Assignment_4_CreditCard_offline_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64fe3cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "791c2d97",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
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       "<p>284807 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'dataset/creditcardfraud-csv/creditcardfraud-csv/creditcard.csv'\n",
    "df = pd.read_csv(path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6dff78c7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
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       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
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       "      <td>0.133558</td>\n",
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       "      <td>149.62</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>...</td>\n",
       "      <td>1.475829</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059616</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127434</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               V1         V2        V3        V4        V5        V6  \\\n",
       "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "...           ...        ...       ...       ...       ...       ...   \n",
       "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
       "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
       "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
       "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
       "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
       "\n",
       "              V7        V8        V9       V10  ...       V20       V21  \\\n",
       "0       0.239599  0.098698  0.363787  0.090794  ...  0.251412 -0.018307   \n",
       "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775   \n",
       "2       0.791461  0.247676 -1.514654  0.207643  ...  0.524980  0.247998   \n",
       "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300   \n",
       "4       0.592941 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -4.918215  7.305334  1.914428  4.356170  ...  1.475829  0.213454   \n",
       "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.059616  0.214205   \n",
       "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.001396  0.232045   \n",
       "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.127434  0.265245   \n",
       "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.382948  0.261057   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0       0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1      -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2       0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3       0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4       0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "284802  0.111864  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731   \n",
       "284803  0.924384  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   \n",
       "284804  0.578229 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   \n",
       "284805  0.800049 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   \n",
       "284806  0.643078  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649   \n",
       "\n",
       "        Amount  \n",
       "0       149.62  \n",
       "1         2.69  \n",
       "2       378.66  \n",
       "3       123.50  \n",
       "4        69.99  \n",
       "...        ...  \n",
       "284802    0.77  \n",
       "284803   24.79  \n",
       "284804   67.88  \n",
       "284805   10.00  \n",
       "284806  217.00  \n",
       "\n",
       "[284807 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Time','Class'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc7ef96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test = train_test_split(df, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f51f5a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "29\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_train.shape[1])\n",
    "print(x_test.shape[1])\n",
    "type(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35c7e167",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3d494ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = tf.keras.models.Sequential([\n",
    "    layers.Input(shape=(x_train.shape[1],)),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(20, activation='relu')\n",
    "])\n",
    "        \n",
    "decoder = tf.keras.models.Sequential([\n",
    "    layers.Input(shape=(20,)),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(x_train.shape[1], activation='linear')  # Use linear activation for reconstruction\n",
    "])\n",
    "        \n",
    "model = tf.keras.models.Sequential([\n",
    "    encoder,\n",
    "    decoder\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a35d9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss ='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6898f208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "2279/2279 [==============================] - 18s 7ms/step - loss: 27.9668 - val_loss: 0.4009\n",
      "Epoch 2/5\n",
      "2279/2279 [==============================] - 14s 6ms/step - loss: 2.8716 - val_loss: 2.5510\n",
      "Epoch 3/5\n",
      "2279/2279 [==============================] - 14s 6ms/step - loss: 3.0143 - val_loss: 0.4821\n",
      "Epoch 4/5\n",
      "2279/2279 [==============================] - 14s 6ms/step - loss: 1.0560 - val_loss: 0.3236\n",
      "Epoch 5/5\n",
      "2279/2279 [==============================] - 14s 6ms/step - loss: 2.0063 - val_loss: 0.4940\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    x_train, \n",
    "    x_train, \n",
    "    validation_data=(x_test,x_test),\n",
    "    epochs=5,\n",
    "    batch_size = 100,\n",
    "    shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cc65fa34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.lineplot(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b234474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 7s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "mse = np.mean(np.power(x_test - predictions, 2), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f5885109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2324373208600132"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.percentile(mse, 95)  # Adjust the percentile as needed\n",
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "660c4900",
   "metadata": {},
   "outputs": [],
   "source": [
    "anomalies = mse > threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3ce17f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Anomalies: 2849\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of anomalies\n",
    "num_anomalies = np.sum(anomalies)\n",
    "print(f\"Number of Anomalies: {num_anomalies}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3ec2f159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the anomalies\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.figure(figsize=(12, 6))\n",
    "plt.plot(mse, marker='o', linestyle='', markersize=3, label='MSE', )\n",
    "plt.axhline(threshold, color='r', linestyle='--', label='Anomaly Threshold')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('MSE')\n",
    "plt.title('Anomaly Detection Results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "817a5cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06NBmnx86dChFRUXk5eW5Hps5cya33norAwcObHbX+vnnnyczM5OHH36YzMxMLrjgAi677LIjrsVms/Haa68xYsQIjjvuOC655BJ++OEH1/Pnnnsu55xzDoMGDWL06NG88sorrFu3jo0bN7bra37mmWeIiIhocnvrrbdcvx9RUVGkpKS0+h5bt24lMzOzyWM333yz6/369u3brjWJiIiIiIh0ll/vnHtSaKCFjf880Wef3R7tScMeP358q89v2bKFCRMmNHns6KOPPuL7pqenu1LYAVJSUjh48KDr19u2beOee+7h559/Jj8/H5vNBsDevXvb1TPgoosu4q677mryWK9evQDj96Gju9533XUXN9xwAx999BH/+te/OvQeIl1C3hYIT4SwOF+vRERERETaQcF5C0wmU5tSy31p0KBBmEwmNm3axNlnn33Y85s2bSI2NpbExETXY+Hh4R5ZS2BgYJNfm0wmVwAOcPrpp5OWlsaLL75I7969sdlsjBgxgtra2nZ9TnR0NIMGDWr2ucGDB1NSUkJOTk6ru+cZGRls2bKlyWOJiYkkJib6rNGdiFsc2ADPT4X0KfD7T329GhERERFpB6W1+7H4+HiOP/54nnnmGaqqqpo8l5uby1tvvcVvf/vbdu0mZ2ZmsmLFiiaP/fLLL51aZ0FBAVu2bOHuu+9m1qxZrnR7dzvvvPMICgpq0r29MWdDuAsvvJAtW7bw6acKXqSb2fwl2Ooh6xdQY0MRERERv6Lg3M/997//paamhhNPPJFFixaRlZXF119/zfHHH0+fPn1cDdna6uqrr2bz5s3cfvvtbN26lffee4/XXnsN6HijtNjYWOLj43nhhRfYvn078+bN45ZbbunQe1VWVpKbm9vk5gz0U1NTefzxx3nyySe54oorWLhwIXv27GHp0qVcffXV3HfffQBccMEFnHfeeVxwwQX885//5Oeff2b37t0sXLiQd999F4ulfWUFIl3GjnnGfV0FVLY8RlFEREREuh4F534uIyODFStWMGDAAM4//3wGDhzIH//4R2bMmMGyZcuIi2tf3Wn//v354IMP+Oijjxg1ahTPPvusq8Y7ODi4Q2s0m8288847rFy5khEjRvDnP/+Zhx9+uEPv9eKLL5KSktLkduGFF7qev+666/j222/Jzs7m7LPPZsiQIVx55ZVERUW5us6bTCbeffddnnjiCebOncusWbPIzMzk8ssvJzU1lSVLlnRobSI+VV0KWcsbfl2822dLEREREZH2M9l70FDn0tJSoqOjKSkpISoqqslz1dXV7Nq1i/79+xMSEuKjFXZNDzzwAM899xxZWVm+Xop0gP5u9xCbv4R3ftfw6/NehRHn+G49IiIiIgK0Hoc21rU7nolPPPPMM0yYMIH4+HiWLl3Kww8/zA033ODrZYlIa5wp7U7Fe3yzDhERERHpEAXncpht27Zx//33U1hYSL9+/bj11lu58847fb0sEWmNMzjvNQIOrIfivb5dj4iIiIi0i4JzOczjjz/O448/7utliEhbFe6Cwp1gDoAxF8PXd0CRds5FRERE/IkawomI+Lud8437vkdDr+HGsdLaRURERPyKgnMREX/nTGkfOBNi0ozj4iyw2Xy3JhERERFpFwXnIiL+zFoPOxcZxwNnQlQfMFnAWgPlB3y7NhERERFpMwXnIiL+bP8qqCmBkBjoPRosARDdx3hOqe0iIiIifkPBuYiIP3OmtA+YDmaLcexMbVdTOBERERG/oeBcRMSfNa43d4p11p1rnJqIiIiIv1BwLtIJl112GWeddZavlyE9VVUx7FthHA+c0fC4qyncbm+vSEREREQ6SMG5n7vsssswmUyYTCYCAwPp378/f/nLX6iurvb10tpswYIFmEwmiouLvfJ53gyonV9bc7fc3FzXeaWlpdx1110MGTKEkJAQkpOTmT17Nh999BF2u9113vbt27n88svp168fwcHB9OnTh1mzZvHWW29RX1/vla9JupDdi8FuhfgMiOnX8LjS2kVERET8ToCvFyCdd9JJJ/Hqq69SV1fHypUrufTSSzGZTDz00EO+Xppb1dbWEhQU5OtldMiWLVuIiopq8lhSUhIAxcXFTJkyhZKSEu6//34mTJhAQEAACxcu5C9/+QszZ84kJiaG5cuXM3v2bIYPH87TTz/NkCFDAFixYgVPP/00I0aM4KijjvL61yY+1FxKOzRKa1dwLiIiIuIvtHPeErsdait8c2u0U9oWwcHBJCcnk5qayllnncXs2bP57rvvXM/bbDbmzJlD//79CQ0N5aijjuKDDz5o8h4bNmzgtNNOIyoqisjISI477jh27Njhev0///lP+vbtS3BwMKNHj+brr792vXb37t2YTCY++ugjZsyYQVhYGEcddRTLli1znbNnzx5OP/10YmNjCQ8PZ/jw4cydO5fdu3czY4aRjhsbG4vJZOKyyy4DYPr06dxwww3cfPPNJCQkcOKJJ7o+a82aNa73Li4uxmQysWDBgiN+Pf/4xz94/fXX+fTTT1072M7XZWVlcf755xMTE0NcXBxnnnkmu3fvdr2n1WrllltuISYmhvj4eP7yl7802dVuTVJSEsnJyU1uZrPxz++vf/0ru3fv5ueff+bSSy9l2LBhDB48mKuuuoo1a9YQERGB3W7nsssuY/DgwSxdupTTTz+djIwMMjIyuPDCC1myZAmjRo1q01qkG2kpOHfuopdkG6PWRERERKTL0855S+oq4V+9ffPZf90PQeEdeun69ev58ccfSUtLcz02Z84c3nzzTZ577jkyMjJYtGgRF198MYmJiUybNo3s7GymTp3K9OnTmTdvHlFRUSxdutSVJv3kk0/y6KOP8vzzzzNmzBheeeUVzjjjDDZs2EBGRobrc+666y4eeeQRMjIyuOuuu7jwwgvZvn07AQEBXH/99dTW1rJo0SLCw8PZuHEjERERpKam8uGHH3Luuee6dpdDQ0Nd7/n6669z7bXXsnTp0jb/HrT29dx2221s2rSJ0tJSXn31VQDi4uKoq6vjxBNPZNKkSSxevJiAgADuv/9+TjrpJNauXUtQUBCPPvoor732Gq+88gpDhw7l0Ucf5eOPP2bmzJlHWFHLbDYb77zzDhdddBG9ex/+9y0iIgKA1atXs2nTJt5++21XUH8ok8nU4XWIHyrcCUW7wRwI6VOaPheRDJZgY9Z5aXbDTrqIiIiIdFkKzruBL774goiICOrr66mpqcFsNvPf//4XgJqaGv71r3/x/fffM2nSJAAGDBjAkiVLeP7555k2bRpPP/000dHRvPPOOwQGBgIwePBg1/s/8sgj3H777VxwwQUAPPTQQ8yfP58nnniCp59+2nXebbfdxqmnngrAvffey/Dhw9m+fTtDhgxh7969nHvuuYwcOdK1Bqe4uDjA2F2OiYlp8rVlZGTw73//2/XrxjvZLTnS1xMaGkpNTQ3Jycmux958801sNhsvvfSSK8h99dVXiYmJYcGCBZxwwgk88cQT3HnnnZxzzjkAPPfcc3zzzTdHXA9A3759m/w6LS2NDRs2kJ+fT1FRkStFvSVbt24FIDMz0/XYwYMHm/w+/vvf/+a6665r03qkG3DumqdOhOCIps+ZzRCTCgXbjdR2BeciIiIiXZ6C85YEhhk72L767HaYMWMGzz77LBUVFTz++OMEBARw7rnnAkYDscrKSo4//vgmr6mtrWXMmDEArFmzhuOOO84VyDZWWlrK/v37mTx5cpPHJ0+ezK+//trkscZp1SkpKYARQA4ZMoQbb7yRa6+9lm+//ZbZs2dz7rnntikNe9y4cW34HWiqta+nJb/++ivbt28nMjKyyePV1dXs2LGDkpIScnJymDhxouu5gIAAxo8f36bU9sWLFzd5b+fa2poW35z4+HhXev/06dOpra3t8HuJH9ox37hv3KW9sZg0R3CucWoiIiIi/kDBeUtMpg6nlntbeHg4gwYNAuCVV17hqKOO4uWXX+aKK66gvLwcgC+//JI+ffo0eV1wcDBAkzTyzmgcDDt3n202GwBXXnklJ554Il9++SXffvstc+bM4dFHH+VPf/rTEb+2xpwp3Y2D2rq6uibndOTrKS8vZ9y4cbz11luHPZeYmNju9ztU//79D8sKcL53TEwMmzdvbvX1zvKBLVu2uC6qWCwW1597QID+Kfco1jrYtcg4PrTe3MlZd66O7SIiIiJ+QQ3huhmz2cxf//pX7r77bqqqqhg2bBjBwcHs3buXQYMGNbmlpqYCxo734sWLDwtyAaKioujdu/dhNd9Lly5l2LBh7Vpbamoq11xzDR999BG33norL774IoCrA7vVaj3iezgD5ZycHNdjjZvDHenrcX7eoZ81duxYtm3bRlJS0mG/T9HR0URHR5OSksLPP//sek19fT0rV6488hfeCrPZzAUXXMBbb73F/v2HZ2qUl5dTX1/PmDFjGDJkCI888ojrgof0YNkroaYUQuMgpYUO/erYLiIiIuJXFJx3Q7/5zW+wWCw8/fTTREZGctttt/HnP/+Z119/nR07drBq1SqeeuopXn/9dQBuuOEGSktLueCCC1ixYgXbtm3jjTfeYMuWLQD8v//3/3jooYd499132bJlC3fccQdr1qzhpptuavOabr75Zr755ht27drFqlWrmD9/PkOHDgWM+muTycQXX3xBXl6ea7e/OaGhoRxzzDE8+OCDbNq0iYULF3L33Xc3OedIX096ejpr165ly5Yt5