DeepLearning V4[Same Size].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48a4b739",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "0d9a99d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, multilabel_confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206691b",
   "metadata": {},
   "source": [
    "# 2. Check the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "61b22970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bench = pd.read_csv('./labeling/benchpress_with_scaled_angles.csv')\n",
    "df_dead = pd.read_csv('./labeling/deadlift_with_scaled_angles.csv')\n",
    "df_squat = pd.read_csv('./labeling/squat_with_scaled_angles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "098732e0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.494753</td>\n",
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       "      <td>0.336074</td>\n",
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       "      <td>0.999994</td>\n",
       "      <td>0.336740</td>\n",
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       "      <th>2</th>\n",
       "      <td>b_correct_down</td>\n",
       "      <td>0.338909</td>\n",
       "      <td>0.297234</td>\n",
       "      <td>0.494922</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.325614</td>\n",
       "      <td>0.295111</td>\n",
       "      <td>0.539178</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.326345</td>\n",
       "      <td>...</td>\n",
       "      <td>117.675792</td>\n",
       "      <td>62.403361</td>\n",
       "      <td>4.246507</td>\n",
       "      <td>124.453244</td>\n",
       "      <td>64.934000</td>\n",
       "      <td>108.079105</td>\n",
       "      <td>153.044650</td>\n",
       "      <td>114.738666</td>\n",
       "      <td>38.354746</td>\n",
       "      <td>61.952723</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b_correct_down</td>\n",
       "      <td>0.342315</td>\n",
       "      <td>0.286979</td>\n",
       "      <td>0.466467</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.325819</td>\n",
       "      <td>0.285868</td>\n",
       "      <td>0.529371</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.325422</td>\n",
       "      <td>...</td>\n",
       "      <td>159.664459</td>\n",
       "      <td>46.911461</td>\n",
       "      <td>26.222707</td>\n",
       "      <td>93.556948</td>\n",
       "      <td>66.509309</td>\n",
       "      <td>107.797629</td>\n",
       "      <td>149.345123</td>\n",
       "      <td>114.492535</td>\n",
       "      <td>38.428589</td>\n",
       "      <td>62.858692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b_correct_up</td>\n",
       "      <td>0.337070</td>\n",
       "      <td>0.292075</td>\n",
       "      <td>0.581475</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>0.290616</td>\n",
       "      <td>0.630095</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.331256</td>\n",
       "      <td>...</td>\n",
       "      <td>140.004166</td>\n",
       "      <td>164.824528</td>\n",
       "      <td>46.450212</td>\n",
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       "            class        x1        y1        z1        v1        x2        y2  \\\n",
       "0    b_correct_up  0.342405  0.293187  0.615661  0.999998  0.333918  0.293168   \n",
       "1  b_correct_down  0.347209  0.290651  0.494753  0.999993  0.336074  0.285710   \n",
       "2  b_correct_down  0.338909  0.297234  0.494922  0.999994  0.325614  0.295111   \n",
       "3  b_correct_down  0.342315  0.286979  0.466467  0.999992  0.325819  0.285868   \n",
       "4    b_correct_up  0.337070  0.292075  0.581475  0.999998  0.329630  0.290616   \n",
       "\n",
       "         z2        v2        x3  ...  left_elbow_angle  right_elbow_angle  \\\n",
       "0  0.662415  0.999998  0.334999  ...        153.626526         170.470252   \n",
       "1  0.560588  0.999994  0.336740  ...        164.207168          44.629975   \n",
       "2  0.539178  0.999996  0.326345  ...        117.675792          62.403361   \n",
       "3  0.529371  0.999994  0.325422  ...        159.664459          46.911461   \n",
       "4  0.630095  0.999999  0.331256  ...        140.004166         164.824528   \n",
       "\n",
       "   left_shoulder_angle  right_shoulder_angle  left_hip_angle  right_hip_angle  \\\n",
       "0            48.070248            105.980023       64.562139       109.855044   \n",
       "1            23.434606            101.919294       63.422402       107.710618   \n",
       "2             4.246507            124.453244       64.934000       108.079105   \n",
       "3            26.222707             93.556948       66.509309       107.797629   \n",
       "4            46.450212            111.923557       68.962632       110.126002   \n",
       "\n",
       "   left_knee_angle  right_knee_angle  left_ankle_angle  right_ankle_angle  \n",
       "0       152.282714        106.709343         37.642478          51.758124  \n",
       "1       154.459397        110.921968         39.437054          57.581308  \n",
       "2       153.044650        114.738666         38.354746          61.952723  \n",
       "3       149.345123        114.492535         38.428589          62.858692  \n",
       "4       149.106618        115.346571         39.358527          63.642535  \n",
       "\n",
       "[5 rows x 144 columns]"
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     "execution_count": 175,
     "metadata": {},
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   "source": [
    "df_bench.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "fe058cdc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
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       "      <td>-0.327615</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>79.634025</td>\n",
       "      <td>33.591396</td>\n",
       "      <td>37.831483</td>\n",
       "      <td>21.485173</td>\n",
       "      <td>160.834280</td>\n",
       "      <td>162.095840</td>\n",
       "      <td>115.512212</td>\n",
       "      <td>136.782411</td>\n",
       "      <td>33.173339</td>\n",
       "      <td>39.736965</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>79.203730</td>\n",
       "      <td>39.950997</td>\n",
       "      <td>48.968212</td>\n",
       "      <td>10.059519</td>\n",
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       "      <td>164.419653</td>\n",
       "      <td>134.658955</td>\n",
       "      <td>134.256535</td>\n",
       "      <td>41.718356</td>\n",
       "      <td>39.468728</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
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       "      <td>156.115926</td>\n",
       "      <td>146.815038</td>\n",
       "      <td>32.683300</td>\n",
       "      <td>33.705884</td>\n",
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       "                  class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral_down  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral_down  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2    s_spine_neutral_up  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3    s_spine_neutral_up  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral_down  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...  left_elbow_angle  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ...         80.806623   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ...         44.327974   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ...         79.634025   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ...         79.203730   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ...        100.755789   \n",
       "\n",
       "   right_elbow_angle  left_shoulder_angle  right_shoulder_angle  \\\n",
       "0          26.355644             5.900469             43.642237   \n",
       "1          26.873086             4.137943             48.484478   \n",
       "2          33.591396            37.831483             21.485173   \n",
       "3          39.950997            48.968212             10.059519   \n",
       "4          28.292219            40.387975             40.373151   \n",
       "\n",
       "   left_hip_angle  right_hip_angle  left_knee_angle  right_knee_angle  \\\n",
       "0      150.990237       151.839009       151.597463        149.620033   \n",
       "1      166.323610       147.648731       158.852685        146.627404   \n",
       "2      160.834280       162.095840       115.512212        136.782411   \n",
       "3      175.491051       164.419653       134.658955        134.256535   \n",
       "4      154.883739       150.977267       156.115926        146.815038   \n",
       "\n",
       "   left_ankle_angle  right_ankle_angle  \n",
       "0         24.727200          34.591380  \n",
       "1         36.455001          37.264337  \n",
       "2         33.173339          39.736965  \n",
       "3         41.718356          39.468728  \n",
       "4         32.683300          33.705884  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_squat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6ca142bf",
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>z1</th>\n",
       "      <th>v1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "      <th>z2</th>\n",
       "      <th>v2</th>\n",
       "      <th>x3</th>\n",
       "      <th>...</th>\n",
       "      <th>left_elbow_angle</th>\n",
       "      <th>right_elbow_angle</th>\n",
       "      <th>left_shoulder_angle</th>\n",
       "      <th>right_shoulder_angle</th>\n",
       "      <th>left_hip_angle</th>\n",
       "      <th>right_hip_angle</th>\n",
       "      <th>left_knee_angle</th>\n",
       "      <th>right_knee_angle</th>\n",
       "      <th>left_ankle_angle</th>\n",
       "      <th>right_ankle_angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.578854</td>\n",
       "      <td>0.459936</td>\n",
       "      <td>-0.178820</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.594528</td>\n",
       "      <td>0.448642</td>\n",
       "      <td>-0.173299</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>...</td>\n",
       "      <td>80.806623</td>\n",
       "      <td>26.355644</td>\n",
       "      <td>5.900469</td>\n",
       "      <td>43.642237</td>\n",
       "      <td>150.990237</td>\n",
       "      <td>151.839009</td>\n",
       "      <td>151.597463</td>\n",
       "      <td>149.620033</td>\n",
       "      <td>24.727200</td>\n",
       "      <td>34.591380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
       "      <td>0.999727</td>\n",
       "      <td>0.595270</td>\n",
       "      <td>0.397050</td>\n",
       "      <td>-0.327615</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>...</td>\n",
       "      <td>44.327974</td>\n",
       "      <td>26.873086</td>\n",
       "      <td>4.137943</td>\n",
       "      <td>48.484478</td>\n",
       "      <td>166.323610</td>\n",
       "      <td>147.648731</td>\n",
       "      <td>158.852685</td>\n",
       "      <td>146.627404</td>\n",
       "      <td>36.455001</td>\n",
       "      <td>37.264337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>79.634025</td>\n",
       "      <td>33.591396</td>\n",
       "      <td>37.831483</td>\n",
       "      <td>21.485173</td>\n",
       "      <td>160.834280</td>\n",
       "      <td>162.095840</td>\n",
       "      <td>115.512212</td>\n",
       "      <td>136.782411</td>\n",
       "      <td>33.173339</td>\n",
       "      <td>39.736965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral_up</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>79.203730</td>\n",
       "      <td>39.950997</td>\n",
       "      <td>48.968212</td>\n",
       "      <td>10.059519</td>\n",
       "      <td>175.491051</td>\n",
       "      <td>164.419653</td>\n",
       "      <td>134.658955</td>\n",
       "      <td>134.256535</td>\n",
       "      <td>41.718356</td>\n",
       "      <td>39.468728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral_down</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
       "      <td>100.755789</td>\n",
       "      <td>28.292219</td>\n",
       "      <td>40.387975</td>\n",
       "      <td>40.373151</td>\n",
       "      <td>154.883739</td>\n",
       "      <td>150.977267</td>\n",
       "      <td>156.115926</td>\n",
       "      <td>146.815038</td>\n",
       "      <td>32.683300</td>\n",
       "      <td>33.705884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral_down  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral_down  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2    s_spine_neutral_up  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3    s_spine_neutral_up  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral_down  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...  left_elbow_angle  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ...         80.806623   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ...         44.327974   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ...         79.634025   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ...         79.203730   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ...        100.755789   \n",
       "\n",
       "   right_elbow_angle  left_shoulder_angle  right_shoulder_angle  \\\n",
       "0          26.355644             5.900469             43.642237   \n",
       "1          26.873086             4.137943             48.484478   \n",
       "2          33.591396            37.831483             21.485173   \n",
       "3          39.950997            48.968212             10.059519   \n",
       "4          28.292219            40.387975             40.373151   \n",
       "\n",
       "   left_hip_angle  right_hip_angle  left_knee_angle  right_knee_angle  \\\n",
       "0      150.990237       151.839009       151.597463        149.620033   \n",
       "1      166.323610       147.648731       158.852685        146.627404   \n",
       "2      160.834280       162.095840       115.512212        136.782411   \n",
       "3      175.491051       164.419653       134.658955        134.256535   \n",
       "4      154.883739       150.977267       156.115926        146.815038   \n",
       "\n",
       "   left_ankle_angle  right_ankle_angle  \n",
       "0         24.727200          34.591380  \n",
       "1         36.455001          37.264337  \n",
       "2         33.173339          39.736965  \n",
       "3         41.718356          39.468728  \n",
       "4         32.683300          33.705884  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dead.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03291a0",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing & Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a4debbf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "bench_data = np.loadtxt(\"./labeling/benchpress_with_scaled_angles.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels = bench_data[:, 0]\n",
    "bench_data = bench_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels = encoder.fit_transform(labels.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "bench_data = np.nan_to_num(bench_data)\n",
    "\n",
    "bench_data = bench_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "bench_X_train, bench_X_test, bench_y_train, bench_y_test = train_test_split(bench_data, labels, test_size=0.3)\n",
    "\n",
    "# 텐서 변환\n",
    "bench_X_train = torch.tensor(bench_X_train)\n",
    "bench_X_test = torch.tensor(bench_X_test)\n",
    "bench_y_train = torch.tensor(bench_y_train)\n",
    "bench_y_test = torch.tensor(bench_y_test)\n",
    "\n",
    "# float 변환\n",
    "bench_X_train = bench_X_train.float()\n",
    "bench_X_test = bench_X_test.float()\n",
    "bench_y_train = bench_y_train.float()\n",
    "bench_y_test = bench_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e36cffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "squat_data = np.loadtxt(\"./labeling/squat_with_scaled_angles.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_squat = squat_data[:, 0]\n",
    "squat_data = squat_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_squat = encoder.fit_transform(labels_squat.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "squat_data = np.nan_to_num(squat_data)\n",
    "\n",
    "squat_data = squat_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "squat_X_train, squat_X_test, squat_y_train, squat_y_test = train_test_split(squat_data, labels_squat, test_size=0.3)\n",
    "\n",
    "# 텐서 변환\n",
    "squat_X_train = torch.tensor(squat_X_train)\n",
    "squat_X_test = torch.tensor(squat_X_test)\n",
    "squat_y_train = torch.tensor(squat_y_train)\n",
    "squat_y_test = torch.tensor(squat_y_test)\n",
    "\n",
    "# float 변환\n",
    "squat_X_train = squat_X_train.float()\n",
    "squat_X_test = squat_X_test.float()\n",
    "squat_y_train = squat_y_train.float()\n",
    "squat_y_test = squat_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "daf4f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "deadlift_data = np.loadtxt(\"./labeling/deadlift_with_scaled_angles.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_deadlift = deadlift_data[:, 0]\n",
    "deadlift_data = deadlift_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_deadlift = encoder.fit_transform(labels_deadlift.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "deadlift_data = np.nan_to_num(deadlift_data)\n",
    "deadlift_data = np.where(deadlift_data == '', '0', deadlift_data)\n",
    "deadlift_data = deadlift_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "deadlift_X_train, deadlift_X_test, deadlift_y_train, deadlift_y_test = train_test_split(deadlift_data, labels_deadlift, test_size=0.3)\n",
    "\n",
    "# 텐서 변환\n",
    "deadlift_X_train = torch.tensor(deadlift_X_train)\n",
    "deadlift_X_test = torch.tensor(deadlift_X_test)\n",
    "deadlift_y_train = torch.tensor(deadlift_y_train)\n",
    "deadlift_y_test = torch.tensor(deadlift_y_test)\n",
    "\n",
    "# float 변환\n",
    "deadlift_X_train = deadlift_X_train.float()\n",
    "deadlift_X_test = deadlift_X_test.float()\n",
    "deadlift_y_train = deadlift_y_train.float()\n",
    "deadlift_y_test = deadlift_y_test.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3122dfa3",
   "metadata": {},
   "source": [
    "# 4. Construct Deep Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "b5c55c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([287, 6])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d43d012e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([668, 143])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "53c59927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([668, 6])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56f3fb",
   "metadata": {},
   "source": [
    "### Bench Press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "85728780",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "30ab9554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 143\n",
    "output_size = 6\n",
    "bench_model = DeepClassifier(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.BCELoss()\n",
    "bench_optimizer = optim.Adam(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0a7e3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nN0ccpFKlShw+fJicnBzc3d0v+ToqKBYRkTInr8bGx8fHyS0RR8objsrNzb2s6yjciIhImaWhqPLFUX+eCjciIiJSrijciIiIlHE1atRg3Lhxzm5GqaFwIyIiUkIsFkuhj5EjR17SdVesWMEDDzxwWW3r1KkTjz/++GVdo7TQbCkHyczJ5XhKFhagcpC3s5sjIiKl0JEjR2w/T5kyheHDh7Nt2zbbMT8/P9vPhmGQm5uLm9uFf1VXqlTJsQ0t49Rz4yAbDyXS4bU/uOPzZc5uioiIlFLh4eG2R2BgIBaLxfZ869at+Pv7M3v2bFq2bImnpyf//PMPu3bt4qabbiIsLAw/Pz9at27NggUL7K577rCUxWJhwoQJ9O7dGx8fH6Kjo5k5c+Zltf2nn36iYcOGeHp6UqNGDd5++2271z/66COio6Px8vIiLCyMW265xfbatGnTaNy4Md7e3lSsWJHY2FhSU1Mvqz2FUc+Ng7icrvDOtRpObomIyJXJMAzSsy9vCvGl8nZ3ddhMn2effZa33nqLmjVrEhwczIEDB+jRowevvPIKnp6efPPNN/Ts2ZNt27ZRvXr1815n1KhRvPHGG7z55puMHz+efv36sW/fPipUqHDRbVq1ahW33XYbI0eOpG/fvvz7778MHjyYihUrMnDgQFauXMmjjz7KpEmTaN++PSdPnuTvv/8GzN6qO+64gzfeeIPevXuTnJzM33//jWEU3+9LhRsHcXUx/1JbFW5ERJwiPTuXBsPnOuWzN4/uio+HY36ljh49mi5dutieV6hQgaZNm9qejxkzhunTpzNz5kyGDh163usMHDiQO+64A4BXX32V999/n+XLl9OtW7eLbtM777xD586deemllwCoU6cOmzdv5s0332TgwIHs378fX19f/ve//+Hv709kZCTNmzcHzHCTk5NDnz59iIyMBKBx48YX3YaLoWEpB7H13BRjEhURkfKvVatWds9TUlJ46qmnqF+/PkFBQfj5+bFlyxb2799f6HWaNGli+9nX15eAgADi4+MvqU1btmyhQ4cOdsc6dOjAjh07yM3NpUuXLkRGRlKzZk3uvvtuvvvuO9LS0gBo2rQpnTt3pnHjxtx66618/vnnnDp16pLaUVTquXGQvJ6bXKuTGyIicoXydndl8+iuTvtsR/H19bV7/tRTTzF//nzeeustateujbe3N7fccgtZWVmFXufc7QssFgtWa/H8kvL392f16tUsXryYefPmMXz4cEaOHMmKFSsICgpi/vz5/Pvvv8ybN4/x48fzwgsv8N9//xEVFVUs7VG4cRDbsJR6bkREnMJisThsaKg0WbJkCQMHDqR3796A2ZOzd+/eEm1D/fr1WbJkSb521alTB1dXM9i5ubkRGxtLbGwsI0aMICgoiD/++IM+ffpgsVjo0KEDHTp0YPjw4URGRjJ9+nSGDRtWLO0tf38LnEQFxSIiUhyio6P5+eef6dmzJxaLhZdeeqnYemCOHTvG2rVr7Y5FRETw5JNP0rp1a8aMGUPfvn1ZunQpH3zwAR999BEAv/32G7t37+bqq68mODiYWbNmYbVaqVu3Lv/99x8LFy7k+uuvJzQ0lP/++49jx45Rv379YvkOoHDjMCooFhGR4vDOO+9wzz330L59e0JCQnjmmWdISkoqls/6/vvv+f777+2OjRkzhhdffJEff/yR4cOHM2bMGCIiIhg9ejQDBw4EICgoiJ9//pmRI0eSkZFBdHQ0P/zwAw0bNmTLli389ddfjBs3jqSkJCIjI3n77bfp3r17sXwHAItRnHOxSqGkpCQCAwNJTEwkICDAYdfdfyKNq99chI+HK5tHX3wluoiIFF1GRgZ79uwhKioKLy8vZzdHHKSwP9eL+f2t2VIO4nL6TmpYSkRExLkUbhxEBcUiIiKlg8KNg7iqoFhERKRUULhxEBdbzw3FuqS0iIiIFE7hxkFcz9pTRJ03IiIizqNw4yB5PTegoSkRERFnUrhxEFeFGxERkVJB4cZBzh6W0uaZIiIizqNw4yAuZ91J9dyIiIg4j8KNg9gVFCvciIiIOI3CjYPY1dxoWEpERApgsVgKfYwcOfKyrj1jxgyHnVeWaeNMBzH/YoJhqOdGREQKduTIEdvPU6ZMYfjw4Wzbts12zM/PzxnNKnfUc+NAtlWK1XMjIiIFCA8Ptz0CAwOxWCx2xyZPnkz9+vXx8vKiXr16fPTRR7b3ZmVlMXToUCIiIvDy8iIyMpKxY8cCUKNGDQB69+6NxWKxPb9YVquV0aNHU7VqVTw9PWnWrBlz5swpUhsMw2DkyJFUr14dT09PKleuzKOPPnppN+oyqefGgVxcLGA1VFAsIuIMhgHZac75bHcfOKv28lJ89913DB8+nA8++IDmzZuzZs0a7r//fnx9fRkwYADvv/8+M2fO5Mcff6R69eocOHCAAwcOALBixQpCQ0OZOHEi3bp1w9XV9ZLa8N577/H222/z6aef0rx5c7788ktuvPFGNm3aRHR0dKFt+Omnn3j33XeZPHkyDRs2JC4ujnXr1l3WPblUCjcO5OZiIQuwWp3dEhGRK1B2Grxa2Tmf/fxh8PC9rEuMGDGCt99+mz59+gAQFRXF5s2b+fTTTxkwYAD79+8nOjqaq666CovFQmRkpO29lSpVAiAoKIjw8PBLbsNbb73FM888w+233w7A66+/zqJFixg3bhwffvhhoW3Yv38/4eHhxMbG4u7uTvXq1WnTps0lt+VyaFjKgTQsJSIilyI1NZVdu3Zx77334ufnZ3u8/PLL7Nq1C4CBAweydu1a6taty6OPPsq8efMc2oakpCQOHz5Mhw4d7I536NCBLVu2XLANt956K+np6dSsWZP777+f6dOnk5OT49A2FpV6bhwobwsGDUuJiDiBu4/Zg+Ksz74MKSkpAHz++efExMTYvZY3xNSiRQv27NnD7NmzWbBgAbfddhuxsbFMmzbtsj77YhTWhmrVqrFt2zYWLFjA/PnzGTx4MG+++SZ//vkn7u7uJdZGULhxKFfbzuAKNyIiJc5iueyhIWcJCwujcuXK7N69m379+p33vICAAPr27Uvfvn255ZZb6NatGydPnqRChQq4u7uTm5t7yW0ICAigcuXKLFmyhGuuucZ2fMmSJXbDS4W1wdvbm549e9KzZ0+GDBlCvXr12LBhAy1atLjkdl0KhRsHcrGo50ZERC7NqFGjePTRRwkMDKRbt25kZmaycuVKTp06xbBhw3jnnXeIiIigefPmuLi4MHXqVMLDwwkKCgLMGVMLFy6kQ4cOeHp6EhwcfN7P2rNnD2vXrrU7Fh0dzdNPP82IESOoVasWzZo1Y+LEiaxdu5bvvvsOoNA2fPXVV+Tm5hITE4OPjw/ffvst3t7ednU5JUXhxoFcT1cwKdyIiMjFuu+++/Dx8eHNN9/k6aefxtfXl8aNG/P4448D4O/vzxtvvMGOHTtwdXWldevWzJo1C5fT+/+8/fbbDBs2jM8//5wqVaqwd+/e837WsGHD8h37+++/efTRR0lMTOTJJ58kPj6eBg0aMHPmTKKjoy/YhqCgIF577TWGDRtGbm4ujRs35tdff6VixYoOv1cXYjGMK2sMJSkpicDAQBITEwkICHDotduPXcjhxAxmDu1Ak6pBDr22iIickZGRwZ49e4iKisLLy8vZzREHKezP9WJ+f2u2lAOpoFhERMT5FG4cSAXFIiIizqdw40C2dW60iJ+IiIjTKNw4kIalREREnE/hxoHyem40LCUiUjKusDkx5Z6j/jwVbhxIPTciIiUjb8XbtDQnbZQpxSIrKwvgkjf+zKN1bhzIts6N/iUhIlKsXF1dCQoKIj4+HgAfHx8sl7krtziX1Wrl2LFj+Pj44OZ2efFE4caBbMNS6rkRESl2ebtf5wUcKftcXFyoXr36ZQdVp4abv/76izfffJNVq1Zx5MgRpk+fTq9evQp9z+LFixk2bBibNm2iWrVqvPjiiwwcOLBE2nshecNSOQo3IiLFzmKxEBERQWhoKNnZ2c5ujjiAh4eHbcXly+HUcJOamkrTpk2555576NOnzwXP37NnDzfccAMPPfQQ3333HQsXLuS+++4jIiKCrl27lkCLC6eeGxGRkufq6nrZNRpSvjg13HTv3p3u3bsX+fxPPvmEqKgo3n77bQDq16/PP//8w7vvvlsqwo2toFg1NyIiIk5TpmZLLV26lNjYWLtjXbt2ZenSped9T2ZmJklJSXaP4uKqXcFFREScrkyFm7i4OMLCwuyOhYWFkZSURHp6eoHvGTt2LIGBgbZHtWrViq192n5BRETE+cpUuLkUzz33HImJibbHgQMHiu2zzqxzU2wfISIiIhdQpqaCh4eHc/ToUbtjR48eJSAgAG9v7wLf4+npiaenZ0k0D9fTM9dUUCwiIuI8Zarnpl27dixcuNDu2Pz582nXrp2TWmTPVQXFIiIiTufUcJOSksLatWtZu3YtYE71Xrt2Lfv37wfMIaX+/fvbzn/ooYfYvXs3//d//8fWrVv56KOP+PHHH3niiSec0fx8XFRQLCIi4nRODTcrV66kefPmNG/eHIBhw4bRvHlzhg8fDsCRI0dsQQcgKiqK33//nfnz59O0aVPefvttJkyYUCqmgYMKikVEREoDp9bcdOrUqdAdQL/66qsC37NmzZpibNWl08aZIiIizlemam5KO61zIyIi4nwKNw6kYSkRERHnU7hxoDMFxU5uiIiIyBVM4caBXE/fTfXciIiIOI/CjQO5qqBYRETE6RRuHEjr3IiIiDifwo0DqaBYRETE+RRuHEg9NyIiIs6ncONAbtpbSkRExOkUbhzINiylnhsRERGnUbhxoDPbLzi5ISIiIlcwhRsHytt+QQXFIiIizqNw40DaOFNERMT5FG4cKK/nJkfhRkRExGkUbhzItv2Cwo2IiIjTKNw4kIumgouIiDidwo0D2QqK1XMjIiLiNAo3DuSqnhsRERGnU7hxIG2/ICIi4nwKNw6kjTNFREScT+HGgbTOjYiIiPMp3DiQq0XbL4iIiDibwo0D2da50bCUiIiI0yjcOJAKikVERJxP4caBVFAsIiLifAo3DuSqgmIRERGnU7hxIA1LiYiIOJ/CjQNpWEpERMT5FG4cyO10uMnOVbgRERFxFoUbB3J3M29njlUL3YiIiDiLwo0DeZxe6CY7Rz03IiIizqJw40DueeFGSxSLiIg4jcKNA7m7mjU3mTkKNyIiIs6icONA6rkRERFxPoUbB/J0U7gRERFxNoUbBzrTc6OCYhEREWdRuHGgvKngWeq5ERERcRqFGwfKKyjOzrViaJViERERp1C4caC8dW4MA3K0v5SIiIhTKNw4kIfbmdupomIRERHnULhxoLyCYtAqxSIiIs6icONAeRtngoqKRUREnEXhxoEsFsuZ/aUUbkRERJxC4cbBzp4xJSIiIiVP4cbB3LVKsYiIiFMp3DhY3rCUNs8UERFxDoUbB9MWDCIiIs6lcONgHhqWEhERcSqFGwezFRRrWEpERMQpFG4cLG9YSuvciIiIOIfCjYOdGZZSzY2IiIgzKNw4mK3nRsNSIiIiTqFw42BaoVhERMS5nB5uPvzwQ2rUqIGXlxcxMTEsX7680PPHjRtH3bp18fb2plq1ajzxxBNkZGSUUGsvLK+gWDU3IiIizuHUcDNlyhSGDRvGiBEjWL16NU2bNqVr167Ex8cXeP7333/Ps88+y4gRI9iyZQtffPEFU6ZM4fnnny/hlp+fu3puREREnMqp4eadd97h/vvvZ9CgQTRo0IBPPvkEHx8fvvzyywLP//fff+nQoQN33nknNWrU4Prrr+eOO+64YG9PSbJtv6CaGxEREadwWrjJyspi1apVxMbGnmmMiwuxsbEsXbq0wPe0b9+eVatW2cLM7t27mTVrFj169Djv52RmZpKUlGT3KE6eWqFYRETEqdyc9cHHjx8nNzeXsLAwu+NhYWFs3bq1wPfceeedHD9+nKuuugrDMMjJyeGhhx4qdFhq7NixjBo1yqFtL4zWuREREXEupxcUX4zFixfz6quv8tFHH7F69Wp+/vlnfv/9d8aMGXPe9zz33HMkJibaHgcOHCjWNrq7nS4o1rCUiIiIUzit5yYkJARXV1eOHj1qd/zo0aOEh4cX+J6XXnqJu+++m/vuuw+Axo0bk5qaygMPPMALL7yAi0v+rObp6Ymnp6fjv8B5qKBYRETEuZzWc+Ph4UHLli1ZuHCh7ZjVamXhwoW0a9euwPekpaXlCzCurq4AGEbpqHHROjciIiLO5bSeG4Bhw4YxYMAAWrVqRZs2bRg3bhypqakMGjQIgP79+1OlShXGjh0LQM+ePXnnnXdo3rw5MTEx7Ny5k5deeomePXvaQo6zuaugWERExKmcGm769u3LsWPHGD58OHFxcTRr1ow5c+bYioz3799v11Pz4osvYrFYePHFFzl06BCVKlWiZ8+evPLKK876Cvnk7S2lgmIRERHnsBilZTynhCQlJREYGEhiYiIBAQEOv/7Hi3fx+pyt3NqyKm/e2tTh1xcREbkSXczv7zI1W6os0PYLIiIizqVw42C2YSlNBRcREXEKhRsH8zwdbjIVbkRERJxC4cbBvNzNWVsZ2blObomIiMiVSeHGwRRuREREnEvhxsHOhBsNS4mIiDiDwo2DeZ2uucnIUc+NiIiIMyjcOFhez02mem5EREScQuHGwfLCTbpqbkRERJxC4cbBvNxPD0sp3IiIiDiFwo2DnT1b6grb2UJERKRUULhxMC83M9xYDe0MLiIi4gwKNw7m5XHmlmrGlIiISMlTuHEwD1cXLObemaq7ERERcQKFGwezWCy2oSlNBxcRESl5CjfFIG/GlKaDi4iIlDyFm2Kg/aVEREScR+GmGGh/KREREedRuCkGnm5ayE9ERMRZFG6KgYalREREnEfhphjYtmDI0bCUiIhISVO4KQbe6rkRERFxGoWbYpA3LJWpcCMiIlLiFG6KQV640To3IiIiJU/hphjYam40FVxERKTEKdwUA0831dyIiIg4i8JNMdAifiIiIs6jcFMMfD3McJOWlePkloiIiFx5FG6Kgb+XGwDJmQo3IiIiJU3hphj4ebkDkJyhcCMiIlLSFG6KQV7PTUpGtpNbIiIicuVRuCkG/p6nh6XUcyMiIlLiFG6Kgf/pYakU1dyIiIiUOIWbYuDnpZ4bERERZ1G4KQa2mpvMHKxWw8mtERERubIo3BQDv9M1NwApWutGRESkRCncFAMvd1c8XM1bm6KhKRERkRKlcFNMVHcjIiLiHAo3xeRM3Y3WuhERESlJCjfFxE9r3YiIiDiFwk0x8dewlIiIiFMo3BQTP08t5CciIuIMCjfFJOB0z01SumpuRERESpLCTTEJDfAC4OCpdCe3RERE5MpySeHmwIEDHDx40PZ8+fLlPP7443z22WcOa1hZFx3qB8CO+GQnt0REROTKcknh5s4772TRokUAxMXF0aVLF5YvX84LL7zA6NGjHdrAsqpOmD8AO+NTnNwSERGRK8slhZuNGzfSpk0bAH788UcaNWrEv//+y3fffcdXX33lyPaVWbVCfQE4npLFydQsJ7dGRETkynFJ4SY7OxtPT08AFixYwI033ghAvXr1OHLkiONaV4b5eLhRNdgbgB1HNTQlIiJSUi4p3DRs2JBPPvmEv//+m/nz59OtWzcADh8+TMWKFR3awLKsZiWz7mbfiTQnt0REROTKcUnh5vXXX+fTTz+lU6dO3HHHHTRt2hSAmTNn2oarBAK9zbVukrXWjYiISIlxu5Q3derUiePHj5OUlERwcLDt+AMPPICPj4/DGlfW+Xm6ApCqcCMiIlJiLqnnJj09nczMTFuw2bdvH+PGjWPbtm2EhoY6tIFlma+HmR0VbkRERErOJYWbm266iW+++QaAhIQEYmJiePvtt+nVqxcff/yxQxtYlvl65u0MrnAjIiJSUi4p3KxevZqOHTsCMG3aNMLCwti3bx/ffPMN77///kVd68MPP6RGjRp4eXkRExPD8uXLCz0/ISGBIUOGEBERgaenJ3Xq1GHWrFmX8jWKXd7O4Oq5ERERKTmXVHOTlpaGv7+5SN28efPo06cPLi4utG3bln379hX5OlOmTGHYsGF88sknxMTEMG7cOLp27Xre4a2srCy6dOlCaGgo06ZNo0qVKuzbt4+goKBL+RrF7kzPTa6TWyIiInLluKSem9q1azNjxgwOHDjA3Llzuf766wGIj48nICCgyNd55513uP/++xk0aBANGjTgk08+wcfHhy+//LLA87/88ktOnjzJjBkz6NChAzVq1OCaa66xzdYqbXxVUCwiIlLiLincDB8+nKeeeooaNWrQpk0b2rVrB5i9OM2bNy/SNbKysli1ahWxsbFnGuPiQmxsLEuXLi3wPTNnzqRdu3YMGTKEsLAwGjVqxKuvvkpubunsGbENS2Up3IiIiJSUSxqWuuWWW7jqqqs4cuSIXa9J586d6d27d5Gucfz4cXJzcwkLC7M7HhYWxtatWwt8z+7du/njjz/o168fs2bNYufOnQwePJjs7GxGjBhR4HsyMzPJzMy0PU9KSipS+xxBBcUiIiIl75LCDUB4eDjh4eG23cGrVq1a7Av4Wa1WQkND+eyzz3B1daVly5YcOnSIN99887zhZuzYsYwaNapY23U+KigWEREpeZc0LGW1Whk9ejSBgYFERkYSGRlJUFAQY8aMwWq1FukaISEhuLq6cvToUbvjR48eJTw8vMD3REREUKdOHVxdXW3H6tevT1xcHFlZBW9O+dxzz5GYmGh7HDhwoIjf8vL52sJN6Rw2ExERKY8uKdy88MILfPDBB7z22musWbOGNWvW8OqrrzJ+/HheeumlIl3Dw8ODli1bsnDhQtsxq9XKwoULbTU85+rQoQM7d+60C1Dbt28nIiICDw+PAt/j6elJQECA3aOk2AqKs3IwDKPEPldERORKdknh5uuvv2bChAk8/PDDNGnShCZNmjB48GA+//xzvvrqqyJfZ9iwYXz++ed8/fXXbNmyhYcffpjU1FQGDRoEQP/+/Xnuueds5z/88MOcPHmSxx57jO3bt/P777/z6quvMmTIkEv5GsUub1jKMCA9W703IiIiJeGSam5OnjxJvXr18h2vV68eJ0+eLPJ1+vbty7Fjxxg+fDhxcXE0a9aMOXPm2IqM9+/fj4vLmfxVrVo15s6dyxNPPEGTJk2oUqUKjz32GM8888ylfI1i5+3uisVihpuUzBx8PC65xElERESKyGJcwnhJTEwMMTEx+VYjfuSRR1i+fDn//fefwxroaElJSQQGBpKYmFgiQ1SNRswlJTOHRU91IirEt9g/T0REpDy6mN/fl9SV8MYbb3DDDTewYMECW33M0qVLOXDgQKndCsFZfD1dScnM0YwpERGREnJJNTfXXHMN27dvp3fv3iQkJJCQkECfPn3YtGkTkyZNcnQbyzStdSMiIlKyLrkIpHLlyrzyyit2x9atW8cXX3zBZ599dtkNKy+01o2IiEjJuqSeGyk6Xw/13IiIiJQkhZtipoX8RERESpbCTTHz087gIiIiJeqiam769OlT6OsJCQmX05ZySQXFIiIiJeuiwk1gYOAFX+/fv/9lNai8UUGxiIhIybqocDNx4sTiake5Zau5yVK4ERERKQmquSlmZ4alVFAsIiJSEhRuipkKikVEREqWwk0xU0GxiIhIyVK4KWZ54SZNNTciIiIlQuGmmPlpET8REZESpXBTzLT9goiISMlSuClmviooFhERKVEKN8XsTM1NLlar4eTWiIiIlH8KN8Usr+YGtJCfiIhISVC4KWaebi64ulgAFRWLiIiUBIWbYmaxWPD1MOtuVFQsIiJS/BRuSkBogBcAu4+lOLklIiIi5Z/CTQloV7MiAEt2HndyS0RERMo/hZsScFV0CAB/K9yIiIgUO4WbEtC2ZkVcXSzsPpbKzvhkZzdHRESkXFO4KQGB3u5cVy8UgG+X7Xdya0RERMo3hZsSclfbSAB+Wn2QjGxNCRcRESkuCjclpGPtECICvUjOyOGv7cec3RwREZFyS+HGkbLSIPlogS+5uFi4oXEEAD+vPoRhaCsGERGR4qBw4yi7F8OrETCp13lPualZFQDmbIrj3QU7SqZdIiIiVxiFG0fxrWT+N6XgnhuAxlUD+b9udQH4Yfl+9d6IiIgUA4UbR/ELM/+bdgJys8972qD2Ubi6WDiWnMnRpMwSapyIiMiVQ+HGUbwrgMvpHcBT4s9/mocr0aF+AKw7mFACDRMREbmyKNw4iosL+Jpr2RQ2NAXQpGogABsOJhZ3q0RERK44CjeO5H96aOqC4SYIgNX7TxVzg0RERK48CjeO5Fe0cNOulrmR5sq9pziZmlXcrRIREbmiKNw4ki3cnL/mBqBmiC9VgrzJyrXSYsx8Rs7cRE6utQQaKCIiUv4p3DhSXrhJjiv0NIvFwtV1QmzPv/p3Lx8u2lWcLRMREbliKNw4kl/RCooB7r0qig61K9o21Pxw0U72n0grztaJiIhcERRuHCnAXIGYExfuhakd6s9397XliwGtiImqQFauld82HAYgIzuXzBxtrikiInIp3JzdgHKlWgxggWNbIOkIBERc8C0Wi4X/NYngvz0nmbMxjmPJmXy3bD9NqgYy9aF2WCyW4m+3iIhIOaKeG0fyrQiVm5k/7/qjyG/rVNccmlp/MJGJS/aSlWtl5b5T7IxPKYZGioiIlG8KN45Wq7P53w0/QhH3jqpWwce2sF/TakE0iAgAYMGWwmddiYiISH4KN47W7E5w9TB3Cd82q8hv+2JAa2YO7cCMwe25o001AH5Ze4iM7Fy2H03GatUmmyIiIkWhcONoFWtBu6Hmz3Oehex0+9cNAxIPwql9YD1TNFzJ35MmVYOwWCx0axRBgJcbW+OSqffSHK5/9y8e/m4VWTlaC0dERORCLIZRxLGTciIpKYnAwEASExMJCAgong/JSoUP2kDSQWjzIPR4wzy+8SdYOAZO7TGfewVC5FVQ53qo0/3M9g3Aom3xPPjNKrLOWtyvZiVfgrzdqV7BhzG9GrFwSzy1Q/1oVCWweL6HiIhIKXExv78VborL9rnw/W3mzy0GmP9d/bX5X4sruLhC7tlbL1igamuo1wPq3gCV6nAoIZ2FW47i7urCa7O3kpiene9jPNxcmDiwNR1qh+R7TUREpLxQuClEiYUbgL/fhoWj7Y9dNQyufgpcPSFunTmraussOLza/ryKtaFuD6jTFarFEJ+Wy787TzBvcxyzNtivgFyzki8Lh12jaeMiIlJuKdwUokTDDcDOBbD0Q8jOgHZDoP7/ztOww7BttlmEvPtPsJ7VS+PhDzWvgdqdWe/Tlhu/MYe1XryhPm/M3WarxbmjTTXSsnJ5rnt9wgO9ivubiYiIlBiFm0KUeLi5FBlJZijaNht2LYS0E3Yv7/CozwzvPjwy5Ake+m4Ni7cds3u9dY1gJj/QDlcXC4ZhcCQxg4p+Hni6uZbktxAREXEYhZtClIlwczar1Ry+2rkAts+DgyuA039kFaNZX/M+bvq7CsY5E98e7lSLGxpH8PS09Ww5kkTXhmF8enerkm+/iIiIAyjcFKLMhZtzJR+FFRNg+aeQkQhARkgjkjoOZ+rJWri5WBg7e2u+t7m6WFg7vAvb4pJpVi0IN1etAiAiImWHwk0hyny4yZOZDMs/h3/ehcwk81ijmzF6vM2YhUf49r99ZOVYaRkZzPqDCWTnGoT4eXI8JZPY+mF81K8FHm4KOCIiUjYo3BSi3ISbPKkn4M/Xzd4cIxf8K0OvDzFqXkuu1cDN1YX/m7aOH1cetHtb1WBvJt0bQ1SIr5MaLiIiUnQX8/tb/3Qv63wrmosE3jvfnD6efBgm9cay6FXcTs8Mv67emcUB64X74+/lxsFT6Vz71mKGfLeapIwzM7MWbYvnmjcXsWib9rUSEZGyqVSEmw8//JAaNWrg5eVFTEwMy5cvL9L7Jk+ejMVioVevXsXbwLKgakt48G9oda/5/K834Ie+kH6Krg3DmHRvG2Y/1pE5j1/NrEc74uNhzpz6fcMR7pm4gozsXBLTshk0cQX7TqTx6u9bnPhlRERELp3Tw82UKVMYNmwYI0aMYPXq1TRt2pSuXbsSH194z8HevXt56qmn6NixYwm1tAzw8IH/vQO9PwM3L9gxDz67Fkv8FjpGV6L+6d3Gq1XwYUL/VtzQOAKAlftO0evDJdww/m/bpXYfTyUzJ7fAjxERESnNnB5u3nnnHe6//34GDRpEgwYN+OSTT/Dx8eHLL78873tyc3Pp168fo0aNombNmiXY2jKiaV+4dx4EVjf3sZrQGTb+bHdK+9ohfNivBZPubYOri4WtcckcPJVOWIAnALlWgy//2Ut8UoYzvoGIiMglc2q4ycrKYtWqVcTGxtqOubi4EBsby9KlS8/7vtGjRxMaGsq99957wc/IzMwkKSnJ7nFFiGgKDyyGmp0gOw2mDYJ5L0Fujt1pHaMr8dsjV/F8j3q81qcxcx+/mj4tqgDw+pytdH77TxZuOcqUFftZuOUoGdnqzRERkdLNzZkffvz4cXJzcwkLC7M7HhYWxtat+ddqAfjnn3/44osvWLt2bZE+Y+zYsYwaNepym1o2+VaEfj/BH6NhyXvw7/twZB3cMtF87bT6EQG2ISuAZ7vVo5KfJ3M3xbH3RBr3fr3S9lrrGsFMfah9iX4NERGRi+H0YamLkZyczN13383nn39OSEjRdsF+7rnnSExMtD0OHDhQzK0sZVzdoMtoM9C4+8KeP+GzTnB47XnfEhrgxXM96jP+jha2Y56n18RZsfcUGw8l8tOqg5xKzTrfJURERJzGqT03ISEhuLq6cvToUbvjR48eJTw8PN/5u3btYu/evfTs2dN2zGo1N410c3Nj27Zt1KpVy+49np6eeHp6FkPry5hGfaBSPZh8p1mH82VX6P46tBgA59lNvHHVQPq0qMLGQ4l8c08Mz0/fwB9b4/nf+H8AiA71Y/IDbanoZ97frBwrVsPAy117WImIiPM4tefGw8ODli1bsnDhQtsxq9XKwoULadeuXb7z69Wrx4YNG1i7dq3tceONN3Lttdeydu1aqlWrVpLNL3vCGph1ONFdIScDfn0Mfr7fXO34PN65rRnznriG8EAvbmpW2e61HfEp3Pzxv8xcd5iM7Fxi3/mTHu//rbocERFxKqf23AAMGzaMAQMG0KpVK9q0acO4ceNITU1l0KBBAPTv358qVaowduxYvLy8aNSokd37g4KCAPIdl/PwDoI7JsO/78HCMbBhKhxeA32/g9B6hb61R+MIfl59iD+3H6NeuD/HU7LYeyKNR39YQ2z9MPafTANg6qqD3N02sgS+jIiISH5ODzd9+/bl2LFjDB8+nLi4OJo1a8acOXNsRcb79+/HxaVMlQaVfi4ucNUTUL0dTLsHTuyEz6+DXh9Bw17nfZu7qwuf3t2SX9cdpkPtEHKtBmN+28y8zUdZsOXM0OIni3dxXb1Q/tgaT49G4bZhKxERkZKgvaWudKnHYepA2Ht6Ab8Oj0Pn4eBStLoZwzC464v/WLLzRIGvd6hdke/ua+uYtoqIyBVLe0tJ0fmGwN0zoP0j5vMl4+CL680p40VgsVgY1qWu7fnTXevi7nqmQHnJzhNsOpyI1XpFZWgREXEi9dzIGRt/gpmPQVYyWFygzYPQ6VmzTucC3luwgxOpmYzs2ZBDCensOZ7KxCV7WLTtGABuLhZubVWVMTc1ws1VmVpERC7Oxfz+VrgRe0lHYO7zsOn0dg3ewXDNs9DqHnDzuKhLrTuQwE0fLrE79m7fpvRuXpVF2+IJ8HKnZWSwo1ouIiLlmIal5NIFRMCtE+Gun811cdJPwZxn4KO2sOVXuIgs3LRaEK3OCS+/rD3Mr+sOM2jiCm7++F/GLdju6G8gIiJXOPXcyPnl5sCab2DRq5BqDi8RdQ3c+D4E1yjSJQ6cTGPmusN0jA7hxg+W5Hu9oq8H/z53HZ5uWvhPRETOTz034hiubuZw1COroeNT4OZlbt/wUXtYMQFOrw5dmGoVfBhybW2aVA2ie6Mzq07XquQLwInULOq+OIeJS/YU29cQEZEri3pupOhO7IKZj8C+0z0wNTrCjeOhQlSR3p5rNfh59UEyc6zc3KIq/SYsY/X+BNvr4/o246ZmlbGcZzsIERG5cqnnRopHxVow4Dfo/ga4+5hr43zcHpZ9UqReHFcXC7e2qsZdbSPx9nClYeVAu9cfn7KWD/7Yyb+7jgNwIiWTtKycYvkqIiJSfqnnRi7NyT1mL07e4n/V2sL/3oGwhkW+RFxiBs/+vJ6bmlXmvQU72HsizfbaW7c2ZeTMTUSH+fFkl7o0qRZIgJe7o7+FiIiUEZoKXgiFGweyWmHVlzB/BGSlgMUVYh6Ea58HT/+LutTq/afo89G/5329ZWQwUx9sh4uLhqxERK5EGpaSkuHiAq3vg8HLoH5PMHJh2UfwQRvYNvuiLtW0alChr6/ad4rJKw6wMz6Zycv389zP68nM0e7jIiKSn3puxHF2LoDfn4RTe83n7YZC5xFFXvxvxppDLNhylOoVfFi2+wQbDyWRlVt4Lc//mkQw/o7mKkIWESnnNCxVCIWbYpadDgvHwLIPzedVWsGtX0FQtYu+VEZ2LqN/20y7mhUZ/8cOth9NKfC8nx5ur5WORUTKOYWbQijclJCtv8OMhyEjEXxCoO8kiGx/yZfbGZ/Cl0v20KJ6MFuOJPHFP2fWxRnYvgYjbyx6IbOIiJQ9CjeFULgpQaf2wpS7IG4DuLhB99eh1b3ggCGkZ6atZ8rKAwCE+Hmw5Nnr2Hw4iSkrDtCtUTid6oZe9meIiEjpoXBTCIWbEpaVBr8MObMRZ4sB0ONNcPO87Etn51rp+Poi4pIyaFI1kI2HErGe/tv82d0tcXdzYf+JNPq3i1RNjohIGafZUlJ6ePjALV9C7EjAAqu/hq97QnLcZV/a3dWFQR1qALD+4JlgAzB9zSEGTVzBiJmbWLz92GV/loiIlB3quZGSs2M+TLsXMhPBPwL6fgtVW13WJVMzc3hxxkb8vdzoFxPJqbQsbv9smd05N7eoSoi/B/XDA+jVvMplfZ6IiDiHhqUKoXDjZMd3wuQ74fg2sw6n07Nw1TBwccyu4Jk5uTQZOY/MnPxTyC0W+O7eGNrXDnHIZ4mISMnRsJSUXiG14b4F0KAXWHPgj5fhm5sgxTFDR55urrSvVbHA1wwDHp28lu1HkwE4npLJxkOJDvlcEREpPdRzI85hGLB+irnoX1YK+FeG276Baq0v+9JHEtMZOXMT8cmZDO5UmwcnrcTP042wAC92xJtr5dSPCGDv8VTSs3OZOKg11541u8pqNViy6zitIivg7eGYHiUREbk8GpYqhMJNKXNsmzld/Ph2cHGH7q85bLp4no2HEvH2cMUCPDV1HWsOJHD23/oOtSsysmdD3F1dqBHiy4S/d/Py71u4rVVV3rilKelZuaRm5RDid/kzvERE5NIo3BRC4aYUykyGGYNhy0zzedM7zR3G3b2L5ePiEjNYuvs4WTlWnvlpg+24h5sLP9zflps/PrOB59rhXXhiylr+3XWC+U9cQ/WKPsXSJhERKZxqbqRs8fQ3h6S6jAGLC6z7Hr7ocmaPKgcLD/Sid/Oq9G1d3TaVHCArx2oXbAC++ncvi7YdIzPHyq/rDxdLe0RExLEUbqR0sFigw6PQ/xdzu4a4DfDpNeb08WL00g0NeLJLHe5oU51gH/d8r49bsMP286GE9GJti4iIOIbCjZQuUVfDg3+ZG25mJMB3t8Kfb4C18N3BL5WLi4VHOkcztk9jBrSvYTs+9aF2+c7dcDCRzJxcHp+8hsnL9xdLe0RE5PIp3EjpE1gFBs0yC4sxYNEr8OPdZm1OMbq/Y01ublGVd25rSusaFagZ4mv3+ra4ZL5dtp8Zaw/z7M8bznMVERFxNoUbKZ3cPM2i4hs/AFcP2PobTOgCJ3cX20f6errx9m1N6dOiKgAPXVOLiEAvRt/UkAq+HmTlWpm26qDt/FkbjpCelVts7RERkUuj2VJS+h1YYU4XT4kDryC4dSLUuq5Em/B/09bx48qD+Y7f0DiCt29rSmpmDhXPmSqenWvl63/30qVBGJEVffO9V0REik6zpaR8qdYaHlh8pg7n25vh96cg/VSJNaF744gCj/++4Qj1XppDq1cWsGrfSbvXPl68i5d/38L/3v+nJJooIiKnKdxI2RAQAQN/hxb9wbDCis/h/Rbw55uQnlDsH9+hVghVgs6/7o5hwPzN8YC5aODuYyn8dXo38uTMHK6wDlIREafSsJSUPbv/hFlPm5tvAngGQJv7oe1g8C2+TTFPpWbx6V+7mbn2EIcTMwDo07wKSRnZLNgST9uaFWhaNYhP/9pNoLc71Sp4s/FQEgB//9+1VKuQfwHAjOxc3F1dcHVx3IrMIiLlkVYoLoTCTTmRmwObZ8Bfb8GxLeYxdx9oOQjaP2L29BSThLQsHvlhDb2aVeHmllXZGpdEt3F/F/qeMb0acXfbSLtj+0+k0eP9v+lUtxIf3Nmi2NorIlIeKNwUQuGmnLFaYdss+OtNOLLWPObqAdc8Ax0eB1e3Ym9CrtWg1vOzbM/rhPmx/WhKvvOe7lqX3cdSWbb7BDe3rEpqZg5f/LMHgOUvdCbU36vY2yoiUlYp3BRC4aacMgzYtdDsydm/1DxWpRXc+hUEVSv2jx/6/Wrmbopj9E2N6NE4gqaj5l3U+8fc1JC729WwPf9h+X6OJKTzeGwdXDRkJSKi2VJyBbJYoHYsDJoNvT8Fz0A4tBI+vxb2/1fsH//+7c1Z+WIX7mhTnUBvdyb0b4Wri4UbGkew+9UePHJdbdu5gd75t3mYt/mo7eeZ6w7z3M8beP+Pnfy541ixt11EpLxRz42UTwn74Yc74egGcPOCO6dAzU4l2oQjiekE+3jg5e4KwIGTacQnZ9K8WhCvz9nKwq3xVAny5s/Ts6pqVvJlWJc6PPfTBpIzcwAID/Di90evsq2hYxgGR5MyCQvwxGJRj46IXDk0LFUIhZsrSGYKTLsHdsw1i43v+gki2zu7VXZyrQbNRs2zhZk8Hq4uZOWa+2kFersz7vZmBHq7M3XlQX5Yvp8P7mzO/5pUdkaTRUScQsNSIgCeftB3kjlclZ1mbsJ5YLmzW2XH1cVCJX/PfMe/uz+GxzpH4+3uSmJ6NoMmrqDPR//yw+kNO2euPVzSTRURKTMUbqR8c/OEvt+aQ1JZKebqxgdWOLtVdjrVDc13rFVkME90qcOf/9eJDrUrEuDlhvfp4S2AhLTskmyiiEiZonAj5Z+7N9z+A0R2gMwk+KoHrPra2a2yGXZ9HR65rjZVg80VkPu3i7TV04T6e/HdfW1ZP7IrG0Zez9u3NgVg57EUkjKyOXAyzWntFhEprVRzI1eOzBT4+QHY9rv5vHo76PikuQmni2vh7y0BR5MymLMxjtvbVMPTreD2pGXl0GD4XAD8Pd1IzsyhZ9PKZOXkEhXixzPd6qrQWETKJRUUF0Lh5gpntcKSd2Hxa5CbZR7zC4dmd5iL/nkHObN1RRLz6gKOJmUW+Jqbi4UnutShVWQwrWpUsG3rkJ6Vy+2fLSWyoi/v39GclMwcfD1cFYREpMxQQbHI+bi4mL01j62DtkPAKxBS4uCfd2F8C1j7g7NbeEGNqwTafq5WwX4zzxyrwZtzt9H3s2U8MWUt8ckZrDuQwF87jrHuYCIz1x1m0rJ9NB45lzfmbivppouIlAj13MiVLScTts+FP14+sxFnzMNw/cslsnXDpdh9LIV5m49yc4uqLN4Wz9PT1gPwfI96LNl5wrZuTh6LBTrVqcSibfkXBJz9WEfqR5j/O5i3KY5vlu4jKsSX0Tc1LFKvTnxyBsE+Hri76t9JIlK8NCxVCIUbKVBuNvz9Niweaz6v2ho6PQc1rzV7e0qp4ymZtH/tD7zcXFj+Qiweri6s3HeKNftPMXb21gu+v0+LKoy8sSEfLtrJp3/uth1/pXcj+sVEFvJOWLrrBP0mLKNfTCRjejW67O8iZUdOrpXUzFwCffKvti1SXBRuCqFwI4Xa/AvMGGxOGwcIrA4t7obmd0FA6Vw0b2d8Ch6uLlSv6GN3/J3523l/4Y5857tYoGvDcGZvjMPf042oSr6sP5hod46bi4UP7mxOt0bn3139+nf/tG0QuvOV7rip9+aK8cA3K5m3+Sgdalfk60Ft9GcvJUI1NyKXqsFNMHQFtHnArMdJ3A+LXoF3G8KP/SH1uLNbmE/tUL98wQZgWJc6fHtvjO154yqBzH38av76v2v58M4WhAd4kZyZw/qDibhY4L3bm7Hr1R70bl6FHKvBmN+2YBgGX/6zh+veWsyOo8lkZOeSlJFNckY2e0+cmYa+9kBCSXxVKQVSM3NYsMXcC23JzhPsOZ7q5BaJ5Fc6iwpEnCmgMvR4E7qMhs0zYfXXsG+J2atzcCXc+jVUa+3sVhZJ02pnio/vbhtJ3XB/2/O72lbnrXnbAbinQxQ3NasCwNg+jZm7KY5DCem8t3AH4xaYvT+f/bWbtQcS2BGfgre7K1k5Vtu1Fm87RnxyJnM3xTGmVyMCvM4MV2w+nES1Ct74e2kIozxYeyAB61n9/SdSs4h2XnNECqRwI3I+7t7QtK/5OLLe3KfqxA74+n/Q9zuIjnV2Cy/I38udYV3qsP9kGr1bVLF7bci1tYkI9Gb9wQQe6Xzm15OXuyvX1gvl9/VHbMEGYOqqg7af07NzCfZx54YmEXy7bD8fLNppe61VZDB3t6vB8j0nWX8wgZd/30Js/TAmDGgFmJt/ZuVaz7uWj5RuK/aetHt+KjXLSS0ROT8NS4kURUQTuP8PiL4ecjJgyl2lbhuH83m0czRv3do034wmi8XCzS2rMuqmRgR62/eq9G1VzfZz90bh+HrYB5EGEQFMH9yBRzvn/zf7qn2nmLR0L7d9upSXf98CwIItR+n+3t+s2neKp6etp+WYBWw+nATAhoOJ9JuwjO//2++Q7yvFa/X+BLvnJxRupBRSQbHIxcjJgsl3ws755k7jfT6H+v9zdquKxeGEdHJyDapX9OHl3zYz4Z89ADweG83jsXVs59V49vd873WxYDd0kadKkDeHEtIBqBfuz/t3NOf6d/8CIMDLjdUvdclXnHosOZNpqw5SL8KfawvYh0tK1jVvLmLfiTRqVvJl97FUhnWpU2DIFXE0FRSLFBc3D7j1K3PLhuw0swdn4RhzKnk5UznI21ao/HCnWrbjHWqH2J33TLd6ADx4dU3bMasBbWtWoGaIr925ecEGYGtcsi3YACRl5DBnUxxn/3srMyeXmz/+l9fnbOWhSatI1IahTmW1GhxJyACgyenFJN+Zv52RMzc5s1ki+ZSKcPPhhx9So0YNvLy8iImJYfny5ec99/PPP6djx44EBwcTHBxMbGxsoeeLOJynH9w5FVrfDxjw91swIRaOld8Vfyv6efLjg+14rU9jWkUG2732wNU1+f3Rq3i2ez1ioioA0DE6hIkD2/DHU53YOqYbL95Qn47RIQVdGi93F9rWNN839Ps1vLdwB/M2xbHvRCqbDyex//TmoJk5Vn5Zd6hI7U3KyGb8wh0cSy54mwq5eLuPpdD/y+Vk5VpxdbHYFn8E+OrfvcQnZTixdSL2nD4sNWXKFPr3788nn3xCTEwM48aNY+rUqWzbto3Q0Pxd0P369aNDhw60b98eLy8vXn/9daZPn86mTZuoUqVKAZ9gT8NS4lCbpsOvj0NGArh6Qqt7oNHNUKVlqV78r7icSs3iaHIGdcP8861wnJiWTY/3/+ZQQjrv3NaUORvjOJGaxXPd62E14LZPl+a73i0tqzLtrELmplUD+WXoVQAcPJXGqn2n6NE4gjs/X0ZWrsHUB9vh4ebCg5NWMnfTUVpGBvPTw+0Bs9fBxUV7aV2q2Hf+ZGe8ua5RlSBvnupahyemrLO9/sYtTbjtrFotEUcrU4v4xcTE0Lp1az744AMArFYr1apV45FHHuHZZ5+94Ptzc3MJDg7mgw8+oH///hc8X+FGHC7pMPwyFHYtPHPMpyLU7WGulxPRxHltK2VOpmaxYu9Jrm8Qli/8LN11gjs+X1bg++5oU40flh/AYoE1L3UhyMeDWz/5lxV7T9G+VkX+3XUCgK8GtaZT3VC7OqC9r93Aoz+sYcXek8x5/Gq74unsXCvL95wkJqqCFqK7gLPvaZsaFRh8bS0GTrQvqp/6UDta16hQ0k2TK0SZqbnJyspi1apVxMaemVLr4uJCbGwsS5fm/1dcQdLS0sjOzqZChYL/B5WZmUlSUpLdQ8ShAirDXT9Bv2lmr42HP6SdgDWT4NOO8GV3WP0NpJ5wdkudroKvB10bhhe4b1XLc4a7zta5XhjRoX4YBrw6awu3fbqUFXtPAdiCDcD0NYfIPaeSOTkjm5nrDnMkMYPF2+JJSMsiPSsXgJEzN9Fvwn98uWQPKZk5HElMR/I799/A/l5uVPT1zHfemN82l1STRArl1HVujh8/Tm5uLmFhYXbHw8LC2Lr1wvviADzzzDNUrlzZLiCdbezYsYwaNeqy2ypSKIsForuYj5wsOLAMVk40F/7b/6/5sDwOUR3NHp3q7SC8sfk+AcDD7fz/1mpePYgOtUPYEZ/CjysPnve8X9YeZu85K+b+tv6I7ee9x9N4ccYiaof68fPD7fnu9PTzsbO38tf246zYe5LZj3WkZiW/y/w25cu5tUvHU7MI9j3TAza4Uy0+WryLzYeTSM/KJTMnlyAfj5JupohNme6Hfe2115g8eTLTp0/Hy8urwHOee+45EhMTbY8DBw6UcCvliuPmAVFXw60T4YmNcN2LEN4EjFzYvRhm/5/Zo/NBK1j0Kpza5+wWlxotqgcBcG3dSjQ+PRvnjZubUNHPkx6NC97nqlPdSoy6sSF3tKmOxQLrztkn67mfN9h+fv+PHSRn5LBmfwL1h8+xHfdxd+WfncfJzLHy/X/7WbH3JDuOJuf7rK//3Uv/L5fnW7ju3N4iwzBsxxLSssjMyS3iHSid8mpt8jx9fV1C/M703DxyXTRhAZ7kWA3u+2YFzUbP57ZPl3IiRQXd4hxOrbnJysrCx8eHadOm0atXL9vxAQMGkJCQwC+//HLe97711lu8/PLLLFiwgFatWhX5M1VzI05zcre5ncOev8ztHHJOzy5xcYe2D8G1L5irIl/BjiZl8N1/+7m3QxQ5VivHUjKpF37mf6f7TqTi6eZK27FmfdNVtUP49r4z+2cdOJnG7I1HWL0vgQBvt0J7ec6ncqAXcUkZeLq5Mvuxjuw5kYqvhxstI4Op9fwsAG5rVZU3bmnKzvhknvxxHesPJTKkU22e6loXgIcmrWLlvlO83Kshw35cR/2IAFthc1n0zdK9DP9lE9fWrcQrvRtTOcj8e7ozPhmwUDvUj8HfrWLWhji7943t05g72lR3QoulPCpzBcVt2rRh/PjxgFlQXL16dYYOHXreguI33niDV155hblz59K2bduL+jyFGykVMpNh22yzLmfP6bVeKtaGmz6C6jGFv1f4bf1h3pm3nY/uamEXfs614WAiPT/457yvd29k7o5eFHXD/Nl2Vm9O8+pB7DyaQnJmDmCOMN7TIYqM7FzbcFceiwW2jO6Gl/ulbzmRkJaFu6sLvp4lV02QazX4/O/dLNxylBV7T3FPhyiG92xQ4LkTl+xh1K/2NTd3xlRncKdaVA3Ov7GryMUqU+FmypQpDBgwgE8//ZQ2bdowbtw4fvzxR7Zu3UpYWBj9+/enSpUqjB07FoDXX3+d4cOH8/3339OhQwfbdfz8/PDzu/A4ucKNlDrb5sBvj0PyEcAC7YaYvTge+oXgCBP+3m3bBgKgXc2KPNGlDjUr+RLi58ldE/7jn53n3+29egUf21o7BWla1Rw+O3c47Fyf3NWSin4eRIX4snLvSbbFpbBk53E+6NecUH9zWN0wDFbtO0WjKoF2QSg+OYNu4/4m2MedBcOuKbAguzice+9e6FGf+89arPFsGdm5fPDHTv7eeZxKfp62ncMBJvRvRY7VoG3NCqrFkUt2Mb+/nb5xZt++fTl27BjDhw8nLi6OZs2aMWfOHFuR8f79+3E5a72Qjz/+mKysLG655Ra764wYMYKRI0eWZNNFHKNuN6i+FOa+AGu/g6UfwJZfoeVAaNgLgqNUeHwZ7utYk64Nw+n4xiLArNFpE3VmduW9V0VxPCWTQG937r0qigcmrcLDzYWxvRtTO9SPptWCmL7moG1Nl1taVqV38ypsPJRIBV8PejWvQnp2Lu/O387EJXvtPruCrwe+nq4cOJnOQ9+uKrB9T/64jvdub866AwlsiUvijTnbbBuNGobBS79s5NtlZk/QydQsDiWk4+nmyqd/7sJqQK/mlTmSmGE3vd5qNZi84gDta1WkxjmrRF+MWRuO2D0PCyy4thHMDVef6lqXp7rWZdaGI3bh5r5vVgLmlhuzH+tYYuFMrlxO77kpaeq5kVJt+zz49dHTvTin+YZCjaugZifw8AXfSlC1tXp2LkJ2rpXoF2YD8PujV9GwcuB5z12x9yQuFmgZeSYAGYbBkz+uY/raQ0wf3IFm1YIKfK9hGNzw/j9sPpLE1/e0oVVkMMN/2cRPqy++9mfioNbk5BrcfzoY5Pns7pZMXLKXpbvtlxb4cmArrqtn/qMwr8clxM+TlS8Wfff6uZvi+GNLPKNuaoiHqwtNR88jOSPH9vqPD7azC4bns/5gAjd+sKTA16Y80JboMH+CfdyxWCzkWg2shpFvY1eRc5WpnhsROUud62HoCnPl43VT4MB/kBoPm342H3ncvKHWtea08no3gI8WTiuMu6sLn9zVkuSM7EKDDVDgInQWi4W3b2vKyJsaEuDlXsC7zpz37X0x7DqWYrtOVMilhdDP/9rNwVPpp68Lef8MfWBSwT1Av6+PIy0rFwsWfj09/f14SiaHE9Kp5O/JqdQsQgO8yLUazNpwhLAAL7ugYhgGD56+dt1wf2JqVrALNgDhAefvuTlbtUJqbO7+cjlZOVZevKE+qZm5fL10L4ZhMO3h9lQJ8uaNOduIqVmBrg3Di/RZZzMMg4ETV5CamcMPD7QtFYHpREpmvsJ4KX7quREpzbLT4fAa2PUH7Du9sOWpvZB0Vk+Ai9vpnp1rzd6d8CZX5NYPpdXk5ft59vR09Fd6N+KF6RuJCPQiJTOHj/q14IFvVpFrGNzSsirf/7cffy83u1ARHuDFwievYdKyfbw2+8z6X+/2bcqPKw7aenDcXCzkFLQV+2kuFvjh/ra8/8cOluw8gb+nG2uGn9mFfWtcEt3G/Q3AwPY1iArxZcQ5G2JuHVO0omjDMIh6bpbdsdtbV2PyivMvxdG4SiCtagTbhvamD27PpGX7eKxzNNUr+PDu/O0cTcrk1T6NcT3PNhpnF5DPebyj0wOFYRhc9foiDiems2DYNdTS+kmXRT03IuWFuzdEtjcfeQwDjm6ErbPM2pyjG8z1c3YvNl+vVA/aPwqN+lzxU8tLg17Nq/D3juNcU6cSt7Wuxs0tqtoFhF8f6YBhQK1KfrStWZHm1YLo/+Vy9pxejPDF/9XH19ONuuH+tvc836MevZtXpUPtEH5dd4Qxv20uNNiAuVP7Iz+sIf70gnzJmTks2XWCbXFJtKgezDvzt9vO3XUshRPnrOUDFHm2V0E1NQM71OC39UdIybTvDRrcqRZf/7uXDYcS2XDoTFF274/+Pd2WVDrVqcT7f+wEzPvZrlbFAj933uYzM9/2HEstMNykZuaQnJFDeAH1QydSMqng6+GwmqBV+05xKMHsfft353GFmxKknhuRsu74Tti5wOzd2fsPZJ9eodfD3xy6CqgMkR2gfk8VJpcRM9Yc4vU5W3msczS3n14nJi0rh7sm/EedMH/G9mls9wv47i/+4+8d55/x5Sh7X7uhyOduOZLEugMJHDyVTmpWDsP/14C+ny1j+Z6TdufterUH2+KSeWzyGnacs1hgQQZ3qsX/datne56SmcPSXSfoVLcSXcf9xe5j5t//p7vWZci1tQFz6YCRMzczomcDJi3bx5r9p/hiQGuurlPJdp0pK/bzzE8bGNypFlWCvbm9dXVbD1FCWhYr955izYFT9G5eldqhZki50GrMz/603tZb1SaqAi/eUJ8mVYOKeAflXGVqKnhJU7iRci0j0dz2YcUESDxnCCD6erhxPPhffC2DlG4JaVkM+X41yRk5/HB/W9YdSODAqTSe+WkDbWtWwMfDjT+3H7OtmuzqYsm3qnLtUD861anEhH/22I55urnwaOdo3py7jagQXxY91emy2rn9aDL3f7OSfSfMqfUdo0OYdK+5rpNhGOw+nkqIryf9Jy5n3YGEAq9RJcgbP083WkcF83Kvxgz/ZSPfLN1H5UAvDidm2M7r06IK79zWDMMwaPnyAk6e0xPl6mJh9UtdbBupnr0xKMBz3evx4DW1OJKYTuzbf5J6ej+yrg3D+PRuc+HYOz9fxoaDiSx48hrCCqhHuvatxbYeODD/bfHfc50JPefcd+dvZ97mo3x3XwwVfB0zVX7y8v1kWw3ubhvpkOuVBhqWErlSeQXCVY+bw1IHlsHBlZCwD1ZPgh3z4L1mUK8HVGsLfpXM3p3wRgo8ZVyQjwff3XdmQdP2tUMA6NownEBvc1bShoOJ3P/NSlIycxjQPpIPF+0C4Jt72lAnzJ+wAE8sFgvbjiazYu9J/tekMr2bVyEmqgJVg72JiSp4KOhi1Anz58+nr+XAyTQmLdvHQ9fUsr1msVhswzZfDGjF53/tJrKiL+/M38aptGzevrUpj09Zaxvm2XY0mVtbVuObpeb2JXnBpmnVQNYdTGT3sVQWbD7K3hOp+YINmAsUTl6+nztjqnPkrFCUZ+zsrazYe4p2tSragg3AjqMpGIZBUnqObdPWuZvi6N+uhu2cORuP8MgPa8jOPXdbDpiy4gCPdI62a8d7C3cA8O2yfTx61muXYv3BBBZtPca7C8xhxlaRwdSPuPL+Ia+eG5ErQfwW+GUoHFpZ8Os1OkKLAVD/f6rTKccysnNJz8rlwKk021TtXa/2sCvQzbUapGfn4leCKyEXJiM7l6SMbEL9vfItuOhiMWuJ8lQO9OLju1py04f5p6G3qB7E6v0J+Y57uLqQlWs97+fnfcadMdX5/vTK0xYL9G8bydeng9U9HaK4M6Y67y/cQZcGYTzywxrb+z3dXMjMOXP9sABPFj7Zib3HUxn962ZuaBJhK9y+M6Y6r/ZubPf56Vm5eHuYtU47jiaz70QasQ3sN5s+W83nfre7J/1iqjPqxoa2wvHitDM+GS9312JbkVrDUoVQuJErlmGYU8v3/AWHVkFmCqSfNIMPp/9vwCvQnHEVWM38f/DsDDCsULUVNOyt4FOOLNh8lMiKPkSH+V/45FJi7qY423T1c3WuF8qIng2JCPLiurcXc+Bkuu21mKgKfDGwNQ98s5J/d51g0r1teGLKWo6nnOnRqeTvSfUKPqzad4palXzZdcx+d/mfHm7PXRP+Iz278E1Qz5211ioymEBvd7bGJZOalUNCWvZ539usWhAzhpgr7xuGwbgFO3j/jx20japIjyYRvDRjI2CuN/TGnK3UjwjgzpjqhPp7UsHXg7SsXBqOmFvgtR+8pibPda9faNvzxCdlsGhbPDe3qFrkUHQoIZ0Or/1BiJ8ny5/vjMt5ZrRdDoWbQijciJwj4YC5MvKab/PX6ZwtoAp0f8NcVyevmDUlHjbNgJx0sLiCV4AZjoK0WaI4ntVq8Pb8bYQHetO5XihjZ29l5d6TvNK7kW0BQ4DV+09xx2fLqFnJj0/uakG1YB9cXCwkZWSz/0QajaoE8s787bx/ejjotT6NualZFSwWsy6ocZVAVu47xa2fmMsvVPL3ZOmz13Hd238WuhVHQVrXCGbqQ+0xDIN/d52g34T/znuuh5sLa4d34afVhziamMEHi3YWeN659UVgvxZSnhA/D1uAq1nJl9mPdWTt/gRa16hgCx9TVx7A38uNdjVDeHXWFm5pVZX/m7aePcdTeaZbPR7uVIsLGfPbZr44q1Zr0VOdiLqMlbHPR+GmEAo3IudhzTVnWx3dBEmHzGPuPubu5Rt/PrO2TkQzaNbP3OV81VdmsDlXRDO46gmo1sZcUdn1/AvfiRSH4ymZ+Hi44uNR8PDaiZRMeo7/h6hKvnx7b0y+6d9Wq8Ftny4lIT2bd29rRuOqgTQYPoe0s+pv/D3dSM7MwcPVhU/vbslLv2y0LbwY7ONOQno2Pz7Yzm5hyI2HEvnf+DObufp4uBLk7Z4vrORpU6MCraOC+XbZfhLTz9/rc66m1YL44f4YVu07xd1fLMfNxUJMzQos2XmCkT0bMLBDlN3aRrH1w+y2zACzePvz/q1oUPn8vyvnbz6abxXt9+9ozo1NKxe5rUWlcFMIhRuRS5CdDn++Dss+yR9mKjc319ax5kLiQXPoyzir697V01xzp8sYs4hZpJQwDKPQNW3yfj3mnfPyb5uZ8M8eOkaH8GG/Fni5uZKQnkV2rkGVIG8GTlzO4m3HAHMRwagQXzzd8q8NVOeF2bY6n92v9sDFxcKbc7fairzP9nyPejxwdS0S0rL4dtk+3pq3Pd85n/dvRWZOLkO/N2t9zq7dycqxUu+l2XZ1ONGhfswfdg3jFmxn3IIdRblV1I8IINjHnWvqVKJh5UB+XHmAxlUC+fjPXfkKtv293PjmnjY0rx5cpGsXlWZLiYhjuXtD7Eho9wis/AL2/g0+IWYPTu3O9uvnpJ6AZR+ZQ13JcZCbCet+gJ0Loc9n5to7IqXAhRbrO/f1p7rWpW64P90bR9gKrvN2dAfsirDrhvmf9/pVgr1tU8Tzhoce7lSbGWsO56vLqVHRHN4J8vHg1lbVbOEmxM+T4ynmgoyd64ViscBQzHBz9uwoDzcXwgPsh7F2xKew53gqczaeWfTwQrYcSQKwzRADmLnuMGDOUOvZtDIz1x1m/cFEkjNyePanDfz6yFV4uDlntXT13IhI8TEMOLAcfnsc4jcDFnO4qtU94OZpDnnt+gOOrDf3xwqoDF5B5tT0qm3AVf/+krJjZ3wKt3+2jPs7RvHgNeevVVl3IIEBE5fzTLd63NHmTH1aSmYOVsNg8LerbbPC5j9xtV3R94Avl3M4IZ2pD7Vj0tJ9tKpRwbZi88ZDiSzYcpQh19a221frtk+WsnyvuXiij4craVm5RAR6FTgF/mJYLNCzSWXG3NSIQB939hxP5bq3F+Pp5sJT19dlUIeo826VcSk0LFUIhRsRJ8hKgznPwuqvi/6egCrQ/G5o0R8CqxRf20RKmaHfr+a305ufFrSf14WG08515+fLbD0usx/ryKCJK4hLMoPN7a2r0bByAKlZufh4uDL8l030i6nOd6envXu4ufDItbVZsus4hxLSOXgqnY7RlZg4sDW5ViNfz8yWI0lU9POw69FyFA1LiUjp4uEDN75vzqT66y2I3wRYwOICIXWgTldzWnrqCchIMKenJx2CP1+Dv94wV1eObA8VaplFyn6h9te3WgEDXIq295FIaXb28FZB+3ld7N5XZxdB148IYPIDbbnz82WcSM3i/qtr2u151bt5Ffy93GlQOYDpqw/x0V0tCPX3si08eCghnTB/T1xdLAX2ypSWBQPVcyMiJc8wCt/nKifT3BR05UTY90/+14MioXpbsxYo6TDs/w8ykyA4EqrFQHAU+IaY6/OEN9Uu6VKm7DuRSo/3/uaWllUZdVOjy77ekp3HueuL/3jk2toMu74uYA6BJWdkExFYdtau0rBUIRRuRMqY4ztg7feQsN+s2zl70cGi8K4AUR2hSisz8ARFQuVm4OH4dThEHCUrx+rQYtzEtGwCvN0ctuO5MyjcFELhRqSMy0g0e2qOrDWfewaYQ1UBlc01eg6uhJQ4SDwE+/6FrOT817C4QoMbofMIqBBV8Ofk5pi9SxrqEikVFG4KoXAjcgXJzTa3mtjzNxzbCumnzJ6f5MNnzgmsBhVqmosNhtaDSvVh92Kz+NnTH9oNhfaPaCHCospIMu9bGe4hkNJJ4aYQCjciwpF1sHA07FxQtPM9/KDGVRBcw1yoMDMZslLMn/1CoXYXCIkGn4rm+j8+Fczp7FfaXlx/vQl/vGxuv9HrY/OeiTiIwk0hFG5ExCbtJBzbBgn7IPkIHD1d0+NXyeytSTwEC0dB6rGLv7bFFcIaQMVos74nuIZZ6xPRHHwrOqb9WWmw+FU4tBp6vGV+XnHISoP1k6HuDeB/eg+nrb/DqX3Q9HYzzG2YBj/de+Y9Hn5w61dQO1a9OOIQCjeFULgRkYtitcLRDeZQ1al9EFrf3D3dw8/sqTm1xxz2Sj4CaSfOPLIL2WAxuAa0eQDq9jCvtfwzWDHBrB/qOMxc+fnsQGAY5u7sZ9f/5ObAdzeb7QLwDob7F52/huhyzH4W/vvYLMYeNAs2z4S5z5mveQaY32PLr5Cdai7QeGIX7PnTfN0nxFyVuuUgczq/go5cIoWbQijciEiJSDwEh1ZC0hFzmvqxrXB4LZzMv39QPrWuMxcvjLoG9i2BOc8DBtzypVk8fXwHLBgJW3+zf1/VNtDvRzPo5NUXhTUyd2s/W9IR+P1JM2h0fBKqtDh/W5IOw7uN7PcLO5/q7WDAb+a5c56DNZMg96x9h9y8zLDj6WeGoqirofW9ZjG4yAUo3BRC4UZEnCojETb+BGt/gMNrwJoNoQ2g3RCz92fx6+ax8/GvfKYg2sXdDDwRTeHjDubMMO9gsyj64HKw5pghoud75ualYAabL7uaQ3Fgho1HV5s9SGD2CK343NwANaCKWZC9fyn4R5jFwtnmnkh0eg6ufhrWT4HVkyC8MVz3on2Qysk0h8zW/QAbphbcm2VxNcNVYDUz5Lj7YE71t0ClulC3u6btC6BwUyiFGxEpNbLTzYdPhTPHTu6G5RPMPbeObTGPtRxo1gdt+RUwzEBQu7MZMPJ6XQ6tgp8fhBNn7fLs6mluXAoQ0czcy+vYVvO5T0Vz+AygcgtzyCgn0+wpit+cv639Z5o9UP99ag6pNbjxIr9rhjlFP+2EWcOTdAhWf2N+XmF8KkLbweY98A25uM88l9UKcevM/1ZursUdyxiFm0Io3IhImZF4yOzpySsUTjxo1v2E1DGLns9lzTVDUUq8GXpC6sCsp2Dll/bnBVaDAb+avTff3mz28JzNMwDa3G/WzmyeAdFd4c4pxVMvc3K32YOVHGcOgeVkmtty5GbB7kVwau+ZcytGm9/Jp4IZeqw5ELceTuw2p58HVYP6PaFON7Mmys3rTIBJOQZT+pk9UnnX6v66GRLP59h2MyyG1DFnw12K7Az483U4uMKcPXb101o76RIp3BRC4UZErjjHtpu9QF5BZu9HhVrgfnpjw33/msXMPiGAYQakqx43p3OD2WPk4QduHiXf7twccwhv2UdnFm28GG5eZvF2QBWz/ikjEVzczOG8nHTznAa94PoxZ74vQE4WzHnG3P4jbzXsWp2h80vmtZIOmT1QfuHm9T39KFD8Vvj5fjOA5al/I3R91Qxitu+ZDekJ4B2k9ZQKoXBTCIUbEZEyKPW4WZCdsM8MXOknT2+8Gm3WLGWnmatTb/yp4GE1gNCG5vR0/3BYPBb++8SchWZxgbCG5nW8Ak8v+nh6SLBSPTixM3/vVh5XT2hwEzTsbYanA8vMHq+EfeaWIWD2MtXtbtZZGbnm5wVWNY9nJJlhKcfcpZvgGtD8Lmj/WNECZUai+XkpR80epgo1C+5hs1rN3qOTu8yeuLOXI7BaYcc8WPkFuHlC2yEQ2S7/NbLSzKFJ31CnDOkp3BRC4UZEpJzLyTQfacfh5B4zPPiFm1PSz+4ZObIe5r14Ztr62TwDzWLt6FgzPCwYAbsWmYs3+oWZPV3JR8yQVZh6/4Pub0BgFTi4yrzO3r8v/B1CG5jDZhFNYctvsPkX83uENzHDy4ldZlA5dw0m7wrm5rE1rjKDStpJ83075pntBTPAtRsKFWubQXD9j2cKzMGs6bp+jFnrlBeUVn1lLgmQk24GsI5PQZO++QNYThZs+938jFrXXfh7XgSFm0Io3IiIiJ2E/WbQOb7dXH06oDI0vsWceXYua+6ZmhnDgMOrzcLovf+YtUo1rjJ7gYIioWItcwXrcyXHmbVT6SfNEOAXZp6fkQA75puBK+140dvvG2p+zvHt9lPvz+UZYA7LFRTIPAOg6R3mTLwtv5rHGvSCDo/Cpunw7/j87/HwMwuzPf3N9luzzRCWesxcluC++UX/DkWgcFMIhRsRESnVUk/AopfNYaycdLP4uUlfc4HGU3vMMqCKNc3aqQo1z0y/z8mEuI2wZzHEbTCH0rxPF19Xa2P2pLi4wdrvzRWmM5PMGqLanc1aIA8fM7At/9xcpPHcobhOz5tLFqz8Ev59//wrd/uFQfO74drnHVo8rXBTCIUbEREpE/IKjX1DSn5l54MrzYUij6yD4EizBqjJrWdez1u5O36LOVR3aq8ZjCLbQ/T1xVIYrXBTCIUbERGRsudifn9rBSMREREpVxRuREREpFxRuBEREZFyReFGREREyhWFGxERESlXFG5ERESkXFG4ERERkXJF4UZERETKFYUbERERKVcUbkRERKRcUbgRERGRckXhRkRERMoVhRsREREpVxRuREREpFxxc3YDSpphGIC5dbqIiIiUDXm/t/N+jxfmigs3ycnJAFSrVs3JLREREZGLlZycTGBgYKHnWIyiRKByxGq1cvjwYfz9/bFYLA67blJSEtWqVePAgQMEBAQ47LpiT/e55Ohelwzd55Kh+1xyiuteG4ZBcnIylStXxsWl8KqaK67nxsXFhapVqxbb9QMCAvQ/nBKg+1xydK9Lhu5zydB9LjnFca8v1GOTRwXFIiIiUq4o3IiIiEi5onDjIJ6enowYMQJPT09nN6Vc030uObrXJUP3uWToPpec0nCvr7iCYhERESnf1HMjIiIi5YrCjYiIiJQrCjciIiJSrijciIiISLmicOMAH374ITVq1MDLy4uYmBiWL1/u7CaVOX/99Rc9e/akcuXKWCwWZsyYYfe6YRgMHz6ciIgIvL29iY2NZceOHXbnnDx5kn79+hEQEEBQUBD33nsvKSkpJfgtSr+xY8fSunVr/P39CQ0NpVevXmzbts3unIyMDIYMGULFihXx8/Pj5ptv5ujRo3bn7N+/nxtuuAEfHx9CQ0N5+umnycnJKcmvUqp9/PHHNGnSxLaIWbt27Zg9e7btdd3j4vHaa69hsVh4/PHHbcd0rx1j5MiRWCwWu0e9evVsr5e6+2zIZZk8ebLh4eFhfPnll8amTZuM+++/3wgKCjKOHj3q7KaVKbNmzTJeeOEF4+effzYAY/r06Xavv/baa0ZgYKAxY8YMY926dcaNN95oREVFGenp6bZzunXrZjRt2tRYtmyZ8ffffxu1a9c27rjjjhL+JqVb165djYkTJxobN2401q5da/To0cOoXr26kZKSYjvnoYceMqpVq2YsXLjQWLlypdG2bVujffv2ttdzcnKMRo0aGbGxscaaNWuMWbNmGSEhIcZzzz3njK9UKs2cOdP4/fffje3btxvbtm0znn/+ecPd3d3YuHGjYRi6x8Vh+fLlRo0aNYwmTZoYjz32mO247rVjjBgxwmjYsKFx5MgR2+PYsWO210vbfVa4uUxt2rQxhgwZYnuem5trVK5c2Rg7dqwTW1W2nRturFarER4ebrz55pu2YwkJCYanp6fxww8/GIZhGJs3bzYAY8WKFbZzZs+ebVgsFuPQoUMl1vayJj4+3gCMP//80zAM8766u7sbU6dOtZ2zZcsWAzCWLl1qGIYZRF1cXIy4uDjbOR9//LEREBBgZGZmluwXKEOCg4ONCRMm6B4Xg+TkZCM6OtqYP3++cc0119jCje6144wYMcJo2rRpga+VxvusYanLkJWVxapVq4iNjbUdc3FxITY2lqVLlzqxZeXLnj17iIuLs7vPgYGBxMTE2O7z0qVLCQoKolWrVrZzYmNjcXFx4b///ivxNpcViYmJAFSoUAGAVatWkZ2dbXev69WrR/Xq1e3udePGjQkLC7Od07VrV5KSkti0aVMJtr5syM3NZfLkyaSmptKuXTvd42IwZMgQbrjhBrt7Cvr77Gg7duygcuXK1KxZk379+rF//36gdN7nK27jTEc6fvw4ubm5dn9YAGFhYWzdutVJrSp/4uLiAAq8z3mvxcXFERoaave6m5sbFSpUsJ0j9qxWK48//jgdOnSgUaNGgHkfPTw8CAoKsjv33Htd0J9F3mti2rBhA+3atSMjIwM/Pz+mT59OgwYNWLt2re6xA02ePJnVq1ezYsWKfK/p77PjxMTE8NVXX1G3bl2OHDnCqFGj6NixIxs3biyV91nhRuQKNWTIEDZu3Mg///zj7KaUS3Xr1mXt2rUkJiYybdo0BgwYwJ9//unsZpUrBw4c4LHHHmP+/Pl4eXk5uznlWvfu3W0/N2nShJiYGCIjI/nxxx/x9vZ2YssKpmGpyxASEoKrq2u+ivCjR48SHh7upFaVP3n3srD7HB4eTnx8vN3rOTk5nDx5Un8WBRg6dCi//fYbixYtomrVqrbj4eHhZGVlkZCQYHf+ufe6oD+LvNfE5OHhQe3atWnZsiVjx46ladOmvPfee7rHDrRq1Sri4+Np0aIFbm5uuLm58eeff/L+++/j5uZGWFiY7nUxCQoKok6dOuzcubNU/p1WuLkMHh4etGzZkoULF9qOWa1WFi5cSLt27ZzYsvIlKiqK8PBwu/uclJTEf//9Z7vP7dq1IyEhgVWrVtnO+eOPP7BarcTExJR4m0srwzAYOnQo06dP548//iAqKsru9ZYtW+Lu7m53r7dt28b+/fvt7vWGDRvswuT8+fMJCAigQYMGJfNFyiCr1UpmZqbusQN17tyZDRs2sHbtWtujVatW9OvXz/az7nXxSElJYdeuXURERJTOv9MOL1G+wkyePNnw9PQ0vvrqK2Pz5s3GAw88YAQFBdlVhMuFJScnG2vWrDHWrFljAMY777xjrFmzxti3b59hGOZU8KCgIOOXX34x1q9fb9x0000FTgVv3ry58d9//xn//POPER0drang53j44YeNwMBAY/HixXZTOtPS0mznPPTQQ0b16tWNP/74w1i5cqXRrl07o127drbX86Z0Xn/99cbatWuNOXPmGJUqVdLU2bM8++yzxp9//mns2bPHWL9+vfHss88aFovFmDdvnmEYusfF6ezZUoahe+0oTz75pLF48WJjz549xpIlS4zY2FgjJCTEiI+PNwyj9N1nhRsHGD9+vFG9enXDw8PDaNOmjbFs2TJnN6nMWbRokQHkewwYMMAwDHM6+EsvvWSEhYUZnp6eRufOnY1t27bZXePEiRPGHXfcYfj5+RkBAQHGoEGDjOTkZCd8m9KroHsMGBMnTrSdk56ebgwePNgIDg42fHx8jN69extHjhyxu87evXuN7t27G97e3kZISIjx5JNPGtnZ2SX8bUqve+65x4iMjDQ8PDyMSpUqGZ07d7YFG8PQPS5O54Yb3WvH6Nu3rxEREWF4eHgYVapUMfr27Wvs3LnT9nppu88WwzAMx/cHiYiIiDiHam5ERESkXFG4ERERkXJF4UZERETKFYUbERERKVcUbkRERKRcUbgRERGRckXhRkRERMoVhRsRueJZLBZmzJjh7GaIiIMo3IiIUw0cOBCLxZLv0a1bN2c3TUTKKDdnN0BEpFu3bkycONHumKenp5NaIyJlnXpuRMTpPD09CQ8Pt3sEBwcD5pDRxx9/TPfu3fH29qZmzZpMmzbN7v0bNmzguuuuw9vbm4oVK/LAAw+QkpJid86XX35Jw4YN8fT0JCIigqFDh9q9fvz4cXr37o2Pjw/R0dHMnDmzeL+0iBQbhRsRKfVeeuklbr75ZtatW0e/fv24/fbb2bJlCwCpqal07dqV4OBgVqxYwdSpU1mwYIFdePn4448ZMmQIDzzwABs2bGDmzJnUrl3b7jNGjRrFbbfdxvr16+nRowf9+vXj5MmTJfo9RcRBimU7ThGRIhowYIDh6upq+Pr62j1eeeUVwzDMncwfeughu/fExMQYDz/8sGEYhvHZZ58ZwcHBRkpKiu3133//3XBxcTHi4uIMwzCMypUrGy+88MJ52wAYL774ou15SkqKARizZ8922PcUkZKjmhsRcbprr72Wjz/+2O5YhQoVbD+3a9fO7rV27dqxdu1aALZs2ULTpk3x9fW1vd6hQwesVivbtm3DYrFw+PBhOnfuXGgbmjRpYvvZ19eXgIAA4uPjL/UriYgTKdyIiNP5+vrmGyZyFG9v7yKd5+7ubvfcYrFgtVqLo0kiUsxUcyMipd6yZcvyPa9fvz4A9evXZ926daSmptpeX7JkCS4uLtStWxd/f39q1KjBwoULS7TNIuI86rkREafLzMwkLi7O7pibmxshISEATJ06lVatWnHVVVfx3XffsXz5cr744gsA+vXrx4gRIxgwYAAjR47k2LFjPPLII9x9992EhYUBMHLkSB566CFCQ0Pp3r07ycnJLFmyhEceeaRkv6iIlAiFGxFxujlz5hAREWF3rG7dumzduhUwZzJNnjyZwYMHExERwQ8//ECDBg0A8PHxYe7cuTz22GO0bt0aHx8fbr75Zt555x3btQYMGEBGRgbvvvsuTz31FCEhIdxyyy0l9wVFpERZDMMwnN0IEZHzsVgsTJ8+nV69ejm7KSJSRqjmRkRERMoVhRsREREpV1RzIyKlmkbOReRiqedGREREyhWFGxERESlXFG5ERESkXFG4ERERkXJF4UZERETKFYUbERERKVcUbkRERKRcUbgRERGRckXhRkRERMqV/wcjWdnawxkHkAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bench_train_losses = []\n",
    "bench_test_losses = []\n",
    "\n",
    "epochs = 500\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    bench_model.train()\n",
    "    bench_optimizer.zero_grad()\n",
    "    bench_outputs = bench_model(bench_X_train)\n",
    "    bench_loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    bench_loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(bench_loss.item())\n",
    "    \n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        bench_test_outputs = bench_model(bench_X_test)\n",
    "        bench_test_loss = bench_criterion(bench_test_outputs, bench_y_test)\n",
    "        bench_test_losses.append(bench_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), bench_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), bench_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.08621600270271301\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = bench_model(bench_X_test)\n",
    "    test_loss = bench_criterion(test_outputs, bench_y_test)\n",
    "    print(f'Test Loss: {test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 3.8660e-01,  3.0802e-01,  4.0827e-01,  9.9998e-01,  3.7464e-01,\n",
      "         3.0289e-01,  4.7746e-01,  9.9999e-01,  3.7588e-01,  3.0245e-01,\n",
      "         4.7748e-01,  9.9999e-01,  3.7684e-01,  3.0232e-01,  4.7720e-01,\n",
      "         9.9999e-01,  3.6446e-01,  3.0789e-01,  4.2888e-01,  1.0000e+00,\n",
      "         3.5863e-01,  3.1103e-01,  4.2891e-01,  9.9999e-01,  3.5249e-01,\n",
      "         3.1448e-01,  4.2889e-01,  1.0000e+00,  3.6988e-01,  3.1342e-01,\n",
      "         7.6044e-01,  9.9999e-01,  3.3822e-01,  3.2875e-01,  5.1280e-01,\n",
      "         1.0000e+00,  3.9760e-01,  3.1603e-01,  5.0874e-01,  9.9998e-01,\n",
      "         3.8809e-01,  3.2095e-01,  4.4122e-01,  9.9999e-01,  4.7420e-01,\n",
      "         3.4207e-01,  7.9640e-01,  9.9999e-01,  3.4242e-01,  3.8399e-01,\n",
      "         3.7442e-01,  1.0000e+00,  6.1456e-01,  3.8073e-01,  7.3465e-01,\n",
      "         1.2434e-01,  3.1173e-01,  4.4796e-01, -1.1705e-01,  9.8480e-01,\n",
      "         6.6893e-01,  3.5571e-01,  4.6902e-01,  3.0262e-01,  3.1424e-01,\n",
      "         3.6011e-01, -5.1097e-01,  9.6467e-01,  7.0543e-01,  3.6146e-01,\n",
      "         4.2956e-01,  3.2784e-01,  3.1901e-01,  3.4846e-01, -5.9755e-01,\n",
      "         9.4133e-01,  6.9883e-01,  3.5065e-01,  3.8083e-01,  3.3068e-01,\n",
      "         3.2167e-01,  3.4632e-01, -5.2162e-01,  9.2191e-01,  6.6938e-01,\n",
      "         3.4962e-01,  4.3865e-01,  3.2091e-01,  3.2063e-01,  3.5140e-01,\n",
      "        -4.8910e-01,  8.8603e-01,  7.1771e-01,  4.0698e-01,  1.3536e-01,\n",
      "         1.0000e+00,  6.2572e-01,  4.4057e-01, -1.3570e-01,  1.0000e+00,\n",
      "         9.7810e-01,  4.8653e-01,  1.9238e-02,  8.9950e-01,  7.3215e-01,\n",
      "         5.7007e-01, -1.5440e-01,  9.7561e-01,  8.4493e-01,  5.0823e-01,\n",
      "         7.6517e-01,  1.8912e-01,  5.7361e-01,  5.8129e-01,  4.4829e-01,\n",
      "         4.8060e-01,  7.7233e-01,  5.0443e-01,  8.5450e-01,  3.9330e-01,\n",
      "         5.4097e-01,  5.6660e-01,  5.2522e-01,  5.7450e-01,  8.2112e-01,\n",
      "         5.3249e-01,  9.4382e-01,  3.1935e-01,  5.5235e-01,  6.2279e-01,\n",
      "         5.7381e-01,  5.3734e-01,  4.3728e+01,  1.3989e+02,  2.3994e+01,\n",
      "         5.9929e-01,  9.0106e+01,  2.5248e+01,  2.9569e+01,  1.6775e+02,\n",
      "         1.5173e+02,  2.6919e+01,  5.4323e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '1.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 4.8189e-01,  3.2936e-01,  7.5606e-01,  1.0000e+00,  4.9183e-01,\n",
      "         3.2935e-01,  8.0948e-01,  1.0000e+00,  5.0023e-01,  3.3046e-01,\n",
      "         8.0961e-01,  1.0000e+00,  5.0886e-01,  3.3192e-01,  8.0900e-01,\n",
      "         1.0000e+00,  4.7069e-01,  3.2990e-01,  8.0400e-01,  1.0000e+00,\n",
      "         4.6402e-01,  3.3119e-01,  8.0410e-01,  1.0000e+00,  4.5715e-01,\n",
      "         3.3266e-01,  8.0414e-01,  1.0000e+00,  5.2068e-01,  3.4194e-01,\n",
      "         8.6585e-01,  1.0000e+00,  4.4489e-01,  3.4448e-01,  8.4830e-01,\n",
      "         1.0000e+00,  4.9889e-01,  3.3774e-01,  7.5929e-01,  1.0000e+00,\n",
      "         4.7219e-01,  3.3806e-01,  7.5162e-01,  1.0000e+00,  5.9151e-01,\n",
      "         3.9009e-01,  7.3145e-01,  9.9999e-01,  3.8486e-01,  3.8576e-01,\n",
      "         6.7214e-01,  9.9999e-01,  7.0446e-01,  4.3343e-01,  7.9661e-01,\n",
      "         9.7703e-01,  2.4765e-01,  4.3558e-01,  5.7601e-01,  9.9152e-01,\n",
      "         7.8904e-01,  3.7562e-01,  7.7216e-01,  9.9249e-01,  1.8052e-01,\n",
      "         3.7603e-01,  4.0592e-01,  9.9878e-01,  8.1900e-01,  3.5742e-01,\n",
      "         7.8821e-01,  9.8346e-01,  1.6202e-01,  3.6010e-01,  4.0367e-01,\n",
      "         9.9688e-01,  8.0265e-01,  3.5630e-01,  7.7690e-01,  9.8192e-01,\n",
      "         1.7319e-01,  3.5313e-01,  3.9147e-01,  9.9551e-01,  7.8894e-01,\n",
      "         3.6248e-01,  7.6240e-01,  9.8501e-01,  1.8426e-01,  3.5691e-01,\n",
      "         3.9636e-01,  9.9614e-01,  5.7567e-01,  4.7837e-01,  2.0629e-02,\n",
      "         9.9999e-01,  3.9302e-01,  4.7712e-01, -2.1787e-02,  9.9999e-01,\n",
      "         7.7756e-01,  5.7291e-01, -5.0692e-01,  9.9789e-01,  1.4675e-01,\n",
      "         5.5243e-01, -5.3342e-01,  9.9844e-01,  6.9342e-01,  6.5632e-01,\n",
      "         2.2668e-02,  8.9857e-01,  2.4184e-01,  6.4512e-01,  3.0678e-04,\n",
      "         9.7553e-01,  6.5621e-01,  6.7011e-01,  7.4513e-02,  9.0410e-01,\n",
      "         2.8295e-01,  6.5945e-01,  6.0941e-02,  9.2789e-01,  7.7892e-01,\n",
      "         7.1678e-01, -2.0622e-02,  8.9218e-01,  1.6294e-01,  7.0401e-01,\n",
      "        -4.2612e-02,  9.3951e-01,  6.9862e+00,  1.2466e+02,  1.1847e+02,\n",
      "         2.3506e+01,  1.5038e+01,  8.9248e+01,  7.9260e+01,  1.5560e+02,\n",
      "         1.5494e+02,  4.1167e+01,  3.9587e+01])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9865', '0.0055', '0.0079', '0.0001', '0.0000', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 2.9229e-01,  3.0615e-01,  5.4755e-01,  1.0000e+00,  2.8300e-01,\n",
      "         3.0848e-01,  6.0485e-01,  1.0000e+00,  2.8411e-01,  3.0949e-01,\n",
      "         6.0492e-01,  1.0000e+00,  2.8525e-01,  3.1038e-01,  6.0476e-01,\n",
      "         1.0000e+00,  2.7818e-01,  3.0906e-01,  5.6201e-01,  1.0000e+00,\n",
      "         2.7575e-01,  3.1025e-01,  5.6201e-01,  1.0000e+00,  2.7237e-01,\n",
      "         3.1191e-01,  5.6211e-01,  1.0000e+00,  2.9578e-01,  3.2439e-01,\n",
      "         8.0369e-01,  1.0000e+00,  2.7240e-01,  3.2987e-01,  6.0643e-01,\n",
      "         1.0000e+00,  3.0329e-01,  3.1346e-01,  6.1857e-01,  9.9999e-01,\n",
      "         2.9936e-01,  3.1353e-01,  5.6107e-01,  1.0000e+00,  4.3788e-01,\n",
      "         3.4754e-01,  8.1543e-01,  9.9999e-01,  2.6922e-01,  3.6477e-01,\n",
      "         3.8933e-01,  1.0000e+00,  5.2843e-01,  3.9636e-01,  8.9138e-01,\n",
      "         5.4665e-01,  1.8107e-01,  3.4959e-01,  1.3414e-01,  9.9782e-01,\n",
      "         6.0593e-01,  4.3175e-01,  8.8777e-01,  7.6639e-01,  1.5846e-01,\n",
      "         2.6399e-01,  3.5240e-01,  9.9819e-01,  6.1240e-01,  4.4420e-01,\n",
      "         8.7723e-01,  6.9897e-01,  1.5953e-01,  2.4010e-01,  3.2740e-01,\n",
      "         9.9286e-01,  5.8675e-01,  4.3811e-01,  8.3826e-01,  6.9499e-01,\n",
      "         1.6633e-01,  2.4072e-01,  3.9688e-01,  9.9040e-01,  5.7831e-01,\n",
      "         4.3473e-01,  8.6566e-01,  7.1477e-01,  1.6852e-01,  2.4975e-01,\n",
      "         3.8373e-01,  9.8632e-01,  6.7742e-01,  3.9923e-01,  1.5073e-01,\n",
      "         1.0000e+00,  5.5826e-01,  4.1836e-01, -1.5047e-01,  1.0000e+00,\n",
      "         9.6341e-01,  4.3292e-01, -1.2105e-01,  9.9594e-01,  6.8811e-01,\n",
      "         5.0499e-01, -5.0330e-01,  9.9961e-01,  9.0447e-01,  5.7127e-01,\n",
      "         8.0426e-02,  9.8041e-01,  6.6443e-01,  6.6470e-01, -6.1753e-01,\n",
      "         9.9855e-01,  8.6944e-01,  5.8893e-01,  9.7081e-02,  9.7270e-01,\n",
      "         6.3142e-01,  6.7888e-01, -6.2880e-01,  9.9467e-01,  9.8131e-01,\n",
      "         6.2197e-01,  3.0749e-02,  9.7634e-01,  7.4600e-01,  7.4161e-01,\n",
      "        -7.6626e-01,  9.9721e-01,  4.4987e+01,  1.7621e+02,  1.1457e+02,\n",
      "         1.9105e+01,  1.7127e+02,  7.6152e+01,  1.0007e+02,  1.3983e+02,\n",
      "         1.2168e+02,  4.3210e+01,  5.1944e+01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.8948', '0.0893', '0.0021', '0.0137', '0.0000', '0.0001']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 7.3149e-01,  3.0955e-01,  3.8184e-01,  9.9998e-01,  7.5372e-01,\n",
      "         3.0925e-01,  4.1096e-01,  9.9999e-01,  7.6232e-01,  3.1159e-01,\n",
      "         4.1082e-01,  9.9999e-01,  7.7095e-01,  3.1409e-01,  4.1045e-01,\n",
      "         9.9999e-01,  7.3457e-01,  3.0439e-01,  4.4142e-01,  9.9997e-01,\n",
      "         7.2960e-01,  3.0344e-01,  4.4145e-01,  9.9996e-01,  7.2524e-01,\n",
      "         3.0269e-01,  4.4144e-01,  9.9997e-01,  7.8090e-01,  3.2948e-01,\n",
      "         4.8274e-01,  9.9999e-01,  7.2882e-01,  3.1301e-01,  6.2817e-01,\n",
      "         9.9997e-01,  7.3645e-01,  3.2475e-01,  3.9984e-01,  9.9995e-01,\n",
      "         7.1707e-01,  3.1765e-01,  4.4235e-01,  9.9984e-01,  7.8594e-01,\n",
      "         3.8605e-01,  3.5664e-01,  9.9998e-01,  6.1672e-01,  3.4420e-01,\n",
      "         6.1516e-01,  9.9982e-01,  8.5176e-01,  3.3076e-01,  4.4449e-02,\n",
      "         9.6832e-01,  5.5357e-01,  3.0088e-01,  7.0152e-01,  6.6608e-01,\n",
      "         8.4142e-01,  2.4656e-01, -1.2493e-01,  9.5486e-01,  5.5800e-01,\n",
      "         2.1685e-01,  6.0575e-01,  8.7333e-01,  8.3371e-01,  2.2743e-01,\n",
      "        -1.7313e-01,  9.0465e-01,  5.5350e-01,  1.9638e-01,  5.7632e-01,\n",
      "         8.6819e-01,  8.2676e-01,  2.2558e-01, -1.4879e-01,  8.7750e-01,\n",
      "         5.5992e-01,  1.9510e-01,  5.5036e-01,  8.6783e-01,  8.2698e-01,\n",
      "         2.3331e-01, -1.2149e-01,  8.5124e-01,  5.6130e-01,  2.0268e-01,\n",
      "         5.8515e-01,  8.5325e-01,  5.6594e-01,  4.3578e-01, -3.2647e-02,\n",
      "         1.0000e+00,  4.5275e-01,  4.1313e-01,  3.1957e-02,  1.0000e+00,\n",
      "         5.3458e-01,  5.6507e-01, -2.9826e-01,  9.9234e-01,  1.8599e-01,\n",
      "         4.8274e-01, -1.9726e-01,  9.8739e-01,  5.9014e-01,  5.8114e-01,\n",
      "         1.0845e-01,  8.4751e-01,  3.8257e-01,  5.3518e-01,  3.3833e-01,\n",
      "         6.2743e-01,  6.0156e-01,  5.7426e-01,  1.6511e-01,  8.9262e-01,\n",
      "         4.1853e-01,  5.4248e-01,  4.0693e-01,  6.5949e-01,  5.8740e-01,\n",
      "         6.2053e-01,  2.0187e-01,  8.8732e-01,  3.5012e-01,  5.5886e-01,\n",
      "         4.4725e-01,  7.2884e-01,  3.6935e+01,  1.2303e+02,  1.2144e+02,\n",
      "         1.7543e+02,  5.2283e+01,  5.1591e+01,  3.2766e+01,  1.3280e+02,\n",
      "         1.7653e+02,  4.1921e+01,  2.4936e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 2.7161e-01,  3.4818e-01,  6.4095e-01,  9.9961e-01,  2.6476e-01,\n",
      "         3.4691e-01,  6.7295e-01,  9.9973e-01,  2.6799e-01,  3.4570e-01,\n",
      "         6.7294e-01,  9.9957e-01,  2.7146e-01,  3.4421e-01,  6.7275e-01,\n",
      "         9.9975e-01,  2.5439e-01,  3.4935e-01,  6.5344e-01,  9.9982e-01,\n",
      "         2.5205e-01,  3.5061e-01,  6.5332e-01,  9.9976e-01,  2.4946e-01,\n",
      "         3.5180e-01,  6.5324e-01,  9.9975e-01,  2.8201e-01,  3.4408e-01,\n",
      "         7.7976e-01,  9.9972e-01,  2.5520e-01,  3.5439e-01,  7.0499e-01,\n",
      "         9.9984e-01,  2.9088e-01,  3.4821e-01,  6.7263e-01,  9.9952e-01,\n",
      "         2.7929e-01,  3.4760e-01,  6.4413e-01,  9.9965e-01,  3.9578e-01,\n",
      "         3.4337e-01,  7.3415e-01,  9.9992e-01,  2.7672e-01,  3.7280e-01,\n",
      "         4.8074e-01,  9.9998e-01,  5.6124e-01,  4.1545e-01,  6.2002e-01,\n",
      "         2.6455e-01,  2.4715e-01,  4.1796e-01,  7.6286e-02,  9.6241e-01,\n",
      "         6.7705e-01,  4.6568e-01,  4.3997e-01,  3.7027e-01,  2.0384e-01,\n",
      "         3.7100e-01, -1.5519e-01,  9.2651e-01,  7.1452e-01,  4.8161e-01,\n",
      "         4.1024e-01,  3.5470e-01,  1.9257e-01,  3.5952e-01, -2.1712e-01,\n",
      "         8.8965e-01,  7.1313e-01,  4.8190e-01,  3.7822e-01,  3.4824e-01,\n",
      "         1.9909e-01,  3.5609e-01, -1.4617e-01,  8.6657e-01,  6.9402e-01,\n",
      "         4.7657e-01,  4.1904e-01,  3.4756e-01,  2.0540e-01,  3.5909e-01,\n",
      "        -1.3406e-01,  8.2651e-01,  6.6610e-01,  4.0584e-01,  1.4783e-01,\n",
      "         1.0000e+00,  5.4761e-01,  4.1997e-01, -1.4846e-01,  1.0000e+00,\n",
      "         9.2139e-01,  4.3692e-01, -2.3401e-01,  9.8874e-01,  5.6664e-01,\n",
      "         4.7610e-01, -5.3571e-01,  9.9800e-01,  7.6458e-01,  5.3763e-01,\n",
      "         2.5196e-01,  9.5517e-01,  4.7891e-01,  6.1215e-01, -3.3679e-01,\n",
      "         9.9556e-01,  7.2359e-01,  5.5223e-01,  3.0347e-01,  9.4609e-01,\n",
      "         4.7175e-01,  6.1567e-01, -3.1732e-01,  9.9047e-01,  8.2225e-01,\n",
      "         5.7655e-01,  2.6741e-01,  9.3704e-01,  4.8588e-01,  7.1287e-01,\n",
      "        -4.0184e-01,  9.9250e-01,  3.2425e+01,  1.7991e+02,  7.5899e+01,\n",
      "         1.3448e+01,  1.0360e+02,  4.2804e+01,  7.6796e+01,  1.6688e+02,\n",
      "         1.4901e+02,  3.3442e+01,  1.0792e+02])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0004', '0.0000', '0.1103', '0.0000', '0.8823', '0.0070']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_5285/658835650.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = bench_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = bench_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = bench_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.669\n",
      "정밀도: 0.990\n",
      "재현율: 0.669\n",
      "F1 점수: 0.798\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = bench_model(bench_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(bench_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "4414bf86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(bench_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5fffb7",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5d1ed0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "352f5823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 143\n",
    "output_size = 8\n",
    "squat_model = DeepClassifier(input_size, output_size)\n",
    "squat_criterion = nn.BCELoss()\n",
    "squat_optimizer = optim.Adam(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "8a9e4e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squat_train_losses = []\n",
    "squat_test_losses = []\n",
    "\n",
    "epochs = 500\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    squat_model.train()\n",
    "    squat_optimizer.zero_grad()\n",
    "    squat_outputs = squat_model(squat_X_train)\n",
    "    squat_loss = squat_criterion(squat_outputs, squat_y_train)\n",
    "    squat_loss.backward()\n",
    "    squat_optimizer.step()\n",
    "    squat_train_losses.append(squat_loss.item())\n",
    "    \n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        squat_test_outputs = squat_model(squat_X_test)\n",
    "        squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "        squat_test_losses.append(squat_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), squat_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), squat_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.10725542157888412\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    squat_test_outputs = squat_model(squat_X_test)\n",
    "    squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "    print(f'Test Loss: {squat_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 5.4234e-01,  3.3718e-01, -9.8448e-01,  1.0000e+00,  5.5388e-01,\n",
      "         3.2688e-01, -9.7680e-01,  9.9999e-01,  5.6174e-01,  3.2601e-01,\n",
      "        -9.7673e-01,  9.9999e-01,  5.6836e-01,  3.2502e-01, -9.7696e-01,\n",
      "         9.9998e-01,  5.3259e-01,  3.2640e-01, -9.6347e-01,  9.9999e-01,\n",
      "         5.2606e-01,  3.2537e-01, -9.6366e-01,  9.9999e-01,  5.2092e-01,\n",
      "         3.2444e-01, -9.6370e-01,  9.9999e-01,  5.8288e-01,  3.1913e-01,\n",
      "        -7.5725e-01,  9.9998e-01,  5.1693e-01,  3.2056e-01, -7.1023e-01,\n",
      "         9.9999e-01,  5.5782e-01,  3.4473e-01, -8.9038e-01,  1.0000e+00,\n",
      "         5.3091e-01,  3.4431e-01, -8.7369e-01,  1.0000e+00,  6.4680e-01,\n",
      "         3.6111e-01, -4.9861e-01,  9.9995e-01,  4.6960e-01,  3.6166e-01,\n",
      "        -4.7506e-01,  9.9999e-01,  7.1446e-01,  4.4088e-01, -7.4442e-01,\n",
      "         9.8796e-01,  3.9288e-01,  4.3068e-01, -7.1833e-01,  9.9825e-01,\n",
      "         6.7252e-01,  3.8171e-01, -1.1780e+00,  9.7258e-01,  4.1854e-01,\n",
      "         3.7994e-01, -1.1914e+00,  9.9585e-01,  6.7142e-01,  3.6899e-01,\n",
      "        -1.2684e+00,  9.2618e-01,  4.1788e-01,  3.6539e-01, -1.2776e+00,\n",
      "         9.8291e-01,  6.6393e-01,  3.6017e-01, -1.2569e+00,  9.1460e-01,\n",
      "         4.2481e-01,  3.5860e-01, -1.2598e+00,  9.7801e-01,  6.5970e-01,\n",
      "         3.6413e-01, -1.1857e+00,  8.3714e-01,  4.2865e-01,  3.6372e-01,\n",
      "        -1.1937e+00,  9.5703e-01,  6.0461e-01,  4.6480e-01,  2.4922e-03,\n",
      "         9.9980e-01,  5.0763e-01,  4.6197e-01, -2.6133e-03,  9.9984e-01,\n",
      "         5.9715e-01,  5.6524e-01, -2.6704e-01,  9.8226e-01,  4.8763e-01,\n",
      "         5.6442e-01, -1.9803e-01,  9.8882e-01,  5.8374e-01,  6.5891e-01,\n",
      "         9.2906e-02,  9.7536e-01,  4.7627e-01,  6.5612e-01,  1.3777e-01,\n",
      "         9.9278e-01,  5.7004e-01,  6.7013e-01,  1.1616e-01,  9.2070e-01,\n",
      "         4.8797e-01,  6.6676e-01,  1.5880e-01,  9.2436e-01,  5.9736e-01,\n",
      "         6.9466e-01, -1.3415e-01,  9.7107e-01,  4.5988e-01,  6.9404e-01,\n",
      "        -9.2982e-02,  9.8591e-01,  6.0724e+01,  4.9794e+00,  2.1204e+01,\n",
      "         5.3975e+01,  5.3103e+01,  1.7448e+02,  1.6786e+02,  1.3745e+02,\n",
      "         1.2521e+02,  8.1228e+01,  8.6424e+01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0016', '0.9979', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0005']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 5.4116e-01,  2.6564e-01, -5.5873e-01,  1.0000e+00,  5.5398e-01,\n",
      "         2.5424e-01, -5.3672e-01,  1.0000e+00,  5.6181e-01,  2.5378e-01,\n",
      "        -5.3677e-01,  9.9999e-01,  5.6822e-01,  2.5326e-01, -5.3689e-01,\n",
      "         9.9999e-01,  5.3200e-01,  2.5346e-01, -5.3023e-01,  9.9999e-01,\n",
      "         5.2509e-01,  2.5251e-01, -5.3027e-01,  9.9999e-01,  5.1892e-01,\n",
      "         2.5156e-01, -5.3024e-01,  9.9999e-01,  5.7801e-01,  2.5335e-01,\n",
      "        -3.1558e-01,  9.9999e-01,  5.1492e-01,  2.5245e-01, -2.8605e-01,\n",
      "         9.9999e-01,  5.5589e-01,  2.7454e-01, -4.6823e-01,  1.0000e+00,\n",
      "         5.2836e-01,  2.7381e-01, -4.6039e-01,  1.0000e+00,  6.4065e-01,\n",
      "         3.0626e-01, -1.2625e-01,  9.9998e-01,  4.6019e-01,  3.0686e-01,\n",
      "        -1.0908e-01,  9.9999e-01,  7.0484e-01,  3.7494e-01, -3.8261e-01,\n",
      "         9.9625e-01,  3.9335e-01,  3.7282e-01, -3.2242e-01,  9.9449e-01,\n",
      "         7.0066e-01,  3.1241e-01, -8.6446e-01,  9.9499e-01,  3.9113e-01,\n",
      "         3.1685e-01, -8.1097e-01,  9.9606e-01,  7.0018e-01,  2.9757e-01,\n",
      "        -9.5356e-01,  9.8483e-01,  3.9097e-01,  2.9956e-01, -9.0274e-01,\n",
