TC1-ConvNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cancer Type Classification using Deep-Learning\n",
    "## S.Ravichandran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document will explain how to use genomic expression data for classifying different cancer/tumor sites/types. This workshop is a follow-up to the NCI-DOE Pilot1 benchmark also called TC1. You can read about the project here, https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/TC1\n",
    "\n",
    "For classification, we use a Deep-Learning procedure called 1D-Convolutional Neural Network (CONV1D; https://en.wikipedia.org/wiki/Convolutional_neural_network. \n",
    "NCI Genomic Data Commons (GDC; https://gdc.cancer.gov/) is the source of RNASeq expression data. \n",
    "\n",
    "First we will start with genomic data preparation and then we will show how to use the data to build CONV1D model that can classify different cancer types. Please note that there are more than ways to extract data from GDC. What I am describing is one possible way. \n",
    "\n",
    "This is a continuation of data preparation which can be accessed from here, \n",
    "https://github.com/ravichas/ML-TC1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part-2: Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import os, sys, gzip, glob, json, time, argparse\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "from keras import backend as K\n",
    "from keras.layers import Input, Dense, Dropout, Activation, Conv1D, MaxPooling1D, Flatten\n",
    "from keras import optimizers\n",
    "from keras.optimizers import SGD, Adam, RMSprop\n",
    "from keras.models import Sequential, Model, model_from_json, model_from_yaml\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us read the input data and outcome class data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read features and output files\n",
    "TC1data3 = pd.read_csv(\"Data/TC1-data3stypes.tsv\", sep=\"\\t\", low_memory = False)\n",
    "outcome = pd.read_csv(\"Data/TC1-outcome-data3stypes.tsv\", sep=\"\\t\", low_memory=False, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          0    1         2         3         4         5         6         7  \\\n",
       "0  1.716923  0.0  1.951998  1.167483  0.667981  1.274099  1.258272  1.837351   \n",
       "1  1.979573  0.0  1.939303  0.946014  0.828050  1.338521  1.215231  2.298950   \n",
       "2  1.681222  0.0  2.016686  0.789298  0.930981  1.167504  1.026718  2.058239   \n",
       "3  1.640044  0.0  1.669994  0.821958  0.426876  1.214174  1.673027  1.904529   \n",
       "4  1.800725  0.0  2.013062  0.743211  0.652487  0.935054  1.102839  2.068075   \n",
       "\n",
       "          8         9  60400  60401  60482  \n",
       "0  1.000251  1.991821    0.0    0.0    0.0  \n",
       "1  1.974058  1.744890    0.0    0.0    0.0  \n",
       "2  1.776646  1.510484    0.0    0.0    0.0  \n",
       "3  0.867674  1.526440    0.0    0.0    0.0  \n",
       "4  1.405575  1.674716    0.0    0.0    0.0  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>60400</th>\n      <th>60401</th>\n      <th>60482</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.716923</td>\n      <td>0.0</td>\n      <td>1.951998</td>\n      <td>1.167483</td>\n      <td>0.667981</td>\n      <td>1.274099</td>\n      <td>1.258272</td>\n      <td>1.837351</td>\n      <td>1.000251</td>\n      <td>1.991821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.979573</td>\n      <td>0.0</td>\n      <td>1.939303</td>\n      <td>0.946014</td>\n      <td>0.828050</td>\n      <td>1.338521</td>\n      <td>1.215231</td>\n      <td>2.298950</td>\n      <td>1.974058</td>\n      <td>1.744890</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.681222</td>\n      <td>0.0</td>\n      <td>2.016686</td>\n      <td>0.789298</td>\n      <td>0.930981</td>\n      <td>1.167504</td>\n      <td>1.026718</td>\n      <td>2.058239</td>\n      <td>1.776646</td>\n      <td>1.510484</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.640044</td>\n      <td>0.0</td>\n      <td>1.669994</td>\n      <td>0.821958</td>\n      <td>0.426876</td>\n      <td>1.214174</td>\n      <td>1.673027</td>\n      <td>1.904529</td>\n      <td>0.867674</td>\n      <td>1.526440</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.800725</td>\n      <td>0.0</td>\n      <td>2.013062</td>\n      <td>0.743211</td>\n      <td>0.652487</td>\n      <td>0.935054</td>\n      <td>1.102839</td>\n      <td>2.068075</td>\n      <td>1.405575</td>\n      <td>1.674716</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "TC1data3.iloc[[0,1,2,3,4],[0,1,2,3,4,5,6,7,8,9,60400,60401,60482]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outcome[0].value_counts()\n",
    "outcome = outcome[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(data):\n",
    "    print('Shape of data (BEFORE encode): %s' % str(data.shape))\n",
    "    encoded = to_categorical(data)\n",
    "    print('Shape of data (AFTER  encode): %s\\n' % str(encoded.shape))\n",
    "    return encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Shape of data (BEFORE encode): (150,)\nShape of data (AFTER  encode): (150, 3)\n\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding \n",
    "# Done run more than once \n",
    "outcome = encode(outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 600,
       "height": 800
      }
     },
     "execution_count": 8
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/Train-Test.png',width = 600, height = 800 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the Test data for validatation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data into training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train/test split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(TC1data3, outcome,\n",
    "                                                    train_size=0.75,\n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state=123,\n",
    "                                                    stratify = outcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us define some parameters\n",
    "\n",
    "* activation to be RELU\n",
    "* batch_size is set to 20 \n",
    "* number of classes is three (chosen a small number for performace) for this exercise. The code that is available from NIH FTP site will model 15 cancer site outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "activation='relu'\n",
    "batch_size=20\n",
    "# Number of sites\n",
    "classes=3\n",
    "\n",
    "drop = 0.1\n",
    "feature_subsample = 0\n",
    "loss='categorical_crossentropy'\n",
    "\n",
    "# metrics='accuracy'\n",
    "out_act='softmax'\n",
    "\n",
    "shuffle = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note epochs should be greather than 10. For hands-on, I have chosen a smaller number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs=5\n",
    "optimizer = optimizers.SGD(lr=0.1)\n",
    "metrics = ['acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_len = X_train.shape[1]\n",
    "\n",
    "X_train = np.expand_dims(X_train, axis=2)\n",
    "X_test = np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please note that the `filters = 128` gave the best results. For the purpose of demonstration via cloud, I might choose a smaller number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filters = 128\n",
    "filters = 128\n",
    "filter_len = 20\n",
    "stride = 1\n",
    "\n",
    "# ALW: Commenting this out because otherwise this line is causing the error in older Keras/TF versions: \"ValueError: Tensor(\"training/SGD/Variable:0\", shape=(20, 1, 128), dtype=float32_ref) must be from the same graph as Tensor(\"SGD/momentum/read:0\", shape=(), dtype=float32).\"\n",
    "#K.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 300,
       "height": 400
      }
     },
     "execution_count": 13
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/TC1-arch.png',width = 300, height = 400 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# model.add  1. CONV1D\n",
    "model.add(Conv1D(filters = filters,\n",
    "                 kernel_size = filter_len,\n",
    "                 strides = stride,\n",
    "                 padding='valid',\n",
    "                 input_shape=(x_train_len, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the topology of the architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 3. MaxPooling\n",
    "model.add(MaxPooling1D(pool_size = 1))\n",
    "\n",
    "# 4. Conv1D: filters:128, filter_len=20, stride=1\n",
    "model.add(Conv1D(filters=filters,\n",
    "                 kernel_size=filter_len,\n",
    "                 strides=stride,\n",
    "                 padding='valid'))\n",
    "\n",
    "# 5. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 6. MaxPooling\n",
    "model.add(MaxPooling1D(pool_size = 10))\n",
    "\n",
    "# 7. Flatten\n",
    "model.add(Flatten())\n",
    "\n",
    "# 8. Dense\n",
    "model.add(Dense(200))\n",
    "\n",
    "# 9. activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# 10. dropout\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "#11. Dense\n",
    "model.add(Dense(20))\n",
    "\n",
    "#12. Activation\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "#13. dropout\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "# 14. dense\n",
    "model.add(Dense(3))\n",
    "\n",
    "# 15. Activation\n",
    "model.add(Activation(out_act))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile and show the model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv1d_1 (Conv1D)            (None, 60464, 128)        2688      \n_________________________________________________________________\nactivation_1 (Activation)    (None, 60464, 128)        0         \n_________________________________________________________________\nmax_pooling1d_1 (MaxPooling1 (None, 60464, 128)        0         \n_________________________________________________________________\nconv1d_2 (Conv1D)            (None, 60445, 128)        327808    \n_________________________________________________________________\nactivation_2 (Activation)    (None, 60445, 128)        0         \n_________________________________________________________________\nmax_pooling1d_2 (MaxPooling1 (None, 6044, 128)         0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 773632)            0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 200)               154726600 \n_________________________________________________________________\nactivation_3 (Activation)    (None, 200)               0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 200)               0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 20)                4020      \n_________________________________________________________________\nactivation_4 (Activation)    (None, 20)                0         \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 20)                0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 3)                 63        \n_________________________________________________________________\nactivation_5 (Activation)    (None, 3)                 0         \n=================================================================\nTotal params: 155,061,179\nTrainable params: 155,061,179\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile( loss= loss,\n",
    "              optimizer = optimizer,\n",
    "              metrics = metrics )\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "save = '.'\n",
    "output_dir = \"Model\"\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "model_name = 'tc1'\n",
    "path = '{}/{}.autosave.model.h5'.format(output_dir, model_name)\n",
    "checkpointer = ModelCheckpoint(filepath=path,\n",
    "                               verbose=1,\n",
    "                               save_weights_only=True,\n",
    "                               save_best_only=True)\n",
    "\n",
    "csv_logger = CSVLogger('{}/training.log'.format(output_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SR: change epsilon to min_delta\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
    "                              factor=0.1,\n",
    "                              patience=10,\n",
    "                              verbose=1, mode='auto',\n",
    "                              min_delta=0.0001,\n",
    "                              cooldown=0,\n",
    "                              min_lr=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is a time-consuming step and smaller sample sizes will not result in good model. \n",
    "Here are the commands for training and evaluating test accuracy score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/5\n",
      "112/112 [==============================] - 60s 537ms/step - loss: 1.5190 - acc: 0.4107 - val_loss: 1.0976 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.09760, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 2/5\n",
      "112/112 [==============================] - 62s 551ms/step - loss: 1.1407 - acc: 0.4107 - val_loss: 1.4496 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 1.09760\n",
      "Epoch 3/5\n",
      "112/112 [==============================] - 55s 489ms/step - loss: 1.2153 - acc: 0.2679 - val_loss: 1.0948 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.09760 to 1.09483, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 4/5\n",
      "112/112 [==============================] - 55s 495ms/step - loss: 1.1007 - acc: 0.3393 - val_loss: 1.0633 - val_acc: 0.3947\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.09483 to 1.06333, saving model to Model/tc1.autosave.model.h5\n",
      "Epoch 5/5\n",
      "112/112 [==============================] - 55s 489ms/step - loss: 1.0705 - acc: 0.3571 - val_loss: 1.0603 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_loss improved from 1.06333 to 1.06030, saving model to Model/tc1.autosave.model.h5\n"
     ]
    }
   ],
   "source": [
    "# batch_size = 20; epochs=5\n",
    "history = model.fit(X_train, Y_train, batch_size=batch_size,\n",
    "                    epochs=epochs, verbose=1, validation_data=(X_test, Y_test),\n",
    "                    callbacks = [checkpointer, csv_logger, reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test score: 1.0602983110829403\nTest accuracy: 0.3157894736842105\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word of caution about the accuracy\n",
    "\n",
