model- breast c p.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "possible-jacksonville",
   "metadata": {},
   "source": [
    "# Breast Cancer Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlimited-nursing",
   "metadata": {},
   "source": [
    "We have given a breast cancer dataset with 33 attributes/ columns and 569 rows.\n",
    "Task is to perform breast cancer preditiction using given dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-desktop",
   "metadata": {},
   "source": [
    "Firstly, we have imported all the important python libraries for the operations and prediction purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "married-queensland",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "undefined-grace",
   "metadata": {},
   "source": [
    "Our dataset is of '.csv' format. So we have loaded it using pandas library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "seven-gateway",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"breast_c_Dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-grill",
   "metadata": {},
   "source": [
    "Now we have loaded our dataset in dataframe.\n",
    "It conrains total 33 columns and 569  rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "every-cyprus",
   "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>Unnamed: 0</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>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>564</td>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>565</td>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>566</td>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>567</td>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>568</td>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        id diagnosis  radius_mean  texture_mean  \\\n",
       "0             0    842302         M        17.99         10.38   \n",
       "1             1    842517         M        20.57         17.77   \n",
       "2             2  84300903         M        19.69         21.25   \n",
       "3             3  84348301         M        11.42         20.38   \n",
       "4             4  84358402         M        20.29         14.34   \n",
       "..          ...       ...       ...          ...           ...   \n",
       "564         564    926424         M        21.56         22.39   \n",
       "565         565    926682         M        20.13         28.25   \n",
       "566         566    926954         M        16.60         28.08   \n",
       "567         567    927241         M        20.60         29.33   \n",
       "568         568     92751         B         7.76         24.54   \n",
       "\n",
       "     perimeter_mean  area_mean  smoothness_mean  compactness_mean  \\\n",
       "0            122.80     1001.0          0.11840           0.27760   \n",
       "1            132.90     1326.0          0.08474           0.07864   \n",
       "2            130.00     1203.0          0.10960           0.15990   \n",
       "3             77.58      386.1          0.14250           0.28390   \n",
       "4            135.10     1297.0          0.10030           0.13280   \n",
       "..              ...        ...              ...               ...   \n",
       "564          142.00     1479.0          0.11100           0.11590   \n",
       "565          131.20     1261.0          0.09780           0.10340   \n",
       "566          108.30      858.1          0.08455           0.10230   \n",
       "567          140.10     1265.0          0.11780           0.27700   \n",
       "568           47.92      181.0          0.05263           0.04362   \n",
       "\n",
       "     concavity_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           0.30010  ...        25.380          17.33           184.60   \n",
       "1           0.08690  ...        24.990          23.41           158.80   \n",
       "2           0.19740  ...        23.570          25.53           152.50   \n",
       "3           0.24140  ...        14.910          26.50            98.87   \n",
       "4           0.19800  ...        22.540          16.67           152.20   \n",
       "..              ...  ...           ...            ...              ...   \n",
       "564         0.24390  ...        25.450          26.40           166.10   \n",
       "565         0.14400  ...        23.690          38.25           155.00   \n",
       "566         0.09251  ...        18.980          34.12           126.70   \n",
       "567         0.35140  ...        25.740          39.42           184.60   \n",
       "568         0.00000  ...         9.456          30.37            59.16   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0        2019.0           0.16220            0.66560           0.7119   \n",
       "1        1956.0           0.12380            0.18660           0.2416   \n",
       "2        1709.0           0.14440            0.42450           0.4504   \n",
       "3         567.7           0.20980            0.86630           0.6869   \n",
       "4        1575.0           0.13740            0.20500           0.4000   \n",
       "..          ...               ...                ...              ...   \n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 33 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alone-discovery",
   "metadata": {},
   "source": [
    "Here we have dropped an unwanted attribute 'unnamed: 0' using drop function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "developed-reform",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Unnamed: 0\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "transsexual-particular",
   "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>radius_worst</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",
       "    </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>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</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>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</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>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</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>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</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>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 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",
       "..        ...       ...          ...           ...             ...        ...   \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",
       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0    ...        25.380          17.33           184.60      2019.0   \n",
       "1    ...        24.990          23.41           158.80      1956.0   \n",
       "2    ...        23.570          25.53           152.50      1709.0   \n",
       "3    ...        14.910          26.50            98.87       567.7   \n",
       "4    ...        22.540          16.67           152.20      1575.0   \n",
       "..   ...           ...            ...              ...         ...   \n",
       "564  ...        25.450          26.40           166.10      2027.0   \n",
       "565  ...        23.690          38.25           155.00      1731.0   \n",
       "566  ...        18.980          34.12           126.70      1124.0   \n",
       "567  ...        25.740          39.42           184.60      1821.0   \n",
       "568  ...         9.456          30.37            59.16       268.6   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "measured-findings",
   "metadata": {},