OfnU1dXx0UXXURCQgJnnnkmixcvZteuXSxYsIAbb7yRffv2AXDTTTfx4IMP8sknn7B582auu+66Ns9mP3jwILm5uU1uzosHDzzwAKmpqUycOJH//e9/bNy4kW3btvHKK68wZswYysvLMZlMvPrqq2zZsoXJkyfz2WefsW3bNjZu3Mhzzz1HXl4eFoulzX8e4uec9eYDpoO5hT93zToXERER8SsKzruhgIAAbrjhBv79739TUVHBfffdx9/+9jfmzJnD0KFDOemkk/jyyy/p378/YNQuz5s3j/LycqZNm8a4ceN48cUXXWnqN954I7fccgu33norI0eO5Ouvv+azzz5r0qn9SKxWK9dff73r8wcPHswzzzwDQJ8+fbj33nu544476NWrFzfccEOr7/XKK69QX1/PuHHjuPnmm7n//vubPH+kr+eqq64iMzOT8ePHk5iYyNKlSwkLC2PRokX069ePc845h6FDh3LFFVdQXV3tmk9+6623cskll3DppZcyadIkIiMjOfvss9v09WdmZpKSktLk5tx1j4uL46effuLiiy/m/vvvZ8yYMRx33HG8/fbbPPzww0RHRwNwzDHHsHLlSjIzM7n++usZNmwYxx57LG+//TaPP/441157bZv/PMTPtTRCrTHXzrlqzkVERET8gcnemY5Ufqa0tJTo6GhKSkpcAZdTdXU1u3bton///oSEhPhohSLup7/b3UxVEfx7ANht8OcNEN23+fMq8uHhgYAJ7j4AAcFeXaaIiIiIGFqLQxvTzrmIiD/ZtcgIzBMyWw7MAcLiITAcsENxlteWJyIiIiIdo+BcRMSftCWlHYyJE67U9t0eXZKIiIiIdJ6CcxERf2G3w/Y2BufQME5NdeciIiLS3ZRkw4GNUFvh65W4jYJzERF/UbgTSvaCORDSJx/5fHVsFxERke5q9Rvw7CT4+g5fr8RtFJwfogf1x5MeQn+nuxFnSnu/YyAo/Mjna9a5iIiIdFdFu4372HRfrsKtFJw7OMdsVVZW+nglIu5VW1sLoDno3UFb682dYjROTURERLqpbhicB/h6AV2FxWIhJiaGgwcPAhAWFobJZPLxqkQ6x2azkZeXR1hYGAEB+ufu16x1sGuxcdzm4NxRc660dhEREeluFJx3b8nJyQCuAF2kOzCbzfTr108Xm/zdvhVQW2aMSEse1bbXONPaK/OhphyCIzy3PhERERFvqauCshzjOLa/b9fiRgrOGzGZTKSkpJCUlERdXZ2vlyPiFkFBQZjNqmDxe86U9gEzoJU/z6/X5zIwMZyMXpEQEg0hMVBdbKS29xrmlaWKiIiIeJSzZC84CkJjfbsWN1Jw3gyLxaL6XBHpWtpQb/7TzgKueXMlI/pE8cWfjjMejE2DnGIF5yIiItJ9OFPaY9KgG2WHajtNRKSrqyyE/auM44EzWjzth00HANhxsKKhS79r1rnqzkVERKSbcNWbp/l0Ge6m4FxEpKvbtQjsNkgcClG9Wzxt0dZ8AKrqrJRUOUpzNOtcREREuptu2AwOFJyLiHR9bUhpzy2pZsuBMtev9xdXGwfOb1raORcREZHuQsG5iIh4nd0OO+Ybx60E54u25TX5dW5plXGgtHYRERHpbpwZgd2oUzsoOBcR6doKdkDJXrAEQdqxLZ62aGvT4Ny1c+5Ka9/rqRWKiIiIeI/drp1zERHxAWdKe79JEBTW7ClWm50l241686EpUQDklByyc15TAlVFHl2qiIiIiMdV5ENdBWCCmFRfr8atFJyLiHRlbag3X5ddQnFlHZEhAZwyIhmAnBLHznlQGIQnGcfF2j0XERERP+fcNY/qAwHBPl2Kuyk4FxHpquprYfdi47i1enNHSvvkgQn0jQsFIMeZ1g4Nu+fq2C4iIiL+rpumtIOCcxGRrmvfL1BbDuGJ0GtEi6c5g/OpgxNJiXYE5860dmiYAaqmcCIiIuLvunFwHuDrBYiISAucKe0DZoC5+WuppdV1rM4qBmDq4ARsNuPxnJJq7HY7JpNJs85FRESk++jGwbl2zkVEuqo21Jv/uD0fq83OgMRw+saG0SvaqL2qqbdRVFlnnOTaOVfNuYiIiPg5V3Ce5tNleILfBOfPPvsso0aNIioqiqioKCZNmsRXX33l62WJiHhGZSHsX20cD5zR4mkLtxpd2qdmJAIQHGAhISIIgP3FmnUuIiIi3Yx2zn2vb9++PPjgg6xcuZIVK1Ywc+ZMzjzzTDZs2ODrpYmIuN/OBYAdkoZDZHKzp9jtdle9+bTBia7HnXXnuSWHzDov3mvMBhURERHxR/U1UJptHCs4953TTz+dU045hYyMDAYPHswDDzxAREQEP/30k6+XJiLifq6U9pZ3zXfmV5BdXEWQxczEAXGux5OjQ4BGTeGiUwET1FVCRZ6nViwiIiLiWcVZgB0Cw4yGud2MXzaEs1qtvP/++1RUVDBp0qQWz6upqaGmpsb169LSUm8sT0Skc+x22DHfOG6l3nzhFiPQntA/lrCghv/OezuC8/3OnfOAIIjqbVxpLt4LEUmeWbeIiIiIJzVOaTeZfLkSj/CbnXOAdevWERERQXBwMNdccw0ff/wxw4YNa/H8OXPmEB0d7bqlpqZ6cbUiIh2Uvw1K94ElGNKObfG0RdscI9Qyml45Tok5JK0dGnVs3+3WpYqIiIh4TfFu474bprSDnwXnmZmZrFmzhp9//plrr72WSy+9lI0bN7Z4/p133klJSYnrlpWV5cXVioh0kDOlPe1YCAxt9pTqOis/7SwAjPnmjaU4d86LNetcREREupFu3AwO/CytPSgoiEGDBgEwbtw4fvnlF5588kmef/75Zs8PDg4mODjYm0sUEem8NoxQW7G7iOo6G0mRwQxJjmzynLMhXE5zO+capyYiIiL+qpsH5361c34om83WpKZcRMTv1dfA7sXGcSvBuSulfXAipkNqrpw757kl1did3dmd49SKtHMuIiIifqqbB+d+s3N+5513cvLJJ9OvXz/Kysr4v//7PxYsWMA333zj66WJiLhP1nKjq3p4EvQa3uJpzhFqh6a0A/SKCsFkglqrjYKKWhIigpXWLiIiIv7Nbm/YZFBw7lsHDx7k97//PTk5OURHRzNq1Ci++eYbjj/+eF8vTUTEfRqntLfQhfRAaTWbc8swmeC4QQmHPR8UYCYhIpi8shpyS6qN4NyV1p4FNiuYLZ76CkRERETcr6oIahzTt5wZgd2M3wTnL7/8sq+XICLieW2oN3fumo/qE01seFCz5/SODiGvrIb9xVWM6BNtjFIzB4CtDspyIbqP25cuIiIi4jFFu4z7yJQWG+b6O7+uORcR6VYq8iHnV+N4wPQWT1u0LR9oPqXdKdlRd+5qCme2QHRf41ip7SIiIuJvnPXmzmzAbkjBuYhIV7FzAWCHXiMhslezp1htdpZsa7ne3KnVju1qCiciIiL+pps3gwMF5yIiXceO+cb9wBktnrI+u4SiyjoigwMYnRrT4nm9Y5w755p1LiIiIt2AgnMREfEKu71d9ebHDoon0NLyf+HJzp3z4sY7547mKZp1LiIiIv5GwbmIiHhF3hYo2w8BIdBvUounLWpDSjsYDeEAckob7ZzHpBv3SmsXERERf9PNx6iBgnMRka7BuWueNhkCQ5o9pbS6jlV7iwGYmtF6cO5sCJdbUo3NZjceVFq7iIiI+CNrHZTsM44VnIuIiEe1IaX9x+0FWG12BiSEkxoX1urb9YoKwWSCOqud/Ioa40FnQ7jSbOObnIiIiIg/KNkHdquRYRjRfNPc7kDBuYiIr9XXwO4lxnFr9eZtTGkHCLSYSYoMBozdcwAikoxvanZbw9VnERERka6u8Rg1c/cNYbvvVyYi4i/2/gT1VRCRDElDmz3FbrezcIszOE9o09s6m8LtdzaFM5kaNYVTaruIiIj4iR7QDA4UnIuI+F7jlHaTqdlTduZXkF1cRZDFzDED4tv0tq6mcI3HqWnWuYiIiPgbBeciIuIV7RihNj49lrCggDa9bYpj59yV1g6NmsJpnJqIiIj4CQXnIiLiceV5kLvWOB4wvcXTnMH5tDbUmzulOHbO95c0N+tcO+ciIiLiJxSci4iIx+1cYNwnj4SI5gPvmnorP+0sBNrWDM4pJcaR1l6stHYRERHxY67gPM2ny/A0BeciIr7UhpT2FbuLqKqzkhQZzJDkyDa/tTOtPafZtHYF5yIiIuIHqoqgutg4jlFwLiIinmC3t6ve/LiMREwtNIxrjjOt/UBpNVab3XjQ+U2t/ADUVbXwShEREZEuwpntF54IwRG+XYuHKTgXEfGVg5ugPBcCQiH1mBZPW7i1fSPUnJIigzGboN5mJ7+8xngwNBaCHLvvxVkdWraIiIiI1/SQenNQcC4i4jvOXfP0yRAY0uwpB0ur2Zxbhslk7Jy3R4DFTK8o5zi1RrPOldouIiIi/sL584qCcxER8Zi2pLRvywdgZJ9o4sKD2v0RydGtNYXb3e73ExEREfEq7ZyLiIhH1VXDnqXGcRvqzae2c9fcqbejKVzz49Q061xERES6OAXnIiLiUXuXQX01RKZA4pBmT7HZ7CzZbuyct2eEWmPOpnC5JY12zpXWLiIiIv5CwbmIiHhU45T2Fjqwr99fQmFFLRHBAYzpF9Ohj3GmtTfdOdescxEREfEDNmtDpp+CcxER8Ygd8437NqS0HzswnkBLx/677h1jpLXnNjvrXGntIiIi0oWVZoOtHixBRrZhN6fgXETE28oOwIF1xvGA6S2etmhr51LaoSGtvWlDOEfNeVUh1JR1+L1FREREPMqZ0h7TD8wWny7FGxSci4h4284Fxn3KURDe/Ozy0uo6Vu0tAmBap4JzY+f8QFkNVpvdeDA4EkLjjGOltouIiEhX5QrO03y6DG9RcC4i4m1tGKH24/YC6m12+ieEkxoX1uGPSowMJsBswmqzk1dW0/CEmsKJiIhIV9eDmsGBgnMREe+y29s439w5Qq35nfW2sphN9IpyNoVrJrVddeciIiLSVSk4lx4rdx0sfxFsNl+vRKT7OrABKg5CYBikTmz2FLvd7moGNy2z4yntTsmuunN1bBcRERE/0sOC8wBfL0C6kC9vhayfIW4ADJrl69WIdE/OXfP0KRAQ3Owpu/Ir2FdURZDFzDED4jv9ka6mcJp1LiIiIv7EuYnQQ4Jz7ZxLA2d6a95m365DpDtrS0q7Y9d8fHosYUGdv4baEJw33jlPN+61cy4iIiJdUU0ZVBqTa1ybCt2cgnMx2GxQYQQEFOzw7VpEuqu6Ktjzo3Hcar1550eoNebs2J7TUs253e6WzxERERFxG+cGQmgchET7di1eouBcDNXFYKs3jgsVnIt4xJ4fwVoDUX0gYXCzp9TUW1m2owCAqRnuCc57xzS3c+4IzmvLoKrILZ8jIiIi4jY9rN4cFJyLU/nBhuOCnb5bh0h35kppnwEmU7OnrNxdRFWdlcTIYIamRLrlY5OdO+eNG8IFhkBEsnHs/OYnIiIi0lUoOJceq6JRcF6SBXXVLZ8rIh2zY75x30pK+0LHCLXjMhIwtRDAt1dvR835wbJq6q2NpjG4msJpnJqIiIh0MQrOpcdqvHOOXR2cRdytLBcObgBM0H96i6ct2mrUm09zU705QEJEMAFmEzY7HCyraXjCVXeuf+8iIiLSxSg4lx7L2QzOSU3hRNxr5wLjvvdoCG9+PNrBsmo25ZRiMsGUQQlu+2iz2USvqGbGqWnWuYiIiHRVCs6lxyo/0PTXagon4l5tGKG22LFrPqJ3NPERzc9A7yhnU7j9jevONetcREREuiKbreHnkx4yRg0UnItTuWPn3OIICLRzLuI+Nlub6s0XOerNpw523665k3OcWm5zHdtVcy4iIiJdSVkOWGvBZIGovr5ejdcoOBeDsyFcn3HGvXbORdzn4Abj31hgOPQ9utlTbDY7i53zzd00Qq2xFEdTuP3NpbVr1rmIiIh0Jc6U9phUsAT4dCnepOBcDM6GcP2OMe41Tk3EfZwp7f2Pg4CgZk/ZsL+UwopaIoIDGJsW6/YlOIPzJuPUovuCyQz11YeXtoiIiIj4Sg+sNwcF5+LkbAjXb5JxX7oP6qpaPl9E2q4N9ebOlPZJA+MJtLj/v+aUGMes89JGwbklsCFVTE3hREREpKtw1Zun+3QZ3qbgXIx0VmdwnjQEgqON48JdvluTSHdRWwl7lhnHrc033+KsN3d/Sjs03jk/5KKb6s5FRESkq9HOufRY1cVGwwWA8CSIH2AcFyq1XaTT9v4I1hqIToX4Qc2eUlZdx6q9RQBM80C9OTQ0hMsrr6HOamt4wtWxfbdHPldERESk3RScS4/l7NQeHAWBIRA30Pi1msKJdJ6rS/sMMJmaPeXHHQXU2+z0TwinX3yYR5YRHx5EkMWM3Q4HGqe2a9a5iIiIdDUKzqXHcnRqrwtN4NsNuRDn2DnXODWRzmtLvflWR0p7hvtHqDmZzSZ6RRujEnNKmpt1rrR2ERER6QJqKxsa1So4lx7H0al9R2Uof3xjJZvqHGm1SmsX6ZzSHDi4ETBB/2nNnmK32xvNN/dMSruTM7U9p9lZ59o5FxERkS7A+TNJSDSEun+CTVem4FxczeD210UC8GNhjPG4ds5FOmenI6W9z1gIi2v2lN0FlWQVVhFoMXHMgHiPLqd3c03hnGntJfvAZvXo54uIiIgcUQ9NaQcF5wKunfPseiM4/zrHUfNatt9IKxGRjmlHSvv4tDjCgwM8upzk5nbOI1PAHAi2eijN9ujni4iIiByRgnPp0Rw153k2Y4TaLwdN2EJijOeU2i7SMTZbo2Zwbag393BKO0DvGMfOeUmjnXOzGWJSjWPVnYuIiIivOYNzZ3ZfD6LgXFzd2vOIdj1UGuqoQ1XHdpGOObAOKvMhKAL6Tmj2lNp6G8t2FgAwdbDnmsE5NVtzDurYLiIiIl2Hds67vjlz5jBhwgQiIyNJSkrirLPOYsuWLb5eVvfg2DnPtzcE53tINg60cy7SMc6U9v5TwRLY7Ckr9hRSWWslISKYoclRHl9SiqPmfH/xIcG5q2O7gnMRERHxMQXnXd/ChQu5/vrr+emnn/juu++oq6vjhBNOoKKiwtdL83+OnfN8ezRBFuOvxOoKR2MqNYUT6Zg21ZvnA8YINbO5+Rno7uQMzvPLa6ittzU8oZ1zERER6Qrs9oafR3pgcO7Z7kNu9PXXXzf59WuvvUZSUhIrV65k6tSpPlpVN2C3N9ScE83UwQn8sPkgq8tjuSwI7ZyLdERtBez9yTjuIvXmAHHhQQQFmKmtt3GgtJrUOEfzR9c4NdWci4iIiA+VH4T6KjCZITq11VPLquuoqbeREBHspcV5nt/snB+qpKQEgLi45scTAdTU1FBaWtrkJoeoKYV6I8U13x7NoKRIhveOYrfdkdaunXOR9tvzI1hrjaA3bkCzp+SV1bAxx/g/aUqG5+vNAUwmU6PU9kZN4ZxXppXWLiIiIr7kTGmP6gsBQa2e+vHqbI5+4Hvu/XyD59flJX4ZnNtsNm6++WYmT57MiBEjWjxvzpw5REdHu26pqa1ffemRHCnt1aZQqgkmKTKYYwcmsMsZnJfnQk25Dxco4oe2fmPcD5wJpubT1RdvM/7tjegT5dUrvs7gPLe0Ud25M629dD/U13htLSIiIiJNuOrNj9yp/Yu1Odjs0Ccm1LNr8iK/DM6vv/561q9fzzvvvNPqeXfeeSclJSWuW1ZWlpdW6EccKe3F5hgAEiODmTQwnlIiKMaYe67UdpF2sNthy1fGceYpLZ7mSmnP8E5Ku5OzY3uTpnDhCRAYBtihZJ9X1yMiIiLi0sZmcAdKq/lldyEAp4xM8eyavMjvgvMbbriBL774gvnz59O3b99Wzw0ODiYqKqrJTQ5R3rRTe1JkMBPS47CYTey09TLO0Tg1kbbLXQel+4xgt3/z/TBsNjuLtzmawXmp3tzJuXPeZNa5ydSo7lyp7SIiIuIjbQzO567LwW6HcWmx9NbOuffZ7XZuuOEGPv74Y+bNm0f//v19vaTuocLYvcuxGrvkiZHBRAQHcFTf6IbUdtWdi7TdVkfzygEzILD5bxYb9pdSUFFLeJCFsf1ivbg4SInRrHMRERHpotoYnH+5NgeAU7vRrjn4Ubf266+/nv/7v//j008/JTIyktzcXACio6MJDe0+V0u8zrFzfsBqZBUkRRm7ascOTGB3djJYgMJdvlqdiP/ZMte4zzy5xVMWOerNjx2UQFCAd6+RpkQ1s3MOmnUuIiIivucKzlveiN1fXMWKPUWYTN0rpR38aOf82WefpaSkhOnTp5OSkuK6vfvuu75emn9z1JznE01ooIXwIAsAkwbGuzq225XWLtI2pfth/2rABINPbPG0hV4eodZYSowjOC8+dOdc49RERETEh+qqoWy/cdxKQ7i564xd8wlpcSQ7yvW6C7/ZObfb7b5eQvfk6Naeb48mKToYk6Oz9Li0WB41G1eirHnb/ecviogvOVPa+06AiKRmTymrrmPVniIApnm5GRxAb0dDuIKKWqrrrIQEGhfklNYuIiIiPuXcIAiKgLD4Fk/70hGcn3ZU99o1Bz/aORcPceyc59mjSWw0zikk0EJUnyEABFTlQbVmxIsc0RZHcN5KSvuyHQXU2+ykx4fRLz7MSwtrEBMWSLAjlf5A43FqSmsXERERX2pcb97CKNp9RZWs3luMyQQnjUj22tK8RcF5T9eoW3tSVNNZy2My0si3Ozrca5yaSOtqK2DnAuO4DfXmvkhpBzCZTK6upk2awjl3zivyjK9FRERExJva0AzOmdI+sX8cSZHdK6UdFJyLo1t7Pk13zgGOHdSo7lwd20Vat2M+WGuMbyiJQ1o8bdFWxwg1H6S0OzU7Ti00BoKNkYoUZ3l/USIiItKzObP3WgnOv3B0aT9tVG8vLMj7FJz3ZDXlUFcJOHfOm159OqpvDFkYtRz5ezd5fXkifmXLV8Z95iktpmLtzq9gb2ElgRYTkwa2XEvlac7mKfsPbQoXq1nnIiIi4iNH2DnfW1DJ2n0lmLtpSjsoOO/ZHPXmNaZgKgg5bOc8KMBMvWOMQfG+zV5fnojfsFkbmsG1IaV9XFos4cG+a7PobAqXq1nnIiIi0lUcITj/Yp3Ryf3YgQkkHBK3dBcKznsyR6f2QlMMYCIx6vC/5NGOpnAm1ZyLtCx7JVTmG2nh/Sa1eNoiH45Qa8w1Tu2wWefpxr12zkVERMSb7PYjBudfOlLaTx3V/bq0Oyk478mcndptRp3poTvnAGkZIwGIq8nCatM4O5FmbZlr3GccD5bAZk+prbexbEcB4Nt6c2ioOT8srT1Gae0iIiLiA5UFUFsOmCA69bCnd+VXsGF/KRaziZOGd8+UdlBw3rM5OrXnWiMBDuvWDjAw0xGcU8rm3fu8tzYRf+KqN285pX3lniIqaq0kRAQxLCXKSwtrXoozrb1Uae0iIiLSBTh3zaN6Q+DhXdi/XGuktE8elEBseJAXF+ZdCs57Mmendns0ZhPEhx8enAeERVNijgVgy8Y13lydiH8o2AF5m8EcAINmt3ias978uIxEzObmG8Z5i7PmvLCiluo6a8MTmnUuIiIivnCkenNnl/aR3TelHRSc92zlBwDII5r4iGAsLQQMVZHpABzYvcFbKxPxH85GcGmTjXFkLWioN0/wwqJaFxUaQGigBThk1rkzjay6BKqKvb8wERER6ZmKdhn3zQTn2w+Wszm3jECLiRO7cUo7KDjv2Rxp7fn2aJIiW+54GNwrA4D6g9ups9q8sjQRv9GGlPa8sho27C8FjJ1zXzOZTM03hQuOgDDHxYPivT5YmYiIiPRIzp1zZ4ldI85GcFMGJRAd1nxvn+5CwXlP1iitPbGV4Dy6r9GxvY89h7X7SryyNBG/UFUEe340jgef1OJpS7Yb/9aG947qMqM/nKntOYfNOldqu4iIiHiZs99NMzvnXzjqzU8b1duLC/INBec9WRt3zs3xAwFIN+WybEe+V5Ym4he2fQ92KyQNg7j+LZ62cEvXGKHWWHJ0C+PU1BROREREvK2FmvOtB8rYdrCcIIuZ2cN6eX1Z3qbgvCdz7pzT+s45jYPznQXeWJmIf3COUGslpd1ms7N4m3FRa1oXCs57u4LzlsapKa1dREREvKC+FkocU6EOCc6djeCmDk4gOrR7p7SDgvOeq7bSMUvQuXN++MgCl7gBxp2pnK27s5p2dxbpqeprYfsPxvHgloPzjTmlFFTUEh5kYWy/WC8t7siSnWnthwbnSmsXERERbyrJAuwQEAoRSa6H7XZ7j0ppBwXnPVeFkdJeSyBlhLa+cx4Ujj3C6IzY27qf1XuLvbBAkS5u749QUwLhidBnXIunLXR0aZ80MIGggK7zX66zIdz+YqW1i4iIiA81Tmk3NUyP2pxbxs68CoICzMwamtTsS7ubrvOTonhXuREwFJpiAFOrNecAJqW2izTl7NI++CQwt/xfqXOE2rQuMEKtMWdDuNzSQ9PanTvne8Fu9/KqREREpMdpod7cuWs+IzORyJDun9IOCs57LsfO+UFbFEDrO+fgSm3vb1ZTOBHs9kb15qe0eFp5TT0r9xQBXasZHDQ0hCuurKOqtlGpSkwqYIK6CqjUhTgRERHxsGaCc7vd7hqhdmoPSWkHBec9V3k7g/NGO+drsoqprK336PJEurSDG42d5YAQGDC9xdOW7Sig3mYnLT6MtPhw762vDaJCAggPsgCwv3HH9oBgiEwxjpXaLiIiIp7WTHC+YX8puwsqCQk0M2tIz0hpBwXnPVejGecRwQGEBQW0fn6cEZwPDjhIndXOit1Fnl6hSNflTGkfMB2Cwlo8zZnSPjWja+2aA5hMJlJiHKntLTaF2+3dRYmIiEjP00xw7uzSPnNIEuHBR4hTuhEF5z2Vc8Y5rc84d3HunJsPAPDjDqW7Sg/mDM5bGaEGsGhb15tv3lhKdEtN4TROTURERLzAbj8sOLfb7Xy5zqg3P3Vkz0lpBwXnPZej5jzfHk1CW4Lz2P4AhFnLiKFMTeGk5yo7ANkrjOPBJ7V42p6CCvYUVBJgNjFpYLyXFtc+zuD8sJ1zdWwXERERb6gqgppS49ixObB2XwlZhVWEBlqY2YNS2kHBec/l6NaeZ49p2855UBhE9QGgvymXdfuKKa2u8+QKRbqmrV8b933GQWRyi6c5U9rHpcUS0UXTsVIcHdv3a9a5iIiI+IJz1zyil6tU8Mt1Rkr7rKFJhDr64/QUCs57qkY750dsBufk6Ng+IaoImx2W7yz01OpEui5ncD649ZT2hVuNqQZdNaUdGnbOc0o061xERER8oLmUdke9+Wk9qEu7k4Lznsqxc55PFEmRIW17jSM4nxhdDKDUdul5aithx3zjuJV689p6m2vk4LSuHJy31BDOWXNekgU2m5dXJSIiIj3GIcH56qxisourCA+yMD2z6/4M5SkKznuiumqoKQEgrz07546mcEOCjF13NYWTHmfXQqivguh+0Gt4i6et2ltERa2V+PAghqVEeXGB7dO7pYZwUX3AZAFrLZTn+mBlIiIi0iMcEpw7d81nD+tFSGDPSmkHBec9kyOlvZYASglvW805uMapJdVlA7App5TCilqPLFGkS9oy17jPPBlMphZPe2e50eV8WmYiZnPL5/lasiM4L62up6KmvuEJSwBE9zWOldouIiIinuLsbxObjs3Ws1PaQcF5z+RIaS8kGjCRFNW+nfPA4l0MTgoH4GeltktPYbPBFke9eSsp7TvzyvnsV2P8x+WT+3tjZR0WGRJIpKNZXU5Lqe0apyYiIiKe0mjnfNXeInJLq4kMDmDq4ASfLstXFJz3RI6d84M2I902MaKNwXlsf8AENaXMTjN+oFdqu/QY+1cb/3aCoyBtcounPT1/BzY7zB6axIg+0V5cYMekxLTQFE4d20VERMSTrPVQnGUcx6bzhWPX/PjhvQgO6Hkp7aDgvGcqb+jUHmA2ERsW1LbXBYa4Ul2nxhvzCH90NL0S6facKe2DZkFA8/9m9hRU8Mkao+zjTzMzvLWyTkl2jFPLKT505zzduFdau4iIiHhC6T6wW8ESjDW8F3PXOVPaU3y8MN9RcN4TNRqjlhAR3L6aWEfH9pFh+ZhMsCOvgoOl1Ud4kUg3sOUr4z7zlBZPeWb+Dqw2O9MzEzkqNcY76+qk3q5xapp1LiIiIl7kSmlPY8WeYg6W1RAVEsCUQT2vS7uTgvOeyDVGLbrt9eZOjuA8vGwPw3sbafEaqSbdXtFuOLjB6GA+aHazp2QVVvLhqn2A/+yaA6Q4d84Pm3XurDlXcC4iIiIe0Kje3JnSfuLwZIICem6I2nO/8p6s0c55m+vNnRxN4SjcwaQB8QAsU925dHfORnD9JkFYXLOnPLtwB/U2O8dlJDAuLdaLi+ucFOc4tcMawjl2zkuyjZowEREREXdyBOe2mDS+Wm8E56f24JR2UHDeMzl3zu0d2Tl3BOcFOzh2oNFFUU3hpNvb6kxpb75L+/7iKt5fYTQ0uXGW/+yaQ0NDuNxDd84jeoEl2KgFK93ng5WJiIhIt+YIzvdYE8kvryUmLJDJg3pml3YnBec9kWPnPI/O7JzvZEJ6LBazib2FlewrqnTzIkW6iOoS2L3EOG4hOH9+4Q7qrHaOGRDHhPTmd9a7KufO+WEN4czmhtR2NYUTERERd3ME50sKIwA4aXgygZaeHZ727K++p2rUrT0xKqR9r41NB5MZasuJqC9iVF9jVJRS26Xb2v492OohIbPh4lQjB0qrefsX/9w1h4aa87Kaesqq65o+qVnnIiIi4imO4PyLvcYUnNNG9fbhYroGBec9TX0tVBcDkG+Pav/OeUCwa5yakdquunPp5ra0ntL+/MKd1NbbmJAe6+rD4E/CgwOICgkAIFcd20VERMQbqkugqgiA9ZWxxIUHccwA/8o+9AQF5z1NhVFvXo+ZYiLaX3MODXXnhTuYNKCh7txut7trlSJdg7UOtn1rHDcTnB8sq+atn43A9cZZGZhM7RhL2IU4d89bbAqntHYRERFxJ8fPFuWWGCoI5aQRyQT08JR2UHDe8zjqzQvs0dgxt3/nHBpSewt2MC4tliCLmdzSanYXqO5cupm9PxlXdsPioe+Ew55+afEuauptjOkXwxQ/bmDibAqXU9zSODWltYuIiIgbOVLad1qNmean9fAu7U4KznsaR6f2PLtRK54Y2ZGdc2PWOYU7CA2yMKZfDAA/7sh3xwpFug5nSvvgk8BsafJUQXkNbyzz/11zaDzrXGntIiIi4gWO4HyXNZGEiGAm9ve/0kBPUHDe0zSacR4VEkBIoOUIL2iGa5zaTgAmqe5cuiO7HbbMNY6bSWl/eckuquqsjOobzfTBiV5enHu5OrYfOk4tJt24L8uBukMCdxEREZGOcgTne+1JnDIyGYvZfzc53EnBeU/j7NRONEnt7dTu1GicGna7a975MtWdS3eStwWKdoElCAbMaPJUcWUtr/+4G4A/zfTvXXNoHJwfEoCHxUGQMd6Ekiwvr0pERES6K1vhbsAIzk8dqZR2JwXnPU3jMWodqTcHo0mUyQx1FVCWy+jUGEICzRRU1LL1QLkbFyviQ1sdKe39p0FwRJOnXlmyi4paK0NTopg9NMkHi3Ov3jEtpLWbTI3qzpXaLiIiIu5RfXAHAGUhfRifri7tTgrOexpHWnuePapjndoBAoIafmAv3ElQgJkJjn9Uy1R3Lt1FCyPUSqrqeHXpbgBumjXI73fNAZKjGxrCHZb9oo7tIiIi4k42K0Hl+wDIHDpSKe2NKDjvadyxcw5NxqlBQ935j6o7l+6gPA+ylhvHg09q8tRrS3dTVlNPZq9IThiW7IPFuV9vR0O4ilorZTX1TZ9UUzgRERFxo+rCfQRQT63dwnFjR/l6OV2KgvOexjHn3Kg570Rw3micGsCkAUZw/tPOAqw21Z2Ln9v2DWCHlNEQ3cf1cFl1HS8vMRoh/mnWIMzd5EpvaJCFmLBAAHKKNetcREREPGfdujUAHDAnMTbdf0fReoKC856m0c55UmQHG8LBYTvnI/tEExEcQGl1PZtySju7ShHfaiGl/X/L9lBaXc+gpAhOHtG9mpckOxpE7j+sY7tmnYuIiIj7bNuyHoDayH7dZqPDXRSc9yTWOqgqBBxp7R2Zce7knHXuGKcWYDEzsb9Rd6555+LX6qphxzzjuFFwXlFTz0uLHbvmMwd1u/ooZ1O4XM06FxEREQ+prrNSlrMNgKjeg3y8mq5HwXlPUmEEzVZMFBFJUmeC88bj1Gw2QPPOpZvYtQjqKiGqDyQ31EG9+dMeiirr6J8QzmmjevtwgZ6R0qgpXBPOnfPKAqjRNAYRERHpuAVbDpJsOwBAQt9MH6+m6/Gr4HzRokWcfvrp9O7dG5PJxCeffOLrJfkXR6f2QnsUNsyd2zmP6QcmC9RXQVkO0BCcL99VSJ3V1unlivjElrnGfebJxigxoKrWyguLjF3z62d0v11zaAjO9x+6cx4SDSExxrFS20XEE2xWOHRShIh0S1+szSHVZMQkprh03y6mC/Kr4LyiooKjjjqKp59+2tdL8U/ljmZw9miCLGaiQwM7/l6WwIZ0V0fd+dDkKGLCAqmotbJ2X0lnVyvifXY7bP3aOB7ckNL+1s97KKiopV9cGGeO7n675gAp0S2ktYNS20XEcwp2wINp8OUtvl6JiHhYZW09P2w66ArOiU336Xq6Ir8Kzk8++WTuv/9+zj77bF8vxT+5Zpwb9eadns8c1yi1HTCbTRzTv6Fru4jfyVljZIIERUD/4wCjNup51675QAItfvXfZpulxLTQEA7UsV1EPGftu1BbBmvfM3rjSPeTvw0+uxHytvh6JeJj8zfnYaqrINHkaB6t4Pww3fOnTIeamhpKS0ub3Ho0Z6d2OtkMzumQcWoAxw5yzjtXUzjxQ84u7QNnQoDxb+Sd5XvJK6uhT0woZ4/p68PFeZZz5zynuBr7oeml2jkXEU/Z+o1xX1sO+9f4dCniASX74H9nwqrX4dPrVb7Qw32xdn/DrnlorFE6J0106+B8zpw5REdHu26pqam+XpJvVTSktbslOD9k5xwa5p2v2F1ETb21858h4k2uevNTAGPX/NmFxsWn62YMJCig+/6X6aw5r6qzUlpV3/RJ5865as5FxJ1Kc4yMJafdi322FPGAqiJ48zwozTZ+ve+XhtIx6XEqauqZt/kg/ZTS3qru+5MmcOedd1JSUuK6ZWVl+XpJvuWacR7VuU7tTvHOcWoNO+eDkiJIiAimpt7G6r3Fnf8MEW8pzoLcdWAyQ8YJALy/ch8HSmtIiQ7hvHHdd9ccICTQQlx4ENDcrHOltYuIB2z7tumvdy/xzTrE/eqq4Z2LIG8TRCTDUb8zHv/hPteUH+lZvt90gJp6G0dFFBsPKDhvVrcOzoODg4mKimpy69EqnMG5u3bOHcF50S7Xf7Qmk4ljBzpT21V3Ln7EeTU/dSKEx1Nbb+O5BcaFp2umDSQ4wOLDxXlHcpRjnNqhwXnjtHalJIqIuzhT2oedZdzv/Ul1592BzQofXQV7lkJwFFz8IZz4AARHw8ENsOEjX69QfODLtcZ0p2PjyowHFJw3q1sH53IIR7f2PGJIigzp/PtF9wNzANRXN6Qs0TBS7ScF5+JPnPXmmUaX9o9W7SO7uIqkyGB+O6FnlMT0jnEG54d0bHfOOq8pNdIURUQ6q64adi4wjqf82ag/rauA/at9uizpJLsdvr4DNn0GliC44C1IHgFhcTD5T8Y58x/QRZgepqy6jgVbjThkcJAjPnBm5UkTfhWcl5eXs2bNGtasWQPArl27WLNmDXv3qg6yTdy9c24JaLjqVdioKZwjOF+dVURlbX0zLxTpYqpLYdci4zjzFOqsNp5esB2Aq6cNJCSw+++aAyQ76s5zig8JzgNDITzJOFbduYi4w54lRjAe2RtSjoK0ycbjqjv3b0seh+UvGMdnPwf9pzY8N/FaCEswehWtecs36xOf+H7TAWrrbQxMDCe8cp/xoHbOm+VXwfmKFSsYM2YMY8aMAeCWW25hzJgx3HPPPT5emR+wWaHSuFKVb492T805NDSFa1R33i8ujD4xodRZ7azYrV028QM75oGtDuIHQUIGn6zOJquwioSIIH53dD9fr85rnB3bmx2npo7tIl7x4qKdjPzHN6zPLvH1UjzLmdI++AQwmRqCONWd+681b8MP9xrHJ86BEec2fT44AqbeZhwveMjInpAewZnSfurIZEzO/jUKzpvlV8H59OnTsdvth91ee+01Xy+t66vIB7sNm91EIZHu2TmHhnFqjTq2m0wmjnF0bV+meefiD5wp7YNPot5q4+n5xq75H6cOIDSoZ+yaQ0Nae+6hae2gpnAiXlBdZ+W/87dTVl3Pp2uyj/wCf2W3N/T5GHyScZ8+xbhX3bl/2vY9fHaDcXzsjTDpuubPG/cHiOoLZfthxcveW5/4TElVHQsdKe1nDTSDtQZMFoju3o12O6pDwXlxcTEvvfQSd955J4WFhQCsWrWK7Oxu/I3E3zlS2guJxIqFhAh37Zw7msI1Cs4BNYUT/2Gtb+gYnHkKn6/dz+6CSuLCg7hoYs+qh0qOcsw6bzY4d2QQKK1dxGO+Xp9LSZURmK7Y040zz/K2GP+XBIRA/2nGY4lDITQO6iohe5Vv1yftk70K3vs92Oph5Pkw+96Wzw0Mgem3G8eLH4WaMu+sUXzmu40HqLPaGdwrggEB+caD0X3BEujbhXVR7Q7O165dy+DBg3nooYd45JFHKC4uBuCjjz7izjvvdPf6xF3KG+rNY8MC3TevOf7wtHZoaAq3bl8xpdW6Ai5d2L7lUFUIobFY+x7NU/OMXfMrj+tPeHCAjxfnXQ0N4aqwH9qVXWntIh739vKGi1/rs0uorrP6cDUe5Nw1Tz+OKoK57f1f+WrDAUhX3bnfKdwJ/3e+0T9gwAw482kwH+FnzKN+Z5SRVRbAT896Z51etjOvnNV7i6ioUe+lL9fuB+DUkb0bsu+U0t6idkdot9xyC5dddhnbtm0jJKSh4/cpp5zCokWL3Lo4caMKI53EmHHuhk7tTs6a86JdRl27Q++YUNLjw7DZ4Zddhe77PBF32zLXuM84kbkb8tiZV0F0aCC/n5Tu02X5Qi/HKLXqOhvFlYdcVFNau4hH7cwr5+ddhZhNEBkSQJ3VzrruWnfuqjc/kS/W7ueDlfu457MN2NKOMx5X3bl/KM+DN84xfsZMHgW/fQMCgo78OksAzLjLOP7xKajsXj8n7sqv4KQnF3P2Mz8y/O/fcNy/53Hl6yt4+JvNfPbrfrYeKKPO2jNmvRdX1rJ4m7FbfuqoFCjabTyh4LxF7d4W+uWXX3j++ecPe7xPnz7k5ua6ZVHiAc6dc6JJinJTSjs40lKCwFoLJfsadteASQMT2F2wlx93FDBraC/3faaIOznqzW2DT+ap77YBcMWU/kT0sF1zgJBAC/HhQRRU1LK/pIrY8EY/ZLl2zvca9aImk28WKdJNvfNLFgDTM5MIspj5ekMuK/cUMSE9zscrc7PKQsj6yTgefCIrvjfS9/PKatgRPoYMgKyfob62bYGe+EZNOfzfb4zNmZg0uOgDCI5s++uHnQXJj0HuOqPD+wn3eWyp3vbk91uprbcRFGCmtt5GVmEVWYVVfL/pgOucIIuZAYnhDEmOZHByJEOSI8lMjqJ3dAimbvT99dsNB6i32RmSHMmgpAhYstt4QsF5i9r902dwcDClpaWHPb5161YSExPdsijxgMZj1NxVbw5gthj/wPK3GuPUmgTn8by9fC/LVHcuXVX+NijYDuZAfqgdztYDW4kMCeDSY9N9vTKfSYkJoaCiltySaob3jm54IqovYIL6KmOXJCLJZ2sU6W5q6218uNIYL3TBhFT2FFTy9YZcY+LJNB8vzt12zAO7DZKGQ0w/ftnT0LPm6wPRZITFG+nO+1dBv2N8uFBpkbUO3r/UmEkfFg8XfwSR7dyEMZth5j1GgL/8BTjmOohK8cx6vWjrgTI+/dVI4/7o2mPpExPK5twytuSWsuVAGVtyy9h6oJzymno255axObdpzX1kSACZvSLJdATsg3tFMiQ5iugw/6zP/tyR0n76Ub2NB7RzfkTtDs7POOMM/vnPf/Lee+8BRmfuvXv3cvvtt3Puuece4dXiM+XOtPZoEt25cw5Ganv+VqPufOBM18OTHB3bN+aUUlRR23QXTqQrcOya29OP49FFxpiPP0zuT3Sof34TdIeU6FDWZ5ey/9CmcAFBENUHSvcZqe0KzkXc5ruNByioqCUpMpiZQ5JY60hnX7W3CLvd3q120hq6tJ9AQXkNO/MqXE/N25rPn9Imw6bPjLpzBeddj90On90I27+HwDD43XuQMKhj75VxPKQeY2RSLHoYTnvMvWv1gce/24rdDqeMTGZEH+MC96SB8a5eTAB2u519RVVsyS1jy4EyV/C+M6+Csup6VuwpOqwhZHJUiCtgz3QE7YOSIggJ7LoTZQoral2NoU8Z6bjwouD8iNodnD/66KOcd955JCUlUVVVxbRp08jNzWXSpEk88MADnlijuENFQ1r7EHfunEOjcWq7mjycGBnM4F4RbD1Qzk87Czh5pP9fEZVuxhGcb46ewuaNZUQEB3D55HTfrsnHUqIdTeGKW5h1XrrPaAqXOsHLKxPpvt75xWgEd/74VAIsZob3jiIowExhRS278isYkBjh4xW6ibUetn1nHA8+yRWAJEYGk1dWw5qsYipGHkv4ps+MuvOp/8+Hi5Vm/fBP+PX/jFFYv3kN+o7v+HuZTDDrHnjtFFj1Ohz7J4jr77aletv67BK+Wp+LyQR/nj24xfNMJhOpcWGkxoUxe1hDxkFtvY2d+eVsyXUG7MYtu7iK3NJqckurXSPJACxmE+nxYQxJjiLTEbRn9oqkX1wYZrPvL+h9vT4Xq83O8N5R9E8Ih9pKKHeUQCs4b1G7g/Po6Gi+++47lixZwtq1aykvL2fs2LHMnj3bE+sTd2m0c54U5caGcNBonNqOw56aNCCerQfKWabgXLqaigJX3eO/d6UDcOmxacSE9ewMj5RoY5xai7PO9yxtuPItIp2WVVjpapj02wmpAAQHWDiqbzS/7C5i5Z6i7hOc71sO1cUQGgt9J7Diqy0AHD+sF6v2FLE5t4yfbEOZBbBXdeddzs8vwBLH7vbpT8LgEzv/numTYeAs2PEDLHgQzjm8r5W/eOy7rQCcNboPGb3aUX/vEBRgZkhyFEOSoziz0eOl1XVsO9AQsDvvS6rq2JFXwY68Cr5cl+M6PzEymCum9Oeiif2IDPFdJuCX64yU9tNGOVLanaNYg6ON/wOkWR3ueDRlyhSmTJnizrWIJzl2zvPcXXMOLY5TA6Mp3OvL9mjeuXQ9274Fu42ymCHMzw0hLMjCFVMG+HpVPuccp7a/pJmdc806F3G7dx2N4I7LSCA1Lsz1+Ni0WFdw/pvxqb5anns5u7QPOh7MFn7ZbeycT0iPJSY0kM25ZXyaHcUsZ9159kpIm+TDBYvLxk/hq78YxzPugrGXuO+9Z/3NCM7XvgtTboakoe57by9ZuaeIeZsPYjGbuGlWhlvfOyokkHFpcYxLa2gOabfbOVBa46hjL3UF7NsOlpNXVsODX23mmfnbuWxyf/5wbLrXS0vzy2tcPadOPSylvZ+ayraiTcH5f/7znza/4Y033tjhxYiH2GxQYVyVN3bOPVBzDsY/Omu9MSLD4ZgBcZhMsP1gOQdLq92/ay/SUVuNlPa5tWMAuOSYNOLUF4HkKOes82Z2zjXrXMSt6q023lthBOcXTOjX5LnxaXE8z05WHlJ76tcajVCrqrWy3lFbPz4tjr6xYTyzYAcLt+VjHzIF06ZPjdR2Bee+t+dH+PAqwA7j/uD+coPeY2DoGUavgXn3wwVvuff9veCx74wskN+M60t6QrjHP89kMpEcHUJydAjTBjc05K6z2vhszX6eWbCdHXkV/OeHbby0eCcXTezHVccN8NrP4V+tz8Vmh6P6RtMv3nHRUfXmbdKm4Pzxxx9v8uu8vDwqKyuJiYkBoLi4mLCwMJKSkhScd0VVhWA3ZpAXEEVipJuD86g+YAkGaw2UZDWpF4oJC2JYShQb9peybGcBZ47u497PFumI+hrY/gMAbxYNJyTQzJXHadccoHeMkdaeU1J9eCMqzToXcat5mw9ysKyG+PAgjh/WtNv1uDQj7XPbwXKKK2v9v+SmaDfkbTJqlQfNYk1WMfU2O8lRIfSNDSUlOoTo0EBKqurYGz2OND41msJNU925Tx3cBG9fYPyMl3kqnPqoZ3Y9Z9wFm78wbtkroc8493+Gh/y4I5+l2wsIspj5k5t3zdsr0GLm3HF9OWtMH77dkMt/529nw/5SXly8i9d/3MNvxvflmmkDm2TpeMKXji7tp45qVNKq4LxNzG05adeuXa7bAw88wOjRo9m0aROFhYUUFhayadMmxo4dy333dZ8Zhd2KY8Z5kT2CgMAgIt09v9lsbgjIm6k7P9bRoVIj1aTL2L0YasspMMez3p7ORRPT3H/Ryk/1clxVr623UVhR2/RJZ1p7yT6wWb28MpHuxznb/NxxfQkKaPojWVx4EAMcO3Cr9xZ7e2nut/Vb477fJAiNZcXuQgDGp8diMpkIsJiZ6tgB/KHKEeBkLTcupopvlOyDN8+F6hJInQjnvWyM0PWEpCEw6gLj+Af/iSfsdjuPfWvUml94dCp9HBe4fc1iNnHyyBS++NMUXv3DBManxVJrtfHWz3uZ/sgCbnl3DdsOlB35jTrgYGk1P+8y/n2fMlLBeXu1KThv7G9/+xtPPfUUmZmZrscyMzN5/PHHufvuu926OHGTxjPOI4M9M5LFmdpesPOwp5zjI1R3Ll2Go0v717WjCQwI4Oqp2jV3Cgowk+DoS3FYantUbzAHgq0OynKaebWItFVOSRULthjfny+Y0KimfPFjcH8vyFru2j1fsafQF0t0r23OlPYTAPhlj7PevKGOdkamEZx/sCcCwhKgvsrYRRXvqyqCN8+D0mxIGAwXvgOBHg48p99ufI/ZOR92LfLsZ7nJwq15rNhTRHCAmetndHCknAeZTCZmZCbxwbXH8u4fj2Hq4ESsNjsfrc7mhCcWcc0bK1m3r8Stn/nV+lzsdhjTL4a+sY126J0lcQrOW9Xu4DwnJ4f6+vrDHrdarRw4cMAtixI3c3Rqz7NHkxTpoVqT+JY7tk9Ij8NiNrG3sJJ9RZWe+XyRtrLbYYsxZ/d721h+d3Q/9UI4hLMp3GHBudkC0X2NY6W2i3TKe7/sw2aHif3jGrqxW+tg2X+hvhp+/I8rOPf7uvOa8oZga/BJWG12Vjm+pvHpDV2bpw1OxGSCjbllVPU51nhw9xJvr1bqquGdi4wyhMgUuPhDCIs78utaYLXZ2XqgDLvd3vqJsekw7jLj+If7jO/XXZjdbudRx6757yeldfmfJSYOiOd/lx/NZzdM5qThydjt8PWGXE7/7xJ+/8pyft7pnk20L5wp7Y13ze32Rjvn/jsuzxvaHZzPmjWLq6++mlWrVrkeW7lyJddee63GqXVVjWacu71Tu5Nz57zw8J3zyJBARvWNBpTaLl1A7joo3UelPZgVppFcPU275odyzTpvrmO7msKJdJrVZnc1grvw6EaN4HbMM7qUA2z5mqN7GcHJmqxi6qw2by/TfXYtBGutEXwlDGZzbinlNfVEBAcwJDnKdVp8RDBH9Y0BYEPQKOPB3Yu9v96ezGaFj64yxmYGR8FFHzSUNHVAfnkNl7z8Myc8voh7Pt1w5BdMvQ0CQo2xe84Ggl3UtxsPsC67hLAgC9dMG+jr5bTZqL4xPHfJOL7781TOGdMHi9nEoq15/PaFn/jNcz8yf8vBI19IaUFuSbVrCkOTevOKPKirBEwQ3U2mT3hIu4PzV155heTkZMaPH09wcDDBwcEcffTR9OrVi5deeskTa5TOKm9Ia3d7p3anVsapgTHvHGCZm67KiXSYI6V9sW0kZ04Y4JrrLQ2cvyf7i5ubda5xaiKdtXhbHtnFVUSHBnLSiOSGJ9a+13BsqyN9/1yiQwOprrOxcX+p9xfqLluNbCUGnwQmEyscP7yPTYvFYm5aajcjMwmAz0sdF05Vd+49djt8fYfRNd0SZHRNTx7R4bdbvbeI059a4iprfOOnPfy4I7/1F0Umw8SrjeN59xkTh7ogm62h1vzyyf2J99Tmlwdl9Irksd+OZv6t07loYj+CLGZ+2V3EH179hdOeWsLcdTnYbO0L0uc65q2PT4tt+vOVc9c8ui8E+HlzSw9rd3CemJjI3Llz2bx5M++//z7vv/8+mzZtYu7cuSQlJXlijdJZjYJzj++cF+8xxqkd4tiBCYCxc97Rq3Ei7lCx7nMA5tnH+dWVbm9y7pznNjvrXB3bRTrr7eXGxa2zx/QhJNDRYKumHLbMNY4djbHMv/6f/6e22+0NzeAyHPXmjmZwE9JiDzt95hDjZ8kP9oRhD080Uvz3rfDOWnu6JY/D8heM47Ofg/5TO/Q2drudN3/aw/nPLyOnpJoBieGc7LgIdedH66iqPUJD0ck3QXA0HFgPGz7q0Bo87Yt1OWw5UEZkSABX+fm0l37xYTxw9kgW3z6Dq47rT1iQhQ37S7nurVUc//hCPli5r82ZO18016Ud1AyuHdodnDsNHjyYM844gzPOOIPBgwe7c03ibo609jw8uHMemWKkIdnqm013HZcWS6DFRE5JNbsLVHcuPlKSTXjBOmx2ExEjT23aqERcUhzdZvc3O+s83bhXWrtIhxwsq+aHTcb35SYp7Zu/NNI+4wbASXOMncvctZwQb/SN8dvgPOdXKM+FwHBIn4Ldbm8IzvsfXsc8vHcUCRHBVNTaKEiYYDyounPPW/M2/HCvcXziHBhxbofepqrWyq3v/8rdn6ynzmrn5BHJfHr9ZP593ihSokPYU1DJ499vbf1NwuJg8p+M4/kPGL0YupB6q40nvjO+hj8eN4DosEAfr8g9ekWFcNepw1h6+0xunJVBVEgAO/IquO39X5n+8ALeWLab6rqWL6xkF1exam8xJtMhXdqhUXCe5rkvoJto90ytyy+/vNXnX3nllQ4vRjykvGm3do8wm40fKA5uMOrO45vuSIYGWRjTL5bluwpZtqOA/o7xMCLelPXzx6QCa+yDuHT2BF8vp8tqteZcO+cinfLByn3U2+yM7RdDZnJkwxNr3zXuR55vBCeZJ8PGT5lW+Q1wAiv2FGK32z0zccWTnHXDA2dAQDD7Cis5UFpDoMXkqi9vzGw2MT0zkQ9W7uNn+3BOZa6j7vx2ry67R9n2PXx2g3F87I0w6boOvc2eggqueXMVm3JKMZvgjpOHcNVxA1x/Zx84ewSXv7aClxbv5NSRKRyVGtPym028Fn56zviZcs1bDY3iuoCPV2ezM7+C2LBA/jCl+zU3iw0P4pbjB3PVcf1586e9vLxkJ9nFVfzt0w38Z952rpzSn4uOSSPikNHMc9caKe0T0uNcY1ldtHPeZu3eOS8qKmpyO3jwIPPmzeOjjz6iuLjYA0uUTqswrrrne7JbOzTMOm+h7vxY10i1I9QbiXhI0epPAchJnkG/eO2at8QZnB8oqTm83sxZc162H+oPmYMuIq2y2ey865htfkHjXfPyg8b4KIBR5xv3oy8GIHn354SarRworSG7uJkLZl1d43pzGsbCjegTTWhQ8zOznant7+WnGw/s+8XoIC7ul70K3vu9kfk48nyYfW+H3mbe5gOc/tQSNuWUkhARxJtXTuSPUwc2uZg0c0gvzhrdG5sdbv9wLbX1raRKB0cYzeEAFjzUZf78a+ttPPnDNgCunT7wsAC1O4kMCeTa6QNZcvtM7j1jOL2jQ8grq2HOV5uZ/OA8Hv9uK8WVDT8HfOGoNz/90JR2UKf2dmj336iPP/74sMdsNhvXXnstAweqfrPLsdmwV+Rhwhmce7BhhXO3vJlxamA0hXuCbfy0s8A/r/6LX9u4ez+ZlavBBEfN/p2vl9Ol9YoKwWSCWquNgoraphk3EUlGCUt9FZTuMzJmRKRNftpZwJ6CSiKDAzit8Q+w6z8Cuw16j234XjpwJkQkYyrP5ffxm3k+bzgr9xT5VzlO+UHY75ju46o3P3y++aGmZCRgMZtYWBhDfVwSAZUHIXsFpE/x+JJ7lIId8NZvoK4CBsyAM582MiHbwWqz8+QP2/iPI2Ad2y+GZy4aR3J085tB95w+nEXb8tmcW8ZzC3dw46yMlt983B/gx/8a32tWvAyTrm/X2jzhvRVZ7CuqIjEymEuOSffuh9vtRjd9a43RJNFa67iva/pYVO9Oddg/VEighUuPTefCo/vxyZpsnluwg535FTz5wzZeWryTi45J48ThyfyaVYzZBCeNaCU4j1Fa+5G45XKP2WzmlltuYfr06fzlL39xx1uKu1QXY7IZDdoKTVHEhXuwQ2Jc6x3bR/eLISTQTH55