{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>172</td>\n",
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       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
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       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
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       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
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       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
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       "      <td>130</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iq3g+kdEUJBWfdBekJOETGV2iXcSQf5P21f+wcrx3XCtNUkqm+zofGUzSoUy3NsmHitv4+NgIDvQn7egxmjaK4pfFOygocLLvwFE2bE3iYLL79jJz3hZ6dWtaKaa+JyWlEhNbvM1Gx5w7Yx9AclY+MUHFxybRQb4kZ5ZMUvydA+l57E/P46Ja3n+i/qfkpHSiT2G/P+0k+/2rur9E7+4v0aFjY+o3iCr3mE9X0qEsYqOKEwgxkYEkHc5ya5N8OJPYSNff0cduIzjQj7T0HHbvS8MYw+AnptPvvm/5cNIqAHx97Dz7UGeuuWcSnW/8jB0JqQzo1aziOnUaTEgE1tHimZPW0UOYkIjS3xAQiK3pRTh3ril6yX7hVfg/8iE+Pe4kf0blS1rLuaVMSQpjTEdjzEZgc+HvbYwx7/xF+yHGmOXGmOVsTC6tmVSwUdc35qulB+n/xiqych34+rgOytbtzcRuMywcdiE/P3k+n8TvZ+/hyncw+3cKnBZHs/P5+rEL+O+1jXnk43VYlkW/DjWIDvPn+lf+YMx3W2hbPxRbJThgLYtZ09ewacMBbrnjUgD27jnM7l0pTPv5MX6c+x9W/LGT1St2ezbIcjTth9/ZsH4Pg+++wtOheI3kQxk8PnIao5++Gts5fH/ltDUprD+QxV2X1vB0KOXKr3FTfGvWJmvhPE+HUuH6944jJjKIAfd8weg353NeXA3sJ6zzM+dt5qrLm3sowvLzw7TfWb9hD3cP1th3olnb0riiUWiJdeFsMWv6ajZt2M8td3QCivf7P/78X6bPfZzlf+xk1Vm83wcocFisWH+QsU9158vXruPnX3exdOU+8gscfP3jBqa+dz3xk26jSYMIPvhqlafDPfNsNvyuf5yC36ZhpRYnsh1/zCD31bspmPMJPl1u9GCAlZOxmbPyx1uVtXDmq0APYBqAZVlrjDGdS2tsWdYHwAcA5v4Omk90Gr5ccoDJfyQBEFcriMTjpiYnHs0jKsR96mZUiL/b9OWko3lEF7ZpEFWVj+52XVXZlXKMhZtdsyqmr07h0qbh+NptRAT50a5eMOv3ZVA7IgBP+zJ+L5MLa0bE1QkhMbU4eZKYlktU6An9D/Un6bhZEElpuUQXFsWLCQvgijau22Va1wvFZjOkZuZTLdiPof2bFr3npvHLqHeSYlzeIjI6uOj2DXBdYYmMKllA9I/fdvDphIW8+/Fd+Pm5NvWF8zYR17o2Vau6/k0uvrQx69bspW37ehUS+5kQHR3OweOmMCcmpREdXXJGwJIlm3jvvVl8/sWj+Pl538ygfyI6MtjtSnBicjrRkcF/8Q53mVm53PfYJB6+twtt47x/evufJv6eyLcrXAnvVjXdx8Gk9DyiQ0re1vFXluw4ygcL9/O/u1ri51O57noM7T+QkGsGAJCzaT0+0TFFy3wioylISXJrHxDXFv9mLan73U8Yux17eAQ13/6E/Q9UjtoE0ZFB7ut8SgbR1d2vgEdVd7WJiQqmoMBJRlYuYaFVMMYw9LhimAPvn1h0yxPA5u3JFDgs4pqWnH3ijaKjw0k8WDz2JSX+9dj3xeeVf+z7au0hpmxw1SCJi6pK4nEzJ5Iy84kKOvX+zd6ayrAutc5YjBUhKjqEpDLt97cX7vcHF+33F8zbWGK/v37NXs7z8v1+dPVADiYXz5xITMki+oRbM6IigjiYkklMZBAFDicZWXmEhQQQExnI+a1iiwpiXnZRHTZuSyGoqmtfUaeGa1ZKr8saMuFr701S2C+8Cp/zXcVxnfu3YkKLZ76a0OpY6YdP+j7fa/6NdfgAjqU/nHS5Y108vlc/QD6vnvmgRc6QMh+dWZZ1YglYxxmORU5iUMcaTH34PKY+fB7dW0bww4pkLMtidUI6wQF2ok44OI8K8SPI387qhHQsy+KHFcl0a+k6KDuc6Tqwdzot3vtlDzd2cB3cxob58/v2NACy8xys2ZNBAy85SR/UuTZTn+zA1Cc70L11FD/8cdDV/11HCQ7wOWmSIijAh9W7jrr6/8dBurVyDerdW0fy+zbXAd6u5CzyC5yEB/lyLM9Bdq5rdV68+TB2m6FRrPdOA23esiZ7E45wYF8q+fkF/Dx7HZ26uE9X3LLpIC+NnMYrbwxyu/c0OjaUlct3U1DgoCDfwarlu6lXyW73aNWqLgm7k9m39xB5eQXMnLGMbt1au7XZuHEPzw7/knfevZ+IiJIHcpVVq+Y1SNh7hH0H0sjLdzBz7ka6dWpSpvfm5Tt48InJXNurddETPyqLmy+KYeq/WjP1X63p3iycH1anYFkWa/ZmEBxgP2ntidJsPJjFiGk7eWtQUyL+wQmOpx2d8jV7bx/A3tsHkBX/C8G9rgHAv2VrnFmZOA4fcmufPnUSu6/pRkK/Huy79zby9uyuNAkKgFbNYkjYl8a+A0dd6/y8LXS7tKFbm26XNuT72RsA+GnBVjq0q+OqtZSTT/Yx10nt4mW78bHbaFS/eHr0jLmbueryplQWrVrVZXdCMnv3uca+GTNPPvYNf/ZL3n3n7Bj7bmpdvajYZbcGoUzblOra9hOzCPKz/W3tiRPtPJJDeq6DNjHecYxTVq79/mEO7DtStN/vXGK/f4AXR/5QYr8fExvGquW7Kt1+v1XTKBL2p7HvYLpr21+wnW4d67m16daxHt/P2QLAT/E76NC2JsYYLj2/Dtt2HeFYTj4FDifL1hygYd1qRFUPZEdCKkfSXPUZlqzYR4M63nvbo+OPGeS+829y3/k3jk2/YW/bDQBTqynkZEFmydu9fLrfigkIJH/WB26vm2rFswZtTS7AOnzgxLeKeJWyzqTYa4zpCFjGGF/gIaD055x5gYl3jaRLk3ZUDwpj7+hpPDt9Ah8v+dHTYZ2Wy5qFE78llR4vryDAz8bo64urV/d9bRVTHz4PgOF9GzL0m23k5jvp1DSczk1dA/CM1SlMXHoQgCviqtPvfNfVo5svjmXYt1vpM24lYNH3/GiaxnpfIaHLWkYQv/EQPUYucT2C9ZaWRcv6vvgbU5/sAMDwG5sVPYK0U/MIOrdwHZT261CDp7/cyNWjl+JrtzHmlpYYYziSkcfd76zCZiAqNICXbmt50u/3Fj4+dv7z1FU8dP9nOB1O+lzXjgaNovjg7Xk0a1GTzl2b8eb4n8jOzmPYfyYBEB0Tytg3B9Htipas+GMXg/q/jTGGDpc0KpHg8HY+PnaeGX4jg+9+A6fDSf/+HWncuAZvvD6NuLi6dOvehlde/o7s7FwefmgCALGx1Xj3vX8BMOjmsezcmUh2di6XdX6SF0bdSqdO3v03/5OPj41nHuvB4Ie/wul00r9PGxo3iOSNDxYS1zyWbp2asG7jAR58cjLpGTnM/3Ubb30Yz/SJ9zJ73kaWr95DWno2U2e67lEd8/TVNG8S8zff6l06NwkjflsaPV9bTYCvjVF9i09Y+76zlqn/cp20jf0pgRnrDpOT76Tr2JX0bxfJg91qM/anBLLznDwyaRsANUL9eXtQ5TlRPV72kniqduxE3W9n4cw9RvILxU/3qP2/yey9fYAHozszfHxsPPNINwY/NsW1zl8VR+P61Xnjw8XENYum26WNGHBVKx5/YRZXDvyI0JAAxj93FQCHU7O5+7Ep2GyG6OpBvPR0b7fPnvXLVj54pfI8jtXHx87wZ27k7sFv4HAWj32vv+Ea+7p3a8PLr7jGvoceLh773nvXNfbdPKh47Ot82ZOMeqHyjH0AneoFE5+QTu/PNhPga+OF7sWPSh/w1RYm3+TajscvPsCMLWnk5Dvp/vFG+resxr8uco1zs7el0bNxWKWoQXI8136/D/93//9wOpxcfV17GjSK5v2359K8RU06d23Om+Nnk52dx1P/cT1eNyYmjLFv3kK3K1qy/I8dDOr/Fhi4+JLGlWK/72O38cy/OzH4yek4nRb9ezajcb1qvPHpH8Q1iaRbx/oM6NWMx1+cx5W3fUlocADjh7lubwoN9ueOAW24/oEpGAOdL6xLlw6uJ7o9cOv53PLo9/jYbdSIDmbMf7t5sptl5ty6DKvJ+fg/8mHRI0j/5P+vN8l9598QEoFvl4E4U/bif/8bABT8/iOOFXPw6dAHW8O24HBgHcsk77vxHuqJSNkYqwzVXY0x1YHXgcsBA8wBHrIs6+TzjI5/7zl8u4ejR+XZ+ZeLqp6/XcSTjl5W6h1R54Qwf+8vzFWujpy7zx93/nzu1T843q7XVng6BI9q+MP/eToEjzKR3v840/KS9/a7ng7Bo7Lvud7TIXhUaPLJHwt7rsj54GdPh+BRVZ6fUbmyf6eo6lNdz8pz2uzR873y71ammRSWZR0CBpVzLCIiIiIiIiJexZuLTJ6NypSkMMa8cZKXjwLLLcs6eVUWEREREREREZFTUNbCmQFAW2Bb4U9roBYw2BjzWrlEJiIiIiIiIiLnlLIWzmwNXGJZlgPAGPMusAi4FFhXTrGJiIiIiIiIyDmkrEmKcCAI1y0eAIFANcuyHMaY3HKJTERERERERMTDVJOiYpU1SfEysNoYswDX0z06A6ONMYHA3HKKTURERERERETOIWV9usdHxphZwK3AJlyPIN1nWVYW8N9yjE9EREREREREzhFlfbrH3cBDuIplrgY6AEuBbuUWmYiIiIiIiIicU8p6u8dDwAXAb5ZldTXGNANGl19YIiIiIiIiIp6nmhQVq6yPIM2xLCsHwBjjb1nWZqBp+YUlIiIiIiIiIueass6k2GeMCQO+B342xqQCCeUVlIiIiIiIiIice8paOLNv4f8+Z4yZD4QCs8stKhERERERERE555R1JkURy7IWlkcgIiIiIiIiIt7GGNWkqEhlrUkhIiIiIiIiIlKulKQQEREREREREa+gJIWIiIiIiIiIeAUlKURERERERETEK5xy4UwRERERERGRc4WxqXBmRdJMChERERERERHxCkpSiIiIiIiIiIhXUJJCRERERERERLyCalKIiIiIiIiIlEI1KSqWZlKIiIiIiIiIiFdQkkJEREREREREvIKSFCIiIiIiIiLiFVSTQkRERERERKQUqklRsTSTQkRERERERES8gpIUIiIiIiIiIuIVlKQQEREREREREa+gmhQiIiIiIiIipVBNioqlmRQiIiIiIiIi4hWUpBARERERERERr6AkhYiIiIiIiIh4BSUpRERERERERMQrlHvhTEePluX9FV7L/tMGT4fgUY4bO3g6BI8KXbzY0yF4VN7vezwdgkf5Dezo6RA8xkSGejoEj2rw0pWeDsGjTMC5/fe3UrZ5OgSP8WlX19MheFRoZpanQ/AoU6uNp0PwqPzkHzwdgkdV8XQA5UyFMyuWZlKIiIiIiIiIiFdQkkJEREREREREvIKSFCIiIiIiIiLiFcq9JoWIiIiIiIhIZaWaFBVLMylERERERERExCsoSSEiIiIiIiIiXkFJChERERERERHxCqpJISIiIiIiIlIK1aSoWJpJISIiIiIiIiJeQUkKEREREREREfEKSlKIiIiIiIiIiFdQTQoRERERERGRUqgmRcXSTAoRERERERER8QpKUoiIiIiIiIiIV1CSQkRERERERES8gpIUIiIiIiIiIuIVVDhTREREREREpBTGqHBmRdJMChERERERERHxCkpSiIiIiIiIiIhXUJJCRERERERERLyCalKIiIiIiIiIlMLYVJOiImkmhYiIiIiIiIh4BSUpRERERERERMQrKEkhIiIiIiIiIl5BNSlERERERERESqGaFBVLMylERERERERExCsoSSEiIiIiIiIiXkFJChERERERERHxCqpJISIiIiIiIlIK1aSoWJpJISIiIiIiIiJeQUkKEREREREREfEKSlKIiIiIiIiIiFdQkkJEREREREREvIIKZ4qIiIiIiIiUwqZL+xWq0iUpLMti9LSdxG9JJcDXxugbmtCyZlCJdhv2ZTL0263k5jvp3DScp65pgDGGzQcyeW7qDrLzHNQM9+eVgU0JCvAh3+Hkmcnb2XggE4fD4tr2UQzpWtsDPTwzPrp1GH1aXUJyRiqtnh/k6XDOCMuyGD1pE/HrUwjwszP6jla0rBNaot2GhKMM/XSt628fF8lTNzbHmOKKvJ/8vIuXJ29mybjuhAf5cTQrn2GfrWNvSjb+vjZeuK0VTWoGV2TXysSyLEZ/s5n4DYX9v60VLeuElGi3IeEoQz9bT26+g84tI3nqhmYYY3hr+na+/XUf1YL9AHj42sZcFhfJj38c4OOfdxe9f8v+DKYMvZjmtUt+treyX3E39obtsfJzKZj+BlbSTvcGPn749H0cEx4DTifO7ctwLPjcM8H+A4uW72PUu7/hdDoZ0LMpQ25s47Y8L8/BE2MXsmHbIcJCAhg/tCu1YorX4QPJmfQZMoUHbmnH4AGtAOh22yQCq/pitxnsdhtT3ry2Qvt0Kspr3c93OHnm8w1s3JuOw2lx7UU1GNKzQUV3729ZlsXorzcSvy7Z1f8729Cybilj3ydryM1z0LlVFE8NbOE+9s3ZycvfbmLJ+CsIL/y3AFi3K42bXlzCuCHn0aN9bIX06Z+KX7KNUeNm4nRaXH9tO4bc0dlt+bKVuxk9fhZbticxftT19Oze0m15ZmYOvW98i8sva8bwx/tUZOj/yKLfdzHq9fk4nRYD+sQx5JaL3Jbn5RXwxKhZbNiS7Nr2R/ShVmwoefkOnn3lZ9ZvScJmDE891JWLznMd0+TlO3j+1Xn8sWofNhs8fM+l9OjSxBPd+1uWZTF64nri1ya51v3B59GyXliJdht2pzH0w1Wubb91NE/dHIcxhte/28wvqw5iM4ZqIf6MGXweUeEB7DyYwVMfrWZjwlEe7teMu3o1qvjOnaJFv+1g1GtzcTqcDLi6LUNuu9ht+bJVexjz+ly27Ehm3Ijr6NmtGQCbtibx3CuzycrOw2Yz3Hd7R3pf3sITXTgt8Ys2MGrUNzidTq4fcAlDhvR0W/7JJ3P5dvKv2O12qlULYvSo26hZMwKAwXe/wZo1u2jfrhHvv/+AJ8I/I/xv/De+cRdh5eVw7NOXcO7d5t7A158q9z6HLbIGOJ0UrF1C7tQJAJjwKKrc+SSmShDYbOROnUDB+t890AuRsql0OaH4LakkHMph9n/bM6JfI0ZO3X7SdiOmbmdkv0bM/m97Eg7lsGhLKgDPTNnOo73qMe2RdlzeMoKPFu4H4Ke1h8grcDLtkXZM/r+2TPo9kf1HciqsX2fap0tn0PPNRzwdxhkVvz6FhOQsZj/fmRG3tGTklxtO2m7ExA2MvDWO2c93JiE5i0UbDhUtO3jkGIs3HiK2WkDRax/M2kHzWsH8MPxSXryzNWMmbSr3vvwT8RsOkZCczewRnRhxc0tGfrXxpO1GfLWRkYNaMntEJxKSs936f3v3ukwd1pGpwzpyWVwkAFdfWKPotZfuaEWtiCqVKkFha9geW3gsee/dT8Gsd/Dped9J2zl+/578Dx4k/+NHsdVqjq1BuwqO9J9xOJyMfHsJE164kukf9GfGgp1sT0h1azP5py2EBPkz55MbuL1vS8Z9vMxt+Ysf/E6n82uV+OzPXurN9+/09eoEBZTfuv/TikTXuP/MJUweejGTFu1l/+FjFdKnU1E09o3qwohbWzHyy/UnbTfii3WMvLUVs0d1cY1961OKlh08cozFG1KIrVbF7T0Op8W4KZvp2KJ6ufbhTHA4nIx8eTofvn4rM755kOlz1rF9Z7Jbm9iYUMY825c+PVqd9DNee+8XLjivbkWEe9ocDicjx89jwth+TP/8DmbM3cL2XYfd2kyesZ6Q4ADmfD2Y229oz7j34gH49se1APz4v9v5+NUBvPTWApxOC4D3PvuNiPCq/PTVXcz4/E4ubFtybPAW8WuTSUjKYvaL3RlxRxtGfr72pO1GfLaWkXe2YfaL3UlIymLROtd6MbhXQ354vitTR3ahS5to3pm2BYDQQD+G3RzHXT0bVlhfTofD4WTk2DlMGHcD0ycOYcbcjWzfdcitTWxMCGOe7kOfK9wTcwEBPrw0/Gqmf3kPE8bfyJjX55KeUbmObx0OJyNHfsWHEx5kxvRnmT5jGdu3H3Br07x5baZMfoofpz1Djx7teGXsd0XL7h58JS+/dGdFh31G+cRdhD2qJpnP3ELOF+OoMujkx/h5cyaR9eztZL1wD/aGcfi0vBAA/6tuJX/5ArJGDeHYh88TcNPDFRi9yKmrdEmKXzYc4dr2URhjaFs3hPRjDpLT89zaJKfnkZnroG3dEIwxXNs+inkbjgCwO+UYF9R3nYB1bBzOz+tdg7wxhmP5DgocFjn5TnzthsAAe8V27gxatH01R7LSPR3GGfXLmmSu7VDT9bdvEE76sQKSj7rvaJOP5pB5rIC2DcJdf/sONZm3Oqlo+YvfbuI//Zq6XV3cfjCTi5q5su0NYoLYfzibQ+m5FdOpU+Dqf43C/oeRnp1P8lH3OJOP5pKZ46Btg7DC/tdg3prkUj6xpBnLDtL7fO++knoiW+MLcaxfAIB1YCv4B0JguHujgjysPYUnds4CnIk7IDiiYgP9h9ZuSaFObAi1Y0Pw87XT+7IGzFu6x63NvKV7uO5y15XAHp3qs3T1ASzLdUIyd8luakUH06hueInPrizKa903xnAsz0GBw0lOngNfH5tXjvu/rE4qHvsahrv6n3bC2JeWQ2ZOAW0bljL2TdrIfwY0x5zwmPcvftnNFe1jiAj2r4iunJa1G/ZRt3Y1ateqhp+vD1dd0Yp5Cze7talVI5xmjWOwndhRYP2mAxw+ksklF3n/VXOAtZsSqVMzjNo1wlzbfvemzPvV/cLMvEXbua6n66S0R5cmLF2xB8uy2LH7MB3a1QEgIrwqIUEBrN+cCMB3M9cXzciw2QzhYVUrsFen5pdViVzbsVbhul+t9HX/WAFtG1ZzrfsdazFvpauvQVV8i9ody3VA4WoREeJPqwbh+NhLrifeaO3GA9SpFU7tmuGudeHy5sxbtNWtTa3YMJo2isLY3PtUv04E9WpXAyA6Mphq4YEcScuusNjPhLVrd1O3ThS1a0fi5+fDVb0vYN4894RVhw5NqVLFNUOsbZv6JCYWJ/MvvrgZgYHeP8b9FZ82l5D32xwAHLs2QZVATEg190b5uTi2rnb9v6MAx55tmHBXUh7LwlRxbeumSiDOo+5JLhFvU6YkhTFm8Am/240xz5ZPSH8tKT2XmNDiaaoxoX4kn3BCmZyeS/RxbaJD/UgqbNMouirzNroSFj+tPcTBNFeC48pWEVTxtdN51O90H7OMuzrXIqyqL+I9ktJyiDluBkRMWADJqSf87VNziQ4vbhMdHkBS4QHNvNVJRIcF0OyEWQLNagXz8yrXwfzaXWkcOJJDUqr3XWVISssl5ri+xYQHnPRgLTqseEccHRZAUlrxv9GXC/Zw7QuLGfbZeo5m5Zf4jlkrEul9fkw5RF+OgqthpR+3s804jAmuVnp7/0DsjS7AmXDyK3LeJulwNrGRgUW/x1SvStLhLLc2yYeziI103fbmY7cRHOhHWnouWcfymfDNWh645bwSn2sMDH5qNv0e/J5JMzeXWO5Nymvdv7JdNFX87HR+cgHdh8Vz1+X1CAv0w9skpeYQc9wMiFL77zb2VSkax+atTiQ6vOTYl5Saw9xVidx0WeWYWZCUkkFMdPFtLtHRISSllC0Z73Q6eem12TzxUI/yCu+MS0rJJDaq+LatmMhgkg5lurVJPlTcxsfHRnCgP2lHj9G0URS/LN5BQYGTfQeOsmFrEgeTM4quoL/+4WL63fU5Dz3zI4eOuI8n3sS13z9+3a9C8gn75+TUHKKPOzaIrlalaL8P8NqUTXR9dA4//raP/7uuWfkHXQ6SUjKJjS7efmMig0lKyTjlz1m78QD5+Q7q1KxcSeukpFRiYotjjo4JIykptdT2kycvpnPnuIoIrcKYsOpYR4oT71baIUz4X8yAqxKIb+uLKdi8EoDcHz/F96IrCHrxG6o++CI5X79Z3iGfdezGnJU/3qqsMym6G2NmGmNijTEtgd+AUm/aN8YMMcYsN8Ys/2COdx38jrq+MV8tPUj/N1aRlevA18f1x1m3NxO7zbBw2IX8/OT5fBK/n72Hve9EVf6ZY3kOPpi1g39f07jEsnt6NiAjO5++z//KF/MTaF47BJvNezfaf2pg59rMeb4zU5/qSGSoPy9P2eK2fM2uNAL87F5Zj+OMMTZ8r30Ux4oZkJb09+0rube+WMkd/eIIrFIy4TpxXB++e/s6JrzQg4k/bmLZuoMeiLBilLbur9t91DXuv9iFn5/vxCdzd7M3pXJdYfw7x3IdfDBzB/++pmTNgTGTNvBYv2Zn5Xh3oomTl9H5ksZuSY6zWf/eccREBjHgni8Y/eZ8zourgd1mcDicJCZncl5cDb77+Fbatozl5bcXejrccvVw/+bMH38lV3eoxZfzdnk6HI9JPpTJ4yN/ZPSwq87qbf6Hab+zfsMe7h58hadD8Rybjap3P0Pe/O+wDrn27b4Xdid/yWwyn7yB7LeepMqdQykxtU7Ei5SpcKZlWTcbY24E1gFZwM2WZS3+i/YfAB8AOL8fbJ1ukF8uOcDkP1wnFHG1gkg8Wnx7R+LRPKJC3KdwRYX4k3Rcm6SjeUQXtmkQVZWP7nZlV3elHGPhZtesiumrU7i0aTi+dhsRQX60qxfM+n0Z1I4IQDzny/kJTP51LwBx9UJJPK5OSGJaDlHhJ/ztw/3dZkEkpeYQHRbA3pRs9h0+xnXPLy56vf8Li5k01HXSMvqO1oCrSNflwxZSu7r7fdue8uWCPUxevA+AuLohJB7Xt8TUHKLC3NfPqBOuHicdd3W5+nHbyfWX1uK+t1e6vXfm8kSuqiS3etja9cLe9koArIPbMCHVKRpogiOwMo6c9H0+vf6FM/UgjmU/VkygZ0B0RFUOphRf6Uw8lE10RKBbm6iIQA6mZBITGUiBw0lGVh5hIf6s3ZzCT4t288qHy8jIysNmwN/Pzi3XtCC6uuszIsKqcHnHuqzdcogLWnnP378i1v3pfxzk0pbVXeN+iD/tGoazfk86tSM9P/39y/m7mRxfOPbVDyXxSHGtjFL77zb2HSM6PIC9KVnsO5TNdSMXFb6eQ/8XFjHpqUtYv/soj01YBUBaZh7x65Ox2wyXn+eds6miI4NJTDpa9HtSUjrRkWWrn7Nq7V5WrE7gq8nLyMrOI7/AQdUqfvzn31eWV7inLToyiIPJxVfLE1MyiK7uXig8qrqrTUxUMAUFTjKycgkLrYIxhqH/17Wo3cD7J1KvdjXCQqtQJcCHKy9zJex7dm3ClBknr3HiKV/O28XkhQkAxNUPO2HdP0ZU+AnrfngASccdGyQdOUZ0WMljtz4X1+TeV3/n330r32yK6MggDiYVzxpKTMkgOrLsFxQys3K57z/f8PCQy2gbV7M8QixX0dHhJB4snjmRlJhGdHTJ2SBLlmzivfdm8cXnj+LnV/lnQ/t2uQ6/S68CwLF7M6ZaFOxwLTNh1bFST37LRsAt/8GRvJ+8eVOKP+uS3mS/8bjrs3ZuxPj6YYJCsTLSyrUPIv9UmZIUxpjGwEPAFKA5cKsxZpVlWRVyyWlQxxoM6lgDgAWbjjBxyUF6t6nOmj0ZBAfYiQpxn54bFeJHkL+d1QnptKkTzA8rkhl0ievg+3BmHhFBfjidFu/9socbO7gOxmLD/Pl9exrXtosiO8/Bmj0Z3HZp5RvIzzaDutZlUFfXVOQF65KZOD+B3hfEsmZXGsFVfIgKPeFgJTSAoCo+rN6ZSpv6Yfzw234Gda1Lk5rBLB7bvahd96cWMPmpjoQH+ZGenU+Anx0/Hxvf/rqP8xuHu93H6kmDutRhUBfXfcUL1qUwccEeep8fw5pdRwv7f0KSJtSfoAA7q3em0aZ+KD/8doBBXV3vTz6aW9T+59VJNK5RfLDrdFrMXpHIF49dWEE9Oz3OlbNwrpwFuApn2tv3xrlxEaZGE8jNgqyS00DtnW8G/0AcM9+u6HBPS6umkSQcSGdfYgZREVWZuXAnY5/o4tamW4c6fD93O+e1iOanRbvo0MZVv+HLccVPL3jz85VUreLLLde0IDsnH6fTIqiqH9k5+SxeuZ8HBpW8JcSTKmLdj60WwO9bDnPtRTXIzi1gza40buvmHbc+DOpaj0Fd6wGwYG2Sa+y7sAZrdhaOfSdJUgQF+LB6RyptGhSOfd3q0aRWCIvHF19R7P7kL0wedinhwX7MfbFb0etDP15DlzZRXpugAGjVoia79xxh7/5UoqOCmfHzOsY9f32Z3jvuhQFF///dj6tYv2m/VycoAFo1iyFhXxr7DhwlKjKImfO2MPbZ3m5tul3akO9nb+C8uBr8tGArHdrVcdVaycnHsqBqFV8WL9uNj91Go/quOjxdOzbkj1V76dC+DktX7KFhPe+qzzOoe30Gda8PwII1SUyct4veF9Vkzc5Ugqv4nnzdr+LD6h1HaNMgnB+W7Ct6/+7ETOrFuLb3X1Yl0iC25NPgKoNWzWuQsC+VfQfSiIoMZubcTYx97poyvTcv38GDT07h2l5xRU/8qGxatarL7oRk9u47RHRUGDNmLmPcWLc70dm4cQ/Dn/2SDyf8m4iIylP8+6/kL/ie/AXfA+AT1wG/rtdRsOwX7PWbw7EsrPSSF2T8r70LUyWQnM9fcXvdOpKET7N25C/9CVtMHfD1U4JCvFpZH0H6I/CgZVlzjavi4KPAMqDlX7/tzLusWTjxW1Lp8fIKAvxsjL6+ePp+39dWMfVh14H28L4NGfrNNnLznXRqGk7npq6M64zVKUxc6pr6dEVcdfqdHw3AzRfHMuzbrfQZtxKw6Ht+NE1j3a9WViYT7xpJlybtqB4Uxt7R03h2+gQ+XlJ5rh6fzGVxkcSvS6HH0wtdjyK7vXXRsr7P/8rUZy4FYPhNLRn6v7Xk5jnoFBdJ58JK/qXZcTCToZ+uxRhDo9ggXrjt5FXhPe2yuOrEr0+hx/BFhY9hLL7fsu+oJUwd1hGA4Te1YOj/XI9h7NSyOp1buu5ZHPvdFjbvy8AYqFmtCs8NKt58l29PJSY8wCuuIJ8q544V2Bq2x+++91yPIJ3xRtEy37teJf/jRyA4Ap9LbsB5aC++d40HwLFiBs41cz0Vdpn52G0886+LGTxsNk6nRf8rm9C4XjhvfLaCuMbV6XZxXQb0bMLjLy/kyju/ITTYn/FDu/7lZx5OPcaDI+cBrqrpfbo2POnTP7xFea37N19Wh2Gfr6fPyF/Bgr4X16RpLe+73emyVlGusW/YgsLHLx839o1YxNRnOwEwfFCc6xGk+c4yjX2VjY+PneGPX8Xd//cZDoeT/te0o3HDKF5/bx5xzWvS/bJmrN2wnwcf/4r09GPM/3ULb77/CzO++benQ/9HfHxsPPNINwY/NgWn00n/q+JoXL86b3y4mLhm0XS7tBEDrmrF4y/M4sqBHxEaEsD451xXXQ+nZnP3Y1Ow2QzR1YN46eni5MZj93fmiRdmMvqN+VQLq8rop7y3TsdlraOIX5tEjyfmFT2C9E99hy9g6sguAAy/tTVDP1rl2u+3iqJz6ygAxk/exK7ETGwGakRU5bnC44aUozlcPyKezGMF2Ax89vNOpo/q6jUXKE7k42PjmUevYPAjX+N0WPTv05rGDSJ5Y0I8cc1i6dapMes2HuDBod+RnpHD/F+38dZHi5j+5T3MnreJ5av3kpZ+jKkz1wEwZlgfmjeJ9nCvys7Hx87wZ27k7sFv4HA66d+/I40b1+D1N6YRF1eX7t3a8PIr35GdnctDD7seuRkbW4333v0XADcPGsvOnYlkZ+fS+bInGfXCrXTqVOGnMKelYP1v+LS6iKAXvsDKy+XY/14qWhb49ASyXrgHE1Yd/9634jiYQOCwDwDImz+V/MUzyZn8LgG3/Ae/7tcDFsc+famUb5LS2M/i26S8kfmzAvxfNjImxLKs9BNea2JZ1tbS3vOnM3G7R2Vl/+nkj8g8Vzhu7ODpEDzL6fR0BB6V//uev290FvMb2NHTIXiMtWufp0PwLJ+y5v/PTra2F3k6BI+yco7+faOzlLV9m6dD8CjTtHKd+J5pppp3zETzlPT7Rno6BI8KeX/+WX0W3+Cj/mflOe3OwVO88u9W1sKZVYwxHxljZgMYY1oAncovLBERERERERE515Q1SfEp8BPwZ1W1rcDD5RCPiIiIiIiIiJyjyjontbplWd8YY4YCWJZVYIxxlGNcIiIiIiIiIh5n1yNbK1RZZ1JkGWMiwPWUP2NMB+DcvelSRERERERERM64ss6keBSYBjQ0xiwGIoEBf/0WEREREREREZGyK+tMioZAL6AjrtoU2yh7gkNERERERERE5G+VNUnxTOEjSMOBrsA7wLvlFpWIiIiIiIiInHPKOhvizyKZVwETLMuaYYx5oZxiEhEREREREfEK9rJe2pczoqz/3PuNMe8DNwIzjTH+p/BeEREREREREZG/VdZEww24alH0sCwrDagG/Le8ghIRERERERGRc0+ZbvewLCsb+O643w8CB8srKBERERERERE59+gJHSIiIiIiIiKlsBvj6RDOKaorISIiIiIiIiJeQUkKERERERERESnBGNPTGLPFGLPdGPPkSZbXMcbMN8asMsasNcb0Pt3vVJJCRERERERERNwYY+zA20AvoAVwkzGmxQnNnga+sSzrPGAg8M7pfq9qUoiIiIiIiIiU4hyuSXEhsN2yrJ0AxpivgWuBjce1sYCQwv8PBQ6c7pdqJoWIiIiIiIjIOcYYM8QYs/y4nyEnNKkJ7D3u932Frx3vOeAWY8w+YCbw79ONSzMpRERERERERM4xlmV9AHxwmh9zE/CpZVnjjDEXA58bY+Isy3L+0w/UTAoREREREREROdF+oPZxv9cqfO14g4FvACzLWgoEANVP50s1k0JERERERESkFHbbOVuTYhnQ2BhTH1dyYiBw8wlt9gDdgU+NMc1xJSlSTudLNZNCRERERERERNxYllUAPAj8BGzC9RSPDcaYkcaYawqbPQbcY4xZA3wF3GFZlnU636uZFCIiIiIiIiJSgmVZM3EVxDz+teHH/f9G4JIz+Z2aSSEiIiIiIiIiXkEzKURERERERERKYT9nS1J4hmZSiIiIiIiIiIhXUJJCRERERERERLyCkhQiIiIiIiIi4hWUpBARERERERERr6DCmSIiIiIiIiKlsNtUObMiaSaFiIiIiIiIiHgFJSlERERERERExCsoSSEiIiIiIiIiXkE1KURERERERERKYTeqSVGRyj9JUTWg3L/CWzn6tgc/X0+H4TH2Sb95OgSPcj7ez9MheJRvXr6nQ/CswDBPR+Axpq7l6RA86nDNmp4OwaPSuj7v6RA8qu7gVp4OwWN8+vXydAgelf+/HzwdgkeZgHP72ufa5zp5OgSPutTTAchZRbd7lKdzOEEhIiIiIiIicqqUpBARERERERERr3Buz8sSERERERER+Qt2m2pSVCTNpBARERERERERr6AkhYiIiIiIiIh4BSUpRERERERERMQrqCaFiIiIiIiISCnsKklRoTSTQkRERERERES8gpIUIiIiIiIiIuIVlKQQEREREREREa+gJIWIiIiIiIiIeAUVzhQREREREREphd2mypkVSTMpRERERERERMQrKEkhIiIiIiIiIl5BSQoRERERERER8QqqSSEiIiIiIiJSCrtRTYqKpJkUIiIiIiIiIuIVlKQQEREREREREa+gJIWIiIiIiIiIeAXVpBAREREREREphWpSVCzNpBARERERERERr6AkhYiIiIiIiIh4BSUpRERERERERMQrqCaFiIiIiIiISCnsurRfofTPLSIiIiIiIiJeQUkKEREREREREfEKSlKIiIiIiIiIiFdQkkJEREREREREvIIKZ4qIiIiIiIiUwm6Mp0M4p2gmhYiIiIiIiIh4BSUpRERERERERMQrKEkhIiIiIiIiIl5BNSlERERERERESmG3qSZFRdJMChERERERERHxCpVuJoVlWYyespX4DYcI8LMz+pYWtKwdUqLdhj3pDP1iA7n5Tjq3rM5T/ZtgCquyfrFwDxPj92GzGS5rWZ3/XteYvAInz329ifV70rEZw1MDmnBh42oV3b2/ZFkWoydtIn59iqvvd7SiZZ3QEu02JBxl6KdrXX2Pi+SpG5sX9R3gk5938fLkzSwZ153wID+OZuUz7LN17E3Jxt/Xxgu3taJJzeCK7NoZ99Gtw+jT6hKSM1Jp9fwgT4dzRixavo9R7/6G0+lkQM+mDLmxjdvyvDwHT4xdyIZthwgLCWD80K7Uiin+Ox5IzqTPkCk8cEs7Bg9oBcCn361n8uwtGAON61VjzGOd8PfzzmHBsixGf7eN+I2HCfC1MXpQC1rWLrmebtibztAvN7nW/xYRPNWvMcYYHvl0PbuTswFIP1ZASBUfpj5+IT8uT+TjX/YUvX/LgUym/OcCmtfynm1g0e+7GPX6fJxOiwF94hhyy0Vuy/PyCnhi1Cw2bEl2/e1H9KFWbCh5+Q6efeVn1m9Jco1rD3XlovNqu733/iensu/AUX787I4K7NGpWbR8H6Pe/83V/x5NGHLDCet+voMnxsazYfshwoL9Xet+9Anr/n3f8cCg8xjcv1XR6w6HkwEPTSMqIpD3R1xRYf05U35bvJ3XXvoJp9Pi6r7ncevgS9yWT/1mBd9NWobNbqNqFT8eH34V9RtGeijaM6f6I0Op2rETVk4Oyc8PI3frphJtYl99D5+ISLDbyVmzkpSxL4DTiV+jpkQ9/gymalUKDh4g8dknsLKzPNCLf8ayLMb8so9FO9MJ8DGM6l2PFtFVS7R7fdF+pm04QnqOg2UPty16/fv1hxm3YD9RQb4A3NQukgGtq1dU+Kdl0dIdjHptDk6HxYBr2jLkto5uy5et2sOY1+awZUcy40b2pWe35gBs2prIc6/MJisrF5vNxn13XELvy1t4ogunzd7lLmz122Hl5+GY8yZW8q6SbTrejK3FZeAfSP7btxQvCK6OT49/g39VMHYcv36BtXtlBUZ/eizLYszC/SzalU6Ar41RV9ahRdRJ1v3FB5m26QjpuQ6WPdDabdnsram881siBkPTyABe7lWvgqI/fet+P8hXb63Cclh0uqoBvQc1P2m75Qv38u6zS3jmvSuo16waOzcd5rOxywGwsLj2jjjadapVkaGL/COVbiZF/MbDJCRnM3t4R0YMbM7ISZtP2m7EpM2MvKkFs4d3JCE5m0UbDwPw+9YjzFt7iO+f7MD0YRdzV/e6AHy7ZD8A0566mI8ebMdLU7fhdFoV06kyil+fQkJyFrOf78yIW1oy8ssNJ203YuIGRt4ax+znO5OQnMWiDYeKlh08cozFGw8RWy2g6LUPZu2gea1gfhh+KS/e2Zoxk0oe8FU2ny6dQc83H/F0GGeMw+Fk5NtLmPDClUz/oD8zFuxke0KqW5vJP20hJMifOZ/cwO19WzLu42Vuy1/84Hc6nV+8Y0o6lMXnP2xg8pvX8uP7/XE6LWYs2Fkh/fkn4jceJiElm9lPd2DEwGaM/HbLSduN+GYLIwc2Y/bTHUhIyWbRpiMAvHpHHFMfv5Cpj1/Ila0juby162Tt6vNjil5/6ZYW1KoW4FUJCofDycjx85gwth/TP7+DGXO3sH3XYbc2k2esJyQ4gDlfD+b2G9oz7r14AL79cS0AP/7vdj5+dQAvvbXAbVybs3AbVav4VVxn/gGHw8nId5YyYeSVTH+vHzMW7mT7nhPX/a2EBPkx56Prub1vHOM+Xu62/MUJf7it+3/67IeNNKgdVp7hlxuHw8m40bMZ987NfDn1fubOXs+uHSluba7sHcfnU+7jf98M4eY7L+bNsT97KNozp+rFnfCtXYc91/cm+cXniHz8mZO2Sxz2GHtv68/eQddhDwsnqFsPAKKGjuDQu6+x95Z+ZC6cR/gtd1Zk+Kdt0a509qTmMvPuFjzXoy7P/7znpO26NAzj61uanXRZz2bhTLmjOVPuaF5pEhQOh5OR42YzYfxApn91LzN+3sD2Xe7re2xMCGOeuZo+V8S5vR4Q4MtLw69h+sR7mfDqQMa8Nof0jJyKDP+MMPXaYcJiyf/kQRxz38XebchJ2zl3LiP/qydKvG6/aADOrUso+PK/FMwcj0+3e8o75DNq0e4M17p/R3Oe616b5+ftO2m7Lg1C+PqmJiVeT0jN5cNlyXx+Q2N+uK0ZT1xWs7xDPmOcDidfvr6CR17qzPP/68nvvyRwYPfREu2OZeczd8o2GjQvvshas34oz7x/Bc991INHXr6Mz8Ytx1HgrMjwRf6RSpek+GVdCtdeGIsxhrb1Q0k/VkDy0Vy3NslHc8nMKaBt/VCMMVx7YSzz1rl2Zl//uo97rqiLn6+r6xHBrgP0HYlZXNSkWtFrIVV8WL8nvQJ79vd+WZPMtR1quvreILyw7+472uSjOWQeK6Btg3BX3zvUZN7qpKLlL367if/0a+o2s2L7wUwuahYBQIOYIPYfzuZQuvu/aWWzaPtqjmR519/vdKzdkkKd2BBqx4bg52un92UNmLfU/eB03tI9XHd5IwB6dKrP0tUHsCzXCencJbupFR1Mo7rhbu9xOCxy8hwUOJwcyy0gKqLkVQlv8cv6Q1x7QYxr/a/3V9u+g7b1Crf9C2KKtv0/WZbF7NXJXNUuusR3zFiRRO+TvO5JazclUqdmGLVrhLn+9t2bMu/X7W5t5i3aznU9WwLQo0sTlq7Yg2VZ7Nh9mA7t6gAQEV6VkKAA1m9OBCArO49PJy3n/ts6VGyHTtHarYeoU+O4db/zSdb93/Zw3eWNAehxaT2Wrjl+3U+gVkwQjeqEub0n8VAWC5ft5foeJQ9mK4NN6w9Qq3Y4NWuF4+trp3vPlixa4J64CwzyL/r/nGP5nA2PeA/s3JWMWdMAyN2wFltQMPaIkifaRbMj7D4YX18oXB9869QlZ5UriXXsj6UEdalcM2jmbzvKNS2rYYyhTY1AMnIcpGTml2jXpkYgkYWzJc4GazceoE6tatSuGe4aBy5vwbz4rW5tasWG0bRRNOaE+8br14mgXm3X8V10ZDDVwgM5kpZdYbGfKbaGF+DctBAAK3Ebxj8QAsNKtLMSt0FWWskPsCzwqwKA8a+KlXWkHKM98+bvOMo1zQvX/dhAMvIcpGSdZN2PDSQysOS6P3n9YQa2qU5ogGu2aETVyrN97Nx8hKiawUTWCMLH186F3eqwavH+Eu2+/2gdvW5qhq+fveg1/wAf7D6uc578PMdZsR/wFLsxZ+WPtypzksIY42eMaW2MaWWM8dilt6S0XGLCi2cBxIT5n/REJTqsuE10mD9Jaa42u5OzWbEjjRvH/sGtry9nXYIrE9msZhDz16VQ4HCy79AxNuzNIDHNuzLtSWk5xFQ7vu8BJKee0PfUXKKP+/eJDg8gqbAf81YnER0WQLMTbo9pViuYn1e5Ehlrd6Vx4EgOSane1fdzXdLhbGIjA4t+j6lelaTD7lOUkw9nERsZBICP3UZwoB9p6blkHctnwjdreeCW89zaR1cP5K4BcXS79Ws63fwVwYF+XNree6cAJqXlEnPcdh0TWtq2X3xiFh0WULTt/2n5jjQigv2od5JporNWeV+SIiklk9io4pkdMZHBJB3KdGuTfKi4jY+PjeBAf9KOHqNpoyh+WbyDggIn+w4cZcPWJA4mZwDwxoeLuXPg+QQEeOftPX9KOpxFbPXj1/1Akg67n2C41n1XGx+7jeCqx637k9fywM3u6z7A6Pd/5z93XVDihKaySElOJyqmeCyPigohJSmjRLspXy/j+qve4p1X5/HwEz0qMsRy4RMZTUFSYtHvBSlJ+ESefJut8er71J+5EGd2Fpnz5wCQt2sHgZ27ARDU7Up8omLKP+gzKCkzj5jg4kOw6GA/kjLzTukzft6aSt9PNvLIDzs5mH5q7/WUpJQM93EwKoSklJLr+99Zu2E/+fkO6tQM//vGXsYEVcPKKJ4Za2UexgRFlPn9jt8mYWveGd+7P8DnumE45n9UHmGWm6SsfGKCixML0UG+JJ0kQVeahNQcElJzuWXSNm7+eiu/7q48F7LSUo5RLbJK0e/hkVVJSznm1iZh6xGOpByjzcU1Srx/58bDPHPHLJ698yduffT8oqSFiDcr01pqjLkK2AG8AbwFbDfG9CrPwMpLgdPiaHY+Xz92Af+9tjGPfLwOy7Lo16EG0WH+XP/KH4z5bgtt64di8+Ls0qk6lufgg1k7+Pc1jUssu6dnAzKy8+n7/K98MT+B5rVDsFXSA3cp6a0vVnJHvzgCq7hfNTiakcu8pXuY++kNxH95E8dy8pk2b3spn3L2mLHy5LMo1uw+SoCfnSY1gjwQVfno3zuOmMggBtzzBaPfnM95cTWw2wybtiWz50AaV3QuOR6cTd76chV3XNeyxLo///c9RIQFENe4ckx1Px39B17AtzMe5P6Hu/HphF89HU6FOvDIvey+uivG148q7V11XJJHPUNov4HU+mQSpmogVkHZT3LOBl0ahjJnSBxT72zBxXWDGTZrt6dDqjDJhzJ4fOQ0Rj999Tl5jGNr2gnnhvnkfziEgu9H4dPz/4Bz59+hwIKEtFw+GdCIl3vV5dm5e0nPKfB0WGeE02kx6e3V3Hh/25Mub9Aiguc/7cXT71/BzC83kZ/rqNgARf6Bsl5CGwd0tSxrO4AxpiEwA5h1ssbGmCHAEIB3H7qMIb1bnlaQX8bvZXJhzYi4OiEkHneVPzEtl6hQf7f2UaH+RbMHwHUF9s+rqzFhAVzRJgpjDK3rhWKzGVIz86kW7MfQ/k2L3nPT+GUnvdJa0b6cn8DkX/cCEFcvlMQjx/c9h6jwE/oe7u82CyIpNYfosAD2pmSz7/Axrnt+cdHr/V9YzKShHYkM9Wf0Ha7iQpZlcfmwhdSuXgXxHtERVTmYUjxzIvFQNtERgW5toiICOZiSSUxkIAUOJxlZeYSF+LN2cwo/LdrNKx8uIyMrD5sBfz871cOqUCs6mGphrr/1FZfUY9WmJK7p3qhC+/ZXvly0j8lLDwAQVyfYbXZT4tHStv3imRNJaTluMysKHE7mrklm8n8vKPFdM0tJXnhadGRQ0ewHgMSUDKKruydSoqq72sREBVNQ4CQjK5ew0CoYYxj6f12L2g28fyL1aldj2eq9rN+cRLfrJ+BwODmSms2t/57E52/eWGH9KqvoiEAOHjp+3c8i+oTbklzrfhYx1QvX/ezCdX9LCj/9uptXPl7utu4nHcrml9/2sHDZPvLyHWRm5/HfVxbyyn8vq+ju/WORUSEkJxZfCUxOTicyuvRaKpf3jGPsqJPusr1eaP+BhFwzAICcTevxiS6e/eATGU1BSlJpb8XKyyNr0XwCO3fl2LKl5Cfs4sDDrnv5fWvXJfCSzuUb/Bnw1coUJq91XUGPi61KYkbx7IekjDyig8o+uTWsSvFhX//W1Rm/sOSUcW8UHRnsPg4mpxMdWfbaQZlZudz32CQevrcLbeMqTy0CW5ue2OIuB8BK2o4Jrs6fVYVMUARW5uHS33ziZ8V1p+C7512fdXAr+PhBlWA45r0zCr5ak8Lkda4+xsVUJTGjOKmYlJlP9Cnc0hQd5EvrmKr42g21Qv2pF+5PQloerWK8ezYhQFhkFY4cN3MiNSWbsONmVuRk57N/11FefvgXAI4eyeGNYYv4v1GdqNesuD5Fjboh+FfxYf+uo26vi3ijsm6ZGX8mKArtBEqdZ2dZ1gfABwDOOQ+cdvXJQZ1rM6izqyL9gvWHmBi/l97to1mzO53gAJ+TnqgEBfiwetdR2tQL4Yc/DjLoMtf7u7eO5PdtqVzUpBq7krPIL3ASHuTLsTwHlgVV/e0s3nwYu83QKNbzV1QHda3LoK6u4p4L1iUzcX4CvS+IZc2uNIKr+BAVGuDWPio0gKAqPqzemUqb+mH88Nt+BnWtS5OawSwe272oXfenFjD5qY6EB/mRnp1PgJ8dPx8b3/66j/MbhxNUpfLcq3cuaNU0koQD6exLzCAqoiozF+5k7BNd3Np061CH7+du57wW0fy0aBcd2tTAGMOX4/oUtXnz85VUreLLLde0YM3mZNZsTuZYTgEB/naWrj7gdVeWB3WqxaDCKtQLNhxi4qJ99G4XzZqEdIID7KVs+3ZW7z5Km7oh/LAssej9AEu3plI/OtDtthFwXYWYvTqJL/6vffl36hS1ahZDwr409h04SlRkEDPnbWHss73d2nS7tCHfz97AeXE1+GnBVjq0q4MxhmM5+a5xrYovi5ftxsduo1H9CBrVj+Cmvm0B2HfwKPc/MdUrExQArZpUJ+HA0eJ1P34nYx/v4tam20W1+X7uNs5rHsVPv+6mQ2tX3aIvX7mqqM2bXxSu+1e7qvo/duf5APy+9iAfT1lfqRIUAM1a1mDfniMc2JdKZHQI82Zv4Nkxfd3a7E04TO26rungS+K3UatO5TwoPTrla45O+RqAqh07EzrgJjJ/noV/y9Y4szJxHD7k1t5UqYKtaqDrdbudqh07k7NmBQD28Go4Uo+AMYTfeS9Hp35T4f05VTe1i+Smdq5Cvwt3HOWrVSn0ahbO2oPZBPnbT6n2REpmflH7+duP0iAi4G/e4R1aNa9Bwt4j7DuQRlRkMDPnbmTsiOvK9N68fAcPPjGZa3u1LnriR2XhXDMb55rZAJj67bC36YVzy6+YmMZYedknrz1RmvQUbHVa49w4H6rVBLuvVycoAG5qE8lNbQrX/V1H+Wr1IXo1DWNtYjZBfvaT1p4oTfeGoczckkrflhGkHitgd2outUO9u3D0n+o3rUbSvgxSDmYSXr0Kf/yyhyFPX1y0vGqQH69PKx7/X37oF264vy31mlUj5WAm1SKrYvexcSgxi4N70omICTzZ18jfsOsumQpV1iTFcmPMTOAbwAKuB5YZY/oBWJb1XTnFV8JlLSOI33iIHiOXuB5DeEvxLI2+L/7G1CddReCG39is6BGknZpH0LmF60CtX4caPP3lRq4evRRfu40xt7TEGMORjDzufmcVNuM60X/pttOb/VEeLouLJH5dCj2eXuh6BOntxY9W6vv8r0x95lIAht/UkqH/W0tunoNOcZF0jvvrR87tOJjJ0E/XYowrMfPCba3+sn1lMPGukXRp0o7qQWHsHT2NZ6dP4OMlP3o6rH/Mx27jmX9dzOBhs3E6Lfpf2YTG9cJ547MVxDWuTreL6zKgZxMef3khV975DaGFj2H8K22aRXFlp/r0e/B7fOyG5g0juLHXyavBe4PLWkQQv/EwPZ5f6lr/by4+2Oz78h9MffxCAIZf37TwEaQOOrUo3vYBZq5MOulsieU70ogJC/DKGUQ+PjaeeaQbgx+bgtPppP9VcTSuX503PlxMXLNoul3aiAFXteLxF2Zx5cCPCA0JYPxzrpPzw6nZ3P3YFGw2Q3T1IF56uvfffJv38bHbeOb+ixn89E+F635jGtcN543PV7rW/Q51GNCjCY+PjefKwd+61v0TEnhnIx8fG48M7cmj90/E4bToc10bGjSKYsLbC2jWMpZOXZoy5evlLPttJz6+doKDA3j6+Ws8HfZpy14ST9WOnaj77SycucdIfqH46R61/zeZvbcPwBZQldiX38L4+YExHFv5R1EyIuiK3oT2HwhA1oK5ZEyf6pF+/FOdG4SwaOdRek3YQBVfG8/3qlu0rP+nm5hyh2tcHLdgHzM3pZKT76T7u+vo1zqCBy6pwRcrk1mw/Sh2myE0wM4LleQRjD4+Np55rAeDH/7KNQ72aUPjBpG88cFC4prH0q1TE9ZtPMCDT04mPSOH+b9u460P45k+8V5mz9vI8tV7SEvPZurMNQCMefpqmjepXPVIrF0rseq1w/fOt7EKcnHMebtomc+gsRR8+R8A7J1uxda0E/j643v3BzjXz8Xx2zcUxP8Pnyvux9auD1gWBT+95amu/COd64WwaFcGvT7dRBUfG89fWadoWf8vNjOl8Gk24xYdYOaWwnX/ww30a1mNBy6O5ZK6wSxJyOCazzZhN4bHOtVwm1nkzew+NgY91I5X/7sQp9Pi0l4NqFk/lO8/Xke9ptVoe0nps4O2rTvErImbsNttGBvc8nB7gsP8S20v4i3MnxXQ/7KRMZ+c5GUL181slmVZd5X23jMxk6LS8ju3ZyPYJ/3m6RA8yvl4P0+H4FHWlpLPbz+XmHYlizWeMzJS/77NWexwzcoznbw8pHV90dMheFTdwZU/0f9P+fSrlOXKzpj8//3g6RA8ynh5Ieby9vt1Tf++0Vns0tiRZ3WRk4Gz7jgrz2m/7vWpV/7dyjqa2ICHLMtKAzDGhAPjLMuqXA8YFxERERERERGvVda7a1r/maAAsCwrFTiHLxOKiIiIiIiIyJlW5pkUxpjwwuQExphqp/BeERERERERkUrJbrzyroiz1qk8gnSpMebbwt+vB0aVT0giIiIiIiIici4qU5LCsqzPjDHLgW6FL/WzLGtj+YUlIiIiIiIiIueaMt+yUZiUUGJCRERERERERMqF6kqIiIiIiIiIlMJuU02KilTWp3uIiIiIiIiIiJQrJSlERERERERExCsoSSEiIiIiIiIiXkE1KURERERERERKYTeqSVGRNJNCRERERERERLyCkhQiIiIiIiIi4hWUpBARERERERERr6CaFCIiIiIiIiKlsOvSfoXSP7eIiIiIiIiIeAUlKURERERERETEKyhJISIiIiIiIiJeQUkKEREREREREfEKKpwpIiIiIiIiUgq7MZ4O4ZyimRQiIiIiIiIi4hWUpBARERERERERr6AkhYiIiIiIiIh4BdWkEBERERERESmFXSUpKpRmUoiIiIiIiIiIV1CSQkRERERERES8gpIUIiIiIiIiIuIVVJNCREREREREpBQ2o6IUFUkzKURERERERETEKyhJISIiIiIiIiJeQUkKEREREREREfEKqkkhIiIiIiIiUgq7SlJUKM2kEBERERERERGvoCSFiIiIiIiIiHiFcr/d4+hlncv7K7xW6OLFng7Bo5yP9/N0CB5le/k7T4fgUc5R//J0CB5lAiM8HYLHOBYu8XQIHlVtf5KnQ/CoiG/u9nQIHmVqtPR0CB7jmPyFp0PwKN9benk6BM86lu7pCDyqw6zlng7Bs+7ydAByNtFMChERERERERHxCiqcKSIiIiIiIlIKmwpnVijNpBARERERERERr6AkhYiIiIiIiIh4BSUpRERERERERMQrqCaFiIiIiIiISCnsqklRoTSTQkRERERERES8gpIUIiIiIiIiIuIVlKQQEREREREREa+gmhQiIiIiIiIipbDZVJSiImkmhYiIiIiIiIh4BSUpRERERERERMQrKEkhIiIiIiIiIl5BNSlERERERERESmFXSYoKpZkUIiIiIiIiIuIVlKQQEREREREREa+gJIWIiIiIiIiIeAUlKURERERERETEK6hwpoiIiIiIiEgpbCqcWaE0k0JEREREREREvIKSFCIiIiIiIiLiFZSkEBERERERERGvoJoUIiIiIiIiIqWwqyZFhdJMChERERERERHxCkpSiIiIiIiIiIhXUJJCRERERERERLyCalKIiIiIiIiIlMJmVJSiImkmhYiIiIiIiIh4BSUpRERERERERMQrKEkhIiIiIiIiIl5BNSlERERERERESmFXSYoKpZkUIiIiIiIiIuIVlKQQEREREREREa+gJIWIiIiIiIiIeIVKXZNi6a/bePWlmTidFtf0a8dtgzu7LZ/42WKmfbcSu91GeHhVho3sS2yNMADeHP8TSxZtxem0uPDihjz6RG+Mlz//1rIsRn+zmfgNKQT42Rl9Wyta1gkp0W5DwlGGfrae3HwHnVtG8tQNzTDG8Nb07Xz76z6qBfsB8PC1jbksLpIf/zjAxz/vLnr/lv0ZTBl6Mc1rl/xsT1q0fB+j3v0Np9PJgJ5NGXJjG7fleXkOnhi7kA3bDhEWEsD4oV2pFRNctPxAciZ9hkzhgVvaMXhAKwA+/W49k2dvwRhoXK8aYx7rhL9fpd4s+OjWYfRpdQnJGam0en6Qp8MpF4uW7mDUa3NwOiwGXNOWIbd1dFu+bNUexrw2hy07khk3si89uzUHYNPWRJ57ZTZZWbnYbDbuu+MSel/ewhNdOC3xv25i1Evf43Q6ub5fB4YM7u62fNnyHYx++Xu2bDvI+JdupeeVxdvKy+N/ZOGijTidFpdc3IRhT/T1+rHveJZlMXrGbuK3plLF187o/g1pUSOoRLvXft7DtFUpHM0pYMXwi4peX74rnTEzd7M1KYuxNzShR1xERYb/j1iWxeivNxK/Ltk19t/ZhpZ1Q0u025BwlKGfrCE3z0HnVlE8NbCF29/2kzk7efnbTSwZfwXhwX589NMOpv92AIACp5OdBzNZ/OoVhAX6VVjfymLRH3sY9c6vOJ0WA3o1Z8hN7dyW5+U5eOKleWzYluIa+5++gloxrv3Xlp2HGf7qQrKy8zDGMPmd/vj7+TD9l228P3ElxkBURCCvDO1OeGgVT3TvlMQv2sioMZNxOpxcP6AjQ+650m35suXbGT1mMlu2HmD82Dvp2eO8omWvjPuehQs3APCv+3vSu1f7Co39dFmWxeiZCcRvK9z2+zakRY3AEu1em7uHaasPubb9py8sev3TxQeZvDIZH5shvKoPL/RtSM0w/4rswhmz6LddjHp9nmub6NOaIbde5LZ82eq9jHnjF7bsSGHcc1fTs2tTD0V6ehYt28Ood5YUbvvNGDLwPLfleXkOnnj5l+LjvmGXFx33bdl5mOGvxZOVne/a9t/ui9Np8fDzc9lzMB27zdC1Q10eu/uik32117EsizHz9hK/I50qvjZG9a5Hi5iqJdq9Hr+faesPczTHwfJHzyuxfM6WVB75fieTbmtGXGzJ7UfEW1TamRQOh5Oxo6fz6ru38tX3DzJn1jp27Uh2a9O0WSyffnUvX055gK5XtOStV+cAsHb1Htau3sMXkx9g4ncPsmn9flYu3+2BXpya+A2HSEjOZvaIToy4uSUjv9p40nYjvtrIyEEtmT2iEwnJ2SzacKho2e3d6zJ1WEemDuvIZXGRAFx9YY2i1166oxW1Iqp4XYLC4XAy8u0lTHjhSqZ/0J8ZC3ayPSHVrc3kn7YQEuTPnE9u4Pa+LRn38TK35S9+8Dudzq9V9HvSoSw+/2EDk9+8lh/f74/TaTFjwc4K6U95+nTpDHq++Yinwyg3DoeTkeNmM2H8QKZ/dS8zft7A9l0pbm1iY0IY88zV9Lkizu31gABfXhp+DdMn3suEVwcy5rU5pGfkVGT4p83hcDJy9Hd8+O4QZnz/BNNnrWT7jkS3NrGx4Yx54Sb69HI/mVu5ehcrV+9i2uT/Mv27x1m3fi9/LN9RkeGftvitaSQczmH2I+cx4roGjJi266TtujYLZ9L9rUq8Hhvmx+j+DbmqdfXyDvWMiV+fQkJyFrNHdWHEra0Y+eX6k7Yb8cU6Rt7aitmjupCQnMWi9cXbxcEjx1i8IYXYasUn4oN7NGTqs52Y+mwnHu3XjAuaRHhdgsLhcDLyzUVMGN2H6R8NZMb87WxPOOLWZvKsTYQE+zPns0Hc3r814yb8BkCBw8l/x8xlxMOdmf7RQD4bdy0+dhsFDiej3/mVz8Zdw7QJN9K0QQRffH/yf1Nv4nA4GfnCN3z4/r+Y8ePTTJ+5gu3bD7q1iY0NZ8zoW+lz1flury9YuJ6NG/fy/XdP8s3X/+GjT+aRmXmsIsM/bfHb0kg4fIzZD7VlxDX1GfHjyffXXZuGM+neuBKvN4+tyrf3xvH9A63p0bIa4+YklHfI5cLhcDJy/M9MGDuA6V/cxYy5m9i+65Bbm9joEMY81Ys+lzf3UJSnz7XtL2bC6N5M//CGwm3/hOO+2Ztdx33/u4nb+7Vi3IfHbfsv/sKIhzoz/cMb+Gzc1fjYXac8d17fmlkf38h37/Zn5YZE4v/YU+F9+ycW7Uwn4Ugus4a05LkedRhZyvrbpWEoX9928r97Vq6DL5Yn01rJiX/EZs7OH29VaZMUG9fvo1adatSsVQ1fXx+u6NmK+Pmb3dq0v7ABAVVcB1xxrWuTnHQUAGMgL7eA/HwH+XkFFBQ4qRZR8kqct/llTTLXdqiBMYa2DcJIz84n+WiuW5vko7lk5jho2yAMYwzXdqjBvDXJpXxiSTOWHaT3+bFnOvTTtnZLCnViQ6gdG4Kfr53elzVg3lL3Hcu8pXu47vJGAPToVJ+lqw9gWRYAc5fsplZ0MI3qhru9x+GwyMlzUOBwciy3gKiIklnpymbR9tUcyUr3dBjlZu3GA9SpVY3aNcNd68LlLZgXv9WtTa3YMJo2isacMPrWrxNBvdrVAIiODKZaeCBH0rIrLPYzYe36PdStU53atSLw8/Xhqp7nMW+++wlWrZrVaNakBrYT+m+MKRz7CsjLKyC/wEH1iGAqk182HeHatpEYY2hTO5iMnAJSMvJKtGtTO5jI4JIn3DXDA2gaE4itEs0e+WV1Etd2qOka+xuGu8b+NPfkWnJaDpk5BbRtGF449tdk3uqkouUvTtrIfwY0p7Ruz/jjAL0vrFGe3fhH1m5Jpk6NUGrXKBz7uzRi3uLdbm3mLdnNdVe6rhT36NyQpav2Y1kWi5fvpWmDCJo1dCWkwkMDsNttWJaFZUF2TgGWZZGZnUdUhPcftK9dt9u17deujp+fD1f1ase8X9a6talVM4JmTWuW2Pa3b0/k/PMb4eNjp2pVf5o2qUn8ok0VGf5p+2Vz6gnbvuOUtv2LGoRSxc8OQOvawSQdLfneymDtpoPUqRVO7ZphhfvAZsz7dbtbm1qxoTRtFFViH1iZuLb94477ujRi3pLdbm1c234TAHp0bsDSVQcKt/19NG1QjWYNXTPlwkNc236VAF86tK0JgJ+vnRaNqpN4KKtC+/VP/bItjWviIlzrf80gMnIdpGTml2jXpmYQkUG+J/2MNxYdYHCHGPx9Ku96IeeOU0pSGGPCjTGtyyuYU5GSlEFUdPF016joEFKSSz8x+3HqCi6+tDEArdrUof0F9enT/RWu6v4KF3VsRP0GkeUe8+lKSsslJjyg6PeY8ICTHqhGHzd9MTosgKS04kTGlwv2cO0Lixn22XqOZpUc3GatSKT3+THlEP3pSTqcTWxk8UFkTPWqJB1237EkH84iNtKVbPKx2wgO9CMtPZesY/lM+GYtD9ziPu0tunogdw2Io9utX9Pp5q8IDvTj0va1EO+WlJJBbFTxiXVMVAhJKRmn/DlrN+wnP99BnZrhf9/YiyQlHSUmOqzo9+joMJKSj5bpvee1qcdFFzTi0u7PcWn35+jUsRkNG0SXU6TlIzkjj5jQ4hOQ6BA/ktIr58lGWSWl5hBz3AyIUsf+4/YP0eFVSEp1tZm3OpHo8ACalTJD7liug1/Xp3Bley8c+w9lERt13NgfGXiSsT/zJGN/Drv3pWGMYfAT0+l337d8OGkVAL4+dp59qDPX3DOJzjd+xo6EVAb0alZxnfqHkpKOEhNTPF5Fx4SXedtv1qwmi37dxLFjeRxJzeT3P7aSmJj692/0IsnpZ27b/25FMp0ah52hyCpWUkqm+z4wMpiklEwPRlQ+kg5lF23XADHVA0k6VJbjvhx270/DYBj85Az63T+FDyetLvH56Zm5zP8tgYvPq1mu/ThTkjPziQk5bv0P9iPpJEm60mxMzCYxI4/LGpa8VVDEG/1tksIYs8AYE2KMqQasBCYYY8b/zXuGGGOWG2OWf/rh3DMV6z82a/oaNm04wC13XArA3j2H2b0rhWk/P8aPc//Dij92snrFbs8GWQEGdq7NnOc7M/WpjkSG+vPylC1uy9fsSiPAz06TmpXryurfeeuLldzRL47AKu6Z5aMZucxbuoe5n95A/Jc3cSwnn2nztpfyKXI2ST6UweMjpzH66atLXHE8myXsSWHHriQW/vws8XOf5bc/trF8ReW/xUlKdyzXwQczd/Dva5qU2mb+2iTOaxTudbd6nK4Ch8WK9QcZ+1R3vnztOn7+dRdLV+4jv8DB1z9uYOp71xM/6TaaNIjgg69WeTrccnXpJc25rFMLBt48jsf+8wlt29THZq+0k2lPy7Q1Kaw/kMVdl3rfzCE5MwocFis2JDJ2aDe+fPUafl7s2vaLlzt5bPQ8bu0bR+1Y77q9uTw4LYuXf9nL4910IU4qj7JUCAy1LCvdGHM38JllWc8aY9b+1Rssy/oA+AAgNXeSdQbiLCEyOrjo9g2A5KR0IqNKDjR//LaDTycs5N2P78KvsCDiwnmbiGtdm6pVXTMOLr60MevW7KVt+3rlEepp+XLBHiYvdg2scXVDSEwtvnqWmJpDVFiAW/uoE2ZOJB03s6J6SPEMi+svrcV9b690e+/M5Ylc5YW3egBER1TlYEpxBj3xUDbRJ0zPjYoI5GBKJjGRgRQ4nGRk5REW4s/azSn8tGg3r3y4jIysPGwG/P3sVA+rQq3oYKqFua5QXnFJPVZtSuKa7o0qtG9yaqIjgzmYXDxzIjE5nejIsifWMrNyue+xSTx8bxfaxlWOKyjHi44OJTEprej3pKQ0oqPKdmXk53nraNO6LoGFY1+nS5uxas1uzm/foDxCPWMm/pbIt8tdty60qhlE4nHTtJPS84gOObtOrgG+nL+byfF7AYirH0rikeL6AaWO/cftH5JSjxEdHsDelCz2HcrmupGLCl/Pof8Li5j01CVEhro+Y+YfB7jKC2/1ANeMt4PJx439KVknGfuDCsf+oOPG/gBiIgM5v1VsUUHMyy6qw8ZtKQRVda0vdWq4tptelzVkwtfen6SIjg51m/2QlJha5m0f4P77enL/fT0BeOy/n1C/btQZj/FMm/h7It+ucN2yeia2/SU7jvLBwv38766W+PlUziRNdGSQ+z4wJYPoSO+/ZflURVevysHjZogkHsoiunppx30nbPvVT9j2L6zDxu2HuLid6yR9+Kvx1K0Zyu39vGJyeKkmrkxm8hpXvZG4mEASj5s5lJSRR/RJbms6maw8J9sOHeOOia5bYw9l5fPgdzt4q19DFc88BfZKdJvomWaM6Qm8DtiBDy3LevEkbW4AngMsYI1lWTefzneWZYT2McbEAjcA00/ny86k5i1rsjfhCAf2pZKfX8DPs9fRqYv7dM0tmw7y0shpvPLGILeaE9GxoaxcvpuCAgcF+Q5WLd9NPS+93WNQlzpFRS27t4nmh99c99ut3plGcBUfokLdK1NHhfoTFGBn9c40LMvih98O0K2N60Dk+PoVP69OovFxFfGdTovZXnqrB0CrppEkHEhnX2IGefkOZi7cSbcOddzadOtQh+/numZC/LRoFx3auOp3fDmuD798diO/fHYjt13XkiED23LLNS2IjQpkzeZkjhXel7x09QEa1A7zQO/kVLRqXoOEvUfYdyDNtS7M3Ui3TqVfJT5eXr6DB5+YzLW9Whc98aOyadWyNrsTUti77zB5+QXMmL2Kbl1KFok7mRqx4SxbvoOCAgf5+Q6WLd9ZKW73uLlDDFMfbMPUB9vQvUU1flidgmVZrNmbQbC//aT3n1d2g7rWKypq2b1tND/85qqzsHpHqmvsP0mSIijAh9U7UgvH/v10axtNk1ohLB5/BfNe7Ma8F7sRHR7AlKc7FSUoMrLzWb71CN3aeud60KppFAn709h3MN21vS/YTreO9dzadOtYj+/nuGYG/hS/gw5tXfU7Lj2/Dtt2HeFYTj4FDifL1hygYd1qRFUPZEdCKkfSXImfJSv20aCO99/21SqubuG2f4i8vAJmzFpJt65lO8lyOJykprlO+DZv2c+WLQe45BLvv8Xl5otimPqv1kz9V2u6Nwt33/YDTm3b33gwixHTdvLWoKZElHLPfmXQqlksCXtTj9sHbqbbJWffxRXXtn/Ufdu/uK5bm24X1+X7Oa4T75/id9KhbY3Cbb+2+7a/9iANC2uSvfbJH2Rk5fHU/R1LfKe3ubldFN/d2YLv7mxB9yZhTFt/2LX+788kyN9eau2JEwX721n8f235+f5W/Hx/K9rUCFSCQsrMGGMH3gZ6AS2Am4wxLU5o0xgYClxiWVZL4OHT/d6yzKQYCfwELLYsa5kxpgGw7XS/+HT5+Nj5z1NX8dD9n+F0OOlzXTsaNIrig7fn0axFTTp3bcab438iOzuPYf+ZBEB0TChj3xxEtytasuKPXQzq/zbGGDpc0qhEgsMbXRZXnfj1KfQYvqjwEaTFJyZ9Ry1h6jDXgDv8phYM/Z/rEaSdWlanc0tX0bCx321h874MjIGa1arw3KCWRe9fvj2VmPAAakd6Z+FIH7uNZ/51MYOHzcbptOh/ZRMa1wvnjc9WENe4Ot0ursuAnk14/OWFXHnnN4QG+zN+aNe//Mw2zaK4slN9+j34PT52Q/OGEdxYCe5L/jsT7xpJlybtqB4Uxt7R03h2+gQ+XvKjp8M6Y3x8bDzzWA8GP/wVTqeT/n3a0LhBJG98sJC45rF069SEdRsP8OCTk0nPyGH+r9t468N4pk+8l9nzNrJ89R7S0rOZOnMNAGOevprmTbwzOXcyPj52hj/Vj7vv/wCHw0n/6y6kcaMYXn97FnEtatO9axxr1+/hwYc/IT39GPMXbuDNd2czY+oT9LiiDb/9sY2r+7+CMYZOlzSjW5eWf/+lXqRzkzDit6bSc/wqAvxsjOpXfHDe9601TH3Q9bjVsbMTmLH2EDn5Trq+vIL+7aN4sHtt1u3L5P8mbiH9WAHzN6fy1i97+fH/2nqoN2VzWaso4tel0GPYAtfYf0fxiWnfEYuY+mwnAIYPinM9gjTfSae4SDrH/X3yfe6qRDq2rE5Vf+989LKP3cYz/+7E4Cenu8b+ns1oXK8ab3z6B3FNIunWsT4DejXj8RfnceVtXxIaHMD4YVcAEBrszx0D2nD9A1MwBjpfWJcuHVwnOQ/cej63PPo9PnYbNaKDGfPfbp7sZpn4+NgZPuwG7r7nbRxOi/59O9C4cSyvvzmduJZ16N6tNWvXJfDg/00gPT2b+fPX8eZbM5jx49MUFDgYdMtrAAQFBfDKS7fj42P3bIdOUecmYcRvS6Pna6sJ8LUxqm/DomV931nL1H+5touxPyUwY91h17Y/diX920XyYLfajP0pgew8J49Mch3C1gj15+1Ble/RnD4+Np559HIGPzrZtQ+8qhWNG1TnjQ9/Ja5ZDN0ubcS6TQd58KnvSc/IZf7iHbz10WKmf3GXp0M/JT52G888eCmDh850bfs9mhZu+8sKt/16hdv+fK68/SvXcd+wy4HCbb9/K65/cGrhtl+HLhfVJTElk/cmrqJB7TD63T8FgEHXtuT63t5/0aJzgxDidxyl1wfrCfCx8ULvekXL+n2yke/udJ0zjp2/j5kbj5CT76Tb22vp36Y6D+jWJjk9FwLbLcvaCWCM+Rq4Fjj+MZP3AG9blpUKYFlW2Z/aUArz59MPykt53e5RGYQuXuzpEDzK1D+3732zvfydp0PwKOeof3k6BI8ygRGeDsFjHNO+9XQIHmWiq3k6BI8y9ev8faOzmKlRuRJ/Z5Jj8heeDsGjbN06eToEzzp29j5ZrCwcc5d7OgSP8rlr4ll9P8SLy+87K89ph17w/r3AkONe+qCwdAMAxpgBQE/Lsu4u/P1W4CLLsh48rs33wFbgEly3hDxnWdbs04nrby+dFM6ceB3ogOsek6XAI39mU0RERERERETOVmdrnfXja0meBh+gMdAFqAXEG2NaWZaV9k8/sCw1KSYC3wCxQA3gW+Crf/qFIiIiIiIiIuL19gO1j/u9VuFrx9sHTLMsK9+yrF24ZlU0Pp0vLUuSoqplWZ9bllVQ+PMFEPC37xIRERERERGRymoZ0NgYU98Y4wcMBKad0OZ7XLMoMMZUB5oAp3XXRVmSFLOMMU8aY+oZY+oaYx4HZhpjqhljzu0bb0VERERERETOQpZlFQAP4nqQxibgG8uyNhhjRhpjrils9hNw2BizEZgP/NeyrMOn871lKed9Q+F/7z3h9YG4alQ0OJ0ARERERERERLyV/SytSVEWlmXNBGae8Nrw4/7fAh4t/Dkj/jZJYVlW/TP1ZSIiIiIiIiIipSnL0z0CgH8Bl+KaObEIeM+yrJxyjk1EREREREREziFlud3jMyADeLPw95uBz4HryysoERERERERETn3lCVJEWdZVovjfp9fWBRDREREREREROSMKUuSYqUxpoNlWb8BGGMuApaXb1giIiIiIiIinmcryzMx5YwpNUlhjFmHqwaFL7DEGLOn8Pe6wOaKCU9EREREREREzhV/NZOiT4VFISIiIiIiIiLnvFKTFJZlJQAYYz63LOvW45cZYz4Hbj3pG0VERERERERE/oGy1KRoefwvxhgfoH35hCMiIiIiIiLiPezGeDqEc0qpJUCMMUONMRlAa2NMeuFPBpAE/FBhEYqIiIiIiIjIOaHUJIVlWWMsywoGXrEsK6TwJ9iyrAjLsoZWYIwiIiIiIiIicg4oy8NUphtjAgGMMbcYY8YbY+qWc1wiIiIiIiIico4pS5LiXSDbGNMGeAzYAXxWrlGJiIiIiIiIeAGbOTt/vFVZkhQFlmVZwLXAW5ZlvQ0El29YIiIiIiIiInKuKcvTPTKMMUNxPXK0kzHGBviWb1giIiIiIiIicq4py0yKG4Fc4C7LshKBWsAr5RqViIiIiIiIiJxz/nYmhWVZicaYKUDjwpcOAVPLNSoRERERERERL2D34voNZ6O/nUlhjLkHmAy8X/hSTeD7coxJRERERERERM5BZbnd4wHgEiAdwLKsbUBUeQYlIiIiIiIiIueesiQpci3LyvvzF2OMD2CVX0giIiIiIiIici4qS5JioTHmKaCKMeYK4Fvgx/INS0RERERERETONWV5BOkTwN3AOuBeYCbwYXkGJSIiIiIiIuINbCqcWaH+MklhjLEDGyzLagZMqJiQRERERERERORc9Je3e1iW5QC2GGPqVFA8IiIiIiIiInKOKsvtHuHABmPMH0DWny9alnVNuUUlIiIiIiIiIuecsiQpAoA+x/1ugJfKJxwRERERERER72E3KkpRkcqSpPCxLGvh8S8YY6qUUzwiIiIiIiIico4qNUlhjLkf+BfQwBiz9rhFwcDi8g5MRERERERERM4tfzWTYiIwCxgDPHnc6xmWZR0p16hERERERERE5JxTapLCsqyjwFHgptP5gjD/qNN5e6WW9/seT4fgUb55+Z4OwaOco/7l6RA8yjbsHU+H4FHOf/X0dAge4zya6+kQPMqnQVVPhyAeZO1b4+kQPMbW5Bx/GFxetqcj8KyAIE9H4FG2qHO7/2c7m0pSVKi/fASpiIiIiIiIiEhFUZJCRERERERERLyCkhQiIiIiIiIi4hXK8ghSERERERERkXOSXTUpKpRmUoiIiIiIiIiIV1CSQkRERERERES8gpIUIiIiIiIiIuIVlKQQEREREREREa+gwpkiIiIiIiIipbAZVc6sSJpJISIiIiIiIiJeQUkKEREREREREfEKSlKIiIiIiIiIiFdQTQoRERERERGRUthVkqJCaSaFiIiIiIiIiHgFJSlERERERERExCsoSSEiIiIiIiIiXkE1KURERERERERKYTMqSlGRNJNCRERERERERLyCkhQiIiIiIiIi4hWUpBARERERERERr6CaFCIiIiIiIiKlUE2KiqWZFCIiIiIiIiLiFZSkEBERERERERGvoCSFiIiIiIiIiHgFJSlERERERERExCuocKaIiIiIiIhIKVQ4s2JpJoWIiIiIiIiIeAUlKURERERERETEKyhJISIiIiIiIiJeQTUpREREREREREphM7q2X5H0ry0iIiIiIiIiXkFJChERERERERHxCkpSiIiIiIiIiIhXUE0KERERERERkVLYjPF0COeUSp2kWBS/gVGjvsHpdDLg+ksYMqSn2/JPPpnL5G9/xW63U61aEKNG30bNmhEA3D34Ddas2UW79o14//0HPBH+GWe/4m7sDdtj5edSMP0NrKSd7g18/PDp+zgmPAacTpzbl+FY8Llngv0HLMti9HfbiN94mABfG6MHtaBl7eAS7TbsTWfol5vIzXfSuUUET/VrjDGGRz5dz+7kbADSjxUQUsWHqY9fyI/LE/n4lz1F799yIJMp/7mA5rVKfra3WLR0B6Nem4PTYTHgmrYMua2j2/Jlq/Yw5rU5bNmRzLiRfenZrTkAm7Ym8twrs8nKysVms3HfHZfQ+/IWnuhCufno1mH0aXUJyRmptHp+kKfDOSMWrTrAqE9W4nRaDOjekCF93f9mefkOnnjzNzbsPEJYkD/jH+1IraggUjNyeWjsr6zfcYTrutRn+N3nF73n7hfmk5Kag8PhpH3zKIbf3R673fsn11mWxZhf9rFoZzoBPoZRvevRIrpqiXavL9rPtA1HSM9xsOzhtkWvf7/+MOMW7CcqyBeAm9pFMqB19YoKv8wsy2LUZ6uJX32QAD8fxtx3AS3rh5dot35nKkPf/4PcPAed28Yy7La2GGNIy8zj0TeWsj8lm5qRVXn1/y4mNMiPo5l5DPtgGXuSsvD3tTHq3gtoUjsUgM9mbePb+TuxLLi+W31u79Wkort9Uov+2MOod351rf+9mjPkpnZuy/PyHDzx0jw2bEshLCSA8U9fQa2YEAC27DzM8FcXkpWdhzGGye/0J7/AyS2PfF/0/sSULK65vDFP/evSiuxWmSxatpdR7y519b1nU4YMbOu2PC/PwROvLGDDtkOEBfszflh3asUEsy8xg6vu/pb6tVx/2zbNoxjxUCe3994//Cf2HczgxwkDKqo7p8yyLEb9bxXxqxIJ8Lcz5v4LS9kOjjD03WWu7eC8GIbdfl7hdpDLo6//xv6ULGpGBvLqQ67t4E/rdhxh4DPzGPd/HejZoXZFdq1MFv2RwKi3fnUd5/ZuwZCb27stz8tz8MSLc9mwNdm17g/vQa2YEH6cu4WPJq0qardl52G+e/8GmjeK5NWPfuOHOVtIz8hh5cx7K7pLp2TR77sY9fp81/rfJ44ht1zktjwvr4AnRs1iw5bC/o/oQ63YUPLyHTz7ys+s35KEzRieeqgrF53n+vvOnLeZ9z77HafTokvHBvzn/s6e6FqZWJbF6O93EL/pMAF+dkYPbErLkxybbtibwdCvt5Cb76Bz8wieuq4hxhg27c/kuclbyStwYrcZhvdvTOs6IfyxPY0HPllPrWoBAFzeqjoPXFmvgnsn8te8/4i0FA6Hk5Ejv2LChw8yfcazzJi+jO3bD7i1ad68NpOnPMW0H5+hR492jH3lu6Jlg+++kpdevrOiwy43tobtsYXHkvfe/RTMegefnvedtJ3j9+/J/+BB8j9+FFut5tgatDtpO28Uv/EwCSnZzH66AyMGNmPkt1tO2m7EN1sYObAZs5/uQEJKNos2HQHg1TvimPr4hUx9/EKubB3J5a0jAbj6/Jii11+6pQW1qgV4dYLC4XAyctxsJowfyPSv7mXGzxvYvivFrU1sTAhjnrmaPlfEub0eEODLS8OvYfrEe5nw6kDGvDaH9Iycigy/3H26dAY933zE02GcMQ6Hk5EfrmDCsC5Mf7U3M35NYPveo25tJs/bSUigH3Peuprb+zRl3BdrAPD3tfPQwNY8fmvbEp/72qOX8sO4Xvz4am+OpOcwe+neiujOaVu0K509qbnMvLsFz/Woy/M/7zlpuy4Nw/j6lmYnXdazWThT7mjOlDuae2WCAiB+dSIJiZn8NL4XI+9uz4iPV5603YiPV/D83efz0/heJCRmsmhNIgATpm2mQ1w0P73aiw5x0Uz4cTMA7/+wiWZ1w5j20pW8dP+FjP5/9u47Oqpq7eP490x6b6QBoXdCFRRBEIOCIgoIVuwF9dV7bdeCHRRsgFiuV8WuWFEQAQEJVYqCCKF3Qkvvvcyc948JSYZQopDMIL/PWlkwc/aZefac/py99/lsPQA7DuTw3eI9fPvCAGa+fAlL1iWRmJxfL3U9EavVxri3ljN1whBmf3gdcxbvYldipkOZ6T9vJTDAiwWfjeKWEZ2ZNHU1AOVWG4++tJCxD/Zj9ofX8dmkobi7WfD39WTme9dU/jWM9OeSC1o4o3onZLXaGPf2CqaOv5TZU0cyZ8ludiVmOZSZPm87gf6eLPjkWm65qhOTPvy9clqT6EBmvjuCme+OqJGgWPDrXnx9POqlHqdi2fpkEpPymT/lMsbd1YOxH/xxzHJjP1zHC6N7MH/KZSQm5bN8fcV28OM2esVGMH/KYHrFRjD1x62V81htNiZ+mUCfzpH1Upe/ymq1Me6NZUx9eQizP76BOYt2smvf0ev+Fvu6/8VN3DKyK5PeXwXAFRe3ZebU65g59TpeGWNP2rVvZT/nuej8Znz7jusmpo6wWm2MmxzP1IlXMfvzW5mzcDu79mY4lJk+ZxOBAd4s+PoObrnmHCa9uwyA735KAOCnT2/ho9dH8srbS7DZTLJyinjtnWV8MuVqZn9+K2mZBaxam1jvdautZdsySUwvZN6Ycxl7dRvGfb/zmOXGfr+Tcde0Yd6Yc0lML2T5Nvt6MnH2Hu4b2JQZj/TgX5c2Y+LsqpuX5zQPYsYjPZjxSA8lKMQlnbFJioSEfTRpGkFMTDienu4Mvrwn8fEJDmV69WqLj489Y96la3OSk6sO7uef3w4/P696jbkuWVqfi3XTEgDMwzvAyw/8jrrbUF6KuX+T/f+2cmzJuyEgrH4DPQWLNqUztGcUhmHQtVkQuUXlpOaUOJRJzSkhv9hK12ZBGIbB0J5RxG90vIA3TZN561O5vHvNE5M5f6Qw+Bjvu5KELYdp0jiUmEYheHq4MfjiDsQv2+FQpnF0MG1bRWJYHJumNW8SRrOYUAAiwwMIDfEjM7uw3mKvD8t3rSezINfZYZw2CbsyaRLlT0ykv31592lC/JqDDmXi1xxkWP/mAAw6P4ZVG5MxTRNfb3fOaR+Op6dbjc/197VfoJRbTcrKbZwprRgX78zhyo6hGIZBl4Z+5BVbScsvq1GuS0M/wv1d/yLseOL/OMzQvk3t+7vWYeQWlpKaVeRQJjWriPyicrq2DrPv7/o2ZeHawxXzH2JY36YADOvblIVrDwGw+1AuvTpGANCiUSCH0gpIzylmz6FcOrcKxcfLHXc3Cz3bh/PLUeuZMyRsT6VJwyBiGgba1//+rYhfsc+hTPzKfQwb2BaAQf1asurPQ5imyYq1B2jbIox2Le2JqJAg7xqthfYezCYzu4genaLrpT5/RcL2NJo0DCQmuqLuF7YkfqXjBVX8qn0Mu8Te4mVQv+aVdT+RgqIyPvl+I/fe0K3OYj9d4tceYmi/ZtW2g7LjbAdlVdtBv2aV63v82sMM69cMgGH9mlVuHwBfzNvFwHMbERroXW/1+SsStqXSpFEQMQ2D7Ms/rjXxK/c6lIlfsZdhA+3J2EEXtmTVuoM1lv+cRTsYHNe68nXXDlFEhPnVfQVOUcLWZJo0CiamYbC9/gPaEv/rLocy8ct3MezSjgAM6t+GVX/sxzRNdu/LoFf3JgCEhfgS6O/Npm3JHDycQ9PGIYSG2Fvf9T6nKQuWHvvC3xUs2pTB0HMqznubBtrPe3OPOu/NLSG/uJyuTQPt6/85UcRvsidzDCC/2ApAfpGViMB/znWP/POdMElhGMZGwzASjvG30TCMhBPNW9dSUrKIjqq6CI+KDCYlJeu45adPX0G/frHHnX7GCwjFzE2vep2XgREQevzyXn64teqJLdGpi/EvSckuISq46mQiKsjrmEmKyOCqnXBksDcp2Y5l1u7OJizAk2YRNZuI//yn6ycpUtLyiI6oaukRFRFISlreX/6chM2HKCuz0qRRzaaz4jpSMguJblC1rkaF+ZKSedRJemZRZRl3NwsBvp5k55We9LPveGExfe74AT8fDwa5YFPnY0nJLyUqoKq5dmSAJyn5J69rdb/syGL4x1t46Mc9JOX+tXnrS0pWEdGh1ZZ7qC8pR12cpWQVERXqU62MT2WZjJwSIkLs08KDvcmo2Fe2bRLML2vsF3AJuzI5nF5IckYRrWOCWLstnay8EopKylm6PomkDMfvc4aU9AKiI6ouqKLC/UjJKHAok5qRT3S4P1Cx/vt5kp1bzL6D2RiGwR2Pz+aqe77jg2rN34+Yu3gXl/VvheGCWbqU9ILKesFx6p5eSHS4/fepqrt9WR9MzmP4vT9w4yM/sXZjUuU8b36ylttGdMLby/V7/KZkFhEddtQ6ftT+LyXzGNtB5pHtoPio7aC4Yp5CfllziOsvaVXXVfjbUtLziY6otvwb+JOSdvTyL6gsU33dr+7nxbu4vFqS4kyRkpbveK4THkBKumPrrtT0qjLu7hYC/LzIzimibasIFq3YTXm5jYOHc9i8I4Wk1DyaNA5m74FMDiblUF5uY+Gvu0hK/evnT/UlJaeEqGrntPbzXsdjVmpO6VHnvZ6kVOzvxwxrycTZe7ho3Gpe/Wk3Dw1uXllufWIuwyauZfTUBHYmO65XcmwWw/hH/rmqkx2hhvydDzUMYzQwGuDd9x5m9Oi/9TGnzawff2Pzpv18/sXDTo3DZRgWPIY+jPWPOZCd4uxo6t2cdcduRbFhXw7enm60aeh/jLn+WVLT83hs3CxefuZKLBbX3UFJ3frwmYsoKbXynzdWsnpTCn26uN7d5NOtf8sgBrcLwdPdwrfr03jq5318dK1rjL1QVwzD4MhWPvrKdoz/bD3DxiygTUwQ7ZsF42YxaNkokLuuaMcdLy3D19ud9k3t75/Jyq0mf2xKYvp/R+Dt5c6tj/5Ex9bhnN+9cWWZuYt38coTA5wYZd2ICPVl0bTrCQn0ZtOONO5//hdmTx3JgeQ89iflMube8zmY7LoXZ3XBMIzKFmMTPl3Pf27o/I8//m3Ymoy3tzttmp85rWZPhxGDY9mzL4ORd31Bw6hAusU2xM1iEBTgzXOPXMzDz83GsBh0i23IgUPZzg63zny9MoknhrZkYOdwfl6fytPfbufje7rQobE/8U/3ws/LjaVbM7j/483MH3Ous8MVcXDCJIVpmpXtCg3DiAR6Vrz83TTN1BPM9z7wPoDJ4hO3O/ybIiNDSKrWfSM5JZvIyJp3hFeu3Mq77/7M5188jKfnmdv091gs3S/DretAAMyknRiBDaj8sQPCMPMyjzmf+2X/hy0rCeuan+on0FMwbflBpq+yN8+MbRJAcnbVHYLknBIighybrkUEeTm0nEjJLnbIMJdbbSzckMr0R3tytLnHSV64msjwAIfMf3JqLpHhtR9DI7+ghHse+YYH7+5P19hGdRGinEaRob4kpVd1yUnOKCSy2l1DgIhQH5LSC4kK86XcaiOvsJTgaq0NTsTL040BPRsTv+aQyyYpvlqXxvQEe0ux2Ghfkqu1EknJKyXSv3Z1BQj2qTrsjejcgMlLD52+QE/RtAW7+G6xvc9wpxahJGVWW+6ZhUSGOC73yBAfkqvdVU7OLKosExbkRWpWEREhPqRmFRFasa/09/XgpXvs+z/TNBnwwFxiKloqjLyoOSMvst9pm/z1RqLCHL/PGSIb+JGUWnWXLzmtgMijmqpHhPmTlJZPVLi/ff0vKCU40JuocD96dIomJMhejwvPa8KWnWmVSYptu9Mpt9qIbRNefxX6CyIb+JGUVnXn+Jh1b+BLUlrBUXX3wjCMym5esW3CiWkYyN5DOWzansamHenE3fQVVqtJZnYRN/1nNp9PdO7NpOqmzd/Jd4vs3Ro6tQxxaNGTnFlUY/8XGXqM7SD0yHbg7bgdVHTt2LQni4ffsI/fkJ1XyrL1Sbi7Wbi4p+scEyMb+JOUWm35p+cTGX708vcjKbXmun/E3EVnZisKgMhwf8dznbQ8Ihs43kSKaGAvExURQHm5jbyCEoKDfDAMgzH/vqiy3HX3flnZ1TWuT0vi+rQE4JtZCS6XjJ326yGm/2Zv+RQbE0BytXNa+3mv4/EuIsjzqPPeUiIr9vcz1ybz5DB7XS/tEs4z39q7Bvt7Vx0HL2wfxrjvd5KVX0bIGdxFUv55ajUmhWEY1wC/A1cD1wC/GYbh1FF3OnVqSuK+VA4eSKe0tJy5c9YQF9fZocyWLft57tlpvPO/ewkLC3RSpHXHtu5nyj56iLKPHsK24zfcYvsDYDRsAyUFUFCz+4tbvxvAyw/rLx/Wc7R/z6i+jSsHtRzQKZwf19j72q/fl0OAt9sxkxT+3m6s35eDaZr8uCaZuNiqgfFW7ciieaSfQ7cRAJvNZN561+/qAdCpfUMSD2Ry8HA2pWVW5i7cQlzf2t0JLi2zcv/j0xl6WefKJ36Ia+vUKpTEpDwOpuTbl/eK/cT1bOxQJq5HI2YusZ/Uz191gF6xkSdsvl5QVNWvu9xqY+m6w7Ro5Lr7yOu7h1cOdBnXKphZmzMxTZMNhwvw93L7S2NPVB+/YvGuHFqEuU5/9FEDWzHzpYHMfGkgA3o04sflifb93c4MAnw8KputHxER4oO/jzvrd2bY93fLExlwTkMA4ro3ZOZy+32GmcsTGXCO/eIrt6CU0nIbAN8t3kvPduGV45McaQp/ON3eFH5I7yb1Uu8T6dQ2gsRD2RxMyrWv/0t2Ede7mUOZuN7NmLnAPpDy/GW76dW1EYZhcEGPJuzcm0lRcRnlVhtrNhymZdOqbpBzFu3i8jjXbe7fqW04iYdyq+q+dDdx5zsuk7jzmzLzF/uFx/xle+nVtSGGYZCZXYTVal/OB5JySTyUQ0xUANdf0YHlX49i0efXM23yFTRrFORSCQqAUYNaM/OVgcx8pWI7WLavajvwPd524FG1HSzbx4Ae9vU97pyGzFy2D4CZy/YxoId9+4h/63IWvT2ERW8PYeB5jXn29u4ulaAA6NQugsRDOVXLf9FO4s5v5lAmrndzZi6wD4o7f+luenVrVLnvt9lMfl6yi8svOjOTFJ3aRZF4MJuDh3Ps9Y/fTtwFLR3KxF3QkpnzNgMwf8kOenVvgmEYFBWXUVhk39evWLMPdzcLrSpak2Rk2ZO/OXnFfDVjPSOHdKrHWp3cqAsaVQ5oOSC2AT/+UXHem5hLgLd7jXElIgK98Pd2Z31irn39/yOZuNiwymlrdtsH2l69M5um4fZtJy23tHLskoT9uZgmBPu5fvcvObvUdo18Cuh5pPWEYRjhwEJgel0FdjLu7m488+y13HHnm9isNkaM6E3r1g15841ZxMY2JW5AF1579QcKC0t48IGpAERHh/K/d/8PgFE3TGTPnmQKC0u4sN8TvDj+Jvr27eis6pwy2+4/sLQ8B8973rU/gnTOm5XTPG5/nbKPHoKAMNz7XIMt/QAet08GwPrHHGwbFjor7L/kwg5hLNuSwaAXVtkfxXRD1UX28Fd/Z8Zj9qZqz17dtuIRpFb6dgijX4eqZo5z16Ucs7XE2t3ZRAV7E9PA+XcOT8bd3cIzjwzijge/wmazMWJIF1q3COfN95cS2z6auL5t2LjlMPc/MZ3cvGIW/7qTtz9Yxuwv72Ze/BbWrt9Pdm4hM+banwDx0tNX0L5NlJNrdfp8efs4+rfpTgP/YA5MmMVzs6fy0UrXbzV0PO5uFp65swd3vGgfnXxEXAtaxwTx5tcJxLYMJa5nY0YOaMljb65i4P0/EeTvyeSH+lTOH3fvLAqKyigrtxH/+0E+fOYiggM8+b+Xl1FaZsU04dzYCK4b6LoXa9X1axHI8j05XDZ1Mz4eFl64rGnltBGfbOX7W+37hUlLDjJ3axbFZTYG/G8jV3UO474+DfliXSpLduXYm/56u/HiZc2cVJMTu7BrFMvWJzHwoZ/x9nJjwt1Vrb+GjVnAzJfsreievb07T767huJSK327RNGvq31bvuvKdjz05mq+X7yXhg18ef2B8wHYfSiPJ979HcOA1o2DePGuqsfS/nvKKrLzS3B3s/Dsbd0I9Kt9C5W64u5m4Zl/9eWOJ2bb1/9L29G6WShvfvI7sW3CievdnJGXteOxl+MZePM0ggK8mfzUJQAEBXhx68guXH3f9xgG9Du3Kf17Va0vPy/dxfsTLndW1U7K3c3CM/f35o4nf7bXfVBbe90/XWuv+/lNGXlpWx57ZQkDb/2GoAAvJj8ZB8Cajcm89dla3N0sWCwGz//7Aoc77GeKC7tF27eDB+bi7eXOhHuqbQePL2DmK9W2g//9bt8OukZXbQdD2/HQlFVV28GD5zulHn9H5br/+CxsVpMRl7WndfMw3vz4N2LbRBDXpzkjB7fnsQkLGXjj5/Z1/5mBlfOvSThMdIQ/MQ2DHD73tfdWMjt+B0Ul5Vx4zSeMHNyBf93qek393d0tPPNQHHc88r39XOfyWFo3b8CbH6wgtl0kcRe0YuTlnXjsxZ8ZeN2HBAV6M/l5+/ackVXInY98j8ViENnAn1eeHlz5uePfWMT2XfYB1f/v1vNp3uQE47c52YXtQ1m2NZNBL/2Ot4f9EaRHDJ+0lhmP2Pffz45ozZivt1FSZqNvu1D6tbPXadzVbZjw4y6sVhMvDwvjRtpvaC1ISOOrlYdxtxh4eViYdGN7lxyXR85uxslGgQb7AJqmaXaq9toCbKj+3vHUVXePM0HpS284OwSn8ujmmk3H64tx7plzMlQXLE+94+wQnMr2f5c6OwSnKf/t2I8HPlu4d2t58kL/ZBGu+VjXemMrd3YEzpOZc/Iy/2QRrtl1qN54nHmJsNPJ/H2ts0NwKsuQ9//RmY7Z+x75R17TDmk2ySWXW21bUswzDGM+8FXF62uBuXUTkoiIiIiIiIicjWqVpDBN81HDMEYAR9oQv2+a5oy6C0tEREREREREzja1HiXFNM3vge/rMBYREREREREROYvVKklhGMZVwCtABGBU/JmmabrucPAiIiIiIiIip8hSu4diymlS25YUrwJXmKa5tS6DEREREREREZGzV21TQilKUIiIiIiIiIhIXTphS4qKbh4Aaw3D+AaYCZQcmW6a5g91F5qIiIiIiIiInE1O1t3jiop/TaAQGFhtmgkoSSEiIiIiIiL/WBbDcHYIZ5UTJilM07wNwDCMT4EHTNPMrngdAkyq8+hERERERERE5KxR2zEpOh9JUACYppkFdKuTiERERERERETkrFTbJIWlovUEAIZhhFL7J4OIiIiIiIiIiJxUbRMNk4BVhmF8V/H6amB83YQkIiIiIiIi4ho0JkX9qlWSwjTNzwzDWAvEVbx1lWmaW+ouLBERERERERE529S6y0ZFUkKJCRERERERERGpE7Udk0JEREREREREpE4pSSEiIiIiIiIiLkFP6BARERERERE5Douhe/v1Sb+2iIiIiIiIiLgEJSlERERERERExCUoSSEiIiIiIiIiLkFjUoiIiIiIiIgch8UwnB3CWUUtKURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJGpNCRERERERE5Dg0JkX9UksKEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXILGpBARERERERE5Do1JUb/UkkJEREREREREXIKSFCIiIiIiIiLiEpSkEBERERERERGXoCSFiIiIiIiIiLiEuh84M/NAnX+Fq/K8+yrISXZ2GM7jF+zsCJzK8AtzdghOZfu/S50dglNZ3pnn7BCcxnpFV2eHIM6Un+/sCJyrtMzZETiNmZXj7BCcytIy1tkhOJVt01pnh+BcDUKcHYHUIYuhe/v1Sb92XTqbExQiIiIiIiIif5GSFCIiIiIiIiLiEpSkEBERERERERGXUPdjUoiIiIiIiIicoSwYzg7hrKKWFCIiIiIiIiLiEpSkEBERERERERGXoCSFiIiIiIiIiLgEjUkhIiIiIiIichwWQ2NS1Ce1pBARERERERERl6AkhYiIiIiIiIi4BCUpRERERERERMQlaEwKERERERERkeOwGLq3X5/0a4uIiIiIiIiIS1CSQkRERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZeggTNFREREREREjsNiGM4O4ayilhQiIiIiIiIi4hKUpBARERERERERl6AkhYiIiIiIiIi4BI1JISIiIiIiInIcGpOifqklhYiIiIiIiIi4BCUpRERERERERKQGwzAuNQxju2EYuwzDeOIE5UYYhmEahtHjVL9TSQoRERERERERcWAYhhvwX+AyoANwvWEYHY5RLgB4APjtdHyvxqQQEREREREROQ6Lcdbe2z8X2GWa5h4AwzC+BoYCW44q9wLwCvDo6fjSs/bXFhERERERETlbGYYx2jCMtdX+Rh9VpBFwoNrrgxXvVf+M7kCMaZpzTldcakkhIiIiIiIicpYxTfN94P2/O79hGBZgMnDr6YoJ1JJCRERERERERGo6BMRUe9244r0jAoBYYIlhGPuAXsCsUx08Uy0pRERERERERI7DYhjODsFZ1gCtDcNojj05cR1ww5GJpmnmAA2OvDYMYwnwH9M0157Kl6olhYiIiIiIiIg4ME2zHLgfmA9sBb41TXOzYRjjDMO4sq6+Vy0pRERERERERKQG0zTnAnOPeu/Z45Ttfzq+Uy0pRERERERERMQlKEkhIiIiIiIiIi5B3T1EREREREREjsPCWTtwplOc0UmK5at2M37KAmxWk5FXdmX0zb0dpq/5cz8vTVnA9t2pTBo3nEvj2gOwdUcyz782j4KCEiwWC/fc2ofBF3dwRhX+kuVrDzL+f6ux2WyMvLQto6/t4jC9tNTK4xOXsnlnOsGB3kwecxGNowIqpx9OzWfI6O+578bu3DGyEwBxN3+Dn68HbhYDNzcL3781tF7r9Fcs/20v499YjM1mMnJILKNvPM9hemlpOY+P/5nN21Pt9R87hMbRQZSWWXnutV/YtD0Fi2Hw5AMXcV63GId5731iBgcP5/DTZ7fWY43+vmW/bmX8KzOx2WxcfVUvRt8xwGH6mrW7mfDqTLbvTGLyKzdx6cCqdeXVyT+xdPkWbDaTPue34anHh2OcASMWL//zMOM/Xmdf/gNaMnq44zZbWmbl8bdWs3lPJsH+Xkx+uDeNI/zJyivhgYm/sml3JsP6N+fZO6ueiHTni4tJyyrGarVxTvsInr3zHNzczuwGZh/e9BRDOvUhNS+LTi+McnY4p4VpmkyYsZNlWzPx9rAw4fr2dIwJqFFu84E8xny1lZIyG/3ah/Lk8NYYhsHWQ3k8/90OSstsuFkMnh3Zhs5NAyvn27g/l+vfWMekmzowqGtEfVbtuEzTZPxn61m2PglvT3deuqcnHZuH1Ci3aU8WY977nZJSK/26RvPUzV0xDIPs/FIefnMVh9IKaRTuy+v/Pp8gf0/yCst49L+/kZRRiNVqctvlbRjRvzkAh9MLeXrqWpIzCjEMeO+xvjQO96vvqtewfN0hxn+w1r7tX9KK0SNiHaaXlll5fMoKNu/OJDjAk8n/6UfjSH9WrD/MpM/+pKzchoe7hcdu7U6vztGV87zw/u/8vtl+XHhwVFcG9W7qjOrVsHx9UrV9XQtGDzvGvu7t1Wzek2Wv74P2fR3AezO28P2iPVgsBk/d1p2+Xe31jbtvFn7eR471Bt+/PAiAh15fwd7DeQDkFpYS6OvJzNcurcfa1p5pmkz4dhvLNqfh7enGhJs70bFJYI1ymxNzGPPZJkrKrPTrGM6T17SrPMZ9sTiRL5cewGKBC2PDefSqtvVdjb9t2cqdjJ80F5vN5Oqh3Rl9az+H6WvW7WPC5J/ZviuFyeOv5tIBHR2m5+cXM/jat7n4wnY8+9iQ+gz9bzNNkwlfbmJZQop9md/RjY7NgmuU27wvmzEf/Glf5p0jefKGWAzD4I0ftrHozyQshkFooBcv3dGNiBBvflp1kA/m7sQ0wc/bnedu7ky7JkH1X8GTME2TCdM2smxDRf3v6n7s+u/NZswH6+zHgS6RPDmqE4Zh8NrXm1i8PhkPNwsxEX5MuLMbgX6elJXbeOajP9mSmIPVamNonyaMvqJN/VdQ5ATO2LNxq9XGuEnzmDr5OmZ/dTdzftnMrr1pDmWiowJ56ZkrGHKJ4wmNt7cHrzx7JbO/vJupr1/HS1MWkJtXXJ/h/2VWq41x/13J1BcHMvv9EcxZsoddiVkOZabP306gvxcLPr6GW4Z3ZNJHaxymv/z+b/Tt0bjGZ3/2ymBmvjPcpRMUVquNcZPjmTrxKmZ/fitzFm5n194MhzLT52wiMMCbBV/fwS3XnMOkd5cB8N1PCQD89OktfPT6SF55ewk2m1k534KlO/H18ay/ypwiq9XGuAk/8MH/RjNn5uPM/nkdu3YnO5SJjg7hpRevZ8hl3R3eX7d+L+vW72XW9EeZ/cNjbNx0gN/X7q7P8P8Wq9XGuA/+YOpT/Zn9+mDm/JrIrgM5DmWmx+8h0M+TBW9fwS1D2jLpiw0AeHm48cB1nXnspq41PnfKwxfw46TL+On1wWTmFjNv1YH6qE6d+mTVHC596yFnh3FaLduaSWJaEfOePI+x17Rl3PTtxyw3dvp2xl3TlnlPnkdiWhHLt2UCMHHWbu4b1IwZj/bkX5c1Z+JPVeu81WYy6afd9G5bMwHgTMvWJ5OYnM/8yZcx7s5zGPvRumOWG/vRH7xwZw/mT76MxOR8lm+w7wumztpGr9hI5r9+Gb1iI5n60zYApi3YRavGgfz48kA+e6Y/r07bQGm5DYDH//c7dwxpy9yJl/LtCxcTFuhVP5U9AavVxrj3fmfqs3HMfusK5izfx64D2Q5lpv+yi0B/Txa8O4xbrmzPpM/sv1VIoDf/e/oifnrzCl5+oDePTVlROc+70zcRFuzN/HeGMeetKzk3NrI+q3VcVpuNcR+uZeqTFzL79cuYs2I/uw4eta9bVLGve2sIt1zelknT7Pu6XQdzmLtyP7MnX8YHT13IuA/XYrXZKuf77Lk4Zr52aWWCAuD1h/ow87VLmfnapQw8L4ZLzqt5juAqlm1OJzG1kHlj+zL2ho6M+2rLMcuN/WoL40Z1ZN7YviSmFrJ8czoAv23PIH5DKjOf6s3sZy/g9oub1WP0p8ZqtTHu1dl88MZNzPn2fmYv2MiuPakOZaKjgnjpueEMGdTpmJ8x5d1F9OzmGom42lqWkEpiSgHzXh7A2Fu7MO7zhGOWG/tZAuNu68K8lweQmFLA8o323+aOy1ry4wsXMWNcf/p3ieSdWfZjR+MGvnz2RB9mvXgR917Zhuc+3VBvdforliWkkJicz7xXL2bsbV0Zd5w4x366nnG3dWXeqxfbjwMJ9vr37hjBrPFx/Dg+jmZR/rw/eycA89ccorTcxqzxcUwf259vluzlUFpBfVVLpFbO2CRFwpbDNGkcSkyjEDw93Bh8cQfil+1wKNM4Opi2rSIxLI53iZs3CaNZTCgAkeEBhIb4kZldWG+x/x0J29NoEh1ITHSgvb4XtiB+1X6HMvGr9jPs4lYADOrbnFXrD2Oa9ovxhSv30TgygFZNXetEvLYStibTpFEwMQ2D7fUf0Jb4X3c5lIlfvothl9rvHAzq34ZVf+zHNE1278ugV/cmAISF+BLo782mbfYT+YLCUj75Zi333tyrfit0ChI27adpkwbENA7D08Odyy/tRvziTQ5lGjcKpV2bhliOWvcNw6C0pJyysnJKS8spK7fSIKzmHWlXk7ArkyZR/sRE+tuXf58mxK856FAmfs1BhlXcER50fgyrNiZjmia+3u6c0z4cT0+3Gp/r7+sBQLnVpKzcxhnQoOSklu9aT2ZBrrPDOK0WbUpnaM8oDMOga7MgcovKSc0pcSiTmlNCfrGVrs2CMAyDoT2jiN9ovzgxDMgvLgfs/0YEVSUlv1h+kEu6hBPm71qJyvg/DjO0b1N7nVuHkVtYSmpWkUOZ1Kwi8ovK6do6zF7nvk1ZuPZwxfyHGNbXfkEyrG9TFq49BNh/i4KickzTpLC4nCB/T9wtBrsO5mK12ujTyX6x7uftjo+X8xtbJuzMoEl0ADFRAfZt/4KmxP/mmEyM//0Awy5qCcCg3k1ZlWDf9ju0CCUy1BeA1k2CKSm1UlpmBeCHhbsqW2RYLAYhgd71WKvjs+/rAuz7Onc3BvduQvyaQw5l4tceqtrX9Yph1aYUTNMkfs0hBvdugqeHG40j/GkSFUDCrsxafa9pmsxbtZ/L+7juReyiDakM7dXQvk20CCa3sOz4+4EWwfZtoldD4jfYL9i+XnaAuwa1wNPDfurrCkm42krYfJCmMaHENA61H/cv6UT80m0OZRo3DKFd6ygsxziQbdp6mIzMfPqc16q+Qj4tFv2ZzNDeje3LvGWofZlnO95UTM0utu8HW4bal3nvxsSvs5/j+ft4VJYrKrFypLV+t9ahBPnZ9/ldWoaQnOmaNyoXrUtmaJ8m9vq3OkH9i8vp2qqi/n2aEL8uCYA+nSJwr2gd2qVlCCkVxxADg6KScsqtNorLbHi4WfCr9luJuIKTJikMw+hjGIZfxf9vNAxjsmEYTj+KpaTlER1RdXEVFRFISlreX/6chM2HKCuz0qSRa1+8p2QUEl2t2W1UA19SMhyznqkZBUSH25t8urtZCPDzJDu3hIKiMqZ+m8B9N3ar8bmGAXc8OY+r7p/JN3O31ZjuKlLS8h2Xd3gAKen5DmVS06vKuLtbCPDzIjuniLatIli0Yjfl5TYOHs5h844UklLt68qbH6zgtut64O3t/JPx2kpJySEqMrjydWRkMCmpOcefoZpuXZpxXs9WXDDgeS4Y8Dx9e7ejZQvXuIN4IimZhUQ38K18HRXmS0rmURdsmUWVZdzdLAT4epKdV3rSz77jhcX0ueMH/Hw8GNQr5qTlpf6l5JQQFVx1QREV7HXMi5PIoKoykUFepFSUGTO8NRNn7eaisSt5ddYuHrrcfkGbkl3Cwo1pXN+7UT3U4q9JySoiOrTaOh/qW3mCWb1MVKhPtTI+lWUyckqICLFPCw/2JqPitxg1sBW7D+fS777ZXPn4fJ68uRsWi8G+pDwC/Dz51+srGT7mF16dtgFrtRZnzmLf9qsd+8L8jrHtFx617XuQnee4fsxftZ8OLULx9HAjN9++X3jjyw1c9fAcHnh1KenZjp/pLCmZRUSHVd/X+Rx7Xxd2dH1La84bWjWvgcEd45dw1ePz+WahY4IfYO3WNMKCvGkW7bpJ65TsEqJCqpJJUSHex7xgi6y2r4gM9iYl274u7Est5I9dWVz7ympumvw7G/fV7rjpClLS8oiKrOqOEBkZSEpa7ZLRNpuNV6bM4/EHBp28sItJyS523MeF+JCaddQyzyomMrRqvYgM9SGl2nox5futXPTwAn5afZB/D2tX4zu+X7afvp1co5vf0VKyiogKq76P9z5msjoypKpMZKh3jWMFwA/LE+lbkYQe2LMhPl7u9HtgHgMems/tl7Um2MUS9a7IYhj/yD9XVZuWFP8DCg3D6AI8AuwGPqvTqOpJanoej42bxYSnr6hxx/mf5O0v1nHrVbHHzJJ+OWkIP/x3GFNfHMSXP21lzcYkJ0RYt0YMjiUq3J+Rd33BhLcW0y22IW4Wg607U9l/OJtL+rV2doj1JnF/Grv3prD0l+dYtvA5Vv++k7V/7HF2WE714TMXsXzqcErLrKzelOLscKQOfL3iEE8Ma8Xi53rzxNDWPP21PSH70sydPDKk5T96/w/2FlRHavhrQgrtmwaz7L9DmPHSQF74ZB35hWWU20z+2JbGYzd05rsXB3AgtYAZS/c5M+zTZuf+bCZ9uo6x99pbzFltNpIzCunWLpwfJl9O17bhvPrxH06Osm59+cIAfnhlEFOfvJAv5+9izRbHrgJzVrh2K4rTodxqklNYxtePncejV7XhoQ82VLY2/Sf7cvoa+vVp7ZDkOJs8OKI9iycP5IpejZkWv9dh2m9b0/l++X4eucb1x6U7Fe/O2o6bxcIVve3duTbuycLNYrB0yqX8MmkgH8/bxYFUdfcQ11Kb28flpmmahmEMBd42TfNDwzDuONEMhmGMBkYDvDv5VkbfctFpCNVRZHhA5d1wgOTUXCLDa38HIL+ghHse+YYH7+5P11jXu4t2tMgwX5Kq9RdLTi8kMsxxQLOIMD+S0vKJCvej3Gojr6CU4EAvEralMX/5Pl77YA15BaVYDPDydOPGKzsQWXGHKizYh4t7NyVhezo9O0XXa91qIzLc33F5p+UR2cDfoUxEA3uZqIgAystt5BWUEBzkg2EYjPl31Tp43b1f0iwmlDXrD7BpWwpxV0/FarWRmVXITf/6hs/furbe6vV3REYGkZySXfk6JSWbyIjanXz8Er+RLp2b4udrv9PU94J2/LlhHz3OaVEXoZ42kaG+JKVXdclKzigkstrdFYCIUB+S0guJCvO1r/+FpQQH1O7OgJenGwN6NiZ+zSH6dHG99f9sNO3Xg0xfZU+axjYJIDm76s54cnYJEUGOTbUjqrWcAHvriyMtK2auSebJ4fZk5KVdw3nmG3uSYtOBPB75zN6vPbugjGVbM3BzM7i4U3jdVewEpi3YxXeL7UnDTi1CScqsts5nFjrcLQOIDPEhudpd9uTMqjtqYUFepGYVERHiQ2pWEaEVv8WMpXu560r7QIJNo/xpHO7HnsN5RIX60K5pMDGR9v3qxT0asWFXBtC8Lqt8UvZtv9qxL6PgGNu+ff8Q1aDi2FdYRnCAvb7J6QXc//ISXnmwD00qWgkEB3jh4+XGwF72boCX9m7K98doXeAMkaE+JGVU39cVHXtfl1F9X1dGcIBnzXkzq+Y90u0lLMibi3s2ImFXJj072O8el1tt/PL7AYexKlzFtCX7mb7C3rUvtmkgydXuoidnFRMR7NhNJ6Jaywmw34k/0rIiKsSLS7pGYBgGnZsFYzEgK7+M0FoeJ5wpMjyA5JSqlh8pKblEhtccNPRY/kw4wB/rE/lq+hoKCkspK7fi6+PJf/41sK7CPSXT4vcyfWkiALHNgx33cVlFRIQctcxDvEmp1l0jJbOIyOCa3beGnN+Iu1//jX8Nt7em2H4gh2c+Xs97D/cixIVaEUxbuIfpFQni2OYhJGdU38cXV7aQOyIixMeh5URKZrHDsWLG8kSWrE/m48f7VA4gO3v1QS7oFIGHu4WwQC+6tw5l095sYiKcP1CyyBG1aUmRZxjGGOAmYI5hGBbghB2XTNN83zTNHqZp9qiLBAVAp/YNSTyQycHD2ZSWWZm7cAtxfWs3Mm1pmZX7H5/O0Ms6Vz7xw9V1ahtO4uFcDibn2eu7dA9xFSdYR8T1asLMihOt+cv30quLve/mtElDWPTZtSz67FpuHtaR0dd15cYrO1BYXEZ+ob3Za2FxGSvWHaJNM9fs9tKpXRSJB7M5eDjHXv/47cRd0NKhTNwFLZk5bzMA85fsoFd3ez++ouIyCovKAFixZh/ubhZaNQ/j+uFdWT7zHhZ9dxfT/nsdzWJCXD5BAdCpYwz7EtM4cDCD0rJy5sz7k7j+sSefEWgYHcKatbspL7dSVmZlzdo9Z0R3j06tQklMyuNgSr59+a/YT1xPxwHe4no0YuYS+12S+asO0Cs28oRPLSkoKqtsNllutbF03WFaNKrdSZ/UvVEXNGbGoz2Z8WhPBsQ24Mc19nEG1u/LIcDH/ZhJCn9vN9bvy8E0TX5ck0xcbAP7tEAv1uzOBmD1ziyahttP4BY+cz7xz9r/BnYJ59kRbZyWoAB7V4yZLw1k5ksDGdCjET8uT7TXeWcGAT4exzw59fdxZ/3ODHudlycy4JyGAMR1b8jM5fYT/ZnLExlwjj0ZHx3my6pN9rvo6TnF7E3KIybCj04tQ8krLCMz136Bt3pzKi1dYHvo1DqsYtuvOPb9mkjcuY7dsuLOjWHmYvtgqPNXJtKrk338ktz8Uu5+cTGP3NSd7u2rmnMbhsFFPRvz+yZ7v/VVCcm0jHGNu8ydWlbs61LzKS23MnflfuJ6ON5IiTun2r5u9QF6dbTv6+J6NGLuyv2Ullk5mJpPYlIenVuFUlhcTn7FMbCwuJwVCcm0qfYkg1UbU2jeMJCoal1FXMWo/k2Y8VRvZjzVmwFdIvlxtX2srfV7sk+8H9iTbd8mVh8mrot92Q/oEslvO+xjdOxNKaDMahLif2b0w+/UoRH79mdy4FCW/bj/y0bi+tXsunAsk14cyZLZj7Bo1sM8/sAghg3u4rIJCoBRA5ozY1x/Zozrz4Du0fy48qB9me/OtO8Hj5GY8vdxZ/3uTPsyX3mQuG5RAOxLruoWvOjPZFpE25OwhzMK+ffba3jlru40j3K84eVsoy5uwYwX4pjxQpy9/ivs46ut35VpX+ePVX9vd9bvqqj/iv3EdbfXf3lCCh/O3cU7D/ZyGGMoOsyH37bYx2wqLClnw+6syt9GxFUYJ2vqZhhGFHADsMY0zeWGYTQB+pumWasuH2bmZ3XWlm7pyl1MmPILNpuNEUO6cM+tF/Dm+0uJbR9NXN82bNxymPufmE5uXjGenu6Eh/kx+8u7mTVvI0++OJtWLRpUftZLT19B+zZRpzfAnOSTl/kLlv5+gAnvrcZmMxkxsA33XN+VNz/7g9jWDYg7vyklpeU89upStu7OICjAi8ljLiIm2vEk863P1+Hr48EdIztxICmX+8fFA/aRo4dc1JJ7ru96+gL2Cz59nwUsXbWHCW8usS/vy2O55+ZevPnBCmLbRRJ3QStKSsp57MWf2bozlaBAbyY/fzkxDYM5mJTDnY98j8ViENnAnxefGESjKMff5WBSDvc+PuO0PoLUCKq7FjpLl29hwqs/YrXaGDHsXO4dfQlv/PdnYjvEMOCiWBI27ef+Bz8mN7cILy93GjQIYM6Mx7FabYwdP501f+zBMAz69mnHmEfr5qku5o41Jy/0Fyxdd5gJFY/lGxHXgntGdOTNrxOIbRlKXM/GlJRaeezNVWzdl0WQvyeTH+pTeVc47t5ZFBSVUVZuI8DXgw+fuYjgAE/ueWkZpWVWTBPOjY1gzK3dKweZOlWWd+adls/5q768fRz923SngX8wKbmZPDd7Kh+t/KleY7Be0fW0fp5pmrzw/U5+3ZZhfwzbde2IrXj04PDX1jDj0Z4AbNqfy5ivtlFSZqVv+zCevsr+CNI/9mQzYcZOrDYTL3cLz45sW+MRpmO+3Er/DmGn5RGkRmSDkxc6CdM0eeGTP1m+IRlvLzcm3N2TTi3sAz4PG7OAmS/ZLzI27snkyXfXUFxqpW+XKJ65tRuGYZCVV8JDb64mKb2Qhg18ef2B8wn29yQlq4gx764hLbsITLjrynZceYG9mf+KjSm88sUGTEw6Ng9h3J098HT/G9uD7+kdhHLp2kNM+GgNNqvJiItbcc/VnXjzy/XEtgoj7twY+7Y/5Ve27skiKMCTyY/0JSYqgP99m8D732+iabXj4IfPDyAs2IdDqfk8PmUFuQWlhAZ6M+HfvWl4uh63Wlp2SrMvXXeYCZ/+aT/WXdSCe67qyJvfbLTv63o0stf37dVs3Vuxr3uwd+W+7t0fNvP94j24WSw8eWs3+nVryIGUfO6f+CtQcay/oCn3XFX1eMon/ruarq0bcN3AUx9U0czIOnmhv/vZpskLX2/l1y3pFY8gjSW2qT3ZMnz8SmY8ZX8M/abEHMZ8an8Ead+ODXj62vb2QaPLbTz9+Sa2HsjDw93gsava0qtd2GmN0dKjz2n9vOqWrtjBhMk/24/7V3bn3tsv5I1344lt34gBF7YjYfMh7n/sq6rjfqg/c779l8Nn/PDTn2zaeqjOHkFq27T2tH6eaZq88MVGft2YWvkI0tjmwQAMf3YJM8b1B2DT3mzGfPgnJaVW+naK4Okb7Y/g/Pfba9ibnI/FgIZhvjx/S2ciQ3x4+qP1/PJHEg0rxntwczOY/tyFpx6w5fQ+j8A0TV74PIFfE1Lw9nJnwp3diK14FPXwZxYx44U4ADbtzWLMVPsjSPt2juTpmzpjGAaDHv2F0nIbwRXJuC4tQ3n+1q4UFJfz1Afr2HXI3kJ5eN8m3DH41Ls+W3q98o/uO7kt67V/ZP+wdiGPuuRyO2mSAioTFecCJvZkRa2vvusySeHyTnOS4oxzmpMUZ5q6TFKcCU53kuJM46wkhSs43UmKM83pSFKc0U5zkuKMc4pJijNZXSYpzgR1maQ4E5zuJMUZ5zQnKc40SlKcmVw1SVGbp3vcCfwOXAWMBFYbhnF7XQcmIiIiIiIiImeX2gyc+SjQzTTNDADDMMKAlcBHdRmYiIiIiIiIiJxdapOkyADyqr3Oq3hPRERERERE5B/NcoLB2OX0O26SwjCMhyv+uwv4zTCMH7GPSTEUSKiH2ERERERERETkLHKiMSm6AQHAFcBM7AkKgB+BvXUbloiIiIiIiIicbU7U3aM78DgwAnirfsIRERERERERkbPViZIU7wHxQHOg+jOFDOytKlrUYVwiIiIiIiIicpY5bpLCNM03gTcNw/ifaZr31mNMIiIiIiIiIi7BME40SoKcbif9tZWgEBEREREREZH6oJSQiIiIiIiIiLgEJSlERERERERExCWcaOBMERERERERkbOaRff265V+bRERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyCxqQQEREREREROQ7D0L39+qRfW0RERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZegMSlEREREREREjsOiMSnqlX5tEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXIKSFCIiIiIiIiLiEjRwpoiIiIiIiMhxGLq3X6/0a4uIiIiIiIiIS1CSQkRERERERERcgpIUIiIiIiIiIuISNCaFiIiIiIiIyHFYDN3br0/6tUVERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJGpNCRERERERE5DgM3duvV/q1RURERERERMQl1HlLCtsv8XX9FS7LCA9ydghOZTQ1nR2CU1mXrnR2CE5lyylxdghOZb2iq7NDcBq3n9Y7OwSnsv1nuLNDcCpz935nh+Bc7m7OjsBpjA4dnB2CU9lWL3N2CE5lhJzl571tOjk7BJF/DLWkEBERERERERGXoDEpRERERERERI7DYujefn3Sry0iIiIiIiIiLkFJChERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyCBs4UEREREREROQ5DA2fWK/3aIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuASNSSEiIiIiIiJyHBbd269X+rVFRERERERExCUoSSEiIiIiIiIiLkFJChERERERERFxCRqTQkREREREROQ4DEP39uuTfm0RERERERERcQlKUoiIiIiIiIiIS1CSQkRERERERERcgsakEBERERERETkOi8akqFf6tUVERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJGpNCRERERERE5DgM3JwdwllFLSlERERERERExCUoSSEiIiIiIiIiLkFJChERERERERFxCUpSiIiIiIiIiIhL0MCZIiIiIiIiIsdhMXRvvz7p1xYRERERERERl6AkhYiIiIiIiIi4BCUpRERERERERMQlaEwKERERERERkeMwdG+/XunXFhERERERERGXoCSFiIiIiIiIiLiEM7q7h2maTJibyLKdWfh4uDFheEs6NPSrUW7Kwv3MWp9OTnE5fzx9buX7n6xIYvq6VNwtBiG+7rw4vCWNgr3qswp/iWmaTPh2G8s2p+Ht6caEmzvRsUlgjXKbE3MY89kmSsqs9OsYzpPXtMMwDN6evYvvfj1IaIAnAA8Obc2FseGUWW088/lmthzIxWozGXpeQ0Zf2qK+q3dSy9ceZPx7q7HZTEYOasPoa7o4TC8ts/L4xGVs3pVOcIAXk8dcROPIgMrph1PzGXLPD9w3qht3jOhU+b7VamPkA7OICPPjvbGX1Ft9ToVpmkyYs49lOyrW/REt6dDQv0a5Kb/sZ9afafZ1/9nzKt9fuzeXl+buY0dKAROvacOg2LD6DP+UmabJS4sOsnxPLt7uBuMHN6NDpG+Ncm8sP8SszZnkFltZ82DXyvdnbspg0pJDRPh7AHB993BGdm5QX+H/ZaZpMmHGTpZtzcTbw8KE69vTMSagRrnNB/IY89VWSsps9GsfypPDW2MYBlsP5fH8dzsoLbPhZjF4dmQbOjet2nds3J/L9W+sY9JNHRjUNaI+q3bafXjTUwzp1IfUvCw6vTDK2eGcFmf7vs80TSZ8v4Nlm9Ptx74bO9Ax5hjHvv25jPlis33979iAJ0e0wTAMHvpoI/tSCwDILSon0MedGU/0IquglAc/3MimxFyGnRfNM9e0q++qnVRdHfdLy208/+VmNiXmYjHgyWvac26b0Pqu3kkt/20f499ags1mY+TlsYweda7D9NLSch6fMJ/NO1IIDvRh8nODaRwdRGmZlecmLmTT9hQsFoMn/9Wf87rFAHDnoz+QllGA1WrjnM6NePbBONzcXPOeXV2t+yu2ZTB51i7Kym14uFt4dGhrerV1veVvmibjP9/AsvXJeHu58dLoHnRsHlKj3Ka9WYx5by0lpVb6dY3iqZu6YBgG8347yNs/bGH34Ty+HRtHpxb2eVdsTGHSN5sq6//Y9Z3o1dG1j33LVu1k/KR52Gw2rh7andG39HWYvmbdPia8Po/tu1KY/OJILh3Q0WF6fn4xg6/7Lxdf2I5nH728PkMX+Vtcc69cS8t2ZpOYUcS8B7oy9srmjP1pzzHLXdQ2hG/ujq3xfvtoX767O5aZ93VmUMdQJi1IrOuQT8myzekkphYyb2xfxt7QkXFfbTlmubFfbWHcqI7MG9uXxNRClm9Or5x2y4CmzHiqNzOe6s2FseEAzP8jmdJyG7Oe6cP0MefzzfIDHMooqpc61ZbVamPcO6uYOm4gs9+9ijlL97Brf5ZDmenzdxDo78mCD6/mluGxTPporcP0l6f+Tt8ejWt89mc/bqFFTHBdhn/aLduRTWJGMfMe6sbYYS0YO2vvMctd1C6Eb+7tVOP96GBPJoxoyeUufGF+Isv35rI/q4S5d3bg+UFNeeGX/ccs179lMF/feOwLj0vbhfD9re35/tb2Lp2gAFi2NZPEtCLmPXkeY69py7jp249Zbuz07Yy7pi3znjyPxLQilm/LBGDirN3cN6gZMx7tyb8ua87En3ZXzmO1mUz6aTe929Y88TsTfbJqDpe+9ZCzwzhttO+DZVsy7Me+Z3sz9rr2jPtm2zHLjf1mG+Ou78C8Z3vbj31bMgB4/fZOzHiiFzOe6MXALhFc3MV+MeLl7sa/L2/Jo8Nb11td/qq6Ou5/9+tBAGY904cP/92DV6Zvx2Yz675Cf4HVamPclEVMfXUYsz+9hTnx29m1L8OhzPQ5mwkM8GLBl7dzy9XdmfTerwB8N3sjAD99cjMfTRrBK+8sq6zflOcv58ePbuKnT24mM7uIeUt21m/F/oK6WvdD/Dz4391dmfXk+bx0Y0ce/3xzvdXpr1i2IZnE5HzmTxrEuDu6M/aTP49ZbuzHf/LCnd2ZP2kQicn5LE9IAaB140DefOB8erR1PMaHBHjxv0d689PLl/Dy3T147N01dV6XU2G12hj36lw+eGMUc765j9nzN7FrT6pDmeioIF56dhhDBtY85wOY8t5ienZtWh/h/mNZDMs/8s9VuW5ktbBoWxZDu4ZjGAZdYgLIK7aSlldao1yXmADCK+4iVHdeiyB8PN0A6BwTQEpOzXldyaINqQzt1RDDMOjaIpjcwjJSc0ocyqTmlJBfbKVri2AMw2Bor4bEb0g9zifaGYZBUamVcquN4lIrHu4W/Lzd6rIqf1nCjnSaNAwkJjoQTw83BvdrQfwqxwvT+NX7GXax/WRz0AXNWLXhMKZpPylZuDKRxlH+tGoS7DBPcnoBS9cc4OpBbeqlHqfLoq2ZR6375X9p3W8U4k3bKD8shlEf4Z52i3fmcGXHUHv9G/rZt/38shrlujT0I7yitcSZbNGmdIb2jLJv+82CyC0qP/623yzIvu33jCJ+o/1CxTAgv7gcsP8bEVS1Tnyx/CCXdAknzL/menImWr5rPZkFuc4O47TRvg8WbUxj6LnR9vW/+YnW/3K6Nq9Y/8+NJn5jmkMZ0zSZ92cKl58TBYCvlxvntAzGy911T4Xq6ri/Oymf89raW9CFBXoR6OvOpv05dVaPvyNhazJNGgUT0zDYvu7HtSX+190OZeJX7GbYoA4ADLqwNavW7cc0TXbvy6RXd3vLibAQXwL9vdi03X7h6u9nbzFbbrVRVmbFlQ+DdbXud4gJJCLI/ju0jvajpMxKaZmtfir1F8T/kcTQC5ra698qjNyCMlKzHG+ipWYVkV9URtdWYfb6X9CUhWsPA9CyUSAtGtZsddihWTCRIT6APZFRUmqltMxa9xX6mxI2H6Jp41BiGoXi6eHO5QNjiV/meLOiccMQ2rWOwmKpuUJv2nqYjMx8+vRqWV8hi5yyWh2ZDcPofoy/loZhOLW7SGpuKVHVTrYjAz1Jyf17iYYf/kilb+vg0xRZ3UjJLiEqxLvydVSIN6nZxQ5lUrOLiazWZSUy2JuU7KoD2rQl+xn64gqe+mwTOQX2i7qB3SPx8XSj3xNLGPDUMm6/uBnBfq51wZKSUUB0g6quPFEN/EjJKHQok5pRQHS4vYy7m4UAX0+yc0soKCpj6vQE7ruhW43PnfDeb/zn9p4Yx9ipu7LUvNO37p+JUvJLiaqWfIkM8CQl/6/V/5cdWQz/eAsP/biHJBf/7VJySoiqtl1HBXsd80Q1Mqjath/kRUpFmTHDWzNx1m4uGruSV2ft4qHL7ScqKdklLNyYxvW9G9VDLeTv0L7vGMe+463/wVVlIoO9HI59AGt3ZxMW4EmziJpdw1xVXR332zUOYHFCKuVWGwfTC9m8P5fkTMfPdbaU9HyiI6ouMKPC/UlJz3cok1qtjLu7hQA/L7JzimnbsgGLVuyhvNzGwaQcNu9IJSk1r3K+O/7zA32GvoefryeDLnTdljT1se4vWJ9K+8aBeHq4XrIuJauI6DCfytdRoT6kZBUfVaaYqNCjy9S+NfD8NYfo0MyeCHNVKWm5REVWdfOJjAgkJa12yXibzcYrb8zn8X8PrKvwROpEbfdI7wCrgfeBqcAq4Dtgu2EYNdZ6wzBGG4ax1jCMtVMX7jhtwdaVWRvS2HS4gNsvaOjsUOrUdf1iWPBCP2Y82ZvwIC9e/d6ehd24Lwc3i8HSl/vzywt9+XjhPg6kFZ7k084cb0/7k1uHdcTPx/GO+uLf9hMW7E1sa9du6i+nX/+WQSwYHcuM2zpwftMAnvp5n7NDqlNfrzjEE8Nasfi53jwxtDVPf21vMvzSzJ08MqTlMe+8yJlP+z5Hc/5IrryTfLY43nH/qt6NiAz25uqXV/PSd9vo2iL4H7UfGDE4lqgIf0be/SUT3lpCt47RuFWr34cTr2L5D6MpLbOyet0B5wVaT4637u9MymfSrF2Mvc71xmOpDzsP5jLp602Mvb27s0OpM19OX0O/3q2Jigxydigif0ltW0IcBu4wTXMzgGEYHYBxwGPAD8CC6oVN03wfe0ID6ze3nNZOjl/+lsx3f9ibMXZq5E9ytS4aKbmlRAb+tRYAK3fn8P7SQ3x6e0c8XbDJ57Ql+5m+wt53NLZpIMnVMsjJWcVEVMueA0QcdQclpdodlgaBVXdarr6gMff8dx0As39P4oKODfBwsxAW6EX3liFs2p9LTLjr3G2KDPMjKb2g8nVyegGRYY7xRYT5kZRWQFQDP8qtNvIKSwkO9CJhexrzf93Hax+tJa+gFIsBXp5upKQXsmj1fpauOUhpmZX8wlIefW0prz16YX1Xr1a+XJ3Md2vtzVVPx7p/pvlqXRrTE+zdF2KjfUmu1r0lJa+UyL/QXSHYp2rXN6JzAyYvPXT6Aj1Npv16kOmrkgCIbRJAcrXtOjm7pLKp7hER1VpOgL31xZGWFTPXJPNkRb/7S7uG80xFv+ZNB/J45DN7H/fsgjKWbc3Azc3g4k7hdVcx+UvO1n3ftGUHmL7Svl3GNjnq2He89b9aC4OU7BKH1gXlVhsLN6Qx/VHHgRddUX0c993dLIy5uurC9PrXfqNZZM2Bx50psoG/Q+uH5LR8Ihs4DhAdUVEmKiKA8nIbeQUlBAd5YxgGY+7vX1nuuv/7mmYxjuPueHm5M6BPS+JX7KZPT9fpq19f635yVjH/mprAyzd1pIkLne9N+2U33y22j7PVqUUISdXGSEvOLCIyxHH9jwzxJjnz6DI+nExyRiH3T1nFK/f0oElkzYHHXUlkeCDJKVUtJ1JSc4kMrzmA6rH8ufEgf6xP5Kvv11BQWEpZuRVfH0/+c7/rDpbsqgwXHr/hn6i2SYo2RxIUAKZpbjEMo51pmnuMeu7Md8N5Udxwnj0bvHR7FtN+S2ZwpzASDuYT4O12zP73x7MlqYCxs/bw3s3tCHPRfuuj+jdhVP8mACzZmMaXS/YzuEcUG/bmEODjfsyDlb+3G+v3ZNOleRA/rj7MqIvs86fmVB3cflmfQuuKp0FEh3rz2/YMhp7XkMKScjbszebmONc5YAN0atOAxMM5HEzOIyLMl7nL9jDxsf4OZeLOi2Hmwp10ax/B/F/30auzvR/ntNeqRjF+64t1+Pp4cOMV9j6sj9zWA4DfEpL46PtNLnWSfrQbekVxQ69q6/7qZAZ3rlj3vf7aun8mur57ONd3t188L92dw1d/pnFZuxASkgrx93L7S2NPpOWXVZZfvCuHFmHeJ5mj/o26oDGjLrAPdrhkczpf/nqIwd0i2JCYe+Jtf18OXZoG8uOaZEb1tc8fEejFmt3ZnNsqhNU7s2gabj+BW/jM+ZXzj/lyK/07hClB4WLO1n3fqH4xjOpnH1NgyaZ0vlx2gMHnRLJhXy4B3sdb/91ZvzeHLs0C+fH3JEZdGFM5fdX2TJpH+jo0nXdV9XHcLyq1Ypomvl7urNiajpvFoFW0a12odWoXReLBLA4m5RDRwJ+5i7Yz8ZnLHMrE9WnBzPlb6BbbkPlLd9KrW4x9nK3iMkwTfH08WLEmEXc3C62ahVFQWEpBUSkRYf6Ul9tYunov53R2re5u9bHu5xaWcc+763n4ylZ0bxFcL/WqrVGXtGTUJfYuiUv+TGLaL7u5/PzGbNidSYCvBxFHJSAiQnzw9/Fg/a4MurQM5cdfE7lx4InHXsgtKOXuSSt55NpYurdx/RZlnTo0ZN+BDA4cyiIyIoA5CzYx6YURtZq3erkfZv/Jpq2HlaCQM0JtkxSbDcP4H/B1xetrgS2GYXgBNUerqyf92gSzbGc2l05Zj7eHhfHDq3ZKw99JYMb/dQZg4vxE5mzMoLjMxkUT1zGiezj3x8UwcX4ihaU2HvrGPrJzwyAv/juqrVPqUhsXxjZg2aY0Bj27vOJRZFVPLBk+fiUznuoNwLPXd2DMp/ZHkfXt2IB+He074Ik/bGfbwTwMAxqF+vD8KPvjiW64sAlPfb6JIeN+BROGn9+Ito1rDjTkTO5uFp6593zueHo+NpvJiIGtad00hDc/X0ds6wbE9WrCyEFteGziMgbe8R1BAV5Mfry/s8OuM/3aBLNsRxaXTv4Tb08L469qVTlt+NsbmHG//RGFE+clMich3b7uv/oHI86J4P4BMWw8mM+/v9xOblE5i7dl8faiA/z0765Oqs1f169FIMv35HDZ1M34eFh44bKqpNqIT7by/a3tAZi05CBzt2ZRXGZjwP82clXnMO7r05Av1qWyZJe9m1OQtxsvXtbMSTWpnQs7hLFsayaDxq+2b/vVmuYOf20NMx7tCcCzI9ow5qtt9m2/fRj92tsfKTfu2rZMmLETq83Ey93COBd81OLp8uXt4+jfpjsN/IM5MGEWz82eykcrf3J2WH+b9n1wYccwlm1JZ9C4lfZH8N5Y9Wi94S+vZsYTvQB49tp2lY9h7Ns+jH4dqh6tPPePlGM2dx/w3K8UFJdTVm4SvzGND/6vm0tdrNfVcT8zr5Q731yLxWIQEeTFK7ce+4kAzuTubuGZB+O44z8/2Nf9wR1p3bwBb364kth2kcT1acnIwbE8Nn4eA2/4iKAAbyY/NxiAjKxC7nx0BhbDIDLcj1eeuhSAouIy/m/MLErL7Emac7vGcN2VnZ1ZzROqq3V/2rID7E8v5H/z9vC/efYn431wX3fCXOxmx4Vdo1i2IZmBj8y3r/+je1ROG/bkQmZOuBiAZ2/txpPvr6W41ErfLpH062Kv7y9rDvHiZxvIzCvhnokraNc0iA8f78u0X3azPyWfd2Zs5Z0ZWwH48PELCAtyzSSmu7sbzz46mDv//TlWm8mIK7rRumUEb7y3iNj2DRnQrx0JWw5x/2Nfk5tbzOLlO3jr/SXM+eY+Z4cu8rcZR0YAP2Ehw/AB/g+4oOKtFdjHqSgGfE3TzD/evKe7u8eZxAg/u/t/GU1d6+5EfbOt2+rsEJzKdtTgXmcbt4Zn7/bv9tN6Z4fgVLb/DHd2CE5l7j72I4HPGu6uOwBfXTM6dHB2CE5lJmx0dghOZYScvcc9AKON6yX76lXQ9f+cgW2OIa9sxj/ymjbAY7hLLrfatqSwAFNM05wEYBiGG+BlmqYNOG6CQkRERERERESktmqbpIgHLqYqIeGDfbDM3nURlIiIiIiIiIgrsNT6oZhyOtT21/au3qWj4v+uMxSwiIiIiIiIiJzxapukKDAMo/IhwoZhnAMUnaC8iIiIiIiIiMhfUtvuHg8C3xmGcRgwgCjsT/gQERERERERETktapWkME1zjWEY7YAjz+fcbpqm0x49KiIiIiIiIlIfDENjUtSnWv3ahmH4Ao8DD5imuQloZhjGkDqNTERERERERETOKrVNCX0MlALnV7w+BLxYJxGJiIiIiIiIyFmptkmKlqZpvgqUAZimWYh9bAoRERERERERkdOitgNnlhqG4QOYAIZhtARK6iwqERERERERERdg0ZgU9aq2SYrngXlAjGEY04A+wG11FZSIiIiIiIiInH1q+3SPBYZh/AH0wt7N4wHTNNPrNDIREREREREROavU9uke8aZpZpimOcc0zdmmaaYbhhFf18GJiIiIiIiIyNnjhC0pDMPwBnyBBoZhhFA1WGYg0KiOYxMRERERERFxKqPWz5uQ0+Fk3T3uBh4EGgJ/UJWkyAXerruwRERERERERORsc8IkhWmabwBvGIbxL9M036qnmERERERERETkLFTbdivJhmEEABiG8bRhGD8YhtG9DuMSERERERERkbNMbZMUz5immWcYxgXAxcCHwP/qLiwREREREREROdvU6hGkgLXi38uB903TnGMYxot1FJOIiIiIiIiIS7AYGjizPtX21z5kGMZ7wLXAXMMwvP7CvCIiIiIiIiIiJ1XbRMM1wHxgkGma2UAo8GhdBSUiIiIiIiIiZ59aJSlM0ywEUoELKt4qB3bWVVAiIiIiIiIicvap1ZgUhmE8B/QA2gIfAx7AF0CfugtNRERERERExLkMjXRQr2r7aw8HrgQKAEzTPAwE1FVQIiIiIiIiInL2qW2SotQ0TRMwAQzD8Ku7kERERERERETkbHTSJIVhGAYwu+LpHsGGYdwFLASm1nVwIiIiIiIiInL2OOmYFKZpmoZhXA08DORiH5fiWdM0f6nr4EREREREREScyWJoTIr6VKuBM4F1QLZpmnrsqIiIiIiIiIjUidomKc4DRhmGkUjF4JkApml2rpOoREREREREROSsU9skxaA6jUJEREREREREznq1SlKYpplY14GIiIiIiIiIuBpDY1LUK/3aIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuITaDpz5t+2d8kddf4XLavHKQGeH4FQZjRo5OwSnCj2U4uwQnMq9ha+zQxAnsf1nuLNDcCrLxBnODsGp9mwrcnYITuXp7+nsEJwm65OGzg7BqVqt3u/sEJzKs2uks0Nwqt9itjs7BKfqFeTsCOSfpM6TFCIiIiIiIiJnKsN0dgR1xHB2AMem7h4iIiIiIiIi4hKUpBARERERERERl6AkhYiIiIiIiIi4BCUpRERERERERI7HtP0z/2rBMIxLDcPYbhjGLsMwnjjG9IcNw9hiGEaCYRjxhmE0PdWfW0kKEREREREREXFgGIYb8F/gMqADcL1hGB2OKvYn0MM0zc7AdODVU/1eJSlERERERERE5GjnArtM09xjmmYp8DUwtHoB0zQXm6ZZWPFyNdD4VL9USQoRERERERGRs4xhGKMNw1hb7W/0UUUaAQeqvT5Y8d7x3AH8fKpxuZ/qB4iIiIiIiIj8Y9Vy/IYzjWma7wPvn47PMgzjRqAHcOGpfpaSFCIiIiIiIiJytENATLXXjSvec2AYxsXAU8CFpmmWnOqXqruHiIiIiIiIiBxtDdDaMIzmhmF4AtcBs6oXMAyjG/AecKVpmqmn40uVpBARERERERERB6ZplgP3A/OBrcC3pmluNgxjnGEYV1YUew3wB74zDGO9YRizjvNxtabuHiIiIiIiIiLH8w8dk6I2TNOcC8w96r1nq/3/4tP9nWpJISIiIiIiIiIuQUkKEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXIIGzhQRERERERE5nrN44ExnUEsKEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXILGpBARERERERE5HpvGpKhPakkhIiIiIiIiIi5BSQoRERERERERcQlKUoiIiIiIiIiIS9CYFCIiIiIiIiLHY2pMivqklhQiIiIiIiIi4hKUpBARERERERERl6AkhYiIiIiIiIi4BI1JISIiIiIiInI8GpOiXqklhYiIiIiIiIi4BCUpRERERERERMQlKEkhIiIiIiIiIi5BSQoRERERERERcQn/qIEzGzw0Bt/efTGLi0l94SlKdmw9btnoV9/CvWFjDtw4vB4jPDWmaTLh6y0s25iKt6cbE27rQsemQTXKbU7MYczHGygptdKvUwRPXtcBwzAqp3+8YA+vfreVlZMvISTAs/L9jXuzuf7llUwa3Y1B50TXS51Oh9UrdjHllfnYbCZXDO/GTXf0cZg+49s/+OGbNVjcLPj6ePLYs5fTvGW4k6L9++pq+X84fzezVx8GoNxmY09SPitev4RgP88an12fTNNk/GfrWbY+CW9Pd166pycdm4fUKLdpTxZj3vvdXt+u0Tx1c1cMwyA7v5SH31zFobRCGoX78vq/zyfI35Oc/FKeen8N+1MK8PKwMP7unrSJsf+On/28k+8W78E04eq45txyWZv6rnaluqp/XmEZj/73N5IyCrFaTW67vA0j+jcH4HB6IU9PXUtyRiGGAe891pfG4X71XfUalq89yPj3VmOzmYwc1IbR13RxmF5aZuXxicvYvCud4AAvJo+5iMaRAZXTD6fmM+SeH7hvVDfuGNGp8n2r1cbIB2YREebHe2Mvqbf61JUPb3qKIZ36kJqXRacXRjk7nDoT+q/H8T3vAsziYtJeeYbSndtqlIl85R3cwhpguLlTnLCOjDcmgM2GZ8u2hD38NIanJ1itpE+ZQOm2TU6oxd8XNPoRvM/pg1lSTNYbYynbvb1GmbDn38QtNAzc3Cnd/CfZ774KtqpB3/yHjSLojgdJGnUxttyc+gz/b/tz9UE+mvIbNqvJgCvacNXNnR2mz5+xjXnfb8XiZsHbx517Hu9DTPNgysqsvPfKSnZvS8ewGNz+4HnEdj9zznGqcx80Gkvrc6CshLIf38BM3n1UAS88rn4cIyQabDZsO3+nPP5TAIwmHfEYdBdGZDPKvn8V29aVTqjBX2OaJhN+3MOybZl4e1iYcG1bOjb2r1Fu88E8xnyzg5IyG/3ahfLk0BYYhsG2w/k8//0uCkutNArx5rUb2uLvbb/02X64gOe+30l+iRWLAd/9uxteHq577zbht8NMe2sdNpvJhZe3ZMioDscst2bpAd5+9leef28gzduFsWlNEt++vwFrmQ03DwvX3duVDt2j6jn6fwgNnFmvXHdr/It8z++LR0wT9l89mNSXnyf8sWeOW9bvwouxFRXWY3Snx7JNaSSmFjBvfH/G3tSJcdOOfWI19ouNjLupE/PG9ycxtYDlm9IqpyVlFrFicxrRoT4O81htJpO+30bvDg3qtA6nm9VqY9KEeUx65wamzbiXhfM2sXd3mkOZgYNj+fz7e/j029HccNv5vDXxFydFe2rqavnfMaglM57ry4zn+vLwVe3o2SbM6QkKgGXrk0lMzmf+5MsYd+c5jP1o3THLjf3oD164swfzJ19GYnI+yzckAzB11jZ6xUYy//XL6BUbydSf7Bcy7/24lXZNg5n1ykBeufdcJny2HoAdB3L4bvEevn1hADNfvoQl65JITM6vl7oeS13Vf9qCXbRqHMiPLw/ks2f68+q0DZSW2w+8j//vd+4Y0pa5Ey/l2xcuJizQq34qewJWq41x76xi6riBzH73KuYs3cOu/VkOZabP30GgvycLPryaW4bHMumjtQ7TX576O317NK7x2Z/9uIUWMcF1GX69+mTVHC596yFnh1GnfM67AI9GTTh44xWkTxpH2ENPH7Nc6thHOXznNRy67SrcgkPwu3AgAKF3P0T2p+9y+K5ryfr4HULvfrAeoz91Xuf0xr1hE1Luvoqs/04g+N4njlku85UxpP57FKn3XYslKASfPgMqp7k1iMSr23mUpybVV9inzGq1MXXiap6aNJApXw7n14V7OLA326FM34EteP2L4Uz6dCjDRnXikzd/B2DhrB0AvP7FcJ6bMohP31qDzWbWdxVOmaXVORhhDSl9+27KZv8Xj8vvPWY566oZlL5zL6XvP4Alpj2WVufYJ+SkUfbjFGwbl9Zj1Kdm2bYsEtOLmPd4D8aObM24H3Yds9zYH3YxbmRr5j3eg8T0IpZvtx8jnvluJw8PbsasR87h4tgwPlxyEIByq8ljX23j+RGtmP2fc/j0ns64uxnH/GxXYLPa+GzKHzzyan9e+nQwq+MTObSvZnKxqLCMBdO307JDWOV7AUFePPRSP8Z/MpjRY3rx3vjV9Rm6yN/2j0lS+PW7iLyfZwFQsjkBi38AbmE1L7gNHx+Cr7+ZzI/fq+8QT9mi9SkM7dUIwzDo2jKE3MIyUrOLHcqkZheTX1xO15YhGIbB0F6NiF+fUjn95W+28J+R7TGO2hd/sWgfl5wTRViA8y9K/oqtmw7TOCaERo1D8PBwY8ClHVm+xPGukp9/VZ2Ki8pq1P1MUZfL/4g5vx9m8LkN67IatRb/x2GG9m1qr2/rMHILS0nNKnIok5pVRH5ROV1bh9nr27cpC9cerpj/EMP6NgVgWN+mLFx7CIDdh3Lp1TECgBaNAjmUVkB6TjF7DuXSuVUoPl7uuLtZ6Nk+nF/WHKzHGjuqq/obBhQUlWOaJoXF5QT5e+JuMdh1MBer1UafTpEA+Hm74+Pl/MZ2CTvSadIwkJjoQDw93BjcrwXxq/Y7lIlfvZ9hF7cGYNAFzVi14TCmab8IWbgykcZR/rRqEuwwT3J6AUvXHODqQc5rLXO6Ld+1nsyCXGeHUad8+1xE/oKfACjZuhGLXwBuoTWP9WZhgf0/bu4Y7h6AfX0wMbH42e/EWvz8sWak1ZjXlfn0upDCRXMAKNu+CcMvAEtIWI1yZtGR+ruBu8eR6gMQdOdD5Hz8FphnzoX6ri3pRDUOIKpRAB4eblxwcQvWLHfcD/hWS64XF5VXHucO7s0mtqJ1aFCoD37+nuzell5vsZ8ulra9sG5YBIB5aDt4+YH/Ua3rykuw7dto/7+tHFvSbowA+/ph5qRipu47o5b7os0ZDD0nwn4cbBpIbnE5qbmlDmVSc0vJL7bStWmg/Th4TgTxmzIA2JdeRM8W9paSvduE8MtG+3JfsSOLttF+tGto3xeE+HngZnHdk8M9WzOJbORPREN/3D3cOC+uCet+rXl+8sOHCVx+Q3s8PN0q32vaJpSQBr4ANGoeRFmJlbJSa73FLvJ31SpJYRhGb8MwbjAM4+Yjf3Ud2F/lHh5JeUpy5evytBTcwyNrlAsb/S+yv/oUs7i4xjRXl5JVTFS1O+BRId7HvEiNDPGufB0Z4kNKlr1M/PpkIkO8aRcTWONzF/6ZzPUXNq3D6OtGWmouEVFV9YmICCQtJa9Gue+/XsPVl7/NO6/H8+Djg+ozxNOmrpb/EUUlVn7dlMbAc1yjGWBKVhHRob6Vr6NCfUk56iI9JavI8TcJ9aksk5FTQkSIfVp4sDcZOSUAtG0SzC9r7BfsCbsyOZxeSHJGEa1jgli7LZ2svBKKSspZuj6JpAzH76tPdVX/UQNbsftwLv3um82Vj8/nyZu7YbEY7EvKI8DPk3+9vpLhY37h1WkbsLrA3caUjAKiG1R1OYlq4EdKhmNLuNSMAqIruqW4u1kI8PUkO7eEgqIypk5P4L4butX43Anv/cZ/bu+J4cInplKTe4MIylOrEq/W9BTcGkQcs2zkq/+jyYzF2IoKKFhqb0GX+farhN79EDHfzCf0nkfImvpmvcR9uriFhWNNr1b/jFTcwo5d/7CxbxL9xQLMogKKVsYD4H1eP6wZaZTv21kv8Z4umWmFNIis2g+EhvuSkVZQo9zP32/l/0ZO5/N31nD7Q+cB0LRVKGt/PYC13EbK4Tx2b88gPaXmvK7OCAjDzK1Krph5GZUJiGPy8sPS5lxsezfUQ3R1IyW3lKjgqhtNUUGepFYcy45IzSkhMqiqTGSQFykViYxWkb7Eb7YnLOZvSCMpx/7+vvQiMAzunLqRq6as44PFB+q6KqckK72Q0Iiq84HQcF+y0h3PB/btyCQztZCu5zc67uesXXqApm1CHJIYIq7qpEkKwzA+ByYCFwA9K/56nGSe0YZhrDUMY+3XKZmnJdDTwbN1WzwaxVCwNN7ZodS7ohIr78/dzb+urHnX8KVvNvPIVe2w/INP1kdc15Pv5tzPvQ/G8cnUX50dTr070fI/YnFCCt1ahbhEV4/TzTAMjqzdo69sR25BGcPGLOCLBTtp3ywYN4tBy0aB3HVFO+54aRl3vbKc9k2DXfrOyl9Rvf6/JqTQvmkwy/47hBkvDeSFT9aRX1hGuc3kj21pPHZDZ757cQAHUguYsXSfM8M+ZW9P+5Nbh3XEz8fD4f3Fv+0nLNib2NZnVvc2+WtSHruXAyMGYHh44t3tXAAChl5DxjuvceDaQWS+8xoNHn3euUHWoYzn/k3SzZdheHji1bkHhpcXAVffRu60d50dWp25bER73pk+kpv+rwfff2K/OB8wpDVhEb48dsdPfDzlN9p2Cv9Hn+8AYFjwGPEo1t9/wsxOOXn5f6jx17Thq5VJjJjyJwUlVjwqunRYrSbr9ubw2g3tmPZ/XVi4KYNVO7NO8mmuy2Yz+eq/f3Ld/9VMyB9xcG8O37y3gVsf6VmPkf3D2Gz/zD8XVZu2vD2ADqZZ+/Zhpmm+D7wPsOv82Dq7FRc04joCrxwJQPHWTbhHVt0Bdg+PpDzNccfsHdsVr3YdafrDfAw3N9xCwmj03485dN9tdRXiKZu2eB/Tl9kzvLHNg0jOrMqcJmcVExHs7VA+Iti78s452O+0RoZ4cyCtgIPphQwbt7zi/WJGvLicb57sw6Z9OTwy9U8AsvNLWbYpFTeLwcXdXOOO+omERwSSmlzVvDk1NZfwagPmHe3iS2OZOP7n+gjttKiP5R8eZP+Mub8f5nInd/WYtmAX3y3eA0CnFqEkZVbdMU/OLCQyxHEslcgQH8ffJLOoskxYkBepWUVEhPiQmlVEaMWdFn9fD166x36QNk2TAQ/MJSbCfodu5EXNGXmRfRDJyV9vJCrM8fvqWn3Uf8bSvdx1ZTsMw6BplD+Nw/3YcziPqFAf2jUNJibS3vz14h6N2LArA2hel1U+qcgwP5LSq+56JqcXEBnm61AmIsyPpLQCohr4UW61kVdYSnCgFwnb05j/6z5e+2gteQWlWAzw8nQjJb2QRav3s3TNQUrLrOQXlvLoa0t57dEL67t6UgsBw64l4PKrACjdthn3iEiO3Et1axCJNT31uPOaZaUUrliMX5+LKP5jNQEDryDzrVcAKFiygAb/ea6uwz9lfoOvxnfQMADKdm7BrUFVK1G3sAisGcevP2WlFK1eivd5F2LNysAtsiERb35pn7dBBOFTviDt4VuxZWfUZRVOWWi4r0Prh8y0QsJOMKhvn4tb8P5rqwBwc7dw2wPnVU57cvRsGjapOei0K3LrMRi37vbWn7bDOzECG1T23DECwjDzjr3c3Ifcj5lxGOtvs+op0tNn2orDTP/N3jI6NiaA5OyqlhPJOaVEBDl2S44I8iKlWuuKlJwSIgPtN1taRPjy4Wj7YMl70wpZus1+4zQy2JMeLYII8bMnsPu1C2XLoQLOb11zcGpXENLAl8zUqvOBzLRCQhpUnQ8UF5ZxcG82Lz9o7w6Uk1nElCeX8+CEvjRvF0ZmaiFvPr2c0U/2IrLR8c+RRVxJbZIUm4AowOVGWMr5/mtyvv8aAN/e/QgaeT35v/yMV8fO2ArysWY49jnMnfENuTO+AcA9qiHRE//r0gkKgFEXNWPURc0AWJKQwpeLExl8bkM27MkmwMf9mBep/t7urN+dRZcWwfy4+hCj4prRpnEgKyZXjV4/4IlFTH/qAkICPFn4clzl+2M+2kD/LhFnRIICoF3Hhhzcn8nhg1mERwYSP28zz73k+MSWA4kZxDS1N4lcuWwnjZuEOiPUv6U+lj9AXmEZa3dk8uqdXeurasc0amArRg1sBcCSP5OYtmAXl58fw4ZdmQT4eFR2XzgiIsQHfx931u/MoEurUH5cnsiNFfPHdW/IzOWJjL6yHTOXJzLgHHsTyNyCUry93PF0t/Dd4r30bBeOv6/9RCUjp5iwIG8Opxfyy5pDfDMujvpUH/WPDvNl1aZUerQLJz2nmL1JecRE+BFY8eSPzNwSQgO9WL05ldgWzj9h69SmAYmHcziYnEdEmC9zl+1h4mP9HcrEnRfDzIU76dY+gvm/7qNX52gMw2Daa5dXlnnri3X4+nhw4xX2EdEfuc3eIPC3hCQ++n6TEhQuLG/mN+TNtB+7fXr1JXDYdRQsmodX+06YBflYMx2P9Ya3DxZfP/v7Fjd8evWjOME+8Gx5RhreXXpQvGEt3t3PpezQ/hrf52oK5n5HwdzvAPDq0Qf/IddQtGwBHm1jMQvzsWU5Xqga3j4YPr729y1uePe8gNLNf1KeuJvkm6q6O0Z+8CNpD998Rjzdo1X7BiQdzCXlcB6h4b78unAPDz7vuM0ePpBDw4onNf2x8gDRFV0bS4rtY/B4+3iw4fdDWNwsxDQPru8q/C3WtXOxrp0LgKV1D9x6DsG2eRlGo7ZQUgj5Ne/+u190I4a3H2U/vVXf4Z4Wo/o0ZFQf+w2TJVsz+XLFYQZ3DWfD/jwCvN2ICHRs7RkR6Im/txvrE3Pp0iSAH/9IrZw/I7+UMH9PbDaTdxce4Npe9rFJLmgTwodLDlJUasXDzcKaPTnc0u/43SScrXm7UFIO5pGWlE9IAx9+W7Sfe57pXTnd19+T/84aUfn6pQfiue7erjRvF0ZBXimTn1jKNXd3oU2nM+/JdnL2Om6SwjCMn7APtRQAbDEM43eovHmBaZpX1n14tVe4chm+vfvS9LufsZUUkfpi1dM9Yj6dzoFbRjoxutPjwk4RLNuYxqCnltgfQXlr1eO3ho9dzozn+gLw7KhY+yMoy2z0jQ2nX+w/d6fk7m7hoTGX8vC9X2K1mQwZ1oUWrSKY+t8ltOsYTd/+bfn+67WsWb0Hdw83AgK8efoFl1p1a60ul//CP5Pp3bEBvi4wUOIRF3aNYtn6JAY+9DPeXm5MuLuqieKwMQuY+ZJ9tP5nb+/Ok++uobjUSt8uUfTrak+w3XVlOx56czXfL95Lwwa+vP7A+QDsPpTHE+/+jmFA68ZBvHhXVe+1f09ZRXZ+Ce5uFp69rRuBTuz6Ulf1v/eqDox5dw1XPD4fTPjP9Z0JqXiKx2OjunDr+KWYmHRsHsLVcS3qudY1ubtZeObe87njaftjhkcMbE3rpiG8+fk6Yls3IK5XE0YOasNjE5cx8I7vCArwYvLj/Z0dtlN8efs4+rfpTgP/YA5MmMVzs6fy0cqfnB3WaVW0ejm+511A4y9mY5YUk/bKs5XTGk79hsN3XYvh40Pk+DcwPDzBYqHozzXkzbJf5KdPHEfYvx4DNzfM0lLSJ41zVlX+lpK1K/Du0YfI92dUPIK0Kv7wN6aR9sAoDG8fwp6ZbB8w1GKhJGEtBT//4MSoT52bu4U7H+7FCw8twGY1iRvSmiYtQvhq6jpatWtAz75N+Hn6VhLWJuHubsEvwJP7n7YfE3OyinjhoQUYhkFouC//frafk2vz99h2rsXSqgee979vfwTprDcqp3mOfoPS9x+AgDDc+16LLe0AnqOnAGBdMwfrnwswGrbG85onwdsfS5uemBeOovTd+5xUm9q5sF0Iy7ZmMujltXh7WphwTVWX1eGT1zHj4e4APDu8VeUjSPu2C6FfO3uCfc6faXy50n6P9ZJOYVzV094KKcjXg1v7NubqN9djYG9J0b+9697AcnO3cNODPXjtP0uw2Uz6DW5B4+ZB/PBhAs3ahdK9T82nVx2xcMYOUg7l8eOnm/jxU/tT4R6deBGBId7HnUfEFRjH68VhGMYJbyuZplmrZxjVZXcPV9filYHODsGpMs/t7uwQnCr092M/MvJsYfj5nryQ/DMFn93NSS0TZzg7BKfas815A866Ak//f964PrWV9cmIkxf6B2v13xXODsGpPLvWHLD+bPL7ucdPFpwNekU9/88e7CXvu3/mNW3A1S653I572/RIEsIwjOZAkmmaxRWvfYCzey8kIiIiIiIiIqddbR5B+h1QfehPa8V7IiIiIiIiIiKnTW2SFO6maZYeeVHx/7O3LaOIiIiIiIiI1InajJKXZhjGlaZpzgIwDGMokH6SeURERERERETOfKbt5GXktKlNkuIeYJphGG8DBnAAuLlOoxIRERERERGRs85JkxSmae4GehmG4V/xOr/OoxIRERERERGRs05tWlJgGMblQEfA2zDsTykxTfPMeri4iIiIiIiIiLi0kw6caRjGu8C1wL+wd/e4Gmhax3GJiIiIiIiIyFmmNi0pepum2dkwjATTNMcahjEJ+LmuAxMRERERERFxOg2cWa9q8wjSoop/Cw3DaAiUAdF1F5KIiIiIiIiInI1q05JitmEYwcBrwDrABD6oy6BERERERERE5OxTm6d7vFDx3+8Nw5gNeJummVO3YYmIiIiIiIjI2ea4SQrDMK46wTRM0/yhbkISERERERERcQ2maXV2CHXCcHYAx3GilhRXnGCaCShJISIiIiIiIiKnzXGTFKZp3lafgYiIiIiIiIjI2a02A2diGMblQEfA+8h7pmmOq6ugREREREREROTsc9IkhWEY7wK+wEXYn+oxEvi9juMSERERERERcT6bzdkRnFUstSjT2zTNm4Es0zTHAucDbeo2LBERERERERE529QmSVFU8W+hYRgNgTIguu5CEhEREREREZGzUW3GpJhtGEYw8BqwDvuTPT6oy6BERERERERE5Oxz0iSFaZovVPz3e8MwZgPepmnm1G1YIiIiIiIiIi7A1JgU9am2T/foDTQ7Ut4wDEzT/KwO4xIRERERERGRs0xtnu7xOdASWA9YK942ASUpREREREREROS0qU1Lih5AB9M0zboORkRERERERETOXrV5uscmIKquAxERERERERGRs9txW1IYhvET9m4dAcAWwzB+B0qOTDdN88q6D09ERERERETEiTRwZr06UXePiYABvAIMq/b+kfdERERERERERE6b4yYpTNNcCmAYhseR/x9hGIZPXQcmIiIiIiIiImeXE3X3uBf4P6CFYRgJ1SYFACvqOjARERERERERObucqLvHl8DPwEvAE9XezzNNM7NOoxIRERERERFxBRqTol6dqLtHDpADXF9/4YiIiIiIiIjI2ao2jyAVEREREREREalzSlKIiIiIiIiIiEs40ZgUp0XLH/9d11/hsgzvIGeH4FTZF73g7BCcKuzbO50dgjhTfr6zI3Aac/d+Z4fgVHu2FTk7BKdq0e7sfgBY+usPODsEp2n+2IfODsGpvJ670dkhOJUR2tTZITiVT8d7nR2Cc2153tkR1C2NSVGv1JJCRERERERERFyCkhQiIiIiIiIi4hKUpBARERERERERl1DnY1KIiIiIiIiInLFsGpOiPqklhYiIiIiIiIi4BCUpRERERERERMQlKEkhIiIiIiIiIi5BSQoRERERERERcQkaOFNERERERETkeEwNnFmf1JJCRERERERERFyCkhQiIiIiIiIi4hKUpBARERERERERl6AxKURERERERESOR2NS1Cu1pBARERERERERl6AkhYiIiIiIiIi4BCUpRERERERERMQlaEwKERERERERkePRmBT1Si0pRERERERERMQlKEkhIiIiIiIiIi5BSQoRERERERERcQkak0JERERERETkeGwak6I+qSWFiIiIiIiIiLgEJSlERERERERExCUoSSEiIiIiIiIiLkFJChERERERERFxCRo4U0REREREROR4TA2cWZ/UkkJEREREREREXIKSFCIiIiIiIiLiEpSkEBERERERERGXoDEpRERERERERI5HY1LUK7WkEBERERERERGXoCSFiIiIiIiIiLgEJSlERERERERExCVoTAoRERERERGR47FpTIr6dEYlKZb/tpfxbyzGZjMZOSSW0Tee5zC9tLScx8f/zObtqQQHejN57BAaRwdRWmbludd+YdP2FCyGwZMPXMR53WLILyzlxvu+rpw/OS2PKwd24Ml/X1TfVfvLlq3cyfhJc7HZTK4e2p3Rt/ZzmL5m3T4mTP6Z7btSmDz+ai4d0NFhen5+MYOvfZuLL2zHs48Nqc/QT5sGD43Bt3dfzOJiUl94ipIdW2uUiX79XdzDwsHNjeIN60ib+CLYbHi2akvEY89g+PpSnnSY5OcexywscEItam/57/sZ/86v9vX/svaMvr67w/TSUiuPvxLP5p1p9vX/6UtoHBUIwPY9GTz7+lIKCksxDIPp74zAy9Od2Yt28t6X6zAMiAjz47UxAwgJ8nFG9U7qdNe/rNzGjQ/NrJw/Oa2AKy9uzZP/d0F9VqtWlq87xPgP1trrfkkrRo+IdZheWmbl8Skr2Lw7k+AATyb/px+NI/1Zsf4wkz77k7JyGx7uFh67tTu9OkdXzvPC+7/z+2b7fvHBUV0Z1LupM6p3UqZpMuH7HSzbnI63pxsTbuxAx5jAGuU2789lzBebKSmz0a9jA54c0QbDMHjoo43sS7Vv37lF5QT6uDPjiV5kFZTy4Icb2ZSYy7Dzonnmmnb1XbW/JfRfj+N73gWYxcWkvfIMpTu31SgT+co7uIU1wHBzpzhhHRlvTLDv+1q2JezhpzE8PcFqJX3KBEq3bXJCLU6/D296iiGd+pCal0WnF0Y5O5w6sXrFTqa8Mg+rzcYVw7tz8x19HaZ/9dlKfpqxDjc3C8Ehfjw5dijRDYMB+O/rv7By+Q4Abht9IRdfGnv0x7s8zxH34dbhXCgtoWTaq9gO7nIs4OGF1+3PYmkQDTYb5ZtWU/bTB5WT3bpdiOdlN4NpYju0h5LPJtRzDf6+5at3M37KQmxWGyOv6Mrom893mL7mz/289MZCtu9OZdLYYVwaZ9+fbd2RwvOvzaOgsBSLxeCeW3oz+OIOzqjCKVm2fDPjx3+LzWbj6pF9GD36UofpH3+8kO+m/4qbmxuhof5MGH8zjRqFAXDHnW+yYcNezuneivfeu88Z4Z8WDZ98isB+/bAVFXPgyTEUbd1y3LLN3n4Hz5jG7Bh6pcP74bfeRsPHHmdT715Ys7PrOGKRv++MSVJYrTbGTY7no9dHEhkewNV3TSOuTytaNQ+rLDN9ziYCA7xZ8PUdzFm4jUnvLuP1sVfw3U8JAPz06S1kZBVy13++Z/rUG/H39WTmxzdXzn/VHZ9zSb/W9V63v8pqtTHu1dl8/PYtREYGMvKW94jr145WLSIqy0RHBfHSc8P56IsVx/yMKe8uomc317wgqQ3f8/viEdOE/VcPxqtjZ8Ife4aDd95Qo1zyU49UJh+iJryOf9wg8hf+TMSYsaS/PZHiP9cSMGQ4ITfeRub7b9d3NWrNarUx7q3lfPTKFUSG+3H1fd8T17sZrZqGVpaZ/vNWAgO8WPDZKOYs3smkqat5/ZmBlFttPPrSQl59YgDtWjYgK6cYdzcL5VYbE975lTkfXkdIkA+vvb+KL2Zu4l+39HRiTY+tLurv5enOzPeuqZz/qnu/45ILWjijeidktdoY997vfDT2YiLDfLn60Z+JO7cxrWKCK8tM/2UXgf6eLHh3GHOW72XSZ+t4/dF+hAR687+nLyIy1JcdiVncOTaeZR+NBODd6ZsIC/Zm/jvDsNlMcvJLnFTDk1u2JYPE1ELmPdubDftyGffNNr75z7k1yo39Zhvjru9Al2aB3P2/9SzfkkG/jg14/fZOlWVe+WEH/j72Q5+Xuxv/vrwlO5Py2Xk4v97qcyp8zrsAj0ZNOHjjFXi170TYQ0+T9H831iiXOvbRyn1fxNhJ+F04kILF8wi9+yGyP32Xot9X4HPeBYTe/SDJD91Z39WoE5+smsPbS6bz2a3POjuUOmG12pg4YS5vvHcTEZGB3HHDVPr2b0vzllXH/jbtovnoy9F4+3jyw7dreOf1X3jhtatZsWwHO7Yl8em391BWauW+Oz/h/Ata4efv7cQa/TVuHc7FCG9E0Qu3YGnWHs9rHqB48r9qlCtb9C22nRvAzR3v+1/D1r4n1q1rMMIb4XHJ9RS9/gAU5YN/cP1X4m+yWm2Mm7iAj964jsiIQK6+4xPi+ramVfMGlWWiowJ56ekhfPTlbw7zenu788qzV9AsJpSUtDxG3v4xF5zXgsCAM2fZW602xo37io8/eoDIyBBGXv0ScXGdadWqYWWZ9u1j+H76k/j4ePLlV0t5beIPTHn9LgDuvGMgRUWlfPPNcmdV4ZQF9OuHV9OmbLt0EL6du9DouefYdd21xywbdPEl2AoLa7zvERVFQO8+lB4+VNfhipyyM2ZMioStyTRpFExMw2A8PdwYPKAt8b86ZtDjl+9i2KX2FgOD+rdh1R/7MU2T3fsy6NW9CQBhIb4E+nuzaVuyw7x792eSmV1Ijy6N6qdCpyBh80GaxoQS0zgUTw93Lr+kE/FLHe+kNW4YQrvWUVgMo8b8m7YeJiMznz7ntaqvkE87v34XkffzLABKNidg8Q/ALaxBjXKVrSPc3DE8PMA0AfBo0pTiP9cCUPT7Kvz7X1I/gf9NCdtTadIwiJiGgfb1v38r4lfscygTv3Ifwwa2BWBQv5as+vMQpmmyYu0B2rYIo11L++8TEuSNm5sF0zQxTSgsLsc0TfILS4kI86vvqtVKXdS/ur0Hs8nMLqJHp+h6qc9fkbAzgybRAcREBdjrfkFT4n874FAm/vcDDLuoJQCDejdlVUIypmnSoUUokaG+ALRuEkxJqZXSMisAPyzcVdkiw2IxCAl03RPWRRvTGHpuNIZh0LV5ELlF5aTmOCZVUnNKyC8up2vzIAzDYOi50cRvTHMoY5om8/5M4fJzogDw9XLjnJbBeLmfMYdCfPtcRP6CnwAo2boRi18AbqEn2fe5ewD2fZ+JicXPHwCLnz/WjLQa856plu9aT2ZBrrPDqDNbNh2icUwojRqH4uHhzsWXxrJ8yXaHMuec2xxvH08AOnZqTGqq/ffYtyeNrt2b4u7uho+vJ61aR7J6xa4a3+HK3Dr1pvz3XwCw7duK4eOPERjqWKisxJ6gALCWYzuwEyM4HAD38wdTvvxHe4ICID+7niI/dQlbDtOkcQgxjULsx4GL2xNf0SrmiMbRwbRtFYFhcTzva94kjGYx9t8pMjyA0BA/MrNrXsC6soSEfTRtEkFMTDienu5cPrgn8fEJDmV69WqLT8W637VLc5KTsyqnnX9+O/z8vOo15tMtKG4AWT/+CEBhwgbcAgJxbxBeo5zF15fwW24l5b3/1ZjW8PExHJ702pHDgYhLO2POzFLS8omOCKh8HRUeQEq6452v1PSqMu7uFgL8vMjOKaJtqwgWrdhNebmNg4dz2LwjhaTUPId558Zv57K4thjHuKh3NSlpeURFBlW+jowMJCWtdidmNpuNV6bM4/EHBtVVePXCPTyS8pSqRFN5Wgru4ZHHLNvw9fdoPncptsIC8hcvAKB07278+sUB4B83EPeIqLoP+hSkpBcQHVGVQIgK9yMlw7F7SmpGPtHh9osPdzcLAX6eZOcWs+9gNoZhcMfjs7nqnu/44Js/AfBwd+O5B/px5V3f0O/az9idmMXIy1yzuXtd1L+6uYt3cVn/Vi65/adkFhLdoFrdw/xIySxyKJOaWUh0A3sywt3NQoCvB9l5jhfx81ftp0OLUDw93MjNLwXgjS83cNXDc3jg1aWkZzt+pitJyS4hKqQqiRIV7HXMJEVkcFWZyGAvUrIdy6zdnU1YgCfNInzrNuA65N4ggvLUlMrX1vQU3BpEHLNs5Kv/o8mMxdiKCihYar+4y3z7VULvfoiYb+YTes8jZE19s17illOXlppLZFRVN6fwiEDSUo5/7J89Yx29+thvRrRqE8nqlbsoLiolO6uAdWv2kpJ8ZiV0jKAGmNlVSTUzOw0jqGaCrpKPH26x52PdYd/nWyIaY4Q3xvvBKXg//BZu7V2v1eDxpKTlEx1ZteyjwgNIScs7wRzHlrDlMGVlVpo0Cjmd4dW5lJQsoqKrYo6MCiYlJeu45adPX0G/fmded6YT8YiIpCw5qfJ1WUoyHpE1z3uj/vVvUj/5GFtRscP7gXFxlKWmULx9e415pJZs5j/zz0XVKklhGMZGwzASjvpbbhjG64ZhhJ38E5xrxOBYosL9GXnXF0x4azHdYhvidlSmeW78Ni6/uL2TIqw/X05fQ78+rR2SHP90hx+6m31XXITh4YnPOfZxTFLHP0PQVdfR+ONvMHz9MMvLnBxl3Sm3mvyxKYmJTw5g2pRh/PLrXlatO0hZuZWvf9rMjHevZtk3N9OmRRjvf1XzAv5Md7z6Vzd38S4uv8j1u3r9XTv3ZzPp03WMvbcXAFabjeSMQrq1C+eHyZfTtW04r378h5OjrHtz/kiubEVxNkh57F4OjBiA4eGJdzd795iAodeQ8c5rHLh20P+zd9/hUVRvG8e/ZzcV0khINgFC74QiNkBpQQVBRQUrdhTLa+9dQcHesIu9+xOlF5UmCKKg9A5CIJAGaZCe3Xn/2JiwBjQKyW7I/bkuLrI7ZzbPSSZTnjnnGTLfeI6G9zzu3SClWsyevoqN6/cw4qpTADi5V2t6ntqG6698j8fu/4aErvHY7b6XlD1qbDYCr3yIkoWTsPaVXdjZ7NiiG1M4/i6KPhxLwMV3QLBvjh6sDul7D3DvmGmMe2gINtux+7ufMvUX1q7bybUjfXuEbHUIat+egPim5M6d4/G+CQoiZtT1pL6qpLTUHlWtSTELcAKfl72+GKgHpAIfAmcf3NgYMwoYBfDWcyMYdYVnUcf/whEd4jH6ITVjP46GIR5tYhq628TGhFJa6mJ/XhER4cEYY3jgoGKYF9/4efnQN4CNW9MpdVoktDv0nXhf44gOJTUtp/x1WloujujKReQOZcXqXfy2MokvJi4jL7+YklIn9YIDuPuWM6or3KMmfNjFhJ3jnk9fuGEtfo6Kiw2/aAelGWmHWxWruJi8RfOp36c/Bct+piRpO3tuHwWAf3wz6p9y5NtodXI0rE9KesXIgdSMPBx/mZoRExVCSsYBYqNDKHW62J9XTERYELHR9Tmhc1x5Qcy+Jzdl/ZYMQuq5h0U2beROWJ3ZtxUTvvTNJEV19L9n9yYAbNy2l1Kni4S2lYdN+gJHZD1S9h7U9315OCI9i5vGRNYjZW8+sQ3ru/ueX0JEqHtoa+rePG5+egHP3H4KTePcI80iQgMJDrRzRg/3NLhBvZrxzRzfGvr92cJdTFzinjeb0DSM1KyKu0Kp2UXEhHsO3Y0JDyQtu6JNWnYRjoiKNqVOF3NWZTDxnsq1LHxd6LkXETrkfACKN67DL8bBn2NE7A0dOPemH3Zdq6SY/MXzqX9Kfwp/W0roGWeT+eozAOQt+J6Gdz9W3eHLURIdE+Yx+iEjPZdoR+Vj/7Kl2/jo3UW8/t5VBARUnOZddV0frrrOfax77P6JxDfz+XtM+PU+B7+egwFw7dxcPnUDwEREY+XsPeR6ARffiZWxm9IF35a/Z2Vn4EraCC4nVmYqVnoytugmuHb6/p1lR3QIKQeNmknN2I8jOvRv1vB0IK+IG+7+H7eP6ku3BN+f1vxXDkcDUlMqRk6kpWbjcFQeDbJkyQbeemsWn35yJwEB/jUZYrWIuuRSoi64AID8NWvwj62YkurviKUkzfO8t37XbtRLSKDDD3PBbscvKpJWH37M7rFPEtC4Ce0mTSlb10Hbb75ly0UXUrr30H9DIt5W1ekep1mW9YBlWWvK/j0E9LUs6xmg+V8bW5b1jmVZJ1iWdcLRSFAAdG4fS1JyNsl7cigucTJz7iYST23l0Sbx1FZMnr0OgO8WbKZH96YYYygoLCG/wH2nfPGyHfjZbR4FN2fM2ciQ09odlThrQueOjdmxM5NdIyD2jQAApbZJREFUu7MoLillxg9rSOxTtWH6Lzw5nAXT72Le1Du577aBnDu4a61IUADkfPMlu64czq4rh5O3cB6hZ7orFgd26oIr7wDOfZ47WhMcXFGnwm6nXq8+lCRtd79sUJakMoYGV19PzqT/1Vg//ovO7WJI2p1Nckque/tfsJXEXs092iT2as7k790nW98t3EaPbo0xxnDqCU3Zsj2TgsISSp0ulq3aQ6tmkcQ0rM+2pCwyy4b5L/ktmZZNfXMIaHX0/08z5m1lSKLv1mfp3CaKpJT9JKftd/f9pyQST4r3aJN4UjyT528D4LslSfToHIsxhtwDxVz/5Hzuurw73TtUTAkwxtD/xCb8utY9Zern1am0ivet0VUj+sQz6f4eTLq/BwO6xDDl1xQsy2Ll9hxCg/wOmaQICfJj5fYcLMtiyq8pJHauuKD5eVMmLRz1PKaN1Bb7J3/FnusuYs91F5G3eD4hZ7jvCwR26IyVdwBn5l/2fUHBFXUqbHaCe/SheKd731e6L4OgricAENT9JEp276y5jsgR6dCpEck797EnOYuSklLmzF7LqX09z102bUjhmSem8+wrlxAZVXEjx+l0kVNWh2Dr5lS2bk7jpJ6e51C+qHTRVAqfvYHCZ2/AuXoxfie5747bmnfAKszDys2stI7/kKsxQfUp/vYNj/eda5Zga93V/aJ+GCamCa69KZXW90WdOzQiKTmL5D3Z7uPAnA0knlq10X/FJU5uvv8bhp6ZUP7Ej9qmc+dm7EhKZ1fyXoqLS5kxcxmJiV082qxfv5NHH/uMN9+4kaioqt2483X7vviczeefx+bzzyNn7lwaDB0KQL0uXXHt30/pXs+aQvu++pL1/fqw4fQBbL1sBEU7drDtqiso3LKZ9b1PYcPpA9hw+gBK0tLYPOx8JSjEp1V1JIXdGHOSZVm/AhhjTgTsZctKqyWyv/Dzs/HIHYmMvOsbXC4Xw4Yk0KZFQ8a/u5iE9g4ST23N8CGduffJWZxx8XuEhwXx4uNDANiXlc+1d32DzWZwNAzhmYcHe3z2rHmbeee582qiG0eFn5+dR+8dwrW3fozT6WLYOd1p0yqGV96aS0KHxgzo257V63Zz871fkJtbwPyfNvHq2/OY8b/KVbBrq/wlC6nXqzfNvp6Fq6iA9CcfKV8W/9FEdl05HFtQPeKefc39qD1jKPj91/JkRMjpgwkfdjEAeQvmsH/6JK/0o6r87DYeuaU3I++fjstlMWxQe9o0j2T8h7+S0DaaxF4tGH5me+59ei5nXPEZ4aFBvPiQ+2QuPDSQq4Z35YL/+wZjoM9JzejXw/1kl/+7/AQuu3MyfnYbjRyhPHVPoje7eVjV1X+AWT9u5Z1xQ7zVtX/kZ7fxyHUnMXL0XFxOi2GntaZN0wjGf76ShNZRJJ4Uz/DTWnPvyz9xxg2TCQ8N4MW73I8l/GzmRnam5PLGV6t54yt3kbH3Hh9AVEQwd13RnfteXsy495YTGRbEuFt7ebObf6tvpygWrt/LwDFLCPK3Me6yikcqn/f0Uibd757G8uhF7csfQdq7QxR9OlYko2f+lnbIqR4DHvuJvMJSSkot5q7J4N2bjqN1XEildr6iYOki6p18Kk0+nY5VVEjGMxVPsmg0wZ3MMMHBOMa+gvEPAJuNghXL2D/1awD2Pj+GqFvuBbsdq7iYvS+M8VZXjrrPrxlDv7bdaRgSwa5xU3ls+gTeXzLN22EdNX5+du58YDB33PgJTpfFWeceR8vWMUx4fR7tOzWid7/2vP7S9xTkF/PwPe5jnSM2nGfHX0ppqZMbr34fgPr1A3ls3Pn4+dn/7tv5HOf6X7B3OongRz8uewTpc+XLgu59i8Jnb8BENCRg4AhcqUkE3eMuHFi6aAqlP8/CuWEZ9vbHE/zge+ByUTzlHcivHXU5/PxsPHLn6Yy840v3ceCsLrRpGc34CQtJaB9HYu82rFm/h5sf+Jbc/YXM/2kLr723iOmfXcfsuRtYvnIX2bkFTJq5BoCnHjqLDm1rx+hhKDvvfeQirh05HqfLxbBhvWjTphGvjJ9KQkIzBiR25dnnviU/v4jbbp8AQFxcJG+9eRMAl454nj/+SCU/v4g+fe9n7JOX07t3p7/7lj5n/8IfCevTh/azv8dVWMiuhx4sX9b220lsPr/2XMeIVIWxrH8umFGWlHgfCAEMkAtcC6wDhliWddjb0Fb6O75bkaOamSDfujNZ07YOfMLbIXhVq/8dG4/1k//oQO14pGV1sHbVjruT1SXpqZ+8HYJXtWwf/M+NjmF7X7rN2yF4TdC973k7BK+q91jlxwHXJSay9j7a/mhY1elGb4fgVV3Xbzx2i50A1panjslrWtPmAZ/8vVVpJIVlWcuAzsaY8LLXOQct9u1x8iIiIiIiIiJSK1QpSWGMCQSG4a4/4ffnY/osyzp2xomKiIiIiIiIiFdVtSbFFCAH+A0o+oe2IiIiIiIiIiL/WlWTFE0syxpUrZGIiIiIiIiI+BqXy9sR1ClVfQTpEmNM52qNRERERERERETqtKqOpDgVuMoYsx33dA8DWJZldfn71UREREREREREqqaqSYozqzUKEREREREREanzqvoI0iQAY0wMEFStEYmIiIiIiIj4Cpfl7QjqlCrVpDDGnGOM2QJsB34EdgCzqjEuEREREREREaljqlo48wmgB7DZsqwWwABgabVFJSIiIiIiIiJ1TlWTFCWWZe0DbMYYm2VZ84ETqjEuEREREREREaljqlo4M9sYEwIsBD4zxqQDedUXloiIiIiIiIgPcLm8HUGdUtWRFEOBAuAOYDawDTi7uoISERERERERkbqnqkmKZpZlOS3LKrUs6yPLssYDnaszMBERERERERGpW6qapPifMeY+4xZsjHkVeKo6AxMRERERERGRuqWqSYqTgXhgCbAM2AOcUl1BiYiIiIiIiEjdU9XCmSW4a1IEA0HAdsuyVD1EREREREREjm0qnFmjqjqSYhnuJMUJQG/gEmPM19UWlYiIiIiIiIjUOVVNUlwHbAEetCwrBbgFWFVtUYmIiIiIiIhInVPVJMXVQA/gkrLX+3E/llRERERERERE5Kioak2Kky3L6m6MWQFgWVaWMca/GuMSERERERER8T6X5e0I6pSqjqQoMcbYAQvAGBP959ciIiIiIiIicuwxxgwyxmwyxmw1xtx/iOWBxpivypb/YoxpfqTfs6pJivHAJCDGGDMW+AkYd6TfXERERERERER8T9lAhdeBM4GOuB+g0fEvzUYCWZZltQZeAp450u9bpekelmV9Zoz5DRgAGOBcy7I2HOk3FxERERERERGfdBKw1bKsPwCMMV/irk25/qA2Q4HHy76eCLxmjDGWZf3nmRdVrUmBZVkbgY3/9RuJiIiIiIiI1Doul7cj8JbGwK6DXicDJx+ujWVZpcaYHCAK2Ptfv2lVp3uIiIiIiIiIyDHCGDPKGLP8oH+jvB0T/IuRFCIiIiIiIiJybLAs6x3gnb9pshuIP+h1k7L3DtUm2RjjB4QD+44kLo2kEBEREREREZG/Wga0Mca0MMYEABcDU//SZipwZdnXw4F5R1KPAjSSQkREREREROTwXEd0zV1rldWYuBn4DrAD71uWtc4YMwZYblnWVOA94BNjzFYgE3ci44goSSEiIiIiIiIilViWNROY+Zf3Hj3o60LggqP5PTXdQ0RERERERER8gpIUIiIiIiIiIuITlKQQEREREREREZ+gmhQiIiIiIiIih+NyeTuCOqXakxQmuk11fwufZWVs8XYIXtVsZGdvh+BVplEnb4fgVVbyKm+H4F3FJd6OwHv87N6OwKsCQgK8HYJX7X3pNm+H4FUN73jF2yF4zWdvZXk7BK+6+JV4b4fgVcbbAXiZTePTRY4a/TmJiIiIiIiIiE9QkkJEREREREREfIJqUoiIiIiIiIgcjmpS1CiNpBARERERERERn6AkhYiIiIiIiIj4BCUpRERERERERMQnqCaFiIiIiIiIyGFYluXtEKqFrz46WCMpRERERERERMQnKEkhIiIiIiIiIj5BSQoRERERERER8QmqSSEiIiIiIiJyOC6XtyOoUzSSQkRERERERER8gpIUIiIiIiIiIuITlKQQEREREREREZ+gJIWIiIiIiIiI+AQVzhQRERERERE5HBXOrFEaSSEiIiIiIiIiPkFJChERERERERHxCUpSiIiIiIiIiIhPUE0KERERERERkcNxWd6OoE7RSAoRERERERER8QlKUoiIiIiIiIiIT1CSQkRERERERER8gmpSiIiIiIiIiByOy+XtCOoUjaQQEREREREREZ+gJIWIiIiIiIiI+AQlKURERERERETEJ6gmhYiIiIiIiMjhqCZFjdJIChERERERERHxCUpSiIiIiIiIiIhPUJJCRERERERERHyCkhQiIiIiIiIi4hNUOFNERERERETkcFyWtyOoUzSSQkRERERERER8Qq0eSbFw0TrGjv0fLpeLC4afwqhRgzyWf/DBHL6e+BN2u53IyBDGjb2Cxo2jABh57XhWrdrO8d1b8/bb/+eN8P+1Rb9sZ+wr83G5LIaflcCoy072WF5cXMp9Y2exblM6EWFBvDj6LJrEhVNc4uSx535g7aY0bMbw4G39Ofm4ePc6JU6eeGkuv65IxmaD2687lYH92nqje/+KZVk8NS+ZRX/kEuRnGDu4OR0d9Sq1e2XRbqauyyS30Mmy27uVvz957T5eWLCbmBB/AC7pHs3wLg1rKvwjtnDResY+NRGX08UFw3sx6rozPJYvW76VcU9NZNPmPbz4/NUMGnhc+bLnXpjMjz+uA+CmGwcx+MzjazT2/2rRsl2MffNn9/Y/qB2jLu7msby42Ml9zy1g3Za9RIQG8uJDA2gSG0py6n6GXPs1LZqEA9C1Qwyjb+vtse6Nj35Hcsp+pk0YXlPd+UeLVqYw9oPf3f0d0JJR53b0WF5c4uS+15ay7o8sIkIDePH2XjSJCQHg7Unr+WbeH9hshoeu7k7vbnEAJP7fVOoH+WO3Gex2wzdPDwTgjpcWs33PfgBy84sJqxfA5Oc896feZFkW4/63kYXrMggKsDPuis50ahpWqd26pBwe+HgtRSVO+nSK5sEL22OM4bXpW/n6p2QiQwMAuH1oG/omRFNc6uLxz9exNikXm4EHL+zASW0ja7p7/1r4qLsIOv4UrKJCsl4ZTcm2TZXaRD0+HntkFNj9KF63guy3nvV4fFrIuSMIH3k7KSNOw5WbU5PhH5Gli7fw8jOzcbpcnH1ed64Y6fm3/MXHS5g26XfsdhsRDerz4OihxDWKAOD1l35gyaLNAFw9qi+nDUqo6fCr1XuXP8RZnU8hfX8WnZ8Y4e1wqsXxrzxEo8F9Kc0vZOlV95O1Yn2lNgPmf0xwXAzOgkIA5p1xDUUZmdSLj6PnR8/gHxGKsdtZdf/z7Jm1sKa78J8tWrSOcWO/dh8ThvfiulEDPZZ/+MFcJk5cjN1uIzIylCfHXkbjxlFs2LCL0Y9/yYG8Quw2w/U3DGLw4BO81Iv/rq6d8x9K3AMPEdq7D67CQpIfeoDCDZW3/z81e/UNApo0Yct55wDguPlWQhMHgMtFaWYmyQ89QGlGek2FLvKv1dokhdPpYsyYL/jg/dtwOBow/IKnSEzsQuvWjcrbdOgQzzcTHyQ4OIDPv/iR557/lpdfug6Aa0eeQUFBMV99tchbXfhXnE4XY16cy/svDccRHcoF131G4imtad0iqrzNxBlrCQsN4vsvRzJjzkZeeGshL40+m6+nrQZg2kdXsi8rn+vu/oaJEy7DZjO89fFSohrU47svrsHlssjJLfBWF/+VRdtz2ZlVxMxrO7I6JZ8nftjJF5e1r9SuX6sILj0uhsHvrqu0bFD7Bjx0WnxNhHtUOZ0uxjz5Pz5492YcjgiGX/Qcif0707p1XHmbuLgGPDXuct7/YK7Hugt+XMv69buY/O39FBeXcvlVr9Cnd0dCQoJruhv/itPpYsxri3n/6cE4Gtbnglsmk9izGa2bNShvM3H2JsJCAvj+w4uYMX8bL7z3Ky89NACApnFhTH5r2CE/+/uftlMv2L9G+lFVTpeLMe8t5/2H++OICuaCB34g8YTGtC5LtABMnPcHYfUD+P7Vs5ixOIkXPlvFS3ecwtbkHGYu2cn0F88kPauAq5+Yz+xXhmC3uQfOffxYIg3CAj2+30t3nFL+9dMfryC0nm/9PBau20tSej6zR/dm1fYcxnyxnq/u61Gp3egv1jNmRCe6tgjn+td+Z9G6vfRJiAbgygHNuOb0Fh7tv/4pGYCpj5zCvtwiRr32O1/f3wObzVR/p/6jwON74deoKWnXn49/uwQibryfjLuvrtQu85kHsAryAIh84BmCTxlAwaIfALA3dBB43MmUpqfUaOxHyul08fy4mbzy9uXEOMIYeekEevdrR4tWMeVt2raP4/3PRxEUHMC3/1vGGy/9wBPPXcDihZvZvDGFj/53AyXFTv7v2g/peWpr6ocEebFHR9eHP8/gtQUT+fiqR70dSrVodGYfQts0Z1qbM4g6uSsnvvk43/e48JBtl4y4m8zf1nq8l/DwjST9bxZb3/qCsA6t6DfzHaa2GFAToR8xp9PFE2O+4r33b8XhiODCC56hf2IXj+N+hw5N+Hri/QQHB/DFFwt5/vlJvPTStQQFBfD0M1fSvHkM6WnZDBv+NKee2pGwsMo3dnxVXTvnP5TQ3n0IbNqMzYMHEtylK40feYxtl150yLZhp52OKz/f472MD94j7bXxAESNuJyYG29iz5jHqzlqkf+uStM9jDGVbi0ZY1ocqm1NWb16B82axhAfH01AgB9DBp/I3LmrPdr06NGO4GD3nbNuXVuQmppVvqxnz/bUr+95ou7LVm9IpWnjCOIbRRDgb2fwgHbM/WmrR5u5i7Zy7qBOAAzs15aff9uJZVls27GPHt2bAhDVoB5hIUGs3ZgKwLcz15aPyLDZDA0iasdBa/6WHM7pFIkxhq6N6rO/0EnGgZJK7bo2qk90iG9dcB2p1Wt20KxpQ+LjG7q3/TO7M3ee57bfpHEU7ds1rnSxtXVrKiec0Bo/Pzv16gXSrm1jFi7aUJPh/yerN2XQtFEY8XFh7u2/byvmLknyaDP35x2ce7p7FNDAPi34ecVuLOvv5w/mFZTw4TdruPHS4/62XU1bvTWTprGhxDtCCPCzM7hXU+Yu2+3RZu7y3Zzbz70bHtgjnp/XpmFZFnOX7WZwr6YE+NtpEhNC09hQVm/NrNL3tSyL2T/vZMgpzY56n47EvFXpDO3RCGMM3VpGkJtfQnpOkUeb9JwiDhQ66dYyAmMMQ3s0Yu6qv79LtC3lACe3cyd6o8ICCavnx9qdvj2qILhHX/LnzQCgZNNaTP1QbA2iKrX7M0GB3Q5+/nDQn0L4tXeQ88Gr8A9/H75m/drdNImPpHGTSPz9/ThtUAKLFniOIjn+pBYElR33O3VuQnp6LgA7/sigW/dm+PnZCa4XQOs2DpYu3lrpe9Rmi7auJDMv19thVJvGQwew/ePJAOz7ZRUBEWEExUZXeX3LsvAPc482CwgPpWBP7bmLvHr1Dpo2jS4/7g8efDzz5q7yaHPyQee8Xbu2IC01G4AWLRw0b+5O5MU4IoiKDCUz80CNxn+k6to5/6GE9h9A1tQpABSsXoU9NAy/hpW3f1twPRpecRXpb7/p8b4rL++gNsG1bv/vE1yuY/Ofj6pqTYppxpjysbXGmI7AtOoJqWrS0rKIjau4i+qIjSAtLeuw7SdOXEyfPrV3aGdaxgHiYkLLX8dGh5K21/Mgk763oo2fn43Q+oFk5xTQrnUM8xZvo7TURfKeHNZtTiMlfT+5+91DIV95dzHnX/MJtz0yjb2ZedQGaQeKiS0bug3gCA0g7UDxv/qMHzZncd4H67ljyh+k5P67db0pLS2H2NiDt/0GpKVX7cKqffvGLPppAwUFxWRmHeCXXzd7HMh9VdrePOKiQ8pfx0bXJ22f57aavjefuOj6APjZbYTWDyA7130hm5y6n/Nu/JbL7prG8jUVd4/Hf7icq4d1JijQtwaVpWUWEBdVkTCMjQomLdNzlFP6QW387DZC6/mTvb+48rqRFesaDCPHLuD8+77jqzmVL9CWb8ggKjyI5nGhlZZ5U1p2EbENKu54xzYIIj270KNNenYhjoiKk1BHRBBp2RWJjM8W7GTok4t56OO15OS5E5rtm4Qyf3U6pU4XyXvzWbczl9RMz8/1NfaoaJx708pfO/elY4+KOWTbqNHjifv0e6yCPAqWuEdVBZ3cB+e+DEp3bKmReI+mjPRcHLEV03yiY8LISDv8Rfn0Sb/T45TWALRu62Dpkq0UFhSTnZXH78u2k5Z67F7QH4vqNXaQvyu1/HV+cir1GjsO2bbHB+M4c8VkEh6+qfy9NY+/RovLzubcXT/Sb+Y7LL/lyWqP+WhJT8v+yzlvA9LSDn/c/2biEnr36VTp/dWrd1BSUkrTprVneivUvXP+Q/F3OChJrTh/KUlLxd9Reft33HIrez/6AFdh5WOZ49bbaTdnPhFDziofVSHiq6qapBiHO1ERYow5HvgauOxwjY0xo4wxy40xy995Z/rRiPOITJn6C2vX7eTakad7OxSvGDY4gdjoEIZf9ynjXp3PcQmNsNsMTqeL1PQDHJfQiG/fv5xuneJ49vUfvR1ujejXKpzvRyUw6eqO9GwWykOzdng7pBpx6ikd6Nu7Ixdf+gJ33f0B3bq2wGY/tuvnxkTWY95nlzDpzfO5//oe3P3UfA7kFbNh2z52puRy+qleHRRWoz5/YgDfPjOQCQ/25fPvtrJsveedxBmLfW8UxdFwcZ94vn+iD5Me7EV0eCDPfuO++35+r8Y4IoK44OmlPPX1Rrq1jPDpqR7/1r7HbiXlijMx/gEEdjkBExhI6AVXk/vZW94OrdrNnr6Kjev3MOIq91Smk3u1puepbbj+yvd47P5vSOgaj91+7PyupcKSEXczs8s5/NB7BNG9j6fF5UMBaH7JEP74cBKT4/uyYPAoen3yLJhjbxuYOvUX1q5LYuTI0zzeT0/P4b57P2TsuCuw2Y7d435dPucPateegPim5M6dc8jlaeNfZtNp/cmeMZ2oSw97GSfiE6p0+9CyrBnGGH/geyAUOM+yrM1/0/4d4B33i/nVMp7I4WhAakpFFjUtNRuHo0GldkuWbOCtt2bx6Sd3EhBQe4f9O6JDSEnfX/46NWM/joYhHm1iGrrbxMaEUlrqYn9eERHhwRhjeODW/uXtLr7xc5rHRxIRHkxwkB9n9G0DwKD+bflmhuccTl/yxe8ZTFy9F4CEuHqk7q8Y/ZC2vxhHSMDhVq0kIrhi0x/WpSEv/rj7b1r7Focj3GP0Q1pqFo6Y8L9Zw9ONNwzixhvcBafuuucDWjQ79F1YX+JoWJ+UjIqRQ6kZeTii6nu0iWlYj5SMPGKjQyh1utifV0xEWCDGGAIC7AAktI0mvlEY23fnsHZTBms37yXx8i9wOi0yswu4/O7pfPL8WTXat0NxRAaTsq9iPmnqvgIckZ51Q2LK2sRG1XP3N7+EiNCAyutmVqzriHSPsIgKD+K0ExuzemsmJ3Z0//5LnS5++HVXeTFNb/tswU4mLnbXjEhoFkZqVsVdodSsQmIiPGsJxPxl5ETaQSMrGh5Ug+OCU5tww+u/A+4RKA9cUFHL5pLnfqG5w3O78gX1B19AvYHnAlCyZT32hhV3z+xRMTj3/c2w9ZJiCpb+SNDJfXFm7cPuaETM+M/d6zaMIfrlT8m48ypc2fuqswtHRXRMmMfoh4z0XKIdlQuoLlu6jY/eXcTr711FQEDFvv6q6/pw1XV9AHjs/onEN6s8TUZ8S5ubLqX1de66E/uWraFefGz5snpNYsnfnVZpnT+ncZQeyGPH59OJOqkL2z+ZQsuRw1kw6FoA9i5diT0okMCGDSjKqNp0OG+KcUT85Zw3C4ej8nF/yZKNvP3WbD7+yznvgQMF3HDDG9x++zl061b7EvN17Zz/T5EXX0rk8AsAKFi7Bv/Yihok/o5YStI8t/963boR3CmBdt/Nxdjt2KMiafHBx2y/+gqPdtnTp9H8zbdJf/3V6u+EyH/0t6lUY8yrxpjxxpjxQCIQDmwHbi57z2s6d27GjqR0diXvpbi4lBkzl5GY2MWjzfr1O3n0sc94840biYqqfCJTm3RuH0tScjbJe3IoLnEyc+4mEk9t5dEm8dRWTJ7tLhD53YLN9OjeFGMMBYUl5Be4hzcvXrYDP7uN1i2iMMbQv1crfl2xC4Cff9tJq+a+e9J2SfdovrmqA99c1YHE1hFMXZeJZVms2pNHSKD9X9WeOLh+xfytObSMqj3F0zonNGNHUkbFtj/rdxL7d/nnFXEXn8rKdl/sb9y0m02b9nDKKZULjvqazu2iSdqdS3JKrnv7/3EbiT2berRJ7NmMyT+4c6ffLdxOj27uGgaZ2QU4ne45d7tScknanUN8bCiXnN2RRV+OYN4nl/DZi2fTvHG4TyQoADq3iiQpZT/J6QcoLnUyc8lOEk9o7NEm8fjGTF6wHYDvlu6iRycHxhgST2jMzCU7KS5xkpx+gKSU/XRpHUl+YSkHyvYD+YWlLF6dStumFSe5P69Jo0WjMGKjfKMuzYh+TZn0UC8mPdSLAV0dTFm6B8uyWPlHNqHBfsSEe84vjgkPJCTIzso/srEsiylL95DY1Z2AObh+xQ8r02jTyJ3gLSh2kl9UCsDiDXux2wyt4zyTv74gb+bXZNw2gozbRlCwdAH1EocA4N8uASv/AK4szwSDCQquqFNhsxN04qmUJu+gNGkbqZcPJO3aoaRdOxTn3nQybr+sViQoADp0akTyzn3sSc6ipKSUObPXcmrfdh5tNm1I4ZknpvPsK5cQGVXxu3Q6XeRku5N3WzensnVzGif19DyGiu/Z8sbnzDruXGYddy7Jk+fQ4opzAYg6uSslOfspTM3waG/sdgKj3Bevxs+Pxmf1I3ute2pT/s4UHAN6AhDWviW2oMBakaAA9zlvUlI6yWXH/Zkzf6N/pXPeXTz+2Oe8/saNREVVTNkrLi7llpvfYejQkxk4qHtNh35U1LVz/j9lfvk5W4efx9bh55E7by4NznGPCgru0hXngf2U7vXc/jO/+pKNiX3YNHAA264YQfGOHeUJioCmFaMkwxIHULR9e8115Fjh7doRdawmxT+NpFj+l9e/VVcg/5afn51HH7mIa0eOx+lyMWxYL9q0acQr46eSkNCMAYldefa5b8nPL+K22ycAEBcXyVtvuucnXjrief74I5X8/CL69L2fsU9eTu/elefv+Qo/PxuP3JHIyLu+weVyMWxIAm1aNGT8u4tJaO8g8dTWDB/SmXufnMUZF79HeFgQLz7uPpHdl5XPtXd9g81mcDQM4ZmHB5d/7l039uG+J2cybvx8IiPqMe5B37iL+k/6tAxj0R85nDlhHcH+Np44s2LnO+zDDXxzVQcAXliQzMwNWRSWuBjw5hrO7xLF/53SiE9/T2fB1hzsNkN4kJ0nz2zupZ78e35+dh596EKuve51nC6LYef1oE2bOF55dToJnZoyILELq9ckcfOtE8jNzWf+/DW8+toMZkx7mNJSJyMuexmAkJAgnnvmSvz87N7tUBX42W08cnMvRj44C5fLYtjAdrRpHsn4j5aT0DaaxJ7NGD6oHfc+s4AzrvqK8NBAXnwwEYBla1J59ePl+Nlt2GyGx289lYgw305K+dltPHLN8Ywc+6P7771/S9rEhzP+qzUktIok8YTGDE9syb2vLeWMW6YTHuJ+BClAm/hwzuwZz5A7Z2K32Xh05PHYbTb25eRz8/M/Ae4LtrNObVb+aFKAGYuTOMtHp3r0TWjIwrUZDHx0UdkjSCvmGp83dgmTHnL3/dFLOvLAR+5HkPbu1JA+ndzzrp//dhMbk/djDDSODObxEe59feb+Yq4dvxybzRATHsgzV3Wu+c79S0XLFxN0wik43plU9gjSMeXLol/5jIzbRmCCgol65EWMnz/YbBStXk7erG+9GPXR4edn584HBnPHjZ/gdFmcde5xtGwdw4TX59G+UyN692vP6y99T0F+MQ/f8z8AHLHhPDv+UkpLndx49fsA1K8fyGPjzq8V+75/4/NrxtCvbXcahkSwa9xUHps+gfeXeLV82FG1Z+aPNBrcl7O3/oAzv4ClVz9YvuzMFZOZddy52AID6P/duxh/f4zdRtqcn9k2wb0t/H7X05w84Una33EVWBZLr7rfSz359/z87Dz8yEVcO/I1XC4X5w/rSZs2jRg/fhoJCc1ITOzCc2XnvHfc/i7gfsrXG2/eyOzZv7F8+Rays/OYPGkpAOOeupwOHWrP083q2jn/oexf+COhvfvQdtb3WAWFJD9Ssf23njiJrcPP+9v1Y++4i8DmzbEsi5I9e9g95rHqDlnkiJh/qn5/xKppukdtYGXUvsJkR1Pp1PneDsGr/K++xtsheJWVvOqfGx3Lsvf/c5tjlLXP94uxVqeUl372dgheFfT1nd4Owasa3vGKt0Pwms/eqtt/+xe73vB2CF5lq3Kpu2PTms43ejsEr+q8duOxV+TlIK7ZNx6T17S2QW/65O+tSjUpjDFtgKeAjkD5LUjLslpWU1wiIiIiIiIiUsdU9bl7HwCPAS8B/YGrqfqTQURERERERERqJ9cxOZDCZ1U10RBsWdZc3NNDkizLehwYUn1hiYiIiIiIiEhdU9WRFEXGGBuwxRhzM7Ab8L0S6CIiIiIiIiJSa1V1JMVtQD3gVuB44DLgyuoKSkRERERERETqniqNpLAsaxmAMcZlWdbV1RuSiIiIiIiIiNRFVX26R0/gPdxTPJoaY7oC11uWdVN1BiciIiIiIiLiVS6XtyOoU6o63eNlYCCwD8CyrFVAn2qKSURERERERETqoCo/RtSyrF1/ect5lGMRERERERERkTqsqk/32GWM6QVYxhh/3IU0N1RfWCIiIiIiIiJS11Q1SXED8ArQGNgDfAf8X3UFJSIiIiIiIuITVJOiRlX16R57gRHVHIuIiIiIiIiI1GFVqklhjGlpjJlmjMkwxqQbY6YYY1pWd3AiIiIiIiIiUndUtXDm58D/gDigEfA18EV1BSUiIiIiIiIidU9Va1LUsyzrk4Nef2qMuac6AhIRERERERHxGS7L2xHUKVVNUswyxtwPfAlYwEXATGNMJIBlWZnVFJ+IiIiIiIiI1BFVTVJcWPb/9X95/2LcSQvVpxARERERERGRI1LVp3u0qO5ARERERERERKRu+9skhTHm/L9bblnWt0c3HBEREREREREf4nJ5O4I65Z9GUpxd9n8M0AuYV/a6P7AEUJJCRERERERERI6Kv01SWJZ1NYAx5nugo2VZKWWv44APqz06EREREREREakzbFVsF/9ngqJMGtC0GuIRERERERERkTqqqk/3mGuM+Q74ouz1RcCc6glJREREREREROqiqj7d4+ayIpq9y956x7KsSdUXloiIiIiIiIj3WU7L2yHUKVUdSfHnkzxUKFNEREREREREqsU/PYJ0P2ABpuz/8kWAZVlWWDXGJiIiIiIiIiJ1yD893SP0z6+NMd2omO6x0LKsVdUYl4iIiIiIiIjUMVWa7mGMuRW4Dvd0DwN8YoyZYFnWq9UZnIiIiIiIiIhXuVSToiZVtSbFtUAPy7LyAIwxzwA/A0pSiIiIiIiIiMhRYatiOwM4D3rtLHtPREREREREROSoqOpIig+AX4wxfz529FzgvWqJSERERERERETqpColKSzLetEYswA4teytqy3LWlFtUYmIiIiIiIj4AqdqUtSkqo6kwLKs34HfqzEWEREREREREanDqlqTQkRERERERESkWlV5JMV/Vfz6m9X9LXyWX/dm3g7Bq/zOP9PbIXiVc+Kn3g7Bq2xtm3o7BK+ysnK8HYLXmI4dvR2CV2V92MjbIXhVi3vrdsmqz97K8nYIXjPihgbeDsGrLp71tbdD8CpXYZG3Q/Cq0J9v9nYIIseMak9SiIiIiIiIiNRWlks1KWqSpnuIiIiIiIiIiE9QkkJEREREREREfIKSFCIiIiIiIiLiE5SkEBERERERERGfoMKZIiIiIiIiIofjVOHMmqSRFCIiIiIiIiLiE5SkEBERERERERGfoCSFiIiIiIiIiPgE1aQQERERERERORyny9sR1CkaSSEiIiIiIiIiPkFJChERERERERHxCUpSiIiIiIiIiIhPUE0KERERERERkcOwXJa3Q6hTNJJCRERERERERHyCkhQiIiIiIiIi4hOUpBARERERERERn6CaFCIiIiIiIiKH41RNipqkkRQiIiIiIiIi4hOUpBARERERERERn6AkhYiIiIiIiIj4BCUpRERERERERMQnqHCmiIiIiIiIyOG4VDizJmkkhYiIiIiIiIj4BCUpRERERERERMQnKEkhIiIiIiIiIj5BNSlEREREREREDsNyqiZFTdJIChERERERERHxCUpSiIiIiIiIiIhPUJJCRERERERERHyCalKIiIiIiIiIHI7L5e0I6hSNpBARERERERERn6AkhYiIiIiIiIj4BCUpRERERERERMQn1OqaFJZl8fTCPSxKyiXIz8aTp8XTMaZepXbjf05h6sYscouc/HpD5/L3n1m0m2XJBwAoLHWRmV/Kkus7V1rfV1iWxbjP17JwdRpBAXbGjTyOTs0jKrVbtyObB95dQVGJkz5dHDx4aQLGGF75diPzVqRgM4bIsECeGnkcMQ2C+CNlPw++t5L1STncfn57rjmzdc137l9a9PM2xr78PS6nxfBzujHqil4ey5et2MlTL3/Ppm3pvDDmPAYldgBgw+ZUHn9uNnl5RdhsNm646hQGn9bRG104IpZlMW5mEgu3ZBHsb2fcea3o2Kh+pXYvz9nJ1JV7ySks5beHTyp//8PFKUz8PR0/m6FBPT+ePK8VjSMCa7IL/5plWYz9aAULV6QSFGjnqRtPolOLBpXarf0jkwfeXEZRsZM+x8Xy0JXHYYwh+0ARd76ylN0ZeTSOrs9Lt/UkPCSgfL012zK5+JG5vHBrDwb1iK/Jrv1rlmUx7n8bWbguw70vuKIznZqGVWq3LimHBz5e694XdIrmwQvbY4wB4NP5SXz+4y5sNuibEM0957er6W5U2aJfdjD21QW4XC6GD0lg1IiTPJYXF5dy37jvWLc5jYiwYF58bDBN4sIpLnHy2PNzWLspDZvN8OAt/Tj5OPfv9tp7viVjXx5Op4vjuzTm0dsTsdt9P2+/Ymky77/8Cy6nxYCz23L+FV08ln83aSOzv9mAzW4jKNiPG+47hfgWEZSUOHn7mSVs27gXYzNcc/vJJHSP81IvjkzAsP/D3vEkKC6i6LNncSVv9WzgH0jgNY9iaxgHLhela5dSMu3d8sX24/oScOYVYFm4dv9B0cfjargHR+b4Vx6i0eC+lOYXsvSq+8lasb5SmwHzPyY4LgZnQSEA8864hqKMTOrFx9Hzo2fwjwjF2O2suv959sxaWNNdqBbvXf4QZ3U+hfT9WXR+YoS3wznqLMti3KQtLNyQSZC/jXGXdKBTfGildut27eeBLzZQVOKiT4dIHjyvDcYYNuzez+Nfb6a4xIXdZnh0eFu6NKt83KgNLMti3LTtLNyUTVCAjXHDW9OpcUildi9/l8SUFRnkFpTy2+geXoj06Fm2JIm3nl+I02Vx5rkdueiqEzyWT5+4hmlfr8FmNwQH+3PbQ4k0axlJ6p5crrvgU5o0c58vtU+I5bYH+3ujC7Wf0/J2BHWK75+R/Y1FSftJyi5ixuXteSyxCU8u2H3Idn1bhPHFhW0qvX9f78ZMvKQdEy9pxyVdGjKgVXh1h3xEFq5OJyktj9lPD2D0VV0Z88nqQ7Yb/fFqxlzdldlPDyApLY9Fa9IBGHlmK6Y80Z9JY/rRr6uDN6ZuAiC8fgAPXZrANYNa1VhfjoTT6WLMC7OZ8OLFTP/iemb8sI6t2zM82sTFhvHUI2dz1ukJHu8HBfnzzKPnMP3z65nw0sU89fL35O4vrMnwj4qFW7JJ2lfA7Nu6MfqcFoye9sch2/Vv14Cvrk+o9H6HuHp8fX0Ck/+vCwM7RfLC90nVHfIRW7gylaSUA3z38pmMue4ERr/72yHbjX7vd54YdQLfvXwmSSkHWLQyFYAJUzbSIyGG714eTI+EGCZM2VC+jtPl4vnPV3NKF0eN9OVILVy3l6T0fGaP7s3oSzsx5ovKFykAo79Yz5gRnZg9ujdJ6fksWrcXgF827WPuqnQmP9SL6Y+eyjWnNa/B6P8dp9PFmJfnMeHZc5n+0ZXMmLuJrTv2ebSZOGMdYaGBfP/5NVx5QXdeePsnAL6evgaAaR9ewfsvDOOZNxbicrlPMl5+fAhT3r+caR9eQWZ2AbMXbKnZjv0HTqeLCc8v5aEXzuDlz8/jpzl/sGt7tkeb3me05KVPz+OFj4Zy7ojOfDj+VwDmTN0MwEufnsdjLw/ko1eXlf8sahN7x5Mw0Y0peOJKir56iYALbztku5J5/6Ng7DUUPHsD9padsHc4EQAT3Rj/0y+h4KXbKHjqWoq+faMmwz9ijc7sQ2ib5kxrcwa/jnqEE998/LBtl4y4m1nHncus486lKCMTgISHbyTpf7OY3f08Fl98Bye88VgNRV79Pvx5BoNevcPbYVSbhRsyScooYPaDJzP6wnaMmbjpkO1GT9zEmAvbMfvBk0nKKGDRRvfv/vmp2/i/gc2ZdM+J3HJmC56ftq0mwz+qFm7KJmlfIbPvPo7R57VizORDnwP16xDJVzd1OeSy2sTpdPH6Mwt4cvw5TPh6BPO/20zSH5kebfoPasfbX13Km59fwgVXdOftlxaVL4trHM6bn1/Cm59fogSF1Bq1Okkx/48czunQAGMMXWPrs7/ISUZeSaV2XWPrE13f/28/a9bmbM5sW/murC+ZtyKVob2aYIyhW6tIcvNLSM/2vMBOzy7kQEEp3VpFYoxhaK8mzP3dfZEWElzxMygocoL7hipRYYF0btkAP7upsb4cidXr99C0SSTxjRsQ4G9n8Gkdmbtws0ebJnERtGvtwNg8+9SiaRTN4yMBcESHEtmgPpnZ+TUW+9Eyb2MWQ7tFu7f9+FD2FzrJ2F9cqV3X+FCiQwMqvX9yy3CCA+wAdIkPJS2n8rq+Zu7y3Qzt09y9/beJcm//WQUebdKzCjhQUEK3NlHu7b9Pc+Ys3122/h7O7dMcgHP7NGfO8j3l6306eytnnNSYyLCgGuvPkZi3Kp2hPRq5fxYtI9w/i5wijzbpOUUcKHTSrWWE+2fRoxFzV7kTll8u3MV1A1sS4O8+BESF+e4omtUbUmnaOIL4RhHuv/fEdsz9yfPkeu7ibZw70D0iamDfNvz8+04sy2Lbjkx6dHePnIhqUI+wkEDWbkoDIKS+u8+lThclJU5MLdj9bV2/l9gmocQ2DsXf386pp7Vk2aKdHm3q1a/4ey8sKC3vV/L2bBKOd4+cCI8Mpn5IANs27q2x2I8We+delP76AwCuHRswwSGYsEjPRiVFuLascn/tLMW1awsmIhoAv56DKV00BQrcoyg5kF1DkR8djYcOYPvHkwHY98sqAiLCCIqNrvL6lmXhH+a+4xwQHkrBnvTqCNMrFm1dSWZerrfDqDbz1u5l6Imx7v1+83ByC0oPv99vHu7e758Yy9w17r9zY+BAYSng/j8mvPK5QW0xb0MmQ49znwN1axpKbmEp6bmVz2O6NQ0lJqz29vNPm9al0Sg+grgm4fj72+l3Rlt+/tEzMVM/5ND7fpHaqkpJCmNMW2PMXGPM2rLXXYwxD1dvaP8sPa+E2JCKC29HiD/pByonKf7JntxiducWc3KTykPFfEladiGxkcHlr2MbBJOe9ZckRVYhjsiKCy1HZDBpByUyXv5mA/3v/J5pS5O59dz21R90NUjL2E9cTMUQx9iYMNIy9v/rz1m9bjclJU6aNvbt5NShpOcWE3vQCYYjLIC0Qxygq+Lb39Lp3SbiKEVWfdIyC4iLOmj7jwwmLbOgUhuPv5GD2uzLKSSmgXtZdEQQ+3IKy9bJ54dlu7nkdN+f5vSntOwiYhtU/J3HNgg6ZMLScdAUHkdEEGnZ7hPaHen5/LY1i4ueWcrlL/7Kmh05NRP4f5C294Dn33t0CGl7D3i0ST+ojZ+fjdD6gWTnFNKuVUPmLf6D0lIXySk5rNucTkp6xb5i5N3fcsrQt6lfL4CBfSuPtvM1mRn5NHRUTOuKjK7Hvoy8Su1mfbOBm4ZP5JM3lnHNHScD0Kx1JMt/2oWz1EXanv1s27SPvWmV1/V1JrwhVnbFyDkrOwMT3vDwKwTXx57QE+fmFQDYYppgopsQdPvLBN35avkIi9qiXmMH+btSy1/nJ6dSr/GhR4D1+GAcZ66YTMLDN5W/t+bx12hx2dmcu+tH+s18h+W3PFntMcvRkZZTROxB+/TYiMBDJikc4Qft98MDSStr88B5bXh+6jb6j17Cs1O3cseQ2jF69lDScoo9fxbhgYdMUhwr9qXnEe2ouEZpGBPC3vQDldpN/d9qrhr6Ee++upib7u5b/n7qnlxuuvQL7h71DWtWHHrUuYivqepIignAA0AJgGVZq4GLD9fYGDPKGLPcGLP83cWHHoLlS2Ztyeb01uHYbcd+2vH2YR2Y/+IZnN2jCZ/N3e7tcLwmfe9+7h0zlXEPn42tDvzeD2fqqgzW7snjmlMbeTuUGmWMKb/LMO6jldx9aZc6tR2UOi1y8kv48t6Tuef8ttzx7iosq/YN/f8nwwYnEBsTwvDrP2fcqws4rlOcx37+vefPZ9G3oygucbL0913eC/QoO3NYB96YOJzLbzqBbz50jygYcFYbomLqce/IaXzw8i+06xx97G/zNhuBVz5EycJJWPtSyt6zY4tuTOH4uyj6cCwBF98BwZXr+dR2S0bczcwu5/BD7xFE9z6eFpcPBaD5JUP448NJTI7vy4LBo+j1ybPolmvd8OXi3dx/bmvmP9aL+4e24eEvN3o7JDnKzrmwCx9OuZKRt/Ti8/eWARDZsD6fTr+KNz6/hOvv6M3TD39P3oFjN6FTnSyXdUz+81VVLZxZz7KsX43ngaz0cI0ty3oHeAeg+LULj2rvv1i9l2/WuecjJ8TUI/WgkRNpB0qICfn7aR2HMntzFg/1a3LUYjyaPpu7nYk/uusFJLSIIPWgO8epWQXENPAcnh7TIIi0zIo7qmmZBTgiKg9hP6tnY65/6RduOa/2jaZwRId63A1NTc/FEV25eNThHMgr4oa7vuL26/vRLaFxdYRYLT7/JZWvf3MPze3cOITUg6ZopOUW4/iXQxqXbMvhnR9389E1nQjw882ZX599t4Wv57mTaZ1bNSBl30Hbf2YBjoNGTYB75JDH38hBbaLCg0jPKiCmQTDpWQXlUzvW/pHFna/8DED2/mIWrkzBz27jtBN9a9v4bMFOJi5OBiChWRipB42iSs0qJOYvf+cxB42cAPdIrD9HVsQ2COT0bjEYY+jSPAKbgawDJUQeYmqQtzkahnj+vWccwNHQc9RbTFmb2JhQSktd7M8rIiI8CGMMD9zcr7zdxTd9SfN4z5FTgYF+DDilFXMXb+OUE5tVa1+OVGR0PY/RD5kZ+URFH/4C+5TTWvLOc+5t2+5n4+rbTi5f9uCo6TRq6tt1mP7k1/sc/HoOBsC1c3P51A0AExGNlXPoaSsBF9+JlbGb0gXflr9nZWfgStoILidWZipWejK26Ca4dh56fr8vaHPTpbS+7kIA9i1bQ7342PJl9ZrEkr87rdI6f07jKD2Qx47PpxN1Uhe2fzKFliOHs2DQtQDsXboSe1AggQ0blNesEN/y2U/JTPzZnWBLaBpK6kH79NTsImLCPafqxRw0cgLcoy/+HFkxeVkqD57nHjE2qFs0j3xVu5IUn/2cwsRl7m09oUmI588ip+iYmNZxOFEx9clIqxg5sTf9AA1jDj/6u98ZbXn1qQUABATYCQhwnwe16RBDo8bh7N6ZRduOtaMGl9RdVb0y2WuMaQVYAMaY4UBKtUX1Ny7p0rC82GViy3CmbsjCsixWpeYREmD7x9oTf/VHZiG5RU66xlZ+KogvGDGgBZPG9GPSmH4M6B7HlCXJWJbFym2ZhAb7H/LCJCTYj5XbMrEsiylLkkk8zn1CsyO1Ygc3b0UqLeN8e3rL4XTu0IikXZkk78mmuMTJzDnrSezdtkrrFpc4ufm+iQw9s0v5Ez9qi0tPjmXSTV2YdFMXBrRvwJSVGe5tf9d+QoPsh6w9cTjrU/IYPfUPXhvRjqj/kNirKSMGtmHyM2cw+ZkzGHBCY6Ys3OHe/rfsI7Sef/n0jT/FNAgmJNiflVv2ubf/hTsYcII72ZB4fCMmL9wBwOSFOxhwgnv0yNxXhzDvtbOY99pZnHFyEx69prvPJSgARvRryqSHejHpoV4M6OpgytI97p/FH9mEBvsd8mQ1JMjOyj+y3T+LpXtI7BoDwICuDn7Z7L4o2Z6WR4nTooGPbged28eSlJxFckqO++993iYST2np0SbxlJZM/s5dPPS7H7fQ47h4jDEUFJaQX+BOZC9eloSf3Ubr5lHk5ReTvs+9PywtdfHj0u20bPqXugY+qHWHhqQk55K2Zz8lJU5+mvMHJ5zq+SSaPbsqpu78tmQXcfHu6v1FhaUUlv0sVv26G5vdRnyLiBqL/UiULppK4bM3UPjsDThXL8bvpNMBsDXvgFWYh5Vb+QLbf8jVmKD6FP+lMKZzzRJsrbu6X9QPw8Q0wbXXK6czVbbljc/LC2AmT55DiyvOBSDq5K6U5OynMNWzcLSx2wmMcifjjJ8fjc/qR/Zad2HY/J0pOAb0BCCsfUtsQYFKUPiwEac2YdI9JzLpnhMZkNCQKctS3fv9HTl/v9/fkePe7y9LJTHBPR0qJiyQZduyAVi6JYtm0cF//XY+bUTPOCbd2o1Jt3ZjQMdIpqxwnwOt3Lmf0CC/YzpJ0a6jg927skndnUNJiZMF32+mR58WHm1278wu//rXn3bQuGkEANlZBTidLgBSknPYvSub2Ma1I0EtdVtVR1L8H+6REe2NMbuB7YDXn+/Uu3koC5NyGfzxRoL8bTw5oOJkbfgXm5h4ifuRei8u3sOMTdkUlrgY8P56hnWK5KaT3Rfus7dkM6hNBKYWDHfs2yWGhavTGHjf3PJHkP7pvEcXMGlMPwAevbwLD7y3gqJiJ707x9Cni/vC5MWJG9ieegCbgUZR9Xj8SnfF44ycQi4YvZADBaXYDHz8wx9MH9vfo9CmL/Hzs/HIXQMZefsXuFwuhp3VlTYtoxn/zo8kdIgjsXdb1qzfw833TyR3fyHzf9rCa+8uZPrn1zN77nqWr9xJdm4+k2a6h0E/9fDZdGgb+w/f1bf0aRvBwi3ZDHp5JUH+NsaeVzG39Lw3VjOprJr1898lMWPNPgpLXPR//neGdY/m5sR4nv8uifxiF3d85T5xbRQeyOsjfPcRlAB9j4tj4coUzrhtJkGBfoy7oWIu+bn3fc/kZ84A4NFruvPgm79SWOykd7c4+nRz/26vG9qeO17+mW/mb6dRw3q8dHtPr/TjaOib0JCFazMY+OiiskeQVjzB5byxS5j0kPuRvI9e0pEHPnI/grR3p4b06eQ+WT2/V2Me/mQtZ49ZjL+f4akrEnx2H+jnZ+OR2xMZefe3uFwWwwZ3ok2Lhox/bwkJ7R0kntKK4YMTuHfsbM649H3CQ4N48TH3Xfd9Wflce88kbMbgiK7PMw8NAqCgsISbHphKcYkTy7I4qVs8F5/j+xXg7X42rr2zB0/c4X78cuJZbWjasgFfTPid1u0bcmLvpsyauIHVy1Pw87NRPzSAmx/uDUBOVgFP3PE9xhgio+tx66N9vNyb/8a5/hfsnU4i+NGPyx5B+lz5sqB736Lw2RswEQ0JGDgCV2oSQfe8CUDpoimU/jwL54Zl2NsfT/CD74HLRfGUdyC/9hRb3DPzRxoN7svZW3/AmV/A0qsfLF925orJzDruXGyBAfT/7l2Mvz/GbiNtzs9sm/A/AH6/62lOnvAk7e+4CiyLpVfd76WeHH2fXzOGfm270zAkgl3jpvLY9Am8v2Sat8M6avp2jGLhhkwGjl3q3u9fXDES9rznljHpHvcx8dFhbXngi43u/X6HKPp0cCdgx1zUjnGTtuB0WQT62RhzYe0bSfunvu0asHBTNgOf/50gfzvjhlfUlDpv/Eom3doNgOdm7WDGyr0UlLjo99Ryhp8Yw82nNfVS1P+d3c/G/93TlwdvmYrL6eKMczrSvFUUH721lLYdYujZtyVT/7ea33/dhZ+fjZDQQO5+/DQA1vy+m4/f/gU/Pxs2Y7j1gf6EhdeOIuFSt5l/Mw/ZGFMfsFmWVeUqhUd7ukdt4tfdt4cOVzfTvrO3Q/Aq1w9zvR2CV9na1r4TgaPJyvLdYpTVzXTs6O0QvGqdf7a3Q/CqFqPneDsEr5ry6rFT3+TfGnFD7StEfTQ5z+7m7RC8q7Don9scw3aefry3Q/Cq5qE3++bdjqOk6IXzj8lr2sC7vvXJ31uVRlIYY6KAx4BTAcsY8xMwxrKsfX+/poiIiIiIiEgt5jwmcxQ+q6o1Kb4EMoBhwPCyr7+qrqBEREREREREpO6pak2KOMuynjjo9ZPGmIuqIyARERERERERqZuqOpLie2PMxcYYW9m/C4HvqjMwEREREREREfE9xphIY8wPxpgtZf9XKkxkjOlmjPnZGLPOGLO6qgMdqpqkuA74HCgu+/clcL0xZr8xpvaUxRYRERERERH5N5zWsfnvyNwPzLUsqw0wt+z1X+UDV1iW1QkYBLxsjIn4pw+u0nQPy7JCqx6riIiIiIiIiBzDhgL9yr7+CFgA3HdwA8uyNh/09R5jTDoQDWT/3QdXtSYFxphzgD8frL7AsqzpVV1XRERERERERI4ZDsuyUsq+TgUcf9fYGHMSEABs+6cPruojSJ8GTgQ+K3vrNmPMKZZlPVCV9UVERERERETEdxhjRgGjDnrrHcuy3jlo+Rwg9hCrPnTwC8uyLGPMYeePGGPigE+AKy3Lcv1TXFUdSTEY6PbnBxpjPgJWAEpSiIiIiIiIyDHLch1x/QafVJaQeOdvlp92uGXGmDRjTJxlWSllSYj0w7QLA2YAD1mWtbQqcVW1cCZAxEFfh/+L9URERERERETk2DEVuLLs6yuBKX9tYIwJACYBH1uWNbGqH1zVJMVTwApjzIdloyh+A8ZW9ZuIiIiIiIiIyDHjaeB0Y8wW4LSy1xhjTjDGvFvW5kLcdS2vMsasLPvX7Z8+uKpP9/jCGLMAd10KgPssy0r9d30QERERERERkdrOsqx9wIBDvL8cuLbs60+BT//tZ/9tksIY0/0vbyWX/d/IGNPIsqzf/+03FBEREREREak1nP9Y61GOon8aSfHCId47uGpI4lGMRURERERERETqsL9NUliW1R/AGHMhMNuyrFxjzCNAd+CJGohPREREREREROqIqhbOfLgsQXEq7tET7wJvVl9YIiIiIiIiIlLXVDVJ4Sz7fwgwwbKsGUBA9YQkIiIiIiIiInVRlZ7uAew2xrwNnA48Y4wJpOoJDhEREREREZFayXJZ/9xIjpqqJhouBL4DBlqWlQ1EAvdUV1AiIiIiIiIiUvdUaSSFZVn5wLcHvU4BUqorKBERERERERGpezRlQ0RERERERER8QlVrUoiIiIiIiIjUPU7VpKhJGkkhIiIiIiIiIj5BSQoRERERERER8QlKUoiIiIiIiIiIT1BNChEREREREZHDcakmRU3SSAoRERERERER8QlKUoiIiIiIiIiIT1CSQkRERERERER8gmpSiIiIiIiIiByG5VRNipqkkRQiIiIiIiIi4hOUpBARERERERERn6AkhYiIiIiIiIj4BGNZ1Tu/Jrvo6zo7gSf8QJ63Q/Cqko+neTsEr/K/7Exvh+BdxfnejsCrTKjD2yF4jWvpQm+H4FXFS3d6OwSvCvy/Yd4OwausyHhvh+A9s772dgReZZ+20tsheJX13D3eDsGrtg163NsheFWrn9YYb8dQnfLvP/OYvKat9/Qsn/y9qXCmiIiIiIiIyOG4jskchc/SdA8RERERERER8QlKUoiIiIiIiIiIT1CSQkRERERERER8gmpSiIiIiIiIiByO0+XtCOoUjaQQEREREREREZ+gJIWIiIiIiIiI+AQlKURERERERETEJ6gmhYiIiIiIiMhhWC7L2yHUKRpJISIiIiIiIiI+QUkKEREREREREfEJSlKIiIiIiIiIiE9QTQoRERERERGRw3GqJkVN0kgKEREREREREfEJSlKIiIiIiIiIiE9QkkJEREREREREfIKSFCIiIiIiIiLiE1Q4U0REREREROQwLJcKZ9YkjaQQEREREREREZ+gJIWIiIiIiIiI+AQlKURERERERETEJ6gmhYiIiIiIiMhhWE7VpKhJGkkhIiIiIiIiIj5BSQoRERERERER8QlKUoiIiIiIiIiIT1BNChEREREREZHDsFyqSVGTNJJCRERERERERHyCkhQiIiIiIiIi4hOUpBARERERERERn6CaFCIiIiIiIiKH4XKqJkVN0kgKEREREREREfEJSlKIiIiIiIiIiE9QkkJEREREREREfIKSFCIiIiIiIiLiE2p14cyff9rMi8/MxOVycc75x3PlyL4eyz//eDFTvl2On91GRIP6PDzmPOIaNQDg1Rdns3jRZiyXxUk9W3HnfUMwxnijG//JoqXbGPvyHFxOF8PP7saoK3p6LF+2YidPvTKHTdvSeWH0uQxKbA/Ahs1pPP7cbPLyi7HZDDdc2YvBp3X0RheOmL3fNdhadMcqKcb5/atY6dsrt+l1KbaOfSGwPiWvX1axILQhfgNvgcB6YOw4f/oUa8fvNRj90bNo6XbGvjIXl8ti+FldGHX5yR7Ll63cxVPj57FpWwYvPH42g/q381KkR2bRr0mMfe0nXC4Xwwd3ZNSlx3ssLy52ct/Tc1i3OZ2IsCBefHQgTWLDmDZnE+99taK83aY/9vHt2xfSoXU0L723lCnfbyJ3fyG/z7y+prv0ny1csoWxL8zE5bK4YGh3Rl3Vx2P5st93MO7FWWzamsaLYy9g0IBOHssPHChk8EWvcVrf9jx671k1Gfp/YlkW477ZzMJ1ewkKsDPuso50ig+r1G7dzlwe+HQdRSUu+nRqyIPD2mKM4Y7317AjPQ+A3IJSwoL9mHR/DxZv3MeLU7dSUurC38/GPUPb0KNdZE1371/zGzgKW5vjoaSIkimvYKVu+0uDQPwvuA/TIA5cLlxbfqV07kcAmKad8B94HcbRnJJvnsW1YYkXevDf1fVj36JF6xg39mv3/n54L64bNdBj+YcfzGXixMXY7TYiI0N5cuxlNG4cxYYNuxj9+JccyCvEbjNcf8MgBg8+wUu9+G8sy2LcpC0s3JBJkL+NcZd0oFN8aKV263bt54EvNrj3Ax0iefC8Nhhj2LB7P49/vZniEhd2m+HR4W3p0qzyfqS2eu/yhzir8ymk78+i8xMjvB3OUbdwyWbGPj8dl9PFBeeeyKirPc/5l/2+nXHPz2DT1lReHHcRg07rXL6sw4kP0bZ1LABxseG89dIVNRr70RJ12/3U79kbV2Eh6eMepnjzhkpt4l54E3tUNMZup2DV7+x9cSy4XAS0bkv03Y9igutRmrqbtNH3Y+XneaEXtZflUuHMmlRrR1I4nS6eGzeNl9+8gi8n38r3s9bwx7Z0jzZt28fx0Rc38tk3t5B4eidee+k7AFav3MnqlTv5bOLNfP7tLaxfu5vfl1e+wPVVTqeLMc9/z4QXLmT656OYMWc9W7fv9WgTFxvGUw+fxVmne16cBAX58cyjZzP9s+uY8OJFPPXKHHL3F9Zk+EeFad4dExFHyQc345zzJvbEUYds5/pjGSVf3FfpffvJw3FtXkLpZ/dQOvNF/BKvq+6Qq4XT6WLMiz8w4fnhTP/0GmbM2VB5W3CE8dSDZ3LWaR28FOWRczpdjHllIROePovpH1zKjHlb2Loj06PNxFnrCQsN5PtPL+fK4d144Z2fATj7tHZMnnAxkydczDMPnE6T2DA6tI4GoH/P5vzvjeE13p8j4XS6GPPsdN595XJm/O9mpn+/hq1/eO774mLDeeqx8zhrYOdDfsbLb83jxOOa1US4R8XC9ftISs9n9qO9GH1xB8Z8tfGQ7UZ/tZExl3Rk9qO9SErPZ9H6fQC8dE1nJt3fg0n39+CMrjGc1jUGgAb1/Xnz+m5MfbAnT13Wifs+WVdjffqvbK2Px0Q1ovi16ymZ/jr+Q248ZDvnz5MofuNGit+5DVt8B2yty5J6ORmUTHkZ15ofazDqo6OuH/ucThdPjPmKdybczLTpjzBjxnK2bk3xaNOhQxO+nng/U6Y+zBkDj+P55ycBEBQUwNPPXMn06Y8wYcLNPPXURHJz873Rjf9s4YZMkjIKmP3gyYy+sB1jJm46ZLvREzcx5sJ2zH7wZJIyCli00X2seH7qNv5vYHMm3XMit5zZguenbTvk+rXVhz/PYNCrd3g7jGrhdLoY8/RU3h1/FTMm3s7071ax9Y80jzZxsRE8NXoYZw3qWmn9oEB/pnxxC1O+uKXWJijq9ehNQHwzdl48hIznRhN998OHbJf6yN0kXzWcXZefhz2iASH9zwAg+r7R7HvrZZKvPJ+8hXOJuPTqmgxf5F+rtUmK9WuTadI0isZNIvH39+P0QZ1ZON8zo3jCSS0JCg4AIKFLPOlpuQAYA0VFpZSUOCkpLqW01ElkVEiN9+G/Wr1+D02bNCC+cQMC/O0MPq0Dcxdt9mjTJC6Cdq1jMDbP0SEtmkbRPN59p9ARHUpkg/pkZteuExUAW6sTcW1wn2RbqVswgfWhfkSldlbqFsjLrvwBlgUBwQCYwHpYeZmV29QCqzeklG0LEWXbQnvm/rTVo02TuPBDbgu1yeqN6TRtHE58o3B3PxPbMHeJZ2Jx7uLtnHuG+67pwL6t+Pn3ZCzLM+s9Y95mBie2KX/drWMsMVH1q78DR9Hqdck0i48kvkkkAf5+DDm9M3N/9Lxob9KoAe3bxGI7xOiwtRv2sC/zAKec3LqmQj5i89ZkMPSkOIwxdGsRTm5BKek5RR5t0nOKOFBYSrcW4RhjGHpSHHPXZHi0sSyL2SvSGHK8+45ax/gwYsIDAWgTV5+iEifFJa6a6dR/ZGvXA+eqeQBYuzdBYH0IaeDZqLQI14417q9dpbhStmFCo9zr5KRjpe9w7wNrmbp+7Fu9egdNm0YTH9+QgAA/Bg8+nnlzV3m0OblHO4LLznu6dm1BWmo2AC1aOGje3J2ci3FEEBUZSmbmgRqN/0jNW7uXoSfGuvcDzf9uP+CkW/Oy/cCJscxd405kGQMHCksB9/8x4QE13ofqtGjrSjLzcr0dRrVwH/eiKo57Z3Rh7gLPc373cS/ukMe9Y0G93v3ZP3sqAEXrVmMLCcUe1bBSu/LREXY/jL9/+XmQf3wzClcuByB/2c+E9D2tZgIX+Y9qbZIiPS0XhyO8/HWMI4yM9MPvnKdO+o2ep7ovTjp3bcrxJ7ZgyIBnGDzgGXr0akOLljHVHvPRkpZxgDhHxRDF2OhQ0jL2/+vPWb1+DyUlTpo2bvDPjX2MCYnE2l9xB806sA8TElXl9Z1Lv8LWoQ/+176D37kP4Zz/XnWEWe3SMg4QF1Mx3NW9LdSuE8+qSNt7gLiYikRibMMQ0jI8hymm780rb+NntxFaP4DsXM87pbPmb2XIQUmK2igtYz+xB+37HI4w0jKqdmLqcrl45uXZ3HfbwH9u7EPSsouIbRBU/jo2IvCQFyeOiIo2johA0rI92yzflk1UaADNY+pV+h7fr0ynQ5MwAvx9+7BoQqOwcg/a9+3fV56AOKTA+tjanoRr+6rDt6kl6vqxLz0tm9i4ipgdsQ1IS8s5bPtvJi6hd59Old5fvXoHJSWlNG1a+QLHl6XlFBEbEVj++rD7gfCKNo7wQNLK2jxwXhuen7qN/qOX8OzUrdwxpFXNBC5HLC095y/HvfAqH/cAiopLOf+y17nwyjeZM399dYRY7fwaxlCanlr+ujQ9Db+Gh752iXvhLZpP/xFXfj55C34AoGT7Nur1TgQgpP9A/Byx1R+0yBGoUk0KY0w0cB/QESg/C7QsK7Ga4jqqZk1fyYZ1u3nrg2sB2LVzHzu2ZzDth3sAuGXUh6z4bQfHHd/ci1HWrPS9B7h3zDSefvgsbLX4Dvt/ZWvXG9e6+bh+n4aJa4vfoFsp+fgOoPbdXZSqWbUhlaAgP9q2qHoy61jz+cRl9DmljcfJXl0y47fU8lEUB9uScoAXpm7l3ZuO80JU1cjY8B92D85fp2Flp/1z+zqgrhz7pk79hbXrkvjkE8/h/+npOdx374c89fSV2Gy+nZA72r5cvJv7z23NGV1jmLUinYe/3MgHN3XzdlhSA+ZPvwdHTDi7kjO58oZ3advaQdP4Y/dcIOWuGzABAcQ8+jTB3U+mYPnPpD/1KA1vv5/Iq64n76f5WCUl3g6z1rFcvj3S8lhT1cKZnwFfAUOAG4ArgYzDNTbGjAJGAbz02iiuuvboDymKcYR53EFIT8slOqZyAaRfl27lwwk/8ub7IwkIcHd3wdz1JHSJp149d7a956ltWLtqV61JUjiiQ0hJq8ggp2bsxxFduXjU4RzIK+KGu//H7aP60i2hcXWEWC1sXQdhS3BvS1baVkxow/KUggmJwjqwr+qflTCA0m+fcH9WymbwC4DgUCioXUMlHdEhpKRX3El0bwu1Z+pSVTkahpCSXjFCJHXvARzRntM0YhrWJyX9ALHRIZQ6XezPKyYirOLO+sx5tX8UBbiHqqcetO9LS8vFEV214m8rVu/it5VJfDFxGXn5xZSUOqkXHMDdt5xRXeH+Z58t3MXEJbsBSGgaRmpWxaiY1Oyi8mkaf4oJDyQtu6JNWnYRjoPuupY6XcxZlcHEe07yWC81q5BbJqzm6cs70TS68ggLX2A/YTD27u7RL649WzBhB+37QqOw9h963+d31s1Y+/bg/GVqDUVaverqse9PMY4IUlOyyl+npWZ5jCj905IlG3n7rdl8/MmdBAT4l79/4EABN9zwBrfffg7durWokZiP1Gc/JTPxZ3fdjYSmoaQeNDrqsPuBg0ZXpB00smLyslQePM99DBjULZpHDlPbRnyPIyb8L8e9nCof9/5cHyC+SSQnHd+S9Zv21IokRdj5FxN29jAAijasxS+mIsnuF+OgdG/64VbFKi4m76f51O/dn4LlP1Oyczspd7oLhPvHN6Nezz6HXVfEF1Q1jR5lWdZ7QIllWT9alnUNcNhRFJZlvWNZ1gmWZZ1QHQkKgA6dGrMraR97kjMpKSnlh9lr6NOvvUebTRv28PSYKTw3foRHzYnYuAhWLN9OaamT0hInK5bvoHnL6GqJszp07tCIpOQskvdkU1ziZOacDSSeWrWLr+ISJzff/w1Dz0wor3peW7hWzab0s7sp/exuXNt+xdbBXdnZxLbBKs4/dO2Jw8nNwNa0i/vryMZg9691CQqAzu3jSNp18LawkcRTak+tgarq3D6GpN05JKfkuvs5bwuJPZt7tEns1YLJ37tPOr/7cRs9jmtc/sQel8ti1oKtDOlf+5MUnTs2ZsfOTHbtzqK4pJQZP6whsU/V/pZfeHI4C6bfxbypd3LfbQM5d3BXn0xQAIzoE19e7HJAlxim/JqCZVms3J5DaJDfIS9OQoL8WLk9B8uymPJrComdK/brP2/KpIWjnse0kdz8Em54ayV3ntOa7i0jaqpr/5pz+UyK37mN4nduw7VpKfau7sOvadwOivLhQFaldfz6X4YJqk/pdxNqOtxqU1ePfX/q3LkZSUnpJCfvpbi4lJkzf6N/YhePNuvX7+Lxxz7n9TduJCqqIoFTXFzKLTe/w9ChJzNwUPeaDv0/G3FqEybdcyKT7jmRAQkNmbIs1b0f2JFDaPDh9gN2Vu4o2w8sSyUxwT2tJSYskGXbsgFYuiWLZtHBNd0d+Y86d2zMjl172bU7033c+341iX2rVgw8J7eA4mJ3LZLMrDx+X5VE61oyxTv32y9JvvoCkq++gLxF8wgddA4AgZ264DpwAOc+z8LBJji4ok6F3U79nn0oTnLX77JHlD25yhgaXDmK3Cn/q7F+iPwXVR1J8eeYoBRjzBBgD+DV57T5+dm5+8GzuPXGj3A5XZx97vG0bO3g7dfn0KFjY/r078CrL84mP7+YB+/+EoDY2Aief/UyEk/vxPJftzFi2GtgoOcpbejdr/actPj52XjkztMZeceXuJwWw87qQpuW0YyfsJCE9nEk9m7DmvV7uPmBb8ndX8j8n7bw2nuLmP7Zdcyeu4HlK3eRnVvApJnuwmpPPXQWHdo6vNyrf8fa/jtW8+74X/06VmkRzu9fL1/mN+J5Sj+7GwB778uxtesN/oH4X/sOrrVzcC79H6ULP8Lv9BuxdT8LLIvS717zVleOiHtbOI2Rd07E5XIxbEhn2rRsyPh3fyKhfSyJp7ZmzYYUbn5wMrn7i5i/eBuvvbeY6Z9e4+3Q/xU/u41HbunNyPumurf5MzvQpkUU4z/4hYS2MSSe0oLhgztw77g5nHHZJ4SHBvHiIxUX38tW7yEuJoT4Rp53HZ97ewnT526moKiUvhd+yPDBHbnlqpP++u19ip+fnUfvHcK1t36M0+li2DndadMqhlfemktCh8YM6Nue1et2c/O9X5CbW8D8nzbx6tvzmPG/W7wd+n/Wt1MUC9fvZeCYJe5HD15WMc/+vKeXMun+HgA8elH78keQ9u4QRZ+OFXfKZv6WVmmqx2cLd7Fzbz5vzv6DN2f/AcC7/9edqFDfLajn2rIcW+sTCLj5HfcjSKe+Ur4sYNQrFL9zG4RG4df7IlwZuwgY9TIAzmUzcK74HtOoDQEXPghBIdjanojVdwTFb/2fl3rz79T1Y5+fn52HH7mIa0e+hsvl4vxhPWnTphHjx08jIaEZiYldeO65b8nPL+KO298FIC6uAW+8eSOzZ//G8uVbyM7OY/KkpQCMe+pyOnSI92aX/pW+HaNYuCGTgWOXuh9FfHHFedt5zy1j0j0nAvDosLY88MVGikqc7v1AB/fp6piL2jFu0hacLotAPxtjLqw9531V8fk1Y+jXtjsNQyLYNW4qj02fwPtLpnk7rKPCfdw7h2tv/gCn02LY0ONp08rBK2/+QELHJgzo24HV65K5+e5P3ce9RRt49e25zPj6drZtT+exsZMxNoPlsrjuqr60bll7/u7/lP/zIur17EPTr2biKiwkY1zF0z2afPA1yVdfgC2oHrFPv4rxD8DYDAW/LytPRoScfiZh518MQN6Pc9k/Y7I3uiFSZeav1e8P2ciYs4BFQDzwKhAGPG5Z1j/u/bKLvq6zk/zDD9Tt5w+XfHxsHBz/K//LzvR2CN5VXLsq5x9tJrT2nQQdLa6lC70dglcVL93p7RC8KvD/hnk7BK+yImvPhf9RN+trb0fgVfZpK70dgldZz93j7RC8atugx70dgle1+mnNsVvoB9h3+anH5DVt1Cc/+eTvrarTPS7AndBYa1lWf+B04LzqC0tERERERERE6pqqJim6WJaV/ecLy7IygWOsDLqIiIiIiIiIeFNVkxQ2Y0z5w7mNMZFUvZ6FiIiIiIiIiMg/qmqi4QXgZ2PMn5MNLwDGVk9IIiIiIiIiIr7Bch2TJSl8VpWSFJZlfWyMWU7FY0fPtyxrffWFJSIiIiIiIiJ1TZWnbJQlJZSYEBEREREREZFqUdWaFCIiIiIiIiIi1UpJChERERERERHxCXpCh4iIiIiIiMhhWE4VzqxJGkkhIiIiIiIiIj5BSQoRERERERER8QlKUoiIiIiIiIiIT1BNChEREREREZHDsFyqSVGTNJJCRERERERERHyCkhQiIiIiIiIi4hOUpBARERERERERn6CaFCIiIiIiIiKH4VJNihqlkRQiIiIiIiIi4hOUpBARERERERERn6AkhYiIiIiIiIj4BNWkEBERERERETkMy6maFDVJIylERERERERExCcoSSEiIiIiIiIiPkFJChERERERERHxCUpSiIiIiIiIiIhPUOFMERERERERkcOwXCqcWZM0kkJEREREREREfIKSFCIiIiIiIiLiE5SkEBERERERERGfoJoUIiIiIiIiIoehmhQ1SyMpRERERERERMQnKEkhIiIiIiIiIj5BSQoRERERERER8QmqSSEiIiIiIiJyGJZTNSlqUrUnKcLTd1f3t/BZpklXb4fgVSaojufACnK9HYF3BYV4OwKvcq1d7u0QvMY0CPd2CF4V0M3h7RC8ykQ283YIXmW8HYAXuQqLvB2CV1nP3ePtELzK3POct0PwquQGQd4OQeSYoekeIiIiIiIiIuITlKQQEREREREREZ9Qx8fji4iIiIiIiBye5XJ5O4Q6RSMpRERERERERMQnKEkhIiIiIiIiIj5BSQoRERERERER8QlKUoiIiIiIiIiIT1DhTBEREREREZHDsJyWt0OoUzSSQkRERERERER8gpIUIiIiIiIiIuITlKQQEREREREREZ+gmhQiIiIiIiIih2G5VJOiJmkkhYiIiIiIiIj4BCUpRERERERERMQnKEkhIiIiIiIiIj5BNSlEREREREREDsOlmhQ1SiMpRERERERERMQnKEkhIiIiIiIiIj5BSQoRERERERER8QmqSSEiIiIiIiJyGJZTNSlqkkZSiIiIiIiIiIhPUJJCRERERERERHyCkhQiIiIiIiIi4hOUpBARERERERERn6DCmSIiIiIiIiKHYblUOLMmaSSFiIiIiIiIiPgEJSlERERERERExCcoSSEiIiIiIiIiPkE1KUREREREREQOw3KqJkVN0kgKEREREREREakyY0ykMeYHY8yWsv8b/E3bMGNMsjHmtap8tpIUIiIiIiIiIvJv3A/MtSyrDTC37PXhPAEsrOoHK0khIiIiIiIiIv/GUOCjsq8/As49VCNjzPGAA/i+qh+smhQiIiIiIiIih2G5js2aFMaYUcCog956x7Ksd6q4usOyrJSyr1NxJyL++vk24AXgMuC0qsalJIWIiIiIiIhIHVOWkDhsUsIYMweIPcSih/7yOZYx5lCZnJuAmZZlJRtjqhxXrUtSLPp1J2Pf+AmXy2L4mR0YdUl3j+XFxU7ue2Yu67ZkEBEWxIsPn06T2DAANv2xj0df+pG8/GKMMUx8YxiBAX5Mn7eFtz//HWMgJqo+zz0wgAbhwd7oXpUtXLSOsWP/h8vl4oLhpzBq1CCP5R98MIevJ/6E3W4nMjKEcWOvoHHjKABGXjueVau2c3z31rz99v95I/wjZlkWT/24m0XbcwnytzH2jKZ0jKlXqd0ri1OYuiGT3CIny/6vi8ey2ZuzeGNpKgZDu+ggnj2zeQ1F/98sWraTsW8sKdv22zPq4uM8lhcXO7nv2Xms27LXve0/dBpNYkOBsm3/5YXk5Ze4t/3Xz8Plsrj9iTnsTMnFbjP079GMu6492Rtdq5JFv2xn7Cvz3f0/K4FRl3nGWlxcyn1jZ7FuU7q7/6PPoklcOMUlTh577gfWbkrDZgwP3tafk4+LB2Dm3I289fEvuFwW/Xq15O4b+3ija//IsizGfb6WhavTCAqwM27kcXRqHlGp3bod2Tzw7gqKSpz06eLgwUsTMMbwyrcbmbciBZsxRIYF8tTI44hpEMS0n5N5d+YWLAvqB/nx2BVdaN80vOY7+A8sy2LsJ6tYuDKVoEA7T406gU4tKtdmWrs9iwfeXk5RsZM+3WJ56PKuGGOY/Usyr327nm179vO/0Yl0buled/GaNF74ai0lpS78/Wzce0lnenSKqenu/SPLshg35Q8WbswkyN/GuIva0alJSKV265L388BXmykqcdGnfSQPDm2JMYaNew7w+DdbyS920rhBEM9d2o6QIPfhf9OePB77ZgsHipzYDHx963EE+vvuTNC6fuyr6/0/mGVZjJu2nYWbsgkKsDFueGs6Na78d/Hyd0lMWZFBbkEpv43u4YVIj46FSzYz9vnpuJwuLjj3REZd3ddj+bLftzPu+Rls2prKi+MuYtBpncuXdTjxIdq2dl9jxMWG89ZLV9Ro7NXtvcsf4qzOp5C+P4vOT4zwdjjVJnzUXQQdfwpWUSFZr4ymZNumSm2iHh+PPTIK7H4Ur1tB9lvPgstVvjzk3BGEj7ydlBGn4crNqcnwpZayLOuwox+MMWnGmDjLslKMMXFA+iGa9QR6G2NuAkKAAGPMAcuy/q5+Re2qSeF0uhjz6iImjDuL6e9dzIz5W9malOnRZuKsDYSFBvL9xyO4clgXXpiwFIBSp4t7nprD6Nv7MP29i/n4haH42W2UOl2Me+MnPn7hHKZOuIh2LaP4dPJab3SvypxOF2PGfMG7E25mxvTHmD5jGVu37vFo06FDPN9MfJBpUx9h4MDuPPf8t+XLrh15Bs8+c3VNh31ULdqxn51ZRcy8qgOPD4jnibnJh2zXr2UYX17SttL7SVlFvLssnU8ubMOUK9pzX9/G1R3yEXFv+4uZMG4w09+9sGzbz/JoM3H2RsJCAvn+o0u48vzOvPDuQdv+0/MYfVsfpr97IR+/cDZ+dvef/tUXdGHW+xfx7ZvD+H1dKgt/3VnjfasKp9PFmBfnMuH585n+yVXMmLOJrdv3ebSZOGMtYaFBfP/lSK688HheeMtdm+fraasBmPbRlbz/0nCeeW0BLpdFVk4Bz72xkA9fvoDpn1xFRmYePy9PqvG+VcXC1ekkpeUx++kBjL6qK2M+WX3IdqM/Xs2Yq7sy++kBJKXlsWiN+1gx8sxWTHmiP5PG9KNfVwdvTHWf2DRpWI+P7z+FqU/258Zz2vLYR6tqrE//xsJVqSSlHuC7FwYyZmR3Rn+44pDtRn+wgieu7c53LwwkKfUAi1anAdCmSRjjb+vJCe0aerRvEBrIm3f1YtrTp/P09Sdw71vLqr0v/8XCjVkk7S1g9n0nMHp4G8Z8u/WQ7UZ/u5Uxw9sw+74TSNpbwKJN7n3EI19v4c7BzZl61/GclhDFewvc+8tSp8W9X2zk8WGtmX738Xx0Qxf87FW/y1HT6vqxr673/68WbsomaV8hs+8+jtHntWLM5D8O2a5fh0i+uqnLIZfVFk6nizFPT+Xd8VcxY+LtTP9uFVv/SPNoExcbwVOjh3HWoK6V1g8K9GfKF7cw5YtbjrkEBcCHP89g0Kt3eDuMahV4fC/8GjUl7frzyXp9HBE3Hvr6LvOZB0i/dQTp/3cRtvAGBJ8yoHyZvaGDwONOpjQ95ZDrivwHU4Ery76+Epjy1waWZY2wLKupZVnNgbuBj/8pQQG1LEmxelM6TRuFE98ojAB/O4P7tWbu4h0ebeYu2cG5Z7QDYGCfVvy8YjeWZbF4+S7atYyifSv3SWqD8CDsdhuWZWFZkF9YimVZHMgvJiaqfk137V9ZvXoHzZrGEB8fTUCAH0MGn8jcuZ4XLT16tCM4OACAbl1bkJpacUHbs2d76tcPrNGYj7b523I4p0Mkxhi6xtVnf7GTjLySSu26xtUnur5/pfcnrt3HxV0bEl52NzGqXuU2vsS97YcRH3fQtr9kh0cb97bvTsgM7NOSn1fsKdv2k2nXMpL2rdx30xqEubf94CB/enRzJ2cC/O10bN2Q1L15Ndqvqlq9IZWmjSOIbxTh7v+Adsz9yfNCbe6irZw7qBMAA/u15effdmJZFtt27KNH96YARDWoR1hIEGs3ppK8J4dmTRoQ2cA9AqfX8c34/sctNduxKpq3IpWhvZpgjKFbq0hy80tIzy70aJOeXciBglK6tXL/XQzt1YS5v6cCEBJcsX0XFDmh7Dr0uDaRhNd37ye6tmpAaqbnZ/qKub+lMPTUZu7+t44iN6+E9KwCjzbpWQUcKCihW+sod/9Pbcac5e4LuFaNw2jZKLTS53ZsHoGjgXvUXJsmYRQVOykucVZ/h/6leev2MfT4GHf/m4WRW1hKem6xR5v03GIOFDrp1izM3f/jY5i71p3I27G3gBNbukfI9GrbgB/W7AVg8eYs2sXVp30j993nBvX9sdt8N0lR1499db3/fzVvQyZDj4t2/100DT3k3wVAt6ahxIQFeCHCo2f1umSaxUcR3ySSAH8/hpzRhbkLNni0adKoAe3bxGH7F8OpjxWLtq4kMy/X22FUq+AefcmfNwOAkk1rMfVDsTWIqtTOKig7j7Pbwc8fDhp8H37tHeR88CpYx2Zthepmuaxj8t8Reho43RizBXe9iacBjDEnGGPePZIPPux0D2PM+X+3omVZ3/7d8uqQtjePuJiKBEJsdH1WbfQcVZK+7wBx0e4TLj+7jdD6AWTnFrIjORtjDCPvm05WTgGD+7fm2ouOw9/PzmO39eGc676iXpA/zRqH8+gtvWu0X/9WWloWsXEVQ50dsRGsXrX9sO0nTlxMnz4JNRFajUnLKyE2tOLCyxHiT9qBkkMmJA4lKct9MXbZV1twWRY39Yjl1OZh1RLr0ZC2N798uwaIbXiobT/v0Nv+7mwMhpH3zyArp5DB/Vpx7UXdPNbNPVDE/KVJXHF+Z3xRWsYB4mIqLjJjo0NZtcHzTkD63oo2fn42QusHkp1TQLvWMcxbvI0hp7UnNX0/6zankZK+nx7HN2X7rkySU3KIjQ5lzk9bKfHBC1SAtOxCYiMrpqDFNggmPauQmIig8vfSswpxRFa8dkQGk3ZQIuPlbzYwZfEuQur589G9vSp9j28W7qR3Z9+b6gCQllVAXNRB/Y8MJi2rkJgGwQe1+cvPKDKYtL8kMv7Od8t207G5Ownma9Jyi4mNqLi4jA0PID2nyOPCKz2nCEd4RRtHeCBpZRdsrR31mLtuH6clNOS7VRmk5Ljf37G3AIzh2glryMwrYXDXaK7tH19Dvfr36vqxr673/6/Scv76dxFIem5xrU9IHEpaeg6xjoqpeA5HOKvX7qry+kXFpZx/2ev42W2Muqovp/XvWB1hSjWyR0Xj3Fsxesa5Lx17VAyurH2V2kaNHk9A204U/raEgiVzAQg6uQ/OfRmU7vDNmzFSO1mWtQ8YcIj3lwPXHuL9D4EPq/LZf1eT4uy/iwk4bJLi4Cqhbz11AaNGVD4hrmmlTovf1qYw8fVhBAX6cdU90+jUJpoTusTx5bR1THrrAuLjwnjitZ9454sV3HjZ8d4O+aiYMvUX1q7byaef3OntUHxKqQVJ2UV8MLw1aQeKufLrrUy6rB1hQbWuTMs/KnVa/LYulYmvnefe9u+dTqc2DenZvUnZchd3jZvL5eclEB/nu4ma/2rY4AT+2LGP4dd9SqPYMI5LaITdZggPDeKxu07jzsemY2yG4xIasWt3trfDrTa3D+vA7cM68M70LXw2dzu3nNe+fNkvG/byzaKdfPrgqV6M0Hu2JOfywpdree++Y7P/Yy9sy9jJ23hzzi4SO0biXzalw+m0+H17Dl/fdhxB/jaufnsNnZqE0LNN5XoftU1dP/bV9f6Lp/nT78ERE86u5EyuvOFd2rZ20DS+8l14OTbse+xW8A8g8u4nCOxyAsUbVhF6wdXsffRmb4cmUmWHvSKzLOs/T1w8uEqotevlozamyNGwPinpFcPRUzPycPxlakZMVAgpGQeIjQ6h1Olif14xEWFBxEbX54TOceUFMfue3JT1WzIIqefOuDdt5M5Qn9m3FRO+PPR8Z1/hcDQgNaViCGdaajYOR+WTyiVLNvDWW7P49JM7CQjw7ekMVfHFqgwmrnFnjBNi65G6v2J6R9qBEhwhVe+jI8SfLrH18LcbmoQH0rxBIEnZxXSO9c0khaNhPVIyDpS/Tt2bh6PhX7f9+ofe9hv+Zds/qSnrt+4tT1I8+tJCmjUO58rzfXfOriM6hJT0/eWvUzP242joWSAtpqG7TWxMKKWlLvbnFRERHowxhgdu7V/e7uIbP6d5fCQAiae0IvGUVgB8NXW1Tw11/2zudib+6K6RkdAigtTMilEBqVkFxDQI8mgf0yCItIOma6RlFuCI8GwDcFbPxlz/0i/lSYpNu3J45IOVvH1nDxqE+M4dyM9+2MbX8913iTu3bEDKvoP6n1mA4y/9dzQI8vwZZRaUT+X4O6n78rn55Z955oYTaOqoXHTPWz5bvIeJv7in6yTEh5KaXVS+LDWnmJhwz2H7MeGBpOVUtEnLKcJRdke5ZUw93hvlHiW1PSOfHze6azk5IgI4oWU4DcpGoPVpH8n63Xk+m6Soq8e+P9X1/gN89nMKE5e57yYnNAn5y99F0TE5igLAERNOalpFkcO0tBwc0VW/qeCIcZ/jxjeJ5KTjW7J+0x4lKWqB+oMvoN7AcwEo2bIee8OKpzvao2Jw7jtUjcIyJcUULP2RoJP74szah93RiJjxn7vXbRhD9MufknHnVbiyK4/EEPEFVapJYYwZYoy51xjz6J//qjuwQ+ncLoak3dkkp+RSXOJk5oKtJPZq7tEmsVdzJn/vLgr33cJt9OjWGGMMp57QlC3bMykoLKHU6WLZqj20ahZJTMP6bEvKIjPbfXK75LdkWjb1zRO0P3Xu3IwdSensSt5LcXEpM2YuIzHR8wJz/fqdPPrYZ7z5xo1ERR0bd8cv6RrNN5e155vL2pPYKpypGzKxLItVKXmEBNirPNUDYECrcJYluy/6swpK2ZFVRHy4757cuLf9HM9tv2czjzaJPZsx+fvNAHy38A96dGtUtu3He277q1No1cy9jb/8wa/szyvmwRu9P9rp73RuH0tScjbJe3Lc/Z+7icRTW3m0STy1FZNnrwPguwWb6dG9KcYYCgpLyC9wJ7QWL9uBn91G6xbuk7N9WfkA5Owv5ItJKxl+lu9MdxkxoAWTxvRj0ph+DOgex5QlyViWxcptmYQG+3tM9QCIiQgiJNiPldvcfxdTliSTeJy7mvuO1IoE17wVqbSMc1+M79mXz62vLeOZ67rTItZ3LtABRpzeisnjTmPyuNMYcHwjpvyU5O7/1n2E1vP3mOoBENMgmJBgf1Zu3efu/09JDDg+7m+/R25eMde/sIS7Lkqge9uGf9u2po04pRGT7uzOpDu7MyAhiim/pbv7n5RLaJC90sVYTFgAIUF2Vibluvv/WzqJncq28wPu6R0ul8Vbc3ZxUQ/3z+XUtg3YnJpHQbGTUqfFsj9yaOWo/JQkX1FXj31/quv9BxjRM45Jt3Zj0q3dGNAxkikrMtx/Fzv3Exrkd8wmKTp3bMyOXXvZtTuT4pJSZny/msS+Haq0bk5uAcXFpQBkZuXx+6okWrf0zal94ilv5tdk3DaCjNtGULB0AfUShwDg3y4BK/9ApakeJii4ok6FzU7QiadSmryD0qRtpF4+kLRrh5J27VCce9PJuP0yJSjEpxnrH4qnGGPeAuoB/YF3geHAr5ZljazKNziaIykAfvwliXFvLMblshg2qD03jDie8R/+SkLbaBJ7taCouJR7n57Lhq17CQ8N4sWHTie+kftAPXXOZt75wv2o0T4nNeOeUT0B+HLaOj6etBo/u41GjlCeuieRBuGV70D+W6ZJ5QrLR8uPP65h3LivcbpcDBvWixtvGMwr46eSkNCMAYlduerql9m8eTfR0e7seVxcJG+9eRMAl454nj/+SCU/v4iIiPqMffJyevfudNRjLHnrraP+mX+yLIux83fzU1IuwX42njijKQllJ9fDPt3IN5e57xK/sGgPMzdlkX6ghJgQf87vFMn/9YzDsiyeW7iHn5JysRvDdSc5GNzu6Can/IYc3cd5/vjLTsa96X4E6bCB7bhhRHfGf7isbNtvXrbtz2fDtr2Ehwby4kOnlU/fmDpnM+98ubJs22/KPdf1IDXjAP0u/YyW8RXz8EcM7cQFg6t24vOPgo7uRe+PP//BuPELcLlcDBuSwA1X9GD8u4tJaO8g8dTWFBWVcu+Ts9iwJZ3wsCBefHwI8Y0iSE7J4dq7vsFmMzgahvDk/QNpXPZY4jsfn86mrRkA3HRVT4ac1v7vQvhXrK1Hb96nZVk88ekaflqTXv4I0oQWEQCc9+gCJo3pB8Da7dk88N4Kioqd9O4cw8OXdcYYw62vLWN76gFsBhpF1ePxK7vgaBDMw++v5IffUmhUVu/BbjdMfKzvYaKoOuN/dO/eWpbFEx+tZNGfj2AddUL5Y0TPfXAOk8e5n4615o8sHnxnOYXFTnp3dfDIFd0wxvDDst08+fEqMvcXEVbPn/bNwnnvvt68OXkD70zbRLODRlC8d9+pRB3h/t9K+Zu7W//l8yyLJyZt46dNWe5HLV7YloR4d/2V8178nUl3uh/FvXZXxSNIe7dvwMPntsIYw8eLdvP5EncNl9M7R3Hnmc3581nlU39L5535uzC4R1Lcc1aLI47Xdnb1PQKwNhz7qpOv99816eOj+nl/x7Isnpi6nZ82ZxHkb2fc8NYklD2a97zxK5l0azcAnpu1gxkr95K+v5iY0ACGnxjDzac1rZaYbGecVS2fC/DjT5sY98J0nE6LYUOP58aR/XnlzR9I6NiEAX07sHpdMjff/Sm5uQUEBvrRMCqUGV/fzu+rknhs7GSMzWC5LK649BQuOPeEaonR3PNctXzuP/n8mjH0a9udhiERpOVm8tj0Cby/ZFqNx5GcXL11rcJvuJeg7j3LHkE6hpKt7uKp0a98RsZtI7BFRBL16EsYP3+w2ShavZycd18Cl2dcjnenkHHnFUf9EaSNpy3zneGo1WBT9w7HZMXRdr9v8MnfW1WSFKsty+py0P8hwCzLsqpUXfJoJylqk+pMUtQG1ZmkqA2OdpKi1jnKSYra5mgmKWqbo52kqG2OdpKitqnOJIX4tppMUvii6kxS1AbeSlL4iupOUvg6JSlqJ19NUlRlusefk3zzjTGNgBLg78fQioiIiIiIiIj8S1WpEjjdGBMBPAf8jvvJHkf03FMRERERERERkb/6xySFZVlPlH35jTFmOhBkWdbRncQkIiIiIiIi4oNcrmNytofPqtLzFo0xvYDmf7Y3xmBZVt2eeCgiIiIiIiIiR9U/JimMMZ8ArYCVwJ8VYSxASQoREREREREROWqqMpLiBKCj9U+PAREREREREREROQJVSVKsBWKBlGqORURERERERMSnuFzejqBuOWySwhgzDfe0jlBgvTHmV6Doz+WWZZ1T/eGJiIiIiIiISF3xdyMpngcM8Axw7kHv//meiIiIiIiIiMhRc9gkhWVZPwIYY/z//PpPxpjg6g5MREREREREROqWv5vucSNwE9DSGLP6oEWhwOLqDkxERERERETE21STomb93XSPz4FZwFPA/Qe9v9+yrMxqjUpERERERERE6py/m+6RA+QAl9RcOCIiIiIiIiJSV9m8HYCIiIiIiIiICP/f3n3HV1Glfxz/POkJKZCEhCZFlKIoLKKACAq2FXXFVdaCuvjbXextF3fFyrK2tax9FctaVrGDKDYUpKyigkgTBASkkwChBdLv+f0xk0YKEZLcG/J9v168mDtzZvKcO3PPzH3mzLkoSSEiIiIiIiIiIaK6MSlEREREREREGjUNnFm/1JNCREREREREREKCkhQiIiIiIiIiEhKUpBARERERERGRkKAxKURERERERESqEHDBjqBxUU8KEREREREREQkJSlKIiIiIiIiISEhQkkJEREREREREQoLGpBARERERERGpQiAQ7AgaF/WkEBEREREREZGQoCSFiIiIiIiIiIQEJSlEREREREREJCRoTAoRERERERGRKmhMivqlnhQiIiIiIiIiEhKUpBARERERERGRkKAkhYiIiIiIiIiEBCUpRERERERERCQkaOBMERERERERkSpo4Mz6pZ4UIiIiIiIiIhIS6rwnRe6zn9X1nwhZBZkTgx1CUC0Y3T/YIQRVn4/nBDuEoApLiw92CMGV2izYEQSNdToq2CEE1TeHLA12CEEVe+RVwQ4hqMIa8e2fhFnXBjuEoCr69ehghxBU65rFBDuEoGrTJjzYIQSVC3YAclBpxKdSEREREREREQklGpNCREREREREpAoak6J+qSeFiIiIiIiIiIQEJSlEREREREREJCQoSSEiIiIiIiIiIUFjUoiIiIiIiIhUQWNS1C/1pBARERERERGRkKAkhYiIiIiIiIiEBCUpRERERERERCQkaEwKERERERERkSpoTIr6pZ4UIiIiIiIiIhISlKQQERERERERkZCgJIWIiIiIiIiIhAQlKUREREREREQkJGjgTBEREREREZEqaODM+qWeFCIiIiIiIiISEpSkEBEREREREZGQoCSFiIiIiIiIiIQEjUkhIiIiIiIiUgWNSVG/1JNCREREREREREKCkhQiIiIiIiIiEhKUpBARERERERGRkKAxKURERERERESq4JwLdgiNinpSiIiIiIiIiEhIUJJCREREREREREKCkhQiIiIiIiIiEhI0JoWIiIiIiIhIFQKBYEfQuKgnhYiIiIiIiIiEBCUpRERERERERCQkKEkhIiIiIiIiIiFBSQoRERERERERCQkaOFNERERERESkCho4s36pJ4WIiIiIiIiIhAQlKUREREREREQkJDT4xz0iB19BWKdeUJBH/vhHcBtX7FUgmqgLRmHJLcAFKPrxWwo/ewmA8GPPIKL3WRAI4PJzKJj4BG7z2vqvxH6KvuA6Irv1xuXnkvPSPwmsXV6+QGQ0sVeMJqx5KwgEKFzwFXkTngPAmqURe/ktWGw8hIWRN+E5Chd9E4Ra7J+F32zk9Se/xxU5+p95KIOHda203Jzpa3n6rq+445lTad8lmZVLtvLKQ3MAcDjOGd6Nnv3b1GfotcI5x31T1jJjxU5iI8O4Z3B7jmgRV6HcYzPW8/6irezILWLOn39VYfnkpdu46b2VvHlZF7q1bFIfoe835xz3vreCGUu2EhMVzr0XdubINgkVyv2wdhej3lhKXkERA7qmcOuQjpgZS9ZnM/qdZeQXBggPM+4873CObpvItz9t55oXF9EmOQaAU45K5ZrT2tdz7arnnOPe1xYyY36GV/c/9eTI9k0rlPth1XZGPT+XvPwiBnRP59ZhR2FmPPjGIr6Yt4nI8DAOSWvCvX/8FYlNoigoDHDHf75n8eodFBUFOKdfW0ac3an+K/gLzJi1nHse/oRAIMDQc3oy4vf9yy2fPfdn7n3kE5b+lMG/7j6fX598ZLnl2dm5DL7wKU45sQt33nxmfYZeKxZ8s4HXnphLIOA48cyOnDXsiErLzZ6+lifv/B+jx55Ghy4pLJq9kbeenU9RQYDwyDAuvKoHR/RsUc/R145Wt95G4oABBHJyWXvrKHKWLK6ybPsn/03UIW1Yds5vys1vPvxyWv31byw6vg9F27fXccS1q+Wo20joP4BAbi7rbhtFbjX1b/fEv4lq04bl53r1T7/2ehIGnexdE2Rlse62URRuzqyv0A/I7K9W88xDMygKOM4YcgQXDO9VbvmkdxbywdsLCQs3YmMjueG2QbQ7NJlNG3byp6Gv0qZdMwC6dGvBDbcODEYVDljKDbfQpG9/Arm5ZN57O/nLllQo0/LhpwlPaY6Fh5Mzfy5b/nUPBAJEHdaJ5iPvxGLjKNy0noy/34LbszsItdh/SSP+Qswx/XB5uWx77O8UrFhaoUzK6McJT06B8Ajyf/ie7c88UK6ffvyQYST94UY2DjuFwM4d9Rl+nXnh0ts466h+ZO7axlH/GBbscERqRYNOUoQd3gtLaUXeo3/C2nQm6uxryHv2zxXKFX45nsCqBRAeQdTwewg7/BgCy7+jaME0imZ/7G2rS28iz/gT+a/cWd/V2C8R3XoTntaa7DsuIbxDV2KH3cTu+6+uUC5/8psULZsH4RHE3fQwEUceR+EP3xJ95qUUzJlGwYz3CWvZjrhr7yf7tovqvyL7IVAU4LXHvuMvD51Es+ax/OPKz+jRrxWt2ieVK5ezp4DP313OoV2TS+a17pDEHWNPJTwijO1bcxj9h0/p3rcV4RENq1PRzJU7WZ2Vx8cjjmTBht2MmbyaNy6rmKg5qWMSF/dM44xnF1VYtjuviFfnZHJ0iCcnis34MYvVW/bwyajjmL9mF2PeXc6bN/SsUO7v7y5nzO860b1tAlc8v5CZP2YxoGsKD01ayTWntWNA1xSmL9nKQ5NW8srVPQA4pkMSz/zxqHquUc3NWJDB6k3ZfPLAKcxfsY0xL8/nzbtOrFDu7y/PY8zlPejesRlXPDyLmQsyGdA9neOPTOOmoUcQER7GQ2/+wLOTljPygiP5dPZ68gsDvH/PIHLyCjnr1imc2ac1rZuH5jFRVBRgzAMf8eKTl5Kelsj5v3+OQf07c9ihaSVlWrZI4r47h/CfV7+qdBuPjv2CY3u0q6+Qa1WgKMArj37HXx8eSHLzWEZfMZlf9WtN60ravsnvLKXjESkl8xKSornpvgE0S41j3crtPHjzNB57d0g91+DAJQwYQHS7dvz469OJO7o7re+6i58uvKDSskmnnEpgz54K8yNbtCDh+H7kb1hf1+HWuoT+A4hu245lg08n9ujutL7jLlZcXHn9Eyup/+YXXyDjyccBSBl2KWlXXc2GMaPrOOoDV1QU4Kl/TuO+p4aQmh7PdZe9SZ8Bh9Lu0NLz+8Bfd+as8712fNb0lYx9ZCb3PnEOAC1bJ/H0uIZxjVOVuD79iTqkHWsuPJPoI4+m+cjbWT+i4hfSTXeMLEk+pN/9L+IHnkb2lE9o/re/s/Wph8mdN4eEM4fQ9OLL2fb8k/Vdjf0WfczxRLRqS8YVvyWyczeaXnULm0deXqFc1j9H4XK8+ieP+iex/U4mZ+ZnAISnphP9q94UZm6s19jr2kuzPuTJae/wyvCG8R2modKYFPVrn9/MzKxDTeYFQ3jXPhTNmwqAW7cUYptAfLPyhQryvAQFQFEhbuMKLDHVe52XU1LMImPAufoIu1ZEdO9H/teTAShatQRim2CJyeULFeR5CQqAokKK1izHmjX3XjuHxXp33i22CYEdW+op8gO38scs0lon0LxVPBGR4Rw3qC3ff1nxYvO9FxZyxkVdiIwKL5kXHRNRkpAoyC/CrN7CrlVTl2/nN91SMDO6t45nV14Rm7MLKpTr3jqe5vGRlW7j8Zkb+EOfFkRHNIw3YeqirZxzTAvMjB7tEtmZU0jmzrxyZTJ35pGdW0iPdomYGecc04Ipi7YCYEB2bhEA2TlFpCVG13cV9tvUuZs4p19br+6HJbNzTwGZ23PLlcncnuvV/bBkr+792jJlrnch1u+oNCLCveO+e8dmZGzz2j7DyMkrpLAoQG5BgMjwMJrEVn68hIIFP6ynXZtkDmmdTFRkBGee1o0pM8rfSWvTqhldDm9BWFjF43rRkg1szcqmX5+O9RVyrVq5JIv01vGk+W1f70Ftmfu/dRXKjX9hAWde3LVc29euUzLNUr02v3WHJAryiijIL6q32GtL0qCT2TZxIgB7FswnPCGRiNTmFcqFxcXR/PfDyRj7dIVlrf42ig0PPwgN55RfImHgyWx736t/TnX1j40j9bLhZO5V/8Du3WXKxDaY656lP2TQ6pCmtGyTRGRkOCed1olZ01eWK9MkPqpkOjensMGe36sS138guz55H4C8HxYQFp9AeEpqhXIlvSPCI7DISJy/jyMPaUfuPK8n6Z7Zs4g/8ZT6CbyWxPY5kT1TPwSgYOkirEkCYc1SKpQrTlAQHg4RkeU+50l/vIkdLz7RYI77mpr50zyydu8Mdhgitaomt4/frWTeO7UdyP6wxBTcjs0lr92OLVhixQarREwTwjr3JrByfsms8OPOJPqm54k4/XIKPhxbl+HWKmuaissq7aLptm/BmlU8WZWIbULk0X0p/HEuAHkfvERk71OJv/8t4q69n9w3nqjrkGvN9s05JDePLXndrHkc2zfnlCuzelkWWZtz6N63VYX1Vy7eyh3DP+auyz/l0j/3anC9KAAyswtokVh6QZaeEEXGrvwar7940x427crnxI5J+y4cIjJ25NGiaWlioUVSNJk7ytc5c0c+6WXKpDeNImOHl8gYNaQjD01aycAxX/PAByu4aXBprnXe6p0MeWgOI55bwPJNodf9NWNbDi1SSo/5FskxZG4rf8xnbsshvVlpmfTkmJJkRFnjZ66m/1HpAJx2bCtioyMYcMMnnHzTp/zfGYfTtMyFfqjJ2LyTFumJJa/T0xLJ2FyzC7NAIMA/H/uUv11/Wl2FV+e2bdlDclrpY13JzePYtqX8Pv55WRZZmXvo0bd1lduZM30t7To1K5fEaCgi09Ip2FR6F7QgYxOR6ekVyrW47noyX3qRQE75ZF7ioEEUZGaQu7RiN/GGIDK9ZvVPv+56trz8IoHc3IrLrr+Rzp9/QdMzzyrpVRHqtmbupnl6fMnr1LR4tmRmVyj3/lsLGH7Oyzz/xJdcPbK0t9mmDTu5+uLXGTniXRZ+3/B60ABEpKZRmLmp5HVhZgYRqWmVlm358DO0nzSdwJ497J7m9SIoWLWCuP6DAIgfeDoR6Q3rca/wlOYUbckoeV20NZPwlMrrn/L3x2n56mRczm5yvpoCQEzvARRt3Uzhz8srXUdEQkuV387MrIuZnQckmdlvy/wbDsRUt1EzG2Fmc8xszgtz19RyyPspLIyooX+l8Ov3cdtKG/mibz8k75E/Ujj5RSJOqrzLZIMXFkbcH+8g/4vxuC3exU3kcSdT8NUnZN/yO/Y8eQuxl4/iYLntEAg43nxqHhdc1aPS5YcekcI/XjqD28eeykevLaEgr+HdTTwQAed4YOpa/jqo4Y3FcSDe+Gojt5zTkS/u7MMt53Tk9re8LylHtIlnyu19eG9kL4ad0JprX/whyJHWnWfeX0p4WBhnH+/t+4UrtxEeZkx/9Nd89vBpvPjJT6zNDL0kTW0Y985sBhx/OC3SG05i7pcKBByvP/U9F15dcfyZYutW7eDNsfMZ/pdj6zGy+hXTpQtRh7Rl55TPy823mBjSRlzBpicaxhfz/RXTufL6F8t4/FGWnjKQ7R9OIuXiS+o5urr1m98dzUsTf88frjuecS/MBiA5tQmvThrOv8ddxBU39ef+2yezO7vmSf2GaONfrmT1OQOxyEhie/YGIPO+O0k69wLavPAmYXFxuIKKvS8PFlvvup6Nl52BRUYRfXQvLDqahKGXs/O1Z4IdmojUUHVjUnQGzgKaAmeXmb8L+FN1G3XOPQs8C5Bzx5m12qcq/Lgziej1awAC65dhSaXdHC0pFbdza6XrRf7mOtzWDRTNmljp8qKFM4g8+xoKeKQ2w61VkScNIeoEb6C3op9/xJLTwB8n1Jqm4rZV/shGzCUjKcpcT/6U0k4xkf0Gs+fxv3rbWrkYi4zC4pNwu7bXaR1qQ9PmsWSV6TmxbfMempbpWZG7p4D1q3bwwI3eo0A7snJ5/LaZXH9Pf9p3KX0kplW7RKJjI1i/ake5+aFq3NxM3pnv7eNuLZqwaWfpRVbGrnzSE2p2B3x3foDlW3IYPm4ZAFt2F3Dt+BU8+duOITd45mv/W88733iJtW6HJLBpe+njHZt25JGWVL7OaUlRZJQpk7E9n/Qkr2fFe3M2cesQr5v/r7s35463vPrHx5Q2gyd2TWHMu8vZll1Asyoek6kvr32+knem/wxAtw7N2LS19JjflJVLWpleEwBpzWLL9ZzIyMot17NiwszVTJu3iRf/1g/zE5KTvl7HCUelERkRRkpiND0PT2bRqu0ckhZax0Gx9OaJbMoo7TmRkbmT9OaJ1axR6vuF6/hu3mpef3c2u/fkU1BYRFxsFCOvPbWuwq11zVLjyMosHWMga/MemqWWb/vWrdrO/SVtXw6P3jqTG+/tT4cuKWRl7uHx22cy4tY+pLeuOOhsqEq56GJShg4FYM/ChUS2aFmyLDK9BQUZGeXKN+neg7hu3ej62RQIDyciJZmOL73C+nvuJqp1GzpPmOivm06nd8ez/ILfUbgldB95TL7wYpLP9+qfs2jf9Y/r0YPYI7vR+dMpWHg44SnJdHjxFVZdflm5ctsnfUD7p8eS+VTo96RMSWvC5ozSnhNbMrNJTYuvsvxJp3XiifumARAVFU5UlPc5ObxrGq1aJ7F+zTY6HVGxB0qoSfzthSSefR4AeUsWEZFW2vshIi2dwi1VD3rq8vPZ/b8vaNJ/IDlzZlGwZhUb/3wF4D36Edd3QN0GXwuaDB5K3OlDAChYvpjw1NJ9Fp6SRtHWagZ9Lcgn5+vpxPQ+kaJtWwlPb0Xa4+O8dVPTaP7oq2z+83AC2yv/3iCyN41JUb+qTFI45yYCE82sr3NuVj3GVK2ibz+k6FvvmbSwTscS0fssihZOx9p0htzdkL2twjoRJ1+KxTQhf2L5uyeW3AqXtaFkW27rhrqvwAEomPYeBdPeAyCiWx+iBg6hcPZUwjt0hZzduJ1ZFdaJPuf/sNgm5P73wXLzXVYGEV16UjDrU8JatIXIqAaRoADo0DmZjHW72Lwxm2apsXw7dQ0jbu9bsjwuPorH3j+35PUDN0zld1f1oH2XZDZvzCa5eRzhEWFs2bSbjWt2ktIiNL+Q7e3inmlc3NPr2jh9xQ7GfZfJ4K7NWLBhN/HR4VWOPbG3hOhwvry+R8nr4eOWMnJgm5BLUAAMO6E1w07wuq1PW7yVcV+uZ/CvmjN/zS4SYiIqjCuRlhhNfEwE81bvpHvbBCZ+t6lk/bTEaGav2MFxhzXl6+XbaecntjbvzCc1IRIzY8GanTgHTZsEf0zhYaccyrBTDgVg2rxNjPt8JYP7tGb+im0kxEaQ1rR8h7a0pjFe3X/KonvHZkz8cg3DTvXWn7kggxc++olXRp1AbHRp3VqmxPLN4i2c068te/IKmb9iG5edFrrjNRx1RCt+XruVteu3kZ6WwIeTF/HwP86r0bply42f9D2LlmxoUAkKgA5dyrd930xdw5V3HF+yPC4+iqfeL63nfTdM4cKretChSwq7d+Xzr1um87srutPpqIpjGISyra+PY+vr3peLhAEnkjpsGNs/+pC4o7sT2LWLwi2by5d/8w22vvkGAJGtWtPh6adZMdz7gr64f7+Scl0/m8KyoeeF/K97ZL0xjqw3SuufctEwdnz8IbFHd6cou2L9s958g6wy9W//1NMlCYqotu3IX7MagMRBJ5O3alU91mT/dT4infVrt7Np/Q5S0uKZNnkZt9x9erky69dsp3XbpgB8+7+fS6a3b8shITGa8PAwNq7bwfq122nRumH0qNo5/g12jvf2ZVzf/iSddzHZn39M9JFHE8jOpmhr+eSaxcYSFtfEmx8eTpO+A8iZ7z3mG940maLtWWBGs9+PYOfEt+q9Pr/U7o/eZvdHbwMQ3asf8Wf9jpwZk4ns3A23J5vAtvIJBouJxWLjvPlh4cQcewL5P3xP4eoVbLq09HhJf34im/982UHz6x4iB6OaXIlvNbMpQLpzrpuZHQ38xjl3dx3Htk+BZbNxnXoRfdPzJT9BWiz66ifI+/d1kJhC5EkXEti8luirvCRF4TcfUPTdZCL6nEVYxx5QVITLySZ//L+CVJNfrnDR10Qc1Zv4u1/F5eeR8/I/S5Y1uf05dt/9J6xpKtGDL6Vo42qa3PYsAPlfTKDgy4/IfedpYi4ZSdTJQwFHzkv/rOIvhZ7wiDCG3dCTR26eTiDgOOGMQ2ndIYn3/rOQ9p2T6dGv6mexly/cwsfjlhAeHoaFwSU3HkNC0+gqy4eqAYcmMmPFDs54dhExEWHcPbh9ybLfvriY8Zd7P0v40Bfr+GhxFrkFAQY9tYDzuqdyzQkVx+loCE7smsyMJVmcft+3xER6P0Fa7NyH5zDhL97P0d153uGMeuNH8goC9O+SzAC/l8yYoZ24d+JPFBU5oiPDGHO+91Obkxds5vWvNhARZkRHhvHwJV1LehqEihO7pzNjQQan3/wZMdER3PvH0u78594xlQn/8J4zvvP33Rn1nPcTpP2PTmfA0d5dp7v/u4D8wgB/ePBLALp3TGb08B5cfPKh3Pb8XM4a5T2ze27/tnRuG7oX7xER4dx582D+eP1/KQo4zjv7VxzeMY3Hxk6lW9dWnDygCwsWr+fav77Bzp25fDFzGU88O40P37wm2KHXivCIMC69sRcPjpxGIOAYMPhQ2nRIYvwLC2jfJZme/ap+hOvzCcvIWL+LiS8vYuLL3q/93PzQQBKbVfv0ZsjZNWM6iQMG0OWTyQRyc1l7260lyzqNn8Cy355bzdoN364Z00noP4BOH0/G5eSy7o7S+h/2zgR+Or/6+re46S9Et2+Pc46CDRtYP+auug65VoRHhHHNzSdy63XvEygKcNpvjqB9xxRefuZrOnVNo++Jh/L+WwuY++1aIiLCiE+IZuRob2DIhXPX88rYb4iICCPMjOtHDSQxqWEd9wB7Zs0kru8A2r75EYHcXDbfe3vJsjYvvs26y4cSFhNHi/uf8HrHhhk5c2eXJCPiTz2DxN9eCMDu6VPY9eF7wajGfsub8yUxvfqR/uwE/ydIx5Qsa/7Ya2y+YRgWE0vKHf/CIiIhLIy8BXPY/fH4IEZdP8b93xhO6tST1PimrL33fe6a9Bz/+eqDYIclckDM7WOEWzObDtwMjHXO/cqft8g5160mf6C2H/doSAoyK/70WWOyYHT/YIcQVH0+/inYIQRVWDVdcRuF1Gb7LnOQCuvaI9ghBNXXOQ1zUMbaEjvojWCHEFRhDW8s5lqTMOvaYIcQVEVnNJwB2OtCTANLeta2Nm0a3mDEtck9/XVo3eGpZR817XxQfqcdvH1pSO63mvSkiHPOfbvXncXCOopHREREREREJGRoTIr6VZN8/xYz64j/S8Nmdj6wsfpVRERERERERER+mZr0pLgG75c6upjZemAVcHD9ZpWIiIiIiIiIBN0+kxTOuZXAKWbWBAhzzu2q+7BEREREREREpLHZZ5LCzP6812uAHcB3zrl5dROWiIiIiIiIiDQ2NXnco5f/r/i3bM4CFgBXmtnbzrkH6io4ERERERERkWDSwJn1qyZJijZAT+dcNoCZ3QV8CAwAvgOUpBARERERERGRA1aTX/dIA/LKvC4A0p1zOXvNFxERERERERHZbzXpSfEa8I2ZTfRfnw2M8wfSXFxnkYmIiIiIiIhIo1JtksK8UTJfAj4G+vmzr3TOzfGnh9VdaCIiIiIiIiLBpTEp6le1SQrnnDOzj5xzRwFzqisrIiIiIiIiInIgajImxVwzO7bOIxERERERERGRRq0mY1L0BoaZ2WpgN2B4nSyOrtPIRERERERERKRRqUmS4vQ6j0JEREREREQkBAVcsCNoXPaZpHDOrQYwszQgps4jEhEREREREZFGaZ9jUpjZb8xsObAKmA78jPdrHyIiIiIiIiIitaYmA2f+A+gDLHPOdQBOBr6u06hEREREREREpNGpyZgUBc65rWYWZmZhzrkvzOzRug5MREREREREJNgCgWBH0LjUJEmx3czigRnAa2aWCWTXbVgiIiIiIiIi0tjUJEkxH9gD3AQMA5KA+LoMSkREREREREQan5okKQY65wJAAHgZwMwW1GlUIiIiIiIiItLoVJmkMLOrgKuBjnslJRKAL+s6MBERERERERFpXKrrSTEO76dG7wNuKTN/l3Muq06jEhEREREREQkBGjizflWZpHDO7QB2ABfVXzgiIiIiIiIi0liFBTsAERERERERERFQkkJEREREREREQkRNft1DREREREREpFHSmBT1Sz0pRERERERERCQkKEkhIiIiIiIiIiFBSQoRERERERERCQkak0JERERERESkChqTon6pJ4WIiIiIiIiIhAQlKUREREREREQkJChJISIiIiIiIiIhwZxzwY6hTpnZCOfcs8GOI1hU/8Zb/8Zcd1D9Vf/GW//GXHdQ/VV/1b+x1r8x1x1Ufzm4NIaeFCOCHUCQqf6NV2OuO6j+qn/j1ZjrDqq/6t+4Neb6N+a6g+ovB5HGkKQQERERERERkQZASQoRERERERERCQmNIUnR2J/NUv0br8Zcd1D9Vf/GqzHXHVR/1b9xa8z1b8x1B9VfDiIH/cCZIiIiIiIiItIwNIaeFCIiIiIiIiLSAChJIRIizKypmV1dS9u6tcx0ezNbVBvbDVVm9pKZnf8Lyh8074mZXW9mS8zsNTMbGex4gsnMbjSzuGDHsb/KtgFmdpKZTfqF6/+iz0GZ9X7x35L6YWbZVczfr329j7813MyerM1t1gUzm2ZmvYIdh9SvYLWP9a02rwX38XeGmNkRdf13RPaXkhQioaMpUOHEZGYR+7GtW/ddRA4SVwOnAsuDHUhtM88vOU/dCDTYJAVVtAEiItJo2sem/IJ67sd5stgQQEkKCVkNPklhZu+Z2Xdm9oOZjfDn/cHMlpnZt2b2XPGdATNrbmbvmtls/1+/4EZ/4MysiZl9aGbzzWyRmV1gZseY2XT/ffnUzFqaWZKZLTWzzv56r5vZn4Idf20ys8vMbIH/XvzXz5o/Y2Zz/OPhrGDHuA/3Ax3NbJ5/fM40s/eBxWYWbmYP+vMXmNkVAP6+neGvs8jM+pvZ/UCsP+81f9sR/p32JWb2TvHdZjP72cweMLOF/uflMH/+UH97881sRjDejOrsva/92QPM7CszW1l8t8Q/eT/o12WhmV0QxLBrnZk9AxwKfAzcBHQ3s1lmtrz4813ZMRLMmPfFvF4uS83sFWARcEeZ4/7vfpnK2r3rgVbAF2b2hV/uNP/9mGtmb5tZvD//WP9Yme8f9wlmFmdmb5nZYjObYGbfWP3frS1pA4AHgXj/8/qj//k1P/47/fdkkZk9Wzy/rKrKmNlhZva5X/e5ZtbRX6XSvxVMZnaJv3/mmdlYM+vtHwcx/jHwg5l1M7N4M5vi12ehmZ3jr9/eb/Oe88tONrNYf9mx/rbmFbcRwa0tmNmf/f21yMxu3GuZmdmT/mfjcyCtzLKq2vFKr3nM7Dj/c/G9/znoXEksZ/plUuu21tWr7LO+1/KL/HovMrN/lpmfbWaP+Pt9ipk19+d3NLNPzLs+mmlmXeq7TrXJKl73nO23Xd/7n/P0YMdYi2qtfQxxZa8FH6mmbSt7njzEzO7w5/3PvGv8kX7ZCse8mR0P/AZ40P87HauMRiRYnHMN+h+Q7P8fi/dBbQ38DCQDkcBM4Em/zDjgBH+6LbAk2PHXQv3PA54r8zoJ+Apo7r++APiPP30qMAu4EPgk2LHX8vtwJLAMSC0+LoCXgE/wknGHA+uAmGDHWk0d2gOL/OmTgN1AB//1COB2fzoamAN0AP4C3ObPDwcS/OnsvbbrgH7+6/8AI/3pn8usfxkwyZ9eCLT2p5sG+72p4b5+29/XRwA/+cvOAz7z35t0YA3Qsux73dD/+fswFRgNzPfbwlRgLd6X9kqPkVD95++bANAHOA1vtHLz9+0kYEBl7V7Z98KfTgVmAE38138D7gSigJXAsf78RCACGAmM9ed1AwqBXkGoe9k2YAfQxq/7LErPX8ll1vkvcLY//RJw/j7KfAOc60/H4PU8qfJvBfE46Ap8AET6r//tt1F3Aw8BTwGj/GURQGKZ/f6Tf8y09/djD3/ZW8Al/vQioK8/fX+w2wPgGLx2twkQD/wA/Aq/LQd+S2lb1grYXmZf/0zl7Xil1zzFx7w/fQrwrj89HHgSOBfv2qlZMN8TP6bKrnGmAb3892EN0Nw/BqYCQ/xyDhjmT99J6XXgFOBwf7o3MDXYdTyA96ayc2EzSgfF/yPwcLDjrMX6tqeW2sdQ/rdXPatr2wJAH3/ZscA8vDY9Aa9nZfF1XqXHfEN5P/Sv8f7bn27koeZ6MzvXnz4EuBSY7pzLAjCzt4FO/vJTgCPKJFUTzSzeOVfpM58NxELgYf8OwiRgG94F9md+PcOBjQDOuc/MbCjexV334IRbZwYBbzvntgA457L8+r/lnAsAy81sJdAFryFvCL51zq3yp08DjrbS5ymT8BIvs4H/mFkk8J5zbl4V21rrnPvSn34VuB7vQh/g9TL/P+JPfwm8ZGZvAeNrozK1qKp9/Z6/rxeXuXt0AvC6c64IyDCz6Xgn8wVBiLs+THTO5QA55vUmOI6aHyOhZLVz7mszewjv2P/enx+Pd9zPpEy755ybWck2+uAlrL70j48ovAvZzsBG59xsAOfcTgAzOwF4zJ+3yMxC4Rj51jm3DsC/e9ge+B8w0Mz+ipdgSMb7QvvBXutWKGNm0/CSjxMAnHO5/rar+1vBcjLeF/fZfnyxQCYwBu+YzsVrx8C7aL/XzAbgXbi3xktKAqwqc8x/B7Q3s6Z4ybpZ/vxxQLB72p0ATHDO7QYws/FA2V5PAyhtyzaY2dS91q+sHa/0mgfv/PGymR2O92U+ssx2BuElAE4r/mwEWblrHOfczDL1ORaY5pzbDGBez8EBwHt4x8GbfrlXgfF+3Y8H3i6zjej6qEQdqexceBTwppm1xGvzVlW3gQbuQNrHhqK6tm21c+5rf7of3vk/F8g1sw8ADsJjXhqRBp2kMLOT8E7CfZ1ze/wLsB/x7sBUJgwv65hbLwHWA+fcMjPrCQzGu8M0FfjBOdd377LmPbPWFdiDl21fV5+xBsnev7HbkH5zd3eZaQOuc859unch/+R1Jl5S4V/OuVcq2VZ170OFaefclWbW29/ud2Z2jHNu6/5Uoh7llZluaN07a0uF/eycm1HDYySUFB/7BtznnBu7d4Gy7Z6ZTXHOjdm7CPCZc+6ivdY7qi4CriNlj+kivMe2YvB6FfRyzq01s9F4d89K1KRMTf7WAcZ+oAx42Tk3qtxM78tXPN4X6xi8Y2UY3t30Y5xzBWb2M6X13btesXUcd7BU1qZXes1j3iOwXzjnzjWz9ng9E4qtwHuErBNej72g2vsax8ym7O+m8N6P7c65HrUVXwh6AviXc+59/xp5dFCjqVv71T42MNW1bburXKtUYzjm5SDV0MekSAK2+QmKLnh3zpoAJ5pZM/MGHDyvTPnJwHXFL8ysR30GWxfMrBWwxzn3Kt4zer2B5mbW118eaWZH+sVvApYAFwMv+ndWDxZTgaFmlgJgZsn+/KFmFuY/b3cosDRYAdbALrxuepX5FLiqeJ+ZWSfzntVtB2Q4554Dngd6+uUL9tq/bYuPCbz9X/YO6QVl/p/lb7+jc+4b59ydwGa8Xkqhoqp9XZmZwAXmjenRHO8u27f1EGOwnGPe8/opeN1hZ1dzjDQEnwL/Z6VjSbQ2s7RK2r3iOpX9DH0N9LPS5/ObmFknvDagpZkd689P8M8VXwK/8+cdAQQjmVFdG1Cs+AJ1i/++VDZafaVlnHO7gHVmNgTAzKItdH8NZQpwvpmlgfc594/lscAdwGtA8RgESUCmfxE/EGhX3Yadc9uBXX4iFrxHIINtJjDEvLFRmlD6yEWxGZS2ZS2BgXutX6Edp+prniRgvT89fK/trMa7bnqlzLVD0FTzWQevLT/RzFLNLBy4CJjuLwuj9LNxMfA/v2fIKvN6lBaP89GQe5VWdi4su29/H6zA6khttY+hrmw9a9q2fQmc7Z//4/F7hu3jmK/J+ykSNMG+U3KgPgGuNLMleBeeX+M1zvfinbyy8HpW7PDLXw88ZV433gi8k/6V9R10LTsKb+CbAFAAXIX3DO7jZpaEV89HzawQ7/nE45xzu8wbDPF24K4gxV2rnHM/mNk9wHQzK6K0e/gavGMhEbgylHvROOe2mtmX5g3glgNklFn8PF5Xxrnm9dnbjDcy80nAzWZWAGTjPY8M3nP8C8xsLnAb3ufjGjP7D7AYeLrMtpv5n4k8vIs88I6pw/HuZk7BG+sgJFSzryszAeiLF78D/uqc2+TfPTwYLQC+wHt29R/OuQ1m9nsqP0ZCnnNuspl1BWZ5hz3ZwCXAYVRs98A77j8xsw3OuYFmNhx43cyKu7fe7t+ZvQB4wrxBFHPweuT9G68L/GK888YPlJ476sU+2oDiMtvN7Dm8MRU24T368EvKXAqMNbMxeO/d0NqvyYFzzi02s9uByeb1AiwAJgIFzrlx/pfSr8xsEF7C4gMzW4h39//HGvyJPwDP+cfQdOp5X+/NOTfXzF6iNIn6vHPueyvtoj0Br3v/Yrzz2qy9NlFZO17VNc8DeMf67cCHlcTyo5kNw+sifrZzbkUtVXN/VHaN85Af50YzuwWvzTPgQ+fcRH+93cBxfh0zKU3iDAOe9udHAm8QQue3X6KKc+FovP22DS+J0SGIIdaq2mofQ91e9ZwNdNlX2+acm23eQOsL8N6XhZS2aVUd82/gtYHX441NEczPuUgFxYPrHFTMH2fCvzs2AW/gyAnBjkvql3/BN8k5906wYwlV5nUd7FX8TKtIY+V/6Y10zuWa1/Pqc6Czcy4/yKFJHbAy41H5X3RbOuduCHJY+0XteEVmlu2ciw92HCL1pcx3nzi8hOQI59zcYMclsr8aek+Kqow2s1Pwun1NxhtESUREpCpxeD9fGol3V/ZqJSgOamea2Si866DVVHzsQUSkIXnWf1QxBm88HyUopEE7KHtSiIiIiIiIiEjD09AHzhQRERERERGRg4SSFCIiIiIiIiISEpSkEBEREREREZGQoCSFiIiIiIiIiIQEJSlEREREREREJCQoSSEiIiIiIiIiIeH/AbkPMUuX3QEIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'age'}>,\n",
       "        <AxesSubplot:title={'center':'sex'}>,\n",
       "        <AxesSubplot:title={'center':'cp'}>,\n",
       "        <AxesSubplot:title={'center':'trestbps'}>],\n",
       "       [<AxesSubplot:title={'center':'chol'}>,\n",
       "        <AxesSubplot:title={'center':'fbs'}>,\n",
       "        <AxesSubplot:title={'center':'restecg'}>,\n",
       "        <AxesSubplot:title={'center':'thalach'}>],\n",
       "       [<AxesSubplot:title={'center':'exang'}>,\n",
       "        <AxesSubplot:title={'center':'oldpeak'}>,\n",
       "        <AxesSubplot:title={'center':'slope'}>,\n",
       "        <AxesSubplot:title={'center':'ca'}>],\n",
       "       [<AxesSubplot:title={'center':'thal'}>,\n",
       "        <AxesSubplot:title={'center':'target'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(16,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='target',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standardScaler = StandardScaler()\n",
    "dataset=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['target']\n",
    "X = dataset.drop(['target'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class evaluate_all_model:\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    from sklearn.naive_bayes import MultinomialNB\n",
    "    from sklearn.naive_bayes import BernoulliNB\n",
    "    from sklearn.naive_bayes import CategoricalNB\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.ensemble import GradientBoostingClassifier\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    from xgboost import XGBClassifier\n",
    "    import time\n",
    "    def __init__(self,x,y):\n",
    "        self.x=x\n",
    "        self.y=y\n",
    "        self.train_test_split()\n",
    "        self.define_models()\n",
    "        self.evaluate_model()\n",
    "        print(\"best model base on Accuracy\")\n",
    "        print(self.best_model)\n",
    "        \n",
    "    def train_test_split(self):\n",
    "        self.X_train, self.X_test, self.y_train,self.y_test =train_test_split(self.x, self.y,test_size=0.33, random_state=5)\n",
    "        sc=self.StandardScaler()\n",
    "        self.X_train = sc.fit_transform(self.X_train)\n",
    "        self.X_test = sc.transform(self.X_test)\n",
    "    def define_models(self):\n",
    "        self.models={'LogisticRegression': self.LogisticRegression(),\n",
    "    'RandomForestClassifier': self.RandomForestClassifier(),\n",
    "     'KNeighborsClassifier': self.KNeighborsClassifier(),\n",
    "    'DecisionTreeClassifier': self.DecisionTreeClassifier(),\n",
    "    'SupportVectorMachine':self.SVC(),\n",
    "    'GaussianNB': self.GaussianNB(),\n",
    "    'BernoulliNB': self.BernoulliNB(),\n",
    "    'GradientBoostingClassifier': self.GradientBoostingClassifier()\n",
    "                    }\n",
    "        \n",
    "        self.modelNames =['LogisticRegression', 'RandomForestClassifier','KNeighborsClassifier','DecisionTreeClassifier','SupportVectorMachine',\n",
    "                         'GaussianNB','BernoulliNB','GradientBoostingClassifier']\n",
    "        self.trainScores = []\n",
    "        self.testScores = []\n",
    "        self.Time_taken=[]\n",
    "        self.best_model_score=0\n",
    "        self.best_model={}\n",
    "        self.less_time=123\n",
    "        \n",
    "        \n",
    "    def evaluate_model(self):\n",
    "        for i in self.models:\n",
    "            start = self.time.time()\n",
    "            \n",
    "            model=self.models[i]\n",
    "            model.fit(self.X_train,self.y_train)\n",
    "            train_score = model.score(self.X_train, self.y_train)\n",
    "            self.trainScores.append(train_score)\n",
    "            print(f'Model:- {i}')\n",
    "            print(f'training score:- {train_score}')\n",
    "            test_score = model.score(self.X_test, self.y_test)\n",
    "            self.testScores.append(test_score)\n",
    "            print(f'test Score:- {test_score}')\n",
    "            \n",
    "            y_predictions = model.predict(self.X_test)\n",
    "            conf_matrix = confusion_matrix(y_predictions, self.y_test)\n",
    "            print(f'Confussion Matrix: \\n{conf_matrix}\\n')\n",
    "            \n",
    "            tn = conf_matrix[0,0]\n",
    "            fp = conf_matrix[0,1]\n",
    "            tp = conf_matrix[1,1]\n",
    "            fn = conf_matrix[1,0]\n",
    "            accuracy  = (tp + tn) / (tp + fp + tn + fn)\n",
    "            precision = tp / (tp + fp)\n",
    "            recall    = tp / (tp + fn)\n",
    "            f1score  = 2 * precision * recall / (precision + recall)\n",
    "            specificity = tn / (tn + fp)\n",
    "            print(f'Accuracy : {accuracy}')\n",
    "            print(f'Precision: {precision}')\n",
    "            print(f'Recall   : {recall}')\n",
    "            print(f'F1 score : {f1score}')\n",
    "            print(f'Specificity : {specificity}')\n",
    "\n",
    "            end = self.time.time()\n",
    "            time_taken=end-start\n",
    "            self.Time_taken.append(time_taken)\n",
    "            print(f'Time required {end-start}')\n",
    "            print(\"***************************************************************************\")\n",
    "            print(\"____________________________________________________________________________\")\n",
    "            print(\"\\n\\n\\n\")\n",
    "            if(float(test_score)>self.best_model_score):\n",
    "                self.best_model[\"model Name\"]=i\n",
    "                self.best_model[\"Time Required on train and test\"]=time_taken\n",
    "                self.best_model[\"Accuracy on train data\"]=train_score\n",
    "                self.best_model[\"Accuracy on test data\"]=accuracy\n",
    "                self.best_model_score=test_score\n",
    "                \n",
    "            if(time_taken<self.less_time):\n",
    "                self.less_time=time_taken\n",
    "    def plot_bar(self):\n",
    "        plt.bar(np.arange(len(self.trainScores)), self.trainScores, color='blue', width=0.25, edgecolor='white', label='train')\n",
    "        plt.bar([x + 0.25 for x in np.arange(len(self.trainScores))], self.testScores, color='red', width=0.25, edgecolor='white', label='Test')\n",
    "        plt.xlabel('Models', fontweight='bold', size = 24)\n",
    "        plt.ylabel('Scores', fontweight='bold', size = 24)\n",
    "        plt.xticks([r-0.25 for r in range(len(self.trainScores))], self.modelNames, rotation = 60)\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    def get_data(self):\n",
    "        self.temp_dict={}\n",
    "        self.temp_dict[\"Model\"]=self.modelNames\n",
    "        self.temp_dict[\"Training Score\"]=self.trainScores\n",
    "        self.temp_dict[\"Accuracy on Test\"]=self.testScores\n",
    "        self.temp_dict[\"Time Taken\"]=self.Time_taken\n",
    "        return self.temp_dict   \n",
    "    def get_dataframe(self):\n",
    "        return pd.DataFrame.from_dict(at.get_data()) \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:- LogisticRegression\n",
      "training score:- 0.8029556650246306\n",
      "test Score:- 0.9\n",
      "Confussion Matrix: \n",
      "[[40  4]\n",
      " [ 6 50]]\n",
      "\n",
      "Accuracy : 0.9\n",
      "Precision: 0.9259259259259259\n",
      "Recall   : 0.8928571428571429\n",
      "F1 score : 0.9090909090909091\n",
      "Specificity : 0.9090909090909091\n",
      "Time required 0.3358793258666992\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- RandomForestClassifier\n",
      "training score:- 1.0\n",
      "test Score:- 0.86\n",
      "Confussion Matrix: \n",
      "[[40  8]\n",
      " [ 6 46]]\n",
      "\n",
      "Accuracy : 0.86\n",
      "Precision: 0.8518518518518519\n",
      "Recall   : 0.8846153846153846\n",
      "F1 score : 0.8679245283018868\n",
      "Specificity : 0.8333333333333334\n",
      "Time required 0.7937173843383789\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- KNeighborsClassifier\n",
      "training score:- 0.8571428571428571\n",
      "test Score:- 0.88\n",
      "Confussion Matrix: \n",
      "[[43  9]\n",
      " [ 3 45]]\n",
      "\n",
      "Accuracy : 0.88\n",
      "Precision: 0.8333333333333334\n",
      "Recall   : 0.9375\n",
      "F1 score : 0.8823529411764706\n",
      "Specificity : 0.8269230769230769\n",
      "Time required 0.1084597110748291\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- DecisionTreeClassifier\n",
      "training score:- 1.0\n",
      "test Score:- 0.68\n",
      "Confussion Matrix: \n",
      "[[33 19]\n",
      " [13 35]]\n",
      "\n",
      "Accuracy : 0.68\n",
      "Precision: 0.6481481481481481\n",
      "Recall   : 0.7291666666666666\n",
      "F1 score : 0.6862745098039215\n",
      "Specificity : 0.6346153846153846\n",
      "Time required 0.019803762435913086\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- SupportVectorMachine\n",
      "training score:- 0.9064039408866995\n",
      "test Score:- 0.86\n",
      "Confussion Matrix: \n",
      "[[40  8]\n",
      " [ 6 46]]\n",
      "\n",
      "Accuracy : 0.86\n",
      "Precision: 0.8518518518518519\n",
      "Recall   : 0.8846153846153846\n",
      "F1 score : 0.8679245283018868\n",
      "Specificity : 0.8333333333333334\n",
      "Time required 0.14313602447509766\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- GaussianNB\n",
      "training score:- 0.8177339901477833\n",
      "test Score:- 0.9\n",
      "Confussion Matrix: \n",
      "[[41  5]\n",
      " [ 5 49]]\n",
      "\n",
      "Accuracy : 0.9\n",
      "Precision: 0.9074074074074074\n",
      "Recall   : 0.9074074074074074\n",
      "F1 score : 0.9074074074074074\n",
      "Specificity : 0.8913043478260869\n",
      "Time required 0.033699750900268555\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- BernoulliNB\n",
      "training score:- 0.8275862068965517\n",
      "test Score:- 0.89\n",
      "Confussion Matrix: \n",
      "[[40  5]\n",
      " [ 6 49]]\n",
      "\n",
      "Accuracy : 0.89\n",
      "Precision: 0.9074074074074074\n",
      "Recall   : 0.8909090909090909\n",
      "F1 score : 0.8990825688073394\n",
      "Specificity : 0.8888888888888888\n",
      "Time required 0.020637035369873047\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Model:- GradientBoostingClassifier\n",
      "training score:- 1.0\n",
      "test Score:- 0.83\n",
      "Confussion Matrix: \n",
      "[[40 11]\n",
      " [ 6 43]]\n",
      "\n",
      "Accuracy : 0.83\n",
      "Precision: 0.7962962962962963\n",
      "Recall   : 0.8775510204081632\n",
      "F1 score : 0.8349514563106796\n",
      "Specificity : 0.7843137254901961\n",
      "Time required 0.3407728672027588\n",
      "***************************************************************************\n",
      "____________________________________________________________________________\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "best model base on Accuracy\n",
      "{'model Name': 'LogisticRegression', 'Time Required on train and test': 0.3358793258666992, 'Accuracy on train data': 0.8029556650246306, 'Accuracy on test data': 0.9}\n"
     ]
    }
   ],
   "source": [
    "at=evaluate_all_model(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Training Score</th>\n",
       "      <th>Accuracy on Test</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.802956</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.335879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.793717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.108460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.019804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SupportVectorMachine</td>\n",
       "      <td>0.906404</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.143136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.817734</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.033700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.020637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.340773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Training Score  Accuracy on Test  Time Taken\n",
       "0          LogisticRegression        0.802956              0.90    0.335879\n",
       "1      RandomForestClassifier        1.000000              0.86    0.793717\n",
       "2        KNeighborsClassifier        0.857143              0.88    0.108460\n",
       "3      DecisionTreeClassifier        1.000000              0.68    0.019804\n",
       "4        SupportVectorMachine        0.906404              0.86    0.143136\n",
       "5                  GaussianNB        0.817734              0.90    0.033700\n",
       "6                 BernoulliNB        0.827586              0.89    0.020637\n",
       "7  GradientBoostingClassifier        1.000000              0.83    0.340773"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# X=standardScaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dataset['target']\n",
    "X = dataset.drop(['target'], axis = 1)\n",
    "model=LogisticRegression(max_iter=2)\n",
    "X_train, X_test, y_train,y_test =train_test_split(X, y,test_size=0.33, random_state=5)\n",
    "X_train=standardScaler.fit_transform(X_train)\n",
    "X_test=standardScaler.transform(X_test)\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model, open('Heart_desease_model', 'wb'))\n",
    "pickle.dump(standardScaler, open('Heart_desease_standard_scaler', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open('Heart_desease_model', 'rb'))\n",
    "sc = pickle.load(open('Heart_desease_standard_scaler', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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