   "source": [
    "Again, we have dopped an id attribute from the dataset for the sake of usefulness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "measured-comment",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"id\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "emerging-plastic",
   "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>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>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0           M        17.99         10.38          122.80     1001.0   \n",
       "1           M        20.57         17.77          132.90     1326.0   \n",
       "2           M        19.69         21.25          130.00     1203.0   \n",
       "3           M        11.42         20.38           77.58      386.1   \n",
       "4           M        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564         M        21.56         22.39          142.00     1479.0   \n",
       "565         M        20.13         28.25          131.20     1261.0   \n",
       "566         M        16.60         28.08          108.30      858.1   \n",
       "567         M        20.60         29.33          140.10     1265.0   \n",
       "568         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",
       "     symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           0.2419  ...        25.380          17.33           184.60   \n",
       "1           0.1812  ...        24.990          23.41           158.80   \n",
       "2           0.2069  ...        23.570          25.53           152.50   \n",
       "3           0.2597  ...        14.910          26.50            98.87   \n",
       "4           0.1809  ...        22.540          16.67           152.20   \n",
       "..             ...  ...           ...            ...              ...   \n",
       "564         0.1726  ...        25.450          26.40           166.10   \n",
       "565         0.1752  ...        23.690          38.25           155.00   \n",
       "566         0.1590  ...        18.980          34.12           126.70   \n",
       "567         0.2397  ...        25.740          39.42           184.60   \n",
       "568         0.1587  ...         9.456          30.37            59.16   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0        2019.0           0.16220            0.66560           0.7119   \n",
       "1        1956.0           0.12380            0.18660           0.2416   \n",
       "2        1709.0           0.14440            0.42450           0.4504   \n",
       "3         567.7           0.20980            0.86630           0.6869   \n",
       "4        1575.0           0.13740            0.20500           0.4000   \n",
       "..          ...               ...                ...              ...   \n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "civic-literacy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                569 non-null    object \n",
      " 1   radius_mean              569 non-null    float64\n",
      " 2   texture_mean             569 non-null    float64\n",
      " 3   perimeter_mean           569 non-null    float64\n",
      " 4   area_mean                569 non-null    float64\n",
      " 5   smoothness_mean          569 non-null    float64\n",
      " 6   compactness_mean         569 non-null    float64\n",
      " 7   concavity_mean           569 non-null    float64\n",
      " 8   concave points_mean      569 non-null    float64\n",
      " 9   symmetry_mean            569 non-null    float64\n",
      " 10  fractal_dimension_mean   569 non-null    float64\n",
      " 11  radius_se                569 non-null    float64\n",
      " 12  texture_se               569 non-null    float64\n",
      " 13  perimeter_se             569 non-null    float64\n",
      " 14  area_se                  569 non-null    float64\n",
      " 15  smoothness_se            569 non-null    float64\n",
      " 16  compactness_se           569 non-null    float64\n",
      " 17  concavity_se             569 non-null    float64\n",
      " 18  concave points_se        569 non-null    float64\n",
      " 19  symmetry_se              569 non-null    float64\n",
      " 20  fractal_dimension_se     569 non-null    float64\n",
      " 21  radius_worst             569 non-null    float64\n",
      " 22  texture_worst            569 non-null    float64\n",
      " 23  perimeter_worst          569 non-null    float64\n",
      " 24  area_worst               569 non-null    float64\n",
      " 25  smoothness_worst         569 non-null    float64\n",
      " 26  compactness_worst        569 non-null    float64\n",
      " 27  concavity_worst          569 non-null    float64\n",
      " 28  concave points_worst     569 non-null    float64\n",
      " 29  symmetry_worst           569 non-null    float64\n",
      " 30  fractal_dimension_worst  569 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 137.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#Generting column info.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "developed-revision",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resident-success",
   "metadata": {},
   "source": [
    "From the info, we found that diagnosis column has categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "destroyed-fireplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      M\n",
       "1      M\n",
       "2      M\n",
       "3      M\n",
       "4      M\n",
       "      ..\n",
       "564    M\n",
       "565    M\n",
       "566    M\n",
       "567    M\n",
       "568    B\n",
       "Name: diagnosis, Length: 569, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appointed-bedroom",
   "metadata": {},
   "source": [
    "Now, we are going use some numpy operationas for numerical computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "complimentary-death",
   "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>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>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "narrative-night",
   "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>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>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "564         M        21.56         22.39          142.00     1479.0   \n",
       "565         M        20.13         28.25          131.20     1261.0   \n",
       "566         M        16.60         28.08          108.30      858.1   \n",
       "567         M        20.60         29.33          140.10     1265.0   \n",
       "568         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\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",
       "     symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "564         0.1726  ...        25.450          26.40           166.10   \n",
       "565         0.1752  ...        23.690          38.25           155.00   \n",
       "566         0.1590  ...        18.980          34.12           126.70   \n",
       "567         0.2397  ...        25.740          39.42           184.60   \n",
       "568         0.1587  ...         9.456          30.37            59.16   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "eight-straight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "564    False\n",
       "565    False\n",
       "566    False\n",
       "567    False\n",
       "568    False\n",
