emg_classification_inception_Rest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InceptionTime Model \n",
    "# on Time Series EMG Classification on Ninapro Dataset [Rest Motion]\n",
    "\n",
    "InceptionTime is a 1D Convolution based Deep Neural Network model which is currently quoted as the state of the art TSC model from the published paper [InceptionTime: Finding AlexNet for Time Series Classification](https://arxiv.org/abs/1909.04939).\n",
    "\n",
    "#### Understanding Data\n",
    "\n",
    "\n",
    "#### Model Accuracy  Results\n",
    "\n",
    "### Choosing labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import warnings\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "# warnings.filterwarnings(action='once')\n",
    "# warnings.filterwarnings('ignore')\n",
    "# warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the Datasets to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_len = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.load('prepared_data/X_all_rest_{}_20.npy'.format(win_len))\n",
    "y = np.load('prepared_data/Y_hot_all_rest_{}_20.npy'.format(win_len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2088042, 250, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.reshape(X,(X.shape[0],X.shape[1],X.shape[2]))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2088042, 50)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[0:1000000]\n",
    "y = y[0:1000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to Binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_y = []\n",
    "for d in y:\n",
    "    if(d[0]==1 or d[2]==1):\n",
    "        new_y.append([1,0])\n",
    "    else:\n",
    "        new_y.append([0,1])\n",
    "y = np.array(new_y)\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Data Augumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge, Shuffle and Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Keep only 8 EMG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import trange, tqdm\n",
    "def rm_columns(X,n):\n",
    "#     X_r = list(np.zeros((X.shape[0],X.shape[1],X.shape[2]-n)))\n",
    "    X_r = []\n",
    "    for i in tqdm(range(len(X))):\n",
    "        d = np.copy(X[i])\n",
    "        for i in range(n):\n",
    "            d = np.delete(d, -1, 1)\n",
    "        X_r.append(d)\n",
    "    return np.array(X_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b82ce199bc04d93b97153731c9e1b4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=800000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94f689021031417eaf585b41b0886e51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=200000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X_train = rm_columns(X_train,4)\n",
    "X_test = rm_columns(X_test,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save and Quick Load Prepared Data\n",
    "###### Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_train_s40',X_train)\n",
    "np.save('prepared_data/y_train_s40',y_train)\n",
    "np.save('prepared_data/X_test_s40',X_test)\n",
    "np.save('prepared_data/y_test_s40',y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_train = np.load('prepared_data/X_train_s40.npy')\n",
    "y_train = np.load('prepared_data/y_train_s40.npy')\n",
    "X_test = np.load('prepared_data/X_test_s40.npy')\n",
    "y_test = np.load('prepared_data/y_test_s40.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = []\n",
    "for d in y_test:\n",
    "    idx = list(d).index(1)\n",
    "    y_true.append(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true_train = []\n",
    "for d in y_train:\n",
    "    idx = list(d).index(1)\n",
    "    y_true_train.append(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train Shape = (800000, 250, 8)\n",
      "Y Train Shape = (800000, 2)\n",
      "X Test Shape = (200000, 250, 8)\n",
      "Y Test Shape = (200000, 2)\n"
     ]
    }
   ],
   "source": [
    "print('X Train Shape =',X_train.shape)\n",
    "print('Y Train Shape =',y_train.shape)\n",
    "print('X Test Shape =',X_test.shape)\n",
    "print('Y Test Shape =',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def countX(lst, x): \n",
    "    count = 0\n",
    "    for ele in lst: \n",
    "        if (ele == x): \n",
    "            count = count + 1\n",
    "    print(count)\n",
    "    return count\n",
    "nb_classes = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94590\n",
      "0 = 47.295  %\n",
      "105410\n",
      "1 = 52.705  %\n",
      "All  100.0  %\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(nb_classes):\n",
    "    c = countX(y_true,i)\n",
    "    sum = sum+c/len(y_true)*100\n",
    "    print(i,'=',c/len(y_true)*100,' %')\n",
    "print('All ',sum,' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "377934\n",
      "0 = 47.241749999999996  %\n",
      "422066\n",
      "1 = 52.75825  %\n",
      "All  100.0  %\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(nb_classes):\n",
    "    c = countX(y_true_train,i)\n",
    "    sum = sum+c/len(y_true_train)*100\n",
    "    print(i,'=',c/len(y_true_train)*100,' %')\n",
    "print('All ',sum,' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InceptionTime Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import sys\n",
    "import subprocess\n",
    "import tensorflow as tf\n",
    "from InceptionTime.classifiers.inception import Classifier_INCEPTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build New Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 250, 8)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 250, 32)      256         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 250, 8)       0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_17 (Conv1D)              (None, 250, 16)      25088       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_18 (Conv1D)              (None, 250, 16)      12288       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_19 (Conv1D)              (None, 250, 16)      6144        conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)              (None, 250, 16)      128         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 250, 64)      0           conv1d_17[0][0]                  \n",
      "                                                                 conv1d_18[0][0]                  \n",
      "                                                                 conv1d_19[0][0]                  \n",
      "                                                                 conv1d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 250, 64)      256         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 250, 64)      0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_21 (Conv1D)              (None, 250, 32)      2048        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 250, 64)      0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_22 (Conv1D)              (None, 250, 16)      25088       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_23 (Conv1D)              (None, 250, 16)      12288       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_24 (Conv1D)              (None, 250, 16)      6144        conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_25 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 250, 64)      0           conv1d_22[0][0]                  \n",
      "                                                                 conv1d_23[0][0]                  \n",
      "                                                                 conv1d_24[0][0]                  \n",
      "                                                                 conv1d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 250, 64)      256         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 250, 64)      0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_26 (Conv1D)              (None, 250, 32)      2048        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 250, 64)      0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_27 (Conv1D)              (None, 250, 16)      25088       conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_28 (Conv1D)              (None, 250, 16)      12288       conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_29 (Conv1D)              (None, 250, 16)      6144        conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_30 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 250, 64)      0           conv1d_27[0][0]                  \n",
      "                                                                 conv1d_28[0][0]                  \n",
      "                                                                 conv1d_29[0][0]                  \n",
      "                                                                 conv1d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_31 (Conv1D)              (None, 250, 64)      512         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 250, 64)      256         concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 250, 64)      256         conv1d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 250, 64)      0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 250, 64)      0           batch_normalization_7[0][0]      \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 250, 64)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_32 (Conv1D)              (None, 250, 32)      2048        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 250, 64)      0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_33 (Conv1D)              (None, 250, 16)      25088       conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_34 (Conv1D)              (None, 250, 16)      12288       conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_35 (Conv1D)              (None, 250, 16)      6144        conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_36 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 250, 64)      0           conv1d_33[0][0]                  \n",
      "                                                                 conv1d_34[0][0]                  \n",
      "                                                                 conv1d_35[0][0]                  \n",
      "                                                                 conv1d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 250, 64)      256         concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 250, 64)      0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_37 (Conv1D)              (None, 250, 32)      2048        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1D)  (None, 250, 64)      0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_38 (Conv1D)              (None, 250, 16)      25088       conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_39 (Conv1D)              (None, 250, 16)      12288       conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_40 (Conv1D)              (None, 250, 16)      6144        conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_41 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 250, 64)      0           conv1d_38[0][0]                  \n",
      "                                                                 conv1d_39[0][0]                  \n",
      "                                                                 conv1d_40[0][0]                  \n",
      "                                                                 conv1d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 250, 64)      256         concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 250, 64)      0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_42 (Conv1D)              (None, 250, 32)      2048        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1D)  (None, 250, 64)      0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_43 (Conv1D)              (None, 250, 16)      25088       conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_44 (Conv1D)              (None, 250, 16)      12288       conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_45 (Conv1D)              (None, 250, 16)      6144        conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_46 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 250, 64)      0           conv1d_43[0][0]                  \n",
      "                                                                 conv1d_44[0][0]                  \n",
      "                                                                 conv1d_45[0][0]                  \n",
      "                                                                 conv1d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_47 (Conv1D)              (None, 250, 64)      4096        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 250, 64)      256         concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 250, 64)      256         conv1d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 250, 64)      0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 250, 64)      0           batch_normalization_11[0][0]     \n",
      "                                                                 activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 250, 64)      0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_48 (Conv1D)              (None, 250, 32)      2048        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_9 (MaxPooling1D)  (None, 250, 64)      0           activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_49 (Conv1D)              (None, 250, 16)      25088       conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_50 (Conv1D)              (None, 250, 16)      12288       conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_51 (Conv1D)              (None, 250, 16)      6144        conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_52 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 250, 64)      0           conv1d_49[0][0]                  \n",
      "                                                                 conv1d_50[0][0]                  \n",
      "                                                                 conv1d_51[0][0]                  \n",
      "                                                                 conv1d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 250, 64)      256         concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 250, 64)      0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_53 (Conv1D)              (None, 250, 32)      2048        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_10 (MaxPooling1D) (None, 250, 64)      0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_54 (Conv1D)              (None, 250, 16)      25088       conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_55 (Conv1D)              (None, 250, 16)      12288       conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_56 (Conv1D)              (None, 250, 16)      6144        conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_57 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 250, 64)      0           conv1d_54[0][0]                  \n",
      "                                                                 conv1d_55[0][0]                  \n",
      "                                                                 conv1d_56[0][0]                  \n",
