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for layer in model.layers:\n", " if layer.__class__.__name__ in ['Conv2D', 'Dense']:\n", " w = layer.get_weights()[0]\n", " layersize = np.prod(w.shape)\n", " print(\"{}: {}\".format(layer.name, layersize)) # 0 = weights, 1 = biases\n", " if layersize > 4096: # assuming that shape[0] is batch, i.e., 'None'\n", " print(\"Layer {} is too large ({}), are you sure you want to train?\".format(layer.name, layersize))" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "Looks good! It's below the Vivado-enforced unroll limit of 4096.\n", "\n", "## Prune dense and convolutional layers\n", "Since we've seen in the previous notebooks that pruning can be done at no accuracy cost, let's prune the convolutional and dense layers to 50% sparsity, skipping the output layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow_model_optimization as tfmot\n", "from tensorflow_model_optimization.sparsity import keras as sparsity\n", "from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks\n", "\n", "NSTEPS = int(train_size * 0.9) // batch_size # 90% train, 10% validation in 10-fold cross validation\n", "print('Number of training steps per epoch is {}'.format(NSTEPS))\n", "\n", "\n", "# Prune all convolutional and dense layers gradually from 0 to 50% sparsity every 2 epochs,\n", "# ending by the 10th epoch\n", "def pruneFunction(layer):\n", " pruning_params = {\n", " 'pruning_schedule': sparsity.PolynomialDecay(\n", " initial_sparsity=0.0, final_sparsity=0.50, begin_step=NSTEPS * 2, end_step=NSTEPS * 10, frequency=NSTEPS\n", " )\n", " }\n", " if isinstance(layer, tf.keras.layers.Conv2D):\n", " return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)\n", " if isinstance(layer, tf.keras.layers.Dense) and layer.name != 'output_dense':\n", " return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)\n", " return layer\n", "\n", "\n", "model_pruned = tf.keras.models.clone_model(model, clone_function=pruneFunction)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Train baseline\n", "\n", "We're now ready to train the model! We defined the batch size and n epochs above. We won't use callbacks that store the best weights only, since this might select a weight configuration that has not yet reached 50% sparsity." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True # True if you want to retrain, false if you want to load a previsously trained model\n", "\n", "n_epochs = 30\n", "\n", "if train:\n", " LOSS = tf.keras.losses.CategoricalCrossentropy()\n", " OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)\n", "\n", " model_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=[\"accuracy\"])\n", "\n", " callbacks = [\n", " tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),\n", " tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),\n", " pruning_callbacks.UpdatePruningStep(),\n", " ]\n", "\n", " start = time.time()\n", " model_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks)\n", " end = time.time()\n", "\n", " print('It took {} minutes to train Keras model'.format((end - start) / 60.0))\n", "\n", " model_pruned.save('pruned_cnn_model.h5')\n", "\n", "else:\n", " from qkeras.utils import _add_supported_quantized_objects\n", " from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper\n", "\n", " co = {}\n", " _add_supported_quantized_objects(co)\n", " co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude\n", " model_pruned = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "You'll notice the accuracy is lower than that in the hls4ml CNN paper (https://arxiv.org/abs/2101.05108) despite the model being the same. The reson for this is that we didn't use the ``extra`` training data in order to save time. If you want to futher optimize the network, increasing the training data is a good place to start. Enlarging the model architecture comes at a high latency/resource cost." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Quantization and the fused Conv2D+BatchNormalization layer in QKeras\n", "Let's now create a pruned an quantized model using QKeras. For this, we will use a fused Convolutional and BatchNormalization (BN) layer from QKeras, which will further speed up the implementation when we implement the model using hls4ml. \n", "There is currently no fused Dense+BatchNoralization layer available in QKeras, so we'll use Keras BatchNormalization when BN follows a Dense layer for now. We'll use the same precision everywhere, namely a bit width of 6 and 0 integer bits (this will be implemented as``<6,1>`` in hls4ml, due to the missing sign-bit). For now, make sure to set ```use_bias=True``` in ```QConv2DBatchnorm``` to avoid problems during synthesis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from qkeras import QActivation\n", "from qkeras import QDense, QConv2DBatchnorm\n", "\n", "x = x_in = Input(shape=input_shape)\n", "\n", "for i, f in enumerate(filters_per_conv_layer):\n", " print(('Adding fused QConv+BN block {} with N={} filters').format(i, f))\n", " x = QConv2DBatchnorm(\n", " int(f),\n", " kernel_size=(3, 3),\n", " strides=(1, 1),\n", " kernel_quantizer=\"quantized_bits(6,0,alpha=1)\",\n", " bias_quantizer=\"quantized_bits(6,0,alpha=1)\",\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " use_bias=True,\n", " name='fused_convbn_{}'.format(i),\n", " )(x)\n", " x = QActivation('quantized_relu(6)', name='conv_act_%i' % i)(x)\n", " x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)\n", "x = Flatten()(x)\n", "\n", "for i, n in enumerate(neurons_per_dense_layer):\n", " print(('Adding QDense block {} with N={} neurons').format(i, n))\n", " x = QDense(\n", " n,\n", " kernel_quantizer=\"quantized_bits(6,0,alpha=1)\",\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " name='dense_%i' % i,\n", " use_bias=False,\n", " )(x)\n", " x = BatchNormalization(name='bn_dense_{}'.format(i))(x)\n", " x = QActivation('quantized_relu(6)', name='dense_act_%i' % i)(x)\n", "x = Dense(int(n_classes), name='output_dense')(x)\n", "x_out = Activation('softmax', name='output_softmax')(x)\n", "qmodel = Model(inputs=[x_in], outputs=[x_out], name='qkeras')\n", "\n", "qmodel.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Print the quantized layers\n", "from qkeras.autoqkeras.utils import print_qmodel_summary\n", "\n", "print_qmodel_summary(qmodel)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "You see that a bias quantizer is defined, although we are not using a bias term for the layers. This is set automatically by QKeras. In addition, you'll note that ``alpha='1'``. This sets the weight scale per channel to 1 (no scaling). The default is ``alpha='auto_po2'``, which sets the weight scale per channel to be a power-of-2, such that an actual hardware implementation can be performed by just shifting the result of the convolutional/dense layer to the right or left by checking the sign of the scale and then taking the log2 of the scale.\n", "\n", "Let's now prune and train this model! If you want, you can also train the unpruned version, ``qmodel`` and see how the performance compares. We will stick to the pruned one here. Again, we do not use a model checkpoint which stores the best weights, in order to ensure the model is trained to the desired sparsity." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qmodel_pruned = tf.keras.models.clone_model(qmodel, clone_function=pruneFunction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = True\n", "\n", "n_epochs = 30\n", "if train:\n", " LOSS = tf.keras.losses.CategoricalCrossentropy()\n", " OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)\n", " qmodel_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=[\"accuracy\"])\n", "\n", " callbacks = [\n", " tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),\n", " tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),\n", " pruning_callbacks.UpdatePruningStep(),\n", " ]\n", "\n", " start = time.time()\n", " history = qmodel_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)\n", " end = time.time()\n", " print('\\n It took {} minutes to train!\\n'.format((end - start) / 60.0))\n", "\n", " qmodel_pruned.save('quantized_pruned_cnn_model.h5')\n", "\n", "else:\n", " from qkeras.utils import _add_supported_quantized_objects\n", " from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper\n", "\n", " co = {}\n", " _add_supported_quantized_objects(co)\n", " co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude\n", " qmodel_pruned = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "We note that training a model quantization aware, takes around twice as long as when not quantizing during training!\n", "The validation accuracy is very similar to that of the floating point model equivalent, despite containing significantly less information \n", "\n", "## Performance\n", "Let's look at some ROC curves to compare the performance. Lets choose a few numbers so it doesn't get confusing. Feel free to change the numbers in ``labels``." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "predict_baseline = model_pruned.predict(X_test)\n", "test_score_baseline = model_pruned.evaluate(X_test, Y_test)\n", "\n", "predict_qkeras = qmodel_pruned.predict(X_test)\n", "test_score_qkeras = qmodel_pruned.evaluate(X_test, Y_test)\n", "\n", "print('Keras accuracy = {} , QKeras 6-bit accuracy = {}'.format(test_score_baseline[1], test_score_qkeras[1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from sklearn import metrics\n", "\n", "labels = ['%i' % nr for nr in range(0, n_classes)] # If you want to look at all the labels\n", "# labels = ['0','1','9'] # Look at only a few labels, here for digits 0, 1 and 9\n", "print('Plotting ROC for labels {}'.format(labels))\n", "\n", "df = pd.DataFrame()\n", "df_q = pd.DataFrame()\n", "fpr = {}\n", "tpr = {}\n", "auc1 = {}\n", "fpr_q = {}\n", "tpr_q = {}\n", "auc1_q = {}\n", "%matplotlib inline\n", "colors = ['#67001f', '#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac', '#053061']\n", "fig, ax = plt.subplots(figsize=(10, 10))\n", "for i, label in enumerate(labels):\n", " df[label] = Y_test[:, int(label)]\n", " df[label + '_pred'] = predict_baseline[:, int(label)]\n", " fpr[label], tpr[label], threshold = metrics.roc_curve(df[label], df[label + '_pred'])\n", " auc1[label] = metrics.auc(fpr[label], tpr[label])\n", "\n", " df_q[label] = Y_test[:, int(label)]\n", " df_q[label + '_pred'] = predict_qkeras[:, int(label)]\n", " fpr_q[label], tpr_q[label], threshold_q = metrics.roc_curve(df_q[label], df_q[label + '_pred'])\n", " auc1_q[label] = metrics.auc(fpr_q[label], tpr_q[label])\n", "\n", " plt.plot(\n", " fpr[label],\n", " tpr[label],\n", " label=r'{}, AUC Keras = {:.1f}% AUC QKeras = {:.1f}%)'.format(label, auc1[label] * 100, auc1_q[label] * 100),\n", " linewidth=1.5,\n", " c=colors[i],\n", " linestyle='solid',\n", " )\n", " plt.plot(fpr_q[label], tpr_q[label], linewidth=1.5, c=colors[i], linestyle='dotted')\n", "\n", "plt.semilogx()\n", "plt.ylabel(\"True Positive Rate\")\n", "plt.xlabel(\"False Positive Rate\")\n", "plt.xlim(0.01, 1.0)\n", "plt.ylim(0.5, 1.1)\n", "plt.legend(loc='lower right')\n", "plt.figtext(\n", " 0.2,\n", " 0.83,\n", " r'Accuracy Keras = {:.1f}% QKeras 8-bit = {:.1f}%'.format(test_score_baseline[1] * 100, test_score_qkeras[1] * 100),\n", " wrap=True,\n", " horizontalalignment='left',\n", " verticalalignment='center',\n", ")\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['Keras', 'QKeras'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "The difference in AUC between the fp32 Keras model and the 8-bit QKeras model, is small, as we have seen for the previous examples. You can find a bonus exercise below, **Bonus: Automatic quantization**, where we'll use AutoQKeras to find the best heterogeneously quantized model, given a set of resource and accuracy constriants.\n", "### Check sparsity\n", "Let's also check the per-layer sparsity:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def doWeights(model):\n", " allWeightsByLayer = {}\n", " for layer in model.layers:\n", " if (layer._name).find(\"batch\") != -1 or len(layer.get_weights()) < 1:\n", " continue\n", " weights = layer.weights[0].numpy().flatten()\n", " allWeightsByLayer[layer._name] = weights\n", " print('Layer {}: % of zeros = {}'.format(layer._name, np.sum(weights == 0) / np.size(weights)))\n", "\n", " labelsW = []\n", " histosW = []\n", "\n", " for key in reversed(sorted(allWeightsByLayer.keys())):\n", " labelsW.append(key)\n", " histosW.append(allWeightsByLayer[key])\n", "\n", " fig = plt.figure(figsize=(10, 10))\n", " bins = np.linspace(-1.5, 1.5, 50)\n", " plt.hist(histosW, bins, histtype='stepfilled', stacked=True, label=labelsW, edgecolor='black')\n", " plt.legend(frameon=False, loc='upper left')\n", " plt.ylabel('Number of Weights')\n", " plt.xlabel('Weights')\n", " plt.figtext(0.2, 0.38, model._name, wrap=True, horizontalalignment='left', verticalalignment='center')\n", "\n", "\n", "doWeights(model_pruned)\n", "doWeights(qmodel_pruned)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "We see that 50% of the weights per layer are set to zero, as expected.\n", "Now, let's synthesize the floating point Keras model and the QKeras quantized model!" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## CNNs in hls4ml\n", "\n", "In this part, we will take the two models we trained above (the floating-point 32 Keras model and the 6-bit QKeras model), and synthesize them with hls4ml. Although your models are probably already in memory, let's load them from scratch. We need to pass the appropriate custom QKeras/pruning layers when loading, and remove the pruning parameters that were saved together with the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow_model_optimization.sparsity.keras import strip_pruning\n", "from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper\n", "\n", "from qkeras.utils import _add_supported_quantized_objects\n", "\n", "co = {}\n", "_add_supported_quantized_objects(co)\n", "co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude\n", "\n", "model = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)\n", "model = strip_pruning(model)\n", "\n", "qmodel = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)\n", "qmodel = strip_pruning(qmodel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we need to define the hls4ml and Vivado configurations. Two things will change with respect to what was done in the previous exercises. First, we will use ``io_type='io_stream'`` in the Vitis_HLS configuration.\n", "\n", "---\n", "****You must use ``io_type='io_stream'`` if attempting to synthesize a large convolutional neural network.****\n", "\n", "---\n", "The CNN implementation in hls4ml is based on streams, which are synthesized in hardware as first in, first out (FIFO) buffers. Shift registers are used to keep track of the last ```` rows of input pixels, and maintains a shifting snapshot of the convolution kernel.\n", "\n", "This is illustrated in the gif below. Here, the input image is at the top-left and the output image at the bottom left. The top right image shows the internal state of the shift registers and convolutional kernel. The red square indicates the current pixels contained within the convolutional kernel.\n", "\n", "![alt text](images/conv2d_animation.gif \"The implementation of convolutional layers in hls4ml.\")\n", "\n", "Lastly, we will use ``['Strategy'] = 'Latency'`` for all the layers in the hls4ml configuration. If one layer would have >4096 elements, we sould set ``['Strategy'] = 'Resource'`` for that layer, or increase the reuse factor by hand. You can find examples of how to do this below.\n", "\n", "**NOTE** Using `auto` precision can lead to undesired side effects. In case of this model, the bit width used for the output of the last fully connected layer is larger than can be reasonably represented with the look-up table in the softmax implementation. We therefore need to restrict it by hand to achieve proper results.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import hls4ml\n", "import plotting\n", "\n", "# First, the baseline model\n", "hls_config = hls4ml.utils.config_from_keras_model(\n", " model, granularity='name', backend='Vitis', default_precision='ap_fixed<16,6>'\n", ")\n", "hls_config['LayerName']['output_dense']['Precision']['result'] = 'fixed<16,6,RND,SAT>'\n", "plotting.print_dict(hls_config)\n", "\n", "\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model,\n", " hls_config=hls_config,\n", " backend='Vitis',\n", " output_dir='model_1/hls4ml_prj',\n", " part='xcu250-figd2104-2L-e',\n", " io_type='io_stream',\n", ")\n", "hls_model.compile()" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "Let's get a nice overview over the various shapes and precisions used for each layer through ``hls4ml.utils.plot_model``, as well as look at the weight profile using ``hls4ml.model.profiling.numerical``. The weight profiling returns two plots: Before (top) and after (bottom) various optimizations applied to the HLS model before the final translation to HLS, for instance the fusing of Dense and BatchNormalization layers." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from hls4ml.model.profiling import numerical\n", "\n", "numerical(model=model, hls_model=hls_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The colored boxes are the distribution of the weights of the model, and the gray band illustrates the numerical range covered by the chosen fixed point precision. As we configured, this model uses a precision of ``ap_fixed<16,6>`` for the weights and biases of all layers of the model. \n", "\n", "Let's now build our QKeras model. \n", "\n", "**NOTE** Using `auto` precision can lead to undesired side effects. In case of this model, the bit width used for the output of the last fully connected layer is larger than can be reasonably represented with the look-up table in the softmax implementation. We therefore need to restrict it by hand to achieve proper results." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Then the QKeras model\n", "hls_config_q = hls4ml.utils.config_from_keras_model(qmodel, granularity='name', backend='Vitis')\n", "hls_config_q['LayerName']['output_dense']['Precision']['result'] = 'fixed<16,6,RND,SAT>'\n", "\n", "plotting.print_dict(hls_config_q)\n", "\n", "hls_model_q = hls4ml.converters.convert_from_keras_model(\n", " qmodel, hls_config=hls_config_q, output_dir='quantized_pruned_cnn', backend='Vitis', io_type='io_stream'\n", ")\n", "\n", "hls_model_q.compile()" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "Let's plot the model and profile the weights her too" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numerical(model=qmodel, hls_model=hls_model_q)\n", "hls4ml.utils.plot_model(hls_model_q, show_shapes=True, show_precision=True, to_file=None)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "For the 6-bit QKeras model, we see that different precisions are used for different layers." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "### Accuracy with bit-accurate emulation \n", "Let's check that the hls4ml accuracy matches the original. This usually takes some time, so let's do it over a reduced dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_test_reduced = X_test[:3000]\n", "Y_test_reduced = Y_test[:3000]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_predict = model.predict(X_test_reduced)\n", "y_predict_hls4ml = hls_model.predict(np.ascontiguousarray(X_test_reduced))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_predict_q = qmodel.predict(X_test_reduced)\n", "y_predict_hls4ml_q = hls_model_q.predict(np.ascontiguousarray(X_test_reduced))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import plotting\n", "from sklearn.metrics import accuracy_score\n", "\n", "\n", "def plotROC(Y, y_pred, y_pred_hls4ml, label=\"Model\"):\n", " accuracy_keras = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred, axis=1)))\n", " accuracy_hls4ml = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred_hls4ml, axis=1)))\n", "\n", " print(\"Accuracy Keras: {}\".format(accuracy_keras))\n", " print(\"Accuracy hls4ml: {}\".format(accuracy_hls4ml))\n", "\n", " fig, ax = plt.subplots(figsize=(9, 9))\n", " _ = plotting.makeRoc(Y, y_pred, labels=['%i' % nr for nr in range(n_classes)])\n", " plt.gca().set_prop_cycle(None) # reset the colors\n", " _ = plotting.makeRoc(Y, y_pred_hls4ml, labels=['%i' % nr for nr in range(n_classes)], linestyle='--')\n", "\n", " from matplotlib.lines import Line2D\n", "\n", " lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]\n", " from matplotlib.legend import Legend\n", "\n", " leg = Legend(ax, lines, labels=['Keras', 'hls4ml'], loc='lower right', frameon=False)\n", " ax.add_artist(leg)\n", " plt.figtext(0.2, 0.38, label, wrap=True, horizontalalignment='left', verticalalignment='center')\n", " plt.ylim(0.01, 1.0)\n", " plt.xlim(0.7, 1.0)\n", "\n", "\n", "# Plot the pruned floating point model:\n", "plotROC(Y_test_reduced, y_predict, y_predict_hls4ml, label=\"Keras\")\n", "\n", "# Plot the pruned and quantized QKeras model\n", "plotROC(Y_test_reduced, y_predict_q, y_predict_hls4ml_q, label=\"QKeras\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "Looks good! Let's synthesize the models. \n", "## Logic synthesis\n", "This takes quite a while for CNN models, up to one hour for the models considered here. In the interest of time, we have therefore provided the neccessary reports for the models considered. You can also synthesize them yourself if you have time, and as usual follow the progress using ``tail -f pruned_cnn/vivado_hls.log`` and ``tail -f quantized_pruned_cnn/vivado_hls.log``.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "synth = False # Only if you want to synthesize the models yourself (>1h per model) rather than look at the provided reports.\n", "if synth:\n", " hls_model.build(csim=False, synth=True, vsynth=True)\n", " hls_model_q.build(csim=False, synth=True, vsynth=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "We extract the latency from the C synthesis, namely the report in ```/myproject_prj/solution1/syn/report/myproject_csynth.rpt```. A more accurate latency estimate can be obtained from running cosim by passing ```hls_model.build(csim=False, synth=True, vsynth=True, cosim=True)``` ( = C/RTL cosimulation, synthesised HLS code is run on a simulator and tested on C test bench) but this takes a lot of time so we will skip it here.\n", "The resource estimates are obtained from the Vivado logic synthesis, and can be extracted from the report in ```/vivado_synth.rpt```. Let's fetch the most relevant numbers:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def getReports(indir):\n", " data_ = {}\n", "\n", " report_vsynth = Path('{}/vivado_synth.rpt'.format(indir))\n", " report_csynth = Path('{}/myproject_prj/solution1/syn/report/myproject_csynth.rpt'.format(indir))\n", "\n", " if report_vsynth.is_file() and report_csynth.is_file():\n", " print('Found valid vsynth and synth in {}! Fetching numbers'.format(indir))\n", "\n", " # Get the resources from the logic synthesis report\n", " with report_vsynth.open() as report:\n", " lines = np.array(report.readlines())\n", " data_['lut'] = int(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[2])\n", " data_['ff'] = int(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[2])\n", " data_['bram'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[2])\n", " data_['dsp'] = int(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[2])\n", " data_['lut_rel'] = float(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[5])\n", " data_['ff_rel'] = float(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[5])\n", " data_['bram_rel'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[5])\n", " data_['dsp_rel'] = float(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[5])\n", "\n", " with report_csynth.open() as report:\n", " lines = np.array(report.readlines())\n", " lat_line = lines[np.argwhere(np.array(['Latency (cycles)' in line for line in lines])).flatten()[0] + 3]\n", " data_['latency_clks'] = int(lat_line.split('|')[2])\n", " data_['latency_mus'] = float(lat_line.split('|')[2]) * 5.0 / 1000.0\n", " data_['latency_ii'] = int(lat_line.split('|')[6])\n", "\n", " return data_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import pprint\n", "\n", "data_pruned_ref = getReports('pruned_cnn')\n", "data_quantized_pruned = getReports('quantized_pruned_cnn')\n", "\n", "print(\"\\n Resource usage and latency: Pruned\")\n", "pprint.pprint(data_pruned_ref)\n", "print(\"\\n Resource usage and latency: Pruned + quantized\")\n", "pprint.pprint(data_quantized_pruned)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the latency is of around 5 microseconds for both the quantized and the unquantized model, but that the resources are signifcantly reduced using QKeras.\n", "\n", "Congratulations! You have now reached the end of this notebook. If you have some spare time, you can have a look at the bonus exercise below, where you will learn how to perform a bayesian optimization over the QKeras quantizers in order to obtain an optimally heterogeneously quantized model.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false }, "source": [ "## Bonus exercise: Automatic quantization with AutoQKeras\n", "\n", "In this bonus exercise, you will learn how to find the optimal heterogeneously quantized model using AutoQKeras.\n", "For more details, you can look at the [AutoQKeras notebook](https://github.com/google/qkeras/blob/master/notebook/AutoQKeras.ipynb). \n", "\n", "Let's first check the estimated energy consumption of the QKeras 6-bit model using QTools. By setting ```for_reference=True``` you can print out the unquantized model energy consumption and compare the two. Note that this only works for QKeras layers. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters_per_conv_layer = [16, 16, 24]\n", "neurons_per_dense_layer = [42, 64]\n", "\n", "x = x_in = Input(input_shape)\n", "\n", "for i, f in enumerate(filters_per_conv_layer):\n", " print(('Adding convolutional block {} with N={} filters').format(i, f))\n", " x = Conv2D(\n", " int(f),\n", " kernel_size=(3, 3),\n", " strides=(1, 1),\n", " kernel_initializer='lecun_uniform',\n", " kernel_regularizer=l1(0.0001),\n", " use_bias=False,\n", " name='conv_{}'.format(i),\n", " )(x)\n", " x = BatchNormalization(name='bn_conv_{}'.format(i))(x)\n", " x = Activation('relu', name='conv_act_%i' % i)(x)\n", " x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)\n", "x = Flatten()(x)\n", "\n", "for i, n in enumerate(neurons_per_dense_layer):\n", " print(('Adding dense block {} with N={} neurons').format(i, n))\n", " x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)\n", " x = BatchNormalization(name='bn_dense_{}'.format(i))(x)\n", " x = Activation('relu', name='dense_act_%i' % i)(x)\n", "x = Dense(int(n_classes), name='output_dense')(x)\n", "x_out = Activation('softmax', name='output_softmax')(x)\n", "\n", "baseline_model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')\n", "\n", "LOSS = tf.keras.losses.CategoricalCrossentropy()\n", "OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)\n", "\n", "baseline_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=[\"accuracy\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from qkeras import print_qstats\n", "\n", "# for automatic quantization\n", "import pprint\n", "from qkeras.autoqkeras import *\n", "from qkeras import *\n", "from qkeras.utils import model_quantize\n", "\n", "from qkeras.qtools import run_qtools\n", "from qkeras.qtools import settings as qtools_settings\n", "from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper\n", "from qkeras import quantized_bits\n", "from qkeras import QDense, QActivation\n", "\n", "q = run_qtools.QTools(\n", " baseline_model,\n", " process=\"horowitz\",\n", " source_quantizers=[quantized_bits(16, 5, 1)],\n", " is_inference=True,\n", " weights_path=None,\n", " keras_quantizer=\"fp16\",\n", " keras_accumulator=\"fp16\",\n", " for_reference=False,\n", ")\n", "q.qtools_stats_print()\n", "\n", "energy_dict = q.pe(\n", " weights_on_memory=\"fixed\", activations_on_memory=\"fixed\", min_sram_size=8 * 16 * 1024 * 1024, rd_wr_on_io=False\n", ")\n", "\n", "# get stats of energy distribution in each layer\n", "energy_profile = q.extract_energy_profile(qtools_settings.cfg.include_energy, energy_dict)\n", "# extract sum of energy of each layer according to the rule specified in\n", "# qtools_settings.cfg.include_energy\n", "total_energy = q.extract_energy_sum(qtools_settings.cfg.include_energy, energy_dict)\n", "\n", "pprint.pprint(energy_profile)\n", "print()\n", "\n", "print(\"Total energy: {:.6f} uJ\".format(total_energy / 1000000.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, lets use AutoQKeras to find an optimally heterogeneously quantized model for us. For more details, check the AutoQKeras tutorial linked above. As baseline model, we'll use the pruned floating point Keras model from above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# These are the quantizers we'll test in the bayesian optimization\n", "quantization_config = {\n", " \"kernel\": {\n", " \"quantized_bits(2,0,1,alpha=1.0)\": 2,\n", " \"quantized_bits(4,0,1,alpha=1.0)\": 4,\n", " \"quantized_bits(6,0,1,alpha=1.0)\": 6,\n", " \"quantized_bits(8,0,1,alpha=1.0)\": 8,\n", " },\n", " \"bias\": {\n", " \"quantized_bits(2,0,1,alpha=1.0)\": 2,\n", " \"quantized_bits(4,0,1,alpha=1.0)\": 4,\n", " \"quantized_bits(6,0,1,alpha=1.0)\": 6,\n", " \"quantized_bits(8,0,1,alpha=1.0)\": 8,\n", " },\n", " \"activation\": {\n", " \"quantized_relu(3,1)\": 3,\n", " \"quantized_relu(4,2)\": 4,\n", " \"quantized_relu(8,2)\": 8,\n", " \"quantized_relu(8,4)\": 8,\n", " \"quantized_relu(16,6)\": 16,\n", " },\n", " \"linear\": {\n", " \"quantized_bits(2,0,1,alpha=1.0)\": 2,\n", " \"quantized_bits(4,0,1,alpha=1.0)\": 4,\n", " \"quantized_bits(6,0,1,alpha=1.0)\": 6,\n", " \"quantized_bits(8,0,1,alpha=1.0)\": 8,\n", " },\n", "}\n", "\n", "# These are the layer types we will quantize\n", "limit = {\n", " \"Dense\": [8, 8, 16],\n", " \"Conv2D\": [8, 8, 16],\n", " \"Activation\": [16],\n", "}\n", "\n", "# Use this if you want to minimize the model bit size\n", "goal_bits = {\n", " \"type\": \"bits\",\n", " \"params\": {\n", " \"delta_p\": 8.0, # We tolerate up to a +8% accuracy change\n", " \"delta_n\": 8.0, # We tolerate down to a -8% accuracy change\n", " \"rate\": 2.0, # We want a x2 times smaller model\n", " \"stress\": 1.0, # Force the reference model size to be smaller by setting stress<1\n", " \"input_bits\": 8,\n", " \"output_bits\": 8,\n", " \"ref_bits\": 8,\n", " \"config\": {\"default\": [\"parameters\", \"activations\"]},\n", " },\n", "}\n", "\n", "# Use this if you want to minimize the model energy consumption\n", "goal_energy = {\n", " \"type\": \"energy\",\n", " \"params\": {\n", " \"delta_p\": 8.0,\n", " \"delta_n\": 8.0,\n", " \"rate\": 2.0,\n", " \"stress\": 1.0,\n", " \"process\": \"horowitz\",\n", " \"parameters_on_memory\": [\"sram\", \"sram\"],\n", " \"activations_on_memory\": [\"sram\", \"sram\"],\n", " \"rd_wr_on_io\": [False, False],\n", " \"min_sram_size\": [0, 0],\n", " \"source_quantizers\": [\"fp32\"],\n", " \"reference_internal\": \"int8\",\n", " \"reference_accumulator\": \"int32\",\n", " },\n", "}\n", "\n", "run_config = {\n", " \"goal\": goal_energy,\n", " \"quantization_config\": quantization_config,\n", " \"learning_rate_optimizer\": False,\n", " \"transfer_weights\": False, # Randomely initialize weights\n", " \"mode\": \"bayesian\", # This can be bayesian,random,hyperband\n", " \"seed\": 42,\n", " \"limit\": limit,\n", " \"tune_filters\": \"layer\",\n", " \"tune_filters_exceptions\": \"^output\",\n", " \"distribution_strategy\": None,\n", " \"max_trials\": 5, # Let's just do 5 trials for this demonstrator, ideally you should do as many as possible\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from qkeras.autoqkeras import AutoQKeras\n", "\n", "autoqk = AutoQKeras(baseline_model, output_dir=\"autoq_cnn\", metrics=[\"acc\"], custom_objects={}, **run_config)\n", "autoqk.fit(train_data, validation_data=val_data, epochs=15)\n", "\n", "aqmodel = autoqk.get_best_model()\n", "print_qmodel_summary(aqmodel)\n", "\n", "# Train for the full epochs\n", "callbacks = [\n", " tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),\n", " tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),\n", "]\n", "\n", "start = time.time()\n", "history = aqmodel.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)\n", "end = time.time()\n", "print('\\n It took {} minutes to train!\\n'.format((end - start) / 60.0))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This model has some remnants from the optimization procedure attached to it, so let's define a new one\n", "aqmodel.save_weights(\"autoqkeras_cnn_weights.h5\")\n", "\n", "layers = [l for l in aqmodel.layers]\n", "x = layers[0].output\n", "for i in range(1, len(layers)):\n", " x = layers[i](x)\n", "\n", "new_model = Model(inputs=[layers[0].input], outputs=[x])\n", "LOSS = tf.keras.losses.CategoricalCrossentropy()\n", "OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)\n", "\n", "new_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=[\"accuracy\"])\n", "new_model.summary()\n", "new_model.load_weights(\"autoqkeras_cnn_weights.h5\")\n", "print_qmodel_summary(new_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check what the best heterogeneously quantized model looks like (keep in mind we only did a few trials, the optimization obviosuly didn't have time to converge at the minimum but yo get the idea!)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hls_config_aq = hls4ml.utils.config_from_keras_model(new_model, granularity='name')\n", "hls_config_aq['Model']['ReuseFactor'] = 1\n", "hls_config_aq['Model']['Precision'] = 'ap_fixed<16,6>'\n", "hls_config_aq['LayerName']['output_softmax']['Strategy'] = 'Stable'\n", "plotting.print_dict(hls_config_aq)\n", "\n", "cfg_aq = hls4ml.converters.create_config(backend='Vivado')\n", "cfg_aq['IOType'] = 'io_stream' # Must set this if using CNNs!\n", "cfg_aq['HLSConfig'] = hls_config_aq\n", "cfg_aq['KerasModel'] = new_model\n", "cfg_aq['OutputDir'] = 'autoqkeras_cnn/'\n", "cfg_aq['XilinxPart'] = 'xcu250-figd2104-2L-e'\n", "\n", "hls_model_aq = hls4ml.converters.keras_to_hls(cfg_aq)\n", "hls_model_aq.compile()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_predict_aq = aqmodel.predict(X_test_reduced)\n", "y_predict_hls4ml_aq = hls_model_aq.predict(np.ascontiguousarray(X_test_reduced))\n", "\n", "\n", "accuracy_keras = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_aq, axis=1)))\n", "accuracy_hls4ml = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_hls4ml_aq, axis=1)))\n", "\n", "print(\"Accuracy AutoQ Keras: {}\".format(accuracy_keras))\n", "print(\"Accuracy AutoQ hls4ml: {}\".format(accuracy_hls4ml))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The accuracy is slightly lower for this heterogeneously quantized model. Due to some randomness in the optimization procedure, you're going to have to synthesize this one yourself!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "synth = True\n", "if synth:\n", " hls_model_aq.build(csim=False, synth=True, vsynth=True)\n", " data_autoq = getReports('autoq_cnn')\n", "\n", " print(\"\\n Resource usage and latency: AutoQ\")\n", " pprint.pprint(data_autoq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: part7a_bitstream.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "6ee8630c", "metadata": {}, "source": [ "# Part 7a: Bitstream Generation\n", "\n", "In the previous sections we've seen how to train a Neural Network with a small resource footprint using QKeras, then to convert it to `hls4ml` and create an IP. That IP can be interfaced into a larger design to deploy on an FPGA device. In this section, we introduce the `VivadoAccelerator` backend of `hls4ml`, where we can easily target some supported devices to get up and running quickly. Specifically, we'll deploy the model on a [pynq-z2 board](http://www.pynq.io/). NOTE: This tutorial requires on Vivado HLS instead of Vitis." ] }, { "cell_type": "code", "execution_count": null, "id": "a62a80f2", "metadata": { "tags": [] }, "outputs": [], "source": [ "from tensorflow.keras.models import load_model\n", "from qkeras.utils import _add_supported_quantized_objects\n", "\n", "co = {}\n", "_add_supported_quantized_objects(co)\n", "import os\n", "\n", "os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "markdown", "id": "b2c75d93", "metadata": {}, "source": [ "## Load model\n", "Load the model from `part4: quantization` (note you need to have trained the model in part 4 first)" ] }, { "cell_type": "code", "execution_count": null, "id": "800575f6", "metadata": { "tags": [] }, "outputs": [], "source": [ "model = load_model('model_3/KERAS_check_best_model.h5', custom_objects=co)" ] }, { "cell_type": "markdown", "id": "3ce14d31", "metadata": {}, "source": [ "## Convert to hls4ml\n", "We'll convert our model into `hls4ml`, with a few small changes compared to the previous use of the same model in part 4.\n", "We target `backend='VivadoAccelerator'` backend: this will wrap the HLS NN model, providing an AXI-Stream interface to our IP. We also specify `board='pynq-z2'`.\n", "\n", "The pynq-z2 board is a popular board with a Zynq 7020 SoC. Since this device is much smaller than the Alveo we specified in previous sections, we set the `ReuseFactor` of all the `Dense` layers of the model to 64." ] }, { "cell_type": "code", "execution_count": null, "id": "53a76ca8", "metadata": { "tags": [] }, "outputs": [], "source": [ "import hls4ml\n", "import plotting\n", "\n", "config = hls4ml.utils.config_from_keras_model(model, granularity='name')\n", "config['LayerName']['softmax']['exp_table_t'] = 'ap_fixed<18,8>'\n", "config['LayerName']['softmax']['inv_table_t'] = 'ap_fixed<18,4>'\n", "for layer in ['fc1', 'fc2', 'fc3', 'output']:\n", " config['LayerName'][layer]['ReuseFactor'] = 64\n", "print(\"-----------------------------------\")\n", "plotting.print_dict(config)\n", "print(\"-----------------------------------\")\n", "hls_model = hls4ml.converters.convert_from_keras_model(\n", " model, hls_config=config, output_dir='model_3/hls4ml_prj_pynq', backend='VivadoAccelerator', board='pynq-z2'\n", ")\n", "hls_model.compile()" ] }, { "cell_type": "markdown", "id": "f7e67d4f", "metadata": {}, "source": [ "We can see which baords are supported in the [documentation](https://fastmachinelearning.org/hls4ml/advanced/accelerator.html). The `VivadoAccelerator` backend introduces the `AcceleratorConfig` section of configuration. Here we can change some details of the interface to the accelerator IP.\n", "\n", "\n", "The `create_initial_config` method (of any backend) can be used to create a template dictionary with the default parameters that you can use as a starting point. In the conversion above, we didn't change any of these settings so all the defaults are used." ] }, { "cell_type": "code", "execution_count": null, "id": "16dbd354", "metadata": { "tags": [] }, "outputs": [], "source": [ "plotting.print_dict(hls4ml.backends.get_backend('VivadoAccelerator').create_initial_config())" ] }, { "cell_type": "markdown", "id": "9c4bf01d", "metadata": {}, "source": [ "## Predict\n", "Run the CPU emulation of the hls4ml NN and save the file to compare against the hardware result later." ] }, { "cell_type": "code", "execution_count": null, "id": "267ecbfc", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "\n", "X_test = np.load('X_test.npy')\n", "y_hls = hls_model.predict(np.ascontiguousarray(X_test))\n", "np.save('model_3/y_hls.npy', y_hls)" ] }, { "attachments": { "part7_block_design.png": { "image/png": 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BAgQIECBAgAABAgQIECAw1gICY2N9eTWOAAECBAiMr0COBvvBD34Qd73rXSOnTGy3bztDdI4Uy6BZ5pUIECBAgAABAgQIECBAgAABAgQICIz5HSBAgAABAgR2SYEzzjhjdiTY1Iap6E5sfFuTQbEMlm0uaLZLNlqlCRAgQIAAAQIECBAgQIAAAQIEtkmgVTqNhttUgoMJECBAgAABAlsgkLce9dSGGbSqP2cR9ee5eRYqen7Qq/6+1OMXKts+AgQIECBAgAABAgQIECBAgACB8RO47ZxD49dGLSJAgAABAgR2gkAGqeam/D4/YJUjujIYlq83v/nN1f48pg6QzT1+U5/z+CwzU77X0yku9fhNlWkbAQIECBAgQIAAAQIECBAgQIDA+AoIjI3vtdUyAgQIECCwUwXqIFUduFq7dm0V8KoDZvm+YcOGePvb3x577713/NVf/dUW13duoC2DYXXwrT5nXeCaX6ypP3onQIAAAQIECBAgQIAAAQIECBBosIDAWIMvvqYTIECAAIHtKVAHp0455ZTYd9994053ulN0Op143vOeV43u+uxnPxsHH3xwvPCFL4x99tknzjvvvCWPFMt6H3300fHABz6wCobV7TjuuOPiq1/9alV+HYA755xz4pC7HlJn8U6AAAECBAgQIECAAAECBAgQINBggY1XqW8whKYTIECAAAECyyuQI7guueSSeN3rXlcFxg499NC48cYb4/3vf3889KEPjRe/+MVx4IEHxje+/o24+z3uvsUnX79+fRVom3vgpZdeWn2dO5Vit9utRqbNzeczAQIECBAgQIAAAQIECBAgQIBAMwUExpp53bWaAAECBAjsEIH//b//d+y1115x5ZVXjqY5HAzjz/78z6pRYhkYy6DZ5OTkDqmLkxAgQIAAAQIECBAgQIAAAQIECBAwlaLfAQIECBAgQGC7CPR7/fjBD35QTaGYJ8g1xzrdTpx44onVlIk5xeLExER17nraw+1SEYUSIECAAAECBAgQIECAAAECBAgQmBEQGPOrQIAAAQIECGwXgXanHb1eL1atWrVR+asmV1VBstvf/vaza4rNnfpwo8y+ECBAgAABAgQIECBAgAABAgQIEFhGAVMpLiOmoggQIECAAIFbBTLYlet7ZfrJT35SBciGw2FcfvnlVUDsmmuuuTVz+ZQjyvbff//IPEsJlHU6nSpfTtOYnzPlcVdddVVcffXV0e/3q/NfccUVSyqvKsAPAgQIECBAgAABAgQIECBAgACBsRZolc6n4Vi3UOMIECBAgACBnSKQtxirV6+OCy+8sJoyMUePZcrt+Zof/Np7773jl7/85ZLres973jN+/OMfVwGwTR1UB8vyvBl0y0CZRIAAAQIECBAgQIAAAQIECBAg0GwBI8aaff21ngABAgQIbFeB0047rRrNVQelMhg2PyBWV2Bz2+v989/POOOM2SBbBsHmP+uT58wyc199/vll+E6AAAECBAgQIECAAAECBAgQINAsASPGmnW9tZYAAQIECOxQgXpkWP3e75VgVbtVjeDaXEUGg8GC++cfNzf/3M/z8/lOgAABAgQIECBAgAABAgQIECBAwIgxvwMECBAgQIDAdhOoA2J5gvx82WWXxQ2/vKEKfOVIriOOOKJaB6weSZZ5ctrDpaT169bHHnvusVH+PLY+59TUVExOTlZF/eK66+KO++23lGLlIUCAAAECBAgQIECAAAECBAgQGGOBpfU8jTGAphEgQIAAAQLbT6AOcr3sZS+LvfbaK+5x2D3iuOOOiwc84AFx//vfP3bbbbd46lOfGtPT01UltmQ6xfsdfb94zWteEzlKLFMGxDLVZWRQLEeovfzlL48DDjyw2ucHAQIECBAgQIAAAQIECBAgQIBAswUExpp9/bWeAAECBAhsd4FTTjkl3vCGN8Qd7nCH6v3b3/52fPe7340vfvGLcdhhh8WnPvWpeOADHzgb2MoK1UGuhSp39NFHx8knnxyrV6+OtWvXzgbE6uMvv/zy+NU7/2q8/vWvj+c///kLFWUfAQIECBAgQIAAAQIECBAgQIBAQwSsMdaQC62ZBAgQIEBgRwtkcCunM8yRYscff3x84hOfmK1C7qunPfyXf/mXeM5znhNf/epXq1Fk9Yiv2cyb+ZAjxb72ta/FE57whLj55pvj1a9+dTU6LLdnMC5Hk+27777x2c9+No466qjNlGIzAQIECBAgQIAAAQIECBAgQIBAkwQExpp0tbWVAAECBAjsQIEMfmWw62EPe1hs2LBhdr2vugq5P4Ng+X7ve987brnllvjxj39cTY1YT8FY593ce06V2Ol24ulPf3pkgO3QQw+NG2+8MdasWRMvfOEL401velN1aAbLllrm5s5lOwECBAgQIECAAAECBAgQIECAwK4vIDC2619DLSBAgAABAitSIINRj3jEI+KnP/1p/OQnP9loqsO5Fc7A2Omnnx5/+Zd/Gddee221a0sDWZn/Wc96VnzsYx+r1iv70pe+FA95yENmz7nUUWhz6+UzAQIECBAgQIAAAQIECBAgQIDA+AlYY2z8rqkWESBAgACBFSGQI7Ry5Nbee++9YH0yMHbcccdVI73qjFsSyLryZ1fGne985/jwhz9crSW23377xcMf/vB42cteFoP+oAqOZeBMIkCAAAECBAgQIECAAAECBAgQICAw5neAAAECBAgQ2C4CGfDKlEGuzQW6Mk+11thgNK1i5s9tm8uf++emk046KX71zr9arTF28cUXx2mnnVaNOvuLv/iLeP3rXx8HHXxQZOBsqeXNLdtnAgQIECBAgAABAgQIECBAgACB8RMQGBu/a6pFBAgQIEBgRQjUwahut1tNpXj11VfH3NdVV11Vfc/A1Y033VitQ1ZXvA6q1d839Z5rl/3t3/5tnHjiiVU5hx122GwALLfnemWZDv7Vg+OlL33ppoqwjQABAgQIECBAgAABAgQIECBAoGEC1hhr2AXXXAIECBAgsKMEMri1evXquPDCC2NiYqJa+2vuuTNwVq8llnn32GOPuOmmm6rg1lJGjWVg7N3vfnccccQRc4ud/Zxl5zle8pKXxBvf+MbqXLM7fSBAgAABAgQIECBAgAABAgQIEGikgMBYIy+7RhMgQIAAge0vkMGtL3/5y9HpdKLf78+O5pp75rnBsQxk5dpgSwmKZRn9Xj/anfZmA2m5v9VuVVM1XnbZZXHXu9517ql9JkCAAAECBAgQIECAAAECBAgQaKCAwFgDL7omEyBAgACBHSVQB7nq9/nnrYNXuT2DZHWgLL/n2mNbkvIc81Nuq9YwK+9ZtkSAAAECBAgQIECAAAECBAgQINBsAYGxZl9/rSdAgAABAttVoJ7OcH5Qan6grJ5ScaHKzA985Xplw0EJfM2MGssy5qYcpZZTONZp//33rz96J0CAAAECBAgQIECAAAECBAgQaKiAwFhDL7xmEyBAgACB7S2wqWDXxRdfHDfeeGN16r322qua3nCiOxGdbmfR6swPfB122GFx+eWXR6/Xi263u8k1zOYWOv/4uft8JkCAAAECBAgQIECAAAECBAgQaIZAtxnN1EoCBAgQIEBgRwvUo8Q2bNgQJ598crz1rW+N9evXz06RmGuPTU9Px2//9m/Hxz72scWnOpw3teLp/3RmTPemqzXM2q0tm3ZxR1s4HwECBAgQIECAAAECBAgQIECAwMoQMGJsZVwHtSBAgAABAmMrcOqpp8ZrX/va2G+//eKlL31pPO6xj4vuRDfWrFkTJ5xwQlxyySWxevXq+N73vregwcYTJY6y1quK5b75Y87680q7dVLFeTt8JUCAAAECBAgQIECAAAECBAgQaIyAwFhjLrWGEiBAgACBHS/Q75V1viYn4ilPeUp85CMf2WQFcvuzn/3s+PR/fDoe+ahHViPH5q9BlgfOX2PsggsuqMo78sgjZ8utp2+cnzcz1CPYZjP7QIAAAQIECBAgQIAAAQIECBAg0DgBgbHGXXINJkCAAAECO0Ygg1Nf+9rX4pGPfGRcf/31kWuKzU1zg1f3uMc9YnJyMjLYtdQA1r3vfe8qb440a89Ms5jrjr33ve+NBz/4wUsuZ26dfCZAgAABAgQIECBAgAABAgQIEBhvAQtyjPf11ToCBAgQILDTBDLA9ZKXvCQOPPDA2wTFslK5vw6C5Rpk11133eyosLlBs7oBg9j4Xz/61ZZe9Mqn0euqa68qn/sxbA3n5d7URIx1yd4JECBAgAABAgQIECBAgAABAgSaIiAw1pQrrZ0ECBAgQGAnCPzyl7/cZFAsq5LTLGbK4NgD7v+AmJqaqkZ+ZVCsDphVGTbzo86XeTvRrXJNdEYrifWH81cY20whNhMgQIAAAQIECBAgQIAAAQIECDRKoFt3Ks1t9dxtcz/PzeMzAQIECBAgQGAhgbyH6PV6sccee1QjweYHu9qd0fM5uS5Yq92KtWvXzubb1P1HOzZ+nqc1aJWoWkRn2Bm9l+DYoDeI9rAdE61RgGyh+u3ofcas7Whx5yNAgAABAgQ2vnviQYAAAQIECBAgkAKjx6s3Y5GdUhIBAgQIECBAYGsE8j6i0+lEv9+Pq668KupAWF1WHfzK92t/fu2CQbH6mLnvdfnXXHPN7OY83+WXXx5XXXXVbUadHXDAAbP5fCBAgAABAgQIECBAgAABAgQIEGimQKs8pT2c/wT33I6qZJm/v5lUWk2AAAECBAhsiUCOBFu9enVcdNFF0e12q9Fjc4/P+412ux2ZL1Peb+QIs3rb/PuP+d8PP/zw+NGPflQF3pZy71KfZ24dfCZAgAABAgQIECBAgAABAgQIEGiWQHd+J1M2v95WvzeLRGsJECBAgACB5RI47bTTqqIyMJYjxzaV8n6jvufIoFgd5NpU3rnb3vOe91Rf60Da3H110G3uNp8JECBAgAABAgQIECBAgAABAgQItErHkfkS/R4QIECAAAECu7RAjgabG2DL7xkwkwgQIECAAAECBAgQIECAAAECBAjMFRAYm6vhMwECBAgQIECAAAECBAgQIECAAAECBAgQIECAwNgKeJR6bC+thhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECMwVEBibq+EzAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA2Aq0N7XE2Nxtcz+PrYKGESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjL3AgiPGBMXG/vprIAECBAgQIECAAAECBAgQIECAAAECBAgQIECgMQILBsYao6ChBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECYy/QKqPChmPfSg0kQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBovIARY43/FQBAgAABAgR2jkD9bE79vnNq4awECBAgQIAAAQIECBAgQIAAAQJNEhAYa9LV1lYCBAgQILCCBFqtVlWbfF+u4Nigd2sDh4NbP/enDZC/VcMnAgQIECBAgAABAgQIECBAgEBzBUyl2Nxrr+UECBAgQGDnCuRszjPBseWoSAbCWuWRn6lSWKe8Mi6W4bDJ8pIIECBAgAABAgQIECBAgAABAgQIpIARY34PCBAgQIAAgZ0i8G9f+WaJXI1Gci3HiLEMimXqltd/nH1uFRQbjUmrNvtBgAABAgQIECBAgAABAgQIECBAQGDM7wABAgQIECCwfQRyxNac2QxLEOyW6tUv208+/cz4X28+I0561z9XJ6+mVcwY2WBYjhmN9erHdIxeg/Jel7ZRidWxVQSsHJsjxfL1qtNPjz9/3d/Ha9/9wZjIIke5IkeU5Su/90fxuJk93ggQIECAAAECBAgQIECAAAECBJoiYMRYU660dhIgQIAAgRUicGoJir3/378Qg8EgPvxfX46/Om0UHIsc3pXrjZXQ1aCKdi1c4Ty+GmmWx5WRZ/l2yrs+Wsr+fLQ73eocJ7/j/dVTQHVwLEtc5hkcs0iJAAECBAgQIECAAAECBAgQIEBgFxGwxtgucqFUkwABAgQI7HoCo3DUYGaAej6N86oyQuy9n/7CTFPaVWBrstWPZzzuYfFXf/r8am2wKs5VAmOdavxXGd01c3yn2nurwnDQ3mhNsVPfcUZ86DNfKgG3Evwq/yY7JcRWomC/8/gnxmv/5FnVSLHc3h7mmLWMwdXPB9Xvt5btEwECBAgQIECAAAECBAgQIECAwHgK6Akaz+uqVQQIECBAYMUJZFDszDJSrE53vtPtSnCqVQWvzvrvr8SrytSHGbLKm5NWNf6rzrnp9zquleG3k951VnzwP784m/HuB+0XvX6vCrzlOU965weqYFldbqvVmc3rAwECBAgQIECAAAECBAgQIECAQHMEOqeW1JzmaikBAgQIECCwvQQyQJVLd5VQV/VpXQlxTZdvq8q3V7/zzHjf574cU2W01kRnMo4/5og49ZlPjjXrp+JHV11bAljtuPCHl8WaNTfEox9w33JEHjlZSuqWcWLt8q8EsoY5lqy8WqMFwjaUz7nlNWWk2Mf+63PRG2QgbBCPOfbY+Kvf/Y248ea1cdGV15V1xvql7B/FmhvXxWPuf1RsKBG1W6rSR+UMyudRvUthEgECBAgQIECAAAECBAgQIECAwFgLCIyN9eXVOAIECBAgsOMERmGmDF2NPmVoK9P/K0GxM//jCyUo1ot2GSF2/NFHxIlPeHDJ1YkHHHpw3LDuprj06l/EcDCMCy+7Im5Yf1M84n4ZHKvCYNV6Y1nSsF9+lqJb7fKj5M2pEF/x7n+Jsz7939XIsHanFb953JHxp098XHS6nTj2HvuXsm6Jn1y1pjrveT+8PK5fuzYeesx9qlFpEzP1HK1ONjpXdVI/CBAgQIAAAQIECBAgQIAAAQIExlZAYGxsL62GESBAgACBHSuQwat8jUZgjUaKZVDsg2Uqw9ze6ayKX7/fveKFT3xwTLQnSmxrKlZNdOJBdzsobrh5Q1x81TXRH/TjghLA+vma6+NhZeRYHldWEhuNFsugWDciR4oNS1Ds1e88Iz5aRorl9IvDEnB74gOOjT961MNi9+F0TJaFxqZ6rXjQYXePNetuiB9cfV0JrHXi+5deHjf88oZ4XBk5VtdzNHVjKUQiQIAAAQIECBAgQIAAAQIECBAYewGBsbG/xBpIgAABAgR2rMBovNho+sQcKZbBrUxPKNMnvqCMFOt0uiVoNZWDvqqpD7slyHXsYYfEL9avix9e9YuMrMUFP748rrtpfTz8mNU5iWIVcSuxr2rE2Iby/sp3nBX/8pnPZIgsbtmwIY5/0L3iRU98TDUyLHplrFq7HTnArFvOdf9D7xS/vPmW+PHVN1Yjy757yY/j2hIce/T9bx2VlqeQCBAgQIAAAQIECBAgQIAAAQIExl9AYGz8r7EWEiBAgACBHSIwVcZyDcq/XBXs5e98Z7zvP78Ug24rJoYlcHX/u8efPOkRUZYSq/K02q1ol1erRLsGJYA1KAGuB1fBsZvj0iuvqQJm3/3RpXHt9dfFox5wdDXpYS4xllMxvubt74mP//fnqzwZMXvcAx4YL37S46JbTdVYom0TZW2zMmpsWEaftVr96LUm4tjDD4tbrv9l/Piqn5eA2VScf8mlcc0Nv4jHPuCYUk6v+JTxYyVAJxEgQIAAAQIECBAgQIAAAQIECIy3gB6g8b6+WkeAAAECBHaoQLeExcqAr/j4586ePe/xxxxepk98yOz3zX0YDAfxgscfF8c/4PAy4quMBSvTIX7oM18uI8tyNbJW9eqXENmHP/OVGPQz6NWKJz3wiBIUe3j0+72SbxC98t4f9Kp9nU6ZhHEm2DUoQbL/8dhj4/j7HV5GkZUxaOXYj3z2G1VVWq1ONZJsc/WynQABAgQIECBAgAABAgQIECBAYHwEBMbG51pqCQECBAgQ2KkC3d6qEr4a3VpMT09FGSwWnRKses4TnxDruvsuWrfBcFXJs1v8yWOfUEZ87VUFx0r8KiZKcGtYgmN1mirfp8t5bmmtiuc99pFlpNjNJRx3Sxl5VsJm7QzLbZy6pQ6dYT9u2WvvePavP7Jae6zVnixrk7WrIN6w7Msgm0SAAAECBAgQIECAAAECBAgQIDD+AgJj43+NtZAAAQIECOwQgXb31tN0uqMvgzLqq1NGaOVorsVSju5qtzPvcDYQlgGx+jX/+MzXzbXEyjlyisXFUpYz0e2U+uRIstE0jhluy/O2WnMqv1hB9hMgQIAAAQIECBAgQIAAAQIECOyyAnqBdtlLp+IECBAgQGCFCWTsq8Snci2wQVlMrF0Fw1ox2b+lClzlWmILpU4ZZdYuga5ua1DWJZuKqeFEdVwJYVWjxzZ1bL8Eu3afmChTKeaor3zep6xWVrbdmnLtsAyCtWO33s1V0K38qOo5KJG8rHInFz5bpG63lucTAQIECBAgQIAAAQIECBAgQIDArixgxNiufPXUnQABAgQINFFgJvCVo8Q65TXd65UA2tJvaTJQtnHwrImI2kyAAAECBAgQIECAAAECBAgQaKaAEWPNvO5aTYAAAQIEll1g2OpVI7PKWLFolUFZZYLD6hwbqqkL2zE56C94zuluju4aRr8zjKnyuT0o5SzjUK4NMTlat6yMGMsBYu0yBeNE1qiaT3HBqtlJgAABAgQIECBAgAABAgQIECAwJgICY2NyITWDAAECBAgQWFwgR4rliLFMcydcXPxIOQgQIECAAAECBAgQIECAAAECBMZBQGBsHK6iNhAgQIAAgRUg0MrFxcp/ct2u/FhPbjhZBopVsahRPGrRmq4qwauJarrEYQzLqK4cflZWDstxaGXdslLYcHT70umX9cj6nXKuTvSjU8qdXrDsdifXHxvEdKlYBsi6sWEUHCvlL+fItAUrYScBAgQIECBAgAABAgQIECBAgMBOFaj7rHZqJZycAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwPYWEBjb3sLKJ0CAAAECBJZNoBqVVo1JG0S3jP6aKOuWtcoosk61bdlOoyACBAgQIECAAAECBAgQIECAAIExFRAYG9MLq1kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIbC1hjbGMP3wgQIECAAIEVL+C5nhV/iVSQAAECBAgQIECAAAECBAgQILBCBfQsrdALo1oECBAgQIDA0gSGw2G026NbmvzcarXmvNzqLE1RLgIECBAgQIAAAQIECBAgQIBAMwSMGGvGddZKAgQIECAwPgKtQdWWqfZETE1ORH+iHTdMTUe3PYhhiYOVuNhsapfPg2F+HcSq3CkRIECAAAECBAgQIECAAAECBAg0WkAPUaMvv8YTIECAAIFdV6BVRon1+/3odLqxatXkrtsQNSdAgAABAgQIECBAgAABAgQIENhhAkaM7TBqJyJAgAABAuMtMGh3Ip+46ZTXsNeOYWcYwzJc6zf+3zuqqQ37vTJ8qwzn6scw+oNBdAa7x3A4iIn2VGSQq9OqhnZFv92LLKjdW1Wyt2N6mEPAWlXZMSill2MytfpT8VuvPi0m4+bqe7+9qowOK2W0Jkb7o1edtzWcrr5H7F69d4f96n3QmyillpTl5yZ3RZWLHwQIECBAgAABAgQIECBAgACBcRbQBTTOV1fbCBAgQIDAThLodDoxGEyXwNewClZlwGwUzxpGd2Kv2KO9dwmE7VHVrt+/Nqb7N5VpEEeBsSq4VfbkMRlAm50OcRTPim43Q285OWJGz9rRHY6+98vhk609Y6K7bxWAa/Wno9deH9O9NSUUV9Yhm5ljsV2CbZnaZSrGKmW5Mx9HG/wkQIAAAQIECBAgQIAAAQIECBAYVwGBsXG9stpFgAABAgR2sMAo3DQ66aAM+ioRrBING5YRZGXkVvk36I6iT7u3VsXuw33KiLFqvFbc1Ckjvdo3RLc/KmE07iyil3cpZXBYJ6ZLUCuDZqPpEnOUWStHnrVGAbH17T2rk06WIV+TvX1jt96BVWCsP9xQgm2/KAPCelWAbroaFpZVGt3+DPpZyfK9O6zqV33xgwABAgQIECBAgAABAgQIECBAYKwFWuVJ7tHj2WPdTI0jQIAAAQIEtrtA3lGMYl2Ro7fyc056mOGr3HxLeWV611lfivd/9JxoV1MkRjz7qfePP37GQ2O30e7qmPyYA7kyVJaBtVEqAa0scCYCNzOAbGbfaPO7PnROnPHRb1dTOEZ7EM/8zfvEi5770PqQKu9MFavyR6G12SJ8IECAAAECBAgQIECAAAECBAgQGHOB0SPTY95IzSNAgAABAgR2kEAJiOVAsJy1sHr0pnxvlekQW2U+xFVlqsR2CZGt6u9W1g+bjM5MZKtTRnCNgmIZTcsAVx3yqseg1d/LbUtGtUbZylSMVfbZHzlKbVimb5wo58u1zXrdMoasW9YVyzplrjJyLFN7MHP7U47PupacJfhWh++qLH4QIECAAAECBAgQIECAAAECBAiMqYDA2JheWM0iQIAAAQJjKTAnGDZas+zWVrYzblYichkUq9Y2y4BcWU+sDqDV4bVbj/CJAAECBAgQIECAAAECBAgQIECgaQICY0274tpLgAABAgS2k8DMkmGz0xbmiLFWGaqV48QytWOq/GxFr0xx2C+vmYFfZVsdsqqjXqPbk5JllNqrZj7UbzNHtkbHtarFyMq+mQFmo1FgORqsnGMmz+jI0YixVjU6rGQvUznmGftlbbLqQ128dwIECBAgQIAAAQIECBAgQIAAgbEVqOcoGtsGahgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFDBizO8BAQIECBAgsCwC85+2KbMYlrXF5hY9Ub7kol7l9qO8csrDTMOy5tdocNjo5+wh9QCyeihYlTt/jHa0WjO3MVnsTBqWUWC9Vg5Vi5gsA8ryVQ1NK987MyuZ1cXVxefqYhIBAgQIECBAgMDWC/Rn7uY69Y1W3ubla/bGbuvLdiQBAgQIECBAYLkFBMaWW1R5BAgQIECAwIoRyDXHqjhadszUkbAVUzsVIUCAAAECBAhsX4H5a7Iu+9nygaSScgrrTLOBsfwiKJYKEgECBAgQILACBQTGVuBFUSUCBAgQIEBg6wRaMV3WDht1zEyXRcq+eeFPIz5YHlguC56Num22rlxHESBAgAABAgR2RYFBLvq6XdNM9Ks1Wsu1O5iM+x9+QNznAfuXIFlLbGy72iucAAECBAgQ2FoBgbGtlXMcAQIECBAgsKIF+r3pOO/Cn8UFF/y8qmeZZVEiQIAAAQIECDRKIB8O2q5pOC8w1u9G/ynHxL2PuVPkjNa7tU1avV39FU6AAAECBAhslYDA2FaxOYgAAQIECBBYaQI5TqzXWhWTw070+4OY7JbFx8rGXiu7ZXLUWPXmBwECBAgQIECAwDIJdMtUioPBoNxvlVmryxTWg+5UDCd70WnnaP28+RIYWyZqxRAgQIAAAQLLKGDG52XEVBQBAgQIECCwcwX6pWMmg2KdjlucnXslnJ0AAQIECBBoikC73a6CYvNHp7Us8NqUXwHtJECAAAECu5yAEWO73CVTYQIECBAgQGBTAhkKe+C97xzxjPJscumg6Q43VNmGUUaOlTQwlWLl4AcBAgQIECBAYLkEpsraYudedE1cfP6VMciHk1qdmOy3Z+6+lussyiFAgAABAgQILK+AwNjyeiqNAAECBAgQ2IkCx93vgHhQeWUyZmwnXginJkCAAAECBBohcHNp5Ts/9LUqMNbOEfs5t7VEgAABAgQIEFjhAgJjK/wCqR4BAgQIEBgbgV7pLMn/lOUmynIUtxnBNZwJZdX9KXVgq/6+pQ7zj6vL29Jy5CdAgAABAgQI7LoC0zNVr9f6mrkjqtde3cYR9atK6d3BRDVaLE80LMXnKDKPKKWGRIAAAQIECKxUAX1EK/XKqBcBAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCyChgxtqycCiNAgAABAgQ2JzAodx35RM6grDoxiG50ZtYAi9boSeb6geXZp3ZmnmRu1zs2V/BSt9dPRi81v3wECBAgQIAAgV1dYPY+qj+vJbN3XPO2+0qAAAECBAgQGH8Bd0Ljf421kAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoAgYMebXgAABAgQIENghAvmccg7aag3KamLlNSxDwVqtVkzHXrGhbN99fi2W+/Gd2Sem55/IdwIECBAgQIDAuApMbLJh0+WmrFvutdwebZLHRgIECBAgQGDMBQTGxvwCax4BAgQIEFixAiU4Fp1WnHfR1fGOD0ZMxlRV1bqDpjMY1bzfmomQtWY2bGWD2qZS3Eo5hxEgQIAAAQK7vsDoPqrdiej3etEfduP+qw+O4+77q7t+07SAAAECBAgQILCFAgJjWwgmOwECBAgQILB1AhO9clyJcXWG0+WtFxs67WrE2MXfvzTyNT3zzLLA2Nb5OooAAQIECBAgcBuB4cYPGHW6w+hNT8dUd5+4pbOnwNhtwGwgQIAAAQIEmiAwc4fUhKZqIwECBAgQILCSBHIaxeFwGIP+IIY5ekwiQIAAAQIECBDYrgKDQT9aOZ11W3fQdoVWOAECBAgQILCiBYwYW9GXR+UIECBAgMD4CEyXu44SB4v2cH10hzdHTHViYmIiup3JGAwG0W3lkLI5qUz1k6kbuTpZlDz1WLLq6xb/6G/b4Vt8PgcQIECAAAECBHa2QLs7Xd1ndbqd6mGk3mCyCoxN9tfE7oMbtrl6GV6rHnYq02NnGpQNgzIrgNXLKg4/CBAgQIAAgRUqIDC2Qi+MahEgQIAAgXETqJ5LLn0mDyzrWbTzKeXWaDH4TvaglNRvTy/S5JlI2SK5Nre7JzC2ORrbCRAgQIAAgTEVaLV71Qj9KEuMnXPRz+I7F6wZ05ZqFgECBAgQIEBg6QICY0u3kpMAAQIECBDYBoEycWL17z73u1vkq1pwbBvK29JDty2stqVnk58AAQIECBAgsPMFctx9PhuUk1afduY58d0LvhitYauMx+/GsDV6OGnn11INCBAgQIAAAQI7VkBgbMd6OxsBAgQIEGisQL90wbTLvzqVlcVmPu6YoVzt8qS0RIAAAQIECBBokkDORF2WFKuCY01qt7YSIECAAAECBBYSEBhbSMc+AgQIECBAYNkE2sPRbcegxMMG/X5ZX6wOko0iVnODZst20rkF1aebu81nAgQIECBAgMBYCwxzFsXyaFJGx+au51pujIbG04/1pdc4AgQIECBAYLMCAmObpbGDAAECBAgQWE6B1szAsFybvdPWEbOctsoiQIAAAQIECBAgQIAAAQIECBBYmoDA2NKc5CJAgAABAgS2UeA2A7bqmRTr6RVnAmfbeBqHEyBAgAABAgQIzAhM5FyKmfLBpNl7rzJYrHyvd40y+EmAAAECBAgQaI7AbfqomtN0LSVAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGiSgBFjTbra2kqAAAECBFaSgBFiK+lqqAsBAgQIECAwhgLD1swwsTKnda41NohVZfBY3oT1yyiy8pIIECBAgAABAg0UMGKsgRddkwkQIECAAAECBAgQIECAAAECBAgQIECAAAECTRQQGGviVddmAgQIECBAgAABAgQIECBAgAABAgQIECBAgEADBQTGGnjRNZkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0EQBgbEmXnVtJkCAAAECBAgQIECAAAECBAgQIECAAAECBAg0UEBgrIEXXZMJECBAgAABAgQIECBAgAABAgQIECBAgAABAk0UEBhr4lXXZgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAwUExhp40TWZAAECBAgQIECAAAECBAgQIECAAAECBAgQINBEgW4TG63NBAgQIECAAAECBAgQIECAAIFxF2i1WrNNbLVv/Ty70QcCBAgQIECAQAMFjBhr4EXXZAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEwUExpp41bWZAAECBAgQIECAAAECBAgQIECAAAECBAgQINBAAYGxBl50TSZAgAABAgQIECBAgAABAgQIECBAgAABAgQINFFAYKyJV12bCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFBAYa+BF12QCBAgQIECAAAECBAgQIECAAAECBAgQIECAQBMFBMaaeNW1mQABAgQIECBAgAABAgQIECBAgAABAgQIECDQQAGBsQZedE0mQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRRQGCsiVddmwkQIECAAAECBAgQIECAAAECBAgQIECAAAECDRQQGGvgRddkAgQIECBAgAABAgQIECBAgAABAgQIECBAgEATBQTGmnjVtZkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0EABgbEGXnRNJkCAAAECBAgQIECAAAECBAgQIECAAAECBAg0UUBgrIlXXZsJECBAgAABAgQIECBAgAABAgQIECBAgAABAg0UEBhr4EXXZAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEwUExpp41bWZAAECBAgQIECAAAECBAgQIECAAAECBAgQINBAAYGxBl50TSZAgAABAgQIECBAgAABAgQIECBAgAABAgQINFFAYKyJV12bCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFBAYa+BF12QCBAgQIECAAAECBAgQIECAAAECBAgQIECAQBMFBMaaeNW1mQABAgQIECBAgAABAgQIECBAgAABAgQIECDQQAGBsQZedE0mQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRRQGCsiVddmwkQIECAAAECBAgQIECAAAECBAgQIECAAAECDRQQGGvgRddkAgQIECBAgAABAgQIECBAgAABAgQIECBAgEATBQTGmnjVtZkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0EABgbEGXnRNJkCAAAECBAgQIECAAAECBAgQIECAAAECBAg0UUBgrIlXXZsJECBAgAABAgQIECBAgAABAgQIECBAgAABAg0UEBhr4EXXZAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEwUExpp41bWZAAECBAgQIECAAAECBAgQIECAAAECBAgQINBAAYGxBl50TSZAgAABAgQIECBAgAABAgQIECBAgAABAgQINFFAYKyJV12bCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFBAYa+BF12QCBAgQIECAAAECBAgQIECAAAECBAgQIECAQBMFBMaaeNW1mQABAgQIECBAgAABAgQIECBAgAABAgQIECDQQAGBsQZedE0mQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRRQGCsiVddmwkQIECAAAECBAgQIECAAAECyyDQHvQjBsNoRyva/fLeH5RShzOvZTiBIggQIECAAAECyywgMLbMoIojQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYmQICYyvzuqgVAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAMgsIjC0zqOIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWpoDA2Mq8LmpFgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwzAICY8sMqjgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGVKSAwtjKvi1oRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgss4DA2DKDKo4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBlCgiMrczrolYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLLLCAwtsygiiNAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFiZAgJjK/O6qBUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAyCwiMLTOo4ggQIECAAAECBAgQIECAAAECBAgQIECAAAECBFamgMDYyrwuakWAAAECBAgQIECAAAECBAgQIECAAAECBAgQILDMAgJjywyqOAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZUpIDC2Mq+LWhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCyzgMDYMoMqjgABAgQIECBAgAABAgQIECBAgAABAgQIECBAYGUKCIytzOuiVgQIECBAgAABAgQIECBAgACBFS8wKDVsDcurvA/Lh2F+kAgQIECAAAECK1hAYGwFXxxVI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQWD4BgbHls1QSAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAChYQGFvBF0fVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIElk9AYGz5LJVEgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwggUExlbwxVE1AgQIECBAgAABAgQIECBAgAABAgQIECBAgACB5RMQGFs+SyURIECAAAECBAgQIECAAAECBAgQIECAAAECBAisYAGBsRV8cVSNAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg+QQExpbPUkkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIrWEBgbAVfHFUjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYPgGBseWzVBIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAKFhAYW8EXR9UIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSWT0BgbPkslUSAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCCBQTGVvDFUTUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHlExAYWz5LJREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKxgAYGxFXxxVI0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGD5BLrLV5SSCBAgQIAAAQIECBAgQIAAAQIEVorAcDgcVaXViuFg5vMWVG6wSN582nrYao9e0Y7MP4hO+ek57EXo7CZAgAABAgR2ooA7lZ2I79QECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI7TkBgbMdZOxMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBOFBAY24n4Tk2AAAECBAgQIECAAAECBAgQIECAAAECBAgQILDjBATGdpy1MxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOxEAYGxnYjv1AQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjtOQGBsx1k7EwECBAgQIECAAAECBAgQIECAAAECBAgQIECAwE4UEBjbifhOTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsOMEtiowNhwOo9/r77haOhMBAgQIECBAgAABAgQIECBAgMAWCbRarahe5ah+fyoGsXf0B3vGxPR07DFsLVpWqzeM7DjqD6J6X/QAGQgQIECAAAECu4BAd2vqeOONN0an04m99tor7nz4A6N3y9qNipmYnNzo+/wv66bmb9my74NyYycRIECAAAECBAgQIECAAAECBAhsXmCi1Y5BrxetbicG/X70h3tHq9OObv/6eMU5/xgvPWnh56UnO624+offjnJIOTZKkG3z57KHAAECBAgQILCrCGxVYGy33XaLVatWxWDt2hhM3RxXXnbBRu29ef3NG32f/2XVHnvM37RF39tR7sYkAgQIECBAgAABAgQIECBAgACBzQq0WmW2n2EZ7tWajHd88Kvxjx8+pxpB1o/pOOFpD4wTn/XQzR6bO/Y/5MhyfBktVgJi0+W9xMkkAgQIECBAgMAuL7BVgbEMimVq77Z77FGCXIMcUz8n7bHnwoGvjXPPOXCJH8tEAEvMKRsBAgQIECBAgAABAgQIECBAoKkCZahX6UIpK2LMplweozPRmf2+0IeJidJtVI7PfpyJUtS29ucsdC77CBAgQIAAAQI7SmCrAmOzlStj6detXxedMiR/S1IZyL8l2TeRd+Gh/ps4wCYCBAgQIECAAAECBAgQIECAQLMEBhkRy3XGylu//OgMZkaM9WNQRpMt1jszPd2b9Vos72xGHwgQIECAAAECK1xAhGmFXyDVI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQWB6BbRsxtpV1GMS2xeO27eitrLTDCBAgQIAAAQIECBAgQIAAAQK7okBOp1herTKALAePdXK9MEPAdsUrqc4ECBAgQIDAMggiKTV7AABAAElEQVSIMS0DoiIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWvsBWjRirHypql0mqb+nPXV9sds+CLd/maNx0OU8597CdzzmVj+0No/MNV1Xvg1Zdj9Hm2/7c5hrctkhbCBAgQIAAAQIECBAgQIAAAQIrSKBX+k3yX6apVukLKf0mg94ghrtPRr+zewxzCbI5qT3v+yBG/SwRvRiWf+3hqBupNZzpd2l3yii0dvTa7VJWyVHWop/KQnvl1R2dd07xPhIgQIAAAQIEFhWYH92Zd3sSnXJfUm40qnL65WdrZtXUduS3TBOjtwV+blVgbIHydsyuXDU2b+7Et3aMt7MQIECAAAECBAgQIECAAAECu5xAtyxl0SudRROly6gKXPUH0Z3oxnTZ1i79Kp35sat531vZ/1JSP/NniG3m+/wOmSw792XHVbtTHqCug2Lze7Kq0vwgQIAAAQIECCwgMP9+ZF7W8ohP2TJd7k+61f1NfdsxL9uCX3fNwFi2NMOGMzdY063RE0zdOiBYnlBaKOWc2hIBAgQIECBAgAABAgQIECBAYJwFeqV7JINj2b/ULbPrtCY6pRNpFMTqTfVLl9LGaX5vytSwFzlHTzcmq26YW3Pf2mPVb03HxKBVAmIlCDfoxUSOSCsZc+DYzEQ/tx7mEwECBAgQIEBgEYHFwjetmRFhed8yeminHmM2d3bDhU+yawbGZtr5rfOujW9d8LPot6aqVnYGo+YMWjmUbvOpXYb5SwQIECBAgAABAgQIECBAgACBcRYYTgyi1+vFqlWr4tvnXxXDQQmKlWhVf7oX3790TbzjQ2fPNH/UT9Kup0gswbRMGdx6z4e/FdEpY8b6/TKV4sYdTu1uN777g2tHeUvZ/TI+Lftp3n7mN6vztPq3BtCqTH4QIECAAAECBBYRGMyOgBplHM7en4y+98qUzZOTk3G/Iw+Ko+51YOy+FeGeXTIwNl0mvc5/377oe/FPHz8n+v09ot3txOSgE4NBuelr1UPHNi2cEwBIBAgQIECAAAECBAgQIECAAIFxFtgt1ke/9JO0ymiu7C9Z29ojJiYmozNYG9/5wcVx7vd+VDW/rB5Wvc8PjMVg9zjjw+fHquGN1VRFZVGPmTTTr1KCbDmN4qCzW/XEdrf011x46eXxw0t+Up23H7vXB3gnQIAAAQIECCxJoFPuWeamfKinXaZs7pcpoYeDMoFiezRQqv+MR8axJTg2GhufkwyOjltK9GeXDIzNRak+F5i8wSsPL1Upg2YLp8X2L3y0vQQIECBAgAABAgQIECBAgACBlS7Qn3nCetDrV8GxifJ0dfafdGfXChuN6Cqrh1VNqd9zGftMgxwyVlK7XaZg7JfVyuZ1VGW+nMKoWl+s5M1Oq0x5XLV9ppxqox8ECBAgQIAAga0QGJSA2GyorLWUsNfiJ9klA2MTw3KjVV7d/qoymn8y+jMW0+3JqsWdds6AvVBaHryFzmAfAQIECBAgQIAAAQIECBAgQGBnCrTbe5apFMuqYhmvKsGqiQyQZSBrw+4xMTkRU53RE9d1IGxYZcwajwJcu3fXxx4Ta8vUi2XUWXkouTXTi1R3Tq0aTIyaV6Y0yie4B2XNjyx/kHnL+7A/fxWzUXY/CRAgQIAAAQKbE5ieWLXJXZODm6rtU/07Vu+91qoyiXPEZD0OaguCZrtkYGy+St5sddrtOPKwg2L13faLVXvUt2jzc858t8bYZmBsJkCAAAECBAgQIECAAAECBMZGYDhVAlTtmO5Nx3cvuS7Ovejn1dSHnTK14j0PuWPc95hfGTV1Zu2wdlklrEoz3//6G6145m8cFa2ZDqdhd/RhONPxNLGhU6ZkvCa+c0FZZ6xsGw56cZ97HRRH3/uAMpViLzqdUYBtbDw1hAABAgQIENjuAsP2HhudI9cYy/uZiy68pKxtenWU53y2Oe2SgbFB3leV14Z2ebqpXZpQbrb65amm+62+Q/yP372/FcS2+ddCAQQIECBAgAABAgQIECBAgMCuLzB6cHhYHhB+xwe+Fud+/8qqSTmi6/5HHxQnPvNBGzexfuJ6ZusrX96KE5/ziNLnMtrQaY3Kq9duz/l43vr+s0tg7MoSFysrfJQNxxx5YLygLldcbGNf3wgQIECAAIHFBepxT+2Z+5hyRK/8e9eZ6+Oc8jBOuxonVkaKDaYj5xDsz9xvzC6FuvgZxJCWYCQLAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAGAhYbGsMLqImECBAgAABAgQIECBAgAABAgQIECBAgAABAgQILC4gMLa4kRwECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJjICAwNgYXURMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQWFxAYW9xIDgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTEQEBgbg4uoCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAosLCIwtbiQHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAGAi0hiVtTTsGg0G02+3Y5673i+svO3fBIvIUrVYrfuWQ+0T5EP1ha8H8i+0c5rlb7eh1ujHd68Vke/fI+nRiQ7Q7nWgt0qJha7DYKRbcPyjnlggQIECAAAECBAgQIECAAAECK1lg99J/0e/3I7rd2LD+5rhl1R1KdVsxMVwbq1btFq2b181Uf9RP0466Q2X0fUN7ZntnGFXfzmD0feYtJsrXffY/KvY56OFVv8+gPR3XXf6NWPezb1ffp9udbeLplP6bdulH2tAbxpqffreUVffn6JfZJlgHEyBAgACBFS0w+ntf7gKqWlY/y6a3fvAb8b5PfCeG/dH9yB/8zjHxp896YMmzifyLtK+7yP5l3/3zy/JGZnnSmz7wrQLx7Yj+RFXgCU8+Mk589rHlpml5ylcKAQIECBAgQIAAAQIECBAgQGCXFsi+otKj9I4PnB1v/eQlVVNWDW+K5/7WMfG/np2dSSXV8bDRt9nv+9z1vnH9T8+rt27y/R3v+3K85yPfKw9Pd2LQ78X/9z9fEC98zrGjvMvUP3PHQ+67yXPbSIAAAQIECBDYGoEdEhjL0WKZplujJ4VG8butqe7omPq+aqLdj3Z/Ktq9m8o9Xjta7V4VFBsucoL6yaatrUFnWJ62kggQIECAAAECBAgQIECAAAECK1mgBKumy6irzrAdU73pmJy6vhrJVSbbicnOYHb8VRlEVqXZcVgz31uZsaTsZumVzpbuzAw6c+NoG1pl5FnJn4PD+jEZw26njC4rsbVyUH+2wKqYLf7RLcfnuet+pS0uwAEECBAgQIAAgU0I7JDA2CbOu02bNgyGZfrEVnQmOjExuSq65UatNz1VTafYLzdfnUVuvEa3ddtQhZkA3zaU4FACBAgQIECAAAECBAgQIECAwHYV6JehYJ0SzMpuku7EROlDmSj9J70q0DRc7KnickxrJmKWgbBNBacyftYpy2zkUhu5v85fNkd7GXqc8rwZGMtlMyQCBAgQIECAwHIJLMNtyuJVqdcYq5cza9/6TNLiB28ix24lKJbPNW2YLq9ev8yXXW6VWuUGr1OCZXlXlndNC6X2YhkWOjiLXyTytvDh9hIgQIAAAQIECBAgQIAAAQIEtrtAK3rVOQbDiTKKq1eCYlWYqUx5WDpPyrYyt89MHerAUz1Dzuh7ezjaX8adlXXdM9X5Zg7L/pEyIm1Yxopln8+w6pAZlBl9Rvu3tf+nzBNU9cD0y/ryEgECBAgQIEBguQREeJZLUjkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIrWmCHjBibL7CtI64ymlcG6Ue/1Y9BWWcsBqMnh1plzuwqLRLuW47zz2+T7wQIECBAgAABAgQIECBAgACBlSQwiBwVNpOGZbRXa2bkVS4K1srRYHP2V9k27lDpt0bdRv2ZfDlJT6ac4jDTaPxYjkKbmZmn9NNEvma+b2v/S55DIkCAAAECBAgst8DGdzzLXbryCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKwQgZ0yYmxZonHl8aTJ/kR0e+XppnzqqaReWWdsKWlZzr+UE8lDgAABAgQIECBAgAABAgQIENhpAvUa66UnJEdy1TPt5Iixmb6UharWHo6Oz5XeM41W/KrHg42ObOWy7/nK/eU9X3Xa9v6Xuv6jEuePQNv28uuaeidAgAABAgSaJOAeoklXW1sJECBAgAABAgQIECBAgAABAgQIECBAgAABAg0WEBhr8MXXdAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAkwQExpp0tbWVAAECBAgQIECAAAECBAgQIECAAAECBAgQINBgAYGxBl98TSdAgAABAgQIECBAgAABAgQIECBAgAABAgQINElAYKxJV1tbCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFhAYa/DF13QCBAgQIECAAAECBAgQIECAAAECBAgQIECAQJMEBMaadLW1lQABAgQIECBAgAABAgQIECBAgAABAgQIECDQYIFdNDA2jGgNymUrr/LearViOBxWrwZfS00nQGCZBG6++ea4+uqrl6m0lV3MYJD/W7ryUv2/6VNTU1XlllrP+rg8KD9v6fErT0KNCBAgQIAAAQIECGy9QDtG/1qlG6U97JRX+T7zauV76VcZvcr+6jQzfS0lf5RXuaWuUl1ObstX5s1X5h62ynunE70oH1rdsqFTtuaxy/f/NbLfRyJAgMD2FMh+h/qV/QlLSf1ef6NseXz2Qyy1D2Ojg5f5S/ZtXXvttSu2v3xL61dfk+uvvz6uuOKKZdZS3MoTqO805tSsbGqX+4HWoPz3c+a/o/12+e9tlWWUfxNHzSlg44/ljkUiQIAAgVog/9C+613vile+8pVxww031JvH9j3/D+Z3v/vd6qbiSU960g5vZ94w/t3f/V184xvfqG4c07/b7c4+8HCve90rXvva10a7nX/aNp/yuGzLhg0b4hWveEV86lOfqm5GH/GIR8RrXv2aOOjggzZ/sD0ECBAgQIAAAQIECBAgQIBAowT++q//Or7zne9Uba4HXdQAq1evjle/+tX1182+d7qdKvC0bt26OPnkk+PTn/50ZMDnkY98ZGT5d77znRftz9hs4du4Y27fVgbqFutX2cbTbfHhW1q/+hq95CUviY9//OOxZs2aLT6nAwjMFRAYm6vhMwECjRfIP7SHHXZYPOYxj6kCNSvtxmG5L1C2941vfGN84hOfiOuuu64KLi33Oba0vKxT3uB84QtfiNvf/vbVTWZu21yqg2K5/3GPe1xcdNFF8YIXvCBud7vbxVve8pZ4yEMfEuedd171faFyNle+7QQIECBAgAABAgQIECBAgMB4C/R6veph2//+7/+u+g8Wa23dF5Hv+aDx97///fizP/uz2HPPPePNb35zPPrRj46zv/nNuON++y1W1HbZf8973jMe9ahHVWWvxL6QLa1fOtep3994pF693TuBLREQGNsSLXkJEGiEwBOe8IQ4/vjjV+xw8+W8CPnUUN4g5Q3gzrhRmpycjJe//OW3adIb3vCG+OpXvxon/P4Js/WqbzrnZ67r/W//9m/x9a9/Pc4888x42tOeVh33m7/5m3Gf+9ynetLrb//2b+cf6jsBAgQIECBAgAABAgQIECDQQIFTTjllo1Znn0M+OPz5z38+/vRP/3SjfZv6UvdF5Oil7Is466yzIvsgcvtv/MZvVH0Rry8z5Pyf//N/NnX4dt/2xCc+MbJ/a6WmLa1f7Z39WJ0yfa9EYFsFFp6baltLdzwBArukQP0URv7x/vVf//X42te+Fg972MOqp17ucY97xOtf//qqXZkvX6973euqQFIOQX/4wx8ed7jDHeKP//iPqzz/9V//VR272267Vduf+tSnxsUXX3yboNO5554bv/M7vxN3utOdYu+9965uIOqbh/o853/v/Mjj991339hrr73iwQ9+cHzmM5+Znbs5p+X7yU9+Es961rOqc2U5Bx10UPzWb/1W3HLLLVV98g/o5ZdfHr/7u79b5ckRSZkn21nPDf1vn/q3eOxjH7vRtfu///f/VjcUWYccTZZPAB1yyCHxmte8pmpLfWyWnzdD9773vat25FSAH/zgB+Okk06qXhsVusCXyy67LJ7+9KfPtjXrmE8gZRvTI59gynpkW+am3JdTCf7+7/9+tXm+x6/8yq9U7cih/VnXnKbwP//zP2P9+vXVaKtf+7Vfq65D3Z5sbwaZ8ppmm/P6/sd//Ed1bB5fX/+8+TvnnHOqa5Km+Xvy4Q9/uKpDBrlyFF5uf8hDHhKX/vDSuVW+zecs85//+Z/j7ne/ezzq0Y+a3V/fBM1uKB8yb53e/va3V151UCz35RNIOZ1ijoiTCBAgQIAAAQIECBAgQIAAgV1TIPsgsv8p+2/OPvvsqn8h+4ayvyGXaJibsq8k+4+yLyP7hg488MDb9A3NzZ+fs8/h/e9/f9ztbneryp6/f/737DfJOv3jP/5j1d/x27/929V0hVlO3RfxkY98ZP5hC37PGXCe+cxnVv1Ndb2f+9znxi/KDD+Zsp8j+7cyz1/8xV/Mbsvt2deWs+jUfTGf/OQn4/GPf3yVZ1P9KdWOOT8W8839+cqU58sRctlvlcZ5HTbXX5QPnmd/YfYrZpuyvzDLmV+/vIYZyPv2t79d9eNkH1L2C2WfUp5vfprfP5f9W3lNNpV3/rG+E0gBI8b8HhAgsJFA/nHK6QPzD8mPf/zjajq7/OOaU9PlkPDPfvaz8bKXvaxaB+pF//NF0e60qz+GX/rSl6pg07Of/ezqj1z+sfvcZz9XPSVz7LHHxunvOT16/V686lWvqv5YZiAsb0zyfBd8/4Lqj3cGxfImJ/+gXnXVVfGjH/1otm4XXHBBPPoxj64CTm9605siRxp97GMfiyc/+clVYCcDLrktn8rJMnPYegbjMgCUda5vAvI9gzi5FlVOs5fH5E3F5z73ubj5lpur4M8PLv5BteZVfUz+Yc2bk6985SvxjN99RvzBH/xBnHjiifGv//qv1ZzReROWNwNp9u///u+RBnlD9Dd/8zdV2bleWdYp/6AvNeXx09PT8Q//8A8xMTExW8dOu1O1Jduba4O94x3vqEzruma7su0vfOELq3PO98i2ZrAy8+crg0YZGMv11DKgmNvyfDlPdpo/8lGPjAzu1Z4f/ehHq0BZBiTzumbKm6H8fumll8Yf/uEfxoEHHBjvOf09kTdvebOabn996l/H2nVr49RTT41nPftZ8c0ynUB61fWuXerfh/z9SLfF0twyMph43/vetyqz3p7veVObo8/qbYuVaT8BAgQIECBAgAABAgQIECCw8gQuvPDC+HwZ0fW85z2v6nv68z//8/jiF79YzUSTfRkvfvGLq0rnA9LZv5D9PqtWrZqdIjF3Zt/AppbNyH6IfOA3R5LN76vYnETmy76r+93vfrfJcpe73KV60HypfRGZL+uQD4PnQ9jZp5UBvr//+7+Pp5QgX7Y7653tyb6w3/u934sjjjii6qPK7fkAeJaR+7LtWa980D23ZVpKm5bim+V973vfi3ywOvvDTjvttKqPMIOA+aDy/P6iur8w6/snf/Inse8++1azFs2v3/nnn1/13WQ7ss8t+yCzrL/8y7+MibIW/QtKP1e2IduaD41vqn/u0EMPrYKGWceltPc2F82GZgmUX5StSmUuz+q4Oxxy30WPL/9lrPLc/i5HLZp3aRmyvP7wrWd+Y/igp79t+OCnvX147FPfOnxL+S4RILB8As9//vOH5Q/O8PTTT68Kzf/e53+fy1M3wzKCafZEZXRS/pUdlqdShiUwU+XJneVpkWH5ozS7LY8vf9SHJRg1LMPSZ48vT7AMyw3D8Prrr5/dNv9D5ikBmmpz/b8/+eWhD33osDwRM7u93AgN3/3ud1d1qOuSda5feWy5iRi+5z3vqY6pf9Rty+/laZQqT36ujzvhhBOG5Y/qsPxRHtbnz33lKaDhU57ylNk2l5uh4THHHDPsTfdm85U/7sOsVxnxlUUumvLYPfbY4zZ1zO2Z6vc/+qM/GpaFXGfPk/vKdIHVuUqQKL9Wn+d6VBvLj7ntzeu3zz77VLtK4Gy2LeWpnlnz+rh8LwGxYRlRN7spf0+yfWUqw9ljyxph1e9OXte8DnVK23S8+uqrN6p3vT/fyw1Q9Tvysyt+NnfzJj/nNciU79mGMupw9nv1ofwoTx1Vv5833XRTvck7AQIECBAgQIAAgUYJDEoXztvfX/pQnvr26nXc004bvu39ZxeD7NsZ9e+MQGa+5212ee17l3l9PjPbR3lHR2ZfzIOe9rbRq/TRZF9N7hkMputs2/A+qs8dZ/qe6trW79tQsEMJENjFBPL/99f9T9nPUafs38i+gDJDTr2p6o/4p3/6p+p73YdTv2c5dV/C7AHlQ3kgfFhmyhlmX0Sdd+7+zX3OvojsF5qfyoPOVV/EL3/5y/m7FvyefT51/fI9+6GyH6WMpNrouOwTKqOqht/77veG+TnrUR5cns2TfSHZt7TUlOdayHf//fef7euq++jq/qm6vvP7i+rysr+wTnXe+fUrwc7qupUHwKuseQ0yb31t83u+ysPqVb75/XMlSFf1V27Jtavr5H3lCsz/e/+2co9xXN5z5P3M75w2fOsHv77RXcyWtMRUis2Kg2otgSULlD8+1RMf+STGc57znNknavKJi3x647oyjDvnUK5TTrOXo6ZyBFamHJmUT6bk9Ij51E4el2XllIA5vPpzZYRWpjxPPvWST7Tk8Ou5qfyPWfU1R2x94QtfqEaVrfnFmrj22mvjmmuuqV65mGmeJ8vJ8g844IBqTugPfehDUf5AV+fNcvL8+Z55yh/zajrIM99/Zqxdu3Z2e+bJcjJlnTNV2/qjbdnGnGKxzpfvRx55ZNx4442z5ecTLjlcP0dc5bnynEccfkQ10i1HgC2Wsq2tdivueMc7VnX8wAc+UI16q+peRudlylF6Wc8cxXfFFVdETv2YKbedccYZ8aiyuGpO85jHzPeoMpYfWfd81aluUz55lJ+zHjmyLIex195X/uzKKAGtaluOBKtTnnf33XePxz/u8fWmuN3et6uecsqRbbVllplPTGX6xje+UfnMHjDzIeucIwHzd+Sggw+av/s23+s2VD7FO7/n5/o9D6jzlJuj2xxvAwECBAgQIECAAAECBAgQILDyBfL/2+cr+51yPfK5KacWzL6L7KvIPoHse8oZiXKZhpxZJ1P20SyUcsRTefg6DjjwgNl+hIXy53kyze1/2FT+up9pU/vmbsvy8nXlVVdGeUC9GhFXHkSO973vfdU5Lrrwoip75skycwahu971rnH8k46P9773vVV7c1mPul7Z3rrN9ba555v/eTHfn//853HueedWh+XMQLlG2A03XF+5Z1/RpvqLMnPdX1ifL8+T9Z9fv6xjt4wM+8M/+MMqa12fZzzjGVUf5HnnnTdrnTNNbap/rjwQPdvm+nzeCWxOYOH/RdjcUbYTIDD2AvkHKgM55emS6g9T/kHKlMOVM3jS6/U2Wt+qDohlnuqPXAkmZSAi15qqj80/clluBsAysJUp/2hlmTld3/xUH7du/bqqrH8s8zbf7dC7xeGHH14FWLLsnFax/oOa5WcgKeuSwbzb3+H2VeDqne98Z5Uny888Z555ZjVd4++f8PtV8Gb16tVRnjaqTl//YZ4bxMpAVNalXtwzP2e+TJmv/nzz+pureuYUkXmeOmWQbL/99qu/LvieebP8XJcsnXJaxnw/6qijZuuYBeQ5jz766Cij0+KN//DGqn1f+PwXqjmlc7h5pixncx65r075uVq7bDCqc9Y9zfMa57QDZVRaFWg7/IjDK/uc9zmvbX1zmeWkTda9TnU75p4nHScnJiub2rLOX79nQDNvtnI6hC1J6ZG/q+vWravKzzbkufM9b84y5TQEEgECBAgQIECAAAECBAgQILDrCuSDufUDw9mK7A/YbdVuVR/AT3/60+o9H7jNfGXEUtzudrer+obe9ra3VY2u+wrmCmTfSfZF5LIUc/sx5uaZ/7nOl+Xnuu11yoeCM9V9EdlXsZSU5X3gzA9U0xNmX1eZiafq48iy6+Ba3deRbc5Xrmd/5ZVXRvZrlVFj1WmynDr/3L6tpdTh/2fvTABtqr4/vu74nrGICs2hUeZ5KpFKaFD9NahIyJBKKqmUpBGVNM+ppEQiUzSIyjyluVSI8mswvXfH//e7z93Xedcb8Az3sTb3nWmfffb+3HvO2WutvdZmnrz4Uo+D6ERmgDnr9OSTT8pRGJRNnRFDKlJXl5u+yK0vtHVg3Znc9eM+GtGoT7LtZB7qIHmM03cw8ZjVKbGttizqsGy7mUeTEiiIgBrGCiKkx5XAAU6Anl9MfKnwwxeaMaJgnSM08krFihcz+elZZl9IttPAlxU7DiyHxgq+xDinWGqy1+RLkC+3W265ReCCLgi5aIwyNKpxbiwaQ2zi6J758+fLhg0b5JWXXzGjhNix+eCDD5IvS+bhHFccTcRRNZUqVTLxiydNmmSL2aWlNQbZzg8LicFAyHawQ2Nf3AUVTk70tOIIHLbxpRdfMl5uCD8pCFeYNPiwHO5jPG0aGjmvF41yrc9qnbxEKg8e7969u9i2Wsb8Dqxhi9e3HRc7Txo7PawL2dPLjl5y/F4Km+xvw9YDIS6NlxvCTu5w0bYMeu/9gk4a22L3sRDOkUfD5O6o7w5XSjMqASWgBJSAElACSkAJKAEloASUgBJQAruVAGV9q6eyBhFeIDuUbfRGdkBszRo1TZQj6qTocUW9DwcRW12IW2/AMhl9h/oSzklvdVc7UnHqqmgUYjQfJhrFaLRj+vbbb40ugl5QO5puu/02E4WI3lHvvPOOcPAwDXosIys7K4eug/qZfv36mQHpjF5ELzObdqYN9hwu8+NLXR4NV36f3zDiXGzUx/H7oJ6osPoiXpvlMLH+ZMt9NJ5xnTpI7uc+TUpgdxBQw9juoKhlKIH9mABfQHPmzDEt5AuIxiwalWg4adGiRZ4tZ152DmjI4UuLH77ItmzeYjontWvXNiH2aKyoVq2aTJgwwZTlfsGxDH6Yhx5l48ePT9bD5uNxWz7XeQ0uEWdZLrv8MjNRJ0fn0CWeyZ7HdXpiXXLxJcZoxjyIeczdO5Xs9XkSmdCzy90WGpvYGfr6669NvQoq3F0/ls0XP9sxbdo00wGxZbMc5u18TWfTjgcffNCEIKSbvTVwsUOWyoPhEdnWsWPHmqrwGojDnfT+stfnfhqaOMqKZdgOp126211Qm/I7znJs+vnnn42RjyE5aVDc0WTrwrCaZP3NN98kjWP0amOoTf5W7aitHS1X8ykBJaAElIASUAJKQAkoASWgBJSAElAC6UOAOgvqqTiQ2C3jU+6n7sjqqWicov6Ceh+GWaROhcdpbLLJ6hKoi+D0HQzZZ3UUO6OTOP3002XlypXCcnhdlkFj0ezZs+XMM88029Sr7EjiQOtGDRsmdVcsi4Y9GqW4bg165ED9D3V0rHvHjh2NkWz5suVGh7Mz9XfXKz++1Hm1atlKMotlmulCxo0bl+M7KKy+yLbts88+S1aJ9eE2vdiaNGliuDDfjvJMFqQrSiAXAmoYywWK7lICSmAbAb74evToIb/AE4cvJBpmnnvuObn66qtz9cBhHvvp1auXMaINHDjQdFzooXVlpyuNNxfdvflSZ9477rhDOBqGL3V2AviC++mnn3J0WAYNGiSLFi0yeVatWmXO3bp1q4kXTaMQy6IR5Pbbbzf5uM1y2IFgh4RzVjHR84nXXrx4sbl2NBY1YQu5ny/ZnU32xc/zeM277rrLxLSmu/6UKVNMKEPOOca5vnZkjiuWQePhrbfeKkuXLjV1ZDtGjx5tRs7UrVvX7GM+fmgEY9jIJ554wsxF1rVr12QTQuHQdjw4Corc3G2tUb2G+X6GDx8uEydOTHYYyRyTu8oVV1xhwmbyu+K5DPP48MMPm3okL7aLKyyTiW3BxLims0ePNrbL3cktqHief+WVV5q52fg7YoeU3oT8HuitR5521FZBZelxJaAElIASUAJKQAkoASWgBJSAElACSiA9CVAPw+g5q35dZfQ+NHZxegzqAmj8og7I6n2oK6DxiLoQ7mdUHXeikY2eVtS7XHfddWZJPYUdcOzOm7pu9RnUw3DAMQ1w1EXQc4r6Ceoibr75ZjPw1607Si3Hvc354p/BdCD0QKNxi4OVn376aaMzsfnYJs4v9v7775t2Ud/EbS6vuPIKydqatUP1t+WlLvPi26VLl6Th78477zQ6q05XdRLq6MivsPoitsvqIKkTZD04yJ3fD3lajzFeS5MS2B0E8vfltJ6J2wb0745rahlKQAkUIQLsVNAwRk8odiaYWrVqZSb15DqNFxy5YsME8kVmU58+fUy4QhpcHnjgAbObrul8qVkDD3fSQ4idmHvuucfMHUYDEsu76KKLpEOHDuY8LieMnyDX97zehD/kTuYpU6aM3HDDDSYP/7BDxOvxBcoODt3o6S5/zdXX5MgzdOhQ47HG+tIlnWUwHjNfsF7ss+73PInlsMOTW0eG+W1errdr2044FxpdyumBxljL9wy6R15+5eUcoSeZ180qWTmsRKIR0/l57LHHzG7m5fdAgxHraDtf9pxePXvJiBEjjMv/EUccYXebETWpPMiMbe3UqVMyX4eLO8j0GdNl8ODBJlQlww7+9ttvxn2fE9Xedttt5nthfQOBgGkH93E7v++fx9mpsYnbHq/jAcjvmOfazibbxBABNGBWrVrVnEJDFvfzPJtS2+4+xt/WmLfGSOcunU1sa57Dtrz80stmPjZ3XlueLpWAElACSkAJKAEloASUgBJQAkpACSiB9Cdg9QMM58eB2Jx3ncYu6kwYQWbYsGFGb8NB0zSoPPLIIzka1bt37+Q8XPYAdRb0fGJUI0Yqyi3lpb+hjoF1Kl++vNH/0HhTuXJlo6sqW7as0X3VqVMnqdNI1WfwWtzn1jU9+8yzctXVV5l53hnthzovGr3at2+fLIeDxqmT4QDgc845x5RBvRSjLNHw17tPb+E0FQHouqhLsim369tjXLI9rAv5Um+UytfO0ca89K4jZxrIjj32WNNmnkvjVV76Ip7nTmRv68drU09EnVMP6L6og2SisY1t5IB4JuolbT3NDtcf7qdnGRPbym1NSiA/Ah78UKz5a/t89kguvyM+FPiDL3NMDfn7l8Xbn+vaY3+MBx9dXf5ZtcR1ZFdXWbG4jHpzvrzy3gLxxR3PkCs71JGeHevtaqF6nhJQAikEONqGI1A4Xxc7G7NmzZITTzxRjjnmmGRO+6JxGzmSB7FiHzEfffSR8eapXr168qVvnw3Mz3V+6MlFrzG+zEuVKmXyuvPx2cM5ozh65OSTTzadBff1uM6X88cff2wmX23UuFHSOOMux7qb8zlG7ym+jN2dHeblMfc57nX3Ne1++1y0x7ifHxoOaSCjcW/kyJE79HJmWawj25ERzJD69etL8RLFc60P2dJYSQ+1s846y17eLHl9Nw8anqwxyn5nzMNkv0uzkfhjjzEsAMMUsmN05BFHmjJ4zJ5jy+Jp7v25rXMfP7nxde9nWbZ8rueW3OXb49xHTzf+Zhs2aJhsrz2uSyWgBJSAElACSkAJKAElcKARiGOA/TNvfSUvvbvQNN2LAWudLqgtPS6rlUBhAwolRuLHne1DoPPZsMql80nREzH3U29+Ja+OW+CUA/3RVSj3+o510OfnYMD8x2MnLp7PwqlPuWNqyV/QPSVql8xva53coStKQAnstwSoJ6HXEr2oNvy1QRgBiHOucxoP6lyoY7CJeoGtW7bKnLlzzK5GDRsZnQo3UvUIqbocdxkF6SRsXpbBvDRacd4tXs+GVbR58lu660QdzpzP58jBZQ42OhieZ8vnemqd3Ody3Z0nr7aZTCl/eK5bD8hB4nnxtafyHOqLfvjhB2PMKkhfZM+zS7cuiZGpaNxj9B/qc3htDpymDtK237bd3WZbFpd2/860232+rqcfAfvet3e37XPEYuhw4J676uIa0v3S+mKP70wL8u+hoHyTUjo+O3MBzasElEDRJsCXChNHn7Ru3TrHC9i+kHjcGlu47k42D+M8uzspzGOP2XVu16hRw+xPPWZfbiyDL8YTTjjBXMadz+zAH9b17LPPtps5lrYcjkyxsZ5Zht1vy3PvswXYY3abS3se11k3dmBoqDr33HNNO7hNj7v169dL586dzT73C5rnpyZ7HbaD8ZvdnSl7jOdwnZ0FenodixE6NI6lJuZx83Bfz4YWdJeZ2/ncRyMkjaJM9nt0n+f+/rnfcnHnsetcutdNofhj99tzud+uW2bu+vM4z0nNw30claVJCSgBJaAElIASUAJKQAkoASWgBJSAEtg/CMRjceNVRJ0Eo9EE/Y5eh62zOgauW/0BBxe3bNmSu5LJ6g+4w+oXrI4jmSmxwjJtWe787nxWJ2HL4GBiu89dJ3ut1HO5nXoNDtw+o8UZyaw815bvzm/LdNfTfU3m5Xk2X7LAPFbc53Ld6s2Y3X3Mnm7rTX0RP3Y7Nb9bX2TPtUv3Mfc1yIA6LruPS7ueWr4ti+20edy87HFdKoFUAvkbxlJz67YSUAIHNIHUF5GFQeMP3Z3ze/HYl7F9SdkXpnt0CMuzZdgXN5fcZ8+z++02z+E+fuw+jmrhCzw1rz1uz+G23WeX7nK4j9uM68yXdWpdWY49j+tMzE+Xci7p9v7ff/8Zw9RIzAHGubxoyOIcXfQiYwhHckhNnDSVxiwmdyfBMrN15NxfnPON22PHjt2uLjyfx9w83PW16+5yeY7dz3Pd2/a7MTtdf3L7/m0ZzGbra69j28xOrW2fzWOvyfOYj+XQc45hKcnMnsvjTPzd+bwIu9nhIrNuy+Ex93pu29ynSQkoASWgBJSAElACSkAJKAEloASUgBJIfwLUH1hdg11afY3VNxTUCnse89n1VN2BLaMg/Q31ThdecGHSE81dpi2b+1g+p6mgbsadqGOxuiHOT0Y9EM+j7im3gcy2nsxDPQmvz8S2U78S9zh6Mau7Yn7yoRGR12Ed8kuczoRlu/UuO8M3N51RbvqivOrAUIq2DDc/5ue2bc+O6ufyuo7uVwKWwI4ZxqznmD1Llwc0ge3V+A6OXXFZPKBB7qPG80WyI4n5evbsaeatsvlTX0x2v40JbLfzWrrPty87axhJPcfmtUt7nNsF7bOdg9R8tgwu8zqWup/bto526S4ndZ0dmb/++suEnWQsZBq/6GlnGW3eslneeOONpNHH3eFgWXZuNcZF5nfgrg+Z2Q4O83LOsfPPP994R9FdPbfE8wviYb8L97VYVup2buVzn21bXsdtOfY6dun2c7Z57JJlJfNh/bXXXjMjw9zHmYedLF7/nHPPkYMOOoi7DDezkrJu9+my6BLY/v1j9+R8AyW37ONO+zFF90vXmisBJaAElIASUAKFJJDoEEExGvdCcYoPk4mU6IPS1N0px36v3U6b/pPTs4t5nGWyn2daoX+UwP5NIJoIHupL3JdRcQwrPvElGp7/HWGlpf2JUs/eN0ibts58W2yfB8Yy007oS5Ltda/vSONpdMklX3Y4KqPfeAshG51Bu6n6mwCu3bb9BeZMXjtV3eZLPEc3Z4XktddHSxgGL3eizoN6Hx+WF1x0sQSd8dGmTbY+Zmnr51r6YRRzt5ePSOblxzKJYuohWNjMJT3wrnsdbckvXXBRB+nV5wa06fxk2bYsKGiS+5Jl5LYvedDh4cfUIKxT1DTEdTCX1b433SznX3hR8kiyfXYP2m/rY5f20P64TP09pbYRv56UXc7vKyaOucf+/lIy6aaLwI4Zxlwn6KoSyI0AX8WMWZ7oq+aWRfelCYFUw0JqtfiiZx6+oDn5qA1JZ400XGrKmwD50BBFYxiT5W25lS5dWqZNm5bDeOMuzea359rzbB4a5+w+zo3Gdfc5Nt/+smT76BXG+dOYcmur5ZHbsf2Fg7bDIbC92GfePhJFhxBdZMWkBJSAElACSkAJKAEloASUgBJQAvspAUp/9erUMp/tjCZ7oM2lSpWQKVOnCJyxTApsL5Amr8pDOc1e8ORKHC1eLMOUk3p6OJEB4xag60gWtcdWZs6Ylm/ZrE7tmjXMFCf5ZtxDB2vVqC78aFICe4uAGsb2Fun96DrJB7m1jyQe3hxxxl0F2U3UYr1vfww0YOaXzIgV5MEcpsbd2uqardHBLvMr44A+xhE5rpTauYlFyDXB1pUvr9XceLv3cX1/Nkq727ozjPLKW9DvP6/zdH96EIglXkD2LjObeF6ZMYIw5ocSDywnoMS2OtvHXuL0bQd0TQkoASWgBJSAElACSkAJKAElkKYErF9YKFG/oATMmt0uSKm7P8o/Vv+xN9rGa0HMlB3VYyYccpO/plR9UPJAYsVtaLPtSs1TqG0rCNtCCoDGw6yHr4B8triClgXph7c73wr6iQO7qRrbXabI7EjhkVpvuDWk7CroiZCSXTcTvnUKQgkoASXgIkDDTQx+zqkeGDTqePU56yK1/So7ERFYFYNBeHa5OiGhUFT8cGE37u3Y716mlmI583x3GcznGNVynuEuKzV/zpxFb4ttY9pmUHS27d9oGGEHAts6AwW135Znz9dlESfAoXscXsfQIvB2tWEiinirtPpKQAkoASWgBJSAElACSkAJKAElkAsByvRWZ5LL4d26y61rYcGp+gZ3XVKP2Ypwf0F6iB3JY8vb08uC6roz19/ZstKJw860c0/lzes3Za9nQyPbbbu0BjNrWLf7dbk9AVVxb89E9xRAIOkoltBF//Nvtny86HuZ+cVqWbTsZ4lm55xMcrviONxCkxJQAkpACSiBnSZgg1NAukBgigZ1jpMmNSpKi+bVpTTmmrMmUpvLjuzTt85Og9YTlIASUAJKQAkoASWgBJSAEtjnBLaXZKhxC8Jl7PWJc+SFCSsklNDBmWgyKS46sZTteEGa9pT2enbWspFyvm7uWwI7+32n1nZff/9Fvf6pPHf3ttV/hHyOBqSUr7hcd2lD6dj2lN19qf22PDWM7bdf7Z5rGG+8jVkxmfXFr/LRZ9/Ksu9/lJOrVpRmjU6Wvp3PkDLF8/9ZRe2du+eqqCUrASWgBJTAfkggp1wXlo/mrsSgjJ/k8TEr5NSqh8npdU6R1k0qSckMv0QQ3Bc+mvshBW2SElACSkAJKAEloASUgBJQAgciAYZQXLX6Pxn5zHSJRKPy1JCLpexBpc0cWAymEXQiLW5Dk+IyYufKshlM8A27kcsyNX8uWXRXGhOI2xGju1hHT8rvZxeL2eXTinr9d7nhO3iil+OFkbITavgNazbKEy98LDPn/Sp3dW8uR1Ys7mTQv3kSyN+CkedpeuBAJvDfpmy56PrX5YQTKkjjhlXlputbm+hVGd6tiEMbl7DqIQ/kn4e2XQkoASWw5wh4N5uyY4nY+k2b1JAzmteQrM1ZMnv+Kpn3xVx57bU/5LWnrpeSxTjTWKKnyFCLmpSAElACSkAJKAEloASUgBJQAkWIQCwhz/gT8swfqzZK/8FjpG3bxtK2xYkSD3olEsG0DX5O5RCT1PhN/niKHMTw864ULyiiU0p+16m6WgQIxDFYtDDJE9u3Ct6iXv/CsN+Rc2OJ+zsz28ldqeJBMuT2tvLe1K+kx71vyIuDWsuhh1bClBMpz4EdKfwAyaOGsQPki95dzYxjuP6Hs3+UU048Qgb0aSWcSDKC5yxH8Qf9QbyI41LIAQm7q6pajhJQAkpACeznBCD/mZSZGZRWTatI+/pV5L6RY+FJtkrat6iyn7dem6cElIASUAJKQAkoASWgBJTAgURg8NMT5eK2NaXlGSdKAOMAs6GL8/l8xjjmgfuXL1UBntMOth0qGtPyS57U8vLLrMfSjoC3gO+3oArv6++/qNe/IL6FPp64fa3nZziC2cVwz7ZvWQ/PBb/c//TnMmLQ/5nLUJ/PcKuachJQw1hOHrrlJmAHFiTuG95v/2V75IuvvpNmdY8TCXglFIogUFVMvLi5sqIx8UFL6Ym4C9F1JaAElIASUAK7h0A8lmkK8loPsGhYvInRkV4IhJs9calWs7p8gvCKFxjDmI6M2j3ktRQloASUgBJQAkpACSgBJaAE9jYBT8yRZ+gY8vx7S+ERVkzatKgl//q9ghlOJPuvLLm+/xPy21/rTNUCwqgZcBBJzCW21eco9vwJ/V4G9HXVTjpO7n+oq8kXtHKV2dr+z05OSbZ9AbpnHxMonDy877//ol7/vfP1h5OYvHBYwf3v80q7lnVkxYLv5L2Jn0n7No3hNMZMqT6lqbFX90590+kqSXTpVCmtS3oRoFXZptV/bpHvfvpTmtQ50YxI8eJl7MfNRcMYP3EYxzQpASWgBJSAEtgTBDgK0nzwvuFoJ67bUY4xxNj3YrhP03rHyNKVq+W//5x4Au532J6ok5apBJSAElACSkAJKAEloASUgBLYEwQ80Nryw/Hno8ctkJuuaZa8zKZNcbm4873y519/G0+x7bzFkjm3rcTgQbT8m5+l5fn94VGybb+uKQElsP8RuP6qlvL063ONUYz3Pp4mrka61127D7BV9Rg7wL7wHWuuNW45dtM4lI8xE5fWIwu/WSMnHn+YFC/ml000mEXxwXGTvE4QxXhiRMuOXUtzKQEloASUgBLYUQKJ8TzJPlxiO3F6BAOgMjMz5SSE+/14/u/SrsXx5gjfajlz7uj1NJ8SUAJKQAkoASWgBJSAElACSmDfEqAfWDQrWw4+vIyEMEA9E4PSe9/2mITiISc8GtxEOHDQF3dCOMW8zjIQs9YvRxqKBj2YDiUK9XhAHn38Penf84J92zC9uhJQAnuMQJmK5eS/eFmnfOMxZvX9fC4klSp77PpFoWDVExWFbykN6ogAiWbusNnzfpQmmMOFNjF6iGlSAkpACSgBJZAuBPhe4ojKxrWPk4/n/ZQu1dJ6KAEloASUgBJQAkpACSgBJaAECkXA6/VhEKDXjE2nTm7V6j+hm9s2b5DjEVLwJZiPn2Vfryo4s+ZQAkqgyBKIwX/FPhfUAJT716geY7lzOcD3bn+70KbMESrfffOL3Hrt6ZKFESoeD/ZiYj9M9enwSo5EcTb1rxJQAkpACSiBvUnAi9CKHAjVtPZR8uxrs/bmpfVaSkAJKAEloASUgBJQAkpACSiB3UqA8wVx4B9TDFruSNQJF+/1BIRKb4nBQoYYTz5/JtRzCJ/hcTzF5k0bxlOkXqv+Zgl/M7OMxzDAHWX6vBnijaT//EIxWgALkQo7oH9fX78QTTen7uv6F/XrF5Z/Yc8v7O+XTmFeH/X2cTwBotDeU3+Pe4qTFqqvi/l61DBW2F/pAXK+eRfhpsnODknJEhlmMj++TQt9kx4g/LSZSkAJKAElsOcJUGgMh+JSDOF+QyFHKNzzV9UrKAEloASUgBJQAkpACSgBJaAE9j4BO6+Y23OMtbA6b+stYqxh2O/FpMycp5k5nDmbE1Y3npSGyWvmRdr1inlM+LhdP39fX3/Xa+6cua/rX9SvX1j+hT2/sL9fOx97YeuxP5+vhrH9+dstbNvoEWaS60UZz8CeXPan5nNO1L9KQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACewKATpNGVsWvMVSIjX5/By4jgzhmAR8QXiNBcXng/ErHhafB5OiwLOMKYJ1Jg/czOg1Fg1kScSbjfX0HkxYaI+jQhrW9vX1zZdWiD/7uv5F/fqFQL9bTi2sYVFnWi/4a1DDWMGMNIcSUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkogbQhEI2Hj+eWDUWzBh4OT9eLUJ5gARZbOGGr22eHtNVsOcDzFYGgzHmM+10D45Nnps+KN2prvWp08hWzfvr7+rrV621n7uv5F/frbSO6btcL+fuNOBNV9U/kiclU1jBWRLyp9qul+Kdl1+yJN3U6fWmtNlIASUAJKQAkoASWgBJSAElACSkAJHHgEEkHVIK574CnCDxPWMO0Q5mfdLiJMQq7nHCRpklgjerrkTPCSoTeINzHnec6DuqUE9gsCMW/c3Ku8G2OSIRHOD4TkbGPeIF8x3AYxCWKfj85hOMypUHhnu1NyC95lPsSf53xlfniRpb/iPFlzd3N2eL3w7dvX19/hpuaRcV/Xv2hfPw+oe213YX+/xmEShfDt6RFrAkr0CfZaK9L7QpZKetdSa6cElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkkCDJ0YCWVLtVZ3I4SiM3fYoql3Yj4xr5zWcqDJF/M4hmU1Iyex6YoS2O8JYEpBWNE9gqCqMI7RqK5GsdQvXQ1jqUR0O38CcfdPJtXyn7qdf1F6VAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSiBnSfgiW11Qin64UsWz5JAPOjMOQajmPH9jDl6Oq8nbAqPC9zKPPQc9Yo3h35v56+tZygBJaAEijoBtWQU9W9Q668ElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUwAFFwGdcQpwme+E5xhRDuMRoPCaRSGr4USef/lUCSkAJKAGHgNv9R5kogZwEbExxjizx0OUSUYs9kZx5dEsJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkogT1KwOdzgiFGqaaDV5gnHobnV1xiHqh34SIW9xYzE5D5MccYd0W9xm8MWwnPsZhfPJxb0JuFc51je7TCWrgSUAJKII0JqGEsjb8crZoSUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkogGsVMQTBoBf0BiUbC4k+ZNIzHYvjUaD0A+eKYUSih9kX4RCazj6EUYTXzIaqiJiWgBJTAgUxAQykeyN++tl0JKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkogbQn4PHC2wteY1mIknjSqXUQMjFDopIhPjh/8RP3bhKvfytcxcLik7jEPTCk8YOW8ePBPGSx6Gac45G6dU9O+/ZqBZWAElACe5KAGsb2JF0tWwkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJVBIAl7PNjXuw3d32K60WBQeYzFayGAUyy1UIrzFgsFMOfXUY6RzxzO2O193KAEloAQOJALbnqgHUqu1rUpACSgBJaAElECRI+CJIVY+P5gDkx8TQJ/x8xP7GSU/xjAhjBTiickmLDZhOxvLLB5LfOyQyeQ29mtSAkpACSgBJaAElIASUAJKQAnsXQLWlytxVRi0aNSyKQbBBs5h5hNBGEU4f8EzzCt+GMiKZXjl44n3yBnNqkggkAHPsAC8yUojd0mJBDMkjHCLcchK/MCVzHwySpaQVmfXkQfu6yzRYMEqYQ+qks4fy0mXSkAJKIFdIaBzjO0KNT1HCSgBJaAElIASSHsCVsykAYxiHwZQmoQIJEbe5PEYhEuv9oYcMPpXCSgBJaAElIASUAJKQAkogb1G4O+NIcz35ZOz+z4tPq9Ppgy/TqLRiES8AWnT5wmznDyyuzGI5VYpeoUN6n+lxEIwYEHGidL+RWNWYv4wLwUepOSSVjakLMg/lINiyOxDaMY4vcwSKYJ1vxeDD/FJClD2YMrSAwOd9Uyz85elZNmtm7xGjsRGa1ICSkAJ7CIBVQXtIjg9TQkoASWgBJTAgUdgm8C0Z9pewKhFeIGZZLzFnNGLZtsbMYsoJpGOJWWjqJx9zWPyweO9zbFzeo/CsbAEgkEZ+8h1GFUZkHN6PwkB0iOznr3eOJmlzF1tznP/KaB27qy6rgSUgBJQAkpACSgBJaAElIASyJfAVsS08EMI8UagnoVckuXzSBgiD+cLk7hPIN1IpoUwqwAAQABJREFUHKIO5ZQADFjJ5HXkogiEH0ZX9MDyxbnHTIyMWFwC+DDFUYI7RRPyVBCHo/GElcydIXXdyl+p+xPbMVjXOO8Zk9cHI5nLwGZ2JuQ2s65/lIASUAJpRiDnEzLNKqfVUQJKQAkoASWgBJTArhJgjH3KaRTVGGs/DoExGg6b4iKRCEY3xs1+DqhMGXu4q5fU85SAElACSkAJKAEloASUgBJQAjtEgIP1mCaNvM4sOdyP+xgycdLI641FLETxBQIN5ZfURG+vKEIsGrkGxz3+vTuUzwurHo1h9C6jnOUY51JrqdtKQAkogfQkoIax9PxetFZKQAkoASWgBPY4ARMeI3EVClTeRCgKd/iM7Ub97dFaWUHOCn1ON4WGLc4dnZ3otQQTIxF9jBWCFI27uzPWxOWTGAS0LAiVbFbElyleb0hYcgzWMoYNwbhG7PNgn3OOz5jQWKImJaAElIASUAJKQAkoASWgBJTAniUQD8PTC8alMOQVen4FIN9EonGJBxz5JITJkgNwBKPo4vPRiOZ4itlaRSRsBv8VgzxE41gWCgnDE6xYzG8MZpIIqWjz7/zSymc5r2vLsbIil+loFDMGO8i5lP0YHlKTElACSsBNwK1Jcu/XdSWgBJSAElACSmA/J0BjGJMfwhhHGzJRqKHQYOLNY9sKEwFIZFnZISNw+X1+8XF0IDyyPAiZEY1EEaIwINnZ2WaEI7ftcZYZioRNyMIAztuZFEMIEJ/fI9lbUUfIMWFUt3gxjpakJ5jHhBQpqDyE6s9h7qLnGFNmJiajRvn8hwj6BRWjx5WAElACSkAJKAEloASUgBJQArudAOWvdjc8beSliY/CSyyR2vRC2HcJIjR8VxPeYrv5tWxG1zKKOIyUbTwYVUj5bgu8uBiCkTKdNWK5sm+3asIhQkZk/mzIfjOmTpRNG/+TIGS/6rUayPFVKptyYswDI9wnM6fL2j9Wy9FHHicNmzYzRj4WGoM8yBCLlL0+nTVD1qz9XY4+trI0btzMGOz8fnjFQb5jhA93Yn6GuvdCiONxDo6cMnmCHH3EMVLl5JOx3zFu8awZUz6QajVqS6Ujj4B86Azy5ADPn1Z+I7+t/U3ObNnaFP310iWQR0NSq3Y996V0XQkoASWQEmxWgSgBJaAElIASUAIHDIEVK1bKmtW/YZShH7HtA1KtenUpd0gZGJwcAYXCE4WLYMAvixYtlFW/rZYg5uiKIZZ9/UZNpFSJDAlAYvEHEAsfMT4orC1ZvFBWr/3TCDkUhmrXqSsVDisP4YhlOuXmCdgZGInhkI7BzsuRj3DxKoUg+83a3YxRiI5hbdKEhyWUTWOcU1LUBOHnekJQohCFragPE1fDMIZqYCLqbLQF6xCyOIoyTFeyRELtE2uFHlJpi9SlElACSkAJKAEloASUgBJQAkogXwJxhE2k3OKLJeQRD1zETHIEnYzoJglCHnKmIHMN5mOsRaSoZBgJi/JQBIP+2p5/D2QyhJD3RGTGe0N2aCChKcj1xwfj05RpM+WG666UTBjEjj+uimzc/J8sW3GddOvaXYY8OEJCkB8vbdtaVn67XE44tZos/mqBNGnRQl5+bbQxqrG4zVu2ymUdOsj333wjJ556sixfvEgaNz9Dnn/lTXM1Gr1SE41iTGwDwnzI9I9mypWXdpDjjq8icxZ9DZMf2gaZE3E/5N0335Ih990rn3y5wJxDo96atX9J+7bnynU9uyYNYyMeeUg2bdkoo9+dlJRzzQn6RwkogQOewM4N3T7gcSkAJaAElIASUAL7D4GHH7hXZk79ULyZGeKHELJ140YJZBSTtuedJ7cNuk+OOupo8ULwwH8ZOexBmTx+vPhLljTeWls3b5GMYgFp1/4iuW3AXXJ4xUpmdN9jDw+VqR9OEU8GysSJW/79V6qeeLK8/PY4CFXH7TQ8yksMH0IvNbfs5Hh+OV5uO1ooR03yPIb54ChHTUpACSgBJaAElIASUAJKQAkogX1FwMwPBoOWTYzaYcPb00DlizsD/yiP0csqr2Qlm3gMawnjEkQeeErBk4orO5jowcVIIbf0vlYaNDlDXn31dXNmCKE7Nm/aLN+tWGi2H3tkiCxdulBmzV8ihx9eUX747mc5q1lteevlF6Vj52tNnheeHiHffL1MPpq7VCoeXk5++OEnOatpbRnz5mty+eVX5lojDmI0hj00OI7BjG+8+oI0anaGfDHnU1k4bx68vmoamZMnDx0+Upo2rCH3DLxD7hx0r/Ea69erq1Q4spLcePMdiDyCKCi0r4FJKBTK9Xq6UwkogQObgBrGDuzvP/fW23dyYjAKX8N2V9w1QCX3k3WvElACSkAJpCsBjrBzJ6/HJ/UbNJZxEz8wuz2YsHn27I9l4B0DpF3LpjJl1qdyRIUjECoREynD3apB40byzvvTJAYvsRhi33/6yUwZPPBWaXNmM5ny2Xw5vHw58WBkX4MGDeX1dz8wIwzXrf5dzmt7jvS8rotMmfGJ+/LbrXt92bhWBMJbCcElMXQSs39BUNwU9MlmCDZBvI0YJoMyYSgL4T2wZD0CGB0YCkUkiCWGR4oX+zwQIlkXOpMxDAe3A544RlUivjz+ReMQuuhKhoQjRmjCSr4pJ718s+pBJaAElIASUAJKQAmkBQF0f4yePIx+TswflRJx9LegLQ77MnCAHa7UDlBiezfJ/rb/5E24h0TZj0SdfCg/9cq5AbPnm7lgsYHuXSKxgB1X+NuzdKkE9iaB5M81cVH+nqlfw9RiJqpFED5McU9Mxj95owkbGIas48M9QtlrwrDukg2ZZRMKCeL+CYdDkGUcNa6PLmRIPshPJsWceyEDN3wcMt9WzFUWhaeZj/c/tp3bGd5W9q7DNXE34ZNaQ3h6bdoi6xAp5Ky7z0OWsAl9mOH3S8nypaVck2aSHdoqk97/QFq1aS+HlyuPEmJy/LFHSvNmzWUcBlJe2vk6c8X33h2PAZdtpEL5gyB7ZUuVKkfBY6yFvP/O23LVVVdiH+qaEkrRwwcD6hxH+2kgm4owis+9+jaeYT554YXnpG69Z42hi3UvW/ZgeXj4k9LlmsukQ4eOMNQtkvlzP5MpM79AnVArhJVEQEcTMYQhGPHfabIBpn+UgBJQAhhwoBCUgBJQAkpACSgBJUACmJZYmp/eUka/U13OqHeaPPX4cBky9FEzKo8iFkctOvHeKVVEpdkZLeWtD6ZJ85qnCD3Fhj70qIlhz/AWQVivwtGQHHXcsXLueRfISy8+VSBkCj8MgREOQWRDGVEISvQU49xiHD1JiY51gEwIQxdHEcKbDGEcmbychAzbO5NYT4brmL/0Z5m34neEWyy+M6drXiWgBJSAElACSkAJpD2BQAwDjahUR+i1pd+u3+f1/WrJOpm//HfjFeODsj0es6Hjcq9aDIOe2F87pEINeWb0HISUS8TSjie8Z4xxL/dzda8S2NcEPN6EL1cchmgkGmg4EDACWar2KRXlhOMrmP1hGHGYKOuYuZ7tAD6IOsYBLCHn0PmLMhBua2NY4wadxMxOiEM0nvEEX9CRa2hkM5EyEtEyOD90foleVkFE/jgM83a98PQTUg3GrOo165pTOP8zkx/37fIVS6VTl25mTrEo5jHzBzOkVr168sTwR1E3yHKYJ3rlyuXStXsP02bKcWxXjZq15fknH5MwGHAes9RE+Yzh/BmS/5mRj0vJUqXlrPPOlb///kcG3NJbtm4eIcVLFccgSgymxPlt2rWTc89pK907Xyl//7Vebuw3QCpXrQo5MoLBlBgMiQGTmpSAElACeRFQw1heZHS/ElACSkAJKIH9jIAX4SjciaP7OILQ7o9D0cBxw4cdfqjUbdJIPpr1idxJ/yr0FjgPmRFUoFjBfyPYUF47vHx5hNloKrNnfYT9cYwgDMFoxcmSffDDCkoIQt7PP34v5cqWc18613WG7qDiw+OnGQ4jI2EUo+AXhL4kk/ONof6c5Nkf5Og/ClIcOSmSgfpkoC7RAgYNRzzwDMPoSA6Q5Nn0HvPhektXrkHM+QXYzjs8Sa4V1p1KQAkoASWgBJSAEkhzAvHEXERxuo4g+TyZ8FChT4kZ9rTHa8/+ppMcJfiilT/J6PcXsTOJUGc+GAly9k9TK+TMEeuVUkc2lucnLIfSPxESDX1DJi9MDJqUQNoSgKeTSYmQiP5AFEadMCJfFJcwjFAnVq5kjDgX9n/aGIsmDuuDgX8YCJgdkwtvekK8WB/3aE/jKRn14DcPeYkGIY+fMlkcc33BUIxbK2l/KwbDG6xmXsyvzOyxxH0CUckkOJKZ5Nw9XMX9aSxvzn1K25cPRq0nnhwlN/XpJi2bN5eSB5WQatVqSbsLO0jnLtdgrudseGOFpXTpEo7sBvmMxvdDypWVLQi3T6NWVlZYYpALixcrboxnYRqqMHDyUMiOW7ZsMbKeMYA51Un+5b4AhE8+Fd4dM1Y6XNxB/Dj34ksulkG39kIYxlfkqmu74bqoKIU6ZLz/wUekxsknynGVT5Cb+/dDhBEWR6N7slhdUQJKQAnkSmDbszDXw7pTCSgBJaAElIASOFAIcARhmJYmhPQ4ouIR8sXsz4wBDFIHBCz4k3EEHz5GkWKWkEWQ/7DDK8inn840QlAAAs/aNb/Li8+OgsAUktmfzZLp02fIQyNGFYiRRjGWf9b5t5tr8XrGEBZBvSDhBII0XMWk1YV3InQiBDIIPIzBnwmD14xxgwosPzWDHZFJzzRNSkAJKAEloASUgBLYHwmwb2UGHiX6Wfu6jbY+NMtZD5T86sSBUdT88zxNSqCoE6BMZe7HhLcUDUpMlHXo2eWHPMZdHIDIfKmJHl1RWIsZRbRVh1slK+oM7PPEHI80j5ceojCYQVY6EzIVDWlM3sTtM/WDB1OLzLFtQxs2Pv1MmbfsR5kze7YsWbxQpk+aKLff1FMWfTVbRj73EkZNwvBlXNVQT1SWBq1wKCyBYgmPTnPRPAYdou40lGXAMy2ei2E8grIWL14mS5YtkceefcbUzw8jemuEbhz79hvS8aouMPBjsGPCm3TEw0OkTNmy8tNPP8iCefPltBq1TBQSDrqkHKtJCSgBJZAXATWM5UVG9ysBJaAElIAS2M8IMGS7OyGiPYQuSEkJTzJvxCfF4TUWj3kknBWCAOWFJxZCcUAfwbm64G6FEYTwukJ4DPh1CaMXMgTG5i1bEb8eexIC3r+bN8lHH82QaGiLLIcg1aRJc/m/y6+CsQsx8xH/g4ITjV52Ymlbp5C/OARAlAnB0M8VjHCkYSzihSkO0lw2QubwukEY3IIezjhGodEviLwoHDtcgMOYvUxyyfk1mMKegyTmwye2KXlMV5SAElACSkAJKAElsD8QMApx9LscJTG6V76tpl/l9RQTKqD3msGJHTekIPuTnD8IIc688GKDGt8Jx5aYI4n9RHfywWOG/Ucq+GkoiKDPyRRHhAImht/WpATSlQDlKic5Sy/nOca/IH7nfvym4/S2wu96yog+zr0Ily0vrWIQtCY83gc/dMhGPBX5ffEI5CEOE/TIwZikzA/ZzchMOOxLeFJGvJkwUGVLpi8ImQ0DCxOeajyXCSa1xJKiHa6Vh1sVZUR/wCfNmzSQxg3rSu+evWTQwP7y1MjHZNgzr8KoVVw2b9yEQZIMicgKemXN6jVSPBPGLhjMMjMDUrx4cdmC+cpiMH5RTozC8Pf777/KQQeVQfh9yHDwKAvQwpeS+AwY8+arEsgIysCbbjHPB2ZZv36tfLNipfz661rMV3akeR589tk8efH5V2TcpMnywqgn5PrrusqMuYskky2FkVFDKabA1U0loARyEFDDWA4cuqEElIASUAJKYP8lYEck2hZSKKMyxAeBhAIYxC2EnkDEexjC1vz+uxxfpQqOU4BCSAvEaKdU5oS7saN2YaaCHLQWAs4xlauYYiHeSdWqJ8krb46TTAwY/HXVKjn7jMZy3539ZcjDDyH0hmMcowiUqvgwQl+ickZJg4uzXl6EIHHi81PoYkooTIwxjsedejjHdvwvlSu8Tq2TDsdJtdG+zTt+suZUAkpACSgBJaAElEARIOBLKL6jGGjExDFRTHGEVKx5wmHOxj78W+2kClL7pCNgsMPgKyjEUw11HBzlg4J72HCEduvdA3PCJkInJuZssgr/fdgEvbQSyJPANsOYY5DyeJ1QoBEYrnj/wS4GWQwyCWOGIlE+MZ6U2MdjcKwyxjHKTtaDzIvRjtHEQEUOPGRiVEGzxCBGGoSsvMT5oZl21IAcSXiB+XGekRMph6FwyosnnHwy7XNwFovIabXryMwZU+TyTlfaMZaycP48qVuvoRkACQu2nFazJiKHTJaOna5BW9AuCJnzv/wSeRoYjzHbHlNB9x9c470xY+SCDh2lYcOG7iPy6AP3y+uvPicDBt6B6CRR6dO9k1zR+Vpp2rS5nHLKadK0QXV58J4BMgT5TCCUHGfrhhJQAkogJwE1jOXkoVtKQAkoASWgBPZbAmGMDHanmK8ElBAhifgwso9KEko6EHoWLlsusz77RG4bcCukLIT1wAjAKAPOm0zbSuA8XwsWLJM5n38utwy8y4wYpADlxVwWfihfYhDwKlWoKN379JdHht4t19/QVw4/vKIZLRjACMREZI9kgcWRPwblzcyJD5t98VjYGOLi/kw5ve1AKQa/MCpMZn74gKlKHPHzTXhHhFLckeRHyI8oyufASdPURJNq1jhU+HECkOxISZpHCSgBJaAElIASUAJFi4CjPkfXDtWm2txOzcWBUns2mas5F+ZlfRiE5UXobir04UVS++Qy0u3/6sOD36ryc9bGDou6q/8C6dGxrjCYtpOccnesF5izTN1SAnuLAIIaOpeKOyEGORCQv1wEHjT7N2BcHuw7clH/JzH3WEQmjrgRnloeycYcYxfdPAqRO7Jl8sgbZRPmXI7GEEkjG5E14EG5CR5l7457QBKRFAVTNJvUps1NRqYJeUvKlLGDxJdh72/nfvF6UBCSF4blOMpLWtTMXjqq+Yx81a1bV+l0dTdpXL8+tr3y3Y+/wVtslJxQpaqULJ6JY12kHwzVc+d+KQ2bNJMZCJ3/6cefylvvvitBeHnSW+3Kyy6Xvjf1lblfzZfaDerJrFmz5PPPPpa33/tAgkFE/0gVBlEHD+a1Hj/uTfn7nz8hiw6SIyodmqiZs/j++x9lPMIp3nn3YLm1bze0Oy73D3kI8mVEDj64tAwe+qj0uO5qaY/50GogpKImJaAElEB+BNQwlh8dPaYElIASUAJKYD8ikDqPQwghCbMxefLGfxAGA4LJ3E9nyPIVy+WZp56QU6tVl67X3wSBicJU3BiUIhA4Nm7aDOEtKgu+miOLlqyQF0cNl+rVa0rPXv2FoW448s9rwiUiZA9DJwYCcm33PvLsqMfkiWEPyH0PjDDhcDgCMjVxJKEXAhoVN/Qmo5HN7MNAR5bLsIpmomVzHAKlMcJBusRxrO50MuWzXJa302frCUpACSgBJaAElIASKHoEcnTBqBenpn5fJ3bEUI3c+mTJfelQz33NSa9f5Am47z9INggTKGKNu/T08vkZ0YL35bZ7k9NwIfKgkbEyM/3Gi4wiGqPYU25yJ6/fbzyzKOcEMeov9bg7b27r9BgLQH5bv3atXNq+pXgR8pTbmzZthQdYDXn+1TeM3Hhpx06ycME8ueiic6RUsBjmOotI/7vukZbntDGDJb2YE+yya66VZd9+LxeeewbmHkNww1CW3DpgoDRv0QptcDw/fSn3NUP1j37lFaldu75UOrIiBL6Eh2iislfCWDfqsWHy+PAHke95GT/1E8kw85o5smP7Cy+WCe+9I72vvVo+m780tybqPiWgBJRAkoAaxpIodEUJKAEloASUwP5NwGtnXU40EzOJyVeffyzHVTxEvMWDUhLS06nVaknf/rdK5259ECMeI/kgdDECTzCjmMyFZ1jlSuXFW6IYBDgPvKxqSp9+/aVLlx6IMy+SjeGOYY78Q4jDQNAnWdlheI9BaPNkyY19+8qgOwdKjz63yaGHlcfoR68JgegmHopS9cEPRjJCKPIixA9loeLhmGSEtkoU9aNHWQhZYhDzMhIxQ7KZGfsyMJqRQiCKlijmHkNkfOTHKExshxCeJyhbkQ/1Mx8Y7UwsIYTnSRl57K6TrisBJaAElIASUAJKYH8g4ARUc7VkX2mDYiUQzrEkel9Z6LehkyalTD+OxgHo4JNzxsYS/TzmYIrxIBICxZml/ZNzy+7VpRJIFwKOp5gRcVxV8ibNYc4APV8YoQthxeLgQM7HRQ8wHwYMZmHuPePYxYGACCNK2QzREiFnQT7DsgREG3fyRbLNvGNhyHkI0OHyynIy+qKJJwFvHHOPYcXch04pflwb5joZOx5eXbDafbtyufy54S8YqupJqVLFMS9YFPcp5Cc8Px569AkZdM8DsnAhvMYaNsOASMhfxhKHgY6Q3xB/UQbfP1QGDLhTvpz3uTRqfAY80jDHIOrNeaIpt6WGEAmFwvLmu+Nh9EM9I/CqQyhHd6p87JGy7n8bzfEb+vYDC7/xtHMGUUIgRAVeeX2MOYVeZDQ2Pv/aG04RDBmiSQkoASXgIrCvukKuKuiqElACSkAJKAElsDcIUMhyp9feGiuQPSizmBGHJrwgZSN4hHHeLk7SzknZ/RCKnnjuZRnx3CvY7+RlOQhvjzyQV3AOJ1P2ofxXx7xjzosiLCInVWaIjAxYzTp17S5XXNvTKZeeZMarLH/hhLIQ6waZBheBEOUNmG0zgTMvvrNDIN2N13UloASUgBJQAkpACSgBJaAElMA+JBAOOQbf8cN7mFpEjLEIYekh+4wf3g3CkGMbojzESB6UvfiBE5cxknEAo5OcFR/lL5QB0xqMWDgXYRlzpER+GuAo7zGlzvtMGZAGMs4zXfXEk6VqooAwjF3Ww5R5mIphwGTjpqebdTuftTVSmW1cJxOhF5s1P9Pk4RVZvwjc4Mw80WZvzj8+eL0xUXZ1DG3bjrNMls+ll3VEPbh0J1sP7nfouo/quhJQAkpgGwE1jG1joWtKQAkoASWgBPZrAtGoE8ve3cjMILyqGJ8Dsg3H43E0rg+hL2zM9yA8vxgj3oe4FgylwfCFnOyZiXYpCk1RngNDF1MIsfG5jykGzzGmMGLhB3CcRjV6fPF4CLMhc1+O5GRP7uJoSMqGMez3BYvD38sJ1ejB3BR+Y3Rzzve4RjnyZDqC8ePHHGUBtIvrgVi2hDHJdRQjI7lN5zk7EtmXiPEPn7PktXVFCSgBJaAElIASUAJKQAkoASWwRwkkPCNDCZcyf8LSFUl4SsFJzMwHGIPjWRxeVPY4o28wJU5POn3FvZiVOZQN2QwRPCBDRWI51b5RzAO9M8m3c9kLLDoMq14GvLyyNm6VHtdegUAjOeVBziedgSghTz79nASLZUB+zHm8wAtoBiWgBJTAThDI+YTciRM1qxJQAkpACSgBJVC0CNhRgbbWjCHPSZ65n6Evolin4cprQhrCGIV9YbiU8bhjKHPCH9IIxgGGZqQfXLpoWKMtLIxQigGM8DPeY8jjFmOS84Hh4hz9B5tXgSkUghEN5dNrbMY798hmFMjreiDQMYSHdmIKRKgZlIASUAJKQAkoASWgBJSAEkhTAgHEO2SkjfY3vohlVD4c2cXISoyccdFNL8JzLCzvPdrNyEP0tOKcyyYlFhzc6E5Tx9wLoxgGK0Jm4gDDdEte1J9zXPth9LqwY0fIkDklOhNeEZUOIuKIkUvVMJZuX6HWRwnsVwRyPoH2q6ZpY5SAElACSkAJKAE3gVTPqoAVrOg9BSMU48mbfQkhivuMecsKVbRQJRIFLRrMTPgKLCnQGQNaIiyHOyQH99tk89jtnMuEZIdY+kyBAMJfoLxseKtFOWMYXM4oLPlYX8TViDL2PFJy4OO26pn9/JOI8gFDGlqHOqKi4sOkY/BfM21zhC/1FEsC0xUloASUgBJQAkpACSgBJaAE9gqBEDzCOA+WNxYyXl4xRMVgdI4gBB4j+8BCBlEIwg+iYUCeoRzEZGWcRIAORMRwBKEYxJowzrfy17a5lBPnOYt99tepF2RONKp9uwu3q4cHLGzkkqQb3Ha5dIcSUAJKYPcQUMPY7uGopSgBJaAElIASUAJpRoAjEuH0JlkQII+uWF78kCB/Xfc/M+ISzm3GyJYakz7NmqDVUQJKQAkoASWgBJSAElACSmA/JzB+WHcTGYN2Lw7c4yDEcY9eY8IhMrw8Iy1Gwpg0rIDE+cecObgKyLgPD5uBktayl1KPpFEM+61xLyWLbioBJaAEdhsBNYztNpRakBJQAkpACSgBJVA4AjYWSIrrF4PnezAJNSRCCoqeWAAfeJH5nDnTfF5nGfYUw7xhHikB7zKfhOTjF3uaEZYRhId8e/DFJvxiHJ5mxRxHM/nkhd7CsP0YV4kPr62eY6ShSQkoASWgBJSAElACSkAJKIE9T6BCCcghtIJlYhIxJEbJ8GUmZCGIJ1kwiGG6MCmJYwE/50s22SAHOUsawpgSm7JtTjBHruL8yjlTipyV86BuKQEloAQOKAJqGDugvm5trBJQAkpACSiBA4cAIi9CtKQwifAkWHKbARSZKCLymNMR4lZCysSaJiWgBJSAElACSkAJKAEloASUwB4nQKMYZBVKI5RXGJ4+BkuYCW+PHW4zFkPMMzuTNYg5W/pXCSgBJaAEdoWAGsZ2hVoRP8eOF/EmXqjrtzgNatt3pFn5YHgvMyI/5onL+X1H4c3skQ9G9sAxzD8T3951O2bfzAku8UTQYx9G8zOhGJOcVz2Lc7/anWPuvza/e5973cZQdu9zrxd0PtwM3Nn3+brHm5NHDBOuupOHmlxXQrRo19bOr2JGoJ0/yX1GIna1e9fOrBf0/RVYVkoPMJVPQecXll9B5etxJZAfgQKfT/mdzGMFPL8Sj9t8Ssn5/HDm19qWnWE38k0xx7/K5ikgt82WXCJSfnI9t5VwyvMwHoN3mNkHIRDvDh/nHkMj/XhMxvAssnOM+RJDJuN+PN/gWRaN+5DNKw2ufVLef7yHESAvuPEZxO53rj9u5DXm8u17P2VCjcx5rpfZtiMtc6ub7lMCSkAJKAEloASUwIFIIJbo8Fkp0oqndnuHmdh+rCcb/bVtegWWTxmRlzHzyCa6qzl7rTt8Fc2oBIoWAY/jKWZ/70F/TonEOZpokpdxEh15zB935JpI4v60jbb3q91OOezyKHNyFKQfKrz+xLbM1kiXSkAJKIH0IaBPqPT5LrQmSkAJKAEloASUgIuAl3FDEsmbIiTa/bpUAkpACSgBJaAElIASUAJKQAnsjwQQEb5QKRZ1Bl6nDsi2hXIgIvNwGY1EzNIOTgz4/eL1YYg7BsPb8yPwWrOJ+3M7HkZ5zMdjmpSAElAC6Uwg/6Hb6VxzrVvaErAeYbFEcOOgdYBKeJBptKq0/erSomIFj1hKi2pqJZSAEtglAtsEqdxPTwhPdkQxvL7oxBaLYonBk6EAPMeicQkwHCKlxIQHWIwjj01S4St3rrpXCSgBJaAElIASUAJKQAkogaJGIOaNQPJhNAxoSjhmMBnBx5Gr/AWEDPEFAsKoRHFE3uFAw1RDGyPw+HxBYxzzYw4z5uU+hm2kbxo9xrwQyLhN4xiVyDSI+Xw+RONwQju6j3txzB/wm4gdWNWkBJSAEkhrAqpBSuuvp4hWjoYwfPTHVUS/P622ElACSiBNCFCY4ocReOOUzPTdkibfjFZDCSgBJaAElIASUAJKQAkogXQnQEMXDWL8pE7bYeseCTmhTW1eu5/b0YTHGffZdRrF7Lpd2uM8FgpHzHH3MVumLpWAElAC6URAPcbS6dso4nWhIYxjVtq1vcXM2RLyBmTW+CGSlZUlwcxMidKV2sxfsy00Vm5NjjEfRqJE4RUQDodlxvSp8u9//6KM4lKzZn2pXOVI85LlC5ejVlKTJ2UOs8mTJ8kRRx0j1U45SVi2B5P80ONg6oypclqNOlKpQnlTBEe/BODqvWDRMtnw559y+hnNUH5cli5bIewo1KpdUzIyAuYl774m8/gwImbJoqWSjbbWqVfbfVj+2vCffDn3U2nVujXOz5BlS5bLmjW/gRVG3oBR7To1pXTpMuLHvDjxxPAdL9oWhofE559+LL/+/pscc8xx0rhhXfEHg6hTjuK321i8eJmsW7cG5YMPMkcxMsjn9UmTBvWl5EGlcf2lEkVHpWat6qY+8+YvlPXr1pr6GFd48x2JNKhTSw4qUxaT+QRky6bNMn3qZNm0aaMcVLqsnHlWG8kMesxIoFg0/wpxDiPzgbsHxh7JjCmT5M/1f6IuZeSEk0+VE6seL9OnTEZA+YBkh7IlM2FRtXOHeVD/o9D+rC2b0a4/JIwRUpnFSki1aqdK+fKH4rt0PEU4EiqK3wu/L3f7wggHoEkJpAuB1Dm9uL1ixVLc5+vM/crnF+f5ioWzpUHj0+XQ8gfJksULzbOwVq26sjUUkU/xTDytVk056sijTLNCoTAMRz5ZsmSR/PXXOml19rmycMFiWb9+LZ4jOYfpHVbmEKlbrz6ehVFZvnyJrPptrQQCQYz4C0j1WvWkXJniOIbxiHHcrahH6pxjNoSG5blo4WI8H8N4PtYwzxPeb3wmbt4alo+mT5Z/N26WQ8qVk7NbnYXnV0Ds/Ri0owhzeYY7ZTvPlTg8jflIQK3kjAv6Y/qwDHN46oQhuC6evZnO/R2PO/udt5CtnS6VgBJQAkpACSgBJaAElIASUAL7MYFkpI3c2zhj+nSpALmxauXjJZN6uRT5MAuTlH3+8SyjaylWrJhUq15bjq9SWSQSlu++/0ayIOudemp14yHmhyovQkMbdXaQFX/48TvZsmVLjuPZkbgsXLRQsv7dIM1angPxLH99Ue611r1KQAkogb1DQA1je4fzAXGVvF53NEbtTPL5/HjZUqk6Xfr2uEaCGSXkaBhGNm3dLCsWL5FO114tDw4baYr0+WFAi+QsPxqNOIplXPfzjz+Rqy6/RCofc6zMXbQcL3MvXMEdReq4MW/Iw0PulVlzvjSGLy8Uy7+u/V0uOa+VdOnZS5o3b2oU1SMeGSpbN26UMe9/gDjJOZXMrISd9+aRBwfLFiiBx74/MUdzFy/8Uq657BJZ+cMvklmhgjz60BCZNuUDyUCnY0t2tsS2ZMlJJ58ib40dJ4cfcaTjto7OxKVtz5Jvvl0hJ51WU5bMmydNT28sL41+h1fMUX7qxmOPPiBTJk2A8ai4hKFENwmdlpdffk2aQTn92KMPyub/NsrYiWxPzGx/OGm8FIexKRudHptefWW0tDzvPHnzjVflrv59pXiJ0lK58gny848/yk29usn99w+VCzteaTpI9pzcllT809A58f1xMvDWG+U/GNmqnVpT/tu8Wb7/Zrk0aNREvpgzG+0O4HuDV0g0S0JZESmWmWF+B36MbDqv/YWyNWurTJ30vmSUKm06Yf+hDVUqV5VXX3tDjj/xRDP6iYbH1PblVifdpwTSicBjjzwgH7w3QQK4B5l4z9CA/+LrY6VVqzPlCdzT//27Ud5+/0Mp4c+UcWPelIfuv1tmfDZPgjA20Yj8B4zbl1/YVjp1u1ZOb9EK98ED8uF7b8NwVDpHUxvCKPbOxKm4J33C6374wWTJKFHM5Nny72YpVioo5553vtx19xA5vGIl8wzMUUDKxohHh8qW//DcSzxPaNR/Z8wrcmu/m6VU6dIwaleRn37GM2Pjv3LvQw/LJR2vMiXQoJ5qZEspOrmZ8wmf3G0GW/g0Nu82ILqmBJSAElACSkAJKAEloASUwAFNwJkfjKEP4/LJjOlyeYfzpUrVqjL7q0VmALEHA59DGFDsg45mwrgxcvftt5oB0KedVku2QOf2zTffSD3IjO9NnirDHhgsf/75Pxk3eYYZnE5jmDPY3UH8wD13yf82bJBxk6abHVlZIXnx+adR5i0ybOST0O04gy0P6C9EG68ElEBaE8hfw57WVdfKpSsBzjHGD01ITvjjnfuZWa+pW27sJXWatJBFK7/HS3mKzPh4tvzw6xppf9ElRqFKb7Fw2E5glpOGUSzDA+LlV1+QJk2ay0+rfpG5c2ebTPRUYBr8wDDZAM+qewbeAVtTBuoakL7du8vRRx0pN954q8lD7404PJ2y4GHF67Hc1ET3cjOhKc7nJKOpaWs2jE1xx/OC+egp1qBxc/nxl7Wy7s9/Zc7ib2XT5v+kd+8eOOZHWSKPPjxEVqxcIjPmzpf3Jk6SmbPnyrRpH8noN99KLX67bVN+k9Plu1/XyuoN/8jv6Mj8uu4vaQUjF41MbE/IeHWgPWgT8zdqfIZ8v2qNrNnwr6z/e6Os/vNvaXF2G/kYHak+110rna7pJguXfytjJ3wgC7/+QfrcdJP07NlbZuI7KSh5cL1PZ30sXS6/TM5u20G++2WdTJgyTaZ/8pl8Ce+5cuXKyq/rnXr+tGa9DH1wuBTPDMhv69bJuv/9LX9s+FtGPf00OmBeadq8heH242/rZP7Slah/SHrBSBf3MoY1YmDzO0ppX0H10+NKYF8S8OK3Gw3HpFGzpvLjmj9l1dr18usff8rvuCdan90Kv2sPjP8ec8/SJs7f+ZBHhskfuK/vGTQo6YHVp3s3KVehktzS/y4jrERxXr1mLXHv/5njM/b9yY4BGo8yxqpv0LgR7v318uOqP+SPvzfJiy+PkRWLFslZLZri3vu3QDSIIC/ZEKz4HOTzZOrUmdL92m7Stet1Mm/JShmHgQJLv/5Wru/bT3p36wYP4Gnm+W28U3N7NdAKlpsljPvixSTsgycxPmieiXfvwRxj/ESxzY8ZOBB3nvHOOgyH5mXEYwH+0aQElIASUAJKQAkoASWgBJSAEigiBDC3MgYDUtTxIprGtsR1CogpH+yjjBmD7PP66NelIfRhP/78syyY/6UZUMnwhgEMZvx87jzp3rWbXHDxxbJy1Vp5b9KHMnXmpzJv6TIpf+ghGDwO+Q5yKArLVTyz1/agrBBkNWSX4cMekvvvu1teGD1WLr+is5mLbFt9dU0JKAElkH4E3E/V9Kud1qhIEDA/oryUmWjBzv7I+KJmCMW1f6yXNu0uMMYohvOihwNDATZq3MxwsRN+5gaJk31u3LRFpk58X67r1VcaNWkmo19+3iiVGe+YHhaHHXqoDBn2mIx66jFZumSBjB0zWj6f87mMGPW8CfllPcEc7w2ENkO9/OgUpKYwPNQ4GscPb7TUsGPMG0CIP2dyHOh7UQZTHN4SVHBTkXz0McdIq3PbyxdfzjH14vFpH05C28+XivDYYFjAo449RlqcdY68P3YMD+ebogiBxsS6MGwZOfHDa5nwkwnjXhTH6N3BkGo2P0MR8pyAH8puhDW8Z+BtCA1ZX+4YPBSRDgPGOw72Kend9xZp0ry53DvwZnNufn988Lcfcs/tUrdhQ7n/wWESCKKThjrwGhUqHCHPvDTa1ItoabTkyCV6/TH8I/Px90BPPyb+LvjdsW2VKh0lLc8+T7795mu0je11+FvjpW1ffnXTY0pgXxPAT9lJWAnAcM1nF3/f7vuS6/S6xC2M50FYKlaqKEMfeUKeHjlcFi2aL2PefEW+/GqOPPn86+ae8ZhnkfMM8KFM94cXw61n7qsIvbZ4LdxnfL7GYUFq0aq1vI0RgZs3bpLHH34gUbm8F3i6JOvL58l9d98mtes3kFsG3ovrsi3OvXt93/7SFELZfXfdbu5XjjTMth6teRTPM83jjO8XrieeXc7Wzr9b7Hm6VAJKQAkoASWgBJSAElACSkAJFDUCdrqJ/OpN+Y76kf8Q1eOj6VOkW58bpWHDJvLy88+Y0yiHUf67s/+NUqtOHbl3yENSCtGMGH2HOp/DyleQUc++bKYLibKgAhKvRx3OfXcPkOdHjpBnnn9DWp/bzpxFnY8mJaAElEA6E9CnVDp/O2lat1gkZBSefoxWYWRBLwwVtH5FEr8mTttJXW8gnilxbASLZUKjCUVsEAYP7PcV8G71w7WbSmCOUnnhqScRC7mKnFq9JsqjkQnX5LUwJIVGHhpxUo1VRtELhe8br78qpcseIueec5Zs+Wed9L7hBnlkFOoQyxLGThZ4OLRpe760OecCueG6rvK/9Wukf7/bMO/VyfB+CEvEgwbhgyvCAwOhzeCBFkLYRqvHtl8Pr2+8lXBNho2MxjiiB8U7+mCskQ88GgDBj/CAVH6jQGOUoiLZj/UN69dL6eLF0YGBEQiGo6XLlkhneFfEYJxiHhreqoHBc0+MMGXn94cdHSqQo/CaMJfi5XAC12FvMol84wjJxlE9PjYI2ueQZJg8jBkNRxLikeVLF8vwUc+a86mQdwxpDPPmkQsuvUxu7n6N/Pv331LqoIOMQp+Fp8459s+mkCxasEgefWIUyqGBDkp41hGs7JLnAZ9JnIsugN9AxJcB8viHuiYT2mWMkAGPbEUZq375RcoefLD5jjxooGN4RLmoP5XyZttxW0QRiXIKiMGdvJauKIE9QIAGKHei3Yj3ayzOOfhw/+AepSGLNwTvJXqMmd8yny0Iser1xmFQypL257eRyRe0kR5du8q///wtN/e7UU6qepTxjorTk5b3E86PhnM+sfwIPxvCje8P4tmDmz+GC+IRg9uDzzbUA8+gw8qVl6ZntJJZmCNMhg5xV9fct+4dXnho8nnCOm5BeNSVSxbKQ088a7Lw/rPPL0R8lEs7XSa9ru8jf/2bJWVL4zmPezg18S6l/MX7nlFyfYkXBh71aBIM48b9C88nZPJkwkgegKconHL9aDKLi9KIjnvcF8MxvDPQQnvni5cFe7eYS/rgZcbEozCpy+wla+WrFb9JCb6s8kvgpEkJKAEloASUgBJQAgcSgbIV68ioNxegu5ht5F6K/0zomTpL9LGWfveHkRE5ANTrocc++loQ3D0U3hN6ApN5F/4cckwdc1bUG5SDj62LcrPln1VL0TN0EuX1/NK23mB+ufSYEtgzBCDq5UhGh4Z7yOhhcCT/X2+OU3PdiCcGIroPQkRDtKHXJKN4Kcz1fKZs/Gu1DESkpC1hrxTzRSV78xZZuZQ6mqeNfoWRd5gYBYTJDm4065SpcNiRrSBOQd6yOh8/BLBI1Cu9EL3k06nj5aU3x0mTRo1RJoQ3CmeUvxLPC1Ow/lECSkAJpBmBQnZR0qw1Wp29QsAfDJpOL5W0Z3UYZAwovHDMv9Vc3w8DUgyGFsYTbnXR4OS8XDFftjn+yficilaz0/XHKIoxIeiwkS/Kzb06S+tmDaR0mZJSo1otadfhErn8ms5Jg0qqUYzF0GjD9O6YV+EW3tG8tNuf/39y6839ZPRLz8vV3Tqb4/xDr7AhjzwmNSpXkuOPO15uuuV2uIHbLnYyW74rHFVDoxHT3C/mSoWDgk7+ZAfAERicnTQ4xRCH+S/MAzYe3k9++e7b7xFebJJ0gKGJKZSNcItgm5kBg6IrlS9XziieXbtyXeXon7mffSZHlcH59AbD99UYI4TGwTWenZjURKPe7Nmfbssf8EkzeHb0uKGfyXpw2bLJUzgnkDFsoZxDDjkM+72yeN5X0vys1lDaR3EpGqeS2c3K4gXzIQxBiClbxvndsHOUT6Jinh5j7GyxgxbEtezcZ2tWr5aXnn/KnP0x5o+bNv1DefwxpzNnOmjsZWpSAkWIQARhFGlYmoN7tmKZUqg57g88Oxs3amhiuadKEsFA0Hhz8jl3930PSs2TTpJjq5wgfW7sj+eJsXAlBRrzPCrNfdvS+RdcLM++9qbZEY7m/qzj/Vfu8ENl5qzft52Yzxo91GgEWzT/K+TyStlDyplnK0NEOs8MJ8xiubLl8QCMycL5c+WsM5snjuV8HgCFMZK3vvAO8xxgCFWmAAQuaGJM+RxAcBaO89nlgUGfXmtBhLKd9s6dJu/O/KFxjsb4BSvXyNsfLJOgHT2QRyF4OuVxRHcrASWgBJSAElACSmD/JFD+qPry6oSFGHgUNrKgHwO1mBjCnskDDX8Yg2fjUaiXaDXbwyIZI4swsRe5vXRrDukfJZC2BKiS4e8WKi/p16+fjBj+WP515UBBZoaFuf75BeRFrhh0IhHkf+eNV6Tdhf9nBiqef9HlcueAO+WNl56VLtd2kfkLvkDGsJTAAGd6oXHQMpP1SOPgxgiie/ggi2VAn0QxjHITDWbU+1idG1bky08/gS7QK6PfekcaNWoK2dZRhFFW06QElIASSHcCahhL928oDeu31dha8LLDkJFQfCu8DhxFIQbomxSOwoMAL8y4N9u4aPuSHecd67bSIOLz+aUVFKfLvvkJRptPZDm8EKZPm4GOww3y5fx58hi8mKLhkAm9lxuibxBeb+nipcj3Cryi4OEEA9iZcOd+B+ESu3TvYhTHNIrRU+PhBwZJmUPLY46dX+QrnFPjtJNyKzLPfclOAXLUgSv6O+++Z/JiMI5Js6bOkus6dTTrDIvIa/7w3ffSr/cNECDCCJN4lHTtfr0MHDREwtlkhtuSimb0IzzwWHM6J/QWgfdFZsG3LMuv17CRvDHmbYRMc/LDP80ooRlGMjXRkNawQWN57e1xJj89AukFsnjZUpM1FIKQg8qwE2SXPBAJZ6F3BI86aLJ5jJ4n9OpITUF0pOjSwQ4aPeq8dmhUasbENnny+6dCnbWnBxtd8yMon5PBfjpzlmRlZcmixYukadMz5aL/64i88CRDO4J0S9GkBIoQARsmtEnThvLymAmIBO8IEDQw817AzzpHCuG5x4SnpIx49FEpX+Ew+XnVD7IMseCrnVYjx/3F59Hb74zLcX4wWAz3CwfvYUZD3L+pyT7Ptm7Jlgxfwsifmim/bXh7hvAMYTnJZ4YRtuKyNcTBE3iOJdqYWzGMT8+5wjCbGHLS88tvnpN4ilP7AiEv07xXAn4+k9gOhluNSJiDMbDN100qM/d1PAkPUmveogcfn4HFY39IRnQdFDrbBgK4z7Pr9KrTpASUgBJQAkpACSiBA4lAGP0t9uswq7Nx/vAYJT27Zk6/iJ5kPgySQkfUKMXDshVe/FslBE/7ODpnCNC2e3Al+nEe9PsOOfJUREBhfxMdQGei2TyvEWMeTUpgHxGgnsKdTAh7GI2497QTjncfyn2diiFm9kLeSejWnIyO3IgDOc7jVBYrliyTrzGf+/AnXzLyUjAjIGeefY68986b0qXr1WZAM+/XeJwKHCsZOcVwsGU0kg2ZEXISjjFaiXEZwxYHMdPg5cNgTRrOULjUrFfbyGu33txH3p08U4498nBTEPVtTkSfnPXLUVndUAJKQAnsYwLba8X2cYX08ulPwIfRILBVwJOHCkgYJMxbmutO3TmqhLGI/TCuRDEKJTFgZKcaxpct59rBAsaP082nc69+MviO/vL8c0/Kg/ASKoYXreONYDsEziU4J9VLzz2FsIs+ufO2GzDKxQkPtv7Pv+Tb5SvkRxilKp9QVcKhsMybM0dee/YZGTv5I3nluaelT9dO8ikmIU3pW+Rbd3dHh15eHHXDZPvn7ITYRAUsU5269eTdiVPRofDAQywMgxRH5MAghc5HEMrqANq+GXOk2RE7PGfNWoT5KlaCq/kmGpEYiq00Qgza+cJ4ArlwDq/UxBF3OfJ7EQITwk/NOg3EizjT69asNdusC+ft8kFZzu9l/R9rxYvQlrVghOP3RQMZFdRculON2nVQTlBW//YrzqXB1H10+3UKXUxG6Y0lRyaFUXfur3LCifLyW++a46vXb5DWTerI0EED5C7ExabRMaxhzgwb/VN0CNBgzBF5TCVLlBA+LsL0IuNQQiR65roTnzdUAHwy8yN56dmn5d0PZ2H5lPS45kqZ+eUiyUyUxXMyETK2ZOnS7tOT68bzM5eHM+8zPBJkze+/SKWjj0nmz2/FhMzBdWvWqYdzPbJu7R8JIQgGPNy79KqllxefJd4Mn9TGs8U8C3EsNZwi9Sy0nYfxPORDAE8MPLeCeAZw9COEMbTfGMojWQlGDAmJcD3IB3s6xLWdSxTuzDMTzy3zPrMvsjyKcT+T88iiu5WAElACSkAJKAElsF8RYP+Q/U/rye8ou6mrd/qpXLI/RQOUCbHmdG33KIMNvy035bMGavbao6i18EISyCnNOYXxN8vYHQNuvV0+m74DF6BoCJ0L5aGCEm5DGf3y8xIsGZR77uiHgZdOKPk/N/wtXy9bIb/89JPUqFFf/MUzZO2a1dsVZ+51ClaQxJxIPtsMZz7ok3j/80N5lfPSZ2ZmyutvTJaO558rF55zukyePE0OqVBBdmGI5XZ10R1KQAkogT1NYHst+Z6+opZf5AnEPBgZgpdtCB3kaZMewsAVR4Fr+7+nX3CrMYaFEU985sQHnbjiaPW2wS35v8ytoYkeUjbx5RyDAenkUzD/11bMcYYDxpiEPKmK1ex4QCaO/0AuQdjFBk2bQtnpeGlh5ip5dOg98tarL8kdg4ZKCJrWnt2ukauv6yLNmjWS006rJk3rniqD771LBt47FOVTOUuvBI/jSQWFKZWysAVtl6j4pYKVIcWsGzrDqTOZOYVQBsuxyY/8HkzAFs2G0td4dYFJQqHNRfXqNWTGR9Pk/6661niQUHG+4MsvpH6DhraIPJf09uIcXTSEMdn6kBMFFdseqwSmISvAcJCJ/OzkcOJVjhxq2ri5vApe1/TsKyVhbOMxznnEprz28rNSq3ZdKQFlvk2pRjHuz8AX36xxI3n1lRfl2l43wfDn1IneaxkwmG3NDkkxLJloyKNQxWTqinVeKyNhJPCbDhrnIPNLeYTXvKnfzXLvPXdL1xtul8MOKY22OkIZT+K6VWJTmDMGCHqumNL1jxLYNwS2DynB+xLGGdyz/K3TKMZklApmLXEcv32GpfAiZODWLVvk5t5dpVOX63CP1pdTT6wsjRvUlfvvu1fuHTzEhDH0J+4Z3gfuxHvCeH7i3qINiAKNM8AAz0nkpeF6+dIVMmf2p3LT7QPdp+a5zucT2xDEc6R+/Uby+ovPSM+ePZL5eb0I6v3ySy9J80bNpVSpYnimYoCF6xlvMxfnCuo1+f3HzS4+L8glOzsmrS8aBIXLf3yoyszxj2A/72fnORdCOzbijBI5m2vKcP/x2rnFnMeMiZXP+p1ySh25wn8kvgAn5K/7HPc6R01qUgJKQAkoASWgBJTAgURg2Khn5MZe3dFkpwNl5m3FVgwyFpMNRQ1Jzgw08vgypXrVwyRIbxR25ArpMWbm4UZfmNM1MEUSUQ/Ya2PXryAFfKLbx1M1KYG9ToB3QG6Jw6cffnCoPIhPfgmBEaF+46/YK80ue367rB6OWnalbIQuGv/uOLmobXtp2KwZooSEjAEr7smU4Q8OkdGvvyp33HO/NKjXUEa/+Jxc36uHS1eHewr6HkdHkwGxC4PRcQ+bQZvQJTkRkDiI09H+ZQSRx7NRypQuIW+Pe18uatNCzmt7jrw7aaZUOaYSBswzukcBApqr7rqqBJSAEtjbBNQwtreJ6/UKJGAMSch1Q/fOcvEVnaQh5seiEWf16t/k+VFPyDGVq5gXM5W6PryQreHHFjzlgwnyv3//J/3uGCQVK1bEbqejkA0Xrh++/caEUxxw9xAZcGMfo4weOPgBY/AqXrKEDHl4mHTrcpVciLnJTji1GjoCzui4CAw2NCzRGBdPmYOGHQUfP+ys56Lo5XEPvd9YYSRuU4nMzkRuHm/Mdnmnq6X/Db3lq7mzpUGDejJr6lSZO/czeWv8VFNGfn8on0TouYXrsL7bFOzOWdzP9rAefniscelunx/hG9mBYbr/oeHSqmVjuapDOxn59DNSpswh8vff/0PdrpfVv/8m4z/81Ck0n79U5t97/yPS9pwW8n/tz5Zhj4+So489xtTtk49myOC7+sukmV+YbRbD8JL8kE0Ic7fRUMY6M4UQapLK+xC8/bwBaa0AAEAASURBVKiEv+yarjLy8SfkERg8H3roEZOH8wXRG3D9unXwJMG5UMhnw/hWuuRB8OYrafLoHyWQLgT4XKChlwYzo1tIsbukHo/h3h5wyw1GSBk05AHzXClzyCEy+KER0rPz5XJhu/PltFq14GEFD0rcBxv+XJ+jqbzfy2MEXyQURXhShMbIysachxskjvCHSxd9JV/MWyKvPTdKTqteW2646Tacm1PQylFYYsM+z/hMu3/YSGnfuol0vLCNjHj6BTm03GHyD8q/oW9f+e33X+XJZ19N3s+5lZW6j3JeFPc8I7IaAazg6qQWke82+VLQq1utvNTGpyDFSl6Cbb4X0YNKQAkoASWgBJSAEijCBG4b0FWuu7Ru0jPLGppst4wKflqouE0xkp7+ENWQYBhjZ66QHaj//bqEhUmZI2vK378tMuv8U8hik+XoihJIdwLQShkjMPUkBaX33xsn/27eKP3vHGz0YT4fIm1A0AxH/LL6119lzJuvyIC775MHILede0ZD+b92beT+Rx+TypUrG9ny4+lTZMhdA2TKJ5iHDCkbkTz+Wr+Oc1yYberIghhIXergskaepIwWhdHs4IMPMgaxi9qdK5e2O0veeWe8HHncsTjHPjHM6fpHCSgBJZBWBNQwllZfR9GoTDCc+Nl4nK5w1Ou8IK0+NxCmtxH6wDgeRBb4GJltOwdZQa9F61Hx88+/yiVt24gH80YxfOPWf/+R6jXryUtvvIAwjTAsYfRKNuIa+/Fidqe3X39O6tapLRUw9w6NMhyhwrCMGajJ1Vd3lieHPyijRjwio195XiZMmSmlSpQ0nl6wB/0/e1cBaEXRhc/NFzQKSEuDNCgtImICKmWCCIoFBoKK9dsFKhZYgIRId5cgBiIlgoCFdEu/vPV/3+zOffddXqEiD5iB+7ZmZ2e/3T0zp+W6G9vL1RMmyD133iZfrVynQnM54EHgRH4rFpXvKmoGTsFqAnJeuWER57St2CL74wghSXEqwiJCIB2gVxlyczns9gJQ/Chhb+QJYClu63ynbNi0STp0uFbi3TGSkpQsj/V7Si6/tFm6mhltRPdXK5XCdRGCTF/f74NXVdT9MVY8lU90k69UtbJMmjJHHnv4fqlRpbzkRSjH48cSpEq1i2TS1BlSs3oVJdAPt53BCh3iqlavLjPnfi29H+4hDWrXFG9+hGs8nqTCvN10+23hEI/0yHMi/CZTqdGDTSvFlHcZcGGiZ+0Fxj7mQ6i4+x58SF574Rnp0/tRKVa8hAqdufybJVKzUmn0Bg/LEwfuLFVaXXWNfDFphuGgMnhGZtfpQ4B0gQrgEN53klSLWqb1J/r4V0uXyuejRsg0hGKNj01T47S7oY3ManOdPHh/d1n83SrQSCRChjL9ogr8DtJKDL7rnUftPGW45vJvv5FqpYuIE3Q2H7w/a1SvKz0feUx6PdgbNJxUPYrgpTWl1iLpGcPBXlS5vEwAE/ToIw9J7WoVJR70NTEhSSqUu1AmT58jterUVOeRlmakuPdp12P7Oj6/FaYVmQ4l2Z8o7qBXhWS1D2NhjUNev32iG7SaYxLqifJuxnF9Cw4ykvb4ZTfAMJDEWOOuq6a1n34tyiAz/UGzZRAwCBgEDAIGAYOAQeAsRADceIbGQ2nTNvLK4OMQuQQxVhC9QM+3MJekMB1hsf9xAe/ndukZm9Wa154H4orZNG/NF7OpZA4bBE4TAtm9v9l0y5bL6VrjP/9EmiDvV6kyxcHn4Mv0wTAbS6YY6QQD8LfeekOWzJ8vl7e6UmbOWiC9e/eSJnVrSjwUW8x5XwAG47fe0TUsp1qx7Fu5qGxx8FT44mFQCKtyadGylYyZOA3yGRhig5d1o23mIitapJBMRyjFjm1bSad2raEomy8lSpXRXTNLg4BBwCCQ6xBwQKiffnaRwy4yti0tBQpdWEcObfkxy7N4CQqeCpatLYe3WtY+WZ6Q7UF2OSSDx6yUEVNWiQuuvuxPl44XS89bG2R79rleQU8LdWjDfVbIYWn7yAcKmpkDeylLkSAGtxsfGQwNhUNmfmCFxbq+2wcyddhDFoRhxZhu0dp9zXUvqhUqxhZPexHqKEvUqGs59YWt6if+xUDLgpSeKmjVn7//JgcO/CX1kZfLg4EYEcfCJdobigdCiAXmh8CXgzMLPbm0so3vYgihyhxMYoNCxVkqLNloQeOAdxU9B1IT0WO8ryosIupToaY+E5i9qfB+9gRcNcA2YCHDQg8MdZ59f357fuNB6C4Kvj2xVr0QlFFsz4WYjPQw04oe1UjEHyqmDh06Bi+O1dIAXnNet3U/YDkiamW0inbRvhtJVzPCh3dOpRKTxNH5jSEuou+P90QvEHptMQxhAGZ/h44myNrVP0ideo2lQIE86AUFygzHpp9sRn0hPqiphM9Y4rrHEJ5y1crlUrZMabnwwtKYfCG3GurQa4bFHUhRCkhHbLyliLTxlRRbuYhrsn+8B2IXwCyPXmKxMXa8a/SM24wJR6UgnzE3oVtFX/GLmjhm3Guz1yBwahCwyVK4cZWzAe+mH6FFFf3AOxtZoo/7oDjme48vVFVjCEaGl2Fh+Fm3N1a1A5KjvgMdmlRVwB9XFP3VggR8VqqQPnJsD0HJT+ODE77vqO8nhO+P9MNNeoJ7YH1uO9yxcvjwUVn3048qX2H+eISCxHfus71q6e3L0KnRhg2+KEMHnTYwiJWrO7yIULpJSjE2b+qr6j5DKjo/uo4QuiweKMYCMDyIgWKsTbcPxZc/KDPf60n+Tdr1/hCJ4S06M27Qvap+uwc+UHRiybCeirJ6KdfJqmg5T1Z1zDGDgEHAIGAQMAgYBAwCZxEClOMcghyH8ylOVSFEUncHblAtMatTjBa3yaMF3chJjSreULKFAnJYW8Wav344ZrmMmrJG8Yicd3bpcIncd4vtkYYq4ZQEYb7bmqgWLVdX9v25xpprqjkz20PepSjDJ/ti4QX5VlMMArkVAc2PZdY/hlJEVnhIQB3S6KYPZeYwi48J14/mzxBqlHImvvfkzWgYyVxh4ASV3CsFxzxIbM1IJEouBH4yGYbey5YtkwqVKknZ0iWVLMhty4QCQZwH2ZIuFP+ATQyHpCcfqaM4WZFEPIrvowyI/B+9yUwxCBgEThMC+D5v6PaRLBv7ACXboAL2t8xhPO2zPk2dy/llNRWxxVby4ZgfZOTkVZD14iYwH+jaqY7cd3NDJdPJeatWTSPiOVnEzoL6Wi7K94cFma7U0sGZLgrs8i3BaxCCRggRMZRB+aj0YzjKEVC/kqq6uPTM1dpEDq5kNVmFC4KaogYgxGWBusquoV9lezN6Ea5vTREqV64g/FkFuWr8VPpg+q26zVB56RtwQAviUQO3dV1OlcMfPD4YR4yloOJZvBM3snipgvboPWCdyz3WeZxSqPNxjJ5r/qj7pZyXuhsq0iJDI7psmNRU3YOJjAUzgEAf8I/KGs4oLNRVD9L98UNAnQ/WOk0vvVTt50SIJRR1w6od7NeCcsaY5h2GgJP9iHlauECPrPqAG1H1ApzJcJ89YaHizYoDzXxpPA1/0OdCBfPCMqgld6BgkoW/tAjUF1E4qWPp/7jYQbSv6uJS+WId0qJZI6sSjnkxUWPRkymfA88DSkg+Cyq29H5oFtU12VWFGdriknBQgK/7zONpwnarl6yjboUHTTEInEYE1PcXcX31XfATAf1gUe92Fsc9oKuRxfr+rXOdYGqsbXzF6rsIfy1pp0RdICwosOmTpSCj0psKL3zn1o7w+fo7B0lTimsqnvlPfX/4Dqmysz6zoKIZzZs3U+fysmSKaEnMQtritvNDqB32H4/6UNP26Mu7QWhjAkegcANdQ9+cAFLdNekFC73DVMFeMG8wjIRNh1NiMV7F4OLJbBdjHMPEKgYRxymwccJVOIjOhFE1syIbR7MwCBgEDAIGAYOAQcAgYCHA2Rvndy4lBwAfi7kYizWr4xrmY9il5mb4w/2YcWGHVoixTlqhUaoypFLzTdSEYMJqERM21Yium7bBqZwyfsSSc0GrYIlzo9hj+1jkIq2dyL1m3SCQGxDI/u0Ev4KOsl6EfsregwUZrYjioNxKfSPgy8B7wS4ZH43FM5Hlc0KWZimr0CIbBQMZi7j1l192mWolhIhL5LS0fMXlhME0t2F5yVz0KYg4xK/cQcUZDDTJ16U39mYuMnCNvA6tNalFM8UgYBAwCORSBIwIKJc+mDO5W4smv049inLXDs9Z/8UbSkhMlN733SUpDEtoT6Yjmx81aoI1wEfu/AfrVIZRgJqYkCi9etyprhvZHCf1HpdHPvpkBHQ3sI45FTetL4hJxx23d9BbaqmZCi8YjPc/HS55YhE68D8sxxIS5MEeXcUHoTP7QqugyDJ85HjsYw42TszwXtgKuMg6Zt0gYBDIhQiA3nTtEkVvQNz5nceAwRn40VAVfvFU9jwABT+Vb/wtnNgfCjFaOook4Ud6EsUHZtgVGlHAcRhtWB6wHCB0KMcMTzA7DQIGAYOAQcAgYBAwCBgEDAIGAYOAQUAhQGNlRu1hxA8dMcmKMASGLKLoY1SU8Ry1jDhuVg0CBgGDQG5DwCjGctsT+S/6Y3t8OW3PJztCHxRZlsW9H9YkHMCs/bQpgXQUJQB9BwJ1qfWs/qTaiiSafTF0njPsYZZeYZJVG1kdi4uJkw633kKHJ3gVWcqYrOr/02MBXEjlvsK9t7v5JghU07fIkIdU0HH5X5Sbbr093WWC6J9SznnwzLT3RLoap3YjFqHbOt12WxiXNAu+U3td07pBwCBw6hHogDj06QrGBoaiZWhS0uKTL9Z4ktPzlGECmCpkeoS3mDWG0CqRJcDxhVoyVSz666FRIiyHQ3BzdiKMrSP1mLCXyRjKQggXksJkkihU1DN3IZIsYgt1tYZNN6dqmT8GAYOAQcAgYBAwCBgEDAIGAYOAQeDcRUCzSecuAubODQIGgbMZgf9Gkn82I2ju7QQE6F1FS/40oeW/O5R6vG658prrw2G4qLRKV6KT+KQ7+Pc26AXmjXXLVdfegBw96T8bHvNDcEuZ7Ql9+XuXy/wsXKN163bpjqd5qNnS4ig40lU+BRsUXF+r+mQJrV12/jZ9KR0Kkcv/uGu6C2ZpEDAI/B0E8Elff0N6jzHlroW2/MjjxbyA+vv+O83n9ByGqA0g56EKywMyR4MLF+KIQFWWbRORivqSRQpCweaVXQeOqvPSaGe2zZgKBgGDgEHAIGAQMAgYBAwCBgGDgEHAIGAQMAgYBAwCZxEC6SX8Z9GNmVvJCgFLgaFTZV2QF9tQLn09pDcX4oQXlhtCR65/PbQncoZByeWzBZEIa5VtcaUoBYgzHHXcul625+WwAgWxVg4pSxEUdhjI4fn/pJoHXgb0LkhfgJd9i/+FoNV5guIvfX+icxil7+u/v5V2z1Y/VALXf/8ypkWDgEHgNCDgiKI3Kl8E+kEa/F8oxfQ1SGfC6xyyoBzLqKR4EdMenmEuh1cCrlRZMryPGt/yIPTjhDfuQCJqxsC3foyNv+TTXlb4Wx+SwyMOvo6/n1HbZp9BwCBgEDAIGAQMAgYBg4BBwCBgEDAIGAQMAgYBg8DZgcC/q7E4OzA59+4CiiZdqGTy2koxen1xWynFEKaPJS1slT7DLA0CBgGDgEHAIHBmIBCZ+1mFoNWWFfBENsUgYBAwCBgEDAIGAYOAQcAgYBAwCBgEDAIGAYOAQeDcQMBIgs6N55z+Lu0cWU53irXfGaOcoLzY0ioyygoTsc2oeA4oxagWo6JMH7dOzORvAO2x8CRVcnSWrmyWBgGDgEHAIGAQyBABl+3qrEeVkNNacwhzhSHEYygGIW1dyJEZEkfIcg2jF60ejgJcx48OZ9rbFSnJUNwI0UjjD66bYhAwCBgEDAIGAYOAQcAgYBAwCBgEDALgkAwIBgGDgEHgrEXAKMbO2keb8xs7cIw5W4LSuu+nWAZk7rsPqJNTQkFp9+gQCQWCMuu9e8SLKFMulxkUc46sqWkQMAgYBAwCpxOBht0GYfy6XynGWj80VHxOH5RfLpnxZg/lAX1N3w8RNjgkqz/qKVHpI09nt821DQIGAYOAQcAgYBAwCBgEDAIGAYOAQcAgYBAwCBgETiECZ6hijObd/EFJQ4twrqt0I1ppo23JM0FOnZPJsXNgd8B+6g6xPLuUAxnM5F3IH+YCpgFY3tOS3otELI5gCvYFFdoBYOPz2+5mWeCU6k2CRT7aQ14ytuMIMi8XLPntx6Mt/rNoIt0ht9cpSUk+YX4vx9/Ia+O3c5JFnst97JsLbeq8NfqiTij/mF+NxQWskpP94nG71b1zGV2f9dg2sbFyn3FPWgmiMV4nECCCou4DOkdcI6gEslRKOnE+hbVUTKam+iQ2NlatU2AbE4Nr+6y6qk7O/PbSOmDWDAKnEgGbnupUVH531vTXjfddlTAdtghDWm68rM8/lbdyKtombaCHkt8fyJDe8DhpA+lERvSDfdK0S9EyUGPSBdIMDxJApqT41TppLmmKqo915tliHb3NY6Q1vE4kvdFt6Xo4FTQujT65kHcrgL6T9ng8IimgRVmVlNRURb/0PcUgRGHAb103I3p7Ylu6favvwRCuTQBD8OgCXQ5Yu9W4pPps0+qQwxpn+FopHCw3MKyn0hlMeT+LHMG4Bt/oEHKQ2UYebox7xID3bYpBwCBgEDAIGAQMAgYBg4BBwCBgEDiTENB8tObHk8CzsVDuxhTKMeCXKKuy+FKHBMF7OikHAw8UJJ+HVCpB8I4O8FzcFwIf5fZ6xQ++jjymw+azrFZz3199/7mvZznrkYpyoqpafDB5ccXGY4nHIQiGkmUJ2Iysy+aLGSGFJeC2ZLde/z8T+3vRIfLjRyB0t8UL1gVstj0s/rGvz+6QfQ9Clsx3Ssh/m2IQyMUI/LMvJBffmOlazhFg2ETSq6kD71FLF0In+n1Q8uDtmPVBz3D8ROp1nBxZsylaQOsERVRKoWzqZ3fYnxpUyiTWS/VZCrLszok8TmWWUkrhBiiYZfGko+iRtXG78JCjcoyCbN6z1wNlGIg8hdYZKcV4NtvX9aLr8JrEVyvH2L4SxGInhc78Z+2jgswFoTInKVBG4ji3qRRjsSYqWEdbphgEDAJnDgKkH5r2ZNRrHtOUlbSE26QjWkmvaYoyNAABUBNMNETa4IL2ngr1EBVIoBeWAiwgbtIte7LK4yysy4lqJL3hMcXwUKGE+tH0iedp2uPzZa89olJf9Z30DfdBpRgL+67vQ+04iT8O0OMAIiVSl5W9aUb6htl3nku6qfqQ/rDZMggYBAwCBgGDgEHAIGAQMAgYBAwCZxUC5O9YIHqC4bulsVC8GGRaVIyxKEUZhIFUirHQiJLFCZ7Rl5KieEsHjkMqFeYh2a7mLcl7amNMvZ+8l17n8TTek0oVi+PlOeRLlTGjuqL1R/G+5EfBn+q6rEP+mIadXoSwop1+5HmUq+GC6l5YL4B7oEH9mVwoqrys/bO4US/uFcpLR9Yc8OKpL5/y2/XhmbXo8DxM9CF/cNopeWC4yuK0Ux2AW1fbXhjGLpn0HN4a6x203j51yPwxCORKBM5SxZj1AWaK+DmuWICqxYbGMiUJuOBZgBEL+ic1cMVlClzODniS4XcGqxJ6n2GMEjiOWcWhr5vN8znhMrp+UGJjPGFB6wnVMtmhBc08zPXoQmFpdEmFYpAKNRaus05G9fR5FABnVo/XVF4aaIP1ODvhQM8BnQpIFu3ZoScZWvBtCa1P7J++rlkaBHILAjn1CNXkQCt4XfYEPNVlTcRP1qM0t9z/3+1HNE1SyiRO/kFDU6H4csL0T9MeTT8irxXJOGh6QfpC2qLpiGYseDwUIFNi0VR9rt5vMTKWQj5T+kRLiiwK70f3N3I9i1OiDml6b+2m8QCLyweLRbiD0QE5Jg6echiwyKjF2CaCqW6LtruDMITAq+TEi+ZEfZ941bhGSzu/xGJcOnEMsK5k/hoEDAIGAYOAQcAgYBAwCBgEDAIGgTMTgVSPpbCI9TPnMngyyPmCCLnx5YIlUrR4KalxUVXwQuDlIPvzQDG1evVa2bd3lwQcQXiWuaVatRpywQXFEWwDnmbKuDwkP61ZK9tRh0UpobAERyZFzisqFzeoj2tgG9aLvhSfzJ41XY4cPSIub5xccnETqVCuBK7mkPXrN8iuXduRC9ryVmPbPhi8n1e4gDRq2FTmL1ygFFou8Kiaf1UXxJ9UeK2VK1ee2jLZsWOH1LukoVxQ9Dxl0EkF2+HDh+X7Zd/KVW1uUOKF6PN1O7l7aclBwKWrbpIPD4GHhcue2uZzzKrYrL1ozzm3bUrqCSussjo7+2NJ6FeI74zT4qOddkQwCVqeYE6HrSjTOb8hGKLY1eeAUa7LIR7DfmcPsqlxWhE4SxVjpxXTM+7iIQyWsK1APrHBqu+zBj6gBJt0Mmjz0CAIF4My/b0HodTBeAQvqswKyThJeqtO/ThqYkB1y6JJrynybpH6zM7Mev/cuTPlyKGDEh8XK5Wr1ZQqVapkfULU0S8XzpESpcpIxYpV4Y0Fl3DcA9V2i76cLRdVr4vBv1i6MzZsXC+H/vpLmja7TO3/ed1ataxTt75aRns9cCJApdimTT8ry5q6dS9O196xY8fkx5XLpMllrdQEYP2Pa+T40UMQ7PokJjaP1KldV/IXLKA8PHzYx8GcgmwKq5cv+0a2bdsqFSpWlpp16kEx6FWThnQXMBsGAYNArkWAenf+1qxZrb7tmjVqp+vrvr37ZMPPa+SKK69TinsvItweOZQiixcvkmNHjkievLFSqTLoXtUqyuOKFn6/b9woW7b/aXuphqQ46FuF8qBv8dbklEqxjRt/BiORIvUvbiDJKanyx2+bZPeObVKvfuN09ObgwYNgeH6Qy1pcrYy8Vq5Yno4+NWrYSLzwAqMHGgut8LIqyksYgwWVYqRf20G/ylWqLLVrgX6B/tIj7WQKc1zSijHG7ZWrOj0tifbpC2AZ58f9xbhjT6Y5U9cgYBAwCBgEDAIGAYOAQcAgYBAwCJy1CNATzAmezY/IHd9/vVRuuvE6yMKqyDcr1igejUaSx48fl3fefE3mQpnljvMgOpJDjh5LkIrlK8qI4eOkcvXqMEgX+eCdN2Qa5HG0YdUhFUO+ZGnU+FKZOHO2iii1cM5s6dmjq3hj4nGdynL4+FHpvbGH3HH77fLG2x/Im/1fkrmzZ8Dw3EoXomRd4FcbNLgEbcyXu7vcikj3MEz3esQH/s7nC4gHfKMu7Tp0VP2dPX2G3Hp7V3l38Ifoi6VEIh975x03Q3l3HLwx0rmAD42W1+l2zpQlneBUhCl0WEWHcWQug+U9UQllFcsEOeS0je8DEOCqYu23N0564fIwCgyePyS7VLiGlY8UcqCEt5VqktuMtIWlFxFjYJTLM00xCORmBM4qxRgFcvROQiRAU7JBgC6wToJFDb5SZ4nEIvZsGlEDgcVYQ0z5D2lYJBw7NpO2tbjTaQs+kalMPQ8KZ91Q6LhxJeaqyY4u6pxgs+cukkcfuFN5bnEgPwJl0kYId+/s0l1e6j9QDXwZCVm1d1YQJhNffrlQbr/pVilXsaJ898NKSylmD5YjR3whm39/XpZ+973l7QUrmYN/HZCb2raRO++/X5o1vxwKKpGBbw2Uo0cOypQZs62JhD0ARMLAMItvvj5AjhxGvekzIw/Jt2i/W8cbZP2f26RIkaIyeNBgmTd7usRjsE+GZU0KBv7KlSvKuElTpESJ0lCIwV0cI0nHG9rKLxBuV69ZU1b+8L1c1vJKGTJijOpTuguYDYPAf4iAzvFkO+goDx7qSvKCjnCZgh9D3nFCx8L1yAKHUlVS7MlsvG3R5LVndDpGduQ5Z/I6aRSZk+eefkbyFygkI0aNSnc78xctksfuu0f2JSQqejt58nzp07O7UnpVrlxNDh89KD9v2gDGoqu8POBdccOTbED/V2XOvLlgHmB+AKCTklMlT7688sxL/aXL7bepCXT/N/rLkYN/yVQwLF5Y5b3+6gCZM3O63HLLzTJw8CfKo4zMx/IVq+TOm2+S7fv+Usqn99//QBaC+YmHd64P9DoZ9JtGCde07SBPPvOKXFi+KBRSFrXnfUUzHpzEk4aTfv264We5yKZfLUC/Phk5Jgfjs35hrAk96bg4wazhkkEwUC53Yhg/Rf/tWUwQlo4s9BSLLMycySOcFoQwBmFkizxs1g0CBgGDgEHAIGAQMAgYBAwCBgGDwBmLAPywVN+9vhillHAGkiEzgleWO06GjxwlTZu3kmXffyurVv4gDRo2Rt5oSAApcwKHdEmjZjJjNuRcQb/s2rNfbr7hGnnw4XtlzqLvYBhpKTZaNGkqoybOhJcZ+CkY1TNqB4sVktElD/a8Vxq3uEo+A5/LvGQu8J6HDhyA4fgGqx7kgo0bXybjpkwFj+uBhxkULeDrdNl54KBaDcCLbNyYkfLU44/J5j2HqYaxcqCBF+wB5VmR4iVl3Phx8lCfvlKhQiXcI9KfUIYAuWZA55emQOIMK5p/1XIWRuIJULnlToGkBLLUkJ00LpP7Yg45PpIUcLsUVQadllJRe2rpyD6ZnJ7tbj/yc4OlRvQWRKQBr69z2QlzeaMEEPKRhbFaWCjuUSy6dqqA/MIUg0BuRuCsUYxRocPwTVSKaRffzICPVP5kVufs3k8nXVv4aI8bjCPMonH0w1vJBYk2vaFIqN2oTg8yRWlVzZP5c3KEkEomClyfeOQeaQzl1JDhn4cvdvDQUdm8aT2UZVYOsMwsQtjvILR540YNlUsaN5NVK5bJGkwE6sP1molHWV59a5Bce1l9efF//eSZF16DhYtTevfsAe+L4vLwo08qge9JOjeE+5nVCj0uGjZuKhOmzoYgXGTrlu2YgFwhfR68T8ZOhtUN7v1tWNX89ONKWbhsrZQuUVx+//0Pubr5xTJm9Ai5vUuXrJo3xwwCpwWBIdPmSd+PPsckGxM3zsg4SeUS73O6wlANefPIihGDJX9e0BVMrsKh9/BtOGDpFVmiFS+Rx87G9af7PADv0pby0dCRCj46au3aeUB2bv1dYhBXPQClFBmSBg0ay7jpcxDSQCQJIReffLSXPPnIA9KpXXt4jsXiXIyJoINeLzxabUVWsaLny/gJo+XBvk9I5YoVMoSPoRsuQdtTZs1Vik40Lcu++lKe7veotL6iocxetERKlSlrGQlkwHgwBAbp19o1K2XR92ulFOnXbxb9Ggv61aXzP6dftEJj+I1TQZ8zBMXsNAgYBAwCBgGDgEHAIGAQMAgYBAwCuRgB7UVFpVgQiq7E5IDMnTxZhn4xUfX6i5HDlGLMD3mYCtGPveQZqehyQZVRHIqnFldcJSOHD7N4SbDyLAHw6yzkQSOLC0qupMRE2b93v1x5XRulxKJSjIqvQuefL02atwhHnKIijuEbKeejUow8J4vlXWTxtzyPoRNpnOkHE+qBmxr7xsszuGDlqvRi88urLzwjw0aNwz3CMQLh9lU7qJeR0aY6eJb/oXyRotqVP21XoQt1TrIYKEpZ/FCw/ZNSt24Z9ewptzHFIHA2InBWKMaURT4IuteZoJ6RQwdXzeyJ2QQ+s8Nn+/4A4tVyGHJBfk3SFkqycrKMfr+nfet+KMKQiQz5WSYN7KVCKVI4SguRjArF2GH5KDbCsXEhAKfhhkvFoiXomTSQQaNUXu2DxcrVra/HWXADxwW8kBAXL5ZfihRqpISyHMxZj4q0yEIhO2n2waMpsgDxij8aMkI+GeSXIZ8OkTqXwEIGSjVUkVIXFJBXXntNetzdXdp2uEM2bPhJvkZ84i8XLYWCFR51ECqfisLJBycVVDb6/T64q5eWq69rrZReAWDOvs2cNlPa4N4vLFkc9xKQShXKStPLr5SZU6cYxdipeCimzRwjYBtjhT1zmLuJ9KRTh6tlwW/bZM6C7yyC4OIMlkf4iyh4x/0I03DLI8/LzLceE3+BPOpgHkyAmevQUltH1D/HVnfv2Sutrr4OpgsIJ0HmAfdf4oLCUrJEI+XxSozISLicYBSwpMVWPEbyq669UcaMGCHbtv4hlapepJSN9NClUoyMCMilVEA4xrKp5eW1F/8nn40cfYI3H6GmYQmV82wbDsXqvMtbtpDxU2fK5Q3ryCeffCgvvPi6aj9SqclzWUKwCJsF+nV9m+ulHOgXwy1UqlhWmsFjjPTrZBRjpMDhsUUNVm6E8SgAhsiHe0YyYsQ39wuinoNWO1IR95zWj6oXaX8csFxjO8TR4Uiy19KOmzWDgEHAIGAQMAgYBAwCBgGDgEHAIHCmI5Cs1CPk32DcjrCFX3w0VAoVKSXXXHuVJB/bLw/37i2vfzBc4jwh5e1FHoqiUyYaIauVgj9bt++WC4oXAQ+HkIbY63DBGBH8eyAEhZftKhRAiEY38kA7oXzzQBFWFKlJPvt4sJQre6E0aNRYgql+xZ8xDQsVW24o6pQizo0IUmTWyGuSQSP/j3ZCaCerwhD9OENdr99zL0jbq1vKirXrpUHdGmg3a2+qrNrNLce0fEX3x43noEJX2i5k4YiIukLUMoDnSJ75mZeHYImIVMQW5d/K3b5g+qt0B8N7YqsPICtVxQ7ZyPeDRUt7GZMsgPcKqk2r2gkcutpt/hgEcg0CZ4ViTCkaIMxbuW4HhHqLJSaQdc6RpHPdlRMDIQWrjBFco9J5UqViOayDmJEAg4A5QFgpUKWCRiXTRNJGCkv5027TkW+wIos28Y3cr9dJz//Oi1aseDEZ8emHclGl8lIL+bVweUlMSFWDrPYw0deIXFKJ5sFgO2HMcImPzystr7pODvy1X1588glYlGAQh2cYC3ONXXd9e7mm9TR54K7b5dDBA/LQI/2kfKUq6ho8fioKcbTc1+mVhwkFRpDDBw9JLAS9xJxl4y8bpAfCObIuC2ML16vfQD5+7y21bf4YBHILAkiLpya2mB/LJ/16SHd8owuWrUL38P2o99d+qXWHqaFBzqtftu2UTk8NlBmDntFHzBIIFIFX18ghH0uFsiWVZykZBxe06Iy1zuIEc8GiLQLpEXZ4/1GZNuELKV2mJHKR1QCDA2bHph2sS/rhR2DwuNh4eeyF5+SayxrJT2tXSY2adXg4XbGsBsF6KG7FOsTxghaEl7ZsJUsXzBf/86+qULaZuWxt+nWD3A36pftAhon065N3T45+pRtbbHLMfGOKgcL9OeBBR6WfA4l9SVPpRebyWJZx6W7KbBgEDAIGAYOAQcAgYBAwCBgEDAIGgbMYAcqOvAi1T2mXHxqoyeNGyg3tboW8ySXXY/lo7z4yauhHctfdd4FNDyKdiE/27N4pHyPVB8MifvvdNzJ/+mR5e/AgxQs6IRykt9g3X34pZQpDxhqk5xF50ZC0aX+zfDpsOHJIx8o7H30mfXv1kOtbNRUvIsNcUqe+tG3fUbrdfS/kBDD3hOLrm6+WSLH84NOoGaPQC/va3NBOPh0xFrxulLwg6hlRCUdelgo/5iVr2fIKeQ1RnyZNnR7mjaNOOec2AbNUrlBC3bePYdRQ/i3FGNtK8xa05JPcZ4pB4GxB4O/oK9S9U2dvqRcioVBiLOywjoS3lGUBPiDs4GeUNdmLbC/j9VTVCsP7QQECdTqJPsv6TQn4/QahYNYWB1CNZNzwObPXiv3qhgDx1jY1pWylClCGiXTtNVAhMPbd3nhITomDp0HbhwcDbY9Mf7eHyjkmsAZIRbIXCk1d8Fq+sv0z4rIFltpRz28fd8JF+up2j8PzwH4f7Bdm4ZTXs0RaC1PfH/SxPN77frny0ubizRcrdWpfLDd06CR3dO2qBKNshALX6FBrnBBwkJ8wZgTq3wJPQniztL8VirGnZNRnQ+Qe5PNJRd+0cu3l/u/KxTUrQBBdTvo81lccELRSKUalVXTbGXWcHou6z999+60UyxeXvhqtWIA1r2fVC8refTtlPrzZ+OpuXL9WFsyfKe07dsQ1g5KQmIo5R5LEIi+bE5MEDkKBQIoULlwA7vD0eDDldCKgY3ifzj6czmvrWNU6BrbyP8V7rOyBsBzY5165/tE3ZPN2xBRHwlUEN7cmv7ZFEdw8qd2BEj5FfvxlozzwyiD59PH7xe+KUd+DtjTK7B6zw197rGZ2/n+9X4WLAKPhZ8gIDILR/XeoGLUBJBz2QaHvkHff/QB07wG5/uorJR60pHqNOtKuw83S/a5u4vbCSwqDqBtMwVeL5knJIvlFkAOMpXSF8vLhZ1+ATgMB0BovxkEPiHMQSrWg8v4KIuRiojRqUBPeZZfL80/2kzHT5+LZ4KExbjjHU8RlJJ9Cqz2MrKpd0h8yThwvip5XWL5eskMxOOwHrQxVni9V0/qTmoJrJiVLTEysTfPQRdDjwoUKgX4loy84jvbo+cYlvZMjSwrmD8zR267D07gulF9+IAZC6aDLMv6nOOPwngTlivbPKgMOKgqpzAu4/LJkxhtI8IvW6MaoLNNwb1zqSQcsHQP2/ECHBEFHFKaRfTDrBgGDgEHAIGAQMAgYBAwC/yUCnLOh2HKD8NxNzed4wBYkcPVvFLt1tGKtYSaqWuHf88tfDHkSjKvg1SL+ZDXP5dySxe+wcueoDfPHIPA3EYiOiRICb0M+J+SOkeoVSsn3i2dk2bKSIdGCHec1av/+iXXt70a5BzDsPtr+bdPP8tPqFTJoyDC8yOCpIJO9unVbmQxjyrvu6ipeyKgoFT125JB8s2S+pKSkyNqf1krjVldKx9vuUHnC/KlgrMA/NW7WWEYjx5gLPCPbZglB+OcEH8fzWrVsLqvXbZCvv/lKft24QaZNmyb9+j4kq1evkXcHD1EeYw2bNkNI/4mKj+P5DIPo9cZkqxRjXRbLkB/fL8579qU35IpmdeW7774V23bUqnSG/g3nGLN5VrC/luzVplfuIGUA4M69fB7IoK14XbDG1qNQOd956wPf7qEQcAYgI8UxOOhBBsFnbzdsy2OCtgGs0xbowD6fdssqIg15dAd5b8hrUnEanfkgJlDteWz5gDtoOaIEbD7eyuiNerZAOAj+mty4fVXVJ/Pn9CFA+UgQkdY4+kFycvo6kouv/M9mGKfpxvgwoYqAfBXxZxGDVn2l+PLV4GKW2eKgHhuII60uKNjkHJAfi84zxuNwMLCE1KCmTiqfQFhJ94gxByUKSkHv1Hb0a6CPc78eOKPrZLfNsImXwTth+drfZMLcRdLv2ZdVX59+9BHph1w6FPyqPjKPUVRhjptf1q+Tn9euk1s7d8PYCdVerEcuv6aNTJ40Np2yi9YnA994Sc6D0PYPeDn8sGKlao3tUylG77PsCgcNXRo2bCSbdhxM93s/IkcalW0cmLb+sVkeRT6zR+/vJnNmTpE77+kp/d+BEhLXc3MEg6aS16b7OQXTtPJRAmrHiferr22WBoHcgEChvG6ZMfAJqQrvJaUEw/uuComILni3A8lQgeDYjK9XyAvDxyl6Q5pythV+07xPRWsz8FZm2EJoxMPK/qsQVnXV+s0yee6X0vfJFxS9feqJPtK7170qmTHbS0Us9cbNr5Bftu6TX3YfkS9XrJWWV14jnTu1lu1btqajW1Q+WcotC38fJrr9nnlBlq3+XpZ/9zUmwGn0K5Xuf1FFewnzuilQcJHe8RQV0oIz3gyLRTc559aet0rYgG1iQWVYAArBaKUYm9LRMMITeO4kfsCO90F6zj5btNIWVuC4CxfDrZliEDAIGAQMAgYBg4BBwCBgEDgpBCjgZ7HmuWlz45NqxFQ2CJwEAkwJQi8tluzkmKoSw7OQVwbfk5Mycugn4oGF+LNP9JaObVtJp+uvlvWIGLLy++9l6+bN4SaqVashI8ZNVrmrFyI/9K8b1surzz2l+E7yryzemBiJgWeYC8u4PHklT4ECkq9QASUTpOyPxurMN9b80sulx329ZPrCxdKjV2+ZOGkMYshAdgv+zQ1+Nz5/fsmLX3yePJIPy3xIOE7+joU8J78/pQDDNnlGaz/U2ajD4y7sI49YrVoV6XRzZ/nfk4+qkI5oRLVD/lApEdWZ5g+VYjkpVIrRVlcbrlrPwTozh69bTi5j6pwmBCiGSydbOU39yM2XPSNHfcq+IBrDl+tVYZMCIKDWz9asI6ySZPWjyvsc/tHyHyOLxCHXmBPmCA7EAPZAMzbq7V7yxfu9oXCChxJMA5JjnTL6/Xvxu0uSsM5x200PJh8GrCCUZXgODqdHUqFZUz9gnoqfEwTYxXbZvgNeIFiqvGawbAja1g1ZfRRUHtGjC+RZ/Zo0vkTuvfcemTBlptzb834Z8/lIlTUGej3l1RXdFnPcjBr1mcTk8coLT/eVTm1bq9/Gn9fI6mXLZcvWHWrwplJs8aLFcCf/RD4aOkquadte+j7QQ5KobLVLThR7nNRw4GZdTh4KFYhL98uDCYSW2FKYzHr1oEBb/8tm2fTndlm09Dt56qlnMJhbAzqFvp5YryQePW5NAKAk5GRjy5Y/pGiRYrprZmkQyJUIuBEk+7wYt4z83yNSuMD5cCVD7kf3MWhIoPBV3mPUjoB+I6xfENZtsbDMHDl+uoyY9bUKi5fdTdEjLKNfduedruNauU4FWADWqNGFYWtVmAoonZTih2Ei4LnVrEkDeeD+e2X2gkVyzz13y7jxY2G1ClqCQjrjholYwQLxUiifV6pXqSQDBr4LA4eADB8xRNELH9rRjIa+JidFpDM1ataWdgin8fwzT4A0gc6CmeBkiSEKMyr0VEtGGNptO3ZI+QoVYVQBGg867YdFYnSJjXOJJ76AHD5wCPcbgueYF55rLvnjz1/lvPPOV6F6ybDEgC4ybG90CSX7JA+6kZAELztYx6XCljHkjhN/Ctp15hc3FWrocwryi6XCKi3VwRjmMeJLDkncic0pGqqvoRksvW2WBgGDgEHAIGAQMAgYBAwCBgFKLhjVIIDIBL6QV3mK0VuMuZf4M8Ug8E8QcILXSveDzseLqB6OQDL2p8meMr0GmTjUpwAupKKNZFpTHUhCUqopk6dKx043ISpRe7npllukE3733/+glIXx6uiRwxUf7gHvR9kURINo1y8lzi8oDz70oAz9bJjsPwwe3i40ivcgRKPi36CgIj8ZYAQm8Hgs2qDbro77EiivqiGKSJLKW+1k38H3MQS+kiHiPEYDSYUcjUvuUz/goteZo0rvCyLCCSOvBOH1FsJ6APnPHu/3uGxEtLAZs7L2ttN9ys1L5hiLzDOmc4y54dHFnxORdhz8BSFPwU+/S/qetGgb6kfg5JYg+Pbx07+S/UnIE+cFii5EncKPoRX58/qtH58ef6wCFp4vgRw/liIjJy2RlOSg5IHoIi/YfZyC/kExGUpWP4cjAf1JQL/w/vKHZ6O8cRmZBb+Qw6fO8aFN/kw5vQhQuZkTufbp7eXpvTq9Z8+4QgLJjjeoWUYJ9UJ0K+Y+frEoKnKjWsv4D4npuV6U9wKUV41qlMEYy7i/IGZQegUZwwow0lrAj0GSijAcFuqKOO5RoGkNnMAZ2wsnP6/CXhFPTcxb3/i4NcDiC1w06WWVeDHyeHbYc6BloaUCFUn0HqPXgRuDZo3adZQ1QwDEPsARAyVa2JmKc2ZOmYJ4yjdLvYsbISQhBt0UCJ3R4YH9X0I4xY/kfy++KsePJkk/hGq8/c7u0rhpM4QsqyvNLq4q7735qvTp9z91XQw92RYqrXTJiOBoCzTWUQo0DBwUQnOCwRGDkxu97sK9Il+m1K17sSz77ivpfv8DuF/L7fVneMFVr1FbX8osDQK5EgGGtgvh/S57XgkZ/+qj0urhJ/Di298IJ/X8vklUqBxDvYAvCe+/Q/p9/LmUKlpYrmxYLVfe19/tlA73Gh+fR/bt2X1CM79sWidFzz9fMRoMReGEsQEZDdIEKqQYRvCiGjWRxBj2dtinlV1kRACfUoIpZgTQ0gtr/65daj+9sVy03gPWgBft0tOLnmsWA9T3qf9J84tryII5M9GnoLLA07Q3spPsPz3JfvtlEzxql0nPh2CZhzGXdTkeRNNfnlu7Th1ZseJb5BnrqfrCsIXr1qxR9CsVg4kLfWNoSfbXQ9ffiOKFtSHb/mrWG2ov59K8zxjcX9Prnhd3nAeYBGTJ7Jes0A44nbwWI0MgUiOYq4jGzKpBwCBgEDAIGAQMAgYBg4BBIAcIMLrB9k0/WPNrez5JU12W9LNVa5/5axDIKQLkZ04oYInJo5HPya74wAMyOgartrj90+yqy9yZ0+Tw4UPy2DMvSokSJSTOgzB8NMJ0xsqWzX/IhHGj5GnkjKYnli7kMemVde9DfWTw4I9kwKvPy4ABAyEadEhSYpLsBR/rJi+PomVehcHD0sOrZ/cuiIB0v9S/uKE6/sdvv8ung96V8hWrQIZoyfSSU5Ll4F8HEFAGRqCU84Hfo0zs/CJFM+QnVUOZ/CGfW7p8ebnj7h4y8uMPM6l17u5+e+hUmbdwNSLz/CIDX+oqRfJmjQXfQT7ao1CKPfK/z2Xrtu2ya+9Befae9nhvcK4hgFkDmMuP8tkaj7GsH9IZ+YrHwl+I5ZLqF6ifpUaJuNFsBhelzY6ofq6tUp9vFQu5/SCAHKxv7PWJ2j39w/uUYDIm1SkdHh6GfX6Z+GYPobOCC14KCj804WMsY90UamnYmVOM6wEsaemhNGlYOHOokKSQl8q6+5EL7NbO3aVho0ZKkPrrH9vkow8HSeXKFWFhA4sFDLIs0cLcqRMnysHjx+XZZ/4n5xezBlpLyRaSPTv+kHFffCF9n31VnnjiUXUPr732phKsFsgfK/0HDoR3xj3S6rpOUrtWNaUc0wM/B4yMBMHcb3m4EZ8IQFTvOMikvaFKsQjQ2CaF3n4IiZWA27ZEo8CX/jBdOneWR/v0lqVffydNmzWRBfPmydcL58ik2YvtVs3idCHgANNExQWVmNHvXkZ9SsEz9iI8pg5J54U3UAoVtZgJc4Cid5AOo6CePzXQEYWTVHryMKxmEIqE8HsDBYr1HsGSUSlSqPTg+2e9b2yLIQ2y7yPiR+P8WIQbVZZfuBbfSybS5beozw9A850Eq68dW3disrRZ0YHSZctJHoRTKFGilMTFOMXHCTe+c06QA9Bk1KxUXoY+8ZTc9cpgEADQbX4sToaMwI/xW1F8iK2uSjBReg54Rya/0FvqVagkSfGwhlLfnHVY/9WKIdpD8f7PhEGe9OfeBx6UTu2uk2GfjZBbOndT+bxWLf9Bpo37Qm65vatSPvEeH4B37K1dukuTJk2U0cHmP3fIxx8OlkoVy4NK4HlY/AiwDsmhw4mgSQHZufVP+WzIR3I8IQVt36EMCfy2wp31aWDAQuZHv2MXliwm3brfKcNGjoJ2DaMqLfbwfPgjrn8dOoZnH5LlPyyT9Yg3P/zDd+GZVl2eeOwZNqTay0gpRq+1O+64Q/o+3le+XfaDNGjUQBZ/uUS+W7pExk2ZrdrnefRo84IGMsZ+ZAmEEtVg4pR4a+iA9SL7n8IYizEwEuA7CesXvmNK3wqrNBpD0EncGQtFYA6MLiOvZ9YNAgYBg4BBwCBgEDAIGATObQQcyOeNySUDEqkCFiTDpb3bLAwCJ4dAyJJfpjsJL5l6z8jbZWPZ5wHfQ46J9f3g2bIrX4z8WC5t3lxKlSqheCcfDMRdyP9MY8fbu3aXd94ZIF999SWaQav0wgJ/RXkDjTRptNjr4Ufk9Reflcee6KfyM69Y9p3ULFcSzBYYLlpxQw7hifHI9j0HlUxk1869cmOba5G2GrE+4uIk6Vii1K1XV4YgkokL7kw07lz1/bdSrUIZ3gAULWwnJPF5Y+WPnYfRD5vBtW+Md0u+98TCfdZ+8pCP9O0rE0Z+Kr6URCWbsPyfTjwrt++hgtQq9r3BM2vQq10RpQV4AqvkVEtT77Ll4Ij3ZVW306y4tbzRzhnWplV9WbDkR9n/yzZ55tFPZMDb3aUgcpeHIJNgQTAwOA5AWQpjV9oqOxGm8+jxZOnd7wPZumOX5C9YVtq1bIrILT5xMhoM3hvy4O+91gcKTYv3Zju6346Q1R8l+8X+eA94fNwKpTwBhgDF4zbl9CHAz9YBGSOfPlwy8Df8wp2+TuWyK5tXNJc9kNPRHS0MDWBwoWKHyWcpEPViZqgGXrjCYp4IIsifJWA9lf3UwtbdCNt1a7trMPZRGeCRpIRUuahmDfnsi4nKqoQDOAXO0WXqhDHSsEFjKXpBMXVIKbM42GNC0b5TZxk44C0Z9M6bMv7zUfIFBLWxeWKtyQBu8trW7eSqK6ZIb+T++nr5SlwaQlgMvirvF1qjwi66cDAJpUJ5wYGFbnRRhV4bcOtQgwLvje1pLwntfh51itzU+U75adOv0rHd1ZLX7UV4x2R5+oXXpEmzy1A1/cQh+lyzfWoR2LZls0ydMNYS8GOA0UqtzK5KJYPy0sE7yPUGTZrCWmofYntvUZZSt93WVQqef56aVPJ91ooo3R4nqda77oAyBaFJocSaMGak7N93QHkE3X3vA7L6++Wy7ue1SoHcudtdeKfzqGuyrlbI6fail5PHfiF/oT/14KVYt2FDKMWgUMM7S0WJLr9sXCdzZs2WPbu3YxJlTdj0pJRfxHmFCstVV10rdRAilN8qlcH87ljaNq0rb/S8Q6ZBycviQ+gDFj++RxZ6RLJwwszvbPDEhfJit3hJ9OYNW5OpCvYfYsjiQn3SrpLFEa4xFxf9PJuCOXnxlf4y4KVn5X/IGUaFKJWmnW/vIv1gsRcCw0RF5q7t2+XWjteB5/BJDBRHx48mSs26deTTMZPUO8JnyvLd0q+kSmneu0+FKixXsaoMeHewNGrWXNFHWhWykHYr5Sq0jG7Qd75jSjmG96pXn8fls1GfK+ZEKejtc75F29Uql1JK+vx58km1i2rJg32flC5334+Zs2o2yz+33NFd1iG0RcfWLSUuPk5SoVB98rkXVd8CCBniwX0y3bNSwkW1RBpJL1s/aCo906gUU98PHnsA7wpzXnIqx9eA98YxyQWlWWoQBh7ADGxfVItm0yBgEDAIGAQMAgYBg4BBwCBgEDAInBsITICMy/IGs/hm8n7k4CkXKFuhvOw7ainqLmt5pQ0I5BSQVTByB8vd9/ZUPyqoho4cJ8GR6cXG2lDVAQN68qaT5i0EXxuAAe2fsmfvbmncqJlqB5JFZdw7BG1kVNKUX1Y/qey6o1sP/O61q0Nph32U4w0ZNRb7tBzCOly0cBH5bcdBbGgGlXXJKZ7ZxQPFUo1qpdW9WJ51liGxvktf9C1a8IVvukqF0vLuy3fJ08+Mli079kqvJ4fJB691l/xxkBGhESrFqLByeinLEjkOpRjr7NyzD0bP8dL/6dukasUiQBvGp4o3RwhGyL0uqlJKyWd0pDBbDycuW0Tst9lwF8QVFCuQX3chtAskQ+G+mRWDQG5EID2Fy409zKhPtkZaH3JqQmB7JOET14cyWWZ3PJPTzrLd8OlQd+RwWYohtwehFGEB4E91QyDJYQpI0isGAmiKYmlAFYwIEsuBjsUVpnMWrnZES5yHARhEO81TTOMePkGdH/2HRJdl+uzZiG3rl21bf5fde/fKJZc0Vh5ckcoDJTSNGvzGTZxsxTm2BejMJUYFFwfSKlUryd7DR5XQvm+fR5WQOLINXnvoyJGK8LMPJOTDR36u6jOcY0YDbWpKAAJsj3wy9DM1cPC8yHJFyxayg0oMjkIoI0Z8rjDNSmnBScsrr74mT/V7WtYiSWrDhk2h5Wf0c2JuBL8KyNP05+jRY7Jz1zbxITefEuzb71lm3WEITz8mo/r516pbV+pe3Ey+XvodrLKSZPSY0dIdk888fE/xjkUrsiL30ZNr0eKl8OJZiZlGQFq2uhYhRj1y6PhB+eW33/BehWT82LFQrMIjicGmc1C2bt8h2+ABVrIMFCF4/xk6L1IpNmLkCPn+1GVgAABAAElEQVRxxXI1iWL75UuWFi+8yVgYFm/r9q1y8NAhGT9xvHy5dKnc/9BDkg8JeaHeAD6gMqDPd7S9TDrjB70XvHssgq0tm/Q3xYkVw+tt3PSnbD6UgD4ctiZiUd+39jQLANdypYrn4A5PbxXSK9IYPF7p3qOH9OjeXTb/8RsmoEelVu364kY+QesZ0/PPJ1NmzoKS0Cm//7ZB0b369Rso5RLvgrHfHbDqGz52olJuWfssj0IyM2RSFIUA3Ro2YriiV8nY7wYdHzrsM0XH+M7yYbL+BcWK49nvVt5bVNaT1o8cPVq1rZRp9jvpBX2jhWGk92tWqLLtF196WZ556llZufJ7uRghbePzxeN83gO80xAKl+9ZkBZk0eM14qbjdHQR4weGKLc3Ri29oOExeOcD6CMt1gKccaPEhWJgnYixKhintrVFGi3WAnQjQ31lS6A+BzJ7GVhsWmeavwYBg4BBwCBgEDAIGAQMAucwApg+qkJRLgsypqul+WMQOKMQABPFcIPk3ckHqpzSWdyAfu91Ff3+p21bfJfehrpKr1pLxNuLg5KlcuUKUqlSefB46etDGpK+frZb1veXVi16Wx/JbL8+fmYufWBkXZDRkudVxrQabvt2o6mSloe7bHm4Dwa4FSuXkVdful0eeXao7N32O6K5vCsDX+8NzzHw1hDgOr2Wwup40lHp8/hnsnv3fvHEFZC3XugmF1Y6X5LxjJ0Q3qRCPhULD8EgeOoYhORUOdN1B+wXxwF+nEV3MwDBMeUGDBSk3sHoF+zMfCym12cxAmemYuwsfiCn49ZSIYz1QCA+4c37sYQwERNAEmEajEwY0A3qfnpK0bMDwkpbaXUq+0n9kc8XQHg3y4OtQqWqUqYcwicqJZ11ZS1s1kL1yP7QU0XnTeN+KrSo8FIKMEpcUags4/7IQZt1kpGkRuXlQR0qB9gO2+O1qVxjqDpLyaaaUX94HgW+VHxk1B99LsPSsW4QHnlW7iDLEyIzjyMKpvMVKoD8Z82VazuF5qacfgQuLF9R7uzRCx2h4odeglmP9IeOHJWxo4aqSUGBwoWlYZPL8D55pcWV18jcGZPlDyhA1q/9URo2bpDhzTF8Ij3FqBRLOHJcvpw3S72TpUqUljbtOyIJqzXR5DfLicfaNT/AwwjKt3r18K5Zyu8MG7Z3uqAQYVEKEyz5nuoyBqEJflyxRm1Wr1lHrr3hJilZuBDedUs5S2+dfQcPyzeL58vXS76UPTt3yIhPB0vXHg9IfMFCagLlYahIdJEu3Cz0rGRxhaw2UlIYttGrvr0/tu1FqADLo4zfE/sUPZF32XifV7igFC1SWLV1Jvwh/aEVnhOJi6vUqKGeJ+kplWK6qG3UI7wVKleVchUq6UPqWTLhMekS3wV6l5EmUPlIRRRpIpX/TsQ0IP2Mp+IJNISFCkeizXeV7wivyVCgpHfsE723rHCdoP+oS+Us3zvYhql6bIPKMV5bT3i5L6MS+T3E5Y2XS1u0VNW0YYGmz7rPXGZV6FBov26KNkM1BjpqvUxqPEqDL6tmzDGDgEHAIGAQMAgYBAwCBgGDgEHAIHDWI8DoG2AFwRP+N7dq8ZEI0gZlCHlTU/4ZAuSnKSOhzBLCEDDDJ4cpZQDMLV4VnmPvvHSXPPHcINmKcJfac6xAfhiWot2jScehOPtctu3YLXkRcWjAi92kSoUSSHUBKQBENpQpeCAgDsIRgHIZPxSgzMVIfjyrQnmCSjsCuQNlDyrcS1YnmGMGgdOMgC2qPM29ONnLR8uhw9sWwTg5snGyFz+b6luCaTcE1xR2xtgeJi54JJASuuDZQVqcbEtCGfrYjzBo0R5i0YgsnPJ69C57O2sBqD5JufWC8GolHAWnSkHFQQElAa6+D93XDZ4nVii1QIRgmcdHj51gK/PS3gS2ESmwVUouXgM/XViHoctY9DW5rsMnJiJ/z8P3d5fE5PRJbEjsmY9p8MdDhIJgnhtZtCKN7fCYA2G/Asprhp4kBDetn+o8bCrIsVTHMbFhkzzPlNOPQJ74GKlatWqmHaFCkxMBKi/4DvfvP0Apd9yIAX1Xj/uUEoj7W7VsIb/8uFx+27JNpkwcK1WqVZWCBQviHUlvUcWJLRVcIYdXPhs+BJ47CEWHmW5n5JJiNlT781STDmURhu/hi1EjpUL58spzix2lIoQTVsYN1yESom+AijUW1W9cc86cubJi5Y94GQPIedVDKe7YTgCTLCsYAyqDTpxXpJC0u/VWKVq6nEweP0J+//03WTRvjrS98UZ4rUEpxmD9/MzC8yfgg/v3QfHC8K3Mv5aU6EOYxgOSeOQIPoe070d9HfbnoUO9wu9fzitYQMqXKWl9KPYnjOkfu5/rCr95TWe4rmHg96y/c3ZaP0cHlO+R6/oY96faHlZcj6QJrE/HKNISPB5VkoPW8E56TSUjkLYOsB6eL2kLFa66D+rxYz8L9zkRwlXtw5YVi5pHnGH6q0gX9kQrLkeNRqgMPArecyQt1BjoJVuLPM5tq6SfllCRRl/ZFNzztIlvSAh5xkhzvWT0MFkPa1xdyep0J74T9hgjGH6gt7gZQmL9fFD8WoYRQXhI49VGTHVM3DkO8KZt7FRD5o9BwCBgEDAIGAQMAgYBg8A5gYAfkSgo+LVny7hni69Q0+vw1jkBhbnJU4FANjnE/ukl03I9WS0luq20Bl7w/9RLZCtFQrSSkytknNJK0E9FjHUVKseUMiTtMNYMk5UOjhxs0LheF4/FlOvNE5Y6pKE+EEPjWP7wWC+qVBoKr8elz/PDkbLhD5VH7PUXH1DvxWPPDJHtu/dJ/sLFVPjE8pWKwpvMKW7wxyxKhgB5jQsCBj5xyqMUTbRDhFmSA+y35TceTTCpHCXTbTHgqi3z5/Qh4IHMw62fGZ9RZp97+PnZfT2HPtu0r+30PSdz5dOMAOksf+37fKgEldPf76mWfp9TOvYdIn5nikx/rycsBPANnaS1wqm4NW+eOLmh001o2nIP154up+JabJOeGEqRBo+J1u3ahwf9yOsxzCE9OLSXWeQxs35uIUBvG04G6d2zaOEc2b39TwVAm3adpGSpMtBJQFyvvIJc0uG2LvLGa69KYlKCzJo8Xm7p0p06i3RFha/DQLZh/RrZvvUPhGUMyeVXXCsXlCpte/nQOouTUacUL1lOKb42/75RRgz9SHo+8phSzukG2aeMlRG6BgZNKIcP7d0vi+bPUjvpKVar3sVh5Vr0vIzfByfcdS9ugMnWVln+9Xz5/rulclnzFnJ+UUyu0ppWa8prktZkwCkGSjEfLJC27Nwjx44cxjVQO40jVfWpGCQGzEvFnIf5Y+OkIpRi0e1GXcZsniIESH/bduiIyTESuEKA4KQ78SksKgwpvd/wjpE/oFkCab7yPMMOSyGY8w7wXQogPAWVYrSk5HejlMKc+HHGfw5NAHOOmqlpEDAIGAQMAgYBg4BBwCBgEDAInAkIkE9igY4DXj/W+r/Z74zkb0pmYV8k+vi/34N/827O/rYuqlhY3nmhuzz67JuyZfsueeQ5KwXMzl17JTbGa+UUq1QMfDYMUg0vfPa/EOYOT0DAKMZOgOQc2mFbhjAmLcdOxv4NMS4ttcnY5ir3O2hBhaVSoNleW6cTJfjPyI3tO4S7YI/7advRkvvwkb+/wslFLDzr2nfspDCJbkkrG/QkJPq42T53EKDHGL4W+XXrLpmEfHdOfDxVq1RHPrCrlVIKjlJYQpEFC64SJUvLtde2kS8XzJblK1dIrYbNkGi1cjqwGI4gMTFBPkeurwDMwSpAKdS27XWqDt87TkK1cN+NmcxNt90u777xgvyxeYvMnbdArmrVUiltdci+6Bxm6S6GDX7i02ZPUcoCtkvPtLgYy9uRXo+pGXxf2I3vQ+S6Nq1l3erv5PixozJj1lQkzr0vunkoJdLyqDFP21/798vhw38hfyG8gDAx84fSx3wgffKjU1QIKm894BOkQsbM2k7A9r/Y4YBG6fob26vQDqS9mvadqmurELp4J51OK4xjLAw2qAimNZuf4XbdnL2jQsCKbW7N5vkNsjAXWZwk45vDMCZFLygGAym/7Nx/RDz8TqFlS5V4HLM5AMMIWLCZvwYBg4BBwCBgEDAIGAQMAgYBg8AZhUCqx0pJkMfOvexDPvMYGJeGol2Kou5Ke5xF7TabZwkC4JSlcqXC8vZz98qjL46U3Zt/g9GxVzzx8dL/ua5SEZ5i5LTjYCEaYF5x28fQdjKCnJgtmHKmIuCDHMRve/UFIENzUVSixSURN6XJRNhOX9c5B2QkRjEW8SKcq6uF8lh3vvSTh9WKRfYQkgtb3wy9R30z/CZoqc9QbbmhUCBL7yzlsfAfCMh5He05Fn3/zDsGqa2Sx1KBeKoFxdHXN9u5CwEqvBKPHpUvhn+EUKQu5Gbyyi3de6hOBvDO0iOLAnv9nlx57fWy6vtvZe/BgwgV94XUePb5dDdERda40cMkCV5ltL5qf2tntdQKLnqB0eNFW2YVKlRIrmzdVqZNmiRff7lAGtSuJQWLnK+uS4WCvm66i0RsUGn3+4YNak/1OvUlPn9+5eHGHQzFGB3Sk4ou5gNknsI4TK7KIzfW+jUr5deNPyulHT6ciNahv4CSS3mKQSl27HiibIP7Pgtd9RmPGumu0hUfJmdUXMTAY7NMieIqjGR295CuAbNxShAg2aUnLeliZMltz+b7Id3VVJ5j2MRXOqhxjWNZLH7ct3xod9GqWHqfRb2uqGGKQcAgYBAwCBgEDAIGAYOAQcAgYBA4MxAgf66iayCyx7HjxyRvXIEzo+Oml6cEAR/eBwa6LH3B+ZI/T4ykJHggPwogPUVeKVOymEpzQd6e+cvdCKlCkYwpBoFzCYH0Eq1z6c7P4XvlQ1cPnppf/Ghwzx+M6tWPwkL+qCDjT6VywZKCxNzgEUUlFYWv9F75L7xGtKBXLwFDukLhMI/pZbqDZuOcQIAKL+b9opIAehyZPHmS7Nm1S3m2dOvWTQrGWd4sVF5Fv0exXnh5de6iQiDynFnTpsBrix+nVVauWiU/b9gkXiiHrrmmtZQqXTZdeEQqiJXCi9piFC88G5tf2kIqV64mqcnHZdhnQ1U8aB7jZCe7svGXjcijlwwaEZT69eqFlWI8T+f9i2yD3yCVYrrUrVMbHqYBSUyA0mv7Nr07vHRBkcyuJh0/In9u3QqiwjxQVt9ZyckY5RE/KtFike+qSoXyUrhgoRPwCzdsVv4TBDTdZS5IFr7Pkb9/3AmE7hX+Mit0ZebPiXeZP+X57IYHGEIt4sdPhz9rnINHGdohI6CXVIpxrOMX5rDjMPIYTlXnYWGKQcAgYBAwCBgEDAIGAYOAQcAgYBA4IxBQeZXBzXh9MRLjjxNv0AfvH+TzBheUL3+hE+7BD/5NF5cdFj/S2JHrrEMD8Nwg/9N9NcucIeD2pj03PkunxyV/JfjkvqeGyM49B8URGyvOuHjZv2+fPPL0h3Lk+GE0jEgr8C5MobwKjDJ/lAfxZ8qZjQBFHsEgUqpA5KYC5VAQAmEJxSiMrKMLZSL86YLsE8LfuVAoOzLFIGAQMAgYBP4BAi5lWQMFFSaQK5Z/K2tX/6CsbRo3u1wqVa0BhZmlEGMeuuiSCk1a5WrV5ZLGzZXi6avF8+XQoUNQsPmUImvS2NHKI6x4iTJyxTVto08/YZt9oAKr/S1QtnljZd/enTJv5hQoknM2qu3ds0ddj5PgChWrndB+djsqVa6uqnjgXXr4rwMnVCcWCQkJshV5xXRuNIZsZK4q/tyYgEf+OCEvXfx8yYfcVqYYBE4WgUu6DgQjEJQ9R3yyE7+9CSIHk0X2HkmU/cdSpPl9H0nzHoNOtllT3yBgEDAIGAQMAgYBg4BBwCBgEDAI5BoEmIKBxem0QrAwIktqyonuPzToJa+vFF8UmmNdRWMC3819XGehIP2/MERXFzN//jUEUpHDPQCZEJVilA0dPuaTB58cKtt3HZC8efLI+6/0kIEvdpOYGI9s2bFLej35GeochUH0v9YF01AuQ4AG+pf2gBIUcpFDx4Jy8EhA9h4TOQK5yIHjAnmJSNO73pHm97yfy3r+33THKMb+G5xz9VW0JYALuX34o/icP+b24Q/DJn7W4MhtUwwCBoE0BIJwTdflAPJlTZs8UXzIh1WqeFFp3fpalUtMH9d5vvQ2l1pp1qZ1GymUJ16SkWtr9Oej4NIeKzOmTJPU1FTJm6+Q3Hbb7fDsPHFiG9kW160cZyJFCueTTp1uUnnJ5s2fI+vXrUc4RTsPU/RJEdupyUkqZ1kehEWMsz3dIg5nu8pwilR0pcAHf9euHSfUV/nVoBy78MJSUqVSWalQobz1K48lfuWjftx33vnFYLXGSfsJzZkdZzkCQYxJiIqNH0aioEtSHW5JCnlg5OQRhx/2kRisAoiZHWIOMvywC9tOFTuboW0d7oLKEsrrdGGfS+JR14uXyRWLKOr4HpKRsywl4La8pDnwmWIQMAgYBAwCBgGDgEHAIGAQMAgYBM4wBJLhI8ZfksspyYi6wuAanlD6PAX0BIOMPKzwouLEBelfvBf8EBluiDY8ULDFMBoONslPmXJmIUBlphcG2d5giiQn7Jc+TwySvTu2iSfOLQNe6irlqpaVC6uUlVeef1jccRfIrh2bpXe/D+QoUnfwoQdc1o9eZNbvzLp/09v0CDDXOgQnkAsGJAWPVMn58c3HIvJZLL5/CSBFEdzKApCV+M4VF7H0EEVnc4k6ajYNAgYBg4BBIEsEIsMLjhn+qcoFFheXR9rdfLvEwk2d4QvpKUalWAgDDtJdZtge697Y4WYZMWq4bP/zVxn7+TD5cdVynOOQ5i2vkmIlSypPsAxPjtgZeY26jZrItytWyeZfN8rkcZ/Lw489izxgWSvHkpMSlTKuTJkLlaWRK4eeZroLtEYpUrS47Ny7W/y45+jiRg42h5OBWb1KieeIS+9Fp5KBRp+EbXYDc3ylwMjgsNl1liLAib0PkzWPyw2Fq0MQZRMehZii493it8fpelZl0ltdkVxYzfHl+l4fiivGCj064/2HkIg6KNPfuxvtYj6IdlVogawaM8cMAgYBg4BBwCBgEDAIGAQMAgYBg0AuRIDRWLxQcJFB8sNINRCEBxj4HB+E3/QSY/FgRwpzeIPH4joLPc1SU5GXDPUCIeREx37mKYuN9UgOMjGoNsyf3IOA9hQ7knBYHnl2tGzZvgueYvEy4MVuUq1CKZUqJzbWLRdVLCLvvtxDnnj6FUTz2QvPsWHywWvdkYfMROrJPU/zn/eEnp9M+zNn0ANWY8gtcUNvRsyxvv9p7/VU+2e+by3/+RXPvBbSmw/8S/3Xgqq0eKQW4E6YG9AgWx//ly530s1YvTnp086aE6Lxx3Bp3ZttLR/Gx95mHhYnBkt8TznOwcIEjzFej3K/Zkg4unG7IMTkAOtBuDTtZcNlpGIhI5A5iGt37uj6zO3EQZ2FygcqIdgeBwPuD2BCQI8cva2vm9F1IvfpwYTeLUxMybbcXq9qj4qHSEWHFQ7OUnY4IbFVAtuopeUVBHd17KdVDgvb5v6cFPaHRZ8beY5uh0ulFMGz0u0qwTEw5vmc4GgcI89nHZWjCv3iuh8eSpwcadyir4m7iDz9nF/XuE+Fd9dvW7ZJXIxbrrnuOqlYuSoE7RDqMwcYsFV4QimWGX6MeFC7QSOp8dNaWffjavl++XJx4X2pVrWStLyipXo2/H4yCmcQRIBgPD3EAMZzc+G7w1NxQIvEV63LnXfLgBeelt0HDsvU6TPk9ttvDn8X1rtjPU/9He2hNRG+UQ/odSw0BWmqLbteNhTcgXcoNk9eTKrx3aEv6HG6d8RFbx21R++PokiZfBL8VHhfp7swhntkiaRJep00Tyly8Pz43aWkWDmz+F25EGIyp3SI1yGNY6hOfsNaucrvlHTRsviz6BzpFOvpwvOc8JJi0fTAi2v7MCmKLNa7iXcHfSaNC1LzFFG8iFGegnAM+v1QFkc4rhW+uiqPk47oetzvw32zD6RLLKSb9P8K4h1g2MyMi9XntGNQiEEpJv4UvJMulX7MhT75yagxYRiq85vy2+aMXlwKejTx4ZSAE+MN3xtclLcNtZqk4va8HniKwYKOoULED4vKJBzD/J9+adFXD/dDw0LTSVMMAgYBg4BBwCBgEDAInEMIYJZn3601f3Mqj37scmgDtxPnR5gaoljnpR3V8z8uwbeoXLGsl3Xh+RTu8cdiTy3VvDOE8AE5bAYn2v1H31SbaIv8Eg0XdRg6dYGoP2n9jzqQw0191zmsbqoZBE5AQL9DIXAsIfCULIyawQIHH1Vi+M1B/sXiAm/kjKF3GLyHcDykeB94heEj8tonsHoQ+cgE3mUu8P0OMExOHA/Bs4Tfrx/fFnm/EBRs/6RE889/t61oOUq43SgCwPxYZ1bR+FrPzhFLvl8kFT+w/eK2dmd6SyEm2mbR9A3P8fAxv/R6ajTCJ+6TwvljpP/TN0mFymUUT6zSdPOx4z2qcmEheed/j8gjzw2Xg3/ulqf7DpMX3rpbChVETipEVqEswwM+nO+NzwHDZtDgMNm3ror3xl6xF4zyQ9mNM5QqfsqvPCevaFOhPvEScknZFAv7QFmllnFwn5ZzUK5FWauWB1vHrFCSXGeJlHtyW8t8uZ/ymaxkrqyvy8nKZ/R7Sxk55eIsWqbHfHD+VPv56QtELX329+pBlBuWYDKEIPwmMfy6YyDHAR2gvCnWFaPwgqRI1dPR3vxqtEM9vEcUDyXhVMSIUt6kAciQEvCCUZYU54cxP55d0JZ3QfJiXU/9/Sd/sr6/sD7in1ziH56bJkX7hw2Z0w0CGgESlF9/+kl27NwiVavXknLlyisNNQWwVI7t2LZV1v20Si64oITUrnuxPi3TJYW/JHSLFsySC0qUlnr16iP/kjU6BCDEXbJkjtSsXV/KlC4DxR2JH0gPBvafoFw4iBxHl7W8UrW9ds0qSUlOlouhfMiq8HySA/5fMm+u7Du8T1UvV66SNIAHjsvFAYF1RH7buEF27toGYTMFsF6pXa+enFe0KPpAwa8lFPfjK1uycJ4KK3dhhcrSENengJh9zImAnHWYI+rC0uWlSo0aiqCri9t/lNCZhBH9XTh/thw89BdC4MVJrVp1pUKVaqrW2pUrQeT8Uqd+g8hT1fqa1SuVAJu46PvigXU//Sh7d++QVle2AQU94TSzIwKBP37/Vb77ZrGaeFZBTrGmzVupZ6smApzNnERpec0N8sdvv8qx4wkQ4MfC86yLGjj198PlyRSGROxw820ydNgQWb7sK7moUmmpWedi9R77A6kSg3CJnHCw8NstVaas/PrrBklEHjAOlGo2dhIX5Pu6B+8NB31OMs72EhMTE1Y4/7J+PRLabpdqoHslS2HiCWWToknAd+/evQhnuVqKFimmvsOcTrw48Zs3Z7qUKVFWqtWubTHuwDYBtGzxgtlSq2Y9KVa8eLoJ4sa1a2Xv/r3SotVV6jtOTU0B3WxwglKMz4b9oMKNwgB+/2qJyfSXi+bK3n278fg9UrFCJfT5EqxzyhCUn1evlm2g73y+XmeM1EC/Cp13nniABQvbYZvffvOV7Nq5XcqWrySNGjaxjoG+cgKbtXJMVbXqoy03JsUe9OPKjk9L0GeNI1Mnvgo6C9Kk5TFpp6Rba9fnA5n2Ti98SyIz3ntQkkHLcKtQInJcCUi73h+o+l9/+pAy3El3stkwCBgEDAIGAYOAQcAgYBAwCBgEDAJnEAI0kHWCnzt69Kji6VITE5TRes1a9aVchQrg4WB0SaUDJOXfLF0CPnW3lChVWpo0uVTJ7Wicy0JhuwfrXy3+Snbs2i7lL6wklzRpGkaCRpeMEKOF/PqAVmqQz6Rh78K506VU6Quleo1aivfUBpm6fgB50BbMnyn7D+yTeMix6tW7REpfWF7xpZSp/Qg5XkLCcWnc5DIlt+D1eA2I02TtqpWqD/UbNFb1yecxvxZlgXt375bLr7wWud+x8wwqVIpd3vE5ePHhBsHwOrkji7Jk+vPWUYTCYzl2PCS9nx2l8oflz5dP+j/VQWpddJEgtRRkBuDmo8RJVcqXkIHPdZXHnxsmf+7YLT2fGCKD3rhbCuaJUVFVHNCOhiBLvLzDc0qmGET6gsji1IpJpDtgoZxz4fgXJAY8P415o/RmkadmuM5ny9+Gjetl9/ZtUq1SVSldvpyqaxnyBmQf3tmf162RgucVkXq2jFPLNL76coGSvZa7sKI0uawFZDKWs4R2AFi6ZGG645RfaRlypDw0w85h59+Rz/wM+fje/Xtg8H6V/LTuR3htpkp9vOdUVp1MUW8yvt9rbnkWhsE82SELZrycrbiW93XDIx/I9Hd7qctNf+9+cePBwJYaz4cKdKe06/MhnnNQlg459zzHTrFiLD0BCoKAsUAMp5an70/6fp2+fpydV071BeS9t/vLtPnzpXGDJjJh8mRlacC75cD40suvyKSxI6VVi5YydvpsNSBnhQQ9GBYsmC+d77hTypUpA0+aVcrrgoQtBmHhJkycKq++/LosWvKVIlJOWLzs2rVTbml3g9z9QE9pAeJDYvjWm2/LsaOHZdLU6VldTlnMzJ4xTfr17S3HE45K3dp1lKB39ZrVUrhgYfl0xEgo1xqjDacMfKu/zJwzB6G6kL8GAt+jx45DGXiRjJwwQ8ogxxQdGjq2bSObNqyX6rVrybrVq+TSy1vJoCGjJA9i/JIIZ6c8WLR4qXS9o7NUgnB58fdrGYAuff8hMJ41Y7I8+Vhf9PeY1KlZC8sE2bBhgzRo2FSmzJwj7w18E5OJYzJ68qwTvr+3gUvS8WMyYdp0EERYBcGbYuhHg+WZZ5+UN954i7dpShYIHExMlmFDPlXKkbx58skdd3bH5JCDLyyvMPM4WUXWhaVKSIniJeTX33+X4iVLy3mFC4evbnkinayyKSh1GzSQWitXyMYNP8nno0fL09XqSH60SgXt+VBoWFZhePZ4/i43J8k+2bNvD95NKM2UH024C9mucKKdDKUNlRYFC56Xbf0zvUIqQ1HQJAofynvvvi2TZsyUpk0vhXcevidbCeR2ey26N3406F4LGTNlFvChJ6GlJMoKA9K1LrfeLJWgVF/ywzooiGDDAwbEg/dryvgJ0v/V12XO4mWw/LPowo49B6U9aN99Pe+R5pdfIQMGDJCEY8dk/MTJyiWKzEVkIWOiLWS5PmvuHHmyz0OSfPyo1ILBAcvqH3+QgvkLyUeffQGGpJm8Dbo3FzQ5LtYLou6XI8cSpGq1ajJq4kzQvWII1xGQWzu2l/U/rpGadevKjz8sV5PSISPHS1w+F5gsy4Mysh9p6ycSHIb/oMqNdDwIzCxjANxOSiqMADijCIgPBgss6u7g1eVEfGxnEEpL7E/FTh++xRR8lzEML4LixgQ+gB8MItXz415eOXp2Eu6NNX2xa2FhikHAIGAQMAgYBAwCBoFzBAFt8a7nSbA1V3eeth2eMWWISFj+E2ZjeSaMlE7aE99uwI4UYCVAAj+d4VUjd+oacJNQhflqrQJ7fXDX7A/36A5ayzRLdn2n9kknucDM9CTPMNXPJgTCHpaZ3VS0C05m9XK83ynz582Wnj26SRzkE1UqVpDDRw7K+l9+li63d5f+b72N8PQhufnGG2Tj+p8hp6opayEraHLZZfLxZ2PBT5FHBQMFpUan66+RdevXwbC2HpRQa6Thpc3ls1GjlTKM4dmilWLsolZs8LNaumQxrnkHFHLl5YdVP6o70LynH7Hsp00eJ8/0e0ISEo9LPcjdEqDEWw85VlMowSifoixlIPjZ40ePyMSZl1OgCBkFvidc24FjH4L/Pnj4kIyfNg8XZqQqtwz6cLA891hvee/Tj2C4afF7OYbutFTU9MWiEy4Em3Em+yUW4VHckG36qO3LqgSZpgLFdt169+PpMLT+U/LGF5D3nu0hFasWlUTg5fVY7QSiFG0+hFWsVKu0vPzKnfLo8yPkry375dNBc+SpJ9oBZr8cRwamEPJ8e+BqRgVLatiDUPfL6r9+z5mnihQ0GXJFHyLFxKvO5fyPfqfefH2AzJk+VVq0aC6jJ80EH4/4L5AB+8HDv/TCczL+izFy+dVXy9hxE5VTAmKHyS2tr5ZNUKjRoeAnvG+XXt5Shg4fqWSuiSD/nfE+//rbxnTHP/sc7zOUeVqBll1P/458puON18u9vSifaSVv9rfkM2MnTVGX0veb2XU9kFdYxfL9CkLRm8qPC9+C2+MVFzwx/JDj4OVHNXjVaRdS+ywvPmWvNwkOhXiXcFoMzqeszo+MhC48e6ePckBE0UF7lGs5KSBBoYE3i37KauNv/QnfwN86+9/oQXYX/uf3mN0VzPFzEoEACG8VKHK+X/a1GsTo9cUBLADCOG/WFKl6UXU1YCbTDTSbwo9z0thR0viSxrL1j82yYf1aRbQo1Kcg/8XXBsoBaN9fe/FZJTCl0PhREJ1ixS+QBx56XBFvtqG9GLK5nCyaN0/u7HyztG3XSX7ZdkAmzJgvE6bPx/oh6fFgH9m+5c+0JiCkbdSoqWza8pf8uvOgfL3yZ3W/fR+8V9UZ/M4bShmxCAqtsZNmy9ylK+H9MUMmj/9c9SetoczXRo8cKpfg3v/csVVWr/zhhIqL5s+V7l1ulTY3dJRftv6FCcNCmffV97J83WYpXqKUqk/XWB3O7YQGsCMVAwyLUiC+8bK88PyT8vHHQ6GMvEvtN38yR+CLIR+piRyVYLd0uUuFo+O7yffwZJVi+ir01FITPuygxRMnl7S44vv+dwotuTp1Zrzo/HjWKTJ+1LCwMiQuTx7VpFKKof3iyGXGa1Fps3Xb5pO+3I7tW5WlGb+3kqWs9++kGzmDTtBhAtllHyYkVStXlpUrvpM9e3apuyCWKYkpsnDONKlSpYqyaNPvhZduTNmUMfj+GzRuJn/u3CZr8P0rayYot1xQxr/29geyHwrMN175n3pmbOqxh+6VC0qWkId6P2l5psLCi+9iZJjFyEvS6orKMt7H8mXfyl0ItXljh1tkPSbEE2ctkAkz58umrQelR8//s3clADZV//8z781qDZF9CVHKvu+pVBRStJdIJCFJ+yLtCAmFkOxrskVIiza/7LRnTdLiX5bZ3pv5fz7n3vu8GWPG2BLny7x3373nnnvu997zPd/924ORgL+YvnR+HUbPbvp5j6F7n65y6N4D3TqR6YrCsFdfpOC0Cos//R+mzxHdW4VPVizDrBmT8M/fBylAUQVBBwpFjWUXNM/Mu0oGz+9zjGGZ9aFc+TzFGNScdUARbc6frq/3VDjVSBwVSGa92WMWAxYDFgMWAxYDFgMWAxYDFgMWAxYDpzcGHrz/HtRvejm+3PgzJlMeW/zRV9j4zU5c3/4WM/Dhg19i+YY1WLLyK0yh0WHhii/w0dIPMGv6O5SbqH+g8+Prr76A9etWY9lnXxvDxHvLv6BMtxSzJtPQQL2BdBQZgdLvCZQCb/L4MajFzCHbtm7D1198ZuQ4yZ5yEP/sk4/QpdOdzG5zG/Vpf2EadW7zl67EZ6u/Q/5zC7n6FGYPUXuOSTK0rhlex1yO91Lq6y+ShrxXXnwGzz39CN6YMsP06+m5Mhrn6bqPaDOyuYxixwJ9OjVHlUvKs35YR5QpzmxWMiIaowmNVXRgzgiIWlQsWwKD+3XAJRXLoHenK9M00yMNBmgYk56KUUoZ/UlG9/RVfGRG5vbSIKbpLIsfesbSgckuU6FSRepWPsdeZsUSSM8mWLJwPi68pJLZ9j6GDXgBGxhFtviTLzCFDrsLln+OD5m5a9aUCeZ9e23Qc/jmmw2HHX9v+hSjEzBGoaPIeJRd/UzfMP2MIuh81M/ovY06imt59xb+LZ9s6W6kL0wJGTmJ8CMAVUEGpBOm6sfo6oKc39IDSVelCDjpRNSf9CJnI5yUiDG+v4cD8esP5TzKsMXh59g9Jw0DWsNoFGfeWuZsdedQhHYSUj0PLDc5ryaLQIpDj9CZHUf4UBtNrHMLFkSBAvkwZtQb6NP3cS5gqZgyYTzKlbuAyveSSNz3f/Ra0CuYVjmqRVILmxZMRTD8E5+M+QsX4O2JUxETlYrhrw/H0JFvwc9jIoxFzzsHrwwcgI53d8K1N9yBjZvW4aOVKxk2/qHxKtAwM5vg4VFbavf8s0+jXr1GeP6F50konAVYZnJW30K3bl3NmKRoVmSaqfHD/YriiCChKV+2JK5u3R4Txr8JX3QMZs+cgzat2qBowfwkPD6ULVMczZq3pIJ4Oo1Odx4Bg4pgcPL5/vHXX0yPuICeO5Mx9s2ReGvMKDRqWC+Uuk2E9fln+6FmrQZ48eWX3PHqOaaieOH8GDHqTXMNETvhMyOIdBfJRBbfefbJRzBjytt4i9drTu8LOQrxtiykw4Ci/BIY8v/R8sXYuuUnHvWhaZOmTF95oWHYxLRpYT1WiGQUoPfOegum+vSYzOz0q/SlOjcvQ+E7dumKQQNewrebNuJDhpg3aXaleZ81Z7U4BzlvS5Usw3eZxXrpFfTTDz+gBKM0A3wJxPyKKU1vPSCf5QA3IhmNs4keXql8cXLnyEHDbFHzDqUZ7xn2PoVHfPpJt847rzDy5T0H74wf59A9FjKePGEcSpUqjeKlyiLhn70hdByKNgvtCj137UmkEfP9eXMwauIME8U5biwFi1pOFJdo4zl58+DZlwaha9cOaHlNGzJ6G/EFU3ouWv6Zme/GOMtn6tFtYxjiuxAOohbmHhgd+NjDD6FR3cbo3/8V7uURNeVfHGldt24PkmmS9xfjs0zMPWsRMqUCXbZo8DsfzVu2xdRJY+lBBixgJHCLq1ugVNHCpNkg3SuBBk2vwMxpU2k8vsMwunqfMqwz5nmgkfaKYgWY25yLEokiO05l2o/IROMRRUcxMvh8T3l9ReRFcExK+8H4N+Pd5GNS7VRfPKYM7sVIMSAP3aNa9xrHfNpObu/5w+6gsAbMGNbT5M43LDZv2fMjdgPw0q1Oos0clAWLAYsBiwGLAYsBiwGLgbMIAyluLSOx8WKFxD8KQmy9t8PZfdhnwGWgmJjCANljlkhhT4bXzOJknhG6pnu+x9uSy4W8593dh103/Q4pJD1gsIrRh8gr3ij4jULQ0WNIFyHQ8Bxwfnu/svsdipjL7om2/ZmBgQwiwqQvE3jvcmY3qvcw7RvovZnet3N20G2USoFsz67daNH8SuSikCOjkuTAgueegyKF6oNqDLw3Zz7aMGKsFLPVpLC8QrnzKa81uwKzZ8xkTfLb2N6PBXPno/m116FE8WLU7wEXUNfVqEFjvMe65W0ZeRZF+UxzR3JdOEiXFcW/vfuTsWTZEowaM46ybCrGjRuPanUbMhV/BPWQQTzx6COoWbM++vXrx9O9LCxAicIFMGrsW8aYoX7ldC98Sfcm/Zey6MjzUfXcdV/KMBWgw+NTTz+Oye+MwTvvTGUk0VXhQ/qPbHsPkM+L+VKkD9J9p/o9CTXj2zBGEuGcQq/UNQfOicOgl243jUWjTcSvNohHH7OpaDMcVANeILJ3YbniGPKycy4LhDnZWCJZkFvASCIOh++iY5zyVoBAqlNDzOdGAEexthjFdIrvjh4iuzXfpL8SpJBIFypcDAWYtWvCuLfQo1dP6l0jMWXi2yz/URIlz6/A8h/7jWOu9Fh6d1u2aUf9cGHzXlxQvjSaNbsM02fMQqsbO2D+nLnmfU5/fNKkSbj2xlt5PdaJ5/uVdlbxts3aYIZ0TPqZz1ZSP7Psc5alcAxieqbevE8/d5yrpP1MSXGqfaUozIug52f0MsSz+kmSHo54kWqGM4K1w2jg4jX8nBtmanLepMZH4L3R3TlXeC7R26bnCPgZ2ScD2bxR3dguBXOGdTfP1yyobjuzba56PB/e+3J0ffB2TPCbKGRaynJ05x9Lq5NiGEs/kHNLXmx2pdC7O1+pqnySSdi7fbNRQCmaU0zJqQQZhM52EA4URmmKbHr4MLPGmUwGP97+MGTJ2yArkPJexFmL1DWtb8T4Nwaj90OPmWtNnjSeCtQ29E5ZbboRkXH5zlC3xuOD50qZL8PXpDeGU9GcHw2pWP3jzz/x5EO9SNNHc/LLM4Sp3/h35dWt0JJ/9919h4ke6937UVS48BLjZXIkg5B3QSmFtXhLiZyUmIxN69Zi4PA3zUKskNRojkHHNR4PjJcL20tBrDHoPmIYKSEPjD27dtEokIsGh1R89+N3uOfebiQ+7F+GB7a/pEo1jB3xmmPkYN/h/ap/9WWIciwNa9MmIU/uPLj8qpY4sP8gHuvTHYnxLDjphkH/vfdvbOZ4X3lthOmflM1RcnsDPYrvIK/n4/31uLcTPv5gEcZOmWPqAen5HW048VFc5oxponchmfm2dzNn9ZIF75LR9KFYydKMMLzhsGd5Oty03i8xrFqIS5Qqgzr1GuPrr2g4XrKIaRNqmndb49R7rOedL18+FC5SAr/u/h1rvvwSjZs147tKLy3SaaVL4Eea25oyeTxWMdIod2wu3NOzN76iYUbvcDnmsRbIcBEOnkAcvu9M2pYR8fqbb8OY1wYYw1gk6cJ0plBsdcMtxPsXWd6qaItSBMiDZ+LoMcibv4CZ//uZrvBRzv/U4AimteDzEqPDd/FaRorOf3c2enXrjL9+/x0P9HkUZZmHW/RMbY4GxDipptymDRswaCiN+lqXZXCiMT9FqRDCHqGebZTrvRZJy5IKO2u8e3bvphEshu8RsJk1JO+5915z/QDbK4qsWvWaGDVsMJJZRTiKBlS+lRwjmXO5xB0BzJumw7q+O4aADGB8l00kGPMC+GnBUoSlEQg4VsNFhfXnoUDfRijk4mfoMW9KjKinH1m3fhdWrd+G5Cgn2YPnHxLWldlMZcoBCxYDFgMWAxYDFgMWAxYDZxMGzi1WDSOmrKTehrwfGUVfipttxFX4U4rIFB1JbsrrSCr4BGu+/cXIC1LiiQ/NCgqWqWac+CSz5i9V2aT1+nPrao89zOr0w44fkurJYkrZqB3kOZ2oFMpN0i5bsBg4iRiQXK33zMg0Er6yAM+QezTzRW0KFy2OCW+NRrkLKqJGLZYGoQLcyPKUMVNpqdj8/Ubc1bmzmX8ygkkuql6jFka9NsSMRLqBTd+sw20dO5vf0iko60f1unUwcojbhuOWbG/6DRu/p3+Y9PZoqNRE8yuvQTxlzYcfoC4r8Q2W7gB/H6TMuA4DXht5mMwq/Zx0Zx7ofjT3ZbRQ3+GOw2qj6/fq3hkfvE9n+ilz0aCJyp7IwZLafYInD5ofZ+CHcCNjx9oNOw0uD/hdQ5Z7r5GhAJWMbz4k90Z4BrhwCglUrVrKnKh3woG0355xJ8KzgBHh0jenhtpnfN2j3dvutjuoR3gVDz3c1+g4pr4zHjfcdDu++uor1lAPMCOSY1T94QfqXrv3MjiQ3jU2NpYpQKvijddfM+vWj99tRtcevQ87PnLYa3yHZDCiHiZM5+uN73j1M736PIayF1Rw1rBjwImMYALPBcSbGnoeAel/+a3ych4V0XzxnpXUd5qjJlCCOmlFWxqDss5hO9WQN2sg+1d2N8H/1uzC15t3ITqCBk4e3+9Z3M3R7H9E0GE5OyBjnQHWsq9ZqQTqVj4vO6cfU9tTYhhT7RMps+CLNZ7ikbIk86l5dkNrqDqmZ3fMJxmHL+Gff0890hv9Xx5s+vLI26HX1p2AfJENfeDP2u1HZnldz9AVSXP0rXfcjhee7IP1GzcimoU0N27ehFlzF6LnvXeDqXOdhThdjyI8WvAEUu7OZBpFpTWMi2GO47btaRh7CBPHjcHd99xtzk+gkUJpwp56fgDqXFKOEWnl0at3X7M4BGiEpabU9HWkD9WcSXKJwJo1X5pmuWiMMqGlXEy1AL87awYOUKEr4lGiaDE0aNyE7Rwj1J7f/8SiRQuNIvlH5qtd+gE9aKgEV7RHSkIy6/DEkfg6hjeN81wquvfRsyGZc0JK5vTKaxEmGQQPJKQw0uQd9nUDYjhnWrW5Do/3vg+TuBDc1qETFcoR+N/XVLSTqOXJl9+cI6Ok+s0OiPB8tvJjGj4iMH7KLDSox8KlJL7ZUaxn53r//bZMjUc3q/F8B5OoiD8nLhZ3d+rCOcL3ge9a+ud5Otyv552iRfP6du2w7YeN2PPnXowdPRKXXKwaevImUQQP5wpD5K+47ApMmDQZ23duNYbiS6oqSknvqpZbZ25693XzzXdg7549+GnLNuZLfpE5iR0mvz5TjCoalaZjr6n7fYjCpDtwRvykbxduvuVWPP9EX3y16n84hzXi1q1bgyms77eeBknRvcxAkWCiE+TvMO2dSaH5f22rVnjiwe70lhqDDvd0Nc9Cc1RRfC8NHIiLK1ZEmfPLokfvB0mzRAfknKDnlTmIBumZfvYZaQlzshcolNeszyBjzFcB8+bNQzwN/qm0IBUrWpxegvXZdzLriu1nWthFxjvvO0aqfbhsAdrddAfrmcVzLqQiNibWXFg0U8Vl8xMPB9mPvAeT6YAgRwIjRKV9nWiYknATRMvWDzuCYoT6YSMyZjIIBlJzIpF9tmzfl8wlccXfEgbEDS6Z+TSZN12W7VPE7MXgpu4jMO/1rtgf68PEkffw6RAvfNcjmIM9QNp3S7fXze9br6iGSfOZptfgwww94w9e24LFgMWAxYDFgMWAxYDFwNmEgVxFG+GNmZTnydkbJWw6wxgl3czRQX5NEJuyz3wnUeEVQV4tIopyAvmyLIGRCtTpIYkyi4/O1n66xodLJVlzvN4VnJbmk5cVV0d2mm5PrA1NmcXwlGGdhW16HRzj94nr6RgHYE87jTCgyA7J54LHWF/rlYGs654JpMqwLBmFESJ1bhiKoBvBma6UUKiHKB5/ddhwPNarB1pc1oSO1nG4iDL/jTfejNvu6kwZikYyFlzKFRdDpTl1tTIgcR6ey8gc1aZWpE8gQF1WfCINWzmNzOY42oJRZwWQQD2XRCJNXSnaPcOINwATgckfs6dRl9XuVsqrEWhJ5/hHH+xt9HidmOlpzRqWCKEMl5/9HS+s/OJzpPBv0tTpaNioPvVyjj5MhsegJvh/Blw6KpIYQa2BsVjRCTQLmV7PQnJ7z+dH87mwXEGEQ2+N/MyufCmZayC8SK8QHacDhCDF5zhAfPDuQPM7Naiq39rvmhGYnUWQ7DqOenp9H624MuTK58CQ96Mg8aajI3xcf0N79Hv4Aeo+qVuh3nPDhnWYOHMeUyyuMhFkOk36gxTWH4+JiTHb0qdIL5q/QH7s+2efcRSXU25cDr3PXE/CjktHYfSy1KHI4VfHwuF49TO9HujNPmV44pqXxbMMv663ffl1fcymO+2JVJbCIH6TI2Opo0nBFdf1NccjcdBp58+JZbNfYiCMjMnA33wO8by3VvcPxbxh9xMXPsx89T7ijnoZTQ92rJIg7fu8YYx3nS5tjAlzNrJdMmuYRSKR9OJ4wK8XIVM4/Lh0SMmRBag7y3XmGMZk5BDhT6ISy4OCJSoyVRcJPMGzZnrHTvb3f4k0ngxcxJCh1fMQoahcoXQI/14qRYd68coyOxN8PnnjK4SXDF1WSkNzhp6pE74ZzYl/RcvWmMo6OXG5cqJx08sYkULFa6idqCRndRgYwkPDkJS+P3zzDTatXYehI8YaY5kU91e0bIV3Z8+AFlTluNX7I0I2YsgrJHwF8ONP32Htuq9Rs0b1sF6PvCmjmN5RY9CQq0VKMolWskNc2X8iQ5InTRxPBiCA77/9BlUrV0W9+g0dIwK7/fH7H9CHNc3ESBctXhx3d74PDz3xrHNB4izSjQpT/xqncOmjxcAQX9f6Hz46GeQE332/CetI9IcyHaLCxaPIEF3Z8lqmZ5yCW+/saHAcI+Uzx6s+ha+ghItsgrwAKtPwIcPO44xIqV1lBc5lfTa/qCgh3Fsnm12fkc2FZxlr//jtNxNN2ILh2oqy8tPSrHfxdMOXon4CTKcghlULcQzfr7YMFR9Oz5lff9mBv/743bz/eliaBzK0VK1TBws/WIq9f/2J6ZMmoCANIkUKF3HapaMBYj46dumO14YNxa87d5h3sewFF6J8hQsNLpRWMQ1k/xVNc/rp/oNYNkzGZVe24HsyAbny5kXTy69C7py5zBzLavyGdhLH337D+b9xPYa88aY5RR5PLVpfh9lzpuHOu7sYepVEOhVFRmMI82kXINO35acfsYFGuEoXVzaGfTF2Rwt5OU4EEozwoHfci9yd8jbTI7IW5Pc//YAL2W/jRnMNs/jT99/jAUaZCgoXc+jeo888T1rlME5JXF8kiPjlokRQf0zCGPLCSqTlz3gcG27ZNAl9mNSObK/ILlNIlkconpn2isiLZmQaWSUyevR4JIPpeBXx+3Ceynh+kTwaEE+meWrkUMl/pL9aC+WpKTCCUxbM6rEws6Zz+2ExYDFgMWAxYDFgMWAx8F/FAHkpyTmKGBH/pG9Bqvsdijg4wv1RV5sGjNxN9svogaRjyAIk6wq8b22fW7oq9UnMviKjWfoLqEEYpLj8ZkhBR7laaanEO2oM8qDXPeUvWdkZk3uud57PYybD+szOptdPds6xbc8cDITeu7Bb0ispfVy1i8qH7T3CpuRvkwONX67ccoSWZrf0Os0ub45Pvv4Wa77+EuvWr2Wmm/fwQM+ujLL5Aq+OGK0X37RVDS7pCRR5IlAd6yjqCwL7XaOHnL35/quNdAVq76fOSadrWnjHzMlhH9/SMX7j2g0YOHS00VNFMdPHZVdfG9LjRUbJ2McMIhkJcGH9HM3mxVWrUQKPwCN0Ir2wUiWUKFPK0Aplu5JiX6kWz2SgulGoxIUVSlAu5gYNIwbkWErw08CRGUS4hosjGca8cw295g/h2oD7Dnm/vcAgLQtOxJh35vF9x8RE46prW2PmZOpW8pyDxk0uQ548ec07qIgxrSfJDCpQZITWCK8OmXQaJqtMrNYJvgMcWCBZ768TouMdl75CfUi3LJ1L+kCDE6GfufCSKqaMkKcryRZGvHXWXSp1K95aaGqVceyCCNWaIFDj7HzziygwGXKkVzH3ITSwuZnP3FbyHi8rnCmtQpokkJ5GzvOiN95zNweO4UN678zAfY1CTTRWQYBj8KfXJYZandiNtKbQE9Q3X0kD3u3L01yKqwgSPxkcSP3w+87vnSdygq5pu8k+BjSZHnzwQdbi+sB5Xh59S5cKQbXB5OWhvL2cQVleKEj3AE0eGYo0h9vfdie6d7yVyuIoPDfoNS6mjjFLk9GJDktrgZaiXSGsiexnPMOvo3JEo9+TjADjhErhRN3z1x/4bvM32LptB+shlST9i8KKD5djHKNf5sz/AGPeHI6eXTph6covkIOETSBCpzFlNKm1wHtQt259jikSvzEtmBSgzjmsjTZrHqMeguh4G9PlmUgMhzFQirk6dWpi+tzFBkdGoaxr8S9AhW0UF2KlO3SIgYxiKdixcxsK5Ctg2qQPE9c49Fw0prcZ+h6TIw5PPkgPAI5d0Q379v9NBmMNft76C84vXRjVqtU2492+fZu5BRllPMJj7pn3EGTOTHnNaL855tAZozBW6jxBnjx5MGb8RNx6fUtcfnkTvLv4Y5Qufp4xbDgEVPWjyCCREfIKd5oTz8KPjz/+lMzlOnPnF1x0CWrWa2iWHncN4f50hqD0OFLErFoZ7Tyfd9j7p/2mbh2ft8ATOs2PTD7EwB4JZBTzQNdSpGG5ihcahuKzlcsZLUnmgNE7ERxPMmmzjwbxCPqDdujUFW++Ngj7DyZg5KsDccttHVC+YiXEMsJSoIVT72oSa9PJOPzPH38Z7zJ5B/0tTzOzkDm5v+VJFkWDmxgk1TUUAy0DXEYeZt5Y/6vfYkR077d06Ih7bm+PHHE58dzA1wwtEQ08GhBdnDJpPKLIBD75UF/Tn57d2LPIigAAQABJREFU/+3bi43rN+GHn7ah0oXn09iWA4uWrMCokWMwe+H7GPfmCHTpeBc++nojUhMTSIMPfy/SG3Z8rmvXJZUvgY/X+2XHDqY+pCOL+95MY255zfs7br8NBw4eCA2/Oh0PZsxbQmZF5InMKCN3I2ikV+rZqOg4JBxgNS8+Z+N9y3dr69afUCB/QSP8xMZGGwOa1oD0EEV6pTnAhI1mbrh8IIPF6CzB/Tm0g+9TSioVGsSnPJk4As6VKN4v3y/+ifeKdF/7mQM7c3z0liKz3K7XG3xxHSeIGSPuYzc+THv9Xt4fMHXBxwjG+BEZNl/Cx3ZI4XP4mMPb2W2LAYsBiwGLAYsBiwGLgTMNA2QNjR4gkjWIxfeluoKPxxV530e6b9V9FSR4kQaM3E+ll7p4NinlYt3jnr7V64eSi7MpGYX8JDk/MnqqqwL8vnW9iUpQH26VAe+0w749aSik+GW/2qdxFyhT3SiOxS/+tX19mnMPnZdmt/1hMZAtDHhKbO8kTyclp7wnnnwCHy2d5x3K+NsUP+aLTtC5XqRYSBHuTkBvf4DO08mJKUxzH28iqGo0bITOPXrg6Scex+iRr2PA8HHImSMPDv6dxDph1L35mIqO7/+W7T8jNx1+E2k88FEuyknHyb///tvIc5S2jOy+k1li8rBsiJ9yniYgKYIRviR3yiBn0pBy4rw9jno8ypn9HmU2E9IP6aF+/f03/Lh5A376aQsq16gjOwazzvxs7iuaerNk0gIvYsfTkznRPVHGiO3pTbyINMfA4UeeXHF4Z/p7aN3iClx9RTMs/vATnFe4qNGlpDIylVc31zh9Pxw6l+SK7knUF8Wz/ncMcZxCITQiC8M/K3CTPgNDB3Q0t5jKrEbGoEGKSdUq9YHp7jwUeuTsj0x1IspSUx1dRUQKnyr780mHQ+Kvet2CoFuE2zP0GtRyP7U85rhnGNOYTdlw7hatzmL45tzMPiKUieyW29HxLtYIp01hyKDB1DlIJcAAHA2UoFINOXPlwH6+r55OVkjZuX0n8uXObe4jijqIg/sZPcb7kq7UO16A77wJuFDEGPUNGcHx6Gfu6dQBH/9vA/XJjmNwRv1nti9Jc40QkeJE7Jl1kPfmp21F+uiIZOcBBRlNLZB+h1V9EHCaI4avRwR1IdPfut9kL8pNfUvb+0cggfRBGXiWvfYY3xQfFjCaTDbSafM/R4B13UF9tqa397xN58fwEYjMfP5F0QAn+uDjeykdY4obmRiJP1lp769juGL2TzkphrH0wxCBFAFPkXGM30Yxpxli4V/DgBZnPYtBgwZh4KuvphmHQ1rCdzkvMs9A3ZuoWMwm1KvXyOQW3rf/HzS/umWWlNGz0Os9eXfmdLS78TZU08JJBloeEH5GCwx+qT8mjx+FJ597CQf3HUTfHvdQkd8FNerUx/mMVmnesDoG9n+K6RVfyeZogboNmmDy22PQvbtqg5HQcBxahKXIl7EvIZmh45q4Uuzz3U4Pam+i3qJjUKVKdXy0Yilu73g3+1L+1lSs+/prVGGElunXJXLhfehcFQZ+b+YMXNvqetRv0MAc1rVFKIa+OgATJ4zG008/TcNZFOo1bIo5kyegZ7f7DIEXkVeEWTRzCRsjHp+zwoE9RkL3JINYKlcutZVyWoaz/AXyYfKchWjX5lq0vfpSLFjwPoqVKWPGLAcljV3GMRk+z2ZYMG8WF2GHpm1avwZ9e3bOFjrue6Avzi93gXmWOjG9cUDpMEUzPU+Jo+lcz+Zo26ttFDmJlm3b4eeff8Avu3Yaxkl1mjxDXDSfc5EixRmheB0WzJtLhngvmeghHHdFnH9+KVzAaDD18/133+Dbzd9h9+6dJh1pHI1AmheKHJsw5g2+90pdB6xa+Sl++uFbxB/cj2aMIN3D+my1ZIQ+A9cB757q1W2IfExHsf/APlzR/GrONcY4HcaVHv509exTE1MxZ9o0XHfDzTS81zG0WnNY8/TVV17i/B9jihQnJiQyV3tX3N65Cxo0aIyKFS9Cs4a1mL72ETzVr//hnWewR8KG+pV3VL1aDTDl7fHoxlpxMTGqmcg0tXwfPVC04ZFA64kM/6KN1WrWwofLF+NOjiuVxEO0bv3q1abOmPeeCk/ymJIQFA5qK6/cxTP7872kgYsMNUkWGTPgsjaPm/dO11r+7vPEC+cPh8dLcj45jHd4X9qOZQpFdmfa+VkbLSJCfIjLQMtFStfgbdWuVBzRPB5nuPj0vbDvENk7hI/DW9k9FgMWAxYDFgMWAxYDFgNnHgZeHjYaD97fjUo413s82am5GqBjkuCQwSnje1cZ2HCQDKpIEfGF+q5eyUmnRraMfR0Oai8+k2lXyLuR3wzxZdyVru/Dz7Z7LAZOLwxIjpY8IyPSc/2fw4svvpjpAL1EeNJzN7gl69ImVLuaueWjXBOkLBak4lnGi/PPr8BSH9QD0TBwETOBLFu2KKSnUgDD+jVrjLwmmUljvPDiKvjy85W49977Kc+xTwpeG9dvQG3q3DxQO0Vlycgn50pOZ2YKS8G82TPR7tY7UKN2PcpzmtkyZACvv/oyZkx5G30fe4a1wJphxjtj0eu+7kiisCc5MIqWHMmnCQcSqOuKMfTAGEB4DcmOcXF0jmT+NxnHAszoJFAGpJw0js2Ytww3tb4Sra5sgjmLPmTd9CKGXki/cjaB9DE0mxg5VzjPLol0RWU+DxpYGByQIoGcINdVgU+5QAlB1+KV6v725GW1khxPLcMJ0fdIf1mtdm3kzp0H++mAfWmLFka36c0hvS+S7yvSaf2jD5firo4deM/S40ZgHd/pGjVrG2PYxZWrYQWDQu688440xy9mNFdmcLz6maaNauO5px7D00epn0k/lmXvvWx2MZGb8030N7/hac4nevcS0SvnDnAOuA9a9iz5oGjNDHKuRPP5aW5J9aG4COlQnDVVNcY01535mUyDmaDGhTQq0+AWGfCeu3th5yrZ/gx6Az/CmTm5rkt/zYeKdd//hjWbfjtCy5O3O61G6gRdJz1jtGf7N0x6xHy0ZWph75ZV2Z6YJ2hYthsXA95C7H1LqRgOingwwAVUIIWuJksKPcSiuEhmFxi5ii/WbDCTUYadrMB4hTDqYO6MGfjr7/144okncW6hQuY0KV1VHHT7D5sxe9Ys9HnqJTz22EOMHIhGfxrJqMtFsSLn4vmXX8E9nTqhRdvbmFKxclaXTHP85cFDcN2VTXHT9W3wBPusVOlicQH4ect2GgD2oGD+cxyckAiJGKcHY9iSJpccyd0cQ88He+HjT1bSW6cRli1dipUff4hp7y5Mf1rYbz/mvjuDUTd/4ZHH+6FUqaLmmDwblN7wpx9/xrxZ0/AkvYv8fCYvMyd1mysvxQ1tr8HLQ0eyxlpZw5ws+3g5XqSCfNknXxnGRjlmdzOqR3mkBRq7olk80LjP5b3NZoRI25aXo3XrazB93gcoVbSQMXbo+noPIo4y6sXr90z7TkrYz8Ul0hjHNIf4MpzQWwyFV/P9UTROVqBFTvmS5SmpVCJZgbwyVQg1ltT/lltuxaCBL3LGHyICMo5pnsaSsW3cpDFKlymN9/i+/fDTj/Tu+o4p9b7HB8uWmfdH9+/n+6QQ9ksqV0Lr62/B3yysO/LVF/HlV18iOmde5hW/ETXqN8De/9vLmlRf4q9/4lGpcvbmZFb3dDoep78RPv7yazKMxC3zeisCKtLzks1kwD4yBu/Omoq9B/bi0Sf6oUTxwmb+GmM76eL33/+IBXOm48lnnsOjD/VmbF8KXqADgCIBzy1YAP1eHIAe93fhs7iB9eMyZ/K8YYjx8dMD7GV6X7Wkp91117Zg7clBqFC+Ij0DgR27duO3XduRJ/+53ilpvmVoFzMqJjxID8MOZDZ79u6Fjz76hAa7hlj2wRJ8+tFyzJq/hDQnkgY3pzaaZ0RM25njdaZMACYazeHpmSqHjB3rlEalHjDXCvKFF8MtBk9kmAFkSOSOaOJaTHpUaqzxbmrTdSAWvM683ry/eQNuZ7pZJ4o4KDcqQvteg827vGL0/WhapUy6eOk0I3N+uOtiBkfsLosBiwGLAYsBiwGLAYuBMxIDjzzWBd1urmp0OhJPIsSEEQKuHOQpRM3ODD4Oy/YvEVpdqDPK9ylUYAocmcTZNjvcj13bNpmtQkyf+Ne29UafpIgIIytJmUGnUgsWA/8VDITrkEyEVRYDjwwyE4iMyJT8wsHTu9L8EL6b8xPo06Mzbr+ZujAasZIY/fXN91sxipmVipcrh5jYAI0Ht+PB3n3w6Rdfsk0trFjxIT5d+RHrdM0xAQ2S8++kc3dvps5fufIz1GaWnBUfrqDhYRneXbTY6ADMHKRRSqD2UqxLJly8YAH27juAvsx8UqxEKWYOk/6eGaFYp337lu8wdepkPPD4s3j+xZfQ+spmuKF9a7w0ZDhKlyxjDCnLVizHgH5PYtGyj02/mt8JCcn46//+QcqfcpR3ypLExuagQc1J75pE5+08uSIxffYcXH/dNWjT8grMXbjU1Mh2iE0aFJ1eP3ye6ZOIIkRLxmVWFJobHT2ACm1nAtGOnYqtnXbRbkRYokuf4Xf7dyPCnHAudui2Q0qc6T2oKCGCIs5kOAkaskpnVlfFFMExCZS5RZAadMZLN1TntzxZCToviWQ8mXpkJaeJ8ULLzNHsfyiKMIrv2eerVhMfMvYwkMB4znIs1G0Z533qI26980483Pt+1nlfjdp162HJ4iVY8dlKTJ8yjW9JCm7j8ceoP0l/fO6cuea4tBCam87dHBrncetnXhhI/cw9uK7tDaiUhRHu0FUPbUXzHgWhcVEBwgIVHK10ONSteEpD93lqOQ0QT5F8/tFUxh9kRpwAjdPXdRuDea91RAJfkylD72b1LkZi0nE7wP40d2/sM8JEha4Y2wM1K5dCtC4YuqgZwrF9MCI1c+B7xOtIbTZ6+mqs3bTdNGeuID61zN/9zPs9+qOn5Cp6jJpTybLkho1NxDN8UQg7ZDdPIgbMZM8m7j0Pe6XzOxZQSGoUFfhKHycPlcxARE7RTaptVK1aTVM3zGsvAi2m4JYO92AoDVivD34ZE8eMxMxFHyGaVjFFPghaXdcOs2bOQY97OuCzr1d7px/Vd7lyFTBj0XI81rsnLm9cG3GMktC7q7e4UaMm6NmnjyHEAa4QXuhueMcekda+Nu1vxtrN36J926uROzYO8Uxv9tDjT6FJs8tJ0BNNcchk3ms46P6mTngHtWrWRbFixegJk2g8Z3Tvgtvv6sp6aoPwGZmXhuynLMc7/b0P0LdPDzSoXpHRebnoCZCC3AwlbnvTrZxjWjCC+PKTj1CtYhlSSXkWEE9kMBpdejnysoCl3gn1n8TwXtWAm7NwOa5r1QI3XHMZln7wIfIWONc1joWP9Ozcvq9nX+OhJKV+kM8uuzSsTJmyxvB0pFSKev7XMJrr4EG+H3EOk5IZpvV86zRsykjJSqxndU5mTc0xx7uK7wCheJkyuP+hJ5Bw8CDOPTe/iSRLoeeVaHOAc0k5fUuQme3Z51F8v2Urvtu4jhFmu7Dv73+MF0luGlLPZx8VL6rCGmQF+E4z8rDgeXzv7sD/vvgCu3fuwJ5fdqJQseLIm78AvXXqIX+h85Avv2PoNoM4wz60oIt+KeKK2QUZacT0MLExZOblrZd2rmd06/K0mz5pEmpXr43C5xU2OFVfqSmOwH9Hhy54Y+irGDViKCa9M55zdQnfEyfNqQxcrdq0x4K576JH57uYV35DRpdIs0+GVRIEs68i0ynOXrAcj5KWNKtbBbE5chgDsA43atIUfZ/on+Zc74doRxSNxYr2FWN6/c13YP23P6A9PfZiGTmbTBrW94mnUb9hE+zfn2Sib8XEal3IrgefwzdwxBwTL2kiuZSSM4J4k4E4Pe+kGo8CQ+M4WfRsdN0YplvQcmZSceihuUB22NvM+DuLwxmfZPdaDFgMWAxYDFgMWAxYDPx3MeBlEBA3KlbI4R8dHY/uSvJIpnCI1cqwWbhBjKpgXuPwDtWFdBKmqxDvxnZpbQIZ9m93Wgycjhg4Wn2olN960TUrVCMpK5DNYOe27bj+2uamvni85DFG2lSoXBMjp8w10V3tbu+ANes3o+21VzA9v/RU8XS8fhZXXNWCEVnxJjqsHeuSb9qwFte3ao7cOXJSwZ6MPk8+h3oNGpu5KBnSOE5zbMnUA0QzK40U7RPGjjJROucVKWrSMvpdx0I5F998Wyfq8Qbho+Uf4Kqrr8KcxSvQu2c3NKxaAXG8RoDXyJU7F2649Q7KiY78Kzyt+uJzVClbnJekXk7O+pTnmlx2BdvmNvKc2vgok+bNryxIi3Hzdc3Rls7js95fjuLUqZ1NkEIj4dT3Pkfzy6tTt5eDzyQ7d+/H1LmfMfKuPgk8I3upv/Dovaf38tYDj0yHqLVriAvSU0JyuleDMjtXz6itSu1ojUlMdAxi0vlqTHrm+pOuUw66N996Fzawnl6bFk35TufGwYQEPNjnMTS78mqjU7n1jk7YxBru6Y/Xrd8oo8uG9h2vfuZa6qYXvEfdtNHPOCVZQp0fxYaHfw/fjhGJEddEikpWudOEPXmLIfUdxJEyAknnosciHaOeH9FlvuUML72I9Eeeiko6FVMCixdiWAwfIE8MPdyjGOgRm2TRiVnUpUOjQZXz/9+ACL5I7jCyd3lF2SlCIR+9dvZuXeuezJ0ZgvOAVMw0fd7mDJvbnacUA+mfmvO0Dg0hhXVjzIThRFMqxXlv3XPo4DFsGT0sz9NENERMK3c6UGSYQAttuKEpXbNj+mmIKK/tERiN44jAQ2vXfIVYGpvKlCqH3HnolcJFPzsgwp1MY8PatatQm6nK/DGOPfpk3f+qzz5DUTIhRUuVMhFmwvGJBLIcJ7K7/1xfWiaOB/59/KUdvwwDYnjEeGpbC6wxFvAmM4zoyeLm0y973v16ePN+Z9HNaXvYu4/jGWA4DUpPf0T7FBmqeetEyKbHaPau7PUhOmoiu1wDu9eLjouR1DiMUwLfAW0nxidREFpNQ3selC1/AevQKde4w0B55+pb+2TcSlSNMTJYngHfHCMz9fnnH6EKhbCceWmwZxsf8xbq/TJOEhnSXoe+eFTL56beSOKScEWbfogL/J9JDfverEGG4Y5iQ+OB6HeExCjm1lYqD6WjbXUXc9vnScC8ofdDlS0kQyW6HUe7KRNb9xhh1rdPRncmZdO1s2LGDl+vdK8WLAYsBiwGLAYsBiwGLAbOVAwUoM7nT9b08qQIqsHNrdL11Xx7kStHun9ymYcdIjtolJ2OUUxGNir5qPHL2EmJVybPem7pKvhj+8awvtiJ+MlDmsGwY4c2D4077Za4bNUYM8p98rF/bUnrUHuo9aG+7JbFwCnHgHRjrgxXv/0bmDWuqxkCc9g4Q5EC24DzrcieSFomUunIveuXbdiyYxcuqVYNOXPnMK0iAk47STVU9eHzNStYAqQmjUzOca/OklPvy89yCEGsW/c1ndZZ3iTakf2c69FQx/mXi6loEt20htJ9SXco8HR5SVTeK+LnSKDyJMpYs5Gp90uULIlC1GWlBe/+0u7N6lfmMmdWZ5+64+kj/hTt8+2Pv9JIqDqMdBRIzdwYWvmSsmawqX7nfRgxbB7mv/8lSpYshtf7d0JM3pyU0fU83MCUUKSYg1cG4BkDyoG98ej+1JvYvuMPXHNVLTzYuZUxoqToZMLaTdvMd8CLDHMfS0zAWQeUwk+gp1/14uJcHZSJiH17xb/N0ex/ZFf/Er8/Huupx6hWrbZJx5le35LRcU/Poe/04GXuOlb9zHHrv0Lz2xmZ5twVbR+nE7v0NkEsm/uyO2R37CQX4SAjV6u7RiEynw9zGSmmVqrwoQBCTldExjLaM5CMdvdPoIE7Gf9729X3exGGx6eO4oXCR3P4thdQqGZvTv0aE2d+YholR+bAnW1roHv7GuZ3+vV45JSvMGH212QBOEDqMO9sVxVdbySNOvwSWe45MnXK8lTbwGLg2DEg4mQUti6RDe9JxMgQbrZRbtgTDSJsAlNgMZ2S+LBrcVZVqVbLLOoq7pldo5j6UwSFrPPhnggn8/5r1a/v4DarezvsZu0OiwGLgZONAdE3jwYFGb2p2mHhIMOSDFgmN7i8tI7TsC0jm5jgjIxi4df1om09YUaFdqvXrmtoiWc4M15G4SdxW/sEoo8husZ78JjLOnUbOoY3lxHXfp+h/zTGUrASw58dCBnvyDkJNX71pb6PQO9mD+zutIsGWnYbTTZeefCjMH9gJ+IeTCfQlTimrGmUMUpJYMFiwGLAYsBiwGLAYsBiwGLAYsBiwGLg9MCAozeT8vfoxqOsL4IIRomcV6w4CpYobWQ2LwUdczCFOkpluvmatetQLmK2E2b5UB0xlS5JoTFL8pVqTsfmyIUadeoZp9koWlGCzNIhmU7GLDnSyiimGkEyfkm2NQYEymfaVjsWFAhdL6MNOejqrxrHIYfdEwUaW3qjyInq+2T2Q/UhKlUoIl2/SfR0BDE3NIRU5SskpLrPvU3z2lj28QYauPagV7+3MaB/Z+TNSzMV2yXT2zT9a6Soov0HE9h2DLZs20UDaV60vLS6cWSVA6rpm8+z6sUljB+CV5LbSbXImt6uX2my+1pFULZmaSsn1a05+9R+yCG3Tr0Gjk5U70C6y6c/zpc+jX5G+ttwONH6mfC+j2Wbw+WQ+Tx9NFEqBU4W4FXzeHfw3UYvIoS07kW9CGuvS4/y7vC7TQ9zeNwBPryzDDKnUMeIDM/i7U04j+ymaGZbOOsxoMVJys34+ET06NIBB7jYpodohsu+MXpchhb+9G2z+zvhYBLuv/cus4Br4U9f92zi1BnSkoZASuEI1vLKSrEcOiHdhpTgIq6egvtU3L+nyJaXj3BtwWLgRGEgGDY3MurzOB2CMuryjNonepBImtety91kTA/3/opjCoohI95CXE6mDMiKCz4KzHieTfEHEtGrWyfEpwtPVzpQpb0Y9dYEYyjyCh6L2dJYveg1I/zQYKd6ckpnoAixaBqY4uPjnTB9thXdIftuRuUJIboHb1vGs2Qdd98hCVXeduhWnNMP/XRfODErH87uRz7OYdQo54mHRRI9Ho1hLOTD7JwqBljeWcqxT9ugyY0ewSiySHksillXJBo7kBOGnDCTKCLQVEfDWLoBhEbibbiD937ab4sBiwGLAYsBiwGLAYuBMx0Dh9VYTS9fZsUfuZrTcDyZwmTSD4lrcyIOwg+n3Sa/x/Ze5JmntlPKxSCVwVmdnbYv+8ti4L+FgWS+5ywnbcSmVEZHeLMvIGGIEOlNCFeOSaZwo+mVxHCQaBq8FLkjeScqipk1ZMzy5B1T+4e/mXFDvUeyFrNkR8lM0RSgJO/lYEq6eNb3iolheRH2Rz069Us+JBxIQtfOHUwb1RyXPKaMIGoTxzIArw0fixy5lZWEg8tCplXEmFK7yZClrB/hoIipLE4Pb/7f3E5XAyw1UWUKKEMTdZJjZWTKDKJc8uq9D4XKFUX/F7rgiYfHskb8bjz92HAMfbozUgoxckw6+QhPBxFjuv2DEVa9nxmFXVt/R5643Oj/1O0of1EJHDTOunwv2ErPRu+Gn86zyW5oWKi8vSs+i0YLfNEcPy8TZMRihGu0MwdO0Yent5VuwtsOv7S3zzuuY+H6Gc/x1zsnlg/hROpnvH6P9tuLmPN5kWP8XjbzWfA1McZT6TsM8LcD7g73gIlY47YeD4PMQqBAK/2MDDg12H2eUdqNFEsJdZzFCxjqMeMNmcYzA497MFfxotR4gsbqvlKZnX5Cjukdt2AxcEox4ClYU1nkr1X7diSwh19exCiSC6sUsydCORx+BX9sNGsg3cTFmywBiXtWIM8aEw1BQiFlsafkzeo877jnYaBQ8ri4GDIoKhhJpuIk3b8iPoQ/k9Ysg4g8b1z222LAYuDUY0AMmNJLtLvlViRR2EgPqpXoI42SQcpj2tK3yc5veRiqLx9D5K+75RZeM61iQjnCRZNFb9U2lRyIjOmijSkSUlId4URGdhn4Y4yjQIQxjokWxrEOnto6KWgYnXaEwekahna6AtwRmmW5WwKX58xAUpcl3NB3BN4bch8FNUaHDevIfAHOKcJCMr3jrr9fqRR9+HBUt5CQmWWntoHFgMWAxYDFgMWAxYDFgMWAxYDFgMXAKcCAn8YFKY8VvHM0ujFPsR9FAUg6IaqzaOhS2rS0wpNJdUi9UZAyYBR1UzJqCSSDptAa5WXqiJSBgfKe0ucrg5KJxKJu6/qbb2b2D+qe6Ljo1Z2S06UcJX1xUUYGNan0vZAVHTGGrpAGX5czRrVIpu2X7k/HBX5dU46XHN/ZBgZHMoBSW5+YyOwo2dTpqflF5QvhtefuRM8nJzAK7Ff07Dca/V64j2USomisSovRHk+MxLbtu1AgJi8GPXUnLqhU0jTQuxagVc5PQ6gn4zv62bRmBO8Ree+d3hXJ7MaYxvdBkYunEuTIG+06+WY0X9Ifl54iXD+T/p3Tu3ki9TPHiwvhW89YRiN3umTaJR+HgTYPjMHcIXebc+cO6Ywg7eGa8kpgxEyK0PEAN74af1+m/Z2JB9O+0SfoDr155top2au7xS9NqLOPtJ0gxJ6kbo5IpjzqxwZBahPpN8K0Vcc/CLNWUjEcw35bXHNdlh16w8iy4VE2kNfF1S3bHLF1+uuJkBsmxM8ICnP/R8TYEfsUJyPfngQvbdhJvH/V8AmH9PcTfsxuZx8DxuMi+6edRmekfX/lgeV5ZnneWN53RoPOigYc6X377+PNwcbx3occcaJiY1hsuGVG6A3tC7JoLqWREBMaOpDdDT5u9RVDwnfV1ddmebZD4/QUySCqHphL8w5tqwvvKSvi1mkrOultZ3QR3bf6zOr9CfELGXXCfYz4l0RlPIgoLzHSS8KY9rt0T4w3vZojyCnKu0opBiKCB1g7OCfvJWi8JiljwecyiHGpTBlC41+Mxqep4XliWUaFyLBgMWAxYDFgMWAxYDFgMXD8GMjKY9y7Qsb1xRzuMChtIPk8cZ4em6bIF0eycT69fo787TCAOsupbcaoDIY9KNrFuYphWMNOP9p+w06xmxYDJxgDXtCN/AsV1RHhCFYmpbwulT6gM2SIkJwWqXc7lTITgbKl4NBb7kQMaULJedsRhpwvnSXQpcINBTKOCWLiotGyhSPPHimqIyWgKLLokORoTkwzg509UWyjtrq+p4dQNJKno3Banbmf/mC6iBrzmOhML6OFu53Z3XvPynm6QE7RNzqAlr2gAAa+cCN6PjUJX2/fje6PjsSoVzohpy+n6W7vvgO477G38NvOvcgTlRMvvtABZcoWRWqCc7WgXjy+Mymh0DCH8vo9S4s7qKBLkNPI+ZS7NS7PWOY2Paav7OpfoqJinHeOr6qHm/ALpz+ud/yU6mfCB3MU2978DoYiuJz7YmCeWQwzukenW2eu6vVKYeaeiIh/qBM5yBSKuRBB1YmMQcYo5tYH9OMfRo26D5PHkt3VNcb9dvo8hs9DXWZ6sqEjrKdHE7zTjrUSs6qvl2mH2Th4Ugxj2bi+bWoxYDFgMWAxYDFgMWAxcFQYkNej8VojB2gENTkuuMJhRh0sf+MBR3tiGDKXO+a2x0B++NaDzmnaIXnwKBm3jK5l91kMWAxYDFgMWAxYDFgMWAxYDFgMWAxYDGSMgXAjW8Yt7N7jxYCytCjqTDXfKtLQNbhfR6ZKHIdffvkTXR8eh6HP3WMiDx94+h1s3bab6ROjMfyFzihzQRFzab9bRMy1o2aZVSXMXpPh0DmaDPfbnacGA/L7kPPHp6McvYfrHxy6uC/WMQt98lYfZ5+nKAm1OPM3rGHszH/G9g4tBiwGLAYsBiwGzggMpMJJP5maymLOzAOh4r4RdKNSbnsDhu+m9y9TQhpbF21hCjKTvUsMj5fqwWPQmQzEHIs2bokynFnLmMGj/bAYsBiwGLAYsBiwGLAYsBiwGLAYsBiwGPhPYUBGMVPvjZFdPoYGXVK+CIY9ezd6PT4cW7f8jF5PjTAlHXbv+Rt5c8diSL8OKFf2PJZHcAxYyRKwCf6AYy6QXJ0pZHHYOp5mir2TflAVxKL4aL2CGuniE90K7of8g72ozfTtTvpA/8ULUAtkwWLAYsBiwGLAYsBiwGLgzMOAjGLJTM0hZifZZdoVYCbDmP5pv9mttCBO3sgzDwn2jiwGLAYsBiwGLAYsBiwGLAYsBiwGLAYsBs54DMTGOjXhFJ2n+lnJFIIrnF8Ig5/pwBpjcdi6Yxd++e0PxET7MfCpO1CxXFHTTogJqMY4zzN/tJD4+Kc+Mv1jRhdldTnS3xmP8NP8BlkqPgQyfab/C7rZd6QTOVtdhG3EWOgVsRsWAxYDFgMWAxYDFgOnMwaYFMIdnkk8byLGHCc2x88nhVyNvOQiyfGJsVPlsehIh8VzospYe4xNveptqk1hQLXF0lcido7YT4sBiwGLAYsBiwGLAYsBiwGLAYsBiwGLgQwwYBNuZICUf3HXgYOszeQatzSMQIqf2VWAYsUKI3fecxD/568mtV4ubhc+r6A5LpE4koYyJAUpEjtydaj2wL94L/bSx4+BGKpPIphDR09VdejcpxvqOFp7jFXMiSlLcfUt6duFTjgDN86mez0DH5+9JYsBiwGLAYuBMwkDMuroz0eGVJ5Z3ra3/0y611N7L+L2zmY/qFOLbXs1iwGLAYsBiwGLAYsBiwGLAYsBiwGLAYuBU4uBcKOY6o3FxgH//BPAfY+Pxe+//4OY2FjE5ogz2z2fHo8DBxPgpyNpSiCIqKgopDKlovmjsUzBROoj0z82Ss3k79Tevb2axUD2MWANY9nH2X//DIe60SnA+aeKLfpzdzPHaIr5MzpE6RGNxdjx0g/6XO967bZgMWAxYDFgMXDCMBAIS+WnHN/JAXr20NtL2zKMWaCXE4sB6y+FFcOUIDHCn0IDYgDJrDGmP0WP6S9Ad6jD1ysx/HSXU3SYt65pSeNfCnfrz4SZ6duCxYDFgMWAxYDFgMWAxYDFgMWAxYDFgMWAxcB/CAORdK41OgTqD6Rf+POvA+j25Gj8vH0nomP8GPx0Nwx6vCviYn3YtmMH7n90LOL3HUCyLxKJQdbuZgoW/SVTZNafhf82BqTjkCZJleP056o/Qt/e3SlSzIkWc1ooA2NYFkav2Rn5bV/zM/Kx2puyGLAYsBiwGPivYSA6ikyon6kO+CcQUytmVn/atpB9DATp+ZaqKDyyg8mpNDQ6qM1+R/YMiwGLAYsBiwGLAYsBiwGLgX8FA1LqiRMWT2f9l/6VR2AvajFgMfAfwYCyzgjkWJvKPJe9+k3C1q2/IleOnHit/924iDXFKlUojqHcjqbeYeeuP9D1kfHYv+/gf+QO7TAtBk4sBk5KjTHFIQmccvfacnJVIiKgH+5Rs/mvfFj1ooMBDw/RJJiCiAjnOUWYqizaYXbzQ/vVhtlHjae9t//I30aZG/Rh6ZL5+HPv38hJIlytWi2ULlOCeU2d9+NIZ0dG+5CUGKQ1248Pl76P4sWL44IKF1Ex7JwRH5+Mjz94H5Vr1EDxEsURSUVnIttHsD7MuvVrsff33Wh6WXPTeMOGtUhMSESt2vUQCCSzeKSKQoZuLDQEtYuPj0dttguHX3/9FetXr8alV16NKCbmXc92v+7eY5pERcegSuXKKFgwvwk7Vviwn6HHwWTl9PVh+fIV+PXXHShasgyaNGoKv48K2hTiMQvNrBayYDCIRYsWolSZsrj4wopGMS6luSBARa8U5/GcTh8vex+//fYbYhkOXbV6LZQqfT6iGdXnJ7K+/OprE2lSu1YNc573oRRta9euRhLHWZPnqK1egXjmE968cQP+2L0DTZu3QFQGePL6sN8WAxYDJx4DSckB/PD9T9i65UdSSYdWiZb4SeSqVroI555XyDC4Wc3/NWv+h4OkZw3qNzpskMc7/8VnK81CgEx2kITj42VLsGfPb8idJz8qVroE5cuUMnR2w0bSmKRE1Kju0lR3ffGl+hEZGYmvvlqFYCCFtNmhT1J0REdHYfVna7D7151o3rIFab4fS5YuIx39BaUvqIA6tevAnwNISGBR4Ci/KSQcJfrFu/S5/SeRNooHiaO3W5Qi8MxxFg42bXiOSDCXAL9OEqjgGMGpU8Z7O3x5MMe9D3cZ8n7ab4sBiwGLAYsBiwGLAYsBi4GTjIFD7NmhLeeSHmfm6puOMA5Wn3WOuHoP6Tn8h5QdZASjDT9JbYGj9UhJMO1TfTFuj+mve4QL2d0WAxliIHP9F1/ADM/ydkpOEkRQ/vK2vWMZfcsxUBFDiv5ZuvA9/L7nL+TMmYP6uJrUx5Xhu+/Ml9SIaKxYvhQ7f/kFZc4vi0YN6prufJQ9pdPyUa6S/mr5ik+oA/sFJUqdjwYNGlAvdkgnpRR76cGUAgjTJb2/aAGKFiuJqlUvQSApibKtMx/XrduIX3Zu42SMQlRkDC6qVJE1r4oSGwGKaIfm3Ib1m7Cb15d8LEjm2KKjo1HgnLyoXqMW9/iMLm/Z0iXYt38fomLiULlyTVQoX9IxplPQW7duDXbs3G3uyXTCj2jq93Lmyou6depgyaL5CHIcwluQ96T7UlrBAPVyaleocDFz9Z2/bEfVGtVQIF8B4iHSjHPPH39j1Ref4JqWrURYzLneNfR9SC8evvfQdkRIx+rgMpXoSZHq3EWt5O9MgfJuOOj5CwIHE3H/k2/h1207kCc6As891xPFyhVBcjQNZsFUlCtbBK+91AMPPvEGftq+DX0eGoE3X7wXqXlz8nkAsQwX0vvG14A6CDrrEgN6FanmTQM87IArX0u8VuluydfSG9g63h6C/p1vPQfNJj0nfad7fM7ONPudFqrVfraAyyGcLbdr7/NUYEAL4ceLF6PHfV2ovIxEWRq1/vjjN/z4w7e48aY7MWTY65kOI4W0U4rfD5YsxR03Xo+KZcvik9XrQwahnHExmD1jKl556Rks/WQVDU6RVMT6sXvPbtx63TXodO89IcPYkAEv4p//+wfT3l1glLVSMCstWXoY/MrzOLj/IKbOWYAULoImLy/7/HbTWtx+2w1Y+81WFCtSBEMHvIDFi95HdI5YRLDd/gMHcWGF8pg86z0UL1na6ZZjv6Flc3zzzXe4kIaz9V+vQv3GTTH+nSkQk8HTMgWlT/tk2VJ0vOVGlC5dGqs2bEYUV0MZxLRQCzfz5szAow/3wb59+1DlkupISIzHpo3rUbd+Q8xZsIiMQRJGvjYI+2iUnD53Xprr6f6HDXqJeNmHGfMWkbFwFs5xo4bj6b4PYvAbbxiiqZOSyQhEaSW0YDFgMXDSMaCosAEv9MOiebPJ0OdwaJWM6TRmv/POJBroryLz7ct0/s+cuwBDOb8P0uOrwdxGh435eOe/mFsJKgvmzMJTjzyI/fsPoGrlGvjn4AF8s2EdmjZtgndmzMPggaQxf+3FzPc+SDMGGcXkhDB86KvY939/kzbP5e1R+EoN4s3XhuLxhx/D4CHDee+paNWyGb7/4UdUqlwFMvbVqtcAb02eaqLnxJSLUVf+88xA65GfdNMAcUn0GdD5rk1M0ooFiwGLAYsBiwGLAYsBiwGLgbMIA3RZpZLQ4RE9x+7MucqzCDn2Vv/zGFixZDG6Uy/mo+xV4cKLqY/bg29/2Izbbu6Aoa8PR1JCMtq3uQqbN21EhYsuxqb161C/YR2MnzzL6MKMwzcVVze2aYHNm79DhYsvxupVX6HRpc0wdsJko5cSktIbwbx9+pb+6vOPPsSd1OmVL18eK79eRxk3xshmiYmJGDLwRTrSv8+aV9HGGHXg730oV+ECyr1TUe6ii4xRRY6Tkm3nz5+DXLE5kcg+U+hMKqhbtw5mv78MHyxahB5dOlC2i0bFCyvhj71/4LuNvNcOt+PlV1+nrBlB/dfLmE+5MzZHLuPcqfNp70KVqtUx9/2luLfTbUhK8dMgJLNcChL/OUCdX041Mw76La9tg/jEBMx/bw7uuOMODBgygva8SBoQA1j79Re465b22Pnn34hkyn8ZFI8HZBRrcv0zRG4kgwAoJwfjM+1u+Zzn0hyXuLuPuoDeNIpt2foLcsRGYsTz96BwhSKmnYyecnCN4b/yZQpicL+O6P3MOOxQ5Nhjb2IgI8ny5ctDHSSNYMZrNNU47jdp35+6TOoO6fAfDq6kTZnaKb/jp+5i6fSnEE1rw9HoP8P7stsWA/8GBk6qYYzVPMw9OXkqOa/pqaM98uf+N8HzNPg3x/BvXtvzjPeUgZ43RlJqNFhzUX4AVDiS2MlVgcTQZ3KNurrDoLMIZTb+CBLvB3vfjzr0Jhk7aZrxmJAy98/ff6dx7LvMTjXHpLAUTHp7NOrUrY9Vn3+GT1euQsMGtRjlFGAkkw8vDBqMJly4n3nmGfTv/xzHm4L7u9yDgsWKo9eDjxvjVqTcHLgwBThmRTdEc0HMyCim8GK1kxEolX1HsE6NDGgBXst45NBQFRkVbZSoEcRJ3fr1MGXOIsTGROKn775Dm2taoGf3npg2c4ZZ6F955WVs/uZbfPLlKpxb8Dz8vG0XmtS+BFMnT8Atd3TinWX+/qdGRGLipAmo16gJvvzkI3y28lMu+vUN8yE8Ll32MTrd1RGd7umEp/q/woUu2jAde2gYfPaJR42hS1EhAXoI8Q4MLsM/Uni/qrOTTLzo1lPpzjHw5ecwbPBAjJ44A61atzIRajLAxZIxUFSHBYsBi4GTjwFFbyZRAKndoClmvTffGIA05+Wx5n1nNf81v8nSk8qQjvNcGfDD4Xjnv+jlx8uX4Z47bkPHe7vjlVde5XWYqpBCyp9/7sHjfR8ywlRQdMYzSIUNQB5+itwNkDR59CkpOQmvDhmMEUMG4K1xk3Hd9a3xwsDnseGbDfhizVrkPSc/ftryFy6tXwWzpkzELbd3oNVeHIWTJ1vd+4KOAEJ5hvfMHSJ98r7jn9kkPdTapDKZxiAmDl4H9K0/F1S9LDM42/mHzHBjj1kMWAxYDFgMWAxYDJyZGPB4OrFODkj16rBS+s6ce1KLEwN+OYwRQvyadBaCMF7O2XGET2YuEFAtQHBO8lMLnUSeVQ6oXnxY0I0UY9yKGoacRs0P+2ExcJpjIDUiivq47qjbkE6FE6cjlfKXdH57//gDP/z4PXU9wKABz9MothbLvliDooULYyudEZs0qIN3Jk5Gxw53UFZLxaCBA+icuBaf/G8tI7mK4Nsft6J5wxqYRr3WzbfeZbCQkWFMc0kQFRONtye8jfqNmuKLL1bi889Xon6DRqbOkXFwpB6qRk0at96bR/nRhy07tuP2G67F/T3uxfwln1BmdEoKSJdXv+GlmDn7XUQyUkzGKGNwofyZyvnbp1d31G58Ba810ch5VA1iDzM//bzlRyN3aq6n0sDToEkTTJq9ELG0+CgSTjpEMw4ae7bs+dvI3CnUCY4f9zb1ag9jy649po25GX7c3bEjChUqjCnU+3Xs3pcO/CVNxigjH7IP9aXxqY/sgUNBpYsURPIBRdH505+6H6ncTnYI1hG7lA5XwBhB871/XwD39R2N7b/+hZionHjxhc4owuiw1GSPnkVRz0BZnZeVXF7ugmIY0P9ePPDYcGzevpupF8dg8LNdkK9ADhrGeF8BGgBJHWNTkoyOIUjdqUCZYAQpocwwTkWqRPVLUp3E+5EuMcYdn2lsPywGTkMMOG/0aTgwO6RTiwFFSUW5BCvIlIMC2cBYksUsXFJimsMZKDrTjzQhPohdO7bh6latkUxCKCONIE++fKjDiKajAXmLLP9gMe7t2Rv1m1yKaVMmhIxaSjNYpGhhvDBgGEa9PthEEkyfOhFfrfoMr7/5jjFq+RnWHOSg5dGg6C8/Cb4WKt1netB+gb61Fin6TIupEzWmHUyP6J5nPCTYVv0lJCSgZOnSvM/rudB/avrQIrjo3Tm48prWKEjmQd4ipUuVRJMmzTBv7myz2JqGmXwcPBiPxQvmoUv3B1C7YWNMm/g2x0X2n+NTNFn/Jx5C9Vq18OIrQ4z3i4x9Yj7y5y+IkW9NCPWs8bu3FtqnDbXVAqdFUMefeexhjHl9CMa+M4P30sa0VdpGpVq0YDFgMXDqMOAZoTU3PYFC9E7gfWc1/41wwvMlXHCqHwbHO/9Fz59/5gnUqlsPLw4cbIxiMtzJY65AgUJ4c+xEc03RSBmijgTy0PVo73NPP4Y3h72K4aMnGaNYgDz1gnffxZUtSUcLFjb09vzzi6FRo0vxHiPVRNtlRPTo8pGuof3ydBMe6G8Q0t6IvnsQvu3ts98WAxYDFgMWAxYDFgMWAxYDpw8GyM4ZUGozB1wmV8JsFtkD3BMO+zq3dGWcW7KS2S/+2E8lff4yVVGgTLXD2todFgP/JQwkUk+1c/tOXH1ta8pN1PvQ6Vv6pDznnIPadLiWYel96pt0/DwaehRBVOaC8mh8RXPMnz3D6JyU8lDy2NWt2xqjmGTAsmXPR9NmVzC7yXshdHgya2gHN7QvOsaPf/bFY8n7C9G1xwOoX78Rpk4YZ5wppTNTOkVPFpTcKihRvBSaX3UNvv9mE2Vf6RFdw4trANdvjd0D6eakk9u1Yyda8F5k6NL9qoRKgUIF6dDeyGtqvmUclJ5L1xdOBIpgU1SacCDQWHSd5KRko0vUtoxd+pZMWalKVVRjtpQB/Z8y7UOpJKOYUYoNwsdnGhzjh65n+mafRjfA53ek7/SXGDhqPn7Z/Rcd6GMx4qXOuOiCInT053NRiBwhJpqhKtTTarwCkdCLKhTAYEaK5c6VCz/v+AVDxs42OkNTCoZt+Lj4WwpixtdS9tefnpH+Qr+1j396/i56iVuPeptL2Q+LgdMSA1IVnQRwXn7G4Ji+C5W62HxH+6NRqHQl+rJHYPeWjcbg4s7NNGMI01ml2X+ifoQ8jE5Uh/+1fkhUBZ5CMMANHxcJ+dVTVYlkUs1kErRY8Z2klfQlYOMAgsxBDEZOZQUxJH5Fi5fAmyNGoljpC1CzZk1jgInmdWXYUbqwzEDGtIkTxyFvrpxoztpe8Qf2o/f9XfDcSy8hJ4m7QnOTWA+rDSObFl3XCvd27oz/2/sXHnzwIaY1LGPuS4uiRm7yF5MwGyMXmV1nf9qra4ERJDOKipc23g1aI4I0kB1M5jEuuFLy8ot44gJqPCR8iGHuYi18fzFKIneOOHMtLbLfffcNOnbpbvrU7wh6p9Rgna8RTBOmCA6uHOZY+IcYDUXDCTcT3h5LpiU/LmvWFPH/9xt69XkA/QeNRg6iPpCQhG/WrcXLw940inKNywMt8kZBzUXUl8E1vHZSnEfScMjoeXTt0hUfvT8Ho6fMQuMGDU3+YKFOxjalUPQU9d659ttiwGLg5GFAdEBCuZhLRXLKPVUOCswqSMcFpnRgitSs5r9oiIQMCQokP4d5uB7v/P+HucrXrl6LQUzBwbBUYxgTRgytkzeb42dgmOIU0vv0YGixS7fEaHe/7z4sX7wAkyZOMylnKYPIFwGbv9+EDvfeh4RU5bd3PNoq166JMUMHkbJzpQokGqNXwL3DoCtQRQlfobAxXp248ElpEsEUFEy7m5Iawz/hUyOjF6IiyngOyafxdtZKmBmc9fxDZsixxywGLAYsBiwGLAYsBs5IDEQEyaCRfzL6AqNMdXg8qozd+z1cvj2RiChUpjq7k5NoCgqUqmycQ/f8vI4DooBsWDdH75TVNVOZGUYgNjCFvKNX2sdTEHt8nnhNgRdBZn7YD4uBY8bA4fKFEU+c1yzLXvV+GiMSu/F0V5mdpIxChQufhzeGj0DRMuVRvXJ1o9vx3nMqrrCRZU46de1KwSnZiXqi4HjxJVXpMD3UdC2917ffbGSb+4xRTbqqGApM1aXXGjyYulxlKaEMxYJj6XVGysQUEfBh8jtv45z8+XD11Vci4e896N3nQbz4GrMSUb5TqkZKrE7kF+VWGZ+SmRHkxx9/pLOl6nc58qV3n0qfn0R9ndLyS+wTxfFH0mBFwbEgnebHjHgdZUuXQeXqNU2qRuHJc2pXH9IDJibxitzvZ0QouyP5iJE4K4nQGL7UTsYztY3gdUz0V5i8G+BJMrw9+9IAXHVpfaxb3xNVKruGdGZA8c43G8fy4ZAn6l2VHIVSqT/O7eVwmTq8+4M8T4ariAD1gNQndOtyDXbsiccDnS9DqTJFEO0GPiirjCBIPKhUiyHqfApxPFfycKXyRTGUkWOvj56Hh+9pa1JWqo5aUrRzfae2GCPt4OiEU/0O3Td6YvZGt1V1T8OY+cH3g8edJma//bAYOF0x4HEyp3R8hphzsoiQap6kXybS/z7xgzv5VzjxYz6BPSr8y423feihRzDo1ZdN58JKJPcniREk0xnJEOwAF60IahBVnFEPq3a7EUc1kEHDR+GB7veiTbPGiM4djepVaqLdDe1wW6fOWZ4vxe4Mpstq1e4mEyXVsnU7PNyzG2ZOfpvpA7uZocswpgXxmecHoupFFVGmbDn0eODhLPvOqIEXifHlp5+iaL7cXIW4qBlqrtX48EXon/3/h2UfLDRGqG83b8CHHyxA63a38TeZdTIBKYlJiI6NMce1T945+VmcM4FeNzI6aZ0IBxP5IHyznRayOdMmom37WxFLhubatjfhkYf6Yvqkt3B350749MvP2IEf5xQsYGqNeWMP7y+rbT+VzGKKvmaE3Sr+TWB4vTx4VFBTmvSMvH6y6tMetxiwGDh+DMggrbn5xWcrUTQ3RXHNSTKYTS9vwfStc/HF5x/96/N/zf9WkT76jcfhsdyxj4Rb9cQEX3y1EoGVn2LitHdRq34DQ4OM4wOzQARIE3PGxRl6JGOeoGD+Akikc4AggnRQBrOArFyEI9GtHKKr/DtAY6MYngg6NMgDUIlmRQsDFCAEvZkCcsDLLxh+RFi3YDFgMWAxYDFgMWAxYDFgMeBgQCn2JcR60QNkV/9ViFT4w0kcQ/4yNc39hS6RidPpv4oIe/H/BAb8Js/7oaEG5fmoF5gT6WI6dq9clrYm/KGWzpaMQUpHL2jY7g1nZyafkp2kj+vTozvaXNoIOXLHoVqVGriuzXW4/e4ulMUY5XMwiZFDTiSU11WhgoVwIP6AmedJNKao5nMMI6o03yVrSSQ795xzcZBRWp5hL71RTH0ZQx7nzMypb6MV9Vly3m513c3o2+dhTH17DDrcdaeJHPNRnvt15w6MHePc00crPmLNsYUYOXKsNyQzFtGdlZ9+guL5aSiSfk7zn/uuZYaqsZNn4NXXx6Jvj864+tJayJk3HypfVJn6ufa4i/eqEimS/QRfso+ieVk3LNmt2UWn/6tbXIO3J00NXe9oNmQMu7z5VXj+qccxe+HSoznlpLYxRjE+o7UbfjK4V4mcjrc2xb4D8Vi98UfEUsMroDnMfCs1pQOO1GtMlMRr0JfX4LvDTY3opLqLCnsmk9U7WrWsae7R/6xorwyeCipIoeFMsr8Fi4HTHQMnxTDmqKl0685EkzeO84vElNsqwli49MVIpCk8KoMcrPKWP5ng5cg+mdc4nfuOMl7zfEo0glSpUNYoC/WEkpDTeGEgOoG/6DcRScUsPT1MvTEabLQI+WQgywJkXLu8eXOs2fw9Pv/kY3pSrMXi+fPw4AO98eWa9Rj2+rBMe1izbiPWb/wGg98cayInFLl1xbXX0Vg2Dbd17GY8S9SB3pKhrw5gysKC2LLtZ6xnwdDKlatk2ndGB8XoywOnYZP6GD9tLpcLxxOEcWJY+fEH6HDLjWlO27x+E3p1u8eEW5c+vww6dbobDz/Z33iXRCvUIR1I2a1FxE+88FZoZEzbIDHpUBTd+g3rsOnr1Rj25gTE0/inYqnNde/TJqFjx7sQGxdLtxzWP5Ni12VQ0vaW9S/qiPnoA7io0sVmoXr0oV6YtWgFShQpeFDnuo8AAEAASURBVETlcta92hYWAxYDJwIDokW1atfFlOkzTHcSQvz+KGMIj1MB4H95/sfEkAZxjEcyRGWFA6Vx9aDKJdUN7e3bqysWLlmGIkWKK6iLwhrXINZzDJIaB1h0WPSTwcJsS6GGDmp0oEOqP4hkCnVJPseTLiLFEXiiXFebSC5Vfh6PRy5ukC5HJZJuUigwcgHZf7rFRSi/otY58iX/+3oTCjF1Dk2TPEYmXtJeBnC28w8ZoMTushiwGLAYsBiwGLAYOMMxoGj83CWqkh910nX7FV5ACJpc1dxwf5udJ+HDJ4ddKup9kXHGcTbedZSiGM0x8Y/HM4e0fJ30CORmOX5HT+Wdy7gVs0mNlfn2u4J7QJkcLFgMHCMGQnYI93wvdWAyQxaDlHOyAq9Gs/SkAerwsoJI5pK/nGkRV2/+zujjNm1Yj4Xz5qJPnz74YvU6DB3+ptuF3utDcyBA41tUtKsiTncZYxjjxEnWPKTjvHRREpcCHJMcvMMhijqsTZvWY+PaDRg8fBydx+VX6UfTK1tg1owpuIt6LSNvsZN9+/ZhJQ1iiQmJWL12NXVyV+Dqtm3ZnaMTVspDycd16tXHlGnTmfnI72Rakn6SY1eNseaXN8UVm77HypUfYzP1aQsXLMQjve839dGG0EAo45jkyYaNGxh9XzQNlSZbE/vwSU7MJkSkJuG5Z59n3bM6UP3v4wXV4xI4EVnEK28txZeICJf+pLq1vJxWh38KPwHqafs++6bBVYobCptCA5kghvK0INWtBRaIcB6up6dPDTCnG59hkDXKZAiLTKHgTfBhv6G3y951AykUykZI8TnnRygji367EWQkqgZ8lOPVxBdkdB9PCbqRZc5R+2kxcPphwKV6p3ZgCmnds/1bkyKKkZkWTjEG5KBi8E7C9eSjT9BDZQ6ZSQ7CEDIujsrby/otJjEsFz4pDQ2QWB6NMlSGz0guPsovW6dhIzRseim6dX8ATzzxKN4aNQLDXqNhzO0yo1ufTs+SKF7/SRpsRJh9TMH5255d+J4L+9Yt21C+dHGz/5MVH+Ot0aMwa/4yjB8zEvd2uBUfrlqPaLNIZtTzkffpOloM4hihoPOdsGsfYqOdRSH8zNoNGmLGwsUMVXYof2x0BFEWMIutj/l6o+h5EuDCrv60TCg117ZtP+Oc3OcYzwlnOTnUoxcppsV58vi3EJUjFk898gANaIlmYduz50/8+O332LFtG6rVqEOBwIdfd//Kvhxm4VBPR7el5y9m7Jxz8mHMpFlof01ztL2qKd5/fynyFTrP5F0+up5sK4sBi4ETjQHRoijmOVcOeJGQhMRkzkkackhPT4f5r/QUPhrntm3dcky3rihX43XMs6PpGDNuyru4pe3VaN38Uixc/inOY23GWNLAKEaM7fu/veYaonlSfGzfvgUFzikQuq6JZM6Sh+CaJocOET4avESTZX1TipyAUj1SwYLkBNS6pCw+Wjo31LfdsBiwGLAYsBiwGLAYsBiwGHAwkLdUdezdvkZclIHsq5LdE4/xKx+v76VGUwSCykBIKS+jmAWLgf8aBlTLPYbynvRvEfKcPgowwQOUYWQYygoC1CtJbyeHxDoNG9IgdCnuZqmP/s8+hTdGDsPQN0YjitmJEuMTjU5JfXIo2LX7F+rDcnJMqhEWxdTz1HMl0rmQ+j1PD/jrrp3IFZPDqAol13n704/p7dFvsn5XpNFrxdBjUcabX3btpl7rO+zcsR3FS5Q0Rpey5S7A+KmzzOm/UO91ZcOaeP6Jh9H/5VfNPtX+UlYljTE35WP9jomjgZ5jdPRtlO7crCuNGzdDs2bNcNd9D6Dfow/hrbfewMDXaRhjT1576fsimVI1NidT9Wv8R4HP9PemFIuly5VD2+tvZtRYXzz0+DPpm5zS3wpM8NFz9MLyJY0ONoL6U4HPn4PRckmghtP8DhnGfA4lV9CKwO/LgURmvVKEmPkdVJQgdRK+g9SLsgyLag4Q9BwMuOd50WCegc0z8PlpGKP4bt4pduMmWHROtZ8WA6cjBrJUKR1p0KRDBjxvh/B2HqlOcbMyx1G7J4+Ag4xyUWFDn4wNJIyKoLFw6jEQMkYS/f1fes78ZTwKh2AG6XEggiYy2OAGepfQm18KzaRk1mshoZTpRyAvfkEEPcZMXt6IAI1E9D7gsxftrFi+IlKo3UziDy28ZGdNe5axJJHlby5y4rbfmz4VN9x4M9P71TLHtbBLkTrglQE0HI3C8y88j3/+OYAH7rsbHTreg8YN6+HCC8rg0sb10f+Zp9C//3Nm4dW7qYVA75rAyzFsjGA8Jq8zgTzf5NWixVoLu/Fe4YDUPllRCNwO0M3F41mU+lCGxBR6mqh9kjGQ8Qb5X54alevUxeIli3DjbbeaXM5JrEm2lt4yVWpW50IVQLRWiTCQwltKWuUxnjtrDm5q145pxepzHKzfwzEpAdiQV17AOBr/nn7hFdSrTW+ZMW/i3i5daMTTuBmqrD7oyRZgez4V47kjnOv89JDEtspHmcp80vnyxGHm3IW4oeVlaNnySsxYsAylihYy9yVGQ2OzYDFgMXBqMKC0F8mcl5rPmnuyfRuhiZcP8phyuNeuXQ9Txo5Gl65dOI1d2qtjpB9JpD+UP0Igw5pSEgpEq/SnmpFRUia483/2/PdxfctmzP1+uYkcLVuyCOmG04WEHRU0zsV6jzLc6/ycLKTcsH49TJ08EZ279+EqTxoVJlQkBZlKlmNJoUdZCt3efKQ1itRSHnl9RygkTIYp0i4/nQjOK5gLc+YtxPWsF3ll82aYt2gxihYtgSr162LF8g/Q9a67TKqNA7yZDas3oVKN2kiQjYuUzs9or1Q3NYk/xVXR6P6Nd51wE0RS6l6SZt238qGT7zArmdo498gDDui3/rzf7m77ZTFgMWAxYDFgMWAxYDFwtmMghjKsUS4TEeITPbk/hBePr3J3HJJ2XVlSgulxgCInJMMrfZoglYpf+o6ZpABGL5Xu+ukvZc7lTk93JXYvKniQtY6cE4PsT/xsDPUXksF37fgpfRf2t8XAv4aBoFLA8x+LdKBu+5GhcUS4kUEsah/apw0ZkSR3KVWiIEIZjCgDnn9+BaTQGPb/7F0HeBXF2v5OTUJAAQGxgGABFaT3LnYFQZCiIMVerr1fr1e9etFfvXr12gsWVBAEBAQVRBBR6UWxIiAKAtKUluTU/31nd05ODilAKAn5Js9md2dnZmfePTs7X48idEijRo1l0kcTpc/Ay/Dbd5S8F82fI82atzDvAmm3hs2ay8eTJkivPr3AC/OD1yRCt/otWjaFkjhd07s8NjDKKHiiwI8p5smQcWPGSv++F0uzNm3BS8s2gu0waMOnHhsirw99Vf5+379REgKvAEOQOIKzo6tVletvvEkef2SIXHvb/XJEFSqKcu4AbYk5hKypKHheVCwnbUz5DoU1UYzfz4kAk1TIDVFSt95J4D3CbAnZtA710LMT+hknUw+CMb75HtTNb+qIwsSPvDDezwscmMI48QBXcAAxDYGuRr/v/OcD0q5JPRk/4QNofJZDW2gbdO7u8tDsfBp2Latc+RS8n5hbOzSqe5jfzheK8c7y3MM35He52HmYGg1fMeoK1Pzuz83GFqOXFiYrGIuhHEVsdlzmov5TBIpAgJ94D39EroW2w7dPYm4VUb84l4u3QtnFO//20yJZteJbI4BYt+JrWbNsIQZLAYR903exIS1WIhDgR48m1EzU1uI612z8IZsfs0duvHKwzJ8zywiC+OH6ZenPMvSlp+XY2rXMB8upw7pgnvKL46Zx778rmzZvlNvvekD6XDRQevXuLxf3v1T69hskXbv3kvGjR4DRK3InTKOZHhjyGOpHpfJhVeQ+xBt76YX/yaK5cxJtmpfJfEzZV7BEsRkhEvuMzjofWk70zjnzkvsTAxHASJZBmJRzvmd/6YaQHwcyiVMTBXi90e9pn3wsc2d9YS5PnzZVvvj8M7n0smsN8zq1Ds99aOv990aZsd9y931mvH0vHiQXX3Kp2bp2v1DGvjcCi5ioPPTo07Jhwx/S9/yzZfny5eY94rv0+fRpck775ugjFhjY2N8QFj3r1/+RZ8vKckye7fuXCab36EmfyqGwIOsFAdmGNWtNG/n1U/MUAUVg3yHAOcVPoVHS3EINOG52vnnw//4rG9avlT6w9Pzxxx9NZ/gufzL5YzkHGnYRUCw8zwnlyB/r18vGdX/Ils2bZeOG9fIn9hRw0a885wbOe4dUyJSR70+RSpWrmPd/za+/GY0xCsVo9VsB13n/IDTFKLijG9chjz8l62C12u/CLrJy2XLQGJhXUX7G9KnS7Yx2pk+ca+n24/ffVsnmjRtlE+ehP9ZL9o7tzhzKeR9TKOfzjMxMeWPkRASJPlJ6dz1LVq/+VfoPvlI+AbE2Z/YXWDv45dOp0+HadqpcdsX10D50ngH7qEkRUAQUAUVAEVAEFAFF4OBGYNOvi2XjykVmjbz5Nxz/MhdMf9Lje2fcXMfaRGVTTYpAaUaAgpkbr75MFs6dZd4Z0kwrli0z/Lhaxx0n6aDrBlx6mXz64UT5auYMI5ia8eknMvPLz+WKa29MuEfsO2CwzPjsEwjD5kDe4wM9NllmfD5dLrvqOtCRDg+PNF8arMuY0uH7nnyo8WPflW2hHXLzXfeDn9cP/LzLpHffAdgPlB69LwZfa7jhI7KObYf0Ho8vvfp6KX9oJXni/x7EucMvplCPyvLka/21aaP8sXadrMe2CTQm023XXSlfffWFoXPDGOvSpcvktReelVq1amH8jrtH8gyzc7JlM3iNqfwx00jSPx+FW7g120qmxRlzjfxAK/gi7drvkkEyZtQ7KEzrOxC2mhQBRaDUIbB/xG8Mjgj5edj6oMbsxEkumflX6pAryx2OOx8+2AEYFOzS0e9qrGRD8+KXVaulZ/dzzPUgrBy2/7lDmjRqIC+//brRLOEFr+uT1gftDa5pqckxcvg70rx5E6lRo6oTkBMMV2qQMPXvP0Cee2KIPAIrN1orjH7/Q8mA9QI1vyis6tWzh0weP0Kuv/Yy+XzuN2C4RsHMRUBRtA69DvNx4+/OfHTxHeee9fixNII+LCBoRUFhGT+cLOvFR1HwUfdQqwTdYF/8+PhTa41LAcusNh3EP7Y1ePAALDy+lp5du0iF8hVgKblN7rj3n9Lh1M7meqpaCtukb+aRw16VZs2aytE1jzJj58IA0YVg2hyDH+bL5dn/PiozsBg59fSzZNKHU+Wmm66Tlg3qij+zPJSEYnDVWE5697sEfYZ2BkbMMc//YqbUr13Dds/sTzvrbCmHWEUcRwj+2cnwrlixvLw/cTLcKp4uPbqeKaM+mCxHHoV+EAMOSpMioAjsFwT8sLgNFkLlN6hXR8aMnyR/v/N26dS8sQQPLS9xuD6ggKtn335GmEW3GXNnzZUGxxwJwT7maxL7mEvbte8o70/60MyJGbDc5rzBCaP8IeUS7//5sBx7b9JkqVnzGFTxQcCG/mCO4PxHQseD+ajOiSfK+xM+kbtuv16aN24gaQjoTO3D8uXLS/+BA0x5gbXtPCgHNKhzjIOby2Q4A/7u3xz5vgQxh3rNPOtYvFatmC6jx46X7ph/ekDoP2b8R3LplZfLOeedLRUzK8iW8Fa5/d6/S+eOrdA0rNcwN2MKQ79cAgRztJOcLxLPYBsHzTUoYJgpjP90LnNB0p0ioAgoAoqAIqAIKAK7jkCKRUpuRVLEWE7aDGvBYs/tlWIuwazYyrr+yrvqS9ys6AO3f7RkWIvQHuz3Yccgdpqh7EWWr/wubxv2RsXsf95G9UwR2PcIrFmzRrqccxboIC+EyH4oSm6TJvBg9OLLbxphz0X9BiMO2Hfw2nG2VEgvJzu2bZPb77xD2rdtYzrHUCGX9LtYfvhmjnQ77xwplwF3eyHQY3ffI63adgA/KU127MgxZXNgvmkUyEmjgX58753XpHH9UxA/upq5Tk9NRikd80hfWJE9CX7etMkfCele0oNGcR0EG/tKN4fXXH+jDLnvHrntltukWvXq4MFFZC6EdvWPq0Xiz9C1qCSZFdLlp982wd3+aundvSvYbKAv4aEsZ2u21K9XX956dxTebcweaJdtz/1ytpxybE2HNjY9g+Uo9qu2Oorjbhb6BE9ncP8YwFiYaJVGwVw6TLhCtF4FTUxhYzr4nDfdcrsMf+cNycIxeXpWaGbb2p291w0y5kM7NB4LQDmVKW5NsXansb1YFg6wMC5wJhkHHMnjxihLsCzgOtG54PYX8yXrhDnBIgWcbOdE/ysCJRABD5j/9nO/W92DsNy4k6tcu4lsWrHArWt/8c4bkOcME16lY5vK5l8W5bkPb68CsjyQlKAT5wmmulJ8/43L8vTR/oCsKXcOXAVGMInHYiH5ddlS2bj2d5hkt5ZD02EmDWHPDh9iuiBZwZjfdbkZDzst0XySH9QoPj78wJoPDywPEoEx+SGCVYTPZYTyI0dB1s6/I+d3GIWwy8a0sb83u2ddJu6iEKTZNpjPPH4A6VPXVYjBBxDuzfBB5+Ii6rooMw24//gxpCCJ99uyIwzm9Cxp07Y1XKDB7Ze74IZNWnIVM1ZqqUVdW2P6CGbiR5Vjt8f8GJngowZbtAHGcE4OGOBzZ0uNWrXkmKPBBEcyCwL0AcsCc+7KFc0x/yEMmjsePCP39Wefea8QLNIoyOR4Y2RcI1+TIqAI7E8E4AIW80HABH7c+b58Tzk3MFhzBO4h5s2fK0fVrClHH1ndxGbkvEQBFhkIfPe5YOXefunLOx414A6Ci1onn4SPF4KrYJpHsrKcOYCLfcaKZGJdljGCMcxfTh/MJcE0Z7QID69WRY6vc7zEOVejHsuEQ86cTsuzIJQYsuEDMR3fB86vtj90V8N5l3kwzpXt2c4550sqKWRlhWUBtBRbtWtj2nU9OJj67D+D+caoROAGA+bcy77DM7p0GfSifP7eNWYmJB6cERMzmtO13IzUc2d4+l8RUAQUAUVAEVAEFIEyj0C1mvURI36JoWK5hsslER26NsG33Ukw5kLnkKV7jKMVjFVhrLOVCxLUdKJZu44r4A4umZ3r6stdELI+BWMeMN657vxt5ddmveioAeOibTexgCzgBpqtCOxDBFJdKX4w9Kq8d0sRXJO3RH5aDgQtK5b+AM8hmyAUa22Uyp2KoLMo7MHrmxPKljlffoU4ZJ3w28d7YOk/0G9R0ojQRNwCemz+3LnSvn1bwxvj+0LFSQrEmFgnRl6ay7ujBxPymdJQJpnH5twbtCnu6w/gbiBIGdaEfDdjmYULhh+He5KfmIYyhi8FkhROS8y8w/kHVdAvnKMdI4xCA5yJlv7wnWzatFmaN2slfjC9KBQjbZjg5bGvvBcLJyXLp7NZdJVI+pX3cfYIvQIeWRj8yjCURjPKIf43+mH5dWwObEODnwnRknoD23ABe6vfGSEQSH6MvWPPeyQdfEymUEICZU53+vfpuP/bKW9vZoQBGjE8p5vjtQtomOYp/DLJCsbcmTkKPuX0MUMgGHMKBHYTD7dV3e0lBKhM3HXQCzJzxLWGF2If215qfq81w8/tCyPmyuuj55k2OZ8M7NFUrunjhFiyr63t//PD58ibY+bjfcYHGouSgb0aydV9Wppv+O52av9YjHFFwZeJs0dKsoKIlGw9LcEI0LKrsOTFV6Ecnnc2ZspT6p0i0RNOMsXNtE6rAfuLdgViMSygncW1FUw5MWv4IjDxg0Mfx9zTuoofDjJo8es3AjE2yA92cqJWCr6BaBfTNrRGzIcW/TYfXdSnKzHDPEZ7AcbA4ZcMie04QjaRrB3Zxu8wXxJaljnXHaYumc/8CFtLCnMR/wLoFxnIqADNG5+c1rm96TeFTPRJzJSKHz/6FHpRIMYFBT71phwZyzbx2PlO4r68zK80xh0o55E2sKBgAnvY7H3uyt9+4FP563ZhwMLJgi/iS2Y896nXTIb+UwQUgf2AAAXveHsxL+SX7PsZACUQgCCrfZsWucXw7johDDFnIte++2bvEvRYyyeS/SI78yndJFIzzilIwTrnWnc64CwLYRzmKSohYN6kkgMJrgxozrVr6/YBeUZIBevwKKgXK1jzYr5kfMkMuNlge3SHkZBQYfJnP5jHvgWgncczzPLmf0Y5n7Tt0NrJAxFGwT9THAtwurqhwpoH/bKKEs5FCAbReWSDgHEEfPyccOgJRocpqP8UAUVAEVAEFAFFQBFQBIpCgHF9kpPDhjZUuJPtrjMT67vkwnvh2DZv75ugkrlkZLIFnLOC/ycECIkWUJbu0bBuxBqTzdgmTSO72m7Bd9QrikCxESBlBzVCw4WjuCc3Ob/jPL9ZXLSxs6gQXReePowvIdBwfGPJH6MIh7QmBU8exGlu16416KkQKlIAAroNvCyWI2+MPLmKsMzq1L4VXCZSGJZj4m5FqHkJzyQO/wptggilEIwC5kCAtCJjZ/McL1ESX4t9J+uOlK6Xgi/sTf/B36Kwziby1Qw1zCz0NWgv2XfSXDS3RQHnpP5JJ9rqbl4ub4sXDHIomoxgUoXEoRd0pNeliUnvMpH/x6GkQdkzmU4nXuyS0z8HP1NhN/6RZmWy/LuQhOTpIZeb+OH0chWJOJZ5Tqn9/z+AEA206nv64VvxKCLol0uPu/32uB7FXD1VKQevXeQ5MvqBMSbYT1KH/Y+M3vFgQUB/ogfLkyxB47DCovT0oAl+mefDgwly8CW9zIfSumIMwoKMH9ggFqTPvTpMymWm7zQaywzmh5kfW350fUYzxfkIBvAlClONBIlaIRS48sPKCZlCL7oM/BtcLDKuTmry4aP97CtvwhwcATNRgXW5T6PjciR++HhPc4wPQhx94D3ozisCrmyycIllqFkTzEhPfDCpLUMG8Y7tO+Sqwf12WriH0acAPh4vvvSmBNPBtk1iXJubpv5ztXhSs/VcEVAEDg4Ekhfb+3tEW7dvl+uvGAiCKgBixnF7wf5w7iXhM3ToW5JRPkOytmfDBeMhIEPg/sII9J2eMuiyQ3CByOF8SWUGUhFQlHAUYVJJt30/QtI1XL5bemrf31HvoAgoAoqAIqAIKAKKgCKwtxAwvHEuIR1N0b3VrLajCJQJBCyPi4PlsU1hWGyRPvPRbUdSssIgm0WajnnGywj2PKcS+bVXXwolxJBL49nS9FDkk2defh1xpBHyg3QYTdM07TICwWBQTjmppsSgkACoYZFXRNV9DC95oeR/nlz3aOjnQ2DpCsRcz4/ic+RkjgI/uuoDP5NTNe1ifBCYQtRaxAD0siJwYBEo6hU7sL3Tu5dIBKLWl1UBvUsHY9RoiMSDEoTAKuSyI6HrYGr07d3bmFZDwmTOrSWhzxs2WisFNJvItkxj7q01GL/vnKyZrKYJr1t/xWlpadKtx4U4h/YHmbRJiVN1mp9uHh2GrcO8TSqQdEgrMTJ9YxB+UXBGJnDqwoFBR92mzKKB1RnvzAdGcy/4VE5Ntr/sozHRSC2g54qAIqAI7CcEgoj52KNXH/hnp45XLvEUZ7BhSJi8mCvphjGtXHnM4x5znCztT0c+CSButNbiQtrMrJjuo/TBvKvJLRp3V9rRmNMfX0K8hRU45nJajDnue5zvCRfqMXyjfOgbLd6oCYlRQAOPDXJRrsueXX0EWk4RUAQUAUVAEVAEFIGShEBqSALjS8100FkHlqS+al8UgYMFgVQKjorfAfCuLujTxwyRbuyTEz2YeGAWRh5YqhJ5cjk9tghYwZFDp9J4jy4myXalUIzuHA9oAn+AXmHSYAnIMDUJctxlq3riLt/A7WQU9gUUwHrxsyCP1Y0Uc0CHoDdXBApDQDlEhaGj1/YIAU7cfgiBfBAgJSmkOG3hI3n+BbAY4+Se5IOcE6bXH0V+3o9qQR2gwMsKxShYohDMCrZ4TyO8cuOOUdDlwde5S9ceyKc1WN5Pu4cut3h/141iQfdkPl2OxcnxReKOATmTXR6aceEa98b9IYsaawtnsd6lW09jas36yYllY3D/6IW2jm0j+XryMS0wCktFXNbFSWHg6TVFoIwjkA6L3S7dLzSKBpxLSfj4IBCjUIuETaqiruOGMRc0zl9RUwciLHeuzL16AI7sfG9c5xQ+dx6A3uktFQFFQBFQBBQBRUARUAQUAUVAEShVCNDV4NnndDP8MdKLySkaC4NPRlazKzlJvqjHRSJAEjonB/xOWlvRtuAAy/2dUDPw98VwNZDURY0vloKHEYVnLR/iy5HHaniv+jMoGCy9UiIQUMFYiXgMpasTvkSUxQL6jZheYSPggm9iCJv8VsHBWoihmvl0Jnx8IwOTZcy0uwuzPsqaAJ9sh/Wwh0gtYQmWMNV1LcN4LxPTBk3Te3jq99npC/XP0BLqmHPUyS/5gn7E2XHcg2Uxhg6CcDo9cEobU2E0wP7EGayU6jJmSGibzGUU81k8km5ghGFGMMfMwjEo8qXVD08SsnqoCCgCu4MA5yIKtKyAntMohfHOdIp5LZ/5K7X95Pqp14o+d+c/+32A33sm63Pdfn/gsNH5o68GfG8wG5tyUGTDAhzWapiaaTmWN+nkmBcPPVMEFAFFQBFQBBQBRUARcKLzwgVcChR2JVk4dZ5SSU8VgYMcAav8SFYXeWEu2y0xaq8RiiVO9aBIBOwM48w4ZJsGjBEW+I5F1t0PBVweadwTNLxXhkhgsiFz4i7NHbATJjVp6T6TpLeS3wYr/VeyEbBvYMnupfZOEcgHAcu4tYzcfIrs9SzrxpExcxyhWN5b0FqOZsYmFpqzUshbQM8UAUVAEVAE9h8C0FijpIxkRTi+axbJ+69zeidFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSBA4FAkcYnB6JTes9SjkAMP6u4+9Oi1oA1MbAWAEVYRJXy0Wv3FQFFQBFQBPYQAcYFY4onfDY6qmfwUG/yPV4nVqXEHB01eC2HyMsPH+xuRRajmh3OmR8NBI3bW0R5TFgYsyGr2WgV2+y5uYn+UwQUAUVAEVAEFAFFQBFQBBQBReAgQoDelpKTpbuSQ5wkX08cp9RL5OuBIqAIKAIHAQJqMXYQPEQdgiKgCCgCioAicDAiYGM4Wtf1UbiotYk6F1bvwual7pPDVpYIVxSpHdRzRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgf2OgArG9jvkpf+GjMaVZwO3kdomdotJBJr5YF5ii0SyoecPv8OON9qUwVNX3+rrp1wq5DRi3BUi5o37Z+9r9zbf7pnP4+Q9rQl4Ho+yb14EkbSlc/e8ZusY6wOcm3rRuIkTFgtHaadgjhk3zG4e9zrPk4/tOdsszlYINHpJEVAEdhMBCl6s8MXuk5tIDiZs555wNIqpAPMAA+Jib/NZj9fYDvN5nJxsOa/Puc5y/LPzEI85N3CesTNR8lzBeYpbEPdlvi0Ti+CeOOeeedyzDVuXx7YM5240YTYeMz6XzbPzOvPi0XDimi2TFnCwsv3nGDmG5I2Y8DrHypS6N5mF/EtDfDNGFePm41wfycJEHYJFGPLYb38QlmDAAeVwm0TiIbcYNBq52US7MhorMxSZcdWOcxNr0hbQvSKgCCgCioAioAgoAopAoQjYNRfXWgibTSLabN54WLiV5uT1BrAi9ovHZ1eKpXk02veDEYEAqDVL9kRBf4XgnIkbLcC4WZrP7kn7cbM0owcEUSiSg9840WHoD9KyeIdJJGHzwFNHHO1yi4H+4p70HBOvsa3ktklv2vNU+pT5LG83luUxy9k6zON5cru2Hq+RNrbXeJ5Mc5pOJf2z9DZpTtvnpMuGHid9ymRp2OTreqwIKAKKgOvvToFQBHYdgTg/dHlS3vOIG8eFQqNAWprEGeMFCd9XN/FjzBObgQ8vmJyFJZbnxnLcfD6fw/xFJa/XJznhkASQ5yR8SJFCsCxITwMTlet216yAe/OhR50YmL/+gB88XzJf0TbyeD0KZjbb9+Jj7SwQwDjGPbnxnIkfVTJuWTa/vpsPs9tXZ6ymmhmDH/cpVnK+63vchI3NtscNaEVF4CBCoKB32A4x+f22848X84MRilHgwmMUxuuO+QHCF8wNEQimTD6uJb9vAT/c/lHYgzmHbfGYZI6dhxLzizsXkTDxIXgx5yUekzDhvBV35xDSKaRZYjYALu7HZOYvl5ixcRFJRPnd+l5wNCKhkGnP6/eJH3Pd9qwsScN8bYRryIsnSZd8mCejmMcjJIrQ7xCOg8jLL6GbiXnRECGugRfvsSspSkGd6ScFeCSgnG+DHxNu1g7MvQEQcph3y6WnSxTzNoAstFkvu4liHhKP6Bu7QdQ1KQKKgCKgCCgCioAioAgoAmahCBhcMl8BUQQOCgSCQZ+Ew67QKxYxdB5pVCbSoRHQXAEqHOKHTzrP8ru4ZyLdZHlhPA9AQ5GOO9zL5lpamk+ys526AvrR8tEsTWvaQUOGz+a2x36xnSj6YvlrvE66lnmke0kDMpHnxn6QtmWydDWVMu1xMBBI0J72HU69Tvo0mOZFX53xm8b0nyKgCCgCLgL5c7YUHkWgUAQKZ3CGKQzDx8ePb2p2KCIBj/sBijsftIjLxPUmYo6hsGFUFtwuP4a0FgjDSouJH0Ivb4BkFEDA/TRWEsxwv9bp6dD+ikZQDh9pfOzJ3KWVQRhf4mg0B+e4jkWCBx9cLxmxKMtjluGHmSkeiUkwGDBWD/xwR0JhUy8SwR7t4gJK5TJZHQFeDAuHgKPpQtsGXObCgwIxMorjuE9xUtzFrzhtaF1FQBFwEOA768e7PH/eQvl9zSoswPNqi7Zt2VwOrVzJLLhJTMR8QZk+eZJs3LTRvOdNm7WSGsfUwnwXle++XyLbtmyVVq3aJBbrXJhbImHx4gWSA4FUyxatsPJ374+5ZsH8OfLX+jXS6czzjPZcHPMSCQoKsGKxsBGGUcngG9RfvWa9mcOCiJ1Vv8EpcvhhVcQfdIRnbHHhggXyx7o1AjoI9AnmPsyVbKtq5YrSsnVbEBoB2bz5T5kxbaps27EdQrl0ady4pdSpU9PMfdmxkCyaP1/WrNvkaPZx3oMgyg81vsqVqkjTFi1l6ocfSoRzLgkXO1k6w0EbcTni6JrSuGF9jDUin06dJhs3rJOatY6X5i1bSZCmX4UkPyb6OXNmyYZNm6VRk2ZSodIheD6Yn1Fny7YQntMM6Xjm6ZKDeZxEnc8VnOVagWFeNt8WzstQdDD/namaNBYjkmGGNz0wRBva0KQIKAKKgCKgCCgCioAisAcIOCYoe1DxQFVxF+BQTLPJWZni3JjfIJdLQ0cXNreULhctXLovBQhQ0LRo0WL57bfVUufE+nLccUeCNg0bZUzytH77dY0sWjBLjqhaXVq0aC0eCqtAo86Y8aWsWvUL6LbjpFXrduCP5cDqDPZqYGiFwAfzgMf1GWi731avkmOPqyOtmjcyvDFapmVnh6EU7yhTkoa1wqk4lTqhqU5eXCTqkY8mTJDatWtL3TonGTqXCqekd2klRv4c+XzoqUz7eKJs2rzZCM+at+4gtWrVMDThAtCpIfDimjdtYZ4ESTnS8lQGZd1FixbASiyS5zpZiHNnzZNN636X087uUgqeoHZREVAE9icCKhjbn2iXkXvhO2UES2EIo2gJYVRCksYOAwhogyRl7OIheLtgRPskKyskGRlBiRohlV9ycnLAQHaEbslNGc0XfMStxkp2dnZCMJYWpPDOWQazPq0Z2AYXCjFYRfADzgUA6+SEcoTlmVgma/sOycgsZzRayJhNTjz3gkBgOzkAggsJLgLwdTcCuh3btksGLB00KQKKQMlAgJZPtIB65qnHZNL49yG8zisYG/7OCGl72ulGU23KRxPk1htvkB0QKNWv10iyQlly63dXg3BoI2+Pel+eevTfsuXPrfL2mAnGospYVrmWWxzt4w//S3J25MjI8R8ampvWXy8+/z/55z13ypP/fdoQ35yPaLnFOYjzTSQaMkCFkf/ko0Pkww8/kmB6GtwMxmXLX5vlpJNOkZfeGi51TqhjBGj//c/D8tHE8SAQMqAF6JVQNuqDOGnRtJm8O+Fj+WL6VLn28gGSlpEJIqmO/Ln1L/n2+2/l8sGD5YEhj2FuTZcXnnpCxk2YCMEaiBOq9CF5Mfc2qN9Qxk6eJldfdolE4rDmwvTHMZDYoAUtUyDdJ13Pv1Aee+ZZubjbOfLD9z9K3fr1ZQkEdm06dZLXhw13Chbwn+N/+j+PyISPPpb+Fw+QJ154wczhMAqW2V9Ml8sG9pSffv9T/Gl0ZcmPiTOPc85lcvkYidbvvPNemT5tmiz7ZanUqXui9OzeVW699VZcB8GGOqnlExX1QBFQBBQBRUARUAQUAUVAEVAEFIFShkAUzLYnQZeOH/+BdOzYSd4c8Y6UyywPus2h6/7voftkxJtD5bTOZ8jb742DV46w9O/ZRb777gdp2KSpLJgzWzqcdoa8PPR1I4QirywMhcd+Xc6V739YIic3aCLfLJwv7Tq2lZdfe0eyI45XE8vzcjyTgC9G/hp4avT2Qd7cZ599JgMvulDqHHeszJjzTUIQxjL08kRF948mvS+33XwT+rRDGjVoKtuytskt118Heru1DB8z3tCJ5Km9OWocvENR0R3kINq3fLmXn3tKNm3YmOf6y88/Lff98255/LEn4Y0E1J9LN5ayx6rdVQQUgX2EALhKe5boTQ+8NjCsHKZU4a24rCcU5VFeUULhNfXqAUDAfVz2QXlxbr8d5mnjw1VYqkDdfhakej6Zlp6gU9yt5sHXC/xapJ2FWU7Bnf9TiGW7RUZ2lJZa0ISJIp6Xh8xbVjFtJjFGcc588xNF2QA2pjSYjDPZ9gIBhxHOc7bHdu319HKZ5tj+Y5n09HLmw00BWEGJrhmDLoM9StMNdI55pm5BlTRfEVAE9jsCXBtzIR2FJWaLNh1kLAVCrtaZ1XRjp6Z8Ml0G9Okn1998vdx574OSCYtUasZt3rxB7r3rDlhUwf1gGO1wYc65DYt7RCswVqecQ2nx6otRIw5WTMZ1q0cee+wReemZJ2To6yOlR89u0MSDhSrmDRIGHrTHOcPnWrBRYO+NB6Rly5YyetJUziiyesUK6dLlHLnhuqvlo8mfGuxgwwXCoaOMgwCNxAUFVxQAUQOP6ca/XS0tO54hQ197zRArzNvwxx+ycsUy4+aCUyuGIG1atZRRE6aY+xjrW+RTUcAf8MiaTX9CGAb3tig49JWhct89d8iq9ZvYlFE4IG7/HvKQfPP1Yvl87nypfviRsmzFKunQop4Me+1lGTD4Csy/nBd3TkFYoAXwLKpWri7vDR8lN9x8i9SqfTwEjTHJzEC8seg2CA5DEqTAMZaDvqI8rHpJ6EVpDQxhWQzWe8sXLjHWYf959CE8YMz5sISbu2G9zPnyS/lp6TJ58YX/mZvDkaR5JoiQYZ4Re8V5vqCvnPuZ2bnjmqMIKAKKgCKgCCgCikBZQ4CLVqYUDwJO5t77z7twjRaCb2we23Ua4w+ZxMVbYcmtAL8uppTljbNNuhuHJi/axzreLUc+F5NdrTq+C5w8/a8I7D4CeSmI3N/VrrVERW+rBAgGbJGV4rB+pLP/Bg0bypdffiFbtm+BUmEaaD/8xlH7g7Fj5JRGTcQH5W8vvJA8/cTjsgRCsRmzZoNuO0p+Xv6rnNqmkbw7/C3pN/By8YOn9/STQ1BmsUyb87UcdUR1WfHzcunUrKGMeGe4XHRJf9CRDmuZtK6XyotItqeMH0YW4tugA1u3aiuz0ad58NbStnUzlMHbBYYdXelPm/6FDOx7sdx4681y2933g94uZ+i09fA+8s+7QW+DRvSB9gvnQAHdAx5gDuhc3MewJ4ELx52zPQSaPCpB0M0U1P3nkYfkmeeelJdefku697jA0Kqmc/pPESgjCFB2g5cM3zO+be7HrQSOnVObCdtkOoxjdHXnME77puN5Z+h9cw9tVRFQBBQBRUARKJEI0LWqTUaABM22RFwuSojcNOSBv0vjFi3kvgcfgXA9YHy208XrYYdVk2deHGqEaf4k6zBbj3u2G6bVKARGlrB54B93y0tPPyHPvTJcTj+nqylurVuT66Yem6DB9L8OouOo2rWlS7ee0O77Jo+SiuM+Fi4pQGTQlQa19Vie2ntrf18rZ5xzrtMP9gcLj0qVK0uT5q2MVatxQ0HiC9cCcKvBNrKycow7WVqTRTGQrduzEvVT+8dzCuQ+Hj9ezjj3fDnyyBqmrRNOqCUdOnSWjz/8IL8qiTwKFolX/XoNpXGzFvLQ/f8Atg5xFSKIWCCxjx6Mixsx5TmFdLQgI4YxCBivv+pSh4BkcDEIOtmmwRfP6NWXX5GpUz81BKO9MYaZYH7YPN0rAoqAIqAIKAKKgCKgCBx4BMgnI/PbB+Y5GVh2S/SMvL7CtkRBPVAE9j8CFIQlb2QA8zdM/WmjQ11El0jHUODEZMJ5FFGeSp68R4WKh0nDxk3lzddeBT3lsH7fffM1Ofb444y7RMfDklc+Gve+dOneUw6vdoShB4+vc5x06nS6TBg72lh6MQb05I8mypnnnS/VqlU33lZq1DoG7u3PlEnjxhbRGzC30ZkI6MlPEY7g6htulrYdT5Vhr79s6oXpbgqJ43vovrvhtr+13Hv/EAjF0k0eab1KcOf/9HMYAy3QgIVNDImQnCwtbZnp9999l/GE8sLQEXJ21wtM0dQ6yfX1WBFQBMomAs7sWDbHrqNWBBQBRUARKOMIGDeqrtCEApYQZGG0+eLmweKbKQfuW79dsEh6X9xfInADQSssm7g4d4Qu0DVF/aISonXJNVdfKSPfeU1ee3esnHXWmZIBc7IcaLxRiFVUikdIDDjsgJyciCz/+SepVvVwU80fDIIngGvgHsRhqeuBNWwYmnVxaPB5sPmD6VLtyOryxssvypy5C+DllsIi1EDcMePCwiW42Bgt6OiFke14oU3IsjCQw1ghMKM/XDcFoGWYmug+9scli6VV2w6JSzGYojVt2QLuEL8EYVjI0gPmdXC2gT5ly78e+ZeMGzdGvlo0z+g3OZZfaehbJqzDEHsNbkI8Xgg2PRACop9RWOQFIOF6Ysh9sujHJYYIyzi0gtx8y82yYNE30vHUzkZzUDC25/73HJ4jiClDmEL7ENCn0FaJvuuBIqAIKAKKgCKgCCgCikA+CNCDSiFeVPKpsUdZR9U8WY6ocTIWazGpclxzqVSjvlm3ZWMdx9U3nb4Utu3RTfNUctbedg2ue8Vjd34DFP0kb7RMzObvy8KY57eW/wkFRExRKCAWlSxNA/GYXNRvgIwbNRr3SgPdlG4svHr06gOXglC0xOvL9+fHpT8gtnMLQzuRruWdGjVrgtjO8ymJM7TbwoULpFWb1sbzU8BVWmwEq7M5X83cqTseKCImb34IuV598xUpf0gFOfPsc+WiAQPlw/HvyZbsLPGlgZaEIC8bhOfXiA/Wu/8lxoozJxIyNGEU8al9aRCI+aH8iOMYY0mDXoyDHKX+Y9xHYs7dSErjOAKsrvnbFTJy5GvyxmjS22fAM4ijWEqhoSZFQBFQBJIRcGbX5Bw9VgQUAUVAEVAEyggC1BqjdRTT7Nmz5OjDMuXIQ9LlyMqZ0v3M042l1Lz5X4Eo8ErlatWMhZLVVKNmm9VMo/LarrgWnvXFDBk78m15+sU3TFBjCzOFYrtSf+3a1fLaK8/L0Beek6sG9ZVPJn8oN91+j4lLxrbYt5nTpsoRFdPk8Irl5OiKATn8kDS56pK+RuPv0adelN9XrZRuZ3WSWkdXlF5dzpQ3hr4IzT8EVAYO3NiPLz+bZrA44tCgVCsXEO4vu6SPEQpSMEiBIq3Hwgz+lZIYD41lGCuNlly0IGOqBuu6nCQLvZRq5pREH4V0TPXqnSKdzzpLHr7v7+41EDIU4uGyj/EbsaUmL4i3mZ9Nh5QSLishqLzyiqvkPw//Wxo0qAfXlY+DgEJ9bDNnzkQ8tQxY/6VJGgSId97xj9Sm9FwRUAQUAUVAEVAEFAFFoAQgYOLKkmGPNSL1mqioxfUgl4xcxdP1YWFbCRiCdqEMIwDv+IjPnLuFcQxdSyOUuvOeBwz9RhqusC0At/i8bmnPouCMkk7Fy9Kn3yBZt3atzJ8/T375ZbkshoDr4v6XGZqWbTCWVyxruxPD2hV4Mb8KrM12QHDFmNkUxlGAFoSyJL2QgFzDu+eB55QqJp416enkLQ4aLM+G8b43/G3pdiFoSZQ9/4Leho4b+dZrcIuYw9vJ3Hlf8sWWw6pUM+d85+kFhR5PLC1JOpU0JjEwoVaoDIotz70hDPxy5gwZPWKYPP3C6whD0NaEOmCjptwuKLKaDug/RUARKDMI5Kp9l5kh60AVAUVAEVAEFAEHAbpDpPs+LKmldZs2MvzdUQlosO5HbCs/NsaoQgkIW0iIM3HHYwY3JpFiYoC5FmZhtEdf6kzGzzoW8SQgmJo0bYZYYjlyxy03yMjxUxF8+Ggs6J32GI/QagM6pXf+v2X7dgQung4iYbssBoHTtl1H6X1Rf6NZRwKBqXnbdvLOiJGSBgsyphwQFOnpmYgz5pVzzz5dzvlhGfzNfy6LF8yVyR9PkTtuv0nmzp0n/4NLSA+IDQrpWrdvK8PHTpIICKJAQrMO3upBpJAAoQtKjp9uGpkscUKhlifOwZJ54VzPDmdLEG06ODnuMkylfP8FQGjB+ouEXzBTbnzwKbmgbVOZ8vl02UENQehbxqI5wB/WcCH4ycaeyesxepc4SpNlPy81efzXoWNHGoUZpsmJJ55knjPzN/6FmGi4Bx4QwmCmyexFi6XKMfUxHhB8+PPyoeSTYu7zz+eSZikCioAioAgoAoqAInBQIkCjDLOgMgtcnCSsxNwF7j4edQiMeSpEhV1GeDoY41VrnAILEmcdCkfohfbAR4uSlMQ1uieQLv44GP9Y35HZXvOEpo5HAuQxMSILUwjeBjQpAnuKQBiKfcmJtJQPdBJ8XkjDOscaGin5euoxaSjSq3D/kXop33MjOAItE4S0GJ7x5Wy4QHxn6EtSpUoV6dD+VKkCjxp+/P7DcG/oN4qGiEmG/jBeM63BQojRZRJ+9swnvUTazqGoKJxyLpMW9MOai0Ky/BIFUXT9uHjREln09bfy5EuvwUsH6sOt4xldusm774yQAZdfZ965tLR03CdslFKDiB8dCpFmRFRAKFrS5SNpTgrILC0ewXHQD7oT/XNc6mOgkD6SJm3UqLHpzm2IDT5hyudy1NFHmHyWJY4F9Te/MWieIqAIHPwIqGDs4H/GOkJFQBFQBBSBXUQgo1y5PCWxnoe1UVPxpvvl119XghBwBEeUmzjH1FillRPOudAGgR7Awt0LxgHzKThjouCM5+lwJfH6u+Olf8+u0qtrZ5k48WM5suYxWPBT6JY/UZHcoRPqniSvDhsJi62YrFmzWs5o3woWVXfLvx5+xAisuNBPg6DnkEMrok8QLuG+QdzTEhE8Z37bdh3MNui6W+Whf9wOC7Rn5b8QjKUFQHRgjBQYsp0MECa0IKP1l43HxnO6X4yjXH7JAyIsAEbD9q1bDSZ0vcj7LoWbjkqHVs6vSiKP5UjMEQsYpMlJJ9eVrv0GygN33Sx33vcQzfIgqAzA9WQMXjfyZ1LUPelkmfMltA6RevboIRf3u1j+ff99Mnz4cOc+6HflqlVk05Y/cY6+weqtZaM6Mn3KBMQd06QIKAKKgCKgCCgCioAikAcBypW4TLUccZpq7cdEBruHa2qsKRlniO7PN676ds974PL5OayqtRqYdnxwO776J7iO279D2/MxaM1Sg0CqWmAOMkBiQeAqcv8/7pOvJu8suE0eHOki0kdU1KRwp6jk0FIQ9hpptsjFlwyWS/v1lHTQuQ8//oyxnAqD0CJ9F0B7aZmHyNY//zT0VwTvOC21Vq5cLpUqVDSCLMqJy6Puji3bIaCC/ImCaryHK1Yslczyh+zUHeNGkXQtyjG9+fpLEoCC47233mDapoxv3R9r5ecffpRlS5fLCSfUksaNW4gX3jx++WVFguZkXdLXlqalu34/OsCY28l0M2OgeeGf0gtam7R4ZvlMefWtMdK725ly/pkdZPyHU6T60TUMnUdXipw/NCkCioAiYBEoela1JXWvCCgCioAioAiUQQQC6QFp17ajjBr22k76qOQP7EDwrSg01OjigkIj47oCxAsFTExcuPug0cZrsC2T6tUOkVFjP4BrwUrStes5smLVWiziHU3VouD1CjX5YBcFTbuq0Pq79qY75eWXn5PfVq4w7ecGMCYRkfuJJ4FjrLrQB1rB2Wvlgn45+eR6IJBAvIBIiISgkQeCwhIbrEeiiUSS0bKDJiE19tgWiRRqFqYm5jds3lw++XiSucRzBpb+ZuFiadq8CXRvd65j2wjSVWI0IAFQjFUxzmqhHfLorTfKup9+lU/HfADBGKzfYpskDYSPD236QKlxC0GDkls4lC1nnXeOSCBTYr50CYSy5P3XX0WQ6WPlnnvvkQzJgoZkSLqfc5bEtm2VeA62aLYMeRhuFk0nSJiSWCposz3VvSKgCCgCioAioAgoAmUEAS4TuUTi2jJpfWlXS/saBYgDEE8WDHHEmDWxkSDEYuL60vZht/ZcImNM5N2HwS6nAlkMMZcY+wkOCRLJtpnI0ANFYA8QCIAmTN7KB2AJhd9yeVgyPj7kfiP0cqy8HAXB1GPj3QS0ZTwaMjTYrnTBoTvxg0bq0Lq1HIb4Xl4Iw7oixpcPtJ4flmEUODOdBPf1M6ZPNfQerbIioGuXfPNtgm5Lg9eRBo0ayafTPzHzABUxSQt+s3ihNGvewrSR/I+uF0PwWEIBWRwv6YTRo6R73/7Ss08fubB3H+nbr6/cePNNUqN2bXnrjVdNW37co1OHzjL2nWEgSWGrCRqUtCeVJklS52ACisKSLUIhGIVjpg/AhMR4SooD28qVysuY9ydJdShDdu1ytvy6ZoMp6w45pYaeKgKKQFlGIJdrVpZR0LErAoqAIqAIKAJAwPgep1DL3UiIUCPu3ocelfXr10r/Hl3lZ9dVHxfln306Rbqf0dZgR4HY9m3bZctfm+WvzX+abetfWySbvtsdGRna8hsXgBUrVpAxEz+VypWrSu/zz5C1q34TPyVeu5BIsNB6i9ZnV1xzg1Srerg899TjpiatqXhtw/oNsmbVavkL2n9bt2yVDfAtT8Li5muvQHytzxN3+fmnn+WlZ/4rx9aqbTQCKRCjQC8H/t43b95g6q39/XezZ3u87gjGHELKxHxItOYcUPB20YBLZdqUj2TOrC9N+RkzpskXX8yQq669MaV03lN00STeh148SLgdftTR0qv/QBn77ggjvCLukO+ZLW9tyMMw/quuu0V690JMNaojIpEAisMFR9wQeiJH1zhSHn300YRwEL4UjSsOZ0HkdiC1YT1XBBQBRUARUAQUAUWgjCKQzHqOwiX4/k5/wjps48rFYJb7ZdOK+bLp18VmfQdeuuvscPd6xDWfFaqZtS0EYxSOMR9L0J2SFZDpfg8FkUC0LGMHKQ5+qLkbaRJHSRGCnXyUDHf6ASZlWOXGpKx8D623EF70wpvHVwu+lXlLfjZlaWHl5DvUz4DLrkTc6kmycN4ckz8dsaY/m/aJDLr0KkM/0h1iv0sGyieTJsrc2XOh8OmVzz6ZAvf+U2XAZVcbAZixEqMlFzYmCvdolTbpw7Hy19Ytcs99j8glg6+EUGyQ9O4zQC7qf6mcd35PeX+U49HDjxAA/3rkCVm77nfjWWXZUqevFJBNnzpZunZqZdrk+7p1yxZDp27euEE2/vGH/LVpk+zYvs3Q71Q2DaCtMJQ9K1asKKPGT3bo7fNOk3WrVyfoQ9NJ/acIKAKKABDYNS6cQqUIKAKKgCKgCByECDhKrz7JAMGQZeiVvNSwo6kWkwbN6/7gAABAAElEQVT16sjUT2fK1VdeKq1bNDYLbqys5dAKGQhqPABCHAjQsFCfP2eunHzMUblIQUh0asfOMnz0OCO08UEv1RvPNnG4ypfzyZix70ufC842rh5GTvhIatSomVsXR1HE02IiqU65md8Q7XBxAQEbXUsE4bLxumuukUf+/S+55c4HjMXU7Fkz5ZSTjwfFHwJ1D38X6Fe5jKD8+NsGWQXrtB5dz4UQDrHV0G4OBHcNGzWVZ196DvHFoDMLwRIFT3O+/ALjqI667jIBVlmHVqokS3/7w/h5pxWY6VcMAjoUoeUYCRpq7TFI8yX9L5al3y+Q8885Uw6BG47tWZvl7n/eJ63bdjCCshz0ja4wLPFkGsO/LHQ3JwCNXbjQz4FJWDweRBmPXHvXgzLqvVGyI5ol26LlJZgGC70g3S06/fM59J2EYfnm9ZWTZ194Sc4f/KKcd8GFsmjRAvlj/UapXr0aNBHby5AhQ+QwCBNt/Dd7b2fvEHN58/RMEVAEFAFFQBFQBBSBsouAWR2ZpR+8IAQRC8hN+3PVhCUnYgzleh3wUPPJlWIV1Q8KZZITrd9c/r3Jpms4MtypHoWlaCIV1W6ioB4oAruBgAfCHpuSj21ecff8LTMF0+CJA4eMxReBK8M4aKsIbp0N7cMQtBF5jTGo+13UV378dp50695FyvnTJCeyTW67++/S4dTTjMJkCBaaPfoPkkU//STdzm4nGRnljUXYHf+8Xzqe0RkeOMKgU+HqHkJzWqKROgugDpUUh738srRu2QyWWxVMn0i/MlFwNmjQpfLCC0/IV9OmScsOHeTEE4+TiZOmyE03XietmjeSQDoIQtCVh2SC3u4/wFi0se782V85dCpPTPJI5zPOlTdHjoH3E1jV4c/jgXUZrqWXC8p74yZJr/NPlwu6nCmjJ30iRxxxhFtPd4qAIqAIYCmBCYnf/91O1LChhk6lWo1k8y+L3Pp2yeEsIXLPnKOKNRrL5t8WG43v3b6hVth/CNhfBD6eTHEEmwUv1qTWF74o4964wjnR/4qAIqAIlHIEaBlGi7AoKGQeQwFup2TLUOgTiSJuGPbz586To2rWkBpHVocgCcIY1GIcsbhLiNhGDDGCqRSyHSOI8UiOcf1gCZZ4nFptjJnlCsBMUHNbO3cfABMiAsIgjPgKrGu0WkFUsU+0BLP6shCZQZuW7i2oHYh7ugIjDw6occdZPYJ/S3/8XjZBy65ps1awlvJDaId8EBJeH2OHAQu0Q+EX5362FYWbRQZLFgjqKDyz9/XwJkj2G8Fx0GrNBEgG4bM1OyoL586RFq1aGuEcGRrmGggWutlIDX5Ml47MIwwUXNHCjquUsEs/0sc9hZlcutA9pR2f6QT+MZ/4BNBpCsZmjrrGeTa4RnfyXLdoUgQUAUVAEVAEFAFFQBEoXQhwPVr5WJf3lMKvsOvggkaEqnkS15ZcozNVrtUEi05n/bhx5QKTp8tFA4P+22sIpP4CUxve9V9cy14vyphhDj8uALqUycOXI09yXPTH4R6UFlTQfsxzNfd9cfKp4Ej6aUtWRL5ZtEiatWhqPJOkgf6iwmMYBBcVGkPgC+bkZMkC0nat20lmht8IyIKkG1GfdBjpNwrJmGJuv6Kkz1ylSiqd0vsJE2lKJ96X47Kf+cYFv6W354HerlFDakHplOU4WsYXC4ftBGCaSbRNeSPf7Til6Ei8F/uFIYCmRAgASsSRGAtckyJQZhDAi9Nt4PMyY+S1fFPzKH+UJAzI0nph+Bx5Y/Q8p1t4Zwde0FSuvchx12pnMWfWE3keZd8cMx9zDj7meM8H9mokV/dpaeaJ3R2Xqwq+u9W0vCKgCCgCioAiUPoRcIRijrtEPz6o+QXjtWUosIHinYkn1qZVU0c4g0U+XQ/yQ21c/KUQJmwvAFMvCrUo5In5M4xvdkso+LG4z+ECPpjG73meuAbJ6OYw9hekdgE/BGQUKEGYFzEad4xfhphhqEtCACt+LPYhMMOKIY57k0Axwif0k5d9IARAFshJJxwvwXonmVvQ9WIUBIaJrxBF7AZIuUCymP5G0e80CO5CGCMJCh+0CBlvzBHGkeAg8REDgcPWEfsM8cjMuasNWCHdJx3atzblKRRjvQj840egRegH0USBW3IK0EoMxAwXPMTNkE0o4mUMN1iI0ZLMib3O+GYOEyO5vlkXoTIFZjHoK7IdBwPgQMJQkyKgCCgCioAioAgoAopA6UMgmZntCrWwQtwr42Co3WTtbbs63Tut75UuaiOlGoH9/UuCEIlCIdBbpEVBBeWLnn2NLA1bsXyadOhAug11EKcrBMKKwiXLNA56QV3BiuvUzp0MbUtBFt3Ye6B1SXqLcQB9oBvDFHCROMV9DV2JQ8Y8YyLN6EVMaSYntjX7RmGZk89yjGlGOq9Ny+ZOu7Bws8qUjDOWOhoPXmDeh+3xvl6Mm+049CmVKakE69D7vK+lY3msSRFQBBQBO8cpEoqAIqAIKAKKQJlDgItkPy2pQDRQm80uui0Q/iAX2SAusMgmYUABl7PgdkyY7CKfBAUFZCQKkhPLGssoVyhDazOvNa9CwQjuy/Ps7Gy5rF9vqrMlVzft0aLsxZeGwdoJllqgBGwfuaeQisIwJp5TiMex0MrNnpO4oXYdNf5AJmAcDhnkCLgcDT36nmffXQU7Y11GoZgPwilrzRaGNVgMQjgm9jeIfnkoiXITxx/KRiDpcuWgPRiGkCyAco6FGcVUxG/7ti3ytysGSdjtg61r90NffUsyy2cY6zX2MxoCEYPnQ+0mDjUCTT9DeBkCyOmzrcs93S6S2HEN2RLPg8/J0ZoEhknPyCHaklvQY0VAEVAEFAFFQBFQBBQBRUARUARKJwIUEkWhSeg3AiIoJYL28YMOIz1IMoj0L+lEKlpaes/SRxR20ctIAAqZVJ5k4rmhv0BjpibGEQtt2yHXXT5QomiTiUI5liedxf0LL78lGXCH6NDLEUMzWxqMfYVepWRlOfSsEXDlQ2+zHdY3NGUSLW1uiH+k9awSJI/ZDmk/Q4u7QjHSxIyPls8wbDO6VwQUgTKIgArGyuBD1yErAoqAIqAIOAiQKKAgiYnHqYlCMZuMgMkVcFmBGK85i34uwKmttnMbVkPNLNjdhbwVjtFyjNc9kHj16d8fwqO8n2UK6/wgWki4UOBDJ4dM7AsTiZzkZPPtWOw5x2jLGtEdqjlNgGgwTbkCPVeiZK7hmC4nPHCvyMSRWbFferlMk2fPqbpHYgUGXRKir0Yc58DnMusm38cPy7gL+/eDEDJ/rAK47tSHQM60A+LNldaxnwFY3eVR6U1W78UVVqGLRQQbw3idZ2M6CpGg82hIpLHXeXFzyuh/RUARUAQUAUVAEVAEFAFFQBFQBEomAj5vSKwLxSgsuJh8Ll1oe0x6kWGmDQ0FksfxoQHaL4XmpUWVBwUdyyqnNs8hT4L7frYNOhHHuRZWEK6BzDLWXbwBEulLDzyi9O0/0NBhFEilpnJQmiTJliy8YhlHgBaDIqVDs1n6OlnAZduy9DbPbTl7Lb89y1i6nHSxpYmjJBY1KQKKgCKQhEBeDlzSBT1UBBQBRUARUAQUgeIjQCsum6wLRXvOPS2jAul+6dL9QpztTEw4Zd1FfClfy9PK7NzzLoBFGwSS1PxLFSQmu8lxBp7nP91qFJqAZQRCsTQSbSlEYqH19KIioAgoAoqAIqAIKAKKgCKgCCgCisBuIRCAl5Bzzutu6DtWTKXv4t4i6LfdupsWVgQUAUVg7yKggrG9i6e2pggoAoqAIqAIFAOBUi752sWR06rLb1xc5CWU4m5Q5IKaMdZgBV1EPuVs8SgEjX74k/eEXPu63Ap0yWhUFnOz9EgRUAQUAUVAEVAEFAFFQBFQBBSBMoUA9BR3KUUL0tu0tV3ylcZijgvDskHP2uHrXhFQBEo3AioYK93PT3uvCCgCioAioAiUGgQ89G0P1xx0bcF4bYwftjvJcftYcA0YoZkUDcNizJ4UXFyvKAKKgCKgCCgCioAioAgoAoqAIqAI7CECjptDumV0Ypv5jOv73MZ2xfVhbmk9UgQUAUVg/yKwx4Ixv3F3xBghyeoDycfJDqHcfGoQ7N/x6d32AIGY+5DgQdjU5iO2wTPp8kuTIrA3ELCaRz5VKNobcGob+wmBXdWsK6g7Zf33Tj/1XB1Y3/TOeTJa7nohOSvp2FOEZCyMtYlxTWnug9hs1iBNP11JKOqhIqAIKAKKgCKgCCgCpQgBrOfIfkqs6/aw61iBmppRl8/BVacPi1GPWZB6dnJobsnUwlene9gZraYIlBIE/EW4p494wFYGreWQX/6dPHYE+I5BKdLyGXd72IjFXZyEO+ep7ikgRneeQnvxBMED9mJr2pQisHsIBPBiemNeCZho8fiQ2vfJY9+LA/37RD9ieEsQo55yh5grY4I6tTNQdycp/WW2M6egPuYXs0ZgDVveqb1LQqgDjYDtqu4VAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgX2KwB5bjO3TXmnjioAicNAjYC1nrOWYHbDNt+e6VwQUAUXAIlCUxR41ib1W+YmV1FLMQqd7RUARUAQUAUVAEVAESikCXNzBK4C6yS6lz0+7XZIRSOXHpPbVn0xbpV7chfO4B5YgLJdqybELdbWIIqAIKAL7GgG1GNvXCGv7ioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCJQIBFYyViMegnVAEyi4CtBBL3souEjpyRUARKC4C9DNNrUf6uI8LjeKp4sgt7G7YaVIEFAFFQBFQBBQBRUARUAQUAUVAEcjDi0nmy9hjxuTiFo1EJI5YQGlBvzlnXixqIosViaIHcYOiKBsIIHIQ4wghbhnzuLfJlrF7W9bubXnW5zH3dvP6nLbYHo+ZH0J/mdhnbvaY8c64RcNh5Ofe3xTAP68PcaqxsY6NR2brMz/g95tr3HP83NtkMUne22u6VwQUgZKJQO4bXDL7p71SBBQBRUARUAQUAUXAIBADkVNY8vg8DhEDYsdJqfvCaus1RUARUAQUAUVAEVAEFAFFQBFQBEo3AksWL5KVvy2XBo2aSo2jaycGE4EgaMMf62Xxwtly1NE15ZSGTRLCoc8+nSK/r10tx9Q4Vtq072AETJFIVOLhiPiDQfFBADT9k8myBmWOO+FEadu2vUDSBeFWovkCD6JRtOEPyMQJ4+WoI46W+o2amLIUUBlBFtr47ruv5ddfVpj89MzyUu/kk6VKlcMlJyciPh+EULGo/PTtt7Jy1S9GsOWHACwUDonX65fKh1WVZs1bmrrbtm6TyR9PlOys7RIMBKVp0xZy9DE10dU0qEtGMfb5sm7NKolAcEZBFxOFfofhXi1atZFJ40c7gjHQnT4KBDFAK0BLT0uXw6pVF9KkG9eskXrAr0rVKgYbtrN5/XqZM/tLOfvc7uIDNhyf+pAkMpoUgZKLgArGSu6z0Z4pAoqAIqAIKAKKQBICfvioLyw5WoLQ8ovExJ/HJr7weoW1qdcUAUVAEVAEFAFFQBFQBBQBRUARKA0IxBCU+cnH/08++GCitOnQSUaNHZ+wagpA0HP/vx6QscOGSsfOp8s7Yz+AkMcrfc87Q77/4Wup37ipLJozT9qfeoa8+sYb4qVgB9ZZ2Tk50qd7N/luyTfSsHEjWThvrrTr1FleeWMETKwKR4WCL1p9TfpoqvQf2E9OqFFbps5aJBUyvBIOU6kR/j0g9Pq/Rx6VmdM+NsewUZMtW7PlhLp15OXXh0m9evVxEy/G9YiMm/iBpEPoFIbFF0zDJC0jKE0at5TRH3wo0yC4u+bKSyUAAdbxtY+TzX9tkO+/WSiXXH6tPPbEf40F2H8ef0I+mjhOfBDWQQIG90UYY3ZIWrVvJyPHfyhXXHoZ+hCBUCuA5iMSzg5LZoVMCOFykOeVbt16Sk4oR8ZPmCAXXtRfnnzyCdCdsFiDEHHB/Fky+NLB8uOys6R8ZtBYyXl8gcIB0quKgCJwQBEoYgo7oH3TmysCioAioAgoAoqAIpBAgERTYRsLUrORNFw0ugvqi4mW9UARUAQUAUVAEVAEFAFFQBFQBBSB0o2Azw8lQQhw6p5cT2Z99YVs2rjeWDzR6ilr2w6ZMvF9qdugISytPEag89Tj/5Yl3y6SqV8ukHdHT5SPZsyXTyZ/KKPeesO1eBJ5+j9D5Nsli+TTOYvl7fc+QJm5MuXjSTLindeLBIuCr1AoKsNef0laNmkpy35dLktgtUVLMKY4CyBRCFfvlCay9LdNsnT1Rpn97VJYa0Xk1uuuBP0XN/0lkXfqqafLT79vklUbt8qvG7bJ8jWbZBSEYixz098ul/adz5S5S36W0ZM+lskYy5IVG6RHr4tdV5Cw/oIwrV37TrJ6a478sn6b/LZ+u/y2NYzyU4zl1/LfN8uy3/+SZWv+lEeffM4Ixb5duUF+XbdNfl69Wf77wlBjMXZYlcNkzIi3ZdXy5QYnjoNWdeFtW4yFnXHhiHtpUgQUgZKNwB4Lxjzu+233+Q6TZZLmAW/Scb7lNbNEIABdB+hi4OOU8vyczvGjZTfbXf6Mkjebr3tFQBFQBBQBRWDvIeCFVl9hG91d0KoM9CBcX4RwY7qvSN72Xl+0JUVAEVAEFAFFQBFQBBSBko+AZWs4XA6vMRCpUruJVK3VyDDj44xXhPXi4cfUk2o1T0oMyHI4Ehl7eMAoTLqVXQws96ygfVE/K9bLm1Jbcq6aOMv40ZJHS97r4VWrS4vGLWTEm2+AhQd6CNvIt4fJMTVryQnHn2jKsOyk8R/IWV17wuXiURCUeeS42kfLqZ06yrjx40hQgfYKyKRxE+T8886TGlUrSdATkuOPqS6nnn6mjB05QnzgDxa2ZYd2SFbWNpk65QO55qabpE2rdvLGm0PR6ah4/bAYi0OpER32ePCGov/pQVJvETnmyGpy7jlnw8Xid+BNkp7DDsKvENryQcjnjYUl3Y96YcT5wrVQKCa/r1onp51xGuhBCNKQ5/dEpVrlTGnVojHiUAfRqh9lUQfWYtlZ2bgfAIjmoKwjpGPMMB9iUwe9UUnzRSQS2irhSBjHECLiXhlpmCsgrPNCCNbg5PoYS0v595CHJBKlS0jUpBUbXDvaREGZJkVAESjZCHCu0KQIFIoANSoc37iFFtuli0XFh9mlRrSQIqAIKAKKQJlEgJp4hW0wKMN1Wo1xjwNNioAioAgoAoqAIqAIKAKKgCKgCBwgBMh0Td7YDTq2IKmyqwzZqEvW7Ap9Q8sw8u/8UCjs0bufjB31LqypnLwRw4dJ7/6DjcUT+8H2fvjpO2nRui0sqii+FQlDONSkRQuZN3eOOLRVTL77ZpE0aenE8AqmpZlyTZo0k28WLTTHhf3LyMiQt4e9JpUqHiadzjhbevTtJxPHj5JQThhCag+EYh7cJwpMnEEyDpoHErLs7GxZvvxnOeLII0zzxAuUoIn7FQ6hLsZn+ZSQbRvBVPXq1eXNV16UhQvnoA0I2NA+x0hrNHscRxvMS2PcNAiz0jAe3p+CLW50l2g29IH71BQM5Aq7brv3QfngwwmyGDHdGKfMuJ5MraDnioAiUAgCEHpjDmDiO5pnc/MLqbxXLuW+0bvdHLUUMGlgauJRfskLiT8TNXOc6cSdzfMrrHklD4HE86MGCOOz4MfqgQZEItkn734s7ON1ftOJUnqgCCgCioAioAjsDQRcZcGim+L3CP7yU75SRdfTEoqAIqAIKAKKgCKgCCgCJQsB8hewtrPrQLu+s53cmXVtrxS8dzwf5W3JsRwTqV7zRFMx6sa2JTekOInGOprKLgJpMXqxyE1k/EYhiAnHvNLwxGNkxvQpuRfzOYr7YEJFoVE8JC0vfAqWT87v1pf355uoSQFXOArBUSRHBg8eIEMeuEO+++E7xOLKkO9/XCLDenSX+TOnShbayUaZWNYOSYeZlrHegjVUEOZXlSofAgusbFhCRSUOiynePiM93QiSYpDqUeH9kEMOhdVVVuK+BR3EYU018s23pGePnlIeL2u/iy6WB++6WUa9PUwGXn6VBCiQgmVVHFZcq1eulBefecZ4CPnyq5ny0UeT5cn/PAkhWAR0Hay9/AGZPu0TqXlkZWhCAlcKrtCX7hf0kFdwj2deeF6uveYKObdjaylfsbI0aNhMunfvKhcPukK8AdihUQCHbcb0T+TwQ8oBV7aB9xtWa90v6CXPDB0GgSIJSWThxbVuHpPHRqs1JtqZ0mLszNPOlgf/ebeMRrw2iRVvrki+jx4rAnsDAYq7+e2004WVNSXO98ZNSnkbxRCMlfKRa/cVAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAEXAevlxlrnWIHcngLksNH3tLbWO9gQiMIiihZK/kAgX4ukncZLQQwEVrQ2oOVXUcmxuIDYBgIjD7bTzz5P3kJ8r4zMTGnbobNUqVo10QStpoxwCTkxxmkG1zwEd4B0CRiOh+E+0AeHh7n3DIVD9K5o2qYLe9cCItFefgdLlnwji7+eL8+88nLi8pnndZWRo96RAZddiTyP7NiRZSxFskJZMnP6dLhezJKvv1koHTp2lj4XDTD3o6UXrbLatO8sb4ycANO2EPKBI1wX+iD08kO41vn0s2Xxj6vk88+myfc/LZWPx4+VO26/SebNmytPPD9UgkEfBH8BadWmg7w28gNYiAEnCOQ47sxgGqzHghIN5yT6md8Byyanu+57UE7v1FK++GKG0NpNkyKgCJQuBFQwVrqe1/7pLbTsTXItxozChDv3+/BBpmaL+QZCGYLaFoHESs/5CCRO909v9S6KgCKgCCgCioBBIAINRrqwiIDAgXcNTYqAIqAIKAKKgCKgCCgCZRwBy8Z2uRwGDbpqi9G7gOs+jsKEGIUPYNJH/ZmmjLUUQ06xEEy+b7Ea0sqlEoGo5a+5vScPLe4LQKbkk0jcZ4Q+hQ4Msa0gijJF+Du1lmIet10ruLX5IQh7QrCuikCwxUBiFw0YLNde2g+WWQF58LH/QihH1/QQmoF954HQLQBBUdb2HciDC0P0KRhIkzWrfpcqlSpD2MQyXsksX162b9lh3BfG4xHsvbJs2VI5pGIlYz2W3H+6J2SiS0cKioa9/qoEguXk7ltuw72CRgC29o/fYcW2VH76eaUcW+sIk+f3+aVu3ZPlrXdHgp7zyurfV8t5p7WX+++/Wx585ElI7tBnCKVo0XZIJvCDRZcfYwqHQrA48xnXi0G070N8sI4d20unTp3khuuukjtuvUlefeUFeer51w0fM4x3nu4TqxxWHmPmG++4mSQmMVjH5ec+MXl8ZmzEz51Y6tU9QS7q1Vvu/fudcutd96YW1XNFQBEo4QioYKyEP6CS0D3nw8APsUfaNKsj02etkM5tapuu5WdaXBL6rH1QBBQBRUARKHsIUCjmQxDnKZ99L60aHlv2ANARKwKKgCKgCCgCioAioAjkQcCKtcgEZ6LBR4xCADDtA7CYicA6xgNhgk9gwYPra1csMOVsPZf/bfL25J8KxvYENa1jEbD2S4zs1fbi5212gXvGBfPBPaAPUi0KmNp16CAVMivIjpwsOfvcrpBYwQYMwjkmCpQaNm0mn3w8UQYM6A/BkxceCqOyGLHDmjZvAaETFOFRpn6DRjIdLh/7Db7UfX/i8uP330vz5q126gfkbLA4c7LT0Idx742UXhdfIs2aNTcWb17cg7G8nnriUXnztRflgX89IBkZ6RBaeXC/sOmzz++Vo46sIdfccIs8MuSfcsOtd0rVqtWNJRtdRVIZshzcKkYxWAq5wlCKpMUYzz3oLz0seiB6ZNzpeuh7LOQo8UPuBWGaK7jDfGDisaGsiXGGwsTMYkMh4a6kICR11998m5zeoa18OuWjXamiZRQBRaAEIaDf6BL0MEpyV6wA7JRTTpS5s7+VdHxXwvigevjVcxOjueRGdLG5ulcEFAFFQBFQBPYPAnR9Az6HfLV4nXRsU3f/3FTvoggoAoqAIqAIKAKKgCJQYhEAzxwu2OjtBq7YsG2FpCEKS51sxD4KmThC4JDTugWCMVra2ES2ODfK04qz2fZ0X1YRKOrXUzgu/mi2BPHLdEQ7uWUL4r/xFxyDO0CIh+BKEcIv1Px87gJZuOQnioqMdZYRmkEYTIvJ/oMGyacfTpCvZs2CG0WRqdM+kxmffS6DGf8rACEWLCkZF+zDSRNl1uz5aA1xvqbPMIKyQZfSFWLelJ2NGGcQMtFN4bixo2XzhjVy9z8elEGDBsslAwaYrX///nJulwtk3OgRRrAVwo3pwpFCMwqtoxHGMYvI5VdcA2u1w+SxRx7Guwt+I4RajDW2fsNmWbdho2zc/Jes+G2VbNj0J4RjAguxK2T+3PmSlRNDHwQWaSvkpWefkhNOrAPeJbDAC832s7OzZdXqNbLpz22ydt0GWfPHBtn8118QAkKYiHtwo5BwV6zHaBV37HEnSJ/+g2TMiHfygqFnioAiUOIRyP3ql/iuagdLAgJntjtOho2YIjk5+NAFESCTHEh8XjUpAoqAIqAIKAIHGgFajGVnh+W7n1fLo7d0PNDd0fsrAoqAIqAIKAKKgCKgCBxgBMDjNglsbrPPBBds3Yq5grBEcvhxLSXuxgzauIJMf02KwF5GAIKTwlKc5k2FJB9cDJLnxl9vBHG1ikr8Ofso9ELBcATlPX4JwKqKidZQHkiHYjQrc10TXnTJIPl68ULpccHZUjEDLhNDOcK4WW3bdTQuF8PRsFw86FL5ftlyubD7GVI+LUO2b98q9/zjful0+ukQSDnWZ+YG+EdXinDgAWutuLz9xmvSrFV7OeqoqojdFTbXeJ3uJPv2GyzPPvmITP/4I2nd6VS4QPQboZ1xaYjeswzLXnXtjfLYv++Xm2+7x6DwxcyZ0qhuLWP5ZnwhQthXLi1TVqz/U/5Ys1a6nXu66YoPuO6AwK1B/ZPkfy++YfIiYbqL9Mjsrz6XJnVrExCalplUPjNdfl69GcfOePhUrIGAU6Lg/8T1b7feLcPffr3gQnpFEVAESiQCiMVoDcp3s3+cQDChVqnVSP74ZVG+lZ2pGHMNpi9OKpVrNpBNv36db1nNLEEIUKGFyf1AOCd8jvwwiNz08PvSrkVtfCgbmktBxnKBuXIEKhk+7CXiBPrkRfpAZsrJoa9e+AOGMK24yfpQLm47e16/8IXNnre7d2pSKyg5pS60eJ2Phc+S+whVa5DMs8OeedTQof9mmrNzncbXnclo2LjHTs6e/C8ufpxNDlwq7u8v9fns7kgi1nl4ARXpLiE5USMqORVRPbloiTyO5h3ebvexuOMv7v13u8MpFXzw8V6akxu6suAhOJ8MTERQ8WNK8snPYNMeQxgy31xFOXcPgojJgwc0fe5KmTX3J3nszrPoVt9JXvselG783NHoThFQBBQBRUARUAQUgTKFANlWlWs3ls0F8J52Bwy2ZfkUlcDPoktF0sDrVzq8KsvHUgXg3UFVy+4rBOzvlWRNmz4vyHuvX21uFTAx8Xho6RtnH4/mOFZPLh1EfmxyohUUBT4UPBm3hmDy8Dxre5YsXDhHmjVvCxejsJjiS4HE8hSk8Tx7R7bMnz9LWrVsL15YkzGxf2wnObHtAFwMmnhmCXotucS+O46Al/XL8p9k3dp1EMq1ljS4S9WkCJRVBMgPOX/wizJz1FUGAkc6Q/6+wx+x5wcOH05U3Lzy3Dtz5PWxC51+ouMDezSVa3o3N+cxl5Fk+/vc8Dnyxtj5NP9GVY8M6tlErunbMpdPZGrh3y7MP8WXUtib6b5MIMBvY+eWtWXanF+kZauGTjBO1wSbgpU4zJvpy5cfTZal8IUpPd1xuWjPndw9+x+DZssBTcWVbBSz87SIKE6ywq0A3n5YzhuBmLPYyn1e5tydQVwZOHxN03Sdz7Y4d2fdvIum3W1tb/yGdveeWl4RUARKBgK5878zEVFYxhQIuvMK5rTZc3+WTvhOYX2088LIlNZ/ioAioAgoAoqAIqAIKAKKgCKgCJR8BKxgbFd7atwNGqGXayrpVrTtWCsoWmMx2fOMzAxpAyux1EShmK2bXi7dlCHvj7G+HL6fQ5fZMqTPjEtE8gYphHMZ8Knt7qtzP+KTHV/nRLPRzb4mRUARUAQKQ0AFY4WhU1avFSD4sOKMzm1PlGeGL5Rnn/5Q2jQ/Vlo1r22CVCLkmPihNRKFAIUfRX6DrJaJZWba8+JA68GH7oAmBDI9kAlrnEKTr4DnZyuFQ9lYqPgkB8/JhxhxMTcyapwe1FHXQ9N3cJTjWABR04fJ64eWjScMTSGUiRd32rCWG7ZH+3lfpMlK4f2JJvmdL7xk/leLej7510rKLcpiKPXnWVT5pKZLxeEB/vns53X9zo+ktK/ti3r/EhZiqfM8zjH2NCPtglDfneeonEijyOysiMxbtAoxML+WH5eul3/+7VTnG+QiaH82qa3uDLDmKAKKgCKgCCgCioAioAgoAoqAIlAyEAiBSUM1c9IxMcTEs44wwlBaZgok+EMOxQNvgeD3IOwJvTpBeOWST4bPE3eVCk3FXfxH/lAoJ2Ta8TMGGOrRdSB5RxHEEovBO9Q1l/eXECy1AuQv4R7WDSJv8dJrbwkFavszMT4Yx0rh356MeX/2Ve+lCCgCBxaB4nK4D2zv9e4HBIEK6Wky5pmL5MuZK+TTOT/L8+/MlJNPqCZtW58sTU4+XA7JLGcEL+yc1T6hIMYeF7fTRQmGitt+0fUPLGe6uMJFBlpl8nqxaDFaPrACM4IwmMBT8wcuMbnnYsKPhUQkhECvAN1qHnmKLRgsHn7FHX/Rz1dLKAIlF4HiW2we4LEV7/XfqfOfzvxJZi/8TRZ/+5vUr3u4dG1VW+679hwJlKOPehTfy/fbqQOaoQgoAoqAIqAIKAKKgCKgCCgCisA+QsAHXg3JGsq0dsV7EIVXTHRjmFzeuE50iUnyepLPTYUC/rFcWprjjtDWoSWWHzwjphjaurBfPwig6DXK1V5EPpXlWc4oY2Of3BdTcR/+ozDM3Hsf3kObVgQUgYMDARWMHRzPcb+OgpFcMiAcO+v0E822bWuWzJi/RqZ99b08++onEqaKSiHJur4qpEgRlw60zn/h4yui88W/jEVFoSlhcZF/KW+geP2Px4o7bRTv/smLrfxHWESuJ6FSVUTB/C9HaDVXjASRY57aXDAmp9TxpV5PLsvj1PJG2GlcXsLqL6Vtlk/EXOLJAUip/d3dLsCRw+5WyVO+uOPHel9TMRBw6bRCWrDzixMzjFp+YVi1Bvxp0FTMEV/Qef4+mIlRX7FV47pyTqvj5OEbzxC4sTchyeht12pGWs+txfvVFNJdvaQIKAKKgCKgCCgCioAioAgoAorAPkLACUqCxsEGgy0W9g4/xUu3GSblpZ/gx8nNt9ed02TBFAVHyeduhXx3LEeBWHIijRaiQjWuBcAe6dqth7lMWt8qVRvBGGN3MJbHAUjsm1qLHQDg9ZaKQClDwM6Ypazb2t2ShECFChlyXqdjpQs2TYqAIlAyEKBMDGtBTYpAqUaAAi3SfEUL1JxhkmRLCMVK9ci184qAIqAIKAKKgCKgCCgCioAioAgceARShWgUOFnLNLrosAIoq4xoXC2i28YzUTG6T6uv1HsXozmtqggoAorATgioYGwnSDSjKARchX185RwNFOrtM0Efw+wRYtPs99W/A20vRj2dkp2APxYqxpSdLhJdLrFdpBS378XHv3g9QYjX4g6hWPX3ur5TKhypw0u5HkuJ0QRHmHnGE4tTcwt2adTWyse6qvjPL8/t9uAkb393t4H8xrQ7bRR3/Af67fdaE6jdGXQJKptX17Cwjlmk4cYVWob4L1FIyIII4Owke909d9+TnWP4OeV8pRy3wpDSa4qAIqAIKAKKgCKgCCgCioAicPAj4FiJWTqooPHa63Zv6aeCyu+//F0RdBkXiLBIs24b91/v9E6KgCJQFhFQwVhZfOr7YMz81MYReJO+isnELCwV1xUaRXCaCkEAQjGaV1A7h8kuhyxD2vEOXUj9Ii7Z9oooVsjlFElPISXzu+R42M7vyv7Jy8894e7ceaf+7+TuMOX3nXI9niIYs4JpZ7nrlW07Yojzx3eQomr71HN7mNJc7oX9dORJCDb27IbwVL5nFd1axR1/POXxFKsze1S55BA2e9L9XYfPKcnZIow/n6SJv5i/nT3pr9ZRBBQBRUARUAQUAUVAEVAEFAFFoCQgUNywBCVhDEX1wbpAtJZi5DEWJ1lrtuK0oXUVAUXg4EVABWMH77PddyNLcDYtK965lcef8H5c6L1TYywVWrhEXizeh3lfDynqhYCSIpHsuDz04jQJx7LNLb029hiCLEWiEfFH/Maq6J+3nSXPvTNLtm+LyN+v7kAutAgeJcUPFGJm/5ktD7/8mbRsfqx07VRHFn+9WkZO/k4yD0uT2wd3kAD9U2OxQkueHaGw/Ot/n0rfsxpJk3rVIS2NSxgCOoppfl25Sd7+YI58t3KLbPwzS06udaQ0POEI6dvtZEkLAlPcMA45jgf3HvHBt/L9j7+glki2L83EyvJ6YKKPAvVrlJdLLmxv+scnwX46yTn6dsnvMvzjryXq86N8TNJx98OrlJPzTm0gxxxdwfSJC0oOkxOg+TmD+x515XVR+MEOBhHXCM0hVFeSnYlTIO5KRmJusCofg07x1jy3vgtxGPdEnH5TQElfcB4rknRvZIRWrINeAMMQfIUH8Of0zIcmnd+ZBx3zsCNuNW/csQazAmbAYu4TxjMY88nP8vxrU2Xa21e54/KjHbSbECiwH0677HQsRmscWJYBV280DQO2gPDZc1Aojn6wRsgIJ3xixovzqNsMLUTZtWXLNsibH8yWFXjOW3fkyHE1q8oRR1SSbp1OkpNqV0WZmERi/G1GxWf6gOeDvzAs3JxznznnyJl8eDqxWBj9C2Dv/MaYT8uhX1fvkI0bt0rLBoeb0rw/mjYJjx1l4gZJJ4cXseH5GOG9CRKMXuA5OtZFvMhniA1Y87lx8Y0wwaZ6xAOhDCzwIrgJcXRyzSX9twcI2F9f0VXxPEwKFIB5Sku2OH8MTPbcfY/wQJ2UyHfPdacIKAKKgCKgCCgCioAioAgoAopACUaAvArSruRF+GIpdJBLP1vOxVYQrunpoK1B65Mfkmb5QPtqfEW1n0R/WWHX7nRFBVu7g5aWVQTyQ4BzRuq8kXqeX72ykadIlI3nrKMsQwhAdIEpz1l9UKBACyduFF5ZaycuqL6Y/6ss/G6NmR7bNz9GJk3/Ud4at9iRFkFWQD4yRGfy7xeny9c/rpGWjY4yrf66bqvMmLNM3p+8RKYvWGbaJby0hGL7M+eulJVr/0xiTItMnr1UBt01Au38Ie2bHStX9mkplQ/NlFdGzZaLbn9P1m3OMsILI1eCgIPtUHgVj1lutsASKkumf7VcNm7abp4miuSbfv1ji8yYvcxcw5AligXh5JnL5JI7R8mUL5Y7LgaBC5ORpbjtUPbEjUIxK2QxhXidm1vH5KX+40zqWuglGPDIMhpdRiiWtBo0KCY14LZLlwG0jMHgscEdIopE0U0jFMMxm7GJ+FDAzDq8h8EKF2seUVHa4Vnau0WxgqbQyanqNsAdFstcJNO6M2GBxRu6wiV7n+Q9f1cUsuFmZuPz5h+Fi18sXCOX3fO2rFz9l3Q5ra5c2buFHFeriiz+fpWM/vgHAwl/k36vH8IlRwAWwc3Ynl0cR/+fvfMAkKLI3vg3aXdZQHKWnHOUJDkpScVEUO/MqAd3pvufnnLmdOac0BPxFBWRYDhJApJzFJQkQYLkuGHS/33VXbOzwy5JUWBfaU93V65f9w499fV7Fdc4x0Xo1vrU3Ltu9ym/f/TlEtz79Deme+bSxEEPybjIhoy4scv2GcB528yhY13ukQPbEtXO1Gc+5FpSu+NGUYyBfRKp0xzrhxJQAkpACSgBJaAElIASUAJKQAkogd+DgJknYUPmd/LRW+Rv6MxM+f0aDiM5IC+/alACSkAJnLYEOGfnzLPZObzYPm4++FR2n3OaGpSAEjibCGTKBL88OQVSvHj49i5iCCMWOvKfmMJIvHznSPIz70xDhm8LHh7cntIEGtYoh8t7NcQbn8xD84aVUKNSIWbF5NmrMHPRRjwwqCMK5U91NYagPI9lokGFMvhg+Hy0b1zVaEKUD2jzRIungM8VGURg2CQWRI89Nxn16lXA8/f2kDQqMECfTsBlXWvhL4+Nxr3P/A9vP9LHaC60DOvfu64Y79Rzzk29wOdfL8eipVvQsV1DU54Ph5ki8Pj97CmDU29E1IywCDD3Xt8OBVOTEZH0fQfCGHjvSLz8/ix0aF1FBLC4lZrc4vLs6AT5TqbVEQUY8z1sq6egI2kUSxhilo/uuRVgMt38vigt8gxyp4D9JBpJCIulGLN6vE7LKXAtytzyfCssIHW7JCFDMoFilAmSEKDyJ4HCGEs3r1MUzeteaOQb00yMDXO5HeWOwg/vCeaMBow4ZW4PNwtz2zWZWE+mZKMbOxvHdNdgzhhavT1iKsqXL4H/PNnP9NkR2/y4bcD5OLSPNl4MZrSGqU/G7shNpgemvxwe22Ib5m04OTHCohST5mPceezzihWkP8PE81bjtQi6fGQ0zqUgfAa3fIYceORHgpst1r6HFnus1MXPe5gOKHn9WYMjUQLJMggjDJpYSdBwehJwL/sRncst/oiMGqEElIASUAJKQAkoASWgBJSAEjg9CGRfHIG/VZ1f1/b3dNaMgdPfJPl5SycpkbAfYVnuJP43/KkYEecRNCgBJaAEzlQCdo7wTO2/9lsJKIFEAmLxxECVncKCIwXIzD8fWGQ3etJSfP7NMtw4oAVaN60aM4S65YpmYu21XtwvTsX7T1yE/fvT8IIISe1aVEHnNjVoy2RCUkDEL6l3UP/WGPToaHw9dS16dqhq0mjhRSs1vp1EQxyKSMPGLoZPlKYHbuuEJL/zEGcEJulP7RpF0a9Hffxn1AIs/v4XNK5bUsQHsRSSzlIcoVDCLSQC2Nff/YBzSxdCg5pljEBFA60kEX6YHh/C0j7XV2MfvVIJ088p4EPDWqXxzbQfTFaKTqPGr8DC77fjpksa4IVhs7Fs4050aVkF99/czggji5dvx7Axy7B0hWMV17o5LaEao1wpccfIQB+GEjb/vBvvjFqI2XPXIyhWR6XKF0bPTtXRt0sT0z/m+WnrPgwbOVus9DYjkhFFnRplMODypmhSr7S4QhBpUsTC7b/sw5ufLMa3834Qi7UwUlMKombVUnj0rrZITU7B9p2H8cp/52D2op/MA27+FD9qVS2Jx+7ugaSA485w1pKfxA3lD3j2nu5GSPtQLKvmL9uAwX9ujpfenIsVq7ehYMFkXCnMr76oMbvmiE+ynzBrDf7z6SJs3b4X54rl2U39mmOVuEeMiivDm69oIEZmwlWYetwnX95ODDzdsv0gzmtQxgh1dEJIsZUP8LwTCxdy7sf5P2zBe5/Ox2N/uwDnFEySKyzXRsYekpvk7Y/nYe/+dNxzc1veuIbFax8vxYz5m5AZTENqagBNapXFI4M6478TFmLWko0IZobxt8fHiKvMfGLl58X9IgIny79o67Zux3ufLcX0OcJJ7sMGtSviuj4NUbdeWXbXuMz8WLgsXbkdA/s1xvNDp+OHdTtQvEQh3Hh5Q5zfqia+nLIcH49ajB27D6JGzXK477Z2KFu8gIwqy+rSVPYHfPAaaFACSkAJKAEloASUgBJQAkpACSiBs58Avci47xbHfrsfa9SZ8rK0WUHgWBk1XQkoASWQxwmoMJbHb4BTOXzjxkwa0IncU0k5h7pp3iNCiREwqE5ERdIKyJ+6PFCtWrMLrw2bju7tq+H6SxuINCHrJ1Fhkgl/iLgwZFA73D5kJIZ9Mgur1u2CXx6o7r61CzJErEh2J+QzIwERSZJQtVZxtDyvBt6XvJecXxkSLdWIFQ9FKZ9YV3mD0mQU3y5chzr1SqJUkVRpT0KCktWrbXUME2Fp2oLVaCr5+AaUEVVkGJTRKLz8vG0fVq7biZv7tzTnrMaRpXiUPfjEtMorC5XxYVC8KNIfpNyD9JQnrvuEhU8KeqQPG376BfMW/ogNm3bhwrZVcHG3+iLApJg2l6/4Gbc/NhaNapfFkDsuEq0mjOEfTcDgIUvx5jM3okShFLEk8mD9z3sxaMhYEX9SMWhgB+TPl4S9uzOwedt+eXp1VgvbuHUvBt4/GpXPLYS/39IeKbLW2JQ56/D3J8bizYcuRZ3qxc0A/u/5USL2hPB/AzuKy4MAMjLSMX/5VnE/6Nim3fnsOBEII7jnpg6S7kd6cJ+IXlulGboNFKsuGePG7WlYsGqzWNFJ8xK3RcS+FUu34sGnvkWvLg1wSbcGmLVoPV7/YC7Klk5Fx+a1zDWZsWgD7ntmDC7sWAc39jvPCG8vfzjVtFf53JLSvwZIIjRSF6HMCXIsgb2rXrE0lizfhpnzf0KbZpUkRiwWnUzyKflEAKtbqSSWr9mJ4WOX4rarznOun1i8hdNC+GTcLFzZ+3wjWoVlPH997gt4MpJxz8DWSJH7MjMYwozFIpLJDXFe7coiDm7Dvt3b0b1jI0TSwoY734xbt3UnbhvyBapULI7/u7krAgEfZojbz78+OA5vPH0x6lYubawbN/28C/MWr8fPW/eI1WITXHZBMj6fvBT/enEK+kkff1i/G9df1UI4QKwMp+DB57/Gfx7r74zI3MSxwenBKSDAdf587i+5+PXlTkFTWqUSUAJKQAkoASWgBJSAElACSuC0JcApDQbOjXAtbf7ONyGX36UesRILyG9qv3hGCcnvKvlprkEJKAEloARyISBTiRqUwG9LwFoq5SSIWbHs17SYU72/pr6zrizd59mHJT49UcgQa5+de9LxtyfGoGLZIvjHdZ0lQYQjifeaNZREjhLLnfrVy6CvuFR8b9R8KePD47d3RT5xS811oGgBZgMtnPjW0sB+zXD93z/GiP+tRN/edWLrjdl8fIjLyAiieGERxdiXHELZsoXNOlrbZe2wbK4L3bx8jvv0f8uM1VnvDjVyqCF7VEgeBMOiaPA+YZu0yJoo4sh3IkY1qlPeedtKKuWEe1Bev/rLNS3QpnEFU4l9Znzz0wUoU6oQXry/N0Kuu8Imla5E3zuHYtjoxbjzzy1N/pfenyOWXX4MfeQS5C/g+O+2ghD1SQpWFKFKFCmANx7qYyyoWLBtkwrYtGsv3vx0Fl78Z29T1+p12/GvWzqga+vq5qGXfeneppa5lLTEY/qjt1+Izi0ruw+35dHl/HqmDRmqKeMTRSz+74PHGcEw/nFDOzSo76wR17l1JSz78ReMn7EWnUQYYxgq461Xq5xYZHU3bhMZV7tacVx55/uoWK6EezvJml3yn2soJ32wN5kIWde2wj+eGI3/e3ockuSGqVejJOpUKYneHWugfKmivIGQnOxDj7a1MHHmWtw64DxjEch2xn67QETLsIhTdajnIkOuy/pNu/HIwB4yPhmrGzq1FutGabJW9WIoU6IAvl+zA11aVXQEOLk32ZvXPpiHYnKvvTLkYhNBzbdLy/LYtvMg3vxoHp4X1rwNfSI2hsRs8O5r26BJg3Imb/OGpdDt+v9g8qy1+OTFa+ATQU6KIzPjkLge/Q47D4ZQvID+k+lejl+1s/9G2H18Zfy7pChm07i+HOMYzN80b3YNSkAJKAEloASUgBJQAkpACSiBPECA0xH8BcSfRHZ97qMN2+/OX0RkruN48h+tLk1TAkpACZztBHKZqj7bh63jO5UE4ifmE4UwO7H5a/ansu9nVd1ePjlFjNCRIZYv978wToSrIB79vz5Ilgl+2vSYdaZcxctZPymKmwe0QcifhBr1zkXjVtXFUkyMrqJSgRsiIbFAE4WCQlqNikXQpa1YjY2ei6AIYAGubyaCGmUafrlwvSdvMILkmCRkaxEBROrlRvEswic9N/ANqNhbUIyTJ8DJs1ahWYMKKFooHyPcjYlHhgKy6JQv8zB6D/oPOlw3FOcPeAsPPDcJ5SqUwr2DLsgqIP33RrwxUcysmSUWWZD+Lv1+C85vVkGG4TVLTwUkunDJ/OKOrzIWL9lkxBi/9GvR95vQuUVlWX/NEcVYOXtHV41JMrAkcZs4Qyy0mjcshwN707BvXxoOHJD94TScJ679Fq/cavKnSaFixQpj2FffY+K0dTh0KGRcV7IuZqAFVrGixfD2qOWYOH0N0sXKitwYgtIY87GP/gyviEuZDm2ylYvgL5CKWiKKMQ83oq5y7jnSRtick+/a9bvQtkVN02aSR/yQSx1lSxZGzYrlxULPWbOLtmFiBigVSC3GekwqksC31mpVKoKRL12Nu2/sjNaNa4g7xjA+GD0LV9/9X4yZsEpy8H4I4uIu5bHll+2YtXiNKRsUu7svJm3E+Q1rokThfKZvKcK81DnnYMSYRZghIlq6sGCg8EVNhHv4ko0uZ0VI3mzpcu1mLd6IFo2rGTege4T1XnEHuv9QGhrWK49VK3ab60YGYbEo9CYloxFFMQapN5+sR5eakg/161SGX0Qx5y4GShY+x9wnq9ZtMLzI/Y/cTH/Pko/4fyvskCiEMTCN/35wYxy3nPLbcrpXAkpACSgBJaAElIASUAJKQAmcbQT4m5e/TflickQ82ZgfzfL71SPzONw49+JsPI4/l1MNSkAJKAElcFQC+vr7UfFo4skS4GQm31AJG192/EdclvyUf8l1YvNkiZ58Oa7V9bhYvKz4YSteHHI5ShZNccQFeajidfKYiWgjNyAjnIFkX4qx2Ch0TooRheJbNhZYvgCidHQtgRZnN152HiZPX40Px61Av+41zTV2rjPlnKi4wkvCwbS0+GqyHWeGg+IqLyj56AJRkpyuxPJMnrtGBKXDuFQsio4nOGPy4O83tBcXe7Sg8qJKuaKoWKGoeaDksyM3TrT7Zc0zilh8vkwSZcwj92jogFibif/GCmWLmmdOtslxc8T5Za2rHbt2Gxd7GVIolBlE1fLFpA3nYZVd52MphRM2lpEh4pMIbR9/tQRjvlrGWKlHuMvfRVT+Jhgo+/ASPHVnTzw5dAIeeHmCiFFRVDi3FPpdJG7+OlQ0+Z666wI8894MSZ8odUh6hWLoL+uE9WxT3aTbj7C9NhJBS7OoeVh2np/ZL/PYTFeX0UyDmmPg4EoUc9dOkxx84Oa1KJgvJj3Z6o/Y05Ui3Tn6UpJxSdc6ZiOHjWL1ddfTn5l16rq1roHk1IgIbaXRUKz2Phu/UgS0qlj+wzaxDtuJQeK2kIGM2b8nZKwvvvkd7nt+PEKiklY5tygu79kEPdvXMKyYl9eZ7dCKjPc413cLirXgx18sMZtf3HoyhKP5zT5gLrRwkLwhEXe99DcpIXY/yCndcHJjEM+bZp02P9epE6ZGqDEp+vFrCZBlRoZzfZKTs0Rl1svrmp6ejnz5RCiVfDzPzMyUv1X5fjDfVb+2dS2vBJSAElACSkAJKAEloASUgBJQAkpACSgBJZDXCZy0MEYrAplfFEsPWgLkFpwUmx7hjKSGPEGAE5p0h8X/NPzOBNw/M67VxfDlVwswafIi3HLthWgklkNexwDHzPoHw2ERhJxrxGJ+j7PGFt888mY6IkVYRCKP+Kc2tYkK4ZcKfGKVFQkFIBoZypctiCu61cR/xy5A97bVxEJMBAbZEPEb0aFW9ZJYunKbnIucIxPbtA7jVwFFDdY5Y8oacPWx8+pUMiKHVybCmYH5mOeriavEPV5+tBELK4ajfeMwPehNkqaTcWHzykgtSAszG1ibBKlbhiOVi8WcrPcVoPWT+SZ0eHnziTgmwtSeA5SfpNvmU8ZEDpkBpIjY5pP8yRG5uyVurSXaXwAAQABJREFUzx4B6io6hqQUovUZjauiqX5jcTe4X0v0vriRGW8S+8BARUfUJysG1RHXhe8/2R8HM9MxY8FGTJi8Ei++Ng5lC3dHq0ZV0YDpj16MtPQQpi/YhK+nLMUzr36DUrK+WSuxSHMqF2uxaKpTv3xG5XpSxePwPGL1Z4YtgxeZRwbkM20HkuQietOwZ2eaSQ+KFV3A64gVBw6mydpwBYylVZhyH4txYPFB4kT7kzJOJC3IInINK5cvimt6t8Fz703FvB82o12TSibDRe3r4ck3JuPArsP4etIalChVHC3EtaQJBC711ROXnm8/0wcHRTyZtXAnvvj2Bzz3+hScK9ezSbPyIlSKUCLtSVa5T5xC+cXKizfNLdc0w596NYLcvubKJUnfeL+FJR/7RiFP7kwxettvkHHdMgqflABFrkHEa0YKWZ7MXFeKwHRH4RVhTb/NhMlvFBIFMVst/+2gKGYDz5NEXNegBJSAElACSkAJKAEloASUgBLI0wSM5xbOIBwt2HS7lx+7GpSAEsiTBOSdYwkyAyhztOI4KRboYMyEM/Drwbwkz3ljbr9BOAMR/Aaj1iqUwNlMgGoBhQD5jli2ejde/u9sdGpZFVf1qCVrOUla3F99koiXRshweVhxg6cnYp1x7eWNRXzw4L0xC92asnaXdamDffvTZT2tRbFIdoFbhohDQz+fjyIFU2RtrUpOOk2VOAYJ2/cFMW/ZRvTqKNZiXDvtOAK/JLOCHHPM5kuf8bKZL1DuHDdtWRmcdK9AKF+mGOYs3Gyqod0b/wunR2Rtrm1oUKOcEVx8IpbUrFoa0xeuN/21WFk9hRbqj7RkqiKWXRNmrjF1sYVY4DglwiyIy0j2Uc6Tk1LQqVUNPHPfxfCLBdvU2RtiRXiQImuace2t5ySd12/qnE0mnXXTgo/XgYGfPDabObY9ZGpckHFUrVAc3837yfgtp4sGhk3bDmCNWH0dK/A+o5GaQSyZKTwFCEDiDxzIQCiYicL5U2PpPdpUQ2FxiTn8q8UYP+0H9Gwn66iZzkthp+tuXg8KiDVR11aV8MK9F5ixTpE+MmuhgiJ+upZxfEWDxSK8HtXKYJKsEUbLOLcqGb90RQqxXxqUgBJQAkpACSgBJaAElIASUAJKQAmcKQT4K5a/gRl0zTCHg34qASVwfATyOQ6UxDuW5LdGEsdXNM/kymWmNM+MXweqBM46AnQBx5C2+xDu//fnIkD50bZJTUz7bjWmT1uJSTPWm236jOXgtmc3V4CShyxaMLnBI5WEg+bVArGecr4/I/J2Uti1pjEiiIfuEZ0yBQqJ1VjPxhg3eRXCYi6W6ZNOSH20POsirv46tqyLEZ8twFvvzcPWLQeQcUhEpkU/4677x2Hzjkz866+9xOJIrHKkLdZNsY59Gvv1UqTLQlk9OtUx619R8GD60baomKxF5HWIDDEpMpZR7lprjhTnFUsuvioheWQfNeZztKMyNlWy95h2+ooLw2U/bsTQTxYiKmri/t0Z+OeLk7F3bwau6VNTehcyfbj+sqb4YfVPePKV/2HPDsdd5NZfDsiaaGvgl05yu7lvc6z8aReee3M89u44JG1I/9ODIpatw0dfLjWCT5p06dWP5+PHDbvNuKNiXTde3FMelvL16lbFXllT7O3P5uKnzbuNvBOS9MnfrUFGZlSsq4qaq8AHZg7VsUFzhCGPuEv0iQUYWYbF0ktsn0x5n1gK+sPJZtS0rLq6b1t8v3qLWHJNwezFmzBpzhrc/eQ4lBB3miGplFfZrv1GSz5ume7GCq+7ZxTe/ngWlq7YjrCsM7dX1lMb+b+leH/sPHFhWQYNapaUGpzrFpTr0q1zPXwydqGIW5nierGq1G86bvIcFIu410fMExb7JV7cdspY/zdzNXZnHkC1+kXMLVdb3FeS9kdfrxThdBOWrtpirLluuKwZ1q7eKdfjW2zfdchYgaUdDsm6bKvx3pdyb5rRS0G5l71i8cfAsdk7n4RigeMkOPetPGvBGEvXAyWgBJSAElACSkAJKAEloASUgBJQAqeQgPwszfrBKnMsYfl9zo1evLg5v7L5O5bHXpkjyJQXdeWXrXi3MS9GS6wGJaAE8iYBmZ4z3xfm68G4kBIOdgIsbyI5YtTEokEJKIGziIAx1pEvv1Xrd4qbv0Mifvjxr5fGyyS/eaQShcNxSxYw0oKs53RPKRQtXEIsxNx0YeHzB8Ra6fi+HsSponyvRnDNRQ0wasJS7N3nrB0Uj/SxO9vj/Y8Kylpbi/D+1xREpIPyoFaxXDG8OuRiNKxX3Khv4QxxV5dMaUec2om2NnHmejSoU04suAqblxv4uHc87uxoDUbrqZMJtC7q3bkqdu3ejQ9GL8K7I2bJWlM+nJOaigcGd0WtyqXNeFl3q6blMeTWjnhJrPK+mDbMWGdFpYMXthMxsEU140qy9XmV8NgdnfH8B9/hq2/fMaoS1wFLLVgAV8saYdQjiWOSCGXDRy2gh0cTvLJeVv/eDUVYrIhgZgjfTP0B7340iyZlJt0votgASe/RoYaxiKJllIevgRjzK6eO4/kkz3ZNyuNfMo63P1uIb2b+iNLFC+BWEfTGTVklYuXROTK1bo2SGDNhBYZ/Lv2X8UREzAoEUtCgdikMGdQlWzdolXhRp5r47LOZaNOsCooXdlw/iqdH4+KS5SfP/gkfjJprHujJk+4br+nTFL3a1DPSVsfzquKizrvw9qfzkXbwAIpIHePeuhbtxc3io3d0wQvvzcbo6cNNu37x81igQDKuurJdtn7oiRJQAkpACSgBJaAElIASUAJKQAkogbOJQEqKrCkvv8dpXebj2uJyrEEJKIG8SYDzjdGTnBvNK8Q8MoEsmE48sBhddBWt3AS71x/pPi2nGgtXbIi9G5bklKRxSkAJnCoCfA7idhSdi8lesZUS54LyH1dgcgqY94/kG4KiCwNTYy7pEr85Yroay7JJls4KrJ9h9ca9+EWs2T4cuwyr1v2Cl+/pgTo1y0gjbl633rAryHiN2Q6TaS8mwV0TzTk58tNpnePJOnJyOe3HlCe3vViuWLtOblpIMSxffUCs2SKoVimfNE3HfQ5IWhAxcJQU9H7auRf7D2SiYa1SJt7qSeYlLuYR4WfHXmDDti2oUKIgSpcsYARCH5UfCUFXEFu2fCP8sl5WrcrlkBTgqKWgBCefB4tW/CLCpQd1qxWTvbTuDkAM65x8zo4Xywkxrk7GoHsxvSJMmuDu2Av+o0k1j240+QDd4+a30a1dA9z+p1axy5Nlf+3eUJabuExMkwW7VqzbJu4evahWrRTyybXiemumL3yrLSQ2WyK6Llq+A7c+NhLP3nshzm9QznTDWeVLOLktZWaEseTHHUiVNd3qVSkqfZL+RSTV7X/E2IJTKLUDjF1JQRLBxq37sFGs92pVKoES4rrR5nIbk52b366Z5mbIqsXJmXieVV6PlIASUAJKQAkoASWgBJSAEvg9CHD+qWjlxtjz0+Jf3Zydy2JFRSo1EgcRdEcvv9U2LDV1H/k78lc3qRUogV9HgD/U5UXmNpe9jk8+vN7U5Qk564I7K4dnVc/1vq3LxfjjrBx6pASUwNlKgHOEYTEV5dwlDRLOka+O7te8iG8/v8MZsjt/F5svdOcR/zgeMuNmvt98ePW/c/De584SPF6ZSPyzvBx/W7/mpmv0YMZAf1j8N/y1j+bifXkx30zryXfjtZc1wa39WmTNg5rc8nHkRKBNie2PMlUey6MHSkAJnEkE3C+6eOHGqDe5jIFuCxkoetngkQJ27TFKQb9lqF6hBLjVr3Yu7npqHP7x/AS8+q8eOLdUcbM2lxXhfss2f01d9aoXNFZQHgo7ObxpERW9zScCVpVyRRxhKaExrsHFQAO8EoWBUsXLmnNS9YklmvXza/89alKvgtOei53XwingRDSu67glNJeFddtkJ1fWZ+Jls4KYez0tZ8qN7OPiFTvRRCz3PKLkpYv7hReGfSdCXzq6n18t4Q7Iuk9MY247vqQkFAhEcV6Dc2N9MDltP+QfO4pifGFt6Mj5KFu6KFo2qCx5XQtD+cfNqc9ZMywp2Yfm9cU6T6KdFGf9NPaPwcllDt0P2xDv5IjUXwTlyhQxFoZMoRUbw4msnWcK6IcSUAJKQAkoASWgBJSAElACSkAJKIE/ioD74z1fShK2bt2PMmXOMT3hy6P2fU/bNSuGcWLcuKaxCbpXAkrgrCdAUYyuVDmPxu+AHZv3iQclx0uT+a6w02ZnPYnjH6AKY8fPSnMqgTOCQNj9ovNZEcc8LR3xvGT1FPludApYC6+Am2J1ioiH63059loUKOz3qFfW6YoP4jzPnDorWsmXsKNmZBVwMxvBTSovkOrFGw9c5IhD1CxEeJLVyUTUcCyoktx91ONYTMniZU4NtgNufUfs3H5F3PLWUo22RSYklHd1JbNuFtPdXFn9DomgI28gRMVyjaJMhls+2T6Bci0zY8wmLRqzJmfg1jJLNDMnyDCMd0p7XewVcDuQ9aaXSD588HV0HHnby8kQEzptfYYZq3baO7aA6RS0xe24eZ18GUHc/cRncg2kbRlD2uEgkvMl4+4bu6FWlRJi6SU4ZH05PliHRdxiiF1ft5/shscnnNw3OZyX2jgOERT5FqaUe+jVqZg840fpchRP3dcdIdn7PM6bbu4wzNuaxjKRFm28Cd2G0mlLJiySXQsv2//YDWnvY3IW0D65b015dlYs1bwB+8+d7bBbgwVigbvXxbVPlPvByW//PmLtsl4NSkAJKAEloASUgBJQAkpACSgBJaAETiUB+V0cljmCfn2b4fU3p+Dpf16GTPdndOxnrNs+rcUojoWCzjyK350POpXd07qVgBI4PQhQDDPTaPIGfjQcxpMfTMb117Q2neO74rGvg9g82OnR7z+yF3am8I/sg7atBJTA70yAk/u0EqL+QpnDWA2JMkErnHBQxIqYmkNtwclIsYLpWcJA9m/SrHh3MNmTJTJ7hLHcYSGpng9zEW/ICHBWqLP5rYUQRRsTslfjxMV9ZglENmPiPi5z3OER/bdpbFcEIY8VwE7wWzND1gJLTnL7wLFS6DJKmpywbsbFhWhUpENh7vXIlUlIM9mo03BjP7jPteOSdjxBxuZNDmDy8JswZ+l6pIf8UqUPLeuXhT9Z/lE1ophUJOISYuJSVsUe6Qfz2MB7hPdKwBX0HLHNkRsv6VILbZtWENeIJVGsZH5bxNnHELnuOs251CU8uHYa+ySynDQWky6zlbOs2Ky5ZSlAythksTxRygjJg6CY9wVcF4zZG9czJaAElIASUAJKQAkoASWgBJSAElACpyEBdy7k6osaYv689Rg9aTG6dGkkL956c10xI+D3IywT4xqUgBLIOwToPtGIY/K3P3Hq9/KSehR9utXPOwBOYqQnOMV7Ei1oESWgBH5XAjHZwAoTia27rwmI5CFSAS2hHN3A51p8Rf2OGmMtgqhP8DmM5xTSssKJfn04+dk/X3xFrFOSvEgyVcf6H2vIbfS4BaDEfiWexyo+vgO2675W4ZWq7ItZ7LEJZGOacM/dh1bHrkpyWFEsvhvx66TFxuUcUAiM4YnjnS2bPTH7uExOj476aYvGMsnDNAPbbdmoaiw6dmD7La4SGWLXJ65ZimM2OMJkXKLlJBka1nTWX7N5s+3dIllrhkmqxNk13ZJ4wrqOGIBbi1s+xo49tZzdtADFxlyDU/GR1ecWn2tFmqAElIASUAJKQAkoASWgBJSAElACSuA3IBAUC7AIQh7+Ivbg7ts6YfCjX8tRGi7u0gpp7m9x6+HErqEXlLdFaWWWmusP6N+ga79DFceS9qyHF/ERk603Nj7oTmB43UXZ3SXs6bDIhIjPOYilJ5zH5j+y1Z51cqz+ZeXM+ciubR905yxwgu3nXGveifW4HplC7oXyuUt1BIL5DISQL+4t7pPBYtekP5myv0OZkHu/+N37ORwSH17C5JvJ3+P90fPw/lP9Yr1w5i1jp3rgEoibzlQmSkAJ5AUCdo0l465PBpyonyXGWxsxG58XGOkYlYASUAJKQAkoASWgBJSAElACSkAJKAEl8EcT4PIKFMUYKsv6Yq8O6YHn3hiHWfPXYdAtfVC8RAHjbYYvidp5G+69fGn3V+oCf/jYj6FMeRKXWnA7nDWP5URYF3JcsYEh69x9EdbV1aLuBFnsHducvPg4VTj1uPVZDzbuZYrlcJbdiJ06HpmyTkX0dCqwzdjq4rLooRI4bgL79u7CM29PFcdJXgx9pD9KlShoylrB/LgrykMZVRjLQxdbh5q3CWR/fyZvs9DRKwEloASUgBJQAkpACSgBJaAElIASUAJK4HQmYNdyt5O3IVlgoFzp/HjmwQH45H9rcef/fYwMWU/ML0pOOHSkCpZpXQGdzoM8St+yeZTJKZ+7xrmzQAgzuEqaK5hFvEGnVNTxHhN1zz0Rq7g5M2Vh1w2Oz7VACstSH6Y2a2Lm1JLrp9e2Z4U3V5GM2sXiYxZtrmlPrCanfbmCTow30+z9Yec8EhtfrIAexBPwONcpEuOXYVK55DyDLPLhHJzs52luMRZw1zUJuvdvit+DG/o3x9W9G8F+HVhRzO5PFsXZWs5+t56t49NxKQEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBM4aAv0urIprZIsFa3YUi5ADNUGKp5HrsUVncSWe51pQE5TAaUhAlhqLLdFC61GKYiqM5XyhVBjLmYvGKgEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBE4LAnYS17Ezior9mEx6y3/GssqqOvE9tQpPfNwZdBw9hi9Ij2tZFbVrSUUdQp6IO3DXdRLXW4sPPhEKGKIeZ5Uwa3EUM7BzLZEkQ3yxI47DHsfyzNYHayHmcyy/EFuQ3e2IrcFeF1u9Pbd7a1hmL7gtp/sjCDiCD6+jMLYWfpbrEbnPsohcxkmPoOJNMRYojlk3q7FIPTAE9E9MbwQloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJTAGUAgIpKYE7j6mCd2ltj1uLnxxKQz4tzjrvmVa2c5QBEHrDWM0b8oFlg8XGdNxCbGMyujzT4UgTdAOdFWIAluYJPUzdh2RPzReQPiji8iJVlXQmCM0dgS0xKEuIRi4NpjXMfMWvJ4ueiZFcUSM+v5UQk4gg/hueLYUXNrohLIToDfABqUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSuA0IcBJW2filpJORLQTr9m8sud/eT5YBN4APpuyEQ8/MxazZi4DZK0lbqSW6Ypib42Yj2femGyQeXw+Eb14yAo8+OSLJXji1UlinxYUsSqEZUs34+GXJiNoLMBYAS3DmDf7tnLpajz20hc4kOGsbWWTw7K2FbeIfDobpcyI1CfyjWg41MG2bNqD+cs3ybFXckWlZfbWDWzOMUazMbrPgcDmjXuxZNl6SeFaYtzIUDbqZNyoXJ7Nmx2n3ctwNZwYAf5Fa1ACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASVwRhCIUgcR4YMa1wejF2P6gvUY9vlSMfASEVFMuYz25Y6kVcOy+N/0NRj+yTzHgkyEJxqCzVq0A29+OAdVyxdGQAQwWopt/OUAJs9YjWCQqooTWF9i2LzjICbP/BEZGSJ/mc4k5sh+Hoibhf/wyyW479lJRsCh7VrgWNZx2avSMyHw0RdzcY8w5G2gQQmcDIG4P8kTK+5x/+C9sucNmNNmY+0N6s3hS+TEWtXcSkAJKIHsBMIhmkvLs8RJfr9Y03W7z177sc/i2z1aX463/vj6+DDHEB93rB6xjN1YzvYpt3LMY9uxeRLbiz9n3vg6cysbX68tn1tetss0m8/2Q/dKQAkoASWgBJSAElACSkAJKAEloATyOgFrIZZoSMRJ3aNtZ7y1zFFHJ9ZzMvigMFi2aiu2/rIdfXo3xbINu/H9pt0IiTtDn2hZXEOIMyt1apXFRV3q4p3Pl2DNhl1GUIuKpdezb32JKtXPxeUXN5VcFMK8YqwlpWT9MGPtZee9JemIue9IihgmpSIga5v5pDNcWYz9MddJ2vaKFZOzZVn4GUMw9lss2qIeqTFLe5MlsmQOxyuWZVKHWd4sQYvjXEzivIpkdYLktf3j9Binkzhbxjgbwq5hG8/DwbjKmckt78ywOSV4HH9uYuOKcbyskv1NkzpYTXz+MBMYqFByEx+S0VAQ6ZKR+eKqcsqxAskXFpKkwPrd1drkyMnPLE5dgk4sBYOZQXOXMDpC6zua47E24XjWB9478Zs7YPudcPqPXzrvuiG166DF9onuSd3B2HSjbv8GA9Q1xn4DiFqFElACfxwBn98XE1Qy5KEmOTn5D+uM113d0vFx7HQjPT0dKSkp8p0tDz3HKd7F5+OxLRtfb26DpBm+LcM87JN9cGL5xDp4HgwGkZSUZPLx2DJkOdbHPLZOU57/ykqwcc6Zc26Pmc/kjcsX3zeWZVt+vz/WRiQcAa+nBiWgBJSAElACSkAJKAEloASUgBJQAkpACRyNgDg+NP+Nnfw9ShcvgFv7t8aYySsxevwK/POm9tlEJ5miwOCrWmH2InGT+Oq3eP/py/HvoVOx72Amnn24o2mGziopv3hkUj4cjrc3O1ovstI+Hb9S6t+EuwY0wIvDF2DJ97+gYIEkXH5BPVxzSUNx3xg2czTDxy4SS7UNOHToEO54fJw0GUZyig8P3N4VSb4kbPh5N975dBEWLNhgKq9cqQQG9m2OxvVKOvMzEjviqyVYuHwPBl7ZBK9/OAVLVm1Cx/b1cO8N7SUPpI4DeGvUTMxauEGqD6J0yUK4pH199LuoIWjr4gt4pPx6vDtqMVat2m7aadykOm6/7nyUK5Zizvnx4VdLsWzZz7htQDM8++5cLP9xKwoWDKBvz4bo37uREcWYb/SkHzF/0U8Y9Kfz8NzrU7Bi9TYZe34MuKgp+vaoS5VQRLEIPP4Atm7bhaGfLsTMuRvlGnlQRcZ3s4yvRd1SMSVPZqKwfuMuDBuzGAsWrkEwHEaZMiXQs2NVDLigCUaOW4E5SzciJOvA3f7wCNFXPMhXuCQeGdxR9Dfh7E1itzQogaMSUGHsqHg0UQkogdOdwPJly7Fp8yZceOGFMUHn9+wzxZ8nnngCc+fOlQen+HdjnF40aNAADz34kBF8rFB0tP4xz/79+/Hwww9j3LhxRjxq3769OS9fvvzRipo0K2ZxP3r0aKxYsQKLFy82ffvoo4+QL1++I+oIyGKyBw8exP3334/x48ebh7POnTubNsuULmMe3NgvilkHDhww8V9//TUOHz4M9u3xxx5H6TKljcDFypn21ltvmQe2ESNGGNHNimisZ8GCBXj00UfN2B566CE0atjI8FFR7IhLoxFKQAkoASWgBJSAElACSkAJKAEloATyJIFj2fyExKwqGopi2py1uLJnfWTIi71tWtbCd3PXwXNDWxG4HGzyyq858Iovw3tu64BBD4zB35/4BrOW78AtfduhYons8yQhEZa4HStkyKx6hrzbm+ETGyexXtq6fhNWrvgR/3x+F3p1qoUenetg2ry1eOPj71ChbCpatahuqqxdtwKKLd6CvXvS0a1dLWREM5CanASfLwXrt+3B4CGjUKl8UfxjYEcRsHyYOm8l7npyNF57qC9qVyts5LsN2zIwf8lqrN24Fb07N8AFnRojKSVgjKk2rNuHQQ+PErHwHNx7XTt483mx/3AYWzfvNu2TyyIZ++2PTULD2mVwz+CuYkEWxlufzcYN93+ID57si+KF8hvLty1bdshaaBtlTHvQs0sdXHxhTUydsxqvDJ+BcsWLoEOrikbM2rh+JxYtWYf7ntyBnp0bo8+FzTBr3jq8/M4MlCpREB1aVEBERLGNMr5B93xuxvf3WzvCL+u9zZq9Bvc+Mg6v/vti1KhQQuy9kvDjxm34+/1fomjRArjzus7Iny+An/fuxc49YqMm16ZurVIou7AIdu/LQLcOzeRFbyklL8pT2jSXTs5lMutYl1DT8zgBFcby+A2gw1cCZzqBZ597FmPGjMGuXWIK/wcGCj60iKIgxY2WYt9++y0KFy5shKXj7RoFpJ49e2LlypUYPHgw8ufPj9deew1t27bF8uXLUaBAgeOq6qsvv0Lfvn2NtVqxYsWwadMmpB1Oy1EYY9+7d+9u2rzjjjtMmVdffRWtW7fG999/j3wp+WLCHvu2atUq/PWvfzV9e+mll9CqdSssWbLEjJVj37BhA8aOHQsKbh988AGuvfZawyYzM9PEvfvuu5gwYYIR4G644QY0bdrUiG4cGPuiQQkoASWgBJSAElACSkAJKAEloASUgBJQAkcjIN4I8fnUNTL/koGLOtQzokifC+vii0nf4+vZa9GjddUjitevUQqXda2Pj79aiPp1q6Fv71oxd4OujnZEmROJyMgUC6Y/t0bT+hVBp0KdW1TG6nXb8c3MtWgnwhhdA9asVhwVyhXGT2u2o3ub6saNIEXAsKg6r7w/FyWLFcDrD19K4zWj8nRoWQG/7B6NN0fMxQv3dzPCT2ZmBtIzgvibtNX2vIomjjZuHMPrH89GamoA7z7RS4Q1Ryxi/fEy0VsjFxorspfv7y6FnJHXq18OfQcPx39GLcBdFNSkTDCYiQzx9nP3de3RSNIZ17lFNaxa/yHGz1iL9i2kbYmMitUb891zYwfUq1tOluAAOrUoj5Vrdpu13dqLMCZOgvDSsLkoIeN79ZFLjbUZ+9SteUUM2jUOb3w0F8/9o6fEQMTERTjnnHx49+FL4D/H8QwVQUVX4gRq1yiOciK4rVi7HRe2rSbtxzwsIiBim2M1Fj9iU61+KIFsBJw7P1uUnigBJaAE/hgCFE7iA0UibvFrWtl0xjNYIcqeMy7+mOfxIdEnM4UYK8awHOtj4N7WY+MYb+PsnnH33nsvRo0aZSy0KAh98cUXRtxiuZtvvjlWhnmPFUaOHIkZM2bgnXfewb/+9S/cfffdmDB+AjZu3AhaV9nA9uP7YOMpzjE0adIEc+bMMRZe//znP03e3CyyPvnkE0yfPt20ed999+Guu+4ybW7fvh0PPvhgzL0h87HON954A0OGDMGdd96JyZMnY/PmzXjqqadMu2yf7hE59osvvhhDhw4VM33nWlEoo7vEzz//3Ah9LGCt7OKvg6lIP5SAElACSkAJKAEloASUgBJQAkpACSgBJZALAcolE6YsRa0aVVGiZEGjI1UQS6ta5YtgwuQVIjjJmlMxKcWxJPLInMSCVZtFMPLiYNpheMWvIGdRnJkUegE60hNQLs0jIFZJAZn7SIoGZTWxEDI9fviT8qFZQ0cUo+LmFYGoUsXC2CdtiXkbkiWyoFSYLC4APc5UiRG1KOGkSN8WLVgt5cvjwP407JFtH7e9aWgka6QtXPmzGSP74xE1KpD/HLRuVtHEOTNZ0pj0Z96itejcspIssJZsxLaIsSMLGQHQ5AtGsHzVOrRodq5Zl4suKTnqMkXyo0m9sli6ZKvDQzInIx9SZOmNJiKKcUk0Br7OXFMYp6UfjFnlhaUGX74U1KpXzqyPJgZ0CArU0pUL4GBGuukBWc1fuA7NZXxpMqZ02dLccTaoVxxLFm814l2S5Js770c0a1UDURHFuFQZ1zKTV9Hl06z8Jq3JcVJA2vKZZap4/ZLlQ5qVjPLiekBtgYhCw9EJOH/3R8+jqUpACSiBU0rg3//+N3r37m0skdq0aWMsj2hJxLB1y1YMuGqAmE8XNdZSZcuWRa9evcD1xB5//HFMmTLFuPTr1q0b6P7vsssuy1EwsgOIF4eeeeYZ9OjRA/PnzzfWUQULFkS1atXwyssvGwsnijUUnygQ9evXz/SBFmClSpUy/T18SB5scgl0W1ijRg1TrxWr4gW2XIph+PDhph2KSlawq1qtKtq1a4cvv/zS9Id9OpaQVLZcWSOO5dZOfPzHH3+MQoUK4aKLLopFV6laBa1atTJt2n5zTGR06aXy5pIE9qNihYo4//zzjTBoC9PHM8d81VVXGbeJdHVpw5ixY7Bz505cfvnlNkr3SkAJKAEloASUgBJQAkpACSgBJaAElIASyEaAFkbxGxM5kR0S94kMa3/ei+/XbMO5sn7W5Jk/Y/K8Lfh29iacW+YczFu6Cbv2U05xAsvxnd3XP1mInzbswH0DO2Hd5h1487MlsvaXzG+4oo/Nfyr2HhHOHFkp66Xr+HZCIliFZMAjxi1Bz4HDcfEtH6H3wI/Q65ZhGCZWXGRhQ1Q67KG7wLjgFTqHwpmmjspli8WlOIcyhWP4ZWZKRfJ/xXLFjCDG9by4vhoFKK6JtlMEq1MRgrLGWEhErxFjZXy3DMdFtw5Dr4HD0PvW98z4IuIakyEtTcYmPa1VtZQRyjhsJ8Ukxz7iX6JX50MxLHpwAgRUPj0BWJpVCSiBU0OAbgOnTZtmRK0//elPuO3W21CocCHTWI+ePUBLMrrsoyUSRZdJkyaZfceOHfHNN98YN4r9+/c3YgzzWEGJ4lmy+BjOLdBN4NSpU3HllVca14DGOktc/P31b38T94c+3HbbbaYuuhlkH1544QUxR08163GxXG6Ba3pRbHvggQdMn2w+K5DZ85z2a9asiQlaVpBivpo1axpLMlpccSFYO8ac6rDljqc9ilvk37x582xVMZ7C3qxZs2JjYN/o9pBtM50bhcbq1atj9uzZxrLPK74CfDRbl4cdrj9WsWJFs97YIw8/YvLSjSKFTeNi0rVuy9awnigBJaAElIASUAJKQAkoASWgBJSAElACeZ6AGHVlC2YeQmKS6ENRwv8mLxXlLIjJ3y7BlKnLRNpJN0tZBDN9Mm/hw9hvVuHGKxqK+ENrKS/WbNqNEeJCsd/lLdG1Y005345PR89FL3FFWKFSUanRUZ58roBkGjnqh+SnGVVU7JSi4jlHukVtJ9Ztr8hNku4P5kMg5MQ6ko+MQaywrAmW19H5xNBJ5lM8ybj1uvPRr3s9qUwUOwkRI6jRFSIzUsISKYuFPJIu7bG/DBS2kv1J0hUvdqdlSDknwR91XAoa8Ui6myRrjoUjQbFEO2AsrCgYCjEEiDUYRXKhFFZn6g56AghL1ynBeU19zOSEsPSV8aw9IjWliIlYknSJa68xcDYuWcYdDgccSy5auUkdt13bGf161IoZ54X97Dn7HzBj8LN/0uYv23bJSMvDz/5Js9bHVFLEYen3CkNLW+anYiZ4pjb9UALHJqDC2LEZaQ4loAROIQErsBw8eNC48rMWXxTAKMCsWLECb7/9Nq6++upYL2iJxDRaNFWpUgXLli3DtX++1ghGVgxivTmJYnyjhMISA8WbtLQ00H3gddddZ+pk+7RoeuSRR4wwRnGNfaCgc80115hyrPvaa681wpCJiPtgGvvLhxy6UWRgO7ZfcVlzPNyyZYsRwVjGil/c161b17gdDMsbQX5v7l/d8W3FH+fYmBtp27TtMZr9bVC/vhEE9+/fL76dz8HPP/9sLOqYzrwU6fhffckXFF/SZJWaP5XJJrB9io60SKMwtlcWSp04cSJGfjpSfGGnGy42r+6VgBJQAkpACSgBJaAElIASUAJKQAkoASVgCXB+JT7Ez1nQwmvCjNXo0LwqnrhLvN84WomTXdL+78mvMWnmGiOM+US6yQhn4KGXJqOMrEt10+WNTL7B/dtg5sKNePD1KXj3SfGMk6X5xDf7mx6H6bpRBKJ8Ob3DneRFlQrFMHHGGvSnMGYEMcfVowxJQhYPu2RFfOeiskiZX0SxqhWLY5Kw6de7YXwyIqIskalPfA6WK1UYi7/fiagM250iQ1AsyZau3Ib6tStkK/ebncj4qlYsKddF+tZThDFObcnARA6UnczVOacIyHxU1Yol8N389fjzJY2MKOaMP3tPUvN7zXgiQXGYyQXdNCiBEySgd80JAtPsSkAJ/PYE+A8zLbEoSvFBh6KMeXtGmipTpgzoavHD/35oRCzmpSDDQJHLCk60XLLHTIt/YOK5DYn5GG9FMVvm0j6XYtu2bZg3b54R19iHJ554Ah988AHoPpEikH1Ao/jDzQamcb2xtm3bmr4zPr5fNl9ue665xfzcbH9sXrZJyzUbb/tg07mPb4vHhpf0j/uc8tu6mNeOxeZlGjdbjum0BmOwY2a67WuIq6tKYH4bf8MNN+Cnn37Cd999Z9YbK1asGGgFyHQNSkAJKAEloASUgBJQAkpACSgBJaAElIASyImAR6yLnC3Ba45MPSxb9gP27DmAbq0qyzpZRl8xlmFBWe+L561b1cLGn7dg2YqtUrUHw0etwPrN+/HY4B7wizAVzgwiJCLSvX+5FGvXbcDwT2fIZAYtvwJGoDFWXDIHQhFLJkBkLTIKONk3WeEKPs63yBwVjam4zpjfEzZWT8a6SUzIPGKt5fekSflDZogBqSVD+l+7ekWZWzqEkV/OxfxFG7Bk6UZjJTXgyhZYvnYrHnnzW2w/cEjG5EXawRCmzlqD4V+vF6spmSuS8im+VONKkWtq0WorQyy9/DIvRsuv6y5rhVUbDuK5t2dg3650M/+ydftBTF60Bp4kZy5mQJ+m4m7yB7z58SwRzCI4tC8DD778Ffbv3Y8rLq4jtncSmNVzGL6QR+oViy2Zx2G8TPlISIE3kmbieRoJpxtLNopdtP/iWaYkZIgJXcisMCb9lPr6X34eflizGv9+4yvs2H8IUYGafiiC72avwidfrTCWZZxZuumKtvj+x0149LWJ2LkvzYzxwM+HMG/6FhySMtyqVyuJw8FMjJu8BPOW/ICFS7dJR+xVkko0KIFjEMjd7OAYBTVZCSgBJfB7EPjss8/wl7/8BVdfczU8f/KgTp06xu3hTTfdZJq3Ag0FIyumnUi/aAlFkSZeqClQsIARezZs2IBmzZrhk08+MX2ggEahp3bt2hg0aJA5ZltWgOLx2LFjjWvHwYMH8/SEA/tD6zkGjo11M2zevNn0ka4if+uQP39+02Yih01um/ny5TN9Yb7Dh5111Uy/pGsUwWhxxn3+1PxHdK1y5crGsu/94e+b9ca4PhnLUkCMZ35EQY1QAkpACSgBJaAElIASUAJKQAkoASWgBJRAIgGZFhk1cRVSZa6iY+u6JpWvK3NdKrrco1DTu31FvDQ0gDHfLkNSighjoxfgz5e2RJXKhUweX5K47ZOMtaumivVSI7z32Xx0alED5coXlfXGnJd9AwHKTrkHzoOYtb744q/Mj3hl7/MHaARlNCWZ9IgV9vskXpQir7iBpBOgNo3L4qLOdfD6x3ORLlNARQqnYsy716Nji/J4/O5ueG7odIydvMqU98sL1AUL5MOAKzqYeilMmT6680VsJiCCF+MpkrGOB/7aBa/8ZyLGTlpmuLCfPaW9Ts2rmTr7dKkra7Dtx/uyxtqwjxcZUa1okQAe+ltX1K5SRuohRaf/Pq/zgrQpGPfhEdHQ5vJJX+z8FbMkSVmDRcr6ZB7Lvk7esUVl5L+rG555ZyY+m/yBqc0XES9FBfOh/2UdTf/ZavNG5fDgoK548YOZ+GLqGpMvn7hkvKBdTTRrc65pt0vzipjbqTZe+3A2DovryIIFi+OrofT0JC/Ui7el+P6YCvRDCSQQkD9FDUpACSiB05cAhSmuX0Wx6ItxX+A/7/3HiFJly5ZFz549jSDD3p+MKMZydKVICzSaXVuhJj1d3m2RJwqKVNy3bNnSrBlGV4Bff/W16cPAgQNRulRp9Ordy+SxZV9//XWUKlUKF10kpvwnERo0aAAKcgz2H3H24ccffwStrege0rZl9yfRTLYi9erVw6ZNm0y98WLcqlWrUKJEiRhb5lu/fr0pyz6xfW42X06m66yP68bdcccd5hp++OGHpjzHxjo0KAEloASUgBJQAkpACSgBJaAElIASUAJKIJGAzNRki7JnFFkevOticG0sHjPepsUKyJzD5A9ucVQySZwy/GbKJcZtoBVpWNonfgRvuaojbr1aRBl/pggzYXTvWhsXdK5pqrJzH7F64w66dmkEbl6xiAqLLdedN3aUjWtucU0z6RzNoaQf993Vw5SijBSNyvIYkkrLrbuu64a/39RNuiF56c9QTKUoQXVuWgGdmvbHzzsOY8PWfahatgRKFRMLMY94L4pkiIdFL/5xc2PcPfA8qUmKkoN4VPK57gRpmXVBq/K4sNV1WL1uH/YdPoQG1YsjkCzrj0leBr5c/qdLW+AqsRxbuWon8ombw9qVSzn9sP2RvPfe0Bn33ES5LYpA0Jk7i4pV3KN3tpe6ZE6IKVLpfQM7ADfzDCggLCJcH82TiifubifHTqO+aKYZX8vm9fCJbNt3HJDx7UWVcsWc8Uk+U4OIeAERELuKCMZt4/pM/LJrP5o2KAGfcGOISPVe4fDPm9shKhsD2Tk8KJGq5GGg6MdRCRzxvXHU3JqoBJSAEvidCfAfWIooBQsWxICrBmDChAngWzuffvqp+ce3ZMmSxprpZLvFh4GFixbGRCHWs3DhQnlbxo927dqZeMZRACpcuLDpw/jx443rx5GfjWRSLNCqiy4D+/TpY/p0MsJP69atsXLlSqxds9bUyzoo3M2YMSNbf2KN/gYHbHP16tVms2Ic3VTOmTMHbdq0ibVAHmvXrjXimBXluK4Y89F1pI2LFZADxlEYa968uVmjjWul2XCst69sPt0rASWgBJSAElACSkAJKAEloASUgBJQAnmbAEUPI3zYvWgk1JNs/BF0qOe4gR4R7XyHjfMxUoLHNYiizMP/7DxUYn5bzu5F5rKHbkmuluUXkYw9ksDOSQqD39TLteRlGQuzPIhjOWYSZd6E62QZZcd0iQ4RvbIeWmG0alARpYtTFBMBSl48NvMuFJpkYy7TB0nzBtxBSJuMZ+BnNbGQa1avrBHFQiG+4OwIWZzz4liTPUloUrssalYtbfobDYmkR2GMQfJ6pD6vWec+a6xOoqRJZWTFvdniVAavWR+NQ/Ig4HKWyljIiJjsLdd7a9WgPEqL6MeiZmysnOOTsTLIK+yoVD4FzZuVTBDFaB3H0VPulLKyudRZzLTjHOinEsidQNwtm3smTVECSkAJnCoC/IeP61bxgSP2j6A0xn9cabl1zz33YOnSpaZ5iljvv/++cednBZv69evjwIEDeP755/HVV1+BohUDyx8rMA/FmT//+c8xsWfEiBF45ZVXzLpjKSkpSDuchn/84x9YsWJFbG2xYcOGmb6df/75pgnb1quvvopQKAS6UYwfy7H6EZ9+yy23oHTp0ug/oL8RqmildmXfK7FfTNyHDBkSnzXHY/bFupfkmmjcFixYYPozevRos1YbRTYKXzYMEleVxYsXR//+/Y311+5du3HFlVcYl5APPfSQycY66T6ySJEi6Nevn7Fq27NnjynD9djYN9suGbAftg0ynjhxIsgtMTCf3RLT9FwJKAEloASUgBJQAkpACSgBJaAElIASUAIkED+JHX9s02hhls3KzC9SiZdySTbJJLZemExHyDyGSCpSWdDHkuI6MeJDVBbxogUYA+d27GYi4j4oS7HuiLFOottFtk+RJkk+A1KHrGXGI1pWSdaoiEVsxeNLcctk9c0r64NFPFIX+2sWTXPcD7J82FikyTyLN1nyJEmypFGskjp9UrcNPOT8Ci3QvK64RW2OvWTw+R0ZKSLji3rDCMjY2RyFPCMv0ciL/RNLLAbTu5i4J3EivkXk3GNEL2esUeFjR0ESzn/SRzpTjJvv4TFt5aRVacO5UrYk+8c6xFDM2UROo+zn0JNOucZftnbIdeV/Rj6UwgG6TXTr5J4Wc9w0KIFjEVC7wmMR0nQloAROKQErilAcSwx8+OAaY08//bQRsMLiV5kiC9ccu/HGG80/+AP6D8C0adNAAYfiEV3/Uag5XmEqNTUVf7ntNlBgY1/oWrFHjx548cUXzXlE/mX+/PPP8cwzzxgrMtbLvlIksuucUdSjKEQrNlpGVatW7bjbTxwz+881za6++mrQuop9oqXa22+/jYYNGyZmP+LcPrCxPzfccIMR6lgH+3z99dcbfu3btzcuIVmYacVEFGObV111lVnDjXHsx3v/eQ+1ataKtUGrvVGjRmHAgAGoUqWKGTMFtf9+8F80atQolo8HZJKTa0XWbfvI6xl/3W1ator0RAkoASWgBJSAElACSkAJKAEloASUgBLI8wQonjDYfbw4ZuOcHDl9MkdcCRFUZHrHrDfGFGs9ZuZ3RL4R+SenSjROCSiBs4iACLYyS3kyQdRYmfVE8UqN8MtPi3OsgSotA3VgfvUUrdAAuzc6lh8mQT+UgBJQAkKAX0N2nS8e80GEgRZHPnlrhu766KKQoVOnTiad+az4ZS2VbLn4NFMolw9aio0bN84IaSwzefJkI5CVK1fO1M16bZ2HDx3G1GlTUaBAASN+ca0vBttHHjM/raWsi8D4sTD9RMO8efNw6OAhtO/QPjbW46kjp/HnFMe64vvP80WLFhkLvBYtWpj1zBgXz4HnrItWaIcPHzbuHZlOU3sKYbYd7hnsNTIncee2Tpsvp7y2jO6VgBJQAkpACSgBJaAElIASyLsE+JuhaOXG2JPL3NOJkLG/V1imiMxneeS3DCfHd2xw5qrsPFa2CfQTaUDzKgEl8JsROJbYFSdzmTYT8x/x92xnwO3eal92bytIrDjXEWXNezOLMU6TPQ2+nOCkx05DzlE4ZqbipPvcArYc1wxjcKdVpGLXei1KSzQJbn+tHyBfQv6QW4F1qRhr347bqUXqCbpHtHaTkDD+2Kl7EHYrsq+102rLCW6Crd/tn+XhZkKmG5HkcqDlV3zgmmXxIUgzOwm0HWOwqZZP2JMhPRBPUCGZn6Nlm60uxtcU04/TlgCvLzcvXvtwLt77fJHpqVcu8J8vbYpbrzzPnNOSksFRl4DXPpqLYZ8vkAlNKSvX/drLmuDWfi2cqkxO98PeMPFxCcd6qyQA0VMloAT+GAIUwBiMr2X331T6MmZISkpCly5djEBjIuQjXnCheGUFFrpfpAtEnnOzwpYtl7inQEMhi/V17949W3J82dT8qdnSWXe8mGf7w7oYz/HYuGyVHuMkXqg677zzzBhOph42w7GxLDfbp8Tm2U8rUjGtcePGiVkMQ/aLwpftS9OmTZ16pQ3Dyb1mtrBlZ6+LjbfnrIfHds90e27z6l4JKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASXwWxM4aWEsKgvmcco6IvuE+dC4PjopNj0iE6EalMApJ+C8UCD3pm3JiYjdh0aNptKcc7AvGOScqrFH+4s/WTpWbGF5K5Dx2IorNt2eMy0xxOcZOnSoKRsvusTyy6s0dL+YkiTimbyF4KePZA/N5Dkyejh2b6BYgewHdvysO76vzGX7lxjPNIo+7733XjZxj/EMLEeXgn379o1ZaTkp2QVAxnGdteHDh8fGR1ErPnABVVuP7Q/Tc+qTLRefz8Yl7uPLW9bMk1g2Po3pieeMSwzHkyexjJ6fGgL2bkr8fswt/tT0QmtVAkpACSgBJaAElIASUAJKQAkogbxOIPF36bF4HJk/IcbOE9p9YoUJ2ROTjzx3CsSKuQex88SZR3cW3lpcyYyKU2X2Xcw0KmsaPbulmO1HrB53PDY/V/HKMRwR7VqK2cxZHTcxsVP3INaem9/Oj9niRzQbq8DJkWTHG1Mjsmew/bf1ySpl9jD73o32IVmsySTY+rJXl72Mnp2GBGT+lWvhyXXjnCk3BnPsrpF3qjttb51T3Y7WrwSUgBL4XQhQOBoxYoRpi4JLonDk9XnQ55I+CCQFZJ2wv5j1xH6PjtEd5EcffQSuqxUf2Ed+6XPfq2cvYx0Xn554HAqGso3P/sNh87GeSy+91NRj67ZpulcCvCc0KAEloASUgBJQAkpACSgBJaAElIASUAJKQAkogbxMQIWxvHz1z9axu/O+ufoSdieGrQWExaAvFlgSZ/aea4BNnDgx10HY687r3axpCzSXjXHyboJ88uY5NcIB3TuOHz8+9gaENGRCvFBhBTKbltOeLh0nTJhgko4nf3z9OdWncXmNgP0LONa4s38jOu/tHKuMpisBJaAElIASUAJKQAkoASWgBJSAElACSkAJKIHTn0D2ma/Tv7/aQyWgBJTAMQkkWlHFF7BfepQH7LFND0aPVzSwJU58T6EqfmMNtr/HK2JZKzjmZ9n47cR7pCWUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACeYeAWozlnWudh0ZqxQ1H9ohGQ0Y4CMmpX/4LimWQSBOgz13aBlk7IQLicaJYwngNZxaBYwlMidfYOZc1w/4gN3PH6m8i/fh1vU60bGJdep7XCCTe/c74s39rSpw1EXP34oHUCXbvnupOCSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJnGkEVBg7066Y9veECXg8frHQiYgIZqd+gb3phzBt5kZMm70WS1ZsRkjWfYpQMKM1j50QPuGW8kaBUCRxuc0za9x+b/Y1vk6092f6+E90vJr/7CLgjSTc//KdFwmHEEjNjyb1KuDCljXRrmk5FCjoh7xTAPn6dAK/PnPW1M4uQDoaJaAElIASUAJKQAkoASWgBJSAElACSkAJKIGznoCd8jrrB6oDzLsEgiJ5eUUWCx8KY/yc1ZgwYx1+/GknqlcvgRbNquGuQd3hE3MIn1fc0kWiYlOmJhFHu1vCZ/jkuC9LHz3aMHNNO9PHn+vANCFvEIhk/wMOBiPIl8+L9AOHMH3+Bnwz7Xu89O5k1K9RFF1aVsf5baqiQEoyMl3z2iT9eswb94mOUgkoASWgBJSAElACSkAJKAEloASUgBJQAmcxARXGzuKLq0NzCFAUOxBMR9/BI1C7ckm0bVkNd9/SFSm0iBDjiaDfK64WxV4sLBZjIo5FZK8hdwK/wzJcuTf+G6SI9vmrwpk+/l81eC18xhNIvH/98v2XmQmkpKSga9tauLB1LaSnhzBz/ipMnL0a//7vXHzxZn+xFvPLCwRntrXoGX/xdABKQAkoASWgBJSAElACSkAJKAEloASUgBJQAr8JARXGfhOMWsnpRID2YfGBK4lNnLER1asVw/13dhfhy0m1e3/ImhCJKQQP/6B1puL7fDofZ6d7Ovc0l779SouXM378uWDR6DxCIOEG9orWFQpGEfX4IP8jKF+Mvnw+tGrbAK3bN8BTL47B+Gk/oneX+vICQR5hpMNUAkpACSgBJaAElIASOH0IyJtdHnkQ9f3aNxxPnxFpT5SAElACSkAJKIHTgEDCFNlp0CPtghL4jQlQB5k+dyNaNqsSE8V+4ya0OiWgBJTAGUmALwh4xVKWgcdmnUWuteg+HbQVd7Pfzl1nRDHxOKtBCSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJnPEEVBg74y+hDiCRgNesKUbTr6DZ0nZnYOXSTWjZvBqCAb3lE3npuRJQAnmJAL8bc9+ioohx8yNkthaNq2PVmh3YmxEy36h5iZSOVQkoASWgBJSAElACSkAJKAEloASUgBJQAkrg7CSgKsHZeV11VHEEJixYjzq1zpU1dJLiYvVQCSgBJaAEjkWg4Dl+1KhUGN/O3mjktGPl13QloASUgBJQAkpACSgBJaAElIASUAJKQAkoASVwuhNQYex0v0Lav19BQBbMgQ8T561Cy5alkCRWEF4akWlQAkpACSgBQ8Aj64bFb4lYMjOB1s3rYtacVdBXCxLp6LkSUAJKQAkoASWgBJSAElACSkAJKAEloASUwJlIQIWxM/GqaZ9PiMCylRtxftPaCIcg6+TILLAGJaAElIASOC4CXj/QqXVNzFm64bjyayYloASUgBJQAkpACSgBJaAElIASUAJKQAkoASVwuhOQKS8NSuDsJGBV34xgFBUmEEMAAEAASURBVIEkP6KRKDwcqvk4O8eso1ICSkAJ/BoCYa+8QSDBH3YeDzJlrbGoL4pI2Ktfnb8GrJZVAkpACSgBJaAElIASUAJKQAkoASWgBJSAEjhtCKgwdtpcCu3IqSIQjUQgXhRlUtcqYmo1dqpYa71KQAmcfQR8Ph/C8j2qQQkoASWgBJSAElACSkAJKAEloASUgBJQAkpACZwNBFQYOxuuoo4hgYC1FXOio9FkwMzp6sRuAig9VQJKQAlkI+CLON+fUY/zfemnpRjfLHC+RLPl1RMloASUgBJQAkpACSgBJXDKCfD9Vnm3Neq+55r4qz77r/8jexNxn2NlxXGT6PHYF2YBr9QblnPG8fHXPPba92jd52HJdWSlGqMElMDpQcD+vdreZP1525g/dB9B0LRvv0UiCDjn9ovMJpzCXoZDYVO7z+87ha1o1UrgZAjIH6z7N2D+HXb/febO4/19/ph/hz/BkwGjZZSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkogZMhYAWGiHqBORl8WuYsJ6AWY2f5BdbhKQEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBM4qAr/CqCQajRpr0bOKR8JgKIZ5vWITo2YxCWT0VAk4BPRPQ+8EJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBI4awlkZmZiy89bzPji3aqetQOWgVEAPHzoMHbv2n02D1PHpgROioAKYyeFTQspASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElMDpSICiUPz21ptvokbNGuC6W7+Ha8Fw1C4mJgIVrbfiIWUlxfpok+26YDy3x7a/HM/xBlqLUQB88603UaZsmVgxWxcj4vnEMsQdLF68GF9//XUsJp6d7Yvd2zTb51ghOYiPs/ntnvl4HH9u4zIyMmLx8Xkocto88XWbSPmIryu+nE3XvRIggWx/k4pECSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUwB9NgKKHFTm4jxd12LfEc8bZMhSF4i3DKlWqjAsuuAA+v89xMcjMvyIktm3PbX+90r7IPaYFj9eHsNW0OBsft7hRfB+Zmf1jYD322LhElLjEvCbjMT5q166N3r17x3KxDttXHtuNcbbvzMzzF154Af37988q6/XE2Nm+2L3lavscKyQH8XE2P9Nte7YPiXFJSUnZxmzLMj5WVvqUKI7ZfKwvvm6ea1AClkDcn6GN0r0SUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSiBP4YAjao8IhKF5MAn64mJRoYkvxdBORctBGFRmvxyznzUmrhniBdhKJ5QGOG+V+9e6NmrpzmOF06cUjl/xsQXqSO3QAHJtBERGcwj4l1YrMN8XumztUcRRUysxzgGE9hR2SJeZw2w+D66OczO9tv2geKYPT6R/nfr1s0IgrafLJtTeSu+sQ1uPPf5HBHRtmvzsIM2ztZFccqyZ1pu8SybWx/iy9lj7snU1s3yDIzzyI1g6orBddL4yXIMth/mRD+UQBwB+xcaF6WHSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBP4YAtQ1fvjhR1x91VWoXLkyihQpgjLlyuHGG27Azp27RRTzIDMziCsuvxJ3/f0fsU7SMmv1up/QpUsXfPzxxzGBZNSoUbjwwgtj+Y7n4KmnnkKvXr2wdOlStG7dGoUKFUL16tUxcuRI0bqixqKqWrVqKFCgADp07IA1a9YYUYyijDhsxLJVK9D3ir4oVqwkChUuig6dumLihClGyaPIRLHqueeeQ48ePUC3he3atUO+fPlQo0YN0wZdBr7yyiuoWbMmChcujPbt25s2jqfvzENRaMyYMUYYY3tWLHr66afNuFasWIE2bdqYuitUqIDHH3/clGHexx57DBMmTMCBAwcMS1rbXXLJJbE6li1bhiuuuAJFixZF/vz5zfjHjx9v0q0Y9eSTT+KSPpeYsbHvvIbXXnttrA6237dvX1MHx1e3bl0888wzZniGoYhtkyZNQqfOnUwf2dbll1+OlStXGqGM14Ahp/FUqlQJbF+DEsiNgFqM5UZG45WAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJK4HcnQCOgJUuW4pxzzsHjTzyJgD+Abdu245FHHsTq1asxZcpUpCQFcOnll+HqAVcbMWngTTcgQ8SUK6+4HB6x0rri8itiFkMbNmzA9OnTY+fHMyAKMCxD8ebGG2/EHXfcgTfeeMOcDxo0CHPnzsVDDz0EroU1ZMgQIxQtXLjQWFstW7US7du0R91atfDSi8/Dl1xIxK5PcZFYrk3+9lu0aN3M9GX58uWYOnUqrhIB8KabbsLgwYPx9ttvm/O//vWvpn0KVvv27cPDDz+MK6+8EosWLTpm9ym6UeDatGkTpk2bZvLznIGCFEWsdevW4brrrsPtt9+OL774Avfffz8qVayEfv37oW3btvjmm2/wyy+/4OqrrzblkpOSzX75suXo0KEDasnYXnn5FSQlJxkhjy4bv5WxUURk4HXiOQW0AQMGYODNA5G/QH4jjK1YvsKIaSVLlsSzzz5rrvO2rVuxZu1aU5bi2rTvphkBj/W9/vrrJv6BBx4wAuGCBQtQToRS5uN1ih/P3/72NyMIcjznnnsurrnmGlNWP5RAPAEVxuJp6LESUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACfyhBGj5dYUIXJeJhVBA9Bx6IBTveahXvy46d+yM5cuXolGjhujfry++E+Hn3nvuETGnDZ5//gWsX78e8+bMNq72aHlE8YRuAa0lk4072gCZhxstpt566y0jFDF/zx49UaJkCYwdO9YIMsnJyabejPR0DBYha+PGjahYsSL+ftffTb7vpn+HsFh+IZAffS7pg85iOfXAv/6F/038KlvzL730Ejp37mxcBNL9YbFixYzFG63QAoGAaSM9Lc20seXnLShbrmy28idzwjXE2BbHeemll2LOnDn4VMS7AVcNMNZrVatWNdZytPJiYD6GO++6E6VLl8bMmTPNOUU4WnLR+owCIa28GCgYHjp0SMTMR4yYyDh7DW6/43akpqZi1qxZKFiwoIlnmm2D+3vuvccIWxMnToytbcY2aLVHi7ZXX3nVWN+Fw2FWbSz4unbtao4pxs2bNw+fffaZCmOGiH4kElBhLJGInisBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUwB9GQBz/GbFk40/rjUs/Wkmli/hkQjSEVWL11ESEMYoyLz7/HObMmokLunYRq7JtxqqLog4tpJh+MoHCDIUaijcUY2xIyZcCimFNmjRBSkqKjUZ5cUXIMrTGolvCb8ZPwKBBt2HH9u2yHloYIU8a/NKfTp0647lnn4+VswcUxdge1yejW0G6DaSlVFJSks2CCiK4MSxavOhXC2McA91NMrDfZEULsP3798fEKbLz+/2xc+ZhXlqg3XrrrdixY4cpz49QKGQ40fWjXW+MYiT59evXL5aP5RloJUfrOLqnZJwVxeyebhJpFUbrL7bLwHzly5c37bAPXHfMXl9y4nhY3rZPl5QHDx6M1W8q0Q8l4BJQYUxvBSWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASOG0IUOD48L8f4vobrgdFrtq1axurL3aQQklmUKyw3GOKIvfee68RYBo3bowbZB0yiigUTZjXiiemwHF+sBz7wLq5t4HxFHziBSumeT1e0xZFMIoxCAFvvP4ahg99xxRNj6RIHsAnOl006lg42Tq5j4g5HEUx2xbbsX3nnoF189iurWUiT/Ij3oLOtklm1vqKccxDwYvHNg+FM1qCvfbaa2YjB5YLBoPGso17ClYMLEMBzgYrWB0+dNikUYhjWVs39zZPMBQ0faG4ZQPTue4ahUMKpbYsLeoo4Nl6yJGBcQw23pzohxJwCTh3ieJQAkrgqAQi8iUdkn9MuWn4fQnEc+d10Gvx+/LX1o5OQO/Ho/PRVCWgBJSAElACSkAJKAEloASUgBJQAidDgKIHXenRxR+tsEaOHIkRI0bgxRdfNIKIFXCYb/euXbj77ruNxdPixYsxdOhQI4ZQELHi2In2gfUysLw9tudWoDIZ5IPp4UjYCEkUyIwlmYhgQ4Y8gL0iJO09cBD79+3F4YN7cWD/XhzKOBDrH+ugeEQxKV7AsWKeFcWYj0LV8Yb4uhLLsb/cbB7u7RjjBTPGsX2mU5DiOfvKuAcffNAIZHQ1SSEwTdw88phWfbbv7Gv8sRXMUvOnGtHr/9m7EkCbqu/9vXvfZJ7nmRAVGUIkQ6gIZSihSChpUJpL8yAVUUqlovKrf5PmUXOaJCRTpmYaNBre/P7ft8/dz33X4z2eyrAW755p7332/u7e5569vr3W+vHHH12ZSiNCzN0vQmqJKJSsY9wxL7ouIk74FytWLAdDnddfdHuUJ/qcL8O2hoBHwIgxj4RtDYEdICDld4g/Avoz+XcR8LjrO5D4Y22NqPx3vwu727YIWH/cFhM7YwgYAoaAIWAIGAKGgCFgCBgChoAhYAgUFgERIz/88APaHn64K8qTHLNmzXLHImc86TJ8xAhH3Mx5Yw4GDx7sSLKvvvoqJ512whGCx5fjLu7gQySLyvfEkE+qY1koSfz9PSGjsrOysxx50/jgAzF79uxINhI63m8bt9npkdN78EZEVcWKFR35pWp6yzBtDznkkKi2BY2Ixkn7+Unjxo3x3HPPOXyVPtpaTrgWKVoEderUcW40VZb/3mSxNnfuXLRs2TLnFp4kzTlhO4ZAARDIv5cWoBBLYgjsywiIkJEPYK8A35fbuqe2LRr7aJJS34uJIfBfImD98b9E3+5tCBgChoAhYAgYAoaAIWAIGAKGgCGwLyMgF4r3TJuG7777zlkDPfPMM7j33nud5ZS32rrzzjsdwfLQQw+hStUqmDp1KqpWrYr+/fs76yURKpI0uvgTAeOP88PNp5NrwLzEk2bR1+R2UKK8119/HRYu+AKnDD4F3379NWT/tCUlE7NmPo4Jt97q0u3JH7LuatqkqXOROHHiRLz4wouYM2eOw/CKK65wrgwHDhyIb775xjVDVmOPPvoobrnllgI1SxZnixYtwrBhw5xVmMjF1atW48knn8yx/JIV4KeffoqLL77YYbrh11+he/7xxx8YO3Zszn08MZlzwnYMgQIgYFrlAoBkSfZvBETK3DX5Ntx/913Ojd/+jca/3/q7p9yOB6bdnWMddsfE8Zjx4L05x/9+jeyOhsBWBKw/bsXC9gwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyB3YWALIgUx2oD3STWrl3bufA755xzMGXKFHcLufxTnKlx48bhggsuwLHHHuvOFy9eHE/83xOOTBszZkwOyaKLIq4KYs2ktC7mF4k0bx2mc9Gi+3uLMW19nCufpnfv3nj8/2bhgw/mok7dBrRYS0JRuv87f+x5zrrNp9uTt3369sGgQYNwww03oGevns4aT/Xt168fHnnkEbbtA/fdiJgqVaoUhLcnFPNrV9++ffHAAw/g5ZdfRvXq1d33VL9BfTz//PMuq8o888wzcfnll7t+IHwrVqqEefPmYcZDM9CuXbuce23vO8qvDnZ9/0Ygjp01oM13EgdlUwctW6c5flv7ea7cPgpTKFJyNr3PpWZlo0rdVvj963lMS7+fCtwXCYCn42ihkWr04W7f/2dL3+3V3ecK9P1jVxu2s99fmxPvwYszz2Avi+SM+Kj191df1l9SUjxXQQR+g9W39QNM62dksnueNrgfkhOTMe2BR5gtWP0RTojnj2Qm4qKslpQvekhl0ew4iz6GJaFQGCGutshKYzDK+ARnWu399nq/uTK3zoLKzWJ9QjRXDrZp6cEYCTFKZzbTqG6ZGdm4btwlKF68BMZcdDnH41Zfuu6GkQ/dN4MBK3Uv1S0UHwTMzEzny4DqwzrGKfonRW3J5o+5gnjqXALHaGYEn0wFz+SPUEZa4DPZlcWyMzMzWDbrzHYmJfFebK7MrXXfoEwixnspjYZ8emT1jLsYuaf2dV/dX/eJp1l2Fu+fnpaNoSf3QfEixXD3gzNdGwb26Y1y5cpj6rR7acUXQkYEf0JPDHTvwvYwXzPb7g8IhOM1loK+p67E7s5VZBmu76tP6ro/554TCezzWi2WmMR+nYWhA/q4/jjlnvscXK4M5kvk8yGD40jPiB1JSC/S0Wmy44NxGhmTqpvqkMWyNP6zstLdWJO/8jQ+SyQaqypH9VPdfJk6DnPQaRzL7agsLPX4u+HqK925iy6/hrlzj5dsjilJZijynMvS2M5G32F3Yu6T57pr9mEIGAKGgCFgCBgChoAhYAj8ewjwfZXvo+Woe9rwzaKYt1fOs/OpiGbYEvqA2SZluVqHghFtnG7rtzUL+V7NJFICSIIpMne2zeeu24chsAcgkHs2l1eF8k+RV6496ZzGrsbx+x+8j1KlS+GQgw9GmP80SP34Lmh9GSkrKmn++YN7R2XZyd04wh+ZYmP1yrVYsXKFi4FWt27tnSxp+8m91Zrm/9JJxorvAXqS+f3YNNs7LujT76sVK7Fq9Wp+N01IcFXNafP2yo09rzZ88cUXWL9+PTp16uQI0Ng0On7n7XdQslRJHHrooQUmN/MqZ185t7X/B9+U52FyaJaCfoH/GSDskfx9pxILU2d9ghmzF7iahNiQISe0wFkDWrnjLA0kiv8dv/uxTzFz9vxAYU992NC+zTFqQOtYeinqd9xlz/PDezfN8+LuOqnqJ7CiqelpoJ4biduO0911KyvHENhpBPwPh0gxiQgdkU96H3Y/YDF6bV2L44UMBp0UUeQJI08weVLJb8MhKro50KW8lqI9emWKzuufrik4ZbyIKCrJZa4sgik+kfeJ3D8gxYLmiWiKiwtjxdKlKFW2HBISwySRSChRGS9SKlpEWOme/sdS9RQhpnt6kWJeZJTaJPGK9TTWz6Xlj6vOOaU72+NFWIjw8qJbi9QLCMKAwNOx6qU6S/8f4ct8FrdNINGXRmwcEeEwZfuSEvL8UU/jcyT4jkg68F92VvCkD9O9c2be1u257mUHhkA0AqlpJIjZv8kXkxjP5AqvsCOQ1BdFJsWxf2lfpFJAHDM9x0I6iWn1c5HOGezYcqno3K5GxrrINT/uo+8Xuy+C2pPsLj3roW2mxh7HhcapJ8d4Czcexc2LYPZjWOkZptYVrbqpnnE8l8QxJPIsNTU1qLMbY2F8ztVV5eknXKJnWbTwFrkknYM6QQS6GDVd3PrYyJUurwNHkLMN0eKft9HnbN8QMAQMAUPAEDAEDAFDwBAwBAwBQ8AQiEUgUIRzbsyJaPsj2nNKyjm6I8qkSeMib+7vqkifpHJ3JFoonwfXtKMsua756bb0i/Xq13HxsqJUarnS7upBtI4xtgzVP8wmaptPU2OzFug4K1hPi/r166NBw/rUZfA2/EpytlJi5CPSEagNTZs2dYTXjpJ37NRxR5ftmiGw0whs1WjvdNbtZ8h5LEWeL+LxNRRoTIIEnaPCLk4WM1TSSXY0iF0C+zAE/kEE/KqKzxcuxsSbr8O336xGsWIlUbVaFVx7062oWr1mrrtTbU4CK0wfx/filedn45effkL9Ro1x0YWXoEGjg/DZFwtx39Q7cP+DD1OJHcKvGzbhvFGnof2RR2LkWWcjjr8Qs2c/heVfLMYlV13rLLJeevZp/G/mDHzz9VrUql8Pp486D507HsEf6XjMX7gQkyfchLWrv0L5ChVRunQ5XHfz7Xjy6dn4YukSJCcXwcABJyGUkYYp986k6XKxXPXVj4zGmoi0L79cjAkTrsfalV+hbJlyKFWmDNt4E75atgwv0lT5jqkP0LSbSn4q+kNkmqbfMxW//rwOl193E8ZPuBllee8sWrA8/+yT2LIlFX37D8Dos89x93OWaVTyv/rqK3j8kRlYtWIFfTtXR8/evTB0xJk55FyuyvFAZFw2CcGXX3oJjz54P9asXoXqNWrhmJ7HY9ioUbHJHYkhsi9Mhi2T23W/bMC1V1yCo4/uht59TuR9tsliJwyB7SLg49fpfe0O9vEi7OObN23E66++xLdHYPS5Y9D1mJ6YcsfNmPPqy841wnCOz94n9CGBzSQkjBITEnl9El7iuE6gJVn/AYMwbPiw7d4z+sLtk25F6VJl8ecff+LN119Bvbo1cee0B/HCSy9i1kPT8Z2eCbVrYeToC9C+Yyc+EeIckbaAfson3syxvOYrFC9Riv7T9byagGrVq/MlNAsvvPgyZs2Yju+/Zf5atTHy7AtwZKfOmDjpdqxYtRJfrV6Nk0/qB1L7uPO+h/lcKcHnxLbMtaxAg9McWDueM7hm6Xkq0XNHBL+JIWAIGAKGgCFgCBgChoAhYAgYAobAP4NA/uqP/FP8MzXbnaUGE9GAxIpuT/T+zt8vg4tI5QpwRzJ48Kmc73PRNm/lyZ4dpY+95hZzk4DTgnlpxrXJ5D8fqzx/Ym/HbczgQtaZD89w3mN073gulM0lWXF0fXgKErmYXrLj0nLldAdbLZK2vaYz8kgl4ZJ1/lHvSIVcrm2c2r1jyRCwlDBBjlU5yKI3WrgM2B3qvPZjr0en3R/2QxFDAcQFDGVmDoAB7jG9YX+AZKfbmH8P3ekit81Qoe6hgQUOfSqWqdEUybzr+tULjBDbFio78x8gINIoIy0Lg044Gsed0A8DhwyjIjgDixZ8hp9oxhtLjEnhe8u143D/A9Mx9vJrHInz2MMP4MTjj8Ur73yEatVq4nkGA73ssitR+4D6eHPOS3jv3TnY8Ms6nDbyLCTxQfXYww+hZo2AcJtx/z0Yf8M1GH3+RRhCAmnRkgUYNOA4PPv0i2je8nCc3PsY9OzTFycNHkKrsFQs+3KJq1erVofjmcdnokTJ0ji+70mIz05DkSJFtkFQimqRz2lU4J/Usyu69zsRJw44FWlbNmHZsqX4nb6aDz6kCYYMHoDTho/iCo0mzrpEFit3T7kNZ5w9xlmLLPzsM3xBTNq27YBRo8/H6jVr6JLtCtSpVQ09T+jvLMIevn8arr3+Kirxz8fJpw7Hpk1/4Zf137kfab8KRD+Z0SJXb9OnTsYNN12PUedeiEGnjcTfG//CuvU/Old10Wn9vsoSKfY1FfzH9zkeXY7ujn4DTiRpF5DwPp1tDYH8EMjk+I9PpItQWoDNpyXVfPbxjl2PxhlnnY/PPv0YZwwfhGOO6YUy5crh7PMuwtwP3sHYUaejzWGtUKVWdY6tOLz+8rNo27ELzr34Cvzw/Xe49upLkZW+BSNGn+NIqh3V4cMPPsDSLxagVesj3POhasUymMkYejdecy1GX3gpTh1Wmy4FFmLAid3x1HNv4PBWrWlBlo0BvbqhZ9/+GHDqaRyf6Vi8cD5+IklfvWZNzCChLf/fI88biyGnD8fiLxdhQL/uePrFt+iWoAteIrFdnOT/Cf313EhHkWJFnOVbAnGQYVi0iOfSJEAv7gURjc1o8USZPxd73Z+3rSFgCBgChoAhYAgYAoaAIWAIGAKGgCGQHwJ+cXt+6fK6Hp1XusDHHnssr2Q55wYOHJyzvyvEWGY2F3Vzkbo0VZ4QU4FaoCt3kCKUCiOZ1AWoDfLw4jzOxMzHk8JJ6NOnD4mxkoW5zXbzqv6is/SXTp2BrNN0JJpMRgUFIa4SqHBQOpF2UkdEbz0RFl2BdH4RyiPJ63p02n1+P4DBIaG25hBhudUy+zwMhWngP0yMBRq2kPynUTLik4Jtxib29Jxvb5vHwNYrLrl97GMIFPb7vfTCC3H7xIl8cPKnpQDK2lb9ptISQiAG/TH2+SDSaM3KJdjw61+49OrbUK5UskO8x3HHua1+ptx9IvfaTF+nk+65A5ddcSUJoBG0GEnH0T06od1hLTFxygTcNv52NKxTA+++/ioa1jsA77/xMvr3PRFPPjoDIVpUZdBl4UeffoKTufIklas7brvlVoy94kacPfoM/pgBnbodg29XrcbkyZNwxXUV8fvvf+Hiq8ajQunijmDu3bufswALcalJlUqVUaZsWZbfx9VVH3JvGC0eoxXLl+OX3/7EpVdcQwuV0vyxycLx2Sc4Ekyu2I5ofxTuv+9e3H3PPVTmZ+PZ557Hhr/+ximnDmNxJA/pZq56jbq4/6FHOHwVtywTn3/0PmY/9TR69CIxxpUgN42/kUFEL8K551/iqqAfZueCLapCKjtaMmk9etttt2HsRZfg3AsuySESMvgjKvGKdL046Dc+zO9LbXrvg48x8tSTMGrMuRh1zsWOFGMVuUImunTbNwR2jEACV1Spj4bZVxUvr169erjzngf4opWN43sfh5effxq//roB9898BEmJ8ejRszee/b9HMOftOThl6FDXH8sy5t10jgvnSpHl/U0ry3vumoLTzjiX13d8f3DBSJWqtTDjkUedO9Mt7MQjhp6K8y+9FGfSyiuObO+xx/Yk4bYOU++YiLaPPYFVy7/k8+o3XDRuPCqWLe5u4J9XKXQnesstN+H8Sy5j/jFunHfv0RPff/cD7qJ12qzHnkb50mWcK8X+fftxTMklZFBJN1Yj+77WepwoxpgkTn6Q83FXqvE6duxY1uGWnLHry7KtIWAIGAKGgCFgCBgChoAhYAgYAobAriMgXYj+pMcSsbOtB67c+qCAZtj1++2JOb2OaFfqFp1XcbvnzJmzw2KEptMJKhVVVLHo7jAzL4bjGPODonwM4OCII3cick5nCyNJSWzDGztug8rf2XpvrVN+Co2tZfu2Kq/IsYLe05NnW7fB3aXzpRYwOIj6lGVZQcuWw829WfJqf3R7tiIR+Z7Y3Mw0krGyyHDYbYtfdH7bd8P6v4GhYq1DwEhBTsG+TQ2onNyRyDLmn5W9e+D8s9gUvvSMuIAg3dWSmjSsFVgvxKyE2NXyZFJcr2FjujCsg2M6tUDnTp3Rucsx6EaCKptK49jn8EcfvIfULSnoeXxf9yLi79ug0cH4du1aKtgT0LLN4VC64WeOxqLPP8OV192CJYvm4+UXZ6Nc5Rq01kpFtx4nYPGC+fjl518w9923sGThx64omb5+/fUa/EjLkzr1GqBWvTo4rvPhLgDlUd2ORZejuiOOyzD0MqS4R/mJ/+Fv2Pgg1K1bF0cfeRi6HdsDHTodRUurHgExRrJvxFljcPqp/TFp0h0oUrwoHqYlW/fjjkdy0WQSB/TdTLxrkzTQy5dEL2CJfJFIZ/wifRULP1uAP/74G31PPNldV/0UY8jf353M42P+x/Pw24bf0affSSS3mJ4WOPnJgnmf0ErnJdxw+2TmO9Ell6VLAbLmV7Rd388RqHPAAW5sxZHREsdau049VKVrT401dX0RaAc2boKUTVsckS0y7MBGTZDOMSIXC4oZ1rx1G9w24UZ3jsl3KCJ8GzZq5NJozMhS9def1+OTue9j+dJljH2WDcXVW732G6z//ltHnh1w4EGoeUA9dO90GDp17AQ9F7p2OZrPKwasXbSY+X/CvI8+wMoVy0k0k4xPT6eF59dY9+P3bjxqjKW7WH3BcySWTI+tcJjuEVRPxTbLbzwrr54NfoKmNpkYAoaAIWAIGAKGgCFgCBgChoAhYAgUHgHNr/xcKyitoBRB4e+9P5ZQ2Nnsf71uW+RSYaSw9VdEuMJIPuqUfIsu7P3zvcE/nCD/9sfgyw6ruPOiK7MVl56GCCY7RuAfthgLbk4jR7dD16ZOwlTK/fTNYrefzmd4Qv7fdJDRPg0BInDJJZfgvTdfcgra3aF0TU3NQLHiiXjjrQ/w+KyHMHfuBzjnjCEoWrwk/jf7VRzYsEEu3DNT+dPCjlu2VCVkkaMN0zRZksiVIH+lkQwiQ9OlW3eMPX8Mvl/3B2OW/UDFdQ+8+/Y7mPPWW6hcpRqaNG+BIknx+OPX9cyZ4oi0YmT01Z64UCKaNmlBU+cEFE9MxntzF2LG9Gl49923cc7IEShesiQem/0KGlCBnxEhqVwFtvPhrUHiqSB/5/1PMWPmfXiXq2Ke/L8nUKJ4cTzx7PMkzA5A16M6oWblinjwwem0gOuFj957Ey+89RHjGSU4Ky65nEuIT8zBXS7kEhhbKSUtxa3W+OOPnwlGNsqUKc80IhECgjs/RfqG335iPuJZtoKLnSQSLj9RnVIJvlxdmhgCuxOBeFo+JjE2YDpfIkSGaUwqnp3GWqJjuTTx4F8omIDIgjGxSCKtyeSOMZMxxugQwf2mZbv4eTyxw+ppfMj6Mzk5iQRYBmMW/sb0CWjeoh2SEpJyfHY3a9bGxewS8VaseBHMeWcuHn/0QT6v5uLskUN4js+rZ18hyfyLM5s8rHVrEnUktbkERXJoi2a0pmSEMt5P7QrzZYleTEmQucvb/fAGZH4cF+SZmx5VqM+33RvYBUPAEDAEDAFDwBAwBAwBQ8AQMAQMgQIhEE2KRe8XKLMl2mkECksM7fQNd3OG/7r+/7W6/7++/27+OrcpzhN/Oe3kjnQ4KTJEol5rx9qobYrbL0/8K8TY9pDNJJOZNykWKBy3l4/Gwtu7VKDzhctdoFtYoh0isOPvd4dZeVEuuvTnJT9FbZsT7/FJ89zqZYI6cJQtVwaj6LpsBGMLbdmyBUd3bIN7J9+OSXffmytfi1Zt3ZNmzhuvoFdvWY0FRM6vG35lvK+SJLTi0blrd/zx1+mYcsfNaHlYa6eAbt32SEy4fhwqVKqENoe3d2V26NCFD6tEWoYdgF49jnHnMrPpJZfK67S0DFpcZVHhnoSRo87FyLPPpxKbpNsRzTCN9Zp45z00w45ZHZCrpsFBRkZ6oAxPoNl2ieKuLLlY+zslE0e3b4F7pkzCrXdMpSVKPPqcNBhP/9+jWL/+BxzUtCkOYzwjPkuRRoW/CIBMklbxLEeWYB531VWu1g5r096NzDmvvYTjaf0lX5dhKuLzs0Y54sijnDXKay8/h959TsrXYkwEXZNDm+OGW6dg+ClyK5lKV4oXIoFWdHKlaOM7j05gpwqMgGLXyX2gLKREhun5oHMJYpEoIr41DkTgki/jtSz8vG4d+3mQR8TV9z9+h4RiRVGsRFFyaDu2gFaZirMnUkzSrj2fCSTTGjRshG606OQQcmVrvAVuSDMdaVembBm6Sjyfz4Wx2LJ5C7p1aI37OZavu/1OhJIScUCDRji6+3Ec+4GrDVlj+rGYxQZms95R/JW7d14fwkJ1UDtj3aDmlT76nJ4TcqcaLUaURaNh+4aAIWAIGAKGgCFgCBgChoAhYAgUHIFoDz7Pv7EEt0x/m4shwzl6EH89p8Ts/1Ttm1ONvXUnFJ/PStJ8GpZVWPyzc8+n87ndNpdDocJ5XCts/bO8hcw2NSvYCee5pmBJ80xV0FjpeWbeA07m1/6McKBHis8MKLDiyfEY0vdQ9Ot9qDNg2AOasMdX4R9+QgYD+JdvPndAlK7VFH98sygHFAXly1t2PPB3fDXvEu3snoTA7v0G81O06jp12/QsG7mvfO7FyOqVy7D+59/Rtn1bpiLRw7g/UnCXK1fWKb+llA5JO0wpXiwZR3bqgom3T8JhRx6DyhVK4vlnZuODD97FY0+9wFhbNNig+8FGTZrh0ccfwXmjzyFhk40u3XvgzOEDsWbtSpw19hJnHZVNxfkxPXviuivHom79erROa0TldTZ+/OE7rFy6GNVr1sQGxhhr064t9evZ2LxlI0mpDJQqVcpZZJWvUA4rVn2DPzenoURyAhXYrDvTRUsCLVkUO2gZ3bL9+usvaNOWpBwHX1oqXcFR2V22XDnnnk3jccSZo3Ab4xh9v+5nXHD+ucQig0p48YDBYI2Xq7gIKRZmhiy6PQ3wjUMZul88rntP3HjD9WjcrB3q1akOurbFV8sWo3Hjg106Z71Gd2/RUorWL8eSALjpxptwUIsjULdWLXd51VdfoBHzue+N7in9/RBOZK3CaNfucDwx+wWcxPhqf2/cgssvv5Ju55Q1qGv0PWzfEMgPgXSS0OrL3josgcHq/OoqjRMRSXqQJJBAdukYbCuOzBKz4YN338Mbr7+Bo7p0xS+//4G7Jt+JPnS1mp1ON6POjH37d1dZXkQ8lS2diF69uuPKK8aiGgnzgxs35P2A777mM2HFCnTu3BGrv1rO59VvaHvkkc59qB5pIr7KMt5guRJJ6M1xeOVlF6N63YY4sEF9N36/oRvGVcx/1FFdUKlSRSxZsYrkeLp7bgT3zz0uw1nB89J95r7kq5vn1hF4fIapXXpuRLcvzwx20hAwBAwBQ8AQMAQMAUPAEDAEDAFDIE8EsqjeznbUV0R9yznpdwzHcdsd73HxYgbuvWUQKlUuJc2Ny5+dEQldEkdlDCU+Zi6n+ZmPBe/muXF+1uuS20cMAiGGHTExBPZUBOIixKknyDZs2IhJ09/Fh/PX4urRXRBXqYSrelIOTRbRi++pDfoP6mWI/Aeg2y33LARE1mzavBnDB/dH9XKl0bhWZTSuUwXVq9fC2WOvoEKcVAtfHry1RQJNqO6a/hDjcCWj1SG10ahmJVx0zkhccc31tBTr7BqXSKuqlm3aIm1TCo7o1JUWY3I7GEKLlq1pzZGAIzt2RiaVx1K23zXtITSnVVnnNk3RqFYF1K9RFke1bY7ly5dgc0oKTjv5BNQoX8JdO6RuZVSrVhNjLr3KWbKce9GlSE3bjEbVy6JS6SR89/0P24DriayNmzdhxKkno3aV0jioVhU0qVsRlStXxegxFzsyTtYxxUuUwDHH9EQKSbPTRo7OsRCJ48uXXp5EyjmSkMeyEpPGXscy1dX2tin3oSHjHx3RshEOJC51KpfG5FtvZF3D7rrHMLqSwuCOex9E44MPQftWB6Nx3UqoVbkkJt58vVP2K22Av+4TuLaTFY/ONW3WEv/33Gt49MEHcO1VF9Naz17qorG1/fwRCMskkuJX4jhrKvVpklGyDNPW9Tf1PfZVccRB/1Of1iQlG0cdfTRuuPJCNKhWFs0b1ED5ChVx3fjbOWby748ij2R95izQSMbx1rhz2sNo3botutKi88CaFVluBXRp14Lk9mJHNm2mRevwwSe650LDahXRqE5FVK1aA2ddcAktz7IweeqDzlK18+GHuueG6tW1XXMsWfolkpLCOOv8ixwxfsgBlVCxTBJ+lvtFYRBj3aVzqo+egR4fnduRuIlWZHwKJxNDwBAwBAwBQ8AQMAQMAUPAEDAEDIFdQ0CkmESfCkWz9qcNGHnV8zisWQ2Mv7Q/KlLxrXlpiARXmC76NQUN/njMHeliov+kt9HCaZFimq9pcbX9bR8DB759GAJ7CQLlypTEzZf2xGHNa2HEVU/im/WpSONzQ57OvLezvaQp/1o14/hQ3CX6W9mkACtbpzl+WxtYhOVX61iLsfzS23VDYHcgIFeKL848I7A8UoExFmO+L0sJ/utvv2D54sVo3aodSpQp7lwXKksaCaHEiCu1dPfSwTg9VGKv/2Edvv/hGxx66GFUKsvSLBhOUiL7+F+K5+NcsukcrTqKMx6R4heJ5PGEj3NzRquyeZ99hMoVq6BmnTp0sUglOV9WJD/9vJ6WV0vRqsXhSCpWjPkChbMebCpDxzqlF5zYIR125QQPQqXdsGE9vvxiER+UbVC0dAlXD91fMc1SU1PRs/vRtF6rjzvuoEs2vkR5xb3qIUU/W+4eqCpLL1NylyjxpJcIwL/+2oSP576HQ5o2cySB6qSyXH3zWHEjLCSb/v4bn340F03pKrEcXU4qXyatctS+sFw40nwtzOCRnuzzrhpVD4mOtaLKxBAoKAKxY1YuEzXudN6PY2315/opV+V5l4J+jIrUUty7FSSeihUtimo1a7s0/vqO6iKyWa4UJduUy5nPZ/M/RqUKlVGrXl1Hnuk+Eo3ZDRt+xtIlX/K50AYly5aiZSrdPHK8aJyqLKVZuGAeKvCZovy6ls3Jktrm26my/PgLnjfBWIwTGxYRjb/eQ6dg7pPn+lO2NQQMAUPAEDAEDAFDwBAwBP4lBPheynfbctQ9baAHoq1vqcHtg7fX7VfFzw+51HObROVqHcoIAIFu67c1C/lezCT+PZgL4gLZNp+/YltD4J9GgD5K2HO5MDkj5MJcnDnuCRzWsjb1Nq2c9xJpQCQyEFPXzYyszfRql5ylmhHNrzacDiI1UQufgWIxHn1cYfZhCBgCewkCwe9TZihwqUhNMz2CUfdKNuz515diwRerMG3c8XyCZCM9OwMJcRF9UqR1//2vW/D7TiUWps76BDNmL3A1k6XmkBNa4KwBrdxxVuQ55X/H737sU8ycPZ8PPD7MuNh8aN/mGDWgNR9uMV9bzu94zPmow4gtbtQZ2zUE9jMERPBI4km4VChXERU6HuXIJsXz8qSZYnl5cidw1xe8TJSpUB7l6ZYskVYYGXzwiPhKYnwfSSKtLxQ3SFYYCSR+xP1IuZ6WRqJHZicUET0ii0QahZPi0YpWIlKmSwLiKRjVlSpWRpUq1ZxinCp6WrJkuFhfcvsm0QuQf393J6I+ZAQid28S1aVMmXLo2OVopmf7OMEQoaf6/Prbb3jx2Scxf95HmDD5bqesT2adUlLVhuA+TonPMlRf1UF19FtPwIl3TEpKohXNsVF10oqkgMTb9kkV1C0jLQ1FSfp16naMU+Cn0FougURXDglI8kH1TCd++q5ExDkykK4jt36HIjGD8uzTECgIAiK43TiNPAfyIsWURpMHXQusx4J9HWtMaDxov8GBjd2xiKZs9veCiCOM2bclIr1UjsSNTZLMrWh5qvJEckVf13OibNnytD7l84rXM2TBSfFjVfthun1s0frw3Pn5/NAzTM8qPY8kWhQg8cfuwD4MAUPAEDAEDAFDwBAwBAwBQ8AQMAT+UwSkCE6li/4iCUVw75MLSOTGoXfnlqCaxulDfuei5NEXTce6779HPENQpEUUQyG6WEtMTKaeKjWof3bgYjEulIXGjWph/M2n0+0/L0V0Rf9pI+3mhoAhsFsQcDpa6rKlcj7+mMaYt2AlZrwwD8N60pgjov/ZLTfahwoxYmwf+jKtKYVDwJmfS6EtDTjFb7UfmJiTdRfrE+iSnQI7IUIYiRSTyIVijmKbx7IWc3m4r6wST4opnfZl7i7xx9H5fVpdl1WKxNfTl+sJu01/b8ZZI04hYR6poEvN+1IBrjhj906fgaQiSU5x7u+h8kXOpZPkOqZjO9YxC7dOmIhG9eu5dFu2bCXxVJyzbBFBwJct7ybNk1I59WE1dc0fb96Y4uolci5IGwEiUr9HHnuCpD4tz3KsZlg2cU2MYOutaJTcWebwZU8iwkxt8/fXOZGZ/vvRsYkhkB8CGu6ynpKIwNbY8C8MGr8S35e178eO9iUitvw4df0zYnGmc9ns9KcOPClIGPWpMSsPjnff/whdsiaxy3qCOxgbuocvU9n8cfS9te9JMO0HT62tN9le/jQ+R8KRdokoz0rncQ4pyFKCJrOgYCdbK3O0mx3UcesdbM8QMAQMAUPAEDAEDAFDwBAwBAwBQ+CfRCCcnYmikfnqjCc+w73XD+Bias5bSXBt3pyCgQNvdLdPRlEqf7gbx4WbcdRL0TqEq7K3Tu98JbPT6aJ/FXr2uwavPnUVNTGxM0mfMNh6Dyu5zxb8yM+tC55jz0q5t9d/z0LTavNPIRD2atZwEGvQ3YdD+4JTO2PMNbMxlMQYsqn3yrEQzVH8/FNV2mvKNWJsr/mqrKKGwPYRcFZnyYk44aSTqajfdliLPEpMDCzZ8ipFFl5zP19GRTtJAK4okmWKCLPdIcnFi6LvyQNpMSZLt3TGWjMF++7A1crYOxA4ceCgXBVNz6DFoywhaRqeVLwIr/k3mFzJ/rEDT+brBiL8ooll7ec1LfLE3D9WKSvYEDAEDAFDwBAwBAwBQ8AQMAQMAUNgWwS0wpoLGsV5ZdPEq1q1EtSrcJ/H5142fZv0WoRJX0ZMEHjWEUkWSKCH8bNPzUvvuOcFXHxWz23K2J0njFjanWhaWYbAziFQuWIJbN4SWI2awVje2PknZN5X7awhYAhsFwG5L5MLMv3Qa19xw7SvAKfR5/XwkVVKtLXHdgvdhQtOmc0bJNEl4kkD+mHLFr0y5Rbnrm07rgwVu8tZunDBgKxH4kmgiRTzLh0LW+84rnDqfUJfvcsxhpncL8ao3m2hQu4vy472HQTYt4/r1StXe2QBqXh+mRyzjnyWjfseLJmR8eknUHtwVa1qhoAhYAgYAoaAIWAIGAKGgCFgCOxTCGRlJXIBM/VMVKOEMhnnndNHRpNAMv/Wfb2O3kcCjzoIbXHtDjEmdogLMTcllnPHSRl/B3jEBdedy/5QAsL0OrJ06dfw873tgba9kB3bSx97nh4d92rJscTZq1thld93EfCammCghSPq4D1by7RnfRtGjO1Z34fVZi9AIIcQkyuzCCkm8si7Y9O+P6/miBSTKF9hSaagpNyfWhHkybGUlNwWIEqp63J96PdF1EWLtwxTvC6RYf44Ok1h9nV/xQXLVPu5r5c6E0Ngf0VAY1FxBn1swf0VB2u3IWAIGAKGgCFgCBgChoAhYAgYAobAjhEIKUA0/2fRQ5r0PgotrbWV8vbj3OfHrIuO04pkyryXL3Pb9t3GuW3OBxcqZ2SkKZgF9TOMYC9F0A4kiy4bCyP5lV+Ysv+dvIVr/79TR7uLIWAI7CoCu0yMbVXGF/whoYe4iSGwuxFQX9yR6Dq5Kkqkr+bTDTOYPoO+mBPpXlBu/zKD4Fgu/pZeSOJoys6oQmScIneVOzLtysSdklN85NidjHzoWnY+Lx7R6fPcz7mBLzTi/ozjy1cpVz5/PjL+SKPluuydTgcY8ZLesCg5xz6BO8uPmOz+9Pa2ejHzWWS5lmcdt5dZ5yPvacqnsrSVcwAniuMU7O1Hn5G2b6fF8gEutwgl6MJSJtPxke9TyR1+MS/O2ylmu6djfYyLBN6TZGfrt7NkNand3M3loe+Dvn/mTpD7yI0HksRamhcXiaMXnWJn6xOd99/YT+NN1N4gdLPu6PtjDC66ZGIIGAKGgCFgCBgChoAhYAgYAoaAIbDrCJCY0uLKOHr6yYxLRVYomNBn0HosIyPk4ru7hdBxDJ1BXUAoLfBoVJSTNhFo6fwL04Ismx6OpCdIYOyxeM5DnSWXPCBR3xXozIJF1klJ8S52exYVQspX+Nm+ny/uOgSW0xAwBAqKQKCXydFRuWzRY9Drbfw5f1zQ8ve9dIbAvvedWosKiUA8ldZFihblShy6OhMpZmII7GUIJCcnI41uKxMYKy5aMjMKyYpFF2b7hoAhYAgYAoaAIWAIGAKGgCFgCBgChoAh8J8hkM2QGV5EcCm0R4ixyESKaQ1rHBdmZ2ZmOPIrMSHJXffpFXM6pIXMTBiKePhJTxdBFhBuPp1tDQFDwBDYVxHYZYuxfRUQa5chIAQUIyzRxd4yYsx6xN6DgMjcePZbvRDLLaa20RLWua3vzdGXbN8QMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAT2IgTiwmkM2SG/JkWx8I1rsZnmD/IBpNDuiqix9LVrXWs28lPnjzzmCuq7srARtEBLTHL+PzLpUjEcH0I8CTWRafIelJkR5/RiCXLlaGIIGAKGwD6KgFmM7aNfrDVr1xEQuaBVNlo9Y2II7G0IZKRnMIYVX45jSDG1w9zZ7m3fptXXEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEMgbgQx6hdHc38/15TNGil5vMZZJgkyaLa8eyOAi8CyullV6/WVmkFRj4rS0TP5lOVJM+xJfpjuwD0PAEDAE9kEEzGJsH/xS97cm7e4fa8VM2pk4P846h3Gc4iOjSabne5JkcLmP2uRM6vmGpBBntKR3q390LratSi93knH8S6cfar+vdP7antS+/b0uilmVns5IvBFRTLGQfIjnQexmOVeKhVsPsbMxxeSaQSSzj/21s/l9u7a3je3f7KTbS7pHnBceaSQvhYMfX8ImQxMSXktMEF6qauDWQnsKE6dzu4Os18rBQHw/iMUrOPaxEOMZC83EEDAEDAFDwBAwBAwBQ8AQMAQMAUPgv0FArg01jdMcMk6Ml4TzQ+nCwiHGFqOkZafioKMvQzgr1R0venOiy9PsqCup+6Feh2EWZFnGyGTuehLSkUySLEyLMOl6ZBmmeanmqAm0HsuiuVmCPM7wmokhYAgYAvsqAkaM7avf7H7cLhFVAVn17yh0Q9Rai2xKZUwn+WXe0yQpUcFTgxU/fitCTJLXS04CGT6d19+0OyeieLESGDr8DEeUhfwyoz2tkftxfUSAhdgBp06+DaXLlsEppw7fo9BQlxHho5Gh/bz6XGEqHNu/o4m3+MQQMrjqbU8SYeDJ5lBkkqHxOOmW67F86VJX1YlTp6NMuWKME8dgyJzszHn9dfz004+oUasu2rZt79IoDy+RUAvSeNJaWEvmvP4qfvjuW9Sr1wBt2x/pzonsBvvL/dPuwicffkiCPANjLroYTZu1dNfz+tA9FGtxdy9AyOteds4QMAQMAUPAEDAEDAFDwBAwBAwBQ2D3IZDFOaeItRzhBC8hnABNkzXH0/xcbhS9rkhzVYnmoQoxEj2/zinDdgwBQ8AQ2EcQiHo67iMtsmbslwikpqZi3LhxaNWqFcqXL48OHTpgwoQJjiD7pwHR6hu5r7vh+mtx8y3j/+nb7XT5Ed27y3f91Vdg4vgbc8qQ5Uqs6D1IK4WkRP9s3sdY9Plnbl8vTN7qJzaPHf/3CLw/92N8Om/Rf1+RmBpceeXluOnmm521U+QdOyZF4Q636d+33MQCg5+2PY0UU0sdHuNvCiYZERJLq/LmfvQREoqXwjG9jkN8UtgReqKzT+Dx6BEj8dQTT2HYyQMw/NTBXOVHYpsEl9y9kx5z/uMTQ/FsdQhbuEDwhG7dcPbpQ/Hc7Nk4bdDJOG3wIA1qZHG8a1LUosVh6NqzD1564QV8/+OPfE7KjUbu14HMUBb05yX3VX/WtoaAIWAIGAKGgCFgCBgChoAhYAgYAv81ArTzoi0YrcIYO0x/XsIJGdAfwn8gO/QbMrMTkUqvIJlxmcjQHyfUWgiphZde3+MWVLIAf+zLsq0hYAgYAvsaAluflvtay6w9+w0Cn3/+OYYMGYIvv/wyWPFCUmfu3Ln4kBYRy5cvJw7bt4bYHSCF5baOZubLli5DiWLFd0eRu7UMWXx4WbZ4McqWr+APkZSUuI0Fjyxa9FIkIuzhx57JsfLRy1ESVxLppclkz0Eggz72EuQWgV+cVnTtaSIrqNIlS7sX7ojXhn+siurfJUuX/sfK3x0FL1uyBKVKlHKTDLlGzBlX3D+0aTOc0HeAuybLrynjr8OXC+fj3fnLUKlCWXzzzffoeNjBeGzWwxg0aDBSUtLdWFW9sgL/i5hy+3gsWbYI73y6iHkqY82a79ClXVM8NWsmTjxlmHtWtWh1OA5ueTjOGXaKG+dupSCfYYGLDhvfu+N7tjIMAUPAEDAEDAFDwBAwBAwBQ8AQ+K8RCHMBZUZmOpdTUs9D70CZGXTLGLW6VBZiIsDo0d9JutwrSilEkY7BxBAwBAyBfRmB3UKMxT4qt+rhfdybBK5UT+PahT1Pabsvf7n7cttkDi63hVu2bHGk2BIqm72rL7/V9ZkzZ6Jln9zE2NYYOwFCUkBLQZ3GqKSvvfQsHn/0UXy9eiWq1KyFUedehC6dO+LP33/HeWefgZOHjkTXozq5jL//uQUXnjcCvU/oi3U/fI+FVGAnJyZi8En9GW8sHtPufwAJSUk5imv/fbBaUKynW2+/BcWLl8SWTVvw+isv4eDGDTBh0l00dknAi88/jcdmzsDXa9egTv36rMeFaN+uLUL08bxo0ZcYf91VdJO2FiVLlUFO5Vg+AABAAElEQVSFiuVw7Y23oladOkin5dxzL76C/3v4QXyzZiXq1K2HM84Ziw5HdcbEiROxbMVyhFatwskDTkR8VgYm3T0d5crmJhK8sj6LViQTJ9xMsq8ERpx5Nl+egAk3XY8S5StiS0oKXnr2GaRt2Yxe/frhnDGXICHOFOr+O/4nt+GYB25ifKKzWIyTuzv+3Xn7bXhx9jPsRknoP2AQhp1+GgnQJEdwXn/lZWhNt3qdjz6W32dgLTiZ6elwHOdfMBafz/8Mt990I75duxrlSKCWofXldTfdjOrsW2lpaa4cWR1FS3ZWHEnpRbiFlogaN+VITJWrUBGXXzeB4+kFrPjic/o9B4afMohGTnTPed903H/3ZBQpXQGp7EevvfAMGjU6CHdOm4arL70U7Y5sjy5dj6XH80DuIDmUlJyMcy6+1MXZUvNffPYpPPbII1i7kuO0anUcfVxvbGTfX7Z8mQscfMrA/gjz5X/yvQ+hdOkS0dXlREBWU3F075iACTdcjTKVqmPTpk14/cXZOPDAxph411RHVj337Av438wH3DiqXacuzr7gEhx+xJGIIwGVq71lyzuy+eabxqNyjRqabeCl557GLI7fb9asQc26dTH8rPPR6cgjMfXO27F88UK6rghj2OCBrl56TiQXLco1fZysZG5BmA8ouSwVx/nC07PR5/jjUbVcKU5kMlC/bjV07nIMXnrmGZwycDA2/PY7li5cgDaduoBeI92E5qXZz+LY4/oyDwlw1rVBnRro1KETXnjhRfQbOIxtC+IMxrt+FKDsXc9mZKTzGaNwzZLge87mAyvT/ahv//dbCwO8xZl/NqsEleufxzo2MQQMAUPAEDAEDAFDwBAwBAwBQ8AQiEYg0KNQw+VOhvx8P6JfoTNDRp/mHJFXOeviR5AuO0yPPpzcZXFeH8+5bXxmmtORZccFMceadr3WLYpU/PFMzjHDKEmCTHNhzuvogCSUkIyM7MxgUSbnx3KlqPlbchI1t7Ig46LorDRfmej62r4hYAgYAvsOAruFGMutJgUSIivXXTRIYcUYTKEEqv2oWJWuLauQd9XPgsnei4D/wd/VFug1QKSXfrSvu+46LKVFivbLlCmD008/HaeddhrGjBmDN954o0C3EOEj+d+M+3HjNVdizCXjMHDIabS6+BKDTjqO7sheQau2bXFIs2a4YPQwvDd3HhX/lTD27GH4Zu1adDu2N5YuXoCqVZ+iAr8YjiNRFk8CK44vIHnFHNOKnMyEOHxKi7alJBTatu2AEaPPQSUpv/lCMvOBabjpunE4a8zFGHzacHyx9Auc2LsbZj/7CmTpcVLPrujZtz8GDRnGl5dUfEni4ZdffkHN2rUx68HpuOGGGzBq7CUYcvpwLGcbBpx4HJ6c/Spat26L5596DEVIxvXu058kSipKlCgZND7qUy9AhNO9IH0xfz4SSPaJGJN8+vFHWLR4ETod1Q1njD6f5NzXuPn6K1Cndn306duH9Wc+kowm/x4CGSR6vLz35qvITD8SF15xDdZ+ze/mhiu42isdp408k0lC+GTeR6hYtZojZkP6nklUzfvkEyQmJyElNR2Dj++Bnn1OxMmDT2PyLForLcRPv6xH5eo1GBcrzGc4LZT0JUdJCsmUfj26oNdJg3AyLZLiMlOwmPl+2/AbWrLPVXriUSQmFUGPE/q4XAr6++lHH2PhF1+gTbv2GDn6XJJX5RijLxMff/ohqlVn/TgoQ4p1x3644LPPUKRoEZLg6Y5Quv/eqRh//TiMINk08NTTsXnzRvzG/t/28CNc/y5atBh69O6DJL7wlyi+rQVnPPuzcwvKfrqI9fzss+kk4zpi+JmjUbZcRUeczbz/Xtx8k8bRZRg8ZCiWr1iKvr264n8cR21at0a/Y7ug54CBGDBoKMnozVhJy9T1P69DDRKI0++7h8+Rq3DWBRy/Qzl+l3yBQQN64pmnX8bhbY/k9mkUKVKUdTyBkw3+NpIk8yJyKSvK6m/F2q9w5tlnk8wKfL9ncLJzcJNmmD51kpuovPHaCzj3rFFY9+tmfl8Mlsw2rfhqOYaPOtcR8iKp9Gxs2qIF7rlzsruNjiXB1xhYGnryKiGRk6A8hm88mU3lC9Lx+2dfEOkWlKPzPGQ+Pe90zwsvvBC33norn0/pbpGALz9In7v/uELswxAwBAwBQ8AQMAQMAUPAEDAEDAFDIF8EqGZy82RyXGjcuDbjVK/kHC6D5zhn5L9YEXGWTbf6YtaCOV2QQrqk9m0OdnofnfGuFKUX0EJOze+cbodzPBNDwBAwBPZVBHaZotKqBYnWLfj15TkgSWnmxG+DBzfCSbRMcM/jnKS7tmMP5l3Dbc/IVdhv75KxYzFx0iSubtn6oy/F6/Dhw6nMvtkpkSfePhFND23qlLQFaXVmGi24xt+CMZddjxEjRyCRSvlux/bC+m++wx13TMD/jngB55Goen/OHAwbOgTH9OiFD99/Gy++9SGKJiegWfPmjG1WDqVLlcWAAQOoN5bLwcyIIjl3DdJELpAo1kioULEq7n9opksga7BsrsyZcPMttI65FueMPgupaam0/jiB9fgeU+6ajMvGlaeVyJ+45KpbULZEsrMg69mzp8u/KTXLKfMvuOxynD7ybFqdZKJHz9747rsfMHXSbXji2edRtUIFlChbDifTqi2D9ROBF0tkpabRM7VICWKqlUWx47tO7TqYPO0BZ6EiM/sP33+X5OEz6NuvDxSzzAdrzd1qO/qnEFDfD4lQIZlUlETQ/Q8+7G4lfuX3dd9h8qTbMYT9IcyAVCItsuIy2G+4z+9ffS5LRC1fopcvW4aff/0ZV151I0qUKkmLpVT0Oq4XUt0qMq0YS0AoD9Zk9epV+P2Pv3HpFdc4cjqO+Xp078E1EEkkSuKcRWNpWjb270cyVsSJi3MFVK5SAdNnPsr4WEHcK8/HZLN+4XAcLThJjomQ4Ut5Bod6WHlp1TmR8blGnn0hLqV1mUilTJXH+2g8VaGVW6lyZdi/T+I1RuIiNorfFS2ZJGs8GSXrsWrVq2L6w7O4joMWW8QxlSvwbrt1Ai649EqMJMEUx/Yf1+t4fPft95h+952oWLECfv/rb1x25bWuvdkZaQj36etukbolDbfcOB7nXnw1zj5b4zcN3Xv1xrqvv8Wdk/kcefolVKhQDuXKlEf/E2m1Sfw1DhXQ2BNPvm5b+JzI2syycyy4iAFJxQrly9CFYgot+FLRsuVhuPo6rgQMCQOuBMxK46o+WvbxmcRpDH+f+QzSd0A3jJs2/c39yG9xNCCF3Ndj2D+K1b9kLSuyVu1JJAkp0Xkv0SSZP2dbQ8AQMAQMAUPAEDAEDAFDwBAwBPZPBIIFh1vDO0c0ZrLsokirGiaxlen0rJzb0t5A868EzpXvvP40tO99ndMHJMX/RQJMc99g7sUo0lwNSZ0P5+fxmlMyUzZJr7gEWpZxHl2/SQsu5OzKOT61upqPSlfAObgPtyE+TPNxE0PAEDAE9mUEdpkYiwalfK0m0YdUxgUrwqWYd0JXX4EPW1r11DiECvWtSrJcGQt4kBkXq6ovYEZLtkcgIHPvwkjT+tXdqhavYJUCVtKhQwcqkQMytmatmk4x69Ps6H5yH7b4S1pd/fobPnr/HaxcsoCkABW7cWF8+/UafPvN6uDFIzGMaVSgH3VEa1x/5Qe4ky7hatepx2sKVCrrlmy+SARKft1ve/eWolpWIbKuaHzwIa5qOqf0Cxd9hg102/jZRx9gzNIF7oUljSbt36xdjXU/fodGhzRBvYYHoNuRzXF01250kdgNXbscTc45Hos+X0jS7Ddaor0PxTGSwl4K6TVrv8GPP3znXnI8DjL20MtPXkSWzitWVTxflmJFdazXsAGJM36HLFsriUowXtKWtBSn4HeEWpQSPDa/He9+BLSyS24Ow3xRPvTQVu670zl9F4cd3o4uNMc7twje1Z1qoOexSLFUuh/046dh44NxQP1G6NqhJd31dUMn/h11THeSLYxhpv7Nl2T5Ho+1GGvQ8GDUb9AARx95GLoyfaeOHZmvB0kc9i8SzukkfnQPBfVVn4lnH1I56vtyrZhEKyXVx724u7rJjSDHERdSpKTwrZ8SEH/ZWEYLy99++Rn9Tj7VnXfjiO1Qv/Qr5FRPEXAiwLIjzwOXOPLhrbLkSlFycNOmru8mMq1W0i2h9eevtP6a9/EHtMZcRKI3IHZWr/ka6zkG6zdojAMOqI9u7VuiC9vb+aiu6HrscRxvGbReXYxfN2zAJ3Pfw1dfznfjl9XB6pUrsJ4kpSRTOBJTtV0W1CLHCIfDyD0HRAAKMw1Simsb66VrDie2NSy/iZQGdP3Y6CDiKBaUEuY/TYT8d6pznqSKIxnKrO5eOr+7RGWKyMwge6lYd5Jkur5UvbU4wFnFaVZlYggYAoaAIWAIGAKGgCFgCBgChoAhUCgEuJaac33OK7nlFBEvPzEOE+56yc1fNXfPS2RRlpEeLDwtVboIOh/RFENG9XXzQ83NpQOSFCmSqCm7m09rwbQ/n1eZds4QMAQMgX0BgV0jxrJTGU9IjhKTsOFrxo/ZxrWhV4IFhAUfqw6r8rUPxa9fL9wm9b4ApLWh4Aj43lHwHLlTyk3Xe2+yFP6Ae+sEpejTpw/69++PCbdMwKz/zQoUytI45yNS4G74+Wd20ywc1rYdikZ41zBXx7RocWjgDpEvEpnZVDszrVwvptNao2zpsk7Jz4o4RXcGldNOIU+lfpimkc7ahWlFnEWL0kjBLeV1HMm3RBIDIg1k+fIHleqyXDm0VWuUSA4FLyp8uVE9EpISGTcpFXPe/gCzHnkAc15/A7Ofepxu8IrgyRfmYP36H6llj0Or1oc7H9OZWalOmX5oi2YuRpOrg5TrEUxUv0T+xVqMeSsyvWgpbbQ7yCyZ4PO8XsSyMkOOUFC5UuQrnyR/xF0y+9gNCOj7ESkiyxyNhXgSpFu/LxKXLoKu/IWnsS8EHTub35uzLqLrhMQIkaExICOjOW/PxWOzZuLdN9/E6FEjXUyxJ158CXXr1XeuFB3xrI4RJUnxWS7fo/97GO/MeQNnPTaSsfOK4wn2yfr1GzhLr2yuQvNBfXUv/Quz74sUc0SO+hWHtKt7FsdZOBGyRnSr1dhG1w+Zb90PPzB9NipVquRqIIsviSszLp4kTDwJNZFitFxMpOUSCUNvgeUSRj5EjmkcSuS6PSEyJkU+rV+/DqwYDuMYTE4sQms1WZIBTZod6sZqkcQ4vPnuh/DtPfuMEShG96TPvvI6fv5xvQYDmrVug+JJGju0aGMdDz74QLqDLIZsYi4rOpFjwl7fnSen1QYRc560TODzJoFjavPfGx2JGeCUhW+/Xcv7leA1WvBFjV/3/ReP53nGnEvV902M9Y/f+zdr1zKWYFlHkun+hZIEWoFprDO+o7tDONORYipT35Pa4d17qr+InN/aJwt1Z8tsCBgChoAhYAgYAoaAIWAIGAKGwD6FwI+bAy8f/c+c5tr13N3Dg/aFktDr3HuQQK7ruUmjkJIs7y8hFJGhgf5zrp9OsqtYkWxce9GxnIz1iOASzPf8OmfvNUScGafLjvhyCZksk4sbQ5wzau4o2bKFfl+oU5DE0yOJyyumzMQQMAQMgX0UgeCJt482zpq1byJw2223QX+S22+/3cWz0b6sZmbNmoUnnnjCxbYJlOX5K4HjyfK0O6ITlbegIr8Rehzb1RFdUmqL1HIWJnyLUHnDBw1A/YaN0H/QKbjg7DPwyvufoGK5ck4BLqWwt+hQfWQtIVG+HYlIMZePLyOHtWyLUJEEHFC3Pnr14ssNJUeBz3JEzKk+I0edxzhC5ztXbV3atcS9Uybi8htvFbeA2nXqOfdtspjRC46sapTH1YeKd7VLkkpXbbL42lmJp/UQjdEocpsmjERq6D6BRZGtKtpZRAuXXvh7ImI9iSMRJN6C6Icfv0dxukUsVqwoz3HFFzv5Lz/9wP6gfilSNBO/0n1ijeq1nIVV8XKlcQbdB541egz+pGvOLkc0w/1Tp+DWO+4meUvyk1WN7c0iRYuVKcWYc+di5Fnn4s+/N6N7x9a4h31y0tRprn/Idaj6cYh9PYnWTrp/QKKIB5ZFmay8AuLul59I8FK85eLPjHGm+knate3I8VEUr738PPr07kXyK9HVWy4GM2idpmeA+ndAGLG+vK427oy0a98ZIRKJNWvWYbw1uUgMqN4EWkVt2ZLqyLYiJYu7uHuKvbcxNZvWY81x52234PrxExFfNBn1D2jA8ds9Mq4DAk7j0cU3Y3GeRFYbExJEHkXwiExI9DwgAmjSvCXee+8tDB05ipgRNGK48PPPaRnYklZ3WUjZlOIssmQhKOs/kVBNmrfAm2+8jCFDTnHN1mRm0YIFaNaspesbwlrPggwSbxq3+YkmWz7GmHvA0MKWXxbLIjFHrNO4kjCehLwsxtTvVM/oZ56RYvkhbNcNAUPAEDAEDAFDwBAwBAwBQ2B/RUDeh7K8ty2CIG8pEq4tdaL5W0Tl4uaXwdmCf8p1vxZmxtNjiqaZVAsEwrAZXngL5+lFczdNO0WiSXTexBAwBAyBfRmB4Im70y1UNira+Rk8L4NjnQv+drpAy7BfIaBf18L8aZVL8As9lvHGhg0b5ixmPITpVEBLRBBUq1bNn97ulkYXSCpWBD179sKN487H8q9WOjeKehH59vsf8dYbrzFvCBNuG4+VdK04/YGHcM0116FC9eo494zTXbl6WaldvSbWrlmFvzenuNbpgiehXKLtfIhYcC7hqNwuWroEetIt2w1XX4gVX60W9UQCKxvfMMbY22+8jiVffomPPv6YZvNy+0ZLIJIBso4pWbIkSlMh35uxkK4edzmWLF9FABKQlhGHr9mG12hdFkfTmAoVy2PV6jX4k7GQEvhilO7ftrZTt7xOyzxf72qydBEZphe5DGKuYyPF8kLsnzsn94lyXxe4y8vC3I/m4vU33mT/jcf3P/+Gu6ZMwfG9+9ANIq182dHr1KqLF2gB9tOGjYxLlUAi+VEsnj/PufVbs2YN3n37bVonkTBjlbfQIkgu/4oULZrTAJFjsbKW1khvv/UWUlJl/SQ3gYGrwFKlSrmk5cuUw9drVmJzCl0b8kV7SxqJGb6Eizzx1orOqovj4IC6B+B51u/n31S/EB59+BH2+UXO9aL6VlLJojjmmGNx2/XjsObr71hvWi1mx+OLL5aIM0Il9u+Vq1ZjE8eFrOIUk2xnpWypYujd90Rcd+1V+HLZSjf+MjLjsHL1t3jrrXfwzbff4f333uPY4e+fG4OpziKvJMmyUqWKo/vRx+L6cWOxfPlKN35pL+XG73vEVvWpWLY88ViVg4dcVHhCXRZjehboLyGchaFDhuDVV17B+x98CNXh1Vdfx/tvv47hJMpEQi2Y/wkGndjP3UfPvSJFiuDkwafwWfEa5n74MVI56Xn51Tl45523MGz4SBqzpTo4ZMnF/wUWjXe52JDl3xb2gb9TNiONRFxq6mZHcKY7cjObz6N01xcnTJhQ4LItoSFgCBgChoAhYAgYAoaAIWAIGAL7KwKhdNpspcfjhTtHub8M6no4G+QELERLsdF4YfJo57qejrsQ5uLG9Ph09xes3+Ti0zjG0NYf/fQHfwzbwRI4hXN/CdQZJFNnoNjZ8maSkz4kbyZcXEkiTLN8hVrgRaRyTq9j/7e/fi/WbkPAENg/ENCz0sQQ2KsQkKWJVrJ4cmz69Ol4+eWXSWz1RJ06dRwh1qBBAwwaNAgLaF2Rn5DbcTL5ngcZr6cJOrRrhka1KqFWpdLoekQLKriXYi4V4RNvHY/J98xEhcpVXPrpDz/B8udhyqRbqOQPYcQ5F9CaJBWNalZCpTLFnHtGxS/LT2TVJWLAK8QnTb0fh9JSRPU4sGZ5HFSnAjq2aYplS5dQmZ6C4YNPRO0qpXFI3cpoVL0syb+aGH3Bpc5yZPLdD6Fp02boxPQH1qyIA2uVR+fWzbBixTJXx3MuvISK/i04qFYFVKrAOv5G1407KXLhlkkFufxPOyEpKCsyub2TpY7Jv4eArIZEiokETiMx0vHIjrj2igvRoEZ5tDyoNomacriWloTJyUmuUmMvv4ZkaTqaN6yOhkzz8nPP4shOXflOnYU//voLZwwdhOrlSqFh7cpodmA1VKlaHRdcPC6wFuMLtbMOjGnenxv/xplDBqJu1dJoWq8KDqlXCRUrVcGYi6/ki3c2Lrx8HFJJstWvXgGViyfjz7/+4Oo3jmHX54PB54hhvoirfumq34E10LB6Rbz6wnPocORR7o7O4ozk2F3TZqIBrTaPaNMEB9WuhDpVSmLiLTeQCIvDeRdfRsJmC9tfFhWKh7HuZ7o23EmRBduUu2egWYvD0PnwQ3Fw3Uqox7ZpDC5Z+iV+//MPjDh1AGpXLoGD61Tm9YqozjF47oW8N4mhyfdMzxm/jTjOGlQriw6tm7jniIivC6+4Cikej2JJ+IsxBWU1qmearPokzhKO32u/gadgyPAz0K93N5ZTDsMG9MFl465Hh85dnCvF1STY5rz+KgkqWn9GCNIhw0bglNOHo3ePTmhcpxKGntQLl187nt9zF06oAjbMWYxpplMASaSFX2A1FuTVCKeBmCPHlT0rK53fc/Cc0/j31mLeYjavPlOA21oSQ8AQMAQMAUPAEDAEDAFDwBAwBPZ5BKRLCrxuUMcl0zCKjgNLMVmLBWE4OB3fJYuxdM4rtRBTMa4V3mB7Iv1a2MWlzt+ryPbKsPOGgCFgCOxtCMRRabXTS+qVxSu/8mpwhGfg6oSI0pN7evxWqN0Ev3z9RV5Z7JwhsNsQiO1/7frdi+cePAMZfOFwnsMUVGgHIquOBZ9/ivLlK6FG7VrMs3MvBj54qd96V4j+lrL08GXqWuA2zV8NtlIwp2xKw+LF81G1Wg1UqlLFKbVVFVnZ/ESF/5pVK9GsaUsk09otVmTBsfiL+YwrVBG16tSOvZzrOLZ+enGSJJBsyatuuTLbwR6JwOpVK5xFYPXadXL6mq+ovu+vlixBydKlSfJW3ub6unU/YOXyFWjVirHqiiTluu7jz/mytPX9/Icfv6Ml1Bq0b9tBi9sKJV8tWYpSZcu4+nkyxxfo3UTKQmrexx+iOQmsRFpKJdJdo6wWo/ut6habX33a19lvfdnbbPmomDfvI1SsWAVVa9ZwMdE0/jQ+f/zxBzcG27Zp79rr76PydzR+M0hi+bSafOj3NJkB3noe2x2du3bDaSS2SpQs5iy69OssUiqFVqiffvIh2rXrgDhOVuT3PZXmYCLTdC9PQvnf5TSO4Sxaon3y8fs44ojOCNH/fGIiVxeyLImsAdOyE1GfBPsDj8xAz14KvMwJGb83v1AgMxQh6Wi1Kuk16A589MwYt28fhoAhYAgYAoaAIWAIGAKGwL+JgN6Zy9Zpht8Zs34b8RqtyLSdb/uRJMFKsFB24LLdBRd2V5RCmRJQtnZzxHN+zpdh/LqKZWu+HUnu7+OL98c7u905bcK2pdv9t8VkZ8789/gHXjsC+y3VPHcH+zWFczBOvY4/f4pb/P3G1DHOjWIK524nnP8AisVtxLO3n4dUrnXtfdoDeOrBEa75ij0myaSnEUlkwy0XzzL0RTond0nME4pMzmUZJsmO6MMYetxJOJ/J+1aPi35cBfkK/JmP/i3fcrILp1xg4AYuJNYi3+BOpuPKF3FLsA8hEGK/73XavfjgyVGuVX40+d/JgK35LxvM5woXkFOxhamzPsGM2QuCerLiQ05ogbMGtHLHWZHniK/v3Y99ipmz5/MBqLxxGNq3OUYNaM0HXExbCvADYDHGYjCzQ0MgxFhCLVsf7kghKc53VkR6iXzIihBMiqsULV4p7hToUYpon8blpXY6PimMFoe1zlHiux9wliupVLEyKlSo5BTsef2wJyYl0GrlsFykhi8/dhubX/X3dVRdYq/H5rfjPQ+Begc0dH3Qk0jRNdS5+o0auVN5fb+VKld1ZKz/3pXGi3d96I+1VX/ROKlWtQaqVKnuXDFEX9/ZfVlPNTiosSvT98O8ypCFVNv2JOF4b618U920VXVF7qjesnHy7fBlKL3+VHbs2PRpcrYsq0WrNjnjQf7ZXXt5gypVqpEwq+ym3Tqn++ie+nN12c74jW6Tt3zduDGF1n1lcNcdt+G+Kbfh40VfoShdo3pJ5v6RnTq7QykFRHApr8R/JzqvP51X7MDs5BCO6NA5p+6eFBMZd+Ul5+OVV14j8VgeRZKLuglYPMmz7YnyKhajiSFgCBgChoAhYAgYAoaAIWAIGAKGwO5DQKSYFnBrnuYXOsrzCh31uEWQdC8SXGc6P/fL6+6azUnz1PH4S105mdlxeOf58cjkgskQ41q7+PNSQOejKNZiSS9buEDz9VdexOZNfyOZHoKaNGvlvDRpzp7IhdSq8/vvvIkf132PurUboGWbNu7evh0qJ5uu+N9/9y0uLP0etRg6od0RHVzc7jDzF0R0jzmvv4ya1WrhwEMOcXNehQZI5aLYd+e8gkMOaY6KWvBLxbqK3MSwIV+v+Ao/rv8enbsd40IQLPpsPj3npDk9X0HuaWkMAUNg/0GgYE+i/QcPa6kh4BDwVl1SYscq1vODSEr39E0pOHPEac4tXGz6RBJvd9//KIoUT+KLROxVKvUjFmX+3n6rlP6a9nU++ljnvOh8dD5/fpst7z944Im5TntlPUNN4c57Z6JEiW0t0nJlsIM9CgH1P4mI2bxcJUT3i7z6T2y/2vjXZpw14hTGuBLZRNKXJFC0TH/wEYRpwSTJy6IsOm1B9mXx5esl60UdR4vqFy1y6xc9QdCYEjm1ZWMq6z0YaVpBEiNyBzrt/oeQQEstkVo7Eo+H0sRi5+vinxGqt0Rl+rR+q/O+Xdr3EkeLr3jGe3vof7PcKZ/GuZvMq248pxapjZ6M04OE34wj/MDydBx0A6aNaZ6szW6fMg0TOeORWw4/AeMuy/S1sq0hYAgYAoaAIWAIGAKGgCGwlyAQ877rV5TzrTxoQMx1zlrceV2VNUwGd9xrt04zrfd8xKDH28kfnC7w57bTkQJndQm3qf/OZd9mBf1OZs+PSMm3uP29/d5CzPenGMBIL3Fel83FjYmO9FGIBMX70vw7OysVaeES2Mw8Is9C7LA58ztvieUtwhhjTBLWPI/zwaxQEhje21mQBUtG3eU8PpiI4i3D/Ph54cXXcdHZwzjHD6EOCa2Nm/5iLPulGHrqMNw04Q6kcJ49qHd3LFu2GI2aNMEX8xei7ZEdMHPW/zg/JxnHebtK7tOjJ1auWI4DDz4Iiz+fzwWfR+Heh/7HK7TlYrulY9iRvPPB+xjMcCIH1KyLd+YtJiFHDFhyPAftk7Nm4YZVV+Ptjz8n2ZbCuibjl9//ZJz1YzHizNMZguAoNozz31tvxt8b/8JTbNOO77ajmtg1Q8AQ2BcRyK1x3BdbaG0yBHYSASmcqcF2iuedzOqSO6U4rSv6DBhAcoKre8QwRUkc3zjiqJx2Lyuy8MhD+R1mnkzmlbjLrJMU5t4E3CnPxQDkISJGfJlayZMXORKd7aTBp0Qf5tQ5i3GDEqOsVnIlsoM9GgGRMd5iMbaivj9tr/+pb0k0DvRSLneKfU46Gem0kkqMT0Qcid1oCbOvezeG3qIq+vqu7Kv+vh6x+XVefdwTZnKfGN3H3dhgIzXG+gwcxMlF7vr6Mal6+3ESe4/o45zyIkSUXBcKwyy6K9VWBJSwiq7vjsZvNInn7xPdXj/G87J28+m11fcXvRUmbuxrEqRKUdyx+8LdoftIS+MEhNfDItAo4XDwGpDFlXxu5iLn9SaGgCFgCBgChoAhYAgYAoaAIWAIGAL/GgLPTjozshh1q55n9sQzUZSh3Tnl5ZyTaqqIV6IdVSoUF+bi0AzO24N5aw5BvKNMUde0YFJzw3EXjUab9p0w7YEZTg+luf7ff//FsAyLXOq77xiPL5csxJtz5zmPM2tWf4dObZvi8RkP4uRhw51V2JRJ4/HViqV47f15qFyxEr799lt0PaIZ/u+xmRg4aHDUXbe/++iD96Hd4e3x0YfvYeH8eWjVuiV1HfTkwsWuN0+6C53aNcf1V16GK69mPHXG/L5w9HBUrV4VY8ZeAc35s4hdmHNcLQg1MQQMAUMgFgEjxmIRseN9DgGnBOZLhH4GnevS3HrybdorhbMU5l7xvE2CApzQqpqex/XMN2Veinmd86SYCvCKdJ2P3te17eXXNdU/mjDQuW2EuBSkntvksxN7LALqEzv67n0fUgPy6h++T6kMueST9D6+j9tu70Nl5lXW9tLv6Lwfd6qHb0t0+tjzsff19U8igXdcj17RWXdp35fn6+Xx03m/r4J9Ou3nN36VxosnIIW1iOxUvsxrP50EoMffp81rK6JOPJh3C6nytkeq6ZpcWYgMc0SYK1AuOyIkGVcmyg2liSFgCBgChoAhYAgYAoaAIbC3IKAwuvIKrtdY7ft1qYH9DMAwSxEJ3nl1oLgrciwRltVNNlNqXVnO5ZydSL5CbvLRPxSy9Pyz2/3zx+ifTBFrKRb5PiKhcTg/C/pbmBMxzeOysrSImv1WH5S/0tk1mcRFJuPcLxw5nxkpN5NaXa1x5JJWdmht0xBKjndzS8UVS09M558WTyewbNlLBuWGlZHCWa3b+o8MLpBWPO/vf1yPi7ofRxeKgacRxGWgfLlSKNu2HZNm47nZz6Jrj+NRp1Ytd6+6tauhc8eOeO7553Di0JEMsZCIF59/GccefQxqVKnkiL26taqhbYcueP6ZZzD41CG8c7Ao1987eitrsnQSfK+9+jzum/E456xhPPjA/WjTtjUJRAZRY+4yZUrhhlsmYcSwQejd9yRar32Jj+e+i1ff/oj3o17ELRgNvN5oMaqJIWAIGAKxCBgxFouIHRsChoAhYAgYAv8SAtEEZDTJtz1SLCC3tlqFyeWFyvAWY7J29SSdtj69muOs3yLtkl92zankTlF/cpHpiD7zp/gvffN2G0PAEDAEDAFDwBAwBAyB3YGAHCFItS8V+4Il6zFvybeu2Ay6UJMkZW1x21CESEjkIjEpzNOp3Nd7NNXn7vq0xxfzmN5dRJRJsh0Vgez4lOB4Fz9FwhVGRG4URuz+hUGPfauQ+CtGWLSEGQsgk4RPBvtjy4OroUHDGrSu4mLU8+/iAstMPHfX+XRDSBefqXRDeOF9SKHrwBfvOsN1cE/uqJ/KkYsWfjMc2VZSl/txso6SxVhcELM6iwk5ROgNJhNJiZwDpgcdktM/xt3i+CCT7F3sJ5NQS03NRlJSEipXroQH7rkTB9atjhZt2iI+kaZrupWbR2ZhBd0qjjz7vICAY7212LLZYYfh7imTXTp5KFmyeAGGn8G6U9xYI1l1aLMWuHfKJDcG83Ol+NB9U1GqZGl0PfY4/P7bH4yVfR7St0xFfDLdTnLsqi7de/ZBdxJ0o0cMwR8bfsV5F1yC+g0b8TpHuBjDyHPAVcI+DAFDwBCIQcCIsRhA7NAQMAQMAUPAENhTEdAERksGtd7NTS4ix95izJNivv5KLzcaOVZlbtWcv2pbQ8AQMAQMAUPAEDAEDAFDYG9HgApyRQ6iC/WFS7/H00+97RqUElfObcPY5LbxWQHRpWN5XfgrXBYp2YlUrgdqsf8986ZT1iMriLGdGSYzQcnKLpzazMducoXtwkeMZ/idLsHuv9OQ5cpQePxFS0kCgiwUznQLFtPCpZEWX9QRY46kYkywjBCZKveFxSGd3S4jFI/4cBoyaTXmCGD6BRTZI2sozf+69b0McZFQCNmRBY5Z2UH/TeTixx69r2a6FOcxRESR5Pnnb+P8kDwbyawEBezm6HHhAnguNTXduR/MpO/Gu+6ehvPPHo6jO3VEUsliaNqkBfoPGIxTThnoxolCC4hAk1tDEWshEnDlKpTDpo0bWUdZezEEAq8VSS7iFmtqX64Ny5evgJS0YGzlFV9MeUV4sXg89fgT6NunL4qSvO7ftz+uv/QCPPbwTJx6+oggVhit7JKZdsKtt6EJY5jVrlUHV467DJs3ZzDGmjwxEc9I6ADXePswBAwBQyAGgcL9wscUZoeGgCFgCBgChoAh8M8hIF5LhJhEVmLRFmP+rrGxxXwed94nsq0hYAgYAoaAIWAIGAKGgCGwzyAgd2l8P6ai3Cvbeca1zi8ci4swHJlKw8TZdKOoayFa2MiCzP+FqFCXKE0gfhs53MlN4XI7vm8n75g7ud0/Nx47e1RoYizSD9mj3K3Vz9S3tHhRItJL8uwdI4IdzveyRA4x+exJw2jZyJxMSl7J5ZPVWBaJKIniZmnBZLTomsoX8SUvIdpPp3vEcBRDqrhb8lCieaVc7GfTr2Mcr4uQkmgMtWvfAfO+WIUPPvwIXyxagDdffQkXnT0SCz/7ABMm382Ome3uEXgfkct+kmGpcuMYENCuIMYB86IysyIknkKPuDryOJyQkDO/DbFOmaxcJl05Llu6AosWLWSMs4d4TvagQLcex+H/nngUQ4ef4cg8nUtJScEdt96EsqVLY82a1Zj38WdofEhTRz7Kyk24mRgChoAhsD0EjBjbHjJ2fi9GIPKS4AzG5Tc8UB7rbOQddy9um1XdEDAE9m4EgufTrrZBLiqiLcDcfD3Kgkzl5kz+ed5ddye5ElCEmliyAolf2cjEir2wzZSrQIVYIkPAEDAEDAFDwBAwBAwBQ+AfRSAkX3eRV9c4WtxkhUo5kish86+AFIhYimVHLHbiEhIZhYlZ4uUaLojPrAqG9eLMv9QI6RDOCJT62QrwVAjJCPiQXS4his/YpTLs/rsEW06mwuKfydhcuUVqWJJXmSKTSIyRi3JkmbY8jouTRVgIaZqCURJ1juROgjgr9vUs5gvxekgxybKT2ZcDMsv373DcRnc9K5GEF62lNm/OomUX05Fw0lwywn1hy6YUJCYmceiQEHPzxhAJqaCujrjTsOL5Dh0OxxHtWmL06FG4+vKLcN9dk3HLnfchgW4XN2/8k8SWCDaxT2H88P23KF2iGOufiUTmTUpKwN9/bVQznIj4WrNmJZKLFHUkmUgxiXf/n5FGYo1pQqEEPDxjOhKKJOHCc8e466rLup9+wMplq7Bs1beoX6+6w+39j+bjvmnTMfvVObifdTtt6FC8M+9LFCEpl02MTAwBQ8AQ2BECRoztCB27ZggYAoaAIWAI7EEIuDhgUfWJPY665Fbexc4FNMlxPuTpjF77+Ynz3c50O7pPfmXYdUPAEDAEDAFDwBAwBAwBQ+DfQiCdyn3FN9KbbqN65dGsST13ax9rKxROpxVJFraEy+PquUoXLBw79YTmLl0qgthM8RGdejoV8oWRwsao8vXe1TrY/XcVuSBfYfEXSRQtmotJ0uJKoVnDSv/P3lUAaFV00bO9S3dIp4BIdyMYNFICSijdSCgqBkpLg0hIGIgFiB2IIkq3dCgGAiJiAdv7nzPvm91vl0Uw8FeZgW9fzZs4b+a9uffMvUMiiHxXXABaDXnCED2vPd6bRJB3R8vBcxAcH4XXpg8yJ6z8pvmOmvT94YpRiPTFjfdtG7UcgVhaiCndVa9MEAeXLMSaRclIuJEUs8GzltQ6YbK8lLyYJCtqvbJgksnxtCq7rkwZxMs1IftP2QqVsObDD9Cla2dapoWY65/t3IXKVar5iC6gguJ8tAp3dO1m6iTia/eunYxT3cSRe0Vr4amyiBRTiIuKxRsrlqFNu9tRtVpNc87+mTJxHJ5dPBePjR6DqPNRGNK/Gzr16ItateqgZMnSqF+zAiaNeRijHhuF6Gj27cuQeW3abusQcAhcfQg4Yuzqe+auxg4Bh4BDwCFwlSJgXYFIPkrNgja52CaQFJPzD610plMuOAQcAg4Bh4BDwCHgEHAIOAT+oQhIqS8X4vFBGVCyVDF071jVlNR/SpicKHBJJIwacQ5BdAGn0KVDnWTxAi2h4H+jifE7//zZ+73i/c5M/aK7/P3A+AO7fxr/lAnoWA/FO3+KBlVqi7Fcdst4B6GVlQidtLwekSCiKBDhvH5e7TGAFlZah4whNsFjwuI9LolnvAYrt4hBAcGIigtGDKMkOTM0tyHYl68pgjmVooGQCJaXx549eqBz116oVrUSYmISsP/gYcyeOQvFixRFMMmmdlxv7MERw7Bl805cX74iPln7ET5aswYvL1+O+OhIyI1hhw7tMXT4cGzcvBWVK1fG+6vexyck01589S3jLtESYV7JvL8BXFftzTdfws/nfsbQe0YiT97c/pex/9AhvPriEtw/agzuHT6IdmoJJMHGsvbxyJojCx4a+zj69eqK5q1bowyJPBAPFxwCDgGHwMUQcMTYxZBx5x0CDgGHgEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BP41CATTV5xZi5fK+xCfqzb/whs6wXIBst7x6c3tKf+4bt8h8OcRSEnMqAXynAhZzlSUQZNZU4yElNYMUwgKDkD0Oa7hRWutELoqjPE8FSZeN5Eu8kcWWAEBdEeYxJhdJGbqp+UxJIGTIk9+ewxtm9YnAxeD0IhwnP01EteXK4unFj1rLMo6demOQ4f2o3nTG5A+PC3ORZ/DsPsfQt0bGtIKzGPr2nbsjF37DqFl8wZIR9el50mYjXzkMdSqewOJM66DRutOf4sxW6KnF8xHhTIVkDdfXvJ3Hib22u2dumPOpAl4YsYkLH1mEV55+z2ky5DGeEuJZnotWrXFayteQd+ud+DT7bvsbW7rEHAIOARSRcARY6nC4k7+GxHQICKGi3SGhvqmzHDwEMBFPemCGbH8adwha4nECTV+lZRP4zjfQqBazNTua60e6+/YRg/k9BnrVszeZ+PFxNLMnGkpjj7KZjFfvzQ0QLfr/9j0tI1h3jJZ171KU0FlUFmUZkhwsDnWID8qOsYslKo4Sk/BDP5pzq58dT2UAoCtg64rT8VVvFiaq5ty+cpo7vfLVwMh/5BU30A8+cQ0hIeGowsXO2XupnzKx6an+yw2/mm4/SuHgG0Daitm8VxfG7NtUznb52/jxPIZ65w9DmI/sY9d7S00JNg8R5uG4k6ZOgXpI9KhZ9/eJh+1CzU/2z4Zxdxj03p8/Dhky54Dt3emawW2L5tXakjY/qJ2btufbXe2z6jMCoqjoPxsW9axPw5TJ09A2rC06NanH+vp9RHFUbDpal/1U/lN+1WCDLqudFVeBV1XsP1a7dv2Jbtv8VU8/31bZ8XXvunTqiPTUP+xZdZ9k1nmA3v2cKZgFGbPf5Y+24MQHh6O81Hx+OSj1fjm+LcoVKgo6tSqwXdL0jvAPiNTJl8/Fobvvf8BTlCYKVi0GKrXqIkwwqZ66R0wZ9YMbNi0xSyW3L//AFSuWs2URxgrPRccAg4Bh4BDwCHgEHAIOAQcAv9WBOI41pay3XMPR5dwPsU6pfQLqkQpBTEkASSLc0UmxqCMlFx8uOAed8Ih8LsQ8Fl2Jb+HsifJq0RSljLrmzN7UhamXugc5XHKpDEhcVg5qxdO08brDM2+IrjomNwyxvNYQbK4QmCCJ69al5lB8WfN+WBkhlbR44Jd5vhy/0iWVNorXn8LCbHxOHRwN3786SeUKVveyKey8lLfCmYfGz16PB568DG6R9yKipWrSMI12cj9ovpUEGXPMWPGY+T9D2H3np0oV64yQkKDWQ/FS00zp9vj8crrbxu5NIHroqlX+ofiXFvs5K/nzakhdw8xW6tDkMyu+xc/u9Scl7zOYuKpZ583x4lKD+/I/XUIOAQcAineMA4Qh8C/FIE4KnxHjhyJ2rVrI1u2HFwgtD4mT5n8u2sjpbY+qv6KckswWYWxPq7alyLaxtWx9qW0t4p3o0j3Kdvth1rppgxKR0SW7rV52DwV179MGqCEhSYZw+uaUb77BkUqm9Ky5dI1m6clz5SmKS/vmfDog5g5dZxv0BGfjExTPAXlaeu7deMGbNuy0aSpcvuXU/GUvwt/PwK2jeiZ6JnbdmSfv84rjn1etm2qndi2oFLruuKJQFGw7Vb3f7ZtK7Zv25SYhp614istxbPPPobT2UQmbdu0iW1lsyHF/NMyCfv9UdoKienw2JbJltsSeYqnPPXz3ZbYNlVu/RS2bdyILVs2mP2Uf1RO255VbuVrg/KzZJvyUHJK0/ZNi4vO2fpavG3+tj5K06TB9BVHeSnOQyOG4vGxj5gsJSjEc5aqthvXrOUixGnR9NZWCOYiw0GcZXful3O4relNGNirK1a+/AK6dWiF7p3bJWJq81L6toxRxL7lLQ0wtF93zpR7Gd07tMZdd7Q15fXeAaA/+Cq48ZYmeOedN/HjD6dh8KXw5Y+FKaD74xBwCDgEHAIOAYeAQ8Ah4BBwCDgEHAJXDAG75lgcLbM02Vvr4yVwVrex/KIsKY/2kn05d9UQUpcqSDD1QTZwzqWRQyWLXu5P99q40hgXL1EKlSpVMXomc80niEvmD2KhQun6sTyvK67IMFmLBXIdMms1pnqky5QBVarVQFgayrm8R/L4xazFtA6Z6u+tP8ZEXXAIOAQcAlcQAW+qwRXMwCXtELjSCOzYsQN33HEHDhw4YLKK5Qd67dpPsGn9Rhw+fJjn6Ff4EkEKbE95TWW2Ly5V1mbPKrUVR4pthaT4XhzvmixC5M/cIwtMRPPHfsxTJ42Uvu5PCp4iXcdJ+Xjl0oKnASnSj/b5TA70rQEUkKKcCZqJxBAbY0kAkgocGEVzddZdu/YgU5bs+JUDpojg1K3ZFFcKeJVzzuLnjPJcWYXQ97MGQ1LIywOF6u6VIfV6mkK4P385ArZ9KmH/fbZWk1cAn50hcvy2umCvh+jZmWueRaElfOLtYlS+aWyLlyw1z9kMzNkmFJQfJ5F5+77+ogO1iWg2ClJBJo7OqY14IXHHHHpWjGzXFAAU6DUikfTyrsUltjFmlxjU7oN9fce/3krdLjycGDnFTlK/8sri3a/6eBHV5nUumn1GpJXy0vvAPx+Ln/qkQoj3akisr62l+oWCZqop7Nl/EFkzZcF5WpSG8l8wBQdZusawX5cqfT3atG2PQOaZQPxnTplI9xR78O7Hm5AzZw58++XXqFurGpYsfQGdOnYw6SV/dwBTJ0/C3v37sWb9JlyTJx/2Hfwct9SthCXPLcYdne/i84pF+QoVUIlWYoO7dURUPBdm5nuNTcAQZBJSUgaRdxJyXHAIOAQcAg4Bh4BDwCHgEHAI/KcQ0BpOPg8TgRx/+w954wJjTFWDLmrZcnlIWLng8mJfGMvKERdeubwzLv/Lw+lisf4s/oYxuljiOi/rMOpVWg9bYPQ/b0ztiyC6+4iOSkDrobMRHB+N12cMRrTEsQSqcAN8T9QntwXHemrdaAn2DC8vn2ZkzwAaklFlQ2H/z7UASbNBYcb2zJBVCZxEa90fynLMyK40V5McGU0mLswQe5xEK+KMeoF46qd0TTqCyMhIc07E1/mzURjctxvdK3r9TGW3IYTrk82e+xTCwsNYCT8lgI3gtg4Bh4BD4C9CwBFjfxGQLpn/HwIdO3bEfiqC9VGW8tbqdfWxnTt/Hqq3nXXJwkkB//HHH2Px3Nn44ovDiKXpd9lyFXDfg6OQO3ceQwxNHPsIKlSsipsbN6W/Zm/Gzazpk0ye/QYOxanTp3Hv4N7o3rMfatSuY/I8d/Ys7u7XC41a3IpbW7c1A56UVhlyizZr2nhkTJ8F586fxXvvvI1ixUtg/JQZiIgIwSsvvoyXlz6HI0cOomjRazFg8FBUrFrduLvbSSue8eNG49uvjyJjxixInzkTxo1+HPmKFDaDqvffeR1PL1yAzz8/bO7t1WcQatera6zanpw+FQf27ub6rSHo2fU2hCTEYPqchciYIcMFeBnFOes8Y+oEpE+bHl269ebCqxNNmc9z8PP6spfpdCIBjZq1xIAhwy+43524cgj4E5eTJjyKTGxHP539CW+/9gZnb4WhbZu26NyjdyI5Fsfn+N7bK/H0Uwvx9TdHUbhwMfTpfzdq1aljrKUmT3wM2bLkwI+//IwP338XRYsVw5QZc0i4TGCbC6UrxQGYNmkMcmTNhZ9//Qkrly2nO4Qw3HbbbejQtbtx1acyKYhQmT1zGla+8qIhY5u1bI2+bL/+4dVXXsZLzz+DI4cPomDBIug7gG20fkOTzpTHRyN9mkz45fwvePeNN3EtZ6u1uq0jnl04B19/9SXOR55HpcpVcc8DDyMrLUVt3wrkSyDwIgNoDcqT+u9EugZNi18jf8Wqd99B0SLXYvqT80x/f335K1j20vM4sG8vri1ZCj169UOd+g3Mtb27d2LyxAn44vODyMR+lzVHdjz66DjkzJuPlY7HB+++gUVPzcdXXx1F/vwF0X/QcL4TamP2jMk4uHeP6X89urQ3xN7sOQsQniYty5uckJIV7GssQ8NGTZCL7yC+opC3YAHUqNcAbyx/GV063W6EnBMnTmLvnu1ocENjkp1xeHvlStzUpAVy5rrGvAOKFS2M2nUa4O3XX8NtHbua90ZsgIQTPoXgMO+dyfemnlkY3VpEe544Eh+RCMsEnyuMxJNuxyHgEHAIOAQcAg4Bh4BDwCHgEHAIOAT+NAJWdtaER2MxRosqT7flWWD93gw435I6Ho9PohjHZTiSy5m/Nz2VS5Nto2nJFUrCKjBIkyo970nSv8nyS1ZvAdS/hVH/YOsjUsyGOOq8EjjxNJj3yxJMVmOB4Qm4lbq86KgLiTHpMSTPi3TzqRZsUm7rEHAIOAT+UgSS3lR/YbJJr11vzx5zLsFfmItL6mpHQB/j+++/35