       "Name: diagnosis, Length: 569, dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"diagnosis\"].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "delayed-chocolate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "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",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "sunrise-affiliate",
   "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>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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  fractal_dimension_mean  ...  radius_worst  \\\n",
       "count     569.000000              569.000000  ...    569.000000   \n",
       "mean        0.181162                0.062798  ...     16.269190   \n",
       "std         0.027414                0.007060  ...      4.833242   \n",
       "min         0.106000                0.049960  ...      7.930000   \n",
       "25%         0.161900                0.057700  ...     13.010000   \n",
       "50%         0.179200                0.061540  ...     14.970000   \n",
       "75%         0.195700                0.066120  ...     18.790000   \n",
       "max         0.304000                0.097440  ...     36.040000   \n",
       "\n",
       "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       compactness_worst  concavity_worst  concave points_worst  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       symmetry_worst  fractal_dimension_worst  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "congressional-library",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>28.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>39.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>188.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>2501.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.16340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.34540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.42680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.20120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.30400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>2.87300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>4.88500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>21.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>542.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.03113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.13540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.39600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.05279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.07895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.02984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>36.04000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>49.54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>251.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>4254.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.22260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>1.05800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>1.25200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.29100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.66380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean         std         min  \\\n",
       "radius_mean              569.0   14.127292    3.524049    6.981000   \n",
       "texture_mean             569.0   19.289649    4.301036    9.710000   \n",
       "perimeter_mean           569.0   91.969033   24.298981   43.790000   \n",
       "area_mean                569.0  654.889104  351.914129  143.500000   \n",
       "smoothness_mean          569.0    0.096360    0.014064    0.052630   \n",
       "compactness_mean         569.0    0.104341    0.052813    0.019380   \n",
       "concavity_mean           569.0    0.088799    0.079720    0.000000   \n",
       "concave points_mean      569.0    0.048919    0.038803    0.000000   \n",
       "symmetry_mean            569.0    0.181162    0.027414    0.106000   \n",
       "fractal_dimension_mean   569.0    0.062798    0.007060    0.049960   \n",
       "radius_se                569.0    0.405172    0.277313    0.111500   \n",
       "texture_se               569.0    1.216853    0.551648    0.360200   \n",
       "perimeter_se             569.0    2.866059    2.021855    0.757000   \n",
       "area_se                  569.0   40.337079   45.491006    6.802000   \n",
       "smoothness_se            569.0    0.007041    0.003003    0.001713   \n",
       "compactness_se           569.0    0.025478    0.017908    0.002252   \n",
       "concavity_se             569.0    0.031894    0.030186    0.000000   \n",
       "concave points_se        569.0    0.011796    0.006170    0.000000   \n",
       "symmetry_se              569.0    0.020542    0.008266    0.007882   \n",
       "fractal_dimension_se     569.0    0.003795    0.002646    0.000895   \n",
       "radius_worst             569.0   16.269190    4.833242    7.930000   \n",
       "texture_worst            569.0   25.677223    6.146258   12.020000   \n",
       "perimeter_worst          569.0  107.261213   33.602542   50.410000   \n",
       "area_worst               569.0  880.583128  569.356993  185.200000   \n",
       "smoothness_worst         569.0    0.132369    0.022832    0.071170   \n",
       "compactness_worst        569.0    0.254265    0.157336    0.027290   \n",
       "concavity_worst          569.0    0.272188    0.208624    0.000000   \n",
       "concave points_worst     569.0    0.114606    0.065732    0.000000   \n",
       "symmetry_worst           569.0    0.290076    0.061867    0.156500   \n",
       "fractal_dimension_worst  569.0    0.083946    0.018061    0.055040   \n",
       "\n",
       "                                25%         50%          75%         max  \n",
       "radius_mean               11.700000   13.370000    15.780000    28.11000  \n",
       "texture_mean              16.170000   18.840000    21.800000    39.28000  \n",
       "perimeter_mean            75.170000   86.240000   104.100000   188.50000  \n",
       "area_mean                420.300000  551.100000   782.700000  2501.00000  \n",
       "smoothness_mean            0.086370    0.095870     0.105300     0.16340  \n",
       "compactness_mean           0.064920    0.092630     0.130400     0.34540  \n",
       "concavity_mean             0.029560    0.061540     0.130700     0.42680  \n",
       "concave points_mean        0.020310    0.033500     0.074000     0.20120  \n",
       "symmetry_mean              0.161900    0.179200     0.195700     0.30400  \n",
       "fractal_dimension_mean     0.057700    0.061540     0.066120     0.09744  \n",
       "radius_se                  0.232400    0.324200     0.478900     2.87300  \n",
       "texture_se                 0.833900    1.108000     1.474000     4.88500  \n",
       "perimeter_se               1.606000    2.287000     3.357000    21.98000  \n",
       "area_se                   17.850000   24.530000    45.190000   542.20000  \n",
       "smoothness_se              0.005169    0.006380     0.008146     0.03113  \n",
       "compactness_se             0.013080    0.020450     0.032450     0.13540  \n",
       "concavity_se               0.015090    0.025890     0.042050     0.39600  \n",
       "concave points_se          0.007638    0.010930     0.014710     0.05279  \n",
       "symmetry_se                0.015160    0.018730     0.023480     0.07895  \n",
       "fractal_dimension_se       0.002248    0.003187     0.004558     0.02984  \n",
       "radius_worst              13.010000   14.970000    18.790000    36.04000  \n",
       "texture_worst             21.080000   25.410000    29.720000    49.54000  \n",
       "perimeter_worst           84.110000   97.660000   125.400000   251.20000  \n",
       "area_worst               515.300000  686.500000  1084.000000  4254.00000  \n",
       "smoothness_worst           0.116600    0.131300     0.146000     0.22260  \n",
       "compactness_worst          0.147200    0.211900     0.339100     1.05800  \n",
       "concavity_worst            0.114500    0.226700     0.382900     1.25200  \n",
       "concave points_worst       0.064930    0.099930     0.161400     0.29100  \n",
       "symmetry_worst             0.250400    0.282200     0.317900     0.66380  \n",
       "fractal_dimension_worst    0.071460    0.080040     0.092080     0.20750  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "loving-litigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.random.normal(0, 1, (100, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "golden-happening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.02262152e-01,  4.79141030e-01, -3.98299049e-01],\n",
       "       [ 9.37658254e-01,  2.45803532e+00, -6.39264488e-01],\n",
       "       [ 1.02514507e+00,  4.89211843e-01,  3.44101904e-01],\n",
       "       [-7.60816165e-01, -5.61884219e-02, -5.45994487e-01],\n",
       "       [-3.68705121e-01, -4.14702789e-01, -5.82743219e-02],\n",
       "       [-2.79186357e-01,  3.65236546e-01,  4.85523186e-01],\n",
       "       [ 8.53298855e-01, -4.19027848e-01, -1.15272562e+00],\n",
       "       [ 6.66148559e-01, -7.84604496e-01, -8.44910003e-01],\n",
       "       [-7.84242415e-01, -1.31270454e+00,  3.66932936e-01],\n",
       "       [-1.04864451e+00, -1.53895439e+00,  6.38268837e-01],\n",
       "       [ 4.56957170e-01,  1.20139998e+00, -7.09833754e-02],\n",
       "       [ 1.24963614e+00, -9.31906874e-01, -3.02861124e-01],\n",
       "       [ 9.15641220e-01, -8.34576903e-01,  1.30078688e-01],\n",
       "       [ 4.08201214e-02, -1.13247767e-02,  7.79669296e-01],\n",
       "       [ 9.25488597e-01,  4.10481121e-01,  1.25618478e+00],\n",
       "       [ 2.27061932e-01,  3.28202438e-01, -2.85698221e-01],\n",
       "       [ 9.22286330e-01, -1.60555201e+00, -2.15442236e+00],\n",
       "       [-1.62334085e-02, -7.42753256e-01,  1.21237384e+00],\n",
       "       [ 2.18866702e+00, -1.02377220e+00, -2.31100982e+00],\n",
       "       [-2.08439121e+00, -1.27777828e+00, -2.03006198e+00],\n",
       "       [-1.91387812e-01, -1.25745834e+00,  1.64813616e+00],\n",
       "       [-6.52383388e-01,  7.55846027e-02, -1.45122338e+00],\n",
       "       [ 3.56919193e-01, -1.10049733e-01,  1.44248998e+00],\n",
       "       [ 7.33976591e-02,  4.51258289e-01,  3.55509639e-01],\n",
       "       [-9.91429818e-01, -6.99580489e-01,  4.76350065e-02],\n",
       "       [ 2.98351150e-01,  1.84510410e+00, -3.20101004e-01],\n",
       "       [ 9.06821465e-01,  9.73594867e-01,  1.22545557e+00],\n",
       "       [ 8.07913008e-01,  1.80732117e-01, -6.71769705e-01],\n",
       "       [ 1.20177896e+00, -1.06389001e+00,  3.64644072e-03],\n",
       "       [ 1.95604443e+00, -5.66085421e-01, -1.84812697e+00],\n",
       "       [-1.23326474e+00, -8.52105975e-01,  8.02099734e-01],\n",
       "       [-6.04093880e-01,  5.02043326e-01, -1.67997652e+00],\n",
       "       [-1.08961576e+00,  1.53046986e+00,  2.08835480e-01],\n",
       "       [ 5.71905649e-01,  5.86792904e-01,  5.82720834e-01],\n",
       "       [-1.33533419e+00,  6.40193273e-01, -7.65686340e-01],\n",
       "       [-3.31154030e-01,  2.13001212e-01,  1.36937497e+00],\n",
       "       [-5.48403836e-01,  8.37014930e-03, -1.39996605e+00],\n",
       "       [ 9.54900311e-01,  2.80337388e-01, -1.18727720e+00],\n",
       "       [-2.00978771e+00, -1.96587291e-01, -7.32683980e-01],\n",
       "       [-2.07229113e+00,  1.76416725e+00, -8.80579725e-01],\n",
       "       [-3.77228316e-01, -7.46720814e-01, -4.09542985e-01],\n",
       "       [ 3.69312308e-01,  6.19837302e-01, -3.11446516e-01],\n",
       "       [-6.54918652e-04,  2.09122425e-01, -2.43229193e+00],\n",
       "       [ 8.71882219e-02,  1.07555302e+00,  1.47879661e+00],\n",
       "       [ 8.71075897e-01,  1.46838787e+00, -7.44529860e-01],\n",
       "       [-2.74940882e-01,  5.34230814e-01,  2.96168737e-01],\n",
       "       [ 3.02096388e-01,  1.01083736e+00, -6.85238107e-01],\n",
       "       [ 1.36793025e+00, -1.20270672e+00,  1.79293459e+00],\n",
       "       [ 7.13271230e-01,  1.38061419e+00, -1.63139745e-01],\n",
       "       [-1.30617017e+00, -4.93690621e-01,  6.29702900e-01],\n",
       "       [-9.57678772e-01,  2.56211124e-01, -3.90883900e-01],\n",
       "       [-6.33695553e-01,  3.28643401e-01,  7.80063843e-01],\n",
       "       [-1.27464223e+00, -3.20380916e-01, -1.25057363e+00],\n",
       "       [ 3.63745589e-01, -3.38815464e-02, -1.72551380e-01],\n",
       "       [-1.04238147e+00,  9.21935680e-02, -1.50527106e+00],\n",
       "       [-6.05463129e-01, -1.03396738e+00, -1.64518141e+00],\n",
       "       [-3.33602258e-01,  3.86630921e-01,  9.01281448e-01],\n",
       "       [ 4.67691026e-01,  1.61938157e+00, -6.64943522e-01],\n",
       "       [ 1.97636729e+00, -4.10542433e-01,  1.80755058e+00],\n",
       "       [-4.76790039e-01, -4.87327244e-01,  1.09243120e+00],\n",
       "       [-1.10571582e-01, -1.58610533e+00,  1.73382421e-01],\n",
       "       [ 1.22504754e+00, -1.08400174e+00,  2.50234752e-01],\n",
       "       [ 3.59661587e-01,  5.74466592e-01,  9.25232636e-02],\n",
       "       [-1.06745241e+00,  1.16594030e-01, -8.17176852e-01],\n",
       "       [ 3.96973443e-01,  2.21713698e+00, -9.69196375e-01],\n",
       "       [ 2.06315750e+00,  2.38917627e-01, -1.96147749e+00],\n",
       "       [ 6.76402273e-01,  8.58728821e-01, -5.30981724e-01],\n",
       "       [-3.25198786e-01, -6.75824915e-01,  2.23986472e-01],\n",
       "       [-1.95810251e-01,  2.77126947e-01,  9.53438094e-01],\n",
       "       [ 1.13335574e+00, -8.25973577e-01,  1.45406536e-01],\n",
       "       [-2.11876253e-01, -1.01111990e+00,  5.95865720e-01],\n",
       "       [-1.26199585e+00, -8.95272163e-01, -6.06022710e-02],\n",
       "       [-2.29409327e-01, -8.66489333e-02,  9.98477988e-02],\n",
       "       [-6.77843301e-03,  9.54022271e-02, -4.99716892e-01],\n",
       "       [ 1.32750645e+00,  1.44379428e+00,  5.69563196e-01],\n",
       "       [-1.51839767e+00, -1.23937847e+00, -1.34825036e-01],\n",
       "       [-1.51484582e+00, -2.18788454e+00,  8.27884108e-01],\n",
       "       [ 1.14388055e+00, -2.49378554e-01,  4.25618146e-01],\n",
       "       [ 9.27730234e-02,  4.67366565e-01,  5.40896651e-01],\n",
       "       [ 1.08458519e-01, -1.03114153e+00, -1.74772360e+00],\n",
       "       [-1.02759296e-01, -7.40036642e-01, -1.04952492e+00],\n",
       "       [-7.04420380e-01,  5.51063865e-01,  3.74178444e-01],\n",
       "       [-4.40370088e-01,  5.60568878e-01,  1.44173108e+00],\n",
       "       [-5.12176413e-01, -6.72630115e-01, -1.14555621e+00],\n",
       "       [-2.46282767e-01,  7.22856811e-01,  6.03084673e-01],\n",
       "       [-1.00139970e-01, -1.44787779e+00,  5.10139005e-01],\n",
       "       [-7.17124139e-02, -7.00865613e-01,  2.31729585e-01],\n",
       "       [-3.82622874e-01,  3.47504360e-01, -5.34246195e-01],\n",
       "       [ 8.29166344e-01, -5.63057799e-01,  9.36094799e-01],\n",
       "       [ 4.28825865e-01, -2.25340694e-01, -1.90296330e+00],\n",
       "       [ 6.74276760e-01,  4.12905345e-01, -1.34784847e+00],\n",
       "       [ 1.71176511e-01, -7.07770048e-01, -1.08843750e+00],\n",
       "       [-9.79735088e-01,  4.14650152e-01,  2.17833754e-01],\n",