LdsOljO4V2Sz54l4woKv3mOYqY68wD70zRjt6+V0aYEWM4kRwRwsqyG3pLppcG4yGd/087cYqe0KzkXa7G3HrvkZo3sTFtToR7B1ji7to37b8JglAI76LSx9krPNC3keIzg/c3QfL664k7Y5GsH1HgORvQBY4ag3H99MMzinqJBAxqfF8vOuQvZGjmVA5ddG3bmCc/cpz4M3z4XKfEgeBb99o91dtIsqarnp3TUs2mpkaP5+Uhp3nzqMoICWA/y48CD+ccZwbnx7NU/N28bJI5LJaOnnwcAQmH47fPYnWPwojP09BPvuZ8fqOitPzTMuQtwwY1CLmR+AMRYxd52RYeYKnJ1B9JEeq3UE3rWHP0cbmioHhMA1SyFhkHu+cIegADPnj0/l3LF9+Wp9Dk/P38GmnFJeWLSTFxYZo5SPGRB/eAltXTWUGo3ilNZ+ZG7LxdixYwf19Yd36RYfc9Sbl9jDiAyPIMDS4R6ARxbf8qxzgOAAC+PT4liyPZ8ft+crOBev2bi/lMTsHyAAAoeebASY0qqUmFAOltWwv6SKkX2jmz7pDM41Tk2kzQoravlmfS5wSCO4gh2QvRJMFhhxTtMXjb4Ilj7J4NJlJHCR/zWFOySlvbiylq0HygFcXehbMnNIEj/vKmRhbSYD+FpN4dypphz+7zdQtMvYybzog3YHvev2lXDNmyvJLq4iJNDMv84eyTlj+7bptaePSuGzNdl8v+kgf/lwLR9cc+xhI/VcjvodLHnCyMr86VmY5rtNwLd+3suB0hp6R4dwwdEtzOq2240RcIsf9cKKTBAQDJZg48KKJQhqK6C6GBb8C87zTB8wi9nEaaN6c+rIFOZvOch/521n1d5iAE4/qvfhLyjJAuxGU8jwBI+sqTtpd3B+yy23NPm13W4nJyeHL7/8kksvvdRtCxM3qfBCMzgn1zi1vcaVQMvh3SsnDYxnyfZ8lu0s4LLJqjsR73h63mbutawBIGbMmb5djJ9IiQrhVyCnuRpXZ7dVdWwXabOPVu2j1mpjRJ8oRvRpdMFr3fvG/YDpRtlIY4mZ0Gc85uwVnGVZwis5p1JeU+8fda71NbDDUUc/+ESgoeP8wMTwI86FnjEkiTlfbeadvDT+EIAx77yu2thNlY6z1hk15vtXQ1g8XPyRK6uhrd77JYu7P11Pbb2NtPgwnrt4HENTotr8epPJxH1njeDnnYtYvbeY13/czeUtNVazBMDMu+CDy+HHp2DClUbDRC+rqKnn2QXbAbhxVgbBAc3smlvr4YubYfUbxq+HnGb8HgcEG4GzJajhuMXHgo2fn494XhCYAw7fbMhZC88fB+s/hCl/huSRHvs9MZlMzBzSixmZxoW0XfkVnD++mYsWjZvBaXPkiNr9v/vq1aub/NpsNpOYmMijjz56xE7u4gPOTu14uN4cIDLZuCpWV2GkuzaTTuNsCvfTzkKsNnvLV0pF3GTrgTJyNy4lIagUa1A0ln6TfL0kv5AS4+jYXtpMEx51bBdpF7vd7ppt3mTX3G6Htc6U9vObf/GYiyB7BRcGLealqlP4NauYyYP8YPdpz49QWw4RvSD5KKBt9eZOGUkR9IkJZUtxL2oiEgmuzjMaw/U/zqPL7tbsdiNFfMcPEBgGv3uvXanP1XVW7v18A28vN8ozZg9N4tHzRxMd2v5RYinRodx5ylD++vE6Hv5mC8cP60VqXAv9FIadDb0ehwPrYOkTcPw/2/15nfX6st3kl9eSFh/GueOayRCoqzIuIGyZCyYznPa4bzrMp4yC4ecY8+HnPQC/e8fjH2kymThmQDzHDIhv/gR1am+Xduc4z58/v8nthx9+4J133uGPf/wjAQF+cCW3p3F0as/zxs65ydRQf9pCU7iRfaKJCA6gpKqOTTmlnl2PCPDfeduZbV4JgCXzhGYzOuRwvaONWec5xc3NOtfOuUh7rNhTxI68CkIDLZzROO1z/yrj+2VAKAw5tfkXDz8HAkIYaN/LSNMuVuz2k9R25wi1jBNcTcZc9eZtCM5NJhMzhiQCJjaHGMG9Uts7wW6Hb++GX982Sih+8xr0Hd/ml+8rquQ3zy3j7eVZmExw2wmDeeGS8R0KzJ0umJDKMQPiqKqzcudH67DbW6inNpth1t+M459fgNKcDn9mR5RW1/H8QqNk8+bZGQQeWiJaVQRvnG0E5pZgOP8N345+m/FX4wLB1q8g6xffrcNJwXm7tDs4nzlzZrMj00pLS5k5c6Y71iTu1GjGucd3zgHiHcF5C03hAixmju5vfFNetqPA8+uRHm37wXI+X7uf2WZHt2BH3aMcWXKrs84dO3+qORdpE+eu+elHpRAZ0iiYWetIaR9ySss1v6ExRnoscJ5lISv3+kFwbrcbgQG4/t+trrOydl8JABPSW683d5qRaaT5f13u6Oit4LxjbDb46i+w7L/Gr09/wlVq0BaLtuZx2lNLWJddQkxYIK//4WhumJmBuZPZj2aziQfPGUVwgJkl2/N5f+W+lk/OOAFSJxqTQhY/0qnPba+XF++ipKqOQUkRnHHUIQ0ZS/fDq6fA3mUQHA2XfAxDT/Pq+g6TkGHU6oNR/+5rCs7bpd3B+YIFC6itPXy2bXV1NYsXL3bLosSNvFlzDkfcOQfNOxfvefibzfQjlwxztlGbNWi2r5fkN3o709pLWklrLz9gpPKJSItKquqY65j/22S2ubUe1n9gHI9sIaXdabTxg/aZlh/ZsOcAVlsbOjb7Uv424wdyS5BRSw+s3VdCrdVGYmQw/VpKXz7EsQMTCAow83WFI/V633L9n9NeNit8cRMsfwEwwWlPGF3P2/JSm53/ztvGpa8up7iyjlF9o/niT1OYOjjRbctLTwjn1hMGA3D/Fxs52FwpFRjZmbPuMY5XvgaFu9y2htYUVdTy8hLjs245fnDTcsz8bfDyiXBwI0Qkwx/mQvpkr6zriKbfDuZA2LUQdi707VqcJXAKztukzcH52rVrWbt2LQAbN250/Xrt2rWsXr2al19+mT59/Gi8R09RbqS1GzXnXmiicoRxaoCrJmX5rkLqrDbPr0l6pK/W5fDNhgOcYHH0yUibbOxASZukONLaD5RWYzs0EAiNhWBH8x/tnou06tM12VTX2cjsFcmY1JiGJ3YtMErPQuNg0KzW32TAdOxRvYkxVXBM3XK2Hijz5JI7b5sjpT19CgRHAPCLI6V9QnospjY2hQoNsjBpQDy77MlUBCUY46X2dYE0XX9hrYePr4FV/zPSnM96Fsb/oU0vLamq449vrOCRb7dit8OFR6fy3tWT6Bvbtgsr7XH55P6M6htNaXU993y6oeUT06fAwJlgq4eFD7l9Hc15ftFOymvqGZYSxUnDkxue2LcSXj4BSvZC/CC44ltIHuGVNbVJTL+GP+t59xnZLL5gt2vnvJ3aHJyPHj2aMWPGGJ35Zs5k9OjRrtu4ceO4//77ueeeezy5VukIb++cu8aptRycD0uJIjo0kIpaK+uySzy/JulxSirruOcz4xv8JXEbjQczT/HhivxPUmQwZhPUWe3kl9c0fdJkUlM4kTaw2+3838/GBawLjk5tGpQ6U9qHn33kXhhmCyZHmup5loVdf6Sas968USlRw3zz9nXanpFp1J2vNju6Tiu1vW3qa+GDP8C694zMsXNfhtEXtumlm3JKOeO/S/h+00GCAsw8dO5I5pwzipDAVuZ6d0KAxcxD544iwGzi6w25fLWulZrymY7a81/fgYObPLIep4Nl1bz2o7FrfusJgxvS+Ld/D6+fDlWF0HsMXP5NQy+WruS424x+Fvt+afg36W2VhVDruJgY06/1cwVoR3C+a9cuduzYgd1uZ/ny5ezatct1y87OprS0VN3auyLnzrm3as6dO+cl+4wxKs0wm00cM0B15+I5D8zdSF5ZDaMTbKSWrTEezFS9eXsEWMyubJvmU9uddecKzkVa8uu+EjbnlhEUYObsMY2yC2srYfMXxvGo37btzRyp7VPNa9m+faubV+pGVcVGp3Yw6oQx0qNX7Gl7p/bGZg4xxnzNLXf8fKHg/MjqquG9S2DTZ0Zpwfn/gxHntOmlH6/ex9nPLGVPQSV9YkL58Jpj+e0EzwdVQ1OiuG668Wf8t083UFx5eAktAH3GwtAzADvMf8Cja3p2wQ6q62yMTo1h5hDHmMO178H//daYTDRwJlz6Rded3R3ZCyb+0Tied7/Re8DbnLvmkb01BrGN2hycp6WlkZ6ejs1mY/z48aSlpbluKSkpWCyeuZomnWC3Y3fsnHulWzsYM1qDIsBua3VH7diBxn9kCs7F3ZZuz+e9FfswmeCxMXmY7FZIGqZ0qg5wjVNrrimcOraLHNE7jkZwp45MISYsqOGJLXONMWMxaZB6dNveLH4gJYnjsZjsJO/5zAOrdZMdP4DdColDIM6YXb31YBll1fWEBVkYmtJC47sW9IsPY0BiOD9ahxoP7PtFdeetqa2Ety+ArV9DQAhc+HbLkwAav6zext8/Xc+f3/2V6jobUwcn8sWfpjCyb7QXFm24fuYgBiVFkF9ew31ftLIrPuMuI01/0+eQvdIja9lfXMVbPxn/fm87IdPIeln2DHx0lZFWP+I8uPBdV9lGlzX5ZqMM7cA62PiJ9z+/yNEboCtmFnRRbQrOP/vsM+rq6lzHrd2kC6kuxmQ1rjxWBsURHuyFUXdtGKcGDU3hftldSE291fPrkh6hsraeOz4yemP8fmIqA3a9bTyRebIPV+W/Uhwd2/c3N05Nae0irSqvqeezX/cDxsioJtY5UtpH/sb4vtlGQeMvBuD4mu84WNpFA9St3xr3jl1zaJhvPrZfLAGHjqFqg5mZSey2J1MS4Kg7z1rulqV2OzVl8NZ5sHM+BIbDRe+3qRFqbkk1F7ywjNeXGf+f3zgrg1cvm0BseNARXulewQEWHjp3FCYTfLhqHwu35jV/YtIQGHWBcTzvfo+s5b/zt1NrtTGxfxyTB8bBd3+Hb+40npx4LZzzIgR49/enQ8LiYNINxvH8B4w+BN6kevN2a9P/kGeddRZFRUWu45ZuZ599tkcXK+3kSGkvtYcSHdm+K9WdEn/kpnCDkiJIiAimpt7G6r3F3lmXdHuPfbuVrMIqekeHcGfKSqOzb1AEjL/C10vzS86mcLnNdc/VODWRVn3+634qa60MSAx3jRAFoKLAqFkFGHWELu2HCB19HtUEM9Ccw87V8924WjexWWGbIzhvrt68jSPUDjVjSBJgYql1mPGAUtsPV1VszNres9TYKb3kY+g/9YgvW7ajgNOeWsyqvcVEhQTw8qXjD+9K7kXj0mK57Nh0AP760TrKa1oIJp3dyHfMg13unRa1t6CS937JAuDW2QMxfXYDLH3CeHLW3+GkOcbsdX9xzLVG48mC7bD2He9+toLzdmvT3yybzUZSUpLruKWb1aod0C6lovGMcy/WecQduSmcyWRikmP3XKnt4g5rsop5ZamRPvXvU/sQsuBe44kZf4VoTZLoiIadc6W1i7SXM6X9ggmHNILb+LGRFps8ChIz2/emwZFsjJ0BQNA6L/+Q3Rb7VhhNskKijZnUDit2d6ze3GlCehzhQRYW1jp+vxScN1VRYDQo2/cLhMTA7z+FfhNbfYndbueFRTu4+OWfyS+vZWhKFJ//aQqzhvbyzppbcdsJmfSNDSW7uIpHvtnS/Emx6TDuMuP4h3+6tRv5kz9so95mZ/agSI7++U+w5i0wWeDMp+G4W9qV7dIlhETBlD8bxwsearEnlEcoOG83P7rsI+1W7gjO8VK9uZMzrb2VnXNoSG1XcC6dVVtv4/YP1mKzw1mjezNl51NQVQS9RsLRV/t6eX7LuXPe6qzzqiKoLvXiqkS6vg37S/h1XwmBFhPnju3b9Elnl/Z27po7VQw1XpdZ8J1RX9yVbP3auB90PFiMUrrs4iqyi6uwmE2MbjxKrh2CAsxMyUjgJ1ujuvOu9rX7SvlBeP00yF0LYQlw2ZdG07TWXlJTz3VvreJfczdjtdk5Z2wfPrr2WNLiw7206NaFBwfw4DmjAHh92W5X5sVhpjq7kS93Wzfy7QfL+Xj1PqIp54nafzTU7l/wFoy52C2f4RNHXwWRKcbot5Wve+9zNeO83dpUhPyf//ynzW944403dngx4mYVDZ3avRqcu8ap7Wz1tEmOeeers4qoqrUSGqSmgtIxzy3cwZYDZcSFB/HPMaXw9pvGE6c95voBUdrP2RAut7ngPDgCwuKhssDYPU8e6eXViXRd7yw3UmJPGJZMfESj779FuyHrJ8AEI87t0HunjT2RrCWJpJrzqN3wOUFj2tjt3RtcKe0nuh5yBlbDe0d1qvfNjMwkvtnQi3xzPAm2AiMgGzC9M6v1fyXZ8L8zjHTliGS49LMjZmNsP1jG1W+sZEdeBYEWE38/fTgXTezX5tnz3jIlI4Hzx/flvRX7uP3DtXx543GHj3KLTIaJVxsp5/PuM/ocdDLd/Invt5JkL+DDyEeIOLjHyAL53XvQ75hOva/PBYYaFzO+vBUWP2JcaAhy/8z6Jqx1ULrPOFZw3mZt+l/y8ccfb9ObmUwmBeddSXmjtPYob+6cNxqnVlfd4uiEtPgwekeHsL+kmhV7CjkuI9F7a5RuY/vBMv47bzsA/zg1g6jvHU1ixl7a9i7I0qzejWrOrTb74TWIMf0cwfleBeciDlW1Vj5Zkw3AhUcfMoLK2Qiu/3EQ1btD758aH86LATP4o+09qpa/3nWC8+IsOLDe6KLdqAmZM6W9vfPNDzU906g7X1w3lLMtS4zU9p4cnBftMVLZi/dAdKqRyu7cHGnBkm35/PGNFVTWWkmJDuGZi8Yypl/H+gB4w12nDGP+ljx25FXw33nbue3EZi48TL4JVrxi/N3b8BGMPK/Dn7cpp5RN61bwYfCD9KkrMHaaL/4Ieg3rxFfRhYz5PSx90vievfwFmHKzZz+vJMuY3hQQAhG+L5fwF226vNR4pnlrt507W98pFS9rVHOeGOHF4Dw8wWhGgr2h1qQZJpOJYxyp7T8qtV06wGqz85cP1lJrtTFzSBKnV34CeZuMHd3Z//D18vxeYmQwFrMJq81OXlkzNWrq2C5ymC/X5VBWXU9qXKirfAswamKdKe0jO5bSDsb3zr2pZwIQlfOjERR3BdscacWpE40O0Q6/OHbOJ3SwGZxTcnQIw1KiWOZMbe/JdecFO+DVk43APLY//GHuEQNzgIe+3kxlrZVJA+L5/E9TunRgDhAdFsh9Z44AjAy5DftLDj8pLA6OdWwMzn/A2K3toI8//5QPgu6lj6kA4jPgim+7T2AORnf56Y6O80ufgOpmfj/dqXG9eRfLzOjKOpX7YbfbsbuxAYO4maNbez7RJEV5sSFcG8epgeadS+e8sWw3q/YWExEcwJyZ0ZgWPmQ8ccL9TX44lI6xmE30cpTEaNa5SNs0NILrh7lxtknuWsjfApZgGHZGpz4jfdAwfrQOw4Qdfu0ijeG2Hp7SXlJVx5YDZQCM62RwDjBjSCI/2RzB0r4VPbPu/OBmIzAvzYaEwfCHrxqmZ7T2srJq1mUbwdh/LhxDgjc3bTrhpBHJnDIymXqbnds/XEu91Xb4ScdcY9TbF+6ENf/Xoc/Z+ePH3Jx9K7GmcqqTRsPl37Tp99XvjPotJGQa/WKWPe3Zz1IzuA7pUHD+8ssvM2LECEJCQggJCWHEiBG89NJL7l6bdJZr5zzKuzvn0KZxaoCrY/vafcWUVnf8aqf0PPuKKvm3o4vr7ScPodfSv0NdJaRNhqMu9PHquo9kR8f2VpvC+XLnfP9q2Pad7z6/B1myLZ/s5jr3i8u2A2Ws2FOExWziN+MObQT3nnGfeZJRx9oJ49Ji+cBqjMmyr3nLrZ2qO6S2EnYtNI4zGoLzVXuLsNshPT7MLVNjZmQmsdeeRC7xYKuDrJ87/Z5+JWctvHYKlB+AXiPgsrkQldKmly7cYmzYjOrr5T5EbvCPM4YTHRrI+uxSXlqy6/ATgiPhuFuN44UPGSWV7fHrO/T79grCTDVsCj+akCvnQnj8kV/nj8wWY4oNGMF5hQc3xxScd0i7g/N77rmHm266idNPP53333+f999/n9NPP50///nP3HPPPZ5Yo3SQ3Vc159CmcWoAfWJCSYsPw2aHX3a10I1T5BB2u527Pl5PZa2Vo9PjuCh6PWyZC+YAOPUxpU+5UUpMGzq2+2LWeXEWfHAFvDAd3joPdnTBmc/dyA+bDnDxyz/zh1eXK2OuFe84ZiPPHJLUNGPNZoX1HxrHnUhpdxreO5p55mMot4dgKtoFe5d1+j07ZdciqK+G6H6QNNT1cMN8c/dkMo3pF0tMWBBLrT0wtX3fSqMre2UBpIyGSz+HiLb36pm/xfiZ0Kjd9y9JkSH87TQjY+Lx77ayM6/88JPGXw5RfYyMghWvtP3Nf3wKPr6aAKx8Yp1C2KXvQ1DX6FrvMUPPMEY51pbD0rb1FesQBecd0u7g/Nlnn+XFF19kzpw5nHHGGZxxxhnMmTOHF154gWeeecYTa5SOsNtdae2FphjiwoK8+/lt3DkHOKa/cXVy1d4iT65IupGPV2ezcGseQQFmHjxjIOavbzeeOPZPkDTEt4vrZno7d86PNOvcWwFbbQXM/xf8dwKs/6Dh8ZWveufze6jnFxk9ZbYeKGe5LqQ2q7rOyoerjM7EFx6d2vTJ3UugLMfYMc84vtOfFRRgZnDfZL60OjpIr36r0+/ZKc4RaoNPbHJx9BfXfHP31DZbzCamZjRKbe8pwfmeZfC/M40a4dSJRlf2dpRu1VltLN6aDxgXjvzRuWP7cFxGAjX1Nu74aB022yHfcwJDYJrjZ4HFj0JNWetvaLPBt3cbN+DF+lNYPvpfpCXFuH/xXY3ZDDP/ZhwvfxFKczzzOQrOO6TdwXldXR3jx48/7PFx48ZRX1/vlkWJG9SUYbIaO1328MSmdW/e4Ko5P3KTwMzkSMCYLSlyJPnlNfzzi40A3DQrgwEb/mt0BI3uB1P/4uPVdT/Jrc06j3YEILXlUOnhgM1mM9KCnxpvpC3WV0G/Y+Hs543nN8+FinzPrqGHWruvuElA7twdlqa+2ZBLcWUdKdEhTBt8SAC0zpHSPuwsCHBPJtvYtFjed6S2s+FjqPHR91C7vdkRajX1Vn7NKgbct3MORnDpmneevdK4YNed7VwAb54DtWWQfpzRPbydZREr9xRRVlNPfHgQo/p0rqTCV0wmE/86eyRhQRaW7yrk/5Y3k7E1+iIjc7MyH356ruU3s9bBp9cZu+bAv+ou5GH777lh1mAPrb4LyjgeUo8xMl4WPeyZz3AG584sO2mTdgfnl1xyCc8+++xhj7/wwgtcdNFFblmUuIFjxnmFPZjIKB/8R+xMay/NPmLDloxeEQBsU3AubXDv5xsprqxjaEoUfxxS3dDQ5JSHPT+zswdy7Zw31xAuMMSYrQuebQq3bwW8fDx8dBWU7Tea9PzmdaND8VEXQO+xRv1pV2mM1c28tNio8RzeOwqAuetyKKlUj5BDOWebnz8+tenYwbpq2PiZcTyq8yntTuPTYllhz2SfOQXqKmDTZ25773Y5sN74Xh8YZgSPDuuzS6mptxEXHsSABPelCU8dnMg+ksi294C6863fwlvnG/1UBs6Ci96H4Ih2v40zpX3aYB9s1rhRalwYf3GMU3vwq83sPzSjyxIAM+8yjn/8T/MXjWsr4J3fwa9vYzdZ+E/kLbxgPZ3fTexHb0cZV49gMsEsx+75qtdbna7UIVVFDd3gYxWct0enGsJdeeWVXHnllYwcOZIXX3wRs9nMLbfc4rqJDzWuN3dDE5Z2C4truLJ7hH/wg5KMbzR7CiqprW+mC6eIw/cbD/D5r/sxm+Df54wg8KvbwFYPQ04zmiyJ27Vacw6e7dheuh8++iO8NAuyV0BguJGKd/0vMPyshvTZsb837lf9z/eNsbqZ/cVVfLnOSHn893mjGJIcSU29jY9X7/PxyrqW3fkVLNtZgMkE5084JKV92zdQUwpRfY1sDzcZmxYLmHi7xhEQ+yq13ZnSPmC6ccHOwVVvnhaLyY19QOLCgxidGtv9U9s3fW4EkdYayDwVLnwbAjsWPM7f7Kg399OU9sYumZTOuLRYymvqufuT9Yf3wBh2NvQaafybW/pk0+cqC43ygG3fQkAoa497hsfyxhMSaOa6GUceRdftpE+BATOMn6MWPOTe93b+7B+e1P1r+N2s3cH5+vXrGTt2LImJiezYsYMdO3aQkJDA2LFjWb9+PatXr2b16tWsWbPGA8uVNnN2asfLM86dTKY2N4VLjgohIjgAq83O7oJunp4mHVZaXcfdn6wH4KrjBjAy/0ujCVJgGJz0oI9X132lOHbOD5RWNz/CxhMd2+uqYOG/4alxsPZd47Gjfgd/WglTb2sSAAAw4lzj70H+Fsha7r51CK/9uBurzc6xA+MZ3juaC482Rgu980uWGsM14kz1nzY4kT6H7r45u7SPPNeo9XSTuPAgBiSG85H1OOyYYM8SKGymk7WnbXXMN2+U0g6N683dP9ZyZmaj1PbuGJyv+wDeu9TIDBh+Npz/eofLIfYVVbL1QDlmE0zLaHsDua7KYjbx0LkjCbKYmbf5IJ/9ur/pCWZzw47wz89DWa5xXJwFr5wI+36BkBhsl3zKX9f3AeDSY9N9s5HVFThrz9e+A3lb3Pe+qjfvsID2vmD+fHXE9Qu+7NTuFD8Q9q86YlM4k8nEoKQI1mQVs+1AOYN7RXppgeJPHvpqM7ml1aTHh3HzsQnwvOMbyvQ7ISa19RdLhyVEBBNgNlFvs5NXXkNK9CGBhzt3zu12o6P1d3+HUsfObOoxcNIc6DO25deFRMHwc2DNm8bueb+JnV+LUF5Tz9s/G3WdVx7XH4CzxvThX3M3sTm3jNVZxYzt555GX/6szmrjg5XG39cLJhwyF7mqqKEee9Rv3f7Z4/rF8n5eBbujJ9C/ZDn8+nbDmCRvqMg3yk4AMk5wPWyz2Vm5x9mp3f1/R2YMSeKa742dc3v2Skw15R1K9+6SVr8Jn94A2I2xoGf810jX7qAFjhFq49JiiQ4LdNMifWtQUiQ3zhrEI99u5R+fbWDKoATiG29EZZxgNM7L+hkWPQITroA3zzXKL6L6wMUf8c2BaDbsX0VEcADXTO2Bu+ZOfccZ2Yebv4D5D8D5/3PP+zov2Cs4bzf3XcKVrsVRc55n9+E8yzbunENDaruawklzft5ZwFuOIGHOOaMIXfRPqCqEpGFwzLU+Xl33ZjGb6OUYCbW/uLlxao5gpLPj1LJXwSsnwYdXGIF5VF8492W4/OvWA3MnZ2r7ho+gurRzaxEA3v0li7KaegYkhjPd0eAsOjSQU0cZc5Xfaa4hUw/0w6YD5JfXkBARzKyhh6QNb/wUrLWQNBx6DXf7ZzsD389MM4wH1rxtNE/0lm3fAXZjLFNUb9fDO/PLKaqsIyTQzPDe7u97MywlipqIVPbZEzDZ6rtP3fkvL8Gn1wN2GPcHOPOZTgXmAAv8eIRaa66eNpAhyZEUVdZx7+cbmz5pMsEsx3jnla8Z31tKsyEhE674FmtCJo99txWAy6f0JzbcyxONupoZdwEm4/+rnF/d857aOe+wdgfn1dXVPPzww5xyyimMHz+esWPHNrl52tNPP016ejohISFMnDiR5cuVwtis8gOAkdae5Kvg3DVO7cgd2zOSnE3hjjD6Qnqc6jord3y0DjDGE00K3GbsjgKc9jhYusdOQFeW0lpTuM6mtZflwifXwYszIesnIz19+l/hhl9g5Hltn1mferTxg1ddZcM8aemwequNV5caKdJXThnQpImUM7X9819zKKtWY7j/czSC+834vgRaDvmxau37xv2o33jks8elGcH5KwXDsQdHQcle2L3YI5/VLNcItaY9P5wp7aNTYwgKcP8+kNlsYkZmNxuptuxp+PJW43jitcb3t06WQVTXWVm6vQCAGd0sOA+0mHn4vKMwm+CzX/fz/cYDTU9InwIDZxqlAdXF0Pdo42JvdF8+/3U/2w6WEx0ayBVT+vtk/V1Kr2HG91uAefe75z0VnHdYu//VX3HFFfz73/8mLS2N0047jTPPPLPJzZPeffddbrnlFv7+97+zatUqjjrqKE488UQOHjzo0c/1S44Z5/naORc/9+QP29iVX0GvqGDuOGEQfOFoNjnmEuh3jG8X10M4m8LlNtcUzpXWvrd9O3Z11Ua64X/Gwpq3ALuR9nvDCph+e/s775tMTRvDSad