BiwiJt2rTo2bMn7uzRHYMHDaJSf5WBKEDmIxwcaKaPWmCAnWFjrvISZ7KAC5N+tnk9leyV0K5De5z79Wc88/QStGvRHJ9u2YpQfpQ/Xv0hMtO6ysyQ4QBA1jAb16+nh+dAhA4JRM6sWVG+fDnc1ak9Ptq8GzmzZUO/HnfhKEmpGfOfMUrsUE7Zsabyyl6DjGBadG1eSxeFO3aiZp266N69J9dmykklfwjmTJ+FiWMfQ/+hI9Dxji44RCV9y+aNsPz1D0jSVcRtLRqjZas26NypKyK5kOkBKt2/++4Y8hbIR0JsEcY/+hAGDh2CO7p0xZ69e9Hu1pux/I2PUL1mDVSrVhOvvvQc/USnxa2tWiMoNtr4jPbBkrgRkaDBjSxktmzajIjQCHTt0Q/r1m/Fjs2bcGPDhujebwBJlmMYO2ok8hUqjFYkY7TGmoIlKxITdDt/OQIiLmWZtHndRuzYuQM1SZ7cfc99OHzoAO57aCR+4aMY0LePsWZaNH8+Jo5+hATm3ShctBt9fe9B25ZsF6+9x7WoamE9LY32bN+GevXqoHuPnmzzWRDKNcj07MNCOEDtnYCt6zZg+47tqNPwZgy/7wEcObQfj/DZn2c/69O7J0kXEtQcHL//+gogsiH6DRyMg4cPYcyjI03bbN22AyI5AF76zFMY88jDGDhshGmjB1mW9q1b4MWV76FunVrY+PGn2LptOxfnrY/efXqzLFnx2ZZNqFy5Oi2rOiAq8hwWLliMW2+5Beu37uAMOv6XIMBBeQL7Fp1KJpmfWdTlStHsB+LTDz8mXrvYH+qiN4mvrNmy832QgEXz59Dd4WMs1324rT2tUVmudm1a4cVlb6EW1/hq0+wWtGndDu3bdyTZHY29uz/D6dMnkTd/PiyYv4D9biTuHn4PChQubPpd29aNsXzF26jKPrfs5Zf5rkqH9u06mndPGrpPlJ92PUNZj5F58370Rb/34AESkf0QJF/zvBbIS2XLlMXC2TNYL76B+EJb89EHGNinO748doqCSCiJvF3o1qs3iTSP7JKf+iqVK9Bl6mSSjZzFx/rpTWg+/rGRfAnFEI94cKU1vps4G9Di5JtYqDWbAym4xEog0ktUt18iDBs2DBMnTkw1lrM8SxUWgrwYjwAAQABJREFUd9Ih4BBwCDgEHAIOAYeAQ+AiCGhYyimwqVz1DVgl5FNe1XhZXhcCKF9rkmyCJoOlWG/4gkSoG9AET95lxtockisxEy3JUkwZ/PHw5+7+4/naO13+Fol/5tZ4JuFDem3qIFPAc7SgCg4JQhq6JHltSk9EIgRaNSxCGii2cy495gWfXGafr2RFhfAgejqRbKe2TKHPW9/aXPpDf4KCuH49yawQsm0JMVxOg/1FpBV7DGI5q9Ls+7wUSRb3tG5JWVnPMCoeRVhe9jwthVHGvKVRs6SIqew5UiwVUNwph8B/CgG+qHwvMemKrL5I32JrmXqlq3tFiLErXWiX/tWNwPDhwzFlyhQzm8ZaWajzDB48mIt/jqZLsnhMevxxVCpX/rKA0odcM1YGD78XXB6JM1hizHH9Rm1RokA2rP9kDarXqotwzloJ8Lkr9E9Ys2I4AYbXQALgHny4+mP0u+sO3NSoKTauW4v3Pt6A9BFULJOciOOUHVtmpSHXbEpT5c+bPz9mzluCNKEawAfgHG+YPnk8ht3/MO7q2ZekBDmGRs3x1bffYubksbj/4dE4/f0pjHx0PC3LIjxfzW2UKt05siKTx4/GoHsfQP8BAwyx1ahpa3z3De+dMg5Va7yGKtWrIEfOPLR2yYHWbdt5RALLY2f4KKWUQW7lQql8D6JGXu7fChUphunzFtEKhcfEbsumDXhzxXI0b9nGmMqndPOWMj13/Nch4LnDS0BGEllPPPUMB9Je2qd//ImWkDMNMRZ1LpqEz6MYfN9I9O3T1wiMahfHjn6F2dMnG2IsPCwcufPmw9xFS9mmJFzGI4pWgSG0FtPMLbUBzf7KRJJq9lNP01UCW1xCM3z/48+YN2sKevfswYwpYLINFy5WHE8ufNZYSEVxseAdW7finVdXkoi9jR+/QEwY/QiGsCw9eg0gaUN6pnFLEjzHMWfmZEOMqQZ5CxTEvGeXUhzwiNY69W4xaSd4o27UuakVShbOgW2bN6ICrcd+b8iTLz/mPrMEoRKK2RfPc9A/ZcJYDBp+P3r3G2j6RZOmt+IbWmbNnTUVWbJkwJnvv8ewkaNoLZaJdU9A02at+NGm8E3SadK4x9jvHkTf/v0T8T359TGD73Mvv4rsOXIha9bsaMJ7gmmhpdlzKYP6oHkXnY80fVsDAp3TjLkc2bPjLM8LX7mlKF++Iu6nZWt4aDhdPtIvfVQ0Lfg8VxdSCIjYzpghE/srZ/L5dAcp83PHDgGHgEPAIeAQcAg4BBwCDgGHgEPAIfD3I5A1bZJaVuIaF6sg6URLL1OUEHDVC2p4SHhJe6yZjJcR5CXGri+QOPHxMu67WBTpGrRsRrwmbHJfxmDy0EgbMHOL/+TvlGkEcXJmHNdfsMtzpNQ3SV411nK+unlLdXiCa3CoyDfKwbR605IPSiuKZJydfK007fILdtkDrZ/tWdwFGG8pdqmIlOVyxw4Bh4BDQAgkvYEdHg6BfzEC+vBVrlzZ1CCEH+yixYubfRpNXDKIFNMCn9+c+gHTHh+Hz2mlIQIrVlNsmO53J08YpbRcHooQShmkfFbQICEkNAQz5y7CzXWrYuPGDZgxZ7FxpRYVFUtrDlmLXXh/vGazMZ9S1xZHWrpQ5q4JB/bup/XXGWz45CPs+WwXz3uK+yNH9uP4N1/SrVwxFCtWFPVqV0HDmxqhLt3P3XBTY1q30aJn6xacOvENNpGY60+3bwryW/3Nl1/g+LFvzLFKEhcThThamsmITgOclIMUEzHFH9VTA60gglusRHESYF6dNOBKR4LuLK14Ei4zrRRJu8O/AIFytCgK5yOxA8cqVetgzrRpHEDSsonWRGd++AHrP/4I+3bt4CDS+wR8QavGY199YWZkxNECqgwtH0VuxcV4ZIwIo1gtBky3m2b2JQmZClyniuNUQ+yIsKlQsRpmTRxPiyTOcGM/EXFVtHhRDoK1eC4JVd6blu3jfNR5Mwdz5/Yd+P6777Hxk49p6bjXuGYQaXz06OfGNajtV6XVxswdHjgnSEpNHT8GXxw6SKvEaDPjTFeOH1e7/v3EWMnixRDGeqjfqW3v27OP5TqDzes/4f5uMwNO6R8+rH73FUqULI5ChQvhphtqocGNt5h+V5eWc/QEgV3sa6dZPt3bd9f2RBJcVqPfsF4ikMnDG2w0ezU1Ukx5JQ/qX/p5woGeawjTURBGJUuVwLXXFjMEWHCkmaJnrtk/iYIA81XefLW54BBwCDgEHAIOAYeAQ8Ah4BBwCDgEHAL/AAQkwUltZaU+6VU8yc8rnKY8Jqq1qOuhQZgJcZ5IKOdIJsRLqcMQGutdiE28yZz+w3+kIxIBJS8jmiSryZbR0R5BJj2WJpqb4Ms/ZUbx9ODkv5xByutmAqgh22g7R1nX5ieiS6SY7rUybQxnj0rXYEk2s0QC5XAzSZjlNPmwTMHU8Um+188Fh4BDwCHwWwh4WtHfiuGuOQT+YQhoNokU6PoIywezDVrjSOuNPUTXbM8tWWIGE3IndjlBiv+OrZrgGlqP3HFXd1xfriLCItKhetni/Ah7Qw2RCPoA68PvPyOG3/DEoEGCseSggj2GM1ky0qJEQaSYZtSovP4WY7oWKGsQ/gsw1mO6X36U43DquxO8IQHl6DZO1mrKU3W+rkxp4/IwlOV5/5PNWLT4GXyyZjWWLe2MdOnT46VX38Kp498y5SCUrVwNacNDzb3BQcEoX7ES3eKFULEeQKIk3vMRzfpoVo2GX3bmjcplA6FOtl5rENNJIgi92T2apaPxkPGJ7RuoBHNWj/T3mh3kwt+HgAgoPU/xlRo4mvbGNiZi9xRJXjKaKFupKjKkS2PaWXh4BIqXKGlITZXS9Cu6ZZSVktyMJpAcU/9Qu9Y2NJTtk+0w2NfwrTWTLMrM/XSRGJAm3KSjtiw/4v5BbSSWVlmnTp1kYw/menk1zMBVcdS3SpctxzxCjHVUMOsSxPtFAMl6TaFtk5uQv2BBdOnRCyVLl0M69rFq1xc2Vm3mfaAG+ztCCC3kVB61YZF5x78VwRZn+l0Yy2H7fYlSpUy/CyYGq9dtxeKnn8Xa1e+z3z2DDBkz4llag31Ha06abJl+ly4iDLEi09lPS5ctS0vQMJw/H0NMvfeJ+riuWQLQv8ga0Ifw3RVCK7DzZ8+bOIF8oPLH/u2JY0iTNp15L5lnREswbxtjMNNziCMJqqDzcnHxDQm9DGkz8B7/XNy+Q8Ah4BBwCDgEHAIOAYeAQ8Ah4BBwCPw/EfhB3u0p57UZMttM0H5lxkBOPqZ+hfqaW4c9ZeTqFZN6ULZjKSmH/z+C8XTkU+tYXZinI2DZOfMypY7Lv4zWi5DIq98KWorD6qOUn9H78R671b3WKuxi1xVX15SWzc9ufytvd80h4BC4ehFwxNjV++z/tTWfPHlyoivF2U88gQEDBxrltRYDXbRoEYmixYkDhli6NpOinxyNIcqoX0dKrkzXfzrzA/bu+gxLX30feXJnMUrnz786TvIohi7NNPjgmjxUoJ869R3TSyJ6ZB2SPl9Bk4eU+lJ297yzIxX2ZVGuTDkM63sX3vt0HbJmymo+0Jq5Yme72AegsY3uDZE1jvap/w8kqVW9Rm0Epw3FtcWKonGzZkn3+c3EScN11bSmU/8B/fDrr1FofEM1riU0CWMnTuO6QEEoUaIUGt3UwCjIlZ9IvgD6iBYPZgcIwoR0nbH+McRfigGLiC1vlg6JSLF7iUEjM1rAML53nhiRdDt7/lxiDEeKJULxt+0co7tMPS89VQ0Kvzt1nCRYODKQNK1d6wYE0syrWNFr0fLWpp61F+OEsl3G+GZniczScxVhExFB93yGeOFzZsNU2xfPqWf+LV0eqn+pbymcPPkt0mZMj7C0aYxbz2CuR+alk9RmRARp0BxKv6A1a9ZhGwWKFimCxs1bsA15pkzKV4SOiD31zWCWT0H7P54+zbXKDmPpineR95qs5txXx0/h7K+RbHthhmAT76Q8ROCmFvzJabmDEDFtB/Ii/WrWYrlIZJeg1Wmjpk1M21bexqKS24SEWKRJk5auKHtzXbIeiKY13M21K2L+rBkYPWEqAkla2X4na1QF3W9mwrEugVxnUBgLT/uc/Mtp7wkgSVepahW89/57aNWhC4SmcNm6YR0qVqps7lWaOifMzDplTL9clSpY9d5b6Ny1M/s58yUWO7btQLkKZWkZStKSoKs8MZzlp6B7zY8WbPFxAXzzJA/xIuRN+r4Hnfxyqkemvj7MUo3gTjoEHAIOAYeAQ8Ah4BBwCDgEHAIOAYcAPRV5Flmx4IRQyoryyCO5Wy4UYyl3B8Wdp/xIWZCCmpmEbRiyJOAowpkQGOfJnn+VpVhSDpIZpQTgRE1uKR1yV/Zjnp4omGVWMJNUfTdJflSQbJtAfZq/K0TpiDSR1+qjtI2Rnopb6aPMffxj9zWBVfomm4auqzzyYhRCvYLWVpPezk7etkSaR5Bx8ihlZJuWzVNpuOAQcAg4BITA5Wu6HF4OgX8QAlK8Spndt18/3HnnneYjqg+pQgA/nDbkyZvH7l50KyuMsIg0tLYIxFsrXzFuFSMjI3HPoD40uYk2s1b0Yc9La7I3VizDrz/9bBTLzz/3NLZv2mQU+uYjTlJs2uQx+IruCp9Y8CweeHQcclyTG4N7dzPlU5mttcgFheE1EXsK+uhrIJSGFifNm7XE6AdH4MD+/WamjAYbJ77+Gh+98w4O7t+LT9auMR963WdIPFrrZMmcmYr7NGjKdb4euW8Ivv7iCxNHdTjxzTf48N13FN2ELFmz4vMjBxFJ94cKGjykDBqAGKU+8Q3hwCaa603ZEOczQZGvZw1mNFtIcTTgMPdY1sTe4LZXHIFPP+W6du++Z4iTE8e+xSyuU3dzs7bGlWbajOnQrGlLjHlkBA7TfWEQyViFLw4exhq2KRGnamMeYcPmb8gyb3CqZ8sRJck0WpHx1NqPP8AHH3j5/HDqFGZPe5wEVxtD1CiOrM1skJ9wtX2dU/pxtGRLnz4dmrdojdEPsX3v2+tdY/4nvvkaH7z1lnHPoL6pAa9H/sQjIl06jcfx7luvchtkrMTuHdzXlEukdCRdliqYPHxEmy2D3VoSzMRjP4tlmczAnvfH0DouHYm95i1a4bGR92Df3n2m34kwU7k+Jkl1+NABrKOLUvVREX16V2jR4bTp0nr9rnELPDRisHHJKjyFxTH2QfVZuWzNzjX95Loy8hynBjJYi1TtK03znuA9EiLa394J773zJtfuW6fLWLPqfazfupHWcv1oUea5pPj04w/RrmVjrivIdcnYN9t17IJV77+LdWs/Mvdof81HH6Ab1ymUy0uFYFqMSsC43CD8zXuJz9+SXr+1nTRpknk/K329G/1/l5uni+cQcAg4BBwCDgGHgEPAIeAQcAg4BK4GBKSLWjG1B16d1ovyL2VyyoXim5ZP7YblUwZQnqKsyHmNknMvJ0hW0zxRK9tb+V73iiT6rZ9IK8+jkBdX+4ofxbWsFaQzsvdL58Oimp/y8CZUagIoZUD+VA7pibwJ59IHWDJNE1k5WZQV0/UQ7iuuZH/pm5SXtR47z/W1FccGlUd5yVPOOcrUkZHRnNBLgoxpKU39Yui5RUuJKF0Fe96m4bYOAYeAQ8Ai4Igxi4Tb/msQsEpWfTil5F64cCHefvtttGzZEgULFjRWLsW4Xle3O7tg86YNl6yXPtgRaSJw/0OP4oERg1CiUB5cXyw/ataohjS0tImllQWHBBhx/4MIjI/FtUXyoXj+nHhz2Uu44aabEMdy8BuM9bQMm/T4ZDwx6ynkzpaFrHMcnl3yEnZu24m5s2detBzU7/N+KZxFSniKfZmlB9EybPLkmShdoSJqVy+HkoWuMflWq1YG+784jB9/+gU9O3VEnszpUDxvLlxfOBdy5syNu4ffZ8o7Y9YTqFK5PKpUKo1r8+VC4bzZUbN6RRzmvZ6JeQCGDB+BaCr2SxW8BjmzpKGbNrraSxHkZk6Bwx8q37l+FP0jxrGcoTwO4/QcEWca4Mj1m5nNI/dxvKZg7zUH7s/fgkC9hg0x7pF7cW2BXKhYthTy5smNMeMmeGQlCa3pM2aZdlGzWnkUz5cTRfPkQP261bHv0F62Q7rwE+ETH8226Ju1xa0GvEEBbAeB3jlV5IabG2PMg/eYNCpcXxLX5MqBx0aPNQSXCJ4wtoegBG+GlgalQcEiVoMQLveI8o0eGI8ZT8xLat+F87J950btmhWw7/AB5sABbxDbl/qGysK+HkaXovfd/xBGkngqmi+76aeVK1dCWBpZQZHgFinL++RrXPd7P25SDbquxXq9GXkeuc172W+nTHkCZSpVQt0aXr/LlzMLy1URO0jg/fzzWfS4owPy5cqCkgXyoHSB7MiV6xoMu+d+liEWs56cg8qVytMirhLrkwvFC+RE1SoVsPfgHlOKwXcPQeQvZ1CU75DsXFQw8tw54yLRc0XhuUsUgRXPtdo63NEFd/XsjVZNG5rn1LHtrbhv2D1oUL+OIS1FHB796iusIUEew7xVh653dkaXLnegJa3dri2YD926tMY9Ix9E7bo3GAtBFSI2JuGy+6YRbtgu9L6lN49LBhGgEsAU3wWHgEPAIeAQcAg4BBwCDgGHgEPAIeAQuDQCoRSf9Iun7KVfMMVv/WyQZVhCEK3KtOXPBorMlLsZl7KafoGR1NWQSIqJkgwv0krklSfISaYWgfRbP01q1XXFFQGmJTyCeBxOryqR586atcYCRdyJEZOczrT9p1waL0W8V/criMCSnGq2PCddlK7xlJlkqjjSKem6ljJIlyENZUmPjFM5NBlVQRM1VR6VzwZdk/cbFUVBWRpCjGmJWLNlUJ76ueAQcAg4BFIiEEDlFV+hvy/oFr3Yfm/IVrAcvj+64/fe5uI7BJIhYNufbbqJCm19BRmiqZiVBVMQP9D6KNdq/xTefLo3qKLnh5SDhsAkSxbFNx983pNAF2M//vgz9n62HVVr1OYVfuD5gdY6ZZ6VjNfm9+4/iCxZcpAIyGY+5iJ/RNDJ6iRKlh5U5OvY9pFALbSl1HxrNekj7x/86yHluIJICQVZqpzjgEZh17ZtyJQ1J/IXyG2O7eDj+HcnsW/3brNWU+bM6RGgQYTKROW6BkDRtKXfsXUzMmXJhkKFC7PCXFuMgwfrdzk2gbNqmK3ierNykpdPM3KMxQ+xVF1FJMpM3czEISEWyvppkKJgZhNxXwSZdz3JT7SJ4P5cQQQCcRsth3LlLYDHZzyJLw4dNOt7FSpYwAxs40kc6ZmwcZg2HxWdYCyRrslbEPnz5/O52ZOln9fOg0SAsj1rvS8NLqON20S2c9bgtlubIEfuPJg5bwH27dmNsPA0KFyggKlbAEkv9alYuuoL5r0ifa1VlLbWSi1OBCoHrNHsFwqmfWfLwbLkNcfBvE8hhpZf4eEkqGltZdwFcvD7/alfsXf3dlSvWdvMqFO8MLV3X9+KZnnTRoQaCzBd8w/+LhjMDDoz2OY6e+q7nFYXS0JK/VcUmQbeO7duZb/LgUKF8psZbOFhXs/7+ptj2L9nL90dVqeVWVq2eZJNLKMduKvfbd7wKfLkL2TwRVyUKX8Ap/6pf/LFwplwtFbjm0n1a9n4ZtRp0AB3de2OjFkymzhmlh3jyyptG61TtR5bSIBHgGntNuGpchrBgs9Vx8ZVBLc/n49l/htQt14drz/6+jh7JKLORRlyu2iu9Fiw5EU0bdaKbjq8d5Z9r+j9p0DbP9PnW3aahw2v9E1a+NkfVL99Wb5qXTZh6IJDwCHgEHAIOAQcAg4Bh4BD4M8ioFGptFbZCpXDmWT6JG+8atKXoM8Zp7OXrseSZTvNJK14yj6db62A3h0qmCieJJO8NFkLlONo19Nv/fD5Do57lZkv3USVlxvXJkfNHf2VCBw/L/1NPNoPedLIs8tmD+Lka+qtqAdqM3w+r0Vh5bT+iCcf1KzLAry+qEfy7D2x2ZBjarkNWt0nCRHxXEJj1asPmzXDEyTzsj9IjWt1N8kTSTqSDCxCSkFy6OpVb+P0qe+5NENaXF+uEvJR7rfyueT5tatWm2UV8uYvgMrVqhkZX7olyaay9JKu4PuTJ7GVnk/KlKuMrNmzGRJMa6CLCFPfNmSWj/CSpZucv4jIUjlUnlXvvskJvwVQ/LrrzDmdj+L1D955DRUrVEOuPHk8KzPK2Ip/hN5xTnIZlIa3NKQFWjx276Qemt249PXlkirq9hwCVwECcjXa/M65+ORlekRjsF8z9lDfsT1jDv8Pf/QC0y8Qs5/fhMUrtpsyBLLgXVpVRJ92lc1xvG9JIfsdn710E55esZUvCt5LHXXX1hXQp31VLylzh+9P4nfc/2Ty/SSqPfl5d+QQ+MciYAknu1VB/ZWwwbRMUW+IlykWf+TIjNl5gFo7+ww/87olMZhxL+Pxk4vMmdKhZm2RYgpevDjT0XTdC9ddV8LsMAev+9r8eBTiI8EUQV1bgWMCQzqpINKHB/nIL++qZtBwIOAjB6xi39ZN522SFSt5A3r/2UFK45rcuc1P+wkkpUy+rI/yVR3IgaEy1ypKCh6RZU3TQ+2aZayuBiGx8cnx4XwiMyNJg4+wMM5Q8hI2W2HrP7AyafoU4klEW1LObu+vR0CkiH0mYSRYOBYkSQQUK17EZCaLK9Pq+fy8eIoPtosArmNX01cgnmCb8dqOd8q2+2gO0m078q6QFOVacrTjMtxJyZKlfGl4G4qVJn1j9eRrK7YvBZCBtafsWnfBYmUZKtDyK7UQwnKpLIqv8sk6MXPmNOynKjtpIX4EFcyHnbsaCAcRhEi6axRBy03ywOqIOCczxDXONLvM809uyqb0BCADUTJvgIpVKvru5zHzivORwP79TviqXoHB7B++2Op3NWrV8h0pU6/8CSSwTMLc0iU67wmnC8goZKGAMG/2DCx6chbWbPkM6emaUQQ5k0F4aDDTquFLK9RszfNhQqb2zFylVR2Uv7YZ04WgYcP6vnuIix6j4pAgffC+gXiH7ioz58hFC74Inqa1p+qghiHn9X5B5JyEGsXxkPa7mMpuKAk7FxwCDgGHgEPAIeAQcAg4BBwC/y0EYrnmk8beGjfbUbE8y2iEzUG9Cw6BiyBgxLCLXNPpoDjKcAn0jsJmpTWiA6IjORmSLS0mBCGctBrHicyyHIumYGjWq+bURYVYn9omOFYSI8Vb33Eg5T1zHBdmSKcoTvwMoKIngRO+pVS2rVct1wvJSygCTTLyW+98gGH97kQ4ZeeiRYvjR3o9+YyTtTt3720m4saw3bdv1hS7P9uK0mXL47Ptu1C+ajU89/xS4+nlLImvVW+/iSGD78aZ0z+xcr9i6vxncfvtdxi9U3AM5UwK61oiwfQpytmaKB5HIkuTx4ONTisOq1d/hE5tW3Md7+vw4cYdnnciCr3hnNi+4oWX8fiYcVj96Rbj3UiTRE/98AvatGqB7j3vQu069YzMP/XxCfjx5zN45Q0uBeEJxr66u41DwCFwtSPgvUGvdhRc/R0CVxABzXSJiYxB755dqKSXm8GkoYiyDeXHe9a8xUjDtY3EEViS7AoWKVnSIgk47sC5s+fQv0dXRPms1WwkDUpCqNSfM+9phIR7vpvtNbf9/yOgQausoGR5FU5Xg3I3KH7DzvL6q0vI8SnXB0tvfrIQFMf0Twoiw9Sez549iwGptGeV9Z/UnmU9JjJp9oIl5pmJuPKeHS1WRUD6iLi/CmMJOo9Pm41JM2klR5kpgC4YFfxnBv5Vebl0HAIOAYeAQ8Ah4BBwCDgEHAL/fgQ0XqaAwaDJeJwmaPY5kvZt3cYhcPkISHzWvGhNgBYlpbXEQiiXyfuIWV9bpJjIIjazmOgoQxJJvtfPugO0a3lpYqNkXwWRagrSD0iHk0D/iprfSOqN1mJstZpQ64ursxcLzJ6FCsR9d/dGheq18PxzL/A+L82ff/4FRw7uM7dOmzQaO3duIVm1HTmz58KRI1+ifo2yWPb8s2hzexejl8hD67IHx07iJNgaqFeuiJlUrTrIS428tShYb0mmzJSFtdVEcbPGOa8/9/RTqFGnPjZw+YDtWzajUuWKhkDTfPPHpz+JWlXK4JEHRuCxseNNOQf17Gqs0gYPfcDEu4wqm3K4Pw4Bh8DViYAjxq7O5+5q/TcjEETzkTYdOpqPfHy0p4i2RZBlSSDN2czaPGYUYq/8PVtZ4BilOOfp3HpbO7qSS56vyq6BibYu/DMRkNWeCKqZ8xeZgbA3xOZfDir/aoJMec1a8LQZWNOI6B8XNNiXMBBC663WHdpf0J5lEfZPa8/q+6Ei9CgkBIio9pFhOv9XPz+9YpSmnp1IzgRaiMrqUFKT117+cY/UFcgh4BBwCDgEHAIOAYeAQ8Ah8H9DgJKWocAMX8BSJCnaPfnY5wH+/1Y+l/E/G4HUZGbrFYhSGC0RSTrx34tzhlIei/fZg9FCLCQBy+cPAD0t4hfGC6PeJoRkl0glWVbF0MpM2+iAGOMdJFZrglO+42paxhW+UDHqpQCtDk/ijQ1Xc6CN7MdjLvltgqV5vSPFiTcuEE98/Q3ufXgMj8/Ty5HnFSRblvTIUKUyJ1jG4K033sRNzVshT55rjJ6rSNECqFu3Hpa/ugwtb+9Ka7cYlC1bBmXLWe9FdOofxLpqqRJlRllXJJhkUv+gfuZN/g0wrhxXvb4M85e8bPQPTy+ab9ZLj6MVnMi7zCzPxGkz0a1TO7Rq1w779u3Gp5+sxgdrN5FwZD60WgugZZkLDgGHgEPgYgg4TffFkHHnHQJ/IQLBVHbffEszkhcc+vhm8tjk4+lSzaz9ZE/8H7ZSlIeGB+OmRi044Ej+WtA1axkkxbkL/ywE9EzUfjTzKiIi3BROa8HpXCAJor86aCAdyp8meEVH/fXEzV9RXlPGsEDTnoNTtGel/09qz8blJNc3i+GgXYJBEF1lhNLHYnQU1zozksxfgUhSGhJ07Dprtj9LFtG+1h5MYTCadKPbcwg4BBwCDgGHgEPAIeAQcAhchQhIUb9tzzFs2H/C1D6ILuBM4Nq/CoEkKFxwCFwMgcSlKhI8giYgUIQQJydzSYvyJXOjUMk8oKMQQ0gFkujS2tdmXXfK86mJ83I9aJfgMERXCC3ClLmPgbPLjGjpD12neGmCVDn6+VqvdzKVv5KlVdLs+fJg4ZxZuL5YXpSrUNlYeMlSLZaJhnBtsQN7P0O3Plz7jLoHyZKBdMlftlJFzJk5zcixgVQYxHOxMHnqj41Rxl7OSkP102RV1SWRofMrSwLzUDkWznuC629nRaPmLRB5PgpDB/XGjGmzEBRKbJhcHGd139SoORpzzex+9BZzhuuK3TNiJIoWK2Fka8naToPlB6zbdQg4BC5AILkG/ILL7oRD4N+HQOKw1PcFlK/lWH40NV4VJ5V4/W+qmlVuey7n5Dc5ecb/b1LMvzQhHHwRKf9T5tgru8ZaKQqfIqY7/PsR0DMxM6q4FemhYM9didIoL4XY6H9me7DlUxm9dfQuJAf/Se1ZgoSelwQJG2JpVXol+5raiSe8sM1QiEnefv7uN6Sttds6BBwCDgGHgEPAIeAQcAg4BP55CESxSGv3nMJLyz4yyva46FBONAtGGM9rLB0XfKG88c+rhSvR/wuBBGsylqAWwxBAGzHKgJEklzoGlEWxYrlNO2ozbJ5xpbh8ci+uGU7ZMDoezQfNRjD9Lr4+rT8iOQdWlloixWQpFs7zDVuNRJBMwRhigiigMyTEhxvSKTQhEk15PSHgnLeOF6PFkkh67a3pJp5ttUlSqDltZEO16+kzn8TQAT3QsG4dRKRNi7Llq6Blq3bo1rsHzv1y1pBu4bIko5ItiHUUmZU7Z05ER3OCpybucuKntG9xuk7rLasIs/qvi6mWJJuKOIulEm/FS6/g1tZtEMryNG7SFCMGg64dF+OOO7uRcON6f1ySQPHHTBiP8qWuQ8HCRTFwyFDe78m4Wis7MMSHu1c999ch4BBwCCRDwGnAksHhDhwCDgGHgEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BBwCFyIQEhpGoiHQkBm66q2NREW8Jpy5n8MgRRswFlWyqvL7Jbr393k3ERGlnw3a9X5J5+w1kWJal0vr06u9pQyyxIoXucQIshpT0HpdIsUuJ8i7isirBg1vxqZdn+OVt1bj/kfG0Q1iMEYMGYhBfbojOIyEmJYB0ARPWWWJ/KL3EZF2gb4lOHReP4UYLarmKyu7juHIFNf8mJ/IraSfSK0E7DuwBzt3bEGXu3qQbIs262/f3LQFnntuAbSuuPD0yDdg6oQxyJYjB44eOYT9u3eZ88YyjVi54BBwCDgEfgsBZzH2W+i4aw4Bh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAlclAlLtByfIJx3VZ1Tux3FVKIU4WquIGDA+3cwZ98chkAoCieSVJbHoYpA8UVA8CSW5NqJVlTydrJzS07uZ7YyrbZDYisfKWX1wnoTUj7yiNcaCjXtFuiKkt5FYumKUD8VoX7LxlgRSfrS4IkVl4sUlkMRiiPctKmbXxLsYZSTPJdG09pI3IXk6rF2rpvn17NUPD98/HPPmz8KEGU8hNCICZ3/5xRBUhhxLCMCXnx9F5vTpTX4mT5JX7D0IDTUsnln+ISbGrDDGPuWLloiPd6y01K+WLF6I0HQZ8cCwYcYzTjAt7E6cPIZ9ew/gwKEvUbR4fnPDhx+uxeIFz2DZm29j8bwncWfnTvho82cI9VYy82XiNg4Bh4BDIHUEHDGWOi7urEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BBwCDgGHgEMgEQFZ4lxXLAfKlMxrztGjmwsOgYsiYNbRMlc9KiohwbPcig6MQIUSuUiS+RgiH2N1McLKZhAot4W+W95fNgaW6Ir33digxUNeVFpkrVoxSrwUxOvaZuo5XLSpXbiVtZh+MT4LM1mCxXHdM7lDLHFdKZJUJOZYgOuuL4OPVr+HTp1uR1CA4sRhx9atqFatRmKixiUizwfKMo7knyzcdE7BlpssWGJ8c57HkWcj8erLL6F12/aoXq2aIRJ1LYh9b+LYx/Ds4nl4+LFRxqXj3X27oVOPXqhdpx6KlyiF+jUqYvwj9+PBR0Yx6UuhmSxrd+AQcAhchQj8rcSYzHldcAj82xGQ6XcsTblDwsI4KODCqRzI6OMuP8rGdcIl23nyj7P6RRBnAckc3g6a7L5M3u3iqRY3DSCiOUgJoz9nxTODCw4e7CKsnKjzmyFeoyK/8HvXMrpE8gYPv+QTBz72HJdltbt/aKu5T38m/On87cwmXyGs+wL77OQO4DeDZnZdxeFP4/+nn/9vg+/vwkIxf2//iJPk8RvBcyaRFEH5SfCQ64hgzqRjlzcz6RRD1zR7UAsP67qJexntR/H1jlLQe0r7QZxh57mn4BPQLDy+t3RseiPbtPzCR8fwvZbog91rp6Hyy671z+inXW+OlOVXHnGcCRjkc5OhYxccAg4Bh4BDwCHgEHAIOAQcAv8VBDS6D6KMF6BFyzk+j+e2YukC6NOuqlfFSwnI/xUgXD3+EALJtS9mSS4SSRSvmJpUC8fO8hwjdR463VhGLZk6lBZWlLqiArnG2AzGi8dr0wdzLTu6JIyTm0RPbxTNu4NCKNf51hmngZkJAfFnjfwYm8BFyZhPABkzf+4pJFEdkbjj3ej7G0f5T2HwgP7o2KkbqlarTDkwBEcPfYW5c+eiSKHCJt0u3XtieL9eWLdxC6rWqIXVq9fgk/WbsPyN11khyq5MIzIyGr/8et6Tqdlvzp+Nwenvf6Q1Whgypg2lS8QQUx+Toe9PHO99953XcebMd7j3gUeRO1c2/8vYc/Awlr24BCNGjcF9I+5FmvAwPPrYBMq4gciZKxdGPz4Tffvcheat25G8K4sgEnkKxrozWUruwCHgEPj/I8D+qckB5vPquSNWmTzdutd3r3QZ9S52wSHgEPgdCEjJrI+qOqqIsdDQkMS7/8jHVqSYR4BxUMMeqZ9IFp3TtZRBpFiQyZ+DDQ4agn0+nBXPzYhJidaVP7bEmPWRfeVzdDn8lxAQ8SbhQ6RXLBdYNmQVz+k4JJiEObearWdIMRJolxOUhggxjywnEeYj3kSYKei6Idt8ft29cwkI4z2phehYkmwsh4LeOfZn44oUs/3AnnNbh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEEiOgAymJFmJfrLUlHRAKeUpn2GVuZniYWLwdFGS5yQz2hQSLxvZT0dBvvXLJMbZX6J1WlL0C/Ykn0r2/OarL9GueUMUyJEJ1+bLgtpli1H3FITFS5cx7UC0v/0OdO3TD21vvQXX5c+Jbne0xrAHHkbtuvUT01yz5gOULpgLpQrkNOdGDh+I6xl3SP/uRqcWxYmZmrTp/xMOi+fPIyFXCzlzZE9My+6073gXXSp+hydnTsXSJc9g7LQnEBJOx4l0NSmcmrZshVtubIRBPe4y7iDtfW7rEHAIOARSQ+BCrXtqsdw5h4BDIBEBKZz1sdZCn7LCiI7Wlgps38yaxIiXuaMZP5Yc83eDbK3IUpJtshQTCSNluixNYmXW7rM4M1n6j6Auswwu2h9HQMSkxqOXtBT741m4O/9NCPh8t1+0yClkFwkdnlWat4B3kM8XSxhnvGlwryCrMcWTr/fQS/hqsdZlcnchAUPvKRFjCnZx4qAgCg5cADmALi8CaAmmoPeXfNkzsjm2f/Sus8LXEC62vGb1xzh58iQKFiyIBg0aoFevXsibN6+J4yzHLGpu6xBwCDgEHAIOAYeAQ8Ah4BBwCDgEZG342ygY6YuETkxCGmOJFUQXQbICk2vEhKAIoyKK475EtQSj6/EmO1pajfZjJoOgAMVgEly7jMnx/hjQ+yFiEuk33SH/Lbp68SB5Uori5a+9QRkxAV8ePYRvT5xA5UpVEZGGZfQF6aPGjJuIkQ88gs2b16NipWpImz4toqgfC6WeTNdvvrkJTp3z1uRLvM8nn8rzUliYdCnJBWSJo8vefMfmokrbW822RJG8+O70T0YnN3jgIHNOE8Q1sVxmJ2Gs3uJnlnj3UFEjyBY8t9Q7TpGXd9L9dQg4BK5mBBwxdjU/fVf3P4SAUSCLlOLHfs7MKQjnoqOdu/Y0lmMiyazP5MtNXFYdmiGkEc/iBXPMbV279TbnAum4LCXhIiLNc78YhDlPTEN4eDg639nTWI6JJAu4xEDncsvl4l0eApaYnD9nFgmMINzZs8/l3ehiOQSIgJ2RN33SGOz9bLeZ5TbjycWISEdXrSSl9Pvkow/xzbdfIl+BwqhTq/Zv4iYSS0LIp5+swbfffo1iha5FucqVzftKizWL6Jo3ezrWfbyW7TUYd99zD0qVLc/3T5x5d6WU25Teq3RVoTBj9hN06UHhgsLSd999hw0bNmDlypV46623kC9fPudO8TefjLvoEHAIOAQcAg4Bh4BDwCHgEHAIOASSI0DxDPKGuHJaH+p5yAORGAvkWlrR9CayYmoPE1nzJQND5SHot0ktRbZxJPfJQ9kfVQ95a40B+QsVRaEixU05RGLF8qeJm8GchBnPgodGhBkrMZ1PSXKZm1L8sS7+jcUbXfJfKqSssuqlX1gY10GL8e6WzkwyqvW65O9JiShcKgt33SHgELiKEXDE2FX88P/tVdfHUB99kUTJg/dxtUvxKJ5mnUTzdMqPqu6T4lkfdYVYfpjtvj7y1voiiNYbjGbujzcm2vLTHIiP3l+NHLlzkRjjmj4shyw04pmGJcfkCs0uWmoySPHHI7hoDaKREGe3bFj7qYlxV4/+Zt0e1U9Kbvleth/5eJp16FhhzaoPmH9udLlLaxRxNhItx2K5hphngcJjltvCo0GK6qprWjBYIcHky3rxWNYesn4S0aNgFfY2LXMylT+yRpF1ioLqb+YhcQSmfZMny++5cuOQhKQeNfTJ0hbGei7C3q61pHJqX3VPmb8wFdY22Dw0wBL2st6LifJmJQWH0rqOUUc9cA/SpkuHofc+iHgONO0gzj5fm9Yf2XptMA4b16+jqX4IuvbobVzYGUtAgwEtfvimjVYDZNAgV/dERsZgHBeFTZc+PYaOeJDl9LVbTmmymFoMQ1UnPivVVddEzpqZXMRO7cues+3Nrnun/HRvtO/56B5h5D0nPn9ferYv6TmaQSXzUPCPb7FVXA2ERQJ7cdgH/Z6xztly23Joa8+Zm/jH9BfioOfM5DBy5EjOQEtLP+H3eXXitZTPXvfaZ+ZfNllC2cGv7XuKa+uuuAqqr9q62n0MZ5TJdaDqpfZm3VCEsB3FMD2RnDqvoK36huIqxPJZ6brdKk3dE8h3wqP334cIPtP77h1hyqr4ylcYGMtSPl8daz0xPXK1e4W1n3yKXHny44b6tRAU4c2ci+a1ts2b4vCB/Shdtgx2bNmImnXqYtGSV8DJdYjytXPdr/oL48iYALRv2gQHDu5FyTJlsHPLVtSq1xCLnn2W/c9bD7FM2QrInCM/+nS6Dc3btUXJ68ubNiScYvh+UztSGVW/pUtfon/5AajYbLwhxYJ5Tf3Stpm9e/filltuweZNm5EmbdIMQpVJ77eUayTqvAsOAYeAQ8Ah4BBwCDgEHAIOAYeAQ+CqQMAqpi5aWcrEFFe1VryMoxLoISSGSgyKdiZojXkFefegVkh75tj+odRqdnmH2S5fOd6sYYaAKEQxjWBPVWOjX3LryYGUCX0ydIhPN2BvlC5AwdNheXojHdvziq80rCyv/ZRBcvrPP51D9y7tKTMnV0tbHd9Ti5/lZNE0tHhLEZi+UDgnXZZXlKQIBJJar6Rj7tm115KddAcOAYeAQ8CHQPI3kIPFIfAvQkAfTIULFa++WTSymb7MYD/qii5FtVGEGyUxSShu9S23A4RAjkiCxHQwBPjcmmnRU8Yw58x53cN0LkaKSYkt4kRl5y7XFhIpRGLHz7Rd9QsJ8b70sbHecED3eAMFDQWo4PflGSQbeV+Ij00ilajZTlTua2DiEU8kP3yEmL3HHqvMds2yyMhIhPoIOBsvta0U5AqW+BBJIpJKwRAiHLgYEiOOxIHIBnNFZJE30rOEmB04iTCRYl7Kd0uU+W4xG5VL+Csfq5wXMZSIC6FSPgqe67ggHKDyPkOWzOac0tTz1jMOTGWQZiL9jj/KV+WYu3CJV14SkyKXdE6knAhNDfa89Z48ckuIaZ04lSt9xoyJ+IjIU720jp2CBr965lGJxFICiQ8+F+Kr9hjjK78ltCJFkrHulhwS7lFRnhWjnoUl3/ScRdKoHCqn8jPPhnnJVaiOdd7iqLxsnQwJxXKmfN6Ko3MKwoQ3J+an8ui6ea68LheBlnyRe0DF371zF7Jl9XyImz7H/FV+3ecfTD58hqbM3JoBOPHQsWkDbP8qt8py/nykrw6W5GaaXhFp4RlqSGARYiLDArmQsS2/vaa1s6x7QIOT+iw7rK4bfFhGBfUfS6Tt37cPGdJlMM9RvVe4q/766TkFh4eQFGX7i2I5WW+1f48sDUTp0tejVZsOJk39mTF5NA4d2INV63YizzXZsH//ETSuUxEvLFmMjrffYYg99RdhonwUnpg6Fnv27cLaLcQzWw58fuRr1K1WGi8/uxhtbu9i4lSrWRvlWd/UbButQKOIX31xBI88MJSsblTiOy+Wbcw/iPw6fPgw3l/1Plq0aJGIl+KYNmclOd9Npm34J+D2HQIOAYeAQ8Ah4BBwCDgEHAIOAYfAVYyAZMsWdz9pZMxlU3sjJFT6jng0H/ikkXdfm96LwhX1RtQVXCpIVUWVBGVjT+dEzcOlbkl2XTK4tF2SMY3ORLL9XxgkI0pHERgago5du1J+9ORYm4XkRel3QrWECMsgvYILDgGHgEPgSiHgiLErhaxL94ohoEGDgj6Ydj95Zt5gISDh8gYA1nro8SkTkS5NBsSQeHnrjRVMMhjd+w7Ara1aYdqk8Xjn9df4TQ5EvwED0bR5a17nQIGDlWg6f544ntfffJUf73C0bXcbOtMVotI1H/LkhTNHIhmiqJieMWUc3nvzNUREpEHLNh05G4aKfrIhUjYHkxSTldFrr7+Jpc8sxNEjB1G4aDH06j8EderVNcSbyLQY1vPxiZPwDsscwsFDq7bMn1ZLoRxMKP+xjz2CWrUboH6Der6SBGLqFFp/MNw9ZATLkUAFPMvx1mtU+IejdftO+PnsWYRzak2fAUM4qEquCPclkrjZum0Hpo0fg6NfHDKK+IyZMuDRcZOwb98BvM0yTZn+BHHy1mNTeZ6aOw+njn+LkY8+hm1bt7Asj+PzwweRNVNWQ16Np5/qvAULJaafcmfKtMnImCEzfvzxJ3z43jsoVbIYHueCq5u3bsf0CWPpPu4rXs+E7Nmz4t4HH8WyFa9i94EDxCIOXbp0FPOJWU89g/RpI1IlXlLmd6ljtUG1xRkzpnEAG4qBA/szL2DaDLrZDAsnARaIlSteRFRkNG5tcxv69e9n3G7OmDkdn+3bi1ASUbe3b4swjvdmzFmItBnT482VK/jMn8aXXx5F7ny07Bk4DA35/ESuTJs8GVkyZ8NPrP8H772N60oVx5jHpxuXnm+sXI4lT/O+Lz7nQrW5cXPTFujZp5cZWM5fOA9vvfYqTn77La4tVQrDh42gRVFZ00Zm8hmkZZoxJGBfW/4KyxrJ9tgGffoNZR1ITrGO04h7eFiEV5/lSfXp26+fqX8UB7Tvvb0czy9eTDLmMAoULoK+g4ejbp1aiRCK1HzznXex9OmF+OLIEeTKdQ0akUyJpOXT/v17DBHbqX07zpgLwhNsJyF0E5pyhpoINZFB69ZtwDML5+ILkjJxfLalylXGvSMfRv48OU2dzDOYNAEZs+XGLz//jNXvvoFixa7F+Gmz8Nbry/EC8T1KnPKwrQ0aOgJ169Zml06A2vPUcaPxBdtz5sxZkT1HVjw6dhLy5svHN0IgVrzxJl5g+b88cggFChXCAN5bvWYd9tx4zJ45Cbt370QYibY7O3Uw/XjeomcwR20hYxZDkq5csdJYNLZo3Qa9+w1BRBjX+6KAk2AsPWW55lnkxXC64DtvvIvGjZrgmpxZ6Ss+HiWLF0KNeg34jJYbYuzrY8ewY+NGNGjS3PiPF+H56rLluKVZKz7/HAb3IoXyon69+li+YgXadu2OeJJcIl8135DMXOKzsTsShBRI12HRggXsZz/zZctzeu8yuly12veu3cparg3bi52kMHjwYIznO1HEogsOAYeAQ8Ah4BBwCDgEHAIOAYeAQ8AhkDoCpK+MnMWpnEY/YSYq8pTmPku+ik+g3EZRTKKVpkbbEO8T5azEleCzTAuN1T2MFe9NVuZsYHvLP2YrvQYdqKBJs5ZGr+VfMF3zD8GUg/9M4HTUP3O7u9ch4BD4jyPgiLH/+AP+L1ZvyJAhmDVrFvmN3yZsbN2rt51ld1PdWldmG9Z+gn17dlGR3wB9BwzFpk2bMKBvN7z16ivImi0b+g8ajvWfrkH/XnehYuWqyJM7r0nvw/feZFkakgS4DyeOf41HH76PCnHgrh7dzfWUH3ZLpEx87AG8+NxzGD7yEeTImQML5s3F/j2foVrVarQ8omtGWpc8v2gBRo9+FN1JUHW4owv20hLktrZNsGzlKlSvWoUK7lij8I+NuQn9774Px459hVEPefnf2bO7sUhZ/+la5MyVh2RabRJntIBh2fZs35GIxSMjh2HFC0txz4OjkDVrDix9bjF2bduCmjVrIa6PLGQSo164w7Q6tLgZzW5tg/adupLIi2QdduH48eMoWqIElnZ8Bt179ESp0h4BEx8Tj+kTx6Lf0Htwjq4EOzS/Bc1uux1tO3RG1PmzxoLq5KmThgxKzVpMBdj46afY89lOVKlaE9369kfOrBlpRRWDLm2b4da27dGh850kRmKxe9d2/PTTT6hT5wa8/dpyRISn4cDrVrqg4yAsONQMOsln+Q0tL6ze5ZyRdZKIzh3btzJtWv0RYD3j9WvXYC9JkpokTXr2HoTPaX0zevQjKFQwN25s1JyL01bFyhw5SWilQbOWrREaEGfWi1s8dzYmjB6FfkPuxe2FC2MX8by9fTMsf+k11KhbFxvWrcMBnqtUuQbdNvZBruyZjAXZU3NmYzRdM/YeOBx3cM25yPO/4uujR4lFAsaOug9PP70Yg+hKMl/+gnjpuUVoQ+zf+PATXHNNPmzauAE7du2gb/Ab0G/QMBw5fAATxj2KglzTqkXLliaN9R9/nFSfPgPxxeesz5iHUaRwPtzSuCUJsfmYOHYUepK4FTG7Zesm01ZfXroCdRo2NAPeJQufwoOPPoTufQah/R3dcD7yLE4e/wYV2J9k3SRXii3btPXIodCwC0gxPY84YiurtS1bN6BClWpofuttnDkXjcVPzcdtzW/Gus3bDBEoK8ztW7di27btqFKjBu5kmTKSfFU5xz32MPrcfQ86devJem9Hu1aN8OqKN1G5SnXc3rIRmrVui9vvJIlEQnfX9i04eeIkcbqG9y7Egw8/hP7D7kPHTp1J/u6mq8Mb8eJr76Ne3TqoVLUGcr6yjPVIh6YtW5l+rDJv4lpc23ZuIzl9E3oTn6+//BzjR49kWyhCfFsYS0OR4QrWclL7+/Z+RqzompMNNZDWqWpbZcqVx4Inpusy1qx+BwN69cSXJ35GEH0rylLzAMvUo5+3CLFnGRiCytWqYda0KYbbUhzhZ/KJv8AxRaKlptJf98mH2njB11mKFiuK/v37Q+SX+qh9D4vc00+uFkUU66dgyTMvEcp8vvP22G0dAg4Bh4BDwCHgEHAIOAQcAg4Bh8DVikDOdJ5XnY3z+xrdhKRCUTn0x4JPn+pj7L3suX8jRnbipS275lwqaNK0ftZri3f2wr+K44JDwCHgELhSCDhi7Eoh69K9YghI0SoFrJk98xsfSU8t6xVDxg4KZut/wTvtU0YHIl++wnjyqQWGNLmlWXO8tfIF/PD9Scx9eglC6BKsWYvmePnF57Dm47VoT8ssWU9kzZYJcxc8TYsOz1LtpzOnMXPGJNzZ/S6joLcuznxZqRRUTMdhMRX0948cjS5dOzO/ANS/8UZULlWIpJqUy8D52GCMHfsY7r73AVr99DXWLM2atyD5dhxPTJ+IGktfppI5gflnxNxFi5g/XSSSBPmV+T85YzLu7N2DPqpJAnEqUQBJNlmiyIWd1kg7TxIqTURaRNOK6bm5JDSmz8btt3su3Fo0a4FKLEcwnVyH0e9crExvVGrfCCaAM46k3JZbuEN0G3fmp7O495HHkT1TGpN/O65ZpDWtEpjvDSSl5jw5B1NmzUMQlfqvr1yGsyRD7uzeG0docXPmx19w38jH6E4wvbG4QavWRtmeOEiSj2je5x/iWa48efJj8bNLjOWVrJAO0SXh99//iIH3jCJRlt48v8ZNmhj8NazMniULMvN3W7t2iUSYycM/4T+5L3eXIsYUPEuZQBQoUATzFj7DcojojMeuzRvw8vMvoEnztqhZvQpyZMmKjFkzm3Jp6BtNsnD82LEYMPwh9OlNdwkMjZu3xHdffYNZs6ejWt36dKUQTCumPHh6yfPExnNbmcBnOp5k2sBhD5GwGGjus3+4WhbmkXQdzrW7enTrZsi3pk0aoXql8pgxdQomT5lBq8AEFC5UGHMWPkuLMFotxTXGNhLDK2iZJGJMbheFY4ECRSELKLk2EPG0c/NGvLB0KRo0boVxo1nuYQ+iL4kchQY3NsYPx0/iyTkzWe4bWLsQjBs/FsOGj0BvEr3yK65nawfK2XNkQ+aMmdGKbeC3gtqd3EIMH3avWWOLCBj3n/VubIoS+bNi7ccfom79hiaJWBJA1+TJhfmLlyAslGUmviW738l28ogpp/rdTSQpVc7p06fgvofG4PTpHzH8wfGJ7blJs2bGfYta8UgAAEAASURBVMWvUfEY89goPPDQKNzVs6/xA9+sCfvjsROYM30qalerihrVa5Bczm7q0YYuET0SiCN/WnUWK1oSs+c9R+xEDgHbNqzF6yteIoF2qx49a5FAIjAQ4SSvtX6bXDjGk/wPFdHECHLXqHdeTpKpkbyuZ1ChQhWMGjMOwSTFhIn6gnGlynuskCHyPGPGDOYedeVo9p8gko5BbDMEn2Wje1a+c6IDSKyxYHr36D2pfvb10a8SH4WpC6P36NEDbdu2xaBBgxItxPzJLxFlej+L6HMkWCJ8bsch4BBwCDgEHAIOAYeAQ8Ah4BC4KhGgEPUbQeopa/Xlv2/PaSqjNCJh/CVQWAv06bbMgmQ8R5c45m+cL5tgqqVkWRbvS5XifWLQvamowxKva8cYnvnuMXld6oZkd6dyQA9L/kGTiiUvmonQFDxT6ss08ThpCRHKlJSRXXAIOAQcAlcKAWk7XXAI/KsQCKPrOauIlXWCPqrJg/cVD6Bi93KCPsgKSrNQsaJUOHsfXnNcuChyXpOXZuuetYbilShRGufPnktUChcvUcYo9xNoXRLDspQuUxFTJ04gORSDCFpn2HWddK+ClPGbt6wngfULLa3amWMRHMFBwShxXWmjTBYX9NnOrfj+u1P4ePX72LVjG5XzXhkP01Ln5LfHTFoiu64tyfxZVQ0oYqgEL3V9RRz7hvnTZZpcNNr6ySTf1I3xgmXZRIX4jh1bjRL+psZNvfSozJZ7xhKlruN1+ZTWECx5kBWK6qCZPQWLFEPBQgXR9IaquKF+fTS4qREa0CqGIzGDSQ+6oryrawcSb7NMnovmz0HTFq2QJg3zuK4U7y+Cm+tWpAVVE9Sr1xANbmnsKdVZL+Myj3n5W9CoJEGsR6FixU0Z5FIvnO72ipcsjUJFi6BZw+q4kdZJNWvXQ6PGzQwmPj4veSWu4JHWhYshCSZSIG+BQontU6SG3CZGR51PlrvnO5sG/nzAe/dsJxF7BpvWrSXZt53l1wyqULr8O4KvvvqcafGZ8FzxkiVJnHhr3el57KB11o/f/4DmrdomS1sHslz7le31FhKeZv0w3q90S5YqjS+PfmGeo55l/oIFjVVXIC3q1JbkNvEcy6rnoLXyTH0YR/kqqC2pPudpJai2eubHH7F5/ScYun+nIew0w+3Lo0dw7Ouj5hnu2rkdp0+dpkVYO3PMR2zS0Pb3BA2SVY/jx49hEl14HjlyhAQZCaEEfs547ccfThvSVpZRajvXEitZYKqP7d6zA2dO/2DwPbxvh+nzKufhA/vogvMb4nodCrId3cI2eVPDG1Gv4c2oT5ItgeuC7aZF3envv8PaD1dhx7athkDUO+Lw51/gu+Pfwq4Pl7IuiiPyT/h6/ZR9isfCV9ilDAZrEdrylcF7/V1J6hmIANOrTSRW0WIlTJnVBiRgBPj8aaiP6BnK4k0Emn4iBi8nmDXPmLbWUivO98DmT9fp5WLylUR2zz33mJ/Siua6bjaobDaoreunoPq74BBwCDgEHAIOAYeAQ8Ah4BBwCDgEHAJ/HAFJVXZt+N+bipFJjSyZJLOlTMPImX76n0TyKmXE33ksOTVlsDoqfxnSxjFyrU+2lEzppEmLjNs6BBwCVwIBR4xdCVRdmlcUgQkTJpj1a/QR1Qf+QsWrR5QFxHuK4Jodn/zN8ogkEMEiK58AWkhJj6tBQDCVycpDCmhZ1OjjLaU1OTJa/0QmkkMREaGGUND1cCrjQ2mNwQgI4JpLSjtlkJL63M+/kDmKQXgaz6UfMzQLrMqtnq5r7PDjmR94ayxJntrGSohFM3mWLluKSmevblL8m/xJmKj8YfyJrABdpCl/EVtRtLDRmk2qozq84olcCQwIxbmzv/IMbXlYZtXVDDx4T4Bvlo6U4ynJMZFvYcEkJ5mGFoVd8+lGPL1wDtas+Qj9enVledLilTdWoUihArjh5puRJVtOLJw/H41oebNu4zq8NXq8sZgT0Ks/4jpRSxZizar38crSF5AufQa8sPxVrqVW3MAmTFNiqOcdRKspj0z0nkkYn8F7H63Fi0sW4aOP1mDFoN64/94heP7Vt1Hq2mtNWn/XH48U80iBIJKdHqYkvngqmGvARcacNzO+vBgwbUrEVDzbzJnvT3O0G4uylasgXZg3eyqIxFTJUsVIAKYhSeLVIpjkpc4riCj66cwZs5+FVnEpg6wDwWliOfgchJm10AoRact2I3xFrMgSLYiEjMU7kOnHnYvzSDEfySHcjetIki4i8kQGxZ09jx9Ofc+CxKIC3YCGB8WyDXtlK1++jKl/MMm2Eye+YeOLN+4M1W6Vzx+Z/WXyZxto2aQp8hcqhI6dO6Ns2QpIlzEbyhcvwK5HMlXEL+sk4jic6+aJKNLx6ZMnCU8cylWpivThSULB9deXMv09jNaQas/PPj0Pq1d9gFf73IUwukV8aeW7LP9x49i9avXqjBvCshNWJlG6bGnzbGJJEqnvpgxBcg6vNqt3i/DmtLtA1l1tw7pP9L/HrqEWxBdNaEQ4iTzv2SqO8jhMN5eZMmQ0hLbXbjyiWpZi6o+hERH4lS5EPYFCrYzE38FDSJcuHff1PkqqNw8uCHo2aiMJsVGoRfeQm7mWGwvLtpO661rbvv3fw1pzzIbUhB17zW0dAg4Bh4BDwCHgEHAIOAQcAg4Bh4BD4OIIWAlTUpyRa30zf5OWDvPpHi5QPfkoJZmAUR41wuvFs5HYKOVC4lYqAK2DbQTf37rvEtcuc776JVJxlx0CDgGHwJVBwNNeXpm0XaoOgSuGgFW22m3yjDzFr6yaLieIMDKKeg4WzPpQvuMYHWtgwKDrIsvCqHjWuRCfyzxZTJyka0OjoI6JMmMJWZ6ERIQhNE14qtlLuV2lai0xVPj4w9Vo3rxpokL98yOHUKLYdWbcUolraAWSsCpUuCgaN23OfOU+ksp1kmXGGoTl0PjmJBX2st6JpzJadT7O/EPDwhHiyz+M5MXp09+Zukm1TVsOY/mSN28B1KhZF4EkSNZ88D5atWxudOYxdLF2cP8elClTnunyhhT6cFnsiWSQez0p+WWx1ZNrTvXsP9S4ULypViXMnvo43Sc+YeK169gFK15eQiu2z3F9mXJch6m6wUVu4tJlyYBefQdxTbfB+IWGVDfVKocFc57AhCkzPcKGFRT2qQXNaBLRoyDXcxnSpUevfndzTaa7SfjRQqp+dcyle7zps+emdvsVPWdN/2V9Y4hIESJ8Vv7kgS2A6mBIIj67atVrI5DWSUUKFUWzpjebdmEIQBFQJDhlrSUiQu3e1l0EWZUqaisheP+dN9G2TSubtNlWr1XfbFetegctW7Uzz0QnvjtxgiRVZtN2dBzLcqj9B9HiTVaOej52Jpeet9qW4ui52zKb+rAsVWuwHUWEoFDBImja5CZTbqWpoDKrvFWqMw63q999Ay1a3WauGXxSIZPMxYv8UTnOnDqFgwcP4EUSVtfkzGzK9vmXXydattlbg9m21UctvjVq1kvEt3nzRp5VIvuRrZ8IoYg0EejRayDX6RqKs7/8atrRnOmTMWrSDNO/CxcpTpK3hbHg1LtD/VnPQKSYfSY2f21FcotcFlbCgf8N8STXm3qWKYPasuoYR+uv62hBun7dGtzZq4+JJiu4PTt3okKlKuado3qprYmwVBAZVrZ8eayhVVu3nt1NvXVe95QpW167lxWUrkhxrWv48tKX8M3Ro54gxbLLstW+F5WY6pAmTRqsfHUl6pBI86+T3EFaC1ybcervbHvVbR0CDgGHgEPAIeAQcAg4BBwCDgGHgEMgNQTiKKddKkhPogmRF9OjXOp+ez0sLIQeiSRv2jNu6xBwCDgE/nsIXKiV++/V0dXoP46Ap2yWwtn+tN4OFcVULusXQJ/GmiRDTstYTKWEQxYaIgB0v0gfKbsVNJCQMttaW+lYl6T41XmRU5qxs/aT9fhg1WoSXWE4feYnzOP6X7d16GQIBKUjJbf/Twr0kLThqFWnPmZMHodzXOdLtlwL587D4X2HjIJdJFyW9GnQgmuKjXr4Aezae4hxqHhneoePHMUHH6xGnMrAfx9/vA7vv/sBSaoQfM/1keZMm4DbOt5h6qQyXpOvAImpV+i2jWQH83n22WewfdsO45s6PcvRnHmMGzUCO3Z+Rtd58XjogXvxHS2XPOJJNUgeLMkgEmD/np3YsHEzAkJkQRZAQiqSREEsMmXK9D/2rgNAiqLp1m26I+eco4pkkJxNiIiAKGBCEFHMqD+IOYMJRcSIAQEVRUURFbMEQVBAogEBAUU+MZEu7e7979Vs3+0dB0dUQjXszc5Mxzfds931uqqUdGC7Bw4aLCuXLJGpk1+V3uddpJkxj59+/FFmzZoJ/R3gjEdF/0gR7EgiMcFn4IdGEjV+cgZe43MiGenM+q356QeZO2+e+kGjID4dGn5RaA4VL1YM9YpI2TKlZPXan2XLjlRk55mUJAnitKdylrG35yQKnMCfuJHA5DMMkjzEQ+M17TM8yQxRKV2mpKxdt0G2wvcV8StcvKg+j/vvuVG+W7Ua7cArGv3q5583yKwvvkBKEKNov6s3+xK/5y9aSLqd2V0euf8OzY/POQNkybKV30nh/InSqf2J8shDj8hvv2+Fzzi/TJ36psye9Zn0G3CJkmEJ0E5iXyZ2SooAWwZtF68r1mgPxoorn32LRIzPnwFtpHzSvVsPGXn3MFmzDmY+QZ6khRNk3fpf5bNP0DfxgEsWLiA9epwtI0aMkJ/WbZT0DJQFLarly5cooVW+dDlZu/pHEKSpqLv3bFw7tTJxfwoVKSKBpJBMf+cN7ffbd6TLjdddgxhubFLrEUQeyOJ4oiYR9ewGknnkncPkhx9XSxoeRwTvh183/U8+/OADWfXdd8Bljo4TamCxH5GQLF6iODTMgtIDZiBvv/UmWfH9Kk0bRhvX//obxv9nigurWBo+xtauWaXjmn75+MQTgB/7hw91Yv/VMYRzjzhiqhjWuKaEM7CFVzq5ZOBAeW/GhzJn7nzU0y8zZnwkc2Bmc8AlgzWPLz79SPqCCGU/ofYe+9l5F/aDttsMmTN7jj6HGR9+IrPmzgMBfSU0PREviMGGwOeITBQf13cxVDID33dVKlWVGe9/LMfVrsEE+HjatF5f5vPPkAIFCsiwYcOkfQcQn3huvMZ+w6O+95AP83efzALsiyFgCBgChoAhYAgYAoaAIWAIGAKGwF4ggHUWlnP8cMXurdqzJweVpfdJjukH626uk7Fw3v2HvsAYJ3bctiMNa+Eo1pqe1RmuY+O/u7V6/DV+Zzx+LBgChoAhcDgggLeeBUPAEIDc1iPAYC6Mwn8XSJhlantA6EuygwJfF7hj56RTTpXbbhwix1UtLfWrV5Cy5crLLXfdr9ojjrhx8d0xBI2zMc++KMk7tkvtqmXkuCql5M0pk+VE+Pqi6UYKmKMQjj/6+PPSsNEJcnKbRlIH8WpVKCEnt20qy1cs8+IhLsu//abrNI8GNSt65d8xQutKn1S33/kgJkYZUrd6Oaldqbi8N22qdOh4sgrpw9AOG/X4M9AyaSJdT24ndaqV0va1b98JZIjnW8rV2R3jzcVFkb5/3x5SuVQRqVOltNSrXlYqgIi7fMgwF12KgZw6HaQNzatdOOBSJUFIfG1F2y+9oK9UL1tUjq9SQRrWLCVly1aQq68fDm0cth/TNz6YHIEmLgNKOIH+ARnA+mzdtlUu7XeuVCheQOpUKiN1a5SVipWrylX/d4sK5y+/7v9QfpocV620lCmaCIJoMwT1yP8AvAGddhifmTNBScIoCLOJLvCcxAB94lG7iKQSJ5JXXj8UfWAr+k4ZKVM8STZA+/DRsc9K/YZNpVO7ptqOYyqVlI7NG8jKFUvRXvjNipFXzJttp7YT+9mDjz0jdeBvrkWjY+V45FetdFG5/67btAqPPPm0JEGLscnxVeR4YHD9VZfJzXfeLe064jkjP4/MA+boLy7PBOTNOjN/EjlBYO2CEjsgeag9xGfBfjZqzFNa79ZNjpNjKpVGfysr7ZvXl9Ugu1z8h8c8LbWPqSNtmhwr9aqhjmWLyagR9+jzvuzaG5TMPK5iKSlXqqj8b9Nv6qfPlemOJGSJ4f8NvUVuu22ojrsGtSpIsxatpEDB/BqNbfLaAiIIcV27eJP9vR76ezuMqWMrl5baFYpL60bHyw/fr5AtMC16yQW9pUrZwlK3Wlmpi3FZvlxFuRr9iHk88tizGCuNpWOrhnJMxZJoJ8Zj68aalm1k3a4ddrOSabXLl5AyhZOgrQkzkwgeaYT3DHBl//f6Cwkjva3kI7XbSMQxsM+c1fdCubD/xXJWt5OkFsrq37uH/N+wm6T9iZ20D9HP2ueffqx9O0ISEARc77795PyL+kvPM0+VWuVLyoC+3eXGW+6Qlq3bqWYlLSKSAOOzxV8ta3d/SpcrK7MWLGECObnzSVK5cmWYS80nlSpVki5dusi0adPkxhtv1PyUDEM8bVvsuLu87Z4hYAgYAoaAIWAIGAKGgCFgCBgChsCBQ4CkFGUNPHLd6eRX3LxIf+jems2TAbjvLJ0WTRgnEjOhrz7qkU8iZBCUg3EdG4IFEwZ+12vYeJnb/VAi5WgekaYJ7I8hYAgYAocoAgl4EWZJ+fewkt7LM2+BWs7sileuL3+ug4DNgiHwLyLQuteT8vbzl0JlDJMDypypPhYXOGlQoTaGAn/g6UeMk4CcgZMKb3Lg3fGE/V48kgc//rBSgkmJUrVydSVjKCSPJ5Fcfh4pQh9LnkbFD9+tALkQlJq1j1XBegSaKqwHJyUsM5F+qaDptGzp19BcgSD/2FpaR+bHPFSbB0LxVT9+75VfsYYSSvRk5QdBUBD7iEjgLVq6GBpK5aQctKeYv0ufjglQCKRHGO2mdlw6dgi1b1Zfzup9ttww7BbVmtG40EbRYwQmI2PpeeT74LfffpUfVq6QZk3gfwmaNY78IWkEPSPpdQYE+zVryKixzwB/TsY831rE83eYeVwBU29NG7eUAkULwgykD9pj8HkFIT/JiJ0C8qQmCoPbpURzdiTJfv1lHeqxUlo0ayNJ0FBi4LMhRgwkAyLQAnLpeI3PcW8COMZsgaQSQyZBFiNliIvnDysrf04eAyGvLko4UWMnDkv2GT5TxQ39YNGi+VIOZGGVGtVBmKRDm8ibiLI8bQv9x5EkRFnMj7gk70iWBfO/lMaNT5CChQtn9jPm++fm32UDMKpfrwm0mEBgoGyWyeC+uz7l+reSY7F6Ecd4vEjIsY0M7plEoDG4dOlCKVy4mFSrUVPr58rQvNGG5OQU+WrObKlbv4EUBnFKMhqPRvsFjwzsI/FleVezyEy2NxlE1tffzJc2rTtCsc4jtF19GT/nGHT9lViGsfuN+JaFphrxde8B1pGmR5cvXSInNG6h2nguLx75DKLQvpy/4EtNW7laNW0703kEqFcP4sLnSFOa1AxjvficYAjVe49E0/RaGP76GHp16SztTzpZBl48CM+tEJ6n56eLuKbsSJGF33wlLVu2AwCsQ9YzZ1rGYf05npwpxtTkNJk/f7Ziw2ftQgawTk5Ol7SUVKldrpi8+OpEmL/sLqlCjTNorOnOQsaO9dtIOnzbBaXzgKfly8lXZO5KZFu8Pu4WV1lluLLsaAgYAoaAIWAIGAKGgCFgCOwvApyVcm1QslpD+Wvt4rjsstZZkMJzQSNPvDJXJr3xrc5To1jrXNijsVzWt7GmgR2MuLTe1xJVGmK9ivUh5sF/rl6MOTWuY42mwa1LkO6pVxfIxDdgoQQViWJzYL+eTWTwOc2zxfNO7K8hcGARYG/kip2SgObnjJVpL1yRo4C4cYA7lCFxfU3ZAzcaz5gxQ8pWqCSN4BsbpzqW6NLAyVtSIP+Z+ckHcLexWeVSTU5oKdWqVRYfZDHLly+VLdiE3AKbUF1wcjGeL132rWzftj3bfZSqa/QtmzdJh1O6SJCFWjAEDIH/BAF4v5Fu/Z+W2a977jncryA1Sxly+138dyuKesR+v8dO+kpefGuRVy9UvF+PJnJ5n2Z6Ho3J8V19n3hlvox/6xu8HPnbnyAXndVYBvfBbzJOs4U9eP1kqcZkS2knhsDRhQCFyhTvkxRTc3ExsoTm/NJhko+Bkwqqhgcxbh3REk+6HHt8PdX2cAQBBei5CfYpPPfB7CEFzxTUH1OHExRvtHqEjaeVxmskxShcDyUF5ISWrUFceRMdCuHTsZPHI/A8YsKV76NePUIgSBKDaux6KnUbNPS+oHxeZ6A23KKFX4MEWCwdTzpV/vrrLxn39NOyGZOYs/tcIKmpIMECWZpCTBPfLkd4lC1bXiqWr5hJ2DEecUiHWby33nlN5s+dLY8+9riSOwGYQeTCxovjg0ZZSemAsokVdxXtSPaeAUkxnufUGnOkgMYn4YBySIoxlAHJUbpUWQmiDGre8B7xo580fif5F4D/NebtYa3J9uuPIz/Yg7iQcmReInZJgX/R4BEIfhCSeC7oa+xnrE8IpAgxZv0dluwfJBYDIHJbtG6ruOhzRn8iAUifbkyPTqT9kNfYj5gfnytNUbZt10nPWbjDKwqCo1iJElK8JIhRankBn3AE/rGQL0MA6VPTQcrG7mWl8/q/szHOuCyHY4D1JI4eKedDe0DM4LzRCS203rynBEosvsOH6dp06KiT8RSYEs2A5+B0PH8+a37cZF7HG0GLCxwXxIrtTYTmUpu2HXT8uOvExdWJuFIDk2SRG7PsN2xzQkIEvv6yJvgsQslBLDsKFy0u7TudnPVMUH+Hk455kJvNMR5JDLIemlZJQ2pLcdyxv+GDscjvjiTjEfpr2r58MHUZhelQEuGseyGQmM+MHS3PPz5Kvlz0Hfx2JWU+i0QQ7m06dJJ09BX6E2P73Rhm+XxnsY0RmEl05GUINuHbtO2Yee7ah+JgdvJKmfnxh1KqbOk8J0IhvIPUHxoEAG4SxbxYZ8Wczw2Yk8jn0YIhYAgYAoaAIWAIGAKGgCFgCBgChsC/jwDlJ44U42bomR9/Ihee3UNqHXOMzF6wSNehIaz3eS8N8oD333lTbh4OKzbbtslxdRrA/UYyNnwPlqZNm8srU96WRx64V7Zs2SqT3pwGbTGsOZGGsglaaOH3x0fdL3//+U+2++OeflxuH3aDPAyf89lX8v8+HlaiIWAIGAJ5IbBPxFgGBGIxuXZe+WfepyA8SoEerpjoLBMW+/KfIOB6INXHQIaBvAjgh52C7AD6KAXnFMxr8KJAiwnmz+CQyBOoO/KDXoAgDAZD7YdpRI4JiMklBAHyjq075JKL+3l5xP6SIGDIB1X0UWPHSaFC+YS+ndLh14uydQrzU6DFweDHhSSQYSkpjvwCGYE4W6E5kkgSCAL2ID4MrGoCNDqoCcXrsZqLX7+AFCCz7snuNT41RuhElZMZ5g99evnggxky8v779X7Tps1k/JvvSrlKlYVaJwMv7K3khhN+ewQEtOdQ/BPPToD5OgjwQYhshxZKUAkaD1/uUOrQ5gSQL1G5c8TDUqXmsSpIj8D8IkM8AUnM1fwdiK7t/yTLFYMuILIajywaTQiGMfHi9xcmvAJChEQY7uM/USX5wEAc2FSHBe85Ys99d8QVy9yXEOMds5LifZjpBJdZxrBOhUaSBp4jju7IwPc0bb/XNn7PfHax9ibgucS3iY300nhtcenj28z4GmJ4OFx4TbHAkeW44PBR/aBYNVOJB/qPu+fSsXyGzPbwHP89+FBXpme/wzGFTrsyQ+wez2P33S2P0PXGESfYDJnX8IxJjqVsT5fBA8+VNPbrHIHmNJ95boL44DjQpfPqm71MXuMO0KzymVdWnV22vOrh6d0P0mcerrlnkdVeJvfaGkVH8IeSNAvdjadNR954xqkgIRPgM45jPgHjj+2h5hjzpPlShmQcAiDIQ3gHIJq89OprekRUDeynrL8vRlymov8HY+UxAt9XWi46XArJe1wgEaeElfah2LPgdZxz4RLEeyUCovjhMU8oKak7BZFeqU9WDsF7b+DZsACEVByj1C5MT3VdW6+7P44Mc0d33Y6GgCFgCBgChoAhYAgYAobAgUKAc2SVJukk3Zu7J/i4WMd6GnfoL5lzWsqcuEOebgn82HznxyQfM2qtBmfHseR6zj/pjI8jvXGHvWjYWOd9YX78xlW3j/lEvXVLFH6Y6NM5c3Icm3Yj2sEJaMd+BfqM2p9g5e8PeljjxRZ4sVy4XmPw1orZ78WiZDvw6fHj7bnNepbOkk1aTKrLfu86JYkvyrleeGGctGzTXr6aN0e+mvulanbRwgvrMPPzLyHr6S/9B10kd498GJtcvU2vv2/aKDcPvV6JMG66Tk1N1vGVgY3Zidh8rZs0sVYNYsyFU7E5E2tfui9ITY3I4/B3/8ioB2TsuBelW/fuOm6wGGXFLBgChsB/gQDfCxnxyg6x3fuqg/pfVChnmXgHxl5rKsuKvR950E3oma9IL5L7NcygEB0ff5RyKi9Stt/3zHQ5y9v5PPYK3fmGXTEEjhYESIoxeIJxr9VO2M4z1WCJCfxJpuh8A9dJZHEyQOE0P848IQXaIWgs9ep9rk44UiFQpk+eCITXPhBINJEWgjYI05Nsowo7A9NRk8YRRmmYWLjvvE8hNrWF9pXQYR4MjhjSeoKUaNSoqbzy1jsgA7375NFIDLJ+1Bo6u8953o24vySCfIgYgN8qBraDpBgDsXBh1jcrUB4wBMb63sKR5aomGPLgkYFl8TqxTiqcX/pcdBHw8jRpSB7GB9bJwpGNAMdSODFBup7VCwSs18dci11/4Fhif/mvAst2YzG+HhyntL3OqsWbqOA5PxzTDC6tG4/uveKOrl3uPt9JLo2758p1R97PK73TcOMY1u9u3LNyuQRqlfpIru3ifi5J7JIhYAgYAoaAIWAIGAKGgCFwQBHgil2XjrEpa4y74tUDWo5lZgj8Wwjs6fqKGz1VE4xrspjsKq86UiaTjE3OH777jjwzaYpu0nx5/HMwzQ+XE0m0iiRy963DpGGTpnL/Q6Mhi/FkOLSOUrJUGWyAflGLYB3zqQwKshmsvxlouSSEjcspKZBrcYM4BibXoffcPlwmvvisvPjSFDnxVFhfSYO1E8husjbcanL7YwgYAobAIYWASZgPqcdhlfl3EIhJgh0tjf1e+xKckDpeKJ1JqGHOcMaZ3ZQsc/FcGTSRF/NnqoRQKkygUZCuZBEmHoxPoggziJ0E4TSh58pz+e3tMWd6lpWCnT7cZcd7SmJhckM5eACTn67dzshWhEvPdKmpadhCt/NrxNmrZpsSY76fODlTM3TImMQH28kj/L/qIsctaRJBhHU9vZuW6YizIHYm0aSlKztbhezkiEQAw0R6nU1txezNY79hHyXp5PpR9hj/7Znr+7ArqBVxRDBUtZRwdu8Iks863jjQ/oPgNMNc0Tn38WVkShv27f3o8rWjIWAIGAKGgCFgCBgChoAhsD8IFK/cSDdpJfhCUqxKY6yVRf5csxBZervg6eqAS3suG3zQ5opAw4n/MjDBTfdnbdrcVR1oByYBm8ECsYVHQkwVx1ljYLoozLBLgmfdJQPz/Ay/913z9Kb9u8p+v69j1bBfecSWJfuch5W/f/hDwpMN+3hNMfqipmWR3YV0uE4IheAqApEysKbMK+h6GWvMSRPGS/EyZaRL51MkdetmGQbzhluSI1IIWmDb/tkmK+A/bORDj0IuA51KyGVIjnkuGny6IZsbp1nXNJTPDZUqp0I8fte1LMcBmkYf9ZcNukRmf/yuvPT6VGnXth2uQ6aFgeoRbvuHX17ttfuGgCGwGwQ4/HwpcRF2/76Ji3hIfM3rjadyK5Wp7ft7Zt9THhIQWSUMgUMPARJGFI5TaM/xyfP4DzXBSBJxYqE+zRDPTV4c8cNz1cqihhVmG/S1xDwpVHfC9H095oaY0/biBMdpbvEYL7x35TE968Q65lOThtlzZLsdGcAj42W2B4AwT7afbWL7SXiRHCRGDPFY8ZzlOj9vTGPhyEbAkV3sYyS/cob46+xXh1pgn3eBY5b9mW1icGYjeY2aY+zbFgwBQ8AQMAQMAUPAEDAEDAFDYNcIKJGA+bOb+2eaTaJU3oIhcJgh4DTFSIrdcustWBMGd/vJBx/p3MRMn+AcC3sSuGZ+Y/JL0vXMXkq8ndm9j2TAIs+Ul1/U5AsXztdjiVKlVfuLBBaJLJJkHGf0g+4C/XbzOtew9FNOP9u64Rlr3QgIsAVzZ8nU1ybKmGfGS/PmrbWOvO+RaXtWX1eWHQ0BQ8AQ+LcR2FnVYw9qsC9sGl6vsPq4v3tN9qByFuWoR8CJpUEpKRacPFDjnC6q8HuOScfBhYgTCffz747xJcYLw532SPx9951kFYXsSkjFvrt7B/LoFhhaTg5B/a7q59rg/IXF18flR2KPRIASXcA8hIkUs/fheagZt7iyONFiIPGV5QnLI8V43ZXHHUwWjmwEXP9xz9ydu1a76+78UD2y3m78cwwwxGu5cbHCceBI6UO1HVYvQ8AQMAQMAUPAEDAEDAFD4L9EIOjDZjnY54ftFKzr4WkMGi/FqjbNdPHF2zQxl57BzacwOydJupEzn3+H3PmVT24d5m22U8E/5uABEBE0Fx6Fv990CPDpg5sC/jLVG3nNRD4MLIdL1nQcGRIysFLlhYxUuW3uOLntOmrRQNsGdTqYIbqfPr58++ljzMp3EqZ9e8r+mKwjPjVJI24SblSnVvzlXX+newn4n+Yakv7zGJxcNiPmk9utPSkzWbFsiSz5erE88vjzkg6fYnCcJ13O7CmvTRovl1zcX/InoS+Hk3VcpaSkoA97mmCq4YX+wrHCQBLPkXGO7KLP7BDiu3V6g8ZNsfE5LDdce6VMnTFTKpUtkU0DzdVLM7Q/hoAh8O8iwAEITWoM9li57s2R8/zfrdaeluZqmzM+5f3U9D4Q4eD+gh+IGloehoAhcFgi4AT+8QQB38lOeyae4EhMDKkWmUtzWDbYKm0I7AIBt2hgn4//bkTvLgCzy4aAIWAIGAKGgCFgCBgChkAcAj5usgRBxQ3XENfL5rVf45wbKLMipUBG9txri+WFN5fpxfzBHdL39Hoy+NyWWZFi37A/TWWFJao1g5lEuhFIkP+tWbxTPF6AOyZ59rWv5aU3FutcPhCIyLndGsoVfXfON9cM7KIhEIdABjpUAiWx6Ls3Dh0mX3wUdzO3ryRjSX7hQM2xvEIadoRPevE5CQYT5Pbh10lCFL6+QBz/snGT/Pj9Klm7erXUb9BEQgUKyeo1P0kwMRG8s0cqu/UpCV+3bmV5JPKcpib9ivmgLUaZNEmzgoUKyVMvTpa+3TtLj1Pby3vvfyily5eHfzEQ0LQAk3eV82qS3TcEDAFD4KAhYK+ogwatZWwIGAK5IUAziiQIdLcTJlIMvOYIs9zS2DVDwBAwBAwBQ8AQMAQMAUPAEDAEDIGjD4FIQpKkQVsLzgqwcOQOcUrkIW8Pk7KiAB+X8ZW+q30JUUnM2CL5E7ZJWiTV8w2m1JYX1+2aJ6GG/5DZQw8tnAJvZemZmjGauRJwHtbQrZECcFgWSEhD/BTUBS4B4PGJBMe/EWIt1v3++/J9f+u4L2XGpznqy1cGCYjEjgkBfE+Hr3acj7z3HshF0nf7SYM2FkmqtJhmmA+dlx+SufqBNlgCP7yGTwSs25TX3pA+556Hz7lydt9zpHffPnLFNddI1arl5IUXn5ZgvkRp0by5vPnKS3BrgbogkOxiSIAG5I6UNJSJsUUNNchqIhh3jjxLR32U8GJc1iEjLCWLF5YpU6dL6ZLF5fSunWXdxs3ePdTHgiFgCBgChzICSow51VhWNP77oVxxq5shYAgcngiQAPNhxxLJsXitsfgdSYdny6zWhoAhYAgYAoaAIWAIGAKGgCFgCBgCBxKBv9YukD+gIRaFOcU/1y2Wv9YupGU4MGGeASRaffODvXIieAr0c/NV7OoUhY9rRqYwzIfE1JLx+3djTAkkXJh+lVC+C87UHHg1C4bA3iEAOYiQhOImYWhV5RV0UzEisYf6YPYzr/D2m1Nky99/yQ033ynde/WRvuf2k169z5Nzz+svZ3TvJdPemqKk1T33PyK//75Jzut5hqz+abWSXfSDPfvzT6VXl45KsrGsHTt2yNat/8iWf7bg84/s2ArSGeYX1YwoBlIAZhXD0FIrWrSIvDr1AylevKT06nqi/Lp2rY6tvOpr9w0BQ8AQ+C8R2OmtSsbfgiFwNCNAf0AkaXhMS/e2gYUwYXHXchI41HxiIOHD+XQ4jfuj9j24cjkWc5JHzNWV50qIJ5dyu+/iMV1iCPbTYRM6r8A6UIsrNx9IvMfADU9eyPziLuz2SPw8xLKi5WxD1h37lhMBmgzZ2+A2PLBPZWAX5e6C2udHv+cE14/FJh3sZg/e889+7cCd0c8cTUSwT4TRV3bl5+7AlXjgc+IYoc141t2NJZbixm78uOb9rHcL3h/R7OkO9Njw7+RjwOsPkZjvAuhzKiCB2AAP6LsQdcfOQXY9N0Ogs2gS3G7OkNnHxNtxqJnk8ieakJjLVbtkCBgChoAhYAgYAoaAIWAI7BoBzkE5jeXMNWs55M1M1YcTNFM4i/WBvErwQ9sFa99wQn6JBHaee6pmTGxiG8HcmyIw+khSfyW8HtNy4TJf57q4nwYewxfJWgclQXstIehq4o5IexACVkb7mevu1395ZW7l7yf+MX9d2XDGWntfQlQJWq+/uVoFs/hazfJlaIQ1b9lCylcqp9xbAH2Vsh2BpuMllwyWMY+Oks8+/FBOOe00eeut6XL9/w2RFg2Pl8TCBRElQ4oWyi99L+iHNSr95yXIN1/Nk2MrlsqqboJfTjyls0x67S3vGgYKSlASrFTJYjLl7elyzhmnyNk9TpfJb78v5SpUykpr3wwBQ8AQOMQQCHDC4ARbh1jdrDqGwL+GgBNeU3AdgrAXMwAVVr/47JNah0suu1IF3LjsWW+Iq1kAu32iUC3nZCMj6k3O427v9VcK1EnEjX3sIUkK5ZMBgwbvdR7xCShYH/Pog1KocGG5aMClKpx3dY6P5wT0/gBIMZBnSvSB7GO7mIcT6nMKR8Lwpeee1QXHwEsHH7YERnz7j9Tv3M341NhREgqEpD/6ki/oTcLVeS76OgkOP22Eow878wlPPf6IFC5URPpe2F9CoZDec8//YFtDGPfUY9rvL7h4EDaBgowBMXMoBkfa8ehIIfrIo013ji+OGY2DyseTW6oxifdIKpwg8zqfxuhHRsiyRcs0n0eeeFaKFisk6djJGsSz4oL84w8/kF9/WS+Vq9aQNm3bKxzMB8l1N2xmWXhBkaxiYJpf1q+TGjVqS6u27fQax/gzTzwuX82ZLWEs/q/5vxulUaPGei+3P+w7QdikYZsoLLj6qstlzpx5snHjRqlZs6a0adNGLr30UqlataqXPyt0cOUCuVXTrhkChoAhYAgYAoaAIWAIGAKGgCFgCACBN6Z9pDhQRsXglmfUeixXoYJs+jMZsh6s8WBCsU6DBjL1g89V1jN/3lypUr2GVKpQVkkxmmV8ZuKr8sT4VzWfnH98oKmfmfAKZGHpSorR1GIYn6JFi8qML+ZlyRlyJrRzQ8AQMAQOIQQCRoodQk/DqvKfIEBhsQshaMjQ7IJHAvhkwdy5Shz3H3Sl+LDDhsSXkmeJUBeHZhhJJAqwVRCOTDj54H0KtCn43hfBvhOiz5m7QAom5Qcx5mq3b8conBTP/vwLKV2unFzY/zKdBNEKREJGVrtdzsSC7UkH8RUEOcd2MVAwTmzYNhJlfG/Mmv2l3iMxdjhq9Wjlj5I/ixcuUGJMZLCaSKBWGB1Ykwzzk8zAEX/RgblRwidffjpTSpYpLf36Xwrb+XjemBT7MKPm0U2sDxZ0s+Z8JflCSUJi7FAkxajNxnEeAGLcPRkijk59EuDcf+/dklSggNxw3Q0g2TFmMNiiIM+IN7GOpMGGPI75EwOyPTkNufhl9mczpXz5itK2YydJCiZKOBXkGtKCi5LuXU6V71Ysk3oNG8o38+dJhxNPkedeekWfWQCkFZ6YrnZC0OhiSE4VORc79FauXIKFTiNZumixtGrXQV54aSLqHZYGTZtIsRJF5Yr+F0uP8y6SBg2xww8km7f/Fn/dayG285BKs5Nffhn198vjzzyNl0FU3w2//fYbSLI58uabb8qMGTOkatWqLB6dZPfmQFz2XmT7awgYAoaAIWAIGAKGgCFgCOSFgCfg50qEc0nvLCsNFbm8VSvvYYYeKaSCfb8/Xfw6T3cpOMGNX80gLta3GZibM0SgCcOAba96tD+GwOGIQM7+m4a1K4MPG2W1r2NhH0G/T1D7oz4pEKLVkqi0a9sy1lzIBiDn4lo8jLUrTY3uLgSwkZakGIP33bM4k5QU1HzdUnl3edg9Q8AQMAT+KwRMRvVfIW/lHjIIkOjhh8JhEkCq0YHJcYjeexGoDeKIHxJHJIfSUr0ffmpWOfJo27btmdoaJJdIcO1KsM+JhwvU8CDBxkAzcgyxubmW5TRA9DrKZ77xH9Yp/hN/j99pDpGB7eA5Q27a/LzutMMSMblhu1IxESIeJMUY+J0fz0ScXtI6xrfHu2p/DxUESNo8/fwrMuaZ8Uposl7UEmPIOcmlFhlJH3Xey35GAi1GjrqjJjyIf0ggBQJBrcdBLGafsyZ2JA9JFJH0Ir48d59lS5fKqpXfa/40Ac94DCSTGYdtYxu5PnHvFd6vW7eB9DyrjyQmhTQur426/05Zsexb+fjLRfLKG9Plw5lfy8cz3pdXJr3E25KSkuXx2yPgIvLYw/fK8pXfyqfzF8vrU9+X9z6bL59/PEOmTBqPsRuQ5s1aSs9efTW9eyfwPUX/CrmFN157Ra4a1E/HORqrdeNuQxLnbNOqVauke/fuuSW1a4aAIWAIGAKGgCFgCBgChoAhYAgYAv8iApRXUT7jZFcsOgB5jq79dIMsrJtgjcpzriFpsUjXg5DzaBrIpLhOpSlFP9d8OeRNPKccgUcGbv4kMcZrdMPAzeHuXoS7LC0YAoaAIXAII6CmFFk/0xw7hJ+SVS0bAvwBz62/Zl33iCDuFmPwwykT/TLxam5m4KghlpgYEihpyKMP3S8fTn8Hwul80v2scyUtA6RQQowEQ37T33lTXhk/XlZDGEwNj9PO6CGXXDZIhcYPP/KglCpZVlK2b5MZH0yDZk6CXHLF1dKjZ08Z9eB98uF77yLfRLlo4KXSrcc5nqAZdWLdWAeSVkmJQZAWUUlOhjNTtJPBOQ4e99w4+WDaVPnt11/kmOPryc033Sq1jzuWsmqdyCAHee/dN2XCuOdk/bqfpXTpstKlW0+5+LLLYnG8CZJAU+z3zb/LLddfLV27nSk9uvdQQf3kN6fJ65NelJ9/XC1Vq1WXa64fKi1atdG8SRg+POoh+fSD6RCuh6Tn2X0w+cFEKWYyjuSbm/xopXfzZ1dk4W6SHBW3Rtw/UooWKS6pydvk3TdfFz/MaPbqfa5cfMnF2jeIL7F7c6r3nNb+9INUq1FTLhl8jbTv1An9XOSRB+6VxCIl4RB3q8z8+AOpUbOWjBrzpDzy0Aj08SQZfOUQGfHAfTBvUEJSt2+X6XC8G8yfT87scbZcdik0tEDy+KAyFIG2YwSaP2MeGyNvTXkNfTIdjnt7ytVDhlI/SQnUMDQR3536ukyeMEHWrv5JqtaqJYOuuE46dmir9R35wAgpULAwHPOmgcyZLsceW0t6nNVXxj3zhGxcvx4T8DQ5tl4jufOue6Rs2bKZ5DS1qxgcGasn+MMJvgscK3O+nCfjnhwjG9atBVGdIsc3bi433nqXVK1YWsfMgxjLxYqVkK1btmn5dY+rLfc/MlYy/BnyHsbxxOefl3U/r5UqNWrIpVddL+3btdJ6L1q8TEaNvEd+XrNK05csWUJuu/d+qVS5ovb5d998X15/+SVZA/yrA/9Bl18r7Tp2ElqoHDN6lCxb8q0kBkNyUd8++p566rnx8sSjD0h+YM42TH1jMnbepeEdQDyHexp8XHQgfcADFyOZY1ow3t+Xrp27SOUypfT+cTWrSaeTOst0aGldcO758seff8mKxYukRceToLnmYTT9ralyWtezpHwJ2IHHQqd2tUrSsX1HmTbtXel17gAsWPgeZEGx9yTqRLI7HWR/0JGgiJMBDdk1P/8ow4YOQcXyqwYbthbqu4TPgSS6e+euXLkS9unfkjPPPFPfJcw7gwQimcFsu3KZ0oIhYAgYAoaAIWAIGAKGgCGw9whQBuDNYHOkxbqEK3auhzinFt9WXWRHo7DyEk6BB1zPzxhnwFxRQEFGylZviKktN4fxmvevXLV6On/ftGYJYnizZabRuTn+OvkCvSdEdVMZ7zK4o3d2oP/m2ua9KAQ0xl7E3jmqlb8zJntzZf/QZ++ipZJYQIel3y8G53ucvTc+cK3Hj7f5Ed+DGDfwpcd1INf1uubXgYKN01j/kQBjcFpfJLhIlqm1mNjaPD5/fo8vkXGZhumZhucM6UjL6/Fx9Yb9MQQMgX8NAf42ZiTw14vyRIzz2Msk63fBveEz3zJaN9hC02NWvINVZb7PvN92vEHUMhNL4kbyA+GqaE9qbaYU9wQli3NIIeBIMSeUdZXjOT9h/OOPfcCfCDMIO09ScxI4TuPrgXtulZeefUZuvnskBOLF5PlxT8sPK5ZKK5BD1OR6YdxTcs9dN8tlV14vfc6/WHaAwKAPH4a01FRZOH++LF+ySE4+uYtcNvhaWQiB9VWX9Zf3pk6RkqVKy1XXDpXZMz+VG66+TBo3bS41qlXTyQpl1Jy4UGiO95UGvwqUObEPSAhmG++99WZ5DsTY0NvukXLlKkIw/6Kc2bmjvPf5XKlQsaKmeQ6Ew7133yKXX3WDXDDwMklPS4Hg/2e9xzZS+4dacEtXfC/9zjpDTup8qpzZszcWEBF5Dn6H7n1whFw6ZJiaz1uxfKmc1aOzvPH2x2h/MxmJfCe+NEn+76bbpWzFCvLiM0/Kd4jTpEUrrTd9Kxnh5T27ff379bx5snLJQmnboaMMGX6brFu3Xkbcc7Mkgsjpd8mlmi1xH3HPPTL4uhvl/Iv6w1zeMunb90yZ8tYMad2ypXz15ZeyeMlSad6qtQwcfCV8VRXH4k5k8ddfS1L+/DoZnj/3S/ke5E3bDp3kuuG3y9p1a+SBEbdLKCMsAy67XFeAHD8fvfcOJs5p6O9DlHwacedwqQl/VZ1PP0P768TnnpaRd90uV1w/XC5E/ZahP5x/zuny5lvvSev27WU+6rIS2k7NW7aVgZddLSWKFpZFi+ZLq9btpFKVapKctkNefuF5Oeu0k2Tm10uVnCHZgg7FX8BMGDku+HFmSfmdO+C+XjBX29mr7/mSmrJDJrz4gvQ+42SZt/BbTevKb9GynVx86ZVStmRRrffzGCes91VDb5Jq1auh3svk3O6nypvvvC9Nm7VCHqdIz959pc8FF0k4PVWWfrtINm36TYmxSc8/IyPuHCGDhxD/AfLdd8ukd68u8vYHM6VZ06bSrEVrKfP661KwYBHpCuIrgHERhIbYN18vwHNZDFLrVLkc7w+SeSOBeY0axyjhyApzIcG20VSjngOGFd8vl0EgtvnO49o7DE3UuvUbybixj6iW6UczpsnVlw+WjZt38CWCPKLy/Q/f4dlfrXiSZOQ7sUGTJvLkmNGaL//oOxDEJ50reySZgDjlYgbQIz4nJgzjxo6Rf/7cgosR2KBPwVEva30YL4D3FLXHaHq1b9++SpYlgiGk37EHH3gQ7zM8x9jCzUtpfw0BQ8AQMAQMAUPAEDAEDIF9QQCCfsw/OUuNTUmzZUJSzF1nPH5opcHJDrJFRkTOkz25gEeKZbtvJ4bAEYQA5TRcS6fBGhADySoGkljqd9zWa4qH/TEEDIGjEwHPPtrR2XZr9WGKwJAhQ+Txxx9XgSwFswwUzmYGypU5K+YHJgrbnPlY5i1+yUngqLYENGSehsD8tttGyHkXXiCJoYCcfNrp0uTYStAaC4NV98voB++XK6+5SYbeMERCIc9ecioIMcqx8+dPgpZIUKpUriZjnnpKVdW7ndVD3pnykvz91yZ5Fv59EiBcPrNnN3ll8iSZNWu21KpRDYQbhP7QrCBxFaYZR9SPBFYwNomPwCfQ31sjMnbsEzLspptk4ABoD+H+KaecJO1bnCCPPzZaHnzoYeTtg9m1+0C+3SxDrh+SKex2pB8XBunQfps1e75cct4ZctmQ64Q4RiMpIBnyyUPQrrl66HCQINeKP1XktJNPl982rJWnHrtfGrd6S14EgTF0+E3Sf+AAzbtjh/bSuE6tTFxZJwrlLew9AkqGkFjEMypYuKiMffYFnbhG0De2/L5RRj/ykPQdcKnu13hw5AgZMuwmED1XQbsoKp07ny4/r10vT0BTqVWL17TwcuXKyFMvTACh5pEbfCrcFaILRJz40ZcLFy8mTz/3Ap49tITQb39fv1oeH/2o9Bt0lfgjqejLAalavYo88fyLsNHvja2v586WKZMnS9eu3UFEpcmD942UK4fdIddcfrkSbp27nCGb1m+Alhnqgv6RAOKlZKkK8vz4lyUYwkIW2XTs0EH7byQMu+aYgJ96alepDQ2vBfO/kVYtm2KBmgHyFns4MXa5A46mBzlPp7l/z4QhdpDgJIqxNOSa69EmXMcfaiyedGp3OaZcEfkaToNPgLnAUCBRypSuKM+/+FLmgjgCraiH7rtfrr3xThkMTU/m3+W07rJ5/S/y5NjHZVjx0koEXT/8bildrJAuls88o5tilx4JyMh77pXrbrxZBoF4CkG9q1u0m2zYsAHmC0fIy5NfQ7lNpEyZElK8WEk5FxpjUSX4sDMHx6pVKuPd8CyIaG+hPm/uLPjnmox3Qk80kLvpEvDcaarVG0dpaTBrkbxdTSvyvmqOAotSJYvBhCJ2vqalStOmJ8jtd90JJVDu6INPsmiaRKGNlgh77njqgNHbtVeqVHHZvn0r4uTdP3UnLggyny8o80DWkhTDC83DEHhXr15dhlx7rVx19dVKiDlhA8kxChjCYZ/MX7lJylarm2thkWD+XK/bRUPAEDAEDAFDwBAwBAwBQyBXBDAf5Zwz4s8vBas3l8SMHObZYKmF96lJw3VCFPN0rn0CaRly95wEuftGbCJDSMA6gvNyP9YpXGOkJoQkDXKE/D7kh+sRzH9BpcX2yWM2jTicPut6Cit1UG2aD6bEuJY1sfZm73rroPxhefsT9rd+Vv7+oP/fp+UGTI4YaofRtCF9XJMUplyM44HnFgwBQ8AQOFoR2IkY4wTCCbqOVlCs3Yc2AiSy4kM8Kca+yz6sW8n4B2YJ8wrUUvkG2idpf2+T9id21ujqIwxkT4PGTZUIWrr4a9n822/QJDkXwt+IbE8OQ4AflAL58unOG04oqIJe65hjNT7rQFOD1avXlAqVqqrgPglkG0mwenUbqmAb8xANFHiTHFG/Xawyzhlon5lWHxZ98xXM3u2AWcQeOoHhfWqVHFevvvz04/eadtm3X8vff/wD7ZM+aoKOEDAoFMgzAtLq26+/kk9hJvL+MY/BFGJvyLtB6gHLxQsXyu9//SXz58yEFtgyaA7Rd1JU1q5dBY24tbLwq69k+z9bpXvPvkrYkAhMykiSRtB6w/JDy98bUiynxp5X0z3/m5PY3POUh2ZMaux45gd80CRsof0gDRNWP4iO+k2agvC8B6b3ItBOWi6b/7cJmlizZOXy5SCOqKkThsbXOvntl/W6I5ItPK7u8UpsJbBPsh+iX3K3pAt8tnXrNtLTEB3loj83adFCxo4eDZ9yqVIAC0mOieq1a2v+cNGrk+hChYrIVpAr7Osrli+SP//+R0iWXfntAs0LBhTkF2hCrQfJlprmlV0HfZTIyrLVAABAAElEQVQhNTWKOuH++vUy+qEH5QeY3kuNYnqOtmMAgVxaB2K4of72pCTvkHyJ+bQOCTEVygwQafTNxbJZX47zX37ZII+ADP5p9Y9KjEdADjOwzzbCuA3DTMTx9espBjT5ybBkxUL5Y/NmmTvzc/kRbWA+EZheWYs8NsD8aK1j6kjVmjWkS8cWcvKJJ0qHk04BCd1ZNdS+XfK1/AkTpHNnfSHLli4BQUdc/fL9j0j78xpoaKZDuxOLazw7EtoR1JNYhzDuGarVrKmmR7mk5jurSJFi8veWv/We+8Nde2oHxl3Akc8vPrDOftpNRKh9bB05DmZVVdMO5/AAiL8e9rzPwLL0dx3PlXDnFaCkqiGMZ7Y+pnGKh+NdhBkOaoP17tNHrr7mGs1X37e4q2WggIwEL4NQKFGvOaLPywDNcy8+d8GOhoAhYAgYAoaAIWAIGAKGwG4Q4Jya83lu6mPgmiBbwDpA5/WxDX2cb0Ywhw7EkVfx8TlvZR4JWO876wnx9+27IXAkIaDmDDEeuAmTIyiC9aHzg+3WckdSe60thoAhYAjsDQIxEVhWEk4o8go5piF5Rdf7VHrnDhumzRLR7lFSi2QIZEOAgl72U2qLUSDszHllRqIcWWXJnqmFCDpcOro15N86EfDnEDQHINz++/ffRSD0LVG8qNo0DcEcWAJkwfTLRJuqf4AUI9tWoXx5lA1hNybRLCM9nZN0+gWLSBhEGLVkyMXxPskuyoCZnloutI9KrQ5fBLt0wqnQoElXTbGAk0SzAZigQ/zPb9AagwAc59v+Qt38GVKuZCmk84TudHDK+xxVCbAXvenX9UgblgoVyoLwovYIJj0Q0DP4/EHEhdIH/Jel+yOyEUSKgLjwo06cFG363y8AJlXagBxJDCYhR0+jpUEDECyJibLlj/9R4i6lCheRABqUinaSxPOh3mihfqepSZpT3JNwpBFbe9Lm3cWhtp2PDCYDIGS/IZYkFBLxzNiDSFj99hueE/pl85bwhcV/6K/c/dWoSWNJSkrS5PwThA84alvFT3JpNsEPwoIkSwADgdqNfuymzICmJLdCFoQ/MzxMiabjPvo8+2kI8RPxnKMw3xdEB/JIKeyNBMHyx/82InIayNGmILEwsUZ/RK7SuHEDmGwMoXbp6B/UgkKTfGnoI35JwUDs1f1MqValkvSDP7NG9etLQZBtDevWRb0wjqGlxmFFTSWtJ8YVjxzrJF5JBKISKAcBde3V7XSpAuJ5AIgaEn35YL6w6fE1kYZ54KdNM4PjXxy1fwK7jRt/Rb1BQDZvJ4XyozDNKyL169dVfwRJSPbZ7C9l8qTx8umnn8qQKy6B37+CMvntGfLHJoxDhBatWkkS8ApHU1A3P8ip2tAYzS/54KuNgVpoDHw38V3FunD3qj4z1C1CDay4wOjud5fkug8XSJAFAhmSL18B2b51uz5LR36tW7dGChUqjIUNHSEnaBnUAuR3fwG/vrNSd+zw8kDZJBTXrV0rJUvAxxlJRTx/9jkUqjRahmILWIAtF0hpIMRUQIB3Tq3j68iCWSA+kQcJPaidyrBhw/QT14Ss+iOPgCRLMCNZiUGOddc2F9/h48739uj5c9jbVBbfEDAEDAFDwBAwBAwBQ+BwRYArIs5hQ1hD69oZc874EIA1F86VYeBB102hjGJY+2Dejfk05+MihTR6QgbWr0ib6OPGVG42hWUYrFvCWMPzPIkbRJGPy92HL5gyYx7PD+bRKs1iXbgGz5rTe7P/+BrZd0PgwCFA+Qz1unQdnEOWtTelOF9g8Wm4GTdnyLmROSWVm8J3jpcz3a7O4/boapS85EHO55nLj2vu3QaubeNCXvnHRbWvhoAhYAhAhmXBEDjMEHj00Udl1KhREOJm/wFkM7ibLANEEXeT+SEADyQlSqtzxmZr4U6CWmTTrt1JZLlk7pyZcsbpXTCB9oiFVT/8IMfXqyct23QQHybc7097R3p2h48l/PimpMJkGcy+qdCeM2YE5q2+kDCJDmA2TqH0/ob2HU+h1Fo++fgD6d79bEnCpCQMLZvffv1VihSF3ySQKO3adlLC4oPp0+AbrJsWGYA2kGqd4Yw+meo2bCx3PfCIXHFeT00/5PobVBjeFm1n22rAf9Spp3XVRUW8PfatySDYIDyf+elHckrXM9TnWRpM0q1b/ZPUhMYKJ04hfGwCsm9POl7bbuOGDeobiliSnNi4cYOEChcAEZJfWuoz9ku16rXklM5dlGxxuyVJNpBE4mSX5AcDSQ7e98dMKgZjk1lOrKl5xkVhEH0kHdpo69evlYJFCknBAvk0DckSZ3uc2omM4wgN9qnWbTrifoLURF06n34itKJo/hALTdQ7DE0wEmEcCxyP/HDx+MfmLbJq5VKZMvVd+MkrhxpGZQMIm9QYgYuqIq1WXf9EcOLHrjYk1UDCKA0+z0iE//PnH7Lq+x9lyrufSbkyRbSstet/wUI4Tb+zbH7Yj0mKUbuT+bVpi3qj3BrVasGEYRevr6Ngkkr0pxYFqVwQpNOAQZfDr9uVqv11Sutm8jTMQ95y70MYJ4lSvUYtOfnU05VIo/YqJ+5p6Wm66GbdXKBJSH7SUS7bQIyoRYZqaRuIP+vHdpNYp0NkkqFc1DNfPr96DRrJRx+9J/0uHoQ0WI7jHjU869Zr5MXFcyFBxny5TifWdZHms09myMBLLtY6+UBwLvrmG2nQoHEMG+RDrVQ8J2KUMygJyrxwo3X7DrLgK/hsQ7+iv7TcgnsPs84qkEAdmtWrJZ9Oe52Nzi2JXTMEDAFDwBAwBAwBQ8AQMAT2GoGSVRrL5p8X5p4O821uhn3i5Tny8turMZ/GvBwbWnueeqxcc2ETTUPpAee4/FO+RjOd/3M6TNqBc3psa9N4mTNkfHESB7e+cBG4frBgCBwNCOimyqOhodZGQ8AQOCoRcL/zR2XjrdGHJwJOoJxb7Tmh5SQ1ERodOlmNCdVzi+uukZjIX6SgtOl0Csy83SfJIH1ole3lCRPkx1VrMMOOStFCBaXbmd3lwbtvkp83/AphNxRSoJmzeMlSFUYzL7XdjBuOIMpF5uyK3KtjoXyJ0qnDifLwAw/Jhv/9DeF/hkybNg3m4D6WiyHAZ5tD8HF2apeu8uBdN8G03gZox/mgwZYgS5evVOKKGl7+6A45sX0rmTRlqjw7drTcd8+dWAIEpUiBJDnzjO5yx203y3c/rYGGTSKuB+Tn9b/Khx9+LEXyo/xOHWUk/JD9syMV+IhMGP8SSI4Vme2g+TgL+4YASUv6giOpMG/2bJnx0SdQzPHJuo2bZfTDj0qvnmfjGYelWMFE6dH9LLntlmEwCfg9NI+4pvPJ+l82gjz5GESKR8qSHAGngn7pkVnUauQ9b9yALAJR8vnHH8lnn38JIsonv/3vDxkL85o9Tj8DOl+Ii3xV+w9kjw91iuLI3ZFRkiPoR9QYKwT/W926d5P7br9e1vy8TtLQ12iDf836dTJn5qe6giTR41fzfV7ZBVB/Xz6/vP/+2xKGtlsKxtDQ/7sO4wtkF0tAPyaBxDT0awUDk9BS4tLVI8ii7MPQaCR5FipQRDXm3n3ndQljZ2dyKjSZrrtaCRwl4pAFffaxzUmqdYd1MdLnL1RAup19joy8c6j8tGodxixMsiD9+g0b5YvPPpUfvlsps2fNRomeph5NqkZRYKFChXSc9OrZS269ZbisXLVaTQaylqvW/Cyff/aFkkwci5XKVZDVq36Uf7btwOIaLxJgT2xUKw7PWQm+GIHH+pFvomkYLrZ5zsA4/oSIDLion3z8yccy68t5IAVFPvrkM5n12Ydy2eVXa38hkd8LRD3HK005kpw6v99F8vEH02XmzFl6fcbHn8gXX3whAwcNBkHpkaZqWhKaebkFvrdQPOoSlgsuuEiqVq2MCsVIMVzPGYg3/dS9//77mcTYyIce2iUp5j1RPtV9++xbqn0tzdIZ3tYHrA9YH7A+YH3A+oD1gf+8D3hTZMzbMVFl4Hn8B+uGjIxUtYag65XINomm/oW9XVtg3QJzVczR+eGMNoDHiaUI1jz07w1LDZoP1jgg0oIJ0IxBBGxd8z6xclmkBUPgaEXgoJsbxQZbbrLN/GBti5Ea9zlakbd2GwKGwL+BQC5irn+jWCvDENh3BOK1HJwg2eVGga+aUcNk1r+H2grcAUNtmLHPT5Qd27dLrSql5JjKZWTyKxOkY8dOuAeNDpBn9zw4RmrWOlZaNKgldaqXkSplisL/092ZAm1qhJDgYKDmDjiFAxaeemG8hKD91rxedamLsq+5bKDcdNd98InWEYsAEiF+efTJF6T2McdJi/q1pHaFklKpcEgeffA+JeoobyduNMnXpHEzmfTGNHnpuefk9puvV2H2mLEvSL36DaVd83pSp1oZqVG+mLRvVl++/84jv0Y/8ZykJCdL3ZrlpX6NcvL2G69Kh86nafuID7WKLOwbAtQwJDlLMqT9iSfK3TddL/XwDFrVryElSpWSW+4aoYQUNYtGjX4WZv8ayakdm8uxldFPK5WUTi0byg/fr1BiiTWglmIYZBgD/WypmUM8JN01iWt8VidB4+m2YddIrQolpEmdqlKiRGm5894HlLwh0RHvByoEc5pBLBSdFiG1qtKR76Njx0mDJidI++b1pW7VMnJspVLS7oQGsnTJtyiX5km8lST7XSI0rfLDH9//3Xi73Dx8iBxfqYwch/rTLGS+xPxaV5JKjn/hoKMZT+eni+3wtKo8k4s0HXnjbffKbTdeJ3WqlpL6Ncuof7Yg8vIIJq+/p6SkSHJymuZP4pHk2MOPA8PGJ0jrFvXleOB8TOUS0q5ZPfnpp1WyIyVNBp7fG2O7sNSpUlaOq1BcypevKNcMvVXJxQceelKaNGkmnVo1ktrAvmb54nJS68by/fcrtQxqfF15wzBJQ7m1y5eQUgUTZPPm/wGvNH2H8D3iQloqzKkC6+QUmkelyUHQgDjnO40aaOwPvc7rJxddfKn06NJBy7uw1xky/Na7pQ00uagp9suGn+XTTz6CsisoOLzHSIxd0H8AtN0GSfczTkbbSsmFvU6X4bffK63bdXBFZ2qMZV6I+0LlM/Y1Pr9ylSrJ69M/lVq1ayjRqGxWXFx+Zb+4+eab8a7sqHfYl9kOBnfUE/tjCBgChoAhYAgYAoaAIWAI7AsCWG9yfuosFeycBaxFxDZ9cV3P9YBaM8AaYldh0yr42V6zEHlyg5onD9i4ern8+uNSTmL1QysamBR7G7pwjXNbna9zvosP5+32MQwOdh/YVR/+t65zLFkwBAwBQ+BIRQAWrmLSy4PcQhZTvFoj+Wvt4oNckmVvCHhCWVX7Ahiteo+Vt58fDNvhHnkT4Kw6LriJDCfaFC7/9MN30MaB5kelatCEgNlCTIC584wTbH5SYUJx/ldz5Pg6dUFclIFQ2/N5xkk1vzMfTphJRDhTd3HFQWLvmWrIdi3uhNolJDN8INmiMYKB5xRU/w0Tct999500btQM2i/5IUCH+TVMymnzmUJ/EnI7tu+QRQvmwgxbYylavLiSdanQJklKxKQVE3tYVY8rLcdXCMXnL5glZcuWl3IVK6omSDq0wWgaj+Wv+ekH+FMqKGXKVdTFhmc6zyuf9bCw9wgQV37O79NbisOP3BNPPCHrf16jGVWqUm2nDGkakKuzFUsWwideaalUtYr2W+LPRR37HI87dmyHhhX8y2Ei6/Vd9suI9DnnHCmJfjtmzOOydvUqyZfklwqVqmg5JGPQdXWBlwK/cySgomFoYaL/MbBfp+OTmBhCX4IKEwL9ly1etEDKli4vVWpW9wgs1IHjir6vXPDMCMIn1o5kWbpsiTQ7oY36/+J9El9B+gZAf0+Aypo6CE5PVVzSokHt3+CBYAIUkb2qoDIRSQe5tGjRNyAL4WcNWpMMJMc1nxhJzT5KfBlIXNOPGtuTAu3HH35YLqVLl5ay9B2IqjIuyyb+q374Xlq2aCtBENLeWARhhbYWKpQkqckRWfjNPClWvBSwq6zvCebPMc/XjBsLJOYY8uXLr9jzexQmDP0xk4vUMsVDkj49uqi51vMvvlJKFII/Lzw31oU+ABk3ZUeKzJ8/R1q3ao96wvdbYgDvIe9dw3eWmkZE3u45E88w/Ah+NW+WtG7dHtp1MHca8qtPRGrEpgHbWhXKyLiJr8rpXTrjOQc1P9aP+TNE4HOBwR/1zs/sN1rKpH4gC79dLr/88gveEWVhnrGBXH/99dK2bVvPnCbi87m7/qIZ7PRn/94T3MNrwRAwBAwBQ8AQMAQMAUPg6EHAF5s+lqzaUDavgzwp53QyNj3kjP+pVxfIpHcWwm8uNqFh/dCvV1O5sleLGFiIiEhcGnDOTtqseNVGcAvgzbv/XrccWWMdEysgAi0WLoO4onnyla9l4uvzNZ8I1Mz6ndVULu/bLJavHQyBg4cA5UOQROkyuMU5T8q0FwZrYartiG8ZkC/FB64PGbSfI+27b0+VylVrSL26x2EdmK4bUCln+hbWjzZu/AWWhmBlJamA1G9QT0qVKo3VlrcOZB4Lv5kvG3/f7q0zsU7n+jUE392F4N+7efOmKoOKIP0ObKL+9KMPZcu2LRJMzCeNGmEjb+3K2JidLiuXLZdN8Jfu1nFcm6t8uGhhadmytbz3wXt6zg28boOuZ1GHm4cTpGLFKlxEy7p1P0uzFq2lZPGiGp9rzm3btsLyy2dyWvfukFnRRUF2LNgGC4bA0YwA3wPdBzwpc1+7AjImvBe810OcVMWNmdiNGFj8LWSAlDx25eAeWNrYlxfIK28t0oLo7bNfzyb4nW2aa8FPTv5Kxr/5DcTrnjz+gp4t5LI+J3ha4Ezh5Ia5ps5+0ZN4Zb9mZ4bAUYWAJ0z2Rg0F2tVrHput/T43E49dJSnQug2EzfjhVm0tHBniv+9eMMyJeNYoJVkVH/jjz8DJSgImNb4Y4UTioASE+K1LlVWhOevKuAGQYgwUpJPAK1KksLTvdLJeU9IF8UiOREF0aLwcxKBedH9Q9AnNWmYSCepriqsGBGrDVKleE2UEYxMgaKmwfJTpiACXjR33DgHVGot7LrkRYi5HThj9mIw2hNaTCw5/9kFOMrlazAcNLUeUMZ5HmIFgZb8BYUX/XTVq18YPBwgWPGPVztIfP+95kxRjn/LyAFmlJFeW2UfXhdn/mqLPuKBm+jiXRl/id91piVOaJ2EIFiyofcz9ULFe3IXGeulYRMb0oRdCfC5CE5A/oSEppkMj9ruMfaDQokySFq3axtrmjccskoht5ljDGEI7SHBTk40/7MwnCSZC68PvXgbMCxKzjNgkOoIf1goVK+uHaXUcIgHHexJIxFQQS5wbNGraXIkwtsnhzqPAPKULJMR4zTNr6ZlKdKQY45CEi2D806/Zc888KeOefkJmfzkfhHYJzcLDnHVNknYdTtRrGVgZeeYxvXKynr23wPDjnUE8fUk+1SxjvRmYhuGWYVcJfRGWKlsS/gLzKSZpaUzjEfnZ30ZwYJ4K0hLPgP1u6rT30Rb2n6w2aqZxf/J698VFta+GgCFgCBgChoAhYAgYAobAAUeAJtV102oOwsAVxOUtp+2c0vqxO46+hhkoCMRKQf/ynGsGznr5iZcZMH38OeNaMAQOHgI5V2i7L4lrXgaSTHM//0L6n3sO/MnXkK+WrFCTom7N/fgjD8oH778LIiuk/Xnb1m1Ss2ZNmThxstSsU0fX5E+MHiXvTpuhmzXpY1zX7Oj/TWFF5fV335MgBtFHILauu2IgNpOGpBYsLP35z9+wqLJCLrzgArl/1ONwDzFS3ps+VfIncWO3tyblGrxZk6byxvSP5JILelPYhDUn/HNHUvARSQwm6jqa7Tjr7D6yfds2eeett+T8C/vLQ6PHqAyMrgTmfTlT+p93tvy06R8piDUz1++UYVgwBAwBQ2BPETBibE+RsnhHDQIUYseHnIJemjb3hzzzd/HxMr/HiC41Y8cfZiUqMu9CQJ8OrZWIDBrQL1chMzVmnho3UUL5gkqKkRxjSMWuN2puwXNSJrHm40Ses/q4kIH6O+It/r4/uhtNsbj0JNMyybnYd16jHyEGuimiWToK323SEQfcPn4ljsSzSL4k+LHK+xm5+O4Z5Sw2Ie65RPCwcsYrXDAfJqV4dphfk+uKgMhJx4kfKlkkPUhYJeAG66QaZOjwJEJ2QPspBJOI1KbkeZQdQUOCTroTQdxyR5gSd9CCYiBJxTyyBZar4wLzX44VfPeC68c+aGZlyMUDL1ANzAh8gLkxyLEUCmTIE89OBPEH8gdjQ7XDsGp15JMjk1xdgiARUzDeWC/t2xhfYYwZhwvx8oI7cqHs1YW734hLKhfXXBkjKPGHI4lBt+jgPYd7PObuu7vnHbN24DE/H+rz/KRXlUCkZhoX8iyd5RCnCM6zBRTMpZGb9POIxuOxAQM+VDaD24Bi1xg3Pjz0yFh54OExOn5JelJ5DYppip/G85qZmSQx5qPNvce8Z+eRcHwu1Ch174b45+QycNfcOcUN+xP2L/X+lGxpDQFDwBAwBAwBQ8AQMAT+EwRiG1UjmDdryLFxVRcwuMEZMbVnnCaNP4rNgPApnLlFPm6ey6k955UJWBdk+D3T7m5nvC/my0yLY5q4dFq+/TEEDgMEApDfjJ/wvLRq20EJpHlfzpamTZupfEotCWEt2KRpC3lr2rtqsWTN2rVyLiyZDL78Epnx+ZdooTd22nVoLy+/9a4kwCoJ13bcUKmWkpA+A+v9G+Hru0Xbk+SF8RMyUfnt119kzZpVOhYjGE+tWnWQKVPf1nIYiflQ+4xr2fW//aPrXsoiJr30vAwfOlTWbfobA9rbwMqRetlF50mp8hXk5cmvyOCr4RICBBzXpbpG59oem7cpG3CbRjMrYl8MAUPAEMgDASPG8gDIbh99CKj2SEz4TyG7aoHEwUDhM7VIGHIjh5hGuQAIm1Vjxsn7M/PAD3wgQXqff4FOBjIvx75EMdH3JYWQFoQFhM6cENB/mSMYWCa1txg8v0nZZ+qc5FD1nDLy3O7HitnlwWmguTYqmcCVAwJJCD9ID2rgkDwIhKBoD5NtFvYdAULLZ/rECyBDQeIQVyU7kKUjaHLmzvixR7LT5M+lp1lNavPlDOMmvAyTm97VCM0kop/FP8+UFGVJNAInmj4QMezPJMUC8FQds6CYLVtObEkSqWYiOx4C6+fIpGyR406YLra8zbzK+pMg6t7rHC07PULyhmwPyRtgk4CxB5xUkw1dj3kwuHHnNMYcEYYkWi9qjDEQW1gWJG/kYY3kjkRjX6f2XnxgfYgjTViyLGLFSTfbG0r0w0xLxDO3ikS8zqryGp8RhrmOQ+bHeywniJ128UHHF7TleKSmHEkmjnss44W74HaFoesb7sj0/OB/Zr/RvGP4uDL1nQRzknzPRfEwo9BAc2Sipw3mYe3i80gCU3Hmw0L1+d3hHgBe7rvmibY48jA+D/tuCBgChoAhYAgYAoaAIWAIHCoIlKzSWKKxeTnnsmHM9SvWaIJ5b1R+Xe2ZcjpU6mr1MAT2BgGuAROxkfuPv3bIjGnvyDPQAOP6euIL45QYY3+njCsdbkC4ZqUVE16rBJOFJ8IX+cTxz1EEpetXPwQBtCzDQCLKBd3AiTGThvXrrxs3ydDOpytZRtKMoTTM7perWEG/68ZzyhSYF9a5ajUFaRkCsbJ5j5ZP3GZMyh9oIYYEXACLdxo4Pf74BrIdbkNG3HWrPD/hdU1P39wQaGiedDOiecbVUy/YH0PAEDAEdoNAdgngbiLaLUPgSEEgvLPcN3vT8IPMwJ/qiOpu6GnmHz+NPMeCE0q7cx519ww0NpyQ2P24uzgZPgw7FNH59DPcpVyPJC18+JFnCKvKOTVeKGj3BPy87jTD+N0FH4gOZ5s5/j74BQ3+rOQuSbYjfUp5gTtwsm5RQYgaaPFtNlIsC599/eZ2NZEwcdi6Y255unvuKe0qTm6kmBcXpjXRrTQ9nm+8KU8+z53TscNwMgoCKExWxEsb1zWUvHEEDo+s427rF9eHdTDkaAS1vLr1ODN2NdZxc8SJHwe8xXP2UUfSsJauLSTF4gmveNvjxJ+EHoOru57Ezt13F4fnTMO4xItHftx1XUTkggHx8PLIPgBJInFBEcQiguPbfXdHzXg3f0i4kQwjYecCyT/XT9w1HnndI7dRFipKrLLwQv3o5y0rG03KRUkUmnMaL/6hxzKOXyAxTl6kWO5PM5aZHQwBQ8AQMAQMAUPAEDAEDIGdEHAzSDcZdec7RcQSgPfcfcbHd5cMZ1zK8y5m8uLHHJpr9WSBf1/Mk5Nh2YWmFDPjx6ULYI2dgUUUfQBzOR+GOXoLhsC/gQBoK6zEsR7Dv5yypdzK59o0HRZTJk18UQoWLSpdTz9N0rdulmuGDJEHx8JCTZrnYiOgcilPc4tr5R3I7GdojRVHGqWisDgNY82YAY2vVLjOTsKmaJUz4Ro3ribBdH8CNDTLwET/hOeelJrVq8oJLVvo+OLGS27CpKyB60n6J6dIK0ArNaw01rAJWLyHQWwxLkrSuHAYhPEHKRwO9HWWAPVPktUYubicKvfcP1JObd1YFnz7rTSpB60xkttc0yIGtmt6a1YuvONCTAE07sqh9ZXOHhhoG4rBvXZ8MRcNkZ00ZDVa3B8vfdyFA/rVyQ+dPNHVxx/xZJWRzAq7d2KsPrH6oycc0PpYZobAgUbA68kHOlfLzxA4ihFwgmJ3jBc8E5Z4smlfYPL21uxLSktjCOwbAiRZHMmU6azzEJ/fOLLK1T03omjf0DiwqeiDjaQYA02luuCuuXN3dKQXdwIy0FQM28ZzvYbdd67tPLr4jKvmMfkFQTXH8AyVUASpyPeUkrRcuVgwBAwBQ8AQMAQMAUPAEDAEjlIESlWtry2PQqLOOXJGbH7Mc4aMjHS5DZbm7hjmNtftfmF0qAvmtVH256AhgB6ULW9uUOQ6jf2qXu2qMvOTd7Pdz3niByFLc/tc+J3Qc2zO27s8f3PyBOl+9rlq7ahbj74y9IZhMumFZ2TAwAFKQnHt+Mu6dfL8uCc1jy8+nykfffiePDbmGb3vNlnP/vRzqVa6ADq+54sPg0K6du8pL0x8WU0jPvrki3L1oAuk68ntJAh3ICc0bIpye8tFAwepphrXm3NmzZSKxZPAb2GsUKMLZXft1h0uBV7bZf3dDW4EJWENmydSt259OaVrD7nvlqEy9b33kU12bF0aOxoChoAhsKcIGDG2p0hZPEPAEDAEDIGDgMDuF5LcHWYhCwGsQ3QxwWU4CTF37jTGHCnmUvA+/RRyzaBxbPHgoLGjIWAIGAKGgCFgCBgChsBRiwDXGJxYY5MaNFewzUx8sc1i0LdRVGC/BTGgt0KzDggZGdC2QQjEdrpSvs+we1sZuO/xaV5k+3vUIeDLsf6iP2/2qHT40k53u053g0oY5vUTQI5lgBxzloF2E11vfffdclm2cImMBmmlmlpIf1KXM+T1VycpMUbSi6TStu1bZRaIr2T4E1/87SJp0+4kOatPX2yuBA2FIUKLJ81bN5NJb0yHXmV6Zvl++PTStSgGQaf2rWX5j2tkzuyZsmLZEpkOn2VDh98gixZ/i/Kf1nKat2wtr0x+TTXNUlNTYc0FPslBDGZA8zIhZiVpV20ifGFonAWgHRYAOTj8jrvlxJYNZdYXX2DsZW0s3VX6Q/6686EY0xzLrK/few8Fwp7/9szrOb4cbOI9LcYaBEhqIoTC3osv6nMaYl4Ef0xDDFIHjUflP4Y96OJeRPtrCPxHCMR+yv+j0q1YQ+AAIMAf9PiPN8GlNBiZ86CSY29jygEozrIwBAwBQ+A/Q4ALAy5CnJaYO3caZKxY/Hee5xaH1y0YAoaAIWAIGAKGgCFgCBgChsDuEaA8gebenF9ejU3WAMHJGvTE/hgCe4AAfYGr1hTiOitDeSUjKQZGFhYI8xbhkjwb/+zTEkz0ya3Dhsh5vbpK7+6nyfIl38g382bLzz+tziyudu3j5MVX3pDJb02XT+ctlZXLl8h9tw9Xn100YUg/2/R3X7hwAUnKX0AKFi4shWBqsXDRQmqZhCSXC61atZWBg66Q16Z/JAP6XyKTobGWStcDGDs0vch0oaQkKVSkiOQrWEDyF8y/R+PH2wyKdoOgZp3q1KkjZ/e5UO64+YY9wsPVz46GgCFgCOSGgGmM5YaKXTvsEIifkGbuOoEAORrec8ODznZuXo2n6bL/MuxpPQ9WHfe1/Qd7J8vBaq/le3AQOHr7Q96Lmd0hnlMDTHl/LJCcBhnTOq0xmlGM7QvQxdfeaYzF1VO3ex0Bu/F2B6zdMwQMAUPAEDAEDAFDwBA4KhHIgMZKhObt6MkJwveNP69UHLi5jHNpzoqffnmJjJ/ylV5PC0Sk31lN5YrejfXcl5dKBDfsWjiKEYipzuwSgbh1Vy5xmNrFaN7nqVxiZL8Uhm7XtLfflbPP7iMt27XHLslUNZmfkRCUh0fcJePHPyc333GfklK0LELtMPoYK12ikFx/ww1y1523y4CrhknV8qXEr2ZEoU8JQoqm+CPp9MMHN2WQs/lwHgrCZxjIM3iaRj6evzI/4tZv0ADXYcofPsSCiOeD64AMXPBDU4xH5kcZHje4c4x5vsjoBiD7YKEZyQhU3lTeh3skEun/bPiNw6V5syYy/d3pmY3X+qFe8bLBzJuH0RcfMI+SeI94GqrhPASAedze75aHiDjcP3g+FiFjDXr92RfzMRaIyUepYasBjzBK33JBeHIEoRt01/e7JpbB/iJA7WUn1XFvJY6o3MKurucW93C/ZsTY4f4Erf5xP6j4IcYPLH5W8cGOGniH9AXdsM8CKj0tDEehIeHR/fDqjzh+lLm7huri3BnDH1QKmP08p5Pd2HlWTvbNEDAEDIF/FwH1AxZXZM7zuFtYYETxTou/4u1qpZ33aJiLkOwLj+wxvTPmwXff7srJLZ1dMwQMAUPAEDAEDAFDwBAwBA4HBMKU4MfCnmrwuPh2NAQONQTen/a2/LXlT7n+5jukfPnyILc8wjc1kiCrvv9epr7xitx27/3eZkoQGCSl0mCukSTXudD0evTR0fLoA/fIAw+MUhOGYcjCfv1lveTDffr7YnzK3UqWKaPfrx08UM6/eJA0bNxU8/jhx5/kmbGjpWqlSpC7JcHPNfKHZtnmTZsgf4OMDh8SYtQkK1KsqObhMIxQMy4u8DwDO2oZPy0tTeV3XMOWq1RZzuvXXyaMexYsTBrIs7BEQe4pgQf53eEeiDHFmnuwXD/oTXVygHA65ax5B5JiPpCYEcgcAiA2dxJI5J2FxTAE/lUEjBj7V+G2wg4GAt4uE0/A601kyXjjpR3gVoWdX97BEHar8EcGL2tHjLFeSnzFfkRJjrlAwXCAmhf8Acd3C4aAIaC88z7BcKB2NB29Gmf7BHtmIocb32Q5NuRpnCyxgEvibTaI6m5Bd82OhoAhYAgYAoaAIWAIGAKGwOGJAOe7v61bBr0TkeLVmkohaNQw/Lp2ibd3PueSH2KFgCRLxO8J7cPQsEnHoiZzn30ee81yZqeF2Z+jBoF/W/Ni8sRnpUmTxlK2XBls8gaphA2R1LxK8kXkov4Xy+OjRspnH7wP/9PwXRbBJ7YoJBlTKF8+GXzFlfLAvXfIkCHXYcEYlblzvpTGx1SDAA0yMvgEwxcJhpJkw+Z/QERFZMP6jdLzjC6QscHsIoiw7f9sk3r16surU17GGMEGc4y0r2Z/LnVqVPGYHrI9kKvlL5BP1m7aov6w2RmojRYMBaHRwrGVNWpIhPETohyPJDa0z6itdhXq99arL8lfW8Mg/6DRpGRb5qg8bPqX882VFhOU0Ndhp7OGS4gQAKfUrDdNrm2a9fbIXK8fqIsZ1PwCOdq1x63guMKSDrkoQ2KMwwzHzvmsGaKQpX72+ggJg0SFLoIk5vF+1ET2xxD4DxEwYuw/BN+K3ncEHBnGXSMB7AzhD6WzbxwMchcKVK5xzw97yIzLEM9pRaARRk0wVVHew2qQFFMby9jZYsEQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQObwSiEQjvIWy3YAgcCQi8POVdCUPeRb6Llo8SQYrR3CFDpSpV5Ld/0pUo63DiKUowRdJBvcQ2hjP+oMFXy6VXXCMJIKGen/A6KCoQURCBRaAxRBItDHOF9Gnth3WmBGihvQ6fYlFc//H7lfLnH5ulUcOmILhCEgSpFkF+z018TYkVlk8faU6WJyDmGCjLC1N2B4208y+6RHr2Ol+vuz8kwcZNeFVJMRJxLJMkXrnS5eT7db/je5xJxpjsz6U9XI/Q44MA05Nj/tdtoJlZBpJiDM6fuauek7fiwep92uoDJ6an+lr1knv37K8hcAgiYL/+h+BDsSrtOQJ0BOpCYqJng1ctKeKd7MeOEry2M/ebcOcJQ1qqZ0oxDerW1BJj0CPe7E71msSZ25lCFe8IJsu6S+UAkGL76qNLK3oY/3Htdhorh3FTjuqqHyiNr6MaRGu8IWAIGAKGgCFgCBgChoAhYAj8Swg47ZOY7o6T30IUEMDm13CAm2nhxwiCXd6izyUGmnCjDIAhDcJ56Kvo94wofCuFqdPiaU7Eouu93P7EFHJyu2XXDIE8EWC/REf04kFm5XgGKIPlGgLolqCslMCg5aMoBTDYOM5AIyBMpv0cfZpHX9CTozkDIezZtJaE3eYgndDLMTboR4+kGPakgzDTnNREIiLBupJXkeOPOwY3+PECCSs/ynUWmNx1N6ZQsJZPU4sJfvgqiw3TpPz5YiONdeAG99gNyOIom9NRGNcOVC6zTcRJ77vCDqOjP1ZxKN4BM2hbxR4InuTuWxFJVcIxQi1W4BNOC2r8QEyDy5/pVcrLRs00xuUYBYEZHzKfT+xiNAzM0QdTqTGoAeAjpOZgE8LiyWYp94sgKg14RaCtqA9HU9gfQyA3BNj/+A7hh99j/VEFyNn7Jm4elJCjKx+UMixTQ+CQQiCEHTOcW6jpRDfBQA35A0AtMgZ35HdOkmmiMQE/TPxuwRAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBI58BCgBoDA5U6Ds5MNHftOthUcAAvviDiQ+DS0y0W81ZWFhEsIgqmjykNaUKDfLcE6tHYEVw4xyty1bt0liUv4jAMVDtwlBkIrpNIkJRjQN5ih94hFjrsaUY8aHTH4rdjEnERYfV7/HCLudrtsFQ+AIQcCIsSPkQVoz9g4BZ0ox/gc/T00mTARcur0rzWIbAoaAIWAIGAKGgCFgCBgChoAhYAgYAobAoYYA5b6UBUDBRYPjvXgeu6TXM2IC5gzVFIP2mRMYx0fysrC/hsARg0B6etbmcJJj6pIEZFgQpFgEZhVzI1ZIIqemwmcYVcwsHFQEdkCXlX7Xvl3wrZYT9WVZ1eIF6rrGh0wiM3YxmsMkUE4irUm92vHJ7bshcMQhkH2EHHHNswYZAjsjQLVwqvUyRKA65swpeld2/5c7Y0xpbPcY5Xk3+4aVPKMfaRHiydgjrW3WHkPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDg0EFCziPtTFacRliMPWGKEbAwOS+j3K84Sk4tG04tqStFdsONBQ4DKekPvnqjPQvxJ2cuBqcr44HyEuWsZnm1Md6omMzNP8OXzN0bGn9p3Q+CIQ8CIsSPukVqDciJAIiIAbWK4CdPAc/fTsDekWKZt45wF2PleIWDE4l7BZZENAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAENhrBPZ7Y24OjUjnOz4tPawaYWGnaqmalF71nPW+/S57r1t79CUIQNBJ1y/1jjlWfYylQOBGn2CBzIcQE4TuAho+P3UrE3MfE0Z+lH3mRnbuIgu7bAgc1ggYMXZYPz6r/B4hgN0rjhTbo/i7iMQfGwuGgCFgCBxtCEQxubaJ8dH21K29hoAhYAgYAoaAIWAIGAKGgCFgCOSOAM0lpofD6tcqMTExWyQSYhHc85spxWy4HMyT0Xf3hzUskTSILRX2GB+Wg9cEaZajFohAhT+n9AfrmIJHC1OZ9CuH7zmi26khcKQhYMTYkfZErT07IUBCC7/L2D2x0y27YAgYAoaAIbAbBBL8PjhUDhsxthuM7JYhYAgYAoaAIWAIGAKGgCFgCBgCRyICNL0Xb2nJnwj5GsiTSJje9mAuMRBSeVv2tsMHH67n5GCyx7GzA4GAL+TDs4hINH8CTFtGJJgRlHB6BM/ME/f7nYpfrDBHgGWWjfU+A2WmUTBiScEMJcmS0xMgQwVjRpbMgiFwBCNgVMER/HCtaR4CPvwQRKK7Vx82rAwBQ8AQMARyR4CmGPw5t5rlHtWuGgKGgCFgCBgChoAhYAgYAoaAIWAIHCEI+P3wI4b1oPukpUZUk4jXw2BT4kmzI6TJh1UzSIrxGUTC6WpCkZWfPG2u7EhO02eWszFc28d/SHCmp0eh+RfW4ytvfylpaemQoUaVKMuZ3s4NgSMNAdMYO9KeqLVHnIljWNpVNJrWryizvv5JTm57HF7s2AaRJ0eWF1+8n8rEOZxb2iM7uhCI7Lfq4v72P2zv2p+Q8f/sXQWAFdX7Pa82KOmS7pKWRhExUCkDFRMLMVB/JqKI+UfsFgGRUhEEBERRQBQQkE5BkZBGUZqNF/9z7swsy7rsgmuA3Atv35uZm+fembn3O/f7PurU5yhkd39ll7ltf3YIZXU9iCB3klGD9ghEF5c3TvKo008BHs6bvRkN6pXMKlt7zSJgEbAIWAQsAhYBi4BFwCJgEbAIWAT+ZQRkxjA1Ekmz+OE3PqdkGl9aXAEkJycjd2KiMYMoc4iBUBARkiIxxguEQlwrZlxvMx2JF/6lPI35+mMIcakYYbQ4vyNSVlocQc7lp12/SJgkizZbkkzLmL+P8pEI66vrCiHG9+IEqQ0VTiH5Rq2mKAsMBP1ITgkj6MpUlCbINoVJDnl5K24yiZ2AG+d4J+587vo7ynW6ApGCj23ys71yZxCJZC0/CQo24hBCgkn/6lsTMGHyd5g6dQnefOYGxBXIhSjx8cWCRhMszAW+n2NBPSrtMZUX4I/U35NxW6/B2LBxE9Zv2Y77b+po+js1gf0R8yHOHRcRn2M2MxpINuUh5tTP73PkCMYFDUVOEjf4mY4FO/HsX4vAcYqAJcaO046x1frrEGjTvBK+mLUWrZtUNpnqBWCDRcAiYBGwCGSPQICzhG8XrkerZuUhSjWntGj2JdoYFgGLgEXAImARsAhYBCwCFgGLgEXAIvBnEBAxFOeSTSKFRK4EAnEmK2kKJSQkGFJMJ0SKRUWicdckYxlCSscKfkNiidRwSC2fCBQSNtQdMxpFPh9/Mz+/P+sVovgUkV9+/vAIL1OA+0fnVE8RXCK/dOyRaCLFlFbnFVJpIlCkmHfd5MkqKr3SKa40n0Ksu0e4ucWcMF/x7Krnel+HgNrDdtFjWJZ1D7j4x9ydrxe1qY/JXy3Ehp+343YSXa892xWhuIAhMdldJsiHuIhOBfXn7r0H0ePBQVi7YQvy5EvAeS1PM9dEUqYwjarxXK9rDEGZmpEYcyUEfqSaNKnE3waLwImEQNZ32InUEltXi4CLgPcY9gb3uc0q4OWh3yI5wl0jlPIGuTsi6+C8dI8cJ7vrR07pXMmu/OzSn9jXs9nwcmI37ihqH8jp8DmKMrKOEsr6crZXczp+/20ATu72a2qdlbH3mGuL3BsGqZFkLFq9Ho/ec5bdUuCBYr8tAhYBi4BFwCJgEbAIWAQsAhYBi8BxiIBHIhlSjESR1n+eRpghu3gs0khBJJi0xtIHaY0ZDSMRUJShiTCTNpaCFIDkXyxKmVqABJyXt9ESSp9Jut/SBlNQfbIKIrQ8TTCVp3RK430rracVdqTrihtnyL6oSefFz6rcf/sadd5MFYi2+ZZ/sMZ1K9NPGDFjH5AuzLKKUdfSi0d6laxaHM++cDN63f8u1q3ZiNsffg+v9LkewTxhIw+V7zH1oU9/GJL2JOOuR4Zg3cZtyJs3L15+shuqVSyMGLXzkihczcOMZa6xXqPaJCRpgIsagyZoMDDExRxCTGNCQZfD7IcQtf18LMKNba7ZPxaBPyKgcaixpPtAv73xzpGjAfQPhJxKOP+BKtoiLAI5QyA+FI/alYtixrwNaS/0nOVoU1sELAIWgZMDgZnz1qJ21RLIk6jdgTZYBCwCFgGLgEXAImARsAhYBCwCFgGLwPGKgLSp9BHRNHHiGLw/dBAm8Xvj+g2myiLCREBJw2ru7FkYPWoEFi2aR/OLJE5EKsnsHrWPdD0lNRXffDUNI4YNwvzZs02eykTaXanhMM0ypmQLw/Rpk/HTTz8Y846ZRY5GYpj6xWf46IMhmDBuNLZs/tmU7RF7K1YsxfKVy0ybRHypbaqbrq/98QesXL7EnBPZ52mdLVk0H9OYZ4Qk04kWgjI9yXYGaa9S1lu8/jzSd8b2BZiuaqUSeLnPDcibJw/WrN2IO3u/i6Qkp6/kU0wuZvRJJfZ3Pvoe1v+8FXly58aLfbqiaoXCJsskWkpkFJqpFKEmP3Ps96PYYywznoa4VDmupl/GOtpji8DxhICnVHM81cnWxSKQIwTiPOmt+51E8vmsFtUxfcYqnNu4Cm0hJyMuLsSXAPdkuLtj/FIdd2niCF8OCt6OFr2ADrdbnFM+2WPAc9RMm/ikRSCnj+2cjj9PJ/PPdkBO7x/b/j+LfGbptHFPz0GZadDvZO7Ske35XDSvkJu7xGbOW4PWTcsbbTGZyjCRMsvInrMIWAQsAhYBi4BFwCJgEbAI/EMISKiblZbKP1QNW4xF4LhDIBQK4PNPP8Od3W9CMC6OJEll/L57J77/YRUuv/wavPjiy0gO+3Bpu/ZY/f0K1DytJuZ/Nwdntj4HA4d86JBLJMcOcNl/dccOWM10VWrVxMqly9G4xRkYOvz9NMJD2lny5ZWeAJFWmjTRRMB89dUUdOl8OcpWrIBZc+cfhlWM6caPGY2e99+L3fv34vS69XDw4AGsWLESjZo2x+hxE4wvsxf6vYDdu37D2PETD0tPiR2eeaIPftu1E6MmfGmuifwZPOhd9H7oPrz4+svUbKOpR6ONkiHpcXXoyGc8y0J+KmAF5dfddeHlj+ciPYvgSVdibgb+VOdM6eol8Oyzt+L+nv2xcd0W3PzQ2/Q51g2nFMxNk5QkxXan4PZHRuDnbTuQK3cCXundFTWrlESqiEcu++P9SabUlGACknkc80eoCcZTnm+xSKJTK9fHmOdrDAkkxFyzjhKtBnMqPsqi7faSReCvQCCnEsa/og42D4vA34qAnslnnl4Krw+agWdemYTGDcugSb2yyJX3FPOSFCGmF0NQR/ydUd3a7FRhHt7uE0/t/M9X2nt1/fkcTuSU7jvyRG5CjuruEbA5yiRHiXM4/rKel2VfM2/ilH3MvynGSd5+zrtFgHlBk14FkV4ix7SQSt5/ADOXbMLX363HolVb0fu2c9OuO7HtX4uARcAiYBGwCFgELAIWAYvAP4uAXz6NWKTxJZR+QvvPVsOWZhE4rhFITY3hf3fejMbNW2HgsBGUcjlaRzt37sSGtWuMT7AX+z2JpYvnY8rsJShdsjjWrFmD8844HR+MGIyrrrrGaI692rcPVq9eji++XYjCRYtjw/rN3DBZB6NGDEGnK69J8/Xlz7DvVutKaaWF4kP4aPhgQ6bNmzUTyxYvQMNGTQzponv4qylTcMuN1+Lm7veg52NP0y+Y41Nr67Zf8PSjPc1GdfkUyy5oDRthfj6W+8qLz+CVl15B/+EfoFP7duZZkV36/+J1rfH1iKxUrhBe7H097nl8MDZv/hV3PjIQLz11g9EIvLcXSbFNO5CQJ4TnHr0W1auWNFA4fs340z5j/4tDw7YpEwQsMZYJKPbUiY1A1Oe8mWNp9khDSIxPwEdvdsH0eZsxbc46vDVqOWpXzIump5fDWY2q0O9YiMRXFGFqF3PjitlTEh8P7lghUca7xE/tCW564cuWH750cxRcQXSO8jiBE5/s79ccjp4T30ize3/+6SF8ot8//3L7Ay4p5k2WdT/qE6b9+Omz1+O7uT9h+U+/onr1kjirWWX0uvtMJMbFQxu9BL3jtvlP955NaBGwCFgELAIWAYuARcAiYBH4Uwj4I0lmne5tVDUCeE5qrebYn4LTJvqPIrD3QBJ2bP0FbS9sq92PiNAXlEwjFitWGIUKF4YUisaPn4QLL+qAsqVKkDVLReWKZdD8rDaYOG4crr2+q9H4+mzCRJzb7mKULlaIWmExVK9QEq1bNsMnY0ahc5frKCOjahNlY8FQAmVlNM3n4umZYvxt90FMmvwFtdBGYFAghkEDB6FWvSZIoDm+COv1RO9HUa9eYzz11JOu9SbHvGOZEoXxzruDHF7mKEzx+ahdJa21x3o/ghHv9cfQwcPRum1bY45QVfIsQR2/3e1s3I24DGOYSBq/b+6OasknswqhDNyhj/0S4AI/zNW7j4v/yjVK4oX/uwF39XqHJhO34I6HBptNr1u2/YbEPHF44Ylr6VOsJBUEmIZmE+Ojjr3E5AAFogp+SgKMPzJHEuCPOOf9UUewQN1dJ577N0TLXNLSk+w0wDiHRsZh0eyBRcBFQE8OV0glWZkrx6cxV573nip/L1iWGPt78bW5HwcI6HbSbZaYGI9zWlTAOWdUoK3hZEyf+ROmzV2DNwZNNc95x46ytCa4E40vagVNsvU7NSUFcWTKnGvmUg7+HP7iyEFGJ2RSB9kTsup/SaVpqTuH+TiOb/90JjnV2MopsWMcav7p2jOhbX/O0OMUw32+aTeYfus7yB0AdWudigtaVMFTd52NuDzxZipCd8xmqmsm1t6EJScVsGktAhYBi4BFwCJgEbAIWAQsAseAQIlyNUxsmf8uVr62RK7Ytm6JmcdaUuwYgLRRTwoEEhMTUbhIEQzu/wYqVKmM+g0bU/5FMopiKO73NiTU6tUrcXO3O4wPKUmnAqEQ6jc4Hf1ffYlxw8a0/g+rVqAr4wT8QbohSSXBxPViwwZ45803DY4Brh9jJGFSKCuT1SUvyCy/yLERQwYiX968aHPehdi1axd63tsDL7zan2b2uAF9/0F8v3Qp+r36JuvjNySWsdQk+RvJNvnYcvxaZS89klnVHt1vxtTJk/DeB+PRrOnpCPOc8stoDcqr43/5O46aeinJqYYUUzvla65qhRJ46fEbce/j7+HnzTu4+T+I3LkSeI6aYlVKGHmoSDEbLAInIwKWGDsZe/0/0OY09d5M2qL9CU5wvg+9omUn170Uikf7s2qYTyZZnFynNNeQoJy7iKJ8F3o7UqLaGWLC4Y+JlJRkM3EKHMEmoKdKr4mXgreBxesHdy+AEbrruvaQpA/e1MeX1o/OVVqudqM5GXv5emm9/NNiHcrIRElxjTTHwdnhQqvKXlL329kZw81QTkgjgLxB4573YHELdDfKpLUnoN00shfpElAR/lSbVV/ZWA65AHg2pFPc7D1NHK9dh9rjtIh7vUwFAjGnnspXQfGcCS7jyWceiV0T3Pp7eNBgqHPebf8hPJ3T3k6fP7RfM1cFtzxPI5PTV+e8u6PIu67vVFYoxImoE5we9hv8mZfiq1LKlp9D7fVq6p1x2pkGrNsv3k4qb3yk1dctjSqd7i+n3l5udNNrzsc4MQyF4g6NLmFm+ouXTZVVkA8pSQcQondZnyb2WhwwjZkx+r2ecYv5j38F3I71vv/jzbXNswhYBCwCFgGLgEXAImAROM4QiFIgbzarch4uBwihCM26aArPhSvl32YqrymrZukSqKcECvIcVwuR3zh91wTfWRd4zYpxPccszTokhWu3CG0iRLgmODTfddYx3qxfSisxLioOuhoUQe5oN0tCLTS0dHCXLV7+9tsikBUCKRxPdOVshs5DPgbQ+QAAQABJREFUD/bE8889n1V0hDj4ZEY0ORBGk0tepf8pZ12b4oppnHU2LXuEnXGewvX+8wM+wIO3XYF2ZzZBfN58qFunATpddimuvelWbvqmTlLyQeTJ5ee6mP6geI9EUlNRsGBBHEhKMmYUD+zfzzVwBLlyJeIAr8Vzk3iUPsMKFSmOfXv3Gi20CAmxEM/rPgnzt3yLRUiuSFbno6xozMjhaNfxMnP9kouvxKP/uxMfDe2PLjfegvkL55o2586Tm2vsMDWcdMibiTd0PP2WpUoDLSyNtAhvbZFc0gxVVMcftmLrt4yrzqZ/tOiM6Xj/43Fo0bQRNZVUBwGs/HSDOrgozXEZ/I4zsYCriaU6mh6WyhWDJ18zB5n82e+Og4ArgPMfIF4kM0PCjnKOA4nEk2OmbNFCyE+89+07QCWwKArkzYNyxfOTMKVkh/jHKPeRCMfnio1iri+x+GTJz4ImjoqPuE7DSGGa2ii9Ca7cKEIBTcQIqJgRB0cg8u8+IDPKG53KHv1fZ5vw0cf/u2PyDjBFeGK6kF6CWQTJK82HcfTtvde8JEqtntQ7Td+eXPKQvPDvvn80PlgxBck6XXlnTOPfHDuXMv6VHFAfU38mV93123mWOL+V5mhqf7jEW6lssAhYBE4+BEQMMPD9mW3QbqLjXx0922b8/RHcZ7sK8sgZF+a/tGwz5zBPexaob70R/uUQ4kRUKvNSoZfGkbPIFCB8VfG/5qiGN9Ob62jeVH9he4Iav8wvDTdTEZ5gJ5l6cXGgF3NcgpzJ0j66Jt/aAWcIz3+4sqyBDRYBi4BFwCJgEbAIWAQsAhYBi8AhBGIkCkpUqE1hL1cbFMrKpJdMj8XINoQpZE+JnmKEf0HsxeMLB6NPL29no9YjjCcfCQyRAN0pkGyLce0ieqxQufoUtmsTnbMopl0FxpKFBS0FwjhI9wtaF8QiKeg9x4++94dJBHDN48Y3mWbyJ9XbEZnJtaM59YeNgEeTKF2cqEgCG/41BGJaeGYIIm5SONbq16ic4cofDzliSTRwDHPYRo/CtCCXuzj7/JZYsPpnzJ/zLebPn4/PJ4zDQ/fdzd/z8NLrA0wh0iSK8l7QvWPGte4hkr46FzKCIV7n4I9jhtLi0n2g4EsQkRxFHLWORIhJPqT26F4UKaa8li1ahGXLluO1AcPIdcnfmB/nXNQeH418H9fc0h0JCQlsDM320WS/MRvoZG3+JlPbKaq1N0MwzhPTm8NM/5xWu565Px+4uzvGf/E1ypcti1RX+8nkfZINf5FhClERqlFtJohSY28PHnxkBLZv34XcufMYvLZt3YlbHxxAv2zdSEaGjBaZ18eZAm1PnpAIyDWQgnv7OgcZjo18jOeckZMW5aT5cRRi8JMGC9tQi8DJgYA7L/N2GKSRNe6TkiaoTfDFnMeDxxt40zlH40mRMz423RjZTLy9B7KX+pCGX+bwe5pd1EEyEbx5Dc1UHx68DA8/m/YGiPO5NpLddNp1lVnwNKK87NPK8fIPultoMmwN9C6nuBpFqq3y0A6fACtNH7gGsYCn0eQ2hHtvnGq4mlderTyNu0Aac+S13Imetr7iaZXj0/YPdZZXEVdj7VAqt/3udW+niZPbH3eOpNF5Xn+69fa7+XoLrJi7hcnT4FI0zf392gbH4LUHUU5qWXYqh5Wu+Di3V9beDh7RaAqHxoMznrxyPE2wgFu+1041O33w6uedC7hbnnxefxEs5ayNTQY3b0sUrztj3amHJvDGNAu3nCieCfzh3Q/eKfttEbAIWAQsAhYBi4BFwCJgEbAI/H0I+EUKUGKrdZlm6r5gArb+tBhJnNQbTirdHP2tD77DkJFzEKLAXQuxqzvURbcrm5rKKa1ZB/Bb6xWthwuVq4u4WJLRwvl17TIKjFlO0FlgSEis9YrSvD1yLoaNdrRcIv4Qru3UAHdc0cjkq+tZBWaTo5BhuXPMeandNhx/CES4AbPXI70w48us6xaOy8NFNN0xcAEdpj5Hirs+D7l8r4+ErYK0KRXikqMct9LaCqBFs5Zo1rw5br/9Djz2SE8MfOd1vPzGQBJVcdi/+wDvA5obDNFHFxe569f/hKJFihmiSuYQQ8FExtntrJkp0NDaeP2GDSiQLx/djHCVT7JYmpzSNjMbSU3p0jzx4/2Rw3gP+vHw/T0QI3mmsPP33/DDiqVY/8Ma1K13OvyJIaxZ+yPlBhe6KQ99xVLZBrZThFx2IW+CHwOGf4LLO7VD+/Pa4NMvp6JEiVJMln3a7PL+R66TvDLBvdElHRIZGqWGoEKcd90c/fFPQM9HBfdGj5EJEa8YjvEZyIdPeOd+PNJnONb9vBOJ3Pz73BNdjE+2+x8eiA0bN6PHg2/h5T7dkDsPNwUwbaor8AimOqRkcjDJZB+MOPUMuaqKUb8jF4t5Psc8uZYr3vLHXFIzp8y+Kd3+8RDwNEY9A1+e3NC7nvE7SoVEyXZb3Pgmxr9yK1JcOZ1HBgXDyYbovuTet3lLRzBj8H1uFifPm8PDIiN29tgiYBE4SRBYsHwLPp66CnqvaYdPCl+s5UoXQpsm5VG5bIFMUNCuOapluxOyTCKc1Ke08Fm5Zjc+njQfP67fjgN7o6hUtiiKFM2DC1tVRJ0qRf5SfH7evhvbf9mL02uUMiY+ZBbk3wzehriFK3Zg1OQViOP8KZW7vuLiEqiuH8K5baoQj0LGfITmXDldKP6Ztm7YnITfaQKiXnWvL/643Ezvr0D3hQ0WAYuARcAiYBGwCFgELAIWAYvAv4+AmZtzeq79iFR6MZsQZT6Jlt6MhotIMfkd0vY2z7du+lprvaKlLJW9HOE717YBmU8nKeb37DClT2B/WwT+QgQ8H89+kk1PP/U0nunbN8vcvW25ElO3uHJAlnF1UaSYnx+jzcgyYtQyi5ApqVy1OqIHkykkj5GYaojZ335NH2LdOe4dpwMrli9DzVp1THppWtVp1AjfTJ+KrjffSLOKqbyXglhKTbB69RvyN8thWdLoMtpk0jZjGSLIIixzzMiR6NS5Cxo0aoo4mkQzbQ7G4cW+T+L9IQPQq88zaEbSbvTw9/C/Hj3SSB2n/tQ8kzYo78X0hJuuZRq4Vi9QoABGfvIlLm/fBpdeeA5Gf/olypQug+RktvfQVt1Mk//XTkp0oWcjeUvTT3c9NgBrN2xGYvwpeOXx61CxUnFD/r/8+I3o8eg7WLt+M+7u0x9vPtONfsZJZulBasN/BgF3374hvPXu072rIE1CBd2bsggmbdSTVe518lCApsvtH4uARSAjAjt++QVTv15g7M2KVJHmz8SpK9H1/g8wffYaRteTU/o8fGAalXbtPvLYF11zrjuzGT5S+CKOyga0HrD8xPF5G8co2l9izvFYv6VZpAdQCl+8eiSnMI6754R7TbTrRedVrkzfOebv0h5YSsByIrI7y28q26uK5uPnpMzHVY5bVXNdcRS0QOIsy5yLGdU4tsr9TmsHbXP7uH0wwG99dNnTSlIREWPzNuQVZ9qgenk2bhcu3YDbHxnKHTm/od3ZNdHtiuaoWLYgVv+8BeOmrVBM0yqiwzZz5xXz00e+s/SRBpb8u2lHZIBtkT1omSPUtwluW2LcMabPh598i57PjTVg+tLvxlE8giyMVP8I40objtmaj+kDLSR5rI+w18e01ZQRQiq12Ay2Ss+66qM8lJda4aPdb2lw6UPDJc6HQ0OWF7Zv/QUzZy/HgWTWnjuPItEDmL5gNW6470N8MmkpZAI9lfkyK44rR99MOLqtdMpijpq4m35nAv1T/cyHcYWLwZ6/aYjcicf2qn5OPys37aA7fPy8P3EBHvi/SWZY0DI6UrS7TkNaHxssAhYBi4BFwCJgEbAIWAQsAhaB4waBTetXYtOG7+nbJh6/rluM7WsXmfVcHNclCUHaFeEaSGtVcVoBarWYNR9rH+GaNUa/YN46TQ3Sbx/TUUpo/I9LGy1o0pMco0kOGYL3VkbOb291otR/LsiYWU4+f67U9KnUBvs5XjDwSR5BYYW+RchmF2T3JZ5yAa1q/WH68tK3EnG86+OPBs1HpxSi1IDs0f1qzJ03y0QMh4NY/9N2vNv/bZStXJNLXh+uvaorPv1sMr759jvKH4L4glpWM6Z8hm633UlijGtwEmtXX3c9pk35HLNnz6HcIQ7TvvoaM2d+i1tv70ExA9fbDJ5GVwrlAkZLjWnHjRqF3QcOoOdDPXFD1+tw9bXX4RrmddWVXXD5ZZ0xevRHHI1+PNvveezcvgmXXtIeP/y0nuckMwhQNvU1zjm7uclfpF2AdUlNScX2HTuxY+fv2Lb9V/N7F31lgfh5wvxCp8Rj0uQvkZg3PzpeeB7Wrl2bTm5lsjtO/3iCCGFKrS3Kg+IoDEoMh5Cb/rmc/lCfZP4JUmVIH2dU0OMcx8RBPsciu/fjtrvexNqfd9BkZQKef/xqVKlanNFiCFMzqEKl0nip753IE18Q69bQrOJ9g7D7V/lvpHyHBGcKcT+gcUfLP0HWI0ZzixHinRLPzfT8xKi5q08cSRV9lMb7KI8YTU2F2ZZjDTLfGdbGBre9GdPruhe83/pWfC+kctykDyJ601+XL7w/G1SWSG2vbOWr396x6u6F9Nf020unuN41L+7Rfof5yEjlsyOJD4SDeqFlE9gV1LKO4tPXupEAi3BcRXH57a/jkjtfNZ8Yx0aqPwGfvPU/fPxqD8ogJYdUpvpzqC3ZFHNCX7ZiwBO6+2zlLQI5RyDMnQFxdJr6YNemyFcg0RBBO3em4JZHP8SzA7/GGY3LOpMN7qDz0Y60gjga7bKT5piC1PS9kMqXuVS/46jSb3bo8cWsaIZ00/YV/lfwFM7iXFMVejlIhV9BNqsDnKDx1Y9k2siIp3q8t7NKmuLKy3tEizAyPq1oq9Bv7DwqD/52n26stlkkmZ0QfBnFmIGxge1WwJtIKY0qp3RqnyFJ+K3WyEeWTqXShEF8KM781mvXqwN/poXXh81EmVKF8N4zl3AHlVrAECvPl1dD7N9LNWXv3cULpunesZuDaLC0YPBwyhc2IoICYoUU+OW0wz025w79jpHuIYXJf5zAGIzUR5xU8i+hZv9wTiQciaeHlbJIH0JspNzdKnibJ9Vmh8YiJpzQB2hKxARN8L3An1GWqT7tdUtz5M9Pf108l3QgjK6PjMKQ8UvQ6YLaBgufVLlFADJjzi9M3VSkmi6MI9q5wkmZJhEKGlsKapn6xYwxHpvrusR03vjRQSrHT4jjR2VwJqD/aUH9bCYKOqO5kTtm0iLwx6Hxkf6s/W0RsAhYBCwCFgGLgEXAImARsAhYBCwCFoFjRYBrUxEGZn3qrn+zyiLd+jW9VZMjJZH2x5ZNJJwuakOtqyBlDvHcrJqKWnVr4d33PzbJOl9zPRav/gGdO52PPCQ7DqYkodfj/4dmLc4063C5FbjsimuxcuVyXNrhHORNzIuDJFPuf7QPmrU887CipdUln2FBylrk22vk8CFo2KAxSp5aCmGqdEp2H6DNU63jL2Gezz/fD1O//BznnHMufYLNwN133ooWDWsgX648rEcyTjklPzp3uZqmHING4yuVqk9zZ3+LWpVKcc1Os4zUPOOCH2efez4Sc+c2dTHEB+UKAaYZ/ek0dG5/LrXHzjeaYyVOLX1YfU+EA0M4coe2kR15gpgjVNyz5OTJOeTTLjkpBff2GowNP29D7ryJeOGR61C1einTt5IpKY76rUq5onjxsetxz2ODsWnzTtz56GC83vdG5M7F/qRsTXnLv6JkIpLv+HkclkCOIera8PNLqMIQc321aOwoKL7kOJkKzUyMzP8EOR5F3HnBkEnsb4UA+1j7GiIiTEXE8Zx+a8wLM5FNCvGUHfEU5Xe8Zkiow6+HKP+UfC2FhKupo0l1dH9UlupntKwkWGP5IZojlYamUz+nDqaOzFL1VF0UPB+BSqvraqt3zomR/d+oUQJQucqUYPAezyqIp1MZnjxVnKCeI6m8oG+PI9S9erKGrBE8WVGx7bYInAQIeJsLfGGamkihBlSIjk/Zbj3GCxaMQ6OapfD51ysRo1MoEU4jJq/E4u+34raL6+HlYXOx4sdNOO+Mqrj7ljOweOUeDH9/Elav+9XsOGreoCxuuKwxSp1a0CFi+KSRheGfN/6G/mMXYd7STUhJ2osyJQvhgjY1ccm5tbkzxtmT9+PPuzH8kwWYPe9H7hSJoE7FMrjl4tNRpQ53tzCIH/mVO1ne+GAmvp6/jnMiP3LRPnWdSiXwdI82AEkQWrlG5Nf9eGHoVExfvIsvvBTkze1D7aol8eh9bQ0ppPosXPoz3hu7EMt/3AZ/XF6cfloJ9OhcFyVYb01C9Pr9cOJCLPx+C+6k5tfLQ7/BplU7cHazirj55rPpcFY1EtXEwF00Cr9t/R2Na5dlg7VvkcSKJgZ88elq/ry0Cc7DFT/+go8+moueN56F3CWcyZxjeRsYNHI+9pLM6XkNd0mxrXt2HMTrH87Atws2UCmKVsUTc6NW5RL4v7vPwQfjl+KbxdsZ34/bnp5odkcWzpsLj9/eBgdJ4OlVuXXtbgwZuwAzl/+AFJpAqF2xHK5rVxcNGpY2Ewn194cT5mLxqj24pXMDvPju1/hhw28omCuEHtc2RcPGlTHqi58w5pPZ+PW3fajGCVWvW89E0WJ5od0lnq8zaV4pCFc1PBrVeIqnKYVE02azozN3ADXLF8MXM37gbjdOZkSKidwiPjs378KAUcsxY9kKJCUnoV6VirixU0PUrF2cFB+7lZ89v+/Gc0O/w6xFP3NcpiBPrjhUr17a9Lt8mgnqzRt2YyjHz6z5P5rJxmmVnPFTwR0/oyauwpylGzkmknFfvy+4JvHTzKMfj93b2qk7y1HwJg4e8eactUSZh4P9tghYBCwCFgGLgEXAImARsAj8Ewi4MkVaqaCA1hSoVRqDu0nTsRDBpRMvygRbTJJThigXvDH66fH2FWrlod+yQuEF2ZzQpj+libibPQPut0SzKs+sWJivz/UJ7eNuOr/8cbvVcGWxXpaZfB8qL5OL2Z7KWWpln/Mcsq2kjXCMCLBP3G7Jhvcw+XKLspE6SBsoyDWvQthdiIfccej5So8LJOCjsVNIYPixYf0abP15Mxo2bYLERG6CliCfQUTHM888i54P9sbypXPQuHFzY7qQIn/6DKPMhUSWj4L7J554Gr16PoaFC+egSeNWlAup0m4FTE68DRhPIZmyBplWHDV6LDfeOppcIiVEWBhzjYxXiWv8X/cmmXpEKQ+oUrUKPpvyFZIpr5k7YwYqV66MoiVLmvqJtFB+Q4eNMPkrD8+0otG2cckTR1ONxA2P1a4C+RIx5euZbAdJOREPLj4mk+PwjzBXSHF3KwdJfJx58aOIjzlypuR0m9Azq/6sMc+a014zY78fxD0PvYN1W7bTp1g8nu3ThbgWNSYu1VXBVGqYcae55F16ZpavXgL9+nbDgw/053jZjrt7DsKrT3RFPHE048XdHH9eu4fMcXKc098Jch7P4GmFSatMIca8vxnzGPa7BJk2Mx9L8EinxYuWYuvWTahcpQoqVqjELETuOETY1q1bsXTJEhQoXBRNG59uxpjKENk0nZqNv+7cgeIlS6Nxk+ZIpCzT02zUGJk+ndd/1fVS5nqcJxg9ykrG+MCfMvlzFC9eArVPq8MxyXLZZ8kEdPqnE1CrXj0UK14ccbyHREhFSXAuW7oEe3btRMszz+LvFTh48CAaNW6YRrBlVbRz52sfN8k8RuQQR9vOT1H6tofknh+TxvQ1jxKv/zPmJb4yQmlap7vfxLiX7qTc14+PXrnNRJN2tYadOLHL73/L+AucM5CmTRm4vdx85/SP+5jLaTZ/a3pnJP+tRdjMLQIWgeMZgSBfdNqpkDHsP0AFbD4ofdLC4tNs/ZZdmLdsIx6SicBWlUlmVeHpKBYv34X7+32OBuUD6HN7az50/SSt5uDWxydg2P9dglyF8phXoUixbk+MR/6C+XD7VY2RP7cf237Zgx2/JaU9yDdu+x239RmDyhWKoeetZ3DXUQCz527CXX0n4vlnLkWV8vnNy+C+ZzXR24dHup3JSVsid4KEsWDp5rQ5mhYnPZ4bTyIojIe7cQJHNi0S3o+FK7eb9ZP4mJWrtuKhF6agWoWCePyOs7H3YBRDJyxH9yc+xeCnOiBPoXyGhNuwdTcJwR144PkJaM92X928EnIlxBlSTC+fjA/6KmWLYN7yjfh2yVa0rFOCL2Fp2vF1xomNFllBvskqlc2HJT9sx/hvVqFz53oG+iBfavtjyfjw0xW4ol3ttO74H9shs5AP3tyCKvDxdDIdIOmz3kwK6tcug1krN+D3PUk478zKrK8fieyuAN9hmn5s+fUAbn9iHE05Fkav7q0McTZ73jbc99wkvPb4JahVrbB5EW7cvAczSERu3L4Hl55TCZdfeBrGfLoUvV6YjPbt9tBn2q/ofvnpSOIL/6URs9Hr5S/x9v91ZNsztj6t2mk/jPYW26ZdSdIs/O33/capq7MzR7uVfPh5yx6an5yE8mUKoectLVgnP80wbkGPp8fh5acvRs1KRTUTwD3/9xnVvOPYp2eQ/+QOH/bv3O9/MWVpBG/+JYxbH/sYVSoWQy/G0cT5W3f8PNv3EuMz77SKBTG7aAGsoumFVqeXRq5QLtaLmngETFO8P94JzsIhrUH2h0XAImARsAhYBCwCFgGLgEXAImARsAhYBP5BBLS+9zRisipWCjzx8SS2SCJUrFyFpEgVpqPwmy4OgtRsSdPooZzilIK50bQ5182UB0VIbClonS5tmiQe58mVaNbUihOgkEEWY7TZNo5m9bwgs3R+ym0CPCciRZt5vasOaeXIDPTXkGSSj7AMfSQr0Zo9ngTYmeec42VpSC4dpBF5lHNow7HIMUdDyqEBRODpnKMtJHN+tO6jY6ZV/idq8MzyyT+czEUeS3j5vc8oX9nOzeMJeOXJm1GzenESRmGkUNghUkVEDi1fGoUjbRSQBZ3qVQsZMuzep0ZgPbXMnh84AY8/dAU3E7s4u1WQVSdHapJ1jVSOgsaVceboHB7VX2lfJaeE8coLffHp2I/Rus3Z+GDsJJPW06567uk++OCDD9CyZSuM/mQC+5puStjvV3e6iFqOK1G1Ri0sX7wQpzdvgeEjPjDXRbhddfFFWLGC12udhuWLFqBRsxYY+v7Io6qXF2na1Om45grJJsth2twlHOsay37tz8fY0R/ixeefwmfTvuXYdVL88st2XNe5Pa656SY0pbblqy/1xf7de/H+mHEkzTj+PbC8ArL4lrxK97cIrShNP0XdMW7MB7MORwq6j0QaKkhDjLcbj50KihRT8O5/5+jk+muJsZOrv21rLQJpTI7ziuMEiTvvpA7tS03mQ5km7/iQ/frbH/DNop9QtWphPuT58CVu0vAJJ4VxZ48z0bxBUUO8yBdYj56foGR+H55+/FK9EriPAahasyw63vE+3hq/CA91bWnok1ffnYl8nLgMffZikg+cDLkvCqOWzYexNIBfGTQDxQvmwpuPXETfY05nNW9SFev2fILBI2fgxYfacadUFKs2rTNkVutmVQ2ZoZhtmlXgb2bK/3oPr9iwkwTKmTireRkTR+09o0VVY7VAL6mXSfDkyR+Ppx9pS1VrajexffXrlsGV3UfgvU+W4e6bTjftibAiSQdS0OO2C9CyYQmTt8pTcF497rTPfZrecNMZuL/v53jgqY+QEB+H+rXKoFq5wujYujKKFS3I2gO5qbrdumVFjJ66CFde3sDs1lHySZNW0q9ZGNecV5P+xlJZ7yh+2vA7et/WGq1aVHPLi6FVk9Km9BpVCqJ8kTz4aY0PHVpU5jkFklAshEpQeGXAVyhUOB5vPn4h+9m5ek6TGtj86z4M/2gmXni0owErhWYvw5zg3nV1fdSrU9aYpmxYozRaXzcA0+cvx8gXrkECd5akaAzwBdyP/bRnZzIKF5TpTaejQrRtrsANSGZypd2ZMX8ES5avQa7cJBLDPiylZt4cat9dcn5tg4OPpJjmAa+PmIFTCvnx2hPnmTxU1TMbVjf1HEgNutd6XcC8olixfhf7vSXOPL0yTSuwHEZszj71diG9PnQGChemU1mOH+WrfFpw/KzfNR5Dhn2Nlx/piDrVi6Jk8TzUbozSB1x1g73i0q2b06FOpzr1UAE2WAQsAhYBi4BFwCJgEbAIWAQsAv8aAumm524d3DNaqKYLWmdJ80taYwoyQR+guS9t5nSCu63Pm+JzDaC4ns8xrqLceM63dMn0zwaLwL+NgAgMSR2CnhRbv11TdmGOeRPccS8La2FuaFXQ6FVa3QKyAhSmlqVfhBT/SaYeNWpDjlaVT7trGTT65eaArAZNG9JMHMkNQ8i5xYQouNm/Zx9u736zYyoxg0A+jsTGa28NRi5pHFG25AWP1PNuW4/MktDeu+bFNYSKe8CaGvmGGB05U1Awdyhvc0e4f+g5kPFYcpHjPah9CmnuuNgvPm4GTlV/UuYhrbusQpLSuhBIpnbXLRdwg/RedLu2DSrRqlLEjJOAsZrk5RNkVzvSGz4nuYk8SqKsIuUkzz/dBW+8NxH33dbGyNDiE3mNchIRdBTncBiRBJWTK4ZkT9Mq5rJm7rPSx4ZI9mQ0G2nK0AhmvIKP4lvNlVnOCGVFtRrUx4yvp2Pzth04lZubFVI5wCeMG0NtrVrEyQxiDg0fXuj3f1hMMmzGdwuozVUSP63bhNaNa2PE0MG4osv16NvvGSxeshRfz5132PVh772La66/iTlTI80lbU1B7h85VdH4lLaZwtB336CWWmPMnTMbSxYvRsN62tTuDLS+z72AZk0a4snHeuPR3o9SxhmkHPFW5C9SAvfc18soJMjiVYpckpAQZueYPLP6I02x9EEWtAL+FN7XvB8oyJJiAo0pIYGKB8myNMpsRX6KCBM+IRYR0j0Wl2jI0HgNNJ1TpoRP1pl0m8ZRq0yb2aUppuGkftDm8b//FmJpKoRl+SiXjoWTqL2qd7Xud1aU5zMGUyeqtvrN4CQ1ybESIK50zXcoZJLu0MXDf3n3wuFn7ZFFwCJw0iCgl452XnS6Y4hpcwrtNstvVIUKRfDM/y405/R80c4cvTSb1nNIMU8DavX633BF25puPBMThU9JRO2axbGEmlZ6qIqwWvLDDlx+/mmGFDOR3T+arGjjiSZGi77fhitJCu3eeRBhmgLUJCkYR/N/JOg+mrjcPC9lB7cItdCGk7ySI9ZGDSshD+OKtDGab3wA6gFelATJiInLEJ+nBJrUKYhcZIr00Ne7U9eXkaS58oJadGxqjP9RAyyAIvSFVbdWCZoV3O7WLsCJYJyZaGQkxdK3If3vauVLYOQrXTB95irMZ/t/pNnEeYs3YvjY7/DAza3Qrs1pph2XnVcbE6etwtxl22jC0TETOWn6WjSsfSry5s3N57/zCipcIDeG0icXfa+iVaPyrI8QddqR/rnv1UHvBr0Ihef85Ztw9YV1sWsXtf84S4roBUxGUmWMpnlF7yWjHV55WGajuuVNNio5MU888p+SG7UrF6PNZKdM4VukEOvGsfDjxp0oVMCxU+2VrW8WrdeXeUnruPcrX+qAEyi+uDh+Op1fH/+7vql50+pFrrfwrAU/4YoL6tMH20H2UaKic0cY1cvrlMLA0XSuzaDJSNHCeTDsk6XEIIim9atwZ5lQ0iSNL03O3r9duBZXXlgbe3cfNJplsr/sI+NVp1oRmotcYvJx8mIBblBZWQVTx6wi2GsWAYuARcAiYBGwCFgELAIWAYuARcAiYBE4DhCQcDsnwfh+Zwbp18GOmUIt4WlphcRXYmIuXNL5iiNovHANHu+QbJnVQ3IKaZmJNBAREZN5nHTBmD+kbEdCfaMh5hJ26aLYn+kQIIxGC0hkVyo1vPLQP9hrT1zHTQKSoRixS7rYf/wZCcuKD/uVeFek9aNX+txoNL20bzg5OUZrP9kITP6YZY7OGBER66PxV6BQUdRv3Ayj3h+Ku+++y4yJD4cMRrny5VGqbEUc2L+PdXfG+5RPP0Wb8y/i5u3ChlgrSZOczVq2wqSJ43HVtTfii4kTcNa5bf9wfcrkSbjqmhsod6TWoyw9SaUqQ9D417hM3Z+EafSP9/ag4axfAIMHvI3GA94x+CnOKQUK4Ml+L+GObl3R8eIO+GHVCsz7bhbGT55piDBZPPozQTI23jYmUCxmylOvqN8UJKfVT8k6JQvULaX7x9HIdExJjn2hm4kjzvyy+98lGZps0n78+u1G3vnJS93NsSP5Yx6esNHTaDBX/8QfDcSsgi6zUJUboMxSpJiC2iYN1YxBMFAB0gSv/RnjHOuxJcaOFTEb3yJwoiPgvjjC7ladpBQ9PRNw701n02kqn6I8rFCyIMqWyWMIJD2B9VIyHxJc7nOKWkUh7KP5wb18KhUtVYg7Wcjn86WqrQd6WOVLDGDlzl0GrQO0oftb6kHkP5V+qVz8Yj5uS2Hw+6juzu9wMgmOpBg+/GwefVqRDIlSe41BZu4URK55pEvf+9rj1YFf47EXp3LnywyULZEfndrVQgf6K+M+D/Pv2Xva4gVqqT3xf6P4cPWhfIXCNP9Y02gJJSfpTRFF2VMLcP8FG8witJNK7/xTEuPpA2wHfGKi+GKMUnMrFGIEPphVE+FwpCBoZcM3RFvO7VvVQsez6cyS8X/YtAe9+47Cy0O+wfktRSL6UaZMQZSjs9MPxy9Es9MuwFKadvxpw3bccVUH7pJgcdzaoR2KT917Ll56bzb6vjEdT776BSqXKUoSqR7atKpqqiG9sjAL3kf8Vbfc7lM9tp/9luzHyPELMGriYr7YHIebwaC744RspGkPc/Fxi0mc1PRVrmrnvIvYlTT96CdR5UbUpiAfiURtbDH7tRgv7VXl9pPqrOTqL70DPxnQDackBLH74C6SXEsw+osl1Dgsh9NrnkqCC5zMsF7cqTbqswX4+IvF3BkUzzHBhO7OtyD9mGlYqZueuZf9Th9oj7z0NScuU1C62Cl0rNsUHVpVRDJLTErZjxETZmHU5Hn0m5ebkwLqJvr3c3KTwokBd10xG3Wl3vKec1iv/n53x5MmFMJRL1nvmykcDPRDsw0bLAIWAYuARcAiYBGwCFgELAIWAYuARcAicJwhoHWsNr5KqGzWtJ4A5xjqKfLKCJ25Lg5Ta0wbY+WX++BBaqrQUoyf6/uLOnTIlBjTRmalVzDE1x/KpTYINx9rWW4EBxmum6SsvxEB/Im6Z8juP38oAz6iIefPXWbamkoiMUiCISBtLYZINsRijFqEInmilOv5RUy4shCl1e/T65WnLMUREEmL1gtGE9EcOOcU1wv6eSwmAr10+pa/LmlTRVkvjZ/Lu3TBgNdfNcSYjke9Pwydr7oOC+bPM8lkwlP+5FZ8vwzX3tjNjH2RW7k4RhvR/9gbr7xMLTNg9ffLccudd2d6Xear1P4I8zdaaOkqZMYwZWk+yj8Hvdsf+fPlQ9u2F9Gq1AE8dPft1NJ6nWJDjWlpcAVxcYf2+Hzc+bjz1tuwY8c23Hn3PahRrVK6HI/xJzW81JOeNhSNMhlNKV/AuS8NMSgxLoVdGgf61m1Dr2cOyRTT2SMHv6uq6CqZYu7iDVi4apvxo2nINfZDToI0/7IKAfpE1LMmLpqCpXR9E4lRMYFYxmi5a8nK3/HOB04/U0BqsgmrXRxgc1b/juQgZX58zuk4NRhGKosK8bqCP4OmnTl5hD+WGDsCMPa0ReBkQSAoW9CcfbSsW5o2pp1HgpmEeADw5eBNWMwDhy8FveREhIX4HeBDa9eeA4ZUkj8yTYHEsUSo+p3IXRd6JCewDE3OfmO8jEFkv2IFqAml/Ltf3QKXX9DQKdKtiIgivmPSQmWaJuz/1CVI2p+M+Qs2Y9xXK9C3/3QUKZCIZg0qkAChI1cSYf37dEQkKRkzF/yMMd+uwv/1/4oaT/lwOh2MKuxhemfG5WSdyralsPK5E6lFJgKEWkjScmLlTd2cWNn/NS8kpld79BqpXiofrmlfD88NnI45S3agRcPiZkrR8bxaeL7/DPyycz/GU3usOLWxGtc5lemEmtJGUatKCQx65mLs25eM2YvXYvz01XjqranInT8XWtYrbcpQOfJdpiDNOWm/+WVLkX1361VNccWF9dIITRMpwx8z6dU5vVCVjTqRyXVeu08MF+T2RSpfUM7kgCf437HxrcRO+c5fnXfar0mCbBgXi8uPnjeeiRX0rdbv3en44NkuJk0iVb5FYHW/RvVsYHLRkBNJqSIN18rfwrFGxXzo/2Q7JCUnYw5f2GOnLsczb0+jg904arudyjEUZHsb4Kp2jY1pRG/MKK3GmHmlexXksRfY1LSgemcMmZ3LGMceWwQsAhYBi4BFwCJgEbAIWAQsAhYBi4BF4N9EIG1tz0oYkuoYK+MRGt4aWFocEr4nJ9MCjNRRGGRaz2h2kZDw/GHpfCiOW2u1iKf8JEJZgg3/DAIiNR54erjRsIuSaFC/+2ixR6YnU41U5cj1CJI4S+VGaY8Q8oskM2SZs+X563F9TB+bDejp+pRDwgluN/tcpkzjRtE0LvSRhapjDTIDqHpIFnfpFdfi8YcfoG+wpShAjazF8+ZiyIfjMGf2HCOvkixTfu2iyWHkphUkBWkkxlifAvkL4kDSAaRQfhSlfNIbv+mvH0xOMvItn+SRkoG5pK5XZ419ybzUJvkQu6jjpebSRR0vQ8//9SBRNwRdqHGmdPLfJ9lWv5deQ43KlVGufEXcdc+DBgfdOXHaGX6MoU2HR0wKv0sM+WMynunI4VS3Np16m+OAa0rV70vBlDHPGFJM18VridjsdG9/SGtMxqDGvXiDK70ze+NpJjKKjvcMNnheTQtXI8ZSUYFEoPCKRGR368+H7ImxeENsxpHoC6vfKIjUMyjCui/7YSvlh5udwmOurJoWofSMS6WVM6NdlolW2bHW9thH6LGWYONbBCwCxxcCYuz54evKfILYR/Xog9SscTS4IsaLF3/rRadPkA94XxI1ocjEk/gCH7TafaBLfpqyK0OHrfNpKlBBD3u9MCIklFZ/vx4NqhQy9oUD1OuuVaYA5n67mnH0eubOI1qxpdtWPvaUEwkYPqGrVimAL2ZuNKRYCvMR4y8yIywDxfyvIDPYznSMdnQTg2hxRgX0692OJFwIs+dvciKxfBaBCJ+d0bwBNGtVHq883I5OaOPxzUzWgS/YKrRR/N3cnyCFOQVlr7ovW70J9WuQONPTkfGCKXQGSy0mBZkpVL3Sf1T7tA/TJLBsxdZkREG/ySVh3wG9KAPIV4AtVt2YT9tmlZEnMQUfTvwOE2etQtvzazqaSczDxwd/kLslPN9gibnpl6x5dbxAP1lBarXN+c7BqfApRRiXWl0sQ5s9RIqpLQESY+WqFMUUEoJ6wXshhVfZm2l11vkYJ7sRamYJMw9nJ75eOoZOcjuCZ/3J7Cv3nMFOmR8qQBMmhWBMNn9pljNC33WslzBK5VadHlefga1b99EhKR2VsiP1kq5Yphi++nadyUVZmtzYHv2m0p7JkYccL/zNF2EcbV23aFqFPsMuRj6Oy3mzf6SzUz+qly2GKbN+dtqmvmd8fUwGrJaZpPNEoXzEV4a0+dsbS4b9MwygErBsvoy9jzmhwWGDRcAiYBGwCFgELAIWAYuARcAiYBGwCFgEjkMEJDB2PiSuKNQ2m3yPsZ6edpBHaEjeLmswzMx8jFYRrc9EuYM1YLSRDskDUlNSKb+QjylKjKSm4xJpx1gFG/0YEJAGTZDaMtVqVEStOjVQtVZFVK5RHtVrVkK1muVRu3rFLD81qpRBHcb14tWqVs78Pq1aBei3xoPZGE/pjCcfyeo7fdU9kjX9uaP5nT5dHOU9HTp1wrChw6ixNRjtOnRCwfz5TJ2Ul+oX1VgjIRVxTXBqw7S5RkGYn2RUHOWACgHGFQGY/nqQQim/LEUxbWbB2wi+bMVKLJw3H9fccLOJFqbFpHMv6oD3h42gjMoxC6q8/Sy77zNPo1Dhgli/fg2WLV1GclDXpYWZWQlZnwuQJNQnGEkyHyMwpIxM8tQwrW8FSQw5H46DaDLLoOIDs0zxxfPjWOHKugTvqnMfB3yUDYOKD7H9iKXuIqnH7fo5+ATY7iw/4b3U8jpAuWmEMj0+aSibTCLh7g+EOd4odyYJr084lmI+Efogi1HOGPMdoHx1n8HGT0ItRF96jk6Z/oYo6nP+ea3L6tsZLVnFsNcsAhaB/zwCZmcDXxKZB3L23K0R5sNJO4PSB/JGuKZDffR95yv0H7kQN15cF/sPHMSz73yD3XuScB3N3MlMXiQQxS2XNsQDz0/CMwO+xa2d6yEv/ZBt2ZWEFSt/xDnNqpMMCeL6DnXx0HNT0OeNqbjx8uYoWTgBSZGDmPndBuzZTVu451VDClmmoeMWo22jU1G+XDHzgpk2ZyvCrFutqkX5EGd5JP7e/WgBWjcqiwrlCppH4uTpP9DmchgNTitlmJcr29ai5tI3GDRqIW7oVJc7SVLRb+BMamal4PJz6UDTbap2uXi7RkQwkS/JMlzzwIc4o3F5tKhbFZVZ9s6k/Zg2ex2Gj1+MsiULoFp5mp1kHsomQGKxfesaGEZ/X5H4IDq0rm7qJuZM5VDZDQM/XoizG5VD1UoFTblTZ6017ahZqZA5rlq2EA4mpdAM4SqUK0XTkMy8Xs2ixjnqLZ0bo/dzH+PpNz/H7V3O5AQikY4+k+iLayN+3Z5k/HEpE+3I0a4ZBaOdp3dJuvabC+4fx36xXuqZv9Wl+i5fZN7OMU2axHRpdMVz0tGA/tRqVC6OYRMW4WLhzJ053S5rhAefH4+n3/gCN3c5A4Xy8rUYTsa38zZhM7XprrqoDvZzB9AI+he74Az2afECVOaL4uuFm4hRCmpWLsQ+Am66uAEeeOkzPPX6ZNx8VUsUZj5iM2ctYnt/2YfLL6prgK9UtqBxSPzxpIUoXLoESdU4NGQenK+ktcvUO33D1Q51mg0WAYuARcAiYBGwCFgELAIWAYuARcAiYBE4DhEIclOyY0aRG5u1KTqHa1jKqemaQDIhJyP5STJECY8lR5DZOxN4bDTJKFeSaTsRayItPFnKcQjVf6JKfncX9CtP3GR4yFQJXhjiPJ7ncBGec/EE+isNrIuv6IJu119PK1Iknfo+x43a3NhNeZA+IrTi6NMuRLnSvt27Od48OZUP69etwSm58prxGqIsKrPr+XLnMySvZFiSAXmp00Mkwmv44AEI5c6FPg/d68jOKCP99dcdWLVsKdb+tB4Vyp5K/AP0QTYN79Hv2KiJUzB44Nu4/Yar8dX8ZYij8PSvuBfkuIQST34yl8mprrpVJa6TzE7ctMo9ko+xsW92pxzMj3EvdTVNHvXJFPOttEYmpsxyEP4gV8uQl0PkxyjPTeVzi23ybEa68dLSu+Pa00zUZV3zPkeST2YoLtNDS4xlCos9aRH47yIQcR9sQZfYOMgdBb74PHxo8knDl2bA29WT9vzTD5rqkwNVam7RMKIBR6bu9K4995zq2Lg3gvc/mIn3R3/HfFKo4pwbD9zVDqUrFuY+Bu5WIMHTtFFl9OxxIV4fMgMTv1xMU3kpZiLVrk0DXNA0SBOGEbRknMf/F8MbI+ag861vkE2RacZE5M2dgK4dG5lJnXxETZ21Hh+MW2C0sqRxpgnYpReehnNbVTMbKGKpAXw1cyXeHzsX9BdqQjwTXnpRTbQ6o4qp93nn1cHPu0MYNmYGhnw0i+mCrHcuPHTfRShdrbBJE6P6eUgvSPmfku1ang1lMbHQC6dWlVL45IvVGEjCLUTChV7GzEO+YY3SePz21mbnlgetHvxt29THexNX44y6lUjk5BaPw8AHvF5krOmsOT/i/TGzuSMiiS81vgRDp+Cy9g1xbuuqRnOtGQnCC2mS8c2Rs3DgYDKK5EvAZwPoPJS5tKhXAo/efTFeGTYLX9z4rnmBa8dMvjyJuJxklIpSvIA/l9nZwp/SmDaBGy6QEkykllbAWOmNd+e7qfH5qHFGP2DuDNs1Sewk4t84ahiq5r5gftY5D78TSJKpRdrx4efOEz9J0iaGCPv4q+9xyTk10LxRGTx+70V4degsTL5tuHlx+6MHkSc363lJc/YwkIcTnm9nr8TI0TPMhENY693Y+ZLGOPfcOiZO08bl8MTdbfDa8Nm4ovs7zkSJO2Xy5k1E1w6NWCvurGFmLRpVw3nn/o43xtD32t5vkTdPAn2h3cSrrCfvD/NCdicCaS9iXVQGXufp2AaLgEXAImARsAhYBCwCFgGLgEXAImARsAj8TQgYiznHsAYVKab1bEJCCEnc/CuThzkJ4RRufE23iVrm4ESOxSgQSqX/MVl4ETkWkw8ELpajPBfmojuR1n1YDRv+ZgR8JEIpSqMPdpJE3C0cog84BXGiCj5q4WQVwtmMrZyNnqxKPvprTZu0oGwoF/bs24ML2renzIxmIim3CXLceb696tZviOnTvsSNN99o5EWpNBC0dMlSnN64IejZBXXqNsC0KZNxw003HHa9fsN6RmbHUYxUmlz0tMm82omES9qfhE9Gj8Klna9Ck8YNHMKJ94Tus5dffBnD3huIxx57DPv37sMD93THNV1vQqtWLVD3tOqM3xBP9umNR3v3RjytXB1rmDLuKTeJpF/sV3Zsm4t7I8gG+rmze/K4Jw+7bjqet2I82y+SjD5w3OuZfzlSNW1kd27W2qxzl1AByuR4T1MmRl23zBMe5VlPvHyk6AEJbFnPVH8SFn+/HUvoeiVC05ixYARVKxdB06rcTM8QDh40336X+Iz6EswYSKLWqp55japTO067/NNMLproR/XHx4bmrJVHVYw6L4aC5evh9/WLjzKFjWYRsAj8HQhoYqWgfQYKeqHoKeDyZTzjzl5ElCnIRh9j87HLf1LZdYJ+6yXpvWYDSVGsWPMLEuJjqFyxKJLdyRMPTf4yi6gy9Cr4Yd2v2Lvnd9StWhGBBObEODIv6JZoarZ56y5s+fVXlClRAsUK5zZEkSmZ8RQinKAt+n4TzSP6UaNCSWM60Ewa1Sw1QfMBTshmr9yIUHwcGlQuxgK8VjtliYTykdlZyodvPF8q1aiFlerGMe5gNZNLe5IrY33cCvBX5sG5voc+wVb8uJ14gBpNpWhP2Gsd685sjA8tAj9vNR1yPj4RLz50EZrUKZFm2s8U7SYJ749i4Zo1yMXJQHm1lXUU9un7ze01Q0TqvPpFk1Svv3Zt2YX1/FQsUwQFi+Y21z2Y/tAipld/qLUKykP9qOCZngzR1LDZnJRxpqTxIvUtqnVz0wdV1z3EnBpyzwp3nIlAc7rJaaKuOb82bd6Frb8loUKRvChUnERh2hWVzkAzk/NXrEU8J/o1K5YmUehgwaJMDl51tu7YhXVbtqFs6dI4lb7bFNQEEWOaH6h9CumnJhHarYzQBGSIM6eUlBRjetN7RYqojHACFpDDNBssAhYBi4BFwCJgEbAIWAQsAhaBfxSBAuXqZilP0vqn/4fzMGz0fFMvCe6vo0WJ7pc3cOvpLq68RQ7XAwXK1jfrQfli+W3dEhPPp4k/gwSQ3ia5t5TvKCffCNeNyve2y+ubeGmLWOfI/rUI/KUIOKtoZ03dvPOb+OTd2zLk78Tw1q3yAS+Thp5rB7P4dVPIqoun+ZUhk7TD9Fpeii+tHCN/IAkhDTEvD91f8k0k4buIMblJoDzbxJH2jYIItPSkWloh9scfEKBXLXPOz2fRGRf34YZiSWEYJNzJIkwZR39SkulRYmeIEJcY85L4PNcY3okM39kRY/GUh2lTcquOD1M2c6guHvFGO4ImxwCJFAUfx8X00Y8j4hIy2Y03kyiTP9dfey2tUe3DmLFjCYHGITURuSE+6HMkkF2vvw57aFZq7PgJlDulYvj7H6LnfXdg7CefolGTZvjiy6m4+vJO+HjsOLRoeRbefe899Hnkfnw8Zvxh10ePGYszW5/DsezkazBMV58ox/zE8WNwS9frseD7jSh1amFTnmdi8Yknn8D4UR9g3pIVJMXuwDfffINpsxZwc3Yug9solndnt+vw5ZSvaeqyLtSuPbt/w9gJk8y9kh0+UhUwwcVZw6FNp8foGsTxC/cl+98Jh95vktORKzLy15gvivZdByFQIGy0xjxxpisOQwo33ytul7vfNDh/Nej2NDmv5JY5lX5J9pZl0DB3uFy889FsvDduKZKTDhqit8tFtXFXlzYmObvfBDOs0uUpukwI5Qp7fvIcCZ82+it48kHnKPO/bvGZX7RnLQIWgf8eAiJknJD2wzwwvbMeQeE9ML2nlEcgZHywuM8bOtfy47RaJJ/c4Fjx5QGLESGW/oFapbw0shytLBOd19PycZKgVIn85mOu68+h6ppT8qHVsE6ZtMv6YdqmeF5mVAluWrvsYXHSZ8M1jYlcpxp9irnBa6cpMI0U00WlTJ/aTXCEr3x54tG03uH186Kqnnp46yX4ARdY5YvmQ/M6h7BTPJdXNEmCuf1oVKeK+X3YnyNUR3h7EHjtKVIyP/TxgvoxY19619RML73OmXhuWWn9mPYjLZX7g6C6cY3J8bTLTo66JFJM4VAZh36VOpX9fqpz/fA47jkSYQ2pXZcxeO30zpcomh/6pA8q2+MnM2t7gD70Au5IlT86BW8xrDZZUsxAYv9YBCwCFgGLgEXAImARsAhYBE4gBA6tNdIq7a5XYISsAQr+uS8+Iv8wjOFJfCUDZjyt2+jhhKe5t14WPEDLJ7TmEeMOd5m1Onyly8MMQeltOHkRyGT0HRMYabwIx6NHfmWVgfaoeqSYSDJjco3Ehsy8SagsoirVdaOgfHTeBEqwk6kxI1cYIdpPlKsLHpD08hkrOikk23zMPEipu7kPmKeP8hZpzUiDJ8z7R6SZykxJTTabTePiaH1H9xGDp40j4k1B6+wwN6OKYBAxrXrIDJ40dERG6LdHUuh3Klk31StjkMaQiBtPuhDl7lwvL10TaRLgxlcRH8pPeXlpQjTNJyxUXvo0qpvMQSq+6qs8gtwgm5SUxA3PzMuN77XFu+6lSV9HbQzOKtDriBPcZ5L6O8iOChi/TjQX6Ptjm9PnF1SHM02y6wc+gQzCiE++wrltWqLAKUHKNxx85CdKJgeTvb53CTTHHhSfcazI7r0HMfHz2bi6Y2uk0FRSkPKwJOYdYx8l0I+ZsItFXUmf269e9RFMMtXyhxKIJUlRlcs4ceYJmr7GWf/W2Aqzr4IkBgPsy4jYGQYRROxO5s02cCxSHIhEqYJpPIbicdXVV2PDulVo3+5C5E3Mg4PhA7j/oV44o/W5ph7Xd70BWzatzeT6eRy77H9WOsxxpJGfPsji0rChQ9CoaVOULFHYaEH60gm6rrzqOrzxUj+88tprGPruQEz4YgYScyWyS5xN+Rd36oAJH7fBHbd2xddzl9DfFscSC3DGbPqSMv8t/2EKYbdavIt5lEJcuTVcXe+9yzyceWwQI1bed4RYhlL3GetXB3hW92VEpgs5dtSf6q+In2a+GDi6zbfuJ8ktD0fDvfRXfrnDW6Umh3lAjYr4QC7T76EA5XluBdLqkdZepxJpXtSChwso2aSjDlnfYUedjY1oEbAIWAQsAkeLgCaPj77xFWYv2sSnfyr63teWSfXC0+vAPpaPFkcbzyJgEbAIWAQsAhYBi4BFwCJgEbAI5BSBCE2JOJsmc5qTTW8R+PcRMMQOhf2Txo/Dvr27kJiQC3VqN0CpsmXIVMQZskdE0YxvpmHTxg0oX6kqGjRqTIKY4mQKnn3SIkl1BPKzvvkKmzb/TEssFdCkRcvDGidCyOcRa+6VVJFIzCfC/CMU6s+cOhkly5RH1SrVDbFCHsOQVCtWLMWmn9eZVIl58qFa9RooVrQ4hfFpOjImvuJt27KRZAnvUT4yGR0AAEAASURBVJFnIt9IVhUuXBR16p1u0qewzC8+/xR7du1CrsQEnFa3ISpWquQSbz6sXLQIm7dtNNpvSqD0yq9IsRJo0qQ5Pv2cWkc89szoiTxMIkkYJPGizczFSpQypN9vO7ahes26KFHqVEOWhVmuyly4YA7Obt2WG2lzLssR9/dc72sh1xIiDcOerwvT0j/+UV8reN/PD/wEkyZ/h8++WYeXnriW7jqCLrmXYlycOLvJD89HxKRIsTt6vkvyaAu2/boXPbq3ozUdEmFkXlTEc72uNmXEIh6V5uTh0SgIOMSKXHHkJESpFSYNxEEjPmT/K6e0Eky2ui5ysv9775s+EYmmPtLn4Uefwl0PPIEF8+fRhGEjWgAKGpItq+vKVNdFcqr3PLLTFMY/IrA+GjvJO/zDd8WKFbFjT7KxTHTn3feRCHbqo3TKy8/vwcNHm3Q6N2j4SEPA6YTK/TNBfS2ZokeAZ5WHrGQpiBxmdbhRXESpUzZ5TkOKiXvzkySOeqSpk+Sk+Zvzu/akgco21CJgEbAI/DUI6AXW+dxaOKdJZVQtVwglisjUn/Zz/LkX419TK5uLRcAiYBGwCFgELAIWAYuARcAiYBGwCKRHQKs0imop8KQuAbUxtGLzc5d9lH6Xedbdlp8+xeG/szUldXh0e/SfQ+Bwwf4fm5emC/HHS3/ijIinqZ9/gdu63UiXEvGoVK489h3ci5U/rsYVl12FF197nSbqDuDyTh2xZvUq1Kh9GhbNm4vGLc/AkA9oti6SRA2aMLW+grihcwd8//0SVKleHcsWL0PLs1pj4OD3aMbN0exS9XR/pA9+VsAxtxjAFPp8uuaKq1ChYiXMmTefPpaC1Pah5hEl8/369cMMXpcRQJFd+w+koBJJhmEjR6FcuYrU0HIIluf6PY9p0z6n9hC1xqRhRrUhEUYtmzXFiI8n4PPPp+Ke27oasqRihcrYd2A3lixehGuuvQHPvvg68wnipef70c/9ZLrPINFEwlDly1daXZKFYz77Arff2BVJ9JEWogZcMomg1APUDJO2G8sJUFP0gvYdsXd/KiZP+hRXX9UFz77yFrXvYvTjloDpi5fi6muuxcbN26mA6miepccju98pYivSBbkwqV2nksFEBAhdh2UZkj0RUtjR5Dq/TXN8Pn2V0Z66t+freLXfzchHX/Z5kSAFQBwIOgRWlOSngrLfTWLnvgfewbbNWxHIewpanVEbIbqY8PsCfMrRhxn7rE69ugiTLBVpZYJbbop7yF43p0PELJnadvFxJG9IWvFp6cQ/hr8y2alwJOJI13UtYNogoscpW3XLTRjOOqOZSa94OpfVdVND5pXC8WXGrquhZjLgHxoVyjJIo9KUwzwURBR75R2qpwOSc15jj2iR4DuaEGUfpA/SECO3RR1m9ifHYHaBLuioaZeMj1+4hV5JSLZGEnHJfW+QnDtoiLWRL93HdgOj+91qMIVrs9BPlzNOOPb+y65Ox9t1S4wdbz1i62MRsAj8txFw3121qxbjhJE7N9JetPqhl89//8Xz3+5g2zqLgEXAImARsAhYBCwCFgGLgEXgREJAphS1s95qjZ1IvWbrmhkCSclh3HvXrWjS8iwMGDKcJgyTaNotAVu37MAWamjJfOGbr/TD98uXYOaiVShauBDWrVuPM5vWwfvDBkEm5xTeeu1FLF22ALMWLEXRYsXw448b0aZ5HYwd+T4uvuJqE0em9YxU3Rw5f2SeTT67U1OjGPvRCDRp2gLffTfbkG91GzQyphIVU2YR61Lja/T4zw2RtnnrVnRudw7uuvVGfDptFjWypIXj+Dc7vX5jjPrkM2rgSLPHKccjYh7ocTOatWyFNwcMMRpNCXTxsWPH7/h5zWoTUQSEfGM1Ob0JPhj3OUkJSl1IgikvaRsp/LRtFwkzaQsBg94ZiKcf74mfNv5irumcfLbddvONKFakCEaO+RC33nUvqlWrbLA0ZimTZBKSFN9REBUm0yz+GEx5XSYFFTxTlOYgkz8eHt6lynRb8vJTN+KB3m/gZ/pcv7v3MLz8xDVIiMvLOnqxDn2LFJOmmEixXCT6nnrsWlQufyr7JIntpklMUpfCKkJ/cl6dDqX+4y9hKhOV0qbTWDhWH3PGHCfJJZFKmQVPC0vxgmyP/NipPMXXcXoiTOnVP1ldVz4pB5PRvVtX5uX5qTpUskiTt98dznsolznpkV6HYugWIElnzIo6Gmaqo6kPSVzhYcx58pxHKpoxyXKPRPylzzvjb/X3lx/Tr5wrV8wcpUOpXM7QYCAsRLNFqSLmaZuJl1WcAJ0ESsvzZAyWGDsZe9222SJgEfjXEeA0gbMc89/Uha9Ot07OBOhfr6CtgEXAImARsAhYBCwCFgGLgEXAImAROOEROLTz3WxD5BqsYOnTuDGe2l8UaFKkSkF5CopVqkuNiih2bFxqWkyXSWbLogSJFPMiFneQgnp6bPJTWyxIDzphRlA4fEO/c87+tQikIeCOk7RjakhRAG1Mtkn14yiC2UzLbDReswvJKRGaDfwFvehribJuQ/ZGU5JwasnCKFe6KA4mxzDxk4no2KEjihcuwOzCqFi+FFq2PoemF8fjumuvN6YUx3w0Em3adkDB/PTzQyF+hbIl0ZKmFEeMGIFOXbqSFEpmff4ouxApJgH73oNhTBgzFkM+HIW3Xg1j8HtD8PLpzU06mUQMuxozAT+1eliLCmVPxUUXXoj+b75Kk4cihgJpTiZk2jFCTbY4v0wbOphFqL0kv2NbSeicff6F9GPOe5PH0UgIRQrnR6ECDQ1UIiGMmcSA/GPxvqVpQrmIMn7NmEY6b4pjWsJyA/TvlUp3FwFqzBgShOfiqT0mLbpqtWoaX2lP93kE7w0bYeoos4xinOR3LJ7kSWbEianIUf6hoUsnph5WDNIQyirEuTjKf5zCAexHtcp58cITN+DePu9h3ZofaF7wLbzc9y6ckocepGJ8flH0lEBVtF27D6LHg/2p7baVbcyDZ5mmWiXH4XuSn0QQm8bucUKca5PP+GXkqZjTDwnudRdB1ld+3HhEnKWJ5Gz+drI4mr8ii5z0mTfcw1fx1G8i3rz4Ok4fFCd9fke8zjF76ZVdODapJUeCK32IJ+kW4IBhVqa89Nf026uPiEMvpK+PqQPHsoIXV791/mhCJO0ec/KXCUWFQ+Pi8PpmzNPPbgvG4tD2jrfx6et3cHT4MfKNu0l4OpqD/rDjG67TnW+ZpNMH3GO+HV9mGXP7bx5njeB/s822VRYBi4BFwCJgEbAIWAQsAhYBi4BFwCJgEbAIWAQsAichAjKIeIhkoPCUgkuRYhLAZwyuvJlSUcfkW8br9tgicEwIeONIhMpRhEMWZigMJ0GVXcidKx4lixbDABJMC+Z/R1OCcdQOkVlAmgjUGCdbseqn1ajfuLEh56ShpPFfr35DLPpurslexNYPq1aiSfMz6KdJRAo1TKj9clqDBli2dAkF/E6a+HgyTBmCygkw7tDB7+CUwkXQotU56Hz1dZg0zvGzJF9Hyk+EkoT8ykskYZjm+tauXYPSZcun5aj2hqnKqXtTfqUUTz7IpPUWoClEnStasjgGvfUaFn43x7RD5Sud4pr4jBeiPTmvzAg1jBSkxeNpZ6UVeBQ/Hu7zDD6bMB6r6PtMhNvxFhISEk2ValQujRf6XI88JOs2bt1BrbBB2L2PPuHIx+jz+64DuOuRIdhAUix37lx45SmSYhUdUux4a9PfWR9pjIXIILdt2x4dOl6K9u0vPuzT5jySrhznuvU0nk7EIF9/fmpoUoEvrf99JJidDwlGc7/IBGT2z5cTsf3Z1dlqjGWHkL1uEbAIWAT+SgR8qSa3ODdP2as3IeZOKo9u44ib2n5ZBCwCFgGLgEXAImARsAhYBCwCFgGLwJEQoEjQ6GBIpOmJNX3BOGp8ydcQtUMoMFQIxTnHxcvXNaampMkiwX2qzzGhFeEW/QiPE6gx8dgs4Ol7pXEWQIqnQWFy+eOfqF3f/RGUk+hMRqGrSB6FMEdjzcpl8O1XE7JBgyQuiZ1YJAVNLn4tm7ga41G8+tY7uOfObmjbuhUS4+NQnyYMO3TshOtv7o6D+w8guv8g8sQn8h4gMUXNKzkKK1GoGMLJKaxYBClJSYjSrFyehFzGnKEIgRgJqsIFC/CyI89QRWTGLrOgNn469iN0uuxKannF0LH9Jej1v/9h8ID+uKnbzWlJdmzfikEDB5j7avacWfh88hd48YWX2AYnXxF6CjOnf4Wi+emXnZqdoNaYWIpOnS7Bm+8Ow1vvvIu7ut+A89u0QRxNRtar1xAX0idY99tvY51dLRs2cMb0GShNrTlpzxmWg4TZeW3Ow7APPkqrz9H8aNKkAdpeeC4effhhvD920tEkyTKOUVozMTxNIOc7EnBVsTw50RFyCUad/khxtZL0XAv5qXdGU5Y1aRLxuSduoznFwdSsW4+7H3qNmmPdTfN79HoTm7b+gkB8PvTtcwP9wJ2KMAdr0C3We1YGjLsPFe5eiHjyK2dke1xKzE2QGnDb4abjY/W4Dp7pRfkK03iJZPAxpuvpzToerabXX9XoiItrwPP9JXaLwS+HcQyp2ZF1TO+njcmv37mN9xVQwO0X6tqZ9DRIac5P6d/DvCcPva6ccvRE+a+HjM/o/3p7bfssAhYBi4BFwCJgEbAIWAQsAhYBi4BFwCJgEbAIWAROAgRSKbkNUEtGpsacXf9S/qLmDNseDMUhiVofh7THpBVAIoIkmM6lP+9BJW0LBR8lweZ6mqkr53zGv178jOft8cmBAMXthzVUR9Q9pHZi0NFqymaAxHzS9NDg1fj1hNWHZXnYgcwbtjmvDeYt/wmzZ83E0iWL8NWXn+OBB+7B4sWL8MKrb5saaOyK6A2SIAqQgFLe4ZhDSOUiIQafs5VXZIHKl4aWfEzJhJvuEWM6kNpkKRm0ppTv8iWLsWTBArz4+gCjxcXbDOe264gxoz/EjfTVJQ01lf3b779h6uTP6AcqguX0edbyzNa44qrr09pjtMJICjZq3gLDPhpntMw8YiJEQkB+ks4462wsWL4WU6d/jVUrl2PKZxPRu3dP/LhyMV584x3jT0wZKo/hoyfyvnUIBdXT7zlqSisx+x9q9yOPP4OWJMjmfjsj+wT/cIwwyc5gMIz4YDySSHBWr1iSPsa64v7eb2P9pi24/eFBxD6Cbdt2ICEhDs+RFKtasYRjclPdf4gZ+Ydr/u8Up/EtjSm/981xkTF4vsHSE2QZ4xyvx3rnaYPHIXrLaZ93rCeKPjqrZ9PJGCwxdjL2um2zRcAi8C8i4GqGuTXwXkgn2wTkX+wAW7RFwCJgEbAIWAQsAhYBi4BFwCJwkiBgtAAoFvx/9s4Dzooi3eLn5pkhSDAhKCAKGMnZgOKaVkwYyCru6rqmt+uaA8Y1ravumhUDBsAAKiJBxawIGEAxr4KAShBUwszN73zVt4c7wzADIjADp/b17b7d1VXV/1s9vl8dzvdRhuACvy3qcyGQrpcgc+SYYyzEnDJWB3TkZLnonQnXdgulZhzgVYRzOY3sGAFzFHgLi2k6KGxdPcNzlZYtdbWxUihbzsVyBhQmsTKhy8QZCk5rIXTRuuW5pHiHCRpVlQzrJJOWISiD7t27Yf99uuMvp5+GoVdcjgfuvws33n43wyMW4eclP5eKv9bud999g7p16zmxyUSj2rUKsOSnxU7E4qynWSuNOd/M9nKOsW0T4JLlRDEbWwnrPTFiOB2YYVxx0XkIJor53gSwYPFCfPnpp5jzv6/QrHlzilIptGmzN0Y8M8Y90k+LF6NX944YOvRiXHntTXzPEjBpzkSzMIW0ukXMWZVzkFlYRxujFRcSkRz/cFBPHHTgfjjn3HNwyT/OxQMP3Ivrb38AoZyjqqgwgq3qMsxgapXjzTWwjh8mEu64UzMc338IrrzsQvzfBZe6Fuy8C/HInFTrUrLk4BXvvpzhCqGcgJf7c7PGJoMZb30p7OQN/s1iDkQ7tAxSgUiB83nt3roJbr72FPxt6MOY+90PTujcqnYt3HLJILRu2dj9XUxkk8yRRsYrvK5iOddeNuuNK5Jkuyx+asUEcy66kvFCN/ouv0hu3MiNy6tUvT99sdXf54/W/bchd6Ki6/l1N8SxPx/8tv2cY/7eP7+mvf3Xyf23i3v7W2ShSK34/9Xy1yNzPkB3zfvwr+Sd2kwP1+2N3Uwh6LFEQAREQAREQAREQAREQAREQAREQAREQAQ2LwJhLqLbv5ovXa/liqA5TczxZcUWPk0gc/9QkWuGi76d7p13n97p+56eiWGjZ7gQiwEu2A84qh3O7duJq4y831tnzNXWTgTKEig/Pdxc5HTjFFyrwoh4DI3nVe123B1V3mOCkQluJlwxvZi3Ak7tZddWe3D+BhCl66t9x454441XcMqf/4IQv1u4tA/p8OrcpVup4NR6j73x1uuT8acz/uSEFHN5zfjgA+y5V5vKx8D+n31yFI48ri+6dN8XoXQxc3yF6UYL4N83XIsnhg/DpVddx9fNxknvHAUlc6A1aLANTj/7/3DddUPx1/87D9szP5ldy+aW8ENsw+UCdPfQ5cNr5iizYnnQ4vE4v0coTqWwV1uGQyW4NEWwaNRb8jfB0GkC7Gt9SyHzdv3tgkvQq2sbTJowzv2YxtzGWB2L6f7bN9we9SgMrvjlV493vdpoumMjMubfOE4Bc8IRIQpR9h9yV8fn0ZhE4PcksJZ/in/PLtWWCIiACIiACIiACIiACIiACIiACIiACIiACGxYAszORNcXQ0lxfdwZTrgKFrAFbH5JpqkYmOOLW5bOsIDFfMsVWz73l9Cz/Gf7BZnlbgswp1g4yEV3W29nBd5d6ea3o73Hc0vjUH5+mDhjopgJXqu5yXJzb312JQyld9YZp+Ldd9+jewsoptjx5ddz8cB9d9DptJObrf37D2bowdfw5rvTUUwD1fhJk/Hmm29g8JC/OEebudoGDhlC0ecFvPP2FLqQIpj0Euu88Rr+cuY5zo22pjE+N/oZLP1lGYZefgVOPmkQBg4chP4DB+DkwSehz7HH4umnn+R7ZwzoMUp67i1PHMvitNPPQv162+DfN93oROholA4mvpdJClzz5v+I+T8swIKFP+GHhYuxkG62LMW2s88+E2++PZXCW5iOuiC+mT0fd995F1ru2gKFOVHMBLiSeAZz5/2ABYuWlNnW9BxrOm8iXphj36V5E/Tr3w+jxzxlqpIT5dZ0T+XnbVneNm+mWE4pt/Hvi5fOq/wMKvs9Ec7ANgYDdJu5WN1Gz1iAW4jK189LluGSy+7Drz/8igLmYatVuza+W/AdTr/0NvzCfHOUGRl6sYDuughKIhm3BTMUIrml+bfPtlS42G3m9LMtmop4G39LyyPGKm5DhgPnZrkdbVMRgepOoHrK2dWdmsYnAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJQ4wgs/t/7bsz1d2xX6lpZNGdmhc9hS/fJRBIpE9GsmLLDQsOK29bW+ePdpU8R8AiYcSmUm0uVMTG3mEkhVtVcT1UVC/f24/ffo88RB7mqtSmELF36M9p07Iw7Hn3UnTtx8Cn4+NPPcfwxh6CILqs4FbQLLr4cPXsd6K6nkikc33cQ85N9gGMO74k6tetiJfNVXXjlP9G1+z6lQwhw8ueHmrMLTzzyINp26IwG227r3GghurnMxVbCPvoNPhW3//vfePWVl/gsGYZbjDp3mzm9rIQYfvGs/7sA1195KS6+8CIEG27NdyyNae+9h/ZtW1mcRtrnWJccajM/1lfzlmD+nDnoe+whFNlSFKxCWLl8Bfbu0B4PjhxNJ9QqYWbqW2+gU9vdkInnQgCyP+t1/s+52IE2gLUoLmQix55hiMezL7gYI59+BslgyWoc1qKpjVLllxUlOO+qxzHvu/ko5G9x8zWnMkxlChddeze+pVD4f1c8xhxkAxk6s9D9wwGbayoisCURCFA553/ON3yxbho0b4elsz/a8J2pBxEQAREQAREQAREQAREQAREQAREQAREQgc2CQP1mbStdTzLJ4N6R0/Do07lQiFQdTjq2A844sSOFBfopcqET41QYfD2iYdPOKIikUVxcjMXzP3X+hqi/MuznDmOcsbtGvI1HRs/0XC6sNfDo9jj3xPY0eji5olK+fnOVVtLFzZbAKmlmTY9YdQ3/zh4n3o1nh53hf83tvRnmL+2aCJVmWDxLLjXnu6/x808/Y88OHRCNRd35EEMiWnHTm7e+995b6NC+CyK1PHHKwu7ll/iKlZgxYzo6duiKUCyMCEMvJihCuTCHFj6Q7ZpIFOD5bDrjXJg2hqiL48hQo+WWnNOsm1/8nEf559bl2MSvDPOaff/9HDrKfnDhINfl/g1d15xc61qcoMffgTirDLmZDXuCfTSn29PPSub8feki+/nXYuZAuxtz5v/A36kubrt2CPbcdUcnsH75vx9wwXUj8PPSBWi24w747/V/wVa1mZOMORdNf00HmXMsFkGGjjLn7kPMjIRIZr2OguWUhBDdZX6x0IxZuvzMXfdbnt9vR/v1J2Bz4eghd+PdJ8/M5Rjz2vRf83Wfnes/popasPHc+cQ0jBjzobuc5syx/37/tV/Hiqr/rueqC4Pf9aHUmAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhURiDD1WdbxLfFMX+xsLL6uiYC1ZmAL0QFaTXbeZdWaNupE6IMHWrOLpdvj3svr56FD82gc9fusLpphmCMMz+Xu+bX4T7CcIRtO3RydUwAS5paw2I5tSznl51LUUkxUcz29i6ZcyuRSKwmim0IbtZ/kGNs0mxndOrUdUN0sdHb9H6f39atr0OaKHbmRcOcKFaLOdFMFGvdorHLJ2Ytt95lB9x0aT86xYow9/uFOOviB/HL8hInilnIWS/nWNKJYgGesHllplnbu83yv+VtlvPN3zg1nCiW79j7bU+ju0RgwxNYJelu+L7UgwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhsJALm6vI9YjA/BZhNzPUdySTdv6IP0gpRml0s98/Huezv6thnMBvhHQyq6KwQXDVmjh2k2Qb/L23nKimeP6eSCrq0WROoSmytfPasO5ok3WIx5sqzsIsmYITp8jLRy4qJwBWVIIUPyzMVjYRL61ieKlc4v6MRbxaH3HU6LJdTdDnjzy5HmDk7rB9zidk+RsfRHfcNR+2tipxoljWbUV7JlDbsnaTkknd13Q+DYU+cszvt2ESlmlQCOTk+EfQ4hflbHXj85YhmKVKlUsgEY5U+zvgxN7nrgayXry1DsfPn5Qn8/eK7mZeNTrGCerj+qiHYvcX2TuQyx577vSlktaRQdv21pzGc4kOY9/08nH3xPbj9xjNQt04MhaECsgTinKC2P/rYv7nft8QFoLQuc5yz3l/OcMZzkmUZDvPVp69DiYmxdJ7F/HqVPoUuisCmI1D2L9SmG4d6FgEREAEREAEREAEREAEREAEREAEREAEREAEREAER+A0ETBRzbi4TuyiOmU5kzp14POEcY36Tfig8y/UVppgVtFCI1Kgy9sHiXzcxLG2hE6mO+A6gaDSCY48/wQklGQonaQpultvM7g3yxgAdXOYYsxIqJ4y5k7/jhwlhJoj5wtzv2PQma8o5snK/w7oOwkSxsy4ehh8oihUwzOW/rhmCVi0aOVHM5kOIrEwcs70JXq3oHLvt6lNw8WX3Yfbc70tzjm3FnGOm+Tv3GAfhjckmU+UjsjlgQ+d04I/PrWzkzMpv1lUR2AQEJIxtAujqUgREQAREQAREQAREQAREQAREQAREQAREYO0IcOneVcz5FEpv4nK+OzYXWIgrvZncwm3Acorl8oqVVv4NB9ZfJsTF/ixXeWmKMHEgZCu+XkqmqtaJf0OPumVzIuDN2t/+RDb/0pzXQaoUKac2lGuLgpYVX68woStpIQ0ZHpGmIASZ68mEI3ODuXqcv/klnPHcYL4O4+cgK63DNpy4FqCQQpHDHVPsOuzw3khRYIkVMS8VXWomjJUtOcEq98KaCGPiWozuMxqh3LGNNcv7zMFkr6o1Ya42X5yzfbjceMv24X2zttli7lLZ58udrLY7PwdX0OdEBikLS2h/YIL2R6by5wnbb8x70qxrGuRtt4zEvLnfIhath5uvOBWtd93BPbvzk5GzMw36e14JJILYq3kT3HDdWfj70Ifx7ZwvcNuDT+Hqvw2mY40OW/vBWVbQGWbi46rijSubG3gmkJtfnE9ZVgvYvHM5GCsf/6r2dCQCm4aAhLFNw129ioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECFBOi/caKTk69M6aqiOOGWglIimUSIopKJYlZ8t9dqoQZzTXri0uqNm3BjYlmAwpwJW74DzI7D4QgSVLlWF8XAXGQppx07gTqv2WQypwDxXDAnellb1r//dH57/j7vdh2WI2C6qIldFokxmcjgor8ei5+WLMdZQ45Fq5238YSwcvfkf40xUiM1LIZV3Bq3DD0J9z0yGued2seJYvn1dCwCmysBCWOb6y+r5xIBERABERABERABERABERABERABERABERABEaiRBAL0gpk45opz4JR9jGwut5N/NuAcZPQbhSy0YYbCFUUnCmXRSIFfZZ32Fn7PnEL5e2vAP2dONHOblRfcggzpaFH0Qqt0sHXqV5XXjoDL7EVx7KPpH3k3ZGI49bieKClejA8/WYSkOV0rKbFs2P2WyYAX83BwnwPx+dfzmRss4ubN3m2aVXK3LolAzScgYazm/4Z6AhEQAREQAREQAREQAREQAREQAREQAREQAREQgS2YQJJOsUiEogZtRBHmArNSXFxcKlz5YQp9RH4usTXpV2FLMsViOwtzaGEPrZjLy4of+lDuLodjk3zYT/GPqx914mUwUOh+mwDDv6bo2su4cIxrHlaIbjHLL8eYlUgmE+44HIogE7e8clm8/sL1a75ZV0RgMyAgYWwz+BH1CCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAlsSgbLLupEwv9PBZaETafZi/q8kogx56Ati5QUsP2tUBWY0B9EXwCy/mBX/ux2ba8y8bOXbtGsqG4eA5WmzkJStd9vTiZbBZHGZjpMoOz/KXLQvgbg7FSyXjzETWJNUuloLOiECNZpAFW9IjX42DV4EREAEREAEREAEREAEREAEREAEREAEREAEREAENnsC6VTWiWBhuoCYFsyJVikqZOGcc8jPNeaDKNU/WLfCYgmsVKotARMnrdx+zUnMKUfjV07A9Aec8pVP/0T5fe667xz0L2equs+vqL0I1HACEsZq+A+o4YuACIiACIiACIiACIiACIiACIiACIjA5kjAluVtjTaDkMtZtO7rteZpMQuNT8datHBwPEFVwPSAQJbHVs3V8YSAoLti/aqIQPUlsLqexRCKES/coYU5BOd2JMDQihmb4CyMu2eh8yz8nnN/lZ73XhALn2fFBJcAlbXcXe7chvwwR5ttlrPMtJ5kMi0n2loAt1xylt8tHPF+0+Ko94sVJnK/pzcV1thSGC5LGVLlJlI494cvm/s7uMYGdEEEajiBclO/hj+Nhi8CIiACIiACIiACIiACIiACIiACIiACIiACIiACmzmBDN1g+ZsJWqkk80NR2LAwe6FwyO09ocsTS0wwyw+JGKaQ5kSwPFHMsKWZo2pjlVAo5HJa2dh8UcwP/7ixxlAT+0mnPYuYOQWtpCl02mZ6p6951sTn0phFYGMRkGNsY5FWPyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAmtNwP/X3EH6xXI+mHL3eov95U7+vl+zMbaXG4mLPZezU6xFL2tfs+LG/Oev+KrOVn8C6zcDzLno9CpOcwt3Z+bGMiVQzhLECTNr1izMn/8DWu2+J5o32R6hUJjiWNKJYfN/WIyZH03DDo23R/sOnWmSzCKRiOOtN9/B3Hlz0LxFS3Ts1AXRUIabuTT55lEgC9LJ9frk1zFn7ndo2nRn7LdPV+cqs7E4BxrHlkylGLLRQjhyo2BjuccCnl6DRDqIlyeO5b3NsNtueyAWY7tEY0KY3W9iXjQYQpz1Xn95PBb8uAC1a9dCu05d0azZjqb44OOPP8KKFSvRpUs3l0/L+s7wfylzTLHPDz+cjkw8ic6d7br35qxkHx9++D5+XvQDDjj4CNBYtY5l/X6/qjrz+WRyP6PlhQtEAgimTawMkH3l/VvIQ8qcbnJYzWiuw1TEE8zKh0gsP550zkob8KqXXva/ps2JxuYLODATTzOo7epkwyvcPhGwv40M4ZjN5Sqjg80hTtGl6C5UPn53sz5EYBMSkDC2CeGraxEQAREQAREQAREQAREQAREQAREQAREQAREQARH4PQjccesNGDPqKfTo2QvPvTge6ZQnc4QiEdx47RV48pmR6Nm9B0aNGUfhJY0Tex+Kz7/4Anu0aYcZ70/HPj0PwH3DHkakMELRjK4xaht9DjsUX375GVrtsTc+mPYeeh6wL4YNf9IuIcP2TQRzQpiJYibi2QeLiSkmrL06+RWcPLg/WjVrijemfQxqaO68iT+WGytL8W7c2Kdx4XnnoXjFcuy1V3usKFmBWR9/jC7dumP02PG4/V83YMmixXhm3EuubXPKBeiIs4VtE8fuuv1f+HXRUjw9dqJ7LnOh3X/3f3HVRf/ArXff8xtEMdfNRv0oKARuvoL5wgjW2GSrSPZlz1hZqUoYq+xeu2ZGNPspb7l8sBMjU9kid0smvNzt40FPGIvkhDH+DKUlp02WfteBCFRHAhLGquOvojGJgAiIgAiIgAiIgAiIgAiIgAiIgAiIwBZOwFwHtiXSdLbwIMqF9iQXX835YWuw5mGwkqWVJj9nUpYrwn6+JFdhvT6S7MCzmmTdPn8pLW8luII+grn7Kri0VqeqWBdfqzZUadMR8GaNfdo8MbGILqBUwg0oEKSYRJeUlYzLoGfGn4irFmeOrTBFnxAtRclUnHnDChgazwSuyudbNhNE8coS7Na2HaZOexfz5s9Fg4bbIBaOIJ5IY8LY57Fby9ZsN+zemZv//S/Moij22pT3sP12O+CbOd+jV7e9MXLkY+g/cAj7C+KO22/EJ7M+wCtvf4DGjRrh2//9D73274YnRo7CgIH9bfguz5U74Ie5wMw15sL8ZZNOWBn1+IPo0a0r3p02BdPefx+dO7VlTQsDmaWoFcBLk1/DyUNOwel//hMuv/p6hKnGBCn6/Ljge1xxwT8QDJMh3/9QLEJHFZ1sdLmZ0Jcgm3COYZrMzFZq70yA52688Rr8+9Zb8MCIJ9H7iN7sx3O0+eOsDvss/55ZoWfO7ZMlWXRt1xJJ/rEz11uginiIGYuZWUlxBtdKrld1qRYrWB9tO7fhfOEvZn+MXfEOMkFv3P7fOc5Yip5p0GCYV8s71qcIVEcC3htYHUemMYmACIiACIiACIiACIiACIiACIiACIiACGyxBGz51VwLYTpKwlzwtmVgc5ikuUquBa0tdlrUsAf3c3qZMOYJCqEwhSknenhiGSUQzmdTdFgjlaEQZqIY5zvFnGgkaqc576ue8aazWL1tG+2ADgxD+PijD6OgoMAJcE88fD923Lk5mu68CwJs3BxJE8eOxUGH98Y2DbelYJfCjk2aoFuPnhj33BgKTxwDX7bnx4zGwYcdgcaNmzjuzVu0QLf9DsTYZ55EPJ5ASUkJxZOsE/ISySTzmgXduTTtQyZWlyxfgUkTX8Bfzvkb9um2Hx4edq97dhO+jEOa91xz+YVot3c7XPPPmxHluVjMcyI1atQE9zz4qBdykRQydLh54RdzgiL7SNE55odwNDHcwg9eeemFuO+O2zHskSdx+BFHufH5LrbqPHnCfPaUhaSk8s8/eZu8pMk2yEllAqYvdlU2KHMnWl4729vvpCIC1Z1A1X9Vq/sTaHwiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAKbHYHtd2yNJju1pnMigQaN90KDpns7kSDFTy+rzYZ9ZFs043I8DWNxtwWzJcybVOKEORPnzOtR2WbulfXZLBSatprLoITzo5hbnA4tm6/FnE2pcJTHPM+9J5RlbYa5zaZUwJRfFnONWTFxaZVTx51a44eFvUtRPLJYhf0GDsa4Z5+l25JfqVE8NepJnNB3AGJsP802s3Ssff7ZJwxVuI8TqMy9FosG0a5je0yZMoUOMxOdgE+++hzdund3grTvcOvQsQumT3nHiWFFRQVuPMXFJU7wMmdRhG4uKwE+92NPDEe9unVx0EGH4Ph+/TFp7DMopuPL5SKjiJfkAD+bORMn9hvohC+7z/KV+cWcYZ6I6J2x++x7lqKYCWjWlwlzJuAkmazrnLP/ilGPP4SHR43BIYccRA+TJ9CYA6+6lhCdfrbxzwzCKeYXW8mRMo1X0LkKzVlY8bahnydOp2Ex50uKzrC4cQyVeFuAvw+3YCbsNmaK41XOq4IAkvzp0wWcBIUm56qIQPUmIGGsev8+Gp0IiIAIiIAIiIAIiIAIiIAIiIAIiIAIbJEEbAHcnAehEEOocUHc3BS23OpcZBuJiDlrMlyEt80cKS4vEkfB5euNNAJ1U1MJ+PIOZQIXCtRmDANzusB5l1wylK4wc4aZMyvqbZzjFvbQvhcWFVF4svMRhAssRJ3dWXmxcIkR3m9z9I9HHYf58+bjE4pOc+Z8h4+mTcUJ/QdTOPOEIuf0ovgWjUad0BSiJcjCEW5Tf2sKU3Rh0bXE1w8ZtlmrqBZK6A6zvF7RaAj169VDcUnCCWNx5iEz0SnGdoImophryNxgfBYLFzni0YfQ+5jjnQPt2OP6UYSLYMyTjzthy5xe06e9TfUvg3pbb+PCJ1b+hJVfnfL263iKYSD/c+/D6LHPvq6yJ5pRkOS4VERABEQgn0A1MGbmD0fHIiACIiACIiACIiACIiACIiACIiACIiACIkACDC1mOYRWcjE/xTxihVxIb7jjnkjznJUgQ81ZiLRQuMiF+0plYk68SqZX4NKpUVx+SZImnCxCpkywpMySYyVLMSDLJTHGKzP3TMMdd3fiQDjqLZNR/nLVomw3lU7h16yJF2EEEsW49P3HcN3FDNnmxDJf+nDVV/tIUxhYn2JuM5WaSyBhSa9YbI7GKerW4nw2B1gmm8LeLZv7s9EzjrGezVUvbxRdUflCGN1ba1MiFKSsWG6yOoVRHHDoH/H48AewVZ062P/Qw9Bgq7rIJhN0YNqrRWsP3ycTjqykEgkKUxTlOMYQ45ZmqD678IPM42U5/KIU6dLpJMMncoyc+5EC3sv3MkpBLZ5Iwvq2Yztnbi57Pz797Et8MmMm/jvsUddHIBZEL4ZlHPHYExhwyulsi7nC2B87du+tq7QeH23ateObG8SF552L1nu2RaMdtnPP4Itj69H0Br01nbOtpMw3aP8YgL+LlXBw7X73DTa4AC1s9rfWDZCOtrQX4jLIOWylxJtuztVq3yOsZ+E8s+Yk43c+jZ1WEYFqS0DCWLX9aTQwERABERABERABERABERABERABERABEdhyCVjoNnOJRSKFFLcoG1AsWDJ3lltudZqRrc/ygOv2uOeJ6Rj+/FdugT2TWYn+vdvi/wZ3LAPPqnsLtkDDpp1QwoV+W9D/6btPvQtlantf7h0xDXc//4n7Es2mMejojji7b1vvYlXrvhK2KiC65ZxK8FGTnCOWL8ymAvUqVyxf3kUXX4R3Xn7eO5H36YQqE1QDFK4ySbq/OL/thrUsJryZs9HKSUNOw5D+x6CwsBBXX3+bc3C5vF4UraJ0eVkus5IVyxHkcYrOMHN8zf72a9QurO3qWq+xojpY9styJ4aZE822b7/9H4oK6yDJsI1Rijj5+c9815jVM1EuWrsOrrzoPI6J4jbFtsWLF+LLWR/j6y+/we6td0a7dl3YfxDz5n7nxrw+H3Xr1sMDjz2N43v/AUcevB/Gv/Q6tm60vSfwrU/DulcERGCzJGD//4CKCIiACIiACIiACIiACIiACIiACIiACIiACFQrAhbKLUCnVia5ki6XErpbAlxgp7slZ6TIclXLRDFbwC9JxhFOLEIkuZgL4cw/FA0wF5iFrqOgZqJaTiwoFccoOkQpQAS4YJ9mA5bHyd8saF1+4LrCZAlsC1FIC2dN7rBWuFnHlW28rLLlErAsYrU4PywLl3ltOCXdZouxN11/g3NrmRDm5cwytxidiCZspRNuS/N7kpMyQ8dYIBjjO0BHTt4WYt38jVIaXT1ZzlF2xJekW6e22Hqr2sgmSnBcn95eWFJaekzIsj7btOmAN155iccZhBkK0aINfvTBR2jfsR0FZLox2dbee+2F1157he8PR817TFD7dMaH6NixEyJsK8ubLA+eXXMvHNuwMKMh7p99+kkcc9yJ6HP88dxOYB6xE/HXs87Cji1a4rHhD/LZ+J7S2da9x34Y+cgDdJl5LII555up1SsZstHe3RTdoRaaMZWk79MYWV2GfjSxMWzhKFk3TeG6oFYhnh73Ehpu0xCHHXIg5s5f6Lim+IzVr3h/R0Lc2UZPKiL8bWJ0itnmn/+t+/V93kiaYTyTQTeuAH8rP+FhhkqvbfTdui3MH8E2+5toc8n25hxUEYHqTkDCWHX/hTQ+ERABERABERABERABERABERABERABEdgCCSz6diYWzZ7pchYtmTMTP8/7xDlomP7IlbgTqbzjaDTG8HAU0fIWwC3Sly02+06XNBdyTceyYvVtwT1swhvXdM3V428WXkkhlhwmfWxCApyepW6zyobhcnpRTEpQKDJRyea1hUG043dmfI6PvviO1yiu8ZwJb1ZCdIudOOAkTBr3PKZOeccJGpMmjsebb76G0/96jrs3xBdi4OBTWOcFTGOdMNt7ZfwEvP3Gqxhy2lmMxEg5Km/LH+O4F0bj52W/4PwLh6LvgJPRt//J6DfwFAw46U848tgT8OyTTzBcI4UtCkHX3Hw7lvy0GH2PPhyz58xx47fzr708EUce0N2N29pOMmTjosULsPSnn7BwwQIs+PFHFC9b5sKomohoz2eiX51atfD02FdQv35DnED32I/z5so1lv/j6FgERMAR0H/nNRFEQAREQAREQAREQAREQAREQAREQAREQASqHwFfxSodWc6FQGeIlQJ3nY4S+haCdCyYuybAPEnpAHOHWSg6XrH/+c0wKxKPTS6jQEAHGLPmcBE+TZeGtbaqmPfBin+f902fIrDxCdiMT1gqO4o+aypphjQM0r1lwpUnAjOvHkWtpCm+5mzkO2Cz2cQmCx2aoiMtnUzi5JMG4psvP8KRvQ9HnYJCFNOZefHlV6D7Pj2dOJVgu30HnYRZX3yOo485GLWjrLOSdYYOxX7778M6ufcxNzATpS0Xn5XH6Qjr0HZvNNmhId1DaTq+6Eqj6yzAMfXvNwB33nYjXp80ET0PPRStd2mKZ58bh7/97Rx02aslInXr0gKaQN26tXD8gEGuvQCf79233kbbXZu771TA6IpLo2evP2DU6HHuHFung4xiG5+xqFYBnnlhIo4/8iD06X0onqKLrEnjxt69+hQBERABEpAwpmkgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAtWIgDkYafRCTmuisFVucNS7TBDLUKBKZ9LOLXX/IyP4PQ0Ty0zZNTEsy2sWejBEBfieR55w3y0soUW/u+aft+C8y2/A9KlTsc8+3RGJUlBmLj8T2Mx5Zvuh19yEiy+7Ch9Mn0bRbD+62Lw8ZiZW5ZcQxSoTy0wgG/HMOB6n2GfEE+sYAjEUCSMZj2PXli2xcGmJex4/jGOrPffEi5PfZh6yNN59523s1Hxnimqr8oM9+MRTfEZ7Hsp83Nvz2DOkcyH7Hhg+kmFVLeTkqjHVqVMbE16d4oYYZp42hffL/7V0LAIiIGFMc0AEREAEREAEREAEREAEREAEREAEREAERKDaEmBguLJjY04hr3guGlvgDzL3UibgOckywRQywaTzh63yi5l3hmKA+58dm1+MZywfk2eoYZOeA8auqIjApiZgohi1n9LZSH2rTAk5UYzXg2GUxEtg1kcXVpE+COfeyglRVKTc+TjFKQuxGArTVUnhzEI1WijEWoUB7E8HmBVzd9m5OMMyhplPzApbQ7gohn32szoUxexkzsHmhDCO0BfR3GhNjGOVkIlRLlwi3zo+iLUdYrhT14Zzs3nhHu285TjLpFPMDRjEAQfs69qzbrycVdad/RUgADKxZ6QUaJcRi4UpprFezr3m1w9zDCkCs72VDN2kKiIgAiKQT0DCWD4NHYuACIiACIiACIiACIiACIiACIiACIiACIiACIjAJiZgopjpT/w/U36Y96vcgDwd14lgUQpLJk6Z4BVm/jATkNLm0qIQZeKwCUYmillxbrJcUxlT27zTuTO81frK1S09WcFBcfFKnH5yf4YtpdhmYhX7yC/DHx3ldDJzcZlAZo4xN0aGcfTdZiaKWUklEu5clOJeSUncOc3svBPT6DoLsY9UTkCz54rS2WbhGa2kKagFcwKYO5H78EWx/HM6FgEREAGfgIQxn4T2IiACIiACIiACIiACIiACIiACIiACIiAC1ZaAubzyi+dL4Rlb2A+kcnnFaJzhWrttTh0whSDjrfyHqDRYHiIrITpQzDFTpjgXCzUIs6CxWBg5FRHYVARs+qXphMpa/MDg6vM1Gy77Ppj4laR4xKCJfBe8OWzhDX2zVLDUaek9Ua6KE98qesbyr0f5OkHm8us7aBDiCTrK6C4z0Su/mObmBCu6OflWURzzrgeDlv/PnfL2/PSFrQTbsuu5NIKlx0mGTLTz1kaAz5E0Ox2Ld74sB3dBHyIgAiJQBQEJY1UA0mUREAEREAEREAEREAEREAEREAEREAEREAEREAER2JgEKIV5oQ+t05xoW1n/5swKUZxK0JEVsuNcKETLQ+Y1UdbRxZRc3vnfqCtFoiEcevjRTqjyWir7acKeOdJSzFnmC19la+ibCIiACGw6AhLGNh179SwCIiACIiACIiACIiACIiACIiACIiACIiACIiACqxGwfHgmjrlicRXLlWyGybXySoDuMHNJRkLM50WnWSKVcWERo5GCvFprfxj2ohyu+YZSR2VZwc2/wZ01l2YuBmRZP5lfq/ru01UIhqGKH7v6PpBGJgIiUIaAhLEyOPRFBERABERABERABERABERABERABERABERABERABGoWgSSdYhHm48oy7GCEObioj6G4uNiJZPYk2XKuM/8rNbQKS7nIiBXWqfRkqXBWaS1dFAEREIFNQkDC2CbBrk5FQAREQAREQAREQAREQAREQAREQAREQAREQARE4LcSKLusGwnzO21ZFlLR8nslMzmhLKeABcq5zoI5QSy7BmeUn4Pst46upjnEfutz6j4REIGaSaDsX9Ca+QwatQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhssQTSqawLnRhmSEVLK2ZCWCadRjAUcUz8XGM+oFzqMeYx88+U3a+v4YujKdugvomACIhANSIgYawa/RgaigiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQPUhkAFFhlysuawTECzx0hosNtVn2BrJFkBg9VnIEIqRkHvyDMMpWo6ySCBCcczzbgUCnM0UzSxEYjaTKQ2t6DvJMrnYiUEqYuY6y3LeW31LXZZiOrN0OsV8YV6vWdb1c4f5qK1+fmEPFN1ybbE/K9ZHKBSCiW42xvySYp1wrn2rZ/da8cdl96Up9Nl3qxcK0xnHJuxZ/Hp+HbvX78uaTOfyrdl1q2/PZ8WO7RkTyVRp3+4CP0IUGssXe24rjgPvsyGmUl4yNn+c1rffT/n79V0ERKD6EPD+ClSf8WgkIiACIiACIiACIiACIiACIiACIiACIiACIlCtCPgL4tVqUBrMFk0gw3iJ+VuAgkyKAo/pUxkKPuFwCCY2mVBTKjJRjPKFJIMXYh0XetEXfEzpYUlSCbM5n6boY6KYE8koJtk52+y+/L6944zr1/q2ze/TxCzrwwQov5QXxey8L4pZ3TTv98Urew4rTsTKPYuJUCZ2WUmyfXtOO2fD9/u1+0z/4iVX7Lr/7DYmOzZxzYp/j/uyhg/3TDkGVsUY5Rcbv7WT30/+dR2LgAhULwJyjFWv30OjEQEREAEREAEREAEREAEREAEREAEREAER+B0IcJmai/i20O8t9tsytplUAjR4pBHjQTEXsjNYxu8RrpAVIOl69XMrpQMFSHPdPBhcRu8LA8MFGZIuWMx7vRZX95O420s/vOX80q86EIF1ImCSDbUcN9mC3HuOxbwmAuVmGG+YNWsW5s//Aa123xPNm+6AKCd2hk4vE2t+XLQUM95/D9tvvw3ad+xsrVNIi+PNN9/BvHmz0axFK3Tq1AURJh+LmhiVMUdWGnG+OG+//hrm/fA9dt65Jbp1ae8Es6ocY2yAYlgIyXgWkyaMxU477oS92rRx4zHHlS9SfTxzFn78cT7SfMAg37Hdd2+NRo0a83bPoZbl+D/7ZAbm/7AIqZygZRTCoQAa1N+aY+7gniWeDmDsC6Ox9NfliEaj6NCpB3ZusSMCOWXs01mf4Yfv51FEI1IKWGk+m4WX3LpBfXTp0hUvTphgza4qudxs/omddmrCsacxf95cdOjcDVtvXb/UabZw0WJ8NG0qeh1+BHKmvdLn8+/XXgREoHoRkDBWvX4PjUYEREAEREAEREAEREAEREAEREAEREAERGADETBHiW1W3MK8uTy4Oub5Rrzz+hSBmkrgjltvwJhRT6FHz14Y8+J4J9yYDBwOhnDjNZdj1FMjsV/XLhg5ZpwLS9i396H4/IsvsHubdpj5/nT02P8A3Pvgw6gVjSBBt1iWYRj7HX0oPvv0Y+y2dzt8MO09HNirJ4YNH1UlIhORbJv8ymScdGIf7NqiOV5590MUFhW4e7MUnqLs5/ZbbsD458cAhQUU5YJYSWGrxS67YsTIp9CoWTMKTSHcdvP1eOGFFxkqMuruDfElTqYT6NKxG0aPn4DXJ07EmWecRvEwgF1b746lSxfji/99hUEDT8V1N93McJBB/PumazFxwgtkQVGcJUy3mAneHdp1wKjnJ+C0QX3deffBPwjJZBZedjbvdO9jj0JJSTFefPZZHN9vEG679z7nEDPH2owP3sOgvn3w7YJfkI1FSt1vqxrUkQiIQHUjIGGsuv0iGo8IiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMDvQ8BsNrlQajSHIGqiGFfDGIiNglgB90CtXE/J3IK537H5ccypk8mynoVho0Mnm405/9naCGm5bv3m1nlPv84636MbqhOB1WeJhSa0Ynm9zK1kJUPXopVglt85P+MJ5tCKME8VrYvJVJxiUAHnoN1X+XzI0uFVvLIEu7Vth6nT3qULbA4aNtgGMYpJ8WQGE557Drvv2orTP0wXVQC33PIvfPr5F3htCl1k2+2Ab+Z8j17d9sZTIx5D/4FD3Jhu//c/8ckn0/HG9I+x3TbbYO6cOTige2c8Mnw4ThlyEtIM3WjF5SmzdytXLAxiGF5OsFGPPYz9eh6At956Cx99+DG6dGpLh2ZOcqI4liSTtp26Yuy4CXS4RTH3+/k45vBeOP2MP2Hia+94Nk+y6UZBb/SEV5Gly82caCasuRCO7Oncc87Avgf2xO13P8Q2GIqRYQ6X/7oCX3/1uRPFXKhE5lvr1GkfPDP2BdaJOIG8uLgEMbrLbLyzFyzmuMJOMHz8kWG4/PzzeO5XinX2V8L+DgB/HtwP2zRugufHPou//eMf2JU8YxxL2H4fXo/Q8RblgeUlTOd+X3ezPkRABKodgdX/Qle7IWpAIiACIiACIiACIiACIiACIiACIiACIiACIrD+BOo3b4/kO1v9AABAAElEQVR6O1noNS50M5Sa5U6qv3MHNGjRcf0bVwsiUAEBE1Xzi+XnMlHHKawUk4I8sM3+L0udKUL1llECOT9TTijioRN38tuo6Nh0GHNGbdtoB4YR7IqRjz+KolpFFNnCGDV8GHbauRmaNt8FATZuoQQnjh2Lgw7vjW0abuuEpB2bNEG3Hj0x7rkxCOXiAY4b+xwOOvRwNKDAZmLUto0aoRvdaBPGPefcYEE+i7m+Kioh1k+sKMHLE1/An844Gz167IvHOQ4Lo2hhDF1ONApY4VCE4l0MBQWek2z77XfAIYf1xheff+pCSRqrEB/O6pvL08ZhTjQrkVjMueK+nz8XBxx0COtZHTq9eL7+1g3RnhxMFLNcYiYuGntzoFl+sHgiSdGRApmNJ9deRc+Rf85+y9132wvt2nXCdVdemn9JxyIgAjWMgISxGvaDabgiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIALrTsCW74NUHkLcwnTpZBhezlwdIYZkC5ozh8qEbfTTuM3kjGwwjhQX1W1zwgVPbrzFNOtJW81l4ExETtCx2WrzyYQZKyYoVVQCubCeCQpkQYvxyUnHaIZrVaj/uBxcoOjTb+BgjBvzHIoTnuFq1IiROPa4voiy2wxHYt6zzz/7BF267cMxhdlXiM6pINp1bI8pU6ZQNGINjvGTWR9j3/324/viDaKwsBBtGXpwyltvOEHJhKp4PF7h+BKJNB574hHmAauPXhSt+nNMk5gDrDhXP2KJ/fKKiU4ZCtVZ9jt79mxs26CBm/3mRkummf8vGOU+6DaEYnw3Iy4vWYRja0QxcNj9D2D6jI8QoNhlrjsr+XnQgnR8hvicCTrrTCyMMuRhgCJZmoBNxKuqmMPPNt6Bq/95Pca9MB7TP5rpRL7y4mdVbem6CIjApidQ9i/Qph+PRiACIiACIiACIiACIiACIiACIiACIiACIiACG51A/eZtXZ8MZOf2wWAy50zxHDZZCg6XTwlh6AW5IIdrEDf8gZspaH1KoIrQeevTtu7dOARMjLGSpvvLnFGJRJyyCtC2dQtMnTzWXUuFvBCJ4XSEAk0QlGmd8ykQKHHupxSFoU69b3V1K/uIU4iyEIVxqmp/POo4XHHB3zHr4xkMp9gAM6a+h+GjxmD61CmuiZKSEmSSabq9GPjPHFkUitL8vk39rV1owzDDEcbjKc5AikEUjey6ubFSFMIasL1EMoEMQyDa+YqKCWbRaBhPj3wMRx57ImIUoXoffQLOP/csPPn4wzhpyOlkkXROLRPDFvwwH/fe9R8XsvTdd6a4XGB33/OgC9VofZjA9darr2DH7esw1mROiCPTw446Fo+OfJohFIfhnDNPR+/9uyNau4B5wzrhj4cdgT+fdS7VMYqLKQrhdIy9+Qrb2JrBUy0EZE4MO/roo/Hf+4fzOSt6klXnEkmOl043K3vs2QYHHXIIbrz6Mox86plVlXQkAiJQYwhU/NerxgxfAxUBERABERABERABERABERABERABERABERCB30DA8o+xMCOQ24ey3oJ7OuAtfmcDMeZ/oiiWKnLumHC2mAoHPTwUzMJ0jmQydLFUUnhnJVervpRhHyo1l0CIwg09XJwwNofoVKTMFM1SbOW8YABACjU55TSXOsy+BylSmZ8rxbkVDlsOLdZJrZ3CaiECrVhusjqFUfQ85HCMeOxB1K5VC/sf/kc0qFeX+bkSsOkdoquKalPpGFIJynEU7sw9FgpTks3rM0AbW5bvSkm8xF23PiIUvaw4lxfDEJp4ll8sPOEnn36J9z+YiX/f/QAF5gzChWEccexxGDViFE4+7SzyYKY/u5fzfEXxSrz60ssuvOmMmTPR88BDcOTxJ1C0oziX8kJNdunRDSPHjHeOT+vXQivS8oUkx7X/gb3wydez8ebLr+CzLz/D+OeexWWXXIgPZ32O2++609U1kbJTjx54YuSTdNalUFRUhCTFrhCf2/KSmehXWQnnhHDjGyOjodfegAP364h33n4HyfVVwSvrWNdEQAQ2CAEJYxsEqxoVAREQAREQAREQAREQAREQAREQAREQARGoUQR8ocLf59b6bQHeHCsBLqyb2OU7uaoSvvxmahQDDfZ3I2DiU5qCmOXUCkZjFFMZxpCCjMmllveqomLimIX3Q5CibKaY93K2ldWcKrqt9JzlzPPD+p005DScNug4hg2M4rqb/+PEIRO+0qwTYx8R5hErWbHchXVM0W0WpFtt9rdfo3ZhbYq/FlbUwivGsHLZcucOs3fAcnvNmf0N6tTZKq/P1YU7C684/KF76GAL4rLz/+bqhphHbP68Ofjq88/wP4pYu+68kztvKFrs0hKPjBztxvj9wkU49ICuGHrR33HtTbeyzyxFqxTbsvCHxjHqxufytOVGYeO10nXffdBtv54Y8qczcOXQy3HfXf/FXXffg4CFQmWJMB9a3XpbOUHQwicWurO5jyqEsfyqdtyyZWsc16cfrrr8fFx25bXlL+u7CIhANScgYaya/0AangiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwLoTcP4ProfnaxD0vlAISNGpUoICig8mIkQyTMTEMn/uF2U6saX0ux9/Bw+Nnk7RgIICGxp8bAecfYIXcpEtl6m/2pdcGL3Vzq/lCQtjp1JzCQRM5MqbfKUup9UeyReWvPlk8zbJHFmxqH3PuJxY+/e7a7W7KjoRoPvK73OfHl2dS2zZL8twBB1jLk8XbzJRLspkY22YK2zySxMxYNBARDi/La/YjI9moEOn9nRE0RxJl1jbtm3w+huvo/8pp7OO5ySb8cF0dOrctbT7sAvHWNZtFaHLbPSokejTbyC6du7gwiqm6Boz59htt96Khx64B9ffcL3L7WWuOBOpohTqTOBqsv3W+Pvfz8N1116FM88fiu0b1uczUVykY8sTFD1eVtecaRa20QRIK2l7n4N03VH826XV7sjwmeLxBIqiBaXj9Q/MrbY+JUTr3UUXXOyebwzzuamIgAjULAL6L2zN+r00WhEQAREQAREQAREQAREQAREQAREQAREQgd9IIMscQZmcM8SEChfKzRb8mX9oTcUW5W3x3Upp+Duec4qbCR9r2tbUoM5vEQR8gcrmmb+VeXALv8ctnfsfNTBXbLE24kQxE4AoYq3D6m2+Y8xyYr05/RPM+HKuazdsIhLHYmEEk8y5deKAkzF58kR8OH2qyxX25luv440pb+G0M852+b9SzFXWf9BJmDRhHGa8P8218ebkyXj73TfRd9Ap7rt9WBjG8uWFF57B0p8W4aJLr8GAwUNwfN/BOLHfYPQbeDJzjR2HF8Y8hRTFLHsXLf+a5Tyz/F3mAjNuJ//5DNTZqj7+feM1njuMQlfxymL8/MsS/LRoIXOS/YglixZh8Y8/Mt9ZEued/Rd8OO095jKLuaF8/dVXuP/OW7FTi2YoZM4xyzFmJcm8Zj8vWYqfFi/Csp9/4RgXu32K+dLWtRjLxs2a4vgBA/DskyPX9XbVFwER2MQE5BjbxD+AuhcBERABERABERABERABERABERABERABEdiABAJ+LrAIFn/3qeuowU57csE9hTCdKvO+/pTHdtp3veQpEXSV0YPjBAXz71gwRTCU29oUWzhfn8Je1+d23bvJCXguJps3VgIMRejNsdz8MkHVzvvOQPvKn9wkGmq17osJXZbja20KTV8u5Kfds3zlchQUFNAFxtxhnNwFsahzoZmzKsZjm++DBvbDt1/NwDHHHIVaDPuYonPysosvQ9fu+/K6Fyi038CT8M3s2Tii94GoHWZ7vP/8Cy7CQQf2dHUyFJTMMeaLgP44H3/kQXTetxsaNW7o3qokxbhYQcwJYCf9+c/47+3XY/yLY3HIEUeiiPnQfiUaZl0jHrrKuIX5MGee83/451WX4bzzL6BjM4Bp06Zht6aNCYeDTzIfYDBCd1gA3y76GfPnz8cRh/yBgiLHQufZil9WYLfWrfDUM885l5qNK1GyAtOmvoNWzdgGRUJX6BqrXbcIn36zkCEnc+d4oaCwEAVF1lbAPbPVtRxlcYp4oQKGyAxwjLEInyeO8y67DM+PHoGV/LmTmTh/zzDDPjKH27prbW5I+hABEdg4BOjqXc//Sq/lOK2bBs3bYensj9byDlUTAREQAREQAREQAREQAREQAREQAREQARHY0gk0bNYeP83+oFS28nnQ/5I7DODeEe/hwdEfuO+WC+wkhjw8vW8H9z2IVcKYSV+2/O2EsRDdY8xftPS7z50gAS52e2XVAvndI97H8GemOGEsw1Bug4/pgLP6ds7V004EKiGQ9eddrg7npVdy8ys3fTO5eRfMeudXCWOWL8wTxrqdeBeee/AvlXRm5jNKMkxkZiu9Oc3N1c8k6cTiyVAk7HKFmThnx3bOnJDLStKY+u672HffHhTTIqzjCXp+Z5SAKACtxLQp76FLj32Zn8zCkYbcvUEKy5Z3rHyxEIe+MzNFJ5iJcyELhcjz6SSfycSt3BttZk1zYoY49gSFvCjFrRCvJzguK/ZMVmj2cufNVebqsw7Tjrlx2HPE00F8/dks/LxsBdq074Q6MY6NdaOFAY6fY7Axkw2rumImMaYcc6+9J4x75/0QizZeK+lUkqKZsfP/3ngNeM/A/nPjt7oW6tHcp5bLzJ5TRQQ2FQGb60cPuRvvPnkmQ4z6Yrv/1nn/HdxUY8vv196SO5+YhhFjPnSn+dfK/ff7r/065lfbIMdr908ONkjXalQEREAEREAEREAEREAEREAEREAEREAEREAENj4BW7g3R1Yw5C1e2yJivpjwe4xoI/1b9N9jqGpjAxAIBKi6bNRiObzoP6NgVFKScM4w6z5Ip5oLsUi3kynAMTqd4nFPtDNnWN2iCHodsI8LY2gCU3mxK4gUCin0HHTQAQzByPCHliuMTdm9LoxiaPXnzFJE8k1ZJnTZu5CmEpWgK83ymdmxX0xMCoUpc7NdC4WYSqacQBaLeM5Mc6VZiUUpTtF5FmBdc6jZcbIk5Z7PxhKhAL7bbi15jdeZZ8zErhDPmyhmxdp1feVe9GiMkjlFQ2srbcpBrli+MysmiFkxgcwEu7Bz/LlT7sOewdozgcw5SXnWnsHqW2jISGjtnKWrWtSRCIjAxiQgYWxj0lZfIiACIiACIiACIiACIiACIiACIiACIiACNYaAL27ZYneWi+DmZFnbUj683Nrep3oi8FsImKDjcubx5rDN15w1ykI1ujx5OYdWkuKNCUnmvLLNPCQmFFv+PU9wyuX6Yh2/mNsqxfacoGyOLdZNMW/XGacMgBmp/PfEr2/t3PvAowjTgRbOGdDMRcV4iWzHc7CZQGYhFkMUzkxkytAdF0yaG4z98H4bv4lhfgYzE+HsOaIMeWghDE3AM9HPhL6EiV78bvnG7LvlU8s3vpmAZcWcZnacpMvNBEQnlFFoo47mip9rzGdnTrF8USzM8Vix/Gt2r2uPPMs8E8dgz6YiAiJQvQnoLa3ev49GJwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAKVEjAxx8Qxc3SZaOOJXXR35QQxFzyNuq6nl/Egp/GaJkQtit9NWLNv3rWcVoQADyxcoF8sVGmYObTSdKX1HXiKE6X8a/4+SudXMBjlOEysSlPMokuMoQXtu21+MVeVtZ1/vvQ6+43HV4UjDDKnmJVEgu3YMa+b3mXf/YeJhmOuPRP//GIsrNjeP47kONn5NAU+v9h1XxSzY5dDLc8pZoJYfvHby3eT5R/n19WxCIhA9SIgYax6/R4ajQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhUWwIWpjBWEMXBhx7BMdJtZrFI80qQbrNUTk2LxSh+eWpcXg0dioAIiMCmJSBhbNPyV+8iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIbiQCDx7EnzxWSsXBzWa7wc1E/YAv3udBxvn8kZ6jZSCNTNyJQPQlkV0VULB1gnHEKzS3lOaYY+rBciNEUQxUG+H5ZsbCFFrpxXUsu/d+63qb6IiACIrBWBPTf+LXCpEoiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAKWw8vl18oJYuYIy99cSEfLGaaVZ00WERCBakpAjrFq+sNoWCIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwJRMw55atq/tZkkrX2P2obZb8iCVLS0vWJUlyXyv4WJXTCGZ/ceaVIILmFjOnmOUV4gI/szKtdq+FiHNbhntX3e+TLjPepyICv5lAbvpwJnpN5L7nzdbf3LTdmOL//KZ/S0PhdOkb524P8h2x1yyT8RKORRhPMcv8XP5bkB9O0QSxMN8tuxahS9PqBXMOMn8sydLcZ/6ZsvuAvXDrUSpyuq1bc+vX/7r1pdoiIAIbm4CEsY1NXP2JgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQA0ikEl7QUbXJAjnn3d1fcWsBj2jhioCIrDlEJAwtuX81npSERABERABERABERABERABERABERABERABERABEaiSwPo7rtgFxTHzWGa4X81g6Ts/1zCS36X/NbSt0yIgAiIgT6jmgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQKUEArmkYf5+TZWrur6m+3ReBERABDYWATnGNhZp9SMCIiACIiACIiACIiACIiACIiACIiACIiACIrBlEKjCEVWanGsD0Qh7kQ/XuvVMOo1QhBnBLDcYBbA4v0dCIWSYZy/EvZ23zYq/z2/cr5fmfZkU85shjSjzivkYsgzFGIqEUVJSwnbDiLAPr02vRiDILH88l04mEWR/yVxfwZzVzB+D1bFTFq4xxTph+87Nip3ncGFj8O9zF/QhAiIgAuUISBgrB0RfRUAEREAEREAEREAEREAEREAEREAEREAEREAERGBTEshmKURR5XEikKk9a1FMEEpTLJo0cSyWLlqI2kVFaNuuM5o2b156dyAQxOuTX8L87+fy/C7o3n0f9mFikydUmciUSaXx6y+/4MP3p2CvPdthu0aNSu9f04ETxnIXTayyMTTZqRn22rsdAhyTCWPJRBLRaBRB6yOe4jgm4vsfv0dBLIY2bTugRavdnDhn/X/++adYuXIlOnbo7IQ5076S6SyCbGvmhx9SOEtiL97ji2Imss346AMsWrgAB/Q6FAitaaQ6LwIiIAKAhDHNAhEQAREQAREQAREQAREQAREQAREQAREQgS2KgC3Mm9RgekPS9jlXikHQYplRUKl5BIKY/PIEnHvGaQjQmbXnLq2weMmP+OzLL9C/38m4/Y47UUJh6vije+OzTz7GbnvugVkzZqBLj33wyBPPIBzIIJ6IY8L4STj/nNOwuDhuShTuuOcuHH/igCpxRMJh59QyZ9drk1/FoL4D0HyXlpgybbpznVkD5uKi5oUXxjyJy88/H8uXL0P7Nm1RXFxMUWsGOnfbB8+NexHm/7rlhpux7Jef8fy4F5BI8Ca6zwoiIR5ncedtt2Aprz3DuqZ/xeNpDHtgGC65+ALc8q+bWJVON/5PRQREQATWREA5xtZERudFQAREQAREQAREQAREQAREQAREQAREQAREQAREoAYQoOELF5x7Bjp22xcfffENRj73Il5560PM/HQuTug3iGELs7j9X9fis1kz8cq7H+GZ5yfhhcnv4LWXJ+GxR4ZRvMogFo1hu+22w8VXXodX3pkBxkKk6FSyVk9vbrUwxatkKoNRjz+E9l26Yc4Xn+H96VMQ4nm/vPXGqzhtcD/0OX4AvpizBKNfnIzxr76LaZ/OxvaNGrtqoaBX38I7migW5Hdzm5WUmIztFXPTJeJZJ4r959brMfTS83H/g8Mx8KRTKXhLFPM5aS8CIlAxAf0jmIq56KwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMAmJBA0SxdzBgW4oG//srt0qZvnrATdWS+vUCDAJa4sXSWuJOhModulihJi/Qy3yGr1/J6CdKOws1DK9c3MS0iB7XI8AZfMyK+3WgPuRMaUCpUtl0BunuYD8GeEzRz/OP/6+hyvLE5j/nfzcNFVf+SrkGUOL+eJRIN6tbBt105g5EK8OHY8jjj8CDTeriHFoyR2b9UCBxxyOF547lmcfPIpDMOYRoeO7bB3mz0RLKwNWrmY78sbqZ/Py8aYf+yP2c/ptaw4heeffx4PP/oY7i+M4JFhD6NDZ4ZrZOjDIHOYDb3oQnTstC+uvPZqa6k0B9kOjRrg7mH35941I2ShJO2d5jHzj1nOMnvvSkpWckwBZJIpFzbyiisuwfBH7sPjjz6OLvvtj2CYucpYL00GKiIgAiKwJgK/99/gNfWj8yIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKw9gRsLZxr206EquguXss3hliuoSBDqK2xfkVtVHHObyvD/EVlSgWiR5nr+iICFRCwWRRneMK1WZA1sdecUibv+KJTBU2WngrSlbVd48YYdtd/8eGHU11YRBO6/DkcosD12Vef0snVhaJRBubKSqVS2HOvtvhw+jQnitk5m+t16tTmdzbN+82ZZUJYVcUX0J4c8Qi2qlcPBx16BPoPPgXjnn8GqSRlZQrFK39dgc8+/hgn9B9UVXPumW3s9lyWPyzCPGQhCmt169ZCMpl04zznjFPx/NMjMfLp8dj/4ENQy8bNa6lEosr2VUEERGDLJrA2f4e3bEJ6ehEQAREQAREQAREQAREQAREQAREQAREQgY1OwDxfcQpQGXODsdgiVv7m3GQ8YQv4mWwAyUBdxDO1EQ3F6E6hqJCrv+oumr9yglaC9pkk/WCpCvxifn0L2pYIRSgM1KECVwfhFNvkgErzkZkjrJLN7197n+iWtTevU0VbKhDCBZdcTtGLQm4lWzQWcsJY0Lml2FgVJcgZfdtd92DhjwvQu+f+aNF8Bxx5xOF45OGHkMwyFCHFpWRxAlEKTIFwEGlOzAzfh6233RbLS1a4vGTmjQxQQItTMFvXkuGLaNuoRx9En76D+P5l0PuPR1GAC7tQjSZwTZ36lrN41m/QsMrm0xyvCWMmilmxsIp2bKEVTax79523MPqpJ3Hrnfege4+ufBX5NyAez4nj9tapiIAIiMCaCSiU4prZ6IoIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMAmIsC1by6Me53Xb9aRB2UX68OZCBfDufzOkHCW3ygerMUQchnUCq7EFVMiuORikyXYhoVicwchVy8YtPvoRKFAkWabWzfdG+lUEmG6UVy1XHC3dKiOqx/gQMJc3M+mV+Lq90O4eigFAFu0NytPJSW9loJGJU3oUg0mYIJtfjExx7m3OGfb7b6rE3fyr5c/TiY47xgWMJXKcr55c7l8nfzvIbbb6w+HYuqn3+A9ikYfz/wIE8c+hwv+cS7ef386brvzfvdC2RjsvQnYBOY+TREsGvXmfiQcRgm/295pY3SQmXgX5rY2rrFZM2di1oczKVYNc0MLUInuddiReGrk4zhlyCmoXYvhGVkcB3e05g//md07nqLzje+TiWOhSIzjCWH3dvY3Abjo72dizPjJaNq0aU4883iZ205FBERABNZEQMLYmsjovAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwCYjUGAL9xQTfpw93Y0hVn4kOWHKBLR7R07Fw2OmeTW4gH7SsR1wxoldcnf4CoWpbJ7Stm3zNsik4ogx9OKC2TPLt+y+2113PvEennhmihMHUnSHndynI04/sROlM4aig5bVKgSnk47Acn/a5Xj42pbpNZddOhRvjK8aFPUgZ7MLUcRdJcR6bqhsoGwH5rAK05XF5Fvo2q0r9u/eCX/58xBcc/VVuPfu/+L2u+5DLEI3WPFKVokjGom6kIo/fj8PhQVF1J3TzuHmXrsU57flFouEKZylkXRurSxCUQpn8WSFwlaW4tnw4cMQYV6xKy/5B8KWw4/3LlyyGF/OmoX/ff0t9u7YDcFYGN/O/taJc+YAM7HLQqBa8UWwLGOkhkJ8v/gux+kCi1K09utZm+Y+q1c7hnseGYX+fY7EkYcehLHjX0KTHZtyvMwxKFHM8dSHCIjAmgnIV7pmNroiAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJQYwmYcmabCQi2+d+pAdAOY4vv5jRbvayqt/o1nRGBtSPAaIV0Nq3azJRVwC3Kczdcf5VzjJmLrLLNxK5sOuc0q6LbCMUlC2VobixzfJkjsqCgADvv2gqpYubd4pzfu10HvDRxXGl+sQiFrw/pJuvStTuFqJw4xfszfDe4A5LmJos5YcpyiHE4vNdzkJUfTglzp417dgyOOb4fjurTF336D0CfAQNw6hnnonGzphgx/AG2E0b37vvi8QfvQ4YhEU0Qsy2bIQdu8ZVx5wyzcym6ODkQ1KlV6AQz68+ey9yalmPMxLGGDRpgxHMvoeHW2+LYw3th7neznShm11REQAREoDICEsYqo6NrIiACIiACIiACIiACIiACIiACIiACIiACm4gARSu6ToLUqWxbJXB5QleaC/e2Mb0YcyUx1FqW4eG4RZg8KcqNS/tuozTBe21j2DirbCUcQzZSgFQFzpJV91lFG4Mtsttmy2i2mZhmY6iqeOMsP2593zK4FFCILb9FsgxTyM3OV1WMUtW1VrXCNwXnnHkapk99H9SckOK78PkX3+DBe+7Azi13QUE4i8EDB2HipEl4Z8o05hiL4IUJL+GNyRNx0p/+wqCiWYZLTGNZSQLzFi7A/B+WsPEQlixdjvkLlmDJTz9TvOIb4BSzVf36R+PHvoCflvyMSy65FEOGDEa/fv3Rt28/nDx4MI7rcxyeHDXCvTU33vQv/PrTQhxz1GH4ki4y0BlGrxtefu1VHH5oTxfaMWHCF4U428/9fhGW/PwrFvy0FD8sWIxflhdzEBTTKMTVKgxhhwa1MPb5cai/9Xboc+Rh+GH+fPDPgYoIiIAIVEpAwlileHRRBERABERABERABERABERABERABERABERABERABNaNQJaibv5md3t5xjZcCM75c7/D8Ucdgubb1cPOjeuja5tdqTuF8cgTo93g+w4+GaeefiaOPbIXWjZuiJNPOAoXD70WPfbrSdGL4QsZsvD1VyagbatmaNdiR2q4SQy9+AK027kJLvvH2XRu8ZTFLq2gPPHwA2jbqTO2a9SIgpaXD9D6ttJ30Kn4ceFCTBg3Di132wPPjHsFiUQC3du1cmPdqWEhzj51ELr22Nc5vix0ooVwnPrO29izZVO0arwN2uzcCLs33RZnnjqARrYknyvEfjz3Z606dTByzCRs3XBbHPfHgzFv3ncVjFCnREAERGAVgQ33l3hVHzoSAREQAREQAREQAREQAREQAREQAREQAREQgXUkwGUrLsSH17AQ7wdLMxNYgCv2/oJ9ivckViVkKtMnI7MhZC4zs75Yye3sqy365xf7GkUM6XCA7psMXWtBF+6NnrT8apUcl2uwkpq6tPkRsDlZvgTp0vJKzrlYvsJ6fGf6Lzz13HiGU8wypOA3+PH7H7BX+w6oXVTkQhFanjBLsXfFNTfgiqtuwDtvv47OnfeheZLhDHneuSl57+FHHosflpYgzBdl5fIVLhyjDSvASpabzEIu2nH5Mmbs80hTEAvw3bN3LEn3WdAY8HuzFs0w/6dfEKHgZSES92i3F56dMMk1MXP6dDRp0gTb7NDYudHsPTZyj414snwXq323UJPmHrNSr14tjHvl9bw6uZc770xFh6s/iVfLG0VFd+icCFR/Apaiz73YtnPeU2+mm5faK6v/fcpd2Eg7G4eNyTY7zo3L/bfbHyNPb8AiYWwDwlXTIiACIiACIiACIiACIiACIiACIiACIiACIiACIrChCZhGZKJSmInNmu+8C3bepaVzgbncYMw9ViYEIrWkfegSCzBfmBVzi5W5znNphjEsql0LGTq3rFjeLyt2j51bTRzjeTtn1629cLkwpZbzLJsbh+0jXLkPMFxihy5dXQhHv39/7zrThwiIgAhsIAISxjYQWDUrAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhuDQJqOsGiE+booOllJplIUpyxPV8qJXhUJTlbHitUrXyysooljviCWYujDeHECZ57xZyR5HCiXayxEp8fdwx5D3a2KkKAwxjR/lZYQh2nhEm1c0Wi0VICr9CZdFAEREIHfiYCEsd8JpJoRAREQAREQAREQAREQAREQAREQAREQAREQAREQgU1BIMqQiGnqXOYQS1FwMrHLjiPcm2hWvtg1UMCKMA+YObzKFxO+LBxk1uKPslh+tGg0gqP6HFe+qvsepDAWKYghHk+u7iar4A7nbqOQlzLhroLrOiUCIiACG5KAhLENSVdti4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMAGJpBKeOKWyVi+O8x3j/nf84dg10w8q0gUs3oZJu8LhP2caPyeCrgwjUccdYzLM1b+PpcbjGJahrn4GFQxv6sKjy1NWSbFTF4MsWh7y4SUXzKZqtvIr1/+OEzRbX2KhXtUEQER2HwJrN9fiM2Xi55MBERABERABERABERABERABERABERABERABERABLZIAuUiJbowjQbCl68sP1h+8b/5+6q0MRPGKis0q61X8UXB9WpEN4uACGy2BCSMbbY/rR5MBERABERABERABERABERABERABERABERABERABNadwIYWlqryY62vY6yCtGnrDkF3iIAIbLYE1lN732y56MFEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAQ2MwISxjazH1SPIwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIisBYEmAsp6+K9ZfiZRoK32BZkuiPbVESgJhFIptMI0CaVynA+c285v+zY5f5i2EPbW/Hr5X8PR5kVjPfkb35d2wfLhU20c+VLgM3nb3bd2rNi47DNin/O2rRjf5zuYt5Hhm9jMJiG7aMFAXccilgnKffd9v7m17Pr4Shge3vO/HH7/dne+vQ3++4zKf/8fh0bltWx7/74Kxt73mPoUAREoJoSkDBWTX8YDUsEREAEREAEREAEREAEREAEREAEREAEREAEREAE1oZAiCJTmuJN2AQu5u9KU4iy41AohFSKIhFPmrBj9bJ5IlWE1zNp1ue9+ZsJRgWxiBOErH9fEPJFpKrGFAp7y87WZzQSRiTsZfQJcWdt2ZisPys2Nit23tq3EmSSsWA4xGfg+BMpd5xNZxDiuUQ86erYsV8ikYg7zLI/K/acfjhIJ3DxXiu5y6V92jiMQfliz2/jcluOhbE0djZOn6Hd54t+5dvQdxEQgepLQDnGqu9vo5GJgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAhsIAKr/rU4F7lpdVl9aXwDdaxmRWADEAhTJDLRxwQbE3tM1DL9y76boGMilO2t2LHVN+HIF6n8a/7QEsmUE9ZMNLL2rFgdk59Mx/LkJ3e6wo/iYjq9KIg5QS7Xj4llaRq98sfi32zjtM31Z/sIBbBEwgljVicej1Ps4piTydJzSXfM57QHz43Rby8YYF2et/5t3NSyHA9fjDMxy65ZsePyi+RJO2c3sRgLE9xsbMbL2nB98poTznLtuMr6EAERqBEEyr/zNWLQGqQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIBHwBeyVhYXY/LkCViycDFq1amNtm07oXmLFq5SMpWiMJXCtCnv4Lu532LnFq3QsXNX5+byBSOf5/Jfl+GTmdOx6667Y7tGjUoFIROC/L78uhXt333zFWzfeCfs2rK1E7msTgmdXt98/SXmzZ1N0SuOevW3QYtddkGDhtuUilBWzwS196dPwU+LF8DEL3OPBYIWTjGIhqzbpl0nJ0itXLkSr05+CSuWLUOdOrXQare9sdvuuyNNIcvEri9nzcKSpQtRXFxC3SyJWKzAmkf9Bltjjz3b4vXXX3L9mkstm1ld6tu+cRNX/8f589ChY1fUqVcPBRT3EskMVq5YgfenvYNeBx6OeNITAV1lfYiACNQIAhLGasTPpEGKgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAisL4HIKpsYQlzgD9D3YsvhGR7LMba+dHX/70nApqoLC2jzlG6lrGduYjY8r2RybqagFyGQjqcsJo0fh7NPG4JYUSFa79ISPy1dhM+/+hyDBp6Kf950s5vrJx7ZG1998Rn2bNMG06dOwf4HHIhHRowGgxtSPEpj0sSX8I+zTsPCn1c6F9adD9yL/gMGUchiyMGcyyzscvOVfdoAxatslg407idNmoAB/fqhefNmmPbBR3zBwHvpOKOL7fpr/4mXJ45DUZQn+Gy//LISu7ZqicdGPYNmzZrT1cWwifzff279DyY8+xQKimpR5KJwRUdYgP136tAeo8e9jEkvvYpzTx/sRL2Wu7bG0l+X4NPPZuGkk4bwWW9HUSiCO2+6Hs++NAEBhmJkAygsKnIiWbce++KJ0WNx2sC+HJr35if4TKl4AkUcIx+BIR2zOKpPHyxbluQ4xqDf4IG48T/3ONdZlGEbX353Kk7ucwzmLl3mhLyyNPRNBESguhOQMFbdfyGNTwREQAREQAREQAREQAREQAREQAREQARE4Hcn4OcisoZzmsPv3ocaFIGNRcDycV34t79inwN64YFHRlJM88L9/frLL/gfxTHL8XXHLf/EZ7M+xuvTPkaj7bbFN998gwO7tcWjjzyA/v0HIMxQgdtuux2u+OfN2KPj/jig854Mt5jC8uUldFtFUbyyBNFoxOX5itN5ll9MmLOcX5av7OkRw9GtWw+8N/UdTHn7LXTo1IVONU+VNnGtS5fuFKZeQIxK9ey536PvUQfjL6f0w9iX30aU11OppBMFu+13IEa/OBGZlOfmMtdYKKcEXnju6ejO6/c/9Jhzk5nja+Hixfjuf5+7cVhYRnPBtW/bgW28QmeYhZXMuro2bmYgwzc//sJPb1wPP/QwLr/07/hq7mL3WBkLF0lB8rSTT8bWjbbFqKcew2nn/B2tW3ruu/xn17EIiEDNI+C9+TVv3BqxCIiACIiACIiACIiACIiACIiACIiACIiACKwTge2a7oEdmu+BWKaEC+RcbOfWZKeW2HGnXdmOWW8ydI+Zg2ydmlVlEdjkBJYvL8b38+bhkEMPpgDEfFyBFGI0QzWoVwudOnWg4yyG50Y/j95HHIHtt6lPOSiJ1q12RveeB2Hcs89ScPKWidu1b4MTTzwBrVvsRNvUcoYNjKOwVoxOqSTdW1G3T1O4CpjhK2+zdymeimPJymJMfHkCzjzzdPTatxuGP/Qgc3xRTGMevwiFrSzFqgzvLwozjxjft5ZNd8BhB/8BX3zxOa9lEea7Z6KebSEOKZgqQSSQYPMU5ehqCzAkYiruPevBhx5Gp1wcsWDabTtsXQfdunZmO3H3e2ToYIvF2EiA7fFekIntbcuyb9v878EQ85HxTKzAPhkakbcVRpmbjOdb7b4HOrTvhuuvvhxpCm5+brJN/qNrACIgAr+ZgPcX7zffrhtFQAREQAREQAREQAREQAREQAREQAREQAREoOYRyHeMccW85j2ARiwCeQQKGT6x0Q474eH778H0aVOc2yljAhPDFYaC9HNRJPr080/QqUd3fvfCB5aUlKBd+47MOfZuaUsmkNk95s6y98JcZFbC0ag7H+S9GQttSPdV/hagihVhiMGRjz+MoqI66PWHw3HUcX0x7rnRbiwhthNnXrFQiG3bmHLtphgice7cOWjEPGZRhldMMF9X/vVoLPb/7N0HYBzF1cDxt3t3klwwxfRqBxeqKQ4QqmmGQDDdDoYECCEfLfCFkMYXAiGhpBIg9BoIoSQkQCghoRfHhWKDMXYoMc0FY8DGxpZ07Xtvdud0Oks6WWfZOuk/cLq73dnZ2d/tneR992bc/mtSNRqU0jnSNDDlMts2WE9uuf4qeWXyiy7DzCpFx5mXXr16SSaddXOT2fs8aVlo2mfLaLPMOuur3ftbUvebbmx0mWk5bd/75LIaBFOPUOv/9NLfyL/+/qC8/upkN3+Z6xQ/EECgagW65FCKvTfeXlNpa/RDM0rJDTR6byXUD3ArFtenIIAAAggggAACCCCAAAIIIIAAAgh0XwFN1CgpzRckw+iCeVZnXQp1UqDGtF7w1gvuuWRGvjfxRvm/c6PrSMWNBLqNXVdq0IwSN9+Ybrck6O2qrDlgx+Kqmj2S1FugeTUpdzG+TrNOzp9wi/zkXBuGTfeTTzerX/qErLNSkZ71PBlfz/RH3ajzV6V0GMJMLpRttxgg45540K9q8d4COnY+5zWQtNthV7ZYp3hhXjOrrrzuOvnuGafKqP33ll5rrCXbbLOjHD1mjJxw8rf0/aGZkLmUTfQluYQGyrQfWQ1yrbH2erIknZOGfMLNlWXBIFcsnaKml767Ah2S0N570XI7r+vT2pjeanR4RV9CDYqldSf3/PEWOWL0WA1CiRytgbHzz/2h3HLzDXLSt76lE43VSL0Oi/jB7Nly5dVXu02fHfesPKXDJV5x9bUugzNZU6dNa1aZ7u7ZJ5+U1VfroxeD42Eb1eTQw4+Q62/9k1x25bVy7jnflv33P0BqNJNt+22Hy6jDj5KTT/qG5JY0aNf1OPX9/fxT/5b1V+vr2tY3rUXPZP+RB8qf7v6L77oemWaLafDQBcjzduA25KJmjmknLMBmAcAv7bKdjDx4f7nwvJ/IXX//pws0aoTN3Sd0nMZsZtnPm8IOeIAAAl1OoEsGxiwoZimp7jNXyWzSRlfieBhhsS53HtEhBBBAAAEEEEAAAQQQQAABBBBAYIUKBMtcZ24eGLOdWeBAr2FridZZtoeVloY6s+tLljVjF8Bra/SiuWbTtFVsyDerkdWL375YFo3mybgL6X4Z9wi0RyCV0suwGtixYhlX5Yqd20mdFyytAaj21LeMrr323l/Gv/qmvDxpvLz46jR57JEH5Yc//K688MIkueyqG/VktqwpC4rpe8eGTtT3i8uISkVXW12GlAaO7G3l4jyazWXrLfDToEEvC0Bb6dNXg1VFJav1LCj2tg6HOP21aXLVDbe5tXl9E+930Cj52713y4knn+wyt+y4Fi9ZJOOefcZlab06dYrsOmJfOXLMsa5P9ha2DC/b1x66/Lrb7tThEO39qu87ne8sqYG6pAYYDzz4EBl5wEHy3ISJ8vLLL8jjD/1dfvL9s2Xqi/+Wq2+41R2j9X3nPfaQW+/+uyZcRME1y/6qq2sK6BUdRpsPddfyw59cKAfsv49M1GBeGGe82UYWyGvpM6fNBlmJAAKrVKBLBsYsU8yCYkGY0t8X+qGXjz6cA/1mjpUg/iBbpXLsHAEEEEAAAQQQQAABBBBAAAEEEEBgJQpE14f8DtOay2WZHEm9+K0zg+ncRJoLphfdGzUjxTK6shoAKC46EFv05evsUnfRPauZYFYS8XWmrE2Y5J7H15/ieFgQRJlhGbsyriXQTBt3H3+P2z3hBwJlBGwIQDs/Q72+mXTDFLa9gQ1LmLFMKT3tbfjCciWrAbSEbpPU+fP22H13Gb7r7nLaqafIeT/+P7n1puvksmtulN79+siC+Z+6oQDtbE/pe+S/b78pa67R372PbB8+YyzUYJh7rv22bKhkUR/ymlWW1veDBawadQhCl7GpF3Nvuvk6TQpLyE80GBdYipqW+Qs+kf+89pq8/fZMGTxokFs2dIut5c6//MUFlOZ9OE9G7rGTXHzR+XLeTy/R/YgLwtn8YEkNJq679urap+g9GSZ6ufd8oPOV2bJEKiF77razZnPtKN/59pny4+9/R26+6Xq56pY7tA3N9NK91dYlZY216sQEM2n9jFB7u5WGJt3lZ7X2cwyWilv22LbbbCdHHHmM/PQnP5TvnXuBBjqjzxQbFtLWUxBAoHoEmv+F0MX6bUGxeTMnxX+muN8DXayHdAcBBBBAAAEEEEAAAQQQQAABBBBAYFUL3PzAFLnlTxN0lLRQ0hp8+OrB28qZX/9Ss25ZXGvDwTu4i/iWOWYBh+LyyTsvu6fF8a4b//KS3HD/y+6Cem/NNBl7yPby7eN2Kd6Mxwi0X8AiNSXnXXs23v3oaNjBtur6Ebcs+GbFJRvo/dbbbie5+owGi6PHkyY9LyederomIuhcWrrsjRnTZbsdhuuj5Ss2D5ftwwfMbP6uh+67T4786nEyfOddpcaCVxosymvA+Xe/+Ln89Z4/umCSZWzZdg2NGReYW3vd9eTMc34gl1x8vpx8yhmykc41VqsZYfYezVrWpgYHLcvNMrQyGoSzYttbseCYZZLVaLacRbq2HjZMl2lgbYkO82jzhmXSmn2mgXMN2gWh3aKgmAXy7PiXp0T9Fvne/50vI4ZvI/969CHdPGqToNjySFIXga4hUPy7vmv0SHuhozTrHyn6QEP19t0d+36B3ezjavk+snQDCgIIIIAAAggggAACCCCAAAIIIIBA9QnYte/iW+HKUHSFKMoHiao0Lm3QqmmdR2mpZm5kpUYvhltGSPHNAPIWCtCL4oGbT8iWWLGLUMXRiqYrUBmbbymzVLPR0pq9Y8PKab3iPrX12LXNjx4rYHPQld7sCqdfthwwFpQpLSkNOtktoe8Lu+WTKTntlG/KpJdfk0Y7v3UkrjffeleuvfI3MmDwQEnpsIYnfP14eeQfj8q48RMlo3OKPfqvx+WZpx6Xb558mg5PGO2jXucbm/vRfJn94ae6y4QsXpyR2R8tkCX6Hgt04jALRIUaBLNiQyi6IQ512d/vv18+WbxEfvD9H8iJJ3xdjj3ua/L1E06Urx17nIwdO1buufMOHSEsGs40pX21TE8LKFmG2P+c8m1ZY4115fLf/sa1a++1IFUrDZqptnBRo8z/+DOZ++En8qHef/TJQk3UCuXMM86UiZNeVc+UC4j/d+Zcufbqa2TQFwa6oJgFz9ywjZ99Lp/O/0Tmzf5QPvnoY1n48afyybz5mlmn+za/+BZYoMy2sQh6zrJP7aPChpHUuQv1fW+ZdLZswEbryTdOOlH+cu89mnJXK5apl9E2KAggUF0CXTpjrLoo6S0CCCCAAAIIIIAAAggggAACCCCAQFcV0AQS+Xjmi4Xu+UvZPuTgnxcq8ACBKhLQGI7MnTVLjvzKSBfATSQCWVLfKNtsu63c8Ic/u+DN6ONOkFf/86aMOWyk9K2rk6X19fKj834qO++6hzQ26OCiOjTh4w//Xb554nEiSy1NIZDzv/9ddzvi6MPkGh2i0AJFfvjEYp57/nirDN9+uGy48SYuYJbQKFJOA1x5TXw4YszX5Ne//o089dijbhOf8VXYXvt++pnnyG8uOl++d873ZP2NNnKBqEkTx8sWm6yuAajebn40q1+rfXznw8/k/XdmypFf3ss1kdIMsyX1adl+hx3k9zffXsgos+EoXxg/XoYNWFfr6Tu8ppdNCKZ3dfLBPAv8NRXLIkulok8D286CYC5Ap/c2V5kFySw4ZsMmnvG9H8gtd9yh/dK50DRYyPxiTY48QqBaBAL9ILLvtnR6sd2sNXAH+fSdKWX31X+zYfaJox9XSflU/2Dxf5iEPsVVP6goCCCAAAIIIIAAAggggAACCCCAAALdWKD0ipVmvDQrGb0+FCWuyPV3vyA33T/ZZaAkEjn5+hE7yLe/umuz6vbErk/5oeaWWdnCgmvuniJ/+Ns4tyaRDeTEo74opxyzc1RTL5i3Wcqtb3NjVla9gGWGtVWCaI67tqr4dXt+9Vr56y2nuKeat+juLUustFj2VkL/e+ONafLRvI9k2PAvSt/evTV4E2o2pQ6nqNEeuzUsbZQXXxovXxy+q9T1qXPvGwv8FBcLDNlVYz9MoLVhgSILDKV1eEPL+Coutr0tr9F5wWw7jRdJvQ3hGO/TsqrqdL4vW1a6rbVj6225tdOowSsrKd2Xjsjo3rf23k1oozbEohvW0A5fb2+/PV1mzZkjO++8mw6bmNBbSodw1H7q4yjLK9RlFuSy2Jr+0GKBLl/c3GL6JK3DMrr9xcfh+2LDObrhGuPjMg9vYR62T1vf0jH5fXCPwMoWsCkBR514nYy/+3T3/mn6vec/N5reAyu7b9H+rB82bmAgV985Ue66b7JbnNU3/AlHDJfTx8a/Zzuxc/qxQEEAAQQQQAABBBBAAAEEEEAAAQQQQKD7C/iLg5bx4uYl6v6HzBH2EAELLKUsAqRlyJZby+ZDogywrA5HaIEqv86CV7W9amT3PUa4uhbosWCU3VuAxwJZPkjlAj4a+LHtLbBmbVigqKU8C1tuQTELRlnJauDagkUuiGX718eWleYDTj7g5irrD2vb+hbqzvz71AXrLEtL++CGbbQJxLT4fdj6QUO3lKFbbqmJYDbUYXSx329vdRvSjdpfm6NM+6JDOVqxx74Nt0B/2DYJm49Mu++trI8WCAz13gzsGC0QVq+ZeHas9tjW6wrfDPcIIFAlAl0yMJbVSRn1+wvLEObiD7dl1yxTlQUIIIAAAggggAACCCCAAAIIIIAAAtUsUCbjKpu07JjATfmV02yyhGaSuAvZev0okYtTyVo5/vYGxRLxhXhrJqcXpDRpTK+qt9IoixEoFliOjLDizTr62II5PrhlbViAy4JiVoqDUC1lNvn1to0VX8ee+wCSLVuqc4id+q1vaICrURIaGCoutRokuuLaW6RXn1q32IJJVvz29ti37/dny3yxZcX7teW2zIY4tEHEQk2BsUwwC2AVZ35awCxv6TFaLCPMssFsvW1jgbKa2hq3znrjs8Oy2q7/eLGMNCsprZvT4Jovvq7va9SXyMOCYlbsePxyvx33CCBQHQLNP8Gqo8/0EgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGAlCiRrauToY4/TucM0sKRzmBWXXCatU4HVaQZVNKSiD4IV1+ExAggg0FUECIx1lVeCfiCAAAIIIIAAAggggAACCCCAAAIIIIAAAl1QwIYtDBN5OfTQQ5tlgfmuJmtCzVCLnvnML79uVdzHoyZKrih+Z1mfvuhUhBQEEOjBAkUfBz1YgUNHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaFGgMKRgPPSgBcqKb5nGnJsjzDa2ubooCCCAQFcWIGOsK7869A0BBBBAAAEEEEAAAQQQQAABBBBAAAEEerRA8TxdHiIbT3bnZ9PTafZ03iydJytb7+bj0rEOfdUVdu/n1LIGfaCsuHHLFLPl2RWYjZWTosbCaE5BnTzMzS2Yt3W6zNXRzLBAh3F0cbt47i/rW1HCmM5JSEEAAQQiAcL3nAkIIIAAAggggAACCCCAAAIIIIAAAggggEAXFQg14FOuZONxDAMNCgUaLKIggAACCLQuQMZY6zasQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEupxAojiTSntn2VIN2bQ+CqU2kZT8ikzb6nJHT4cQQACBygQIjFXmx9YIIIAAAggggAACCCCAAAIIIIAAAggggMAqFbD5vmr0pjExiZPHVml/2DkCCCDQlQU6JTDmR37VUW3dsef0mwp5G+U2H8/OWBBpWm+LfFJwIBkdj9aWxOPGlnwDoqmm1aEggAACCCCAAAIIIIAAAggggAACCHQ/Ab04lLNZhPSakl7wLy2JePYgGzQu1EtOebvuZPMNuQUrdig5a1sHqCvtAs8RWCkC7Z1jzDrTuCSeYyxkRq2V8uKssJ1E18m57r3CQHt2QxaGyce/N/P6u6vw62vZ36U9FQqJnvrKc9wIIIAAAggggAACCCCAAAIIIIAAAggg0OUFmGOsy79EdBABBKpMoFMyxqrMgO4igAACCCCAAAIIIIAAAggggAACCCCAAAJVI8AcY1XzUrWzo+SvtBOKagisEAECYyuEbWKkPgAAQABJREFUkUYQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFVI8AcY6vGnb0igEB1ChAYq87XjV4jgAACCCCAAAIIIIAAAggggAACCCCAAAJOIJ/L6ZR8eclmbWq+vCRbmJcPKgQQQACBSIAcTc4EBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgigUsYywMAncE/r6KD4euI4AAAp0qQMZYp/LSOAIIIIAAAggggAACCCCAAAIIIIAAAggggAACbQnkSlaSz1ICwlMEVqgA77AVykljCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACXVWAjLGu+srQLwQQQAABBBBAAAEEEEAAAQQQQKAnC+Sj73MHlkhhI8RFo8TpA51Eqag0SijpoFaSkpdcNif5UB/l8kU1OvbQWsjqf4H2w3ad1HbtsU7gpH3RJa5jxW3z/fNiDR5XKBCf93k91RolJ4k4oSibjB/EdxXuhc27jACfH13mpaiKjsTnS9gQ9TaXaqHXcZ3AfpsVfoG2UK9nLiIw1jNfd44aAQQQQAABBBBAAAEEEEAAAQQQQAABBLqqwHJex87nokgZ84t11ReUfiGAQFcSIDDWlV4N+oIAAggggAACCCCAAAIIIIAAAgggUCRg33S3m33z3WeKRQEAzaFx9SxxK8wn9RZVzedz+t14X9dV4QcC1SdQHBjTTMVsGJ33icK53VKGSPUdJj1GAIEOCBQyxaLfg00tRJ8T0e/MpqU8WlaAHM1lTViCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQDQUIjHXDF5VDQgABBBBAAAEEEEAAAQQQQAABBLqdQF4zZNytVu9rNY8s0DyyUBJ6dStILJLGRFbSqZxkdP6vjMsw63YCHFAPEsjn82I3Sxzr3SuQ9+Z8Jhk3x11C59Jr+ZJuRodTDEKdE09vFAQQ6MYCNqeYm1fM3uuhTn1pWdKWLRZKRu8aP/xM1kwsdQCBzYnp1vlsMre4x//gU7LHnwIAIIAAAggggAACCCCAAAIIIIAAAggggEBXEshlmy5iH3/EMLnq9uckoaOmhUkNjOlwoaXFgmE2v1g2mxU/31hpHZ4jgED3FrDPgVQylF/f9Kicduxu7mBb+Ljo3gjtPDoCY+2EohoCCCCAAAIIIIAAAggggAACCCCAwEoUsMnD3Lfc9Zvw7nHJvvPFkzClJKFzjNkt0KuAOiNTSWWeIlBdAkEioydzWuq122MO301q9YL33//5qkgmKzVh6bxCIumM5km6zBB912imGQUBBHqCgP89qYHxRFJSDTl59l+TJRf0llEH7KRBcv0YaRYB8vV7gk3bx9iMpe2qrEUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDobIEw0KHS4pLRQNe5J+8tD/xzqvzjiemSamGo0NqapBtCsbGhoRAg89tzjwAC3VsgqVli+hEgjz/zktz14Ety3ml7aUQsCoqRMdbya69cK740RduiR/ZTR8XVSGXxN3lW/H5pEQEEEEAAAQQQQAABBBBAAAEEEECgewjk9WpeoJkxQRjoXEv6uPnX3sVyZuy771YymkUjeV3iMmX0cpfNRbaCS9b2YUX7o5e5KAh0soDOFSY1UqdneZ2ecr02Xk0uv+AwueL6J+XfL7wh5515gKy5Rh9Z2qino11y1fnF7Bpsn951rl/d/mJ4/B7M+cvN8QXpMP5QCOKkuqQm3lnJxM8T8Xr/PBW/rRvi9TVxu8ubc+oTWH13NIGvWSlcFo/7GcTrff99fwsfas227sCTCq/6Z0v634EeVLRJKn49WmskKPkM1ikmXfHO6ZL1rbXT2nL/urS2fpUv1/NYE8QkVZvUTwmRjz9aJD++6mFZmugvF19yoqyzri2NSvSrMz7x/ELupcK3CIIIIIAAAggggAACCCCAAAIIIIAAAggggAACnSlgcYLN1u8nv7/gcPnzw1Pka+fcI/WNGUlohMCCtmGi+YXvfHcfTjEXXfjPhXEER4edtBJagFxLEGrEUEsi28vdZ0O/PgqaZ+IIWioXRVQaw2h5KhfVa39gJNpfLojaCePIVi4XXXbPl15+Dxpcf8JctL9cHPAPgqVueT5f6+4r/hFWFhla1eePHxa0NYemw4vdc5FbEPtm8k2BodbaaGt5+1//tlqpZJ3/2kdrbWjgXKOAuTii2qemQU4ePVwOG7ULAwm3RlaynMBYCQhPEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBriEQBbwSknZZklkdSHH0V7aXMXprq6zqwEZbfVsh6+IULc0nbdZcU+ZWlLuVjVO4fCZXs8r6xIcTfRgiVOeotJ2y1FQ/bsF3ozAfYslyv+O4I4Xt/XZ+vb9vrcN+fQ+/z8UByDAfOedi93z8+iWksgCjf31WHXPbPdAZxZyAj39boDCXzetnhJ1QdvNn9qo7gq6+ZwJjXf0Von8IIIAAAggggAACCCCAAAIIIIAAAggg0MMFLFCjQ4rqz4xe+E7pIwt+WdZIS6W15S3Vre5lzY/fc/ihJH3mT1gSQPPHnI8r+IBa3o/R5xvwFUvuC/VzpQGIKKBROvRryead/rTSwOiqPn/K9T/vA2FxYMw/z8Wvc9thzU7n7/Qd2Fnmzrz49LcAWSJlT9oOqHV6x6poBwTGqujFoqsIIIAAAggggAACCCCAAAIIIIAAAggg0AMFXABA80T0ArjNPmZXxVd18GKVvgpKYMGTtA0npx2pVZcoKGbPAh1K0XpnAcQocKUhRFOTXGNewhrb2FWzqq4k/HMbgk+XZS37JhmI3sXtRvX8T9vMbRI17xfrMHbRgsBnnsXt62xQbo0FMKyfNqCi1azVxy7A6SNtJe0VGl7OB9V+bkSvVlsHnXSvexhP1pbIx2GOILqvNDy0gl6Gtg6gzLr29iA60nzoQ7/Rdu3dukwnuvVqjLr1y8vBIYAAAggggAACCCCAAAIIIIAAAggggAAC3U/AAkoWBnnoqaly3uWPyZMT3tJnPhLl70Wuv+tlufS6cQ4g1GBX3qYli1ff+eA0ufCqpyVr8QUNWk157QP56ZVPSTpnYa+Wg2K2/MVps+Unv39KFjVEc4bZsnLFB2veef9zmfjKrEL1ag9iFQ5kJT6YOWuRvPTqXA0yJtxtJe6aXXUTAQJj3eSF5DAQQAABBBBAAAEEEEAAAQQQQAABBBBAoJsIWBRFbzaXkv1nc2W5+bJCXWi3Hl7yGt0KdDi9rDrccd80eX7cDLnzb5P1mcvjchlZDZqTZc922WEzeeq5qXLHnye4gJjGUly1SZM/kGvuniiDNltTshpha9Qr5XPnfCxPj5smjY1+rjGrGgXJdKtCWTBngYx7bpos1biYJqEVimWMRVljKV3WdLOL8C5bTO8feOQF+cVlj7ost8KG8YP4ZddXPBoUr6P3pe1W3XMLXLZxe+jhF+Tnlz0k9VrJbt2vlHnl7ZwrOu9sbjW7WczXbpTyAgTGyhtRAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCLCNgcXnabMm2uzJ23UL5++A7y9jsfycz3PtYeWiirKWowbGh/OWLkUPnD316Wt9771AVc6jXT65Ibn5OtBq0jxx66nQutWBjLsrdCbTeXy7o27OJ5ziJaJcUNfxiN11iypuWnjT5aoTEcazub0QX62KYyKzOdWcsN9vClGfOjIFCBQDz4ZgUtsCkCCCCAAAIIIIAAAggggAACCCCAAAIIIIDAihOI0xl0VrGSNkufl6zuIU8tU8zKo09Nlw3X7iffPHY3ue/Zt+Xuf0yRc08ZKXVFsSzLJzr5ayPk6Snvy8+veVRu++VY+dVNz8uizz+Xq396SBwUswwdDVqFNdKgD5OhDtFnwymGgQ7XuOyMV42JrGQSOc36CqVGq9316DSZ9Op78sNjd5VLb3hcXpr5oayxel859oDt5djDtpNkfBX+9odmyJPT5svnuo8zL3lY54xLSO8+tXLx/45wQbn35izUoR/Hyfgps6Qxk5Udhmwk3zpqZ9lq23VcLywb7a6Hp8q0l+fJWcfvIL+6+TmZ8c582W/PLeXcb+3mwoHvfLBA/vDXl2XC5HelMbdUNlp3dTl49yFy7BE7ufUWNHxt2iy56a/jZepbH7lj/dIOA+T0r+4m663fSzIaqavR/+55aLK88MZcOfWYXeWqm56TqTNmS12/WvnaoTvKkQcN0+1ELPnuzgdfl/FvzJLTjttJrr3+SXl1xgfSZ+215KgDt5LjDtnR1fFBiHfmLtDje0EmTn5PMnp8w7bQ4ztyR9limw1cPXsVrH8fvrtQbrr3BR2ycqY0pDOy0YYbykF7DJTRB20ndz88RcZpQHTB0kY576K/2MsmNauvIT8/c6Rt7NqtTVnPqrmUeZ8XkuSieon4c6Laj3plvmL+nFyZ+2RfCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh0SEBzriStE4M988I7MubL20o+DGX/3QfJY+P+Iz/M5CRMNA8s1GgU58en7infvvBR+cEvHpIJU+fIaccM16DRah3af+lGM2d9Ki+9NkvO/d0jctg+Q+XQUdvJMy++JVf9abxssv4asvsum7mAzzYD15JJ660lixYtli/vNUTyOmJjXV2Ny1J7Z9Z8+Z/z75cvbLa2/PCUEVJbUyPjxr8t/3vR/XLZzw+UrYdsrOGPUN6fvVAmz5gjP/j1YjloxEZy1MitpKZvbzdU4zuzFsi3fnq/bLBmPznrhN2kb59APlmwRD76pCnDaooGxb73iwdk2NAN5Cen7y0ZDTzd8rep8q0L/iI3/+IoWU+3tbEc3/tgoUzSYOLsDxfLkXsMltEHbiOPT/6v/P5P/5Z11l5NRuwy0AWz3p+zWF589X25YN5iOXrPTWXMgVvLo1Pelevumigb9u8n++86yHG5vunxDdy0v5z7P3tJjR7fMxPflrMufliuufRIGTJgLReknDn3Ezn7ggdl7bX6yne/sav0ru0lsxc0yrxPlroA43ZbbqhztL0nCxY1yEgNlgUavAzqVnPDTyY0YJRMEh4qPT95vqwAgbFlTViCAAIIIIAAAggggAACCCCAAAIIIIAAAggg0EUFanT+roee+o8sra+XQ/cb4gIqo/bfWu5+ZKo8psGWA3cf3Kznjfps6BYD5WhX52XZZpuhctSoHV0wJaoYBdIyibRkw6b5xZo1UvwkrwMv5pOWoCR5DcbUagpbriEj3/rml2XHrdfSgFFG9tp5oEx7/W555NnpsqcGxnLay223Xlc2mNBPpr2RkoM12JTTFiwfLatxq6vveFnW7Le6XHf+4ZLT56FeuR+x00B5d8FSuf3e1+Ty/xvoehBqllp9erGc9vU9NTi1WdQr64j248rbn5HVNF3u8ku+In1SNa5zGjfSrDcX63J5Rbf87SXp339tufR7h0tCA4aWzbblNpvLV//3T3LXg1PljGP3kJQuy+uPdCYv3znxSzJ8q02sedntiwPl9RlzdR62d2TEjtofY8gvFVlcLz/SjLHhwzaWrPblS18aKq//5zZ5fMJ02U8DYxqrlKv/NEnWXq2X3HjBKDd8pI1EuffOm8mcTxrkhjv+Lb/78SHuGK65dYLUrhbItZeMkpq62uj44p+Beg0bso6sv0E/ef2dj2XfvYe7NQkXdtR553R2uVCHw9RwY7Ptet6T6Hzuecfd/iNGqP1W1EQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDoAgKP6DCKwzR7aP11NcNJy6CN1pChX1hHHnzmjWV6Z6ESu700fbYGgpLy+ecaKstpZpkuWxEXyHPaVl1tygXFbD9WEvrfEM3+qtddWbDIpiqzfaUzFqZzu9caOqeZPk5oiGz85Jmy246bycKFS2WxxpoWfpaTT+uXyk4abJo8bbYLLlnbNkdZQoM/e7lgW9S2tZfXYNrLr38g+2om15qpWm050Hq2RutoYMr2k9d+Tp0+R/beZVPpVRdKIk6uWm/NPtr3DWXS1PddUMy2sXm8alOh7BwHxWz4ykADZoM27q8Zb0uibfWYbA62Xpr15oNitq3ta+iA9aR+aRRkTOrxTdAssj2GD5TPP1sqny224xP5VA90+NYbyNQ35kV91f69MPU92e9Lg6SPBsWs+97TgntWLFswr8NcmoN7rttYMUmb+83+oyBQTsCfT+XqsR4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZUuYAGPQINB/v699xbIdJ3/6iAdtvDxf7+pIRHNx9LUqEH915SnJ70lHy1skP6rR9lGUeBJ5Aadu+tNHWrw3NMOlEuve0xuufclOVXn3bLIS5iMAzjZlCRymgbVzpIJda6xIKX5YZbLlNCZuTT4pLeE5mjpVF0SaOApm2+URByryem+krowF+j+rGOu5GWRplRldDjIv/3jJbnv0Zdde7lsVpKJGhd4siyxlEWItE3LU0tpcC+woFTcgh1DgwaIGuuTstn6G7qmU/pcm1QzzUvT9DOr25jWISg1HW3T9fu7YF0isKU2p1hS1urVS17/aL7rv+3Kjsb6aiWvPVIZvdfsNj3eUK2tbSuB9TERte+StXSZHW5OM+pSQR+XrVa/RPugaXF3/2OC3PPoJG28VnIarUsGJqb11dHCXI06wVuQC2XgBuu747NjTmv/3Rxa1intR6KxUVIa8QssQKj9CbUjgcJaBpoEOqSkzl3mJkCz6hQEWhEgMNYKDIsRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEVr2ABcWs+PuHn3vTBcn+8ewMefip13VIQA3YaKCkJhNd7r7vsRly8tHbaR0N0Oimb703V+5+eKp8/YgvyiF7D5b/fvCh/OnBKfLlXbaUAZv1XTkHqDEoH0xywZzCXvMS1oSS1cDRN4/9ohx/2E6F4FRRleihBn9sqEDnoMdlYSsfHNPwkAsSLbRsOC22D1sbaMQojsvpQg0xaRBr4WIbctDWavaV/mftNGhqW2/NIrP2LJDnsq8suqfFssIk0ICTDR+py3I21mNc7LFlzLl96A9N5tJAl2WpWb0o4FWj7Vp7p4zdRY4ftYsLgrk4lm9D7y0wFujccFkNbH22SI9B+2dBsZSLeFlfrWjjOsSj7U8PzC2xviXsYKzozgPmGIss+NmmgJ1/FAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEuqSABVUKRR8/8twbMmK3LWTcnafJ+HvOkGfuOlWevec0eeavp8n2O2wiz4yb5rKmbCjBbENWLrzyKdlg3dXk5NE7ugDOmcfuJhus1U8uuu6BQrPuQdigARe9lSuW8RVoQChhASEd1i9sdDe72G5BJQvyZEJdrr3QsJcGpHRB0iJGIuv2s2yyRov7iOWpZXVlHw1ibbXJ2vL0S++5AJAL82hdCwal43t7bO2mNYqV0f3acwuBWYjK4lcpDQ4O+cIa8vSE19xzXWwzbrlbWtdrM1KnQyNuvN6aMuHF/7o6mnum7dRI/vOcTJv+gWw3dCMXJLPO5dTC5uyyksxYJl3SBdMC3atleFl7VgLdPqHzstkh6iG7DDELtCXymnOmr5Xl34U6l9nAgWvIk5NmuWOw9YGus/7X682KhTTrNEA4aLN15Nnxr7mDTxQFxSKvGs1Yq5XV+2k2oG5vfUhqHedijWjgTztujygItCnAWdImDysRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEVqWAzxSzYMjEV96TeZ8slv13HahRIQ2NaNAnChNFPRy5x1B5d9bHMnXa+27BbX9/WWbOmi8X/+9BLtOqIa3hIg1EnX/mfvLG2/Pk9j+P6/RDy+XS2nUN32h0Z7MN+2l2VoPc+9gr8uJrH7ibRXhOGb2zTH9zjlx845Py4TydZEyr1zfm5LkXPtRst+kuQcqCYTa/lhULFNnNisWPLIHqhEOHyYz/fiS/vmWSzP94qQscvTfvc3l64lsukGZ1R395iEyZPktuu/dlaVS/xUtz8rObnpBFSxvkmIO3tCquWGZaGA+lGCdnueVBPHGZ33dcPepP6cJ4pfX75KN30uObKxdf/5TMm6+TjFlgTCN2416epcf3aiHQduoxX5IZM+fLb296Wj5aEA1xOWvufHlq0psuaGfztQ3VudsaNLPsgSf/K/9+ZZZMe3uBC8y5g4x9fL+4R6AlAYZSbEmFZQgggAACCCCAAAIIIIAAAggggAACq1QgCPxlK5tbqOXvdvulNs+ML26uocIlYL+UewQQqC4BC6VY8e/y6JlFfx586jXpX1sj++20qaULuRUpH1bRoMnBIwZrUOUpuf+pN6Wmrk7+cP8rcuLhu8qgTdZynxSpVK2bX2voF9aW0YfsKjfeN1VG7DpENtt4TZ1/q07r9NY2NfOoOBoU797f1ehnUkIjNHX1mi/VO5Ba/byyZb4kNBcq1CEGk2FGajS7S2cIcylbaZ0YbN+9hsrUGXPkxjvGyyLdfvV+dfLwDSfIl3bZTC49+0C54rZxcuQ/X9Pd27xZgfSqq5FjjtzTfapZppUda18NsmnCmo0q6IpljNnu9/7SULnwjBr51W3Py33/elWSuSWunZH7bCf77zJIhyAUOXL/HeSzhXm5/e9T5Oa/jHPr+662tvz49MNk2yEbuvZM3+YTC3VuMis6dZkLSln2VyqhwzHqcfkS5Oulzl4G3ciGULShK62fSc2Ha6iJtreMrj12GiyXfVfkij+OkyMem+r2G2qKWW89vuNHj4g+xfX123OnTXQeuL3l93dMkIcen+J2k0n0la/ofHJ7fnGwaFKZjNhhsBy1/6dy4y0Py1IdArJXv3XkHzcf75LFsmpm/aQg0JaAZixqaHYlFNvNWgN3kE/fiU7maJf2FrP3TPShEf0UWXPAMAsYawnk03df0TdhVM+WWAnj+tEzfiKAAAIIIIAAAggggAACCCCAAAIIdF8Bf13IXzla9kivuXOS3HHfi3o9SS8o6dXhrx+xo5x+7C7LVlyOJXZp6lpt97b7XnJbBXq194Sjviin6RxA7hp84PvlG229f74G9wgg0F4B//5q5X3lr2gXYuJRZpHk45BIYXn0drXLyxY48q1m9YFNS6VxKg3Q6M0NSKh9s4C8xXxsPERd4S+dFzLWfPd9Q8Xd003cR5Dbtw4Q6AJlNdEWcX+zcb8scFRcSpubM2uBvP/RpzJwo3VlnXX6uKECLbhkrVldF5eyRuL2LDBmxe3SVRCZ/tZ8aVyyVLYbspHkdY4vt979jPqpk4DJ62/NkV61tTLQgoY63GH02RZViptxT2xARWthmYCTHZf1Ia5sfbTij8+yu8w5fnVc/229P75Benxr6fHFPO71sPVuge7wnbfny8JFn8pWW24uqVodMlEr2uvljsY2Kmyoj3W57d+6skw/dRmlKwnYqxToaxXI1XdOlLvum+w6l9U35AlHDJfTx+7c6Z2NQradvht2gAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAewRc6KP1ihaMaVZ8KCZa6DOo7Jmras1pEMW3asEaF8XSB1FQKb5MboEW9zAKii0TENPVrviG7Pq+FXuubVrQxgVrdB4sGzrRsqesxKMPFgJGPpBlGVZWbG6u4rLeRmvIBnrzJdSIWyIO1NkQh66PRdu4gJhWLhy3rtty0NpRX/SxhSBciTtkmWiaziZbDtlA+6yZaa7j1oDeNPqk04NpQCvuvC7SGb2iIGG8zFqzIKAdlz3WkSndfdMWulCLP+4av//4FbBjKz6+wnaFB9H2AzbXYxC76c5yWZ3LLAaz44jNo5rWcTVSUL/PaDk/EWhZID6TWl7JUgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEqknAB4pa7LMFXzSq4+fq8kEql25k6zTS4zPFbPvix/a8WbH6dotL3rLNLEYT/YgynOJ1xXe+f3ZxvnCB3qWbRbVsWbrQMQu4NQXqXH/cPqK6RdXcAvfc1sexsEL/4rm33HFHcaTmQbGoOQ2YFR2QX6b3rg/apo4Q6YrFyFxQTJ/5XdkKO4ziEvlZ/4trNbVTXLf0cZO9NloIiuXiYGbTa2NBSNe+db1k/6Vt8hwBEyBjjPMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoIoE/OB8PlMsDi/5IU59KpY/IguYFMd7NGPKZU1ZvEWbcIlU2oTNUWj1dHYtv2VTNlVhiT7wMR7fZvw8iLvjpg7S6JfPlPJTCfkm3FyITbvQ/esTjTS5RXGbKR89izeywJTbjd773dqq4mqFxz44VOwRbxQUpVS5lnxdayzuU2F58fZuXzr4nasTV7RttDR7ZvspatO3FbhQhIfTyZKKIxNF9V2D8X5dl12krdAx94LlQw2O2X7jA/ZzUkY+Jf1xDfIDgeYCzc7Z5qt4hgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED3ESiOy3afo+JIEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLqZQJxxlE81P644w8hnZoU+08nX8hlkPjMpThfxc4CFUpqB5jds5V4zllyJ283Fz0O/H59SFj/32SlZn+qViZcko3b8HGO5uN/5OAfLV/cpar6dpl6VLon71TyHqzDXmd/Ob5WL+6n5W9GqePOcr+A3KHQkWlG62lfz9/44/POmHK7IOczZrGVafLv+PlqqhxvvIXbNtfb6+NfT35frmG+f+x4vwKnS408BABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBniFAxljPeJ05SgQQQAABBBBAAAEEEEAAAQQQQAABBBBAoMoF4jyP0gyjOOOpKQuk6ZE7YF/f38cKTbVKMtDKKjXvRyHjKm6/9Llvzs+I5qbbcgtbacdvULhv6mlhUYsPmrfnM7Va27rQT99WXLGpftMjX6U998u0W9gozhQr12xhffSg1fb861lwL+yIBwi0KVA4xdqsxUoEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEqlyAwFiVv4B0HwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoH0CBMba50QtBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBKhdgjrEqfwHpPgIIIIAAAggggAACCCCAAAIIINC9BeLvdefjo/RzykhOFwSS15sEWckEGXsqkgwlV9O9RTg6BBBAAAEEEOi4ABljHbdjSwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSoSIGOsil4suooAAggggAACCCCAAAIIIIAAAgj0GIG8ZYRZyUZ3+VR07zLF9GGQth960/QwzRYL84lofS6pCWRc8oow+IkAAggggAACpQJkjJWK8BwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBbClTl12f894XCkrGlC8v9N4dafcmIB7ZKwwoEEEAAAQQQQAABBBBAAAEEEECgKwgU5hKLO1P6XGoLV4Cy+jiUhOTzecnmcpKzW4XHYJed8mFW5zCLryMFOp9ZYI91TWBr276+FOe5VdgLNkegZwos83bvmQwcNQJVKRDa78k2ymf6+7M2Xp/V37OS09+1+vtbUjX6ezfO/m5j+xWxqioDY/7PDrOyon+XuOKXR2n00TJ+IoAAAggggAACCCCAAAIIIIAAAghUo4C/NO7v/TE0PY/DVHptSINWeqEoDEPJ6eNkIlEmbOXbav3eAmuhXqCztq0EGhSzm5W8DvPoH7sF/EAAAQQQQACBdglYUMxCOzoQsiRWUiCstGNVGRjLScYdRxhq91XQfwMoWqqYbnzp0kNter5yYo5N++MRAggggAACCCCAAAIIIIAAAggggMDyCWjoKd6g9PJVtNyuA9kju84TJPMaqNKLRIFmigUJyWRzYjOQVVJsrzkLhiUaJZ/LS06DYbn85zrjWaDLk26/bbVPxlhbOqxDoG0Bu2BOQQCB6hSw35Plir3H7fdkItn02zLIh03zhZZroML1pX9ZVNjcytk8q6Ew+97PpMkfyovTPtD0uugw0pZ2pyURZ5K11ptE3ofQWqvBcgQQQAABBBBAAAEEEEAAAQQQQACBVSmQ1eyvqMRfic7Hl7E0+OVKqAEqDViFQVomT58XDcNkqzRSNmXGXLn67pejeh38mdTrTJOnf+iCYpaNZpljL70+R665K243KL2+5Psb77AwB0gHO8BmCPRggaTPhOjBBhw6AtUqUCY8I4361RXLFLOg2PiXZq6Sw6zKwFiY17Emleu1196WO+99QfJxClig39yxki+MUOmeLvMjU/J3yjIVWIAAAggggAACCCCAAAIIIIAAAgggsEoFkjpPmCtB/G3yfCruT7Q8kaiVbNbWRQGqRklJMpnSL0x/LtNmzJTXp74T1+/gXT6QwIZmDOs0AKezmCUaZPqMD+SN1z90DVoWWfPS/IJTXgN3FAQQ6JhAtvnbqWONsBUCCKwSgXLfC/FxHN+5ZO/Vpb5+qX4BJadZZKVfOvG1Vux9VQbGbGhnm1/MvqljYz1n9b9oHOlAI42hpsuX+cODD9YVexbRGgIIIIAAAggggAACCCCAAAIIILCiBcpkjFhQLKe3REozx/Q+mUq560OBBaw0KOXnButotzRHTLPFcpLXIRr9fGJ2/ckCYgFBr46ysh0CCCCAAALNBCwoZsX9XtcvoqyMUpWBMTermEYP63Ui1cZEqH+Q2Dd49CYN+geQjvcctv2XE3GxlXFqsQ8EEEAAAQQQQAABBBBAAAEEEECg4wI5fwHHf/+5JEErqV+UtnnF8nodKJfLuKCYBa5sO9skXxiKsWN9yGXSer3JZrLX2c70y9n2ZWydZEznHIuuO+XKtV/m+lTHesVWCPQMAf/27xlHy1Ei0L0E8hq3aavkNMPbis0p5u4tU0y/4FKrQyOH2Xq3rLN/VGlgrDlLqH+I2B8+22+1oey4xUaS0UlW2yoaR6MggAACCCCAAAIIIIAAAggggAACCHRhAZtf3pXWhlLUbC7L5LKRhF6cNlumvPFZlDGmQaxt9frQ8C02ruzosnl5efoceeWND11gzBrbbqtN5Ytbru/a1Vy1kvabX8pfWcNBlXSCpwgggAACCKxagbDtsFMu/v0exnNk2e9LG7LYgmJ77rD5Sul72z1cKV1Y/p2ECpfXcZ41uU5vOUkGKclqptjWW20p3xw7TCdva7n4P1f8fcu1WIoAAggggAACCCCAAAIIIIAAAggg0HUEfKpY8286Z/UCUEK/dG5fgM7cPVWmz5wkDfUNOopijX5xemM5ZezOFR/C1Xe9Iq++MUfb1ABcrla233IzvfY0XK9D2RQfFTdPAwgggAACCPQ4geZfI1k1h1+VgTGjKh4nOhtPxmpZYytnBMpV82KxVwQQQAABBBBAAAEEEEAAAQQQQKCnCDRNlNFyYMyCYqKrbLqvnF4byqR1CCYdVcge2zWiSkvxNSZrM9AIXC5u14Ji5fZA3KzSV4DtEUAAAQQQ6ByB6gyM2V8e7q8Ly/3S7LH4Lw2bY8yijTb2s5WmyGP8yP/Fwl8mzocfCCCAAAIIIIAAAggggAACCCCAQFcV0BBX3LU4RBXPRaIRqmi5ZnFF14fsrkGX2fK8hFrPLv2E/jpQVHu5f1rwK5G3dpctrm3fj8LqpitRblGF+y80ywMEEEAAAQSqSKAaprIq+Y1dRbp0FQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHlEKjOjLHlOMCWqvrvG7W0jmUIIIAAAggggAACCCCAAAIIIIAAAqtewDK/ohLfFzKw4uerfEQg37+4m4X+rXo7eoAAAggggAACrQuU/AZvvSJrEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEKhmgR6ZMUY0sJpPWfqOAAIIIIAAAggggAACCCCAAAI9QqAwh1c8x5ikosMuLF/VV3hKxiSyOc+KCxlkxRo8RgABBBBAoMsIlPzG7jL9oiMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIrFCBbpoxRrxvhZ4lNIYAAggggAACCCCAAAIIIIAAAgisdIGS6zuFOcWallvOlj3L67pcvN4eu2StQv2V3vFoh6t6/6vosNktAggggEDPFmj6Ld11Haqhj11Xj54hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUjQCBsap5qegoAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAJQIExirRY1sEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGqESAwVjUvFR1FAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoRIDAWCV6bIsAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFA1AgTGqualoqMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKVCBAYq0SPbRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKpGgMBY1bxUdBQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKASAQJjleixLQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQNUIEBirmpeKjiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCFQiQGCsEj22RQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqBoBAmNV81LRUQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUoECIxVose2CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACVSNAYKxqXio6igACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUIkAgbFK9NgWAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgagQIjFXNS0VHEUAAAQQQQKArCeRyOdedBQsWyOxZs8t2LZvJysfz50s+n3e3shtUWMH2Y8XufV/9c7/OVeAHAggggAACCCCAAAIIIIAAAggg0IMECIz1oBebQ0UAAQQQQACBFScQhqELOp199tmy7bBtyzZ83XXXyuAhQ1ZKUMw6EwRBoU9LliyRs846S7baaisZNGiQnHTSSfL+++8X1vMAAQQQQAABBBBAAAEEEEAAAQQQ6CkCyZ5yoBwnAggggAACCCCwogXS6bQLQGWz2bJNb7LppjJixAhXrzhoVXbDDlawLDEL3lk5+OCD5fXXXxcL4vXq1Usuv/xyefrpp2Xy5MnSr1+/Qr0O7orNEEAAAQQQQAABBBBAAAEEEEAAgaoRIDBWNS8VHUUAAQQQQACBriaQTCbbnQF26KGHyqhRo1wgzYYy7OzgmAXFbPjG+x+4X8aNGyd//vOf5fDDDpcgDFygbLvttpNLLrlEfvnLX3Y1VvqDAAIIIIAAAggggAACCCCAAAIIdJoAQyl2Gi0NI4AAAggggEB3F7DgkwW4MpmMTJs2Tfbaay/p06ePDBgwQC688MJmh//Xv/5VRo4c6eb7sm0saGXFtjv66KNljTXWcNvutttu8sQTTzTb9rLLLpODDjrIZXjZeqt7yimnFOq0NGeYC75pEOyPf/yj9O/fX4466ihJJBMuO2yLLbaQPffcU+6///5OD9AVOskDBBBAAAEEEEAAAQQQQAABBBBAoAsIEBjrAi8CXUAAAQQQQACB6hcYPXq0WFbYrbfeKha8uuiii+Suu+4qZJTNnTNHJkyYUBi2MEyEMn36dNl7771l9uzZctVVV7ltN9UhF60dq+vLa6+9Js8995yMGTNGDjjgAFf3K1/5il9d2EdhgT6w4JsF7t58802x7LDSYvuZNWuWW2zDLlIQQAABBBBAAAEEEEAAAQQQQACBniDAUIo94VXmGBFAAAEEEECg0wVs3q7999/fBaMsSGbzd917770yduxYt++sBp8ss8yCUBa0sts555wj66yzjhvq0DK8LJBlwa899thDzj//fPnXv/7ltrV1S5YskZ///OdyzDHHLHMstl1pcRljug8LflmGWGnZfvvt5Q9/+IMsXLhQVl999dLVPEcAAQQQQAABBBBAAAEEEEAAAQS6pcCyV1G65WFyUAgggAACCCCAQOcKWCaXD1BZ0Gvw4MGyaNGiZju1Ocl8HRtK8cknn5R99tlH5s6dKx9//LF8+OGH8vH8+bLvvvvKxIkTC9taMK2mpsYFzQoLyzywPlhwzEpLGWEWpEskEoU6ZZpjNQIIIIAAAggggAACCCCAAAIIINAtBMgY6xYvIweBAAIIIIAAAqtKwIJPFrQqLba8pYCUr7e0fqlks1m5+eab5bbbbnMBqnQ6LalUyi2vra31VcUCanV1dYWgWmFFGw8s8GZzilk2WENDQ6FmY2OjJMKEC8bZ/nv16lVYxwMEEEAAAQQQQAABBBBAAAEEEECguwuQMdbdX2GODwEEEEAAAQS6pIAFu6zYkImfffaZLF68WCxoZVlm9fX1smDBArfegms+88staOcPC4rZdltvvbW8++67hbYsiGfrZsyYIWuttVaLQb127oJqCCCAAAIIIIAAAggggAACCCCAQNUJEBirupeMDiOAAAIIIIBAdxCwANWQIUPkgQcecJlgNvShFT/UomWPWWDLnnckMOaN9tprLxcEmzlzppvXzJZbBtmECRNk7733dsvaymzz7XCPAAIIIIAAAggggAACCCCAAAIIdAcBAmPd4VXkGBBAAAEEEECg6gQs4HXhhRfKpEmT5Pjjjy9kdVnm2N133y2/+c1vCsfkg2aFBe14YME02+7EE090mWFjx44VC4598vEncuyxx8rChQvl3HPPLQTf2tEkVRBAAAEEEEAAAQQQQAABBBBAAIGqFyAwVvUvIQeAAAIIIIAAAqtKwGda2VxdLRU/95ivZ8Mn2mMf6Dr66KPlnnvukWeeeUYGDBjgssNsTrDTTz+9WT2rb3OOtZU5ZuuKb34f66+/vjz66KMye/ZsGTp0qKy/wfry7LPPyp133inDhw8v9KWl/rMMAQQQQAABBBBAAAEEEEAAAQQQ6G4C0eQW3e2oOB4EEEAAAQQQQKCTBSzAZVlft956q9x8883L7O3BBx8sLLN6Z555ppx11lmFZf7BmDFjxG4255dldG255ZYuSGbrfdaXte+DXrbcB73ssS8tLfPrLABm84y9/PLLsmTJEtlt193cPGO23u/D1+UeAQQQQAABBBBAAAEEEEAAAQQQ6M4CBMa686vLsSGAAAIIIIBApwlYsMuX4sd+Wel9ceCqpWCUZXNtscUWzTazbXxdv709b6n49S2ts2W23gJkVnwbvm23kB8IIIAAAggggAACCCCAAAIIIIBADxAgMNYDXmQOEQEEEEAAAQQ6R8Bnjfn70r34AFQ6nXar/NCKPohVvJ0tK37u2/J17bl/3NDQIHfddZev4u79Or/wmGOOkbq6Ov+02b2v6++breQJAggggAACCCCAAAIIIIAAAggg0I0FCIx14xeXQ+uuArkKD6wpw6FDDbWcqND+poL2V22xJvtvkaXdC/FvN1WLFTn/WmRp98JueP6FgX6m6nnh70stAokOuiZVo1Gt0rW6XVHWma0tfd5si1x0AlozdTW1cucdf2q2uvTJ6COPlKy2n6jRfa+IwvlfmWILr/9yNYj/cnEtUxn/ZUiWa0FPP//iz9/lMiuurF98qKiU2bz0Xweh/WJypcyGFXWKjRFAAAEEEEAAAQQQqF4BAmPV+9rRcwQQQAABBBDowQKPPf54m0efTTdqoE3/1NMLunm7qJuo7AJp4K+ztrnX1ldWuLmw/9Zt27MG//YotV6H8691m/asqfbzzz5HKylBhZ+/leybbRFAAAEEEEAAAQQQQGBZAQJjy5qwBIFVI+D/vR2UfueztDuVZXzlpFz7pftr/jys7LpqhXu3rIzm/VneZ5UdPfvHf3nPuOb1Of+aeyzvs55+/gUlvx8Cy1ZroyRqUjqXWFav5+bFLsrmy9Rvoym3qtKEB/9rrtx+WlvP/luTad9y/Nvn1Fotzr/WZNq3vOrPv0SFv8Er/Pu1xZTjZvRe2P9e8DvMxLX4Z38zLp4ggAACCCCAAAII9HgB/kLu8acAAAgggAACCCDQXQVsjjObRywIki2N5LhSD9tfrl2pOy3aGfsvwlgFD/FfBehFu8S/CKMjDyuMjNqXFCop5b7YkPdfPctHr3Qujos1DalYyd7ZFgEEEEAAAQQQQACB7idAYKz7vaYcURcXqNd/uIb6n735ml2k0H/A2nc9g+ZLlzka/33QZVa0c0G59tvZTIer+e+vdriBCjdk/xUCVrg5/hUCVrg5/hUCVrh5pf7lMsRa6l4YpgqLK//9UWiqQw/Yf4fYChtVev7gX6Ds0AP8O8RW2Kj6z7/KzgD7ckJnltACYtrFtN7Mumlvic7cLW0jgAACCCCAAAIIIFC1Ak1/M1ftIdBxBKpLwIJiOtuLzFmwQG594BV5+onX5PMlDZIL6/Rfsho0y9e0fUCJyr5xmstX+A/k6r+y0bZvubUcfzmhttdXdl0putrT9h7aXsv+2/Ypt5bzv5xQ2+s5/9r2KbeW86+cUNvrOf/a9im3lvOvnFDb63v6+de2Tvm1Zfz6JlNy2f+NkiFbr+2yhMs3SA0EEEAAAQQQQAABBHq2AIGxnv36c/SrQMDCUhNe+UAuveoh2WunAXL7Vd+W1XvrQg2KJWy4qzJxq0oHw8q475F2/MArnny+zD/sy/WM/ZcTant9Hv+2gcqs5fwrA1RmNedfGaAyqzn/ygCVWc35VwaozGrOvzJAZVZz/pUBKrO62s+/ModXdnW582fya/+VH115n5z/7S/LLsM2lXTcov/Hfpl/XpTdPxUQQAABBBBAAAEEEOhuAs1GcutuB8fxINAVBSa++oH8/Oqn5fyz9pazTz7QBcWymgQWBcXKRy0y2bxUcuuKJvQJAQQQQAABBBBAAAEEOiaw/ZYDNSi2r/zsqifkhakfuNEpbISKXPxfx1plKwQQQAABBBBAAAEEuq+A/xJZ9z1CjgyBVS1gQ+/k87JUs8FeeHWeXHrtY3LeGXvJFkM3lgadiLtGw9O99NboquWl3EiJ9Vovm9O5A1K2bUoSuWhoxWwcU0tko++EZhPWou460MpabGYzK4lc03wzbsFy/kgUvoO6nBvG1SsbCFL737HdFrZi/wWKDj3Av0NshY04/woUHXrA+dchtsJGnH8Fig494PzrEFthI86/AkWHHnD+dYitsFFnn3/+7+98PGR5Wv9Ot5KP//5O6sgQFZUyQ3nW6z8gth82UC44I5QLr3xUfnz2AbLzVptKKqdnjv4bxOYfoyCAAAIIIIAAAggggECTAIGxJgseIdA5AhoUy2kka/6Cernwiifkou/tJ8OGbCw6mZjOLZaWr5z0W/n006WSlXoJdRzFwNLH2ihDtt1Kbrvs625ARHsDl/l3chstsQoBBBBAAAEEEEAAAQSqXcD+DWFl+y0GuMyxn171tPzsrH1lZ/0iHkGxan916T8CCCCAAAIIIIBAZwgQGOsMVdrs4QKl3wgNJUwm5O77J8rIXdaWYfrtzZxGs8KFS+Wgoy+VzzUkFuq/WHOZRq2nb8lE21/pfHfqZNn3y1PliUd+JUtyOanxKWb5GueeLXmejONsoe7HSmOZ9l2lNn5k85VlnFUayGs7bNhGx+NV7L+8UVs18G9Lp/w6zr/yRm3V4PxrS6f8Os6/8kZt1eD8a0un/DrOv/JGbdXg/GtLp/y6Tj//LCvLSmEyND/LV61bbH/td2axsJjOVCwZ/efAdsO+ID89TeSCy/4pF519kAzfcsPO3DVtI4AAAggggAACCCBQlQKd+xd6VZLQaQRWrEAuo/8U1/8ffuI1OeHI3VzjSf2384nfv0UyGgxLBBqysiEVa1IShAnJa9SsrVsmk5XGhgb59bWPiMbbKAgggAACCCCAAAIIINCDBRqyDfrPjSj8Z1/A23GbAfKz746U71/6aA9W4dARQAABBBBAAAEEEGhdgIyx1m1Yg8AKEQhrom+QNujcX2v37yU5/QKpfan0jbffk0AjZOmEzhqmAa7GbPR2TEnbb8tsYk3Xr5em/Ee/lHqwNuajY9E/hiWe20DHZHT10qF+RzVM6sxgGnTTKok4c6yjB5fIVRZPz1a2uc6R1tGeR9ux/8r88K/Mj/OvMj/Ov8r8OP8q8+P8q8yP868yP86/yvw6+/zLxH9f60jprqTSdW6ECMsgC/UP/7QfwqGyw2h169pAR3TQecxq4rmGM2Gj7DhkPVnSsMCFy+J8tla3ZwUCCCCAAAIIIIAAAj1NoO0r8D1Ng+NFoBsKBBo4y+UyksklJZGoMCrVDX04JAQQQAABBBBAAAEEVqSAGzZdo1FBGIWkEn6oxRW5kxba0oEoXAn0QXSzARYpCCCAAAIIIIAAAgggUCpAYKxUhOcIVCzQSvApsFQx/TKn/etUq4R5/QerZl/1aozehplENBdBLmg7JSoMM66HeVlTrGZo6WZagqDR3adscgEtjak4Y0wz0CZO/UjemDxNM8cSOsdYNNeBq9SBH2GusjnGchX+69x/E7cDXXebsP+OykXb4V+ZH+dfZX6cf5X5cf5V5sf5V5kf519lfpx/lfl19vnnZxQL9YtoVhKScX93f+GLg2SnLfpJ306eY8zr+IEdwpz+e0D/5q50pAffLvcIIIAAAggggAACCHQ3AQJj3e0V5XgQKBGw4ROnzpgr9z/ymmaO5aQhjAJnJdXa/ZTAWLupWqzY2RdmWtxp0UL2X4TRgYdcmOwAWtEmnH9FGB14yPnXAbSiTTj/ijA68JDzrwNoRZtw/hVhdOBhVz//snFeVpCP/nmd1KEVA80SO6S2VgNjW3fgiNkEAQQQQAABBBBAAAEEOlOAwFhn6tJ2DxfwmV9xBplmiNkg/7X2D2X7L0jqT12W+DxySvWViQ//VPqWZFQt1rXW0oh9z5FsNivZml6ufqBLreXAIl9a8ja3gJZ0Ip7cIB8FwGp1voFEJiONmShTLBeu7gJkQW6R+wd7Loy2q4kSzCQbZ6S5xlr4kYn318Kq9i2Ku9e+ysvW8qrLrmnnEvbfTqiWq+Hfsku7l3L+tZuqpYqcfy2pLMcyzr/lwFq2KuffsibLtYTzb7m4Sitz/pWKLOfzTj7/gjAX/V2tmWJ5/SJaXocxt7/zezUulVpLJ/N/ny9nt9tbPR8P1RjGc53lNXXMlqV15Ag79JJ/XrS3WeohgAACCCCAAAIIINBtBeIr9t32+DgwBLqcQC6f1X8s5yWvAaa8C3k1ddH+TWsXPopvfm2YSOg8BZW/ZbNZHYpRdxRqW9YHCgIIIIAAAggggAACCHRcwLLD3N/2GhTzxZbZTUcypyCAAAIIIIAAAggggEAXEyBjrIu9IHSn+wtkklEmV0b/lWyTYmfyvd1B12QXy+4Hfi/OA9O5w7I+w6vBrc8EGkZL+hnFOu7UL/u5DBu8kQzadgvJ5rISpqLgWCobZY7lbS60NkqOYFobOqxCAAEEEEAAAQQQ6GkC6dqE5DJZeePV2TL1jdmaraV/Y+vf7kHYqDfVaIqX9TQajhcBBBBAAAEEEEAAgS4pQGCsS74sdKo7C2QyOrSh+1ap/QtZg1KWJWbZYzrkoRWfxZXLa2aXPdfsMitBwg286DZxCzr4I5lKybAt15XjjhkuCf2Hus8Zq4kflJsDg3/XdxCezRBAAAEEEEAAAQS6pUD0NTaR27PjZdqbcyTr/8DulkfLQSGAAAIIIIAAAgggUP0CBMaq/zXkCKpNIKlBMBtWJZXTub6y+gVSjYxZcCwXZ4hJnLmViP6JHcTLE3EiVz6ps4tp0CynQ7Xo9AWSWM53cYNGttJ5HdNFv75qQS4/OPT9jXEAAEAASURBVKMPeGXjQFy1sdJfBBBAAAEEEEAAAQRWhUAv/ds+o3MB5xt1ZIhcnWSDBv17X//KDvxf2KuiV+wTAQQQQAABBBBAAAEEWhPw18RbW89yBBDoBIGcZodZZpgNpbi8xQXFstE/ssPlDIot776ojwACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAdxLgsnp3ejU5lqoQSGUTYsMp2hiGmUxGJ+SOv0kaD6WYiDO2dEYCdzz5xOLoPg6iBVIrSZ2nLBHPVba8B51JLpW03kTnPbBUtUQ81EvWzy2W52NheU2pjwACCCCAAAIIINCDBezv6FBHggh1RIeiLLFEtpekdISH7PJ/F64HY3LoCCCAAAIIIIAAAgh0vgB/one+MXtAoJlASuNOdXWhbDt0Yw1wJSWhYyE2v6X0eUqS8S2haWHFtzBIakCtUXYePtANpdiscZ4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAqwIExlqlYQUCnSNgU4WldRjFm397qqRqNACWTUttPqsJXL0lr/OJWeaW3cJcdEtmNDCWTohOK+Zuyfq07L7VIPn+SYdo1lgoKa1ntyAbuFu5Xidyodgt1Ewx9wFgs4PrzTLVfLZauTZYjwACCCCAAAIIIIAAArFAXucI1r/j85Y1phlj9vd7MssoDJwfCCCAAAIIIIAAAgh0VQECY131laFf3VYgrYGwvA6bGOq779/3XSB77jRYs8OiYRODKFS1zLEHQUKSqRqpq0nI6FG7yU2//aaboyzebJn6LEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFlBfga27ImLEGgUwXqEjrHWDYjeb0Pw4Rc/LOTJKVBL5t3LAgDSemttOQ1wyyT020sq0vrfm7ZXul6SescZbX6nyvJbHSfJ95d6sdzBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRMgMAY5wECK1kgndahD1Mpqc9mJaH/1WqgKysa8EroEIcaE7MgWEslpXOOaeKYLNWxGO3e2kgEuoGNzUhBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsgIExsoSUQGBFSsQJGslo7GvlAa1XAZYTjQ8pkXnJND4mAbJWsn40oCZhcwSKd3Ais5LZtODhVITPdcto9LK9vFa7hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ6KkCXEHvqa88x40AAggggAACCCCAAAIIIIAAAggggAACCCCAAAII9DABMsZ62AvO4a56gcDSvrTkbRhELY0uXUwkF0Rx6lQ+zghza1v4kY838Kt8g1HemV/KPQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJQIkDFWAsJTBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB7ilAxlj3fF05qlUq4DO+SuPOPtMrShnziV5+aSLOJLNZw9oqfooxCaMt06FPQWu+XTKeciwbL87EOwqCpDRqdlmYyUmoWWp+vc561tZuWddFBfI5f75FHQzC5udBpd0ubb8hnZZPP/lUNt5wA8lks1K6v0ScCen3m9W58YpLaXvJREJsk3QmOmFrkgnJ6SYttV3cTkcfl+ufaTY2NMjCzxbK2v3XlkSJZ0v9T2fSUptKuX6n9Xhz2Ywek87+p8eSiY/LjtNKqYdbyA8EEEAAAQQQqGoBm/c3CAP9/a+/7/VvAfv7KG/zA+sfNfGgEJ18fKX/HrC/aOxvjxX7d2EnHwTNI4AAAggggAACCCCw0gT4S3mlUbMjBBBAoPoFrrvuWhm2zZYuwFMaFOvI0VkALKMXjSxw9PprU+XBvz9SCB6VBqE60v7ybmP7vOGG62WroUOWCfq11Jb1v0aDYmk9hsWfL5Yffu8cGb7ddrLF4M3lhBNOlFmzPnCblcQLW2qKZQgggAACCCCAAAIIIIAAAggggAACCCCwEgTIGFsJyOwCAQQQ6C4CQzYfLPvss5/LikolU4UgVkePz4JrlsVlmVaXX3mlPHD//TJn3kcuU2tVBMZqNctr8KBB7hjzmgkmeoxtlZTWt5LRLLEDDzxQ3pwxXb79v9+R3n16yzVXXSX77D1CXpzyivTr07ddgba29sU6BBBAAAEEEEAAAQQQQAABBBBAAAEEEKhcgMBY5Ya0gAACCPQYgYMOPkhGjTrEBcRWxLCAFhSzrKtEHGDK56OhIW15Y06Xlwxl2NnQGp+Tgw46WEZ95SvSqMNGliuW7WblwQcfkImTJsif7/mL+oySlGaRHX7EES7z7Oc/+6lc/tvfufbCBL92y5myHgEEEEAAAQQQQAABBBBAAAEEEEAAgc4UYCjFztSlbQQQ6PECv/ntb2XUIYfI5FemyJ577C6rrdZLttpiiNzzlz9LVoftu/yKy2XIoM2lX7++st8+I+Sd996VRs1UevyJJ+SAkfvJB++/5zKqDNLmrrJtzvvJeXLiCcdLSueyyGvg6LLLL5cjjjhcJr34gtvHWmv1k6FDhmjbl+ncFjnJanuW0fSrX/9KDvnKwfLq1Fdlrz13k3XXXlO+ddI33Wtk7f7jn4/K/vvtLX371sk6/deUQw87RP77zn/FQlU+YPXQI/+Q/UeObOqTrnt9+nQ5Zsxo6b/WGu44dtfjfEz7b1lgtn+X+aWBrtemTZWxY8fKBuuvK2utubpsP2xbueSXl7j2f/WrX8hTTz4uny1epF4H6T72k6PHjHF9a+uHzyr7z5sz5LjjjpPNBwxw/dhkow3kGyeeIB9+NE+Da4Hrb73OHXbEkUfKd875rjRqMM5uZjrzvzNl5P77yG233+qCdI88/Ijst/9InZpj2SCWZbgV36xvFsS7/fbbZa011pTDDj9Cp/9L6uuUl4EDviAj9tlXHnn4YZtuhIIAAggggAACCCCAAAIIIIAAAggggAACXUBg2at+XaBTdAEBBBDoLgJv/meGTBg/To4b+1X5n1NOkdPPPF1uv+UP8s1vHC8vTpwo48ePlwsv/pksWrRELvnZhXLM6KNk4gsvya677iovvvSC3Hjj9XLhzy52HIHO3l6vWUxXajDtrDO/rQG0rM6pHsqM16fJY4/9U6a/Pl1OPf1UOevs78izzzwrP/zRjySVSGnds0RryvRp02Tc88/pPo6WsV87Tk497XTpt1o/F9h5ZtzzcvQRh8kuO+8qN9/6B0k3puUXF18kI/bYQya88KJstMGGYsMGzpz5tvx7/HMugGTBshlvvCn77b2XDB4yVK74/e+lprZO7rv3Xjn88EPlscefkN12+ZJmSjXKtGmvykEHHCDrrLuu/EIDdH36riZzZs+R9999VxI6v9jOu+ws//zHP2Xu/I/liKNHS1KDTbW1te06DSwwNWXKq7L66qvLxb+6RGqSNTJr9ly57Ne/kLGjx8hjTz7pMs/qtL3Rx4yW4489ToYMHSxnnHKaLKnPyuijNJgVJmTM2ONc0Outt96Q8RP+HQX0ymSsucCj1pn59tuy/fY7LtPfL3xhkDzz/LPOoCZVU/HQk8vsgAUIIIAAAggggAACCCCAAAIIIIAAAgggsFwCBMaWi4vKCCCAwPIJZDV4tWjpErnnimtkX82CsgylUQcfJmuv11/u1QDSjP+8Kb3ral2gKdNQL2d/92x5//33ZcAmm8iYo46RP91xh/zfTy6QGs1CSmoA6Zrf/U7SWu/UU093zxtsHiwtNhzhj8//sRz/9RNdoOvII46SDzXw9ItLL5EzNDCW0XoWWFtSv1QuuuQXGgw6ym1j2U+27QXn/UQ22nAjDWY95oJEOR3GcK8995ahQzeXX156qVyuWWnpjGataQaYFZc1JaGc+4MfyNrrrCvPaCDOl9FHHiUjRuwlF55/vjzy6D+ltqZGvvvdc6RX7z7y7HPjpf8a/VxQz2eh2Sxde+w+Qjbd9HZ5Zcpk+cYJJ0qtzu2l8S7xQxX6tkvvff+/qgGwY8d81WWp2bEmdfstthiqGXIHySuvvCI7bLedC+wdO2asPPvE0/KTc8/V7LoRcsUVV8gHH7wvTz/1vPTSwJVl6+V1x/l8VttIaHCs7VQv27+VWR/M0iy9LUu7pwG4IZJrSEt9fdq5+vrLVGQBAggggAACCCCAAAIIIIAAAggggAACCKwUAYZSXCnM7AQBBHqqQE7H0LOAy8gD9ncZSDbMXu/edbKGZmrttvse0qdXFBQzn802+4LoGHzykmaKpTWgdsYZZ8j7s2bJPx55yAWiLID1x1tvkX322U8223QzF9Cy9ix4Y0GzY8Ye54JilqFlAZgjxxwp8+Z+KBMnTnBzXoUuC6tOh0g81G1rwS8rFqB64YUJcvhRo3X0wJRry7KbBm62qQuOPffcM265tRnqLQgSbj8W5Hviqcd0yMcDZO6cefKRZnvZ/ccffyJ77b23PP/vcZLUYQxtrq7nxz0vh+owg6v17RNluul+re8p3Z/NVWZZYzU1KTEvK3as5YJiVs8N1RjPSzbz3ffkpptulO9oIPB/TjpJ/vjH2yyyJjOmT3PHqHE2ddWMu6t+LxtvspmMOugg+cONN+hwjr+UoVsNdcdkRoG7aVCs7ZiY7V4adXhGKxZ0zLewgQ+smbEPBLoN+IEAAggggAACCCCAAAIIIIAAAggggAACq0SAwNgqYWenCCDQUwQSiVAzj3TOKQ2a+GwhCwjZvFf2AWzBH1serYsCVS4wo8u232F7Gb7DF+WGa693mWbPPv2sZpjNkFPPOM1lU5mhBYYCbatP774uS8oFmTTYZG2vpsuszJkzS9IanLJ6tSldp/u3YsMHWnBr0eIlktF0sM0HDXTLLSjl+quRpP7919IMtg/ccvthASAbutACTPX1GUnXN8r1N14rW201WDO0NpdBgzeTwdrO1Vde4epZrMjaz+vQjEN1bjUL4Ll29Ph8O3ZvATHLRkton2wf3spVLvPD6t/xpz/K1lsPkWt+f4XMmzdPPl/yuaa36YZ6DNZXO+a0Wjt79fn+j77v5m/bcafhcsKJJ7n92TF7G+tTS8WWF99696pzr0Gfvn2lIQ6SWTDQitWb//E897hXr97uuNwTfiCAAAKdIGCf51b8Z9Ty7sJ90SCeF9IeW7HPM/+ZtrztUR8BBBBAAAEEEEAAAQQQQACBripAYKyrvjL0CwEEEFCBE0/+pjz1zFMa3Jqjc3/dJJtsuokcfPAhLpvJ5vyyAJJlJdU3/j971wFeRbV1961J6EWKFAGliiCICIJSRH2odJBmV7piQf3l2fA99flsiAJ2uoAIogLqs6NYsCFNRSygFAuCUpPc+q91Zk64uQkkoQayd757Z+bMqWtuZs7sdfbemUJrp0TZRaIGSs3UtDSHNIM1WrKQVErDeVa4BRZfUZBIlpSipVc66ihVqrQh0BKts1jOtAfi6447R8m27Tvkzy3bZGd6SP78a6v8tW2H/I00kmwBxPwik/fH75vMMftAxa0lrGyfYuhf1LVioyLWKmbt+dy2JNr4ueuOO6Vblx7y5VfL5fkXZsn052fJg6NH5yjCdv/eslXuuPU2qdOgviz5colMmTwxX23lqAwJtOyjJdiJJzWSH3/8wdRDstP2/dtvV0mFChXgTtIhBHOrQ9MUAUVAETgQCJDY573HLm44EHXyfpYf690D0ZbWoQgoAoqAIqAIKAKKgCKgCCgCioAioAgcKgSUGDtUSGs7ioAioAjsAwJXXXmVlC9bTh5GbLF5r86Tiy++BESQY4FmCRhaW2Ui7thHH31kCCcqRqP4fPn5l5IC0qtN27PgzjCUa+sklejCsOYJJ8ibb75F/4bG0IqxttIzQ/L54k+kxWmnZVlx0arLWr8Fgl45sVEjmTt3bpZ7xDiIsCDq5DYKYo31lyqZJic1aiwL5r1s6iY9Z0kxEnFWSpcrLbAbM8QcKb78uB40Vl7IvG7dL9KiVUtjKUd8wPDJC9On26qztrTJGzDoCslIz5C33npX+l18sdx8002y6vvVxoovK2M+dzg+KqNPP/00+R51/LT2JzNGFt+VkQn8FkvbdmcZAs0SjvmsWrMpAoqAIlAgBHjv533GWTCRcyFEgSrTzIqAIqAIKAKKgCKgCCgCioAioAgoAorAUYyAEmNH8cXVoSkCisCRj4AfVmF9+/WX8Y+OkfSdu2TokKsN+cWRJbq3YuyvYYMGydo1Pxml6NyXXpIJzzwll158qRRLTTUWY7mhQWKJVlTXjrhePv5wkfzzn7eA5ArL5k2b5fLLLpPNf22RG0AcWaF1Wjy+2+XjqH+NkqWwurr88svkZ5BTVMhu275N5sx+QcaMfsSQaLQ8u+XWkbJ02TIZOOBK2bBxgyGT1q1dI3NefMFWLS1atpSdW3fI+HGPyvxX58sH7y3KOre3HXRJ6tSpLZMmTJANGzYa67YFCxbIU08+maPY2LFjZf5LL8uEKVOl0rGVZfz4J6Ralapycb9+sh1kWUHFWGYAr8FDhhnLsMsuudQQZH9v2yaXX3KJ/PnnJrn+xhH5IvkK2rbmVwQUAUUgEQG7mCADlr6M9XggZNu27cKPiiKgCCgCioAioAgoAoqAIqAIKAKKgCJwNCGgxNjRdDV1LIqAIlDoEIjCPSDdDpJ8stZRThwYxKoC6WVjwnDL2GLeQNBYPVlXfLS6unroMIE/Qjmv4/mGzCGZRbHurRjHrGTx4jJ42FBp2LiRlCiRKhf3vlBOO62FPPjQg8ZNogeWBKkgyAKIMWatCmjtRHINzcqQqwbJXaP+Jc+CTCuNuFk1q1UBUfa+TJ08VZo3a2baYzn22dYRQKyubl17yLSpz8nHHyyS+nVrI4aZT8qVLSPXXjtc0sOZZnzM16t7T5n07CR5/dVXpU6NGpKCPteufYK88cY7EoJ1GR9GvXv1kf4gk/496i7p2q2bXHRJX9Muv4gP28/tQ3Lq0THjZMumTXLC8TWkVOkSct3wa2Fl96jBjS4aKcu/Woq675Abbv4/6dCunQQw8ACs5WbOniMbQOrddMMNxtIs6AtgjE4cNl43ftguhdgnfoghr1vlYyrIjOkz5DfEc2sMF42VK5SXDxYtBFk3WZo1aSJBF3dTiX4pAoqAIgAE7P2fYCTe23hs7z3cJoq9H+VW1sZIfOqpJ6VuveMTi+Vr39zPsLiBwn3e9S66qL/07dvbWCEz3faHzzP22R7bhRo8tmnMz37aZx+PWcbezwN4/rAc89t7LPPsSZiPddn8JABvvulGadK4odQ+oaYMuJILL37NV117akPTFQFFQBFQBBQBRUARUAQUAUVAEVAEigYCjuavaIxVR6kIKAKKwCFH4MlnnkabT5uYL3GQKlb59/P6DYYwo6UXheRZx47nSTqslozSEHGronGvIVR++WW9IV+GXH21yZv4RQsBEjPkygYNHixXwmps0cKFUrdBA6laubIhnFgfFaZjxz4mY8ePFx8UnsbVIdqOk7jD1gsXiv838ha5+Z8j5UO4ZCwOou2Upqe4rgl9hgyiAnMbLKH83oDpgrU2u7BPH+mJz08//SirVn0rjU9uItWqVjP10qUhiSu237d/P+lz0UWy5KslsGbYJG3atEfsraDJZ5SliC82eepUKGAdnNgvowQFsUZxxurgZRLcL+Y79x/nyk8/r5OFH3xg+k4yj+npiHnGuvk5qfFJsmnL38ALHiOBF/vlw7jr1asnGxD/zAjwHAKCceCQweaQLh9J7FFYB+tMFquMPqNNG1n908+y9KuvZMfOHdLq9NMNfizPWGQqioAioAgkIsB7B+8rJKGiuLcY4gc38z1tWZb3Xd6HsIwi657EY5ax96u69erIP849L7GpfO3znmjvcbxPxiIRoateCu+/XGQQx7OK4sN9jX1nfyi8x7GsPebzgos3+GwzdeFZwH0Kn4UmzSnKUJgOBm5dJlMuX+ZZgns2t8SsbVvcc79bJddcdz0WhBSTx8eOk/ZtWsunny6RsuXL5FKDJikCioAioAgoAoqAIqAIKAKKgCKgCCgCDgJKjOkvQRFQBBSBg4hAAEo8Sgykjx/WYFQkUhlK9SD3SVpZZaElWEikkEahQnJXKCR33/MvOaH6cfKPs89hVTkkAoWkDwpJSikQTR3atzdkjlVQMp3tBNE+00iKUalolaDsBxWaKbAGo3KzQ9t2LGKEyktjmYadTJxbuPA9qX7ccVnlwbKhLWeM9evUlRNq1TLjYf2UKMZKYRscFxWgp5x8cpZSle0mtx9gWXzYVx/6TGUvCph6GOMsUUx5JMRcAqsdyCmmWesFjof1sS5eC47FKHeRh/1GcpI4uBBP1klLL6PwdcdjlcS2UNRVJJvYasjL+ptifBQqkm35LDxyNmir0q0ioAgUQQQS78W873twk4zivsQt7+28n9l7tb37OXf77GSSIY1wn+S24zkdzaegcHpwn+M9zottBqyVHeKOfYB1Me7fvGGa5xYqjuOZwjR7T2QZe0NlP1mewucb+2tJQKbxvs57p33OeUC42Xskz+9JeE9lPRG0PWvW8/LpF5/JzOnPS59ePSUDz8oLLrhAmjY5Wf5z/91y3333o059zdkTlpquCCgCioAioAgoAoqAIqAIKAKKQFFHwL5bF3UcdPyKgCKgCBwUBEiIGWUeFIsOOeRsbWMkTiiWILLpGRkZ0u/CC6VqpYry6eeLZfRjY7MIKJuH21SU94K0Ah9jFJBUqDqKQ6ocHWG7No1WBYmKVhJIiW1zn0IiiR+SQiw/b95LckzZ0rLow0Vy48j/M2QViSUqYRlDDFnRvtO2TScJxrIUYkCCK4T6aGlAsYpQQxois2nLzce6WHZXRiYUn9NlyqQJ8ty0yfLcc1OyfaZOmQjlsUNgWeUxrcxIUBlCyx2PY02BtlkxhEShk8dR3nKsLM8+sl/Wym5XRsi0P3nyBJnG9pM+M2ZMk12wDmMZYmDHSxypEGYfrLANFUVAEVAELAK8X/C+wPvFzz/+JH379Jbjj6shZUuVkOpVj5Vhw4bJZsQp5Hm6Dezbq5eMvOUWY0lryf8fvl8t5519tsycOd3c35h3/vx50vn8C2wz+d6+/PKL0vSkhlKieIo0A8G/YP7LaJvPrKghwHj/HD36YenVo7ssX75C2p55hpQvXVIaNagnLyCuJO+bTz7+hDSoU0cqVigHcq6DrF37s7n/sxPs208/fA/XjH2k9vG1pDyeKTUwziGw0P1j0x959pP3VGLG7UsvzpVypUpLtx49zPOPVs916tSXli1ayvx58/cYVzPPRjSDIqAIKAKKgCKgCCgCioAioAgoAopAkUBAl1IWicusgzyqEMiis62S3VH07x6jTd+dckTtxdzx0NcdxW6dIzAayeO1Jwrn1pJAtnckY6gcpHCbfGzzpaakyIjrR0jvC3shVlhLqV6zhiFZLJlk85GUGjpkmHTu0tmsyme6IX+4et+K3cfWkjO2D2yfYvvCfZuH+9YarHmzFoiXNUlatGwtlatUysrPcnTpZcq4l8yOKZwwVhJzyWLbIUa2DOui2ONoBMTUjBkShmKWBGDUJdpsXV7wbl079ZBAWcT6Ql+oNKWC1BJU3EZsZmzZpu0zk9kv2w+bzmPb3wjipM2Y/hzyOASftY6wVXrARHY+v5tEizvuz1iHlcR924Y9d/i2ef3/OBfRZ//PfBmmqxFxCVy491RRBBSBA4MAFwVYa9/FsH4qXaas3AUL4RIlSsh6uNB96MEH5OuvV8p7739gYjv26XOh9L/4Iql7Qi0ZPGSo7ARZ1qfXhVhs4JceF/bhDU48uFetWbNWPvjgPaElVqJwgUKy+Ly4ByJx4fsLpU/PC+W8886XUXf9W3bu3CY34xmEp4PUrd9Agli8EInE5btvV8nCd96R71d/J1cPHio3XHOtPDPxGRlw6SXyOZ5FX3z+ufz7X6OwYCBd/v3vu6Rfrx7y1dKlEgnTatonK5avlDIlS8m999wjacXS5Nf1G+VBjHPV19/IuwsXSjDFibPJ/F4QYInica2ag9h+g/ynNm/hEGXG+pcW2ILYaifKRx9/bBZMqMVYInq6rwgoAoqAIrCvCHgwPfYmzIHjeAGJe9x3A0ytHdvofa1dyykCioAioAgoAorA4UIgp6bycPVE21UEFAFFQBHIsmgiFC1any6nxVpkET4kWpIJFhI4zU47VZrJqcZ9IAmlvYkljPaWJ9s5kEykUqpXqyLH9b7QtUyjBRjUpTiRV3vZ6tqHg5IlSsqC/70hfPUkMZZD2BGIjWeT4/x+JiS3n4MYo/UCrgvJSGuxtp9NanFFQBEoIggkLpzo06ev9Onbz1hmkQ6i9RVjRXbp2FG+/PJLkEDNpUePC2XQVe/LrSP/KS1btpJx48bJho3rZeGijyQFzwKPD0sLolDP4X7kBZGVTCzlpMVwH0eZOCxbb0d8yRNPbCBzX3rJoB8IBhAvsqmcdmozqQ03uaFQWFKRRtmVni6PPjpWzj6rgzk+q+M/pFrlivLyy3Pl29U/SICWXSDhtu7YJrfcdLP8/Ms6qXpsFUOO9ezRUzp37YLnh8e4mo1gcUeDk06SjueeIyu//loaYd/nEmKxJCLPG4dLRyzw4KKLPzdtkhMbNHRiZlJhifFzfHXr1ZVYKCI7YXVdungJ0z/9UgQUAUVAEVAEFAFFQBFQBBQBRUARUASSEVBiLBkRPVYEFIHDi4BrIWYtVuDoL1t/IrJ34idb5iPwwBJf1uVf1LWCIvlCUivRCil5eLQes5ZOyef255hKSBJP5NzITVlSzOWk9qfqPMsSD2JB+zeLTW6F7NiTLeqS8+6tjuS89ti6LMt1vLguFLpgNLHhcs1kazqCtl7XBaS7Ota5Auz/0f3/dwRdIe3qUYjAz+vXybtvviWff/aZsXjKCMFi0++VNWt/lBYtTsOuT8Y+9ph8/NFH0rVzZ1n/2wZ56smn5CQQaHFYi+E2LXE/STGfeRZ4sZ8oHhBLicIyUZBiJLG+WrpEbvnnP42bQt7VoiDCTm7cRBo0qI+6nHIhWIxRiqWlSbt27WCpButc1JGamirFYeV26qktDKkVgGVYKBSTOrXrm/xLl3whNbp1N8QYE379/Xd54/XX5QuOEy5sYetFU2f59tuvpXGjxqZeUzDpK4L7K+/HcVjCRSKJtsBORlq1+XlPBmaxcM7zSdXpoSKgCCgCioAioAgoAoqAIqAIKAKKQBFGQImxInzxdeiKgCJQ+BBItHwiiZNIhtGtYTLvkpiHcVcsobankZFoK6gYayhriebyIlRM0p0VdKIHVYgHe0xLAD8IsmQLNfYtkexK3M+tYwW2mINCmKplkoO5YZvYPhW8eRFzufWpcKU5ivTdhLRzbNXpB/t6Fy4stDeKwMFFgPEQrdXYrBnTZcjQIVKzVk2pV9chlMw53LPTd+2CG1kQX+SP4Prw1ttvlf79+kuzU5rKFVcOMOQUe8rbNIkqumikxVnMJbLsKJItsJhOq7K/ECcxAoLs+Fq1DEnmRRvWbWHlSpXxHHKI8mg0bCy9vCCe6PKQdwe6mU2B61/Wk5aaYoi1KNr1I/5lJJppSCq2Y59ds2c9L1cNGmDG2aB+Q6FlGq3G+PyKua4fuQCDrhSTJY70uMdnrMRKli5lLNeivO/iWRQDURYRv/z220YDRGrxYsnF9VgRUAQUAUVAEVAEFAFFQBFQBBQBRUARyEJAibEsKHRHEVAECgMC1l871H+mO5FIpomfEjYECJRiUAzu3BWWTz7/Qd7/bI18tWINFG6wZqIiEJq3mDe412HEoURTUQSKKgKg93IM3QvFOy08GterIm3OPF7aNz9eSqcFYEmC/0Iot+P4l4m4lpyOI7UcVWiCIqAI5AOBZGKell0k18N4dt12++3SFVZVEyZNph9FQ5j9vukPmTN7Fp57zgoEPr127twuI2+5RerUrSNfLvlKJk18Vq649ArTuh+kES2v6KbQw/0kyeFaEQ9UWpWVKAYSCeW2bt0KazO4JEQ6eoZzPtmxY4eULV/BqT8YxPOWiwRARLFLLlNOMs6QWbT+ikUMyWVdOjIPSS9apnlxn7l55C3SDeOc/NxzcPsIizXk3/zXXzLnxTkSxU2HbZJES+4rOxCJgUAjCYbGG8CN4tq1a4wVdZh105UkFlJ8s+o7OeaY8pIS2PtcwAxIvxQBRUARUAQUAUVAEVAEFAFFQBFQBIosAkqMFdlLrwNXBI4MBHx+KLugcUvPjMhbH/8kiz5ZKytW/yZN6pWXM087XkYOPRfn/XBjBzd/MC0KHRnD0l4qAocHAZreJQmV0Jn4//poyVp599Of5PEpH0nTBjWk9WnHSYfm9SQN+mUf3ZdREW6+kirQQ0VAEdgnBOgal5amdEm47pefpUWrm4wlLr0E0lp2xnPTzP+c10drKhBOsJ8dcNUVxsLqg0WfyK23/h9ieN0kbVu1kTr165p/T5JUJK/iYLRzsxBL7Kjj8jCMtvxS54Ta8tqrr8vQoVeLB+Vjoahxebh8+TJp07a9IczQJfM8Jglm9kGmkQCDOa+bzi1jnHmdeGcg/ixRxzRakq3fsF5OOfUU0w3GN/PAXevMmdMT8qENnsW5ZEnxO2QXCcYzzmgt/37/Xfnxh+/gsrGesa6GM2H57JNPpHXrNgavg+FaOLlPeqwIKAKKgCKgCCgCioAioAgoAoqAInBkIqDE2JF53bTXikCRQYCWYiTFeg+ZIifWqSwtT6kutww7XQJppQ0GVvHHlfRcVe/XGEhF5rehA90XBHK60qSSOzXVL+e2qSttW9aQEP7fFn31q7y/eI1MmLpIZj16CSzKoJCmnprkmIoioAgcEASMK1aQY+SbaQH27FPPSMfzO0v1KlXk5bkvyZTJk8hKGxeDAVhvjh3zqMx9ca68Nv91qV6tijz77EQ5DSRTjwt7yqdffinF4d7QElG0GMvN6iqx47T6omtGytDBQ2XEiBtkDNq45trhsmXzFrnissukWLHiZMNMHhJbJN1onc19DyzLGJ9MIrAyQ2V0hWist3GbIWHGfZh5mS0tw+Igx+rUri0Tnn1WunTvKcdVrSbz5r8s48eOM/miILxYL8U+282B+0VrMhKJKdheOWCgjHvsUbniksthfTZdSpcpJdddc438ve1vue2ftwGGnPe6xLp0XxFQBBQBRUARUAQUAUVAEVAEFAFFoGgjoG+NRfv66+gVgUKPABa/yzuwFCMp9q8RF0jH9idBiZ9qlGZWcUZCzLqaouJsb59CP2DtoCJwiBGgkpsCnTQsR3xSLC1Fzm51vNw5vIM0rF1B3v98tfl/g23HIe6ZNqcIHN0IMCYiLcFoHDXuyaflt183Sv1aNaVsmZJy3bXXyX8ffBjuCmPGReCyZcvkzjtukxtuvFnand3eWI35fAGZPmu2/LpxgwwfNqzAYJl4ZCC2aLk1DOWHXn2NjLrjVjkGJFON6lWl8cknS5Nmp5p6jXWXa8XFewGPKbQQs0JXhrkJ89jPeIzzz02b5MR6daR8udJy/XXXyyMkxkC+efGx7ZDUS/6Qn6OVHaVKxUoyB+Thb79vlIb160i1KpXl/YXvyrMTJ8rJzZpm5cutP5qmCCgCioAioAgoAoqAIqAIKAKKgCKgCKjFmP4GFAFFoFAh4KdbJoqXNmA0UPHIws82SKtTamG1OXSE5rw3KVLSbmVczF1tbgrn+rVbiZfraU1UBIoYAlRGU+iezOdwZBKFBzR4c5PWp9SH5dhPcla7k6CRNtn06yAiwHtcdsnF92X2DHp0CBGg27/9EUvqJNZBd39RLO5o16Yt3Az+BleAn4o3NSAng5QK+gOSCYsrK1v/2m53s7YN69aXTX9sNsf2+XfdNdfCeupaEz/MptFiyw+rs0Rh3C+KEyPMI+PGjpXbb7tNPv/sU2nZ6kwpV8axzDaZkJfWYU8/9ZT5xMNhk2y+kL5hw8asY+aL4tP5gs5w0+o4OGaaHwtXzsI4163bIJ98+ImUKl9GTjqpsfhBsoUy4dIRMcjYX96G2F9rzWYrZiw2UImIyUYrO7hTbH2GrP5pjSxdukxC6ZlwRdnS6Y4tkLT1uPe3pGQ9PEIQ8MWcG2TUjXkZd59dMffC+nb/qxwhI9JuKgKKgCKgCCgCioAioAgoAorA4URAibHDib62rQgoAnkisGNnWFas2igjB7YAKUatx/4pJvNsUDMoAopAFgLNm9aSJ2d+LBmhmBRPBVumoggoAgcNAb/PJ81bNod/wwMzPXeNQR0rLJBJlgizA7BEFI9JQvF8pQqVpGPH82DA5TGxz2xebuN24UpiYgH2WT/jqQV8QWnd5gzjWpEEF90+Mt32l1XSwsxahdsmmC9xDhBBAS+sXBud3EhdJ1qQdKsIKAKKgCKgCCgCioAioAgoAoqAIpAvBA7Mm3e+mtJMioAioAgUHIGPPv1BGtcpI8HUElgnTtElwQVHUUsoAvlDIGaJ55hDQKcW90v9OhXlw09Wy/mt60ncn8OkKX8Va679QsA1lNivOrTw/iNwsJdlpEdCEoCVWATWUhSPG//L9rygvFQc8bjCIKNmTJtmtsFA9mk/Y4IxZljvPn2leFoxEGhetB2TAGKVkaRKNrAqaPu233ZLV62W/PLBOoz7JLtC6COt6Wi1yv5Onz7V6QfIskSJo78kD3v37S8p2JLYi8PCjJZ17Ku1jkssk7i/v/1PrEv3DwMCricB0LimcY84Fo+uARnSDvZ/6GEYszapCCgCioAioAgoAoqAIqAIKAIHDYHsb8gHrRmtWBFQBBSBfUPg/c/XyhmnnWAKx+Buyg0vsm+VaSlFQBEoEAL0VNXqtJrywac/Sacz6rlqyAJVoZkVAUUgnwiQ9KGlFmNmBgMkp5KpqXxW5GajNVYM5Njs52eBcIqYuhNrYP1sr1OnrpKWkiIe5KdEQIwz9hndPCYKiav9EdQIyy4P+hLG+AJwtxgXP4457gj6aQR9eHH2C4YgS27LA1xS0M+uXXpKsESaOe0FWR8OR008suT8eqwIKAKKgCKgCCgCioAioAgoAoqAIqAI7AmB7G+8e8ql6YqAIqAIHCYElq5cKzcPPAeryenqCQpDyTA98UQdpVjc4yjqwu7C8kDEXTGcZdhiFXm6kvgwXUJt9ghCIOxxV+C7/zbxgFdan1ZPnp74kXiRxthjKocOgQCsaciN8O4VtreyQ9e8tpSEACJnJaUU7JDuAfcmPE+yilZjlrTKlp8/hgIIia3UNL+8+vobBSjlZE0mxZiaW4y0glZMMox1R0BykSSzQyI5xnNBEF8LXnP6m0zEeZAnUYxdHSGBZVm+roxtLLES3T9iEAi7lswejxO3zu8J4jeDy+/eG23ssSNmQNpRRUARUAQUAUVAEVAEFAFFQBE4rAgoMXZY4dfGFQFFIC8EIlgJnprqxwp6N+fe9Yp5VafnFQFFoAAI0NWanxYZ2EKHrXKIEaAen7gb3b/e+w4x+jmbi7gu3HKeyV9KLJYv+iahsqSLvp8WWwkVF4rdaJIJeDIRRou3REHUs8RD3S9iCHh8zkPI/g5IijGJfHPST6WIIaPDVQQUAUVAEVAEFAFFQBFQBBSBfUFAibF9QU3LKAKKwCFDgKvmEZZEoJc3kmwp5ok7isNU15wi6s90MsYCbh+tYjG7gs09qRtFQBHIhoDzf+J1Y7V4ocinVUcmqJlwEBn13ygbWgf6ABSkqTIqBBsKX2h7jZELbmMwsFE5zAgUmNdK6q8XVs8FEZ8n6aK7xEBB6ijMeb1J44slse+xeHYizLef7AfCl6kcwQh4fc7/AxyOmlF4QRTz/rgLPxNyrDb9CB6idl0RUAQUAUVAEVAEFAFFQBFQBA4hAkqMHUKwtSlFQBEoOAJeaDuMMrJg+sSCN6QlFAFFIAcCJt4RyWkorPdTJ52jbk3IG4Gp85bL1LlfSGZoV96ZNcdBRyAWcwjLfW0oZljO/Jfm/102OcrYUU8SUcjYatkkCS+fb/+YrWhMX3uy4XukHbius71x53fgiaVIyRLFpFevJtK/U8MjbTTaX0VAEVAEFAFFQBFQBBQBRUAROMwI6BviYb4A2rwicMgRgKKNym4vfM8YtzRJiqdD3p+kBqM+1yTFtQSLIcYOU2IMcASxp80BvmyMsajVH2ZZitkcauJikXC21oIue6o9ooXK3iRucd5bJj13xCLgt67KvI6COob/wzj+PPgv5KVPUlsfseMstB2PB4yF3tqNf8uDTy/Cfc8jD9zZTWpWO0YiMHfJQZQU2oFoxxQBRUAROLAI+NwYYzGv4xkgivne77/vkGcnviPLPv1Rrr6mnRxXqRSsx5wYZDFJMR3wuhMXz0Ge78bdGJ0xPixNTFynnzw28fAOLBxamyKgCCgCioAioAgoAoqAIqAI7CcCe9eQ7mflWlwRUAQUAUVAEVAEFAFFIH8IMMbSD+v/kuv+NV9ObVJd7r7xfKletZwhxXy+7G7l8lej5lIEFAFF4OhEwOf3SuXKJeXem7pK86bV5dq7F8i6TdvhCFhfb4/OK66jUgQUAUVAEVAEFAFFQBFQBA4sAvrmcGDx1NoUAUVAEVAEFAFFQBHYJwRSYFow5sl3pVunU6TLuY3FH6CtHv78PnFsIPapWi2kCCgCisARj4DxHoBRxCVgPvQ0GoVbxZ1pImdf0FT6nd1YRo9ZKDG6zEx0e+rB3ZMfFUVAEVAEFAFFQBFQBBQBRUARUAQSEFBiLAEM3VUEFAFFQBFQBBQBReBwITD9tWVo2ivdzmkAd2BQAMP1F93eUkysRbOnX4qAIqAIKAJEIOD3m3sk75PdOpwktKx9/vWvJRRWx7/6C1EEFAFFQBFQBBQBRUARUAQUgb0joMTY3vHRs4qAIqAIHF0IRDLFGwtLLBIWnz4Bjq5rq6M54hF45qVlMmzQWeKBaQQ/8QgivOETi0YRGzJ6xI9PB6AIKAKKwL4iEAlkCj9WYtGY0P2sN+J8QjAUGzqgjUyf9YUUQ4wvBKE1Hw9ikfGjoggoAoqAIqAIKAKKgCKgCCgCikAiAniFUFEEFAFFQBEoKggE/I5yiPqicCgsXve4qIxfx6kIFGYEMtIzpULZkhKDpRg/XvoKU1EEFAFFQBHIE4EYSLKy5dJk+4508WpIxjzx0gyKgCKgCCgCioAioAgoAopAUUdAibGi/gvQ8SsCikCRQsAvEWe8Hr/QQxtjdqgoAopA4UDA4w2JBwpdL7S6XvxzRsSxEvPHXC0vE1UUAUVAESiCCMTiKc6o3fug3zWi9cYd8/c40r3iEUxvJIxbpdfnulOk+S3Fzecc6LcioAgoAoqAIqAIKAKKgCKgCBR1BNSRVlH/Bej4FQFFoEghwJhFVri6WkURUAQKDwL8/0TIHBVFQBFQBBSBAiJAK1uK3+cs/Clgcc2uCCgCioAioAgoAoqAIqAIKAJFDAFVvxSxC67DVQQONwIegYswdIILeBnjykbNsV5vovTxZ2Q3gcNDX8zl8b3p5qwnmubkclcCh90KAhE3n60myybqQK0DKNxkUtQdpl1JTTipLPL7PIgrFpVdiMURCASEOHv9KRKJw50i3LXFoUii+BirA6ZkHsbngMRjznWIxZ1xRyX7Y4NWZ1acLK5Fmk3E1rqEM1v3su6u32nHY13GuYHP3OaMVZvdj6Evfn/23wXjiyRKyOf8ory47iQZisHSJhqLSoakIkaTR/ye7OUTy+q+InC4EfDB2gEhACEx7IkgMo7pEi0hVBQBRUARKMoIBJKe93a6GLUWYTE/5hsimZjr8EnvcTPE3YmKczctygjq2BUBRUARUAQUAUVAEVAEFAFFIBGB7BrOxDO6rwgoAorAQUAA+gppPWScpEJDQaIiGguaVrxitMES87rESswhvlSRsX8Xwa6gJoFEMiotLU0yMjJAikGBBA1SNB6VGEmpOFTyvB7RKMjKqPh8cOUGZVI4AuLMC6KMeZKE5em2iFsryJpDbJwkbr0gviyZ5ZBuTr1Zlmyu/t/Wyfop9tiW9SQ1ZI9jYBXY1zj+zBasGtvln4oioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJKjOlvQBFQBA4tArDs8kmKeEFYgCbB6l6HSaGFDyVTSpptEHsU2Ag5K3/d88mWYh63fCoDSkCifqecxALmGFEm3G3RsLjwJQ3TWIuBRIoDp/Il4vLhR/fJhx+OA/Z+CfhLSdyTCuIrFTAFQVqlSjFfeRBoQZzDsTdF0lJhs4JtSgDEmdcvvpSywDMgxYoh1geOg8E0kGjIlxIQnydFgoHi4gukSGoKypvjVAmkpBjXRilBlImmSyiyQ9LDEclM3yaZoa2wWsuQzF0hicQyJSNzm4TD6RJO3yXhaEh2hrZLPJIp6ZmZIO2QJ3MnyNQQjtNBsEUkHSRfNJopoXBUIiDxwqFdpp7MzAyUz5TMnTulbt0zZcBl4yVY+njEHVGCzP2H0I0ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIlAkEVBirEhedh20InD4EIBBkrw2fqD4YRhGIySXz8py0LfTvSuluD4WOw949vB19iho2Z9gWfX5p/fL2+/eCtxLiN9bGq4saZ0XAeUYAakVANEEcslDZ4l0Q4gPzPuiEQayxzkQa/RWGYGFWdAXAPmEfCDG4rQ4A0EVj5tSWYh5GOvDuLeE9RkvNIQWax7k8xi3RjG0AnI0gHMhHwi6MEhStAeLr1g8IlH0g/2JguyKRSMg0zLRnzC2IXMcxg+Jx5EIiDG0H2Ffo2HJBKnGMhHkj8VgGYc2V6x4SyZPuVyuvHohOuH63DQ90i9FQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARKGoIWFOKojZuHa8ioAgcJgQCjBEVBSUCg64M7IbQjwjIsF0eWAX5QpIKQiwIr4oxP9z4mXhQIGjAnURh+cVPHLEk+KGlGD/2OB0ECD+eGK2SUkCKkIzhLY4mVPxwP+eHrv0S08MRxEBDWbp85CfxnLPPtMIszniJGT+E0HxATr238A64TcQoYNEVg9VVFGP1ekFwkexCviDsyAJgLL0gqTz4+PwhkGew/EJ6DHRZBO4WaZEWj9KFIlKx9SHNT1wZ3AyEl4GTQcFwbbyo0wuXjBEQV14vyDTEf4uD9GKcsHicdYNUC+Fih7GP/ORE/bAy83rgRtMDF5so6/PjmqLqAAg+cnxszePFtwmmxl6wdUZmYqy0qBQPwhIONoY+EGDxCKzKPBEJoKqlKz8G/WetCAvz9dO+KQKKgCKgCCgCioAicGgRyHJpndRsLCm2G0/vKW9iUZZjPpvX7iceJ+bfU3pinuT95DqTz/PY1pt4LjnNHtv67JgT021dNo+tLzmPTbf5E7eJ53RfEVAEFAFFQBFQBBQBReDwI6AWY4f/GmgPFIEihQCtgOKuO7seI54F2RWVeY8NBukCsgV8SudrnzEvsa88McRYGOUFjo2hFQNJQ4mDNAFLAqsjEFxI8+dhIITugACLwSWgV8Y+MhouA1NkwNCrQRrFQBLlUTivzhWy8zFgDb5RIqFtcmqjgdLy9GEgxUA8gTQjAUbiD5SSOaZ1F4WuLJnuActF66tYjAQVLbxAUmVZgiEeGdKcmF6M7eWTIKzJPCAwSWZ6cGFJjDkC4gusaFlfGVyrKIhMR2kRDaNt5IviWlDM5QQLF4vsMuQnLocRH64XlRA2XwwEHhJgeRgy6T7EO/OACSR1993qhTLpuRvJw6J9k838Npya9FsRUAQUAUVAEVAEFAFFgAjYOZxFwx5zmygkjRh7Ni+xeSxxlJw/sV7mscd2y/yWoLJ1JddhjxPLMM22yfTkczyfnGaP97RlmcRxcz85L/NYseOxeWKYiPqOsncKO1bdKgKKgCKgCCgCioAicCQjoMTYkXz1tO+KwBGIQNwDwgTsjB8+FH2IARWHlQ/NxrzBVJAgeNmO7TQvm+l8iSQRA4KDnIh9BQ+7XJWNKRZD7CsadnXpPtKg4YPrv4Uv3yvbYA0VSEFmrxtzDFZkRsjEJAnfVfnSuuSLT6RYWjGQaiTlSLTlzJtUtNAd0lUhJe5zxx1xxk38zJAwrIC/jLRqPkAqVmpo8povWnlRYJ7FF3qrVEhURph0V0Fir0duCJGWctIdWy7aedl8Xtil8WJanUoc7hy9tDQjLwfh9c4uZbId0kWjVTTwhFVOJKZZhUSVKifJgjcfk183fA8yFhZxARBt/LGoKAKKgCKgCCgCioAioAhkIfDVV1/JhvUbpOFJDaVWrVpZ8ytmWL9+vSxbtkwqV64szZo1M3PExHlXViUJO5yLvbrgValWvZo0adLEnGGZKNwxvPraq9K8eXM59thjs9K5s3LFSvn9j9+lQ4cOJp1tZiCW7Omnn26O9/S1ZMkS08dGjRqZvtt87MOGDRtk6VdL5ZgKx0iLFi3MHJL5f/31V2nYsKEcd9xxhuhjXspvv/0mn332mdSoUUNOPvlkkz8cDsvKlStl+fLlcGbgMxiceOKJ2XAIhUKyYMEC2bVrlynDturUqWPqVFLMwKBfioAioAgoAoqAIqAIFDoEkjWQha6D2iFFQBE4uhCgxRJJCvIrtDIylkZMg4lQEG7w/AE/CDEvyBtYGrkkzN4QAP+FF1N8QKnwE3OZjyjiUsF4KE+JJLiImTB1lox9apKxNvOibWuNlmclR1AGYhWN0oElqSrGGHOFWLukGFNIiPFDBQbJJ6swsNlZmp9EsQ8UrM81f3RwCPtA80nMx33Wxw+ty9Bw8mmUcv6i+LFwj8L6k38T7GNimlNn9vp4iZFNRRFQBBQBRUARUAQUAUUgFwTuuece6dqtqwwaNMictfMrzqvuuOMO6dSpk9x6663mXOK8K5eqTNIbb7xh6uvfv3+OOeSUKVPkggsuMPnsfJCE1DnnniOffPJJVv57771XRo0alWsTLGflP//5j3Tturvv7J/93H777dKlaxdhXVa43717dzNWjpPC/Fwkx/y9evWSW265xUnDJPKUU06Rli1byqOPPioPP/ywIdQGDx5synGe/Nprr0mVKlXkmmuukcmTJ8vo0aOFxNmwYcOy5rsms34pAoqAIqAIKAKKgCKgCBQqBNRirFBdDu2MIpAfBHa/CDq5k4+dVIRzMkKXdzDJkVTG48JLZDi6A3Gc0mUXY32BcEixFjRuNSa/U/SgfMfg+c4LsmOHpMusRy9H12BNhHhTwShc7WVGZdrYa027QbIu6FsAL6TcR/cdQWwpvgtHkN1a/5BoYwysOPJGi6XINlgfRUKIKOUPwI1eiskfQA6On5RNovhQlwllBtrlwfvvkVIlS8rgYdeZLPffc6eUq1xVMjMz5ZW5cySCWFjdu54v1914K9wIok3E5or70BEIXfdRolEH0If/O0rKla0gO7fvkDf/95qcUK++PDzuKbgytAMx2Q/4F2OuUaJuPC0f/VNC4PTF8E+Oh8StLlkFzGClZ9wm0joPPJkXMb2sEK/kVa4RgE3SkKSXU7OTmwQWLb84OqYnjpKY22NzDUjCQbIUKy5zxXPsJuvyuXVZd40xXuQE4s5UkFiHm5BVJ45tW2yOVZv62YCKIlBIEaCXWX6Sf6UmpB76zBh/KoqAInBwEKDr373JbtvnveXSc4cLAW9gp9O0L2LmjB7MLSkpES7AgXMCWPpQ7H00aszoMTdwb7jOXNBkyf0rF48D2TPa3w/ngbhZx535YfY8h+7Ig/lmlJMfupjOZ7ONGzeWhQsXGqspWodRzBz4lVcMOWRJJJJByfNDO+eyTT3zzDPStm1bef/992Xx4sXG6ot5WO6Zp5+RRo0byciRI+W///2vKXLVVVcZCzISU1aY386rbZrdJs73uE/rrkWLFsnGDRulStUqZlFXJBKRl19+2ViscRwkvpzYwmKIqw8++CBb/iheLF588UUhDhwr2+f2wgsvlOFsymhvAABAAElEQVTXDJfSpUub/k+aNEmuvPJKQ6Cdc845QpKsffv2MmfOHNM9Lij7a8sW+ebbb02bHDPrUtl3BOLuu8OeauB/n/0P5P8y/70JOX/7+f3976luTVcEFAFFQBFQBBSBoxcBJcaO3murI1MEckWAL8orv/tTnn5hiTmfEnJd7oHqoOCV0WwP1pdXwlK/bnU5uXEldyXmXl4UnS5l64p9r6QHRvY0AqLNGB1hn2OLuMRUgNZoIFLc0GOmDhMbiwRLgtBCjUkxKA6+Xb5MgqmpWWeXwdXKl8uelXbtz5FrrrtJfvpxtfz3nlvgGqW+nN+5y25iJ6uEs0NLsy8/+1y+WbkcK0zPkIHDrpHyFSoddFIsqRv5OrSk2O+/b5JSpUpJmp9EYlz++OMPKADKGkurYDBo0qh4CBBT0IDZUQSB6aaH4Urmr7/+kmMrVTZ5SIrR4ouYbP5zs2zfvl1SU1IlrViapKWlya6t26VcpQqmr2x3w2+/SiVgxdBmW7dvMy5pKlaqJNu2bZM43G+WO6acUXawL3Rbs2nTJuP+ksoKKjsSFSX5AkAzKQKKgCKgCCgCioAiUMQR4ByMVk/FixeXCRMmyG233WbmW9OmTZNatWpJzZo1ZceOHWY+yPkWyZ9EIYFk3VuThPrf//4ns2fPNq4Hn3rqKWNxxTka8xQvUVweeughueyyy6R3797GTeNHH30kH3/8samSxJslsJiwJ1LJzvl4vhLmipwLTpo8yfSd5yZOnCjHH3+8nHDCCWYeyTmvLVOxYkUz7504aaLJz3Y4bubneDnvtEQaLeYsUcZ8V1xxhQwdOlTWrFkj6enpsnHjRmOxxnMU5i1/zDFy5plnOgn4tu1mJehOgRDYy9tigerRzIqAIqAIKAKKgCKgCCQioMRYIhq6rwgcBQjYFXV25at9kaDFgSGAvMVk6eq/ZdlqvHziRVKiMK+CWELMWhwdLCh8IMY8aV8hphjc7OHlOApCheLLIqxKmBfggH8r+hvHCt9SkoE7VQrNmSB+uFhkt70gxCipLM5heLASFCeCIPhS8K4eCoDMgTUUQpCZccexcpjlPIxxlSDm5RskGl/v4Xwx6axIrRq15Ilnp8CqLIqX2k7yxcfvyVysJu3ctQesq0DzZdcLmJqDINtIwlWoeKxMnT7drHZNx8phum082BZjCUPL3y5NyIDLnNlzpGyp0tL/4r6yFDEdXn55npyPVbC/gXjqdMF5iAEWdBQeyE9rsUSxEESB95bNf8hjY5+Uu++921h92Xx0lbl95w554fk50uqMVvLl55+a+A2bftssg68eRI0BlBA75OEHRsuIG4Yj5kMNWfzxYvnk009l1J23y/wFr8tWEG7DrxlmqvwZyogJk6ZINDMs26GUaHV6S+kL1zcSzIVNtZ3QrSKgCCgCioAisAcE/InuhXPJYy3DczmlSYUBAddCKxYryWkNxLHYSkd8UYoP8zhH3HknzXONOLmtZa6bWOQ2JG5oYUXLrTFjxhi3iZyHT506VS666CIhcUUCivnsNhkkSx6NHz9eSsIDw3nnnSd///W3DLt6mJAcCwQChjTigqt+/foZC6uLL75Y/vzzT+O6sEGDBslV5vuYbV9yySUyduxYQ3Sxj9MxB2fahx9+aNplHgq3HAfPPfbYY8Z9IvPPmDEjKz9jidkFV8xLQs+O71tYgtGSrXr16iaN1nVsl/2n20Xbju28LWuPdasIKAKKgCKgCCgCioAiUDgQcGaHhaMv2gtFQBE4RAjw5c+wRIeovdya2df4Xez62d1vk7O63SUduv9LzsJ+h+6O2xUfV7DiRbVDj5Hyj56j5Oxud0r7Ljjf4w7TBb7YJovzgg93glkv+gm3RaTVwMpRiomFBhKvdOlykgkrO7pRjIEISha+SHOFKV+CG57USMJQMlAKHSGW0PEYVuaehFgI33y3igOVVStWSVWQepWwcpgrcFetWgVrrz9Nic8//QJudTIQgPxreevtd2TNunXGci8TbiY/+WyxrPgadUBoQbYdFl9vvf2WrPz6W+N65viataTm8TVBNlaHtVdEvvvuOylRqoQhxUwhfEVAgPL3SWUMrf94PbG+F/V5JIwyxJWKm/nz5kmJYqnyn//eIzfeeB3cVpbBOav0srXpVhFQBBQBRUARUAQUAUUgvwiQsFqHud3SpUvll3W/yOeffy6XX3a5mX9xjkvhnDn5w/mZlVmzZhlLMObp06ePsRpjXDFLLDGdwphd33//vRwD6yob04vp+bEWY75kufTSS+Xnn3/GHHW5rPlpjSyB5wdad1HYP9tHu2V+O1bm51htfpax/UzcD4fDJk+LFi0M8UeSj64VN2zYIKeddpohBNu1a2esz9gO57SJ9bAuFUVAEVAEFAFFQBFQBBSBwoGAWowVjuugvVAEDiACzspXvM45dcJTohfxFQKeCKyq+ILm/NvHPI7LwFjAcaXohloQD2JyHUxJwQrdC9s3k74Xnw6CA31DY3w/jiEUBIysJMTAYhC/+37dZeAECSApBOssShCWYrQE83odAsUDn4okUTwCS7MIXz53YBUn4ovBF18MW1p1xaNhifhTDRcYAAmULPaFlS+vJFfc93XUG5MUWJ55mO5atCWTL8xDsagn1u3BgBiPy0phJMcMliAUG5xYT16eP082/7FZVq/5QTqdf778+OOPsurbVVIRbhGXLfsGMRpOkjdBhvlglfU2tjWPqy5jH/mfjPznSJkxc7qJQ0HSkFdqy59bZMxjY6Vi5YrywXuLZHXTk+XCXj2lVPES4gP5Vrd2XckAwXZs1apZSoMUXKsYrtUYrN5NS0mTLbAQq1GzJrAFSUblAn4bVKpQCXFqs+YyfeZMufX2O6XOCbXkfPTXl5rdGtDirltFQBFQBBQBRSAvBMJx2EMnPLOT82uMsWRECtdx3OPMX1Nj2+Tsq8bL3MeHC6aBmFvC2gdzuBhcDXCeVzKSKp2uG4+Jm0c+eGqYxDjJpMT0tdjv95s5Vvfu3YUxwsqUKWPIn3Lly5n5lyWUiGOy8H+H6cvgdeCLL76Qp59+2iHTEF+ra9euQpeMgwYNyprzsa777rvPkGKcb5LESrS2Yn38cN63t/9L9sOe5/yQfad1Gt0qMv4Xt+yX7XPilvnZN/aVY+3YsaPJz77RIox5WTePrRVY586djcvwd99910DA8+eee64hxt555x0z/gULFpixMubZ5MmTTT79UgQUAUVAEVAEFAFFQBEofAjoG0DhuybaI0XggCJAsqlpw2ri9zQx9Uag+KFEPI6LmbjXIcas60XvQSbGApIpDU+oAhIFOgm8V/tAbJE8sqtDDckFd332ZdR0NuGLZTywRvIYBRZcKMLCyMTJMul8eUVgb7xEk6nygPCJu4SWUy6hIt01CBjsgH/FYypL+XLlQXi9LaFdGVK3bm356otlRhHQvEUzmTVzlmzdsVUag+CqeEwFrIgtIb/+/odkhiPy/Q/fyy+/rJfRDz2A7RooGCbJF1Bw+FMCcs2wofLTmrUyevQY6QJlwvmdzzftdof7GUrWdcYFCoWj4oebnV49e0pVWKt9ALc9P/24xuSj4iEYdOKfcbXu6XDHeGKDE+XrVSsQ1P0zWI49IP++6y4pXba0ya9fioAioAgoAopAQRDw+TGHyLl2piBVaN7DiACJDD9IGM4rOBckKUaJMBgtxJkTwhId80MK55ucItIqPeLOFZ0zRfOb8yzOr0gAXXLxJdK3X18pVqyYPPzwwwYQnrfkEPeTxZJHEyZMMOVuvPFGQyqxDK2pvvnmG/nxhx/l+BOON9fo/YXvyxNPPCHvvvOujB031rhrpItCCuvndUz8JLdn83GbNZfE/pVXXik9MY9k38eNHYf/6ZixWGOdiR9av/GYriNtfrpDJAY8x4+tl2Pgfo8ePYwXhcWLFwvdJ1qx+c466yzp0KGDjBgxwnwef/xxJcYsSLpVBBQBRUARUAQUAUWgECKgxFghvCjaJUVgfxDwu0qdiM958/eD/zq1yTHSrEklU63XtZiysb083l0mPRguZra7gq6p1v50Yi9l/Vih+9cOrLzEi2r3Gx7HCt5UmT/2SrjNw8s4XlB73viEKT1v9HCzRU5YusFSLOJYjDGExNvz7hO/q/HgeUr7LneYrcdbUt556S687Dorg72wjCM55gnT6guYONlNXv0CAiAhSSLCwE4anthYXnrpBanXoJ6ULFVKduzaJRnpmVKjWnXs75A1cE9z0UV9ZdLU6bDOy5RzO54LVzUTJTUlBdcvIj/98JNs/H093EdmSsWKFYzV2Nq1P8uy5cvkmPJlEe8t5yOHSgkj7jYciknN6jWkavVqUD6shrvF712XlVHEL/tTfl7zs6RgRfPrIPCKpaTKWR3aQQHmle9/nIU+qUbTAVO/FQFFQBFQBAqKQBRW5/4AlOcwZ/e6SvHEOmA0olKIEfDzAmEOEQ+kysvjBmLRlBh31n1HTDCumX3emLwyZrjsBMkxa/xweCPIlHAwAlLMsTYPmMlQIR7gQe4aiS3rKrFd+3ZSrlw52bp1KxY1dclXyySPGLd39uzZxn1i27Zt4TY7lEVG3XvvvfLkU0/KAw88IOm70mXQ4EEycOBAaduuLVyPN5RGjRrJyJEj5b7/3Gfcb5NsonBr9/fUEZ63fW91eitjhca+X9Dpgqzyth7OOxPzt2zZ0uTftm2b0BqMRBrPEw/m5Zgo/S/qL19++aUs/mSxcTPOc8xDyY0w5HgysQqQdWXNdU1u/VIEFAFFQBFQBBQBRUARKCwI5NRSFpaeaT8UAUXggCAQ5csiX+zc2rzUFEBicC1D8YhjQWZf2ux5c/IgfOF11Fh0wdDLvCxG+QKK90qSYiSu7AskV/SSs0kWvoNCX2WEL8FRuFW0fTcvqYJjnOe4WDyO+mlhRjc6tCSLZiGRXHMRPU4A+uSmJ8nb774uTRrBuhDXo0SJYlK2fBkDTMOG9WX9LxukVKnS0vr0logd9qZ89PFiHJeS6tWqSB+szn1+9guSAneG5cqWhdvFk+Ufv/4qEyZOlBS4Rbz08kuNW0tWZq+xRTzxuHLlSlAwOBe4RPGSsGKD+x64WKxYoaIs+fIrGTfucTn22ErGPc6rr78OK7UxUhwB3ocMuFLKVChnq9StIqAIKAKKgCJQIARoMUahJRH3I5iTqBw5CNDVtQ8LZ5y5JPrtkhYmXin24UQga17JSxs15535BqdCRV04h+aHczJ+Vq5caY4T59h2f09YzX1prmzZskXu/vfdUqVqlWzzPcarff755+XBBx+UETeOMOdojcY6GWNs3Lhx0r9/f2GMs6ZNmxqyiedIOlniaU/t8jzjz5r5JF4ebN+Z36bzncHWw3pJajE/3Ud+/fXXWedYhvksDvRowX69//77QheJPN68ebNkZGSYOXAJuAi/7IrLZOjQoSbGGNtb98s6Y2lXv359Mz6Sa9YzButX2QcEcM1UFAFFQBFQBBQBRUARONAIKDF2oBHV+hSBw4xA1LWIsq4R8XonUfPC77z17z5vtQBOTIaQ3zn2OYsfD9oo4C0PghdYNBTxBWGoBLc2SCOZRYGhl1FcIBQZwz+A3gqJ2zVzPuaLgPoyvA1eauH/P+A3K4HFh3pAuqWAKAvCTWM4FcoRutHxYbUmS8J0jjlQkkfZhOfxiizTnn/RpCMShdnOeHm+m8/JwYPxE6cZEo/DiPHF2VW8uBlNLCySfDPmvmqSHEeV2KUWBsJzhUoS+lOrZk0ZPx5xN1w5s92Z0qZ9G3PUt+9FNhnxvU4xHyZQcUCpAOKq9RmtkWAOzdd553WUjv/oaPbj+F0SVf48kxUr9phuEEfdcZvJz68zzmxlPtxn/AZ+bHtMa9yksTm25ZmmoggoAoqAIqAI7AsCkXBMlq9YKps3rpUGDRtLjeNqm2qimK/QTd+mDZtk6VefSTUo/E8+uSnmAI5L6t1tOXMHe+zF5Msf8GFeIPL6gvlSrXpVaYh6g4jTyfrSMyOy6O03pNEpp0h1uBeOIw5sKAwLG0mFpfVS+fvP3+XMdh1kxcplEoblTbPmLWzVuW6peF+5crmsgWX1iSc1kdrHVzeLh5jZg/nK2rXrZPW3y6VCxfIgHk5FLKSV8uuv66Vevfpwb1fbzKXoitAPxf6vv22UZUuXSoXKVaRR01NMeT7ev/16Jfq2wsy/Tj2lmRxfux7maXA9BzfInA/FMK5QzCfvvLFANv+1VYoVL4a2mkutWjXNPAtV7FEC7nyK8z8jmDiE4a6Zi2MAC7D0gsQAoQXXiIa0oEsEiJ+rqyiwIjdTEMz9ugx/WuY+NggEh19efGy4WVAVAb47cU1Kop1Ow5/F7FLkg6cHCEPvmqlQ9stnqkz88tkJdGJiwj7C+RqJI8ptDLNbD2L7OuJMzPd3fh3xY4IMiaF+ilccjw+eWHFznDj/chLsd/5e933Axw+rOtBjaARWTsCdwrm1EROnmLNfEmeMx+ucd07y2wNX2k9Ky5anGVKMJZ0pplN+ABYwkRR7CG63GQNs0aL34e6Q7yCORVXPnt2k43nnyiWXXiQrV6w0vzEPAiDjp4trHjKk5+62nPacY1qLeSSA/yvGqSU5yr7E8BLAhVYB/G5S4Np7166YZIbSJSXI3wmIUpDf9HAQRCxhSiicIX6z8s6pDz7b8ZuOSBjvGvPmvYTymdK8eXOTl1/sV7/+/WTqlCmyfv0v0qZta6duzIt37kwHSdZcJjz7DLCiy3di4OKYVUPBdnLiXbDy1rV8wUrtzn2428cbl9sZ+49q77/mxRLn7LHtM34H5h0F24McJsC2qFtFQBFQBBQBRUAROPIQyN9M+cgbl/ZYEVAECjkCfG99ecxAoaLAWIwFvcZ27dWx1xhiLAPv5XzpzEuoXLLiBbsWJ8sGwTu7eSG3r1E2z4HYcjUyCS62bV232HqDUEzxPcwoiMwLmT2jW0VAEVAEFAFFQBEorAhwkcWTYx6UeXOex6KMNjJn3ntQnjsTET7r77v7Dpn13EQ566xzZMaL86DsTlbE5hxZBKuB3nj7Xbm0by+pX/d4+XjJt5g7QGEPUqFEsVSZM2um3H/fKHl70efih