diff --git a/Adegoke_Toluwani_Bolu/LogReg Algo challenge.ipynb b/Adegoke_Toluwani_Bolu/LogReg Algo challenge.ipynb
new file mode 100644
index 0000000..4f75654
--- /dev/null
+++ b/Adegoke_Toluwani_Bolu/LogReg Algo challenge.ipynb	
@@ -0,0 +1,1709 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'C:\\\\Users\\\\USER\\\\Desktop\\\\PYTHON\\\\LogReg Algo challenge'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "os.chdir('C:\\\\Users\\\\USER\\\\Desktop\\\\PYTHON\\\\LogReg Algo challenge\\\\Python')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer=pd.read_csv('data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>Unnamed: 32</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>843786</td>\n",
+       "      <td>M</td>\n",
+       "      <td>12.45</td>\n",
+       "      <td>15.70</td>\n",
+       "      <td>82.57</td>\n",
+       "      <td>477.1</td>\n",
+       "      <td>0.12780</td>\n",
+       "      <td>0.17000</td>\n",
+       "      <td>0.15780</td>\n",
+       "      <td>0.08089</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.75</td>\n",
+       "      <td>103.40</td>\n",
+       "      <td>741.6</td>\n",
+       "      <td>0.1791</td>\n",
+       "      <td>0.5249</td>\n",
+       "      <td>0.5355</td>\n",
+       "      <td>0.1741</td>\n",
+       "      <td>0.3985</td>\n",
+       "      <td>0.12440</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>844359</td>\n",
+       "      <td>M</td>\n",
+       "      <td>18.25</td>\n",
+       "      <td>19.98</td>\n",
+       "      <td>119.60</td>\n",
+       "      <td>1040.0</td>\n",
+       "      <td>0.09463</td>\n",
+       "      <td>0.10900</td>\n",
+       "      <td>0.11270</td>\n",
+       "      <td>0.07400</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.66</td>\n",
+       "      <td>153.20</td>\n",
+       "      <td>1606.0</td>\n",
+       "      <td>0.1442</td>\n",
+       "      <td>0.2576</td>\n",
+       "      <td>0.3784</td>\n",
+       "      <td>0.1932</td>\n",
+       "      <td>0.3063</td>\n",
+       "      <td>0.08368</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>84458202</td>\n",
+       "      <td>M</td>\n",
+       "      <td>13.71</td>\n",
+       "      <td>20.83</td>\n",
+       "      <td>90.20</td>\n",
+       "      <td>577.9</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0.16450</td>\n",
+       "      <td>0.09366</td>\n",
+       "      <td>0.05985</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.14</td>\n",
+       "      <td>110.60</td>\n",
+       "      <td>897.0</td>\n",
+       "      <td>0.1654</td>\n",
+       "      <td>0.3682</td>\n",
+       "      <td>0.2678</td>\n",
+       "      <td>0.1556</td>\n",
+       "      <td>0.3196</td>\n",
+       "      <td>0.11510</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>844981</td>\n",
+       "      <td>M</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>21.82</td>\n",
+       "      <td>87.50</td>\n",
+       "      <td>519.8</td>\n",
+       "      <td>0.12730</td>\n",
+       "      <td>0.19320</td>\n",
+       "      <td>0.18590</td>\n",
+       "      <td>0.09353</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.73</td>\n",
+       "      <td>106.20</td>\n",
+       "      <td>739.3</td>\n",
+       "      <td>0.1703</td>\n",
+       "      <td>0.5401</td>\n",
+       "      <td>0.5390</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.4378</td>\n",
+       "      <td>0.10720</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>84501001</td>\n",
+       "      <td>M</td>\n",
+       "      <td>12.46</td>\n",
+       "      <td>24.04</td>\n",
+       "      <td>83.97</td>\n",
+       "      <td>475.9</td>\n",
+       "      <td>0.11860</td>\n",
+       "      <td>0.23960</td>\n",
+       "      <td>0.22730</td>\n",
+       "      <td>0.08543</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40.68</td>\n",
+       "      <td>97.65</td>\n",
+       "      <td>711.4</td>\n",
+       "      <td>0.1853</td>\n",
+       "      <td>1.0580</td>\n",
+       "      <td>1.1050</td>\n",
+       "      <td>0.2210</td>\n",
+       "      <td>0.4366</td>\n",
+       "      <td>0.20750</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "5    843786         M        12.45         15.70           82.57      477.1   \n",
+       "6    844359         M        18.25         19.98          119.60     1040.0   \n",
+       "7  84458202         M        13.71         20.83           90.20      577.9   \n",
+       "8    844981         M        13.00         21.82           87.50      519.8   \n",
+       "9  84501001         M        12.46         24.04           83.97      475.9   \n",
+       "\n",
+       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0          0.11840           0.27760         0.30010              0.14710   \n",
+       "1          0.08474           0.07864         0.08690              0.07017   \n",
+       "2          0.10960           0.15990         0.19740              0.12790   \n",
+       "3          0.14250           0.28390         0.24140              0.10520   \n",
+       "4          0.10030           0.13280         0.19800              0.10430   \n",
+       "5          0.12780           0.17000         0.15780              0.08089   \n",
+       "6          0.09463           0.10900         0.11270              0.07400   \n",
+       "7          0.11890           0.16450         0.09366              0.05985   \n",
+       "8          0.12730           0.19320         0.18590              0.09353   \n",
+       "9          0.11860           0.23960         0.22730              0.08543   \n",
+       "\n",
+       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+       "0  ...          17.33           184.60      2019.0            0.1622   \n",
+       "1  ...          23.41           158.80      1956.0            0.1238   \n",
+       "2  ...          25.53           152.50      1709.0            0.1444   \n",
+       "3  ...          26.50            98.87       567.7            0.2098   \n",
+       "4  ...          16.67           152.20      1575.0            0.1374   \n",
+       "5  ...          23.75           103.40       741.6            0.1791   \n",
+       "6  ...          27.66           153.20      1606.0            0.1442   \n",
+       "7  ...          28.14           110.60       897.0            0.1654   \n",
+       "8  ...          30.73           106.20       739.3            0.1703   \n",
+       "9  ...          40.68            97.65       711.4            0.1853   \n",
+       "\n",
+       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+       "0             0.6656           0.7119                0.2654          0.4601   \n",
+       "1             0.1866           0.2416                0.1860          0.2750   \n",
+       "2             0.4245           0.4504                0.2430          0.3613   \n",
+       "3             0.8663           0.6869                0.2575          0.6638   \n",
+       "4             0.2050           0.4000                0.1625          0.2364   \n",
+       "5             0.5249           0.5355                0.1741          0.3985   \n",
+       "6             0.2576           0.3784                0.1932          0.3063   \n",
+       "7             0.3682           0.2678                0.1556          0.3196   \n",
+       "8             0.5401           0.5390                0.2060          0.4378   \n",
+       "9             1.0580           1.1050                0.2210          0.4366   \n",
+       "\n",
+       "   fractal_dimension_worst  Unnamed: 32  \n",
+       "0                  0.11890          NaN  \n",
+       "1                  0.08902          NaN  \n",
+       "2                  0.08758          NaN  \n",
+       "3                  0.17300          NaN  \n",
+       "4                  0.07678          NaN  \n",
+       "5                  0.12440          NaN  \n",
+       "6                  0.08368          NaN  \n",
+       "7                  0.11510          NaN  \n",
+       "8                  0.10720          NaN  \n",
+       "9                  0.20750          NaN  \n",
+       "\n",
+       "[10 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.duplicated of            id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+       "0    ...          17.33           184.60      2019.0           0.16220   \n",
+       "1    ...          23.41           158.80      1956.0           0.12380   \n",
+       "2    ...          25.53           152.50      1709.0           0.14440   \n",
+       "3    ...          26.50            98.87       567.7           0.20980   \n",
+       "4    ...          16.67           152.20      1575.0           0.13740   \n",
+       "..   ...            ...              ...         ...               ...   \n",
+       "564  ...          26.40           166.10      2027.0           0.14100   \n",
+       "565  ...          38.25           155.00      1731.0           0.11660   \n",
+       "566  ...          34.12           126.70      1124.0           0.11390   \n",
+       "567  ...          39.42           184.60      1821.0           0.16500   \n",
+       "568  ...          30.37            59.16       268.6           0.08996   \n",
+       "\n",
+       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+       "0              0.66560           0.7119                0.2654          0.4601   \n",
+       "1              0.18660           0.2416                0.1860          0.2750   \n",
+       "2              0.42450           0.4504                0.2430          0.3613   \n",
+       "3              0.86630           0.6869                0.2575          0.6638   \n",
+       "4              0.20500           0.4000                0.1625          0.2364   \n",
+       "..                 ...              ...                   ...             ...   \n",
+       "564            0.21130           0.4107                0.2216          0.2060   \n",
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+       "566            0.30940           0.3403                0.1418          0.2218   \n",
+       "567            0.86810           0.9387                0.2650          0.4087   \n",
+       "568            0.06444           0.0000                0.0000          0.2871   \n",
+       "\n",
+       "     fractal_dimension_worst  Unnamed: 32  \n",
+       "0                    0.11890          NaN  \n",
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+       "..                       ...          ...  \n",
+       "564                  0.07115          NaN  \n",
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+       "567                  0.12400          NaN  \n",
+       "568                  0.07039          NaN  \n",
+       "\n",
+       "[569 rows x 33 columns]>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
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+    "cancer.duplicated"
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0    False      False        False         False           False      False   \n",
+       "1    False      False        False         False           False      False   \n",
+       "2    False      False        False         False           False      False   \n",
+       "3    False      False        False         False           False      False   \n",
+       "4    False      False        False         False           False      False   \n",
+       "..     ...        ...          ...           ...             ...        ...   \n",
+       "564  False      False        False         False           False      False   \n",
+       "565  False      False        False         False           False      False   \n",
+       "566  False      False        False         False           False      False   \n",
+       "567  False      False        False         False           False      False   \n",
+       "568  False      False        False         False           False      False   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0              False             False           False                False   \n",
+       "1              False             False           False                False   \n",
+       "2              False             False           False                False   \n",
+       "3              False             False           False                False   \n",
+       "4              False             False           False                False   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564            False             False           False                False   \n",
+       "565            False             False           False                False   \n",
+       "566            False             False           False                False   \n",
+       "567            False             False           False                False   \n",
+       "568            False             False           False                False   \n",
+       "\n",
+       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+       "0    ...          False            False       False             False   \n",
+       "1    ...          False            False       False             False   \n",
+       "2    ...          False            False       False             False   \n",
+       "3    ...          False            False       False             False   \n",
+       "4    ...          False            False       False             False   \n",
+       "..   ...            ...              ...         ...               ...   \n",
+       "564  ...          False            False       False             False   \n",
+       "565  ...          False            False       False             False   \n",
+       "566  ...          False            False       False             False   \n",
+       "567  ...          False            False       False             False   \n",
+       "568  ...          False            False       False             False   \n",
+       "\n",
+       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+       "0                False            False                 False           False   \n",
+       "1                False            False                 False           False   \n",
+       "2                False            False                 False           False   \n",
+       "3                False            False                 False           False   \n",
+       "4                False            False                 False           False   \n",
+       "..                 ...              ...                   ...             ...   \n",
+       "564              False            False                 False           False   \n",
+       "565              False            False                 False           False   \n",
+       "566              False            False                 False           False   \n",
+       "567              False            False                 False           False   \n",
+       "568              False            False                 False           False   \n",
+       "\n",
+       "     fractal_dimension_worst  Unnamed: 32  \n",
+       "0                      False         True  \n",
+       "1                      False         True  \n",
+       "2                      False         True  \n",
+       "3                      False         True  \n",
+       "4                      False         True  \n",
+       "..                       ...          ...  \n",
+       "564                    False         True  \n",
+       "565                    False         True  \n",
+       "566                    False         True  \n",
+       "567                    False         True  \n",
+       "568                    False         True  \n",
+       "\n",
+       "[569 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                           0\n",
+       "diagnosis                    0\n",
+       "radius_mean                  0\n",
+       "texture_mean                 0\n",
+       "perimeter_mean               0\n",
+       "area_mean                    0\n",
+       "smoothness_mean              0\n",
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+       "fractal_dimension_mean       0\n",
+       "radius_se                    0\n",
+       "texture_se                   0\n",
+       "perimeter_se                 0\n",
+       "area_se                      0\n",