    "The output loss and accuracy from smalller sample sizes (for example, n = 50) will not reflect the real learning. For good accuracy, we need to use the whole dataset. Here are few epochs from the original dataset modeling (Train: 3375; Validate: 1125)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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uN+mRa8+oh+Rvy3YIUmvseW2QB5e5ldps/lik1B36FIGOU2qsSPdmPSr5vNreVFet932cHnF7X/7Hmh2/rj3/o82Sx/W6m1Oojr2Y6joy7GfX2eC8tczv6iehjQ/Rut5M7Po7saXTdW+djdaT2XhwBfPH/n2f1/Weka3uy1rkhF7+cmDeUHLzu7cP83VP0GMvsla2OZ3HI+P1rq1+3eJXM73rg7eIk939BYyTFmZcM3K9jNmMk8B5yQW8hN8zjmfB6mLUT0G5E/RIyB/Y+v8PUWdzkxo7hyY33wllQhPlP5wCzgkCBWaDBJsCJyeLxneUZcFIm7Pxpk3Fd59CcXZWM4DP6EI2JmVB7686Z2woQc7Xa0YmNrYfJNfKYsfFlpaMgMqtyA+K2D4jY96CgCA+kNehZmflBfRA6RLACSFkIF4irgF5396YZMi2N/3gVpAvbu4aVFxazPWFfnxxKbKlwM7Yu5QH53vTd3zNh6Nu86l1leju+tnk2/Uo5/OG9mScbuzBtQj1OalNPfvLd5npoW0sh9SxA1BcR1IDJ3WNVWejdm6YD0H6V7+a/SW+v4eW9VZA67V1fmVPIbaarq1y0pfhU9k6u7WeROLLU/kI1oagdkpsIPEF2JfBcjO9/KV13lCA+d23D5B1FcYg5Sw9umGPOz0UQf1qo3d98BZx8gR/0XmqxcmYUlzTPO3mwrUheTd+w3RB5mtbY3M9E8lcx+h+zzhuM/uRW4vVuhj0U0juhHWZkB7YinILqbMKusjMhRw6ePZRYFV+UwxWaH5dnEC7PHIMFVWzQ+dkZHzB/dlxYjl0Yk93gClZSR9WUbIBysl4SwE+QDaFOVtW7Jwwqh44vQsXQgi5mh5xTfLdVpNEwHE6A3qYguaEYntzXqtu7FGm+uLuctr2YwrTE6Plp0SNPFiqybSu8euYVj1m+ayNG9qLxpky2cSqW+M5ROtYlDP9tLSO0Dobt3Pr/J5AUw1Wwt+0OZ92bcpDHkHb8rfhU/U6G6gnOxzWnhr/Fox1Vdd/ApXrzu55Q0H3FQKwD8j5dGc9zvCXJP5U4/BMRa73vuct4uQpfg/ESZ9KXPu6R/nT2TipScTuYr+1DbFne95deGe/XzH6Rf3imP8coLs/T36E1MWQnzoguVPWZcTuJ2wzA9iCYmnxhfdEIfMdqZjMIjh8YKbt1p/SFb1LC1/vW8EitkNGj8QOqNxB0PZqcq3jyvlRzc45RtOjhWwbEgRMn9rQsVIugXI2lLOhnE2TXBS/esTG0wDzviL613IS0p5rZ316S3Dy5cPVElJHWcWgu27UDTFBnotqDbm3tiv34hqmQQ8on6PtZWqiFR2r106M99qSbx6uKTuTHyteZyuonbv66Q5c/3s32TUSW2Z86nA9qe12+vRfT+bDlnVu5/mNN5uH9T+bg/OGgrZXy3VlPzlfj2OEscQcG+hXPjWbtfIWcfJJ/pK1ZS2umeNLbSztr3YLyM3TFQzs94zjGWTOgLoY9NNjcp3tinyHbYwuNLOYes4TtvH1I8W1fqWCPN5s3AsR27T2k7Ondd16hxaVOwZq/7Jc+1MpdtDaY+eJsfRoo0sAJ4SQgRg1ruF5f6ZygNTcniIfaXP5LvmIHY6dy6TNtE6KCV5bqsnkXjEnlvTYk88L7WVqwA08f6N17AiU1hFaZ6eg/nfcT5uprLf9WP4o1+L5xutsaw3uiwfPQHz7282jm1P/uuEr5Tp5A5Xrz7F5Q0H1K+e6Ukx5jh5HWe0w+0r41DHuVz6964P3iJPP8BfpI20PimtiTyN2/1TjoaXX9Yzp94zjJWxdZG16tkH9FPbnJ6xL6MA2GrA6mxuEPjbvySXf/aWL2zm4P1j5Xgn32iCgSfsSHPyPFjg5/R4Vd319J8dwRmGvQeTjLrEOuT5E5/YCMh+A4gWrP3xhtI/KIWhb93/eO2NiY3mHJhwjKhcgBbZhN0FtJ87t5nS9JvJWQhDZin6voEcrOtZLgiUhhJzDiHGtJe+vSJzP5CW0Pc0fLh+l9dCBDV5cOxn1FZQHo/EGBa/2scm26DHJ1vM5bJdlLIvsvEEO+nBzJbqE3+826+zJqV2QOnYAdKy5MVn6ZvwAsXPTfAjSvz7h483LUQrrbWXtF6y/1FeivcdMbF+rzlaZ2jpy7IovT0P2JKG9lkOmpO6NY4teE53F7tG+DJFb6OgvyLytVPwFnV9dH637ALlfmP8mPa5ijr0/Ljeka6jBrzxivY9SbE/m/4/EySZ/qfh9QiZO4nHtQ18f+MJs+1AuRnwoXGtDMKTfN/T7ZnFciXUx1jnup7g/N+mxh/XA1g0geLQ6xut0GZQOepVxxrAc0BlOnWiRc4nONPIcJFY5154kzyUYSp/bvWVRi9Mu19rfHZDAurXpkUxEujhqhG3bQUgC4CKjuhoyLXII049vbGPTd8sMvVC5hdq7M9Je7Adpe7idx9ajneMBnBBCxmLEuAbnfS36DDmHX/ih7aktXP4O8keubmohqLHs9op5MKj9pvwreVD/lrpvtcN0D9cDy+etdtEiOJBLC+IkTxtyi29CdewFyPi28UfEmwCgzkbt3Dof3TZu4HpbWfvN+dh0eLK0ITqVa796nY3Uk/i+4hpCf591EL30b/d/X76yL2uWE3r5ywy8P6r6Cza/i1y83sq+5dqqxJWe+7yzmPKC/wOUG6hfNcU1gLeLkzP9/B6Lk01xbTk8nGVq/hy2vfnKKAzp92C/ypvFcQWoi2E/bfDnU+N4+IQthgaqSvL5S+hC27GgSBvvbOd3W1OEkL8P49r7Mno+p28SQgghhJDh4YEtIdejm9v5XRm+OUAIeWX8eMZagYwI61hCCCGEEDI8rQe260fleLhECCGEEEJeCNaxhBBCCCHkJdjzhC0hhBBCCCGEEELIYQrfZR0wyHdCE9IF+j2pwQNbQgghhBBCCCGEEEIIGQQe2BJCCCGEEEIIIYQQQsgg8MCWEEIIIYQQQgghhBBCBoEHtnv5eNwf34+fm3WP9IN2JoQQQl4f5nNCCCGEEEJgXuXA9vbzfc4XLt/+e/zc57a1/c/HF7CZ0PEUf1n44/HzK2269sz70sbn41dl/vf4vf973AyZFjkIVF9E7utzux+Q6pz8KrMA/DJz1s4vpkeN1b87tUcIIVfhx7Pfnw9T5lJu/x73Oac+Ht+Pe2GMt69JtqjHzjqiF8sYN5tHOeTi8S3U6yay8d/jvsyXzumA6+gEutW7DXXdQn0ddY4b5DCX+EvP/YdH1f/+BL3jGuPk4XOBmbr/fQT+nO23we/P0GNM3tNPUa7J+7392c2xv356+/N6YJtVcmII55Jf0et5YKu/yvf9+PnadPvSjeZnxciy8MpPiejG5PfbtW85yXT//uVdk7EYGxlUDgLVF5UTn0nGIrZJdZaDzkAPiIydX06POjLPDOCEkL/EmHFtyiv3JS/cpjdXk3FK/nA5/MdtWm4uR2T1QPPMSUy1xmfQf8DF49uo101VdE7e71eS36U+6FrvNtR1QnUd9Y4b5DCX+AsaT1G5mbr//T16x7Xe7Y1KV7+fQfxPH1hy/az+KzEwPpRq8Psz9HgF3sVPUbr6QeN5Tj9/lnrAyXn1reb/nv4cPmFrKzWMc4nhnlCsx0aP0cBWmoRlgm+54tBdd3rEwSvtF5U7BtoeJGculmlhBAsSoGrniFH1QGAAJ4T8NUaMa+aYarWFFF6NevTO0yYyLqNGQHjK+Dxa87nJk2rA0XiP+uAJ9W6mrkPW0bPiBkG50F8M0H5NuQNx/JXpHdcYJ8NrMJD/uX4z50PInjgd33PON0bkPfwU5ao43tmfzVrA7mM3rQe2eiI9P3GrjzCvT+E6JS0HdEr4jwjrOzjWJMjhpjPAKqftTUFkk5kMkvTb8QBNTs7LgUtsVHIk/133zGSJHshhICp3gLq+E7BcZmztY67ZOWRcPTCyAVwCzerrNjoeyiVQzoZyNpSzaZKL4tfLFKaiX2lj7u636JHNM0Y9JH9btkOQWmPPa4PxLXMrtdn8sS+pO/TpAB2n1FiR7s16VPJ5tb2prlrv+zg94va+/I+vOX5de/5H2CSP63U3p1AdezHVdWTYz66zwXlrmd/VT0IbN+P6PL3ezbS1dx31jhtPYf54p+/zut4zstV9WYuc8Af8Jebo/mO3/52MxJ1tTucxit29a6v/t/jVTO/6gHEyvIaC+Z99noHMoen3J+jRi5fwe8ZxGLutzv5s1biimxHvd5M/sLWVEVQhN6mxc6gSvqIyoclgP5zxnBMExpsVDTYFTs61FxTjy4KRNmejTJuK7+MOszhoxbi6kA3nWtD7axsZG7ox25uM5aC3Ua4VVN8Gu0zID4rYPiNj3oKAID6Q16FmZ+UF9EBBggQhhLwSLxHX5iK1+Oag5OLD+Wj6wa0gX9zcNWizZDHXF/oxrqXIlgI7Y+/S+OZ703fWzYejbvOpdZXo7vrZ5Nv1KOfzhvZknG7swbUI9TmpTb35lO/m00PbWA6pYweguI6kBk78zaqzUTs3zIcg/atfzf4S30c5q95dEb2suq5xHS30ihtP5yNYG4LWtYntxS7AvgyWm3l5f5lB9xVVuZ3+9zRsO6SHfKhfbfSuDxgn99gS9z+dzzVfyhoXm07XYtmq35++fo8yut8zjmPY8yh09edFRmpK14bUm/GDAodJD2znAS0T4QvPiLPZRUhomOwjzarUNkHwxMyviw3V79HpKXhnJ2Nx6FxfOqH+/TkQxnKogz7Bkcv6LoByMl7TLyxkU5izZcXOCaPqgdO7cCGEkKvpEde0qArqEg84TmdADl2Expxg55k5rwUF8RGm+uLu+tp+HGR6YrS82TfGV6rJtK7x65hWPWr5vKG9aJwpk02sujWeQ7SORTnTT0vrCK2zcTu3zm8nzq53s2t48pmmdXRK3LA5Nf4tGOsKtXu3+WnlMn+JObr/2OF/Bc7wlyT+VOPwTEWu976HcXLPWFr8zz/Udf05f5KH5sp+lfH7znq8s9+vGP2i9tzvPwe5NI539GfH1z2qG9187InhWXY/YZsZxBYU86+P78nEmO9IxWQWQY8DM5/ceETv0kTqfT9IrKSTaOmR9IvKHQRtrybXOq6cH9XsnGM0PVrItiHBw/SpDR0r5RIoZ0M5G8rZNMlF8atHbDwNza/gp3NE/8acZOYR1876Lr3g+t9f0EkdZRW17rpRN8QE44tqDbm3tiv34hqmQQ8on6PtZWqiFR2r106M99qSb/auKY+SHyteZyuonbv6KUhmblvrsRz5dhrXkY7zvLhxOsnHXqf5jTfNqN17zU8zl/mLzX57NfrfJcgYK3t20K98etcHjJPhNQzRf7//oXOYjO/k9duH0DeG8nvGcYjWdvr6c+g/h0G+wzZGFLKLkOc8YRtfP+PA1gxe8m5aUz85e1rXrSc1UbljoPYvy7U/lWIvjD12nhhLjza6BHBCCBmIUeOafpVS9BH5IjsOXur5SD7S5vJd7iOUAHYf0mZaJ8UEry3VZHKvmBNLeuzJ54X2MjXgBp6/0Tp2BErrCK2zU1D/O+6nGOIrsd171bvlOUXX0TPixrmIHb7dPLo59a8bvlKPXxOoXH+u8xeLI/ZC/e9K1jHOvhI+XYb7lU/v+oBx0r+Gs9//ZCxYv2kf/fU4gzH9nnEcozWOH/Bnsb2R639a6oUa0IFtNBB1NmfM6nd/aXHjHNxXXr4nxb02CGjSvhjJ/2iBk9Pvk3DX13eyM4tg78SqHvd/3jvl0qe8Y5HaQGTbC6+MPR3xgtUfvjDaR+UQUH1b7LIihWkmQKnt9Ls8PD1E3lgYKlvR7xX0aEXHekmwJISQcxgxrsnHmeLaJVdbrEicz+Sllnz0cHJpPXRggxfXTkZ9BY0v0j8oRrWPTbZFj0m2ns9huyxjWWTnDXLQh5urpO5cdPbk1C5IHTsAOtbcmCx9M36A2LlpPgTpX5/w8eZlJ7GexXp37Reov2T9ltY3sI56x41rSDekyyFTYp/YJnpN1pHYPdqXIXILL+4v2mctnjbIKYD/XY6OUX6Y0ooDDX7lEc/fUYrtyfy7sTBOGuzxP53fVEbHBvp9kx5XMaTfN/SLxmdUbmFkf15wsqV5CDjszx/6+mBOZKxo/wjrga0opkbI4BlvMa4OZpVxiloO6JxAnWiR+839KuNkrFXOtSdGWoKh9LndWwwlTrtc2/fuwPRjFF4beiAXy6WLo4YE7aVNK1AJ+j0YS7+iqyHTIoeA6YvLLdTenZH2Yj84Yuex9WjneAAnhJCxGDGuhbnZwyx0DTlHUtQBeWYp/IL8kaubWghqLLu94viC2m/Kv5IH9W+p+1Y7TPdwPbB83moXLeYDubSwT/K0Ibf4JlTHXoCMbxt/RLyZAeps1M6t89Fz4ybA9e7ab93HanWdUllHZ8SNKwj9fY4FEh/0b/d/X76yL2uWE/6Av6D7ClROAeL41Ux5wf8Byg3Ur5riGgDjZCe/r/rfdBjt95nTo8Xve55vnMWQfg/2qzCOG5zgz2q/sM2u/hw+YYuhgWrAZHIWutB2LCjSxjvb+d3WFCHk78O49r6Mns/pm4QQQgghZHh4YEvI9ejmdn5Xhm8OEEJeGT+esVYgI8I6lhBCCCGEDE/rge30aLgHD5cIIYQQQsgLwDqWEEIIIYS8BHuesCWEEEIIIYQQQgg5TOE7oAN6/pgPIVdDvyc1eGBLCCGEEEIIIYQQQgghg8ADW0IIIYQQQgghhBBCCBkEHtgSQgghhBBCCCGEEELIIPDAdi8fj/vj+/Fzs+6RftDOhBBCyOvDfE4IIYQQQgjMqxzY3n6+T/7C5Y/Hz698qfPn48u8H6LjKf6ycL2928/n41dl/vf4vf973AyZFjmI23+Pn/tsS7WnG5+1eULkvj63+wGpzsmvMgvALzNn7fxietRY/btTe4QQchV+PPv9+TBlLuX273Gfc+rj8f24F8Z4+5pkMT3a6oheLGPcbB7lEDRfnky9biIb/z3uy3zpnA64jk6ga727UlmXYDxor/+uiQfvRF9/cWtu9QOkvfL84v7i2vHicz+/H5HecY1x8vi5ABD/0H1yb7k/w3v6KUrfOA7GU3gfgMbn1vzRyHpgm108E0M4l/yK3kkHtrqR+P1+/ELBQhZe+SmRWnty//7lXRPdjESOykHIa2XcX9tcfsk43RgDp0LlxGeSsYhtUp2lcAn0gMjY+eX0qCPzzABOCPlLjBnXprxyX/LCbdp0J+OU/OFy+M/Pf4+byxGIHrW8fwZTn59BngtA8+Dp1OumKjon7/crye9SH4ie3epdj/K6BOOBo7X+uyIevBN9/UXmPYxPGvcL7dXmF/UXPdh1/azxWHyw92Z/QMR+PeNa7/ZGpa/fg/EP3Sf3lvuDvIufovT1ZzSetuX9envy+rb80Uz4hK29WIZxrrOKdWlXNxJYsBB7pIHGo9qeu+70iJNxPNm43DHQ9iA5Mwi3F7pC1c4Ro+qBwABOCPlrjBjXzDHVagspvGp6NNYRXZBxGTUCQu86okZrPjc5qwYcnPeoD06qdyvrsiUeNNV/V8SDt6Kzv5hzXpg7YH4xf8n75Rl7jZHoHdcYJ8NrCC3xLyGzT07oLffivIefonSO42A8xf0ejM+t+WMPrQe2etI8P3GrjzCvT+G6wVsO6JTwHxHWJ0GsSZCk5yZoldP2ps3IJjMZJOn3UFLzT9UR44pMyZGA9kQP5DAQlTuAPGmjNjXu+cBymbG1j7lm55Bx9cDIBnBJYKuv2+h4KJdAORvK2VDOpkkuil/ZuDYaol9po+Dul/UA6wijHpK/LdshSJ97XhvkwWVupTabP/YldYc+HaDjlBor0r1Zj0o+r7Y32Xe97+P0iNv78r/+wfHr2vO/AkLyuF538wXVsRdTXUeG/ew6G5y3lvld/SS0cTOuz/71bmt9P5OJB/hYdvb7DNxeS9a57/O63jOy1X1Zi5wwrL8YMUr6MPcL2PxiY7FfP0LulDFsczrrIjbxrq3rpMWvZnrryDgZXttNJv7FoH32ljubl/B7xvEMB+Kp6fdoeyKH5o+d5A9s7UEKOlA3qbFz6CbAV0AmNBnsh5sI5wTBRMyKBpsCJ+faC4rxZcFIm7NRpk3F926H0YW5jjGv84LKG861ALUnTuHrNSMOGtsPkmvFDzzrWA1QuRX5QRHbfjLmLQgI4gN5HWp2Vl5ADxRdU0fmlBBCBuMl4tpcpGYPEwWzkNuA8r7mlShf3Ny13AavytyPfjzL9b/ko5y9S3lwvjd99+18OOo2n1pXie6un02+XY9yPm9oT8bpxh5ci1Cfk9rUm0/5jl89tI3lkDp2AIrrSGrgpK6x6mzUzg3zIUj/6lezv8T3UU6od7F1GVGIB2j9t6vfp/ERrA1B9UpsL+MG9mWw3MzA/jLFF4kLrg0XL+I3ehbQ+UX9ReXWOCS2k76na7Hs85F4kOonOSKOG5hfbRTj2g4YJzvYEqmHFNsvUnrLPQt7POP4PeN4DrVDazyt5X2kPTB/7CY9sJUJWLAXjyYrU/HQwbOPNEdFNzwx8+vCgHvg0WlpTxx0fa3ob+u83S/0hbaHOujZgVkcTzZ6hk1DQDkZb2lBBMimMGfLip0TRtUDp3fhQgghV9MjrmmxFNQlHnCczoBuTko5Aa4j5rxmbNj3MfVzdzlt+5Gx6YnR8lOiRh4s1WSqn69Pqx6zfNbGDe1F40yZbGLWcNEconUsypl+WlpHaJ2N27l1fjvRu96F16UHfFixYNR/e/otcGr8WzDWFWr33fNzlN7+4vi6R/Pk7JLE0kPzm9svuOsSk+d51TeRmvYhG2f4SxJ/DH8xqcj13vcwTh4cS0v8Q/2zt1yGd/b7FaNf1C+6+M8eTvHnxnha9XusPSh/HGH3E7aZQWxBMf/6+J5MjPmOVExmEWQDcQXRY5mAkLzepYUPt5fRI7EDKncQtL2aXOu4cn5Us3OO0fRoIduGBAXTpzZ0rJRLoJwN5WwoZ9MkF8WvHrHxNDS/gp/OEf0zOUnzlWEPs45w7UxPJcy4/vcXdFJHWUWtu27UDTFBnotqDbm3tiv3Yl0a9IDyOdpepiZa0bF67cR4ry355t6a8izyY8XrbAW1c1c/BcnMbWs9ttC0LgXtv/3TevHcNPf7bObNaTA2+TuyPWr3vfNzmM7+YreXrqGj85tfyyGo3HMI7WDaGPQrn9465tszYmHp3hvFyZXG+Nc7Phwe/ykM7PeM401kbd7o9wtJe2D+OATyHbYxMlC7AH/OE7bx9X7FdUl/udfaT64967r1zisqdwzU/mW59qdS7AW0x84TY+nRRo82CCFkJEaNa/pVStFH5Is4efxNxFzej5GPtDnZ3EcoAexcJm2mdVJM8NpSTSb3ivqU9NiTzwvtZWrADTx/o3XsCJTWEVpnp6D+d9xPMcRXYrv3rHet9iea44FHPcbl+30+4tvfbh7dnPrXDV/pU0+fSWd/ERsY8eCn6hdt84vlRGmzl9/3YZ3n2VfCTynifuXTuz5gnPSv4bTHPzRH9pZ7PmP6Pd7v28VxEzue7s/7Rnti+135owHowDYaiDqbM6Y+FuzJJd/9pcZwDh4oNU1EENCkfVHe/2iBk9PviXDX15PvzCLoN7EZ/R2iM75hWyi359tKf/jCaB+VQ9C27v+8dxLExvIOTThGVC5ANrSZAKW2E6d1c7peE3lrAYlsRb9X0KMVHeslwZIQQs5hxLgmXwcQ1y652mJF4jycd+28r/nD5aO0HjqwwYtrJ6O+gvJgpH9QvGsfm2yLHpNsPZ/DdlnGssjOG+SgDzdXSd256OzJqV2QOnYAdKy5MVn6ZvwAsXPTfAjSvz7h483LTmI9i/Xu2i9af9nrEo0Hapdd9V9+H/B8ZCzheJdDpqTujWOLXpN1JHaP9mWI3MKw/vLh7BDZRl4T2yXBnt/d/qLzEe2RR2COvT8uN6Rja/Arj3j+jlJsb55LxsmQ3fVQdV04estdwZB+39Dv28XxiEw83eX3QjY+780fDawHtrJg1AgZPOMtxtXBrDJOAcsBnQHUiRY5l8BMI89GWOVce7LJWIKhBsf13pIcxWmXa8feHZAgm7a/IP2AzjFTbm9Cv0dullFdDZkWOYTpxze2seW+FBmVW6i9OyPtxX6QtofbeWw92jkewAkhZCxGjGthbvYwC11DzmFuyBylvK+2cPk7yB+5uqmFoMay2yvmwaD2m/Kv5EH9W+q+1Q7TPVwPLJ+32kWL+UAuLeyTPG3ILb4J1bEXIOPbxh8Rb2aAOhu1c+t89Ny4CXC9u/Zb97HSuoTjgaO1/kP2Ac8m9Pc5Fkh8sMZY2Zc1ywkj+4vqsehQac9Rm1/MX6ZDvkVG+uxhlzOY8oL/A5QbqF81xTUAxsljft8S/xZq++SF3nJXMaTfg/0qbxXHsXiK+31DfG7MH82ET9hiaKAaeHH1RhfajgVF2nhnO7/bmiKE/H0Y196X0fM5fZMQQgghhAwPD2wJuR7d3M7vyvDNAULIK+PHM9YKZERYxxJCCCGEkOFpPbCdHg334OESIYQQQgh5AVjHEkIIIYSQl2DPE7aEEEIIIYQQQgghhyl8d3xAzx/zIeRq6PekBg9sCSGEEEIIIYQQQgghZBB4YEsIIYQQQgghhBBCCCGDwANbQgghhBBCCCGEEEIIGQQe2O7l43F/fD9+btY90g/amRBCCHl9mM8JIYQQQgiBeZUD29vP9ylfuJz8WrAA/GKwjqco9/H4+ZX2Ph9f5n1p4/PxqzL/e/ze/z1uhkyLHMTtv8fPfbal8OvGZ22eELmvz+1+QKpzdzu/mB41Vv/u1B4hhFyFH89+fz5MmUu5/Xvc55z6eHw/7oUx3r4mWUyPet4/g2WMm82jHILmy5Op101k47/HfZkvndMB19EJ9K138XUuVNeRzIl/Pzs+Fwe89XZYD5Klq780zBuaF6pyDfuPv0HvuMY4eTxO4vVBV39ujM+vzXv6Kcplcbyp31p9f3LeXw9ss4tsYgjnkl/RO+HA9v5l38sjC6/8lIhuTH6/H7+ZiZX7Qb+im7GRQeUg5LUy7q9tLr90Y/0ZOhUqJz6TjEVsk+rc1c4vp0cdmWcGcELIX2LMuDbllfuSF25TEZaMU/KHy+E/P/+5TYor6gA9ann/DKY+P4M8F4DmwdOp101VdE7e71eS36U+ED271bvoOp+priPdrIX+q3HBGJ++se+ur+tL+u69eSOd/QWcNzQvoHIN+4+/Ru+4xjjpXUOR1yL1QXd/bovPf4l38VOUS+K4o7VfkS/V96fn/fAJWztJDONcGjCuP7DVSSs50xIAb7mk6647PeJJjItBXO4YaHuQnBmsT7JzxKh6IAyzxgghpBMjxjVzTLXaorRBWajm/ROQcRk1AkLvOqJGaz43OaEGfAXeoz7oW+82rXNkHZmvtda6vf5lPGfUju9L7/3RjnlD8oKAyi04+cOx8gXoHdcYJ8NrRyi218Gfm+LzH+M9/BTlqjje2G+1vkf7PUDrga2eIM9P3OqjxOtTuG5QlgM6Jf2PEOk72KYxxHjyro7f3lREbTLTYk76PWCM9gM4sVHJkfx3iTITK3ogh4Go3AHknbRqoeqA5TJj62/nkHH1wMgGcEl0q6/b6Hgol0A5G8rZUM6mSS6KXy9TmIp+pQ2yu1/WA8j7glEPyd+W7RCkzz2vDfLgMrdSm80f55K6Q5+i0XFKjRXp3qxHJZ9X25vsu973cXrE7X35H+90/Lr2/I94Sh7X626+oDr2YqrryLBfbtMBzVvL/K5+Etq4Gdfn2fVubp1j68jwYRlzUk/a63+YWDh//Nn3eV3vGdnqvqxFThjWX3bMm+iCzCkqN3PWPqMV0X2b03lMYnfv2rqeWvxqpveaYJwMr+2luk8+y59duyO8UfESfs84ngGM4039IvX9jvzRSv7ANjeoeQBuUmPn0EXuD0wmNFn0H84gzgkCg0hfrhAKNgVOzrUXFOPLgpE2ZweZNhXfux1GJmdzTkHGljeuLmRjkhf0/qpzxoYSlIwFIGOJ7QfJteIHnnWsBqjcivygiO0zve2svIAeKF0XNSGEDMBLxLW5SC2+OSi5+Gje17wS5Yubu5bb4FWZ+9GPXbn+l3yUs3cpD873pu/snA9H3eZT6yrR3fWzybfrUc7nDe3JON3Yg2sR6nNSm3rzKd+7p4e2sRxSxw5AcR1JDZzUNVadjdq5YT4E6V/9avaX+D7KWfXuQnadN6wj9T/xGzcm50/xGwELWieufiV7GZGdrsWyz+cjWBuCjjexvdgF2JfBcjMD+0vzvMkYkDlF5RRZf2GsuhZ7POmbHKhfbRTj2g4YJw/YsmWffIY/I3XYU7HHPY7fM47nUDvU4nhDvzJfIju1J3ay/Rnq9wjpga1MwII9KB28OYDQwYuPFnuGgidmfl0cSPp+FEA2K7n2ZofO9aUBz7+fmVjUUU5w5JApWW2OmAOUk/HCjnnAzgmj6oHTu3AhhJCr6RHXtAgK6hKPo4UQukko5QQ07y95LSiIjzD1c3c5bftxpOmJ0fJTokYeLNVkqp+vT6ses3zWxg3tReNMmWxi1a3xHKJ1LMqZflpaR2idjdu5dX47cWa9W1znk88g6+jrHvmFs7G91vzDXzd+N//ycMkePzg1/i0Y6wq1e5f52cMp/tI4b+ictsz9Tj9ZOMNfkvhTjcMzFbne+x7GyR5jAfbJvf25w2HtO/v9itEv6hf9/KeRU/wZiONov2JTWeerb071gl0f9sv7JrufsM0YcguKJaXCe2Ig8x2pmMwi6HFg5pPTT66XDK/354kKieyQ0SOxAyp3ELS9mlzruPbaOcdoerSQbUMWu+lTGzpWyiVQzoZyNpSzaZKL4leP2Hgaml/BT+eI/pmcpPnKsIf5hrdrZ32KRXD9lw9XS0gdZRW17rpRN8QEeS6qNeTe2q7ci3Vp0APK52h7mZpoRcfqtRPjvbbkm71ryqPkx4rX2Qpq565+CpKZ29Z6LEHbLa1zcB2Z4yvZP2SYWDgfjqxzO89vvHlF7X54fvZylr9EFOfNrRNor4LKOS6zZ5HQz80xgn7l03tNME6G145QbK+nP1fj85UM7PeM400kNgf7ldcFNl65IO8j32EbowqYi/U5T9jG159zYCu2ae0nZ0/ruvWkJip3DNT+Zbn2p1L62XliLD3a6LqoCSFkAEaNa/pVStFH5Is0bFDyeT9GPtLmZHMfoQSwc5m0mdZJMcFrSzWZ3CvqU9JjTz4vtJepATfw/C2+idSxI1BaR2idnYL633E/xRBfie1+rN5F1zm0jsSehr/8QHFEdOtbt+9DfPvbzaObU/+64SvlOnkDletPf39Jqcwbmhfg/DFe7FlY53n2lfDpS9yvfHrXB4yT/rVjFNd1J39ursMuYEy/Zxxvw4rje/u1XpfD6vcA0IGtOIG3ONXZnFL6uK8nl3z3ly5G5+D+YOV7otxrg4Am7YtS/kcLnJx+/4O7vr7zklkEeydW9Ji+p23rVx9fNgysslCA8slPbLxg9YcvjPZROQRt6/7PeydLbCzvHoRjROUCxG6ZAKW262jnV9CjFR3rJcGSEELOYcS4Jh/3i2uXWoGNb1AEO+9r/nD5KK2HDmzw4tpJ2wvrKygPRvoHxbv2scm26DHJ1vM5bJdlLIvsvEEO+nBzldSdi86enNoFqWMHQMeaG5Olb8YPEDs3zYcg/S9PnAQ2byfWs1jvrv3a9VfTOgfWkfhQ/LHd6cChEDcE9dG4rauQ2BTpsKyhWI/YJnpN1pHYPdqXIXILg/pLAjJvrk0oL7TI1fzpKubY++NyQ2qTBr/yKMa1HRTbk/l3Y2GcDNG2nB7bmMB98kF/3lWHXcGQft/QL+N4No439bsiti+sjQUkf7SyHtjKwlIjZPCUWJTUYmWVcQOzHNA5gTrRIveb+1XGSblVzrUnQWQJhtLndm8xlhhuubbv3QH5MYp4fKnzST+gc8xI0F7bzEyufk/MLKO6GjItcgjTj29sY8v9aAIqt1B7d6a3ncfWo53jAZwQQsZixLgW5mYPs9A15By5IqyU99UWLn8H+SNXN7UQ1Fh2e8U8GNR+U/6VPKh/S9232mG6h+uB5fNWu2hRHcilBXaSpw25xTehOvYCZHzb+CPiTQVQZ6N2bp2Pnhs3Aa53135tH4PX+QKwjqZ9ytaWPb7p0MaX6WGXXoT+PscCiQ/6dxizavuyZjlhUH+B5w3NC6icx3VPuWFMecH/AcoN1K+a4hoA4+RRv5e5c/Pk+75fHyx09ufm+HwhQ/o92K/COG7ITbScr4U+G9n4GXk/fMIWQwPVwEmlN7rQdiwo0sY72/nd1hQh5O/DuPa+jJ7P6ZuEEEIIIWR4eGBLyPXo5nZ554ZvDhBCXhg/nrFWICPCOpYQQgghhAxP64Ht+lE5Hi4RQgghhJAXgnUsIYQQQgh5CfY8YUsIIYQQQgghhBBymMJ3pQYM+F2nhOyGfk9q8MCWEEIIIYQQQgghhBBCBoEHtoQQQgghhBBCCCGEEDIIPLAlhBBCCCGEEEIIIYSQQeCB7V4+HvfH9+PnZt0j/aCdCSGEkNeH+ZwQQgghhBCYVzmwvf18n/qFy7evf4/77/bFzr8/5V8N1vEUf1n44/Gj7X0+vsz70sbn43fu8/