      "         9.8757e-01,  6.9343e-01,  2.8888e-01, -9.4090e-01,  9.8279e-01,\n",
      "         3.9823e-01,  2.9223e-01, -8.9314e-01,  9.8599e-01,  6.8936e-01,\n",
      "         2.9452e-01, -8.6998e-01,  9.6798e-01,  4.0191e-01,  2.9840e-01,\n",
      "        -8.1891e-01,  9.7679e-01,  5.9505e-01,  4.5411e-01, -5.7482e-03,\n",
      "         9.9990e-01,  4.8737e-01,  4.5277e-01,  5.6139e-03,  9.9995e-01,\n",
      "         6.6938e-01,  5.6264e-01, -4.0536e-02,  9.9749e-01,  4.0719e-01,\n",
      "         5.5727e-01, -1.0414e-01,  9.9906e-01,  6.8332e-01,  6.4978e-01,\n",
      "         4.7129e-01,  9.9401e-01,  3.7723e-01,  6.4720e-01,  2.9364e-01,\n",
      "         9.9877e-01,  6.6103e-01,  6.6469e-01,  5.1141e-01,  9.6346e-01,\n",
      "         3.9841e-01,  6.6335e-01,  3.2288e-01,  9.8442e-01,  7.3779e-01,\n",
      "         6.8541e-01,  3.0432e-01,  9.8670e-01,  3.1751e-01,  6.8247e-01,\n",
      "         9.8980e-02,  9.9507e-01,  4.1396e+01,  3.9234e+01,  4.7651e+01,\n",
      "         3.6673e+01,  3.3427e+01,  1.7731e+02,  1.7353e+02,  1.1467e+02,\n",
      "         1.0889e+02,  4.8865e+01,  5.0617e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0001', '0.0000', '0.0693', '0.0000', '0.8602', '0.0000', '0.0704']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 6.2697e-01,  3.3034e-01, -4.1296e-01,  9.9961e-01,  6.2426e-01,\n",
      "         3.1508e-01, -3.8153e-01,  9.9949e-01,  6.2373e-01,  3.1398e-01,\n",
      "        -3.8171e-01,  9.9944e-01,  6.2280e-01,  3.1273e-01, -3.8204e-01,\n",
      "         9.9942e-01,  6.1626e-01,  3.1539e-01, -4.4688e-01,  9.9970e-01,\n",
      "         6.1020e-01,  3.1442e-01, -4.4713e-01,  9.9976e-01,  6.0332e-01,\n",
      "         3.1338e-01, -4.4728e-01,  9.9979e-01,  5.8280e-01,  3.1055e-01,\n",
      "        -2.0840e-01,  9.9955e-01,  5.5597e-01,  3.1199e-01, -5.0063e-01,\n",
      "         9.9967e-01,  6.1341e-01,  3.3986e-01, -3.4310e-01,  9.9909e-01,\n",
      "         6.0296e-01,  3.3975e-01, -4.2841e-01,  9.9954e-01,  5.2904e-01,\n",
      "         3.5195e-01,  2.3510e-02,  9.9678e-01,  4.0388e-01,  3.6647e-01,\n",
      "        -5.2984e-01,  9.9946e-01,  5.6149e-01,  3.8145e-01,  3.0547e-01,\n",
      "         2.2088e-01,  3.1369e-01,  4.5530e-01, -5.3461e-01,  9.2411e-01,\n",
      "         5.8603e-01,  3.5849e-01,  5.1087e-01,  3.2106e-01,  4.2606e-01,\n",
      "         3.9840e-01, -3.8210e-01,  9.2702e-01,  6.0205e-01,  3.4811e-01,\n",
      "         5.6030e-01,  3.0656e-01,  4.4711e-01,  3.8643e-01, -3.9797e-01,\n",
      "         9.0332e-01,  5.9993e-01,  3.4789e-01,  5.5902e-01,  3.1335e-01,\n",
      "         4.4946e-01,  3.7628e-01, -4.0374e-01,  9.0329e-01,  5.8754e-01,\n",
      "         3.5166e-01,  5.1429e-01,  3.2618e-01,  4.4561e-01,  3.7877e-01,\n",
      "        -3.7707e-01,  8.5369e-01,  3.4490e-01,  4.7458e-01,  1.7798e-01,\n",
      "         9.9949e-01,  2.4573e-01,  4.8671e-01, -1.7758e-01,  9.9985e-01,\n",
      "         5.3753e-01,  4.6472e-01,  1.8240e-01,  8.8882e-01,  3.5036e-01,\n",
      "         5.5409e-01, -6.9735e-01,  9.9396e-01,  4.1840e-01,  5.7495e-01,\n",
      "         5.4887e-01,  9.3164e-01,  2.7226e-01,  6.5125e-01, -3.9875e-01,\n",
      "         9.9228e-01,  3.8725e-01,  5.8639e-01,  5.8455e-01,  8.8886e-01,\n",
      "         2.3918e-01,  6.5909e-01, -3.5144e-01,  9.7183e-01,  4.9320e-01,\n",
      "         6.1562e-01,  5.8956e-01,  9.3335e-01,  3.3634e-01,  6.9949e-01,\n",
      "        -3.9517e-01,  9.8783e-01,  3.6839e+01,  9.4637e+01,  1.7709e+01,\n",
      "         4.3420e+01,  2.9514e+01,  1.2847e+02,  1.5713e+02,  1.5740e+02,\n",
      "         1.4212e+02,  3.5601e+01,  3.5899e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0428', '0.0022', '0.2823', '0.0102', '0.4739', '0.0411', '0.1195', '0.0280']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 5.3807e-01,  2.7273e-01, -9.6897e-01,  1.0000e+00,  5.5158e-01,\n",
      "         2.6109e-01, -9.5320e-01,  9.9999e-01,  5.5967e-01,  2.6065e-01,\n",
      "        -9.5314e-01,  9.9999e-01,  5.6687e-01,  2.6018e-01, -9.5336e-01,\n",
      "         9.9999e-01,  5.3011e-01,  2.6098e-01, -9.3915e-01,  9.9999e-01,\n",
      "         5.2372e-01,  2.6057e-01, -9.3933e-01,  9.9999e-01,  5.1888e-01,\n",
      "         2.6017e-01, -9.3933e-01,  9.9999e-01,  5.7910e-01,  2.6163e-01,\n",
      "        -7.4639e-01,  9.9999e-01,  5.1678e-01,  2.6268e-01, -6.7993e-01,\n",
      "         9.9999e-01,  5.5389e-01,  2.8243e-01, -8.8224e-01,  1.0000e+00,\n",
      "         5.2730e-01,  2.8230e-01, -8.6429e-01,  1.0000e+00,  6.4578e-01,\n",
      "         3.1744e-01, -5.3331e-01,  9.9997e-01,  4.6554e-01,  3.1759e-01,\n",
      "        -4.7354e-01,  9.9998e-01,  7.3051e-01,  3.8867e-01, -8.7995e-01,\n",
      "         9.9658e-01,  3.8251e-01,  3.8320e-01, -7.5267e-01,  9.9780e-01,\n",
      "         6.8605e-01,  3.2161e-01, -1.3671e+00,  9.9029e-01,  4.0058e-01,\n",
      "         3.2164e-01, -1.2567e+00,  9.9553e-01,  6.7832e-01,  3.0558e-01,\n",
      "        -1.4667e+00,  9.7041e-01,  4.0553e-01,  3.0564e-01, -1.3567e+00,\n",
      "         9.8318e-01,  6.7167e-01,  2.9719e-01, -1.4341e+00,  9.6425e-01,\n",
      "         4.1218e-01,  2.9934e-01, -1.3313e+00,  9.7814e-01,  6.6862e-01,\n",
      "         3.0420e-01, -1.3646e+00,  9.2902e-01,  4.1516e-01,  3.0553e-01,\n",
      "        -1.2598e+00,  9.5856e-01,  5.9593e-01,  4.5403e-01, -3.9122e-03,\n",
      "         9.9984e-01,  4.9467e-01,  4.5212e-01,  3.6301e-03,  9.9986e-01,\n",
      "         5.9464e-01,  5.6004e-01, -1.2268e-01,  9.9376e-01,  4.8641e-01,\n",
      "         5.6025e-01, -1.0251e-01,  9.9467e-01,  5.7578e-01,  6.5669e-01,\n",
      "         3.4427e-01,  9.8858e-01,  4.7926e-01,  6.5261e-01,  3.5492e-01,\n",
      "         9.9387e-01,  5.5903e-01,  6.6853e-01,  3.7667e-01,  9.4017e-01,\n",
      "         4.9487e-01,  6.6436e-01,  3.8712e-01,  9.2724e-01,  6.0234e-01,\n",
      "         6.9532e-01,  1.2443e-01,  9.8206e-01,  4.5881e-01,  6.9344e-01,\n",
      "         1.2717e-01,  9.8796e-01,  5.2197e+01,  1.6406e+01,  3.5326e+01,\n",
      "         6.1848e+01,  5.6602e+01,  1.7914e+02,  1.7065e+02,  1.3630e+02,\n",
      "         1.2253e+02,  6.6998e+01,  7.5839e+01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0003', '0.9955', '0.0000', '0.0004', '0.0000', '0.0000', '0.0000', '0.0037']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 5.1873e-01,  4.4547e-01, -7.5814e-01,  9.9999e-01,  5.2606e-01,\n",
      "         4.3631e-01, -7.6041e-01,  9.9998e-01,  5.3400e-01,  4.3509e-01,\n",
      "        -7.6036e-01,  9.9998e-01,  5.4279e-01,  4.3320e-01, -7.6057e-01,\n",
      "         9.9996e-01,  5.0976e-01,  4.3675e-01, -7.4241e-01,  9.9998e-01,\n",
      "         5.0479e-01,  4.3606e-01, -7.4260e-01,  9.9998e-01,  5.0024e-01,\n",
      "         4.3521e-01, -7.4267e-01,  9.9998e-01,  5.6436e-01,  4.2311e-01,\n",
      "        -6.1884e-01,  9.9997e-01,  5.0227e-01,  4.2881e-01, -5.3336e-01,\n",
      "         9.9999e-01,  5.3745e-01,  4.5149e-01, -6.9180e-01,  9.9998e-01,\n",
      "         5.1494e-01,  4.5136e-01, -6.6748e-01,  9.9998e-01,  6.3522e-01,\n",
      "         4.5668e-01, -3.8387e-01,  9.9990e-01,  4.6024e-01,  4.5931e-01,\n",
      "        -3.6117e-01,  9.9999e-01,  6.9882e-01,  5.2417e-01, -5.8609e-01,\n",
      "         9.6675e-01,  3.9012e-01,  5.2477e-01, -5.3287e-01,  9.9562e-01,\n",
      "         6.8525e-01,  4.8798e-01, -9.5808e-01,  9.4429e-01,  3.9566e-01,\n",
      "         4.8524e-01, -8.9364e-01,  9.9516e-01,  6.9101e-01,  4.7596e-01,\n",
      "        -1.0230e+00,  8.9282e-01,  3.9052e-01,  4.7036e-01, -9.6030e-01,\n",
      "         9.8268e-01,  6.8147e-01,  4.6736e-01, -1.0107e+00,  8.8058e-01,\n",
      "         3.9895e-01,  4.6322e-01, -9.4530e-01,  9.7913e-01,  6.7629e-01,\n",
      "         4.7105e-01, -9.5947e-01,  8.0187e-01,  4.0450e-01,  4.6743e-01,\n",
      "        -8.9443e-01,  9.6360e-01,  5.9247e-01,  5.5692e-01, -1.9973e-03,\n",
      "         9.9968e-01,  4.9008e-01,  5.5350e-01,  1.8949e-03,  9.9989e-01,\n",
      "         7.3308e-01,  5.8544e-01, -4.5887e-01,  9.9617e-01,  3.6264e-01,\n",
      "         5.7366e-01, -3.9865e-01,  9.9818e-01,  6.8587e-01,  6.5255e-01,\n",
      "         4.4998e-02,  9.8976e-01,  3.7536e-01,  6.5323e-01,  6.6613e-02,\n",
      "         9.9776e-01,  6.6176e-01,  6.6388e-01,  8.9604e-02,  9.7287e-01,\n",
      "         3.9797e-01,  6.6794e-01,  1.0863e-01,  9.8703e-01,  7.3648e-01,\n",
      "         6.8632e-01, -7.1937e-02,  9.7293e-01,  3.1695e-01,  6.8476e-01,\n",
      "        -4.4667e-02,  9.8994e-01,  2.6865e+01,  2.2748e+01,  3.8991e+01,\n",
      "         6.0687e+00,  6.4893e+00,  1.0764e+02,  1.3044e+02,  1.5031e+02,\n",
      "         1.3213e+02,  4.1898e+01,  4.4776e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(4)\n",
      "예측 확률: ['0.0003', '0.0000', '0.0177', '0.0000', '0.9819', '0.0000', '0.0000', '0.0000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_5285/3174289499.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = squat_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = squat_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = squat_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.588\n",
      "정밀도: 0.982\n",
      "재현율: 0.588\n",
      "F1 점수: 0.735\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = squat_model(squat_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(squat_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "e57f97ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(squat_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50889ab8",
   "metadata": {},
   "source": [
    "### Deadlift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "43352d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d9c8d471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 143\n",
    "output_size = 8\n",
    "deadlift_model = DeepClassifier(input_size, output_size)\n",
    "deadlift_criterion = nn.BCELoss()\n",
    "deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "2a73ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deadlift_train_losses = []\n",
    "deadlift_test_losses = []\n",
    "\n",
    "epochs = 500\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    deadlift_model.train()\n",
    "    deadlift_optimizer.zero_grad()\n",
    "    deadlift_outputs = deadlift_model(deadlift_X_train)\n",
    "    deadlift_loss = deadlift_criterion(deadlift_outputs, deadlift_y_train)\n",
    "    deadlift_loss.backward()\n",
    "    deadlift_optimizer.step()\n",
    "    deadlift_train_losses.append(deadlift_loss.item())\n",
    "    \n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "        deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "        deadlift_test_losses.append(deadlift_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), deadlift_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), deadlift_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.09784714877605438\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "    deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "    print(f'Test Loss: {deadlift_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 5.4232e-01,  3.2144e-01, -1.0854e+00,  9.9999e-01,  5.5346e-01,\n",
      "         3.1023e-01, -1.0759e+00,  9.9997e-01,  5.6161e-01,  3.0934e-01,\n",
      "        -1.0758e+00,  9.9997e-01,  5.6878e-01,  3.0822e-01, -1.0761e+00,\n",
      "         9.9993e-01,  5.3254e-01,  3.1097e-01, -1.0640e+00,  9.9997e-01,\n",
      "         5.2603e-01,  3.1069e-01, -1.0642e+00,  9.9997e-01,  5.2115e-01,\n",
      "         3.1020e-01, -1.0642e+00,  9.9997e-01,  5.8284e-01,  3.0388e-01,\n",
      "        -8.5808e-01,  9.9994e-01,  5.1902e-01,  3.0731e-01, -8.0472e-01,\n",
      "         9.9998e-01,  5.5830e-01,  3.2985e-01, -9.9130e-01,  9.9999e-01,\n",
      "         5.3205e-01,  3.2995e-01, -9.7631e-01,  9.9999e-01,  6.4599e-01,\n",
      "         3.4793e-01, -5.9829e-01,  9.9995e-01,  4.7229e-01,  3.4836e-01,\n",
      "        -5.7003e-01,  9.9999e-01,  7.3353e-01,  4.1616e-01, -9.1262e-01,\n",
      "         9.8997e-01,  3.7904e-01,  4.1005e-01, -8.6164e-01,  9.9787e-01,\n",
      "         6.8188e-01,  3.5692e-01, -1.3587e+00,  9.7798e-01,  4.1374e-01,\n",
      "         3.4881e-01, -1.3425e+00,  9.9383e-01,  6.7233e-01,  3.4243e-01,\n",
      "        -1.4492e+00,  9.3355e-01,  4.1915e-01,  3.3369e-01, -1.4356e+00,\n",
      "         9.7448e-01,  6.6166e-01,  3.3583e-01, -1.4166e+00,  9.2300e-01,\n",
      "         4.2881e-01,  3.2920e-01, -1.4060e+00,  9.6782e-01,  6.6007e-01,\n",
      "         3.4049e-01, -1.3555e+00,  8.5637e-01,  4.3207e-01,  3.3475e-01,\n",
      "        -1.3412e+00,  9.4319e-01,  6.0593e-01,  4.6743e-01, -1.6991e-03,\n",
      "         9.9973e-01,  5.0856e-01,  4.6413e-01,  1.4507e-03,  9.9978e-01,\n",
      "         5.9091e-01,  5.6779e-01, -1.7044e-01,  9.8771e-01,  4.9467e-01,\n",
      "         5.6624e-01, -1.4819e-01,  9.8810e-01,  5.8272e-01,  6.5814e-01,\n",
      "         2.4884e-01,  9.8331e-01,  4.7854e-01,  6.5672e-01,  2.5230e-01,\n",
      "         9.9194e-01,  5.6948e-01,  6.6873e-01,  2.7650e-01,  9.1663e-01,\n",
      "         4.9199e-01,  6.6814e-01,  2.7898e-01,  9.1184e-01,  5.9966e-01,\n",
      "         6.9624e-01,  2.5233e-02,  9.7902e-01,  4.6041e-01,  6.9484e-01,\n",
      "         2.1029e-02,  9.8572e-01,  6.1236e+01,  1.0987e+01,  2.6977e+01,\n",
      "         6.6133e+01,  6.2379e+01,  1.7111e+02,  1.6403e+02,  1.3385e+02,\n",
      "         1.2025e+02,  8.1019e+01,  8.0572e+01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0008', '0.9991', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 5.4973e-01,  2.5562e-01, -9.7611e-01,  1.0000e+00,  5.6290e-01,\n",
      "         2.4369e-01, -9.6065e-01,  9.9999e-01,  5.7066e-01,  2.4300e-01,\n",
      "        -9.6061e-01,  9.9999e-01,  5.7716e-01,  2.4240e-01, -9.6084e-01,\n",
      "         9.9999e-01,  5.3930e-01,  2.4346e-01, -9.4605e-01,  9.9999e-01,\n",
      "         5.3209e-01,  2.4273e-01, -9.4621e-01,  9.9999e-01,  5.2598e-01,\n",
      "         2.4242e-01, -9.4620e-01,  9.9999e-01,  5.8726e-01,  2.4408e-01,\n",
      "        -7.4235e-01,  9.9999e-01,  5.2270e-01,  2.4571e-01, -6.7585e-01,\n",
      "         9.9999e-01,  5.6609e-01,  2.6514e-01, -8.8576e-01,  1.0000e+00,\n",
      "         5.3853e-01,  2.6489e-01, -8.6691e-01,  1.0000e+00,  6.6052e-01,\n",
      "         3.0306e-01, -5.2780e-01,  9.9997e-01,  4.7031e-01,  3.0483e-01,\n",
      "        -4.6415e-01,  9.9998e-01,  7.4583e-01,  3.7843e-01, -9.0322e-01,\n",
      "         9.9690e-01,  3.8059e-01,  3.7138e-01, -7.5184e-01,  9.9766e-01,\n",
      "         7.0633e-01,  3.0786e-01, -1.4278e+00,  9.9205e-01,  4.0207e-01,\n",
      "         3.0867e-01, -1.2813e+00,  9.9583e-01,  6.9908e-01,  2.9359e-01,\n",
      "        -1.5348e+00,  9.7661e-01,  4.0617e-01,  2.9194e-01, -1.3856e+00,\n",
      "         9.8527e-01,  6.9258e-01,  2.8419e-01, -1.5053e+00,  9.7148e-01,\n",
      "         4.1489e-01,  2.8533e-01, -1.3640e+00,  9.8159e-01,  6.8989e-01,\n",
      "         2.8993e-01, -1.4278e+00,  9.4181e-01,  4.1825e-01,  2.9151e-01,\n",
      "        -1.2865e+00,  9.6649e-01,  6.1134e-01,  4.5148e-01, -6.1887e-03,\n",
      "         9.9980e-01,  5.0440e-01,  4.4855e-01,  5.9903e-03,  9.9984e-01,\n",
      "         5.9766e-01,  5.6168e-01, -6.8439e-02,  9.9464e-01,  4.9506e-01,\n",
      "         5.5975e-01, -4.4868e-02,  9.9552e-01,  5.7590e-01,  6.5405e-01,\n",
      "         4.6674e-01,  9.8882e-01,  4.8169e-01,  6.5114e-01,  4.6323e-01,\n",
      "         9.9433e-01,  5.6004e-01,  6.6577e-01,  5.0490e-01,  9.3648e-01,\n",
      "         4.9735e-01,  6.6296e-01,  5.0003e-01,  9.3121e-01,  5.9683e-01,\n",
      "         6.9498e-01,  2.4617e-01,  9.8236e-01,  4.5854e-01,  6.9136e-01,\n",
      "         2.2596e-01,  9.8939e-01,  5.2973e+01,  1.9301e+01,  3.4532e+01,\n",
      "         6.2200e+01,  5.8982e+01,  1.7959e+02,  1.6613e+02,  1.3971e+02,\n",
      "         1.1872e+02,  7.4911e+01,  7.3241e+01])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0001', '0.9985', '0.0000', '0.0006', '0.0000', '0.0000', '0.0000', '0.0007']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 5.2103e-01,  3.4730e-01, -9.6210e-01,  9.9997e-01,  5.2941e-01,\n",
      "         3.3624e-01, -9.6934e-01,  9.9995e-01,  5.3638e-01,  3.3484e-01,\n",
      "        -9.6918e-01,  9.9994e-01,  5.4453e-01,  3.3318e-01, -9.6937e-01,\n",
      "         9.9989e-01,  5.1654e-01,  3.3646e-01, -9.5717e-01,  9.9997e-01,\n",
      "         5.1308e-01,  3.3542e-01, -9.5743e-01,  9.9998e-01,  5.0942e-01,\n",
      "         3.3420e-01, -9.5747e-01,  9.9997e-01,  5.6590e-01,  3.2519e-01,\n",
      "        -8.1475e-01,  9.9993e-01,  5.1268e-01,  3.2668e-01, -7.5355e-01,\n",
      "         9.9999e-01,  5.3472e-01,  3.5221e-01, -8.7921e-01,  9.9996e-01,\n",
      "         5.1936e-01,  3.5125e-01, -8.6275e-01,  9.9998e-01,  6.4312e-01,\n",
      "         3.5559e-01, -5.6036e-01,  9.9991e-01,  4.7013e-01,  3.5707e-01,\n",
      "        -5.3289e-01,  1.0000e+00,  7.0694e-01,  3.9947e-01, -8.0818e-01,\n",
      "         9.6226e-01,  3.9796e-01,  4.1312e-01, -7.9772e-01,  9.9781e-01,\n",
      "         7.0760e-01,  3.9336e-01, -1.2549e+00,  8.1447e-01,  4.0044e-01,\n",
      "         3.8548e-01, -1.2885e+00,  9.8900e-01,  7.1124e-01,  3.8684e-01,\n",
      "        -1.3438e+00,  7.0012e-01,  4.0200e-01,  3.7558e-01, -1.3823e+00,\n",
      "         9.5494e-01,  7.0020e-01,  3.8098e-01, -1.3397e+00,  7.0119e-01,\n",
      "         4.1345e-01,  3.6936e-01, -1.3715e+00,  9.5255e-01,  6.9548e-01,\n",
      "         3.8391e-01, -1.2642e+00,  6.2849e-01,  4.1602e-01,  3.7256e-01,\n",
      "        -1.2944e+00,  9.2744e-01,  6.0626e-01,  4.6227e-01, -3.6715e-03,\n",
      "         9.9974e-01,  5.0874e-01,  4.6109e-01,  3.4487e-03,  9.9986e-01,\n",
      "         6.4015e-01,  5.6378e-01, -1.9743e-01,  9.6584e-01,  4.5850e-01,\n",
      "         5.6087e-01, -2.0370e-01,  9.8551e-01,  6.3186e-01,  6.5608e-01,\n",
      "         9.9098e-02,  9.4548e-01,  4.5962e-01,  6.4998e-01,  1.0890e-01,\n",
      "         9.8840e-01,  6.1528e-01,  6.6977e-01,  1.1355e-01,  8.1250e-01,\n",
      "         4.7424e-01,  6.6294e-01,  1.2729e-01,  9.1884e-01,  6.6113e-01,\n",
      "         6.9123e-01, -1.2230e-01,  9.5045e-01,  4.2489e-01,  6.8473e-01,\n",
      "        -9.8340e-02,  9.8165e-01,  6.0058e+01,  6.1680e+01,  4.7047e+01,\n",
      "         5.6304e+01,  4.8899e+01,  1.7568e+02,  1.7602e+02,  1.3470e+02,\n",
      "         1.3226e+02,  6.4643e+01,  6.5362e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0000', '0.0346', '0.0000', '0.4670', '0.0000', '0.0420', '0.0011', '0.4553']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 5.4789e-01,  3.1058e-01, -7.5189e-01,  9.9999e-01,  5.6072e-01,\n",
      "         2.9908e-01, -7.3564e-01,  9.9999e-01,  5.6875e-01,  2.9866e-01,\n",
      "        -7.3555e-01,  9.9999e-01,  5.7478e-01,  2.9806e-01, -7.3576e-01,\n",
      "         9.9999e-01,  5.3785e-01,  2.9876e-01, -7.2691e-01,  9.9999e-01,\n",
      "         5.3086e-01,  2.9820e-01, -7.2707e-01,  9.9998e-01,  5.2474e-01,\n",
      "         2.9761e-01, -7.2703e-01,  9.9998e-01,  5.8467e-01,  2.9575e-01,\n",
      "        -5.2859e-01,  9.9998e-01,  5.2039e-01,  2.9587e-01, -4.8674e-01,\n",
      "         9.9998e-01,  5.6293e-01,  3.1863e-01, -6.6445e-01,  9.9999e-01,\n",
      "         5.3507e-01,  3.1854e-01, -6.5282e-01,  9.9999e-01,  6.4479e-01,\n",
      "         3.4371e-01, -3.2027e-01,  9.9997e-01,  4.6533e-01,  3.4273e-01,\n",
      "        -3.1368e-01,  9.9998e-01,  7.1856e-01,  4.0921e-01, -5.8367e-01,\n",
      "         9.9675e-01,  3.9343e-01,  4.0671e-01, -5.6402e-01,  9.9501e-01,\n",
      "         7.0718e-01,  3.5292e-01, -1.0122e+00,  9.9548e-01,  4.0111e-01,\n",
      "         3.5335e-01, -1.0090e+00,  9.9626e-01,  7.0700e-01,  3.3588e-01,\n",
      "        -1.0962e+00,  9.8445e-01,  4.0193e-01,  3.3861e-01, -1.0959e+00,\n",
      "         9.8727e-01,  6.9694e-01,  3.3159e-01, -1.0722e+00,  9.8271e-01,\n",
      "         4.0978e-01,  3.3286e-01, -1.0775e+00,  9.8565e-01,  6.9248e-01,\n",
      "         3.3669e-01, -1.0121e+00,  9.7059e-01,  4.1330e-01,  3.3828e-01,\n",
      "        -1.0122e+00,  9.7733e-01,  5.9907e-01,  4.7816e-01,  6.0423e-03,\n",
      "         9.9991e-01,  4.8883e-01,  4.7550e-01, -6.2193e-03,  9.9994e-01,\n",
      "         7.1161e-01,  5.6549e-01, -3.8049e-01,  9.9837e-01,  3.7541e-01,\n",
      "         5.5935e-01, -2.8212e-01,  9.9924e-01,  6.8703e-01,  6.4884e-01,\n",
      "         1.2246e-01,  9.9566e-01,  3.7591e-01,  6.4727e-01,  1.3805e-01,\n",
      "         9.9885e-01,  6.6290e-01,  6.6311e-01,  1.6412e-01,  9.6974e-01,\n",
      "         3.9839e-01,  6.6230e-01,  1.7116e-01,  9.8138e-01,  7.3913e-01,\n",
      "         6.8638e-01, -2.8483e-02,  9.9046e-01,  3.1766e-01,  6.8260e-01,\n",
      "        -2.8131e-02,  9.9579e-01,  3.6201e+01,  3.6969e+01,  4.0146e+01,\n",
      "         3.1634e+01,  2.5793e+01,  1.4681e+02,  1.5713e+02,  1.3701e+02,\n",
      "         1.2410e+02,  4.7562e+01,  4.7889e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 0., 0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0037', '0.0000', '0.1555', '0.0001', '0.6994', '0.0040', '0.1373']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 5.3616e-01,  1.1587e-01, -5.4009e-01,  9.9988e-01,  5.3447e-01,\n",
      "         9.7927e-02, -5.1179e-01,  9.9986e-01,  5.3438e-01,  9.7150e-02,\n",
      "        -5.1195e-01,  9.9986e-01,  5.3404e-01,  9.6247e-02, -5.1222e-01,\n",
      "         9.9985e-01,  5.2299e-01,  9.5783e-02, -5.7048e-01,  9.9988e-01,\n",
      "         5.1422e-01,  9.3508e-02, -5.7075e-01,  9.9990e-01,  5.0414e-01,\n",
      "         9.1076e-02, -5.7097e-01,  9.9990e-01,  4.9422e-01,  9.1204e-02,\n",
      "        -3.0834e-01,  9.9989e-01,  4.5007e-01,  8.7015e-02, -5.7678e-01,\n",
      "         9.9988e-01,  5.2063e-01,  1.3088e-01, -4.5768e-01,  9.9982e-01,\n",
      "         5.0291e-01,  1.2736e-01, -5.3672e-01,  9.9990e-01,  4.5735e-01,\n",
      "         1.6082e-01, -1.8677e-02,  9.9917e-01,  3.1180e-01,  1.6302e-01,\n",
      "        -6.1461e-01,  9.9964e-01,  4.8784e-01,  2.4084e-01,  3.9149e-01,\n",
      "         5.0462e-01,  2.5373e-01,  2.6756e-01, -9.6691e-01,  9.4771e-01,\n",
      "         5.4753e-01,  1.6565e-01,  6.5500e-01,  5.3453e-01,  3.6425e-01,\n",
      "         1.5492e-01, -1.2797e+00,  9.4226e-01,  5.5676e-01,  1.4870e-01,\n",
      "         7.2715e-01,  4.5905e-01,  3.7671e-01,  1.3430e-01, -1.3445e+00,\n",
      "         9.2670e-01,  5.4387e-01,  1.4126e-01,  7.4382e-01,  4.4431e-01,\n",
      "         3.7440e-01,  1.2464e-01, -1.3506e+00,  9.2569e-01,  5.3505e-01,\n",
      "         1.4771e-01,  6.7142e-01,  4.2691e-01,  3.7469e-01,  1.3178e-01,\n",
      "        -1.2857e+00,  8.9729e-01,  2.9158e-01,  3.5766e-01,  1.9105e-01,\n",
      "         9.9863e-01,  1.9334e-01,  3.6274e-01, -1.9077e-01,  9.9967e-01,\n",
      "         4.0061e-01,  4.8068e-01,  5.9195e-01,  8.1381e-01,  3.0556e-01,\n",
      "         4.9913e-01, -4.5615e-01,  9.9261e-01,  3.7962e-01,  6.0485e-01,\n",
      "         9.7919e-01,  8.3040e-01,  2.5576e-01,  6.4683e-01, -5.3081e-01,\n",
      "         9.8979e-01,  3.5397e-01,  6.2055e-01,  1.0084e+00,  7.9062e-01,\n",
      "         2.0791e-01,  6.6962e-01, -5.4999e-01,  9.6866e-01,  5.1350e-01,\n",
      "         6.5145e-01,  9.5607e-01,  8.4678e-01,  3.5844e-01,  7.0506e-01,\n",
      "        -7.3825e-01,  9.8124e-01,  4.3507e+01,  5.9300e+01,  1.5405e+01,\n",
      "         3.0915e+01,  2.7987e+01,  1.7954e+02,  1.6243e+02,  1.3107e+02,\n",
      "         1.3410e+02,  4.2437e+01,  3.8713e+01])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1., 0., 0., 0., 0.])\n",
      "예측된 데이터: tensor(5)\n",
      "예측 확률: ['0.0000', '0.0032', '0.0000', '0.2244', '0.0000', '0.5381', '0.0020', '0.2323']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_5285/2186968047.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = deadlift_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = deadlift_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = deadlift_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.602\n",
      "정밀도: 0.955\n",
      "재현율: 0.602\n",
      "F1 점수: 0.738\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = deadlift_model(deadlift_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(deadlift_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "41d1840a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5AMBSHlwRBkAe++GHH4xhw4YZlStXNnx8fIySJUsazZo1MxYsWGCkpqa6jrvWcokTJkwwypcvb/j5+RnNmjUzYmNjsy319/LLLxstW7Y0ypQpYzidTqNatWrGxIkTjeTkZMMwDCMtLc2YOHGi0bBhQ6NkyZKGv7+/0bBhQ2PhwoU5qv/qsnjXehw/fjxH59i/f7/Rv39/IywszChevLhRqlQpo23btsby5cvdloT8/fffjXHjxrmOq1GjhvHMM88YWVlZbueTdM3lH/96D82WSzQMw3jjjTeMqlWrGj4+PkajRo2Mjz/+ONtyiWvXrjXat29vhISEGD4+PkalSpWMBx980EhISMh2jb8uKfj2228bjRs3NpxOp1G6dGljwIABxsmTJ92OiYqKMvz9/bPVNm3aNCMn/6/gz8slmrnWPfjtt9+MwYMHGzfddJMREBBgdOjQwThy5Mg1lzl89dVXjapVqxre3t5u7zM8PNzo0qXLNa/55/P88ssvRlBQkNGtW7dsx911112Gv7+/cezYseu+VwDwFIdh8Ls9AAAAwNOYMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAYK5Td/+jUe5ekSAEC/7X3R0yUAgHxt1u1Z1add/rrg/RtMYg4AAADYgM1+hgIAAECh5iAXNsOdAQAAAGyAxBwAAADWcTg8XYFtkZgDAAAANkBiDgAAAOswY26KOwMAAADYAIk5AAAArMOMuSkScwAAAMAGSMwBAABgHWbMTXFnAAAAABsgMQcAAIB1mDE3RWIOAAAA2ACJOQAAAKzDjLkp7gwAAABgAzTmAAAAgA0wygIAAADr8OFPUyTmAAAAgA2QmAMAAMA6fPjTFHcGAAAAsAEScwAAAFiHGXNTJOYAAACADZCYAwAAwDrMmJvizgAAAAA2QGIOAAAA6zBjborEHAAAALABEnMAAABYhxlzU9wZAAAAwAZIzAEAAGAdEnNT3BkAAADABkjMAQAAYB0vVmUxQ2IOAAAA2ACJOQAAAKzDjLkp7gwAAABgAyTmAAAAsA7f/GmKxBwAAACwARJzAAAAWIcZc1PcGQAAAMAGSMwBAABgHWbMTZGYAwAAADZAYg4AAADrMGNuijsDAAAA2ACJOQAAAKzDjLkpEnMAAADABkjMAQAAYB1mzE1xZwAAAAAbIDEHAACAdZgxN0ViDgAAANgAiTkAAACsw4y5Ke4MAAAAYAMk5gAAALAOM+amSMwBAAAAGyAxBwAAgHWYMTfFnQEAAABsgMQcAAAA1iExN8WdAQAAAGyAxBwAAADWYVUWUyTmAAAAgA2QmAMAAMA6zJib4s4AAAAANkBiDgAAAOswY26KxBwAAACwARJzAAAAWIcZc1PcGQAAAMAGSMwBAABgHWbMTZGYAwAAADZAYg4AAADLOEjMTZGYAwAAADZAYg4AAADLkJibIzEHAAAAbIDEHAAAANYhMDdFYg4AAADYAIk5AAAALMOMuTkScwAAAMAGSMwBAABgGRJzcyTmAAAAgA3QmAMAAMAyDofDkkduxMTE6D//+Y9KliypkJAQ9ejRQ3FxcW7HpKamauTIkSpTpowCAgLUq1cvnT171u2YEydOqEuXLipRooRCQkI0ceJEXblyJcd10JgDAACgSNuxY4dGjhypL7/8Ulu2bFFGRobat2+vixcvuo4ZN26c1q9frzVr1mjHjh06ffq0evbs6dqfmZmpLl26KD09Xbt379by5cu1bNkyTZ06Ncd1OAzDMPL0ndmAX+NRni4BAPTb3hc9XQIAyNdmnygM6rfSkuskv3nvDb/2119/VUhIiHbs2KGWLVsqOTlZZcuW1erVq3X33XdLko4cOaI6deooNjZWt956qzZt2qSuXbvq9OnTKleunCRp8eLFeuSRR/Trr7/Kx8fnutclMQcAAAD+JDk5WZJUunRpSdL+/fuVkZGhdu3auY6pXbu2KlWqpNjYWElSbGys6tev72rKJalDhw66cOGCDh06lKPr2uxnKAAAABRqFi3KkpaWprS0NLdtTqdTTqfzb1+XlZWlsWPHqlmzZqpXr54k6cyZM/Lx8VFwcLDbseXKldOZM2dcx/y5Kb+6/+q+nCAxBwAAQKETExOjoKAgt0dMTMx1Xzdy5Eh99913euuttyyo0h2JOQAAACxj1TrmkydP1vjx4922XS8tHzVqlDZs2KCdO3eqQoUKru2hoaFKT09XUlKSW2p+9uxZhYaGuo7573//63a+q6u2XD3mekjMAQAAUOg4nU4FBga6Pcwac8MwNGrUKL3//vvatm2bqlSp4ra/SZMmKl68uLZu3eraFhcXpxMnTigyMlKSFBkZqW+//Vbnzp1zHbNlyxYFBgYqIiIiRzWTmAMAAMAydvzmz5EjR2r16tX64IMPVLJkSddMeFBQkPz8/BQUFKQhQ4Zo/PjxKl26tAIDAzV69GhFRkbq1ltvlSS1b99eERERuvfeezVnzhydOXNG//d//6eRI0deN6m/isYcAAAARdqiRYskSa1bt3bbvnTpUg0aNEiSNHfuXHl5ealXr15KS0tThw4dtHDhQtex3t7e2rBhgx566CFFRkbK399fUVFRmjlzZo7rYB1zAMgnrGMOwA7sto556XtXW3KdxJX9LblOXmLGHAAAALABm/0MBQAAgMLMjjPmdkFiDgAAANgAiTkAAACsQ2BuisQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADrEJibIjEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgGRJzcyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgHUIzE2RmAMAAAA2QGMOAAAA2ACjLAAAALAMH/40R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwAVs05p9//rkGDhyoyMhInTp1SpK0cuVK7dq1y8OVAQAAIC85HA5LHgWRxxvzd999Vx06dJCfn5++/vprpaWlSZKSk5M1e/ZsD1cHAAAAWMPjjfmsWbO0ePFivfrqqypevLhre7NmzfTVV195sDIAAADkOYdFjwLI4415XFycWrZsmW17UFCQkpKSrC8IAAAA8ACPN+ahoaGKj4/Ptn3Xrl2qWrWqByoCAABAfmHG3JzHG/Nhw4ZpzJgx2rNnjxwOh06fPq1Vq1YpOjpaDz30kKfLAwAAACzh8XXMH330UWVlZalt27a6dOmSWrZsKafTqejoaI0ePdrT5QEAACAPFdQ02woeb8wdDocef/xxTZw4UfHx8UpJSVFERIQCAgI8XRoAAABgGY835lf5+PgoIiLC02WgkIq+v7163N5QNSuX0+W0DO05eEyPz/tAP/58znXMgsf76vZbaql82SClXE7TlweP6//mfaAffjorSRrY7Ra9OvPea56/0u2P6tffUix5LwAKtyWvvqytWz7R8ePH5PT1VaNGjTV2fLQqV+FzVygcSMzNebwxb9Omzd/+B9q2bZuF1aCwavHv6lr89k7tP/SzihXz1oxR3bRh0Sg17jlLl1LTJUlfH/5Fb23aq18SflPpoBJ6fHgXbVg4UrW7TlNWlqG1n3ylLbu/dzvvKzPula+zOE05gDyzb+9/dU+/Aapbv74yr2RqwbznNXzYEL334UaVKFHC0+UByEceb8wbNWrk9jwjI0MHDhzQd999p6ioKM8UhULnzlEL3Z4/MO0N/bLtKTWOqKgvvjoqSXr9vS9c+08kJGrGS+u1953HFB5WRsdP/k+paRlKTctwHXNTqQC1vrmmhs9YZc2bAFAkLHplidvzmU8+pTYtInX4+0Nq0vQ/HqoKyDsk5uY83pjPnTv3mtunT5+ulBRSSOSPwABfSdJvyZeuub+Er4/u636rjp/8n06e+e2axwzoerMupabr/U8P5FeZAKCU33+XJAUGBXm4EgD5zePLJZoZOHCgXn/9dU+XgULI4XDomei7tfvro/r+aILbvgd6t9CvXzyn87HPq32zCHV56EVlXMm85nmiekTq7U373FJ0AMhLWVlZmvP0bDVq/G/VqFHT0+UAeYNv/jTl8cTcTGxsrHx9fa97XFpamtLS0ty2GVmZcnh551dpKOBemNxHdauXV9vB2X9b89amvdq654hCbwrU2Pva6Y2n79ftg59XWvoVt+NuaVBFdaqW15D/W2FV2QCKoNmzZujojz9q2crVni4FgAU83pj37NnT7blhGEpISNC+ffs0ZcqU674+JiZGM2bMcNvmXe4/Kl7+5jytE4XD3Ed6q3OLemo35AWdOpeUbf+FlFRdSEnV0RO/6r/f/KSEnXN05+0N9c7m/W7HDborUgeO/KKvD/9iUeUAiprZs2Zq547ten35GyoXGurpcoA8w4y5OY+PsgQFBbk9SpcurdatW+ujjz7StGnTrvv6yZMnKzk52e1RrFwTCypHQTP3kd7qfntDdXxwvn4+ff66xzscDjnkkE9x959f/f181OuOf2v5utj8KhVAEWYYhmbPmqltW7fo1deXq0KFip4uCYBFPJqYZ2ZmavDgwapfv75KlSp1Q+dwOp1yOp1u2xhjwV+9MLmP7unUVL3HvaKUi6kqV6akJCk5JVWpaRmq/K8yurtDE22NPaz//Zaif5UL1oTB7XU5LUMf7zrkdq67OzRRMW8vvblxryfeCoBCbvYTM7Tpow16YcFC+Zfw1/9+/VWSFFCyZI5GPAG7IzE359HG3NvbW+3bt9fhw4dvuDEHcuLBPi0lSVteG+u2fdjUlXpj/R6lpV9Rs8bVNKp/a5UKLKFz53/Xrq/i1WbQc9nWKB/UI1IfbDuo5JTLVpUPoAh55+03JUlDBrl/odnMWTG6866e13oJgELC4zPm9erV07Fjx1SlShVPl4JCzK/xqL/dn/Brsu4avShH52oz6Pm8KAkArungoThPlwDkKwJzcx6fMZ81a5aio6O1YcMGJSQk6MKFC24PAAAAoCjwWGI+c+ZMTZgwQZ07d5Ykde/e3W3myDAMORwOZWZeew1pAAAAFDzMmJvzWGM+Y8YMDR8+XJ999pmnSgAAAABsw2ONuWEYkqRWrVp5qgQAAABYjMDcnEdnzPlVBgAAAPAHj67KUrNmzes254mJiRZVAwAAgPxGMGvOo435jBkzFBQU5MkSAAAAAFvwaGPet29fhYSEeLIEAAAAWIjA3JzHZsz5NQYAAADw/3h8VRYAAAAUHV5ehLNmPNaYZ2VleerSAAAAgO14dMYcAAAARQvTzOY8uo45AAAAgD+QmAMAAMAyLABijsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAKNJ27typbt26KSwsTA6HQ+vWrXPbP2jQIDkcDrdHx44d3Y5JTEzUgAEDFBgYqODgYA0ZMkQpKSm5qoPGHAAAAJb5a4ObX4/cuHjxoho2bKiXXnrJ9JiOHTsqISHB9XjzzTfd9g8YMECHDh3Sli1btGHDBu3cuVMPPPBArupglAUAAABFWqdOndSpU6e/PcbpdCo0NPSa+w4fPqzNmzdr7969atq0qSRpwYIF6ty5s5599lmFhYXlqA4ScwAAAFjG4bDmkde2b9+ukJAQ1apVSw899JDOnz/v2hcbG6vg4GBXUy5J7dq1k5eXl/bs2ZPja5CYAwAAoNBJS0tTWlqa2zan0ymn05nrc3Xs2FE9e/ZUlSpVdPToUT322GPq1KmTYmNj5e3trTNnzigkJMTtNcWKFVPp0qV15syZHF+HxBwAAACWsWrGPCYmRkFBQW6PmJiYG6q5b9++6t69u+rXr68ePXpow4YN2rt3r7Zv356n94bGHAAAAIXO5MmTlZyc7PaYPHlynpy7atWquummmxQfHy9JCg0N1blz59yOuXLlihITE03n0q+FURYAAABYxqp1zG90bCUnTp48qfPnz6t8+fKSpMjISCUlJWn//v1q0qSJJGnbtm3KysrSLbfckuPz0pgDAACgSEtJSXGl35J0/PhxHThwQKVLl1bp0qU1Y8YM9erVS6GhoTp69KgmTZqk6tWrq0OHDpKkOnXqqGPHjho2bJgWL16sjIwMjRo1Sn379s3xiiwSjTkAAAAs5GXDr/7ct2+f2rRp43o+fvx4SVJUVJQWLVqkb775RsuXL1dSUpLCwsLUvn17PfHEE26J/KpVqzRq1Ci1bdtWXl5e6tWrl+bPn5+rOmjMAQAAUKS1bt1ahmGY7v/444+ve47SpUtr9erV/6gOGnMAAABYxoaBuW2wKgsAAABgAyTmAAAAsIyDyNwUiTkAAABgAyTmAAAAsIwXgbkpEnMAAADABmjMAQAAABtglAUAAACW4cOf5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMs4RGRuhsQcAAAAsAEScwAAAFiGLxgyR2IOAAAA2ACJOQAAACzDOubmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAAy3gRmZsiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMuwjrk5EnMAAADABnKUmH/44Yc5PmH37t1vuBgAAAAUbuTl5nLUmPfo0SNHJ3M4HMrMzPwn9QAAAABFUo4a86ysrPyuAwAAAEUA65ib+0cz5qmpqXlVBwAAAFCk5boxz8zM1BNPPKF//etfCggI0LFjxyRJU6ZM0ZIlS/K8QAAAABQeXg5rHgVRrhvzJ598UsuWLdOcOXPk4+Pj2l6vXj299tpreVocAAAAUFTkujFfsWKFXnnlFQ0YMEDe3t6u7Q0bNtSRI0fytDgAAAAULg6Hw5JHQZTrxvzUqVOqXr16tu1ZWVnKyMjIk6IAAACAoibXjXlERIQ+//zzbNvXrl2rxo0b50lRAAAAKJwcDmseBVGOlkv8s6lTpyoqKkqnTp1SVlaW3nvvPcXFxWnFihXasGFDftQIAAAAFHq5TszvvPNOrV+/Xp9++qn8/f01depUHT58WOvXr9cdd9yRHzUCAACgkGDG3FyuE3NJatGihbZs2ZLXtQAAAABF1g015pK0b98+HT58WNIfc+dNmjTJs6IAAABQOBXUNcatkOvG/OTJk+rXr5+++OILBQcHS5KSkpJ022236a233lKFChXyukYAAACg0Mv1jPnQoUOVkZGhw4cPKzExUYmJiTp8+LCysrI0dOjQ/KgRAAAAhQQz5uZynZjv2LFDu3fvVq1atVzbatWqpQULFqhFixZ5WhwAAABQVOS6Ma9YseI1v0goMzNTYWFheVIUAAAACqeCmWVbI9ejLM8884xGjx6tffv2ubbt27dPY8aM0bPPPpunxQEAAABFRY4S81KlSrnN6ly8eFG33HKLihX74+VXrlxRsWLFdP/996tHjx75UigAAAAKPq8COv9thRw15i+88EI+lwEAAAAUbTlqzKOiovK7DgAAABQBBObmbvgLhiQpNTVV6enpbtsCAwP/UUEAAABAUZTrxvzixYt65JFH9M477+j8+fPZ9mdmZuZJYQAAACh8Cuoa41bI9aoskyZN0rZt27Ro0SI5nU699tprmjFjhsLCwrRixYr8qBEAAAAo9HKdmK9fv14rVqxQ69atNXjwYLVo0ULVq1dXeHi4Vq1apQEDBuRHnQAAACgECMzN5ToxT0xMVNWqVSX9MU+emJgoSWrevLl27tyZt9UBAAAARUSuG/OqVavq+PHjkqTatWvrnXfekfRHkh4cHJynxQEAAKBw8XI4LHkURLluzAcPHqyDBw9Kkh599FG99NJL8vX11bhx4zRx4sQ8LxAAAAAoCnI9Yz5u3DjXn9u1a6cjR45o//79ql69uho0aJCnxQEAAKBwKaBhtiX+0TrmkhQeHq7w8PC8qAUAAAAosnLUmM+fPz/HJ3z44YdvuBgAAAAUbqxjbi5HjfncuXNzdDKHw0FjDgAAANyAHDXmV1dhKSgS//uip0sAAJ04f8nTJQCAapYr4ekS3OR65ZEihHsDAAAA2MA//vAnAAAAkFPMmJsjMQcAAABsgMQcAAAAlvEiMDdFYg4AAADYwA015p9//rkGDhyoyMhInTp1SpK0cuVK7dq1K0+LAwAAQOHi5bDmURDlujF/99131aFDB/n5+enrr79WWlqaJCk5OVmzZ8/O8wIBAACAoiDXjfmsWbO0ePFivfrqqypevLhre7NmzfTVV1/laXEAAAAoXBwOhyWPgijXjXlcXJxatmyZbXtQUJCSkpLyoiYAAACgyMl1Yx4aGqr4+Phs23ft2qWqVavmSVEAAAAonJgxN5frxnzYsGEaM2aM9uzZI4fDodOnT2vVqlWKjo7WQw89lB81AgAAAIVertcxf/TRR5WVlaW2bdvq0qVLatmypZxOp6KjozV69Oj8qBEAAACFRAEd/7aEwzAM40ZemJ6ervj4eKWkpCgiIkIBAQF5XdsNu5zh6QoAQPol8ZKnSwAA1SxXwtMluJm0Mc6S68zpUsuS6+SlG/7mTx8fH0VERORlLQAAACjkvIjMTeW6MW/Tps3fLkGzbdu2f1QQAAAAUBTlujFv1KiR2/OMjAwdOHBA3333naKiovKqLgAAABRCN/S180VErhvzuXPnXnP79OnTlZKS8o8LAgAAAIqiPPuhZeDAgXr99dfz6nQAAAAohBwOax4FUZ415rGxsfL19c2r0wEAAABFSq5HWXr27On23DAMJSQkaN++fZoyZUqeFQYAAIDCh1VZzOW6MQ8KCnJ77uXlpVq1amnmzJlq3759nhUGAAAAFCW5aswzMzM1ePBg1a9fX6VKlcqvmgAAAFBIEZiby9WMube3t9q3b6+kpKR8KgcAAAAomnL94c969erp2LFj+VELAAAACjkvhzWPgijXjfmsWbMUHR2tDRs2KCEhQRcuXHB7AAAAAMi9HM+Yz5w5UxMmTFDnzp0lSd27d5fjT0NChmHI4XAoMzMz76sEAABAocCqLOZy3JjPmDFDw4cP12effZaf9QAAAABFUo4bc8MwJEmtWrXKt2IAAABQuBGYm8vVjLmDOwkAAADki1ytY16zZs3rNueJiYn/qCAAAAAUXgV1xRQr5KoxnzFjRrZv/gQAAADwz+WqMe/bt69CQkLyqxYAAAAUcg4RmZvJ8Yw58+UAAABA/sn1qiwAAADAjWLG3FyOG/OsrKz8rAMAAAAo0nI1Yw4AAAD8EyTm5nK1jjkAAACA/EFiDgAAAMuwoIg5EnMAAAAUaTt37lS3bt0UFhYmh8OhdevWue03DENTp05V+fLl5efnp3bt2unHH390OyYxMVEDBgxQYGCggoODNWTIEKWkpOSqDhpzAAAAFGkXL15Uw4YN9dJLL11z/5w5czR//nwtXrxYe/bskb+/vzp06KDU1FTXMQMGDNChQ4e0ZcsWbdiwQTt37tQDDzyQqzocRiFcB/FyhqcrAADpl8RLni4BAFSzXAlPl+DmuR3HLLnOhFZVb+h1DodD77//vnr06CHpj7Q8LCxMEyZMUHR0tCQpOTlZ5cqV07Jly9S3b18dPnxYERER2rt3r5o2bSpJ2rx5szp37qyTJ08qLCwsR9cmMQcAAEChk5aWpgsXLrg90tLScn2e48eP68yZM2rXrp1rW1BQkG655RbFxsZKkmJjYxUcHOxqyiWpXbt28vLy0p49e3J8LRpzAAAAWMbhsOYRExOjoKAgt0dMTEyu6z1z5owkqVy5cm7by5Ur59p35swZhYSEuO0vVqyYSpcu7TomJ1iVBQAAAIXO5MmTNX78eLdtTqfTQ9XkDI05AAAALONl0XKJTqczTxrx0NBQSdLZs2dVvnx51/azZ8+qUaNGrmPOnTvn9rorV64oMTHR9fqcYJQFAAAAMFGlShWFhoZq69atrm0XLlzQnj17FBkZKUmKjIxUUlKS9u/f7zpm27ZtysrK0i233JLja5GYAwAAwDJeNvx+oZSUFMXHx7ueHz9+XAcOHFDp0qVVqVIljR07VrNmzVKNGjVUpUoVTZkyRWFhYa6VW+rUqaOOHTtq2LBhWrx4sTIyMjRq1Cj17ds3xyuySDTmAAAAKOL27dunNm3auJ5fnU2PiorSsmXLNGnSJF28eFEPPPCAkpKS1Lx5c23evFm+vr6u16xatUqjRo1S27Zt5eXlpV69emn+/Pm5qoN1zAEgn7COOQA7sNs65gu+OG7JdUY3q2LJdfISM+YAAACADTDKAgAAAMt4yYZD5jZBYg4AAADYAIk5AAAALGPRMuYFEok5AAAAYAMk5gAAALCMHdcxtwsScwAAAMAGSMwBAABgGS+GzE2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWYcbcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBlSYXPcGwAAAMAGSMwBAABgGQdD5qZIzAEAAAAbIDEHAACAZcjLzZGYAwAAADZAYg4AAADL8M2f5kjMAQAAABsgMQcAAIBlyMvNkZgDAAAANkBiDgAAAMswYm6OxBwAAACwARJzAAAAWIZv/jRHYg4AAADYAIk5AAAALEMqbI57AwAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZ8nJzJOYAAACADZCYAwAAwDLMmJsjMQcAAABsgMQcAAAAliEVNse9AQAAAGyAxBwAAACWYcbcHIk5AAAAYAMk5gAAALAMebk5EnMAAADABkjMAQAAYBlGzM2RmAMAAAA2QGIOAAAAy3gxZW6KxBwAAACwARJzAAAAWIYZc3Mk5gAAAIANkJgDAADAMg5mzE2RmAMAAAA2QGIOAAAAyzBjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLMGNujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAyTmAAAAsAzf/GmOxBwAAACwARJzAAAAWMaLwNwUiTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbsFVjnp6erri4OF25csXTpQAAACAfOBzWPAoiWzTmly5d0pAhQ1SiRAnVrVtXJ06ckCSNHj1aTz31lIerAwAAAPKfLRrzyZMn6+DBg9q+fbt8fX1d29u1a6e3337bg5UBAAAgLzks+r+CyBYf/ly3bp3efvtt3XrrrXL86XcPdevW1dGjRz1YGQAAAGANWzTmv/76q0JCQrJtv3jxolujDgAAgIKNdczN2WKUpWnTptq4caPr+dVm/LXXXlNkZKSnygIAAAAsY4vEfPbs2erUqZO+//57XblyRfPmzdP333+v3bt3a8eOHZ4uDwAAAHmkoM5/W8EWiXnz5s114MABXblyRfXr19cnn3yikJAQxcbGqkmTJp4uDwAAAMh3DsMwDE8XkdcuZ3i6AhRE77y1WmveflOnT5+SJFWrXkMPDB+h5i1aebgyFFS/JF7ydAkoAD5a9442rVurs2dOS5IqVamqvlEPqOmtzXU24bSG3tPlmq97ZMYcNW9zh5WlooCqWa6Ep0tws+vH3yy5TvMapSy5Tl6yRWP+1VdfqXjx4qpfv74k6YMPPtDSpUsVERGh6dOny8fHJ1fnozHHjdixfZu8vLxVKTxcMgx9+ME6LV+6RG+tfV/Vq9fwdHkogGjMkRP//WKHvLy8FFahkgxJWzev1/tvLtcLS95ShUqVdSHJvYnZvP5dvf/mCi1/f4v8Stir4YI90ZgXHLYYZXnwwQf1ww8/SJKOHTume+65RyVKlNCaNWs0adIkD1eHoqJV69vVomUrhYdXVnjlKho9ZpxKlCihbw8e8HRpAAqxm5u1UtPIFgqrGK5/VQzXfcNGydevhOIOfSNvb2+VKnOT2+PLzz9T8zZ30JSjwHJY9CiIbNGY//DDD2rUqJEkac2aNWrVqpVWr16tZcuW6d133/VscSiSMjMztfmjjbp8+ZIaNGrs6XIAFBGZmZnauXWzUlMvq3a9Btn2x8d9r2M/xumOLj2sLw5AvrPFqiyGYSgrK0uS9Omnn6pr166SpIoVK+p///ufJ0tDEfPjD3G6b0Bfpaenya9ECT0/7yVVq1bd02UBKOR+OvqjJo6IUnp6uvz8/PT4rOdUqXK1bMd9snGdKoZXUZ36jawvEsgjXnxHjSlbNOZNmzbVrFmz1K5dO+3YsUOLFi2SJB0/flzlypX729empaUpLS3NbVuWl1NOpzPf6kXhVblKFb397jql/P67Pv3kY019/BG9tuwNmnMA+epflSpr3