2KfffgN1tYXde8kA68egsUpZ8u4MQ/Ilk2bZc6CN3M2kJASgVXKw/fdLQvmzpUzO5wrz780H0TH7onUA/fcKXPmzJKz27aRma+8JuMeuV9ewXHb9mfJ7PlvGksrupGj3H/PKJkyZTLiJXWUmfNfN2mtYVW+bv06qQsiLQym6oYVy+TSAUPl4cfGmn4zrtf7b74pw9FvkhMn1Gsgf4LcW736WxAIl8vosY+bevL7RZKNRN1ZPW83Ezou+Hp77j1mgY0PZEZmLnMshhPzoYzAnbi1+vGDADHzMpBiXpwL7yRhA0skfHh5uYDJcnH57dvRmO+F2XBJbYDaTYoljnP2C7PdQxJZu39Xu/PE5a233nIPLXmx+2ydOrVBVjnk3o2uxRivA0k2u+hp3ivzsM+ytBTzywuzZqEtMo6czSdfJTvD98rMmTNMHdbjgEOKUc3BMjGZMWMGthQee+TFOXOxddphKut3CDIuk4uY+px2uUDPLzt27HCyJXyzz7a9t9560/ymVn33nWz+c7M0BymWgri4jlhLPEuUJlSiu4qAIqAIKAKKgCKgCCgChxUBJcYOK/zauCJQ9BBg6DO6N+S7N1+raUlk3nnxvoh3YNkOPRPPFcPKVHJeXsSP4kLLOFaxUrxR57bF1Z6UAJg1vlSHBCs+kc9P8zK8Q7MOusbZrRNy8iPFlNufrxDibg0ZeAViJyAuGoiwRKGSZexTU6RU6eKG6Esk7hLzHa37jAdH4YbIxMwqWV4S9wRPqigCioAioAgoAoUNgThsiEBaNWjcWD5b8oX89eefsK6qiKcXbHPgNu2NBfPkxMZNjaLe56NV0N6Fi2jownkmyLRWINoWf/SBfPDRx9KmdUvMTxAhFRX858HR0ubMlnLXqLtk1N13mgqvHTZYjjm2qgy5+iajeA/jwRoDgZC3YGEO5k0NTj4FcZA+RlzOTXBD7MSXzQiH5d03/ycNGjRANXw605uAF3mbyscffyI/b/hNalSvYmJyRUMRWTBvgTRGPTS1svOYvhf1k8tAhJUoVkwCiC06BZYyNw67Us4//zzp0PF8mnTJiBHDpcUZrWTic7ONtQ0t1bb88Yd8/8PqPLsfdqdTvpgzz3MNwtBTTOaAZdQbNFZevhAsgzh5dGPphqzLAVw+GOiBfKHlGsgbgEFX4rFMEjkixeFizwPzve2oPgNzTA8oMdI0qbiStNSL+1P32sc8DMZgueYszJI4CBHELYuzjxB/PFP8uNawG9pr/Xmd9EcsEevW4xKzUe9Op2jctRxzu2EuszmTP0Im5gmahWksHsXvMwiMKLDTM1v8CJ3tPn4nk2nmJ+321V5CgGSsE7nlJNrjQdtmH23jZ5BNrL91dNODFwjTO3eoZm7OfZTxcCUeh8IMrMMZFurH9bF1sjPsC469/OExnce8hDjHe0CyZGuPLxuIZdygbkOR2shJLs+2wy3q8+QSZze5zoN57HF/jwezjb3Vvb/t7/75WWDtNXG29sj0wcOLhx8A2XReKLojUVEEFAFFQBFQBBQBRSAXBNyZbi5nNEkRUAQUgYOEQNcRTxv3jsbqKu74/Pd706GPikg63KFwBWkQShBu/TlcY2TvVJSuUpDvzZfuggIEio+Edx/7zpy9xP4dsa0gFEK9+l8EpQffnJMlKoHUFKNI4kpnFUVAEVAEFAFFQBEo/AgE6A4Qf5VASpUoWVqmg9C67oZbQLSIvDBjilSrUVNqHl9H0rf9la/BcL6Qvn2XvPnqPJkwfTZ4I788P32qnN6imbE2D8O86dhqVeS+h8bKwMv7SZceF8jqVd/Ip599JK+9tRgu4OBsGkQd5xqcD+UlhoiD8r18xWOlZKmyMn3aJLlhxEhTbM7MKVL1uOPQ/9qy6++/4GoaEVcxsMpVqiEu6DEyZ+Y0uXbEjcbCbPKUZ6XacdWk5gl1JT0j3cyxWMnw6/8PBITTD1pz9bn4Mhl57WD55Ze1xko+IxSVDet+ljv+dY9E4QKRFnDse2nEHW3R6gyDY15jSDxvCTnHqsiZb3EBFV1epoHDsupxWyZxzjfvkUHmWpKY637jGIOfNxaU+Y8ONnzIK48OFA9j0KKwwTexsK2wiG4Np5Pb7w3MQzQ9Ir379Da/3xDceyYKY3VNmzJV0sqAoCMplY2pSMyJffIW1ELwstotdh0iClteGPvBbmRXSPr064u9nMLfx4zpMyRYwrXQYn38cdi6zUV2y7FdHts2I8gEi0LTX2Zhus1PIJifW44neUrPNuwYTZ34YppZlccECM9zF6SZs4ONiiKgCCgCioAioAgoAopAoUGA0z8VRUARUAQOGQJh3HW4ujfgycS7I5Q9WAEcg//9DChoPFiZnOKaHHmxcpVCtYXz7ukwXihp0u3KXT+XEPJ9Eys/+f6KhcRmxStfROnWx4tYF0a8PAuBBdr+ihcMXKcLOuVZDV0SUTF2NItVWFmlHWONWM0C4yr4uNoXAkc5uCS5rbk1p/P9ZdvJdwHNqAgoAoqAIqAI5AMBTyQDc4YwrKZSpfell8mzY+6XYTeNFKzfkZkzpks/EEGLP17kLNrBwpcQJyd7EbpimzptgpQrXxbxNs+XXdu3yo3Dh8oDDz8kJWCVAmpKMjIypWuXC+S1bp1k6MDB8vdfW2B1dSPcLtaEtRhdM3OC45A3e2nKnEqBS2rjphHWPhdffoU8AbeQV19/K1y6icyY9px079VHln75ubEGCtMVNOqPgiDr2aePPDP2URlx083GWmfuCzOlR+/+8sUXX4CEcuZenK+xbh/mXP4grKEwZ/t2+UrE/opJzWo1cRyXNBBhVatVlyeeeEKq1KorzU451fQrgLldBCSK1+/M6/Y0DlqU0KooCmuPCN0eehlfDHMoWHlxLhXDhfChOzG4UeRULgqLMeYP0jAEx2GQHJxpBGG5lcpiMB8Lw5Vl2FfMLLxKw/WwPIiZN7ruAGmN54UFoIcM6F6E0829SRSBcTnl83LmCnw96CxxykQ7nJtyRrs/Yi3yo7CAo2VcIOLi6VqOwSmhqZ7XiBJGXC66+vMgBm9+hP2mNwbGRwMaZstydtaMKR1wjsvFl11mfpfWjaCtm3F/U9LcPnG8e8PLaiCSjOBsXSSo2J4VX8Ajl+A3nShsj/N8H+b/jD+Xld8t504/nXcE2xfbrgMVQHRP2HQ2wHM2v7luOGZ/kmDMsnJjGQrLMD/eESgmv73kJF7tvjlb8K8c7RWwCuOhsoBlErMf7vZplOdI1o5NyLH1gMnMxD0kgHsF7wkx3INUFAFFQBFQBBQBRUARyA2BxGlgbuc1TRFQBBSBA4oAbzqvjL5KEAdbsBBZ/PQ4g5dQq3CguxmKeYfEfvcBE50E/S6UCCSuZA+FNsjG3++RCsdcJcXTTjEKK/si7sMFJTm2W9tQKIejnVIEFAFFQBEoogiQ/KGQkOrRu4+Munm4rFzxjRQvXUK+XrFcnp/1snz68YeGFAjB3SB8N+eJ1Aszn5NuPfsZV9Gdu/eWW264RmZMmSBXDBgiQRBMtLphvLFR99wvTRrWl5q168q1sFLb30UgXbr1lpFwa/jtNyulRPESsuyrpTJ5+ouy5IvPTJ9TU4MgTsKmnZ59LpG7Rt4sy5Z9BTfQpWXF0qUyddYrhhjjM97iEoRFHYV9y0gPyfVXXyXNWraSdh07GmKM5NiYx5+R664eKl06tJEUWPA0hTvG3r16y8VXDTQzAFPBHr7YFl1jw0YORBXJKodgMIucqAt3DlEv5owgKUiKUUxMKnfRE3ggYwnX+YYn5IXHhpjzL40eYLapCcRE1+ufRSthuW/YebJ89RZznp649yZ2nrqnPPRxQFm26neDEUkxygocP/X8l7b7Jm1fvkAPmmKenetl3arFxs04E6K+BAYJx7SMo4QjO0EMBOQPuLG8+f/WAKg8yAG4fKzdsLVkBmsY0jTmdUjRYDjN1EfajOLxVDfYR0CUJoof1orPvPi1uR68lsnEWWJe7juxxJJT93zMdhOF7ZMw5e/x6TkrEk+Zffv7yHFiHxOS+5tX/cn597HZrGJ5tZeVcQ87+9ufw96+L8MdmfuPHHdZVeM2EadgsUoXmvTE+unStYYUo1tVL36XvDepKAKKgCKgCCgCioAikBsCeb/R5VZK0xQBRUAR2EcEfJ6IVCxJC6KgpAXgDocr+cCeRP1U0Hgl4q6ApXKDwthiWISLF33nBSjmY5h0ivtC5L4YBbEy0AjqR0XYhQKBvuWz1romLfV0cuv3AUQgFishO8Jr5e+fb5BK5ftI+TIXIZh5WdMC1yDDaeYBbE2rUgQUAUVAEVAEDhwCvmAqrKk8eFbtkBTMNc674B8yfdIYKVm6nLRt31Yqli+HyQtii2KhBz8JPEuunVj5zXcgm76Wsc9OMZZVHli1nN+tl7w4ew5idQ03pBgLQp0rj44eLcdUriRrf/5elq9YIY0bnYypTMGUuZmuCRstf9JSfNKxS3eZNulpQ3a1OauDVDymLMgUPItdQoltk8AoluqT8zt3kmlTp0mZcmWl/VlnSbkyJTE7c0gHa/lOooeuFNNDHrn4oj6ya+tfMuvdecYai3UFUvzS/pxz5auvv5PFHy6SpcuWyP9eXSAjbrhBFi9ZJmPHP85sexQfsD+7x+2YtsVhMYY5Ia4E+0dXd1FsA/G/5R/db5SMSCkJwPos6t1h6npr/n1m63WZqwhNhXabl0hKMoxmipiJOn2y7Ns/ZO4rn5l2LPG0pw5yLro3gfrdnI4jxpiP81rGvoWs/GaDrFy10WBvEvb1C54WqPivWDJDXp7wX9TndCie9DvxxC2RBTeYaMtfoY489PD/wCZmd32Ysxt+6TbgOPlz8++GcNrp1htwY5vFGESXgmvB60I3ldmE18olqkw2lxjMlifhwFzbhOPk3VzrT8xk5vpI2EN/9pdcTmyK+8n9zav+5PzJ9RX0OK/28qpvf/tz2Nv37f33682k5SD+CxErkm5YA37nvS89VhwWpnndrfNCT88rAoqAIqAIKAKKwNGKgBJjR+uV1XEpAoUYAa5apF7Gz5ds+LXhaulYgqKmEHddu5aEAF+U+bLNbWpqaQkEj8PKzOWyadM02bbrI6lc/kopXeIctxSVRnlolpLq10NFQBFQBBQBReBQIBBDnFN/gsutiy+9QgZf0R+uA1Plv6PHwxIJ7vEwV6H+34fVO3mpWqdPfUaKlUiVO26+3jwjydv8+usG+f677xGX6xepedyx4G+88sF778mEp56UOf97WyY/86QMu/xieeezpZJqiYh8Dt6fRFT0v+QKGXhxT0ktVlzufXCsqcV5XhsuAW37QA45o+jZ7yIZcuWVkoK8D9z7H2PNltiscX2NvHQPN/CSfvIdYqG98c57INsq4pnvKKAZVywYDEgYxy1at5ZW7c6SocNHyJ23/1Oefmq8jB0LYiyJS0lsg12hezzOJ8zHnS+wbWO15k4fUlJSYTFGR9t0HuiSUe7aJ4SdNeTRy48MEC+ISLpS7HbDWOMy0uctI6+Mudw0+cojV6NOkTkLlpg5DC1KuIBnb+K0tOccxNLnxpY1JATJU/ymBAu2vK67wz2XzvsMXVnGYBnH2HDGWhFj25sYL5TodCqsxkL8Xe99eKgzaAgx9p1Zve7/ggdkHIWkHEnSCK6zIa34j5AgTCNxyvh1XoPDXi42yyWVT6gq191kooxELYmQKN2Ws11sVYouAj78Xvkb4P+gsW7zOL8HHyx7I7COVVEEFAFFQBFQBBQBRSA3BA4KMWanpVyxSOEKPPp39uZ3AowVah7khbrc7bPduodWr4oVhXF8PFHHtCSOGELOFN32wM2fVY891q0icDARcH+v7vtiFC/EPrhX8SJ2gg8/zUjSz/lg9iQ/dTMWARUOPtdlDV7hXcn+wmtT93sLCy/+C3tctxY2VpjHtQhzdQpZzXgQI4Ev9058BiijYk5wbYSWcMR9YXftx6Cg2e0qxq5yNhnd+rMqdneMsgX72fImZLIv4nwBT8zrYIbVzLmsiLXnuGJxT/UmNHFAdy2e/K1ZYX+MNV5Cmj13oLaWHEuJV0HclWVQoBSXUPo6+WXD/VKs2EdSo3J/KKnqoLkki7+s+7PtXN7/ILYt9p3KM4pNS6zFWW2P5w8TUS3jvTjqHlPksHwlLezO0Ye8VqTnQ7OVo87sCQ5CHut2yn1Oh6wPU/d6BF0FUwgr72nRmSYZ0v6KhyQj4KxET6H2EZLpdaYRfgMyQ2tE5cOnr4cSAvc7YB7BMzpR8lYlJ+bOuU/LisMpeV8f5/dIx6GUDBTYEfJK6cyYXDD8UdnlT5N5jw7CKJzzveHui8rB58ZfZ/J7onhW4BJBNQzlDpS+7g86iBg7FLuAwPYj7MLBGDsUWtgWROw9zZbJ+35VsPptvXbLGEL7Ja6ia7/q2Evh7L/W3DLuZ/9zq1LTCg0CcUnBHTGI+E2IlQQ/zy1bt4UbwlKSnp4hHc+7wMS9itGcHfHBzFwul+c/5wycF0TwP/sCYnV1QlyvVq2amzHy/4ukxkP3PyBTpk2SUaNGya6MDLnuusFy6aCh0vL0VtKwwYnSpvVp8p97/y3/uusuc4eOYw4Z99hZzp7h4v8zyS6SbVxrdHrL02C5XRr9z5Qunc8DOYR7BO4+cZJ6/Fc2QYOc/XZnnSslS5SUHbt2SIfO3fCkBgECF2QhzGA5JnBMCPXlk4GXXSTLv/pcXn17kVSsXMV570J9zBMGCcR/URtLjG7MKLVPbCyx9HwopjFED8hAxgHz+GBtFaaCGzHDZIupPx7H/REESEbsT3OMkGqOYFxIxjkQR8CAYaMYVstMMxm3zFsM+MGKBFaAnArakLPw2Sh+1BnHtTZziKwgVW69SRvn7p6UmHCYwmuPm3aIA4BgJMaFJglLPx6IGa5FW0KRAu1646kmfwz1CX+r/l1Oedd1or39xzwO1rY5Dz0qwDrOzpf21GicrglBIKC35rlk59lx11MDf4esKit2V46KHGeLPG/mWvb65MjnJOSF5x6K7U7GhCpCUhYV4bIT8GziXIXdSXmNf3dOZ88SvjadcZAThb+ZvQlJwsIkh7s3+9u+nQ/tCdOQ6/oz7GY0LhRxjXgb8tBvv4oioAgoAoqAIqAIKAK5IOBotHI5oUmKgCKgCCgCeSNgFcv+BEYvHApjMa/zAm2JNVtTbkSaPXekbhOVDSVKHi9bt0ex8BivwFG4OsLn723vSTz8AeKO9YAFWV/xBOFL0yVGrSIjjhXGBqskxcaRiklh7XcEGgIq6ajPoX7X5cWgxIWlRC7xcqj4oZI1gutDYZB7ShzaMV53Kim5Wpv/Bz4q/6AwtsoPw5k5elFTpqh+kbvlz90LfAPAh/v+AJTRxJS4Mf4Fdl2OsajCpONWBA47AryP8Z5nrQtogfDJ0lXoF6hs3POSzyd32Nxfkchn2avzZsn2bVvl5lv/LTWqVUD5qCnP+++q1WvlpVnT5Y4775TbbrnBWKndee/95p5aplw5ufPeB+W6AZdJ7x49pG7DhrhX27tqcovZj0m80eLKKuzZ1kdLvgF5gHsQPxgbhfdzEmfMZ5/ftLRYvOJbkFsYLdgtzlWoX7bKfZYfcuUV8sn778vk2XPFByukTb/B5R6IrOKwMkstVswsBLp6wKVy5eBh0qx5S9Qt8uP3P8gz4x+RmvWwMCaP5zv79M4r94I8dO+Z7AvSOvYcYfrNvr479wHJQL0cCl9iyT3YhVNhY7EWNFYjXW96RmaNHmj6NPvBq0w+cGCmvijW5/S4aTxKp8r9AztK/05NUB8Hi89+SApu5OzjZ19vluXfbjR9JMYN6lWUZidVkzA7sB8Sjzrx6DJi26XWMTdhodhOpza33qDzmIaLSXccIANjWMQy9+3NctUlF2LhCgL77kU8WLhWAyRmqr8UfisxCbsx5XA1nFKF7CFV0JhZxopoL+NPPsX/pURJjpmWV/sFbS+xLd3PiUBeeO8u4fxePVxAjf+/TCkupzasuvu07ikCioAioAgoAoqAIpCAwP7N0BMq0l1FQBFQBIoiAlQ0GWWUa2HD44DrzoP72deXHv0I+X1VofvCinUoVfBKKkGfEwNkFyxmMjJnytb0L6Vy2YFSpmwrgIEcVn/jC4AccFxsHv0oHb4RUlHK36VraA3FWoZxj5QSSDFuwvyuSWuUGkqIHxavzJ8BRdnssVdB6QcLTpA6mdvpwiggQUQ55yUMBbdDQRqQHX/7ZCd0SamI7RAKRSSOek09UHBSoi6x5hwdju/8KZj3vWeO4pk2X5QgtLbpsbBsTU2RKeOvAd4EAthEA7AI88rMx64zylMPLB3irpWBKbiHL2spFnJNQn3WRHQ/Fbp7aE6TFYEihQCf5Q5Z5Pwf77b6hnsu3rvwb03lLAmf3ITlSYqRVJo18zlpdtopUu3YcuaY+VOCuF/ihnnppZfLU4/cL489+og8N22KzJnzKmKCBcxcgvl69egur700QwZd1V8WfbZC/FDQ0woqL+Ftm8r4AOPsoCG7MAehv2AtBv8dOEfiJoB7OM/7EHMqFaSbzRcOh/HM9oFIcx7MPty7UnEPs+N9bf5LEtq5TTq1bQ2GyZ3doG8XXXGFPPQYrF8h639ZJ13OPdswVp6UoIT+3i5NTm0qEybOzqv7xt0hibiAS/SE0W/eFsNxWHzhORTD84ekWCjp7TUGSy+6QmTI2ZCEYUkVMe4caWRHq7UA+spr6cHzLQ4zKm8Q4+PljKXLqadWlZZNqhvCLGJNpPLsae4Z4lHUDVgypi5H7LItwDfT3N8bNqgiV/RtyZlR7gXzmUprLYoH1y0WbSt+4EuhjRclNes5aw6R0bEc+6zXY/Lwgze6iXvZ8LIndNF5iqEat0jCqb1UcuhO8TdRELEkcH7LWItHm5+LhBIlr/YL2l5i3bqfEwHnrpQzfU8pWOKA3272a7anvJquCCgCioAioAgoAkUXgaRXi6ILhI5cEVAEFIF9QSBoAj1DWWYVSVAqUYkU4spl99y+1HuklkkNVoe1GJRYILmoqIrC1Q2VBz5DkAQkGvpR1v/2T7h2ai8Vyw4SX1oVZ6jISxdKKgcXgTCUg+a6uC5HU+CLisqGPVmMJfYG0UUQQw4xSKAc8qTimsIqEjHOYe0A5SPSubq6TPFUHMOCDPskzhy1XGItRW/fuPDk/wAU0H6suA+Huep/Nw64ZUBBi99/YuLu0zn3XO2Q1UvnzKApioAisC8I8H9q0sxZxqLKxiuyVrasj//DE6fPBnEUMUQLibBEoYWZJaSen/uqIUV43mTDM9GdJkjtE06QP3aE4N5PZPj1N2ExjVML27Sk1MRpDpHENp6ZOjMrPbG93PYnPDcL8w8QJ7jneN0GM0GKBWCxyn48OWUG+hU19T373Au498SyxsJFPWyfbfLDunjMfjG+1E+//W3GxHoomOKgLJ4BeHSTeOL4Z7/6Jp77Afn5+9Wy8fdN0rxFaymB9RHE1mLqlM75zXsgn09kYrjhrc45RGG46SXpZ7kQQ5SZxTW8t8ICF1ZStMiNIgPb4fOJRCBXJ0Vdi2daOFPYfxKcPtfCOQB+iW4mD5R4SDYCL7pQJ7liCZKIZbb2sSE/6g2FophbYmwAlGOlWHrIYpOVjuvsR74gB8h8NoM5yvlln0G0GuS15fImyu6ZmXvhcxbVFEWg0CLAuSvueOjf7l9yoe2sdkwRUAQUAUVAEVAEDjkCSowdcsi1QUVAETiQCDCmBa1gqPAPQhFP5Q2FxJRV7nA19P+zdx0AV47t/zrrHe2pSCm0t0qpPhXCR4uUUIq0SBmRGT6bSJOE9hANKiPzb2WlnbKV0tCgesc57znn//tdz3O/73lP72pRuq8677Pued3rea7ffV0XhTbcJc1d04eTaJKIQpmxY8dAu8YvfQb003xHPzNCipcoIb2v63M4szvq0/IFThR/RjG4Btnq7EKHZgyFUF449QhBoEOH7BSt/LHvHdmZulwqlBsgJYpc4NSLMphjTO7C/sY+oH0N50buZDQNKFSkQJLXFFQGKKjLRub68PbLbFnEXFD+GkE5O1w2RIWMGVJI3n5lGHWY0F546goOE1BuUip80FDIx4p1uOk5mT/2evgAFek8aBwAMDg0x7+FY/tLoVAAPrQmSCo0xT5+7kY8gyAO0ajpQAoSPQP5jMqTXh3MHyMCzCVuPppTmRpWuUTP77ZR0MotnKmeD/2e5AM0mJBBLQIAxOlhCIzBYswXlPiSxampQBbB/aRkaJKgj+RF5GA0FJHzLr0fzcR4Im+89iDmtBA0JQrptSbsnuV00HkR7c/+ynkrLSUkb7+5QP0cEfRs0KCRVDm1CvqpI6Dn3BpL9OdCYbPj3B5mNFHht99YKCdXrCg1atRWATnDI5S89+ZCqV2/vlSsVEky3Pk4ivDffbdONm34Wdqcd4Gms2zZUknZlyJNm56loGtsfjynMJdC4G+++VJS0/ZJi5at1AylEeJSkM555ZulX0oY+TRp1lzefPNtBXs59/tRzwzwlsJpBelxr+IpFcHLkPyy4Tdtj6wwESld5gSUpYlj6jK+MPb6X80BA9xwfJDMOwTPzeYXnptxETIDnjdBjgU7+pN1IAXO+bFkgCreM9rSHNfuNKlBTdp6kfmHJnDp48tZT4IuwEKgJBu58zY35cQS02T5WRpn/4kzH6MU6P/Z08iev5Mv0yJvVGstNjimLJ2XMJeRV8yD4BoMxkr16qfrz8xJsXWMLVvsuXILa5BuqEA+tNyn5fYBRATIE0Aepmasjc/1lUlNMP6j6TT6s45ibVowqj+0cJ11rCvWJo7/NF+KvA4t3RAm8rkjBksy/VRHQ7IXXsyYcHI+4Fh+8z8dm7EOQW8Q/tmoxQYGod08jtMtMCuWebE1L9h5EOAgXIvB46fTfsaFJ95sNYGI+owDz9z+6/EkQeMuivo64Q1Al1tuX32zVH7fskXq1zxNTqlyqgTou4xLACq14ZcNsnL1SilXvpw0atQox7k6p3Rff/11qVy5stStW1fbgGEIVC56Y5E0bNhQypUrBzPMAPqwLrF8K1eulK1bt8p5552Xeb1v3z7MyU33y5Pzuc7/iEtKT0+XhQsXyp9//inJSckah+uZ0fQiSLl69WqsJd/oWtCsWTOpWbOmnjM+8+ezjRs3Sr169aRKlSq8rcS8Nm/aLF8v/VpOOukkaBo2lmXLlskW8KtWrVpaRxOWx19//VVWrVwl5U8sr/xi2kzD0sFzAD3kACOb2YLzFM4PNPoB5maDWw5YDlgOWA5YDlgOHJscyHpjODbLb0ttOWA5cJxzgEIcCp/o34gCFO7SJdFkUSjkgBERgGcKTkBIwrDcIU3hDYnCYQNa8NoIxHhOMsAbzZ5R5h4blvmZ+Es+/gjC7WTpP/B6Lcen//ehlK9QQXpdc63mx49iloFkgLr4vPThMf4n4C0ihaAFtjt9E2qS5W8qA7zP3A3OOnqSAQzslt83jZCEKmdIoUDZY/KjlXIO+pPj0YCkpgkp6FEhHfqJaWsjGCGwQLrvztulSJGicuOQoU6fRD8xxH7CPhZP7OPUyGNfZrrmOoQt7wEIBPMi+oVBJ9Yg3DHPf8gG6TkCpqzcnVTYZiosRj4EwgxRkMU+zX/caY/i6DXLQ9EP8bUgQJxkv5Mibis5tTapHMyRYIwjXPIhUXMemxLvk/jMnMc+P5rOnf7gaDw4IubcS+e02v7PtX0cHG7/h3F32Fc5Z5E+eOtN6d/3GklITJJTq9WUPXt2y9o1K6V7j97yyJNP6UaD+P6nGhiIy/6HSVfee+c96dm5g5xWtap8tuxbPHH6K3N4ddYMeeTBe+SjL1fgCn0CeW/ZvFU6X9RWeg/oq8AYwYFnnnhYUvelysy5CzVc7B+OIYahBuJzo56SnTt3SrN5zaGVmqBCRhU2QjpMf23PjXxaduzYIa8ueFv6XH2Ftn0GQDPKpNP3pEogydGaoAbfRe3/C82LoLy1YCHCIYCqvKBfYZI/s1kLmfXa65IAbZL9gA23H8eW0Z4fPxzQ/ohuou8BmDN7de+SrfIczwzDmXL0hMnwvWUA62zBDvqCAPLNA3pLOsYD+348TaWWmfMKFP/omLzmu5pqkKH0HLecX9y9G5n34ytG0IXrEPYLKIDJuYN+HMOuSphiVWggHvl+VygR6zfTj0/oOLx+7NFHZe78eXLhua3kDWx48BAwdOmBBx6QqTOmSuvWrRV8SsAcnB8tXrxYOnXqJFWxPqxfvz4zON8rZs2aJUzz66+/zuzLBJ4uvPBCGTBggLRt21bDM0xKSgrK80ZmfHOigJfb3/m8Z8+ewnIRqOJasGrVKunTp4+MGTNGwbE6deooYFW9enXV5OvXr59cc801Mn78+EzQ6uGHH5b58+dLmzZthOU3xLzuufcemThxopbxzTffFIadN2+egnhvvfWW1kPXJMwD9913n0yfPl3rsWjRIk0mpzFr0rfHAnDAefUrQEAbxHLAcsBywHLAcsBywHKg4Bzgt1umcKHg0WxIywHLAcuBo4MD1BQjKEbiOW0TKViAD1MKgSk0piCYAhDe54/gADd6ByE48YRD+jFL0MqEia0ZgTfu0qYVDgW0IHUKQNgfgeML7rRGNkoRLUOWRCoCrQqIcLQMjMt8KUzjkcJmHg2wEZvfsX9OH2tlwXdqpcRI6zOdzjuCc2odcYt21JcO3m4GhlYM1xRN5S9sYcyjhcIQjrIduUObWidsYxL7HLVz2K8MuKCABLsowqelByF498uqteukVIlSCM899pRp0qSeo+FIQDf77n0I8xDXgTUICDMG+iH6uALCTtbMPndSjSpoA7ljBp5YJMhmcneU+7xoD1CEzlpAxLVoDtMLKWIEdeWGd/b5SAAagBg7fi/aC90+DYUKBaApgehw7wIQBBqc2N0eAjhKggstpbDZ4u5cHtRfw8+wW4fYRKIZaAN2IxDLqRJqvXL+4PEhkdN7c0+CPnBIHq/b9+GDjdpT4JjyycdxgHaCEpmST326wb8b+kpBiPlrq7vxvUhPm5STjFLWHOTe2O+Aroq2E7l58A3yn9atZfzEmQjjAK07t22Xn3/+QedUsykgNgE1n4n+poARBNAzpr4oZ7U5V7747FP5/KulcmbjBjq3Mb3HR4yUFs0ayz133yP333+/ao3ddP0AKQ/tsptuvVu1xVRIjT4SjqZiDFGbNDuHVchNTUbwkIJuCso99IcHbS/H1xPmGQWZqbmJBmefgBB8w+/bFUyjptnUyS/I/+65W77/fSd4BZ95GKPU4OjX80pp2epsmf3a20jdEcDD/ZCSqacBsA0PoLRiyXIgkwNdu12VeR57gqldkhKSYm8dlnNqwXS+ohv6urOO/NsE7cbFV9Adh1hGMFZ9smju45hTcY7rIKYD+hTjekpNMSX4wiRhL4bOB95gRLrc/Jy8/MwAxPPJRPWPyW0cWPuQUAKed755NAInyPtj+8GPGuYWJBHvu0wTPYA/ZnmjxiD9rDoahEgg8/1HX3wOIMXsQRPcBSxIxpDIEJDPNY0chsaaQy5f9J3KWTPcB3ke6B+2fu368tH7H8vvG7bKiRWqgP8ie4J7Zc7CuUJgyY/OHeAGmwLQhAkTpGXLlvL555/LJ598Ii1atNBY7LejR42SetAovuOOO+Txxx9XoKpvv75ywgknyN13350tdZp6zqmv812K93ns37+/AlQzZ87Ua97fvn17NkDu6quvluuvvx6bkYpo+gS5+vbtK507d5bzzz9f77Fu9VGujz76SDXXTjzxRL1PUPq1116DVnWDzLKRF7xm2M2bN6smGfNNS0sTasqRXyybKWdmRHtycBxwXnYKENf0fzfooQ27AuRng1gOWA5YDlgOWA5YDhzLHFBgLKeXzWO5UrbslgOWA8cPB2g+kYJygmLLv/xSxo0bJb/99osEU9OlTr2GchcEshUrnqIMGfn0I1K8aCnZl7JX5s1+VZKwm/vKK66U7r376PN4EII3KQA24Nhbi+bJdHxI//rrz1L+hJOkXcdLpXf/ARo3tz/UIiJt37FN7r39VrmoXQfpcImzy5zg2L+PolI4uTIkKe6O9nw+SKPRNJi/2QAtsxoOknGMseTFZ8fKO2+/IVu2/S7VYZJn6B33SLWatRQUe/rxB6Vls1bS4txzHA0DCHfGjRgOoVKCDBg4WJ4d87SsW7tK/X/0uqqzdoUXJk6DrC4B92haLztIwABPPfmglC1VTnb99ae88+YiqYadz8+MeV7jUosrN6KYAMMkV4oTI+QYbgGEjCkQHhKHmDuiv6NpBrDC0JwREDDiObEJ7tInEbxwTtyy5VUIJ2SefwkaEoAk5aQtxiGFIavEcxendG7gL0RUmecHc+Ja7so1asQV3Bjwjgg8y8Nraj7ED/mSRQDe41kGBGwGVI1PnG2Tlzwo91aPT8kBbNmvMgBcb964Udre9z8IOllGaJIheKkTysoJMJXFeS+nPqjvi9CIQQKQyUZl8Ruvy/NTXka9fDLpxfFyVrPnHW0ypFWyVGl5bMRY6XfNldKly6WyeuUyWfL5x/L2+1+gjwDAZBqqBZLVX+JLTN6QGJ4bIKiNYygpKRGmKNNQFgrDAwBtnWehIDZBAMFiHNVSRR9MR17czMDo9FOkJl3RGNomTofVsGws3bzgjj2WMZa8bnlj79nz44cDZk5mH/Fi3HTsdFm2yitgrHfymGyzxTiwC5qDPv+/HdFHnVG//zvEkcn3wEp5+EITUMDsoBs/qNRplg+OW/oUw0jeLzNuGOE8QWCf8xXToG8xTQqh1beY3SUOsgAAQABJREFUGy2MMFxODrOF7f3KdKzcIK9o2rBM8SIyecpUufPeYVw0ZcrUaVK5SmWpUqmKmissqNxgwYIFQqDq2Wefleeff15BMqdNsdaULi0jRoyQ7t27S7du3WTNmjUKnhFkonYWwzEfnpv8TFzDT3Of2r80f9ihQwddT839MmXKSNmysEbgEkE4ky5v9e7dWwYNGiQ//vhj5n2CcIzTvHlzmTRpkgJ3DDt9xnSpUqWK/mjakelwfWJYmmScPHmy3HnnnXqPgNspp5wip512GjSx9+g6k0NXZbKWLAcsBywHLAcsBywHLAcsB/5hDigw9g+XwWZvOWA5cFg54CALPggOlKi+ATKaG46mjt7SP5AV4gOPAkLnHnyFO4QP0pyoYPtEc4rp3COApRox+KDkjt94YTBFHSRTDCdW1t/48qvAGHVIgBD0668+kaZnNpMrrrhagumpMumF56Vr+3by8VcAH+Cs4vOPlsAnwDdy9jnny9C7h8mvv/wkt905VPYGozKgf18yIvMD3ORIk4uU8U8Y+5w8/ODdMvimgdKz1zWSBoHsxo0bJBXCGQ/CRMN7dDcyDPioJpkPO3nDIQhtIdT54fv10qXDhXLhxQ4oRuEZ654pPDeZHY7jfpL7ggvKtMXdbsOixPNai5dzt4gpeVDCCeXQfgm6UxsKGm408gIsptMQEHdTK3HHt2cbfmAy+KIONJwnR+1fCt3SMXAeh5m4aRNflFuH3i+V4LNo9owpcvGF58nC95ZAKFJRPnj3PSlVqow0a91K25v+ZT5b8gVMbhaSG9BfzzzzLJlZaqYULVYCO5a7qkCFmlp+atKBHQQvKAyMpU8/Xyor4Qfk7LNbSL8bB0ux4qWEmlM0hch+6DgYR69zQQJG51QAGS4EiTgiXS9AEHR5BQC8GI9egFte+qTBvQz4xmIzUGGMgh8v1Cp1DETS0Z4ihdGfI1CKSoAwkW1LzSMCE0yfUwbT/XLl77JkzY9qis+LcpGMJk4gc4LR2/v9Cbn9AyXSZ2YHPscVqRC8wTSoUV7qNKgpAQg7CXaQwjhXQr9a9u0OWbtyPeYWjsVkvW18i6UalQQn9GH/S01UkgHI4LlNap5aUZrULa+88vqcfu+BDxoNF4XGHfnGC7QfNRqYRASqdx7wIorxoTEcdmgbUTuRcweFfylkUCFfpm+cBIRjO6SAfzxmqupBc43kcZHCAOKdWOFEmQATUqfWrCv169WHdiPakSCRSzoczYV79LDB8cCP9J8dBz+KhYtIuwvayd4dO+XOWwZJyuhRUgQbDsLpIQWfLmvfXhbBNNbAa/rIH9gccOutt0n1qlWQRAQmr6DJi75H8MoIMuOyA2jnrDgRdz1TgTjAKmoDpyMPmlQk0ZQiic8JFvgBPvMer0k0qUkgg8xU86B4FkYZotDso+awH0CDlgM8IIv8aCc101VATT7NxP45DjngrmNuzc00hF52xHjhjFGnX+uEcMRy+vsTxsjWTDOnaTCUNQ1wsQGr1U8QjtBZ0msz3/IOKYIJhQB6GIvV62NuwLrgaEBLCI65QFGA5tQCDcF84svPDMIcBO0e3Pfz/QN+UH30qZUnGb7nHCjqrrsRLqrQYsssn6vRluM7Vc5J5XjXaFwb32pmnXRmv6zeYPIhfMolmL7RWPL8eiX9uPmgFt716p4yevRouePeezXSzEnTpPvlV8mSJUv0PSXHwsXcJGD09NNPS8mSJeWSSy4RAkkDBw7UzRCxWridL+0s8zrPk6uuukpNH95+++3q2ysWvOK5odzWicTERDn55JNlFLTQqlSpIk0aN9F2Zlz9YR1QLeCYtJjmt99+i80VqQpixebBOL169ZKnnnpKwS4+I9h15ZVXyqeffqpp8h7ryXWiR48emvddd93F2zJjxgytE/nF56bOsXXRgPbPAXGAm1oOivLr+AeVqI1kOWA5YDlgOWA5YDnwb+GA8yXxb6mNrYflgOXAfhzIAKqjO+TxYcAP5CzBjRvU/WCATFbJfGDn9gUddQXR+2VUwBsExe647TYZ8fRTORQGiQAwUHLlTc0uGZFnysZvBH1I9L/hVnzUQ5iMevKD9bwLLpYqFUrKcjjLPgPOwikcLQezKOMnTRG/G2br9j9kAgS8BMb40Uotg3giSDFmxGNyy233ypChN6kQNwybPT7s3k7FLBqEgDaQAEADwlg6W1ctM2iy0dfBkiWfSL8eV0gfgBiDb7mTBVPB7REBxeILnsd15gd6lszBkbHwmn2CAmWVrLvXTCs2LK9zIOyhlUJJp0DInATBNIX3Lj9NWm4HNIIbmlzc/df3UrIEwrlmgXJI9qi5RbOaAQre0FemQENm8O3D5Oo+/XEvqj6TWjc/Q54f+7Q88fRI3bUeX3D2L2o8ERhoBrNCJ1aoJCUAbnW89HIITwCLxMhZ0b3Ud1dsGhQMVapcRSZOn6PNw76uFNdtjYAujDY4/9I7AXI6AbxAxyio8QN0CYYA20Az5qKuw7BjniZFacjRESC+9dowTTYR/ZpajwkQPLUbPBpwiANUGMCKfKAGXBDogpq5g+ZT93PPkNmLVupY8rrAhKkWgbi8KJRpftMRgJh8DDCWHN0roQ4NFBgzVY9Nj/PYstWbZPb8pVqeMBA5mqn0o/7s84dqKis2r5zOTT2pzUCKwsRV53ZeaVynvHS5nX5MEuXVp3qhvzivXx1uehb9xC/TRvTJsb+c1+l/TjauypjHlbSyH7A+bFtSgiv4TUDbvjcPbYcxzL7hTu9OGvhr/CvS99bT416QIYMHysUtzpRAkWRp1LCxCjG7X9dPATJj+jUzMk6Yp/qXQz5zXp4hl3a9Uh9f2uUqGTb0Vnll2mS5GiAYQSYS03j48aelbvWaqt04+NahmZqQIdo9cwFQDfwP/PnkvXelYkmYvCPay7kKSPRF7TvJ8xMn6/z9DxTJZmk5YDlwCBxQjU/MU8WAcYUBfCm5C6sXgBjnzhCBby51WB/MnH0IWf6ronKOJwA0dOhQWbp0qVDr6quvvlK/YgR6CkJ8x5g9e7ZcfvnlumZ06dJFNbMmTZ6kWlomDa4TTz75pAJT1apVEwMs5QaAmXg5HV966SVN+6yzzpJC2JzRCO/8XZHvwBtvzFxmYtNNT09Xn2RNmzaViy66KDNJrlncuEYttltuuQWb6ZZJiRIl1F/ZQvikJDBGIp9IDM963obvmnh+ffbZZ/otwu8RUmz+esP+sRywHLAcsBywHLAcsBywHPjHOWCBsX+8CWwBLAcONweyi4sT4EOBYAVtxVSq0QAaCNkl6B4KBiCkN2bYjGYHJAY5FiwEocKhkB++ZOpCY0AlthSeolzZyKgYuTeNgDVbmJiLWKfqO3fukMcefkh++eEHSQ+lOqHgS2bT779LY+RFM2q1a1YTQDYANqjLEZXGTc6UMcMfgrA2TQGL+P2IoWBYVqxYKn9s3yKdL7uEm4BBELDjw59heemBVgM1O8LpEcpVFRgLh9JlxZefyeLX58hT48ZLh4svgjA8Q9L30+hiekeSyF/0CRfwM+0bVhAKGhOohTap6TYqgGcbI15mW/MaP30Wz6HsZacuSTRQCTeToaFBEzKMS/mT0+98RBmVnGvHF9tOlIPaMg4o4wY4Kg/0GUZa9tU38ueOPXIJTPckASsKA2Aii2vUaSDfr/8OmlfQ4HLBgdiK+IHIRiGEh6EnaBSCpVFoBsGcJHTQcI1G8GTfsU4wN5b8AFxq1qqGNNBf/RzbpuGcdnFlNQqCM56ajoNWUiDqmP6JhguB0+ir8NPkB3jrh2ZSMJgOrTDkg7bxAHgieB1AGB2jaC4PQJQM7LT3QDPJq/vroSnmKarF8vkyJAP+XqIYZwkQOmYQgPMkiR87973Yge91NcQirpOxjEjebRzAXOSQoyEWdcFSA9iFvEUx5hz/IBrOAFBu/6J/sTDyTYXUk+CjB2pQ7HFeaLyxPtFDHH/5RSfwSPK6GlohaCRFAIbxrhd84nBwer4Gw2jBPJQ5JhgGQjk2KfmE/pPg2asBI9QsAwUxLtmvwp5Ceu2Fbhgp6kNn4lHbC1VFJux+6JV63/yhNgWBez5rfc65svzb7+STDz+UlatWyOJFC2XokNvli2Ur1fQVzbYRWI2nMMD/5ctXy4qVa2XUhEnKZ38STLy16yivznpZrujRFxoY6C8Kivph/vMJKVW2pHz343pZ+vXX8MnSCH2DwN7+acfndaSvW7Y+W6a8/BraK4Qx62x2YAsFuMEC15YsBywH/kkOZJ+/8iuJajdjfkvAOkV3W9Af0yjGFVki1lNOS0l4ptMT3mkwJWJN4YYRhD3OhzyBG4I4ycnJ0h7avgSb6I/rggsuUHAoP/6b519jniegRPOJJPrt6tixo5ob7NWzV+a7Ec0WPvLII2q+keYMGa9x48YmmXyPBKcM2NS2bVvZsGGD+vv6EmbVFy5cKIMGD5Zly5fLCy+8kBmOidL0IjXZdu/eLR9//HG2fJgef0lJSWqakX7SCIyde+65MP9YSgExAn/8mbDkEetHfhUtWjSTX3xOkI1hLVkOWA5YDlgOWA5YDlgOWA4cnRywwNjR2S62VJYDh40DBMUoPOd3GT8it363NHvaEARQPunKlVV4nD1A9qvQIc4afpg3G3rHrfL++5DM5iB0zZ6bc0XrGV78IYAXT/oMwndUTbq0u1DKnVxRevbpJ3UbNpBEfNg2qV0TmjEhfc64heG8nkQTXdQ2C9AUFwCGYEqaeArjWdz3Kz9qd2zbCiZlyEkVKmhcal3Q9FzKvlQJIz0o1WQjmrWjbJtmvgDByYZfN7hmXFj+uAyyxfx3XKxdPx/ASnUgFMtQXQASAAZyJfoCCe+QcHAX+JXlCyLX8P/wA46jEMzJ7d69U0tSokRp3TFM0270P0PBHIX+6oOGnROkO9jdHcNm53AGdyW7ghUfwDYfNIj8iJcBDaNYigcPHEEMACiUweeCQk549jiw2hXmR11BDEaZ5s8OScFfFGMlDPA5gnyYtoJrBOfwLwJUxQh7wtAMZV40ecl77MvzRvaFkEezcUz/MW3N07mnVUSG0+evUFN3BAtNfTPHLhGZPMhVjEIIN5wbnuZeHXLqmUcSeT4yu7zzDJTHQ84zeZFJ3wCW5CHN+UGpThaMvkHN9jE+24D0+jOD9fgXEcwCEEExNQ9IGS4KE89O5kfifRX8xqXJvqhaj+hz7CIEvpq1/A9+raTvgEEy7J475cWxo2TkiNHiB9DJ8LHE9mR/mD1zkiQk+eWe225GWwN8BF+2bvld1sM01YZff5WqVStr/1ny8RJ5acLzMmfRe/LC+NFyc/8+8s6SpZKIAtIkHE0p/pNEs6hcJ/yeRBXWsnk9uMdxRG1rv7uh4J8so83bcsByoOAc4FpMLedzB4+VmSMHaESzrgbwCgbMG+aDQ9JpyHhojXll8VPQkIUNWm6gsMT3BL5PR6UnzCle1f0qBbVoovBAaOrUqZoONa64PnDd2Lx5s6xfv142wAR5lSpVNLlPP/lUCDy9+eab8A88TjW+lgPIMutYfnnynSvWTCHjnX322fobMmSIanzRJCTzIPE5y0INr7Vr18pnn36moJx5T4nNl/cMD2iqcfSo0coXphNyFy6G5zcC6ZprrtF0aSkill+xaWpA+8dywHLAcsBywHLAcsBywHLgqOLAIYq4j6q62MJYDlgOkAMeV9LoCvQyIFD2uQJ6fOdJeiC7AJYKGRT24ttVKdNSYnZ5qPNQ/+YjGY4JmdNpCGbNHnpylP5yek7/OSRjaq9V1zEK3BnTZPFxMuADhh/yW//YKWvw0T1j3kI56SQHwNq48WcFxSJqTpLCYi/8gv0GAQh2eobTce2T33//Dab/kiQRoBg/fjPiJLUUMLds2Vr8yUny2mtz5LLOlynY5Qn7pVBiMgSo2JMMsCEADRUftH0g7gVg4ZMEf6LUqttQHnl8uFzbvYuEUlPkpiHwP5ArX+NrdnReR7AHG9wDzgKNEIVFHE0W10uSfLJkpLy1+Ha5bdAaSU7YLb9sfVFCwR8hvKCjcgglXATWmMij3y1vZAekVfjJ0Q+MERQjWNCsWQsMGp+8+/5i6dCxs44hCtm3Q7OwWLFi6LPUHvTLlq1bJIQxGECHJpCxa+c2OblCRfQ1P/oaeAihCgUwEY5bDK0wfHyxP6tZwxyAYPUHiPT80OZhuBCdepFc4CjiMtYHLTSSF+m99+p94LEjvInChBTTvqDTEJSZmpPF5L25j0KDzBkffnf6iPgcTaUMePUiEONDf6fSVyriUGsuI0gQEK3uzh3cnc95BEWS5g1PkkIJzXGNXkIsmOQiXmZcOzf3/xt15y9fxEGbudN/5dotsn7NZg3sWoTcP6J7h+k7MxwyRp4e/OpWP0nq1j5d6x2M7sk1bkEeBPKRntLfGwniOj36ATZWrlhK0tA+HmjlUSmQBKhRj3+587HrSQzabhDqIgmv6jD45K3Xhms4j9sX0pFs2053SgLrgbnk3Tn/0+deVxMvFIhKKnLPVH1wNdc0EP5QG9DLhgLFaoPRrwxNQlWrVcfxzYX8vNACjCcVAiL5ea/Mlku7XCnNz2qSLciTjz8hs6ZOlLvuvhvC6QwZOKCnXN27j5zduoXUq1tDWjRtIg8/cL888L//ad9gH4kgX4K2OvW7/DGJso9yfPixEYFEYJDjiH1S+y/MMXoxhlgvbgExz9nHTXiG5SVZmIh0glwzMHYNiOlTIAybJ9C2jr9I8g/D211D9cL+OS44wH5ECqHvxvrbM5XnHE/QheOgIOaQ2VcZlmPLkIlLgDoB/ZHDkflxXWF45styGJNuLBLHqg/q6LFj1qQXf2RcbgbiJg2TDtNl+tzUww0ZJi8Tl9cMa+rE8CSWleNSxwxOTF1MOU18XvPHtcGE5zNesx7x4U28/Y6ZOyOcdgCrUVbUH5q4TJfzmh9tQOLwxKtddiKPUPQwNz5hgnQV1qWQO3+qYjTWMb6HcG8CfWopX7Knclxfmb7auk1r1RZLSUlRzSkyhSCPmTdzYhLnar6XvPzyy9K9e3dpAXPRsfTwww+rFtlDDz6ENg1Ln759pE+fPnLeeedJkyZNpEaNGurT67HHHtNozCuv/AiKmTxNPgxvwKjatWvrbQJZBKz47IorrlDNtI8++gib3U7SeyxzbDzG571W8A9LDbC0tDRpB3/FJgzBPkOGXwTkGJb+1DrAkgCJ4bgOWrIcsBywHLAcsBywHLAcsBw4ejmQ9WZ39JbRlsxywHLAciBXDtCUIoGvokWKQlDikTcXzpPefQfqh+yQG69XrTQVukKWQkHU/336f/L24jfkovPPly2bN8nYEU9KB/p3wkdwPCjGTKnZkQTQrF37S2T4Iw/ImWc0kIqVT1VB69oVa6RO/doq1GJYfjQbn2csEwUuZ57VQmbOWyA94OsgZd9euet/zgc/P6b5/FijsArMISxz4QeVwTtyKln89gD58POXsMMYtYJpsqTCZ0q1ys1k284Z8seuSRBE7YZfNscEn6k6BY3pwRTZu+8nKVEMWmaU9h/FZHajFylWWFqde66MfeZJafGfNnAyX1wWATj99P/el1cWva9tW7lyFVk0f570u3GIlCxeVObNnCrLl30t5cqfpD65AhCK0jn9T99/D+AUgsxkBwzKq/oU8gcg9GPfYX/L7EMuQ6OUIoIgRtSjERAZflPcSUDB+VGvyQENGNjR6nIbU2NjzAAITIBKJIVPHW99UeaN7aOacV1um+CkAZ9g80ZcC7BF5NJbJyHjsLw9qrecUaMsniMvBx9yU8NjB3PIvI4/MQpzsNDoEFgy/uWl8v26LQ4YgvIeKNWpXkauvayGRose4ltPQj7lpylHkjlSV3I3MEoKvilUZsv8k8R+YITbNw3oLb3gH68efIth4pJNMEP10riRUrl6VUlMNoB39tJyc8G8+TNl566dcsfdD8rJJ5XOFuD7H36F77HpMuy+++S2m26AgNIr/3vsSZgJDUmx4iXkvkeGy43It/MlHaU+fMBQu5bEeZPgVbzaBudJlpcCcJY9BF92f+7egY0N9J0JoBGS86JFCktSoaIKKHB8mLGhIBnCkRiOgnQV8Gt6BE09kpqSKn/8sQ3dFs8pHHXHTzGMS9Y1c3xpKvbP8cABZ1ODMTnqDmi34pzTzPgpSN9wxrwTmedMmz++izC+AdliQTg+4zRl0meeJNzOlzRdRCAoRtK6ADwgqXaNLs56qdA9xxdBKxJD8Zzl5Dmf8UciwMU5LOICeKZsseE5h8SHN4CfCa+JHcAf4FxK1PIiYXQ7J/jLOSGeCPzpHICxPPep67GNh8B6WDrdOg5zDSJgw8WCkXgvBC0cfT3S4HsY1jmwgHMRtb2PZ+I7Bcm8N6xbt07PDSBknufGI86hr7zyiuzcuVOGDRsmlSpVyhb0e7zrTJ8+XQiQDbx+oM65w58cjneFqBQvXlxGjhypgBo1uurXr69xWZa88mWepF69esmAAQPkzDPP1OuffvxJqC12+umn66Y33qTfMAJiCxYsUHOR27dvV+CKPsmYP99zmF8smEUtN95nGTiGSHyu1+59vYk/3333nZ6a8ppw5rk9Wg5YDlgOWA5YDlgOWA5YDhx9HNC3SfMCd/QVz5bIcsBy4LBxgN9zqlXCD9+YH2eBWDUOygVUNhATJja8PjSBDuaI9A8jUZhK4KsQfNzcdfc9+N0hp1csK7WrniSNmp6FHa8J4oN/IW4YpsDkvHPbysPDhgKwOVEa1a8FwW55efChhyEcgTDISF9iysePZP6eePJpqVmjmjRtVktqnnaiVCtTQkY+/j9JRLpJ2JYcDaeiHBm6I5vCmUSAdNgrinIlSMP6Z8jMua/JzCkTZdhdt6rwiMInI5CKye6oP/UBCKE8iib5sB8WzN8H32p/yPSXr5QPPwUoxrvoOt4wIQFoGaGBypfuLqdVGAvBwzkSSgPiAdQARsogaGMaED7jX0ZwE8614+F49BJaGpqBUSmEne1jIHTxo53rn1pO6lQpL4P6Xgth0F3SolF98CEo9z1wv0rvGp1+stSpXE7mv/qKtDm7FTypQdsKoBjppptvlvR9f0rlCmWkTJFC8tfuP1UIww3JFMbEUwLy9cJHGeQy2u/JP+fnhqSGEH5hlIA/uhTkL839cQrgL4xoBG8y4Jsq6BRFEwjiPn/ecJL+PBgTqkXqS4O2017kC0ACgqiE0A74admNRPawF2ibS0aqBCBcLIIbDKNjh0f8qLXKH3f75/VLQH30h7AJ+FEOReEu/XP5XH9jbk1zPwCNYx2pLeDxpqJXpQI0cX4BCLcO5adzJefLXH4JKKfzg58b5gWhrD89iJ3jAfCDvtvgzw0/025Z12xrCOXcpPU5xlgIGn78sY3486PdfRnJmDuwIYA/7U2JeMZ2pLqf6Q/ukUik/thh4A+O7UfeoGybN22SDm3PlcplS0ilk8tK49o1IBCPytTps5W3FLLHE4Hs6VMnS+NGDaRShf01PHtdfa1s/v03eQYbDqZOniijxjwHzVH4YEQfgFhdLrv0Ermw7TkysP81Oiezj1DjS7Vb9s8OJig5o7iEsEs+/j+pVaWi1KxWRWpUrSx1cezd62qkwR3/nEEwPoHSss9Q6O0DEMD0k6HeGKW2J+dd3OdawP741ZJPpc6plaRW9VOlRpUKUhNp16tZVYWe1FKjWcXYnymKPf47OWCAHdYuFtQytWW/Yhg+47yWHzGsCU+QiMSxR00xBaCQjrnPZ7zHZ7HvBhTGa//OYTwyTiwRYCMIxfhm/HLM80cy5TFHhjPhqWXG/M0zUz5zj+Vi+qZsIZcPBxI+tqw5nzvzIOdC/jBMVbM0IQ0aP/hBp19/OcfNuhvGewpnQwJpaC2dJ8PQ6Nc1Efd17dPgThtyafHo+3FWGsfjGeUB9AdmQB/OzyQezfuIbiBAuNyI/rzoJ6xixYrOOwDi6rsAjj179oSVht/l8ccfV/OGDJtcKFmfM8+uXbuqb7MePXooaMZ4vG/KkVuevL8J6xm1tghyccPR6VVPVy2xV2a/khlt3rx5sm3bNgXPWL4TTjgBFiZOkpvxHkYy+RmNMFNXA75xHJLII1MmaqIZYllNecPumKMlCkPmuT06fDpQPhg+2qPlgOWA5YDlgOWA5YDlwOHkAMQszovg4UzUpmU5YDlgOfB3cYAfnwTGSAMH3ya9+t4k33z5hTQ/+z8QwPvk1tvvwW5RaCWlw4QQQIxy5SvI1NmvQ0tnPfxKJMqplU9R04chmFZM8GZ94JryEywLhoJStuwJMm3mfNkTSpGvP/9Uqp3WEB/VZRQIo7B14rRXtBzud7NMnPEKzCnCd1l6ugqSateuK9/+9LsKa5g2BWzHImHPLMRM4Dck+NQ6SkvZIjNnd5Tvf1oDn0OODB7sAF8gaaLsxJGrQPhRTU4p9JgUSfo/+TNlsuz9c534EgvrLm1yIjX112OCHRSqk0IZITn55FPkrfeXyB/bt8oP330vTc5qiYoDHIK5UFKp0qXl06+/hV+NtVK6dBn4sjgBbAtqn+FzamjVrFkHWnbfwM8aeEcwCEAA5achmIhj3yOPY2nK7Pl6ScFMWlo6BJVZQpfYcIfrPBCg+S22J01YOeOMglwVCuckHGN5URcCe0EgakZ8ZnyEOdzLv3SuRUgI47LeUZQX4NExRZib/FB7oJAWSlGYa/7Z0lMIzmbjcc6idwQWMeWn9d/J5q3bVbu1KMYwhy7ZbEwuxpaYJqlmvPK6I0BHQCMcNGFOP+1U2bEzDcCrV2686XYAUwS9g5qgo4Hlk0nT56igPw2AYSKcr42fOB39HfNkDukZ4SKSk3EvTpXnps5QQI3+61gHEjXDKEQfP2UG5mqCjg4Zc4rduveSy6+6Rm9SEMf52uf3aPjx02dBm41ak05aGW6fhkFLNXVq8jBpgitZp/bsX8cBtvfqFcvl9y2bpEbtOlIRc3wsbdmyVdas/kbKliknNetiA4Q7J8aGiT8nsMQ584N33pQKJ1WSajDvRqCKcyqGn3zywWKsA/XUh6mCUy74tG71atmMcrQ+93z5dvUK1WhvSO3OPIhgVVIi4Hb4TP3wg3dk547tUrRwUalbr5GcUqUy5iICwxFZuWq5rFuzWsdvvXr1pWadepmgOcv2ww/rZPNvG6RurfpSHgACiUODoNj2LVtkzZrlUrxUGWl6ZjOtxw94n9q08RepV7sh/LxWyDV8k0aONo8GKMAfDFOMRJHzr7gF+ePtw5cg7815QkdhEPUwZhVNUtT8100R4O2lQybInJF9FMyb93QfDcLRa5bUdoMmwMQx2uXZAZgPnBRcBVaT3HF3nDt3LrTZg5nzOnluiODQnDlzsj03z8yR4d9Y9IbO77wXG5/X1atXx7s4zJgjrTvuuEPzMWEMKEWNM25cIBDHcK+++qr69GK4+PWGaZL47N1339Vz+g7binHaomULBcbMc8alSUSGNUCXRoj5wzCsI8Mwf17zZ8rCoCwPifcY1qxRetP9w/g08xj/nGlZshywHLAcsBywHLAcsBywHDi6OOB+ChzeQmWJDZwz/RCBeCxS0BdCfNXg9ReFMi+Q/HSMpawcYu/ac8sBywFywBFeC3wyKOmBY4njCLvfs75zneccbQgTxoccyYw692EOh/0SyCHMwd/yu5JzarkoUWCJ8wBVXECRTB8UegnBKzQBUCQKXVnnJNSn1Tln625pGDbEBzo+hl1NAfovC0JwFEHY02vU1AQcTQZEjQLEgopJ2JMdaAilZ2B3aDKe4SMZH+l+X5I0bX6OCsQoJErFj8IbOMFRqXcIwhqSFyhROkEU7FwmU80ua3OksAxbszXs3/qHzcfyqo07h6dgWRZRE4zPKXoyO6j12gkCMX9m2J3bVsikVy6R7ds2wtQSgqM6qgSm0RFJ4zlm+zyq7eOV0iVaSOnk2rIlMEX2/PWGpAagZZcB3kd/QyzCB6bhTTZuGbX/asIxwhHyGpmiDTzQM2I94i0hEWBhMXj04Re/mphcCnrkDnQlgB1BbmdHpiVLlwUoRu0ZpO5LzAQ/ObDgZkxq1q6lURjaG0APRX8Moa9x9zH7i5dSOfo6gSBlH4T01EIj/9HlNAyPxqSU3/W9RV77vQTgstcok3vZb4OrWTfYBxPYthgPCbBdWARsTzdsVjEkU3VuhNH/HS2EBJn3dH/EwX1EnT32dq0TATzOHeFoSOaN7guBE3TlaG8QvAkAFAm6nSsDJhZJATMv6VVOf1h3RHcnKiShaaUiXw9AJh/yyovYxtRH9EBrjv7rhH6yosk4J69wijF8JAl6bZo8y0BKg3ZfKsoUyEB7A/iBrqDOI1Gcq9YSikfyub7AIuAfWQSxGvoGehv5zef6F22O/kJNNC8AVgrZoIenT7xmrMaxh3OWA8Jn1ZvXBAB4JFeqVjtNf16dW8lntj/KAC1apxZu5jywbAgQxfyVE7jPaYU85jPV2EO5vIGsOZXFU+yUeVMzhj0NE2iiH4gcCJ6W9Jj5B92BTZaBuZLhOCjYQxzrcKayWQAs52pzl0khuM7fJj0KM1EC1MsNxfGnWTrXiRho7GJRjAuOE/a5WMqpzrHP7fmxzQHOq2OeelLmLVgozf/TSubOm6/jzBGQwyzoPffI3LmzpHWLljJr3iJ0MGeEsK/HkgH2Axxn2ETx8bvvS/cunaVq1arywRcrkSY0SxVU88qsyZOFJube/+wbAMXOuNq+40/p3PEi6Tegj1z034tk5PDhsmvHDpmLcuVF0HGUtxYulEGI54cmS/XTq8uOXdtl3fpvpXuP3vLEMyPlP03qyYYNG6BxWR2bF0LyLQCyq3pdJ8NHPaugGTXDhj/8P5gBniut27SWV15fDPDLGR/0E/rwA8PklekT5by2F8rUuQv1XejJBxH+tVel5X/aAHB/F28JGGd0MInwj9x/j8yeMV1aI/zL817Lq/gYb+6ECP6QyF76AEzLKKLtEAz7sKZgXUn7CxtxMK9EnXmdWuqkMN5fOH4xi6nmG7XRnWsEZRi0E2cL/cqExm0GbPYiScnA+wkgEIbWdHL7o5uCcnvo3ufGFg82hXgxkXsjabqG+7EOQfEX8xvnn4OnRE5WaIuoH5rE2KQQxsYVh5w51vQ793UZFq2haYdNNHx/5vQe3f8DIHthgtgg5mpAKYjjsoPrMf9xquS6jkJgbuWmNLe93FQ8mj7HhBPRgzUkO4H3WmSCTnxirk148I73M1/knHAJCcyHYUx9cark8JObIKLQ1uaxVq0a+sv+nFdOXiZ/53nOfxnGlI/xnDXC5OXEMelwkwX5QXLu6Sn+OHWKfW6e2OOxyQF6WA6jj3nxEqPgKTcasPnR1PoaEd89j81q2lJbDlgOWA5YDlgOHHccyP5Ge9xV31bYcsBy4FjnQLwzerOD2/jsIPjAb/UwhMxFixZTPwLOM+dDlmaACFJxJ+nA664GQON8zMbyhRpAz46fKH6YRaSvDUP85I/97GfUWPNKCn65gU04FdbjHgWs+2sjmJSPoqP7zR+FoEo1MBy2yY8/zJXZcy5ToT/lKPpRiGI7YsLs5Xc0N/iEkZFgcikpnzxIShZtKxt2TZTdqZ9ImmeTpMMHWaK/jBt5/3bITJXJGAlQ5s1j4yQEUMz0C+7MZx8wfiuCAKF6de+SrSLsv3yeiOOYF6ZIoeTkbM8P9ILCfub57qtPqrDHbU5NxuQVmybHCvu0AXQpDCM5/sgIzkG3Bn05ATv5qU0Xi3tlFxLFpnr8nKt/Q0gkya/43eJeRTsdfkLWUiBCN5AP5t+nckPi6tDFypMKAuTEzkMZAGaDaUG5YcC18P2XpX1lMiFU9uxL0zCXFsZ4hcaGIzE0jzP7Mm8w3YLknxVZ5JqrL8u85IkPglWWIwBB6VhojBWBmawjSWYsMo8DLv+RLJhN+2/jAIH+GjVryeeffAxfSduhLX6iggBBaDi+v3gRtKjqqOYVAZswkbR8yIc5csbUidKieUtZsuwL+ebrL+Ws5k11bBAcf3zEWPlPswby4LA75P6HYNYZ7yOD+/aS8jDxdvud9+v8SmCKgtj8iHPM7TcNkJatz5VnJ04DMINNiYj3119/yo/frde5vNtVPQCE9YeZaYBNGFezZ06Wm28cIBd3vETOO/+/0LoM6bxeDz6evvz8M9WILgYNOa5b1Hx7582F8P/UUMEyjhFqqVGbuE69BvLlF9Cg/mO7nFCmpM531Ip7503wrEHDzDUjvzoU7Ll5o8oemmsqteKQGfhLnjnPzXuiP4Hvg44kW4FvNwBBMWqbJkHLPy8yG8hyDQMpOdc9tgPnPufotB26AQDDnMuda3rxD/AuwMk/A+rH2h5mG46rBmdwL7OuFwYoRi04taqAtYP1zJMQHokD/cp5QYrA5y43KXS7vJuWIT4tbuSYNWsW3lf4PgCtd2cHQ3ywg77u1KlDtrjkL3nB48zpM6Qw+rQly4EjxQEMvUxin7NkOWA5YDlgOWA5YDnw7+BAloT331EfWwvLAcsBy4FsHAiGoIOADxgKEcZPhqBIP2YcsYECARACEBwLQCDSsTOEsqqFky0JveDua1LIBQb04iD+cEf3sUCUHZFVkIOo1pOjAcOLJFm6ZKTMf/cmrYZulsWZwalYO2xmxi5san+Rz0bA4mqNMCAERgINnsSEelK1wmOyJ/FV2bLrZUj5tyOyAcZcgaP6MkMyJh1KfqI7EB85eUvywTFF7IfsbySexwMHl1/RPVt9qEUWRh+mXzP20UMlk2cQAkJXluYmCT9WyIcKVqSEMNuPwEsi8sZ1Rop0GTxC9qD9HXLHEPy2EN8JUlAHkMQLsOSj5wcAIIOgEd3FF03T4J6o2/5H+q2DHRD9jr0OclE16RcGuOLoRiksq+U5Un8i7kBIgAYdKQhNRuicShKY3fHmZyUd2oFzn+mDOxwHUek8+DkNN2PkYPiy0tM8/6THAFHaFO4wyTPSATyE/p8kJPql46WXaqx4YTwgUmggJAKscubVA0i6QEG7dLsyW7gwbYyC2P8TYzTPsgXKduH0y2y37IXlQAE5YISdpcvC516xYgC0pqiJZs6bs6a9JJUqVoJJwtMkZc9uzOPoa2Z5yyP9v/b8JW8tmi+TZsyFOtpImTzxRWneopmrTxKR0gCRHh0+Uvpde5Vc0uUKmGpcLl989rEsevdjR/Md6yXfW7gWQB3TyUm1O/fPNAWg9ubffpPzLjhfNVTpb4/gdemSRaVUk0bQGgrBxOktWEtgbhGAsx+mTLv36Cm33zJIfv3lF12bOI8TyC9VtpzUK1JUpk6aKINvv0vXqpenTZUKMK14GjTfdu3areXLFj65qNbvjqFDAZyF5eXpU+TkSicjfDXZuWvX/gWOuxNyq5ecAT+ZHMr4cU2K+BwN2RDWEfpahFdC8IKLiRMh5HdAfG8EXsgA7vtQv/mjrnV8aWJd63T7807dANy8PmqAap3NG3cdJ2j40sSchmkmCdYC6AI1L3I0onIPwfmS701eWAqgl1lO1zQBjLdMbLpivEObn6jdpnlgs1aIq5qrCU1tNFKQOydALhuhsQxgFGuz+gnVJ3n/iQK9o5YsVy+mocrrOOLNQCMmoF7U0et+ZXf0EfDZzU8fMhyA0wCtJKBtqAi/Hykoud/dgt3AO0mvnr2yhaXWnKMtBl74sGnCjI9soeyF5cDh4QDfKaN4t/PAbyvXCp47hrIPT/o2FcsBywHLAcsBywHLgX+GAzm9tv4zJbG5Wg5YDlgOHAEO0CwQNwUbzbBYIIJAAJ9zl3EAJow6XXZFHFhA2bVKMyDocHZeR2O3DB5MeQ9NLnIwOR5yHAOO0QDZ/y2+VT78v6chRco5WZddWQ8d9sVcxzBAeemRomUuE19yVdkbXAEzPqUhMCyJXfOIkosaTRhqSRTeGOFPVuLH+Bkq1K6DA0iYmrD/JWD3Nvka23fN8wM9csc/tR5pApEmkQiOkddBXOdEkAdnESV+ccTypas/NO7cBqgCAZoKAxkU9XF252cd46L/6y/ZR8mPnPB0FXTjOQUsBMVyYG+O/CFWlAicsQDKKjnGz+umCuAxLjt07KzDj/0jO0EghApxPnU2GWR/ekhX4FP7dtn7P9NzBLAHqH12SAWxkY9XDjhmz7A/AxPfpd17ygujh8ugm4fqWJg1a4pc2q27LP3yc+ztoA/I/DXG/Jhrp095QUqVKSvnXniRbNu9W+4aMlBS946S5CLQ/sW4pw++dh06y4UXvyw39OkpO7dtkZtvu0Nq16+LMZ6BOdoBW+JB6pzaqHDRZGiaVZSJ48fK6VVPl0YNmyA+zARiTHO+oVYv0yEoRvCIoMm6VasktC9FKlY+Vd+DqNVMovZPj2v6yNhnnlRgjPdmTnlJOnXpLqu+/oyXme9PPPdhAut+7XUyfvTTQmCMNHPKRIB94NnSr/W6oH+0BJw8cyECRLFLkwmWAZCLfsdYR85dxkcn5ytqMPnxnkcy8xo3dXy15g9Zs/ZXjRPEe15e5M1n9wJNCZPHK9ZtxbxP31Q0N+iT1eu3ywuzlx36pA2Tj0wvAz5Zf17zWeY2K68LCIW42MQQYC4AjBH5fd2PMnTIj3iDyxv547vwaXVawvpwBeVdAkxDkzJc5M2bTo13vjdUVBOR8cmRxy/OW6Hjg/EM0MxzUkH6sBMy579eb6VsD4x/L4KwL837Jtsze2E5cNg5gL6/cv1WTfZQ+/JhL5tN0HLAcsBywHLAcsBy4KA5YIGxg2adjWg5YDlwLHDAmNahcJXaTTSFY7R2jGCXRz4PY/epMcVo6uaH5ASPIAimOTTEzUcwYuLlftxP0px70H/iCeVCMfgHT7lje2+qX95++wb54qsXYc4PgkPIX5QvbnUcURrCIr7uqoRPC4jzEMipRGaSRGEIwgQYA/5CUrdKSvrXkhr8BeYs/5K/UjdIUmJlKV20PoRYlVU4pym4Nvoi8Ini9RQFkIBdycyb4FlcmZ0cj86/BLdMv4sFunjPue8yLLP4jjkohs3yF5X58IBPqD3AtPyBkNM0YB/by/FXBnbiGSnMXd8gD/3EgEL+wvLR+P7wIee0ZObGb/fEtK/KzxAFunAaDxCOHtXnDNP598GZbj2dg8+VuGa4oG4UqHKJJPT5JK+898INqizJkMak1eIXBoL/YcWZIU/Nn2DSFFg+hK8mqBGEmqPTXubpgR5N3+TgMsLj2DRUboy5kAJX08SO3y60OVQEjCA6Nk7seay5ztjzrDD7l19F2bAHxj6W2e+yItgzy4HDxgH2eWy7gNZzhlx22eXy4F1DZAWAo8JFi8uKFctkzquLZOmnH6q5PgJHxuepGX2mIAbkptbsnJdfkY4dO8E3YES6desm9902SF6dNV2uuPoaBAfQAPN1BK+GP/2M1KlRQypWrCw33XY7SoE5lFrF0PriWkvK6v9OjnhzcR6YvwC6Ro59Vm4dPFAuatNaCsHkae06DaRLl8vl6uv66xhl0DDWUeYZhmbPjQP6SROYdmx7bitsmMicWJB7BDy4TO4ZcqN8s3y5FIUG3aqVy2XG3DdlyJcfQ0OY5gcdH5RmPrsM+dw/dLB8swLhYbrahP/qqy+y1nJT1hyORbD+nN3+NtST2hgY86g7wQ+PrzA0wcKSHEiXC9vdhmtsaIIGETW4GG7x/BF6TKayEtouAt+O7W8eKwufvRF+uLyyePSNOp+lY64NYU3jqtR+0PO63l3dponMWPSJxg9Hctnx45Z1f59Z2SthgCDAmSgIOEjfiTgu/3aLgmU+VxM7e6yCX/ky9mKt9sG8J/x8ThwvHryzkqIGoTILsc6WXGecvlOsfE15YsQbeD9zwueWYxTgaadry8u27T/ouuTxFHfTd8QF6k8pt8gFuG/4U4CgOQaxYESObLE3/yYOFHHHr35/IM+ANxWappiD9cWIk3T8SvA3FcxmYzlgOWA5YDlgOWA5cEgccN50DykJG9lywHLAcuDo5QAFvbpTGIAAj7yOBSd4j8T79O0RT9meI0w4J2lxfKQ8rvfPIY/AR8EjxQHBog/fu0O++AKgWGGnUJD1KLk4SVZJKS/HM+64h0hGd03rQyO/IyjGNgEAtm/fV/JX2noI/yIQctHsUAIEYhmSFlov2/9YBaFYWSlZqAl2pVfH7vbSRFtg6fL4WrZMfyUPY4G0LIbbs6OZAxSkRl3VLhp4jGZKFp0BEYEGAIV9nIbMmDqa68OyqcCa8ygKTO0FKinwnKCYc+TmA2ecxgJlavLKzANuJY0ZLF46WjiAULNQP9U4cHwUMg9nc4Ib9aAOHE+WLAdy40AEuwRCAI24gaZQUgAgTCeZBgCicNGi0urcCyQpKQmAEDSvNFyW6dnc0lu1aqV8s2yZPDNmnPuO4ZG2/70YfpimSq8+fWCKEFpnLrgx/LEHAXiUlY2bNsjKZUul/hmNVfuIGmPU+okAICoIUTNt6bqf5ZMPP5SVAPUWL3pdbr/9Fvnyyy9k3PPP4T0HADbS5Fp6eYf2si9tn0ye85aj3eOu6yafRKim/rd9B5k9fZIUATh4znkXSIkS8E3mAv8mnDkmwuT0f9t3kldmTJJChYpKG/CsRIliTtomUB5H4Gw6zjMAhFPbjuCjF1peBLOogUWtZJpuDAP8IsjCTU48mjkpAo1oasaZuYj+tUgE1Uhe2AbkBif81zBepJUZFzzOD7jJ7znnF7YVcSn1N8uMOOfgwPnNbDzRwhzEH5ri1r7AioEXeGt1UnGntSzg1HnTJEhI0nKjzfkvP3LeM8B3rF3kFakg8fJL10mnIKHyCOOWJ48Q9pHlwBHjgHmf4ZxB4rjS8a7jyoJiR4zxNmHLAcsBywHLAcuBI8yB40vCeISZaZO3HLAcsBw45jngClggwqFsx/Edhu+9SPg3bl6HzyinhpCxKEExzCE3Hkzv42uRt/CA5n3UFwqE/264vcGNAMS+kLTg92omygvjewJQDM6pEAeC9QzH5FPYWwiCpF0wPfUedkh/LclJp0HQ1kCSYOLHIRQAoIL6GWGejhzIfWYP+XIAvliyk4NYZKjfFpjIcts36HcEa6nwb7YXzZTo3s+MqztlVe6nt4J0MAdKcqLhzGn4kM8V6pqOoKH+hX+iTkekThXJAzCnzYAX4QsGO/UhhKWpL5Jhowf8ppDF54OWgwpb4CclL3LbJzOIO+44dv4OIlAehZA6Uz5JZBwCX2p/UcDvaL9QqA1hNAXBKBQF+j4I4Z3nAL4gpKbwmlorNPHmQVyGodAajpEQx+GO0ZCk5grPmZbvEAe6BZf/jl7y78mjy5U9pH+vbtAkD8gjT4+G/z0Yi3WBGJqUA1yVZ2WnTX5REpMT5f677gGOgTGOTrxl++/y/bfrZS3Aq6qnV0R6Xvngo8/kpfEvyiuL3pLJz4+TG/r2kcWfLpWkmK+0nEAZwFuavzsNY1xyrcX4AUB0duuzpVWbVjLwxhvkrjuGyovPjZXRY8aKBxqsEWxC6XF5Z/lh3Wp5+6MlUqas8e2ZvTpRaEhc2fVKubpPP0kuVEiefPgJDmhMZQSnzCwWEwfz3FVXdJdre1+jIOITjz6BdT7dBVny5hVT4ftDwFsM9QgCvMMcgfqw3vR6xWMwnIz5IgHpYTMN8uJ49oF/AW8QgBnmUoQMYn7yI8yCkX1wDV0q8KLj0OehDQc9Lsyfr40cAP2qdHlt3LXiCyfInAXLJOQB4Alm0yRf3sRZKHeKYg4Lw6GXL90AUvCNBl6FEpz1NuJuGMg9hbyfBPGuxPTCshOTYRBTr1seU6w4FlOzn80UgaaZeMHDLOQsx4yiGfCHGUJi/lKqqRcJuHOxySfHWAW/mVMfLnhsrD05dLkDiW/DWg4cCgfSXKfKQbyvkfxSSDIwdxAww/QEcj90eGrJcsBywHLAcsBywHLgmOFAzCfXMVNmW1DLAcsBywHLgb+LA67AJQQEzPnwO9CMHUFTasoPsiflK0nN+AVSGgqLEmGiEcIiAGGx5FXTlRCFQ7hFc0mRMHx2RFMlJRXmFlOWSXLhGpLgq40d/KcgDdh0tIKSWPbZ86OMA+zdRnshCgFt1BVMZsonIemj9kZEzaUd/Z2Z/pCoJUZAK5bS0tJUEE6Ai8+CwZCEAAQkwEwcQTHHt1FEUlNheghaNyYc01BQjFoeEHITPCNQQK2xAB2pQQicnp4OcNwrfmijGA3e2LztueXAkeJAixYtpGiRolh/9slF7S+m5UOMYZj4w2JIH43pWKdyJTxaMG+OXALfpWc1baZ915MI8AWaPiOeeExmTH9Jht07TNLT0uWWG66Vq3r3k1at2kid2vWkVfOG8vgDd8v9Dz+CceGAQ7nmE/MgflyyrBxXNWrVxbKbpmOTfsWu791TVkAr7e3/WyInlD8JGlVpkgjwJhzKPgdx7mrRpg20xYrJvr175Lx2FysIrmB+3BzAYlArqvnZraUIeLZnD8JffLFqdRGgovZXfsQk33n1PpjypdY5Xg/AbwI753e+I5Pv784Zhp0bTkpUtnPeSwCkAUQLQZOKGmWcUwmisJwkgmIsG7XOGJ63IwhLDLFBrfIAys5QoF4RfSfpnP/ms7Ej6poqXLt6k6xa9zvmL0ebrWbVE6RBzROx/8fZMJFz4vnfDXgwr6LSXtkuFUsMkkDUAdwyjKAedSRFXCew0dAe5f+cDzbIdVdfChAx789+P4Cx0+o3lGCgFPq3X9K5oQNkfJjpxSH8OXRgzGnPQyiCjWo5cNAc8ABQJ6U7lsYlEPTImbVPwpziWAbITZP2oDO0ES0HLAcsBywHLAcsB/4WDuT9hvy3FMFmYjlgOXC8cyB2Fz/NGVJoQeFFBDtvKZihYISaFyTcPiTKFEgfZCquD/KDjP1PRKMwKkuYQEEQCXux8ZfaGTkLqxhOec0/PHcFL2wLJbchTGxznxpk5LEHO7q9EOL8lbIJZpC+lt2pa1VDLADn9FHusIQJKUdI4iRo+BqJBpx8XQ2jCARgHvSJME7oTyQ1uE72ZayBL7IyMHVVR5KLVpdEfzkUyS2QWzx7yI8DzniKD+VTDT/If10hps/17ZYIYWoyojienhgrriO4CflcwWFEVQcpR3bbBe16eIjl5s9NF1oFJPqiIVGgqmPcAwGG1gX5oh9G3fL43fpo4IP6U7B+FnVVK2nB640xNzial8gvBdEpu0yIQosCGg7pGImc7+jZhsdOvWByzS0Xx1TUDAyjmulUEz7fnEABVxDqgUYFiRoT2cmk5h5djQiCW6RYs4W8zk9w6cX4ZVwTT+cPlIk+6iCXRnyaVqQ2SVgSAHwbM3AUGDHtJJinIxBG4lxPwIzCfK4BBNcIpFGbzIt68JqCbZpnYzgNm49gWhM+iv8c6vpzFFftX1M09jkCNCQPOvUXy9eqthdHUEo0Q4KYVziNpKPf0+RiLPkJAqN/h9HQixbOkZ27t8nNdw2TShXKIU0m6ozD7378Rf2M3fvAg3LnHYMlAWPmkUefwlrnlRKlS0E7baz0va6ndLysm9Sv3wDvQDABmAewZPpVGDoMg/tfK9f06idnnNlUi/bjDz/LC+OekSrVasLnVbKCYh9+8LHMnP825pdk2b5jDwClkCQne6V4EY5ZANS+RAlG06BJhQ0sKNvXK1YpoMcE+SoW8SVJELNXAuYwvQcNLeiLYu6Fbhcmri+WrXbe3TQ8wCiE49tGfpSC+pMwMyB9nnCDDOYTzOGcJ5IBZhUCy1OgsMFyBdz1SqL05SVSGO8LnF78oXS55IaRMnfs9eo3du4zgwH04D7aKwSg3Yf6/feGMeKBmcaPnx8gTRpUYG6SEdeeejPmT74aV67q3nMzl8iKn7eJFxt7SE2qnyz9ujbBWf480AgF+tNlv1CmHxgNQhPgo25j5KnhNx7W3E3a9mg5cPxxgO+gsSJiIXMAAEAASURBVMTNEs7cFXvXnlsOWA5YDlgOWA5YDhwbHLCr+LHRTraUlgP/Wg5QCEN/VCrkgECUclsXA1NTXMdaxX20XQMyvhl4ND8+M/dZX3N+JOvIfCiQo6CP3236y/yA4w3kzl+sQAgCJpXhsWAxGASDZKXhhGE4ZKFEK0EqNEOcv/askR27psmu3TNgOvF7PKf5H5qNQ1lYJgjXKYCM/3FXt/6wE59aYyTd7Q0TdDRRBIUVCM6pcbJTdu57C0K9WZKSvlHDxX+q6k3751/HAfZpWt3LjUyfMs8dUIbAjNtRzYO/4cgxEYQEnRpU+oOPPZaP5JTTKYQZfgSPeE4fRtRuiKe9KZgg0dHVMiMekhdBOuYBmXT1Ipc/TN+AX2SHA3LBHBnm3oQEAl4QRLt8MsfYpJzyMUxWOnzu9wPQduNRq4vpkVgmA8IxTqZ2mNnowLmAmyFQX5pqYxoMQw2xAJhXBPf4jGH4KwixDsyXaVE7x8wpvOaPZh/NPabHa8M7E4bXCQngrWr5ZbUXn1NTiHH4yyk80yaZNJkO4zCspaObA2wz+sdjn2Vzmf7ONYv92vio88FWaNQ1ixpbI/ZdakjSF9b0iZPkzGYtpUqVk7HWO32Caz7HbI+efWXr79tkxFPDZfpL42X4sy+IH6BxBuIzj3YdOsp557aVQdf10vcEjifTn2Lziz+nCdMtmzZJx4vPkUonFJXTK5SUFmfURJ4+mThzHuomsmjBa/Lnjt1yUeuzpN4p5aXmyaWl3mkVZehN16O+AIfcOYD9O57Yhx2zYa5/LwRxroFhoV7xFB8+/vnhvgZuhvcEzkcwuurOGZw/CIrxGcF1nfNwrs9RZkJ7eLWwZDlgOWA5YDlgOWA5YDlgOWA5YDlwHHKA3wOWLAcsBywH/jEOUEhEZ/dRCFyCEAoFPM4O2wyY9EmEoPZYowgE3xRnU4FFTQe5PjIoLA0HIdTeX9Z9hKpoYKL9hVU5Zcidxlo07MxWijjtALEXLrMKHa+woU8QhNoi6QStcOOss3pLqdLVJC11OQCMRAlFU1SYn4i2jUJqFcV2Zg/ygawOQm+nfPQiQvI7+8QVROO1B5pj9JkSjTjLVUBNLQYlMbGwlEg6Cz5fmkKAXVTxO4a3dGQ54IOQkWQUmExuRrFJncSYm3p0wpvn8fGyBS3QBToaWpuaYex76k+H8TI1B3CX5qXU1xdAaZiOgliUARgK5XbKoxcH8cfRtMw9YgadcIH8LCZoH36egEcS0nzSfvBYCWG+WzB6AJy1Jarvmy43jFaB8ssj+gHwhdAZxeTIjUBCHV9SjpSShTzSquMwzJMYU6C3Xh8OyBmCfIwjBaVdn3AYifo882gSA58IMNFkYQLArD9T0uSdtxbKn3v2qYnDRo2bSqVKlaC5BV5B6yI/MApiZnn7rUVS8eSTpU7d+hivEDIDvU7L8MqS99+RqrVqyckVKzo+xdTHj0dWr1wlf2zbLK3OaauC6pWrVmicJs2aQ4BP7bEoNE1D8u6br8uuv/ZKABpjTZu1AMBQCfOGy1i3dvsfvKpp9v57i2XPvr0A6BKlYcMzpVq1U7Q+UU+CrF25UpavXKHAVqOGZ0jturVQCwe0ozm2NWtWys8//SK1G5whp59aWcvIDRws24bfNsma1cvkxBPLQZOnoZY15/COJtyGjb/LurUrNHydOvXRHE7/2L/c9s7RwAFiOxNnvqwgWE5mO71Yu8ZPma4gVYSLXg4URj/3A7mfNe91fRoGKG6IoBf7UpXKJ8r2PXuRjshNN92sYBjfhUgKTuFdaPK0WQrwMMxLANkKspEmDBXVV197UyeR775bKzu3/SGNzmoOgBkaXQDymNamP3Y7gDEuCEAbYvoE5njv+RdfUh6YZ+ZIP2nUipuA5yQFCwGgTXhxooLNJpw5xoc39/M/mvcXrPsosxfrP0FK8eAlQ6dyPNejSckJnwbNNy/eG9Mw/4dQnzDi8Z0kI41gPpcFAJy8RrQw3k18Gek6zzIM4/pdDTiTqj1aDlgOWA5YDlgOWA5YDlgOWA5YDvy7OWCBsX93+9raWQ4c9RzwQVqRkpYhjz7+oHzwzvvyw/c/SJ06teWijpfIgOshQD7GyOzqphCHQiHVkoAgjDvOzbNjrEp5FpfCJmzCVoEbZWznnHuXnNPyPo1TtEgNaLOkww/Lt7IvbRmAzx2ENFQTzAPBoQq68kwdgjfwzgkXUn4m+06SQkUaQ9BXBYJq+tcopClQ4MeyWLIcOJo4ABkyzH7C4wvGBsc/wRX2U/rNcrSNPKpdQlDMaEjmVn4V/eofaMwBYKGvGZIK8HPQ7sgpHWpMEHiiVsviBQvk+v7XAVxOklOr1YB2505Z9+0qaLNcJ08+/Uy+oBjTX/z2m9Lz8s5S9bQq8tmK9agThO0AsrxQwZg65SXZ8OtP8u4nX6GMyBfI3c6dO6VLu7bSd+AN8p9W52geo556VHb/sUvmLHpHAbRP3ntHel/TQxLhi+w0mH/766/dcnP/ntKz9wB5fPhTOVUr896H7y6Wftf2AMhXSKrVqCXbdmyT9evWyTXX9JJHh4+U5o3qyq8/fi81G8CPDtpg3Zo10q1HLxnz3HjNmxo/Tz/xkCyc95r8p815MhvghqM95AB2jz80TGZPmyjnXXCBzJr/BgTvGdnCz5z7mmq6sZ1ZZ4Z/ZcZUaXvB+TJj7gLMZ5lFtSdHKQcIMOtYzWVB4XrE8ZcTGfCKABjPCSIRemJ6QQDGCYEkbASi772szy+jhcb7Aah8pQNIo0lR1XYEmBVCPwpQ1asAFEYHCyQiLrSt2f891bku0k8fACNqneE5y2XypHYYgbD9ys17OdRf32dQJr7PkJi2gmMuP+LfceLDY5k+osTyGFow4gaUz3kv6HzLBJzjnQwmbOfxPtj52shrMU+Z0NjMYEGxLGbYM8sBywHLAcsBywHLAcsBywHLgeOEA1lfZsdJhW01LQcsB44uDnyzYrn07HGVfAvhJaQzKJxfPv7kM/n4i89l1bercY1d9scQUfhC4UwYwqN77xgipUqVlluG3qNC4ZyqwV3iFICRaNqHgqtjgUwpodgnkLlJCWBU/z1/jNRv3AfFp3CKgjwPBIMB/JpA4FcPdftV/ty9FL5LNuAHPx/RBIn6nZSM5piodg+6gtcR+vvAj4yMoCQFTpRiyfWlVMkzscOb+h1ZIra8hJjHAi+PpTLmpvGVed8FJ10XW5lVy3yeeefgTgAjofXh5wfRmZXxZeZXDTGa84LgF9qIzA/yX/QSP6BY+LVys0ugusAhUd4CatflmY4AZpOInJOg5RSlJizeuCIAhFX5EVpt7MEh+ObhUKF2qRfm9jAgnNJRM4pqDIbcUz6mr6G0DJxAGy4zI9YQoFTWjSwBsSbhatT5aFMMlB6Mys03AZxq3UomTJ0NUA5au0AE9vz1l6xbtxYAHk3/OeCORsjlzyszp0jTFi3lq6+/lCVLvpTmzZuoKUSm9czY56VZkwZy/333y3333acg2I3X95eyFSrK4FvuckBC+h7iUEd+ELcjrkduHNhPWrVpLeMnzgSAh9YGoPDnrl3yEwCt/OiWwajTuRfIS1OmajxqvP2xbZv8tvEXBQQvv7Kb9Ol/o2rH8dnUadPk1hv7yqWXdJLW550PHtBrWkBq1msgn3/2qfz55y4pXrwkTLElSkooTd5e9Lo+S08jvzALxYXfCxCvSLESmK+SJA3A5dsLFyB8PQATQeSfX+nt86OBA45pwJixF1coP0AgAkkR2Obr1b1btqccM1yPgHPLuAlTpVDhrHQSABjT/KICUTjGE+9z/ScIxqMBrwwoxuuUfelyfZ8eMHeI4e7mFZvOJGiZERQz4Bc1UFFSjC+aScXc4YJiHN9e3DDvHCYvppXTPZMH8ySxjjTvGAaQ7JTDmC/NvuFlv/AsRB5Er4ukDMycJBilRKHhp3HRQ3rtC2Ecsgju+4FOqvrEQbgC0BbLgOYXYEhJLgxfZNCgU21Q+Ibj0RBTJziWiB/dmXK98KNu+fkYM/Ht0XLAcsBywHLAcsBywHLAcsBywHLg38EBSFUsWQ5YDlgO/H0coIYYTScGYMZr3740BxRb+y0lNpBUoBz80URRMENmTJ4sTTs9nWfh6AeDyhIhmjCEwJhCLQOUEHCiYMYR3ABOgTCIxDjc5UyfXxQi0UcPd3AboVFshhQgGbDKPGe8EAQumpYrKOI5n1OIRWGRH07df/7xR/njjz9U64k+LzJQb+7aJrFsFEYZARjjmnxiy8OwjEsy/kf04h/6w+qiqCpkQ1XhyF6kVPGAdO0wR047vT1kWCH8KKA3P6egCYFknNSQ8uVqQEi8TXalQIMs+B00LrYiPOuX6LaTE94xdsTd7xWkqL+BlCjaEO0HEEHTdXhvQtrj8cUBmrtz+pgzXcTW3sibzZh3jmGMN4R1hn9s8AM+5zjNixTbRwDjZ4oaHH74LAK2K689M1CCNFEIQa9JZf7I6zQ5+lY01iBj01ftLqZnNBuyZLuccDDXwBcW/R25Zt2oXaLaJrifk48ymlB0zCN6ZfOmjdL2ooc1uwTYTqT2SPGSJeWslmfHFiHbuZkPmca+XX/ClONceX76qxIYN1amTJwgzZqeoeE5FxcrURxaWqOlX69u0rlzJ1mzarl88tlH8t6HX8H8qZOfzn+uthu1rDIwH2+CucJ7H+ig87gzl3ulZJky0qRcOdXCii0Q+RNLv23cJEPuvUjDsYx8XrZ8eSldpqwGu+m2u3SeNfXoetXVcgeAsV83/KxhGYcC9NInnCglS5aRl158Xu66+z41Qzdr8otyymmnycmVTpV9u3doOrr+oK65ha9cNSt8bDnt+b+DA12vuipbRTJoRhHvEtScTC4GZAZa44eTkosmS+crr8QrEgBjmv7zOUD34czjWE4rFCIADdAO721t+4+SBWMGKcg//5kBus8AU7GuBXgVk0uHjMdrRpp89tJgCWECJmQXq3F2LPPBlt1ywHLAcsBywHLAcsBywHLAcsByoGAcsMBYwfhkQ1kOWA4cJg6EIVgmKEZo46FHH8jUFCtesrhce10f6XFVL7n15kHywaefOAhMPvkShKLJHwoo33/3fZkyaYJs/PlnmO9Ll3oNm8rd99wr5cufpELi4Y/+TwqVOkFSUvbJG6+/ClNdhaTzpV2kV9/+yCVrh3ZslhSkpME/xfDhj0P7qww0KvbKu/DJU6tmNXlq5LPyzfJVMvyh+2Xjxp/1eckSxeThJ56ROfPmy8rVqyAATpQe2FWegPTHvjAFZgAJEGURBe0EyCLY2j3qqcckqVgpFbi+Pv8VSd2bIh06Xyo33jQU0JEDpGXF/GfOiAvA4hP4Cb7AzdEp5U+Uy7sukNKlGwElg2jJ1UghPx2KQyOi8A+WUEJO8LVBOk3w+0FS09fI3pTfIAiHoUXwwQc/UMUTT5bCSY0kIakm2paAGBABoge6W5+CR1N/QCRAFCjOzjE/E8weDwsHjK8wk5jRBMu87zYDFLeU4p+baxP/QI+RSGFZtnafRKa9jz4IbTAX7UqCTytSRiRJVq/djH5EH0+AOKDls279Rpk1LQPdJ0MykhyQ+UDzzQyfj9oPRLJOUNdXH3GbmtVPkQbVKzhgmBkWLsMSXLl5Bq7pvw8zgfonpA81kt/VGvO4mpWJCONFGaj45gFAD+iHN6B96UQoSjAIyFsqUsJQQZ1Nhs7RCH6hPyInnVRBXnh+glSuVksaN8L4xRjyQaONxA0MQZh0I1gVTzQR6AHvp06fJCVKl5bzz/+vpOzZI3fePFDC48YpkE8Ajn4DO3ZsJ4s6tJd+vfvIjh3b5bZbh8BvV0X1A+aYKMzeHlSaO+nE8jIO6Zx8eg1pfEZjrWcECDwF3jmVJ7Z8ZcufIC89N0ZOr3KqNGrSVOcSrRf8vDmbMuDjEOlEACqQfvzhJ2xyiErlCpX1ms8ygEImoP7tr7hCJowZJTcPGaZ9adaMmdL+ki6y6puvnbkKaw83W4SQVoHDu5scNDP759jmAMZdu3Yd9qtDAKAvAd0MjoGshWq/cAdzI4qx17Hjpbr+mc05senkB9zHhj06z535IOJzJkZvyJnXA+76Tm3gmMU/a4eBO01xmuQ7StCTjC06eF/AvBqJoh2SobuL+5x2OS8GEDAMv4wRX7K+i0L12GFH9uno6GSRLZXlgOWA5YDlgOWA5YDlgOWA5YDlwGHjgAXGDhsrbUKWA5YDBeEANQRuGzpURowaAQkFhBGUVkCoQVDs8SeeVKHwk089I42bNS7wbmvVFIPA49tV38iZzVpIl25XS0owVaa9MF66tv+vfLJ0JQSbIl/CPOPylcvl7HPayqDb7pXNGzfIgw/QpJdPruvXRwUqRmvL1IVYDIVc33z1paxesUzObNpCesMUV/kyJSQVJr4ub3+edOpyuVx17XUA3PbKL9+tk23btkqzZi1l7qwpUrhIMbnkssthnCtDTRSZdM3RAGO8/uKzz7R8Lc9ujfIMlk0bf5FHH7xbKlWqIl27dgWI9M+DY6p5g/YCe6Vq1TZyRdfJUqhQOac6BTFTR0Eh+EkgM8FfEoDhmQAL8Sv8C/i3QoKR3UivkRQtVINieqSL/kFCv2HbsK0sHb8coKB51brNsvYndECQAcYSXMQtGk7W8YoBp0QgZdV3W+XHNVsgCIU+Y8DtT87jA/4LWXieBFjLee4CY5C8SjRppRDb5TziynnVdBcDGmAs7CKJiRgRsdS6/e3OJVEzkCecpMJ21a4C0Ne6w3163+cCdpHobvl44SgFbDIA+HCKjSXGU4AJE+LoCZPlxv595OL/NJOEIklyRsPG0qXzZXLVNddJNBPgjo2NYcg5COAbTQO+PG0K5r4rNEC7Tl1kKICxiRPGSu8+N2gYPiA4+ehTI6V29epy6qmny+Ahd2p49bGGMhM8iyVq1I587gUZfMMAaXd2UylUvKjUq9dQLruks3QHuBYfPjYuz58ZN1GGDOwt7c49C3GLS8O6DeRixL2u3wC0P/iB9On3MDERJtn+2ieD+l0tDZueJW0uytIyI2AXQdm6XdlL7rn9Vlm9eqUULlxYVi1fBj9hb8otX3+uWoB+ACCczg4kvNEkjC+3vT42ORBrgpA1yMALQxhaYzzSPOqRIGqUG79gPMbSsQ+MxdbmwM8TAglqkYCbA/7vOcwXAZhsxngOYiOAvk3AhCzXECj0y4d4zmkTUxrM2DozizWleOA8tzEsBywHLAcsBywHLAcsBywHLAeOZQ5YYOxYbj1bdsuBI8AB6OJgp7NIheoNVZBL7Z1YyjhEYY8vkiZ1qlZ2QDEIeBT3gOZD8xbN/5+9qwC0omij3+33Ht3SKYhKNwISAoqElAFKi4SChGJ3IBYgElIiIYr1Cyq2hCCilEqqCBYYIAovbv7nzO7cd999BaJIzOh9WzOzM2d3Z5fvzPk+RfxQ6VGuUjnsZxQhzOxFbB4PDBch5CFJBdMs/0STA0ZaPyRMVEYMHjEKgiWXUgAwa+s27aR65dLy0cdrpVnTxoqMKVgwr0ydMQuqJbrZCsu+H3bLpCfGy6DBAxX5AjtnhhSGy0TYTNTs76JQjD07f6E6TgPU7l075c9DyXLzXROkWMEkGFOp/qLigHFE3FK8WHEpWLiQ9OjRA2UytlufxEM3aMjvQf9IfJUpU1qmwGDtg3KDBrDVKz6UN5a+Lpd17aaMYbpc7kvLYMa62S6GH6IRCGFRYK3GbGnbAI9D5CVhLLKuM1qiTPshpx/7oTpRM6nh/hIlOIebeXl/NK3bRTp3nA+jcD7sQVLTsRmfJ+t+pvcfFqkoyKoxqnhiQgXhLz0fd1tt4hr9IFmkGAxc0fLqCA/qlejZoePAPrQFmSmmIa8QyspXXbSkWTkaBDIrvqxrlL7fuhbp21at8dvZncsWUkWJI+YjQZonfFiNRynu/Opyh3FBaeAMw2Ugk583N1IEFSjyx4nnEfe+E8HOODYEJVW5Tg3bLgdV5r/zJ5KQY6mgi/cdxq2ApQx14TkKwz1gBIOYIoNt8jhgE136I4zPGlMEhBofOZpxuSePHXNHEJNMHUeBANSwQSdJGbd4nH+q/U67XgfucV4BDtNu3PuMg2Ql67pEaLAHOURFV4tWrWTjVzvkk9WrZDMmDDAe1phRo+XjTzfI1GlTEd8LyhdgGJt02a+27pAvvtgqT82Zrw67vG65uFNXeWXJy9Jn4A3ic+M9gokDYYdXJox/GHG68sm33+6ULzZvlurVz1fuHtNw3JfJFVwYsb7ayqZtO2XtypWqXe+8sUzGjh4ln27+UqY8PSW2OZnWL2rdQrbs/FZWfvS+bP1iM2ITvSW33TJGNm7YiHF1Dq4F3PhCBkxCtU+fXpL8xwF5+Z2PgBOUx2T97bEEDnQxZoak7aWdZf68mZIfJFuLtu2kENzjMX4ex1RiQ3TpevFo82dqsNlxQhGgIjPHlOndEp/beo4opHQiSBWJ39jktu8fkuAuKONjnx8OPfZrNrZIhnVb+JlhX+yGJuJ4Xidf3nEJXxOKDHfgfcxxMFOygyBawyXdRDNHbB+sCSix5XQf1LntU6qxF+MqlzpRPanetdih4pqlH0LHHWpigs6b3dLH2IlM9mXyg9hi8gbx3YAUwpimPgM584Ft0S8MdRRjI55rxidzYD/gxyiKcRN16ekGxIRjI4syRCNPo06FHTFdsWvLamEDkNUhtS+20zgPxmWO+3yHEWue2ySDgEHgVEfAPMin+hU07TcIGAQMAgYBg0AsAtomE7vPrBsEDAJnMAIBe0YtDTu/7N4AV2QZwfAf578HvDBbjIOh8sOV79nWCdQPw3GvqxCHBsqq+x64X5a88AIsHNh/FCMUY3Vx1rQDBNPPP/8oTz7ysHyzaxdmAXPWNqgckEu//fqLMsrQUF6/QRPMIKZBiAagiDRs0kKmT54kR+AKLDExKZMxSccsY9yemnWs+DkW8eWUipWrIuZMRbm0VUNpBSNzG7gUa31ROxhCYH6jYQwGkeySNnCx/Vawe4FqKklKlSsDwxboJVhREhK8Ks7NX8l/HSMplt1Zj30/cSIhRSM87GDihl2oZatx0qHVA6gsxrJ0dFalY2+AKXFGI0AyXBG7MfcXxyY+H0wO+jpFirpuxDqJMG2+pHqDZI565lEuS2OxquHo/nDMyCmxbUx6ScNxtzY15OqezdCGdONrmPZa9M2LcY72az9+7GKngU8r4ynNq+yiNkqjQlVvSsphqJ0SMYoyP3NlTH4/FRIwHoMwdJPRj0scz/gsM5EcY0yyJi1aSONmF8rgoSPkrrtuk1lTJ8vUp6elnzuuDhJmLyyci/h/brljzAh2FgZxlxw4+Jts27xJfvjxRylfurhq37o1q2QuXBsuXvaRzJs9XQZfc5Ws+WK7hNJSMTnBMlcr9452XyyDu6VKY7uatmwNV7Kj5aabxsicaVNkylMgxmKG1fjrScKAmLVo2Ub9+g8fI/fcOlrmzJgqE2fMUa8U3g+9e3aXXTu2ynsfrVZjLK+rG+0hkRjGhVGTAnDBruk7QIYPvFqRhA88OlldV2Kox29FutoB4GgAzy1/HJRm8xRHIPp8xvWDsUazOxaX1Wz+gwj40/BsYgzgmNTs2mkgsa0P2JA95mkeLezkCIpscKW4GsoxvmNS8R3qxQQlkwwCBgGDgEHAIGAQMAgYBAwCBoEzBwHLOnLm9Nf01CBgEMgFAafbNiTA0syQUQ4GtYlJuc64jsmb1WoEc3dpYLxv/BPy1OSn5OZbbqLlWwIpflm8cIEsXgxFFgyXnBCsLMVZVRK3jwZJFJeenTtKhfIV5JqB10qdWrXFCwNygzo1wI35oRKwZhy7oChz4BcBcUaRhScBCpAwlA0wpnM7Y2/RBJRzoT0kw2BhVwYUEnGKHMN5P1qzTubNmS4fffihjBjST/LkKyALX31LzqlSBUHeUeYoEg1oNOj606w4Ol4Qa5zrzDYdb1Izy1k/quRsZUUgwMbusg38av41D1KmEtN5Z9ir8ut9rmCyJOKN0bXTJKlVB8ZwOylBmd5Qy5hKstyv+5R1Ps6tzjnp8tnl0sd5A+l1rJJXULKlmH3ZVWH2//cI2Oq+EJRHNWuUQ3taqTZRkanUOjZRFE8OhfHsbNr6o2zd9pMiZiJQLJ1TsRhc6pWytnFfH0/Shtbs6giBIGLykPVC4phSpVIpxDO0XHZRwIVhRDxp1n2OUUipOCMkdFgggvsWt6gTDyroJln6xiTujQojwnjg2nSly9nf8Hy65PWl1nGcRqUElE3l4wyyRhNg9iG1oBtEKqOUO0QothIw/tE9YkKCG8rbsFQ95zxR8bxA/lDtEE88MS/b9+qLi6Xr5b2kYf06UOlCp2Jfj8cmPCbPzZ4hd99zj4rlOHJof7l6wBBp3fICaVi3hjSqX1vuuOUWuf/++2ObFV0PYAz0wM2hJjSp8EoFltXPqwUsc/9kJdlHgovlXWiXA32tdk51DPEBiaDtEchIBvW5Sr7avF7efG8VYiMWVcQpx2CWsVSp0eZIixbNJNHnkSOI99jx0ktVnvSjmdeONX/mGsye0xkBxsPMOeX2/su5dG5Hc1Os5VY+N0VbbvXnVt4eFqHwtogrd9BSyuqJD9GBMFNDLdyoEgtHMBkCg5SP4xLcyTLp7wqt2NIxSZ2kyjGkeTCJgOS+9aGgipg/BgGDgEHAIGAQMAgYBAwCBgGDwBmAQO5WhjMABNNFg4BBIB0BNywLOg6KB7aGgG3wTM9hGRrSt49tjRwMjZBUYI0ePVq2bPtSFsyfD8kEDCGUTpAMss9ZqmSpo6qc2Q8dOiQ7d+yQJf97W4qXKIZydJP4PWJhWa54YENWhvEf9u6Bq0UaTizD9c8//yBJhQpKYr68URVA7ElphKdLMK3i8vrgujHNUqkxHw3L1w4dKYOGjYZKIyQXXVBPZkx6XB6fPBVltLk6tsbM6yT2glB4MFYNDczEiG4VqSSjesHNpY2JVipkruXo9rBu22ubVUC1ETtpr7NtcmpV2+eYHzvy5C8pXTpORByiGFLMqsH8NQj8awjw9qxfo5g0rs5nGpw5DJ9qiMA9qY2csSdnDK/pi8KKGLPImojUqV5SBvWsJxCVHneKGmizqSnE5wXJaw0vSlR54IgIQnJJj7GzJQ3P9bLJg5V6DKvS5UaOE05Z8uRQEPB2YasK9ddy7xcdErHCsSIAt6MexPlKpdhWJf24UoHKscILo3BqGt0G0tibnjQxxj2jhg6Sa4cMl9r1GipS7BuMn7OnTZJK1c6Dq0GMR+nFomtUW7364iI59OchufWOB6RM6aJqPFdxy3Cxtu/8Tpa+8oLcC+XvbTePspRWEx5X5ZMQp+uehx6TG67rL5d37yY1atfJdA1duEjDB1yDOGcDpXHT5hgPw4i1+INMnTRBKlQoHx2jog2KW7kRfbqy3wBpghiPLgx23+MdMG/mNKl8djWlGhwy8Br5eOVHMv+FV8UNAm4fVMYOGMQTExOkaLFimERhA2rXy3fVui07lAKP67mlY82fW33muEHAIHD0CKRBMevBtyXTixMGYn6VNYqFlAsCTDCIcDzEPjXUhuXQYU4SwCcOvr88dP96fJ+36rzmj0HAIGAQMAgYBAwCBgGDgEHAIHDqIJCV3ePUab1pqUHAIPCPIxAMJcNoEICxlnGomGgMTP+5Qy45nl8ASiWqmIJYhmDAnTZ1urz99rtyWbfuUqlCZXHDfWCVKlWlT+++8tn6z1QLcvvjh0ogX94kcSa4ZNmyVxGPzAGjcFhG3zgyWtTmleTjFauh7lqBc7vltwOHZObER6VL18tVPgcIqvjkhoHZSxdbMIo6YYkP+tPdaO3cvlXWfbIeBm1a3B2SfASGapBujEdDzIoWKyLf7flB/khGTCBlU81cP4ku3TYagd1wDUeSjKQYk4vbMHZz200l3VEnfc3iCsDwE4Io0HKzRisQ2oRp3BEXZ2ijb9YerOLq46dc1mHnxa3vhTrkaihVUEIZj2g4xwqL4efALG3rZxnU0+uBWg0d5M/ax7nbcH1p7wOw9jGeG/cEfgoqVMvE7Yz7rFrCQcwJx8+ylNN9Wvq2dSbWQrIVcV5gBONk8KONc8XzmvTfIBDCzcWfCgoHEsiLnwcX0AElGH+4W3A9oaWCooBLK2aMpWziegLu7YRwGpSRqerHO4gESSLKuaAeON5fAIGCcvoxRqL64fYjgXcIvzS45/LiVg0jNpiLMcBwP6p+Ypx1476lOCKI8dbPY/bzRZdfXPe6fNbPiSV/ELx5vei7is/lljy4r/lLcoXUj2OED/G+0qCQ8nktA3HslSRZqMmxX3/9VTp2aCdlzyospfBr3KCeJKDs/EUvqDhcseX0OhV6ixY+J/WhFCtbphjGEpBvMYxjnz79QEbtkScfe0SeX/CcPPbYZEnCmE7ijCrbnj26SQecc9h1feESMr19Do6vvLp4N7Bd3S9uL6UL5pGK5UpK/VrnS364mV3w/Iu6Gdkuv9/7k1x+WWcpW6qwFC9eQBqfVwnaW7c8t/hFKL8c8ubS1zDuH5BLWjWR8xHLslb1qlKzajm589axqn3RipWyx3K3y/eLGzEpwxw4owkXKMNIZW2zn0eXP1qRWTnBCPAe078Q4pNynQS03sdryMTtzElf97+5dOK9msPPGuE4ymX9y9yejHs4BmoCl8r2NCgwmficqv0Ovqxz+qFfvPez+WXXLr2fz7D1029mvW0tM7Y281YQMcX4c+D7jD8MkBjng5IMKewRH1SgeOfzZz17iMSI8ZM/KzAj9F8uKoL5JYExF99zYfvnxncAf/zG47cAQ63xWP48GIPYXX4kWJc9c6PMHoOAQcAgYBAwCBgEDAIGAYOAQeC0ReAfmD992mJjOmYQMAicAARcmK7bvFlz9ePpHDDs+gN+oQfHeLdWWTWH6giqInyJThk75la587Yx8uiDd0MVFpAR2F69amW0GI3CF13aUe6/c6wM/36vpKSmSr0GjeS2ex5SarFIFq4LSXTRlaLbnoXsgpE7ZKsKAlBu9O/VTZJTUiTJ65PDKUekMZQKo8bdpeobMfYWGTygv5xfvija45eNO76TUiVKRNujV3ScMZJgbCP75IYBWsVKwjqN0fg/SqDpcn9niepAetI9EY3StjGIO7Fq/+EKM6kFuAcrOWlwUpnsHdYBp61yYRVM2rWdpdZhNVY93Fb7aIBSGa2FPm5ZpdBXGLSYYKtSCT2316x91lHUG/f2onqIP4vMpdIRBntYwEJpIBHQNtg9lUoH9neTTiEESApbcbFo9MT1jN4ZfB5it6xOuXE/Mp4Y70Peb+n3F+4Z3iDHmWhszinRdSIT43xZSxiEscqhJUTyxya3/SC2dGI7SfpllbSRWxttwT+rvjkxVoQZR9G+4Z00djPZkjWOiVm5YmV9fFRJji15/S3E1XHId1/vkN8P/in1GzYWnw7CY+OsSQJVN/5EwNa9uvQdKKtwbippUQ/XtcvFKmdXkV/+SFOlR4wep4zkLMvjXhimmebMX6KWfoytaInMXoCYktHzhWXRy6+LH0bxH3Z/I/t/OyDn16wjBWDAJlERCliGflUBS+P8msDgtXl5+ftQv4Vkz7dfy/79+6QJVGdMaKWkpqbJ3l/+UtvxfzDlwSYUnDJnIdtnDRS63cSN52J7Y49T7Ru7nVv++POa7ROLwJdfbJYfQdzWOr+OlChdRrnd1Nf2xz175MuvNmFCSwmpXbueemlY1zz7NpJo4bP80fvLpXTZ8nJu9RoqM9/hjFf3/rvLpGbtBlKsRHE1/oQCISiaXPLFli3y6y/7EZO0rXy5cbMkpyYj3mkTVZbEKt16vvPOG5KKbwoPnvXadRpKpbPPzr4h9hHdvxrn1pLSFSsqZT7dnzKO349798oXX2xAW0A2N2yi4pZu/mKjfP/dbjn/nJpyVtkykidPAiYVWWThLz//LBs2rIO70eJo2wVWvzAeb920Sb7YtlmNL40aNZUq55yrxlrlxtTlwfdQsrz7znL589AfiqSv16CpVK5cxZpkk2sPMmbgNxmxDKJNIUzUScCLH3MlFNGux5zYEnpCEct1HDldlk4ZhnaKdB45U2Xj2Ld00rVqvQv3Ybxc+cwgkPvqcnOgMMkgYBAwCBgEDAIGAYOAQcAgYBA4gxCIMy2eQT03XTUIGASyRoAxbuhuRrmcYRZtKbCYj6DLWmZdOPe9UaLFzupVxsb0cgFYMfJ4SMIw9k7u59KuBcNQco25CUTUdcNky5aN0rBRM8QY88iw60dAmYCYMzBS0VherHAhmbdwvez+epuKL1a6dFlleKLxhQRYPDnmgeqCRqoFi55Xhq0ADFv6nDXhCmzHnp9k376fZOe2bdKgfmO4ZMyD81j9qQL3XSsQg4zb/LE7WRmrlWoMhte5i5co9QUNdTrfM3NmK4MtXSvq86ajdexrjHHkiKTJ1/vWS6ES5+PqgkDAj7Oz2b6QwNiNbdj7kBwwJzMqHO+BJPyURowHcMQiCOzJ9eif9Tqx7fTYtkCwzPDsv7XN2d9Win/9WPXRgG0ZpS0jOs+pk2WqDkBkxn1sSzqRxjzUmsEEqPY7xSe7f94iR478xigiwJBtYOusdjC/SSczAvo64VoryV/MNq5wekq/P7gP9AbugUQlIFDPGw5TKRi0iSrc6OlF/8aaUhbkUM4Lso7JaY+fibjvaJj1g+l/6ekRIFeULRb3oxeknUOWPDNMkVt0W8v2Um3Bp02tY1s/Z1SPMbmhoHBCEedHhgiM3SR0mFxQ8v6dlOCOSLWqZ0sAJJuHSmGbnA/hGSMp5oxRg7H+CGKXBTA+ZZ+sYxmvSva5rWc9/bgm4tiuSpXKS8WK5axxGSAqXhOG99hEEoyjlheEaAhjTDCQCmWYT86uXAH9qoxjuq0aHz3+xNYSu67zx+6LXY8/Hr8dm5fruR2Pz2+2/00EHnv0cagG/ycXXtgCBOwb4sM9w0cLT5WMH/+gLMZ7/sJWreXFl17JphkZ72y/PyJrVq2Vq6/sK+UrVZD1GzbjPYOJGVBsUkm5aNHL8vADj8gHq9dYqm/cv3u/+1au7HyZ9BsyRNq07iCPPfSQJKelyAuvvaWI/A9AiI26Yah69qpXqy6/H/hFtm3bKtf0u1YmPDE5m3ZZux9FjL+30L/mzZvJi3ArTRIuhGdW9e/hB+SFhQvkwrZt5aVX/6cUkuMfeliWv7pEWrZtLy++vhxxAUGKsQMgme+9+y55ccHz0rJ9O+DRFGODU5rVryt79u6WaiDA+f4dOWSY9Bk0RCZOnCQeTA567Y3lMvb6QUBTpFq1c+XPw3/IFzcMk4H9Bsl9Dz+eaUJLfGe8Qes51S5rk70cFF1yWYc7VLswbUree+kRScN3hMsm2uPrCFFxD1WeE353MT8GKl2obMN/KHI+GEpE/EeMq2ggVcVeDLzwjo3xFPusU8dXZ7YNAgYBg4BBwCBgEDAIGAQMAgaB0xiBeMvkadxV0zWDgEHgZERAk18kg1SCwSJIoy/UWDpWRE7tJlmkFQPMl5g3r1zQ7EJVhEoGxtrRicoMugbk8uyq1dVu5VoHddCQQ7dDnJ0dm0iKMdGdolKPgcjTbdXKsbPOKiWlSpWKKsmohOB56dYMp4qSXCTfsiK36GKM+T0wRum6eU6VH8fYPyeW2ZVn3qNJRFjZfmAoWvHRg7Jh/fOSkJgXJwIR6UhSroZ8MCY5HB7EBvKpeBwuXz4EsU9EHirnvOJNKI46cBzWJP7n8+ZFP7FNF0YwVCUl5VEGe8aQo+IvKU9eZdDyeuHMDudFRVAEHpGU5BRgkyZpmCkfCqbA9RvdTaZCxfebMr77ER8pHPKrOEqhoF/Nyg/BeJ+WdhCGvgDivGFWPY30uMYhqPECyBPE7zDc1TlBTgSx7+CBvTD0/YZ24bSwnwdhrHQ4SLqaZBA4cQhAACIB2+jK8QDcuiR4qGiDQRbHKCILQuFwNIo23Nby0WsPCO3HjFvG5+l4EokoKi/y4Pm0niVQi2DP+/a5XI2TmtDW5+DoSIUXFSguPOOx45XOczxLrQ6kizvGXKS7RT9AInEXSk6VawddnaF6urilSsQFMGc897zkTUpS+d0YrznO/9Pty3Bys3HKIcB7ovq558ialSvgsnO/lCxeFAQKyKNAWJaDUDr3vHOjfVL3ovXGjO7LamXhvGekQeOm8vkna2TdmtXSEOtKtYr39viJ0+SipjXloXtvlzugTOeEl3FjbpBSZUrJmLF3qPvTg3vV4U9V73l+b9w69gZp0ryVzJi3EO9kkmwh+eOPP2QvVJC5JU4OqX5edflk1Qr57fdfpXiRQqp/QfZv2etyXq2ainyj2tSF9zZT9Vp14GL6A/nh+z1SpmRZtY/k0rtvLZNza9VQzyGfMw9IpB5XXSN9Bw6T/HnxfQCibMGCBTJ6+BDp0qmDNLuwtdw2apg0vuBCmTl3gRpXSEDt2/er/Pzd12qiE1/Fx5LU6AZSPITvQY5FDjBmSt2VythgGb/VWC8nITAfrzO/FzjBB6uK1OO+DApiXB+q9XVSZe1xWu8zS4OAQcAgYBAwCBgEDAIGAYOAQeD0RiDzvypO7/6a3hkEDAK5IIDIDEpTE4FKgyobWBztEpYB1kE/Nv9CipJbijyBIQOzrTVpltvpYo2fsa7OuK7rJSFVpEhRKVgwP6pD3CFPgjKekOxiSjmSJiOHDpA0pUBQu9QfGqq8UJxNnzlXEpJQBgai+MR9sHepxHWeVyu+SGYxHW39KrP9R5No+px6OzZP/Hq8XYeGH7bBBRdpZcu0k+93vqOubwoMZcm/7QXpRWMRyKxwfriVSpREVwFsezH72wWCCcZlD9xa+vJIXocf8Yd8khwAgQfizB0g8eWGjgwqk6BHEkioYTa8hz4OsXTi5nGALAukHlGkGUxbWIIsBDEVgUUfJinlPirVfwiEFogxuDkLIb5datoRGMEswsyP7WS4kgrCaJgGlUwolUTZnziOJdxXBXCt/KkBkArYxpJxYtKCf0JRhu1AMvYflhBiNIUlRQpi6fSDqKQXSZNOYgTsAUDpONKbSW0gTZgcjqh61HyQUhGlZ1NutsLONDzbuNBazsjsjOuDpOLRqLV/54/LNrSmkO1CYnu7jp0pLj8IWqQUJ58v6DRB6tK1ItUQimRSMa2QAcrLACCA2VcpMiwyGfXocRFjix8W3AiVtigT9Xyo2F91ipz/xI3fdAOXN2+CNX7hGMkAtq9bj6vUkqq22GTFD/IAWrQdqjKtaNN5WP54ks/nBrFlxVRUxm1UR3IskgoVKyziPa+4JlP1jAMZArFOtjAV44Byp4b3F/8Lx9wDLBhP9GWq7N/ekRHOf/tspv4sEChS7CzJl6+gzJ83V2697Vb1HbBg3my4QiwD1dfZcuTwXxapkwUplgCmikStvo+SEct0+RvLQATNk5l4Rz43e7bUrddEfFA88p4sVySvPD5hgvTv1086X9ZLNu/YLCtBoH3w/kq8b9E4KN0deJ5DgRQsMTkEsqUff/xRbulwsUWK4T3Gd3GRYnmkWPHa0XEvi26pXXwei5coJfnzF5IF6N/o0aPVNwv7V6ZcWSlfsar8cfAASDG8FzFWcXJPkWIlkb+wLF74vIwde7PC49nZM6Vk6dLAo4ocOnQQrqQZ+1TkxjGjMGTBtTTGhQA+enpc1UfG3ThUdn+3V+o3CcpPbHunzhg7oPAEK0WX1kWL5sevLog1qNL1h1I2HUjBgEeVG9/rJN5c+LbgGBhRMi/EcY3kkwN8B8BnrRfjFMXEGBLFjUkC2ER/oHDHpIOQO1GWPj0IsRit18DiJ29SZ+SoTME5y/xv4nAJ4wWSinGUpBjLc3zLMeXy/AbsF5IHCnwn3DRG7HGdxY5TrJxjs8xBg4BBwCBgEDAIGAQMAgYBg4BB4O8hYIixv4ebKWUQMAicQgjoGF5TZz8HY06q5TqRZBaIFRp4qUzzgPTq1LMHDCYZh0VrljEMQZjV/XcSSSm6VFL1d0f99ixtXRfVGU4Y2/5u/bqe7JY0ElNtFoKKqnv3ubJ8yRD55rsPYLABeeVLgnGJBFYCDENUuPnQViq7oMCAYd8Jt5pOKMcUPihPJZmHU7DhnJAxjJAJeDH2E0kxmpxIRMIEBEsWDfmalCSGNP6rOEqwO7mBRwCqL86qpzukiAs0AAxpkRCUZ9gXUedAG3DMxbppZCSvgaUvnAi3eHAgFyahQMMelzCA4xoGQYj4HAm4xiRR6BIqL64vFGTOIlKx1FVQ/XnEin6UHVpm/8mGAO8n/siFkWPGXaKayHvLheseT57T7sr7TSXeiyc62cSQ5mPA2SrjsBfPCgluPgfqeUC7eK/TMMukXQjGttgF94A02MYmS1GFftOQi/K5WspjC2exTjezAZDkOmlFRdduV+hdcUsrL504Hi8JFlex2mRbNOmgnmtc0DQYu7nugMG7y2U9siqGfel9YAY9MUHXlU0hs/sMQ4DPHGMVdoHyaebUiTJuHIggPKxLFi2QHlf2kfXr1+eIiCbF9DfFc3NnSMFChaVDp25KxTwGLhAj4ZlKicTvC75/L+3cTdp3eElGDO4r+//4RUYi9h4V6ySlfXzXIg9JoBAHL2yXLFlKnn1mmlSuUllq1akTbY9SjEe3sl/hPd/tit4ye9okueWWcUoJGtu/hMTE6BiZmJAgKYiR2rPXNSr/zeNuUc/Oyy8skit695d169ZJgi9RfSvxjPxWSUVsViYfXJbu+XYX3uUiZcuVU9slQKbNnjZFKpUrJQ0Qx4xJK/+Jx9FM7lEKeuDhV+MSyC882+yT9S3G7wiMnaiX143jfxjHMDRgm58eDpTDPgyQ5MoTMERaI6xqCr4xSKphHd8bTFxFs1QeNfnAfnWog3/zD3TsOAdYRLQFjf6btZhiBgGDgEHAIGAQMAgYBAwCBgGDwIlAIKMF+ESc0ZzDIGAQMAicQARojKbhSRuyEqAUo1sgzlxW5A9IFCYf3AR173E51EmWgiy2iXS1RGUZZ0DTsHIsSSvYfDAodb+8B+qnKSZjYuwOxhCjsV+rwzLmOJatjPUrYzyNgTD9pIbOktZdXpXWqM6F6dE03kdgxFHGY1tJEkRQDkViYdY294fRriCs854EH9oYFIQ4Ukkb8qksZFJ1cIlgSPoY94dBTrDIYeBGtSGxD0cKgnCji6eIilzGfAVpRFLJei3ZfIHawz44SRKgLcSf59Lns0g3blvmrzAIN0WmoMdM4QhcVKm6GZ2E1zbWTKaymD8nFQLxhkRrW98duTWVsWkcKkaivs68+3D/xCmlcqsn++Px7cuYM4BnjcmrmFycGc/92jnDYH3lvYjnLRd+vXmP6YoMC1PFoJ419QfPqTXwuEEWM+HpVcv4+zlEAHJIR6+Yy7meHE7xjx+KbYnTVmD84ycxFZ4RCPBVwHfQFb16yUN33SSfff65FChQQL74Yos8u/i1XImxCN5n/JZQ7hfxQL/84gK5tAvIWsiBLu3YRcaMvB5xxeZJ3759xJ1gPaV8r983YaI0Or+SVDy7qgwfeZMiwjggUKWkn226VKQs6+lpz8j1w66TDi0vlPwF8sj5NetI5y5dZODQEWoMyelCKbId/buy11XyyD3j5FP0L39M/9Z9+okqznciRzIqrxkr7Mqresl9t4ySzzda+Tdv/kzmLwEeyB+EgptjKBWbjBmqia5kME/DB/WWRo0bIkZZW4XJ5IlT5Ca4U+zY+iJJypsoNeCm8XIQjv3791XfXMpvbA4dSKCnAriXDeJ970vEdxcUbSSy+J6nwjYBA6kPkw0cIOuYz+tjL9CDIIl6rALCUNCBOTQRueyGKYg9yAkJINbtmI8Ol0Xq2Z8tUOcmyaNDO8iO7fvUOfzRcTXrRupYj1kfRbuccEWLc6/f8Yf4of514L7g9ee4zDCQJPRMMggYBAwCBgGDgEHAIGAQMAgYBE4eBAwxdvJcC9MSg4BB4F9AwO0F6QQyiuQYkzKwgGBRpBiN2PjRUEaXPxFYLkh+xSYSaCzDZLkByng8Nm9W6y4YxlG9mrUdoesxnjMmkfSxq/8HSLGYiu1VTbaRfGMv3CC8gghypIz0OLFSeGG/7qOuQfUbG8SDLwoqMJSBiZY87tfdQJ9ilSOWwkdlUX9IZjHRkKiTnjXOa2Bhap1HHbeghkHJyk1sOFNeuyGiO0YmfU66fYtNNN6pZNcTsdUm3GdhYR02fw0CJwIBDwy8vIcpROUj47fvy+zOrZ8fUL1WFvtB+OeIvezObPYbBE5/BBjLT6m48T3Qpn0HeREkVpHCRaVlyzZYFj4qAOhKkO/C3bt2ypbPN8jE6XPV+5PvuLaXXoY65ytizHpf4vnHO37qxAlSuGgx2b17h2zZvEEa1kSsL9SjCCoMEMwboIIMCukWrVrL51v3yJrVK+Srrzap2Ge3jBstGzZskKdmPJtrG1lnApRgF118qSyYMwPnLZ6hf4zfp2OnKvfGeIcyHmr7jp1lycJnVVzQVq3aSZGihTAphrH+LCU4Fbok0tzITwz79ewqh5MPy/xXlqn3O78hWrdtL5u3fycrV38imzd9Jm+9/hrIwuGyfu2HMmXGHJvez74LdNfYrvttIJUsOtwJGRff9Xyr87shGApI6663w2Wu5Qrb4bYm9rz34gPqm4HxHPkJF0a7FYHHwTcm8VuPyfqLvIiVumHbj/LSqxus/fq7Rm1l8SdjdZkyuKh+xzdKKtTvxONoJ3RkqsjsMAgYBAwCBgGDgEHAIGAQMAgYBE4IArYF8YScy5zEIGAQMAjkioAmQPSMXuUqh9wH+BXaLLRBI9eK7AxaoUXjFJNSH8HAQkJGuS6y89HYo45jf3xiXk3mxB/LbZsxPJi0cky3I7acblPsvn9qXZ9PuxbzEwflM4jqKSb7NWAHKwqLFYRL002WQg6O05BduU+Lx8eBAzHWn0goo2WJbhmZokq7KKOm91n59fEQT6QKxNSD9gZVvTTQWYetu4EGsLjrFT2/td/hoNsolEEbLS93cfl1dWZpEPgHEAjZ44iuyvasGH1GchGMqWIYnnC72g+V7Q7SiZg6JhkEDALHjwBdKbog/RnYf5D06n2V5EnKIxMemYDJF5Ai5ZI02UUvfzNnTQP54pI7bxpluQ7Gy2n/r/tkO9Rnu3fvQUyvclAuOeSTdR/LXLg1XLLsA5k/9xkZM3igfAQXhQ68mIKQkPITIYVxy0CKBSJBTNqBC2Oo0lq0vlAuaFpPhkE9duvN4+Ci8CmZNHUuiBdLtZZVU6mqUoQMiKFrruknfa7pHe0fpghFi+gJMcEgJgTxBQnCaUC/gdKn79Xq/I8+/IiEoQij+0Pm9YNxIqHmgetVpgFQpG3btUvefOc9OatoYUXwUVVPt82crHJBk/rSvGlDGTpkqNx9200yfcbT8ujTcxH/K2dmifG/wlBaJQLLENTfThCGpJf8cIus+hX5EzHXvOJE7DWeh4pwzvgJ4DOD3fACS8yFAuGVKP97aiC+8awPBkRIU0Sk3/YQEFXc4vD8JZ+JJ2gpy/3HOc566eyXCjvEUuX1xYcj1vXXVBR+s2IQMAgYBAwCBgGDgEHAIGAQMAicJAgYYuwkuRCmGQYBg4BBwCBgEDAInNoIaJee0V7YdmDbs2J0d3YrltvP7I6a/QYBg8DxIBAKWa7uqB6u16SJFCxYUI4cPiyt2rcHuWORKDnVT1UYn1FMrZFXl7wol/cZIHXqN8LEjwDc+oEWAbHy5IQHZNG8mXL7PQ9JypEUGTVskPS77gZp1qKl1KhdT5o3qC6P3n+33HrXfepUVGRR4RWCy2IfYmWGQFY53CDMQDRxwg7dHp9fuy4mBeXePvZPKaxRZ4PGjTP0jydzQ7pKgokxuKhYo5I1aH6pAABAAElEQVSc/XHhvHUbNZa8efNJcnKytIfrRpKAbpB0aYgpxvYxpQX8MqR/b9m8caO89vZKxBYrpVwcU4HmplobxF5KSqpS6EObh7a7peo51SVMKRcn1TAYWA4JzVbKbraJ8VcZX4xJXRu4I0z0JkLd7wcp5sGkGGu2lEVWIhOysjxF6pwIxDBfnF9AkXkEUt0IyNAw+qTjKDKWGXw9i8/rU3FPOclGH+M5/06KoP+sg7FqeT7VD9SrJyb9nTpNGYOAQcAgYBAwCBgEDAIGAYOAQeDfQ8AQY/8etqZmg4BBwCBwEiOQtZHNMkOh2XasIrc949opjDNC4Z6lKNOCFrVT/bFL5mz3Qs7oGeyiuh16mV6jtWbn17GTMhkv4+uLK09FG1PEft3l2j4ru/lrEPg7CGjBQXosGosZ05qBkK0A+zt1mzIGAYPA8SHg9XgV0cNaSI6t+fQzECfWu4EES26JCmw38r/20hL5468jMg5KrrNKl1ZEE8u6QbLs2fmVLFmyRMbd94iMu3UMVGFOufvu+0ASheCuMa888NDDMnzYEOnU7WqpeW4NxD8EiQIixwcyLJAWkrFwPXh1v6FSt0F98YPk2bXjO3lmymSpVKWqFkpn20z2L9WRopTxJJPYPzcUVmw3+0dFmSbCuCRRpskbuiH+dNMWtR2ky0SWs9/XSlWGsw7rO1jWfvypLFj8iuRz+eTAvt8V0ZaU4JEkKO+GXz9Ueve5Vho1aoh3rsj2nd/IzJnTpUqVKuJDzDVdX3YdcKLN7758r1Lvsf1UzzG16XSLWqYEHPL+q4+qGGJqB8ZTXkcneDcOrX9ip1XEL51HT5O3Hh8qkbSIdB091cou+eW1x/uSD5PuY2bjc8Qvj1zXQeTyRiDUglCcHY2mV1WV9R+XpToMBvMq99x+uHpkqlOtosCrrlK1ZV3Q7DUIGAQMAgYBg4BBwCBgEDAIGAT+CwQMMfZfoG7OaRAwCBgEDAIGAYOAQcAgYBAwCJwwBKimokqKsS+p/qISKi0tTSmmMk/ayNwskkt+uD1cvOBZqVu3gZQtX17o7lnFHcMS/8tVfQbJpMlPydMTn5AXnpsnL7/5vuQtmN8mp8LStWcvWfb6GzJswNWycu0mi6gCucO62b4f9u6Ryy69SNVF14OH//xTatZrIM8tfjVzg7LYQ8USFWEklmL7h01FTJHkUi6WSSiBTVJKKajHVBnsIynG/lAF5oBKTRNnPNV77y6VI0dS5eLWjcAs+lTsxDDy9evbRx6bPE1+2LNXenRuI9DVidfrkUOHjkjt+nVk9pL/ZdHSzLvYRhfaFAlasUvpipb7mNgWN9xNUnjmxAHVLuCt3DfamcBL0iukKsR+McZr1J0tdrOPOkVAhIEblCb1KoKErKhcWibmMs9Gl81uadFgOIo2e/BLtjN6MT+HAjfdl+zKm/0GAYOAQcAgYBAwCBgEDAIGAYPAiUXAEGMnFm9zNoPASY8ABUIMQcW505xVG1U+YN/pkdApk2IQyGgJgoMi65i9iDislZBYM7ezNx4eLa7Z5bPOY4c6i2lfxvy8LzOmjMczHuOWfdzuR+bjZo9BIB2BCC2lOSQVNyaH4/H3r64vaEvG3HAPllPieKvGXN0M+znUm2pgzqECFwftHBLM4DkcPfkOUQ3iyKDkyRm/Y+0BCQAayxlDyQOfayRLSBqkgRwgqaAIhJhKQYHEbJnVUw2Buc8tiDYZl1kYc5OxvbjOd8WzCxapa867jCSVC/dHxgS3eNj/0mvL1NJvxxBNzxOWipUryf6Df6ldI2+8US11/aDj1Pas+S9Ei0yf/zzW6XgQLUBblix9D+4URX78fq/8+vNPUqNefcmbx417FN9leI/xmQgj3hdTfPvmPLsQ9zMIIZB/EUUeqWwW2QWXjHOfW6R2qGEIf2bMmae2wwzqCvUYySTioZ4yNPXZ+QvVcf3UfbvvgFVhFn9JOb329juSlhySH37cLfv2/yJNmjSNkkGhIPCMY4ZIUjIF0Dn2JWjHQfNyHEZsLkQ5U58cjoilWE/As1oERQ7iQ5V1OV2IMcbykqiWDBPnxb9svST1oNRLRjA4S+2GPuG6hZypErL/5YtoYFCe0c0h8uO6eHABQtEPXlXdMf+xemMVI4Fn6+t5a53YZH/vhKGw84Chc4U8mUX+J7ZF5mwGAYOAQcAgYBAwCBgEDAIGgZMSAUOMnZSXxTTKIGAQMAgYBAwCBgGDQEYEqAKh5zc/fKwpRYRtuOe6zwfjeSZDfcbyWtkSYswfpIQEn1KmMN6OUoowphFcqmnXaW4Y4amoYeK5p0x+TPIk5JGrB16L86GsZfFXx/mHJBPPMenx8ZIvT37pPxiuzGB8njF1koqVdO3QEYp84vkZ14jKHcYKinX35qBPNDtpZQ/VIWwf2xCfdP4QyAIP2uTBzI40fxDkQlASE6kIQsymmDpjy2uVzJwZUxDfKUH6XTtE5Z05bbIUQPypa/oOis1u1k9zBHC7K0USySdSYpnub5ArmjCjUumfTuSJcAo8QyKly5eDIq2sUkaRFCNXpAkqPpd8ZuPbB+ZM3b985kg68d7ncxbNpyuwG+60JwJEjx9nhxTJlOhCuytJ+QqVVW1oikpUp6nOxZxDx96KJ/h0Fu4PowIXSGtL6RaRAIgsF/rP65B5NMD1w3H26/UnhoufpBCwfPWJIQIvkxgfdc0ir08aJi7Ec+PxAEhCF2eEmWQQMAgYBAwCBgGDgEHAIGAQMAicUQgYYuyMutymswYBg4BBwCBgEDAIZIeAI85wnClfbspDqBRiU24Ks9i8GdezNrqHwwEJBOFqLBKCmzA4LKMhHyQTyajU1FSQTjnHyNFEEPPzR8M5E5VSJJ80KcZ1ulILeeDAjJZjJJIFmzZskERvgiK8lCszdST9jya7Pv1skyT5EqXvoMGK9Pp8/WeSlGCpOpj7nnvulnwFCsjY0WPVcd0Hul+jqzoa9kmWhakYQWJbuM1lbNIKLw/YQt1OkmJ0S8f8mhRTyjP0if2PTWob+9as+0x8Lq9qL6CV1R+vk8KFixpiLBasM2CdXBfvlZTDaTLs2qsR4yvzgOBBEKvpM+eKB3G1NLF0tNBYT1JWua1xI4T7VhFIyGI9dnjW7aGAx44cSZHrB/dBPCySZ1SFZWzfswsWgzhHXSSOUIcDbQ2SrLYJZSrJYtM/RYjF1kllmNfLtpFYjD2C7Vy5J6t9uhgFXBxyqQhjX73oFy8Jcfd4MUYFbSW7hsFBYBySivhnKQAbGjilCEtFtQg1Jkmoi+3iWVCFNbqok+EPFGpR5jFjs82WQcAgYBAwCBgEDAIGAYOAQcAgcJoiYIix0/TCmm4ZBAwCBgGDgEHAIHB6IUDCKAzDsxOSEiqiSE5RRUWCRxFJysibfZ9JHtHAzKWKIQQjMkkkqrFUedRDYoD1kSTT8YZYI9Vb02YvFC/207+ZLp/V2XiOEFgGi8wKwGXbQrQZrs/cIKdQz66dOyV/vgKKkOO2PjfVaewPf6yfajIXJXI8I9oUn7RhX5GClNkgsd2U+zC/Jvy4HuK2TfLperQCjRiwjN4mtiadeQioe4TPFNSX3Xr1xrOSkckJQ5HpBPHEe+xYSbGs0AzzvotL3OfCc6JcD2IZhE8+Ek24O8WXJ1G6X9kLHA4UVGCIHFmonCwSmPe/NRiwndzHZ0U/L3Gn/Mc2+QgGFZnIcen4eSbd3ndfvke1ke4OKTzzEzZy+hkvj8pDNRnHn86jZonHkabGkIADxBpw9WHc8oF8d2JcCSgFqkM+mj4c1xPjomLcVBXmj0HAIGAQMAgYBAwCBgGDgEHAIHCGIGCIsTPkQptuGgQMAgYBg4BBwCCQMwKRXCRjjFuTc8rCUpuhQGZDeIbDuWxoYmfDpi0y6ZGH5JtvdkixosUlf4F8Mv6JyXJWyTI51rB61WqZNf1p2fPN1xKE0qxmw6Yy5pY7pFL5UvL7/v0ydtRIuRLuA9u3v0iRYn8lB2TU8L7StUdX6XxZT5n8+ARJSEyS668fBmO9B+q1jAQSDdkko5g8UGwxkWya/NRTipQaccMNcMf4hGzeuAEEm0eu6tYVyhiXPDN3niIbli57S15aNE9279ohlSpXkWE33iSNmjZHXhjZUXe8S0Qau0lSfPThSpk3Z4b8sPtbFS+sRv3GMu6Ou6Vc6RKIReSQQNghy99aJgvnPSt7vv1Wihc7Sy7u3FWGoB+sV8VWoiU/JlFpw0SS7JcDf8hdN4+Rjl06S+euPdV+TTaoDfPntEBAk10+EE4dL+387/cpToGqz8/7GvwO7k2HuKHyootCJvJjnbt0tTay+cv7OX6U4b7jSbkMi9GqIxgOPCSm7GFBP1GaX4yvR+/XFcTHSAw57YoIBlI4CFISq0pJhsqhC7WK2icKhjD24Lgb3fV5fCC/0pDXKQkhSxnLiQTBNAQic6Be5At5E8VvV+EBCQn/jFZ95q9BwCBgEDAIGAQMAgYBg4BBwCBwRiBgiLEz4jKbThoEDAIGAYOAQcAgEI+Al263QBDRPRp5kQD9bP2DiYIOChFCCIzj9VqE0dFWnxXxQoN5Wmqa9L7sEul6+ZVyVd8B2E6GAmub/PTDT7kSY59+sloaNGoiV/buKykpR2Te7FnSq+slsmb9BilcvLicX7uWjB42QN5bsVZKliktN/S+UhFJHTrOBz4O2bTxc0kCMcYUT4pxn6VWoavHCBQYljWfxNW6taslX1JepWJpesGF8sorr0lSnnzSs1cvKLksMm3RvNly7113y/Axt0ivq/vI1u1fSY8u7eSVNz+Spo0bgCSAQoZykZiktzdtXi/1GzaWy7pfCUVJWObOmilXdG6v+hUAKTD96Yny6Ph75fobb5Wr+w2Gu8ZU9OubmJqsVU32sf1eEHvszzc7dspVV3SViy7uoEgxfV2Ol2zIdHKzwyBgEDguBMifkUSkauzFh68BGU9y0SLSYivWhFwY+XVKCYRVfEK9bZYGAYOAQcAgYBAwCBgEDAIGAYPA6Y+AIcZO/2tsemgQMAgYBAwCBgGDQBYIkBQjCeKiogBLhyPnzyJXLsILV7ziAKKpO26+TcY/PhEyBh9acDiLVqTvuqDbdEXQkXxhii7tLGG089ud2+X3336TMbc/JIXzJdgKLbpYtGUldt6sFqNvukUCdFeIWGQkpFq27STVSueXT9etlUZNLpDRN90mq957T4YOHiSXQlH1yZoVsnzVehWTJ6v6jnWfG+Rgnbp1pGjRwlIwf0G5tFNnyZOYKIfTwjLhoQdl3J13y+ChI3C+sHSCMmbfT/vkqScmSKNFzyuXiiTH4lMgmCYjR4xWpBkoNnW41cWXSZWS+YSxzWrUqSeTHn9Uho28TcbedJM6nhXJxgOxZB+VYqtWfyKDr7lCho0YKtePvFkpcUiIaXJMVWb+GAROEQTCGOO0Ku1omhyv8DqaMlnlOdp6oOdSxSNQmTLB6aFagu5SSziPBcvF2Qb27rjhIAJlKT1IwjmsFCsggqiLHNa5QyU4TFTLkK0OxVEcD0sC/DLCeyZcNOY8/lu1mL8GAYOAQcAgYBAwCBgEDAIGAYPA6YKA+RfA6XIlTT8MAgYBg4BBwCBgEDgmBKgUYype/my1DLryqGV2f5y5uCSji6/YBKpNap9TBbtsS66KvxObI+M6YxxRaBUM2y7E7MOuiFWeJN55tepKpWrV5eKW9aV9u3bSsnU7adOmnXINlrG2zFs//rBHHn/4QfkW7gQDIAVDdA0JI/G+n35QLgVJGE2ZNVcuatFU1q36UJ5ZsEhKlSprkU62AixzrUe/hzHLVPww9MMBn2gexEoKAtMvt2yQ337ZJ2tWfChbv/xSwoE0ccPV4s6dX6Nt30dJMRU/LO50jhDav/8nmQLya/uO7XCfFkCdlrLsp+/3KleLfxw4BDXZFaokSS0P4kSF0AbiGZt4jAQj1XFbN2+Qfj3/Jw9Pelp6wOUjkybESI45lYu7XJjS2MrN+hmPAOOFkZhygtxhLDE3YlupuFx+6z5y4b5M8wdVHD8/YuzxefT5XFFXimc8gLkAQFKMrhYZ35BDqBP/yrWHAqtkxsddkWK5VGkOGwQMAgYBg4BBwCBgEDAIGAQMAqcxAoYYO40vrumaQeCURMCOueEOWTOFaThKTcU8YSgNaCRi/AqV7Fg/UcdajBmBhPnQamn+GAQMAseOgIt+p5BS7a+DBKwfORwSH6yNbnAzgWPzBqjqOpn+RGylgFYwBEA4eWCZDkAz8PPXm2FIjY4oWTY7EBcTKD6THrf0fjd8Kd409mb5YLUHhtpUpUrTx7JakpBhE+NVHYg0pLJzPAzD+vvhqnXy3Nxn5IP335Mlzy+ShKQ8cDm4QipVKptVtdF93Tt2krLlK0jfQddKLSipfIn5pH61CkpVwXhGLtRPF4Ik6OD9URIRg8eHGGAcWTn+cn8E5ydBFAKxpmOe6ROQMKLtmWorpvhtnZ+jtFUPXFmmpsq+fT9DHuKRC5q3AJWI/+z77Lwa56tYZqzP5XZnIrJ4Drale8fOUqZsWbhJHCD16zcEHgWk1tnlJAiF3f4ff4R13A83kyWj7SUZRwJMx0NjPUzEX+9zeoCDMyy//vqLIgZ5nP3nteE5gyzPi2WSQeAoEfB4rO8TDrMkbyiQCgcsgtaHQHqhoEWc8d7yed0gr0PqeScJq+63OGLnKE8bzUZCjveuTnw+jy0da/5jqz29e9Z5tHIsyvqr8ReDA5fsR1z7k4K6BoxPOOzGZgSTEUJxLlhdUULcwsKfa+zIY+uHyW0QMAgYBAwCBgGDgEHAIGAQMAicGggYYuzUuE6mlQaBMxaBuueWk1XrdkuL5lVgsKQh5IyFwnTcIHDCEaBI6JONe6T2eaUVYXPCG/Avn5DqpSCMpG6QQRAvKVInp1M6bRdf2eXRMa/0cQrExj86QR7Cj8kJcimn1KrXTGXvpYwk1mit6Rebt5REuB8cMnyUDMYvNTUgl7RuJJMffUgmTpuWoXoSQB4QSiR0/CCgvv7mG3nhf+9IuXIlFAn09bffwX0gFVKWgZix0Ib0vVpq1K4n59aoIaOvHyzvr9soRQoVUvU6YYx2oj6SSgkJPqGqJTbR6E5yyZeQoEis+G22hUQY1WCKhALp5sGvabNWakpDhUpVpF37DiDooNwiCaVIOZAIOA/XuS82kaw7+Ptvsuubr+XF19+RMqWLKsJu748/K7eIJBQubNNWSHK9+/Zb0qP7ZVFCTBNgsfXxPLyGLFet+nlyx33jgUdPSTt8QEaOHof7xGeTiGFxoT2x1yi2HrNuEMgKAf388pjHTfbXIsK4TcKbrg51IimmiVceU4SsPvg3l6z9ZL5nqSKNTSTPNSbsP8df3X69jM3PMYWJSlByYYoUU9IxD8oxRmFsbrNuEDAIGAQMAgYBg4BBwCBgEDAInOkImH8inOl3gOm/QeAkR6Blo2qy+lO4+YLygm6IGOOHPzgiUj+LKcMOzvg1s35P8qtpmneyIwCvdMKfJ5KmfmSK1n62VZpfUB3xWk6HpEzD6Ii1DEJJ5IB8LARlEfcoJQLVCNn8XJGA5PTT9cYvGcuGP8X+8Msrux+Z/xy+zIJQuO3atV3WrlsvaaiOQ54f5FdKSpoUKlaEPcg2oWYQPg55+83/SQpieh0+kia3jr2ReyUVcceYHn/yUdn53XcyfcYzcu+990uxsuVk6IC+6hj/uGDMZ0wekkcBjMfxicZqGqV5nEbu+G04hxOXzyuFChaSPXt3y19HUsTh82E7STp3vUzuvPNW+fLrb3APOiQFnNuu3d/J8rffUe7QIpbNO8MpSW4VAGnnxDVcvuxVEHVQO0KBc9ONNyAfVCMg8LwgxS7p2EUeu/922f3dXiAMYg/Hvvhqa4a6uBFLlpH4an3hBbLwxZdl9jOz5UHEQKNqh30i4ZeVYT5ThWaHQSADAnxmYn8ZDsYdi82n1+Pz/7vbfOZif9mNi0e7P7aurJ7n4+6NHrdt17VOaIEZt4zfjPFubo/7XKYCg4BBwCBgEDAIGAQMAgYBg4BB4JRHIAfzyynfN9MBg4BB4DRAoFnd0rJ158/K8HsadMd0wSBwaiEAwmjzVz/JBXXKnJaz7d0MQnOKpb8O/yXX9rlcKhbJI9XLnSXnli2COGBlZMSY2zP1xG0TOFRbJEFlNu72e+WOm0dKnbNLw9VgKWnUuJn4khKVamvdJ2vlsYful8cmzZDipUqpuubMXyJffrlZJj72sHJpqFQXkF1oV4nxJ+TxKGGk1uO2QZpRBXLjuNslLSVZqpYtJiXyeuXn/T/LpKlzpDbcO7ZtVleqlioq51YoLu0vqCc7d2xTp4mPB6bP7YS6jv26/Y6xUrNySal7Tjlp3BT9SqQjUBjEQdBNnDpbqiIuW+N658o55YtK5ZKF5JH77tJVRJea9KJCR7uca4S6Fr68TObPAll49zilYKEwRStZooXNyimPAK+5ua6n/GU0HTAIGAQMAgYBg4BBwCBgEDAIGAQMAkeFwKlnETqqbplMBgGDwCmLgK360jEh8uZ1S+0aJWTdZ99K++bV4R6LLnLSpQNw7KUUDOwvDaAqbscp23nTcIPAiUWARmD+LDdVDsyzt+M4heHizuOQTz7dIg1rlJU8ebxKneQA2XE6pQgUWNBBwX0hXABiLffe2cGv/iMQOL7Va9BINm7/Rg7/eUi++nKL1K3dQBLzJqnxj4RObNLkjiZ8Ro8dLQMH9Jdt276Uhg2aYtAUGTWKqjEr7T/4l1pRrsigxipbqoRs3bVH7QsgzteC5xdbx9GOrGKMUSlGcmzevAVRZRkLPDd/oVXObl/VyhXlwzXr1b7YP9OemSVPT5spWzavl0KFiknZCuVxb1pzuFRMstjMWGcbXHDLOGLE9TJk8CD5AnjUBh4exGcaM3aMnTssBfIlyLznX5Q0v18+X7dWzjnnXClYuHCUxFto94sFiNWSV161y1rbtWpWl517oFwmYEjatV000ymyQteaJEuZtJtNXled9DigySH9rnV74XLUH1ZluM+Ne4PqOb2kuo6JdbF+lucxLFTiXfnlli/ly6++QliokNSvW1/OOa8qrp/lGlOXJ/b63LHlrVqsa6HXY5cuuFlGUag/rWujxzSdh8+B7mds+9R1xHdD7HPDttD1KJcso12Rst/ETGPEuvXzxfOyrzoGHfN5PVAm2vd7fHt0u7Jbatx5nGWVUjPu2c6ubE77NbY6T+x59L6clrq/Og+v19El6547urz/XC62jj/ehvxxjDfJIGAQMAgYBAwCBgGDgEHAIGAQMAhoBP6bf6nos5ulQcAgYBDIBQEanFo0qiRr1++GYQ4mbBirtHEn3kijjVC5VGkOGwQMAjYC2jCql4yzxTgtJMW87rCs/GyPtGxcWRmcQ+n2c4Pff4SAi6QDDOQ07ufNX0AaNW0ueQvmVWMiiYr4RMM1jfUcM7nkdoHChaRho6bicMPdIV0exvx4PPYXXx+3reNwl5hFwB6OyXRHyDp1fLCs6shuH+9DunusXbehVKxUCf0EaYl9JAbi4w+xDraB8c5ciF2WkJRHGjRsIt4ExC/D+UnQxScfYslRTVa4WLEs2x+f/3Tb5n3Da8T7geu8llySxOEyOg7YRJfuvz/NuoeYh4n3GvOSDGLS9wzXWTePMQ8W6r5rXr8O4uA1k7kzpsi0yY/hnV5bRt9wg3qfayKKefW7neWP5n3O/Ko/YD94fzNxm3Vyoozuqyas2H7dPradx/VzQzKL/WBeRcphXfXRXrKc7j/Pw219TtaliTcei83HbdbDxDKxiefU+/TSjWvBxPs91rWnPh/7pRPXSWJ7EJOPS62qjN2Ozc9y9905TiY98ZCqgv3Xx/X5dd16yf3Mw6XCB2Q0E587/Yzp2H+sL34fj91/5y0y4YG7o9dElUd98YnvHhLg/NENKhOf/TDctwZBqLJ+JuKm19UO+w/PRSKT6bZx46Rx40ZS6qyzpGWLC+TJJ5/AdcHkjyDc5qK8SQYBg4BBwCBgEDAIGAQMAgYBg4BBwCjGzD1gEDAInFQIwMyl2sNQEUx+zFRv1LiMTJmzSh55+n1pXhPuv+qcLa4Cbsw2RyYYUpTaIzaqvVXU/DUIGASOAgEaxV0hK8aUy++RQHJQPv5ym3y8fo9s2HlAbh/RDsZJGsOPorLTPct/HMeQBn9t1NWKlABUNzS8JyNe17X9+2S4AsqoDCNwAq7x5OlzofzzKIM2De66ngwFctlgGRJejGk24rp+ciTVum9ii3kRM2z6zLlw0ej7W+dgm5mUQgwG+TSca8jA/lB7pWWKE+SDMuypaXMlCYowpth+xa6rg/YfN8gxGtCzOx6b93RcJ5kTex+xj6l4l5J0BQWhuuwPgQQiOWa/h7nKxOvPstY6ySiombCt7yUHXJNiF9RbQXVPktzi8R5XXS79+g8BmZtPERcLFi6Q0cMGSedOHeXC1m2Rlwo0EE1akWeTmuB7ckys2yKnLDIM3AkIE6sIz00yR7fXarPVHrY3thw/H7wkl4IgXHBuK+SeU3wY9xhTj8mNNqJGu7+cpKN2R8kxTciwbpI2mjy0cll/NUHGLbYtvQywROM9jKUaCOKYQxLw7UNBXRDrTvSFhBkJIkUY4rkIAa8ArhsVlRyb6VKUajySxal+YMk+oZEBP1AFJiSdcOWhMt0uRYqdJf6IS7y4F5hi26V22H/8aAtJsTTU4fO6xO9HvC5X+hhCckw9SzgPCbEIzs/nKoy2uTw+LAPC5+2Lrduh2iwgabbin9DF3jf6nKo+4JLeHlwTNNHt9KA+9Bn9VUQdCTTgnDm5ZNOmzeh7RCY8+pgiZsm/rV27RtZ//rns/GaHzET8RN4ifhK3NtGWuR6zxyBgEDAIGAQMAgYBg4BBwCBgEDgTEDDE2JlwlU0fDQKnOAL5Enzy6tSr5P1138vHa7fLMy9+ItWqlZYGtcvKhY3PUbOl9QxtOIY7xXtrmm8QOHEIcOY9PwTSUlJl09b98tHKnbLj2wNStUp+PFuV5Obh7SVPkkfgRQ0KMmQ0j9eJuzhZnEmTGlwG/JZywwOjNA28LhjGr+h9TaZSTriZczhgqE6AO0zbmEwjOa3lejtToWx28DwhGMsjqLPz5T1hFM+ckXWTVKGRm8qtY0nK6A2LPtUhHrhIpGqEBv+uV1yu1DDOuPsPhyQhXxLOQ9LGUsT5oWjxgMkNIz5evGqN7VfKGhjOszp+LG09FfOq592+7k8+/iDIisJy8NBBee/d5WBmRIbdMEIu7dJdnpjwgHyw/C0op9wyePhI6dq9h+ruY48+IIUKFJFDiHP37rKl4sSgcN2QG1Cmqzw+4X5Zvmw5iBC3XG/Xw0K8x24cNS5KQnC7R++rZdzIIbJnz3dq/+bNiGM3/gHZ9fUOKVS4iBQuUkTuefBRqVixYo4wT3j4HikCl5tszztLXxeXzyPdelwp/QYOVsTLE+PvlUL5i8hfKUfk3eVvytlVzpYJE6fKUxPHq3KHjyTLW0tfhXrSJQOuHSo9ul8pjz1yt7yz/D0oD70yfPj10qlrT0VWPTXxIUlKKiCHUw/Le28vlyqVq0mHTp3l/XeWoc7pwvvOi3uWadqUJyTlz79k5Lg7ogQQsc+Tx42x1rqJ2Yb8eQvJwT8Pyvtvvy3V4N7ziclTFCm0dOkr8sL8ebJ79zdSoUIlGT5ylDRr3grPgls2b/xMxj/8gOz5dpeUKFFS8hYoIA/c94iUq1xJkV//e+UFWbxwvnyN45UqVJYRo8dJswsvxHUQmTrpSdm1favs3LldBvTqKV5nSCZNnyN58ubN4PpRg+7zedXqrKlT5Q3gtP+XH6Va9fPkttvvkipnn6NIsEfuu0eaIFZh05atlHKTz/+kJx5W6yPH3CyTHh8v277agrY5pd9V3fBcu2X6M8+KC4QZx67YxOefadKTiGnoTkAdHnl1yWLxp6ZK18uvlOtvvDmaXRNk0R1Y4XA0pN9VUuj8G8iSo88g9vDewgIeB9Jk7qxZcuVVvaTNhS1ji5l1g4BBwCBgEDAIGAQMAgYBg4BB4AxF4D8mxqxpndbf9CvgtP5dpIwXNKlgrmT6QbNmEDAI5IiA/fhIyH5sOMMZ82+V8ftkII3CYhlzYadVyYlYRkyYh62WQbdladWDkzeItmMWdQjqg06tq8jFbapIalpYVqz6Wj7Z+KPMXvCeRDCTOzUZ7rNgkAmHjiiXWlF3PjCAWskyRgWQRyV7t1sftnMFteU1YrXAHbEVERGfyhF0WbIZZ9gC2IH2WjPPE5QhWM2yR86wM6O8xhm26lMGadZkS+Ii9jLssPrtCluGtewYCJh+VTuy+6PbpY+HaTGOTbbixeFMUXvhMM8+auVz2bPjwzZMYRi2g5iO7vJasajcAX1lYitNX484LbwiYuEF65R9Hnu/jaPunx7vPbbBPgCjrZX00raEO6z7xqXuZJS2+xXS/bP7BYdP6Y1Ra9Y25s2rrRDuFSaXfV3Ddjth7lf700trg53dDh37Lv76RxVEVj6nwN+nStZ2wFZAhhx+9U7z0kqXU7LvO3FkvH/ARKhSYfu6idj3SSjj/ULlBVPQvv8cmN0fAYniQ3WMT+WAe0QmjRvvRxos2a7zqhWXtq0qydgRzSR/QoJSCogD6hrcckn8g6IBl4UjEFT1ROyBxmnfNxGoC/7LxHGF95S+r/i4qO8ILJkgvlDJZd/gEdswy9uPriJJIp3MiUoKEgtcxpI+3OdL9ErnLp1zab513aj0sdKx9VddZrBRPhTr0LFrLucC3rnmyJhB82ic6EClCO9nPjKdOl6WMWPcFq8fAFHn80CpwgQHjJnOb9+mKm9Wx1XBf/GPfu/93VPo+/fvlncDFasNTvl01VrZtHmLtG5/iVx//WjZ9Nl6Gdy/v3TovFSKFykkN9w4FkqbT+SGwYOlfr16UrZsRfn4409k+5Yt0q7tRTJkxEjZuHGTDB7QW9q2v1RKliojo28eK5+uXiXDrxssNetfIBXKlEhvqrpI2MRYsvOLrRJICUjpUqXwPk+RHh0vks7de8qdV/eR5JTDirw58OtPUrF82fTyWHOpmIDpu9Z/8qls2fS5tG3TRkaPHSu7v9sjD999q4T8yTIEfVq3Zp1s3bxRGjVpKMOHDJW8cCPqwDv+09Xr5Evsb9XuIhk8YoR8vna9DO/XS15/5SUpXuIsGTlmtHz80YcyeNAAWV23iZQvdZasXrFKvoSStnHT5jL42iHIV1JKlqssfXtdLpdf2VcaNm6iiNwgyJ2pT06S4aNuwh2I5xXkLhOxDx6xvnT8UGB9umKNbEbcNZYbNmS4FCxYBJ8Fbnl21jR54J47ZRRItf4DhqL9m6Rn567y6rIPpF6jhnJF1454zrvJNX0GYEwPysbPP5PfDvyqiLHp06bLE+PvB5E2Uq7q01+2btsm3bp3lFdefhMYNJVmTZrJ0peeF58vUXr26IkBPU0SvQmqbUotlw6tWqMi7ZH7b5f5c+bI6HF3S7nypWTJC/Ol4yVt5e2V66V0yZKyYsUKyQdyrkW79mpsooJs/ecbVfnrQaI3aNhYXlrykuTLV1Au69oD+IM4h6pUT2aKPSWVckwfrVgn27ZskpYg9IaNGCs7v/1aHnzwfqkAMq79xZ1A4qWrFGPLj3/oPtmxY5c0PjckBfDNOOi6G6RPn36IozhGPvjgPQxIEZk+ZYo0bdoUpC/ejXbhYxsFY89o1g0CBgGDgEHAIGAQMAgYBAwCBoFTGYFcLHSnctdM2w0CBoGTEQGHbbjX/JTTJhaiBgqb6IjyHZYdP9qVIFwd+eBiqHOLKuoXcVszocm/0O5Gsy9dCNE+b08+jpbliuZ1tOHc5gmiefz2qKj5seggadm2JGjviBo4cR4aVXhOJu0CEl6KMiSb98rUJpsXTDfYaz7ENuBnqIQbcfXGH4+2yz6gcY3PBxudSvo6aAuRJgo1MaZpEObjui/uesTXq3miKB9pZyBGKsU1SHfTY+Mb0P3T54kWtIpjgrtK9kJCcfl1fVau9L8e+7y6fo9dv67HZR8PxVcQ196IzUPpZun7RJ9J79fbqTgBsQvZ941L90tniF/q4/EV2fsd0RvSKqgN/Tq7vp66H+wfj3nB16l22Hjp46yFzwl5OB6PAIgQjM9UH7nxEMIuapJBwCBwmiJQ6eyq8uTTMyUB40LHTt3k5Vdell/275Pps+ZCseOWizt3l0UL5srKj96X3tcMEhfGhDLly8vEabMxbkTk4i495DUQLYcPHpIHF7yEMSMil1zSReYueE5WoUyFq3tlQI6KPT/cb46+fqDUbtBQLgIpt3XbF3Loz0Ny1/2PSYE8mGDCl2kXjEFQX5HwzSnR/WOhQoXlqRlzoT6CG1i8eH775WeZNW2K9B88XI1pxUHePIu20VVhEGOcF0olul8uX6GyTIRaiqlDxx6y/I3X5AhUXuPnLwFp4lB4LIZa6eOVH0oFqIwY765EydIyc8FCNS2BLhZBUUvTZhfKc3OekSZNm2DChkPegGLrSPJfcnXfAZmazvOG0Se6CyV+JUuXllnzF4OYst5E8DQoEx66V2667W4ZBCUe8by4fUfZ/f2P8hRUWLfcc7/8fuAPufOBCSCaEFsQ/e1w6WWSkEBcw/LkhIfk+ptuk+uHDQf55ZRL8dLe9/0PMuXJCVCNLZV6DRtIkSLFpUDBQtK1x+UgSXPGl6rSObOmg/C8WwYMGYZvL2DV+TJpUu98mQhV4ROTpyksExISo9eKk5LYR58vQZFgJBKLFS8JpWEh6XlFbxuTrM+rCH9gSPeQFSqdLdPnLgRWdPUpsvGzz+SFefOkI/rLyUiMA6fulRiUV7xP8suaOHDtddfJ+PET1NHHnpgodWvXUC+7j0B40guBujlwdOTo0TLhkUdV/LEg2m6SQcAgYBAwCBgEDAIGAYOAQcAgcOYgEGdiO3M6bnpqEDAI/DcI+GDQYArZSiUIwqxkMzoJAYuZSLZHJzjDUcdpyCdZkwRFB40XqXbwEWXMx36nXS8NTiS9qMTC3HiUtg0dNmPDKBtMeuZ9EIaf2KQNRREYZpgQ7cM6bCtLXFZxCdoEgxNBQEIwxLgQfyMEQ5cmzjTRYhVGPXZ/qFKITZqg03sdbtJPaJ9WBNmZoYVTa3AOZu/JZgFlkpWsGl1aOmjv1e1OL23jY/c3HGX2rPO4glZ9NHQlwvAIv1DpRbNYwwRxlTRBB9uqSiFdr3V5MfPfql8TNKk2w+TVzFqUQbL7qy+jXV+0FXZ9GpZ43K2zg9Sz8yXY7bMFfzD06vbpnHFL+7x6r5uWy5ik4dX3k0Ov2LdNgk2kBVAMtwcMnTGFs1rVTK3GQefR++kTCkk/P27bkBe08YrYjGxEP094aGhnDtnCQBpjmTQRyXUH49rguHIxh3JKlRcJwsgcQHwbC2l9+eA7jUXSk32/6fZA55N+zKwZBAwCJzUCVSqUl0SMUXRZyee/YqUqUqpMBRXrihJRkuPnVT9Xkg+ngYwgoeOQqudUQ16oiOk2EGNLlSpV5KwSpRSJw/1ur0fq1qovR44cydT3FL9TroHC6vAfB+X191eCw4jIudVqSGWQVBc3ryct27ZHzLH20rZtO/IbuaYg2lSnTkMQKRx3SFMFpWGjxnDl97hyn0cXoufVrAGiHyolKoSQn31l3spVK2Npuex0YCAvV7aclCpdCnWA8MIEHKbzzqslqSkpChu+52ued45w8giVWi4wNlTKjhh2A1w39pbxf02XpDw+uOubI506d5YkuGKMTwGQc0qlyxhhOFj17Cpw04g4XHhRkGjbtHEz1F9/ydo1K6Em26Ty8jxff71d9v/0g9Soda5Uq1pRWrVoIC3btJNWrS6Stpd2Qky0kGza8rkc/P1XWb9mhYz4yipLVek3u7bLz9/vUa5XiQH7Z/34nZFzWrt6Da5VsvTo2hXxyviOIDYhqQ5c9u7eowpTARa0v7+4w4vrTwJQv2usMxBP/bP2ZPWXxBjJ0zDuo7IVyiHGGjTXiC/Gcyeg7SnAj7hl9xrd881WcfjwLQaVe6sLW+OMuL5uL+qqgFLoLeo4+PsBixTjuxPnc0GSim5JcppfvLbryKzaZu3LDbHsS5ojBgGDgEHAIGAQMAgYBAwCBgGDwMmHgG1iPPkaZlpkEDAIGASyQsC26yv3brDHwPhi5fKCEGPSChcSHjSqRJOW+ljZoruPd4X2sxQYpQoirkooABIJRqGcEkmKDMne1v1w2pKocBwxol39xRNmGerihpYw2bjYvEk0mz5PdEfcStRVo13eBYVfCoxMfgSZKpgPBrBc7EL00MYukaAipaXtZVQixSaSiEwR28SliZyIdvGmGcNoByxTWFCZxdJrioMJBi6bSFOt4AmsC64JOMYhYgppF572eSIat2h5lQ1/rPPqrWO9fdhNGmKTwWeF4KsvbxbGUl23WmoXitlIDuOJtTAtekgR28VYPPEKb2Xq/syX34VrmKZUIMyvYVXKBWDuQMWsi4oQf9gviSSMqe60XTjqy2ehxxrSUxhtVdc8fZdZMwgYBE4BBCIgBjiEcBxQ6huMV2F7jCSxwbhYTFRmkezwY+aDA0QC3a+qGHIgGrj0QIVFgiSMFwRVXnTbqkfiWBgG9+kpu3Zslbc/XCXFixZHXsSQg1u89z7eIPPnzZQP331bXlq8UAoi5tWCV5Yhlmi12OKZ1jkxxolJMow3xbGZpJNLycURpw7jLftAd5xhjI9020zihXlJEPENRRUYy6T5/VY57OUkEAdmApBUUphgm3HTlJqJ/cU2ySriRNxatr9Yihc/S56bO1PaXdJZ1q5eIXc/uFrlIZEUm1R7cE4mfr+4EG8rETEcmYjdvn0/qcG5QZNmClPd3lp16qj6iOm7q9fLrFlzZNUH78rIF/pJfrgxfPG1N+WXn37GeB2Wuo0vkCTEWqNLQxdmplQ/7zzx4np5sC/OE6U6b05/UhCbDZVK0SIlgCX6gr4zqfh/qF+RWPioCeLbR50PZGlamu1+WuU8tj9OXjskxrZze3BP4doo17HYz3ssxZ+aY4XngLBb98lqlPXIZT27yRVwF3nPfffKwsUvKVIMDZXCJUrIgQMH+RFA0NV9wkpzJ8VyPLU5aBAwCBgEDAIGAYOAQcAgYBAwCJyCCBhi7BS8aKbJBoFTGYEwDEskrL7culdefGMLLXJy74g2MNq4lIEiGdYiukTa/80BeWbJZxL2OKR3p1qYJQ0jGox2QRjxaaxygcjwoC7OCOd/NOZ71Mxfa8YxzHgwqDAOD2cDM69DAjDgMfQS3fRQJUPDVBrKMXHmujL2pLlggKJtigY1EkEu7FcTi7EPJANGTZ4vCYQJZ3+/uWq7TJy9Wt5feB32eIWKJxIhQRri6I4OlZEE8tqMQlogVXyIgaNmnDN4DtqB00gKMHFj2+XwwfiINmHnd3sOyQG4qGpUqxzmwXtRF9quGY1sboJQGAHrlYXQanMAxI82rtHIxPIunhCJ7QpTyoTk8cKYiW20QLUnzWYYqfR6Y9VXMuPZz+X9BQOR08qvCuGPrktvR6Bcun3iO1IwIZ/cOqSJcl214Yvv5c3lW9XM7BRchwgMlqUKFZBLWlSTUlUKKltbPhgvSTKGbEkeDXgedDgQQWwutNmJafrqXJDeaUzDqIeKOBoniTmvK5UBShEAYys5NpcTqidcp6+3/i6LXt+A+nF9oTp0wbdhiaJ55aLWZ0v5MoWArm2QQ/kAVIAeFcOE9xvuKxjkWCcNoaloP+9PIkjXXB60m7j5gCnL0djK5OS9x0y8l+Dv8Z4nXweuDnlkXCf0A6Qt8M0qnlUExlsaGyNwXOnA/RMGKcnkdfuUUlITv1rJmIR2Msv33x+Ug4eSpU71sup68yLyWVm6eotMnv0x7s+hOK9HPPQlhhRCP5jU1WSDELcvAXUFoBBMxH3I89CdYjh4BFyvV1JxXUiSInieKufFOknMMIizt1fvlOdeXCeLJvQWV16UhfGRzyZVBOyLSacvAiROOL7QrRlTvGuz/7rnehxXbcNzyrb+14mkEhOJBkUwYRzRYxjHiH8zKaKBOKAJ6v2kiB5sY0QjKUHihsQPE/MyDpQXL0AHmHGS73xHhTmwIpHUUavIRzJEjdM26ePFmMLYoEzc70Adg/v0kq82r5c331kBUqwYxlQ/CCeMrSDbkhLcMnjwdTJk6DBJSU6R9m1ayIxpU+WJiZNUHdn9YRv3/wQyCYnvVAd8ze7bv1/yFywgeZLwlsY4xbhV7JdSxtrjKdVv6vsA960fpFgCXOsxj05Uw1l9Z3m8z/1Bde+QdLMwDEMZhfESAyWvYY8r+8jLiL21d+8eqVmvvtSt20BVZV1rC0/uIOZMdIFIRTsJOlx+bENpBzxbtmwjzkSnVKlcRdpd3IE51flUIW7hPZSIfg0beh3iwg2Dy8aAtG/ZUJ6e+Lg8OGGieqdUqXy2dO7cQZULom+ajAtiYoTLZcXfI23JVuV2tzVv3pKNlmXLl0o3kExUtzHt//lHKVSkIF+Q4oMi63ByMq6xD+fEexrYHTzwG2KwQX2H9y0JMyfuG+1GW1WQzR8Sr8RdXTfg4gUmvCzWLx3HbIrLJRe3lXWrVyrOKwAF2IJFC2XxkufRblTC9xdSx0s6yszZs6JV0PkArzwn/2T1To5mNCsGAYOAQcAgYBAwCBgEDAIGAYPAaYeAsViddpfUdMggcHIjQFKJxMIP+/+SNRu/k/Wbf5b31n6rjES65TTkvLnya/l0015Zue5r+e0PujKyjnJWup5VTMN7EMZFEhMkxbhO0oQECuxOyjjPUto4z6UmxSyjvj4j85BIoHGKRioaYmj058xy2H5APDB2Cc/H9tPNTxDHaOQqXSy/tGxU2SZvrCGVRk6SCZrAsWbQQ+sE4xdJMZ2UoY51oq00ODKRFFN9RFWvfbhT7p38AdqM9uEHG9RRJSsvqBVb3kUCRxtEiY3CDefVpBaNQYokQ0eJSxAEj2oD9tOwV/qsAtKiYSVlwItvgK5LL3fuPihrPv9B/s/eVQBoVXTtZzvoZWHppRvpVBpFuiQEQUBCkBDs+FTsbhQUQUX9MEBRUFRUQrokpUGQ7tze/Z9n7jvLy4oLiH76ywzc99bcmTNn5saeZ55zerW7wmSVLHsPHMPshZvMfhAbK6PkN3M3YMD9n+C7eex7tc1n85JMWqQ76cU/JVN/SqpLSeBLsAAsymz71Zzw+zFgH/vp133HsYDjTaBYGA136t8fFm1B//um4IeFmzl+KIQvCRRTkv06mGPVlq9tjV0ljQ0lozeOG4039XsQjcEyvlN8srA4bohxctidlWRM/j0DnMao7RddFKgByJToM0gaQJeglgy0wWyLxqZE+fjrVbj3+W9MXp/d3WwXjM7GvivGfGKDeW204IDs1sawzZw6JjBSTAi5oTIGexascSwDbghBNSWBZTZZcPnaK0vTOBqCSV8tN6d0rfpIfejSv1sDMmJr7AgQ+6eBYtK8HesZt//OXhEgZtg3FEJrvUcMsO93b/1V8um5570LTOWsm88rLgL9KYpJOq/FA8r0PuTEEwIs9h0isEPvBwFaAmqUT3oO9IGjOp/EZ4ieAXqehZGpNKB3T8z94Xu89f4ndDscSnd2B7kcwsnjx7F18wZzTs8NgWgpBNaTWW727NnPqwbJNueHHzB/1mwDMu07uA+jn38WrdteZ65Ve/XMkuzJLFfvCSWBf5Jb7dIxM5nCnNG71gP61Aa97/U8VBIIbAA1o0NdqwkUXnk39h2In9dtwORJ/0XHzj3M+8VXla9Ub2XZePag5DMymuduCiIjQ9GOcd1G3Xsb3SduMNkSWf9uAm4zp03H9q2b8OOcWfZy6FxyYhKyZsuGyKxZ0KZTFzz24N3YuHatJwOFUIyxuTO/NftxcfGIicmHX7ZvQVxcgtGB0buvTekF+zbEvGrQpClefe5pHNh/yByd/sWnmPfjLPTqN9iAg4UZc+7TSZNw/NhxMyb+O34cli9ZzHeivm34vcRxEJU7N7axzoQ4byKS3sPnSnqeBDOGrPpHseB4+Zl3n59CgzjGdC5jGjzsdtzYuzf73Fe+vi3MLA/vPVo4tigef/ppM87stTqt74/feyfbfG7tNOA04DTgNOA04DTgNOA04DTgNPDv08C5/zL597XTtchpwGngH6IBBVFPUhwmGeE4g7dejUKYMXsDDTycGR1E3g6NWAk0OH09bzMqVyxCwz2NdjTMh/K8lkQaTBSXLECABek4mnet5XSSZkAH4xQNtFrifItyaEkkI0YoQCLRgDgaVJJ4LI7lpYlRxiWUwJQWlZ8WQiMvw4Mkh9Cowzyc8ywkhOAV5SATKJIggWJxKdpF5coFcdewJoYhpDBPmpishY6FzGJcBAqBINgSwBnm8XSVpyWJ+ydo/JOBMYmyJnOmtVkYWywpVW4ZWT8BlxTGedKM8mAx5FhBigySmSzqZtr6qEfKy1n+AWwza0a85GEd8QJSZFSiETOelqVw5g+lZYgwGWUkW42yplA2mqQQTLkUFKtqxWK4d2hjyPugcb1Fw6FdGwSI5dESZZaPv1yOWmXzcLZ4VraDABfrk7fClIhw3HzTVRg1pBmeHtkSE57vihy5QzH2nfkI53lqmv8EdLLdXIJopdWSREZSCvs6jm1IYKwRgTFa1Kfq8dPME892isll44hJB16iUVN65IBJ4zhK4Xi7/cZGuG9QYzx219V44+mOyJctEG++M9frd5Z5WmUaSSQNt8lU03LStyRxfMjloGbGy6DMHqQrL7KjKHMSdc3eorySKRUnNZa4TpWbxjQyEjiomM0socynxe7bdSjLCBQLgfeCWqj6AjgjP1T9w4EVylntwfECuVQmr2fsMdWpuGACsFKoiwTu65iC4VSvHIv7hl/DsUDjtfqWMeySgjhA2LcpHNsCMxJlGeRYTyIrjKZS+ZRCAMeGliR2eoJuOCo3iAHqdI2M4NKrFhPzjsbhaxuXxZSZ62kQj+PNyRNMYo659OdqgDAExxrvP+o8ns+kpCD1J9mF7Akt//QkQFmsECU7YcEAK3p+nCNZV346ZcHoc2T7f3vIANB8wOu9Z8Gyv7IxYjl5QIj37hFQo8WAc3zf6F1H8rbJo2eDsC69S/QeMWCFnjVkWRsWEE8G87mrOGTCIVJ5v+t95jGEWIZvAoPAmK+mfY5Dhw/jmgb1UKlYQZQtEcvYYsXxwD134uixk7i5f18UL5QH5UsWRpkShVAwf34MGXrreVUhVnHjZs3wwAN3o2SRfKhbpQKi6Nbwscce5/uSz0vJS9axXDsqlpSeXWKGk/eGgOQEvlfFCOPzj5MuPNacAEHGswoP94En0gcnPPB+Uxwzw4jiWJVbXIE+CQl8R3M7T1ROtGx5Ldt+Gn36kT2udySLzZikQ/Wz9/7ge87oWuAYn8U8xwLw3HOvoFL1mqh/VXWULV4A5YvmR0NuryFQduTYCTLvrkfhfFEoXTgfKhaNQcEChTHitjtZXzJefX0sY65V4rU1eT4GJQrnRZ06vHb9atPHAimHj7wD8adOo1SBKETnCMcBgpSa1CHgMGMKI540+o03EZolEleUjkXpIvkx9Oab8cCoUWjauKFhCt4/6lHzPVC5VBGUjo3Bp59NReNGDRFMeaQHAaV33HkXkhNOoUTeHIiJyoa9e/YaoDFjfbrHqQIysDl+UhMMwCrGYgrHUjL7iz98LzEDkyYcZUzq81fGjDGTkDp36IiiRWLNGC1ZrAR69boRiwnYRZOtTkWrhD+wZKzR7TsNOA04DTgNOA04DTgNOA04DTgN/H/WgDeF7v9zC5zsTgNOA/+vNKAZ1rR9mCSjSbP6xXDfU99g78GTyB+T3RhFlq3bjZMn49Gkdix+l3V74QAAQABJREFUXLnVzAT/6efdmDj1J9w9ojki6V5Rboc0gVgkorcnL8HR02m4rW8tc+yjLzdgw6YjuIGspTfe/R4bt+1HVHQU2jYojg5tqxiDlWyzX/ywBYtX/Yr+XemKaPzX2Lj9IOrXLoU7BzQEMQCsWHsQH01eiHUb99AwFo4q5fNiaN8GiKIxKSBEBsVULFq5F599swEvjGxu2iRj2LZfj+Ddz5aTofQL0mhsKl86P3p1qI5qFfMbZpjaHUdW1oGjyRg38XvWswunCAYWoRHrmivL4rpryuCTb9dj8cod1Eca7njic6SFRpAhFYhHRjbLvL8FOjLt3n0Eb3+2GsvWb8bJE/EIisiGmpUKYHivq5E9ghnYvtOcvf3Ua/OM3nt2r0E9E+ygMVQMvRdG/4jWjcqidr3CbOM+fPHNSjx95zUEbEzx6T9iIikJIhK4Nmv+JowgACbwSkwr5VdsGhnd1F9iVCnlzBFGfRbCzLmaRU6wJTwVn369FstWHcCALjXxyvjvsYn9cVX9KhjZtyb7OxTb9hzEuxNXYNm6fdTrcVQoXQA3dm+CMsWzCb+ju0ka1EzpZ36CRAH0JQFZShp/cquZm4qoXL4gfqDMCadTMPX7tfhp42H0ua4q3nh/BtZt2ovGTa7Crb2qG2aVxsOkKYvNeAgh6FStYgyG9m6J3DlCQHszxxWBOJb945KtePfzNfhl50EUKpAb/TrXMPWq3zVRPi4xDR9/OQ+nOGO/d9ez+/ONj5cY12XdOlUmi03K817Tv+w8iXenrcaCpevZR0B0oexoWrcwurWqhclfb+BY20bwNwkjHplsDJFZGKPngVuaYdGanfjs2w149vZrjAz6WbF2N96dvAY/s30ChWvRVWf/rpVomI4mEMZ7im2YSgbaip/3oG/HOnhl4lKs37QdERFBuK5FNVzf+gqy7qhv2lHVXtqL0bRmcbz5wUIsXrMbtSoW4FjVQPHcTKZX7Db+FA1oHPE2NcZsb9T/KcX+oUIEXIUx/tLol5/nmAhHn/43n7ccuUezoJgy2/fBuS4Ug0TGcjFEAs9hCD/XNf7HVq9Zic0bfjaHSpcphwoVK/uf/lu2x70x2tjl+918i3nmvvzCC8iaNTu69+xj2vlXCiXXf0oaQx9M/sIAMtr3wJoAfMhYVeluMX36/nT6dzwvUCgAb3/0GQEn9gUfuBaY+GDy5yqCfeqBIGKSTfniWzJ/9OzlhI6wMGzfc9ScN+8YiqD3pLaTCSzJJePa7buwf99+rF63CjWq12WMMe99J9ZWZkmstHz5CmAS27KJ/RwaHoGiRYtpSoF557z70acE9+ggWIAdAZUA3/vg/Q+nGrZxHCeeKIl9POnTaXpZmee43o9qzzS6fdT4FOj00RczOBnAAlveu0TXSi8Cd/dR/jYdOpty/QFP5bEphDpJpn70/TLx46nmWptXY1zfFFmyZ8GYtybixfjxWL1yBXLnyY9ixQqbPgjht8+67fuwe+9ebF6/HlWr1+JzOQLhoYTFqatQtmP02Lfx0pj3sHTxfOTJWwAlSpbgg5pTHvhMFqZUtGQpsqV/Ms95A4SGeew3K6P/WqBp3ugYTP1qNo4cOYTtWzbRVWRNTd9J7/+8BDFnL+b7ZN0aROfJiwIF87NjPeBVY0H1lihZGrNZJz9zTOLcG59evfFo69TYk77fUb+xL/QKjI+ne9+IUIx/bxL1o28UrxDpPCNL1Uza0MORA+yjKR9zwguBNuolLZAznZh4hVmb8c49l5wGnAacBpwGnAacBpwGnAacBpwGLm8NeBa3y1sHrvVOA04D/0MNKE6JDEkpREnEcahUqjDBg6yYQdeJfQlIMNQVvvxqIwGqIsiVne4IOas3NSEepUoXxrotx/DJ9KW4sUNtgga0vTBvPN0Ivc9YZZ1bVTVGfT3Udu0+iuXL12LHL7+iVfOqaNWqJtav24nRk5aRtRWKTs3LIokZN+7YhZWr1mPUnj1oUb802jevhADG90gmzWnT+kO47/GPUbJMLG4VI4yxxt6ctACD7voE45+4DtlzhTAmE2db7zuKn1YRlKDdRbPSD7Hu4Q9/jtgCOXFX/waUMxVzFu/APY9+jjEPd0LhEnnNLPy9jB922xPTkCNfTgzseSUiI7IyLscpgmWcxU5jUPXiebEoT06sYYyx5k3KkS3FWGdy4+dj4/xelwXIAERj0LodJ2k4D8Ww7lcboOTIwUN47/PVuOfJjzH6wU4GrMpGg2X9BoXxwPPfokCBCAJh5Qg4peKBp78y7qUa1itAZhxwiC6UVqzaZQxWAkGUgoWeMMlsadwf0qj3/ewtZP4FoUalIuw3iioWF/svnv2cRoTMsMHYb6nckPErnp2QxphswaE0fHP7l+0nsHzNFvxn30G0ql8S7VtWpAExjG76wHhrRzBy1BcoWTg3RvaujlAaA2ct3IaR97+FVx/sgnJkGcjmSUIVTV803NJ2JpYYaQvUJ81/ZFgl0wiayLq0BPGcjiXRRaGJnUNEbceeU1j+0zr8unsPWjL+WbumNWjUiyTrgGNlzX48/Pg0lC5TCPfcco0xsL314WLcdNfbGP9kd+TLlYWAViAWbdiD+1+djiurl8MNnZoi6VQiXnlnDmUIQJECnKnOdofS4LrhlyS6ETtj9JWeldYR0A2LIDNOFkzpjfl/2bkPw0ZNQ0zeXOjfr75xW3jqVBxdVJ4yxuUqBAgXL4vC4eO70KxRZWNcjSTtQ3GBDuw/gZUEWGUP1P2yfssh3PnkDJQuVxwjySRLZZyftz9ehqGjpmPc452RJzqLqXPz9mNYsGwn9v5yEG05/tpfXRLzlvyCcR/MR5E8kahXsygBkVCE09JJPgWi8mdHaI5smLd4J+pWKmbAMhNzzbNHcv+fYYS0rKoU06vSMMcGVR3AMemJymcTu4W2dNnIue2Nc6Iypn/O9+ML4fa72S5VD7qvZCw2MgqcZExCJYbS4z3LfTIIBUTLIG2TAZb0UGEyw8qe4FrnZEyWkVnu62ScTzc887jOy7Wo+lJGa7G9bNwf5QshuiEW4eIFC5A9q5gYmadAISJUaSrphopFJBajYh+ZWEQ0YAexPIFmio2kbSWxHZMTCe5zX//UN/GkvoYSkFNx0odxl8a8ctGmY5p40KRuVWzbshUVK1YiizYBa9auRo9+A/Hcy68ak7iABMmvpLZID0oWiLPAjzno+6GpnveHx+4J44MpnhRo6VuMK4EI1i2ezaN3nRab2DuYPesHZInIgn43D6VegTnf/QCBCz17D+D1ntFfzC656vO/1pZxKWvfvAlTBN+sugHOJMqpMc/WmWMB1Ltv9DMfn0U8GkJAR8nDzLx7WvmUdD5FSDl1YcrxMpkz2lcOMbBVpRZNalHfCXTSfkyeKMQ0bMQtJT6bmTfNNzHAO8Z+Zv8rdqJimHn9o2c79c8xWqpceZPNyEHoRoV67VFpTJqZ4amXMyg4pnkoWPmYDDzmA820b9rBvtJEES9p2wMHLUiotZb4+Hh8MuVTLFq8GC+++pr5NtFYUJI6zHvIK8Q8a/hSMXt2bFhQUgc1lkziGIugjLVqeZMqTAvYHr07BaTlY4y2mCujffklA+8d3rvSsc6L5Va3Tj3zLjBHybb2rlUfqw62yzz4BLLz/uLAUBwwMcHPShyPGtshlCdv7lxkW9XQUGCbdO8F0E0mWeYcp/oeq1i+PONxkt3F54iS3ofJjAFq98XMU1t1n+kdrX5i4fpNTwK6dP9rEpD6VyLquWPYzdS1TZ4Mdu/MOoVse8WwVbnSfbBm5nBs6EoTD04zU5jYWlO+P0BvTrgfpwGnAacBpwGnAacBpwGnAacBp4HLSgPeX2eXVZNdY50GnAb+SRqQTa1JvRKYOX8z+nepyiDuSWQo/YIn72iablBRPKXw8EA0rlMUM+ZuQp9OtY2RXzaOabPJoKGBs03TMjR00NZDC4hcG8VxlnHPNhXRqEkJ09xm1WPIhDqNd6csQ9cWZdPtY/G0TA68vjqurFKKhloCK/L7x/TmpIXIS7c/L97fxtixdLhi6Rj0GvQBJk1bi0F9atLwQiMUrdE2bomue/m9RciVMwtefaitdgmCAA1rlMSth07jjU+W4JG7Whlb0FsfLSOQE4Dn72mOHFnpFpJGQmO3Yxtk9ClRPAqF6I7w582BaFKnFN0bsl3M47Nvm7LP9ZOYQOM2DUGNqxcmi6ewAShkTwoN4szxIjEYSEbR5h376a4qnwGqGlYvgbZX78GL7ywkAys/PpmxlrPRj+OtRzsawIP2I8P+8ox4nmyqV4wwJXrfM0l1bN99grIHIB/jrikla7a2MaKxUT6jljHO8do5BFAWLt1G11kxNGYTxGEWGeAUM2Vgt+poUK2UAX1UvFhzYz5ciKiorHjxP21M2eqlhjVjMezQfrw1ZT5evLeLZ0SjHUyQgOzdqtJnozPX6EdGb7kclJFs/opfsXD5L6hQlqCaDHUcQHLpqfrrVSlt+omHjQF3/OSfkCdPNjx7j9wSUlaWVaFkXnQZ/i7em7oEt93YiIZc4I1Jc1G0UG48RmafVJRG70+lYqPR985JjNWWLV2OC90Q2PHSu/PMjPnn726LrLl8r21P/RzrQJlS2Vh2dqzdvAfNCSj6bH+mCmt8NbZYyjfmPbII8mTFc/c2M+1TaZXL5UePW97HxE+XYSTBXJtkxL+1dz1Uq1AMCayvQfVYsi/3MSbbTjRmXDEljbUUxaQjCFq6WF7GcjtmytW9qLppOrbF/SPXxjasMS5ZKaqM9Vp7hnGfkv+HkhtjO/s0jTeEBa0yVi8wJUmAJN1cphL81DV0EmfGXxLd3IUIOWKy14udojznSgK9ZHTWM0dJa2O4Zn57vXWfqSIEisl4rXMyxCu/Sh4z/r/mHGs15fzej56XYlYKCFPyB/B0/+u4QDEDNAms4r6JY8VzctHHkWbGvGIIJSZ67uyC5eOVyb+NcpHX9foe6NF7EHLl8p5HUz75EDf37Y72HbugYaMGhpEiQEzt8EAF6tUHltnjGcExPSNUj8AFPUN0nUDFJE7QMNusV0nMH+URSKH3kWL36drzJV2nsWiZXefL/68+T3X17NburCamUb+aIJJG8OOVN99BNoLxWSMiDagrACUjg+isi/+CHd1b9WtWIAsYjF31IoqXLJvezwlkBA/o39OMF40Z9avGhLZDOW5eev0tZMmS5aKl0jhSWVpssuPXHrN5dNyOO3vO/5rTp09jaK8+fGeS7az7jfL5p3cnfpx+vY7b87YsAzb55FBdBtjy7SdqlgqTzattscAUZ2zQwB50mc2XY4YUyckWL4x5C1kjI9PPWMDarvXs4X8DbKVn8m2IFWjyUQZ9C+odLGBO7qO9GGJ6WnnJgWJWE27tNOA04DTgNOA04DTgNOA04DRw+WrAZ2G7fBXgWu404DTwv9VAcAqNqbS9iDmgRcBHi8ZV8D7ZTMtW72KA9j2IjgpC7WpF8P2i9XRpSLCExlCaI8noKouv5qwgQ2sLqlctgVO0q3z9/SrUu6IACkZnJRBDAySNpKGMuYOgSDStX9bERpEBRC51rqoVix/mrsDa9XtRpmQ+hmAKQHhwNgPCKIaIjC3B/BHQtnbjPnRuURE5aHBOkyE6mC6bsgfTfV5hLF37KxtQi/kFsmi2e5KJkyUgSO4Pu7WqhmMUTuUJGKO9CVWq5MP7dK8YEqz4LGRYsA0dW1ZGniw08vE6yS2bciIpc5rxLNdOorAEyBDNcsIU54lJZWaW5NJP5ejyXQfi6EpxJ10SHkH8yQTDlpORas+hUyhenAWpSiJXI3rXx+qfd2DEE1OxZ/8p3HdLU+QtnJN6F8jkxSbTrHfZddN8SJiVI4CGStnBQgheHTggtlO4AaMCg+J9YoZSP2QWsRF9hnyAk4ah5YFTxYpE4f6hTZmPxmMCAQEhcrsUgbrVSxnAMZl6SmZ9Yor89NM+dGxaASfJqFNK5NtL9VYqkw+ffLmGxnOfEY66Zlgvw6CRxV7MwBTmE7DJFW4cNNFcr/hnMkIX4zi4c3BTwxvQSAsKyoF61QSK8TwN2gFp7B/+W75+G7rShWAWdSiTyitAl5q1KhbHynX7DTtLRvSN64+gX9faVKyM+NQLfQ4WLxqF2CLRRgCpT9cGcVCJsyBAUCnQp1ea/SknO4b1Eh1BAI2La9YdQOuWtRCem+wcMW3Y8MA0j98kOyZzE1jlrH3eWKFcBO4EUXcyEHLYGn2YPKz4580HcUOHGojghSmUUYb7GLajZrmCWLthrymLZkT2M/siIAEVKvE+Y9+FEsSVqLEF8uD4CcV+IRhDq2OagAWCYqd5LoTurn7ddNiw9QSICrxIDtAoYiLj0kts/N+Ygnxy+G4nhLDPPMk4JqlyxdUTSSyFDDcDmsmyapLX73+j6F7VUh91/sJLLyBnztxIizuKqZ99ZJgvA28Zims6dsQzzz6Bb6ZPM4DzwFuGoXW76zg2COYQONBY9k9sPp+bqZg7ax7eHT8W2zdvJiiTyBhHdXHHfQ8itmBeHD54ELcOHYQefW9Gi+ZkQbL+kycTccetA9D+uo5ofm1bvPTii4jMmgW3DMrcleIzzz5Ft4E5kHT6FL7+ejpFCUJfMqfatGmHF59/AjO/nG5c8A0cPBSt23YiQy3RsFKe5XWRkdmQRobWF5/LXWgo+g0cwrZ1xIvPPm7aq/bdzPa2v64zG5WMW2+7y2PM8BkgEK1Tp+sw9JZ+2LRpowHGVq1ejaeefBS7tm8lQJENeWJi8PjjT6FgbFGjIj1fztaWnpfA/Pk/4o3XX8XeX38lIBKPK6rWxKiHH0E0Xc5ZptCMGV/h/bfHY8umDShcuChatWmN3hfgZlKsnd27f8XDD96P5i1aoGOnLv7dddltd73+hrPb7GN1pRJUCSULasLED8z7VExBsRoteHL2RX/dntwizl2ylu9bDzAWCGWB0gCylbp062G+E/wl0DtYkysiwuTT+OISXwmXlCzj2xYSzPuoYxdOKuE7WHKdDyy65Pr5TtA7p2PXbgTAPballUXrIAL9WcMJdPJGE5szIzDtn/dc26nUub7L9P4xspobWM/ujHfyua52x5wGnAacBpwGnAacBpwGnAacBpwGLjcN0FzgktOA04DTwN+rgZjcEahE5tC387dgy9bddIFV3BggxRoQw0DMLLncKlE0F8oUi8bkGT+jWpUSBHxOYMsv+zG4B4EIX5IxUzP0I8LDTBkGaKCxSrOZw8Ppoo4ZDh09wZnL+YwBJYTuuMTKEaglI4zMJwkEWRLIWohlzCULAKn4ELr+yhoZhrWMzSQDqAw8MsLIGKZYJ7TLGVDqo69W4pOvV1MOuh8j0KE8MpaJqSRQ4zQt70msK5bu5ySr2mlANp6TIVigmBhkxvUQ8wn8MIYigXcSJJPEKg0oNnPhFjw+ehYKFsiGooWjKStnc9P9nlLc6QQTc8Vrm+JipTEOVi3c9+wMlCtdENfSjaD0SNuyYcsJ4NBMcRFR1GYl6UxJcaboPelMYlm6VkmsDanEpqG96iI4Szhn/AegANlfJYvRsE+9SA4xmpJZYSiBJH/Gk649lZDIWDRJmPLtGnz6/RrTT4n0Hae+FDwUJDdRLGPRqt249+kp7JtUulDy+ubrj4bY6s16ZJ96CKcLtICwcOTNmQ0lS0WZ40nE20xME4I8Nqmt0v3JOMaT48HYgpRX/pmY9Cumm9xd7T94wpOJ/ap+io7yZrsH8XqVpn7Mx/ZaF2W6/kJTAhUsgC62UE7WIQ9goaY8CWD1JExQfWSTxnKYb1/gl8aqjiVTPrUjP5lvXiu8K1JYcPZs4VizaY8tIn2t7g7RPei7wrB9bAf7culYMPWmMZyS7AHCHG7/L1JMySo+OQMQU7y66ccDW1YYPYl16Ovuf0xbxH7QuF+6cAHjDy3FNU2a4pZhd2D5wkUYNrg/Gk+bjNy5ozFo6AgsnD8XI28ZyDiBVzFOEN2uJfE5lKFjDEuE5a1auQw1atVDx849cPLUcbz39gR0btkUPy5djmw5c6JyjRq4bXBfVJ+3GDmicmPELX2wbfMmXNPiHY6PQALXS41BG8gcGFs8fz5WrViKa1u0xeChI7GIct/SryemtWyNvDH5MGzknZg75wfcMqAPqtWohcKFCpk+WbxA1y1Hs8bX8rrbsGzpMgwZ1BfTp36S3t4FbO/tQweyHXUJRBfis8l7+ui+0djcuG4dkuJTUCi2ON24pqBbm2vQlsBFl+430pVmMnWwAvsO7EO+QoXN++L32EdrVixD7bpXoWixEnTZGId3xo5Fp5bNMWvRCtM348eMxqOPPYgBt4xEtxv6ID7hNPbt3mHYsMF0b5tZ2sIYTt07tEJTgmLtO3VjVt2xl2nis6d1m45nN958E+gd7zH9zLtDjD3259/BGJNwYgPaseI9Az3ZgiND0LZ9R/Pclpz+KVWTeC6QReh/3Z+9HRYeSnC5M98RGQS0FWVE0uzxS1iHZwlFy5btDIgp9pd/8uLAseP5UrtYUMy/HLftNOA04DTgNOA04DTgNOA04DTgNOA0cCEacMDYhWjJ5XEacBr4yzQQTKtlIK3PbRuUxbMT5iE+JRH3DyGDh7aRoLQIBHFmeARRohBSPMToaH31FXhh/I84dCgOU79dwmDv0WRxlTRGFrlYCqJBJSIwDMnxJwwLKI2ueQROJLCeZMabCqJhODvd9JB4Q/CE+VLiPXd7nF6cJjYJWTWhIaGsP9zEgNJ1IRSGNmXD5UkJiEOWbBSOzBIVHBIQyvBDBNxosBNQIejhpi610LNlVZMlNcQD0MiHMoCRwIlgGsRkdD96/ARSaCiV+VYAhFJocLi3QdqT4s2kESQjPmVcF2oW9PlmPktHyv/yewtRnwDjfYPrG+BI4NfJk0l0g7eRwAoN5DSEyfAUQDpV3KlkvDpxCQoXiMa2jfsxa9ZGNGtW1rDFQllYKBsv/pPkpCYkBFlJHhqmdsi0Fc+f6DwRhm0iWxqjjxCIYRsEkAQkI54MpOp1iiE6m2cYJkfOg1rS4qk3ujlKov6YWS6yZMg2bCTWRooIQR5CSoEJ6EsXh+1bVUKY+kcdwnSCIJtAukAaSsuXjsJ/7u7AesWeCjfGcC8m2xnjcq2aJZE1C2eiE+SULZDwqSnHhMBi3BSxuXTI1i/gL4QAmADOk8dOIp4iCShiSBdTr7okeza6w2IbVFIqx9FpsrxCyPiivZbGecZo4vUn6NozK9mBiYwBJfBTYFQq20W8ljoTy4D8MeqLPCUuZ+DEIDLUOPpx/Pgx6oHVKIgV2YWSl9mNgdgwFKhxxYmJp/LTQjRjnkAYAbU0Nkg9l8r7SuM1jYDioRNHGSOOu7wXFGNNPRmfRkA2koUypXFMBKUSwAyMZB95sqTbTUVx431kxyslMvWkqqN5TxQpls8w9NTtJpkBoC2vv7yD/4BfdRyTZeylyrhNGQUcJfGc4gWKeZcBRzJj07vy3L9m/PidErB2zz134+VXxqqzqIZTfmd/u1m302u/Peh3RLGUlDQmChUti9cIyii179AeX375EY4ePoCxb7/P50gAj3XEZLoP/G7m9+hxfTfzjDSZ/X6sC7ZbyLQSqBtAhFrPh2YtOqB8sTwEvJaherWauPXWOzH/++/Rp1cvtCS7a8HcH+jWdonuUF7jjRu/Yn93U8zTosXL4KXXXjfXXduyDaZ/NgnHKPf49z80479thw74cNL7+P67WejVs4fHdOPDr3CR4nh1zBsGtJIMX037GCeOH8G4dz4w13UiU+zTKR9jJtvb84bupr1qSypBL5JTMGwIQbN61cl6a8JYgqtx6PAxjLzzfuSOijLP7rbt2poJDIqjJnxcsbMs4GEbFMD7auCQ4dwV84XvDOa79ppWKFk0PxYsWorqtWrimWeewgiy1YYMv93ksddqrWeEfxLAwzuW90wo5vw4HwN6dsKQkXdgyNBbmS1jbv8rL4/tgAw3lN6vej+IGWaBEz1ZtJ+xr/4KDXmTYbx3uvrHjAEJ5Evat8m4MzR3iI54ebzr+YznPWOeoYzvpaS8AtVsbDkd08QLFa1zmuxhgLQ/eUyobCU/sc3+X/WjPvP6SzXoeXt2TXJLrTyapOKS04DTgNOA04DTgNOA04DTgNOA04DTwF+tAQeM/dUaduU7DTgNZKqBIAI/mundsHYpfL94B3JFh6NQwTzGKC2mizEg0VgiA2cQjaOtCaApTtLHXy3H9/M2oed1tYxRx7qFU2Vy9aVYMes270bJMgUMsKXjK9fuYnnBqFAiP41NOiKDkAdY0S5u7NayiMokU7BAbixYvgvXta1Iwz/rpoyKP7V6/R5ULluA4JkHDJlCfD+6rmSJvJi1ZCtubFPVyCVHcsaIxzVFQgSBHJVXtFAOk69LxzrmanpiIkjh5dG2zN/ZCCKl6CBTIOXyYtr4BDdHf/uTRnBCjLMDB4/jinLVKANZW6lkV7HdXy/YbICUYKExTDJoh9Lw+OCbPyIxLgljnu6Mtz5chucJUJaoUBBF8jMmltCXTJLALwGHAhDKse0pST9h596jKFIwF8ElggyXaMiTsTCEBupisVH4buEm9kcVMxaM5Zo4gwBUeknjsRTkyh7B2GAFaSAVYCmDogT3ufLLpA2ZnZLBMJg/BfJFYd6yX9GxXQ2y/TQexO4Dlq/dgerli7BPCbKx34qQnTd36Q60b0RgUcAKr91/+DQ2btmHqpUoG9EWAa+R4cFY9NNWunhLoau4IDJPUsksScKaLftRo1LhdJEEmJUmm3Le4m2MmUd2E+tRMqAbt1W+QLzoqCzGwK5zOibgzYC1RGtN7CYimBqf+Rg3b9FPe3AjQ+BpPAVzrGhcr1q/24xrXX8xSYzOEDL2BCRt2LwXTWoVN4xFAYLqO4O6XUyB/4C8BUrUQArvDe/5k/n9llHcDHZejkvGgCvK/tRg+BNSEgea0St1W6xkGY4D717WsChWrDiiC8em1yJgtHQZxj9iHCEluRpUjD//xKFigORdu3Zg9AvPGTeD8QkJHDNhREvjsWfHLwCBMaVX33yb7mnr4NEHF+K1N8ejaNGixu2sf3nn29bYLFu+vMmmbcX9iS1aHPkKFKJsAlwpH3VfsWJVJCTEs610t8txqnFWtEQJji0PLAjmdYUKFaH7w/zpVQpoKMEYT7oulExgxUYTQ1djs1+vrjh+8gQ+mfytyV+qbAWULF2asSnroXGza9CgURNcTZAuQKCY+l4TH86RJPNhgnhPPvIw3SRuMm5vTX9QV7t370Tw8mAc2n+AbC+6c7yAZHRAwVcsWYCv6SLy8edfMkwxzufgs+HcMlxAsS7LP1QD3jOFY5jjWm4YTf/zHhZgJvBM7wPDeOZzWXHrlLw4YWRie1nMMffjNOA04DTgNOA04DTgNOA04DTgNOA04DRw6RrQn1kuOQ04DTgN/M80kMwYEja+jypNCQhGAq26QUSDHh3ZHLf0vNIYRmXoNdwZMl1SCeykEOk4Fpds+EqN6lbBJ1+t52z9IFzblG7/aCRNo3EpnmwalZ1KoEAzr596azYO7TyBUBpI5y3Yii++/BnXXk1wgcwYEVmS0xgriYbOBBqHDZmDNtk0Go61tGtdFku37sSb/11CkC0FR4/E4dFXvsOh04G4rh1dNzKvYmQAZCaRnZRCg66W3m1rYeOGI3jotW+w8/ApGq7J4klIxvc/bsXHM1aT2cSYOWRP9ezUEBt2JOIlglIH95BFQuPvXgJK835cwzhRBEmogLLFSiIt8TQ+n7Eci5evxc+rNxDcyPxfEBkNKfQLWDgmGp/OWI9Dx+MJsgVjzuJfuL+WBjgax2lvpTNBEbkw6dvV+G7JGtw+ohnCCUoO7VuP4GRuPPLil3Q7lsw2UTTmDebM9kDmTxNbiYtik2kJDpahnbPAaUyuWq0w2xaMlWv20LgsAFBGfAFUmvEeSiYSt1mgt2hbfRDEJdiLZ0YmkuaTC0JI5oWKbxYamJV1R6JH5zpYv+MEXhg7Fwf2nSaDLxXHKd/sBRvx2TfLkEiw6TTlJHePiwBVMcloTEwhc4vsLYqqLuP4SGLcMhq/uaO4aMm0NmrRxP0UNjCZLC5vfLJvmZewEfUXh66tqmPN5sN4e9IC6iEZR4/F4ZFXv8VhhlLr2KEm8xBMYJywjleXw5KftuOjr8SmSSUr5RQee2U2Y5OxF+Q+i3SkUFZWu25FHIkLxXsfLUAq4yYdZNy3Ua/MoNy8ijSUFDIZRWcSQaxbu+rYRmDthddmYf+JUxzfqfh111HMWbSBoBTzc7wWJyAXx/tj+persGLFDixduYd6J3hHKQwkI4oZl56trmDMsh14bdJ8xHMgHD4eh1GjZ5JNGIDurQmAiEIVKDDRuwO54SXf9QikO06yJk0+5k3xAS37dh9FHBmQta4sjJPUdCBlDDJjQ8ANlwDqU8vfndTRWsxoELOODCmzcFxSVwGpiTi4bSn2bFqKXRvpWm/bTxe1HNz6E/yXQ5tXoEGNsqyPOksjQKVBmNlyHv1EEtzKwmdlRMBhZA04glO8D+L0LKPs8QQj1bIQPtMEkMvoniZXnHwehNDfqQzxGZPYlZqY0KNzZ/y6Zz96DxiAMW+9jXHv/RdBYaF0KatrVKr3jE4j6px0Og45sin64h9Livcl95upeugyifGYJSLClCcgWCmJ7gf1gBDoJNBALiSDyQhmc5mfXchLNeFADzMxTBRjSuWGsqwAXmfdKCaxrwf26Uk3iisx5YuvCXDnN6BXGMfnzFnz0af/IPy6nfEVhwxGzQplTEwwIwB/JFfGpNhzba+9Frt/3UVdDcTo18dh3NsfML4egWkKd/Ag3ZHyvoiKymMmU2S8/vf2NSQUZ/EQ47npmembD/F72d3x/8ca0DjR2FJcMk24EeClRexBJb0DlEf3rCYuKGltgTJzwP04DTgNOA04DTgNOA04DTgNOA04DTgNOA1csgY4v90lpwGnAaeBv08DMnyKfUOswhgEIznTP4nGdrFvPKOoGF008NJYGB7BgzQedmpRHp9MX4QGtUsiN1lCchOYRPdvDCFmwB7lDyI4dn3barjpvk+MkfgUDU91KhXBwB41TVnETTzDJ/PKvqqkta6T+7O2jcuRYRCHDz5ahfemLKNLQxo7c0TgPrp5LFuS8Z4oozfL27vW/tavVhCjRlyNF9/9Fl//uIFGWtluGcMpRxS60A1ggMqnkbdutQK4/aarMG7SHEyduZKsJJUXhBb1y+Dqq8pTJ8CV1fOibbOKeOvjpWQnxTO+WSimvCkj++8nsYOS6Y7w9v4N8dDLX6N9/3foVi2E7LMw3DWoEe56fDrrkRsqYOuvR8m+W4SeHWugWqX8hC8IKhGAfOy2Zhhw3wd46Z25uGNAQ6MnXUN7nQEgVbvkU0rjcbGgUskaigwJQ8NaJTBr8Xa0aV7euBAUY8wwlnixbN5iNyl5JkDqnBCdDNyBBExlEPTyMh87SMCnYnqlsPxGNYsh8NZmeG3CLEz7YTWxBQF0qciaKwLXt/NYZCpHDJE0WhkNU0QGRua5lCTQNZhjs13jUjh89BTenbIU705ehlQej8oZif8MbY5SsdmM/KrnuublsHvPSYz9YBHenLSM4GkAQbWqZInFe4Z8ZaIKalWMQYvGFfDup0vw3mfLCfwG4qZutRFAX40CB2QwVRLjqGntQgjoXx+vvLcAn/+4zYynoNR4XNuwHJrUKWPyNaxRBK2bVcAbHy1BnHHbGInPXutl7iHdR1YP7RpXwikCaGMmLcH7U38yzLPsWUNx/y1Xc1xHUTT+4xiSLtXndowTbjD1ZPwR8CuA8wcCz2Kt1ShfID1LilhSPtd/6Qf/4Rtyn8chawzVetz4cD9KzY5gkk4uNj32+ON49IknjS4NJpdJAU26jsnkrGc8F4NE4ztILECOw1QC/3o+ytDun0J4k1pXifFkNIWShqS+8k8CxQ6S4bRh4yZM/ORLxtGLNi4Vd/66h8B4IsftmTL7d++C0uUr4YobqmE443t9NXcx8jB22cUkjUUlMcA0zpMJ4ImdlUCWmo4lM7ajjoeFh3OcEwyjzGLNmmcDjwsUS+YkAj69zX2g4yHUgYAwjVmPOaw12ZDM3//Grli5bAk+/4bxFgsW5HuC9VEGscKCs0ai381DMWDQMLo/DcC1japhwhuv49FnXjTtNi4Vmc8/7dt3EBs3bcBHn01H/vz52adp2E1WXcLpeNO/da9qhEDK/O2ML9Cmfed0/fuX4b8tfSgGYbWaddHl+hswsGdHurY9iRG332nKE0Di0r9HA2KzyxWwulV9q2+DRI75sLAwA37pmPfdE4hTJ0+Z92E47wXFIjXMsX+PKlxLnAacBpwGnAacBpwGnAacBpwGnAacBv52DZyxePztojgB/uka8P5Y94yC/tuXKrctSwYCGbkyS8rjGWs9Oc51jUAHa1TOrCx7TvVnlvwNoefKq/PnkiOzMi/ncwKKZMVt2ay8WaQLsQfSWWQ0DMn4mcSZ+Q3rxmJWnUHGUB9EtokAsBCyi04dPkzgjIDDtRUQTvBEKSyIbBSWnZYShFDRwXh9y3plcG3dMlj580HEFMmCPDnCz4wxDrU7+tU3iynAZ/gWKKYUSiNtXzJ1+rS9Aqt+3o+sEWEoUSyKZ8iWoour+CAyiZjn+OljCAlLYqwqAgS8NoXHGtaKRYOaN2HPgVPYd/AYCueOQEyBKMNKS1QmGqw1D7x14xJo16Q4Nm89hCOn4lG5VAxZGpwZznPGoxibMeymxmbhIZPkTlKzy4VTaKxLVyb5LO4ylGs2erWK+TB1TC/8TNnDGJyqSInsxpg+d9LNJrsi2xQrmhPfvNPfd723Em0jtlAWfG2PU9wOzcqYxahGSA0TIQ8vUR6aphmzTLtp6NGmMgb+ZzK27zqMonSnSGnRlgBOh/qe+zT1oX/Snaz7ik7UCBQ2MovOJ/vq8WgknhvARjWKolGN3ti15xj27D+MEgWjyG7L6RWnYrmoXXIRaeux29cS6NSSnlin0aOvnjQCdiNZ/8ibvBxisSnRji4E1Wz36lQVN7SrgNUbD5jxULxoDj6PvBn9RONMHv0O710LfTqVw/qtB+m2syCyEHhKS61u5GKtSKScoZTx/oH1MLxzeWzYeRiVyhVBGFllPeji0CbVTYd2Bqhp2rgitGzdegBHTyXQTWZBGljPtFPsuLtY3l0DrrKXm/V1BMu02KR+69KmGpcqWP3zXmSJDEOxItm9dlAuDwgKJLB6lVmkUyXCkGb9yMhWZh2S7OlELBfJOXXWOnRqzvhvvnFo8BfGzwtI72+fnszVf9+PT2wEE0hX2vsLQU4+c/IXr4q9W37iEbHs9CtA1txmynYmpbfnzCH/rfT7wh7UvUo3pl5t3PQd9w0XA5wLtBSIozGbxueH2I1ialIsPcZMIsHJJB1nZyCB8d9Ok4WpO0N55HWPJnXeS7yei+IRKQURsA4gIy4slECTGczmcPqPwJ8s2Qjs8nn67VdT0W9Af4JTKbht2ECTh+JwLkIKnnr2SWzZvhVz5y1FVHQeLJ43B0P69cGUqdORIuasyua9pOe7XKvGE6hSEvCmOgSwJSgf5UrSQ9bEMpPMAvj49BQbzLSF51mOeQ5Tbh1L4TNe1wjU0r3jtZegAu9Zcs9wmmCD8EyTuE5mTEqxQAf0uQHz5s3Fe5O/YPy8COzcv5fM5DA+DyOxZ+dWukQ8guq161FX5PPFnTYAY9asjBfoS+d630dyIkZgWACmTf8cfW+5GQknkjH81mFsD2Uh6pgzWyRa0SXjY489jvLV66Mo3WgK416/bg3KlCln6tI3g/pc3w0COzQZIIAM0QZX1cOE/36Km7p3xKlTJ3DfQ4+ljxsr06WuOToyLUIsPoHhcrspQDKILoMFImpsphjXf959n2khmZ20N0JmeTI9l7n8mV7Kk5davW5/75tPejp/af73nPQaynvD40R7wujVEh6huJu8d/VW1f3GRdsRfDYradt7BfFusQ8Ec0Z57cD3DnBU+c78/1zZeIWpfK8oiaVuk9r6/719ti1u7TTgNOA04DTgNOA04DTgNOA04DTwz9DA2X9R/TNkclL8wzQg442SNQJo327/EVH9jU3+25mBYsqnRXls3XY/owwCxfzLzXg+477Ky2xRflveufLpfGay67xLf1wDwT4Dry1BLrTGT/mJMazyoFalM+yU9PM0XCXbMcsnHHEiVK8QbUAxm+di1mJxVS6f34BiYmnYFCxjN8+tWrsXhQrkMkCOBWOs8Sx/HjJoyuZHTJ6cJsSQjmdMuqYYAbdqFQsQFLswo2OKD707FwCssWqN4qqrQuk8KBmbm8QdGcgvrPyMMl7MfqmiUWhStzQmfrrqjLH6Ygq4gLwF8+dA7YqxiM7FGGj/4xRIlo4dDxoDv5eyZ8uCWpVjDSjmn0eEOf8lZ45spi2RpEgaFhDHWGbrMkWjUZOgWDCBDDve/Ms/37Z/2VU4rovHMhZcJu04X3ny/jV97iYCL2Ho3qry+bK783+CBmRs132uRe7XxICy7yYVL+aUgBfLErTvJxmdMy4CPSIiwnH7Xf/Bf+4cgfJFYlCpWAzq1quPyCyRBNNSsGjhArz09BN48fUJyBkVRUAnBePe/Qhr16zES88/acah6qcQ/B9I9hddlQpYoQzxZJ1JHiXlkex6biUSdJHscrealJzIvJrQYrKZPIRmzI7aoOt1rYCac7XXlEsdSA/Sifa1fPXlVBw5dAitGl+JikXzonLxAqhYrCDuv2M4jhw/jn49OqMkn93liuRD1dL5kZduFoeMuNsAQZIzRIhZhhQZGYm77n4AD9x/OyoUzo8qpQqgzlVXEQwkiMe61YbnRr+JSpWroEGdSqhUPB9i8+bCC0894muP73vBJ6MAD+lMfaRvq3pX1sUHn32FD955G6MeuCdD7X/9rmQRc096FyimteJ1GsBMKKlLTgNOA04DTgNOA04DTgNOA04DTgNOA04DTgP/Eg1wMjf/kv8fJFUTVawqjmzXjOzMU+7YK0wGSXZ4x6rfzfz6h4vw9uSljNnjWVNu6HwFBnWry/y+mYbpV/rwP9tS97d9umYuZkN9KGOTTRn37fE/spZByBrvMl7vX4/dtuuMef33LYDnL7M9r3NKv1enzZdxnZmcynvWed94S2EgJ8V4uvK61zF30kAyo87oMGP5f8Z+mm+mrZ1ZrBm2MnYFE0yJKVkFu7euPE81MswD0cWrYM+W5TRP+uT1lZtOYThPKX/8tK1PvA2mtFAjTyDdhT3EmE5zl+0wzISnbm+JGgTGEoV8MRkmmjbYt8+Mm40f5m8hG6ovWVVeeal0hahk9WJ2LvBHYIIFIRTPSYbDeau34aEXZjJ+TxoeuLUJGlQtYZgSwqykP7tO9oFYusZL9kF0pnKVr2TrOHPmt1txZEN8N3srZ54nmJMBqT6GA+NzKYUmJaJJvbJIoR843SfBpJ3IkC1AUcyEtHRqhcn+l/wk0+2TWCIRBPqCGd/LPxnGi/+BP7AtY60M/Hb9B4q4hEu8+8PXrSwns/7kWR8jjbfg78rrP77OL5it/7f1nv9a/xx2PHoySj4lOxa9vd/+iiGmFGSZC1TE6QQygRhPLIzgQNB5wNcLGeO/rfXcR86pN8rzJt1JvkfwXCk1JA3d21blt0Ets2+ZWpYxlkq5MzLGTMa/+MfKoeeWxrFljDW8fiymjh/gY4x5zxqJYhlj2jbgFrtP4I3YoYoTZpMFobSvc/qOEpjkf9zmtWt7L8WdPo1lS+ajXt0GCKJLW4EhAkdSWb4HlmjMnZmcoveKni1K5tnCZ419N4u5qToFVhkXcL53rvZNft85gWdBvJdtHsmiJDAmhCC0dKNrVJbao+TfXluejgvk811uvlUMw4nPXd2DimEp2fQ8Fmhnyzh4cD/Wr1mLGjVqIxuBdompOpVUn3Tsn6wsJwisrVq1AjWq10UYGT9Whzav8iURyFv442yCZNUQlTu3pwvf+1S683eTaOuyOtV3y1nfE76C7bix9Vzs+nyMMf92SG9n3ltWAN+D4mIrtvntd4Tdv8i1N3ou8iK/7Geeen4HL2bT99yz4/xiLjV9LlbYpST73PWVoTL90/kYVenfSf4XXcT2pY6/81Vl7//MGGO6t+zzImN5Gt8at21uegNzPxzE1xLZjrzn5UTgcki2f+x9nsqYpmLwXnXdGCyY7HkLuBz04NroNOA04DTgNOA04DTgNOA04DRwoRrwzc+90Owu3+WsAcUAmfntTBw8dBDZs2dHlSpVULx48YtWyYwZMxAbG4vSpUqbmeMqQH/IfjnjS1ThLOsCBc9mAa1btw779+1HgwYNsHrNagNmXXGFBU/PButMWT6DlK6TQbB69eq/kXHFihXmmM5Nnz79N+f9DxQuXBgVylfA+g3rsWvXLmPs8j8fxRn0desKkJVx8GwjhX8+t33pGuh0TTnGFStO93T5EMN4Rv5GUVu67ETtmpZFzSsKGqOwYobJJRktfMx/tpHTXpPZ2hhJ/ZgD1lBYrmgM7h/aBBXIRMibOysNt17fC6dLpF8z5ZMJTDG1aJcxY9xu+9cn4z7/n3Ed6H/yHNtJ8QH4YeEOxiSLM4wMpIZ7uXzAWDjHf4OapeifyYsTJdzGGNDpUk0ynjGhn6PwP+lQGGOaBdEXpGGoXaolM4NM6g8Zzwwo8jffbxYA9RfRgjVaK9nxkuYDEPzzZty2bct4POO+xpOtJ+O5C9m3stm81lR7IWXSxGguswCX7I1ZCAwIkNcxe9yW7dZ/vgb0XpNhWIkqN89BgULWqKzjBgzTBpMFxfzPe2e8fCpJ57Jky4r6DZuYUx6Y5IFiBsChi0wzln2W1zNAjscS00Ue2ENITEJx+AfxYZhKl4oCuQyw5bsnQhh3TtgTSY988JnqzDMwiQdTCagLuFJ70gEqPSO5LzDLttuuA30glmUJ65qMgI7cAmpsC6wP4rPJH+yKjs6LBo1jjA4ltn/yz2ePq161R+4na5NVJ/BOSfXqfaRFbVUbta5HfYYSXLRAn1xOylAtvE3x3iywQRGRwEkXhkFGHUu/Ro+24v/R2rpPFBi66eefsfynxQgPC0eFSpVRslRZ00//I1FcNU4DTgNOA04DTgNOA04DTgNOA04DTgNOA04Df6kGHDD2l6r331O4wKM+ffrQ5VIESpUqhYMHD2Lz5s244YYbMGbMmAtqqOIhfTvzW7Rp0walS5fGmjVr0q+Ta6UJEyYwHtLPnIW9yhi2ZBjau3cvGjdujKFDh6JR40Z44okncIiukWbOnGmuPRcbTMdCaIh69NFHcfTo0fS86ZVxQ+UcOXIE3333Hbp27UqDmxfYXDO8tZ0lSxYDgAXT6NWlSxeMGzcODz30ECZPnozQUMWDOJPq1KmDWbNmGSOWDFkOHDujmz+2Za2TnuGX5kMDLKVwPJRlHKayPsN8om/muQ8PohXWg3zIP0CpkvlRqkR+Y3MlLEbLMCWhIVLRsC42ySDrb+y3zLO8OULQtHoRlktGFMu2kFMSLZuK82WTQAwlGZW9bQ8w8Y76WGK2yfZgJuvskQF47h4ZrzNAXGTWKQURZDYpjSwebiaLwRNKYzBjtFFMxgvy4pZ4mf783xBVwopNFLWLaNeFSmKN+/59cqHX/jn5vPGosmzf+pdr5bJre87Kbff91zZvZnnS8/sGfEoG5kD6+QvYsPXZrD5M96xxbs9lXIf6gnSlMsaeUkqgb9zx2ac7NsOoNHncz5+rAQEuFjTSWiCSkj2mbeURsCOgJoVs2wF9epEtlWziEuq8TSFEp14f9x5CIwSci2fge+7qMaXOZJwvSxrW8yTYRzkR08seJxxki8Op0wkY3L8nx5LnzjAjuPP6mxOQjW5GlfR4UJ1KktW0w9cWc9DvR22z7bZrv9PpOiCs5j3vddI+hilekkTk5IAgya1NX71mx7cvGU6djM9U/vc++Ci9roxyaN+WK50ryR2j7SOt1dokI4w5ze8GL18ibyfDEhNixiRZzvV94131x38tC1BlJ7NSAXOhfD8IuBOgGMzvG/XZVbVrYvuOLahQrqz5Flq7ej26974JL770ijkvhqCS5BcIGyzAkeC/ZQ7qnAX99HxRM5VXdYQxjqZiSKoeAXA6rrxi9QnAFMjpP5ZV1oWkAH4j6BtO32iG5ch9o1PWoXP6Nkshu9WyB1Wmfz2BPrDZThAQ41BJwKplTaa3iR2ZwvtJ5fu32fsGFPB7JgaZjimf6WtvuJtyFXdPYK197ksWyaA+suCuzum47TcQoFbZKlMuk41OuW37w5al/lT5PJXeRj0LCBFj1P138hs3K26/5wGWRdDaPj94XnL6J9seeyyFaK4Fpa3uDACu/qWcSqYe37jXfhLbM+qB/yCSwPvdd91j7hErp71G+ZS0r/KS1Gf8ZrIuVb2zHFOUNVXf6zxgQWh7zq6Nnjm+Hrz/Xv7NMRtbNm1CmQpl0apNa9x3591m7KWy70I4ZjO+C20Zbu004DTgNOA04DTgNOA04DTgNOA0cHlowAFjl0c/X3IrBw0aZACqDz/80PxBrj+Wjx45io2bNp63bGsUEPj1zjvvGHbVsmXLsHDhQrMtwEx/AL/yyiuoWbMm7rnnHjzyyCOGTda/f3+IsXXXXXeZegRa/dnp5MmTpkjJMeHtCRgxYoRpm4A5GQkSjfFIRo00owMLylk5rCFBa7ttz7m104DTgNOA04DTwN+lAQPU0Jjc6frrjQipPsDGypNG16xhWSII9njuC4NCzjaM23zp63OhweknCQRFhqNDl240WnNbIEuG/OHhPoar3zX/pM3zyf9PkvWPyCJgx4ARHBNyk6lvFsu2E2AloEYgTOcevdD9xn7ImY1jg99ukyZNwq0D+qJTp46o36DxWYBMINnBBoygQALBBGfoe0qgi8r2j9Wmb70EHzglYC6Ik48CCVhqG5zLISxWZQmgs64wz9fOZB9QFJAqYNJj2pljBD/03RZKACSNQGMKFwuKCegS4CUASkCT5LBAYaoAGR9IJnmVtBYoY+8OAX3mOPVnQTDrolvHBVQlJxOE85VjMvMnhd+wOqe64+LizTYhHlO2ZEmTLrm2YJyYxqZutktsvjROiLAgpMpUncEEwDwg1Qeisc0CmdWXar9clQpEErtR84nWr12L7LlyGZHU/8qjdK7vV51Tf5hx42u0B4RSd+YqsB2MI8jvZQvQW1alBYlV7gZ6XMieNbsZS5I1IyDmK8rIKZZpgOjYTKrX6ldjSzEJJROLTAeh7bV2venndTqJJ595hq6zCQ4yhuGiH+dh0bx52Ll5G14fO5b9LTA4iXK7P4Ot3tzaacBpwGnAacBpwGnAacBpwGngctSA+4vgcuz1i2zzsWPHsGfPHrRu3dpcqT9ytUTljkLtqNrnLU1/xOoP4RMnT2DatGl4//338eqrr+L111+H2FYCzJRiYmLw0ksvoVu3bmZZuXIl5s6diwULFqSztOzM1vNW+gcySA4Bb5pxbEExFSPWmNqrdsggoG3/5P2R7h3z3/bP47b/PA3YWDspvhnr6awAH4PG9o5lTqX4DFvppAYani4qZYiJEuS7PAW+2FlBtOYxpaTH0rq08i84hlt6fbbFXr0+Qg8NeN59FeALrhEkv12S0/z+dT9J1nTo07Ptr7+uxv9xyRnGw+/214Xmu1jxMzLF/mg9f/C6JG9YkXnjjX/fLpljtFq69I/TgN5lMry3a9eWBuYzrA4rqIz93nstyBjOiRdkmmx//16mAD5n2nfoZAzcGUEBXSMmyz85nU/+3zPo/5Pb5C+bABcBLGLirFixFC88/wy2b96EnDlzIWeuKDzx2FMoVKI4br11mAFodK14hJ27dccdQwdg545tPNKYSyBeeizZvrkAAEAASURBVPYxZI/Oh8OHDuDrr74kMBKEwUOHo2Wb9njhuafwLT0NBBB8Gzz0NrRu1cJ8Oz39zJPIlTM3Th49gm+/+ZKgTwhuGjQM7dt3wJOPP4jZ9AYQSFBk8LBb0aJVB77FMn+fPvE0y8sVjbgTRzHj808REp4VHQnM3tT/JgPOPUM5onj+2NFj+O6br1C+VAk89/IYPPXsk8hDN5on6Tlg2rRP6fI4HDcNHsL7pBNeePoxfMf2CFwZMmwE2nXqbNr71MP/QVS+WBznN/F3M75AuXIV0KR5S8yYPhUvjx7jx+BKxbg338Dh/ftw533/4bVnkmHk8UYUxvTis08hR3R+KF7d919PozeGMpTtVZN52vSvMOndCdi2eQMnhxXBQOqwfsPG7Is09ttyPPXEo9j5y1bkZtvyxOTFqIceRcGiRYktBuAr6uG/E9/B9q1bUbBILAYNv4OuyK805T77/PNYvW6tAa17dO+CYIKXL499B7mishl9Zfy+FStNaewbYzDji8+wb98+lK5QHrfffjfda1ZinwLPPfQIatSth8ZXN6fKPObwKy++wL4NwrDhIzBuzMtYt3Y1AdJgdO98nYmn+Nob45A1O2P68W8D/2RBtWc4TnLkiEJgQgo+m/whv81T0LZjFwy7dTjHx5kYgRZ8s2WciE+hd4seiKo4VP6p+d3uMZvVl9zBmxPGoQ3B3eZXX0PZ3TvL6s2tnQacBpwGnAacBpwGnAacBpwGLlcNOGDscu35i2h3jhw5kD9/fgNmlStXjkHqa6RfnfGP6PQTfhsGzOLfn+PHjzexydq2bQuBbcOHD+fM4QRoBrkAJaX27dtD53v06GFcJooppjot4OQ/I9evikvatGVrrT/KLRBmC/VvY0Y3isrjf71/Xnu9WzsNOA04DTgNOA38HRrwvVpp9D7bAG1lsUCPDM+ekfnSjMVhdMunOhX7S4BbRnBMrBVbp5Xhn7Q+n/z/JFn/iCxiSokpFpeQjJ4d2qBN1+vRuWtPJMWfxsb1P2Pfgb0GGBNrTCCOWFSaU/HzuvVI4jXRBMI8Fk8aFs6fh1WM+9qo2TW4echIrCTQ1r/fDWjTqh2y58iFwcNvx5w5P2DkoL6otXAp8hYoiMXz55OxxGsaNcOAwcOxePFiDLm5Dz776D3kyRODQQSi5s2dhSH9+2DOkuooQo8BmaVlixbh51UrcFWDhhhy+710/7gDjz18L9KS4nDTwMFkCam+VahRqx569x+Egnk8ptRyXreG1zVo0BTDR9xFOZZgCOX88rPJiM6Tl7LfhoXz5mDYoH6oVqsO8uXJh6VLlmD16ndQs05dI3sOtrFoyRLoe31H9O7dB1Vr1DKiptJV5svPPkFA6vbfiK6xb4CdxBSsoOeE5ctXoFa9eujT/2bkIGAo4PKdMa/hsccexc0j70LX67tj3fq16HZdS3w89RvUrF0P3du3QMtOXdG9V1+v38jGOnD4APIXKYKJE97Ek6MewMARdxrXl2vXrcENnVrik8+m40rq6KqrGuELAk2RdBfe4bpunOCQYFybCqDydwnpL/gTo+6lq/PxuOP+h5GvYGF8+P4EdGlzLab98COKFCmKBYt+RM7cuXFNixZkqdFlMx8Aq5YvN0w3bVepVgt5Y/LxWz+SoCVdl5uxFWaeE/71aFvPDKWlnBC3ZuVPaHRVYwOsbtm6GU8QmCxVOhbNW7QzzyqB+hmfJS8/+yg20yV7rfJpyJEtO/rfPJgT7Qjq3jUSP/zwvfwv4q2xbxhgLIS6lktPl5wGnAacBpwGnAacBpwGnAacBpwGLl8NOGDs8u37i2r5WLoekTvFWrVq8Y/obLjiiitwPV0zDR48+LzlyKWOZq1PnDiRbng4k5yGAcX1EjAmsMy/DIFocqlYrFgxFC9eHPfee296+RaASj/wJ21YMEtr/VEu1ph/Xfa8qpszZ85vDH2KQSYWnGT3v+5PEu/yLSYjM8Y3czwl3W77ewYN7/hv8qVfd5Eq/Y0cGa5PZ25lOH6hu+crP2M5v8l/th58IYB4lW2wTx+W4ZaxvD97PwMj70w//NkV/U3l/Ub/vyPHheb7ncsv+PAfreePXnfB9+EFt8Bl/As1kNFwfK6q/FkXjHB0rix+x+xzxe+Q3yY9v/kSOUkiafymuN8csBf8Kevzy595Nf4MFunlQvR3domZ6yctI1Pz7Iv/8j25paMTRWzasAH7OUHp/gcfQ0RkBN3teVWrvVpsrCsdTSa4cc/wgaheswaubt7CZAxk5wpIKVmyJF55bRzZYgHo2L4Npn36MWPQHiYzZyKZ/oFo3a4Dpn/6Ib6f9R26de9NUC0IRYuXxitj3jDliI01c8ZUnDpxDBPe+4hypKEdJ0h98vEkzJ49Fz1v6G7y2R8OqbOS3PdFRUVj3NvvIT4+nvHLInD6wD6MffUV9B04jDHxglCwQBG898EkA/KFhIUZV5FBwaEoWrQUxk542zDoWrRuhy+nfoiTZJ6NnfAewkKD0L5jR7qQfB8/fD8L13ftZtwAFsyfBxM/+NDcJakEVVIZS69+42aYMH4cqteqa8Ce6V9MwREy1G7sO8B8E0pg+20owFFJ38SKlVW4SAG89c4HLNtrWSJjiD377NMYcfe9GEAmHbEbXBPfGrt/3c02vYRc9NRwkCy3Bx5+DNnIuGIpJv6ZypS7yKcffRy33v0wbhk8SIfI1GuHvdt2YPTLz6PuVY1Qu2Y1gny5kYPswE4dOrA/znw/WHeY5kLfTxwJnmPHjsGd99yLfjf1Y9yyVLRv1QR1alTD6BeexLMvvszRlISIMI6rtDiE+N7/yYmnEB4Wzni/Sah7JevMlY2sxJzo2LFTevHnurfkttMD1lNQLDYWo99628RKS0lMxU+L5mPSxPcJjFFuPVzOkb779nsqV3EvU3Fjv754nPrQffz0M8+iZrWq6gjMmTsbYSFnuK/DRtyKp598xjDZXMyxcyjVHXIacBpwGnAacBpwGnAacBpwGvgXa8ABY//izv0zm9aCM0G3b9+O7777DqtWrcJnn31GVzu3mjhh7777bqZV6Q/YtWvWmusUY0zxE0I4Y7lNmzYmboUAN3/w6fHHHzfMsg003CgWWfXq1U35yuOfL9NK/6KTioEmd5D+SSyyv9LFo39dbttpwGnAacBpwGnAacBp4I9oQCwwscbKlK+EEkWLoVHdK9DsmhZo1KQZrm7R2sSjEtgkgEAgjthlXVs3x+Gjh/HJ9JnG9bWZ7ERQLIkxrPIVKMDvH7maVhyqAMQWLY6YAoW4zRhXBDNSCcAXL1kWCacTzHnF/spfqJDJb8A3gnD58xVAfjKR9K0osETrsmUqwsZ/zaydkqFcuUoG9AoT6MW2VatdB88/9zQSEulYkDKVLlvOFGEZcKpX3gpiOfnK1EdZ1V6xn/LkzcfJT4Sb2Hbls3LoG0+gaYUrKnmyMyifXD4qNFp/Mt8G3NgZz7zwimFFTXjzdbKxunA7TGjVWeKrTJvEpipRqrQv1pnnYnQV3STu3b0TCwnerFuzxsTHUv6t23/Bzu1bUbJUOcQSMGp6ZRU0ufpaNGrUBNe0bMtYWqlYs3Yljhw8gIU/zsbGNcsoJ2ONhYZjy5YN+HXndlvtRa2XLJyHU8dPEODsaPo0NVWMsFSU5fj5hX8TKPHLHMlkiplt9l0I43dJX4xOZo5dzE9yEuOrsQz1W3GCrhqLHpstkABuJJmO8ZkWt23rRsMKEyrfiAw5gWLSsybaGaSe4/bIwUNnyhB4z/MuOQ04DTgNOA04DTgNOA04DTgNOA1cnhpwwNjl2e8X1Wo701UXNW3aFE2aNMGIESPM8uKLL+J8wJj+QH59zOuGaTVy5EjzB69YY4pbtn79emzatMnMOlY+AW+jR4/GN998w1mqY3HDDTdAscYEpOkP5b/7D1i5fZRrSf8kmfx15H/ObTsNOA04DTgNOA04DTgNeBq4VMZc5oy082lZwJFSCFk+c+cvw5g3XsX8ObMx6L/9kJ2u5yZ/MQ0lS5c1eTSJqXfP67Fpy1bMnDWHLrULgtiQ+RZTBgFYkeFZDDdZ0IL5DuKxYAIjhplGEIIYE/cJkgQoPivdVROEEqtLSeUH8FwoAS21SksKgbOQkDDDoAq5gKYGE2gKjeD1XHsMJE68ImBFYQwTTG77VLK+H1MIogkc01qASWBgsLmOTj/p+pNgDL9Bg3g8SGAM9ZRG9lOIhCAYJBeHukY4l8RSe8WOFKOr6dVXIy/BvQnj30KzFm0xb8lCfPP4E8xH9h3zZZZC2Vb6FkSgj8F0gHHJ2HjUqF0bkWGRZOslmMur1ahO2UMQFpjKfluKie+/gx++/RrDPxmMbFnvwqQpnxNQ20vhgggM1kWOSA+AS2GHlS1biiBdhJEHvtijmcnkf+706RMcLEGM45bb9Cl71DQ+lIy71BSBWFyzH5OTqN9k9WUIwULP/WYa+9vGODVauwCWchCZc8TdTAoKCjV9Ik8OItSFsO9OJcWbOKln4EV/aYEyBEmXE8wD+7MrGXFdbuiDhx8ehfc/el+Dy4Bm0Xnz4uDhg94+Lw9m7DO5VXT42Nm6dHtOA04DTgNOA04DTgNOA04DTgOXgwYcMHY59PIlttECUhaUEoClVImBt+12ZlVodvGUKVPQs2dPXHnllenXqLxRo0bhrbfewpNPPom4uDjjVrF///5o1qwZKleujAoVKuCRRx7hH7YPZ1bFX35OQJ7aqj/QrR5spdKPkjEK+bbtObd2GnAacBpwGnAacBpwGvgnaCAoJJixwhIMQCR5hjAO1rCRd+LYqSS0bFQLr730PJ4f/YYR9ea+PbBiyWLGklrAuFt5CHiIgUWAiGiXwAuxebzvwzMtE9glUCyIQIO+/VLoatC6qNZ1SjZ+meQAwZQUuhS0yYtzRVBL1/K7S+VllgR87d6505SpfGKq/UrGVWT2LIyjFWnc41lQTKw2fadJB0raFkgmgE+Lkr71BLB5bgUFtnHik1lSGY/Kuz6QdSQRFBMgJCAniNde17UHPvnvRE74+tXExa1SraYpL+OPADzrTlHtE9MqiMBTCteSoS5jaklLpUqXw7Wt2hCg844LtBNYJ4AuIkc29O03iPGzhuA0iVrN6lXBmFdexKNPvUCALRAlipdE2zYtDKhnVZuSckbHGWXKbL92vYYGuJs9eyZatmzPPvHK2bdvL10j5jJ9J68JcSePEz8j3Cp5+R18gi4po6NjMiv6nOdSkj2d2u9s9YUBJDkMkkTPO09q0botli9ZZPoxiYDX++9NNAuDqRETpGYJjrVs1Qqvc+KdxoqSiVXG8p0bxfMo1512GnAacBpwGnAacBpwGnAacBr4F2rA+6vgX9gw16Q/TwMyUPTt2xfz5s1LN1KI5aVYYKVLlz5vRZOnTGbMiYN49NFHTTm9e/eGXcQI+/DDD42BYsiQIWb90ksvmT9q89AQ8/bbb+Ppp5/GihUrTD36Y1kAVfofzdw/V9If5kE0QMhdzr59+85ajjGuhj1vr7Xl6RotOq9jMpIoqU4tSTRe7N+//6zlENumpGtcchpwGnAacBpwGvi3akAu22jZN67b7FofkloM7sFzaTTC22PKbxfzwalryaSxC1+r6dtpKWTmcN8upnzmtfu6Rm7RLmX5t/bLxbRL3yobydb/8ce5ZEUFirDE+FwJ5ntJcaAE0tx0Y1/8MHc+xv/3UwJMWbHvwGHs2X8Qp0+fJpiTbEAh7xtJTLCzJwbZbyGtxb7yvqkEgKWYvtNx46qQ7CYBPUr2Gm0bAEidzmSYWsxj1xbA8kCuVFP+vFnfYybjgJFrhn2Hj+MVxr3q0KETwoh/2W84XSdQSvUkE2Cx9aWXR5BL28HyTuCr2wjAH3kssEkxsJKINlF0A4oJyNEioGrjz2vxMYGY63v0MWCggMD0eqQkJguKadvqRts25cwWiXaU/f5778S6DZvJGFPUvGBs2fYLvmcb1W/z2G+pBCh5Z+AE3RyqjdmzZ0UWXtu2bTs8OeoubNi0BQkEzUjqwuatvHbmt/ymlYtCIE+eaJa3HScTCAoSHPKOe/q2cth1jshwNGzUDE8//hT2HjqJlMBwfDr1C8z9cQ569x/EfgmhC8ri+PTzL7HvWBwS00IZz+2/WLxsqScjFZXKJV9MAbpj34q4xBQk+Zhjuo/PlSSjvrcN69AHpppvdJ8OdY3A1nOlYUNuYVy66ykXC+FzKEDXi+YnthiXwkUK49EnnuDYCPH0z3rUcgeKnUub7pjTgNOA04DTgNOA04DTgNOA08C/XwPn/kvo399u18KL0ICMBdu2bUPr1q2RM1dOZMuWja5ZyhpjweTJk89b0oQJE1C3bl3ky5fP5NUfuFpkMOjVqxd27dqFp556ChMnTsQbY9+gy5dw80exMrds2dLUe+ONN6bXE0w3MzZZo4Pd19r8Ac11Mo0XCxcuRKFChc5aunfvnp4n3Wji+4Nbf4xbI4jK1v75yit1AeCgv3xu22nAacBpwGnAaeD/owYsy8K+Fz3DuuJC8d1L27MYP3pv2te0d54TWmiglqFb3xNi4Ah8EFPIxHjSe5bHbPwlvXvN+9cHmngu8jxmh5288kfXf7fOLagng70Wf3DAGPH/BwIqbtiRY8fRv1c3FI7OhgqxMahSIgYFCxbGsDvuNWDJl19NxdFDh9GiQR2UKxiNSsULokqpIrj7tqGmf5MITFgASe76NB7kdlD9JtBCIJgHDHkNMv3OPHJ9aJj37G8lgWChlEffYirPy6dtTU7yJiZ5JZz5FUimcaV2qI6mLVrisf/cifKx0aheoSjZbXlx/0OPGYaXmGtKAqQ0xqw7xSSCJmKb2eMqM5hj17gB9E2I0nWS1fQZ2yjmkpIXU80b79oXayw6Ojeat2zD785E3Nh3kAEDpYOMKZRsNY+BxvGuca77gPQzsdhCfDfNS6PHo1r1WmhcswIqFo9ByYK50LRuVWxYv9b0W78buqBY/hym36qVLoAChQpj6Mi7DOD3wug3cUXVGmhcvyYqFMuH0oVy4+r6VbF50wbTN5Jn+J33kH0Vj7KxeZAnZxgOyK0gk/0eNju+H/Xny2PHITwyAjUrFkXZItEYOWQA7nvoYVzZoBEB1XiMvOdBgnAJqFyqIEoVyoWpH3/M+F5NqDev/QmJCbiFrEQxBEtxvBXKEY59e/YaHfnXZbf5mDDjSWNK8cVs0rPFf/9c4Jj68blXxhALS0HHDh0Zj60IQsLCUbxMafTo1RvzFi2hW8hcpu/tc8WW79ZOA04DTgNOA04DTgNOA04DTgNOA5efBgL4h8e5p+z9ybpQNVHFquLI9p/OW3Lu2CtMHkl2eMeq383/+oeL8PbkpQhM8YCSGzpfgUHd6jK/94fUGdTPt2Vb6v1t+7vluhNna8AOEbGltmzZgt27d6NunbrGTc3ZOc+9pz+2ZSg7X5JBQi5ZMibVr7rtOWNAkUGBi85p7Z8utD7/a+y2ZBDw5i/v+crzP++/bYNLpAQkMypDMK687nXMnTSQnoPOlvf/2LsKAKuqrrtfTdIdCkODgYCBhNgYICAdCiIyII2K3UFY2IqIvy0WivnZICIiSCmiImGLhQgMMy//tc69Z7jzGGbocm+Yd+vkunXuXmfvbeveVcuEz7n+fXZWLu4HKpGCmDpcuW4T+WXl4mKqwixvNLFC7Sby64oFmE3rttctF1PMi8mvhxUBRUAR2P0IMB7QFjP98Zyb/Pw8eWqaM9aIhxLSu0NTjA2OMQ2C0YSRYMxZiQdpgRSXqrWbym8rmMdN4CTbbb+2HQjDY8ghQxjgfXZ8r0ky/dFsWGZwv2PFwkbQE9i+INaax753+Q6mi72HEEc0E3GMBmQPzH83kzxJ4fuUBBAU3Hw/5uVtkowSmYYQIfHhxIYCSQCFNhXVX37xlXy5dAkU1wlpdlRzObRRXWPJRLd9Jj4U9u/bUvT7kWTBpAfuAgkTkuxhI0xX7rljgpTILCn9LhgEsqS4/hVdfnHY0OrOuvMjKfPbb7/I0kWLpFXL4yUN7gcpPA9MQ8sqkk8FJM7JQs6YgkQnzylJSu/Yh+MNEqU47Th3m134BeDaLhrNNdcHy2U99hq3JBuvK8eiy+knr4tkoScAXg+UXj27S/nyleTBhyfJym+/NhZfNWrVye+D10LLW4fdb0gql4BjuymWvCVZRSLNCtvC+5Bx0BKwOLJDTxI5zNm+7fFSt149uev+R0CowarMbaN3nBpHvDKKg5FrOQf8rLA9rIc45G7MhbeEz6RqlepyECydUhGLjfcaj//y8w/yzdKvMBY/TlJg1cV9xBM2bzgXOC+RuMz//FOTt2bt2mafIZbYWFdYh7032UYeTyYj2U72hefuz99/kx9Xr4ab86NQ3+YxeBCuIKMg975dulRKgSCselB1CRhyK45Ids4YHY9ZI3nu9U1SkISjJRttm4gkT4PfH3ZIywRisFGAC9uY8Iyft8yLaw6DV57bdn0fkI9eGCYhXK8sj8/bcATfGKEUhxAFDgGcwwNN8t8r7ndwHLHfSFi37vqQzHlp8IHWXe2PIqAIKAKKgCKgCCgCioAisNMIbDa92emitIADFQGrACMx1ahRI/PHvpJECuBD2s7I3Vr/jeIBH8POh7ff5GNZ3KZ4y7dleJUs3EcrLvNRbD7eN1tx2bw2H5dURnCmLtvFcpLT2HZ483Cd+1kP03Pd5iuuPKv8YBnedW6rKAKKgCKgCCgCBwoCfMd5Fex8x9LKe8G8OVIS5I4IiTEQYYhFFILC36y7FkV8r6bD8sQo8KHg9oMcots+hxyLS6sjD5Pvf/xZGtRrABdycVm25Avpnz1AbmLsJJRFixN/cMvJM/sTtlTmz5k1S9LS02VgbJixGvt45gypBIv6fjJot3eF47E4nA6SJOIYpxLiQFU8+VRznmzlPEZCKC0NRIyHGOJxQ9y4BAqvA5ZByx0zTsKSJBnJlTjcC0ZwbYRSNpMPHJelp6fBksux3CKZEwqiLlwXvDYoNv4ZrxEKiVivsGxeb5awYpm8Bpm+ToNGhlRjerYzGgub68YSYrYs46KPbcc1yD9eh04ejBfRd5bFtlli0NsWTpzygX1g8+DNECSMyKaNOfLa9Bdl3rxP5Y6778cx4gDLMJdwM4W7PyR3TV3sMwqxBKHZhzaHEaMrFW1i/LIgCI0Wrdvkt4MxziwBVq16DeEfy2Cb2TcSefEo0rBvKT5p3qKVIUVsnDHbHnv/2nuTy4Rr3eU2M3/BPHYsXB6WeBXKV0S7GD8OVxHbj7rDYZCAuA7qIyYwOo42ERf3vIGUIjHjB7+ILhlLUl5TvE7o7dBrAWYrpWtKn8uksc+WBOU1FkHhlhArjFhjvTz37BOJWfJDUbSVlZEUsxMpSIqFgHOEjVJRBBQBRUARUAQUAUVAEVAEFIH/LAL7NTHmj8JlTwwfVwlnRic+0fC5j48wM3ezkHO6ZyaCF1JxwV1WAVBw77Zv2Y/Ubc+xe1JaC65NmzZJnz59jFUXP3aT5fnnnze7LIFm8xXVDy/B5E3HdSpBcjflyrl9zzUzyb3HWRHLf/KJJyUjmGHqTT6evG0S4ce737vO47btbNcmfGOnckYr9kfEsQZjGr/9vrbXmc+ZGSwJKF2wLwYXO37ccT7M5C1SAhugCAii7BQoEmKSGnNmzEYCnvKKLsActTNHY1GfpIVgBcaZ4gIyExgWKa4+IwDlRBoSxt30UXcJlUuR2Xf7QWu5toMVQYW3gzk1myKgCOxLCJhHVdLzLIAHcRyPqBRfLpSvIQnjcRuCojrhj+DJhQN8HhtxHtiMr5PAOCJBS1ikjSMdxQett58ET5Jyu7inXwx5vMLnJWo3u1g/zPTNOttIYWpWTQM2HuF7xLxL2Bauu68La0gEJ3VQTEM571rixEE8JEA8BZA2AE1wzJ2EEsVzn4rrEAqgMp3KZcZGsiQB66ZYpb/TElrDgLRAvjQEaLLkg2NB45AIcYy3+A6mQp9KbRM/DJU//vRUR0GPY/xn3QRad2qOVUkC7yEHH9Mt/PhxjgzZAsV1t1595ILsoVLSjXP1yktTZfCAbGl7Vk855eQ2xmLMhzodKYizOXk4wHhMFETOMsvknyhJAPQxiPqISR6V5hAf9qWkwJUetokRsbIKeW8Z9jzwbUrZ3ArbLrv09NOkc/bzvIMSMYQCL4EE3ejhGuQ5p9VgAoSiI7ZkW56zF8l2TkhqoQTnssYaSRScFQpO3WbB/jwzVrHtcA9hk+fX3EruktcXhYRLfvtwX3Kb11K+gIhwymRiTlzCBCvvce4G8Qk7IkNgRPNiMqB/n/zsdoVj6BRYBt390GNSBq69y5UthfToBfb5cD2ZIlE++7W5r06dLIPtoti2br7FeW3wiLPML8ueZfQ9YiwWQSoiIT89wnlhObVNM7gmjMqE2++ReiDnKCTHCM+5vbub7fwfw9bgmYSy7p30uGSWSMP1Zj/F4uYaNO3Ccaf9wDGUivI24xjn9ZK/CazQJkN28VnGCxR5bZ/s9czSSArRDaQ5f+5y/YZNMnxQPxCgmwmw/LZi5empL6A8Nw8u2DjuEV7DUfOgQn3oD0mosEtg+mzDQMxR+AwNYxnl1xl2YeqZROF+MXvQBZKb51gPmoTuT0ZKhtz94P9JydK4DoAhHjO4hsIS8aWZ52Eq+meJMW8+u25IMbSXbeajluQZx/PMY0kx+wxUUsyipktFQBFQBBQBRUARUAQUAUXgv4uA/RrbrxHgRzI/fMzHz37dk/2z8XTv0rNnT3xw42sckkz80aqMirVdKayzV69eW9TFOiJwNeMlsnZlvSwrHd3MM4WGoVihoq/o24hKC8agCGCmMXVeVBQUJTFXo8Gy/Qyw7ipEElbh4FGQFFYOchXYTSIsApIrgHNABYZVDhRI5NmgXoUKJgqbwnaYdWpeIcXlN4l264/Tjh2twvZnR/NrPkVAEdiHEcCji1YMfB/RagmmB0U2lgYxRq9PLSqEz7eQUa5DlYuJCSTIdkaov7bkWmAny2I7SIpF0egHJ46Tkhll5O+8jfL2a9PNZIrBQ4ZL+85dZOJtN8s7/3sPfU/IsBEjpH2nbsYFW2HvYSqJabU1e/ZH8uSUh2TV6pUmPufhhx8hV1x3g9Q4OEt+/+03GT3iQjn3/EFy+umnm9hNa9f+I2OGDpKze3SVdh27yt13jIertzQZNGzUFnAZV3sgtBhDadGC+TJx3M3y3cpvpXwFxDhiPKgbxkmtWlky5vJrnHbinUXirXP3PjJixEi4b1uB83C8sUK5Y8KNUqpEacmFxfr/Xn/NvJ2GDB8lnbp0kwljr5O33ngb71q/jBg5Sjp27m76ctv466V82cry74Z18r+338T1EZTsC4dIh7O7yZ23jZV333wDBiV+yR4x2uQhWUhFOrHZTBw63aLruY8/+FAeevg++e2XnyRnQ440PfJoxFy6SSpVqmywoaXc9FdekOeeegzup1fKwQfVlHZntZfzBw0xhXCsSgW+VxwiDuMXXDC/rflFbrjqKjm17WnAoKc32QG/bkkLdtSHcUi3ngWJMe5LYEyVACuVmpkuk594FucJ9y0shaJkwfewpKamyMfzlprnRGExr3r0ObdAi/h8IVlDL37BdGfSk81XGOETBeFkr0UWlHw9Fih8BzaCaH+nbj3Q/qKfk7ZottUQy3gOUZK37bNua+kjiEVGy7aze3QvdPzOiVx+kPK0/OK1EE/AopT3Ip/rHJYWvG1sNbpUBBQBRUARUAQUAUVAEVAEFAFFYIcQ2LYvoR0qevdnwicTZtnCnUeCiisf3O78Ko/mzZMIrGOKEs4j3ZvCj+KdkcI+nnemvJ3Ny/4kErXw4YoZmW4sAm+Zk55bYI559+3MOok3unfx+7MK/bCmUnTKi0vy60zGa6fx5zRgSDTvN1mx5CMo+5zewIDRiI0FY2N8RX2Om6Gff/peRl7zg6Tkz3h30if/JkBAHVz/WEytrYg+cga5Q3T5zXWO1CivKIH6scBhEpNRzH5PSwlK6WqtZNIz8wscT96IwVYtCK1N2JciE59fAprNsaAIMegNJGoJuuSMe2g7AUXJzgjQ2JnsmlcRUAT2YQT8iVLyxdd/yyZjBYNJM3huxuK467HkpISA+/6NuZM16tZrBmveqOTFQ1K5fktJ8/8rvyxbgHFEmikhmvy+Lub1Tesfr1CJnAaihE9tugizT2f7FOdrg68EcEZ41ntzFr5OUozK59mfzJfFCz6Xk047xRA6C+fOlwsG9JUzXp4m5cqVB0E1XD79aKZkD7hAZjdrLTWqVgBD58Tb8ZZMRTsnTMyf87E0AcFzNpTk4XCu/N+Ux6VL+7Pk00VLpGyFCnJIk2YycsgF8v7seVK5YmUZPnigrFr5nUw6vZOxLPts/gLEGHOstL3lB8E6GssyvJdpVdO905nSoUt36dP/AhNvbBniif391xrJqlnTkGJpaY7FDy1zvl76tYRBZlWvXt0oyonlZ/MWyBL0+7S2p8rgUSPlc9Q7aHB/mTbtBalStZqMuGi06ffA/v2k6bHHS7VK5eXzz+bLAsTRantGOxk8bLQs+GwucBkgL7z4slQuX1YuHHGRzIJ7w6HZ2dLs6GOkcpXqkgpLPEtKePtDR4TzF3wsLVq0kNp14fIRWE164D7p0uEMmTVviaTiBE/B9o03XS3ZQ0bJgEGDZf2/a+WHH37AeAVn3SVHvSQlJ8Nw4kwE19ay5avlnLNPl1PanmTIvmQCLWnei7dpB8y6JSYDALNDxw4F+sXxG4lWEiW8NZnWTD4y1nbbcAMVKG3nNzhxiWNfkvB0s2gsxWyxGDJ16NjJbpll1OOykH2J4Obntcb7H1yQcVFoMwRTYG0FqzkSRPgPrwwOSWaP74plOizAunTrhnY7WBZXJu9n3hfEn9aAydvJ+ZOP8xnsxzfaWe07GKvS5PQGS5RPAtEQYbDmpSAymFkCbbPUH0VAEVAEFAFFQBFQBBQBRUARUAR2BQL7NTFmAXB84Adk4Vc/y4Ivf5Q4/N8XJclESVFpd8exLRQd21mJV6GynVl3S3Kj9MJXMmdSJ1uLscLk/u5s+1kelQ9B15VTcp20FmObbHv4oe2V5PTeY9uyjl6aZNVK/S0vPnEvYifQUQx0ju7dZHkrxqGg0HNXEORUsMzBct/dLyMgRTHEDMo5e8AN8uv6CsAT7npcxYDxt2VKLE75U/C2trPeg1AEl696lDz16gJTylZ/4KqQeQjbVKS1RFTQJejie5kYoxXEzkhgJ4m1nalb8yoCisDuRcAXyzTPf/hYdp5jdDtGxTJJMaNULfj8ICnmfUfw/UKiiO+YBJY7K3zfXTbmMrlj4p14GWxWbLuPU+MSje8kY1UFRfOxXe4vtkqm51/NrNpwJ/co3oUB6dCpu7z1+svy719/yyOPT4XC2icdYcn1xLPPyKwZ70vfvueYfhb2/mMbR192tamXXWa8ohNO6yoNssqDMJstzY45Vi6+5GqZO+MjubBvHzkdSu1PZs2Qtz6aBxNo/Mc711q9JDeeSnSrHF/5zdfyz59/wEJsgpTOSDP52qEsxg/jH1035uTkmgkheSDG6OKteavj5RRYqcVhacLzEsW7/eCsLNNvYta+cw955aXnZO0ff8qUJ57D6yEm7TucLY89/YS8/97/pH/ffob4rJlVS26/d7KkApdOsCR79dXpsmHdOnns8afhojEk7c7uIdNeeFZmzfxAevU+zyj/2fYUO/PF7RiJmJGXXJVPYvJdedKpZ8LirZJ89ukcOfqYY2C5doMMGX6RXHrVtRLO3SSZiCeWh/hoFF5f5vyhH3bdkikfzwKRee45MvyiS+TCoUPz05uV/8gPxx32DjVDNzJCXjEWSxgDYb8hdXlv45wYMg1jUI799qTwXFpvCfY8euvnGC5ZeN3yfqEFJdvNNoctU+5JTAs4fq8wDbu1O75dSM7ROzXbw3axrqKEZBifF9ZFZvJ2ct7k4xyf83lL3CwJ5s3Dc083rP49fB69bdB1RUARUAQUAUVAEVAEFAFFQBH47yBQUIO+n/Q7z341QyNDN0c+kA4UfwIEBT64bAyPrXUHaomtHdoj+83H/s7UhA/KfUkcvQVQhSKxMNmivzvZfpbHWCqsj+vJ5dv9tj07Wd0WXUqLb4AixAelBq4746priyQFdlABEEVbA3RVQ04sucEFUvM4lBS0DovkmNgx1pWXL2Fv16IVF5ttEpyC/QhgEwV5l0DsDrqY8rlEXnK1dhsqGCiQ3dgc2BmIOwq9AKwuKJtjoJjNPf5TnHKIFoMUKlEpVExR+cOZ3VRgYV632a8/ioAicOAh4Pf9i5kIVK6n4lmF54D7XsqDpU8Uz+tSsD3wCp/PFCppHeIlKBXqHok4Ou7zLkmxnWQQ5i3KrIOPKyAJlHNY/VoYoMBtGkiWRC7aR3Ffl0zONvD5ZNtijhfx48TpjEvdBnWhLEdekHuwOZFaWVlS7aCD4SrQeR/78bxvekRT2bhxoyG7CpuUQnKGxNZPP/8qd902TlYtX454QWHJo6Ye5f7ww2ppelRzkFsJuW/KZDmhVWv5ZMH18vBDD0qdGlWNkpxEGp+thbXfkmJ8bteq30Dq1Gsopx93pBx38qlyMgiltqedZogBvlf4rPb7MZMEck73jrJ2A9xETnsN5xAvTjzPnVhWcalTv55kZARl3b8bQailSO1ateDysSbON+LIAQ+Wc0TjZohFusm0j5NoGiFPOqyxE3h3s65atepKlerACkQZt/0InHTYIYdKzr+5qJ1EqmMNQ+LLKyRY/1jzm4wfe7Os/m6FbMrd4JCCaOKaNWtkyRdLZe0/6+Feugfih0UkLY2ROp1zHACBaa5JjgF4vaEtccQ0S4GF9oLPF8kbb3STu+6+G+4c4doO1dLFZRAxU71C66gDWbzElnfd9tm+/y2BY5c8Xlh6m293LUkmUfj82BZh+9lmu9yWPLszjW2HXe7OukzZuE9wF5pVLjFtoUCVCcTSNZaVHIMacsw5zihnKoqAIqAIKAKKgCKgCCgCioAioAjsagT2Sw0xvz+py6KyuzBFzK4GSctTBJIRYDyQnE1QoFFpVszM1qCrWItgRi60eCiqcAIxvw6426KCJxqlkg9qAzf2A9RobhK7zM+RtFLwOO+RIAPaYxmjArMYBQ7rsfeVUfZAeUexFnDbqgBKatQu29yMg1Mk+2NmJbv9sjOZE3DryZnHwQBnZUNxDOVjbCvk7S5rnBakCCgC+xQCJDZsnMYgYksl61cdMibIaTZ47uL5iNb/8u1ikwyPDkNkeDtkLb28+7zroST9LZ/2V155pXz04bt49O9qUqOgUtnbDu96UakY74f97tWpnVSHO8N+F2TLoXCbmJFZSo49rK4pxljCGG7KsSSOg2irUKGSt4qtrnOsxmc0x2spcI333uwF8tQTU+SDd96SEbAIywRx9MJrb8shhzSCVRXiCYGk69ujk6xY/rX8b+ZnUqFiGUMk2HZyUgoni2zcmGssbuhazZZPt8F8v3JyhLF+wfuOxJMR7LPvta02dhsOxODOrvMZbaVarVpyXvYgObRxY0mHRdiRhzaScCQsv/zyoymlUiW4f8T7JgGrnxhYLuMaDoQISTevGAt3YOOH9VAUrNkvP/8MUowTf2jJSAuo5PFCwfzesnRdEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEth2B/ZIYs/NnDzu8ivQOYma3ZJoeBxObzNKZbbh1EFStsHVs9Mg2IBDzGfdKeZFV0uCgUZgt7hBHNhaXxJ2A6j6f62LRl2os3J57c4V06dcLCkLOSN+6cFb5wXUbSzS1BAitkKREnds0EoBmkpJvOeZsJv/SQsErxmUPZsz7whvk7gcelpEjRngPb7nuan6vnvOM9DvraKgd15s0MetCETPr96b4kyw+bFs4+z6CGfZUJlqrsTADvQdCEqKlBpSPtI6gOzMVRUAROLARgL2P6aBf8qTJIVUkM+a46Iv6HAueoEtS8dlAMj3VPJcd+4UALI8QCAc2CyAothOmGKy1vBIAKXTL2Ftk7PhxIDlgmYRnMcXyZxyP8NlEcoc5T+z+EA9vgzBn8aMZpwe2ti2LpfvCf9f9g3heS+WZaW/LQdXLm0Srf1ojORv+BbmDWoBFAqTThf37y2GHNZLDe/eUQQMHyLsffy5VKpTeslDPHs5XiKAQ6zauRIlUGTZsmAweMkRyc3PlxFbHyr333C0T77rb5BrQ7xxYTy2Ud977UCqVLZlv6WZIS2Ods639Zp+33m9PEwuumvfc1vP9tXadLINLyGdeflVq1cqCFWJCfli9Ei4Tc+HmMSbHHXcCSK6EfPD+/+SMdmejbMQPwzW2NeE7idffkU0Ol9vuvFPO69VN/s0Jy+VXXI55NA5Zu7W8un//Q6A4T9DFWaTu7R7bGLo72o5i++d6JrCjTEwTM1XhCelWWfD5uqPt0HyKgCKgCCgCioAioAgoAoqAIqAIEIGtf63vB/i0POJgaYU/qLCMOOqm/aDh2sT9HwF8q8f9TSWS6Cghl4jKJ8bg1MoR58qMxEHM4Jv+g28ekZvHnm8UoEUBkIcYWFTWWnor5F7g4W28W5NVepxwTr0cebUbrh8hA3s2L6p64+6R+serLkvIwO6HgGRye+MuGY9ib4pVmGytDVTEQs9shG6ruM59DG5PXixJb721YnS/IqAI7McI5NPfeAbQLZ0fExUYTyx51PPL1wvNc6Fy/Sbyx6pFkh8j0j74dgEGQTxQaTGUAlLMnd6wC0rdNUXQYi4tMxOEXUDeeWu6DMi+wMTDGjNiMICDJR2slyh33nazLP/2G5kx93MpX7GyzJo9R4YMOEemTX+tyIaEQbzRapoTNFat+Fb+WvM74oa1MpZQMcTd4v7MEqgfGA3L7i8fffCeTJ3+tqTCCutvxCOLwOorIyNDSpYqBQJp76OXkZlhDL/fe+cN6Xf+ILicDMulIxAPDGQqz3Op0pnSoWMXufHqK6XRYU0c8gxpvvv6G6nXqBHIT9fTAa4HkmKc0GEncrQ57jh56uU3pG+XjhLLXSdX3zi+SGz1oCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCOw4Atuoat/xCnZbTii6rYLc0hC7ra59vOBk90Db6+quuPzFHd9ZeFg+20w3f1QS2Vgl3G/XvXVsb/+8eXd23baVk1epLkwtYJ2VdCUmnG2/n8q8IAi0iKTgoo06esatNiWVxBoJHTdF2DJkdkf+lV94ET6/Y6kWJCsE8QXSDDkEp1ISgXVbBDHHvALIC0hKgLHbGEMtLIyX44s56UkuURy7Bmd9b/xaxbWtO4gYMZRIBFYJYCCjcEVJKw2KcbGIZQCacRJ6NAZItugwCfVHEVAEDkwE8PjiozOWgCVvIFWCLmMWcC1fE+ahzHcQbKtwLAIrHUfcB27Bx+V2Y0RCzAqti/ItLtxyuR3Hs5ou9RLb4GoxDpMLP9ppyD7z7MOzDcQTrZV8sI71uSQcY2ExHlUMZSfwbiWbw5Ykx+rhO40xrq685ga58rIRMuGmq+ASMCIjRl8in87+EO+MsHw692O5/ZYb5LGpL0v58hXR4Lg89tQLckrLpnLnnbfKsJFjDFnE1yHLp3VYHkgvtiGAF0w8FjGuDfNyNsq5fXrIxpxcycxMk40b1kvz5q3k4kuvRruDMv3laZKXG0YMsqNQEM4DscOyR5++8tDkR2CdBceJ2JdI4J2akmpIKcZYI66oQBIg93wg8xjDKcrTiLxRtImnPAIc/GkgJuHa0O/6yGQcMbowRHL8RRFXDlWCyKPb4xDKM256se6V0iXS5KrrbpDLRgyTm6+7FpjGgNVlMuvjGZLCEwOrwLvuuV+GDh4oLZo2ktJlysoGuJ489dRT5Onnp0s4DHeRqJCeDfw4f1GQZiZOHOpn/446orE89+I06dWtk+TivN14k5JjXvz39XVeb0VJCPcDx1e0lgzxOoTQ5TOFMbV8cPdqrl9cC6kpsGZ1XYH6kYa3A914e4XXKN2G8p7liJMuS3l9cWnFu12cxVpx7ffnOa5K2QeOz9keuv7mZC5u21iJbJNdZ1rGt+WQNFLs1AAXC7fxiSD7gtErPCXwG4GPaxVFQBFQBBQBRUARUAQUAUVAEVAEdhUC0DF4vp52VamFlMNqytVqKmtXLyrkaMFd5Ws2NjvYsr9/WFLwoG5tgUDyKeRHqFd43O679dZbpUSJEjIEboz2FWH7Fi9eLAsXLjQf+EceeSTijRyS3+Y93U6L18SJE42CbyjcPhG/8ePHS7ly5SQ7O3vbmuTqJWK+KKy/gtKq64Mya+ogEGMFz8+2FeZNVXT+RBIxFhXHdZgPyovKdZvITysL3lPJzUmBEoL9rVyrmaxZvgCKTkdRYS3WqJjcm1IYMUYyjAoiWicwBg1n7lMYcoaEGWfkU5FMxYwSY3vz7GndisDeRcASYpagSsA3cxzPjyp1m5rnXTSfGNs97bT1uvMNDFHmJcba9J4k0x/NNiSNUXK773Or0KYrMirJGS6RxwNoLxXpXkU4163rQideFZTveP7hMWjSenvGvBS6nly/foMsnP+ZHHf8iYa0iYDEobWzE9/Lfe6jEKOEBwnn1AlyCW10orPBOhmzD/j+4Hs0BmU9LcEoTjtAWKGeNb/9Lqu+XymHNW4mZTKtc2yTzJB3UTyzg6iY5yUQwhsIfWY7WV4CxAFJxBhicBpSAAp5uoPMQ3yvNMQrIyHHuJLorVMgehYFWUXyL2piTDqEl9+d1BIFIcVy+EdiimLKRbtpzUYFv1dI/LEda//NkWVfLpHmLVuZ8tNTUkwfof835FcELokZJ/SzOZ/I4U2aSrXKZSUHhKAdixEXuockTkHkJUEWSk01S0PMpWBCCwi/tJSC9YdtzDRvo3R9jyFg78OtVVgcseTDOQ/imo2TrHbF3ru87gzxjSWF1yPJsQji2gVxzUeRZ8nihbLsqyUY0wTlyKOOlpq165l7nel5X3Ks45SzeZzm3QZ1zKT5wmfJ3XdMkNRQqgwcMlxi7kSj/ARJK+m4r6JoDyX/WkadvFe82yTMbL2mTUhDoizfJXdSuZs37X3r7gnR1SjHdalY4t4EBix3q4J7nBPs2vefJLOeu1B8hiFHPvTLO0lhq/n38wPWVSWfU5R4BJPc8Cxt3fUhmfMSrIBVFAFFQBFQBBQBRUARUAQUAUWgAAL7r8VYgW7oxrYi8NlnnxlibPDgwWZ2J/NRMeMV+3Fr9xV33Kbb0WXDhg3lZwScr1+/vokRtRSxTvr37y9TpkwxRe7u+pPbbfs/c+ZMSYWiatjw4SbJBx98INWrV992Yiy54D21nXAVaQVPq1u7X4JGObi5MVRYeiUG5okWVwks+ZGd4Cx4iA0tlhIrqKjz5t0T61GX+LN1QRVk2sgYOFQS07Ul1MRGCUJFaIKaLChK2acY1gN7uf223bpUBBSBPY9ADBaxFKiPzZIWPzGwVfZ5Z3buwz98XnMCAIkvKrmpkKZQWWzJJ5I5VjFNaxHuJ59C65MUMjceScCai8JJBKVLpskJJ7ZBek6moYGdE5vRWDW5BBEt6+gSkkRVAEp1n7WahtaaCv446qB7xgQst2jBBk09tvH+QPoEXiJBWFBXr1LO/BlXgq4C1zYJun1HqASHgjsCC6+UFL/k5pFwC0oarMLYDxIIATzXI2Fak4ikp2UY4iAtNd3gk4J0jhvHAPoMMoztgAUdiQa2xxKCdPNIsWQi+83jYaTlBAtHvew0ib+EK444SOXLl5UWrdqYfMEAyD2Ugy4KmgsPlLDcA7mXDqKu5XHH41wFBIZweIemoGy0IRUkR4SEGAgSnL4wicBgGsY/qBhp6PaX7zJawoULGghtboiu7ZMIWOJ7a43zgaAJh3NxjjffhwH3HqIVIXwL5F+bqbiG4yCY0kCk5eGaP6nlkbJixQppVL8hygjLsmVfybnnD5RbJ96P2wzWVC5JzjGsnxexKwW27f1qD2K5eOHnuEcKEtSewwVWSc6Nu+kaSYer0zFXXoN7LgrCyiGQSfBTuJ3C+wzXr3Wjyn3O2HpzvwsU7G5EXABTIs7naQRE903XXA53qtXkosvH4D4rLJfuUwQUAUVAEVAEFAFFQBFQBBQBRWDHEFBibMdw229zvfDCC+bjlMoZ/lkSaG92aODAgcK/0qVLmzY9+uijZrtXr15y0kkn7bU22hmwVOgRp+SZ43sTM617MwJBKGrpnpLEFy0DIljnvhD+8jDTWEURUAQUgQMFAcday3GdRoLKWmVYUowED8kw7o+AZaJlUgyWStnZfZ13PhTUXgkg/QNTnjDWVrRcoiI6YNwLYnwAYsoot7GfVlmG8AIhZS02DLkEQojvStabAgX+xn83IFbYeQJjL1MfraaCIIrsBJfJjzyF+GGwriLBlkIrroLUE9+3lGAaLVOwBKHFJqelBUEGxGUdyqdlGNvAeqmoZ71050bBbrMeh+UayUPyXiTS0DzzHmc6YkWiLA8WcaloA/cZYgxp16/fKNl9e5nAnHzv23abwvEzadJTklkyA7g4ZBr3M00MHQ7Cuo1tNW1AP+JYISnmFadMh/Sz+2nRwfxWSAiSOONS5cBCgNZPvH7DkTxcK7hm6GIQhNiGjTlSAvHrzJgc1yfvrU2bNhliiZaFMJ2ULt16yDnnD5Vy5UsZwnjai8/K8IH9pX3HznLiSSeYe3V70eK1/8jjU7c5WwAM+ldffinpiPuXmwsLTrg1DfJ5gRuCY+RNOZtM/+gqkkLiLDMjExOVHPIsRhZ7O4SWct9+vUwySqwz9/J2ZNWkioAioAgoAoqAIqAIKAKKgCKgCBSLgBJjxUK04wmoLLnuuuukcqVK8s+6dfLGG2+YwkaPHi3dunWT66+/Xt58803zUXnxxRdLly5dCq2MM0NDUIDxA3bGzBly3333yQ8//GA+OI844gihe8TKlSvL2r/XyoALBsiFF14obdu2NWWtXbtWLrjgAundu7d07txZxo4da2Z6XnTRRfl1mQ/x/K3NK2xfxQoVEA8kR6ZNm2baOXLkSNP2m2++WV555RWzb8yYMfltZx66G1yH/rJvlEsuucQcv+mmm/IxsP2l0o3HKZaAOv/884UWbatWrcKHMDRVkHnz5sktt9wiy5cvx0zt8lIB7aKrw5o1a5rj3h+WY2MdfDTzI7n3vnvl+++/N3g1adJExt4yVqpVr5av8GI/Hn74YVm5cqWxCOvatatxNWnbw7KtcoyKRrv922+/Cfvevn176dGjh6PQMEf39o+riIMrJ4rfncXLLRpLpLqudMJQDBYqmLEeNpFTMMsdOowQTQkgyGok6lpcuJt7fJFA+7yShzZaq4UwZvIzrg0VklSEpsA90EZMuQ8grhotALgvFHBisHnL0HVFQBH4byAA+qZAR+lCkPwD1Lb7hTUCY19RuLRWTySJvOs8zn2pILRI+GAAId17nWPIrSSeBs/IgKSnZogfS77nSIpRSIpR/MjA/VziDWKsokgqcTxCYb1UiJs4RtgX8IekW6/eUIQ7sZTsu9NJm8D4IzMfZ7q55XGOQUgOcdvvvl9IilFo9ctnOsx90agY+kRnbpj4QBIKTQjnISHyxjGW4HsfcyNApoXgUjEKyyzkw39aZKWBaKPFms+11CEsJBZtP4xlCwg02B1L7/P6YbyA6miBg/eGV2LoI+uisM18veahDSTASGYFYBHEYUscDfGDsGPT2ZaMjCAmbjglkQQj6UUSjWWFkCcXxGNGZsi0n6kitApEfi855vTRKUN/9w4C3uuZLZhw23gpX66C5G7YKK+9+hLI0VQZMHiEtDurg9xz5zh5983XJQTydejIi6Rdh06YsJMmd95ys6SWKiubEGPvTRwPwopr8LCvvLqLAABAAElEQVTh0qFzVxk/7iZ57803JI5x15ARl0jH9meYjiaiYRl5yWXmfvPhogvBsorjzotGDDbjW8YmW7Rovtx+6zj5fuV3Uq5seSmDsfjNN42TGnXrIB9j2uFetgO5fPj8MvF2fhdkyOChoySAa/Ou226R1JJlcG3GZfq0F3Fdh6Uj2jbq4ktk3MTbZf4Xi3FthqRH764SQlse+L8nETOwhNw74WbJLIvvnbV/y8x335K69RrIrfc8JPfef5+8+to02YDvgvoNGsqlV14vhx3SwJDiN914rbRoeYKcfOpJJsaaH7HE7p14Gzwb+GUkvovuuPseWfbNt3hOrJJzevWQVFjG3TPpMSkFcpqkurX4tN2xZD6ff7itYLnqPMe4MBa2vPG3IsarQBHHt5JNdysCioAioAgoAoqAIqAIKAKKwH6MgKMB2Y87sC823RJZVPZ8+umn8sUXXxiiatSoUbJgwQLp06ePPP/881KxYkW59NJL5cMPPxSSQcccc4whZpKJqhQotyi0hvn888+lTZs2Uq9uPcnZlGMInZNgVfXVV19J2XJlhfG5+vfvL/Pnz5cqVaqY9VWrVsnZZ59tFFBz5swxxJUlfZLrYj32GNMuWrRITjvtNENesS8k2KZOnWqIKRJadDfI/rDtBx98sDAPY4WRmBsxYoRZ79mzp3Ts2NH0l4SY7e+xxx4r1ao6BJW3HcSLs0xZHoUxQ9gGWpARJ+LLNL///rtUrVrVkHMmoftjyTSWMW/+PGndurX0P69/Pl6nnHqKfIkZr6zz7rvvlmuuuUZ4bhg7jLNdV3+/2pREN4oUWx7XOTOWsmzZMunQoYPpJ5UT+7pUrX+k+EAOUSrVOcwsf1r9lVkm/1D9GcasZjjFgvIl+ei+tw3dZb67K7rZCkPRaHUb1K2mwaWP0W0iobGmcBW++15PtEWKgCKwuxFI4jmMJUMC7AWfdw7Vs7tbsGfLp4KYpFf7dp2cisnGeISxd0huOX+OlZjn8HavpqSG5PQzO4EQghIez2KvGFLBeQ3l72a8MJJTVFzTyoTWZBQfyDdORAmiHK5T6BIx7sYMIilGUorWNySMDAGIbUNMYfIHSSUSVnSdmwqXkOx2MqlhCk36Yf1sP101FiZeRTzrj6LtrIthzXwk3pBt0ybGkQIpiXbB2aSpn0SfIepQKMm7CFgyjkFMm8J+SUsPIV8UBAXdQyIfyUe8yGjtZoXpk+Czh3S5lxD4fC7G+IsWyCmnnC7DR40xk7iGDjxX3mp/Fiy7KsmwUZfK7FkfytALzpOmC5dJlUoVZe4nn8iCJQvl5LZnyIjRl8onsz6SQef1ktdefknKYuLXhSNGy8cfz5KLhwyQ1kcvkIpVq8NgjNc+Y9KRfCYhm5DvvvlaIjlhqVq9hmzKC0vPs06XM7t0k559zgMJnidfL/1S/l73lxwUzTJEt4n5B+I6WRbheyENFmAU3j+fYRxv2nfKaTJs5CWyCkTb+LHXSp3adaV585by5isvgAgrach2ieVhjIwoumjTp+jXokWLpXmr1nIhiMCM9BJy+y3XyVOPTZYRl18rBx1UQ156+nHpcFob+XDGJ1K9Vi355KOP8O1SEd4hTjAkOOmreSgnDe5JeZ8ce2wrmTaVxFt56dSlB8iuPMdCFPcGn23JQlxIAPLeoVtTWsTadIwxZicQ2HzEMwSLVkoUlnm0JlVRBBQBRUARUAQUAUVAEVAEFIH/DgI+fJQX1Fzspr6zmnK1msra1YuKraF8zcYmDVv29w9Lik2/ryWwkFKJceaZZ8pff/0lc+fONQoQKoDq1K0jWVlZ8v577+Mj1GEeypQpYyygSGolC8tjWVbsNpckckqULCGf4EOSRBP3kZTikpZMN954o6m7Xr16JnunTp0wczlDnnrqqXyljJf4sXVwyfwknxiXjGQZpRY+ZOvUqSOMt8V9zEsXiLTeImlFAovWYiTRKEzDPPyz/WXbvP1l37iPQkVYy5YtjVUbSTceW/rlUjm88eHyzz//5Ltb9OJhMnp+WNbWjufAXU3JUiWhdPjYkIiMGUbrucsvv9zksaQg81usnnnmGdM+9q1atWrSr28/6dmrp7EWY16Ln+1Dft3unRXzwWIJVgmtuj4os6YOgtu/zefS0+ztWC06v72hg64+LYBzULlBUyjtOKseClA3RtfPq5YWWmccs95DIJEqZDWTP39YAEsAJxldFBpxt92tPb4I2g4m1cxLiLdJYUvoSqAccTLQ8EBFEVAE/psIkEin2FhAMTzXyD1UadRKfl0+2xAZTord8+ut17bDuCHDWIDWSW16T5Lpj2abmI6GyOdDDcJQiZSt8DXOwUJ+t1D0JrmXpSLZWocxbbLiODk/XiEOaYOxjJUC+cBAsTy6cKQYN3BuQlOXr6Bint0DL2mE5JUv5Lh5jrnmVVapbYgvpGL8Llpc2fcs37t0YUixFltct64NqVyH3t4Iy/eEdjL7kvtrMbf9Tj6O0pzCPL8sn2WTCKOQXCBpZvdzH/sYRjw061rR9pntZHprRca0tpxkazG2zR0yMpnK3kDAPce26u5dO8u//6yTt9//wIwTSRI1O6SWZGXVkedffQcTc+gSNCY1a1SScbfeK1379JIeHc+UTRiLvvbeTElDQXSPelSTRlILxNMLr7zuXNsop071CjL29jul97l94fYTFojusyAEkmwTDN/PPL45rNEC8tq7H8lXy76RU1sdLd/8+DssDzME1RrhdUyXozavfY7Y9mMUL/16d5YMxOib9OhTpg+9OreDy9J/5fX3ZuG5QxeIQenZ9WxJh1XY5MeekN6d2xtrtIemPAkCynGjyvuib7eO8tMvP8n7nyww8c7oZqBmjYqwdLtahg8fimsfccfAEB/btKGcCtLtxgl3ytlnnSlnwhVkdvYg89wIwvKtd3dYouGemPLUcxINRKQ70pQvW0EefvxpgR2YeUbxXqCl6hb3p/v86XD+I/LpNMQH5nOEcdX42OE6B4heAak9AnGEbx1/myHeI0nn15t0f1i37wdMAzPNjUcca9zWXR+SOS8N3h+6oG1UBBQBRUARUAQUAUVAEVAE9igCajG2m+Cm0sYSNI0aNcqvhbOAs7KyzJ8lxZjukEMOMbOlbZ78DFghmWbT/vrrr3LDDTeYANyMP2CVQ6tWrcJMzuYm2+OPPS7NjmwmH2Em5tNPPy2WFPOWSTKHddml95gtk5ZZNi/3sR1169aVGjVq5OdlvsaNG0vOxo1mH7cbNGjAhSFhuKwFUiwLfbZ94L5DDz3UWIJxnWL7Tcs2EmCvv/56PuFE/OrXry9NmzY1lmcnnniinH766YhPwhnZDjnnlOL8FocX+7x69WqDHUnL7t26m/qZzyoIWRL7zD/iwLgKXKdLx1dffVUeeeQR45rSW6/Nk7xvX9imwpUzY71SqU4z72b+eshn0/mlYtZRUKhAswhhHC8KZ8XvTYH3qgJCxat1/2WJTaOMpeYRfaaC1l5fXCapRQqUpRuKgCLw30DA77qaTbg8TSyKOFgHYNepFLcKZI4/kvvouEjE+xpKY0vW7AwM1pUZyZ4wYngZ9214TzvkGRTThQhfTdRX4xVr3rdMYtwncknrD7QtCAW2eR+7LhZJOlFIfJEQC6UgDijqZBksi+vczzwknSjctwUA5sjmH2JlMMOuCKzOvRZim1M5a2wD456RACDxwPLZFxJgfNekQslPTA35BbIwBSSGJcLYTrbPkl9cWvwZe4puGimWMGPZdAW3BRHgNEV/9xICtDKqU7+eOd9sAvngrFp15aCDa5prmKQYSZxDGjXGRLYcXFuO+78aGBfTAQGHW7xGa9epJ5WrHWTWzXgFZR162BGy/p/1uAaczhlilEQXmPw+nTvIv+vXyavvfgj3okGp1/BQqV27trQ97kg58ZS2csJJp8gpZ7QX2l/aa5ql8BpKFtbPPwqXvPfYpxju2xCuyzjIppKlysAqbROuy4L5WTYvVd5zvG8Oh7vyIGYfcH3u7I9l07oc6dS1e/59ROLwiKZHyorvlptxNeuMYIzN+5wWbXlRuBzFkm6wKQHeQ2gTx3g+tIuEOeuk69bChONcP6ziOJ43/lDZX/qP3Nq4FS5LWS6fT/s7KVYYHrpPEVAEFAFFQBFQBBQBRUARUASKRqBQYswqkYvOuuNHC/+c2Vxe3NUOWBV2cnpHXbA5/b625v3AJJZW7H6647Pr9hg/9Ggtlbyfxy2hxLJoDZaVlWVicDVr1sy4ESJZRWFeponhI5ZLlleyZMl88oikAT9WKTxu0xdWJ9NwP+MI2HUq1dhOm9ccwA+3bRrvkgQUxZJvrJ/CcpP7yzSMgbZ06VJDPtHNpCW92H9arU2ZMkXef/99+b//+z8pVaqUvPPOO9KwYcP8+k3h+LF4Mf+pp54qWcBr0KBB0rRJU2OJVg8f/JS///7b5GW8MYNB0oVFV4zsm3VlyTzWvSKJtWQcWIb5aN/NU7rtjFC2pzCBGajZDfsw5zAUG37EZUi4rhRTE2EcSchPyx1Xil4ykBl8rq+xynC/+Nvy+ZKwJhauQsWW7xSuv4qAIqAIKALbikA0/3nq5IgaggfkDzepUMZzlm9pxujC0xj7nee4fZ4HLJPH9IVIIn9ig3OQhI0lU6gsTnYRaC2CoR5GXB/mKfgiJNGTLEbp7Nlpy7e7+E4hqeSQZE4BTGO23f7YtPkjJHSXrs78PmdYai1c4obUQu/NElYizEho3PcUuS5KgiSYs5q/dI6hBnuAE0TcNFtbsJ0Ws8JJsc2AYKjlseBC2a61Ca1dWKnFhUMhe8yusx3OEIn5nNbYJYcQ3rzYyk+ztXbr/j2EAK5trxjilwwpx1kYI+fimuCYKUbTLJ5HHGOOAEwVI3GwummIRcfrA9854INAGGFcjvhhYiYgRTHuzTPFJ+KMfYixchruo0AU1kwgemMRXPZp0h8WXquWL5XXP/wY7sirmLF+Oup8f+bH8swzU2QmxskjB0/F+L+EvDAdsb7qNjRxzDg0TEiuGe/asaofLguj0VzUz3iEecYFaEKipk2MY+ZznycJjBuZjvdfgA+NeBj94lPDIdU5vA8GQOii33yaxZA+d+PfOLhJKpQrg6VLvGMZggUXCUIK44SlwG05ye8AvzdYDiziAngwBmHOC0M5CcVKoG1psKxDbEFzLyEfcHa444LPq0BKquHBAogzKCTX+Jxg43izUnCOvOIjyeY+I62VqPe4risCioAioAgoAoqAIqAIKAKKwIGNQMEvhAO7r/t972hJReKI1kpdunSRGgfXMFZmtGiyShd2kjG/DjvsMBMXbMCAAfLHH3+YWdiWkNoXgWCcLsZPowtGLynGtpLgortGui1844035KeffpISJUrInXfeabrCfnn/LAG3FsQX8SKR1rVrV8nKyoI7mjzJwaxdEnOMPUbi780338zPzwItwWXLNJXgh2kPP/xwE2Nt3LhxMn78eHsof2lJufwd++AKSTEGGfdeM/tgM7VJioAioAgccAhQr17UHzuMx3P+O4lkEv+ovOWfiiKgCOy/CJCc4hiVbgURhs5MZDNx8kAUcVwahCcEjiN5qxs3hbAOS9DiCUJLsezzesriBfPhhvEjxPqtDOsqkGlgVGlNllmmlGRfOFKeev5lWfDtz5IG14cP3XsXSChO8nIwC8AVKeP/sQ62gxO8ODHNWIyCrLOuT4M4TusrpmU6jocpJLwNMY5tEklmnExiEMIxJa2vSOoyxt6xzdvgYReU/73xirEIY9pQSkj+/PNPySxVwliMse4//vjNPOPYhij6+Oefv5s+WQ6LfUOhpg7rZpQWlZZINgcK+cnLzYEFWp5swiTBHJTLvzxYc3r/cnLyZOz4W01fCilCdykCioAioAgoAoqAIqAIKAKKwAGOgBJj+8EJthZZjA1Gcuall14yH6rhSFiGDh2KD0nEHsAXJNPdfPPNQreKjIs1ftx4BLs+SM4991zzAWo/bPe1Lp9zzjkyY8YMmTZtmlEWrFmzRvhHIpDyzTffGEsx225avbHP5cqVMzjY/XZpCZ+MzEyD14svvlgAL6bLzc01VmcdOnQw8cVWfLfCZpeFCxcapYHF3R7gNjGk1R7JtHvvvVeuvfZae3ifXcZwbTCe2E+rFkvEnyorV66Qb5cvhyWc37i82Wcbrg1TBBQBReAAQ4Dvp6L+9tX39AF2GrQ7isBeQSAVHE+qscrMA9EVkbBvk+T5NkocpFgYY0xwShKGlVMMhpOMTxvHWDcAK8pY1CfDsgfK7I9myeRnXqZZlfyJyV9r/vxLNm6KyndwTTgT42hQUvgLSTgvAtIsJpmZ6egniTWSW+CXYIlK60saUNHlIIksrIG4CuE7Au5k4XKQabh04nnhML0N4M8PkovPrkqVKsnKVT/KP+tz4f6QnBWPk0hjPqRHeQFYhaXBYq3NyafIgxNvB/n1DyzbRKa98qp8+OEMOX/AYJBsEalZq768Pm26/L02V6Kwhn362SdlAcbgsDkTGIFKKoKwVTuorHy34ivJjcJ9PEhCWloyVpkflmuO1Rot15y/OPoMr6toByYMoo+0P6WTXNiomT+zn8fcPz8s4vjn+LQ0jWcHVBQBRUARUAQUAUVAEVAEFAFF4D+CAL+IVPZxBCxBQzd+JGJGjhyJmaIVpGrVqtKyZUt8+GYaUmwGPjavv/56mTx5slSuXNmQOy9Pe1kWL14sE26dYNJYV4r7UpdJXHEGaYsWLaQK+lSlShVD6I0ePdoQWhs2bJCePXsaN4bsNy3KDj74YLl0zKX4uHWnwXo6xNn1lPT0dLnuuutkxIgRhkTz4mXdIz766KMm3ln9BvWlfPnyhiwjuViY2FmzXB511FEm1tikSZMMsVZY+n15n3FptSV0+3KTtW2KgCKgCCgCioAioAjs1wgwzhYlSAsxuFg08eXAWjGWLa2jOD61MWGZNopJcPwOeOv16bL299/krDbHSJNaVeWIugdhWU2uvGQESKr1kt23lxxcIVMa1agsjetXkWqIWTb68mscKy7kt2UGwBwZt4VoAwk04yLdEPYOMWSsU2kNZqxVnUl3bK/9Fhly0RihNdYhNSpK5VJB+R0WXpycZyy7mA5lMb5gBJPY7n/4MUnPSJcmjWrK4XWqyNDsfnLlDTdJmxNPMfVecfXNkoD7xcb1D0K7y8ur016Q448/Cd8vpLQcC7VhqC8cyZNGB5eXKmVTZQ0wsDHJTCLPD+P4RSKMMwZcATPJQB/cTNJLAv9oAef94z66duSSMc5UFAFFQBFQBBQBRUARUAQUAUXgv4UAwgXhaylJuGtXz1pmmeVqNZW1qxdhJmDRUj6riUnAPP98v3iL9Acqm8f+8o8fdEXJunXrZMHnC6RNmzbGDQutyIqS5HNpCR7mST7GffZ4Ycd4fE8L20NM6ELxu+XfyVFHH2VcKRKrbWljMl6WFLP9oCtK1jF79mwTh6xc+XL20M4t3TuLs36h/pBWXR+UWVMHSdQl7na08AQ+4IsSGwMsxKnHEKg93ORxqVz3KFmzcoG7XfhCY4wVjovuVQQUAUVgZxEI+PHeMvYNVB6HoRxm7E5YYsDdGRW2x/ecItMfvQD2De44wA0CFrDPc4QgSkkJYqIILZ8TkpoCWwk86mMmBhdi7yAn342pcFkWhWuylFQo22E5EYbLNcbMovszujq75557JD0lTQYOzjZdYqyfwmJqUdlNxTlfO3lQdqdgvEF3ajatPc5JN0wXhwKb7uDYBuOezX3f0UWbn+Wwr3g35uWFESMVcY3cWENshLcNjIXGskMgDsJQWPNdT/dzbDuV8Oybo9DfPKL05t/Z86T5FYHdgQCjBlI2f3g5BBDvXCOIB0uB3ZNZpuMxsHHTJnM/mXuOsbM8kuxedc2va2T5t19L86ObSygN8bZcf4M+PHf8uPd8jO9F94t4KJB0s+QX71Uv2WTvJRJovOeS429F8eyiC0g+C3hfMj4gyzWTrkCMRfA8C6E+knBBPAN+/uUX+fHH1dK4YRNJL5FuvD7wnsbdbHrz7dKvpCwmp1WsUtls4/Y34jzPaO3md753fKH8+94+y5yUzm8clm6Ujv0x3n5+MAgv5APYJN8oFn+zUdiPG4u3sEP7wz6Lm+1nPMJY0X5p3fUhmfPS4P2hC9pGRUARUAQUAUVAEVAEFAFFYI8iYL/I9milWlnhCPAjkX9UKHmF+yh2P+NtnXDiCSYt5jkaUsebvrh1kkwsy9Zly7f5iiPmbLo9uWR76RaSf1Zsuy0uhe3nMeJ14kknmsPEK1mc2bkJOfnkk5MP6bYioAgoAoqAIrDLEIhCYUyi6oabr5cZ738oK+ACrUGDBtKx09kmjmZxFQVBCnFIQFIsEPSBNILjMrzW+D7k/hQoo0ka4dXnEFWoi+RTsgJ9wbw5UiKzJKpziLGt1WtJMZZBUozCsiwhZt+ooRAU7mDojCIdlVPRTqU4CTFakHA/98XoCg35U3CMbbRCJXfIJe24b+z1V0pGRqZcdNmVpl4Sb1TQU2x/7LbZiR9L1tltXSoC+zsCJKhCiPdFUovi3gL53fLheeCVypVhKVa9OtI5RBvspNzDMdw3jtt1EmCg481+3pMJkmTYRyF5TUlxl/Yes0tzED+06CIpRrFLkmJW6OGCeTgGD8O9YYWKlaRchYqg+2CZhXvZjt9NTDKQd/UOaZS/z5bBpSHu3LYG4coRjKGKIqAIKAKKgCKgCCgCioAioAgoArsMASXGdhmUO14QPxz5kUiXgeedd56ZRe4tjcf5kfnkk08a94A8Zj8qrTWVN31x68yTk5Nj6srLw+xufOB6hYqwxx57bLussrz5d/V6MlFn8crZmCO9+/TeojrGH+NMVi9eNtHW8LJ42rJtel0qAoqAIqAIKAK7CoGFC76Q7AHnyRdfLnWKxPv9z98/kXnzP5fFX3wp/kRL/OEQ495Q3IVVOafAgioKE4gQlNDwsAbFMRTTWMIowBBQtBwLQsnt6sXNxBlaWIFDc4goKJb9yPT4E8+ByEJ+l5wKoB4n3pBTrf1lM9BEkGy0NMM66mH5FO6ntZpZxwqtuxKomCSXIcJApkEnbuqLh2mhwnYwFhEzIS3a6kMe4/rMxAwi6ecz1mSLvoQFSemyxnIujv5SUe9VzqM0Uy96apb6owjsDwiEXSIrxVpKuhZd4nMZnyhMQSH5fBcucx9ijJl7DwSzaxCV31W/Lcfdw7TRsGNVyV05G9fLkIF9jTUm7y3GIPPKE08/x1sRdTh73ceBN8l2r/tAkpOcJwFOEsyS8gE+l5Lam4jDGhTPMj6jHCLNaV8s31LW2Y7wAQXxASfTDxwnDxd309lG0h6XksCDi6HPzDrs8+Lu8zSwKzroFKu/ioAioAgoAoqAIqAIKAKKgCJwACBQ8AvpAOjQ/twFWi716NHDfPR5+8GPQCqUSI4lC49tr7CsjIwM6d27d4GZm7YckkPJ7gbtsb2x9JJZlrjiPn5k9+3bF0q2gl+6Nn1KyHHnxDYTJ5s3uQ8xzmx3FX07gmdyebqtCCgCioAioAgkI0BLsfPPO1eWfbUM7gbh6hCux/ByMgwT3QU+/djj0rJby+RsBbZJigURM+eDD2bKs08+It+vXiUbcjbK0Uc1l8uuuk4qIEbnurX/ypiLhkjPc86Xk089CcpokX/+2SiXjx4i7Tt2kLM6d5GJE241sX8GDRleoPzkjTiU8ffeNV5KZpSR9Tn/yltvvi516zWQux6YJG+/NV2eePRRWbniO6lXv6EMGXGRtGrltP/z+Z/JHWNvllWrv5PKlapIufIV5PpbJsDqu6ZRgk978QV5cerTyPutNGh4iJx3/kA5/qRTJYyJLf/3yAOyfNlSY73S/5yupkl33P2gPD7lASlTspxsysuRN996U6J5udLx7LNl2MhLk5ut24rAAYGAIYtwD9KiiyQ4LTCLE5JMtCLl8yU1M0O69umDdThnhAvWqGWLiitkB4+znXQRSxfvZjyN8bW1ajNj7aRvFlqVUiKYpGfjiu1g1ZpNEVAEFAFFQBFQBBQBRUARUAQUge1GYI8RY17CIWnC4BaNDsBdRjTmxJSgb3h8ExYUdxZgwZ378RZJGzSfcTm6dOtWZEe2mBvt5i0yU2EHQY516ty5sCP5+0xdhZSfxEPlp9/WlaTv4uKzoa35/bbtwb4QiMLi+mAvHbN0K7b7bMU+fMjnl293FrFkDC9LtBmXTvlTe91MrpN/O+HfF0TsBQkjthhm+uOOo7upoiSVU/shnNtLMi+Z+AskUorKLmG3nxHMsKfwfEE3IZlYT/HBtKAYyap7uHGTxXzV6jUx2Kz6Yp74S6Saubjpse0nY4upUg8rAoqAInCAIFDwDUN3h7Tiojr7xhuuka+XL6fPP8QAS5GhQ4dK/wEDZczoUfLue+9tW/99eXBH6JOvvvxUjm5xtHTq2hEWY2F59NHHpEvH02XW5wulTIWSUqdRAxlxYT95/5N5UqFsBRmcfY789MNqeWDKFFhxRWT+gk8kMy0DlhVDTb0+lAt1OiwrnPeGbQwtVT6eOVMWL14iLY47TrIHDZZKlavKI5MmyYSbrpWLLr1Uzul3nny1bJl0aX+KvPjy23JM69bSveOZ0ql7L+l9LqzgUd+3y76Un37+UarXrCWPPvyAjL/5Bhl20WXSu19f+XLhQunRvbO8MP0dad3yGGnevLk898JzkpFeQrqAxCOJWKJUSfl0zhxZuvgrOeGUU2XwoCHozw9y87ixUuXg+tKpUwd1pWhPmi73WQSMNShax/FgQQk5m0kWUAESWSTE3GGjP+n+LFiGYynFffzOIiHF0Vq79g65nJzWuw0Pp64kt8vu37ZlnBarfsQlQ3kYvSITLMHcstn2gk9HtJMfeUwVxEQ2rljLLre/3EUJxZx2MYaYH6ZiPuBCopBLr1icfImgEErMIUAdtEhzn2sc2KooAoqAIqAIKAKKgCKgCCgCioAi4CKwR4gxuuvzWiAl8wjJZ4N6fcakCGLGIT9qXD1/cjLdVgT2CgKWFBszZoxMnDhxC+JKEmmmXVRIUPypmxxiqmwdSQ2NgEakGHIKd+WokcPk9lvvcArYyV/oYB2jhAhmHW+hjNm2wjMyQpKLpKDq8Gt7tm15NZUioAgoAv9VBEiKXXbpGLljovs854AGA5thw4fLjTeOhdI2IhNuu11mND8ahBUsyIqREAi1OCZXXDj8YpMyBldklOPadpL6NcrJ3E8+lmNbHSejL75KFnwyR4YNOE9OP6O9fP7ZJ/L6+3OMkn0L7bQpofAfvu8Yu6ty1ary8OPPSSreT3kIbHZ+33PkoiuulaFDhpp34BntusiaH3+WB++7U8pVLi/r/v5Lrr5hAiy8MkzBiXgXM65j3lvH3iCjLrtKsi8cAes3KO7P6iy//vGXPHD3rXJc65fk0CbNpGz5ilK6ZGnp2LmHcQMXhLULocuqV1fumjRFfG58o9mffCpvvDpNunbuZFw4et0tFt4j3asIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKAOYt7wkQTBBmd5ZeuVrNJBjNKbLaeDDTuNqRSI5UrtUYcSgKEgkx/5YuBYssUA8WQAD2eAW2t3cjupOXjdd6cHvr3hfShxJ5hmk6FDPyjRJuC+bWTr11ZqjSWovmAjFfCZBiUBIWZ7UVS5H5X62RyrWbOcHSOS3WIzEGIC9SCqY3SXH/+ZEvvi3WXgm029yvdD+J9VieVK3VRDAB1yg2twhyUWRb9KAioAgoAv8lBAqaOiRgJXFY3Voi6aVFcnMRUyuG52ocVlHHwnWYTwISknp16rkxgAo+uxmrK5noicLlIi0vfv/rT7kVVlc/rFwlG3M34r0SQBywgKz57Rfjlowl3Xn//XIirLdmz/tMHsR63azqO3Qi4ngfNDn8MEmDUUsM9X+5eJ6s/XONzJ39kSz/2omVxnb9+MMqWHGtknqNDpOsOvXl5GMbyylntJPjTmwrbdueJiG076slX8ifazfInI9moJzF+dbXK1d+J7/8+L1pH8eMnMPhzOMgnniBRhG3DAHO6mbVBGKYOJUWlDBcxWVklpCcvE0gFeEqbod6p5kUgT2HgBsqK7/CuDWncvfYeFz5CWgu9h+WzaNp97lqgiJybM1tYlPwebt54pZnv4FQLcX+w5eRdl0RUAQUAUVAEVAEFAFFQBHYKgLFadi3mnFHDvy9etE22ZqUqXmE+dbxw0n+mlVLdqQqzaMI7F4EoCi89OKL5f3/4dN8C2IM09ohftelonHtgu94OH8RyYAbxE3FN63ZYbVk5qtTjauYqGMQkJ8pyXNM/v6trXBGPlUEdIm1IxIIBOW37xaK68lGkhU7O1Km5lEEFAFF4L+AAAx15aorr4A7QrhKDKVKIvyv6Xb3bl2lR88+cK94vUydOlUSbrwxThzhK4WkGCV/abbgDjcYlJycHOnQ9kTJql1b+mYPlIaNDpfM0mWlReN6kou4WywjAIv7tLQ040IssmGjlC1X3pBHKdalmFvetizMO44knatb/uP330y2Zse2koxU0FR4uQTxnjii2ZGILYR2w4/be7MXyDNPTpEZ770jL03tJyUzM+WF196WP37/FQGFcqVp8xawoE5B/9hfvxx6eGMTR9WSgXYCDd3BhfBH/oC4kC2LmtkmjiWbTUcvA2AZVRQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUAR2CYE9igxBjsb48GnOHsvo+iAgiOM2cGUZH2+s9cc0p8dQCAZz+0tQvEHYtDQ3XrnneZvS/xchF0GK4agEgibLq263iOznh8Gez2HONsyn7Mn5D1BWCex5ZXwFneE9yjSJzFnAcRk8BZR0P6yYF5uRfyI9QANaBiWYqmIFYGI7Q6XB4Uky6F7UxVFQBFQBBSB4hHwIYbOLbeMM3933T1Rrrj0EpMpCiLs6Scfl6effRLWUO5DHw/XGCyf8B+7Clp2B1zLkjCO5fz7r3y3cqW89PoHUr1qOVPeyp/WyAbsJzvkD4CsAtnU/5w+cuihDaRR9y4yZFC2vDXjMzn4oIoSR90cZ5GUKk5sGsboiSGfH2084bhT4SI4TerC0q39mafllxNHw5mO5FYAluWDLxwiAwdmmypOaN1C7rn7Lrn+ltvEn54qDevVlzPO6gi3kBGQeEEY0+XmxwhjOZxQwnizMUOCcQIK+oXJUqFgSIJ4KQbQDlRjJqaE0N/UVLy3bICh4jqlxxUBRWC7EDCGWtuVo2Bid56YJJdj9xdMrVuKgCKgCCgCioAioAgoAoqAIqAI7BkE9igxtme6pLUoAorAziDw/RdzYWkgUrpuU/nlu8+MzpZxyvJgFUC3NgHVZOwMvJpXEVAEDmgECpJNxpLJ7e+okaPlm2+/BiH2hEQQa8uYYJEU4wMWUrVSFYewQhHJlsiggUyaKGKSpaRnIAZrwMTWGpg9wJBKlwwfbCYxBEB4xWGxdedtY+XH1avlf7M+kbJlK8jnny+UUYP7ywuvTEfMyyiIpzjqcso0BW/lh8QUXSnGENPLD8stZknJSJMz23eSG68ZI40b1JFaDRsa0uzXH36Ub7/7Rg6qUUN+/2WNtGhzHNxFBmXThg2G8CtbtqxkZmTIaW3PlOuvHAN3i3WkIfKSCFvz40+y6rvlcvzppxtyrWq1arLsyy/gFjICQ7sQPEXCUgzt5h9JMRKICUz6YNssicj1ZNy20i3drQj8JxHY3+4Rkt8U+/wzswacXfqrCCgCioAioAgoAoqAIqAIKAKKwE4jsJ8SY+7s6p3u/n+1gOKVYUUj81/Hvzj8SB9BrNKRRJLRlcLeitAVkz3sNe8yBSX9xN3yk3bbTYRiKSjJrrOKCbUQKOmXXKRJBKAMZUlQ2uYlIhJ0+5NIsmQoWJluKQKKgCLwH0aAcRk9Yl8DdtekhydLjx495eFJk2ThggXy868/Sp2sutKyZWu55rqrpc/Fb4gTRjJQwALK2v1SUZxWIgPuGW+Qa0FM3Tn+RrgWjMiFw0fL3FkfmLhl8+fPkYm3j5dnnp8ulSpUgJVVUCZNfkxOP6G53HfP7TJ42Gi8hqLg5YqzH8brCvX5YOaRgMVawrx7HEuz+x54QC4ZdaE0P+YIKQs3jnmRsLE0vuzKq6R0ubIycEA/uHzcKKlpqbJh43ppAbeLoy+5TEL+uNx//2S56OIhsDw7UkqmZkoMhBbjrY28+ApDjBGrESNHSXbfXlLn4IrGBeSS5SvxGkVsMbpVBKlHAixqljFYMbNtmLjB7eT3nQVel4rAPoCAc82C5iGBjes+CGtJPyw3o2HHQpTWk3l5eZKSkiLhsHNPpaaEDNm9N5qfb+Hl27lxv8/1ACJblJM8YC3Yy6KPFkyrW4qAIqAIKAKKgCKgCCgCioAioAhsLwI+uEzbQk3OXTZuw/YWuLX0LDMPH4KsrDhXiuWzmphimOef7xdv4TjOv8WerdWq+wtDAKqjwnZv8z7Fvzj83KBgCbghhBgrKxBjrbreL7OmXijRYtxXFWuQFU8KOpZ85pIUs8EkRSE8exUt6B48ZknFeofK7yu+gDsrP9yaOsQYVDhwhbNzCpKiK9ejioAioAjsxwhYBbDbhTgsnPyIv2WFrgkjkUgBa60AntGk06Kwgjqp92R59fFsjHIcAig/n/vc9oGg4vgs4QvK+vUbZdHn86RF6zZQrjvjNj8IJuOKEC8eky4BKysQRqmp6djG+wjtofVVLEzn1iC+Au6IDK5z+ayPw2WhV6zFG+OIUUg8cV8cJFkoNVXywglZjDaULldBateujRhjNITDcbT/119+la++WSrNmx8nmbBwC6YE0TYQcsifgFvEGCzb5s2dI5WrHiw1ahxkysdep56UVOCEtGhPEH0yVssgDPzoN10phnGMhF8YLzRav6XCiiyARDHrltKUoj+KwL6FgI0ZSCI3gvsdtyIIsri5Z1JxP8VdC022OgRCLALfqSTRaK3pxNbb+f5sj1XlriLGcMM7DU8ixmDjWWSHLF42ES1F+bwkfjYeoT3GZRxWpJSO/R+E6/LBIM3xTMCzM+F2JL8/JtWB92O/H/AWMJ2LR3wm7mPrrg/JnJdgVayiCCgCioAioAgoAoqAIqAIKAIFENisrfHs3tWkGItmmfDOtk3idxUw0K0Y2fKzacs921SwJtoKntsLzPbjT0UZ//xQvHFJoXKQs2IpRpEGJVkACi8quZhue4T5HSVgwSXL8JZn03G/d53bRcnWyvDu35zfIcTyt32cCcxbbdv6VOyHuy+p/PyK3BVHL5C/l7Pxt0cSUCyytamCWC5Y+qGITWfb82OXbVs/tqdOTasIKAKKwIGIgJcUY/9ICgU9RJnpM57RpIOSlcDmWNJPHISSI3HJLJkurU5o4247E49iZuYDJjC4yaAWFXBS5j3oJMQ7GJXlE2Ju7lgg3aWk3B3uIgALL4q1WDNvE7wCGO+LdTEG5pHHHO2mRj8cXgvbcalapbL5cw8aIsy8B5Gfrylaf7Vo2coedpfO+yWB8UDQjANQI+phuUHGvITQVSSPUTkeYkEgxShKihkY9Gc3ImAJh+Qq8mDdlYrxLElpElwRuB6l+9Gvv14uX3zJCX5+OfywI/DXwDwDSJDz2uW4NeDerAmQYiR9UrAvL7zJjLkCGD/yGZIAwe1YRoJcxnXPdFbsWJakEZk2HmP9hlBDWV7xox0ktZnHGWf7ZfJD9+F+9MkFg4eiLt5HMUPa8R4jcce0nNVovg1xw0fRVwpJcv4x9mAKSDxaulmLTfY9DT65caua8nwgrRlHMA3kOIXH6WYVgJlt4sG8xI/CZyHrs0S6sw8EOclwlxQ3CfVHEVAEFAFFQBFQBBQBRUARUAQUgZ1AwPlC2YkCNKsiUBwC9qPdfFQjsV1aUoz5uc+SYvY49xdOPPFIQWEeWw+PeMvwkmx2/5gxY6R06dJy9dVXFyyokK3NCoQtD9rytjyiexQBRUARUAQUgX0bgfUbN8rwgf0kQisuKsupCffIY088j314P0NpTR07SSkVRUARcBAg0e0HWUQSiNZeFJJS3H9y66Nl+YqV0rB+I8nDvm+XfS39+veV2+99yKSLI14gCWeSQYz3R3LIWHviKMeWhhQiKQWhG1Pen5YUs+Nd5iOZxfuSFmZmDhNu0RS4ZCzsXqXb1WDQIZhpBcq8c+d8DMIK1mrxC839z7LTUlMMoRXA88AQbmwD9sdAit183ZWSUTJTLoU7VwrdP9Iq1emDQ1iT9CIO5L0CJPaQjhhZYjEIEpHENoVEHQ2cHAswTG4DDjzGepnu6itGS6kypUx9gNVYijKfzc91FUVAEVAEFAFFQBFQBBQBRUARUAR2BAElxnYENc2zXQjwA58f1Js/5AvOYLX7ufSSWKwkebuwim3ZNi0VB6zTS1olp1myZImUK1fOUTRAoUBSbmti28+lJepYHsVbx9by635FQBFQBBQBRWBfRCAtJU269e4NN21O6/Sdti+eJW3TvoqAHySTcX+IBnIdXj8NIcU4gWd36yHnnT9IKlSsaKylpk97TgZnXyjtu5wjJ5zQ2hBJJNAC9D8KRotED7cpfhBLJMN8wTS4WIWrUJSdB/enJLWMO1R3PIpBMiy2QIqBzzaWXiDKgiCyEXbPkFzJ5BgJbhJRvM9JZPH45MeeNmNhFIU6kQ8rruMOWI5xvE4n2nDBijwJSZEvv1wmFSpVlVy46QsmHJeshqDDOJp9IMEVj8J9PtobIibAYr2TDLkdt5HBFNfaDDFrSX5RiKMh2UC+cR/LoavWb5Ytl7IVK8OlN2hElE/yPi9Md7R0sVqQyDcF6Y8ioAgoAoqAIqAIKAKKgCKgCCgC24iAEmPbCJQm2zkEZnw4QyY9PEm+/fZb8wHetGlTufHGG+Wggw4yH7csfdy4cVKhQgX5+++/5ZVXXjFuFnt07y5Dhw0rtvIJEyZIqZIlJReuXJj3sMMOk379+snYsWNl1apVUr58efM3fvx4eemll/Bh/6Upv2vXrkYJMHnyZClTpkyh9dA9zO23326INLp/fPbZZ43LmN5QJo4eNbpIUq3QAnWnIqAIKAKKgCKwDyDAuFxntDsbLXEUzAHXLaFtmrXKMEp7u1OXioAiYBD4fN5cuff2CbJy1XKMMStKKXgiuHnCnVK9+kEyasyVJg1dCFK69DxXhg8dKqtWLJc2rVuYfffeNV5KlSgr//y7Tt596w24QA3IkOEj5fR27eSeiePlzdf/ZyyoLsjOlm49+mD8HJeJt98iFcpVkrXr/pZ3330bd65fLsgeJGd16iZ33XGLvP/WW3CB6JNBw0dLh468tzcL21KyZAaIuggszHDPw0X23XeONePhwUNHYzwucu9dE6RERikJR8My7cWpsBKLSs8+PWXwkIvlofvukm++WirfLv9Gzj+nm6QFEnLXA5OlJPp954QbpXTJcrIxd6O8gzbUq9dAJt43WR689155bfqr8tfaP6VBvbpy3U3j5OCaNU2jbh93gxx1TAs5/qRTjAvKMAiv++6+HXGxQjJg8HC5/567ZdlXXyBE2VLp16OrpCCW4t0PPyalS5UEFmo1tvnM6poioAgoAoqAIqAIKAKKgCKgCOwIAkqM7Qhqmme7EKB11dzP5soxxxwjPXr0kE05m+ThyQ9L27ZtDUHFwjjzc8aMGbJo0SKz/4orrpAVK1bIlVddhRgFcRkxYsRW62Tejz76SBYsWCAnnniijBo1Ch/+JeXMM8+UXr16Sf/+/Q2RtWzZMvnrr7/k+OOPlxdeeMF8hPM4hTEhtiZ0+Thz5kyhldkZZ5whdMNIsu3aa6+VrKwsIbmmoggoAoqAIqAI7G8IMA6RI46lirVY2d/6oe1VBPYUAnR3aGJrgTDq062jdOjSXXqcA3ekIJK+QjyxNWt+McSPJZUZjw/DWPl66TITW5cTwigkuWZ9/JksWfC5nHrKyZI9fJQsXLBQzu/XR9qf1VnKYkLXyIsvlY8/+EDGjLxYjmrZVmpUrSpzP5krixcvkVPOOFMuHDpKFn8+XwYNHCgvTpsulSuVl6EXXSwzP/xIhlwwQI7AJLTatWvDwso1CUW9GzfBIsu1CmNIwvnz50tmeibG4XCXCq7sk5kfywKMxU9ue7oMHTpSfvz+e7nu+hulWs2G0rL1cTLt+acls0RJ6XR2Z8kEsR7C+JkWY3Nnz5VFGCe3OfEkOf+CQVK+QmW55por5bknH5NLLrtCqtesIc8/+4ycdvIJ8trb70qdOvUxdv9YSoLkO/HU08w4PRz3mfYEUd75g0ZIi1bHyxvTX4Ibxgzp1r2HCFxBpsLtI4WuFjc/v8wuieN7IwTzOX53EHOf+5WbcJ9z1tuDkxrTAbCfeWBuZ3Ylx4S06XSpCCgCioAioAgoAoqAIqAIKAIHJgJKjB2Y53Wf6hWJq8svv9y4ITRuY+BupV37dsY6bPbs2dK6dWvzEct0tOx64oknDFHG7T///FMmTpyYT4zxo5b7vcJ9dP1SFQoDWnNxmyTY+vXr5aYbb5Ky5cpukYeWaayrS5cu+UXZD+bk8pmA7meyQIJNmjTJBP9mmg+grKD1mRJj+RDqiiKgCCgCioAioAgoAgckAiTF6OYvCIusZV9/DSuov+Xya8dKpbIlJByOyZntzjLH6RYwNTUk4UjUxCDblJMLoupcOfro5tL6hJOBDeP5OS68D+bY8rEnQazFpV3X7vLKS1PljzVr5OHHn4Xll8ipp50lTz33jHzy8QzJgtVYAOPPrNp1ZeL9D0sqjrc9o4MZi67/5x+Z8sTTyOOX09t3lueee1I+nT1TsrJqGyLMxA+kj0fWDlLOxPYCGeYV1heGK8OatVD+g1MkE4djcL04+5M58uLUZ9HOp6RihSqSDmKsw9ndUVAY8cxCEsmLmGKqVDtI7p/ypMDBJNwwxuW8vr1k1KXXSvaw4cZtY9v2HaXNkY1lyoP3ye333O+t2uCRythoYLRCIL/S09Pk6GOOhbeGilIG4/XT2nVCXXDTiDE+XUByuQUxhrwJWMFxjJ4eQuPhyhEJwYA58d8Yy6yA4DyOGDlSJoybYPoRdV1ZFkijG4qAIqAIKAKKgCKgCCgCioAicMAioMTYAXtq962O/frrr8atIQkruia05BP3c50fuSS3mjRpYpZsPT/OW7ZoKXSTuGnTJidwN9ImC/OT1GrcuLE5xHIaNWpkZsk2P7a5tINLmpNOOknandnOxCZgeqbhn21HcpnebabhzNQGDRoYdzOsi/tKlSolGzdu9CbVdUVAEVAEFAFFQBFQBBSBAxABYykGsisXFkZ16jeU2jVrS9vWTeWU09vBuulEad/hLPQaY0t/HO4KERMMBA9Jo/PginA9yLGXXntb0sFmRTG+DYCk8vkSUr9hA4x3HWLJD8OlunXrSrWqsCpDHXEQNQFfXA4/5Aj5d92/ZqxLq6Z6tWsivhdZHr+QTMqqXUcqIw+JLT/K9Ad8cgRciudsyDPxwpx4XXCfCEssjl9p4YWF0GLMK+vX55qYZ4fWzAKxhHF4Xq6ZDFayVBlZv/H/27sTQKvKeu/j/z2ecwCRQVFQQUZFBOdZEZyHtDTNMvV6s0yrt0F7s9FrdRtuWlneW2b13qw0h9RyLhUVGQRUBGWQQRBFFBBDpnP2+P7+z1rreDxwQDgInMP36e6zpzU867O23L33b/+fZ6XmUNO2lS5pirAwr1kqnQtzgalLYXSHffYaaNkwN1rKnh79uK1UcHjmhz8S5gTLKQhMy27ovvvYnJfnWL2CxKYtp8nIiuqPB2NlHVu5qBBSP6Tz6C6lbdbomEpeBub39f59XR9gs9pH0ecq80BMfdtgk69buCmh2Aa1WAABBBBAAAEEEEAAgXYnoI9QNAQ+WAH/kHvKKaeEoREvu+yyUBF2++23hyqskj4ke/Nfsnr45MMWJpVbPseAP+7VWv5cSyFWsnz0YV8hlj4Ue5swYYJdfvnlNmfOnDCcYv8B/W327NnhufDBW9v062R9v91S8217H5oun4RrLa3D4wgggAACCCCAAALtQ6Bc8rCqHH6oVamW7bFxk+2CT11m8/Q+88ovXGIHD+5rM2dMUyimQEnvZ/1HX5/+t/Nszksz7O6HnrCuO3VX4FMMzyUi1aqCKlUu+cWbj6zg70trarK69h+NeTSkMEi3/b1oUcMJphUi+XvQcgjP9N5U+/F1fOhxD48aFDrprkYeLGm7vs1oXd9W8p63pO00b7W1tQrWtLz247lbPpd/z/tv76NXavk2/LYHhb5tr4zzSrWM+uXNK+bWrF6pW1WNztAjPOdhljcPBP24kuMND+pPEnp5YpdRp30fGqlR8669axP2EY5bnwni7SXrJ9c5hWnhc4DOTxgisaLPGZ4A+sUrwppeHCluydCXyX2uEUAAAQQQQAABBBBAoP0LEIy1/3O81Y/Q5/V68cUXwzCEPuxg3759QzWYh0zhQ7auk/b66683fmj3xxYuXBjm/+rYsWOyyFrXSSCWjT+Qhw/r+sC8oyYDv+KKK+y+++6zxW8uDvOOXXfddWF9D+C8Na0a89stNd+2z0OWhGFJiLa+dVraFo8jgAACCCCAAAIItD0BD7xCxZd+vNWhNmv/5wtfsNvvuddemDXfOu7Yzf77hl+GYfl82MV/u/CTmjfsebv/gYdtj147hfWiai0PnjxgUugTD2foQU9JgVISGGlkRT2naiYFPdmsj6ygkEuhkbeMKpz8ZnI/eR/sVVk+f5i/N81pmUw++lGXr+PDDnpI5s3DPf/RmYdPvmwUnEXPJX/LCtu85VR9FQI2re8BnDfFdRoqUbGXrx/v0x/3fhR13N6GH3Wc/tba44/9M+zL+6vkzBYvXmI7dNJ7+rICwlyNvf322754sFG37G19ZgjBm2/LEeQUNql1/RjCj+WafG4IKzf541V23sMGBX8l9bdBgVi9rv3S0Pyi7f/kv64Na7cUtDXZNDcRQAABBBBAAAEEEECgnQm0nAS0swPlcLaegAdKHix5QOWB0upVq0Mll3+A9i8G/EN58uF+9OjR9vDDD4cPxW9qjoXrr7/ezj///PC8f1BeXyvpl7zefFvTp08Pc4CF8E33V6xcEX716sMfevP5xebPnx+GdfT7SdDlt1tqyfb9ed9Hsu2WludxBBBAAAEEEEAAgfYhkFRE+dG8PGumTRg3Jrxf9QqpiirBSgp7/H2mB1qXXXKhTRg72n775zutpq7O3tJ72qVLltqKd94JIZBXTfn2/OKhjG+jtlZDE2pdf49ZVhWWh2L+uL9XziqI8+bBlwdb/pY4CrYyYfnwvlQPJnNv+XPJCAq+Xqj0inKtsPy63lN7cZriM21XfdOIDeHHa146pubhWUbDNu6yyy42f95cKxbrw+MeAIZ9xWFVTU0+rFfTsYMdfdwI+/m1P7Tly94OVWX/eOg+G/3UE3bxJZ8N6+7Wp4/dd8/dtlIm7nDbLX+wyc9MDP3zY3eXnXbe2Ra88rLWL4T7vqIHgDQEEEAAAQQQQAABBBBAoLUCBGOtFWT9DQrssMMO9p3vfCdUb/kH6p69etpRRx1lPlRiJf7A7R/e/VegJ5xwgl111VUhuNpzzz2ta9eu9qMf/miD+wjDLoYvC6LhYv6lScgvuuiiUCW2sz5U+3533313+8bXvx4+wH9d1z5vWbdu3cLwjYsXL17vPppu3xf0LwG8vzQEEEAAAQQQQACB9i9QUPgVVVelbaXmmP3URRfYwD49bXD/3XW9q+3ecze78sqrFGjl7IF777FlixfZ6ccerOf72N6D+mqusAH27W99I1RZhVEIippzt1Qfhlf00ElvLq0aKsOioRR92EUPqaoaFrCqeclCcKV5vvx+RUMEZrVOqP7SkIX5dDVUnPlZUJymoQmLmgssG4ZXlNhL7AAAQABJREFU9GHJfQ6vBoVLNTVJpZjm/PLarxBoReFXMcx1Fm3fh1r0ISF9GPEwZVfKhzSv2he/8tUQ7A3ctavt3DFri/T+WW+/1aJ++nUUllXshl/9ymqyFdtnYG8b0r+3XXrJJ+ybV19txx53YpjL7Fvf/g+z4hrbd1A/67fbzvbg3+62o48doaqzYggEy/L56lXfsjUrV1n/Xl2tR5daW6QRIDww837REEAAAQQQQAABBBBAAIHWCKT0Bf/6y3Bas/VNXLd7n2FhTe/ZsgVTN3ErrLatCITQS7/89LBq0qRJNnz48DAsoVdchSFc/IsAXU499dQQYP3xj3+0GTNmhDkc+ujXpMky/mvYdTVfN3kZ+7Le/L4v71VnL7zwgh1yyCEhJPPnk+qw5Hayzrq2nWyr6faTbftxlfWr3WRYxrXWj//LKqc094KmCT/qnF/bU7d91krxPBJrLb+NPJDMs7DrwINs0dznwkTuoWtVcvRt5BTRDQQQaKcCIz/+G7v35kv96+rwBXxymPo+OjSNvEZDAIGtJOBvMb2CK1RSeTCTqbElSxbblKmT7aBDjrRunTo09iy8x81HVV7+GzAfJjGr98KhlRtCMOTVYWWNSZj2oRL1vDevlGpoaAjvgXOaX6u+vhiW9ed82WS0Ag/F/H1o9P40rfBK24qrtrw6LCyv+15d5ff9x1xVLZ/R9n1oRH/v6+sWFIaFKi+vVNPx+Tajucj0QzMFcBlP4xSslTTsYLmscE73U/phW9hF3OeUQrkQVinI82MKFWf+47eSArt8xl5d8o69Mu9l23+/fS2r9+Y59dXDMw/rfLTEKVOm6odw3W2P3XuG6jg/Ru+nh2xhu5V339v7sfjcYx72JUM7hoP1pSvRP5Af/ne9377jsjBXmU8tVo3/AU3+HU2Wb2/Xyf9/0KspHFqlGFkffc6NNv6uy9rb4XI8CCCAAAIIIIAAAgi0WiCaJbnVm2EDCLQsEH24NevSpYudeOKJjQsmgVQSePlwi8ljgwcPblzObyTLvOfB+I4/1/z55L5XivmlaUv24Y81vd10maa3fVtJGJZc+/N+XP4/GgIIIIAAAggggED7FgjVYvGPm3yuMZ/3q0f3rnbKSSeFKqok4ArzhOmHU6k4qPG4Jh8+cZVC8JMOoZEHYv5DrpICLws/GCsr9PEhFH24RJ/HrOxJlZrvx5uHTsloBR4KNX3/G0KiODj3ubi8+fxfUXWVgit/QPOZaTXL1nio5sMmVkIo5k95mOVBVhKK+WMhaFKfKhqq3LeZTudC0FUoRCGeB1g5hX8hoNNwj7W1NRpOMgrXfFtayeoV5u3avbMu+4f9RfuKgrtiPAT6sKH7+MOyURCnwK9a8OONhlsP4ZgeD8/r+HN67+3nIQRz4dF3/2QUMHrz9/bKzbTBcJc/CCCAAAIIIIAAAggggMA6BQjG1snCg1tSwD9Qe8jkQy527KgJuZs1n5PsggsvCOFUs6fC3XvuuWddD7/vx/yDff2aervo3y7SL2cLjV86JBvwD9g333yzderUKXmIawQQQAABBBBAAIHtSMCrnDJeLaX3jUVVdeU0h25ZQxr6bQ/KfOjBTh06mAc+HjCtq3lolgRsfu3hlbckJPLnU5vpR1cNek/7uU9dqIDNrNBQsGy8r6Rff/rzX5UiJfc8eIvmF0seyej9r7dMHLT5VbEYBXLhcW0vCej8fqEQVSolP07zXMoDNT/OqCXX8d1mV0klXmISAkYtk1wTdDUD4y4CCCCAAAIIIIAAAgi0SoBgrFV8rLw5BDwU80qsW/58Sxgapfk2O2gC7/PPPz98YPcQ6/1UeTXfxobuex8+8YlPhDkLkl/jJuv4PAbJcIlNK8aS57lGAAEEEEAAAQQQaN8CHn5588DHQzEPyooqvfL5wnw+sNra2jAMoodnXuz1biAUVmt1rhPVQ0Xbej9/MypJO+vcj1nVAyyNWZgEXO9nXV8mLnhrXDwZqq/xAW4ggAACCCCAAAIIIIAAAm1YgGCsDZ+89tJ1D5u8JQHZuo7r7LPPfs+QMetaZlMf81+2+pcaZ5111jpDt6ZhWPIr2E3dF+shgAACCCCAAAIItD0BH+bQ5wDz5re95TJZhU7JXF96LxtXlIUnt/KfnMZvPO3Ms6P5xVSstfYcW+uv4NrK3Wf3CCCAAAIIIIAAAggggMAHKrBtBmPx0B3+y8juffdfC6CscexpCGwtgWxFk4trqJtBew2xcY/er58E18ddiV+XybA0zb5vyHbvq3nav6jloy9TWux/NW2fu/IK+9kP/0thoA9h0+KSm/REPGVFi+umdWylBs1BocPJa1qHUpioocXFeQIBBBBAYDMJ+A8xqvp3t5rSfD+6ncwVtJk2z2YQQKA1AhoeMRoVUG/0kvd68fZ86MTGEQObP9mafTZZd2MrtsJQjU2GMtzY9ZvserPc1L9ordqO3p42juzQvBovbFjDWlb1GbFiZSvrPay/j035jXWPatmqvrAyAggggAACCCCAAAIItH2BbTMY24ArVTsbAOLpD1Sgca4D34snV/EnbsVJYb/KtUJLxd9AJBVxeQ1ls4FILFpRQ99MnDLLdhs0TF+MNkvXwhKt+892Q0NRamDLsBf/ciEu5ov6xV8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNq4QOu+Yf+ADr5aXlcY0GRncUVZk0e4icAWE6ika8O+UhkFSGld9Ov+97QWXr75dMVW69esG24FO3K//vbUg5oU/QNoqQ2WjCka0zEUdFzFnP8oOj7AJPH7APrEJhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2BIC22QwtmzB1C1x7OwDgVYJeP5V0CWKyXxTUaWV34paNAZiQ7lehWVpO/YTv7Enb/u8qsDWPzZiRRO5pzVnRS5Uoyl328xlW+scfibpsq7DblX15iPPaF559aXJk9xEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKANC2yTwZhpdPj1t9aNUb/+bfMsAusXaPrqzL9n0TjwSvIxzRHjLZuptfCMhib0ic839Oqt8YU0j1nS1lq++t69Jsu93+vKBirGyups1Se5USLm8zm825I7yQG++wy3EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoCwJrfefeFjpNHxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYWIFttGJsYw+D5RHYcgLptSoam+XLjQVV8dxccYVWxXJrDba4rl4X05rYa72tcQfrXarFJ+NKthafD/Vt3nc/rqRKrOWleQYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbYi0Owb/bbSbfqJAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwMYJbKMVY+R1G3caWXrLCqz/9VmJn66qQsxbShVm6W2p8qq6/v5byqvEMuq45kjzarfkgN5XvVs4ZP4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBNCmzgG/Jtss90CgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGNFiAY22gyVkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGiLAgRjbfGs0WcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGNFiAY22gyVkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGiLAgRjbfGs0WcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGNFiAY22gyVkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGiLAgRjbfGs0WcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGNFshu9BqsgAAC6xVIV+OnU00Xq1omldLFrJw83/Tpbel2pWpp/5/3c1vv67bkRl8QQACBVgrUdsjY60tX2K49d7RqpWJWiX+/FP9b/J7/t9LKfW2d1XVMrWjVVOt+z1Vuxb7DqunW9b/xfLa2H6y/aQJb+fxlrJWvn0076nazVtrPX7lkhZLep2bTlouPzP+tzGWzViqZLV36tnWprbNc0azgC2RTlo3feJdb989Hu3HkQBBAAAEEEEAAAQQQQCAS4CMCrwQEEEAAAQQQQGAbELjk3EPtht8/alV+lLANnA26gAAC25JAtVK2dCZruXzWMum0VUrvRt1FpWLZjNn1/zvGLj7nQCu/+9S2dAj0BQEEEEAAAQQQQAABBLYhASrGtqGTQVcQQAABBBBAYPsVOOf0YTZq0mv28D9m2Gkj9jLTF72hpaJKk9ZWTLV12dZ+193qih2v4qO1XYGtfv54/bTmxVMUn39wTcels+Vqyqr6R0FFYVaulOyxR2dYB4VnHz1zf1tTLFs+HvagnIndq/wetDX+rIsAAggggAACCCCAQHsT4BNCezujHA8CCCCAAAIItEkBrxT7xuUj7M77nrF7H53eJo+BTiOAAAIfhEAuFw2emFTUpjREubeUkrLHxs21W//+jH3j0uPCgJVZLx+jIYAAAggggAACCCCAAALrEaBibD04PIUAAggggAACCGwpAR8erPcunexX/3mG/eBXT9jTz8+2L336ONut6w7m3wEXq2284iSu9NhUTw2etqmrhvUyrf6unN+TteYEeHVPa1qqjZ8/n8GVtukC4Z+PTNoy5ejfgaoqxha/ucJ+/qcJGlaxZL+49mLrvGM2UvaFk38uKvELp3ES4E3vA2sigAACCCCAAAIIIIBA+xEgGGs/55IjQQABBBBAAIE2LOCVEBoJzHbt3tl+9d0z7dYHptiXvn2HFdYULK3QrGStTJa2tk08JOSmdqPSymAhVWnd5G2p1n6xvp0P5VZNSn028QWQVAht4uoqLUqSkk3cQivPX6WN/+e7iWqbbbVMuhC2la5ElWMp/efcsUOtXXjukfbREwZaJZPSHGQphWTRf+f6J5OGAAIIIIAAAggggAACCLQoQDDWIg1PIIAAAggggAACW06gWq5aTl/sahYdK+n2eacdYOeetJ9V89E3vJlWBjtb7kha2FMrg4lWV9zElSYt9G6DD1erpQ0us/4Ftu439anU1t1/tZUVj63uf6qVJWvV1pWspUhq1v+fxwaeraSj//7SFgVjpn8PMyqlDdW0Jc0pJt9y0SyXixLIssXnG/cNyPI0AggggAACCCCAAALbpwDB2PZ53jlqBBBAAAEEENjGBHyuHG/KxELLKseo5tJWr3tbN9KI+tPW/7Y2mEilWvm2uZUVR23dv3Fou008kNaev1YXXG7v528Tz9vmWq2cjMUZn4d8OmMlhWNVBd7NXxuFQtky+c21Z7aDAAIIIIAAAggggAAC7VGglZ/w2yMJx4QAAggggAACCGwNgWpjKObFPf6lr7dcHJRtjR5tyX2u1jxBac0hVFNOqwpEQ6LFaWCpXLJcNmc1lQaraqzJYq7OnESLW1lFITWqCMmpiKShvsHy2bzVa8hDX9XnbCuqhKQ2nmOoPl0MFSbpdN4q2k5aX6w3balSRY+bZZu9O14TqlD0eCFjnl1WK0VLqz8Fbfud1SXrukNt2EwpEw3Vly1H222sT8pEtzItJEOlZPKsVDRUXKUaf6PvY8WpZeIx+Oq1fk7HlGpQEKB+pLJRkJoMEViOK6LSlWj9bFwxs0ILe7/r5OXHl9LT7pLL5HRf5uXogL33TlLWwv6/cjXqTyaulEo3ztUUumV5x1dryEbHm40PuD72y8Xr6wjCcqnYuxoPiWfxdlPxdT4uyFsVH34uqZCMj7+aXhW2ky11DNcWb6eUiiqIKnFFYp3Ol7c1uahD+VK0wXKz8+2FmA0adi+bjs5bUcft4jq9wctfi95KMXM2PKvXQTl6oPnImsUYQJFMWC9++Wr5cNcK2eh8Nl9PZ/M9y+fjZFwvt9CycaVd8jqpJOe5sYIt2kGlcb/RflJxP5PFUq0cyjLqzdb7m44rHvVqDp1I/n1MQrGSnx+99pwvox8UxKdr63WYPSOAAAIIIIAAAggggMA2LdDso/823Vc6hwACCCCAAAIIINBOBTyI8HmofMi9Bx+faeOmLLHjjhpgJxzZO/qOWwFPSoGUf+3/2zues6VvNdiVlxwRNArKcGpqaqyhocH+8OBke3XhSvv6Z4622nzOJk5ZYH9/bJp948sn6AtzfV/eQkDw7Iuv232Pv2RFDcvmLasQqmNd1vbZZw8bfnB/28G/j1dYkclEb5/vf3Kq/fqWZ+3B310S+qwkLqz3Qf1J5tia/2rRlv5rlR16YNf17qqs5NCrEJ+ftcTu+ef0EIx994sjtE5Gx5a1QrFgWfV5wcK37abbn1YaVGOfOHM/23uvnRTKFkP4tt4dtPEnPRRzH3fKKA297/E5NmbiXDt1+P424vBd2vjR0X0EEEAAAQQQQAABBBBAAIH1CXywn+DXt2eeQwABBBBAAAEEEEAgFsiUc5ZVtVNJJS43PzDNJj07y+64+2kFF15To2ouq1EoltdfsyMG9bInR0+zu/72vKrEvJKqovUqNnX26/aXO5+2wb3rrGO61jKljC19e7WNnbwgVJR5pVVLc129vmSFPTp+VgjecionSikAe31Jvf3sxgfsnMtvtD89PMnKqgrzihTP1nr27G0HDh3YZHv+tlrzw2Wq4WIZpXW6aCakcCmp4sUvXtkVLlpWdWtax6M+raPl/OIFZuGiSimvFitqEb/UFkqW1xBxDzw6wa75+V9V+uXlX6oT0up+UV1duFRUkuQXy9dYQQe86o23bPLEGfb084vsgSfnqf9pWWasJlerACxrDz0xyyY8t8CenDLHlq1ZY2l551V6U5SVX5L+euWRX7wQyy8FhWp+8Uokv5S1T7/k1K1wUaVWTpeqjtcv5hVxuqR0jsNF/Uj5sHjhubIVdNMvOQ2Z55e8SrX84pVYfklVc+HilVfhotK3gi5aIlzyOtd+KagCzy8Z7ccvobLMq8vi/VtKg5P6JdugNVdaOZfVcKVlu+PO8fb8c3PtT3eNtVSoOovOS1bH5hetFC5e2OaX+lx9uKxWSadfEievfAsXvVC8+ktdCpdMVRWLukSvZq/Ji/qtTUUt7le95hn0S+P+dCxeLaYIL1yS8+11bn5xw8hRfQxVhvG6sgwvr+S4dbctN69c9EuqKjtdaAgggAACCCCAAAIIIIBAawSoGGuNHusigAACCCCAAAIIbBYBH5HPR417ZvpCe3PxcrvotGF2+4NTbe6CFTZgzx0U5URNhU62/5Bd7aMnD7U//e1ZO3z/PWxgv+62WmMe/uCmJ2zIwF3tgjOOCJlAwTMQD6M0D1HFAzYFRSHjWEeP/TmvILryM8dYt841GnYxGpZtzeqRdu3vH7Pf3/GMddKwiecdPzQkEgcP7m4HDz5WFWTpMPReQ0sbXse+NuWhrIK6isZCrGg/OR87cgPNowOvDPNhI8u6DD9sTxs1fr6dNHJAWLOiY/XjfXTcbDtoWB8bM+2V4JMMTbeBzbeLp92nJJvpc96yJctW2Xmn7Wd/uneazZ23zPoP6t4ujrG9HETGU+0mzQNqGgIIIIAAAggggAACCCCwqQIEY5sqx3oIIIAAAggggAACm02goMqhrNKxf4yZZz127mIXnHe4/X3MXLtv1FS74tNHWVmBmLdC1kthzD71yYPt8edetf/633F203fPsBv+ONHe+VfZ/uc7Z1hJ8ys1qLor00FVTaq6qqRVbaQQyJsHZV411nxIxZxCJ79kfS4tBXQaiDAsX1ubt+98/lSb/eod9vs7J9k5xw9RdZoCvBcX2l0K7q77xumh5iulB//6j+k24YU5dun5w+1XN422Wa8ss5EjB9pX/v0oW/jqv+zmv0+yxybNCsc5tM9u9plzD7ZBA3qGURjzuZRCGrOli5bZb+6caGOnLLL6hqL16tnNTh8x0D724aF2y99fsqemz7e316ywL33/kRBk7ViXtmu+eLyl80lwoOosNa8+0wKqS+qo2x3tlGP729d+/KAt0xCUXbvWqCqsauMnz7Xlq4o24qh+Nm7qAqusKVmDqtB8Pqc6VY29vOgtu/WOKTb2uflhfrK9+ve0Sz96hA3bq4tMvd4tbbfdP8OemfKaXfbRg+3G26bZ5DlzrFu3Tvb5Cw6yIw/sZ/c+OFtDOc60FcuW2N579rArLh9hu3bvYj6Vms8LNeP5N+zmu5+zF19+U0MaZuyQoT3t8x8/3HbutWMY5jCnKqE7HnzBpjz/pl163sH2iz89ZTNfXmLDjxtoxx/W326/Z7p97TNHWZdueVUP6tSpYsunE/vDHWPtrbdX2TcuPUmBq1duRT7J3GglvY40cGeozrv3oVnWaadO9qkLj7KHxsyxe0e9YFcMGh4cQ7mXblXlsmDhcvuDqhif07Cba+oL1nPXHTX04l72yTMOUPgYhZCvv77MbrpztE2Y9lY4f77M6SMG2cfO2D/Ma1fQi2vegjV2p/o3cepCzfdWbwP67mwXnnuAHTS0r05ZdP7+pWrFm26faM88M0PLyLpmBxuyVy+7+spjVXFXtbeW19uNt06wiZPmat8Vq+tQa/sM7GnXfPXE8HxdiIa9Ci8KUeOXf3RMbfBvPOWezhgNAQQQQAABBBBAAAEEEGi9AMFY6w3ZAgIIIIAAAggggEArBbIaLq5BX/CPnjTHzjpliEIEs5FHDrRHn5wWgjG/7y35gr8ml7FvXnqsffk/77NvXPeYjX1+rn3uwiNstx4KghSOeIGJr+KBWDI/V9jAJv45+dhB9tu/TLBnpi62Q/fvYYuXvmPPvvCalTTsnu8rp6Bn/mvL7Nlpr9rVv3zIzjxssH34xH0s3yVvryxcYl+85l7r23tn++YXjlcPKvbcpDdD32/47sU2cE8NC6g+v7potV35nbttp5062xcVpuVrcgp3VuuiIQi11rC9e9jkKV1s+fI1dtIx/UPQUqcAUFNkeZYXmo42XDcPEIb028l279k1zKN28TnDNL+Yae61mXbYQb2tg0KV5m3eG8vs8m/fa3vt1t2+/tljLa2djBr3sn31x/fZL68+w/oOrAurzH9tuT0/83X7/v+Mt4+cNNg+dOqedv+jL9n3bhhlZ5282GbOWmYXnjXUakoNdsOfxtl//HKU/e57ZyuwM5uicPHb//WIDR7Yw76pwKyoyr4/3vWMXf4f99hvfnqede9cp8q9qr32xgp7dvpr9s1fLLfTRvS1j5yscLJDyvYesKtNmfHPcEwXnXugNRQ1fKECsFWrKwrsXrSPnbZP9CIQTjUuMapomEZvXkXnrdCQtjHPzrPzPjI0vF5GKmwb9fTLdoWeyyog1GkJVYavLnrHPnfN36zXLp3tcxcdZp3qau0NvQbeWlawor8GNIzkq2+stK9cc7vt7Ofv4iOstjYXhvJcuqwhnCsPY2fPW2Nf+u6DNqxPnV112QhL50r2+PiX7Vs/fcR+ec0Ztk+/3UJg9/XrHgiv4y9dfJjV1dXY8kLeJs9YYpr+zeoU1n7zurs1p17FvqZt5HJpW6VQc/KMxTqeinWo1cmNDs8PkYYAAggggAACCCCAAAIIINBMgGCsGQh3EUAAAQQQQAABBLaCQKFi/xwz24qFBjv7hL2sTl/sf2zEPnbvPybaE+NfsmOO2Dt0KqN5p7w1qCps3712sVNPHGR/e/hFGzS4l512+lBr8ERIuUBe6yvXsKzClqyP0biBlszl5Uv61FdVVZp5y8RDJPbv0U3VZGl7Z/kSVeT0sILP7ZRT5ZUX5GglD7aqmussqxtf+OgwO+ogDbmoVtRj1/zySdupW1e7/utnhkq2tFK+Ew7Yx+a/Xm+/uOtR++lVp1mNdvfrW8ZaViHVL/7jw1bTqSYMAenBXrmkebdUJXRA/872z112tBmqmPrQ8D1UaZaxNVWfI0tzkHmCE5rHgdG8Vn6drq4OF3XdjjuyfzD+97OH2durizZec4v9WBVvlbLmHdP+/ZLT7ZTCpZt+O8p6d6uxX6oCz4PF+nTRDj24l135/Qfst/eNtR9deVpUWVZYY6lVK+3zV5xsh+zXUwZlO/TAHnbGBbNs7LhZdttPLlAll+b9qq3acp2Y63/9mMKiNdalR539713jrWv3jP38W+qD5uny4zlgn93svP/zR/vz36bYly88zFbpuFarlK6++I79+8cPsmMP3ycMi5nLKIlU+9AxA+2fT0y3T521fzjfGmvSHn7iFSuUs/ah4w4Mc8JlsjKMk8Kc5rLzVvXAVNePjJ+igGmFnXxMP82pVrKTThxst6tK8anRc+344QPUr5LWLduNN0+wHevy9uNrPmSdamusk86Jn5sGoSUVaTfcMsEytZ3sZ1efY500HGeSTRX1GvJ5x6qaG+u2W5+0/jun7WffPS0EqjXq13GH9LfPf+seu+WWZ+yH3+4V+jV3/jK7SsHv0UcPChWOOhw7VmFoB01aVlH15Py5b6qS8Xg7Vuv6SzSrvpx+zF6qbFMmph37XHfefJ666Dpctd0/8fG03QOg5wgggAACCCCAAAIIILAtCUSfnLelHtEXBBBAAAEEEEAAge1OQFNo2UOjZtjQvXez3VRxk9bcXX17dbb+e3TX47PW8sjEQ+NNnv6mgoGqrVytQeoUPnghUPzUWuu05oFiuRjN8RVvJFShKTAKBUgepHk4oYPI52ts5KFDLKv8xavIvI1/bp4dccCetqqhwVasKNhKpT3/Wt5ghwzrbTNnLw5DM3pwM3HKPBtx2ABV/ORULRQFLx665H2MQDW/nfZ9amc+3GFaVXY5BSLvp1W07mkj9rKFbyy3F2cvs3+Mfcm6de1ohw3pHY6r+Taem7HIDh66m73zToOtXFmw1avK6nvRDtx3F3th+uth8bLA3aFG6eDBGgLRW0mPddCBd+pYa0MH7aaqNw0x6HO86fh22amjlk/b7FeWWFFh4rTZi+yEIweEcLFYjJK97jvW2YFDd7fnpr0ewkYdYthHRhVrpxwRhY35fF7DLnrYVbUzjhtiS95aZc9MWyKf6PzfP2qmQro9rYfCt5JglbnKKrp46OeXkk5cUZeHn5xtg/bcyXbduXPof5/e3a1/n+72z3HzrNggc/3P52l7esrLNvzgfraDQjEX9+P08+GhZTIs56Sp823EEQOtY11OgVkxrOuhWC6cs7B5DZ+4wA7bv7e99ZYqAd9aY/9SNdnSpfV28L69bLq/FmLT7t12sFvvm2qPT3o5VNJlNSRiThjaVDiWrl062J/vnWKPTZxjDeqDvy68eSjWHpsb+8X/O0lCzubH6eGgz0VWKUevpebPcx8BBBBAAAEEEEAAAQQQSASoGEskuEYAAQQQQAABBBD44AQ0V1Tz5gFWoaAv9RWCLVj0tk2ftchOPmKAPTlhniq/oi/6e+/axcZMnG3LNGzdTgoLKtmqgoKCZUt5+91dz9obGr7wm5cdZz/53ZN2+93P2Gc+eogqavQluoaX8zCmpKEGS6raymjOrIoSkrQCFU2QFap9mvYnpxSkRmGGVwhpIctraLySkpaywoiSHm/QXFxZRSId8ztaVcPZVRRgVDws07tpD2i0uJUaStpXpzBMoFdx+a6qmiesqkDsr/c+a3fd95xV8h60aPlKlJplFKbVKrjJaFsZBUh9du+gSjSzDvLyAMADQJdIqc8ZVajpcCynQf9S6Xx4vqKEKaf5sgoxr1fKRc2DBJ8vrIM6oTCnpsZ6dE7ZUcN2tUefmmIzVZF02rFDFN6oaV6rVAhdMjoXqoISQamUtrsfnGF3av4tD79S1kHDRhZEU9Zxq/+qWsrkcqoky1hRKWDGS9LUN58nzoMkr47yydPqM1pQoy5mvOSpVKvgIiurtJVXFi1d7Kj55Lr76bCc+ucBlnisc4c6e2HmW6bp08JwgjU64g6pmrDPWg0v6UNlevBYVV8H7tnFhihM/fNDk23ovifZXFVSvfzqK/bZiz6sPujYFJb4VSbOSnRYoeV1TAteLyiYfNVOGjHEnnx6XgiXSvUVG9Crt416cqq9vUKVbdmOGirRh/lcab12zes1oCpABWpuLgjLKqTzgMzWyEYBVb/dFf55xaL6XNW8cx302nM9n0+tQfeLxdV2z4PPad606cG17POHqYIwo7o/d85kcuYZ4Xe+frL94v+Nsf+8/vEQCA3Yo5Odc+q+dsoJQ82nk/vxVWfY9Xr+2usftZ/oQHtr/raPnT7MTtTca368WUdSK/uEa96SsUije23wb3ziolfsOvtf8vOglvaUfTtr+s8ytHJ8nsO/c3rEX1k0BBBAAAEEEEAAAQQQWFtg+/vUsLYBjyCAAAIIIIAAAghsYQEPxbyax0Mxbw+PmR+CgUfGvGSPjdeQivqO24OCfLVe80FV7P5Rc+zicw7wSMhqMjUaTnCp3fb35+2Csw60U4/eS8HaCrtDFTbHHz7QBvXpaopjNqqlFaD4/nxfHmKoFCiqCNK75byqdZ6c9JqCtqrmF9s9BC4hLNLyLTXPTfzZnIIiH3Lv0o8fZuefNsxWKw/TXQ3RGK2pLCzMEeZfYfs2l6uizJuHYh64KH7T+vp6W/1SjZhCJ91X1ZXPa+XNt13RUIMqHYseaOFvxSubVL113BF72HW/G63t5+3qy4737ErVV80/Evg+UvaZ8w+3c8/QEIXqr3K76Li1fAic4hAi9GEdDomPB27ranVKvdx61eqSNeiQU3F1ndsUCkXr2CEa8tDX9WNu3prsXvOt9bVr/zDO3lm5xu5/4mXbubsq4Ybu0riKDl3VVo13ww1lWvbA4y+F2/98Yprd//jsEFClKgoktYK7/O3R6XbJxw+xnIK8rJK4FfXFEIpFG9AJUmedvagzndEND7j+pQo733Zy2B4SppNwSoMrelXhpz9+qH3ktP1CVuV07pmcgeR1O3jPbvar751pZVXpPfviYntg1PN27U2jrXP3Hjb8wF30Gu9m/3PNmVZQwPjstIX299Gz9PyTVtu5syrbdmncZ8XHE/UWv96iO/xFAAEEEEAAAQQQQAABBLZvgfiT0vaNwNEjgAACCCCAAAIIbFkBrxQLLaXERZdRoyfbCYfvYY/f/gUbddvnbPQtl9lTt11uD//lS7b/IUPssXEzrKKh7VKquilrIrFrb3zc+vTqZBefd4gm2ErbJZ88wLp1r7Frb3jYMqqiyquqJ6/JmHKeGa2dq6x1sPVKQtbo0bT6UlG1k1edaBA8yzWkbPTYmTZ+yot20sg9FYCoAkmlYF6Z5VUamuoszOvkFWOevaTVSQ85/HYIkLTdfn172FOqevMQpFbhVkbzeHlhWihO03IqKFP8lbHevbvamKcXaJ4yMxUuWUHBS1VbKisl86mxGqoF26Xbjjr+kvrplUgaolAJm4rHdF8VS7qU1RG/pNSxVDmjAK6ki6qz1CsVudkJx+xjw4YMthOPPdD6qNpKTKHCLltt0HIKddRHD95679HVHnl6ttJLf97DKW1B4Y8X43jok/IqPF9Wc4r5RdlRuKRU7uaXjErpcgrf1PtwUQfUyQb1TcGjyt78eHv27GQTJs0IlWGei+W1zGqFYM++NM+GDumn5bR7PVZRuZevJ7Ywb1coJpSJV88VdD5OGjHQdumYsbvvftYeVaXXh0YM8I6FSypVb3U5HYCfIF2qqeiS1XE9MWaq7bf/UHv49i/ZqFs+b4//+XJ7/NZLbfQdl9sR+/eyR8e+aCu9alDznw3cbScb+/R8yxQUhsnWg8ViSRVzDQ6ojqjCr88ene2p8fP0uotfF3qdVAWmASdVwahzoWEY+/XpYQ+Pnqm+RF30yjoPLbWVcFFarDA1qpxTrZ/VdazaMYfvYj/46umWq6uxMZOmh2Eo9dIMYWK6S94OO7qf/fCrJ2mbJZvwzMuqLvQuqbJPF68a9AsNAQQQQAABBBBAAAEEEEDgXQF9JKMhgAACCCCAAAIIILBlBZJKMd/rpKmLbMnS5XbCYX2iPEOBS1olV6VCKYQlxxyyhy14dalNnTU/dPJ3d060V3T/6stPDFVcxaIqfPS/b10+0mbPW2w3/+2ZsNzG/Mlo+ERPsiZpbqsxk2fbE0/PtQefnGlf++n9ds0vHrHBA3e1L14wUvuLhmP0aq5kbqmm+/EKLm+hCkg3VTBm/3b2ATZD82n94KZH7fWla0KOsrq+wUZNnGt3PDTHvLrMl7/4owfbrLmL7Ye/H6th/BTQKS15bdE79sTEBWHuJN9un55dbFV9we74x0s2/oU37fnpSxQybbhp1MO4ZewnXzvOrrzkIFNhVqhaqno61qxd8rFDQl9+8tvxtnR5fRi68J2VCpM05OBt901+z9LrcvAF3Kql5tyfPHN/mzz9Nbvx9jGmkSrtXysr9r1fPaywqWrnatjApPlwgxtqJx45SMMTPh+GvfzIccPC4iVV0mUVkOlMrbX6szNfszcXv20nDN8r2JcVuCbFVd634YfuYYveeMemvfS2jsPs8o8fYXPmL7Xrfj9e88NpPjs99pqeH61hP726zCv/PnXuoTZr3hL7kSrylmqZko5p4cKVGqbx1cZz9Gm5ztEwlj/+9RP25lsrdO5TmntOwfC4Oer/C6HCcI2Czxv/Mkn+y0JI6fNmPfHMS9ag18ywvXuoWrBiv/7L09rXUoWhXulo9viEGXq8ZPsO2inc9yE6/eLVau9WrK3FwAMIIIAAAggggAACCCCAwHYpoI99NAQQQAABBBBAAAEEtqxA1suNvGnMxPsfecY61e1qxxy8j9UrA/GKGW/ZvFdtmZ01cojddPMEe2D0AuvYcUe79d6pduG5R9huGm4u41VVGv4urRRp3/497Nxzjrbfa4jFIw/pbwP27Koqq1Vh3MKajpr7SZVNocpJlTw++mDT1qBQoSbbya77xWgFC6oY0/8675C3/v172lc+c6yddkIUtqRVtRWqcbz7vm8lWh5OFFW5VfSaHy+div4vhCclpS1HHtrXrr7yI/bffxxvD3zuJqutrbN6hRydOtbahWcfHvrkNUPHHz7ASp8t2q9unaCqoukKVlTpVVNnI4/oa8cdtodcauyoY/a0U2ccbDf/eayGICypoihrD/6/T6mKLQ5/PCVRU3YSApJ6H4xRQ0GK0rulx6LlclrO50YrlDQMYL6DpTUHmOrYrEbLqLjJjjxwgF3z5az95lb1+bPPKlypUVBXsY5dMnaeAq2MtlMqqBpLlWj5amPq5nvQRaGYqssqKi0rqTKwRvOHrS6t0aluCMMOFpUkNWgOrNNPGGJvLVttt9wzxf5075Rg2b3rDnb1F060vfbQXGuyzPgcXZqPrZrREIfajboWWjLEpO+voqEgTz9hX7tF83YdffAetvPOdWGerpzslXepqU+qBPSmWsJwfcdDU63TjjvYaUftEYKxbE00iGFalWA+SdzJI4faL2+Zao8+MtUO73+s7XtIX/vC50+y39/6pN376LgwnKef+1OP209VeP001GJV50jze32hajfcPNYefOpFnT9V9tV2sJGH97UTD+mj10PVhh24p13zrTPtv38zyj4xWkM5ytQDrKzmTrvw7EN1zHoJFdM2ZsICu/PvU6NqOZX65TQ32yfOOtRGHrmXXikpGzNutv1VAXClWhvOi2acs4vOPNROHNnXSl4Vp/8mQksyRT/5NAQQQAABBBBAAAEEEEAAgSCQ0ge6+FM0IgggsFkEkv+iou+FtMnom7cjP3aTPXX7pfoSqvGJzbI7NoIAAggggEDbEHjv//8rq8rLA6hahR1lBUA+/J6/K61XmBGCseT/nyYH5+PDqfncXF5ApKIi8yqossITL0zKeVygOZ68Osjv+7B83pLNlFTi4897SOLhmM/h1bRltH1/LKNt+1CBGn0wVBAVtaxXEpX9j1pewZi3lIIkb0WFbH4cVQ2n55mURv0L1x7+eIt6rSHx1Cnf5oI337EFS5baoN49rFvnTgpEdCw+0p8Pu6dt1Oh9gjvMfHWZ5qtabfsO7qVl0laj/fvj5TCEoo5Xwy96SuTb9wqnQtwvTSTmu9UQiVElUSYb9aCkoRC9pVJRUlJRMOXVbdVqNhx3bXhWQxcqVMkqhCrF1V5p9fn1xe/Ya0tWWb9ena1bj45h+MWcnwjfnoYW9Cm0yl72puZDS3qrNFTC+fE0rqjzW1ZwlNYxVLQ/7VZ5kIa6VGCX1TF75drUma9ahw4dbYDmztJUaFbQmI6hfzpeP25fx699PEV3DsMi+ro+h5vuzpj2hn35+/fZdd88xQ7aZzdfQBe1THS+ojtaPQ7GfLhNr/Ly/Cirvmdjv3Q5Cs6qchORj1gY9rtSmxOLucf8RW/Z8mWr7ADtJ5vXg2oVJXAZ9SVd0EbVoZcWLrNly1fYkL33sLyG+szofGX1+lut7frrt1bbdtcly1dZ7507W5euHtxqQ/F5Di7a1uR5C0P/9huwq447EwJYHxqxTsNk1mu8zckvvWE1tVnbb8+d5K3AUSfS/xvIJMFYdPqDV+gof9qlgA8r6s2HG41a9O/c8HN+a2Pv+kzyINcIIIAAAggggAACCCAQC6Q0mXU1fJjXJ02/piGAQCsFou+Jwpci0ZaiT6oEY610ZXUEEEAAgTYu8N73mcnwgT4snH+hny7Wh+OrpGtDcKAJqsL9jE96peZxjgcTSfDiXwRrOqYQDvnzHip4cJKPg6n6dFwB5CmXmg+1mIRhPgxiMuRheFJ/FFeEm+kkUFA442FHKq5syzYbbrCgkEPRTYhfNJOTchoPcDQ/lIIXD1y8cMyboqdwrYH1oiBId8s+j1S83SRw8976W3HPo8Jbcm3L5/qqj9+f76j1qypzq6h6yjO3hhCGVa2jng/Vb3HgpUgk7K/Gt6VbkaqO38f+UyvH2/PbXsHkAYwHhhXNT+XNq8ZCRVyxEIKerCYw81W8QquiyqW07vvzHhj5dTmrai5tOtXoEzlWU1HA5HOxeStp4jQPdNKqcHL/nCZo8/DN++uBmW9ft7RkSlVsOo86Pm/F2E8zl0Xhk14PKrCyogK88BlGB+/7/78/ut/efLvebvn5x0LQpqnHQstkIgHVXIX7hfj1UKN9+OvJK8rcPGSmWiJ5fXlw5cddr/2H0FILer/LCjBzSukadLxlJWe1qZoQDPrLtOzHpZW8P6H0S1cF3feu+Hxh/ngUbKpaL+LWMesItV4lXxOe97nx/LgKMok0tI1y0eoyUXDsE4j5Fht08b7XhJ1pdyHk9P1G/0n4c968Ai1qjTeSB7huRwIEY+3oZHIoCCCAAAIIIIAAAltEIJuEYcn1FtkrO0EAAQQQQAABBBDYrgUKqobJq6yr6eAFKZ+QSyFFzkezW8f3+F7Bo9IeBSkaujAOFjx08FbQkH3+fjbriZTWTWt4PQ9yvDLK9+GVPR4ZlZSOrSsY8xAqaR6YeEspBCnEwV2cz0RP6G9WCYRnPh6K+VxaPg9UWlVvPq/Uuppv35dNKcjxIMirwyoermQUdMUH4cGKb8crirJKv/w4vSu+Hw/FkubHXomDI89FfP2SQipv1TiR83sZ9d+bNhu2E+7EfzwU8+17WJVVWFSQVUalX76G99Wrn7KeQGldr7arxglLCMO0Tlrpn4cxIQzyUCbEOO/uQauFfpV1nN58P0nz9YraXzj32qE/4+fOYzLdUoWUHlOy44GU2/o+Q0CnA/VQUIuqzykNu1m0H934lI2d9LJVdJ6+95Xjwy78ZRQ2Gu6t+0+cJ0WBmL9k3u1eWMFD15wX3+k6vBy077L6XdLxeLiY94DNx5zUegW9WDXSofomB91XgZzmZJNR7FnWgyWV8vjryl+TGVXK+bF58yC2rMfCa1v3PYgNrw9R+Dn0xbIqCUz668NfVn15BZTefI4yPz/e3MVv1uu14lVwNAQQQAABBBBAAAEEEEAAgXULxB+p1/0kjyKAAAIIIIAAAgggsHkEoi/vk215MOBf6GdULeZf9pd9TEFvad1usmhZ95MWogTd9UDFhw9s2vLJUH4hXNEzKuHx6qckEWoMDzxYUZgQEp8mG2isrEn2H+82VHapn+VmwY8nEEn0ECqdvF/aXioeYrDJpsPNXFWpiC+jdMQDD812pr9+0W09Ht1Tv3Q7VC/FQypG8YfCFoUpocUeXiHi63jY5s2Hooyuw1W0D/fV3Wgv0eOZOGEJj/lthV/unzeNwadtKnYJ4Zhpe175Fjqkvz7fV1Qfp/55iCh/33PYq/YTzo0vHqtk/FlfIAmA4iEeG7en2cw8yAlNyzX6h5X0qONqu6rH0pO6xP0OeaiW9wgvLbhzjutjIw/a2YYM7GdddsxGJgrMUh7qqZXj/Sa7SiqsfMHwOvM+NmnJ6fOpxgp6zs+Fh39Jy/trx/vy7kON1W0hldKC+YCiFbVedP60Tvy68tdkFMJFGwivK1Xdqe4u7KLkQ25r+8k5811535Pz7DAeXCbHkVSGedjrzZcNr9nYKzzIHwQQQAABBBBAAAEEEEAAgfcINPtK4T3PcQcBBBBAAAEEEEAAAQQQ2OYEiirN84qswWHuLQ0FqcAorTDMA1C/piGAAAIIIIAAAggggAACCCDQkgCfGluS4XEEEEAAAQQQQAABBBDYJgXyuZyGMFQ4lu0QhhP0TiZVgdtkh+kUAggggAACCCCAAAIIIIDANiOgMT5oCCCAAAIIIIAAAggggEDbEShqEjAfUjMXhsX0eb7endzN52+jIYAAAggggAACCCCAAAIIINCSABVjLcnwOAIIIIAAAggggAACCGyTAilN/pXRpRjPrdV0+ER/joYAAggggAACCCCAAAIIIIBASwJ8amxJhscRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTalUDahyDxlly3q6PjYBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIBdKpVCrcTK6RQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKA9CjDHWHs8qxzTNiKQTPyeNr+VLlctU/auRWG06jTjfkb3w1P+yAYHOE3Wi1fnCgEEEEAAAQTagAD//7sNnCS6iMA2KpB8flh398rp5N+XeDl9+ChpZJhUpn7dK/AoAggggAACCCCAAALbucAGv4Lfzn04fAQQQAABBBBAAAEEEEAAAQTajEAmY7amVLSafK7N9JmOIoAAAggggAACCCCwJQWoGNuS2uxr+xBIfrCZlH6l/IGqVVMVqyqKLqcKkUP1vR9UM3GBWWPl2PahxVEigAACCCCAAAIIIIBAKwSq8fQIWY1Q4a2szx7jJs23A4f2a8VWWRUBBBBAAAEEEEAAgfYrQMVY+z23HBkCCCCAAAIIIIAAAggggMB2KPD403Pt2MP6bodHziEjgAACCCCAAAIIILBhAYKxDRuxBAIbJ+BD+4eLSsBUJVbRnXIlbTvU1Nnkqa9bQzUbLkUt5pdorrGoquzdecfCE/xBAAEEEEAAAQQQQAABBNYrUCmUzQepWK0hKArZlK1avsZemP6ajVAwpqnGaAgggAACCCCAAAIIINBMgGCsGQh3EdjcAv4fWU5/fnrVKfb9G0bZ5GmLwi7S8ZAnm3t/bA8BBBBAAAEEEEAAAQS2H4FMLmMZ/TAvnUqH3+f94a5JdvKRA61TXZZgbPt5GXCkCCCAAAIIIIAAAhshQDC2EVgsisD7ESjrQ6lfKhrd3y8a4t/v2OBhvexrV5xh19/wgM17aZ6Vql5NpidTqhvzS1Rm9n52wTIIIIAAAggggAACCCCAQBDIVFdYurpKMxxXbfJLr9ojE16xi889JDyX5hM/rxIEEEAAAQQQQAABBNYS4G3yWiQ8gMBmFlD2pQzMCro+dEhXu/pzw+2anz/WWDm2mffG5hBAAAEEEEAAAQQQQGA7EsikM5bS5fmXFtr3fznKvvelkda1S0erlLYjBA4VAQQQQAABBBBAAIGNEEhV1TZieRZFAIFWChS0/qQZi+1n1z9g3778GNt7/35W0X+F2aKGPlFUnTJfouVW5j/ZlnF4BgEEEEAAAQQQQACB9iaQqomOKK3RKNTSKQVhGqEiV9Kv7/T5YfmyNfbHe8bZQxMWKBQbYQfs2z8sl6lq+bQW9IVoCCCAAAIIIIAAAggg0ChAMNZIwQ0EtoyAx176CGsvvzjfvnPD4/a1L33E9hu8o0rK0qYfelouu/6sOgy/uGW6yl4QQAABBBBAAAEEEEBgKwtUmgVbhWLFCoWKTZw420Y9PdemzVxgpxwzyM7/6BHWTZViUXxmRjC2lU8cu0cAAQQQQAABBBDYZgUIxrbZU0PH2quAf1D13236yCaTZyy0b/7gfltT32BZhWLZTM7WFLLrPfSK+XxkNAQQQAABBBBAAAEEENgeBLKpeEzEalw5lmqwmnzOhu7fx4Yf1s9OPqCvdeqo51QY1tDQYPl8PmKppsKIFNuDEceIAAIIIIAAAggggMDGCBCMbYwWyyKwGQSSX3Amm8p4+ZgnZX6hIYAAAggggAACCCCAAALvQ8A/RnhkFmIwvxOPmNg4WwLB2PtQZBEEEEAAAQQQQACB7VFg/aUp26MIx4zAByyQ5F/pMKCiduYTizVt6x9JkQCtqRW3EUAAAQQQQAABBBBo9wKeenlLfmKnoSbU/FNECMVK7/08kXze2NDHirAR/iCAAAIIIIAAAgggsB0KvPcd9HYIwCEjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghsHwJUjG0f55mj3IYE3v3lZpxLxw9UUtEvQdPNK8ia9T35vWizh7mLAAIIIIAAAggggAAC7VIg+T1rcv3eg0xnozmIk88JKQ2hGFqVj/vvleIeAggggAACCCCAAAKRwLrfWaODAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDsTSGli3ncLWNrZwXE4CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCQCVIwlElwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi0awGCsXZ9ejk4BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBRIBgLJHgGgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoF0LEIy169PLwSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCQCBGOJBNcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALtWoBgrF2fXg4OAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgESAYSyS4RgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaNcCBGPt+vRycAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAokAwVgiwTUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEC7FiAYa9enl4NDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIBAjGEgmuEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE2rUAwVi7Pr0cHAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQCJAMJZIcI0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINCuBQjG2vXp5eAQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSAYKxRIJrBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBdi1AMNauTy8HhwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkAgQjCUSXCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLRrAYKxdn16OTgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFEgGAskeAaAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAA7eT+EAAAA0SURBVAEEEECgXQsQjLXr08vBIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJAL/H8920FygdTGsAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "id": "3412fa7c", "metadata": {}, "source": [ "## Synthesize and make bitfile\n", "\n", "Now we will synthesize the model, export the IP, and create a bitfile! The `VivadoAccelerator` backend design scripts create a Block Design in Vivado IPI containing our Neural Network IP, as well as the other necessary IPs to create a complete system.\n", "\n", "In the case of our `pynq-z2`, we add a DMA IP to transfer data between the PS and PL containg the Neural Network. If you want to create a different design, for example to connect your NN to a sensor, you can use our block designs as a starting point and add in relevant IP for your use case.\n", "\n", "Our block diagram looks like this:\n", "\n", "![part7_block_design.png](attachment:part7_block_design.png)" ] }, { "cell_type": "code", "execution_count": null, "id": "555b1243", "metadata": {}, "outputs": [], "source": [ "hls_model.build(csim=False, export=True, bitfile=True)" ] }, { "attachments": { "part7_floorplan.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "1bd13389", "metadata": {}, "source": [ "The floorplan of our NN placed on the `pynq-z2` is shown below, with the hls4ml Neural Network highlighted in purple. You can reproduce this yourself if running the tutorial with a local Vivado installation by opening the project at `model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.xpr` in the Vivado GUI and clicking \"Open Implemented Design\".\n", "\n", "![part7_floorplan.png](attachment:part7_floorplan.png)" ] }, { "cell_type": "markdown", "id": "f3d55bac", "metadata": {}, "source": [ "Let's also inspect the final resource usage after placement:" ] }, { "cell_type": "code", "execution_count": null, "id": "b00c1adc", "metadata": {}, "outputs": [], "source": [ "!sed -n '30,45p' model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.runs/impl_1/design_1_wrapper_utilization_placed.rpt" ] }, { "cell_type": "markdown", "id": "aac3541d", "metadata": {}, "source": [ "## Preparations for deployment\n", "First, you'll need to follow the [setup instructions for the pynq-z2 board](https://pynq.readthedocs.io/en/latest/getting_started/pynq_z2_setup.html).\n", "Typically, this includes connecting the board to your host via ethernet and setting up a static IP address for your host (192.168.2.1). \n", "The IP address for the pynq-z2 is 192.168.2.99.\n", "The default username and password is xilinx.\n", "\n", "Next you'll transfer the following files from the earlier part of this notebook into a directory on the pynq-z2:\n", "- bitfile: `model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.runs/impl_1/design_1_wrapper.bit` -> `hls4ml_nn.bit`\n", "- hardware handoff: `model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.srcs/sources_1/bd/design_1/hw_handoff/design_1.hwh` -> `hls4ml_nn.hwh`\n", "- driver: `model_3/hls4ml_prj_pynq/axi_stream_driver.py` -> `axi_stream_driver.py`\n", "- data: `X_test.npy`, `y_test.npy`\n", "- notebook: `part7b_deployment.ipynb`\n", "\n", "The following commands archive these files into `model_3/hls4ml_prj_pynq/package.tar.gz` that can be copied over to the pynq-z2 and extracted." ] }, { "cell_type": "code", "execution_count": null, "id": "3ca20d05", "metadata": {}, "outputs": [], "source": [ "!mkdir -p model_3/hls4ml_prj_pynq/package\n", "!cp model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.runs/impl_1/design_1_wrapper.bit model_3/hls4ml_prj_pynq/package/hls4ml_nn.bit\n", "!cp model_3/hls4ml_prj_pynq/myproject_vivado_accelerator/project_1.srcs/sources_1/bd/design_1/hw_handoff/design_1.hwh model_3/hls4ml_prj_pynq/package/hls4ml_nn.hwh\n", "!cp model_3/hls4ml_prj_pynq/axi_stream_driver.py model_3/hls4ml_prj_pynq/package/\n", "!cp X_test.npy y_test.npy model_3/hls4ml_prj_pynq/package\n", "!cp part7b_deployment.ipynb model_3/hls4ml_prj_pynq/package\n", "!tar -czvf model_3/hls4ml_prj_pynq/package.tar.gz -C model_3/hls4ml_prj_pynq/package/ ." ] }, { "cell_type": "markdown", "id": "06d6bd96", "metadata": {}, "source": [ "To copy it to the pynq-z2 with the default settings, the command would be:\n", "\n", "```bash\n", "scp model_3/hls4ml_prj_pynq/package.tar.gz xilinx@192.168.2.99:~/jupyter_notebooks\n", "```\n", "\n", "Then you can navigate your web browser to http://192.168.2.99.\n", "You will see the JupyterHub running on the pynq-z2. Open a terminal and extract the tarball.\n", "\n", "```bash\n", "cd ~/jupyter_notebooks\n", "tar xvzf package.tar.gz\n", "```\n", "\n", "Now open the notebook `part7b_deployment.ipynb` on the pynq-z2 JupyterHub" ] }, { "cell_type": "markdown", "id": "f876eff5", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: part7b_deployment.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "033cc4d9", "metadata": {}, "source": [ "# Part 7b: Deployment on PYNQ-Z2\n", "The following section is the code to execute in the pynq-z2 jupyter notebook to execute NN inference. \n", "\n", "The following cells are intended to run on a pynq-z2, they will not run on the server used to train and synthesize models!\n", "\n", "First, import our driver `Overlay` class. We'll also load the test data." ] }, { "cell_type": "code", "execution_count": null, "id": "89c67e4f", "metadata": {}, "outputs": [], "source": [ "from axi_stream_driver import NeuralNetworkOverlay\n", "import numpy as np\n", "\n", "X_test = np.load('X_test.npy')\n", "y_test = np.load('y_test.npy')" ] }, { "cell_type": "markdown", "id": "551c5cd6", "metadata": {}, "source": [ "Create a `NeuralNetworkOverlay` object. This will download the `Overlay` (bitfile) onto the PL of the pynq-z2. We provide the `X_test.shape` and `y_test.shape` to allocate some buffers for the data transfer." ] }, { "cell_type": "code", "execution_count": null, "id": "cfb786f3", "metadata": {}, "outputs": [], "source": [ "nn = NeuralNetworkOverlay('hls4ml_nn.bit', X_test.shape, y_test.shape)" ] }, { "cell_type": "markdown", "id": "5fde9b2d", "metadata": {}, "source": [ "Now run the prediction! When we set `profile=True` the function times the inference, and prints out a summary as well as returning the profiling information. We also save the output to a file so we can do some validation." ] }, { "cell_type": "code", "execution_count": null, "id": "1fd6dee7", "metadata": {}, "outputs": [], "source": [ "y_hw, latency, throughput = nn.predict(X_test, profile=True)" ] }, { "cell_type": "markdown", "id": "1983e7d7", "metadata": {}, "source": [ "An example print out looks like:\n", "\n", "```\n", "Classified 166000 samples in 0.402568 seconds (412352.6956936468 inferences / s)\n", "```\n", "\n", "Now let's save the output and transfer this back to the host." ] }, { "cell_type": "code", "execution_count": null, "id": "981ffced", "metadata": {}, "outputs": [], "source": [ "np.save('y_hw.npy', y_hw)" ] }, { "cell_type": "markdown", "id": "b9e92d1e", "metadata": {}, "source": [ "Now, go back to the host and follow `part7c_validation.ipynb`" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: part7c_validation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "005ae126", "metadata": {}, "source": [ "# Part 7c: Validation\n", "We executed NN inference on the pynq-z2! Now we can copy the `y_hw.npy` back to the host we've been using for the training and synthesis, and make a final plot to check that the output we took on the board is as expected.\n", "\n", "The command to copy it back is\n", "\n", "```bash\n", "scp xilinx@192.168.2.99:~/jupyter_notebooks/y_hw.npy model_3/\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "fee790be", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import plotting\n", "\n", "%matplotlib inline\n", "from sklearn.metrics import accuracy_score\n", "\n", "y_hw = np.load('model_3/y_hw.npy')\n", "y_test = np.load('y_test.npy')\n", "classes = np.load('classes.npy', allow_pickle=True)\n", "y_hls = np.load('model_3/y_hls.npy')\n", "y_qkeras = np.load('model_3/y_qkeras.npy')\n", "\n", "print(\"Accuracy QKeras, CPU: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_qkeras, axis=1))))\n", "print(\"Accuracy hls4ml, CPU: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hls, axis=1))))\n", "print(\"Accuracy hls4ml, pynq-z2: {}\".format(accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_hw, axis=1))))\n", "\n", "fig, ax = plt.subplots(figsize=(9, 9))\n", "_ = plotting.makeRoc(y_test, y_qkeras, classes, linestyle='-')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hls, classes, linestyle='--')\n", "plt.gca().set_prop_cycle(None) # reset the colors\n", "_ = plotting.makeRoc(y_test, y_hw, classes, linestyle='-.')\n", "\n", "from matplotlib.lines import Line2D\n", "\n", "lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--'), Line2D([0], [0], ls='-.')]\n", "from matplotlib.legend import Legend\n", "\n", "leg = Legend(ax, lines, labels=['QKeras, CPU', 'hls4ml, CPU', 'hls4ml, pynq-z2'], loc='lower right', frameon=False)\n", "ax.add_artist(leg)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: part8_symbolic_regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "79933ff7", "metadata": {}, "source": [ "# Part 8: Symbolic Regression" ] }, { "cell_type": "code", "execution_count": null, "id": "ede2226f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import sympy\n", "import matplotlib.pyplot as plt\n", "import hls4ml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.metrics import roc_curve, auc, accuracy_score\n", "from tensorflow.keras.utils import to_categorical\n", "from sklearn.datasets import fetch_openml" ] }, { "cell_type": "markdown", "id": "d9e2b159", "metadata": {}, "source": [ "## Load the LHC jet tagging dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "ee6d96bd", "metadata": {}, "outputs": [], "source": [ "data = fetch_openml('hls4ml_lhc_jets_hlf')\n", "X, Y = data['data'].to_numpy(), data['target'].to_numpy()\n", "print(data['feature_names'])\n", "print(X.shape, Y.shape)\n", "print(Y[:10])\n", "\n", "LE = LabelEncoder()\n", "Y = LE.fit_transform(Y)\n", "Y = to_categorical(Y, 5)\n", "\n", "Y = 2 * Y - 1\n", "print(Y[:10])" ] }, { "cell_type": "code", "execution_count": null, "id": "0502aea8", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5, random_state=123)\n", "\n", "scaler = StandardScaler().fit(X_train)\n", "X_train = scaler.transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "# PySR (or any genetic programming based SR) not happy with too many training data\n", "X_train = X_train[:8000]\n", "Y_train = Y_train[:8000]\n", "\n", "print('X_train.shape: ' + str(X_train.shape))\n", "print('Y_train.shape: ' + str(Y_train.shape))\n", "print('X_test.shape: ' + str(X_test.shape))\n", "print('Y_test.shape: ' + str(Y_test.shape))" ] }, { "cell_type": "markdown", "id": "7ec86106", "metadata": {}, "source": [ "## Perform SR with PySR (if installed)" ] }, { "cell_type": "markdown", "id": "57e7896d", "metadata": {}, "source": [ "If you want to run `PySR` (a genetic programming-based symbolic regression software), please see https://github.com/MilesCranmer/PySR for installation and intructions.\n", "\n", "Below is an example configuration script to run training in `PySR`, where one can specify the allowed primitive functions `unary_operators` `binary_operators` (e.g. `+`, `*`, `sin`) and constraints `complexity_of_operators` `constraints` `nested_constraints` in the equation seacrhing. The training results will be stored in a `.pkl` file that contains the final equations selected by the training strategy `model_selection`.\n", "\n", "We also provide an already trained PySR model `sr/example.pkl` in the following sections for demonstrating the HLS implementation." ] }, { "cell_type": "code", "execution_count": null, "id": "96a651dd", "metadata": {}, "outputs": [], "source": [ "from pysr import PySRRegressor\n", "\n", "!export JULIA_NUM_THREADS=32\n", "\n", "model_pysr = PySRRegressor(\n", " model_selection='accuracy',\n", " niterations=40,\n", " timeout_in_seconds=60 * 60 * 1,\n", " maxsize=40,\n", " select_k_features=6,\n", " binary_operators=['+', '-', '*'],\n", " unary_operators=['sin', 'sc(x)=sin(x)*cos(x)'],\n", " complexity_of_operators={'+': 1, '-': 1, '*': 1, 'sin': 1, 'sc': 1},\n", " constraints={'sin': 20, 'sc': 20},\n", " nested_constraints={'sin': {'sin': 0, 'sc': 0}, 'sc': {'sin': 0, 'sc': 0}},\n", " extra_sympy_mappings={'sc': lambda x: sympy.sin(x) * sympy.cos(x)},\n", " loss='L2MarginLoss()', # (1 - y*y')^2\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "5f4d9501", "metadata": {}, "outputs": [], "source": [ "model_pysr.fit(X_train, Y_train)" ] }, { "cell_type": "markdown", "id": "846e710b", "metadata": {}, "source": [ "## Prepare symbolic expressions in strings first" ] }, { "cell_type": "markdown", "id": "c7aaf105", "metadata": {}, "source": [ "We provide a trained model for the HLS demonstration.\n", "\n", "**If you have `PySR` installed**, you can directly load the trained expressions from the output file `sr/example.pkl`.\n", "`PySR` allows custom functions to be defined, such as sc(x):=sin(x)*cos(x) in this example, they need to be re-defined through `extra_sympy_mappings` and a new `sympy` class when retrieving the equations for evaluation." ] }, { "cell_type": "code", "execution_count": null, "id": "d3d5d2cd", "metadata": { "scrolled": true }, "outputs": [], "source": [ "from pysr import PySRRegressor\n", "\n", "model_pysr = PySRRegressor.from_file('sr/example.pkl')\n", "with sympy.evaluate(True):\n", " for i in range(5):\n", " print('Tagger {} = '.format(i) + str(model_pysr.sympy()[i]) + '\\n------------------------------------------')\n", "\n", "# Re-write custom operator defined from PySR config: sc(x) = sin(x)*cos(x)\n", "model_pysr.set_params(extra_sympy_mappings={\"sc\": lambda x: sympy.sin(x) * sympy.cos(x)})\n", "model_pysr.refresh()\n", "\n", "\n", "class sc(sympy.Function):\n", " pass" ] }, { "cell_type": "markdown", "id": "699d2e05", "metadata": {}, "source": [ "There are two options for evaluating math functions in `hls4ml`, one is using the standard HLS math library (`func`), another one is using approximation with user-defined lookup tables (`func_lut`) for resources saving. We will define the lookup tables (table range and size) for `func_lut` later.