f+73EzZFrkIG7/PX7usy2FXzc+a/OEyH19bvcDUp2TX2UWgF9mztr5xfSosfp3p/YIIeQq/Hj2+/NhylzKzc/33497YYxLbYDpUc/7Z1CtX9B8eTL1uols/Pe4L/OlczrgOjqBvvXuVevc3ffW22E9SJau/oLOW8/9R4vcn6B3XGOc7BFfsPjnbD33We4XXUd4fH593tNPUS6J4yu1fN46vnp7u1gPbLOHVhNDOJf8it4JB7a6iXAJ8ucL1VEWXvkpkanN78dvZsLk/v3Luya6GRsZVA5CXivj9vT8knG6MQbOh8qJzyRjEdukOstBZ6AHRMbOL6dHHZlnBnBCyF9izLg25ZX7khduU3GVjFPyh8vhPz//uc2MK9YAPWp5/wymPgv1C5oHT6deN1XROXm/X0l+l/pA9OxW7164zvWNfTfudX1J34c3oSSmr7+A84bG095yf5DecY1x0ruGIq+F4p/ETyfn5W+VN/rF4h8Yn/8g7+KnKJfEcQ/pv8d53UKtvd2ET9jah1TDOJcGls7FugQc12ZLYtTJKDmTjFMS8M22p9rZ6DMOhrjcMdD2IDlnT8s2ew46q3aOGFUPhGHWGCGEdGLEuGaOqVZbFDcyM9W8fwI76peF3nVEjdZ8bnJGDfgCvEd90LfevW6d29dlPGfUju9L7/3RsXnruv9wPDs+X0HvuMY4GV5rphT/zNhprRlsHZlz9Sb5/T38FOXiOI7k85bxVds7QOuBrZ5cz0/c6iPC61O4zhiWA7rB+4/Q65MgppJiFFfQB+1Nm5FNZlrMSb8HiiB5R6ft9WKjkiP57xJlJkz0QA4DUbkDyDu5alPjng8slxlb+5hrdg4ZVw+MbACXg+PV1210PJRLoJwN5WwoZ9MkF8WvlylMRb/SYaK7X9YDyPuCUQ/J35btENrrl4kgDy5zK7XZ/DEyqTv06Rkdp9RYke7NelTyebW9yb7rfR+nR9zel//xYseva8//iLHkcb3u5guqYy+muo4M++U2E9C8tczv6iehjZtxfZ5d7z5nndvXh4mFbq8l69z3eV3vGdnqvqxFThjWX/bPW/f9Byh3NqL7NqezXcXu3rV1PbX41UzvNcE4GV5rphj/ZH1EOVzGkvjpgfhXi89P4iX8nnE8Q4v/dTyvU8B9wF7yB7b5zlRxN6mxc2iS8Q0iE5os5g+nqHOCQFHpywWCYFPg5GTR+I6yLBhpc3aQaVPxvdNhZh31cenFScVB40nd0IVsTN6C3l91zthQgpKxAMQBYvtBcq34gWcdqwEqtyI/KGL7jIx5CwKC+MB+OysvoAeKHUwIIeR1eYm4NhepxTcHJRcfzfuaV6J84WqP7AavSmP9UsqD873pu2+nolM2n1pXie6un02+XY9yPm9oT8bpxh5ci1Cfk9rUm0/5fj49tI3lkDp2AIrrSGrgpK6x6mzUzg3zIUj/6lezv8T3Uc6qdxeets7nOnH1K9nLyBxN12LZ5/MRrA1Bx5vYXvQD9mWw3MzA/tI0b733H6jc05F4kPq55Ig4bmB+tVGMaztgnDxoS2m7FKM0/zobOx0ln8ZvhC40raMFJD4/ldH9nnE8B+p/UD5vGB9aH+wmPbCVCViwO9NBmQsvdPDiI8OeAeCJmV8XBtxCP1Umg95dwN5+pGN64sLe9MwOnetLE65//6ADnODIIVOy2hwsBygn4y0F5ADZFOZsWbFzwqh64PQJ4IQQMg494poWX0Fd4gHH6QzoJqGUE9C8v+S1oCA+wtQPXr8sGHmwVJOpfr4+rXrM8lkbN7QXjTNlsolVt8ZziNaxKGf6aWkdoXU2bufW+e3EmfXuU9e54L+J4sbv2pSHS/b4wanxb8FYV6jdu8zPHk7xlz3z1nn/AcvZnOEvSfypxuGZilzvfQ/j5MGxVHz96x7FO2c7u9ZoXEcdDmvf2e9XjH5Rv+jiP3u4Ko6LrZB8jo4Pbe8Iu5+wzRhyC4qlwYb3RHHzHamYzCLYf2Am47Ccwl03+hG9Swtf7y8BIiCyQ0aPxA6o3EHQ9mpyrePK+VHNzjlG06OFbBsSLEyf2tCxUi6BcjaUs6GcTZNcFL96xMbT0PwKfjpH9M/kJM1Xhj3MN7xdO+tTVILrv3y4WqKtfokJ8lxUa8i9tV25F+vSoAeUz9H2MjXRio7VayfGe23JN3u8CduT/FjDWrp6D7VzVz8Fycxtaz2WoO0+eZ0blPztqcyHI4EO8ndke9Tuh+dnL2f5SwQ6b73tdZldTcJYYo4N9Cuf3muCcTK81kwh/tn9luwakp2blvj8dAb2e8bxJmKby9+B7VYifwbHB7d3BOQ7bGN0YOaifs4TtvH1I8W13bcshLgfsU1rPzl7WtetJzVRuWOg9i/LtT+VYgetPXaeGEuPNnq0QQghIzFqXNOvUoo+Il+ktJFJyOX9GPlIm5PNfYQSAK9fUoLXlmoyuVfUp6THnnxeaC9TA27g+RutY0egtI7QOjsF9b/jfoohvhLb/Vi9O8Y6F0S2b92+D/HtbzePbk7964av9Kmnz6S/v6Tg89bbXtfZ1WYdz+wr4dO/uF/59K4PGCf9azuoHdga936g+Gqvo+b4fAFj+j3jeBu2/4VY48hdR8aXa+8A0IFttFDV2dxg9TFjTy757i9djM7BfaVuk6JBQJP2xZj+RwucnH7vhLu+vvOSWQSHJjbu2xqfQ3TGC7mF/ITFC1Z/+MJoH5VD0Lbu/7x3ssTG8q5AOEZULkACfSZAqe30u248PUTeWEAqW9HvFfRoRcd6SbAkhJBzGDGuycdN49olV1usSJyH866d9zV/uHyU1kMHNnhA/QLlwUj/oHjXPjbZFj0m2Xo+h+2yjGWRnTfIQR9urpK6c9HZk1O7IHXsAOhYc2Oy9M34AWLnpvkQpP/lSZLA5u3Eehbr3bVfu/66ap0nqI+me4prkDGH9loOmZK6N44tek3Wkdg92pchcguD+ktCZt60z1o8PUHucubY++NyQ+rLDX7lEc/fUYrtyfwzTtoyC042H/8+dD0EczzbNJSLyKyjXfH5Cob0+4Z+GcdN/0sRm9oxt2l8K/n2drMe2MpCVSNk8Aa3DF4X6yrjDGI5oHMCdaJF7jf3q4yTUVc5154ksSUYSp/bvcUIYpDl2oF3B4IxWnpIP6BzzEjQTscbot9TtIxddDVkWuQQph/f2MaW+9JwVG6h9u6MtBf7Qdoebuex9WjneAAnhJCxGDGuhbnZwyx0DTlHrvgr5X21hcvfQf7I1U0tVOuXSh4Mar8p/0oe1L+l7lvtMN3D9cDyeatdtFgO5NLCOcnThtzim1AdewEyvm38EfFmAaizUTu3zkfPjZsA17trv7aPXbXO5WDDvy869LBLL0J/n2OBxAf9O9Klsi9rlhMG9ZeWeeu9/0DlrmbKC/4PUG6gftUU1wAYJydf3e33LfFvORSc79v9YusIjs8DMKTfg/0qjOOG3Eboi5HtZlrO4ZD2dhE+YYuhgWqQovYZ6ELbsaBIG+9s53dbU4SQvw/j2vsyej6nbxJCCCGEkOHhgS0h16Ob2+UdGb45QAh5Yfx4xlqBjAjrWEIIIYQQMjytB7brR+V4uEQIIYQQQl4I1rGEEEIIIeQl2POELSGEEEIIIYQQQshhCt+pGjDgd50Sshv6PanBA1tCCCGEEEIIIYQQQggZBB7YEkIIIYQQQgghhBBCyCDwwJYQQgghhBBCCCGEEEIGgQe2e/l43B/fj5+bdY/0g3YmhBBCXh/mc0IIIYQQQmBe5cD29vPd/wuXvz7TL3RWPh9flryHjqf4y8Ifj5/fclu3n8/Hr8r87/F7//e4GTItchC3/x4/99mWwq8bn7V5QuQa7Jf8KrMA/DJz1s4vpkeN1b87tUcIIVfhx7Pfnw9T5lJu/x73Oac+Ht+Pe2GMt69JFtOjnvfPYBnjZvMoh6D58mTqdRPZ+O9xX+ZL53TAdXQCXetdR3X9tuwD4Ljh4oC33nroQWz6+kvrvJXjPbpfOGtfMSa94xrj5FPipOJsvca/TL+N5ypt9dUr855+inJFHMfjLp4XTvXn9cA2u8gmhnAu+RW93ge2yeTIoqpttkSm/JSIbkx+vx+/uSDl7t+/vGuim+EoqByEvFbG/bXN5ZdurD9D50PlGuwnCyPQAyJj55fTo47MMwM4IeQvMWZcm/LKfckLt2nTnYxT8ofL4T8//7kizBWTgB61vH8GU5+fQZ4LQPPg6dTrpio6J+/3K8nvUh+Inl3rXWT9wvUfGDccuhF0ba7rS2Q7HKqQkK7+4midt1q8R/cLZ+0rRqd3XGOc9K6hoHFS35wI87fKx/2i8RTu9+/xLn6K0tWfHWgcb4nP1fae4c/hE7ZWkTKQc6lBTi7WzWATIvYoysg4dWNi21Pt7PSInScOhrjcMdD2ILmM/fYUJFU7R4yqBwIDOCHkrzFiXDPHVKstkAKsmvdPQMZl1AgIveuIGq353OQZNeCAvEd9cGK9i6xfH6P+w+OGvf7l9WfUju9Lb39pnDcg3rccCLyjb/SOa4yT4bVmSnGyIdYlGPE0oDU+vzjv4aco18VxLO7i7a2c5c+tB7ai4PLErT7CvD6F6wZvDdAtcv8Ren0SxJoESXpuglY5bc+17weIOWAk/XZMdPUJFBuVHEnehVredbftqXogh4Go3AHkSRu1qXHPB5bLjK19zDU7h4yrB0Y2gEuiW33dRsdDuQTK2VDOhnI2TXJR/HqZwlT0O7ShAPK+YNRD8rdlOwTpc89rgzy4zK3UZvPHvqTu0KcEdJxSY0W6N+tRyefV9ib7rvd9nB5xe1/ex9eEX9ee/xUQksf1upsvqI69mOo6Muxn19ngvLXM7+onoY2bcX2eVu9W128I3KesnWTM9vofJhbOX4/i+7yu94xsdV/WIicM6y8t84bFe3QsXXz8BET3bU7nMYrdvWur/7f41UzvNcE4GV5rphgnxc+jHC5jObCfXmmMz2fzEn7POJ4Bj+NYH3h7K2f5c/7A1h6koAN1kxo7h24C/EHKoJPF/OGM5JwgMJL05QJBsClwcq69oBhfFoy0OTvItKn4PuYwK/KDGLbOC7qQDeda0PurzhkbujHbm4ylAGiUa8UPPOtYDVC5lbz9ZMxbEBDEB/I61OysvIAeKMXFTwghL8hLxLW5SC2+OSi5+Gje17wS5Yubu5bb4FWZ+9GPZ7n+l3yUs3cpD873pu++nQ9H3eZT6yrR3fWzybfrUc7nDe3JON3Yg2sR6nNSm3rzKd8rpoe2sRxSxw5AcR1JDZzUNVadjdq5YT4E6V/9avaX+D7KWfWuUFm/IaK/tX4jCnFD68TVr2QvI3M0XYtln89HsDYEHW9ie4kvwL4MlpsZ2F/QecPi/dzeEnOVaF01yl2DvR7SNwtRv9ooxrUdME4etKW0XYpRmn+djZ2Okk/jN0JtbP8JqPV7Cfa4x/F7xvEcagcgjqscEHfR9lbO8uf0wNYfvL3INFmZgwkdPPtIc1R0wxMzvy4MuEcenY6oGnl26Fxfuunx72cSOeqgJzhyyJSspI/YpiGgXJOTyqYwZ8uKnRNG1QOnd+FCCCFX0yOupUWVx9GiCDmsFUo5Ac37S14zCsJ9TP3cXU7bfmRsemK0/JSokQdLNZnq5+vTqscsn7VxQ3vROFMmm1h1azyHaB2LcqafltYRWmfjdm6d306cWe+21HSIbDVu+G+iuPG79uThkj1+cGr8WzDWFWr3LvOzh1P8BZg3jYf+3BdiTgK6X9i/rzjDX5L4U43DMxW53vsexsmDY6nEqK975OfOdtVPpCBxD5Gp8M5+v2L0i/pFF//Zwyn+vDf/5uJuY3sd/Nlk9xO2GUNuypaSWHhPJsZ8Ryomswj2JraY2jhE79Ik6P15QkPSIGfpkfSPyh0Eba8m1zqunB/V7JxjND1ayLYhC9/0qQ0dK+USKGdDORvK2TTJRfGrR2w8Dc2v4KdzRP9MTtJ8ZdjDfMPbtTM9lTDj+t//EXypo6yi1l036oaYIM9FtYbcW9uVe7EuDXpA+RxtL1MTrehYvXZivNeWfLNXTdmL/FjxOltB7dzVT0Eyc9taj5k4fdCastqfjrP9U33DxMLkY6/T/MabZtTuXeZnD2f6i0c8bxrPfNut5NZhCOoHw/iLEsYS08agX/n01jHfHuMkRClOmv2W7DoBjashPj+Xgf2ecbyJ3nG3KHeWPyPfYRsjA7UH85wnbOPrfYrr2lMVYpvWfnL2tK5bJ/uo3DFQ+5fl2p9KsR1+j50nxtKjjR5tEELISIwa1/SrlKKPyBdpKsByeT9GPtLmZHMfoQSwc5m0md8sLASvLdVkcq+oT0mPPfm80F6mBtzA8zdax45AaR2hdXYK6n/H/RRDfCW2e6d6F16/5blvjhsre9bBGYh+324e3Zz61w1f6VNPn8mJ/rKCzJs1jjxoThwtd67zPPtK+ClF3K98euvIOOlf20EpToqdjHs/xXgI5tKm+uq5jOn3jONtSB9Ye9jcVNo7y5+hA9tooapCzpj6WLAnl3z3lxY3zsF9pW7TRAQBTdoX5f2PFjg5/Z4Id319JzuzCLoFqsoCa5+AjD0dsVPoD18Y7aNyCNrW/Z/3ZIDYWN6hCceIygUU7Ke20++68fQQecPhVbai3yvo0YqO9ZJgSQgh5zBiXJOvA4hrl1qB3VaA2Xlf84fLR2k9dGCDF9dORn0F5cFI/6B41z422RY9Jtl6PoftsoxlkZ03yEEfbq6SunPR2ZNTuyB17ADoWHNjsvTN+AFi56b5EKR/fcLHm5edxHoW6921X6D+QtevyGXiwK64IaiPRnuey5DYFNprOWRK9I5ji16TdSR2j/ZliNzCK/iLAM+b2NSLpTO6joD9Aip3OXPs/XG5IbVJg195xPN3lGJ7Mv+Mk7bMgvhdNk5+6HoI5ni2aSjnIe3V4qNQ7PdihvT7hn4Zx804rut3T9xF8sJZ/rwe2OpAxQgZvM4X4+piXWWcApYDOidQJ1rknIFMY8xGWOVce7LJWIKhGne954ysrxOnXa4de3eg/O6C9AM6x4wE7XS8Ifo9csvYRVdDpkUOYfrxjW1suS8NR+UWau/OSHuxH6Tt4XYeW492jgdwQggZixHjWpibPcxC15Bz5Iq1Ut5XW7j8HeSPXN3UQlBj2e0V82BQ+035V/Kg/i1132qH6R6uB5bPW+2ixXwglxbGSZ425BbfhOrYC5DxbeOPiDcDQJ2N2rl1Pnpu3AS43l37zfjYjvVbqv/guPF/06HNcl906GGXXoT+PscCiQ/6dxizavuyZjlhVH/ZMW+leC+g+4Wz9hW9mfKC/wOUG6hfNcU1AMbJg37fEid1nW/3i/06ivvpHfH5Kob0e7BfhXHckBMbInEXbO8Z/hw+YYuhgSq3CP8gutB2LCjSxjvb+d3WFCHk78O49r6Mns/pm4QQQgghZHh4YEvI9ejmdnlHhm8OEEJeGD+esVYgI8I6lhBCCCGEDE/rge36UTkeLhFCCCGEkBeCdSwhhBBCCHkJ9jxhSwghhBBCCCGEEHKYwndBBiA/ZkXIq0C/JzV4YEsIIYQQQgghhBBCCCGDwANbQgghhBBCCCGEEEIIGQQe2BJCCCGEEEIIIYQQQsgg8MB2Lx+P++P78XOz7pF+0M6EEELI68N8TgghhBBCCMyrHNjefr5P+sLl/x733+0LnX/v/x43Uy5Ex1P8ZeGPx4+2+/n4Mu9LG5+P37nvUr+oHMTtv8fPfbal8OvGZ22eELmvz+1+QKpz8qvMAvDLzFk7v5geNVb/7tQeIYRchR/Pfn8+TJlLuf3z8v73414Y4+1rksX0qOf9M1jGuNk8yiFovjyZet1ENlxtusyXzumA6+gEuta7Dmj9gusDr/9cHPDa66EHsenrL23zVo27IgON7538pXdcY5w87C9oPQTXTaA/N9Rhr897+inKJXG8tz/vPE+EWQ9ss4dWE0M4lzPub9cDWzcJv+HTHjdnh9/qhkIWXvkpEd2YuLZ/Mxs3uX//8q6Jbka/qByEvFbG/bXN5ZdurD9Dp0LlxGeSsYht7IPOQA+IjJ1fTo86Ms8M4ISQv8SYcW3KK/clL9ykDjBqHMkfUh+4TbjWBYAetbx/BlOfn0GeC0Dz4OnU66YqOifv9yvJ71IfiJ5d611k/TasD7T+04NdN+719RJjem/eSF9/cbTMWzXuOtDxvau/9I5rvdsblb5+D9ZDsBzqz3h7f4138VOUvv7c3/+w9uT1YX2LnSc2ED5hKwqkG41hnEsmsWexbrZn28BH7CGTZ91TpF0pAG+5ttx112842f+XTDYudwy0PUjOPPycHB4pdH2qdo4YVQ8EBnBCyF9jxLhmjqlWWyAHttW8fwIyLqNGQOhdR9RozecmvWvAF+E96oMT611k/UZY/WL1n73+ZQ7PqB3fl97+0jBvUNxFx/e+/tI7rjFOhtcQTJsZeRaVQ/0Zb+/v8R5+itLXn/v7HxifG167m9YDWz1pnp+41UeY16dw3eAtB3RK+I8I6zuS1iTIJsdN0Cqn7U1JcZOZDJL0uzupib6RUyx9BHI+xmsC/FP6zGRJH8hhICp3AHmSoKzvBCyXGVv7mGt2DhlXD4xsAJeD49XXbXQ8lEugnA3lbChn0yQXxa9sXBsN0a90mOjul/UA8r5g1EPyt2U7BOlzz2uDPLjMrdRm88e+pO7QpwN0nFJjRbo361HJ59X2Jvuu932cHnF7X97H14Rf157/EXfJ43rdzRdUx15MdR0Z9rPrbHDeWuZ39ZPQxs24Pk+rd6vrNyRXJ2Jjsdf/MLFw/voH3+d1vWdkq/uyFjlhWH/B5w2Ku/D4xvUXGcM2p/O4RS/v2po3W/xqpreO1faMuMY4aeDGCr25asod8Ge035N5Cb9nHM/Q2//Q9kSu9TyxkfyBrT1IQQfqJjV2Di1yfAVkQpPBfriJcE4QTMSsaLApcHKuvaAYXxaMtDkbZdpUfO93GDWo6DMV9XFhH6ML2XCuBb2/6pyxoTiFr9eMOGhsP0iuFT/wrGM1QOVW5AdFbJ+RMW9BQBAfyOtQs7PyAnqgpIufEEJem5eIa3ORWnxz0CzkNqC8r3klyhc3dy23wasy96Mfz3L9L/koZ+9SHpzvTd/BOB+OLrWQ6O762eTb9Sjn84b2ZJxu7MG1CPU5qU29+ZTvmtTaLpZD6tgBKK4jqYGTusaqs1E7N8yHIP2rX83+Et9HOaveFSrrVwHqRLT+U7nVr2QvI3M0XYtln89HsDYEHW9ie3BfBsvNDOwv2LyBcbdhfKP7i5XP0kNr1K82inFtB4yTHWyJ1ENCQW6XP6P9Po3R/Z5xPEdv/4Pb0/rUza27h5wnNpMe2MoELKTOKoiz2YqHDp59pDkquuGJmV8XBtxCPwBf90hH10f+CYvZoXN9acHn3xd5w4aog54dmMXxpOAwbBoCysl4SwsiQDaFOVtW7Jwwqh44vQsXQgi5mh5xTYuloC7xgON0BnSTUMoJaN5f8lpQEB9h6ufuctr2YzfTE6Plp0SNPFiqyVQ/X59WPWb5rI0b2ovGmTLZJLW9I5pDtI5FOdNPS+sIrbNxO7fObyfOrHcbazqsnhRy9Z9/mOfG7/qWh0v2+MGp8W/BWFeo3bvMzx5O8Rdk3qYYU427TeMb21+S+FONwzMVud77HsbJg2PpcFg70ejPHQ5r39nvV4x+Ub/o4j97OMWfe/sf1l7beeIOdj9hmxnEFhTzr4/vycSY70jFZBbB7gMzs72y3qWFr/fnCQ2J2svokdgBlTsI2l5NrnVcOT+q2TnHaHq0kG1DgoLpUxs6VsolUM6GcjaUs2mSi+JXj9h4GppfwU/niP6ZnKT5yrCH+Ya3a2d9ik9w/e8v6KRWsYpad92oG2KCPBfVGnJvbVfuxbo06AHlc7S9TE20omP12onxXlvyzd015Unkx4rX2Qpq565+CpKZ29Z6zMTp01pTov2iMQ6VO515c7rO7Ty/8aYZ1b/L/OzhTH/xSOcNjLsHxzeMvyhhLDF1AP3Kp7eO+fbC8VfvvWOc1HaBegiVi8jOzc72nsPAfs843kRv/0vaM/UoxZ0dIN9hGyMDtYuf5zxhG18/dGBr6PETfXRuQmzT2k/Ontb1D0MPVO4YqP3Lcu1PpdgLaI+dJ8bSo40ebRBCyEiMGtfk3fH4I/JFmg58cnk/Rj7S5mSTj9jh2LlM2kzrpJjgtaWaTO4V9SnpsSefF9rL1IAbeP5G69gRKK0jtM5OQf3vuJ9iiK/Edu9U7+48sEViFxbj9qyDMxDf/nbz6ObUv274Cqo/KtefE/1lxZ43W2dZI74Nj4xvFH/ZWHWefSV8+hz3K5/e9QHjpH8NB62HmuumFduf97f3PMb0e8bxNnr7n9Ge2B4+T9wJdGAbDUSdzRlTHwv25JLv/lJjOAcPlJomIgho0r4o73+0wMnp90S46+vJd2YR7J9Y5/TRa6cJTPsQnVsLvqw9HfGC1R++MNpH5RC0rfs/750EsbG8QxOOEZULcHbLvaOkthOndXO6XhN5awGJbEW/V9CjFR3rJcGSEELOYcS4Jh93jmuXXG2xInEezrt23tf84fJRWg8d2ODFtZNRX0F5MNI/KN61j022RY9Jtp7PYbssY1lk5w1y0Iebq6TuXHT25NQuSB07ADrW3JgsfTN+gNi5aT4E6V+f8PHmZSexnsV6d+0XqL8K61f7rK2PWW5X/ac+Gu15LkNiUzje5ZApqXvj2KLXxDZi92hfhsgtvIK/CKV5i3XW9ZHKNo1vYSh/8Zhj74/LDenYGvzKI7bPUYrtyfwzTib30XpoV90kZPx5d3vPZki/b+hXx+/FKr3GON7bn6VukOvBnMwxJ5Q7wHpg6xoOHq2O8Yy3GFcHs8o4BSwHdAZQJ1rkXMFjGnk2wirn2pMiagmGGhzXe0shJU67XNv57sDi5Esb0mciJ/2AzjEjQXtpMy78FsRhyv22ySFMP76xjU3fXTD0QuUWau/OSHuxH6Tt4XYeW492jgdwQggZixHjWpibPcxC15BzmBsyRynvqy1c/g7yR65uaiGosez2inkwqP2m/Ct5UP+Wum+1w3QP1wPL56120WI+kEsL+yRPG3KLb0J17AXI+LbxR8SbGaDORu3cOh89N24CXO+u/WZ8rGH9ttSTsZ1TuenQZm3L6dDDLr0I/X3WVfTXv93/ffnKvqxZThjVX1rnDYi7Qn18Y/uLz5QX/B+g3ED9qimuATBOHvN7tB5C5VB/xtu7niH9HuxXYRxP5Hr7swKdJx4gfMIWQwNVLij9QXSh7VhQpI13tvO7rSlCyN+Hce19GT2f0zcJIYQQQsjw8MCWkOvRze38rgzfHCCEvDJ+PGOtQEaEdSwhhBBCCBme1gPb9aNyPFwihBBCCCEvBOtYQgghhBDyEux5wpYQQgghhBBCCCHkMIXvng4Y8LtOCdkN/Z7U4IEtIYQQQgghhBBCCCGEDAIPbAkhhBBCCCGEEEIIIWQQeGBLCCGEEEIIIYQQQgghg8AD2718PO6P78fPzbpH+kE7E0IIIa8P8zkhhBBCCCEwr3Jge/v5PukLlz8eP/e5bcfv/d/jZsqF6HiKvyzs2v2VNj8fX+Z9aePz8asy5X5ROYjbf4G+j183PmvzhMh9fW73A1Kdk19lFoBfZs7a+cX0qLH6d6f2CCHkKvx49vvzYcpcyu3f4z7n1Mfj+3EvjPH2NclietTz/hksY9xsHuUQNF+eTL1uIhv/Pe7LfOmcDriOTqBrvetA1y8mh+4X9u0rSDt9/aVt3nrG3d5+Py694xrj5GF/aaiHBPr9Ht7TT1EuieOwn7q58/3daq/hHGk364FttrOJIZxLfkWv84GtHsC5DcRq/JtMdM1ZZOGVnxLRjcnv9+M3M1ly//7lXRPdjI0MKgchr5Vxf21z+aUb689QX1ROfCYZi9gm1VnsHOgBkbHzy+lRR+aZAZwQ8pcYM65NeeW+5AXJ+a4YS8Yp+cPl8B+3Gbm5HIHoUcv7ZzD1+RnkuQA0D55OvW6qonPyfr+S/C71gejZtd5F1m/DOkf3C/v2FaSVrv7iaJm3nnG3tx6vQu+41ru9UenrL2A9NEO/P867+ClKbz+A4jjsp7IenJxXt2qdEI+v4RxpN+ETtnbjwziXGLhrsZ7XN3CeCLmfTozH4gi33GS5606POHjFToHLHQNtD5IznXZaQCWbWlTtHDGqHgjDrDFCCOnEiHHNHFOttqgc5CjVvH8CMi6jRkDoXUfUaM3nJt1rwNfgPeqDE+tdZP0KRTl7XcvchDUhKkeO0dtfGuata9w90e8Hp3dcY5wMryGYNsvlWfp9F97DT1EujOMGSb/mWrD7SHDr5XDN69N6YKsn1/MTt/oI8/oUrjOG5YBOWf9RYn1nxpoE2eQ4Q61y2t4UHDaZyXBJv7uLoLq+KfKakiP5705lJlX0QA4DUbkDyDsKSACG5TJjax9zzc4h4+qBkfU5WfCrr9voeCiXQDkbytlQzqZJLopfL1OYin6lwsrdL+sB5H3BqIfkb8t2CNLnntcGeXCZW6nN5o+HSd2hTxHoOKXGinRv1qOSz6vtTfZd7/s4PeL2vvyPuTl+XXv+R90kj+t1N19QHXsx1XVk2M+us8F5a5nf1U9CGzfj+jyt3q2u35minL2u07lB5S5i/hio7/O63jOy1X1Zi5wwrL/g89Yl7i6c6fcHEd23OZ3HI+P1rq15s8WvZnqviWp7RlxjnDRwY7XqIfr9du1yv2ccz4DH8Rj7fEjasw9xS3MsdPfl/IGtrbSgirtJjZ1DlfUNIhOaKPXhlHBOECgxGyTYFDg5115QjC8LRtqcjTdtKr53O4wYVBbeNEbpU8Y8XbPkdSFn7gl6f9U5Y0MJhr5eMzKW2H6QXCt+4FnHaoDKrcgPitg+o3Ze2lLEB/I61OysvIAeKEgwIYSQV+Il4tpcpBbfHJRcfDTva16J8sXNXdu5CVr70Y97uf6XfJSzdykPzvem76KbD0fd5lPrKtHd9bPJt+tRzucN7ck43diDaxHqc66vpUbUa1/yunBOVA6pYweguI6kBk7qGqvORu3cMB+C9K9+NftLfB/lrHpXqKzflYqc1n+rv+T3C6jcNXwEa0PQ8Sa2B/dlsNzMwP6CzVvHuCuc6fddkHgQxk4hPbxD/WqjGNd2wDjZwZbZeoh+L4zj94zjOdQOaP6t+ekiI7Wia0PqyPgBABvbfw6RHtjOA18mwheeEWezC49wgNlHmqOiG56Y+XWxQY89Qu8HHzcOmWRxIFO/2aFzfenE+/fnABfLoQ56ekCbklXWSVdAuazdLGRTmLNlxc4Jo+qB07twIYSQq+kR17T4CuoSDzhOZ0AOa4VSTkDz/pLXgoL4CFM/d5fTth/9mJ4YLT8lauTBUk2m+vn6tOoxy2dt3NBeNM6UySZW3RrPIVrHopzpp6V1hNbZuJ1b57cTZ9a7aE1XlUP3Cy37ijKnxr8FY12hdu8yP3s4xV+QeZtiTJe4K3TW4wx/SeJPNQ7PVOR673sYJw+OpVgP0e9H9fsVo1/Unl38Zw+XxfGY/PnQ1z2qB52dyz7vqPa3g91P2GYGuwXF/OvjezIx5jtSMZlF0OPAzCenn1wvTYDe94PESjrZlh6JHVC5g6Dt1eRax7XXzjlG06OFbBuy6E2f2tCxUi6BcjaUs6GcTZNcFL96xMbT0PwKfjpH9M/kJM1Xhj3MN7xdO+u7+YLrv1r4ZZE6yipq3XWjbogJ8lxUa8i9tV25F+vSoAeUz9H2MjXRio7VayfGe23JN3vXlEfJjxWvsxXUzl39FCQzt631mInTB6opUTkPNMahcqeTfOx1mt9404zavcv87OFMf/FI561j3BWepMcxwlhijg30K5/ea4JxMrzWhLZbqofETvT7YfyecbwJ1OZJv+b4Qr+wOMWeyHfYxojidlHznCds4+t9i2uxgdVe7nqJnD2t69aTmqjcMVD7l+Xan0qxF9AeO0+MpUcbXQI4IYQMxKhxTd5tjz8iX6TpICeX92PkI21ONvcRSgA7l0mbaZ0UE7y2VJPJvaI+JT325PNCe5kacAPP32gdOwKldYTW2Smo/x33UwzxldjunepddP02rXMB9W9U7mzEt7/dPLo59a8bvtKnnj6TE/1lxZ63bnFXeYYex1nHPftK+BQa7lc+vesDxkn/Gg5aD9HvR/F7xvE2pA+svcQOYlOjJvgprpeTakjowDYasDqbM6Y+ZuzJJd/9pUHAObhvJPn+E/faIKBJ+2JM/6MFTk6/d8JdX9/xySyCbhOrwTYa24zo3FbICRl7OuIFqz98YbSPyiFoW/d/3jtoYmN5hyYcIyoXIIVuJkCp7fQ7Pzw9RN5YQCpb0e8V9GhFx3pJsCSEkHMYMa7Jx57i2iVXW6xInIfzrp33NX+4fJTWQwc2eHHtZNRXUB6M9A+KVu1jk23RY5Kt53PYLstYFtl5gxz04eYqqTsXnT05tQtSxw6AjjU3JkvfjB8gdm6aD0H61yd8vHnZSaxnsd5d+wXqL3T9onJCYb8QgMo9BYlNob2WQ6ak7o1ji16TdSR2j/ZliNzCK/iLUJq3WOfMekP3H016XMUce39cbkht0uBXHrHeRym2J/PPOJncb6qH6PfR/av8vqHfeM70GuM47qcf+vrA1nMs2WQi3P3S/O9mPbCVDtQIGTzjLcbVQa8yziCWAzonUCda5H5zv8o4GXWVc+2JMZdgKH1u9xaDitMu1/a8OzAF1/X1weT