pK3dOliir7Y/qnmzp6qmAWvuTXnaWmp2vnpJt1z3zAPVgogP9lilOWFF17QV199pVGjRunxxx9X9ep/NEFr167Vbbfd9revjYmJUVBQkNvjmadjrCgbhVDx4j6qVClcEXXr6eFxE1SzVm2tfmOFp8sCUMgVL15cYRUqqXqtCEU9+LCqVK+pD9e86XbMF9s/VVpqqm7v2NVDVQJ5gxlzc7ZIzBs0aKBvv/022/ZnnnlG3t7ef/vayZMna/z48W7bsrxIy5E3srKylJ6e7ukyABQxRpahjAz3f3u2bFynm5u1UlBwaQ9VBSC/2aIxN+Pr63vdY5zO7GMrLJeIGzF/7nNq1qKlQsuX16WLF7Vp4wbt2/tfLXx5iadLA1CILX95vprc0kxly5XX5UsXtePTTfr2wD7NeHah65jTJ0/o0MGvNG3OAg9WCuSRghpnW8BjjXmpUqXkyOHwf2JiYj5XA0iJief1f489ov/9ek4BJUuqZs1aWvjyEkXe1szTpQEoxJJ/S9Tc2VOUeP5/8vcPUOVqNTTj2YVq/J9bXcd8+tEHKlO2nBr/J9KDlQLIbx77gqHly5fn+NioqKhcnZvEHIAd8AVDAOzAbl8w9OXRJEuuc2u1YEuuk5c8lpjnttkGAABAwedglsWU7WbMU1NTs33YLjAw0EPVAAAAANawxXKJFy9e1KhRoxQSEiJ/f3+VKlXK7QEAAIDCweGw5lEQ2aIxnzRpkrZt26ZFixbJ6XTqtdde04wZMxQWFqYVK1hDGgAAAIWfLUZZ1q9frxUrVqh169YaPHiwWrRooerVqys8PFyrVq3SgAEDPF0iAAAA8kABDbMtYYvEPDExUVWrVpX0xzz51eURmzdvrp07d3qyNAAAAMAStmjMq1atquPHj0uSateurXfeeUfSH0l6cHCwBysDAABAnnJY9CiAPNqYHzt2TFlZWRo8eLAOHjwoSXr00Uf10ksvydfXV+PGjdPEiRM9WSIAAABgCY99wZAkeXt7KyEhQSEhIZKke+65R/Pnz1dqaqr279+v6tWrq0GDBrk+L18wBMAO+IIhAHZgty8Y2nf8giXXaVql4C237dHE/K8/E3z00Ue6ePGiwsPD1bNnzxtqygEAAICCyBarsgAAAKBoKKhrjFvBo4m5w+GQ4y//df76HAAAACgKPJqYG4ahQYMGyel0SpJSU1M1fPhw+fv7ux333nvveaI8AAAA5DEiWHMebcyjoqLcng8cONBDlQAAAACe5dHGfOnSpZ68PAAAAKxGZG7KFl8wBAAAAHjK9OnTXZ99vPqoXbu2a39qaqpGjhypMmXKKCAgQL169dLZs2fzvA4acwAAAFjGYdH/5VbdunWVkJDgeuzatcu1b9y4cVq/fr3WrFmjHTt26PTp0+rZs2de3hZJLJcIAAAAqFixYgoNDc22PTk5WUuWLNHq1at1++23S/pjHLtOnTr68ssvdeutt+ZZDSTmAAAAsIzDYc0jLS1NFy5ccHukpaWZ1vXjjz8qLCxMVatW1YABA3TixAlJ0v79+5WRkaF27dq5jq1du7YqVaqk2NjYPL03NOYAAAAodGJiYhQUFOT2iImJueaxt9xyi5YtW6bNmzdr0aJFOn78uFq0aKHff/9dZ86ckY+Pj4KDg91eU65cOZ05cyZPa2aUBQAAAJaxalGWyZMna/z48W7brn53zl916tTJ9ecGDRrolltuUXh4uN555x35+fnla51/RmIOAACAQsfpdCowMNDtYdaY/1VwcLBq1qyp+Ph4hYaGKj09XUlJSW7HnD179poz6f8EjTkAAACs47Do8Q+kpKTo6NGjKl++vJo0aaLixYtr69atrv1xcXE6ceKEIiMj/9mF/oJRFgAAABRp0dHR6tatm8LDw3X69GlNmzZN3t7e6tevn4KCgjRkyBCNHz9epUuXVmBgoEaPHq3IyMg8XZFFojEHAACAhW5kjfH8dvLkSfXr10/nz59X2bJl1bx5c3355ZcqW7asJGnu3Lny8vJSr169lJaWpg4dOmjhwoV5XofDMAwjz8/qYZczPF0BAEi/JF7ydAkAoJrlSni6BDff/JJiyXUaVAyw5Dp5icQcAAAAlnHYLzC3DT78CQAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiHyNwUiTkAAABgAyTmAAAAsIwd1zG3CxJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIfI3BSJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgHWIzE2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiHyNwUiTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIB1iMxNkZgDAAAANkBiDgAAAMuwjrk5EnMAAADABkjMAQAAYBnWMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMBCROZmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyDqbMTZGYAwAAADZAYg4AAADrEJibIjEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDoG5KRJzAAAAwAZozAEAAAAbYJQFAAAAlmGSxRyJOQAAAGADJOYAAACwDF8wZI7EHAAAALABEnMAAABYhi8YMkdiDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZozAEAAAAboDEHAAAAbIAZcwAAAFiGGXNzJOYAAACADZCYAwAAwDKsY26OxBwAAACwARJzAAAAWIYZc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIB1iMxNkZgDAAAANkBiDgAAAMuwjrk5EnMAAADABkjMAQAAYBnWMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYh8jcFIk5AAAAYAMk5gAAALAM65ibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADTgMwzA8XQRgN2lpaYqJidHkyZPldDo9XQ6AIoh/h4Cih8YcuIYLFy4oKChIycnJCgwM9HQ5AIog/h0Cih5GWQAAAAAboDEHAAAAbIDGHAAAALABGnPgGpxOp6ZNm8YHrgB4DP8OAUUPH/4EAAAAbIDEHAAAALABGnMAAADABmjMAQAAABugMQcAwGaWLVum4OBgT5cBwGI05ijUHA7H3z6mT5/u6RIBFGKDBg265r898fHxni4NgA0V83QBQH5KSEhw/fntt9/W1KlTFRcX59oWEBDg+rNhGMrMzFSxYvzPAkDe6dixo5YuXeq2rWzZsh6qBoCdkZijUAsNDXU9goKC5HA4XM+PHDmikiVLatOmTWrSpImcTqd27dqlQYMGqUePHm7nGTt2rFq3bu16npWVpZiYGFWpUkV+fn5q2LCh1q5da+2bA1AgOJ1Ot3+LQkNDNW/ePNWvX1/+/v6qWLGiRowYoZSUFNNzHDx4UG3atFHJkiUVGBioJk2aaN++fa79u3btUosWLeTn56eKFSvq4Ycf1sWLF614ewDyEI05irxHH31UTz31lA4fPqwGDRrk6DUxMTFasWKFFi9erEOHDmncuHEaOHCgduzYkc/VAigMvLy8NH/+fB06dEjLly/Xtm3bNGnSJNPjBwwYoAoVKmjv3r3av3+/Hn30URUvXlySdPToUXXs2FG9evXSN998o7ffflu7du3SqFGjrHo7APIIv7NHkTdz5kzdcccdOT4+LS1Ns2fP1qeffqrIyEhJUtWqVbVr1y69/PLLatWqVX6VCqAA2rBhg9vYXKdOnbRmzRrX88qVK2vWrFkaPny4Fi5ceM1znDhxQhMnTlTt2rUlSTVq1HDti4mJ0YABAzR27FjXvvnz56tVq1ZatGiRfH198+FdAcgPNOYo8po2bZqr4+Pj43Xp0qVszXx6eroaN26cl6UBKATatGmjRYsWuZ77+/vr008/VUxMjI4cOaILFy7oypUrSk1N1aVLl1SiRIls5xg/fryGDh2qlStXql27durdu7eqVasm6Y8xl2+++UarVq1yHW8YhrKysnT8+HHVqVMn/98kgDxBY44iz9/f3+25l5eXDMNw25aRkeH689U50I0bN+pf//qX23FOpzOfqgRQUPn7+6t69equ5z/99JO6du2qhx56SE8++aRKly6tXbt2aciQIUpPT79mYz59+nT1799fGzdu1KZNmzRt2jS99dZbuuuuu5SSkqIHH3xQDz/8cLbXVapUKV/fG4C8RWMO/EXZsmX13XffuW07cOCAa54zIiJCTqdTJ06cYGwFQK7t379fWVlZeu655+Tl9cdHvd55553rvq5mzZqqWbOmxo0bp379+mnp0qW666679O9//1vff/+9W/MPoGDiw5/AX9x+++3at2+fVqxYoR9//FHTpk1za9RLliyp6OhojRs3TsuXL9fRo0f11VdfacGCBVq+fLkHKwdQEFSvXl0ZGRlasGCBjh07ppUrV2rx4sWmx1++fFmjRo3S9u3b9fPPP+uLL77Q3r17XSMqjzzyiHbv3q1Ro0bpwIED+vHHH/XBBx/w4U+gAKIxB/6iQ4cOmjJliiZNmqT//Oc/+v3333Xfffe5HfPEE09oypQpiomJUZ06ddSxY0dt3LhRVapU8VDVAAqKhg0b6vnnn9fTTz+tevXqadWqVYqJiTE93tvbW+fPn9d9992nmjVrqk+fPurUqZNmzJghSWrQoIF27NihH374QS1atFDjxo01depUhYWFWfWWAOQRh/HXYVoAAAAAliMxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMARRZgwYNUo8ePVzPW7durbFjx1pex/bt2+VwOJSUlGR6jMPh0Lp163J8zunTp6tRo0b/qK6ffvpJDodDBw4c+EfnAQDkDI05AFsZNGiQHA6HHA6HfHx8VL16dc2cOVNXrlzJ92u/9957euKJJ3J0bE6aaQAAcqOYpwsAgL/q2LGjli5dqrS0NH300UcaOXKkihcvrsmTJ2c7Nj09XT4+Pnly3dKlS+fJeQAAuBEk5gBsx+l0KjQ0VOHh4XrooYfUrl07ffjhh5L+3/jJk08+qbCwMNWqVUuS9Msvv6hPnz4KDg5W6dKldeedd+qnn35ynTMzM1Pjx49XcHCwypQpo0mTJskwDLfr/nWUJS0tTY888ogqVqwop9Op6tWra8mSJfrpp5/Upk0bSVKpUqXkcDg0aNAgSVJWVpZiYmJUpUoV+fn5qWHDhlq7dq3bdT766CPVrFlTfn5+atOmjVudOfXII4+oZs2aKlGihKpWraopU6YoIyMj23Evv/yyKlasqBIlSqhPnz5KTk522//aa6+pTp068vX1Ve3atbVw4cJc1wIAyBs05gBsz8/PT+np6a7nW7duVVxcnLZs2aINGzYoIyNDHTp0UMmSJfX555/riy++UEBAgDp27Oh63XPPPadly5bp9ddf165du5SYmKj333//b69733336c0339T8+fN1+PBhvfzyywoICFDFihX17rvvSpLi4uKUkJCgefPmSZJiYmK0YsUKLV68WIcOHdK4ceM0cOBA7dixQ9IfP0D07NlT3bp104EDBzR06FA9+uijub4nJUuW1LJly/T9999r3rx5evXVVzV37ly3Y+Lj4/XOO+9o/fr12rx5s77++muNGDHCtX/VqlWaOnWqnnzySR0+fFizZ8/WlClTtHz58lzXAwDIAwYA2EhUVJRx5513GoZhGFlZWcaWLVsMp9NpREdHu/aXK1fOSEtLc71m5cqVRq1atYysrCzXtrS0NMPPz8/4+OOPDcMwjPLlyxtz5sxx7c/IyDAqVKjgupZhGEarVq2MMWPGGIZhGHFxcYYkY8uWLdes87PPPjMkGb/99ptrW2pqqlGiRAlj9+7dbscOGTLE6Nevn2EYhjF58mQjIiLCbf8jjzyS7Vx/Jcl4//33Tfc/88wzRpMmTVzPp02bZnh7exsnT550bdu0aZPh5eVlJCQkGIZhGNWqVTNWr17tdp4nnnjCiIyMNAzDMI4fP25IMr7++mvT6wIA8g4z5gBsZ8OGDQoICFBGRoaysrLUv39/TZ8+3bW/fv36bnPlBw8eVHx8vEqWLOl2ntTUVB09elTJyclKSEjQLbfc4tpXrFgxNW3aNNs4y1UHDhyQt7e3WrVqleO64+PjdenSJd1xxx1u29PT09W4cWNJ0uHDh93qkKTIyMgcX+Oqt99+W/Pnz9fRo0eVkpKiK1euKDAw0O2YSpUq6V//+pfbdbKyshQXF6eSJUvq6NGjGjJkiIYNG+Y65sqVKwoKCsp1PQCAf47GHIDttGnTRosWLZKPj4/CwsJUrJj7P1X+/v5uz1NSUtSkSROtWrUq27nKli17QzX4+fnl+jUpKSmSpI0bN7o1xNIfc/N5JTY2VgMGDNCMGTPUoUMHBQUF6a233tJzzz2X61pfffXVbD8oeHt751mtAICcozEHYDv+/v6qXr16jo//97//rbffflshISHZUuOrypcvrz179qhly5aS/kiG9+/fr3//+9/XPL5+/frKysrSjh071K5du2z7ryb2mZmZrm0RERFyOp06ceKEadJep04d1wdZr/ryyy+v/yb/ZPfu3QoPD9fjjz/u2vbzzz9nO+7EiRM6ffq0wsLCXNfx8vJSrVq1VK5cOYWFhenYsWMaMGBArq4PAMgffPgTQIE3YMAA3XTTTbrzzjv1+eef6/jx49q+fbsefvhhnTx5UpI0ZswYPfXUU1q3bp2OHDmiESNG/O0a5JUrV1ZUVJTuv/9+rVu3znXOd955R5IUHh4uh8OhDRs26Ndff1VKSopKliyp6OhojRs3TsuXL9fRo0f11VdfacGCBa4PVA4fPlw//vijJk6cqLi4OK1evVrLli3L1futUaOGTpw4obfeektHjx7V/Pnzr/lBVl9fX0VFRengwYP6/PPP9fDDD6tPnz4KDQ2VJM2YMUMxMTGaP3++fvjhB3377bdaunSpnn/++VzVAwDIGzTmAAq8EiVKaOfOnapUqZJ69uypOnXqaMiQIUpNTXUl6BMmTNC9996rqKgoRUZGqmTJkrrrrrv+9ryLFi3S3XffrREjRqh27doaNmyYLl68KEn617/+pRkzZujRRx9VuXLlNGrUKEnSE088oSlTpigmJkZ16tRRx44dtXHjRlWpUkXSH3Pf7777rtatW6eGDRtq8eLFmj17dq7eb/fu3TVu3DiNGjVKjRo10u7duzVlypRsx1WvXl09e/ZU586d1b59ezVo0MBtOcShQ4fqtdde09KlS1W/fn21atVKy5Ytc9UKALCWwzD75BMAAAAAy5CYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDhRBlStX1qBBgzxdRqGzd+9e3XbbbfL395fD4dCBAwfy9Pzbt2+Xw+HQ9u3b8/S8BRl/lwEUJjTmQCFy9OhRPfjgg6patap8fX0VGBioZs2aad68ebp8+bKny/tbe/fu1ahRo1S3bl35+/urUqVK6tOnj3744YdcnefAgQMaOHCgKlasKKfTqdKlS6tdu3ZaunSpMjMz86l6KSMjQ71791ZiYqLmzp2rlStXKjw8PN+uZ7XWrVvL4XCoRo0a19y/ZcsWORwOORwOrV27Ntfn//777zV9+nT99NNP/7BSACi4inm6AAB5Y+PGjerdu7ecTqfuu+8+1atXT+np6dq1a5cmTpyoQ4cO6ZVXXvF0maaefvppffHFF+rdu7caNGigM2fO6MUXX9S///1vffnll6pXr951z/Haa69p+PDhKleunO69917VqFFDv//+u7Zu3aohQ4YoISFBjz32WL7Uf/ToUf3888969dVXNXTo0Hy5RsuWLXX58mX5+Pjky/mvx9fXV/Hx8frvf/+rm2++2W3fqlWr5Ovrq9TU1Bs69/fff68ZM2aodevWqly5co5fFxcXJy8vMiYAhQONOVAIHD9+XH379lV4eLi2bdum8uXLu/aNHDlS8fHx2rhxowcrvL7x48dr9erVbk3nPffco/r16+upp57SG2+88bev//LLLzV8+HBFRkbqo48+UsmSJV37xo4dq3379um7777Lt/rPnTsnSQoODs63a3h5ecnX1zffzn891apV05UrV/Tmm2+6Neapqal6//331aVLF7377rv5XodhGEpNTZWfn5+cTme+Xw8ArELMABQCc+bMUUpKipYsWeLWlF9VvXp1jRkzxvT1iYmJio6OVv369RUQEKDAwEB16tRJBw8ezHbsggULVLduXZUoUUKlSpVS06ZNtXr1atf+33//XWPHjlXlypXldDoVEhKiO+64Q1999dXfvofbbrstWxJco0YN1a1bV4cPH77eLdCMGTPkcDi0atUqt6b8qqZNm7rNIl+8eFETJkxwjbzUqlVLzz77rAzDcHudw+HQqFGjtG7dOtWrV09Op1N169bV5s2bXccMGjRIrVq1kiT17t1bDodDrVu3lvTHCMjVP//ZoEGDsiXDb731lpo0aaKSJUsqMDBQ9evX17x581z7zWbM16xZoyZNmsjPz0833XSTBg4cqFOnTmW7XkBAgE6dOqUePXooICBAZcuWVXR0dK5GfPr166e3335bWVlZrm3r16/XpUuX1KdPn2zH//zzzxoxYoRq1aolPz8/lSlTRr1793YbWVm2bJl69+4tSWrTpo1rJObq+6xcubK6du2qjz/+WE2bNpWfn59efvll176r/10Nw1CbNm1UtmxZ1w9KkpSenq769eurWrVqunjxYo7fKwBYjcYcKATWr1+vqlWr6rbbbruh1x87dkzr1q1T165d9fzzz2vixIn69ttv1apVK50+fdp13KuvvqqHH35YEREReuGFFzRjxgw1atRIe/bscR0zfPhwLVq0SL169dLChQsVHR0tPz+/HDXXf2UYhs6ePaubbrrpb4+7dOmStm7dqpYtW6pSpUo5Om/37t01d+5cdezYUc8//7xq1aqliRMnavz48dmO37Vrl0aMGKG+fftqzpw5Sk1NVa9evXT+/HlJ0oMPPugakXn44Ye1cuVKPf7447l6r1u2bFG/fv1UqlQpPf3003rqqafUunVrffHFF3/7umXLlqlPnz7y9vZWTEyMhg0bpvfee0/NmzdXUlKS27GZmZnq0KGDypQpo2effVatWrXSc889l6sRp/79+yshIcHth4PVq1erbdu2CgkJyXb83r17tXv3bvXt21fz58/X8OHDtXXrVrVu3VqXLl2S9MeIzsMPPyxJeuyxx7Ry5UqtXLlSderUcZ0nLi5O/fr10x133KF58+apUaNG2a7lcDj0+uuvKzU1VcOHD3dtnzZtmg4dOqSlS5fK398/x+8VACxnACjQkpOTDUnGnXfemePXhIeHG1FRUa7nqampRmZmptsxx48fN5xOpzFz5kzXtjvvvNOoW7fu3547KCjIGDlyZI5r+TsrV640JBlLliz52+MOHjxoSDLGjBmTo/OuW7fOkGTMmjXLbfvdd99tOBwOIz4+3rVNkuHj4+O27er1FixY4Nr22WefGZKMNWvWuJ2zVatWRqtWrbLVEBUVZYSHh7uejxkzxggMDDSuXLliWvfVa3z22WeGYRhGenq6ERISYtSrV8+4fPmy67gNGzYYkoypU6e6XU+S239PwzCMxo0bG02aNDG95p/fx9X/9k2bNjWGDBliGIZh/Pbbb4aPj4+xfPnya96DS5cuZTtXbGysIclYsWKFa9uaNWvc3tufhYeHG5KMzZs3X3Pfn/8uG4ZhvPzyy4Yk44033jC+/PJLw9vb2xg7dux13yMAeBqJOVDAXbhwQZKuOb6RU06n0/UBuszMTJ0/f14BAQGqVauW2whKcHCwTp48qb1795qeKzg4WHv27HFL2m/EkSNHNHLkSEVGRioqKupvj83tPfjoo4/k7e3tSmmvmjBhggzD0KZNm9y2t2vXTtWqVXM9b9CggQIDA3Xs2LEcXS8ngoODdfHiRW3ZsiXHr9m3b5/OnTunESNGuM2ed+nSRbVr177m5wr+nCRLUosWLXL9Pvr376/33ntP6enpWrt2rby9vXXXXXdd81g/Pz/XnzMyMnT+/HlVr15dwcHB1x1v+rMqVaqoQ4cOOTr2gQceUIcOHTR69Gjde++9qlatmmbPnp3jawGAp9CYAwVcYGCgpD9mu29UVlaW5s6dqxo1asjpdOqmm25S2bJl9c033yg5Odl13COPPKKAgADdfPPNqlGjhkaOHJlt1GLOnDn67rvvVLFiRd18882aPn16rhu/M2fOqEuXLgoKCnI1fn8nt/fg559/VlhYWLZG/uroxM8//+y2/VrjMaVKldJvv/2Wo+vlxIgRI1SzZk116tRJFSpU0P333+82x34tV+usVatWtn21a9fO9j58fX1VtmxZt2038j769u2r5ORkbdq0SatWrVLXrl1Nfyi6fPmypk6d6prlv/p3Kykpye3v1vVUqVIlVzUuWbJEly5d0o8//qhly5a5/YAAAHZFYw4UcIGBgQoLC/tHK47Mnj1b48ePV8uWLfXGG2/o448/1pYtW1S3bl23D/nVqVNHcXFxeuutt9S8eXO9++67at68uaZNm+Y6pk+fPjp27JgWLFigsLAwPfPMM6pbt262FNpMcnKyOnXqpKSkJG3evFlhYWHXfU316tVVrFgxffvtt7l/8zlg9oOB8ZcPil6Lw+G45va/fuAyJCREBw4c0Icffqju3bvrs88+U6dOna7724LcuN4PODlVvnx5tW7dWs8995x27typ/v37mx47evRoPfnkk+rTp4/eeecdffLJJ9qyZYvKlCnj9nfrenLbWG/fvl1paWmSlG9/LwAgr9GYA4VA165ddfToUcXGxt7Q69euXas2bdpoyZIl6tu3r9q3b6927dpl+/CgJPn7++uee+7R0qVLdeLECXXp0kVPPvmk2/rV5cuX14gRI7Ru3TodP35cZcqU0ZNPPnndOlJTU9WtWzf98MMP2rBhgyIiInJUf4kSJXT77bdr586d+uWXX657fHh4uE6fPp0tYT9y5Ihrf14pVarUNe/jX9NsSfLx8VG3bt20cOFC15dFrVixQvHx8dc899U64+Lisu2Li4vL1y846t+/vz7//HMFBgaqc+fOpsetXbtWUVFReu6553T33XfrjjvuuOYHU81+gLkRCQkJGj16tNq3b6+uXbsqOjr6mvcbAOyGxhwoBCZNmiR/f38NHTpUZ8+ezbb/6NGjbsvu/ZW3t3e29HfNmjXZlty7ugrJVT4+PoqIiJBhGMrIyFBmZma28YSQkBCFhYW50kszmZmZuueeexQbG6s1a9YoMjLyb4//q2nTpskwDN17771KSUnJtn///v1avny5JKlz587KzMzUiy++6HbM3Llz5XA41KlTp1xd++9Uq1ZNR44c0a+//uradvDgwWwjQH+9t15eXmrQoIEkmd67pk2bKiQkRIsXL3Y7ZtOmTTp8+LC6dOmSV28jm7vvvlvTpk3TwoUL//YLj671d2vBggXZfmNwdbWUa/0Qk1vDhg1TVlaWlixZoldeeUXFihXTkCFDcvQbDgDwJL5gCCgEqlWrptWrV+uee+5RnTp13L75c/fu3VqzZo3bGt5/1bVrV82cOVODBw/Wbbfdpm+//VarVq1S1apV3Y5r3769QkND1axZM5UrV06HDx/Wiy++qC5duqhkyZJKSkpShQoVdPfdd6thw4YKCAjQp59+qr179+q555772/cwYcIEffjhh+rWrZsSExOzfaHQwIED//b1t912m1566SWNGDFCtWvXdvvmz+3bt+vDDz/UrFmzJEndunVTmzZt9Pjjj+unn35Sw4YN9cknn+iDDz7Q2LFj3T7o+U/df//9ev7559WhQwcNGTJE586d0+LFi1W3bl3Xh1YlaejQoUpMTNTtt9+uChUq6Oeff9aCBQvUqFEjt2UD/6x48eJ6+umnNXjwYLVq1Ur9+vXT2bNnNW/ePFWuXFnjxo3Ls/fxV0FBQZo+ffp1j+vatatWrlypoKAgRUREKDY2Vp9++qnKlCnjdlyjRo3k7e2tp59+WsnJyXI6nbr99tuvuQTj31m6dKk2btyoZcuWqUKFCpL++EFg4MCBWrRokUaMGJGr8wGApTy4IgyAPPbDDz8Yw4YNMypXrmz4+PgYJUuWNJo1a2YsWLDASE1NdR13reUSJ0yYYJQvX97w8/MzmjVrZsTGxmZb6u/ll182WrZsaZQpU8ZwOp1GtWrVjIkTJxrJycmGYRhGWlqaMXHiRKNhw4ZGyZIlDX9/f6Nhw4bGwoULr1t7q1atDEmmj5zav3+/0b9/fyMsLMwoXry4UapUKaNt27bG8uXL3ZaE/P33341x48a5jqtRo4bxzDPPGFlZWW7nk3TN5R//eg/Nlks0DMN44403jKpVqxo+Pj5Go0aNjI8//jjbcolr16412rdvb4SEhBg+Pj5GpUqVjAcffNBISEjIdo2/Lin49ttvG40bNzacTqdRunRpY8CAAcbJkyfdjomKijL8/f2z1TZt2rQc3d8/L5do5lr34LfffjMGDx5s3HTTTUZAQIDRoUMH48iRI9dc5vDVV181qlatanh7e7u9z/DwcKNLly7XvOafz/PLL78YQUFBRrdu3bIdd9dddxn+/v7GsWPHrvteAcBTHIbB7/YAAAAAT2PGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAGyiU3/zp13iUp0sAAP2290VPlwAA8rVZt2dVn3b564L3bzCJOQAAAGADNvsZCgAAAIWag1zYDHcGAAAAsAEacwAAAMAGGGUBAACAdRwOT1dgWyTmAAAAgA2QmAMAAMA6fPjTFHcGAAAAsAEScwAAAFiHGXNTJOYAAACADZCYAwAAwDrMmJvizgAAAAA2QGIOAAAA6zBjborEHAAAALABEnMAAABYhxlzU9wZAAAAwAZIzAEAAGAdZsxNkZgDAAAANkBiDgAAAOswY26KOwMAAADYAIk5AAAArMOMuSkScwAAAMAGSMwBAABgHWbMTXFnAAAAABsgMQcAAIB1mDE3RWIOAAAA2ACJOQAAAKzDjLkp7gwAAABgAyTmAAAAsA6JuSnuDAAAAGADJOYAAACwjherspghMQcAAABsgMQcAAAA1mHG3BR3BgAAALABEnMAAABYh2/+NEViDgAAANgAiTkAAACsw4y5Ke4MAAAAYAMk5gAAALAOM+amSMwBAAAAGyAxBwAAgHWYMTfFnQEAAABsgMQcAAAA1mHG3BSJOQAAAGADJOYAAACwDjPmprgzAAAAgA2QmAMAAMA6zJibIjEHAAAAbIDEHAAAANZhxtwUdwYAAACwARJzAAAAWIcZc1Mk5gAAAIANkJgDAADAOsyYm+LOAAAAADZAYg4AAADrkJib4s4AAAAANkBiDgAAAOuwKospEnMAAADABkjMAQAAYB1mzE1xZwAAAAAbIDEHAACAdZgxN0ViDgAAANgAiTkAAACsw4y5Ke4MAAAAYAMk5gAAALAOM+amSMwBAAAAGyAxBwAAgGUcJOamSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAA6xCYmyIxBwAAAGyAxBwAAACWYcbcHIk5AAAAYAMk5gAAALAMibk5EnMAAADABkjMAQAAYBkSc3Mk5gAAAIANkJgDAADAMiTm5kjMAQAAABugMQcAAIB1HBY9ciEmJkb/+c9/VLJkSYWEhKhHjx6Ki4tzOyY1NVUjR45UmTJlFBAQoF69euns2bNux5w4cUJdunRRiRIlFBISookTJ+rKlSs5roPGHAAAAEXajh07NHLkSH355ZfasmWLMjIy1L59e128eNF1zLhx47R+/XqtWbNGO3bs0OnTp9WzZ0/X/szMTHXp0kXp6enavXu3li9frmXLlmnq1Kk5rsNhGIaRp+/MBvwaj/J0CQCg3/a+6OkSAEC+NvtEYfCANyy5TtKqgTf82l9//VUhISHasWOHWrZsqeTkZJUtW1arV6/W3XffLUk6cuSI6tSpo9jYWN16663atGmTunbtqtOnT6tcuXKSpMWLF+uRRx7Rr7/+Kh8fn+tel8QcAAAAhU5aWpouXLjg9khLS8vRa5OTkyVJpUuXliTt379fGRkZateuneuY2rVrq1KlSoqNjZUkxcbGqn79+q6mXJI6dOigCxcu6NChQzm6Lo05AAAALONwOCx5xMTEKCgoyO0RExNz3fqysrI0duxYNWvWTPXq1ZMknTlzRj4+PgoODnY7tly5cjpz5ozrmD835Vf3X92XEzb75QYAAADwz02ePFnjx4932+Z0Oq/7upEjR+q7777Trl278qs0UzTmAAAAsIxV65g7nc4cNeJ/NmrUKG3YsEE7d+5UhQoVXNtDQ0OVnp6upKQkt9T87NmzCg0NdR3z3//+1+18V1dtuXrM9TDKAgAAgCLNMAyNGjVK77//vrZt26YqVaq47W/SpImKFy+urVu3urbFxcXpxIkTioyMlCRFRkbq22+/1blz51zHbNmyRYGBgYqIiMhRHSTmAAAAsIwdv/lz5MiRWr16tT744AOVLFnSNRMeFBQkPz8/BQUFaciQIRo/frxKly6twMBAjR49WpGRkbr11lslSe3bt1dERITuvfdezZkzR2fOnNH//d//aeTIkTlO7mnMAQAAUKQtWrRIktS6dWu37UuXLtWgQYMkSXPnzpWXl5d69eqltLQ0dejQQQsXLnQd6+3trQ0bNuihhx5SZGSk/P39FRUVpZkzZ+a4DtYxB4B8wjrmAOzAbuuYl4l605LrnF/ez5Lr5CVmzAEAAAAbsNnPUAAAACjM7Dhjbhck5gAAAIANkJgDAADAMiTm5kjMAQAAABsgMQcAAIBlSMzNkZgDAAAANkBjDgAAANgAoywAAACwDpMspkjMAQAAABsgMQcAAIBl+PCnORJzAAAAwAZIzAEAAGAZEnNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADrEJibIjEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgGRJzc7ZIzD///HMNHDhQkZGROnXqlCRp5cqV2rVrl4crAwAAAKzh8cb83XffVYcOHeTn56evv/5aaWlpkqTk5GTNnj3bw9UBAAAgTzksehRAHm/MZ82apcWLF+vVV19V8eLFXdubNWumr776yoOVAQAAANbx+Ix5XFycWrZsmW17UFCQkpKSrC8IAAAA+YYZc3MeT8xDQ0MVHx+fbfuuXbtUtWpVD1QEAAAAWM/jjfmwYcM0ZswY7dmzRw6HQ6dPn9aqVasUHR2thx56yNPlAQAAIA85HA5LHgWRx0dZHn30UWVlZalt27a6dOmSWrZsKafTqejoaI0ePdrT5QEAAACW8Hhj7nA49Pjjj2vixImKj49XSkqKIiIiFBAQ4OnSUIhE399ePW5vqJqVy+lyWob2HDymx+d9oB9/Puc6ZsHjfXX7LbVUvmyQUi6n6cuDx/V/8z7QDz+ddR3T+uaamjaiq+pWD9PFy+latX6Ppr20XpmZWZ54WwAKof379mrZ60t0+Pvv9Ouvv2ru/Jd0e9t2ni4LyDMFNc22gsdHWa7y8fFRRESEbr75Zppy5LkW/66uxW/vVKv7nlXXh15UsWLe2rBolEr4+riO+frwL3pg+htq1HOWuo94SQ6HQxsWjpSX1x//gNSv+S+tW/CQPtn9vW7t95TuffR1dWlVX7MevtNTbwtAIXT58iXVqlVLk/9vmqdLAWAxjyfmbdq0+dufnLZt22ZhNSis7hy10O35A9Pe0C/bnlLjiIr64qujkqTX3/vCtf9EQqJmvLRee995TOFhZXT85P90d/t/67sfTyvmlc2SpGO//E+Pz1unN56+X0++/JFSLqVZ94YAFFrNW7RS8xatPF0GkG9IzM15vDFv1KiR2/OMjAwdOHBA3333naKiojxTFAq9wABfSdJvyZeuub+Er4/u636rjp/8n06e+U2S5PQpptS0DLfjLqdlyM/XR43rVNLn+3/M36IBAECh5vHGfO7cudfcPn36dKWkpFhcDYoCh8OhZ6Lv1u6vj+r7owlu+x7o3UJPju2hgBJOxR0/oy4PvaiMK5mSpC27D2tU/zbq07GJ1n7ylULLBOqxBzpJksqXDbT8fQAAUCARmJuyzYz5Xw0cOFCvv/76dY9LS0vThQsX3B5GVqYFFaKgemFyH9WtXl73Pbo02763Nu3Vrf2eUrshc/XjiV/1xtP3y+nzx8+vW788osdeWKf5j/VV8p4X9M0HU/XxrkOSpKwsw9L3AAAACh/bNuaxsbHy9fW97nExMTEKCgpye1w5u9+CClEQzX2ktzq3qKcOw+br1LmkbPsvpKTq6Ilf9cVXR9U/+jXVqlJOd97e0LV//hvbFNpyomp2nqoKbR7V+u3fSJKOn/yfVW8BAIACjXXMzXl8lKVnz55uzw3DUEJCgvbt26cpU6Zc9/WTJ0/W+PHj3baFtHgkT2tE4TD3kd7qfntDtR82Tz+fPn/d4x0OhxxyyKd49v+ZJPyaLEnq07GpfklI1NdHfsnzegEAQNHi8cY8KCjI7bmXl5dq1aqlmTNnqn379td9vdPplNPpdNvm8PLO0xpR8L0wuY/u6dRUvce9opSLqSpXpqQkKTklValpGar8rzK6u0MTbY09rP/9lqJ/lQvWhMHtdTktwzWuIknj7murT3YfVlZWlu5s20jRg+/QwEmvM8oCIM9cunhRJ06ccD0/dfKkjhw+rKCgIJUPC/NgZUDeKKhpthU82phnZmZq8ODBql+/vkqVKuXJUlDIPdinpSRpy2tj3bYPm7pSb6zfo7T0K2rWuJpG9W+tUoEldO7879r1VbzaDHpOv/72/z6E3L5ZhCYN7SBn8WL69odT6j3uFX3yxfdWvhUAhdyhQ99p6OD7XM+fnRMjSep+5116YvZTnioLgAUchmF4NOrz9fXV4cOHVaVKlTw7p1/jUXl2LgC4Ub/tfdHTJQCAfD0+H+GuevQmS64T/2wnS66Tlzz+4c969erp2LFjni4DAAAA8CiPN+azZs1SdHS0NmzYoISEhGxLHwIAAKDwYFUWcx775cbMmTM1YcIEde7cWZLUvXt3t5toGIYcDocyM1mTHAAAAIWfxxrzGTNmaPjw4frss888VQIAAAAsVkDDbEt4rDG/+pnTVq1aeaoEAAAAwDY8+jndgjr/AwAAgBtD/2fOo415zZo1r/sfJzEx0aJqAAAAAM/xaGM+Y8aMbN/8CQAAgMKLwNycRxvzvn37KiQkxJMlAAAAALbgscac+SIAAICix8uLHtCMx75g6OqqLAAAAAA8mJhnZWV56tIAAADwEIYmzHksMQcAAADw/3j0w58AAAAoWvicoTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyzJibIzEHAAAAbIDEHAAAAJYhMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAAyzBjbo7EHAAAAEXazp071a1bN4WFhcnhcGjdunVu+wcNGiSHw+H26Nixo9sxiYmJGjBggAIDAxUcHKwhQ4YoJSUlV3XQmAMAAMAyDoc1j9y4ePGiGjZsqJdeesn0mI4dOyohIcH1ePPNN932DxgwQIcOHdKWLVu0YcMG7dy5Uw888ECu6mCUBQAAAEVap06d1KlTp789xul0KjQ09Jr7Dh8+rM2bN2vv3r1q2rSpJGnBggXq3Lmznn32WYWFheWoDhJzAAAA4Dq2b9+ukJAQ1apVSw899JDOnz/v2hcbG6vg4GBXUy5J7dq1k5eXl/bs2ZPja5CYAwAAwDJWffgzLS1NaWlpbtucTqecTmeuz9WxY0f17NlTVapU0dGjR/XYY4+pU6dOio2Nlbe3t86cOaOQkBC31xQrVkylS5fWmTNncnwdEnMAAAAUOjExMQoKCnJ7xMTE3NC5+vbtq+7du6t+/frq0aOHNmzYoL1792r79u15WjOJOQAAACxj1WqJkydP1vjx49223Uhafi1Vq1bVTTfdpPj4eLVt21ahoaE6d+6c2zFXrlxRYmKi6Vz6tdCYAwAAoNC50bGVnDh58qTOnz+v8uXLS5IiIyOVlJSk/fv3q0mTJpKkbdu2KSsrS7fcckuOz0tjDgAAAMt42fALhlJSUhQfH+96fvz4cR04cEClS5dW6dKlNWPGDPXq1UuhoaE6evSoJk2apOrVq6tDhw6SpDp16qhjx44aNmyYFi9erIyMDI0aNUp9+/bN8YosEjPmAAAAKOL27dunxo0bq3HjxpKk8ePHq3Hjxpo6daq8vb31zTffqHv37qpZs6aGDBmiJk2a6PPPP3dL5FetWqXatWurbdu26ty5s5o3b65XXnklV3WQmAMAAMAyNgzM1bp1axmGYbr/448/vu45SpcurdWrV/+jOkjMAQAAABsgMQcAAIBlrFrHvCAiMQcAAABsgMQcAAAAlvEiMDdFYg4AAADYAIk5AAAALMOMuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyDhGZmyExBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsIwXkbkpEnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhsDcHIk5AAAAYAMk5gAAALAM65ibIzEHAAAAbCBHifmHH36Y4xN27979hosBAABA4UZebi5HjXmPHj1ydDKHw6HMzMx/Ug8AAABQJOWoMc/KysrvOgAAAFAEsI65uX80Y56amppXdQAAAABFWq4b88zMTD3xxBP617/+pYCAAB07dkySNGXKFC1ZsiTPCwQAAEDh4eWw5lEQ5boxf/LJJ7Vs2TLNmTNHPj4+ru316tXTa6+9lqfFAQAAAEVFrhvzFStW6JVXXtGAAQPk7e3t2t6wYUMdOXIkT4sDAABA4eJwOCx5FES5bsxPnTql6tWrZ9uelZWljIyMPCkKAAAAKGpy3ZhHRETo888/z7Z97dq1aty4cZ4UBQAAgMLJ4bDmURDlaLnEP5s6daqioqJ06tQpZWVl6b333lNcXJxWrFihDRs25EeNAAAAQKGX68T8zjvv1Pr16/Xpp5/K399fU6dO1eHDh7V+/Xrdcccd+VEjAAAACglmzM3lOjGXpBYtWmjLli15XQsAAABQZN1QYy5J+/bt0+HDhyX9MXfepEmTPCsKAAAAhVNBXWPcCrluzE+ePKl+/frpiy++UHBwsCQpKSlJt912m9566y1VqFAhr2sEAAAACr1cz5gPHTpUGRkZOnz4sBITE5WYmKjDhw8rKytLQ4cOzY8aAQAAUEgwY24u14n5jh07tHv3btWqVcu1rVatWlqwYIFatGiRp8UBAAAARUWuG/OKFSte84uEMjMzFRYWlidFAQAAoHAqmFm2NXI9yvLMM89o9OjR2rdvn2vbvn37NGbMGD377LN5WhwAAABQVOQoMS9VqpTbrM7Fixd1yy23qFixP15+5coVFStWTPfff7969OiRL4UCAACg4PMqoPPfVshRY/7CCy/kcxkAAABA0ZajxjwqKiq/6wAAAEARQGBu7oa/YEiSUlNTlZ6e7rYtMDDwHxUEAAAAFEW5bswvXryoRx55RO+8847Onz+fbX9mZmaeFAYAAIDCp6CuMW6FXK/KMmnSJG3btk2LFi2S0+nUa6+9phkzZigsLEwrVqzIjxoBAACAQi/Xifn69eu1YsUKtW7dWoMHD1aLFi1UvXp1hYeHa9WqVRowYEB+1AkAAIBCgMDcXK4T88TERFWtWlXSH/PkiYmJkqTmzZtr586deVsdAAAAUETkujGvWrWqjh8/LkmqXbu23nnnHUl/JOnBwcF5WhwAAAAKFy+Hw5JHQZTrxnzw4ME6ePCgJOnRRx/VSy+9JF9fX40bN04TJ07M8wIBAACAoiDXM+bjxo1z/bldu3Y6cuSI9u/fr+rVq6tBgwZ5WhwAAAAKlwIaZlviH61jLknh4eEKDw/Pi1oAAACAIitHjfn8+fNzfMKHH374hosBAABA4cY65uZy1JjPnTs3RydzOBw05gAAAMANyFFjfnUVloLif3sWeLoEANCpxMueLgEAVC3Ez9MluMn1yiNFCPcGAAAAsIF//OFPAAAAIKeYMTdHYg4AAADYAIk5AAAALONFYG6KxBwAAACwgRtqzD///HMNHDhQkZGROnXqlCRp5cqV2rVrV54WBwAAgMLFy2HNoyDKdWP+7rvvqkOHDvLz89PXX3+ttLQ0SVJycrJmz56d5wUCAAAARUGuG/NZs2Zp8eLFevXVV1W8eHHX9mbNmumrr77K0+IAAABQuDgcDkseBVGuG/O4uDi1bNky2/agoCAlJSXlRU0AAABAkZPrxjw0NFTx8fHZtu/atUtVq1bNk6IAAABQODFjbi7XjfmwYcM0ZswY7dmzRw6HQ6dPn9aqVasUHR2thx56KD9qBAAAAAq9XK9j/uijjyorK0tt27bVpUuX1LJlSzmdTkVHR2v06NH5USMAAAAKiQI6/m0Jh2EYxo28MD09XfHx8UpJSVFERIQCAgLyurYbdjH9ht4SAOSpM0mpni4BAFQtxM/TJbiZtDHOkuvM6VLLkuvkpRv+5k8fHx9FRETkZS0AAAAo5LyIzE3lujFv06bN3y5Bs23btn9UEAAAAFAU5boxb9SokdvzjIwMHThwQN99952ioqLyqi4AAAAUQjf0tfNFRK4b87lz515z+/Tp05WSkvKPCwIAAACKojz7oWXgwIF6/fXX8+p0AAAAKIQcDmseBVGeNeaxsbHy9fXNq9MBAAAARUquR1l69uzp9twwDCUkJGjfvn2aMmVKnhUGAACAwodVWczlujEPCgpye+7l5aVatWpp5syZat++fZ4VBgAAABQluWrMMzMzNXjwYNWvX1+lSpXKr5oAAABQSBGYm8vVjLm3t7fat2+vpKSkfCoHAAAAKJpy/eHPevXq6dixY/lRCwAAAAo5L4c1j4Io1435rFmzFB0drQ0bNighIUEXLlxwewAAAADIvRzPmM+cOVMTJkxQ586dJUndu3eX409DQoZhyOFwKDMzM++rBAAAQKHAqizmctyYz5gxQ8OHD9dnn32Wn/UAAAAARVKOG3PDMCRJrVq1yrdiAAAAgKIqV8slOvjVAwAAAP4B2klzuWrMa9ased3mPDEx8R8VBAAAABRFuWrMZ8yYke2bPwEAAICcKqhLGVohV4153759FRISkl+1AAAAAEVWjhtz5ssBAADwTzlET2kmx18wdHVVFgAAAAB5L8eJeVZWVn7WAQAAgCKAGXNzOU7MAQAAAOSfXH34EwAAAPgnSMzNkZgDAAAANkBjDgAAAMs4HA5LHrmxc+dOdevWTWFhYXI4HFq3bp3bfsMwNHXqVJUvX15+fn5q166dfvzxR7djEhMTNWDAAAUGBio4OFhDhgxRSkpKruqgMQcAAECRdvHiRTVs2FAvvfTSNffPmTNH8+fP1+LFi7Vnzx75+/urQ4cOSk1NdR0zYMAAHTp0SFu2bNGGDRu0c+dOPfDAA7mqw2EUwnUQL6YXurcEoAA6k5R6/YMAIJ9VC/HzdAlunttxzJLrTGhV9YZe53A49P7776tHjx6S/kjLw8LCNGHCBEVHR0uSkpOTVa5cOS1btkx9+/bV4cOHFRERob1796pp06aSpM2bN6tz5846efKkwsLCcnRtEnMAAADAxPHjx3XmzBm1a9fOtS0oKEi33HKLYmNjJUmxsbEKDg52NeWS1K5dO3l5eWnPnj05vharsgAAAMAyVn2ZfFpamtLS0ty2OZ1OOZ3OXJ3nzJkzkqRy5cq5bS9Xrpxr35kzZxQSEuK2v1ixYipdurTrmJwgMQcAAEChExMTo6CgILdHTEyMp8v6WyTmAAAAsIyXRZH55MmTNX78eLdtuU3LJSk0NFSSdPbsWZUvX961/ezZs2rUqJHrmHPnzrm97sqVK0pMTHS9PidIzAEAAFDoOJ1OBQYGuj1upDGvUqWKQkNDtXXrVte2CxcuaM+ePYqMjJQkRUZGKikpSfv373cds23bNmVlZemWW27J8bVIzAEAAGAZO37zZ0pKiuLj413Pjx8/rgMHDqh06dKqVKmSxo4dq1mzZqlGjRqqUqWKpkyZorCwMNfKLXXq1FHHjh01bNgwLV68WBkZGRo1apT69u2b4xVZJBpzAAAAFHH79u1TmzZtXM+vjsBERUVp2bJlmjRpki5evKgHHnhASUlJat68uTZv3ixfX1/Xa1atWqVRo0apbdu28vLyUq9evTR//vxc1cE65gCQT1jHHIAd2G0d8wVfHLfkOqObVbHkOnmJGXMAAADABhhlAQAAgGW8ZMMhc5sgMQcAAABsgMQcAAAAlrHqmz8LIhJzAAAAwAZIzAEAAGAZO65jbhck5gAAAIANkJgDAADAMl4MmZsiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZAnNzJOYAAACADZCYAwAAwDKkwua4NwAAAIANkJgDAADAMg6GzE2RmAMAAAA2QGIOAAAAy5CXmyMxBwAAAGyAxBwAAACW4Zs/zZGYAwAAADZAYg4AAADLkJebIzEHAAAAbIDEHAAAAJZhxNwciTkAAABgAyTmAAAAsAzf/GmOxBwAAACwARJzAAAAWIZU2Bz3BgAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAy5OXmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALEMqbI57AwAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZ8nJzJOYAAACADZCYAwAAwDKMmJsjMQcAAABsgMQcAAAAlvFiytwUiTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZRzMmJsiMQcAAABsgMQcAAAAlmHG3ByJOQAAAGADJOYAAACwDOuYmyMxBwD8f+zdeZxP9f////trhnnNmNU2xljGvq/RMtlrskQbElFDKNlC1k9vyyimlIRCi2xRlkpvRImQyBtFZZkYI8lSkdHQLGbO7w8/r28v4zCjmfM6M3O7vi+vy8XrnOc55/E6vPWYu+d5vgAANkBiDgAAAMswx9wciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgGb750xyJOQAAAGADJOYAAACwjBeBuSkScwAAAMAGSMwBAABgGeaYmyMxBwAAAGzAVo15amqq4uLidOnSJU+XAgAAgFzgcFjzyots0ZhfvHhRvXv3VpEiRVS7dm0dO3ZMkjRo0CC9+OKLHq4OAAAAyH22aMzHjBmjvXv3atOmTfL19XVtj4qK0tKlSz1YGQAAAHKSw6L/5UW2ePhz5cqVWrp0qe644w45/vFvD7Vr11Z8fLwHKwMAAACsYYvG/Pfff1doaGim7RcuXHBr1AEAAJC3sY65OVtMZWncuLHWrFnjen+lGX/nnXcUGRnpqbIAAAAAy9giMZ88ebLatWun/fv369KlS5o+fbr279+vbdu2afPmzZ4uDwAAAMh1tkjMmzZtqj179ujSpUuqW7euPv/8c4WGhmr79u1q1KiRp8sDAABADuHhT3MOwzAMTxeR0y6k5ruPBCAPOnUu2dMlAIAqh/p5ugQ3X/30pyXXaVatqCXXyUm2SMy//fZb/fDDD673n3zyiR588EH93//9n1JTUz1YGQqSObNm6pa6NdxeHe9r5+myAORzaz5epv7RD6tTmybq1KaJhvV7XDu/2ZppnGEYGjt8gO5t1kDbtmz0QKVAzuALhszZYo75U089pdGjR6tu3bo6cuSIHnnkEXXs2FHLly/XxYsX9dprr3m6RBQQlatU1ey333W99/a2xf9FAORjJUJLqVe/wQovW16GIW1Y9189P2aIZr77gSIqVnGNW7nsvTzbbADIGlsk5j/99JMaNGggSVq+fLlatGihJUuWaP78+frwww89WxwKFG9vb5UoUdL1Klo07/0zGIC85fYmLXRrZDOVKRehsuUjFP3kIPn6FdHBff/vX5LjDx3UR0sXacjoGA9WCuQMh0WvvMgWcaBhGMrIyJAkffHFF+rQoYMkqVy5cvrjjz88WRoKmGPHflbru5rJ6eNUvfoNNHDIMJUuHe7psgAUEOnp6dr65XolJ/+tmrXrSZKSk//WlJj/U/+hY1SseAkPVwggN9miMW/cuLFeeOEFRUVFafPmzZo9e7YkKSEhQaVKlfJwdSgo6tatr5jnYxVRoaL++OM3vTX7DfWO7qHlH/9X/v4Bni4PQD6WEH9Izz79uFJTU+Xn56exk15V+YqVJUlvz3xFNevUV2SzVh6uEsgZXszJMmWLxvy1115T9+7dtXLlSj333HOqUuXynLoVK1bozjvvvO6xKSkpSklJcdt2yeEjp9OZa/Uif2rSrLnr19WqV1fduvXVvs1dWv/ZOj3YsbMHKwOQ35UtX0Gvv7tUFy4kaeuXX2jqpHGaMvMdnfj1F+399n+aOXepp0sEYAFbL5eYnJwsb29vFS5c2HTMhAkTFBPjPuduzH/G6bmxE3K5OhQEPbp21u13RGrQkGc9XQryIJZLxM36vyFPqXSZsvJxOvXfFe/L4fX/HgnLSE+Xl5eXatdrqJdmzvVglcgr7LZc4jeHz1lynTuqhFhynZxki8TcjK+v7w3HjBkzRsOGDXPbdsnhk1sloQC5ePGCjv/yi9rfd7+nSwFQwGQYGUpLTVX3J55Wmw4d3fb1j+6svoOG6/Y7W3ioOgC5xWONedGiReXI4hyjs2fPmu5zOp2Zpq3wBUO4GdNeeUnNW7RS6fBw/f77b5rzxuvy8vZS23YdPF0agHxs3pwZanxHE4WWCtPFixe1af1a/fDdLj0/dZaKFS9xzQc+S4aGKSy8jAeqBXIAU8xNeawxZ21y2M3p06c1ZtSzSjx3TkWLFlODWxppweKlKlqsmKdLA5CPJZ47q6mT/qOzZ/6Qv3+AKlaupuenztItt0Z6ujQAFrP1HPObRWIOwA6YYw7ADuw2x3xHfKIl17m9crAl18lJtptjnpycrNTUVLdtQUFBHqoGAAAAsIYtvvnzwoULGjhwoEJDQ+Xv76+iRYu6vQAAAJA/OBzWvPIiWzTmI0eO1MaNGzV79mw5nU698847iomJUXh4uBYuXOjp8gAAAIBcZ4upLKtWrdLChQvVsmVL9erVS82aNVOVKlUUERGhxYsXq3v37p4uEQAAADkgj4bZlrBFYn727FlVqlRJ0uX55FeWR2zatKm2bNniydIAAAAAS9iiMa9UqZISEhIkSTVq1NCyZcskXU7SQ0JCPFgZAAAAcpTDolce5NHG/MiRI8rIyFCvXr20d+9eSdLo0aP1xhtvyNfXV0OHDtWIESM8WSIAAABgCY+uY+7t7a2TJ08qNDRUkvTII49oxowZSk5O1u7du1WlShXVq1cv2+dlHXMAdsA65gDswG7rmO9KOG/JdRpXzHvLbXs0Mb/6Z4JPP/1UFy5cUEREhDp27HhTTTkAAACQF9liVRYAAAAUDHl1jXEreDQxdzgcclz1u3P1ewAAAKAg8GhibhiGevbsKafTKUlKTk5Wv3795O/v7zbuo48+8kR5AAAAyGFEsOY82phHR0e7ve/Ro4eHKgEAAAA8y6ON+bx58zx5eQAAAFjNhpH5hAkTFBMT47atevXqOnjwoKTLszqeffZZffDBB0pJSVGbNm00a9YslSpVKkfrsMUXDAEAAACeVLt2bZ08edL12rp1q2vf0KFDtWrVKi1fvlybN2/WiRMn1LFjxxyvgVVZAAAAYBmHHSNzSYUKFVJYWFim7YmJiZo7d66WLFmiu+66S9LlWR81a9bUN998ozvuuCPHaiAxBwAAQL6TkpKi8+fPu71SUlJMxx86dEjh4eGqVKmSunfvrmPHjkmSdu/erbS0NEVFRbnG1qhRQ+XLl9f27dtztGYacwAAAFjG4bDmFRsbq+DgYLdXbGzsNWu6/fbbNX/+fK1bt06zZ89WQkKCmjVrpr/++kunTp2Sj4+PQkJC3I4pVaqUTp06laP3hqksAAAAyHfGjBmjYcOGuW27skT31dq1a+f6db169XT77bcrIiJCy5Ytk5+fX67W+U805gAAALCMVTPMnU6naSN+IyEhIapWrZoOHz6se+65R6mpqTp37pxban769Olrzkn/N5jKAgAAAPxDUlKS4uPjVbp0aTVq1EiFCxfWhg0bXPvj4uJ07NgxRUZG5uh1ScwBAABgHRsuyjJ8+HDdd999ioiI0IkTJzR+/Hh5e3urW7duCg4OVu/evTVs2DAVK1ZMQUFBGjRokCIjI3N0RRaJxhwAAAAF3PHjx9WtWzedOXNGJUuWVNOmTfXNN9+oZMmSkqRp06bJy8tLnTp1cvuCoZzmMAzDyPGzetiF1Hz3kQDkQafOJXu6BABQ5VDrHl7Miu9/SbLkOvXKBVhynZzEHHMAAADABpjKAgAAAMs4bDjH3C5IzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADrEJmbIjEHAAAAbIDEHAAAAJZxEJmbIjEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAdYjMTZGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIfI3BSJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgHWIzE2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiHyNwUiTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlmGNujsQcAAAAsAEScwAAAFiIyNwMiTkAAABgAyTmAAAAsAxzzM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWYY65ORJzAAAAwAZIzAEAAGAZB7PMTZGYAwAAADZAYw4AAADYAFNZAAAAYB1mspgiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswxcMmSMxBwAAAGyAxBwAAACW4QuGzJGYAwAAADZAYg4AAADrEJibIjEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYw4AAADYAI05AAAAYAPMMQcAAIBlmGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMswx9wciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAA1iEyN0ViDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMOwzAMTxcB2E1KSopiY2M1ZswYOZ1OT5cDoADi7yGg4KExB67h/PnzCg4OVmJiooKCgjxdDoACiL+HgIKHqSwAAACADdCYAwAAADZAYw4AAADYAI05cA1Op1Pjx4/ngSsAHsPfQ0DBw8OfAAAAgA2QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAANjM/PnzFRIS4ukyAFiMxhz5msPhuO5rwoQJni4RQD7Ws2fPa/7dc/jwYU+XBsCGCnm6ACA3nTx50vXrpUuXaty4cYqLi3NtCwgIcP3aMAylp6erUCH+bwEg57Rt21bz5s1z21ayZEkPVQPAzkjMka+FhYW5XsHBwXI4HK73Bw8eVGBgoNauXatGjRrJ6XRq69at6tmzpx588EG38wwZMkQtW7Z0vc/IyFBsbKwqVqwoPz8/1a9fXytWrLD2wwHIE5xOp9vfRWFhYZo+fbrq1q0rf39/lStXTv3791dSUpLpOfbu3atWrVopMDBQQUFBatSokXbt2uXav3XrVjVr1kx+fn4qV66cBg8erAsXLljx8QDkIBpzFHijR4/Wiy++qAMHDqhevXpZOiY2NlYLFy7UnDlztG/fPg0dOlQ9evTQ5s2bc7laAPmBl5eXZsyYoX379mnBggXauHGjRo4caTq+e/fuKlu2rHbu3Kndu3dr9OjRKly4sCQpPj5ebdu2VadOnfT9999r6dKl2rp1qwYOHGjVxwGQQ/g3exR4EydO1D333JPl8SkpKZo8ebK++OILRUZGSpIqVaqkrVu36s0331SLFi1yq1QAedDq1avdps21a9dOy5cvd72vUKGCXnjhBfXr10+zZs265jmOHTumESNGqEaNGpKkqlWruvbFxsaqe/fuGjJkiGvfjBkz1KJFC82ePVu+vr658KkA5AYacxR4jRs3ztb4w4cP6+LFi5ma+dTUVDVs2DAnSwOQD7Rq1UqzZ892vff399cXX3yh2NhYHTx4UOfPn9elS5eUnJysixcvqkiRIpnOMWzYMPXp00eLFi1SVFSUHn74YVWuXFnS5Wku33//vRYvXuwabxiGMjIylJCQoJo1a+b+hwSQI2jMUeD5+/u7vffy8pJhGG7b0tLSXL++Mg90zZo1KlOmjNs4p9OZS1UCyKv8/f1VpUoV1/ujR4+qQ4cOevrppzVp0iQVK1ZMW7duVe/evZWamnrNxnzChAl69NFHtWbNGq1du1bjx4/XBx98oIceekhJSUl66qmnNHjw4EzHlS9fPlc/G4CcRWMOXKVkyZL68ccf3bbt2bPHNZ+zVq1acjqdOnbsGNNWAGTb7t27lZGRoalTp8rL6/KjXsuWLbvhcdWqVVO1atU0dOhQdevWTfPmzdNDDz2kW265Rfv373dr/gHkTTz8CVzlrrvu0q5du7Rw4UIdOnRI48ePd2vUAwMDNXz4cA0dOlQLFixQfHy8vv32W82cOVMLFizwYOUA8oIqVaooLS1NM2fO1JEjR7Ro0SLNmTPHdPzff/+tgQMHatOmTfr555/19ddfa+fOna4pKqNGjdK2bds0cOBA7dmzR4cOHdInn3zCw59AHkRjDlylTZs2Gjt2rEaOHKlbb71Vf/31lx5//HG3Mc8//7zGjh2r2NhY1axZU23bttWaNWtUsWJFD1UNIK+oX7++Xn31Vb300kuqU6eOFi9erNjYWNPx3t7eOnPmjB5//HFVq1ZNXbp0Ubt27RQTEyNJqlevnjZv3qyffvpJzZo1U8OGDTVu3DiFh4db9ZEA5BCHcfVkWgAAAACWIzEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBFFg9e/bUgw8+6HrfsmVLDRkyxPI6Nm3aJIfDoXPnzpmOcTgcWrlyZZbPOWHCBDVo0OBf1XX06FE5HA7t2bPnX50HAJA1NOYAbKVnz55yOBxyOBzy8fFRlSpVNHHiRF26dCnXr/3RRx/p+eefz9LYrDTTAABkRyFPFwAAV2vbtq3mzZunlJQUffrppxowYIAKFy6sMWPGZBqbmpoqHx+fHLlusWLFcuQ8AADcDBJzALbjdDoVFhamiIgIPf3004qKitJ///tfSf9v+smkSZMUHh6u6tWrS5J++eUXdenSRSEhISpWrJgeeOABHT161HXO9PR0DRs2TCEhISpevLhGjhwpwzDcrnv1VJaUlBSNGjVK5cqVk9PpVJUqVTR37lwdPXpUrVq1kiQVLVpUDodDPXv2lCRlZGQoNjZWFStWlJ+fn+rXr68VK1a4XefTTz9VtWrV5Ofnp1atWrnVmVWjRo1StWrVVKRIEVWqVEljx45VWlpapnFvvvmmypUrpyJFiqhLly5KTEx02//OO++oZs2a8vX1VY0aNTRr1qxs1wIAyBk05gBsz8/PT6mpqa73GzZsUFxcnNavX6/Vq1crLS1Nbdq0UWBgoL766it9/fXXCggIUNu2bV3HTZ06VfPnz9e7776rrVu36uzZs/r444+ve93HH39c77//vmbMmKEDBw7ozTffVEBAgMqVK6cPP/xQkhQXF6eTJ09q+vTpkqTY2FgtXLhQc+bM0b59+zR06FD16NFDmzdvlnT5B4iOHTvqvvvu0549e9SnTx+NHj062/ckMDBQ8+fP1/79+zV9+nS9/fbbmjZtmtuYw4cPa9myZVq1apXWrVun7777Tv3793ftX7x4scaNG6dJkybpwIEDmjx5ssaOHasFCxZkux4AQA4wAMBGoqOjjQceeMAwDMPIyMgw1q9fbzidTmP48OGu/aVKlTJSUlJcxyxatMioXr26kZGR4dqWkpJi+Pn5GZ999plhGIZRunRpY8qUKa79aWlpRtmyZV3XMgzDaNGihfHMM88YhmEYcXFxhiRj/fr116zzyy+/NCQZf/75p2tbcnKyUaRIEWPbtm1uY3v37m1069bNMAzDGDNmjFGrVi23/aNGjcp0rqtJMj7++GPT/S+//LLRqFEj1/vx48cb3t7exvHjx13b1q5da3h5eRknT540DMMwKleubCxZssTtPM8//7wRGRlpGIZhJCQkGJKM7777zvS6AICcwxxzALazevVqBQQEKC0tTRkZGXr00Uc1YcIE1/66deu6zSvfu3evDh8+rMDAQLfzJCcnKz4+XomJiTp58qRuv/12175ChQqpcePGmaazXLFnzx55e3urRYsWWa778OHDunjxou655x637ampqWrYsKEk6cCBA251SFJkZGSWr3HF0qVLNWPGDMXHxyspKUmXLl1SUFCQ25jy5curTJkybtfJyMhQXFycAgMDFR8fr969e6tv376uMZcuXVJwcHC26wEA/Hs05gBsp1WrVpo9e7Z8fHwUHh6uQoXc/6ry9/d3e5+UlKRGjRpp8eLFmc5VsmTJm6rBz88v28ckJSVJktasWePWEEuX583nlO3bt6t79+6KiYlRmzZtFBwcrA8++EBTp07Ndq1vv/12ph8UvL29c6xWAEDW0ZgDsB1/f39VqVIly+NvueUWLV26VKGhoZlS4ytKly6tHTt2qHnz5pIuJ8O7d+/WLbfccs3xdevWVUZGhjZv3qyoqKhM+68k9unp6a5ttWrVktPp1LFjx0yT9po1a7oeZL3im2++ufGH/Idt27YpIiJCzz33nGvbzz//nGncsWPHdOLECYWHh7uu4+XlperVq6tUqVIKDw/XkSNH1L1792xdHwCQO3j4E0Ce1717d5UoUUIPPPCAvvrqKyUkJGjTpk0aPHiwjh8/Lkl65pln9OKLL2rlypU6ePCg+vfvf901yCtUqKDo6Gg98cQTWrlypeucy5YtkyRFRETI4XBo9erV+v3335WUlKTAwEANHz5cQ4cO1YIFCxQfH69vv/1WM2fOdD1Q2a9fPx06dEgjRoxQXFyclixZovnz52fr81atWlXHjh3TBx98oPj4eM2YMeOaD7L6+voqOjpae/fu1VdffaXBgwerS5cuCgsLkyTFxMQoNjZWM2bM0E8//aQffvhB8+bN06uvvpqtegAAOYPGHECeV6RIEW3ZskXly5dXx44dVbNmTfXu3VvJycmuBP3ZZ5/VY489pujoaEVGRiowMFAPPfTQdc87e/Zsde7cWf3791eNGjXUt29fXbhwQZJUpkwZxcTEaPTo0SpVqpQGDhwoSXr++ec1duxYxcbGqmbNmmrbtq3WrFmjihUrSro87/vDDz/UypUrVb9+fc2ZM0eTJ0/O1ue9//77NXToUA0cOFANGjTQtm3bNHbs2EzjqlSpoo4dO+ree+9V69atVa9ePbflEPv06aN33nlH8+bNU926ddWiRQvNnz/fVSsAwFoOw+zJJwAAAACWITEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcKIAqVKignj17erqMfGfnzp2688475e/vL4fDoT179uTo+Tdt2iSHw6FNmzbl6HnzMv4sA8hPaMyBfCQ+Pl5PPfWUKlWqJF9fXwUFBalJkyaaPn26/v77b0+Xd1379u3Tww8/rEqVKqlIkSIqUaKEmjdvrlWrVmXrPHv27FGPHj1Urlw5OZ1OFStWTFFRUZo3b57S09NzqXopLS1NDz/8sM6ePatp06Zp0aJFioiIyLXrWa1ly5ZyOByqWrXqNfevX79eDodDDodDK1asyPb59+/frwkTJujo0aP/slIAyLsKeboAADljzZo1evjhh+V0OvX444+rTp06Sk1N1datWzVixAjt27dPb731lqfLNPXzzz/rr7/+UnR0tMLDw3Xx4kV9+OGHuv/++/Xmm2/qySefvOE53nnnHfXr10+lSpXSY489pqpVq+qvv/7Shg0b1Lt3b508eVL/93//lyv1x8fH6+eff9bbb7+tPn365Mo1mjdvrr///ls+Pj65cv4b8fX11eHDh/W///1Pt912m9u+xYsXy9fXV8nJyTd17v379ysmJkYtW7ZUhQoVsnxcXFycvLzImADkDzTmQD6QkJCgrl27KiIiQhs3blTp0qVd+wYMGKDDhw9rzZo1Hqzwxu69917de++9btsGDhyoRo0a6dVXX71hY/7NN9+oX79+ioyM1KeffqrAwEDXviFDhmjXrl368ccfc6V2Sfrtt98kSSEhIbl2DS8vL/n6+uba+W+kcuXKunTpkt5//323xjw5OVkff/yx2rdvrw8//DDX6zAMQ8nJyfLz85PT6cz16wGAVYgZgHxgypQpSkpK0ty5c92a8iuqVKmiZ555xvT4s2fPavjw4apbt64CAgIUFBSkdu3aae/evZnGzpw5U7Vr11aRIkVUtGhRNW7cWEuWLHHt/+uvvzRkyBBVqFBBTqdToaGhuueee/Ttt99m+3N5e3urXLlyOnfu3A3HxsTEyOFwaPHixW5N+RWNGzd2m4t84cIFPfvss64pL9WrV9crr7wiwzDcjnM4HBo4cKBWrlypOnXqyOl0qnbt2lq3bp1rTM+ePdWiRQtJ0sMPPyyHw6GWLVtKujwF5Mqv/6lnz56ZkuEPPvhAjRo1UmBgoIKCglS3bl1Nnz7dtd9sjvny5cvVqFEj+fn5qUSJEurRo4d+/fXXTNcLCAjQr7/+qgcffFABAQEqWbKkhg8fnq0pPt26ddPSpUuVkZHh2rZq1SpdvHhRXbp0yTT+559/Vv/+/VW9enX5+fmpePHievjhh92mrMyfP18PP/ywJKlVq1auKTFXPmeFChXUoUMHffbZZ2rcuLH8/Pz05ptvuvZd+X01DEOtWrVSyZIlXT8oSVJqaqrq1q2rypUr68KFC1n+rABgNRpzIB9YtWqVKlWqpDvvvPOmjj9y5IhWrlypDh066NVXX9WIESP0ww8/qEWLFjpx4oRr3Ntvv63BgwerVq1aeu211xQTE6MGDRpox44drjH9+vXT7Nmz1alTJ82aNUvDhw+Xn5+fDhw4kKVaLly4oD/++EPx8fGaNm2a1q5dq7vvvvu6x1y8eFEbNmxQ8+bNVb58+RtewzAM3X///Zo2bZratm2rV199VdWrV9eIESM0bNiwTOO3bt2q/v37q2vXrpoyZYqSk5PVqVMnnTlzRpL01FNPuabIDB48WIsWLdJzzz2Xpc97xfr169WtWzcVLVpUL730kl588UW1bNlSX3/99XWPmz9/vrp06SJvb2/Fxsaqb9+++uijj9S0adNMP9Ckp6erTZs2Kl68uF555RW1aNFCU6dOzdYUp0cffVQnT550++FgyZIluvvuuxUaGppp/M6dO7Vt2zZ17dpVM2bMUL9+/bRhwwa1bNlSFy9elHR5is7gwYMlSf/3f/+nRYsWadGiRapZs6brPHFxcerWrZvuueceTZ8+XQ0aNMh0LYfDoXfffVfJycnq16+fa/v48eO1b98+zZs3T/7+/ln+rABgOQNAnpaYmGhIMh544IEsHxMREWFER0e73icnJxvp6eluYxISEgyn02lMnDjRte2BBx4wateufd1zBwcHGwMGDMhyLVd76qmnDEmGJMPLy8vo3Lmzcfbs2eses3fvXkOS8cwzz2TpGitXrjQkGS+88ILb9s6dOxsOh8M4fPiwa5skw8fHx23blevNnDnTte3LL780JBnLly93O2eLFi2MFi1aZKohOjraiIiIcL1/5plnjKCgIOPSpUumdV+5xpdffmkYhmGkpqYaoaGhRp06dYy///7bNW716tWGJGPcuHFu15Pk9vtpGIbRsGFDo1GjRqbX/OfnuPJ737hxY6N3796GYRjGn3/+afj4+BgLFiy45j24ePFipnNt377dkGQsXLjQtW358uVun+2fIiIiDEnGunXrrrnvn3+WDcMw3nzzTUOS8d577xnffPON4e3tbQwZMuSGnxEAPI3EHMjjzp8/L0nXnL6RVU6n0/UAXXp6us6cOaOAgABVr17dbQpKSEiIjh8/rp07d5qeKyQkRDt27HBL2rNjyJAhWr9+vRYsWKB27dopPT1dqamp1z0mu/fg008/lbe3tyulveLZZ5+VYRhau3at2/aoqChVrlzZ9b5evXoKCgrSkSNHsnS9rAgJCdGFCxe0fv36LB+za9cu/fbbb+rfv7/b3PP27durRo0a13yu4J9JsiQ1a9Ys25/j0Ucf1UcffaTU1FStWLFC3t7eeuihh6451s/Pz/XrtLQ0nTlzRlWqVFFISEi2pjdVrFhRbdq0ydLYJ598Um3atNGgQYP02GOPqXLlypo8eXKWrwUAnkJjDuRxQUFBki7P7b5ZGRkZmjZtmqpWrSqn06kSJUqoZMmS+v7775WYmOgaN2rUKAUEBOi2225T1apVNWDAgExTLaZMmaIff/xR5cqV02233aYJEyZkq/GrUaOGoqKi9Pjjj2v16tVKSkrSfffdl2nu9z9l9x78/PPPCg8Pz9TIX5k68fPPP7ttv9b0mKJFi+rPP//M0vWyon///qpWrZratWunsmXL6oknnnCbx34tV+qsXr16pn01atTI9Dl8fX1VsmRJt2038zm6du2qxMRErV27VosXL1aHDh1Mfyj6+++/NW7cONdc/it/ts6dO+f2Z+tGKlasmK0a586dq4sXL+rQoUOaP3++2w8IAGBXNOZAHhcUFKTw8PB/teLI5MmTNWzYMDVv3lzvvfeePvvsM61fv161a9d2e8ivZs2aiouL0wcffKCmTZvqww8/VNOmTTV+/HjXmC5duujIkSOaOXOmwsPD9fLLL6t27dqZUuis6ty5s3bu3KmffvrJdEyVKlVUqFAh/fDDDzd1jRvx9va+5vbr/bBwhcPhuOb2qx+4DA0N1Z49e/Tf//5X999/v7788ku1a9dO0dHR2S/YhNnnyK7SpUurZcuWmjp1qrZs2aJHH33UdOygQYM0adIkdenSRcuWLdPnn3+u9evXq3jx4m5/tm4ku431pk2blJKSIkm59ucCAHIajTmQD3To0EHx8fHavn37TR2/YsUKtWrVSnPnzlXXrl3VunVrRUVFXXM1FH9/fz3yyCOaN2+ejh07pvbt22vSpElu61eXLl1a/fv318qVK5WQkKDixYtr0qRJN1XblS9Gul66WqRIEd11113asmWLfvnllxueMyIiQidOnMiUsB88eNC1P6cULVr0mvfx6jRbknx8fHTfffdp1qxZri+LWrhwoQ4fPnzNc1+pMy4uLtO+uLi4XP2Co0cffVRfffWVgoKCMi1z+U8rVqxQdHS0pk6dqs6dO+uee+655oOpZj/A3IyTJ09q0KBBat26tTp06KDhw4df834DgN3QmAP5wMiRI+Xv768+ffro9OnTmfbHx8e7Lbt3NW9v70zp7/LlyzMtuXdlFZIrfHx8VKtWLRmGobS0NKWnp2dqoENDQxUeHu5KL838c3m7K9LS0rRw4UL5+fmpVq1a1z1+/PjxMgxDjz32mJKSkjLt3717txYsWCDp8prp6enpev31193GTJs2TQ6HQ+3atbvutbKjcuXKOnjwoH7//XfXtr1792aaAnT1vfXy8lK9evUkyfTeNW7cWKGhoZozZ47bmLVr1+rAgQNq3759Tn2MTDp37qzx48dr1qxZ1/3Co2v92Zo5c2amfzG4slpKVpbGvJG+ffsqIyNDc+fO1VtvvaVChQqpd+/eWfoXDgDwJL5gCMgHKleurCVLluiRRx5RzZo13b75c9u2bVq+fLnbGt5X69ChgyZOnKhevXrpzjvv1A8//KDFixerUqVKbuNat26tsLAwNWnSRKVKldKBAwf0+uuvq3379goMDNS5c+dUtmxZde7cWfXr11dAQIC++OIL7dy5U1OnTr3uZ3jqqad0/vx5NW/eXGXKlNGpU6e0ePFiHTx4UFOnTlVAQMB1j7/zzjv1xhtvqH///qpRo4bbN39u2rRJ//3vf/XCCy9Iku677z61atVKzz33nI4ePar69evr888/1yeffKIhQ4a4Pej5bz3xxBN69dVX1aZNG/Xu3Vu//fab5syZo9q1a7seWpWkPn366OzZs7rrrrtUtmxZ/fzzz5o5c6YaNGjgtmzgPxUuXFgvvfSSevXqpRYtWqhbt246ffq0pk+frgoVKmjo0KE59jmuFhwcrAkTJtxwXIcOHbRo0SIFBwerVq1a2r59u7744gsVL17cbVyDBg3k7e2tl156SYmJiXI6nbrrrruuuQTj9cybN09r1qzR/PnzVbZsWUmXfxDo0aOHZs+erf79+2frfABgKc8tCAMgp/30009G3759jQoVKhg+Pj5GYGCg0aRJE2PmzJlGcnKya9y1lkt89tlnjdKlSxt+fn5GkyZNjO3bt2da6u/NN980mjdvbhQvXtxwOp1G5cqVjREjRhiJiYmGYRhGSkqKMWLECKN+/fpGYGCg4e/vb9SvX9+YNWvWDWt///33jaioKKNUqVJGoUKFjKJFixpRUVHGJ598kq17sHv3buPRRx81wsPDjcKFCxtFixY17r77bmPBggVuS0L+9ddfxtChQ13jqlatarz88stGRkaG2/kkXXP5x6vvodlyiYZhGO+9955RqVIlw8fHx2jQoIHx2WefZVouccWKFUbr1q2N0NBQw8fHxyhfvrzx1FNPGSdPnsx0jauXFFy6dKnRsGFDw+l0GsWKFTO6d+9uHD9+3G1MdHS04e/vn6m28ePHG1n5T8E/l0s0c6178Oeffxq9evUySpQoYQQEBBht2rQxDh48eM1lDt9++22jUqVKhre3t9vnjIiIMNq3b3/Na/7zPL/88osRHBxs3HfffZnGPfTQQ4a/v79x5MiRG35WAPAUh2Hwb3sAAACApzHHHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGygkKcLyA1+DQd6ugQA0J87X/d0CQAgX5t1e1b1aX9/l/f+DiYxBwAAAGzAZj9DAQAAIF9zkAub4c4AAAAANkBiDgAAAOs4HJ6uwLZIzAEAAAAbIDEHAACAdZhjboo7AwAAANgAiTkAAACswxxzUyTmAAAAgA2QmAMAAMA6zDE3xZ0BAAAAbIDEHAAAANZhjrkpEnMAAADABkjMAQAAYB3mmJvizgAAAAA2QGIOAAAA6zDH3BSJOQAAAGADJOYAAACwDnPMTXFnAAAAABsgMQcAAIB1mGNuisQcAAAAsAEScwAAAFiHOeamuDMAAACADZCYAwAAwDrMMTdFYg4AAADYAIk5AAAArMMcc1PcGQAAAMAGSMwBAABgHRJzU9wZAAAAwAZIzAEAAGAdL1ZlMUNiDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAdvvnTFIk5AAAAYAMk5gAAALAOc8xNcWcAAAAAGyAxBwAAgHWYY26KxBwAAACwARJzAAAAWIc55qa4MwAAAIANkJgDAADAOswxN0ViDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA5zzE2RmAMAAAA2QGIOAAAA6zDH3BR3BgAAALABEnMAAABYh8TcFHcGAAAAsAEScwAAAFiHVVlMkZgDAAAANkBiDgAAAOswx9wUdwYAAACwARJzAAAAWIc55qZIzAEAAAAbIDEHAACAdZhjboo7AwAAANgAiTkAAACswxxzUyTmAAAAgA2QmAMAAMAyDhJzUyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgHUIzE2RmAMAAAA2QGIOAAAAyzDH3ByJOQAAAGADJOYAAACwDIm5ORJzAAAAwAZIzAEAAGAZEnNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAdQjMTZGYAwAAADZAYg4AAADLMMfcHIk5AAAAYAMk5gAAALAMibk5EnMAAADABmjMAQAAABtgKgsAAAAsw1QWcyTmAAAAgA2QmAMAAMAyJObmSMwBAABQoMXGxurWW29VYGCgQkND9eCDDyouLs5tTHJysgYMGKDixYsrICBAnTp10unTp93GHDt2TO3bt1eRIkUUGhqqESNG6NKlS1mug8YcAAAA1nFY9MqGzZs3a8CAAfrmm2+0fv16paWlqXXr1rpw4YJrzNChQ7Vq1SotX75cmzdv1okTJ9SxY0fX/vT0dLVv316pqanatm2bFixYoPnz52vcuHFZvzWGYRjZK93+/BoO9HQJAKA/d77u6RIAQL42m7hcPPp9S65zZkG3mz72999/V2hoqDZv3qzmzZsrMTFRJUuW1JIlS9S5c2dJ0sGDB1WzZk1t375dd9xxh9auXasOHTroxIkTKlWqlCRpzpw5GjVqlH7//Xf5+Pjc8Lok5gAAALCMw+Gw5PVvJCYmSpKKFSsmSdq9e7fS0tIUFRXlGlOjRg2VL19e27dvlyRt375ddevWdTXlktSmTRudP39e+/bty9J1bfYzFAAAAPDvpaSkKCUlxW2b0+mU0+m87nEZGRkaMmSImjRpojp16kiSTp06JR8fH4WEhLiNLVWqlE6dOuUa88+m/Mr+K/uygsQcAAAAlrEqMY+NjVVwcLDbKzY29ob1DRgwQD/++KM++OADC+6GOxJzAAAA5DtjxozRsGHD3LbdKC0fOHCgVq9erS1btqhs2bKu7WFhYUpNTdW5c+fcUvPTp08rLCzMNeZ///uf2/murNpyZcyNkJgDAADAMlYl5k6nU0FBQW4vs8bcMAwNHDhQH3/8sTZu3KiKFSu67W/UqJEKFy6sDRs2uLbFxcXp2LFjioyMlCRFRkbqhx9+0G+//eYas379egUFBalWrVpZujck5gAAACjQBgwYoCVLluiTTz5RYGCga054cHCw/Pz8FBwcrN69e2vYsGEqVqyYgoKCNGjQIEVGRuqOO+6QJLVu3Vq1atXSY489pilTpujUqVP6z3/+owEDBtwwqb+CxhwAAADWseEXf86ePVuS1LJlS7ft8+bNU8+ePSVJ06ZNk5eXlzp16qSUlBS1adNGs2bNco319vbW6tWr9fTTTysyMlL+/v6Kjo7WxIkTs1wH65gDQC5hHXMAdmC3dcxDey+z5Dq/ze1iyXVyks1+qwAAAJCf/ds1xvMzHv4EAAAAbIDEHAAAAJYhMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYhsTcHIk5AAAAYAMk5gAAALAOgbkpEnMAAADABkjMAQAAYBnmmJsjMQcAAABsgMQcAAAAliExN0diDgAAANgAiTkAAAAsQ2JuzhaJ+VdffaUePXooMjJSv/76qyRp0aJF2rp1q4crAwAAAKzh8cb8ww8/VJs2beTn56fvvvtOKSkpkqTExERNnjzZw9UBAAAgRzkseuVBHm/MX3jhBc2ZM0dvv/22Chcu7NrepEkTffvttx6sDAAAALCOx+eYx8XFqXnz5pm2BwcH69y5c9YXBAAAgFzDHHNzHk/Mw8LCdPjw4Uzbt27dqkqVKnmgIgAAAMB6Hm/M+/btq2eeeUY7duyQw+HQiRMntHjxYg0fPlxPP/20p8sDAABADnI4HJa88iKPT2UZPXq0MjIydPfdd+vixYtq3ry5nE6nhg8frkGDBnm6PAAAAMASHm/MHQ6HnnvuOY0YMUKHDx9WUlKSatWqpYCAAE+Xhnxk+BOt9eBd9VWtQin9nZKmHXuP6Lnpn+jQz79dc/zK159Wmya11WXoW1q16XvX9r+/ez3T2MdHz9Pyz3bnWu0ACqYPlizWgnlz9ccfv6ta9Roa/X9jVbdePU+XBfxreTXNtoLHG/MrfHx8VKtWLU+XgXyq2S1VNGfpFu3e97MKFfJWzMD7tHr2QDXs+IIuJqe6jR3UvZUMw/xcfcct0vpt+13vz/31d26VDaCAWrf2U70yJVb/GR+junXra/GiBXr6qd76ZPU6FS9e3NPlAcglHm/MW7Vqdd2fnDZu3GhhNcivHhg4y+39k+Pf0y8bX1TDWuX09bfxru31qpXRM4/dpSbdp+joF7HXPFfiX3/r9Jm/crVeAAXbogXz1LFzFz34UCdJ0n/Gx2jLlk1a+dGH6t33SQ9XB/w7JObmPP7wZ4MGDVS/fn3Xq1atWkpNTdW3336runXrero85FNBAb6SpD8TL7q2+fkW1vzYnhry4rLrNt6vjemiXza+qK8WDdfjD9yR67UCKFjSUlN1YP8+3RF5p2ubl5eX7rjjTn2/9zsPVgYgt3k8MZ82bdo1t0+YMEFJSUkWV4OCwOFw6OXhnbXtu3jtjz/p2j7l2U76Zm+CVm/6wfTYmFmrtfl/P+licqqiImto+phHFFDEqVnvb7aidAAFwJ/n/lR6enqmKSvFixdXQsIRD1UF5CACc1Meb8zN9OjRQ7fddpteeeWV645LSUlRSkqK2zYjI10OL+/cLA952Gtjuqh2ldK6u9f/+6GwfYu6anlbNd3R9cXrHvvi2+tcv94bd1xF/Jwa+ngUjTkAAPjXPD6Vxcz27dvl6+t7w3GxsbEKDg52e106zQoZuLZpox7Wvc3qqE3fGfr1t3Ou7S1vraZKZUvo1JaX9dfO6fpr53RJ0vuv9NFnbz9jer6dPxxV2bCi8ils259xAeQxRUOKytvbW2fOnHHbfubMGZUoUcJDVQE5h3XMzXm8m+jYsaPbe8MwdPLkSe3atUtjx4694fFjxozRsGHD3LaFNhuVozUif5g26mHdf1d9te47XT+fcP8P3ivzPte8j7e5bdu94jmNnPqh1mz+0fSc9aqX1dnEC0pNu5QrNQMoeAr7+Khmrdra8c123XV3lCQpIyNDO3ZsV9duPTxcHYDc5PHGPDg42O29l5eXqlevrokTJ6p169Y3PN7pdMrpdLptYxoLrvbamC56pF1jPTz0LSVdSFap4oGSpMSkZCWnpOn0mb+u+cDnLyf/dDXx9zavo9Digfrf90eVnJqmu++ooZG9W+u1hRss/SwA8r/Hontp7P+NUu3adVSnbj29t2iB/v77bz34UMcbHwzYXF5Ns63g0cY8PT1dvXr1Ut26dVW0aFFPloJ87qkuzSVJ698Z4ra977hFem/VjiydI+1Sup7q0lxTnu0kh8Oh+F9+16ipH+ndj7bd+GAAyIa27e7Vn2fPatbrM/THH7+reo2amvXmOyrOVBYgX3MYxvW+SiX3+fr66sCBA6pYsWKOndOv4cAcOxcA3Kw/d2b+plgAsJqvx+dHuKsyfK0l1zn8SjtLrpOTPP7wZ506dXTkCMs/AQAAoGDzeGP+wgsvaPjw4Vq9erVOnjyp8+fPu70AAACQf7AqizmP/ePGxIkT9eyzz+ree++VJN1///1uN9EwDDkcDqWnp3uqRAAAAMAyHmvMY2Ji1K9fP3355ZeeKgEAAAAWy6NhtiU81phfeea0RYsWnioBAAAAsA2PPqebV+f/AAAA4ObQ/5nzaGNerVq1G/7mnD171qJqAAAAAM/xaGMeExOT6Zs/AQAAkH8RmJvzaGPetWtXhYaGerIEAAAAwBY81pgzvwgAAKDg8fKiBzTjsS8YurIqCwAAAAAPJuYZGRmeujQAAAA8hEkT5jyWmAMAAAD4fzz68CcAAAAKFp4zNEdiDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGOebmSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWYY65ORJzAAAAwAZIzAEAAGAZAnNzJOYAAACADZCYAwAAwDLMMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMswx9wciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGeaYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzDHHNzJOYAAACADdCYAwAAADbAVBYAAABYhpks5kjMAQAAABsgMQcAAIBlePjTHIk5AAAAYAMk5gAAALAMibk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMswxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGOebmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGMOAAAAy3g5HJa8smPLli267777FB4eLofDoZUrV7rt79mzpxwOh9urbdu2bmPOnj2r7t27KygoSCEhIerdu7eSkpKyd2+yNRoAAADIZy5cuKD69evrjTfeMB3Ttm1bnTx50vV6//333fZ3795d+/bt0/r167V69Wpt2bJFTz75ZLbqYI45AAAALGPHOebt2rVTu3btrjvG6XQqLCzsmvsOHDigdevWaefOnWrcuLEkaebMmbr33nv1yiuvKDw8PEt1kJgDAAAg30lJSdH58+fdXikpKTd9vk2bNik0NFTVq1fX008/rTNnzrj2bd++XSEhIa6mXJKioqLk5eWlHTt2ZPkaNOYAAACwzNVztXPrFRsbq+DgYLdXbGzsTdXctm1bLVy4UBs2bNBLL72kzZs3q127dkpPT5cknTp1SqGhoW7HFCpUSMWKFdOpU6eyfB2msgAAACDfGTNmjIYNG+a2zel03tS5unbt6vp13bp1Va9ePVWuXFmbNm3S3Xff/a/q/CcacwAAAFjGy6I55k6n86Yb8RupVKmSSpQoocOHD+vuu+9WWFiYfvvtN7cxly5d0tmzZ03npV8LU1kAAACAbDh+/LjOnDmj0qVLS5IiIyN17tw57d692zVm48aNysjI0O23357l85KYAwAAwDIOGy7LkpSUpMOHD7veJyQkaM+ePSpWrJiKFSummJgYderUSWFhYYqPj9fIkSNVpUoVtWnTRpJUs2ZNtW3bVn379tWcOXOUlpamgQMHqmvXrllekUUiMQcAAEABt2vXLjVs2FANGzaUJA0bNkwNGzbUuHHj5O3tre+//17333+/qlWrpt69e6tRo0b66quv3KbKLF68WDVq1NDdd9+te++9V02bNtVbb72VrTpIzAEAAGAZGwbmatmypQzDMN3/2Wef3fAcxYoV05IlS/5VHSTmAAAAgA2QmAMAAMAyDtkwMrcJEnMAAADABkjMAQAAYBmr1jHPi0jMAQAAABsgMQcAAIBl7LiOuV2QmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACW8SIyN0ViDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANZCkx/+9//5vlE95///03XQwAAADyN/Jyc1lqzB988MEsnczhcCg9Pf3f1AMAAAAUSFlqzDMyMnK7DgAAABQArGNu7l/NMU9OTs6pOgAAAIACLduNeXp6up5//nmVKVNGAQEBOnLkiCRp7Nixmjt3bo4XCAAAgPzDy2HNKy/KdmM+adIkzZ8/X1OmTJGPj49re506dfTOO+/kaHEAAABAQZHtxnzhwoV666231L17d3l7e7u2169fXwcPHszR4gAAAJC/OBwOS155UbYb819//VVVqlTJtD0jI0NpaWk5UhQAAABQ0GS7Ma9Vq5a++uqrTNtXrFihhg0b5khRAAAAyJ8cDmteeVGWlkv8p3Hjxik6Olq//vqrMjIy9NFHHykuLk4LFy7U6tWrc6NGAAAAIN/LdmL+wAMPaNWqVfriiy/k7++vcePG6cCBA1q1apXuueee3KgRAAAA+QRzzM1lOzGXpGbNmmn9+vU5XQsAAABQYN1UYy5Ju3bt0oEDByRdnnfeqFGjHCsKAAAA+VNeXWPcCtluzI8fP65u3brp66+/VkhIiCTp3LlzuvPOO/XBBx+obNmyOV0jAAAAkO9le455nz59lJaWpgMHDujs2bM6e/asDhw4oIyMDPXp0yc3agQAAEA+wRxzc9lOzDdv3qxt27apevXqrm3Vq1fXzJkz1axZsxwtDgAAACgost2YlytX7ppfJJSenq7w8PAcKQoAAAD5U97Msq2R7aksL7/8sgYNGqRdu3a5tu3atUvPPPOMXnnllRwtDgAAACgospSYFy1a1G2uzoULF3T77berUKHLh1+6dEmFChXSE088oQcffDBXCgUAAEDe55VH539bIUuN+WuvvZbLZQAAAAAFW5Ya8+jo6NyuAwAAAAUAgbm5m/6CIUlKTk5Wamqq27agoKB/VRAAAABQEGW7Mb9w4YJGjRqlZcuW6cyZM5n2p6en50hhAAAAyH/y6hrjVsj2qiwjR47Uxo0bNXv2bDmdTr3zzjuKiYlReHi4Fi5cmBs1AgAAAPlethPzVatWaeHChWrZsqV69eqlZs2aqUqVKoqIiNDixYvVvXv33KgTAAAA+QCBublsJ+Znz55VpUqVJF2eT3727FlJUtOmTbVly5acrQ4AAAAoILLdmFeqVEkJCQmSpBo1amjZsmWSLifpISEhOVocAAAA8hcvh8OSV16U7ca8V69e2rt3ryRp9OjReuONN+Tr66uhQ4dqxIgROV4gAAAAUBBke4750KFDXb+OiorSwYMHtXv3blWpUkX16tXL0eIAAACQv+TRMNsS/2odc0mKiIhQRERETtQCAAAAFFhZasxnzJiR5RMOHjz4posBAABA/sY65uay1JhPmzYtSydzOBw05gAAAMBNyFJjfmUVlrzi9PasJ/wAkFt+P5/i6RIAQOWKOT1dgptsrzxSgHBvAAAAABv41w9/AgAAAFnFHHNzJOYAAACADZCYAwAAwDJeBOamSMwBAAAAG7ipxvyrr75Sjx49FBkZqV9//VWStGjRIm3dujVHiwMAAED+4uWw5pUXZbsx//DDD9WmTRv5+fnpu+++U0rK5eXAEhMTNXny5BwvEAAAACgIst2Yv/DCC5ozZ47efvttFS5c2LW9SZMm+vbbb3O0OAAAAOQvDofDkldelO3GPC4uTs2bN8+0PTg4WOfOncuJmgAAAIACJ9uNeVhYmA4fPpxp+9atW1WpUqUcKQoAAAD5E3PMzWW7Me/bt6+eeeYZ7dixQw6HQydOnNDixYs1fPhwPf3007lRIwAAAJDvZXsd89GjRysjI0N33323Ll68qObNm8vpdGr48OEaNGhQbtQIAACAfCKPTv+2hMMwDONmDkxNTdXhw4eVlJSkWrVqKSAgIKdru2nnkzM8XQIAKPFimqdLAACVK+b0dAluRq6Js+Q6U9pXt+Q6Oemmv/nTx8dHtWrVyslaAAAAkM95EZmbynZj3qpVq+suQbNx48Z/VRAAAABQEGW7MW/QoIHb+7S0NO3Zs0c//vijoqOjc6ouAAAAoEDJdmM+bdq0a26fMGGCkpKS/nVBAAAAyL+yvSRgAZJj96ZHjx569913c+p0AAAAQIFy0w9/Xm379u3y9fXNqdMBAAAgH+LZT3PZbsw7duzo9t4wDJ08eVK7du3S2LFjc6wwAAAAoCDJdmMeHBzs9t7Ly0vVq1fXxIkT1bp16xwrDAAAAPkPyyWay1Zjnp6erl69eqlu3boqWrRobtUEAAAAFDjZevjT29tbrVu31rlz53KpHAAAAORnDoc1r7wo26uy1KlTR0eOHMmNWgAAAIACK9uN+QsvvKDhw4dr9erVOnnypM6fP+/2AgAAAMx4Oax55UVZnmM+ceJEPfvss7r33nslSffff78c//h3AsMw5HA4lJ6envNVAgAAAPlclhvzmJgY9evXT19++WVu1gMAAIB8jFVZzGW5MTcMQ5LUokWLXCsGAAAAKKiytVyig59wAAAA8C/QTprLVmNerVq1GzbnZ8+e/VcFAQAAAAVRthrzmJiYTN/8CQAAAGRVXl0xxQrZasy7du2q0NDQ3KoFAAAAKLCy3JgzvxwAAAD/lkP0lGay/AVDV1ZlAQAAAJDzspyYZ2Rk5GYdAAAAKACYY24uy4k5AAAAgNyTrYc/AQAAgH+DxNwciTkAAABgAyTmAAAAsAwr/ZkjMQcAAABsgMQcAAAAlmGOuTkScwAAAMAGaMwBAABgGYfDmld2bNmyRffdd5/Cw8PlcDi0cuVKt/2GYWjcuHEqXbq0/Pz8FBUVpUOHDrmNOXv2rLp3766goCCFhISod+/eSkpKylYdNOYAAAAo0C5cuKD69evrjTfeuOb+KVOmaMaMGZozZ4527Nghf39/tWnTRsnJya4x3bt31759+7R+/XqtXr1aW7Zs0ZNPPpmtOhyGYRj/6pPY0PlkvqUUgOclXkzzdAkAoHLFnJ4uwc1rXyVYcp0hzSre1HEOh0Mff/yxHnzwQUmX0/Lw8HA9++yzGj58uCQpMTFRpUqV0vz589W1a1cdOHBAtWrV0s6dO9W4cWNJ0rp163Tvvffq+PHjCg8Pz9K1ScwBAAAAEwkJCTp16pSioqJc24KDg3X77bdr+/btkqTt27crJCTE1ZRLUlRUlLy8vLRjx44sX4tVWQAAAGAZq1ZlSUlJUUpKits2p9MppzN7/4Jw6tQpSVKpUqXctpcqVcq179SpUwoNDXXbX6hQIRUrVsw1JitIzAEAAJDvxMbGKjg42O0VGxvr6bKui8QcAAAAlrHqiz/HjBmjYcOGuW3LblouSWFhYZKk06dPq3Tp0q7tp0+fVoMGDVxjfvvtN7fjLl26pLNnz7qOzwoScwAAAOQ7TqdTQUFBbq+bacwrVqyosLAwbdiwwbXt/Pnz2rFjhyIjIyVJkZGROnfunHbv3u0as3HjRmVkZOj222/P8rVIzAEAAGAZL9nvqz+TkpJ0+PBh1/uEhATt2bNHxYoVU/ny5TVkyBC98MILqlq1qipWrKixY8cqPDzctXJLzZo11bZtW/Xt21dz5sxRWlqaBg4cqK5du2Z5RRaJxhwAAAAF3K5du9SqVSvX+ytTYKKjozV//nyNHDlSFy5c0JNPPqlz586padOmWrdunXx9fV3HLF68WAMHDtTdd98tLy8vderUSTNmzMhWHaxjDgC5hHXMAdiB3dYxn7XtqCXX6X9nBUuuk5OYYw4AAADYAFNZAAAAYBmr1jHPi0jMAQAAABsgMQcAAIBlvKxayDwPIjEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMMcc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlSIXNcW8AAAAAGyAxBwAAgGUcTDI3RWIOAAAA2ACJOQAAACxDXm6OxBwAAACwARJzAAAAWIZv/jRHYg4AAADYAIk5AAAALENebo7EHAAAALABEnMAAABYhinm5kjMAQAAABsgMQcAAIBl+OZPcyTmAAAAgA2QmAMAAMAypMLmuDcAAACADZCYAwAAwDLMMTdHYg4AAADYAIk5AAAALENebo7EHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlSIXNcW8AAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIa83ByJOQAAAGADJOYAAACwDFPMzZGYAwAAADZAYg4AAADLeDHL3BSJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYg4AAADLOJhjborEHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAMc8zNkZgDAAAANkBiDgAAAMuQmJsjMQcAAABsgMYcAAAAsAGmsgAAAMAyDpZLNEViDgAAANgAiTkAAAAs40VgborEHAAAALABEnMAAABYhjnm5myVmKempiouLk6XLl3ydCkAAACApWzRmF+8eFG9e/dWkSJFVLt2bR07dkySNGjQIL344oserg4AAAA5xeGw5pUX2aIxHzNmjPbu3atNmzbJ19fXtT0qKkpLly71YGUAAACANWwxx3zlypVaunSp7rjjDjn+8SNO7dq1FR8f78HKAAAAkJOYY27OFon577//rtDQ0EzbL1y44NaoAwAAAPmVLRrzxo0ba82aNa73V5rxd955R5GRkZ4qCwAAADnMy2HNKy+yxVSWyZMnq127dtq/f78uXbqk6dOna//+/dq2bZs2b97s6fIAAACAXGeLxLxp06bas2ePLl26pLp16+rzzz9XaGiotm/frkaNGnm6PAAAAOQQh0X/y4schmEYni4ip51PzvB0CQCgxItpni4BAFSumNPTJbj56qc/LblOs2pFLblOTrLFVJZvv/1WhQsXVt26dSVJn3zyiebNm6datWppwoQJ8vHx8XCFKAjS09P11uzXtW7NKp0584dKlAxVh/sfVO8nn+YhZAC5YsmCd7R18wb98nOCnE6natVtoL79h6hcREW3cft/2Kt335yhg/t+kJeXtypXq64Xp82R8x9LDAN5Bf9JNWeLqSxPPfWUfvrpJ0nSkSNH9Mgjj6hIkSJavny5Ro4c6eHqUFAsnPeOPlz+gUaM+Y+WfbxGg4Y8q0Xz52rpkvc8XRqAfOr773bpgU5dNfPt9/TS9Ld06dIljRrST3//fdE1Zv8PezV66NNqdNuden3uEr3x7hI90KmbHF62+E84gBxki8T8p59+UoMGDSRJy5cvV4sWLbRkyRJ9/fXX6tq1q1577TWP1oeC4fs936lFy7vUtHlLSVJ4mTL6bO0a7fvxB88WBiDfevG1OW7vR/7neXW+t6UOHdyveg0bS5JmTZ+ihx5+VN0e7+0ad3WiDuQlBObmbPHjtmEYysi4PC/8iy++0L333itJKleunP744w9PloYCpF6Dhtr5v2/089EESdJPcQe197tvdWfTZh6uDEBBcSEpSZIUGBQsSfrz7Bkd3PeDQooV0+C+j6nzvS017Ole+mHvt54sE0AusUVi3rhxY73wwguKiorS5s2bNXv2bElSQkKCSpUq5eHqUFBEP9FXSUlJevjB9vLy9lZGerqeHjRE7drf5+nSABQAGRkZmvXaFNWu11AVK1eVJJ08cVyStPCd2Xpq0LOqXLW61q9dpZGD+urtxR+pbLkIT5YM3BQvJpmbskVj/tprr6l79+5auXKlnnvuOVWpUkWStGLFCt15553XPTYlJUUpKSnu24zCcjrt9QQy7O+Lz9Zq3aer9ULsy6pUpap+OnhAr74cq5L//0OgAJCbZrwySUePHNZrb853bTMyLi+c1uHBzmrb4UFJUtXqNfXdrh1at2ql+vR/xgOVAsgttmjM69Wrpx9+yDyP9+WXX5a3t/d1j42NjVVMTIzbttHPjdOY/4zP0RqR/02f9oqin+ij1u3aS5KqVK2mkydPaP7ct2jMAeSqma9M1o6vt+jV2fNUMjTMtb1YiRKSpIiKld3Gl69QSb+dPmlpjUBOIS83Z4vG3IxvFpaBGjNmjIYNG+a2LcUonFslIR9LSf5bXletcuDl7S0jg3XxAeQOwzD0+tRYbd28UVNnzVXp8LJu+8NKl1HxEqH65eejbtuPH/tZt0U2sbBSAFbwWGNetGjRLK8NffbsWdN9Tqcz07QVvmAIN6Npi1aa9/abCgsrrUqVqyru4H4tWTRf9z/Q0dOlAcinZrwySRs/X6uJL01XkSL+Onvm8oIH/v4Bcvr6yuFwqEv3aC14Z7YqV62mylVr6PNP/6tffk7Q+MlTPVw9cJOIzE157Js/FyxYkOWx0dHR2To3jTluxoULFzTnjenatPEL/Xn2rEqUDFWbdveqz1P9VbgwX3KF7OObP3EjUZH1rrl9xH+eV5v2D7jev79wrv774Qf663yiKlWprr4Dh6pu/VusKhN5nN2++fOb+HOWXOeOyiGWXCcneawxz0005gDsgMYcgB3YrTHfEZ9oyXVurxxsyXVyku3mmCcnJys1NdVtW1BQkIeqAQAAAKxhiy8YunDhggYOHKjQ0FD5+/uraNGibi8AAADkDw6HNa+8yBaN+ciRI7Vx40bNnj1bTqdT77zzjmJiYhQeHq6FCxd6ujwAAAAg19liKsuqVau0cOFCtWzZUr169VKzZs1UpUoVRUREaPHixerevbunSwQAAEAOyKNhtiVskZifPXtWlSpVknR5PvmV5RGbNm2qLVu2eLI0AAAAwBK2aMwrVaqkhIQESVKNGjW0bNkySZeT9JCQEA9WBgAAgBzlsOiVB3m0MT9y5IgyMjLUq1cv7d27V5I0evRovfHGG/L19dXQoUM1YsQIT5YIAAAAWMKj65h7e3vr5MmTCg0NlSQ98sgjmjFjhpKTk7V7925VqVJF9epd+8sXrod1zAHYAeuYA7ADu61jvivhvCXXaVwx7y237dHE/OqfCT799FNduHBBERER6tix40015QAAAEBeZItVWQAAAFAw5NU1xq3g0cTc4XDIcdXvztXvAQAAgILAo4m5YRjq2bOnnM7Lc5+Sk5PVr18/+fv7u4376KOPPFEeAAAAchgRrDmPNubR0dFu73v06OGhSgAAAADP8mhjPm/ePE9eHgAAAFYjMjdliy8YAgAAADxlwoQJrmcfr7xq1Kjh2p+cnKwBAwaoePHiCggIUKdOnXT69Okcr4PGHAAAAJZxWPS/7Kpdu7ZOnjzpem3dutW1b+jQoVq1apWWL1+uzZs368SJE+rYsWNO3hZJLJcIAAAAqFChQgoLC8u0PTExUXPnztWSJUt01113Sbo8HbtmzZr65ptvdMcdd+RYDSTmAAAAsIzDYc0rJSVF58+fd3ulpKSY1nXo0CGFh4erUqVK6t69u44dOyZJ2r17t9LS0hQVFeUaW6NGDZUvX17bt2/P0XtDYw4AAIB8JzY2VsHBwW6v2NjYa469/fbbNX/+fK1bt06zZ89WQkKCmjVrpr/++kunTp2Sj4+PQkJC3I4pVaqUTp06laM1M5UFAAAAlrFqUZYxY8Zo2LBhbtuufHfO1dq1a+f6db169XT77bcrIiJCy5Ytk5+fX67W+U8k5gAAAMh3nE6ngoKC3F5mjfnVQkJCVK1aNR0+fFhhYWFKTU3VuXPn3MacPn36mnPS/w0acwAAAFjHYdHrX0hKSlJ8fLxKly6tRo0aqXDhwtqwYYNrf1xcnI4dO6bIyMh/d6GrMJUFAAAABdrw4cN13333KSIiQidOnND48ePl7e2tbt26KTg4WL1799awYcNUrFgxBQUFadCgQYqMjMzRFVkkGnMAAABY6GbWGM9tx48fV7du3XTmzBmVLFlSTZs21TfffKOSJUtKkqZNmyYvLy916tRJKSkpatOmjWbNmpXjdTgMwzBy/Kwedj45w9MlAIASL6Z5ugQAULliWZtXbZXvf0my5Dr1ygVYcp2cRGIOAAAAyzjsF5jbBg9/AgAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAANYhMjdFYg4AAADYAIk5AAAALGPHdcztgsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAA1iEyN0ViDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYB0ic1Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAANYhMjdFYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAdInNTJOYAAACADZCYAwAAwDKsY26OxBwAAACwARJzAAAAWIZ1zM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxhwAAACwAaayAAAAwDI8/GmOxBwAAACwARJzAAAAWIjI3AyJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhjrk5EnMAAADABkjMAQAAYBkHs8xNkZgDAAAANkBiDgAAAOsQmJsiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAOgbkpEnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLMMfcHIk5AAAAYAM05gAAAIAN0JgDAAAANsAccwAAAFiGOebmSMwBAAAAGyAxBwAAgGVYx9wciTkAAABgAyTmAAAAsAxzzM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAADWITI3RWIOAAAA2ACJOQAAACzDOubmSMwBAAAAGyAxBwAAgGVYx9wciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA3QmAMAAMAyDov+l11vvPGGKlSoIF9fX91+++363//+lwuf/vpozAEAAFCgLV26VMOGDdP48eP17bffqn79+mrTpo1+++03S+twGIZhWHpFC5xPzvB0CQCgxItpni4BAFSumNPTJbhJvmTNdXyz8STl7bffrltvvVWvv/66JCkjI0PlypXToEGDNHr06FyqMDMScwAAAOQ7KSkpOn/+vNsrJSUl07jU1FTt3r1bUVFRrm1eXl6KiorS9u3brSw5f67KEuTLzxv4d1JSUhQbG6sxY8bI6bRX0oC8I8iXPzu4efw9hPwqO0n2vzHhhVjFxMS4bRs/frwmTJjgtu2PP/5Qenq6SpUq5ba9VKlSOnjwYG6X6SZfTmUB/q3z588rODhYiYmJCgoK8nQ5AAog/h4C/p2UlJRMCbnT6cz0g+6JEydUpkwZbdu2TZGRka7tI0eO1ObNm7Vjxw5L6pXyaWIOAACAgu1aTfi1lChRQt7e3jp9+rTb9tOnTyssLCy3yrsm5nwAAACgwPLx8VGjRo20YcMG17aMjAxt2LDBLUG3Aok5AAAACrRhw4YpOjpajRs31m233abXXntNFy5cUK9evSytg8YcuAan06nx48fzwBUAj+HvIcA6jzzyiH7//XeNGzdOp06dUoMGDbRu3bpMD4TmNh7+BAAAAGyAOeYAAACADdCYAwAAADZAYw4AAADYAI05AAA2M3/+fIWEhHi6DAAWozFHvuZwOK77uvpreQEgJ/Xs2fOaf/ccPnzY06UBsCGWS0S+dvLkSdevly5dqnHjxikuLs61LSAgwPVrwzCUnp6uQoX4vwWAnNO2bVvNmzfPbVvJkiU9VA0AOyMxR74WFhbmegUHB8vhcLjeHzx4UIGBgVq7dq0aNWokp9OprVu3qmfPnnrwwQfdzjNkyBC1bNnS9T4jI0OxsbGqWLGi/Pz8VL9+fa1YscLaDwcgT3A6nW5/F4WFhWn69OmqW7eu/P39Va5cOfXv319JSUmm59i7d69atWqlwMBABQUFqVGjRtq1a5dr/9atW9WsWTP5+fmpXLlyGjx4sC5cuGDFxwOQg2jMUeCNHj1aL774og4cOKB69epl6ZjY2FgtXLhQc+bM0b59+zR06FD16NFDmzdvzuVqAeQHXl5emjFjhvbt26cFCxZo48aNGjlypOn47t27q2zZstq5c6d2796t0aNHq3DhwpKk+Ph4tW3bVp06ddL333+vpUuXauvWrRo4cKBVHwdADuHf7FHgTZw4Uffcc0+Wx6ekpGjy5Mn64osvFBkZKUmqVKmStm7dqjfffFMtWrTIrVIB5EGrV692mzbXrl07LV++3PW+QoUKeuGFF9SvXz/NmjXrmuc4duyYRowYoRo1akiSqlat6toXGxur7t27a8iQIa59M2bMUIsWLTR79mz5+vrmwqcCkBtozFHgNW7cOFvjDx8+rIsXL2Zq5lNTU9WwYcOcLA1APtCqVSvNnj3b9d7f319ffPGFYmNjdfDgQZ0/f16XLl1ScnKyLl68qCJFimQ6x7Bhw9SnTx8tWrRIUVFRevjhh1W5cmVJl6e5fP/991q8eLFrvGEYysjIUEJCgmrWrJn7HxJAjqAxR4Hn7+/v9t7Ly0uGYbhtS0tLc/36yjzQNWvWqEyZMm7jnE5nLlUJIK/y9/dXlSpVXO+PHj2qDh066Omnn9akSZNUrFgxbd26Vb1791Zqauo1G/MJEybo0Ucf1Zo1a7R27VqNHz9eH3zwgR566CElJSXpqaee0uDBgzMdV758+Vz9bAByFo05cJWSJUvqxx9/dNu2Z88e13zOWrVqyel06tixY0xbAZBtu3fvVkZGhqZOnSovr8uPei1btuyGx1WrVk3VqlXT0KFD1a1bN82bN08PPfSQbrnlFu3fv9+t+QeQN/HwJ3CVu+66S7t27dLChQt16NAhjR8/3q1RDwwM1PDhwzV06FAtWLBA8fHx+vbbbzVz5kwtWLDAg5UDyAuqVKmitLQ0zZw5U0eOHNGiRYs0Z84c0/F///23Bg4cqE2bNunnn3/W119/rZ07d7qmqIwaNUrbtm3TwIEDtWfPHh06dEiffPIJD38CeRCNOXCVNm3aaOzYsRo5cqRuvfVW/fXXX3r88cfdxjz//PMaO3asYmNjVbNmTbVt21Zr1qxRxYoVPVQ1gLyifv36evXVV/XSSy+pTp06Wrx4sWJjY03He3t768yZM3r88cdVrVo1denSRe3atVNMTIwkqV69etq8ebN++uknNWvWTA0bNtS4ceMUHh5u1UcCkEMcxtWTaQEAAABYjsQcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHUGD17NlTDz74oOt9y5YtNWTIEMvr2LRpkxwOh86dO2c6xuFwaOXKlVk+54QJE9SgQYN/VdfRo0flcDi0Z8+ef3UeAEDW0JgDsJWePXvK4XDI4XDIx8dHVapU0cSJE3Xp0qVcv/ZHH32k559/Pktjs9JMAwCQHYU8XQAAXK1t27aaN2+eUlJS9Omnn2rAgAEqXLiwxowZk2lsamqqfHx8cuS6xYoVy5HzAABwM0jMAdiO0+lUWFiYIiIi9PTTTysqKkr//e9/Jf2/6SeTJk1SeHi4qlevLkn65Zdf1KVLF4WEhKhYsWJ64IEHdPToUdc509PTNWzYMIWEhKh48eIaOXKkDMNwu+7VU1lSUlI0atQolStXTk6nU1WqVNHcuXN19OhRtWrVSpJUtGhRORwO9ezZU5KUkZGh2NhYVaxYUX5+fqpfv75WrFjhdp1PP/1U1apVk5+fn1q1auVWZ1aNGjVK1apVU5EiRVSpUiWNHTtWaWlpmca9+eabKleunIoUKaIuXbooMTHRbf8777yjmjVrytfXVzVq1NCsWbOyXQsAIGfQmAOwPT8/P6Wmprreb9iwQXFxcVq/fr1Wr16ttLQ0tWnTRoGBgfrqq6/09ddfKyAgQG3btnUdN3XqVM2fP1/vvvuutm7dqrNnz+rjjz++7nUff/xxvf/++5oxY4YOHDigN998UwEBASpXrpw+/PBDSVJcXJxOnjyp6dOnS5JiY2O1cOFCzZkzR/v27dPQoUPVo0cPbd68WdLlHyA6duyo++67T3v27FGfPn00evTobN+TwMBAzZ8/X/v379f06dP19ttva9q0aW5jDh8+rGXLlmnVqlVat26dvvvuO/Xv39+1f/HixRo3bpwmTZqkAwcOaPLkyRo7dqwWLFiQ7XoAADnAAAAbiY6ONh544AHDMAwjIyPDWL9+veF0Oo3hw4e79pcqVcpISUlxHbNo0SKjevXqRkZGhmtbSkqK4efnZ3z22WeGYRhG6dKljSlTprj2p6WlGWXLlnVdyzAMo0WLFsYzzzxjGIZhxMXFGZKM9evXX7POL7/80pBk/Pnnn65tycnJRpEiRYxt27a5je3du7fRrVs3wzAMY8yYMUatWrXc9o8aNSrTua4myfj4449N97/88stGo0aNXO/Hjx9veHt7G8ePH3dtW7t2reHl5WWcPHnSMAzDqFy5srFkyRK38zz//PNGZGSkYRiGkZCQYEgyvvvuO9PrAgByDnPMAdjO6tWrFRAQoLS0NGVkZOjRRx/VhAkTXPvr1q3rNq987969Onz4sAIDA93Ok5ycrPj4eCUmJurkyZO6/fbbXfsKFSqkxo0bZ5rOcsWePXvk7e2tFi1aZLnuw4cP6+LFi7rnnnvctqempqphw4aSpAMHDrjVIUmRkZFZvsYVS5cu1YwZMxQfH6+kpCRdunRJQUFBbmPKly+vMmXKuF0nIyNDcXFxCgwMVHx8vHr37q2+ffu6xly6dEnBwcHZrgcA8O/RmAOwnVatWmn27Nny8fFReHi4ChVy/6vK39/f7X1SUpIaNWqkxYsXZzpXyZIlb6oGPz+/bB+TlJQkSVqzZo1bQyxdnjefU7Zv367u3bsrJiZGbdq0UXBwsD744ANNnTo127W+/fbbmX5Q8Pb2zrFaAQBZR2MOwHb8/f1VpUqVLI+/5ZZbtHTpUoWGhmZKja8oXbq0duzYoebNm0u6nAzv3r1bt9xyyzXH161bVxkZGdq8ebOioqIy7b+S2Kenp7u21apVS06nU8eOHTNN2mvWrOl6kPWKb7755sYf8h+2bdumiIgIPffcc65tP//8c6Zxx44d04kTJxQeHu66jpeXl6pXr65SpUopPDxcR44cUffu3bN1fQBA7uDhTwB5Xvfu3VWiRAk98MAD+uqrr5SQkKBNmzZp8ODBOn78uCTpmWee0YsvvqiVK1fq4MGD6t+//3XXIK9QoYKio6P1xBNPaOXKla5zLlu2TJIUEREhh8Oh1atX6/fff1dSUpICAwM1fPhwDR06VAsWLFB8fLy+/fZbzZw50/VAZb9+/XTo0CGNGDFCcXFxWrJkiebPn5+tz1u1alUdO3ZMH3zwgeLj4zVjxoxrPsjq6+ur6Oho7d27V1999ZUGDx6sLl26KCwsTJIUExOj2NhYzZgxQz/99JN++OEHzZs3T6+++mq26gEA5AwacwB5XpEiRbRlyxaVL19eHTt2VM2aNdW7d28lJye7EvRnn31Wjz32mKKjoxUZGanAwEA99NBD1z3v7Nmz1blzZ/Xv3181atRQ3759deHCBUlSmTJlFBMTo9GjR6tUqVIaOHCgJOn555/X2LFjFRsbq5o1a6pt27Zas2aNKlasKOnyvO8PP/xQK1euVP369TVnzhxNnjw5W5/3/vvv19ChQzVw4EA1aNBA27Zt09ixYzONq1Klijp27Kh7771XrVu3Vr169dyWQ+zTp4/eeecdzZs3T3Xr1lWLFi00f/58V60AAGs5DLMnnwAAAABYhsQcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzoICqUKGCevbs6eky8p1169apQYMG8vX1lcPh0Llz53L0/PPnz5fD4dDRo0dz9Lx5mcPh0IQJEzxdBgD8azTmQD4THx+vp556SpUqVZKvr6+CgoLUpEkTTZ8+XX///beny8uWSZMmyeFwqE6dOtk6btOmTerYsaPCwsLk4+Oj0NBQ3Xffffroo49yqdLLzpw5oy5dusjPz09vvPGGFi1aJH9//1y9ppUqVKggh8OhqKioa+5/++235XA45HA4tGvXrmyff9u2bZowYUKO/zADAHlFIU8XACDnrFmzRg8//LCcTqcef/xx1alTR6mpqdq6datGjBihffv26a233vJ0mVly/PhxTZ48OduN7fjx4zVx4kRVrVpVTz31lCIiInTmzBl9+umn6tSpkxYvXqxHH300V2reuXOn/vrrLz3//POmzeu/9dhjj6lr165yOp25cv4b8fX11ZdffqlTp04pLCzMbd/ixYvl6+ur5OTkmzr3tm3bFBMTo549eyokJCTLx/39998qVIj/nAHI+/ibDMgnEhIS1LVrV0VERGjjxo0qXbq0a9+AAQN0+PBhrVmzxoMVZs/w4cN1xx13KD09XX/88UeWjlmxYoUmTpyozp07a8mSJSpcuLBr34gRI/TZZ58pLS0tt0rWb7/9JknZaiqzy9vbW97e3rl2/htp0qSJdu7cqaVLl+qZZ55xbT9+/Li++uorPfTQQ/rwww9zvY6MjAylpqbK19dXvr6+uX49ALACU1mAfGLKlClKSkrS3Llz3ZryK6pUqeLWSF3t7NmzGj58uOrWrauAgAAFBQWpXbt22rt3b6axM2fOVO3atVWkSBEVLVpUjRs31pIlS1z7//rrLw0ZMkQVKlSQ0+lUaGio7rnnHn377bdZ+ixbtmzRihUr9Nprr2Vp/BVjx45VsWLF9O6777o15Ve0adNGHTp0cL3/7bff1Lt3b5UqVUq+vr6qX7++FixY4HbM0aNH5XA49Morr+itt95S5cqV5XQ6deutt2rnzp2ucS1btlR0dLQk6dZbb5XD4XDN4Tebz9+yZUu1bNnSbduN7q3ZHPNZs2apdu3acjqdCg8P14ABAzJNCWnZsqXq1Kmj/fv3q1WrVipSpIjKlCmjKVOmmN3STHx9fdWxY0e3miTp/fffV9GiRdWmTZtMx3z//ffq2bOna3pVWFiYnnjiCZ05c8Y1ZsKECRoxYoQkqWLFiq4pMVc+p8Ph0MCBA7V48WLX51y3bp1r35U55n///bdq1KihGjVquE3dOnv2rEqXLq0777xT6enpWf68AGAlEnMgn1i1apUqVaqkO++886aOP3LkiFauXKmHH35YFStW1OnTp/Xmm2+qRYsW2r9/v8LDwyVdnkc8ePBgde7cWc8884ySk5P1/fffa8eOHa4pIv369dOKFSs0cOBA1apVS2fOnNHWrVt14MAB3XLLLdetIz09XYMGDVKfPn1Ut27dLNd/6NAhHTx4UE888YQCAwNvOP7vv/9Wy5YtdfjwYQ0cOFAVK1bU8uXL1bNnT507dy7TDzFLlizRX3/9paeeekoOh0NTpkxRx44ddeTIERUuXFjPPfecqlevrrfeeksTJ05UxYoVVbly5SzXL2Xt3l7LhAkTFBMTo6ioKD399NOKi4vT7NmztXPnTn399dduP6T8+eefatu2rTp27KguXbpoxYoVGjVqlOrWrat27dplqc5HH31UrVu3Vnx8vOszLlmyRJ07d77mD0Tr16/XkSNH1KtXL4WFhbmmVO3bt0/ffPONHA6HOnbsqJ9++knvv/++pk2bphIlSkiSSpYs6TrPxo0btWzZMg0cOFAlSpRQhQoVMl3Lz89PCxYsUJMmTfTcc8/p1VdflXT5X40SExM1f/58j/6LAwBclwEgz0tMTDQkGQ888ECWj4mIiDCio6Nd75OTk4309HS3MQkJCYbT6TQmTpzo2vbAAw8YtWvXvu65g4ODjQEDBmS5ln96/fXXjeDgYOO3334zDMMwWrRoccPrGYZhfPLJJ4YkY9q0aVm6zmuvvWZIMt577z3XttTUVCMyMtIICAgwzp8/bxjG5XsgyShevLhx9uzZTNdbtWqVa9u8efMMScbOnTvdrnX1vb6iRYsWRosWLVzvs3Jvr1wjISHBMAzD+O233wwfHx+jdevWbr9/r7/+uiHJePfdd92uJ8lYuHCha1tKSooRFhZmdOrU6brXvfI52rdvb1y6dMkICwsznn/+ecMwDGP//v2GJGPz5s3XvAcXL17MdK7333/fkGRs2bLFte3ll192+2z/JMnw8vIy9u3bd81948ePd9s2ZswYw8vLy9iyZYuxfPlyQ5Lx2muv3fAzAoAnMZUFyAfOnz8vSVlKis04nU55eV3+KyE9PV1nzpxRQECAqlev7jYFJSQkRMePH3ebxnG1kJAQ7dixQydOnMhWDWfOnNG4ceM0duxYt6Q0K7J7Dz799FOFhYWpW7durm2FCxfW4MGDlZSUpM2bN7uNf+SRR1S0aFHX+2bNmkm6/C8NOSUr9/ZqX3zxhVJTUzVkyBDX758k9e3bV0FBQZmeKwgICFCPHj1c7318fHTbbbdl63N4e3urS5cuev/99yVdfuizXLlyrntyNT8/P9evk5OT9ccff+iOO+6QpCxPb5KkFi1aqFatWlkaO2HCBNWuXVvR0dHq37+/WrRoocGDB2f5WgDgCTTmQD4QFBQk6fLc7puVkZGhadOmqWrVqnI6nSpRooRKliyp77//XomJia5xo0aNUkBAgG677TZVrVpVAwYM0Ndff+12rilTpujHH39UuXLldNttt2nChAlZavz+85//qFixYho0aFC268/uPfj5559VtWpVt2ZWkmrWrOna/0/ly5d3e3+lSf/zzz+zXauZrNzbq12ps3r16m7bfXx8VKlSpUyfo2zZsnI4HG7bihYtmu3P8eijj2r//v3au3evlixZoq5du2Y67xVnz57VM888o1KlSsnPz08lS5ZUxYoVJcntz9aNXDkmK3x8fPTuu+8qISFBf/31l+bNm2daHwDYBY05kA8EBQUpPDxcP/74402fY/LkyRo2bJiaN2+u9957T5999pnWr1+v2rVrKyMjwzWuZs2aiouL0wcffKCmTZvqww8/VNOmTTV+/HjXmC5duujIkSOaOXOmwsPD9fLLL6t27dpau3at6fUPHTqkt956S4MHD9aJEyd09OhRHT16VMnJyUpLS9PRo0d19uxZ0+Nr1KghSfrhhx9u+h5cj9m8ZMMwbnisWUN49UOIWbm3/9a/+Rz/dPvtt6ty5coaMmSIEhISrjsHvkuXLnr77bfVr18/ffTRR/r8889dD27+88/Wjfwzec+Kzz77TNLllP7QoUPZOhYAPIHGHMgnOnTooPj4eG3fvv2mjl+xYoVatWqluXPnqmvXrmrdurWioqKu+WUv/v7+euSRRzRv3jwdO3ZM7du316RJk9zWry5durT69++vlStXKiEhQcWLF9ekSZNMr//rr78qIyNDgwcPVsWKFV2vHTt26KefflLFihU1ceJE0+OrVaum6tWr65NPPlFSUtINP29ERIQOHTqUqTE8ePCga39OKVq06DXv49VptpS1e/tPV+qMi4tz256amqqEhIQc/RxX69atmzZt2qSaNWuqQYMG1xzz559/asOGDRo9erRiYmL00EMP6Z577lGlSpUyjc3JRPv777/XxIkT1atXLzVs2FB9+vTJVjoPAJ5AYw7kEyNHjpS/v7/69Omj06dPZ9ofHx+v6dOnmx7v7e2dKTVdvny5fv31V7dt/1ziTro8ZaBWrVoyDENpaWlKT0/P1ACFhoYqPDxcKSkpptevU6eOPv7440yv2rVrq3z58vr444/Vu3dv0+MlKSYmRmfOnFGfPn106dKlTPs///xzrV69WpJ077336tSpU1q6dKlr/6VLlzRz5kwFBASoRYsW171WdlSuXFnffPONUlNTXdtWr16tX375xW3cje7ttURFRcnHx0czZsxw+/2bO3euEhMT1b59+xz7HFfr06ePxo8fr6lTp5qOuZLQX/1n61pLYV75Mql/+82faWlp6tmzp8LDwzV9+nTNnz9fp0+f1tChQ//VeQEgt7FcIpBPVK5cWUuWLNEjjzyimjVrun3z57Zt21xLAZrp0KGDK2G888479cMPP2jx4sWZks3WrVsrLCxMTZo0UalSpXTgwAG9/vrrat++vQIDA3Xu3DmVLVtWnTt3Vv369RUQEKAvvvhCO3fuvG4DV6JECT344IOZtl9p4K6172qPPPKIfvjhB02aNEnfffedunXr5vrmz3Xr1mnDhg2u9beffPJJvfnmm+rZs6d2796tChUqaMWKFfr666/12muv/asHaa/Wp08frVixQm3btlWXLl0UHx+v9957L9Nyije6t9dSsmRJjRkzRjExMWrbtq3uv/9+xcXFadasWbr11lvdHvTMaREREa71w80EBQWpefPmmjJlitLS0lSmTBl9/vnnSkhIyDS2UaNGkqTnnntOXbt2VeHChXXfffdl+9tfX3jhBe3Zs0cbNmxQYGCg6tWrp3Hjxuk///mPOnfurHvvvTdb5wMAy3hwRRgAueCnn34y+vbta1SoUMHw8fExAgMDjSZNmhgzZ840kpOTXeOutVzis88+a5QuXdrw8/MzmjRpYmzfvj3Tkn5vvvmm0bx5c6N48eKG0+k0KleubIwYMcJITEw0DOPy8nsjRoww6tevbwQGBhr+/v5G/fr1jVmzZt3U58nqcon/tGHDBuOBBx4wQkNDjUKFChklS5Y07rvvPuOTTz5xG3f69GmjV69eRokSJQwfHx+jbt26xrx589zGXFku8eWXX850HV21TJ/ZcomGYRhTp041ypQpYzidTqNJkybGrl27sn1v/3mNq5cUfP31140aNWoYhQsXNkqVKmU8/fTTxp9//uk2xuxeRkdHGxEREZm2X+3KconXc617cPz4ceOhhx4yQkJCjODgYOPhhx82Tpw4cc1lDp9//nmjTJkyhpeXl9vnlGS6BOc/z7N7926jUKFCxqBBg9zGXLp0ybj11luN8PDwTPcFAOzCYRjZfOIHAAAAQI5jjjkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYQCFPF5Ab/BoO9HQJAKA/d77u6RIAQL426/as6tP+/i7v/R1MYg4AAADYgM1+hgIAAEC+5iAXNsOdAQAAAGyAxBwAAADWcTg8XYFtkZgDAAAANkBiDgAAAOswx9wUdwYAAACwARJzAAAAWIc55qZIzAEAAAAbIDEHAACAdZhjboo7AwAAANgAiTkAAACswxxzUyTmAAAAgA2QmAMAAMA6zDE3xZ0BAAAAbIDEHAAAANZhjrkpEnMAAADABkjMAQAAYB3mmJvizgAAAAA2QGIOAAAA6zDH3BSJOQAAAGADJOYAAACwDnPMTXFnAAAAABsgMQcAAIB1mGNuisQcAAAAsAEScwAAAFiHOeamuDMAAACADZCYAwAAwDok5qa4MwAAAIANkJgDAADAOl6symKGxBwAAACwARJzAAAAWIc55qa4MwAAAIANkJgDAADAOnzzpykScwAAAMAGSMwBAABgHeaYm+LOAAAAADZAYg4AAADrMMfcFIk5AAAAYAMk5gAAALAOc8xNcWcAAAAAGyAxBwAAgHWYY26KxBwAAACwARJzAAAAWIc55qa4MwAAAIANkJgDAADAOswxN0ViDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA6JuSnuDAAAAGADJOYAAACwDquymCIxBwAAAGyAxBwAAADWYY65Ke4MAAAAYAMk5gAAALAOc8xNkZgDAAAANkBiDgAAAOswx9wUdwYAAACwARJzAAAAWIc55qZIzAEAAAAbIDEHAACAZRwk5qZIzAEAAFCgxcbG6tZbb1VgYKBCQ0P14IMPKi4uzm1McnKyBgwYoOLFiysgIECdOnXS6dOn3cYcO3ZM7du3V5EiRRQaGqoRI0bo0qVLWa6DxhwAAACWcTgclryyY/PmzRowYIC++eYbrV+/XmlpaWrdurUuXLjgGjN06FCtWrVKy5cv1+bNm3XixAl17NjRtT89PV3t27dXamqqtm3bpgULFmj+/PkaN25c1u+NYRhGtirPA/waDvR0CQCgP3e+7ukSAEC+Npu47N95niXXubCi100f+/vvvys0NFSbN29W8+bNlZiYqJIlS2rJkiXq3LmzJOngwYOqWbOmtm/frjvuuENr165Vhw4ddOLECZUqVUqSNGfOHI0aNUq///67fHx8bnhdEnMAAABYx2HNKyUlRefPn3d7paSkZKnExMRESVKxYsUkSbt371ZaWpqioqJcY2rUqKHy5ctr+/btkqTt27erbt26rqZcktq0aaPz589r3759WboujTkAAADyndjYWAUHB7u9YmNjb3hcRkaGhgwZoiZNmqhOnTqSpFOnTsnHx0chISFuY0uVKqVTp065xvyzKb+y/8q+rLDZP24AAAAgP7NqVZYxY8Zo2LBhbtucTucNjxswYIB+/PFHbd26NbdKM0VjDgAAgHzH6XRmqRH/p4EDB2r16tXasmWLypYt69oeFham1NRUnTt3zi01P336tMLCwlxj/ve//7md78qqLVfG3AhTWQAAAFCgGYahgQMH6uOPP9bGjRtVsWJFt/2NGjVS4cKFtWHDBte2uLg4HTt2TJGRkf8fe/ce33P9/3/8/t7Ye5htTttMWg45zPlDsZyznCqJElFIKqEix8phUvt8qI9TolKkKFLpQ6WPKIcsH4eoHJaxPj5ixJiGnV+/P/y8v72bFxvvvd6vbbdrl9fl4v16v96v1+P9Vnrs7vF6viVJUVFR+umnn3TixAnXMWvXrlVgYKAiIyPzVAeJOQAAACxjxy8YGjp0qJYuXarPPvtMZcuWdc2EBwUFqVSpUgoKCtKgQYM0cuRIlS9fXoGBgRo+fLiioqLUokULSVLHjh0VGRmphx56SNOmTVNSUpJeeOEFDR06NM/JPY05AAAAirV58+ZJktq1a+e2f+HChRowYIAkacaMGfLx8VHPnj2Vnp6uTp066fXXX3cd6+vrq9WrV2vIkCGKiopSmTJl1L9/f02ZMiXPdbCOOQAUENYxB2AHdlvHPLD3Ykuuc/bDhy25jicxYw4AAADYgM1+hgIAAEBRZscZc7sgMQcAAABsgMQcAAAA1iEwN0ViDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZEnNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADrEJibIjEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgGRJzcyTmAAAAgA2QmAMAAMA6BOamSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYhsTcHIk5AAAAYAMk5gAAALAMibk5EnMAAADABkjMAQAAYB0Cc1Mk5gAAAIANkJgDAADAMsyYmyMxBwAAAGyAxBwAAACWITE3R2IOAAAA2ACJOQAAACxDYm7OFon5pk2b1K9fP0VFRem3336TJL333nvavHmzlysDAAAArOH1xvzjjz9Wp06dVKpUKf3www9KT0+XJKWkpOjll1/2cnUAAADwKIdFWyHk9cZ86tSpmj9/vt566y2VLFnStb9ly5bauXOnFysDAAAArOP1GfP4+Hi1adMm1/6goCCdOXPG+oIAAABQYJgxN+f1xDwsLEwJCQm59m/evFnVq1f3QkUAAACA9bzemA8ePFhPP/20tm7dKofDoaNHj2rJkiUaNWqUhgwZ4u3yAAAA4EEOh8OSrTDy+ijLuHHjlJOTow4dOuj8+fNq06aNnE6nRo0apeHDh3u7PAAAAMASXm/MHQ6Hnn/+eY0ePVoJCQlKTU1VZGSkAgICvF0aipBRj3RU99sbqdZNobqQnqmtuw/p+Vmf6cB/T7iOmfN8b93evLYqVwpS6oV0fb87US/M+ky//HpcktSgVhWNGniHbmtcQxWCy+i/R5O1YMVmzf3gWy+9KwBF2YdLl+jdhW/r5MnfVat2HY17boIaNGzo7bKA61ZY02wreL0xv8TPz0+RkZHeLgNFVOu/1dT8ZRu1Y89/VaKEr2KG3a3V84apSY+pOp+WIUn6Yd//9OGX2/S/Y6dVPqi0nn/iTq1+fajq3DVJOTmGmtStqt+T/9DAF97VkaTTatGouua+0EfZOTmav2yjl98hgKJkzZdf6JVpsXphUowaNGikJe+9qyGPD9Jnq9eoQoUK3i4PQAFxGIZheLOA9u3bX/Enp/Xr1+f7nKWaDLueklAMVCwXoP+t/7uiB83QdzsPXvaY+jeHa9vy5xR592QlHjl52WNmjOulOtVC1eXxOQVZLgqp09te83YJKKT69r5f9eo30HMvTJQk5eTkqGOHturz4EMaNPgxL1eHwsbfNjHsRdWe+dyS6yTOvNOS63iS13+rGjdu7PY4MzNTu3bt0s8//6z+/ft7pygUeYEB/pKk0ynnL/t8aX8/PdythRKPnNSRpNOm5wkK8Nfps5c/BwBci8yMDO3bu0eDBj/u2ufj46MWLW7Tj7t/8GJlAAqa1xvzGTNmXHb/5MmTlZqaanE1KA4cDoemj7pPW344qL0Hj7k999j9rfXSM90VUNqp+MQk3TnkNWVmZV/2PC0aVdN9HZvq3qfmWVE2gGLi9JnTys7OzjWyUqFCBSUmHvJSVYAHMWJuyuvLJZrp16+f3nnnnasel56errNnz7ptRs7lGylAkmaO76V6NSvr4XELcz334Zfb1KLPxRGXA4d/1/v/eEROv9w/v0bWqKzlMx7TS29+oXXf77eibAAAUMTZtjGPi4uTv7//VY+LjY1VUFCQ25Z1fIcFFaIwmjH2fnVtXV+dBs/WbyfO5Hr+bGqaDh7+Xd/tPKgHRy1Q7Wqhuuf2Rm7H1Kkepi/eGK53Pt6ifyz4yqLKARQX5YLLydfXV6dOnXLbf+rUKVWsWNFLVQGewzrm5rw+ytKjRw+3x4Zh6NixY9q+fbsmTJhw1dePHz9eI0eOdNsX0nqsR2tE0TBj7P3qdnsjdRw8S/89euqqxzscDjnkkF/J//vPpG71MH355lNasmqrJs9dVZDlAiimSvr5qW5kPW39Pk63d4iWdPHmz61b49S7Tz8vVwegIHm9MQ8KCnJ77OPjo9q1a2vKlCnq2LHjVV/vdDrldDrd9jl8fD1aIwq/meN76YEuzXT/iDeVei5NoRXKSpJSUtOUlp6pm6pU0H2dmmpd3D6dPJ2qKqHBenZgR11Iz9RXm/dIuji+8uWbT+nrLfs0+/31rnNk5xg6eZr7IQB4zkP9B2rCc2NVr1591W/QUO+/964uXLig7vf2uPqLAZsrrGm2FbzamGdnZ2vgwIFq0KCBypUr581SUMQ93quNJGntgmfc9g+e+J7eX7VV6RlZatmkhoY92E7lAkvrxKk/tHlngtoPeFW///+m+97oJgopX1YP3nWrHrzrVtc5/nv0lOrcOcmy9wKg6OvcpatOJyfr9ddm6+TJ31W7Tl29/sYCVWCUBSjSvL6Oub+/v/bt26dq1ap57JysYw7ADljHHIAd2G0d85qjvrTkOgmvdLHkOp7k9Zs/69evr0OHWP4JAAAAxZvXG/OpU6dq1KhRWr16tY4dO5Zr6UMAAAAUHazKYs5rf7kxZcoUPfvss+rataskqVu3bm4fomEYcjgcys5mTXIAAAAUfV5rzGNiYvTEE0/om2++8VYJAAAAsFghDbMt4bXG/NI9p23btvVWCQAAAIBtePU+3cI6/wMAAIBrQ/9nzquNea1ata76m5OcnGxRNQAAAID3eLUxj4mJyfXNnwAAACi6CMzNebUx7927t0JCQrxZAgAAAGALXmvMmS8CAAAofnx86AHNeO0Lhi6tygIAAADAi4l5Tk6Oty4NAAAAL2FowpzXEnMAAAAA/8erN38CAACgeOE+Q3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlmDE3R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDDPm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMswY26OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDDPm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBjDgAAANgAoywAAACwDDd/miMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzDjLk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIAN0JgDAADAMg6Hw5ItPzZu3Ki7775b4eHhcjgcWrlypdvzAwYMyHX+zp07ux2TnJysvn37KjAwUMHBwRo0aJBSU1PzVQeNOQAAAIq1c+fOqVGjRpo7d67pMZ07d9axY8dc2wcffOD2fN++fbVnzx6tXbtWq1ev1saNG/XYY4/lqw5mzAEAAGAZO86Yd+nSRV26dLniMU6nU2FhYZd9bt++fVqzZo22bdumZs2aSZLmzJmjrl276pVXXlF4eHie6iAxBwAAAK7i22+/VUhIiGrXrq0hQ4bo1KlTrufi4uIUHBzsasolKTo6Wj4+Ptq6dWuer0FiDgAAAMtYtY55enq60tPT3fY5nU45nc58n6tz587q0aOHqlWrpoMHD+q5555Tly5dFBcXJ19fXyUlJSkkJMTtNSVKlFD57+4NWwABAABJREFU8uWVlJSU5+uQmAMAAKDIiY2NVVBQkNsWGxt7Tefq3bu3unXrpgYNGqh79+5avXq1tm3bpm+//dajNZOYAwAAwDJWJebjx4/XyJEj3fZdS1p+OdWrV1fFihWVkJCgDh06KCwsTCdOnHA7JisrS8nJyaZz6ZdDYw4AAIAi51rHVvLiyJEjOnXqlCpXrixJioqK0pkzZ7Rjxw41bdpUkrR+/Xrl5OSoefPmeT4vjTkAAAAsY8dVWVJTU5WQkOB6nJiYqF27dql8+fIqX768YmJi1LNnT4WFhengwYMaM2aMatasqU6dOkmS6tatq86dO2vw4MGaP3++MjMzNWzYMPXu3TvPK7JIzJgDAACgmNu+fbuaNGmiJk2aSJJGjhypJk2aaOLEifL19dWPP/6obt26qVatWho0aJCaNm2qTZs2uSXyS5YsUZ06ddShQwd17dpVrVq10ptvvpmvOkjMAQAAYBmrZszzo127djIMw/T5r7766qrnKF++vJYuXXpddZCYAwAAADZAYg4AAADL2DAwtw0ScwAAAMAGSMwBAABgGTvOmNsFiTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGR8ic1Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALCMD4G5KRJzAAAAwAZIzAEAAGAZZszNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAAlnGIyNwMiTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlfIjMTZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADeUrM//Wvf+X5hN26dbvmYgAAAFC0kZeby1Nj3r179zydzOFwKDs7+3rqAQAAAIqlPDXmOTk5BV0HAAAAigHWMTd3XTPmaWlpnqoDAAAAKNby3ZhnZ2frxRdfVJUqVRQQEKBDhw5JkiZMmKC3337b4wUCAACg6PBxWLMVRvluzF966SUtWrRI06ZNk5+fn2t//fr1tWDBAo8WBwAAABQX+W7MFy9erDfffFN9+/aVr6+va3+jRo20f/9+jxYHAACAosXhcFiyFUb5bsx/++031axZM9f+nJwcZWZmeqQoAAAAoLjJd2MeGRmpTZs25dq/YsUKNWnSxCNFAQAAoGhyOKzZCqM8LZf4ZxMnTlT//v3122+/KScnR5988oni4+O1ePFirV69uiBqBAAAAIq8fCfm99xzj1atWqWvv/5aZcqU0cSJE7Vv3z6tWrVKd9xxR0HUCAAAgCKCGXNz+U7MJal169Zau3atp2sBAAAAiq1raswlafv27dq3b5+ki3PnTZs29VhRAAAAKJoK6xrjVsh3Y37kyBH16dNH3333nYKDgyVJZ86c0W233aYPP/xQN9xwg6drBAAAAIq8fM+YP/roo8rMzNS+ffuUnJys5ORk7du3Tzk5OXr00UcLokYAAAAUEcyYm8t3Yr5hwwZt2bJFtWvXdu2rXbu25syZo9atW3u0OAAAAKC4yHdjXrVq1ct+kVB2drbCw8M9UhQAAACKpsKZZVsj36Ms06dP1/Dhw7V9+3bXvu3bt+vpp5/WK6+84tHiAAAAgOIiT4l5uXLl3GZ1zp07p+bNm6tEiYsvz8rKUokSJfTII4+oe/fuBVIoAAAACj+fQjr/bYU8NeYzZ84s4DIAAACA4i1PjXn//v0Lug4AAAAUAwTm5q75C4YkKS0tTRkZGW77AgMDr6sgAAAAoDjKd2N+7tw5jR07VsuXL9epU6dyPZ+dne2RwgAAAFD0FNY1xq2Q71VZxowZo/Xr12vevHlyOp1asGCBYmJiFB4ersWLFxdEjQAAAECRl+/EfNWqVVq8eLHatWungQMHqnXr1qpZs6YiIiK0ZMkS9e3btyDqBAAAQBFAYG4u34l5cnKyqlevLuniPHlycrIkqVWrVtq4caNnqwMAAACKiXw35tWrV1diYqIkqU6dOlq+fLmki0l6cHCwR4sDAABA0eLjcFiyFUb5bswHDhyo3bt3S5LGjRunuXPnyt/fXyNGjNDo0aM9XiAAAABQHOR7xnzEiBGuX0dHR2v//v3asWOHatasqYYNG3q0OAAAABQthTTMtsR1rWMuSREREYqIiPBELQAAAECxlafGfPbs2Xk+4VNPPXXNxQAAAKBoYx1zc3lqzGfMmJGnkzkcDhpzAAAA4BrkqTG/tApLYfH9Z7HeLgEAAACXke+VR4oRPhsAAADABq775k8AAAAgr5gxN0diDgAAANgAjTkAAABgA4yyAAAAwDI+TLKYuqbEfNOmTerXr5+ioqL022+/SZLee+89bd682aPFAQAAAMVFvhvzjz/+WJ06dVKpUqX0ww8/KD09XZKUkpKil19+2eMFAgAAoOjwcVizFUb5bsynTp2q+fPn66233lLJkiVd+1u2bKmdO3d6tDgAAACguMj3jHl8fLzatGmTa39QUJDOnDnjiZoAAABQRLFcorl8J+ZhYWFKSEjItX/z5s2qXr26R4oCAAAAipt8N+aDBw/W008/ra1bt8rhcOjo0aNasmSJRo0apSFDhhREjQAAACgimDE3l+9RlnHjxiknJ0cdOnTQ+fPn1aZNGzmdTo0aNUrDhw8viBoBAACAIs9hGIZxLS/MyMhQQkKCUlNTFRkZqYCAAE/Xds12H/7D2yUAgGqHl/V2CQAgf5t9a82Yz+Mtuc60O2tbch1PuubfKj8/P0VGRnqyFgAAAKDYyndj3r59+yveTbt+/frrKggAAABFlw+rspjKd2PeuHFjt8eZmZnatWuXfv75Z/Xv399TdQEAAADFSr4b8xkzZlx2/+TJk5WamnrdBQEAAKDoyveSgMWIxz6bfv366Z133vHU6QAAAIBixWP36cbFxcnf399TpwMAAEARxIi5uXw35j169HB7bBiGjh07pu3bt2vChAkeKwwAAAAoTvLdmAcFBbk99vHxUe3atTVlyhR17NjRY4UBAACg6GFVFnP5asyzs7M1cOBANWjQQOXKlSuomgAAAIBiJ183f/r6+qpjx446c+ZMAZUDAACAoszhsGYrjPK9Kkv9+vV16NChgqgFAAAAKLby3ZhPnTpVo0aN0urVq3Xs2DGdPXvWbQMAAADM+Dis2QqjPM+YT5kyRc8++6y6du0qSerWrZscf/p7AsMw5HA4lJ2d7fkqAQAAgCIuz415TEyMnnjiCX3zzTcFWQ8AAACKMFZlMZfnxtwwDElS27ZtC6wYAAAAoLjK13KJDn7CAQAAwHWgnTSXr8a8Vq1aV23Ok5OTr6sgAAAAoDjKV2MeExOT65s/AQAAgLwqrCumWCFfjXnv3r0VEhJSULUAAAAAltu4caOmT5+uHTt26NixY/r000/VvXt31/OGYWjSpEl66623dObMGbVs2VLz5s3TzTff7DomOTlZw4cP16pVq+Tj46OePXtq1qxZCggIyHMdeV7HnPlyAAAAXC+HRf/kx7lz59SoUSPNnTv3ss9PmzZNs2fP1vz587V161aVKVNGnTp1UlpamuuYvn37as+ePVq7dq1Wr16tjRs36rHHHsvfZ2NcWm7lKnx8fJSUlFQoEvPdh//wdgkAoNrhZb1dAgDIP1/zEQXv5XUHLbnOcx1qXNPrHA6HW2JuGIbCw8P17LPPatSoUZKklJQUhYaGatGiRerdu7f27dunyMhIbdu2Tc2aNZMkrVmzRl27dtWRI0cUHh6ep2vnOTHPyckpFE05AAAA7Muqb/5MT0/P9Q316enp+a43MTFRSUlJio6Odu0LCgpS8+bNFRcXJ0mKi4tTcHCwqymXpOjoaPn4+Gjr1q15/2zyXR0AAABgc7GxsQoKCnLbYmNj832epKQkSVJoaKjb/tDQUNdzl5sqKVGihMqXL+86Ji9s9pcbAAAAKMqsWpVl/PjxGjlypNs+p9NpzcWvEY05AAAAihyn0+mRRjwsLEySdPz4cVWuXNm1//jx42rcuLHrmBMnTri9LisrS8nJya7X5wWjLAAAALCMw+GwZPOUatWqKSwsTOvWrXPtO3v2rLZu3aqoqChJUlRUlM6cOaMdO3a4jlm/fr1ycnLUvHnzPF+LxBwAAADFWmpqqhISElyPExMTtWvXLpUvX1433nijnnnmGU2dOlU333yzqlWrpgkTJig8PNy1ckvdunXVuXNnDR48WPPnz1dmZqaGDRum3r1753lFFonGHAAAABay4zd/bt++Xe3bt3c9vjSb3r9/fy1atEhjxozRuXPn9Nhjj+nMmTNq1aqV1qxZI39/f9drlixZomHDhqlDhw6uLxiaPXt2vurI8zrmhQnrmAOwA9YxB2AHdlvH/NUNhyy5zrNtq1tyHU+y2W8VAAAAijK+TN4cN38CAAAANkBiDgAAAMv4EJmbIjEHAAAAbIDEHAAAAJax46osdkFiDgAAANgAiTkAAAAsw4i5ORJzAAAAwAZIzAEAAGAZHxGZmyExBwAAAGyAxBwAAACWYcbcHIk5AAAAYAMk5gAAALAM65ibIzEHAAAAbIDEHAAAAJbxYcjcFIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBlmzM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWIRU2x2cDAAAA2ACJOQAAACzjYMjcFIk5AAAAYAMk5gAAALAMebk5EnMAAADABkjMAQAAYBm++dMciTkAAABgAyTmAAAAsAx5uTkScwAAAMAGSMwBAABgGUbMzZGYAwAAADZAYg4AAADL8M2f5kjMAQAAABsgMQcAAIBlSIXN8dkAAAAANkBiDgAAAMswY26OxBwAAACwARJzAAAAWIa83ByJOQAAAGADJOYAAACwDDPm5kjMAQAAABsgMQcAAIBlSIXN8dkAAAAANkBiDgAAAMswY26OxBwAAACwARJzAAAAWIa83ByJOQAAAGADJOYAAACwDCPm5kjMAQAAABsgMQcAAIBlfJgyN0ViDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZozAEAAAAbYJQFAAAAlnFw86cpEnMAAADABkjMAQAAYBlu/jRHYg4AAADYAIk5AAAALMMXDJkjMQcAAABsgMQcAAAAlmHG3ByJOQAAAGADJOYAAACwDIm5ORJzAAAAwAZIzAEAAGAZvvnTHIk5AAAAYAMk5gAAALCMD4G5KRJzAAAAwAZIzAEAAGAZZszN2Soxz8jIUHx8vLKysrxdCgAAAGApWzTm58+f16BBg1S6dGnVq1dPhw8fliQNHz5cf//7371cHQAAADzF4bBmK4xs0ZiPHz9eu3fv1rfffit/f3/X/ujoaC1btsyLlQEAAADWsMWM+cqVK7Vs2TK1aNFCjj/9iFOvXj0dPHjQi5UBAADAk5gxN2eLxPz3339XSEhIrv3nzp1za9QBAACAosoWjXmzZs30+eefux5fasYXLFigqKgob5UFAAAAD/NxWLMVRrYYZXn55ZfVpUsX7d27V1lZWZo1a5b27t2rLVu2aMOGDd4uDwAAAChwtkjMW7VqpV27dikrK0sNGjTQv//9b4WEhCguLk5Nmzb1dnkAAADwEIdF/xRGtkjMJalGjRp66623vF0GAAAA4BW2SMx37typn376yfX4s88+U/fu3fXcc88pIyPDi5WhOFn54SL1uqOZFr3+qiTpRNJR9bqj2WW3uA1fe7laAEXdh0uXqMsdt+uWJg3Ut/f9+unHH71dEuARrGNuzhaN+eOPP65ffvlFknTo0CE98MADKl26tD766CONGTPGy9WhOEiI36O1n3+iiOo3u/ZVrBSqN5etcdt6Pfy4/EuVVpNbb/NitQCKujVffqFXpsXq8SeH6sOPPlXt2nU05PFBOnXqlLdLA1CAbNGY//LLL2rcuLEk6aOPPlLbtm21dOlSLVq0SB9//LF3i0ORl3bhvObETtDjI55XmYCyrv0+vr4KLl/RbfvPd98oqm20/EuV9mLFAIq6995dqB739VL3e3uqRs2aemFSjPz9/bXyE/6fiMLPYdFWGNmiMTcMQzk5OZKkr7/+Wl27dpUkVa1aVSdPnvRmaSgGFsz5h5o0b6mGf2t+xeMO/bJPvx78Rbd3vseiygAUR5kZGdq3d49aRP3f38z5+PioRYvb9OPuH7xYGYCCZoubP5s1a6apU6cqOjpaGzZs0Lx58yRJiYmJCg0N9XJ1KMq+++YrJR7Yr9i5i6967Po1n6nKjdVUu14jCyoDUFydPnNa2dnZqlChgtv+ChUqKDHxkJeqAjzHp7AOgFvAFo35zJkz1bdvX61cuVLPP/+8atasKUlasWKFbrvtyrO86enpSk9Pd9uXkZ4hP6ezwOpF0XDyRJIWvf6qXvjHXPn5Xfnfl4z0NG1ev0Y9+z5qUXUAAKC4sUVj3rBhQ7dVWS6ZPn26fH19r/ja2NhYxcTEuO17/JlxGjLiOY/WiKLn0IH9SjmTrLFD+rn25eRka99PP2jNZ8u19Ist8vn///59v3Gd0tPT1PaOO71VLoBiolxwOfn6+ua60fPUqVOqWLGil6oCPIe83JwtGnMz/v7+Vz1m/PjxGjlypNu++OMssYira9DkFr3y5odu++a9MkXhVSN0zwP9XU25dHGMpVlUGwUGl7O6TADFTEk/P9WNrKet38fp9g7RkqScnBxt3Rqn3n36XeXVAAozrzXm5cqVkyOPM0bJycmmzzmdTjn/Mrbid+aP66oNxUOp0mV0Y7Wabvuc/v4qGxjstj/pt/9p308/aPxLs6wuEUAx9VD/gZrw3FjVq1df9Rs01PvvvasLFy6o+709vF0acP2IzE15rTGfOXOmty4N5Mv6Nf9S+Yohati0hbdLAVBMdO7SVaeTk/X6a7N18uTvql2nrl5/Y4EqMMoCFGkOwzAMbxfhabsPk5gD8L7a4WWvfhAAFDB/mw0ubz2YYsl1mtcIsuQ6nmSz3yopLS1NGRnuM+KBgYFeqgYAAACwhi2+YOjcuXMaNmyYQkJCVKZMGZUrV85tAwAAQNHgcFizFUa2aMzHjBmj9evXa968eXI6nVqwYIFiYmIUHh6uxYuv/sUvAAAAQGFni1GWVatWafHixWrXrp0GDhyo1q1bq2bNmoqIiNCSJUvUt29fb5cIAAAADyikYbYlbJGYJycnq3r16pIuzpNfWh6xVatW2rhxozdLAwAAACxhi8a8evXqSkxMlCTVqVNHy5cvl3QxSQ8ODvZiZQAAAPAoh0VbIeTVxvzQoUPKycnRwIEDtXv3bknSuHHjNHfuXPn7+2vEiBEaPXq0N0sEAAAALOHVdcx9fX117NgxhYSESJIeeOABzZ49W2lpadqxY4dq1qyphg0b5vu8rGMOwA5YxxyAHdhtHfPtiWctuU6zaoVvuW2vJuZ//Zngiy++0Llz5xQREaEePXpcU1MOAAAA5MfkyZPlcDjctjp16rieT0tL09ChQ1WhQgUFBASoZ8+eOn78uMfrsMWMOQAAAIoHu65jXq9ePR07dsy1bd682fXciBEjtGrVKn300UfasGGDjh49qh49enjwU7nIq3+5ceknkr/uAwAAAKxUokQJhYWF5dqfkpKit99+W0uXLtXtt98uSVq4cKHq1q2r77//Xi1atPBcDR470zUwDEMDBgyQ0+mUdPGvCZ544gmVKVPG7bhPPvnEG+UBAADAw+wawR44cEDh4eHy9/dXVFSUYmNjdeONN2rHjh3KzMxUdHS069g6deroxhtvVFxcXNFpzPv37+/2uF+/fl6qBAAAAEVJenq60tPT3fY5nU5XIPxnzZs316JFi1S7dm0dO3ZMMTExat26tX7++WclJSXJz88v1xLeoaGhSkpK8mjNXm3MFy5c6M3LAwAAwGoWReaxsbGKiYlx2zdp0iRNnjw517FdunRx/bphw4Zq3ry5IiIitHz5cpUqVaqgS3Xh5k8AAAAUOePHj1dKSorbNn78+Dy9Njg4WLVq1VJCQoLCwsKUkZGhM2fOuB1z/Pjxy86kXw8acwAAAFjGYdE/TqdTgYGBbtvlxlguJzU1VQcPHlTlypXVtGlTlSxZUuvWrXM9Hx8fr8OHDysqKsqjn43NlpwHAAAArDVq1CjdfffdioiI0NGjRzVp0iT5+vqqT58+CgoK0qBBgzRy5EiVL19egYGBGj58uKKiojx646dEYw4AAAAL2XFl7CNHjqhPnz46deqUKlWqpFatWun7779XpUqVJEkzZsyQj4+PevbsqfT0dHXq1Emvv/66x+twGH/9+s0iYPfhP7xdAgCodnhZb5cAAPK3WQy7y6I+rfGNhe/PYJv9VgEAAKAos2Fgbhvc/AkAAADYAIk5AAAArENkborEHAAAALABEnMAAABYxkFkborEHAAAALABEnMAAABYxo7rmNsFiTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAA1iEyN0ViDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYB0ic1Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAANYhMjdFYg4AAADYAIk5AAAALMM65uZIzAEAAAAboDEHAAAAbIBRFgAAAFiGLxgyR2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIfI3BSJOQAAAGADJOYAAACwDF8wZI7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsBCRuRkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsIyDKXNTJOYAAACADZCYAwAAwDoE5qZIzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAAKxDYG6KxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyzJibIzEHAAAAbIDGHAAAALABGnMAAADABpgxBwAAgGWYMTdHYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZZgxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiHyNwUiTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIB1iMxNkZgDAAAANkBjDgAAAMs4LPonv+bOnaubbrpJ/v7+at68uf7zn/8UwLu/MhpzAAAAFGvLli3TyJEjNWnSJO3cuVONGjVSp06ddOLECUvrcBiGYVh6RQvsPvyHt0sAANUOL+vtEgBA/ja7ozAty5rr5Od9N2/eXLfccotee+01SVJOTo6qVq2q4cOHa9y4cQVUYW4k5gAAAChy0tPTdfbsWbctPT0913EZGRnasWOHoqOjXft8fHwUHR2tuLg4K0sumquyNLqRlArXJz09XbGxsRo/frycTqe3ywFQDPHnEIoqqxL8yVNjFRMT47Zv0qRJmjx5stu+kydPKjs7W6GhoW77Q0NDtX///oIu002RHGUBrtfZs2cVFBSklJQUBQYGerscAMUQfw4B1yc9PT1XQu50OnP9oHv06FFVqVJFW7ZsUVRUlGv/mDFjtGHDBm3dutWSeqUimpgDAACgeLtcE345FStWlK+vr44fP+62//jx4woLCyuo8i6LGXMAAAAUW35+fmratKnWrVvn2peTk6N169a5JehWIDEHAABAsTZy5Ej1799fzZo106233qqZM2fq3LlzGjhwoKV10JgDl+F0OjVp0iRuuALgNfw5BFjngQce0O+//66JEycqKSlJjRs31po1a3LdEFrQuPkTAAAAsAFmzAEAAAAboDEHAAAAbIDGHAAAALABGnMAAGxm0aJFCg4O9nYZACxGY44izeFwXHH769fyAoAnDRgw4LJ/9iQkJHi7NAA2xHKJKNKOHTvm+vWyZcs0ceJExcfHu/YFBAS4fm0YhrKzs1WiBP9ZAPCczp07a+HChW77KlWq5KVqANgZiTmKtLCwMNcWFBQkh8Pherx//36VLVtWX375pZo2bSqn06nNmzdrwIAB6t69u9t5nnnmGbVr1871OCcnR7GxsapWrZpKlSqlRo0aacWKFda+OQCFgtPpdPuzKCwsTLNmzVKDBg1UpkwZVa1aVU8++aRSU1NNz7F79261b99eZcuWVWBgoJo2bart27e7nt+8ebNat26tUqVKqWrVqnrqqad07tw5K94eAA+iMUexN27cOP3973/Xvn371LBhwzy9JjY2VosXL9b8+fO1Z88ejRgxQv369dOGDRsKuFoARYGPj49mz56tPXv26N1339X69es1ZswY0+P79u2rG264Qdu2bdOOHTs0btw4lSxZUpJ08OBBde7cWT179tSPP/6oZcuWafPmzRo2bJhVbweAh/B39ij2pkyZojvuuCPPx6enp+vll1/W119/raioKElS9erVtXnzZr3xxhtq27ZtQZUKoBBavXq129hcly5d9NFHH7ke33TTTZo6daqeeOIJvf7665c9x+HDhzV69GjVqVNHknTzzTe7nouNjVXfvn31zDPPuJ6bPXu22rZtq3nz5snf378A3hWAgkBjjmKvWbNm+To+ISFB58+fz9XMZ2RkqEmTJp4sDUAR0L59e82bN8/1uEyZMvr6668VGxur/fv36+zZs8rKylJaWprOnz+v0qVL5zrHyJEj9eijj+q9995TdHS07r//ftWoUUPSxTGXH3/8UUuWLHEdbxiGcnJylJiYqLp16xb8mwTgETTmKPbKlCnj9tjHx0eGYbjty8zMdP360hzo559/ripVqrgd53Q6C6hKAIVVmTJlVLNmTdfjX3/9VXfddZeGDBmil156SeXLl9fmzZs1aNAgZWRkXLYxnzx5sh588EF9/vnn+vLLLzVp0iR9+OGHuvfee5WamqrHH39cTz31VK7X3XjjjQX63gB4Fo058BeVKlXSzz//7LZv165drnnOyMhIOZ1OHT58mLEVAPm2Y8cO5eTk6NVXX5WPz8VbvZYvX37V19WqVUu1atXSiBEj1KdPHy1cuFD33nuv/va3v2nv3r1uzT+AwombP4G/uP3227V9+3YtXrxYBw4c0KRJk9wa9bJly2rUqFEaMWKE3n33XR08eFA7d+7UnDlz9O6773qxcgCFQc2aNZWZmak5c+bo0KFDeu+99zR//nzT4y9cuKBhw4bp22+/1X//+19999132rZtm2tEZezYsdqyZYuGDRumXbt26cCBA/rss8+4+RMohGjMgb/o1KmTJkyYoDFjxuiWW27RH3/8oYcfftjtmBdffFETJkxQbGys6tatq86dO+vzzz9XtWrVvFQ1gMKiUaNG+uc//6l//OMfql+/vpYsWaLY2FjT4319fXXq1Ck9/PDDqlWrlnr16qUuXbooJiZGktSwYUNt2LBBv/zyi1q3bq0mTZpo4sSJCg8Pt+otAfAQh/HXYVoAAAAAliMxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQdQbA0YMEDdu3d3PW7Xrp2eeeYZy+v49ttv5XA4dObMGdNjHA6HVq5cmedzTp48WY0bN76uun799Vc5HA7t2rXrus4DAMgbGnMAtjJgwAA5HA45HA75+fmpZs2amjJlirKysgr82p988olefPHFPB2bl2YaAID8KOHtAgDgrzp37qyFCxcqPT1dX3zxhYYOHaqSJUtq/PjxuY7NyMiQn5+fR65bvnx5j5wHAIBrQWIOwHacTqfCwsIUERGhIUOGKDo6Wv/6178k/d/4yUsvvaTw8HDVrl1bkvS///1PvXr1UnBwsMqXL6977rlHv/76q+uc2dnZGjlypIKDg1WhQgWNGTNGhmG4Xfevoyzp6ekaO3asqlatKqfTqZo1a+rtt9/Wr7/+qvbt20uSypUrJ4fDoQEDBkiScnJyFBsbq2rVqqlUqVJq1KiRVqxY4XadL774QrVq1VKpUqXUvn17tzrzauzYsapVq5ZKly6t6tWra8KECcrMzMx13BtvvKGqVauqdOnS6tWrl1JSUtyeX7BggerWrSt/f3/VqVNHr7/+er5rAQB4Bo05ANsrVaqUMjIyXI/XrVun+Ph4rV27VqtXr1ZmZqY6deqksmXLatOmTfruu+8UEBCgzp07u1736quvatGiRXrnnXe0efNmJScn69NPP73idR9++GF98MEHmj17tvbt26c33nhDAQEBqlq1qj7++GNJUnx8vI4dO6ZZs2ZJkmJjY7V48WLNnz9fe/bs0YgRI9SvXz9t2LBB0sUfIHr06KG7775bu3bt0qOPPqpx48bl+zMpW7asFi1apL1792rWrFl66623NGPGDLdjEhIStHz5cq1atUpr1qzRDz/8oCeffNL1/JIlSzRx4kS99NJL2rdvn15++WVNmDBB7777br7rAQB4gAEANtK/f3/jnnvuMQzDMHJycoy1a9caTqfTGDVqlOv50NBQIz093fWa9957z6hdu7aRk5Pj2peenm6UKlXK+OqrrwzDMIzKlSsb06ZNcz2fmZlp3HDDDa5rGYZhtG3b1nj66acNwzCM+Ph4Q5Kxdu3ay9b5zTffGJKM06dPu/alpaUZpUuXNrZs2eJ27KBBg4w+ffoYhmEY48ePNyIjI92eHzt2bK5z/ZUk49NPPzV9fvr06UbTpk1djydNmmT4+voaR44cce378ssvDR8fH+PYsWOGYRhGjRo1jKVLl7qd58UXXzSioqIMwzCMxMREQ5Lxww8/mF4XAOA5zJgDsJ3Vq1crICBAmZmZysnJ0YMPPqjJkye7nm/QoIHbXPnu3buVkJCgsmXLup0nLS1NBw8eVEpKio4dO6bmzZu7nitRooSaNWuWa5zlkl27dsnX11dt27bNc90JCQk6f/687rjjDrf9GRkZatKkiSRp3759bnVIUlRUVJ6vccmyZcs0e/ZsHTx4UKmpqcrKylJgYKDbMTfeeKOqVKnidp2cnBzFx8erbNmyOnjwoAYNGqTBgwe7jsnKylJQUFC+6wEAXD8acwC20759e82bN09+fn4KDw9XiRLuf1SVKVPG7XFqaqqaNm2qJUuW5DpXpUqVrqmGUqVK5fs1qampkqTPP//crSGWLs7Ne0pcXJz69u2rmJgYderUSUFBQfrwww/16quv5rvWt956K9cPCr6+vh6rFQCQdzTmAGynTJkyqlmzZp6P/9vf/qZly5YpJCQkV2p8SeXKlbV161a1adNG0sVkeMeOHfrb3/522eMbNGignJwcbdiwQdHR0bmev5TYZ2dnu/ZFRkbK6XTq8OHDpkl73bp1XTeyXvL9999f/U3+yZYtWxQREaHnn3/ete+///1vruMOHz6so0ePKjw83HUdHx8f1a5dW6GhoQoPD9ehQ4fUt2/ffF0fAFAwuPkTQKHXt29fVaxYUffcc482bdqkxMREffvtt3rqqad05MgRSdLTTz+tv//971q5cqX279+vJ5988oprkN90003q37+/HnnkEa1cudJ1zuXLl0uSIiIi5HA4tHr1av3+++9KTU1V2bJlNWrUKI0YMULvvvuuDh48qJ07d2rOnDmuGyqfeOIJHThwQKNHj1Z8fLyWLl2qRYsW5ev93nzzzTp8+LA+/PBDHTx4ULNnz77sjaz+/v7q37+/du/erU2bNumpp55Sr169FBYWJkmKiYlRbGysZs+erV9++UU//fSTFi5cqH/+85/5qgcA4Bk05gAKvdKlS2vjxo268cYb1aNHD9WtW1eDBg1SWlqaK0F/9tln9dBDD6l///6KiopS2bJlde+9917xvPPmzdN9992nJ598UnXq1NHgwYN17tw5SVKVKlUUExOjcePGKTQ0VMOGDZMkvfjii5owYYJiY2NVt25dde7cWZ9//rmqVasm6eLc98cff6yVK1eqUaNGmj9/vl5++eV8vd9u3bppxIgRGjZsmBo3bqwtW7ZowoQJuY6rWbOmevTooa5du6pjx45q2LCh23KIjz76qBYsWKCFCxeqQYMGatu2rRYtWuSqFQBgLYdhducTAAAAAMuQmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4UUzfddJMGDBjg7TKKnDVr1qhx48by9/eXw+HQmTNnPHr+RYsWyeFw6Ndff/XoeQszh8OhyZMne7sMALhuNOZAEXPw4EE9/vjjql69uvz9/RUYGKiWLVtq1qxZunDhgrfLu6Jvv/1WDofjstv333+fr/P06NFDYWFh8vPzU0hIiO6++2598sknBVi9dOrUKfXq1UulSpXS3Llz9d5776lMmTIFek0r3XTTTXI4HIqOjr7s82+99Zbr92v79u35Pv+WLVs0efJkj/8wAwCFRQlvFwDAcz7//HPdf//9cjqdevjhh1W/fn1lZGRo8+bNGj16tPbs2aM333zT22Ve1VNPPaVbbrnFbV/NmjXz9NpJkyZpypQpuvnmm/X4448rIiJCp06d0hdffKGePXtqyZIlevDBBwuibG3btk1//PGHXnzxRdPm9Xo99NBD6t27t5xOZ4Gc/2r8/f31zTffKCkpSWFhYW7PLVmyRP7+/kpLS7umc2/ZskUxMTEaMGCAgoOD8/y6CxcuqEQJ/ncGoPDjTzKgiEhMTFTv3r0VERGh9evXq3Llyq7nhg4dqoSEBH3++ederDDvWrdurfvuuy/fr1uxYoWmTJmi++67T0uXLlXJkiVdz40ePVpfffWVMjMzPVmqmxMnTkhSvprK/PL19ZWvr2+Bnf9qWrZsqW3btmnZsmV6+umnXfuPHDmiTZs26d5779XHH39c4HXk5OQoIyND/v7+8vf3L/DrAYAVGGUBiohp06YpNTVVb7/9tltTfknNmjXdGqm/Sk5O1qhRo9SgQQMFBAQoMDBQXbp00e7du3MdO2fOHNWrV0+lS5dWuXLl1KxZMy1dutT1/B9//KFnnnlGN910k5xOp0JCQnTHHXdo586deX4/f/zxh7KysvJ8vCRNmDBB5cuX1zvvvOPWlF/SqVMn3XXXXa7HJ06c0KBBgxQaGip/f381atRI7777rttrfv31VzkcDr3yyit68803VaNGDTmdTt1yyy3atm2b67h27dqpf//+kqRbbrlFDofDNcNvNs/frl07tWvXzm3f1T5bsxnz119/XfXq1ZPT6VR4eLiGDh2aaySkXbt2ql+/vvbu3av27durdOnSqlKliqZNm2b2kebi7++vHj16uNUkSR988IHKlSunTp065XrNjz/+qAEDBrjGq8LCwvTII4/o1KlTrmMmT56s0aNHS5KqVavmGom59D4dDoeGDRumJUuWuN7nmjVrXM9dmjG/cOGC6tSpozp16riNbiUnJ6ty5cq67bbblJ2dnef3CwBWIjEHiohVq1apevXquu22267p9YcOHdLKlSt1//33q1q1ajp+/LjeeOMNtW3bVnv37lV4eLiki3PETz31lO677z49/fTTSktL048//qitW7e6RkSeeOIJrVixQsOGDVNkZKROnTqlzZs3a9++ffrb3/521VoGDhyo1NRU+fr6qnXr1po+fbqaNWt2xdccOHBA+/fv1yOPPKKyZcte9RoXLlxQu3btlJCQoGHDhqlatWr66KOPNGDAAJ05cybXDzFLly7VH3/8occff1wOh0PTpk1Tjx49dOjQIZUsWVLPP/+8ateurTfffFNTpkxRtWrVVKNGjavW8Wd5+WwvZ/LkyYqJiVF0dLSGDBmi+Ph4zZs3T9u2bdN3333n9kPK6dOn1blzZ/Xo0UO9evXSihUrNHbsWDVo0EBdunTJU50PPvigOnbsqIMHD7re49KlS3Xfffdd9geitWvX6tChQxo4cKDCwsJcI1V79uzR999/L4fDoR49euiXX37RBx98oBkzZqhixYqSpEqVKrnOs379ei1fvlzDhg1TxYoVddNNN+W6VqlSpfTuu++qZcuWev755/XPf/5T0sW/NUpJSdGiRYu8+jcOAHBFBoBCLyUlxZBk3HPPPXl+TUREhNG/f3/X47S0NCM7O9vtmMTERMPpdBpTpkxx7bvnnnuMevXqXfHcQUFBxtChQ/NcyyXfffed0bNnT+Ptt982PvvsMyM2NtaoUKGC4e/vb+zcufOKr/3ss88MScaMGTPydK2ZM2cakoz333/ftS8jI8OIiooyAgICjLNnzxqGcfEzkGRUqFDBSE5OznW9VatWufYtXLjQkGRs27bN7Vp//awvadu2rdG2bVvX47x8tpeukZiYaBiGYZw4ccLw8/MzOnbs6Pb799prrxmSjHfeecftepKMxYsXu/alp6cbYWFhRs+ePa943Uvv48477zSysrKMsLAw48UXXzQMwzD27t1rSDI2bNhw2c/g/Pnzuc71wQcfGJKMjRs3uvZNnz7d7b39mSTDx8fH2LNnz2WfmzRpktu+8ePHGz4+PsbGjRuNjz76yJBkzJw586rvEQC8iVEWoAg4e/asJOUpKTbjdDrl43Pxj4Ts7GydOnVKAQEBql27ttsISnBwsI4cOeI2xvFXwcHB2rp1q44ePZqvGm677TatWLFCjzzyiLp166Zx48a5EtXx48df8bX5/Qy++OILhYWFqU+fPq59JUuW1FNPPaXU1FRt2LDB7fgHHnhA5cqVcz1u3bq1pIt/0+Apefls/+rrr79WRkaGnnnmGdfvnyQNHjxYgYGBue4rCAgIUL9+/VyP/fz8dOutt+brffj6+qpXr1764IMPJF286bNq1aquz+SvSpUq5fp1WlqaTp48qRYtWkhSvsab2rZtq8jIyDwdO3nyZNWrV0/9+/fXk08+qbZt2+qpp57K87UAwBtozIEiIDAwUNLFuexrlZOToxkzZujmm2+W0+lUxYoVValSJf34449KSUlxHTd27FgFBATo1ltv1c0336yhQ4fqu+++czvXtGnT9PPPP6tq1aq69dZbNXny5GtuYGvWrKl77rlH33zzzRVng/P7Gfz3v//VzTff7NbMSlLdunVdz//ZjTfe6Pb4UpN++vTpPF0vL/Ly2f7VpTpr167ttt/Pz0/Vq1fP9T5uuOEGORwOt33lypXL9/t48MEHtXfvXu3evVtLly5V7969c533kuTkZD399NMKDQ1VqVKlVKlSJVWrVk2S3P7duppLr8kLPz8/vfPOO0pMTNQff/yhhQsXmtYHAHZBYw4UAYGBgQoPD9fPP/98zed4+eWXNXLkSLVp00bvv/++vvrqK61du1b16tVTTk6O67i6desqPj5eH374oVq1aqWPP/5YrVq10qRJk1zH9OrVS4cOHdKcOXMUHh6u6dOnq169evryyy+vqbaqVasqIyND586dMz2mTp06kqSffvrpmq5xNWZzyYZhXPW1Zg3hX3/QyMtne72u5338WfPmzVWjRg0988wzSkxMvOIMfK9evfTWW2/piSee0CeffKJ///vfrhs3//zv1tX8OXnPi6+++krSxZT+wIED+XotAHgDjTlQRNx11106ePCg4uLirun1K1asUPv27fX222+rd+/e6tixo6Kjoy/7ZS9lypTRAw88oIULF+rw4cO688479dJLL7mtX125cmU9+eSTWrlypRITE1WhQgW99NJL11TboUOH5O/vr4CAANNjatWqpdq1a+uzzz5TamrqVc8ZERGhAwcO5GoM9+/f73reU8qVK3fZz/GvabaUt8/2zy7VGR8f77Y/IyNDiYmJHn0ff9WnTx99++23qlu3rho3bnzZY06fPq1169Zp3LhxiomJ0b333qs77rhD1atXz3WsJxPtH3/8UVOmTNHAgQPVpEkTPfroo/lK5wHAG2jMgSJizJgxKlOmjB599FEdP3481/MHDx7UrFmzTF/v6+ubKzX96KOP9Ntvv7nt+/MSd9LFkYHIyEgZhqHMzExlZ2fnaoBCQkIUHh6u9PT0K76H33//Pde+3bt361//+pc6duyYa+zkr2JiYnTq1Ck9+uijl11q8d///rdWr14tSeratauSkpK0bNky1/NZWVmaM2eOAgIC1LZt2yteKz9q1Kih77//XhkZGa59q1ev1v/+9z+346722V5OdHS0/Pz8NHv2bLffv7ffflspKSm68847PfY+/urRRx/VpEmT9Oqrr5oecymh/+u/WzNnzsx17KVvSb3eb/7MzMzUgAEDFB4erlmzZmnRokU6fvy4RowYcV3nBYCCxnKJQBFRo0YNLV26VA888IDq1q3r9s2fW7ZscS0FaOauu+5yJYy33XabfvrpJy1ZsiRXstmxY0eFhYWpZcuWCg0N1b59+/Taa6/pzjvvVNmyZXXmzBndcMMNuu+++9SoUSMFBATo66+/1rZt267YwEkXb7AsVaqUbrvtNoWEhGjv3r168803Vbp0af3973+/6mfwwAMP6KefftJLL72kH374QX369HF98+eaNWu0bt061/rbjz32mN544w0NGDBAO3bs0E033aQVK1bou+++08yZM6/rRtq/evTRR7VixQp17txZvXr10sGDB/X+++/nWk7xap/t5VSqVEnjx49XTEyMOnfurG7duik+Pl6vv/66brnlFrcbPT0tIiLCtX64mcDAQLVp00bTpk1TZmamqlSpon//+99KTEzMdWzTpk0lSc8//7x69+6tkiVL6u6773Y17Hk1depU7dq1S+vWrVPZsmXVsGFDTZw4US+88ILuu+8+de3aNV/nAwDLeHFFGAAF4JdffjEGDx5s3HTTTYafn59RtmxZo2XLlsacOXOMtLQ013GXWy7x2WefNSpXrmyUKlXKaNmypREXF5drSb833njDaNOmjVGhQgXD6XQaNWrUMEaPHm2kpKQYhnFx+b3Ro0cbjRo1MsqWLWuUKVPGaNSokfH6669ftfZZs2YZt956q1G+fHmjRIkSRuXKlY1+/foZBw4cyNdnsG7dOuOee+4xQkJCjBIlShiVKlUy7r77buOzzz5zO+748ePGwIEDjYoVKxp+fn5GgwYNjIULF7odc2m5xOnTp+e6jv6yTJ/ZcomGYRivvvqqUaVKFcPpdBotW7Y0tm/fnu/P9s/X+OuSgq+99ppRp04do2TJkkZoaKgxZMgQ4/Tp027HtG3b9rLLMfbv39+IiIjItf+vLi2XeCWX+wyOHDli3HvvvUZwcLARFBRk3H///cbRo0cvu8zhiy++aFSpUsXw8fFxe5+STJfg/PN5duzYYZQoUcIYPny42zFZWVnGLbfcYoSHh+f6XADALhyGkc87fgAAAAB4HDPmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYQJH85s9STYZ5uwQA0Oltr3m7BACQv826Pav6tAs/FL4/g0nMAQAAABuw2c9QAAAAKNIc5MJm+GQAAAAAGyAxBwAAgHUcDm9XYFsk5gAAAIANkJgDAADAOsyYm+KTAQAAQLEWGxurW265RWXLllVISIi6d++u+Ph4t2PatWsnh8Phtj3xxBNuxxw+fFh33nmnSpcurZCQEI0ePVpZWVl5roPEHAAAANax4Yz5hg0bNHToUN1yyy3KysrSc889p44dO2rv3r0qU6aM67jBgwdrypQprselS5d2/To7O1t33nmnwsLCtGXLFh07dkwPP/ywSpYsqZdffjlPddCYAwAAoFhbs2aN2+NFixYpJCREO3bsUJs2bVz7S5curbCwsMue49///rf27t2rr7/+WqGhoWrcuLFefPFFjR07VpMnT5afn99V62CUBQAAANZx+FizXYeUlBRJUvny5d32L1myRBUrVlT9+vU1fvx4nT9/3vVcXFycGjRooNDQUNe+Tp066ezZs9qzZ0+erktiDgAAgCInPT1d6enpbvucTqecTucVX5eTk6NnnnlGLVu2VP369V37H3zwQUVERCg8PFw//vijxo4dq/j4eH3yySeSpKSkJLemXJLrcVJSUp5qpjEHAACAdSyaMY+NjVVMTIzbvkmTJmny5MlXfN3QoUP1888/a/PmzW77H3vsMdevGzRooMqVK6tDhw46ePCgatSo4ZGaGWUBAABAkTN+/HilpKS4bePHj7/ia4YNG6bVq1frm2++0Q033HDFY5s3by5JSkhIkCSFhYXp+PHjbsdcemw2l/5XNOYAAACwjkUz5k6nU4GBgW6b2RiLYRgaNmyYPv30U61fv17VqlW76tvYtWuXJKly5cqSpKioKP300086ceKE65i1a9cqMDBQkZGRefpoGGUBAABAsTZ06FAtXbpUn332mcqWLeuaCQ8KClKpUqV08OBBLV26VF27dlWFChX0448/asSIEWrTpo0aNmwoSerYsaMiIyP10EMPadq0aUpKStILL7ygoUOHXnWu/RIacwAAAFjHhuuYz5s3T9LFLxH6s4ULF2rAgAHy8/PT119/rZkzZ+rcuXOqWrWqevbsqRdeeMF1rK+vr1avXq0hQ4YoKipKZcqUUf/+/d3WPb8aGnMAAAAUa4ZhXPH5qlWrasOGDVc9T0REhL744otrroPGHAAAANa5zjXGizI+GQAAAMAGSMwBAABgHRvOmNsFiTkAAABgAyTmAAAAsA4z5qb4ZAAAAAAbIDEHAACAdZgxN0ViDgAAANgAiTkAAACsw4y5KT4ZAAAAwAZIzAEAAGAdEnNTfDIAAACADZCYAwAAwDo+rMpihsQcAAAAsAEScwAAAFiHGXNTfDIAAACADZCYAwAAwDp886cpEnMAAADABkjMAQAAYB1mzE3xyQAAAAA2QGIOAAAA6zBjborEHAAAALABEnMAAABYhxlzU3wyAAAAgA2QmAMAAMA6zJibIjEHAAAAbIDEHAAAANZhxtwUnwwAAABgAyTmAAAAsA4z5qZIzAEAAAAbIDEHAACAdZgxN8UnAwAAANgAiTkAAACsw4y5KRJzAAAAwAZIzAEAAGAdZsxN8ckAAAAANkBiDgAAAOuQmJvikwEAAABsgMQcAAAA1mFVFlMk5gAAAIANkJgDAADAOsyYm+KTAQAAAGyAxBwAAADWYcbcFIk5AAAAYAM05gAAAIANMMoCAAAA63Dzpyk+GQAAAMAGSMwBAABgHW7+NEViDgAAANgAiTkAAAAs4yAxN0ViDgAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiHwNwUiTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLkJibIzEHAAAAbIDEHAAAAJYhMTdHYg4AAADYAIk5AAAArENgborEHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxBwAAADWITA3RWIOAAAA2ACJOQAAACzDjLk5EnMAAADABkjMAQAAYBkSc3Mk5gAAAIANkJgDAADAMiTm5kjMAQAAABsgMQcAAIB1CMxNkZgDAAAANkBiDgAAAMswY26OxBwAAACwARJzAAAAWIbE3ByJOQAAAGADJOYAAACwDIm5ORJzAAAAwAZIzAEAAGAZEnNzJOYAAACADdCYAwAAwDoOi7Z8iI2N1S233KKyZcsqJCRE3bt3V3x8vNsxaWlpGjp0qCpUqKCAgAD17NlTx48fdzvm8OHDuvPOO1W6dGmFhIRo9OjRysrKynMdNOYAAAAo1jZs2KChQ4fq+++/19q1a5WZmamOHTvq3LlzrmNGjBihVatW6aOPPtKGDRt09OhR9ejRw/V8dna27rzzTmVkZGjLli169913tWjRIk2cODHPdTgMwzA8+s5soFSTYd4uAQB0ettr3i4BAORvszsKqwz51JLr/Dbv3mt+7e+//66QkBBt2LBBbdq0UUpKiipVqqSlS5fqvvvukyTt379fdevWVVxcnFq0aKEvv/xSd911l44eParQ0FBJ0vz58zV27Fj9/vvv8vPzu+p1ScwBAABQ5KSnp+vs2bNuW3p6ep5em5KSIkkqX768JGnHjh3KzMxUdHS065g6deroxhtvVFxcnCQpLi5ODRo0cDXlktSpUyedPXtWe/bsydN1acwBAABgGYfDYckWGxuroKAgty02Nvaq9eXk5OiZZ55Ry5YtVb9+fUlSUlKS/Pz8FBwc7HZsaGiokpKSXMf8uSm/9Pyl5/LCZn+5AQAAAFy/8ePHa+TIkW77nE7nVV83dOhQ/fzzz9q8eXNBlWaKxhwAAACWsWodc6fTmadG/M+GDRum1atXa+PGjbrhhhtc+8PCwpSRkaEzZ864pebHjx9XWFiY65j//Oc/bue7tGrLpWOuxhajLJs2bVK/fv0UFRWl3377TZL03nvveeUnFQAAABQvhmFo2LBh+vTTT7V+/XpVq1bN7fmmTZuqZMmSWrdunWtffHy8Dh8+rKioKElSVFSUfvrpJ504ccJ1zNq1axUYGKjIyMg81eH1xvzjjz9Wp06dVKpUKf3www+uofyUlBS9/PLLXq4OAAAAHmXDdcyHDh2q999/X0uXLlXZsmWVlJSkpKQkXbhwQZIUFBSkQYMGaeTIkfrmm2+0Y8cODRw4UFFRUWrRooUkqWPHjoqMjNRDDz2k3bt366uvvtILL7ygoUOH5jm593pjPnXqVM2fP19vvfWWSpYs6drfsmVL7dy504uVAQAAoDiYN2+eUlJS1K5dO1WuXNm1LVu2zHXMjBkzdNddd6lnz55q06aNwsLC9Mknn7ie9/X11erVq+Xr66uoqCj169dPDz/8sKZMmZLnOrw+Yx4fH682bdrk2h8UFKQzZ85YXxAAAAAKjFUz5vmRl6/18ff319y5czV37lzTYyIiIvTFF19ccx1eT8zDwsKUkJCQa//mzZtVvXp1L1QEAAAAWM/rjfngwYP19NNPa+vWrXI4HDp69KiWLFmiUaNGaciQId4uDwAAAB5k1TrmhZHXR1nGjRunnJwcdejQQefPn1ebNm3kdDo1atQoDR8+3NvlAQAAAJbwemPucDj0/PPPa/To0UpISFBqaqoiIyMVEBDg7dJQhIx6pKO6395ItW4K1YX0TG3dfUjPz/pMB/77f0saffXW02rT7Ga31721YrOeeulDt3397m6up/rdrpsjQnT2XJo+WfuDRvx9uSXvA0Dx8eHSJXp34ds6efJ31apdR+Oem6AGDRt6uyzguhXWNNsKXm/ML/Hz88vzGo9AfrX+W03NX7ZRO/b8VyVK+Cpm2N1aPW+YmvSYqvNpGa7j3v74O704b7Xr8fm0TLfzPNXvdj390O16bsZK/efnX1WmlJ8iwitY9j4AFA9rvvxCr0yL1QuTYtSgQSMtee9dDXl8kD5bvUYVKvBnDlBUeb0xb9++/RV/clq/fr2F1aCoumfY626PH5v0vv63/u9qEllV3+086Np/IS1Dx0/9cdlzBJctpUlP3qWez8zXt//5xbX/5wNHC6ZoAMXWe+8uVI/7eqn7vT0lSS9MitHGjd9q5Scfa9Dgx7xcHXB9SMzNeb0xb9y4sdvjzMxM7dq1Sz///LP69+/vnaJQ5AUG+EuSTqecd9v/QNdm6t31Fh0/dVZfbPxZsW99qQv/PzXv0KKOfHwcCg8J1g8fv6CyZZz6fneixv3zEx05fsbqtwCgiMrMyNC+vXs0aPDjrn0+Pj5q0eI2/bj7By9WBqCgeb0xnzFjxmX3T548WampqRZXg+LA4XBo+qj7tOWHg9p78Jhr/7Ivt+vwsWQd+z1FDW4O19Sn71GtiBD1HrVAklTthory8XFozCMdNWr6xzqbekGTht6l1fOG6ZZescrMyvbWWwJQhJw+c1rZ2dm5RlYqVKigxMRDXqoK8CACc1Neb8zN9OvXT7feeqteeeWVKx6Xnp6u9PR0t31GTrYcPr4FWR4KsZnje6lezcrqMND9h8J3PvnO9es9CUd17ORZrXnzKVW7oaISj5yUw+GQX8kSenbaCq37fr8kqf/4Rfp17ctqe0stfR23z9L3AQAAihavr2NuJi4uTv7+/lc9LjY2VkFBQW5b1vEdFlSIwmjG2PvVtXV9dRo8W7+dOHPFY7f99KskqUbVSpKkpJNnJUn7DyW5jjl5OlUnz6Sqali5AqkXQPFTLricfH19derUKbf9p06dUsWKFb1UFeA5rGNuzuuJeY8ePdweG4ahY8eOafv27ZowYcJVXz9+/HiNHDnSbV9I67EerRFFw4yx96vb7Y3UcfAs/ffoqase36j2DZKkpJMpkqS4XRf/Cvnmm0JcTX25wNKqGBygw8eSC6ZoAMVOST8/1Y2sp63fx+n2DtGSpJycHG3dGqfeffp5uToABcnrjXlQUJDbYx8fH9WuXVtTpkxRx44dr/p6p9Mpp9Ppto8xFvzVzPG99ECXZrp/xJtKPZem0AplJUkpqWlKS89UtRsq6oEuzfTV5j06deacGtSqomnP9tCmHQdcq64kHD6hVd/s1iuj79OwqR/obGqapgzvpvhfj2vD9l+udHkAyJeH+g/UhOfGql69+qrfoKHef+9dXbhwQd3v7XH1FwM2V1jTbCt4tTHPzs7WwIED1aBBA5UrxygACs7jvdpIktYueMZt/+CJ7+n9VVuVmZml25vX1rAH26tMKT8dOX5aK9ft0t8XfOV2/KAJ72naqB76ZPYQ5eQY2rzjgO4ZOldZWTlWvRUAxUDnLl11OjlZr782WydP/q7aderq9TcWqAKjLECR5jAMw/BmAf7+/tq3b5+qVavmsXOWajLMY+cCgGt1ettr3i4BAOTv9fkIdzVHfWnJdRJe6WLJdTzJ6zd/1q9fX4cOsfwTAAAAijevN+ZTp07VqFGjtHr1ah07dkxnz5512wAAAFB0sCqLOa/95caUKVP07LPPqmvXrpKkbt26uX2IhmHI4XAoO5svbQEAAEDR57XGPCYmRk888YS++eYbb5UAAAAAixXSMNsSXmvML91z2rZtW2+VAAAAANiGV+/TLazzPwAAALg29H/mvNqY16pV66q/OcnJfKMiAAAAij6vNuYxMTG5vvkTAAAARReBuTmvNua9e/dWSEiIN0sAAAAAbMFrjTnzRQAAAMWPjw89oBmvfcHQpVVZAAAAAHgxMc/JyfHWpQEAAOAlDE2Y81piDgAAAOD/ePXmTwAAABQv3GdojsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDGHAAAALABRlkAAABgGW7+NEdiDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZZgxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZZgxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZZgxN0diDgAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZQjMzZGYAwAAADZAYg4AAADLMGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsIwPkbkpEnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYxofA3BSJOQAAAGADJOYAAACwDDPm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMs4RGRuhsQcAAAAsAEacwAAAFjGx2HNlh8bN27U3XffrfDwcDkcDq1cudLt+QEDBsjhcLhtnTt3djsmOTlZffv2VWBgoIKDgzVo0CClpqbm77PJX9kAAABA0XLu3Dk1atRIc+fONT2mc+fOOnbsmGv74IMP3J7v27ev9uzZo7Vr12r16tXauHGjHnvssXzVwYw5AAAALGPHdcy7dOmiLl26XPEYp9OpsLCwyz63b98+rVmzRtu2bVOzZs0kSXPmzFHXrl31yiuvKDw8PE91kJgDAACgyElPT9fZs2fdtvT09Gs+37fffquQkBDVrl1bQ4YM0alTp1zPxcXFKTg42NWUS1J0dLR8fHy0devWPF+DxhwAAACWcTis2WJjYxUUFOS2xcbGXlPNnTt31uLFi7Vu3Tr94x//0IYNG9SlSxdlZ2dLkpKSkhQSEuL2mhIlSqh8+fJKSkrK83UYZQEAAECRM378eI0cOdJtn9PpvKZz9e7d2/XrBg0aqGHDhqpRo4a+/fZbdejQ4brq/DMacwAAAFjGx6IZc6fTec2N+NVUr15dFStWVEJCgjp06KCwsDCdOHHC7ZisrCwlJyebzqVfDqMsAAAAQD4cOXJEp06dUuXKlSVJUVFROnPmjHbs2OE6Zv369crJyVHz5s3zfF4ScwAAAFjGhouyKDU1VQkJCa7HiYmJ2rVrl8qXL6/y5csrJiZGPXv2VFhYmA4ePKgxY8aoZs2a6tSpkySpbt266ty5swYPHqz58+crMzNTw4YNU+/evfO8IotEYg4AAIBibvv27WrSpImaNGkiSRo5cqSaNGmiiRMnytfXVz/++KO6deumWrVqadCgQWratKk2bdrkNiqzZMkS1alTRx06dFDXrl3VqlUrvfnmm/mqg8QcAAAAlrHjOubt2rWTYRimz3/11VdXPUf58uW1dOnS66qDxBwAAACwARJzAAAAWMaGgbltkJgDAAAANkBiDgAAAMtYtY55YURiDgAAANhAnhLzf/3rX3k+Ybdu3a65GAAAABRt5OXm8tSYd+/ePU8nczgcys7Ovp56AAAAgGIpT415Tk5OQdcBAACAYsCO65jbxXXNmKelpXmqDgAAAKBYy3djnp2drRdffFFVqlRRQECADh06JEmaMGGC3n77bY8XCAAAgKLDx2HNVhjluzF/6aWXtGjRIk2bNk1+fn6u/fXr19eCBQs8WhwAAABQXOS7MV+8eLHefPNN9e3bV76+vq79jRo10v79+z1aHAAAAIoWh8NhyVYY5bsx/+2331SzZs1c+3NycpSZmemRogAAAIDiJt+NeWRkpDZt2pRr/4oVK9SkSROPFAUAAICiyeGwZiuM8rRc4p9NnDhR/fv312+//aacnBx98sknio+P1+LFi7V69eqCqBEAAAAo8vKdmN9zzz1atWqVvv76a5UpU0YTJ07Uvn37tGrVKt1xxx0FUSMAAACKCGbMzeU7MZek1q1ba+3atZ6uBQAAACi2rqkxl6Tt27dr3759ki7OnTdt2tRjRQEAAKBoKqxrjFsh3435kSNH1KdPH3333XcKDg6WJJ05c0a33XabPvzwQ91www2erhEAAAAo8vI9Y/7oo48qMzNT+/btU3JyspKTk7Vv3z7l5OTo0UcfLYgaAQAAUEQwY24u34n5hg0btGXLFtWuXdu1r3bt2pozZ45at27t0eIAAACA4iLfjXnVqlUv+0VC2dnZCg8P90hRAAAAKJoKZ5ZtjXyPskyfPl3Dhw/X9u3bXfu2b9+up59+Wq+88opHiwMAAACKizwl5uXKlXOb1Tl37pyaN2+uEiUuvjwrK0slSpTQI488ou7duxdIoQAAACj8fArp/LcV8tSYz5w5s4DLAAAAAIq3PDXm/fv3L+g6AAAAUAwQmJu75i8YkqS0tDRlZGS47QsMDLyuggAAAIDiKN+N+blz5zR27FgtX75cp06dyvV8dna2RwoDAABA0VNY1xi3Qr5XZRkzZozWr1+vefPmyel0asGCBYqJiVF4eLgWL15cEDUCAAAARV6+E/NVq1Zp8eLFateunQYOHKjWrVurZs2aioiI0JIlS9S3b9+CqBMAAAAo0vKdmCcnJ6t69eqSLs6TJycnS5JatWqljRs3erY6AAAAFCkOhzVbYZTvxrx69epKTEyUJNWpU0fLly+XdDFJDw4O9mhxAAAAQHGR71GWgQMHavfu3Wrbtq3GjRunu+++W6+99poyMzP1z3/+syBqBAAAQBHBFwyZy3djPmLECNevo6OjtX//fu3YsUM1a9ZUw4YNPVocAAAAUFxc1zrmkhQREaGIiAhP1AIAAIAijsDcXJ4a89mzZ+f5hE899dQ1FwMAAAAUV3lqzGfMmJGnkzkcDhpzAAAAmOILhszlqTG/tAoLAAAAgIJx3TPmdnR622veLgEAlJya4e0SAEDhwX7eLsFNvtfqLkb4bAAAAAAbKJKJOQAAAOyJGXNzJOYAAACADZCYAwAAwDI+BOamrikx37Rpk/r166eoqCj99ttvkqT33ntPmzdv9mhxAAAAQHGR78b8448/VqdOnVSqVCn98MMPSk9PlySlpKTo5Zdf9niBAAAAKDp8HNZshVG+G/OpU6dq/vz5euutt1SyZEnX/pYtW2rnzp0eLQ4AAAAoLvI9Yx4fH682bdrk2h8UFKQzZ854oiYAAAAUUazKYi7fiXlYWJgSEhJy7d+8ebOqV6/ukaIAAACA4ibfjfngwYP19NNPa+vWrXI4HDp69KiWLFmiUaNGaciQIQVRIwAAAIoIZszN5XuUZdy4ccrJyVGHDh10/vx5tWnTRk6nU6NGjdLw4cMLokYAAACgyHMYhmFcywszMjKUkJCg1NRURUZGKiAgwNO1XbO0LG9XAABScmqGt0sAAIUH+3m7BDdjPo+35DrT7qxtyXU86Zq/YMjPz0+RkZGerAUAAAAotvLdmLdv3/6Kd9OuX7/+ugoCAABA0eXDqiym8t2YN27c2O1xZmamdu3apZ9//ln9+/f3VF0AAABAsZLvxnzGjBmX3T958mSlpqZed0EAAAAouvK9JGAx4rHPpl+/fnrnnXc8dToAAACgWLnmmz//Ki4uTv7+/p46HQAAAIogRszN5bsx79Gjh9tjwzB07Ngxbd++XRMmTPBYYQAAAEBxku/GPCgoyO2xj4+PateurSlTpqhjx44eKwwAAABFD6uymMtXY56dna2BAweqQYMGKleuXEHVBAAAABQ7+br509fXVx07dtSZM2cKqBwAAAAUZQ6HNVthlO9VWerXr69Dhw4VRC0AAABAsZXvxnzq1KkaNWqUVq9erWPHjuns2bNuGwAAAGDGx2HNVhjlecZ8ypQpevbZZ9W1a1dJUrdu3eT4098TGIYhh8Oh7Oxsz1cJAAAAFHF5bsxjYmL0xBNP6JtvvinIegAAAFCEsSqLuTw35oZhSJLatm1bYMUAAAAAxVW+lkt08BMOAAAArgPtpLl8Nea1atW6anOenJx8XQUBAAAAxVG+GvOYmJhc3/wJAAAA5FVhXTHFCvlqzHv37q2QkJCCqgUAAAAotvLcmDNfDgAAgOvlED2lmTx/wdClVVkAAAAAeF6eE/OcnJyCrAMAAADFADPm5vKcmAMAAAAoOPm6+RMAAAC4HiTm5kjMAQAAABsgMQcAAIBlWOnPHIk5AAAAYAMk5gAAALAMM+bmSMwBAAAAGyAxBwAAgGUYMTdHYg4AAADYAIk5AAAALONDZG6KxBwAAACwARJzAAAAWIZVWcyRmAMAAAA2QGIOAAAAyzBibo7EHAAAALABEnMAAABYxkdE5mZIzAEAAAAboDEHAACAZRwOa7b82Lhxo+6++26Fh4fL4XBo5cqVbs8bhqGJEyeqcuXKKlWqlKKjo3XgwAG3Y5KTk9W3b18FBgYqODhYgwYNUmpqar7qoDEHAABAsXbu3Dk1atRIc+fOvezz06ZN0+zZszV//nxt3bpVZcqUUadOnZSWluY6pm/fvtqzZ4/Wrl2r1atXa+PGjXrsscfyVYfDMAzjut6JDaVlebsCAJCSUzO8XQIAKDzYz9sluJkf96sl13ki6qZrep3D4dCnn36q7t27S7qYloeHh+vZZ5/VqFGjJEkpKSkKDQ3VokWL1Lt3b+3bt0+RkZHatm2bmjVrJklas2aNunbtqiNHjig8PDxP1yYxBwAAQJGTnp6us2fPum3p6en5Pk9iYqKSkpIUHR3t2hcUFKTmzZsrLi5OkhQXF6fg4GBXUy5J0dHR8vHx0datW/N8LRpzAAAAWMbH4bBki42NVVBQkNsWGxub73qTkpIkSaGhoW77Q0NDXc8lJSUpJCTE7fkSJUqofPnyrmPyguUSAQAAUOSMHz9eI0eOdNvndDq9VE3e0JgDAADAMlZ986fT6fRIIx4WFiZJOn78uCpXruzaf/z4cTVu3Nh1zIkTJ9xel5WVpeTkZNfr84JRFgAAAMBEtWrVFBYWpnXr1rn2nT17Vlu3blVUVJQkKSoqSmfOnNGOHTtcx6xfv145OTlq3rx5nq9FYg4AAADL+FgVmedDamqqEhISXI8TExO1a9culS9fXjfeeKOeeeYZTZ06VTfffLOqVaumCRMmKDw83LVyS926ddW5c2cNHjxY8+fPV2ZmpoYNG6bevXvneUUWicYcAAAAxdz27dvVvn171+NLs+n9+/fXokWLNGbMGJ07d06PPfaYzpw5o1atWmnNmjXy9/d3vWbJkiUaNmyYOnToIB8fH/Xs2VOzZ8/OVx2sYw4ABYR1zAHYgd3WMX9n22FLrvPILTdach1PYsYcAAAAsAFGWQAAAGAZUmFzfDYAAACADZCYAwAAwDIOG67KYhck5gAAAIANkJgDAADAMuTl5kjMAQAAABsgMQcAAIBl7PjNn3ZBYg4AAADYAIk5AAAALENebo7EHAAAALABEnMAAABYhhFzcyTmAAAAgA2QmAMAAMAyfPOnORJzAAAAwAZIzAEAAGAZUmFzfDYAAACADZCYAwAAwDLMmJsjMQcAAABsgMQcAAAAliEvN0diDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZozAEAAAAbYJQFAAAAliEVNsdnAwAAANgAiTkAAAAsw82f5kjMAQAAABsgMQcAAIBlyMvNkZgDAAAANkBiDgAAAMswYm6OxBwAAACwARJzAAAAWMaHKXNTJOYAAACADZCYAwAAwDLMmJsjMQcAAABsgMQcAAAAlnEwY26KxBwAAACwARJzAAAAWIYZc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgGX45k9zJOYAAACADZCYAwAAwDI+BOamSMwBAAAAGyAxBwAAgGWYMTdnq8Q8IyND8fHxysrK8nYpAAAAgKVs0ZifP39egwYNUunSpVWvXj0dPnxYkjR8+HD9/e9/93J1AAAA8BSHw5qtMLJFYz5+/Hjt3r1b3377rfz9/V37o6OjtWzZMi9WBgAAAFjDFjPmK1eu1LJly9SiRQs5/vQjTr169XTw4EEvVgYAAABPYsbcnC0S899//10hISG59p87d86tUQcAAACKKls05s2aNdPnn3/uenypGV+wYIGioqK8VRYAAAA8zMdhzVYY2WKU5eWXX1aXLl20d+9eZWVladasWdq7d6+2bNmiDRs2eLs8AAAAoMDZIjFv1aqVdu3apaysLDVo0ED//ve/FRISori4ODVt2tTb5QEAAMBDHBb9Uxg5DMMwvF2Ep6WxDDoAG0hOzfB2CQCg8GA/b5fgZtMvpy25Tuta5Sy5jifZYpRl586dKlmypBo0aCBJ+uyzz7Rw4UJFRkZq8uTJ8vOz179QKNo+XLpE7y58WydP/q5ateto3HMT1KBhQ2+XBaCIWrJogTZ9+7UO/zdRTqe/6jVopMeGjdCNEdVcx7waG6Od277XyZO/q1Sp0qrXoJEeHzZCN95U3YuVA9eGdT3M2WKU5fHHH9cvv/wiSTp06JAeeOABlS5dWh999JHGjBnj5epQnKz58gu9Mi1Wjz85VB9+9Klq166jIY8P0qlTp7xdGoAiavcP29X9vt6a+/YSTZ/9prKysjTmqcd14cJ51zG16kRqzIQX9e6Hn2narPmSpNFPPa7s7GxvlQ2gANhilCUoKEg7d+5UjRo19I9//EPr16/XV199pe+++069e/fW//73v3ydj1EWXKu+ve9XvfoN9NwLEyVJOTk56tihrfo8+JAGDX7My9WhsGGUBdfizOlk3du5rWbOX6hGTZpd9piDB+L1aL/79P7HX6jKDVUtrhCFjd1GWb47YM0oS8ubC98oiy0Sc8MwlJOTI0n6+uuv1bVrV0lS1apVdfLkSW+WhmIkMyND+/buUYuo21z7fHx81KLFbfpx9w9erAxAcXIuNVWSFBgYdNnnL1w4rzWrV6pyeBWFhIZZWRqAAmaLGfNmzZpp6tSpio6O1oYNGzRv3jxJUmJiokJDQ71cHYqL02dOKzs7WxUqVHDbX6FCBSUmHvJSVQCKk5ycHL024x+q37CJqtW42e25lSs+1Buv/VNpFy6oasRNmj7nLZUsWdJLlQLXzochc1O2aMxnzpypvn37auXKlXr++edVs2ZNSdKKFSt02223XfG16enpSk9Pd9tn+DrldDoLrF4AAArCrOkvKfFQgua88W6u56I736lmt0bp1KnftXzJu4p57lm99tZ78uP/d0CRYYvGvGHDhvrpp59y7Z8+fbp8fX2v+NrY2FjFxMS47Xt+wiS9MHGyJ0tEMVAuuJx8fX1z3eh56tQpVaxY0UtVASguZk1/SXGbN2jWG4tU6TIjKgEBZRUQUFY33BihyPqN1C26pTZ9u04dOnX1QrXAtSMvN2eLGXMz/v7+V/1ruvHjxyslJcVtGz12vEUVoigp6eenupH1tPX7ONe+nJwcbd0ap4aNmnixMgBFmWEYmjX9JW3esF7/nPu2KoffkKfXGIahzExuMAaKEq8l5uXKlZMjjzNGycnJps85nbnHVliVBdfqof4DNeG5sapXr77qN2io9997VxcuXFD3e3t4uzQARdTM6S9p3VdfaOr0WSpdpoyST11c9KBMmQA5/f119Lf/6Zu1X6lZ8ygFlyuv308c1weL35bT6VTz21p7uXrgGhCZm/JaYz5z5kxvXRow1blLV51OTtbrr83WyZO/q3adunr9jQWqwCgLgALyr4+XSZJGDHnEbf/YCS+q813d5efn1E+7dujjD9/TH3+cVbnyFdSwSVPNWfCeypWvcLlTAiikbLGOuaeRmAOwA9YxB2AHdlvHfOvBFEuu07zG5ZcctTNb3Pz5Z2lpacrIcP+fWWBgoJeqAQAAAKxhi5s/z507p2HDhikkJERlypRRuXLl3DYAAAAUDQ6HNVthZIvGfMyYMVq/fr3mzZsnp9OpBQsWKCYmRuHh4Vq8eLG3ywMAAAAKnC1GWVatWqXFixerXbt2GjhwoFq3bq2aNWsqIiJCS5YsUd++fb1dIgAAADygkIbZlrBFYp6cnKzq1atLujhPfml5xFatWmnjxo3eLA0AAACwhC0a8+rVqysxMVGSVKdOHS1fvlzSxSQ9ODjYi5UBAADAoxwWbYWQVxvzQ4cOKScnRwMHDtTu3bslSePGjdPcuXPl7++vESNGaPTo0d4sEQAAALCEV9cx9/X11bFjxxQSEiJJeuCBBzR79mylpaVpx44dqlmzpho2bJjv87KOOQA7YB1zAHZgt3XMtyeeteQ6zaoVvuW2vdqY+/j4KCkpydWYly1bVrt373bNm18rGnMAdkBjDsAOaMwLD1usygIAAIDiobCuMW4Fr86YOxwOOf7yu/PXxwAAAEBx4NXE3DAMDRgwQE6nU5KUlpamJ554QmXKlHE77pNPPvFGeQAAAPAwIlhzXm3M+/fv7/a4X79+XqoEAAAA8C6v3vxZULj5E4AdcPMnADuw282fO/9rzc2ff4sofDd/2uILhgAAAIDijlVZAAAAYBkHU+amSMwBAAAAGyAxBwAAgGVYGdsciTkAAABgAzTmAAAAsIzDoi0/Jk+e7Priy0tbnTp1XM+npaVp6NChqlChggICAtSzZ08dP378mt7/ldCYAwAAoNirV6+ejh075to2b97sem7EiBFatWqVPvroI23YsEFHjx5Vjx49PF4DM+YAAACwjk1nzEuUKKGwsLBc+1NSUvT2229r6dKluv322yVJCxcuVN26dfX999+rRYsWHquBxBwAAABFTnp6us6ePeu2paenmx5/4MABhYeHq3r16urbt68OHz4sSdqxY4cyMzMVHR3tOrZOnTq68cYbFRcX59GaacwBAABgGYdF/8TGxiooKMhti42NvWxNzZs316JFi7RmzRrNmzdPiYmJat26tf744w8lJSXJz89PwcHBbq8JDQ1VUlKSRz8bRlkAAABQ5IwfP14jR4502+d0Oi97bJcuXVy/btiwoZo3b66IiAgtX75cpUqVKtA6/4zGHAAAAJaxah1zp9Np2ohfTXBwsGrVqqWEhATdcccdysjI0JkzZ9xS8+PHj192Jv16MMoCAAAA/ElqaqoOHjyoypUrq2nTpipZsqTWrVvnej4+Pl6HDx9WVFSUR69LYg4AAADL2HFRllGjRunuu+9WRESEjh49qkmTJsnX11d9+vRRUFCQBg0apJEjR6p8+fIKDAzU8OHDFRUV5dEVWSQacwAAABRzR44cUZ8+fXTq1ClVqlRJrVq10vfff69KlSpJkmbMmCEfHx/17NlT6enp6tSpk15//XWP1+EwDMPw+Fm9LC3L2xUAgJScmuHtEgBA4cF+3i7Bzc+/pVpynfpVAiy5jicxYw4AAADYAKMsAAAAsIzDllPm9kBiDgAAANgAiTkAAAAsY9U65oURiTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMYcAAAAsAFGWQAAAGAdZllMkZgDAAAANkBiDgAAAMvwBUPmSMwBAAAAGyAxBwAAgGX4giFzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAdYjMTZGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIfI3BSJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALERkbobEHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALONgytwUiTkAAABgAyTmAAAAsA6BuSkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAOsQmJsiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAMM+bmSMwBAAAAG6AxBwAAAGyAxhwAAACwAWbMAQAAYBlmzM2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAANYhMjdFYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAdInNTJOYAAACADdCYAwAAwDIOi/7Jr7lz5+qmm26Sv7+/mjdvrv/85z8F8O6vjMYcAAAAxdqyZcs0cuRITZo0STt37lSjRo3UqVMnnThxwtI6HIZhGJZe0QJpWd6uAACk5NQMb5cAAAoP9vN2CW6s6tP883EnZfPmzXXLLbfotddekyTl5OSoatWqGj58uMaNG1dAFeZGYg4AAIAiJz09XWfPnnXb0tPTcx2XkZGhHTt2KDo62rXPx8dH0dHRiouLs7LkorkqS35+QgIuJz09XbGxsRo/frycTqe3y0EhZbeUCoULfw6hqLKqT5s8NVYxMTFu+yZNmqTJkye77Tt58qSys7MVGhrqtj80NFT79+8v6DLdFMlRFuB6nT17VkFBQUpJSVFgYKC3ywFQDPHnEHB90tPTcyXkTqcz1w+6R48eVZUqVbRlyxZFRUW59o8ZM0YbNmzQ1q1bLalXKqKJOQAAAIq3yzXhl1OxYkX5+vrq+PHjbvuPHz+usLCwgirvspgxBwAAQLHl5+enpk2bat26da59OTk5WrdunVuCbgUScwAAABRrI0eOVP/+/dWsWTPdeuutmjlzps6dO6eBAwdaWgeNOXAZTqdTkyZN4oYrAF7Dn0OAdR544AH9/vvvmjhxopKSktS4cWOtWbMm1w2hBY2bPwEAAAAbYMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwDAZhYtWqTg4GBvlwHAYjTmKNIcDscVt79+LS8AeNKAAQMu+2dPQkKCt0sDYEMsl4gi7dixY65fL1u2TBMnTlR8fLxrX0BAgOvXhmEoOztbJUrwnwUAz+ncubMWLlzotq9SpUpeqgaAnZGYo0gLCwtzbUFBQXI4HK7H+/fvV9myZfXll1+qadOmcjqd2vz/2Lvv8CjK/f//r00gm5BKCyGI9N5ERI0IgSNSFRAQQVRAQEUiTerXQwnFeLDQVLBSFJCi4gFsFGmCHIqAUiKEIAcpeggEA6TP7w9+7MclDCSYzE6S5+Nce13szD0z7x09+M4r99y7ZYt69+6tTp06uZ1nyJAhat68uet9ZmamYmJiVKlSJfn5+alBgwZavny5tR8OQL7gdDrd/i4KCwvTjBkzVK9ePfn7+6t8+fJ6/vnnlZSUZHqOvXv3qkWLFgoMDFRQUJAaNWqknTt3uvZv2bJFTZs2lZ+fn8qXL69Bgwbp4sWLVnw8ALmIxhyF3ujRo/XKK6/o4MGDql+/fraOiYmJ0YIFCzRnzhzt379fQ4cO1RNPPKGNGzfmcbUACgIvLy/NnDlT+/fv1/z587V+/XqNHDnSdHzPnj112223aceOHdq1a5dGjx6tokWLSpLi4uLUpk0bdenSRfv27dOSJUu0ZcsWRUVFWfVxAOQSfmePQm/ixIl68MEHsz0+JSVFL7/8stauXauIiAhJUuXKlbVlyxa98847ioyMzKtSAeRDq1atcps217ZtWy1btsz1vmLFipo8ebKee+45vf3229c9x/HjxzVixAjVrFlTklStWjXXvpiYGPXs2VNDhgxx7Zs5c6YiIyM1e/Zs+fr65sGnApAXaMxR6N111105Gn/kyBFdunQpSzOfmpqqhg0b5mZpAAqAFi1aaPbs2a73/v7+Wrt2rWJiYnTo0CFduHBB6enpSk5O1qVLl1SsWLEs5xg2bJj69eunjz76SC1bttSjjz6qKlWqSLoyzWXfvn1auHCha7xhGMrMzFR8fLxq1aqV9x8SQK6gMUeh5+/v7/bey8tLhmG4bUtLS3P9+eo80NWrV6tcuXJu45xOZx5VCSC/8vf3V9WqVV3vjx07poceekgDBgzQlClTVKJECW3ZskV9+/ZVamrqdRvzCRMm6PHHH9fq1av11Vdfafz48frkk0/0yCOPKCkpSc8++6wGDRqU5bjbb789Tz8bgNxFYw5co3Tp0vr555/dtu3Zs8c1n7N27dpyOp06fvw401YA5NiuXbuUmZmp119/XV5eVx71Wrp06U2Pq169uqpXr66hQ4eqR48emjt3rh555BHdeeedOnDggFvzDyB/4uFP4Br/+Mc/tHPnTi1YsECHDx/W+PHj3Rr1wMBADR8+XEOHDtX8+fMVFxen3bt3a9asWZo/f74HKweQH1StWlVpaWmaNWuWjh49qo8++khz5swxHX/58mVFRUVpw4YN+vXXX/X9999rx44drikqo0aN0tatWxUVFaU9e/bo8OHD+uKLL3j4E8iHaMyBa7Ru3Vpjx47VyJEj1bhxY/3555966qmn3MZMmjRJY8eOVUxMjGrVqqU2bdpo9erVqlSpkoeqBpBfNGjQQG+88Yb+9a9/qW7dulq4cKFiYmJMx3t7e+vs2bN66qmnVL16dXXr1k1t27ZVdHS0JKl+/frauHGjfvnlFzVt2lQNGzbUuHHjFB4ebtVHApBLHMa1k2kBAAAAWI7EHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhxAodW7d2916tTJ9b558+YaMmSI5XVs2LBBDodD58+fNx3jcDi0YsWKbJ9zwoQJuuOOO/5WXceOHZPD4dCePXv+1nkAANlDYw7AVnr37i2HwyGHwyEfHx9VrVpVEydOVHp6ep5f+7PPPtOkSZOyNTY7zTQAADlRxNMFAMC12rRpo7lz5yolJUVffvmlBg4cqKJFi2rMmDFZxqampsrHxydXrluiRIlcOQ8AALeCxByA7TidToWFhalChQoaMGCAWrZsqX//+9+S/m/6yZQpUxQeHq4aNWpIkv773/+qW7duCgkJUYkSJdSxY0cdO3bMdc6MjAwNGzZMISEhKlmypEaOHCnDMNyue+1UlpSUFI0aNUrly5eX0+lU1apV9cEHH+jYsWNq0aKFJKl48eJyOBzq3bu3JCkzM1MxMTGqVKmS/Pz81KBBAy1fvtztOl9++aWqV68uPz8/tWjRwq3O7Bo1apSqV6+uYsWKqXLlyho7dqzS0tKyjHvnnXdUvnx5FStWTN26dVNiYqLb/vfff1+1atWSr6+vatasqbfffjvHtQAAcgeNOQDb8/PzU2pqquv9unXrFBsbqzVr1mjVqlVKS0tT69atFRgYqM2bN+v7779XQECA2rRp4zru9ddf17x58/Thhx9qy5YtSkhI0Oeff37D6z711FNavHixZs6cqYMHD+qdd95RQECAypcvr08//VSSFBsbq1OnTmnGjBmSpJiYGC1YsEBz5szR/v37NXToUD3xxBPauHGjpCs/QHTu3FkPP/yw9uzZo379+mn06NE5vieBgYGaN2+eDhw4oBkzZui9997TtGnT3MYcOXJES5cu1cqVK/X111/rxx9/1PPPP+/av3DhQo0bN05TpkzRwYMH9fLLL2vs2LGaP39+jusBAOQCAwBspFevXkbHjh0NwzCMzMxMY82aNYbT6TSGDx/u2l+mTBkjJSXFdcxHH31k1KhRw8jMzHRtS0lJMfz8/IxvvvnGMAzDKFu2rDF16lTX/rS0NOO2225zXcswDCMyMtIYPHiwYRiGERsba0gy1qxZc906v/vuO0OSce7cOde25ORko1ixYsbWrVvdxvbt29fo0aOHYRiGMWbMGKN27dpu+0eNGpXlXNeSZHz++eem+1999VWjUaNGrvfjx483vL29jRMnTri2ffXVV4aXl5dx6tQpwzAMo0qVKsaiRYvczjNp0iQjIiLCMAzDiI+PNyQZP/74o+l1AQC5hznmAGxn1apVCggIUFpamjIzM/X4449rwoQJrv316tVzm1e+d+9eHTlyRIGBgW7nSU5OVlxcnBITE3Xq1Cndc889rn1FihTRXXfdlWU6y1V79uyRt7e3IiMjs133kSNHdOnSJT344INu21NTU9WwYUNJ0sGDB93qkKSIiIhsX+OqJUuWaObMmYqLi1NSUpLS09MVFBTkNub2229XuXLl3K6TmZmp2NhYBQYGKi4uTn379lX//v1dY9LT0xUcHJzjegAAfx+NOQDbadGihWbPni0fHx+Fh4erSBH3v6r8/f3d3iclJalRo0ZauHBhlnOVLl36lmrw8/PL8TFJSUmSpNWrV7s1xNKVefO5Zdu2berZs6eio6PVunVrBQcH65NPPtHrr7+e41rfe++9LD8oeHt751qtAIDsozEHYDv+/v6qWrVqtsffeeedWrJkiUJDQ7OkxleVLVtW27dvV7NmzSRdSYZ37dqlO++887rj69Wrp8zMTG3cuFEtW7bMsv9qYp+RkeHaVrt2bTmdTh0/ftw0aa9Vq5brQdarfvjhh5t/yL/YunWrKlSooJdeesm17ddff80y7vjx4zp58qTCw8Nd1/Hy8lKNGjVUpkwZhYeH6+jRo+rZs2eOrg8AyBs8/Akg3+vZs6dKlSqljh07avPmzYqPj9eGDRs0aNAgnThxQpI0ePBgvfLKK1qxYoUOHTqk559//oZrkFesWFG9evXS008/rRUrVrjOuXTpUklShQoV5HA4tGrVKv3xxx9KSkpSYGCghg8frqFDh2r+/PmKi4vT7t27NWvWLNcDlc8995wOHz6sESNGKDY2VosWLdK8efNy9HmrVaum48eP65NPPlFcXJxmzpx53QdZfX191atXL+3du1ebN2/WoEGD1K1bN4WFhUmSoqOjFRMTo5kzZ+qXX37RTz/9pLlz5+qNN97IUT0AgNxBYw4g3ytWrJg2bdqk22+/XZ07d1atWrXUt29fJScnuxL0F198UU8++aR69eqliIgIBQYG6pFHHrnheWfPnq2uXbvq+eefV82aNdW/f39dvHhRklSuXDlFR0dr9OjRKlOmjKKioiRJkyZN0tixYxUTE6NatWqpTZs2Wr16tSpVqiTpyrzvTz/9VCtWrFCDBg00Z84cvfzyyzn6vB06dNDQoUMVFRWlO+64Q1u3btXYsWOzjKtatao6d+6sdu3aqVWrVqpfv77bcoj9+vXT+++/r7lz56pevXqKjIzUvHnzXLUCAKzlMMyefAIAAABgGRJzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMgUKqYsWK6t27t6fLKHC+/vpr3XHHHfL19ZXD4dD58+dz9fzz5s2Tw+HQsWPHcvW8+ZnD4dCECRM8XQYA/G005kABExcXp2effVaVK1eWr6+vgoKC1KRJE82YMUOXL1/2dHnZsnv3bnXo0EElSpRQsWLFVLduXc2cOTPbx2/YsEGdO3dWWFiYfHx8FBoaqocfflifffZZHlYtnT17Vt26dZOfn5/eeustffTRR/L398/Ta1qpYsWKcjgcatmy5XX3v/fee3I4HHI4HNq5c2eOz79161ZNmDAh13+YAYD8ooinCwCQe1avXq1HH31UTqdTTz31lOrWravU1FRt2bJFI0aM0P79+/Xuu+96uswb+vbbb/Xwww+rYcOGGjt2rAICAhQXF6cTJ05k6/jx48dr4sSJqlatmp599llVqFBBZ8+e1ZdffqkuXbpo4cKFevzxx/Ok9h07dujPP//UpEmTTJvXv+vJJ59U9+7d5XQ68+T8N+Pr66vvvvtOp0+fVlhYmNu+hQsXytfXV8nJybd07q1btyo6Olq9e/dWSEhIto+7fPmyihThP2cA8j/+JgMKiPj4eHXv3l0VKlTQ+vXrVbZsWde+gQMH6siRI1q9erUHK7y5Cxcu6KmnnlL79u21fPlyeXnl7Jd6y5cv18SJE9W1a1ctWrRIRYsWde0bMWKEvvnmG6WlpeV22S6///67JOWoqcwpb29veXt759n5b6ZJkybasWOHlixZosGDB7u2nzhxQps3b9YjjzyiTz/9NM/ryMzMVGpqqnx9feXr65vn1wMAKzCVBSggpk6dqqSkJH3wwQduTflVVatWdWukrpWQkKDhw4erXr16CggIUFBQkNq2bau9e/dmGTtr1izVqVNHxYoVU/HixXXXXXdp0aJFrv1//vmnhgwZoooVK8rpdCo0NFQPPvigdu/efcPPsGjRIp05c0ZTpkyRl5eXLl68qMzMzGzfg7Fjx6pEiRL68MMP3Zryq1q3bq2HHnrI9f73339X3759VaZMGfn6+qpBgwaaP3++2zHHjh2Tw+HQa6+9pnfffVdVqlSR0+lU48aNtWPHDte45s2bq1evXpKkxo0by+FwuObwm83nb968uZo3b+627Wb31myO+dtvv606derI6XQqPDxcAwcOzDIlpHnz5qpbt64OHDigFi1aqFixYipXrpymTp1qdkuz8PX1VefOnd1qkqTFixerePHiat26dZZj9u3bp969e7umV4WFhenpp5/W2bNnXWMmTJigESNGSJIqVarkmhJz9XM6HA5FRUVp4cKFrs/59ddfu/ZdnWN++fJl1axZUzVr1nSbupWQkKCyZcvqvvvuU0ZGRrY/LwBYicQcKCBWrlypypUr67777rul448ePaoVK1bo0UcfVaVKlXTmzBm98847ioyM1IEDBxQeHi7pyjziQYMGqWvXrho8eLCSk5O1b98+bd++3TVF5LnnntPy5csVFRWl2rVr6+zZs9qyZYsOHjyoO++807SGtWvXKigoSL/99ps6deqkX375Rf7+/nryySc1bdq0Gyajhw8f1qFDh/T0008rMDDwpp/38uXLat68uY4cOaKoqChVqlRJy5YtU+/evXX+/PksP8QsWrRIf/75p5599lk5HA5NnTpVnTt31tGjR1W0aFG99NJLqlGjht59911NnDhRlSpVUpUqVbJz612yc2+vZ8KECYqOjlbLli01YMAAxcbGavbs2dqxY4e+//57tx9Szp07pzZt2qhz587q1q2bli9frlGjRqlevXpq27Zttup8/PHH1apVK8XFxbk+46JFi9S1a9fr/kC0Zs0aHT16VH369FFYWJhrStX+/fv1ww8/yOFwqHPnzvrll1+0ePFiTZs2TaVKlZIklS5d2nWe9evXa+nSpYqKilKpUqVUsWLFLNfy8/PT/Pnz1aRJE7300kt64403JF35rVFiYqLmzZvn0d84AMANGQDyvcTEREOS0bFjx2wfU6FCBaNXr