\n", "\n", "We have the equations in the `sympy` format, now convert them into strings: `expr` for using the standard functions and `expr_lut` for using the approximation with lookup tables. We will re-parse `expr` and `expr_lut` from strings in `sympy` format for the `hls4ml` converter." ] }, { "cell_type": "code", "execution_count": null, "id": "7219a874", "metadata": {}, "outputs": [], "source": [ "expr = []\n", "expr_lut = []\n", "for i in range(5):\n", " expr.append(str(model_pysr.sympy()[i]))\n", " expr_lut.append(expr[i].replace(\"sin\", \"sin_lut\").replace(\"cos\", \"cos_lut\"))" ] }, { "cell_type": "markdown", "id": "0abcba26", "metadata": {}, "source": [ "**If you don't have PySR installed**, you can also write your expressions directly in strings and parse in `sympy` format, which can then be fed to `hls4ml` converter. Here again, `expr` for using standard math library, `expr_lut` for using approximation with lookup tables." ] }, { "cell_type": "code", "execution_count": null, "id": "3356d1e6", "metadata": {}, "outputs": [], "source": [ "# Expressions from 'sr/example.pkl'\n", "\n", "# Expressions that will use Vivado math library\n", "expr = [\n", " '-0.1630426*(sin(-0.75052315)*cos(-0.75052315) - 0.84283006)*sin(2*x14 - 1.03665108)*cos(2*x14 - 1.03665108) - sin(x14 - (0.9237657 - 0.11933863*x3)*(-x15 + 2*x2 - 0.3817056) + 1.761264957)',\n", " '(-(0.5822144*sin(0.83811*x14)*cos(0.83811*x14) - 0.5324657)*(sin(0.3923645*x2)*cos(0.3923645*x2) - 0.63548696) + sin(x14 - 0.3923645*x15 + x3 + 0.51168373)*cos(x14 - 0.3923645*x15 + x3 + 0.51168373))*(0.561041303633489*sin(x15) - 0.47277835) - 0.84055585',\n", " '0.49239117*(sin(x3)*cos(x3) + sin(x15 + 0.76784414*x3)*cos(x15 + 0.76784414*x3))*(sin(-0.13417026)*cos(-0.13417026) + sin(0.5180547)*cos(0.5180547) + sin(x2)*cos(x2)) - sin(x14 + 0.25715914*x15*x3 - x2 - x3 + 0.66443527)',\n", " '0.41071504*(0.9298677 - sin(0.59376544*x15))*(sin(x14)*cos(x14) + 5.2546763*sin(0.71913457 - x3)*cos(0.71913457 - x3))*(-sin(2*x3)*cos(2*x3) + sin(5.2546763*x14 + x3 + 0.77032656)*cos(5.2546763*x14 + x3 + 0.77032656) + 0.32492808) - 0.863786752431664',\n", " '(1.0745832 - sin(-x14 - 0.4094719)*cos(-x14 - 0.4094719))*(-0.15737492*x15 - sin(x14 - 4.2594776)*cos(x14 - 4.2594776) + sin(3*x14 - x3*(x14 - 4.1772995) - x3 + 3.087878)*cos(3*x14 - x3*(x14 - 4.1772995) - x3 + 3.087878) - 0.690204005690814)',\n", "]\n", "# Expressions that will use look-up table approximated math functions\n", "expr_lut = []\n", "for i in range(len(expr)):\n", " expr_lut.append(expr[i].replace(\"sin\", \"sin_lut\").replace(\"cos\", \"cos_lut\"))" ] }, { "cell_type": "markdown", "id": "788ee608", "metadata": {}, "source": [ "## Then parse the strings to sympy expressions" ] }, { "cell_type": "markdown", "id": "03fc8284", "metadata": {}, "source": [ "Define the lookup tables for approximating math functions. The table range and size can be customized for each function to be approximated, they depend on how much precision can be compromised to save more resources." ] }, { "cell_type": "code", "execution_count": null, "id": "920e2326", "metadata": {}, "outputs": [], "source": [ "from hls4ml.utils.symbolic_utils import init_pysr_lut_functions\n", "\n", "# For functions approximated with look-up table, define the table range and size\n", "function_definitions = [\n", " 'sin_lut(x) = math_lut(sin, x, N=256, range_start=-8, range_end=8)',\n", " 'cos_lut(x) = math_lut(cos, x, N=256, range_start=-8, range_end=8)',\n", "]\n", "init_pysr_lut_functions(init_defaults=True, function_definitions=function_definitions)\n", "\n", "lut_functions = {\n", " 'sin_lut': {'math_func': 'sin', 'range_start': -8, 'range_end': 8, 'table_size': 256},\n", " 'cos_lut': {'math_func': 'cos', 'range_start': -8, 'range_end': 8, 'table_size': 256},\n", "}" ] }, { "cell_type": "markdown", "id": "8be93891", "metadata": {}, "source": [ "Parse `expr` and `expr_lut` to sympy expressions." ] }, { "cell_type": "code", "execution_count": null, "id": "96f61066", "metadata": {}, "outputs": [], "source": [ "# Use sympy to parse strings into sympy expressions\n", "for i in range(len(expr)):\n", " print('expr =\\n' + expr[i])\n", " print(\"----------------------------------------\")\n", " print('expr_LUT =\\n' + expr_lut[i])\n", " print(\"========================================\")\n", " expr[i] = sympy.parsing.sympy_parser.parse_expr(expr[i])\n", " expr_lut[i] = sympy.parsing.sympy_parser.parse_expr(expr_lut[i])" ] }, { "cell_type": "markdown", "id": "f7548c93", "metadata": {}, "source": [ "Use `hls4ml.converters.convert_from_symbolic_expression` to convert sympy expressions and compile." ] }, { "cell_type": "code", "execution_count": null, "id": "46ff4b5e", "metadata": {}, "outputs": [], "source": [ "# Use hls4ml to convert sympy expressions into HLS model\n", "hls_model = hls4ml.converters.convert_from_symbolic_expression(\n", " expr, n_symbols=16, output_dir='my-hls-test', precision='ap_fixed<16,6>', part='xcvu9p-flga2577-2-e'\n", ")\n", "hls_model.write()\n", "hls_model.compile()\n", "\n", "hls_model_lut = hls4ml.converters.convert_from_symbolic_expression(\n", " expr_lut,\n", " n_symbols=16,\n", " output_dir='my-hls-test-lut',\n", " precision='ap_fixed<16,6>',\n", " part='xcvu9p-flga2577-2-e',\n", " lut_functions=lut_functions,\n", ")\n", "hls_model_lut.write()\n", "hls_model_lut.compile()" ] }, { "cell_type": "markdown", "id": "08682628", "metadata": {}, "source": [ "## Compare outputs: PySR vs HLS vs HLS(LUT)" ] }, { "cell_type": "code", "execution_count": null, "id": "39269441", "metadata": {}, "outputs": [], "source": [ "test_vector = np.random.rand(1, 16) * 4 - 2\n", "# print(model_pysr.predict(test_vector))\n", "print(hls_model.predict(test_vector))\n", "print(hls_model_lut.predict(test_vector))" ] }, { "cell_type": "markdown", "id": "08795fca", "metadata": {}, "source": [ "## Compare performance on the dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "05894f0b", "metadata": {}, "outputs": [], "source": [ "# Y_pysr = model_pysr.predict(X_test)\n", "Y_hls = hls_model.predict(X_test)\n", "Y_hls_lut = hls_model_lut.predict(X_test)\n", "# auc_pysr=[]\n", "auc_hls = []\n", "auc_hls_lut = []\n", "for x, label in enumerate(LE.classes_):\n", " # fpr_pysr, tpr_pysr, _ = roc_curve(Y_test[:, x], Y_pysr[:, x])\n", " fpr_hls, tpr_hls, _ = roc_curve(Y_test[:, x], Y_hls[:, x])\n", " fpr_hls_lut, tpr_hls_lut, _ = roc_curve(Y_test[:, x], Y_hls_lut[:, x])\n", " # auc_pysr.append(auc(fpr_pysr, tpr_pysr))\n", " auc_hls.append(auc(fpr_hls, tpr_hls))\n", " auc_hls_lut.append(auc(fpr_hls_lut, tpr_hls_lut))\n", "\n", "# print('PySR acc = {0:.3f}'.format(accuracy_score(np.argmax(Y_test, axis=1), np.argmax(Y_pysr, axis=1))))\n", "# print('PySR auc = {0:.3f},{1:.3f},{2:.3f},{3:.3f},{4:.3f}'.format(auc_pysr[0],auc_pysr[1],auc_pysr[2],auc_pysr[3],auc_pysr[4]))\n", "print('HLS acc = {0:.3f}'.format(accuracy_score(np.argmax(Y_test, axis=1), np.argmax(Y_hls, axis=1))))\n", "print(\n", " 'HLS auc = {0:.3f},{1:.3f},{2:.3f},{3:.3f},{4:.3f}'.format(\n", " auc_hls[0], auc_hls[1], auc_hls[2], auc_hls[3], auc_hls[4]\n", " )\n", ")\n", "print('HLS_LUT acc = {0:.3f}'.format(accuracy_score(np.argmax(Y_test, axis=1), np.argmax(Y_hls_lut, axis=1))))\n", "print(\n", " 'HLS_LUT auc = {0:.3f},{1:.3f},{2:.3f},{3:.3f},{4:.3f}'.format(\n", " auc_hls_lut[0], auc_hls_lut[1], auc_hls_lut[2], auc_hls_lut[3], auc_hls_lut[4]\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "002643a3", "metadata": {}, "outputs": [], "source": [ "def plot_roc(y_test, y_pred, labels, model):\n", " color = ['blue', 'orange', 'green', 'red', 'purple']\n", " for x, label in enumerate(labels):\n", " fpr, tpr, _ = roc_curve(y_test[:, x], y_pred[:, x])\n", " if model == 'pysr':\n", " plt.plot(\n", " tpr,\n", " fpr,\n", " label='{0}, PySR, AUC = {1:.1f}'.format(label, auc(fpr, tpr) * 100.0),\n", " linestyle='solid',\n", " color=color[x],\n", " lw=1.5,\n", " )\n", " if model == 'hls':\n", " plt.plot(\n", " tpr,\n", " fpr,\n", " label='{0}, HLS, AUC = {1:.1f}'.format(label, auc(fpr, tpr) * 100.0),\n", " linestyle='dotted',\n", " color=color[x],\n", " lw=1.5,\n", " )\n", " if model == 'hls_lut':\n", " plt.plot(\n", " tpr,\n", " fpr,\n", " label='{0}, HLS LUT, AUC = {1:.1f}'.format(label, auc(fpr, tpr) * 100.0),\n", " linestyle='None',\n", " color=color[x],\n", " lw=1,\n", " marker='o',\n", " ms=1,\n", " )\n", " plt.semilogy()\n", " plt.xlabel('True positive rate', size=15, loc='right')\n", " plt.ylabel('False positive rate', size=15, loc='top')\n", " plt.tick_params(axis='both', which='major', direction='in', length=6, width=1.2, labelsize=12, right=True, top=True)\n", " plt.tick_params(axis='both', which='minor', direction='in', length=2, width=1, labelsize=12, right=True, top=True)\n", " plt.xlim(0, 1)\n", " plt.ylim(0.001, 1)\n", " plt.grid(True)\n", " plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=12)\n", "\n", "\n", "plt.figure(figsize=(15, 15))\n", "axes = plt.subplot(2, 2, 1)\n", "# plot_roc(Y_test, Y_pysr, LE.classes_, 'pysr')\n", "plot_roc(Y_test, Y_hls, LE.classes_, 'hls')\n", "plot_roc(Y_test, Y_hls_lut, LE.classes_, 'hls_lut')" ] }, { "cell_type": "markdown", "id": "7beb92ea", "metadata": {}, "source": [ "## Run synthesis from command line" ] }, { "cell_type": "code", "execution_count": null, "id": "e4047f52", "metadata": {}, "outputs": [], "source": [ "!source ${XILINX_VITIS}/settings64.sh\n", "!vitis_hls -f build_prj.tcl \"reset=1 synth=1 csim=0 cosim=0 validation=0 export=0 vsynth=0\"\n", "!cat my-hls-test/myproject_prj/solution1/syn/report/myproject_csynth.rpt" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: plotting.py ================================================ import itertools import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.metrics import auc, roc_curve # confusion matrix code from Maurizio # /eos/user/m/mpierini/DeepLearning/ML4FPGA/jupyter/HbbTagger_Conv1D.ipynb def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation='nearest', cmap=cmap) # plt.title(title) cbar = plt.colorbar() plt.clim(0, 1) cbar.set_label(title) tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2.0 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") # plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') def plotRoc(fpr, tpr, auc, labels, linestyle, legend=True): for _i, label in enumerate(labels): plt.plot( tpr[label], fpr[label], label='{} tagger, AUC = {:.1f}%'.format(label.replace('j_', ''), auc[label] * 100.0), linestyle=linestyle, ) plt.semilogy() plt.xlabel("Signal Efficiency") plt.ylabel("Background Efficiency") plt.ylim(0.001, 1) plt.grid(True) if legend: plt.legend(loc='upper left') plt.figtext(0.25, 0.90, 'hls4ml', fontweight='bold', wrap=True, horizontalalignment='right', fontsize=14) def rocData(y, predict_test, labels): df = pd.DataFrame() fpr = {} tpr = {} auc1 = {} for i, label in enumerate(labels): df[label] = y[:, i] df[label + '_pred'] = predict_test[:, i] fpr[label], tpr[label], threshold = roc_curve(df[label], df[label + '_pred']) auc1[label] = auc(fpr[label], tpr[label]) return fpr, tpr, auc1 def makeRoc(y, predict_test, labels, linestyle='-', legend=True): if 'j_index' in labels: labels.remove('j_index') fpr, tpr, auc1 = rocData(y, predict_test, labels) plotRoc(fpr, tpr, auc1, labels, linestyle, legend=legend) return predict_test def print_dict(d, indent=0): for key, value in d.items(): print(' ' * indent + str(key), end='') if isinstance(value, dict): print() print_dict(value, indent + 1) else: print(':' + ' ' * (20 - len(key) - 2 * indent) + str(value)) ================================================ FILE: pruned_cnn/myproject_prj/solution1/syn/report/myproject_csynth.rpt ================================================ ================================================================ == Vivado HLS Report for 'myproject' ================================================================ * Date: Mon Jun 28 13:48:42 2021 * Version: 2020.1 (Build 2897737 on Wed May 27 20:21:37 MDT 2020) * Project: myproject_prj * Solution: solution1 * Product family: virtexuplus * Target device: xcu250-figd2104-2L-e ================================================================ == Performance Estimates ================================================================ + Timing: * Summary: +--------+---------+----------+------------+ | Clock | Target | Estimated| Uncertainty| +--------+---------+----------+------------+ |ap_clk | 5.00 ns | 4.363 ns | 0.62 ns | +--------+---------+----------+------------+ + Latency: * Summary: +---------+---------+----------+----------+------+------+----------+ | Latency (cycles) | Latency (absolute) | Interval | Pipeline | | min | max | min | max | min | max | Type | +---------+---------+----------+----------+------+------+----------+ | 1044| 1044| 5.220 us | 5.220 us | 1028| 1028| dataflow | +---------+---------+----------+----------+------+------+----------+ + Detail: * Instance: +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ | | | Latency (cycles) | Latency (absolute) | Interval | Pipeline | | Instance | Module | min | max | min | max | min | max | Type | +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ |dense_array_array_ap_fixed_16_6_5_3_0_42u_config17_U0 |dense_array_array_ap_fixed_16_6_5_3_0_42u_config17_s | 7| 7| 35.000 ns | 35.000 ns | 7| 7| none | |conv_2d_cl_array_array_ap_fixed_24u_config12_U0 |conv_2d_cl_array_array_ap_fixed_24u_config12_s | 40| 40| 0.200 us | 0.200 us | 40| 40| none | |conv_2d_cl_array_array_ap_fixed_16u_config7_U0 |conv_2d_cl_array_array_ap_fixed_16u_config7_s | 229| 229| 1.145 us | 1.145 us | 229| 229| none | |dense_array_array_ap_fixed_16_6_5_3_0_64u_config21_U0 |dense_array_array_ap_fixed_16_6_5_3_0_64u_config21_s | 3| 3| 15.000 ns | 15.000 ns | 3| 3| none | |dense_array_array_ap_fixed_16_6_5_3_0_10u_config25_U0 |dense_array_array_ap_fixed_16_6_5_3_0_10u_config25_s | 2| 2| 10.000 ns | 10.000 ns | 2| 2| none | |conv_2d_cl_array_array_ap_fixed_16u_config2_U0 |conv_2d_cl_array_array_ap_fixed_16u_config2_s | 1027| 1027| 5.135 us | 5.135 us | 1027| 1027| none | |softmax_array_array_ap_fixed_10u_softmax_config27_U0 |softmax_array_array_ap_fixed_10u_softmax_config27_s | 7| 7| 35.000 ns | 35.000 ns | 7| 7| none | |pooling2d_cl_array_array_ap_fixed_24u_config16_U0 |pooling2d_cl_array_array_ap_fixed_24u_config16_s | 19| 19| 95.000 ns | 95.000 ns | 19| 19| none | |pooling2d_cl_array_array_ap_fixed_16u_config6_U0 |pooling2d_cl_array_array_ap_fixed_16u_config6_s | 903| 903| 4.515 us | 4.515 us | 903| 903| none | |pooling2d_cl_array_array_ap_fixed_16u_config11_U0 |pooling2d_cl_array_array_ap_fixed_16u_config11_s | 172| 172| 0.860 us | 0.860 us | 172| 172| none | |relu_array_array_ap_fixed_64u_relu_config24_U0 |relu_array_array_ap_fixed_64u_relu_config24_s | 0| 0| 0 ns | 0 ns | 1| 1| function | |relu_array_array_ap_fixed_42u_relu_config20_U0 |relu_array_array_ap_fixed_42u_relu_config20_s | 0| 0| 0 ns | 0 ns | 1| 1| function | |relu_array_array_ap_fixed_24u_relu_config15_U0 |relu_array_array_ap_fixed_24u_relu_config15_s | 18| 18| 90.000 ns | 90.000 ns | 18| 18| none | |relu_array_array_ap_fixed_16u_relu_config5_U0 |relu_array_array_ap_fixed_16u_relu_config5_s | 902| 902| 4.510 us | 4.510 us | 902| 902| none | |relu_array_array_ap_fixed_16u_relu_config10_U0 |relu_array_array_ap_fixed_16u_relu_config10_s | 171| 171| 0.855 us | 0.855 us | 171| 171| none | |Block_proc_U0 |Block_proc | 0| 0| 0 ns | 0 ns | 0| 0| none | +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ * Loop: N/A ================================================================ == Utilization Estimates ================================================================ * Summary: +---------------------+---------+-------+---------+---------+------+ | Name | BRAM_18K| DSP48E| FF | LUT | URAM | +---------------------+---------+-------+---------+---------+------+ |DSP | -| -| -| -| -| |Expression | -| -| 0| 32| -| |FIFO | 96| -| 5518| 13608| -| |Instance | 4| 5414| 47025| 231790| -| |Memory | -| -| -| -| -| |Multiplexer | -| -| -| 36| -| |Register | -| -| 6| -| -| +---------------------+---------+-------+---------+---------+------+ |Total | 100| 5414| 52549| 245466| 0| +---------------------+---------+-------+---------+---------+------+ |Available SLR | 1344| 3072| 864000| 432000| 320| +---------------------+---------+-------+---------+---------+------+ |Utilization SLR (%) | 7| 176| 6| 56| 0| +---------------------+---------+-------+---------+---------+------+ |Available | 5376| 12288| 3456000| 1728000| 1280| +---------------------+---------+-------+---------+---------+------+ |Utilization (%) | 1| 44| 1| 14| 0| +---------------------+---------+-------+---------+---------+------+ + Detail: * Instance: +-------------------------------------------------------+------------------------------------------------------+---------+-------+-------+-------+-----+ | Instance | Module | BRAM_18K| DSP48E| FF | LUT | URAM| +-------------------------------------------------------+------------------------------------------------------+---------+-------+-------+-------+-----+ |Block_proc_U0 |Block_proc | 0| 0| 2| 11| 0| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0 |conv_2d_cl_array_array_ap_fixed_16u_config2_s | 0| 208| 3799| 6334| 0| |conv_2d_cl_array_array_ap_fixed_16u_config7_U0 |conv_2d_cl_array_array_ap_fixed_16u_config7_s | 0| 786| 10465| 38710| 0| |conv_2d_cl_array_array_ap_fixed_24u_config12_U0 |conv_2d_cl_array_array_ap_fixed_24u_config12_s | 0| 1300| 11809| 53724| 0| |dense_array_array_ap_fixed_16_6_5_3_0_10u_config25_U0 |dense_array_array_ap_fixed_16_6_5_3_0_10u_config25_s | 0| 549| 2736| 17875| 0| |dense_array_array_ap_fixed_16_6_5_3_0_42u_config17_U0 |dense_array_array_ap_fixed_16_6_5_3_0_42u_config17_s | 0| 1394| 6159| 59765| 0| |dense_array_array_ap_fixed_16_6_5_3_0_64u_config21_U0 |dense_array_array_ap_fixed_16_6_5_3_0_64u_config21_s | 0| 1167| 4332| 35083| 0| |pooling2d_cl_array_array_ap_fixed_16u_config11_U0 |pooling2d_cl_array_array_ap_fixed_16u_config11_s | 0| 0| 2006| 2705| 0| |pooling2d_cl_array_array_ap_fixed_16u_config6_U0 |pooling2d_cl_array_array_ap_fixed_16u_config6_s | 0| 0| 2008| 2709| 0| |pooling2d_cl_array_array_ap_fixed_24u_config16_U0 |pooling2d_cl_array_array_ap_fixed_24u_config16_s | 0| 0| 2868| 3803| 0| |relu_array_array_ap_fixed_16u_relu_config10_U0 |relu_array_array_ap_fixed_16u_relu_config10_s | 0| 0| 16| 855| 0| |relu_array_array_ap_fixed_16u_relu_config5_U0 |relu_array_array_ap_fixed_16u_relu_config5_s | 0| 0| 18| 859| 0| |relu_array_array_ap_fixed_24u_relu_config15_U0 |relu_array_array_ap_fixed_24u_relu_config15_s | 0| 0| 13| 1231| 0| |relu_array_array_ap_fixed_42u_relu_config20_U0 |relu_array_array_ap_fixed_42u_relu_config20_s | 0| 0| 3| 1998| 0| |relu_array_array_ap_fixed_64u_relu_config24_U0 |relu_array_array_ap_fixed_64u_relu_config24_s | 0| 0| 3| 3032| 0| |softmax_array_array_ap_fixed_10u_softmax_config27_U0 |softmax_array_array_ap_fixed_10u_softmax_config27_s | 4| 10| 788| 3096| 0| +-------------------------------------------------------+------------------------------------------------------+---------+-------+-------+-------+-----+ |Total | | 4| 5414| 47025| 231790| 0| +-------------------------------------------------------+------------------------------------------------------+---------+-------+-------+-------+-----+ * DSP48E: N/A * Memory: N/A * FIFO: +---------------------------+---------+----+----+-----+------+-----+---------+ | Name | BRAM_18K| FF | LUT| URAM| Depth| Bits| Size:D*B| +---------------------------+---------+----+----+-----+------+-----+---------+ |layer10_out_V_data_0_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_10_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_11_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_12_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_13_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_14_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_15_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_1_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_2_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_3_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_4_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_5_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_6_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_7_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_8_V_U | 1| 38| 0| -| 169| 16| 2704| |layer10_out_V_data_9_V_U | 1| 38| 0| -| 169| 16| 2704| |layer11_out_V_data_0_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_10_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_11_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_12_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_13_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_14_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_15_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_1_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_2_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_3_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_4_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_5_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_6_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_7_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_8_V_U | 1| 33| 0| -| 36| 16| 576| |layer11_out_V_data_9_V_U | 1| 33| 0| -| 36| 16| 576| |layer12_out_V_data_0_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_10_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_11_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_12_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_13_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_14_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_15_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_16_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_17_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_18_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_19_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_1_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_20_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_21_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_22_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_23_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_2_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_3_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_4_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_5_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_6_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_7_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_8_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_9_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_0_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_10_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_11_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_12_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_13_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_14_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_15_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_16_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_17_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_18_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_19_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_1_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_20_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_21_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_22_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_23_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_2_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_3_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_4_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_5_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_6_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_7_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_8_V_U | 0| 7| 0| -| 16| 16| 256| |layer15_out_V_data_9_V_U | 0| 7| 0| -| 16| 16| 256| |layer16_out_V_data_0_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_10_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_11_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_12_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_13_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_14_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_15_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_16_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_17_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_18_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_19_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_1_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_20_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_21_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_22_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_23_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_2_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_3_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_4_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_5_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_6_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_7_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_8_V_U | 0| 5| 0| -| 4| 16| 64| |layer16_out_V_data_9_V_U | 0| 5| 0| -| 4| 16| 64| |layer17_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_42_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_43_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_44_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_45_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_46_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_47_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_48_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_49_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_50_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_51_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_52_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_53_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_54_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_55_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_56_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_57_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_58_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_59_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_60_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_61_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_62_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_63_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_42_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_43_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_44_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_45_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_46_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_47_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_48_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_49_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_50_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_51_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_52_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_53_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_54_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_55_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_56_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_57_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_58_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_59_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_60_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_61_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_62_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_63_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer24_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer25_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer2_out_V_data_0_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_10_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_11_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_12_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_13_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_14_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_15_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_1_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_2_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_3_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_4_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_5_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_6_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_7_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_8_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_9_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_0_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_10_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_11_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_12_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_13_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_14_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_15_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_1_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_2_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_3_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_4_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_5_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_6_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_7_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_8_V_U | 1| 49| 0| -| 900| 16| 14400| |layer5_out_V_data_9_V_U | 1| 49| 0| -| 900| 16| 14400| |layer6_out_V_data_0_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_10_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_11_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_12_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_13_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_14_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_15_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_1_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_2_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_3_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_4_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_5_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_6_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_7_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_8_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_9_V_U | 1| 40| 0| -| 225| 16| 3600| |layer7_out_V_data_0_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_10_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_11_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_12_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_13_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_14_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_15_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_1_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_2_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_3_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_4_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_5_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_6_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_7_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_8_V_U | 1| 38| 0| -| 169| 16| 2704| |layer7_out_V_data_9_V_U | 1| 38| 0| -| 169| 16| 2704| +---------------------------+---------+----+----+-----+------+-----+---------+ |Total | 96|5518| 0| 0| 39470| 6240| 631520| +---------------------------+---------+----+----+-----+------+-----+---------+ * Expression: +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ | Variable Name | Operation| DSP48E| FF| LUT| Bitwidth P0| Bitwidth P1| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ |Block_proc_U0_ap_ready_count | + | 0| 0| 9| 2| 1| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | + | 0| 0| 9| 2| 1| |Block_proc_U0_ap_start | and | 0| 0| 2| 1| 1| |ap_idle | and | 0| 0| 2| 1| 1| |ap_sync_done | and | 0| 0| 2| 1| 1| |ap_sync_ready | and | 0| 0| 2| 1| 1| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_start | and | 0| 0| 2| 1| 1| |ap_sync_Block_proc_U0_ap_ready | or | 0| 0| 2| 1| 1| |ap_sync_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | or | 0| 0| 2| 1| 1| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ |Total | | 0| 0| 32| 11| 9| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ * Multiplexer: +---------------------------------------------------------------------+----+-----------+-----+-----------+ | Name | LUT| Input Size| Bits| Total Bits| +---------------------------------------------------------------------+----+-----------+-----+-----------+ |Block_proc_U0_ap_ready_count | 9| 2| 2| 4| |ap_sync_reg_Block_proc_U0_ap_ready | 9| 2| 1| 2| |ap_sync_reg_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | 9| 2| 1| 2| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | 9| 2| 2| 4| +---------------------------------------------------------------------+----+-----------+-----+-----------+ |Total | 36| 8| 6| 12| +---------------------------------------------------------------------+----+-----------+-----+-----------+ * Register: +---------------------------------------------------------------------+---+----+-----+-----------+ | Name | FF| LUT| Bits| Const Bits| +---------------------------------------------------------------------+---+----+-----+-----------+ |Block_proc_U0_ap_ready_count | 2| 0| 2| 0| |ap_sync_reg_Block_proc_U0_ap_ready | 1| 0| 1| 0| |ap_sync_reg_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | 1| 0| 1| 0| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | 2| 0| 2| 0| +---------------------------------------------------------------------+---+----+-----+-----------+ |Total | 6| 0| 6| 0| +---------------------------------------------------------------------+---+----+-----+-----------+ ================================================================ == Interface ================================================================ * Summary: +-------------------------------+-----+-----+------------+------------------------+--------------+ | RTL Ports | Dir | Bits| Protocol | Source Object | C Type | +-------------------------------+-----+-----+------------+------------------------+--------------+ |input_1_V_data_0_V_TDATA | in | 16| axis | input_1_V_data_0_V | pointer | |input_1_V_data_0_V_TVALID | in | 1| axis | input_1_V_data_0_V | pointer | |input_1_V_data_0_V_TREADY | out | 1| axis | input_1_V_data_0_V | pointer | |input_1_V_data_1_V_TDATA | in | 16| axis | input_1_V_data_1_V | pointer | |input_1_V_data_1_V_TVALID | in | 1| axis | input_1_V_data_1_V | pointer | |input_1_V_data_1_V_TREADY | out | 1| axis | input_1_V_data_1_V | pointer | |input_1_V_data_2_V_TDATA | in | 16| axis | input_1_V_data_2_V | pointer | |input_1_V_data_2_V_TVALID | in | 1| axis | input_1_V_data_2_V | pointer | |input_1_V_data_2_V_TREADY | out | 1| axis | input_1_V_data_2_V | pointer | |layer27_out_V_data_0_V_TDATA | out | 16| axis | layer27_out_V_data_0_V | pointer | |layer27_out_V_data_0_V_TVALID | out | 1| axis | layer27_out_V_data_0_V | pointer | |layer27_out_V_data_0_V_TREADY | in | 1| axis | layer27_out_V_data_0_V | pointer | |layer27_out_V_data_1_V_TDATA | out | 16| axis | layer27_out_V_data_1_V | pointer | |layer27_out_V_data_1_V_TVALID | out | 1| axis | layer27_out_V_data_1_V | pointer | |layer27_out_V_data_1_V_TREADY | in | 1| axis | layer27_out_V_data_1_V | pointer | |layer27_out_V_data_2_V_TDATA | out | 16| axis | layer27_out_V_data_2_V | pointer | |layer27_out_V_data_2_V_TVALID | out | 1| axis | layer27_out_V_data_2_V | pointer | |layer27_out_V_data_2_V_TREADY | in | 1| axis | layer27_out_V_data_2_V | pointer | |layer27_out_V_data_3_V_TDATA | out | 16| axis | layer27_out_V_data_3_V | pointer | |layer27_out_V_data_3_V_TVALID | out | 1| axis | layer27_out_V_data_3_V | pointer | |layer27_out_V_data_3_V_TREADY | in | 1| axis | layer27_out_V_data_3_V | pointer | |layer27_out_V_data_4_V_TDATA | out | 16| axis | layer27_out_V_data_4_V | pointer | |layer27_out_V_data_4_V_TVALID | out | 1| axis | layer27_out_V_data_4_V | pointer | |layer27_out_V_data_4_V_TREADY | in | 1| axis | layer27_out_V_data_4_V | pointer | |layer27_out_V_data_5_V_TDATA | out | 16| axis | layer27_out_V_data_5_V | pointer | |layer27_out_V_data_5_V_TVALID | out | 1| axis | layer27_out_V_data_5_V | pointer | |layer27_out_V_data_5_V_TREADY | in | 1| axis | layer27_out_V_data_5_V | pointer | |layer27_out_V_data_6_V_TDATA | out | 16| axis | layer27_out_V_data_6_V | pointer | |layer27_out_V_data_6_V_TVALID | out | 1| axis | layer27_out_V_data_6_V | pointer | |layer27_out_V_data_6_V_TREADY | in | 1| axis | layer27_out_V_data_6_V | pointer | |layer27_out_V_data_7_V_TDATA | out | 16| axis | layer27_out_V_data_7_V | pointer | |layer27_out_V_data_7_V_TVALID | out | 1| axis | layer27_out_V_data_7_V | pointer | |layer27_out_V_data_7_V_TREADY | in | 1| axis | layer27_out_V_data_7_V | pointer | |layer27_out_V_data_8_V_TDATA | out | 16| axis | layer27_out_V_data_8_V | pointer | |layer27_out_V_data_8_V_TVALID | out | 1| axis | layer27_out_V_data_8_V | pointer | |layer27_out_V_data_8_V_TREADY | in | 1| axis | layer27_out_V_data_8_V | pointer | |layer27_out_V_data_9_V_TDATA | out | 16| axis | layer27_out_V_data_9_V | pointer | |layer27_out_V_data_9_V_TVALID | out | 1| axis | layer27_out_V_data_9_V | pointer | |layer27_out_V_data_9_V_TREADY | in | 1| axis | layer27_out_V_data_9_V | pointer | |const_size_in_1 | out | 16| ap_vld | const_size_in_1 | pointer | |const_size_in_1_ap_vld | out | 1| ap_vld | const_size_in_1 | pointer | |const_size_out_1 | out | 16| ap_vld | const_size_out_1 | pointer | |const_size_out_1_ap_vld | out | 1| ap_vld | const_size_out_1 | pointer | |ap_clk | in | 1| ap_ctrl_hs | myproject | return value | |ap_rst_n | in | 1| ap_ctrl_hs | myproject | return value | |ap_start | in | 1| ap_ctrl_hs | myproject | return value | |ap_done | out | 1| ap_ctrl_hs | myproject | return value | |ap_ready | out | 1| ap_ctrl_hs | myproject | return value | |ap_idle | out | 1| ap_ctrl_hs | myproject | return value | +-------------------------------+-----+-----+------------+------------------------+--------------+ ================================================ FILE: pruned_cnn/vivado_synth.rpt ================================================ Copyright 1986-2020 Xilinx, Inc. All Rights Reserved. -------------------------------------------------------------------------------------- | Tool Version : Vivado v.2020.1 (lin64) Build 2902540 Wed May 27 19:54:35 MDT 2020 | Date : Mon Jun 28 13:59:34 2021 | Host : geonosis.cern.ch running 64-bit CentOS Linux release 7.9.2009 (Core) | Command : report_utilization -file vivado_synth.rpt | Design : myproject | Device : xcu250figd2104-2L | Design State : Synthesized -------------------------------------------------------------------------------------- Utilization Design Information Table of Contents ----------------- 1. CLB Logic 1.1 Summary of Registers by Type 2. BLOCKRAM 3. ARITHMETIC 4. I/O 5. CLOCK 6. ADVANCED 7. CONFIGURATION 8. Primitives 9. Black Boxes 10. Instantiated Netlists 11. SLR Connectivity 12. SLR Connectivity Matrix 13. SLR CLB Logic and Dedicated Block Utilization 14. SLR IO Utilization 1. CLB Logic ------------ +----------------------------+--------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------------------+--------+-------+-----------+-------+ | CLB LUTs* | 123948 | 0 | 1728000 | 7.17 | | LUT as Logic | 120268 | 0 | 1728000 | 6.96 | | LUT as Memory | 3680 | 0 | 791040 | 0.47 | | LUT as Distributed RAM | 0 | 0 | | | | LUT as Shift Register | 3680 | 0 | | | | CLB Registers | 43435 | 0 | 3456000 | 1.26 | | Register as Flip Flop | 43435 | 0 | 3456000 | 1.26 | | Register as Latch | 0 | 0 | 3456000 | 0.00 | | CARRY8 | 13270 | 0 | 216000 | 6.14 | | F7 Muxes | 256 | 0 | 864000 | 0.03 | | F8 Muxes | 0 | 0 | 432000 | 0.00 | | F9 Muxes | 0 | 0 | 216000 | 0.00 | +----------------------------+--------+-------+-----------+-------+ * Warning! The Final LUT count, after physical optimizations and full implementation, is typically lower. Run opt_design after synthesis, if not already completed, for a more realistic count. 1.1 Summary of Registers by Type -------------------------------- +-------+--------------+-------------+--------------+ | Total | Clock Enable | Synchronous | Asynchronous | +-------+--------------+-------------+--------------+ | 0 | _ | - | - | | 0 | _ | - | Set | | 0 | _ | - | Reset | | 0 | _ | Set | - | | 0 | _ | Reset | - | | 0 | Yes | - | - | | 0 | Yes | - | Set | | 0 | Yes | - | Reset | | 1069 | Yes | Set | - | | 42366 | Yes | Reset | - | +-------+--------------+-------------+--------------+ 2. BLOCKRAM ----------- +-------------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-------------------+------+-------+-----------+-------+ | Block RAM Tile | 42 | 0 | 2688 | 1.56 | | RAMB36/FIFO* | 0 | 0 | 2688 | 0.00 | | RAMB18 | 84 | 0 | 5376 | 1.56 | | RAMB18E2 only | 84 | | | | | URAM | 0 | 0 | 1280 | 0.00 | +-------------------+------+-------+-----------+-------+ * Note: Each Block RAM Tile only has one FIFO logic available and therefore can accommodate only one FIFO36E2 or one FIFO18E2. However, if a FIFO18E2 occupies a Block RAM Tile, that tile can still accommodate a RAMB18E2 3. ARITHMETIC ------------- +----------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------+------+-------+-----------+-------+ | DSPs | 5386 | 0 | 12288 | 43.83 | | DSP48E2 only | 5386 | | | | +----------------+------+-------+-----------+-------+ 4. I/O ------ +------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +------------+------+-------+-----------+-------+ | Bonded IOB | 274 | 0 | 676 | 40.53 | +------------+------+-------+-----------+-------+ 5. CLOCK -------- +----------------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------------+------+-------+-----------+-------+ | GLOBAL CLOCK BUFFERs | 1 | 0 | 1344 | 0.07 | | BUFGCE | 1 | 0 | 384 | 0.26 | | BUFGCE_DIV | 0 | 0 | 64 | 0.00 | | BUFG_GT | 0 | 0 | 768 | 0.00 | | BUFGCTRL* | 0 | 0 | 128 | 0.00 | | PLL | 0 | 0 | 32 | 0.00 | | MMCM | 0 | 0 | 16 | 0.00 | +----------------------+------+-------+-----------+-------+ * Note: Each used BUFGCTRL counts as two GLOBAL CLOCK BUFFERs. This table does not include global clocking resources, only buffer cell usage. See the Clock Utilization Report (report_clock_utilization) for detailed accounting of global clocking resource availability. 6. ADVANCED ----------- +-----------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-----------------+------+-------+-----------+-------+ | CMACE4 | 0 | 0 | 12 | 0.00 | | GTYE4_CHANNEL | 0 | 0 | 24 | 0.00 | | GTYE4_COMMON | 0 | 0 | 6 | 0.00 | | ILKNE4 | 0 | 0 | 8 | 0.00 | | OBUFDS_GTE4 | 0 | 0 | 12 | 0.00 | | OBUFDS_GTE4_ADV | 0 | 0 | 12 | 0.00 | | PCIE40E4 | 0 | 0 | 4 | 0.00 | | SYSMONE4 | 0 | 0 | 4 | 0.00 | +-----------------+------+-------+-----------+-------+ 7. CONFIGURATION ---------------- +-------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-------------+------+-------+-----------+-------+ | BSCANE2 | 0 | 0 | 16 | 0.00 | | DNA_PORTE2 | 0 | 0 | 4 | 0.00 | | EFUSE_USR | 0 | 0 | 4 | 0.00 | | FRAME_ECCE4 | 0 | 0 | 4 | 0.00 | | ICAPE3 | 0 | 0 | 8 | 0.00 | | MASTER_JTAG | 0 | 0 | 4 | 0.00 | | STARTUPE3 | 0 | 0 | 4 | 0.00 | +-------------+------+-------+-----------+-------+ 8. Primitives ------------- +----------+-------+---------------------+ | Ref Name | Used | Functional Category | +----------+-------+---------------------+ | LUT2 | 52029 | CLB | | FDRE | 42366 | Register | | LUT3 | 41635 | CLB | | LUT4 | 40010 | CLB | | CARRY8 | 13270 | CLB | | LUT6 | 12631 | CLB | | LUT5 | 10697 | CLB | | DSP48E2 | 5386 | Arithmetic | | LUT1 | 4899 | CLB | | SRL16E | 2816 | CLB | | FDSE | 1069 | Register | | SRLC32E | 864 | CLB | | MUXF7 | 256 | CLB | | OBUF | 210 | I/O | | RAMB18E2 | 84 | Block Ram | | INBUF | 64 | I/O | | IBUFCTRL | 64 | Others | | BUFGCE | 1 | Clock | +----------+-------+---------------------+ 9. Black Boxes -------------- +----------+------+ | Ref Name | Used | +----------+------+ 10. Instantiated Netlists ------------------------- +----------+------+ | Ref Name | Used | +----------+------+ 11. SLR Connectivity -------------------- +----------------------------------+------+-------+-----------+-------+ | | Used | Fixed | Available | Util% | +----------------------------------+------+-------+-----------+-------+ | SLR3 <-> SLR2 | 0 | | 23040 | 0.00 | | SLR2 -> SLR3 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR3 -> SLR2 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR2 <-> SLR1 | 0 | | 23040 | 0.00 | | SLR1 -> SLR2 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR2 -> SLR1 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR1 <-> SLR0 | 0 | | 23040 | 0.00 | | SLR0 -> SLR1 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR1 -> SLR0 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | +----------------------------------+------+-------+-----------+-------+ | Total SLLs Used | 0 | | | | +----------------------------------+------+-------+-----------+-------+ 12. SLR Connectivity Matrix --------------------------- +-----------+------+------+------+------+ | FROM \ TO | SLR3 | SLR2 | SLR1 | SLR0 | +-----------+------+------+------+------+ | SLR3 | 0 | 0 | 0 | 0 | | SLR2 | 0 | 0 | 0 | 0 | | SLR1 | 0 | 0 | 0 | 0 | | SLR0 | 0 | 0 | 0 | 0 | +-----------+------+------+------+------+ 13. SLR CLB Logic and Dedicated Block Utilization ------------------------------------------------- +----------------------------+------+------+------+------+--------+--------+--------+--------+ | Site Type | SLR0 | SLR1 | SLR2 | SLR3 | SLR0 % | SLR1 % | SLR2 % | SLR3 % | +----------------------------+------+------+------+------+--------+--------+--------+--------+ | CLB | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLBL | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLBM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLB LUTs | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Logic | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Memory | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Distributed RAM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Shift Register | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLB Registers | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CARRY8 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F7 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F8 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F9 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | Block RAM Tile | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | RAMB36/FIFO | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | RAMB18 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | URAM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | DSPs | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | PLL | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | MMCM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | Unique Control Sets | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | +----------------------------+------+------+------+------+--------+--------+--------+--------+ * Note: Available Control Sets based on CLB Registers / 8 14. SLR IO Utilization ---------------------- +-----------+-----------+---------+------------+----------+------------+----------+-----+ | SLR Index | Used IOBs | (%)IOBs | Used IPADs | (%)IPADs | Used OPADs | (%)OPADs | GTs | +-----------+-----------+---------+------------+----------+------------+----------+-----+ | SLR3 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR2 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR1 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR0 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | +-----------+-----------+---------+------------+----------+------------+----------+-----+ | Total | 0 | | 0 | | 0 | | 0 | +-----------+-----------+---------+------------+----------+------------+----------+-----+ ================================================ FILE: quantized_pruned_cnn/myproject_prj/solution1/syn/report/myproject_csynth.rpt ================================================ ================================================================ == Vivado HLS Report for 'myproject' ================================================================ * Date: Mon Jun 28 12:56:37 2021 * Version: 2020.1 (Build 2897737 on Wed May 27 20:21:37 MDT 2020) * Project: myproject_prj * Solution: solution1 * Product family: virtexuplus * Target device: xcu250-figd2104-2L-e ================================================================ == Performance Estimates ================================================================ + Timing: * Summary: +--------+---------+----------+------------+ | Clock | Target | Estimated| Uncertainty| +--------+---------+----------+------------+ |ap_clk | 5.00 ns | 4.373 ns | 0.62 ns | +--------+---------+----------+------------+ + Latency: * Summary: +---------+---------+----------+----------+------+------+----------+ | Latency (cycles) | Latency (absolute) | Interval | Pipeline | | min | max | min | max | min | max | Type | +---------+---------+----------+----------+------+------+----------+ | 1044| 1044| 5.220 us | 5.220 us | 1028| 1028| dataflow | +---------+---------+----------+----------+------+------+----------+ + Detail: * Instance: +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ | | | Latency (cycles) | Latency (absolute) | Interval | Pipeline | | Instance | Module | min | max | min | max | min | max | Type | +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ |dense_array_array_ap_fixed_16_6_5_3_0_42u_config14_U0 |dense_array_array_ap_fixed_16_6_5_3_0_42u_config14_s | 7| 7| 35.000 ns | 35.000 ns | 7| 7| none | |conv_2d_cl_array_array_ap_fixed_24u_config10_U0 |conv_2d_cl_array_array_ap_fixed_24u_config10_s | 40| 40| 0.200 us | 0.200 us | 40| 40| none | |conv_2d_cl_array_array_ap_fixed_16u_config6_U0 |conv_2d_cl_array_array_ap_fixed_16u_config6_s | 229| 229| 1.145 us | 1.145 us | 229| 229| none | |dense_array_array_ap_fixed_16_6_5_3_0_10u_config22_U0 |dense_array_array_ap_fixed_16_6_5_3_0_10u_config22_s | 2| 2| 10.000 ns | 10.000 ns | 2| 2| none | |dense_array_array_ap_fixed_16_6_5_3_0_64u_config18_U0 |dense_array_array_ap_fixed_16_6_5_3_0_64u_config18_s | 3| 3| 15.000 ns | 15.000 ns | 3| 3| none | |conv_2d_cl_array_array_ap_fixed_16u_config2_U0 |conv_2d_cl_array_array_ap_fixed_16u_config2_s | 1027| 1027| 5.135 us | 5.135 us | 1027| 1027| none | |relu_array_array_ap_fixed_64u_relu_config21_U0 |relu_array_array_ap_fixed_64u_relu_config21_s | 0| 0| 0 ns | 0 ns | 1| 1| function | |relu_array_array_ap_fixed_42u_relu_config17_U0 |relu_array_array_ap_fixed_42u_relu_config17_s | 0| 0| 0 ns | 0 ns | 1| 1| function | |softmax_array_array_ap_fixed_10u_softmax_config24_U0 |softmax_array_array_ap_fixed_10u_softmax_config24_s | 7| 7| 35.000 ns | 35.000 ns | 7| 7| none | |normalize_array_array_ap_fixed_64u_config20_U0 |normalize_array_array_ap_fixed_64u_config20_s | 1| 1| 5.000 ns | 5.000 ns | 1| 1| function | |relu_array_array_ap_fixed_24u_relu_config12_U0 |relu_array_array_ap_fixed_24u_relu_config12_s | 18| 18| 90.000 ns | 90.000 ns | 18| 18| none | |normalize_array_array_ap_fixed_42u_config16_U0 |normalize_array_array_ap_fixed_42u_config16_s | 1| 1| 5.000 ns | 5.000 ns | 1| 1| function | |pooling2d_cl_array_array_ap_fixed_24u_config13_U0 |pooling2d_cl_array_array_ap_fixed_24u_config13_s | 19| 19| 95.000 ns | 95.000 ns | 19| 19| none | |relu_array_array_ap_fixed_16u_relu_config4_U0 |relu_array_array_ap_fixed_16u_relu_config4_s | 902| 902| 4.510 us | 4.510 us | 902| 902| none | |relu_array_array_ap_fixed_16u_relu_config8_U0 |relu_array_array_ap_fixed_16u_relu_config8_s | 171| 171| 0.855 us | 0.855 us | 171| 171| none | |pooling2d_cl_array_array_ap_fixed_16u_config5_U0 |pooling2d_cl_array_array_ap_fixed_16u_config5_s | 903| 903| 4.515 us | 4.515 us | 903| 903| none | |pooling2d_cl_array_array_ap_fixed_16u_config9_U0 |pooling2d_cl_array_array_ap_fixed_16u_config9_s | 172| 172| 0.860 us | 0.860 us | 172| 172| none | |Block_proc_U0 |Block_proc | 0| 0| 0 ns | 0 ns | 0| 0| none | +-------------------------------------------------------+------------------------------------------------------+---------+---------+-----------+-----------+------+------+----------+ * Loop: N/A ================================================================ == Utilization Estimates ================================================================ * Summary: +---------------------+---------+-------+---------+---------+------+ | Name | BRAM_18K| DSP48E| FF | LUT | URAM | +---------------------+---------+-------+---------+---------+------+ |DSP | -| -| -| -| -| |Expression | -| -| 0| 32| -| |FIFO | 96| -| 5728| 14972| -| |Instance | 4| 340| 38113| 248240| -| |Memory | -| -| -| -| -| |Multiplexer | -| -| -| 36| -| |Register | -| -| 6| -| -| +---------------------+---------+-------+---------+---------+------+ |Total | 100| 340| 43847| 263280| 0| +---------------------+---------+-------+---------+---------+------+ |Available SLR | 1344| 3072| 864000| 432000| 320| +---------------------+---------+-------+---------+---------+------+ |Utilization SLR (%) | 7| 11| 5| 60| 0| +---------------------+---------+-------+---------+---------+------+ |Available | 5376| 12288| 3456000| 1728000| 1280| +---------------------+---------+-------+---------+---------+------+ |Utilization (%) | 1| 2| 1| 15| 0| +---------------------+---------+-------+---------+---------+------+ + Detail: * Instance: +-------------------------------------------------------+------------------------------------------------------+---------+-------+------+-------+-----+ | Instance | Module | BRAM_18K| DSP48E| FF | LUT | URAM| +-------------------------------------------------------+------------------------------------------------------+---------+-------+------+-------+-----+ |Block_proc_U0 |Block_proc | 0| 0| 2| 11| 0| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0 |conv_2d_cl_array_array_ap_fixed_16u_config2_s | 0| 27| 2826| 7354| 0| |conv_2d_cl_array_array_ap_fixed_16u_config6_U0 |conv_2d_cl_array_array_ap_fixed_16u_config6_s | 0| 105| 9439| 42854| 0| |conv_2d_cl_array_array_ap_fixed_24u_config10_U0 |conv_2d_cl_array_array_ap_fixed_24u_config10_s | 0| 5| 9548| 48911| 0| |dense_array_array_ap_fixed_16_6_5_3_0_10u_config22_U0 |dense_array_array_ap_fixed_16_6_5_3_0_10u_config22_s | 0| 83| 1531| 39567| 0| |dense_array_array_ap_fixed_16_6_5_3_0_42u_config14_U0 |dense_array_array_ap_fixed_16_6_5_3_0_42u_config14_s | 0| 6| 4281| 52236| 0| |dense_array_array_ap_fixed_16_6_5_3_0_64u_config18_U0 |dense_array_array_ap_fixed_16_6_5_3_0_64u_config18_s | 0| 0| 2801| 24971| 0| |normalize_array_array_ap_fixed_42u_config16_U0 |normalize_array_array_ap_fixed_42u_config16_s | 0| 40| 676| 2016| 0| |normalize_array_array_ap_fixed_64u_config20_U0 |normalize_array_array_ap_fixed_64u_config20_s | 0| 64| 1028| 2972| 0| |pooling2d_cl_array_array_ap_fixed_16u_config5_U0 |pooling2d_cl_array_array_ap_fixed_16u_config5_s | 0| 0| 1497| 2135| 0| |pooling2d_cl_array_array_ap_fixed_16u_config9_U0 |pooling2d_cl_array_array_ap_fixed_16u_config9_s | 0| 0| 1495| 2131| 0| |pooling2d_cl_array_array_ap_fixed_24u_config13_U0 |pooling2d_cl_array_array_ap_fixed_24u_config13_s | 0| 0| 2148| 2939| 0| |relu_array_array_ap_fixed_16u_relu_config4_U0 |relu_array_array_ap_fixed_16u_relu_config4_s | 0| 0| 18| 1755| 0| |relu_array_array_ap_fixed_16u_relu_config8_U0 |relu_array_array_ap_fixed_16u_relu_config8_s | 0| 0| 16| 1751| 0| |relu_array_array_ap_fixed_24u_relu_config12_U0 |relu_array_array_ap_fixed_24u_relu_config12_s | 0| 0| 13| 2575| 0| |relu_array_array_ap_fixed_42u_relu_config17_U0 |relu_array_array_ap_fixed_42u_relu_config17_s | 0| 0| 3| 4350| 0| |relu_array_array_ap_fixed_64u_relu_config21_U0 |relu_array_array_ap_fixed_64u_relu_config21_s | 0| 0| 3| 6616| 0| |softmax_array_array_ap_fixed_10u_softmax_config24_U0 |softmax_array_array_ap_fixed_10u_softmax_config24_s | 4| 10| 788| 3096| 0| +-------------------------------------------------------+------------------------------------------------------+---------+-------+------+-------+-----+ |Total | | 4| 340| 38113| 248240| 0| +-------------------------------------------------------+------------------------------------------------------+---------+-------+------+-------+-----+ * DSP48E: N/A * Memory: N/A * FIFO: +---------------------------+---------+----+----+-----+------+-----+---------+ | Name | BRAM_18K| FF | LUT| URAM| Depth| Bits| Size:D*B| +---------------------------+---------+----+----+-----+------+-----+---------+ |layer10_out_V_data_0_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_10_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_11_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_12_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_13_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_14_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_15_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_16_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_17_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_18_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_19_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_1_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_20_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_21_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_22_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_23_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_2_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_3_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_4_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_5_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_6_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_7_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_8_V_U | 0| 7| 0| -| 16| 16| 256| |layer10_out_V_data_9_V_U | 0| 7| 0| -| 16| 16| 256| |layer12_out_V_data_0_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_10_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_11_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_12_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_13_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_14_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_15_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_16_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_17_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_18_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_19_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_1_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_20_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_21_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_22_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_23_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_2_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_3_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_4_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_5_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_6_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_7_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_8_V_U | 0| 7| 0| -| 16| 6| 96| |layer12_out_V_data_9_V_U | 0| 7| 0| -| 16| 6| 96| |layer13_out_V_data_0_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_10_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_11_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_12_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_13_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_14_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_15_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_16_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_17_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_18_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_19_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_1_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_20_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_21_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_22_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_23_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_2_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_3_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_4_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_5_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_6_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_7_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_8_V_U | 0| 5| 0| -| 4| 16| 64| |layer13_out_V_data_9_V_U | 0| 5| 0| -| 4| 16| 64| |layer14_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer14_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer16_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer17_out_V_data_0_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_10_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_11_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_12_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_13_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_14_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_15_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_16_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_17_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_18_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_19_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_1_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_20_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_21_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_22_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_23_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_24_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_25_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_26_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_27_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_28_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_29_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_2_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_30_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_31_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_32_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_33_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_34_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_35_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_36_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_37_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_38_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_39_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_3_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_40_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_41_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_4_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_5_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_6_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_7_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_8_V_U | 0| 5| 0| -| 1| 6| 6| |layer17_out_V_data_9_V_U | 0| 5| 0| -| 1| 6| 6| |layer18_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_42_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_43_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_44_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_45_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_46_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_47_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_48_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_49_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_50_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_51_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_52_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_53_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_54_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_55_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_56_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_57_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_58_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_59_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_60_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_61_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_62_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_63_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer18_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_10_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_11_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_12_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_13_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_14_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_15_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_16_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_17_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_18_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_19_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_20_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_21_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_22_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_23_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_24_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_25_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_26_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_27_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_28_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_29_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_30_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_31_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_32_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_33_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_34_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_35_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_36_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_37_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_38_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_39_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_40_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_41_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_42_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_43_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_44_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_45_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_46_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_47_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_48_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_49_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_50_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_51_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_52_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_53_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_54_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_55_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_56_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_57_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_58_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_59_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_60_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_61_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_62_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_63_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer20_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer21_out_V_data_0_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_10_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_11_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_12_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_13_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_14_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_15_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_16_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_17_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_18_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_19_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_1_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_20_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_21_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_22_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_23_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_24_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_25_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_26_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_27_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_28_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_29_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_2_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_30_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_31_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_32_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_33_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_34_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_35_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_36_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_37_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_38_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_39_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_3_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_40_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_41_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_42_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_43_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_44_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_45_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_46_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_47_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_48_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_49_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_4_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_50_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_51_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_52_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_53_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_54_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_55_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_56_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_57_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_58_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_59_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_5_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_60_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_61_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_62_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_63_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_6_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_7_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_8_V_U | 0| 5| 0| -| 1| 6| 6| |layer21_out_V_data_9_V_U | 0| 5| 0| -| 1| 6| 6| |layer22_out_V_data_0_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_1_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_2_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_3_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_4_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_5_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_6_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_7_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_8_V_U | 0| 5| 0| -| 1| 16| 16| |layer22_out_V_data_9_V_U | 0| 5| 0| -| 1| 16| 16| |layer2_out_V_data_0_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_10_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_11_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_12_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_13_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_14_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_15_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_1_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_2_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_3_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_4_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_5_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_6_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_7_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_8_V_U | 1| 49| 0| -| 900| 16| 14400| |layer2_out_V_data_9_V_U | 1| 49| 0| -| 900| 16| 14400| |layer4_out_V_data_0_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_10_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_11_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_12_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_13_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_14_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_15_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_1_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_2_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_3_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_4_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_5_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_6_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_7_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_8_V_U | 1| 39| 0| -| 900| 6| 5400| |layer4_out_V_data_9_V_U | 1| 39| 0| -| 900| 6| 5400| |layer5_out_V_data_0_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_10_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_11_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_12_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_13_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_14_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_15_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_1_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_2_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_3_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_4_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_5_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_6_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_7_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_8_V_U | 1| 40| 0| -| 225| 16| 3600| |layer5_out_V_data_9_V_U | 1| 40| 0| -| 225| 16| 3600| |layer6_out_V_data_0_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_10_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_11_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_12_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_13_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_14_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_15_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_1_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_2_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_3_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_4_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_5_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_6_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_7_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_8_V_U | 1| 38| 0| -| 169| 16| 2704| |layer6_out_V_data_9_V_U | 1| 38| 0| -| 169| 16| 2704| |layer8_out_V_data_0_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_10_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_11_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_12_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_13_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_14_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_15_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_1_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_2_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_3_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_4_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_5_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_6_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_7_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_8_V_U | 1| 28| 0| -| 169| 6| 1014| |layer8_out_V_data_9_V_U | 1| 28| 0| -| 169| 6| 1014| |layer9_out_V_data_0_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_10_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_11_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_12_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_13_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_14_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_15_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_1_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_2_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_3_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_4_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_5_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_6_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_7_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_8_V_U | 1| 33| 0| -| 36| 16| 576| |layer9_out_V_data_9_V_U | 1| 33| 0| -| 36| 16| 576| +---------------------------+---------+----+----+-----+------+-----+---------+ |Total | 96|5728| 0| 0| 39576| 6316| 457276| +---------------------------+---------+----+----+-----+------+-----+---------+ * Expression: +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ | Variable Name | Operation| DSP48E| FF| LUT| Bitwidth P0| Bitwidth P1| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ |Block_proc_U0_ap_ready_count | + | 0| 0| 9| 2| 1| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | + | 0| 0| 9| 2| 1| |Block_proc_U0_ap_start | and | 0| 0| 2| 1| 1| |ap_idle | and | 0| 0| 2| 1| 1| |ap_sync_done | and | 0| 0| 2| 1| 1| |ap_sync_ready | and | 0| 0| 2| 1| 1| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_start | and | 0| 0| 2| 1| 1| |ap_sync_Block_proc_U0_ap_ready | or | 0| 0| 2| 1| 1| |ap_sync_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | or | 0| 0| 2| 1| 1| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ |Total | | 0| 0| 32| 11| 9| +-----------------------------------------------------------------+----------+-------+---+----+------------+------------+ * Multiplexer: +---------------------------------------------------------------------+----+-----------+-----+-----------+ | Name | LUT| Input Size| Bits| Total Bits| +---------------------------------------------------------------------+----+-----------+-----+-----------+ |Block_proc_U0_ap_ready_count | 9| 2| 2| 4| |ap_sync_reg_Block_proc_U0_ap_ready | 9| 2| 1| 2| |ap_sync_reg_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | 9| 2| 1| 2| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | 9| 2| 2| 4| +---------------------------------------------------------------------+----+-----------+-----+-----------+ |Total | 36| 8| 6| 12| +---------------------------------------------------------------------+----+-----------+-----+-----------+ * Register: +---------------------------------------------------------------------+---+----+-----+-----------+ | Name | FF| LUT| Bits| Const Bits| +---------------------------------------------------------------------+---+----+-----+-----------+ |Block_proc_U0_ap_ready_count | 2| 0| 2| 0| |ap_sync_reg_Block_proc_U0_ap_ready | 1| 0| 1| 0| |ap_sync_reg_conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready | 1| 0| 1| 0| |conv_2d_cl_array_array_ap_fixed_16u_config2_U0_ap_ready_count | 2| 0| 2| 0| +---------------------------------------------------------------------+---+----+-----+-----------+ |Total | 6| 0| 6| 0| +---------------------------------------------------------------------+---+----+-----+-----------+ ================================================================ == Interface ================================================================ * Summary: +-------------------------------+-----+-----+------------+------------------------+--------------+ | RTL Ports | Dir | Bits| Protocol | Source Object | C Type | +-------------------------------+-----+-----+------------+------------------------+--------------+ |input_2_V_data_0_V_TDATA | in | 16| axis | input_2_V_data_0_V | pointer | |input_2_V_data_0_V_TVALID | in | 1| axis | input_2_V_data_0_V | pointer | |input_2_V_data_0_V_TREADY | out | 1| axis | input_2_V_data_0_V | pointer | |input_2_V_data_1_V_TDATA | in | 16| axis | input_2_V_data_1_V | pointer | |input_2_V_data_1_V_TVALID | in | 1| axis | input_2_V_data_1_V | pointer | |input_2_V_data_1_V_TREADY | out | 1| axis | input_2_V_data_1_V | pointer | |input_2_V_data_2_V_TDATA | in | 16| axis | input_2_V_data_2_V | pointer | |input_2_V_data_2_V_TVALID | in | 1| axis | input_2_V_data_2_V | pointer | |input_2_V_data_2_V_TREADY | out | 1| axis | input_2_V_data_2_V | pointer | |layer24_out_V_data_0_V_TDATA | out | 16| axis | layer24_out_V_data_0_V | pointer | |layer24_out_V_data_0_V_TVALID | out | 1| axis | layer24_out_V_data_0_V | pointer | |layer24_out_V_data_0_V_TREADY | in | 1| axis | layer24_out_V_data_0_V | pointer | |layer24_out_V_data_1_V_TDATA | out | 16| axis | layer24_out_V_data_1_V | pointer | |layer24_out_V_data_1_V_TVALID | out | 1| axis | layer24_out_V_data_1_V | pointer | |layer24_out_V_data_1_V_TREADY | in | 1| axis | layer24_out_V_data_1_V | pointer | |layer24_out_V_data_2_V_TDATA | out | 16| axis | layer24_out_V_data_2_V | pointer | |layer24_out_V_data_2_V_TVALID | out | 1| axis | layer24_out_V_data_2_V | pointer | |layer24_out_V_data_2_V_TREADY | in | 1| axis | layer24_out_V_data_2_V | pointer | |layer24_out_V_data_3_V_TDATA | out | 16| axis | layer24_out_V_data_3_V | pointer | |layer24_out_V_data_3_V_TVALID | out | 1| axis | layer24_out_V_data_3_V | pointer | |layer24_out_V_data_3_V_TREADY | in | 1| axis | layer24_out_V_data_3_V | pointer | |layer24_out_V_data_4_V_TDATA | out | 16| axis | layer24_out_V_data_4_V | pointer | |layer24_out_V_data_4_V_TVALID | out | 1| axis | layer24_out_V_data_4_V | pointer | |layer24_out_V_data_4_V_TREADY | in | 1| axis | layer24_out_V_data_4_V | pointer | |layer24_out_V_data_5_V_TDATA | out | 16| axis | layer24_out_V_data_5_V | pointer | |layer24_out_V_data_5_V_TVALID | out | 1| axis | layer24_out_V_data_5_V | pointer | |layer24_out_V_data_5_V_TREADY | in | 1| axis | layer24_out_V_data_5_V | pointer | |layer24_out_V_data_6_V_TDATA | out | 16| axis | layer24_out_V_data_6_V | pointer | |layer24_out_V_data_6_V_TVALID | out | 1| axis | layer24_out_V_data_6_V | pointer | |layer24_out_V_data_6_V_TREADY | in | 1| axis | layer24_out_V_data_6_V | pointer | |layer24_out_V_data_7_V_TDATA | out | 16| axis | layer24_out_V_data_7_V | pointer | |layer24_out_V_data_7_V_TVALID | out | 1| axis | layer24_out_V_data_7_V | pointer | |layer24_out_V_data_7_V_TREADY | in | 1| axis | layer24_out_V_data_7_V | pointer | |layer24_out_V_data_8_V_TDATA | out | 16| axis | layer24_out_V_data_8_V | pointer | |layer24_out_V_data_8_V_TVALID | out | 1| axis | layer24_out_V_data_8_V | pointer | |layer24_out_V_data_8_V_TREADY | in | 1| axis | layer24_out_V_data_8_V | pointer | |layer24_out_V_data_9_V_TDATA | out | 16| axis | layer24_out_V_data_9_V | pointer | |layer24_out_V_data_9_V_TVALID | out | 1| axis | layer24_out_V_data_9_V | pointer | |layer24_out_V_data_9_V_TREADY | in | 1| axis | layer24_out_V_data_9_V | pointer | |const_size_in_1 | out | 16| ap_vld | const_size_in_1 | pointer | |const_size_in_1_ap_vld | out | 1| ap_vld | const_size_in_1 | pointer | |const_size_out_1 | out | 16| ap_vld | const_size_out_1 | pointer | |const_size_out_1_ap_vld | out | 1| ap_vld | const_size_out_1 | pointer | |ap_clk | in | 1| ap_ctrl_hs | myproject | return value | |ap_rst_n | in | 1| ap_ctrl_hs | myproject | return value | |ap_start | in | 1| ap_ctrl_hs | myproject | return value | |ap_done | out | 1| ap_ctrl_hs | myproject | return value | |ap_ready | out | 1| ap_ctrl_hs | myproject | return value | |ap_idle | out | 1| ap_ctrl_hs | myproject | return value | +-------------------------------+-----+-----+------------+------------------------+--------------+ ================================================ FILE: quantized_pruned_cnn/vivado_synth.rpt ================================================ Copyright 1986-2020 Xilinx, Inc. All Rights Reserved. -------------------------------------------------------------------------------------- | Tool Version : Vivado v.2020.1 (lin64) Build 2902540 Wed May 27 19:54:35 MDT 2020 | Date : Mon Jun 28 13:06:58 2021 | Host : geonosis.cern.ch running 64-bit CentOS Linux release 7.9.2009 (Core) | Command : report_utilization -file vivado_synth.rpt | Design : myproject | Device : xcu250figd2104-2L | Design State : Synthesized -------------------------------------------------------------------------------------- Utilization Design Information Table of Contents ----------------- 1. CLB Logic 1.1 Summary of Registers by Type 2. BLOCKRAM 3. ARITHMETIC 4. I/O 5. CLOCK 6. ADVANCED 7. CONFIGURATION 8. Primitives 9. Black Boxes 10. Instantiated Netlists 11. SLR Connectivity 12. SLR Connectivity Matrix 13. SLR CLB Logic and Dedicated Block Utilization 14. SLR IO Utilization 1. CLB Logic ------------ +----------------------------+--------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------------------+--------+-------+-----------+-------+ | CLB LUTs* | 118931 | 0 | 1728000 | 6.88 | | LUT as Logic | 115875 | 0 | 1728000 | 6.71 | | LUT as Memory | 3056 | 0 | 791040 | 0.39 | | LUT as Distributed RAM | 0 | 0 | | | | LUT as Shift Register | 3056 | 0 | | | | CLB Registers | 30702 | 0 | 3456000 | 0.89 | | Register as Flip Flop | 30702 | 0 | 3456000 | 0.89 | | Register as Latch | 0 | 0 | 3456000 | 0.00 | | CARRY8 | 14273 | 0 | 216000 | 6.61 | | F7 Muxes | 578 | 0 | 864000 | 0.07 | | F8 Muxes | 80 | 0 | 432000 | 0.02 | | F9 Muxes | 0 | 0 | 216000 | 0.00 | +----------------------------+--------+-------+-----------+-------+ * Warning! The Final LUT count, after physical optimizations and full implementation, is typically lower. Run opt_design after synthesis, if not already completed, for a more realistic count. 1.1 Summary of Registers by Type -------------------------------- +-------+--------------+-------------+--------------+ | Total | Clock Enable | Synchronous | Asynchronous | +-------+--------------+-------------+--------------+ | 0 | _ | - | - | | 0 | _ | - | Set | | 0 | _ | - | Reset | | 0 | _ | Set | - | | 0 | _ | Reset | - | | 0 | Yes | - | - | | 0 | Yes | - | Set | | 0 | Yes | - | Reset | | 1413 | Yes | Set | - | | 29289 | Yes | Reset | - | +-------+--------------+-------------+--------------+ 2. BLOCKRAM ----------- +-------------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-------------------+------+-------+-----------+-------+ | Block RAM Tile | 34 | 0 | 2688 | 1.26 | | RAMB36/FIFO* | 0 | 0 | 2688 | 0.00 | | RAMB18 | 68 | 0 | 5376 | 1.26 | | RAMB18E2 only | 68 | | | | | URAM | 0 | 0 | 1280 | 0.00 | +-------------------+------+-------+-----------+-------+ * Note: Each Block RAM Tile only has one FIFO logic available and therefore can accommodate only one FIFO36E2 or one FIFO18E2. However, if a FIFO18E2 occupies a Block RAM Tile, that tile can still accommodate a RAMB18E2 3. ARITHMETIC ------------- +----------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------+------+-------+-----------+-------+ | DSPs | 353 | 0 | 12288 | 2.87 | | DSP48E2 only | 353 | | | | +----------------+------+-------+-----------+-------+ 4. I/O ------ +------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +------------+------+-------+-----------+-------+ | Bonded IOB | 274 | 0 | 676 | 40.53 | +------------+------+-------+-----------+-------+ 5. CLOCK -------- +----------------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +----------------------+------+-------+-----------+-------+ | GLOBAL CLOCK BUFFERs | 1 | 0 | 1344 | 0.07 | | BUFGCE | 1 | 0 | 384 | 0.26 | | BUFGCE_DIV | 0 | 0 | 64 | 0.00 | | BUFG_GT | 0 | 0 | 768 | 0.00 | | BUFGCTRL* | 0 | 0 | 128 | 0.00 | | PLL | 0 | 0 | 32 | 0.00 | | MMCM | 0 | 0 | 16 | 0.00 | +----------------------+------+-------+-----------+-------+ * Note: Each used BUFGCTRL counts as two GLOBAL CLOCK BUFFERs. This table does not include global clocking resources, only buffer cell usage. See the Clock Utilization Report (report_clock_utilization) for detailed accounting of global clocking resource availability. 6. ADVANCED ----------- +-----------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-----------------+------+-------+-----------+-------+ | CMACE4 | 0 | 0 | 12 | 0.00 | | GTYE4_CHANNEL | 0 | 0 | 24 | 0.00 | | GTYE4_COMMON | 0 | 0 | 6 | 0.00 | | ILKNE4 | 0 | 0 | 8 | 0.00 | | OBUFDS_GTE4 | 0 | 0 | 12 | 0.00 | | OBUFDS_GTE4_ADV | 0 | 0 | 12 | 0.00 | | PCIE40E4 | 0 | 0 | 4 | 0.00 | | SYSMONE4 | 0 | 0 | 4 | 0.00 | +-----------------+------+-------+-----------+-------+ 7. CONFIGURATION ---------------- +-------------+------+-------+-----------+-------+ | Site Type | Used | Fixed | Available | Util% | +-------------+------+-------+-----------+-------+ | BSCANE2 | 0 | 0 | 16 | 0.00 | | DNA_PORTE2 | 0 | 0 | 4 | 0.00 | | EFUSE_USR | 0 | 0 | 4 | 0.00 | | FRAME_ECCE4 | 0 | 0 | 4 | 0.00 | | ICAPE3 | 0 | 0 | 8 | 0.00 | | MASTER_JTAG | 0 | 0 | 4 | 0.00 | | STARTUPE3 | 0 | 0 | 4 | 0.00 | +-------------+------+-------+-----------+-------+ 8. Primitives ------------- +----------+-------+---------------------+ | Ref Name | Used | Functional Category | +----------+-------+---------------------+ | LUT2 | 53834 | CLB | | LUT3 | 29466 | CLB | | FDRE | 29289 | Register | | LUT4 | 28455 | CLB | | LUT6 | 17197 | CLB | | LUT5 | 16487 | CLB | | CARRY8 | 14273 | CLB | | LUT1 | 5418 | CLB | | SRL16E | 2032 | CLB | | FDSE | 1413 | Register | | SRLC32E | 1024 | CLB | | MUXF7 | 578 | CLB | | DSP48E2 | 353 | Arithmetic | | OBUF | 210 | I/O | | MUXF8 | 80 | CLB | | RAMB18E2 | 68 | Block Ram | | INBUF | 64 | I/O | | IBUFCTRL | 64 | Others | | BUFGCE | 1 | Clock | +----------+-------+---------------------+ 9. Black Boxes -------------- +----------+------+ | Ref Name | Used | +----------+------+ 10. Instantiated Netlists ------------------------- +----------+------+ | Ref Name | Used | +----------+------+ 11. SLR Connectivity -------------------- +----------------------------------+------+-------+-----------+-------+ | | Used | Fixed | Available | Util% | +----------------------------------+------+-------+-----------+-------+ | SLR3 <-> SLR2 | 0 | | 23040 | 0.00 | | SLR2 -> SLR3 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR3 -> SLR2 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR2 <-> SLR1 | 0 | | 23040 | 0.00 | | SLR1 -> SLR2 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR2 -> SLR1 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR1 <-> SLR0 | 0 | | 23040 | 0.00 | | SLR0 -> SLR1 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | | SLR1 -> SLR0 | 0 | | | 0.00 | | Using TX_REG only | 0 | 0 | | | | Using RX_REG only | 0 | 0 | | | | Using Both TX_REG and RX_REG | 0 | 0 | | | +----------------------------------+------+-------+-----------+-------+ | Total SLLs Used | 0 | | | | +----------------------------------+------+-------+-----------+-------+ 12. SLR Connectivity Matrix --------------------------- +-----------+------+------+------+------+ | FROM \ TO | SLR3 | SLR2 | SLR1 | SLR0 | +-----------+------+------+------+------+ | SLR3 | 0 | 0 | 0 | 0 | | SLR2 | 0 | 0 | 0 | 0 | | SLR1 | 0 | 0 | 0 | 0 | | SLR0 | 0 | 0 | 0 | 0 | +-----------+------+------+------+------+ 13. SLR CLB Logic and Dedicated Block Utilization ------------------------------------------------- +----------------------------+------+------+------+------+--------+--------+--------+--------+ | Site Type | SLR0 | SLR1 | SLR2 | SLR3 | SLR0 % | SLR1 % | SLR2 % | SLR3 % | +----------------------------+------+------+------+------+--------+--------+--------+--------+ | CLB | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLBL | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLBM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLB LUTs | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Logic | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Memory | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Distributed RAM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | LUT as Shift Register | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CLB Registers | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | CARRY8 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F7 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F8 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | F9 Muxes | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | Block RAM Tile | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | RAMB36/FIFO | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | RAMB18 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | URAM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | DSPs | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | PLL | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | MMCM | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | | Unique Control Sets | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | +----------------------------+------+------+------+------+--------+--------+--------+--------+ * Note: Available Control Sets based on CLB Registers / 8 14. SLR IO Utilization ---------------------- +-----------+-----------+---------+------------+----------+------------+----------+-----+ | SLR Index | Used IOBs | (%)IOBs | Used IPADs | (%)IPADs | Used OPADs | (%)OPADs | GTs | +-----------+-----------+---------+------------+----------+------------+----------+-----+ | SLR3 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR2 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR1 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | | SLR0 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | +-----------+-----------+---------+------------+----------+------------+----------+-----+ | Total | 0 | | 0 | | 0 | | 0 | +-----------+-----------+---------+------------+----------+------------+----------+-----+