      "                                                                 conv1d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 250, 64)      256         concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 250, 64)      0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 64)           0           activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            130         global_average_pooling1d_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 377,346\n",
      "Trainable params: 376,066\n",
      "Non-trainable params: 1,280\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    " clf = Classifier_INCEPTION('', (X_train.shape[1],X_train.shape[2]),nb_classes=2,\n",
    "                            verbose=True,batch_size=1000,nb_epochs=25,nb_filters=16,\n",
    "                            depth=8, kernel_size=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continue Previous Training Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 250, 8)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 250, 32)      256         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 250, 8)       0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_17 (Conv1D)              (None, 250, 16)      25088       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_18 (Conv1D)              (None, 250, 16)      12288       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_19 (Conv1D)              (None, 250, 16)      6144        conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)              (None, 250, 16)      128         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 250, 64)      0           conv1d_17[0][0]                  \n",
      "                                                                 conv1d_18[0][0]                  \n",
      "                                                                 conv1d_19[0][0]                  \n",
      "                                                                 conv1d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 250, 64)      256         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 250, 64)      0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_21 (Conv1D)              (None, 250, 32)      2048        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 250, 64)      0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_22 (Conv1D)              (None, 250, 16)      25088       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_23 (Conv1D)              (None, 250, 16)      12288       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_24 (Conv1D)              (None, 250, 16)      6144        conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_25 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 250, 64)      0           conv1d_22[0][0]                  \n",
      "                                                                 conv1d_23[0][0]                  \n",
      "                                                                 conv1d_24[0][0]                  \n",
      "                                                                 conv1d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 250, 64)      256         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 250, 64)      0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_26 (Conv1D)              (None, 250, 32)      2048        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 250, 64)      0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_27 (Conv1D)              (None, 250, 16)      25088       conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_28 (Conv1D)              (None, 250, 16)      12288       conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_29 (Conv1D)              (None, 250, 16)      6144        conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_30 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 250, 64)      0           conv1d_27[0][0]                  \n",
      "                                                                 conv1d_28[0][0]                  \n",
      "                                                                 conv1d_29[0][0]                  \n",
      "                                                                 conv1d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_31 (Conv1D)              (None, 250, 64)      512         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 250, 64)      256         concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 250, 64)      256         conv1d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 250, 64)      0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 250, 64)      0           batch_normalization_7[0][0]      \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 250, 64)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_32 (Conv1D)              (None, 250, 32)      2048        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 250, 64)      0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_33 (Conv1D)              (None, 250, 16)      25088       conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_34 (Conv1D)              (None, 250, 16)      12288       conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_35 (Conv1D)              (None, 250, 16)      6144        conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_36 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 250, 64)      0           conv1d_33[0][0]                  \n",
      "                                                                 conv1d_34[0][0]                  \n",
      "                                                                 conv1d_35[0][0]                  \n",
      "                                                                 conv1d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 250, 64)      256         concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 250, 64)      0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_37 (Conv1D)              (None, 250, 32)      2048        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1D)  (None, 250, 64)      0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_38 (Conv1D)              (None, 250, 16)      25088       conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_39 (Conv1D)              (None, 250, 16)      12288       conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_40 (Conv1D)              (None, 250, 16)      6144        conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_41 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 250, 64)      0           conv1d_38[0][0]                  \n",
      "                                                                 conv1d_39[0][0]                  \n",
      "                                                                 conv1d_40[0][0]                  \n",
      "                                                                 conv1d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 250, 64)      256         concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 250, 64)      0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_42 (Conv1D)              (None, 250, 32)      2048        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1D)  (None, 250, 64)      0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_43 (Conv1D)              (None, 250, 16)      25088       conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_44 (Conv1D)              (None, 250, 16)      12288       conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_45 (Conv1D)              (None, 250, 16)      6144        conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_46 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 250, 64)      0           conv1d_43[0][0]                  \n",
      "                                                                 conv1d_44[0][0]                  \n",
      "                                                                 conv1d_45[0][0]                  \n",
      "                                                                 conv1d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_47 (Conv1D)              (None, 250, 64)      4096        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 250, 64)      256         concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 250, 64)      256         conv1d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 250, 64)      0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 250, 64)      0           batch_normalization_11[0][0]     \n",
      "                                                                 activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 250, 64)      0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_48 (Conv1D)              (None, 250, 32)      2048        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_9 (MaxPooling1D)  (None, 250, 64)      0           activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_49 (Conv1D)              (None, 250, 16)      25088       conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_50 (Conv1D)              (None, 250, 16)      12288       conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_51 (Conv1D)              (None, 250, 16)      6144        conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_52 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 250, 64)      0           conv1d_49[0][0]                  \n",
      "                                                                 conv1d_50[0][0]                  \n",
      "                                                                 conv1d_51[0][0]                  \n",
      "                                                                 conv1d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 250, 64)      256         concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 250, 64)      0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_53 (Conv1D)              (None, 250, 32)      2048        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_10 (MaxPooling1D) (None, 250, 64)      0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_54 (Conv1D)              (None, 250, 16)      25088       conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_55 (Conv1D)              (None, 250, 16)      12288       conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_56 (Conv1D)              (None, 250, 16)      6144        conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_57 (Conv1D)              (None, 250, 16)      1024        max_pooling1d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 250, 64)      0           conv1d_54[0][0]                  \n",
      "                                                                 conv1d_55[0][0]                  \n",
      "                                                                 conv1d_56[0][0]                  \n",
      "                                                                 conv1d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 250, 64)      256         concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 250, 64)      0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 64)           0           activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            130         global_average_pooling1d_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 377,346\n",
      "Trainable params: 376,066\n",
      "Non-trainable params: 1,280\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "clf.model = load_model('best_model.hdf5')\n",
    "clf.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "800/800 [==============================] - 378s 472ms/step - loss: 0.1191 - accuracy: 0.9538 - val_loss: 0.2506 - val_accuracy: 0.9119 - lr: 0.0010\n",
      "Epoch 2/25\n",
      "800/800 [==============================] - 393s 491ms/step - loss: 0.1149 - accuracy: 0.9556 - val_loss: 0.2137 - val_accuracy: 0.9207 - lr: 0.0010\n",
      "Epoch 3/25\n",
      "800/800 [==============================] - 391s 488ms/step - loss: 0.1117 - accuracy: 0.9567 - val_loss: 0.2532 - val_accuracy: 0.9173 - lr: 0.0010\n",
      "Epoch 4/25\n",
      "800/800 [==============================] - 378s 473ms/step - loss: 0.1076 - accuracy: 0.9585 - val_loss: 0.2748 - val_accuracy: 0.8932 - lr: 0.0010\n",
      "Epoch 5/25\n",
      "800/800 [==============================] - 373s 467ms/step - loss: 0.1049 - accuracy: 0.9597 - val_loss: 0.3719 - val_accuracy: 0.8763 - lr: 0.0010\n",
      "Epoch 6/25\n",
      "800/800 [==============================] - 377s 471ms/step - loss: 0.1013 - accuracy: 0.9609 - val_loss: 0.4331 - val_accuracy: 0.8832 - lr: 0.0010\n",
      "Epoch 7/25\n",
      "800/800 [==============================] - 382s 477ms/step - loss: 0.0978 - accuracy: 0.9622 - val_loss: 0.2027 - val_accuracy: 0.9256 - lr: 0.0010\n",
      "Epoch 8/25\n",
      "800/800 [==============================] - 375s 469ms/step - loss: 0.0961 - accuracy: 0.9630 - val_loss: 0.3788 - val_accuracy: 0.8997 - lr: 0.0010\n",
      "Epoch 9/25\n",
      "800/800 [==============================] - 383s 478ms/step - loss: 0.0924 - accuracy: 0.9644 - val_loss: 0.2057 - val_accuracy: 0.9237 - lr: 0.0010\n",
      "Epoch 10/25\n",
      "800/800 [==============================] - 377s 471ms/step - loss: 0.0908 - accuracy: 0.9651 - val_loss: 0.3060 - val_accuracy: 0.9065 - lr: 0.0010\n",
      "Epoch 11/25\n",
      "800/800 [==============================] - 376s 470ms/step - loss: 0.0872 - accuracy: 0.9666 - val_loss: 0.2684 - val_accuracy: 0.9026 - lr: 0.0010\n",
      "Epoch 12/25\n",
      "800/800 [==============================] - 385s 481ms/step - loss: 0.0852 - accuracy: 0.9672 - val_loss: 0.2570 - val_accuracy: 0.9202 - lr: 0.0010\n",
      "Epoch 13/25\n",
      "320/800 [===========>..................] - ETA: 3:30 - loss: 0.0781 - accuracy: 0.9703"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-18f29238e70f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_metrics\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mplot_test_acc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mF:\\The Stuffs\\Awear\\Final_Project\\myotron_control\\InceptionTime\\classifiers\\inception.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mplot_test_acc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m             hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=self.nb_epochs,\n\u001b[0m\u001b[0;32m    123\u001b[0m                                   verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)\n\u001b[0;32m    124\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     64\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m    853\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[1;31m# No error, now safe to assign to logs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 855\u001b[1;33m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    856\u001b[0m         \u001b[0mepoch_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m    387\u001b[0m     \"\"\"\n\u001b[0;32m    388\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m       \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'end'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_process_logs\u001b[1;34m(self, logs)\u001b[0m\n\u001b[0;32m    263\u001b[0m     \u001b[1;34m\"\"\"Turns tensors into numpy arrays or Python scalars.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy_or_python_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    266\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\tf_utils.py\u001b[0m in \u001b[0;36mto_numpy_or_python_type\u001b[1;34m(tensors)\u001b[0m\n\u001b[0;32m    521\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[1;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 523\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    615\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 617\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    618\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    615\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 617\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    618\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    517\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 519\u001b[1;33m       \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    520\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    521\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[1;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    959\u001b[0m     \"\"\"\n\u001b[0;32m    960\u001b[0m     \u001b[1;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 961\u001b[1;33m     \u001b[0mmaybe_arr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    962\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    925\u001b[0m     \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    926\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 927\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    928\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    929\u001b[0m       \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "df_metrics = clf.fit(X_train, y_train, X_test, y_test, y_true,plot_test_acc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\moham\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:1813: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: models/rest_model_250_92\\assets\n"