5pIujht+uFagE/b4Mr+8gYHqgcgjTj29sY8t9eTIqt1B7d0bai/0gbQ+389h6tHM8gBNCyFiMGNfC3OxhFrqGnMPckDlKeX8pEIP8kaubWghqLLu9Yh4Mar8p/0oe1L+l7lvtMN3D9cDyeatdtJgP5NLCPsnThtzim1AdewEyvm38EfFmBqizUTu3zkfPjZsA17trvxkfQ9cvvM7R/QIqdw2hv8+xQOKD/h3GrNq+rFlOGNVfWuftaNyN6LnPO4spL/g/QLmB+lVTXANgnDzm93A9tEC/D7jM78F+FcZxQ67BT9UuYZslP62dI+0mfMIWQwPVGYMZFF1oOxYUaeOd7fxua4oQ8vdhXHtfRs/n9E1CCCGEEDI8PLAl5Hp0czu/e8M3Bwghr4wfz1grkBFhHUsIIYQQQoan9cB2/agcD5cIIYQQQsgLwTqWEEIIIYS8BHuesCWEEEIIIYQQQgg5TOE7pQPO+FEfQq6Cfk9q8MCWEEIIIYQQQgghhBBCBoEHtoQQQgghhBBCCCGEEDIIPLAlhBBCCCGEEEIIIYSQQeCB7dl8PO6P78fPzbpH+kE7E0IIIePCPE0IIYQQQgjMqxzY3n6+T/vC5dvXv8f993+P358P8/7C7efz8evkZBy/93+PmyETo+Mu/gLxx+NH2/x8fJn38X73jC/L7b/Hz322udrdjc/aZCFyX5/b/YBU5+TXmwXgF5yzdn4RPVb/rsgRQsjo+PGsllcv4Tbl/GmM3497YYxofTBRz+dnsIxxs3mUQ9A8eDL1eohs/Pe4L/OlczrgOjqBrnWsA2vP2dpfP1k5t769dVQf3zXx4J3o6y/A/DbsA4Qz95evSe+4xjh52F967pMVPE7S74nw9DguNOwDJtB8flLeXw9ss0loYgjnkl/R63lgq+19P37c5ubm9C/pKBuN+5d3TV5b3XjIAi0/TaIbGDeG31yyB/vdN74M8loZ99dmjy8Zpxtj4PSonPhWMhaxTaqzHHQGekBk7PxCesj8MYATQv4SY8a1KV/cl3h/m4qrZJySF8D6YKGWz89g6vMzyF8BaH47nXo9VEXn5P1+Jfld6gPRs1sd68Dak/Uf+qWud6NffSPeXV/XjcSO4mHE8+PBO9HbX6D5RfcBGquw/NFbj1dB9O4Z13q3Nypd/UVe23OfLNfBOEm/t++/G739APM/cB/gIeNE8jkq10z4hK19+DSMc8kknlWsFxOqs4vrNw42cZEXo5NWcrolAN5su+P97htfK2h7kJxZ9EwLLVi4AFU7R4yoxzBrjBBCOjFiXDPHVKstKhtupZrPT0DGZeR+hN71QY3WPG1yZg04MO9RH/SuY8H2TJ+y1rC9rmVuzFrvinjwVpzgLy3z65PZB6wU80dvPV6H3nGNcTK8dgS0vVQOXUf0e+ve+9HbDzD/M+egVF+i+fzMvN96YKsn1/MTt/oI8/oUrjOG5YBu8P5HjfRJEGsSRDk3QauctjdtRjaZyZhJvw0HY1lKCVX6bT6UE1uWHM4/zc9MLNrvrvG1Ie+kqe2Nez6wXGZs7WOu2TlkVD2yAVwKwdXXbbQfyiVQzoZyNpSzaZKL4tfLFKai3+4NtwDkc8Goh+Rvy3YI0uee1wb5bZlbqc3mj5FJPaFPhek4pcaKdG/Wo5Knq+1N9l3v+zg94va+/I9tOn5de/5HNyU/63U3X1AdezHVdWTYz66zwXlrmd/VT0IbN+P67FrHwu0ZvimvTeo/e13bcwPGgytwey1Z577P63rPyFb3ZS1ywqj+0jS/IdU+S/mjux79EN23OZ3HI+P1rq15s8WvZnrXB4yT4bW9HNsng+voCXrs5SX8nnE8w/44LjrZ+wA0n5+c9/MHtvnOVHE3qbFz6OL1DSITmizmDzcRzgmCiZC+XMEUbAqcnCwa31GWBSNtzg4ybSq+jzmMkJ2o+Z7hsOJQOQfQBZ9rz6H3V9tkbI32u2N8EH6AWsdqgMqtyA+P2L4lY96ChSC+ktehZmflRfQ4NFeEEDIYLxHX5iK1+KZfqT5wQPlc80WUB27uWm6DV2XuRz/u5fpf8kzO3qX8Nt+bvvt2Phx1m0+tq0R3188m365HOU83tCfjdGMPrkWoz7m+lhpRr33J68I5UTmkjh2A4jpy84PV2aidG+ZDkP7Vr2Z/ie+j9K5jW9pTvxJ/cPecn8QH/Ata163+InsUkZ2u+XJYPLiKj2BtCKpXYisZN7Avg+VmRvUXBzq/IbJeKvMrY821cYIefbH1kxwRxw3MrzaKcW0HjJMHbInufwE5aB3R7817OIzjOSD/iynsA9B8fnreTw9sZQIWCoMyFQ8dPPtIc1R0wxMzvy4OEF0eoRenyU1ms0PNjp8bkwY8/35mYtF+T3D4kCmpbY6YA5Qr2TpBNo85W1bsnDCqHr0COCGEjEOPuKbFV1CXeMDxNwNyWCuUYj2az5d8FRTER5j6ubtctf3I2PTEaPkpUSO/lWoy1c/Xp1WPWT5r44b2onGmTDax6tZ4DtE6FuVMPy2tI7TOxu3cOr+d6F3HNrT3dY/m29nOXkP+myOuHTev8tBIML+6Xvw5KfgkwKnxb8FYV6jdd8/PUXr7iwLMb0ztvlCS6azHGf6SxJ9qHJ6pyPXe9zBO9hhLj30ysI7o9+a9Qxj9ovbs5z+NnOLPjXG8tA8QmyL5HJU7wu4nbDOG3IJiabDhPZkY8x2pmMwiKB2EwRQn0+43N26xTylA6H0/mKxE9kL7bRzfXtD2anKt48r5W83OOUbTo3hP/NL0lQ0dA+USKGdDORvK2TTJRfGrFPMuR/Mm+Okc0T+TazQPGfYw3/B27axPpwiu//0fwZc6yipq3XWjHogJ8ldUQ8i9tV25F+vSoAeUp9H2MrXOio7VayfGe23JN7vUlB3JjxWvsxXUzl39FCQzt6111granilXsmtIPDdN8eAKko+9OuTvyAao3XfPz1F6+0uGUpwQoP7ceuq1v7yGcD2YYwP9yqdm21YYJ8NrR0DbQ+WSuaHfm/dgGMebyNpc+8/vA+R1gY1XwpiByh0C+Q7bGB2YmXye84RtfP30A1vTLrknJkW2dTw5u6P9toxvP+g8leXan16xF9oeO0+MpUf9HiGEvCKjxjX9KqXoI/JFivVBTC6fx8hH2pxs7iOUAHaOkjbzm4WF4LWlmkzuFfUp6bEnTxfay9SAG3heRuvYESitI7TOTkH977ifYoivxHY/UseC7YmdDD/4geKD9FEbnzWOqxDf/nbz6ObUv274Sp86+Ux6+4tFbX7BWNFtf3kd6zzPvhI+VYn7lU/v+oBx0r92jL7r31pH9HvrHgbjeBuW/7l5aN0HKNZ4LVC5BqADW3ECL9moszlj6mPGnlzy3V9qDOfgvpFu00QEAU3aF2P6Hy1wcvq9E+76evKdWQRdJrayIYsXmP5QhSEvcvjGbiE/sS39InII2tb9n/eOg8yFvHsQjhGVCxA7ZwKZ2k6/O8zTQ+SthSayFf1eQY/1vozhkmBJCCHnMGJck4/xxbVLrcCu1Qchdj7XvODyTFoPHdjgxbWTUV9B+S3SPyjetY9NtkWPSbaep2G7LGNZZOcNctCHm6uk7lx09uTULkgdOwA61tyYLH0zfoDYuWk+BOl/eZIksHk7sZ7FOnbtF6+r7Pbc5jfaQ0wbuUI8ENT3or2MSb6+fz4ylkjXZQ3F+saxRa/JOhK7R/syRG5hYH8JQObXtVl6km5F5HLjcjTpcRVz7P1xuSG1SYNfecR6H6XY3rymGSdDtC2nxzYme/+LyiUU1lGTHlcxpN839Ms4bvrfrn2AIrav+LyCyjWwHtg6I9iP8854xluMq0XNKuMMYjmgM4A60SL3m/tVxsmoq5xrT4LDEgylz+3eYgQxyHJtx7sD6qDb632s4KLf1zLf17FF961FVEOC+9avPbn1ftvkEKYf6djGlvsRBlRuofYujrQX+0vaHm7nsfXYOB7ACSFkLEaMa2HO9TALXUPOYW7IHKV8rrZweTnIC7m6qYWgxrLbK+a3oPab8qrkN/1b6r7VDtM9XA8sT7faRYv5QC4t7JP8a8gtvgnVsRcg49vGHxFvZoA6G7Vz63z03LgJcB279lv2Mai9ZbNblJsOY3yZmr5Iff9sQn+fY4HEB2uMlX1Zs5wwrL+0z29xH9B9f3k9U17wf4ByA/WrprgGwDh51O9l7gr1QbNc2zqi3zv2+D3Yr8I4nsiFudmjcGCL5nNUrpnwCVsMDVS5oPTG6ILcsfBIG3/RzlxThJC/BuPa+zJ6nqZvEkIIIYSQ4eGBLSHXo5vb5R0ZHvoTQl4YP56xViAjwjqWEEIIIYQMT+uB7fpROR4uEUIIIYSQF4J1LCGEEEIIeQn2PGFLCCGEEEIIIYQQcpjCd/8GVH8ciJAXgn5PavDAlhBCCCGEEEIIIYQQQgaBB7aEEEIIIYQQQgghhBAyCDywJYQQQgghhBBCCCGEkEHgge3ZfDzuj+/Hz826R/pBOxNCCCHjwjxNCCGEEEIIzKsc2N5+vk/7wuXb17/H/fd/j9+fD/P+Airno+Mu/gLxx+PHtfl4fD6+zPvSxufjV2Vc3/d/j5sh0yIHcfvv8XOfba52d+OzNlmI3Nfndj8g1Tn59WYB+AXnrJ1fRI/VvytyhBAyOn48a8mXT+M25fJpjN+Pe2GMbXm/ns/PYBnjZvMoh6B58GTq9RDZ+O9xX+ZL53TAdXQCXetYB1zfI/3C68jNnb8eO+hBbPr6i4vf3vya7TXsA4Su/vcn6B3XGCd7+Avmp2Bca6w3qvXLn+A9/RTl6XHcAZ/TtOwXzozj64FtNglNDOFc8it6PQ9stb3vx48LDjenf1ZHVC5BFmj5aRLdwLi2f3PJ3t2/f3nXZCyGQ6FyEPJaGffXpueXbsA/Q+dD5cS3krGIbVKdZQEFekBk7PxCesj8MYATQv4SY8a1KV/cl3h/mw5Zk3FKXmjM+7V8fgZTn59B/gpA89vp1OuhKjon7/crye9SH4ieXetYcP1C/cLrSOJJ6Ofa/149SJau/uLQDbx7/TqfkhviTTe6D+jtf3+Q3nGtd3uj0tVfYD8F45q011BviC7F+uUP8i5+itLVnx1QHHdg5zTgfsHRW4+E8Alb+/BpGOfSwHJSsV5JqCuonEMDUWmylsB2s+2u8+H0jZ0sDpq43DHQ9iA5Z0fLNnsObKt2jhhRj2HWGCGEdGLEuGaOqVZbIHm/ms9PQMZl5H6E3vVBjdY8bXJmDTgw71EfnFjHFtfvsX4TOdNHnxgT3obe/mLPkay9ai2f2QesnOh/r0zvuMY4GV5rpuSnB+OaOb4D9csr8x5+inJdHEfOacy5yq2Fs9blQuuBrZ5cz0/c6qO/61O4zhiWAzrF/Efd9Z0Ua/CyyXGKrXLa3rSYN5nJSEm/jQd8JsWE6oHKqS1LE+Wf0tt2V32Rw0BU7gDyDhkSWGG5zNjax1yzc8ioemQDuPO3bU3YaD+US6CcDeVsKGfTJBfFr5cpTEW/3RtuAcjnglEPyd+W7RCkzz2vDfLbMrdSm80fI5N6Qp+e0XFKjRXp3qxHJU9X25vsu973cXrE7X35H8d0/Lr2/I9kSn7W626+oDr2YqrryLCfXWeD89Yyv6ufhDZuxvV5Wh1bWr8H+rXrRMPXpQ+gnnwK88eVfZ/X9Z6Rre7LWuSEYf3FjttIDqv2eZL/nY3ovs3pPB4Zr3dtzZstfjXTuz5gnAyvNVPy0wNxLbef3lu/nM1L+D3jeAY8ju/uw+me7BfOXJcL+QNbW2lBFXeTGjuHLkrfIDKhySL9cAo4JwgUkL5cIAg2BU5OFo3vKMuCkTZnB5k2Fd/HHEawJsAClNMFX5DT+6ttMraWvoyFIg4Q2xmSa8UPUOtYDVC5FfnhEdu3ZMxbsBDEV/I61OysvIgehwM4IYQMxEvEtblIzR4mCpW8D+VzzRdRHri5a7s3LXM/+nEv1/+SZ3L2LuW3+d703XHz4ajbfGpdJbq7fjb5dj3KebqhPRmnG3twLUJ9zvW11Ih67UteF86JyiF17AAU15GbH6zORu3cMB+C9K9+NftLfB/lrDpWKK3f1n6ROlH9VPzLteH8Ln7D4Fo+grUhaL2a2ADcl8FyMwP7i9phjQeig8zddM2Sn5D1EsaWhJ7+93Rs/dLDNtSvNopxbQeMkwdtWfJToSWuVeNkY/3ydEb3e8bxHGoHII6r3OKji/8F+hvk9gtnrsuF9MDWH3zqrII4m72oQwfPPgocFd2wQvPr4oXf66MAxUC1AMnNjp8bkwYy/77IG7ZGHeB0R5mSmvSRBl0fUA61tSKbx5wtK3ZOGFWP/oULIYRcTY+4lhZVHnD8zYAc1gqlWI/m8yVf1QpCmKmfu8tV2490TE+Mljc9Rn4r1WSqn69Pqx6zfNbGDe1F40yZbGLVrfEconUsypl+WlpHaJ2N27l1fjtxZh1bWr+7+83XiV/3yH/cXOw9iDg1/i0Y6wq1e5f52cMp/uIfHrl2nH3loaCinWv3hVP8z+YMf0niTzUOz1Tkeu97GCcPjqXiy/viWi5OTrm6vX6xeWe/XzH6Rf2ii//s4RR/3hHHlfI5TXG/cOa6XNj9hG1mAJuy+dfH90Qh8x2pmMwiKBoYBZpMByAn9inJ6H0/mKykwdDSN7EXKncQtL2aXOu4cv5Ws3OO0fQo3hN/M31lQ8dAuQTK2VDOhnI2TXJR/CrFvMvRvAl+Okf0z+QazUOGPcw3vF0761Mngut//xMlUkdZxaC7btQDMUH+imoIube2K/diXRr0gPI02l6m1lnRsXrtxHivLflml5qyI/mx4nW2gtq5q5+CZOa2tc4ycfpkffBgv4mc2V5pnp5M8rHXaX7jzeZu/Z/Fmf7iUcthUH8n+t9zCP3XHBvoVz4127bCOBlea6bZT0t2DUnHJ6/dX788h1C/ofyecbwJ1OZZOR1nYb/wDD2Q77CNEYXsRf2cJ2zj62Md2IoNW8eTs7t13XoHAJU7BjpPZbn2p1fsBbTHzhNj6VG/Rwghr8iocU2/Sin6iHwRtD5Qcvk8Rj7S5mRzH6EEsHOUtJnWSTHBa0s1mdwr6lPSY0+eLrSXqQE38LyM1rEjUFpHaJ2dgvrfcT/FEF+J7d6pji2u32P9JmtQ7G709dMSb05DfPvbzaObU/+64Svl+ncDlevPif6yIn2U2gNjxYn+9yzWeZ59JXxaEvcrn971AeOkf20HJT89GNesOGHHDrFhaT6ey5h+zzjehvSBtWfNDbZfeIIe0IFttFBVITcIfczYk0u++0uVdA7uD1a+r8S9Ngho0r4Y0/9ogZPT751w19cT7cwiOD1Q+VTkxDZQOwEZuzti59EfyDDaR+UQtK37P++dBJkLeScnHCMqFyD2ywQytZ1+J46nh8gbC01lK/q9gh7rfRnDJcGSEELOYcS4Jh/Pi2uXWoEN1weKnc81L7g8k9ZDBzZ4ce1k1FdQfov0D4p37WOTbdFjkq3nadguy1gW2XmDHPTh5iqpOxedPTm1C1LHDoCONTcmS9+MHyB2bpoPQfrXJ3y8edlJrGexjl37Ber/yvpF+lWZ2jqar8cfm5w2fIX48jQkNkVjW9ZQPL44tug10VnsHu3LELmFV/AXQe0S7VVjxK+Qee3gf5czx94flxtSmzT4lUes91GK7c1rkHGyQNFPsbimY4PipCOOHcZ8XM6Qft/QL+O4Gcd1/QLnNC37hSY99rAe2OpAxQgZvE6XQeliXWWcQSwHdIqpEy1yzkCmkWejrnKuPVn0SzBU4673loUvTrtc2/HugDro9nqfYHJROWMR1ZDgvrVnBDSHfv/LLKM2MWRa5BCmH+nYxpb7cnFUbqH2Lo60F/tL2h5u57H12IgXOiGEvDojxrUw53qYha4h58htKEr5XG3h8nKQF3J1UwtBjWW3V8xvQe035VXJb/q31H2rHaZ7uB5Ynm61ixbBgVxaECf515BbfBOqYy9AxreNPyLeBAQ+4HDzG9sdtXPrfPTcuAlwHbv2m/GxxvWL9AvXicvmudLeFYT+PusgeunfYcyq7cua5YRR/eX/psM2v63a+Ir7gBP872qmvOD/AOUG6ldNcQ2AcfKg37f4KRjXmvbTwZyMk399hvR7sF+FcdyQm/w0jgexLLxfmDk1jodP2GJooBpwUV2NLsgdC4+08RftzDVFCPlrMK69L6PnafomIYQQQggZHh7YEnI9urld3rnhoT8h5IXx4xlrBTIirGMJIYQQQsjwtB7Yrh+V4+ESIYQQQgh5IVjHEkIIIYSQl2DPE7aEEEIIIYQQQgghhyl8p2pA4UedCHk56PekBg9sCSGEEEIIIYQQQgghZBB4YEsIIYQQQgghhBBCCCGDwANbQgghhBBCCCGEEEIIGQQe2J7Nx+P++H783Kx7pB+0MyGEEDIuzNOEEEIIIYTAvMqB7e3n+7QvXL59/Xvcf//3+P35MO8rt0lm+uLn78e9JOuh4y7+AvHH40fb/Xx8mfeljc/H79z37/3f42bItMhB3P57/Nxnm6vd3fisTRYi9/W53Q9IdU5+vVkAfsE5a+cX0WP174ocIYSMjh/Pinn1KhryOVQfrNTz+RksY9xsHuUQNA+eTL0eIhv/Pe7LfOmcDriOTqBrHetoaa+6jjyZ8ny4OOCttx56EJu+/oLOm1ubvp8U+sX2l2PE5+fQO64xTvaIL73jGjK+vfv91+Q9/RTlmjg+AeV9aHwn5/31wDZ7GDUxhHPJr+j1PLDV9r4fP25ybk7/vI6y0Nym7mu+f3OTUg1swvS60tMkuoFxY/jNbPDk/v3LuyZjNgIaKgchr5VxL/o6vnQD/hk6HyonvpWMRWxjH3QGekBk7PxCesj8MYATQv4SY8Y1MJ9LXoDqg41aPj+Dqc/PIH8FoPntdOr1UBWdk/f7leR3qQ9Ez251rKOlPWgdgfFADyJcP+v6khjTe/NGuvsLNm+SL8I4pv4Q94v6i8gNEZ+fT++41ru9Uenq9yfENXR8+/b7r8+7+ClKV392tORf6buY9x0t/nxq3g+fsLUPn4ZxLg0sJxXrhUBl6g+MRR2h5HTShm5gbLvrfLg+4smOiwVc7hhoe5Ccs7dlmz0BvGrniBH1GGaNEUJIJ0aMa7vyeWUjo1Tz+QnIuIzcj9C7PqjRmqdNzqwBB+Y96oPedWxDe63rqBgP7PUvc/iOhxPncYK/IPNmxiD7tStI/oh4dny+gt5xjXEyvNZMl7iGj48Htvb996K3P6N+6oDyPjq+hn730npgqyfI8xO3+ojw+hSuG5TlgC65+Y8a60m2NQmyyXEGWOW0vcmYm8yUKJN+exijNaE6+fLmQ2xZcjh5l3Z5qse2u+qLHAaicgeQd3yRghaWy4ytfcw1O4eMqkc2gIufrb5uo/1QLoFyNpSzoZxNk1wUv16mMBX9Svnc3S/rAeRzwaiH5G/LdgjS557XBvltmVupzeaPc0k9oU/b6Dilxop0b9ajkqer7U32Xe/7OD3i9r78jxc7fl17/keMJT/rdTdfUB17MdV1ZNjPrrPBeWuZ39VPQhs34/rsWsc2tNe8jorxwF7/w8TC+eP3vs/res/IVvdlLXLCqP4Cz5sRy2Qspf1C0V9S0P3H2Yju25zOdhVdvWtr3mzxq5nea4JxMrzWTI+41jC+LmM+gZfwe8bxDGgcB/M+PD68393kD2ztzgUdgJvU2Dk0yfgDkwlNks6HU9Q5QaKoS4DBpsDJufaCYnxZMNLm7CDTpuL7mMMIrh346Y/ZqUuHhLrgC+3p/dU2GVvLmIyFIo4S2xmSa8UPUOtYDVC5FfnhEdu3ZMxbsBDEV/I61OysvIge3RY1IYQMwEvENSCf1+oDKJ9rvojywM1dQ4pGk7kf/diV63/JMzl7l/LbfG/67q75cNRtPrWuEt1dP5t8ux7lPN3QnozTjT24FqE+5/paakS99iWvC+dE5ZA6dgCK68jND1Zno3ZumA9B+le/mv0lvo/Su46F22tcR4K0XYgHWv+tfiV7GZmj6Zol/1w+grUh6HgTW4ldgH0ZLDczqr844HnTOCTxw/Xl4kr8hlBCxV+U5v3Hs5B4EMZOIT3sQP1qoxjXdsA4edCWFT+F1kfD+LS9xeeVyJ6XMrrfM47nUDvU/BTN+63+XO33AOmBrQx6IXVWQZzNHkDo4NlHmqOiG56Y+XVxIuv1UQDIqMjmbnH8nIwmZv/+7DixHOooJzh8iHM8cWjD9iGgHGprRTaPOVtW7Jwwqh79CxdCCLmaHnFNi6CgLvE4WghB+dxRivVoPl/yVbcNydTP3eWq7UcSpidGy0+JGvmtVJOpfr4+rXrM8lkbN7QXjTNlsolVt8ZziNaxKGf6aWkdoXU2bufW+e1E7zoWbm/ymaZ1VK39/E2g68/JysMle/zg1Pi3YKwr1O675+covf1Fwebt6x7FB2e/Iwf8Ieg+xeYMf0niTzUOz1Tkeu97GCcPjqXqp8D6ODS+8j65xDv7/YrRL+oXXfxnD6f4MxLHwbzfNL5+ed9k9xO2GUNuiy3/+vieKG6+IxWTWQR7F3gAYlTtv/40r9in1Jbenyc0JC0CLH0Te6FyB0Hbq8m1jivnbzU75xhNj+I98UvTVzZ0DJRLoJwN5WwoZ9MkF8WvUsy7HDCfK6J/JtdoHjLsYb7h7dpZn6ISXP/lw9USUkdZRaO7btQDMUH+imoIube2K/diXRr0gPI02l6m1lnRsXrtxHivLflml5qyI/mx4nW2gtq5q5+CZOa2tc5agdvbsY6cfVprz2FiYfKx12l+400pavfd83OU3v6SIZk3s9/SOnTs8JfL7GoS6meODfQrn95rgnEyvNZMj7h2cHzDxEllYL9nHG8itbnMLZD3R/Jn5DtsY2QA9qJ+zhO28fVnHNjKKXn8kTobsWHreHJ2t65b70ChcsdA56ks1/70iu3we+w8MZYe9XuEEPKKjBrX8Hw+07SRyeXzGPlIm5PNfYQSwM5R0mZaJ8UEry3VZHKvqE9Jjz15utBepgbcwPMyWseOQGkdoXV2Cup/x/0UQ3wltvuROhZvr3kdNR9syFj26tET8e1vN49uTv3rhq+U698NVK4/vf3Fwpg3sZUx9z+lfNLsL1fa1WYdz+wr4dO/uF/59K4PGCf9azvoEteOjW+0mnFMv2ccb8Py05zOTjaw4ZHx2f3uBjqwFSfwFrE6mxusPu7rySXf/aWbIufg/mDl+yLca4OAJu2LUv5HC5ycfv+Du74+AZNZBGcHKvl4Sqxrbixim9bEnLW7I17Y+gMZRvuoHIK2df/nPXkkcyHv5IRjROUCxM6G3QS1nX4XlKeHyBsOr7IV/V5Bj/W+jOGSYEkIIecwYlxryecrhfogxc7nmhdcnknroQMbvLh2MuorKL9F+geFrPaxybboMcnW8zRsl2Usi+y8QQ76cHOV1J2Lzp6c2gWpYwdAx5obk6Vvxg8QOzfNhyD96xM+3rzsJNazWMeu/eJ1VbY9YB0FtMQD9dFCW09FYlNor+WQKalnY5voNVlHYvdoX4bILQzsLwHZefvQ64ENpe2d+UN1qMXnEZhj74/LDalNGvzKI56/oxTbm+eIcbJAwU8TCnENGZ/ar3GffAlD+n1Dv4zjWT9NdNb1m8o2jW+h1O9e1gNbXShihAze4JbBa5JaZdzALAd0BlEnWuTcAjWNPCu3yrn2JIktwVAX93pvSWTitMu1He8O6GRtr/fxjSwB2JJJF6SMB3SimbBtO0Hr9xnNMmoTQ6ZFDmH6kY5tbLkv1UflFmrv4kh7sb+k7eF2HluPjeMBnBBCxmLEuAbnc7A+8Cnlc7WFy8tBXsjVTS0ENZbdXjG/BbXflFclv+nfUvetdpju4XpgebrVLlosB3Jp4ZzkX0Nu8U2ojr0AGd82/oh4swDU2aidW+ej58ZNgOvYtd+yj8Ht1dYRHA+mQ5vlnvTZwy69CP19jgUSH/TvMGbV9mXNcsKw/tIwb6pvKJv0C/uLzEkhPg/ElBf8H6DcQP2qKa4BME4e9PuT4hoyvtZ98lUM6fdgvwrjuCE3U8v7M/XxPSHvh0/YYmigygWlN0YX5I6FR9r4i3bmmiKE/DUY196X0fM0fZMQQgghhAwPD2wJuR7d3M7vzPDQnxDyyvjxjLUCGRHWsYQQQgghZHhaD2zXj8rxcIkQQgghhLwQrGMJIYQQQshLsOcJW0IIIYQQQgghhJDDFL5TNaDwo06EvBz0e1KDB7aEEEIIIYQQQgghhBAyCDywJYQQQgghhBBCCCGEkEHggS0hhBBCCCGEEEIIIYQMAg9sz+bjcX98P35u1j3SD9qZEEIIGRfmaUIIIYQQQmBe5cD29vN92hcu377+Pe6//3v8/nyY95Xbf4+f+zwGHcfn4wvYdOi4i79A/PH4cX0/Hq4987608fn4VRk3xvu/x82QaZGDQPVF5L4+t/sBqc7JrzcLwC84Z+38Inqs/l2RI4SQ0fHjWTGvXsVtyvnTGL8f98IYofpgpZ7Pz2AZ42bzXA65ZnwL9XqIbPz3uM/zOew6OoGudayjpb3aOmqvT69db+9AX39x8+XtA8z2GvYBUHtNcn+B3nGNcbKHv7TUOf3rjXeIk+/ppyhPj+MeiD9j4zs5jq8HttkkNDGEc8mv6PU8sNX2vh8/bnJuTv+sjvrrfU7ua7v/pRvSz8pkyAItP02iGxg3ht9MoJL79y/vmozFKBBROQhUX1ROfCsZi9gm1VkK4kAPiIydX0gPmT8GcELIX2LMuDbli/sS72/TZiEZp+QFpD7wqOXzM5j6/AzyV44rxrdRr4eq6Jy8368kv0t9IHp2q2MdLe0h62hPXXfdevv79PYXPZB3r1/rfskN8aa7cR9Qbc+Byv01ZP56xrXe7Y1KV7/XnIrXOVNM61tvoHJ/hXfxU5Su/uxoiaeIP6PjOz2Oh0/Y2klnGOfSwHJSsQ5uyHx+JMjVDmNLTif66AbGtrvOh9M3nuy0X1TuGGh7kJxZ9EwO31IQC1U7R4yoxzBrjBBCOjFiXDPHVKstkPqgms9PQMZl5H6TK8bn0ZqnTc6sAQfmPeqD3nVsQ3vgOmqq6y5eb3+fE/wls/+tzrm5D0DbO9Dvi9M7rjFOhteaqdU5Z9Qbbxgn38NPUXr7c0M8hfwZHV9Dv3tpPbDVE+T5iVt9RHh9CtcNynJAtxj9R431JNuaBFmszgCrnLY3GXOTmYr1pN8exqgFqgh5ArM80WLLksN9uAlfnuqx7a76IoeBqNwB6vpOwHKZsbWPuWbnkFH1yAZwKQRXX7fRfiiXQDkbytlQzqZJLopfL1OYin6lw0R3v6wHkM8Fox6Svy3bIUif2GsL41vmVmqz+eNcUk/o0zY6TqmxIt2b9ajk6Wp70/jX+z4uT8ftfXkfSxN+XXv+VxxJftbrzh5QHXsx1XVk2M+us8F5a5nf1U9CGzfj+uxaxza0h64jfCxgPLgCt9eSde77vK73jGx1X9YiJ4zqL5l5QnKY3Sfa3v5+z0bGsM3prKPY3bu25s0Wv5rprWO1PSOuvX2c9JExFuzXpd7YJfdcXsLvGccz4PEU8md4fHi/u8kf2NqdCzoAN6mxc+ghlz8wmVA30aEDfThFnRMkirpiPtgUODnXXlCMLwtG2pwdZNpUfB9zGMG1U336w1+wiV4huuAL7en9tY2MrWVMvv4z4iixnSG5VlB9G+wyIT88YvuWjHkLFoL4Sl6Hmp2VF9Gj26ImhJABeIm4NhepxTf9KvUBlM81X0R54OauwZugmLkf/diV63/JM4a9q+Obc9/03V3z4ajbfGpdJbo7+S0ftutRztMN7ck4nR7BtQj1OdfXUiPqtS95XaizyiF17ADoWHNjcvOD1dmonRvmQ5D+1fdmf4nvo/SuY+H28HWE1nXV9XYpH8HaEFSvxFYybmBfBsvNjOovDrXDGg9EB1lb0zVLfkLWiz2/aHv7+n0Wtn7pYQfqVxvFuLYDxsmDtpS2sz6Hx0k0/o0eJ8f2e8bxHGqHajwF/blhfFi/B0gPbGXQC4VFZg4gdPDsI81R0Q1PzPy6MOAW+mmhGKhipiAvkxiPZWJ2/NyYdHPk358dJ5ZDHeUEhw+p6bsAyjXaOj+/FTsnjKpHrwBOCCHj0COuaREU1CUeRwsh5LBWKMV6NJ8v+SooiI8w9XN3uWr7kYTpidGg6ETGV6rJ9PW+fKses3zWxg3tReNMMXRbiOYQrWNRzvTT0jpC62zczq3z24nedSzc3uQz1XVkYtR1yHpr4NT4t2CsK9Tuu+fnKL39RfE3764dZ195KKho5+J9tL0d/WY4w1+S+FONwzMVud77HsbJg2Mp+hwYJ9H4h8qBvLPfrxj9on7RxX/2cIo/I/EU9Oem8fWL4ya7n7DNGHILiqXFF94Txc13pGIyi6B0EAazw6i5cYt9Sm3p/XlCQyJ7ZfRN+kXlDoK2V5NrHVfO32p2zjGaHsV74pemr2zoGCiXQDkbytlQzqZJLopfpZh3OZo3wU/niP6ZXKN5yLCH+Ya3a2d6KmHG9V8/FMohdZRVNLrrXj0AjS+qISS3re3KvViXBj20/1qeRtvL1DorOlavnZjILjnf7FJTdiQ/VrzOVlA7d/VTkMzcttZZK3B72DrKEc8NtN6uJPnYq0P+jnRF7b57fo7S218ylOKE0Npfrb0FVO45hLHE1Bn0K5/eOubbC8dfvfdOcdLH6ZPP1WKnTvVGg9y1hL4xlN8zjjeR2hzz56Pj6zLXC8h32MboQjMX9XOesI2vX3lga0528K4RQs7u1vUPQ19U7hjoPJXl2p9esR1+j50nxtKjfo8QQl6RUeOavOsdf0S+SFN9kMvnMfKRNieb+wglQLYGMeqkDWN8pZpM7hX1KekhfbXm6UJ7mRpwA8/LaB07AqV1hNbZKaj/HfdTDPGV2O5H6li8vX3raKIe46xxXIX49rebRzen/nXDV/rUyWfS218spI9Se62xotbeAir3PNZ5nn0l/PQh7lc+vesDxkn/2g4qdc6+OGmN1wKVey5j+j3jeBvSR9oe5s9Hxmf3uxvowFacwFvE6mxusPq4ryeXfPeXboqcg/uDle+LcK8NApq0L0r5Hy1wcvr9D+76+gRMZhGcHahU3/s/70kcGZu8s5HaSmRbD36zdnfEC1t/IMNoH5VDQPVtscuK2DkTyNR2+r15nh4ibzi8ylb0ewU91vsyhkuCJSGEnMOIcU2+7iauXWoFdm0jE2Lnc80LLs+k9dCBDV5cO1n1VYIxvkj/oJDVPjb5Fj0m2Xqehu2yjGWRnTfIQR9urpK6c8npnpz6JlLHDoCONTcmS1/DD1A7N82HIP3rEz7evOwk1rNYx6794nVVtj1gHaldGuu6iXx9/3xkLOF4l0OmpJ6NbaLXZB2J3aN9GSK3MLC/BKhdKrFU5r+UN3yQ9gRU7tnMsffH5YZ0bA1+5RHP31GK7cn8M07aMgviz7l2BCBOpohvIPEPlXsyQ/p9Q7+M4/l4Cvpz0/gWzojj64GtLFQ1QgZvcMvg9SmVVcYNzHJAZxB1okXOFTymkWflVjnXnhyiLcFQ+tzuLYtanHa5tuPdAZ2s7fU+yYTpj1Zs9+NfHZ6Q8YBONCPBfevXDlb6fapLv2ITQ6ZFDgHTF5dbqL2LI+3F/nLEzmPrsXE8gBNCyFiMGNfCnOthFrqGnCNXhJXyudrC5eUgL+TqphaCGqvcnjm+oPab8qrkN/1b6r7VDtM9XA8sT7faRYvlQC4tnJP8a8gtvgnVsRcg49vGHxFvFoA6G7Vz63z03LgJcB279lv2Mbg9YB211nVIff9sQn+fa12pf60xVvZlzXLCsP4yHbb5bdXGV94HoO2193sVU17wf4ByA/WrprgGwDg5+cxuv1/ze0rukKsWJxfQ+DdinPQZ0u/BfhXGcUNuBvTn+vieEMfDJ2wxNFAVFum7ogtyx8IjbfxFO3NNEUL+Goxr78voeZq+SQghhBBChocHtoRcj25u53dmeOhPCHll/HjGWoGMCOtYQgghhBAyPK0HtutH5Xi4RAghhBBCXgjWsYQQQggh5CXY84QtIYQQQgghhBBCyGEK36kaUPhRJ0JeDvo9qcEDW0IIIYQQQgghhBBCCBkEHtgSQgghhBBCCCGEEELIIPDAlhBCCCGEEEIIIYQQQgaBB7Zn8/G4P74fPzfrHukH7UwIIYSMC/M0IYQQQgghMK9yYHv7+T7tC5dvX/8e99//PX5/Psz7IR+PHyf7eHw+vsz7ITru4i8Q19u7/Xw+flXGjfH+73EzZFrkIG7/PX7us83V7m581iYLkfv63O4HpDonv94sAL/gnLXzi+ix+ndFjhBCRsePZ1hefTK3KedPY/x+3AtjhOoDNM+cxDLGzeZRDrl4fAv1eohs/Pe4L/OlczrgOjqBrnWso629Wj3u5sRfZ6V6vGlfQfbS11/c/HtxMt8e7gdCNT57Mn/fX3rHNcbJ436P+l/H9dGwn/4bvKefolwTxyeQ+DxRP6+bQOUaWQ9ss4tnYgjnkl/R63lgq+19P37c5Nyc/oiOuuFwr/mFJkIWaPlpklp7cv/+5V2TMRsbHlQOQl4r4/7a7PEl43RjDJwelRPfSsYitkl1loPOQA+IjJ1fSA+ZPwZwQshfYsy4NuWL+xLvb1NxlYxT8gJSH6D54ySmGuIz6D/g4vFt1OuhKjon7/crye9SH3StYx2t7U1rKVePS5wI/VfjQtye+igQN8hhevuLPmjhXr/GRckNyWYf9IPlnvpUJT6/qb+IbXrq27u9Uenq9w3+13V9uGvofvqv8S5+itLVnx2Yn05I38X47DHJ5uqDDVSumfAJW3uxDONcGlhOKtaRRCn964YDCyo6aSWnq7bnrjt9YyeLgyEudwy0PUjODNbTQgsWLkDVzhEj6jHMGiOEkE6MGNfMMdVqC6Q+iOidf01kXEbuR3jK+Dxa87TJmTXgwLxHfdC7jm1sr1aPm76XkV3YETcIygn+ktn/JocJqB+0xuc385fecY1xMrzWTNH/TlgfMa7/wzXCC/AeforS259BPxVa4nOtPlhA5fbQemCrJ9fzE7f6CPP6FK4zhuWAbvD+o8Z6km0WSzJprqAP2puMuclMgSDpt/GAz6SaKOVdo+UpHGQiRKbkcEB7oi9yGIjKHUCeyEEcG5bLjK19zDU7h4yqRzaASwJbfd1G+6FcAuVsKGdDOZsmuSh+vUxhKvqVNgrufose2fxh1EPyt2U7BKkh9rw2GN8yt1KbzR8jk3pCn4rRcUqNFenerEclT1fbm+ql9b6P0yNu78v/+gfHr2vP/woIyc963c0pVMdeTHUdGfaz62xw3lrmd/WT0MbNuD671rFN7SH1veHD0kepTmyMG0/B7bVknfs+r+s9I1vdl7XICaP6S2be07WH+0FzfB7MX0T3bU5nXURX79qaN1v8aqZ3fcA4GV5rpuh//ddHTBcdOvASfs84ngH105b4jJ7/tZ4TNpI/sM13poq7SY2dQzcBvkFkQpNF+uEmwjlBYCTpyy3wYFPg5GTR+I6yLBhpc3aQaVPxfcxhBNdOacOmC3jVpT4RKn+0PRmTr/+MOHJsZ0iuFT9ArWM1QOVW5IdHbPvJmLdgIYiv5HWo2Vl5ET0OzRUhhAzGS8S1uUg1N24LlfpAqeaP6Qe3gjxwc9fgojFmrhv0414uDy55Jmfv0vjme9N3d82Ho27zqXWV6O762eTb9Sjn6Yb2ZJxu7MG1CPU519dSI+q1L3ldmKtVDqljB6C4jtz8YHU2aueG+RCkf/Wr2V/i+yi969iG9tQ/VxsW6nv1P/Eb14bzp/iNgAQZQy1uPJ2PYG0IWq8mthI7APsyWG5mVH9xqB3WeCA6yBxP1wJZyA8a47MwqL9Y6yE97ED9aqMY13bAOHnQlhX/67s+Ymw/uw57POP4PeN4DsxP8fiM1gdwHbGX9MBWBr1QGJS5qEMHL370yJsgeGLm14UBt9BPC6VAJf2KI6991CZC7hfGhLaHOvIJDh8yJbXNEXOAciVbJ8jmMWfLip0TRtWjVwAnhJBx6BHXtPgK6hIPOP5mQA5rhcZYb+ePOV8FBfERprrh7vrafiRhemK0/JSoMb5STab1il+ftOpRy9MN7UXjTJlsYtZm0RyidSzKmX5aWkdonY3buXV+O9G7jkXbQ+txx9c9uu5ee/YB3Knxb8FYV6jdd8/PUXr7i+Jv3l07zr7yUFBsZ8wPJj9qis+D+ksSfwx/ManI9d73ME4eHEvV/3quj4iDvv/Ofr9i9Iv6RRf/2cMp/oz4KRifxaZIfYDKHWH3E7YZQ25BsTTY8J5MjPmOVExmEZQOwmAKwUL0DRb/St4+pQABt5fRN7EXKncQtL2aXOu4cv5Ws3OO0fQo3hO/NH1lQ8dAuQTK2VDOhnI2TXJR/CrFvMvRvAl+Okf0b8w1Zn5w7UxPJcy4/qsbmSxSR1lFrbtu1AMxwfiiGkLure3Kvbg2adADytNoe5laZ0XH6rUT47225JtdasqO5MeK19kKaueufgqSmdvWOmsFbE/909d1BanHS/Z3ODvuqVFPJfnYq0P+jnRD7b57fo7S218yJGsP9gO51hifR/QXJdTPtDHoVz6lGLwHxsnwWjM7/G//+gi5LI4UGdjvGcebSG0uc1uPz/K6wMYroT+jcodAvsM2RgdmLurnPGEbXz/7wDalZCe51zqeXHvWdetJTVTuGOg8leXan16xg9seO0+MpUf9HiGEvCKjxjX9KiX5mJ5xz2THRqaeZ+QjbS6P5T5CCWD3IW2mdVJM8NpSTSb3irmupMeePF1oL1MDbuB5Ga1jR6C0jtA6OwX1v+N+iiG+Etv9SB27tz3rdQ6xp+EvP6U4siNunIv49rebRzen/nXDV+rxawKV609vf7GQPqL2GvygOT4P5y8bqy6zr4SfHsH9yqd3fcA46V/bQbP/HVsfG+Pl3IUx/Z5xvA3DTx3N8VmxxmuByjUAHdhGC1CdzRlTHzP25JLv/tJNkXPwYDFPExEENGlfjOl/tMDJ6fdOuOvrEzCZRfD8QJWfCLFNe8Itt+fbVH8gw2gflUPQtu7/vCePZC7k3YNwjKhcgNg5sxjUdvpdN54eIm8sNJWt6PcKeqz3ZQyXBEtCCDmHEeOafB1AXLvUCuxSfdCSZx5OLq2HDmzw4trJqK+g8UX6B4Ws9rHJtugxydbzNGyXZSyL7LxBDvpwc5XUnYvOnpzaBaljB0DHmhuTpW/GDxA7N82HIP0vT5IENm8n1rNYx6794nUVVhfn6nG3SY72GtMbP/vixjWIbpEOyxqK9Yhji16TdSR2j/ZliNzCwP4SoHaJ9qpKgx/EtjHWZcBw/uIxx94flxvS8Tf4lUc8f0cptjfPEeNkgRb/67E+FqTf0v0rGdLvG/plHM/4qdxrjM+K2N6qD2JQuQbWA1tZMGqEDJ7xFuPqIlxlnJKWAzqDqBMtcr+5X2WcjLrKufZkk7EEQw2O673FCGKQ5dqOdwd0srbX++QmTILxJhdPhowHdKKZcnsT+n1zs4zaxJBpkUOYfqRjG1vuS8NRuYXauzjSXuwvaXu4ncfWY+N4ACeEkLEYMa6FOdfDLHQNOUdcHyD5Q23h8nKQF3J1UwtBjWW3VxxfUPtNeVXym/4tdd9qh+kergeWp1vtosV8IJcW9kn+NeQW34Tq2AuQ8W3jj4g3M0Cdjdq5dT56btwEuI5d+y37WEtdXK3Hl01xqb0d+4pnEvr7HAskPujfkc6VfVmznDCsv0yHbX5b2fEhfrBQi8+D+4vPlBf8H6DcQP2qKa4BME4e9HvY/05aH47afvpqhvR7sF+FcdyQm6nFZ49qfTCDyjUTPmGLoYFq4MV1Fbogdyw80sZftDPXFCHkr8G49r6Mnqfpm4QQQgghZHh4YEvI9ejmdnlHhof+hJAXxo9nrBXIiLCOJYQQQgghw9N6YLt+VI6HS4QQQggh5IVgHUsIIYQQQl6CPU/YEkIIIYQQQgghhBym8J2qAaP+SBUhe6Dfkxo8sCWEkM6g/6zXEkIIIYQQQggh5L3hgS0hhHQG/We9lhBCCCGEEEIIIe8ND2wJIaQz6D/rtYQQQgghhBBCCHlj/u/x/wGX3K9oohlp7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 1000,
       "height": 1000
      }
     },
     "execution_count": 21
    }
   ],
   "source": [
    "from IPython.core.display import Image\n",
    "Image(filename='Img/TC1-Acc.PNG',width = 1000, height = 1000 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "tc1results = pd.read_csv(\"Output/tc1results.txt\", index_col='epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch'>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 372.103125 262.19625\" width=\"372.103125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2020-11-07T03:59:17.630995</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "tc1results.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to save the model/weights?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Saved model to disk\n",
      "Full model also saved to Model/tc1.full_model.h5\n"
     ]
    }
   ],
   "source": [
    "# JSON JSON\n",
    "# serialize model to json\n",
    "json_model = model.to_json()\n",
    "\n",
    "# save the model architecture to JSON file\n",
    "with open('Model/tc1.model.json', 'w') as json_file:\n",
    "    json_file.write(json_model)\n",
    "\n",
    "\n",
    "# YAML YAML\n",
    "# serialize model to YAML\n",
    "model_yaml = model.to_yaml()\n",
    "\n",
    "# save the model architecture to YAML file\n",
    "with open(\"{}/{}.model.yaml\".format(output_dir, model_name), \"w\") as yaml_file:\n",
    "    yaml_file.write(model_yaml)\n",
    "\n",
    "\n",
    "# WEIGHTS HDF5\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"{}/{}.model.h5\".format(output_dir, model_name))\n",
    "print(\"Saved model to disk\")\n",
    "\n",
    "# ALW: Also saving model using model.save('path/to/location') as model.save() is different from model.save_weights()\n",
    "full_model_hdf5_file = '{}/{}.full_model.h5'.format(output_dir, model_name)\n",
    "model.save(full_model_hdf5_file)\n",
    "print('Full model also saved to {}'.format(full_model_hdf5_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "The calculation was carried out on a NIH Biowulf GPU node. Model weights were saved in Python HDF5 grid format. HDF5 is ideal for storing multi-dimensional arrays of numbers. You can read about HDF5 here.\n",
    "http://www.h5py.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "# Open the handle\n",
    "json_file = open('Model/tc1.model.json', 'r')\n",
    "\n",
    "# load json and create model\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "\n",
    "# load weights into new model\n",
    "loaded_model.load_weights('Model/tc1.model.h5')\n",
    "print(\"Loaded model from disk\")\n",
    "# loaded_model_json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mimicking the process of external set\n",
    "\n",
    "Note this is a demonstration of how to use external data for inference.  \n",
    "\n",
    "When you bring in an external dataset. Make sure you follow the following steps:\n",
    "\n",
    "a) Make sure you do the same operations that you had done to the data set \n",
    "b) scale the inference dataset in the same way as the training data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "chosen_idx = np.random.choice(38, replace=False, size=5)\n",
    "# X_test[chosen_idx].shape\n",
    "# Y_test[chosen_idx].shape\n",
    "# Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_mini.shape (5, 60483, 1)\nlen(y_minip) 5\n"
     ]
    }
   ],
   "source": [
    "X_mini = X_test[chosen_idx]\n",
    "y_mini = Y_test[chosen_idx]\n",
    "# df_trimmed = X_mini.drop(X_mini.columns[[0]], axis=1, inplace=False)\n",
    "# X_mini = df_trimmed\n",
    "print('X_mini.shape', X_mini.shape)\n",
    "print('len(y_minip)', len(y_mini))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_mini.shape (5, 60483, 1)\ny_mini.shape (5, 3)\n"
     ]
    }
   ],
   "source": [
    "print('X_mini.shape', X_mini.shape)\n",
    "print('y_mini.shape', y_mini.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "acc: 40.00%\n"
     ]
    }
   ],
   "source": [
    "# evaluate loaded model on test data\n",
    "loaded_model.compile(loss='categorical_crossentropy', optimizer='sgd', \n",
    "                     metrics=['accuracy'])\n",
    "score = loaded_model.evaluate(X_mini, y_mini, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "source": [
    "You are viewing the Jupyter Notebook from ML-TC1 GitHub repository, https://github.com/ravichas/ML-TC1 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Unsupervised learning plots (PCA and tSNE)\n",
    "\n",
    "### *This section was based on Dr. Andrew Weisman's code template. Check out Andrew's Gihub here, https://github.com/andrew-weisman*"
   ]
  },
  {
   "source": [
    "## Unsupervised learning plots (PCA and tSNE)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Load the new tc1_library (`.py` file), which currently contains just the unsupervised learning plotting function"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tc1_library\n",
    "\n",
    "# In case it was already loaded (for Andrew's debugging; you can leave this in here and it won't hurt anything)\n",
    "import importlib\n",
    "importlib.reload(tc1_library);"
   ]
  },
  {
   "source": [
    "### Decode the outcome matrix back into a single vector (remember earlier that we one-hot-encoded it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "outcome[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let us explore some outcome values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_decoded = [x.argmax() for x in outcome]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0]"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "### Perform the PCA and tSNE using scikit-learn"
   ],
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the PCA and t-SNE using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top 10 PCA explained variance ratios: [0.38811713 0.12041275 0.0499922  0.03027331 0.02528322 0.02271728\n 0.01852503 0.01623522 0.01457019 0.01271466]\n"
     ]
    },
    {
     "output_type": "display_data",
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Ha2trfPvttwgMDMxUv9WrV8emTZswatQoWFtbw8PDQ3MzXrly5TBhwgTUqVMHjo6OuHv3Lr7++mvNuY0bN4aXlxecnJxgZ2cHABg3bhyMjY3h6OiIvn37fvLfRWbGT05OxtSpU2FnZwcnJyeEhIRg0aJFAFJXQTlx4kS+WoOZyFAJ1Jn9TYuIKItCQ0NRr1493Lx585MrPeS2rl27omzZslmufmbk/Pnz6NWrl9byYkRZsXr1arx58wY//vijvkMhMnhifQdARIWXvb09Hj16lKdjXrt2DTY2NihevDhOnz4NHx8fblNL+cb333+v7xCI6D9MgomoUAkKCkKnTp0QHh4OV1dXrF+/XrNBCBERURpOhyAiIiIig8Mb44iIiIjI4DAJJiIiIiKDk+k5wQMGDMDx48fh4OCgWW9x7ty52LRpk2bJnkWLFqFVq1Zf7MvOzk5rH3UiIiIiotzw6tWrDHeMzHQS3K9fP4waNQp9+vTRah83bhwmTpyYpWDc3d3h5+eXpXOIiIiIiLKqevXqGbZnejpE/fr1YWNjo7OAiIiIiIj0JcdzgtesWYOKFStiwIABiIyM1EVMRERERES5KkdJ8PDhw/H8+XPcunULzs7OmDBhwieP9fb2RvXq1VG9enWEhobmZFgiIiIiohzJ0WYZjo6Omj8PHjwYbdq0+eSxQ4YMwZAhQwB8em4GEREREeUvCoUCb9++RVJSkr5D+SyJRAJXV1cYGRll6vgcJcGBgYFwdnYGABw9ehTly5fPSXdERERElM+8ffsWFhYWcHd3h0Ag0Hc4GVKr1QgPD8fbt29RvHjxTJ2T6SS4e/fuOH/+PMLCwuDq6op58+bh/PnzuHXrFgQCAdzd3bFx48ZsB09ERERE+U9SUlK+ToABQCAQwNbWNktTbjOdBO/bty9d28CBAzM9EBEREREVTPk5AU6T1Ri5YxwRERER5XunTp1CmTJl4OHhgSVLluS4PybBRERERJSvKZVKjBw5EidPnsSDBw+wb98+PHjwIEd95ujGOCIiIiKiD8X8cxkRh/cjJTwcYltb2HTuBsuv6uaoT19fX3h4eKBEiRIAgG7dusHHxwflypXLdp+sBBMRERGRTsT8cxmh272REh4GQI2U8DCEbvdGzD+Xc9RvQEAAihYtqnnu6uqKgICAHPXJJJiIiIiIdCLi8H6o5XKtNrVcjojD+3PUr1qtTteW05v1mAQTERERkU6khIdnqT2zXF1d8ebNG83zt2/fwsXFJUd9MgmmQk2lUmH79r1fPO7du3d4/PhZHkRERERUeIltbbPUnlk1atTA06dP8fLlS8jlcuzfvx/t2rXLUZ9MgqlQ27PnMFatOAp/f//PHjdn9hrMnbM2j6IiIiIqnGw6d4PA2FirTWBsDJvO3XLUr1gsxpo1a9C8eXN4enqiS5cu8PLyylGfTIKpUFGr1YiJiQGQWgU+dOASypWahQXzN3zynFevXuHtKymiwt1w/fqtPIqUiIio8LH8qi7s+w2B2NYOgABiWzvY9xuS49UhAKBVq1Z48uQJnj9/jhkzZuS4Py6RRoXKvn1HcejA7zjqswV79hyGQFUfRmJT+L80gb+/P9zc3NKds3CBN2xl3SEUGmH5Mm/s3V857wMnIiIqJCy/qquTpDe3sRJMhYZarcaBfeeRlFAB167dwKEDl2BtWRkAYCvrmGE1OK0KLBZLIRSKERlelNVgIiIiA8AkmAqNffuOAin1YG/dAuPHLYJAVV/z2ofV4A+lVoE7aJ7bW7fE8mU78ypkIiIi0hNOh6BCIa0KbG01GgDwxj8S0lKXIIi/rjlGIIrHtq0HMGfuJABAWFgYLl++AFcXlVZfAYE38PTpU5QqVSrvLoCIiIjyFJNgKhTSqsBp6tdeixThJuzdv1jT9vPyjYiJidc8t7Ozw/0HlzLsL6cLcBMREVH+xiSYCjy1Wo2N6w/C0rQ7wiPva9pDwpNx/fotVKtWGXK5HGf+fASVSoj4+HiYmZkBAIRCzggiIiIyREyCqcBTq9WYOLkPlErVR6+0gKOjPQBg9S9bIRW3glAowbIfN2LuvPFf7PfRo0coW7ZsLkRMREREWTFgwAAcP34cDg4OuHfvnk76ZBJMBZ5QKETbtq0++bpcLsdfZx7DQdYUAHDl3wCtanBGIiIi0K5tPxw5uhXly5fTecxERESUef369cOoUaPQp08fnfXJJJgKvbQqcBoLSYcvVoMXL1qPyuV+xo9LtmLn7p/yIkwiIqICb8TLcYhQRqZrtxFZY13xFdnut379+nj16lUOIkuPEyKpUJPL5Th54hZMjG0hl8dALo+BkdgMly4+R3x8fIbnRERE4N7tOJhKHRAa5Ih79x7kcdREREQFUzWzyhBDpNUmhgjVzKroKaJPYyWYCrX4+Hg0auwJtVp7FYhyKI24uLgMp0QsXrQeVuYdAQB21q1ZDSYiIsqkTjbtcT72MqBWatqEAiE627TTY1QZYxJMhZZarcaokZOxddsamJiYZHjMjRs3UbXq+2+naVVgR1sbAIBIZIzg/6rBnBtMRET0edZiGRpa1MW5mItIgRJiiNDAoh5kYpm+Q0uH0yGo0Pr999N486oI1qzeluHr167dQPdu3yMsLEzTtmD+CsTEAsFhvyE4PPWhUgOzZy3Lq7CJiIgKtE427SEQpKaY+bUKDDAJpkJKrVZjy6bfUbxoT/x15jHkcnm6Y35evhtVyi3HwgXrNG3jxg9BbNxN1G8MbNjcCRs2d4L31i7Y6P1jXoZPRERUYKVVgwUQ6KwK3L17d9SpUwePHz+Gq6srtmzZkuM+OR2CCpXExETExMTg2rVbSEmuBoFUAKm4FVb/shUTJg7THHft2g1Eh7vDwdYWD+8nISwsDHZ2dti86QDKlJiJvy8dwOQprhCJRJ8ZjYiIiDLSyaY9bifc01kVeN++fTrp50OsBFOh8uPSDRgzeiG2bPodNlZfAQAszIunqwb/vHw37KybAwBk5h2xcME6xMXFwfdKMMxMnSAWNMWWzXv1cg1EREQFnbVYhl/cl+XLucBpmARToREfH48r/wQgNNAJkeFOEAgEmtfSqsHA+yqwUChCeMR9REU/wcP7SZg3dznMJe0BADJLL/zvmC+USmWGYxEREVHBxiSYCo2flnnDXNIeRZw6IjL6Xxhb7NA8bJ0vIj4+AQAwf97PUKqiEBR2AI9fbMNL/8NIiE+Gz