       "       [-3.96140839e-01, -4.80044197e-01,  1.75806323e-01],\n",
       "       [ 1.31838435e+00, -6.24432938e-01,  1.06692090e+00],\n",
       "       [-5.06506933e-02, -6.80610546e-01,  1.25457047e+00],\n",
       "       [ 1.37735955e-01, -2.89671693e-01,  5.17845607e-01],\n",
       "       [-6.70714359e-01, -1.09573807e+00,  1.42670466e+00],\n",
       "       [ 1.33774847e+00, -8.27973974e-01,  1.20992373e+00],\n",
       "       [ 5.92065591e-01,  8.36426682e-02,  6.74714953e-01]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "therapeutic-benjamin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "automated-person",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conventional-cemetery",
   "metadata": {},
   "source": [
    "Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aware-gnome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "plt.scatter(df.texture_mean, df.area_mean)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "painful-treasury",
   "metadata": {},
   "source": [
    "Joint plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "green-wonder",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "sns.jointplot(df.texture_mean, df.area_mean)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pacific-animation",
   "metadata": {},
   "source": [
    "Count Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "activated-organ",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diagnosis', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"diagnosis\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-danger",
   "metadata": {},
   "source": [
    "Correlation Matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "bright-speaker",
   "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>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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>-0.311631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>0.323782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>...</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.119205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.051019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.003738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>0.170581</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.499316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.687382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.514930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.368661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>0.147741</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.438413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>-0.311631</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>0.767297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>0.679090</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.732562</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.497473</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.303379</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.715065</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.531062</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.049559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>-0.097317</td>\n",
       "      <td>0.386358</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>-0.066280</td>\n",
       "      <td>0.068406</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.076218</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.128053</td>\n",
       "      <td>0.164174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.111690</td>\n",
       "      <td>0.409003</td>\n",
       "      <td>-0.102242</td>\n",
       "      <td>-0.083195</td>\n",
       "      <td>-0.073658</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.068956</td>\n",
       "      <td>-0.119638</td>\n",
       "      <td>-0.128215</td>\n",
       "      <td>-0.045655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>0.674172</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.726628</td>\n",
       "      <td>0.296092</td>\n",
       "      <td>0.548905</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>0.039830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697201</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>0.554897</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.085433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>0.735864</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.800086</td>\n",
       "      <td>0.246552</td>\n",
       "      <td>0.455653</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.223970</td>\n",
       "      <td>-0.090170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.757373</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.538166</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.017539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>-0.222600</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>-0.202694</td>\n",
       "      <td>-0.166777</td>\n",
       "      <td>0.332375</td>\n",
       "      <td>0.135299</td>\n",
       "      <td>0.098564</td>\n",
       "      <td>0.027653</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>0.401964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.230691</td>\n",
       "      <td>-0.074743</td>\n",
       "      <td>-0.217304</td>\n",
       "      <td>-0.182195</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>-0.055558</td>\n",
       "      <td>-0.058298</td>\n",
       "      <td>-0.102007</td>\n",
       "      <td>-0.107342</td>\n",
       "      <td>0.101480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>0.206000</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.250744</td>\n",
       "      <td>0.212583</td>\n",
       "      <td>0.318943</td>\n",
       "      <td>0.738722</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.421659</td>\n",
       "      <td>0.559837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204607</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.483208</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.590973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>0.194204</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.248396</td>\n",
       "      <td>0.570517</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.342627</td>\n",
       "      <td>0.446630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186904</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.440472</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.439329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>0.376169</td>\n",
       "      <td>0.163851</td>\n",
       "      <td>0.407217</td>\n",
       "      <td>0.372320</td>\n",
       "      <td>0.380676</td>\n",
       "      <td>0.642262</td>\n",
       "      <td>0.683260</td>\n",
       "      <td>0.615634</td>\n",
       "      <td>0.393298</td>\n",
       "      <td>0.341198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358127</td>\n",
       "      <td>0.086741</td>\n",
       "      <td>0.394999</td>\n",
       "      <td>0.342271</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.452888</td>\n",
       "      <td>0.549592</td>\n",
       "      <td>0.602450</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.310655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>-0.104321</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>-0.081629</td>\n",
       "      <td>-0.072497</td>\n",
       "      <td>0.200774</td>\n",
       "      <td>0.229977</td>\n",
       "      <td>0.178009</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.449137</td>\n",