16u98nJyUZGRobbmPj4eMPpdBoTJ050bevYsaNRp06dG547ODjYGDhwYLZruap+/fpGsWLFjGLFihkvvPCC8emnnxovvPCCIcno3r37DY/94osvDEnGtGnTsnWt6dOnG5KMjz/+2LUtNTXViIiIMAICAowLFy4YhnHlHkgySpYsaSQkJGS53sqVK13b5s6da0gyduzY4Xata+/1VZGRkUZkZKTrfXbu7dVrxMfHG4ZhGL///rvh4+NjtGrVyu2f35tvvmlIMj788EO360kyFixY4NqWkpJihIWFGV26dLnhda9+jvbt2xvp6elGWFiYMWnSJMMwDOPAgQOGJGPjxo3XvQeXLl3Kcq7FixcbkoxNmza5tr366qtun+2vJBleXl7G/v37r7tv/PjxbtvGjBljeHl5GZs2bTKWLVtmSDKmT59+088IAJ7EVBagALhw4YIkZSspNuN0Ol1zujMyMnT27FkFBASoRo0ablNQQkJCdOLECbdpHNcKCQnR9u3bdfLkyRzVkJSUpEuXLumpp57SzJkz1blzZ82cOVPPPvusPvnkEx0+fNj02Jzegy+//FJhYWHq0aOHa1vRokU1aNAgJSUlaePGjW7jH3vsMRUvXtz1vmnTppKu/KYht2Tn3l5r7dq1Sk1N1ZAhQ9zm5Pfv319BQUFZnisICAjQE0884Xrv4+Oju+++O0efw9vbW926ddPixYslXXnos3z58q57ci0/Pz/Xn5OTk/W///1P9957ryTddHrTX0VGRqp27drZGjthwgTVqVNHvXr10vPPP6/IyEgNGjQo29cCAE+gMQcKgKCgIElX5nbfqszMTE2bNk3VqlWT0+lUqVKlVLp0ae3bt0+JiYmucaNGjVJAQIDuvvtuVatWTQMHDtT333/vdq6pU6fq559/Vvny5XX33XdrwoQJ2Wr8rjZwf22WJbmmcWzbts302Jzeg19//VXVqlXL8oBprVq1XPv/6vbbb3d7f7VJP3fuXLaulx3ZubfXulpnjRo13Lb7+PiocuXKWT7HbbfdJofD4batePHiOf4cjz/+uA4cOKC9e/dq0aJF6t69e5bzXpWQkKDBgwerTJky8vPzU+nSpVWpUiVJcvt362auHpMdPj4++vDDDxUfH68///xTc+fONa0PAOyCxhwoAIKCghQeHq6ff/75ls/x8ssva9iwYWrWrJk+/vhjffPNN1qzZo3q1Knj9gBmrVq1FBsbq08++UT333+/Pv30U91///0aP368a0y3bt109OhRzZo1S+Hh4Xr11VdVp04dffXVVzes4eo89jJlyrhtDw0NlXTjJrhmzZqSpJ9++ilnHzybzOYlG4Zx02PNGsJrH0LMzr39u/7O5/ire+65R1WqVNGQIUMUHx9/wznw3bp103vvvafnnntOn332mb799lvXg5s5ebj3r8l7dnzzzTeSrqT0N/ptCwDYBY05UEA89NBDiouLu2GqfCPLly9XixYt9MEHH6h79+5q1aqVWrZsed0ve/H399djjz2muXPn6vjx42rfvr2mTJnitn512bJl9fzzz2vFihWKj49XyZIlNWXKlBvW0KhRI0nSb7/95rb96pSYvz4IeK3q1aurRo0a+uKLL5SUlHTTz1uhQgUdPnw4S2N46NAh1/7cUrx48evex2vTbCl79/avrtYZGxvrtj01NVXx8fG5+jmu1aNHD23YsEG1atXSHXfccd0x586d07p16zR69GhFR0frkUce0YMPPqjKlStnGZubifa+ffs0ceJE9enTRw0bNlS/fv1ylM4DgCfQmAMFxMiRI+Xv769+/frpzJkzWfbHxcVpxowZpsd7e3tnSU2XLVuWpUn+6xJ30pUpA7Vr15ZhGEpLS1NGRkaWBig0NFTh4eFKSUm54Wfo1q2bJOmDDz5w2/7++++rSJEiWZYWvFZ0dLTOnj2rfv36KT09Pcv+b7/9VqtWrZIktWvXTqdPn9aSJUtc+9PT0zVr1iwFBAQoMjLyhtfKiSpVquiHH35Qamqqa9uqVav03//+123cze7t9bRs2VI+Pj6aOXOm2z+/Dz74QImJiWrfvn2ufY5r9evXT+PHj9frr79uOuZqQn/tv1vTp0/PMvbqt6T+3W/+TEtLU+/evRUeHq4ZM2Zo3rx5OnPmjIYOHfq3zgsAeY3lEoECokqVKlq0aJEee+wx1apVy+2bP7du3epaCtDMQw895EoY77vvPv30009auHBhlmSzVatWCgsLU5MmTVSmTBkdPHhQb775ptq3b6/AwECdP39et912m7p27aoGDRooICBAa9eu1Y4dO27YwElSw4YN9fTTT+vDDz9Uenq6IiMjtWHDBi1btkxjxoxxTXUx89hjj+mnn37SlClT9OOPP6pHjx6ub/78+uuvtW7dOtf6288884zeeecd9e7dW7t27VLFihW1fPlyff/995o+ffrfepD2Wv369dPy5cvVpk0bdevWTXFxcfr444+zLKd4s3t7PaVLl9aYMWMUHR2tNm3aqEOHDoqNjdXbb7+txo0buz3omdsqVKjgWj/cTFBQkJo1a6apU6cqLS1N5cqV07fffqv4+PgsY6/+xuSll15S9+7dVbRoUT388MOuhj27Jk+erD179mjdunUKDAxU/fr1NW7cOP3zn/9U165d1a5duxydDwAs48EVYQDkgV9++cXo37+/UbFiRcPHx8cIDAw0mjRpYsyaNctITk52jbvecokvvviiUbZsWcPPz89o0qSJsW3btixL+r3zzjtGs2bNjJIlSxpOp9OoUqWKMWLECCMxMdEwjCvL740YMcJo0KCBERgYaPj7+xsNGjQw3n777WzVn5qaakyYMMGoUKGCUbRoUaNq1arZXgLxqnXr1hkdO3Y0QkNDjSJFihilS5c2Hn74YeOLL75wG3fmzBmjT58+RqlSpQwfHx+jXr16xty5c93GXF0u8dVXX81yHV2zTJ/ZcomGYRivv/66Ua5cOcPpdBpNmjQxdu7cmeN7+9drXLuk4JtvvmnUrFnTKFq0qFGmTBljwIABxrlz59zGREZGXnc5xl69ehkVKlTIsv1aV5dLvJHr3YMTJ04YjzzyiBESEmIEBwcbjz76qHHy5MnrLnM4adIko1y5coaXl5fb55RkugTnX8+za9cuo0iRIsYLL7zgNiY9Pd1o3LixER4enuW+AIBdOAwjh0/8AAAAAMh1zDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGCuQ3f/o1jPJ0CQCgczve9HQJACBfm3V7VvVpl3/Mf38Hk5gDAAAANmCzn6EAAABQoDnIhc1wZwAAAAAbIDEHAACAdRwOT1dgWyTmAAAAgA2QmAMAAMA6zDE3xZ0BAAAAbIDEHAAAANZhjrkpEnMAAADABkjMAQAAYB3mmJvizgAAAAA2QGIOAAAA6zDH3BSJOQAAAGADJOYAAACwDnPMTXFnAAAAABsgMQcAAIB1mGNuisQcAAAAsAEScwAAAFiHOeamuDMAAACADZCYAwAAwDrMMTdFYg4AAADYAIk5AAAArMMcc1PcGQAAAMAGSMwBAABgHeaYmyIxBwAAAGyAxBwAAADWYY65Ke4MAAAAYAMk5gAAALAOibkp7gwAAABgAyTmAAAAsI4Xq7KYITEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA7f/GmKxBwAAACwARJzAAAAWIc55qa4MwAAAIANkJgDAADAOswxN0ViDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA5zzE2RmAMAAAA2QGIOAAAA6zDH3BR3BgAAALABEnMAAABYhznmpkjMAQAAABugMQcAAABsgKksAAAAsA4Pf5rizgAAAAA2QGIOAAAA65CYm+LOAAAAADZAYg4AAADrsFyiKRJzAAAAwAZIzAEAAGAd5pib4s4AAAAANkBiDgAAAOswx9wUiTkAAABgAyTmAAAAsA5zzE1xZwAAAAAbIDEHAACAdZhjborEHAAAALABEnMAAABYxkFiborEHAAAALABEnMAAABYhsTcHIk5AAAAYAMk5gAAALAOgbkpEnMAAADABkjMAQAAYBnmmJsjMQcAAABsgMQcAAAAliExN0diDgAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAyTmAAAAsA6BuSkScwAAAMAGSMwBAABgGeaYmyMxBwAAAGyAxBwAAACWITE3R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwARJzAAAAWIbE3ByJOQAAAGADNOYAAACwjsOiVw7ExMSocePGCgwMVGhoqDp16qTY2Fi3McnJyRo4cKBKliypgIAAdenSRWfOnHEbc/z4cbVv317FihVTaGioRowYofT09GzXQWMOAACAQm3jxo0aOHCgfvjhB61Zs0ZpaWlq1aqVLl686BozdOhQrVy5UsuWLdPGjRt18uRJde7c2bU/IyND7du3V2pqqrZu3ar58+dr3rx5GjduXLbrcBiGYeTqJ7MBv4ZRni4BAHRux5ueLgEA5GuzJwpL9f7Ekuv8b173Wz72jz/+UGhoqDZu3KhmzZopMTFRpUuX1qJFi9S1a1dJ0qFDh1SrVi1t27ZN9957r7766is99NBDOnnypMqUKSNJmjNnjkaNGqU//vhDPj4+N70uiTkAAAAKnJSUFF24cMHtlZKSkq1jExMTJUklSpSQJO3atUtpaWlq2bKla0zNmjV1++23a9u2bZKkbdu2qV69eq6mXJJat26tCxcuaP/+/dm6Lo05AAAALONwOCx5xcTEKDg42O0VExNz0/oyMzM1ZMgQNWnSRHXr1pUknT59Wj4+PgoJCXEbW6ZMGZ0+fdo15q9N+dX9V/dlh81+uQEAAAD8fWPGjNGwYcPctjmdzpseN3DgQP3888/asmVLXpVmisYcAAAAlrFqHXOn05mtRvyvoqKitGrVKm3atEm33Xaba3tYWJhSU1N1/vx5t9T8zJkzCgsLc435z3/+43a+q6u2XB1zM0xlAQAAQKFmGIaioqL0+eefa/369apUqZLb/kaNGqlo0aJat26da1tsbKyOHz+uiIgISVJERIR++ukn/f77764xa9asUVBQkGrXrp2tOkjMAQAAYB0bfvHnwIEDtWjRIn3xxRcKDAx0zQkPDg6Wn5+fgoOD1bdvXw0bNkwlSpRQUFCQXnjhBUVEROjee++VJLVq1Uq1a9fWk08+qalTp+r06dP65z//qYEDB2Y7uacxBwAAQKE2e/ZsSVLz5s3dts+dO1e9e/eWJE2bNk1eXl7q0qWLUlJS1Lp1a7399tuusd7e3lq1apUGDBigiIgI+fv7q1evXpo4cWK262AdcwDII6xjDsAO7LaOeZl+yyy5zpn3H7XkOrmJOeYAAACADdjsZygAAAAUZFatypIfkZgDAAAANkBiDgAAAMuQmJsjMQcAAABsgMQcAAAAliExN0diDgAAANgAiTkAAACsQ2BuisQcAAAAsAEScwAAAFiGOebmSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAAy5CYm7NFYr5582Y98cQTioiI0G+//SZJ+uijj7RlyxYPVwYAAABYw+ON+aeffqrWrVvLz89PP/74o1JSUiRJiYmJevnllz1cHQAAAHKVw6JXPuTxxnzy5MmaM2eO3nvvPRUtWtS1vUmTJtq9e7cHKwMAAACs4/E55rGxsWrWrFmW7cHBwTp//rz1BQEAACDPMMfcnMcT87CwMB05ciTL9i1btqhy5coeqAgAAACwnscb8/79+2vw4MHavn27HA6HTp48qYULF2r48OEaMGCAp8sDAABALnI4HJa88iOPT2UZPXq0MjMz9cADD+jSpUtq1qyZnE6nhg8frhdeeMHT5QEAAACW8Hhj7nA49NJLL2nEiBE6cuSIkpKSVLt2bQUEBHi6NBQgw59upU7/aKDqFcvockqatu89qpdmfKHDv/5+3fEr3hyg1k3qqNvQd7Vywz7X9ss/vpll7FOj52rZN7vyrHYAhcuunTs078MPdPDAz/rjjz80beZb+scDLT1dFpBr8muabQWPN+ZX+fj4qHbt2p4uAwVU0zuras6STdq1/1cVKeKt6KiHtWp2lBp2nqxLyaluY1/o2UKGYX6u/uM+0pqtB1zvz/95Oa/KBlAIXb58STVq1FCnzl00bHCUp8sBYCGPN+YtWrS44U9O69evt7AaFFQdo952e//M+I/13/WvqGHt8vp+d5xre/3q5TT4yX+oSc+pOrY25rrnSvzzss6c/TNP6wVQeN3fNFL3N430dBlAniExN+fxxvyOO+5we5+WlqY9e/bo559/Vq9evTxTFAq8oABfSdK5xEuubX6+RTUvpreGvLL0ho339DHd9Pa4x3Xst//pveVbtOCLH/K8XgAAUPB5vDGfNm3adbdPmDBBSUlJFleDwsDhcOjV4V219cc4HYg75do+9cUu+mFvvFZt+Mn02Oi3V2njf37RpeRUtYyoqRljHlNAMafeXrzRitIBAMj/CMxNebwxN/PEE0/o7rvv1muvvXbDcSkpKUpJSXHbZmRmyOHlnZflIR+bPqab6lQtqwf6/N8Phe0j66n53dV1b/dXbnjsK+997frz3tgTKubn1NCnWtKYAwCAv83j65ib2bZtm3x9fW86LiYmRsHBwW6v9DOskIHrmzbqUbVrWlet+8/Ub7+fd21v3ri6Kt9WSqc3vao/d8zQnztmSJIWv9ZP37w32PR8O346ptvCisunqG1/xgUAwFZYx9ycx7uJzp07u703DEOnTp3Szp07NXbs2JseP2bMGA0bNsxtW2jTUblaIwqGaaMeVYd/NFCr/jP068mzbvtem/ut5n6+1W3bruUvaeTrn2r1xp9Nz1m/xm1KSLyo1LT0PKkZAAAUHh5vzIODg93ee3l5qUaNGpo4caJatWp10+OdTqecTqfbNqax4FrTx3TTY23v0qND31XSxWSVKRkoSUpMSlZySprOnP3zug98/vfUOVcT365ZXYWWDNR/9h1TcmqaHri3pkb2baXpC9ZZ+lkAFGyXLl7U8ePHXe9/O3FChw4eVHBwsMqGh3uwMiB35Nc02woebcwzMjLUp08f1atXT8WLF/dkKSjgnu3WTJK05v0hbtv7j/tIH6/cnq1zpKVn6NluzTT1xS5yOByK++8fGvX6Z/rws603PxgAsmn//p/Vr89TrvevTb2ydGuHjo9o0ss3fg4GQP7mMIwbfZVK3vP19dXBgwdVqVKlXDunX0O+kAGA553bkfWbYgHAar4enx/hrurwryy5zpHX2lpyndzk8Yc/69atq6NHj3q6DAAAAMCjPN6YT548WcOHD9eqVat06tQpXbhwwe0FAACAgoNVWcx57JcbEydO1Isvvqh27dpJkjp06OB2Ew3DkMPhUEZGhqdKBAAAACzjscY8Ojpazz33nL777jtPlQAAAACL5dMw2xIea8yvPnMaGRnpqRIAAAAA2/Doc7r5df4PAAAAbg39nzmPNubVq1e/6T+chIQEi6oBAAAAPMejjXl0dHSWb/4EAABAwUVgbs6jjXn37t0VGhrqyRIAAAAAW/BYY878IgAAgMLHy4se0IzHvmDo6qosAAAAADyYmGdmZnrq0gAAAIDteHSOOQAAAAoXZjOb89hUFgAAAAD/h8QcAAAAlmEBEHMk5gAAAIANkJgDAADAMgTm5kjMAQAAABsgMQcAAIBlmGNujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGeaYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzDHHNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZZhjbo7EHAAAALABEnMAAABYhsDcHIk5AAAAYAMk5gAAALAMc8zNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAAlmGOuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyzDE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIY55uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhjrk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMswxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFjGi8jcFIk5AAAACrVNmzbp4YcfVnh4uBwOh1asWOG2v3fv3nI4HG6vNm3auI1JSEhQz549FRQUpJCQEPXt21dJSUk5qoPGHAAAAJZxOKx55cTFixfVoEEDvfXWW6Zj2rRpo1OnTrleixcvdtvfs2dP7d+/X2vWrNGqVau0adMmPfPMMzmqg6ksAAAAKNTatm2rtm3b3nCM0+lUWFjYdfcdPHhQX3/9tXbs2KG77rpLkjRr1iy1a9dOr732msLDw7NVB4k5AAAALHPtlJC8eqWkpOjChQtur5SUlFuue8OGDQoNDVWNGjU0YMAAnT171rVv27ZtCgkJcTXlktSyZUt5eXlp+/bt2b4GjTkAAAAKnJiYGAUHB7u9YmJibulcbdq00YIFC7Ru3Tr961//0saNG9W2bVtlZGRIkk6fPq3Q0FC3Y4oUKaISJUro9OnT2b4OU1kAAABgGS+LFmUZM2aMhg0b5rbN6XTe0rm6d+/u+nO9evVUv359ValSRRs2bNADDzzwt+r8KxJzAAAAFDhOp1NBQUFur1ttzK9VuXJllSpVSkeOHJEkhYWF6ffff3cbk56eroSEBNN56ddDYw4AAADLWDXHPC+dOHFCZ8+eVdmyZSVJEREROn/+vHbt2uUas379emVmZuqee+7J9nmZygIAAIBCLSkpyZV+S1J8fLz27NmjEiVKqESJEoqOjlaXLl0UFhamuLg4jRw5UlWrVlXr1q0lSbVq1VKbNm3Uv39/zZkzR2lpaYqKilL37t2zvSKLRGIOAAAAC9lxHfOdO3eqYcOGatiwoSRp2LBhatiwocaNGydvb2/t27dPHTp0UPXq1dW3b181atRImzdvdpsas3DhQtWsWVMPPPCA2rVrp/vvv1/vvvtujuogMQcAAECh1rx5cxmGYbr/m2++uek5SpQooUWLFv2tOmjMAQAAYBmHLFqWJR9iKgsAAABgAyTmAAAAsIxV65jnRyTmAAAAgA2QmAMAAMAyeb3GeH5GYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYxovI3BSJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIZ1zM2RmAMAAAA2QGIOAAAAy5CXm8tWY/7vf/872yfs0KHDLRcDAAAAFFbZasw7deqUrZM5HA5lZGT8nXoAAABQgLGOublsNeaZmZl5XQcAAABQqP2tOebJycny9fXNrVoAAABQwHkRmJvK8aosGRkZmjRpksqVK6eAgAAdPXpUkjR27Fh98MEHuV4gAAAAUBjkuDGfMmWK5s2bp6lTp8rHx8e1vW7dunr//fdztTgAAAAULA6Hw5JXfpTjxnzBggV699131bNnT3l7e7u2N2jQQIcOHcrV4gAAAIDCIsdzzH/77TdVrVo1y/bMzEylpaXlSlEAAAAomPJpmG2JHCfmtWvX1ubNm7NsX758uRo2bJgrRQEAAACFTY4T83HjxqlXr1767bfflJmZqc8++0yxsbFasGCBVq1alRc1AgAAoIDIr/O/rZDjxLxjx45auXKl1q5dK39/f40bN04HDx7UypUr9eCDD+ZFjQAAAECBd0vrmDdt2lRr1qzJ7VoAAABQwLGOublb/oKhnTt36uDBg5KuzDtv1KhRrhUFAAAAFDY5bsxPnDihHj166Pvvv1dISIgk6fz587rvvvv0ySef6LbbbsvtGgEAAIACL8dzzPv166e0tDQdPHhQCQkJSkhI0MGDB5WZmal+/frlRY0AAAAoIPiCIXM5Tsw3btyorVu3qkaNGq5tNWrU0KxZs9S0adNcLQ4AAAAoLHLcmJcvX/66XySUkZGh8PDwXCkKAAAABVP+zLKtkeOpLK+++qpeeOEF7dy507Vt586dGjx4sF577bVcLQ4AAAAoLLKVmBcvXtxtrs7Fixd1zz33qEiRK4enp6erSJEievrpp9WpU6c8KRQAAAD5n1c+nf9thWw15tOnT8/jMgAAAIDCLVuNea9evfK6DgAAABQCBObmbvkLhiQpOTlZqampbtuCgoL+VkEAAABAYZTjxvzixYsaNWqUli5dqrNnz2bZn5GRkSuFAQAAoODJr2uMWyHHq7KMHDlS69ev1+zZs+V0OvX+++8rOjpa4eHhWrBgQV7UCAAAABR4OU7MV65cqQULFqh58+bq06ePmjZtqqpVq6pChQpauHChevbsmRd1AgAAoAAgMDeX48Q8ISFBlStXlnRlPnlCQoIk6f7779emTZtytzoAAACgkMhxY165cmXFx8dLkmrWrKmlS5dKupKkh4SE5GpxAAAAKFi8HA5LXvlRjhvzPn36aO/evZKk0aNH66233pKvr6+GDh2qESNG5HqBAAAAQGGQ4znmQ4cOdf25ZcuWOnTokHbt2qWqVauqfv36uVocAAAACpZ8GmZb4m+tYy5JFSpUUIUKFXKjFgAAAKDQylZjPnPmzGyfcNCgQbdcDAAAAAo21jE3l63GfNq0adk6mcPhoDEHAAAAbkG2GvOrq7DkFye/n+HpEgBAiZfSPF0CAMg3qKinS3CT45VHChHuDQAAAGADf/vhTwAAACC7mGNujsQcAAAAsAEScwAAAFjGi8DcFIk5AAAAYAO31Jhv3rxZTzzxhCIiIvTbb79Jkj766CNt2bIlV4sDAABAweLlsOaVH+W4Mf/000/VunVr+fn56ccff1RKSookKTExUS+//HKuFwgAAAAUBjluzCdPnqw5c+bovffeU9Gi/7cuZpMmTbR79+5cLQ4AAAAFi8PhsOSVH+W4MY+NjVWzZs2ybA8ODtb58+dzoyYAAACg0MlxYx4WFqYjR45k2b5lyxZVrlw5V4oCAABAwcQcc3M5bsz79++vwYMHa/v27XI4HDp58qQWLlyo4cOHa8CAAXlRIwAAAFDg5Xgd89GjRyszM1MPPPCALl26pGbNmsnpdGr48OF64YUX8qJGAAAAFBD5dPq3JRyGYRi3cmBqaqqOHDmipKQk1a5dWwEBAbld2y07dynD0yUAgFLTMz1dAgCoTFDRmw+y0MjVsZZcZ2r7GpZcJzfd8jd/+vj4qHbt2rlZCwAAAAo4LyJzUzluzFu0aHHDJWjWr1//twoCAAAACqMcN+Z33HGH2/u0tDTt2bNHP//8s3r16pVbdQEAAKAAuqWvnS8kctyYT5s27brbJ0yYoKSkpL9dEAAAAFAY5doPLU888YQ+/PDD3DodAAAACiCHw5pXfpRrjfm2bdvk6+ubW6cDAAAACpUcT2Xp3Lmz23vDMHTq1Cnt3LlTY8eOzbXCAAAAUPCwKou5HDfmwcHBbu+9vLxUo0YNTZw4Ua1atcq1wgAAAIDCJEeNeUZGhvr06aN69eqpePHieVUTAAAACigCc3M5mmPu7e2tVq1a6fz583lUDgAAAFA45fjhz7p16+ro0aN5UQsAAAAKOC+HNa/8KMeN+eTJkzV8+HCtWrVKp06d0oULF9xeAAAAAHIu23PMJ06cqBdffFHt2rWTJHXo0EGOv0wSMgxDDodDGRkZuV8lAAAACgRWZTGX7cY8Ojpazz33nL777ru8rAcAAAAolLLdmBuGIUmKjIzMs2IAAABQsBGYm8vRHHMHdxIAAADIEzlax7x69eo3bc4TEhL+VkEAAAAouPLriilWyFFjHh0dneWbPwEAAAD8fTlqzLt3767Q0NC8qgUAAAAFnENE5mayPcec+eUAAABA3snxqiwAAADArWKOublsN+aZmZl5WQcAAABQqOVojjkAAADwd5CYm8vROuYAAAAA8gaJOQAAACzDgiLmSMwBAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIYp5uZIzAEAAAAboDEHAACAZbwcDkteObFp0yY9/PDDCg8Pl8Ph0IoVK9z2G4ahcePGqWzZsvLz81PLli11+PBhtzEJCQnq2bOngoKCFBISor59+yopKSln9yZHowEAAIAC5uLFi2rQoIHeeuut6+6fOnWqZs6cqTlz5mj79u3y9/dX69atlZyc7BrTs2dP7d+/X2vWrNGqVau0adMmPfPMMzmqw2EYhvG3PokNnbuU4ekSAECp6ZmeLgEAVCaoqKdLcDNzS7wl1xl0f6VbOs7hcOjzzz9Xp06dJF1Jy8PDw/Xiiy9q+PDhkqTExESVKVNG8+bNU/fu3XXw4EHVrl1bO3bs0F133SVJ+vrrr9WuXTudOHFC4eHh2bo2iTkAAAAKnJSUFF24cMHtlZKSkuPzxMfH6/Tp02rZsqVrW3BwsO655x5t27ZNkrRt2zaFhIS4mnJJatmypby8vLR9+/ZsX4vGHAAAAJZxOKx5xcTEKDg42O0VExOT43pPnz4tSSpTpozb9jJlyrj2nT59WqGhoW77ixQpohIlSrjGZAfLJQIAAKDAGTNmjIYNG+a2zel0eqia7KExBwAAgGW8ZM1C5k6nM1ca8bCwMEnSmTNnVLZsWdf2M2fO6I477nCN+f33392OS09PV0JCguv47GAqCwAAAGCiUqVKCgsL07p161zbLly4oO3btysiIkKSFBERofPnz2vXrl2uMevXr1dmZqbuueeebF+LxBwAAACWseM3fyYlJenIkSOu9/Hx8dqzZ49KlCih22+/XUOGDNHkyZNVrVo1VapUSWPHjlV4eLhr5ZZatWqpTZs26t+/v+bMmaO0tDRFRUWpe/fu2V6RRaIxBwAAQCG3c+dOtWjRwvX+6tz0Xr16ad68eRo5cqQuXryoZ555RufPn9f999+vr7/+Wr6+vq5jFi5cqKioKD3wwAPy8vJSly5dNHPmzBzVwTrmAJBHWMccgB3YbR3zOduOWXKd5yIqWnKd3MQccwAAAMAGmMoCAAAAy3jZcZK5TZCYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhjrk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMqTC5rg3AAAAgA2QmAMAAMAyDiaZmyIxBwAAAGyAxBwAAACWIS83R2IOAAAA2ACJOQAAACzDN3+aIzEHAAAAbIDEHAAAAJYhLzdHYg4AAADYAI05AAAAYANMZQEAAIBlePbTHIk5AAAAYAMk5gAAALCMg8jcFIk5AAAAYAMk5gAAALAMqbA57g0AAABgAyTmAAAAsAxzzM2RmAMAAAA2QGIOAAAAy5CXmyMxBwAAAGyAxBwAAACWYY65ORJzAAAAwAZIzAEAAGAZUmFz3BsAAADABkjMAQAAYBnmmJsjMQcAAABsgMQcAAAAliEvN0diDgAAANgAiTkAAAAswxRzcyTmAAAAgA2QmAMAAMAyXswyN0ViDgAAANgAiTkAAAAswxxzcyTmAAAAgA2QmAMAAMAyDuaYmyIxBwAAAGyAxBwAAACWYY65ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACzDHHNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZfjmT3Mk5gAAAIANkJgDAADAMl4E5qZIzAEAAAAbIDEHAACAZZhjbo7EHAAAALABWzXmqampio2NVXp6uqdLAQAAQB5wOKx55Ue2aMwvXbqkvn37qlixYqpTp46OHz8uSXrhhRf0yiuveLg6AAAAIO/ZojEfM2aM9u7dqw0bNsjX19e1vWXLllqyZIkHKwMAAEBuclj0v/zIFg9/rlixQkuWLNG9994rx19+91CnTh3FxcV5sDIAAADAGrZozP/44w+FhoZm2X7x4kW3Rh0AAAD5G+uYm7PFVJa77rpLq1evdr2/2oy///77ioiI8FRZAAAAgGVskZi//PLLatu2rQ4cOKD09HTNmDFDBw4c0NatW7Vx40ZPlwcAAIBckl/nf1vBFon5/fffrz179ig9PV316tXTt99+q9DQUG3btk2NGjXydHkAAABAnnMYhmF4uojcdu5ShqdLQD7w466d+njBh4o9sF//+98f+tcbMxXZoqVr/3tz3tTab77SmdOnVbRoUdWoVVvPRQ1W3XoNPFg18pPU9ExPl4B84OO572nTd2v166/xcjp9Vbf+HXouaqhur1jJNSYlJUVvTX9V69d8pbTUVDW+t4mGjfqnSpQs5cHKkV+UCSrq6RLcbDl8zpLr3F+tuCXXyU22SMx3796tn376yfX+iy++UKdOnfT//t//U2pqqgcrQ0F2+fIlVateQ8PHjL3u/tsrVNSLo17SwmUr9M7cj1Q2vJwGP99f5xISLK4UQEG2Z/dOPfJoD835cJHeePNdpaen6cUXntHly5dcY96c9i9t3bxB0TFvaOY783T2f3/onyOHeKxmAHnDFol548aNNXr0aHXp0kVHjx5V7dq11blzZ+3YsUPt27fX9OnTc3Q+EnPk1L0Na2dJzK91MSlJDzS9W7PmfKDG9/BQMm6OxBy34vy5BHVo1Uwz35mnO+68S0lJf6rDg001bvJUNX+glSTp12NH9eSjHTT7w4Wqw2/xcBN2S8y/tygxb0Jifmt++eUX3XHHHZKkZcuWKTIyUosWLdK8efP06aeferY4QFJaWqpWfLZUAQGBqla9pqfLAVCAJSUlSZKCgoIlSbEHryyM0Ojue11jKlSsrDJhZbX/p70eqRFA3rDFqiyGYSgz80qytHbtWj300EOSpPLly+t///ufJ0tDIbdl0waNHf2ikpOTVapUac2c875Ciue/n8AB5A+ZmZma9cYrqtegoSpXrSZJSjj7PxUtWlSBgUFuY4uXKKmzZ/lvJPIfL76jxpQtGvO77rpLkydPVsuWLbVx40bNnj1bkhQfH68yZcrc8NiUlBSlpKS4b8soIqfTmWf1ovBo1PhuLfjkMyWeP68vPluml0YO0wcffaISJUp6ujQABdC0qZMVH3dEb763wNOlAPAAW0xlmT59unbv3q2oqCi99NJLqlq1qiRp+fLluu+++254bExMjIKDg91e0157xYqyUQj4+RVT+dsrqG79BnppwmR5e3tr5edMrwKQ+6ZNnaKtmzdq+uwPFVomzLW9RMlSSktL059/XnAbfy7hrEqyKgvyIYdFr/zIFol5/fr13VZluerVV1+Vt7f3DY8dM2aMhg0b5rbtUoYtPhYKIMMwlJrGSkEAco9hGJr+6svavGGdZsyZq/Byt7ntr1GrtooUKaJdO7ar+T8elCQdPxavM6dP8eAnUMDYuoP19fW96Rin05ll2koGq7IgGy5duqgT/z3uen/yt9/0S+xBBQUFKzgkRPPef0dNI/+hkqVKKfH8eS1fukh//H5GDzzY2oNVAyhopv1rstZ+86Vefm2mihXz19n//9mqgIAAOX19FRAQqPYdO+utaVMVFBQsf39/TX/1ZdWp14DGHPlTfo2zLeCx5RKLFy8uRzYn/yfkcN1olktEduza+R8N7N87y/Z2D3fSqJfGa9z/G6EDP+3T+fPnFBwcolp16qpP/+dUu04964tFvsRyiciOZo3rXnf7mHGT1fbhTpL+7wuG1n37pdJS09T43vs0bNRYlSzFVBbcnN2WS/wh7rwl17m3Sogl18lNHmvM58+fn+2xvXr1ytG5acwB2AGNOQA7sFtjvj0u0ZLr3FMl2JLr5CaPTWXJabMNAAAAFGS2m2OenJys1FT3h+uCgoJMRgMAACA/YRlzc7ZYLvHixYuKiopSaGio/P39Vbx4cbcXAAAAUNDZojEfOXKk1q9fr9mzZ8vpdOr9999XdHS0wsPDtWABX7IAAABQULCOuTmPPfz5V7fffrsWLFig5s2bKygoSLt371bVqlX10UcfafHixfryyy9zdD4e/gRgBzz8CcAO7Pbw546j1jz82bhy/nv40xaJeUJCgipXrizpynzyq8sj3n///dq0aZMnSwMAAEBuIjI3ZYvGvHLlyoqPj5ck1axZU0uXLpUkrVy5UiEhIR6sDAAAALCGRxvzo0ePKjMzU3369NHevXslSaNHj9Zbb70lX19fDR06VCNGjPBkiQAAAMhFDov+lx95dI65t7e3Tp06pdDQUEnSY489ppkzZyo5OVm7du1S1apVVb9+/RyflznmAOyAOeYA7MBuc8x3xl+w5Dp3Vcp/y217tDH38vLS6dOnXY15YGCg9u7d65pvfqtozAHYAY05ADuwW2O+65g1jXmjivmvMbfFHHMAAACgsPPoN386HA45rvn6p2vfAwAAoOCg0zPn0cbcMAz17t1bTqdTkpScnKznnntO/v7+buM+++wzT5QHAAAAWMajjXmvXr3c3j/xxBMeqgQAAACWIDI3ZYtv/sxtPPwJwA54+BOAHdjt4c/dv1rz8OedFfLfw58eTcwBAABQuOTXNcatwKosAAAAKNQmTJjgWpTk6qtmzZqu/cnJyRo4cKBKliypgIAAdenSRWfOnMn1OmjMAQAAYBmHw5pXTtWpU0enTp1yvbZs2eLaN3ToUK1cuVLLli3Txo0bdfLkSXXu3DkX78oVTGUBAABAoVekSBGFhYVl2Z6YmKgPPvhAixYt0j/+8Q9J0ty5c1WrVi398MMPuvfee3OtBhJzAAAAWMZh0SunDh8+rPDwcFWuXFk9e/bU8ePHJUm7du1SWlqaWrZs6Rpbs2ZN3X777dq2bdstXMkciTkAAAAKnJSUFKWkpLhtczqdru/P+at77rlH8+bNU40aNXTq1ClFR0eradOm+vnnn3X69Gn5+PgoJCTE7ZgyZcro9OnTuVoziTkAAACsY1FkHhMTo+DgYLdXTEzMdUtq27atHn30UdWvX1+tW7fWl19+qfPnz2vp0qV5cw9M0JgDAACgwBkzZowSExPdXmPGjMnWsSEhIapevbqOHDmisLAwpaam6vz5825jzpw5c9056X8HjTkAAAAs47Dof06nU0FBQW6v601juZ6kpCTFxcWpbNmyatSokYoWLap169a59sfGxur48eOKiIjI1XvDHHMAAAAUasOHD9fDDz+sChUq6OTJkxo/fry8vb3Vo0cPBQcHq2/fvho2bJhKlCihoKAgvfDCC4qIiMjVFVkkGnMAAABY6FbWGM9rJ06cUI8ePXT27FmVLl1a999/v3744QeVLl1akjRt2jR5eXmpS5cuSklJUevWrfX222/neh0OwzCMXD+rh527lOHpEgBAqemZni4BAFQmqKinS3Dz04kkS65T77YAS66Tm0jMAQAAYBkbBua2wcOfAAAAgA2QmAMAAMA6ROamSMwBAAAAGyAxBwAAgGUcROamSMwBAAAAG6AxBwAAAGyAqSwAAACwjB2/YMguSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAA6xCZmyIxBwAAAGyAxBwAAACW4QuGzJGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZAnNzJOYAAACADZCYAwAAwDpE5qZIzAEAAAAbIDEHAACAZVjH3ByJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAAKxDZG6KxBwAAACwARJzAAAAWIZ1zM2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMA6ROamSMwBAAAAGyAxBwAAgGVYx9wciTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswxxzcyTmAAAAgA2QmAMAAMBCROZmSMwBAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDHPMzZGYAwAAADZAYg4AAADLOJhlborEHAAAALABEnMAAABYh8DcFIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBnWMTdHYg4AAADYAIk5AAAALMM65uZIzAEAAAAbIDEHAACAdQjMTZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDKsY26OxBwAAACwARJzAAAAWIY55uZIzAEAAAAboDEHAAAAbIDGHAAAALAB5pgDAADAMswxN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlmGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsA6RuSkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAOsQmZsiMQcAAABsgMQcAAAAlmEdc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABh2EYhqeLAOwmJSVFMTExGjNmjJxOp6fLAVAI8fcQUPjQmAPXceHCBQUHBysxMVFBQUGeLgdAIcTfQ0Dhw1QWAAAAwAZozAEAAAAboDEHAAAAbIDGHLgOp9Op8ePH88AVAI/h7yGg8OHhTwAAAMAGSMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAABsZt68eQoJCfF0GQAsRmOOAs3hcNzwNWHCBE+XCKAA692793X/7jly5IinSwNgQ0U8XQCQl06dOuX685IlSzRu3DjFxsa6tgUEBLj+bBiGMjIyVKQI/7cAkHvatGmjuXPnum0rXbq0h6oBYGck5ijQwsLCXK/g4GA5HA7X+0OHDikwMFBfffWVGjVqJKfTqS1btqh3797q1KmT23mGDBmi5s2bu95nZmYqJiZGlSpVkp+fnxo0aKDly5db++EA5AtOp9Pt76KwsDDNmDFD9erVk7+/v8qXL6/nn39eSUlJpufYu3evWrRoocDAQAUFBalRo0bauXOna/+WLVvUtGlT+fn5qXz58ho0aJAuXrxoxccDkItozFHojR49Wq+88ooOHjyo+vXrZ+uYmJgYLViwQHPmzNH+/fs1dOhQPfHEE9q4cWMeVwugIPDy8tLMmTO1f/9+zZ8/X+vXr9fIkSNNx/fs2VO33XabduzYoV27dmn06NEqWrSoJCkuLk5t2rRRly5dtG/fPi1ZskRbtmxRVFSUVR8HQC7hd/Yo9CZOnKgHH3ww2+NTUlL08ssva+3atYqIiJAkVa5cWVu2bNE777yjyMjIvCoVQD60atUqt2lzbdu21bJly1zvK1asqMmTJ+u5557T22+/fd1zHD9+XCNGjFDNmjUlSdWqVXPti4mJUc+ePTVkyBDXvpkzZyoyMlKzZ8+Wr69vHnwqAHmBxhyF3l133ZWj8UeOHNGlS5eyNPOpqalq2LBhbpYGoABo0aKFZs+e7Xrv7++vtWvXKiYmRocOHdKFCxeUnp6u5ORkXbp0ScWKFctyjmHDhqlfv3766KOP1LJlSz366KOqUqWKpCvTXPbt26eFCxe6xhuGoczMTMXHx6tWrVp5/yEB5AoacxR6/v7+bu+9vLxkGIbbtrS0NNefr84DXb16tcqVK+c2zul05lGVAPIrf39/Va1a1fX+2LFjeuihhzRgwABNmTJFJUqU0JYtW9S3b1+lpqZetzGfMGGCHn/8ca1evVpfffWVxo8fr08++USPPPKIkpKS9Oyzz2rQoEFZjrv99tvz9LMByF005sA1SpcurZ9//tlt2549e1zzOWvXri2n06njx48zbQVAju3atUuZmZl6/fXX5eV15VGvpUuX3vS46tWrq3r16ho6dKh69OihuXPn6pFHHtGdd96pAwcOuDX/APInHv4ErvGPf/xDO3fu1IIFC3T48GGNHz/erVEPDAzU8OHDNXToUM2fP19xcXHavXu3Zs2apfnz53uwcgD5QdWqVZWWlqZZs2bp6NGj+uijjzRnzhzT8ZcvX1ZUVJQ2bNigX3/9Vd9//7127NjhmqIyatQobd26VVFRUdqzZ48OHz6sL774goc/gXyIxhy4RuvWrTV27FiNHDlSjRs31p9//qmnnnrKbcykSZM0duxYxcTEqFatWmrTpo1Wr16tSpUqeahqAPlFgwYN9MYbb+hf//qX6tatq4ULFyomJsZ0vLe3t86ePaunnnpK1atXV7du3dS2bVtFR0dLkurXr6+NGzfql19+UdOmTdWwYUONGzdO4eHhVn0kALnEYVw7mRYAAACA5UjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEUWr1791anTp1c75s3b64hQ4ZYXseGDRvkcDh0/vx50zEOh0MrVqzI9jknTJigO+6442/VdezYMTkcDu3Zs+dvnQcAkD005gBspXfv3nI4HHI4HPLx8VHVqlU1ceJEpaen5/m1P/vsM02aNClbY7PTTAMAkBNFPF0AAFyrTZs2mjt3rlJSUvTll19q4MCBKlq0qMaMGZNlbGpqqnx8fHLluiVKlMiV8wAAcCtIzAHYjtPpVFhYmCpUqKABAwaoZcuW+ve//y3p/6afTJkyReHh4apRo4Yk6b///a+6deumkJAQlShRQh07dtSxY8dc58zIyNCwYcMUEhKikiVLauTIkTIMw+26105lSUlJ0ahRo1S+fHk5nU5VrVpVH3zwgY4dO6YWLVpIkooXLy6Hw6HevXtLkjIzMxUTE6NKlSrJz89PDRo00PLly92u8+WXX6p69ery8/NTixYt3OrMrlGjRql69eoqVqyYKleurLFjxyotLS3LuHfeeUfly5dXsWLF1K1bNyUmJrrtf//991WrVi35+vqqZs2aevvtt3NcCwAgd9CYA7A9Pz8/paamut6vW7dOsbGxWrNmjVatWqW0tDS1bt1agYGB2rx5s77//nsFBASoTZs2ruNef/11zZs3Tx9++KG2bNmihIQEff755ze87lNPPaXFixdr5syZOnjwoN555x0FBASofPny+vTTTyVJsbGxOnXqlGbMmCFJiomJ0YIFCzRnzhzt379fQ4cO1RNPPKGNGzdKuvIDROfOnfXwww9rz5496tevn0aPHp3jexIYGKh58+bpwIEDmjFjht577z1NmzbNbcyRI0e0dOlSrVy5Ul9//bV+/PFHPf/88679Cxcu1Lhx4zRlyhQdPHhQL7/8ssaOHav58+fnuB4AQC4wAMBGevXqZXTs2NEwDMPIzMw01qxZYzidTmP48OGu/WXKlDFSUlJcx3z00UdGjRo1jMzMTNe2lJQUw8/Pz/jmm28MwzCMsmXLGlOnTnXtT0tLM2677TbXtQzDMCIjI43BgwcbhmEYsbGxhiRjzZo1163zu+++MyQZ586dc21LTk42ihUrZmzdutVtbN++fY0ePXoYhmEYY8aMMWrXru22f9SoUVnOdS1Jxueff266/9VXXzUaNWrkej9+/HjD29vbOHHihGvbV199ZXh5eRmnTp0yDMMwqlSpYixatMjtPJMmTTIiIiIMwzCM+Ph4Q5Lx448/ml4XAJB7mGMOwHZWrVqlgIAApaWlKTMzU48//rgmTJjg2l+vXj23eeV79+7VkSNHFBgY6Hae5ORkxcXFKTExUadOndI999zj2lekSBHdddddWaazXLVnzx55e3srMjIy23UfOXJEly5d0oMPPui2PTU1VQ0bNpQkHTx40K0OSYqIiMj2Na5asmSJZs6cqbi4OCUlJSk9PV1BQUFuY26//XaVK1fO7TqZmZmKjY1VYGCg4uLi1LdvX/Xv3981Jj09XcHBwTmuBwDw99GYA7CdFi1aaPbs2fLx8VF4eLiKFHH/q8rf39/tfVJSkho1aqSFCxdmOVfp0qVvqQY/P78cH5OUlCRJWr16tVtDLF2ZN59btm3bpp49eyo6OlqtW7dWcHCwPvnkE73++us5rvW9997L8oOCt7d3rtUKAMg+GnMAtuPv76+qVatme/ydd96pJUuWKDQ0NEtqfFXZsmW1fft2NWvWTNKVZHjXrl268847rzu+Xr16yszM1MaNG9WyZcss+68m9hkZGa5ttWvXltPp1PHjx02T9lq1arkeZL3qhx9+uPmH/IutW7eqQoUKeumll1zbfv311yzjjh8/rpMnTyo8PNx1HS8vL9WoUUNlypRReHi4jh49qp49e+bo+gCAvMHDnwDyvZ49e6pUqVLq2LGjNm/erPj4eG3YsEGDBg3SiRMnJEmDBw/WK6+8ohUrVujQoUN6/vnnb7gGecWKFdWrVy89/fTTWrFiheucS5culSRVqFBBDodDq1at0h9//KGkpCQFBgZq+PDhGjp0qObPn6+4uDjt3r1bs2bNcj1Q+dxzz+nw4cMaMWKEYmNjtWjRIs2bNy9Hn7datWo6fvy4PvnkE8XFxWnmzJnXfZDV19dXvXr10t69e7V582YNGjRI3bp1U1hYmCQpOjpaMTExmjlzpn755Rf99NNPmjt3rt54440c1QMAyB005gDyvWLFimnTpk26/fbb1blzZ9WqVUt9+/ZVcnKyK0F/8cUX9eSTT6pXr16KiIhQYGCgHnnkkRued/bs2eratauef/551axZU/3799fFixclSeXKlVN0dLRGjx6tMmXKKCoqSpI0adIkjR07VjExMapVq5batGmj1atXq1KlSpKuzPv+9NNPtWLFCjVo0EBz5szRyy+/nKPP26FDBw0dOlRRUVG64447tHXrVo0dOzbLuKpVq6pz585q166dWrVqpfr167sth9ivXz+9//77mjt3rurVq6fIyEjNmzfPVSsAwFoOw+zJJwAAAACWITEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsgMYcKKQqVqyo3r17e7qMAufrr7/WHXfcIV9fXzkcDp0/fz5Xzz9v3jw5HA4dO3YsV8+bnzkcDk2YMMHTZQDA30ZjDhQwcXFxevbZZ1W5cmX5+voqKChITZo00YwZM3T58mVPl3dDvXv3lsPhMH399ttv2TrPhg0b1LlzZ4WFhcnHx0ehoaF6+OGH9dlnn+Vp/WfPnlW3bt3k5+ent956Sx999JH8/f3z9JpWqlixohwOh1q2bHnd/e+9957rn9XOnTtzfP6tW7dqwoQJuf7DDADkF0U8XQCA3LN69Wo9+uijcjqdeuqpp1S3bl2lpqZqy5YtGjFihPbv3693333X02WaevbZZ7M0fYZh6LnnnlPFihVVrly5m55j/PjxmjhxoqpVq6Znn31WFSpU0NmzZ/Xll1+qS5cuWrhwoR5//PE8qX/Hjh36888/NWnSJNPm9e968skn1b17dzmdzjw5/834+vrqu+++0+nTpxUWFua2b+HChfL19VVycvItnXvr1q2Kjo5W7969FRISku3jLl++rCJF+M8ZgPyPv8mAAiI+Pl7du3dXhQoVtH79epUtW9a1b+DAgTpy5IhWr17twQpvLiIiQhEREW7btmzZokuXLqlnz543PX758uWaOHGiunbtqkWLFqlo0aKufSNGjNA333yjtLS0XK/7qt9//12SctRU5pS3t7e8vb3z7Pw306RJE+3YsUNLlizR4MGDXdtPnDihzZs365FHHtGnn36a53VkZmYqNTVVvr6+8vX1zfPrAYAVmMoCFBBTp05VUlKSPvjgA7em/KqqVau6NVLXSkhI0PDhw1WvXj0FBAQoKChIbdu21d69e7OMnTVrlurUqaNixYqpePHiuuuuu7Ro0SLX/j///FNDhgxRxYoV5XQ6FRoaqgcffFC7d+/O8edatGiRHA5HtlLusWPHqkSJEvrwww/dmvKrWrdurYceesj1/vfff1ffvn1VpkwZ+fr6qkGDBpo/f77bMceOHZPD4dBrr72md999V1WqVJHT6VTjxo21Y8cO17jmzZurV69ekqTGjRvL4XC45vCbzedv3ry5mjdv7rbtZvfWbI7522+/rTp16sjpdCo8PFwDBw7MMiWkefPmqlu3rg4cOKAWLVqoWLFiKleunKZOnWp2S7Pw9fVV586d3WqSpMWLF6t48eJq3bp1lmP27dun3r17u6ZXhYWF6emnn9bZs2ddYyZMmKARI0ZIkipVquSaEnP1czocDkVFRWnhwoWuz/n111+79l2dY3758mXVrFlTNWvWdJu6lZCQoLJly+q+++5TRkZGtj8vAFiJxBwoIFauXKnKlSvrvvvuu6Xjjx49qhUrVujRRx9VpUqVdObMGb3zzjuKjIzUgQMHFB4eLunKPOJBgwapa9euGjx4sJKTk7Vv3z5t377d1Tw/99xzWr58uaKiolS7dm2dPXtWW7Zs0cGDB3XnnXdmu6a0tDQtXbpU9913nypWrHjDsYcPH9ahQ4f09NNPKzAw8Kbnvnz5spo3b64jR44oKipKlSpV0rJly9S7d2+dP38+yw8xixYt0p9//qlnn31WDodDU6dOVefOnXX06FEVLVpUL730kmrUqKF3331XEydOVKVKlVSlSpVsf1Ype/f2eiZMmKDo6Gi1bNlSAwYMUGxsrGbPnq0dO3bo+++/d/sh5dy5c2rTpo06d+6sbt26afny5Ro1apTq1auntm3bZqvOxx9/XK1atVJcXJzrMy5atEhdu3a97g9Ea9as0dGjR9WnTx+FhYW5plTt379fP/zwgxwOhzp37qxffvlFixcv1rRp01SqVClJUunSpV3nWb9+vZYuXaqoqCiVKlXquv9O+Pn5af78+WrSpIleeuklvfHGG5Ku/NYoMTFR8+bN8+hvHADghgwA+V5iYqIhyejYsWO2j6lQoYLRq1cv1/vk5GQjIyPDbUx8fLzhdDqNiRMnurZ17NjRqFOnzg3PHRwcbAwcODDbtZhZuXKlIcl4++23bzr2iy++MCQZ06ZNy9a5p0+fbkgyPv74Y9e21NRUIyIiwggICDAuXLhgGMaVeyDJKFmypJGQkJDleitXrnRtmzt3riHJ2LFjh9u1rr3XV0VGRhqRkZGu99m5t1evER8fbxiGYfz++++Gj4+P0apVK7d/fm+++aYhyfjwww/drifJWLBggWtbSkqKERYWZnTp0uWG1736Odq3b2+kp6cbYWFhxqRJkwzDMIwDBw4YkoyNGzde9x5cunQpy7kWL15sSDI2bdrk2vbqq6+6fba/kmR4eXkZ+/fvv+6+8ePHu20bM2aM4eXlZWzatMlYtmyZIcmYPn36TT8jAHgSU1mAAuDChQuSlK2k2IzT6ZSX15W/EjIyMnT27FkFBASoRo0ablNQQkJCdOLECbdpHNcKCQnR9u3bdfLkyVuuR5Jrnni3bt1uOjan9+DLL79UWFiYevTo4dpWtGhRDRo0SElJSdq4caPb+Mcee0zFixd3vW/atKmkK79pyC3ZubfXWrt2rVJTUzVkyBDXPz9J6t+/v4KCgrI8VxAQEKAnnnjC9d7Hx0d33313jj6Ht7e3unXrpsWLF0u68tBn+fLlXffkWn5+fq4/Jycn63//+5/uvfdeScrR9KbIyEjVrl07W2MnTJigOnXqqFevXnr++ecVGRmpQYMGZftaAOAJNOZAARAUFCTpytzuW5WZmalp06apWrVqcjqdKlWqlEqXLq19+/YpMTHRNW7UqFEKCAjQ3XffrWrVqmngwIH6/vvv3c41depU/fzzzypfvrzuvvtuTZgwIccNbFJSkr744gu1bt1aJUuWvOn4nN6DX3/9VdWqVXNrZiWpVq1arv1/dfvtt7u9v9qknzt3LlvXy47s3NtrXa2zRo0abtt9fHxUuXLlLJ/jtttuk8PhcNtWvHjxHH+Oxx9/XAcOHNDevXu1aNEide/ePct5r0pISNDgwYNVpkwZ+fn5qXTp0qpUqZIkuf27dTNXj8kOHx8fffjhh4qPj9eff/6puXPnmtYHAHZBYw4UAEFBQQoPD9fPP/98y+d4+eWXNWzYMDVr1kwff/yxvvnmG61Zs0Z16tRRZmama1ytWrUUGxurTz75RPfff78+/fRT3X///Ro/frxrTLdu3XT06FHNmjVL4eHhevXVV1WnTh199dVX2a5nxYoV2V6NRZJq1qwpSfrpp5+yfY2cMJuXbBjGTY81awivfQgxO/f27/o7n+Ov7rnnHlWpUkVDhgxRfHz8DefAd+vWTe+9956ee+45ffbZZ/r2229dD27+9d+tm/lr8p4d33zzjaQrKf3hw4dzdCwAeAKNOVBAPPTQQ4qLi9O2bdtu6fjly5erRYsW+uCDD9S9e3e1atVKLVu2vO6Xvfj7++uxxx7T3Llzdfz4cbVv315TpkxxW7+6bNmyev7557VixQrFx8erZMmSmjJlSrbrWbhwoQICAtShQ4dsja9evbpq1KihL774QklJSTcdX6FCBR0+fDhLY3jo0CHX/txSvHjx697Ha9NsKXv39q+u1hkbG+u2PTU1VfHx8bn6Oa7Vo0cPbdiwQbVq1dIdd9xx3THnzp3TunXrNHr0aEVHR+uRRx7Rgw8+qMqVK2cZm5uJ9r59+zRx4kT16dNHDRs2VL9+/XKUzgOAJ9CYAwXEyJEj5e/vr379+unMmTNZ9sfFxWnGjBmmx3t7e2dJTZctW5bl2zb/usSddGXKQO3atWUYhtLS0pSRkZGlAQoNDVV4eLhSUlKy9Vn++OMPrV27Vo888oiKFSuWrWMkKTo6WmfPnlW/fv2Unp6eZf+3336rVatWSZLatWun06dPa8mSJa796enpmjVrlgICAhQZGZnt695MlSpV9MMPPyg1NdW1bdWqVfrvf//rNu5m9/Z6WrZsKR8fH82cOdPtn98HH3ygxMREtW/fPtc+x7X69eun8ePH6/XXXzcdczWhv/bfrenTp2cZe/VbUv/uN3+mpaWpd+/eCg8P14wZMzRv3jydOXNGQ4cO/VvnBYC8xnKJQAFRpUoVLVq0SI899phq1arl9s2fW7dudS0FaOahhx5yJYz33XeffvrpJy1cuDBLstmqVSuFhYWpSZMmKlOmjA4ePKg333xT7du3V2BgoM6fP6/bbrtNXbt2VYMGDRQQEKC1a9dqx44dN2zg/mrJkiVKT0/P9jSWqx577DH99NNPmjJlin788Uf16NHD9c2fX3/9tdatW+daf/uZZ57RO++8o969e2vXrl2qWLGili9fru+//17Tp0//Ww/SXqtfv35avny52rRpo27duikuLk4ff/xxluUUb3Zvr6d06dIaM2aMoqOj1aZNG3Xo0EGxsbF6++231bhxY7cHPXNbhQoVXOuHmwkKClKzZs00depUpaWlqVy5cvr2228VHx+fZWyjRo0kSS+99JK6d++uokWL6uGHH3Y17Nk1efJk7dmzR+vWrVNgYKDq16+vcePG6Z///Ke6du2qdu3a5eh8AGAZD64IAyAP/PLLL0b//v2NihUrGj4+PkZgYKDRpEkTY9asWUZycrJr3PWWS3zxxReNsmXLGn5+fkaTJk2Mbdu2ZVnS75133jGaNWtmlCxZ0nA6nUaVKlWMESNGGImJiYZhXFl+b8SIEUaDBg2MwMBAw9/f32jQoEG2ljy86t577zVCQ0ON9PT0W7oH69atMzp27GiEhoYaRYoUMUqXLm08/PDDxhdffOE27syZM0afPn2MUqVKGT4+Pka9evWMuXPnuo25ulziq6++muU6umaZPrPlEg3DMF5//XWjXLlyhtPpNJo0aWLs3Lkzx/f2r9e4dknBN99806hZs6ZRtGhRo0yZMsaAAQOMc+fOuY2JjIy87nKMvXr1MipUqJBl+7WuLpd4I9e7BydOnDAeeeQRIyQkxAgODjYeffRR4+TJk9dd5nDSpElGuXLlDC8vL7fPKcl0Cc6/nmfXrl1GkSJFjBdeeMFtTHp6utG4cWMjPDw8y30BALtwGEYOn/gBAAAAkOuYYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA0UyG/+9GsY5ekSAEAJ/3nT0yUAgPyKeroCd1b1aZd/zH9/B5OYAwAAADZQIBNzAAAA2JSDXNgMdwYAAACwARJzAAAAWMfh8HQFtkViDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA5zzE2RmAMAAAA2QGIOAAAA6zDH3BR3BgAAALABEnMAAABYhznmpkjMAQAAABsgMQcAAIB1mGNuijsDAAAA2ACJOQAAAKzDHHNTJOYAAACADZCYAwAAwDrMMTfFnQEAAABsgMQcAAAA1mGOuSkScwAAAMAGSMwBAABgHeaYm+LOAAAAADZAYg4AAADrkJib4s4AAAAANkBiDgAAAOt4sSqLGRJzAAAAwAZIzAEAAGAd5pib4s4AAAAANkBiDgAAAOvwzZ+mSMwBAAAAGyAxBwAAgHWYY26KOwMAAADYAIk5AAAArMMcc1Mk5gAAAIAN0JgDAAAANsBUFgAAAFiHhz9NcWcAAAAAGyAxBwAAgHV4+NMUiTkAAABgAyTmAAAAsA5zzE1xZwAAAAAbIDEHAACAdZhjborEHAAAALABEnMAAABYhznmprgzAAAAgA2QmAMAAMA6zDE3RWIOAAAA2ACJOQAAAKzDHHNT3BkAAADABkjMAQAAYB0Sc1PcGQAAAMAGSMwBAABgHVZlMUViDgAAANgAiTkAAACswxxzU9wZAAAAwAZIzAEAAGAd5pibIjEHAAAAbIDEHAAAANZhjrkp7gwAAABgAyTmAAAAsA5zzE2RmAMAAAA2QGIOAAAAyzhIzE2RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxBwAAADWITA3RWIOAAAA2ACJOQAAACzDHHNzJOYAAACADZCYAwAAwDIk5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADLkJibIzEHAAAAbIDEHAAAANYhMDdFYg4AAADYAIk5AAAALMMcc3Mk5gAAAIANkJgDAADAMiTm5kjMAQAAABsgMQcAAIBlSMzNkZgDAAAANkBiDgAAAMuQmJsjMQcAAEChFhMTo8aNGyswMFChoaHq1KmTYmNj3cYkJydr4MCBKlmypAICAtSlSxedOXPGbczx48fVvn17FStWTKGhoRoxYoTS09OzXQeNOQAAAKzjsOiVAxs3btTAgQP1ww8/aM2aNUpLS1OrVq108eJF15ihQ4dq5cqVWrZsmTZu3KiTJ0+qc+fOrv0ZGRlq3769UlNTtXXrVs2fP1/z5s3TuHHjsn9rDMMwcla6/fk1jPJ0CQCghP+86ekSAEB+RT1dgbuSvRZbcp2z83vc8rF//PGHQkNDtXHjRjVr1kyJiYkqXbq0Fi1apK5du0qSDh06pFq1amnbtm2699579dVXX+mhhx7SyZMnVaZMGUnSnDlzNGrUKP3xxx/y8fG56XVJzAEAAGAZh8NhyevvSExMlCSVKFFCkrRr1y6lpaWpZcuWrjE1a9bU7bffrm3btkmStm3bpnr16rmacklq3bq1Lly4oP3792frujz8CQAAgAInJSVFKSkpbtucTqecTucNj8vMzNSQIUPUpEkT1a1bV5J0+vRp+fj4KCQkxG1smTJldPr0adeYvzblV/df3ZcdJOYAAACwjFWJeUxMjIKDg91eMTExN61v4MCB+vnnn/XJJ59YcDfckZgDAACgwBkzZoyGDRvmtu1maXlUVJRWrVqlTZs26bbbbnNtDwsLU2pqqs6fP++Wmp85c0ZhYWGuMf/5z3/cznd11ZarY26GxBwAAACWsSoxdzqdCgoKcnuZNeaGYSgqKkqff/651q9fr0qVKrntb9SokYoWLap169a5tsXGxur48eOKiIiQJEVEROinn37S77//7hqzZs0aBQUFqXbt2tm6NyTmAAAAKNQGDhyoRYsW6YsvvlBgYKBrTnhwcLD8/PwUHBysvn37atiwYSpRooSCgoL0wgsvKCIiQvfee68kqVWrVqpdu7aefPJJTZ06VadPn9Y///lPDRw48KZJ/VU05gAAALCODb/4c/bs2ZKk5s2bu22fO3euevfuLUmaNm2avLy81KVLF6WkpKh169Z6++23XWO9vb21atUqDRgwQBEREfL391evXr00ceLEbNfBOuYAkEdYxxyAHdhtHfPQvkstuc7vH3Sz5Dq5icQcAAAAlvm7a4wXZDz8CQAAANgAiTkAAAAsQ2JujsQcAAAAsAEScwAAAFiGxNwciTkAAABgAyTmAAAAsAyJuTkScwAAAMAGSMwBAABgHQJzUyTmAAAAgA2QmAMAAMAyzDE3R2IOAAAA2ACJOQAAACxDYm6OxBwAAACwARJzAAAAWIbE3JwtEvPNmzfriSeeUEREhH777TdJ0kcffaQtW7Z4uDIAAADAGh5vzD/99FO1bt1afn5++vHHH5WSkiJJSkxM1Msvv+zh6gAAAJCrHBa98iGPN+aTJ0/WnDlz9N5776lo0aKu7U2aNNHu3bs9WBkAAABgHY/PMY+NjVWzZs2ybA8ODtb58+etLwgAAAB5hjnm5jyemIeFhenIkSNZtm/ZskWVK1f2QEUAAACA9TzemPfv31+DBw/W9u3b5XA4dPLkSS1cuFDDhw/XgAEDPF0eAAAAcpHD4bDklR95fCrL6NGjlZmZqQceeECXLl1Ss2bN5HQ6NXz4cL3wwgueLg8AAACwhMcTc4fDoZdeekkJCQn6+eef9cMPP+iPP/7QpEmTPF0aCpDhT7fSlo9H6Pctr+nXdTFa+kZ/VasQajp+xZsDdPnHN/Vw8/rX3V8i2F9Hvp6kyz++qeAAv7wqGwD04fvv6o66NTT1lSmeLgXIFSTm5jyemF/l4+Oj2rVre7oMFFBN76yqOUs2adf+X1WkiLeiox7WqtlRath5si4lp7qNfaFnCxnGjc83Z/zj+unwSZUrUzwPqwZQ2P380z4tX/aJqlev4elSAFjA4415ixYtbvhTzfr16y2sBgVVx6i33d4/M/5j/Xf9K2pYu7y+3x3n2l6/ejkNfvIfatJzqo6tjbnuufo/er+CA4vp5Xe/Upv76+Rp3QAKr0uXLur/jR6hcRMm6713Znu6HCDX5Nc02woen8pyxx13qEGDBq5X7dq1lZqaqt27d6tevXqeLg8FVFCAryTpXOIl1zY/36KaF9NbQ15ZqjNn/7zucTUrh2lM/7bqN3aBMjNvEqsDwN/w8uSJatosUvdG3OfpUgBYxOOJ+bRp0667fcKECUpKSrK4GhQGDodDrw7vqq0/xulA3CnX9qkvdtEPe+O1asNP1z3Op2gRzY/prf83fYX+e/qcKpYrZVXJAAqZr79crUMHD2jhJ8s9XQqQ+wjMTXm8MTfzxBNP6O6779Zrr712w3EpKSlKSUlx22ZkZsjh5Z2X5SEfmz6mm+pULasH+vzfD4XtI+up+d3VdW/3V0yPmzSog2Ljz+iTL3dYUSaAQur0qVOa+soUzXnvQzmdTk+XA8BCtm3Mt23bJl9f35uOi4mJUXR0tNs27zKNVbTs3XlVGvKxaaMeVbumddWy73T99vt51/bmjaur8m2ldHrTq27jF7/WT9//GKfW/WcosnF11a0arkd23CHp/+bInfjuFf3rg280ec6XVn0MAAXYgQP7lZBwVj26dXZty8jI0O5dO7Rk8UL9Z/dP8vYmfEL+xRxzcw7DuNn6E3mrc+fObu8Nw9CpU6e0c+dOjR07VuPHj7/h8ddLzEObjiIxRxbTRj2qDv9ooFb9Zyju+B9u+8qUDFTJkAC3bbuWv6QXpy7T6o0/69eTZ1XptlLycxZ17W9Up4LejX5CzXu9rqP//UN/nGPqFdwl/OdNT5eAfOjixSSdOnnSbdu4f45RpUqV1advf1WtVt1DlSG/8it68zFWqjzMmiDr6BvtLLlObvJ4Yh4cHOz23svLSzVq1NDEiRPVqlWrmx7vdDqz/KqPphzXmj6mmx5re5ceHfquki4mq0zJQElSYlKyklPSdObsn9d94PO/p87p15NnJUnxJ/7ntu9qI3/o6GklJl3O408AoLDw9w/I0nz7+RVTcEgITTkKBBJzcx5tzDMyMtSnTx/Vq1dPxYuzHjTyzrPdmkmS1rw/xG17/3Ef6eOV2z1QEQAAgDuPT2Xx9fXVwYMHValSpVw7p1/DqFw7FwDcKqayALADu01lqfLiV5ZcJ+71tpZcJzd5fB3zunXr6ujRo54uAwAAABZwOKx55Uceb8wnT56s4cOHa9WqVTp16pQuXLjg9gIAAAAKA4/NMZ84caJefPFFtWt35YnZDh06uD0MYBiGHA6HMjIyPFUiAAAAchkPf5rzWGMeHR2t5557Tt99952nSgAAAABsw2ON+dVnTiMjIz1VAgAAACxGYG7Oo3PM+VUGAAAAcIVH1zGvXr36TZvzhIQEi6oBAABAXiOYNefRxjw6OjrLN38CAAAAhZFHG/Pu3bsrNDTUkyUAAADAQgTm5jw2x5xfYwAAAAD/x+OrsgAAAKDw8PIinDXjscY8MzPTU5cGAAAAbMejc8wBAABQuDCb2ZxH1zEHAAAAcAWJOQAAACzDAiDmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAAyzDH3ByJOQAAAGADJOYAAACwDIm5ORJzAAAAwAZIzAEAAGAZAnNzJOYAAACADZCYAwAAwDLMMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhjnm5kjMAQAAABsgMQcAAIBlCMzNkZgDAAAANkBiDgAAAMswx9wciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgGeaYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAACzDHHNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAZZhjbo7EHAAAALABEnMAAABYhsDcHIk5AAAAYAMk5gAAALAMc8zNkZgDAAAANkBiDgAAAMuQmJsjMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswxxzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYY26OxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwjBeRuSkScwAAABRqmzZt0sMPP6zw8HA5HA6tWLHCbX/v3r3lcDjcXm3atHEbk5CQoJ49eyooKEghISHq27evkpKSclQHjTkAAAAs43BY88qJixcvqkGDBnrrrbdMx7Rp00anTp1yvRYvXuy2v2fPntq/f7/WrFmjVatWadOmTXrmmWdyVAdTWQAAAFCotW3bVm3btr3hGKfTqbCwsOvuO3jwoL7++mvt2LFDd911lyRp1qxZateunV577TWFh4dnqw4ScwAAAFjm2ikhefXKbRs2bFBoaKhq1KihAQMG6OzZs65927ZtU0hIiKspl6SWLVvKy8tL27dvz/Y1SMwBAABQ4KSkpCglJcVtm9PplNPpzPG52rRpo86dO6tSpUqKi4vT//t//09t27bVtm3b5O3trdOnTys0NNTtmCJFiqhEiRI6ffp0tq9DYg4AAADLeDmsecXExCg4ONjtFRMTc0s1d+/eXR06dFC9evXUqVMnrVq1Sjt27NCGDRty997k6tkAAAAAGxgzZowSExPdXmPGjMmVc1euXFmlSpXSkSNHJElhYWH6/fff3cakp6crISHBdF769TCVBQAAAJbJi/nf13Or01ay48SJEzp79qzKli0rSYqIiND58+e1a9cuNWrUSJK0fv16ZWZm6p577sn2eWnMAQAAUKglJSW50m9Jio+P1549e1SiRAmVKFFC0dHR6tKli8LCwhQXF6eRI0eqatWqat26tSSpVq1aatOmjfr37685c+YoLS1NUVFR6t69e7ZXZJGYygIAAAAL2XEd8507d6phw4Zq2LChJGnYsGFq2LChxo0bJ29vb+3bt08dOnRQ9erV1bdvXzVq1EibN292S+QXLlyomjVr6oEHHlC7du10//336913381RHSTmAAAAKNSaN28uwzBM93/zzTc3PUeJEiW0aNGiv1UHjTkAAAAs45A1c8zzI6ayAAAAADZAYg4AAADLeBGYmyIxBwAAAGyAxBwAAACWsWod8/yIxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwjBeRuSkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAliEvN5etxvzf//53tk/YoUOHWy4GAAAAKKyy1Zh36tQpWydzOBzKyMj4O/UAAAAAhVK2GvPMzMy8rgMAAACFAF8wZO5vPfyZnJycW3UAAAAAhVqOG/OMjAxNmjRJ5cqVU0BAgI4ePSpJGjt2rD744INcLxAAAAAFh5fDmld+lOPGfMqUKZo3b56mTp0qHx8f1/a6devq/fffz9XiAAAAgMIix435ggUL9O6776pnz57y9vZ2bW/QoIEOHTqUq8UBAACgYHE4HJa88qMcN+a//fabqlatmmV7Zmam0tLScqUoAAAAoLDJcWNeu3Ztbd68Ocv25cuXq2HDhrlSFAAAAAomh8OaV36U42/+HDdunHr16qXffvtNmZmZ+uyzzxQbG6sFCxZo1apVeVEjAAAAUODlODHv2LGjVq5cqbVr18rf31/jxo3TwYMHtXLlSj344IN5USMAAAAKCOaYm8txYi5JTZs21Zo1a3K7FgAAAKDQuqXGXJJ27typgwcPSroy77xRo0a5VhQAAAAKpvy6xrgVctyYnzhxQj169ND333+vkJAQSdL58+d133336ZNPPtFtt92W2zUCAAAABV6O55j369dPaWlpOnjwoBISEpSQkKCDBw8qMzNT/fr1y4saAQAAUEAwx9xcjhPzjRs3auvWrapRo4ZrW40aNTRr1iw1bdo0V4sDAAAACoscN+bly5e/7hcJZWRkKDw8PFeKAgAAQMGUP7Nsa+R4Ksurr76qF154QTt37nRt27lzpwYPHqzXXnstV4sDAAAACotsJebFixd3m6tz8eJF3XPPPSpS5Mrh6enpKlKkiJ5++ml16tQpTwoFAABA/ueVT+d/WyFbjfn06dPzuAwAAACgcMtWY96rV6+8rgMAAACFAIG5uVv+giFJSk5OVmpqqtu2oKCgv1UQAAAAUBjluDG/ePGiRo0apaVLl+rs2bNZ9mdkZORKYQAAACh48usa41bI8aosI0eO1Pr16zV79mw5nU69//77io6OVnh4uBYsWJAXNQIAAAAFXo4T85UrV2rBggVq3ry5+vTpo6ZNm6pq1aqqUKGCFi5cqJ49e+ZFnQAAACgACMzN5TgxT0hIUOXKlSVdmU+ekJAgSf9fe/cdH1Wd/X/8PQlkEkIKIEkIHUKXJihGqRopVgRFEDQgoCChSl2XElpcXEVAARWlLShNcQEbwgJGkKWrlAghyiJFJQIGTCG5vz/4MV+HcCWB5M5N8nr6uI8Hc+fOvWeubvbwzpnPqHnz5tqyZUveVgcAAAAUEbluzKtVq6akpCRJUu3atbV8+XJJl5P04ODgPC0OAAAAhYuXw2HJVhDlujHv1auX9u3bJ0kaPXq03njjDfn6+mro0KEaMWJEnhcIAAAAFAW5njEfOnSo689RUVE6dOiQdu3apYiICDVo0CBPiwMAAEDhUkDDbEvc1DrmklS5cmVVrlw5L2oBAAAAiqwcNeYzZ87M8QkHDRp0w8UAAACgcGMdc3M5asynT5+eo5M5HA4acwAAAOAG5Kgxv7IKS0Hx247XPV0CAOjX39M8XQIAqEIpp6dLcJPrlUeKEO4NAAAAYAM3/eFPAAAAIKeYMTdHYg4AAADYAIk5AAAALONFYG6KxBwAAACwgRtqzL/88kv16NFDkZGR+umnnyRJixcvVnx8fJ4WBwAAgMLFy2HNVhDlujFftWqV2rVrJz8/P+3Zs0dpaZeXAzt37pymTp2a5wUCAAAARUGuG/PJkydr7ty5evvtt1W8eHHX/rvvvlu7d+/O0+IAAABQuDgcDku2gijXjXlCQoJatmyZbX9QUJDOnj2bFzUBAAAARU6uG/OwsDAdOXIk2/74+HhVq1YtT4oCAABA4cSMublcN+Z9+/bV4MGDtX37djkcDp04cUJLlizR8OHD1b9///yoEQAAACj0cr2O+ejRo5WVlaV7771XFy9eVMuWLeV0OjV8+HANHDgwP2oEAABAIVFAx78t4TAMw7iRF6anp+vIkSNKSUlR3bp1VbJkybyu7YalXvJ0BQAg/fp7mqdLAABVKOX0dAluRq5LsOQ60x6oZcl18tINf/Onj4+P6tatm5e1AAAAoJDzIjI3levGvE2bNn+5BM3GjRtvqiAAAACgKMp1Y96oUSO3xxkZGdq7d6++++47RUdH51VdAAAAKIRu6Gvni4hcN+bTp0+/5v4JEyYoJSXlpgsCAAAAiqI8+0tLjx499O677+bV6QAAAFAIORzWbAVRnjXm27Ztk6+vb16dDgAAAChScj3K0qlTJ7fHhmHo5MmT2rlzp8aOHZtnhQEAAKDwYVUWc7luzIOCgtwee3l5qVatWpo4caLatm2bZ4UBAAAARUmuGvPMzEz16tVL9evXV6lSpfKrJgAAABRSBObmcjVj7u3trbZt2+rs2bP5VA4AAABQNOX6w5+33nqrjh49mh+1AAAAoJDzclizFUS5bswnT56s4cOHa+3atTp58qTOnz/vtgEAAADIvRzPmE+cOFEvvPCC7r//fknSww8/LMefhoQMw5DD4VBmZmbeVwkAAIBCgVVZzOW4MY+NjVW/fv30n//8Jz/rAQAAAIqkHDfmhmFIklq1apVvxQAAAKBwIzA3l6sZcwd3EgAAAMgXuVrHvGbNmtdtzpOTk2+qIAAAABReBXXFFCvkqjGPjY3N9s2fAAAAAG5erhrzrl27KiQkJL9qAQAAQCHnEJG5mRzPmDNfDgAAAOSfXK/KAgAAANwoZszN5bgxz8rKys86AAAAgCItVzPmAAAAwM0gMTeXq3XMAQAAAOQPEnMAAABYhgVFzJGYAwAAADZAYg4AAADLMGNujsQcAAAAsAEScwAAAFiGEXNzJOYAAACADZCYAwAAwDJeROamSMwBAAAAG6AxBwAAgGW8HNZsubFlyxY99NBDCg8Pl8Ph0OrVq92eNwxD48aNU7ly5eTn56eoqCgdPnzY7Zjk5GR1795dgYGBCg4OVu/evZWSkpK7e5O7sgEAAIDC5cKFC2rYsKHeeOONaz4/bdo0zZw5U3PnztX27dvl7++vdu3aKTU11XVM9+7dtX//fq1fv15r167Vli1b9Oyzz+aqDodhGMZNvRMbSr3k6QoAQPr19zRPlwAAqlDK6ekS3Mz6KsmS6wy8u+oNvc7hcOjDDz9Ux44dJV1Oy8PDw/XCCy9o+PDhkqRz584pNDRUCxYsUNeuXXXw4EHVrVtXO3bsUNOmTSVJn376qe6//34dP35c4eHhObo2iTkAAAAKnbS0NJ0/f95tS0vLfWCSlJSkU6dOKSoqyrUvKChIzZo107Zt2yRJ27ZtU3BwsKspl6SoqCh5eXlp+/btOb4WjTkAAAAs4yWHJVtcXJyCgoLctri4uFzXe+rUKUlSaGio2/7Q0FDXc6dOnVJISIjb88WKFVPp0qVdx+QEyyUCAACg0BkzZoyGDRvmts/ptNdYz9VozAEAAGAZq5YxdzqdedKIh4WFSZJOnz6tcuXKufafPn1ajRo1ch3z888/u73u0qVLSk5Odr0+JxhlAQAAAExUrVpVYWFh2rBhg2vf+fPntX37dkVGRkqSIiMjdfbsWe3atct1zMaNG5WVlaVmzZrl+Fok5gAAALBMbtcYt0JKSoqOHDniepyUlKS9e/eqdOnSqlSpkoYMGaLJkyerRo0aqlq1qsaOHavw8HDXyi116tRR+/bt1bdvX82dO1cZGRmKiYlR165dc7wii0RjDgAAgCJu586datOmjevxldn06OhoLViwQCNHjtSFCxf07LPP6uzZs2revLk+/fRT+fr6ul6zZMkSxcTE6N5775WXl5c6d+6smTNn5qoO1jEHgHzCOuYA7MBu65i/9fWPllzn2TsrW3KdvMSMOQAAAGADjLIAAADAMlatylIQkZgDAAAANkBiDgAAAMt4EZmbIjEHAAAAbIDGHAAAALABRlkAAABgGSZZzJGYAwAAADZAYg4AAADLkAqb494AAAAANkBiDgAAAMs4GDI3RWIOAAAA2ACJOQAAACxDXm6OxBwAAACwARJzAAAAWMaLGXNTJOYAAACADZCYAwAAwDLk5eZIzAEAAAAbIDEHAACAZRgxN0diDgAAANgAiTkAAAAswzd/miMxBwAAAGyAxBwAAACWIRU2x70BAAAAbIDEHAAAAJZhxtwciTkAAABgAyTmAAAAsAx5uTkScwAAAMAGSMwBAABgGWbMzZGYAwAAADZAYg4AAADLkAqb494AAAAANkBiDgAAAMswY26OxBwAAACwARJzAAAAWIa83ByJOQAAAGADJOYAAACwDCPm5kjMAQAAABsgMQcAAIBlvJgyN0ViDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZBzPmpkjMAQAAABsgMQcAAIBlmDE3R2IOAAAA2ACJOQAAACzDOubmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYhm/+NEdiDgAAANgAiTkAAAAs40VgborEHAAAALABEnMAAABYhhlzcyTmAAAAgA3YqjFPT09XQkKCLl265OlSAAAAkA8cDmu2gsgWjfnFixfVu3dvlShRQvXq1dOxY8ckSQMHDtRLL73k4eoAAACA/GeLxnzMmDHat2+fNm3aJF9fX9f+qKgoLVu2zIOVAQAAIC85LPqnILLFhz9Xr16tZcuW6c4775TjT797qFevnhITEz1YGQAAAGANWzTmv/zyi0JCQrLtv3DhglujDgAAgIKNdczN2WKUpWnTplq3bp3r8ZVmfN68eYqMjPRUWQAAAIBlbJGYT506VR06dNCBAwd06dIlzZgxQwcOHNDWrVu1efNmT5cHAACAPFJQ57+tYIvEvHnz5tq7d68uXbqk+vXr6/PPP1dISIi2bdumJk2aeLo8AAAAIN85DMMwPF1EXktlGXTcgOXvL9XyZe/pxE8/SZKqR9TQc/2fV/MWrTxcGQqqX39P83QJsLmlC+cpftMGHfsxSU6nU3XrN9KzA4aoYuWqrmOSz/yqN2e9ql3/3aY/Ll5QhUpV1L1nX7W85z4PVo6CpEIpp6dLcBN/+DdLrtO8RilLrpOXbJGY7969W99++63r8UcffaSOHTvqb3/7m9LT0z1YGYqSkNAwDR46XO+t+EBLl6/SHc3u1OCYATpy5LCnSwNQSH2zZ6ce7txVr8/7l6bNfEuZly5p5OB++uOPi65jXop9Uf879oMmvzxTby/5QC1aR2nS30focMJBD1YOID/YojF/7rnn9P3330uSjh49qieeeEIlSpTQihUrNHLkSA9Xh6KidZt71KJlK1WuXEVVqlTVwMFDVaJECX2zb6+nSwNQSL302ly1f/ARVakWoeo1amnk2En6+dRJHT50wHXM/m/36tHHu6l2vfoKL19BPZ55Vv4lA/T9n44BChKHRVtBZIvG/Pvvv1ejRo0kSStWrFCrVq20dOlSLViwQKtWrfJscSiSMjMz9cnH6/THHxfVsGFjT5cDoIi4kJIiSQoIDHLtq1e/kf7zxWc6f+6csrKytHH9J8pIT1Oj2273VJkA8oktVmUxDENZWVmSpC+++EIPPvigJKlixYr69ddfPVkaipjD3yfoqSe7Kj09TSVKlND0mW+oekSEp8sCUARkZWXpjdem6dYGjVW1eg3X/nFTXtakv4/Uo+1ayNu7mHx9fRX7j9dUvmIlD1YL3DgvvqPGlC0a86ZNm2ry5MmKiorS5s2bNWfOHElSUlKSQkND//K1aWlpSktz/4CV4e2U02mvDzqgYKhSpaqWr1qtlJTftf7zzzT2b6P0zoJ/0ZwDyHczX56iHxKPaMZbC9z2z3/zDaX8fl4vz3pLQcGl9NXmjZr44gi9Nne+qkXU9EyxAPKFLUZZXnvtNe3evVsxMTF68cUXFfH/m6CVK1fqrrvu+svXxsXFKSgoyG17+R9xVpSNQqi4j48qVa6suvVu1eChL6hmrdpa8q9Fni4LQCE3859T9fVXW/TK7HkqGxLm2n/i+P+0euV7GvH3ibrt9jtVvUYtPd2nv2rVrquPVi3zYMXAjWPG3JwtEvMGDRq4rcpyxcsvvyxvb++/fO2YMWM0bNgwt32GN2k58kZWVpYyWBkIQD4xDEOzXolT/OaNevWNd1QuvILb86mpf0iSHA73HM3L21vG/x8BBVB42KIxN+Pr63vdY5zO7GMrrGOOGzFj+itq3qKlwsqV08ULF/TxurXaueO/mvPWO54uDUAhNfPlKdrw+SeaNG2GSvj7K/nM5c9V+fuXlNPXV5WqVFX5CpU0/R8T1W/gCwoMClb85o3a9d9tmvLK6x6uHrhBBTXOtoDHvmCoVKlScuRw+D85OTlX56Yxx40YP/Zv+u/XX+uXX35WyYAA1axZS71691XkXXd7ujQUUHzBEK7n3jsbXHP/iL9PUvsHH5EkHT/2o+bNfk3f7tuj1D8uKrxCJXXpHq37OjxkZakowOz2BUNfJ5615Dp3Vg+25Dp5yWON+cKFC3N8bHR0dK7OTWMOwA5ozAHYgd0a8+2J5yy5TrPqQdc/yGY8NsqS22YbAAAAKMxsN2Oempqq9Ks+bBcYGOihagAAAJCXWMbcnC2WS7xw4YJiYmIUEhIif39/lSpVym0DAAAACjtbNOYjR47Uxo0bNWfOHDmdTs2bN0+xsbEKDw/XokWsIQ0AAFBYsI65OVuMsqxZs0aLFi1S69at1atXL7Vo0UIRERGqXLmylixZou7du3u6RAAAACBf2SIxT05OVrVq1SRdnie/sjxi8+bNtWXLFk+WBgAAgLxEZG7KFo15tWrVlJSUJEmqXbu2li9fLulykh4cHOzBygAAAABreLQxP3r0qLKystSrVy/t27dPkjR69Gi98cYb8vX11dChQzVixAhPlggAAIA85LDon4LIY18wJEne3t46efKkQkJCJElPPPGEZs6cqdTUVO3atUsRERFq0ODa34r2V/iCIQB2wBcMAbADu33B0M6k85Zcp2nVgrfctkcbcy8vL506dcrVmAcEBGjfvn2uefMbRWMOwA5ozAHYgd0a810/WNOYN6lS8BpzW8yYAwAAAEWdR5dLdDgcclz19U9XPwYAAEDhQadnzqONuWEY6tmzp5zOy79iSU1NVb9+/eTv7+923AcffOCJ8gAAAADLeLQxj46Odnvco0cPD1UCAAAASxCZm/JoYz5//nxPXh4AAACwDY825gAAAChaCuoa41ZgVRYAAADABkjMAQAAYBkW4DNHYg4AAADYAI05AAAALOOwaMuNCRMmuL5f58pWu3Zt1/OpqakaMGCAypQpo5IlS6pz5846ffr0Db3/v0JjDgAAgCKvXr16OnnypGuLj493PTd06FCtWbNGK1as0ObNm3XixAl16tQpz2tgxhwAAADWsemMebFixRQWFpZt/7lz5/TOO+9o6dKluueeeyRdXvK7Tp06+vrrr3XnnXfmWQ0k5gAAACjyDh8+rPDwcFWrVk3du3fXsWPHJEm7du1SRkaGoqKiXMfWrl1blSpV0rZt2/K0BhJzAAAAFDppaWlKS0tz2+d0OuV0OrMd26xZMy1YsEC1atXSyZMnFRsbqxYtWui7777TqVOn5OPjo+DgYLfXhIaG6tSpU3laM4k5AAAALOOw6J+4uDgFBQW5bXFxcdesqUOHDnr88cfVoEEDtWvXTh9//LHOnj2r5cuXW3pvaMwBAABQ6IwZM0bnzp1z28aMGZOj1wYHB6tmzZo6cuSIwsLClJ6errNnz7odc/r06WvOpN8MGnMAAABYxuGwZnM6nQoMDHTbrjXGci0pKSlKTExUuXLl1KRJExUvXlwbNmxwPZ+QkKBjx44pMjIyT+8NM+YAAAAo0oYPH66HHnpIlStX1okTJzR+/Hh5e3urW7duCgoKUu/evTVs2DCVLl1agYGBGjhwoCIjI/N0RRaJxhwAAAAWsuNqicePH1e3bt105swZlS1bVs2bN9fXX3+tsmXLSpKmT58uLy8vde7cWWlpaWrXrp1mz56d53U4DMMw8vysHpZ6ydMVAID06+9p1z8IAPJZhVI5G9+wynfHUyy5zq0VSlpynbxEYg4AAADr2DEytwk+/AkAAADYAIk5AAAALOMgMjdFYg4AAADYAIk5AAAALOMgMDdFYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYh8jcFIk5AAAAYAMk5gAAALAM65ibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDIE5uZIzAEAAAAbIDEHAACAdYjMTZGYAwAAADZAYg4AAADLsI65ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACxDYG6OxBwAAACwARJzAAAAWIfI3BSJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWYR1zcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgHWIzE2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGdYxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwEJE5mZIzAEAAAAbIDEHAACAZZgxN0diDgAAANgAiTkAAAAsQ2BujsQcAAAAsAEScwAAAFiGGXNzJOYAAACADZCYAwAAwDIOpsxNkZgDAAAANkBiDgAAAOsQmJsiMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAAAswzrm5kjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAOgbkpEnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAMqxjbo7EHAAAALABEnMAAABYhnXMzZGYAwAAADZAYg4AAADLMGNujsQcAAAAsAEacwAAAMAGaMwBAAAAG2DGHAAAAJZhxtwciTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAlmHG3ByJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAdInNTJOYAAACADZCYAwAAwDKsY26OxBwAAACwARJzAAAAWIZ1zM2RmAMAAAA2QGMOAAAA2ACjLAAAALAMkyzmSMwBAAAAGyAxBwAAgHWIzE2RmAMAAAA2QGIOAAAAy/AFQ+ZIzAEAAAAbIDEHAACAZfiCIXMk5gAAAIANOAzDMDxdBGA3aWlpiouL05gxY+R0Oj1dDoAiiJ9DQNFDYw5cw/nz5xUUFKRz584pMDDQ0+UAKIL4OQQUPYyyAAAAADZAYw4AAADYAI05AAAAYAM05sA1OJ1OjR8/ng9cAfAYfg4BRQ8f/gQAAABsgMQcAAAAsAEacwAAAMAGaMwBALCZBQsWKDg42NNlALAYjTkKNYfD8ZfbhAkTPF0igIWzvsIAAFeNSURBVEKsZ8+e1/zZc+TIEU+XBsCGinm6ACA/nTx50vXnZcuWady4cUpISHDtK1mypOvPhmEoMzNTxYrxPwsAead9+/aaP3++276yZct6qBoAdkZijkItLCzMtQUFBcnhcLgeHzp0SAEBAfrkk0/UpEkTOZ1OxcfHq2fPnurYsaPbeYYMGaLWrVu7HmdlZSkuLk5Vq1aVn5+fGjZsqJUrV1r75gAUCE6n0+1nUVhYmGbMmKH69evL399fFStW1PPPP6+UlBTTc+zbt09t2rRRQECAAgMD1aRJE+3cudP1fHx8vFq0aCE/Pz9VrFhRgwYN0oULF6x4ewDyEI05irzRo0frpZde0sGDB9WgQYMcvSYuLk6LFi3S3LlztX//fg0dOlQ9evTQ5s2b87laAIWBl5eXZs6cqf3792vhwoXauHGjRo4caXp89+7dVaFCBe3YsUO7du3S6NGjVbx4cUlSYmKi2rdvr86dO+ubb77RsmXLFB8fr5iYGKveDoA8wu/sUeRNnDhR9913X46PT0tL09SpU/XFF18oMjJSklStWjXFx8frzTffVKtWrfKrVAAF0Nq1a93G5jp06KAVK1a4HlepUkWTJ09Wv379NHv27Gue49ixYxoxYoRq164tSapRo4brubi4OHXv3l1DhgxxPTdz5ky1atVKc+bMka+vbz68KwD5gcYcRV7Tpk1zdfyRI0d08eLFbM18enq6GjdunJelASgE2rRpozlz5rge+/v764svvlBcXJwOHTqk8+fP69KlS0pNTdXFixdVokSJbOcYNmyY+vTpo8WLFysqKkqPP/64qlevLunymMs333yjJUuWuI43DENZWVlKSkpSnTp18v9NAsgTNOYo8vz9/d0ee3l56eovxM3IyHD9+coc6Lp161S+fHm34/jqbABX8/f3V0REhOvxDz/8oAcffFD9+/fXlClTVLp0acXHx6t3795KT0+/ZmM+YcIEPfnkk1q3bp0++eQTjR8/Xu+//74effRRpaSk6LnnntOgQYOyva5SpUr5+t4A5C0ac+AqZcuW1Xfffee2b+/eva55zrp168rpdOrYsWOMrQDItV27dikrK0uvvPKKvLwuf9Rr+fLl131dzZo1VbNmTQ0dOlTdunXT/Pnz9eijj+q2227TgQMH3Jp/AAUTH/4ErnLPPfdo586dWrRokQ4fPqzx48e7NeoBAQEaPny4hg4dqoULFyoxMVG7d+/WrFmztHDhQg9WDqAgiIiIUEZGhmbNmqWjR49q8eLFmjt3runxf/zxh2JiYrRp0yb9+OOP+uqrr7Rjxw7XiMqoUaO0detWxcTEaO/evTp8+LA++ugjPvwJFEA05sBV2rVrp7Fjx2rkyJG6/fbb9fvvv+vpp592O2bSpEkaO3as4uLiVKdOHbVv317r1q1T1apVPVQ1gIKiYcOGevXVV/WPf/xDt956q5YsWaK4uDjT4729vXXmzBk9/fTTqlmzprp06aIOHTooNjZWktSgQQNt3rxZ33//vVq0aKHGjRtr3LhxCg8Pt+otAcgjDuPqYVoAAAAAliMxBwAAAGyAxhwAAACwARpzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQdQZPXs2VMdO3Z0PW7durWGDBlieR2bNm2Sw+HQ2bNnTY9xOBxavXp1js85YcIENWrU6Kbq+uGHH+RwOLR3796bOg8AIGdozAHYSs+ePeVwOORwOOTj46OIiAhNnDhRly5dyvdrf/DBB5o0aVKOjs1JMw0AQG4U83QBAHC19u3ba/78+UpLS9PHH3+sAQMGqHjx4hozZky2Y9PT0+Xj45Mn1y1dunSenAcAgBtBYg7AdpxOp8LCwlS5cmX1799fUVFR+ve//y3p/8ZPpkyZovDwcNWqVUuS9L///U9dunRRcHCwSpcurUceeUQ//PCD65yZmZkaNmyYgoODVaZMGY0cOVKGYbhd9+pRlrS0NI0aNUoVK1aU0+lURESE3nnnHf3www9q06aNJKlUqVJyOBzq2bOnJCkrK0txcXGqWrWq/Pz81LBhQ61cudLtOh9//LFq1qwpPz8/tWnTxq3OnBo1apRq1qypEiVKqFq1aho7dqwyMjKyHffmm2+qYsWKKlGihLp06aJz5865PT9v3jzVqVNHvr6+ql27tmbPnp3rWgAAeYPGHIDt+fn5KT093fV4w4YNSkhI0Pr167V27VplZGSoXbt2CggI0JdffqmvvvpKJUuWVPv27V2ve+WVV7RgwQK9++67io+PV3Jysj788MO/vO7TTz+t9957TzNnztTBgwf15ptvqmTJkqpYsaJWrVolSUpISNDJkyc1Y8YMSVJcXJwWLVqkuXPnav/+/Ro6dKh69OihzZs3S7r8F4hOnTrpoYce0t69e9WnTx+NHj061/ckICBACxYs0IEDBzRjxgy9/fbbmj59utsxR44c0fLly7VmzRp9+umn2rNnj55//nnX80uWLNG4ceM0ZcoUHTx4UFOnTtXYsWO1cOHCXNcDAMgDBgDYSHR0tPHII48YhmEYWVlZxvr16w2n02kMHz7c9XxoaKiRlpbmes3ixYuNWrVqGVlZWa59aWlphp+fn/HZZ58ZhmEY5cqVM6ZNm+Z6PiMjw6hQoYLrWoZhGK1atTIGDx5sGIZhJCQkGJKM9evXX7PO//znP4Yk47fffnPtS01NNUqUKGFs3brV7djevXsb3bp1MwzDMMaMGWPUrVvX7flRo0ZlO9fVJBkffvih6fMvv/yy0aRJE9fj8ePHG97e3sbx48dd+z755BPDy8vLOHnypGEYhlG9enVj6dKlbueZNGmSERkZaRiGYSQlJRmSjD179pheFwCQd5gxB2A7a9euVcmSJZWRkaGsrCw9+eSTmjBhguv5+vXru82V79u3T0eOHFFAQIDbeVJTU5WYmKhz587p5MmTatasmeu5YsWKqWnTptnGWa7Yu3evvL291apVqxzXfeTIEV28eFH33Xef2/709HQ1btxYknTw4EG3OiQpMjIyx9e4YtmyZZo5c6YSExOVkpKiS5cuKTAw0O2YSpUqqXz58m7XycrKUkJCggICApSYmKjevXurb9++rmMuXbqkoKCgXNcDALh5NOYAbKdNmzaaM2eOfHx8FB4ermLF3H9U+fv7uz1OSUlRkyZNtGTJkmznKlu27A3V4Ofnl+vXpKSkSJLWrVvn1hBLl+fm88q2bdvUvXt3xcbGql27dgoKCtL777+vV155Jde1vv3229n+ouDt7Z1ntQIAco7GHIDt+Pv7KyIiIsfH33bbbVq2bJlCQkKypcZXlCtXTtu3b1fLli0lXU6Gd+3apdtuu+2ax9evX19ZWVnavHmzoqKisj1/JbHPzMx07atbt66cTqeOHTtmmrTXqVPH9UHWK77++uvrv8k/2bp1qypXrqwXX3zRte/HH3/MdtyxY8d04sQJhYeHu67j5eWlWrVqKTQ0VOHh4Tp69Ki6d++eq+sDAPIHH/4EUOB1795dt9xyix555BF9+eWXSkpK0qZNmzRo0CAdP35ckjR48GC99NJLWr16tQ4dOqTnn3/+L9cgr1KliqKjo/XMM89o9erVrnMuX75cklS5cmU5HA6tXbtWv/zyi1JSUhQQEKDhw4dr6NChWrhwoRITE7V7927NmjXL9YHKfv366fDhwxoxYoQSEhK0dOlSLViwIFfvt0aNGjp27Jjef/99JSYmaubMmdf8IKuvr6+io6O1b98+ffnllxo0aJC6dOmisLAwSVJsbKzi4uI0c+ZMff/99/r22281f/58vfrqq7mqBwCQN2jMARR4JUqU0JYtW1SpUiV16tRJderUUe/evZWamupK0F944QU99dRTio6OVmRkpAICAvToo4/+5XnnzJmjxx57TM8//7xq166tvn376sKFC5Kk8uXLKzY2VqNHj1ZoaKhiYmIkSZMmTdLYsWMVFxenOnXqqH379lq3bp2qVq0q6fLc96pVq7R69Wo1bNhQc+fO1dSpU3P1fh9++GENHTpUMTExatSokbZu3aqxY8dmOy4iIkKdOnXS/fffr7Zt26pBgwZuyyH26dNH8+bN0/z581W/fn21atVKCxYscNUKALCWwzD75BMAAAAAy5CYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgAzTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDhRRVapUUc+ePT1dRqHz6aefqlGjRvL19ZXD4dDZs2fz9PwLFiyQw+HQDz/8kKfnLcgcDocmTJjg6TIA4KbRmAOFTGJiop577jlVq1ZNvr6+CgwM1N13360ZM2bojz/+8HR513X48GF17dpVFSpUUIkSJVS7dm1NnDhRFy9ezPE5Nm3apE6dOiksLEw+Pj4KCQnRQw89pA8++CAfK5fOnDmjLl26yM/PT2+88YYWL14sf3//fL2mlapUqSKHw6GoqKhrPv/222/L4XDI4XBo586duT7/1q1bNWHChDz/ywwAFBTFPF0AgLyzbt06Pf7443I6nXr66ad16623Kj09XfHx8RoxYoT279+vt956y9Nlmvrf//6nO+64Q0FBQYqJiVHp0qW1bds2jR8/Xrt27dJHH3103XOMHz9eEydOVI0aNfTcc8+pcuXKOnPmjD7++GN17txZS5Ys0ZNPPpkv9e/YsUO///67Jk2aZNq83qynnnpKXbt2ldPpzJfzX4+vr6/+85//6NSpUwoLC3N7bsmSJfL19VVqauoNnXvr1q2KjY1Vz549FRwcnOPX/fHHHypWjP87A1Dw8ZMMKCSSkpLUtWtXVa5cWRs3blS5cuVczw0YMEBHjhzRunXrPFjh9S1evFhnz55VfHy86tWrJ0l69tlnlZWVpUWLFum3335TqVKlTF+/cuVKTZw4UY899piWLl2q4sWLu54bMWKEPvvsM2VkZORb/T///LMk5aqpzC1vb295e3vn2/mv5+6779aOHTu0bNkyDR482LX/+PHj+vLLL/Xoo49q1apV+V5HVlaW0tPT5evrK19f33y/HgBYgVEWoJCYNm2aUlJS9M4777g15VdERES4NVJXS05O1vDhw1W/fn2VLFlSgYGB6tChg/bt25ft2FmzZqlevXoqUaKESpUqpaZNm2rp0qWu53///XcNGTJEVapUkdPpVEhIiO677z7t3r37L9/D+fPnJUmhoaFu+8uVKycvLy/5+Pj85evHjh2r0qVL691333Vryq9o166dHnzwQdfjn3/+Wb1791ZoaKh8fX3VsGFDLVy40O01P/zwgxwOh/75z3/qrbfeUvXq1eV0OnX77bdrx44druNat26t6OhoSdLtt98uh8PhmuE3m+dv3bq1Wrdu7bbvevfWbMZ89uzZqlevnpxOp8LDwzVgwIBsIyGtW7fWrbfeqgMHDqhNmzYqUaKEypcvr2nTppnd0mx8fX3VqVMnt5ok6b333lOpUqXUrl27bK/55ptv1LNnT9d4VVhYmJ555hmdOXPGdcyECRM0YsQISVLVqlVdIzFX3qfD4VBMTIyWLFniep+ffvqp67krM+Z//PGHateurdq1a7uNbiUnJ6tcuXK66667lJmZmeP3CwBWIjEHCok1a9aoWrVquuuuu27o9UePHtXq1av1+OOPq2rVqjp9+rTefPNNtWrVSgcOHFB4eLiky3PEgwYN0mOPPabBgwcrNTVV33zzjbZv3+4aEenXr59WrlypmJgY1a1bV2fOnFF8fLwOHjyo2267zbSG1q1b6x//+Id69+6t2NhYlSlTRlu3btWcOXM0aNCgv5zXPnz4sA4dOqRnnnlGAQEB132/f/zxh1q3bq0jR44oJiZGVatW1YoVK9SzZ0+dPXs2219ili5dqt9//13PPfecHA6Hpk2bpk6dOuno0aMqXry4XnzxRdWqVUtvvfWWJk6cqKpVq6p69eo5ufUuObm31zJhwgTFxsYqKipK/fv3V0JCgubMmaMdO3boq6++cvtLym+//ab27durU6dO6tKli1auXKlRo0apfv366tChQ47qfPLJJ9W2bVslJia63uPSpUv12GOPXfMvROvXr9fRo0fVq1cvhYWFuUaq9u/fr6+//loOh0OdOnXS999/r/fee0/Tp0/XLbfcIkkqW7as6zwbN27U8uXLFRMTo1tuuUVVqlTJdi0/Pz8tXLhQd999t1588UW9+uqrki7/1ujcuXNasGCBR3/jAAB/yQBQ4J07d86QZDzyyCM5fk3lypWN6Oho1+PU1FQjMzPT7ZikpCTD6XQaEydOdO175JFHjHr16v3luYOCgowBAwbkuJY/mzRpkuHn52dIcm0vvvjidV/30UcfGZKM6dOn5+g6r732miHJ+Ne//uXal56ebkRGRholS5Y0zp8/bxjG5XsgyShTpoyRnJyc7Xpr1qxx7Zs/f74hydixY4fbta6+11e0atXKaNWqletxTu7tlWskJSUZhmEYP//8s+Hj42O0bdvW7d/f66+/bkgy3n33XbfrSTIWLVrk2peWlmaEhYUZnTt3/svrXnkfDzzwgHHp0iUjLCzMmDRpkmEYhnHgwAFDkrF58+Zr3oOLFy9mO9d7771nSDK2bNni2vfyyy+7vbc/k2R4eXkZ+/fvv+Zz48ePd9s3ZswYw8vLy9iyZYuxYsUKQ5Lx2muvXfc9AoAnMcoCFAJXRkBykhSbcTqd8vK6/CMhMzNTZ86cUcmSJVWrVi23EZTg4GAdP37cbYzjasHBwdq+fbtOnDiR6zqqVKmili1b6q233tKqVav0zDPPaOrUqXr99df/8nW5vQcff/yxwsLC1K1bN9e+4sWLa9CgQUpJSdHmzZvdjn/iiSfc5ttbtGgh6fJvGvJKTu7t1b744gulp6dryJAhrn9/ktS3b18FBgZm+1xByZIl1aNHD9djHx8f3XHHHbl6H97e3urSpYvee+89SZc/9FmxYkXXPbman5+f68+pqan69ddfdeedd0rSdceb/qxVq1aqW7dujo6dMGGC6tWrp+joaD3//PNq1aqVBg0alONrAYAn0JgDhUBgYKCky7PdNyorK0vTp09XjRo15HQ6dcstt6hs2bL65ptvdO7cOddxo0aNUsmSJXXHHXeoRo0aGjBggL766iu3c02bNk3fffedKlasqDvuuEMTJkzIUeP3/vvv69lnn9W8efPUt29fderUSe+8846io6M1atQot5nkq+X2Hvz444+qUaOGWzMrSXXq1HE9/2eVKlVye3ylSf/tt99ydL2cyMm9vdqVOmvVquW238fHR9WqVcv2PipUqCCHw+G2r1SpUrl+H08++aQOHDigffv2aenSperatWu2816RnJyswYMHKzQ0VH5+fipbtqyqVq0qSW7/bV3PldfkhI+Pj959910lJSXp999/1/z5803rAwC7oDEHCoHAwECFh4fru+++u+FzTJ06VcOGDVPLli31r3/9S5999pnWr1+vevXqKSsry3VcnTp1lJCQoPfff1/NmzfXqlWr1Lx5c40fP951TJcuXXT06FHNmjVL4eHhevnll1WvXj198sknf1nD7Nmz1bhxY1WoUMFt/8MPP6yLFy9qz549pq+tXbu2JOnbb7+9kbd/XWZzyYZhXPe1Zg3h1R9CzMm9vVk38z7+rFmzZqpevbqGDBmipKSkv5yB79Kli95++23169dPH3zwgT7//HPXBzf//N/W9fw5ec+Jzz77TNLllP7w4cO5ei0AeAKNOVBIPPjgg0pMTNS2bdtu6PUrV65UmzZt9M4776hr165q27atoqKirvllL/7+/nriiSc0f/58HTt2TA888ICmTJnitn51uXLl9Pzzz2v16tVKSkpSmTJlNGXKlL+s4fTp09dcMePKEoeXLl0yfW3NmjVVq1YtffTRR0pJSbnu+61cubIOHz6crTE8dOiQ6/m8UqpUqWvex6vTbCln9/bPrtSZkJDgtj89PV1JSUl5+j6u1q1bN23atEl16tRRo0aNrnnMb7/9pg0bNmj06NGKjY3Vo48+qvvuu0/VqlXLdmxeJtrffPONJk6cqF69eqlx48bq06dPrtJ5APAEGnOgkBg5cqT8/f3Vp08fnT59OtvziYmJmjFjhunrvb29s6WmK1as0E8//eS27+pxEh8fH9WtW1eGYSgjI0OZmZnZGqCQkBCFh4crLS3tL99DzZo1tWfPHn3//fdu+9977z15eXmpQYMGf/n62NhYnTlzRn369LlmE//5559r7dq1kqT7779fp06d0rJly1zPX7p0SbNmzVLJkiXVqlWrv7xWblSvXl1ff/210tPTXfvWrl2r//3vf27HXe/eXktUVJR8fHw0c+ZMt39/77zzjs6dO6cHHnggz97H1fr06aPx48frlVdeMT3mSkJ/9X9br732WrZjr6y6c7Pf/JmRkaGePXsqPDxcM2bM0IIFC3T69GkNHTr0ps4LAPmN5RKBQqJ69epaunSpnnjiCdWpU8ftmz+3bt3qWgrQzIMPPuhKGO+66y59++23WrJkSbZks23btgoLC9Pdd9+t0NBQHTx4UK+//roeeOABBQQE6OzZs6pQoYIee+wxNWzYUCVLltQXX3yhHTt2/GUDJ13+EqBPPvlELVq0UExMjMqUKaO1a9fqk08+UZ8+fVxLNpp54okn9O2332rKlCnas2ePunXr5vrmz08//VQbNmxwrb/97LPP6s0331TPnj21a9cuValSRStXrtRXX32l11577aY+SHu1Pn36aOXKlWrfvr26dOmixMRE/etf/8q2nOL17u21lC1bVmPGjFFsbKzat2+vhx9+WAkJCZo9e7Zuv/12tw965rXKlSu71g83ExgYqJYtW2ratGnKyMhQ+fLl9fnnnyspKSnbsU2aNJEkvfjii+ratauKFy+uhx566C+XybyWyZMna+/evdqwYYMCAgLUoEEDjRs3Tn//+9/12GOP6f7778/V+QDAMh5cEQZAPvj++++Nvn37GlWqVDF8fHyMgIAA4+677zZmzZplpKamuo671nKJL7zwglGuXDnDz8/PuPvuu41t27ZlW9LvzTffNFq2bGmUKVPGcDqdRvXq1Y0RI0YY586dMwzj8vJ7I0aMMBo2bGgEBAQY/v7+RsOGDY3Zs2fnqP7t27cbHTp0MMLCwozixYsbNWvWNKZMmWJkZGTk+B5s2LDBeOSRR4yQkBCjWLFiRtmyZY2HHnrI+Oijj9yOO336tNGrVy/jlltuMXx8fIz69esb8+fPdzvmynKJL7/8crbr6Kpl+syWSzQMw3jllVeM8uXLG06n07j77ruNnTt35vre/vkaVy8p+Prrrxu1a9c2ihcvboSGhhr9+/c3fvvtN7djWrVqdc3lGKOjo43KlStn23+1K8sl/pVr3YPjx48bjz76qBEcHGwEBQUZjz/+uHHixIlrLnM4adIko3z58oaXl5fb+5RkugTnn8+za9cuo1ixYsbAgQPdjrl06ZJx++23G+Hh4dnuCwDYhcMwcvmJHwAAAAB5jhlzAAAAwAZozAEAAAAboDEHAAAAbIDGHAAAALABGnMAAADABmjMAQAAABugMQcAAABsoFB+86df4xhPlwAA+m3H654uAQDka7Nuz6o+7Y89Be9nMIk5AAAAYAM2+zsUAAAACjUHubAZ7gwAAABgAyTmAAAAsI7D4ekKbIvEHAAAALABEnMAAABYhxlzU9wZAAAAwAZIzAEAAGAdZsxNkZgDAAAANkBiDgAAAOswY26KOwMAAADYAIk5AAAArMOMuSkScwAAAMAGSMwBAABgHWbMTXFnAAAAABsgMQcAAIB1mDE3RWIOAAAA2ACJOQAAAKzDjLkp7gwAAABgAyTmAAAAsA4z5qZIzAEAAAAbIDEHAACAdZgxN8WdAQAAAGyAxBwAAADWYcbcFIk5AAAAYAMk5gAAALAOM+amuDMAAACADdCYAwAAADbAKAsAAACswyiLKe4MAAAAYAMk5gAAALCOF8slmiExBwAAAGyAxBwAAADWYcbcFHcGAAAAsAEScwAAAFjHwYy5GRJzAAAAwAZIzAEAAGAdZsxNcWcAAAAAGyAxBwAAgHWYMTdFYg4AAADYAIk5AAAArMOMuSnuDAAAAGADJOYAAACwDjPmpkjMAQAAABsgMQcAAIB1mDE3xZ0BAAAAbIDEHAAAANZhxtwUiTkAAABgAyTmAAAAsA4z5qa4MwAAAIANkJgDAADAOsyYmyIxBwAAAGyAxBwAAADWYcbcFHcGAAAAsAEScwAAAFiHxNwUdwYAAACwARJzAAAAWIdVWUyRmAMAAAA2QGIOAAAA6zBjboo7AwAAANgAiTkAAACsw4y5KRJzAAAAwAZIzAEAAGAdZsxNcWcAAAAAGyAxBwAAgHWYMTdFYg4AAADYAIk5AAAALOMgMTdFYg4AAADYAIk5AAAALENibo7EHAAAALABEnMAAABYh8DcFIk5AAAAYAMk5gAAALAMM+bmSMwBAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAAy5CYmyMxBwAAAGyAxBwAAACWITE3R2IOAAAA2ACJOQAAAKxDYG6KxBwAAACwARJzAAAAWIYZc3Mk5gAAAIANkJgDAADAMiTm5kjMAQAAABsgMQcAAIBlSMzNkZgDAAAANkBiDgAAAMuQmJsjMQcAAABsgMQcAAAA1iEwN0ViDgAAANgAiTkAAAAsw4y5ORJzAAAAFGlxcXG6/fbbFRAQoJCQEHXs2FEJCQlux6SmpmrAgAEqU6aMSpYsqc6dO+v06dNuxxw7dkwPPPCASpQooZCQEI0YMUKXLl3KcR005gAAALCMw+GwZMuNzZs3a8CAAfr666+1fv16ZWRkqG3btrpw4YLrmKFDh2rNmjVasWKFNm/erBMnTqhTp06u5zMzM/XAAw8oPT1dW7du1cKFC7VgwQKNGzcu5/fGMAwjV5UXAH6NYzxdAgDotx2ve7oEAJCvzQaXy/ZaZsl1fpn/xI2/9pdfFBISos2bN6tly5Y6d+6cypYtq6VLl+qxxx6TJB06dEh16tTRtm3bdOedd+qTTz7Rgw8+qBMnTig0NFSSNHfuXI0aNUq//PKLfHx8rntdEnMAAABYxo6J+dXOnTsnSSpdurQkadeuXcrIyFBUVJTrmNq1a6tSpUratm2bJGnbtm2qX7++qymXpHbt2un8+fPav39/jq5rs79DAQAAADcvLS1NaWlpbvucTqecTudfvi4rK0tDhgzR3XffrVtvvVWSdOrUKfn4+Cg4ONjt2NDQUJ06dcp1zJ+b8ivPX3kuJ0jMAQAAYB2HNVtcXJyCgoLctri4uOuWN2DAAH333Xd6//338+495xCJOQAAAAqdMWPGaNiwYW77rpeWx8TEaO3atdqyZYsqVKjg2h8WFqb09HSdPXvWLTU/ffq0wsLCXMf897//dTvflVVbrhxzPSTmAAAAsIxVM+ZOp1OBgYFum1ljbhiGYmJi9OGHH2rjxo2qWrWq2/NNmjRR8eLFtWHDBte+hIQEHTt2TJGRkZKkyMhIffvtt/r5559dx6xfv16BgYGqW7duju4NiTkAAACKtAEDBmjp0qX66KOPFBAQ4JoJDwoKkp+fn4KCgtS7d28NGzZMpUuXVmBgoAYOHKjIyEjdeeedkqS2bduqbt26euqppzRt2jSdOnVKf//73zVgwIDrJvVX0JgDAADAMnb85s85c+ZIklq3bu22f/78+erZs6ckafr06fLy8lLnzp2Vlpamdu3aafbs2a5jvb29tXbtWvXv31+RkZHy9/dXdHS0Jk6cmOM6WMccAPIJ65gDsAO7rWMe1nelJdc59fZjllwnL9nsXxUAAAAKMzsm5nbBhz8BAAAAGyAxBwAAgGVIzM2RmAMAAAA2QGIOAAAA6xCYmyIxBwAAAGyAxBwAAACWYcbcHIk5AAAAYAMk5gAAALAMibk5EnMAAADABkjMAQAAYBkSc3O2SMy//PJL9ejRQ5GRkfrpp58kSYsXL1Z8fLyHKwMAAACs4fHGfNWqVWrXrp38/Py0Z88epaWlSZLOnTunqVOnerg6AAAA5CmHRVsB5PHGfPLkyZo7d67efvttFS9e3LX/7rvv1u7duz1YGQAAAGAdj8+YJyQkqGXLltn2BwUF6ezZs9YXBAAAgHzDjLk5jyfmYWFhOnLkSLb98fHxqlatmgcqAgAAAKzn8ca8b9++Gjx4sLZv3y6Hw6ETJ05oyZIlGj58uPr37+/p8gAAAABLeHyUZfTo0crKytK9996rixcvqmXLlnI6nRo+fLgGDhzo6fIAAACQhxhlMefxxtzhcOjFF1/UiBEjdOTIEaWkpKhu3boqWbKkp0sDAAAALOPxxvwKHx8f1a1b19NloJAa/kxbdbynoWpWCdUfaRnavu+oXpzxkQ7/+PM1j1/9en+1u7ueugx9S2s2fSNJ6vFQM7098alrHl/pntH65beUfKsfQNHz/tIlWjj/Hf366y+qWau2Rv9trOo3aODpsoCbRmJuzuONeZs2bf7yX9DGjRstrAaFVYvbIjR32Rbt2v+jihXzVmzMQ1o7J0aNO03WxdR0t2MHdm8jw8h+jpWf79b6rQfc9r0V+5R8ncVpygHkqU8/+Vj/nBanv4+PVf36DbVk8UL1f663Plr7qcqUKePp8gDkE49/+LNRo0Zq2LCha6tbt67S09O1e/du1a9f39PloZB4JGa2/rVmuw4ePaVvv/9Jz47/lyqVK63GdSu6HdegZnkNfuoe9Zvwr2znSE3L0Okzv7u2zCxDre+oqQWrt1r1NgAUEYsXzlenx7qo46OdVT0iQn8fHytfX1+t/mCVp0sDbprD4bBkK4g8nphPnz79mvsnTJiglBRSSOSPwJK+kqTfzl107fPzLa4FcT015KXlOn3m9+ueo/uDd+hiaro+/GJvfpUJoAjKSE/XwQP71bvvc659Xl5euvPOu/TNvj0erAxAfvN4Ym6mR48eevfddz1dBgohh8Ohl4c/pq17EnUg8aRr/7QXOuvrfUlau+nbHJ0numOkln2yU6lpGflVKoAi6LezvykzMzPbyEqZMmX066+/eqgqIA85LNoKII8n5ma2bdsmX1/f6x6XlpamtLQ0t31GVqYcXt75VRoKuNfGdFG9iHK6t9f//bbmgVb11fqOmrqz60s5OkezBlVVp1o59f77ovwqEwAAFDEeb8w7derk9tgwDJ08eVI7d+7U2LFjr/v6uLg4xcbGuu3zDr1dxcvdkad1onCYPupx3d/iVkX1fk0//XzWtb/17TVVrcItOrXlZbfj3/tnH321J1Ht+s5w29/z0UjtPfQ/7Tn4PyvKBlCElAouJW9vb505c8Zt/5kzZ3TLLbd4qCog7xTU+W8reLwxDwoKcnvs5eWlWrVqaeLEiWrbtu11Xz9mzBgNGzbMbV9Ii1F5WiMKh+mjHtfD9zRU274z9OMJ9//D++f8zzX/Q/cPce5a+aJGvrJK6zZ/57bf389Hne+7TeNm/TvfawZQ9BT38VGduvW0/ettuufeKElSVlaWtm/fpq7deni4OgD5yaONeWZmpnr16qX69eurVKlSN3QOp9Mpp9Ppto8xFlzttTFd9ESHpnp86FtKuZCq0DIBkqRzKaluq61c7X8nf8vWxD/WromKeXvpvXU7LKkdQNHzVHQvjf3bKNWrd6turd9A/1q8UH/88Yc6Ptrp+i8GbI7E3JxHG3Nvb2+1bdtWBw8evOHGHMiJ57q0lCStnzfEbX/fcYv1rzXbc3Wunh0j9dHGfTqX8kdelQcAbtp3uF+/JSdr9usz9euvv6hW7Tqa/eY8lWGUBSjUPD7Kcuutt+ro0aOqWrWqp0tBIebXOCbPXtOm56s3Ww4AXFe37j3UrTujKyh8CMzNeXy5xMmTJ2v48OFau3atTp48qfPnz7ttAAAAQFHgscR84sSJeuGFF3T//fdLkh5++GG3mSPDMORwOJSZmempEgEAAJDHmDE357HGPDY2Vv369dN//vMfT5UAAAAA2IbHGnPDMCRJrVq18lQJAAAAsBiBuTmPzpjzqwwAAADgMo+uylKzZs3rNufJyckWVQMAAID8RjBrzqONeWxsbLZv/gQAAACKIo825l27dlVISIgnSwAAAICFCMzNeWzGnF9jAAAAAP/H46uyAAAAoOjw8iKcNeOxxjwrK8tTlwYAAABsx6Mz5gAAAChamGY259F1zAEAAABcRmIOAAAAy7AAiDkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMAyzJibIzEHAAAAbIDEHAAAAJYhMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyBObmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAIk5AAAALENgbo7EHAAAALABEnMAAABYhhlzcyTmAAAAgA2QmAMAAMAyJObmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAAyzBjbo7EHAAAALABEnMAAABYhsDcHIk5AAAAYAMk5gAAALAMM+bmSMwBAAAAGyAxBwAAgGUIzM2RmAMAAAA2QGIOAAAAy3gRmZsiMQcAAABsgMQcAAAAliEwN0diDgAAgCJty5YteuihhxQeHi6Hw6HVq1e7Pd+zZ085HA63rX379m7HJCcnq3v37goMDFRwcLB69+6tlJSUXNVBYw4AAADLXN3g5teWGxcuXFDDhg31xhtvmB7Tvn17nTx50rW99957bs93795d+/fv1/r167V27Vpt2bJFzz77bK7qYJQFAAAARVqHDh3UoUOHvzzG6XQqLCzsms8dPHhQn376qXbs2KGmTZtKkmbNmqX7779f//znPxUeHp6jOkjMAQAAYBkvhzVbWlqazp8/77alpaXdcN2bNm1SSEiIatWqpf79++vMmTOu57Zt26bg4GBXUy5JUVFR8vLy0vbt23N+b264OgAAAMCm4uLiFBQU5LbFxcXd0Lnat2+vRYsWacOGDfrHP/6hzZs3q0OHDsrMzJQknTp1SiEhIW6vKVasmEqXLq1Tp07l+DqMsgAAAMAyuZ3/vlFjxozRsGHD3PY5nc4bOlfXrl1df65fv74aNGig6tWra9OmTbr33ntvqs4/IzEHAABAoeN0OhUYGOi23WhjfrVq1arplltu0ZEjRyRJYWFh+vnnn92OuXTpkpKTk03n0q+FxhwAAACWcTis2fLT8ePHdebMGZUrV06SFBkZqbNnz2rXrl2uYzZu3KisrCw1a9Ysx+dllAUAAABFWkpKiiv9lqSkpCTt3btXpUuXVunSpRUbG6vOnTsrLCxMiYmJGjlypCIiItSuXTtJUp06ddS+fXv17dtXc+fOVUZGhmJiYtS1a9ccr8gikZgDAADAQg6L/smNnTt3qnHjxmrcuLEkadiwYWrcuLHGjRsnb29vffPNN3r44YdVs2ZN9e7dW02aNNGXX37pNhqzZMkS1a5dW/fee6/uv/9+NW/eXG+99Vau6iAxBwAAQJHWunVrGYZh+vxnn3123XOULl1aS5cuvak6aMwBAABgGS9rFmUpkBhlAQAAAGyAxBwAAACWsWod84KIxBwAAACwARJzAAAAWIbA3ByJOQAAAGADNOYAAACADTDKAgAAAMt4MctiisQcAAAAsAEScwAAAFiGwNwciTkAAABgAyTmAAAAsAxfMGSOxBwAAACwARJzAAAAWIbA3ByJOQAAAGADJOYAAACwDOuYmyMxBwAAAGwgR4n5v//97xyf8OGHH77hYgAAAFC4kZeby1Fj3rFjxxydzOFwKDMz82bqAQAAAIqkHDXmWVlZ+V0HAAAAigDWMTd3UzPmqampeVUHAAAAUKTlujHPzMzUpEmTVL58eZUsWVJHjx6VJI0dO1bvvPNOnhcIAACAwsPLYc1WEOW6MZ8yZYoWLFigadOmycfHx7X/1ltv1bx58/K0OAAAAKCoyHVjvmjRIr311lvq3r27vL29XfsbNmyoQ4cO5WlxAAAAKFwcDoclW0GU68b8p59+UkRERLb9WVlZysjIyJOiAAAAgKIm14153bp19eWXX2bbv3LlSjVu3DhPigIAAEDh5HBYsxVEOVou8c/GjRun6Oho/fTTT8rKytIHH3yghIQELVq0SGvXrs2PGgEAAIBCL9eJ+SOPPKI1a9boiy++kL+/v8aNG6eDBw9qzZo1uu+++/KjRgAAABQSzJiby3ViLkktWrTQ+vXr87oWAAAAoMi6ocZcknbu3KmDBw9Kujx33qRJkzwrCgAAAIVTQV1j3Aq5bsyPHz+ubt266auvvlJwcLAk6ezZs7rrrrv0/vvvq0KFCnldIwAAAFDo5XrGvE+fPsrIyNDBgweVnJys5ORkHTx4UFlZWerTp09+1AgAAIBCghlzc7lOzDdv3qytW7eqVq1arn21atXSrFmz1KJFizwtDgAAACgqct2YV6xY8ZpfJJSZmanw8PA8KQoAAACFU8HMsq2R61GWl19+WQMHDtTOnTtd+3bu3KnBgwfrn//8Z54WBwAAABQVOUrMS5Uq5Tarc+HCBTVr1kzFil1++aVLl1SsWDE988wz6tixY74UCgAAgILPq4DOf1shR435a6+9ls9lAAAAAEVbjhrz6Ojo/K4DAAAARQCBubkb/oIhSUpNTVV6errbvsDAwJsqCAAAACiKct2YX7hwQaNGjdLy5ct15syZbM9nZmbmSWEAAAAofArqGuNWyPWqLCNHjtTGjRs1Z84cOZ1OzZs3T7GxsQoPD9eiRYvyo0YAAACg0Mt1Yr5mzRotWrRIrVu3Vq9evdSiRQtFRESocuXKWrJkibp3754fdQIAAKAQIDA3l+vEPDk5WdWqVZN0eZ48OTlZktS8eXNt2bIlb6sDAAAAiohcN+bVqlVTUlKSJKl27dpavny5pMtJenBwcJ4WBwAAgMLFy+GwZCuIct2Y9+rVS/v27ZMkjR49Wm+88YZ8fX01dOhQjRgxIs8LBAAAAIqCXM+YDx061PXnqKgoHTp0SLt27VJERIQaNGiQp8UBAACgcCmgYbYlbmodc0mqXLmyKleunBe1AAAAAEVWjhrzmTNn5viEgwYNuuFiAAAAULixjrm5HDXm06dPz9HJHA4HjTkAAABwA3LUmF9ZhaWg2P/5Pz1dAgAAAK4h1yuPFCHcGwAAAMAGbvrDnwAAAEBOMWNujsQcAAAAsAEScwAAAFjGi8DcFIk5AAAAYAM31Jh/+eWX6tGjhyIjI/XTTz9JkhYvXqz4+Pg8LQ4AAACFi5fDmq0gynVjvmrVKrVr105+fn7as2eP0tLSJEnnzp3T1KlT87xAAAAAoCjIdWM+efJkzZ07V2+//baKFy/u2n/33Xdr9+7deVocAAAACheHw2HJVhDlujFPSEhQy5Yts+0PCgrS2bNn86ImAAAAoMjJdWMeFhamI0eOZNsfHx+vatWq5UlRAAAAKJyYMTeX68a8b9++Gjx4sLZv3y6Hw6ETJ05oyZIlGj58uPr3758fNQIAAACFXq7XMR89erSysrJ077336uLFi2rZsqWcTqeGDx+ugQMH5keNAAAAKCQK6Pi3JRyGYRg38sL09HQdOXJEKSkpqlu3rkqWLJnXtd2wo7+keroEAFB4KV9PlwAA8rXZ10mOXJdgyXWmPVDLkuvkpRv+V+Xj46O6devmZS0AAAAo5LyIzE3lujFv06bNXy5Bs3HjxpsqCAAAACiKct2YN2rUyO1xRkaG9u7dq++++07R0dF5VRcAAAAKoRv62vkiIteN+fTp06+5f8KECUpJSbnpggAAAICiKM/+0tKjRw+9++67eXU6AAAAFEIOhzVbQZRnjfm2bdvk68sKBAAAAMCNyPUoS6dOndweG4ahkydPaufOnRo7dmyeFQYAAIDCh1VZzOW6MQ8KCnJ77OXlpVq1amnixIlq27ZtnhUGAAAAFCW5aswzMzPVq1cv1a9fX6VKlcqvmgAAAFBIEZiby9WMube3t9q2bauzZ8/mUzkAAABA0ZTrD3/eeuutOnr0aH7UAgAAgELOy2HNVhDlujGfPHmyhg8frrVr1+rkyZM6f/682wYAAAAg93I8Yz5x4kS98MILuv/++yVJDz/8sBx/GhIyDEMOh0OZmZl5XyUAAAAKBVZlMZfjxjw2Nlb9+vXTf/7zn/ysBwAAACiSctyYG4YhSWrVqlW+FQMAAIDCjcDcXK5mzB3cSQAAACBf5God85o1a163OU9OTr6pggAAAFB4FdQVU6yQq8Y8NjY22zd/AgAAALh5uWrMu3btqpCQkPyqBQAAAIWcQ0TmZnI8Y858OQAAAJB/cr0qCwAAAHCjmDE3l+PGPCsrKz/rAAAAAIq0XM2YAwAAADeDxNxcrtYxBwAAAJA/SMwBAABgGRYUMUdiDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZRszNkZgDAAAANkBiDgAAAMt4EZmbIjEHAABAkbZlyxY99NBDCg8Pl8Ph0OrVq92eNwxD48aNU7ly5eTn56eoqCgdPnzY7Zjk5GR1795dgYGBCg4OVu/evZWSkpKrOmjMAQAAYBkvhzVbbly4cEENGzbUG2+8cc3np02bppkzZ2ru3Lnavn27/P391a5dO6WmprqO6d69u/bv36/169dr7dq12rJli5599tlc1eEwDMPIXen2d/SX1OsfBAD5LLyUr6dLAAD52mxweWZ8kiXXGdS86g29zuFw6MMPP1THjh0lXU7Lw8PD9cILL2j48OGSpHPnzik0NFQLFixQ165ddfDgQdWtW1c7duxQ06ZNJUmffvqp7r//fh0/flzh4eE5ujaJOQAAACzjcFizpaWl6fz5825bWlparutNSkrSqVOnFBUV5doXFBSkZs2aadu2bZKkbdu2KTg42NWUS1JUVJS8vLy0ffv2HF+LxhwAAACFTlxcnIKCgty2uLi4XJ/n1KlTkqTQ0FC3/aGhoa7nTp06pZCQELfnixUrptKlS7uOyQmb/XIDAAAAuHljxozRsGHD3PY5nU4PVZMzNOYAAACwjJesWS7R6XTmSSMeFhYmSTp9+rTKlSvn2n/69Gk1atTIdczPP//s9rpLly4pOTnZ9fqcYJQFAAAAMFG1alWFhYVpw4YNrn3nz5/X9u3bFRkZKUmKjIzU2bNntWvXLtcxGzduVFZWlpo1a5bja5GYAwAAwDJ2/H6hlJQUHTlyxPU4KSlJe/fuVenSpVWpUiUNGTJEkydPVo0aNVS1alWNHTtW4eHhrpVb6tSpo/bt26tv376aO3euMjIyFBMTo65du+Z4RRaJxhwAAABF3M6dO9WmTRvX4yuz6dHR0VqwYIFGjhypCxcu6Nlnn9XZs2fVvHlzffrpp/L1/b9lcZcsWaKYmBjde++98vLyUufOnTVz5sxc1cE65gCQT1jHHIAd2G0d87nbfrDkOv0iq1hynbzEjDkAAABgAzb7OxQAAAAKMy87DpnbBIk5AAAAYAMk5gAAALAMgbk5EnMAAADABkjMAQAAYBlmzM2RmAMAAAA2QGIOAAAAyxCYmyMxBwAAAGyAxBwAAACWIRU2x70BAAAAbIDEHAAAAJZxMGRuisQcAAAAsAEScwAAAFiGvNwciTkAAABgAyTmAAAAsAzf/GmOxBwAAACwARJzAAAAWIa83ByJOQAAAGADJOYAAACwDCPm5kjMAQAAABsgMQcAAIBl+OZPcyTmAAAAgA2QmAMAAMAypMLmuDcAAACADZCYAwAAwDLMmJsjMQcAAABsgMQcAAAAliEvN0diDgAAANgAiTkAAAAsw4y5ORJzAAAAwAZIzAEAAGAZUmFz3BsAAADABkjMAQAAYBlmzM2RmAMAAAA2QGIOAAAAy5CXmyMxBwAAAGyAxBwAAACWYcTcHIk5AAAAYAMk5gAAALCMF1PmpkjMAQAAABsgMQcAAIBlmDE3R2IOAAAA2ACJOQAAACzjYMbcFIk5AAAAYAMk5gAAALAMM+bmSMwBAAAAGyAxBwAAgGVYx9wciTkAAABgAyTmAAAAsAwz5uZIzAEAAAAbIDEHAACAZUjMzZGYAwAAADZAYg4AAADL8M2f5kjMAQAAABsgMQcAAIBlvAjMTZGYAwAAADZAYg4AAADLMGNujsQcAAAAsAFbNebp6elKSEjQpUuXPF0KAAAA8oHDYc1WENmiMb948aJ69+6tEiVKqF69ejp27JgkaeDAgXrppZc8XB0AAACQ/2zRmI8ZM0b79u3Tpk2b5Ovr69ofFRWlZcuWebAyAAAA5CWHRf8URLb48Ofq1au1bNky3XnnnXL86XcP9erVU2JiogcrAwAAAKxhi8b8l19+UUhISLb9Fy5ccGvUAQAAULCxjrk5W4yyNG3aVOvWrXM9vtKMz5s3T5GRkZ4qCwAAALCMLRLzqVOnqkOHDjpw4IAuXbqkGTNm6MCBA9q6das2b97s6fIAAACQRwrq/LcVbJGYN2/eXHv37tWlS5dUv359ff755woJCdG2bdvUpEkTT5cHAAAA5DuHYRiGp4vIa0d/SfV0CSgA1n64XOtWL9fpkyckSZWrVteTPZ/T7ZHN9fv5c1r8zmzt/u82/XL6lIKCSymyZRs93WeA/EsGeLhyFBThpXyvfxBg4v2lS7Rw/jv69ddfVLNWbY3+21jVb9DA02WhAPK1xXzE/4k//Jsl12leo5Ql18lLtkjMd+/erW+//db1+KOPPlLHjh31t7/9Tenp6R6sDIXZLWVD1KvfYM165z3NnLdUDW+7QxPHDNaPR4/ozK8/K/nXX9RnwDDNWbxKw16cqF1ff6XpL03wdNkAioBPP/lY/5wWp+eeH6D3V3yoWrVqq/9zvXXmzBlPlwYgH9miMX/uuef0/fffS5KOHj2qJ554QiVKlNCKFSs0cuRID1eHwurO5q11R2QLla9YWRUqVVHP5wbK16+EDh34RlWq1dDfp7yqO5u3Vnj5imrUpJminx2o7V9tVibfTAsgny1eOF+dHuuijo92VvWICP19fKx8fX21+oNVni4NuGkOi7aCyBaN+ffff69GjRpJklasWKFWrVpp6dKlWrBggVat4ocQ8l9mZqY2ffGJUlP/UO16Da95zIULKSrhX1LexWz2O0EAhUpGeroOHtivOyPvcu3z8vLSnXfepW/27fFgZQDymy06DMMwlJWVJUn64osv9OCDD0qSKlasqF9//dWTpaGQS0o8rGH9nlJ6err8/Epo7NTpqly1erbjzp39Te8teEsdHursgSoBFCW/nf1NmZmZKlOmjNv+MmXKKCnpqIeqAvKOF99RY8oWjXnTpk01efJkRUVFafPmzZozZ44kKSkpSaGhoX/52rS0NKWlpV21z5DT6cy3elF4VKhURW/MX64LKSmK37Rer0wZq2mz3nFrzi9cSNH4ETGqVKWaevTu58FqAQBAYWaLUZbXXntNu3fvVkxMjF588UVFRERIklauXKm77rrrL18bFxenoKAgt23ujJetKBuFQPHixRVeoZJq1K6rXv0Gq1r1mvpoxRLX8xcvXtDYF56XXwl/jZ06XcWKFfdgtQCKglLBpeTt7Z3tg55nzpzRLbfc4qGqgLzDjLk5WyTmDRo0cFuV5YqXX35Z3t7ef/naMWPGaNiwYW77fjpf6FaAhEUMI0sZGRmSLiflfx/WX8WL+2j8P2bIh9/CALBAcR8f1albT9u/3qZ77o2SJGVlZWn79m3q2q2Hh6sDkJ9s0Zib8fW9/hrATqcz29jKr2msY47rmz93hpre2VwhoWG6ePGiNq3/WN/s2anJr87RhQspenFoP6WlpWrEuKm6eOGCLl64IEkK+v9pFgDkl6eie2ns30apXr1bdWv9BvrX4oX6448/1PHRTp4uDbh5BTXOtoDHGvNSpUrJkcPh/+Tk5HyuBkXR2d+S9c/Jf1fymV/k719SVavX1ORX5+i22yP1ze4dSjhw+bc4vZ940O11C1Z8rNBy5T1RMoAion2H+/VbcrJmvz5Tv/76i2rVrqPZb85TGUZZgELNY9/8uXDhwhwfGx0dnatz882fAOyAb/4EYAd2++bP7YnnLLlOs+pBllwnL3nsX1Vum20AAACgMLPZ36Gk1NRUpaenu+0LDAz0UDUAAADISyxjbs4WyyVeuHBBMTExCgkJkb+/v0qVKuW2AQAAAIWdLRrzkSNHauPGjZozZ46cTqfmzZun2NhYhYeHa9GiRZ4uDwAAAHmEdczN2WKUZc2aNVq0aJFat26tXr16qUWLFoqIiFDlypW1ZMkSde/e3dMlAgAAAPnKFol5cnKyqlWrJunyPPmV5RGbN2+uLVu2eLI0AAAA5CUic1O2aMyrVaumpKQkSVLt2rW1fPlySZeT9ODgYA9WBgAAAFjDo4350aNHlZWVpV69emnfvn2SpNGjR+uNN96Qr6+vhg4dqhEjRniyRAAAAOQhh0X/FEQe+4IhSfL29tbJkycVEhIiSXriiSc0c+ZMpaamateuXYqIiFCDBg1yfV6+YAiAHfAFQwDswG5fMLQz6bwl12lateAtt+3RxtzLy0unTp1yNeYBAQHat2+fa978RtGYA7ADGnMAdmC3xnzXD9Y05k2qFLzG3BYz5gAAAEBR59HG3OFwyHHV1z9d/RgAAAAoCjz6yw3DMNSzZ085nU5JUmpqqvr16yd/f3+34z744ANPlAcAAIA8RgRrzqONeXR0tNvjHj16eKgSAAAAwLM82pjPnz/fk5cHAACA1YjMTfHhTwAAABRpEyZMcH328cpWu3Zt1/OpqakaMGCAypQpo5IlS6pz5846ffp0ntdBYw4AAADL2PULhurVq6eTJ0+6tvj4eNdzQ4cO1Zo1a7RixQpt3rxZJ06cUKdOnfLytkjy8CgLAAAAYAfFihVTWFhYtv3nzp3TO++8o6VLl+qee+6RdHkcu06dOvr6669155135lkNJOYAAACwjMNhzZZbhw8fVnh4uKpVq6bu3bvr2LFjkqRdu3YpIyNDUVFRrmNr166tSpUqadu2bXl1WySRmAMAAKAQSktLU1pamts+p9PpWqb7z5o1a6YFCxaoVq1aOnnypGJjY9WiRQt99913OnXqlHx8fBQcHOz2mtDQUJ06dSpPayYxBwAAgGUcFm1xcXEKCgpy2+Li4q5ZU4cOHfT444+rQYMGateunT7++GOdPXtWy5cvz5d7YIbGHAAAAIXOmDFjdO7cObdtzJgxOXptcHCwatasqSNHjigsLEzp6ek6e/as2zGnT5++5kz6zaAxBwAAgHUsisydTqcCAwPdtmuNsVxLSkqKEhMTVa5cOTVp0kTFixfXhg0bXM8nJCTo2LFjioyMvMmb4Y4ZcwAAABRpw4cP10MPPaTKlSvrxIkTGj9+vLy9vdWtWzcFBQWpd+/eGjZsmEqXLq3AwEANHDhQkZGReboii0RjDgAAAAvdyBrj+e348ePq1q2bzpw5o7Jly6p58+b6+uuvVbZsWUnS9OnT5eXlpc6dOystLU3t2rXT7Nmz87wOh2EYRp6f1cOO/pLq6RIAQOGlfD1dAgDI12Yx7Df/S7HkOg0qlrTkOnnJZv+qAAAAUJjdyBrjRQUf/gQAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAACsQ2RuisQcAAAAsAEScwAAAFjGjuuY2wWJOQAAAGADJOYAAACwDOuYmyMxBwAAAGyAxBwAAACWITA3R2IOAAAA2ACJOQAAAKxDZG6KxBwAAACwARJzAAAAWIZ1zM2RmAMAAAA2QGIOAAAAy7COuTkScwAAAMAGSMwBAABgGQJzcyTmAAAAgA2QmAMAAMA6ROamSMwBAAAAGyAxBwAAgGVYx9wciTkAAABgAyTmAAAAsAzrmJsjMQcAAABsgMQcAAAAliEwN0diDgAAANgAiTkAAACsQ2RuisQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMuwjrk5EnMAAADABkjMAQAAYBkCc3Mk5gAAAIANkJgDAADAOkTmpkjMAQAAABsgMQcAAIBlWMfcHIk5AAAAYAMk5gAAALAM65ibIzEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMOMuTkScwAAAMAGSMwBAABgISJzMyTmAAAAgA2QmAMAAMAyzJibIzEHAAAAbIDEHAAAAJYhMDdHYg4AAADYAIk5AAAALMOMuTkScwAAAMAGSMwBAABgGQdT5qZIzAEAAAAbIDEHAACAdQjMTZGYAwAAADZAYg4AAADLEJibIzEHAAAAbIDEHAAAAJZhHXNzJOYAAACADZCYAwAAwDKsY26OxBwAAACwARJzAAAAWIfA3BSJOQAAAGADJOYAAACwDIG5ORJzAAAAwAZIzAEAAGAZ1jE3R2IOAAAA2ACJOQAAACzDOubmSMwBAAAAGyAxBwAAgGWYMTdHYg4AAADYAI05AAAAYAM05gAAAIANMGMOAAAAyzBjbo7EHAAAALABGnMAAADABhhlAQAAgGX4giFzJOYAAACADZCYAwAAwDJ8+NMciTkAAABgAyTmAAAAsAyBuTkScwAAAMAGSMwBAABgHSJzUyTmAAAAgA2QmAMAAMAyrGNujsQcAAAAsAEScwAAAFiGdczNkZgDAAAANkBiDgAAAMsQmJsjMQcAAABsgMQcAAAA1iEyN0ViDgAAgCLvjTfeUJUqVeTr66tmzZrpv//9r+U10JgDAADAMg6L/smNZcuWadiwYRo/frx2796thg0bql27dvr555/z6S5cm8MwDMPSK1rg6C+pni4BABReytfTJQCAfG02uPxHhjXX8Sue82ObNWum22+/Xa+//rokKSsrSxUrVtTAgQM1evTofKowOxJzAAAAWMbhsGbLqfT0dO3atUtRUVGufV5eXoqKitK2bdvy4Q6Ys9nfoQAAAICbl5aWprS0NLd9TqdTTqfTbd+vv/6qzMxMhYaGuu0PDQ3VoUOH8r3OPyuUjXm1svz6GDcnLS1NcXFxGjNmTLb/AQOAFfg5hMLKqtGaCZPjFBsb67Zv/PjxmjBhgjUF3IBCOWMO3Kzz588rKChI586dU2BgoKfLAVAE8XMIuDk5TczT09NVokQJrVy5Uh07dnTtj46O1tmzZ/XRRx9ZUa4kZswBAABQCDmdTgUGBrpt1/rtk4+Pj5o0aaINGza49mVlZWnDhg2KjIy0suTCOcoCAAAA5NSwYcMUHR2tpk2b6o477tBrr72mCxcuqFevXpbWQWMOAACAIu2JJ57QL7/8onHjxunUqVNq1KiRPv3002wfCM1vNObANTidTo0fP54PXAHwGH4OAdaKiYlRTEyMR2vgw58AAACADfDhTwAAAMAGaMwBAAAAG6AxBwDAZhYsWKDg4GBPlwHAYjTmKNQcDsdfbnb+9i8ABV/Pnj2v+bPnyJEjni4NgA2xKgsKtZMnT7r+vGzZMo0bN04JCQmufSVLlnT92TAMZWZmqlgx/mcBIO+0b99e8+fPd9tXtmxZD1UDwM5IzFGohYWFubagoCA5HA7X40OHDikgIECffPKJmjRpIqfTqfj4ePXs2dPtK3klaciQIWrdurXrcVZWluLi4lS1alX5+fmpYcOGWrlypbVvDkCB4HQ63X4WhYWFacaMGapfv778/f1VsWJFPf/880pJSTE9x759+9SmTRsFBAQoMDBQTZo00c6dO13Px8fHq0WLFvLz81PFihU1aNAgXbhwwYq3ByAP0ZijyBs9erReeuklHTx4UA0aNMjRa+Li4rRo0SLNnTtX+/fv19ChQ9WjRw9t3rw5n6sFUBh4eXlp5syZ2r9/vxYuXKiNGzdq5MiRpsd3795dFSpU0I4dO7Rr1y6NHj1axYsXlyQlJiaqffv26ty5s7755hstW7ZM8fHxHl+PGUDu8Tt7FHkTJ07Ufffdl+Pj09LSNHXqVH3xxReKjIyUJFWrVk3x8fF688031apVq/wqFUABtHbtWrexuQ4dOmjFihWux1WqVNHkyZPVr18/zZ49+5rnOHbsmEaMGKHatWtLkmrUqOF6Li4uTt27d9eQIUNcz82cOVOtWrXSnDlz5Ovrmw/vCkB+oDFHkde0adNcHX/kyBFdvHgxWzOfnp6uxo0b52VpAAqBNm3aaM6cOa7H/v7++uKLLxQXF6dDhw7p/PnzunTpklJTU3Xx4kWVKFEi2zmGDRumPn36aPHixYqKitLjjz+u6tWrS7o85vLNN99oyZIlruMNw1BWVpaSkpJUp06d/H+TAPIEjTmKPH9/f7fHXl5euvoLcTMyMlx/vjIHum7dOpUvX97tOL46G8DV/P39FRER4Xr8ww8/6MEHH1T//v01ZcoUlS5dWvHx8erdu7fS09Ov2ZhPmDBBTz75pNatW6dPPvlE48eP1/vvv69HH31UKSkpeu655zRo0KBsr6tUqVK+vjcAeYvGHLhK2bJl9d1337nt27t3r2ues27dunI6nTp27BhjKwBybdeuXcrKytIrr7wiL6/LH/Vavnz5dV9Xs2ZN1axZU0OHDlW3bt00f/58Pfroo7rtttt04MABt+YfQMHEhz+Bq9xzzz3auXOnFi1apMOHD2v8+PFujXpAQICGDx+uoUOHauHChUpMTNTu3bs1a9YsLVy40IOVAygIIiIilJGRoVmzZuno0aNavHix5s6da3r8H3/8oZiYGG3atEk//vijvvrqK+3YscM1ojJq1Cht3bpVMTEx2rt3rw4fPqyPPvqID38CBRCNOXCVdu3aaezYsRo5cqRuv/12/f7773r66afdjpk0aZLGjh2ruLg41alTR+3bt9e6detUtWpVD1UNoKBo2LChXn31Vf3jH//QrbfeqiVLliguLs70eG9vb505c0ZPP/20atasqS5duqhDhw6KjY2VJDVo0ECbN2/W999/rxYtWqhx48YaN26cwsPDrXpLAPKIw7h6mBYAAACA5UjMAQAAABugMQcAAABsgMYcAAAAsAEacwAAAMAGaMwBAAAAG6AxBwAAAGyAxhwAAACwARpzAAAAwAZozAEUWT179lTHjh1dj1u3bq0hQ4ZYXsemTZvkcDh09uxZ02McDodWr16d43NOmDBBjRo1uqm6fvjhBzkcDu3du/emzgMAyBkacwC20rNnTzkcDjkcDvn4+CgiIkITJ07UpUuX8v3aH3zwgSZNmpSjY3PSTAMAkBvFPF0AAFytffv2mj9/vtLS0vTxxx9rwIABKl68uMaMGZPt2PT0dPn4+OTJdUuXLp0n5wEA4EaQmAOwHafTqbCwMFWuXFn9+/dXVFSU/v3vf0v6v/GTKVOmKDw8XLVq1ZIk/e9//1OXLl0UHBys0qVL65FHHtEPP/zgOmdmZqaGDRum4OBglSlTRiNHjpRhGG7XvXqUJS0tTaNGjVLFihXldDoVERGhd955Rz/88IPatGkjSSpVqpQcDod69uwpScrKylJcXJyqVq0qPz8/NWzYUCtXrnS7zscff6yaNWvKz89Pbdq0caszp0aNGqWaNWuqRIkSqlatmsaOHauMjIxsx7355puqWLGiSpQooS5duujcuXNuz8+bN0916tSRr6+vateurdmzZ+e6FgBA3qAxB2B7fn5+Sk9Pdz3esGGDEhIStH79eq1du1YZGRlq166dAgIC9OWXX+qrr75SyZIl1b59e9frXnnlFS1YsEDvvvuu4uPjlZycrA8//PAvr/v000/rvffe08yZM3Xw4EG9+eabKlmypCpWrKhVq1ZJkhISEnTy5EnNmDFDkhQXF6dFixZp7ty52r9/v4YOHaoePXpo8+bNki7/BaJTp0566KGHtHfvXvXp00ejR4/O9T0JCAjQggULdODAAc2YMUNvv/22pk+f7nbMkSNHtHz5cq1Zs0affvqp9uzZo+eff971/JIlSzRu3DhNmTJFBw8e1NSpUzV27FgtXLgw1/UAAPKAAQA2Eh0dbTzyyCOGYRhGVlaWsX79esPpdBrDhw93PR8aGmqkpaW5XrN48WKjVq1aRlZWlmtfWlqa4efnZ3z22WeGYRhGuXLljGnTprmez8jIMCpUqOC6lmEYRqtWrYzBgwcbhmEYCQkJhiRj/fr116zzP//5jyHJ+O2331z7UlNTjRIlShhbt251O7Z3795Gt27dDMMwjDFjxhh169Z1e37UqFHZznU1ScaHH35o+vzLL79sNGnSxPV4/Pjxhre3t3H8+HHXvk8++cTw8vIyTp48aRiGYVSvXt1YunSp23kmTZpkREZGGoZhGElJSYYkY8+ePabXBQDkHWbMAdjO2rVrVbJkSWVkZCgrK0tPPvmkJkyY4Hq+fv36bnPl+/bt05EjRxQQEOB2ntTUVCUmJurcuXM6efKkmjVr5nquWLFiatq0abZxliv27t0rb29vtWrVKsd1HzlyRBcvXtR9993ntj89PV2NGzeWJB08eNCtDkmKjIzM8TWuWLZsmWbOnKnExESlpKTo0qVLCgwMdDumUqVKKl++vNt1srKylJCQoICAACUmJqp3797q27ev65hLly4pKCgo1/UAAG4ejTkA22nTpo3mzJkjHx8fhYeHq1gx9x9V/v7+bo9TUlLUpEkTLVmyJNu5ypYte0M1+Pn55fo1KSkpkqR169a5NcTS5bn5vLJt2zZ1795dsbGxateunYKCgvT+++/rlVdeyXWtb7/9dra/KHh7e+dZrQCAnKMxB2A7/v7+ioiIyPHxt912m5YtW6aQkJBsqfEV5cqV0/bt29WyZUtJl5PhXbt26bbbbrvm8fXr11dWVpY2b96sqKiobM9fSewzMzNd++rWrSun06ljx46ZJu116tRxfZD1iq+//vr6b/JPtm7dqsqVK+vFF1907fvxxx+zHXfs2DGdOHFC4eHhrut4eXmpVq1aCg0NVXh4uI4eParu3bvn6voAgPzBhz8BFHjdu3fXLbfcokceeURffvmlkpKStGnTJg0aNEjHjx+XJA0ePFgvvfSSVq9erUOHDun555//yzXIq1SpoujoaD3zzDNavXq165zLly+XJFWuXFkOh0Nr167VL7/8opSUFAUEBGj48OEaOnSoFi5cqMTERO3evVuzZs1yfaCyX79+Onz4sEaMGKGEhAQtXbpUCxYsyNX7rVGjho4dO6b3339fiYmJmjlz5jU/yOrr66vo6Gjt27dPX375pQYNGqQuXbooLCxMkhQbG6u4uDjNnDlT33//vb799lvNnz9fr776aq7qAQDkDRpzAAVeiRIltGXLFlWqVEmdOnVSnTp11Lt3b6WmproS9BdeeEFPPfWUoqOjFRkZqYCAAD366KN/ed45c+boscce0/PPP6/atWurb9++unDhgiSpfPnyio2N1ejRoxUaGqqYmBhJ0qRJkzR27FjFxcWpTp06at++vdatW6eqVatKujz3vWrVKq1evVoNGzbU3LlzNXXq1Fy934cfflhDhw5VTEyMGjVqpK1bt2rs2LHZjouIiFCnTp10//33q23btmrQoIHbcoh9+vTRvHnzNH/+fNWvX1+tWrXSggULXLUCAKzlMMw++QQAAADAMiTmAAAAgA3QmAMAAAA2QGMOAAAA2ACNOQAAAGADNOYAAACADdCYAwAAADZAYw4AAADYAI05AAAAYAM05gAAAIAN0JgDAAAANkBjDgAAANgAjTkAAABgA/8PIAp1vfULuc4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x4800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(deadlift_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyTorch",
   "language": "python",
   "name": "pytorch_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}