7GLMDN10vTHajAREVHhxTnBVCikVYEdbdoCAFycGmDU9y1QsWL5dMfu3b8BkZGRuHz5CnZttYZIZAVr+0uQK10QHLFJ+9i9DzFkaO88uQYiIiLKO0yCqVBIqwKnsbdujR+XbMbuvcvTHWtubo6FC9bg+G+XUbX8zxAIhIiMuoJ/r+zUmkJBREREhRenQ1CBl5iYCB+fM4hLuI/gsDMIDjuD0IgLuH/vDR48eJju+OjoaJw/+xA2Vk006xiqFbVx5PDxvA6diIiI9ISVYCrwjI2NsWt3Ruv41kOxYm7pWpcsXo+YaCGqV26tabOR1cLOHavQqXMbVoOJiIjymTdv3qBPnz4ICgqCUCjEkCFDMGbMmBz1ySSYCjyRSAQvL69MHRsdHQ3fKwFIlkfj7sMNkMtD4VHKHhKpBElJ0Xjx4gVKliyZyxETERFRVojFYixfvhxVq1ZFbGwsqlWrhmbNmqFcuXLZ71OH8RHle0sWr4edrCeKOtkBAJTKZIjNdmDb9kV6joyIiKjwiIpMwcI5bzHjB1fIZDlPN52dneHs7AwAsLCwgKenJwICApgEE32On99NuLkVgVQqxalTl+FkZ4OYuPevBz5+jpcvX6J48eJZ6letVgMAp08QERF9xOdIBMJCU/C/wxHoM9BBp32/evUKN2/eRK1atXLUD5NgKtTUajXmz9sE16K2WPXLHOzctTiDo+rA0dExy33/MG8lZDILjBk7KOeBEhERFRJRkSm4fD4WajVw6UIs2nW20Uk1GADi4uLQuXNnrFy5EpaWljnqi0kwFWp/nDoLlaIGnj95idDQUFSoUEEn/aatSywUpmD4CDmMjY110i8REVFB53MkAqr/fi1VqdQ6qwYrFAp07twZPXv2RKdOnXLcH5dIo0JLrVZjk7cPbKzqwsaqIxbMX6ezvtPWJZaIW2Dd2h0665eIiKggS6sCK1NSnytTUqvBUVEpOepXrVZj4MCB8PT0xPjx43UQKZNgKsT+OHUW8sQqEAgEMDaywPMnAgQGBua437QqsJmpMyzMPfDnH/chl8t1EDEREVHB9mEVOE1aNTgn/v77b+zatQtnz55F5cqVUblyZZw4cSJHfTIJpkLpwypwGl1Vgz/enY7VYCIiolS3rsdrqsBplCnAzevxOeq3bt26UKvVuHPnDm7duoVbt26hVatWOeqTc4KpULp+/Qaev3gGJ4fNWu13H/+L+Ph4mJmZZavf+Ph4XLr4Ao7WjaFQpP6Dlpg448TvxzBiZF/ODSYiIoO2Yl3WVlrSJybBVChVr14Nt27n7GeSjMTGxuLreu6A+oxWeylhKcTHxzMJJiIiKiCYBBNlgZOTE+bNm6DvMIiIiCiHOCeYiIiIiD5L/dHNbvlRVmNkEkz0GTExMfoOgYiISK8kEgnCw8PzdSKsVqsRHh4OiUSS6XM4HYLoE5KSklC7VlP8ddZHs1/5x06ePI0WLZpx62QiIiq0XF1d8fbtW4SGhuo7lM+SSCRwdXXN9PFMgqnAGjJ4LFb9shhSqTRX+l+5cjNKFZuFBfPXY+26H9K9HhQUhEkTlkMAEVq0bJIrMRAREembkZERihcvOKs+ZBanQ1CB9M8/V/HwnhTLl3vnSv9JSUm4eP4FrGWeeP404002Fsxfh4qeS+C90SfLPxEpFApdhUpERETZwCSYCqRVK/ahhFt//H3pNRITE3Pc3/jxc+Hre13zfOXKzTA1agMAsLHsgPk/aG+yERQUhGdPAGMjCygSq+CPU2c/2ffHCXJiYiKqVqmH2NjYHMdNRERE2cMkmAqcf/65irjoUhAIhDAzbpfjanBoaCju307Gz8t3A3hfBTY3cwOQmuh+XA1eMH8dbCw6AgBsZHU/WQ0ODw9H3bottF77+edNcHMej6VL1ucobiIiIso+JsFU4KxasQ921s0AAOZmrjmuBi+Yvw42Vt8iOqI4fH2vY/lPG5AU74GIyIeah1DtiRnTl+Pff6/h0KGjePpYDWNjCwCAQCBAYmzFDKvBixaug7lJJ+zffwxAahX48sVXsLetCj/fUFaDiYiI9IQ3xlGBklYFltq8//6WVg2eOXNMlvsLDQ3F4wdyONlZwd66OX5evgFTpvaHZ7nXHx1pDgeHzvj5p9148OAOHOw9ERy+GcHBYYDaEip1NNasPqt1g1x4eDju302Es0MT7NuzCt26dcDPP2+CmXE7AIClaQcsXbIeCxZOztZ7QURERNnHJJgKlPv3nsLR5R2Egh2aNpkUiInO3nbFC+avg7VlJwCAUChCdHhxKBQKdOrUNt2x589fRkKsJ0oUq4ahoxxRqlRxjBl1BE623yIs8iJGfV9M6/hFC9dBZp46ZUKQUhc7dx7A5Yuv4GjdCgBgKnXUVIMtLCyyFT8RERFlj0Cth5WPq1evDj8/v7welkhLaGgoendfCSe7/po2lUoJuWAD9h9Yke747zqPgal4JAABYpNXw9JKAmVCX4hEJlCr1YhNXo3DR1dBIBAgPDwcPbsth5PtAM359x5Pg71NJ5ibvV/DMCExBBWrPcbiJdNz9VqJiIgM1afyTlaCyWC9evUW7iUBsXjXR69IoFQqIRKJNC1pVWCz/6ZhRIa7IioiEW4uJgBS5wXLEyvjj1OpUyLm/7AGFtJWUCqTNX042LaBk9tVVKmSoDVaiRI1cucCiYiI6JOYBJNeXLzwD/z9A9Grd2e9xVCjRhXUqFEFy5evwdixw7WS3o+tXX0IdtYjNc8jI99CqQqFucVmTZtArMbOnb5o0bIJynq6w9T0slYfRQF8Xbc1WrdupvNrISIioqxhEkx68cuqA0hKVKF7jw6fTT5z28OHj7Bv9zVYWVlj0KCeGR4THByMiIhgCO22a9rKeJojPCIcq9YM1eyis2vnrxCJUivFw4b1zfXYiYiIKPuYBFOeO/vXRUSFuSA88i22bN6LIUN76y2WJYu2wNNjCnyObkT//t0yTMgdHR3x17n9n+1HqVTi4IFLANTo1r0DhEKuPkhERJSf8X9qynPr1h5GWMQ7pCgVOHzoEpRKpV7iePjwEUIC7SESGUOkboxt2z6f6ALA8d/+QGhoaLr2bdv2Q6RuDJGqEXbuOJgb4RIREZEOMQmmPJVWBTY2Mke50v0Q+A7YsnmvXmJZsmgL7KxTt0aWWXrB56jvZxPylJQUrFxxCAvma2+hrFQqcezIVcgsvSCzqoDDv/4DlUqVq7ETERFRzjAJpjyVVgUuVeI7SCV2EIvN9VIN/rAKnOZL1eBN3rthIe2Ixw8VCAkJ0bRv27YfYjRBXPy71H5YDSYiIsr3mARTnomMjERcfAiMjAUwMjIHAJQq0QVBQa/x7NmzPI3l6NFTMLUIRaJyq+ZhYnYV//x9LcPjU1JS8PvxW7CyKAMbi05YuGA9gPdVYKHQHBf/HQu5IpbVYCIiogKAN8ZRnrG2tkbx4iVha/6tpk0qsYOLS2U4OTllub/Q0FDY29tnK5bp08dm6fhN3rthJEhd2szY2FJTDU5JSYGtnRh3bq9C/To/48WbeahUqSKsjUQIDg6Gs7NztuIjIiKi3MVKMOUZtVqNkJBACCSHkCLapnlIzRLx778ZV2A/RS6Xo1HDDrh//2EuRfveh1XgNGnVYBcXF8ycNQJODrVhbuYKa6tSWLZ8InbuWsEEmIiIKB9jJZjyjEAgwOEjW3TS19q12+FRbDqWLtmKnbuW6aTPT7l48TJiYkIhFmvH7ud3FykpKVi8aBPsZH0AABamHbF0yXosXDQlV2MiIiKinGESTAWOXC7HmdMPYW/VGMGBD3D//kN4eXnm2niNGzdE48YNM3zt6dNnCHxrBUfb1O2TTaUOuHEtDDExMbC0tMy1mIiIiChnOB2CCpy1a7fDRNQCAGBn3RpLl2zVWywzZiyDMsUSwWHnNI+EBBkWLFilt5iIiIjoy1gJpgLlwyowAIhExggOdNBZNTglJQViceb/WSxdOhWxsbHp2m1tbXMcCxEREeUeJsFUoKxZvQ1REbZQJvtq2lQqB8ycvhxHfTbnuP/27ftg+vTR+Prr2pk6vnjx4jkek4iIiPIek2AqUDp1bo6atd6mazc3r5Tjvv38bkKZXB2/rNyf6SSYiIiICiYmwVSguLu7w93dPVf6/vmnXXCwGYKwqL/w999XmAgTEREVYrwxjgipVeDIcDcIhWLYWzfDLys/vX0yERERFXxMgqnAevr0Gby9d+W4nx3bD2LM6AWwt05dcUIgECI2uhT+/vtKjvsmIiKi/IlJMBVYixZ649CBK0hKSsp2H0qlEjt3/ImwEBESk8I0D3NpFSxbul13wRIREVG+wjnBVCA9efIUgW9lsJQ2x8qVmzF16qhs9bNt236YS1qjbCkhBMY7ULN2Nc1rUmlNXYVLRERE+Uymk+ABAwbg+PHjcHBwwL179wAAERER6Nq1K169egV3d3ccPHgQ1tbWuRYsUZrFizbB3rovRCITXDx3HGPHJkEikWSpD6VSCZ+jvrC1+h4AEBp9BmPGDsrSOsFERERUMGV6OkS/fv1w6tQprbYlS5agSZMmePr0KZo0aYIlS5boPECij6VVgUWi1K2KpUZtsHJl1tcI3rZtP0TqxprnxoJvsEkHc4yJiIgo/8t0Ely/fn3Y2Nhotfn4+KBv374AgL59++LYsWM6DY4oI6lV4Paa5xbmxXDx3PMszQ1OqwLLLL00bVaWZXH8t5tISUnRabxERESU/+Tod9/g4GA4OzsDAJydnRESEqKToIg+RaFQICwsGFaW+/BhqmpknAw/v5uoW7dOpvoJCwuDo6MxjIy1K7/OZhIEBwejSJEiOoyaiIiI8ps8m/zo7e0Nb29vAEBoaGheDUuFjJGREX47viPH/Tg6OmLz1mU6iIiIiIgKohwtkebo6IjAwEAAQGBgIBwcHD557JAhQ+Dn5wc/Pz/Y29vnZFgiIiIiohzJURLcrl077NiRWpXbsWMH2rdv/4UziHLXnTt39R0CERERFQCZToK7d++OOnXq4PHjx3B1dcWWLVswdepU/PnnnyhVqhT+/PNPTJ06NTdjpXzq+vVbml8E9OnChb/RretIxMXF6TsUIiIiyucEarVandeDVq9eHX5+fnk9LOWSju1HwtHJGhs2LtDL+MHBwXB0dMR3344B5N+iXOWrmPfDRL3EQkRERPnLp/JObptMOfLnn+egSKqCV8/FCAgIyPPx4+LiUL9eGxw4cBjxMWVhZuoM3yvBrAYTERHRZzEJphzZsO4obGUNYGvVEfN/WJ/n4y/7cSPKlVqE+fM2w966CQDAXNIey37cAACQy+V5HhMRERHlf0yCKdv+/PMckhMqQiAQwMjILM+qwcHBwRj9/TTExcXB90oQrCyKQ2JcEfEJQQAAM1Mn+F4JxsWLl9Gieddcj4eIiIgKHibBlG1pVeA0eVUNXjB/He7eUmHa1EUwl6SuSFLGoyfuPVqJ4LAjCA47gthYJSaM/wkS8Tc4d+5SrsdEREREBUuebZZBhcvt27fx/PkTODtt1mq/+/gKYmJiYGlpmSvjBgcH49ljFYo49sbpP6agTrUBAAAjsSmKulbAD4sboWjRorh16w5W/vQA9jZNsXbNGjRqVC9X4iEiIqKCiUkwZUulSpVw686pPB93wfx1sLboBLFYAkuzCnjp/z+YSv/bpEVVBCt+3oG9+9Zg+7alcLAZBoFAgMRYL5w7d4mJMBEREWkwCaYCI60K7GhrAQAo49ELcYpVmDyltuYYmawurl27gZhwdzjYiQAAdtaNWA0mIiIiLUyCKVekpKRALE7/8YqOjoaVlVW2+kyrAqcRiyVIiioGR0cnlCxZQtPescNQmEq7Iz7h/QYeYRG2rAYTERGRBpNg0rnXr1+jy3cDcOXqGQgEAk17YmIiatdqirPn/gdnZ+cs9Xnt2nUolckwsz6i1e5kosDdu4+0kuAOHRshNvbFRz24wtraOsvXQkRERIUTd4wjnRsyeDrevvJAv8G26NKlvaZ98aLVuHKpOIq4X8W69fMz3d+ZM+cx5vt5+PeqT5ZuuBs/fi46d26Br7+u/cVjiYiIqHDijnGUJ16/fg3/VxI42NbH7p1nkPYdKzExEZcuvIK1VVm8eCZEYGDgF3p6b8O6I/AqvRBLl3x5+TWlUol167YgNDQUD+7IsWL5bujhex4RERHlc0yCSacWLtgIO6uOqU9S6uDQof8BAFau2AxT4zYAABvLDpj/w7pM9XfmzHkkxVeEqdQBN66FISYm5rPHb92yD1s2/YUpkxbB2rIz7t55hxnTF2b/goiIiKhQYhJMOpNWBRaLpQAAG1lN7N55BgkJCbh04RXMzYoCAIyNLDJdDd6w7ohmQw4L046frQYrlUr4HPOFZ8np+OfvZ1Aqk2FpXhY+R69DqVTq4AqJiIiosGASTDozd85qmJnUQ2JS2PtHXGkMHDgBifHFER5xX/OAsjSmT/vps/2lVYHTbq77UjV465Z9EAuaQiyWwMqiCu4/2oJSJb9D8aK9MGvmYp1fLxERERVcXB2CdKZR45oIDXn8UasRbGzrwtZG9lG7DHb2XT7b39IlqyFCRQSF7dW0xSUkYflP6zDvh6lax6ZVge0sRwMASpXogr99p8HYyAJ2tpXgc3Q35i9QQiQSZfPqiIiIqDBhEkw606fP55ParDrmsxMpKSnp2iUSSbq2tCpwGrFYAge7KoiLD4C5WRGUcOuFrVv2YPCQPjqNkYiIiAomToegPJfZ1RoG9h+Ld+8CYWVlpfUwMTFJd+y5c5chNv4XcclbEBG3Ab43ZyM+IQgvXv+G+IQgSCSO2L//HOcGExEREQBWgkkPxo6ZjYaNaqNjx9afPObevQeICCuOxYs2Y+euz88dBoC9+zZo/uzv/wbHjp7+4NXUKRpCYV0oFApOiSAiIiImwZS3QkND8eShGq9f/YEOHVpp7Sj3oR+XbIWT3UCEBP4PDx8+gqdn2UyP4eZWFKPHDNRVyERERFQIcToE5alFC9ZBZtERKnktHDt2IsNj7t17gNBgRwiFRrC3bovFizZnqu9VKzfj4EEfXYZLREREhRSTYMozoaGhePRAARNjK1hb1cSObX9kOD/4xyVbYW+durGGSGSMkEB7PHz46LN9Jycn4/SpB9i98y/uEEdERERfxCSYcp1CoYBardZUgQFAIBBkWA3+sAqcJjPV4NW/bIXEqBWgrIt9+47q/iKIiIioUOGcYMp1w4dNQ+UqZfHgXiIcbU2hUikAAFYWVbB96zqtucEREdEo7RkHYLdWHxaWdp/sPzk5Gef+egp7WTMA7ti/9xd0797xk/ONiYiIiJgEU656+/Yt3r42x8sXl1C1WikIBEe0XnczKoLk5GTN2r/169dB/fp1sjSGpgqc5r9qcI8enXIcPxERERVOTIIpVy2Yvx62Vl3wNvAPvPZ/jv371+PKlWv46qtaOulfuwqcysaqKqvBRERE9FlMginXvH37Fq+eG8HJzgwRkc8REZWILZt3Y8P6g7j8zyFIpdIcjxEUFASXIkYwMt6l1W5sIUFkZCRsbGxyPAYREREVPgK1Hm6lr169Ovz8/PJ6WMpjw4bOQGxYF0TFPEN8wjs42FXFjXuzULncLFSrcw/Tpn+v7xCJiIiokPtU3snVIShXvH37FvfvxkKuiMXL17+hmGtzmEodITUuDRNja1y68AqJiYn6DpOIiIgMFJNg0qmoqCjMmL4QSqUK33WtBNcSf8DZqSIEgtSPmmfp3nj64hBMjdtg5YrMbYJBREREpGtMgkmnFi9ah4vnQwCoMXrMQAS8jYGrcxvN66ZSRyhS4lkNJiIiIr1iEkw6ExUVhTu3YuHmMhALF2xEcnIyzMyFkFjtwT9+g+F7cwp8b05BQuJzvHy7HhEREdi4cbu+wyYiIiIDxNUhSGcWL1oHK9MOMBKbwv+lCYKCgrBt+woAgFwuh0qlSneOkZFRujYiIiKi3MZKMOlEWhVYIrEFANjKOmLhgo2a142NjSGRSNI9RCKRvkImIiIiA8YkmHQirQqcJq0a/Pr16xz3feLEGTx79jzH/RARERGl4XQI0omwsDCYWPyGDxedtrZLwj//3ECxYsWy3a9KpcLa1Ufg4GiObdt/zHmgRERERGASTDqyZevPudLvnj2HIRY0RuDbJ3jy5ClKly6VK+MQERGRYeF0CMq3VCoVDh24BGvLyrC3bo/FizbpOyQiIiIqJJgEU761Z89hCFT1AQAikQkC38rw5MlTPUdFREREhQGTYMqXPqwCp2E1mIiIiHSFc4IpX7p58xZiYsJgJN6i1R7y7DHi4+NhZmamp8iIiIioMGASTPlStWpVcf7iXn2HQURERIUUp0MQERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBkesi07c3d1hYWEBkUgEsVgMPz8/XXRLRERERJQrdJIEA8C5c+dgZ2enq+6IiIiIiHINp0MQERERkcHRSRIsEAjwzTffoFq1avD29tZFl0REREREuUYn0yH+/vtvuLi4ICQkBM2aNUPZsmVRv359rWO8vb01CXJoaKguhiUiIiIiyhadVIJdXFwAAA4ODujYsSN8fX3THTNkyBD4+fnBz88P9vb2uhiWiIiIiChbcpwEx8fHIzY2VvPn06dPo3z58jkOjIiIiIgot+R4OkRwcDA6duwIAEhJSUGPHj3QokWLHAdGRERERJRbcpwElyhRArdv39ZFLEREREREeYJLpBERERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcJsFEREREZHCYBBMRERGRwWESTEREREQGh0kwERERERkcnSTBp06dQpkyZeDh4YElS5booksiIiIiolyT4yRYqVRi5MiROHnyJB48eIB9+/bhwYMHuoiNiIiIiChX5DgJ9vX1hYeHB0qUKAFjY2N069YNPj4+uoiNiIiIiChX5DgJDggIQNGiRTXPXV1dERAQkO44b29vVK9eHdWrV0doaGhOhyUiIiIiyrYcJ8FqtTpdm0AgSNc2ZMgQ+Pn5wc/PD/b29jkdloiIiIgo23KcBLu6uuLNmzea52/fvoWLi0tOuyUiIiIiyjU5ToJr1KiBp0+f4uXLl5DL5di/fz/atWuni9iIiIiIiHKFOMcdiMVYs2YNmjdvDqVSiQEDBsDLy0sXsRERERER5YocJ8EA0KpVK7Rq1UoXXRERERER5TruGEdEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBkcnO8ZRzt29exeRkZHp2p2cnFC6dGk9RERERERUeDEJzieWbVmG1xXeQWxqpGlLDE5A/YSvsGT6Yj1GRkRERFT4cDpEPrF40mIYvxHDvoaj5mEeIMXM0TP0HRoRERFRocMkOJ8oUqQISqYUhyJeAQBICIpHLcdaMDc313NkRERERIUPk+B8ZNbwmYg5Gg4ASPKJw5Rhk/QcEREREVHhxCQ4H0mrBse8iEZtp5qsAhMRERHlEt4Yl8/MGj4TjTo0xsFzu/QdChEREVGhxUpwPlOkSBH8feIyq8BEREREuYhJcD5kb2+v7xCIiIiICjUmwURERERkcDgnmLT4+/tj/NKJMLM202pPiIjH6tm/wMnJSU+REREREekOk2DS4urqiiRREsT9TLXab0+8i0GrhiI6IArTek5Bq+at9BQhERERUc4xCTYA3w3pApG99l91QmwCejTqhm4du2m1C4VCdKn7LfbdPgKrSjIAQMjVIDi1LgLzRtZIWZ2CZo2b5VXoRERERLmCc4INQPHiJZDUTgn0N9Y8Ei2S0KRukwyP7/VtL4guqDTPA8+/hXPDIoh7GoMWFb6BkZFRXoVORERElCtYCS4Adh/agyR5Yrr2r2t8Dc/Snl88f/rwqfhuSXdIBkkBAMlRyShn7PnJVSg+rAYnxyVBVs4GAoEAKaeSMfKnETm7GCIiIqJ8gElwAbDn5F4Ie5pA8EFb2JkQlPMol6nzZTIZqlhVwpPwl5DYShF3NBIzx/7y2XN6fdsLB8f+ChOFCEIrKavAREREVKgwCc4FCQkJOHbSB4A63WvNGzaHra1tlvqb2G8Cfnz8M6wb2QEA1Co1JAlifFXrq0z3kVYNFnwr/GwVOI1QKETvpr0gMZbggt8F3Dh0EyM3sgpMREREhQOT4FwgFAqx+sgayHppJ5oRh0LQrH7WbyprUr8x1h1eB3VDNQQCASL+DMW8HjOz1EdaNfjy5r/hPf/zVeA0Xdt1AQDUq1kXl69eZhWYiIiICg3eGJcLJBIJ2tZpC7GJGFalZLAqJYPU0RS1i9XM9m5wIzqPQNT5cKhVasieWmSpCpxm+vCp6FShfZZjsLGxQbuW7bI8HhEREVF+xSQ4l4wfNBby4wma57FHIjFzxIxs99ekfmOY35Mg4nQIxnUfk60+ZDIZJo6cmO0YiIiIiAoLJsG5RCKRoEGJ+oj3j4M8Ro5yRmWyXQVOM6LzCAguqbJVBSYiIiKi9zgnOBeNHzQW7ad3QpJpPGaOWZXj/prUb4yva2YtAY6IiMD5v8+naxdAiJbNWkAikeQ4LiIiIqKChklwLpJIJGhYoj4CwwJzXAX+sM+sSE5OxqLDS2DbyUmrPWpfKFo2a6GTmIiIiIgKGibBuWzSsIlISUnR2/jOzs6o6VYDwUUjYWRhDACI949D+6/asQpMREREBotJcC4Ti8VISkrCuLnjoYRS+0U1MLrP9/Ao6ZGrMcwaPhN9NvSHbe/UarD8t3iMXzwuV8ckIiIiys+YBOcBMzMzPIt4DrPR1lrt4RuD4GDvkOvjOzs7o5S6JIJjIyGPlKNBifqsAhMREVGmKBQK7Fi3Fi9uXAeMjNGu/wDU/vprfYeVY1wdIg8IBAL0bNwDUdcjNW2JIQmoblcVlpaWeRLDrOEzEXMsIrUKPJhVYCIiIvoytVqNSf36wvXCnxggUKCfIg6X58/Bkd279B1ajjEJziNd23eB0d/vnyccjcG04VPzbHxnZ2d4pJRgFZiIiIgy7Z9Ll+AVFQYPSwsAgFAgQAcnO1w8sB9qtVrP0eUMp0PkkbRq8Pbre2FS1CRPq8Bpls/4CcbGxnk6JhERERVcN/6+jFoWZunaLeXJSExMhKmpqR6i0g0mwXmoa/su2DNmLxJMYjBtVt5VgdPo8oN67949REVFpWt3cnKCh0fu3uhHRERkaNRqNX47fBhXT52E0EiMDv0HolrNmrk+rle16nj493l8LTHRao81MoJUKs318XMTk+A8JBAI0LtJT9x+dCfDKvD2gzvw9NXTdO3VyldDp1Yd8yLETFuwfiECq4ZBLHn/EUoIjEdTVUPMn/yDHiMjIiIqfH4YPxYlnz1CPxsZlHI1fGZNw9OuPdFtwIBcHbdRs2YY5b0BJeMT4GRmCrVajbNhUajSpj0EAkGujp3bmATnsS7tu6BL+y4Zvvb05VPcqv4AUof3FdvYR9FwCXLJq/AybcmkxRi0cyjsejhq2iLWB2Pa/LyvcBMRERVmz58/h8nD+6jtZAcAEAsE6Oxsj3WHD+HbPn0gFucsnUtISMCJo0eQnJSM1p07QyaTaV4TCAT4adcerF2yGGFPHkMtFqPB4GFo1SF/Feeyg0lwPjJ1xBR0/qELLIa+rxILL6kw9OcheowqY+7u7iiW6IboxESIpWIkvItH3aJ1CvTcICIiovzI99JFVJWmv6eniEqBwMBAFC1aNMPz4uPjccrHB0KREM3btsvw/+irly9j17w5aG5qBAuhEAv270GDwcPQtsv7gp1UKsXEeYXvV16uDpGPWFhYoJZDDSQGJwAAYh5Fo22VNjn+hpdbZg2dgaijoQCAJJ84TBwyUc8RERER5X8qlQoJCQmZXl2hjFd5PEtSpGsPgRB2dnYZnnP21ClM79gOxgd2QrhnO6a0b4tLZ89qHaNWq7Fr8SKMcbGDp7UMJa0sMayIA85t8UZCQkLWL6yAyZ/ZlQGbPGwSOv/QBdKhplCflmPIz4NzZZwbt2/A56//pWs3NTbFlFGTM9VHWjU46EV4pqrAk+ZPRnRKtFabSqlC85rf4Lt232U+eCIiogJq08/L8fCvMzBLUSBOIsU3/QagZcfPTy2oWqMGtltZo3JCIhxMU29GuxMVC4catTK8OU2hUODYqp8x2sVe0+YFYOWypfiqQQOIRCIAwLNnz+ChlKeb21tHLMDFc+fQonXrHF5t/sYkOBcEBQVh0IwhsHK20mqPCYrGmlmrUaxYsU+em1YN/vuiL76t0j7XqsBikRjnYy/Brr2Tpk2tUkOwLf03zc+ZNXQGvvmuOQ5d2P3FY1VCFUJbR8PE5v06xWE+wfBw52oSRERU+O3fthWS82cwwv59frB73WoUK1UK5cqX/+y5S7ftwKof5iHy2ROoBEJ4Nm2B8SNHZXisn58fqqqV6dorqpW4ffs2qlatCgAwNzdHXAbnx6rVcP1gXnBhxSQ4Fzg6OkJsJkJKX6HWtyv1KsDNze2L508eNglN2zfFkNO5UwUGgIrlK6LINmekmAghNEqdFRN+IgQL+83NUj/u7u64/PulTM0FnjF8Orou6wGTgamJtypFBfsAG1SpWCXL8RMRERU010+dxFAb7QLZd062OLBhPeatWfvZc6VSKaYuXvLFMeRyOQ5v2YywNwG4GRQMgUCAvp6lYWFshHioYW5urjnW2dkZ4dZ2iFMoYG5kBABQqFTwhRH6f/VVNq6wYOGc4FwgEAgwuM0gRP0doWmLuhaB3k16ZWo5EQsLC1z560quzwWe2n8yIn9PndOrUqpg89IKNatlfc1BBweHTB1nY2ODiublkRyRBACI+D0Uk/txHjERERkGoTIlXZuJSIRbZ89gQod22Ll+fY7HmDf6e7SLDsXMGpXxfeXy6F22FNbevo9kpRLPpRYoXbq01vGz163HHpEptgWGYVdgKDYkqjBt3foCv/xZZjAJziWtmrWC9IZYM+ld/A/QpX3m573mxYevYvmKsA+0hUqhQuQfYZjQa1yujzlj+HTEHY1iFZiIiAyOadFiiE6Wa7XdCglDM0c7DJeZQnDyf9jtvTHb/YeHh0P86jlczN7/OmtlYoyyttZYHBqDGevSJ9nW1tZYtnMXph0+hvEHj2DVoV9RzN092zEUJJwOkUvSqsGr/9kAGAvQ/xNV4AHjBiJKGqPVplSkoKJtBcyfmvvLkUztPxnjf5sM20Br1Byd+zvPpFWDr+7xw+p+K3J9PCIiovxi7Nx5mNy7JxrGJaCUuRT/BobgYUQUxlZJnQ9cx8YK60+dRK8hQ7PVf0hICBwyqKGVsDSH55CRcHZ2/uS5H06TMBSsBOeiVs1aQXJd/Nkq8DcNmiGhkhzGA8w0jwTHJHzX5ts8ibFi+YowfyDNkypwmhnDp6NUVAlWgYmIqNCQy+WYPX4s2lXwQlvPMhjctAm2r12jdYyVlRXWH/sfrIeMwKK3oXA1N8X4qhUg/KBIJlLIP+460zw8PPAUonTt1xVq1KlbN117dHQ01ixZgtkjh+PAzh1QKLJ2c3xBx0pwLhIIBBjZaQQSkhM/Ob2ha/uu2DNmL1At9blKoYJ9oC0qlq+YZ3Ee2XoYQmH2vw8tW7cM997dB6B9jUXMXbBo6sJ0x9vY2GDb6q3ZHo+IiCg/USgUGNqhPb4zUqNPtQqIlcux5f5jvDu4DwfMzNC1X3/NsUKhEC3atkNKihIxWzdo9ZOiUgE2Ga/7mxlGRkZo0LsPdm3fgk721hALhTgREoGizVrAxsZG61j/16+xaPAA9LA0hbOZKR4cOYCx//PBir37YWycfmOOwoiV4FzWrGEztG/e7pOvCwQC9GzcA1HXIwEAkb+HYmr/zK3Tqys5SYABoELZiggqEYbkfmrNI7RmFNycM97BhoiIqDA5vHcP2oqUKG0tAwBYGBtjTOXyeBsZiWsnfs/wnFbt2+O6jSP+CYuEWq3G27h4rAwMx7DZc3IUS8cePdF3w2b87loSv9oVQfOlyzFs4qR0x/0ydzakoSE49Owl1tx5gD9evEKd+Cjs27olR+MXJAJ1Zrcr0aHq1avDz88vr4fNt9RqNdqNaQ/pcBnuT76JqpWqal5TKlLQqEIjDO41SI8RflnH0Z1h/L25puId80sY/vfzsXy72x0REZGuzB45HH3iI9O1r7l1DyGmFihfvz4q1qqNlu3aaTaqAFL//z9z8iQunzoJl+LF0WPQYFhYWKTrRy6XY+3ixQh6cBdqgRDlGjRE32HDc3QTfWsvT0wrVwpO/91El6xUYtn123D9uj4WbNqc7X7zo0/lncxQ8oG0avDsGfNQpHNRKOq+/1CHHQnHV9XqfLGPXiN6I9oyFh/+e0iRK1HBygtLZ395XcGcGtphCFZcWg3r+naIeRCNdtXy73bPREREuuTo5o5AvwA4f7AqQ7JSibvhkejr5IBqAS9wb9ttjNq5Hcv37NOsrS8QCNCsVSs0a9Xqs/1PHzQQrROiUNTcDABw64/jWB7wDhMXLMhWvDExMSgmMdYkwEDqUm2NXF3wb1xG22cUTsxSdOjvq39j12+7IRRpTy8wUhlh5Q8rPvuNrWv7rnjj/wZn7p4F/pu7rpQr4RTiAC9PLwQEBGDEvJGwdLDUOi8mJAZrZ6/Bt22+xYaILZDVfj/nJ+xwEPq176u7C/yMFo2bY+Nob6jrqYEzCgz6OX9XromIiHSl17BhmPLdaXwvMYHJf5XeBb43MbtWVRT5L3