       "      <td>0.345007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128121</td>\n",
       "      <td>-0.077473</td>\n",
       "      <td>-0.103753</td>\n",
       "      <td>-0.110343</td>\n",
       "      <td>-0.012662</td>\n",
       "      <td>0.060255</td>\n",
       "      <td>0.037119</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.389402</td>\n",
       "      <td>0.078079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>-0.042641</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.005523</td>\n",
       "      <td>-0.019887</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>0.507318</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>0.688132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037488</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.215204</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.591328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.093492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.219122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.138957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.079647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.617624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.810455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.686511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>0.511114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "radius_mean                 1.000000      0.323782        0.997855   0.987357   \n",
       "texture_mean                0.323782      1.000000        0.329533   0.321086   \n",
       "perimeter_mean              0.997855      0.329533        1.000000   0.986507   \n",
       "area_mean                   0.987357      0.321086        0.986507   1.000000   \n",
       "smoothness_mean             0.170581     -0.023389        0.207278   0.177028   \n",
       "compactness_mean            0.506124      0.236702        0.556936   0.498502   \n",
       "concavity_mean              0.676764      0.302418        0.716136   0.685983   \n",
       "concave points_mean         0.822529      0.293464        0.850977   0.823269   \n",
       "symmetry_mean               0.147741      0.071401        0.183027   0.151293   \n",
       "fractal_dimension_mean     -0.311631     -0.076437       -0.261477  -0.283110   \n",
       "radius_se                   0.679090      0.275869        0.691765   0.732562   \n",
       "texture_se                 -0.097317      0.386358       -0.086761  -0.066280   \n",
       "perimeter_se                0.674172      0.281673        0.693135   0.726628   \n",
       "area_se                     0.735864      0.259845        0.744983   0.800086   \n",
       "smoothness_se              -0.222600      0.006614       -0.202694  -0.166777   \n",
       "compactness_se              0.206000      0.191975        0.250744   0.212583   \n",
       "concavity_se                0.194204      0.143293        0.228082   0.207660   \n",
       "concave points_se           0.376169      0.163851        0.407217   0.372320   \n",
       "symmetry_se                -0.104321      0.009127       -0.081629  -0.072497   \n",
       "fractal_dimension_se       -0.042641      0.054458       -0.005523  -0.019887   \n",
       "radius_worst                0.969539      0.352573        0.969476   0.962746   \n",
       "texture_worst               0.297008      0.912045        0.303038   0.287489   \n",
       "perimeter_worst             0.965137      0.358040        0.970387   0.959120   \n",
       "area_worst                  0.941082      0.343546        0.941550   0.959213   \n",
       "smoothness_worst            0.119616      0.077503        0.150549   0.123523   \n",
       "compactness_worst           0.413463      0.277830        0.455774   0.390410   \n",
       "concavity_worst             0.526911      0.301025        0.563879   0.512606   \n",
       "concave points_worst        0.744214      0.295316        0.771241   0.722017   \n",
       "symmetry_worst              0.163953      0.105008        0.189115   0.143570   \n",
       "fractal_dimension_worst     0.007066      0.119205        0.051019   0.003738   \n",
       "\n",
       "                         smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "radius_mean                     0.170581          0.506124        0.676764   \n",
       "texture_mean                   -0.023389          0.236702        0.302418   \n",
       "perimeter_mean                  0.207278          0.556936        0.716136   \n",
       "area_mean                       0.177028          0.498502        0.685983   \n",
       "smoothness_mean                 1.000000          0.659123        0.521984   \n",
       "compactness_mean                0.659123          1.000000        0.883121   \n",
       "concavity_mean                  0.521984          0.883121        1.000000   \n",
       "concave points_mean             0.553695          0.831135        0.921391   \n",
       "symmetry_mean                   0.557775          0.602641        0.500667   \n",
       "fractal_dimension_mean          0.584792          0.565369        0.336783   \n",
       "radius_se                       0.301467          0.497473        0.631925   \n",
       "texture_se                      0.068406          0.046205        0.076218   \n",
       "perimeter_se                    0.296092          0.548905        0.660391   \n",
       "area_se                         0.246552          0.455653        0.617427   \n",
       "smoothness_se                   0.332375          0.135299        0.098564   \n",
       "compactness_se                  0.318943          0.738722        0.670279   \n",
       "concavity_se                    0.248396          0.570517        0.691270   \n",
       "concave points_se               0.380676          0.642262        0.683260   \n",
       "symmetry_se                     0.200774          0.229977        0.178009   \n",
       "fractal_dimension_se            0.283607          0.507318        0.449301   \n",
       "radius_worst                    0.213120          0.535315        0.688236   \n",
       "texture_worst                   0.036072          0.248133        0.299879   \n",
       "perimeter_worst                 0.238853          0.590210        0.729565   \n",
       "area_worst                      0.206718          0.509604        0.675987   \n",
       "smoothness_worst                0.805324          0.565541        0.448822   \n",
       "compactness_worst               0.472468          0.865809        0.754968   \n",
       "concavity_worst                 0.434926          0.816275        0.884103   \n",
       "concave points_worst            0.503053          0.815573        0.861323   \n",
       "symmetry_worst                  0.394309          0.510223        0.409464   \n",
       "fractal_dimension_worst         0.499316          0.687382        0.514930   \n",
       "\n",
       "                         concave points_mean  symmetry_mean  \\\n",
       "radius_mean                         0.822529       0.147741   \n",
       "texture_mean                        0.293464       0.071401   \n",
       "perimeter_mean                      0.850977       0.183027   \n",
       "area_mean                           0.823269       0.151293   \n",
       "smoothness_mean                     0.553695       0.557775   \n",
       "compactness_mean                    0.831135       0.602641   \n",