     ]
    }
   ],
   "source": [
    "best_model = load_model('best_model.hdf5')\n",
    "best_model.save('models/rest_model_{}_92'.format(win_len))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "metrics_df = pd.read_csv('df_metrics.csv')\n",
    "best_model_df = pd.read_csv('df_best_model.csv')\n",
    "history_df = pd.read_csv('history.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>precision</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>recall</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.962095</td>\n",
       "      <td>0.961858</td>\n",
       "      <td>0.963274</td>\n",
       "      <td>4974.996405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision  accuracy    recall     duration\n",
       "0   0.962095  0.961858  0.963274  4974.996405"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Loss'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2jZqaGi644AJ+8IMfsH79em6++Wba2trIzMzkwQcfZN68eSxfvpyCggJ++9vf0tjYyDe/+c3h/zxFRELIWhuSOfeDZYxJiCkldU3trN5Rzwd7OkjfWENaso/0lCTS/WsT3Odu6lQi/DzAXXM76lr8Izs9kp7djTS0dHQfl5+ZwtTROVw0cyxTRmcztcglPaNy0uJqjcVQVFRUsGDBaV6HIRJniZBHbrnlFv785z9zxhlncM8993DmmWdy7LHHctlll1FdXc0ZZ5wRVCK0bt06nn32WTIzM/nc5z7H888/T2ZmJnv37mX58uXs3LmTX//613R1dR2275xzzun3vGeddRb//M//zObNmzn55JO5++67aWlpobi4mB/84Ad84xvf4Fvf+hbnn38+jzzyCO+//z6f/vSnefjhh/n85z/Pfffdx5NPPhnKH5mIyJC1dXSxbFMNL67exYtrdlO1v9mTOMbnZzB9bC5Hj81h+thcpo/NpbQgMyzrJ8LNWsvuhlZWVdfxYVU9q6rrWFVdz/Z9PX62by8d8BwpSYb05KTuyljpPbaHJk4HH0tL8XU/Jz350ATrkClWPaZZpST1+LrHtKxQJ2LWWqr2N7N+dyMbdjWyzj+tbcPuRhpbDyY8hVmplI/O5uPHj2NqUQ7lo7OZMjqHkdmpCZ/wiES7+EqEjjByEy4LFy7km9/8JrW1tSxZsoTnnnuOb33rWzzxxBPk5ubS3h7cHODRo0dz/fXXk52dzdq1aznllFP46KOPOOWUUwA3yvPDH/6Q//mf/zlsX0VFxSHnsj3Gvo866igACgoKeOutt1i8eDG5ubm0trYCHPIaV155JQDTpk3jqquu4vTTT2fMmDEUFRUN/QckIjJMdU3tVKzbzaLVu3jloz00tHaQnuLjtCmjuGpOCZF+u9neZdm09wBrdtTz8tpdBAalMlOTOGrMwcTo6LE5HDUml+y06Plza61la20Tq6rr+bDKJTyrquvZ29jafczEkVkcX5LPp0+awNHjclm/+gOOnnFc94LslvYuWtrdIu2Wdv/X/gXcLR2d3Y+1th88tqGlwx0bOK69kxb/lKlQ8BkOXZfSK3FK6bFGJSXp8MfdfkPtgXY27G5gw+5GDvSotjUyO40po7P55KzxlPtHd6aMzqYwO/ZKBouIEz2/mWOYz+fjiiuu4LbbbuPSSy/lZz/7Gaeccgq33XYbixcv5tlnnz3iOerq6vjud7/L1q1bATj33HOx1jJ9+nQeffTR7mOuvPJKPv/5zx+277/+67/YsWMHAFu2bKG2tvaQ+ADuuece8vPz+d3vfseGDRu46667ul/jrbfe4pxzzuGBBx6gtraWL33pS+Tn5/Pf//3f3HTTTSH9eYmIBGNbbROLVu/ixTW7WL6plo4uy8jsNC6aOZZzphcxf8rIqFgY3dLeybpdDazZUc+aHQ2s3lHPM+9X8+Cyrd3HTCjMZPqYXH+C5BKl4hEZYR8x6OjsonLPgR4JTx2rq+tp8I9oJPsM5aOzWXDUKI4Zl8sx4/KYPjaHnPSUQ85jq5M4dfLIsMTY1WVp6zyYUPVMtNo7D64/afevMWk/ZA2L7f68vbPHmpUex7T32tfY2nHo/u7zdNLW2UVuegpTi3K4YnYJU4rc6M6U0a76lojEFyVCIXLjjTcyadIk1q9fz6ZNm7jtttt44IEHKCwsJDk5uXv0pT+5ubnMmzePWbNmkZWVxYgRI6iuruaGG27gxRdfZP78+XR0dPDd736X888//7B9s2fPJj8/n5NOOonp06czceLEw17j7LPP5uqrr2bJkiVkZWUxZcoUqqur+elPf8rnPvc5fvjDH5KZmcn9998PuCl/X/7yl7u/FhEJp64uy8qquu7kZ+3OBgCmjM7m1tMncc7RRRxfnB91U8/SU5KYWZzPzOL87n2BaVVrd/gTpJ0uSXp+9c7uxeo5aclM6zGtbvrYXI4qyiEjdWjJXUt7Jx/tbODD6oOjPGt31NPqH3FJT/ExfWwuHz9hHMeMy+PYcXlMKcr2PJn0+QzpviTP4xCRxKNEKERKSkq6p8CVlZWxdu3aw47pPX2tJ2MMjzzySJ+P/epXvwpq31NPPXXYvnvuuaf782OOOYaVK/suJvHSSy8dtq+jo4Mbb7yRpCT9cRKR8Ghp7+TNyhpeWL2Ll9bsYndDKz4Dc8oK+M5F0zlnehFlI7O8DnPQjDEUj8ikeEQm5xx9cGrxgdYOPuoePapn7Y4GnninisbWLYCb3lU2MsslRj2m2I3NSz9k9Ki+pZ3V/mRnVXUdq6rq2bCnsbtwRE56MseOy+PakydwzPhcjh2Xx8SRWSQn+SL7gxARiWJKhCJs+fLl/Mu//Mth+6+66qqgCipEyre//W2WLFnSZ3IlIjIctQfaeHntbl5cvYtX1++hqa2TrNQkzjhqFOdML+LMo0bH7TSkrLRkZpWOYFbpiO59XV2W7fuaWe1PjtbsqOeD7ft59oMd3cfkZ6YwbUwO+RmprNlZz5aapu7HRuWkcey4XM49uohjxuVy7Pi8iEy7ExGJdUqEImzu3LkDjgxFix/96EdehyAicWTjnkZeXLOLRat38faWfXRZGJObzmWzxnPO9CJOmVxIWnJijj77fIbSwkxKCzM5/9gx3fsbWtr5aKcbPVrtn2K3tq6eo8fmcsWJxRwzPo9jxuUyOifdw+hFRGKXEiERkTDo6OxiX1M7B1o7yExNIistmYyUpKhb3xIunV2Wd7fuY9GaXby4eheVew4AMH1sLl88s5xzjx7DseNzNWoxgJz0FGaXFTC7rMDrUERE4lJcJELWWv0xjbCe5blFEoG1lobWDmoa26hpbGVvYxs1B1oPfn3AbWsa26g50Ma+pjb6+m8SSIqyurfJZKUlkZmWTHZqMplpSf59bn/g8ay0ZDJTk8lOSz54jrQkUpN8UfP7r6mtg9fW72XR6l28vHY3NQfaSPYZTp5UyLUnT+Cco4soHpHpdZgiIiJAHCRC6enp1NTUUFhYGDVvBuKdtZaamhrS0zUdYzhaOzpZsXkfr6zbw6vr9rBnfxOj319CTnoyuenJ5KankJOeTE56CrkZbpvTx/7c9BTSkqPnzXAsaWnvpPZAGzWNbeztkdTUHGhjb3dSE9jfRltn3/1OctOTGZmdRmF2KpNHZTN3YiqF2WmMzE4lKzWZpvZOmlo7ONDawYG2TpraOmhsdfsaWzvY29jGgdomDrR20NTaSWNbR59JVF+SfeaQxCozLZnstCTq9rVw94ZldFnr/3D/d7ssh3zd2dXzsYOP2+7jLF1dhz/34GMHj23t6KKjy5KTnsyZR43mnKOLWHDUKHJ7lWIWERGJBjGfCBUXF7N9+3b27NkT8dduaWlJ2GQgPT2d4uJir8OIOZv3HuCVdXt4Zd0e3qysobm9k5Qkw+wJBRT4ksjMy6ChpZ2q/S2sbWmgoaWDhpb27maN/UlJMocnTmn+xCmjx/4e29yMFPIyUhiVkxZ3ZWuttdQ1t7OttpmttU1srW1i+74m9jS4JCcwctPQozt8T2nJvu7EZlR2GtPG5FKYncrILLevMDuNwqxURmanUZCVSmpyaCtxWWtpaXf9TpraOjjQ2smBNn8i5f+8yZ9UHeiRYPXcNndY0to78RlXwcxnXE8xnzEYA77APmMwxpDkC+zr+/HA5z4fh37d4/j0FB+nTh7JnLKCkP9MREREQi3mE6GUlJQ+e+ZEQkVFBSeccIInry2x4UBrB29W1vDqepf8BCo9lRZkcsXsYk6fMopTJheSlZZMRUUFCxbMPuwc1loOtHXS0NJOfbNLjBpaOqhvaae+pYP65vbuhKm+5eDju+sbu/f37I7el5HZqYzLz2BsXjpj8zIYn5/B2Px0xuVnMC4vg1E5aSRF2dqWlvZOqvY3s622iW3+ZCeQ+Gzb10RDy6FJzojMFIpy0ynMTmVmcb5LbPwJTaE/6QkkOpmpSZ6OsBljyEhN8veTGVrXenc9nRrawEREROJIzCdCItHEWstHuxp45SOX+Ly1uZb2TktGShKnTi7kxnkTOWPqqEH1RTHGkJ3m1oaMzRtaXB2dbnShoaWDuh6J0/7mdnbWtVC9v5nquhYq9xzgtfV7D0uckn2Gotx0xvmTI5csuaRpXH4G4/LTyctICWny0NVl2dPY6k9wmg6O7PiTnV0NLYdMH0tL9lE8IoPSgkzmlI2gpCDTfYzIpKQggxxNzxIREZEelAiJDNP+pjZe27CXVz7aw6vr97CrvhWAaWNy+Kw/8ZldNsLT0sDJST7yM1PJz0yl5AjHWmupb+mgen8zO+qaqdrfwo79zd3J0ttb9rGzbgcdvebrZaYmMTbv4ChSzxGlQALVewpeQ8vB6Wvb9x1MdrbVNrFtXzNtHQfX5Bjjyi2XjMhkXvlISgpc0lPqT3hGZaclTEU2ERERGT4lQiKD1Nll+WD7/u61Pu9v20+XdQvmT5syijOmjuK0qSMZm5fhdahDYowhz79+aPrY3D6P6eyy7G1s9SdL/hGl/S3dydOaHQ3sbWw97HkjMlMYl59Bks+wrbaJfU3thzyek55MaUEmU0bncPb0IkpGZFDiT3bGj8hI2D4zIiIiEnpKhESCsLu+xVV3W7+XJev3sL+pHWNgZnE+XzxrCmdMHcVxxXkkJyXGAvEk/1S5otx0+lsl19rR6Z92dzBBqvYnTZ1dlmNnjHWjOSMyu0d28jI1fU1EREQiQ4mQSB/aOrpYsaWWV9ft5ZV1e1izox6AkdlpnD2tiDOOGsX88pEUZKV6HGn0SktOYkJhFhMKg18PJSIiIhIpSoRiSKChY1ZqctRV8IplLe2dbNp7gMo9jVTuPsDKqv28WVnDgbZOkn2G2WUj+Nfzp3H61JFMH5OrdSgiIiIicUCJUJTp6rLsrG9hS00TW2oOsNm/DXx9oK2TJJ9hZHYqo3PSGZ2TxujcwDaN0TnpFPm3I7NTE2aqVjBqD7T5k51GNuxudJ/vOcC2fU3d1ceMgQkFmXxi1nhOnzKKU8tHkp2m/yYiIiIi8Ubv8DzQ0dlF1f5mNtc0sbV3slPbdEilrJQkQ8mITCYUZjJ3YgHj8tNpaOlgV30Luxta2VHXwvvb91NzoO2wTvTGQGFWWneSVJST7k+W0hgVSJhy0xmVnRY3zQ87uyxV+5rZsKeByt3+UZ49LvHpuTA/LdnHpFHZHFeSz2WzxjN5VDblo7OZODIr7pqLioiIiMjhlAiFSUt7J9v3NbF5r0tuAqM7W2sOsH1f8yGlh9NTfEwoyGLiyCzOnDaa0oJMygqzmFCY2V1h60jaO7uoaWzrTpB2N7Swq76VPQ0t7K5vZXdDK6ur69nb2EqvqscAFGSl+hOknqNKLlEqyk1jRGYqmanJZKS4Jo8pScbThpNNbR1s3HOge1Sn0j/Cs3HvgUMSyZHZqUwalc35x45l8qgsykdnM3lUNuPzMzTFTURERCSBKREahpYOy+rqejeaE0h29ro+KNV1zYeM0OSkJVM2Motjxudx0cyxbhF5QSZlI7MYnZM27KQiJcnHmLx0xuSlD3hcZ5el5kCrPzlySdKuwOcNLmHasHsvexpaD+sT01OSz3QnRRkpSWSmJpGe0uPzHvt7HtffNpBkpaf6uj/3Gdjb2NZjGtvBpKdqf3N3LD4DpQWZTB6VzRlTRzF5VDaTR2cxeVQ2+ZkqZiAiIiIih1MiNERX3vkmyzc3wYtLuvcVZqUyoTCTkyYWUFp4cFRnQmEWIzJTPB1BCUjyGf/aonQgr9/juros+5raupOk/U3tNLd30tTWSUt7J81t7vPm9k6a2zrctr2L5rYOdjW009zmjnH7O2lp7+r3tfqT7DOHJGOZqUlMHpXNnLIRXD2qhMmj3XS2CYWZ6i8jIiIiIoOiRGiILpgxhtKUBs6cM8Of7GSSkx4/PVB8PkNhdhqF2WkcTd9NNQejq8vS0uFPnvzJVFOPROmQxKnH/qKcNCb7p7ONzUuPimRSRERERGKfEqEh+uy8iVS0b2HBzLFehxITfD5DZmoymam65ERERETEe/FRKkxERERERGQQlAiJiIiIiEjCUSIkIiIiIiIJR4mQiIiIiIgkHCVCIiIiIiKScJQIiYiIiIhIwlEiJCIiIiIiCUeJkIiIiIiIJBwlQiIiIiIiknCUCImIiIiISMJRIiQiIiIiIglHiZCIiIiIiCQcJUIiIiIiIpJwlAiJiIiIiEjCUSIkIiIiIiIJR4mQiIiIiIgkHCVCIiIiIiKScIJKhIwxScaYHxtjdhhjGo0xjxljigY4/nxjzAr/sSuNMdf0enyUMeY+Y8weY8xeY8wjxpji4X4zIiIiIiIiwQh2ROh7wPXAdcDpQDHweF8HGmPmAX8HXgNmAz8GfmeMubbHYQ8BE4HzgHOAccCTgw9fRERERERk8JKPdIAxJhX4CvBla+0i/76rgU3GmFOttW/0eso3gTestV/1f73WGDMR+D5wnzEmBzgL+Li19l3/+X4EPGuMKbTW1oTiGxMREREREelPMCNCxwM5QEVgh7V2M7AZOK2P46cAS3rtexcoM8aUAi1AI3C9MSbXGJONG2naAOwbVPQiIiIiIiJDEEwiFFi7U9VrfzVQ0sfxfe0v829HW2vbgRuABcB+oA433e4Ca21XEPGIiIiIiIgMyxGnxgGZQJc/gempFUjv4/j7gD8YY54GngCOBb7hfyzVv50GrMStPeoEfgg86Z9q19DzZMaYW4FbAYqKiqioqAgi5MhobGyMqngktul6klDS9SShpOtJQknXk4TScK6nYBKhZsBnjEm21nb02J8GHOh9sLX2XmPMBOAeXFGErcDtwK+BOmPMacAPgBJrbTWAMeZSYAtupOhXvc53F3AXwOzZs+2CBQuC/+7CrKKigmiKR2KbricJJV1PEkq6niSUdD1JKA3negpmatw2/3Zsr/3jOHy6HADW2h8AubgpcpP85+jEJTsnAzsCSZD/+P3AOqB8ELGLiIiIiIgMSTCJ0PtAA3BGYIcxpgy37ufV3gcbY75ojPmFtbbDWlttrbXApbhKco3AdqDIGDO6x3MycQnT+qF/KyIiIiIiIsE54tQ4a22rMeYO4HZjzF5gN3AH8Iq1dqm/vHYBUGutbQPWAv9njFmB6yV0DfAZ4Fz/KZ/BTZf7izHmG0AbrrR2M3BvSL87ERERERGRPgTbUPU7wAPA/cBi3BS3y/2PnQrs8G+x1r4I3IYrhLAa+ARwsbX2Ff/jjbg+QnuA54CXAQOcZq2tH/Z3JCIiIiIicgTBFEvAXyTh6/6P3o9V4BKZnvvuBu4e4HxbgCsHE6iIiIiIiEioBDsiJCIiIiIiEjeUCImIiIiISMJRIiQiIiIiIglHiZCIiIiIiCQcJUIiIiIiIpJwlAiJiIiIiEjCUSIkIiIiIiIJR4mQiIiIiIgkHCVCIiIiIiKScJQIiYiIiIhIwlEiJCIiIiIiCUeJkIiIiIiIJBwlQiIiIiIiknCUCImIiIiISMJRIiQiIiIiIglHiZCIiIiIiCQcJUIiIiIiIpJwlAiJiIiIiEjCUSIkIiIiIiIJR4mQiIiIiIgkHCVCIiIiIiKScJQIiYiIiIhIwlEiJCIiIiIiCUeJkIiIiIiIJBwlQiIiIiIiknCUCImIiIiISMJRIiQiIiIiIglHiZCIiIiISDywFjZWQFen15HEBCVCIiIiIiLx4MPH4d6PwwePeB1JTFAiJCIiIiIS67q6YMnP3OdrnvE2lhihREhEREREJNatew52r4YRZVD5MrQ1eR1R1FMiJCIiIiISy6yFV3/qkqCLfg4dzS4ZkgEpERIRERERiWWVL0H1uzD/azDxdEjPh7XPeh1V1FMiJCIiIiISq6yFV34KuePhuGsgKQWmnu+mynV2eB1dVFMiJCIiIiISq7a8DtuWwryvQHKq2zftImjeB1vf8Da2KKdESEREREQkVr16O2SNglnXHdxXfjYkp2t63BEoERIRERERiUXbV8DGxXDqlyAl4+D+1CyYfJZLhKz1Lr4op0RIRERERCQWvXo7ZIyA2Tce/ti0i6BuG+x4P/JxxQglQiIiIiIisWbHB64gwsmfh7Scwx+fej4Yn6bHDUCJkIiIiIhIrFnyM0jLhbm39v141kgoPVWJ0ACUCImIiIiIxJI9H8Hqp2DOzZCR3/9x0y6C3augdmPEQoslSoRERERERGLJa//nqsKd8oWBj5t2kdtqVKhPSoRERERERGJF7Sb44BFXICFr5MDHjpgAY2YoEeqHEiERERERkVjx+i/Al+RKZgdj2sdg61Jo3B3WsGKREiERERERkVhQVwXvPgAnXAu5Y4N7zrSLAAsfPRfW0GKREiERERERkVjwxv8DLMz7SvDPKToW8ks1Pa4PSoRERERERKJd4254+88w8yq39idYxsC0i2FjBbQ2hC28WKRESEREREQk2r35G+hogflfG/xzp10Ena2w4cXQxxXDlAiJiIiIiESzplp462449jIYWT7455eeDJmFmh7XixIhEZFwWb8I7v8kdHV6HYmIiMSyZb+DtkY47etDe74vCY66ANa9AB1toY0thikREhEJlxV/dNMQ6rZ5HYmIiMSqlnpY9ltXBrvomKGfZ9rHoLUONi8JXWwxTomQiEg4dLTCxlfc5zUbvI1FRERi11t3Q0vd0EeDAiYtgJRMTY/rQYmQiEg4bHkD2g+4z2sqvY1FRERiU1uTK5JQfg6MnzW8c6VkQPnZ8NHfoasrNPHFOCVCIiLhsH4RJKVCSpZGhEREZGje+TM07YXTvhGa8027GBp2QPW7oTlfjFMiJCISDhsWQdl8GDVViZCIiAxeRyu8/kuYMB8mnBKac049D0wSrH0mNOeLcUqERERCbd9m2LsOys+FwnIlQiIiMnjvPeBGb04P0WgQQMYId5NO64QAJUIiIqG3fpHbTjnPJUL7t0F7i7cxiYhI7Ohsh9f+D8bPdkUOQmn6xe5m3Z51oT1vDFIiJCISausXwYiJUDjZJUJY2LfJ66hERCRWrHwM9m+F078JxoT23Edd4LZr/xba88YgJUIiIqHU3gKbXoUp57o/XoWT3X5NjxMRkWB0dcKSn0HRDJi6MPTnzyuGcSdoehxBJkLGmCRjzI+NMTuMMY3GmMeMMUUDHH++MWaF/9iVxphrejx2gzHG9vPxx1B8UyIintnyGnQ0u2lxAAVKhEREZBBWPwU16+H0r4d+NChg2kVQtQLqd4Tn/DEi2BGh7wHXA9cBpwPFwON9HWiMmQf8HXgNmA38GPidMeZa/yF/Acb2+vh3oBn45VC+CRGRqLF+ESSnu8WoAOm5kF2kREhExCtNta4XT3uz15EcWVcXvHo7jJwK0y8J3+tMu9htP/p7+F4jBhwxETLGpAJfAb5trV1krX0HuBqYZ4w5tY+nfBN4w1r7VWvtWmvtg8BPgO8DWGubrbU7Ax9AJi4R+pq19v0QfV8iIt5YvwjKTnON6wIKy9VUVUTEK8t+B89/Gx64AlobvY5mYOv+AbtXwWlfB19S+F5n1FFuxkKCrxMKZkToeCAHqAjssNZuBjYDp/Vx/BRgSa997wJlxpjSPo7/X+BD4K4gYhERiV41lVBb6dYH9VQ4WSNCIiJe2bgYskbBljfg/sugpc7riPpmLbz6U8ifAMdeHt7XMsZNj9v0KjTvD+9rRbFgEqFi/7aq1/5qoKSP4/vaX+bfju650xhzHPBJ4N+stV1BxCIiEr02vOi25eccur+wHA7sSeg/NiIinmjeD9tXwKzr4Yp7oOod+PMlbrpctNm4GKrfgdO+BknJ4X+9aR+Dro6Df7sSUDCJUCbQZa1t77W/FUjv4/j7gKuMMVcaY5KNMccDgU5Qqb2O/SqwzFq7OPiQRUSi1PoX3FSDQKW4gMJyt63V9DgRkYjavARsJ0w+C46+BK5+EPashXsugoZdXkd3qFdvh9zxcNw1Rz42FIrnQNZoWPNMZF4vCgWTbjYDPmNMsrW2o8f+NOBA74OttfcaYyYA9wAPAVuB24FfA91jkcaYdOBy4MsDvbgx5lbgVoCioiIqKiqCCDkyGhsboyoeiW26nmKbr7OVeRtfZcfYhWzo9e+YeaCWucDq155hd1FDROLR9SShpOtJQimS19OUdQ8wxpfOaxubsJsrgFTyj/l3Zqz8b1rvWMD7x/2A1vSREYllIHn7V3HCltdZX34zVa+9GbHXnZp7AqM/+gdvvPQCXUm9xytiw3CuJ2OtHfgAY+YCy4BSa+22Hvs3Ab+11v5vP89Lxk2F2wFcDDwB5FtrG/2PX+TfV2St3R9MsLNnz7YrVqwI5tCIqKioYMGCBV6HIXFC11OMW/cCPHgFfOYJKD/70Mc6WuG/x7jGeGd+OyLh6HqSUNL1JKEU0evpl8e7wgCf+suh+7cugwcuh4x8uO5pKJgYmXj6c98nYOdK+MoHkJoZudddv8j9HD71KEw9L3KvG0L9XU/GmLettbMHem4wU+PeBxqAM3qcuAy37ufVPl70i8aYX1hrO6y11dZlWpfiKsn1LNVxGvBOsEmQiEhU27AIUjJhwrzDH0tOg/xSFUwQEYmk2k2wb5ObFtdb6Ulw/dPQ2gB/uhD2rIt8fAFVb0Ply3DKFyObBAFMPB1ScxK2etwREyFrbStwB3C7v1HqLOBh4BVr7VJjTKoxZoy/zDbAWuALxpjPGGPKjDHfAj4D/EevU58ArAzdtyIiMaFxj/vjFE+sdeuDJp4OKX0tncRfQluJkIhIxGz0L0GfdGbfj487AW54Frra4Z4LYeeHkYutp1d/Bun5MOemyL92cpqrdPrR36GrM/Kv77FgG6p+B3gAuB9YDGzBre8BOBU3/e1UAGvti8BtuCasq4FPABdba1/pdc6xQM0wYheRWPT8t+GP50Nnx5GPjRU1lbBv8+HV4noK9BI6wnRkEREJkcrFkFsMI6f0f0zRMfDZ58CX4gooVL0TufjAJV8fPQsnfx7SciL72gHTLnKVTbe/5c3reyioRMg/ze3r1tqR1to8a+1V1tq9/scqrLXGWlvR4/i7rbXl1tpMa+1ca+3zfZxzprX2WyH7TkQkNtRuhMadsKn3vZEYtv4Ft+3dP6inwnJoa4TGKKtSFOte+A/488XQ1uR1JCISTbo63d+ZyQtcz5yBjJwCNz4H6blw78dh69KIhAjAkp+5qWkn3Rq51+xtyrkuEUzA6XHBjghJImupdwv4OntXUBcZgvpqt135mLdxhNL6F2DkVBhR1v8xgZLamh4XWpUvu4aAj302vkYZRWR4qt91jVP7Wh/UlxFl8Nl/QPZoV7hgY0U4o3P2rodVT8LcmyFjRPhfrz/peTDpDFjzt4SbtaBESI7sma/AnfPhx8VuStML/wGrn4aGnV5HJrGms8ONBhmf61vQ3ux1RMPXdgC2vA5TjlBtJ9BLSIlQ6HR1uemGBZNh3T/g2a8l3B9xEelH5cuAgYkLgn9O3ng3TW5EGTxwpasGGk5Lfg7J6XDyF8L7OsGYdpErLLF7jdeRRJQSIRlYU60bKp16Acy+0XUgXnYnPHIt/Owo+L9j4dEb4M07YNtbrkywSH8ad4LtgmM+AW0N8NFzXkc0fJtehc62gafFgZunnpSmRCiUGnZARzOc8gU47Rvwzp/hlT47OohIoqlcDGOPg6zCwT0ve7QroDB6Ojz8KVj9VHji27cZPvgLzP4sZI8Kz2sMxlEXAgbWPut1JBEVTENVSWQfPu7e5J35bRg70+3raIUdH7hFdduXw/YVbmgXICnV/eIpnnPwI6/4yPNzJTHUVbntzKtgyxuw8lE49jJvYxqu9YsgJQtKTxn4OJ/PTY+rqYxMXIkgkFQWlrsbNfXVUPEjyBkDJ17vbWwi4p2Wevf+5NQvDe35mQWutPYDV8Cjn4VP3AkzrwxtjK//EnxJQ48x1HLGuPdsa/8GZ3zT62giRomQDOy9B6FoxsEkCFypxZI57oPPu331O/yJkf9jxR9h6R3usZyxUDzbnxjNhXHHQ0pGpL8TiQb1/kQorwSO/SQs+50bdcws8DauobLWJUKTFrj/F0dSOBn2fBT2sBJGrT+pLJzsbrZc8v/gwG742z9DdhEcdb638YmINza/5mawBLs+qC/pea5B9sPXwBO3QnsTnHhDaOKrr4Z374cTPgO540JzzlCYdhG8+F3Yvw3yS7yOJiI0NU76t3sNVL8Dx3/qyMfmjoWjL4HzfgA3/gO+tR1uWQwX/BTKTnPFFhb9J/zpfLfW6K4F8PdvwgePuJ4ymtefGAKJUO44d3etqz180w4iYe86qNsKUwYom91TYbm73rWoPzRqKiE5A3L8bySSUuCKP8OYGW7K7vYVnoYnIh7ZuNg1uC45aXjnScuGTz3ipj4/8xVY+tvQxPfGr1xVu3lfDc35QmXax9z2o797G0cEaURI+vfeg+BLhhlXDP65SSkwfpb7CJSEbNxz6KjRuw/A8rvcY1mj/CNG/pGjcbPcLyCJL3VVkJrt7rSNmekqra181M2RjkWBstnlR1gfFFBY7pK/uq1QMCl8cSWKmkr3c/T1uKeXlg2ffhT+cC48eCXctOhgxT4RSQyVL8OEecGN1B9JSgZc9QA8fiP849/cyNBpXx/6+Rr3wIo/uSniIyYMP75QGlkOI49y0+NO+pzX0USEEiHpW2eHW8Q3ZWHoFvFlj4JpF7qPwGvsXu1PjFa4+byBuxDG55qcXfJrN5VO4kN9lRsNCqwZm3ElLP5h7A7Dr18Eo6YHH3t35bhKJUKhULPBLWjuLXu0m9Lyh3NdGdybX3T7RCT+7d/qfjfMvil050xOhcvvgb/+E7z0fVfx9Mx/H9r656W/gY4WOO1roYsvlKZ/DF77RWxPWx8ETY2TvlW+7Bo/BjMtbqiSkt3aozk3wSd+C196G