+       "smoothness_se                0\n",
+       "compactness_se               0\n",
+       "concavity_se                 0\n",
+       "concave points_se            0\n",
+       "symmetry_se                  0\n",
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+       "radius_worst                 0\n",
+       "texture_worst                0\n",
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+       "area_worst                   0\n",
+       "smoothness_worst             0\n",
+       "compactness_worst            0\n",
+       "concavity_worst              0\n",
+       "concave points_worst         0\n",
+       "symmetry_worst               0\n",
+       "fractal_dimension_worst      0\n",
+       "Unnamed: 32                569\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>843786</td>\n",
+       "      <td>M</td>\n",
+       "      <td>12.45</td>\n",
+       "      <td>15.70</td>\n",
+       "      <td>82.57</td>\n",
+       "      <td>477.1</td>\n",
+       "      <td>0.12780</td>\n",
+       "      <td>0.17000</td>\n",
+       "      <td>0.15780</td>\n",
+       "      <td>0.08089</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.75</td>\n",
+       "      <td>103.40</td>\n",
+       "      <td>741.6</td>\n",
+       "      <td>0.1791</td>\n",
+       "      <td>0.5249</td>\n",
+       "      <td>0.5355</td>\n",
+       "      <td>0.1741</td>\n",
+       "      <td>0.3985</td>\n",
+       "      <td>0.12440</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>844359</td>\n",
+       "      <td>M</td>\n",
+       "      <td>18.25</td>\n",
+       "      <td>19.98</td>\n",
+       "      <td>119.60</td>\n",
+       "      <td>1040.0</td>\n",
+       "      <td>0.09463</td>\n",
+       "      <td>0.10900</td>\n",
+       "      <td>0.11270</td>\n",
+       "      <td>0.07400</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.66</td>\n",
+       "      <td>153.20</td>\n",
+       "      <td>1606.0</td>\n",
+       "      <td>0.1442</td>\n",
+       "      <td>0.2576</td>\n",
+       "      <td>0.3784</td>\n",
+       "      <td>0.1932</td>\n",
+       "      <td>0.3063</td>\n",
+       "      <td>0.08368</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>84458202</td>\n",
+       "      <td>M</td>\n",
+       "      <td>13.71</td>\n",
+       "      <td>20.83</td>\n",
+       "      <td>90.20</td>\n",
+       "      <td>577.9</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0.16450</td>\n",
+       "      <td>0.09366</td>\n",
+       "      <td>0.05985</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.14</td>\n",
+       "      <td>110.60</td>\n",
+       "      <td>897.0</td>\n",
+       "      <td>0.1654</td>\n",
+       "      <td>0.3682</td>\n",
+       "      <td>0.2678</td>\n",
+       "      <td>0.1556</td>\n",
+       "      <td>0.3196</td>\n",
+       "      <td>0.11510</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>844981</td>\n",
+       "      <td>M</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>21.82</td>\n",
+       "      <td>87.50</td>\n",
+       "      <td>519.8</td>\n",
+       "      <td>0.12730</td>\n",
+       "      <td>0.19320</td>\n",
+       "      <td>0.18590</td>\n",
+       "      <td>0.09353</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.73</td>\n",
+       "      <td>106.20</td>\n",
+       "      <td>739.3</td>\n",
+       "      <td>0.1703</td>\n",
+       "      <td>0.5401</td>\n",
+       "      <td>0.5390</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.4378</td>\n",
+       "      <td>0.10720</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>84501001</td>\n",
+       "      <td>M</td>\n",
+       "      <td>12.46</td>\n",
+       "      <td>24.04</td>\n",
+       "      <td>83.97</td>\n",
+       "      <td>475.9</td>\n",
+       "      <td>0.11860</td>\n",
+       "      <td>0.23960</td>\n",
+       "      <td>0.22730</td>\n",
+       "      <td>0.08543</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40.68</td>\n",
+       "      <td>97.65</td>\n",
+       "      <td>711.4</td>\n",
+       "      <td>0.1853</td>\n",
+       "      <td>1.0580</td>\n",
+       "      <td>1.1050</td>\n",
+       "      <td>0.2210</td>\n",
+       "      <td>0.4366</td>\n",
+       "      <td>0.20750</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "5    843786         M        12.45         15.70           82.57      477.1   \n",
+       "6    844359         M        18.25         19.98          119.60     1040.0   \n",
+       "7  84458202         M        13.71         20.83           90.20      577.9   \n",
+       "8    844981         M        13.00         21.82           87.50      519.8   \n",
+       "9  84501001         M        12.46         24.04           83.97      475.9   \n",
+       "\n",
+       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0          0.11840           0.27760         0.30010              0.14710   \n",
+       "1          0.08474           0.07864         0.08690              0.07017   \n",
+       "2          0.10960           0.15990         0.19740              0.12790   \n",
+       "3          0.14250           0.28390         0.24140              0.10520   \n",
+       "4          0.10030           0.13280         0.19800              0.10430   \n",
+       "5          0.12780           0.17000         0.15780              0.08089   \n",
+       "6          0.09463           0.10900         0.11270              0.07400   \n",
+       "7          0.11890           0.16450         0.09366              0.05985   \n",
+       "8          0.12730           0.19320         0.18590              0.09353   \n",
+       "9          0.11860           0.23960         0.22730              0.08543   \n",
+       "\n",
+       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+       "0  ...          17.33           184.60      2019.0            0.1622   \n",
+       "1  ...          23.41           158.80      1956.0            0.1238   \n",
+       "2  ...          25.53           152.50      1709.0            0.1444   \n",
+       "3  ...          26.50            98.87       567.7            0.2098   \n",
+       "4  ...          16.67           152.20      1575.0            0.1374   \n",
+       "5  ...          23.75           103.40       741.6            0.1791   \n",
+       "6  ...          27.66           153.20      1606.0            0.1442   \n",
+       "7  ...          28.14           110.60       897.0            0.1654   \n",
+       "8  ...          30.73           106.20       739.3            0.1703   \n",
+       "9  ...          40.68            97.65       711.4            0.1853   \n",
+       "\n",
+       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+       "0             0.6656           0.7119                0.2654          0.4601   \n",
+       "1             0.1866           0.2416                0.1860          0.2750   \n",
+       "2             0.4245           0.4504                0.2430          0.3613   \n",
+       "3             0.8663           0.6869                0.2575          0.6638   \n",
+       "4             0.2050           0.4000                0.1625          0.2364   \n",
+       "5             0.5249           0.5355                0.1741          0.3985   \n",
+       "6             0.2576           0.3784                0.1932          0.3063   \n",
+       "7             0.3682           0.2678                0.1556          0.3196   \n",
+       "8             0.5401           0.5390                0.2060          0.4378   \n",
+       "9             1.0580           1.1050                0.2210          0.4366   \n",
+       "\n",
+       "   fractal_dimension_worst  Unnamed: 32  \n",
+       "0                  0.11890          NaN  \n",
+       "1                  0.08902          NaN  \n",
+       "2                  0.08758          NaN  \n",
+       "3                  0.17300          NaN  \n",
+       "4                  0.07678          NaN  \n",
+       "5                  0.12440          NaN  \n",
+       "6                  0.08368          NaN  \n",
+       "7                  0.11510          NaN  \n",
+       "8                  0.10720          NaN  \n",
+       "9                  0.20750          NaN  \n",
+       "\n",
+       "[10 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer = cancer.drop(['Unnamed: 32', 'id'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1e003a982b0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3Pv8RGnhqw67Y76RNid6urjGANxDmmic28ZUH1rP41e0YOpInYtn6nQnZh5fM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\nUMrv397D3BvOYn+lhzsXbojxF9Yzc+yZmIwKo16N9hWeX/09fyvoxQvX9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5QAACAASURB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iW8XrJIienQ9TzxRbPv+uaBs//05JJqVkwKp7ofvFjdVEexNUWthQM7oQGDYvZsd3joxe7ZEv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M72MOhxWHOPdHQGPgJr75P97Rz5P/ft6hxvZOTBtS/Vp2JHex+WV6ucbs+rd5QL+sBFfwJn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NZlPJcv/DSKdvDITpX2gDmT7FjpoNUNAHKSUNtnxyzPHSZG/hr4WG7dBrWIdd4qVvQ4Sd\nhaBDRuOW3R/QjvQrzsFu0fjHukrTWD7YiOqhEA4qB7yrQDnjdV0VTk58FXyVytHuKY1njw27M/04\n7a+Nv/duTqhtju7X68y4oWzss2gSYspbbBj1KnbCeIMaFj2fXOcmZpwksM4flSryeNxFsZRt6zVf\ncsWpPXlp1RZyXTZToMskRlvTsHdKKR+RUr4vpfyn8bO7g6SUF0kpu0kpbVLKYinl36WU1VLK06SU\nPaOvNdF9pZTyainlEVLKvi2FvQ5UvP4g1Y3NrFi7DS3kAz0Ck5aoNBOIf9n/eQ9EwkgpocvhZpso\nk1Ra6a9MOJpmrGnq2Sk6GvJ6pt/XmZv8PhKC5gZE0AcLRqu06hcmxOvnjf09pUihxc/ZSuq2Vv0d\nT52pVFu/iRTzva03kTHzk3uGj5mvnneDRC91i/t1ahG0qm+wvfUHujWtp499J31yIpR8+Qh93z4f\nT8P3sVR0swXUgUG1L8iD762nW44NS7N312OmHiHs8JBltoky2ZtUrlOvnkM65PTbGnU+2R7hpBIH\nIYeHjIa9KwDlsFoo657N8m8qaIt4rMl+QGJpl2+nyl576nR46Bj1WrkWImH1+vfT4f8NUJo7wgpj\nF8Tn7jT97hk1S2038JRChketO4z9DMd78aB4KvawOxEhP96mEL+Y/T3nPPMtl89fw42n5GNb3KKM\n8bVrlKGcsFb5b4WfGW+s5ben9aJb9k/vjKDrkqqGZrbWNlHV0Iyum8/+gUZbI8urhBB3oVKlE9Ow\nd9k6ymT3BIIRnvrXBh47IwMxN6Hlyeg5Sgyhrlz1VG6sVF48WwaaxYyEmaTBSIFq6bm1JhiDmqZq\nlSMh1Yu2pXJwopfXU6r2s7lAD7fuFY4aJ6J+a3wfY2JMFPwa+Sj843byGiu59YzFaDYrp8/6mv4l\n2dx6xmIKXYLKJkkfZy6ZjZXx66x8CM6dnT4CLQS8f2equNjoOVC1DpEQWTdbQB0YBMMRrju9J10D\nPyAWTdjlmCk1K8Keaf6NTfYuVd+o15YOw3bi3Y1hAE7uDsG6IjIaNu/miPZnQKmHp1duZEOVjyML\n3Xv9+iYdjK6Dvwbqt8Xb8bVUpjay16a+nZrVNvccuOQ9CNSr70H552pcjqZC48pTXV+Mud5YI7x3\nGwz9LfroeSoV21+rHKEt5/gx8yn2ZMdut7zWj9saSR8EyCxQa51orfId71RRXuvnyvmreOHyE4lI\nSYbNQn6mAw2ZNnJuCoQeHLQ1DNkfOBG4E3gg+nN/W2/KBDRN8vDwbmjGYATRlNjJ6sv9wgQ1mIyZ\nj7RkIJx5+/aGTTovRn/lRM/tb55T2w0iYTX5LbhAiWosvUXVE/UermqW//Nc/Nix88GWATlR3b0x\n85LOLcfOh/zeavL7x+3wxXPKYDEmxk9mIye/CZevgHGLwe5SKVnuQvoW2Sl02ynOdbJmSz0XzNvA\nL2Z/z+/e2MZXNRrV58yJX6uxksawRnj0/NTfTVjSpmizeLK6VmJknbiSZY9cFwVZDnOS2w/JsGsc\n6fJjXTRhl2OmHDOfiLsAq7WtvmITkz2kch1YM9QivQN4d2OIUjf0cEPIWbjXI8tATAn7/XWVu9nT\nZL/D0D/ZtjpuKEOrnTGIhFK3uwvVdotNrSWMdcHSW1Sa9Xu3KY2Tcx+Daz6Hcx5R64j1byLdXamw\ndCUy+U2luXLW3alz/KIJqo9zlOJcJ41hS9qMNukuItx9IKvOWMyl7/hYs6UeUAb29roAJ9+7gvNn\nfcim6kZkusi5rpsCoQcJbVotSCl/2V43YhInHA5TGNyGpmnpB6C8nnDtf0CzIR1uhCPbTL82aR1N\nUxPLpctS64lATYB1W5InPyNNadxiWLMAhv0NzrxTTWKfPQ2lg6M9ErtDRo5KgdLDoOuIVXOg5+kq\n8lvQRxmnIb/qA65pSCkJaRnYmxvjKpjR1Ct7UwXZso6Z447j6oX/iXlq77uwH6+uLueyQTnkTnqd\nsBT84NW5+SWl9nr7WS9TVujAYnOo362pqtUUbZy5qZF1k/0eIcGqB9L/zfN7RcdMC9izsFoz9s1N\nmhzcVH0T61zR3tQ1Sz7ZFuH8I9T7oLMIz/Z/oYUD6Hvxec93Oyjp4mT5NxVcdvLhe+26JnsBQ//k\n3MeS20u6lkXYPgAAIABJREFU8tNnpFlsyZlfxYNg+P+p74HNpcbjyW+orDZhUY1iz7obKTRE5Vql\nn1Ierbz0lCKERrfmH+H5aAvVi5emHe8DwTD9S7K5/fSu9MyzscOvo507l5xX4+sNfcwCtpPP1+U+\nZryxLUWzxDB2y2v9NNbsQLydXvclGM4yBUIPAn7WiC2EmBB9/UO6n/a9xYOHcFinsiGADNShOdyq\n5i6NNwxhgUgQqYdNQ9mk7TRVRWuP0hgZVgeccLl6v/NbWPonZQgvvQXWzINIELybYPsXsPRWCNTB\nkb9Sx53/FAz5LdT+CM31sHM9NFQi3r4Je6heKVwmTj5LrgIEWcEqjnJ6eX1qL9685iRmXnQcg/JD\n3PWrLI4Ib0B7+RLs80bQVVOp4VWNIbSsQursReiuQvV9cBUo8bJ0359QU2pk3WS/JhSKkC38CM3W\nypipwWdPI5tqER3UssfEZLdUfgM5HZOC/f7mMBEJJ3ZV74OuQgAce1nkC6B/SS6fb6qlPhDa69c2\n6UAM/ROjzMpQtF44OjUjbcw8yCxKzmr79R0q/frN65XOyctRo7dxB7w0FRqroHYT4q0bwOKIp2IP\nuTZqVEcg2KCi0xBLoU7CU4rT6eTF0V3pq/1Ihh7gULckO9uDnPIWtVevZdUZi7lyWQCvX2dgqYfZ\nEwcmaZbcc0E/Hl+xIX5Ku552fSSDfrqyk9en9qJ/STz1u6VAqFnTvP/zcyPLmdFXc9XRToTDOptq\nGunhDGMN1ivjYsU9qfWdY+Yqb50mELZM01A22T27ax0VDsYnnZa1xxVfqwlw1CwVqTVSm92FMOgS\nmH9e/JznPg5CqJZS3s1w8TsqvTuxDdWoWXDiFUoQJJ1x7sqDly/D7t2M3VOKZ9yL6OFmLM8k1O1H\na5w9SyYzb+JSPquyEoro3PTSl1x3Ru9orZCm0sTHLohHzD2l6n12d3B2af2705qqp0mnJBzWCYYC\nuJq9ynkzalbcEWM8l1KHEy5TzkWLmX5tsg/w10bH2ZIOOf27G0N0yYBeHvU+6CwCIKNhM35Pzw65\nZmv0L/Hw2hfb+PD7nZx5TLe9em2TDsTQPzG0RyLNqWnQRkba8tvhnAdVdtmk15RDPqsbzBmR6iSf\n/AaMnAmB2vjY7atQpVz5vZWD2zguYQ2QVgNl1Cxcdgti5w744V9wzPmwcDQi+nnOmHksXquzdG0V\n/93eyCtXDeWortm8ctVQ/KEIwbDOve98w5ot3tiv7Q1qlKTTRqn+FsuC0fT1lPLMqDlMfUs57xMF\nQs2a5gODn7UClFLOFkJYgHop5V9b/rTzPR4U1AeCHMIOMiKNiEhI1detfzMufHDxUhj/kjJYZEQt\n6DM8+/q2TfYHWmsd1VSl3lvsqia5pSLlyEfVZGRMaFZnPLV56O9VLWjiOV+9ApoSjODMgvTR4+zu\nrXqEY7VKY+eDuxBRtwnLojTp4dG649r6BqY++xnXLFzDBQNLkmuFLFYoOkaln//+a/VaWKa+P/Vb\n1eStt+h4ZzgW0tQmmXROfM1BXMEaNW4uuFC1Lxt2pyoNGP6AcnTU/KgydRzZuz2fiUmHUPODes3u\n0e6nbo5IVmwOc0JRvHNk0KWMZUfj3q9bPrLQjcOq8dGG6r1+bZMOxNA/aaxUa1PPoemd3k071fo1\n5IeGbapGOeRXTst0+9dtUdHpjFxVozzlTTXHr3xIOeAXTUy/BohqoDBucUwNm+V/QYT8aq3Rf3xK\neylt0UT+/Eul8WOkS1f7ggTDETJsGg6rYOrQw5Iize4uXZEtdV9GzYp35/BuxrNkMs+PP5JXrhqa\nZAibNc0HBj/bxS6ljAghRgIPtuP9HJTousQtfVjtLlV4Z7HGv9zln8d6wPHb1SoFWw+rCdeMdpn8\nFHbVOkrXlfLkiVfCx4+piSr3EKj4r5oMjXoh72aV/mT0SGylXzI2l/p38SCglZp7PaKM87Hz472Y\nPaUqbWv1PPjokbixbm9FOCRad1zZpNKZymv9eJy21FohTVPp2KCi3HVblJHsq1L38Mtb4hF22H1P\napNOhR6JkB2uQegh9Tf0blY/xpgJatx8dzqM+D9zzDTZd9T8qF6z2j/S+un2CE1hGJwwREVsWUQs\nzn2iiG21aPQuyuJD01g+sGipfyJE690weg8HzQ5hPyCjGZGW1vd3F6oIsh4V/7I64Mx71TG70h45\n8Up47eqk2ubYMa0c69SU87s410lEl5w3a2VS1Peoblm8cPmJWDToQj026UW4uhKc8i4VtfUUZmXg\nePXi+DWj53UQpiAruR1hMBwxa5oPANq6cvhQCPGoEOIXQogBxk+73NlBgq5L6poC2Bq3wts3qJqN\n6u/TR90sNhUVcxeZqYQmPx0jdSoRQ+CqqUqlUi//i0qx9pSo52/pLckTgTGh/ftBZdSGmlqvBzbq\nmBq2pd/HVwUn/y9s/Eh5hK/5XL1+9ZISDoO499iR0+p1qs+Zwx0rquhfks3rU3vRN6uB16f2wmVP\nM6wZEeO5I+O1VSdMU+2ljAg77L4ntUnnIfo3Fc8MU22hhNbquClPvRkyC/fNfZqYQDyynNW13U/9\nwZYwNg36JTbFEIKgq2ifKGIDlHXP5rvKRnY2Nu9+Z5P9B8MB7SlRAp8tI64jHwXvViUIGvJBzQYV\n5X31SrWGaNE9I5bBdtptEPTF65nfvF6tJ7RW1i+eUpj0Otgzk9tMjZoFRI341nR/NAtX/uIQ3r2s\nDy7/dh4e0Z3+JdmxqK+vOYLLrlHk/wH7M2cgHjoGnvwlVv9O7vhXHV9X+OPXTDxvGtFQu9USi1Ib\ntKxpNun8tNVYPgk4Grgds3XUHqPrkk3VjXjC1YgvF6vBJRIEzQYXPps8oIyZhxQWFdkzVXxN9oRd\ntY4yjMPyz+HbdwBNpRa1TMkeM095lE+9WT2DhWWq/jdxn7HzlVL7KTcqQ3fZbapeNGmfBcrba3NB\n0VEq9erRQeq15+nQtZ86T/GguMBYi3PIsfPxFxzHLSvD9C7MZPHYHvR1VuPa+RV9V9+Gp+H71LTp\npqpUte9PZsNpf4ZgUzwle1eOBZNOhfRVoa24C0bNhNxDwe+FCS+riAbEnklpz0IW9DGjyib7lpof\nlGqw1bH7ffeQD8rDHN0FMlr40IP7qH0UwNHdVcnDxz+Y0eUDlsRI8+++gMmvq8yJQ4fAnHPU3P7m\n9cp57i5UGT+ZeXDB0/D7r1RpoYzO1dnd05dtadZUA3vMPLVOJiqUNfyBeNlNRjYEG9U+axakPVZ8\n8xY3DM3GFWkk3wkDs2p5dmR+zGAur/VTXr4F0SLLTHthHA+OKOaJVfXJbSzTteOMkpdp58lJg5LS\nuhNrmk32D8zWUfuQhkAzpZYahMUOfS9Qg4uRknr+k2oRaERLbE6EsKgejSYme8KuWkcZxuFhJ8Og\nS6Hm+3g90rA7VapTqEkJzv3zXmUIL5qgJr7zn4RJS9T+violSPeLP0Dh0fF02GV/jp/HUwqfPKmM\nYqG1Lgyy9BZlrH8yWy0wP30SOek1wlJjc32YUJNGfqSeR0Ydir1pB2L+qCThD/H+nXDOg+iuwlgt\nUleasSRGjIsHqcjywtHJ4l8FR6lJr6UYmqmc3emQuo74xfXKwfjs8GQRxFP+qKLNrnwI+tBcufv6\ndk0Odqo3dEhUucKns75GZ+pRqZ+FXEVk7VytImza3o1kHZbvxmmz8NGGakb0675Xr23SAbQmfGl0\nn2jYBqGAMn5fmJA6tw+7U22XqCzJlmO21aXWFYnztHczeDeqzhsTX1HrhkgYPnwYfvxARZG/fB56\nnRkvAXNkK+e35xDkkKsQzQ3KiLa51FrGmgHd+iISrz96DjkuFwvGHsJ1b+3A6w/R3ZW+vtqpRfjT\n8KOpbA6hjXubSDCAy+UiI6dICYu2QNMEvYuyeOWqoQTDEexWC3mZdlPcaz+jTcayEKIIuBPoLqU8\nSwhRBgyRUv69Xe7uAEbXJRmheiyBGtDyUgUMXr5M1Y++fjVMeRMZbELkdIyKpslBQGLtroGuq4lr\n4qvKe/vOzUqB0lCXfGFCPE1q+V+VoMai6CQ4aqY63lDDNqj4Snl4jboko+beU6oULz96BLZ8qKLF\n6dKdgw3xyXXSEmhuRA77G5WWQp7590Yu69NM3utRYbHxi+NK28bx0UlZhoNJCpSvT+1F38RaqaG/\nTzXWXxivVDsL+rTek9qkc6DrCBlW9XCvXpn8d1w0SY2dS29BTnkT6e6OuSwx2efU/gjd+7f7aT8o\nDwMwII0/L+gsRNPD2Jt2EHS3v7DYrrBogt5dzbrlA4JdddSA5M9a6X0cc5hLPXW9u2iSMmhP+4sq\nCUusP87MV8Zw/TZYcnXyuZdcFTfCDS5eqozrQZcgmnYmrxEg/bph8WQYdieupbfw6OgFTP9wB/ll\nGWkVsMPCxrinPknpy/zKVXkUZKV3SGmaSKllNtm/aOsK8FlgKWC4Db8Fft/Gcx4UVPuC2EVEReT0\nSPrBJacHjJqFtNiVoWwu2E3aC2Py+/vp8P8GqLYMJ0xTn/3jdhXhvXipmogMoS9DCRtUylSi8rWB\nd7NqCzV6bmpdUrAx4foRdX4j5drYr2FH/Dx6BGb/AjF3JJU7tjHt+Jy4oQzKU5zu+pkFhIUtSYHy\nz8t24B2VkDaV+LskHttYAf7qeE2Wu8j83nVGfJUIGWn97xgdO7HY0aymvoPJPqa5Qc31HSDu9cGW\nMF0ccFgaofd4+6h9l4r9404fFfWBfXJ9k3ZiVx01Wn7WWqeLUJNaFzRVty4OuuQqlb1mHDNqFrwy\nTWWbObLi/ZUTj3MmZA15SpXqttGtI3GNUDxIrTdyD2vdmPduxrZ4PKPLMrhjRVXymiHqIKgl2xTs\nOghp6yoiX0q5SAhxM4CUMiyEMJ+Y3aBHIuRaAuCrVxtaUwi0ZoC7COHMMxfsJu1LU5UStxp2p0oN\ntGepdNaRM5Wy5Ef/D4Zep/Y97TYQGjKrO8J4TvVI672Z67cqpelxi5VB7a9VKdWDpsTFvxLTn42U\n6xOmKcMcVN2p1QFXfwqahT4WF1LK5Gv5a9Nf312UMqGt2VLP1Lfg+Snv4iDcuoqnr6pDWruYtC9S\nDyM0K1hb+TtaHKr1mCt/392kiYlBBylhR3TJv8rDDCxQQ1pLgk4Vbnb4ytv1uj+Vsm7xuuVRx5nj\n6n7L7oQv3YXxciupqxKtly9LKHGaD648eOuGqJBoK4rY3s1Kf+LaNSqSLHU4/a/qs3/epwzpBaOT\njws1xf898lH1RTAMcmON4C5U6w4jHby160d/r0KXoKoxRK27JzmXLENE4llmwheiONeZElk2BbsO\nbNpqgfmEEHlEq+yFECcCdW2+qwMUQ/laD3ixRppVDbIjWxkeo2al9nDTbMqQsdr27Y2bHHjoujJO\nl96iegkvHK0mizUL4LzZ8D/XQe0PKp0JAa9eiXhpavw5rd+Wvjfz6DlK2XL9mxDwqrqkpbeoWmZn\nXlz8q2Xq9Fl3K4O5/HNlKJ/yR3XszMEw7zxstRuwhX1x8SZQgmSjW3h+xy6AnBKElqpAWdUYot7S\nJa7i2VKgbOSj6ncSwuyr3AnRdUlVQzONTX7lSAwHVA1burHTYldjp9k1wKQzEFPCbl9j+eudOt7m\n9CnYAKGMPCQaGY37xlg+NC+TTIfF7Le8v7Mr4UubU6VPL71FzdlLrlbG6cRX4tlpK+5RBuwpf4SM\nHGVMp1PE9pSqMV1Y1Lp4ydXxNcQJ05SmSOJx5z6u5oKLl6ryr4pvwHOIuv74xWqNMPLR+LrDXQh2\ntxKCHL9YOe8Trx89b1FuNq9cNZRD8tyIrOQss7YKdhnz2NbaJqoamtF12Q5/IJOOpq0riT8ArwGH\nCyFWAgXAhW2+qwMQXZesr2igZ67AGvBDpFkZI/+8BzKLlCpvogiBuwhpcyHs7n196yYHAi3FOWQk\n1Wj9ZLZq6TTvvGThjXAAzn1MGdNfPq+e0/yecOpNsOJuNRlmFijP8cqHlMHrKYUMj5rAQk0qWvvm\n9TDsjvQe6kgQzrwLhv1NZVoY4hvG50uuQgx/ADnsb6r+dPBlkFOs0qZHzVTGkbsIckrAYiUv08KT\nkwbFUrFjE5rTooSfIiHI7qZqlI2+y0Z0+60bUvsvm+xTjPHz8w2VTOjrRIQDygBZuwQGXZI6dlrs\nCHvWvr5tExNFBxnL/4rWKx/Xmv6gZiWUkYejcUu7XvenommCPl2zzbrl/R2jo0ZizfKEV1SYLNSo\ntCMMcS7vZtV+smWtcMVXaq2w9BaYulQZ0wjVWuoftyuh0DHz4euXod+Y9E71ia8oQ7epWtUy12+L\nzv2FULsJDjlRlZQZ92gIgJ30u+TocvRzOXY+woh4G+uWsfOxWzQKMqNq275qldotI2B1omUW/GzB\nLmMea7ku6V2UZQp+dXLaaiyvBV4BmoAG4FVU3fLPQgjRG3ghYdPhwJ8BD3AZYDREvUVK+dbPvc6+\noNoXZGOFlz7UxpUCDdXrd/8Ey29XBrPVAZoVmZGjDGVzsW7SVtKJc0xakmq0HneRqvNJJ7yxYHTc\nA/uP22HUY8pAPutu5QEG+OxpJcTRf2LccF4zX33229Vw1j2AVNHh4y5SKVv+WhXNlcQnudYEQmwu\nhDVDGelGGyjjnt79k5psL10G7qL0CpROC1rl13FxEU+pShXPKY7//kZ9dsVXsXOZ7HuqfUFeW72Z\nG/pHEM8kOnPmw44vIatIPY82J9JiQ2TkmGOnSeeh5gflPLS72vW0/9wS5sgc8OxCOyjkzMexjyLL\noFKx523axDavn+4e5+4PMOl8tOyoYXMqfZH55yXPw8b82ZqeiDNXGa2+CrW2cBeqqO+5jysnttUO\nJSdAU0364/21EG5WbSmH/19c8MtTClPfgspv4o79lQ+pGuipS0GG1ZrFuzHJqBcvTFAG+NBr4Yy/\nKhG+N/+g1hK/eU5lfno3xVtaReuWtcKynyXYVe0LJmmpGH2dX7lqqCkA1slpq7E8F6hHKWIDXATM\nA0a3esQukFKuB44DEEJYgK0oY3wq8KCUcr/t4RwMRzizpwvRFFCeMT2i6kJfvkwZzE8PU19YzQpZ\n3U1D2aT9SCfOUf19at1OOrEkdyF0OVxFiI3a4zPvURG8BRckGN+vQb8Lkx1BIx+FqnVq4qnZoM4T\n9MEp/5+9M4+Pqyr///vMTCaZmSyTvUsaurCWVSiLoCCyi7TsbVmrIvpF9ItfF/wqP1RUBJWvqIiy\niFJ2kJ2CQBGqlrVYbKFQ2kKbplvSNPs+c8/vj5PbWTIzmZlMMpPkeb9eeU3n5t47Z5rnnnOec57n\n83w30mE9/x54++7BAiHROUX9XSZEumN75IAYXpbCzqEihgJla/1gFc77zzNtv+uUyO8dno8lZJ2+\nQJCvH1mEevtOs8DhGAjTW3kfHP4F+OdNZpGmtx1VNFnCr4XcovljE8mSQdr7NCt3BDl7VuLz+jyV\n+Jo/yOhnp4Jdb/nVDU2ce1hN1tohDJPwihodOwbPKcLH4f6u+HnBx1xlHGV7F9peiD/9JuOEx8or\nrpljnOr8EigsgLP/ZHaoW+rM746/xjjRYBzpjgYz/1j9sJn/RM9LbKfeFiR1uuHFH5r0MZsHF5o6\n0NG1nx9cmPZCel8gKOJgY5ThemP7aK0v01q/PPBzObB3JhoGnABs0FpnR8Yxg1iWpsKjzGT9vnNM\nofb7zzN1bWccC76qkER+frEp2i6OspApYolzLLtxcM6utzwyL6lmjslFuuesyLyhokmDnc6mdbFr\nKx53tRmclt1oVqI7dsQoG3Ex1B4R+tzlNw/OhZ53K1TsawbBJd8Kteez1xqH3i5L4UqQNxTsj71a\nra34+VhCTuBxO/Hk58MBZ5u+0+5DDzjb1N08+r/D+s+qoW8oCKNJ00dQmNkay69uCRDQ8fOVbfoL\nqnB3N6CCvRn9/GSZVualqMAlecvjiXiCX/Y4XDoTFtwfOy95QHV60LVlswBl/r38ZrPb7K8NzUOW\nfAt+f7gZ+1s3w6e/BZ+4yMwBnv4G/O4wc449J3jqSjj26tjzkmOuCrWrs9FE1B2ycHCblEosbJYi\nbtdgLRURBxsbDNcjWzkg6gWAUupIYPkw72mzAHgg7P2VSqlVSqm7lFKl8S7KNSxL09rZhbtnJyra\nSXjkEjPJcziNWqC7GEprTSi2IGQCywIrMNgZ7Ggw5Z++8JwJkV60BLSOFMw67urBq6pPXWmcy+gB\nJF7YVdmeJvTwuKuNQnGCck+7qV9hdrAvfQa+8Q4sehaq9jPPhR1+Hd6e4642K9nz7zO5VfFw5sV2\nitu2DHbOFzyQ+F7CqGGEEfvMYscjlwzuQ11ucLrQ+Xb/KYKIQg7R3w3tW01/m0H+UR/A44J9yxKf\n1+cxivD5nVsy+vnJ4lCK/SYX89qGnaaigTD2iSf45d/D7LqWzTT5+Wf+Ab6+0uQ32wKe9q5z9LXt\n20xKjb/WnLf0Wjjjt3DOXYPnIU9eYUo8fvLrsXObj7lqYCE8kNiptx346DmI3SatY7fVCqQlAjpc\ncTAhewzXWT4SeFUptVEptRF4DThOKbVaKbUq3ZsqpdzAXOCRgUN/AGZhQrS3ATfFuOZypdQKpdSK\nxsbG6F9njZbuXvyOXpQV56F1ukwIiLccPMVG2U+YUIyo7XY1wvM/iK1aDUZ1UqkB5enD4bnvmBCo\nLz4P5XvFtlkrOHgAse8b/X7XeqO2veRbxtYdrtjneSsi23fCtWbwBDMwe8qMCFi8FWlXgRnsoiMy\nLMvsZrdsNo76+fcMXu1+6ccmLOuU682iwaJnRdwrCUarz23q7MMXbI3fh1pBcOShXB7pP4WkGNX5\nQvNG85pBcS+tNcs2BziwHPKG6Kb6PSbSIpt5y/tPKWZraw91u7qy1obxQM7Mc23Br+hKFI6B9Bdt\nmUUiX6WZAyz/tdm5XbTE1Euef+/g+Uiw30StXfSY0TWpX2F2jK04EWGlM0zYdrzay/5aswMca75R\nNNmM93Y4tl1eyt7JvvARrIueIJDnMyKn0XOG539g5lYpEq6lsvzq43n8imNE3GuMMNzErlMz0orB\nnAb8W2u9A8B+BVBK3QE8E32B1vp24HaAOXPmZH/50rLQ3bsotYKonoFqWl983oR8hCsGO1xGuTXf\nJxO9CcqI2m6gz+ThdO4I1UHsbjYOcn+3cUStYGgwql9hwpYA/ntV7LwjOxLCDm/a53Tj7M67FV7/\ngxkUfZXm2PJfm+ta6kydxBP+nzkvXDBj/n3mnEVLTFuc+eY5Ca/TuOABc89Y7WmpM4PqZUsjv3ss\nYbNLl5jd9GC/GdjfuN2sQtv/L6/dCmf8OjVHOVpp3BvDaR+HjFaf6yRIqUcBeabcR543lK/e0QDO\nPLTbZ8TfBCEJRnW+MAJK2BvbLOrbNWfsMfS5u2stZ9NZnlwCwGsbmtij3Je1dox1cmaeGy34ZQWM\nA7l2yUDpxwFdElvA61PfNLu0fR3Q3WJqKV/yNKDNeN/8sRnD7XH6vLvh1Btg+39M+lascb/xA5OK\nNe9WeOlHZu5i/66/yzi1r/3OvIYpYHPW7Wb8t3OebQf4nzeZyhr5RfDwJTha6nD4a9EX/BV1xm9N\nZFp3c8jBPu3GNP/rlIh5jUGG5SyPYD7xQsJCsJVSk7XWA9tMnAW8O0KfmxkGJunK4YL8QrOzFe4c\nzL3FhKQc911TUy7QY/KUBSHT2OFS4U6wLaZRPMXUPg70xh6MtDXYsZ13qwlbfuEac4+yWcbpvPvz\nJv/+2G+HFLXDRb7qVxgn+v7zzQBqO+79Xcapbq0LOccXPhJZdsIW1fjyy4PLV9jPUqyw6VjCZnef\nbgb4khqT63rgOYPFxjwpPIuxHPIFD8jOdKawLEqtFhQWtNSH7MK2xcIqyPOZhR/vEPGogpANmtab\n1+KpGbvlPzYbQaDDkkjPD+SXYikXBVkqHwUwxV9AqTePVzc0seCIGFFJwtjDFvzq2AF3fi40zh6y\nMOQoR5VqYu4t8OYdZp7Q3w1PfNUINj7x1aj0mkvN8YcuMju9Z90Oj18+WKTLDskOr9gx/17oaTUR\nY/UrzPzjlOuh+gAzp3G5YcMrofmLcoQc/dnzQgrbA21R959rzr3nzNB3F02TCUfOzeaUUl7gJOCx\nsMO/CAvtPh74ZlYalyydA5P0gpKBwuox8j5PuwGKqtG9nSgpTyNkGjv82LIGC3nNu9UIcHjKzUDy\n3PcGh2nPvQX++SsjlnT6TWbX9/SbwO0zjnL9CjM43XOmqT/YUmfKRkWXngoX07DVtm3H/S+nm3v0\nd8Dy35oB7bKlJs85VthVf7dxQr+0FP77P6ZNFXvD3N/Gdk7jiZDY4hzdTbHFxrpTEKKJ5ZA/uDCt\nEC0hBp0NKKvfLGzYkyUITZLyvOi+DulDhdylab2ZC+QXZuyWyzYHmOKDycls0ioH/Z7KrO4sq915\ny02StzzeiB5nbQGv46+JnU98yELo2gmv/d6M+U6Xea2ZE7pHSx30tZt/16+AF68x4/03VkaGT9vn\nlk6Hr71pfhfoMw6v/fv6FWYXuXkj/O5Q+PPnoPaTUDLNpEDmeWDOInN//x4x5wzB0lmiaTLBybn6\nGlrrLqA86tjFWWpOegS6B8qYtJnQjZgT9h5QDlTZTClzImSW6N3OfU435ZGUMjnKeR6TAxzu6Nlh\n2r5Ks+P83PfMSuux3zU7z55SM7g8+sXQIAQDipFOM4DEU7m084dste3oHWxXgVHZtgfWCx+Jc97A\nSm5nY+RO7vz7BsKqHZEh0Pauerz7DOVMJ0Mm7iHEp7/b2Gt/V5x85QDKUyZ9qJC7NH2UUXGvnoDm\n1S0BTpyW/DX9BdmttQyw/5QSXt3QxPqGDvaqLspqW4QMEj3OdjebOUdJTfz5gNsXOeZHl3Ty15rw\na5uOBmjaYMK47fBpG38tNK4197V3os/8Y2i32l9r3i+9NtSGrp0mdPzWoyLDxqNLVg3cv4sCtnz+\ncYpQrpa5AAAgAElEQVTzLCr9xeQVVUnk2ARD/tqZJhgwORgHL4CWTdC2NbbAgDPflDkR5VYh00Tv\ndq5dAovnGqfDP82U2HE4Ih09e7f3rlNMbtFx3xnI/emGdx6Avi7YudYMWuH4a82x8xbHV7ksmWZU\nLd9ePFjYY+4tRrgrfAV62Y1m9ztaPEQ5Ix1lMK8PXQhbVhghsYY1IZXKWCIk4SvC8RQ9UwmvysQ9\nhPjkecxCSJx+VDvzzTmCkKs0rYOizIVgv7U9SE8Q5qRQIa3PU5HVMGyAA6eaesv/WLczq+0QMkz0\nOPvOA3DKz0wecqyxsbvZpM7EU7H215r5xDsPhK45bzG8/FNz7PwYc4h3HjD3BTOXee33pkbyN1aa\niLil10bmNHeGRX6tXQLLfmG0TKbOGRSJ1zLvbi556CMue7SOVvcknEXV4ihPQGQ5PpMEA6b+m2tg\n8lY6wyj6nn1HpFjR/PtMCKzbm932CuOTZHc78zyxBZO2r4IP/2ZUoZ1uM/AtnmtykMLFMvY5HT53\no3FmLMs44Rf+Fe47N3K1+G//C0f9F3ziQti1wexyd+wwA9Ybt8HJ10W2t36FEey4+HGzulw02YTg\ndjTAxU/EX622Q6AvW2pyqcJFSCzLhItrbRYTvJWhQT463ziV8KpM3EOIjb3wGOwzOxHnLQ6VjvLX\nouffh/KVycKEkLv0tpu+bu9TMnbLZZsD5DngwBSkFfo9VeT17sLR34WVl515R2VRAVP9Hl5Z28CX\nPjUjK20QRoBosS+X20SjLbtxsLjW+YuN0OfJP4k9jlcfYHZ3179oKmKc+nNAm/H7uKuhfG8TiXbp\nMxDshdYtZg5x7HfgH7809/HXmvkGgLvQpJLZi/z+WjMfX/7byLrKa5cYwa6SyWbuMPBdtNONdpRw\nywUWbpeTcp9blKsnKOIsZwJbDTcYMO/7OkyYR2ejWfH69LfMzpozz+y+FU0yjrKsTgkjwVDhx2Bs\ntn17pGDSeXeDxw9dTUboIs9jHMzednNOS12oxFJJjXGyd20crGz9xRdMVEVnYyisasdqM1CW72VE\nNoomm+sPWRhb7bKjARreNyFXp1wfWhXetSH2d7NXlaMXBezQ7HgiXNGDfKpK1rEmChNEDXtEsSyT\nxtLRYMTf8rxGhf2SJ419djejfJVSQUDIbZo2mNcMinst2xzggHIoSGH21lcwoIjdWU+3f++MtSVV\nDq4pYekHDXT3BfG4nVlrh5BhbLEvm44dpu+25wu2mKev2uj1oGKP40qZOfORX4GXrhscqn3+PWYX\neO2SkPN91FdDzvVJPwZXvtEKQsGdJwwWFC2cZATGnvtO5Gc73YO+iwJKgVIfA/P8BhnnJyjylx4u\ndn7o0980OyDdzXDvWSac9fnvm4f9nzcZB/qJ/zIPqqdMHjJh5Bgq/BgGh2oXVkFfJyyeF6qL3L7d\n2HfLpsHhVPnF5ni0eN1DF0J/p7H/hy4KObl2LcT7zjEiG3d/3uQMldRA6SxYcH/s0Kq5t5gdb5tl\nNw4WLAs/J1YIdCIRLntg9E8L7UanSibuIYSw+9T+HrPgsuRbRgzuscvNToIVMLsFLim/IeQ4GVbC\n3tJusa7Z4rAUA1f6d5ePym4o9sHT/PQFLF77SEKxxzX2HKSjwcwDnvgvE3HZsT2UexxLVNThglN+\nGhICiw7Vfvji0I5wSx08fIn597Qj4f7z4JY5ZqzobTPObyxBUSyjzRK+2zzvVrMpYKdwRWOPSXee\nCDcfMDjlSxj3yM7ycOlqhJevN06xtmKrAduy9V94zkzyRIxGGEmS2e2MDtU+5qrBju+DC00otu20\nvnFbaKX3zD+Y3b6YokvBwavGx11tBrbo8hCn32Ryml0e829PKeSXmF3tz/0Snv1OpKBYR4MRy4mu\n72iLgsQKgRYRrrGF3aeedsNgm3zyCqNamucxCzaCkMvYO8sZqrH8j3oTvZZMyahw+jzmgoKOLRlp\nR7rsN7mYfJeDlz9o5LP7ioL9uMWeg3xpqRG8tYVFwYjb9neb+YS949vdbN5//tdm0yk8tSoc+3j4\n+5Ias8gfsWh/0YC6dYzd62DARCSdflMoBe2lH5m5hZ3CFU28Bfd45wvjDvHahoNlmYf+qK8OqFur\n2A+3r9KEEPoqJL9OGB2iw6KicUaFahdNim27OmhClv7xKxPmdP955nh3s9nZizUY9XfBRY+ZZ8Pp\nNu/jDXxls4zDe+9Zg+/zpaVw/PdNCHd4+HR4ZIZlwRm/NvlG8UKj4oWlK2Wul53g3CLQZ3YPrGD8\nxZj8Ill0FHKfpvUmqiZDURDLNgeo9MC0FKtQBd3FWI78rO8s5zkd7D+lmJfXNqC1RinJ/xy3OBxQ\nFGMO0r7dhGmf9GOTrtjfY0o2nXaDcawdeUYPpbs5ccqV/Z44824rGMqRPmShmYcXVpl0yN4OE6UW\nvhAP8RfQc33B3U4FlRDxEUP+N9MlGIAd7xoBr/wiEyq4473Y6n+F1QMDpuTXCTmAZZmQI1txumYO\nFJTGtl2Hy/wcsjCyDNrym41IXbRq9Tl3ml3iF39oBrX7zzO5QfZuc/T927cZpzrWQBTsC+2QX/Wu\neY2up5xMCHSssPS5t8Cz35VQqlzDssziiX8P8z5mJQG39KXC2KBpPRRlpmxUf1Dzr/oAh1aadb6U\nUIo+T2XWnWUwodj1zd1saOzMdlOETGFZxgFu2WxeE4UzdzSYsOwnvmrm0W/eYeYKfzkdfnMw/OVz\nppRT3ZuDQ7WjVbLt0O1Y4wQaSqbCZ642KZF3nWJ2oFu3wNIfmWi36GvibWblctULCREfFcRZTgfL\nMqrXr9wAJXsYGfyLHoPK/YwAQXTJm+KpMrkTcoeuRrOT+9KPTBjUWbfBi9eGBqaaOUYl++InAAXe\nCjPYBPtDtl2/Av72XbO7d+kz8LU3jYhdoNc4yOH5RsdcFXl/GBDnuBeW/tCkL9jHa+aY8lJffD40\nI8xETnHVbPjSi/D1fxvFbm2Z2tJ27rKQG3Q2wlt3mUgHHTQ2eOEjxi7s/tQhO8rCGEBrI0iYoXzl\nt7YH6ehPrWRUOP0F5VmvtQxwWK0Jo/3bu9uy3BIhIyTjrNnOdNsWo2tSWAVzf28W4E+41oRgR+cm\nH3oxFBTDBY/A5cvMPCOvwJx/+Stm7vLGbaAcg0tSnn8vdDZBX7cJyQ6/92NfNvOTslmJdV3CSUYH\nJtb/SzILCMMlkSaLkDFk1pEOnY1mZ+6z10Bng3mww9X6zrkLfOUmnKSwOjdWnwTBxg4pCs/tWbvE\nOI/zfm8iJez8YttBufBRk2oQXgqio8EsAj36xVA406Ilg/ONPKWh+4fnKDld5h5tW0LlHKLVLxfc\nD1X7ZyakqKPBDNThZa3+fl3uhFIJZlf5kIWmrnJ4vzr/XvN38paBN4WaOYKQLdq3Q0+ryanMAM9/\n3I/bAYemWZWu31NF8Y43MtKW4VBemM8+1UU8vWobV352r2w3RxguQ+Xz2s70gwvh3LuMo/zZsJQu\neyzu3BGaR7TUmbn1X043C6WfvTZyPLAFQD9zNbz+RzjwnFAOMpi0h4cvMtoq8VIj3YXJV7FItepF\n+HeOrsCR6fDoXA8RHyfIznKqWJZR+3W6zQ6H/QBDaEWsoGRg90OJoyzkHtEhRXZuUP0KM7mLFuJ6\n6ELj0Lq9IVGORUvMa6AnpCoZfi/7Nfr+tirl898395x7C7z0YzOonvijweqXD16QmRXSrsaQo2zf\n+6krTSiWPKO5gw7G7lcfusj0q1pLrrIwNtjxnnktHX5NYa01z28McGhVaiWjwunzVOLqb8PZ2zrs\n9gyXo2aWs3Z7O+t2tGe7KcJwGcpZC3em3UVmzI0e55+60kSg2djaJ2COxzr/1J9DgR9qjzDjxX3n\nmbmFnf5la6vES430VaYWtZZK1YvR3O3N5RDxcYQ4y6nS1Qg4jGBXPEEvp8uEhuSnqMIhCKNBdEjR\nOw+YXCB/bXwhruIp8K9fwwk/DInVuPKNcvVZt0fea/69IQVtf63Jb47ObZ5/H0w6yDjf9SvMjmJn\nw8itkMYb0MtmJQ6lEkYXy0rcrxb4s9MuQUiVHe+a19Lpw77V6p0W2zs1R01K/x69PpM77WldP+z2\nDJcjZ5ahgKdXSSj2mMd21uwUqkVLTOrMbvXrsLE30G0Wj+Lt9kIoQtM/PfGcpLMBdq4114X/Pvz8\n5TcPTv+af5+pwDGSAlijudubToi4kDKyRJ8qygVo4wzHKpHjrzXh1w63lDYRcpPwsg59HSavbsWd\nJozJXxvbpls2wWFfhGCPEbOzQ4vOut2EVS161uwyW0HzbMxZFMo3CvQYJ2fREvP7PI8poQYhtWsr\naFaSY312JlZI4yliuwtFNTKXcOYZYbd4/arbl722CUIq7HjP9HMZWDR/4eN+HMCRw6hS01towsG9\nrevoqDps2G0aDqVeN7OnFPPMf7byzRP3ElXssYy3Ei563Ih12qX+witXhI+9rfXGmY3VvxdNhq+v\nBCwzfy6eYuYowd7Y57dvN87w2XdE/t7WVrFrLP/9uoG5zR6w80PjXIdHJ42EknS8+cZI7PamGiIu\npIX8b6ZCMGCk7pvWQfcuk9t5/uIoYYF7jGPgqxBjFXITe3AI9hlnccqhcPw1psMFuOQpU7oBQvlB\ny26E7qbBYhmv/g5624yC5S1zTPhTZ6OZJD58Cdx6pAmR6m01+UT+PYx4k8Nhfir3NfXHPX4oqR28\nA73g/syskMZbffXJ6mvOEAyYiU5+0WChxPPvgUKZAAhjiB2roXSPjNzq+Y8DHFAOxcOYa/cXVBB0\n5uNt+TAjbRouR80s56Odnby3tS3bTRGGg8Nh+mzbUYbIsOPwsXf5zWYeED3On/lHo31yzzyjg1JS\nYxzaomqjJj//vsHVLJbfbFLAulvgvLtDu9tFk4zz/sXnzU63byDkur/LRMM580JtT1ZJOlWxrtHe\n7U0lRFxIC9lZThbLgq4m83CG76xd8pTZVbMCJtcugzUVBSHjxBKeuOhxs/sbfmz+vXDcd8xK8N+v\nMyu0ed7BoUWHLByc4/zEV+HcP5tno2OHcZ5fudHsInvKQtcGA0ZV3j6nZQvsP2/wDnQmOn5Zfc19\nuncZe3jyCphxrBGVc7rMjnJ+kVQUEMYOgV7YuQ72P2vYt1rfHGRdi8VX9h/mjZSDXl8Nnhxylu99\nfRP3vLaJG889KNvNEYZDMEHYcfTYa4dnL1piyka2bYWl14bEvbQOjcuWBY0fmMozp1wfqpXc3WKc\n4BN+ZEpT+qrNfMNVYMq1he9wn3e3iXa77djIHW+HY2hxMrsNqYp1yXxj3CF/uWSwH5Zg7+DVs8Vz\nTUfRtM48EOIoC7lMrMGh+aPBxx66yDiwD10UGsTsMOlwovOF7Ou95ebZuOsUc4+1SyIFLuxnyj7n\n+e9D9X5mIcqVD2UzQjvQmUJWX3MXyzILNnb/uvJe+P3hpi5msM+kCwjCWKFhjVlAz4C41xPrTAj2\npzJQrrm3cGrO7CwX5rv41J4VPPHOFnZ1inLvmGYokanwsddXYX5c+XDfuXDPmaE5RnSosj1fWbvE\nzCPsWsldO81ivtsHJ/7YLNo//wOw+gfP0R+5NJS+Ey20lUxucbpiXTLfGFfIXy8Z7IfFCsZ+sJQD\nSmdKQr2Q+8QaHGLtGNviV+FhRJ5yEy4VfsxbHnuQdLiGVsiMpU59yEIpeTAR6WpM3L8WDkPZSBBG\nm81vmdfKfYd1G0trnljXzyGVUJaBwIpeXw3unp24epuHf7MMcMr+k+gNWDz4Vt3QJwu5Szphx7Gu\nmX9fKOTZsuI7s3leE9HWtsUoYNsL8vHGECsY+d6eYySjJC2lmQRyMAxbKbURaAeCQEBrPUcpVQY8\nBEwHNgLna61Hr7e3HxaHM3bSvjPP5GLKypGQ68QSnogrrFUQql3Y3Qx/+64Jd1q0xIROdTbC24tN\nPml0rfE8T2KBi3gDkK9SSh5MRAJ9CfpXt5SLEsYWm98Ab8WwNRFWbA9S36GZn6FyxD2F0wDw7XqP\n1smfysxNh8G0Mi8HTC1m8aubuPzTM3E5ZQ41Jkkn7Dj6GitgdofXLonUFIk1JnQ3m2Oe0sjjzjjC\nWm1bI9/bcwzbYY8OsQ538kdTrEvIWXK1Zzpea32I1nrOwPvvAS9prfcCXhp4P3ooZR6O/zwI598b\nJTxzr3mgxVEWxgKxVnNLZ8ZeFS4cENd44r/Mym1Hg8k7LppiwqQnHwJHfQVWPxpZe3nZL8x9Eq00\nx1vRLayWCI2JiFLw/rNx+teK7LZNEFKl/k2o3MfY9TB4fF0/BU74ZIYCK7pL9kKjKGpYkZkbZoBT\n9p/E9rYeHlu5JdtNEYZDOmHH9jUut0nJWrvEHLdDnR3OwfMIW9wrvBZz+Jxl0G71vfDmHZHn2XOM\ncIf9qnfNa3QuspRmEgCltc52GyIY2Fmeo7XeGXZsLfAZrfU2pdRk4BWt9T7x7jFnzhy9YkUGBoNA\nP3RsHxDvcprJXF8bHDQftCWCXhObEal1kTHbTUSsUgkQu3zCUGUVWjYbFclornoXiqfGvzaWaMb8\n+8xAlcwuYni7bHXL/m7z78JJshMZn9yzW8syNTODfSZczpEHTqdRxi7wmxJkgpCLthuL9u1w0z4w\n5zLY/8y0b9Mb1By+uJ3DKuHbh2aueTNf/wF93kmsOfm+zN10GGit+eFT79HW3c8r3zkej9uZ7SZl\nmty225EonZTqZ1oW/N++Rs36mKvMjnF3M0ydYxzgzsZQmctlN5qF+wUPGF2T/u7EcxZPuankMZzv\nl43/o9xAaroNkIszSg28oJTSwG1a69uBaq31NoABh7kq+iKl1OXA5QC1tbXRv06dQD80vDc4vLSg\nBLavgncegDN+LY6yMGwybrtDYa/mRhPrWLxzbRKFKCW6djhqkbEc7Xm3wks/MoPo+fdA9QHiMI8w\nGbFby4JdHw2u0Xn+vbDpNTjwLECcZSGzjGifu/Ff5rVqv2Hd5pkN/bT1wYnTMtCmMLpK98G/5RVU\nsA/tzH4oqVKKC46s5cdPr+GOf37EN07IUMz5OCTjdpuO0vNIfOb8++CT34C9TjTaJeHHC6tNCSmr\n0tQsP/cviecLseYdieYwyTDUPEgY9+Ti0sgxWutDgdOArymljk3mIq317VrrOVrrOZWVGQiP6Nge\ncpTBvD58sQmrev77JhxVwjCEDJBx2x1NhhOilK5aZCx1yievMCvS9nPasT297yMkTUbstqvRqLFH\nK5g+fBHs9znpY4URYUT73HUvmEX18j3TvoXWmj+v7qO2CA7OcBZCp39fnMEefLvezeyNh8G+k4o5\nYnoZf1i2ge2tPdluTs6ScbtNV+k505/50IVw5JdDjnL4cbstoi4tZJGcszat9daB1wbgceAIYMdA\n+DUDrw0j3hArEFuASOvYeQ2CMBFJJucn08QTB7PFPlrqTAivkPsE+uKrsVtB6WOFsYUVhHUvwpRD\nTepWmry9I8i7Oy3mTh922vMgukr3QysnZZtfzOyNh8kFR9ZiWZr/efgdglZupQeOW7Kh9BzvM+Mp\nWYvqtJAD5NRMRCnlU0oV2f8GTgbeBZ4CLh047VLgyRFvjMMVvySOrGoJQojRXvGNJw7W3Rz6t53H\nLOQ2Lnfs+t3yNxTGIlvehu5dJvdyGPx5dR+FeXB8TYbaFUbQXURH+YFUfPyU0V7JEaqLC7j0k9N5\ndUMTt/1jQ7abMzFIpnTSaH2mM2/02yIISZJrHl818C+l1H+AN4ElWuu/ATcAJyml1gEnDbzPPHZ9\nt5bNkF9kch8jlFnvkXqfwvgm/Bmwax3mGrFCv+fdGlLIlOd07OApB/908/eLVjCVv6Ew1nj3USNQ\nN+WwtG9R12bxt48DnFILBSMku9A66Wjyu7bllCo2wGf2qeSomWX83wsf8q91O4e+QBge2VB6jveZ\nhZOyqzo9FuY+QtbIKQUcrfVHwMExjjcBJ4zoh8cSHfjCC7Do2QE1bJd5mF2y2yGMU7Ih9pEO0eJg\n9g7k2XeKGvZYwrKg8QN4+Xr41Dfh0mfMTpfTbSIU5G8ojCX6e0x5ydqjjBBRmtz0Vg8uB8ybmcG2\nRdFeOYegy8OktffSXn3EyH1QiiiluOxTM9nWuoYvL17BPV86gjnTy7LdrPHLcIQ2R+IzR7stNmNl\n7iNkDbECm1iiA38+2TywZTNMiKk4ysJ4JhtiH+kSHvpdNMn8lM2AkhpxssYKtr2tXQJ/OhF+c5Cp\ntel0yd9QGHu8/xT0tMDep6R9i/d2BnlyfYB5M6C8IINti8JyFdBccyLldc9S0LZx5D4oDXz5Lr53\n6r6UevNY9Oe3WL5edphHlGwIZ8X7zGyJeI2luY+QFcRZtsmG0IEg5BLyDAijidibMF6wgvDPX0HJ\nNJh0UNq3+cWbPRTlwTnpC2knTVPtaWjlZOrqW0b+w1LE73Xz/c/th9+bx8V/eoPb/7EBS0S/hJFC\nxiJhCMRZtsmG0IEg5BLyDAijidibMF5Y/VdoXAuHXAAqvWnV0o39LNsc5Lw9oXAUgtgC+X52TTuF\nqo8ew9e0auQ/MEXKC/O5bu4BzJlexvXPfsC5f3yV1fWt2W6WMB6RsUgYAnGWbbIhdCAIuYQ8A8Jo\nIvYmjAd6WmHptVA2C/Y4Jq1btPZqfvDPHqYXwdwRzFWOpnHmmfS7S5j+1k9MWcwcw+N2ctUJe/HV\n42ayobGTM275F5fe9SYvvb+D/qAIMAkZQsYiYQgkMcwmm+ICgpALyDMgjCZib8J44IVroKMBPver\ntHaVtdZ855VudnZrfvUpyBtF87dcXhr2PJ+pa+6gcsMjNO55/uh9eJIopThu7yoOn17Gc+9u56X3\nd/CluxspLnDxmX2qOGJGGYfWlrLPpCKcjgwXpRYmBjIWCUMgznI4triAIExU5BkQRhOxN2Ess+ph\n+PdiOOBcqNg7rVv8ekUvL2wMcNls2Nuf4fYlQcuU4yjZtpwZb11HW/VR9BbVDn1RFvC6XZxzaA3z\nDp7CO5tbWLGpmWUfNvLUf7YC4MlzMrPSx8zKQmZU+JhZ4aOm1ENNqZeqonwc4kgLiZCxSEiAOMuC\nIAiCIAipsGMNPP0NqD4APnFRWrf44zu9/PbffZw0Dc4cxfDrCJSDrft/hVmvf5/9/v4F3j31rwTy\nS7PUmKFxOR3MmV7GnOllaK1paO9lXUMHGxo72NbazZsfN/HMf7YSHlSe51RMLvEwrdRDbbmXg2v8\nHLZHKbMqC8WJFgRhSMRZFgRBEARBSJbmTXDv2eDywLHfAUdqU6megOanr/Vw75p+jp0CVx4EKos+\nW7+nkrpDvsUe//45Bzx3Dh8e+zu6yvbPXoOSRClFdXEB1cUFfGrPit3H+wIWDe097OzopbG9l50d\nfTS299LQ3suq+lYeeHMzACWePD67bxUnz67m2L0r8eXLlFgQhMFIzyAIgiAIgpAMW9+BB+ZDbwec\negN4y5O+NGhpXqoLcMPrvXzUanH2LFi0HzhzYHOzq3RfNh36v9Ss/i0HLZlL0/TT2brfl+gsz7In\nnwZul4OaUi81pd5Bv9Nas721hw8b2lmztY0X1+zg8ZVbcDsdfGrPck6YXc0J+1YzqWQEC10LgjCm\nEGdZEARBEAQhHlpD03pY8Wd48zbwlMIpP4fS6UNcptnZrVndGOSNbUGeXN/P9k7NVB9cdyQcVjU6\nzU+WrtJ9Wf/JX1CxaQlldc9TsfEZOv370LDnebRM+Qw9xTPGnOMcjVKKyX4Pk/0ejtu7iqClWbu9\njbc2NfPvTc38fW0jP+Bd9ptcxCdqSzloagkHTC1hqt+D35uHGuPfXxCE1BFnWRAEQRAEIZyHL4Gu\nXdDXAc0bobsZlBNmHQ+HfQEKSnaf2t6n+frSbroDmp6gpicAXf2axi5NT9Cc41BwaCUs2hc+OQlc\nOSq0a+UV0rDnfHbucQYlO17Dv+VlZqz4KfBTAu5ien1TCbj9WK58Nh/033RWHJztJg8Lp0Mxe0oJ\ns6eUcMlRe1Df3M3KumZWb23lyZVbuP+Nut3n5rscVBTm43U78eQ5KXA7yXc5cCjFL887iKoi2Y0W\nhPGI0jlYW2+4KKUagU1ZbEIFsDOLnz8SyHeKZKfW+tRMNgZGzHZz9W8n7UqNTLQrm3abq/+vySBt\nzx52+8dKn5vL/9+52rZcbRcMv225bre5/H+fCuPle0DufJcRsd2xyLh0lrONUmqF1npOttuRSeQ7\njV1y9XtKu1IjV9uVLGO5/dL27DHW2p/L7c3VtuVquyC325YJxsv3Gy/fA8bXdxkv5GggkCAIgiAI\ngiAIgiBkD3GWBUEQBEEQBEEQBCEKcZZHhtuz3YARQL7T2CVXv6e0KzVytV3JMpbbL23PHmOt/bnc\n3lxtW662C3K7bZlgvHy/8fI9YHx9l3GB5CwLgiAIgiAIgiAIQhSysywIgiAIgiAIgiAIUYizLAiC\nIAiCIAiCIAhRiLMsCIIgCIIgCIIgCFGIsywIgiAIgiAIgiAIUYxLZ/nUU0/VgPzIz0j+jAhiu/Iz\nwj8jgtit/IzCz4ggtis/I/wzIojdys8o/AgDjEtneefOndlugiCkhdiuMBYRuxXGKmK7wlhE7FYQ\nRo9x6SwLgiAIgiAIgiAIwnAQZ1kQBEEQBEEQBEEQohBnWRAEQRAEQRAEQRCiEGdZEARBEARBEARB\nEKIQZ1kQBEEQBEEQBEEQonBluwGCIAiCIAiCIAipsmZrG9c/+z49/UH+5+S9OXpWRbabJIwzxFke\nDpYFXY0Q6AOXG7yV4JDNemEMIrYs5Apii8J4RWxbEDLK5l1dLLzjdbTWFOQ5ueRPb/L4FcdwYE1J\ntpsmjCOkl04Xy4KGNXDniXDzAea1YY05LghjCbFlIVcQWxTGK2LbgpBxfvjUe/QFLK6bdwA/O/NA\nij15XP3oKrTW2W6aMI4QZzlduhrhwYXQUmfet9SZ912N2W2XIKSK2LKQK4gtCuMVsW1ByCjvbKUx\nIDQAACAASURBVG7h7x80MPeQKVQXF1BY4OL8OTWs2dbGsg/luRIyhzjL6RLoCw16Ni115rggjCXE\nloVcQWxRGK+IbQtCYt7+Cyw+E9a9mNTpi1/biCfPySmzJ+0+dsysCkq9eSx+bdPItFGYkIyas6yU\nOlUptVYptV4p9b0Yvz9WKfVvpVRAKXVu1O9qlVIvKKXeV0qtUUpNH612x8XlBn9t5DF/rTkuCGMJ\nsWUhVxBbFMYrYtuCEJ/1S+GZb8KmV+G+c2HryoSnt/X08+yqbRw9qxyP27n7uMvp4Jg9K/jHh400\nd8pClJAZRsVZVko5gd8DpwGzgYVKqdlRp9UBi4D7Y9xiMfBLrfV+wBFAw8i1Nkm8lbDggdDg5681\n772V2W2XIKSK2LKQK4gtCuMVsW1BiM/LP4eiyXD2HVBQAi9cAwnyjv/+fgM9AYtj9x78/Bw9q4KA\npXn+ve0j2WJhAjFaathHAOu11h8BKKUeBOYBa+wTtNYbB34XoXYx4FS7tNYvDpzXMUptTozDAVWz\n4bKlomwpjG3EloVcQWxRGK+IbQtCbJo3wZYVcOil4C2Dg+bDm7fDlrehZk7MS55/bzul3jz2rCoc\n9Lvp5V4qi/L5+wcNLDiiNsbVgpAao9VLTwU2h72vHziWDHsDLUqpx5RSK5VSvxzYqY5AKXW5UmqF\nUmpFY+MoJfY7HFBYDf5p5lUGPSENsmK70YgtCykyYnYrtiiMMFnrc8W2hWGQE3OFkWDNk+Z1j0+Z\n11kngtMN/3kw5ul9AYtXPmzksD1KcSg16PdKKQ6u8fOv9TvpDQRHqtXCBGK0eurB1gzJ6rq7gE8D\n3wYOB2ZiwrUjb6b17VrrOVrrOZWVEtYkjB3EdoWxiNitMFYR2xXGIuPWbj98DspmQvFk897thWlH\nwruPxhTAW1nXTHdfkINr/HFvefC0Err6gqysaxmpVgsTiNFyluuBaWHva4CtKVy7Umv9kdY6ADwB\nHJrh9gmCIAiCIAiCMFoEA0bMq3r/yOMzj4fuXfDxskGXLF+/E4eC2VOK495230nFKOCtj3dluMHC\nRGS0nOW3gL2UUjOUUm5gAfBUCteWKqXsZbTPEpbrLAiCIAiCIAjCGKPxA+jvhoq9I49P+QTkeeCD\nZwZd8uqGJmZWFuJ1x5ddKsx3Ma3My5sbxVkWhs+oOMsDO8JXAs8D7wMPa63fU0pdp5SaC6CUOlwp\nVQ+cB9ymlHpv4NogJgT7JaXUakxI9x2j0W5BEARBEARBEEaALW+b12hn2ZkHUw6DD54FK6T72x+0\nWL2llb1jCHtFs8+kIt7e1EwgaA15riAkYrTUsNFaPws8G3Xs2rB/v4UJz4517YvAQSPaQEEQBEEQ\nBEEQRoctb4O7EIqmDP5d7VGw6V/mnGmHA7B2ezu9ASumCnY0+04q4sU1O1izrY2DEuQ3C8JQiBSj\nIAiCIAiCIAijy9aVULEXxFC1pmYOOJwRodgrNxvBruScZZPT/KbkLQvDRJxlQRAEQRAEQRBGD8uC\nnWvBv0fs37sLofrACGf5nboWSjx5VBTmD3n7Mp+bqqJ83pK8ZWGYiLMsCIIgCIIgCMLo0boZAr1Q\nEjMD01B7FDSth8YPAVi5uZlZlYWoWDvRMdirukjKRwnDRpxlQRAEQRAEQRBGj53rzGsiZ3nakeZ1\nzZO0dvfzUWNnUiHYNrMqfTS097KjrWcYDRUmOuIsC4IgCIIgCIIweuw0u8WUTIt/jq8SqvaH1Q+z\nOoV8ZZuZFebcVfWtaTdTEMRZFgRBEARBEARh9Nj5IeQXm59EzDgOdn5Iw/oVAOxR5k36I6ZXeHEo\nWF0vodhC+oizLAiCIAiCIAjC6LFzLZRMja2EHc70Y8DhonL9I/g9eRR78pL+iHyXk5pSL/+RnWVh\nGIizLAiCIAiCIAjC6LFzHRRPHfq8ghLY4xgO2/Use/l1yh8zs8LHqvoWtE79WkEAcZYFQRAEQRAE\nQRgt+jqhsxGKJid1urXfXLx0c57zlZQ/amalj+aufra0dKd8rSCAOMuCIAiCIAiCIIwWLXXmtbA6\nqdPr8/fiteBsTm+5H2dfe0ofNbNSRL6E4SHOsiAIgiAIgiAIo0PzJvOapLP84a4g1wcuwBdoYeq7\nt6b0UbVlXlwOJc6ykDaubDdAmJhYlqaps4++QBC3y0m5z43DkVyReWFsI397QUgPeXaEiY48A+OE\nlgFnuWhSUqevbbZYrWfSOOlYJq/5Ezunn0FX2eykrs1zOqgt97JKFLGFNBFnWRh1LEuzdkc7X168\ngvrmbmpKPdxxyRz2qS6SQW+cI397QUgPeXaEiY48A+OI5k3gzIcCf1Knf7grSLUHmva9CP+uVcx6\n7WpWn/Y4OJJzY2ZW+Hjj411YlhZbEVJGwrCFUaeps2/3YAdQ39zNlxevoKmzL8stE0Ya+dsLQnrI\nsyNMdOQZGEe0bIKi6qHLRg2wdpdFbREE8wrZtu8iCne9x5Q1dyT9cTMrC2nvCbCxqTPdFgsTGHGW\nRxHL0jS297KluYvG9l4sa2LK2PcFgrsHO5v65m76AsEstUgYLeRvL6TLRO8/5dmZeEx0m49GnoFx\nRPNG8FUldaqlNR+3WtQYnS7aq4+grepwalb9jrzuxqTuMbPCB8DqLZK3LKSOOMujhB0+dNatyznm\nxpc569blrN3RPiEHP7fLSU2pJ+JYTakHt8uZpRYJo0W8v33Q0hPyWRCSQ/pP6TcnGmLzg5FnYJyg\ntdlZTlLca3unpjcIU32hYzv2XIDD6mPqu39I6h5TSz3kORXvirMspIE4y6OEhA+FKPe5ueOSObsH\nPTvvqNznznLLhJGm3OfmtosPi/jb33jOQfx0yZoJ+SwIySH9p/SbEw2x+cHIMzBO6G2D3nYoTG5n\neWOrBcCUwtCxPt9kWiZ/mup1D+DsHdoBdjkc1JZ5ZWdZSItRE/hSSp0K/AZwAndqrW+I+v2xwM3A\nQcACrfVfo35fDLwPPK61vnJ0Wp05kgkfmigqjw6HYp/qIh6/4phx/13HG8O1UYdDUeFz8/8+Pxu/\nJ4+W7n5+9fxaVm5u4YdnSCidEJuh+s+J0HdKvzmxkDnDYOQZGCe0bTOv3vKkTv94wFme7I08vmva\nKZRuXUbFxifZsc8lQ95nRoWP1z8SkS8hdVJ2lpVS+cA5wPTw67XW1yW4xgn8HjgJqAfeUko9pbVe\nE3ZaHbAI+Hac2/wEWJZqe3MFO3wofPALDx+aaCqPDoeisig/280QUiBTNupwOPjJM2viPguCEE2i\n/nMi9Z3Sb04cZM4QG3kGxgHtW81rks7yxlYLtwMqIiPw6SmeTnfRDKrXPciOvS8eUixsRkUhS99v\noG5XF9MrfAnPFYRw0gnDfhKYBwSAzrCfRBwBrNdaf6S17gMeHLjHbrTWG7XWqwAr+mKl1GFANfBC\nGu3NCYYKH5KQKyHXyZSNSiidkCqJbEb6TmE8InMGYdySxs7yZB/EWgNqmXIsvuYPKGj7aMj7zBCR\nLyFN0gnDrtFan5riNVOBzWHv64Ejk7lQKeUAbgIuBk5IcN7lwOUAtbW1KTZv5BkqfEhUHicuuW67\nNpmyUQmlGx+Mpt0mshnpO4VUGQt9rswZhGjGgt0mRYo7yx+3WkyJsxHcVjWHyWvvpqz+JbaWzEp4\nn2mlHlwOI/J1xsFTUmmxMMFJZ2f5VaXUgSleE2sWnKyk4xXAs1rrzYlO0lrfrrWeo7WeU1lZmWLz\n0sSyoGMHtGw2r9agTfEI7PChqaVeKovyI5wDUXmcuGTFdhMRx64zaaOJngVhbDBidhvH/uLZjPSd\nQqqMhfkCyJxBiCTn5grp0rYN3EXgGjqcPmhp6trMznIsAgXldBdNp3Tz0iHv5XI6qC0XkS8hddJx\nlj8FvK2UWquUWqWUWq2UWjXENfXAtLD3NcDWJD/vk8CVSqmNwK+AS5RSNyS+ZBSwLGhYA3eeCDcf\nYF4b1iQ1AMZCQlOFnCCBXYuNCiNOGv2q2KWQ82R4vgBi98IYpn0beMuSOnVbp6bfIu7OMkB7xSco\n2vlvXL3NQ95vRrmPd7e0ovXELcEmpE46YdinpXHNW8BeSqkZwBZgAXBBMhdqrS+0/62UWgTM0Vp/\nL402ZJauRnhwIbTUmfctdeb9ZUuTrh0XjoSmCjlBArt2FFaLjQojSxr9qvSdQs6T4fkCiN0LY5i2\nLSmJe0FiZ7mj4iCqPn6c4h1vsqv2lIT3m1Hp46UPjMjXHuUi8iUkR8rOstZ6E4BSqgooSPKagFLq\nSuB5TOmou7TW7ymlrgNWaK2fUkodDjwOlAJnKKV+rLXeP9X2jRqBvtDAZ9NSZ46niag8CllnCLsW\nGxVGlDT7VbFLIacZgfkCiN0LY5S2bTD54KROtctGTU3g1/YUz8JyuCne8caQzvLMClOsefWWVnGW\nhaRJp3TUXIzg1hSgAdgDU/84oWOrtX4WeDbq2LVh/34LE56d6B5/Af6SaptHBJcb/LWRA6C/1hwf\nJSZajUVhFEjTrsUWhYwwgv2q2KiQNXJgvgDyDAg5QLAfOhuT3lmub7fIc0BZgq057XDR5d+L4h2v\nD3k/W+Rr9ZZWPn+QiHwJyZFOzvJPgKOAD7XWMzAK1csz2qqxgLcSFjxgBjwwrwseMMdHAbvG4lm3\nLueYG1/mrFuXs3ZHO5YleRjCMEjDrsUWhYwxQv2q2KiQVbI8XwB5BoQcoWMHoFNylqs8sctGhdPl\n3w9v81qcvYnFu1xOB7VlXlbXi8iXkDzp5Cz3a62blFIOpZRDa/2yUurGjLcs13E4oGq2yTkK9JkV\nYm+lOZ4hEq0Cx6ux+PgVx2Q1LEtWrsc4adh1srY4XNsQ25oAZKBfjWUn0l8KWSXL8wXI3TkDyHMw\noWjfbl6TdJY3DzjLQ9FZui9VaIoa36al5rMJz51e4WPFxl1orVFK7EwYmnSc5RalVCHwT+A+pVQD\nEMhss8YIDkfa4hxDYa8C24ObrXS5T3VRztYWHarNwhghRbtOxhaHaxtiWxOIYfSr8eykuMAl/aWQ\nXbI4X4Dcrcssz8EEoy21Gsv17RaHVw19Xk/xTDQOCptWDeksz6jw8fcPGti8q5vacm9S7RAmNuks\na84DuoCrgL8BG4AzMtkoIf4qcFOnEQTJxRqLQ7VZGJ8kY4vDtQ2xLSEZ4tmJUkr6S2Hckowt5eKc\nAeQ5mHC0bzOvSTjLXf2aXT0wKQl/1nIV0Fs4lcKd/xny3BkVRthL6i0LyZKys6y17sTUTP6M1vpu\n4E5AerUMM9QqcC7WWMzVlWthZEnGFodrG2JbQjLEsxOnQvpLYdySjC3l4pwB5DmYcLRtBYcLCoqH\nPHVLh1HCrkpy87e7eJZxloeooVxb5t0t8iUIyZCOGvaXgcuBMmAWMBX4I0boS8gQXreDp7+wN363\nRUOX5qevNNLY0b97FTgXayzaK9fhA18urFwLgGWZWp8jkC+XjC0O1zbEtoRkiNdvOhwO6S+F8cdA\nvz6JXp7+wt5cu3Q7Kze3AYNtKRfnDCDPwYSjfRt4y0ANPf+obzfOcnUSOcsA3SWzKN36Cvkdm+kt\nqo17Xp7TQU2ph9VbWpK7sTDhSSdn+WvAEcAbAFrrdQM1l4VMYVn429dT+txCaKljmr+WP8+7m+bC\nvSJWgUerxmKy4hv2ynV07lG2V64nPJYFDWvgQWNPu5VYq2Zn1GFOZIvDtY1Y19928WGUevIy0n5h\nHDBEvzka/WUqQkXSXwrDIqxfd7bUceCAvX/hWWjs6I9pS9msyxzv2ZDnYILRtgU8yeYrmx3i6qR3\nlmcCUNi0KqGzDDCjopB/1zWLyJeQFOk4y71a6z7buJRSLkBqD6RJ+ADicTsJWJqSYDP5tmMD0FKH\n/8lLKfnSUpSjaNTbl6z4Rq6uXE94uhpDjjKY1wcXGmXWFAVn0lUtTdY24t3f4VDsVVnI/ZcdSUN7\nL02dffxm6Yd886R9RAhmAmPbi2VZlOoW3FnsN1MVKpL+UkiV8P6xytFGXgx7f3DRC7Q5y3LKloZ6\nNuQ5mEC0bYOiyUmdatdY9ie5vtNbWINWTrzN79M0/fMJz51R4ePltQ3UN3czrUxEvoTEpOMsL1NK\nfR/wKKVOAq4Ans5ssyYG4QNIZWE+3z11H77z11U8cP5UptkDoE1LHSo4+qnhqZabyObKtRCHQF9o\nQmXTUmeOp8BwVUuHso2h7t/c3c8Fd74REa63Zlt7TpQ+EUYf215+/eJaLj16BjPz2pmcxX4zndI8\n0l8KyRLdP664ch8qYti7m0DO2dRQz4Y8BxMErU0YdvUBSZ2ebI3l3bd35NHrm4Kv+f0hz51ZaUS+\n3t3SKs6yMCTpxGB+D2gEVgNfAZ4FrslkoyYK4QPIVz8zi+/8dRX1zd00dGkTKhuOv9bkmo4yIr4x\nDnC5M2JPI61aOtT9xRaFcGx7OeewaVz96Cq2dlhZ7TfFPoWRJLp/dLoLYtp7QOVeaoo8GwIAvW3Q\n35VSjeVkQ7Btegpr8TZ/MOR500q9OEXkS0iSdNSwLa31HVrr87TW5w78e2KHYVsWdOyAls3m1bKS\nuix8APF78nb/+6evNNJ0xt2hgdDOMfVWxvhoTWN7L1uau2hs78WyMvunyNVyE0IKeCuN/YTZk17w\nAM2qJNJuhrDjkZ7wDHV/scUJRBJ9qm0vdt+ZTL85kv2l2KeQNGnMGaL7x619vkH23nTG3TQTX2V4\npOcL8ZBnQwBMCDakVGO5KklxL5ueolryu7bj6k0s3uV2GZGvVfXiLAtDk44a9ueBnwB7DFyvAK21\nHloHfjwQrSrsKYfGD9ISTwpXgWzp7t/975Wb27jsb3DdaY8xe1IhToImfKWrMULFODwM8ZzDplHu\nc9PdF2BKiQeXKzPCTSK+MQ5wOIw9XrYUAn1op5uNPV4uvuXV3X/Te754ONPVDlTzR5DnNau/pTOh\nbOZuexuOamncXOew56nKkccpsyt5fk1jzPuLLU4ALAu6d5nyIg9dmLBPte3R7jvtfvPnZzzFTL+D\nPKVReZ6wW49sfyn2KUQQrwJBmoKL0f1vfWsvv307wOUnPUKNPx9fnkL3BSilDayCQfeKDuM+eXYV\n15w+G6dDjXiesDwbAgDtW81rCjWW09lZBvA2v0/bpE8mPHdmhY+VdS0i8iUMSTo5yzcDZwOrJ9yO\ncqxB7pKnkhNPsgdOywJtnN8Kp5t7vng4F9/1Fn98ZQO/PPeg3aHYjR39FJZNwtG9Ke6g2tTZtztf\n7+pHV0WoBO83qTgjA5+Ib4wTHI7d9rizvZeL71oeEe6c19eC6tsGS74VsrV5t4LHD74KIP0JT9xc\n5Cofjsb3d9t3nr+W359/P1/DOMzR97dt8ekrj8bb34xL9+NwdaMoxKzZCWMau39t3xqyQwj1qV9a\nCg4n9HeDDlLhMos8P3/uA2485yCufnQVAJMczbjvuXRQn9nU2T+i/aX0lcJuEjnEQwkuxpgr4HJT\n7q2I6H8ffXsz3zhhH3730lquP8ZF4SOXUpjA+Q4P4/7END+XHj1jtwZEqvoTqSLPhgCktLOcao1l\nm96iPcxHJOEsG5GvRra0dFNTKnnLQnzScZY3A+9OOEcZYg9yHTsSiidZlqa1u5fitnU4X/k5HPkV\neOpKIzzjr2X6ggd4+sqj6eqz8LidPHbF0fQHLNwuJxW0oP4Uf1DtCwR35+uFOz5fuefthKIyqSoa\ni/jG+CI6nO8T04qZ6rXgkSsibe3JK2DRs7vPS3fCEy8X+eWvzsYR9Ty5Hr6A3y16keZ5B+JwOAbd\n34GmtH39iJbBErKE3b+e+YeYfaoO9qF2bjR2GdZ/3njOgfQFNA9/5ZOU00L+X06O2Wf2BYpS7i+l\nrxTSIpFDnEBw0QoGoWENjqi5Av5aHAseYJ+q/SL631JPHrfMqyXvzycNuWAf3u9/9TOzBj0HQ4nR\n2QynIoI8GxOc3TvLZUOemmqNZZtAvp9+dwm+JPKWZ1SERL7EWRYSkc7s8rvAs0qp/1VK/Y/9k+mG\n5SSxBrnOxriiMvaOWn39ZpwPXQCHLAwNfmAmfA8upNRqZWqplzJfPlVFBUwt9VJZlG9UXBM44vZA\nlUoeqd2ms25dzjE3vsxZty5n7Y72UctdErJPdP7YNZ+pRAV6Y9uajrQje8Jj22jEJClOHl68XGQV\njP2ZVqCHzr5g7ElYvEloVyPCGMfuX7ubY/apqr97t6MMhPrPwE6qi/KZ4veQTyBun5lqfyl9pZA2\niSoQxBFc1E43Pa07cMSZK/DgQhzdOyP6X5fLQZ7uT6raQXi/H66RYpOM/oQ8E8KwaNsG+UXgGnrR\nJNUay+H0Fk7Dm4Qidm2ZD4dCRL6EIUnHWf4Z0AUUAEVhP+OfWIPcOw/A/PtiisrYO2pVXszg5SmN\nPaj1dUAwMNjRGELFuNznpqooPyXhjESKw9kS/xBGFzuc2rabKYUOaNsSx9aSXNa1ww7vPBFuPgCe\n/ia0bEK31FGpWnnuG8dw28WH8YlpfsDYqHbmx/zMdU39bGrqiq2ynUQZLLHjMYrd3y2/GebeEilI\nN/9e6G2N/bfv74Lmj6F9Bzjj95mp9pdDqbOLnQlxSTR2xxBcZMEDtDhKaG3vSDxXsPu58IVJpZJS\ngQ/v9+08/3CS0Z+I90xsa+2WZ0AYmvZtKYl7pVJjOZyeoj3wtqwDK5DwPCPy5WW1iHwJQ5COs1ym\ntT5ba/1DrfWP7Z+MtywXiTXIHf/9kHjSVe+a14GQ0L5AkMrCfDwerzk3zo4J7dsiHY07TzTvPeUx\nB1Vb3dXhUEwpMTl39sA3VB5pIsVhWTGeGISHUy+/+ngq/MXw5h1w5h8jbW3+feAbrMAek/Ad35o5\nJoRw8VzUzQfi/svJVHV/xGNv1/HtU/bh5NlV3HHJHJyFlQTn3z9IzfXapdvxup2xdzmGWECSnY8x\njN2/djTA36+D02+Cr/8b/aWl9Honx+8/mzfC7w6FP50Ive1x+8xU+8tEfaXYmZCQOA7xbpGvGHOG\nrj6L1n5H4rmCUoMXJp/9Lpx/z5DVM8L7/UNqSlKaN9jEeybqm7vlGRCGpm1LykrY6aS19xTW4rD6\n8LR9NOS5Myp8rN7SykTMLBWSJ52c5aVKqZO11i+kcpFS6lTgN4ATuFNrfUPU74/FiIcdBCzQWv91\n4PghwB+AYiAI/Exr/VAa7R4+UarCEQqX4WJeA7hdTr5xwl5859mNXH/G3ZS/+SuzYxKWh8TcW0Bb\nIeVX2B1ypb+0lJaiPfEuemFAzCgf5auMyM10uRzsN6k4bh5pdH5R3oBcfrSisVIq5opxMjlMwtgj\nIn/MKjCLPi9fD6dcbxzkwmoomZZ8HnD4ju8xVw0KISx/+lIuP+kR/vvRVTz8lU8yqbgAh0PRXLwX\n9ac9ht9t0dCl+enfGmns6Ke8MJ+gNjt3EfbsqYD595tQxfCc5YGJYbydD7HjMUCc/nVnZz/XPPkf\nfnH8NErm3RoKxbZF6F76kbm+pQ7uPQv95ZcJfOFFVNAovzsLK3EM2HEq/aVSKq76u9iZkJBEcwX7\n91FzBrfLSaezlJZ5d+N/Pc5cQTkHp6KsXQKAteg5LMsaZPORzQr1+5VFBYOeA4DG9t64+cjxKiK0\ndPfLMyAMTds2mHJIUqemU2PZpme3yNcHdPv3TnjuzAofyz5sZGtrD1P9KSZICxOGdJzlrwHfVUr1\nAv0kUTpKKeUEfg+cBNQDbymlntJarwk7rQ5YBHw76vIu4BKt9Tql1BTgbaXU81rrxEXURoo4jnEs\nyn1uZlT4aGjvpVGXUXjiT8lze3Bc/LhZOW7fbnZQTv5ZzJCr/r4ezrjzvUi1Sp8aFA4QTzgjlgrx\n4i8eEVPR2KkY0Rq6Qg5jT+zO+HXsiV0y5HngwkdM2SlfZUx7rvIq6pu70VrvnoAV5btxFFWx8J63\nd9vjrRceyi/+9j4vrGmIUGkFWNvQyc0vdnP5SY8wudCBv6iQgpLq3RPDka4FLYwwMfrXvkCQhvZe\ntvWW4SmbTt6iJahAn9lyeOxyqF8ROrmljv6eTj5754a4Kr/J9pcnz67ijxcdxlfvfTviXuU+N9ta\nu8XOhMSkMFcAM18IBArY3lRM8ck/w+F0wUWPQ8/AXOGN20J9dHT/unYJ24/+EUf/YV3SytbRz0Hc\nqgVh94lVEeHGcw7iV8+vBeQZEBIQ7DcaPynsLB9Rld5H9fmmoJUTX/MHNM2Ym/BcW+RrdX2rOMtC\nXFJ2lrXWCfOTlVL7a63fizp8BLBea/3RwDkPAvOA3c6y1nrjwO+sqM/7MOzfW5VSDUAlkB1nOQUc\nDkWxx8mfP+fD/+RZu1eIrfPvwfHPX5sVYX8turAa5a+NHAD9tazd2Tdo5+KxK46mqqhg92mJlClj\n7X5cctebPPm1o3n4K58kELRwOR1UFebTHFbn2SbZGrpC7pK0cmmKE7vwe2srSEXXNhx2uZ8LHzE7\nIVH23NClI2zKsjTrGjv4zdIPueHsA5lc4qEgz8GPn36PF9Y0AJE7dsBAjdxaAp48Vnf08+iyzfzs\nrHIqi8w9h1MLWshNvG5H7D60r8OEbIcTp9+0d7tS6S9tG3zo8qMIWnp3X+kYqEsrdiZkEgea6p6P\nmPzMBYMjJzoadkfQ6M7GmPOFbQOlduLNFWziPQPxoiXsSCAwz0iZN8/MHyyLDQ2d/Or5tazcbKZj\n8gwIcenYAegRrbFsox0uer2T8LRuGPLc2nIvCli7vZ1TD5iU3gcK456RqLVyT4xjUzElp2zqB46l\nhFLqCMANDHoClFKXK6VWKKVWNDbmjjJuqdWK/8lLI0JSHQ9fjD7tBvRV79LzhZf4xWudNJ1xd0TO\nUXD+/Vy7dHvEveqbu+npD60lDJU3F2+Xra0nyI+eepdjf/kK59/2GusaOyj15EWIPsXKYRJBm5Fh\npGx3JPMqw++9ub4uFBYNsOxGM8kLs+fWMxdz+9ttETZlT85eWNPARX96kxP+bxnbWnt2UpCTwQAA\nIABJREFUOyk2oTxRi0uPnsFPnlnD/Ntf5yfPrOHSo2dgWaFnIlq8bKhcPLHp9BmtPtcfrw8tm4WO\nElcMnh+730wmzzhWf/nCmgZ2dvRF9JWWpRPamdhU7pOT84Wuxsh+dKB8nz73z+gvPAeV+2Kh2Njj\npWVe5Hyh6Yy7+ekroe8RPVewSfQMxJsvbG3pZu2OdjY2dXLWrcs58ud/5/zbXiMQ1EwqKaCxoxeI\n39fK85A5ctJuk2UUaiyH0+ubiqd13ZDn5bucVBbls66hPf0PE8Y96YRhD0WsuJ9Yx1LqMZVSkzGO\n+KVa60GjgNb6duB2gDlz5qTXG1uWyQdKNxQ1BvHKP6mWOnjiv+iZdzevb+zk9Y1wzUBoaaW/mGaK\naex4PeKymlIPLgW7Onvp7guiIeZK8EOXH0XA0rgciq98ejq3/XNjxD027uzknMOm8cKahoidl0Q1\ndAMBi7UN7XzlnsiQxL0qC2nu7k+55qIQIiO2G4O08iqTfAbC713lVZE2Xr/C7IZc/IQRr+vvotBX\nyA3nTKPEEyo3pdD8edHhOAfet3b3UV6Yzz+++xksCwKWxfbWHha/thG3ywh+RdcGvfrRVTx0+VG7\nPzqVWtDJhB0K8RmtPjd+H7qJdmcJWz7/OD5nkIYuDb2VNHZETiJth3Zra3da/aXX7Yw4335+YtkZ\nENemgLTq0wqZZ9i2OwJzhXhK//ZcgQUP0Fq0Jxff9RaVhXlcc9IjVHkVxYU+vv/cVlZubtt9mT1X\naGjvoac/iFMpPG4nAUvHfAYevPwoHEpx8uyqiMXKmlIPTZ19XPXQO/xk3gGDotSeuvKYhHnPHreT\nHW29Ec/DbRcfRoXPjcPhkGcgRUZqrjAq7K6xPLSznG6N5XD6fFMoblyBCvaayhsJmOr3sL6hI/0P\nE8Y9I+Esx3qA64FpYe9rgK3J3lApVQwsAa7RWr8+1PlpYStM2sIZtnDQgLJ12tjqvVEhU3Q3Q0sd\n/td/xX3zf05Tew8NXZrfvtnF10+sweGAuxbNYUtzD163k66+IFVF/5+9Mw9vqsz++OdmbdItpbRs\nbRUYRKuCUFZ11BEFERAQC8oOKuCG47g7og44jOtvHEcR0BEo27CpbCIKI+ooLiCujIgCtpWlpTRt\n06bZ7v398fbeJM1NN4qg5Ps8PCF3Sa5yct5z3nPO92vBryj8WOTirpVf8mxuV92d4ENl1Vw3dzsZ\nKTZeGpsDwLwPDoTNFz0w8Oywe7z+QJ2zfAfL3FqirN5zc94Olt3Um9GvfBJLNk5BNHp+V5bh2D4o\n3Sdmj31VkNIBWnQQ50OCQ0VOJC3BzCsjsmjr0LFxV5H4Pa0YC4DRkUXCpHeAVsiywlGXh0qvn8JS\nN89v3Uuxy8OLo7tRVuXD5fFz7+qvNJuaOzaHFJuZg+V+3f+egKwgy0q9M6m1ESNpOsloqM+N5kMr\ni0n84nmyrvwbFW6FDmk2ShQTCyf1pOCYW/ObGS1s/GX9t9x4cYcm+ctqXyDsevX3o2dnRRXVujb1\n2i0XhtlbzFf+inGSYgXenU3ywKdYPrIdRVUKq3ZX84fs1rQ1x3FHv858e8hFWoKV6f06cUaqnYAC\nj73+tcb98NKY7iRH0Vf+udTN3au+ZG6N/av3qPFCYalb2zQKvc/tDdAuJVj+q70BuWBiT2as/Sbs\n9zB18U5mDM5m1obdsd/A6YRGVJaPR2NZhSe+HZIiE1d+AHdK5zqvbZdi4+1vjxCQFW3zPoYYQnEi\nkmU9fAZ0kiSpPfAzcD0wuiE3SpJkAV4H8hRFWXXCnrA2w2QNIzU3bQmf5WzsjrIqIRG6sF7zgiD2\nqpHYsS8ZjN2ZT6Yjiy65S7l9/TcUVfh46OqztYUmI8XGvLE5OKt83LXyS9ISzJyTWM32Wzpx0CXz\n+LZidhWUk5Fi04K7wlI3tyzZyfKb+3D5Oa1xun08s3kPxS4PTrdPe8Tac0a1Z5oUFIoqPLqLbOjx\nWLJxaqHRc5XuY6ISrM4eO7Igd5Eg5XAdCTK2O7JIG7OGNdcmY1gxHBLSRdt1KEuxauMqnPn4vdUc\ndlbh8gR0CWKOVQqbrB1cTVuykxVT+mCMwk78Y3Elld5Ao4OuGBnYSYaez313Nlz9FChK0L9G86Ff\nrwzznziyMAzL42fzmWF+c/7YbvzpwhTOTKxg/aSzeGTLYa0Kp+cvl4X4y0Uf7WdETnCft67fjywr\nuL36NlXtC8Q2Zn4raGisAI2LFxoQKxgWDiQzNFbYms/m3cX0z05n+c29cVb5uGXp52G+tYXNTG52\nHG04SoIhngHZaWzeHey+CGWzVn3tlEs6UlLp1eaRM1JsVHnD/WJGig2bxRjGnq0QXrm2W4y6vwdH\nTdIe+w2cRqg4CAYzWKNyAWs4Ho1lFZ4EMelpL/uh/mTZYcMbkCk4VsWZNYRfMcQQihORLHtrH1AU\nxS9J0u3AZoR01KuKonwrSdJMYIeiKOskSeqJSIpTgCGSJP1FUZRzgZHAJUCqJEkTaz5yoqIoXzTr\nU0dpgcIf8p/TlB1lgwESWwvN0JT2ULpfLH6FO2DUkgiJHfOqMUy5chVHcXDXyi/Dd2RrEt+0BDOv\nXBVP0tKrSHLm08aRxStDFvHQh1YmXtQh7OsLS93IisLdq74Mq9St/6KQeeNySI23kJ5oJcVmrvlP\njGxNXXJjb0oqvbqJSkmlN+L7miPZaDAx1XHe81uGHnNpnVqaPncw4QWRBHsr4fCXwQQaaloDDyCp\nx5z5ou160LPIqZ2QJROmt+4PZyl2ZPHDMR8JqZFtgPev+YoZg7PD2l1DUVgqmIf/9d99zBnTnVtr\nBYPqBlBjg64YSdNJRm2fq+pzLxgY6V8TW8OYNWJDp7JY+FAdibLkN8aTP/A17d80LcFMW+9+HBvE\nzPP5jiwWDF3EpDeh2OXj6eu6hD1SYakbf0Bm1PyPNV/5/Nbv6ZbpYHq/TrRvGY9CeCcDBP1mnFlf\nms9gkE7KxsyJ9Imnrb9tSKwAjY8X1Fhh2EuQ1BZKfmhQrLB5dzFv7y7ihl5nRGw05n20j3/2i8Oy\nKld7hhdyl3I7ImHOSLHx4ujuPLbuW+2egKwQbzXxxxVfaKzwDw/KRgYWTOypdQLlTe4V0WK95Mbe\nYXbujEIcqm7WN+dvoDH2eNra7slE+SGwtxBa4fXgeDSWVXjsbVGQsJX9UO+1Kgv2D0WuWLIcgy4a\nnSxLknQR8IWiKJWSJI0FugP/UBTlJwBFUfro3acoypvAm7WOPRLy988Q7dm171sCLGnsczYa0Vqg\nTCGJRUN2lPV2kn1uWJoLE9YLWR2VwTWKxE73NlaqJIm0hPCWKTXxnXlFa1LXXxuhY/vE6E1MXhXe\nYp2RYsNkkFh2c29tbinRYkLqlhExf9y5VaJua+r+o5Ws2VnAkyO6aDOjaqX7H1u/D3t8vWSjsQtT\nU2ZJY/OnkWjM/C4AkkHoLNtSROtfXLJInoe9FGmnZnvknPLSXPy37eKRbcXMvvQ+DEe+DrIX5y5m\n6UcVXNczNcymu2Um8fBlaZzbqhqP4ucHly1qcKXO0i2/uQ8HnW6tU0JlYg0Nuvx+mSKXB19Axmw0\nYLcYqPSE/z9o9GZCDM2L2j5XJ/nl3zfApE1CdmTtLXD1/8Hmh8S5KP6zc6qJbplJ7Coo5+HL0nCs\nzQ37TMfaCeSNfYuPjhh56q1If2k1Gfjw/j9gsxhJspp5YkQXDjmrmbok0l/WZtJ++rouPH1dl7Ax\ngqev64LZEF2zuT40NbA/kT7xtPa3DYkVoO54wZ6mX3H2uYXv3T4HLr233ljh/FZWzdb1qrhTcpKC\niXLNPeZVY0SscHFHnG4fBokwNmujQSIpzsTKqX2xGCWOVHjCRq3mjc2hjSMOv6wwfs5HEbFCqJ3P\n3fZjxO8hVGqqvk6NxiS/DbXH09p2TyYqDjZYNup4NJZVKEYLPlt6w5LlGl6LvUUurshunCpIDKcH\nmjJg8xJQJUlSV+A+4Ccgr1mf6mRAbYEKYZhUpRo01LejrO4kv3IFPHeeeC3aDcaaxVUyiPbCMWvg\n9h1iF1n9PhWOLKSSvcQ7vydvWEu6ZQZbVjJSbNhMEuemmXSfw1VZSbHLo7VLqTN4eR/t55KntjFq\n/sccKfdQ4fXrzh+rC1PtBXfT14f486BsHHYzCyb2ZO1tF7H0pt58tv8oEy5sXy+DdmMZmaPNktau\nYh/vPacD1LnKdil20hKt0YMBWYaqEpGILBwkXq2JorrsLo20U9C1XUUykpttx/DeUyLxnrgRBszG\n8P5TTOyWRLLNzOppfZk3Lofrc9rxylXx5LyTS9wLXUleOpCu1oO8NPqCMJt6Nrcrc7cJAvy3dxfh\nl2XuXvUlUxfv1JUs8ftlvjtSwch527m0hsU4/5ibf2zZG2Z/oZsJH97/B43kLhYw/UKo7XOjJASU\nFcKRb0TiYE+Ba+fDHTshsY2uDVoIsPAa4Tszkoy6n+mscDF18c4IfzlnTHf+sWWv5isNBomAjJYo\ng75vUf3m65//TEaKjcU39mLLny7hmdyu2CxGXv3vPuaOzWkwS7uK42G0P5E+8bT2tw2JFaDueEEv\nTpBlkTjHpwlZyQbECqG2XuUNaPbVLTOJNeM60rWVJWqsoCoKHHWJfzO1k+Iv67/loiffZeS87Ryp\n8PCPLd9HdLgFZPD5Zd1YYelNvTUfn5ZooVVSHK/dciHv3XsZeZN7seij/Vprd7TfQGPtvjH2eFrb\n7slEecOT5Z8rFNKaQfLYE9+2QYzYdouJFvGWGMlXDFHRlDZsv6IoiiRJQxEV5X9JkjShuR/sF4fB\nINqjbtoSfb6ovh3l0J3kjB6iUuJ1QXxLsZh6K8DvgVU1MiidB8HIPFg5XiQll94vWrXLCuHjl0js\nMZGZV7RnyAIxi7xwYg5J5XsxVB/RfY6khHjmjcshwWriP3dfitVk4J1vD9H9zFRW1Mzg/f2dPTw6\n5NyoLYG1W1O7ZToY3r0dY2rtLC/7+ADzPjhAt0wHMwZnkxpvoa3DRuukuLBkoykkSk2ZJY3Nnx4n\nqoqDM8kgXleOF+MD7z0pZpdD7dbeUswpf/wSXHCDCPDiW3KoyijYsfdsFH9CcOaVs7niX4LJdeYV\nrZl9RQsMiwaFfadp5Wg6T3ybJTf2BgkOl1XzxKbvwpJiu9nI3LE5TKtV6VODriKXRzsHwg5uXfo5\nCyb2ZOXOwjD7aygZWAwnALV9riTp+1efW3Q5jMwTGzemOFhcMyufuwiqjgYJ6Wyp8M4jJPeYyN8G\ndCDVZoiq+d0/O50Hrz4HgyTx3/v/gKworP38Z/plt2JETgaHy6pJT7Lg1UkKavsWs8lA/+x0hnZr\nxw0vB33l3LE55H10gJU7C9lfUsXKqX1RFKXBFeLjIaE7kT7xtPa3DYkVIHq8IEnRZ/Ulo+hS6zxI\n2H3tWOG9p4P+1p4KOxeR3OH3zLyiPfEt7Lw8rgfPbfmO2ReZSF2fKzYsdZ7BkZjAgok9aZcSh91s\nZOufLiXRamRfSRU3XtyBETmZzN32o0bGFcqQ3ahYYVwOWSl2St0+xrzyCWkJVqZd1pEbL+5AlTdA\nqyT9DdzG2n1j7PG0tt2TBUURfCitz6/3UrdPoaRaOe7KMgiSrxYFb4PsB0Pd6U7b5LiYfFQMUdGU\nZLlCkqQHgbHAJZIkGQFz8z7WSYLBEEnQEQo9Ao7QHWV1JzmjB1z+SLCl0JEFY18HmyM4jwdaMqFM\nfhvJdVgkJ6HEHnFJdLKYWTGlD+mJVtKN5cQtHyuCxGteCPv8kiGLeGjTQSZf3JF/bPme6f3OwheQ\n6dQ6Oax1+skRXTBIsHpaX0oqvczd9qO2y6sGcKGtqdP7ddJaqCC4szxjcDZ8cIBdBU6mLt4JwIf3\n/yFi4WvKwtSUWdLY/OlxIloVpEVHUdHb8S9hw1VHIc4By3Kh/SVwyT3BgM6RRcqwPA7JDt0AbW+J\nT5u3T11/rX57tzMf2e/lrhWCkmDm0HPDdDyfvq4L05Z8TlqihWU39cZokCISD19AP7lRWS5jgdEp\nhFCfK8uR/nXYXJEcr54o/N7weSJRduaL9z53OCHdsLlQeQTMds5KMmHY/FCEr1RGLSXTnsXtyV7G\n/etTzTc+m9uV33dO4/Zlu8KS3dQEszarqdfJIMsKrmo/Dww8h/GvfhrmK6fV+MqVOwt5e3cRjw5R\nwtiD68PxBPYn0iee9v62vlgBoscLUq1uh9qz+n2nw4C/Qt414bFCi45w2f1CYaBWnJBtsyIlx+N0\ne/nHkEziFvUX13z4XIT9O4cuYtKKfRS7fDyb25Vn/ruH2/7wO6q8fu4J4TZR26U7pScwb1xO02KF\nxTu1MSCVe0KNF0DEDOiMiDbW7htjj6e97Z4MVJcJX90YjeVmqiwbZC9xrgKqk9rXeW27FDsf7C1G\nURSkBsxVx3B6oSlt2KMAD3CjoiiHgXbA0836VKcqQneU//iNeA0l61B3kvVm75YMF7tbtZODPRvF\nPJ6aKKvXr7sdzPG4ZRNPbPqO74tcGFS90cIdgvijps3VM3YDN71Vyebdxdy96kseu+Y8nt/6PQZJ\n0tWlrfTKXDd3O7M27OaeAZ3pn52uVeYMBolWSVZmDT2PFVP6kNnCprtoqVW8bpkO5o3LYfW0vkiS\nFNEmpS5MoahvYVIX4ca0LDblnhhCoNpuKBxZIlEZ9Kyw6YqDYsEz1owBnHVVMFEGjWAJJYBz6KKw\nNsXAyGU8suUwD1+WRur6mnv02rsdWewt8XLfVZ2Z3q8TdquJvMm92P7g5Syc1ItAjX29vbuI0a98\ngsVkjGgvNxsNujan3hsLjE5R1PavkzaJY6snBv1exeHwGec3poX7zTemiQ4dXxWSwSD8a4ivZMBs\nAvY0kAwaURwIn3b3qi8prfRFJLtfFZYzY+033HdVZ7plOiJ8S0mll/GvfkqZ26frKx015Il6qgPF\nFR5+Lq2iuMKj22LaFP+p4kT6xJi/bQDqihdC/V7teGH780G5qFBk9QomyhAWJ8gGC4fKhJQTAU/w\nmpBYQZn+BV8PfI1Jb1ayq6Bcs/n7rjoHX0DRWLQhGCtM79eJvUWu44oVVL3lBRN7smJKH+aNy9F+\nR9HsuLF23xh7jNnuSUDFLysbpcKTIGiQbGU/1ntt2+Q4qrwBbXM+hhhC0aTKMqL9OiBJ0lnA2cDy\n5n2sUxh17SirO8lel36VTjIGK24ZPeAPD0NSO5AQVZLQe5z5yAYTD2w8yGPXnIvVJGGMM8Ntn4k2\nLjkA2/8J+9/nmytXaVIohaVuPH6Z4govLo++Lm1FdZCJ8v41X7Fyal/izAYOlbmxmIx4/QEmLfwM\ngHnjciJ2Yftnp9M6OY6N0y/GH1C4bVmQnbg2UUZTSJQaTUzVxHtOK9QnYaLa7ruzRZtfUjsxPiAZ\nILUTuA7D2tuEjY5aIuzYlqJr579rYWFHaSZthq+jVbxEwGDhuzIrxa5i0aKdkC6Sl8TWMPY1eOdR\nkdQ4sqgcvpiln1Yypm8a964O2tXT13XhqbcE67Va8dhV4ESW5TDpktR4C+kJ1og27TljuvPy+/ti\ngdGpjtqVZr8n3MbUDZaEdGiVLboT3KWigla4Q1yb0p4Kjx+zMZ64yW+JllU5IGbmvliOIaMXVR79\nypWelqwqc3Pv6q9YMaUPkiRhlESSnBpv0apgRRUe3YqVrCgsmNgzjEkbaBDJ0PGQ0J1Inxjztw2E\nXrxQu+KsN6tf/nNkrJByhvCbqq0DOPNRDCZuX5uvsVtvubVrZJyw+SE8E95myILPw75GjQdS4i26\nv4esVDv31KhyNCVWUOWljpR7wuTc5ozpTordHNWOG2v3jbHHmO2eBJQfFK8NSZabs7JsbwuArWwv\npZlX1Hltq6Q4AA4crSI9Me74vzyG3xSakiy/D/xekqQUYCuwA1FtHtOcD/arg5qMxCWLP50Hhc9s\nOrLAaIaRi+G9p6DvbVpVRHJkifnPrY8FF0FHFt+XymzeXcy3h1zMHX0BpooCWDku2II1Mo/Kvnfz\n+Kqfta/JSLFx4Ggl0/t1ihq8FVUEd87Ezq/MyHnbtUVp1dQ+LJjYE7vFiC8g88LoblprYv/sdKb3\nO4vr53/MjMHZzNqwu865oqYuTPXNkkZjyozNn+qgIRImBgOknQ2XPRCmp8w1L8An88TxMWtg6QgR\nrA2fL2bwddqtjQaJ7w+Vcf2GPXTLdHB3/7No67Cy9KbeSNXH4MrH4fUpYXbMgL9CyV4sNju5PVIj\nqhz3rhbyUlMX79SkptbsLOBopVeX1f3sVomsnNoXf0DGVMOGfecVnbjXdHYsMPo1wWSFyZuFXNSH\nz0HR/2DMaqg6BnnDwu30PzPBVUQVVv76UQUzLi6GgD/Ytu3IQhm1hDIpif1HK3R9o56WbKjMjS+g\nMPZfH2v2lje5F3FmI6un9aXaFwjzlRkpNhZO6onHL3Pv6lrz9QmWBs1kHm9g3xif2FjW7Zi/PQ7E\npwl1DMko4oJQP5rRQ2xUjloK254IixXCbL1wBziy2FeuaLrJF3dwYNGJE+Tf30eVOUXX5hPjzOSX\nVOmeO+R0a6MHtWOF/tnpPDLkXG2ka+vuIxGKGS+P74FfjpQLvHXp5yy7qXfU/z1N3TDXs8dYrHAK\nQKsst6z30p8rZEwStGiGfFU22/FZWzSostw6uSZZLqmkV/sWx//lMfym0JRkWVIUpUqSpBuBfyqK\n8pQkSc2refxrg14yMnKxmDHK6iUWxsQ2ooXVFAdXPCaSjtB2qrW3inbXpbngyMKXu4SFHwWrxWcn\nVGJYNC6CgMk28U2KXSKYC50z+vuoC5j95u6IxUut0KnISLGx/2iltpClJVgpLHWH7QL/fWRXlt/c\nG5PRgCwrjJr/cVjFJRR6c0XNvTCpTJl/f2cPI3IySY234Pb6aZtsw2RqymTBbxwNkTwDcJdEknyt\nu11UM1aMgRu3iHtkGTwVoiNCJagLCeSkTfcz7tL7aHn9+VycYaSyspSDRSXM31nO80MyYNGUSCKx\nkXmwNBezI4tzJ7xdZzurOgbw8KBsTdJEPX5z3o4wEqVQwjlHM7R1xfALIdSnquSHI16FaqdInN+4\nJdJOBz2LHN+Kme8eJTfbTrwrP1IffMVYzOPe4vmtByKl8MblYDUFdZL1ZG5q+8oj5dVhsjjP5nbl\n7yMvIDXBQoLVhF9WmLhge4SNLru5d4N8J/wySWlT5fpiWrWNRLSNywkb4eh3olsnLgX2boGOl+nH\nCqpP3vwQyqgl+KoMQam0S1tgWDYkwr9KkzbhdPsjZJxeHtcDm9nA81v3RvweXhrTnUfWfqs9eqj9\nd8t0MOHC9lw//+Ow38r7e46QN7kXZqOBeKsRv6zg9up3cRRVeLBZTFFtuznsXs+u8yb3IiHOhM8v\nx+z2l4KWLNefhBZWyKTbj09jORSCEbv+ZLllghWjQeKnksrm+eIYflNoUrIsSVJfRCX5xppjp88A\noF47a1WxaF8N1aj9eg10yRVJhhrstewMW2dC/5n67YOOM2D6Lqgsweyr4G+Xt+GRP7Tkn5+UY1B0\n5p2d+Uiyn1lDz8NuMWq6s8UuD3arkb8O74Isy1ryYDYZcFX7wwiT5o3N4eE3vtE+ctplHbmrpu0K\nxKJ218ovWTGlD8dcXty+4MLndPtOClFGSaWXv7+zhwkXto8Ids9pnRRb+GqjPsmz+q6rabdWAl6k\npHZi4VMDuIwewu7j0yA5SzAS//4uJNnPwPYGpKoSkuJttIk3MndIKxSDrP8d1iTxWYU7MCpeXbvy\nBWTmjcshNd5Cm+Q4FNANwg463Vw3d3tMP/PXCNW/+tyic+Hal0XrXul+QTS3ZnJNJ0KIr1V9aEp7\nDEYzf7s8QMBgBre+FJVB9lHs8vDM5j3MGJyNw2amyhugTXIcSVaz1pGgAH/duFsjNdLzlbUJje5e\n9SXLbu5DlcdHsTcQ5i9VFJa6MUpN110+EWgs+3BMq7YeRBt7cR8T3A+h6/+7s0Xnzo6FYgRGDsA5\nV8OmB6LHCmlnw4QNSF+t4Oy2F/Da9R054s4kzqwvlUbAx/hXPyUtwRpm8w67CaPBoPt7aJlgiRor\nTLusoy4fyoKJPZm77Ufu7n+Wpk0+Y3C2rq2XVHppk3xi211r27W6wTX+1a9idvtLovyQWOON9Y+P\nFFbIzSIbpcIT34bkw9sFI3cdxF1Gg0R6opUDJVXN9+Ux/GbQlGT5j8CDwOuKonwrSVIH4N3mfaxT\nFHq7wqOWiupc76nh7Ncj88BXKV4VRRAhjcwT14W0BYa2D+L8SWjbmuJgzWQkZz52Rxb3jVyC1xCH\nVafl1S0bsJgM3F3DYtk/O53nrr9A2zVNq5m9UCsADruZ1269EJ9fxmw04AvIPJ3blcNlbp59+/s6\nqsVyxMI3d9uPum1X0XQTm6sK4fUHGJGTGbFYq8ybsfaqWqhP8kyFMcp19pbQeRA+TJjLCpCUQDiB\nzIqxItG9+pmwKrM0fL74zJpqteTIQspdpD+icOxHQXSz+SHyy/y8OLp72Cz830d2xWwy8MBrX5OW\nYGV6v078Lj0hahAGjZPZieEUQO1qcr/HRMeN5muXCAZ2RRE64LVHBZBg0RAkZz4m1Qfr2Fp+mZ8n\nR3Rh0Uf7AREkdUyLJ8lqxmQy0DopjpJKL7Is89g15/HoEMGOajLC07ldqaj2UVThoW1ynH7FrLya\nkkovszbsjpoo2CzGRs8in8hKbmPZh49H0uo3j2jV46S2YhY5lL392pchOVNo3IcqC0z9oO5YoXS/\niBU6XSkqx858WtcwvevZvGIQ67q6TqpYPa0vHdPtLLupN0UVHkoqvfzrv/u484qzMBsNwY32WrFC\ncpQ4weXxM7x7O3YfqtC60+Zu+zHCn6u/v+5ZXWr9r2teG69t13obXDG7/QVQcajhGssuha4Nu7RB\n8NrbYvK5MFcfxWdLq/PaVklx7C+OVZZjiESjk2VFUd4D3pMkKb7m/T5genM/2Cloo5jhAAAgAElE\nQVQJvXbWFWNgwoZI9uuV48WMZ/nPsH66OGaywTod1utBz4pzZjt4yuG18DZVaeVYzJM2o4xcjBQy\ni6SMXMzq73x0aKWwcFJP4kwGSqt8YTqHL4/vgdVk0CRN1DYkgPxjVREthIYoFY+AokQkyLsKnCz6\naL8m4VOb+EZd5BpahWjoQqmea0xwd1qjPskzCLZWD50TnqBc8wJsnYl86X0Y5GqkvGv0tTsvvT+S\n0f31KcK2Q4+tmiAkqI58HRkE9v8rZcPyuGfjQZ6/oTULJvbE5fHj8ctktLCTO3c7aQlW7hnQmfvX\nfEVagjWirTC0bRZOvE3EWlGbEaH+dcDsoB1Cja8dG5ybr+1Dx6+FzQ9H+uDxa8NsreraxTh9yaTa\nzUzvd1aEVnentAT2Frs0X6VyNDy/9fuITpY5Y7rTPzs9TINW3axRNx2jbSg6bJawKrbJaCA9QV9z\nFur3ocdrh42V04lp1daBaGMvY9ZEslm/drM4brEHE2MQm4y1Y4qGxAorxqBM2IAUYvPyyMWUmVK1\nf99umQ6mXdaR1HgLaYlWDjo9YbwPc8fmsG5XIfM+OED/7HTuu+rsiFghmu23TLByw8sf82xuV80+\ndhU4WfrxT+RN7sWxSi8llV4WfbSfO684SyNoVDeJmtKtUJft17brho6OxdDMKD/YoBbsar9CUZVC\nq8zm+2pPvEry9WODkuX//hCTj4ohEo0e8JQkqa8kSbuB/9W87ypJ0pxmf7JTEdHaVEMrbaHH3ccg\nOSN4zluhf13q7wAF3rpPkH3otVsHPCzZl4B/wpsE7tiFPHETbxa14IIzW/LAa19zxf+9z96iyghi\npJvzdvBTSVXYsZ9KqvippEq3hbCtI05jtYTgHPThsmoyUmzsKnBq7Vqrp/XlsWvOo22yjfJqPyPn\nbaf33/7D8DkfsudIhcb6Gq0KoVYAIRgMDp/zIRc9+W7EZ4QiNd5CeqK1yZIqpx3qkzwDEeAtGS5I\n5sa+JkiVBswWSeyejRhWjsNU/rM4ltpJzOSHSEOR0l7fts32yGPVpWKD6bZPYcjzWrWk0taG/wUy\nKHb52H2ogkkLP6PaFyDObOBwWTWFpe6w9r9dBU6eemsPs4aex9Y/Xcq/p/Rh0Uf7NUIaOLE20Rib\njaEBCPWvUZjWNdmy2seRwqtpNccVJOQJG/Hd/gW+iW9z0Nqee1Z/zU/H3FqiDEGfVOTyhPmqETmZ\nTFuyU7eT5daln/PnQdkRvnLuth+1EZXa/nLl1L50bpUIwN5iFyPnbeeSp7cxct529ha7otpOXT60\nOeywsXI6xyNp9ZtHtDhBkqLHCaGSaHBcsQKKjHvcRuTpX1A1dgMPfShT4ZGZNzaH/tnp3DOgM7M2\n7Oa6udvZV1ypJcoQlEvrfqYo7Y3IyaTgmDsiVohm++XVQj5NtX8VK3cW8sSm/9HWYeO8tkk8PCib\nf2z5PixecLrrjxNqoz7br23XVd5AzG5PBsoPNqiyfKiy+ZiwVWjJcnkDSL6S4qj0BOq0uRhOTzSl\nDfs5YACwDkBRlC8lSbqkWZ/qVIVeO2vnQWAw6bevVhaL3V9V6iTOoX9dyQ+C2AuC0ii1rpEUmZ6t\n4bL5e3kmtyvpiVa6tzfy3aEK5o3pRltLJfFGN/8Y3JbHtxWHSUnZLUZtN9lhM5OeaMUZRRP0UFm1\npp2Y2cJGwTG3Vql7YXQ3Sit92C1GqrwBWsRbtJbFulryGlKFaExbn8Eg0TZZzCjXZkKOSQJFgSph\nos7Slf8cPkunBngJ6eL6VweE3+/MF22E2+eIubovlovEueVZYnygrFDftn215n8cWcLGKw6LVtpr\nXoD4ViiD/g8sdi5o4WPLjR044HSRlmDm3tVf8dyoC0i2mclIsUVUBnYVOJm08DNev/VCXnrrB26/\nvBO7D1Ucl000tEoXa0VtZoT614BP354w6B/3e/SPG4zcsu4w916VTYJkpKyqik03dibO4OGMEVk8\nuPlQmK/0B+Qw+2qbHMeMwdl0Sk/Q9WGyorBoci9KK70k28w8vfk7dhU4mbvtx6j+0mCQKK7wNMp2\n6vKhzWGHjWUfPh5Jq988oo29IEWPE9S/qzwQxxkr/FQa4KY1+cwYnM1/9zm55XJx+m/Dz8NUXcKW\nGzvglk2UKFZdu0pPtDJvXA6d0hNQFH1uiPJqPwsn9cJZJarFz2zew7TLOorqtI79n5Fq1+KF4XM+\njLDXFVP6NLrqW5/t17brpow/xHCcCPiEjf/CGssq/NYUAsY44sr21Xtt62ThLw8craRlQmwNjyGI\npiTLKIpSUKtF4fToYbGnCdZKf7XYJZYkIU2y6QER9IfOLKutpRCUOnnn0cjrRrwCUkiy/cXySIbh\noXOg/Gc6pWTybG5X2jpsLNm+n8Fd2/HvTw8w+yITKavFrFOOI4tXhiziprdgV0E5GSk2FNBaV9XW\nwhmDz4067+mwi8Qk3mqkQ1o8fx50Dr6ATEBWwliyXx7fA6i/Ja8hLX6NbeszmQyc0zopppXYGNQl\nIWW2wZhVgmROUfTnikt+EHN0cUni3J6NIrC7YiZsfxFyFwVn7lRGeEUO2rYjS0hO2VJg3W3B1sIJ\nG5Def4r4LtdrrbdnO7JYkbuUvUomSXYrS7bv58kRXbTKQG1bSraZKa7wIgErpvQBwGwyYDJImiZo\nQ+yjMcRFsVbUZoY9DcavB8UPGES7/juPaBrcjF8nfG/tUYHh84WWbO3jQ+cgIfHM1e0oM0q4PD46\n8hOJSyZoNrZwWB4TNwZ9pckYZMPulunAYjJgMRqQQNfuFAVMBonWSVb8Mjww8Bxu6HUGm74+hM8v\n6/pLaLzt1OVDm8sOG8M+HNOqrQPq2MuXK6DbGLGZbrKAT8d2h82FLY+I+3IXwfvPCKmoZooV0pOs\nvDIhh6MuD+98c4h7usmYVo4GZz5xjiwSRy5j2u/PZO4HB7THV/3pS9t+YEROJme10ueGSLGbURRI\nT7KSEm/hkSHZVHr8vDC6G3Pe/QGPT9/+o9lrQNH/jdVV9W2I7de2a4fNErPbXxKuI4DSwGS5+SvL\nSAa89tbYyutPljWt5ZIqepwZk4+KIYimJMsFkiRdCCiSJFkQ88r/a97HOkWhyKJlKlTDcOgcqDwi\nEmOVFTjOIZKBGh1EzHZ4/TpxT+WR4HW2FmCOg9IDoh3VaIaADyXOgTToWXGfr0q8fvAsgf5/44lN\nu5l5RWvu6ZvAd8XF3HVhC1LXh8w6OfNJXT+Bh69cxZ0bfLw8rgcOu4nceR9rAeCEC9sza8O3EbN0\nT47owucHjtHWYWPDF4Vc3aWdRsqxYGLPqMQY9SXDdVUh1CpeQFFYMLEnz2/dq7XR1rdQxrQSG4lo\ns3Q3viNIY0KJZ0bmiWvURCWUXGbC+vBk2hQHPSaKxVBlKU5oJQI+1d5tKcKWE9tAWX5QT9yZD5VF\ncNZVETOqllVjqL5yFVOXHuTZ3K7EmQ2kthAzddOW7CQtwcwzV7clK9mEVynn+Rsu4HCZRwuAmjL/\n1pgqXWPnPGOoB4ospKFC/WvuIrj0XlGZMBhhWa7ofgi1qZQzoefN4v2wl8Rn+arAEg+VxVisqaT6\ni5Elifi1E8JsLPmN8ZqvnDs2h7QQX3XfVZ05VullxtpvdOfjXxrTnSc2/Q+HzcLYvmdw69IggdHS\nm3pr3BEQaUeNtZ1oPjTFZqbIJWtat3O3/aixd59oO4z53ygwGITyxfkjhL2Gxgpf/Tvcdg2GoC+U\nJLjyL7DkWv1YwWiGD54J3l9PrDBq/scMyE7j/wZn4PZXcfeFSZgWXR1m/6aVo/njhLfZ8M2RsJnl\nFZ/+pM3oR7P9So+PcrcYk7llaTgZ42PXnKfpMUPD4oU4s6Heqm/trh+bpfE+OGa3vzDKVdmo+pPl\nnytkDBKkNjNJupCP+qHe69ISrRgkUVmOIYZQNCVZngb8A2gHFAJvA7c150OdsnAdDgZyIF7X3lqj\nQztW/AEx76kmymNWQyBkhkllDwaYWJNsrL1NJCNbHhXJyOS3oUUHqDwq2gs/eBb63MLBKiOvXBVP\n6nqxmJ7vyCIw9nXduaUubWwsnNSLlglmKj3B3dfQmc/iCi8zBmeTGm8h2Wbm5ff3cUe/Tsza8C0P\nDDxHIwUDsFuMUXdw2yTb6lzkolUhIJLQQ9WBLnZ5Yu1RzY1os3Teykh95ZXjRRdF31tFu99/ZgaD\nuorDcMm94u8X3ACrJ4p7xr0RZCmeuDGYTKv2DnD7Dtj6l+B7R5b4vCgzqul2SZunnzX0PIa++BH9\ns9NZNbU3aVX7MK0cBs58zI4sfEMX4Y3/HQaDQnm1l8Nl1Tyb2xWn28fcbT82qDW1MVW6WCtqM0PP\nv66aEPSv03eJYyrZl4rbd0J1mZDr6z5ObL6A2MSpLsO6arC4Z/LmKL7SzsJJPTnq8lLh9Wu+yuMP\naDqyhaVubT6+Q1o8Xr/M05u/o7jCG+ErhW/11GlHjbUdPR+aYjOHkZGFsgzfdWXnmB2eTFQeqTtW\nUHHHzmCl2F8NloTosYLBBOePDPpiR5aIFVI6QFV4rLDPKdMtM4nZF5mIzxtAfB32b1R8LJrcCwmw\nmgxYTQa6n5mqxQmhtp/ZQnRTKCiUuLxU+2TuWvlNmO3ftfLLOnXEo8ULLeOttIy3Rq36Ruv6yZvc\nK4zANOaDTzFUHBSvDWzDTrOBsdFsSnXDaxfyUQZ/NbIpeiZuMhhIS7SSfywmHxVDOJrChn0UobF8\n+iHg0082bCnB944sMdt587til9doEfOhofNIF/0xuFvsOhzBdCk5D0BiOzHvbEuBHhNxWdOorqrm\nzA3hlRFj6T7duaWvDrm5c8OnzBubQxtHnLb7Gjrzqc7WTbusIy0TrNzRrxOgcO+AszlW6Q1b7OrS\nVG5IS57ebq7e3N69q79ixZQ+sfaoE4Fos3SyvoY3fje8cYv+jN3mh2DiRhTZj6SeNxiDbYZR5ukU\noxkpvpWQAYpPE1XCr1dDVh/RBm62BzVFXUWkJtp466bOxBsDOBJ9dMtM4u3dRdzR20GbTaPDfguO\ntRMoGPga+ceSaZloZfmnP/H27qIwluz6WlMbU/GLtaI2M+ryr44skIxB/oeL/hhSnTMGGbExiPPF\ne0QXUKj9Vhbr2uR3xR6GLHhf05J12Cw1AVNlRMDvDYg2QZvZyB2XdyLObIzwlSA6FOqyo6bYTm0f\nquc/71/zFSun9tVmo2M4SWhorGC0CDZsSRKJsqei7lhh3e0i4d78kBgzkH1ioygkVigzteTBdYU8\nfFkaqetz67X/gGRmQkiyqRJ86tm+0SBhNorxlskLd4SxXqsoLK1bR7w+24+2mRmt6+e1Wy+M+eBT\nGY2oLBe65OZtwa6BJ74tEgpxFfupSjmnzmvTEuP4qSRWWY4hHI3ev5EkKU2SpIckSZovSdKr6p8T\n8XCnHIzmIAOwilASI7V91VsFNgdsrZlZTjlDzG92HgSXPyIWulcHCAkUySgWRZXp0myHtx+GgEdU\nRmpw2Gsj3qjDuv3ekwRGLgljJi4ZsojHtxVTWOpm6pKd+GWF5Tf3ZsufLqWtw6axQXbLdGjMmJc9\ns40bXv6Y/GNuvAFZC/ZUzN32I09f1yWM/XLe2ByMBrHjqwZy7VLsGrFGfYhWxQMa/BkxNALqLF0o\ni/U1LwimSj27Lj8ozodeP2wuxCXDsJdQQMiYqOclg2DTHjBbsMDn5oXdq4xaiqQgZKbU30DeUKEV\nak0SbeALB4lz/R6DkYuxe45y9obhZC7qReKSq1g93MH62/qSbkc3GHVYZOwWI7fUMBhDMImY3q9T\nva2pjWUFbordxxAFdfnX8etEYjF6lbCNzQ8JW9l4t5iJS0gHJHjtRvFqS4H49HAb+fC5CHuuGJ7H\nI1sOA2j+8milh6KKak1GD8J95aVPb+P6lz+mvNqv6ysB1uwsiFAVCPWXcPy2E81/KooSs8OTjYbG\nCooCzgPCNqvLwF1Sd6yQkA5pZ4suHrMd5ADsfFVUlQ0mfC3P4b53K9hVUE66XarX/v0jl/HcR8fC\nEtCpi3fiqCFUhHDb/8Mz73H9/I8prvCQlmCNYL2GmpZqiyHCj84bl0OKzQw0zfaj2bvPL8d88KmM\n8kLhu61J9V5aWHHikmUAWwNIvlrFKssx6KApbdhrgQ+ALTSC2EuSpKsQ7dtG4BVFUZ6odf4SBNN2\nF+B6RVFWh5ybADxc8/ZxRVEWNeG5jx8JrcVCFjpTN3IJIIs2KV+VSCTkgCBTGvikOJZ3jVjkrlsA\nxd+JuTq1evbGtOBOsWQQkhCuIijdD+Z4Qf5RuIPMO3fj98dF7gy7iqi2tcY7ehOJJpkvD1Xx+Fvh\nbNhev0yZ28+0JTu5sEMqy2/ujV9WMBkM3PDyxxGViQUTe7JmZ0HYTHOxy0NaopWVU/viC8jsK65k\n0UcHGHh+G9q3jMduNdIyvnELVWzm8xdGqISU3wtIUF4gNmVqk3MNnSMS37SzRYJiMIkA0FsJy0eB\nMx9JTZ5Hr4RlI4VNu4pE62BGD7jqKdEtUTNPJ1kTa5i33cLmP3xOtBOuHB+px7z2VvGbWjgovJNi\n5WjOHPsWBilet0ri9Bpw+n1aF4WKwlI3nVsnoKDwc2lV1ApErFp8EqHnX8esFn4w4BF6yY4zhf2E\n+tDXbhb2I/uF/RmM8OVyOO+6cBsp3AGfzMM/4U28/gCK0cL45T9qvhKEnVR5Aoz91ydc2CGVvMm9\ncPsCJMaZGd1AX5mRYuPOfmfROsnKv6f0ISArlFZ6Ka/24S4OkJ7op22yDZPp+HoNY/7zFEZDY4WA\nV7RRX/qgGB94bUrdscKgZ0VskJwlWq57TIQeN4rKdFkhFZY23PqH33F77xTSEy269i9P3ITXH8At\nGylREpn7wX/DHr2w1E1RhYcXR3fD61donRwXESfcsvRzZg09j+e37o2w/bljc/D7FVolWVh6U2+K\nKzxU+wJUeQIUOt1NihUgZu+/WpQVio3LenSLvQGFI5UKf2jX/I/gtbcGIK4BJF/pSXGUVvmoqPaR\nGGeu9/oYTg80JVm2K4pyf2NukCTJCLwIXImYc/5MkqR1iqLsDrksH5gI3FPr3hbAo0APQAF21txb\n2oRnPz4YTdDqPJi0SSxykhE+fQW2Py/OdxsLPW+KZKdU5XiqSsJJlFTSpPg0scv81UpRdUtpLxzM\noa8hdzHIPiyKF6vJIBIXleTm0vtRWnTkaHkVs98r5p4B5zB/52EeviyNdHs6RVUK83eWIwHVvgBP\nXHs+LRMtWrtztBYql8fPhAvbs+ij/dpMsyoBtedwBS3izWz6+hBDu7ULWyQbQqAUitjM50lAqITU\nkW9EcObMF5WMsa+LDRuTBcVbJdqlzx8ZSVKTkB6cHd3+Igx8QrAYo4hW2JIfBKu28yfYvVaQdyVn\niKAvNCFX7b9wh74ec5QZ64rKSg7JqXQbtQzDitHa5zmHLkI2pTJ33f/ISLEhKwrrb+tLpwQPRsVL\nQCnjufePMfeDA3Xaa4wA5iTBaIL0c0Uy4feKzRm3E5ZeF86wvmNhJPFci47CJ0/aDASEH5UD4rM2\nPSBmSGv85c/lfl76tJQHL0tnzuBWHHSlaXJ7GSk2DpdX88S159PGYaPE5UVWFNxe/apWNF9pMxt4\n8LWveXt3Ef2z07n98k5hBEnzxuVwTuuk49qEifnPUxhhsYJPbDZ+Mv/4Y4WU9uLzqo4KMjC3UyQh\nthYQl0KKQSLFUoZkN0B1iWCK/+ifglsiPg05oRXFUgpfHKng3d2HefAy2H5LJw665LDfQEBWMJkM\n+OUAR8qrdW0/K9VOscvDM5vFPHNWqp3iCg+JcSYKSt20TLCy/JMDfHrAyT0DOnPXyi+aHCtAzN5/\ntSgrhPiW9V52uFJBAVqdgMqyYrTijUvDVla/1nKrmrU//1gV57ZNbv6HieFXiaYkyxskSbpaUZQ3\nG3FPL+AHRVH2AUiS9G9gKKAly4qiHKg5J9e6dwDwjqIox2rOvwNcBSxvwrMfP4w1FbYFA8Wur7r4\nAfS9I5hYQDipBwQXRvWcOqec2EbMK3W6MnhN3+lCduLYj7D2VjEXqgaLEzYI5tjNf0bas5EzHFm8\nOHIpb/x0lBf6xWFeJZ4h05HFiyOX8cxH+7UEIW9yL+5dLbSJo80hF1V4WLOzQJtdLqn0YrMYGfT8\nfzUSrmmXdYwgtTnR2p4xNCOqisNJvfZsFJW7CRuhrAAJCa76GywaHJ2kJqOHkJJSq79qa6GazHQe\nJIjAVo0Pdk/Utn/1uJ4es+zXrR4fcsn4bVBk70jl0DfISjYTkMz86LTw+Lr/UezysGBiT4ySTKbv\nJ8yLxmgkYHfnLsVZ1Y5/7/z5lNZEbqjW828O1cdE+/+rA+DWT2Dl2FrEc+OEzezZGO5DKw6JanLf\nO8RsZqg8z6ilYiym+Duk16dwhquI2SMXY9hyH8l7NtKmRm7voQ+tTO/XGZ8sc/uqLzUVgBlrv2HG\n4OwG+0qrycCYVz7nyRFdKK7wMiInU2PKhmCr6/HaXsx/nuIwmsQmIQjFi+aIFcw20d3zxi0isR74\nNHhc8N7T0HsqUqjU1Mg8wRR/xaPahpPBkYVpyCK+OGBh1oUGzEsHkuzMD/sNTLyoA4lxJkqrfNy7\n+quotn/I6dZIv34sruSelV+yq8DJ6ml9uW7udjJSbLw4uju/Pytd21SHpuvRx+z9VwpnPrTuUu9l\nmmxUM2osh8IT3wZbef3JcnqNfFRBLFmOIQRN6QO7E5EwuyVJKpckqUKSpPJ67mkHFIS8L6w51hA0\n6F5JkqZIkrRDkqQdxcXFDfzoJkCWwecWDkAlMVJhMEYn9YjC9kuLjvD+U+B1hS+Q3cYIiZ1acjqs\nHAeHvxKt3b2navPOppVjuPYsM+ZV4azGppWjufJM0aZUWOoOI6OZu+1HnhwRPof85IgurNlZwIQL\n23Pf6q94YtN3WIyCIXPeuBzSEqzcu/orjAapwazBdSE28/kL2q6KUBsOhTNfzMwF/KK1taygbpKa\ni/4Y1AFVz60cL6oYIF5XjQ/eo/dZ8Wlw7cuirbD2LPX2f0bM2JUMWcT8neVUeQMYJAPV1pb8tyiO\n6esLOVrl44GBZzNjcDZxZiOpUkXE78G8agyP9ktnxZQ+zBicjSzX3ps7+VBZX4fP+ZCLnnyX4XM+\nZM+RCm3W9VTBCbFbvzdIRFSXPw1936JjMFHW85krxsChL0Wl7vJHICEdw8pxQTt1Crm9fwzJpGWC\nhduX7YpQAWiIrxw1/2NmbdiNy+NnxuBsrCYDT13XhbbJcc3iK/UQ859Nwwn3ubIsZumdBeJVMjRP\nrADB1u6L/igqzGtvFbas54uPfC1GE9SKdY2t39YzIcI3pq6fwBNXteWpt/Zw46IdtEyw1Gn7edsP\nEGc2cO+qrzSi0NXT+pJsM9Mt00FhqZvbln1O62a0/9Pd3n/xWOF44fcI+49Pq/fSE6KxHAKvvY3Q\nWlbqXkdbJYkNnJ9KYnPLMQTRFDbsxCZ8j55Ha2jk16B7FUWZD8wH6NGjx4mJKmUZjtXMPDiygoQZ\n6iKlKPpsw/Hpov1V71zFIeg6OnLxNBhFa2q0BTW0MrdCVF8kWZ+BMyMpONMTytK6q8CptVB1SIvH\nLyv4AzJ/HpTNXzeKov99V3UOax9UWYUtJgMLJvbEbjFq0jzFLk9sfqgJ+EVsV4UsQ9FuIeegZ48m\nW3DTJgqjtVYFjk+LtLeEdEg/R7S/JrYOtmxH+yx7qqiUbJ0Z1BRNSIfPFsCuJWJur+a4