       "concavity_mean                      0.921391       0.500667   \n",
       "concave points_mean                 1.000000       0.462497   \n",
       "symmetry_mean                       0.462497       1.000000   \n",
       "fractal_dimension_mean              0.166917       0.479921   \n",
       "radius_se                           0.698050       0.303379   \n",
       "texture_se                          0.021480       0.128053   \n",
       "perimeter_se                        0.710650       0.313893   \n",
       "area_se                             0.690299       0.223970   \n",
       "smoothness_se                       0.027653       0.187321   \n",
       "compactness_se                      0.490424       0.421659   \n",
       "concavity_se                        0.439167       0.342627   \n",
       "concave points_se                   0.615634       0.393298   \n",
       "symmetry_se                         0.095351       0.449137   \n",
       "fractal_dimension_se                0.257584       0.331786   \n",
       "radius_worst                        0.830318       0.185728   \n",
       "texture_worst                       0.292752       0.090651   \n",
       "perimeter_worst                     0.855923       0.219169   \n",
       "area_worst                          0.809630       0.177193   \n",
       "smoothness_worst                    0.452753       0.426675   \n",
       "compactness_worst                   0.667454       0.473200   \n",
       "concavity_worst                     0.752399       0.433721   \n",
       "concave points_worst                0.910155       0.430297   \n",
       "symmetry_worst                      0.375744       0.699826   \n",
       "fractal_dimension_worst             0.368661       0.438413   \n",
       "\n",
       "                         fractal_dimension_mean  ...  radius_worst  \\\n",
       "radius_mean                           -0.311631  ...      0.969539   \n",
       "texture_mean                          -0.076437  ...      0.352573   \n",
       "perimeter_mean                        -0.261477  ...      0.969476   \n",
       "area_mean                             -0.283110  ...      0.962746   \n",
       "smoothness_mean                        0.584792  ...      0.213120   \n",
       "compactness_mean                       0.565369  ...      0.535315   \n",
       "concavity_mean                         0.336783  ...      0.688236   \n",
       "concave points_mean                    0.166917  ...      0.830318   \n",
       "symmetry_mean                          0.479921  ...      0.185728   \n",
       "fractal_dimension_mean                 1.000000  ...     -0.253691   \n",
       "radius_se                              0.000111  ...      0.715065   \n",
       "texture_se                             0.164174  ...     -0.111690   \n",
       "perimeter_se                           0.039830  ...      0.697201   \n",
       "area_se                               -0.090170  ...      0.757373   \n",
       "smoothness_se                          0.401964  ...     -0.230691   \n",
       "compactness_se                         0.559837  ...      0.204607   \n",
       "concavity_se                           0.446630  ...      0.186904   \n",
       "concave points_se                      0.341198  ...      0.358127   \n",
       "symmetry_se                            0.345007  ...     -0.128121   \n",
       "fractal_dimension_se                   0.688132  ...     -0.037488   \n",
       "radius_worst                          -0.253691  ...      1.000000   \n",
       "texture_worst                         -0.051269  ...      0.359921   \n",
       "perimeter_worst                       -0.205151  ...      0.993708   \n",
       "area_worst                            -0.231854  ...      0.984015   \n",
       "smoothness_worst                       0.504942  ...      0.216574   \n",
       "compactness_worst                      0.458798  ...      0.475820   \n",
       "concavity_worst                        0.346234  ...      0.573975   \n",
       "concave points_worst                   0.175325  ...      0.787424   \n",
       "symmetry_worst                         0.334019  ...      0.243529   \n",
       "fractal_dimension_worst                0.767297  ...      0.093492   \n",
       "\n",
       "                         texture_worst  perimeter_worst  area_worst  \\\n",
       "radius_mean                   0.297008         0.965137    0.941082   \n",
       "texture_mean                  0.912045         0.358040    0.343546   \n",
       "perimeter_mean                0.303038         0.970387    0.941550   \n",
       "area_mean                     0.287489         0.959120    0.959213   \n",
       "smoothness_mean               0.036072         0.238853    0.206718   \n",
       "compactness_mean              0.248133         0.590210    0.509604   \n",
       "concavity_mean                0.299879         0.729565    0.675987   \n",
       "concave points_mean           0.292752         0.855923    0.809630   \n",
       "symmetry_mean                 0.090651         0.219169    0.177193   \n",
       "fractal_dimension_mean       -0.051269        -0.205151   -0.231854   \n",
       "radius_se                     0.194799         0.719684    0.751548   \n",
       "texture_se                    0.409003        -0.102242   -0.083195   \n",
       "perimeter_se                  0.200371         0.721031    0.730713   \n",
       "area_se                       0.196497         0.761213    0.811408   \n",
       "smoothness_se                -0.074743        -0.217304   -0.182195   \n",
       "compactness_se                0.143003         0.260516    0.199371   \n",
       "concavity_se                  0.100241         0.226680    0.188353   \n",
       "concave points_se             0.086741         0.394999    0.342271   \n",
       "symmetry_se                  -0.077473        -0.103753   -0.110343   \n",
       "fractal_dimension_se         -0.003195        -0.001000   -0.022736   \n",
       "radius_worst                  0.359921         0.993708    0.984015   \n",
       "texture_worst                 1.000000         0.365098    0.345842   \n",
       "perimeter_worst               0.365098         1.000000    0.977578   \n",
       "area_worst                    0.345842         0.977578    1.000000   \n",
       "smoothness_worst              0.225429         0.236775    0.209145   \n",
       "compactness_worst             0.360832         0.529408    0.438296   \n",
       "concavity_worst               0.368366         0.618344    0.543331   \n",
       "concave points_worst          0.359755         0.816322    0.747419   \n",
       "symmetry_worst                0.233027         0.269493    0.209146   \n",
       "fractal_dimension_worst       0.219122         0.138957    0.079647   \n",
       "\n",
       "                         smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "radius_mean                      0.119616           0.413463         0.526911   \n",
       "texture_mean                     0.077503           0.277830         0.301025   \n",
       "perimeter_mean                   0.150549           0.455774         0.563879   \n",
       "area_mean                        0.123523           0.390410         0.512606   \n",
       "smoothness_mean                  0.805324           0.472468         0.434926   \n",
       "compactness_mean                 0.565541           0.865809         0.816275   \n",