/5lE3z6MVcBav9WeP0X4Xt9ibz6Ksgdf/DrGZe77Ycx2FOotcEVfDhStbieAonQ3vXhiSmRdHa4qkv9jfYUToZPPeq6pT9wufv3EpH4V7nYbSefGdrzJiXDJ34Hs66DV38Kz//74Kf1N++D5Xe7yqkjp4Q2vlCZdhHYTlj3vNeRRIQSoaGwFip+QvG2p72OJHzeewAyC4/cGyXUMgvcG8uz/h2mXQwbXlYj13hSX31oIlQw0RXQ+OBR72Iaqk2vumlug0mEMgvdtECV0B6+uq3u518wwLS34hPhintg54fwyPX6XSKSCDYudkWaRk0L/bl9SfCxX8Lcz7mRnWe/5vqZBWvZXa51xHCm1oXb2BPcusu1f/M6kohQIjQUxkD1O5RufSw+++Y01bopajOucMPBXpm6EFrrYNsy72KQ0Olsd01488Yfun/mlbB7Fexa5U1cQ7X+BUjNgZKTg3+OMW5USInQ8NVsdNvAKFt/pi6Ei38JlS/B019SYRaReNbVCRsrYNKZ4Wvb4fPBBT+B+f/sKuQ+9fngCuC0NrhqukddBGOODU9soeDzuVGhDS9BW5PX0YSdEqGhmnsLqe11sDoOR4UCvYPCOS0uGJPPBF+K6xgvsa9hJ2APLxV69KVgklzRhFgRKJs9ecHgbxYUlquXUCh09xAKohDCrGvdfP73H4KXfxDeuETEOzved9PPhlM2OxjGwNnfhTO/436vPH4TdLQN/Jy3/gAt++H0KB4NCph2kWtWvXGx15GEnRKhoZp0Fk0Z4+Ct33sdSei9/xAUHeuqenkpLQfK5sG6F7yNQ0Kju3R28aH7s0e5P1orHxvcFAMv7V7jvp9gq8X1VFgO9dsT4k5bWNVWQlquqzgZjNO/6XqALPkZLI/D39si4tY3g+vtFm7GuMaj5/03rP4rPHIttLf0fWxbE7z5a/e3bvyJ4Y9tuMrmQ1oerH3W60jCTonQUPl8VI+7wE3bqn7P62hCZ/daqHrbjQaFa1h5MKYshL0fud4rEtt69hDqbeaVULcNti2NbExDFSibPZj1QQGBEYzajaGLJxHVbHCV94L9PWUMXPgzOOpC18NszTPhjU9EIm9jhesjFqpqt8E49Ytw0c/c7JWHroK2A4cf8869rnDL6d+MXFzDkZTiphV/9Fzc971TIjQMO8ec5Rp2xdOo0PuB3kFXeh2JM3Wh267XqFDMq/MnQr3XCIF7c5qS6RrsxoINL7pR06F0BO8uoa11QsNSU3nk9UG9JSXDJ//g+pU9dhNseTM8sYVD7SZ48Cq4c77WOYn0pbURti5164Mibc7NcOlvXRGd+y+HlvqDj3W0whv/z/U1mnBq5GMbqukfg+Za2BpDvyeHQInQMHSkZLuGWCsfcwUGYl1nB7z/F1cpLpJ3UwZSONm92UmQMo5xrb7aNVNNyz38sbRsNyd59V+PPM/aay317g/DUEaD4GCVMyVCQ9fR6kYQh9IoNTUTrvmL6/300NWw56PQxxdKHa2uVO8dJ7s7zjtXQlON11GJRJ8tr7tKkuFeH9Sf4z/lbrRsXw73XXrwfeH7D7kZEad/w5u4hmry2ZCUFvfT45QIDdfcW1xjrHfv9zqS4du4GBp3el8kobcpC2HzEne3R2JX/XZXOru/qUwzrnSLXCtfimxcg7WxAro6hrY+CFzSlzNWBROGY99msF2DHxEKyCqEzzwOSalw/yehfkdIwwuZTUvcCNDLP3Sj4xfe7vbv3+ptXCLRqHIxJKdD6SnexXDsZXDV/e6GxZ8vcUWClvzcrQvyYqRqONKyXdGqtc/G9Si0EqHhKjrGDXeu+IMr2xjL3nsAMgpc4hFNpp7nqthtesXrSGQ46qsHnko2+UzXZyfap8etf8EtIi2ZO/RzqIT28AR+dgP1EDqSEWXwmcdc8v3A5dBSF5LQQqJxDzz5T/Dnj7kRoU89ClfeCyUnucfrtnkbn0g0qnzZTT1LSfc2jqMugE/9xf2e+s1JsH+LWxsUDeuuB2vaRa5n284PvI4kbJQIhcKcm90dyg0veh3J0DXvc1m/172D+lJ6quvXoulxsa2uqu/1QQFJKa7b9kfPuX4L0cha9/988gIX71AVTlYiNByB0bTCScM7z9jj4Kr7YM9aePjT3veF6+qCFX+CX892U65P+wZ8fqm7GQRuOh9oREikt7oqV1jJq2lxvU0+y406d3VA0QyYer7XEQ3NUReC8cX19DglQqEw/WLIHgPL7/I6kqGLlt5BfUlOdaMF61+I6+HZuNbZDo273NS4gcy40vUuWBOlHa13fQgNO9w6uuEoLHeLUONhbaEXaivd6GHGiOGfa/JZ8PHfuOm3f73NuxLuO1fCH8+Dv33VVb267XU4+z/cmqaA9Hx3U2i/RoREDhHodxNN08/K5sEXlsO1T8bmaBBA1kjXNFyJkAwoKQVm3+juFMfqvP/3HoLRx7g7pNFo6vnuDWgcD8/GtYYduGaqR0iESuZC/gRYGaXT49Yvctvyc4Z3nu7KcTH6+8JrQ6kYN5DjroZzvuduCC36j9CdNxitDfCPb8PvznCV4T7xO7j+GRh11OHHGuNGhTQ1TuRQlYsha7RbrhBN8sZHT/GpoZp2kbsJGKdtTJQIhcqJ17uy02/9wetIBm/PR1C1Inp6B/UlUKFLzVVjU3212w40NQ7c9TfjCleQoHF32MMatPWLXKPhnDHDO49KaA9PTeXw1gf1Zd5XYe7nXNPDN38T2nP3xVpY/RT8ei4s/Q3Mug6++JZLygb6PZxfqhEhkZ66utyI0OQzo/c9TCybdpHbxumokBKhUMkZA0d/3FWP66uZVjR770EwSa6pZbTKHg3jZrnysRJ76ra77ZFGhMAlQrYLPnwivDENVvN+10B5uNPiwI16mSQlQkPRdgAaqodWOnsgxsD5P4bpl8Dz33ajQ+GybzM8eCU8cp2b4nfTi3DxLyCz4MjPzSvRGiGRnnb5S8pHy/qgeFMw0fXNUyIkRzT3Vmitg5WPeh1J8Lo64YNA76DRXkczsKnnQ9XbrqKSxJbAiFAwidDoaW6NRLRNj9u4GGzn0PsH9ZScCiMmKBEaitqNbhvqRAjAlwSX/d4VaHnyn1xzxFDqaINXb3eVpDa/Dgt/BLdWQMmc4M+RX+L+zkRTlTsRL1W+7LaTFngaRlybdhFsWxqX77+UCIVSyUmuOsjy38fOov7KxW79RjQWSeht6nmAhQ2LvI5EBqu+yi3yTu+jmWpfZlzpkt5oWkOzfpFbrD5+dmjOV1geXd9frAgkj6FcI9RTSjpc8yAUTHKV5HatCs15u3sC/cAl019cDqd8AZKSB3eevEDlOE2PEwFcIjT6mOFPWZb+TbvIzdRY95zXkYScEqFQMsY1WN31IWxd6nU0wXnvAVd5aWqU9Q7qy5jjILtIZbRjUf0RSmf3NuNywETP6GpXl79s9lmDf+Pan8JyV/3MqyplsSqQPBYMs3T2QDJGuNK3qdmu4epwko5DegI1w6cecQ0X84qHdr78CW6rggkyXF1d0NbkdRTD09bk3m9NjqJqcfFozEzIK43L6XFKhEJtxhWQnhcbpbQP6R2U5nU0R+bzuSl8lS+7cswSO+qqBm6m2lvuOCib75qrRsPo6s4PXPnvUKwPCiicDO1N/op6ErSaSsgZB6lZ4X2dvGLXcLWtySVDgy11flhPoK/D55cN/6aTeglJKOxaDXedDr+eA+0tXkczdFvecK0/lAiFlzFuVKhyMbQ2eh1NSCkRCrXUTDjhWljzNDTs9DqagX34BHS2xsa0uICpC6G1Hra+6XUkMhj1VcGtD+ppxhVuxKT63fDENBgbQlQ2uydVjhua2srwrA/qS9ExcPUDsG8TPPyp4N8w7lwJf1zoegIVHevvCfSfh/YEGqqsUZCcrkRIhqarE17/f3DXGVC7Geq3u/crsWrjYkhKc+v6JLymXeTeM1a+5HUkIaVEKBxm3+h+2bx9j9eRDOz9h2D00TD2eK8jCd6kBZCUqulxsaSjzZXCHmwidPTH3b91NEyPW78Ixp0Q2n4QSoSGpmZDeKfF9TbxNNfbZ+ub8MTN7nd7f1ob4Pl/9/cEqoRL74Qb/tZ3T6ChMsaNVmlqnAxW7Sa452OuV9aU8+DL77gy9Cv+6HVkQ1f5MpSeHJqbDDKw0lMgoyB6G54PkRKhcCic7O4cr/ijexMYjfasg+1vRXfvoL6k5cCEeUqEYkmgmepg1ggBZOS7P9YfPj7wm89wa6p1/1dCOS0O3PSu5AwVTBiM5n2uTG64CiX059jLYOGPYc0z8Ny/Hj5d01pY/bSrBvfmr+GEz8AXV8Dx14Tn96t6CclgWOtuzP52nlvDfOmdbp1a9miY/VmX5IeqKEgkNeyE3atVNjtSkpLhqAvc+684Wp6gRChc5t7q1hSsfcbrSPr2vr930Iwo7h3Un6kLoWb9wTK6Et26S2cPYo1QwMwr3f+jTa+ENqbBqHzZVcspD0HZ7J58PnfTRCNCwasJY+nsIznl83Dql+Ct38Prvzi4f99mePAqeORaV2Thxhfgkv8XXE+goVIvIQlWw07Xs+qZr0DxiXDbG4cm6Md/2k0tW/Enb+McisrFbqv1QZEz7SJXvn/za15HEjJKhMKl/BwYUQbL7/Y6ksN1dcL7D7sSrjlFXkczeIHFxute8DYOCU59ldvmDqFK1pSFkJYLH3g4PW79IjcdYPys0J9bidDg1PpHzyI9IhRwzvfh2Mvhxe/BO/fCkp/Bb052bwrO+2+49RUoPSn8ceSXQNPe2K/4JeG16km442TXD+uC/4VrnzpYbCMgswCO+YR7TxBri+A3LobMka5tiUTGpDPdTIa18TM9TolQuPh8MOdm2PoG7PzQ62gOtdHfO+i4a7yOZGgKJkHhFFj3D68jkWB0J0JDGBFKSYfpl7gpSe3NoY0rGIGy2eVnu2aboVZY7kYU4miaQVjVbADjczeZvODzwaV3wMTT4ekvwUvfhynnuJ5Ap34xdKXVjySv1G3rtkfm9SS2NO+Dx2+GR2+AERPhc0vgpM+567cvc26Ctgb48LGIhjks1roRoUkL+v++JPRSM93fw7V/j5vWD7p6wun4T7vM+a3fex3Jod570DWGPOoCryMZuqkLYcvrsXcHKxHVVblRnWCbqfY28wr3R9qLxHfHu+7Oe6jXBwUUloPthH1bwnP+eFNT6aaFeVnuPzkNrnoAZt8E1/xleD2Bhio/kAhpepz0suEluONUNxp05r/DTYtg1NSBn1M8x1U3fOsP0dGuIBi7VsGB3Vof5IVpH4OGavf3MQ4oEQqnzAL3Ju6DR9wdmmjQvN9V/IiV3kH9mbrQ9Q7YWOF1JHIk9YPsIdRb2WmQPcab6XHrXwQMTD47POcvnOK2mh4XnJoN3qwP6i09Fz72czjqfG9eX72EpLe2A/C3r8H9l7nr8+YX4Yx/CW6U0hhX7XbnB1D1TvhjDYXKl91W64Mib+pCt8Y8TpqrKhEKtzm3uKaJ7z3odSTOqhjsHdSX0lPcKIOmx0W/ofQQ6smXBDMuh/UvDL6p5XCtfwHGnwhZheE5f+BNvRKhI7PWFUjxan1QNMkZC75kVY4TZ9tyuHO+q1R7yhfdWrVxJwzuHDOvhNRsWPGH8MQYapUvw6hpw7vJJkOTWQATTo2bMtpKhMJt7EwoORneujs65lO+9xCMmj74X5LRJinFDYmvXxQdP1fpX3318P9YzbgCutph9VOhiSkYB2qg6u3wTYsD9wclo0CJUDAO7HHNlAuiYETIa74k939KvYQSW0cbvPhfrnlvZ4frWbXwv93aysFKy3HJ0IePR88Mlv60N7uS35M0GuSZ6RfD3o9g73qvIxk2JUKRMPcWdyczMJTrlb3rYfvy2Osd1J+pC6FxJ+x83+tIpD+BZqrDXUMx9jgYOTWyzVUrXwKsWwwfToXlSoSCUeNxxbhokz9BI0KJbOeH8Puz4LWfu/XIt70OZfOHd87ZN0JHi7thGs22vuni1Pog7xx1odvGwfQ4JUKRMP0SyBoNy+/yNo73/L2DZsZg76C+lJ8LGJXRjmaBZqrDHREyxo0KbXk9cpWy1i9ypVnHhnn0tLBcTVWDEUgWCyd5G0e0UC+hxNTVCa/9An5/prsReM3D8PFfD70YTU9jZkDxXDfFLpqLJlQuBl8KlM3zOpLElV/iblDGQRltJUKRkJzqujevfwFqN3kTQ6B3UPk5kDPGmxhCLXuUW7+x/nmvI5H+dJfOHsYaoYAZl7vtygiUeO3q9JfNPif8pVkLJ7sKPKqAOLDaSvfmJ1A6OtHll7gbDR1tXkcikVK7Ef50Ibz4XTcj4vNLQ1/9dfaNrmH55iWhPW8oVS6G0pMhNcvrSBLbtIth+1uuaW8MUyIUKSfe4PpfeLUQcWOFe7N1fIz2DurP1IVuHUfjbq8jkb7UV7ttKBKhgkmuzGskpsdVvwvNta7pcLgFpnrVbgz/a8Wymg2uf1CkevVEu/xSwB682SDxy1o3SvPb+bB7DXziLrjyPsgaGfrXOuZSyBjhXi8aNe6GXStVLS4aTLvIbT/6u7dxDJMSoUjJHecWl71znzfdwAO9g6bGcO+gvkxd6LbrF3kbh/QtMI0tLwSJEMCMK2HXh7BrdWjO15/1L7gbF5GYgx5IhLROaGA1qhh3iDx/CW0VTIhv9Tvggcvhb/8MJXPg82/AcVeFb51vSoZbc7TmGWjYFZ7XGI5AywwVSvDe6OmuYW+MrxNSIhRJc2+Flv2uKkskNe938zhnXD60ajLRbMxMV0pW0+OiU321K3OelhOa8x3zCbfObeUjoTlff9a/4EafMgvC+zrgRrpA64QG0tXlL52tinHd1Eso/n34ONxxMmx+HS74KXzmycg07z3xs9DVAe/eF/7XGqzKxa7S5tjjvI5EjHGjQhtfgZY6r6MZMiVCkTThVBh9tCuaEMmFiKuedBVWYr13UF+McdOXNrysufLRaLg9hHrLHuWmRKx8PHxl0xt3u6lx5RGYFgeQmgm5xRoRGkhDNXQ0KxHqKbcYMKocF4+aauGxG91HYTn802tw0q3hX68YMLIcJp4Bb9/j1ktGC2td9d1JZ7gS8uK96Re71hYxPCtHiVAkGeNKae/8wC0wi5T3H3KNx8bNitxrRtLU86GtwZXUlOhSXxW6aXEBM66Euq2wbVlozxuw4SW3jcT6oIDCyUqEBhIYLVMPoYOSU91ouKbGxZf1L8Idp7ieaWd9B2583iUmkTbnJndtbXgx8q/dnz1rXaU8lc2OHsVzIGtUTE+PUyIUaTOuhLS8yJXS3rvBvWGMl95BfZl4BiSlwjpNj4s6dVWh7/w97SJIyQzf9LgNiyC7yE27jJTCclepKZpL1nqpu3S2EqFD5KuEdtxobYRnvgoPfNIVK7jlZTj9m94VBznqQsgeA295VOCpL4FejFofFD18Sa5y4fpF0NHqdTRDokQo0tKyXVKy6q+RqXT2/oNu0ffMq8L/Wl5Jy4ay07ROKNp0tMGB3f4pPCGUlu3+SK96MvTTITs73IhQJMpm91RY7uZYN9VG7jVjSe1GSM6AnBAn1bFOvYTiQ0sd/O50NxXt1C/BrRXer4FJSoFZ17n1ktFyjVW+DIVTDq6Pk+gw7WI3K2fTq15HMiRKhLww52Y3p/LtP4f3dQK9gyafHT+9g/ozdaG7a6wF59GjIVA6OwxvXmdeCc37oPKl0J636m1X0CSS0+JAleOOpGaDKyoRyeQ0FuSXuOmn0bSOQwZvzTOuT9bVD8J5P4yeokYnXu9mkrx9j9eRuNGGza9rWlw0mng6pGbHbHNV/VXxwshy9595xR/dHehw2fSK+yMZj0USeptynttqelz0CPQQCvUaIXD/fzIK4IMQT49b/4KrShfpqReBKV9KhPpWU6lpcX3JL3XVvWK8oWHCW/20axQc6uaow5VX7NbgvnOf98WIti51BVPUPyj6pKS7WRRr/x6+IkZhpETIK3NvdXfMPwrjArP3HoT0PDeNKN4VTISRR2l6XDSp8zd6DGXVuICkFFdK+6PnoLUhdOfdsAhKToKM/NCdMxj5E8CXrESoL50dsG+TEqG+5JW6rQomxK6WOjfl6+hLonMd7+wb3RRnr+/2b1zsfkeWzfc2DunbtI+56ySShcBCRImQV6ac5+7mLf99eM7fUueG24+Nw95B/Zl6nhs6D+UbYxm6en8z1XBMjQM3Pa6jOXTVahp2wY73Yco5oTnfYCQlu8Z0SoQOV7fVjXqomerh1Eso9q173k2VP/rjXkfSt8lnu/cqK/7obRyVL0Px3ND1pJPQmnoe+FK8T5iHQImQV3xJMPsm2LwEdq0O/fm7ewd9OvTnjlZTFro/KJWLvY5EwN9MNS98f7hKTnJ/oEM1PS5QJjYwzTLSCsu1xq0vKp3dv0BzTSVCsWv1U64M+vjZXkfSN5/PNVjdvAT2rPMmhgN7YccHWh8UzdLzYOJpLhGKseqnSoS8NOs6SE6Ht+4O/bnfe8hNFRsfp72D+lJ6snvjrelx0aEuDD2EejIGZlzhpkyEogLj+hfcG5KiY4d/rqEonOwWTMfgHOuwCiRCGhE6XGoWZI7U1LhY1drobsBMvyS6C4GccK272//2n7x5/Y0VgNX6oGh30j/BKV8EG1t/w6L4f14CyCxwU9fef9hNZQuVmkrYtjS+ewf1JSkFys+CdS/ozWQ0qA9DD6HeZlzpful++MTwztPZ4UYSy8/x7v9MYbkbxa2v8ub1o1XNBkjLhayRXkcSnfJLYL8SoZi0/gX3f/7oS7yOZGDZo1yM7z0AbU2Rf/2Ni92Iw7gTIv/aErypC10jXl+S15EMihIhr829GdoPuBGcUHkvAXoH9Wfq+W7B3o73vI5E6qvCUyihp9HToGgGrHx0eOfZvhxa67ybFgcqod2fWn/FuES6qTMY6iUUu9Y8DVmjoPQUryM5stk3uRu2q56M7Ota625STTwj5t5gS2xQIuS1cSdA8Rx46/ehGcXo6oT3H3JzaXPHDv98sab8HMC4O23inY5WOLAn/IkQwMwroGrF8NbXrH/BVSSatCBkYQ2aEqG+1WzQ+qCB5JdC3faYm5ef8Nqb3eyFaR+LjTf4E06FUdNgxR8i+7p717ubalofJGGiRCgazL3V/bHfVDH8c216NXF6B/UlayQUz4Z1//A6ksQWzh5CvR17OWBg5WNDP8f6F91d2fTckIU1aDljICVLBRN66mh10760Pqh/+aWueuKBvV5HIoOx4SU3GyRaq8X1ZowrpV31NlS/F7nXrXzZbbU+SMJEiVA0OPrjbsHr8hAUTXjvQVcw4KiLhn+uWDV1IVS/68ohizcCiVC41wiBS7bK5sPKR4Z2V7y+Gnat9I8mesgYNwVMI0IH1W4CrHoIDSTPX0K7TtPjYsrqpyBjRGz1xZl5FaRkRraUduXLUDAJRpRF7jUloSgRigbJaXDiDbDuOdi3ZejnCfQOmvHJxOkd1JcpC91W0+O8E1jwn1scmdebcYVLIKrfHfxzvS6b3VNhuRKhnmoDFeOUCPVLvYRiT0erm7Uw7SJX5CdWZOTDsZ90o++hLPDUn4422PyapsVJWCkRihazP+u2w7nTsuqvbopEIvUO6suYGZAzTmW0vdSdCEVgRAhcRaOk1KEVTVj/glvLNHp66OMarMJy2L/FvQGQg0mh1gj1LzAipMpxsWNjBbTWw/QYmRbX0+wb3ZS+UPVvG8j25e61JmlanIRPUImQMSbJGPNjY8wOY0yjMeYxY0zRAMefb4xZ4T92pTHmml6PG2PMt4wxW4wxB4wxrxpjjh/m9xLb8ord3aF37oX2lqGd4/2HYORUGH9iaGOLNca4LseVi/WG0it1Va7caVp2ZF4vY4Qb0fnwcVcwJFid7VBZAVPOjY6qZIXlrhz4vs1eRxIdairdtOGMfK8jiV4Z+W46tHoJxY7VT7t/s0lneB3J4I2f5Yo8rfhj+At0VC4Gk+QadYqESbAjQt8DrgeuA04HioHH+zrQGDMP+DvwGjAb+DHwO2PMtT0O+0/gX4GvALOAKuDvxpgwtaCPEXNvheZaWDWEnig1lbD1zcTrHdSfKQuhrRG2vO51JImpvjoyFeN6mnEFNO5yBUOCtXUptDVEx7Q4UOW43moqNS0uGOolFDs622Ht3+Co8920+Fg0+0bYvdr9/gynypdd8aP0vPC+jiS0IyZCxphUXMLybWvtImvtO8DVwDxjzKl9POWbwBvW2q9aa9daax8EfgJ833++bOBfgK9Za/9qrf0I+BzQikuKElfZaa485fK7Bv/c9x9K3N5BfZl0BiSlaZ2QV+q3Rz4Rmnq+a7w5mOlxGxa5jukTTw9fXINROMltlQg5tZWqGBcM9RKKHZuXQMv+2KkW15djP+lGtMJZNKGp1q351PogCbNgRoSOB3KAisAOa+1mYDPQ13jlFGBJr33vAmXGmFJgPpAOdNe6tdbWW2snWmtfCT70OGQMzLnZ/eff/nbwz+vqcg1ZJ50ZuTUZ0S41yw2nr9M6IU/UV0f+WkxJh+mXuGkn7c3BPWf9ItcfIy1KBqMzRripYEqEoLURGna4ilEysPwSTY2LFaufcmXyY/kNfmoWHHc1rP5r+Mq2b3oFsFofJGEXTCIUKPtU1Wt/NVDSx/F97S/zb0cDU4E9wEnGmKXGmF3GmH8YY44OLuQ4d9zVkJozuFGhza+6O/CJ2juoP1MWujvKe/WmMqICzVTzIlQxrqcZl7upbsH0karb7qZ3TDk3/HENRmG5egkB1G50W02NO7L8Urf4vnm/15HIQLo6Yc3fXIuHlAyvoxme2TdCZxu890B4zl+52I3wJ/qaZwm75CCOyQS6rLXtvfa34kZ2ersP+IMx5mngCeBY4Bv+x1KBXNwI069w0+h2Av8GvGqMmW6t3dPzZMaYW4FbAYqKiqioqAgi5MhobGwMSzzlo05n3MrHeDP7QtpTjzw3dtqa/2NkUhZv7M6hK4p+Pl5Lb87jZGDDc79he0n0T0MI1/UUaenNOzkZWFtdz85Ifz+2i1NSR1C/+Les2jNiwEPHVj/PUcDyffk0RdHP/aj2LApq3+HNYcYU69fTqN2vcwzw1qb9HNhT4XU4UW3U7nr3s3rpSQ5kTwzLa8T69RQN8vet5Pimvayy5eyJg5/l8XnHkLbkDpa1zXBT8wdhwOvJWk5e9RwNOdNZteS14QcqcW84v5+CSYSaAZ8xJtla29FjfxpwoPfB1tp7jTETgHuAh4CtwO3Ar4E6oB2XXN1mrV0MYIz5NLANuBb4ea/z3QXcBTB79my7YMGCQXx74VVRUUFY4jlmHPzmWeZlbIDTvj7wsS318NoyOP5qTj97YehjiXUbf0551wbKo+i66U/YrqdI2/waLINpc89m2uQFkX/91k8x6q3fs+Ck49xUs/48dBfklTL3ws9EV4GRpHfgpZdYcMqJw5qyF/PX06tvwWqYc96VbiqO9K8qB1b/L3PKR8O0BWF5iZi/nqLB35+F5AyO+fhXIldRM5wKvwaP38SCEgvlCwb11AGvp70b4JXdpJ/zryyYM7jzSmIazu+nYFL4wMTjsb32j+Pw6XIAWGt/gBv5KQEm+c/RCWzp8ZyVPY5vATYB4bmVFWtGTYVJC+CtP0Jnx8DHrv6regcNZOpC2PKGSxglMuqr3daLqXEAM69wUzZWP9X/MR2tbg56tJTN7qm7clyCT4+r2ej6gSkJOrK8UrdV5bjo1dXl1i+Wnx0fSRDA9IvdmsZQF03YuNhtY3kdlcSMYBKh94EGoLvgvTGmDLfu57A6tcaYLxpjfmGt7bDWVltrLXAprpJcI66sNsCcHs9JByYDCf6Xv4c5t7h1P0da6/DeQ1A4xZWYlMNNWQhdHQd/sUr41W13W68Kd4w93v2f+GCA6nFb33Tl1aNtfRCohHZAzQatDwpW1khIzlDBhGi2/S1o3AlHX+p1JKGTnAazroWPnnO940Kl8mXIn6BCKRIRR0yErLWtwB3A7f5GqbOAh4FXrLVLjTGpxpgx/jLbAGuBLxhjPmOMKTPGfAv4DPAf/vNtBu4HfmuMOccYMw34E27E6P5Qf4Mxa+r5riTqQEUTaiph6xtw/DXRd1c7WpSc5HoQqHpc5NRXQ3q+d3fyjXE9hba8djAp6239IkhKjZ6y2T0VTASMEqFa9RAKmjH+XkIqoR21Vj/lfudMjbMp7Cfe4JpAv3NvaM7X2Q6blmg0SCIm2NVt3wEewCUqi3FT3C73P3YqsMO/xVr7InAbrgnrauATwMW9SmPfjCuffT/wDq6a3JnW2jDVYYxBSckw+7Nu+s6ej/o+5v2HAQMzr45oaDElKRnKz3H9hLq6vI4mMdRXRb6HUG8z/L+ePuyz77NLhMrmR+e0q5QMdxMkkROh5n3QVAMFSoSCpl5C0ctaWPO0e3Ofnut1NKE1osz9jX3nz0eeyh+M7Stc5c/JKpstkRFUIuSf5vZ1a+1Ia22etfaqQNJira2w1hprbUWP4++21pZbazOttXOttc/3Ol+rtfYb1tox/mPOttauCul3Fg9mXe/uIL119+GPdXW5JqqTz4Q8j990RrspC1055x3veh1JYqjb7v01WTgZxs/ue3rcvi2w9yMoj8JpcQGFkxM7EaoJlM5WM9WgqZdQ9Kp+x/3bTL/E60jCY/aNrufXuueGf66Ni10FumgcrZe4NLh6hxJZWSNdB+f3HoLWhkMf27zE/WJVkYQjKz8HMJoeFyleNFPty8wrYddK2L3m0P0bFrntlPMiH1OwAr2ErPU6Em8EkkBNjQtefqkbRWs7rJireG310+BLhqMu8DqS8Ji6EHKLQ1M0ofJlGDdr4IqfIiGkRCjazbnFDRO///Ch+9970DUbm3aRN3HFkqxCKJmrRCgS2lugaa/7o+i1Yy4DkwQfPHLo/vUvuukc0fwmu7DcNcg8sOfIx8aj2kp3V3hEmdeRxI5A5bj+1sWJN6x164Mmng6ZBV5HEx6+JDjxepfEDKfaZfN+qHpb64MkopQIRbviE93dkeW/P3h3uKXe/WI99rLY704dKVPOgx3vQcNOryOJbw3+0tnRMCKUPcpNHV352MH1Ye0t/rLZ50V3gZFErxxXs8GteUlO8zqS2JFf4rZaJxRddn0I+zbB0dHf1HtYZl3nbjy9fc/Qz7HpVVd4QeuDJIKUCMWCube6NQ2b/NXKVz+l3kGDNfV8t13/grdxxLtACVWv1wgFzLgC6rbCtmXu6y2vQ3tTdK8PgoOjVQmbCFVqfdBg5SkRikqrn3Kjm9M+5nUk4ZUzxs1Qefd+d8NpKDYuhtRsKJ5z5GNFQkSJUCw45hOQWQhv/d59/f5D7k2CflkEr+gYV8lM0+PCK9BM1euqcQHTLnL9VVb6iyZseBGS013FuGiWXwq+lMRMhKz1J0JRPHUxGuWMcdeMCiZEl9VPw4R5bs1vvJtzEzTXDtzMeiCVL0PZaZCUEtq4RAagRCgWpKS7Yee1z7r6+lteh+PUO2hQjHHToSoXQ0er19HEr3qPm6n2lpYD0y6EVU+6/hTrX3B/aFMzvY5sYL4k10xwOPPtY9WBPW5dpEaEBseX5EZi9ysRihq717rZHPE+LS6g7HRX8n4oRRNqN8K+zVofJBGnRChWzL7RbR+9ATBwnHoHDdrU86H9gEskJTy8bqbalxlXuruUb/3BjbBMifJpcQGF5Yk5IhT4ntVDaPDUSyi6rHkaMDD9Yq8jiQyfz71X2bYUdg2yI0rlYrdVIiQRpkQoVuSXwtQLXEWuSQsgLwqqcsWaiae7aVGaHhc+dVXRd22Wnw0ZBfDS9/1fn+NtPMEqnOzuknZ1eh1JZAVGwTQ1bvDySzU1LpqsfgpKT3bTFhPF8Z+CpLTBjwpVvuwSef2/lwhTIhRLTvqc28661ts4YlVqppsWte75xO3PEm71VdEzLS4gKQWOudSNBhZMjp0/tIXl0NmWeG9saza4tS6Bxf8SvPxSVxmzo83rSKSm0lWMi9cmqv3JLHAVbd//C7Q2Bveczg437X/ymZryLxGnRCiWTDoDvrjC9UeRoZm60JUyTcQpR5FQXxU9hRJ6mnGl20ZzE9XeErWEdm0lFEyEpGSvI4k9eSWAPbhWT7wTKBiQKNPiepp9o1vnFyhScyTV70BrHUxS2WyJPCVCsWbkFN0xGY6pC9123T+8jSMetbe4zvbRmAiVngzn/RBO+bzXkQSvOxFKsIIJNZVaHzRU6iUUPVY/BeNPPPhvkkiK50DRDFjxh+BmX1QuBoyb9i8SYUqEJLHkl8Loo7VOKBzqo6yHUE/GwKlfcv/+sSJ7NKTmJNaIUFeXWxcVK9MXo013L6EEm04ZbfZtcQ28E6VaXG/GwOzPws6VUPX2kY+vfBnGHe+m1YlEmBIhSTxTzoOtb0JLndeRxJfuHkJRtkYoVhnjEoJESoTqq6CjRYnQUOWOd807E21dWbRZ87TbJtr6oJ5mXumao771h4GPa6mH7W+pWpx4RomQJJ6pC6Grw92FktAJjAjlRlnVuFiWaCW0a/3TADU1bmiSUyFnrEaEvLb6aRgz0611S1RpOS4ZWvUENNX2f9zmJWA7tT5IPKNESBJP8VzX62bdC15HEl+6EyGNCIVMYbl7U9ve4nUkkRFI+tRMdejUS8hbdVWwfTkcncCjQQGzb3QjvO8/1P8xlYshJQtK5kYuLpEelAhJ4klKdr1k1r/g1iRIaNRVQcYIV6ZcQqOwHLCu0mEiqNkIyRluVEOGJr8E6pQIeWbt39z26Es9DSMqjJnhbjyu+GP/RRMqX4ay+ZCcFtnYRPyUCElimnq+a05b/Y7XkcSP+urorBgXywJrZRJlelxtpfueffrTNGT5pe7/YqI14o0Wq5+CUdNdhVeBOTe531+bXj38sX1b3P/5yZoWJ97RXxtJTOVnu0XFqh4XOvXblQiFWqIlQjUboGCS11HEtrwStwayYYfXkSSext2w5Y3ErRbXl6MvdTMFVvzx8Mc2LnZbFUoQDykRksSUWeCG7NVPKHTqq7U+KNTS8yBrdGIkQp0dsG+z1gcNl3oJeWfNM4DV+qCeUtLh+E+7KYMNOw99rPJlyBkHI6d6E5sISoQkkU1dCDs/gHrdOR229mbXTDUaewjFusLyxGiqun+LG8lQ6ezhyfP3ylLluMhb87T7/zr6aK8jiS6zb3T/t9+97+A+2wkbX3GjQWoSLx5SIiSJa+pCt12v6nHD1t1DSKWzQy5RegnVbnRbjQgNT2BESAUTIqupFjYtcb2D9Mb+UIWTYdICePvP3WvXchoqoWW/1geJ55QISeIafbR74651QsOn0tnhU1gOB/ZA836vIwmvQLKnHkLDk5IBWaM0IhRpa591oxxaH9S32Te6Rr/rFwEwYt97bv+kBZ6FJAJKhCSRGeNGhTZWJE6flnCp8ydCeRoRCrnACEltnE+Pq6mEtDzIGul1JLFPvYQib/VTrmLf2OO8jiQ6HXUhZI+BFX8AoKD2Pdd0Vv/fxWNKhCSxTV0I7Qdgy2teRxLbAiNC6v8SeoEyvPG+TqhmAxRO0rSiUMgvcXffJTKa97sbakd/XNdvf5JSYNZ1bkRo12py6z9StTiJCkqEJLFNPN01cFyndULDUl8FGQVqphoOI8pcqfd4XydUW6n1QaGSXwp12/tvYimhte4f0NUO0zUtbkAnXu8Sxb/+Ez7bofVBEhWUCEliS8lwydC6f+hNw3ComWr4JKe5N7bxnAi1t7g1LVofFBp5pdDR4taWSfitftr9/ht/oteRRLe8YtfMfMf7dPpSoeRkryMSUSIkwtTzXOneveu8jiR21VWpdHY4FZbHdyK0bzNgNSIUKuolFDmtDbDhRZh+Mfj0luqIZt8EwP78Y12PIRGP6X+tyBR/GW1Vjxu6+ipVjAunQC+heB21DCR5hZO8jSNe5CkRipj1L0Bnq6rFBWvyWXD0x9kxdqHXkYgASoRE3N3T0ceon9BQtTVBc62mxoVTYTm0NULjLq8jCY9ARTxNjQuN7l5CKpgQdqufgqzRUHKS15HEBp8PrryXvaM0LU6igxIhEXDV47a8Ef+9WsKhYYfbKhEKn0J/ghCv0+NqNkDmSMjI9zqS+JCe5z7USyi82ppcFbTpF4MvyetoRGQIlAiJgEuEbCdUvux1JLGnbrvbao1Q+ATWzsRtIrTxYLInoZFXqqlx4bbhRWhvgqMv8ToSERkiJUIiAMVzIGOE1gkNRX2122pEKHxyiyEpLY4ToQ0qlBBq6iUUfmuedm0DJsz3OhIRGSIlQiLgpjWUnwsbFkFXp9fRxJZ6/4iQiiWEj8/nRkzisalqayM07oQCFUoIqfxSNzUuXgtseK2jFT76B0y7CJKSvY5GRIZIiZBIwNSF0FQDVe94HUlsqa92d0VTMryOJL4VTo7PEaFAoQSNCIVWXgm0NUDLfq8jiU+Vi93P9+hLvY5ERIZBiZBIwOSzwPhcc1UJnnoIRUZhOdRugs4OryMJrcAol9YIhZZ6CYXX6qcgLc815BaRmKVESCQgs8B1ul6vdUKDUl+t9UGRUFgOXe1QF2dvbAOJkKbGhVZ3LyGtEwq5jjb46FmYdiEkp3odjYgMgxIhkZ6mngc7V0LDTq8jiR3125UIRUJ35bg4WydUWwk54yA1y+tI4kv+BLdVwYTQ2/wqtNTBdFWLE4l1SoREegpMc9jyurdxxIq2Jmjep0IJkRCvJbRrNmhaXDhkFkBKpkaEwmH105Ca7aZTi0hMUyIk0tOY4yA1Bza/5nUksSFQOjuv2Ns4EkFmoWuSGXeJUKUSoXAwxk2P27/F60jiS2cHrP2bK66Tku51NCIyTEqERHpKSobSk2CzRoSCUl/lthoRCj9j3KhQPCVCTbXQXKuKceGiXkKht/UNV1306I97HYmIhIASIZHeJsyDvR9B4x6vI4l+3YmQ1ghFRGF5fK0Rqt3otgUaEQqLQC8hCZ3VT0FyBpSf43UkIhICSoREeis7zW21TujINCIUWYXl7g5/e7PXkYRGjXoIhVVeiRtxa230OpL40NUFa56BKeequIdInFAiJNLbuOMhJUuJUDDqqtzaFTVTjYzAWprASEqsq9ngeneNKPM6kviUX+q2mh4XGtuWQeMuTYsTiSNKhER6S0qBkrkqmBCM+ipNi4ukeKscV1vp3qyrF0t4qJdQaK15GpLSYMp5XkciIiGiREikL2XzYPdqOFDjdSTRTc1UIyuwliZeEqGaDVofFE7dI0Jx1oTXC9a6stnlZ0N6rtfRiEiIKBES6cuE+W679Q1v44h2ddshT4lQxKRlQ87Y+CiYYC3UbNT6oHDKLoKkVI0IhULVO655tJqoisQVJUIifRk/C5LTVUZ7IG0HoGW/CiVEWryU0G7cDW0N6iEUTj6fG7HdrxGhYVv9V/ClwFHnex2JiISQEiGRviSnuXVCW7ROqF+BZqq5aqYaUYWT4yMRqvWPamlqXHipl9DwWevWB006AzJGeB2NiISQEiGR/kyYDzs/hOZ9XkcSnVQ62xuF5a6hY1Ot15EMTyCZ04hQeKmX0PDt/AD2bVa1OJE4pERIpD9l8wALW970OpLoVOdPhLRGKLICa2pivYR2TaWbahSobCbhkVcKjTuho9XrSGLX6qfBJMFRF3kdiYiEmBIhkf6Mn+1KpaqfUN8CU+NyNCIUUfFSQrtmAxRMhKRkryOJb/n+RLNuu7dxxCprYfVTUDYfsgq9jkZEQkyJkEh/UtKheLb6CfWnfjtkjnQ/J4mc/Anu7nSsJ0K1G7U+KBK6ewmpYMKQ7FkLNevhaFWLE4lHSoREBlI2380Pb6nzOpLoU1+t9UFeSE6FERNiOxHq6nKJkNYHhV93LyGtExqS1U8BBqZd7HUkIhIGSoREBjJhHtgu2LrU60iiT10V5KlinCdivYR2fRV0tCgRioTccWB8KpgwVKufhtJTIKfI60hEJAyUCIkMpHiOW9Ct6XGHq6/SiJBXCstdsQFrvY5kaLorxqmZatglpbh1fLE4Na6ry9vX37sedq9StTiROKZESGQgqZkw/kQVTOitu5mqKsZ5onAytDcdLFgRa9RDKLJisZfQpiXwg5Fwz8fgrbtdA95IW/2U207XtDiReKVESORIyuZB9XvQ2uB1JNGju5mqEiFPxHrluJpKSMmEnLFeR5IYYrGX0Lp/uCl9jbvg2a/Dz46CP18MK/4EB/ZGJoY1T7tZAWoRIBK3lAiJHEnZfLCdsG2Z15FEj0ApXr1B8EY8JEIFk8CnP0ERkVfiprJ2dngdSfC2LXej8V9YDre9Aad93a1L/NtX4fapcO+l8M694WssXLsJdrwP01UtTiSe6a+QyJGUnAS+ZK0T6ql7REhrhDyRMw6SM1xCEYtqNqhQQiTll7ibOQ0xMpWyvQV2vAclc8EYKDoGzvoOfOlt+KfXYN5XYN9mePpLcPsUuP+T8O790LwvdDGsecZtVTZbJK4FlQgZY5KMMT82xuwwxjQaYx4zxvRbQsUYc74xZoX/2JXGmGt6PX6RMcb28aESVBJ9UrNg3AmwWeuEutVXua2aqXrD53OJRCyOCHW2w/4tWh8USd29hGJketyO96CzDUpPPnS/MTBmBpzzXfjyu3DrK3DKF2HvOnjqC/DTKfDAlfDeQ8NvebD6KRh7HIwoG955RCSqBdvS+3vA9cB1QA1wB/A4ML/3gcaYecDfgf8HfAaYBfzOGJNsrb3Pf9ixwLvAhb2e7sFqSJEgTJgHb/7aFQlIzfI6Gu/VV6mZqtcKJ8POD72OYvD2b4WuDlWMi6T8CW4bKwUTAu0Kiuf2f4wxMO5493HO96D6HVj1JKz6K6x/HpJSofwcOOYTMPV8SM8N/vXrtkPVCjj7P4f+PYhITDhiImSMSQW+AnzZWrvIv+9qYJMx5lRr7Ru9nvJN4A1r7Vf9X681xkwEvg/0TIRWWmt3huB7EAm/svnw+i/cvPXJZ3odjffqqrQ+yGuF5bDmb26EJSnF62iCF5jOp6lxkRPo9xUrI0LblrsRw+xRwR1vjFtPNP5EOPcHsH2FPyl6Ej76OySlwZRzDyZFadkDny8wLW66ymaLxLtgpsYdD+QAFYEd1trNwGbgtD6OnwIs6bXvXaDMGONvcc2xwJpBRSripZKTwCRpnVBAfbUqxnmtsNyt+9i3xetIBidQOlsjQpGTkg5Zo92UxGhnrStMU3LS0J5vDJTMgfN/BP+8Cm58HmZ/1iVHj98EP50Mf7nWJUltB/o+x+qnYfQxMFLXqEi8C2ZqXGDdTlWv/dVASR/H97W/zL8dbYypAqYBJxpj3gdGAW8B/2Kt/SiYoEUiLj3XzRdXPyGnfjtMONXrKBJbz8pxsfSGrWYDpOVBZqHXkSSWWOklVLsRmvZC6RAToZ58PrfOqPRkWPhj2LYUPnzCrf9Z87Qr4T51oRspmnIepGRAwy7Y+iYs+Lfhv76IRL1gEqFMoMta295rfyvQ1wKB+4A/GGOeBp7Ajf58w/9YKjDZ/7w04Bb/vu8AS4wxx1prD1knZIy5FbgVoKioiIqKiiBCjozGxsaoikfCa1JSKcXb/sZrLz1PV1JayM8fK9dTUkczp7XUsXFvC1tjIN54ldxez3xgw/Ln2b7j8F/F0Xo9zdywguTUUbzzyiteh5JQjm5LJ7t2HcuHeE1E6noas+MlpgHLd/poCsfrZX0MZl9A/v7VjNrzGqPWvUzqqifp9KWzd+RcOpPSGYdl+YFx4Xl9AaL395PEpuFcT8EkQs2Az1/soGcTgjTgsHFla+29xpgJwD3AQ8BW4Hbg10CdtXadMWYksM9a2wVgjLnMf9y1wM96ne8u4C6A2bNn2wULFgzqGwyniooKoikeCbOxLfDQXzl9UgZMPD3kp4+Z62nPOngNJp1wOpNmLvA6msT2zgjK8y3lfVw3UXs9vVsLZSdFZ2zxrO0lWLaCBaefPqT+TRG7np5+AtLzmHvBZ8LcZ+ps4Euut9KW10ha9SRFq5+G5loYOZW5F17rptlJWETt7yeJScO5noL5LRMYS+/dAnwch0+XA8Ba+wMgFzdFbpL/HJ3AFv/jNYEkyP91E7CRvqfaiUSH0pMBozLa9f5mquoh5L3C8tgqod3e4qZnqXR25OWXQmcrHIjy4qzblrlqcZFqtpuUDJMWwMW/hG+sg+uegqvuVxIkkiCC+U3zPtAAnBHYYYwpw637ebX3wcaYLxpjfmGt7bDWVltrLXAprpJcozHmUmNMgzFmVI/n5ABTgVXD+WZEwiojH8bOVMGE7maqKpbgucLy2Gqqum8TYFUowQux0EuoeR/sWRua9UFDkZTikqJRR3nz+iIScUdMhKy1rbi+Qbf7G6XOAh4GXrHWLjXGpBpjxvjLbAOsBb5gjPmMMabMGPMtXD+h//A//gpQD9xnjJnpP9+jwF4OltcWiU4T5sP2t9yd7URV5x8I1oiQ9wonQ0M1tDZ6HUlwuktnT/I2jkSU7y/aWrfV2zgGsu0ttx1qxTgRkUEKduz5O8ADwP3AYtwUt8v9j50K7PBvsda+CNyGa8K6GvgEcLG19hX/4/uAc4B2XEnuCtxao7OstQn87lJiQtk8N72k6m2vI/FOfRVkjYLk0BeMkEEKjKzUbvQ2jmAFpvFpalzk5cfAiNC2Za5NwfgTvY5ERBJEMMUS8BdJ+Lr/o/djFYDpte9u4O4BzrcGuHgwgYpEhdJTAOPKaJfN8zoab9RXaVpctOhZQnvsTG9jCUZtJWSOdNNMJbLSciA9H/ZH84jQMhgzA1KzvI5ERBJEhFYjisSJzAIoOiax1wmpmWr0KPBPMYuVdUI1lVof5KVo7iXU2e5G2ktP9joSEUkgSoREBqtsPmxbDh1tXkfijboqyFMiFBVSs1xSGiuV42oq3bom8Ub+hOidGrdzJbQ3QclcryMRkQSiREhksCbMg45mqH7H60gir7UBWutUKCGaFE6OjUSotQEadyoR8lKef0TIWq8jOdy25W5bohEhEYkcJUIigzXBvzYoEafHdZfOLvY2DjmosBxq1kfnm9ueAgUdVCjBO/kl0NboylRHm21L3e8VjTaLSAQpERIZrKxCGDXdFUxINPUqnR11CsuhpQ6aar2OZGCBUSutEfJOdy+hKCuYYC1sXeZd/yARSVhKhESGomye+8Pd2e51JJEV6CGku7bRo2fluGhWExgRUg8hz3T3EoqydUJ1210/LPUPEpEIUyIkMhRl86H9AFS/53UkkRWYGpcz1ts45KCYSYQ2uMIOqZleR5K4AolQtBVM2LbMbZUIiUiEKRESGYrAOqEtCbZOqH47ZI1WM9Vokl8KvuToT4RqKzUa5LWMEZCSFX1T47Ytc3EVHet1JCKSYJQIiQxF9mgYORU2J9g6ofpqrQ+KNkkpMKIs+hOhmg1aH+Q1Y6Kzl9DWpVB8IiQF1eNdRCRklAiJDNWEee4PeGeH15FETl0V5KliXNQpLI/upqpNta5SmUpney+/NLpGhFobYdeHKpstIp5QIiQyVGXzoa0Bdn7gdSSRoxGh6FRY7qaedXV5HUnfAkmaRoS8lxdlI0JVK8B2aX2QiHhCiZDIUCVaP6HuZqqqGBd1CidDR8vB8ubRptafCKmHkPfyS9zoXGuD15E425YDBopnex2JiCQgJUIiQ5U71r2xS5R+Qt3NVJUIRZ3CKW4breuEajaA8bm1TOKt7l5CUTIqtHUpjJ4OGfleRyIiCUiJkMhwlM2DLW9CV6fXkYRf3Xa3VQ+h6BPtJbRrKt3alORUryOR/AluGw3T47q6YPtbmhYnIp5RIiQyHBPmu+liuz70OpLwC0y70hqh6JMzxpUfjtaCCbWVmhYXLfIDI0JRUDBhzxporVciJCKeUSIkMhxlgXVCCTA9rruZqhKhqGOMWycUjSNC1roETYUSokPWaEhKjY5EKNBItVSJkIh4Q4mQyHDkFbt1D4lQMKEu0ExV05uiUmF5dCZCjbuhrVGls6OFz+d+b0XD1LityyBrFIyY6HUkIpKglAiJDNeE+bD1jegtXRwq9dVaHxTNCsth/xboaPM6kkMFkjMlQtEjvzQ6iiVsW+amxRnjdSQikqCUCIkMV9k8V45292qvIwmv+ipVjItmheWuH8u+zV5HciiVzo4+0dBLqHE37Nuk9UEi4iklQiLDFegnFO9ltOurlQhFs2itHFezAXwpbhRCokN+KTTugvYW72LoXh90sncxiEjCUyIkMlwjJrg7rPG8Tqil3lV30tS46FU4yW2jLhGqhIKJ4EvyOhIJCPQSCpTE98LWpZCUBmOP8y4GEUl4SoREQqFsvhsRstbrSMJDzVSjX8YIyBwZnYmQKsZFl8DoXJ2HleO2LYdxJ0BymncxiEjCUyIkEgoT5kFTDexZ63Uk4VHvv3OsRCi6FZZHVy+hri6o3QgFk7yORHrq7iXk0Tqh9hbY8R6UzPXm9UVE/JQIiYRCdz+hOJ0e1z0ipB5CUS3aSmjXb4fOVo0IRZuccWCSvOsltOM96GzT+iAR8ZwSIZFQGDHRvbmI14IJdVWAgZyxXkciAymcDI07obXB60icwOiUSmdHl6Rkd1PDq8pxW5e6bbFGhETEW0qERELBGDcqtDlO1wnVV0G2mqlGve7KcVEyPa67h5BGhKKOl72Eti135dSzR3nz+iIifkqEREKlbD4c2A1713sdSeiph1BsiLYS2rUbISVTI4nRyKteQtYebKQqIuIxJUIioTJhvttuicN1QvXVWh8UCwomAia6RoQKJrsRU4ku+SXuBkdne2Rft6YSmvZCqRIhEfGeEiGRUCmcDNlFbnpcvKmrgrxir6OQI0nJcHf6o2VEqKbyYH8jiS55JWC7DhZCiZRAI1WNCIlIFFAiJBIqxrgy2vHWT6ilHtoaNCIUKwonR0ci1NkO+zZrfVC06u4lFOHpcduWQnoejDwqsq8rItIHJUIioVQ2Dxp2uLUR8aK+ym21Rig2BHoJeZ2M798KttNNjZPoE0iEIl0wYdtyVy3Op7cfIuI9/SYSCaWy09w2nvoJKRGKLYXl0FpHSnudt3F0V4xTIhSVAv+fI9lLqKnWNZ3W+iARiRJKhERCaeRUyBoVX/2E6vyJUJ4SoZjgn4qW2RThtR+9dfcQ0tS4qJSS7tY01kUwEdq+wm1L1EhVRKKDEiGRUDIGJpwaX/2E6qtRM9UY4h+ByWj2OhHaAGl5kFnobRzSv0j3Etq2FEwSjJ8VudcUERmAEiGRUJswH+q3w/4tXkcSGvXb3Z3jpBSvI5Fg5JeCL4WcBo9LaNdWuqRMpbOjV6R7CW1bDmNnQmpW5F5TRGQASoREQq1sntvGSxntuipVjIslviSYupDx1X+HRf8JXZ3exFFTqfVB0S6/BOq2Q1dX+F+rs91NjVPZbBGJIkqEREJt1HTIKIifggn11VofFGsu/xNV486H138JD1zuFqlHUnuLe4Ot9UHRLa8EOtugcVf4X2vnSuhoViIkIlFFiZBIqPl8bp3QljhIhKx1VeNUMS62JKeyfuptcPEvYdMS+P2ZsGtV5F5/3ybAqnR2tMuf4LaRmB6nRqoiEoWUCImEQ9l8V5Y20j06Qq21HtoalQjFqhNvgBuehfZmuPtcWP1UZF5XpbNjQ36J20aihPa2ZW4ESqPLIhJFlAiJhMME/zqhWC+jHSidrTVCsav0JLj1FRg9HR65Dl76QfjXDXWXzlYiFNXyIpQIWQtbl0HJ3PC+jojIICkREgmHomMgPS/21wnV+0sw5xV7G4cMT+5Y+Ozf4YRrYcnt8NA10Lw/fK9Xs8H100rPC99ryPClZUPGiPBPjavbDg3V6h8kIlFHiZBIOPiS3KhQzCdC291WI0KxLzkNLvkVXPQzqHwJfn8W7PkoPK9Vu1Hrg2JFJHoJda8P0oiQiEQXJUIi4TJhnls0Xu9xY8vhUDPV+GIMzLkZrn/Grf/6/dmw9tnQv07NBlWMixWR6CW0bRmkZEHRseF9HRGRQVIiJBIu8dBPqK4KcsaomWq8mXAq3FoBI8vh4U/B4h+HrpdMa4Mrx1w4KTTnk/DKL3VrhKwN32tsXQrFJ0JScvheQ0RkCJQIiYTLmJmQlhvbZbTr1Uw1buUVw2f/Acd9Cl75H/jLp6Glfvjn7S6UoBGhmJBXAu1N4es11doIuz7U+iARiUpKhETCxZcEpSfH9oiQegjFt5R0uPQOOP8nsO55uPts2LtheOes9SdCWiMUG/JL3bYuTJXjqlaA7VL/IBGJSkqERMKpbD7UrIeGnV5HMnjWuqlxSoTimzFw8j/BdU9BU41rvrru+aGfLzAiVKCpcTGhu5dQmNYJbVsOGCieHZ7zi4gMgxIhkXCaMN9tY7GfUEsdtB9QA8REMfE0t25oRBk8eBW8+tOhrRupqXTJc2pmqCOUcAh3L6GtS10Pq4z88JxfRGQYlAiJhNPY4yA1OzanxwWq3WmNUOLIL4Ubn4cZV8DLP3QNWFsbB3eOmg0aDYolGSPc76hwVI7r6oLtb2lanIhELSVCIuGUlOzeBMTiiFB9ldvmqplqQknNhMvugvP+G9b+De4+x/UFClZtpQolxBJjwtdLaM8aV6ZdiZCIRCklQiLhVjYP9qyFxj1eRzI43YmQRoQSjjFw6hfhM09A4064awFsePHIz2uqheZ9UKhCCTElryQ8xRK2LnXbUiVCIhKdlAiJhFvZaW4ba6NCdVVgfK6PkCSmyWe6dUN5JfDAFfDaLwZeN6TS2bEpvyQ8a4S2LYesUTBiYujPLSISAkqERMJt3AmQkhl7iVB9NWQXqZlqohtRBje9AEd/HF78Ljx2I7Qd6PvYGn/pbZXOji15Ja44Sij6SPW0bambFmdMaM8rIhIiSoREwi0pBUrmxl7BhPrtKp0tTmoWXP4nOOd7sOpJ+MN5sG/z4cfVVrpRxBFlEQ5QhqW7l1AI1wk17HLXiNYHiUgUUyIkEgkT5sPuVeHr3h4O9dVaHyQHGQPz/xk+/Zh7w3zXAthYcegxNRvcm+rkVC8ilKEKJEKhLJiwbZnblp4cunOKiISYEiGRSCib57Zb3vA2jmAFmqnmqWKc9DLlHLhlMWSPgfs+AW/+5uC6oRpVjItJ4egltG0ZJKW5FgIiIlFKiZBIJIw/EZLTYfNrXkcSnEAzVY0ISV8KJ8PNi2DaRfD8t+GJW6GtySVCWh8Ue7JGuaQllJXjti1z6yOT00J3ThGREFMiJBIJyWlQPAe2xEgi1F06W2uEpB9pOXDFvXDWd2Dlo/D7M13yrBGh2OPz+SvHhWhqXHsLVL+nstkiEvWUCIlEStl82Pmh67MS7eqUCEkQfD44/ZtwzcNuTRlA4SRvY5KhySsJXbGE6nehq12FEkQk6gWVCBljkowxPzbG7DDGNBpjHjPGFA1w/PnGmBX+Y1caY64Z4NjLjTHWGFM2hPhFYseEeYA92GQwmgVGhPKUCEkQjjofbnnZJUUT5nsdjQxFKHsJBQolKBESkSgX7IjQ94DrgeuA04Fi4PG+DjTGzAP+DrwGzAZ+DPzOGHNtH8eOBe4cdNQisah4NiSlxsY6oXp/M9VsNVOVII2c4qbJpaR7HYkMRV4pHNgD7c3DP9e2ZW6tWNbI4Z9LRCSMjpgIGWNSga8A37bWLrLWvgNcDcwzxpzax1O+Cbxhrf2qtXattfZB4CfA9/s49o/AyqGHLxJDUjLcOqGYSISqXRKUlOx1JCISCd29hLYP7zzWukRIZbNFJAYEMyJ0PJADVAR2WGs3A5uB0/o4fgqwpNe+d4EyY0xpYIcx5vPAWOAHg4hXJLZNmAc7P3BV2aJZ3XZVjBNJJPkhKqFdUwlNNa6JtIhIlAsmEQo0Eqnqtb8aKOnj+L72l/m3owGMMVOB/8ZNtWsLJlCRuFA2D2wXbF3mdSQDq6/W+iCRRBKqXkLd64M0IiQi0S+YRCgT6LLWtvfa3wr0NRn8PuAqY8yVxphkY8zxwDf8j6UaY5L9x/yvtfaDIcYtEpuK54IvJbrLaFvr1gipYpxI4sgZCyZp+JXjti2F9DwYOTU0cYmIhFEwCwCaAZ8xJtla29FjfxpwoPfB1tp7jTETgHuAh4CtwO3Ar4E64N+BLuB/gwnQGHMrcCtAUVERFRUVwTwtIhobG6MqHokNJ2RPxnzwHO+knHXI/mi5npLbG5nf3sSGPS1sj4J4ZGii5XqS2HFSWiH1H61gTVLFYY8Fez3NWbuYlsxyVr76asjjk/ih308SSsO5noJJhAK3h8b2+BxgHIdPlwPAWvsDY8yPcVPhdgAXA53AFuAG/3PrjDFwcFRqlTHmv621P+p1rruAuwBmz55tFyxYEETIkVFRUUE0xSMxovMieO0XLDjlRNeU0i9qrqedH8LrUH7CaZQfu8DraGSIouZ6ktixaSoZto2iPq6boK6nplqo2EbWydez4PQjHCsJTb+fJJSGcz0FMzXufaABOCOww9/zpww47JaPMeaLxphfWGs7rLXV1loLXIqrJNcILACOwRVhOB74rP+pF6JS2pIIJswD23lwLn20CTTGzCse+DgRiS/D7SW0fYXban2QiMSII44IWWtbjTF3ALcbY/YCu4E7gFestUv95bULgFprbRuwFvg/Y8wKXC+ha4DPAOf6z7el5/mNMYFGJVustbUh+r5EolfJSW4u/ubXofwcr6M5XL2/fK7WCIkklrwSaNgBne2QlDL4529b6n63jZ8V+thERMIg2Iaq3wEeAO4HFuOmuF3uf+xU3PS3UwGstS8Ct+GasK4GPgFcbK19JWRRi8SytGwYdwJsed3rSPpWX+1vplrkdSQiEkn5pa6qZX2fs96PbNtyGDsTUrNCG5eISJgE1S3RXyTh6/6P3o9VAKbXvruBu4M892u9ny8S98rmwZt3QNuB6HvTUFflKkipmapIYunuJbQNRpQN7rmd7W5q3InXhzwsEZFwCXZESERCqew06Gp3d1CjTX2VmqmKJKLh9BLauRI6mt3UXxGRGKFESMQLJSe56WfROD1OPYREElOgQMpQegl1N1JVIiQisUOJkIgX0nNh7HGuYEI0sdatEVIiJJJ4ktPctNj9Q0iEti51I0p5+t0hIrFDiZCIVybMg6oV0N7sdSQHNe+D9ia9mRFJVHklUDfIqXHWuhGhkrnhiUlEJEyUCIl4pWw+dLYd7L0RDQI9hLRGSCQxDaWXUN02V3Zb/YNEJMYoERLxSukpgIHNr3kdyUGBsrm5aqYqkpDySlzlyK6u4J8TKPqiESERiTFKhES8kpEPY2ZEV8GE7kRII0IiCSm/1FW0bNwZ/HO2LoWULCg6NnxxiYiEgRIhES+VzYftb0FHq9eROHVVrjN8zhivIxERL+SXuu1gCiZsWwbFJ6r3mIjEHCVCIl6aMA86WqDqba8jceqrXBLkS/I6EhHxwmB7CbU2wK4PtT5IRGKSEiERL0041W2jZZ2QegiJJLZ8fyIUbOW4qrfBdql/kIjEJCVCIl7KLHDz6qMlEaqr0vogkUSWmgWZhcFPjdu6DDBQPDusYYmIhIMSIRGvTZgH25Zjutq9jSPQTDVPFeNEElpeiSuJHYxty2D0dFf8RUQkxigREvFa2TzoaCanYYO3cTTvg45mjQiJJLpgewl1dbpiL5oWJyIxSomQiNcmzAMgf/8qb+PoLp2tNUIiCS2v1E2Ns3bg4/ashdZ6KFWhBBGJTUqERLyWNRLGHs/4qr+5NTpeqVMiJCK4EtodzdBUM/BxW5e6rRqpikiMUiIkEg0u/S1Jnc3w8KegrcmbGAIjQnlKhEQSWn6QJbS3LYes0TBiYvhjEhEJAyVCItGg6GjWTP867Hgfnvr8kaekhEO9v5lqdlHkX1tEokewvYS2LXWjQcaEPyYRkTBQIiQSJWpGzoVzvgernoRX/jfyAdRXQ85YNVMVSXTdvYQGqBzXsAv2bdb6IBGJacleByAiPcz7iluAXPEjGHUUHHNp5F67brsqxokIpOdDWu7AvYS2LXNbVYwTkRimESGRaGIMfOwXUDwXnvwnN1UuUuqrtT5IRNzvoSP1Etq2DJLSYOxxkYtLRCTElAiJRJuUdLj6Adfd/aFroGFn+F/TWrdGSBXjRASO3Eto2zIYdwIkp0UuJhGREFMiJBKNskfDNQ+5JqcPfxraW8L7es37oKNFiZCIOHkl/U+Na2+B6vegVNPiRCS2KRESiVZjZ8InfgdVK+CZL4e3klzddrfV1DgRAddLqLUOWuoOf6z6Xehq1/ogEYl5SoREotnRl8CZ34EP/gKv/V/4Xqe+2m01IiQi0KOXUB+jQiqUICJxQomQSLQ7/Rtw7Cfhpe/D2mfD8xr1/hEhJUIiApBX6rZ9rRPatgwKJkPWyMjGJCISYkqERKKdMfDx38C44+HxW2Dnh6F/jfpq8CW7tUkiIv31ErLWJULqHyQicUCJkEgsSMmAqx+C9FxXSa5xT2jPX1elZqoiclDWKEhOP3xEqKYSmmqgZK43cYmIhJASIZFYkTsWrn4QDuyGR66FjtbQnbu+Ss1UReSg/noJbVvqtiUaERKR2KdESCSWjJ8Fl94BW9+Ev30tdJXk1ENIRHrrq5fQtmWQngcjp3oTk4hICCkREok1x34STv8XeO9+ePM3wz+ftW6NkEaERKSnvnoJbV3mqsX59PZBRGKffpOJxKIF34Lpl8Ci/4B1LwzvXE21rplqXnFoYhOR+JBfCk17oa3Jfd1UC3s/0vogEYkbSoREYpHPB5+4E4qOgcdvgt1rh36u7tLZGhESkR7y/SW0Aw2Xt7/ltlofJCJxQomQSKxKzYJrHnaVnR662t2tHYruZqoaERKRHvICTVX964S2LQOT5NYqiojEASVCIrEsr9hVkquvhkeug872wZ+jTiNCItKH7l5C/kRo6zIYO9PdhBERiQNKhERiXckcuORXsHkJ/P2bg68kp2aqItKXnLHud8P+bZiuDqh62xVKEBGJE8leByAiIXDcVbBnDbz2fzD6aDjp1uCfW69mqiLSB1+SK6tft43slE3Q0axESETiihIhkXhx1n+6ogn/+DcYWQ6TzwruefXV6iEkIn3LL4X9W8lNy3NfKxESkTiiqXEi8cLng0/+HkZNg0dvgL0bgnte3XatDxKRvvl7CeXVrXGf5+mmiYjEDyVCIvEkLQeuecjN63/oKmjeN/DxgWaqenMjIn3JL4WGHeTvX6XRIBGJO0qEROLNiAlw1f2wbws8+lno7Oj/2KYa6GzV1DgR6Vt+CWBJbd+vREhE4o4SIZF4NOFU+Nj/wcbF8Py3+z+uvsptlQiJSF8CvYQASpUIiUh8UbEEkXg161rYsxbe/DWMngazbzz8mDolQiIyAH8voU5fOkmjj/E4GBGR0NKIkEg8O/f7UH6O6y+0acnhjwdGhLRGSET6klsMGOpzp0KS7p2KSHxRIiQSz3xJcPkfoWAyPHIt1G489PH6KldYIUvNVEWkD8mpcNzV7Bh7jteRiIiEnBIhkXiXnucqyQE8eDW01B98rL4acsa50tsiIn35xJ3sLjrD6yhEREJO735EEkHhZLjyXqithMdvgq5Ot7+uStPiREREJCEpERJJFBNPhwv+F9a/AIv+0+2rr1IzVREREUlIWvkokkjm3HSwktyoaW5q3PSLvY5KREREJOKUCIkkmoU/hr3r4JmvgO1U6WwRERFJSJoaJ5JokpLhintgRJn7WmuEREREJAEpERJJRBkj4FOPwLSPQYm6xYuIiEji0dQ4kUQ1shyufsDrKEREREQ8oREhERERERFJOEqEREREREQk4SgREhERERGRhKNESEREREREEo4SIRERERERSThKhEREREREJOEoERIRERERkYSjREhERERERBJOUImQMSbJGPNjY8wOY0yjMeYxY0zRAMefb4xZ4T92pTHmml6PH22M+Ycxpt4Ys8cYc6cxJm+434yIiIiIiEgwgh0R+h5wPXAdcDpQDDze14HGmHnA34HXgNnAj4HfGWOu9T+eDbwI1AJzgUuA04A/DfWbEBERERERGYzkIx1gjEkFvgJ82Vq7yL/vamCTMeZUa+0bvZ7yTeANa+1X/V+vNcZMBL4P3AdMwCVJt1hrD/jPd5f/cRERERERkbALZkToeCAHqAjssNZuBjbjRnJ6mwIs6bXvXaDMGFNqrV1lrb2yRxI0FbgWeGGQsYuIiIiIiAxJMIlQsX9b1Wt/NVDSx/F97S/zb0f33GmMeQ/4CBgJ/HMQsYiIiIiIiAybsdYOfIAxnwH+bK1N6rX/ZWCjtfbmXvuvA/4AfBp4AjjWv50IzOs5lc4YMwvIAn4CjAKOs9Y29TrfrcCtAEVFRSc+/PDDQ/g2w6OxsZHs7Gyvw5A4oetJQknXk4SSricJJV1PEkr9XU9nnnnm29ba2QM994hrhIBmwGeMSbbWdvTYnwYc6H2wtfZeY8wE4B7gIWArcDvwa6Cu17HvABhjPglsBy4FHux1zF3AXQCzZ8+2CxYsCCLkyKioqCCa4pHYputJQknXk4SSricJJV1PEkrDuZ6CmRq3zb8d22v/OA6fLgeAtfYHQC5uitwk/zk6gS3GmDJjzMd7Hb8DqAHGBx+6iIiIiIjI0ASTCL0PNABnBHYYY8pw635e7X2wMeaLxphfWGs7rLXV1s29uxRXSa4RVzL78Z59iPxV5UYBq4f+rYiIiIiIiATniFPjrLWtxpg7gNuNMXuB3cAdwCvW2qX+8toFQK21tg1YC/yfMWYFrkz2NcBngHP9p/wbsBF4wBjzz7iKdL8C3gSeC+l3JyIiIiIi0ocjFksAMMYk4woaXA//v717DbWsLuM4/v05mmXGIBllTDlMUOicQjOKMqfxTRGU0bsoGEJFqEGjgTQwS80SKroQSV66CEpBdyNwuuAYFUYl2SQWRmrSJDbFpNZpaJinF2tN7Dmdy177bM9ynf39wGZz/vvM5hnOc357P2ev9V8cB9wO7Kyq/Um2A3cA51bVnvb7LwTeT3P43G+BK6pq98jzbQY+BWwHCvgWsKuqjjqHaJE6/go81OH/92Q7GdjfdxFaN+wnTZP9pGmynzRN9pOmaal+OrWqnrPcPxxrENLikvxypd0opHHZT5om+0nTZD9pmuwnTdNq+mmcc4QkSZIkaV1xEJIkSZI0cxyEVueGvgvQumI/aZrsJ02T/aRpsp80TRP3k+cISZIkSZo5fiIkSZIkaeY4CHWUZEOSa5P8JckTSb4+