8s+EA+4qqiAsP4pyxfZo8Nz4tlkFJEVTVWQ+v9+xhsgorWbzN+H57waGN4Er2GXXgACPO88jnD0iIICYskJK6AAr6n248uMZLjeGsdbBjnnF8WHBupLR7ot7caUZ3qjtf9DB84yhHmPJno5THDU30ft5VSFWdleSoEIamdL+SwZF9o7lqqjG69+5P1lN66MXC4++DiF4w7Ez4/acQFked1cYT3xu75n+cOoDoUF3gB4wSQZPFKMPZ+1Pn30/BeYe1O1fmiy++YO7cuUyePNkT62nVY489xlVXXcUf/vAHAJ577jm+/PJLXnnlFe644w6vr6crs1ccxISR1u6zJhdxjquRZTnG6IpWujVmJBnfNHbmV1BvtRFw6O6D9Ejrs0t4YZGReXHfmSOIXvsyHNwAoXFw/D99vLqewzlOrdm09qi+xjd0a42RsROV0vqb2e1G6tx3f2tIhe8z3mjqlzqhcws96gL4/h9Gr4vc9ZA8onPv14O9tMT4d3fxMWmEBFqaPGcymbhgQir3f7mJd5ZncdHEnjsm5/0VWdjtMHlQPGnxh3yPW/eBcZ822WN9McxmE+P6xfLD5oP8GHkSZ/CWMbbtlEeMXgyetHeZ0RE7PBF6j2ny1ArHzrk755sfakpGAg/ajVIB+/5V/ll3brcbO5WLHzF+PeMumPr/3BKYQ0OX9hlDEt3aMb8r+fPswXyzPpfdBZU8+NVm/nX2yIYnk0fALZubZCB8tGofuwsqiQ8P4rJj072/4K4qfiCMudgYq/bDfXDZFx3/e2ithxIjo0jBefu1OwLq06cPkZHev/pWW1vLypUrmT17tusxs9nM7NmzWbZsWbOvqampobS0tMmtp7CXNTSE89nOeVgchDoawRS2ntreJzaUkEAztfU2soqaSZ2VHqfeauP2D9ditdk5dWQKJ/Sth/lzjCeP/6fx90u8Irm1tHZLgNFgB45cd57zK7x2Grx/qXFuZG8450W44rvOB+YA4Qkw5BTjePUbnX+/HmrlnkJW7y0mKMDMJcc0H3ifM7YvQRYz67JLWJ9d4uUVdg31VhvvrWihERzAOkdK+0jPpLQ7jXWktn9T1AcSBkN9FWz42KOfCTR0ac84oUnwc6C0mr2FlZhNMLZfjMc+PjIkkN7pmWTZEh11527Y7fMmux2+vbshMD/+Ppj2F7cF5tB9680bCwm08OC5owD4v5/3smxHQdMTGv3drKm38p8ftgNw7fSBhAd3rp6/25n2F7AEGRk/OzvRALw02xjPZgmCyCNcsJfDtDs4f/TRR7n99tvZs8cDM21bkZ+fj9VqpVevXk0e79WrF7m5uc2+Zs6cOURHR7tuqak9qKOzY5RaVXD8YbseXuVKbW89OLeYTQxIUGq7NHhx8S427C8lOjSQf5wxHL66HeoqjO7doy/y9fJ6FGeH9g7POi8/CJ/9CZ6fZnzTDwiBabfDn1bAqPPdOl7Kldr+6ztG6ry020uLjVrzs0f3afHiblx4ECeOSAbg7R7aGG7h1jxyS6uJDQvkhOFNfzbhwEY4sB7MgUZKrQeNdwTnK/YWYXf+3+iN1HZXvXnTEWrOXfOhKVFEhng2ZXjGkET/TG232WDubbDsv8avT34YJt/o1o/YlV/B7oJKAi0mJg9KcOt7dzXHDIjnoonGBbI7P1pLVa212fPe/SWL7OIqekUFc3ELFx57tOi+MP4K4/iH+4wLSB3hTGmPSXPv9/ceot2/Y+PHj6e6upoBAwYQGRlJXFxck1tXcuedd1JSUuK6ZWVl+XpJ3lFTjrne2OEyR/h4pqWrKdyR684zeqkpnBh25Ve4asL+dtowEnMWGGM+zAFw2mP6z97Lese0snMOLTeFq6+BpU8adeWr/gfYjdrbG1YY455aKXXpsAEzIDrVaAC0+Qv3v383t7egkm82GBe8rziu9UZJFzpqrD9ds5/K2nqPr62redvRCO7csX0JDjjkIvg6x2zzjBM8nuUzqm8MAWYTB0pryEk/E0xmo3t5/nbPfWjBDijYblx8GDCjyVPeqDd3mjkkydUUzrrLi43wOuvr243O7JjgjKdg4h/d/hHzHCntR/ePI6IH7BDfcfIQUqJD2F1Q6fr5obGqWitPzTP+TdwwM8O3G1dd2XG3QGC4UR62+cuOvYfqzTul3f9aL7zwQrKzs/nXv/5Fr169vFbDkpCQgMVi4cCBpt0YDxw4QHJycrOvCf7/7d13eJRV9sDx75Rk0nshjZoQinSkIyAq9i5WBPu6u/a1rqur+7O77lp314KKK4iIbe0oTZr0TiChQ0hI73Xm/v54Z0IC6ZmZdyY5n+fhYUgmc+5AmMx577nnWCxYLDqVdOvJsWuufAkKDtN3LW1pChdt3znPlp3zrsxmUzyycCtVtTYmpkRxxaBweOtP2ifH/N4l44hE8xxl7cdLqqix2k7tSF03Ts2enCul/VD/6XEo0HZhiR8G577g+hnkRpN2bm7pc9rZuUGuacTVWc1euR+bgjP6RtM3tvkjbGN6R9IjMoCDeeV8s/UY00d2neq0rKJKFqdp70euGXXS87bZXN6lvT5/XxMDE0LZcriQdbm+XJJ8ltbUafPHcNaTrgnqKGnvMe6Us+3rXTjf/GR9ooM4EDwMqsCQuQmqSsDi4Y3Pjm6AtW8DBrjsPzDkapeEcZS0d7Yu7U0J9vPhmctO4+YP1vPOr/s4f1AcQ5LC6j7/3zUHySmpIiHMn6u70GtVmwXFwJjfwa9/1+aep56n/Vxti7rkXKoT2qPN20+rVq1iwYIFPPzww8yaNYuZM2c2+OUqvr6+jBgxgl9++aXuYzabjV9++YWxY8e6LK5XqtepPSbEX9+1tGWcmn3nPCNHkvOu7JN1h/ltfz7+PiaevWwQhuUva+eTQxK1UmjhdlGBFnxMBpTSEvRT1C9rz94Bcy6G+ddriXlQN7j0X3DrYtcn5g5DrwcMsH95i0dqxAlFFTV8ul7bDb6thV1z0JqRXW3fPe9qM88XrD+MTcHpPcNJjjkpGTy0GoqPgCXklC7mrjKiu720/UDBiWM/Wz4BW+PlvR3WxAi10qpadmZq/X1G9nD9zrnBYGBA/9M4ZIvGqKxwyMPnnSsFi+wXTIZc47LEvKyqlt/2aRdJpvTrGsk5wJn9Yrl0aDw2BQ8v3Ep1rTZBpLSqln8t0zaJ7jkrxSXj/TqVcXeBJRSO74Ttn7f962XnvEPa/N3Zr18/Kir0adh1//3388477/Dhhx+ya9cu7rzzTsrKyuq6tws7+855Ljo2g3OI0EZctG6cmvYGJ+N4KTZbO8+5CK+WVVTJc9/tAuDBaakkWQ/Dqte1T57/ovd14u0kjEbDiaZwhc3MOj/0G/x7gpYUmyww8QG4awMMvc69RxHCkiB5qnZ703/dF9fLzVt7iPJqK/26BTOhlWdUrxyRiNloYOOhQvZkd40jSTabYr79IoZj5nsDjpL2/heDj3sukDt2qTccLNB2uvzDoSSzY02dmlJZDAdXardPOm++6VABNgVJEf51rxmuVr+0Xblzxnt77P1Fm0Nv8tWO9rjIqr15VFttdI8IoHeUC44PebAnLhpIRKAvaVkl/NuekH+wcj/5ZdX0igrk8mEJLTyCwD8cxt+l3V76rDY2sS0kOe+QNr9bev7553nggQdYunQpeXl5bu2GfvXVV/Pyyy/zxBNPMHToUDZv3swPP/xwSpO4Lq/0RKf2GE9JzkuztXKzZvSIDMBsNFBebeVYsTRy6mqUUjz+5XZKqmoZmhTGzLE9tJnmthroex70u0DvJXZpcfYqnMzmZp1bq0DZYMCl8Me1MPUJ/S6oOBrDbfpYG+simlVjtfHBygMA3DKhV6uPrMUE+zG1v7Yz11Uaw63IyOVIQQUhfmbOH3RSJ+LaatjxpXbbDSXtDiPsTeHSsooptZpOdIjf5ILGcHsXa52YI1NOVMfZrbM3gzvdDbvmDmN6R7LeoB13qkxf5ra4bWazwaK/ardH3X7iOJALLKkrae+8I9SaEhHoqzWRBV5fnM76A/n8xz6S9d6zUmRUb2uNvhMCorTqs7Y2mJTkvEPa/B167rnnsnr1aqZOnUpMTAzh4eGEh4cTFhZGeLjrzxf98Y9/5ODBg1RVVfHbb78xerSbyiS9SZlW1p6jQvTfOfcPg4BI7XYL5aU+JiO97Fd407vIDow44dttx/h5VzY+JgMvXjkY07b59s7e/nDeC3ovr8uLszeFy2qsKVxwnFbemjQGZn0H0z/U/4dy3/O0NxalWZCxSN+1eIFvtx4jq7iS6GALFw+NP/UO2Tvg/fO1cYYndfC9xr57/PnGo1TWuKiM2oN8sk67CHHZsIRTm0plLNKaEQZ1g54T3bam2BA/EsP9sSnYfKjwRGl72rdQUeDcYOk/ab+ftGsOsP6A47y5+5Jzf18T1u7jAbAc39riRoButn8G2du04w4TH3BZGKVU3XzzyV2opL2+iwbHcVb/GGqsiuve/Y2Sylr6xgZx4eBGXttE4yxBJ75Pl73Y+uknlcVQob0O1FXViTZpc0O4JUuaLpHatm1bhxYjnKT0RFn78GD3lJU1K6IPlOdp3V3jhjR71+SYINKPl5JxvLRTz+UUDRWUVfPkVzsA+P3kZPoG18CHj2ufnPywNBXxAI5xapmFjfyANhjguvluXlELzL7amc7Vb8DGj7RSX9EopRTvrtAuns4c2+PUzuMZP8Ons6C6RCtnNvnAGX+q+/QZKdEkhPlztLCCH7ZncWknLhvNLa1i0U5HI7hGdj632kvaB13Z9iZKHTSiRzhHCipYfzCfCVOHQMxAOL4Dti+E0291ThCb7UQzuJOS8xqrjU2HCgHtLL47DTntNA4ejqGH8TgcWgMpZ7s1fotqq2Dx37TbE+51aQf/3dklHCuqxM/HyNjekS6L48kMBgN/u/Q0ftu3nJIqrXLq/rP7YjJ2rSqCDht5s/YztPgobHgfxtzZ8tc4GsMGRJ7SLFK0Tpt3zidNmtTg1/Dhw9m9ezcPPvgg99xzjyvWKNqq7ERZu+4753Ci7K0VjZlSYmTWeVf0t293kldWTd/YIH4/pQ/88jSU50J0PxjzB72XJ4C40BbGqXkiR2n7nh+gJEvftXiwNfvy2X60GD8fI9eNPulC2Pr34ePpWmLumL6x+G8NyqVNRkNdp/bOXtq+cMMRaqyKIUlh9I876Y1nZfGJRmmD3FfS7uCYd77hYIF2wWyYY+b5XOcFydyovTZbQqB7w2a8OzOLqaixEhbgQ59o9x5nmZJ64tx5VYYHlravn601Ng3qppULu9CSNK16clyfqC49Liwu1J/HLugPwODEUKYNbHyyk2iGjx+c8aB2+9e/Q1Ur3ptLSXuHtfvgxfLly5k5cyZxcXG8/PLLnHnmmaxZs8aZaxPtZCvxoDPnUG+cWsvJebJ9dE+6JOddxrI9OXy+8SgGAzx/xWAsxzZqV2gBLnhF2wEVunMk51mNnTn3VNGpWqm9sjo3Qelk3rPvml8xPJGIQPv/N5sNFj0B39yr/f0NvgZ+vxrG36t9/uu7IP3EcYHppydiNMBv+/PZ10knbiil+GSdvRHc6Y2MYtr1P6ithKi+LVaJucJwe3K+6VAhVpuCQdPBaNZGdx1Pc04Qx8WH5KlaBUU9jvnmI3uEY3TzDmVSRAD7g4YBUL7Hw5LzyiKtLBhgyqPgG+DScPXPm3d1147qzqd3jOWDm0Z1ubP3TjPsBgjvpR2Z/e3fLd9fkvMOa1NynpWVxfPPP09KSgpXXXUVISEhVFVV8eWXX/L8889z+umnu2qdog1s9rL2QmMYYQE+LdzbDSLtTeHy2jDr/HgpSknH9s6urKqWxz7XjsPMGteT4QnB8M192ieHXg89x+u4OlFfXVm7NyXncGL3fOOcU85KC9iXU8rPu7SfGbdMsI9Pq6mAz2bByle1P09+FC77N5gtcNZftURdWeHTG7XED+37w3EUab49ge1s1uzLZ39uGYG+Ji4a0sjZVUeX9kHTtZ1rN+vXLYQgi5nSqlqtc35QNKTYS883O2lqgaOkPaWx8+ba2XZ3njevL6DvJABCCnZoVQyeYtXr2hncyBQYeoNLQxWV12iVEyBHA+1G9Yo4cdFRtJ3J58RkgVWvQUVh8/eX5LzDWp2cX3TRRaSmprJ161b++c9/kpmZyeuvv+7KtYl2Mtgbwtn8PaRLZ93OecvJee/oQIwGbd5uTmkj85RFp/LSj7s5WlhBYrg/fzonFda+rTXM8QuDs5/We3miHkdDuNzSqrrZsV5h4KXgG6zNXD+wQu/VeJz3VuwH4Kz+MfSODoLSHPjgQtj5FRh94LK3YfIjJ5JNgwEufh36nAk15VrJu/3C6zX23eTPNhzxru+RVnI0grt4aAKBlpNa9hQfg332HdtBV7p5ZRqT0cCw7mEArLcnaAy9Tvt9y/yOTy0ozoSsrYDhlDPdSinWH9R2zt193txh5OBBHLDFYsKK7eBqXdZwipIsWP2mdvusJ8HU5lZPbfJrRg5WmyIlJoikCNfu0Isu5LQrILq/VgWyqoXcT5LzDmt1cv79999zyy238NRTT3HBBRdgMnXdcywerbocU41WUmgI9pARc44z52U5LV7N9vMx1f1AycjunKWRQrPhYAEfrj4AwLOXDSKwMhuWPKN98uynILB1c5aFe0QG+uJrMqIUZHvTqEPfwBPJ0sY5+q7Fw+SXVbNw4xEAbp3YG3J2w7tT4eh67QLZjV/CkKtP/UKzL0yfo5Vul+fCf6+A0hzO7BdDTLCFvLLquqZpnUVheTXfb9f6Flw7qpGS9u0LAQVJoyGil3sXV8/w7lpivNGRnPedpk0tKDuuNfbrCMeueeLpp7w+H8grJ7e0Gl+zkdMSQjsWp51G9oxgg32kWu72X3RZwymWvaBdxEo8Hfpd6PJwjvPmU7pol3bhIkYTnPln7faaf2kXcZsiyXmHtTo5X7FiBSUlJYwYMYLRo0fzxhtvkJub68q1ifawN4OrUj4EhuhTWnYKSzAE2n9QtGL3vK4pXCc9tyigqtbKIwu3opR2zvWMvtHw46NQXQqJo2DYjXovUZzEYDDQra4pnBcl53CitH3nV84fK+XFPl5zkMoaG6clhDCa7fDe2Vqn3fCecOvP0HNC019sCYbrFmijcgr2w9yrMNeW1zWGc+wydxafbzxKda2NAXEhDGos+awraXd/I7j6Rtp3rR272Jh8YLD9AktbZxWfrIku7XDivPnQxLBTu/27ia/ZSFGsNl7Xtv9XXdbQQG4GbPhQu33WUy4/6mCzKZbtsY9Qk/Pmwtn6XQjxw6CmDFa80vh9bFat8SHIGLUOaHVyPmbMGN555x2OHTvGHXfcwSeffEJ8fDw2m41FixZRUuKhcyW7GvvVrBxCiQ7xgDFqDhFtOHceY28KJzvnndZbS/aSfryUqCBf/nJhf0j/WUucDCa48BUwtrtXpXAhr+zYDtobitjTwFoFWxfovRqPUFVr5cPV2sibp3tsxfDfK7SSxaTRcOsvEJXS8oMEx8INn2sjczI3wYKZXD1c64j8a3ouh/PLXfkU3EZrBKe94bx2VNKpx8Vy9sCxLVrztYGX67DCE4YmhWE0wOH8Co47Klwcpe27v4eyvPY9cE0F7LeX7TeWnO93zDfXp6TdIeq0MwGILk3Tvp/1tPhprTdD33Pd0j9le2YRuaXVBFnMjOzhIZszovMwGODMv2i3170HRUdPvU/JMbBWa6+FIZ13pKartfkdcGBgIDfffDMrVqxg27ZtPPDAAzz//PPExMRw8cUXu2KNoi3qxqiFeEandoe6cWr7W7xrsoxT69R2Z5Xw1tIMAJ66+DTCfKzw3QPaJ8fcCd0G6bg60Zz4MK0pnNftnBsM0hjuJF9tziS3tJK/BHzB8I2Pga0GBl4GN37dtiMlUclw3afgEwAZP5O04hEmJmuzlTtLY7iNhwrYk12Kn4+RSxqb4e7YNe8zFQL1nSsd7OdDajdtxJujMRjdTtOOINhqYFs7L04dWKGVZ4ckaBe6TuI44366Ts3gHEYPHcx+WywmbBTt1nH3/Mh67YIzBpj6pFtCLk7T3v9NSI7C1ywXuIUL9DkTeozXLnQvf/HUzztK2kOTXN5foTPr0P/e1NRUXnzxRY4cOcK8efOctSbREaUeNuPcwbFz3oaydhmn1vlYbYqHF26lxqo4e0As5w/qps3OLDgAwfFa4ynhserK2gu9bOcctHJjk0VrOHhss96r0ZVSijnL9/BPnze5xWZP1ibcD1fM1ubatlXiSLjqA63yZctcngj8AoAFGw5Ta/X+xnDz1moXGS4cHE+I30kTUJQ6kfAOnu7mlTVuRI8woF5TONCmX0D7S9sdI9T6TjulPDunpIr9uWXaNbDu+u6cx4b4sdtfG2OXvbWDZ+zbSylYZE/Ih14HsQPcEnbJbq1y8kw5by5cpf7u+caPTq2GlfPmTuGUS2smk4lLL72Ur7/+2hkPJzrC3qndY2acOzh2zltR1t7HnpznllZRWF7tylUJN/tg1QE2Hy4k2GLmb5echiEvA1b8U/vkeS9o51iFx4r31jPnAAERMMBe3dXFG8Ot3p7OXwof41LTKpTRrHVfP+vJjh0n6TsNLvwHACm7/80d/kvILq6qSxi8VXFlDd9szQSaaAR3ZJ32htQnEFLPc+/imuAoad5QPzkfdBWYfLVu61nb2vaAStU7b37uKZ/eYD/fnhobTKgHjG+1dtd6JViOrNJnARk/w8EV2sXAyY+6JWRuaRVbjxQCMEnOmwtX6jEWks/Wjmwsfb7h5yQ5dwqpe+lsHDvnhBLjUWfOWz9OLchirksCpLS98zicX87LP+4G4LEL+tMtxALf3q+VWqacA/0v0nmFoiWOWedemZzDidL2bZ9BdZm+a9FL3l56f3Upo41pVBoDMVz/2Ym/l44aMRMma/NwH1HvMs24jnlrvbsx3FebM6mssZESE9T4rvBWe0l7/wu1yQAeYEQPbZ07MouorLFqHwyIOHHxYPPctj3g8V1QdBjMftBz4imfXlc331zfXXOHpGHnAJBYtYfaMjc3gLTZ4Oe/ardH3w5hjVzQcYHle3JQCgbGhxDrSe/9ROd05uPa79sWQPbOEx8v0PqYSHLeMZKcdzKqzMPL2svzoKKwxbsnx9qbwkly3ikopXj0821U1FgZ0ztCm4e8bQHsX6694TvvRZd3shUd181bG8I59JgA4b2gqth+HrSLObia2nem0q32KEdVFAXXfAt9pjg3xqSHYPhMDChe83mD0j3Lvff7BfjEfnHhmlHdT20EZ62BHZ9rtwd5Rkk7QGK4PzHBFmqsiq1H6jVFc5S2b50PtW2oSnOUtPeaBL6nzs5ef8Ax39wzmpAN7NePg8RhQrFvg5tL27ctgOztYAnVjoq4ieO8+ZRUKWkXbhA/FPpfDKgTI3BBds6dRJLzTqa2WJstm6tCiQry1Xk19ViCIEjr4tua3fPkaGkK5+1sNsXh/HKW7j7OM9/uYkVGLhazkecvH4yhsgh+1HbYOONBXecCi9ZzNITLLa2mqtaq82rawWiE4TO0212ttH3bZzDnYsyVBWyx9ea1Xv8mru8w58cxGOCCVyD1fCyGGt72+Ts/L1vu/DhusO1IETsyi/E1Gbm8sUZwexdrF5wDo6H3ZLevrykGg6Fu97xupBpoDeuCYrU1p//Y+gdsZoRaeXUt2zOLAW3OuCcwGQ0cDR0BQMHOxe4LXFsFi/9Puz3xPq1awR1hrTaW75H55sLNpvwZMEDaN3B0g/YxSc6dQpLzTkaVaFdPKy2Rus0abVLdufN9Ld41JVaawnmLyhoraVnFfLM1k1d/TufueZs4/9VfGfDkD0x8cQmz3l/Huyu0Lv33n92XnlGBsPhvWn+EqL4w7m6dn4ForfAAHyz2LsDZRVU6r6adhlynNS47tFobgdXZKQXLX4aFt4C1mkW207m6+i9MnzLSdTFNZrjiPfLDhxBmKOPsjb/HWtjI2B0PN88+Pu3c07oRHtjIxW5HSftpV3hcZ2JHcr6x/rlzk7nezPNWlraX58ORtdrtRpLzzYcKsdoU8aF+JNgv3nkC3+QzAAjPWeu+oOveg6JDWnPT0b9zW9hNhwsprqwlLMCHoUlhbosruriYfjDkGu324v/TjorZq3clOe8Yz/ppIjrMWK5dPVWBHtgQJKIXHFzZpo7tGdklrl6VaKXC8mr25pSScbyUvTll9t9LOZxfjq2JyVS+JiM9owJIjgliTO9Irh/dA45s0N7EAFzwdzB7UIWHaJbBYCAu1I8DeeVkFlXQPfLUElePFxKn9TjY8z1smgPn/J/eK3Kd2mr45j7Y/F8A1sddxx37z2dI94i65M1lfAMImPUZB/5xBj05RukHlxP0u5/AL9S1cZ1k25EiPrWPgrt2VPdT71BVCru/0257UEm7g+Pfd8PBApRSJ0ryh90Aq17TdsNLj0NQCzutGT+Dsmnj00ITT/n0ifPmnrFr7pA86jzY8BB9aveSmZVFfLdurg1YWQTLX9JuT3kUfNx3oWKJvaR9Ut9oTEY5HibcaNLD2lGOvYtPXPDzCwP/MD1X5fUkOe9Maiox12jJrCnEA0ub6prCtbxz7ph1nllUSWlVLUEW+VZ1B5tNkVlUcUoCvi+nlNzSps8oBvuZSY4JIjk6iD71fk8K98dsqlegY7PCt/cBCgZfA73OcP2TEk4VF+rPgbxysry1KRxoDdD2fA9bPoEzn+icF4gqCuHTGVpfB4ORmnNe4LZFPbFRw60TertlCX6hMXw58A2u234LMYVp8Mn1cMNCMHtQP5RGVFRbuWf+JmptivMHdWNM70YSz7RvtbnfEb0hYbj7F9mCgfGhWMxGCspr2JdbRh/7UTGiUyFhJBxdr+38j/tj8w9Uf4RaIxxl86d7SDM4h7DY7hw1JZBgPcqetT8Sf/FM1wZc+RpU5ENUqlad40aOiQhy3ly4XUQv7efp+tknGiHKrnmHScbTmdjHqFUrE4EhHrhz3oZxamEBvkQFWcgtrWLv8VKGSKmWU1XVWjmQW16XfJ9IwsuoqGn6LHF8qB99YoLo0yAJDyQ6yHJqs6TGrHsXjm3Rds86845lJxYXpjWFy/TiJl+knKP1wCjN0pIPx4i1zqLgIHx8FeTuBt8guPJ9Pi1IpaB8O4nh/kwbGOu2pZw3cTSzNj7Ep75/I+jAr/DF7+CK9zo2ts3F/u/bnezLKaNbiB/PXjao8de2bfaS9kHTPbKZpa/ZyJCkMNbuz2fDgYITyTlos7ePrtdmno/9Q9Prt9ZoO+fQ6Ai1Wqutrmze03bOAQqiR5GQ9QXVGcsBFybnJVmw+k3t9llPuvWIw7GiCnYdK8Zg0HbOhXC7Mx7Uds2r7cdQJTnvMEnOOxP7WY88Qon2xFEabRinBpAcE0huaRUZkpw7hVKKVxbt4estmc2WovuYDPSMDCTZnoQ7fu8dHUhgRyoYio/BL3/Tbk99EoLkjYQ3inN0bC/04p1zk1lLUFa8ojWG60zJ+ZENMO9q7WJtcDxcNx9b7CDe+8cyAG4a36thNYuLpXYLxi9pKHccuZc5lpcw7fgcguPg3Gfdtoa2WLQzm49/086a/336EMICGqmqKM2BvUu024M9r6TdYUSPcC05P1jA9NPrjfQ67Qr44VE4vhMyNzW983/4N61cOyASEkac8um0rBLKqq0E+5npa5+w4klCB5wJWV+QWLSeyhorfj4u6sOz9HmorYCk0ZB6vmtiNBXavms+LCms8b4IQrhaSDycfiusfkP7c3gPfdfTCUhy3pmUai/SuSqEGE8ao+bgGKdWUaA1mWmhk2lKTDBr9uVLUzgn+XT9YV5fnFH352CLWdv9bpCEB9I9IsA1b95/fAyqS7SSyhE3Of/xhVt4/axzh2E3aMl5xs9QdKTR87ReZ+fX8PntWqIQOwiumw+hCSzZlc2+nDKCLWauPt09c5fru2ZUdx46NIhnfe/iL1X/gDVvamf/x93l9rU053hJJQ8v3ArA7Wf0ZnxyVON33PE5KCvEDz9REeaBRjbWsR2086D9L4TtC7Udr6aSc0eX9uSzwXhqYrvOPkJtRI9wjzzrnDj0bFgM/TjImt37GXdasvOD5KafmPxw1lNur6JYIiPUhCeYcD9s+EDbPZed8w7z3Loy0XaeOuPcwTdA28mBVp07d3RszzguTeE66khBOX/7ZhcAd52ZzNrHprL1r+fw5R/G8/JVQ7hzch/OHhBL7+gg1yTmGb9