GtZiODa0IiNvy4FOPnzst03w/u30eR0EAUdXw/V7iyzBLXr/siPj4eZmZmWY43JiYGLlZW6dodTU1RpU7h3yQjDZNgHSrqXBTPjF7Cruf7rYhVChWMtqi/+JOFQCDApNGTkLQqGdcCbsO0iBkij4di1cDlAAAXFxeoJYCin3aCrf4FKFKkCIoUKYJtY7ZDXSt1LKVcCedQB3iW9dT9hX7C0A5DMMd7HvpX68MqMBERGQwrKyvM2LwN6+bPQ8yrlwiOjIbQykqTAKdxNJUi7OmTDPt4dP8+ti//CcK4GChNzdF77DiUr1wZj+7eQWnj9P+nOkONwMBAeHh4ZNjf9nVrcPfPPyGSJ0NtbYNB02eiTLlyAFLzhlDz9NMuzkfHYWKPHlm9/AKLmUomqVQqVGlUFfa1nbTaw5+EYP6QH9CmZRu4ubmheLIbIhPiITZN3X4w8kQolvdbmulxJgwej46zOsNkoERTBQZSk+R+3/SB99XtkNVKrfZG+0agf5Pemg05+n7TBxv/3QpZbRtE+IRg9cCcr8MbGRmJhw8fpmsXCASoWbOm1tymFo2bw+f3YxjUk1VgIiIyLEXd3LB40/ubyqYN6A+VMkFr+TO1Wg2lUfqVF148f46NY0ZhqIs9xKZGUKoSsXnSOAxcuQbV6nyFPdu3wFWhgJFQCOP//t99IxChaNGMb0Dfvm4NjP44geHWlgCkUKpSsGr0KCz69QhkMhkEAgG+HTseaxYvRGeZOcyMxPg9LAql2neCnV32V6coaJgEZ5JQKES7Nu1w56tHkDq+n0NjvMoIrZq/n8sza9hMDNg2BHY9naBSqOAQaJel5c5MTU1R1+1rHN94EluHeWu91rF1R2wfsxOolfrc6IoQtSfWQrmGXijyVeo/hJCIEATeDkDwmUDI+sqyf8H/CQgMwPCNo2DbTHtr5NjDkfjn4GWtJBgA1i/P+ZaPREREBV37AQNxdMEcdHay17T9LyQcbSZNT3fs9p9/wiBnO4j/W61JJBSiv5Mddq5agR4jRuFacAgiQkOgVKkhEABF7O1Rrk0HmJiYZDj23dOn/0uAoemvu7U59nhvxMjJUwAA9Ro3gVflKjiwbSsSYmLRY34flCxZUpdvQb5n8ElwYmIikpOT07WbmJhAKpVqtU0eNgmdf+gC6dDUJDj6TiS6fN1Za4mxD6vB0X9GZKkKnGbC4PGIWRqjqQKn+bAaLBAAA5r2gZubG6rXrIHkPmqIjEWwhwuUyUp4JJaAi4tLlsf+WPly5VHB3gvyagIIjVKvM+5FLNq0aA4jI6Mc909ERFQY1a5bF6EDhmLt3t0Qx8chViBAnECI8O1bcf3vyxg8fgJMTU1x6fx5vLx1Gyal3LTONxaJoIyOxtpJE7C0oqemovwuPgH7BSaY8P3oT46duuGGqVabk6kUf719q9VmY2OD4RMm6uaCCyCDT4IHjhuEd3bBEJu8fytSklLgFuWCnWt3ah1rYWGBWg41cCc4tRosPK9C35V90vU5a9hM9PMeCOckp2xtemFqaopl837M8DVNNRjAt6s6AwCmDZyCMb9NgF2n1Kka7w76Y9u4TVke91Om9J+M8b9Phm0HRwCA8mQSRi0dqbP+iYiICqO2XbqgbZcuuHDmDP76cTHGOFrDSJiCoDt+GNetC4ykUlSLj4FNfAxi5HJYfrBJRbxCgcCYeHQ3M9GaUuFiZgrTwHAolcp0v8amUcmsoVQpIfqgSPdPeBTqdu+f7tiXL1/i9vXrKF+58ifnF3+JWq3GpfPn8fj2LdRs0BCVqhSMHWENPgn+YdI8DNs/CnZd38/1DdsfhHmDMr5zM60aLP9Kji5fdc5wowk3NzeUTvbAiH7DM+zj0ZNH2OOzN127WCjGtO+nfnanFoFAgKFtB0Olfn+znZenF5y2OCBZrgTUgEdicZQuVfqz150VFbwqwHGbHeQKFRLexKNZ2aYGs5sMERFRTvl4b8AIl/fTIpzMTNFGHg2/1y9R37MUvCxMseb2fQyt4AlbiQSRScnYGBSO4OhoyEq7p+vPSK1CSkrKJ5PgwTNm4Zcxo9BdZg5HUyn+DY/CXXtn9GneXHOMSqXCnO9Hwez5E3gZi3BksxKRbsWxYP2GT/abkYSEBEzq0wvVE+NQzkyKi6dPYF+RYli00TvHm3HlNoNPgj1KesA12hnxySkQmYiQkpSCYvGuKF68eIbHp1WDLx/7G323pa8Cp9mw7NNzYy3NLXEp7G+tVSTUaiB5Q0ymphi0btY6XVtaNVigAhYO/uWLfWRVWjVYHCBkFZiIiAySSqXCmZMncfX8OXhVrYp233XJ1GpIRvHxgKn2kmTlrK1w/k0AAMBWIsGIiuVw9PkrPEpOQY1WrZEcehWTirvi5Ks36OP5vrClVKmQaGX9yfnAAFCmXDksPHQEezZuRMjbN6jbtS96t2yptVLVns2bUD3gFbycUm+E8wTwJOwdtq7+BYPHjtPqLz4+Hvu3bcXb58/RoHUbNGjSRNPXqh9+QC+REvb2qTfttzSV4m5IAA7s2IHu/dNXnvOT/J2i55GZQ2cg8lgoACDqWBhmDpnx2eOnDJ+MBcN/yPY3HBcXF5Q1Kg21ChCbGkFsaoT4OzEY3G5wtnd/8fL0glOIA1winHRaBU5TwasCbF5bsQpMREQGSS6XY3T3rgjZ8Au+DfaHYP8ujOzQDlFRUV88V/HRPUYA8DQqRmtzDZmJCfp5lkbJipVQo2EjNJcaw93SAlKxGDsePkFgfALuhUXgp3dhGP7D/C+OKZPJMHLKFMxbvQbNWrVKl188uHwJXjLtvQdKW1rg+TVfrbZXL19iQsf2cD/3B7qFv8PzFT9iyqCBmpWpol88g/1H11fB2gr3Ll34Yoz6ZvCVYAAQCUV4/dcLBAUHQRwqxLTNqUlwhH8Edv20A46OjlrHm5ub4+vaX+dozNkjZqHPxgGw6+0EtVoNk2titFvVNkd9zh81L1d/elg3dy0sLS2/fCAREVEhs3PDerSVJ6CkrTWA1ESvaHIy1i5cgBnLfvrsuS369cfBNSvxraMdhAIBopKTsSs8GpWk2jev/REagW++H4/w4GDYiVOnJHxXqgQC4xNw+V0QniYmY9bhY3Bzc0NUVBQsLS2z/f++WiCEWp3BPgYf9bdu3hyMcbKB0X/tDextYBL8Fn8c/w0t2raDKoOpEykqFQQm+b9gxiQYgLu7O4p7lIDN6A+nJ6hhttosXQL8OUlJSRg3dzwEJtofqBRFCsb0Gg2vcu9Xe3BxcYGHsjhC42IQfzcGJU3c0GRYM5jZaC+sLX+RjD8OnMrU+MXdM57CkRMKhQJyuRwAIJFIIJfLIZfLIRKJIJFIdD4eERFRfvTy5g00tNLeYEJmYoI4/1dfPPebNm1hYWWFrVs2Q5CcDIuiJbF23Vac+e1/WHvoIMzlSYgzMkGNTl3QtGVLhIaGYuXuHfCQpZ7vbGaKbz2K45fIOJw5ehSP/joNS2UKooyMUbdLd3Tu3fuLMbx58wZ/nz2LEqVLo0bt2qjXoSMubtmABnbWmmOuRkShRteP+goPh5Gt9nXXsrXGzpMn0aJtO1Rv0RL/Hj6AOjZWiJUrIBQAJ8Oi0GnBpC/GpG9MgvH+ZrONV7bCpnbqxPXIc+GY3HncF87UJpFIEJQQBOOBFlrfrCI2BcO1iGu649OqwWaRUiyYtwBdlvaA2cD3H8a4F7FoVqx+Nq9KN5asWorfn52EuaO5pk0NQHlbjvPHzukvMCIiojwklEggj0vQbFYB/Lf5hThzy4XWqVcfdepp/5/+XZ+++LZ3HyQlJUEikWhyB3t7e3i0bY89vx1DSxsrxCsUOBqTgKJf1YP43GkMt5dp+ji8Zzt8PTxQs06dT469dNpUKG76oabUGLeSFdhpaoHF23Zg5+NH2HjhHNyhhj8EcK7zNcb27Kl1riqDe5UikpNhXTx1/4AuffthydOnGHPoIDzMpEgBEC2zweASJTL1vugT5wT/p1ObTpD4pv5Fq9VqmN0zQdMGTbLcz7AOQxF1MVzzPDE0EdVsqsAqgz26XVxc4B5fFANa9oe1tTWqWFVCUnii5nXFiUSMHvB9Nq5Gd8YOHQNLC0vY9XbWPITFRGjbpA2uXLmCK1eu4M6dO3qNkYiIKLd1HTYCB4PDtdpOh0WiUZduOepXIBBAKpWmm5bQ//vR6L95O/6tUA0vGn6D+b8eRdjTJ6hnK9M6roODLXy2bf1k/+f+/BPWt6+jm5MdSlhZorGDLfqK1fh59kyMnDoNPxzxQbNVazH716MYO3tOuvMrNP0GvhHRmudqtRp7wqLRe+QozfNXt2/hx5qV8X3FchhXsRwmOdti3vChOXhX8gYrwf8RCATo26w3Nl3dAVW8KstV4DTNGzfHhtEboa6fOs8m8VgMevfthQcPHqQ71srKCpuWeWs++NOHT8V3S7pDMkiaWgUu2yTDm9Du3buX4QYfrq6uWZq+kRkWFhao6VAdd4MfQ+poCrVajRenn+P3ViqceHoGaqUaJmdFOLnzd52OS0REpG+RkZFYNmUy5G/fACIhEmzssCY6EcbxcVCamqFWlx5o2b59ro3v5uaGMTNmap4LlSkAtOfgioRCqP+btpiRs0cOo/d/KzeksZaYIP7lCwCAVCpF6dKfvqG+74gR2JqiwLozf0IkT4bSSoYBS3+CvX3qL+c3btxA5ZRkmIjeT5kwMzJCkfBQ+Pv7w83N7VNd6x2T4A90atMJ20fvhFhggqZDsl4FTjOsw1CsuLgaJuXMUM2mCpZ5/4QXRf1hZPb+JwV5jBxlQz3gvWyjpk0mk6GKVSU8CX+ZWgX+MeMq8Oj5YyForv3zRPTDSIysOgxlPMrA745funMcbB3QrUPXbF3PlGGTNTvlBZ0LQNGWxeDwVer86fATIZg3Nv03RyIiooJMrVZjxoD+GCwVwcJBBiB1t7bTTi5YsH5DroypUqlw/swZvHz0EHW/aY4yZctqvW7tUQrBT+/D0fT9agz3o6JRtlWHT/YpNjGBIk4Fk49uYMvohraMREdH483z5zAyMoJKKkW1Zt+gcrVqmtdjoqJg8dG9dbdCw3HzlT8Chw6Gkb09eo+bgIr5cAMNJsEfEAgEGNFhGMzNLL588GekVoO9Efc4GlNnTEFCQgL6bOwP2/bvq7Th24OwYFL6JU6mD5+KZiOao2v9Lp9cimz6iGn48ckK2DSy07RJbovRp0tvHPA5gMMJPrCq/H5usTIpBbYnLVHSrQTevnubrr8izkVQs1rNT17Ph9XgxL/iYNc7NQFWKVWweWmFmqM/fS4REVFB5Ofri0rJ8bCwstW0uZiZQvDiGaKioiCTyXQ6XlRUFKb27Y2vlXKUlprgtxO/Yb+nF2b/vFLzi/HombMwsXdPVA+NQFlzKW7EJuCVYxEsGTjok/12GTIUh8aOQg8XB03b05h4uNd8P4dYrVbDz9cXwUFBqNewoWYKp0qlwpTePTHYzBhWFqnrEvv+7wg2REVh2MTUG9/qfP01pv4oQK3/+rofHomboWGYX6sqBAIBVGoFvCeNx/ANm1AymzvS5RbOCf5IyyYtUa923Rz3M6LTMNS2rwErKys4OzvDQ1USijgFgP+qwEZl4ODgkO48mUyGcZ3GfHYucNMGTWB+T6JZoy/mfhQ61eoAkUiE7h27w/KpGUyd3z+S/03ED6PnYd9v+7EyZB02KLdqHitD12Hv//Z98XqmDJuM53MfYUa/aVCfTr2OyD/CMKF39qaNEBER5WcB/v5wFqdPk2wFQHh4eAZn5MzPM2dgiJkR6thZw9nMFB2c7OD25AH+PHFCc4xUKsWaQ4dRcuI03K5ZD3XmLcKybds/u8ObZ7lyqNBvINaFxcAnKAxbgsNxy6MsRkyeisDAQJw7dw7D2rXBzbkzodi0Fou7dMauDakbfv154nfUVytg9cFyZzVtrPDk3F9QqVQAUhcF+GbocKx/F4oX0TE49uIV+nqW1iTuQoEA/ZxssWvVSp2/ZznFJDiXNGvYDPM/qPTOHj4TMcdS/9HEHonArOGf3pCjx7c9vrghxYhOwxF5PrU/wVkl+nXtl/pngQDdG3ZF9M1IAEBKYgrcE4vC3d0dc8bOhvFjMay9bDUP48dizB335ekMFhYW2LRwI9q1aIfWlVsh5kFUahW4KqvARERU+NRt1Ai+ySnp2l+JjODu7q7z8ZLfvoHlR//317WzwaXj/9NqEwgEqN+oEYZNmIiqNWpkqu+OPXpimc9v+M57K2Yd9sHYeT9gyoD+2NK/N+7Nnw3h65dQy5NQwc4Gg5zt8OzIIfj7++PpvXvwMDNN1595SgqSkpI0z1t16oTZh44gtE0niB2dIfzoJj+JWIyUmJjMvhV5hklwHkmrBicExn+yCpwVadXgmHuRmipwmu4du0N8KfXPUUfDMHNoasJtZWWFataVkRSWugJFUngiqlhVyvRPOnVq1oFAIMCwPkPx5qcXrAITEVGhZWdnB9dmLXD4XQiSlUrEyuXYGhCCZgMHfbbyml1KUfqULEmphJE0fRKaHSKRCG5ubjA1NcWPUyejc2I0ujnZoaVbEUyoWhEvomMRGJ8AAGhra4Vft23FV02a4lp0XLq+YkykkH60S5yVlRV6DhwEF6/yiFcotF4LTUyETXHd72WQU5wTnIdmD5+Jem3rY/OJtejYpyOiHGIhEL7/tqRIVKCMqBQ2r9yUqf5GdBqOKQunoN/JflrtadXg7f/u0VSB00wbPhXfLuoGyWAp4o/GYPrUqVm+DrFYjD8PnoaTk9OXDyYiIiqghk2chHvNW+DI9m0wlkgwfOlwFC1aNFfGKvVVPdy/fA5eVu/X5T8YHIE+sxflqN8TR4/g7IH9ECvkENraY+ScuYh99hR2H6w1DADdSpfAr09for9XGSQrVTCRSlG1Rg0cdC2Gm8FvUdnaCnKVCoeCw9F8+Pfpd5r7z7DpMzCvb2/0kZnBwVSKt3Hx2JugwE/jJ+ToOnKDQJ02sTQb5s6di02bNmmWyVi0aBFatWr1xfOqV68OP7/0KxgYgtevX6NYsWK4duMapl2aA7vW7yvCYfuCsLn3hiz9zBIQEIAiRYqka1er1ajRuCZ+3XYoXX9Tl0zDrYoPUPG2J36ctiS7l0JEREQ6olarsWLuHAT6XoWVWolwYwma9RuAVp06ffHcN/7+2LhoIZQR4UiRSPDdsOGo+dXX8Nm/H292bUWL/5ZIS0pJweqQKJgJBRjhZKvVR4IiBXseP8Pg8mWx/m0wpu0/BDs7O6hUKhw7sB83zp6FWCpF12HD4Vmu3GfjiYqKwo41qxHm7w+X0qXRZ/gImJmZffac3PSpvDPHSbC5uTkmTpyok2AMTddx3aAeZgyhSIiUxBTI9kmxeVHmqsCZoVAoYJTBTi/R0dGo2bgmrv51Ved3txIREVH2KRQKxMTEwMbG5pPV1g+Fh4djZrcuGOVkAxORCCq1GvuCwvDN9Nk4vHolhpmbaB3/Ji4eG8NjMNnZVmsO8qb7j2FiKkW0uRXajBiFZq1b6/za9OVTeSenQ+SCE3+cwNK9yyBzkWm1Rz6IwNnDf2mej+0+BjN+nwe7Vg6IOByCn4Z4Z2u8M+fOID4hPl17uTLlUMqjVLp2Kysr+J71zXAXOyIiItIfIyMj2NpqV2nDwsKwbdVKxAQFwbl0afQdMVJTWd3+yyr0s7XQrAMsFAjQw8kO3t4bIUpKAj5Kgouam8HN2hHboiLhEREGJyFwQyVAsS7d0bR9B3h4eEAoNIxbxnKcBK9ZswY7d+5E9erVsXz5clhbW3/5pEKuaaOmWH1qLaQD3yeZyZFJqCathp37d2HtiXWQuVvjhe9zqKXAi0tPIQ9JxnzlAiQnJaNf675o2rBppsdbsXMl5G0Bgej9N8aoGxEYrxidYRIMgAkwERFRAeD/+jWWDBqAvrYWsJVI8ObqJYw/fw7LDxyCubk5ot69g61EonWOQCCAOCkRCgsrqNVqrYryncho1O7WDp2698CDBw8QGBCAmbVrw8IiZ3skFERfTPWbNm2K8uXLp3v4+Phg+PDheP78OW7dugVnZ2dMmPDpSc/e3t6oXr06qlevjtDQUJ1eRH5jbGyMZmWbIu5FrKYt7mgUZoyYjl5desLB0QH2A1xg5mmB6ovroPayeqi/oyni+6YgyCQUFctVzNJ4c76fDQSoYeNlq3k4RNiia/suur40IiIiyoabfn6Y0q8PpnX9DjOHDcXrV68ydd7GRQsx0slGk+gWNTdDL1MjbP1lFQDAuXRpvI3T/jVYqVJBaW6BHuMnYH1ACGLlqas1PI2OxV9Gpmj3XWp+UK5cOTRp1ixbCfDTx48xqXdPTG7bChM7d8S+LVuy3Ie+fTEJPnPmDO7du5fu0b59ezg6OkIkEkEoFGLw4MHw9fX9ZD9DhgyBn58f/Pz8NDfSFWaj+o+E8mTqGnrJkUkoLykHOzs7GBsb45tyzRD3PBbunUri1eFnmnPkMXJ4ZmP5tJpVa8LmhSVUytSFq6P8ItCrSc9MzSUiIiKi3HX96lX8On0yBqiSMFAqQvf4CPw4eACCg4O/eK4qMjzdlsfOZqYIeZaaP/QdMRL7EhQI+m95sziFAuvfhaL/pMmoWqMGxm3biRMu7thuZIbgb9pgxd59EItzNhEgMjISK0YOxyCBAkNsLTHMwgRJPoewd/PmHPWb13I06SMwMFDz56NHj6J8+fI5Dqiw+LAanFYFTjOy7wgoTyXBvKgFYl/GIiUpdTHumC9sovE5E3qPQ+Tp1M0zxH+DVWAiIqJ84sDaNejrbA/Rf3NtTUQi9LezwraVK754rtJEio/XMIhTKCCxSV3xwczMDD8dOITrlWpgu0CC3xzdMH7rDnj+l5MVLVoUM35ajgWbtqDf8OFf3IwrM3ZvWI8e1uaa6wGAejYyXD9xPMd956UcfRWYPHkybt26BYFAAHd3d2zcuFFXceU6lUqFf//9V7Pt34fKli2rk2r1qP4j8b/hv6FKscqws7PTtKdVg/96fglOcEDUsTBYtrLJVhU4Tc2qNWGzyxKRvuHo36QXq8BERET5hCgxAUIz7dWaZCYmiA8N+eK5nYYMxd4Fc9HDyQ4CgQBKlQrbgiMwaekqzTEWFhYYM3OWzuP+lNCAADiaStO1i+TJeRaDLuQoCd61a5eu4tCLCcsnwvRb7RvEIs6FwnvYeq0k+NGjR5izfh7MrLR3bYkPj4f3oo2fvMnM2NgY33cYiQZ1GqR7bWTfEdjf/QD6tOmFG49v4vnel9gweWW64/qM7IMg01CtOzWVKUoUTSmCrau0599M6D0OI6eMQtfTrAITERHlF2pLSygU8TD64P/ykIRE2JT3/OK5tevVg3LaLGz03gCj5GQoLSwwfNUauLq65mbIn1WjcRP4bfNGDVuZpk2pUkFllbo4QmhoKKKjo1GiRIl8vdKEwS6RJhQKMajzQBwxOw5LLxkAQKVUwc5XhprVamodW6JECcQIYyDo836ZEbVaDflqxRdXWejctnOG7cbGxlgxeTlqVqsJf39/bNq3KcMq8PcDv8f0y3Mga/U+KQ8/FowxLUanO7Zm1Zo48+ufrAITERHlIwMnTcHqUcMxwNEG5kZGCEtMwo6YRPw4Zmymzv+6YUN83bBhrsaYFa07dMCEI4chjAhHVWsrhCUlYXd4LAYu/RET+/SGRUggrAA8Fxvh27ET0KBp5le8yks52iwju/LLZhlKpRLtxneAxfep6/GFnwjGgq/molb1WumOXbFpJf5yuwTzkql3UEaeD8PEkmPQrFGzXI+z2/juUA01glAkhEqhgnizCnuX7wYAHPA5iEs3LgEfJb7ujm6YODz9JiZJSUlISEhI125kZGSQy6MQERHlBX9/f2xf8TPkkZGwcnPD4PETCvSGVSqVCr8fOwrfM2dg51IEvUeMwPJpU9EhOhTWkvdFw7UBIZj761G9Ls3KzTIyIBKJ0LFmexy+9xssPK0QfTES58UXcN73AgDATmaHgT0GAEidvnB6yp/ASAuo1WqY3jFBs0G5nwADwIRe4zH9jzmwbeWAyN9D8VP/91sdmxiZ4Fmp15DVttG0JYYmwuKieUZdYfm65fjtxQmYO3+Q8KoB5XU5zh09m2vXQEREZMjc3Nwwe8VKfYehM0KhEG07dUbbTqm/eCuVSshfvYC1s53Wce0sTfHrrp0YOOp7fYT5Wfl3okYe6d+tP2KORcD/fy9h2tYS/1a7gX+r3cDl8r4473dec9yHS5tFXQjHiI7D8izGGlVrwPaVFZTJStgH2qJS+Uqa19q3bAcTX5HWnaOJx2IwddiUDPsaO3QsLCWWsOvqpHmInMWY8n3GxxMREVHBdtPPD1MH9Mf0nt2xcNJERERE6HwMtVoNQQaTC0xEIiQnJup8PF0w+CRYJBKhX9O+CP7tHRy/cobEVgKJrQSJl+IwZ+RsrWNH9h2BlJNJqVXgPJgG8aEJvcbjzlg/TOk/SatdIBCgf4t+iL4aCSC1ClzNpsonf3YwMzNDHZdaSAhMXVhbrVZDetsYLRo3z90LICIiKmRCQ0Ox03sjfH79FXK5PM/HV6vV+PfyZWxduwYP7t/P8JjL587i2PTJ6J8SjwFGajR/+xxTe3ZHfHx8hsdnl1gshtLeAclKpVb7iag4dOzVW6dj6YrBJ8EAMLDHACwY9QOir6R+M0pJSkHRGBd4lPTQOs7Y2BjtqrTBiE7DoFQqtR4ZLbWmSzWq1sDSMUu0qsBpPqwGf64KnGby0ElI8okDAERdDMewDkNzJWYiIqLCavfGjVjRsxuKnPoflDs3Y3SbVnj0iUQ0NyQkJOD7Lt/i/uJ5KHnuD/wxcQxmDB+aLh85tnEjen2wRrHMxARdTY2wd5O3zmOasGw5VkfG40JIGO6GRcA7MAxeXXvA2dlZ52PpgkHPCU4jFArR47seODjmV6hrqxHlE4YlQ9ZkeOyR/x2BqhSw5I9lmraEiAQ0KtoAi2YuzNU4O7Rpn2F7WjV4xe+r8ZVNzS9OPk+rBt8MvJ9aBR7IKjAREVFmhYSE4MHhAxjg8n5Vp/JqNdbMnolfDh/Nkxh+mf8DeghS4GCfenN/EXMz3Ax4jcN79+C7DyqvooR4CCTa9wkVszDHhcePdR6Ti4sL1v/2O67+8w/CQkMxs3FjAMCWtWuQEBODDr16o2jRojofN7uYBP9HIBBgUKsBWHV+XYZV4DQTRkzASv+1sK5nq2kL3xKEiSMmZGk8lUqF4VNHABLtVR1S5AoM7DgAX9X6Kkv9tW/ZDhu2bsDULZmb2zt56CTUbFsby2f+lKVxiIiIDN3JI4fR1EJ77wChQACbuBhERUXlyaoPUc+fweGjDSuq2Miw7dxZrSRYaWqWOl/3g1WkXsXGoVi1rOUZmSUQCFD7668BAFcuXcKeeXPQ0coUZkZibDl3Bu7tOqHfqFG5MnZWMQn+QJvmbbC01VLMWLP6k8e0aNwcG8d4Q1039QOVHJGEimblYWNjk+Hxj58+xu5je9K1i4ViRCtioBoghlD8flZK2PYgeBTPOAFPExwcjB83LoMa2hPQvSp5ZXpOkpmZGXYt34GqVapm6ngiIiJKZSmzRmyKMl17skAAExOTDM7QPVUGm1CkqFQQSrS3Rf52xEjsmD8XPZ3tYCQUIjwpCQcTU7Bq0OBcjU+tVmPPj0swuoi9JgHv5SLBlv8dRVi3blo76eoL5wR/QCAQ4MLxC5+sAqcdM7T9EERdTp0/HHc0CjNGTP/k8ZbmlrgU9jceNn+ueTz45jl8n13DlP6TEPl7mOZYeYwcZcWlv7h1skwmw72I+wjoFqb1uB/2IEvr8DEBJiIiyrpWHTviZKIcqg9WQwhPSoKgqDuk0vTbCeeGSk2bwS8yWqvNJyQcHQYM1GqrXb8+uv20ArslVtimFOF8yXJYtu9ArscZGBgIV0Vyug286psa4+yJE7k6dmaxEvwRkUj0xWPSqsHJXp+vAgOAs7MzPI3LIEQZBSOL1G9nUVciMKztIFSpWAUOO22hSFFBKBYi5kgENoxe+cXxTUxM0KR0Y1x8cwXmxVLX+417Fovm5b+BsbHxF84mIiKinDAxMcH3P63AmrmzYRsfh2QBgKLumLFyVZb7iouLw5lTp2BmZoZGzZpBLM5catZj4CCsDQ3FhksXYKtWIlRsjNrde6NazZrpji1fqRIWem/Kcmw5YW5ujtgM2sNTVHB2dMzTWD7FoHeMy4mTf53ChMUTcPngpc8mwUDqt6E+G/rDtrdT6goOq6Phs+ooBAIBbt29hUmnp8OikQwu/7PDmrm/ZGr85ORktJ/aEbKRqVXjqDWh+N+PR5kEExER5aGIiAhIJBKYmpp++eCPHP/1EM56b0ADiRiJKuC8QoUxy1fAs3z5TPehUCgQGRkJOzs7CDOYIqFP04cMRtvoENj/V3WWK5VYFRaL9b+fyNNYuWOcjrVo3BymJtIvJsBAajXYQ1UCIXHRiL8Xi2EtBmp+HqhcoTIcdtgi8GAQNoxfmenxTUxM0LhUI1x8fQVQAM29mjEBJiIiymOZyQMyEhcXh7PeGzDCxV7TVlWtxi8zpmONz/8y3Y+RkdEXp1Hqy6xVv2DJpImIf/YEQpUKKnsHzNiwMd8k64U+CZ6xdCZC4kK02lRqNb72qoMB3Qdku1+BQIAGdRtk+vjZI2ahz4b+kEZK0H5VO63XJvebiB2/7szyh3jMwNE4O/UcoBZg5I8jsnQuERER6c+ZEyfQQGKk1SYUCOCanIB3797BxcVFp+Md//UQzu3bC3FSIhRm5ug8YiS+bthIp2N8TCqVYt6atVCpVFCpVJme6pFX8lc0ucDY2AjB30TB1Pn9zxQRf4agpFvJPI3D2dkZxROLoVWLVukmiVcqXwk/l1+e5T5NTEzQtExjGBkZswpMRERUgEjNzBCZwUZbyWrofIWJc6dP4/EWbwx3tAUsUvveunA+bB2dUNbTU6djZUQoFOab6u+H8l9EOjZxyEQk/7c7GgCoVWpYPDJDg68zX8XVlY1LN6BDq4w3vMiuCUMnYMzA0Trtk4iIiHJXk+bNcV6u0lphIl6hQLSdPWxtbT9zZtad2Lkd7Ry0p230dLLF3jWZuw+psCr0SbCZmRm+KlILCYEJAIDIv0Ixuot+Fmn+uAKcX/skIiKi3CUWizF6+Qr8EpWAw0Fh2BMUhh1qY8xcvVb3Yynk6fIFE5EIyvh4nY9VkBT66RBAajW405zvIB0iTa0CD8/7KnBm+L/xx/l/zqdrFwlF6NapW6aWbyMiIqL8LyEhAaeOHIalhQUCTU3RokcvtGjbNlfGkrgUQWzwG1h8MHXSPy4eRWoZ9n4BBpEEp1WD/9j9FxZ3ma/vcD4pISEBay5tgF077fXzEnZFo3vn7nqKioiIiHRJqVRifI/u6GWkhpOZKVQi4NjaVUhJTkKbb7/T+XijZs7GtF490FkqR0lLc9yLjMEfQmOs+N6wp1MW+ukQaSYOmQiPEHe9zAXOrLJlyqKstBTMipjDsoQVLEtYAdEqDGo/IF9OKCciIqKs+/3oUTRRJcPJLPWmfaFAgE5Odvhrz+5cGc/W1ha/HPVB0DdtsNvSHsouPbH60K+QSCS5Ml5BYRCVYCC1Grxzw059h/FF0wdNw6ijY2H3rRMAQHhJjR4re+g5KiIiItKVW//+gy4yy3Ttxom5N0dXIpGg77BhWTonJSUF169fh0QiQcWKFQvdfUgGkwQXFGXLlIVLuCMSk5WIfxCDHvW7sApMRERUiFSu8xXu7tyMKjYyrXa51Ew/AWXgnwvnsXfxIlSFEskANohNMPmXNSheooS+Q9MZZlf50PRB0xD5W2hqFbgTq8BERESFSeuOHfGX0ASB8akrV6nUahwJCkOTnr30HFmq5ORk7F24AGOdbFDfyR7NnOwxxsYcyyeM03doOsUkOB8qW6YsbP1l6MYqMBERUaEjEonw89598CtfDVtThNgqlKDejDm5clNcdpz98zTqG2vnH2KhEK6J8QgICNBTVLrH6RD51Pbl2wx+wjoREVFhZWpqinFz5ug7jAwJBUKoM2hXQV2o5gWzzJhPSaXSQvVBIyIiorzx4vlzzBgyGNO6dcG0QQPx+MGDLJ3fqFkzXJCroP5gNzu5UolAM0u4uLjoOly9YRJMREREVEi8fv0aK4YOQo/EKAyUCNFHHouNo0fiyaNHme7D2NgY/ebOw8qQKPwVFIrfg8KwNiYJk35emXuB64FA/WGan0eqV68OPz+/vB6WiIiIqFCbO/p7dI4IhFT8fsarXKnEHqkMC703ZakvlUqFO3fuQCKRoGzZsroONc98Ku/knGAiIiKiQkIRFamVAAOAsUgEdVxslvsSCoWoXLmyjiLLfzgdgoiIiKiQkDo4IlYu12pLTEmBSGatp4jyLybBRERERAXYhzNbB0+ajI2h0Yj5LxGOlSuwITgCg6dM1Vd4+RanQxAREREVQE8ePcL6ubMhjgiH0sgILlWrY9zcefhhzz5sWf4T4oODIHEpgpnL18DZ2Vnf4eY7vDGOiIiIqIBJSEjA+A5tMc7JFqL/NtZ6GhOHe56VMGnhQj1Hl798Ku/kdAgiIiKiAubI3j3oYGaiSYABoJSlOYJu+kEP9c0CiUkwERERUQET/OYtHKXpd5YVKRRQqVR6iKjgYRJMREREVMC0+O47nI2I0WpTqdVQWNtAJBLpKaqChUkwERERUQHjVb48lNVqwicoFAmKFLyOjcPKgFAMmjlb36EVGFwdgoiIiKgAmrxwEe7evo3fD+6HvbMLfuzXH+bm5voOq8BgEkxERERUQFWoVAkVKlXSdxgFEqdDEBEREZHBYRJMRERERAaHSTARERERGRzOCSYiIiIyADExMdi6aiXCXr2Ema0d+o+fACcnJ32HpTesBBMREREVcjExMZjY9TvUuHsdtk8f4fXJ45jarDGGt2uD676++g5PL1gJJiIiIirktq5ciX4WElwPCYPMxAQTq6WuKKFWq7F2+hS47NoLZ2dnPUeZt1gJJiIiIirkwl69gIOpFI8jo1CvyPspEAKBAL3trbFrzWo9RqcfTIKJiIiICjlTOzvEyuUQC9OnfpbGRogND9dDVPrFJJiIiIiokOs3djy2hkRBoVIhWanUeu1WZDSqNW6ip8j0h0kwERERUSHn4uKCses3Ap7lMf3aLbyIjoVKrcbfYZH4V2aPtp076zvEPMcb44iIiIgMQMlSpbB2/wEkJCRg/7at+OfJE9Tt3h99mjeHQCDQd3h5jkkwERERkQExNTXFgJGj9B2G3nE6BBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwmAQTERERkcFhEkxEREREBodJMBEREREZHCbBRERERGRwBGq1Wp3Xg9rZ2cHd3T2vh6V8LjQ0FPb29voOgwoRfqZI1/iZIl3jZyr3vXr1CmFhYena9ZIEE2WkevXq8PPz03cYVIjwM0W6xs8U6Ro/U/rD6RBEREREZHCYBBMRERGRwWESTPnGkCFD9B0CFTL8TJGu8TNFusbPlP5wTjARERERGRxWgomIiIjI4DAJJr07dOgQvLy8IBQK090hu3jxYnh4eKBMmTL4448/9BQhFUSnTp1CmTJl4OHhgSVLlug7HCqABgwYAAcHB5QvX17TFhERgWbNmqFUqVJo1qwZIiMj9RghFSRv3rxBo0aN4OnpCS8vL6xatQoAP1P6xCSY9K58+fI4cuQI6tevr9X+4MED7N+/H/fv38epU6cwYsQIKJVKPUVJBYlSqcTIkSNx8uRJPHjwAPv27cODBw/0HRYVMP369cOpU6e02pYsWYImTZrg6dOnaNKkCb9gUaaJxWIsX74cDx8+xJUrV7B27Vo8ePCAnyk9YhJMeufp6YkyZcqka/fx8UG3bt1gYmKC4sWLw8PDA76+vnqIkAoaX19feHh4oESJEjA2Nka3bt3g4+Oj77CogKlfvz5sbGy02nx8fNC3b18AQN++fXHs2DE9REYFkbOzM6pWrQoAsLCwgKenJwICAviZ0iMmwZRvBQQEoGjRoprnrq6uCAgI0GNEVFDws0O5JTg4GM7OzgBSk5qQkBA9R0QF0atXr3Dz5k3UqlWLnyk9Eus7ADIMTZs2RVBQULr2hQsXon379hmek9HCJQKBQOexUeHDzw4R5VdxcXHo3LkzVq5cCUtLS32HY9CYBFOeOHPmTJbPcXV1xZs3bzTP3759CxcXF12GRYUUPzuUWxwdHREYGAhnZ2cEBgbCwcFB3yFRAaJQKNC5c2f07NkTnTp1AsDPlD5xOgTlW+3atcP+/fuRnJyMly9f4unTp6hZs6a+w6ICoEaNGnj69ClevnwJuVyO/fv3o127dvoOiwqBdu3aYceOHQCAHTt2fPKXLKKPqdVqDBw4EJ6enhg/frymnZ8p/eFmGaR3R48exffff4/Q0FDIZDJUrlxZsxzawoULsXXrVojFYqxcuRItW7bUc7RUUJw4cQJjx46FUqnEgAEDMGPGDH2HRAVM9+7dcf78eYSFhcHR0RHz5s1Dhw4d0KVLF/j7+8PNzQ2HDh1Kd/McUUYuX76MevXqoUKFChAKU2uQixYtQq1atfiZ0hMmwURERERkcDgdgoiIiIgMDpNgIiIiIjI4TIKJiIiIyOAwCSYiIiIig8MkmIiIiIgMDpNgIiIiIjI4TIKJiIiIyOAwCSYiIiIig/N/yEdcPtPfgokAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import sklearn.decomposition as sk_decomp\n",