P7EdD60/\nxG2Xn0WK3YzL42f9rkJu6+3gokHp7DlawSObDlPs8rH0pt5YJX2W79IKF6Pm79LaYdMSTy3m4MZo\nPZ9MNLvdyrLomPliufCr0fypuzT8fcUhMS5gNDXOZ9ZKuk0BN/GyTFpCUDFA7b5RfaXaat06OQ63\nL8CDV5/D3978n6Zt/MLobnh8MrM27NZ85oujuzP192fS/cxUHDYzTrePNTsLYr7yJOKE+lzVx6qa\n9p0HwdVPBZUCjidWsCQEfapqv3VtRprtogodKlnlzCfOIOte76qsZFeBEwCjQdK1/VZJcZRX+/jL\nNedR6fWTlmjRNJlD44S1u36mX3YrzEYRKzy/da/22bFZ46bhF40VmgPlNbJRDUyWDUDLE5Qse+Lb\nYvS7sVQdxhvfJup1douJxDhTjOQrhjA0iWFEkqR2kiRdKEnSJeqfem4pBDJD3mcABxv4dcdzb/Oi\nqhhK98E7j4hgzlUk5ogGPQt3fC5a/UbmhVfIRubBlsfg9alisaxdPdvyqCDukAPhO89yIHhdKEKD\nxdAF05GFYtBn4GxhM9ItUzARrtlZwJwx3bVd4mKXB7vFyF837uaHIhcDnvsAowQ39DqD/xvZVUuU\nIUhqc/9A0XI4Y+03WjXlvqs6kze5V2x+6FRB7cqGWkGtKhZB3HtPRjJdX/OC0PpUgygd9lSGzRWE\nYBM3ivGBUHvL6CGYihcPF63Zi4eL9xk9dD9LGbVUkDcpsqiMfPhckCE7Z4K4tnAHbH4IZ8BCkdKC\na3OyeOE/ezEZDDy5aTc3n+0hcclVWP7ZhfM3Xcvqax2sv/1CbGYjHsWk+3s45BL/L9R22JJK7wn5\nJ2gqGqP1/JtCVbHYxOlzC3wyDyx2ITEWan+5eSKZVt+rPnTd7WIu1FfVMJ+pk3Sbir8lYfFVLLg6\nXvOXoSoAuwqczNqwG49fZub6b9lXXMnhsmpu6HUGW/50CbOGnoer2s/dNS3cgFZdG93nTGZt2K35\ny+n9ztKYgWP4jUH1saofveAGePM+kbgOelb4TkUWI1WNjRVWjhPz+CDsV7X3gC+63dfuxnBk4cWo\ne31lQCSwGSk2rCZDhO0rikJhaRWDnv8vsqKQaDVx31XnRLRZ37/mK6Zd1lFjj5+x9hvuu6oz3TId\nsVnj0wllheJV7WyoA4UuhRY2MDcpK6kfGiN2Q1qxY4zYMdRCoyvLkiQ9CeSlRBgAACAASURBVIxC\nzBur0ZsCvF/HbZ8BnSRJag/8DFwPjG7gV24GZkuSpHr7/sCDjX3uZoHfKxa8PRsFYcyA2WIRcpeC\nNRGOfi8WoIkbofKouHbrTHHtRX8Ec5zQVDSaBbnSf2aKzx0wG2QfjH0N3nlUfP6P26DjZeG70Y4s\n0bL69p/Ffepi6MgiMHIp83ZUcMv4dUiuI6KV8Yvl0Hsq5nce5uHLHuHODT5uvLgD6YkWFk/uRUBR\nOFxWzRObvmNXgZMbL+5ARoqNar/MpIWfsWJKH92gvU1yHGNe+SRCH/m1Wy/UrdCdtvOXJwu1KxuO\nLCEZlZ4dJM1y5gv7GzAbWp0rSLn+MxOueTFY1SjcEdwMSjlTaNd+uUwQdsWniYBPZcJ25osgrnYL\n7Npbxf1Lc0UCNO51wAAVB5ECPlgwQJ/wK+BBnrAROeDHoxgpDiRz3/IvtMrEw4NkpuQkheuIOvMx\nrhhNyk1bkBPSOeRPwZ67NNhuqMqqvRVsXzsVk9DTlvXV7xWbhFsfE3borRKVtgkbRRLtc4s59ise\nhf6zRGvrB88ENZcNRkjtJJKOUOKkkXnw2SviO2o6I5SRS5C+Xh3U+7anis0ap9DrnjnwNYYsKNdU\nAPIm96LM7aOoQhAaqf7SaJC44eVPNHkdq8mg6zOLKzxh/nLakobNLMd8568QtYkJbSnBmOGiPwob\nDfhElbgxsULhDrHx2LIzTN4sfg+JrYWSAUTGCsPmCjWNWpJVZcPysJosIt4o3S82Tl1F4n5Z+J6X\nxnSn0uMHCIsVZr/5HQ8MPFvzR26fnxKXR9fmQ0e+1BhhxZQ+Ue04Zuu/QajJcgMryyeqBRvAaxfJ\nclz5PsraXFzntemJcbE27BjC0BSCr2FAZ0VRPA29QVEUvyRJtyMSXyPwqqIo30qSNBPYoSjKOkmS\negKvAynAEEmS/qIoyrmKohyTJGkWIuEGmKmSff3iMFn0KxfxLUWL1Bu3BNuuBvwVlAD0mSZYs9+Y\nFh68fficuPfyR4Jt3I4ssfBdeq9oh1VZr9Wk3FcFKGLRVK+VJBj0LH57GlOzi5A2z9KYLxnwOHy9\nGvZspEv/vzFjcDZPbPqO6f06MWPtNxHBeGqClZfGdOdwWTUZKbaoBGDR5pV9/sj5T3X+sjaDZWOZ\nMGNoBGpXNpz54v1NW4QNhybDK8bC5LeEvbiKhCxP6GiBqwjiWwkSmS+WQ9/bgrbceZBIWsa+prFo\n67YCOs4ItmWXH4S1t9UQe92iT/j1xXLwezAsvx6DMx+TI4v0YXmAsC/VBs9INul/n19Uij0Bhb1k\n0WHC2xhlL7LRwkPrCsKkgupKQk9W8Hbasr6q/jW+FZis4t9yy2PQf6Z4b7SEs6mPzIOcybB6YvDY\nhPWC7Ch0I/O9p4VP3P++uMdfjT++FaYu1yFtezI4u3zRH8XrriWc3dLCiil9cLp9lFb6NCZfFaq/\ntJjCW1WfqqnE1b62dvdCYakbt8+PLEe3qZjv/JUi1MeCsMHOg4SdJWdAdbkgrqtRtKBFR7GO21ME\nD0ltki5VYk/t3FFJF9XzCemCcLR2rKAEhP8eNlf8ffJmlKR2JMl+pOKvgwoE17woZKu2PsaZw14V\nHRIeP0ddsm6coICmmaygUOUNNNjmAd0Nopit/0ahJcv1s2Hnl8ucm1LvZU2G3+ogYLI1TGs5ycpn\nB47hD8iYjCeo1B3DrwpNSZb3AWagwckyQA179pu1jj0S8vfPEC3Weve+Crza6CdtTsiyqJ+n/k7I\n47idwcBtzKqgBI/KEJx3TfCcKgEBQeKNcW+I3eXajJirJgRnN9UKoDprBDB9l0g83KWw6V6xiALW\n2z8TO8S1F9vcRXDwC/LL/Uxd/A0ZKTYyW4hZzamLd4bNGD311v+Y3u8sWiZYeGlMd/75n0gNxaev\n60LBsSrdxVEBDjrdpCdYNQ1Rp9vL4bJqns3tqs02n4rzl78pRJFcwucWGzfj18HmPwfld+xpopIx\n6FnRIfHe0zDkeRHYKYpIWCqPwLB5sGR4uJ0vHhZua50Hic9V4cgSHRebHwq367oIv66cGfyemuPJ\nb4zn4StXcecGH0+O6MJf1n/L3we3053tU4wWDpdXh7G1A/TPTufOKzrz7SGXZs+LJ/ekJU5wekWQ\na08Dg+GkBm+nLeurLVXoevefKTZfPplXY2PD9dnU9fS5Kw4L+wu1QRAyOwNmw5v3QOEOzLd/Btui\n+MuqUvLL/Yya/zEZKTYWTurJvLE5TF0S6S//eOVZ2rldBU6e3vwdL43N4ZaQa+eM6c4L/9kb9jgZ\nKTYOOqtBEbOhNosRv6zg88vav3dJpZe/vyNmRdVZ57+/s4e/Du8S852nMuxpQoavdJ9ISE1x0O8R\nsdardpyQHrlRPvb1CL/HyvGiwrz2Fv3OnZXjhT/XixVu3yG+b8sjWpwg3fmlSGBCZamGzhHXu4rY\nW+LFYopnwYf7ue+qs5k/LocpIXHC09d1IcVu5rWdBYzqdQbJdhOZLcTxUG6TaDYvSZJGVBi6EWk2\nEosTfosoq1nrjXVv9Fb7hcZyvxOgsaxBkvDY2wqSr3qQnhiHX1Y4VFZNZosTRM8dw68KDU6WJUn6\nJyJdrAK+kCRpKyEJs6Io05v/8U4R6LW1hurNhpLK1GYIDj2X0SPYMogidEGjJQwJ6fqJgMGE9MYt\nEceRDPqMmKsmEBi3jmJnAhunX4yjZk4u3mJi1bS+/FzqpqTSq7UW7j5Uwayh57Hp60M8MPAc3L6A\nkOExSPxUUsVTb+0B0E2i71i2i2KXh7ljczi7Jqk45KzWdqdDCcJOtdbX3xRqVzYgmLQuzQ3K6Qx6\nVpDJ2FLBUy7aA92lIlmpOAxLrhXyaGrioQSi23mNrSnj1yEd+Tq8tfqTecHXK/8SNj4QYd9J7VDk\nAAad30WXNjaW39QL2VXMjN8ncbjMTdboNZiXjdC+zzl0ER45kYNOd0T3w9u7i5g19DwtCbVbDDgq\nfkD6V2S7ekml76SSbJ12Ws+yLMjcVB9787vh/kzdXAn1oe7SyPY+n1vf9ou/CyYSDfCXzvIktj94\nOYoCAVnmqMvL0pt6U1zhifCXfx95AU9cez5tHTZ+Kqli8UcHNNLEfcWVLNn+ExMubM/uQxWaH3w2\ntytWs4HRr3xCWoI1gkjx5fE9SEvQJ046FRncY6gFf3VkQpqQHrTjAbMjba/qqH6sYDCK6q8pLjJe\nSEgX5/VsvnR/ePLsyAIk/VGZcW8QGLkMh7UNuP3c0OsMWsRbUBSFhZN6YZAgICu8/P4+PtpXwhPX\nns+R8mqe2LSfW//wO0wGA4sn98JgkKLa/JMjuvDYum+460oxrqBuZvbPTueOfmfF4oTfIsoKG9yC\nrQCt40/s43jj2zRIazk9Kai1HEuWY4DGVZZ31LzuBNbVOnfqs/IdD/TaWlVNxA+fA3tLMUNUWSyq\ncaGLlpoU6O0kR6vEJbYVicnY18UCqs4f970N6ZOXkXMXY1g1LjwhKT+oz07szOdolZ9XPzzA9H5n\nMWr+x9qCNHdsDn/d+D9tDhREUtAywcLNl3TQdEPnbvuRBwaezaSFn2nXqeyYZ7dOZF9xJU+9tUf7\nnGlLdrJyal/MRoNWjVE/+/41XzFr6Hm//fnLkwl7mkj6am/ubH1MnHfmw7YnBEOrjGB4dx0Jn3cb\n94b4e2hSGxqUJbbWtTW/Asr4TVjwiYTEYICBT4rzPSaK38vQOfDxS+Ht3jXPWFhlpLUNDOrvSW1B\ndGShSGbauH/E9PpY7Z7AqGWUj38Hd2UFheUBHn+zmGdy5TDWdxUZKTYMBgNpiVZkWSFQcQRJr139\nxi0kBWSWj2xHUZXC49uK2VVQfkrON/9mUNvHel1ixn7YS8IGFVn4Sr1KcEaPYJtqYutIv3npffDe\nU+JzG+AvS90BUhKsOKt8YZ0FL43prusvZUXRNhPtFiP9slvx/Na9/HnQOZrP3Fvk0irE6YlWDpdX\naxJVMwZnRxAp3py3gxVT+ugSJ62c2vfE/lvEcHzQixc+fkkkvAaD6DbTsz1VMk0vVhg6R3S1hSbF\nGT2EzrIzP2L+WBm5hEqsJKjXq3YfZYNexsD3ZDLw6fe0zZokq5nvjlQwrVZHxd4iF62T45i44DNm\nDM4Ok1p79+5Lo9r8n2p0mNUNefWeETmZWicGxOKE3xTKChqULBfUyEa1PsF5qcfeFseh/2LwVSGb\no39Zqxqt5Z9Kqrjodyf2mWL4daDBybKiKIsAJEm6U1GUf4SekyTpzuZ+sFMKoW2toTu+jiy4Ymaw\nlVovAVaTA79bt4rB2NeEFqI2g7RYJCXHfgpPXlQSj4KPMJxzNcrEjfh8fvySCfs7D9S0yc7V3WF2\nJCbw2DUZjJy3PYJkZtbQ83h+616mXdYRh82MAlhMBiYt/CxsgUyJN0dIRc3asJvlN/cJS6LVz/YH\n5BrmzMjZ5vYt43/785cnEwaDSDRu2qLN77J6otaKp7VQLxgoNnxM1shRgWM/ClsKlUcrPxgkkbEk\n6leGJSNmfCL5DiGZ4+uV0GUUyqX3iyR64JNI3koYvQq8FaKS/dW/yeg1BWnR2PAA75N50OcWLMf2\nwPrpYc9pXDGanwa+RrkxhWe27aHY5cMgBTXEQ6ty88blIMsyxyo9HCn34PCW00avs8Prwrp4GJnO\nfDIdWbwyZBE3vQXFLl8seDtRUH1sRg/o9yhYk8Jb/IfNFRwMeUMjfahKINf/cUGWVNtvmu1wxWMo\nA/6KLBkxfvoyFHwU1V8mJyYgG0yMn/NRmL+8ZennEf6yyhsgzmzAIEn/z96Zh0dVnQ38d2ZLZrIn\nJGFJgoiKgoIKdaNf3bei4sKmslUtLrXWtm51a13aaq3VWmtBbZVNVFQURcWtakXFgqAiFdkkBDEJ\nIfs2mZn7/fHOzWx3skn283uePJOZnHvnTOa97z3veTeuf+7TZll7fNZYklxOnrvi2OYNx8sXriUv\nw91sQJjnDv/dpKi8Hl/AWn8arfQJ1XQz4bI8/tpQOktL6wQQXTl5vmz0RK8VXrpK0r/OexSWBVO+\nTrtb2u6Fy/uUBZCYjlp5C9Un3k/irFfB34SyO0TuDzjJUua3VwVIy0hg1Y0nRqQBXGFhxC689Cgc\nNhvZyQkRsntEfjouh43TRuZwwdj85tSBf36wjQvG5jdvMhWV1+NxhfRoPPnX64RejmGIZzlnZKtD\nd1SJThvUBZ5lgMTq7dRljoo7LtPjwmFTuiK2ppmO5CzPAv4a9dpsi9f6DmZYa/SOr1U+8tJZ8JPX\nxIvm9EihjbR8MKx7vqJsUu014JMfu1Meo0Olls6CmS/K+78wB1VRiCu9gPpzF1D5w9tIe36KGDaT\n50cUwTGmLSEhNRejsiHuDSk6BPC+SaPJTk6gqFxCWd/fVMzZh+dFhEndN2k0qYkOHHZl6cFz2G04\n7TbLv3kS7H0//7K7sdkgOVd+rymWQi8m4SHU4b06w3nv3lBV4eaK2MMkzHXC/eB0x3qGz3kYp02h\nXrslVGTu1Dtg89vwf9dBQwXqyQmRC7vGavj8Gamw/cNfoUwDyZzT8qtDOXun3GF5DaW7Alz5rHgi\nEp029tR4I3qDDkxNJCfFSV35d3gbfSQmJ/Hgm98yZ2wqg6zCF/dujZhD1stSHdmRmqsXb52FwxXy\nHPsapAhcuMFhCy6urXRo1oFwzXoZY8qX+bels2DWKzD/LFRFIfb0AonM2bvVUl/6pzyFPTmbpirr\nCr/7DfDE6EuzvoM5/rj9s/D6DC6b/3HEhuP8D7fz85MO5G/vbOaCsfnNujFeIcV4+lNv2PRwwmXZ\nLFoYnW9vtU5I30821bMOtJZzgNTBMOkJcGdKX/FoeX92psi7J4OBgZKIjcfA5IXYNjwvm0RhBUer\nz1uAzZPDwNTIfvPxWtiVVDXy66Wfcd+k0c0F7orK67n97EOob/Jx9UkHctXi0MbRIxcfyaKPdjSf\nIy/DTZ03FKETT/71OqGXU18u64Wk1ttGFVYFSLRDeiffXpvbR1VubdFYttkUOakJ7NTGsiaIra0D\nlVIXKqVeBoYppZaH/bwLlHXaDHsCZljr8TfGz0c2Sc4R78aKX8uNbMWvpVJ2nB7I+JukLYoCPl0I\nDx0OvsY4N0sVs+Oc9uJMKgIJrD11KU3/dwONyXnUzVxJ3c8+Y+vEF6lIOQBsNpwOW3NvZRNZkKmY\nEMDrn5MeiSaTxhXE7DBf/9znZHhcuJ2Kf0wf23xuM7w7Jzmhuapv+N8emzmOAUn9KBezJ2DKryl/\nZghg3jhJIUjKkY2fvHGhY2pKxJCdsQxO+z1k7A/b3pVwWJBWZ6vnyUJw9gp5XD0PVbFDFokrb5ae\nyYvOh/2OhZrvQu18ILSw8zXAuMvEq1K921rua0vEK26GhIeTXkBJnXjg9s9OClYpdkX0Bs1NceIu\n38Twl84lf/5RpC0+kz+Md7B0Yx1lZ8+P7Gc6dbFsFETNYWROgq7M2pl4sqWDwPKrQ3o1b5xsDpqy\nVLbFWocafijbLJuMVvLjb4yQO9vSGdSdek+MvvzqrGVc9VY9ZXW+ZkM1nLwMNy67LUZfXrn4Uy4Y\nm9887qc/2r/ZWDDH3Pj859x21igGpyfy27NHMTovjXkzRG+G93I23+exmePISU6w1J96w6aHEy7L\n4fn24VitE5rqpehc+Tdx5DwAezZJy6k3bpXx8eR9/C9Rz06PkfuaIy7FlzGMhhkr8F3zBdXTX+fr\nQAFpnlABQZ8vwLcV9RhgeQ1U1Dc1rwGykhOaZXdAciI79zbEyP5Viz/lzMMGNR//2MxxDM3yNJ/7\n+bU7mRu1htDrhD5A5U55bEMYdmFVgIFJUhS+M/G6czGwtanXcnZKAjvKajt3QppeQ3s8yx8Cu4EB\nwP1hr1cDn+/LSfU4zLDWhGTrfOTw146/EZ6dEWkULJsDs1+FKYukZURzKNYC6a1oViWesgga9kLV\nLuuCHTZnKE/aDKmtKCQ/Gf7wXhXTjsokL8PNqQ+833zYqhsHkeY2qGnwxVSsnDt9LA6bavYimxSV\n15MTVlwobquogEGTX3FIbgrPXn5sc5n98GrY/bKqb08jOixbqZDnIzw00MxrrikRr/HHc+GMP4oR\n0lAJ+UfD4gsiDZnV88Jalf1eIiNKNoaK31UUSpGZWa+EXjNJzoHM/WXRePLtYixbyb3ZIzQ8JDzM\nW7L0Q6nOvruygf2zkxiSnhAhc6n+vSS8NCvGWzz51KVc9nopt566lEHJNrLTU3E6HJFe+OAc7M4E\n0HLbedhsYHNE5slHF5F7797YXrITH4Fll8t3Nutla/nBFsprBqgoJIEm/leXSYLTEaEvAX5/nkFl\nQ1OMvpw3Yyx2G5a6MNyAjasv/QEammBIhuTK5aYkNsup22XnhauOi6iG3W+rovd2wmUZ2r5OeHa6\ndCGw2aX/9zPTreV8ygLRtTXF8eVdYblWaPD62FCTQr3Xgdcf4PKFawFYdeOJkCSGspmnfNz+WTxy\n8ZERXmKz8BaITPsDBss+3cUTs39AwDDwuOyWsj88OykixBuIkOsMt1PLeV+jImgsJ7fuWd5RFWBg\nJ/ZYNjHsLrzubNyV21sdm5OSyOptfdsPqGk77clZ3gHsAI5VSuUCPwj+6X+GYfg6Y3I9CptNQk/D\nb06rHozMIUovkMW/1W5vxQ746BFp84AhRotpKJtjnp0uOcyrHowJleK8R+G9e2DkRPlbZRH8+26o\nKcFmBHhw4n789o3dXHh0aEf6tJE5KKXYXVnPzH99QnZyQkTV1tte3EBpTSP3TRodUaArL8NNmtvJ\nEfnplNY04ooTDqiUwuvz43AkMDjdWtP1u6q+vQGbU4xgs18nhPLiZrwoxu7qeXDCTWIkv/VbOPFW\n6QNqji9aI3nIP7ousvftOQ/DmifFkH7nThlXUSiG8Mm/E2M8vGfowvNCx056Es5/DF74afNrvimL\naVQuktIL5LjV8+QaaaiA6u+wbXieu0+Zg9+fhZcG6gPuGJnzlzdZXpODkm2s21nFL15p4rGZ4xiY\nkgIYscXRpi0Rb5GmczHTXVY9KLUbfA2R31vRGpGfmS8CSjzNpjwBvH6zRAY8c3Houzv/MSm4FKUz\n7TW72S/Fza6m5ObTH5GfzjUnH0ijL8DsJ/7LcftnsfCSoygLFjr861tf8/OTDuS0kTm8sTG0oSJe\ntYRmHRkwsNSXAYOIEOq26EatP3sp4R0JLDb5yBhmvU5IHwoLJ4qBMeF+Ccku2xwp52bLNHdWjL5k\nyiJ483apYXL8jTFyn56UQKJhY0h6ItcsWQ+E1gm7ymVT8qG3v+a+SaNJTnDwt3c2c9tZI8lKcpGZ\n5GLuu1sj1gn+AJw8Mpe9tV6yUxLi9ly221TzJpFJtFxrOe9jNPdYbvneaRgGO6sCjBraBXMiWBG7\nakur43JTEqlq8FFZ10Sax9kFM9P0ZNqds6yUmgz8GXgX2b/8m1LqesMwntvHc+t5RFcZrimB9Pxg\nDqdHQlqrv7Pe7a0vF8O4+AvJwWyoiO0FWlEIdWWSv/nR3yW8NeCTReH6RXDYlNgKmU4PvHk7jtP/\nyJmHDaKirgmQG+A1Jx/E75Zv4IYzDm7OPwZi+s9e/5zke/7kyf827x7f89r/+MuUMbgcNj7dURbT\nO/Qf08fyzsbdTBhj2Rpb05Owan1mVrsOx8yhzx0lFaxdSeLVPfO+UP/vcNk+6IyQoWweb+bomY/P\nTJdjakslnNYsxmTVM/S52eKBNq+npjpwJDD/8wCXzHqDBKMBVbxBNpFMg/uk23HM/zGOikIS0gtI\nnroYkkdKPl8QmyPB8pocmObhk9+ciLKFezJUpBc+rPeyppMJ16+JaaAyYr+3mhLwNcn3sXhy5PGb\nVsgGz6xXZHOmqV42hp6bHakzXUkQ8FNVU0NSajp5Ge7m9k1PrNrOLRNGUlRezzmHD2ZGlK7cuLua\nBZccFdES575Joymva+SZOcfgN8Bph39cfCRXRuVtuhxKh1D3F8Jl2dzkm71CiiTWlgLKep1gphJU\nFIp8X70mVs4rCkU/PjcbJv49aFQfAAE/vPlbMZStqmknZVPVGODqp9ax4JKjWLezgtNG5nDDGQez\n6btqPC47+ZluZh03jIamQHPBOnNjKC/DzV0TD+XZtUXkZbj5+0VHUu/1NRfyeud/3zH5BwUxERl/\nnXY4bpfOs+93VO6U/soJqS0OK60zaPDDoC7q0NToGUzmrrclrUHFv6+b7aN27K1ltCe9ayan6bF0\npMDXrcAPDMMoAVBKZQNvAX3PWA4ExCsRvmiOXkj7vKGb2ewV4oWL3kWesgBevU7GVBTKRRqnjzJ+\nr7zHaXfK73aXnH/qIusKmWc/BJtWEDjlboYNSCLV7WDVjSeilOJ3yzcw67hh7Nxb37zbOzAt0TJM\nKj/TzTNzjqGivqm5h+iNZxyCJ8HGAblpbCmu5Ok5x+APGCileGfjbo4enq0Xf70Bq1Ym8UKeq3ZJ\nDp25wAr44JPHxPBNy4tMJYjTeqc5R8+sGH/Ow+JlriiURd0160Jjo4+t3h2xOHSkFzBnxkvstaWR\nGoDE8EI5Fr2e1TMXi7GUlt9s4KqkbIxpS0JtooLVaG2r55EzZqpcb+Ehf+HF0TSdT7ieTcqWHsuN\nNVC/17KIHIYfbBa9xEdMEH1p+MUgSUwLGcoQqTPThrC7OEBuaigc9I6Xv2TWccPYXdlAXoY7rq6s\nrG9qrmhdUd/En17fxP1TxqCUYnBKAoXldbgcigXBvrN2pUhwKDI9CTq0tK/SprVCo+TeA8x511q2\nm8JyJPPGxe+hbAREJ6cMljHeOvDVyYZRvLXC7BWU7PFSVF6P025j1Y0n4rTb2Fpaw5JPdnDB2HwG\nB9zc+Pzn3D95TKvrhMwkJ40+A7fLwfNrizjvyCF88HUJpx82mCU/lbVCRZ2XVLeTdLdeJ/Q7KndK\nPZRWEpELu6htlElj8hBs/kYSanbSmBLfnW2mIhburWN0njaW+zsdcZfYTEM5SFkHz9OzMb1xj58C\nDx4qj8UbJE8IIHWILKjNcCsQ73FNiRgGZuGjCfdLoSQzhMoc+98nxPAILy406UnxhCw8F/42VkJU\n68tlEWhVJKSiUPKo0wsoqjHwJNjJTEpgSIYHwzC4YGw+Nz7/Oa99sZtHLj5SwqGUsizasXNvPVMf\n/ZjLF65l3c4K8jLcFO6t47tKLwdmJ3PM8BycNoXTbsOuYMKYPF3wqLfgs+itaQTEGA6Xv4mPhAp4\nmQusjP1CBbsePUFufKZsJ6ZbF6Ixc/RSBslYMxw7vUCiJB46QjwsVsc2RS7QqCjEUVtC8Xe7uPCp\nLVRMDCvIFc9Y93vF0DKx2VDZB0sKxCUrZU7v/xkOPAX+/QdZ5Gq6h2g9+89TRDaciWLwWhSRQ9lh\n5a1S88GUhRET4EfXSw7+Q0eIvCakxObLVRRCYhqGPZFH11aR4LA3h39eMDaf+R9uJ9Fp475Jo7Hb\nrHVlRV0Tly9c26wvS2sa2VxSw5R5H7G5tIaCDA/ZKYkkOKSlVILTxoDkxOY6Dpo+Rry1Qv1eMZrT\n84NrhYSQvFYWWct2bZguGn+tyLnZ4gxC6wRlFxl/eCwsOEfeKym4AZ8y0FovBvyg7ORluEl02hmS\n4aHJH+CJVduZddww7nplI3tqGiMqtIeTl+Fma2ktUx/9mLte2ciWklpO+ct7zH7iE2Ydtx+HDErh\nx2OGYEPy9p12Cb3eLytJrxP6I3u3Q0rrm847qoLGcie3jTJpTJZijEnlX7U4LrzXskbTEc/y60qp\nlcCS4POpwKv7bko9BCtv3DMXh9pATFsiO8eebJi+DMq3iUE7fZnkDJnhp5MXwPv3yS7x8TdC5gES\nIrrzQynmNf0FCb2uLZW2ES9eGfWe02WRX7bZeoc5MQP/Rc9jUzkR1SPNIhlF5fWcPDKXh4O5R067\nigmTemDKGNI8zmbvsxlitfjjHXy4rYxlV43X+US9lUBAvMPRsmN34RYZiwAAIABJREFUwRu3iDy7\nM8TAfft30p7JxAzLjvBS7Ai1QckbF5tbb/ZFnrpYKr2bY0dMgFPvBG8NXLVa8v/Na8UscDfxkYjw\naaDZgE53BVi3s4qfvAp3nvkCB2Q68RuQbHVNlG8HR2LkeerLZFEZPrb4C/n8Zi9qTdcTT8+e+w/J\n1zz1Tuk7a/bsPv56cHmkqNyWN0P6MzE9VKzOPI+Z2xkexppeAO4Myo1Erj314ObIGFNfXjA2n6uf\nWkd2cgIPTju8TbryHxcfSU2jj+zkBH66YE1IX3bR4k/TzcST4Qn3i+c3Z6REq0SvFU69M1L/nTtX\nXr94qTwm5UDtg/DW7cFoiDzRbY1VMf3meekquWYuejbUeSOmQKidLE8goqK6P2xTvai8npLqxuYK\n7dF96s3aJubvyz7dxbwZY0l3OympbmRQeiI5KVF6V9M/MQyR1WHHtzq0sCqAAnK7oMAXQENyHgYK\nT/lX7C04Pe64RKedNLdTt4/SAB0wlg3DuF4pdT7wQyRn+VHDMJbt85l1N1beuPAQ06cvlBArT7YY\nuWa/ZbP9zI/vk9fXzIdjrhAvh9k6J7zy8ItXyA5yUrbsPlu9J8jxFz8nxkp4/+a187EddRlDUz0R\nu7cZbieNaYk8d8WxZCW5KK32cvnCtRyRn85vzxnJXRMPxeOyU+f143TYWPnFdyy45Cj21nppaPLj\n9QW4/IThnHP4YAIB2fkLBAzKar3trljZ0eM0+4C6Ulh5S2zIX3KuREE8Mz001vQKhz/3e8U7ZxrV\n/iYpvvTsDPEW22zBYjMZ4EoRmR83W4yX128K5jkPBQxYdF6sUf2j68QAqiyS68H0ooQb2AqyDBdH\n5KeybmcVZz9RxbvXncAfX/uSuVMWhVqkhId8X/DPyP9DvOs5KVuiQ9qIluV9TLzvxTNAjILwisBT\nF8Hnz8FHD4W+61UPSu68Z4D1ebIPEeMjXGf+919k/OAS0nOymr87U19mJrm47ayRzH13K9c+vd5S\nVz7+/nb+PHkMuamJfLOnlttf+pLSmkYevugIahp81Hl9lFbTbJB8X3nRMtfDiSfDTk9onZCcK1E7\nTXWxa4VTfiebhP99AkacEfl3c52w8NzQhnvG0PjrBEei6Ptz50rdE7NTQXIOrH6MAaMnk5WT3yw/\nSQl2DspN5v7JY6iob+LtjcXNRvKfV27iromHUpDlobzWS5M/wC0TDmFQWiJ/fWszFx1TQHltU/MU\n9tbKpmO9t/1yqmW8j1FfLhGVyQNbHVpYZTDALbUeugLDnojXMxBPRcueZYDc1AQKtbGsoWOeZYBV\nQBNgAJ/su+n0IBwWOXHhxkRFodwka0vg6Ytid5UvfVNuXPv/H6QODlX9Nce8dFXI67HyZln4me9h\nsSOMv0mqw4bfSM97FEadi2qoBEcipaQ3tyEprmrkpwvWxOwKr9tZwR3LN3LNyQeSleyiorSWO5Zv\n5IoThjdXzL7u9BH88tn1ES1TMj0udlc1UFLdSFmtl+fX7uSXp45oNRQ7EDDYVFwdMZfHZo7TIdxd\nhc8rnova4kgvsisptnJwTBTE/uDwSNXq8HY9UxbC+Y9KWJ87C/51Ruz7XrJS3tfMoQvPNQ4vBGYW\nDjMjMVByXSRly+500MD2pBfw+Nnzuex1KK1pwmVX/Pacw/Dbq3GYBcHqy8VQrikBR+Q2tWF3oSyu\nLSM5F+XOatO/UstyJxBPz7o88GSUp/iZ6SIrHxEpQytvFo+dVQ5zfbm1zmysxldTyu5AqqW+NFvk\nWOnKdTsrOHlkLtct/aw5r/OI/HTqvX5ueuGL5nMsuOQonHYbxVUN7dKZ4WiZ6wW0tFYw1wmBgNSD\nMPUthNYKs1+F126U9nnR0RHh64SaEimWZHPEb7FnGKLr3RmxnQrOnYt69x446wFKSScQCLCnVjbR\nw+X+pXW7uGvioeyfncRX31Vz3bOfNVfABnj/hhOZPC6feq+f217aEBaNdgR2m6KkqpE6r5+hWW0L\nwdYy3gfZG2zNlDKo1aGFVf4uy1c2aUjOx1O+qdVx2SmyIarRtDuJSik1BTGQJwFTgNVKqUn7emLd\njlnNMjxXyPRk5I0Tb4XhlxxLq13epnq5cS2eHLppRo9JHyr5SjNehJ2rAQUzlsm588aFDJP37pVw\nWNPLYh6/bI548uwuaKrnvEdWMf7ef/PZzsrmGw9IYY7rn/ucv0wdw7wZY8lOcZGV7OJPr3/VnKOc\n7nZSVF7PFScMbw69Mo/961tf83VJDRc9vppJcz/irlc2Muu4YTzw5ibKalsOYS2r9cbM5acL1rR6\nnGYfYS7kitaI/Dw5QYwLjGBv5LCcuTWPww9/CRP+IgbG38ZKyGB01epnZ0hv5CcnwJ5N1rnHiely\n3qmL4ufQRRcCm/gIeKvlmqksiqm0nfXyLO48ZSAPTBnDz55axx3Lv4D6SvEqNtXJeY6/EWP6CzHt\nKipsafgmR9UImDwftWkl1HyHUbGTpsrvKKmso7S6kUDAiPlXalnuBKz07NRFUgwpnsyEP88+GC74\nl8j59Bcidefpd8caJxE6sy6uvrzx+c/5y5QxXHPygQxOT4zQlUBziovJFScMbw7XBshOTqC4qoEL\nH/u43TozHC1zvYCW1gojJkidh8pCWS9Y5hL7ZFOxbk/L64SfvCoe4to9sk4YMSH0fhMfkagJb41s\ndNbvje1U8OIVcPiF+JoaOO+RVawvqmw2lCEk91eeOJyhWR4q6rzc9crGCENZ8p1tDEpLjJH3Oq+f\nGf/8hKmPfsxtL22guKqBivrW5VTLeB+k3DSW2+ZZ7mpjuTG5gMTqHdiaWvYa56YksLuyHq8v0EUz\n0/RUOuJZvoX+UA3bZousZhnwSXgTRHraZq+I4w12SF7mxUvBkyWP790bWeir6lsINMlC78DTYcHZ\nkSGHCWmAAT+4TH63upEm58rcbDZ+uH8mT6/dhcdlt6xkWVLVyF2vbOSRi4/kva9KmHXcsOYWKGZ/\nRNNoDueCsflcvij2pnrbWSPx+vwt/hu9Pr/lXFo7TrOPiG53Ft43uK400uMLcNwvYPEFka+1ZLRY\n9QSfsjDUQzy9QIyYeJ6X9AJIK5B+oXaX5Ka2UKTmwCwXv/v3Tm49IYtDc1w4ar8FV3JMaGPAMCJ2\nApt8AQLuzEjv+pp/Sju2J85EVRTiTC/AfvZ8bl7l49pTD47xbGhZ7gRMPXvpW7LQr94tETk1xZEy\nY0Y7JGWLblz1oHjabA4JYX1yQpj8LQLCCtWFE6Yz7Q7pJR9XX1Y3cttLG3hw6uFcdeIBEe2iclJC\nfZWBGL0ZbTy3R2eGo2WuFxAtw3u3SoRLUi4cfwM8cWbra4URE0QuL1kpOnDVg6GiiGbBu7q9kWkJ\nUxbAj++V9UnVLnj7Ljj5tzBghKTPxEk78fpFhqzu9eY64ddLP+P+yWN4+KIjuPqpdREeX4dNUW8Y\nrcr79c99zjNzjmk1d1/LeB+k2bPccoGvuiaD0vquN5YbUvJRGHgqN1MzYEzccTmpiQQM2FVRz7AB\nughFf6YjxnL/qIYNkS1kAgE4+wG5MT3549CNyOmREMDwIjTHXCkLvqpvrfOPakrEwHC6Q2Go65dE\nLuTfvVfyjVbeLK/npFjfaMu3w4ARqPoK7jzOxqaS1OZKluE3oLwMNxX1TRSV13PV4k+57ayR/Hnl\npuYWKMOyPMydPpbSYIGP8GOjvSggN7OsJBcuR8uJJi6H3XIurR2n2UdEb/qE9w22MqTtjlDxrvHX\nitHaUjpC0ZpgT/BXg5s2dnjtplAP8YpC6f05dVHkQs/MWT7nYXj9Jryn30PAAJc7E/WT11C+Bsv3\n/aaiieuPDJD18uTI6yo5R55XSPso30/exJYW3NUOBBhQtxVbU41UpDUZd6lct+f+Qz7PqgfJenkW\nc05dGlmoKYiW5U7CZpNFVSBbvp8nzoTJ80N6tale2kCFh5QG+8ZStQs+eiRSd74X1J3mxouVzsw+\nmIC3jhvPOKhVfXntM+u55/zDuO2skRwyMAW3y0F6ooO508dyRXAT0dxsjGc8Q9t1Zjha5noJ4TKc\nkBysWK1ChnLeOMnfnP6CyN9798o6YOIjIvMn3AiLzo/Vj8f+TNYJdXvghTmR9SNqSyU1KzwNpvgL\naZ1Xvt1a9j1ZNPrkaWty/+uln0XIfaLLTk2Dj3MeXsU95x/WJnn3xwboxKBlvA9S/o04iaILbUZh\nVsIe1MV2aEOyRIF4yr9q0VjODWsfpY3l/k1HjNzXlVIrlVKzlVKzgRXAa/t2Wj2EQEA8HBU7xQvn\nCYZ2hns7lJK8yn+dLobtj66Dz5+WsNDoEMCXroLzH5cd5oyhcgNMzpEql2Z7HjNM9ujL5XWzCFHV\nt+Kxiw71eu9e8NZCxQ5cSy/m1hOymfvuVu6bNLq59YOZizT33a1AaFd53c4KLl+4ll8v/QyfAQ+9\n/TWJThv/CLaZMo81vSjhmK+31mc5K8nFYzPHRZwvvBqnpgswN33MFibB/sPNhvRPXgu1VAr4xctx\n0u0ihx88IB6McLmbslA2d8znx1wp/WwXnCO59aahbLJphXiNT/8D/PxTuGgpJKaKQfOOeKDtgSYS\nF07A9tAYVMlGeON2ke+w9w1MXczQTA9ZL8+Kva7GXxt6v4pClD8shK+uFNu7f5TrecWvQ9eYssGa\nJ0PPT7odknPI8ShLz4aW5U7E7FPrb4KJf5fvxtSrL18jOs5sA2V+544EaeEXT3e+d69s0ljpTL8P\nx1OTGJXmbZO+dNpt3PXKRtwuB9kpCVQ0+Hjo7a+57ayRPDPnGBKdNh6YMqb5HKbxHE5bdWY4WuZ6\nCeZaoWqXPE8dImlYpqF80u0izw+PEx304z+LnL/9OymEFJ1itfxqOPNeqRvh90oodnJOSC8/OUHO\n40iU85uYYd3v3RujP5m6CNYuwOOQaBmz4nVb5V6hmPmvTygqr+f+N77m/smty3uis/UlppbxPsje\nrW0q7rW9UozlvOTOnlAkTe5s/HY3nlbaR+UE20fpIl+ajlbDvgAYTzuqYSulzgD+CtiBxw3DuCfq\n7wnAAmAs4q2eahjGN0opJ/A4cGRwvgsMw/hje+fdbszeidHhq+GetvHXhipcgzwunSUFOeKFQQV8\noRY25uLNlRx7nuVXi1ExYoIUCAv45cZoVczI6YaS/0FFITkeRWlNI26XnXvOP4yCTA9bS2v588pN\nzblHeRlu6rz+5t8fmzkOwzB4Y2MJb2ws4Yj89GaPc16Gm5zkBJ667OiI4l6/OOUgBqe5Wy3AYbMp\nRuSmsOyq8brSZU/EZpNF3b9Ol0XX6ffA6b8XGU3OgTEXwXv3iaGblC35wTa7jDnjj1D9Lbxxayi9\nYO9Wa4+GM0mMm4BP8veiUhLs5VsjozWsipIlZuCuLmw9lzW9AMMettDyecUwN0PFzWOenSHn37Qi\ndM1NuJ+SOsPSs6FluZOI1rUXL4WXfha7IWIWgjNfq9ol7XUiWpsFv8fpLwTDtnOsdaYChv0Iu9HU\nZn0ZvoD3+vzN+tLkiPx0nplzDA6bImDAokuPZvueWh56ezOlNY3MmzG2TTozHC1zvYDW1grjr42V\n0WdnijzXlIhOtNJpvoaQt/nipSLPVuc5+yEJ/3ZnyCa93SXnfefOkP5sqgNPJhx+IS6njbwMN+t2\nVjD/w+0svuxolIKtJS3L/e7K+mbv77qdFdzz2lfNXudUtyNmjfDLU0dEtLOMh5bxPkjZFhh8RKvD\ntleIsTy4q522ykZjcl6rxnK6x0mCw8b2Ul3kq7/ToWrYhmE8r5R60zxeKZVpGMbeeOOVUnbg78Cp\nQBHwX6XUcsMwNoYNuxQoNwzjAKXUNOBepIfzZCDBMIzDlFIeYKNSaolhGN90ZO5txqp34tMXSl6S\nGboar3BRxjAxKOKFAFot7KzO4/dKzpOZjzdigjx/dkaksb3yFjj1Dvh2Pekpydx21kjuWL6R0ppG\nll89noFpiZTWNAIh4zg3NYFVN57YfGMqq/U2h0KZHue8DDfLrx7Plj21EZUq580Yy4icFByOtgUm\n2GxK92nuyThcIltHXw7PXwKT/iXydfofQgam6S1OLxDjw9cYWcXdZONLsSHXk54UozpeSsKUBfDq\ndaFzmLnMZlGy4PvazPeNVwnW/H3iIzgcYarN4RJDvw0Fo/wZw3n01bK4ng0ty51AtK51etq0IUJ9\nuWzAWI1VNpG35BypMWH2r2/WmbfCqXeinJ5W9eW86WMZlJ5Iuju0gLcKHTWN7ujK2lbHtwctcz2c\n1tYK3pq4+cNMXhDSdy2tFd67V1K3rM6TlhcZwj3tqVCnA7PLwDkPw6s3wml34jdszZvhdV4/KYkO\n0t0uahv9Lcp9tMyv21nBXa9sZPnV49ld2RizRjgwO7nN8q5lvA/RUCkpAqmDWx26rTJAViK4O9qX\n53vQkJxPaukacRYoazm1KcXgdDdbSmu6eHaanka7w7CVUpcrpYqBz4E1wNrgY0scBWwxDGObYRhe\n4GlgYtSYicD84O/PAScrpRTSnipJKeUA3IAXqGrvvNtNvN6Jfm8oBzR5oHUl4MoiWYxNnh8VvrpI\nbnrR51TK+jzu9JBhDGKwvPcn8TibFYyDYazUlWGc/nu+rk5k7rtbKa1p5LGZ40h3u5p3bVfdeCIv\nX30cByfXk9lUwhBnNdlJTmw2FTcUyhcwYipVXr5wLeX1TWj6CJ5s8RSvnicylZAqnox4m0FOT6iS\n9fKrQyHQeeNg9DQxXCfcH5LRxip44aexnsLz5sm4xHQxmk1WPSjGdNi14zevnVUPxoYXnjtXoi7M\n93v7d6imsPw5T7CHudU1FtVX2u/wcPd5Y3Tbkq4kWteaxkM40RsiZrXh6u+sx+4NRioUrZFNmQn3\nw88+idKZe/A3NbWqL0emNpDZVIKtrkS8iMQPHbXUl4vW4g+g5amv0tpaIS0vfseA9++TcdE6bcqC\nyLVC0RpZV1idJ3oD/umLRD+HdzowZb52DzYCzH13K79e+hkD00LGsCn3n/zmRP59xUhGJlWRaVRg\nQxKP9RpB0yb2bJHH1LxWh26v9DOkm1KBG1MKcHircNV91+K4wWmJbCmu7qJZaXoqHdnPuQ4YZRjG\nnnYcMwTYGfa8CDg63hjDMHxKqUogCzGcJwK7AQ/wSysvtlJqDjAHoKCgIPrP7Sde70SHK5QDWrVb\nbnJmaJS5iHvnTrm5nXBTZPhqfXmkUWCe054ghnV4AZvJ88WIjr4Jb1oB468Rb3P4OWpLKVFZ/OLZ\nzyw9GdkpCfHDxXJGYrPZLEOhwkOvTHSlyn3LPpfd9mKzibF59OWRsmzVu9Y0WnzigWj2kICECb50\nVSi3zvTmXbLSejFZ/Z20iZrzriwOzVSEmhIxyM9+CNLyMByJGPZEeb2iMBRemJQtfRxf/01knrR5\nnYZ/vrT8WI+32VfaPGbaElypuWSbOd1txcy3jS6g1sfZZ3IbrWvNzZLw3t7nzpXWO9eskxA/U8da\njZ26CFb8KnT+ojUiZ7NXREQqUFtKlZHJ3eceqvVlP2Of6tzW1gopg2N72p/zMCz/mcjm+GvgjVtk\nQydjmBjFvobYtcInj8mG+7PhFbEXwau/ihxXUSg6OrrTQVDmq53ZljJvsymyk5xQslnLfA+l29cK\nbaHMNJaHtDp0W0WAY1oumN1p1KcMBSBp75d4k+L3gx6c7mbV1jJqG30kJXSDC1zTI+jIim4r0N5s\nd6st9eg6ifHGHAX4gcHAMODXSqn9YwYaxqOGYYwzDGNcdnZ29J/bj1XvRLPlDshCytcg3riLloYK\nJJmLuPQCcHkg91AxHupKpdJwtLd56iIp9OWtEePA3An2ZMni22onOdxLZt541y+hqMrfsicjXrhY\nXSkQCoUakuEhOyUhIvQqHF2pct+yz2W3Q5Pwx+bDvXm7LPLCZW3iI+DOEiPFfM2TBT9fC1kHhLx5\npkE7e4WEY8Xz6pqRGI3Vkd7o12+Qwk7l21EOF46krNBcitbIQtDXCO//STal4l2nJnaH3LzD32PN\n4zBuNvziMwmZTBkoebA1xc0exFYxDarHT4EHD5XHko1tP74Xs8/kNlrX1pRA0gCpUm5+V2/dLnnM\n9kTJQzYNiZoS2TSZvkyifczICKtNyWjP9Pol7K4JaH3ZD9mnOre1tQJIvvDFz1uvEzxZcN6joj/r\n9oDdKcdEF/T80XWweWWkx9iTaS3rtaWxujso8w67Xct8L6VHrBVao2wzKHurPZbLGwJUNMKQLi7u\nZdKQsh+GspO8Z32L44YEZXubzlvu13Rkm+Q3wIdKqdVAo/miYRjXtHBMEZAf9jwP+DbOmKJgyHUa\nsBe4CHjdMIwmoEQptQoYB2zrwNzbTkstdyDYo/YW8ca9fWeoImu4d6O2FFCRIah542TBnnWQFPZ4\n8/ZQP9pzHoa3fis3v7Mfgg3PRXrczErEdifMfEnG1ZbC6nmUHXUdd78uN7S4u7rxwsV83tixQczQ\nq/B8JF2psg9iVm4NZ9MKOOMeMVqSssXgVA7x5JoLvckLpDVUbTGcOy/kYTHzjdML4CevYkxdjIr2\nrKyeF8pXTsqV9inhnt+Jj4jn2LzuUgdbF2s65Y7412k47kzx8oR7TY6+UsLFSr+y9Ka06iGOt7i8\n7K1Q2zlNy4Tr2qZ62TwxDPkJ9xif9ygsnSHHBKMOKN8uHrqaklBodv5xoifDaztMWSQGuNnHNkxn\nan2p+V60Za3w6g3wf7+Wau8R64TFIvPu9FBtEpNjr4m8z7//Z1lnhBvaM5dLj3pzjWHqzaQcMcJn\nLIPaPc0yz9GXU9tkaJnXdB5lW6SNmrnpEwezEnZ3hWEb9gQaUgpI2bOuxXFD0sVY3lJazWF5aV0x\nNU0PpCPG8jzgHeALoK3uk/8CByqlhgG7gGmIERzOcmAW8BEwCXjHMAxDKVUInKSUWoSEYR8DPNiB\nebef8D7L0fi8oYq946+VVjgXLQVnolSnDgTgLwfLDTT85mOGBP7ic2klEV3sa8L9ofccc6EUqpm9\nQqph2+yw+jH46CEpyHT67/GnDGbjkQdz++vfsW6npHLnZbhxu+yUVjdGVpdsKVws7r9AV6rsF9jj\nyEbAGxnyb/ZfPvUOMVSciaGicy9eHhsSGyws03jm/SROuF9y6Vwp4KuHcbMJJA9CTXoS5Qx6Jpr7\nNTvl3O7M0KLTytidtiRyTEvYbJB9sLTK8jfJzTx5INSXddzg7cDiUmOBqWsrdoq85Y2Dk38ri31l\nA5sDnvtJqIK6tyZU1MjE1J9J2ZIPaupNfxN8+FeoK8c4/feU2QawY/Tt3P16Ket2Vml9qfn+tHWt\nYMp0wC8dAmx2WSf8fG2sHik4ChZMjHy9+AvxKK+8WTbkG6ukyN2MZeLNU3ao3gUvXSlG9uT5QWM8\nQzoCrJ5H0ejbtcxrOo89m+U+3QrNxnI3eZYB6lMPIO27D0PrawsGpiZiU7ClRBf56s90xFj2GYbx\nq9aHhQjmIF8NrERaR/3LMIwvlVJ3AmsMw1gO/BNYqJTagniUpwUP/zvwBLABCdV+wjCMzzsw732L\neVOJqtjbvMCuKRaDNikHLnldFnABv/RL/uQxCeGOV0m7pljOUbcX3r1XwkyTsuFfp0UW+yr+Atul\nb+FITae0RlLC8zLcLLjkKIqrGnnwza+YMzaVwck2GpqSSUzNwWZW8g43NqJDVqPQlSr7OIGAhEFb\nGbpV30YunswQaLONT3DThunLJITQ74VJT8jOsmFIqPRRP8WhDKo9+aTU7ZTXmuqoTMznhhcLuenM\nQ9jPV4wq3yZe46Y6yNhfPMnhRnC4BycQkNBxwwj1QG/NYA4EYj3I05fJYvPcf4i3etWD8hnbavB2\nYHGpaYFwvTr/bHntktchbSicdndIj9qdkt4S/n+vKJRQVoD/3Afb3w9VXQ8a2ar4CzwzV/KLxV81\ne8G0vtR0KlYyba4VQDzIdhdc/h959FYHC9cNtV4jZB8iPZobq2WdUFkk7f1OuAnevSeyfoPZynLh\nuZBeQNnZ83l0VZWWeU3n4PeJsXzwhFaHbq8IYFOQ6+mCecWhLm04mUVv4q7cQn3GCMsxDruNgWmJ\n2lju53TEWP53sMjAy0SGYcdtHRX8+6vAq1Gv3R72ewPSJir6uBqr17sdM08p3k3Fkw0//hPUlYkC\nWXheZCh1Q1Uw/zhHPHVmL0RnkjzWFEOgSUK33g32vrW4cSq/lxG5ORG7ugYG9y77nD+Md5D18uTm\n9w1MfQpyDoHL3sLwefEpJ+Wkomqb9E5wf6ahAowApA6CWS9DU4OEXL9xu3hDrIzod+6UYw+/UHKU\nzLZQeePgjD9BRREsm9N8jH3qYpJcblgaah9ln7KUXx6XSZ69AlVfFdNaypeQhi1pQKRc2mxybcUp\nvNSiwRwdMp2cA9W7rT9bTUnbDN7W9ICmfVj9P9OGivc/Wo+e9yisXwQHnSH602YPFlvbI3oTLPsz\nu23+dulL49K3CPga8Skndc4M0lAdKvah6ae0pCOMAIyeBK/dFFtgceoi2Yw0o9fMNYKvUdJT6spk\n82/9Ejj2Z7JOOPzCSGO5ohAGHIRx7QZ8yomfVO4+T68RNJ1E+XbwN8pGTytsqwyQ6wFnNyrT+rQD\nAUjZsz6usQwwOM2tjeV+TkeMZTN8+jdEFumKKbrVp2ktT8lmE69XTXHICAB5fHaG5Nxd9KzkE5mL\n9RET4PjrY/vRHnOFHBvHgxW9q7urvI45Y1NDN8Hg+9qeuQgue4uAJ4dNxdUxOUa6XU4/xO8LyWR4\nleiiDXDmPeLFcyRKbp0rGfaGVSKGoKfPF5Kz8ddKiLVZCRtkU+eZi1Hn/iPCUE1uLOXgV64KhRWG\nXyMvXUVgxitsrqmOlcuO5glHh0yPvzZ07ZnnMUN5Uwa3zeBtTQ9o2ofV/9PnlU2NaD26bI5EBiw6\nL9RPedF5kXJc+lVMf2blcJGd3DZ9aVz6Fl/VuPnpgi+1rtSzcaVFAAAgAElEQVR0jJZ0RE2pbOSc\n/ofYAovPTJec5MqiqDXCDbBoWuQG30d/h6N+Kilg4aQXgNONSs7FCeQEX9ZrBE2nUPI/eYwu6GnB\n5r0B8rsxBBvA6xmIz5lM8p71lBw4Ne64vAw363dW4PUFcDn0vb0/0pFv/UZgjGEYw5Dw6M+QHOP+\nh5mnlJ4vj9ELZMMvYaVWoVQJyRL+Gb5YP/zCUDEvc9xLV0kV36pdsdUt43iwXA47g5NtcXMpy2q9\nMX0Rf7pgDWW1Os+y31HzXWQv74pCWDoT9hsvuaMPHS7h//Xl8MH90ubMrL5qVmZvqgvJpTtDDGgr\n2UsKk9VwQ9Xs2Rw13gj4reWyo3nCZjikSZz3ZcBBbSvuZdKaHtC0j+j/Z0t6tG6PPFptfCydKe3M\nwqtgW+jMlvRlwNeodaXm+xNPR5i6LJ4uAos1QpS+Xn61vJ42JLZThl4jaLqSkv8BSlo1toDXb7C9\nMsDQlK6ZVlyUoj51OCmlLRf5ys/04AsYbC7R/Zb7Kx1Z1d1qGEaVUuqHwKnAk8A/9ums+goOd6Qh\nYZJeIEWO/L7Im1W8G6ZSUiU7KVt2p6/dII9xFvRZSS7SUpKt39fhwuvz676IGsHfZC1zZk9j8/ny\nqyXc1WwJdc16abeUmAbp+0kEhNkOyuawlj1bWCBLuKybLaSixm+vDFjLZbTRa56/tbDp6BYv8a5N\np1sbvD2JlvRorXQAiKs7M4ZB7mEt6syW9KVPObWu1HQepi6LowMxAm1bIyRly0amK0mvETTdR+n/\npGWUM7HFYd9UBvAZUNDdxjJQn3YA7srN2Jrih1kPGyAluzfsquyqaWl6GB1ZEZracgIw1zCMlwBd\nzcaKpOxIQwLk8dy5oQV5+M0q3g2zpkR+2ujBstkUiWm5kn9kscus+yJqmrE7WzZCTEzvh1ngKyFF\nxjx6vFReTcuHGS9KX3GHK1bmJz4ikRRWhuqqByWUMGx85bkL+M3K3dZy2Za+plaEh0NeuwEGH9mx\n82i6lnh6dMoCydeEFnRncVDPxteZLenLOmeG1pWazsPUZeuXxOhAKbC4q21rBM8AqN8rqV96jaDp\nLoo3ti0Eu1wqYXe7ZxkxlhUGyXvi1w3OTU3E47LzhTaW+y0dyVnepZSaB5wC3KuUSqBjRnfvJhCQ\n3EkzB8mdJUVoonOSPJnyutkuoupbyS86815pbzLtKXj6IjFG1i+J7at8wT8lDHH2q0Ev4M425UXa\n7HbIHWWZJ6X7ImqaSR4Y25N26iKpwh5OeoHIn7mgAqj+NlRFuqEcHj1BXj9iOhx7dagnclOdFKRJ\nHhiSR6c7VPSmaA2snocxczk+bHxV6uX2Fd9RWtNkLZdtzROOvkbNMeF5ze5MnW/c0wj/3pSSdjiJ\nqbF69IsXpAJw8Rey4RJdiG7KQvFyGEarejOevkxDaV2p6Tjhsmx3SRG6pvpIWcwZCWc/IJFmFz8P\njZVSDfudO6X//NTFYPaoX78kVl9Pni/ncWfKe1YXS1eCVvSZXiNo9ilN9dJj+dDWszK/LvdjA/K6\nOWcZpCK2gSKldC1Vg46zHGNTiv2ykvi8SBvL/RVlGEbro8IPUMoDnAF8YRjGZqXUIOAwwzDe6IwJ\ndoRx48YZa9as6bw3CARiq/FOWQjv/UkqUYZX54XYsVMXS/uHhr2hFjh+rywAnR4JbVU2QEmLqbfv\njK2U2Zbqvy1+BIOyWq/ui9hxOuWf1emya4XfJ7nLfq/8vvFF2O//IqpZM/ERMTI9mbLJU7IxtIAz\nF2zv/zlUiTVvnOSLpg+Fih3SCipz/0h5tTBmA6hmufS4bKQHKlFtWPjFYHWNTl0s14y9I3uEfYae\nLbdW39s5D8Pmt+CwC0JGgtmyzJEoXQPqKyTaQdkkdLVqF3y2BEZPizSgO6A3ta7sMfRs2Y3GSpbN\nVmY1JbGyaDV+2hIYMEIqYvubxNj+36uQMwLS8iRaR9ng1RuCfZx/973lPTR9Lff7iN4ltx1l11p4\n7CQ44TcwdHyLQ696s471xT4eO6mL5tYK+398M17PQDae9lTcMYtX7+CNjcV8ecfpOO39ZkNdX/BB\n2r1qNAyjDngh7PluYPe+nFSPx6oa77MzJJdz04rY6rzZB0tVy5piCV39fGnkwi/8JmpWGU4vkF3m\nt++Ek2+X9j6n/yHUB7Yt1X9bQPdF1DRjd0g49hNnhmR61sshz3B9eWiBd9lb4uEzDeW8cVJcye+F\nM/4ox25aIWPtwQJ2RWtCfUXD5TXay4uEqGSnJMRfOLZ14Wd1jT5zsVyH6UO197inYvW9Lb9adN8X\nz8v311gtFdgXnBNpUK/4pcji+iVS8Oi4a2SjxuzH3Naq6VFoXanpEFayHN7KzEoWHYmRETkJqbBn\nU+zmkdmR4OKlsOZJkfcBB+0TeTfRcq9pF7uDYcyZw1sd+vXeAAU9wKtsUptxCJlFb6P8jRh2a5kf\nNiAJry/A18XVjBqc1sUz1HQ3/drF0mHiVeMNb1ESXp23viy0sAMJc42uaGnRDxSXWzzKT022vlG2\nVv1Xo2kr0TKtbLDYor25KXOmoXzS7VG9QRdLf/Fv10W2mGpLtepwOtoeKt7nMc9RUwwuT4c3mTSd\nTEu6teAo0aNWrcZMgzotLzYKJ1xntlcONZqO0to6IVoW60pDrc9MLl4a2zLNlPVnpks+v5Z3TU/g\nu8+lwFwr91av3+CbygBHtm5Tdxm1mSMZUPgaKaXrqBp4jOUYs8jXF0WV2ljuh2j3SkeIV423vjzy\nuVmdN/qmGa+iZVQ/UAxiey8uv1q8J22p/qvRtJVomY5XSMbhCo0df61Fb9CL5feVN4cM5fBj20pH\n20PF+zzmHGpL9eKxJ9OSbjVbkrWkPx3u+DrTPJfWm5quoLV1QrQsWum8eC3TzLWCM0nLu6ZnsPtz\nSbdSLUfumpWwe0JxL5O69EMwsJH23Ydxx+SmJpLksvO5LvLVL9HGckewqsY7ZWGoMmt0Vd3om6YR\nkB3j2SvEy5w3Tv4e3Q8UI36bCF21V7MviZZps5CMVaVoc2y8fsqGAdOXhWT84qXyvCV5DQTE61ux\nUx6d7o61hwr/PNF9yc95WD6XXjz2XKx0q/m9mT1k68slZ3nqopAOHTFBXm+qi29c6Grnmq7ESpYn\nPiKpVFayaGVc2+zWawVzM9Pu0PKu6X78PijeIHVJWmFjmVTC3i+1syfVdgJOD3XpB5C+6724Y2xK\ncVBuCh9vLevCmWl6CjoMuyNYVeN1Z0lFyzPvjS1GZN40n75Q8olsTljxs8h85ZRB4E6Xljbm8XWl\n8vfwm2F6gVS9VHqfQ7MPiSfT8SpF54yUathW8ml3QUNlKHwwvIK2FfHyk6cvC4UltnfhZ36e8FoB\nq+fBiTfrxWNPJloOzWrYZz8g8jhtCXz2DPzoelga1jVgykJIHSy581YymTJIZCEtX+era7qGaFk2\nq2FPetK6YGH4OsEsYuf0wAsWhRYTUuS8BlreNd1PyUYpRjvgwFaHflHqJ8EO+T0oZxmgJmsMuVuX\n4qjfg889wHLMoUPSWPjxDr6tqGdwuttyjKZvorVoR4nueWx3xO9vGH7TnPRkqMowhPKVE1IgaUDk\n8fG8LC9dKUZEXWnMtDSaDtNemU4ZHCufUxfLgtAq3zievMbLTzYXhNdukMf2VnW1O6SYV+YwGHS4\nGFzfo4K8posIl8O0PEgdFJLHnJFwzOUhQxlCBRYxrGXynIdF5y44R+pHaDRdRbgsp+TG3uOjx4b3\ngf/xnyQvOXqtkJgWuiaS4qwRtLxrupJdwZSr7INbHfp5qZ/9U6GnFZSuyRoDQPruD+KOOXSI5Cp/\nsGVPl8xJ03PQnuWuwrxpVuy0DpvyW+RRmjfP2a9Klcv68siiSTr3UtOd2Gyxld7fvQdOu6t9+cbx\n8pP9XllYft856mJefQebLdg3OY58aZ2p6c2E66u2rBVMHTxrBVQWannXdA9Fa2QTp5V7bcAw+LLM\nz0lDumhe7aAhdT+aXOlkFL3Nnv3PtRyTn+Emze1k1ZY9TBn3Pdcmml6FNpa7GjMvKTpsKl4epc0m\noYgvXhl7TCuFFDSaTie60jvAuNntk/H2XhOa/k1r8qJ1pqYv0Fa9WF8Ge76KrJodb6xG0xkU/Vda\nl7WiX7dVBKhrggPSu2he7UHZqM4+koxd72DzNRBwJMYOUYpDB6fyweY9GIaB0veTfkMPC4ToB1iF\nVpu5mNFFjgJSCAFll9Cq6FArZe+ez6DRmFh5hd+7N7a4Vkv5xlbXxNTFoevBvA40GoivQ5U9pDu1\nztT0dtoi54GA6OD37o2V96mLdX0GTedTXwF7voYBI1odumGP3MsP7KGdl6pyj8buqydt93/ijjl0\nSBpltV6+/LaqC2em6W60Z7mrsSqkZN7QrIocmTmWq+dJb0V3hoRarZ4nOZgaTXdi5f2oKZFiS/GK\ng0UTfU0EfLDyFti0IvY60GisCic1VsNjJ4Z058zlWmdqejdtkXOzK0FNiYRfm/LeVCc6WOtMTWdT\n+LE85o5sdWhPLe5lUptxCD5nMlk7XqU8/1TLMUcWZGC3KV7+7NvmHGZN30dr0u4gupCSzRa/yFFd\nqRgaJ94svWufnCCPuqqvpicQz/vhzoxfHMwK85pwuCSse9MKeb214mCa/km4DlWEqqaDPK68BU64\nSetMTe+mNTl/+kIpqDhtiRjMz0yX9IOUwaKDNZrO5pv/yEZOG4p7fbHHz7AeWNyrGZuDqpyjySxc\nid1bbTkk1e1kTF4aL67bhT9gdPEENd1Fl3mWlVJnAH8F7MDjhmHcE/X3BGABMBYoA6YahvFN8G+j\ngXlAKhAAfmAYRkNXzb1LiFfkKLxoTVs9dRpNV7GvZbOl60CjscJKZjatgAn3a52p6TvE041N9Xp9\noOk+dqySfGV7y/nxjX6Dz0r8nDm0i+bVQSqGHE/mrrfJ2rGCkgOnWY754QHZPPTOZj7eVsb4A6zb\nTGn6Fl2iTZVSduDvwJnASOBCpVR0zMalQLlhGAcADwD3Bo91AIuAKwzDGAWcADR1xby7FDOcNZzo\nojXt8dRpNF3FvpTN1q4DjSaaeDKjdaamL9GSbtSyrukOGqpg92eQe2irQ9cX+2n0w+isLpjX96A+\ndTgNSXnkfr1EOi9YMHZoBh6Xnec/Leri2Wm6i67SqEcBWwzD2GYYhhd4GpgYNWYiMD/4+3PAyUpK\nzZ0GfG4YxmcAhmGUGYbh76J5dx0tFf7SaPoL+jrQtBctM5r+gJZzTU/jmw/ACMDA0a0O/ehbPwoY\n1cONZZRib/5pJO/9gpSS/1oOcTlsHDd8AC9/9i3fVfatIFeNNV0Vhj0E2Bn2vAg4Ot4YwzB8SqlK\nIAs4CDCUUiuBbOBpwzD+FP0GSqk5wByAgoKC6D/3fHSodb+l18vuvkRfB72GHiO3WmY07aTHyG57\n0HLe7+lxcrvlLXAkQs4hrQ79eLeP4WmQ7OyCeX1PKgb/iJxtzzFkwz/4KvcoyzFnjx7EO18V8+j7\n27j97NaLm2l6N12lZa2akUXHN8Qb4wB+CFwcfDxPKXVyzEDDeNQwjHGGYYzLzu6lO606lKpf0idk\nd1+ir4NeQY+SWy0zmnbQo2S3PWg579f0KLk1DNj8JgwaA/aWLeAGn8GnxX4O6+le5SCG3UVZwZlk\nfPseqcWrLcfkpCbywwMG8NQnO9hT09jFM9R0NV2laYuA/LDnecC38cYE85TTgL3B198zDGOPYRh1\nwKvAkZ0+Y41Go9FoNBqNRhNJ2RaoLIQhY1sduq7Ej9cPo3tRLayygjPwJmYxdM3d0s7SgomHD8Hr\nC3D/G1938ew0XU1XGcv/BQ5USg1TSrmAacDyqDHLgVnB3ycB7xiGYQArgdFKKU/QiD4e2NhF89Zo\nNBqNRqPRaDQmXwXbOw4Z1+rQdwt92BWM6kXdzAx7AsUHXkzy3i8Z8uU8yzGD092cMWogT39SyLrC\n8i6eoaYr6RJj2TAMH3A1Yvj+D3jWMIwvlVJ3KqXOCQ77J5CllNoC/Aq4KXhsOfAXxOBeD3xqGMaK\nrpi3RqPRaDQajUajCWPjS9IyKjmn1aFvfuPjsCxI6gX5yuFUDTyGyoHHkvfZX0kq+8JyzAVj88hI\ncnHLsg14fYEunqGmq+iyhBfDMF41DOMgwzCGG4bx++BrtxuGsTz4e4NhGJMNwzjAMIyjDMPYFnbs\nIsMwRhmGcahhGDd01Zw1Go1Go9FoNBpNkIpC+PZTGHpcq0O3lPvZVhngmIFdMK9OYPfBP8HvSuWA\nD36JzVcf83ePy8Hs4/Zj4+4q7n9jUzfMUNMV6OoQLREIQE0xVOyUx4DeNdJoegT62uxb6O9T05vR\n8qvpT2x4Xh4Lxrc6dPmWJmzA+EGdO6XOwu9MZteoy3FXbWfY6tstey//YL9MTjkkh3nvb+Pfm0q6\nYZaazkYby/EIBKBkIzx+Cjx4qDyWbNQ3QY2mu9HXZt9Cf5+a3oyWX01/wjDg04WQOwpSW7aAA4bB\nss1NjMmGzMQuml8nUJt1GKX7n0fOtufJ2fKs5ZgZx+zH0CwPP39qHZuLq7t4hprORhvL8agrhacv\nlHATkMenL5TXNRpN96Gvzb6F/j41vRktv5r+xM7VsHcrHHBqq0M/KPKzs9rglLwumFcnU7r/+dRk\nHsqw//4Oz97YGsMuh43rThuBw6aY/cR/KSqv64ZZajoLbSzHw+cN3fxMKgrldY1G033oa7Nvob9P\nTW9Gy6+mP7F6HjiTYGjrIdhPbmgk3dV7Q7AjUDaKDvsZfkcSI967Cru3KmbIgOQErjt9BBV1XibP\n/YitpTXdMFFNZ6CN5Xg4XJBeEPlaeoG8rtFoug99bfYt9Pep6c1o+dX0F8q/gY0vwkGng9Pd4tCv\nyvy8U+hnwn7gtHfJ7DodvyuNosOuJqG2iOEf3WiZvzw8O5lbzxpJrdfHWX/7gKdWF+IPxI7T9C60\nsRwPTzZMWxK6CaYXyHNPdvfOS6Pp7+hrs2+hv09Nb0bLr6a/8MEDoGxwyDmtDv3TJw14HHD2sC6Y\nVxdSl3EwxQdMI6twJQO/esJyzH5ZSfzh3MMYPiCJm5d9wcn3v8u/PthOSVVDF89Ws69wdPcEeiw2\nG+SMhMveknAqh0tufja9v6DRdCv62uxb6O9T05vR8qvpD5T8Dz5dACMmQNKAFoe+9U0T7xT6uXQk\npPTBAIuyoRPwVHzN0LX3UDPgcGqyj4wZk5WcwG9+fAirt+3l1Q27ufOVjdz1ykaOGpbJ2WMGc+ah\nA8lKTuiG2Ws6gjaWW8Jmg+Tc7p6FRqOJRl+bfQv9fWp6M1p+NX2ZgB9W/FpCr8dMa3Fojdfgjg8b\nKEiBc/qYV7kZpdg16nKGr76Vg96/ms8nvIIvMTNmmE0pjh2exbHDs9hVUc9HW8v4aNsebn1xA799\n6UvOO3IIN5w+gpzUXlwqvJ+gtz41Go1Go9FoNBpNLB/8BXasgh/8FBLT4g4zDIPr361nV7XBzw4D\nRx+2MALOJHaOvgZnQxkj3r0CW1PLxbyGpLuZNDaPP08awz3nH8Zpo3J5cd0uTvjzuyxevQPDIv9Z\n03Pow6Ks0Wg0Go1Go9FoOsTa+fDO3TDseBh+ctxhhmHwwJpGXtvu4ycj4dCsLpxjN9GQOoxdh15J\nyp5PGfXGhSRUF7Z6jFKKoVlJzDx2P+6bNIb9s5O4ZdkGpj++mp17dbupnoo2ljUajUaj0Wg0Go1Q\n/R0s/zm8fA0MPhLGXwtKWQ4NGAa//7iRhz71cko+nLd/F8+1G6nKPYbCMb/GXbWdMS+fQcGnfyKx\nartlpexoBqYlcvOZh3DpD4fxaWEFpz/4Pos+3kFAV8/uceicZY1Go9FoNBqNpr9RuwdKv4I9m6Fy\nJ1QWQdk2+PZT+fuo8+HImWCzNhc27fVz8/v1rC0OcPYwmDMqrk3dZ6nJPoKtx/yR3M1LGPzlPIZ8\nORevO4fq7COpzRzV/NPkjq2Qr5TilENyGZOXzmP/2catL27g1S92c8c5ozgwN6X1N/c3Bfu6NwIG\nuDOlfoIuMLhP0cayRqPRaDQajUbT1zAMqC+H6t1QtRvKtohxbP7Ul4fGKrtUuk4aAIdOggNOgdRB\nYacyqPJCYVWAT4v9vPFNE6t2+Ulxwa8Oh5Py+p+hbNLkzqZo9DU4GspILV2Lu+JrkkvXkVX4evMY\nb+IAajNHUZcxgvrU4dSnDacxOR+fK5XslAR+c+bBvLOphMUfF3LqA+9z1LBMTjkkh8MHuSlwN5Id\n2IO97GvYs0k2N0o3QcUOCPgiJ+NIgIz9IedgyD5EHgccBEnZkJgOdm36tRf9H9NoNBqNRqPRaHo7\nnzwG//kL+BvF2+hriDWmElIgfSgUHCd9wTOGQlo+eLLAZo855RlP72VnVYB6n0F4hPBAj2LWKDs/\nHmYnLaGfWslRGK7BVKYOpjL43NZUS0LVNyRWbSexcjvuqu2kffchtkBTxHEBmwu/M5kfKMUNHgOv\nP4Bvd4DEb+tJVJFjm7BT6x5Cem4B5P8AUodIpXIU1FfIxkhlERR+DF8ui51kQio4EiVaYPRkOPXO\nzvln9CFUX6zAppQqBXZ04xQGAHu68f07A/2ZItljGMYZ+3Iy0Gmy21O/Oz2v9rEv5tWdcttT/69t\nQc+9+zDn31t0bk/+f/fUufXUecH3n1tPl9ue/L9vD33lc0DP+SydIru9kT5pLHc3Sqk1hmGM6+55\n7Ev0Z+q99NTPqefVPnrqvNpKb56/nnv30dvm35Pn21Pn1lPnBT17bvuCvvL5+srngL71WfoKOgNc\no9FoNBqNRqPRaDSaKLSxrNFoNBqNRqPRaDQaTRTaWO4cHu3uCXQC+jP1Xnrq59Tzah89dV5tpTfP\nX8+9++ht8+/J8+2pc+up84KePbd9QV/5fH3lc0Df+ix9Ap2zrNFoNBqNRqPRaDQaTRTas6zRaDQa\njUaj0Wg0Gk0U2ljWaDQajUaj0Wg0Go0mCm0sf0+UUv9SSpUopTaEvZaplHpTKbU5+JjRnXNsD0qp\nfKXUv5VS/1NKfamU+kXw9d78mRKVUp8opT4LfqY7gq8PU0qtDn6mZ5RSru6e6/ehp8piT5Wpni4X\nSim7UmqdUuqVnjSvjqCUOkMptUkptUUpdVN3zwfad70o4aHg/D9XSh0Zdsys4PjNSqlZXTT3dl1T\nPWn+7b3ulFIJwedbgn/fL+xcvwm+vkkpdXpnzz3O5/lGKfWFUmq9UmpN8LUu1237Sp67cG6/U0rt\nCv7f1iulfhz2ty75XvflddTTaE3nxruulFL7KaXqw76XuV0992ja8Fl+pJT6VCnlU0pNivpbl+vn\nlvien8Uf9r0s77pZazAMQ/98jx/gR8CRwIaw1/4E3BT8/Sbg3u6eZzs+zyDgyODvKcDXwMhe/pkU\nkBz83QmsBo4BngWmBV+fC1zZ3XP9np+zR8piT5Wpni4XwK+Ap4BXgs97xLw68DnswFZgf8AFfAaM\n7AHzavP1AvwYeC0oM8cAq4OvZwLbgo8Zwd8zumDu7bqmetL823vdAVcBc4O/TwOeCf4+MihLCcCw\noIzZu0GOvgEGRL3W5bptX8hzF8/td8B1FmO77HvdV9dRT/vh/9s793g7pzOPf38kjVskhBoNehJl\nGEFIKBMiJZOiJZ2RqU4ZUj51Ga3qDJ12pB304vZp6dSlpDWhVOs6Ij6VKD2oCiFOchKhkjiGyohb\njZgil2f+WM+WN7t777P3Pvuc/e7k+X4+72evd71rrfdZ73rWetfledeuos2tUK/asuXU7KPKvLQB\newE3AJMy/k1pn3sjL35tRbPLY0M9YmW5h5jZQ8AbRd4TgevdfT3wmT4VqgeY2TIzm+vut4FFwFBa\nO09mZiv8tL8fBhwK3Ob+LZWnUuRVF/OqU3nWC0k7AJ8CfuLnyoNcdbI/sNjMlprZ+8AvSGXfVGqs\nLxOBG1xnZgODJW0PfBK4z8zeMLM3gfuAw/tA9lrrVG7kr6PeZfN0G3CY14eJwC/M7D0zex5YTNK1\nPNDnbVuD9LkvZStHn5VrA+tR3qimzS1Xr/JGt3kxsy4zmw+sKYrblPa5Aj3JS9BEYrDcO2xnZssg\nNcbAh5ssT124Wc4+pJn/ls6TkklrB7Cc1GAuAf5oZqs8yEukl+T6Rq7KLW86lWO9uBz4GmtfmENy\nIlc9DAVezJznWfZyOlkuD03PW5V1Klfy11jvPpDRr79Fqg9Nf/aOAbMkPSnpFPdretvWjRx5eXZf\ncnPm6zKm6s3SyTbqr0d5oxo5y9UrgGFKnwA9KOng3ha2G3ryzPNWXj2VZxNJT0iaLalVJsvXC2Kw\nHJRE0hbA7cBZZva/zZanp5jZajMbCexAmt3bvVSwvpVqwyKPOpVHvZD0aWC5mT2Z9S4RtFX0tZVl\nL1AuD03NWw11Klfy11jvciV7CcaY2b7AEcAZksY2QYZaycOzuxrYGRgJLAO+7/59LlsD6lHeqEbO\ncmGWATuZ2T74p0CStmywfLXQk2eet/LqqTw7mdlo4PPA5ZJ2boxYQXfEYLl3eKVgmuO/y5ssT01I\n6k96cdxkZne4d0vnqYCZ/RFoJ31vNFhSP7+0A/Bys+TqRXJRbnnXqZzpxRjgaEldJDOtQ0krzc2W\nq15eAnbMnOdZ9nI6WS4PTctbjXUqd/JD1fXuAxn9+iCSSW8u9MrMXvbf5cCdpMF/Xtq2WvWhzzCz\nV3zSZA0wlbWm1n0qW4PqUd6oRs6S9crN318H8AnbJcCuvS5xeXryzPNWXj2SJ9PWLCW1m/s0Urig\nPDFY7h2mA4Vd904E7mqiLDXh36z8FFhkZj/IXGrlPG0rabC7NwXGk75N+g1Q2G2wpfJUA00vt7zq\nVF71wsy+YWY7mFkbaeOVB8zsuGbL1QPmALso7Xb8IVKe8rqTZzmdnA6coMQBwFtunjkTmCBpKzcj\nneB+vUoddSo38tdR77J5mkSqD+b+n1Pa1XcYsAvweDVbpxIAAAxRSURBVG/KXoykzSUNLLhJz28B\nOWh3nVr1oc8o+tb3b0nPrSBbn5RrA+tR3qimzS1Zr7x+bgwgaTjp+S/tI7lL0ZP3R1Pa5wrUnRfP\nwwB3b0OaVH+61yQN1sVysMtYKx/AzSSzlZWkWaOTSd993A88579bN1vOGvJzEMksZD7Q4ceRLZ6n\nvYCnPE8LgG+5/3DSS3gxcCswoNmy9jCfudTFvOpUK+gFMI61u2HnRq468nEkaafZJcC5zZbHZaq6\nvpDM5650+TuB0Zl0TvIyWQx8oY9kr6lO5Un+WusdsImfL/brwzNpnet5ehY4ogk6NJy0o+08YGFB\nt5vRtjVKn/tQtp/5veeTBgzb93W5NrIe5e2gRJsLXAAc7e6S9Qo4xnV5HjAXOKoF8rKf69U7wOvA\nwkzcPm+feyMvwF+7zs3z35ObnZcN6ZAXQhAEQRAEQRAEQRAETphhB0EQBEEQBEEQBEERMVgOgiAI\ngiAIgiAIgiJisBwEQRAEQRAEQRAERcRgOQiCIAiCIAiCIAiKiMFyEAQ1I2mwpH+qM26bpM83WqYg\nCIIgCIIgaCQxWA6CoB4GA3UNloE2oObBcuG/H4OgWYQOBkEQBMGGRQyWWxhJ4yTNcPfRkr7ebJmC\nDYaLgJ0ldUi6VNI5kuZImi/pfABJ+/n5JpI2l7RQ0giPe7DH/aqkyZKuKCQsaYakce5eIekCSY8B\nB0oaJelBSU9Kmilp+3ICSmqXdJmkhyQtcnnukPScpO9kwh0v6XGX55rCgEjS1ZKecLnPz4TvknS+\npLmSOiXt1uBnGzQJSf/lurVQ0inuV5UOSvqi14F5km6XtFmF+0xz/fqNpKWSDpF0nevptEy4CZIe\ndV27VdIW7v8tv9cCSddKkvu3S7rY9fn3kg7uzecV5A9Jp0k6oUFp/Vsj0gmCWim2QCvuJwRBXxKD\n5RyiRE1lY2bTzeyi3pIpCIr4OrDEzEYC9wG7APsDI4FRksaa2RxgOvAd4BLgRjNb4HEfNrORZnZZ\nN/fZHFhgZh8HHgN+BEwys1HAdcB3u4n/vpmNBX4M3AWcAYwAJksaIml34FhgjOdlNXCcxz3XzEYD\newGHSNork+5rZrYvcDVwdjcyBK3DSa5bo4EzJQ2heh28w8z2M7O9gUXAyd3cayvgUOCrwN3AZcAe\nwJ6SRkraBpgCjHddewL4Z497hd9rBLAp8OlMuv3MbH/gLODf634SQcshqZ+Z/djMbmhQkjUPlsP6\nImgQbdRhgRYEvUEMlnOCz6ItknQVMBf4aZlVrcMlPSPpt8DfZfw/mHXzVYtJmWsr/Hd7X2Xr8BWJ\nsqsOvppysa+e/FrS/r5qsVTS0R5mY6VVxcKK4qnuv4Wk+zMrbxOL8jjV8zVL0qYNfZBBM5jgx1Mk\n3d2NNHgGuAD4G9Lg45I60l4N3O7uvyQNdO+T1EEaSOzQTfzp/tsJLDSzZWb2HrAU2BE4DBgFzPE0\nDwOGe5zPSprr+doD+KtMunf475Okl3qwfnCmpHnAbJJ+7EL1OjhC0sOSOkkTLnt0c6+7zcxIuvmK\nmXWa2RpgIUmnDiDp3CN+rxOBj3rcT0h6zO91aNG9QjdbGH9PPiPpen+v3iZpswoWDe2SvifpQeAr\nks6TdHbmWl3WNZIuAjZ1v5vKhXP/dawvyuSry+V81Ps2+3o+lkg6LRPuz6yU3P/PrD4y9/6ukkXH\nbEnbNbRAgnVQshK7x5/3AknHVlO2SlzqcTolHVvJnyILNPf7iKR7XX8vychUUgckbatk5TPHjzHu\nf4in2yHpKUkDFf3joBJmFkcODlKnZg1wgJ9v7b8bA+2k1a1NgBdJHTgBtwAzPNxk0moDwDTSykch\n7RX++y+k1bJCugMryGPAEe6+E5gF9Af2Bjrc/xRgirsHkFY+hgH9gC3dfxtgscvbBqwCRvq1W4Dj\nm/3s46hbXxe4+/vAqWXC/QWwBHga2Nz9xhX01s+PB67KnP8aGOfuFRn/PYFHa5CxHRhd5p7tpAH8\nl4ELS8Qd5nq7lZ9PAya7uwvYxt2jgfZml0ccPT9cR34LbJbRkXHV6iDwPLC3uycD0yrc64M2OluX\nsteAo4CbS8TdBHgF2NHPzwPOy8hc0PltgK5mP9c4atbDNtL7d4yfXwecA/wO2Nb9jgWuy5R5tv08\nDzg7c+1id38FeBnYnvS+fgkYAuxOsmzo7+GuAk5wd1b3K4Uz4LPd5KsLON3dlwHzgYHAtsBy958A\nXEvqL2wEzADG+rVCn2hTYAEwJHPvo9x9Cd4niaPX9PMYYGrmfFCVZXsMyQptY2A74L9dF8v5j2Pd\nd/Zk0iT3IG8DX8i0gSV1APg5cJC7dwIWufvuTP3agtRnjf5xHGWPWFnOFy+Y2Wx3l1rV2g143sye\ns1Sbbqwx/TnAFySdB+xpZm9XCPs+cK+7O4EHzWylu9vcfwJwgtKqx2OkF29hIP89SfNJA5+hpEYQ\nl7/D3bHy0bq8TXoZAswETtLa7ymHSvqwX7sW+CZwE3BxibiQXrQjJW0kaUeSOXcpngW2lXSg36e/\npO5W77rjfmBSQV5JW0v6KLAl8A7wls9SH9HD+wT5ZxDwppn9n9J36AeUCFNJBwcCyyT1Z60pf0+Y\nDYyR9DG/12aSdiV1FAFe8zo3qVwCQcvyopk94u4bgU9S2armlxXS6ol1TZZK4bLWF5XIyvKYmb1t\nZq8C70oaTGUrpVJWH5D6KjPcHX2K3qcTGO8rqweb2Vvu313ZHkSa/FttZq8ADwL7VfAvxf1m9paZ\nvUuagC9Y2pTTgfHAFa6v04EtJQ0EHgF+IOlMYLCZrSL6x0EF+jVbgGAd3gGQNIz0HeR+Zvam0oYv\nhQ6SVZHOKtzEXpKADwGY2UOSxgKfAn4m6VIr/23TSh+QQ1rxfs/TWCOpoDcCvmxmM7MRJU0mzSiO\nMrOVkroy8r+XCbqaNEsctBhm9rqkRyQtAH5FmsF9NKkbK4DjJR0OrDKznyuZ6/1O0qHAw8Aq7/hM\nAy4nrcp1klYM5pa55/tKnxf8h6RBpPbrcpLZar35eFrSFGCW0j4BK4EzzGy2pKc87aWkl2uwfnMv\ncJp3Yp4ldcrXoRsd/CapU/QCSZcHFsevBTN71dvSmyUNcO8pZvZ7SVP9Hl2kTl6wflH8nn+bNNAt\naeKM9x3KUHjnrmHd9+8akv4KuN7MvtGNTJXCvWtmq7uJX60sF5rZNevcOG34OB440Cez2lnbp8j2\nVVYT/dpexdufUcCRwIWSZvmlasq2FOX8S1HcfyyUdTkd2IikM38qSuciSfd4HmZLGh/946AS0ajk\nk1KrWu3AM8AwSTub2RLgH8rE7yLNAN8CTCSZh+ArZn8ws6mSNgf2BXqyEchM4HRJD3il3xX4A2mF\nZrn7fYK1s3/BeoSZFW++8cOi8yW4fnlH6uOZa4cVhS25EmdmWxSddwBjq5RvXMbdTqpDpa79khIr\nM2Y2uUy6bRn3EyRzsaDF8dW2UhYEVemgmV1N2vCtmntNzri7SKuGpa49QIlVFjObQlpdLPYfl3G/\nRqxMtCo7STrQzB4lvednA18s+Ln1wq5mVvdEYYb7gbskXWZmyyVtTTJBfQFYKam/r5pVCtcoZgLf\nlnSTma2QNJQ0gVmN1UfQB0j6CPCGmd2otB/O5CqjPgScKul6YGtSG3oOaRxSyn8oPZxwJJlHfwm4\n1GUfaWYd3ofuBDrdSmg3SX8i+sdBGWKwnEPMbF6pVS0ze1dpY4t7JL1G+r5uRIkkppJeao+TXnCF\nWedxwDmSVpJW/3r69xI/IXXG5voK9qvAZ0gmt3dLegLoIA3ygyAIgiDonkXAiZKuAZ4j7cA+kwZa\n1RQoZ11DspC4Fpgvaa6ZHVchXEMws1lK/1CwjpUSVVh9BH3GnsClktaQdOB04LYq4t1J2vxtHsly\n4mtm9j+Syvm/zroWaG/WIeuZwJWuN/1IA/bTgLN8oLqaZM79K+BzRP84KIPWWhIEQRC0HpKuBMYU\nef/QzP6zGfIEQQFJ5wJ/X+R9q5l195dnwQaKpDbSxkalJsKDIAiCPiYGy0EQBEEQBDkgBstBEAT5\nIgbLGzhK/4s4oMj7H/17jiAIgiAIgqpws9phRd7/WrzRURDknegfBwVisBwEQRAEQRAEQRAERcT/\nLAdBEARBEARBEARBETFYDoIgCIIgCIIgCIIiYrAcBEEQBEEQBEEQBEXEYDkIgiAIgiAIgiAIivh/\nUepYiETLAaAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 955.5x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(cancer, hue='diagnosis', vars=['radius_mean', 'texture_mean', 'area_mean', 'perimeter_mean', 'smoothness_mean'])"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "LE=LabelEncoder()\n",
+    "cancer['diagnosis']=LE.fit_transform(cancer['diagnosis'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "cancer.diagnosis=[1 if each == 'M' else 0 for each in cancer.diagnosis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_dat=cancer.drop(['diagnosis'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "Y=cancer['diagnosis'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X=(X_dat-np.min(X_dat))/(np.max(X_dat)-np.min(X_dat)).values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test train splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test=train_test_split(X,Y, test_size=0.15, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train= X_train.T\n",
+    "X_test= X_test.T\n",
+    "Y_train= Y_train.T\n",
+    "Y_test= Y_test.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Function definitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#initializing parameters\n",
+    "def initialize_theta(dimension):\n",
+    "    theta_others=np.full((dimension, 1), 0.01)\n",
+    "    theta_nut=0.0\n",
+    "    return theta_others,theta_nut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#a sigmoid function is otherwise known as a logistic function and the formula is g(z) = 1/1+e**-z\n",
+    "def sigmoid(Z):\n",
+    "    g=np.zeros(np.size(Z))\n",
+    "    g = 1/(1+np.exp(-Z))\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#simplified cost function and gradient descent(to minimize cost function to achieve fitting parameters)\n",
+    "def cost_and_grad(theta_others, theta_nut, X_train, Y_train):\n",
+    "    Z=np.dot(theta_others.T, X_train)+theta_nut\n",
+    "    g=sigmoid(Z)\n",
+    "    loss=-Y_train*np.log(g)-(1-Y_train)*np.log(1-g)\n",
+    "    m=np.size(X_train, 1)\n",
+    "    cost = (np.sum(loss))/m\n",
+    "    \n",
+    "    #applying gradient descent to minimize cost function in order to fit parameter(theta)\n",
+    "    d_theta_others=(np.dot(X_train,((g-Y_train).T)))/m\n",
+    "    d_theta_nut=(np.sum(loss))/m\n",
+    "    grads= {'d_theta_others':d_theta_others, 'd_theta_nut':d_theta_nut}\n",
+    "    return cost, grads"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#fuction to update cost, parameters and plot the logistic regression graph\n",
+    "def update(theta_others, theta_nut, X_train, Y_train, alpha, num_iter):\n",
+    "    cost_l = []\n",
+    "    cost_l2 = []\n",
+    "    index = []\n",
+    "    \n",
+    "    for i in range(num_iter):\n",
+    "        cost, grads = cost_and_grad(theta_others, theta_nut, X_train, Y_train)\n",
+    "        cost_l.append(cost)\n",
+    "        \n",
+    "        \n",
+    "        #formula:repeat{theta=theta-learning_rate*(d/d_theta)cost}\n",
+    "        #where: (d/d_theta)cost = 1/m*summation(sigmoid-y)x for all i=1 to m\n",
+    "        theta_others=theta_others-alpha*grads['d_theta_others']\n",
+    "        theta_nut=theta_nut-alpha*grads['d_theta_nut']\n",
+    "        \n",
+    "        #initiating the steps of the cost i.e for 10, it will be in '0, 10, 20'\n",
+    "        if i % 10 == 0:\n",
+    "            cost_l2.append(cost)\n",
+    "            index.append(i)\n",
+    "            print (\"cost after % i: % f\" %(i, cost))\n",
+    "            \n",
+    "   #plotting the cost against iterations graph\n",
+    "    parameters = {\"theta_others\":theta_others, \"theta_nut\":theta_nut}\n",
+    "    plt.plot(index, cost_l2)\n",
+    "    plt.xticks(index, rotation='vertical')\n",
+    "    plt.xlabel('Iterations')\n",
+    "    plt.ylabel('Cost')\n",
+    "    plt.title('Logistic Regression')\n",
+    "    plt.show()\n",
+    "    return parameters, grads, cost_l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#building the prediction function\n",
+    "#z=transpose(theta).X\n",
+    "#prediction = 1 if sigmoid(Z)>=0.5\n",
+    "#else prediction = o\n",
+    "\n",
+    "def predict(theta_others, theta_nut,  X_test):\n",
+    "    Z=sigmoid(np.dot(theta_others.T, X_test)+theta_nut)\n",
+    "    Y_pred=np.zeros((1, np.size(X_test, 1)))\n",
+    "    n=np.size(Z, 1)\n",
+    "    \n",
+    "    for i in range (n):\n",
+    "        if Z[0, i]>=0.5:\n",
+    "            Y_pred[0, i]=1\n",
+    "        else:\n",
+    "            Y_pred[0,i]=0\n",
+    "    return Y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cost after  0:  0.692836\n",
+      "cost after  10:  0.566320\n",
+      "cost after  20:  0.471511\n",
+      "cost after  30:  0.397116\n",
+      "cost after  40:  0.346511\n",
+      "cost after  50:  0.312452\n",
+      "cost after  60:  0.288972\n",
+      "cost after  70:  0.272392\n",
+      "cost after  80:  0.260523\n",
+      "cost after  90:  0.252030\n",
+      "cost after  100:  0.246077\n",
+      "cost after  110:  0.242120\n",
+      "cost after  120:  0.239803\n",
+      "cost after  130:  0.238887\n",
+      "cost after  140:  0.239218\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RhsIcYJiZDTazHGASMC12ATMbFvPwQuDdCPuRNNGrc3tun3gM/7rtY9x6/ggW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\nyoY8ioQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train accuracy: 90.89026915113871 %\n",
+      "test accuracy: 87.20930232558139 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Building the logistic regression function by applying previously built functions.\n",
+    "\n",
+    "def logreg(X_train, Y_train, X_test, Y_test, alpha, Iter):\n",
+    "    \n",
+    "    dimension=np.size(X_train, 0)\n",
+    "    \n",
+    "    theta_others, theta_nut=initialize_theta(dimension)\n",
+    "    \n",
+    "    parameters, grads, cost_l = update(theta_others, theta_nut, X_train, Y_train, alpha,Iter)\n",
+    "    \n",
+    "    Y_pred_test=predict(parameters['theta_others'], parameters['theta_nut'], X_test)\n",
+    "    Y_pred_train=predict(parameters['theta_others'], parameters['theta_nut'], X_train)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    print('train accuracy: {} %'.format(100-np.mean(np.abs(Y_pred_train-Y_train))*100))\n",
+    "    print('test accuracy: {} %'.format(100-np.mean(np.abs(Y_pred_test-Y_test))*100))\n",
+    "\n",
+    "logreg(X_train, Y_train, X_test, Y_test, alpha=0.25, Iter=150)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "classifier=LogisticRegression(random_state=42, max_iter=100)\n",
+    "print('test accuracy: {}'.format(classifier.fit(X_train.T, Y_train.T).score(X_test.T, Y_test.T)))\n",
+    "print('train accuracy: {}'.format(classifier.fit(X_train.T, Y_train.T).score(X_train.T, Y_train.T)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Adegoke_Toluwani_Bolu/LogReg Algo challenge.md b/Adegoke_Toluwani_Bolu/LogReg Algo challenge.md
new file mode 100644
index 0000000..7238993
--- /dev/null
+++ b/Adegoke_Toluwani_Bolu/LogReg Algo challenge.md	
@@ -0,0 +1,47 @@
+Logistic Regression as the name implies is not a regression algorithm but a classification algorithm.Logistic Regression representation: 0<=sigmoid(Z)<=1Example of Logistic Regression use-cases are:
+Classification of email as spam or not spam
+Classification of breast cancer as Malignant or benignIn building logistic regression, i used the Wisconsin breast cancer dataset as the data to build the algorithm.LabelEncoder could have been used to convert my dependent variable 'Y' from string to integer but label encoder might make a mis-representation of the values i want for my classification and end up making a mistake with the prediction, so a simple 'if' statement worked the miracle since i wanedt my Malignant to be 1(i.e there is presence of breast cancer in the patient) and Benign to be 0(i.e there is absence of breast cancer in the patient)In Building a logistic regression we have to basically go through four processes:1. Building the sigmoid function: The sigmoid function is represented as g(Z)=1/1+e**-Z  but Z= transpose(theta).X : in order to
+
+fit the parameter theta, we need to pick initial values for theta which is the reason for my function initializing theta as the parameters.
+2. Building the cost function: usually looks like:
+cost(sigmoid(Z), y)={-log(sigmoid(Z)) if y=1 and -log(i-sigmoid(Z)) if y=0 
+
+but
+
+it can be simplified to give us 
+
+cost(sigmoid(Z), y) = -ylog(sigmoid(Z))-(1-y)log(1-sigmoid(Z))
+
+for logistic regression, the cost function = 1/m* summation for all i=1 to m (cost(sigmoid(Z), y)) which will finally give us
+
+-1/m[summation for all i=1 to m (ylog(sigmoid(Z))-(1-y)log(1-sigmoid(Z)))]3. Gradient Descent: In order to fit our parameters theta to the data, we need to minimize the cost function and in order to minimize the cost function, w emake use of the gradient descent.
+
+it is represented as:
+
+repeat{theta = theta - learning_rate * d/dtheta cost}
+
+where d/dtheta cost = 1/m * summation for all i=1 to m(sigmoid(Z)-y)x
+
+imputing it into thet theta formular, we have:
+theta=theta-learning rate/m *summation for all i=1 to m(sigmoid(Z)-y)x
+
+in order to implement it well, we make use of the vectorized implementation which looks like:
+theta=theta-learning rate/m * transpose(X).(g(x.theta)-y)4. Finally, we build the prediction function
+
+0.5 has been chosen as the threshhold
+
+we just need to initialize our prediction and build a simple if conditional statement:
+ 
+if sigmoid(Z)>=0.5:
+	then prediction=1
+else:
+	prediction=0
+In order to complete building the Logistic Regression algorithm, the 'logreg' function was defined, it made use of the already defined fuctions which was applied on the dataset to perform its function and xmake its prediction.NB: The cost function is used to penalize the algorithm for making a false prediction, in order to minimize this cost function,
+we make use of gradient descent.We could decide to go further by:
+
+5. Regularizing logistic regression cost
+6. Regularizing logistic regression gradient
+
+These can be achieved by using advanced optimization
+
+but i didn't use it in the codes since they are just extra.Thanks to geekforgeeks and Stanford Machine Learning Course by Andrew Ng for the Knowledge of codes and mathematics imparted.
\ No newline at end of file