ob2gNRrjwFY8uaRXNiw/z8lnnDpF9oOdEQMGmj/VeTccoBateh09v1BLzlHPg5u8hVBv/9e6v2rSEa0d316V/x4WD4wi2mHmv6HQODH9E++BPj2vj3TyEUooHF2wlv6yaAXEhPHBO36bv7OjS7sG75gDDu2vJ+d6cMgrKTuoZ4ph5vm0B1DRxoa2ZEWoA6+3N4DxlvvnJDCFxHPftjtGgOLjJRfPOf3lau1DT9zzoMdY1MZpQVWtlZUYuICPUhM4CI+GiV7WRkv0v0Xs1Xk/eIXcmpR6enEObzp3LrHPnsNkUDy/cSmlVLSN6hHPvWX2JCfFr3RlxpyzAqpVQAoy6A+KGuCeucIluIZ1k5xxguP0c6qaPtO9Tb2SthW/v15JdlFZeeM08sGhlxtuPFrF6Xx4mo4FZ43rqssQAXzMXD9UuzP69dBqMvlP7xBe/g31LdVnTyT5cdYBle3KwmI28es3QpkeR5u3VzmsbjDDwcvcuso3CA33pEx0IwMZDBQ0/2XsyhCRAZaHWHPFk+fu0ngVGM/Q585RPK6Xqds5HurrzfwdUJmoJs+ngSuc/+OF1sOtr7Xth6hPOf/wWrNtfQFm1lehgCwPiQtweX4gGBl0JN36lJeqiQyQ570zsDeFyCPPMsnY4UdreinPnyfad80P55VQ206RMNO/j3w6yMiMPPx8jL181xP3lhzu/0t7k+YVqI2aEV3PsnOeXVXv//8v+F2ljX4oOe0yS2CaVxdr58vWzAQNMexbOf7lBQ6r3Vmi75hcMiiNexznUjnFkP+7IJn/iX2HgZWCrgU9ugGNbdVsXwJ7sEp79Pg2AP1/Qn5Tmzk87dvt7T4Fg9zXWa68RdaXtJyXnRtOJGcWNVY7s+Un7vfvYRsciHc6v4HhJFT4mg0f3hIk+bSoAA6u3sD+3zHkPrBT8/KR2e8h1EDvAeY/dSkt2O0raozF64LECIUT7SHLeiViLswDHzrkHNoSDE8l5K3bOo4MshPiZsSnYl+PEH6pdyKG8cp79TnvT+fC5/egVFejeBdhssPxl7faY33vNjGPRtFB/H/ztjZW8epwagI/fidJkx7lRb1F0BN4/Tzszb/aHq/97SuftrKJK/rclE4BbJ/bSa6UAnJYQymkJIVRbbXy+KRMu+492rKC6BD6+Uuswr4OqWit3z9tEda2NKanRzBjTTDMjpWDrfO22h5e0O4zsoZWcbzg5OYcTpe17f4HizIafS2++pN2xaz4oIdR1jdacwD9lMgD9DYdYuS3deQ+cvggOrgSTRbeLzieScylpF6IzkeS8E6kpzgagwBhGiJ+H9vqLbP2sc4PBULeDkZEjpe1tZbMp/vTZFipqrIzuFcHMsT3dv4jd38HxHeAbDKPvcH984XQGg+FEx3ZvT85Bm9EK2tzqsjx919Jax7bAu2dB9nYIjIGbvtUafJ3kg1UHqLUpRvWKYHBimPvXeRLH7vm8tYdQJl/tgkLMQCjNhv9eoTUKdbOXfthNWlYJkYG+vHjlkOaP+2Ru1Kq+zP7Q7wL3LbIDhtt3zrccLqS61tbwk5F9IGkMKNuJiw4AVSVwYIV2u5ERalB/vrlnnjevExxLYUAvjAZFzo4lznlMm/XETOfRd+jSTPJgXhn7csowGw2MT2micaEQwitJct6JKPuZc6t/lPvOE7dV/XFqqolZXvWkxNjPnWdLU7i2en/VAdbuzyfA18RLVw5xf9mbUrD8Re32qNvA33PPJYq2iessTeEAug2C+GFaifXWT/ReTcv2L4fZ50HJMYjuD7f90uiYq7KqWub+pu1G3zpB311zh4uHxOPvY2JvTplWZu0fBjd8BiGJkJcOc6+GGvd9T61Iz+Vde9n/i1cObrlXy9YF2u/9zq870+/p+kQHEhbgQ1WtjZ3Hik+9wzD77vmmj0/8TN63FKzVWqVbZOMdztcd8Nz55icz9JoAQFj2b5RV1Xb8Abd+ql109guFCfd1/PHawTFCbWTPcEL8fHRZgxDCNSQ570RM5VrXToI8uMQpwv4msbKoVbskyY7kXHbO22RfTikv/qCVsz92fn+6Rwa4fxEZP2s7fD4BWrmt6DQ6VVM4OLF7vnFOqy4a6iZzE8y7FmrKtIZet/wIYd0bveuC9YcprqylZ2QAZ/X3jLPRwX4+XDQkDtB2zwFtRu4Nn2mJzpG18NktWpM7Fysoq+aBBZsBuGFMd6a29HdkrdVGjwEM8o6SdtAqXUbYu7avP9DIz9yBl2mv0XnpcGS99rE9P2i/p0xrdDRYfll1XaPWER7cDM4hpN8UAEYbdtZ1N2+3mkpY8ox2e8L9LY6EdZXF9hFqZ0qXdiE6HUnOO4vaanxrigAwh3jGG7FG+fhruyTQuqZw9uQ8PVuS89ay2hR/WrCFqlobE1OiuH5042/eXUopWGbfNR95MwRK2V1n0mnGqTmcdqWWoOSkwZF1eq+mcbnpWul3dal2Vvva+U32cLDaFLNXHgDglgm9PKpZ1DX20vbvth2jqKJG+2BMf+35mCyw+1v47k8uvUiilOLRz7eRXVxFn+hA/nx+K5p57V+mjSv1j4DkqS5bmyuM6Kkl0Kd0bAetAqD/xdrtzf/V+oSkL9L+3MR5c8f59eSYICICfZ2+Xmcz9JoIQD/DIdbsaPl9R7PWvas1kAyO1+2oVkW1ldX7tCM4ct5ciM5HkvPOwt6pvVYZCQqN1nkxLYhsfVM4R3K+P7eMGquthXsLgHd+3cfGQ4UEW8y8cMVgfY447F+m7YKZ/WDc3e6PL1wqLtS+c17YSXbO/UK0HUSAjR/qu5bGFB2Fjy6D8jyIGwrXzNWa2TVh0c5sDuWXExbgwxUj3H8etjnDksJIjQ2mssbGV5uPnvhEj7FwxbuAATa8f6KRpAssWH+EH3Zk4WMy8Oo1w/D3bUVDs232kvaBl4HJu8qIT+ycF6Aau+jhKG3f/jkcWq31APANgh7jG308xw786T09f9ccgKAYykL6YDQoyvYsa/zvoDUqCuFX+/fllMe0zQYdrN6XS3WtjYQw/7r3SEKIzkOS886iTCtxyiOEqBD9xuW0Sv1z5y2ID/UnwNdErU1xMK/cxQvzfunZJbzy0x4A/nLhAP1GJy17Sft9+EyvGDck2sbREC6zs5S1w4nS9u2fayPKPEV5Pvz3cm23LjIZblioXUxoxru/ag03rx/dnQBfz2oOajAYuGZUEgBzfzvUMFEacDGcb3/tWPJ/sPEjp8ffn1vGX/+3A4AHzknltIRWTJCoLodd/9Nue0mX9vqGJIVhNho4XlLFkYJGql16TNCOR1QVw7cPaB/rcyaYG98VPzHf3PPPmztYkicBkFq5hbSsdvawWfkqVBRAdD8Ycq0TV9c2S9K0zZgp/aI9t7+QEKLdJDnvLEq1F+tcFeq5M84dHB3bW7FzbjQaTpw7Py5N4ZpTa7XxwIItVFu1kUBXjdRpx+zgKji4Aky+MP4efdYgXMrREC6rs5S1AySNhqi+UFMOOz7XezWaqlL4+Cqt3D44HmZ80eIRkU2HClh/sAAfk0GfCQ2tcNmwBHzNRtKySthypKjhJ0fdpp3lBfjfPbDnR6fFrbHauHf+ZsqrrYzpHcFtE3u37gv3fK8dJwjrrn2feBk/HxMD7RchGh2pZjRqs7oBcnZpvzdR0l5ZY2XbUe3fbFQv70nOzb210vaxxp11I8japDgT1vxLuz31STDpc9FLKVV33lxK2oXonCQ57yzsO+fajHMPT84ds85bsXMOkBztSM7l3Hlz/rV0L1uPFBHiZ+Z5vcrZ4cRZ86HXQ2iCPmsQLuUoay8or6Gi2qrzapzEYIBhM7TbLtixbbPaavh0Bhxdr006mPFFk83f6nN0H794SAIxIU2XvuspLMCXCwZpjeE+cTSGq2/qE9rOpLLCgllwZINT4r7+SzpbDhcS4mfmlelDMbX2LL6jS/ug6Y02SPMGI+2N2xpNzgGGnrQTnHJOo3fbcriQGqsiNsRCYriHV+nV11NLzvsbD7F+ZzvOnS99HmortNFzqec5eXGtl3G8lKOFFfiajYzrI71chOiMJDnvLOxj1HIJJSbYM9+Q1akra9/fqqY/ybH2pnCSnDdpZ2Yxry1OB+CpSwYSq9eb8iPrYd8SMJp1GzEjXC/Ez0yA/Zxup2kKB1pCaDRrCXH2jgafqrXayDheyrdbj/HKT7u546P1XPP2ahZuOILV5uTmZTYrfHEH7F0MPoFw/WcQ06/FLzucX873244BWiM4T3bN6Vpp+9dbMik9ebyVwQAXvw59pmqVDHOvgtyMDsVbdyCfN5Zoj/Hs5YNaf+SnLA8y7A3SvLCk3cGRnK9vKjkP71mXwJIwosmpL46vH9kzwrtKqoOiqYnoC4Dl6BqKymta/7U5e2DTf7XbZz+l6wUax67/2N6RreuVIITwOp51GE20m630OEYgR4Uy3tN3zsN7AgbtfFtZLgQ138DOsXMuHdsbV12rlbPXWBXnDIjl0qE67lY7ds0HXwPhPfRbh3Apg8FAXKgfe3PKyCqqpHd0J2lKFBSNSj0fw66vOfzLf/g24R52Z5WwO6uEjJxSqmtPbUq5Zl8+/1m+lwfOSeWcAbEdT1iUgu8e1ErrjT5w9UeQOLJVX/rBqgPYFExIjmJAfPPn0vU2qlcEvaMD2ZdTxv+2ZHLtqJOqAkw+MH0OfHABHNsMb4yA4DgI66FVEITbf3f8OTSxyUZtxZU13Dd/MzYFlw9P4MLB8a1f6M4vwFYL3QZDdGr7n7DOHCPPdmcVU1JZQ3Bjs7EnPgBZ22D0nU0+juO8+eleMELtZD59zoD8PYwy7GB5eg4XDWnl98Hip7UqjtQLoPsY1y6yBXXnzVM9vPGvEKLdJDnvJKqLsvED8gglMsjDR5v4+EFoEhQd0krbW0jOU2KDAdibU4rVplpfithFvLE4nV3HigkP8OGZywbpt5uRuRnSfwSDESber88ahNvEh/mzN6fMq5vCFZXXkJZVzJ7sEtKyStiTXUJU1mn8i68J2r2Qf2ydQhUnXk8DfE2kxAbTLzaYvt2Cqaiu5Z1f97Mnu5Q7PtrA0KQwHjo3tWPlpkufg/XvAQa4/O1Wj+0qrqxh/rrDANwy0bN3zcHeGO70JJ79Lo1P1h46NTkHsATB9Qu0EXJZW6HkmPbr8JpGHtCojelsJHH/54oyMguqSIoI5KmLB7ZtoY6Sdi/eNQeICfEjKcKfw/kVbD5cyMSURn7u9pkCjxxs8jGsNlVXFj+yp/ecN6/TcwKse5cxxp28k3a8dcn54XVaM0CDUTtuoaPiypq6iyNTZL65EJ2WJOedhLU4G4AKn0h8TF5wWiGyt5ac5+1t8Up0Urg/vmYjVbU2jhZU0D0ywE2L9HxbjxTy5lLt/NzfLj1N334Dy+1dlk+78kTTP9FpdbMfnThW6Pll7ZU1VjKOl9Yl4GlZJezJKiGr+NQLC0YGkGmJJN6QxyM991LW9xL6xgbTr1sIieH+p8wMnzG2J28v38vsFQfYfLiQ6975jYkpUTw4LZXBiWFtW+iaf8OyF7TbF7wMp13e6i+dv/YwpVW1pMQEMbmvd+yqXTE8kZd+3M2WI0XsyCxiYHwjndODYuCO5VrX+sIDUHAQCg9Bof13x5+tVdrPlKJDWkPKep4AHrGYsJkS8Zvfs17yXu92UOyp5coFB+0XAgxw2hWu+UtwoxHdwzmcX8GGgwWNJ+ct2JNdQkllLUEWM/26BbtghS7WYwIA/Y2H2bx7LzbbkFP+PzegFCyyJ+RDr2/V0RJXWpmeS61N0Ts6kB6RgbquRQjhOpKcdxb2hnDWAC9pEBLRB/YtbVVTOLPJSO+oQNKySsjIKZHk3K6q1soDn27BalNcMDiubaWazpa9A9K+AQxwxp/0W4dwmzj7md1jjSS4erHaFAfzyrRS9OySupL0A3llNHUsPCHMn37dtJ3wft2C6RsbTMzOW+DXF7nJfzmc2fz3c6i/Dw9O68fMcT15Y3EG89Ye4tf0XH5Nz+X8Qd24/+zU1s0i3vop/PCwdnvKn+H0W1v9vGutNt5fqTWCu2VCL685CxwZZOGcAd34dtsxPll7mL9d2sRYM4MBAiO1XwkjTv28zab9DKxL3A9A4SEqc/aTc3gP3VQuvgYrlBzUfu1vJIbZXtFVf9c9J037XK+JEKLj66uTjOgZwZebM5tuCtcCx3zzYd3DMHvDJsDJgqJR0f0w5KSRUrmVrUenMjQprOn7p/8Eh1Zp3xuTH3XbMpviOG8uXdqF6NwkOe8kzBW5ABiaaOLicdowTg0gOSaItKwS0rNLObOfzM0G+MeidNKPlxIV5MvfLjlN38Usf1n7fcAlXn0uU7RefKhn7Jyv3pvHZxuOsDu7mPTsUqoaORcOEB7gQ2o3bQe8b2wwqd2C6Rsb1PjZW/8Z8OtLsH+Z1rgyouUy8ZhgP56+5DRum9ibfyzawxebj/Ldtix+2J7FlSMSueesviQ01YRsz0/wpf2c7+jfwRkPtvbpA/D99iwyiyqJDPTl0mHeNSHh2lHd+XbbMb7cfJTHzu/fviZXRiMEd9N+dddGnVltipnvrOG3ynyGJwbz6XU9MBcf0XbcT959Lz4KtZWQl679Otkg7y5pdxjRXTsnvulQYbuOiK07oCX1p3tjSbudoedEyEljjHEXi9OON52c26zw81+126N/p/vkEaUUS3Y7zpt7yfs8IUS7SHLeGVhr8K0uBMAntJu+a2mtuo7trU/OQTq2O2w8VMDby7W/u2cvG0REoI59BnL2wI4vtNttTCqE9+rmSM51PHP+1eaj3G+vHnHw8zFqybc9AXf8ig6ytH5HOaw79DkT9v6idWme+pdWrykpIoBXrh7KHZP68PJPu1m0M5tP1x/hy02Z3DCmB3+Y0ofIoHrHTw6tgU9v1JqODZoO055rUzdopRTv/roPgBlje+Dn410dnMf1iaw7C/3ttmNcOSLRKY/7n+V7+W1/PgG+Jl65ZgTmiECI6AGMP/XOtdVagt5Y4u4f3qbjBZ4stVswQRYzpVW17M4qaXPTQMfO+cie3tcMrk7PCbDuHcYad/LQ7uPcf3bfxu+3dT4c3wl+YTDhXneusFE7MovJKakiwNfE6b28+O9fCNEiSc47g7JcDCisykBAmJdcUa2bdW4fp9bCm9GUGO18m8w6187P/unTLdgUXDYsgXMG6nxB5te/A0rrZNtN5x184TaOUVR6Jefz1x3ikc+3oRScP6gblwxNIDU2mO4RAc2fI22t4TO05HzzXK2k1dS2H5ep3YJ558aRbDhYwEs/prFmXz6zV+5n/rpD3DqxN7dO7EVw0R6YO12bn5x8Nlz6lrYL3AbrDxaw5UgRvmYjN4zxvgkJRqOBa07vzks/7uaTtYeckpxvO1LEKz/tAeCvFw+kZ1QL53PNvlp1RCsqJLyZyWhgWPcwfk3PZcPB/DYl50cLK8gsqsRsNDRfCu7pemrnzvsZD3PkyGFySqpO7dVSUwmLn9FuT3xAu0CjsyVpWkn7+OQoLGbvugAnhGgbLzw0JE5hP2+eTwjRIV5yHju8p9b9tLq0bkZ7c1Lss84zjpeiWjEbvTN76cfd7MstIzbEwl8vamPnYWfL3wfb7N2MJ8mueVcSZ985L6qooby6toV7O9f7K/fz8EItMb9hTHfeuHY40wZ2o2dUoHMSc4DU8yEgEkoytSS9nUb0CGfebWOYc/MoTksIoazayqu/pHPtC59Q9t7FUFkESWO0sWFNjAJrzjvLtV3zK4YnEBXk4WM0m3DViERMRgPrDxaQnl3Soccqr67lnvmbqLUpzjutG1c5aSe+s3CMVGvruXPHrvnAhFACfL14XycwCmIGADDKmMbS3Y28/1j3DhQf0br/j7rdzQtsnOO8+ZnSpV2ITk+S886gVDuHlKtC9e3W3RZmX635DrSqtL1nZCAmo4HSqlqyi6tcvDjPtXa/tvsG8PzlgwkNaPubeaf69RVt/mvy2RA/TN+1CLcK9vMhyKK9Sc8sdN/u+ZtLMnjqfzsBuP2M3vztktOcl5DXZ7bAkGu12xvndOihDAYDZ/SN5us/TODN64YzIqKaN6x/I7A6lwxDDz7v/zK1Jr82P+6B3DIW7dImddwywXt3fWNC/OqSjk/s4+Da6/++3cW+HO3i5bN6jpb0UI7kfH0bk3Nvnm9+Cvvu+RjjTpbaz3HXqSg80UNlymPa6Fed5ZdVs+lwIQCTZb65EJ2eJOedgX3nPEeFEuMtyTm0qSmcr9lID3uX9vTjHdtZ8Vbl1bX8acEWlILpIxP1n3NaeAi2zNNuT3pI37UIXTh2z7PcUNqulOKlH9N46cfdANx7VgqPntfPtcnXsBna77u/h5LsDj+c0WjgghR/Pgt+mZ7GbI4Sy3UVD3H/14c455/L+XbrMWxNtZVvxOyV+1EKpqRGkxzjhaOt6rl2lHaxduHGI1TWWNv1GIt2ZjP3t0MAvDJ9KOF69uLwUEOTwjAa4EhBBdltmLSw/oAXzzc/Wc+JAIw17mR5eg411npNJFf+EyoLIbo/DLlGl+Wd7Nf0HJSCft2CiQttoqmkEKLTkOS8M7CXhefiRTvn0OamcCmOpnDZXfPc+fPfp3Eov5z4UD8ev3CA3suBFf/Umlj1mgRJo/RejdCBY5xaZpFrO7YrpXj6m528uUR7rXj0vH7ce1Zf1++KxvSDpNFadciWuR1/vJoKmHcthuztEBhD1J3fcvsF4wgP8GFfThl/mLuRi99cwbI9OS0e3yksr2bB+iMA3Dqxd8fXprNJfWOIC/WjsLyGH3dktfnrj5dU8vDCrQDcNrEX45O9ZKyomwX7+ZDaTTtr3trS9qLyGnbbjxt4dTM4hx5aU8BU4xF8K/NO/D0UZ8Kaf2m3z3oSjJ5xtnux/by57hfkhRBuIcl5J1BTrO3o5KpQYkL0L8FqtXaMUwPIyOl6yfnKjFzmrD4IwItXDiGksfFP7lScCZs+0m7LrnmXFRfi+p1zq03x2BfbeH/lAQD+dslA7pjUx2XxTjH8Ru33jXO05pXtZa2FBTdpc5MtIXDDQiyxKdw6sTfLH5rCPVNTCPQ1sf1oMTNnr+Xad9aw8VDTydPHvx2iosZK/7gQxvWJbP+6PITJaGD6SG33/JO1bSttV0rx4IKt5JdV0z8uhD9Nk3GOzRnZxnPnGw8VoBT0jgr02r4GDQRGQozWr2WUMa3uPDdLn9NG6nUfC33P1XGBJ1htimV7tNJ7OW8uRNcgyXknUFWo7TIUGcMIbM+MWL3U7Zzva9Xd6zq2d7Gd85LKGh76TNsRun50dyakeMCO0MrXwFoN3cfVnd8TXU9cmGOcmmt2zmutNh74dDPz1h7GaICXrxrCjLE9XRKrSQMuBd9g7XXq4Mr2PYbNBl/fBXu+B7MfXPsJxA2u+3Swnw/3nd2X5Q9N4ZYJvfA1GVmzL5/L31rFrR+uZ3dWw6M81bU2Plx1AIBbJ/TqNOeqp5+ehMEAq/flcSC3rNVf9+GqAyzbk4PFbOTVa4ZKN+sWOHa/W3vufF1nGKF2snrnzhftzMaanaaNTQQ466k2jTN0pc2HCyksryHEz8wwb+6SL4RoNUnOOwGr/SxkjV+Ud71Ji6yXnLdiR6qr7pw/+90ujhZWkBThz2Pn99d7Odoxig3va7elQ3uX5jhz7oqGcFW1Vv4wdyNfbs7EbDTw2rXDnDYDu00sQTDoCu12exrDKQWL/qKVxRtMcOX70LORWdtAZJCFv1w4gCUPTmb6yESMBvh5Vzbnvrqc++dv5nB+OQDfbM3keEkVMcEWLhoS395n5nESwvyZ1FdreNXaxnB7skt49vs0AB47vz99Y7377L07DO+uJdk7jha16nx/pzpv7tBLO3c+3qQ1EEyf9yAoG/S7ELqP1nlxJzi6yZ/RNxqzSd6yC9EVyP/0TsBQppU82QK9rItnWHftzWpNOZS0fMawT3QQBoPWuTSvtGt0bF+6+zjz7CWeL105hECLB4ywWfW6VvqXMBJ6T9F7NUJHjuZEzi5rr6yxcvucDfy4Ixtfk5F/3zCCCwfrmIQOs5e27/xK6+bcFiv+Aavf0G5f8gb0O7/FL0kI8+fFK4fw032TOH9QN5SCzzcd5cy/L+WJr7bztn182sxxPfE1d64f49ec3h2AzzYcprrW1ux9q2qt3D1vE9W1NianRnPjWO+b866HxHB/YkMs1NoUW+xdwJtSVWtl8xHtPqd3puTcfu482XCEc4zr6Fe4HBtGmPqEzgtryFFyPyVVStqF6Co610/1LsqnMhcAY7CXvXibfLQEHVrVFM7f10RiuJYMpB/v/LvnRRU1PLJwGwCzxvVkTG8POFdalgfr3tNuT3rIY0r/hD7i7WXtzmwIV1pVy6z317JsTw7+PiZmzzqdswbEOu3x2yVhuHZGtbYSti1o/ddt+AB+eUq7fc4zMPS6NoVNjgniretH8PUfxzMxJYoaq2LO6oOkZZXg72Pi+tHd2/R43mBq/xiigizkllbzy67mO+S/9MNu0rJKiAz05cUrB3tX5ZiODAbDiXnnzfQ1ANh2pIjqWhtRQb70tE9M6RQCIiD2NABe938bgAW2SfxW4gHHxuyOF1ey/WgxBgNMkhFqQnQZkpx7O5sVS3UhAL6h3fRdS3u0tSlctL20vQsk50//bydZxZX0igrk4XP76b0czZq3oKYMug2GlHP0Xo3QWTf7znlJZS2lVbUdfryi8hpmvPcba/blE2QxM+eWUZ7RY8FgaNgYrjV2fgXf3KfdnnAfjPtju8MPTgzjo1tGM/e20Qy1nzudOa4nYQGdb1SYj8nI9JHa8YV5zZS2r0jP5d0V+wF48crBxAR7UTNUDzCih7YLvuFA88n5OkdJe4+Iznfxw37u3GIto9rgyyvVV3Dnxxvrjo/ozTGDfXBiWOdoxCeEaBWvSc6feeYZxo0bR0BAAGFhYXovx3OU52HEhk0ZCAjXeXepPdo6Ts1+nrCzJ+eLdmazcOMRexOswfh7QqO/ikJYq+0wcMaDsmsuCLKYCfbTjlpkdXD3PK+0imvfWcOmQ4WEBfgw97bRnlVGO3g6mCyQtRUyNzd/331LYeGt2hnW4TfC1CedsoRxfaL44vfj+PWhKTzUiTuSX3261rX91/ScRhOlgrJqHliwGdCaZE7t74U/+3RWf+fcZmu658v6ztgMzsE+7xzAMOb3RCf0JL+smls+XEdJZY2OC9OcKGmXXXMhuhKvSc6rq6u56qqruPPOO/VeimexzzgvIIiokECdF9MOsnN+ioKyah79XCtnv21i77odDt2tfRuqiiFmgNY0Rwgg3r573pGmcNnFlVzz9hp2HismKsiXT24fw+DEMCet0EkCIqD/Rdrt5nbPj26ET67Xphn0vwgu/KdTL2QZDAaSIgIwGjvvxbEekYGMT45EKViwvuHuuVKKRz/fRnZxFb2jA3n8ggE6rdK7DYwPwc/HSGF5Dfua6Ixvs6m6ju4edaHMWXqOB79QCIrF54z7eOfGkcQEW9iTXco9n2zG2sxFC1ersdr4NV07sijnzYXoWrwmOX/qqae47777GDRokN5L8SxlWnKeq0KJDvbCsqc2jlNLjtWS8/TjJS3c03s9+fUOckurSI4J4r6z++q9HE1ViVbSDjDxATB6zUuHcLFuoR0bp3akoJzp/1lN+vFSuoX4Mf+OsfTrFuLMJTqPo7R92wKobqT0NWcPfHwlVJdCrzPgivfA6AFVL17I0Rju0/VHqLWeaAy3YP0RftiRhY/JwGvXDPOMqiIv5GMy1l0A23Awv9H7ZOSUUlRRg7+PiQHxHvp/siP8w+HO1fC7FeAfRlyoP2/fOBKL2cjitOO88EOabktbdyCf0qpaooJ8GZQQqts6hBDu16nfYVdVVVFcXNzgV6dTqp1JylWhxHhjch7ZW/s9f582C7gFjnFq2cVVFHtA2Zmzfb/tGF9vycRkNPD3q4bg5+MhbzzXvQsVBRCZAgMv03s1woPE1806b/vO+f7cMqb/ezUH88pJivBnwe/G0sdeHeORek6E8J5aBcnOrxp+rugIfHQZlOdB/DC4Zi6YvfA12UOcMzCW8AAfsoorWbZH+zm3P7eMv/5vBwD3n53KaZK0dMhIR2l7E/POHfPNh3UPw6ezjvEKTYCgEzvTQ5PCeOmqIQC8vXzfKZUb7uI4bz6pb0ynrpIRQpyqk77aap577jlCQ0PrfiUlJem9JKdzzDjPxUt3zkO7g9GsdUEuyWzx7iF+PsSGaM+zs5W255ZW