    "tc1_library.run_and_plot_pca_and_tsne(TC1data3, outcome_decoded)"
   ]
  },
  {
   "source": [
    "## Create a binary dataset instead of a multi-class one"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Current outcome distribution of the 150 samples"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "pd.value_counts(outcome_decoded)"
   ]
  },
  {
   "source": [
    "### Get the indexes of the data that correspond to classes 0 or 1 only (excluding class 2)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_indexes = np.where(np.array(outcome_decoded)!=2)[0]"
   ]
  },
  {
   "source": [
    "### Recreate the data structures of the same types that was used in the original analysis above, except this time with just two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC1data2 = TC1data3.iloc[binary_indexes,:]\n",
    "outcome2 = outcome[binary_indexes,:]"
   ]
  },
  {
   "source": [
    "### Decode the new outcome matrix just like we did above, and print out the outcome distribution of the new set of samples"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1    50\n",
       "0    50\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "outcome_decoded2 = [x.argmax() for x in outcome2]\n",
    "pd.value_counts(outcome_decoded2)"
   ]
  },
  {
   "source": [
    "### Check our new dataset by performing the same unsupervised learning that we did above"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top 10 PCA explained variance ratios: [0.49686232 0.04021868 0.03354305 0.0322151  0.0264985  0.01835517\n 0.01706732 0.01658542 0.01310743 0.01183897]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "tc1_library.run_and_plot_pca_and_tsne(TC1data2, outcome_decoded2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pick up with the Jupyter notebook from the cell that splits the data into the training and test sets, but now replacing TC1data3 with TC1data2 and outcome with outcome2\n",
    "* Work through the notebook until at least the model has been trained (the cell with history = model.fit(...)), resulting in a binary classifier\n",
    "* Apply gene/feature importance tools to the resulting model in order to determine which genes best contribute to discriminating between cancer classes 0 and 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving two-class datafiles to disk\n",
    "### Save the data and labels to CSV format in the repository's data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC1data2.reset_index(drop=True).to_csv('Data/X_two_classes.csv')\n",
    "\n",
    "pd.Series(outcome_decoded2).to_csv('Data/y_two_classes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test that we've exported the data correctly by reading them back in and running the unsupervised learning analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top 10 PCA explained variance ratios: [0.49686232 0.04021868 0.03354307 0.03221507 0.02649849 0.01835583\n 0.01706931 0.01658852 0.01310649 0.01187091]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "X = pd.read_csv('Data/X_two_classes.csv', index_col=0)\n",
    "\n",
    "y = pd.read_csv('Data/y_two_classes.csv', index_col=0, squeeze=True)\n",
    "\n",
    "tc1_library.run_and_plot_pca_and_tsne(X, y)"
   ]
  },
  {
   "source": [
    "You are viewing the Jupyter Notebook from ML-TC1 GitHub repository, https://github.com/ravichas/ML-TC1 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "\n",
    "## Trying Deeplift again now that we've made the keras/tensorflow stack as close as possible to what they suggest"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "nonlinear_mxts_mode is set to: DeepLIFT_GenomicsDefault\n",
      "For layer 1 the preceding linear layer is 0 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "Heads-up: current implementation assumes maxpool layer is followed by a linear transformation (conv/dense layer)\n",
      "For layer 4 the preceding linear layer is 3 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "Heads-up: current implementation assumes maxpool layer is followed by a linear transformation (conv/dense layer)\n",
      "For layer 8 the preceding linear layer is 7 of type Dense;\n",
      "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
      "For layer 11 the preceding linear layer is 10 of type Dense;\n",
      "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
      "Heads-up: I assume softmax is the output layer, not an intermediate one; if it's an intermediate layer, please let me know and I will prioritise that use-case\n",
      "For layer 14 the preceding linear layer is 13 of type Dense;\n",
      "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n"
     ]
    }
   ],
   "source": [
    "import deeplift\n",
    "from deeplift.conversion import kerasapi_conversion as kc\n",
    "\n",
    "# The following commented message from the output of the convert_model_from_saved_files() function that follows isn't true for layer max_pooling1d_2, whose next layer is flatten_1, but I assume this is okay because flatten is not a \"real\" layer (no connecting weights)? If flatten is indeed fine, then this is fine, because the next layer is dense_1, which, according to the output, is an allowed, linear layer.\n",
    "\n",
    "#Heads-up: current implementation assumes maxpool layer is followed by a linear transformation (conv/dense layer)\n",
    "\n",
    "deeplift_model = kc.convert_model_from_saved_files('Model/tc1.full_model.h5', nonlinear_mxts_mode=deeplift.layers.NonlinearMxtsMode.DeepLIFT_GenomicsDefault)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "find_scores_layer_idx = 0\n",
    "deeplift_contribs_func = deeplift_model.get_target_contribs_func(find_scores_layer_idx=find_scores_layer_idx, target_layer_idx=-2)\n",
    "\n",
    "# #Compile the function that computes the contribution scores\n",
    "# #For sigmoid or softmax outputs, target_layer_idx should be -2 (the default). This computes explanations\n",
    "# # w.r.t. the logits. (See \"3.6 Choice of target layer\" in https://arxiv.org/abs/1704.02685 for justification)\n",
    "# #For regression tasks with a linear output, target_layer_idx should be -1 (which simply refers to the last layer)\n",
    "# #Note that in the case of softmax outputs, it may be a good idea to normalize the softmax logits so\n",
    "# # that they sum to zero across all tasks. This ensures that if a feature is contributing equally to\n",
    "# # to all the softmax logits, it will effectly be seen as contributing to none of the tasks (adding\n",
    "# # a constant to all logits of a softmax does not change the output). This is discussed in\n",
    "# # https://github.com/kundajelab/deeplift/issues/116. One way to efficiently acheive this\n",
    "# # normalization is to mean-normalize the weights going into the Softmax layer as\n",
    "# # discussed in eqn. 21 in Section 2.5 of https://arxiv.org/pdf/1605.01713.pdf (\"A note on Softmax Activation\")\n",
    "# #If you want the DeepLIFT multipliers instead of the contribution scores, you can use get_target_multipliers_func\n",
    "# deeplift_contribs_func = deeplift_model.get_target_contribs_func(\n",
    "#                             find_scores_layer_idx=find_scores_layer_idx,\n",
    "#                             target_layer_idx=-1)\n",
    "# #You can also provide an array of indices to find_scores_layer_idx to get scores for multiple layers at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "#things_to_save = [deeplift_model, deeplift_contribs_func, X_train]\n",
    "things_to_save = [X_train]\n",
    "with open('Data/X_train.pkl', 'wb') as f:\n",
    "    pickle.dump(things_to_save, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('Data/X_train.pkl', 'rb') as f:\n",
    "    things_to_save = pickle.load(f)\n",
    "X_train = things_to_save[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-6ccf708abd7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# X_train.to_numpy()[:N,:].reshape((N,60483,1)) --> (N,60483,1) -->\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mscores_0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeeplift_contribs_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_data_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m60483\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress_update\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# # X_train.to_numpy()[:5,:].reshape((5,1,1,60483)) --> (5,1,1,60483) --> ValueError: Cannot feed value of shape (1, 1, 1, 60483) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# # X_train.to_numpy()[0,:]] --> (60483,) --> ValueError: Cannot feed value of shape (10,) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[0,:]], batch_size=10, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[0,:]] --> (60483,) --> ValueError: Cannot feed value of shape (1,) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[0,:]], batch_size=1, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[0,:].reshape((1,1,60483)) --> (1,1,60483) --> ValueError: Cannot feed value of shape (1, 1, 60483) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[0,:].reshape((1,1,60483))], batch_size=10, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[0,:].reshape((1,1,60483)) --> (1,1,60483) --> ValueError: Cannot feed value of shape (1, 1, 60483) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[0,:].reshape((1,1,60483))], batch_size=1, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[:5,:].reshape((5,1,60483,1)) --> (5,1,60483,1) --> ValueError: Cannot feed value of shape (1, 1, 60483, 1) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[:5,:].reshape((5,1,60483,1))], batch_size=1, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[:5,:].reshape((5,1,1,60483)) --> (5,1,1,60483) --> ValueError: Cannot feed value of shape (1, 1, 1, 60483) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[:5,:].reshape((5,1,1,60483))], batch_size=1, progress_update=1000))\n",
    "\n",
    "# X_train.to_numpy()[:N,:].reshape((N,60483,1)) --> (N,60483,1) --> \n",
    "N = 2\n",
    "scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[:N,:].reshape((N,60483,1))], batch_size=1, progress_update=1000))\n",
    "\n",
    "# # X_train.to_numpy()[:5,:].reshape((5,1,1,60483)) --> (5,1,1,60483) --> ValueError: Cannot feed value of shape (1, 1, 1, 60483) for Tensor 'inp_input:0', which has shape '(?, 60483, 1)'\n",
    "# scores_0 = np.array(deeplift_contribs_func(task_idx=0, input_data_list=[X_train.to_numpy()[:5,:].reshape((5,1,1,60483))], batch_size=1, progress_update=1000))\n",
    "\n",
    "# #compute scores on inputs\n",
    "# #input_data_list is a list containing the data for different input layers\n",
    "# #eg: for MNIST, there is one input layer with with dimensions 1 x 28 x 28\n",
    "# #In the example below, let X be an array with dimension n x 1 x 28 x 28 where n is the number of examples\n",
    "# #task_idx represents the index of the node in the output layer that we wish to compute scores.\n",
    "# #Eg: if the output is a 10-way softmax, and task_idx is 0, we will compute scores for the first softmax class\n",
    "# scores = np.array(deeplift_contribs_func(task_idx=0,\n",
    "#                                          input_data_list=[X],\n",
    "#                                          batch_size=10,\n",
    "#                                          progress_update=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = X_train.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(112, 60483)"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(60483,)"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "tmp.reshape((112,1,60483))[0,0,:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(60483,)"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "X_train.to_numpy()[0,:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[[1.55144077, 0.        , 1.64929815, ..., 0.        ,\n",
       "         0.        , 0.        ]]])"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "X_train.to_numpy()[0,:].reshape((1,1,60483))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1, 1, 60483)"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "X_train.to_numpy()[0,:].reshape((1,1,60483)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(112, 60483)"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(5, 1, 60483, 1)\n(5, 1, 1, 60483)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.to_numpy()[:5,:].reshape((5,1,60483,1)).shape)\n",
    "print(X_train.to_numpy()[:5,:].reshape((5,1,1,60483)).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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