emotion_detect2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 28709 images belonging to 7 classes.\n",
      "Found 7178 images belonging to 7 classes.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d_3      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">903</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_4 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m1,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d_3      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m65,664\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m)              │           \u001b[38;5;34m903\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,781,255</span> (56.39 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,781,255\u001b[0m (56.39 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">66,567</span> (260.03 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m66,567\u001b[0m (260.03 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> (56.13 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m231s\u001b[0m 514ms/step - accuracy: 0.2710 - loss: 1.8120 - val_accuracy: 0.3482 - val_loss: 1.6356\n",
      "Epoch 2/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 323us/step - accuracy: 0.3594 - loss: 1.7226 - val_accuracy: 0.2000 - val_loss: 1.5949\n",
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hoisaline\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m226s\u001b[0m 505ms/step - accuracy: 0.3208 - loss: 1.6915 - val_accuracy: 0.3677 - val_loss: 1.6165\n",
      "Epoch 4/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 252us/step - accuracy: 0.2656 - loss: 1.7299 - val_accuracy: 0.4000 - val_loss: 1.3767\n",
      "Epoch 5/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 517ms/step - accuracy: 0.3425 - loss: 1.6632 - val_accuracy: 0.3730 - val_loss: 1.5991\n",
      "Epoch 6/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 260us/step - accuracy: 0.4062 - loss: 1.4989 - val_accuracy: 0.5000 - val_loss: 1.3703\n",
      "Epoch 7/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m233s\u001b[0m 519ms/step - accuracy: 0.3494 - loss: 1.6502 - val_accuracy: 0.3768 - val_loss: 1.5845\n",
      "Epoch 8/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 258us/step - accuracy: 0.2969 - loss: 1.6855 - val_accuracy: 0.2000 - val_loss: 1.5250\n",
      "Epoch 9/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m228s\u001b[0m 510ms/step - accuracy: 0.3538 - loss: 1.6453 - val_accuracy: 0.3806 - val_loss: 1.5794\n",
      "Epoch 10/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 245us/step - accuracy: 0.3281 - loss: 1.7498 - val_accuracy: 0.3000 - val_loss: 1.6528\n",
      "Epoch 11/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 513ms/step - accuracy: 0.3573 - loss: 1.6277 - val_accuracy: 0.3955 - val_loss: 1.5620\n",
      "Epoch 12/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 327us/step - accuracy: 0.3906 - loss: 1.5263 - val_accuracy: 0.2000 - val_loss: 1.8608\n",
      "Epoch 13/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m231s\u001b[0m 516ms/step - accuracy: 0.3579 - loss: 1.6303 - val_accuracy: 0.3890 - val_loss: 1.5664\n",
      "Epoch 14/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 256us/step - accuracy: 0.3438 - loss: 1.6281 - val_accuracy: 0.4000 - val_loss: 1.4442\n",
      "Epoch 15/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 513ms/step - accuracy: 0.3649 - loss: 1.6176 - val_accuracy: 0.3895 - val_loss: 1.5619\n",
      "Epoch 16/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241us/step - accuracy: 0.2969 - loss: 1.6267 - val_accuracy: 0.5000 - val_loss: 1.3500\n",
      "Epoch 17/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m227s\u001b[0m 508ms/step - accuracy: 0.3587 - loss: 1.6168 - val_accuracy: 0.3980 - val_loss: 1.5566\n",
      "Epoch 18/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 244us/step - accuracy: 0.4219 - loss: 1.5061 - val_accuracy: 0.4000 - val_loss: 1.3539\n",
      "Epoch 19/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m233s\u001b[0m 521ms/step - accuracy: 0.3627 - loss: 1.6122 - val_accuracy: 0.3989 - val_loss: 1.5498\n",
      "Epoch 20/20\n",
      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 262us/step - accuracy: 0.3281 - loss: 1.6064 - val_accuracy: 0.1000 - val_loss: 1.7022\n",
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 403ms/step - accuracy: 0.4051 - loss: 1.5389\n",
      "Test Accuracy: 0.3983\n"
     ]
    },
    {
     "data": {
      "image/png": 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8+eef5fb16aefKkOHDlWaNWumODs7K23atFGeeOIJJTMzU1EURSksLFSeeOIJJTIyUvH09FTc3d2VyMhI5aOPPqpyjEI0BhpFuWzuphBCXEV69uyJn58f69ats/dQhBBCCCFEEyVrvIUQV629e/cSFRXF9OnT7T0UIYQQQgjRhEnGWwhx1Tly5Aj79u3jzTffJCUlhXPnzpkrqQohhBBCCGFtkvEWQlx1fvrpJ2bNmkVxcTHfffedBN1CCCGEEMKmJOMthBBCCCGEEELYkGS8hRBCCCGEEEIIG5LAWwghhBBCCCGEsCEHew/AGgwGA3FxcXh6eqLRaOw9HCGEEAJFUcjOziY0NBStVq5zW4P8vRdCCNGQWPK3vkkE3nFxcYSFhdl7GEIIIcQVLly4QIsWLew9jCZB/t4LIYRoiGryt75JBN6enp6A+g17eXnZeTRCCCEEZGVlERYWZv4bJepO/t4LIYRoSCz5W98kAm/TdDMvLy/5QyyEEKJBkSnR1iN/74UQQjRENflbL4vOhBBCCCGEEEIIG5LAWwghhBBCCCGEsCEJvIUQQgghhBBCCBtqEmu8hRBCCCGEEFc3vV5PcXGxvYchmhhHR0d0Ol2d9yOBtxBCCCGEEKLRUhSFhIQEMjIy7D0U0UT5+PgQHBxcp4KpEngLIYQQQgghGi1T0B0YGIibm5t0kxBWoygKeXl5JCUlARASElLrfUngLYQQQgghhGiU9Hq9Oehu1qyZvYcjmiBXV1cAkpKSCAwMrPW081oVV/vwww8JDw/HxcWF/v37s3v37kq3Xbx4MRqNptzNxcWl3DaKovDss88SEhKCq6sro0aN4vTp07UZmhBCCCGEEOIqYVrT7ebmZueRiKbM9PNVlxoCFgfe33//PQsWLOC5555j//79REZGMnr0aHP6vSJeXl7Ex8ebb+fPny/3+muvvcZ7773HJ598wq5du3B3d2f06NEUFBRY/h0JIYQQQgghrioyvVzYkjV+viwOvN966y3uvvtuZs2aRefOnfnkk09wc3Nj0aJFlb5Ho9EQHBxsvgUFBZlfUxSFd955h2eeeYYJEybQvXt3li5dSlxcHL/++mutvikhhBBCCCGEEKKhsCjwLioqYt++fYwaNap0B1oto0aNYseOHZW+Lycnh1atWhEWFsaECRM4evSo+bXo6GgSEhLK7dPb25v+/ftXus/CwkKysrLK3UQTpi+Gvx6DQz/YeyRCNGw7P4E1z4LBYO+RCCGEEFe6tB++vwtSz9p7JE1SeHg477zzjr2HISphUeCdkpKCXq8vl7EGCAoKIiEhocL3dOjQgUWLFvHbb7/xzTffYDAYGDRoEBcvXgQwv8+Sfb7yyit4e3ubb2FhYZZ8G6KxOb0G9nwBfz8uAYUQlclLg5X/gm3vwsU99h6NEEIIcaXdn8Hx3yFqmb1HYleX17+6/Pb888/Xar979uxh3rx5dRrb8OHDeeSRR+q0D1Exm1c1HzhwIAMHDjR/PWjQIDp16sSnn37KSy+9VKt9Lly4kAULFpi/zsrKkuC7KbuwS70vyITkExDU2b7jEaIhitkKKOrjM2uhZX+7DkcIIYS4Qkasep9+vurtmrj4+Hjz4++//55nn32WkydPmp/z8PAwP1YUBb1ej4ND9WFbQECAdQcqrMqijLe/vz86nY7ExMRyzycmJhIcHFyjfTg6OtKzZ0/OnDkDYH6fJft0dnbGy8ur3E00YabAGyC28iUNQlzVojeVPj6zxn7jEEIIISpjCrxN91epsrWvvL29y9XDOnHiBJ6envzzzz/07t0bZ2dntm7dytmzZ5kwYQJBQUF4eHjQt29f1q5dW26/l08112g0fPHFF9xyyy24ubnRrl07fv/99zqNfcWKFXTp0gVnZ2fCw8N58803y73+0Ucf0a5dO1xcXAgKCmLSpEnm13766Se6deuGq6srzZo1Y9SoUeTm5tZpPI2JRYG3k5MTvXv3Zt26debnDAYD69atK5fVroper+fw4cPm5uMREREEBweX22dWVha7du2q8T5FE1ZSpK4HMondab+xCNGQnSsTeMcdgJxk+41FCCGEuJy+GLIuqY9tHHgrikJeUUm93xRFsdr38K9//Yv//e9/HD9+nO7du5OTk8MNN9zAunXrOHDgAGPGjGHcuHHExlb9b/nCCy8wefJkDh06xA033MAdd9xBWlparca0b98+Jk+ezNSpUzl8+DDPP/88//73v1m8eDEAe/fu5aGHHuLFF1/k5MmTrFy5kqFDhwJqln/atGnMnj2b48ePs3HjRm699Var/ps1dBZPNV+wYAEzZsygT58+9OvXj3feeYfc3FxmzZoFwPTp02nevDmvvPIKAC+++CIDBgygbdu2ZGRk8Prrr3P+/Hnmzp0LqFdiHnnkEV5++WXatWtHREQE//73vwkNDeXmm2+23ncqGqeEQ6AvLP1aAm8hrpQVB6mnQaMFn1aQHg1n10PkFHuPTAghhFBlxYFirNWTkwDFBeDoYpND5Rfr6fzsKpvsuyrHXhyNm5N1VvK++OKLXHfddeav/fz8iIyMNH/90ksv8csvv/D7778zf/78Svczc+ZMpk2bBsB///tf3nvvPXbv3s2YMWMsHtNbb73Ftddey7///W8A2rdvz7Fjx3j99deZOXMmsbGxuLu7c9NNN+Hp6UmrVq3o2bMnoAbeJSUl3HrrrbRq1QqAbt26WTyGxszidmJTpkzhjTfe4Nlnn6VHjx5ERUWxcuVKc3G02NjYcusW0tPTufvuu+nUqRM33HADWVlZbN++nc6dS9fpPvnkkzz44IPMmzePvn37kpOTw8qVK3Fxsc0vo2hETNPMw4eARgeZsZB50b5jEqKhMWW7QyKhy83q4zNrK91cCCGEqHeXZ7nlfK5Kffr0Kfd1Tk4Ojz/+OJ06dcLHxwcPDw+OHz9ebca7e/fu5sfu7u54eXmRlJRUqzEdP36cwYMHl3tu8ODBnD59Gr1ez3XXXUerVq1o3bo1d911F8uWLSMvLw+AyMhIrr32Wrp168Ztt93G559/Tnp6eq3G0VjV6pLM/PnzK72ysnHjxnJfv/3227z99ttV7k+j0fDiiy/y4osv1mY4oikzZbjbjITCbIiPUp/rNqnKtwlxVTGt744YBm2vg61vw9l1ahcArcXXV4UQQgjruzzwzjgP/m1tcihXRx3HXhxtk31Xd1xrcXd3L/f1448/zpo1a3jjjTdo27Ytrq6uTJo0iaKioir34+joWO5rjUaDwUZdgjw9Pdm/fz8bN25k9erVPPvsszz//PPs2bMHHx8f1qxZw/bt21m9ejXvv/8+Tz/9NLt27SIiIsIm42lo5IxMNFyKUprxDusPLY1r/qXAmhClFKU04916GIT1A2cvyEuF+AP2HZsQQghhknmh/Nc2XOet0Whwc3Ko95tGo7HZ97Rt2zZmzpzJLbfcQrdu3QgODiYmJsZmx6tIp06d2LZt2xXjat++PTqdetHBwcGBUaNG8dprr3Ho0CFiYmJYv349oP6/DB48mBdeeIEDBw7g5OTEL7/8Uq/fgz3ZvJ2YELWWEQs5iaB1gOa9IDcZdn0s67yFKCv1DGTHgc4JwgaAzlENwI//AafXQvPe9h6hEEIIUSbQ1gCKmvEWNdauXTt+/vlnxo0bh0aj4d///rfNMtfJyclERUWVey4kJITHHnuMvn378tJLLzFlyhR27NjBBx98wEcffQTAn3/+yblz5xg6dCi+vr78/fffGAwGOnTowK5du1i3bh3XX389gYGB7Nq1i+TkZDp16mST76Ehkoy3aLgu7FbvQyLB0RVaDlC/TjwK+Rl2G5YQDcq5jep9WH9wclMftzUWY5F13kIIIRoKU+Ad3K3816JG3nrrLXx9fRk0aBDjxo1j9OjR9OrVyybH+vbbb+nZs2e52+eff06vXr344YcfWL58OV27duXZZ5/lxRdfZObMmQD4+Pjw888/M3LkSDp16sQnn3zCd999R5cuXfDy8mLz5s3ccMMNtG/fnmeeeYY333yTsWPH2uR7aIgk4y0argvGzHZYf/XeMxh8I9SKzRf3QLvrKn+vEFeLsuu7TdqOUu8v7YW8NHDzq/9xCSGEEGWZAu3wIWrXGgm8AbXquClwBRg+fHiFLbbCw8PNU7ZNHnjggXJfXz71vKL9ZGRkVDmey+t1XW7ixIlMnDixwteuueaaSt/fqVMnVq5cWeW+mzrJeIuGq+z6bhNZ5y1EKYMeoreoj1uXCby9m0NgZ7Vty7kN9hmbEEIIYWLQl/bwDjdWxZbAW1xlJPAWDVNhtjqlHMoH3q1Mgbes8xaChENQkAFOnhB62XSztteq96dlurkQQgg7y44HQ4lat8d0XpeTCMX59h2XEPVIAm/RMF3ap2brvFuCV0jp86aM96V9UFJon7EJ0VCYqpmHDwbdZSuHyq7ztlHxFSGEEKJGTNlt7xbg1ky9YAzSy1tcVSTwFg1TrGmaeb/yzzdrq35glxRA/MH6H5cQDUn0ZvW+7Ppuk5YDwNEdcpMg8XD9jksIIYQoK8PYSsw7DDQa8GlpfF4qm4urhwTeomEyre82VTI30WhknbcQACVFpb8DrSsIvB2cS58/vab+xiWEEEJczpTx9mllvG9Z/nkhrgISeIuGx2BQq5bDlRlvKA3GZZ23uJpd3APFeeAeoBZSq4hpnfeZdfU3LiGEEOJypsy2KeCWwFtchSTwFg1P8nEozFKnyQZ2ufL1lmUKrMnaVXG1MrcRG6rOBKmIqa3YhV2Qn1EvwxJCCCGukGmcau4TZryXwFtcfSTwFg2PaZp5i95XFowCCO4ODq6Qnwapp+t3bEI0FOfKBN6V8Q2HZu1A0ZcG6kIIIUR9M081l4y3uHpJ4C0angu71fuwARW/7uAELfqoj2Wdt7gaFebApb3q44oKq5XVzljdXNZ5CyGEsAeDobR6ubcx4+1rXOstgbe4ikjgLRoeU8a7bP/uy8k6b3E1O79d7Yfq0xL8Iqretuw6b0Wx/diEEEKIsnISQV8EGh14NVefM2W8pZd3nQwfPpxHHnnE/HV4eDjvvPNOle/RaDT8+uuvdT62tfZzNZHAWzQsOUmQdk59bMpqV8QceEvGW1yFzOu7q8l2A7S6Rl2akR0HScdsOy4hhBDicqastlfz0iWELj7g7GV8/YJdhmVP48aNY8yYMRW+tmXLFjQaDYcOHbJ4v3v27GHevHl1HV45zz//PD169Lji+fj4eMaOHWvVY11u8eLF+Pj42PQY9UkCb9GwmKaZB3QCV5/Kt2vRDzRaSI+BrPj6GJkQDYdpfXfr4dVv6+gCEUPUx2fW2mxIQgghRIUuL6wGl/Xyvvqmm8+ZM4c1a9Zw8eLFK1776quv6NOnD927d7d4vwEBAbi5uVljiNUKDg7G2dm5Xo7VVEjgLRoWc//uKqaZA7h4QZCx4vkFmW4uriK5KZB4WH1cVWG1skzVzWWdtxBCiPp2eSsxE3Pgfb5+x9MA3HTTTQQEBLB48eJyz+fk5PDjjz8yZ84cUlNTmTZtGs2bN8fNzY1u3brx3XffVbnfy6eanz59mqFDh+Li4kLnzp1Zs+bK84CnnnqK9u3b4+bmRuvWrfn3v/9NcXExoGacX3jhBQ4ePIhGo0Gj0ZjHfPlU88OHDzNy5EhcXV1p1qwZ8+bNIycnx/z6zJkzufnmm3njjTcICQmhWbNmPPDAA+Zj1UZsbCwTJkzAw8MDLy8vJk+eTGJiovn1gwcPMmLECDw9PfHy8qJ3797s3avWyDl//jzjxo3D19cXd3d3unTpwt9//13rsdREBSWjhbAjc2G1agJvUNuKJRxW13l3ucW24xKioYjerN4HdgaPwJq9xxR4x+6Ewmxw9rTN2IQQQojLmTLa3mHln7dlxltRoDjP+vutjqNb5S0+y3BwcGD69OksXryYp59+Go3xPT/++CN6vZ5p06aRk5ND7969eeqpp/Dy8uKvv/7irrvuok2bNvTr16/aYxgMBm699VaCgoLYtWsXmZmZ5daDm3h6erJ48WJCQ0M5fPgwd999N56enjz55JNMmTKFI0eOsHLlStauVWfNeXt7X7GP3NxcRo8ezcCBA9mzZw9JSUnMnTuX+fPnl7u4sGHDBkJCQtiwYQNnzpxhypQp9OjRg7vvvrva76ei788UdG/atImSkhIeeOABpkyZwsaNGwG444476NmzJx9//DE6nY6oqCgcHR0BeOCBBygqKmLz5s24u7tz7NgxPDw8LB6HJSTwFg1HSSHEHVAf1yjwHgC7P5N13uLqYsn6bpNmbcA3AtKj1cC94422GZsQQghxOdMa7koz3jYIvIvz4L+h1t9vdf4vDpzca7Tp7Nmzef3119m0aRPDhw8H1GnmEydOxNvbG29vbx5//HHz9g8++CCrVq3ihx9+qFHgvXbtWk6cOMGqVasIDVX/Lf773/9esS77mWeeMT8ODw/n8ccfZ/ny5Tz55JO4urri4eGBg4MDwcHBlR7r22+/paCggKVLl+Lurn7/H3zwAePGjePVV18lKCgIAF9fXz744AN0Oh0dO3bkxhtvZN26dbUKvNetW8fhw4eJjo4mLEy9qLN06VK6dOnCnj176Nu3L7GxsTzxxBN07NgRgHbt2pnfHxsby8SJE+nWrRsArVu3tngMlpKp5qLhiD8I+kJw8we/Gvzwm9qNJRxWs3hCXA3M67stCLyhtK2YrPMWQghRny7v4W1yFa/xBujYsSODBg1i0aJFAJw5c4YtW7YwZ84cAPR6PS+99BLdunXDz88PDw8PVq1aRWxszf69jh8/TlhYmDnoBhg4cOAV233//fcMHjyY4OBgPDw8eOaZZ2p8jLLHioyMNAfdAIMHD8ZgMHDy5Enzc126dEGn05m/DgkJISkpyaJjlT1mWFiYOegG6Ny5Mz4+Phw/fhyABQsWMHfuXEaNGsX//vc/zp49a972oYce4uWXX2bw4ME899xztSpmZynJeIuGo2wbsRpM08G7ufqhnRELF/dAm5G2HZ8Q9pYRq2atNTpoNdiy97Ydpc4QOb1WnYJXk98xIYQQoi4UpeLiamDbwNvRTc0+1zdHywqbzZkzhwcffJAPP/yQr776ijZt2jBsmHph/fXXX+fdd9/lnXfeoVu3bri7u/PII49QVFRkteHu2LGDO+64gxdeeIHRo0fj7e3N8uXLefPNN612jLJM07xNNBoNBoPBJscCtSL77bffzl9//cU///zDc889x/Lly7nllluYO3cuo0eP5q+//mL16tW88sorvPnmmzz44IM2G49kvEXDYQ68q58+Y9bSeOXuvEw3F1cBU7a7eS+1wKAlwq8BnTNkxkLKKeuPTQghhLhcbjKUFAAa8GpR/jVT4J2bZP1e3hqNOuW7vm8WXtSePHkyWq2Wb7/9lqVLlzJ79mzzeu9t27YxYcIE7rzzTiIjI2ndujWnTtX873enTp24cOEC8fGl3X927ixfkHj79u20atWKp59+mj59+tCuXTvOny9f7M7JyQm9Xl/tsQ4ePEhubq75uW3btqHVaunQoUONx2wJ0/d34UJpO7pjx46RkZFB586dzc+1b9+eRx99lNWrV3Prrbfy1VdfmV8LCwvj3nvv5eeff+axxx7j888/t8lYTSTwFg2DokBsmYx3TZkCb1nnLa4GtVnfbeLkDq0GqY9lurmoo82bNzNu3DhCQ0OvqGxbmWXLlhEZGYmbmxshISHMnj2b1NRU2w9WCGE/5h7eoeDgVP61q7yXN4CHhwdTpkxh4cKFxMfHM3PmTPNr7dq1Y82aNWzfvp3jx49zzz33lKvYXZ1Ro0bRvn17ZsyYwcGDB9myZQtPP/10uW3atWtHbGwsy5cv5+zZs7z33nv88ssv5bYJDw8nOjqaqKgoUlJSKCwsvOJYd9xxBy4uLsyYMYMjR46wYcMGHnzwQe666y7z+u7a0uv1REVFlbsdP36cUaNG0a1bN+644w7279/P7t27mT59OsOGDaNPnz7k5+czf/58Nm7cyPnz59m2bRt79uyhU6dOADzyyCOsWrWK6Oho9u/fz4YNG8yv2YoE3qJhSI9Rr3hqHSG0R83fZwq8L+4Ffe3bEQjR4ClKaUVzS9d3m5jWeUtbMVFHubm5REZG8uGHH9Zo+23btjF9+nTmzJnD0aNH+fHHH9m9e3etCuoIIRqRyiqaw1Xfy9tkzpw5pKenM3r06HLrsZ955hl69erF6NGjGT58OMHBwdx888013q9Wq+WXX34hPz+ffv36MXfuXP7zn/+U22b8+PE8+uijzJ8/nx49erB9+3b+/e9/l9tm4sSJjBkzhhEjRhAQEFBhSzM3NzdWrVpFWloaffv2ZdKkSVx77bV88MEHlv1jVCAnJ4eePXuWu40bNw6NRsNvv/2Gr68vQ4cOZdSoUbRu3Zrvv/8eAJ1OR2pqKtOnT6d9+/ZMnjyZsWPH8sILLwBqQP/AAw/QqVMnxowZQ/v27fnoo4/qPN6qaBRFUSx904cffsjrr79OQkICkZGRvP/++zWqrrd8+XKmTZvGhAkTyl0dnzlzJkuWLCm37ejRo1m5cmWNxpOVlYW3tzeZmZl4eVk4/VI0DAe/h1/mQYu+MNeCbJzBAK+3hvx0mLseWvS23RiFsKekE/BRf3BwgafOg6OL5ftIPgkf9gOdEzwVU+PKq6J2rpa/TRqNhl9++aXKE8I33niDjz/+uFxhm/fff59XX32Vixcv1vhYV8u/qRBNxtZ3YO1z0G0yTKxgGu93t8PJv+DGN6Hv3FodoqCggOjoaCIiInBxqcXfRiFqoLKfM0v+Llmc8f7+++9ZsGABzz33HPv37ycyMpLRo0dXW5EuJiaGxx9/nCFDhlT4+pgxY4iPjzffqmsQL5qYC8Y1J5ZMMwfQakurm8t0c9GUmaaZtxxQu6AbwL89eLcEfRHEbLXe2ISoxsCBA7lw4QJ///03iqKQmJjITz/9xA033GDvoQkhbMlc0byCjDdIxltcVSwOvN966y3uvvtuZs2aRefOnfnkk09wc3Mzl8KviF6vN1fMq6xHmrOzM8HBweabr6+vpUMTjdmF3eq9JYXVTFpK4C2uAufqsL7bRKOBtteqj2Wdt6hHgwcPZtmyZUyZMgUnJyeCg4Px9vaudqp6YWEhWVlZ5W5CiEYks5Ie3iYSeIuriEWBd1FREfv27WPUqFGlO9BqGTVqFDt2VB70vPjiiwQGBpr70lVk48aNBAYG0qFDB+67774qC67IH+ImpiALEo+qjy3NeEOZAms71XWwQjQ1+pLSDHVt13ebyDpvYQfHjh3j4Ycf5tlnn2Xfvn2sXLmSmJgY7r333irf98orr+Dt7W2+le3XKoRoBCrr4W0igbe4ilgUeKekpKDX66+oThcUFERCQkKF79m6dStffvllleXZx4wZw9KlS1m3bh2vvvoqmzZtYuzYsZWWrpc/xE3Mpb2AAj6twDPY8veH9lDbJOWlQOrZajcXotGJPwiFmeDsDSE96raviKFqEcP0aPl9EfXmlVdeYfDgwTzxxBN0796d0aNH89FHH7Fo0aJyrW4ut3DhQjIzM823sm1jhBANnKKUViv3lsBbCAdb7jw7O5u77rqLzz//HH9//0q3mzp1qvlxt27d6N69O23atGHjxo1ce+21V2y/cOFCFixYYP46KytLgu/GrDZtxMpycIbmvSF2uzrd3L+t9cYmREMQvVG9D78GtLq67cvZU12eEbNFnW7erE2dhydEdfLy8nBwKH/KodOpP8tV1Xh1dnbG2dnZpmMTQthIXhoUG/s6e7eoeBtzL+9kKMoDJ7f6GZsQdmBRxtvf3x+dTndFD7nExESCg6/MVJ49e5aYmBjGjRuHg4MDDg4OLF26lN9//x0HB4dy1U3Lat26Nf7+/pw5c6bC152dnfHy8ip3E43YBVPgXYv13Sbmdd476z4eIRoa0/ruuk4zNzFNN5d13qKWcnJyzP1UAXOP19hYNWu1cOFCpk+fbt5+3Lhx/Pzzz3z88cecO3eObdu28dBDD9GvX79y7XOEEE1Ixnn13iO48qKgrj7qbC4oXQ9eSwaDoU7vF6Iq1vj5sijj7eTkRO/evVm3bp25bYjBYGDdunXMnz//iu07duzI4cOHyz33zDPPkJ2dzbvvvltplvrixYukpqYSEhJiyfBEY2TQqz24ofYZbyizzlsKrIkmprig9OJUXQqrldV2FKx5FqK3qPuvbZV0cdXau3cvI0aMMH9tmoU2Y8YMFi9eTHx8vDkIB7VtaHZ2Nh988AGPPfYYPj4+jBw5kldffbXexy6EqCfmwmrVzEr1aQmJh9Xp5gEdLD6Mk5MTWq2WuLg4AgICcHJyQqPR1GLAQlxJURSKiopITk5Gq9Xi5ORU631ZPNV8wYIFzJgxgz59+tCvXz/eeecdcnNzmTVrFgDTp0+nefPmvPLKK7i4uNC1a9dy7/fx8QEwP5+Tk8MLL7zAxIkTCQ4O5uzZszz55JO0bduW0aNH1/obE41E0nEoygYnDwjqUvv9hPUFNJB2FnKSwCPQakMUwq4u7IKSAjVjUIsTkgoFdgbPUMiOg/PbSiudC1FDw4cPr3KK+OLFi6947sEHH+TBBx+04aiEEA1KdYXVTMyB9/laHUar1RIREUF8fDxxcXG12ocQ1XFzc6Nly5ZotRY3BTOzOPCeMmUKycnJPPvssyQkJNCjRw9WrlxpLrgWGxtr0YB0Oh2HDh1iyZIlZGRkEBoayvXXX89LL70k67quBqb+3S361G3tqquvGkwkHVWnm3ceb53xCWFvpv7dEUPVdmDWYGorduBrdbq5BN5CCCGszVxYrQYZb6hTgTUnJydatmxJSUlJpcWZhagtnU6Hg4NDnWdS1Kq42vz58yucWg5qW7CqXH4V3NXVlVWrVtVmGKIpMPfvrsM0c5OWAyTwFk2Ptdd3m7S7rjTw5hXr7lsIIYSwJONddvta0mg0ODo64ujoWKf9CGErtc+VC2ENF+pY0bwsWectmpqCTIjbrz621vpuk4hhoNFByilIr930PiGEEKJS5sC7VdXbSUsxcZWQwFvYT3YipMcAGnWqeV2ZKpvHH4Si3LrvTwh7i9kGigH8WldfnMZSrj6lF7zOrLHuvoUQQlzdFKXmxdV8jYG5XAQWTZwE3sJ+TNnuwM7g4l33/fmEgVcLUMpUSheiMTOv77ZyttvEtLb7zDrb7F8IIcTVqSADCrPUx9Wt8Ta9npciiRPRpEngLezHGv27Lyf9vEVTEr1Zvbf2+m4TUz/vc5ugpNA2xxBCCHH1MU0bdw8AJ7eqt3X1KU3AZNStl7cQDZkE3sJ+TIXVTMGyNZgDb1nnLRq5nCRIOqY+Dh9qm2MEdQP3QCjOlYtVQgghrKemFc1NZJ23uApI4C3so7gA4qPUx9bMeLcapN5f3AP6EuvtV4j6Zsp2B3cD92a2OYZWC21HqY9lnbcQQghrqWlFcxNTAbZa9vIWojGQwFvYR3wU6IvUKUi+Edbbb0AncPaGohxIPGK9/QpR385tVO9ttb7bRNZ5CyGEsLaaFlYzkYy3uApI4C3so2wbsTo2oy9Hq4WWxkrNMt1cNGamwmqth9v2OG1GgkarTmvPvGjbYwkhhLg61LSVmIkE3uIqIIG3sA/T+m5r9O++nKzzFo1dWrR68qF1KO1PbytuftC8t/pYst5CCCGswTRlvMZTzSXwFk2fBN6i/ilKaSEnmwTexkAldqd6LCEaG1O2u0VfcPaw/fHaGqubyzpvIYQQ1iDF1YS4ggTeov6lnVN7NeqcICTS+vsP7aXuOycR0qOtv38hbO2cjft3X67dqNLj6ovr55hCCCGapoIstY831HyNt/TyFlcBCbxF/TNNMw/pAY4u1t+/owuE9lQfS4sk0dgYDLbv3325kJ7g1gwKs0p/P21EkVkoQgjRtJkKq7n6gbNnzd4jvbzFVUACb1H/TIXVWtpgmrmJrPMWjVXSMfWKv6MbNO9TP8fUaqGNqbr5Wpsd5q9D8bT5v79ZvlumEgohRJNlLqxWw2y3ibQUE02cBN6i/pWtaG4rZdd5C9GYmNZ3txwIDk71d1wb9/Mu0Rt4deUJDAq8vuokeUUlNjmOEEIIO7O0h7eJrPMWTZwE3qJ+5WdA0nH1cYt+tjuOKahPOQW5KbY7jhDWZlrfXV/TzE3aXgtoIOEwZCdYffd/HY4nNi0PgNTcIr7dJSdWQgjRJJkCZ29LA2/JeIumTQJvUb8u7QUU8A0HzyDbHcfNDwI6qo9NGXYhGjp9MZzfpj6ur8JqJu7+ENpDfWzltmKKovDxxrMAdA7xAuDTzecoKNZb9ThCCCEaAMl4C1EhCbxF/TL37x5g+2PJOm/R2FzaD0U54OoLwd3r//g2aiu24WQSJxKycXfSsWR2P5r7uJKcXShrvYUQoimSwFuICkngLeqXuX+3DaeZm8g6b9HYmNZ3hw9RC57VN9M677MbQG+9NdgfbVCz3XcMaEWApzP3DW8DwCebzlFYIllvIYRoUkxVzS0uriaBt2jaJPAW9UdfApf2qY9tWVjNxJTxjouCojzbH0+IuqrvNmKXa94bXHzU/qum39U62h2dxt7z6TjptMy5JgKA2/q0INjLhYSsAn7ce9EqxxFCiKudwaDw2A8HefKng/Zr3ViUC3mp6mNvSwNvUy/vVCjMse64hGgAJPAW9SfpmDqN1tkLAjvZ/ng+rcAzBAzFELff9scToi6K8krrEUQMt88YdA7QZoT62EptxT7eeAaAib1bEOTlAoCzg457h7U2vn6WohKDVY4lhCXWHEvkQppclBVNx77YdFbsv8gPey9yNC7LPoMw9eB28VZ7c1vCxVu9+AulWXMhmhAJvEX9MQUVLfqAVmf742k0ss5bNB4XdoK+CLyaQ7M29huHFdd5H4vLYsPJZLQauGdo63KvTe3XEn8PZy5l5PPLAcl6i/p1IS2Ph5cfYPQ7m1myPQaDwU7ZQSGs6PeoOPPj1ccS7TOI2lY0N5Hp5qIJk8Bb1J/66N99OVnnLRoLUxuxiGHqRSN7aXuteh93AHKS67Srjzepa7tv6BZCuL97uddcHEuz3h9uOEuJXrLeon51be5NXpGe534/ypTPdnAuWaa2isarRG/g78Px5q9XH7V+W8gaMbUCs7SwmokE3qIJk8Bb1B9z4G15YbW8ohKKa3Nibgq8L+wGgxRxEg1YtJ36d1/OMxiCu6mPz66v9W5iUnL565CafTEVU7vc7f1b4ufuRGxaHr+VydQIYWthfm4sv3sAL03ogruTjj0x6Yx5dwufbJKLQKJx2nY2ldTcIrxdHdFq4ERCtn2WUtS2sJqJ9PIWTZgE3qJ+ZMWrVy81Wmjex6K3Rqfk0v8/65i9eI/lxUKCuoCTJxRmqWvMhWiI8tPVIoBQ//27K2Kqbl6Hdd6fbj6HQYHhHQLoEupd4TZuTg7cPcSU9T6DXqb7inqk1Wq4a2A4qx4dypB2/hSVGPjfPye49ePtnEiw0/pYIWrJNM18fGQofcP9ADtNN69tKzETyXiLJqxWgfeHH35IeHg4Li4u9O/fn927d9fofcuXL0ej0XDzzTeXe15RFJ599llCQkJwdXVl1KhRnD59ujZDEw2VKdsd2AVcvCx666ebzpJdWMKW0ylsOJlk2XG1utIMu0w3Fw1VzFZAAf/24BVi79GUrvM+uw4Mlmf/krIKWLFPXbd9//C2VW5718BW+Lg5ci4llz8PSdZb1L8Wvm4snd2P1yZ1x8vFgUMXMxn3/lbeWXtKCv+JRqGgWG+eWj6+RyjXdQ4CYM0xO0w3r2vg7WvMeKdLxls0PRYH3t9//z0LFizgueeeY//+/URGRjJ69GiSkqoOiGJiYnj88ccZMmTIFa+99tprvPfee3zyySfs2rULd3d3Ro8eTUFBgaXDEw3VBePFGQunmSdlF/Dz/kvmr99ac8ryrLd5nbcUWBMNVNn13Q1BWD+1+0BeKsQfsPjtX26NpkhvoE8rX/pF+FW5rYezA3MGq23GPlh/RopcCbvQaDRM7hPGmgXDuK5zEMV6hXfWnmb8B1s5dDHD3sMTokobTyaRXVhCqLcLvVv6cn3nYEBt55ieW1S/gzFVNbe0lZiJZLxFE2Zx4P3WW29x9913M2vWLDp37swnn3yCm5sbixYtqvQ9er2eO+64gxdeeIHWrctXtlUUhXfeeYdnnnmGCRMm0L17d5YuXUpcXBy//vqrxd+QaKBqWVhtyfYYivQGOoV44e6k48ilLMunTpkqm5/fAfbqaylEVRrK+m4TnWPpWE5bNt08M6+Yb3aqmYrK1nZfbsbgcDxdHDidlMNKexUEEgII8nLhs7t68/60nvi5O3EiIZubP9zGK/8cp6BY6oSIhun3g+psoXGRoWi1Glo2c6NjsCcGBdafsHCmYF0U50Ou8Xi1zXibAvb8NCjMts64hGggHCzZuKioiH379rFw4ULzc1qtllGjRrFjR+XZxBdffJHAwEDmzJnDli1byr0WHR1NQkICo0aNMj/n7e1N//792bFjB1OnTr1if4WFhRQWFpq/zsqStVh1cmoVHFwOw56CwI7W339xPsQfVB9bkPHOLSzh6x3qCfwjo9px6GIGH244y9trTnFdpyC02hpWfm7eG7QOkB2nFv2o7R8DcfXKz4AjP8GZ9ervSPepENDeOvvOioOUU2r9g/BrrLNPa2h7HRz/Q13nPfypqrfVl0BBBuSns2rLYfqWHKOTn4GRmRdgY6a6ht34OoXZoHMCRzdwdAVHV7wc3fiyeT5bYvK4+NdaDPld0DqZXjfdu5u3L33OTe09LoQVaTQaxkWGMqhNM1744xi/H4zj003nWHM0kVcndTevnxWiIcguKGbdcTXYHRcZan7++s5BnEjIZvWxBCb2blE/g8k0toZ08gRX39rtw8VLfW9+upo9D+psvfEJYWcWnbGkpKSg1+sJCgoq93xQUBAnTpyo8D1bt27lyy+/JCoqqsLXExISzPu4fJ+m1y73yiuv8MILL1gydFGZcxth+R1gKIZzG+COFdCit3WPEXdA3b9HEPiG1/hty/dcIKughNb+7lzXKYj+EX4s3X6eEwnZ/HMkgRu713AtrJMbhPSAS3vVdd4SeIuaMBggZjMc+EYNQEuMS19O/gVb3oTQnmoA3nUieATU/jjRm9X7kMjan6jYgqnA2qW9sPkNtUBhfkb5IDrfGFQXlWYlJgOTnYA8YGXND9cP6OcIFAB/WzBOnTMMfQKGPWHBm4SoXjMPZ96b1pNxkaE8/cthzqXkMvnTHcwYGM4Tozvg7iwXfYT9rTmWSGGJgdYB7nQJLa2hc13nYN5bf4bNp1IoKNbj4qiz/WDMrcTC6tYW06elMfCOlcBbNCk2/auRnZ3NXXfdxeeff46/v7/V9rtw4UIWLFhg/jorK4uwsFquJbmaxR+C5XeqQbGTh/oht3Q8TP3WulNey7YRq+EHcbHewKKt0QDMHdIarVaDj5sTs6+J4N11p3l77SnGdA1GV9Osd8sBagBxfjt0n1yb70JcLdLPw8Hv4MAyyCyzxiygE3S5BS7tU7PAcQfU26r/U4PUyCnQ4QY1E2sJ8/ruodb7HqzBuzkEdla7Aax/qUZvKXLwILHIlTydJ+3Cw9C6+oKrj3pBwcV47+wJhhIozlNnwxTlqvfF+USdi+NsfDLBrgqDWrqhKc6HYtPreebtKMoFjMtG9IVwaLkE3sJmruscRL8IP/7z1zF+2HuRxdtjWHs8kf/d2p1r2lnv3MbWtpxO5sHvDnDvsDbcO6xmy0BEw2eaZj4+MhRNmXOsrs29CPF2IT6zgG1nUri2U1Blu7CeuhZWM/Fpqc6UlHXeoomxKPD29/dHp9ORmFh+jW1iYiLBwcFXbH/27FliYmIYN26c+TmDsUKug4MDJ0+eNL8vMTGRkJDSDGZiYiI9evSocBzOzs44OztbMnRxufQYWDZJzVSFD4HJS+HHmepa02W3wW1fQccbrXMsc2G1mq/v/vtwPJcy8vH3cOLWXs3Nz88ZEsFX26I5k5TDn4fimNCjeRV7KaPlQNjxgVQ2t6aob9UCXJ1usvdI6q44H47/CQe+Ll1vDeDsDd0mQs87IbRX6YWjnGQ4skIN+OIOwOlV6s3ZCzqPVzPhrQaDtpoyGopSeryGUlitrDH/gz1fqBfmKgqiy3xd7OTJ8De2EFdUwMs3d6XDgFYWHy4sp5Bpr24gP0vPV336MqJjYMUbKgroiyDxCHw+ErLt0DJHXFW8XR15bVIkN3UPZeHPh7mYns+dX+5iSp8w/u/GTni7Otp7iFWKz8znoe8OkJFXzNtrTjGhRygh3hZeJBQNTmpOIVtOpwBq4F2WRqPhus5BLN1xntVHE+sp8K5jYTUT6eUtmiiLiqs5OTnRu3dv1q1bZ37OYDCwbt06Bg4ceMX2HTt25PDhw0RFRZlv48ePZ8SIEURFRREWFkZERATBwcHl9pmVlcWuXbsq3KewgtxU+GYi5CSq7b2mLgM3P7j9B+h4k5pB+v4uiPqu7sdSlDIZ7wE1fIvCp5vOATBjYHi56VFeLo7MG6oW6Htn7WlK9DVs9WIqsJZ8HPLSavYeUbmkE/DrffDTbCgprH77hkhR4NJ++HMBvNEBfp5bPgi+9Qt4/CTc9LZaJ6DsbA2PABhwL8zbCA/shiGPqScahVnq1PQlN8G73WHtC5B8svIxpJ6FrEvqmueWDfDzrvUwmPI13PIxjPkvDHsS+t0N3W+DdqOgRR9o1gbcm/HboSTiMgsI8HRmUi3XEzbzcOaugeoJ13vrT1fewUCjAQdnaGZsVVaUbcyCC2FbQ9sHsOrRoUw3/px+v/cC17+9ibX26JdcQ8V6Aw9+e4D0vGIACksMvLNGWrY2BX8fSUBvUOja3IvWAR5XvG6qbr7uRCL6+ugYYc2Md9n9CdFEWFzVfMGCBXz++ecsWbKE48ePc99995Gbm8usWbMAmD59urn4mouLC127di138/HxwdPTk65du+Lk5IRGo+GRRx7h5Zdf5vfff+fw4cNMnz6d0NDQK/p9CysoyoVvJ0PqGTVQuPMncPFWX3N0gduWQOTtoOjh13th16d1O17qWbUlkc4ZQrrX6C3bzqRyLD4LV0cdd1aQNZs5OAJfN0eiU3L55cClCvZQAXd/aNZOfXyhZn3nRRVOGhfh6gvV2RONSW4K7PgIPh4Mn4+AvV9CYab6+zDsX/DwQZjxuxpc1mTaeEAHuPZZePgQzPwLet6lZr4zL8DWt+DDfvDpMNj5sZopLyt6o3rfop9ai6CRMhgUPtl0FoA510TUaS3h3CERODtoORCbwbYzqVVv7OwFDsb/o5yGG/iIpsXD2YEXJ3Tlh3sGEuHvTmJWIXOX7uXh5QdIq+/WTTXwxuqT7D2fjqezA29PiQTgx30XOJMkFaMbuz+iSqeZV6R/az88XRxIySniQGy67QeUacx4S+AtRIUsDrynTJnCG2+8wbPPPkuPHj2Iiopi5cqV5uJosbGxxMfHW7TPJ598kgcffJB58+bRt29fcnJyWLlyJS4uLpYOT1RFX6JmKC/tVaeK3rkCvC77sNY5wIQPof996tf/PAkbX619Gy5Ttju0p5qhqoFPN6sn8FP6huHr7nTF6x7ODub1ae+tP02xpVlv6eddd6fKVM1KPWOVXRbrDbbrN6ovUav3f38nvNkRVi2EpKPqBaGuk+CuX9XAecRCiwoAlqM1ViWf8AE8fgomfQXtx6gV9eOjYOW/4M0O6lKOwz+p09tN67sbShuxWlp9LJEzSTl4ujhwR/+6nXAFerpwu3Ef762rJiun0YCncfqkTDcX9axfhB//PDyEe4a2RquB36LiuP7tzQ0qoF13PNE8g+y1Sd25pWcLruschEGB11ZWMRtHNHhxGfnsjlFn8N3UveLA21GnZaRxyc6a+piVYc5413WquQTeommyOPAGmD9/PufPn6ewsJBdu3bRv3/p2t2NGzeyePHiSt+7ePHiK/pzazQaXnzxRRISEigoKGDt2rW0b2+lVj1CpSjw5yNqwOTgok4rD+hQ8bZaLYx5BYb/n/r1xv/CyoVqlWdLlS2sVgNH4zLZcjoFnVbDnGsiKt3uroGt8Pdw4kJaPj/tu1izsZim8so677rJTSk/a8AKgXdmfjE3vbeV/q+sY/0JK54cpJyBtc/D213UmR7H/1CLCYb2hBvfVKeST/oS2oyofj22JRxdoeutcPv38NhJGPuaukZc0cPp1bBiDrzeDk6vUbdviOu7a0hRFD7eqP4MTB/YCk+Xuq91vWdoG5x0WnbHpLHzXDVZbw9j4J0j/b9F/XNx1LHwhk78cv9g2gV6kJJTyIxFe0jMKrD30LiUkc9jP6qtPGcOCmdsN7WOzpOjO6DVqBfM9p2vhyyosIk/D6nZ7n7hfoT6VD4767rO6mfk6mOJlS/fsYaSQsg2Jt58LK/xUY708hZNlBXPNEWDtuG/auEojRYmLYKW1RQ602jU3r1jXlW/3vUx/D5fzRxawhSgtazZ+u7PN6tX5m/oFkKYX+VTb92cHLhvuLq+84P1Zygs0Ve/c9MY4vZDsf1PihqtU6swV5SGOgfeJXoD87/dz8nEbIpKDNy/bD/7rTElbvfn8EFv2Pq2GpS5+sGA++Hebera7L5z66d9l7s/9L8H5m2A+XvV1lc+LdV1ySX5ar/T5r1sPw4b2X42lYMXM3F20DJrcOUXyywR7O3ClL7qiVe1WW9z4J1klWMLURuRYT58b5x6fikjn5lf7SG7oNhu4ykqUT9XM/KKiWzhzcIbOppfaxfkaa7D8OrKE7YNxoTNmKqZj+tRcbbbZFj7AJx0WqJTcjmbnGO7AZl6eDu6gVuzuu3L1MsbJOstmhQJvK8GexfB5tfUxze+ZVm18gH3ws2fgEYHUcvgxxk1D1rz09ViZqCuYa3GpYx8/jikXi29x1hArSp39G9JkJczlzLy+WHPherH49ca3APVashxB6rfXlTs1D/qvamwVeq5Ou3uxT+PseV0Cq6OOvqF+1FQbGD24j11n6554k/1vuVAtWr/YyfVmRzBXeu237rwbwcjn4GHDsKsf2DgfLj1M9A17IrIVfl4o7o0ZGrfMPw9rNdt4t7hbXDUadh+NpW9MVUURPQ0dtTIloy3sC8/dyeWzOqHv4cTx+OzuO+b/RSV1GKmmBW8vuoEB2Iz8HJx4IPbe+HsUL7uwiOj2uPkoGV3dBobTyZXshfRUJ1LzuHIpSx0Wg03dL2yq1BZni6ODGyjBsKrjtpwurkpQPauYw9vE3Nlcwm8RdMhgXdTd/xP+Osx9fGwp6DPLMv30WOaWtlY56wGM99OhsIaXDW9uFe992utVoGuxqKt0egNCoPaNKNrc+9qt3dx1PHACGPWe8MZCoqryXprNLLOu66KC+DMevXxwAfU+zpkvJfuiGHpDrVdyDtTe7B4dl96hPmQkVfM9C93E5+ZX/uxphjHNeoF6DwBHK6sF2A3Wi20GgSj/wMdb7D3aGrt0MUMtp5JwUGr4e4aXCyzRHMfV3NW7r31VfyMmTPessZb2F/LZm58NbMfbk46tp5J4V8rDtV7RnnNsUQ+3xINwOu3RVY4eyzUx5WZg8IBNetdLxWvhdWYst1D2vnTrAYXPK/von5O2nSdt7UqmpvIOm/RBEng3ZTF7lTXkioG6DUdhi+s/b463qhWQHfyUFsuLZ1QfVsu8/ru6vt3Z+YVs3y3+uF6j7FwWk1M6RtGqLcLiVmFfLurBh/Oss67bmK2QnEueIZCl1vU53ISarUGa/OpZF744xgAT47pwOguwbg5OfDVzL60CXAnLrOA6V/uJiOvFgXXinIhyzjtzb+d5e8XNfLRBjXbPb5HKC18rV+V/b5hbdFpNWw+lUzUhYyKNzIF3pLxFg1EtxbefHhHL3RaDT8fuMQbq+uviNmFtDwe+yEKUDsMjO5SeTb0/uFt8HRx4ERCNr9F1bBDiLA7RVHMgXdl1cwvN8rYwzvqQobt6g+YK5rXsbCaiQTeogmSwLupSjoB306BkgJoPxZufLvuU38ihsL039V1N5f2wlc3QFYVFexNwW0NAu9vdp0nt0hPx2BPhrbzr/GQnB10PHitGlh9tPEs+UXVZL1bGQPvCztrVyzuamdqI9ZhjPpz4Gb8v0o9a9FuziTl8MC3+9EbFG7t1Zz7ylxs8XV3Yumc/gR5OXM6KYe5S/ZW//96OVMW3tVP7VEvrO5MUg6rjqnB7r0WXCyzRMtmbtzcozkA71e21ts01VzWeIsGZESHQF65tRsAH244yzc7z9v8mEUlBuZ/d4CsghJ6hPnw1JiOVW7v4+bEfcPV3903V5+qWa0UYXdH47I4l5yLs4OW66u4sFJWkJcLPcJ8AFh73EZZb6tnvE1TzW3/uyNEfZHAuynKioNvJkJBBrToqxZT0zlYZ98teqtrUz1D1PXbX42BtOgrt9OXwKV96uNqAu/CEj2Lt8cAMG9oazQWXiCY1LsFYX6upOQUsnRHTNUbB3UDR3coyITkExYdp14pirpGviFRlNI2Yu3Hqvfmdd41n26enlvEnCV7yC4ooU8rX165tdsV/+fNfVxZOrs/Xi4O7D2fzoPf7aekpm3jAFKMQZpku23mk01nURS1Ym77IE+bHeeBEW3QamDdiSSOXMq8cgOpai4aqMl9wnh0lNqh5dnfjrD6qG1/Rl/55zgHL2Tg7erIB7f3xMmh+lO8WYMizLVSlu2UzGJj8Icx231tp0A8nGt+bmeqbm6z6eYZVurhbSIZb9EESeDd1ORnqEF31kVo1g6mfQ9OVp4CGtgJZq9U+x2nx8CiMZB4rPw2iUegOA+cvSCg6qvuvx64RHJ2ISHeLoyr4bSpshx1Wh4aqQZYn2w6S05hFZXXdQ4Q1ld93JDXeR/8Dl4Nh/1L7T2SUgmHIOuSWrE0Yqj6nDnwrlnGu6jEwL3f7ON8ah4tfF359K7eVxT9MekQ7MkXM/ri7KBl7fEk/u+XwzVfK2m6ENBMAm9buJSRz68H1Kmp9w+3TbbbpHWAh/lz4f31FWS9TRnv3BTLuy4IYWMPXduWqX3DMCjw0PID1unYUIGVR+L5alsMAG/eFlnjpR+uTjoevla9OPDBhjN2rcQuqmcwKObAu6bTzE2uNwbe28+kVn2eVFvm4moSeAtRGQm8m5LiAlh+ByQdU7NAd64A9zq2dKiMbzjMXgWBXdRM01djS4upQWkbsRZ9q+yPbDAofGZsITZ7cASOutr9SN7SszkR/u6k5xWzxJg9r1RjWOd9erV6v+MjNdPcEJw0ZrvbjARHF/VxM2PQlVZ94K0oCv/+9Qi7otPwcHbgyxl9qy0K0y/Cj/en9USrgR/2Xqz5WklzxrttzbYXFvliyzlKDAoDWzejZ0vbt2SbP6ItGo1akfdEQlb5F92aqW0SUSBXppvXp82bNzNu3DhCQ0PRaDT8+uuvVW4/c+ZMNBrNFbcuXbrUz4DtQKPR8PLNXRnRIYCCYgNzFu/hnJVbOsWm5vHET4cAddbYKGOAVVOT+7Sgtb87ablF5paeomHaF5tOXGYBns4ODO8QaNF72wZ6EOHvTpHewCZrV7LXF0O2ekHAehlvUy/vdCjIqnpbIRoJCbybCoMefpkH57eqWeY7V4BvK9se0zMYZv6pBtcFGbBkPJzbqL52wRjUVtO/e/2JJM4m5+Lp7MDUfrUvyOGg0/Kwca33Z5vPkVXVVXtzZfMGHHgnnzLeH1dnDzQEpvXd7ceUPmfBVPMvt0bz/d4LaDXw/rSedAiu2fTk67sE899bStdKfrWtgqUNl0s1Bd7ta3QMUXNpuUUs361OKbx/hG2z3Sbtgjy5oWsIAO9fXuFcq1PbBIJUNq9nubm5REZG8uGHH9Zo+3fffZf4+Hjz7cKFC/j5+XHbbbfZeKT25aDT8sHtvejewpv0vGJmfLWb5OxCq+y7sETPA9/uJ7ughN6tfHlidIdajc/0vi+2RlttbML6TEXwru8SjItjxbPFKqPRaMzTzVcfs/Kyh6xLaiFfBxfwsOyCQKWcPdU6LVBauE0Ia7FTnScJvJsCRYGVC+HYb6B1hCnfQHC3+jm2mx/c9Su0HqFWu152m9rCzJTxDqu6f/enm9VM6R0DWuHpUrdexuMiQ2kX6EFmfjGLtlYRnDXvo2bIMmMbZiVkg758IHvoe/uNxSQrHuKjAA20H136vCnjnXqmysz8uuOJ/Odvtaf70zd2ZkRHy/4wT+3XksevV4PoF/88Zq7oWiFFKW0lJlPNrW7xtmjyi/V0a+7NNW1rXgixruaPVC/y/H04/soe756myuYSeNensWPH8vLLL3PLLbfUaHtvb2+Cg4PNt71795Kens6sWbVoc9nIuDs7sGhmX1r6uXEhLZ/Zi/eQa4Xpvv/96ziHL2Xi6+bI+9N61nrW2JiuwUSG+ZBXpK94SYewu2K9gb8Pq+cs43tYviwPSqebrz+RRLEldVOqY55m3sI6PbxNZLq5sJVf74NPh8LptfV6WAm8m4Jt78DuT9XHt3wCrYfV7/GdPeD276HTONAXwQ93qVcnNVpo3rvSt+2PTWdPTDqOOg2zBofXeRg6rYZHjIVsvtwSXXkbKmcP8I1QHzfEAmvpMaAvk3E4/JMajNuTqahaiz7lr2b7GXs3F2RCXmqFbz2RkMVD3x1AUWBav5bMruX/9QMj2jJjYCsUBR77IYqtp1Mq3jArTr0IpNGpSyKE1eQUlpgLId4/vI3FhRDrolOIF9d3DkJR4IPLs94epsrmDfBCmqjUl19+yahRo2jVqvLZWYWFhWRlZZW7NVb+Hs4smd0PP3cnDl/K5IFv99cp+PnrUDxLdqgVn9+a3INQH9da70uj0fDUGDXr/e2uWM6n5tZ6X8I2tp1JIS23iGbuTgxuU7tlhD1b+tLM3YnsghJ2naumJawlrF1YzUQCb2ELigLRmyH+IOjqlvSzlATejV3Ud7D2efXx6Feg2yT7jMPBGSYthh53qtONAIK6qFOFKvHZJnUt2c09mhPk5WKVYYztGkzHYE+yC0v4YksVWW9TwTfTlO6GJMU4poCO4OID2fEQs8WuQ+LkP+p9h7Hln3d0BW/jEoEKppun5BQyZ/Fecov0DGzdjBcndKl1sKbRaHh2XBdu7B5CsV7hnq/3cvhiBVWuTdPMfcPBwalWxxIV+3bXebIKSmjt717jNjbW9JBxOcnvB+OITikTGJguBknGu9GIi4vjn3/+Ye7cuVVu98orr+Dt7W2+hYVZqUewnUT4u/PljD64OGrZeDKZpy0pGllGTEouT61Q13XfO6yNxbOIKjKojT/D2gdQYlB4Y3UD/Nt4lTPN9LqhWwgOtZzZoNNqzD2911hzurk5423l308JvIUtZJxXaxJoHdTlsvVIAu/G7PRa+H2++njQgzDwfvuOR+cAEz6AgcYxdbix0k2jU3LNPYDnDW1ttSFotRoevU7Nen+1LZq03Eqy3gHGdXANMeOdbCwgFtQVuhincB76wX7jKcqF6E3q4/Zjr3y97HTzMgqK9cxbupdLGfmEN3Pj4zt71XoapIlOq+GtyZEMatOM3CI9M7/aXT4AA2klZiOFJXrzxax7h7VBp62/bLdJ1+bejOwYiEGBDzeU+Xkz9/KWwLuxWLJkCT4+Ptx8881Vbrdw4UIyMzPNtwsXGv9az54tfflgWi9z0ch31lo2tbugWM/9y/aTU1hC33Bf8zIca3jSmPX+42Bcxe37hF0UFOtZfVT9fKvtNHOTsm3FanPRp0LW7uFtYpq1lh5j3f2Kq9v57ep9aC/rd36qhgTejdWl/fDDdDCUQLfJMOpFe49IpdHA6P/AY6dg+L8q3eyLLedQFBjZMZB2Vu4BfH3nILo29yK3SM+nmyqptm3OeNewSnZ9Mme8O0D3yerjY79Dcb59xnNuI5QUgE8rtZXc5SpoKaYoCgt/Psz+2Ay8XBz4cmZffNysk312dtDx6V296RLqRWpuEdMX7SIpu6B0A3MrMalobk0/779EkrHt3809m9ttHA8a13r/cuASF9Ly1CfNvbwl8G4MFEVh0aJF3HXXXTg5Vf254OzsjJeXV7lbUzCqcxAv3dwVgHfXnWb57ppn9F768xjH4rPwc3fi/Wm9ap39rEiXUG8mGAO7V1c2wAvTV6kNJ5LIKSwh1NuF3nXsJHFNO39cHXXEZRZwNM5KSzdMxc98rFzUVzLewhbOb1PvWw2s90NL4N0YpZ5Vi5gV50Lr4TDhwypbdtmFZ1ClBTZScgr5cd9FwLrZbhONRsMCY9Z7yY6Yiiu0NoaMt397CBug9sQsyi6d7l3fyk4zr+j/tILK5h9tPMsvBy6h02r46I7etAnwsOqQPF0cWTyrH62aqYWKZi7aU9p/NkUqmlub3qCYL2LNHdIaJwf7fd70bOnLkHb+6A0KH200/syZMt4NsViiuMKmTZs4c+YMc+bMsfdQ7OqO/q2YP0L9/Hz61yNsOFF9O7zfoi6xbFcsGg28PaUHwd7WWaZV1mPXdcBRp2HL6RS2namkloaoV6Zp5uMiQ9HWcbaRi6OOoe3Vwpirj1rpMzNDrTVgbgFmLRJ4C1swZbxbDa73QzewaE3UyM/zIC8FgrurFcwb2TrWpdtjKCoxEBnmQ/8IP5scY0SHQHqE+VBQbODjjRVkvf3bAxr13zG3AZ1YKEr5jLdWC92NrXbsMd3cYIBTq9THl6/vNvEzTTVX/51XHonn9VXqxYMXxnfhmna2qXwd4OnM0tn98Pdw4lh8FvOW7qOwRF+mlZhMNbeWvw/HE5Oah6+bI9Pq0PbPWkytA3/ad5FLGfmS8baTnJwcoqKiiIqKAiA6OpqoqChiY9WT5IULFzJ9+vQr3vfll1/Sv39/unbtWp/DbZAeu749t/Zqjt6gcP+y/Ry6mFHptueSc/i/nw8D8MDwtgxrH2CTMbVs5sYd/dXM5asrT1hvOrKoleyCYtYZL8rUdZq5yfWd1YuVq49Z4TNTXwKZapszq081N60ZL8hQi7gKUVdZ8ZB2DtBAWP96P7wE3o1NUS5c2qc+nvJNlcXLGqK8ohKW7lSvjN4ztLXNqiKXzXp/s+s8iVkF5Tdwciu9MtuQpptnJ0BhllqR2xTQdjNONz+zBnIrrhxuM3H7ITdJ7Q3fclDF25jWeKed5cjFdB79/iAAMweFc+cA2/aSb9XMncWz+uHh7MCOc6k8+d0uFFN1VWklZhWKovCR8eLVzEERuDk52HlE0Cfcj4Gtm1GsV/hk49nygbcECfVm79699OzZk549ewKwYMECevbsybPPPgtAfHy8OQg3yczMZMWKFVd9tttEo9Hwv1u7M6SdP/nFemYv3kNsat4V25nWdecW6ekf4ccjo2z7+TZ/ZFvcnXQcuphpbmEl7GP10USKSgy0CXCnc4h1llqM7BiITqvhREJ26ZKd2sqOB0WvtrP1sHLRTWcPcDNWcM9o/PUdRAMQa8x2B3cFV596P7wE3o1N8klAAfcA8LVtUGMLP+69SEZeMa2auTHaxlWRh7Tzp2+4L0UlhvKFmExM67xTGlDgbRqLX0TpTIbAjhASqa7nP/pz/Y7n5N/qfdtRlc+s8GmlVoYsKeDpJavIL9YztH0Az9xYwXpwG+ja3JvP7uqNk07LyWMH0aCguHiDe/31mG7KNp5K5nh8Fm5OOmYMajifOaYK59/vuUCCwVt9Ul8E+el2HNXVZfjw4SiKcsVt8eLFACxevJiNGzeWe4+3tzd5eXncfffd9T/gBsrJQcvHd/amc4gXKTlFzPhqN6k55ZdIvfDHUU4kZOPv4cT703padV13Rfw9nJk7RF0K9sbqk9bt+SwsYppmPj6yudWSFb7uTvQNV9eK1znrXbaHty2WPcp0c2FNdpxmDhJ4Nz5Jx9X7iopcNXAlegNfbFVbiM29JsLmVZHVrLe6lnv57gvqlNSyzOu8G1DgbWpv5t+h/POmrHd9Tzc/aezfXdk0cwCdAwYftS+6R24MbQM9+OB2258YljWorT9vTYmkjVY9QUl0DKu0xoCwjGmpxu39WlqtQJ41DGjtp15Y0xv4cMtFtfUeyHRz0Sh5ODuweFZfmvu4Ep2Sy5wle8kv0gPw64FLfLf7AhoNvDOlJ4FWar9ZnbuHtqaZuxPRKbn8sFeyjQBnk3N4/vejnEzIrpfjpeYUstW4zt5a08xNrjNON69zW7FMG/XwNpHAW1iTOfCuZBanjUng3dgkHVPvAzvbdxy1sPJoAhfS8vFzd2JS7/pZJzqwTTMGtm5Gkd7AB+svy3qbK5s3oAJrpox3wGWFwbpOBI0WLu6GtCr6k1tT+nlIOqpOe287qtLNDAaFI4XqWsMuzsl8OaMPXi6O9TPGMm7qHsqcjuqJ6rYMX76zoEqwPVxIy+OZXw836BPafefT2B2dhqNOY85+NRQajYZHRqm/J1/vPE+2o3E6ohRYE41UoJcLS2b3xdvVkagLGTz43QFOJWbzf7+o67ofHNnOZjUzKuLh7GDuIvDO2tPkFZXU27EboiOXMrntkx0s3h7D7Z/v5HxqbvVvqqO/jySgNyh0a+5NhL+7Vfd9vbGt2O7oNNIra71aE+ZWYjY6r5PAW1hLXlppHFXZ8kkbk8C7HpxKzOZieh3X0Jg00oy3oih8uknNdk8f2ApXJ129HfsxY4/TH/deKL92riG2FCtb0bwsrxCIGKY+Pvxj/YzllDHb3XIguFVeBO+ddafZmalOWZvVUU+rZtY9ObBEL3c1M3DWEMLTvxxmlbUqtlpRYYmeD9afZtRbm/hmZyz/9/NhEjILqn+jHZiy3RN7tbBJ9eS6GtzW39wZ4UimcXw51VeGFqKhahvoyZcz+uDkoGXt8UTGf7CVvCI9g9o0MxcVrE+3929FmJ8rydmFfLUtpt6P31DsjUlj2mc7ScstQqfVkJpbxMyv9pBWl4C1Bn6PUouWjY+0brYbIMzPjY7BnhgUWF+DivqVMlc0t9FSJNN+TccRorZid6j3/u3BwzbFKasjgbeNnUnK4cb3tjD67c3siUmr+w7NgXfjynjvOJfK4UuZuDhqmT4wvF6P3Sfcj6HtAygxKLy3/nTpC6bgNjse8jPqdUyVSqlkqjlA9ynq/aHv66eAlLmN2JhKN/kt6hLvrTtNjKJOWQspuWT7cVXF2EosuHV3DAo8+N0BvthyjoJivX3HZbTtTApj393CG6tPUVhiwNlBS4lBYfH2GHsP7QpnknJYezwJjcY2bf+s5akxHRnZMZAExQeArJSL9h2QEHXUJ9yP96b2QKOBgmID/h7OvDO1h82XZ1XEyUHLY8YlW59sPFu3zGgjtfV0Cnd9uZvswhL6hfux+tGhZZYE7DEvCbC2Sxn57IlJR6OBmyJDbHKM67uYqpvX4SJ1hkw1F42EnaeZgwTeNvfuutMU6xVyi/TMWLSbXefqUJU6Px2y1TWs5mxtI/HZZjXbfVvvMPzc63+dqKnC+c/7LxKdYpwe5uIFnsaryKaA157yM0rXp1bUCqvTTeDgqvbLjttv27EUZEHMVvVxhxsq3ORAbDpP/HQIgC7deqlPplZQxK6+KIr5+LffMJLrOwdRVGLg5b+OM/z1jXyz8zxFJfYpEJSUVcBD3x3gji92cS45F38PZ96d2oP3p6nVoL/ddZ7cwoY1jfNLYz2G6zoF0drKfditSafV8O7UHhS7BgKwfs8hm50IC1FfxnQN4dWJ3Yls4c0nd/Yi0NN+M07GR4bSKcSL7MISPtpox894O1h9NIHZi/eYi4Yumd2PNgEe5iUBB2IzeHj5AfQG618M/9NYVK1vuB8h3q5W3z+UTjfffCql9heozcXVZKq5aODOb1Pv7VRYDSTwtqlTidn8eUj94OwR5kNekZ6ZX+1h+9la9o1OMq5F9m6pBo2NxMmEbDaeTEargblDIuwyhh5hPlzbMRCDAu+uLRNkN6QCa6bg3zO04v9fZ0/oaAyCbV1k7ew6MBSrLblM7cLKOJ2Yzd1L91JUYmBUpyCmjhmhvpB+HkrslBHJSTK2YtPiENCWj+7oxasTu9Hcx5WErAKe+fUI1761kRX7LtrkJKkiJXoDX22L5to3N/H7wTi0GrXN2rrHhjGhR3NGdQoivJkbWQUl/NiA1nonZxeyYr86e6EhZ7tNPF0cua5vdwC0uYk88dNB6T0sGr3JfcL4bf419AmvfKlPfdBqNTw1Rv1buWTH+SsLlTZRv0Vd4r5l+ynSGxjbNZjPp/c2L5NrG+jJF8YlAauPJfLCH0et/plTWs3c+tPMTbqEehHq7UJ+sZ6tp2txbmowQKZxlpGtMt7Sy1tYQ2E2xKvtbhtdxvvDDz8kPDwcFxcX+vfvz+7duyvd9ueff6ZPnz74+Pjg7u5Ojx49+Prrr8ttM3PmTDQaTbnbmDGVT29tLN5dexpFgbFdg1k+bwDD2geY+3TW6gPOXFitca3vNmW7x3QNtuv630eNWe/fDsZxJslYkbQhFVhLLl9YrcIpfabp5kdWgN6GGVLzNPMrq5kfi8tiymc7SckponOIlzoF0jsUHN3UXp72WodlunDh0xIcnHHQaZnStyXrHx/GC+O74O/hzIW0fB778SDXv72Jvw7FY7BhAH4gNp0JH27jhT+OkV1YQmQLb3574BqeH98Fb1e1+JxWq2HONerFqEXbYurtgkB1vt4RQ1GJgZ4tfejdytfew6kR3yD15CxQk8mfh+J5//JiikKIWhvWPoABrf0oKjHw9poGMEPMxr7dFcsj30ehNyjc2qs570/ribND+do0fcP9eGeKuiRg6Y7zfGo817GGs8k5HI3LwkGr4YZutplmDmqRyuuMWe81tWkrlpOgXqTXOoCnjcbp7AFuxqKCkvUWtXVhFygG9RzRu4XdhmFx4P3999+zYMECnnvuOfbv309kZCSjR48mKaniwgx+fn48/fTT7Nixg0OHDjFr1ixmzZrFqlWrym03ZswY4uPjzbfvvvuudt9RA3E8Pou/Dsej0cAjo9rj4qjj07t6M7JjIAXFBuYs2cOmU8mW7bQRBt7xmfn8FmXKnF2ZOa1PXZt7M7pLEIoCb681rvVuUBlvU2G1Dny5NZqeL63h4eUHKCwpM/2rzUhwawa5yXBuo23GoS+B06vVx5cF3ocuZjDtc7XATLfm3iyb2x8PZwe1dZcpM5561jbjqk6q8f+0Wflp+s4OOmYMCmfLkyP419iO+Lg5cjY5lwe+3c9N729l/YlEq2YqMvKKWPjzYW79eDtH47LwcnHg5Zu78vP9g+nWwvuK7Sf1DsPHzZHYtLy6t3WxgvwiPUt3qhdP5g1pbbW+sTbnqZ48dvZUCyi+teYU/xyOt+eIhGgyNBoNT41RL1T/vP8ipxLrp52WPXy2+Sz/98thFEUtBvvGpMhK22Pe0C2EZ25Ua+78758T5vOduvo9Ss12X9PO3+bL80xtxdYeT7T84q8pEPYKBZ2DlUdWhkw3F3V13lhYzY7TzKEWgfdbb73F3XffzaxZs+jcuTOffPIJbm5uLFq0qMLthw8fzi233EKnTp1o06YNDz/8MN27d2fr1q3ltnN2diY4ONh88/VtHFmWyrxrDOxu7BZCh2BPAFwcdXx8Zy9GdQqisMTA3Uv3ssGSSpKNsLDaV9tiKDEo9I/wo0eYj72Hw6PXtUejgb8OxXM8PqthVTY39vDO927DO8aMwm9RcUz/cjeZecXqNjpHtbUYqEXWbOHCLrWegKsftOhnfnrf+XTu+HwXmfnF9Gzpwzdz++Nb9oSgmdp2xm7rvFOMx61ofTzg6qTj3mFt2PzkCB6+th0ezg4ci89i9uK9TPx4O9vP1HIJiJHBoPDj3guMfHMT3+2ORVHUauDrHx/OnQNaVVoYydVJx5391aqtn2+pp1ZxVfhp3wUy8opp6edmLrzTKHioY/UqSWPW4HAAHv0hiiOXZGqiENbQs6UvY7oEY1DgtZUN4G+mlSmKwltrTvHfv9UZcPcNb8ML47ugraao3ZxrIswzlx7/8WDtlxOWGccf9TDN3KR/az88XRxIzS3iQGy6ZW82F1azUUVzEwm8RV01gMJqYGHgXVRUxL59+xg1qrSnr1arZdSoUezYsaPa9yuKwrp16zh58iRDhw4t99rGjRsJDAykQ4cO3HfffaSmVl6ErLCwkKysrHK3huTIpUxWHk1Ao+GK9h/ODjo+uqMXo7uohZ/u+Xofa2syvUdRGl3GO6ugmG93qR+S9wxrGOtEOwZ7caNx2tY7a0+VZrwzY6Ewx44jw5zx/v2SJ9mFJbTwdcXD2YFd0WlM/GR7aUs603TzE3/aZsynjNPM211vvoK961wq07/cZa7q+vWc/ubp0mZ+poy3nQJvc8a7bZWbebk48uh17dny5AjuGdYaF0ct+2MzuP2LXdz++U72W3riAZxIyGLKZzt44qdDpOUW0T7Ig+/nDeDNyZH4ezhX+/7pA1vhpNOy73x6rY5vLXqDwhdb1eB/7pAIu1RRrjUPtbgahVk8fV0rhrYPoKBYvcCZlNUw27UJ0dg8MaYDOq2GtccT2WuNTi0NhKIovPTncd5bp/4deWJ0B54a07HGM36evqETN3YLoVivcM/SfZxIqP156dG4LM6l5OLsoK2Xi5+OOi0jO6qfnxZPNzctLbNVYTUTCbxFXRQXwKW96uPGlPFOSUlBr9cTFBRU7vmgoCASEiqfIpmZmYmHhwdOTk7ceOONvP/++1x33XXm18eMGcPSpUtZt24dr776Kps2bWLs2LHo9RVXWHzllVfw9vY238LCrPgLn3AENrwCB76p9S7eMWa7x0eG0i7I84rXnRy0fHB7L27oFkyR3sB9y/ZV3284J1HNQmq0V/Z4bqC+2xVLTmEJ7QI9GN4+0N7DMXtkVDu0Glh1NJEDKRpwN/bys2dl8+J8tTAZ8OER9dfymRs78+O9Awn2cuFMUg63fLRdzd417w2+EVCcByf+sv5YThr7dxvbiG09ncKMr3aTa+wlu3h2X3V6+eXsnvE2Bt41/P3wdXdi4dhObH5iBDMGtsJRp2H72VRu/Wg7sxfv4Whc9ZnS3MIS/vv3cW58byt7YtJxddSxcGxH/npoCP1bN6vx0AO9XBjfQ81sfGnHrPeaYwmcT83Dx82RSb3ttwaqVly8wUGt/OyQl8z703rSOsCd+MwC5n29r8G0lBOiMWsT4MHkPupnw//+OdEkihjqDQoLfz7Mom3qZ+8L47vwwIiqL+BeTqvV8ObkSPqF+5FdWMKsr/YQn1m7InSmomqjOgVV/LfWBq7vbGorZuHSK1MgbKvCaiYSeIu6uLQP9EXgEQR+9k0E1ktVc09PT6KiotizZw//+c9/WLBgARs3bjS/PnXqVMaPH0+3bt24+eab+fPPP9mzZ0+5bcpauHAhmZmZ5tuFC1asBhx3ADb9Dw4ur9XbD1/MZO3xRLQaeOjaiqe8gnqF8b2pPRkXGUqxXuGBZfurXo9oynb7tQFH+7UVqamiEoP5j9jdQ1tXO1WrPrUN9GRiL/XE4ZV/TqCYst72DLxTzwAK+TovYos86Nrci9FdgugU4sUvDwyiY7AnydmFTP50BxtOJZdmvQ9bubp5yhk1c6x1hDbXsuFEErOX7KGg2MCw9gEsmtkXN6dKTgTMgbcd1niXFJZeea9kqnllAr1ceGFCVzY8PpwpfcLQaTWsP5HEje9t5YFl+zmTdOWsAkVR+OdwPNe+uYnPNp9Db1AY0yWYtY8N455hbXCsZD1gVUwV//85Es+FtDyL328NpkKIdw1oVfn/c0Ol0ah/VAFyEvF2deTLGWrLn6gLGfxrxaEmESQIYW8PX9seZwcte8+ns+64BcvlGqBivYGHlx9g+Z4LaDXw+qTuzBgUXqt9uTjq+Gx6b9oGehCfWcCsr/aQVVBs0T4MhtJp5uPqYZq5ybAOATjptESn5HI22YKZdJk27uFtYprKbq/iraJxKzvN3M51ayw6O/T390en05GYWH4qSmJiIsHBlU+H0Wq1tG3blh49evDYY48xadIkXnnllUq3b926Nf7+/pw5U3HmzNnZGS8vr3I3qwmJVO/jD6nTuy30trFV1c09mtOmmt63Djotb0+OZEKPUEoMCvO/O8BfhyoJvs3ruxvHNPPfoi6RmFVIoKczE3rU3x+PmlpwvXrisDs6jYs64x8Me1Y2N64xP14SDGhYcF178xS3EG9Xfrh3IIPbNiOvSM/cJXv5g2vU951dr7bRshbTNPPwa1h9No95X5e2DPtsem9cHHWVv9dUXC07DopyrTemmkiLVqtVOnmWBl8WauHrxquTurPm0aGMjwxVawEcjuf6tzfx2A8HiU1Vg+GYlFxmfrWH+5btJyGrgDA/V76a2ZdP7upNc5/a91rtGOzFkHb+GBTMF63q077zaeyPzcBJp2X6wPB6P75VeBr/DmWrM4gi/N35+I5e6LQafo2K4+NNdir8J0QTEuztwqzB6oXC11adaDDdGCxVUKzn3q/38eeheBx1Gj64vRe39anbDEofNycWz+pLgKczJxKyuffrfRSVGGr8/r3n04nPLMDT2YHhHQLqNBZLeDg7MKitOktr1VELppubM94y1Vw0YA2gf7eJRYG3k5MTvXv3Zt26debnDAYD69atY+DAgTXej8FgoLCwsNLXL168SGpqKiEhtmuhUKmAjqBzgsJMSI+x6K1RFzJYfyIJnVbDg1Vku8ty0Gl5a3IPbu3ZHL1B4aHlByquimle393wC6spisLnW9TM2exrIq5owdEQhHi7mouh/HLReIHEngXWjNn2U/pQeoT5MKJD+an5Xi6OfDWzH7f2Un9OHlyVRZxHVzXYPLLCeuMwthE74jmY+5ftp1ivcGO3ED6+s1f1/49ufmpBNoA067VVqRHTbAX/tnW+mtk6wIP3pvXkn4eHcF3nIAwKrNh/kZFvbuTupXu5/p3NbDqVjJNOy0Mj27Lm0WGM6GidpRRzh6hToH7Yc4HMfMsyJXVlynbf2qs5AZ7Vr0tvkEzrvHNKTxwHtfXn+fFdAHh91UlWV7esRwhRrfuGtcHb1ZFTiTn8csA6lbzrU65xOvi6E0k4O2j5bHofq7XtauHrxlcz++LupGP72VSe/OlgjWfb/H5Q/bcc3TW46gvdNmBxWzFFsX0PbxNTYF+QCfkZtj2WaFr0xXDB2PbazoXVoBZTzRcsWMDnn3/OkiVLOH78OPfddx+5ubnMmjULgOnTp7Nw4ULz9q+88gpr1qzh3LlzHD9+nDfffJOvv/6aO++8E4CcnByeeOIJdu7cSUxMDOvWrWPChAm0bduW0aNHW+nbtICDU2lwa2q0XkOm3pa39GxOhH/N+1XrtBpevy2S23q3QG9QePT7KH45cLH8Ro0o473xVDKnEnPwcHbg9v42/jCug3uHt8HP3Ykd2carynbMeOfHqf+/Z5Tm5bLdZTk5aHnztkjzEoZP0nsDYDhopermeWkQuxOAe3cHUGJQuLlHKO9O7VHzqdP2WuddSSuxuugY7MXn0/vw2wODGdLOnxKDwppjiRSVGLimrT//PDKEBdd3sOrJ0dB2/rQP8iC3SM/y3fV3ZT86JZfVxpMt05T3RslY2bxs4A3q1Pm7BrRCUeCR76M4FtewCnIK0dh4uzly/3B1ltNHG+xU16OWMvOKufPLXew4l4qHswNLZve74mJ3XXVt7s3Hd/bGwTjb5vVV1V/YL9YbzLMe66Oa+eWu66QG3lEXMkisSUHKnCQoKVBrD3k1t+3gnNxLe3lnWnF5qWj64g9BcS64+ECA/WMoiwPvKVOm8MYbb/Dss8/So0cPoqKiWLlypbngWmxsLPHxpdOlc3Nzuf/+++nSpQuDBw9mxYoVfPPNN8ydOxcAnU7HoUOHGD9+PO3bt2fOnDn07t2bLVu24Oxsp6yLebp5zQPvfefT2XQqGZ1Ww0MjLT/512k1vDqxO1P7hmFQYMEPB/lpnzH4NhggyRgUBnWxeN/17fvd6ofi5D5heLk4VrO1/Xi5OPLQyLacNqjrvZX0GLXImR1kXTgCgDawPUPa+Ve6nUajTkN/dWI3VioDKVG0aOMPkH3xWN0HcXoNKHqOG1pyUQlgcp8WvDm5R6X9SyvUzE6VzatpJVYXkWE+fD2nP9/PG8DEXi14f1pPvp7Tr9qlJLWh0WiYe42a9V68PYZifc2nKNbFl1vPoShwbcdA2gZeWRCy0TD28ib7yozNs+M6m5dr3L10Lyk5lc+6EkJU7/b+LdFq4FxKbs0CtQYgObuQKZ/t4EBsBt6ujiyb258BFhTCtMTQ9gG8cms3AD7aeJavd1a9PnnrmRTS84rx93BiUBvbjKkqgV4u5rava4/XIOttmvbtGaq2OrU1mW4uasM8zXwQaOultFmVajWC+fPnc/78eQoLC9m1axf9+/c3v7Zx40YWL15s/vrll1/m9OnT5Ofnk5aWxvbt25kyZYr5dVdXV1atWkVSUhJFRUXExMTw2WefXVE5vV7VIvB+x7i2e1KvFrRs5larw2q1Gv57Szfu6N8SRYEnfjrI93ti1VZXxbmgc1arWTdg6blFrDuhfmBP7tvwqyLf3r8V7n7BZCjuaBSDXSpyX0rLxjdf/UMyZtjwGrUvmdK3Ja/PHMU21J/V375+h7iMul00iNnxEwBrDb24c0BL/ndrd8vbSZkD73peS2vKeNsg8Dbp37oZb06OZFxkaI1bzNTGhJ6h+Hs4E59ZwN9VFVy0ktScQn7cq17ku3tow2j7V2vmjPeV08kddVo+vL0X4c3cuJSRz71f76OwRCqdC1Fbni6OdAxWa+zsO2+/Nog1FZeRz5RPd3AiIRt/D2e+v2cAkcZA01Zu6xPGguvUThvP/XakyqUuf0SpRdVu6BZi2QVvK7q+i3ruvbom67wz66miuYkE3qI2Gkj/bhP7h/4NUUgP9T7+YI0KrO2JSWPL6RQctBrmj7SsBcXltFoNL9/clRkD1WmRT604zKYtm9QXA9qb+yo3VL8fjKNYr9C1uZf5D3JD5uSg5ckxnTitqNOkMo2Z5/r03arNOGlKKNQ406t79xq/b1j7AFqPnA3A0Pz13PLh1hq1wKrIok0naRa/BQDXrjfx0oSutatEb4+p5opS2krMilPN7cXZQceMgWoF18+3nLN5Je5vdsZSWGKgewtv+kf42fRYNudRecYb1MJHX8zoi6eLA3vPp/P0L0ek0rkQddC7lS/Q8APvmJRcbvtkB+dScmnu48qP9w6st3OUB0e2Nc9mfGj5AfbHXvlvVVCsN7eVtcc0c5Prjeu8d5xNJbu6iuz1VVjNxBR4p0tlc1FDBgPESuDd8AV1Bo0O8lIgK67azU1ru2/rE0aYX+2y3WVpNBqeH9+FWYPDAdi1a6v6QiMorGaaHm9q19UY3NAtmDQ3NdO3b+/Oej12bGoep4/sA0Dv28biaTBhAydhcHSnpTaZ5jlHmPzJDjadSrZoHx9uOMO6Vb/gqcknx7EZcybfWvuMrj1aiuWlQkEGoCnNuDdydwxohYujliOXsth5Ls1mxyko1rN0RwwAdw9pbdNMfr3wLG0nVpm2gR58eHsvtBr188pUCFIIYbnGEHifTMjmtk93cCkjnwh/d364d6BFdXjqSqNREyojOgRQUGxg7pK9RKeU7/yx/kQSuUV6mvu40qulb+0Ppiiw/mXY8EqtOvO0CfCgtb87RXpD9ecSGfXUSszE19RSTDLeooaSjqkF+RzdITjS3qMBJPCumKMrmHo7JxyqctOd51LZfjYVR13ds91laTQanr2pM3OviaCDVg1m9+ZV3rKtITiZkM3hS5k46jRM6GHjQhtWpNFo6NitLwCF8ccq7NtsK++tP0046sUdt9BaXFhxckPbeTwA9/ruJbdIz+zFe/hhb/XFRxRF4e01p3h91UlGafcD4N71BjTaOhQL8zNOVc5PU4u11QdTRXPvMPV3twnwc3cyX7z6cqvtAsOf918iNbeI5j6ujO3asD9fasQ01Tw3GfQllW42tH0A/75J/X175Z8TrD9hQfscIYSZKfA+GpdJQXHDW7pxNC6TKZ/tIDm7kI7Bnvxwz8A6tX2sLQedlg9u70X3Ft6k5RYx86vd5epM/G6cZn5TZEjtZpuZnFkHm1+HTf+DuP0Wv12j0dS8unlGfU81l8BbWMg0zbxl/wYzY1gC78rUYJ23oii8Zcx2T+3b0uof5hqNhqdv7MRAD/XD78NjTnzRgLMzK/arFwhGdAjEz93JzqOxTKsOPQFoyyVeW1k/1c3PJefw8/6LtNUYW7GYLvZYqtttAFynbGNiZAB6g8KTPx3i7TWnKp1GqygKr606ybvrTgMKEz0OA6DpcEPtxmDi5F5a3bS+st6maeb+1rvw1RCY2t2tPZ7E2WTrXwwyGBS+MAb1c66JsNuaQqty91cr7KKowXcVZg4KZ1q/MBQFHvouilOJ2fUzRiGakBa+rgR6OlOsVzh0sXZLnWzp5T+Pk5FXTI8wH5bPG2DXVonuzg58OaMvYX6unE/NY87iPeQVlZBVUMz6k0lAHaeZKwpsfKX0672LarUb0zrv9SeSqi7waQqAvet5qrkE3qKmyhZWayCawJmWjdQg8N5xNpXd0Wk46bTcP8I2U1w1hhICCtUPmVOGFrz813E+21zPhatqoERvMPfynNi78UwzNwvoCEC4JoENxy6xO9r22dp3153GoEAvN/UPLv7ta7ejiGHgEYQmP503eqaYW7y8u+40T/x06Io/nIqi8NKfx/l4o/pz9OYwJ7wK4sDBBVoPr+23U8qU9a6vdd42aCXWELQO8GBUJ7XFzaKt0Vbf//oTSZxLzsXTxYHJfevpxMnWtDpwN7YHrGK6OagXNl8Y35X+EX7kFJYwZ8ke0nKL6mGQQjQdGo2mwU43LyjWs8+4nvrNyZH4uNk/IRDg6cySWf3wdXPk4MVMHvz2ACsPJ1BUYqBNgDudQ+qw7vzMWri0V10qCXB4Ra16XvcI88Xfw4nsghJ2VbbUSVFK23rVV8bbFOAXSi9vUQOKUqaw2mD7jqUMCbwrU03grSgKbxsrmd/evyUh3jaaupR6Fo2hGMXJg4kjBwLw379P8NHGhtU3c8uZFJKzC/Fzd7J6P8x64RUKTp44avS00iTw37+P27To0unEbH4/GAcohCt1zHjrHKDrJAA0h3/gyTEd+c8tXc1rWGcv3mMukmIwKPz7tyMs2qYGci9N6MJEd+NyitbDwanuNQrqvcCaDVuJ2dvcIepFjJ/2XbR6UPiZcfbMHf1b4eHcMKZgWYVH9eu8TZwctHx8Z29a+rlxIS2fe7/ZR1FJ/bRwE6KpaKiB98ELGRSVGPD3cKZ1Pa7prk7rAA++mNEHZwct604k8e/f1KKu4yOb177ORtls94D71JpAJflw6HuLd6XTahjVyTTdvJIq7HmpUJynPvaup2SLk1vphVXJeovqpJ6F3CTQOUFoL3uPxkwC78oEq70XyboEOVdOWdx6JoU9Mek4O2i5b7gNCzolqf2ZNYGdWHB9Bx4dpWZFX1t5kgMVVMa0F1NRtfGRoTg5NMIfK41GrRoPdHFMIOpCBv8cqbztR129s/Y0igJTOjiiK85Wr1D71eHnqPtk9f7kP1CQyR39W/HFjD64OurYcjqF2z5RC8v86+dDfLMzFo0GXp3YjbsGhsOplep724+p8/cF1H/gXQ+txOylf4Qf3Zp7U1hi4JtqesBaIupCBruj03DUaZg5KNxq+20QPI3rvLNr9vvr5+7EFzP64OHswO7oNJ79TSqdC2GJXsbAe39seoP63THNXOsf4dfgCkf2buXHu1N7otFAofFi3/gedZhmfnoNXNoHjm4w+BHoo3Y8Ye+iWhVZK7vOu8L/0wzj3yPPEHCox+n7Mt1c1JRpmnnzPuDoYt+xlNEII6R64uxZGkAklM96m4pSgZotCvKy4X9o0nH1PrATAA+PasfNxg/nL2ww/bQ2MvOKzUU4JjXGaeYmxunmU8LVaqOvrTxhk+zXsbgs/jocj0YDD3QzFqPxiwCHOkyDC4kE/w5QUgDH/wBgZMcgvr9nAP4ezpxIyGbEGxv5Ye9FtBp4a3IkU/q2hJwkuLhX3Ye1A++0elgSoS+G9BjjcZte4K3RaJg7RF3rvXRHjNWKF5kqeY+PbE6wd8P5g2QVFmS8TdoHefLetB5oNLB8zwW+2hZjm7EJ0QR1DfXGyUFLWm7RFdW67Wl3jBp492ugbRLHdA3m+XFdAOgb7lv7SuuKAhv/qz7uOxc8AtSL8Y5ukHwCYndYvMvBbf1xddQRl1nA0bisKzeo74rmJhJ4i5pqYP27TSTwrkol0803nUpmf2wGLo5a7h3e2rZjMGa8y7YSmzdUzYyuPJLAxfQ82x6/Bv44FEdRiYGOwZ50CW34vbsrZZzq3c89GX8PZ2JS8/h2l/X7RZqWKNzYLYSWenWmQK3Xd5toNNBdLbJWdmpZ9xY+/HL/INoEuFNUYsBBq+H9ab24pafxAsmpVYACoT3BK6RuYzAp21LM1tmP9BgwlKitIrzs1/vUlm7oFkKItwspOUXmyrd1cSEtj38OxwNw99CIOu+vwalF4A3qhar/G6te4Hz5r2Mcuphh5YEJ0TQ5OWiJbOENNJzp5sV6g3ks/Vs3zMAbYMagcP566Bo+n96n9js5vRriDqiB9qCH1OdcvKGbugStNkXWXBx1DGuvTutefbSC2UP1XVjNRAJvUVMSeDdCFQTeZbPddw1oRaCnjbNF5ox3aeDdOdSLQW2aoTcoLNkeY9vj14Cpmvmk3i0a3HQuixgz3g5pp3j0OjV7+t76M2QZ10dbw+GLmaw5lohWA4+Mag8pJ9UX6hp4g7m6OdFbyvWfD/NzY8V9g3hgRBuWzunHjd3LBNgn/1Hv61rNvCzfVurU+eI8yI633n4rYmol1qyNevGhCXLUaZk1OByAL7aeq/NUzi+3RmNQ1JZaHYMb8YWyylg41bysuUMimNynBfcPb0vXUG8rD0yIpqvsdPOG4MilTPKK9Hi7OtI+0NPew6lSl1Dv2hd+K7u2u9/darbbxDTd/NhvkJti8a5N081XV9RWrL4Lq5lI4C1qIiMWMmPVc9GwfvYeTTkSeFelgsB7w8kkDl7MxNVRxz3DbLi2G6A4H9KM7cPKBN5Q2mpo+e4L5BRW3q/W1s4m53AgNgOdtnH17q6QqbhZymmm9AqldYA7ablFfLrJelOm31qjBto392hO20APSD5Z/th14RsOLQcCChz+qdxLPm5OPDG6I4Pa+Jc+WZwP5zaoj601zRxA56gG32D7dd4pTXd9d1lT+rbE3UnHqcQcNp+2/ATKJCOvyNzjfd4QG8/WsZdaZrxBndr/6sTuPD66Q9166YrG4cTfcGq1vUfRJPRuqQbee2MaRuBtWt/dN9yvaf8un1plzHa7l2a7TUJ7qjd9EUQts3jXIzsGotNqOJGQzYW0y2ZX1ncPbxPp5S1q4rxxeUVIpLp0uAGRwLsqwd3V+/QYyE83ZrvVE/3pg1rh72HjghLJJwAF3PzLX8VE7ZXd2t+d7MISfjSeSNvDCmNRteHtA+zaH9MqvFuCgyvoC3HIiuVfY9QM+BdboonPzK/z7vedT2fDyWR0Wg0PXWsMFE0ZW38rBN5QWmTt0A/Vbxu9Wc1Ke7UoLSZoLfVVYK2JthK7nLero7omH/jCuD67NpbtiiWvSE+nEC8Gt21mreE1LOaMt+WBN9C4Z+2ImoveAt/fAT9Mhwt77D2aRs9U2fx0Ug6ZedabJVZbpsB7QAOeZl5nl2e73f2v3MZcZO0rMFhWs8bX3Ym+4er/6xVZb3Pgba+p5udtv5RNNF4NsH+3iQTeVXHzK/0lTzjM2uNJHL6UibuTjnuG2jjbDVcUVitLq9Uwy5j1/mpbDHpD/X8A6Q0KP+9vxL27L6fVlmZOk09yXecg+ob7Ulhi4K3Vp+q8e9MShYm9mhPu7672oTRl5ayVse18M2gdIfEwJB6telvzNPMx1p+mXXadty014VZil5s1OBytBracTuF4fAXFbqpRWKJnsXFpyryhEU03wPQwtjPMSZQTM1G5lgOgzbVqy6VvbyudfSRqpZmHs7k42P4L9s166w1Kgy+sZhUn/4H4qIqz3SZdJ4KzF6RHl85ws8D1ndULmeXWeStKmeJqrSzeZ52YzskLs6Ago36PLRqPBti/20QC7+oYs95K/EFz4DRjUDh+7nWoQF1TFRRWK2tir+Z4uzoSm5Znripen7afTSEhqwBvV0eu7dQIe3dXxLjOm+QTaDQa/u8G9aLHT/svciLB8mDHZNe5VLaeScFBq+HBkZdluz1DwcVKa23d/KDd9erjqrLeilKmjdhY6xy7rGbGC1O2DrybcCuxy4X5uTG2q7o+/8tadDT4LSqO5OxCgr1cuKl70yxEB4CHMeOtL5QTM1E5nSNMXgLNe0N+Onx9K2ResveoGrVexunm++1cYO1EQhbZBSV4ODvQOaQJ1rGA8tnu/vPAvZIZTE7uEDlVfVyLImumdd57YtJIzy1Sn8xPh6Js9XF99fA2cXQFd+P5pkw3FxXJSSo9N2w5wL5jqYAE3tUJ6QFA/IldHIvPwsPZgbvra21kFRlvADcnB+7or179W2SH1mJle3c7O+jq/fg2YVprbcx+9Gzpy43dQlAU+N8/J2q1S0VReMt40WZy3zDC/NzKHcPUP9xqTNPND/9U+dSy+Ci18JmTB0QMse7xoX6mmuelQV5q+eM1cabWYr9FXSIpq6DG71MUhc83q1PUZ18TjqOuCX/0O7qoFX2h1tPNxVXCyR1u/1FdqpJ1Eb6ZqAYVolZM083tvc7bNM28dytfHJrqZ93JvyHhkPo3fOCDVW9rmm5+8p9yhVdrIszPjU4hXhgUWGXKepsCXvdANRCub1YqsGYwKBQU68kuKCYtt4iEzAIupOVxNjmHEwlZZOQVWWGwot6Z2ucFdlGTUQ2Mg70H0OAZC6wVX4wCpjFrcDi+9ZHthgorml9u+sBwPtt8jt0xaRy6mEH3Fj71MrTsgmLzh3CTmGZuUibjbfLE6A6sOprAxpPJbDuTwuC2FayjqsL2s6nsik7DSadl/ogyAaK5ormV1nebtB+jTi3Lugix2yH8miu3OWnMdrcZAQ42WJtvCoTTo0FfAjobfNSYgnqv5uoJ9FWgZ0tferfyZd/5dJbsiOGJ0R1r9L6Np5I5nZSDh7MDU/vVczEce/AIhoJMyEmAwJr9G4mrlHszuOtn+PJ6SD4O306F6b/aJ6Bo5PoY1wNHXcigRG+wW9BrCryb7DTzcmu7q8h2mwR2gpaD1POB/V/D8KcsOtz4yFCOx2fxW1Sc+vfDXhXNTXxawqW95sA7M6+Yl/46RnJ2IcV6A8V6A0UlBor0SrmvS+/V50uqWaKp0UD35t4MaRfAkHb+9Gzpi5OD/S/k5BfpOXAhnfiMAvqG+9GymZu9h9SwNNA2YiYSeFfHGHiH6S8S6FzC3GvqKdudnwFZxmlvVZw4Bnu7cFP3EH6NiuPLrdG8O7VnvQzv78PxFBQbaBvoYe7f2SSYAu+UU2q2WKsl3N+dOwe0YvH2GF755zi/P3BNjaukls12396/JaE+ZU7mko1Tza2d8XZ0gc4T4MDXak/vCgPvv9V7a7YRK8szVC1UV5KvFkFpZoOaCOZWYldHttvk7iER7DufzrJdsTwwoi1uTtV/jJuy3dP6heHl4mjrIdqfR6B6YSsnyd4jEY2BT0u4cwUsGgsXdsJPs2Hy17a5YNiEtQ3wwNPFgeyCEk4kZNO1ef2fGyiKYg68+zfVwPvEX5BwWM12D6om223SZ7Yx8F4CQx6z6Gd7XGQIr648wc7oVBIyCwi2V2E1k8sy3ou3x5hnYNaFg1aDo06Lo06Dg05LWm4RBy9mcvBiJh9sOIO7k46BbZqZA/EIf/d6qZWSllvEnpg09saksScmnSOXMstdNGgX6MHIjoGM7BjYtGd51FQDLqwGEnhXS+8eSIbGj2ak8Vj3Irzd6umk1ZRx9WpROm2yEnOuac2vUXH8dSiehWM7Eext497ilE4zn9irkffuvpxvOOic1GrfWRfNH/APjmzLin0XOXIpi98PxnFzz5q1Ttt0Kpl959NxdtBy//DLgk9bZbwBuk9RA++jv8HY19Vg3CTzkjpFDU3penBr02rBrzUkHVXXedsk8L561neXdV3nYFr6uRGblseKfRe5a2B4ldsfuZTJ9rOpOGg1zBocUT+DtLc69PIWV6mgLnD7clh6s3ph8s9HYPz71i882YRptRp6tfQ1/92zR+B9NjmH1NwinB20dGtKSQETRYFN/1Mf97+n5lNpO4+Hlc3UhM7p1dCx5hfdW/i60Tfclz0x6fxxMI67cxtAxhsgIxZFUfj5gHo+OntwBJFh3jjptDg5aI1BtBYnBw1OOh2ODmpgXf710ucuT6gkZBaw9UwKW04ns/V0Cqm5Raw9nsTa4+oF3eY+rgxt78+QdgEMatOs9r3Yy1AUhQtp+eyJSTPfzibnXrFdsJcLIT4uHLqYyemkHE4n5fDp5nN4uTgwvIMahA9rH1B/M3QbivwMSDiiPpbAu3H663A87iWtuFaXxvig2vfPtZi5sFrF67vL6tbCm34RfuyOTmPJjhieGmPbqZUxKbnsiUlHq4FbahiANho6BzWDmnRMXYNt/IBv5uHMvcPb8Pqqk7y+6iRjugbj4lj1uvay2e67BrQi0KtM8FucD+nn1cfW6OF9uVaD1SnYWZfg9Co1A25yyljNPKx/xe1HrKVZG2PgfQawQYBvmmrexFuJXU6n1TB7cDjP/3GML7dGc3v/VuiqmIHxubH92E3dQ8rPuGjK6tDLW1zFWg2CSYvgh7vUC5eewTDyGXuPqlHp3UoNvPeeT2fGoPB6P/4uY7a7V0vfplN7pqwTfxqz3Z4wcH7N3+fgDD3ugO3vqUXWLAi8Acb3aM6emHR+O3iJu/3t1MPbpEwv7/2xGZxPzcPNScdj17fH3dl6YU2wtwuTerdgUu8WGAwKx+Kz2HJaDcT3xqRzKSOf73Zf4LvdF9BqoHsLH4a282dI+wB6hPnUqJaK3qBwPD7LnM3eE5NGUnbhFdu1C/Sgb4QffcN96dPKjxa+rmg0GjLzi9l8KpkNJ5LYcDKJ9Lxifj8Yx+8H49Bq1N/HER0DubZjEO2DPJpWoqwiF3YBCvi1Kb0A38BI4F0FvUHh3bWnGK+Ecy0HcE0+Un8Hr6aw2uXmXBPB7ug0vt0Vy4Mjazb9tLZ+3q9eXbymXUC9ZNfrXUAHY+B9AtpdZ3569uAIvt5xnksZ+Xy94zx3D6162cHa40kcupiJm5OOey/PdqeeARRw8QH3gIreXjdaLXSbBNveVaublw28Teu7O4yx/nHLMk0BT7NRZfOrNOMNcFufMN5ac4qY1DzWHU/k+i4V/4G5lJHPn4fiAZhbX0UhGwLJeIva6nQT3PQ2/PEwbH5dLSDVf569R9Vo9Gll38rmu8414fXdBgNsrEW226T3TDXwPrMW0mPUGX41dGO3EF74/ShHLmVRSDTOAN72z3j/vE/Nvo/pEmzVoPtyWq2Grs296drcm/uGtyGvqIRd0WlsOaUG4qeTcoi6kEHUhQzeW38GD2cHBrZppgbi7QJo1cwNjUZDfpGeqAsZaqB9Pp3959PJKSwpdyxHnYZuzb3pG+5H33A/erfyrTRz7e3qyLjIUMZFhqI3KERdSGf9iSTWHU/iREK2MZhP57WVJ2nu46pOSe8UyMDWzapNHjVKDXyaOUjgXaU/DsZxNjmXGNe2oADxB+vv4KbAO6hLjTYf1SnIoumntWUwKKww9u6e1JSKqpVVQYE1AFcnHQuub8+TPx3i/fWnua1Pi0qnFhkMpdnuGYPC8fe4rICZuaJ5B9tNZew+RQ28T69WK4C7+UFhDkRvUl+31fpuE1tWNteXQJqayb0aA293ZwfuGNCKjzee5Yst0ZUG3l9tjUZvUBjctpldpn3ajWS8RV30nqnWB9jwH/jnSXVmUNdb7T2qRiEyzAetRr3oF5+ZT4h3/c2yafLru0/8CYlH1OKpAx+w/P3N2kDrEWo/731LYNRzNX6rn7sTQ9sHsP5EEkqGvaeaG9eWF2ax6dAZwJlbe9Xv+aibkwMjOgQyooPa2iw+M58tp1PYejqFrWdSSMstYs2xRHOr3xa+rjTzcOZYXCbF+vJF3TydHejVylfNZof70SPMp1ZBsU6roXcrP3q38uOJ0R25lJHP+hNJbDiRxLYzKWrSaOd5vt55HldHHYPbNmNkxyBGdgxsOkm0Bty/20QC70qU6A28t07NqPXoNwx2va5WPC0uKL9e1hYUBRKPqo9rmPEuO/100bYY7ujfqsYFwCyxMzqVSxn5eLo4cL2xv2OTc1lLsbIm9mrBoq3RnEjI5sMNZ3j6xoorzq86msBxY/u5eRVlGk2FwfytXFitrKAuENRV/UN97DfoM0v9g6svAt8I2x4bygTeNsh4Z5wHQ7FawM2riV4AqsaMgeF8buxocPBCBpFhPuVez8wv5rvd6pTAemuB2FBI4C3qaugT6s/Pni/gl3vArRm0HlZ/xy/KUy+cnlkLEz6o8bmAvbk7O9ApxOv/2zvv8KjKtP9/z0ySSZ/0Bgkh9Bo6AouggKEK2BALRSyr4rsuuiqvin1BRZefLgv7KsW2tlVsKEgXEUFK6CVAIAHSQ3qfOb8/nnnOzCTT50zJzP25rrnmZObMOc/JlHPu5/7e3xsnrlbh0KUKTOnvvsA7v7wehVUNCFQKGKjrKe4zGGW7/+x4m6Qh97HrgMMfAWMXAwG21wBPH5CCP05fRLBG18PbU+ZqvJd3bTHUjQVoieyJEV2sOLu7mGR1CO4Ykoo7hqRKsvRfckqw+2wpDlwqx+Vr9bh8rR4AkBChwtDOMRiWHoMh6dHomRRpsVzMUTpEheDe6zrh3us6ob5Jg9/Ol2L76WJsP12MgsoGo3r13smRGNcrAfNGpiO2daKovdBUC1w9zJYp493++Db7Ki6U1iI6NBC33nAdcDQGqC9nEuQOg1y785piti9BYVdwdPuQVLy15SxyS2ux40wxxvWSPzD+6iDLdk/tn+ybMhXAION9hk2CGGSklQoBz0zqiXnr/sAHv13CnBHp+r7cOjRaEf/YygLr+8y1nzPMeLuS/ncAW44zufmQ+QYy80muNw3igXdlPqtpl7M9D5eZx3Zhsno/JEkdjJszU/D14St4/9dcvDvbuKPBZ/vzUNukQY/ECIzp7oJyBm9GkppT4E04iCAAk94AakvYxOVndwPzN0qdTlyGKLL9/fycvm3Tsf8C45537X5lZHCnaJy4WoUDl8oxpX+y2/a7L7cMAKu1DQnyseuT098zzxRVJDDiEce302MSa7dYU8gy6HYoOSb0TsTaQKYoaAmOQYAn23hGpQG1xegolOBPA290SeDqKIay9EfGdkVtYwv255ajqqEZg9KipfpsdxISpMS4XokY1ysRoijiVEE1tp8uwvbTxTicX4GTBVU4WVCFK9fq8fasAW4dm62s25OLk1er0D81CgNTo9AjKcK4jv7yH4C2hSVjPKXGsAH/vGK1QotGi3e2swv7h8Z0QXhwoP5k6w65OTdWi8mwK1gJUwVgtq5H7/u7c2UfVm1jC346zupFfVZmDjBTBkEJNFaZrBEd0z0eo7rGokmjxVs/t82KbzxWgLNFNYgMDsACc5lGKePt4sC7720ABNZGpDwXOGsQeLua0Bi9Iz+XhctFGQ+8/auVWGsWjGYu5T8eK8CVinrp8aYWLdbtuQgAuH90Z983VGkNz3g3VrJJH4JwBIUSmPl/QPpooKka+Pg2+X/LDCk6CXx4M/DlXBZ0K3S5Ef57104Y7KE6732+2r/bMNt93cNAiBPZfGUgMGgOWz6w1q6XhgYFYGpaMwCgSJHg+BhkoDGCXYN2FIpxy0Dvvh4NUwXghp4JmD6gA1JjQj1+PhYEAb1TIrHwxm74+pFROPDseDw3hSlqNp8oREOzxqPjM0V+eR1e+v4kvjx4Gc9/cxxT3/0V/V7cjNtX/4bXNp7Ej8cKUH1GV0bZaaRXd6NwKPBeuXIl0tPTERwcjOHDh2P//v1m1/36668xZMgQREVFISwsDAMGDMBHH31ktI4oiliyZAmSk5MREhKC8ePHIyfHcyearw9fwaWyOsSGBWHOCJ17YnJ/dl941PUDsNNYzZC5I9OhVAjYe6EMJ65Wyjqsn44Xoq5Jg85xYRjkazIuQwKC2KQH0KbOG2A/Wosnsffmm+yrOH5F/39u0WixQpftfmB0BtQhJtrPaVr0dc9y9/BujbqDvo/3pmeAulJApQbSRrh2vwD74XNVnbcfG6sZ0idFjZFdYqHRilj3q36y7YejV1FY1YCECBVuHpDiwRF6iGA1oNTJ5UhuTjhDYDBw5ydAYj+gthj46Bb5+8PXXwN+ehpY/Scg9xcgIBgY8wxw2zr2fGn7DLxPXK1CfZP7LuJ9tr771LcsIaNSs8DbWQbNYYrKi7uBkrN2vXRMIpvIPF0fhRaN1vmxOMjZRvYeZ4ZXoUdShMfG4QvEhquw4E+d0SEqBLVNGuw8U+LpIbXh+6NXAQAZcWG4vns8IoMD0NCsxR8Xr+G93bl45JNDOP4bSyx9XNgBq3edx+8XylDX1GJpsx7B7sD7888/x6JFi/DCCy/g0KFDyMzMRFZWFoqLTZ+IYmJi8Oyzz2Lv3r04evQo5s+fj/nz52Pz5s3SOm+88QbeeecdrF69Gvv27UNYWBiysrLQ0NDg+JE5SLNGi3d12e4/j+midwf3RMY7wXT9sCU6RIVgUl8ms1z760UZBwV8JfXu7uDxGTuXY6HOGwD6dlBjhi6g+fuPpyCKzCzj2+yruFBSi6jQQMwblW562xWXWJ11QIh7XEH7z2L3PNvdbQKb9XYHrqrz5oG8q+vU2wG8fvuzP/JR3dAMURTxf7+wrNy8Uem+2VLHGoIAROiy3iQ3J5wlWA3c81/WxuhaLvDJbUBjtfPb1WqYydW7g4F9qwFRA/SaBjy6H7hhMZDUj61Xdp6t207oEBWCxEgVWrQijl6ucMs+CyrrkVdeJ7VQ8hm0WmDn62zZ2Ww3JyoV6JbFlg+us+ulXYKYiuF8cyz2XihzfiwOsruElfhlRlR5bAy+hCAImNyPxQ4bjxV4eDRt+eEIG9MD12fgw/uGIXvJTdj2xBgsvz0T91yXhszkEAxUsNht3eUULPvpNO78v9/R94XNmPT/dmPx18fwxYF85BRVQ6sVLe3K5dgdeL/99tt44IEHMH/+fPTu3RurV69GaGgo1q41LVkZO3YsZs6ciV69eqFLly74y1/+gv79++PXX38FwLLdK1aswHPPPYfp06ejf//++PDDD3H16lV88803Th2cI3x18DLyy+sRF67CPdd10j+RPIDdFx4HNM2uHYQTGW+AtRYDgO+OXEFxlTyTF/nlddh7oQyCAMx0s3ukRzDjbG7IEzf1QJBSgd/Ol2Hn2RI0a7T4fzpDvoeu74KIYDPBLQ/m47q6pz6598367B/gHpk5x1WBdylJzTljuseja0I4ahpb8Pkf+fj1XClOF1YjNEiJu4d1sr4BXyVcV+ddQy3FCBmISALu3QCExrEJ+M/uBlra9tu1mfz9wHs3At//D1BXxsqO7v0GmPUxEK373kalsd9uTaO+3rsdIAiCFPwecJPcnGe7+6SozZ972yMnv2HGvnJluzlD7mP32Z/YVY6jrGSGnZfFOHybfVW+8djB+ZIa/F7O6ss7QGb1iR8zpT9LJm07VeRWpYo1LpTU4GRBFQIUAibqOrgoFAK6xIfjtsEd8eqMfvh2ZiiChWY0B8di1sQbMblfEpLVwdCKwKmCKny6Pw9P/fcoJvzjF2S+9DPueX8f3vr5DLadKkJlnYtjulbYddXf1NSEgwcPYvz48foNKBQYP3489u7da/X1oihi27ZtOHPmDK6//noAQG5uLgoLC422qVarMXz4cLPbbGxsRFVVldFNLrolRmBY5xg8PLaLsTlHdGcgKIKdAEvtk+bYhVarD/YcyHgDwMC0aAzuFI1mjYiPfr8ky7A2HGamaiO7xKJDlPtcSj2GocGaGVJjQqWs9rIfT+PLA5eRV16HuPAgzB1pIeAp5YG3i+u7OcFqfbCtCAC6jnPPfgFmfgbIKzVvqGSST4ACb7ATEJ9sW7fnIlbvYpMcs4amQh3qQxeg9kIZb5fyyy+/YNq0aUhJSYEgCDZNlDc2NuLZZ59Fp06doFKpkJ6ebnbS3iuJ7QLc/SUQGMbaMm74Mztn20N1IXvdmglAQTYzy8paCjy8B+hyg/G6CqX+N7Tdyc2ZFNhddd4+Wd+t1QK7dNnuEY8AIVHybbvrOKa4a6gETmyw/XUVLPC+IsZh03HP1ANvOHQFl0VmGBpQdZkZEhJOk9lRjY7RIahr0mDnGe+Z0PjhKMt2j+oaZ7afOe/fHdh5FB4c0xX/unsw9i4eh98Xj8PqewbhoTEZGNY5BiGBSlQ3tuDXc6V4d/s5LPjgALaccu81gl2Bd2lpKTQaDRITjd2yExMTUVhoPqtQWVmJ8PBwBAUFYcqUKXj33XcxYcIEAJBeZ882ly5dCrVaLd1