8ecvtwNw56Q+DLE3ftJddRmsso+CmviA7ASKBhzj1I61sax9d1YJV/17NZlFlfSJDmTBHeNIivDwbtBGIwy7Qbtdv7S9LE9LzIuPaBewrv8MLDJvuyMsZhNXDLc3hlt7iBqrjXvnb6a82sroXhHcfkZvnVfo/Rznztc3kZx3yvnmrXDxkHjuPjMZgMe+2FZ3kcKdlqTZz5v3k/PmQnQ1uibnjzzyCAaDodlfaWntLyt69NFHKSoqqvt1+LA+V0BdqbJQmw+ep0KJDPTC5NxkhjD7bNpWlranxNibwmV3nuRcKcVfvtxOflk1/boFc9fUZL2XdMKGD6A8V9sxHHSV3qsRHsZR1t6WcWrbjxZxzduryS2tol+3YObfMbbucTze0OvBYIRDqyA3Qzvy8fGVkLsHQhJgxhcQ6AEd5juBa0Zppe2L047zxFfb2XK4kBA/M/+4eigm2U3ssOHdteR8X04Z+WXVp3zekZSe3hmbwbXg3rP6ct5p3aixKu74aINbO7gfzi8n/XgpJqOBicmSnAvR1Zj1DP7AAw8wa9asZu/Tu3f7r45bLBYsFi9MWNuguvAYgUCZb6T3vlmJ7KN1az++Szun2YLkmCBWZOSSkdN5kvOvt2Ty/fYszEYDf58+BIvZQ3anayph5Wva7Qn3axdThKjH0RAuq5Vl7RsO5jNr9jpKqmoZkhTGhzed7l0jwULitTGCe37Qjnvk7ILMjdr51RlfQFjnq9DSS3JMEKN6RrD2QD7z1moX15+5bBDxYf46r6xzCA/0pU90IHtzyth4sICzBpzoen+sqIIjBRUYDTCse9dLzo32n8WH8svZkVnMrR+uZ+HvxxFkcf3PwKX2YxwjuocTGuDj8nhCCM+i6855dHQ0/fr1a/aXr68XvWnTgbJ3a6/19+KdmqTR2u9Ln4OCAy3e3XHuPD27czSFO15cyRNfaeco7zozhYHxHnSOctNHUJoFoUkw5Fq9VyM8UFwbzpyvyshlxntrKamqZVTPCP57yyjvSswdHI3hfvuXNjbNJ1ArZY/uvOPN9HLNqBMXOy4flsBFQ+J1XE3nM9Ix7/xQw9J2R0n7gPgQtySknijA18y7M0cSHWxhd3YJ936yyS0d3B0l7ZOlpF2ILslrzpwfOnSIzZs3c+jQIaxWK5s3b2bz5s2Ulnae3dP2MJRrV1htgV78Ij7uLkgYoTUcm38D1DRfHpviSM47wZlzpRSPfbGNoooaTksI4fdT+ui9pBNqq2HFP7Xb4+8BsxcmUcLl4uzl6KVVtc02aVycls2sD9ZRXm1lYkoUH948imA/L90VSjkHguy7jEYfuOa/kDhS3zV1UucPiqN3dCD9ugXz1CUD9V5OpzPCXrK+4cDJybl9vrmnjPLUSVyoP2/PGIGv2cjPu47z4o+u7eBeWWNl1V5thNqZ/WSEmhBdkdck50888QTDhg3jySefpLS0lGHDhjFs2DDWr1+v99J05VupvYibg734RdxsgelzICAKsrbBN/eDavrqtGPn/GhhBeXVte5apUss3HiUn3cdx9dk5O9XDfWsjrhb5moNroK6wbAZeq9GeKgAXzOh/lqS3VRp+3fbjnHHRxuorrVx9oBY3p050rtHYJl8YOKftFL2K96BPmfqvaJOy8/HxC/3T+Lbuyd678UcD+ZoCrflSCHVtScmpqw70Innm7fRsO7hvHTlYAD+s8y1HdzX7MujssZGXKgfqbHSVFKIrsiDMoHmffDBByilTvk1efJkvZemH5sNv+pCAHxDu+m7lo4KTYQrZ2uNlrbMhfXvNXnXyCALEYG+KKU1svFWx4oqeMo+Fujes1NI7eZBP4itNfDrK9rt8feAj5c06xK6cOyeZxaeWvWycMMR/jh3IzVWxUVD4nnr+uGe01OhI0bfDg/tl9GCbmAwGLy3p4qH6x0VSHiAD1W1NnZkFgFQXFlDWpY2enZkF2wG15hLhibwxylao9Y/f7G9rrLA2Rwj1CanxmAwyPe8EF2R1yTnohEV+ZiwAhAYEafzYpyg9yQ466/a7e8fgcNrm7xrcrSjtN07z50rpXjos62UVGpNsW6f6GFjgbYtgMKDEBgNI2bpvRrh4RzJ+cnnzj/+7SAPLNiCTcH0kYn882oPqw7pKHnzLLycwWCo2z13zDvfdKgQm4LuEQHEhsiFWYf7z+7LtIGxVFttLungrpRisWOEWqoXH1UUQnRIJ3qX1AXZm8EVqCCiQoN0XoyTjLsbBlwCthr49Ma653iy5Fjt+XrrrPNP1h3m1/RcLGYjf79qCGZPSlhsVvj179rtsX8EXw+fPS10F2fvnl0/OX/31338+YvtAMwa15PnLx8su59CeKARjqZw9uS87ry57Jo3YDQa+MfVQxkQF0JeWTW3zVlPaZXzjtbtyy3jUH45viYj45O9uMmvEKJDPCgjEG1WpiWuuSqUmOBOMjLOYIBL3oSoVCg5BgtuAuupP/zqmsJ54azz3NIqXvhmC6B4cFpq3Rl6j7HjC8jL0M7Tnn6L3qsRXiDesXNeWIFSitd+Sef/vt0FwO8m9eHJiwZglMRcCI/k2Dlff7AApVS9+eZy3vxkjg7uUUEW0rJKuPeTzdic1MHd0aV9dO8IArtoh3whhCTnXs0xRk1LzjtR6ZklGK75GHyD4eAK+PnJU+7iSGi9cdb55hXfsdRwB8sCHuGmhCN6L6chmw2Wv6zdHvMH7d9CiBZ0Cz2xc/7CD7t5ZdEeAB44uy8Pn5sqZyeF8GCDE0PxMRnIKaliX24Zmw8XAnC67Jw3Kj7Mn7dvdHRwz+bFH3c75XHrnzcXQnRdkpx7sarCLAByCSG6s+ycO0SlwGX/0m6vfgO2L2zw6ZQYLWk8mFdOVa3V3atrv0NrmPDb7wgzlNHDdhjTnIvgi99BaY7eK9Ok/Q9ydoElVGt4JUQrOHbOV+7N5d/L9gLw+AX9uWtqiiTmQng4Px8TA+NDAZiz6gCVNTbCA3zoE+1hVV0eZHj3cF68Quvg/u9le1m4oWMX2suqavltfx4g582F6OokOfdiFfbkvMgY7t1jiZrS/yKYcJ92+6u74Piuuk/FhlgIspix2hQHcp3blMVlDq9D/fcK/FQlv1pPI6//jYABtsyDN0bC+ve1nWu9KAXLX9Juj74D/EL1W4vwKo4z50ppJ1OevWwQt3pak0MhRJNG2kvb59vHhI3oESEX1lpw6bAE/jClDwCPfr6NDQfb38F9RUYuNVZFz8gAestFESG6NEnOvVhNkZacV1oidV6JC535F+g9GWrK4JProVIb9WIwGE6UtntDU7gjG+C/l2OoLmWldSCPWh4j/KrX4NZfoNsgqCyEb+6F2dO0We962PODFts3CMbcqc8ahFeKC/UjyGLGaIBXpg/hutHd9V6SEKINHOfOK2u0C8SjeklJe2s8cHZqgw7uRwrat1mwdLd2TFFK2oUQkpx7MceZ81r/TtzV02iCK2ZDaBLk79VKwO27y3VN4Tx9nNrRjfDRZVBVzIHg4dxa8wBjUxO1BlmJI+C2pXDu81pSfGQt/GcS/PhnqHLjRQelYNmL2u3Tb4UAaQQkWs/Px8Snd4zlf3dN4LJhiXovRwjRRo7k3GGkNINrFaPRwCvTh9I/LoTc0mpu/XA9ZW3s4K6UYkmadrRtSj9JzoXo6iQ592Km8lztRmAnfzEPjITpc8Bkgd3fwQptzJdX7JxnboaPLoWqIug+jt+rh6jAr+EPYJNZ26n+4zoYcCkoq3bO/s1RsOt/WuLsant/gcyNYPbXxqcJ0UYD4kPqzq0KIbxLTIgfSRHa8RSL2chp8n+51QItDTu43ze/bR3c07JKyCquxM/HyOheclFEiK5OknMv5lupJefmkFidV+IGCcPhAnsX8cXPQMbPpHj6rPOsbVpiXlkESWM4dN4H7My1YTYamJDSSLVDSDxM/xCu/wzCekDxUZh/A8y7BgoOum6dSsEy+1nzkTdDkDSjEUKIrmakfd750KQwfM3y9rAtEsL8+c+MEfiajPy0M5uXf2p9B/fF9hFq4/tE4efTCfsHCSHaRF59vZVSBNQUAGAJ66bzYtxk+I0wfCagYOGtpFoKAdiXU0atVcdGao3J3gEfXgwVBZB4Oly/gMX7tLNoI3uGE+Ln0/TXppwNf/gNJv4JjD7aWfA3R8Ovr0BttfPXeuBXOLxGq0wYf7fzH18IIYTHu3x4Aj4mA1eNTNJ7KV5pRI9wXrhyEABvLd3LF5ta18Hdcd5cStqFECDJufeqKMCMdq4pKCJO58W40fkvQfxwqCgg/sdbCfWppdpq43BBhd4rO+H4Lnting8JI+CGheAXwhL7DNMprWn44uMPU/8Cd66EnhOhtgJ+eQr+MxEOrHTueh1nzYffCMFd5EKPEEKIBiamRJP+zPlcOUL6RrTXZcMSuXOy1sH94YXb2HiooNn7F5XXsOGgdp/JMkJNCIEk597L3gyuSAUQGRai82LcyGyBqz+CgEgMWVt52X8OoEjP9pCmcDm74cOLoDwX4obCDZ+DXygV1VZW77PPMG3L1fHoVJj5P7jsPxAQBTlp8MH58OUfoCyv4+s9tEbbOTf6wPh7Ov54QgghRBf24DmpnD0glupaG7fP2cDRwqY3D5an52BT0Dc2iMTwADeuUgjhqSQ591ZlWnKeq0KJCbHovBg3C02EK98Hg5Gzq3/metMvZOR4wLnz3HQtMS/LgW6DYcYX4B8GwOp9uVTX2kgI86/rMt9qBgMMuUZrGDdilvaxzf+FN0bAxjkdm43umGs+9FoIk1JGIYQQoiOMRgP/vHoo/boFk1taxW0frqe8uvEO7kscJe0yQk0IYSfJuZeqLc4GIJdQooO6WHIO0HsSnPVXAJ40f0jNgd/0XU/eXvjgQijNhthBcONXDcaRORq+TOkXjcFgaF+MgAi46FW4ZRHEnqadZ//6Lnj/PO2Me1sd3QAZP4PBBBPub9+ahBBCCNHAiQ7uvuw8VtxoB3ebTbFst4xQE0I0JMm5lyrLPwZAngolPMBX59XoZNzdZCVMw9dg5fpDf6kr9Xe7/H32xDwLYgaekpg3mGHqjKvjSaPg9mVwzjPgE6g1c/vPGfDTX6C6rPWPs9ze/X7wdIjo1fF1CSGEEAKAxPCAug7uP+7I5u+LGnZw33q0iLyyaoIt5lPmzAshui5Jzr1UZWEWAGU+ERiN7dyJ9XYGA2XnvUa6LYEoWx5qwSywNl465jL5++GDi6AkE6L7aYl5YGSDu2QcL+VoYQW+ZiNj+0Q28UBtZDLDuD/CH9dC/4vAVgurXtO6uqd91/LXZ23TZsZjgIkPOGdNQgghhKgzokcEz12udXB/c8levtx0tO5zS+wVdRP7RuFjkrfjQgiNvBp4KUdZe5VfI/Oyu5DucTH80XofJcofw8GV8POT7gtecFA7Y158BKL6ao3bGpkR7jhTNqZ3JAG+ZueuITQRrv4vXDsfQrtD0WH45FqYdy0UHmr66xxnzU+7HKJSnLsmIYQQQgBwxYhEfjdJ6+D+0MKtbLJ3cHeMUJss582FEPVIcu6t7CXcVv+unZz7mIzYIvvyp5o7tA+sfgO2L3R94MLD8OGFWjIcmWxPzBv/Aes4b36mK8ekpJ6rzUafcB8Yzdqu+JujYeWrYK1peN/jabDza+32xD+5bk1CCCGE4KFpqZzVX+vgftucDWw9UsiWI0WAjFATQjQkybmXMlVoZ5gNTSSEXUlKbBA/2kaxpccs7QNf3aXNGneVoqNaYl54CCL6wMxvmpwPXlxZw/oDjhmmLv638g3QmuT9bgV0Hwc15bDoCe08+qE1J+7368uA0srhYwe4dk1CCCFEF2c0GvjnNSc6uF/7tvYzeVBCKDHBfjqvTgjhSSQ591J+VdqMa3No40lhV5IcEwzAJ0EzodckqCmDT66HyiLnByvO1BLzggMQ3lPbMQ+Ja/LuK9NzqbUpekcF0jMq0PnraUxMf7jpO7jkLfCPgOM7YfY0+OqPcHjdicqCMx50z3qEEEKILi7I3sE9MtCXsmorAFNk11wIcRJJzr2RUgTWaLuxfmGSnCfb54bvya2EK2dDSCLk74Uv7uzYDPCTlWRpZ8zz90FYD23HPDSh2S9ZoteZMoMBhl0Pd22A4TdqH9v0Ebx3Figb9D0X4oa4d01CCCFEF1a/gzvAmf1jdV6REMLTSHLujSqL8EE7RxwcFa/zYvSXYk/O07NLUAGRcPUcMPnC7m9hxSvOCVKSrSXmeRla47VZ30BYUrNfYrMpltTNMNXp6nhABFz8Otz8I8TUK2E/4yF91iOEEEJ0YSN7RvDxbaP559VDGZoUpvdyhBAexsmto4VblGkJX4nyJzIsVOfF6K9XVCBGAxRX1pJTWkVMwgg4/2X4392w+P8gfhgkT21/gNIcmHMx5O7RduVn/Q/Curf4ZTuPFZNTUkWAr4lRvSJavL9LdR8DdyyHzXPBJwASR+i7HiGEEKKLOr1nBKf31HsVQghPJDvnXkiVamPUclUIMcEWnVejPz8fE90jAgDIyC7VPjhipr2cW8HCW7SxZ+1Rlqsl5jlpEByvJebhPVv1pY4ZpuOTo7CYTe2L70wmH+3vZfBVeq9ECCGEEEIIcRJJzr1Qef4xAHIJJSpIknM40RQu/XjpiQ+e9xLED4eKAvh0BtRUtO1By/NhziVaQ7XgOK2UPaJ3q7/ccd58iswwFUIIIYQQQrRAknMvVGZPzouM4fj5eMCOrAdwNIXLqJ+c+/jB1R9BQCQc2wLfPgBKte4By/O1HfPs7RAUq3Vlj+zT6vXkl1Wz6XAhIDNMhRBCCCGEEC2T5NwLVRVmAVDuo/M5Zg9S1xTueEnDT4QmwpXvg8EImz+G9bNbfrCKAvjoUsjaBoExWmIeldKm9Szfk4NS0K9bMPFh/m36WiGEEEIIIUTXI8m5F7KWaOXS1X5ROq/Ec5zYOS879ZO9J8HUJ7Xb3z+szfpuSkUhfHS5ttMeEAUzv4bo1Davp66kvZ+UtAshhBBCCCFaJsm5FzKUaQ3hbAGSnDv0sSfnuaVVFJRVn3qH8fdA/4vBVgOf3gilx0+9T2Ux/PcKyNwI/hFaYh7Tv81rsdoUy/bYR6jJeXMhhBBCCCFEK0hy7oXMFbkAGIJjdV6J5wiymEmwl49n5JSeegeDAS59C6L6QkkmfHYzWGtPfL6qBD6+Eo6uB/9wLTGPHdiutWw+XEhheQ0hfmaGdw9r12MIIYQQQgghuhZJzr2QX1UeAD6hkpzX16expnD1WYLh6o/BNwgO/Ao/20vdq0rh46vg8G/gFwo3fgXdBrV7HY4Ramf0jcZskv9iQgghhBBCiJZJ5uBtlCK4tgCAgPB4nRfjWeqawmU3kZwDRPfVdtABVr8Bm/4Lc6fDodVgsSfmcUM6tA4ZoSaEEEIIIYRoK0nOvU1VCb5oZ6qDoyQ5ry+5qY7tJxtwiXYGHeCrP8DBlWAJgRlfQPywDq0hu7iSHZnFAEySEWpCCCGEEEKIVpLk3NuUaY3GypSFqPBwnRfjWRw753ubKmuv78wnoNcZ2m3fYLjhc0gc0eE1LNut/fsMSQwlKsjS4ccTQgghhBBCdA1mvRcg2qaqKAsLkKtCiQ6W5K8+x855ZlElpVW1BFma+fY2meGqD2Hdu9D3XIgb7JQ1LE6TEWpCCCGEEEKItpOdcy9TknsUgDxDKKH+PjqvxrOEBfjW7Va3avc8IAImPeS0xLy61saKDK2Tvpw3F0IIIYQQQrSFJOdeprwgC4ASUwQGg0Hn1XieuqZwrUnOnWz9wXxKq2qJDPRlUEKo2+MLIYQQQgghvJck516mulBLzit8I3ReiWdqdVM4F1hqP28+KTUao1EunAghhBBCCCFaT5JzL2Mr1c401/hF6bwSz5QS24amcE7mOG9+ppw3F0IIIYQQQrSRJOdexlimJYAqUMZ0NSZZp7L2w/nlZBwvxWQ0MDFZ/m2EEEIIIYQQbSPJuZfxqcwDwBQcq/NKPJMjOT+cX05ljdVtcZfu1i6ajOgeTmiANOoTQgghhBBCtI0k517Gv1pLzn1Du+m8Es8UHWQh1N8Hm4J9OWVui7vEft58cj/ZNRdCCCGEEEK0nSTnXia4tgCAgIg4nVfimQwGg9ubwlXWWFm1VxuhJufNhRBCCCGEEO0hybk3qSrFn0oAQqLidV6M53KMU3NXU7jV+/KorLERF+pHamywW2IKIYQQQgghOhdJzr2IrUQ711ypfIiKjNR5NZ7L3U3hltq7tE9OjZHZ80IIIYQQQoh2keTci5TkHQUgl1Aig/x0Xo3nciTnGW5IzpVSdefNp6TKeXMhhBBCCCFE+0hy7kVK8jMBKDCE42uWf7qmpNhLy/fnllFjtbk01t6cMg7ll+NrMjI+WWbPCyGEEEIIIdpHMjwvUpGfBUCpOULnlXi2+FA/An1N1NoUB/Nc27HdMUJtdO8IAi1ml8YSQgghhBBCdF5ekZwfOHCAW265hV69euHv70+fPn148sknqa6u1ntpblVTlA1ApUWS8+YYDAb6uKm0fcnuE+fNhRBCCCGEEKK9vGKrLy0tDZvNxn/+8x+Sk5PZvn07t912G2VlZbz88st6L89tbKVaIljjJ2ebW5IcE8TWI0WkZ5dy7mmuiVFaVcva/fmAnDcXQgghhBBCdIxXJOfnnnsu5557bt2fe/fuze7du/nXv/7VpZJzU7nWeIwgSQRbUtcULsd1O+cr0nOpsSp6RgbQOzrIZXGEEEIIIYQQnZ9XJOeNKSoqIiKi+fLuqqoqqqqq6v5cXFzs6mW5lG9lLgCm4FidV+L5UmK0pnDp2a5LzpdKSbsQQgghhBDCSbzizPnJMjIyeP3117njjjuavd9zzz1HaGho3a+kpCQ3rdA1Amq0EmpLeDedV+L5Uuw753tzSrHalNMfXxuhpiXnU/pJci6EEEIIIYToGF2T80ceeQSDwdDsr7S0tAZfc/ToUc4991yuuuoqbrvttmYf/9FHH6WoqKju1+HDh135dFwuxFoAQFBEvM4r8XxJEQH4mo1U1do4WlDh9MffdayE7OIq/H1MjO4lDfqEEEIIIYQQHaNrWfsDDzzArFmzmr1P7969625nZmYyZcoUxo0bx9tvv93i41ssFiwWS0eX6RlqKghESzJDoyU5b4nJaKB3VCBpWSWkHy+he2SAUx/fsWs+PjkSPx+TUx9bCCGEEEII0fXompxHR0cTHd265mZHjx5lypQpjBgxgvfffx+j0Ssr8tutouAY/kCVMhMVKQ3hWiM5Joi0rBIyjpcytb9zz+kvSZPz5kIIIYQQQgjn8YqGcEePHmXy5Mn06NGDl19+mZycnLrPdevWNc5fF+YcxR/II5Q4Px+9l+MVtKZwx0h38qzzwvJqNh7SjhhMlhFqQgghhBBCCCfwiuR80aJFZGRkkJGRQWJiYoPPKeX8Zl+eqDQvE4AiUzjxBoPOq/EOKbFaUzhnJ+fL03OxKegbG0RiuHPL5YUQQgghhBBdk1fUhs+aNQulVKO/uorKwiwAyszSfKy1HLPO9x4vder3iqOkXbq0CyGEEEIIIZzFK5JzAbXF2QBUWSJ1Xon36BkZiMlooLSqlqziSqc8ptWmWLZHO1YxRc6bCyGEEEIIIZxEknMvoUq13VprQJTOK/EevmYjPexd2jOcVNq+9Ugh+WXVBFvMjOgR7pTHFEIIIYQQQghJzr2EudzeBC9IdmvbIsVe2p6e7ZzkfMlu7d9hYt8ofEzy30cIIYQQQgjhHJJdeAlLVR4A5hDnjgTr7LSO7c5rCld33lxK2oUQQgghhBBOJMm5lwisyQfAPyxe55V4l/pN4TrqeEkl244WATBJRqgJIYQQQgghnEiScy8RatPmagdFxum8Eu/iSM7Tj5d0+LGW2UvaByWEEhPs1+HHE0IIIYQQQggHSc69gLW6gmDKAQiLTtB5Nd6lT3QQBgMUlNeQV1rVocdautvRpV12zYUQQgghhBDOJcm5FyjIOQpAtTIRHilnndvC39dEYrg/0LFz5zVWG8vT7cm5zDcXQgghhBBCOJkk516gKCcTgAJDKGazSefVeB9nNIXbcLCAkspaIgJ9GZwY5qSVCSGEEEIIIYRGknMvUJavJefFJpmr3R7OaAq3ZLfWpX1S32hMRoNT1iWEEEIIIYQQDpKce4GqwiwAyn0idF6Jd3JGU7ilaVpJ+2Q5by6EEEIIIYRwAUnOvYC1WNu1rfaL0nkl3qkuOc9u38750cIKdmeXYDRoO+dCCCGEEEII4WySnHuDMi05twZIYtgejuT8eEkVRRU1bf76JWna3//w7uGEBfg6dW1CCCGEEEIIAZKcewVzRS4AxiBJztsjxM+HbiHaXPKMdpw7X2o/by5d2oUQQgghhBCuIsm5F/CrygPAJ7SbzivxXu1tCldZY2Vlhvb3L+fNhRBCCCGEEK4iybkXCKrNB8A/PE7nlXiv9jaFW7s/n4oaK7EhFgbEhbhiaUIIIYQQQgghybmnU0oRZisEIDgyXt/FeLETyXnbds4X28+bT0mNwWCQEWpCCCGEEEII15Dk3MOVVVQSZtASyrCYBJ1X471S7Ml5W8+cO86bT06V8+ZCCCGEEEII15Hk3MPlZR8BoFYZCQyVBLG9UmKDAThSUEF5dW2rvmZ/bhkH8srxMRkYnxzpyuUJIYQQQgghujhJzj1cce5RAIqMoWCUf672igj0JSJQG4O2L6esVV/jGKF2es8Igv18XLY2IYQQQgghhJBsz8OV5x8DoMQUrvNKvF9bm8ItsZe0nykj1IQQQgghhBAuJsm5h6suygKg3FfKqjvKce48Pbvlc+dlVbX8tk/rki/nzYUQQgghhBCuJsm5h7OWaLu31X5ROq/E+yW3oSncqr15VFttJEX40yc60NVLE0IIIYQQQnRxkpx7OENZDgAqMFrnlXi/lBitKVxrknNHSbuMUBNCCCGEEEK4gyTnHs6nMhcAY5CUVneUY+f8YH45VbXWJu+nlKprBjdFzpsLIYQQQggh3ECScw/nX50HgG9YN51X4v1iQywEW8xYbYoDueVN3m93dgnHiiqxmI2M7S1n/YUQQgghhBCuJ8m5hwuuLQAgIDxO55V4P4PBQHJsyx3bl6RpRwnG9YnEz8fklrUJIYQQQgghujZJzj1YjdVGuCoEICQ6Xt/FdBLJ0S03has7by4l7UIIIYQQQgg3keTcg+UVlxOOlkSGRibovJrOIaVu57zx5LyovIYNB7VqhSkyQk0IIYQQQgjhJpKce7CCnEyMBoUVI8YgGaXmDI6mcHubSM5/zcjBalMkxwSRFBHgzqUJIYQQQgghujBJzj1Yce5R7XdDCBjl7LMzOMap7cspo9ZqO+XzjvPmU1JldJ0QQgghhBDCfSQ592DlBVkAlJrDdV5J55EQ5o+fj5Fqq41D+Q07tttsimV7Tsw3F0IIIYQQQgh3keTcg9UUacl5pUXGeTmL0WigTxNN4bYdLSK3tJogi5mRPSP0WJ4QQgghhBCii5Lk3IPZSrVd3Bo/OW/uTCkxjTeFc3Rpn5Acha9Z/msIIYQQQggh3EcyEA9mLNPOPxMo55+dqammcEt228+b95O/byGEEEIIIYR7SXLuwXwrcwEwhcTqvJLOJdneFK7+znluaRVbjxQCMFnOmwshhBBCCCHcTJJzDxZYkw+Ab2g3nVfSuThmnWccL8VmUwAs252DUjAwPoTYED89lyeEEEIIIYTogiQ591BKKYKtBQAERcTpvJrOpUdEAD4mAxU1VjKLKoAT582lS7sQQgghhBBCD5Kce6jiyloiKQQgJDpR38V0MmaTkV5RgYBW2l5rtbF8j5w3F0IIIYQQQuhHknMPlVNURgQlAFjCpKzd2eo3hdt0uJDiylrCAnwYmiQz5YUQQgghhBDuZ9Z7AaJxBblZJBsUNgwYA2SUmrNpTeGySM8uJa+sGoBJfaMxGQ36LkwIIYQQQgjRJUly7qFK8zK1340hhJjkn8nZTsw6L6H8iBWQ8+ZCCCGEEEII/UjW56EqC7IAKDWHE6LzWjojR1n7jsxiqmptGAxwRl85by6EEEIIIYTQh5w591DVxVpyXmWJ1HklnVOvqECMBqiqtQEwNCmMiEBfnVclhBBCCCGE6KokOfdUpVr38Fp/2c11BT8fE90jAur+fKaUtAshhBBCCCF0JMm5hzKVa8k5QZKcu4rWFE4zpZ8k50IIIYQQQgj9SHLuoSxVuQD4hMgYNVdJidXOnUcHWxgQJyf7hRBCCCGEEPqR5NxDBdTkA+AXLsm5q0y2N4CbPjIRo4xQE0IIIYQQQuhIurV7oKpaK2G2QjBCYESc3svptEb3jmTzE2cT7Oej91KEEEIIIYQQXZzX7JxffPHFdO/eHT8/P+Li4pgxYwaZmZl6L8slckuriTIUARAUEa/zajq3sABfTLJrLoQQQgghhNCZ1yTnU6ZM4dNPP2X37t0sXLiQvXv3cuWVV+q9LJc4XlROJMUAGIKkUZkQQgghhBBCdHZeU9Z+33331d3u0aMHjzzyCJdeeik1NTX4+HSusuTCvGzMBm3+NoHSrV0IIYQQQgghOjuvSc7ry8/P5+OPP2bcuHHNJuZVVVVUVVXV/bm4uNgdy+uw0rxjAJQZgwk0++q8GiGEEEIIIYQQruY1Ze0ADz/8MIGBgURGRnLo0CG++uqrZu//3HPPERoaWvcrKSnJTSvtmKpCe3LuE6HzSoQQQgghhBBCuIOuyfkjjzyCwWBo9ldaWlrd/R988EE2bdrETz/9hMlk4sYbb0Qp1eTjP/rooxQVFdX9Onz4sDueVofVFmcDUGWJ1HklQgghhBBCCCHcQdey9gceeIBZs2Y1e5/evXvX3Y6KiiIqKoq+ffvSv39/kpKSWLNmDWPHjm30ay0WCxaLxZlLdgtDWQ4A1kBpBieEEEIIIYQQXYGuyXl0dDTR0e1reGazaQ3T6p8p7yzMFVpybpBmcEIIIYQQQgjRJXhFQ7jffvuNdevWMWHCBMLDw9m7dy9/+ctf6NOnT5O75t7MryoPAJ/QbjqvRAghhBBCCCGEO3hFQ7iAgAA+//xzpk6dSmpqKrfccguDBw9m2bJlXlm23hylFIG1BQD4h8fpvBohhBBCCCGEEO7gFTvngwYNYvHixXovwy0qa2z08iuDagiKlORcCCGEEEIIIboCr9g570r8fU30sJQB4BMSq/NqhBBCCCGEEEK4gyTnnkYpsHdrR7q1CyGEEEIIIUSX4BVl7V1KTQX0OgPKjoN0axdCCCGEEEKILkGSc0/jGwAzPtd7FUIIIYQQQggh3EjK2oUQQgghhBBCCJ1Jci6EEEIIIYQQQuhMknMhhBBCCCGEEEJnkpwLIYQQQgghhBA6k+RcCCGEEEIIIYTQmSTnQgghhBBCCCGEziQ5F0IIIYQQQgghdCbJuRBCCCGEEEIIoTNJzoUQQgghhBBCCJ1Jci6EEEIIIYQQQuhMknMhhBBCCCGEEEJnkpwLIYQQQgghhBA6k+RcCCGEEEIIIYTQmSTnQgghhBBCCCGEziQ5F0IIIYQQQgghdCbJuRBCCCGEEEIIoTNJzoUQQgghhBBCCJ1Jci6EEEIIIYQQQujMrPcC3EkpBUBxcbHOKxFCCCGEEEII0RU48k9HPtqULpWcl5SUAJCUlKTzSoQQQgghhBBCdCUlJSWEhoY2+XmDail970RsNhuZmZkEBwdjMBj0Xk6TiouLSUpK4vDhw4SEhEjcThhb4nbuuHrGlridO66esSVu546rZ2yJ27nj6hlb4nbuuHrHbgulFCUlJcTHx2M0Nn2yvEvtnBuNRhITE/VeRquFhITo8k3W1eLqGVvidu64esaWuJ07rp6xJW7njqtnbInbuePqGVvidu64esdureZ2zB2kIZwQQgghhBBCCKEzSc6FEEIIIYQQQgidSXLugSwWC08++SQWi0XidtLYErdzx9UztsTt3HH1jC1xO3dcPWNL3M4dV8/YErdzx9U7tit0qYZwQgghhBBCCCGEJ5KdcyGEEEIIIYQQQmeSnAshhBBCCCGEEDqT5FwIIYQQQgghhNCZJOdCCCGEEEIIIYTOJDnXwUUXXcS5557b6Od+/fVXDAYDW7du5e6772bEiBFYLBaGDh3qlrhbtmzh2muvJSkpCX9/f/r378+rr77q8rjLli3j3HPPJT4+HovFQlJSEn/84x8pLi52eeytW7fWfSwvL4/ExEQMBgOFhYUuj2swGE759cknn7g8LsAHH3zA4MGD8fPzIyYmhj/84Q8ujfvaa681+nwNBgPHjx93WdytW7eybt06pk6dSlhYGOHh4UybNo0tW7a0K2ZbY//yyy+MGzeO4OBgunXrxsMPP0xtba1TY7TmtWLr1q1MnDgRPz8/kpKSePHFF10et7KyklmzZjFo0CDMZjOXXnppS0/ZabGXLl3KJZdcQlxcHIGBgQwdOpSPP/7Y5XF3797NlClTiI2Nxc/Pj969e/P4449TU1Pj0rj1ZWRkEBwcTFhYmMuf74EDBxr9P71mzRqXP1+lFC+//DJ9+/bFYrGQkJDAM8884/Ln/Ne//rXR5xwYGOjy5/zjjz8yZswYgoODiY6O5oorruDAgQMuj/vpp58ydOhQAgIC6NGjBy+99FKj92tt3Na+11i6dCnDhw/HYrGQnJzMBx984PK4x44d47rrrqNv374YjUbuvffeJmM6O/bnn3/O2WefTXR0NCEhIYwdO5Yff/zR5XFXrFjB+PHjiYyMxN/fn379+vGPf/zD5XHrW7lyJWazudnXN2fFXbp0aaP/h7Oyslz+fKuqqvjzn/9Mjx49sFgs9OzZk9mzZ7v8Oc+aNavR5zxw4ECXP2fHa6bJZCIuLo6bb76ZvLy8Jp+zp5g8eXKr/v87nRJu98UXXyij0agOHz58yuduuukmNXLkSKWUUnfddZd644031IwZM9SQIUPcEve9995Td999t1q6dKnau3ev+uijj5S/v796/fXXXRo3Pz9fvfXWW2rdunXqwIED6ueff1apqanq2muvbXfc1sau75JLLlHnnXeeAlRBQYHL4wLq/fffV8eOHav7VVFR4fK4f//731V8fLz6+OOPVUZGhtqyZYv66quvXBq3vLy8wfM8duyYmjZtmpo0aZJL45aUlKiIiAg1a9YslZaWprZv366uuOIKFRsbq6qrq10ae/PmzcrX11c99dRTKj09XS1dulT169dPPfDAA06LoVTLrxVFRUUqNjZWXX/99Wr79u1q3rx5yt/fX/3nP/9xadzS0lL1u9/9Tr399ttq2rRp6pJLLnHbc37mmWfU448/rlauXKkyMjLUP//5T2U0GtX//vc/l8bdu3evmj17ttq8ebM6cOCA+uqrr1RMTIx69NFHXRrXobq6Wo0cOVKdd955KjQ0tMn7OSvu/v37FaB+/vnnBv+3m/q/5czne9ddd6nU1FT11VdfqX379qn169ern376yeXPuaSk5JTXsgEDBqiZM2e6NO6+ffuUxWJRjz76qMrIyFAbNmxQZ5xxhho2bJhL43733XfKbDarf/3rX2rv3r3qm2++UXFxcU2+L3DWe419+/apgIAAdf/996udO3eq119/XZlMJvXDDz+4NO7+/fvV3XffrT788EM1dOhQdc899zQazxWx77nnHvXCCy+otWvXqj179qhHH31U+fj4qI0bN7o07saNG9XcuXPV9u3b1f79+9VHH32kAgICOvQzoi3vJwsKClTv3r3VOeec0+zrm7PiLlmyRAFq9+7dDf4fW61Wlz/fiy++WI0ePVotWrRI7d+/X61atUqtWLHC5c+5sLCwwXM9fPiwioiIUE8++aRL465YsUIBatKkSSowMFB98cUXauDAgeqyyy5r8jl7ikmTJrXq/7+zSXKug5qaGhUbG6v+9re/Nfh4SUmJCgoKUv/6178afPzJJ590SnLe1rgOv//979WUKVPcHvfVV19ViYmJ7Y7b1thvvfWWmjRpkvrll186nJy3Ni6gvvjii3bHaU/c/Px85e/vr37++We3xj3Z8ePHlY+Pj5ozZ45L465bt04B6tChQ3Wf37p1qwJUenq6S2M/+uijp1wA+vrrr5Wfn58qLi52Soz6mnqteOutt1R4eLiqqqqq+9jDDz+sUlNTXRq3vpkzZ7YqOXfl6+P555+vbrrpJrfHve+++9SECRPcEvehhx5SN9xwg3r//febTc6dFdeRnG/atKnJWK6Iu3PnTmU2m1VaWlqr4joz9sk2b96sALV8+XKXxl2wYIEym80NEoivv/5aGQyGRi+GOCvutddeq6688soGH3vttddUYmKistlsHY7rcPJ7jYceekgNHDiwwX2uvvpqNW3atEa/3hXvcVr75tyV768GDBignnrqKbfHveyyy9QNN9zglrhXX321evzxx1v8P+esuI7kvLXv85wV9/vvv1ehoaEqLy+vVXGdGftkX3zxhTIYDOrAgQMujft///d/ymAwqLS0NHX11VerZ555Rr322msqISGh7t/hhx9+UEOHDlV+fn5qypQpKjs7W3333XeqX79+Kjg4WF177bWqrKys7jErKyvVXXfdpaKjo5XFYlHjx49Xa9eurft8Yz//vvjiC1V/T9rxvTZnzhzVo0cPFRISoq6++uq692YzZ85UQINf+/fvb/Lv05mkrF0HZrOZG2+8kQ8++ABVb8z8ggULsFqtXHvttR4Vt6io7OyqhgAAE/VJREFUiIiICLfGzczM5PPPP2fSpEntjtuW2Dt37uTpp59mzpw5GI0d/2/Rluf8hz/8gaioKEaNGsXs2bMb3N8VcRctWoTNZuPo0aP079+fxMREpk+fzuHDh10a92Rz5swhICCAK6+80qVxU1NTiYyM5L333qO6upqKigree+89+vfvT8+ePV0au6qqCj8/vwZf5+/vT2VlJRs2bHBKjNZYvXo1Z5xxBr6+vnUfmzZtGrt376agoMBlcdvDlbGbey1zVdyMjAx++OGHJl/LnBl38eLFLFiwgDfffLPF+zr7+V588cXExMQwYcIEvv76a5fH/d///kfv3r355ptv6NWrFz179uTWW28lPz/f5bFP9u6779K3b18mTpzo0rgjRozAaDTy/vvvY7VaKSoq4qOPPuKss87Cx8fHZXGbeh07cuQIBw8edFrck/9/rl69mrPOOqvBfaZNm8bq1asb/Xq93uO4MrbNZqOkpMTpr1stxd20aROrVq1y+utWY3Hff/999u3bx5NPPtnkelwRF2Do0KHExcVx9tlns3LlSpfH/frrrxk5ciQvvvgiCQkJ9O3blz/96U9UVFS47Tk7vPfee5x11ln06NHDpXFLS0sB2Lt3L9dffz3vvPMOCxYs4Pzzz6+7z1//+lfeeOMNVq1axeHDh5k+fTr//Oc/mTt3Lt9++y0//fQTr7/+et39H3roIRYuXMiHH37Ixo0bSU5OZtq0ac2+/jdm7969fPnll3zzzTd88803LFu2jOeffx6AV199lbFjx3Lbbbdx7Ngxjh07RlJSUpsev93ccglAnGLXrl0KUEuWLKn72MSJExu9SumsnfO2xlVKqZUrVyqz2ax+/PFHt8S95pprlL+/vwLURRdd1KES79bGrqysVIMHD1YfffSRUqrtV1TbG1cppZ5++mm1YsUKtXHjRvX8888ri8WiXn31VZfGfe6555SPj49KTU1VP/zwg1q9erWaOnWqSk1NbbCz6uy4J+vfv7+688472x2vLXG3bdum+vTpo4xGozIajSo1NbXJq8XOjP3jjz8qo9Go5s6dq2pra9WRI0fUxIkTFaDmzp3rtOfn0NRrxdlnn61uv/32Bh/bsWOHAtTOnTtdFre+1u6cuyK2UkrNnz9f+fr6qu3bt7sl7tixY5XFYlGAuv3225ssl3RW3NzcXJWUlKSWLVumlGp858AVcXNyctTf//53tWbNGrV27Vr18MMPK4PB0OwxGWfEveOOO5TFYlGjR49Wy5cvV0uWLFFDhw5tcTfQ2d9bFRUVKjw8XL3wwgtuibt06VIVExOjTCaTAtTYsWOb/VnljLj/+c9/VEBAgPr555+V1WpVu3fvVv369VOAWrVqVYfjKtX4e42UlBT17LPPNrjft99+qwBVXl7usrj1taWs1RXvr1544QUVHh6usrOz3RI3ISFB+fr6KqPRqJ5++ukmYzor7p49e1RMTIzavXu3Uqp1/+ecETctLU39+9//VuvXr1crV65UN910kzKbzWrDhg0ujTtt2jRlsVjUBRdcoH777Tf17bffqh49eqhZs2a5/DnXd/ToUWUymdT8+fNdHnfcuHFq1qxZKigoqMHrVnV1dd377fqVnM8995wC1N69e+s+dscdd9RVzJSWliofHx/18ccf132+urpaxcfHqxdffFEp1fqd84CAgAZVjA8++KAaPXp03Z+lrL0LGjdunJoxY4ZSSqn09PRT/gM4ODM5b0vcbdu2qaioqFNKWlwZ99ixY2rXrl3qq6++UgMGDHBK8tZS7Pvuu09dffXVdfd1VnLeUtzG/OUvf+lwKX9LcZ955hkFNHjxPH78uDIajU2e5XNG3PpWrVqlALV+/foOxWtN3PLycjVq1Ch14403qrVr16rVq1erK664Qg0cOLDJN3jOiq2Udr4/JCREmUwmFRAQUPeD55NPPnFaDAdnJufOiFtfW5JzZ8devHixCggIUB9++KHb4h46dEjt2LFDzZ07VyUkJLSYvHU07mWXXaYefvjhuj+3Jjl3RtzGzJgxo8kyfmfFve222+rOjDps2LBBAS2WujvzOc+dO1eZzWaVlZXV7P2cEffYsWMqJSVFPfjgg2rjxo1q2bJlatKkSWrq1KmNlpc7K67NZlMPPfSQ8vPzUyaTSYWHh6u//vWvClBr1qzpcNym3mu0Jzl3Rtz62vrm3JmxP/74YxUQEKAWLVrktrj79u1TW7duVW+//baKiIho8SJyR+LW1taqkSNHNiiPbu3rjCvex55xxhlNJp3Oinv22WcrPz8/VVhYWPexhQsXKoPB0OL7EWc+52effVZFRka2akOmI3HT0tKU2WxWMTEx6sUXX1RbtmxRF110kQoLC1M333xz3fvt48eP133N7NmzVUBAQIPHfuKJJ+p6a2zZskUBp2ywXHrppXXH1lqbnA8YMKDBfV555RXVq1evuj9Lct4Fvffee3VXbR577DHVp0+fRn/AOjs5b03cHTt2qJiYGPXYY4+5NW59v/76qwJUZmamS2MPGTJEGY1GZTKZlMlkUkajUQHKZDKpJ554wmVxG/PNN98oQFVWVros7uzZsxVwSpOPmJgY9fbbb7ssbn0333yzGjp0aIditTbuu+++q2JiYhrsXFZVVamAgAA1b948l8Z2sNls6ujRo6q8vFzt3LlTAQ3ORzkjhlJNv1bMmDHjlMR48eLFClD5+fkui1tfW5NzZ8VeunSpCgwMbLKxkavi1udoklNbW+uyuKGhoXWvYSe/jr333ntufb5vvPGG6tatW7P36WjcJ554QpnN5gYfKy8vV0CzTeGcEbu+M888U1166aXN3sdZcR9//PFTelgcPnxYAWr16tUui+vgqP6pqqpS33333SlvqtsTt7n3GhMnTjzljfHs2bNVSEhIkzGdEbe+tr45d1ZsR9POb775xq1x6/vb3/6m+vbt67K4BQUFda9Rjl8Gg6HuY7/88otbn++f/vQnNWbMGJc9X6WUuvHGG1WfPn0afMzxnmDPnj0uje1gs9lUcnKyuvfee5uN54y4Dz74oAKUwWBo8LPJUVX22WefnbIZ1lhiXf+1qTXJ+YcffnjK68Snn356SnJ+8uvdP/7xD9WjR4+6P+uVnMuZcx1Nnz4do9HI3LlzmTNnDjfffDMGg0H3uDt27GDKlCnMnDmz2bE0zo57MpvNBmjn3VwZe+HChWzZsoXNmzezefNm3n33XUAbFdGR8WItxW3M5s2bCQ8Px2KxuCzu+PHjAW3kk0N+fj65ublNnj1yRlyH0tJSPv30U2655ZYOxWpt3PLycoxGY4N1OP7s+B5zVWwHg8FAfHw8/v7+zJs3j6SkJIYPH+7UGM0ZO3Ysy5cvbzDOa9GiRaSmphIeHu6yuB3hjNhLly7lggsu4IUXXuD22293W9yT2Ww2ampqmv1+62jc1atX172Gbd68maeffprg4GA2b97MZZdd5rK4jdm8eTNxcXHN3qejccePH09tbS179+6t+9iePXsAWnwdc9Zz3r9/P0uWLGn1a1lH4zpey+ozmUwALv3eqh8rISEBX19f5s2bx9ixY4mOjm533Jbea4wdO5ZffvmlwccWLVrE2LFjm12nXu9xnBV73rx53HTTTcybN48LLrjAbXFPZrPZWnz/1ZG4ISEhbNu2rcHr1u9+9ztSU1PZvHkzo0ePduvzdcbrVktxx48fT2ZmZt05bNBet4xGI4mJiS6N7bBs2TIyMjKc9rrVVNza2lrmzJnDoEGDOOecc+r+jbds2UJUVBRAu3os9enTB19f3wY9Ampqali3bh0DBgwAIDo6mpKSEsrKyurus3nz5jbH8vX1xWq1tvnrOsztlwNEA7fccosKDw9XJpNJHT16tMHn0tPT1aZNm9Qdd9yh+vbtqzZt2qQ2bdrUoXPBLcXdtm2bio6OVjfccEODkQvNXR13Rtxvv/1WzZ49W23btk3t379fffPNN6p///5q/PjxTonbXOyTObOsvbm4X3/9tXrnnXfUtm3bVHp6unrrrbdUQEBAh3frW4qrlDYybuDAgWrlypVq27Zt6sILL1QDBgzo0Gix1sRVStvJ9vPzc9rfb0txd+3apSwWi7rzzjvVzp071fbt29UNN9ygQkNDnVKV0VxspZR68cUX1datW9X27dvV008/rXx8fNrVob8jrxWFhYUqNjZWzZgxQ23fvl198sknzY7JcVZcpbQr6ps2bVIXXXSRmjx5ct19XP2cHaXsjz76aIPXstZ0ye1I3P/+979q/vz5aufOnWrv3r1q/vz5Kj4+Xl1//fUujXuy1pa1dzTuBx98oObOnat27dqldu3apZ555hllNBrV7NmzXRrXarWq4cOHqzPOOENt3LhRrV+/Xo0ePVqdffbZLn/ODo8//riKj49vtiLCmXF/+eUXZTAY1FNPPaX27NmjNmzYoKZNm6Z69OjRYklsR+Lm5OSof/3rX2rXrl1q06ZN6u6771Z+fn7qt99+a/fzbc17DccotQcffFDt2rVLvfnmm82OUnNWXKVU3d/BiBEj1HXXXac2bdqkduzY0WLcjsb++OOPldlsVm+++WaD+9Qvg3ZF3DfeeEN9/fXXas+ePWrPnj3q3XffVcHBwerPf/6zS+OerC0VOh2J+49//EN9+eWXKj09XW3btk3dc889ymg0tmqKTUfilpSUqMTERHXllVeqHTt2qGXLlqmUlBR16623uvw5O9xwww0NzlW7Ku4XX3yhfH191ZtvvqnMZrN666231N69e9WKFStUt27dVGBgYKPvt1vaOVdKGzkYHx+vvv/+e7Vjxw41c+ZMFR4eXlcJmJeXpwIDA9Xdd9+tMjIy1Mcff6zi4+PbvHN+2223qdNPP13t379f5eTkNNs7xpkkOdeZ4+zt+eeff8rnJk2apIBTfjmjlX9TcZ988slGY9b/ZnVF3MWLF6uxY8eq0NBQ5efnp1JSUtTDDz/s1ASuub/r+pydnDcV9/vvv1dDhw5VQUFBKjAwUA0ZMkT9+9//dtp//uaeb1FRkbr55ptVWFiYioiIUJdddlmDUWOuiquU1ijruuuuc0qs1sb96aef1Pjx41VoaKgKDw9XZ555ZrNloM6MPWXKlLrv69GjR6vvvvvO6TFa81qxZcsWNWHCBGWxWFRCQoJ6/vnn3RK3R48ejd7H1bEbG4MC2qxVV8b95JNP1PDhw+v+Xw8YMEA9++yzrWpu6cyfB21JzjsS94MPPlD9+/dXAQEBKiQkRI0aNUotWLDA5XGV0poaXX755SooKEjFxsaqWbNmtXpEUUdjW61WlZiY2OajXx2NO2/ePDVs2DAVGBiooqOj1cUXX6x27drl0rg5OTlqzJgxKjAwUAUEBKipU6c2e9a8NXFb+17D0ejP19dX9e7dW73//vtuiduR90Edid3Uv8XMmTNdGve1115TAwcOrPt/PGzYMPXWW2+16v2IM99PtiU570jcF154QfXp00f5+fmpiIgINXnyZLV48WKXx1VK2zA466yzlL+/v0pMTFT3339/q/vfdDR2YWGh8vf3b/MRxvbEvfDCC+vu/9prr6kBAwYof39/FRcXp6ZNm6YA9eqrrypoe3JeUVGh7rrrLhUVFdXoKDWltDPmycnJyt/fX1144YXq7bffbvDeozXJ+e7du9WYMWPqGlW7a5SaQakOzG0SQgghhBBCCCFEh8mZcyGEEEIIIYQQQmeSnAshhBBCCCGEEDqT5FwIIYQQQgghhNCZJOdCCCGEEEIIIYTOJDkXQgghhBBCCCF0Jsm5EEIIIYQQQgihM0nOhRBCCCGEEEIInUlyLoQQQgghhBBC6EyScyGEEEIAMGvWLC699FK9lyGEEEJ0SWa9FyCEEEII1zMYDM1+/sknn+TVV19FKeWmFQkhhBCiPknOhRBCiC7g2LFjdbfnz5/PE088we7du+s+FhQURFBQkB5LE0IIIQRS1i6EEEJ0Cd26dav7FRoaisFgaPCxoKCgU8raJ0+ezF133cW9995LeHg4sbGxvPPOO5SVlXHTTTcRHBxMcnIy33//fYNY27dv57zzziMoKIjY2FhmzJhBbm6um5+xEEII4V0kORdCCCFEkz788EOioqJYu3Ytd911F3feeSdXXXUV48aNY+PGjZxzzjnMmDGD8vJyAAoLCznzzDMZNmwY69ev54cffiA7O5vp06fr/EyEEEIIzybJuRBCCCGaNGTIEB5//HFSUlJ49NFH8fPzIyoqittuu42UlBSeeOIJ8vLy2Lp1KwBvvPEGw4YN49lnn6Vfv34MGzaM2bNns2TJEvbs2aPzsxFCCCE8l5w5F0IIIUSTBg8eXHfbZDIRGRnJoEGD6j4WGxsLwPHjxwHYsmULS5YsafT8+t69e+nbt6+LVyyEEEJ4J0nOhRBCCNEkHx+fBn82GAwNPuboAm+z2QAoLS3loosu4oUXXjjlseLi4ly4UiGEEMK7SXIuhBBCCKcZPnw4CxcupGfPnpjN8jZDCCGEaC05cy6EEEIIp/nDH/5Afn4+1157LevWrWPv3r38+OOP3HTTTVitVr2XJ4QQQngsSc6FEEII4TTx8fGsXLkSq9XKOeecw6BBg7j33nsJCwvDaJS3HUIIIURTDEoppfcihBBCCCGEEEKIrkwuYQshhBBCCCGEEDqT5FwIIYQQQgghhNCZJOdCCCGEEEIIIYTOJDkXQgghhBBCCCF0Jsm5EEIIIYQQQgihM0nOhRBCCCGEEEIInUlyLoQQQgghhBBC6EyScyGEEEIIIYQQQmeSnAshhBBCCCGEEDqT5FwIIYQQQgghhNCZJOdCCCGEEEIIIYTO/h9zPbooPUCL1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_test.iloc[0], label='Original ECG')\n",
    "plt.plot(predictions[0], label='Reconstructed ECG')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.legend()\n",
    "plt.title('Normal ECG')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c61e421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x475 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "plt.figure(figsize = (6, 4.75))\n",
    "sns.heatmap(confusion_matrix(anomalies, anomalies), annot = True, annot_kws = {\"size\": 16}, fmt = 'd')\n",
    "\n",
    "plt.xlabel(\"Predicted label\", fontsize = 14)\n",
    "plt.ylabel(\"True label\", fontsize = 14)\n",
    "plt.title(\"Confusion Matrix\", fontsize = 14)\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d57de4c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lab_env",
   "language": "python",
   "name": "lab_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}