enFYqYskW5PUIrfX9l2bhiXJ9UluWrD2+iS/TjKf5DdJ3thXfRqWJfrpF4tk1U1LPYdmW5LnJrm5fb90IMnuJHMjj5tPGtsY/TRRPjkIdXclzfWUdgDbgE3AN/osSIM2R7P3/SkLbj/vsygNRxpXAxctWD8duA34GnAm8B3g20m2rn2VGopl+inAacA7ODqrdq15kXrKS3IMzTUiXwy8BXgN8A/gR0mebT6pizH6aeJ88hyhDpI8jeZN6yVV9eV2bTPwAHB2Vf2sv+o0REk+DGyrqtf1XYuGJ8kW4As0A/W/gB9U1YXtY9cDL6mq7SPffwdwf1VdtMjTacat0E8vAv4AbKmqB/qrUkOQ5EzgbuD0qrqvXTse+DvwLuBszCeNaYx++ikT5pOfCHVzBvAsYM+Rhap6EHgQOKePgjR4c8B9fRehwXo18EfgpTR/kBl1DiNZ1dqDWaWlLddPc8A88NBaF6VB+hPwJuD3I2uHgQAnYT6pm5X6aeJ8chDqZlN7/+cF6/uAF6xxLVof5oBTk9yV5JEkP0zyyr6L0jBU1a1VdUFVPbLIw5swq9TBCv00BxwAbk2yL8neJLvaQ1ako1TV36rqe1V1eGT5EuDpwPcxn9TBGP00cT4ZYN2cAByuqv8sWD9I88OQxpbkGcAWYCPwPuA8mheCO5Oc1mdtWhdOAP69YM2s0qS2AicCu4E3AJ8DrgI+1GdRGoYk5wHXAp9sD20ynzSxRfpp4nw69kmscz2aB45JcmxVHRpZPx74Z081aaCqaj7JScDBqjoIkOSdwFnAu4GLeyxPwzdPk02jzCpNagdwYlUdaL/em2QjcHmSK8sTjrWE9nXtRuCrwKXtsvmkiSzRTxPnk58IdfNwe3/KgvXn8/8f8UorqqrHjgxB7deHgXvx8ACt3sOYVZqSqjo08ibjiL00581uXPuKNARJLge+BHwe2DFyaJP5pM6W6qfV5JODUDf3AI8D/9vhq901bjPw435K0lAlOSvJY0lePrK2gWZTjnt7K0zrxU8YyarWuZhVmkB7HuOnFyy/Ati3yBsQiSSXAtcAH6yqixf8Vd58UifL9dNq8slD4zqoqoNJrgM+kWQ/8ChwHXBnVd3Vb3UaoHtodhy8IclO4AngMuBk4DM91qX14bPAr5JcBXwFeDvwKpqtRqWuvglcneRumq1qt9Pk1Xv6LEpPTUleBnwU+CJwY5LnjTz8OOaTOhijnybOJweh7j4AHAfc0t7fDuzstSINUlUdaq+k/THgu8AzaX6Bt1XVo70Wp8Grqr1J3krTX5cBvwPefOQaDFJHHwcO0bwGvpBmO9v3VtWKV27XTHobsAE4v72NuqKqrjGf1MGy/QR8hAnzyQuqSpIkSZo5niMkSZIkaeY4CEmSJEmaOQ5CkiRJkmaOg5AkSZKkmeMgJEmSJGnmOAhJkiRJmjkOQpIkSZJmjoOQJEmSpJnjICRJkiRp5vwXGptEz09saaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ari35SdcTSVt+aeQQUVQBvZ3Q0eR1JCLiF00uUTpUdqn7ed/zHgYjIhJfIyZK1tou4LvA14wxVxhjzgHuBZ601j5vjMk2xkwPtxEH+H/AFOA9QEf4semRBWqttQeAXwE/MsZcYIy5EPgB8PPwY+Daid9qjPk7Y8xC3BpLxbi1mUQmhroNUDIPcou9jsTRWkoiEi3cyOHI1NWQmQe1auggIukj1gVnv4BbRPZu3GhRLXBd+LHVQD2w2hiTB1wDFAIvhvdHvgaXzX0QN2r0R+C3wF9xnfEAsNY+gmvO8CngZdw8p8uttQ2jfoUiqap+E1Qs9TqKAcHwMmdKlEQkoqkGcifRk10MM1ao852IpJVYmjlE2nd/KvwV/Vg1YAbtyog+ZojntOIWoH3fKY65C7grlvhE0k7rETi+D87/sNeRDOgfUdJUQREJO1YLk2e776tWwVNfg64WyCnyNCwRkXiIdURJRJKpfqPb+qWRA0BhGZiARpREZEBTDUye5b6vWgk2BPtfOuVTRERShRIlET+KNHKYvsTTME6QkemSpRYlSiIChEJwbO/AiNKMc92HKXvV0EFE0oMSJRE/qtsApadDbtDrSE4UaREuItJSD33dA4lSbhCmn6V5SiKSNpQoifhR/UZ/NXKIKCpXoiQiTrjjHZNmDeyrWgX710FfjychiYjEkxIlEb9pOeQaJvhpflJEsBKa672OQkT8ILzYbP+IErhEqacd6qOXUBQRST1KlET8JtLIoXypl1EMLVgBXcddVysRmdiaatycpOKZA/uqVrrt3mc9CUlEJJ6UKIn4Td1GwEC5jxo5RPS3CNeoksiE11TjRpkzswf2FU2HyXPU0EFE0oISJRG/qdsAU87w5zokkURJne9EpKn2xLK7iFmrXUMHa5MekohIPClREvEbvzZygEEjSkqURCa8wWsoDVa1EtoboWF30kMSEYknJUoiftJc71ru+rGRA7iud+CaTYjIxNXTAa0HYdLskx+rWuW2ahMuIilOiZKIn/i5kQNAVh7klWiOkshEd2yv2w5Veld6GuRP0TwlEUl5SpRE/KRuo+siNf0sryMZXrBSpXciE11kDaWhEiVjXPmdOt+JSIpToiTiJ3UbYMp8yCn0OpLhBStUeicy0fUnSkPMUQJXftdUo9FnEUlpSpRE/MJafzdyiAiWa0RJZKJrqoWsfCiYOvTjs8LzlPap/E5EUpcSJRG/aKmH1kP+beQQEayE9gbo7fI6EhHxSlONK7szZujHpy9xiVStGjqISOpSoiTiF3Ub3NavjRwi+tdSUkmNyITVVAOThim7A8jIghnnqvOdiKQ0JUoifpEKjRxgUItwld+JTEjWwrFhFpsdrGoVHNoKnc1JCUtEJN6UKIn4Rd0GmLoQsvO9juTUgpVuq0RJZGJqb4Tu1uEbOURUrQQbgv0vJicuEZE4U6Ik4gep0sgBBkrvlCiJTEynag0+2IxzwWRoPSURSVlKlET8oPkAtB3xfyMHgNwgZBcqURKZqGJNlHIKoXyJGjqISMpSoiTiB6nSyCEiWAEtSpREJqRIojSpauRjq1bBgXXQ253QkEREEkGJkogf1G10JSrTF3sdSWyCFRpREpmommqgYBpkF4x8bNUq6O2E+k0JD0tEJN6UKIn4Qd0GmLYIsvK8jiQ2RUqURCasWDreRVStdNu9zyYsHBGRRFGiJOK1/kYOZ3sdSeyCFdByEEJ9XkciIsnWVDNyx7uIwmlQMk8NHUQkJSlREvHa8X2u3W4qNHKICFaA7YPWw15HIiLJ1NcDx/fHPqIEMGuVS5RCoYSFJSKSCEqURLzW38ghlRIlraUkMiEd3+/WRhpNolS1CjqOQsOuhIUlIpIISpREvFa3EQKZUHam15HELljutup8JzKx9He8i7H0DlyiBLBXbcJFJLUoURLxWt0GmLYQsnK9jiR2GlESmZhiXUNpsJK5rkue5imJSIpRoiTipf5GDilUdgeQXwoZ2W6hXBGZOI7VQiDLzVOMlTGu+50634lIilGiJOKlY7XQ0ZR6iZIxUFSuESWRiaapBibNhEDG6J43azUc2wvH9eGKiKQOJUoiXupv5LDU0zDGJFgJzfVeRyEiydRUM7qyu4j+9ZQ0T0lEUocSJREv1W10ZSyp1MghIlih0juRiaZpFIvNDlZ2FmQXap6SiKQUJUoiXqrbAGWLIDPH60hGLxguvbPW60hEJBk6j7s236PpeBeRkQkzzlWiJCIpRYmSiFdStZFDRLAS+rrcHCsRSX9NtW47lhElcG3CD22FjmPxikhEJKGUKIl4pWmP+4Q2ZROlcNcrld+JTAzHxpsorQQs7H8pXhGJiCSUEiURr6RyIweAokiipM53EoP2o/DDN8GLP/A6Ehmr/jWUxlB6BzBjhVtcWw0dRCRFKFES8UrdRrcW0bRFXkcyNkElShKjnk6490bY/yK8+hevo5GxaqqB3GLImzy252cXQPnZUKtESURSgxIlEa/UbXDd7jKzvY5kbArLwASUKMmphULwmw+7UYRgJRx9zeuIZKzG2vFusKpVcGA99HbFJSQRkURSoiTihVAI6jen7vwkcF2sCsuUKMmpPf5PsP0heNO/wVnXuVGJUJ/XUclYNNWMrePdYFWrXBOYuo3xiEhEJKGUKIl4oWkPdKVwI4eIYAW0KFGSYTz/v/Dcd+C8D8Pqj0LJXOjrhuP7vI5MRisUcs0cxj2iFFl49tlxhyQikmhKlES8kOqNHCKCFRpRkqFt/y088o+w4Cq44stgDJTMc481qvwu5bQedEnueBOlgilQerrWUxKRlKBEScQLdRsgIwemLfQ6kvEJVipRkpPtfR5+fbtbYPTaH0Igw+0vDSdKR1/3LjYZm/F2vBts1ip3j4RC4z+XiEgCKVES8ULdRpi+GDKyvI5kfIrKoasZulq8jkT8omE3/PIGl0S/517Iyht4rKgcsvKVKKWi/kRpzvjPVbUKOo/BkZ3jP5eISALFlCgZYzKMMV82xtQbY1qNMQ8YY8pieN48Y0ybMWbGoH23GWPsMF8/HnTc4SEe/8LYXqaIj4RCUL8p9ecngXszDNBc720c4g+th+Hua8FkwM0PQEHpiY8b4+YpqfQu9TTVAgaKZ47/XFWr3FbrKYmIz8U6ovRF4FbgFuBiYAbw4KmeYIw5A3gMyI966D6gPOrr80AH8N/h55YBU8PXGnzcN2OMV8S/jr4G3S1pkihF1lI64G0c4r3uNvjFu12ydOOvXEI0lJK5ahGeippq3Acj8VjOYPJsKJyuRElEfG/ERMkYkw18HPictfZxa+3LwA3ABcaY1cM85+PAOuBY9GPW2g5r7cHIFy6R+jzwSWvtpvBhi4Fe4IXBx1pr28bwGkX8JdIWN9UbOQAEy91W85Qmtr5euP99bqT0XXfBjOXDH1s6z73p7utNWngSB00142/kEGGM636nhg4i4nOxjCgtBYqA6sgOa20NUANcNMxz3gp8CPhUDOf/KrAVuHPQvsXAa9ba7hieL5Ja6jZAZi5MXeB1JONXFB5RUovwicta+OOnYPej8Navwfy3nPr4knkQ6oXje5MTn8RHPFqDDzZrtWsTf0yt4kXEv2JJlCLzi6Jra+qAIYuVrbVvttbeN9KJjTFnA9cCn7XWDm5/sxjoNcb83hhz0Biz3hjz3hhiFfG/ug0w/Sy3YGuqy8qF/FKNKE1kT30d1v8ELvwEnPuBkY+PdL5rVEOHlNHTAS318el4F9G/npJGlUTEv2J5p5YPhKy1PVH7u4DccV7/73HldU9E7T8TKAX+CVeW9xbgLmNMprX2ruiTGGNuB24HKCsro7q6epxhxUdra6tvYhGfsH1ceOBlDk6/jFdHeW/49X5aYYJ07tnCVh/GJsOLx/1UdvAJFu78FoemXcKOjIshhvNldzWxGtj9wiMcOJAGHxZMAPlt+zgP2H6wncPD/D8e9f1k+7gwI49Dzz/I7qNT4xGmpBG//r2T1DSe+ymWv1IdQCCcpAwuKs8BxjxnyBiTC1wHfGyIhy8Fsq21kZ7Dm4wxs4BPAiclStbaOwmX7q1YscKuWbNmrGHFVXV1NX6JRXziyCvwZCczzr2KGUvXjOqpvr2fDpxBYUu9P2OTYY37fnrtCVj7HZh9EWU3P0BZrJP8rYX1hZxeEuB03TOpYddj8BIsWv1WFs08b8hDxnQ/7V9NZcteKnUfSBTf/r2TlDSe+ymW0rtIAXF51P4KTi7HG43LgGzgN9EPWGu7BiVJEVsYptRPJGWkUyOHiGCFSu8mmoNb4b73wpQz4Pq7R9cJzRgomaPOd6kksobSpDiW3oFrE354O3Q0xfe8IiJxEkuitAloAS6J7DDGzAZmA2vHce2LgJettccG7zTGZBpj9hljPhF1/Apg2ziuJ+K9ug1uwc0pZ3gdSfwEK6C9AXq7vI5EkuH4frjnXZBTBDfdD3mTRn+OknlaSymVNNVAZh4UTovveatWAhb2vRjf84qIxMmIiZK1tgv4LvA1Y8wVxphzgHuBJ621zxtjso0x08NtxEdjGW6UKPp6vcDDwBeMMW83xpxmjPk08F7gS6O8hoi/pFMjh4jIWkotWnQ27XUcc0lSV4tLkopnjPiUIZXOg2N7oS966qv4UqTjnTHxPW/lcghkQe2z8T2viEicxLrg7BeAe4C7gSeAWtz8IoDVQH14OxrlQOMwj30C+B7wbdwo0nuBd1trHxvlNUT8I9QHBzenx0Kzg/UvOqvyu7TW2w333QwNu+CGu2H64rGfq2Qe2D6XLIn/NdXEt+NdRHY+VCxV5zsR8a2YPtYOj/J8iiHWRbLWVgNDfsw0wmNLTnG9Lly3u8/HEp9ISmjYBT3t6TU/CSBY6bZKlNKXtfDbv4Oap+Cd34e5a8Z3vv4W4a8NfC/+ZK1LlGZfmJjzV62CF74HPZ1uuQERER+JdURJRMYr0sgh3UaUisJ9XprH09tFfO0v/wpbfgVv+AKcfcP4z1cSTo7U0MH/2o9Cd2t8F5sdrGoV9HVD3cuJOb+IyDgoURJJlroNkFUAU073OpL4yg1CdhE0a45SWnrpR/D0N2D5bXDRp+NzzoIpkBNUQ4dUkKiOdxH9C88+l5jzi4iMgxIlkWSp2wDlSyCQ4XUk8Res0IhSOnrlT/DHT8Ppb4a3fj1+k/mNgZK5GlFKBU173DZRI0r5JTB1geYpiYgvKVESSYa+Xji4Jf3K7iKC5ZqjlG72r4cH3g/lZ8N1P45/p8ZStQhPCcdq3TYRzRwiqlbC3hdcwxsRER9RoiSSDA2vQG9H+jVyiAhWqj14Ojn6Ovzi3VAwFW78FeQUxv8aJfPg+D7XTU/8q6nG3QfZBYm7RtUq6DoOh3ck7hoiImOgREkkGdK1kUNEsAJaDrqRM0ltbY1w93WufffND8Z/kdGI0nlgQwNzYMSfmmoSV3YXUbXKbTVPSUR8RomSSDLUbYDsQig9zetIEqOo3L2xbjvsdSQyHj0d8Msb4Ph+eM+9iW08os53qaGpNvGJ0qQqKKpQoiQivqNESSQZ6ja4uR6BNP0n17+WksrvUlaoDx78IOx/Ca79wUA3skQpmeu2mqfkX309LmlOVMe7CGPc/Vb7nFu3SUTEJ9L0XZuIj/T1wKGt6Vt2B670DtT5LlVZC4/8I+z8PVzxZVj0jsRfM78Ecos1ouRnx/e7keJEjygBzFoNLXVu3pqIiE8oURJJtCM7obczfRs5wKBESZ3vUtJz34EXvw+rPgIr70jONY1x5XdHX0/O9WT0+jvezU78tSIjmLUqvxMR/1CiJJJo6d7IASC/FDKy3SfCklq2PgiPfQEWXQ1v+rfkXrt0HjQqUfKtSKONRLYGj5i2yC1CrHlKIuIjSpREEq1ug3sDEJmTkY6MCS86q0QppdQ8A7/5G9d17J3fT/4cukiL8J7O5F5XYtNUA4HMgTmIiRTIgJnnK1ESEV9RoiSSaOneyCEiWKlEKYXkt+2Fe9/jyqpu+AVk5SY/iNJ5gFWLcL9qqnUd6QIZyble1UpXqtx+NDnXExEZQZq/cxPxWG83HNoGFUu9jiTxisqVKKWKloMs2fyvkJkLNz3gGit4QS3C/a2pJvEd7wabtdpt972QvGuKiJyCEiWRRDqyA/q60ruRQ0Sk9E7tff3vhe+T3X0UbrwvOfNPhlOqFuG+lozFZgerOMfNdax9NnnXFBE5BSVKIok0ERo5RAQrXVKoshn/O/IKHXmV3t+XeZMhr0QjSn7U2QwdR5ObSGflunty7/PJu6aIyCkoURJJpLoNkFOc3o0cIoLlbqu1lPyvYRft+UmYoB+L0nkaUfKjZLYGH6xqlfu92dOR3OuKiAxBiZJIItVtgIqzXVe4dBfpjNVS720ccmp9PdC0h/b8GV5H4mgtJX/qbw0+O7nXrVoFoR44sD651xURGYISJZFE6e0KN3KYAGV3MGjRWY0o+drRPRDq9VGiNNfdM93tXkcigzWFR5SS2cwBYOZ5bqs24SLiA0qURBLl8Hb3yehEaOQAUDANTECd7/yuYReAv0rvAJr2eBuHnKipxpUN501O7nXzS9zis5qnJCI+oERJJFEmUiMHgIxMKJwOzSq98zW/JUqR+Xsqv/OXphrXyMGLsuGqlbDvRQj1Jf/aIiKDKFESSZS6DZA7Kfk1/l4KVqj0zu8adkNRBX2Z+V5H4kRGlNTQwV+O1XrXOr5qFXQ1u9JlEREPKVESSZS6DW6h2YnQyCEiqEVnfa9xN0w5zesoBuQWQ/4UtQj3k1DIzVHy6kOeqlVuq3lKIuIxJUoiidDTCYd3TJyyu4hgpbre+Zm1rvRuyhleR3Ki0nnQqNI732g96NZE8ypRmjQTgjOUKImI55QoiSTC4W0Tq5FDRLDClcx0NnsdiQyl7Qh0HvdfolQyTyNKftLf8W62dzFUrXQNHaz1LgYRmfCUKIkkwkRr5BBRFG4RrlElfwo3cmDK6d7GEa10rrtnutu8jkTAuzWUBpu1yt0TkVhERDygREkkEeo2uLa6k6q8jiS5+tdS0jwlX+pPlHw4ogTqfOcXTTWAcSVwXumfp6Q24SLiHSVKIolQt9GNJk2kRg6gRMnvGnZDVsHAyJ9fqPOdvxyrdf+WM3O8i2HqQtfoQ/OURMRDSpRE4q2nA45MwEYOAEXlbqtEyZ8adrmOdwGf/ervX0tJiZIvNNV4v6xBIAAzVypREhFP+eyvpUgaOLQNQr0Tr5EDQFYu5JdqLSW/atgFpT6bnwSQUwSFZep85xd+SJTANXRo2AVtDV5HIiITlBIlkXir2+C2E3FECVzJjpo5+E9PBxzb57/5SRElczWi5Ac9ne7f7ySPFpsdbNZqt933grdxiMiEpURJJN7qNrpRleIZXkfijWClRpT8qPE1wPqv411EyTzNUfKDY3vd1g8jShXLICMHap/1OhIRmaCUKInEW92GidnIIaKoXHOU/MivHe8iSudC22HoavE6konND63BIzJzoPIcdb4TEc8oURKJp+52OLJz4pbdgRtRam90JTziHw27ATPQYc5v1CLcH46FF5ud7IPSO3Btwus3ao0tEfGEEiWReDq0FWzfxGzkEBHUorO+1LDLreuVled1JENTi3B/aKqBzFzXXMMPqla55jgH1nsdiYhMQEqUROKpbqPbTugRJbUI96WGXf4tuwO1CPeLSMc7v5QOzzwPMCq/ExFPKFESiae6DVAwdWBUZSIKVrqtRpT8IxSCxlf9nShlF7j5bWoR7q2mWn90vIvImwRlZ6qhg4h4QomSSDxN9EYOMJAkqvOdfzQfgJ52t9isn5XM04iSl6z1zxpKg1WthP0vQV+v15GIyASjREkkXrrboOGViV12B27x0Owild75id873kWUztUcJS+1H4XuFh8mSqugu9XNARURSSIlSiLxcnAL2NDEbuQQEaxQouQnja+6rd8TpZJ50N4Ance9jmRiOlbjtn7peBdRtcpt9z7nbRwiMuEoURKJFzVyGKBEyV8adkFusZs/52fqfOctP62hNFhxJRRXKVESkaRToiQSL3UbXEvdSNe3iSxYqUTJTyId7/w+d66/850aOngikij5qZlDxKxVrvOdtV5HIiITiBIlkXip36jRpIhgObQe0uRrv2jY7f+yO4DJc9xWI0reaKqF/CmQU+h1JCerWul+pyiJFpEkiilRMsZkGGO+bIypN8a0GmMeMMaMuBqdMWaeMabNGDMjav+Vxhg7xNeMQcfcZIx5xRjTYYx53hhz7uhfnkiSdLXCkVc0PykiWOEW3m077HUk0tnsWrVPOd3rSEaWne9GI/Vm2Bt+7HgX0T9PSespiUjyxDqi9EXgVuAW4GJgBvDgqZ5gjDkDeAzIH+LhxcAGoDzqqy783DcCPwa+DpwDbAEeM8b4vMBeJqyDmwGrEaWIyFpKKr/zXuNut02FESVw5XdqEe4NPydKU+ZD3mTNUxKRpBoxUTLGZAMfBz5nrX3cWvsycANwgTFm9TDP+TiwDjg2zGkXA1ustQejvkLhx/8B+KW19k5r7Q7gw8BR4EOjeXEiSdPfyGGpl1H4R1F4npbWUvJeQzhRKk2BESVwDR1Uepd8fb1wfL//Ot5FBAIwc6USJRFJqlhGlJYCRUB1ZIe1tgaoAS4a5jlvxSU1nxrm8cXAjqEeMMYEgAuirhcC1p7ieiLeqtvgkoOi6V5H4g/9I0r13sYhrpFDIBNK5ngdSWxK5kHHUeho8jqSiaV5vyuX9euIErh5So2vQusRryMRkQkilkQpMm8o+qPhOmDmUE+w1r7ZWnvfUI8ZYzKABcByY8wmY0ydMea3xpj54UMmAQWjuZ6I59TI4UT5JZCRoxElP2jY7ZokZGR5HUls+luEa55SUvm1Nfhgs8JFLBpVEpEkyYzhmHwgZK3tidrfBeSO4Zrzws/LwY06ZQNfAJ4yxiwO/wzQGev1jDG3A7cDlJWVUV1dPYaw4q+1tdU3sUjiZPS2c2HDbmoKl1ObwP/fqXY/nZ81meZXN7Iju9rrUCa0c2s30pFXztaoe8ev91N+WyPnAdufeZjDZS1ehzNhlNc9xnzg+Z31dNZWj/r5ybifTKiHCwPZ1D17P68dDib0WuItv/5+ktQ0nvsplkSpAwgYYzKttYN7/eYAbaO9oLV2lzFmCtAUmZNkjLkG2Au8F/jJoPMPNuz1rLV3AncCrFixwq5Zs2a0YSVEdXU1folFEqjmaXjaMmf1Ncw5Y03CLpNy99OeeeTRR1kqxZxu+nrhqYMULLvmpHvHt/dTTye89DEWTcthkR/jS1d/fhJezWTl5ddCRixvDU6UtPup9lxm9uxnpu6NtObb30+SksZzP8VSercvvI1eRbOCk8vjYmKtbRzUuAFrbTvwOq607iguIYrb9UQSSo0chhasUOmd147VQl936nS8A8jKheKZ6nyXbE017r/7GJKkpKpaBfWb3JIMIiIJFkuitAloAS6J7DDGzAZm4xosjIox5mpjTMvgVt/GmCLgDGCbtdYCz0ZdL4BrSz7q64kkXN0G17ygcJrXkfhLUblr5mCt15FMXA0p1ho8onSuOt8l27Fa/3a8G6xqlWs6cWCd15GIyAQwYqJkre0Cvgt8zRhzhTHmHOBe4Elr7fPGmGxjzPRwG/FYPAk0Az83xiwJn+9+oAH4efiYbwC3GmP+zhizEPg+UAz8cFSvTiQZ1MhhaMFK6OuC9qNeRzJxNexy2ymneRvHaJXMcyNKSrKTx89rKA028zwwAS08KyJJEeuCs18A7gHuBp4AaoHrwo+tBurD2xFZa5uANwI9uBbg1bhSuzdYazvDxzyCa87wKeBlYBFwubW2IcZ4RZKj87hrV1u+1OtI/CdY4bYqv/NOwy4omOoW6kwlJXPdvy0l2cnR1QLtjamRKOUGoexMqH3W60hEZAKIqRg53MThUwyxLpK1thowwzxvyMfCi8i+bYRr3gXcFUt8Ip6p3+S2GlE6Wf9aSnVQvsTbWCaqht2pV3YHAy3Cj74OBaXexjIRNNW67aQUKL0DV3634R7o60mdtvcikpJiHVESkaGokcPwguF+LC113sYxkTXuhimnex3F6JVEEiXNU0qKVFhDabCqVdDTBge3eB2JiKQ5JUoi41G3wXWKKpjidST+U1gGJsONKEnytTW6cqpUHFGaPNvNQ1FDh+Q4Fh5RSplEaaXbauFZEUkwJUoi41G/UaNJwwlkQNF0JUpeaUzRjncAmdlqEZ5MTTWQE0yduWzBClcmqERJRBJMiZLIWHU0uTkUauQwvKJyNXPwSn/HuxQsvQM3T0kjSsnRVONag5shpxv706zVUPucOiOKSEIpURIZKzVyGFmwwq2lJMnXsAsyw4u3pqKSee6DCL0RTrym2tQpu4uoWgntDUqmRSShlCiJjFV/IwclSsMKVqr0zisNu6H0NFcCmYpK50FXM7RpVYiECoXcHKVU6XgXUbXKbV/4HuxfB91t3sYjImkppvbgIjKEug0wqQryS7yOxL+C5dDdAp3Nbv0TSZ6GXTA9hduyD+58VzjV21jSWesh6O1MvRGlKWfAtEXw0g/cF8Yl12WL3df08LZ4RmqVFIqIryhREhmr+o0aTRpJZC2llnolSsnU2+XmnSy+bsRDfSuyllLjawNdziT++jvezfE2jtEyBv7mGRf/oW1waKtrF16/EbY/NHBcbvHJydO0hZCV51XkIpJClCiJjEX7UfdG9JxbvY7E34IVbtt8AKbO9zaWieToHrCh1Ox4FzGpyrWXV+e7xOpfQynFSu8AAgEomeO+Fl41sL+zGQ7vgENb4OBWl0RtuNutvQSu9XzpaScmT2WL3e8rjT6JyCBKlETGon6j22pE6dSKwovOap5ScqV6xzuAjCyXLGmyfmI11QAmdZt+DCU3CFXnu6+IUAia9oRHnra6UagD62DbrweOyZt88ujT1AWQlZv81yAivqBESWQs+hs5LPUyCv/rT5TU+S6pIolS6WnexjFepeHOd5I4TbVuJCXdk4FAwN1PpfNg0TsG9nced0lTZOTp0FZY/xPo7XCPmwz3gUN/8nSW+72vRcZFJgQlSiJjUbfBTX5OlQUavZKVC/lTtJZSsjXshuAMyCn0OpLxKZkHe593LcJVEpUYTTWp1/EunnKL3ZpMs1YP7Av1uQS9f/Rpq7sPtz7gHs8ugr95ypX8iUhaU6IkMhb1G6FyuddRpIZguUrvkq1hV2qX3UWUzoPuVmg9DEVlXkeTnppqYO4ar6Pwl0B4FGnK6XDmOwf2dzTBgfVwz7vg5Z/BG//FuxhFJCm0jpLIaLU1wrG9UL7U60hSQ7ASWpQoJY21bkQplRs5RAxuES7x19PpOlKmYiMHL+RNhtPeCKe/2TWH6OvxOiIRSTAlSiKjVb/BbdXIITbBCo0oJVPLQbd2VVqMKM11WzV0SIzj+wCbemsoeW35rdB2GHY94nUkIpJgSpRERivSyKH8bE/DSBnBCmhvdJ9eS+KlQ8e7iOIqCGRqRClR+luDz/YyitRz2pugqALW/9TrSEQkwZQoiYxW3QYomQt5k7yOJDUUhddSalHnu6ToT5TSoPQuI9O9ideIUmJEEqWJ3MxhLDIyYdnN8OqfXRm2xFfN01Qc+IPXUYgASpRERq9+k8ruRqN/0VmV3yVF46uQXTjQmj3VlahFeMI01UBmLhSqUcaonfNet91wt7dxpKNHP89pr/4Iutu9jkREiZLIqLQcdHX9SpRiF6x0WyVKyRHpeJcu7bQjaylZ63Uk6SfSGjygtwKjNqkKTrss3NSh1+to0seRV6B+IwHbB3Uvex2NiBIlkVHZ85Tbzr7Q2zhSSTCy6KzWUkqKdOl4F1EyF3raVbqZCMdq1fFuPM651f1ee/XPXkeSPjbfByb81nTvc97GIoISJZHR2VPtFiicvsTrSFJHThHkBPVGNxm629yIZzo0cogoDbcI1zyl+LIWmmrVyGE85r8FCqbBy2rqEBehEGz+Fcy9lLb8Ktj7gtcRiShREhmVPWth9kVuQUKJXbBCI0rJ0Piq26bbiBKo8128dTRBV7MSpfHIyIJlN8GuR1VaHA97n3Mf9Jx9A8eLF8K+F13yJOIhJUoisWqqcR2O5lzidSSpp6hcbySSoWG325am0YhS8UzIyFZDh3hTx7v4OOcWsH2w4R6vI0l9m++FrAJYcCXHixdA13E4ssPrqGSCU6IkEqs9a912zsXexpGKgpXQrNK7hGvY5er7I6Mw6SCQoRbhiaA1lOKjZK77m7DhZxr9GI+eTtj2W1j4Nsgu4HjxIrd/7/PexiUTnhIlkVi9/qSrR5863+tIUk+wAloPqjtUojXsciMEWbleRxJfahEef/2JkkaUxm35ba7a4PUnvI4kde16xI0gnX09AJ25Za5t/T7NUxJvKVESiYW1bkRpzsXp03Y5mYLlYEPQesjrSNJbw6vpNT8pItIiXJ/Yx8+xWsgvdc1WZHwWXAV5JbD+J15Hkro23weF0wdK242Bmeer8514TomSSCyOvAJth2Gu5ieNSWQtJXW+S5xQCBp3p1fHu4iSudDbCS2a5xY3TTUqu4uXzBxYeiO88kdoPex1NKmnrRF2PwZnXXdio6SqVW6kTmXb4iElSiKx0Pyk8QlWuK063yXO8X0umUjXESXQPKV4UqIUX+fcCqFe2PgLryNJPdt+7f7bnX3DifurznfbfZqnJN5RoiQSiz1PupXY9cZibCIjSup8lziRjnfpmCiVhBMltQiPj75eOL5fHe/iaeoZULXaralkrdfRpJbN98G0RVC2+MT905dAVr4aOoinlCiJjCTUBzVPaTRpPPImQ0aOEqVEatjltulYeheshMxcjSjFS/MB9wm+PviJr+W3ubl0NU95HUnqaHwN9r8ES64/ef5vRhZULleiJJ5SoiQykoObofO41k8aD2PCi84qUUqYhl0uIc0v9TqS+AsEYPIcdb6LF7UGT4xFb4fcYjV1GI3NvwIMnPWuoR+vWgkHt0BXa1LDEolQoiQyEs1Pio9gpRKlRGrY7cru0rUrY+k8jSjFy7Fat1Vr8PjKyoMlN8COh12DAjk1a13Z3ZyLoLhy6GNmrnQL+h5Yl9zYRMKUKImMZM9amDIfiqZ7HUlqC5armUMiNexKz7K7iJI50LTHlcLK+DTVgMmA4AyvI0k/y2+Fvm7YfK/Xkfjf/pfcv+klNwx/zMxzAQN7tZ6SeEOJksip9HZD7bMaTYqHYIVrD66JzvHXccy1r0/HRg4RJfPcG1Al2+PXVAOTZkJGpteRpJ+yM2HGua78Tr/rTm3TvW7u4cK3DX9MbrH7b6rOd+IRJUoip3JgPfS0K1GKh2Cle6PbrpKUuGt81W3TOVFSi/D4aapVx7tEOudWN8KrJgTD6+12bcEXXAm5wVMfO/N82PeSRpPFE0qURE5lz1rAwOwLvY4k9RWVu61GBOKvv+NdGidKahEeP1pDKbEWXwPZRa5VuAzt1ceho+nUZXcRVauguwUObUt8XCJRlCiJnMqetVC+BPJLvI4k9fWvpaRV1uOuYRcEstJ7lKCoHDLzoFGd78alqxXaG5QoJVJ2ASx5F2z7jUsG5GSb7oX8KTDv0pGPjSw8qxE68YASJZHhdLfD/hdVdhcvwQq31YhS/DXshpK56T3nJBBwr1EjSuOjjnfJcc6t0NsJm+/3OhL/6TgGux6Bs65zayWNpHgmFFVonpJ4QomSyHD2Pe/m1Gj9pPgonOY6balFePyle8e7iNK5mqM0XlpDKTkqlkL5UjV1GMr2h9zf1iXXx3a8MW49JXW+Ew8oURIZzp61EMh09dEyfoEM12K9RaV3cdXX4xZiTef5SREl89wb/b5eryNJXf2J0hxPw5gQlt8Kh7e5pkAyYNN9UHo6VCyL/TlVK6F5Pxzbl7i4RIagRElkOHvWQuUKyCn0OpL0EaxQ6V28NdVAqHdiJEql8yDUA8f1ZmnMmmpdo4G8yV5Hkv4WXwdZBW5USZymWtj7LJx9/egWx54Znqe0T6NKklxKlESG0nkc6jZoflK8BStUehdvDbvddiIkSup8N36RjnejeZMqY5MbdB3wtv4aOpu9jsYftvzKbc969+ieV7YYsgvV0EGSTomSyFBqngEbUqIUb0UV6noXb/2twU/zNo5k6F9LSZ3vxqypRo0ckmn5bdDTBlsf8DoS71nryu6qVo/+HszIhBkrlChJ0sWUKBljMowxXzbG1BtjWo0xDxhjymJ43jxjTJsxZkbU/tOMMQ8ZYxqMMUeMMfcbY6qijjlsjLFRX18Y3csTGaM9a92K4TPP8zqS9BKscOth6NPV+GnYDYXT3Qr26a6wzJUyaURpbKx1Xe/UyCF5Kpe70ZD1WlOJug3QuNuV3Y3FzJVuzpf+fkgSxTqi9EXgVuAW4GJgBvDgqZ5gjDkDeAzIj9pfADwKZABvAN4MTAH+ZIzJCR9TBkwNX6t80Nc3Y4xXZHz2rHWTRzNzvI4kvfS3CFf5XdxMlI534MrFSua65hUyeq2HXMtqJUrJY4xrFV6/Eeo2eh2NtzbfBxnZsOgdY3t+1UpX6bH/pfjGJXIKIyZKxphs4OPA56y1j1trXwZuAC4wxqwe5jkfB9YBx4Z4+HKgCrjJWrs5fL5bgEVAeLYei4Fe4AVr7cFBX22je3kiY9B6xH1qpbK7+IskSi1KlOLC2omVKIFahI+HWoN7Y8m7XIXCyxN4VKmvB7Y8AGdcMfZGIjNWgAmo/E6SKpYRpaVAEVAd2WGtrQFqgIuGec5bgQ8BnxrisReBt1prB4+dhsLbyL+excBr1truGOITia+atW6r9ZPiTyNK8dXWAJ3HJkYjh4iSea58TC3CR68pvNjsJM1RSqq8yXDmO93is90T9PPe156A9gY4+4axnyOnCKafpYVnJaliSZQi84uie/rWATOHeoK19s3W2vuGeeyAtfbxqN2fBdqBp8M/LwZ6jTG/N8YcNMasN8a8N4ZYRcZvz1rICbrFAiW+isrdVolSfPQ3cphII0rzXDv0Y7VeR5J6IiNKk6pOeZgkwDm3uvmZ237jdSTe2HyvSxhPe9P4zjNzJexf70aoRJIgM4Zj8oGQtTb6ruwCcscbgDHmDuAjwEettY3h3WcCpcA/AZ8H3gLcZYzJtNbeNcQ5bgduBygrK6O6unq8YcVFa2urb2KR2J23/VHaC+ez9amnRz44idLlflqdVUzDznXsstVeh5LyyuseZT7w3KtH6dpfParnpur9VHysmWXA5uqHOFq63OtwUsqCHc8zObuU556J/yfyqXo/JY21nJs/g94nvs2G4zNGPj6NZPS2s3r7wxycfhm7n342pucMdz9NbS3izJ421v/hJ7QEJ9AHRDIu4/n9FEui1AEEwknK4FqHHGBcY8jGmM8D/w582Vr7nUEPXQpkW2tbwj9vMsbMAj4JnJQoWWvvBO4EWLFihV2zZs14woqb6upq/BKLxOjYPqiuJ//ij7Fm1RqvozlB2txPO2dRUQgV6fBavPbo45CZx6rLr4PA6FZ7SNn7qXURbPxHllQWwMo1XkeTWvZ8FaafkZD/7yl7PyVT7t/Co59jzcKpUHam19Ekz4Z7INRN5RWfoLLq/JGP5xT30/HTYfvXWD6tV//+JWbj+f0Uy1/WyBLo5VH7Kzi5HC8mxpiAMeZ7uCTp/1hrPzf4cWtt16AkKWILw5T6icTNnsj8JDVySJhgpUrv4qVhl1s/aZRJUkormArZRWoRPhaRxWbFG0tucF3fJlqr8M33weQ58Vluo7gSiqvU0EGSJpa/rpuAFqB/ZrsxZjYwG1g7xut+B/gg8D5r7VcHP2CMyTTG7DPGfCLqOSuAbWO8nkhs9qyF/CkwbZHXkaSvYDk0j+kzFonWsGtiNXIA125Zne9Gr7fLfUChRMk7BaWw8G1uvk5Ph9fRJMfxA+7v6pLr3b/deKg63yVK1sbnfCKnMGKiZK3tAr4LfM0Yc4Ux5hzgXuBJa+3zxphsY8z0cBvxERlj3grcgRtNeiT83MhXbri872HgC8aYt4cXp/008F7gS2N8nSIjs9b9Qp9z0cT6hD7ZghXQcRR6Or2OJLX1dLouZhMtUQLX+U4jSqNzbB9g1fHOa8tvg87jsP13XkeSHFvuBywseXf8zlm1EloPqqGLJEWs7wa/ANwD3A08AdQC14UfWw3Uh7exuDm8/Zfw8wZ/Rc75CeB7wLdxo0jvBd5trX0sxmuIjF7ja259H5XdJVaw0m21ltL4HH0NsFB6mteRJF/pPDi2F3q1gkTMtIaSP8y+yC2avP4nXkeSHJt/BTPOdf9m42XmSrfd+0L8zikyjJgSJWttr7X2U9baKdbaYmvt9dbahvBj1dZaY+3JLawGPbZ/0L4bw/uG+ro7fEyXtfbz1trZ1toca+3Z1toJ2lNTkmZPtdtq/aTE8muL8Bd/AE/8p9dRxK6/NfgEHVGyIX2iPBpNe9xWiZK3jHGtwvc+C0d2eR1NYh3c4hZvX3J9fM87baFbwmPvc/E9r8gQVF8kErFnLQRnuE/7JHEiI0rN9d7GMVh3O/zl3+Dpb0Jn88jH+0HDbrediCNKkX+jmqcUu2O1kJEDhWVeRyJLb4RAJryc5k0dNt3rXueZ18T3vIEM1xhin0aUJPGUKKWbv/77xOuoEw+hEOx5ypXdxWvCqQwtGBlR8lFDh22/hq7j0NcNr/7Z62hi07DLdX/Kzvc6kuSLlPEcfd3bOFJJUw1MnqX5l35QOA3mvxU2/sI12UhHoT7Y8gCcfrlrYhFvM1fC4e3Q0RT/c4sMot+Y6eTQdlj7X/DXf9Oq1aN1eJtrMKD5SYmXUwQ5xf4qvVv3Y5gy33U83PkHr6OJTcMumDJBF1zML3X3kBo6xE6twf1l+W3ub87O33sdSWLsedI1XIh32V1EZD2mfS8l5vwiYUqU0snT33TbtiOwW30vRqV//aSLvI1jogiW+6eZQ/1mOLAeVrwf5r/F/dvxe5MAa6Hh1Yk5PwnUIny0rHUdEtXxzj/mXgqTqtK3AmTzr9yHGWdckZjzVy53ZX37tJ6SJJYSpXRxdA9sfQDOv8PVoG+4x+uIUsvrT7oJ4sUzvI5kYghW+GdEaf1dkJkLZ18PC66CrmaoGesScUnSXAc9bRN3RAnUInw0Oprcfa0RJf8IBGDZLW7kJd1KSLvbXPvzM98BWbmJuUZ2AUxfooVnJeGUKKWLZ7/tPl254ONuqHv3o9B6xOuoUkNfD9Q+A3PV7S5p/JIodbW4Tz7PvAbyJsPcNZBV4P/yu4nc8S6idB4c35++czziSa3B/WnZTWAy4OWfeR1JfO38g/sgJ1FldxFVK101gN8rACSlKVFKBy0HYcPdrpNOsByW3QyhXth8n9eRpYa6jdDdqvlJyVRUAa2HoK/X2zi2POD+3694v/s5KxdOfyPs/KNr8OFXkY53E31EyYYGkgAZXqSN+mSV3vlKsALOeLOrAEmnecWb74PimVAV6/KaY1S1Eno74eDmxF5HJjQlSunguf/nEqMLPu5+njofKle45Mlab2NLBXuedNvZmp+UNMEK9ya39ZC3cay/C8oWw4wVA/sWXOUmIde97F1cI2nY5dYRmcitniOd7zRPaWSRZFJzlPznnFuh7TC88ievI4mPlkPw2l9hybsT32Gxf+FZrackiaNEKdV1NLmOXWdec+L6P8tugiM7/P1mzy/2POneLBdM8TqSiaN/LSUPy+8OvAz1m1z3qcEt4U9/kytj3fGwZ6GNKNLxbiK3so/8vtM8pZE11bhOgblBryORaKe90f0+TJc1lbY+4D4ES3TZHUBRmSsn1TwlSSAlSqnuxR+40qELP3Hi/sXXugnqaupwaj2dsPcFmKP5SUnlh7WU1v0YsvLdJ5+D5U2G2Rf6e55Sw+6JPT8JIL/E/b/SiNLI1PHOvzIyXbn8q3+BY3u9jmb8Nt8H5UtdZUsyVK1yC8+qekYSRIlSKutqhee/69pvTl984mO5xbDwbe7TnZ5Ob+JLBftfhL4uzU9KtsiIUku9N9fvPA5bH4SzrnP/VqItuAoad8ORXcmPbSRdLa61+kSenxShznex0RpK/rbsZrd9+efexjFeh3e6Ufqzb0jeNWee75ZESbfOgeIbSpRS2cs/daV3F31q6MeX3uTeEKbrgnbxsGet6zo0K8GTTuVEeZPdiKdXI0qbfwU97bD8fUM/Pv+tbuvHfzuNr7rtRB9RAjdPqVFvkE4p1AfH96mRg59NqnIleBvu9r7BzXhsvs/9PV18bfKuWRWZp6TyO0kMJUqpqrcLnv0OzLoQZp439DFzLnGdZzaq/G5Yrz8JFctUu59sxkBRuTdzlKyFdXdB+dlQec7QxxRXQsU5/kyU+jveKVGiZC4074eeDq8j8a/mA67Zj0aU/G35rW6k+NU/ex3J2IRCsOV+mPcGKJyWvOtOmQ+5k7TwrCSMEqVUtele90v1ok8Of0wgAGe/B157wq03IifqanFrMGj9JG8EK6HZg9K7/S/B4W0DLcGHs+BKd3/4Yb2nwRp2uU9tJ8/xOhLvlYQ736lF+PC0hlJqOOMK18Vy/U+8jmRsap9xI5fJLLsD9z5n5vkaUZKEUaKUikJ98My33Cfi895w6mOX3ghY2PTLZESWWmqfA9un+UleCVZ4U3q37i7ILoLF1536uAVXue0rf0x8TKPRsMu96c3M9joS75WGO9+pocPw1Bo8NWRkuXL53Y/678OZWGy+D7ILB8qWk6nqfPd7sf1o8q8taU+JUira/pCbuHjRp0ZuD1wyx5XnbfyFusJE2/MkZGS7T6Mk+YIVrplDMu/LjibY9mtY8i7IKTz1sVPnQ+lp/ut+p453AyIjSmroMLymWjcCWTzD60hkJOe817XW3nC315GMTk8HbP8tLHw7ZOcn//pVq9x23wvJv7akPSVKqcZaeOqbUHo6LHhbbM9ZdpNLrLQo24n2rHVJUlae15FMTMEK6OuG9sbkXXPTvW4l95HK7sB9CLHgSnefdBxLeGgxCfW5Zg7qeOfkTXLrA2lEaXhNNS5JysjyOhIZSclcmLvGdb8LhbyOJnav/Am6mk9eaiFZKpZBIEvvcSQhlCilmt2Pw6Etbt2kWFe9XvQONySuNZUGtB+Fg1u0fpKXghVum6zyu0gTh8oVMP2s2J6z4Co3EX7344mNLVbHal1yqRGlASXz1Br4VJpq1PEulZxzKxzfC6//1etIYrf5V645j1dl7Fl5ULHUrYkoEmdKlFLN099wnexG88lNdgGceTVs+41be0mg5inAan6Sl/oTpSTV49c+Cw2vwIphWoIPpXIFFEzzT/c7dbw7Wek8jSidyrFaNXJIJQuudKOkqdLUoa0BXn0cznoXBDK8i6NqJdS9rHUjJe6UKKWS2mfd0PLqj46+jGLpzdDT5uqIxZVTZRUM3x5aEq8oyYnS+rsgpxjOvCb25wQCsOCtrmWvH/4A9ydKKr3rVzLPdQDtbvc6Ev/panWLcSpRSh2ZOa4J0yt/gtbDXkczsm2/caPuS673No6ZK91oe/1Gb+OQtKNEKZU89XXInwLL3jv651atdG8otKaSs2etW2RWdfveKZzmJpknI1Fqa3QfEpx9w+gnGy94G3S3unvGaw273O+A/BKvI/GPSOc7ld+d7Fit26rjXWo551aXfKTC3+tN90LZYpi+2Ns4Ik2Z1CZc4kyJUqqo3+Q+1V55x9i6yhjjPqWqfUZvKJrr3BtOrZ/krUBG8had3XiP+7RxNGV3EXMucu3Edz4c/7hGSx3vTqbOd8NrCidKWnMrtUw5HWZdAOt/6u+mDg2vwoF13o8mARROdV1KlShJnClRShVPfQNygnDuB8d+jrPfAybgWoVPZHueclvNT/JesDzxzRysdfX+M1fCtIWjf35mDpz+JlcKE+qLe3ij0rALppzmbQx+U6K1lIalxWZT1/LboGlPeD6tT235FWDgrBHWpEuWmStdi3AthSJxpEQpFTS86sqGzv2Aa4c7VsWVMPdS2PhL79/weWnPWsidBGUxdj6TxImspZRIe9a60YZYWoIPZ8GVbq7H/pfiF9dotR+F9gaNKEXLDULBVI2UD6WpxnU8Valm6ln4dvd36uWfeh3J0Kx1i8zOvWSgMY/Xqs6HjqMDczlF4kCJUip45pvuU+2Vfzv+cy27CZr3u8VWJyJr3Wufc1Hs7dUlcYKVcPxAYj8BXH8X5E12bfLH6vTL3TodXna/U8e74alF+NAiHe9GWphc/Ccr182p3PGwm2PpN/tecIn4khu8jmRAZOFZrackcaR3in53fD9sus81cCicNv7zzb8Scosn7ppKTXvg+D6tn+QXReWuG2NXc2LO33rYvdE4+0b3xmOscoPuk9Mdv/eurKNhl9uq493J1CJ8aE01KrtLZefc6uZWbvql15GcbPN9kJkHC6/yOpIBpae51ur7tJ6SL7U1wP23waFtXkcyKkqU/O7Z7wAWLvhYfM6XlevWO9j5e+g4Fp9zppJI5zIlSv7Qv5ZSgsrvNtztukeNpYlDtAVXukT78I7xn2ssGnZBRrY6mA2lZC60HtQ6cYNZ65o56H5JXWWLYMZ5rvzOT/Nuertg669dkpRT5HU0A4xx3e/U0MGfdjwcbiefWlM/lCj5WVuD+wV51rthUlX8zrv0JujthK0Pxu+cqWLPWiicrk/l/SJY6baJaOgQCrkmDrMvis//7/lvddudfxj/ucai8VX3iamXizr6VWmk853K7/q1HobeDo0opbrlt7kPSfxUTrb7ceg85o9ud9GqVro5qamwBtVEs/0h96HW9NSaH65Eyc9e+B70dMCFfx/f81Ysg2mLUmONhniy1iVKcy5Wzb5fBMvdNhEtwl//q5ujsfy2+JyvaDrMONe7eUoNu5TgD0ctwk+mjnfp4cyrXcfb9T5q6rD5XiiY5ppD+c3MlW6r8jt/aWt0HYcXXZ1y77+UKPlVZzO8eKcb2p46P77nNsaNKh1YD4d3xvfcfnZ4h+tcprbg/lEUTpQS0flu3V1ucdaFb4vfORdc5VZ+P74/fueMRW83HN0DpUqUhqQW4SfrT5RUepfSsgtcufz2h6CjyetoXAy7HnUtwTMyvY7mZBVLISNH5Xd+s/NhsH0u8U8xSpT8at2PofM4XPjJxJx/yfUQyISNdyfm/H4UmZ+khWb9IzPHtXaOd+ldc71b92jZTe4a8bIgPHE52eV3TXvcHxl1vBtaTqErqVXp3YBj4cVm41m2Ld5Yfpsrl/fDqNK2h1yDiSXv9jqSoWXmQOU5SpT8ZttDbuHr6Uu8jmTUlCj5UU8HPPf/3LB25TmJuUbhVDj9za6jXl9PYq7hN3vWujIUvXHwl2BF/EvvNvzcJRbn3Brf8045DabMT375nTrejUyd707UVONGbLPyvI5Exqt8CVSthj//C/zgDbDpXtdQwQub73O/A8uXenP9WMw8H+o3ufdS4r22Rvf+68yrU67sDpQo+dPGe6DtMFyUoNGkiGU3ueu8+ufEXscPQn1Q87TK7vyoqCK+Xe9Cfe6T17mXDkzyj6cFV0LNM24B2GRRojSykrmaozRYU4063qWTm34Fb/kvV5b/mw/DNxbBX/41uWXATTWuqcTZ1/v7DW/VKgj1wIGXvY5EAF75g/vgctHVXkcyJkqU/KavF575bzdpfPZFib3W6Ze7sqcNE6D8rn4TdB1XW3A/ClbEt/Tu1T+7RZXj0RJ8KAuvcr/0dz+WmPMPpWG3Syj91IrXb0rnuTmInQlakyvVNNWqkUM6ySmC82+Hj7wE730IZp4HT30DvrUE7rvZTZRPdAvxzfe77VnvSux1xmvmeW7rp06BE9m2h9yHNuVnex3JmChR8putD8KxvW5uUqI/scnIcnOVdj3iWpGnsz1Puq1GlPwnWAEdR+NXJrHux1BYNtDOO97Kl7mkJZnld+p4N7ISn7UI7+2Cnk7vrt18QIlSOjIG5l0K7/klfHwTrP6Iq5b46VXw3VXw0o8Ss56Yta7b3awL/V++nl/iygPV+c577Ufd+68ULbsDJUr+EgrB099wrbvPuCI511x6k1uQc/OvknM9r+xZC1MXQuE0ryORaP2LzsZhntLx/W6kZ9l73QcBiRAIwIK3wqt/SU4NvLVuREmNHE4t0vnOD+V3vV3w4yvgGwvg6W9Cd1tyr39sH2DV8S7dTZ4Fb/pX+OQOeMf/c7/z/vBJ+MZC+NNnoeHV+F2r7mW3lptfmzhEq1rpEqVQyOtIJradf3DvMVO07A6UKPnLrj/BkZ1uNCmQpP81ZYug4hw3L8pPK3/HU2831D6n0SS/iiRK8WgR/vLP3H28PM5NHKItuBJ62uG1JxJ7HXALJ3Y1K1EaSX+LcB+MKP35i+6N5ZQz3Pf/fbZr0JOsyeXHatxWI0oTQ1YeLLsZPrwWPvA4nPFmeOmH8J3l8PNr4JVH3NzN8dh0n2u7vegd8Yk50apWus7BRybQEih+tP0hNwJZsczrSMZMiZJfWAtPfd39YTvzncm99rKb4NBWN48nHR1Y51aoV6LkT8FKtx3viFJfr0uUTntj4ktDZl0IOcXJaRPe38jhtMRfK5Vl57uSSK9HlF55BJ7/Lpz3YfjAY/D+x2DaQnj0c/DtZfDiDxLfsUyLzU5Mxrj5Odf+ED6xDS79PBzeDr+83t17z3x7bE1o+nrctID5b4G8SXEPOyFmnu+2+9Qm3DPtR+H16pRcZHYwJUp+sWetWwD2go8nfxG3xde6T4o23pPc6ybL60+CCcDsC72ORIYSWXR2vA0ddj3iRqUS1cRhsMxs96ntK390CVoi9SdKGlEakdctwpvr4KE7YPpZriQKoOp8uPVhuPX3LnH546fhf5bD+p8kbmmGphr3O71wemLOL/5XVAaXfAb+fgtcd5f7QOrxf3Jleb/9CNRvjv1cr/0V2hvcnOZUUTIXCqbBXs1T8swrf3Rldym4yOxgSpT84qmvuwnoZ9+Y/GvnTXadvLbc793aDIm0Z63rtpIqn4RNNDmFbnRmvC3C19/lRhROf3N84hrJgitdE4pEf2LZsBuyCtxrk1PzskV4qA8e/JD7HXrdXZCVe+Ljcy6C9/0Jbv61myv58MfhOytg4y/in2w31bpR1WSVcIt/ZWTB4mvg/X+Cv3naJTtbHoDvX+Tm0W190JWnn8qmeyGvxI3Wpwpj3IcU6nznnW0PhcvuErQeaJLot6gf7F/vuoKs+sjJf1yTZelN0NHkPgFIJ91tsP8lld353XhbhDfVuOYK59ySvBHZ0y5zn9onuvyuYZcru9Ob3pGVzoP2Rug4lvxrP/V1qH0arvza8B0KjXH3zQf/Au+5D3KCbgTqu+e7N6/xmnjeVKOyOznZ9LPg7d+GT+2Ay//DjcA/8H741llQ/X+h5eDJz+lsdu8LFl/rRtJTycyVcKw2vuv0SWw6msJld+9I6bI7UKLkD09/A3InJadkaDhz17ih+Q1pVn6393m38JwSJX8Llo9vjtL6n7pfxufcEr+YRpJT5P7d7Px9YhuhqONd7PpbhCd5VKn2Waj+Mpz1bjj7PSMfbwzMv8JNvn/3zyEjGx78APzvatj+2/EnTE216ngnw8ub7NqKf3QD3Hi/S6CqvwzfPNMlTnufH/idtuN30NuZWmV3EVWr3FbzlJLvlT+5914p3O0uQomS1w7vcG+0zv+wt4tJBjLcH/jX/hKfNs1+sedJCGQN/MIUfwpWjL3rXV+PWzT5jCuguDK+cY1k4VVu3bNDWxNz/u52OL5XiVKsSsOJUjI737UfhQc/6EZwrvrG6D49NQYWvR3+5hm49keunv9Xt8CdF7s3GmNJwDua3OLaGlGSkQQCcMblcPMD8NGXXQOS3X+GH7/Zlea9/DP34WnJPJixwutoR698CWTmaZ6SF7Y9BMUzoXK515GMW0yJkjEmwxjzZWNMvTGm1RjzgDGmLIbnzTPGtBljZkTtzzfG3GmMaTDGHDPG/MAYUxh1zE3GmFeMMR3GmOeNMeeO7qWliKe/5eYfnP83XkcCS28EG3L1yOliz1qYcS5kF3gdiZxKsNKVfYxlcvvOP0DbYVjuwYjsGW8BTOLK7xrD66BosdnYTJ4DmOSNKFnrJsa3Hobrfjz2D7sCATjrOvjb5+Gd33cLhv7yBvjhZfDqn0eXMKnjnYxF6Ty44j9dWd5V33Jz7n73Udj7rBtNSsXyqYws90ZdI0rJ1XHMNQBJg7I7iH1E6YvArcAtwMXADODBUz3BGHMG8BiQP8TD3wcuBK4C3gasCe+LPPeNwI+BrwPnAFuAx4wxU2OMNzU01bgGCstvcytJe610HlStTp81lTqaXMtzld35X7ACsNB6aPTPXfdj98nVaZfFPawRFU5163Xs+H1izt+42201ohSbrFwonpG8zncv/RBe+QO86UvxWSckIxPOvgE+8hK8/X9cAnb3tXDXW2DPU7GdI5IoTVLpnYxBdoGbBnDHs3DbH2H1R+HcD3od1dhVrXQd/rpavY5k4kijsjuIIVEyxmQDHwc+Z6193Fr7MnADcIExZvUwz/k4sA44NsRjlcCNwN9aa5+31j4FfBB4T/gxgH8AfmmtvdNauwP4MHAU+NBoX6CvPfs/rm316o94HcmAZTe5T7H3veh1JONX+6wbIVOi5H+Rjm6jnXTb+Jorr1x+qysf9cKCK+HQloE3qPHUsBswA4upysiS1fnu4BZ49POuy+LKv43vuTOy3Hy7j74MV37d3Vs/vQp++raRy4iaat1Wc5RkPIyB2RfA5f8OBaVeRzN2VSvB9rnlVyQ5tj8EwRmpWa45hFhGlJYCRUB1ZIe1tgaoAS4a5jlvxSU1nxrisQuAEPDMoH3PAH3AhcaYQPiYwdcLAWtPcb3U03IIXv45LH1P+NN0n1h0tSsF3Hi315GM3+tPuvrkGelZtZlWIv8GRtv5bv1PIJAJy94b95BituBKt92ZgI6RDbtce9WsvPifO12VzIWjCZ6j1N0G97/PTYq/+ruJKy/JzHaf5n9sI1zxf+HwTvjx5W6Uabg3fk01rpVzbnFiYhJJJTPOBQzs0zylpOg8nlZldxBbohSZXxT9DqYOmDnUE6y1b7bW3neK8x221vYMOr4XOBw+3ySgYDTXS0nPf9cNTV7w915HcqKcQrc42NbfuDcDqWzPWpi1KvVamk5E/YnSKBqJ9Ha5MtH5b4EiDxfWLJkL085MzDylhl0quxut0nmu7Lb9aOKu8cfPuJH3a38ABVMSd52IrFxYeQd8fKNbyPbAy/CDN8Avbjh54dCmGo0miUTkTYJpi7SeUrK88gj0daf8IrODxbLgSD4QGpzYhHUBY1n0Jx/oHGJ/5HyROU3Rxwx7PWPM7cDtAGVlZVRXV48hrPhrbW0dMpbMnlZWPv99jk5ZzfYt+4B9SY/tVIo5k2Xd97Dj11/l0PRLvQ5nTLK6j3HBkR28VnQu+3xyP4zXcPdTWrCWiwLZHNj+Aq93LYrpKdMOrWVReyObslbQ5PF/l9l5ZzKr9gGefey39GTH6ZN8G+Kiw69QlzmH1xLw+tL1fiptaOcsYP2f76clOD/u55926EkW7bibmlnvpqY2BLXVcb/GqZ1NxvL/R+WB3zPz9YfI2vUnDk9dTc3s99BeUMV59TtpLZzL9iT/v03X+0m8Ec/76fTMmZTVPMnTT/wFjEcl2hPE4i0/pCi7lOdebYXXqr0Op9947qdYEqUOIGCMyQyP/ETkAGMZcugIPzda5Hwdg34e6vGTWGvvBO4EWLFihV2zZs0Ywoq/6upqhoxl7X9BXwfTrv2/TJt+VtLjGpG9BGp/wMLO9Sxc8yWvoxmbLQ8AMO+y25iXBu0p4RT3U7rYMoOq4gyqYn2Nd/0XTJ7N2Vd/zPvFWM+YBHf+igumtsCyd8TnnMf2wpPdzFz6BmauWBOfcw6StvfTkXLY+p8snzUJzl4T33M3vgbP/gBmrmT2Lf/L7GQtbjykt0LHf8Lz32Xac99l2kvPuc55XQ3kL7+BaUn+f5u295N4Iq730+RD8JtHWLNwmlszShKjsxme2gQr3s+aS9/gdTQnGM/9FMu7i8hwR3nU/gpOLo+LxT5gmjEDab0xJhOYFj7fUVxCFK/r+Ut3Ozz/v3D65f79B2sMLL0Jap5KzAT1ZNizFnKKoXyp15FIrIKVsZfeHXkFap92HSO9TpIAys92nffiWX7XsMttVXo3OpNnuyY58W7o0NvtFoUNZMC1P3Qd6ryWNwku/Rz8/Wa48O/d/RfqUWtwkcGqVrrtXrUJT6hdj0BfV1qV3UFsidImoAW4JLLDGDMbmI1rsDBaz+BGsgavAHphOJZnrLUWeDbqegFcW/KxXM9fXv4ZtDfCRUP1ufCRpe8BDGz8pdeRjM2eJ2H2hd51QpPRKyqPvZnD+p+4hYSX3pzQkGJmjGvq8Npf4ze3r0GtwcckMycxLcL/8iWo2wDv+A5M8tl02fwSeOMX4eObXZe8xdd5HZGIf0yqcn9flCgl1raHXAfbGed5HUlcjZgoWWu7gO8CXzPGXGGMOQe4F3jSWvu8MSbbGDM93EZ8RNbaA8CvgB8ZYy4wxlwI/AD4efgxgG8Atxpj/s4YsxC3xlIx8MNRv0I/6e2GZ78Nsy4Y+ITDr4pnwNw1sPEXEAp5Hc3oNNW6kTC1BU8twQq36OxI91tPh7svF77NrWPkFwuuhN5OlyzFQ8NuyJ2UnGYB6aZkXnxHlHY/Ds99x3WgW/i2+J033gqnuhhzCkc+VmSiMAZmnq/Od4nU2ewWx170dn9UecRRrK/mC8A9wN3AE0AtEPnIajVQH97G6oO4UaM/Ar8F/grcEXnQWvsIrjnDp4CXgUXA5dbahlFcw3823+c+Mb/wk15HEptlN8Pxva4EL5VE4lWilFqCla5bTnvjqY/b/lvoPOYWRfSTqtUusYnX4rMNu2DK6WnTYjWpSudB4+vxWTi7uR5+82EoWwyX/8f4zyciyVe1Co7vg+P7vY4kPe161JXdpckis4PFlChZa3uttZ+y1k6x1hZba6+PJC3W2mprrbHWVg/xvMhj+6P2t1pr3xc+V6m19sPW2o6oY+6y1s611uZZay8IL3SbukJ98My3YPoSOO0yr6OJzYIr3Tyfjfd4Hcno7FkLBVNh2kKvI5HRCIanJY5Ufrfux1B6Gsz22bJqGZmuVfmuR6AvuknoGDTsVtndWJXMg67jIyfdIwn1wW9ud6OY1/3YtekWkdRTdb7bqvwuMbY/5MobZ57vdSRxl17jY36243du3Y2LPpk6nxBn5cFZ18L237lFxFKBtW6h2TkXp85/Z3Eiaym11A9/zKHtrnxi+fv8+f93wZVutKv22fGdp/M4tB50I0oyeqXz3Ha885Se/qb74OUtX4Wp8W81LiJJUnYWZBX4r/zuyC546hvuQ5lU1dXqyu4Wpl/ZHShRSg5r3T+E0tPcjZRKlt4MvR2w7TdeRxKbht3uDabK7lJPsNJtTzWitP4uyMiBpTcmJ6bRmncZZOaNv/tdw6tuqxGlsSkJJ0rjmae09wV44j9h8bWuDFlEUldGJsxY4a+FZ48fgJ+9wzWK2fqg19GM3a5H3PzcRXFaGsNnlCglw6t/gYOb4YK/T70ubJXnwNQFsCFFyu/2POm2SpRST8FUCGQO3yK8uw023et+GeeXJDe2WGXnw7w3uERpPPNj1Bp8fCZVuYUlj74+tud3NLlW4JNmwlXf9OfopYiMTtVKOLQNulq8jsRVDdxznYtl8hx48iupO6q0/SEoLPN/k7IxUqKUDE9/w31avuR6ryMZvciaSvtfdEPEfrdnrVvPZvIcryOR0QpkQOF0N3l+KFt/DV3NsOL9yY1rtBZcCc37oX7j2M/RsMsljVoPZ2wys12SM5bSO2vhdx91JaDX/hhyi+Mfn4gk38zzwYZg/0vextHbDfe91/2ev/5n8KYvuakZqTiq1NXquoIufHvqDQTESIlSou19HmqfgdUfdX+8U9GS692ns35v6hAKuY53cy7RJ8CpKlgxfOnd+rvc6KbfP7U64wq34Ol4yu8adkHJXMjIil9cE81YW4Sv+zHseBgu+xeYsTz+cYmIN2ac6343e9nQwVr43Udc9cvbv+MqEBa8DaadCU9+NfVGlXY/6sru0myR2cGUKCXaU9+A/FI45xavIxm7ojI4/XJX9tTX63U0wzu0xZXMqOwudQUrhi69q98EB9b7t4nDYAWlbq208SRKja+q7G68xtIi/NA2eOQf3VyzVR9JXGwikny5QSg709tE6a//5paKufQLsPQ9bl8gAGv+DzTudpUTqWTbQ1AwzbVfT1NKlBKooHWPy7ZX3gHZBV6HMz7LbnJNEuK1mGYi7FnrtnN81jZaYhdJlKLf3K67yzVJODtFylcXXAmHt4+t9Kuv1z2v9LT4xzWRlMyD7hZoOxLb8d1tcP/7IG8SvPP7adm9SWTCm7kS9q/z5kPfdT+Gp74O59wKF3/6xMf6R5VSaK5Sd5sru1uUvmV3oEQpoar2PgjZRXDuh7wOZfxOf7MbGdt4t9eRDG/PWig9faDNtKSeYAX0tLm5SBFdLbDlflh8DeRN9i620Zj/Vrcdy6jSsVoI9WhEabxG2yL8kc+6ksd3fh8KpyYuLhHxTtVK9zfm0JbkXveVR+APn3LVOVd+4+TKiFQcVdr9mOuKnIaLzA6mRClRGl9j2uFn4NwPuE8oU11mtpur9MqfoP2o19GcrK/HrV0z9xKvI5HxiCS5g8vvttwP3a2u7C5VTJ4F088aW6KkjnfxUTLXbWOZp7T1QXj5Z3DhJ2DepYmNS0S8E5njujeJ6ykdWA8PvA+mL4Hr7nKtyoey4G0wbVHqjCpte8h1q5212utIEkqJUqLsfoxQIBNW/q3XkcTP0pugr9u9cfWbAy+7N9Oan5TaiiKJUrihg7Wu7K7sLLcGRipZcJVb3LD18Oie158oqfRuXCbNcp0DRxpRaqqBh//eTfS+9HPJiExEvFI8w3XGTdZ6Skf3wC+uh4IpcOOvIKdw+GMDAbgkRUaVutvdiNLCt6V12R0oUUqclXfwwvnfd40Q0sX0xVB+NmzwYfldZH7SbM1PSmn9I0rhFuF1L7s1yFbc5v8mDtEWXAVYNwo7Gg273OTYVCkz9KuMTJcsnWpEqa8HHng/YODaH6nLoMhEMPN89yHWeNa6i0X7UbdWUl8P3PRgbO8HF77djSqt9XkHvN2PQU972pfdgRKlhOrO8emimOOx9Gb3xvVgkut7R7LnSVfq5NeFSCU2ReVuGym9W3cXZBXAWe/2LqaxKjvTvVHf+fvRPa9ht8ru4iXS+W44f/03Vxbz9m+7ckkRSX9VK906acf2Ju4aPR3wyxvg2D54z70wNcbf6ZFRpYZdsO03iYtvvLY/BPlTXIfXNKdESUbnrOsgIxs2+GhNpZ4O2PeiWz9JUltmtqt5bj7gVi7f+iCcda1r65pqjHGjSq9Xj24l+IZdMOX0hIU1oZTMg6PDtAh/9S/wzH+7uW9pvAaIiETpn6eUoDbhoT749Yfc+5Jr7oRZo2ydHRlV8utcpe522PWoK7sbbr5VGlGiJKOTX+I6em35lVtd2g/2vQB9XUqU0kWwwn3at/lXbmh/xfu9jmjsFlzp5vW9+ufYjm9rdGuBaUQpPkrnuQ5XrYdO3N9yCH7zYZi6EK74sjexiYg