SU+VraTC4UzQ+f/A6zB+ZbvyEQqGv83al3LwyH2iqAZQGdcYOwC/EP9mX5/QPoyiK+OoQl5n7QbYb0NdeW8h4A8CjY7tCHRKIM0XVePE71gLOqETBFLymytWO5oYMnsvuu06QR6pmK66o8S7VbSs8CQiOlG+77ZiZAzsgNiwIVyrqsedcGRQCcN+ozp4elmehlmIupba2FpmZmVi5cqXNr7njjjuwbds2rFmzBmfOnMGnn36KHj3c+DsoBx0GAXd+DCgCgRNfM+8MWy78W5qAPe8A7w4BjnzKHht4D/DYQRZUmSv/4b+h7S7wZueZg3nXpFIsV+KT9d0nN7BrkGA1ayEmJwolMGQeW7bHZE3Xw7spPBU1jS3Yftq9AZpWK2LD4Su4IsaxB5qqmUcC4TSCIEhdCH7wIrn5D7r67qmWOiSY6d+dpA7GxL7JWDypF754aASOvXgTNv7Pn/DazL64bXBHdE0Ix8C0KBeN3DRucTWPiIhAdnY2/vjjD7z22mtYtGgRdu7c6fD2Fi9ejMrKSumWny+vBEsQBNM9sN1R581l5nHdnarDval3IjpEhaC8tknKVjvKgUvXcKmsDmFBSkzU1Y/7PLHdAAisrVttqdnV1KGBWHgDuzBq0miRENGqRMEUUsbbjfXJXW4EHtoN3PJv9+0T0E8e1ZXKd3KUHOH921jNkOBAJe4dof/cTe6X3KbNnd9BUnOXMmnSJLz66quYOXOmTetv2rQJu3btwo8//ojx48cjPT0dI0aMwKhRo6y/2NvociMwczVb3v9v4Ne3La+fsxVYNQLY8jwLFDoMBu7fDkxfCYRbcYfm5wlXKu1cQO/kSKgCFKioa8aF0lqX7qu0phHnimsAAEPTfSTw1mqAXW+w5eselTfbzRl4L1PBXf4DKLDBOLixhl0TARjUn/kPfJvt3PWlvey/WI4rFfUIUoVCDNNNruqy8ITzTO3H5ObbTxV7hTHZmcJqnC2qQZBSgZv6mIk/NC1A3j62bKV/d4BSgT4patw9vBOW356JrYvGoEt8uMyjtoxdgXdcXByUSiWKiowzCEVFRUhKMh+QKRQKdO3aFQMGDMATTzyB2267DUuXLgUA6XX2bFOlUiEyMtLo5hbcEnhzYzXH6rs5AUoF5utk0Gt+zXVqxvm/B1i2e3K/ZMuZXF8iKFRfZ2cl633viE6S/P7RG7pa7m8uivqMt7uNwZL769t7uQtVhD4AKpOpDQ+1EjPJPdd1giqA/aQ/YK6NnT9BUnOv4rvvvsOQIUPwxhtvoEOHDujevTuefPJJ1Ndbri91ZWmZU/S7DZioa/G07WXg8Mdt1ym/AHw6G/jkVqb6CYsHpv8LWLAV6DjYtv3wCUa5O0PIRdl5oK68zcNBAQpkdowCABy86NqM5B+6bHePxAjzUtT2xgmDbPd1Mme7OeEJzMwPsM1kjfsMBEdh0hB2/bLjdIlba2S/PqS/HhWidea0FHjLRt8OkUiLCUV9swY7Tnve3Zxnu6/vHm+6SxAAFB1jE5oqNZDYx42jcwy7Au+goCAMHjwY27Ztkx7TarXYtm0bRoywvT2RVqtFYyMzJencuTOSkpKMtllVVYV9+/bZtU23IAXeR+2v67IVyVjNsfpuQ2YNTUW4KgDnimuw66xjX6D6Jo3kcHirL/fuNoUNBmsAyzZ+cN8wvHFrf9xrLdtdXcB+IASFvnbP1+EBcrlMBmvUSswkceEqfHL/cLw/ZwgyU6M8PRzPQxlvr+LChQv49ddfcfz4cWzYsAErVqzAf//7XzzyyCMWX+fK0jKnue5hYNTjbPm7/wHO/MSWm2pZML5yOHDmR5ZVHLGQycoH3m2fqSb/nfNGqXnlFXaMH95s8unB6Tq5uYvrvHl99/AMX8p289ruha6dMOcma0e/sO7UzwPcqDT0TIpEj8QINGm02HTCPbLk+iYNfjzGfs9vGdSBmQ8ajotwGiO5+VHPmOdxRFGU6runZdogM0+7jpVQeDl2S80XLVqE9957Dx988AFOnTqFhx9+GLW1tZg/fz4AYM6cOVi8eLG0/tKlS7FlyxZcuHABp06dwltvvYWPPvoI99xzDwD2Jj/++ON49dVX8d133+HYsWOYM2cOUlJSMGPGDHmOUi5iu7EWUM218gURrXGilVhrIoIDcccQdpGyxqDHrz1sPlGImsYWpMaEYJivSLhsxUpLMUO6JoTjjqGppksUDOHbiu4MBKgsr+sryG2wRq3EzDIkPQbjeydaX9Ef4BJeuXsuEw6h1WohCAI++eQTDBs2DJMnT8bbb7+NDz74wGLW29WlZU4z/kUg8y7WCuzLecDut1gd9+63WNvIjBuAh38Dsl5zLICK1QXeNYVAg5dk+zlXDwHaZqDwGJMht2Jwmr7O25Xs9zVjtRMbWGlBcBQw/CHX7it9NJscb6oBjn1peV2DwBsAbta1VHWXu/mWU0WoaWxBh6gQVlJAgbdLmNKPBbnbTxejttFzcvMTV6uQW1oLVYAC43pZuK6R6ru9LFlrBrsD71mzZmH58uVYsmQJBgwYgOzsbGzatEkyR8vLy0NBgX72q7a2Fo888gj69OmDUaNG4auvvsLHH3+M+++/X1rnqaeewmOPPYYHH3wQQ4cORU1NDTZt2oTg4GAZDlFGlAFAUl+27Aq5uaZZX8flpNScM39UOhQCsDunFGcK7e87yk3VbhnY0XpQ6WvYmPG2C561cKexmqeR02BNq9G3JiOpOWGJCF3Gu7akXfVA9lWSk5PRoUMHqNX64LNXr14QRRGXL182+zqPlZbZiiAAN7/D+iK3NLBMd/VVFhTM+oS1IHPm9z44Um8UWOZlWe9ig3OjiRr0QTqDtXPFNaioa3LJECrrmnGqkE1I+ETg7c5sN8A+vzzr/cday0aBrQPvTBZ4771QhiKZWtdagsvMbxnUgV2PUuDtEvqkRCI9NhSNLVpsc7N5niE8231jzwSEq8yUuWq1Zo3VvBWHzNUWLlyIS5cuobGxEfv27cPw4cOl53bu3In169dLf7/66qvIyclBfX09ysvL8dtvv2HWrFlG2xMEAS+//DIKCwvR0NCArVu3ont3L81mSXLzbPm3XX6BzZAHhQNqeeR0qTGhyNIZEqy1M+t9taIev55jxmJ+42ZuSJztGW+b8YSxmqeRM/CuzGct/ZQq/UmXIEwRFs9KOkQtC74JjzJq1ChcvXoVNTX6zOjZs2ehUCjQsWM7P78oA4Hb17PsYWAocMOzwKP7gV5TWWDjLJLBmpcF3iWWA++YsCBkxLF+y4dclPU+cKkcoghkxIUhIcLLkjWOcPxr92W7OZmz2Tm16Bhw5aD59VoF3qkxoRjSKRqiCHx/xLVZ7+LqBuzOYdejMwd2MBoHBd7yYig33+ghuTmTmbN9T9NN8Jik9Awz/AsIAZIHuGdwTuIWV3OfIom3FLPBAdJeuMw8vqd9NWBW4K3FNmRfQWlNo82v23D4CkSRzSKnxfqhQzJvKVZTJJ8jNw/i/SrjzaXm553vt8lbicVktItaHsKDKJQs+AZY72RCVmpqapCdnY3s7GwAQG5uLrKzs5GXxy6CFy9ejDlz5kjr33XXXYiNjcX8+fNx8uRJ/PLLL/jb3/6G++67DyEhIZ44BHkJCgXmfAc8kweMeQoIlPGYvLWlmGHgbWaCWmor5qI6b5+SmRtmu0cudF+7zNAYoO8tbNlSazFurmaQGJruJrn5d9lXodGKGJAahQzuQh2l89SpuES9vGVmis7dfMeZEtR4QG6enV+By9fqERqkxA09LHR+0PXvRupQIKB9GCtS4G0vhs7mcn/RJWM1eWTmnMGdopHZUY2mFi0++d22mUHD3t23+ZupGkcVAUTqjr1EplYuUissPwq8o9NZ5rGpxvmeytL/j2TmhA1QnbfLOHDgAAYOHIiBAwcCYP4vAwcOxJIlSwAABQUFUhAOAOHh4diyZQsqKiowZMgQ3H333Zg2bRreeecdj4zfJSgUTrUBNQvPeHuT1FzTYjwRYKbdmasD732+FHgf+y97j0OigWFuynZzuNz8+FfmEw2tMt4AMKV/CgIUAo5dqcT5krZ1/nLx9SHWtuzWQR30D/IJgKYa6uUtM72SI5ARH4amFi22nXJ/ZxAuMx/fKxEhQRaSLO1MZg5Q4G0/Cb0ARSDQUCG/vEVGYzVDBEHAAl17oY9+v4iGZuv1jofzK3ChpBYhgUpM7mfBTdDXkQzWZKjzrq/QB57+5MgdYCALL3PSlFBqJeZH/z/CccjZ3GWMHTsWoii2ufFSs/Xr12Pnzp1Gr+nZsye2bNmCuro65Ofn46233vKNbLer8UZn82sXWdkPx8w5kgfe2fkVaNbI2w2mtrEFx65UAvCBwFvTAvyi69s98jH3Zbs5HYcCiX2ZT8GRz9o+31SnL9mJ0me8Y8KCMLpbHADXZb1PF1bhZEEVApUCpvY3kB0HBut/4ysuuWTf/oogCJjaj7ubu8e1nqPVitio2+fU/hbiD1E0CLwt9+/2JijwtpcAlT4jLbfBmosy3gAwqW8SktXBKK1pwnc21OJ8dfCy9Dqzpgb+gGSwJkOdN88IRKS4/6TqaeSq85ZaiflRjTzhONTLm/AFpN/P895jFMgDbR74lOcCLW0N1LrEhyMyOAANzVqcKpDXlf1Q3jVotCI6RIWgY7SZcrimOmMTOG/l+H/Z+TEkBhj2oPv3LwjAENadCAdMmKxV6gwQVZGs/tyA6QNYFvq77CsQXSD53qDLdt/QI6Ftn3aq83YZU3STHLvOlKC6wX292g/mXUNhVQMiVAEY0yPe/IrXLrIWvYpAoMMQt43PWSjwdgRDublcNNczczVA9ow3AAQqFZg7Mh0AM1mz9OPY0KyRjDL8rnd3a+TMeEv13X4YNMoVeEutxCjjTdgAZbwJXyAqjZlfaRr1dbaepkSXKMgYAwRFsHZqJtqsKhSCy+TmvL57uKVs949PAv8aDmT/R9Z9y4qmBdhlkO1WRXhmHP3uAALDWJKA185yDGXmrQwDJ/ROREigEhfL6nD0cqWsQ9JoRXyTzQLvW0yZ/FLg7TK6J4aja0I4mjRabDnpvsnrH3Txx4Q+iVAF2CAz7zCIeWy0EyjwdgRXBN6lZ5n7bkiMvi5RZmYPTUNokBKnC6vx2/kys+ttPVWEqoYWpKiDMSIj1iVjaTfY0cvbKpKjuR/Vd3MMMzaO0ljNZjcNt0cQluBtmJz1FiAIT6JQ6k0qvUVuLk0k97Q6Qe2qwHvfBRvquy/uZveb/xeoLZV1/7Jx7Es2aRESAwx7wHPjCI4E+t/Olv9YY/wcl3Kb6LgTpgrAhN7st1Zuuflv50tRVNUIdUggbuhpIvtJgbfLEARB6um90U1yc41WxMZjbKLcops50C5l5gAF3o7BLesLsuUzWJNk5r3laT9iAnVoIG7XZbDf333B7Hr/Pch7Jfph7+7WcElz1WUW+DkDN2jzx4x3DPMYcCrjzV8bFg+ERDk9JMIPIKk54St4m7M5l28bBd6mDdYGuSDwbmjWIDu/AgAw3FyCoLFaH5DVXwN+fl62/cuGYW33qP/xXLabw03WTn1vbErJlRZm2nhyd/PvjzL3cbngpmrTMpNNZz8p8HYpvK3YLzklqKx3vdx834UylNY0Iio0EH/qGmd5Za7KaEfGagAF3o6R2Ie5NNeWyNemhhurJcovMzdk/qjOEATWIuBccVsHyuKqBvxylhlo3GLoHumvhMbos2ZmXFtthjLewLVcx2sUeSsxMlYjbIWk5oSvIPXylqnDhjNoNfpxJPQ0GJtpZdiA1CgoFQIKKhtwtaJeliEcya9Ak0aL+AgV0s21O+VZ+cBQAAJw5D9A7m5Z9i8bv/+LlRmGxgJDPZjt5iRnsnpZbTNw+GP94yYczQ0Z3S0eUaGBKKluxF4Likp7qG1swabj7LfbpMzccDwUeLuE7okR6J4YjmaN6Ba5+fe6zPrEPkkIVFoIUauusutJQQGkDnP5uOSEAm9HCArVB09yyc2LuKO5/MZqhqTHhWF8LxZIrtuT2+b5DYevQCsyaZjUK9HfkUNu3lwPXLtkvD1/Qt1RV6PY5HiNIrUSI+zFMONNfV6J9gz3tXDWJ0MOuKN5QDDrpSyZkJqeFAgNCkDvZGYoKlfW27B/t2BOJcgTGqnD9cZhP/wVaGk0vb67KT4FbH+FLY97AVB5yTUXz3ofXAdodU70FTzj3VZqDgBBAQpJlvytribbWTYdL0R9swad48IwMDXK9EpSL+88+o13Ebyn98ajru3V3qzRYtNx7mZuo8w8qR8QrHbpuOSGAm9HkbvO21Bq7mIW/KkzAOCrQ5dxrVbvQmrYu/tWc7OL/oh0UeGEwVrZOQAicwMNs+DS6KsolM7LzamVGGEvXK2iaQQa5DX9IQi34k0txfi5MK47+22PN8jGm1E0yV3nzft3X2epvltKaPRmgW1YAjuP7PGC3vGaZmDDn9lkdNcJwKA5nh6Rnj4zWTBTkQec384es5LxBvTu5puOF9rUttYaXx9m16MzB3YwP7mi1l2rUi9vl8Hl5rtzSlFZ5zq5+W/ny3CtrhmxYUG4LsNKe8B22L+bQ4G3o/DAu/Co89tqqGQ1xIA+yHMhwzvHoE9KJBqatfjPfr0859iVSpwtqoEqQCF90QjIk/GWjGh6uKyG3+vh5kCOGqxxqTm1EiNsJTAEUOlmw8lgjWjP8AnHmkKgQd62XHbDEwX8eiWqk9513Uw/ZTnrvJs1Wmk7wzpbMIAtNlAShkQBE5eyv3950zmjTzn49R/MJyg4Crj5Xe+6LggKBTLvYssH1gLNDfpyHbX5wHtIp2ikqINR3diCHaeLza5nCwWV9ZIJ8MyBFsoeqZe3y+maEI6eSRFo0YrYfNJ1ZVu8m9LkfskIsCQzB9qtsRpAgbfjJPdn93JkvLlJSWQHt5hGCYKA+0ezrPcHv11EUwuTEvHe3Tf1SYI6JNDl42g3xFl2bLUJSSbtx0GjMy3FtFpqJUY4hiQ3pzpvoh0THKlXcJR5OOttOJEMsKw3/102IzfnGe+TBVWoa2pxavfHr1SivlmDqNBAdEuwIM9urSTseyuQMZZNEGx8wnPS5IIjwK7X2fLkN4FIL0x0cGn+2Z+Ay/vZcmAY870xg0IhYJrOZM1Zd/NvDl+FKALD0mOQGmOlVRTPwl+jwNtVTO3vWnfzxhYNNp8oNNqXWWrL9O0M00a4ZDyuhAJvR0nqx+4r89mHwBmK3VPfbciUfilIiFChuLoRG49dRWOLBt/qZptu8/fe3a3hs/rXLrFabUdofaHij0gZbwcC76orQEs9oAjU13QRhC1QSzHCV5BMzDwdePOA1uCaxUpLsQ5RIUhWB0OjFXEk37myDy4zH5oeY77zSm0pUFtsPDZBAKa8zbLzF3YAx79yahwO0dLIJObaFqDXNKDf7e4fgy3E9wA6/Ym1ud3+KnvMRA/v1szQyc23nyl22AVbFEV8fYh317HB5DfaoM6bcAmTdfX7e86VGpWoysXus6WobmhBYqQKQ9OtyMzz9rL7+J5AmBXncy+EAm9HCVbra1YLncx6F5s4ibmYoAAF5o5MBwC8vzsXO04Xo6KuGYmRKusW/v5GWBzrrwnR8QseKePtz4G3E728eYYnpjOgDJBvTITvQ4E34St4Q0sxrUa/f8PSOH5us+C6zuXmh/Kck5tzY7Xhluq7+XVVVCdj07LYLsD1T7LlTYuB+gqnxmI3O5exZEtoHDDlH94lMW8Nz3rn72P3ZozVDOmZxFywm1q02HzcMZXRiatVyCmuQVCAApP62aAGIGdzl5MRH47eyZFMbn5CfvXYD0f1MnOrbYzbscwcoMDbOeQyWCs2MABxI3cNS0NwoAInrlbhtR/ZSWrGwA5Q+nvv7tYIgoHBmgN13poWfZbXH3t4c/hFY0We/a6ypWSsRjhIhK7+j6TmRHuHZ7w9KTW/dhFoaWBZ4+h0/eP83GbhHDk4jQXeBy6WO7x7jVbEHxd54G2pvtuCYe2ov7BzSW0xsO1lh8diN/l/AHtWsOWp/wDCvdxotdc0NkHAsWCsxhEEQTJZ+/aIY+7m3OR3Qu9E28oe+bi8odWeD8O9nzYek1du3tCskVqVWXUzB9pt/24OBd7OIFvg7f6MNwBEhwVJ7uX55UxCfRu5mZvGiozOIhWXmHNpQIhFYxKfJyweUEUCEIHytq3sLMIDb2olRtgLZbwJX8EbnM15YM0dzTl8crr0rNna6cFSxrsCWq1j9dWnCqpQ3dCCcFUAeiVHmF/RUglfgIoFvgAzD7t8wKGx2EVTHfDNn5l0u98dQO+bXb9PZwlQAQPv0f9tQ+ANADdnsuDpt/NlKK5qsGuXzRqtZLJ1qy0yc0Bf55u3F2istmt/hO3wdnG/nS9DWY18Lfl2nC5GbZMGHaJCMCgtyvLKDVV6U+t2WN8NUODtHHIE3jUlQF0pAMEjMuT7dK3FACCzoxrdEi2cyPwZZ1qKSRcqXQGFH3/lBMHxOm+e4fFnczrCMSjjTfgKhuU6Ztp2uRypvrtVB5aYLoCgBBqrgGrTGbHeKZEIDlSgsr4ZF0prHNo9l5kPSY+27HxsrUVr59FA5mwAIvD940yZ5kq2vczOexHJwOQ3XLsvORk8T7+sti41B4DUmFAM7hQNUQS+O2KfydrunBKU1jQhNiwIo7vZqAiI6w5Ed2YJjvM77Nqf29j6IvCfO4EW+euj3UV6XBj6doiERiti8wn5JrJ/OMp7dyebbxvHyd/PJq+i0wG1jRMzXoYfRwEykKQLvMsvON4jls/KxnRmLRzcTJf4cEzozTJCdw7z42ysNWyQ0Zml1CBD4O846mzOW4mR1Jywl/AEdl/jXHsbgvA4UWn6tl2V+Z4Zgzmj0IAgdh1juE4rApUKZHaMAuB4WzEeeA+zVN8tirYpCW96FQiJBoqOAftWOTQem8jdrd/+ze+yfbYXYjoDA+9lPjd21NRO17mb2xt4f32IydNvHpCCQGstpTiCAPSYzJbPbrJrf26hugj4dYWxQ3w7ZUo/9r5uPOacaz2ntrEF2077j8wcoMDbOcJi9TOAhccc24aH6rsNefuOTHxw3zDcOdS22Uy/hGe8yy/YP2NZQsZqEjEOZLybavV97qmVGGEvvMdrDWW8iXaOQqlXDXlKbi718DYR0NpgsMbl5gcu2h94i6KI/RdtMFarugo0VgKKAMsT3mFxwARdjfeOvwMVLpjMaKwGvn2ELQ+aA3SbIP8+XM3N7wJPXdCrh2xgcr9kKBUCjl6uxIUS29QNlfXN+FlX63vLQDvLHntMZPdnN3tODWKOsz8B0JVWFJ306FCchcvN954vQ6kMcvNtp4vR0KxFp9hQ9O0Qaf0F7dxYDaDA23mclZt7QeAdERyIMd3jrUs8/JmIZFafLGqAcjtduXnG25+N1Tg8411+wfbXcBf0kBiLPUQJwiS8j3dDpePtAAnCW/Cks7lWow+q43u2fd4GLxQeeB90wNn8XHENymubEByoQL8OUeZX5NdVsV1ZJt4SA+5htaLNdcBPT9s9Jqv8/BwzFFWnATe9Jv/23YEg2O2+HheuwuhuzJjN1p7ePx0rQFOLFt0Swm0LwgxJGwGo1Kx00x01+/ZweqN+udhLA+/LB4Hl3YFj/7W4WlpsKDI7qqEVgU0OutYb8oNOEWGTzLy5HrhykC1T4O3HOB14e8ZYjbATQXDMYE0UKeNtiCM13lJ9N2W7CQcIjmLyXIAM1oj2j9TL2wMOzhWXTDuac6RzpIWWYjpn8wsltSi3sx8w7989KC0aQQGW6rstGKu1RqFgRmuKAODMRuMgyVlytgIH17PlGSuBYDuDyXaOodxcNGO4Z8jXh5nM/JZBHe1PBCkDgW7j2fLZn+x7rStpqAIu7NT/7a2B97Ev2Pnx8MdWV+Xu5rwFmKNUNTRj59kSADbKzC8fALTNLBEW3dn6+l4KBd7OIgXeR+1/rVEdkucy3oSNSBcVdtR5VxcATdWAoNAHnf4M/x/UFLETki1QKzHCGQTBwNmc6ryJdg6fgLTXJ0MOJKPQboAyoO3z0qSA+XNkdFgQusSHAQAO25n1tqm+G7D/uiqhFzDyMbb841NAo2PGb0bUXwO+021z+J+Bztc7v812xoTeSQgOVCC3tBbHrlj2Qcovr8P+3HIIAjBjoA1BmCl4nfcZL6rzPreVmb4F6XrJF58y6/rvUYpOGN9bYLJObr4vtxzF1fa51huy5UQRmlq06JoQjp5JNhg7G8rM27FClwJvZ0nqz+5Lz7B2EfZQmQ801QCKQArK2gOOOJvzC5Xozqw1h78TrAbCdGZXtkr2qZUY4Sxcbk7O5kR7x5MtxaT6bhMyc0AfeNeWAHXme3VLdd52GKyJooh9uWUAbAm87ch4c65/CojqxPxEdi61/XXm+OkZoPoq8zUZ94Lz22uHhKsCMKE3qwu3JjffoMt2j+wSi2R1iGM77DqOOeuXnLK/Zamr4AqKQXOZqqKxCqi87NkxtUYU9QF3bbHVCeqO0aEYkBoF0Um5Oc+Y2yQzBwyM1dqvzBygwNt5IpJYICFqbZopMoKfxOK6M5kM4d3EOZDxlurhSGYuYdgSxxaolRjhLNTLm/AVuPKnptB21ZBcSI7mZgJvVbjecNbCeXJIJxY42+Nsnldeh6KqRgQqBUmubhKtRr9ve5SEQaHA5OVs+fdVjqkYOad+AI5+xpRuM1Z5pGONtzBd19P7+yNXoTHTu10URSnwtttUzZCQaH1Q5g3u5i1NQM7PbLnPTP01jLfJzasLgXqDiTIbzKKnSnJz060DrVFR14TdOaW6bdmgcNC0AJf/YMtpfhh4r1y5Eunp6QgODsbw4cOxf795e/z33nsPo0ePRnR0NKKjozF+/Pg268+bNw+CIBjdJk6c6MjQ3I8gGMjNs+17rSOzsoTn4MFz2Tnbe35K0jwKGiViM9i9LVJJUdQH6CQ1JxyFenkTvkJwpH4iqczNWW9zPbwNsUFuPkiX8T6SX4FmjdamXfP67syOUQgOVJpf8dpFVoceEGy6Dt0S3W8Cek9nJqo//NUxd+zaUuCHx9nyyP8B0obbvw0f4vru8VCHBKK4uhG/Xygzuc7h/ArkltYiJFCJiX1td043SY9J7P6MF9R5X/yFZbjDE4EOg/XX+t4WeLdOGtqQRJykk5v/cbEcRVX2y803nyhEi1ZEz6QIdE0It/6CshxmgBgUbn7ir51gd+D9+eefY9GiRXjhhRdw6NAhZGZmIisrC8XFpqUJO3fuxOzZs7Fjxw7s3bsXqampuOmmm3DlyhWj9SZOnIiCggLp9umnnzp2RJ7AUYM1MlZrX6hTgcBQVq9z7aJtr6GMd1vsyXhXF7ByDEFp/0UUQXAo4034ElJw68bAW6vVm6ZZuvC1wWAtIy4MUaGBaGzR4uRV27L2ttd3n9SPQ2EhQDfHxNeBoAjgygHg4Dr7XiuKLGCvLWHt1m74X/v372MEBSikmuBvs6+YXGeDrnf3xL5JCFOZ8A6wh+66pN2lPayThSfhMvMek5mJH1dgeFtLseLWgfdxqy/pEBWCQWlMbv7TMfuz3jxTPi3Txnp+noVP7Mv+l+0Yu0f/9ttv44EHHsD8+fPRu3dvrF69GqGhoVi7dq3J9T/55BM88sgjGDBgAHr27In3338fWq0W27ZtM1pPpVIhKSlJukVHW5ASeRsOB96ebyVG2IFCob/gsbXOW8p4U+AtIQXeNmS8+YVldLr1tjAEYQ4KvAlfwhMtxSouAS31gDLIsqOwDd0/FAq9XNzWOm9e3z08I9byis4a1kYmA+OeZ8tbXwaq7fjNOP4VcOo7Vss7czX5uuiYoXM3/+lYIRqajVUEjS0afK+r9Z05sIPzO4vtwq7TtC3M2MxTaLXA6R/Zcq+p7J5/Jvln1FvgGe4Og43/tsIUnUR8o52Bd2lNI/ac4zLzZNteVKgr/UjqZ9e+vBG7Au+mpiYcPHgQ48eP129AocD48eOxd+9em7ZRV1eH5uZmxMQYz1ru3LkTCQkJ6NGjBx5++GGUlZmWpABAY2MjqqqqjG4ehQfexaeAFhsbymta9DPClPFuP9hjsFZ/jRlVANQKyxDDjLc1d09qJUbIAUnNCV+CTwC7U2rOJ5FjzTiac/gks5V2Z9xg7ZANgffVinrkl9dDIehfZxY5EhpD7weSBwCNlcDmxba9pqoA2PgEW77+b0DKAMf372MMTY9BsjoY1Y0t2HnGWB2743QJKuqakRChwqiucfLskGe9PelufuUg82FQRQLpOkf7RN1nsvQMoGn23NhawwPt/ney+5IzrD7dCpP7sfPqHxevobDSdrn5T8cLoRWB/h3V6BQbZtuLeMbb3wLv0tJSaDQaJCYmGj2emJiIwkLbLmiefvpppKSkGAXvEydOxIcffoht27bh9ddfx65duzBp0iRoNKbra5YuXQq1Wi3dUlNT7TkM+YlKY71itc22z2RdywU0jUy6HNXJpcMjZMSelmI8GxGR4nf9Oy0S3RmAwC5qakstryu1EiNHc8IJKONN+BKecDa3pb4b0J8jK/MttuXSO5uXW+3x/MdFJjPv20GNcGtSZDlatCqUwLQVzBzt+FfAuW2W1xdF4Pu/AA0VLBEz+gnH9+2DKBQCbtZJilu7m284zBy+Zw7sAKVCphZRvK1Yzs+2+/HIzekf2H23CXq1njqN1ShrmoDyC54ZV2s0zfrr2e43ASo1i2UseDRwktUhGJrOvsf2ZL1/OKJ3M7cJUdSbHfpb4O0sy5Ytw2effYYNGzYgODhYevzOO+/EzTffjH79+mHGjBn44Ycf8Mcff2Dnzp0mt7N48WJUVlZKt/z8fDcdgRmMDNZslJvzGab4nu2+XsGvsEFGJyE5wJKxmhGBwUCUbrLMmty8lDLehAzwwLu2xDHDJILwJgxVQ+76PFtzNOeExgBh8WzZQkY+s2MUlAoBRVWNuGolW/b7BV19d7qV+u6WRv05w1klYcpAYNiDbHnjE0Bzvfl1D38M5GxmMvyZ/6YuNSaYPoDJyLedLkZVA8v2XqttwvbTLAM+c5AMMnNO6jAgJIZNhOT/Lt927YHXd/econ9ModB/f+ztguQqSnNYoB0UwZKAiX3Y47bKzXX1+xuPWm4XxymqasB+3UTaFFvczAGg6ipzXReUPlGaa1fEFxcXB6VSiaIi46xBUVERkpIsOxEuX74cy5Ytw88//4z+/ftbXDcjIwNxcXE4d870RblKpUJkZKTRzePYG3jzWdnE9v8h8iv4j2ZpDqvhsUQp1XebJUbXt95a4E2txAg5CIsHILC2j9ZUFgTh7USlAUoVU81VuinxYK2HtyE2tN4MCVKiTwq7djtw0XzPbwDYb2t9d2kOcyRXqYFIGy/qLXHDs0BEMlMo7n7L9DoVecCmxfr1qXTQJL2SI9AtIRxNLVqp9/MPR6+iWSOid3IkeibJeB2vUALdbmLLnnA3LznLrl2UQUDXCcbP8Wt+b3E25wF2Yh+WREzqy/62oaUYwNzNBQE4lFeBKxUWJqd0/HisAKIIDEqLQocoG/u187HE92CJm3aOXYF3UFAQBg8ebGSMxo3SRowYYfZ1b7zxBl555RVs2rQJQ4YMsbqfy5cvo6ysDMnJNsoQvAG7A28yVmuXRHViFzwt9UBlnuV1JQdYChrbwDM25RaczZvrgQrdRSW1EiOcQRmgz8LVUJ030c5RKJmJFOAeublWa9Chw4bAm5/zrJRk2VLnXVrTiPMltQAgyVrNYtgpRpBBthwcCUx6nS3/uqLt8Wi1wLePAk3VQMdhwMjHnN+njyIIAqbrTNa+08nNv+a9u+XMdnN4WzFP9PM+/T277zymbZmhtxmscQdznum2M+OdGBmMoTolii3u5t9LMnM7JsZ8qL4bcEBqvmjRIrz33nv44IMPcOrUKTz88MOora3F/PnzAQBz5szB4sV6M4rXX38dzz//PNauXYv09HQUFhaisLAQNTWs9qempgZ/+9vf8Pvvv+PixYvYtm0bpk+fjq5duyIrK0umw3QDPPAuOmFbTQm1EmufKAP0smdrdd6U8TaPLc7m5RcAiECwGgiTyXSF8F8idHJze1yKCcJbcWedd2U+66GrCARiMqyvb6fB2sE884H3H7o2Yj2TIhAVaqWzhZTQkPG6qtfNQLcsJsf9YZGxIeiBNUDuL0BACHMxd6R9mR9xcyYLsH87X4p9F8pwOK8CCgG4eYAM6oTWdLmRfV7LzrnXCwEwLTPnSC3FvERqzr8zPBOfqAtubWgpxuG12rxFmDmuVNTjUF4FBAGYYmt9NwAU6hKa/hp4z5o1C8uXL8eSJUswYMAAZGdnY9OmTZLhWl5eHgoK9P/8VatWoampCbfddhuSk5Ol2/LlywEASqUSR48exc0334zu3btjwYIFGDx4MHbv3g2Vqh21YojpwkwTWuqtO402N+gzfZTxbn/YUufdXA9cu2S8PqHHll7ekrFaN3myF4R/QwZrhC/BVUBWgltZ4Oe6OCuO5hwbvVB44H2qoBq1jaYTFvt0gfdwa/27AXmM1VojCMDkN1lwfelX4Min7PGy88CWJWx5wkt6BQJhlrTYUAxKi4JWBP76eTYA4Pru8UiIcIF8ODgSSP8TW3an3LzqKnM0h6A3eTOEfzavXQSaat03LnNIUnOdxDyhFwCB+aHUFJt9mSET+yZBEIDs/Arkl9eZXY/XgQ9Lj0FipB3vuZTxtlym3F5wyNVr4cKFuHTpEhobG7Fv3z4MHz5cem7nzp1Yv3699PfFixchimKb24svvggACAkJwebNm1FcXIympiZcvHgR//d//9fGOd3rUSj0szHW5OalZ1mtYUi0/mKQaD9ILcUsZLzLzoFla6P0EldCD79IKTtvvlaejNUIOQnX+ZCQ1JzwBfjvojWfDDngAbQtMnNAH3iX51psS5SsDkGKOhgarYgj+RUm1+GB97DOVuq7AddkvAEguhMw9mm2/PNzQE0J8M3DTAWQPhoY+oC8+/NhuMkaN9STpXe3OTwhN+fZ7tRhepWVIeHxumtC0TaTXldSVw5UMbm/9J0JCtVfn9lY550QESxNjP103HzWm2fEp2baoXBoqGSTFID/ZrwJC9ha5204K0uZvPYHN/qy9KMpOcD2oPfYFFFpTAamaQSqLptehytHqJUYIQckNSd8CXdKzYvtDLwjkplLsqix7OMBYLCuPvSgiTrvyrpmnC6sAgAM7WylvruxBqjQqcxcUcI3YiG7ZqsrA96/Ecjfx45x+krqTGMHU/onS23DwlUBuKm3ZWNmp+D9vPN+Z0GmO7AkM+dIcnMPG6zxiaqoNFbSx+HZbzvk5tyhfKMZufmlslocvVwJhQBM6mvHe84z8pEdWccEH4B+LeTE5sDbRbOyhHuQMt5njeu9DOHyP3LjNo1CCcR0ZsvmMjal5GhOyAhlvAlfgkvNawqBhirX7otPMlvr4c0RBAO5uRWDtbQoAKbrvP+4WA5RBDLiw6zLkfl+whJc4wmiDASm/oMtV+iMVSf+nWXDCZuJC1fhT13Z+zO5XxJCglxYFx/dCUjowyaAcra4bj+c+grg4m623HOq+fW8xWCNB/4JfYwflwJv2+vQJ/ZJgkIAjlyuRF5ZW7k5z3aP7BKHuHA7yoh9zFgNoMBbXqTA+6jlVlNkrNa+ickAFAHMybTKTO9Cw4w3YRpLdd6iqA/ISWpOyEF4Aru3sW6NILya4Eh9qZo1Xxln0Gpt7+FtiK2BdyeWxTp06Rq0WuOJbN7v17b67lYmUa4g7Tpg0Fy23O0mYOC9rtuXD/P81F6YNSQViya44fqohy7rfdYNdd45WwBtC/ueWKr5l1qKedhgrbWjOUdqKWZ7xjs+QoURXVg5yEYT7ubczXxapp3dqgqO6sZEgTdhirgeQEAwC8iu5ZpfzxUGIIT7CAjS96E2JzeXMt4UeJvFsM67NTXFQGMVIChsc9ElCGtE6DLe1ZTxJnwErgZypdy86jLQXGu7ozlHGpvlwLtncgRCApWoamjBuZIao+f09d0eMlYzxeTlwO0fALevpzIyB+maEIHXb+uPJLUbejJzg7Nz2yz6DcgCbyNmKdsNeFHG26CHtyE84116xq7/2ZR+Orn5MeOE1LniGpwurEaAQkBWHztLCwp1gXeybxirARR4y4syQP8BLsg2vU5Dlb7/sz2zx4R3YWk2X9NC2VpbsNRSjGdwotKAgHbU3YDwXgxdzc2ViBBEe4L/hroy8Ob13bFdmdzaVqRzpGXX9UClAgNSowAY13nXNrbg+JVKALYaq+mCCFcrCQOCgD4zgKAw1+6HkIeUQaz8oLEKyPvNdftpbgBytrJlS/XdgP7av6YIqC1z3ZgsodXqA//Wgbe6I6v51rZYnTgzJKtPIpQKAcevVOFiqd6x/Qedm/nobnHWWwIa0tKkT25Rxpswi7U6b/4hikj2GaMAv0Sq8zaR8a64BGiamPohKs2942pPWAq8DVuJEYQc8MC7pYE5pRJEe4dnlV0pNbe3vpvDA+/Ss4BWY3FVqZ+3QeB98NI1aLQiOkaHoENUiPX9kZKQMIVCAXS/iS27sq1Y7i6mDIlIAVIGWl5XFQ5Ep7PlYg8ZrFVcZONVqvQKTo4g6LPedsjNY8NVGNlKbi6Kot7NvL+d/dpLz7JraVUkEOU7XgoUeMuNtcBbMlajk0O7xlLGmz8W242ZiBGm4YF3xaW2ciZqJUbITVAoO4EDVOdN+AbucDa3t5UYJ6oTu6jXNOrdxs1gKvDeb4/MvLaMZQ8B8lUh2sLl5md+cp3a6fQP7L7nFNtKECS5uYcCby4zT+jJ1Lqt4VlwO5zNAWBKP1bDzd3NzxRV41xxDYKUCkzoY2f75EKD+m4fKuugwFtuDANvU19wMlbzDaTA+3Tb95lLc+LJjdsi4YlAUDjrac/7NHKolRjhCiS5OdV5Ez6AoUGllayywzgaeCuU+okBK3LzgTpn89zSWpTVNALQB942GauV6K6rotIAVYR94yR8n4yxbBKo4pJremdrNcDpH9lyLyv13RyppZiHDNak+u6+pp93oKUYAGT1SYJSIeBkQRUulNRIpmpjesQjMtiOUhXAwNHcd+q7AQq85SehN3O8rr8GVJroT0wZb98gtisz/mqoAGpLjJ8rIWM1mxAEvVlP616v1EqMcAWSwRr18iZ8gKg0fVa5Ml/+7YuiY47mHElubrlONCo0CN0SwgEAh/Iq0NCsQXZ+BQBguE313SQzJywQFAZkjGHLZ36Uf/v5+4G6UlYX3WmUba/hyTdPGaxJGW8z35kk+1uKAUB0WBBG6drFbTxaIMnMp2XaKTMHfLKVGECBt/wEqIB43RfKlNycMt6+QWCIvkan9QwqZbxtx1Sdd4uBNJGk5oScUMab8CUUSn13CFfIzSsvA001LJlgqT2SOeIslGS1wlBunp1fgSaNFgkRKnSKDbW+HymhQddVhBm669qKndkk/7a5zLz7RNsNCLmUu/iUZ8w+zTmac+J7seRSbYndE9VTdXLzNXtycamsDsGBCozrmWDf+ETRWGruQ1Dg7QrM1XnXlOiyowLVIfkCksGawUWFKFLG2x5MBd7luUx+HhShD5QIQg4Mnc0JwhdwZZ13iYOO5hw++WxD4D1ICrzLjeq7BVtqOynjTViDB96X/2DX4nIhigb13TbKzAH2nVIEsvbDrlCrWKKpFii/wJbNSc2DQvWma0XH7Nr8TX0SEaAQUFHXDAAY1zMRYSoTdeSWqMxnJqiKQJ/rAEWBtyswF3jzOqTodGpF4QsY1nlzqgvYD6mgcCxD4G8Y1ihyeH13XFefMtQgvIAIXeBNUnPCV+CdH0ot11E7hKP13Rw++Vx61mpWj2e8j1yuxK/nSgEAwzNskJmLIpXwEdZRd9Bdm4tAzs/ybbf4JPOoCQgGuo6z/XXKQH0pXZGbDdZKTgMQgbB4IDze/HqSwZp9cvOo0CCM7hYn/T21f7L9YyzQZbsTerIWfj4EBd6uwFzgTbOyvoUpGR1fju5M/adtwVTGm1qJEa4iXFfjTVJzWfjll18wbdo0pKSkQBAEfPPNNxbX37lzJwRBaHMrLKT3w2F4xttUW0ZnKXYy8I7tAghK1kO5usDiqhlxYYgODURTi9Y+Y7XqApYZE5RUmkRYpvskdi9nnffpjew+4wb7E2qJ3NnczQZr1mTmnCT7W4pxpuhah4UFKXGDvTJzwGeN1QAKvF1DUl8AAru4qza4oKA6JN/CVEsxnnWgUgLbiNWZq1UXAI01bJlaiRGugjLeslJbW4vMzEysXLnSrtedOXMGBQUF0i0hwYELM4LhDqm5vT28OQEqIKazbluW5eaCIEhZbwCIDg1E1/hw6/vg11WxXWmym7BMD13gfX4H0NwgzzYN24jZi6cM1qw5mnMS+xmvbwdT+ydj9rA0vDKjL4IDHWir66PGagBgp+iesImgMCYhKT3D5BLcSZeM1XwLLhOqLQbqyoHQGP3FBblx20ZINBAaC9SVsZqj5P4GUnMKvAmZoRpvWZk0aRImTZpk9+sSEhIQFRUl/4D8Ea4MqikEGqqA4Eh5tuusozknrgfLxpeeBbrcYHHVQZ2isfVUMQBgaHoMFAp76rvpuoqwQnImEJECVF8FLv4KdBvv3PYq8piyVVDog3p7SOBSbjdLzW3NePPnS88w01s7JraCA5VYeosTQbMPB96U8XYVreXmokhSc19DFQ6o09gyv0ChjLf9GMrNRZGk5oTr4IF3Q4V8GQ/CbgYMGIDk5GRMmDABe/bssbhuY2MjqqqqjG6EAcGR+s91mYxZ76orzK9EEaA3WXIEU8owMwxO02e8barvBvRBC11XEdYQBKB7FluWQ27Oe3enjQDC4iyvawo+WVR6FtA0Oz8eWxBF663EOOqOrEWatsWm769s1JUDlXls2VpWvh1CgberkALvbHZfeZnVOSkC9IEG0f5pbbAmZbwp8LYZQ4O1ujIWFEEgczpCfkKiWd9jgLLeHiA5ORmrV6/GV199ha+++gqpqakYO3YsDh06ZPY1S5cuhVqtlm6pqaluHHE7gSus5JSb8/rumC7OmRvZEXhnpkYhSMkuS22q7waohI+wD56ZPrvZ+TZezsjMASAqjXVv0Ta7xqPBFNWFQH05y9JbU7IIgj7wdUBu7jBFupryqE5ASJT79usmKPB2FVLgrXPm49nuuO4+59Dn1xheVNRfY7JzgGTS9sAD7LJz+gtHdSrrlU4QciIIJDf3ID169MBDDz2EwYMHY+TIkVi7di1GjhyJf/zjH2Zfs3jxYlRWVkq3/Hw3t95pD/DJSzkDb2fruznSpID1wDs4UIk3b++P/53cE31SbJDMazX6gJ4y3oQtdL4eCAwFqi7r5cyOUFcOXPqNLTsaeAuCfsLIXYEt309sNyAw2Pr6UuBtv8Gaw/iwzBygwNt18A9MZR77gtKsrG/CA+/SM/r+3REp8tXZ+QOGUnPDVmIE4QrCdUZeFHh7BcOGDcO5c+azPSqVCpGRkUY3ohU8uJVTas7bnzrbQ1fyQilh10JWmD6gAx68vott/buvXQRa6pmKhZu4EYQlAkOYAzkAnN3k+HbObgJEDTMgi053fDvuNljjAXSijRNVSZ4MvH3P0RygwNt1hETpv4yFR8kAxFfhFyUlZ/Qz+vFkrGYXPPAuP0/13YTr4WaX1dTCyhvIzs5GcrIDfV4JPa5wNpfDWA3QeaGkGm9TLvh1VXwPQOGAczLhn/SYyO6dqfPmbcQczXZzuIFZsZsM1vh+rBmrcfh6hcedl+bbio9nvMnV3JUkZ7IZ2YIjBhlvkkP5FHw2v+oKcPmA7jGq77aLaF2mov4akL+PLZNUn3AVJDWXjZqaGqNsdW5uLrKzsxETE4O0tDQsXrwYV65cwYcffggAWLFiBTp37ow+ffqgoaEB77//PrZv346ff/7ZU4fgGxj6ZGg1zgehcjmac+K6A5X5bHK60wjnt8chw1rCEbpPBCAAVw8DVQVApJ0Tf011wLltbNnZwFvKeLsp8La1lRgnvherB68rZedMPnHtKloa9WUuyZTxJuyF13lfOWRQh0QZb58iJAqI0P1o89lTynjbR1AoENmRLefvZ/cUeBOugjLesnHgwAEMHDgQAwcOBAAsWrQIAwcOxJIlSwAABQUFyMvLk9ZvamrCE088gX79+mHMmDE4cuQItm7dinHjxnlk/D5DVBqTW2saWYsjZ6m6ysxgBaU8ZrCSF8pZ57dliJS9o8CbsIPwBKDDYLacs9n+15/fzkoc1GnOZ2V5S7FrF4HGGue2ZQ1Nsz4WsTXjHRSq72rgDrl58Snmoh4SDUR2cP3+PABlvF0JD7xztrATYkAIEJXu0SERLiC+B1BdwGrYAMp4O0JsF2Z2Ap2UiaTmhKuQaryLPTsOH2Ds2LEQLcgP169fb/T3U089haeeesrFo/JDFEr2G1p8knllOFvvzOu7Y510NOcYeqHICWW8CUfpMRG4cgA48xMweJ59r+Uy815TmUGaM4TFMhVWTRELijsOdm57lijNYQ7qqkh9+YctJPVl/hGFx4GuTvY+t4ahzNzZ/62XQhlvV5KkC7yba9l9Qk9AQf9yn6O1FI96eNuPYVYlMAyITPHcWAjfJlyX8a6hjDfhQ8hZ5y2nzBzQT0bLWePd0qQ3kyMlIWEv3XVtxS7sZNJxW9G0AGd/YsvOysw5ktzcxc7mhv277QlqeXbcHc7rPm6sBjgYeK9cuRLp6ekIDg7G8OHDsX//frPrvvfeexg9ejSio6MRHR2N8ePHt1lfFEUsWbIEycnJCAkJwfjx45GTI6NJiKcIjzeWStCsrG8SZyAtD44CwuI9NpR2i2HgHdvFZ2c6CS8gQlfjXU013oQPwVVCpTLIuYtlcjTn8Mnoynz55LRl55gkVRXps5JUwoUk9mFS8ZYGIHeX7a/L28v8aEJigNTr5BkLl5sXubjOW3I0t1FmzknsZ/x6V1Koa8FMgbeezz//HIsWLcILL7yAQ4cOITMzE1lZWSguNi3b27lzJ2bPno0dO3Zg7969SE1NxU033YQrV65I67zxxht45513sHr1auzbtw9hYWHIyspCQ0OD40fmLXC5OUCzsr6K4cVJfA8KGh3BMPCm+m7ClfCMd20xM6IiCF+A/26WmW/NZjOSJ41MgXdoDBAax5blanlm2KKVzrmEvQiCgbv5T7a/7vQP7L7HJEApU7WuuwzWJGM1OwNv3lKs9CwzP3MVWi2TswM+62gOOBB4v/3223jggQcwf/589O7dG6tXr0ZoaCjWrl1rcv1PPvkEjzzyCAYMGICePXvi/fffh1arxbZtzBFQFEWsWLECzz33HKZPn47+/fvjww8/xNWrV/HNN984dXBeAQXevo9h4B1HxmoOEdvFYJkCb8KFhMUDEABRC9SVeXo0BCEPcknNRVHvKixXxttwW3LJzQ0Db4JwhO66wPvsJhb0WUMUDdqITZVvHNwc0NWBt72txDiRHYBgNVOYyN0S0JCKi0BTNTOK9OEEjF2Bd1NTEw4ePIjx4/XF9QqFAuPHj8fevXtt2kZdXR2am5sRExMDgLUfKSwsNNqmWq3G8OHDzW6zsbERVVVVRjevxSjwJqm5TxIWq5/Np8DbMaLSAIVu9tiHf3AJL0AZAITpvq/kbE74CnzCsqYQaHDimqi6QF5Hcw7v9iFb4E3GaoSTpP8JCIpgxmYFh62vX3iUlUsEhgJdbpBvHPE9AQjMoLemRL7tGlJXztreAvZPVgmCe+TmvL47oRegDHTdfjyMXYF3aWkpNBoNEhMTjR5PTExEYaFtFzBPP/00UlJSpECbv86ebS5duhRqtVq6paba4c7nbjoMYV/s2K76tlOE75E6zPiesA9loG6SSgBSBnp6NISvIxmsUZ034SMER+o/187IuXlAG5MBBKicHxeHG6zJUYMOUMabcJ4AFdD1RrZ8ZpP19U/pZOZdbgQCQ+QbR1AYEJ3Oll2V9ebbjUpj2Wt7cYfBmqGjuQ/jVovtZcuW4bPPPsOGDRsQHBzs8HYWL16MyspK6Zafny/jKGUmPB74825g3kaqQ/JlZqwCHtgOpMlktuGPzP6cfVcMZecE4QokgzXKeBM+hBxyc7nruzlSL+/Tzm+rqZb1PQYo4004B3c3t6XOW2ojNk3+cfDA1lWBNzduS7BTZs7hdd48OHYFBTpjNUOlsA9iV+AdFxcHpVKJoiLjLEFRURGSkpIsvnb58uVYtmwZfv75Z/Tvr3er46+zZ5sqlQqRkZFGN68mpjMQYfn/Q7RzQqKADi7sv+gPhMf7/Ewn4SWE6wJvyngTvgSXhjsVeMvsaM7hgXd5LmsF5gw8eA9L0JeNEIQjdLsJEBRA0TGgwkISr/wCa/clKNlr5MbVBmuOOppzEvvqtyOK8oypNZTxbktQUBAGDx4sGaMBkIzSRowYYfZ1b7zxBl555RVs2rQJQ4YMMXquc+fOSEpKMtpmVVUV9u3bZ3GbBEEQBOEQFHgTvgj3GHFGai53D29ORDIruxM1QPl557Yl1XeTzJxwkrBYIHU4Wz5rQW5++kd2nz6KufTLDVduuKqlmKOO5pyEXmyCoq7MNefN2lKg+ipbdnSM7QS7peaLFi3Ce++9hw8++ACnTp3Cww8/jNraWsyfPx8AMGfOHCxevFha//XXX8fzzz+PtWvXIj09HYWFhSgsLERNDevlKAgCHn/8cbz66qv47rvvcOzYMcyZMwcpKSmYMWOGPEdJEARBEByuQCKpOeFLOCs1F0Wg2AWO5gArtZPLYI2M1Qg5MXQ3NwdvIyanm7kh/LNccto2h3V70Gr13xmeubaXwBC9oqbQBQZrPNsdkwGoIuTfvhdhd+A9a9YsLF++HEuWLMGAAQOQnZ2NTZs2SeZoeXl5KCgokNZftWoVmpqacNtttyE5OVm6LV++XFrnqaeewmOPPYYHH3wQQ4cORU1NDTZt2uRUHThBEARBmIQy3oQvwi+My8471qO+uhBorGSZLVd0l5CrpRgZqxFy0kNX5537C9BY3fb5mhIg73e23HOKa8YQ2wVQBgFNNUBlnrzbrrgINNeyNl0xGY5vRzJYc0XgravvTupveT0fwKHu7wsXLsTChQtNPrdz506jvy9evGh1e4Ig4OWXX8bLL7/syHAIgiAIwnYo8CZ8kag0dnGtaQQq8pi/jD2UuMjRnMOl8KWU8Sa8iLju7DNffgE4vwPofbPx82d/AiACyQMAdUfXjEEZyJz/i44xuTl3OZcDLjNP6MnaaTpKYl/gxAYXBd7+Ud8NuNnVnCAIgiA8juRqXuQ6oxiCcDcKpb4rRNk5+1/vqvpujuRs7kRLsbpy1mvccHsE4QyCoHc3NyU3527mrpKZc1xlsCbVdzsoM+fwoNiVUnM/yHhT4E0QBEH4F7zfcUs90Fjl2bEQhJw4U+dd7CJHc068QS9vR6TwgH6M6jTWu5wg5KCHQZ234WezsZplwQHXycw5iToFh6sCb2cVIlxqXnoWaGl0bluGNNezbQKU8SYIgiAInyMoFFDpLtqrSW5O+BCxPPB2IKss9fB2Ue10VCcDKfwlx7ZB9d2EK0gbAajUzLX78gH94+e2sc9rTIbrP3M8MOaTS3LhrKM5J7IDEBzFOhPwln5yUHwSELVAWLxftF6mwJsgCILwP8IT2D3VeRO+BM942ys1F0X9xbSrJNwKpX58jsrNqZUY4QqUgUC3CWz57E/6xyWZ+RQmSXclPPAuPet8r3tOUy2rXQecl5oLgkE/7xPObcuQAm6s1s/1/2MvgAJvgiAIwv/gcnMKvAlfwlGpeU0R0FDBHM1jXeBoznHWYI2M1QhXwd3Nz+gC75Ym4Oxmtuzq+m6AGbepIgFti2MeDaYoPg1ABMISgPB457eXpAu85azz9iNjNYACb4IgCMIfkQzWqJc34UPwoLmmEGiww7+AZ7ujOwOBLmzl6kxLMVHUS80TKfAmZKbrOEBQsu9CeS5w6VfWXi8sHug41PX7FwT5DdaKucxcpu+LlPF2ReDt+8ZqAAXeBEEQhD8iZbwp8CZ8iOBI/We7zI6sd7Eu8Ha1hDtel/F2JPCuLtRl5ZWuzcoT/klINNBpJFs+u0kvM+8xmZVJuAOu5JBLyi2XoznHsJe3HB1BtBr9GCnjTRAEQRA+ilTjXezZcRCE3DgiN3d1fTcnzsDZ3N4Ld54FjO3i2qw84b9wufnpjcDpH9myO2TmHLkN1uQyVuMk9GLlKHVl8qjFyi8AzbVAQAgQ29X57bUDKPAmCIIg/A/unkpSc8LX4BewDgXeLs54x3ZhGevGKn0/blshYzXC1XTXtRW7uBuovgoEhQOdr3ff/qWWYjJkvEVR/sA70CBAliMrX6gzVkvs4z5VgYehwJsgCILwP8J1Nd5krkb4GtzAzFapuSga9PB2ccY7QAXEdGbL9srNyViNcDWxXfTfHwDoOt696gr+2a7IYz3EnaG6EKgvZxNdcTJ+r6U672POb8vPjNUACrwJgiAIf4Qy3oSvYq/UvKZY72ge54baaUO5uT3wLCBlvAlXwuXmANBrmnv3HRqj92godrJXNs9Ix3aVd/IgScaWYhR4EwRBEIQfwDPeDRVAS6NHh0IQssKloGXnmXmRNSRH83QmJXU1jhisabUGBnCU8SZcSI/J7F4RyDLe7kYuuTl3HpdLZs5JlLGlGA+8kzOd31Y7gQJvgiAIwv8IiQaUQWyZ5OaELxGVBihVgKaRSVat4a76bo4jLcUqLgIt9ey4oju7ZFgEAQBIHQ6MfxGYsQoIiXL//uUyWHNV6z0eeJeeBZobHN9OdRE79woKv5pMo8CbIAiC8D8EQZ/1rqbAm/AhFEpWqwoAZeesr+8uR3MOr6EttSPwlmrQuwPKAPnHRBAcQQD+9Feg/+2e2b9cLcXkbiXGiUwBgqMAUWPfd7g1PNsd2xUICpVlaO0BCrwJgiAI/0QyWKM6b8LHsKfO2109vDk88K4tAerKbXsNz94lyCybJQhvg38Pncl4a5r1ihK5peaCoK/JdkZuzh3N/ai+G6DAmyAIgvBXyNmc8FVieeBtxcBMFIESNzmac1ThgDqVLdsqN6dWYoS/EN8TgADUlTLjQ0cozQG0zYAqUv9dk5NEGQzWJGO1/s6Ppx1BgTdBEAThn0SQ1JzwUXjG25rUvLYEqL8GQDBuo+Rq7JWbUysxwl8ICgViMtgyV3rYCw+IE3qzDLXc8Cy6My3FKONNEARBEH4Eb9tCUnPC17BVau5uR3MOz66X2NBSrKVJn7mnjDfhD/DPeZGjgbeLHM05SQbO5qJo/+sba1jXBYACb4IgCILwCyjjTfgqXGpeUwg0VJlfz9313Rx7Mt7l5wFtCxAUAag7unZcBOEN8IDZ0ZZikrGaiwLv+J7Mjby+HKh2YOK6+CQAkU1+hyfIPjxvhgJvgiAIwj+hGm/CVwmO1Cs6yixkvd3taM6xp6WYZKzWyzWyWYLwNpw1WJNaibko8A4M0U/uFTlgsOanMnOAAm+CIAjCX6HAm/BlbJGbu7uHN4cH+pX5THZqCTJWI/wN7t5ffBrQau17bV05UHVFtx0Xfme43NyhwFtXG57sX8ZqAAXeBEEQhL8SwWu8iwGtxrNjIQi5ie3K7m0KvN2c8Q6NAULj2LKljDygr3MlYzXCX4jJAJQqoLkWqLhk32t5tjsqDQhWyz82Ds+mO9JSrIAy3gRBEAThX4TFAxAAUQPUlXl6NAQhL7yO2lxgW1Oi+9y72dGcY6vc3FBqThD+gDIAiNd9J+11Npfqu/vKO6bWJOqCZnsz3poW/TH5WSsxAAjw9ADciUajQXNzs6eHQRCyExgYCKVS6elhEET7QhkIhMbq+qUW+Z3JC+HjWJOaS47mnVgLI3cT3x249KvlwLupFrh2kS1TxpvwJxL6MEl20Umg5xTbX2fYSsyV8Ix3aQ7Q3AAEBtv2urJzQEsDEBQORHd23fi8FIcC75UrV+LNN99EYWEhMjMz8e6772LYsGEm1z1x4gSWLFmCgwcP4tKlS/jHP/6Bxx9/3GidF198ES+99JLRYz169MDp06cdGV4bRFFEYWEhKioqZNkeQXgjUVFRSEpKgkDmMwRhOxFJLPCuLvJL2Rvhw0i9vM+zUgpFq8lZT9V3c+J08vZSCy3FSs4AEJk6JTzeLcMiCK9AMlhzNOPtImM1TmQKEBIN1F9jvyUpA2x7Ha/vTuwLKPxPeG134P35559j0aJFWL16NYYPH44VK1YgKysLZ86cQUJC22xBXV0dMjIycPvtt+Ovf/2r2e326dMHW7du1Q8sQL5kPA+6ExISEBoaSoEJ4VOIooi6ujoUFxcDAJKTkz08IoJoR4QnMqkc9fImfA11KqsT1TQCFXlATKvskqfquzlcSmsp403GaoS/IrUUsyPw1mr13xlXS80Fge3j4m4W7NsceB9h93460W13dPv222/jgQcewPz58wEAq1evxsaNG7F27Vo888wzbdYfOnQohg4dCgAmn5cGEhCApKQke4djFY1GIwXdsbGxsm+fILyBkJAQAEBxcTESEhJIdk4QtsIN1hzpRUoQ3oxCCcR2YRfuZefaBt6e6uHN4TXe5ReAliYgIKjtOsVkrEb4Kfx7WXYOaGkEAlTWX1NxkRmyKVXMoM3VSIG3HXXePOPtp4G3XTn+pqYmHDx4EOPHj9dvQKHA+PHjsXfvXqcGkpOTg5SUFGRkZODuu+9GXl6e2XUbGxtRVVVldDMHr+kODfVA/RJBuBH+GScfA4KwA17XXVPs2XEQhCuwVOft6Yx3RDIQFMHMDcvPm16HMt6EvxLZAVCpAW2L5c4Ehkj13T2ZQZur4S3FeDBtDVGkwNuelUtLS6HRaJCYmGj0eGJiIgoLHc8WDB8+HOvXr8emTZuwatUq5ObmYvTo0aiurja5/tKlS6FWq6Vbamqq1X2QvJzwdegzThAOEM5bilHGm/BBYnng3aqOuraUeRtA0NdauxtBsC43lwJvyngTfoYgAIm6z72tcnN3OZpzuBy+6AQLqq1RXcA6KQhKv/1Oe0VV+6RJk3D77bejf//+yMrKwo8//oiKigp88cUXJtdfvHgxKisrpVt+fr6bR0wQBEH4BBG6ieTqIs+OgyBcgWSwds74cZ7tjkrzjKM5x1JLsfprQPVV4/UIwp+w12CNS75dbazGie/Fguj6chZUW4Nnu+N72O6C7mPYFXjHxcVBqVSiqMj4AqWoqEjW+uyoqCh0794d586dM/m8SqVCZGSk0Y2wjfT0dKxYscLTwyAIgvAOpIw3Bd6ED2JOau4tEm7eP7zURODNa9DVqUAwXecRfgjPChfZGni72RMhMFj/G8Oz7ZYoOMru/VRmDtgZeAcFBWHw4MHYtm2b9JhWq8W2bdswYsQI2QZVU1OD8+fP+7U7syAIFm8vvviiQ9v9448/8OCDD8oyxk8//RRKpRKPPvqoLNsjCIJwO1KNd5FtUjlC4pdffsG0adOQkpICQRDwzTff2PzaPXv2ICAgAAMGDHDZ+AjopeY1hUCDgR8OzzB7qr6bw/dfYqKlWDGvV6X6bsJP4QE0nyizRFMtMyoE3Cc1B/TZdVvqvAsp8LZbar5o0SK89957+OCDD3Dq1Ck8/PDDqK2tlVzO58yZg8WLF0vrNzU1ITs7G9nZ2WhqasKVK1eQnZ1tlM1+8sknsWvXLly8eBG//fYbZs6cCaVSidmzZ8twiO2TgoIC6bZixQpERkYaPfbkk09K64qiiJaWFpu2Gx8fL5vR3Jo1a/DUU0/h008/RUNDgyzbdJSmpiaP7p8giHYKdzVvrgMaTfuKEKapra1FZmYmVq5cadfrKioqMGfOHIwbN85FIyMkgiP1qo4yg6y3p3t4c3jGuyyH9Ro3xFuy8gThKXiNd2We8cSZKYpPg/W8T3Bvz3se5NvibO7nxmqAA4H3rFmzsHz5cixZsgQDBgxAdnY2Nm3aJBmu5eXloaBAr/O/evUqBg4ciIEDB6KgoADLly/HwIEDcf/990vrXL58GbNnz0aPHj1wxx13IDY2Fr///jvi4934wfEykpKSpJtarYYgCNLfp0+fRkREBH766ScMHjwYKpUKv/76K86fP4/p06cjMTER4eHhGDp0qFFvdKCt1FwQBLz//vuYOXMmQkND0a1bN3z33XdWx5ebm4vffvsNzzzzDLp3746vv/66zTpr165Fnz59oFKpkJycjIULF0rPVVRU4KGHHkJiYiKCg4PRt29f/PDDDwCAF198sU0WZMWKFUhPT5f+njdvHmbMmIHXXnsNKSkp6NGDzZp/9NFHGDJkCCIiIpCUlIS77rpL6m/NOXHiBKZOnYrIyEhERERg9OjROH/+PH755RcEBga2MQp8/PHHMXr0aKv/E4Ig2iFBYcxZGSC5uZ1MmjQJr776KmbOnGnX6/785z/jrrvuklUpR1jAlNzc047mnOh01vqopQGouGT8nBR4u6lelSC8jZBoICKFLfPvrDm4QsRd9d0cKfC2IjVvqAKu5bLlpP6uHZMX45DX/MKFC42CKEN27txp9Hd6ejpEK/K9zz77zJFhOIwoiqhv1lhf0QWEBCplc59+5plnsHz5cmRkZCA6Ohr5+fmYPHkyXnvtNahUKnz44YeYNm0azpw5g7S0NLPbeemll/DGG2/gzTffxLvvvou7774bly5dQkxMjNnXrFu3DlOmTIFarcY999yDNWvW4K677pKeX7VqFRYtWoRly5Zh0qRJqKysxJ49ewCw8oRJkyahuroaH3/8Mbp06YKTJ0/a3Xt627ZtiIyMxJYtW6THmpub8corr6BHjx4oLi7GokWLMG/ePPz4448AgCtXruD666/H2LFjsX37dkRGRmLPnj1oaWnB9ddfj4yMDHz00Uf429/+Jm3vk08+wRtvvGHX2AiCaEdEJAJl1Szw5kEK4RLWrVuHCxcu4OOPP8arr77q6eH4B7FdWa9dHnjXlgG1JWzZ04G3Qsm+c0XHmdyc9x4WRYMe3pTxJvyYhF7MZLDoBJA6zPx6RR4KvHlLsdIcoLnBvGkaz4hHdgRCzccXvo4bmrx5H/XNGvRestkj+z75chZCg+T5t7/88suYMGGC9HdMTAwyMzOlv1955RVs2LAB3333ndmJEoBlj7ms/+9//zveeecd7N+/HxMnTjS5vlarxfr16/Huu+8CAO6880488cQTyM3NRefOnQEAr776Kp544gn85S9/kV43dOhQAMDWrVuxf/9+nDp1Ct27M5lZRkaG3ccfFhaG999/H0FBQdJj9913n7SckZGBd955B0OHDkVNTQ3Cw8OxcuVKqNVqfPbZZwgMDAQAaQwAsGDBAqxbt04KvL///ns0NDTgjjvusHt8BEG0E8ITmetzNbUUcyU5OTl45plnsHv3bgQE2HYebGxsRGNjo/R3VZUVuSXRFkM5N9DK0TzMM2MyJK47uygvPQP00F131BQxV3NBoR8/Qfgjib2B89usO5t7KvCOSAZCYpizeclpIGWA6fVIZg7AS9qJEY4xZMgQo79ramrw5JNPolevXoiKikJ4eDhOnTqFvLw8i9vp318v+QgLC0NkZGQbebYhW7ZsQW1tLSZPngyAud1PmDABa9euBQAUFxfj6tWrZuv3srOz0bFjR6OA1xH69etnFHQDwMGDBzFt2jSkpaUhIiICY8aMAQDpf5CdnY3Ro0dLQXdr5s2bh3PnzuH3338HAKxfvx533HEHwsK84OKEIAjXEK5rKUZSc5eh0Whw11134aWXXrLrt3/p0qVQq9XSLTU11YWj9FFaS81LdBJuT9d3c0y1FONBRkwXv207RBAAbDNYE0X3txLjCIJBP28Ldd5krAbATzPeIYFKnHw5y2P7lovWweCTTz6JLVu2YPny5ejatStCQkJw2223WTUeax2ECoIArVZrdv01a9agvLwcISEh0mNarRZHjx7FSy+9ZPS4Kaw9r1Ao2pQnNDc3t1mv9fHX1tYiKysLWVlZ+OSTTxAfH4+8vDxkZWVJ/wNr+05ISMC0adOwbt06dO7cGT/99FOb8gmCIHwMbrBGGW+XUV1djQMHDuDw4cOSAkur1UIURQQEBODnn3/GjTfe2OZ1ixcvxqJFi6S/q6qqKPi2F6mX93lmYOYtjuaceN1EjFHgTcZqBAHAoKXYCRZgmypXrS7UKUSUQJwHvtdJ/Vg5S6GlwFuX8U723/puwE8Db0EQZJN7exN79uzBvHnzJKObmpoaXLx4UdZ9lJWV4dtvv8Vnn32GPn30s2oajQZ/+tOf8PPPP2PixIlIT0/Htm3bcMMNN7TZRv/+/XH58mWcPXvWZOYjPj4ehYWFEEVRqofPzs62OrbTp0+jrKwMy5Ytky7MDhw40GbfH3zwAZqbm81mve+//37Mnj0bHTt2RJcuXTBq1Cir+yYIoh0jZbzNK30I54iMjMSxY8btZv71r39h+/bt+O9//yuVKbVGpVJBpVK5Y4i+izqVGZhpGoGKPO8LanmgUHpWH1gUu7kfMUF4K/E9WMlFfTk7R0Uktl2Hy8xju3pGIWIt461p1v/u+HnGm6TmPkS3bt3w9ddfIzs7G0eOHMFdd91lMXPtCB999BFiY2Nxxx13oG/fvtItMzMTkydPxpo1awAwZ/K33noL77zzDnJycnDo0CGpJnzMmDG4/vrrceutt2LLli3Izc3FTz/9hE2bNgEAxo4di5KSErzxxhs4f/48Vq5ciZ9++snq2NLS0hAUFIR3330XFy5cwHfffYdXXnnFaJ2FCxeiqqoKd955Jw4cOICcnBx89NFHOHNGP9OelZWFyMhIvPrqq1KbPIIgfBgp8KaMtz3U1NRI7UIB1u0iOztbKu1ZvHgx5syZA4ApmQzPGX379kVCQoLU1YLKeVyIQgnEdmHLZee8L+Md24Vl6hqr9KoTb5scIAhPERiiNx0sNuMc7imZOcewpZgpQ+2SM4CmCVBFAlGd3Ds2L4MCbx/i7bffRnR0NEaOHIlp06YhKysLgwYNknUfa9euxcyZM006s99666347rvvUFpairlz52LFihX417/+hT59+mDq1KnIydG3Mvnqq68wdOhQzJ49G71798ZTTz0FjYY5zffq1Qv/+te/sHLlSmRmZmL//v1GfcvNER8fj/Xr1+PLL79E7969sWzZMixfvtxondjYWGzfvh01NTUYM2YMBg8ejPfee88o+61QKDBv3jxoNBrpopEgCB+GZxCqqcbbHg4cOCC1CwWARYsWYeDAgViyZAkAoKCgwKrHCOEmuNw8fx9Qq1N2eEKSaooAFRCjUzyUnAa0Wl1PYlDGmyAAA7m5GYM1rhDxVOAd35NNntVfA6oL2j5vaKwmU2en9oogWuv11Q6oqqqCWq1GZWUlIiMjjZ5raGiQ3LaDg8mgg7CNBQsWoKSkxKae5t4CfdYJwkGKTgKrRrCeqU9flG2zls5NhGPQ/9RBtr0C7F7OLpBLTgPqNOCvx6y/zl18ehdwZiMw6Q2g203AOwMAZRDwvwWA0vdKAwnCLnYsBXYtAwbcA8xY2fb5VaNYtnn2Z0CPSe4fHwCsHM5+W+76Euh+k/Fzm/4X+H0lMPxhYNIyz4zPhdhzXqKMN0EYUFlZiV9//RX/+c9/8Nhjj3l6OARBuANurlZ/DWhptLwuQbRHeMabtxJL6Om5sZjC0GCNy8zjelDQTRAAaykGmJaatzTpy0c8lfEGDOTmJib0yNFcggJvgjBg+vTpuOmmm/DnP//ZqEc6QRA+TEg0oNCVm5DBGuGL8MCb4y313RzDlmKSbJZk5gQBwKClmK4Uw5CyHEDbzOqn1R7s+CAZrLWaHBBFCrwNoKlEgjCAWocRhB8iCMxgreoy6+UdRe2qCB8jtnXg7WUZ7zhdxrv0jF6BQsZqBMGIyQACgoGWeuBart4sEdDXfSf09mz9NA+qW7cUq8wHGirZ5La3/e54AMp4EwRBEIRksEbO5oQPEhwJhCfp/473sqCWB961JUDeXrZMxmoEwVAo9SqV4lYGa552NOfw/ZflAM0N+se5sVp8TyAgyP3j8jIo8CYIgiAIHpTUkLM54aMYys15TbW3oArXy2SrrrB7yngThB5Jbn7K+HEu7fZ04B2RDITEAKIWKDEYIw+8k/t7ZlxeBgXeBEEQBBGewO4p8CZ8ldiu7F6dCqgiPDsWU8QZTAYEhXu2XpUgvA2ppVirGmrJE6Gve8fTGkEAkrjBmsEYDVuJERR4EwRBEIRUV0pSc8JX4fWV3ppJNjR8S+jl9/1+CcIIUxnvunLvUojw4N+wzruAjNUMIXM1giAIggjX1XhTxpvwVfrfAZSfBwbc5emRmMYw4+0NQQRBeBPc5b/sHGt7GaDSZ7uj0piPg6eRWorpAu/6a0BlnvFzfg5lvAmCIAgigmq8CR8nNAaY/CaQMtDTIzGNoeMxGasRhDERyUBwFCBqgNKz7DGpvttLglqppdhxXRsxXQAe1QkIifLYsLwJCrx9nLFjx+Lxxx+X/k5PT8eKFSssvkYQBHzzzTdO71uu7RAEQbgcXuNdTYE3QXiE1lJzgiD0CIJBnbcu080Db2+ZqIrvCQhKlumuukr13SagwNtLmTZtGiZOnGjyud27d0MQBBw9etTu7f7xxx948MEHnR2eES+++CIGDBjQ5vGCggJMmjRJ1n2Zo76+HjExMYiLi0NjY6Nb9kkQhA/BXc1riwGt1rNjIQh/JDQGSOgDqNRA8gBPj4YgvA8uNy/WBdze4mjOCQzWl4wUnQAKeX03OZpzKPD2UhYsWIAtW7bg8uXLbZ5bt24dhgwZgv797f8gx8fHIzQ0VI4hWiUpKQkqlcot+/rqq6/Qp08f9OzZ0+NZdlEU0dLS4tExEARhJ+EJAARA2wLUlXl6NAThn9y/BfifwyRLJQhTcCVI8Sk2QcyN1rxFag4YyM2PUcbbBBR4eylTp05FfHw81q9fb/R4TU0NvvzySyxYsABlZWWYPXs2OnTogNDQUPTr1w+ffvqpxe22lprn5OTg+uuvR3BwMHr37o0tW7a0ec3TTz+N7t27IzQ0FBkZGXj++efR3NwMAFi/fj1eeuklHDlyBIIgQBAEacytpebHjh3DjTfeiJCQEMTGxuLBBx9ETU2N9Py8efMwY8YMLF++HMnJyYiNjcWjjz4q7csSa9aswT333IN77rkHa9asafP8iRMnMHXqVERGRiIiIgKjR4/G+fPnpefXrl2LPn36QKVSITk5GQsXLgQAXLx4EYIgIDs7W1q3oqICgiBg586dAICdO3dCEAT89NNPGDx4MFQqFX799VecP38e06dPR2JiIsLDwzF06FBs3brVaFyNjY14+umnkZqaCpVKha5du2LNmjUQRRFdu3bF8uXLjdbPzs6GIAg4d+6c1f8JQRB2oAwEQmPZMtV5E4RnCAoDwmI9PQqC8E4SeFB7Eqi4CDTXAgHBQEyGR4dlBG8pduUQUHJa9xgF3hz/dDUXRaC5zjP7Dgy1qUVGQEAA5syZg/Xr1+PZZ5+FoHvNl19+CY1Gg9mzZ6OmpgaDBw/G008/jcjISGzcuBH33nsvunTpgmHDhlndh1arxS233ILExETs27cPlZWVRvXgnIiICKxfvx4pKSk4duwYHnjgAUREROCpp57CrFmzcPz4cWzatEkKKtVqdZtt1NbWIisrCyNGjMAff/yB4uJi3H///Vi4cKHR5MKOHTuQnJyMHTt24Ny5c5g1axYGDBiABx54wOxxnD9/Hnv37sXXX38NURTx17/+FZcuXUKnTp0AAFeuXMH111+PsWPHYvv27YiMjMSePXukrPSqVauwaNEiLFu2DJMmTUJlZSX27Nlj9f/XmmeeeQbLly9HRkYGoqOjkZ+fj8mTJ+O1116DSqXChx9+iGnTpuHMmTNIS0sDAMyZMwd79+7FO++8g8zMTOTm5qK0tBSCIOC+++7DunXr8OSTT0r7WLduHa6//np07drV7vERBGGFiCSgrhSoKQTgRRkEgiAIgkjQGRBWXQYu7WXL8T0ApReFczz7nrOFKchCogF1R8+OyYvwonfKjTTXAX9P8cy+//cqm9G1gfvuuw9vvvkmdu3ahbFjxwJggdett94KtVoNtVptweT9KQAAFs1JREFUFJQ99thj2Lx5M7744gubAu+tW7fi9OnT2Lx5M1JS2P/j73//e5u67Oeee05aTk9Px5NPPonPPvsMTz31FEJCQhAeHo6AgAAkJSWZ3dd//vMfNDQ04MMPP0RYGDv+f/7zn5g2bRpef/11JCayVj7R0dH45z//CaVSiZ49e2LKlCnYtm2bxcB77dq1mDRpEqKjowEAWVlZWLduHV588UUAwMqVK6FWq/HZZ58hMDAQANC9u75tyauvvoonnngCf/nLX6THhg4davX/15qXX34ZEyZMkP6OiYlBZmam9Pcrr7yCDRs24LvvvsPChQtx9uxZfPHFF9iyZQvGjx8PAMjI0M9azps3D0uWLMH+/fsxbNgwNDc34z//+U+bLDhBEDIRngAUgQzWCIIgCO8jJBqI7MB6dx/7kj3mTTJzQD8ejc5vKamfTQlHf4Gk5l5Mz549MXLkSKxduxYAcO7cOezevRsLFiwAAGg0Grzyyivo168fYmJiEB4ejs2bNyMvL8+m7Z86dQqpqalS0A0AI0aMaLPe559/jlGjRiEpKQnh4eF47rnnbN6H4b4yMzOloBsARo0aBa1WizNnzkiP9enTB0qlUvo7OTkZxcXFZrer0WjwwQcf4J577pEeu+eee7B+/XpodQZJ2dnZGD16tBR0G1JcXIyrV69i3Lhxdh2PKYYMGWL0d01NDZ588kn06tULUVFRCA8Px6lTp6T/XXZ2NpRKJcaMGWNyeykpKZgyZYr0/n///fdobGzE7bff7vRYCYIwATdYqyn07DgIgiAIwhTcwTx3F7v3FmM1TkSSvmwLIGO1VvhnxjswlGWePbVvO1iwYAEee+wxrFy5EuvWrUOXLl2kQO3NN9/E//t//w8rVqxAv379EBYWhscffxxNTU2yDXfv3r24++678dJLLyErK0vKHL/11luy7cOQ1sGxIAhSAG2KzZs348qVK5g1a5bR4xqNBtu2bcOECRMQEhJi9vWWngMAhYLNTYmiKD1mrubccFIBAJ588kls2bIFy5cvR9euXRESEoLbbrtNen+s7RsA7r//ftx77734xz/+gXXr1mHWrFluM8cjCL8jgilvUGN+so8gCIIgPEZCL+DcFkDUXRt7W+AtCGxMub+wv6m+2wiHMt4rV65Eeno6goODMXz4cOzfv9/suidOnMCtt96K9PR0CIJgtoe0Pdt0GkFgcm9P3OyUW9xxxx1QKBT4z3/+gw8//BD33XefVO+9Z88eTJ8+Hffccw8yMzORkZGBs2fP2rztXr16IT8/HwUFBdJjv//+u9E6v/32Gzp16oRnn30WQ4YMQbdu3XDp0iWjdYKCgqDRaKzu68iRI6itrZUe27NnDxQKBXr06GHhlZZZs2YN7rzzTmRnZxvd7rzzTslkrX///ti9e7fJgDkiIgLp6enYtm2bye3Hx8cDgNH/yNBozRJ79uzBvHnzMHPmTPTr1w9JSUm4ePGi9Hy/fv2g1Wqxa9cus9uYPHkywsLCsGrVKmzatAn33XefTfsmCMIBeMa7mjLeBEEQhBfSOtBO6GN6PU+SaBBsU8bbCLsD788//xyLFi3CCy+8gEOHDiEzMxNZWVlm5cB1dXXIyMjAsmXLzNYA27tNfyI8PByzZs3C4sWLUVBQgHnz5knPdevWDVu2bMFvv/2GU6dO4aGHHkJRke21iePHj0f37t0xd+5cHDlyBLt378azzz5rtE63bt2Ql5eHzz77DOfPn8c777yDDRs2GK2Tnp6O3NxcZGdno7S01GQf7bvvvhvBwcGYO3cujh8/jh07duCxxx7DvffeK9V320tJSQm+//57zJ07F3379jW6zZkzB9988w3Ky8uxcOFCVFVV4c4778SBAweQk5ODjz76SJK4v/jii3jrrbfwzjvvICcnB4cOHcK7774LgGWlr7vuOixbtgynTp3Crl27jGreLdGtWzd8/fXXyM7OxpEjR3DXXXcZZe/T09Mxd+5c3Hffffjmm2+Qm5uLnTt34osvvpDWUSqVmDdvHhYvXoxu3bqZLAUgCEImwhPYPbmaEwRBEN4IbykGAGEJQHi858ZiDj45oFQBcd08OxYvw+7A++2338YDDzyA+fPno3fv3li9ejVCQ0OlOtTWDB06FG+++SbuvPNOsz2d7d2mv7FgwQJcu3YNWVlZRvXYzz33HAYNGoSsrCyMHTsWSUlJmDFjhs3bVSgU2LBhA+rr6zFs2DDcf//9eO2114zWufnmm/HXv/4VCxcuxIABA/Dbb7/h+eefN1rn1ltvxcSJE3HDDTcgPj7eZEuz0NBQbN68GeXl5Rg6dChuu+02jBs3Dv/85z/t+2cYwI3aTNVnjxs3DiEhIfj4448RGxuL7du3o6amBmPGjMHgwYPx3nvvSbL2uXPnYsWKFfjXv/6FPn36YOrUqcjJyZG2tXbtWrS0tGDw4MF4/PHH8eqrr9o0vrfffhvR0dEYOXIkpk2bhqysLAwaNMhonVWrVuG2227DI488gp49e+KBBx4wUgUA7P1vamrC/Pnz7f0XEQRhDxGU8SYIgiC8mLgegKDzQvI2mTmn8/VAUDjQ/SbWqpOQEETD4lUrNDU1ITQ0FP/973+NAry5c+eioqIC3377rcXXp6en4/HHHzdqWeXINhsbG42yqlVVVUhNTUVlZSUiIyON1m1oaEBubi46d+6M4OBgWw+VILyG3bt3Y9y4ccjPz7eoDqDPOkE4Sdl54N1BQGAY8KzzPiBVVVVQq9Umz02EY9D/lCAIv+efQ4HSs8CIhUDWa9bX9wQNVUCAit18HHvOS3ZlvEtLS6HRaNpc/CcmJqKw0LEMgSPbXLp0qdROS61WIzU11aF9E4Q309jYiMuXL+PFF1/E7bff7rAknyAIGwnXfceaa4HGas+OhSAIgiBMkT6a3Xc23RXHKwiO9Iug217aZTuxxYsXo7KyUrrl5+d7ekgEITuffvopOnXqhIqKCrzxxhueHg5B+D6qcGDm/wH3fA0ogzw9GoIgCIJoy02vAA/tZlJuol1hVzuxuLg4KJXKNgZeRUVFZo3TXLFNlUpltl6cIHyFefPmGZnpEQThBjJnWV+HIAiCIDxFUBiQTG7h7RG7Mt5BQUEYPHiwUeslrVaLbdu2Oey27IptEgRBEARBEARBEIS3YFfGGwAWLVqEuXPnYsiQIRg2bBhWrFiB2tpayXF5zpw56NChA5YuXQqAmaedPHlSWr5y5Qqys7MRHh6Orl272rRNgiAIgiAIgiAIgmiv2B14z5o1CyUlJViyZAkKCwsxYMAAbNq0STJ+ysvLg0KhT6RfvXoVAwcOlP5evnw5li9fjjFjxmDnzp02bVMODPsnE4QvQp9xgiAIgiAIgvBO7Gon5q1YsnHXarXIycmBUqlEfHw8goKCIAiCh0ZKEPIjiiKamppQUlICjUaDbt26GU1+EQThGaj1lfzQ/5QgCILwJuw5L9md8W5vKBQKdO7cGQUFBbh61fm+rAThrYSGhiItLY2CboIgCIIgCILwMnw+8AaYgVtaWhpaWlqg0Wg8PRyCkB2lUomAgABScxAEQRAEQRCEF+IXgTcACIKAwMBABAYGenooBEEQBEEQBEEQhB9BmlSCIAiCIAiCIAiCcCEUeBMEQRAEQRAEQRCEC6HAmyAIgiAIgiAIgiBciE/UePOOaFVVVR4eCUEQBEEw+DnJB7p2eg10vicIgiC8CXvO9T4ReFdXVwMAUlNTPTwSgiAIgjCmuroaarXa08PwCeh8TxAEQXgjtpzrBdEHpuK1Wi2uXr2KiIgIWdopVVVVITU1Ffn5+VYboXszvnIcgO8ci68cB0DH4o34ynEAvnEsoiiiuroaKSkpUCiosksO5Dzf+8JnjOMrx+IrxwH4zrH4ynEAvnMsvnIcgG8ciz3nep/IeCsUCnTs2FH27UZGRrbbD4EhvnIcgO8ci68cB0DH4o34ynEA7f9YKNMtL64437f3z5ghvnIsvnIcgO8ci68cB+A7x+IrxwG0/2Ox9VxPU/AEQRAEQRAEQRAE4UIo8CYIgiAIgiAIgiAIF0KBtwlUKhVeeOEFqFQqTw/FKXzlOADfORZfOQ6AjsUb8ZXjAHzrWAjvxJc+Y75yLL5yHIDvHIuvHAfgO8fiK8cB+Nax2IJPmKsRBEEQBEEQBEEQhLdCGW+CIAiCIAiCIAiCcCEUeBMEQRAEQRAEQRCEC6HAmyAIgiAIgiAIgiBcCAXeBEEQBEEQBEEQBOFC/DbwXrlyJdLT0xEcHIzhw4dj//79Ftf/8ssv0bNnTwQHB6Nfv3748ccf3TRS0yxduhRDhw5FREQEEhISMGPGDJw5c8bia9avXw9BEIxuwcHBbhqxeV588cU24+rZs6fF13jb+8FJT09vcyyCIODRRx81ub63vCe//PILpk2bhpSUFAiCgG+++cboeVEUsWTJEiQnJyMkJATjx49HTk6O1e3a+z2TA0vH0tzcjKeffhr9+vVDWFgYUlJSMGfOHFy9etXiNh35jLryOABg3rx5bcY0ceJEq9v1tvcEgMnvjCAIePPNN81u0xPvCdH+aO/nesB3zvd0rvf8+0Hneu871wO+c76nc711/DLw/vzzz7Fo0SK88MILOHToEDIzM5GVlYXi4mKT6//222+YPXs2FixYgMOHD2PGjBmYMWMGjh8/7uaR69m1axceffRR/P7779iyZQuam5tx0003oba21uLrIiMjUVBQIN0uXbrkphFbpk+fPkbj+vXXX82u643vB+ePP/4wOo4tW7YAAG6//Xazr/GG96S2thaZmZlYuXKlyeffeOMNvPPOO1i9ejX27duHsLAwZGVloaGhwew27f2eyYWlY6mrq8OhQ4fw/PPP49ChQ/j6669x5swZ3HzzzVa3a89nVA6svScAMHHiRKMxffrppxa36Y3vCQCjYygoKMDatWshCAJuvfVWi9t193tCtC984VwP+Nb5ns71dK6XC1851wO+c76nc70NiH7IsGHDxEcffVT6W6PRiCkpKeLSpUtNrn/HHXeIU6ZMMXps+PDh4kMPPeTScdpDcXGxCEDctWuX2XXWrVsnqtVq9w3KRl544QUxMzPT5vXbw/vB+ctf/iJ26dJF1Gq1Jp/3xvcEgLhhwwbpb61WKyYlJYlvvvmm9FhFRYWoUqnETz/91Ox27P2euYLWx2KK/fv3iwDES5cumV3H3s+o3Jg6jrlz54rTp0+3azvt5T2ZPn26eOONN1pcx9PvCeH9+OK5XhTb7/mezvVq9w7KCnSub4s3nFd85XxP53rT+F3Gu6mpCQcPHsT48eOlxxQKBcaPH4+9e/eafM3evXuN1geArKwss+t7gsrKSgBATEyMxfVqamrQqVMnpKamYvr06Thx4oQ7hmeVnJwcpKSkICMjA3fffTfy8vLMrtse3g+AfdY+/vhj3HfffRAEwex63vqecHJzc1FYWGj0P1er1Rg+fLjZ/7kj3zNPUVlZCUEQEBUVZXE9ez6j7mLnzp1ISEhAjx498PDDD6OsrMzsuu3lPSkqKsLGjRuxYMECq+t643tCeAe+eq4H2vf5ns713vV+GELneoa3nld87Xzvr+d6vwu8S0tLodFokJiYaPR4YmIiCgsLTb6msLDQrvXdjVarxeOPP45Ro0ahb9++Ztfr0aMH1q5di2+//RYff/wxtFotRo4cicuXL7txtG0ZPnw41q9fj02bNmHVqlXIzc3F6NGjUV1dbXJ9b38/ON988w0qKiowb948s+t463tiCP+/2vM/d+R75gkaGhrw9NNPY/bs2YiMjDS7nr2fUXcwceJEfPjhh9i2bRtef/117Nq1C5MmTYJGozG5fnt5Tz744ANERETglltusbieN74nhPfgi+d6oH2f7+lc713vR2voXO+95xVfPN/767k+wNMDIJzn0UcfxfHjx63WPIwYMQIjRoyQ/h45ciR69eqFf//733jllVdcPUyzTJo0SVru378/hg8fjk6dOuGLL76waSbMW1mzZg0mTZqElJQUs+t463viDzQ3N+OOO+6AKIpYtWqVxXW98TN65513Ssv9+vVD//790aVLF+zcuRPjxo3zyJjkYO3atbj77rutGg9543tCEK6mPZ/vffU7S+d676a9n+sB3zzf++u53u8y3nFxcVAqlSgqKjJ6vKioCElJSSZfk5SUZNf67mThwoX44YcfsGPHDnTs2NGu1wYGBmLgwIE4d+6ci0bnGFFRUejevbvZcXnz+8G5dOkStm7divvvv9+u13nje8L/r/b8zx35nrkTfiK+dOkStmzZYnEG3BTWPqOeICMjA3FxcWbH5O3vCQDs3r0bZ86csft7A3jne0J4Dl871wO+d76nc713vR90rm+Lt55X2vv53p/P9X4XeAcFBWHw4MHYtm2b9JhWq8W2bduMZiMNGTFihNH6ALBlyxaz67sDURSxcOFCbNiwAdu3b0fnzp3t3oZGo8GxY8eQnJzsghE6Tk1NDc6fP292XN74frRm3bp1SEhIwJQpU+x6nTe+J507d0ZSUpLR/7yqqgr79u0z+z935HvmLviJOCcnB1u3bkVsbKzd27D2GfUEly9fRllZmdkxefN7wlmzZg0GDx6MzMxMu1/rje8J4Tl85VwP+O75ns713vV+0Lm+Ld56Xmnv53u/Ptd71tvNM3z22WeiSqUS169fL548eVJ88MEHxaioKLGwsFAURVG89957xWeeeUZaf8+ePWJAQIC4fPly8dSpU+ILL7wgBgYGiseOHfPUIYgPP/ywqFarxZ07d4oFBQXSra6uTlqn9XG89NJL4ubNm8Xz58+LBw8eFO+8804xODhYPHHihCcOQeKJJ54Qd+7cKebm5op79uwRx48fL8bFxYnFxcWiKLaP98MQjUYjpqWliU8//XSb57z1PamurhYPHz4sHj58WAQgvv322+Lhw4cl989ly5aJUVFR4rfffisePXpUnD59uti5c2exvr5e2saNN94ovvvuu9Lf1r5nnjiWpqYm8eabbxY7duwoZmdnG313GhsbzR6Ltc+ou4+jurpafPLJJ8W9e/eKubm54tatW8VBgwaJ3bp1ExsaGswehze+J5zKykoxNDRUXLVqlclteMN7QrQvfOFcL4q+c76nc73n3w8613vfud7asbSn8z2d663jl4G3KIriu+++K6alpYlBQUHisGHDxN9//116bsyYMeLcuXON1v/iiy/E7t27i0FBQWKfPn3EjRs3unnExgAweVu3bp20TuvjePzxx6VjTkxMFCdPniweOnTI/YNvxaxZs8Tk5GQxKChI7NChgzhr1izx3Llz0vPt4f0wZPPmzSIA8cyZM22e89b3ZMeOHSY/T3ysWq1WfP7558XExERRpVKJ48aNa3N8nTp1El944QWjxyx9zzxxLLm5uWa/Ozt27DB7LNY+o+4+jrq6OvGmm24S4+PjxcDAQLFTp07iAw880OaE2h7eE86///1vMSQkRKyoqDC5DW94T4j2R3s/14ui75zv6Vzv+feDzvXed663dizt6XxP53rrCKIoio5mywmCIAiCIAiCIAiCsIzf1XgTBEEQBEEQBEEQhDuhwJsgCIIgCIIgCIIgXAgF3gRBEARBEARBEAThQijwJgiCIAiCIAiCIAgXQoE3QRAEQRAEQRAEQbgQCrwJgiAIgiAIgiAIwoVQ4E0QBEEQBEEQBEEQLoQCb4IgCIIgCIIgCIJwIRR4EwRBEARBEARBEIQLocCbIAiCIAiCIAiCIFwIBd4EQRAEQRAEQRAE4UIo8CYIgiAIgiAIgiAIF/L/AaOcWnMzS/hrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 402ms/step\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.15      0.04      0.07       958\n",
      "     disgust       0.00      0.00      0.00       111\n",
      "        fear       0.17      0.08      0.11      1024\n",
      "       happy       0.25      0.42      0.31      1774\n",
      "     neutral       0.18      0.25      0.21      1233\n",
      "         sad       0.15      0.10      0.12      1247\n",
      "    surprise       0.10      0.11      0.10       831\n",
      "\n",
      "    accuracy                           0.19      7178\n",
      "   macro avg       0.14      0.14      0.13      7178\n",
      "weighted avg       0.17      0.19      0.17      7178\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hoisaline\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\hoisaline\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\hoisaline\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Input\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  # 添加這行\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "\n",
    "dataset_path = 'fer2013'\n",
    "train_dir = os.path.join(dataset_path, 'train')\n",
    "test_dir = os.path.join(dataset_path, 'test')\n",
    "\n",
    "# Step 3: Data preprocessing\n",
    "# Data Generator\n",
    "train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=30, zoom_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Create data generators for training and testing sets\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir, target_size=(48, 48), batch_size=64, color_mode='rgb', class_mode='categorical'\n",
    ")\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir, target_size=(48, 48), batch_size=64, color_mode='rgb', class_mode='categorical'\n",
    ")\n",
    "\n",
    "# 加載預訓練模型\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(48, 48, 3))\n",
    "\n",
    "# 凍結預訓練層\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# 添加新的分類層\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(7, activation='softmax')(x)\n",
    "\n",
    "# 建立模型\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# 編譯模型\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "# Step 5: Train model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    epochs=20,\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=test_generator.samples // test_generator.batch_size\n",
    ")\n",
    "\n",
    "# Step 6: Evaluate model\n",
    "loss, accuracy = model.evaluate(test_generator)\n",
    "print(f\"Test Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "# Step 7: Plot training history\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Model Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.legend()\n",
    "plt.title('Model Loss')\n",
    "plt.show()\n",
    "\n",
    "# Step 8: Predictions and confusion matrix\n",
    "y_pred = model.predict(test_generator)\n",
    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "y_true = test_generator.classes\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_true, y_pred_classes, target_names=test_generator.class_indices.keys()))\n",
    "\n",
    "conf_mat = confusion_matrix(y_true, y_pred_classes)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=test_generator.class_indices.keys(), \n",
    "            yticklabels=test_generator.class_indices.keys())\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Step 9: Real-time emotion detection (using camera)\n",
    "def emotion_detection_from_camera():\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "        faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)\n",
    "\n",
    "        for (x, y, w, h) in faces:\n",
    "            roi_gray = gray_frame[y:y + h, x:x + w]\n",
    "            roi_gray = cv2.resize(roi_gray, (48, 48))\n",
    "            roi_gray = roi_gray.astype('float32') / 255.0\n",
    "            roi_gray = np.expand_dims(roi_gray, axis=0)\n",
    "            roi_gray = np.expand_dims(roi_gray, axis=-1)\n",
    "            \n",
    "            prediction = model.predict(roi_gray)\n",
    "            emotion_label = test_generator.class_indices.keys()[np.argmax(prediction)]\n",
    "            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "            cv2.putText(frame, emotion_label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
    "        \n",
    "        cv2.imshow('Emotion Detection', frame)\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_2       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │ input_layer_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16928</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ emotion_output      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>)         │    <span style=\"color: #00af00; text-decoration-color: #00af00\">118,503</span> │ flatten[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ gender_output       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">33,858</span> │ flatten[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_2       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m,    │        \u001b[38;5;34m320\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16928\u001b[0m)     │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ emotion_output      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m)         │    \u001b[38;5;34m118,503\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mDense\u001b[0m)             │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ gender_output       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │     \u001b[38;5;34m33,858\u001b[0m │ flatten[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mDense\u001b[0m)             │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">152,681</span> (596.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m152,681\u001b[0m (596.41 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">152,681</span> (596.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m152,681\u001b[0m (596.41 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Input, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# 多輸入模型\n",
    "input_layer = Input(shape=(48, 48, 1))\n",
    "\n",
    "# 共用特徵提取層\n",
    "x = Conv2D(32, (3, 3), activation='relu')(input_layer)\n",
    "x = MaxPooling2D((2, 2))(x)\n",
    "x = Flatten()(x)\n",
    "\n",
    "# 主任務（情緒分類）\n",
    "emotion_output = Dense(7, activation='softmax', name='emotion_output')(x)\n",
    "\n",
    "# 輔助任務（性別分類）\n",
    "gender_output = Dense(2, activation='softmax', name='gender_output')(x)\n",
    "\n",
    "# 定義多任務模型\n",
    "model = Model(inputs=input_layer, outputs=[emotion_output, gender_output])\n",
    "\n",
    "# 編譯模型\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss={'emotion_output': 'categorical_crossentropy', 'gender_output': 'categorical_crossentropy'},\n",
    "    metrics={'emotion_output': 'accuracy', 'gender_output': 'accuracy'}\n",
    ")\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.backend as K\n",
    "\n",
    "def focal_loss(gamma=2., alpha=0.25):\n",
    "    def loss(y_true, y_pred):\n",
    "        y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())\n",
    "        pt = K.sum(y_true * y_pred, axis=-1)\n",
    "        return -K.mean(alpha * K.pow(1 - pt, gamma) * K.log(pt))\n",
    "    return loss\n",
    "\n",
    "model.compile(optimizer='adam', loss=focal_loss(), metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow_hub'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow_hub\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mhub\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n\u001b[0;32m      4\u001b[0m input_layer \u001b[38;5;241m=\u001b[39m Input(shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m48\u001b[39m, \u001b[38;5;241m48\u001b[39m, \u001b[38;5;241m3\u001b[39m))\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow_hub'"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "from tensorflow.keras.layers import Input\n",
    "\n",
    "input_layer = Input(shape=(48, 48, 3))\n",
    "base_model = hub.KerasLayer(\"https://tfhub.dev/google/vit_base_patch16_224/1\", trainable=False)(input_layer)\n",
    "output_layer = Dense(7, activation='softmax')(base_model)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=output_layer)\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'previous_layer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [5], line 13\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m multiply([input_tensor, se])\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# 添加 SE 模塊到模型\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m x \u001b[38;5;241m=\u001b[39m Conv2D(\u001b[38;5;241m128\u001b[39m, (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m), activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m)(previous_layer)\n\u001b[0;32m     14\u001b[0m x \u001b[38;5;241m=\u001b[39m se_block(x)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'previous_layer' is not defined"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Reshape, multiply\n",
    "\n",
    "def se_block(input_tensor, ratio=16):\n",
    "    channel = input_tensor.shape[-1]\n",
    "    se_shape = (1, 1, channel)\n",
    "    se = GlobalAveragePooling2D()(input_tensor)\n",
    "    se = Reshape(se_shape)(se)\n",
    "    se = Dense(channel // ratio, activation='relu', use_bias=False)(se)\n",
    "    se = Dense(channel, activation='sigmoid', use_bias=False)(se)\n",
    "    return multiply([input_tensor, se])\n",
    "\n",
    "# 添加 SE 模塊到模型\n",
    "x = Conv2D(128, (3, 3), activation='relu')(previous_layer)\n",
    "x = se_block(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Sequential' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [6], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConvLSTM2D\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# 使用 ConvLSTM2D 處理時間序列\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m Sequential([\n\u001b[0;32m      5\u001b[0m     ConvLSTM2D(\u001b[38;5;241m32\u001b[39m, (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m), activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m, input_shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m48\u001b[39m, \u001b[38;5;241m48\u001b[39m, \u001b[38;5;241m1\u001b[39m)),  \u001b[38;5;66;03m# 10 為時間序列長度\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     Flatten(),\n\u001b[0;32m      7\u001b[0m     Dense(\u001b[38;5;241m128\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m      8\u001b[0m     Dense(\u001b[38;5;241m7\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msoftmax\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      9\u001b[0m ])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Sequential' is not defined"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import ConvLSTM2D\n",
    "\n",
    "# 使用 ConvLSTM2D 處理時間序列\n",
    "model = Sequential([\n",
    "    ConvLSTM2D(32, (3, 3), activation='relu', input_shape=(10, 48, 48, 1)),  # 10 為時間序列長度\n",
    "    Flatten(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(7, activation='softmax')\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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