3pi2CnCDsS1Ci9Ojn3aLVb/6PsX0IEwjAJZ/x76jSq392f5snyAdMSpRk9JbdDO2NsOsRryNx9qwFkzH6T23En4KVcPyAK7srXwoVy7yOaOyqVrq2+rF2v+tv5KARpbgomeO2gxs6hEIuSepqhXfdBVl53sQmIt4IZLjmQInofPfc/4MX/tc18lr1d2M/z8J3uA9y/DiqtP0h93dt1oVeR5IUSpRk9Oa9wc0l2eiT8rs9a6FyOeQUeR2JxENRuVus9fA2WJFCLcGHEsiA+W+BXY/FNgKrRCm+SsJrKQ1u6PDsf8PrT7iRpGkLvYlLRLxVtcr9nek4Fr9zbvsNPPo5Vzp3+X+M71yRdZX8NqrU0+HWhJogZXegREnGIpABZ9/gVmRuOTTy8YnU2exag2v9pPQRrACsW6x58XVeRzN+C66CruNQ89TIxzbsgsxc175Wxq94JgSyBkaU9r0Ef/1316lp+W1eRiYiXpp5PmBh/0vxOV/ts/DrD8PMlW5eUiAOb6/7R5V81AHv1T+7cuYJ0O0uQomSjM3Sm8H2weZ7vY2j9lkXh9ZPSh/BSrdd8u5TrzmRKuaugaz82MrvGnZD6Wlpvy5F0mRkwuTZbkSp4xg8+H6XiL/tv1Ov3byIxM+MFa5kPx4NHY68Ar98D0yqgvf8Mn7lvP2jSq/4Z1Rp20OQVzKhlmJRoiRjM+U094nMhnsSvxbBqexZCxk5MOM872KQ+KpYBoVlcN7tXkcSH1l5cNpl8Mof3fyYU1HHu/iLtAh/+ONu7tu1P4a8SV5HJSJeyi6A8iXjT5RaDsHd17k12G5+IP5LlPhpVKmnw81NX3jVhCm7AyVKMh5Lb3KfdBxY710Me9ZC1fmQletdDBJf0xbAp3e5bbpY8DbXya9uw/DH9HTCsVp1vIu3knluvtv2h+Cyf4KZ53odkYj4wcyV7v1LX8/Ynt/VCr94F7Q3wI2/cqPX8dbfAc8Ho0qv/gW6WydU2R0oUZLxOPOdkJkHG+725vptjXBoi9qCi/+dcbkr89j58PDHHH0dbEiJUryVznXbuZfC6o97G4uI+EfV+dDbAfWbR//cvl64/zY4uAXe9ROoPCfe0Q1YdDVMXeD9qNL2hyBv8oSb6qBEScYuNwiL3gFbf+2GZJOtZq3bKlESv8ubDLMvPPU8JXW8S4zT3gRnXgPv/H58JliLSHqYGVl49rnRPc9a+MMn4NXH4cpvwBlvjn9sgwUCcEl4rtL2hxJ7reH0dLpudwuucmWGE8jEKTKUxFh2k2vosOP3sORd4ztXKOS6g7UfdV8d4W17Y9T3Te774/tdZ7RUXmdHJo4FV8Gf/gGO7IKpQ4waNe5229LTkhtXups8y62XJCIyWLAcJs0KLzz7kdift/a/4OWfwUWfTt4SFouuhqlfcaNKi65OfsOf1/4K3S0TZpHZwZQoyfjMutB1etl494mJUl+vS2iGTXaOQnvTifs7mlwHu6GYDPepfH6JW+hs8myoXObKaSbQpEJJYQve6hKlV/4wdKLUsBuCM9wkYxERSbyqlfDaE26UKJZOmBt/AU/8Byy5Ad7whcTHFxEZVXrgfW5UafG1ybs2uGvmTpqQFTx6hynjEwi4pg7V/xd+cFk46WmEzuPDPycj2yU7eSUu8Zm2cOD7wfvzSweSo5xilc1Iaiue4UY/d/4BLvzEyY+r452ISHJVrYTN97k5oqXzTn3sa3+F333UJQtv/5/kLzHg1ahSbxe88ie3kO4EK7sDJUoSD8tvg5qnIZDpylyGS3Yi+7MLtIaJTEwLrnQLnjbXu7KPCGvdiNLSm7yLTURkoonMU9r3wqkTpYNb4L5bYMp8uP7nkJmdnPgGi3TAe+D9yR1Veu2v0NU8IcvuQImSxEPRdLjt915HIeJ/C65yidIrf4RzPzCwv6XetV3ViJKISPJMXQC5xW49paU3Dn3MsX1wz7tcA6ub7nfHe2XR1TD1q+FRpXcmp9Jm20PuNU/AsjtQ1zsRkeSZusCt6xPd/a6/451ag4uIJE0gADPPH37h2Y5jLknqbnNJUnFlUsM7SSDDjSod2ZmcDni9Xe6DvQVXeTOK5gNKlEREksUYV363Z+2J8/gawh3vlCiJiCTXzPNd6+32oyfu7+2C+26Gxlfh+ruh7Exv4ovWv67SV1y34ER6vdqV3U2wRWYHU6IkIpJMC66CUA/sfnxgX8MuyC50ZawiIpI8VYPmKUWEQvDQ30LNU3D1d2Guj8rOkjmqFCm7m7smsdfxMSVKIiLJNGMFFEyDnYPm9TXsdvOT1ORERCS5Ks6BQNaJ5Xd/+RJsfQAu+2dY8m7vYhvOoqtdY4knv5q4UaXebrecxfwrJ2zZHShREhFJrkAGzH+LG1Hq7XL7Gnar7E5ExAvZ+VB+9sCI0os/gGe+BcvfBxd+0tPQhtU/qrQDdvw2Mdd4vdqViE/QbncRSpRERJJt4dtcl7s9a6GrFZr3q+OdiIhXqlbCgZdh22/gT5+BM66At37N36P8Z77TjSpVJ2iu0vaHICc4ocvuQImSiEjyzbnYzUna+Xs3URg0oiQi4pWqldDXBfe/D8qXwnU/hgyfr6CTyFGl3m7392n+WyEzJ77nTjFKlEREki0zB05/E+z8o5uQC0qURES8Ell4dvIsuPE+yC7wNp5YJWpUac+TKrsLU6IkIuKFBVdB22HY+AswASiZ63VEIiITU+FUeNdP4NaHoXCa19HELlGjSpGyu3lviN85U5QSJRERL5z+Jtdpac+TMHn2hC9vEBHx1JnvhElVXkcxeme+01UkxGtUqa/HLYo+/y36u4QSJRERb+QWu7lKAKVq5CAiImMQyIBL/k94VOl34z/fnieho2lCLzI7mBIlERGvLLjSbdXxTkRExioyqvRkHEaVtj0E2UUquwtToiQi4pUFV7o/SDPP9zoSERFJVZFRpcPbxzeq1NcT7nb3FsjKjV98KUyJkoiIV4qmw2dec+sqiYiIjFU8RpX2rA2X3b0jvrGlMCVKIiJeyszx96KGIiLif4EMuPgz4xtV2v6QW+PvtMviGloqU6IkIiIiIpLqFl/jmgM9+dXRjyr19cKO38MZV0BWXmLiS0ExJUrGmAxjzJeNMfXGmFZjzAPGmLJTHL/CGPOMMabdGLPbGHPLoMduM8bYYb5+POi4w0M8/oXxvVwRERERkTTUP1dpG+x8eHTPrXkKOo5qkdkosY4ofRG4FbgFuBiYATw41IHGmKnAo8DLwDnAt4EfGWMuDx9yH1Ae9fV5oAP47/A5yoCp4WsNPu6bo3lxIiIiIiITRmRUabTrKvWX3b0xYaGlohETJWNMNvBx4HPW2settS8DNwAXGGNWD/GUDwLHgY9ba3daa/8HuBv4NIC1tsNaezDyBeTjEqVPWms3hc+xGOgFXhh8rLW2bZyvV0REREQkPY1lVKmvF3Y8DGe8WWV3UWIZUVoKFAHVkR3W2hqgBrhoiOMvAtZaawensdW4xGqo630V2ArcOWjfYuA1a213DPGJiIiIiAiMflSp9mlob9Qis0OIJVGaEd4eiNpfB8wc5vihjs0HSgbvNMacDVwLfDYqsVoM9Bpjfm+MOWiMWW+MeW8MsYqIiIiITFyBDLjkM7GPKm17CLLyVXY3hMwYjskHQtbanqj9XcBQq1HlA51DHMsQx/89rrzuiaj9ZwKlwD/hyvLeAtxljMm01t4VfUFjzO3A7QBlZWVUV1ef6vUkTWtrq29ikdSn+0niSfeTxJPuJ4kn3U9xYEs5L6+C0B/+mXWHimDIoi4woT5Wbf41xyYtY/uzLyY5yOQYz/0US6LUAQTCSUrvoP05wFBzhjrCjxF1LIOPN8bkAtcBHxviHJcC2dbalvDPm4wxs4BPAiclStbaOwmX7q1YscKuWbNmpNeUFNXV1fglFkl9up8knnQ/STzpfpJ40v0UJ6Vfgl9/iDVlrbDo7UMfs2ctrD3OtDW3M+3MNUkNL1nGcz/FUnq3L7wtj9pfwckldpHjhzq2FdfkIeIyIBv4TfQJrLVdg5KkiC0MXeonIiIiIiKDLb4WSk+DJ08xVylSdnf65UM/PsHFkihtAlqASyI7jDGzgdnA2iGOfxq42JgTlpq/FHgmah7SRcDL1tpjg59sjMk0xuwzxnwi6rwrgG0xxCsiIiIiMrFFOuAd2go7f3/y46E+1+3u9MshOz/58aWAERMla20X8F3ga8aYK4wx5wD3Ak9aa583xmQbY6aH24gD/Ai3BtL3jDELjTEfBW7EdbcbbBlulCj6er3Aw8AXjDFvN8acZoz5NPBe4EtjfJ0iIiIiIhPLqUaVap+FtsNaZPYUYl1w9gvAPbj1kJ4AanHziwBWA/XhLdbaQ8AVuERoA/AR4BZr7V+jzlkONA5zvU8A38MtVrsNlyS921r7WIzxioiIiIhMbIEMuPgzblTplT+c+Nj2hyAzT2V3pxBLM4fIKM+nwl/Rj1UDJmrf88B5I5xzySke68J1u/t8LPGJiIiIiMgQFl8La7/q1lWafyUEAq7sbvvv4IzLIbvA6wh9K9YRJRERERERSTUZmeFRpS0Do0p7n3Nld4ve4W1sPqdESUREREQknUXmKlWH5yptewgyc+H0N3sdma8pURIRERERSWcZmXDxP7hRpZ0Pw47fwelvgpxCryPzNSVKIiIiIiLpbvF1UDIPHv57aD0Ei672OiLfU6IkIiIiIpLuMjLhks9Ax1FXdnfGFV5H5HtKlEREREREJoLF18HUhbDwbSq7i0FM7cFFRERERCTFZWTCh/4KAaUAsdB/JRERERGRiSI73+sIUoZK70RERERERKIoURIREREREYmiRElERERERCSKEiUREREREZEoSpRERERERESiKFESERERERGJokRJREREREQkihIlERERERGRKEqUREREREREoihREhERERERiaJESUREREREJIoSJRERERERkShKlERERERERKIoURIREREREYmiRElERERERCSKEiUREREREZEoSpRERERERESiKFESERERERGJokRJREREREQkihIlERERERGRKEqUREREREREoihREhERERERiaJESUREREREJIoSJRERERERkShKlERERERERKIoURIREREREYmiRElERERERCSKEiUREREREZEoSpRERERERESiKFESERERERGJokRJREREREQkihIlERERERGRKEqUREREREREoihREhERERERiaJESUREREREJEpMiZIxJsMY82VjTL0xptUY84AxpuwUx68wxjxjjGk3xuw2xtwS9fiVxhg7xNeMQcfcZIx5xRjTYYx53hhz7thfpoiIiIiISOxiHVH6InArcAtwMTADeHCoA40xU4FHgZeBc4BvAz8yxlw+6LDFwAagPOqrLnyONwI/Br4ePscW4LHwuUVERERERBIqc6QDjDHZwMeBj1lrHw/vuwHYY4xZba19NuopHwSOAx+31oaAncaYc4BPA4+Fj1kMbLHWHhzmsv8A/NJae2f4eh8G3gB8CPjP0bxAERERERGR0YplRGkpUARUR3ZYa2uAGuCiIY6/CFgbTpIiqoELjDGR6y0Gdgx1sfAxF0RdLwSsHeZ6IiIiIiIicRVLohSZN3Qgan8dMHOY44c6Nh8oMcZkAAuA5caYTcaYOmPMb40x88PHTgIKRnE9ERERERGRuBqx9A6X4ISstT1R+7uA3GGO7xziWMLHzwtvc3CldNnAF4CnjDGLwz8zzDmGuh7GmNuB2wHKysqorq4+9StKktbWVt/EIqlP95PEk+4niSfdTxJPup8knsZzP8WSKHUAAWNMprW2d9D+HKBtmONzovZFfm6z1u43xkwBmiLlecaYa4C9wHuBn0Q9Z6TrEZ7LdCfAihUr7Jo1a2J4WYlXXV2NX2KR1Kf7SeJJ95PEk+4niSfdTxJP47mfYim92xfelkftr+Dk8rjI8UMd24pr8oC1tnHwHCZrbTvwOq607iguIYr1eiIiIiIiInEVS6K0CWgBLonsMMbMBmbjGixEexq42BhjBu27FHjGWhsyxlxtjGkZ3OrbGFMEnAFss9Za4Nmo6wVwbcmHup6IiIiIiEhcjZgoWWu7gO8CXzPGXBFu9X0v8KS19nljTLYxZnq4jTjAj4CpwPeMMQuNMR8FbgS+Gn78SaAZ+LkxZkn4fPcDDcDPw8d8A7jVGPN3xpiFwPeBYuCH8XjRIiIiIiIipxLrgrNfAO4B7gaeAGqB68KPrQbqw1ustYeAK4BluEVlPwLcYq39a/jxJuCNQA+uBXg1rtTuDdbazvAxj+CaM3wKt3DtIuBya23DmF+piIiIiIhIjGJp5kC4icOnwl/Rj1UDJmrf88B5pzjfDuBtI1zzLuCuWOITERERERGJp1hHlERERERERCYMJUoiIiIiIiJRYiq9k9F7dNtB/uv5Dp44vpXFlcWcNaOY06YWkpmh3FRERERExO+UKCVIwBgCBu5fv5+fPlcLQE5mgIXlQc6qLOasymIWVxZzelkhWUqeRERERER8RYlSgrxpURlZh/O46OJL2NPQytYDzWw5cJwtB47zmw0H+PnzLnnKzgywcHqRG3UKJ09nlBWRnankSURERETEK0qUEiwjYDhtWhGnTSvi6mWVAIRClj2NbWw9cJyt4eTpdxvruOeFvQBkZwSYPyh5OquymDOmF5KTmeHZ67DWcryjh8MtXRwJfx1u6Qxvuzja1t3/et1omiEjEP3zifuNMWRE7Q8YQyDg9vdvDe77QfsDxp0jNyuDKYXZlBbkUFqYzZTCHHKzvPvvJCIiIiLpQYmSBwIBw7yphcybWsg7lg4kT7VH209Inv6wuY5fvuiSp6wMwxllRf2jTmdVFjN/etG4k4Ku3j4aWrs53DyQ9Bxp6eJIaxeHm932SHMnR1q76OmzJz0/JzPAtGAOJQU5GFxC1WctfSH3mvqsJWTtwPch6At/b61134csIUt4674i+8aiIDuD0kKXOJUW5DClMJuSgmxKC3NOSKpKC7Mpyc/WvDEREREROYkSJZ8IBAxzphQwZ0oBbzu7AnBJx76jHf0le1sPHOdPWw9y70v7AMgMGE4vK+KsymB/ArWwPEhOZmDY0Z/BydDhli6Od/QMGU9pQTZTi3KYWpTDvKmlTC3KYVpRrttXmMO0oHusKCcTY8yQ5xgva12yNJA4hbeh8D5rae/qo7Gti8bWbhrbumho7aaxtZujbV00tnVz4FgHm/cfo7Gtm75hMq/J+VkusSpwI1IuqQonVuEEq7QwmykFOQTzEvd6RURERMQ/lCj5mDGGqtJ8qkrzuXJJOeCSh/1NHf2jTlsOHOfx7Yf41br9AP3lad19oZPOFxn9mVaUy7yphaycW8q0cDI0OBEqLcz2RYMJV5oHGRiGHTgrhKrS/BHPFQpZmjt7womUS6IaW11idbRtIMnaebCZxrZujrUPnUBmBgylhdnMmVLAwvIgC6cHWVge5PSyQpX8iYiIiKQRJUopxhjDzJJ8Zpbk85azBpKnuuOdbNl/nG11x+nps4OSn4FEKJGjP34XCBgm5WczKT+b06YVjnh8T1+IprZul1iFR6waWt1crMMtXew+3MovX9xLZ49LSDPCI4ILy4MsmF7EovIgC8qLmB7MnbD/zUVERERSmRKlNGCMoXJSHpWT8rhi8XSvw0kLWRkBpgVzmRbMHfaYvpCltrGNnQdb2FHfzI76Fl6ubeLhTXX9x0zKz2LhdJc0RUagJuLok7WW1q5ejrZ19381Dvr+xH1dtHf1MXtKAfOnF7FgehHzy4pYMD1IcX6W1y9FREREJgglSiJjlBEwzJ1ayNyphbw1PLoHcLyjh1cOtrDzYDM76pvZXt/CvS/uo6Onr/950aNPC8uDlAVzUmb0KRSyHOvo4WhbF0fbevrnhB1tHUiAmtoj88Xc11DloOBa5JcWuIYbJQXZzCnNJzcrg9cb2vjD5np+Ee4GCVBenMv86UX9CdSC6UHmTS1UO30RERGJOyVKInFWnJfFeXNKOG9OSf8+L0efrLV09Ybo6gnR2dtHZ08fnT2h8LaPzt6B74c65nhHz0kjP03t3cN2JSzKyaSkMJvJ+dmUF+dyZkWQksJsSgvcvtLCbEoKcvqTo/zsjGETRGstB5s72XmwhVfCXzvqm3nm1Yb+LoyZAcPcqQXMnx4MJ08ukaqclJcyiaeIiIj4jxIlkSQYbvSpubOHnfUDo087TjH6REs3z7XvGEhiopOe3hBdPX0nPd7VG8KOsdV6ZsAQzMvqH+05bVohkwuyTxgBKi3IYXJBVv82nut9GWMoL86jvDiPS+dP69/f0xdiT0NbOIFqZucQiWdRTiZn9I88FTF/epD504sozvO2fK+7N0RbVy+tXb20dffS1tWHDf8POjGvMyfsG/xQJAE0/T9HjjGDjjnxutHH7GsJ8erhVrIyDJkZAbIyDFmBAJkZhqyMAFkZATICSjRFRGTiUqIk4qFg7tCjT3uPtrOjvpmd4dK9DXubONDUQ86+GnKzMsjNzCA3K0BuVgY5WRnkZgaYlJdFbjCn//Gc8OO5mQF3TFb4OZmDvg9vc07a557n1zWmsjICnFFWxBllRRBupw/Q0tnDrkMt7KgfGIF6eFMd97zQ239MeXFuf+IUGX06VfmetZb27r6B5Karj5auHtq6Bu/rDX/fR2v4sdb+fQMJUWtn77AliJ545slTPmwMUcmTITMQICtzIKlyPwfICpgTkqzMgPt+8HOzMgJUleSzYnYJZ1YEfdFdU0REZDhKlER8JmPQmlqDR5+eeOIJLr30Ug8j87+i3CyWzyph+ayBxNNaS/3xzvC8sfAI1MEWno4q35s3tZDSwuz+5CaSFLV198Y0ImcMFGRnUpCTQUFOJkU5mRTkZDKzIJ/CnIH9hdluf2FuJoU5meRnZ5ARMCdcI/JtZKTphMvbyCb8WOTnGJ4/+JgtW7dyxoKF9PZZekMhevosPX0hevssPaHwts/t7+0L0RuydPeF3Pd9lp6Qpac31P/c3lCInl5La2/voOe650V+7uoN9a/dlpsV4OwZk1gxezIrZpVwTtVkNesQERFfUaIkkiI032ZsjDFUTMqjYlIely44sXzv9SNt7DzY3J9ENXf0MCk/mxkl+QMJTTjBcd8PbCPJT2RfXlYGgRQqVctt2MmapZVJv+6h5k7W1TSxrvYo62ub+N6Tr9MXeg1j4IxpRSyfPZkVs1zyNLNE88xERMQ7SpREZELKygj0d9CT5CkL5nLlkvL+RbTbu3vZuPcY62qbWFfbxMMb6/o7HU4tynFJ0+wSVsyazCKV650kFLI0tbv13Q41d9LU3s3UwlxmT8mnvDhP88xERMZBiZKIiHgmPzuT1adNYfVpUwA3R2/XoRbW1TaxvuYo62qb+NPWgwDkZWVw9sxiVswqYfnsya5cz+PmHInSF7I0tnVxuLmLI+Ek6HBLF4dbOjnU3MXhli6OhPf1DtOCMjsjQFVpPrNL85ldWsDsKQXhbT4VxXkpNQIqIuIFJUoiIuIbGQHj2uOXB3nvylkAHDzeybrao6yraWJ9bRP/++Rr9D1hMQbmlxWxfNbk/rlOMyb7u1yvty9EY1u3S3zCCU8kCTrSnwR10tDaTd8QCdCk/CzKinKZFsxh3tRSyoK5TCvKYVpRLmXBHCblZ3O4pZOahnZqG9vY09BGTWMbT+1uoKt3oJFIdqZrrDG7tMAlUuF5kbNKlUSJiEQoURIREV+bXpzLVUsquGqJ63DY1tXLpn0D5Xq/21jHPeFyvWlFOf1J04rZk1lYPrpyvVC4aUVPX4ju3oEmFyfuC9HdO9Cwors38viJ+5o7evoTH5cQddHY1jVkc5DSgmymFuVQFnRdGacFc/qToKnhJGhqUU5M7fdPm1bI6nknv66DzZ3UNLZR09Ae3kaSqCMnJVGzSvKZVVrAnCmRrRuRKg/mJjSJ6ukL0R5uotLePdBQpa2rr//nwVsM5GRmkJMZIDsjQE6W22Znum6e2ZmR7902OyNAblaA7IyME/dnuk6Nfk6yRST5lCiJiEhKKcg5uVzvlYMtrK91pXrrapr445aBcr2F5UUEjAknPPaEhGdwQtTdFxpyFGesjIEphTnhEZ8czqosdolPMJeyohymhROhKYU5w7anj5dAYKCpybBJVEMbNY0uidrT0EZtYxtrdx+h+xRJVKScb39LiA17m/pb6bd3h5OdcKv89u5e2rr7aO8Kb6OSnsixo2mfn5MZwMIJ8Y3rv5GhP5nKDidfOdGJVmaAvKwMJudnj3khbRFJHUqUREQkpWUEDIsqgiyqCPLeVbOBE8v1dh5sJiNgKMzNJCtjYMQha9C6T5E3yFkZbp2o/u8HHTvweOQcA88/6byZAfKzMny7FtlgJyRRp5342OAkak9jG7WN7cMmUTzz7LDXyM/OID/cPj8/O5OC7AyCeVmUF+f27y/IcftPOK7/+Ezyw10mI+eKNKqw1iW53b3uq6t3YJTP/dzXv6//sf7vXXLW1TP4+BPP0dXT1/9YZ0+IprYettU109jWPWySlpMZcElTVAJVMmjB7khyVVKQTTA3U4mViA8pURIRkbQTXa4nY3NiEjXlhMdCIUt9cye1DW08vW4j5y5b4hKa7EFJT7h1fiK77xljwuV3I5clxpO1lrbuPo62dtPY1sXRtm4a27o5Gv5qbO3maHj/60daOdrWTXt335DnygwYJg9KogYSqpwTRq5KCrKZnJ/FpPzshI9CJkN3byj8362r/79bQ2s3O1/vZl9OTdRyDAPrz0USZiWXkmhKlERERGTUAgFD5aQ8Kifl0b0/kzXzp438pDRijOl/015Vmh/Tczp7+lwy1XpicjCwzyVXWw8cp7Gtm5bO3mHPVZiTyaT8LCbnZzM5nEBNzs8O/+ySqZL8bHdMgfs+LzuxyWRHd9+JSWProNcXnUy2dtPSNfzru3/XtlNeK7LI9+A17Qpz3ehjJKEanGid8HOuWyOvMCfLjWZmZw45985aS1/I0hfZRn9Zt6B2yFp6Q5ZQyG37Qifv63/MWvr6Bs4JrkQ4NysjPFqaQV541NTtDygh9JASJREREZEkyM3K6E8uY9HdG+JYu0sumtq6OdreTVN7D8fC3x9r76Gp3T1W09BGU/upk6uczMDJiVWB207Kz6YknGBNDidZRbmZtHT2jjhiFtk/3IhZVoYJj5S5MsSZk/MHRs0KB42ehfe9+PwznHPeatq6emkNf530fWcvrV19tHb10BaeC9fa1Utjazstnb20dbtjhmufHy0/O4OAMfSGQoRCuG38piyOmTEukYokUHlZGeRlZ5Iftc99n9mfbJ2YeGWecI78bDfPLjcruaOwqUiJkoiIiIgPZWcGXNOPYG7Mz+npC3GsvYdj7S55aYp8H0ms2rpdctXew46DzTS1dXO8o2dUSUH0HKy5UwvjOgcrJ8Mwtch1ehwPay1dvaGoJMslV61dfbR2npiAgZvzmBEwZBgz8H34KzNgCBhDZkZ4GzAEwvtP9bwh94W/wI3EdfT00dHdR3t3ZNtLR0+Ijm7XHKU9/HhH//e9HGzu6X9u5HmjaYhSUpBNeXGuK68tzqV8Ul7/z+XFuZQFcyf8It9KlERERETSRFZGYNRJRihkae7soam9h6Nt3RwLJ1LNHT0U5WambFc/Ywy54bK20sLxJV2porcvRHtPH53hpKu9u4+Ont7+RCqSVDW2dnHgWCf1xzvY29jO8683njQaGTAwrSiX8km5VBS75Kl8UFJVMSmXKQU5ab3umhIlERERkQksEDBMCpffzZlS4HU4Mg6ZGQGCGQGCuVmjfm5rVy/1xzo4cKyD+uOd1B/roO64S6Z21Dfz5x2HTlhzDVxZ5fTiXMqLBydQ4e+LXTJVnJeVEon1UJQoiYiIiIhMcIU5mZxeVsTpZUVDPm6tpam9h7pIInW8g7pjneGfO3ippolDzfUnzQvLy8roH5X6zBXzWTJjUhJeTXwoURIRERERkVMyxvTPRVtcWTzkMX0hS0NrF3XHXBIVSabqj7vRqUCKjSwpURIRERERkXHLCBjKgq4RxLIqr6MZv4ndykJERERERGQISpRERERERESiKFESERERERGJokRJREREREQkihIlERERERGRKEqUREREREREoihREhERERERiaJESUREREREJIoSJRERERERkShKlERERERERKIoURIREREREYkSU6JkjMkwxnzZGFNvjGk1xjxgjCk7xfErjDHPGGPajTG7jTG3RD1+mjHmIWNMgzHmiDHmfmNMVdQxh40xNurrC2N7mSIiIiIiIrGLdUTpi8CtwC3AxcAM4MGhDjTGTAUeBV4GzgG+DfzIGHN5+PGC8OMZwBuANwNTgD8ZY3LCx5QBU8PXKh/09c3RvkAREREREZHRyhzpAGNMNvBx4GPW2sfD+24A9hhjVltrn416ygeB48DHrbUhYKcx5hzg08BjwOVAFbDMWtscPt8twF7gfGAtsBjoBV6w1naP/2WKiIiIiIjELpYRpaVAEVAd2WGtrQFqgIuGOP4iYG04SYqoBi4wxgSAF4G3RpKksMixk8PbxcBrSpJERERERMQLsSRKM8LbA1H764CZwxw/1LH5QIm19kBkZGqQzwLtwNPhnxcDvcaY3xtjDhpj1htj3htDrCIiIiIiIuM2YukdLsEJWWt7ovZ3AbnDHN85xLEMdbwx5g7gI8BHrbWN4d1nAqXAPwGfB94C3GWMybTW3jXEOW4HbgcoKyujuro6hpeVeK2trb6JRVKf7ieJJ91PEk+6nySedD9JPI3nfoolUeoAAuEkpXfQ/hygbZjjc6L2RX4+4XhjzOeBfwe+bK39zqCHLgWyrbUt4Z83GWNmAZ8ETkqUrLV3AncCrFixwq5ZsyaGl5V41dXV+CUWSX26nySedD9JPOl+knjS/STxNJ77KZZEaV94Wz7oe4AKTi6xixxfHrWvAmjFNXkgPFfpu8CHgf9jrf3q4IOttV0MjEJFbAHeM1Kw69evbzDG1I50XJJMARq8DkLShu4niSfdTxJPup8knnQ/STwNdz/NGumJsSRKm4AW4BLgbgBjzGxgNq5DXbSngfcZY4y11ob3XQo8M6jBw3dw3fHeZ639yeAnG2MygT3AN6y1g9uBrwC2jRSstXZqDK8pKYwx66y1K7yOQ9KD7ieJJ91PEk+6nySedD9JPI3nfhoxUbLWdhljvgt8zRjTABzGjQY9aa19Ptw+vAQ4Gu5S9yPgM8D3jDHfAt4I3AhcEQ72rcAdwJeAR4wx0wdd7pi1ttMY8zDwBWPMa8B24GrgvcCVY3mRIiIiIiIioxHLiBLAF4As3IhSFvAI8Hfhx1YDT+BGjaqttYeMMVfgFprdANQCt1hr/xo+/ubw9l/CX4O9N3yNTwBN4XOUAzuBd1trHxvVqxMRERERERkDM1AdJ/FmjLk93GhCZNx0P0k86X6SeNL9JPGk+0niaTz3kxIlERERERGRKLEsOCsiIiIiIjKhKFFKAGNMhjHmy8aYemNMqzHmAWNMmddxSWoyxpxpjLFDfF3odWySOowx3zfG/DBq3+XGmI3GmA5jzGZjzFu8ik9SyzD300tD/J764XDnkInNGFNmjPlp+L3SMWPMo8aYxYMe1+8niVkM99OYfj8pUUqMLwK3ArcAFwMzgAe9DEhS2mJc///yqK8XvAxKUoNx/hW4PWr/IuB3wP3AMuC3wEPGmDOTH6WkilPcTwZYCNzEib+nPpn0IMX3wutp/gY4A3gHrjHYceAvxphS/X6S0Yjhfhrz7yfNUYqzcLv0BuBjkTWiwutO7QEusNY+6110koqMMf8GXGytvcTrWCS1GGPm4pZsWAy0A49baz8Yfuz7wHxr7ZpBxz8B7LbW3j7E6WSCG+F+mge8Csy11u7xLkpJBcaYZcDLwCJr7Y7wvhzgKG4JmQvQ7yeJUQz30zOM8feTRpTibylQBFRHdlhra/j/7d1PqFR1GMbx75NGZYIILSrsD+2iW4uMooVRqzYltIsWErVLLArShSUZVlARRSSRUZukVm0iqGiR0cJNgVyk2kQkiUiBaHG7dLlvi3NuTOf+c8arx7HvBy7D/Gbzwrz3OfPOnPM78DOwqY+CNPYmgO/7LkJj6U7gJ+Bmmi9rBm1iIKdaX2FOaXFL9dMEMEVzSxBpOb8A9wE/DqzNAgHWYz5pOMv108j55KC08ja0j7921o8C15zjWnRhmACuS3IwybEkXya5ve+idP6rqv1V9WhVHVvg5Q2YUxrCMv00AZwA9ic5mmQyyVPtKTHSf1TV71X1aVXNDiw/DlwKfIH5pCGcRj+NnE8G2MpbA8xW1d+d9WmaN0w6bUkuA24A1gFPA5tpDhYHktzYZ20ae2uAvzpr5pRGdROwFvgcuBd4C9jN/BvLS/Mk2Qy8BLzWnjplPmlkC/TTyPm0+izW+X81BVyUZHVVzQysXwL82VNNGlNVNZVkPTBdVdMASR4GNgKPAdt6LE/jbYomlwaZUxrVFmBtVZ1on08mWQfsTPJceUG0FtEe0/YBHwHb22XzSSNZpJ9Gzid/UVp5R9rHqzrrVzP/Z2RpWVV1cm5Iap/PAofxFASdmSOYU1ohVTUz8CFkziTNNbvrzn1FGgdJdgLvA28DWwZOnTKfNLTF+ulM8slBaeUdAk4B/+5Q1u56dz3wdT8laVwl2ZjkZJJbB9ZW0Wwacri3wnQh+IaBnGrdgzmlEbTXUL7eWb4NOLrABxSJJNuBPcCuqtrW+VbffNJQluqnM8knT71bYVU1nWQv8GqS34DjwF7gQFUd7Lc6jaFDNDsmvpNkK/AHsAO4Anijx7o0/t4Evk2yG/gQeAi4g2YrVWlYHwPPJ/mOZiveu2my6ok+i9L5KcktwIvAe8C+JFcOvHwK80lDOI1+GjmfHJTOjmeAi4EP2sfPgK29VqSxVFUz7d3IXwY+AS6n+Se/q6qO91qcxlpVTSZ5gKa3dgA/APfP3YNCGtIrwAzN8e9amu16n6yqd3utSuerB4FVwCPt36Bnq2qP+aQhLNlPwAuMmE/ecFaSJEmSOrxGSZIkSZI6HJQkSZIkqcNBSZIkSZI6HJQkSZIkqcNBSZIkSZI6HJQkSZIkqcNBSZIkSZI6HJQkSZIkqcNBSZIkSZI6/gE66lnzYcOZHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_df[['accuracy','val_accuracy']].plot(figsize=(14,8),title='Accuracy',grid=True,fontsize=15)\n",
    "history_df[['loss','val_loss']].plot(figsize=(14,8),title='Loss',grid=True,fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit",
   "language": "python",
   "name": "python38064bitcaed634bb72a48a5bf9eda0dc4f94389"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}