       "concavity_mean                   0.448822           0.754968         0.884103   \n",
       "concave points_mean              0.452753           0.667454         0.752399   \n",
       "symmetry_mean                    0.426675           0.473200         0.433721   \n",
       "fractal_dimension_mean           0.504942           0.458798         0.346234   \n",
       "radius_se                        0.141919           0.287103         0.380585   \n",
       "texture_se                      -0.073658          -0.092439        -0.068956   \n",
       "perimeter_se                     0.130054           0.341919         0.418899   \n",
       "area_se                          0.125389           0.283257         0.385100   \n",
       "smoothness_se                    0.314457          -0.055558        -0.058298   \n",
       "compactness_se                   0.227394           0.678780         0.639147   \n",
       "concavity_se                     0.168481           0.484858         0.662564   \n",
       "concave points_se                0.215351           0.452888         0.549592   \n",
       "symmetry_se                     -0.012662           0.060255         0.037119   \n",
       "fractal_dimension_se             0.170568           0.390159         0.379975   \n",
       "radius_worst                     0.216574           0.475820         0.573975   \n",
       "texture_worst                    0.225429           0.360832         0.368366   \n",
       "perimeter_worst                  0.236775           0.529408         0.618344   \n",
       "area_worst                       0.209145           0.438296         0.543331   \n",
       "smoothness_worst                 1.000000           0.568187         0.518523   \n",
       "compactness_worst                0.568187           1.000000         0.892261   \n",
       "concavity_worst                  0.518523           0.892261         1.000000   \n",
       "concave points_worst             0.547691           0.801080         0.855434   \n",
       "symmetry_worst                   0.493838           0.614441         0.532520   \n",
       "fractal_dimension_worst          0.617624           0.810455         0.686511   \n",
       "\n",
       "                         concave points_worst  symmetry_worst  \\\n",
       "radius_mean                          0.744214        0.163953   \n",
       "texture_mean                         0.295316        0.105008   \n",
       "perimeter_mean                       0.771241        0.189115   \n",
       "area_mean                            0.722017        0.143570   \n",
       "smoothness_mean                      0.503053        0.394309   \n",
       "compactness_mean                     0.815573        0.510223   \n",
       "concavity_mean                       0.861323        0.409464   \n",
       "concave points_mean                  0.910155        0.375744   \n",
       "symmetry_mean                        0.430297        0.699826   \n",
       "fractal_dimension_mean               0.175325        0.334019   \n",
       "radius_se                            0.531062        0.094543   \n",
       "texture_se                          -0.119638       -0.128215   \n",
       "perimeter_se                         0.554897        0.109930   \n",
       "area_se                              0.538166        0.074126   \n",
       "smoothness_se                       -0.102007       -0.107342   \n",
       "compactness_se                       0.483208        0.277878   \n",
       "concavity_se                         0.440472        0.197788   \n",
       "concave points_se                    0.602450        0.143116   \n",
       "symmetry_se                         -0.030413        0.389402   \n",
       "fractal_dimension_se                 0.215204        0.111094   \n",
       "radius_worst                         0.787424        0.243529   \n",
       "texture_worst                        0.359755        0.233027   \n",
       "perimeter_worst                      0.816322        0.269493   \n",
       "area_worst                           0.747419        0.209146   \n",
       "smoothness_worst                     0.547691        0.493838   \n",
       "compactness_worst                    0.801080        0.614441   \n",
       "concavity_worst                      0.855434        0.532520   \n",
       "concave points_worst                 1.000000        0.502528   \n",
       "symmetry_worst                       0.502528        1.000000   \n",
       "fractal_dimension_worst              0.511114        0.537848   \n",
       "\n",
       "                         fractal_dimension_worst  \n",
       "radius_mean                             0.007066  \n",
       "texture_mean                            0.119205  \n",
       "perimeter_mean                          0.051019  \n",
       "area_mean                               0.003738  \n",
       "smoothness_mean                         0.499316  \n",
       "compactness_mean                        0.687382  \n",
       "concavity_mean                          0.514930  \n",
       "concave points_mean                     0.368661  \n",
       "symmetry_mean                           0.438413  \n",
       "fractal_dimension_mean                  0.767297  \n",
       "radius_se                               0.049559  \n",
       "texture_se                             -0.045655  \n",
       "perimeter_se                            0.085433  \n",
       "area_se                                 0.017539  \n",
       "smoothness_se                           0.101480  \n",
       "compactness_se                          0.590973  \n",
       "concavity_se                            0.439329  \n",
       "concave points_se                       0.310655  \n",
       "symmetry_se                             0.078079  \n",
       "fractal_dimension_se                    0.591328  \n",
       "radius_worst                            0.093492  \n",
       "texture_worst                           0.219122  \n",
       "perimeter_worst                         0.138957  \n",
       "area_worst                              0.079647  \n",
       "smoothness_worst                        0.617624  \n",
       "compactness_worst                       0.810455  \n",
       "concavity_worst                         0.686511  \n",
       "concave points_worst                    0.511114  \n",
       "symmetry_worst                          0.537848  \n",
       "fractal_dimension_worst                 1.000000  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-fundamentals",
   "metadata": {},
   "source": [
    "Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "imperial-trader",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 10))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precious-edmonton",
   "metadata": {},
   "source": [
    "Pair Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fourth-dublin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2af6e8e12e0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAAN2CAYAAAC1rRuNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydeXxU1fn/33cmM5mZ7AtZSEggJGyBsBgVLfBVopZaXHFptVhb+qX9tjRYu9Ba0SpWi1WsiK1F6aJdRItLpZRaQQv+6gbKHpYQSCBk3zNLZrn398fkXmYyd0ICCVk479drXpCZufeemTn3Oec553k+j6QoCgKBQCAQCAQCgUAgGLoYBroBAoFAIBAIBAKBQCA4N4RjJxAIBAKBQCAQCARDHOHYCQQCgUAgEAgEAsEQRzh2AoFAIBAIBAKBQDDEEY6dQCAQCAQCgUAgEAxxhGMnEAgEAoFAIBAIBEOcYenYzZs3TwHEQzzOx6PXiP4pHufx0WtE/xSP8/joFaJvisd5fvQK0T/F4zw+wjIsHbv6+vqBboJAEBbRPwWDGdE/BYMV0TcFgxnRPwWDgWHp2AkEAoFAIBAIBALBhYRw7AQCgUAgEAgEAoFgiBMx0A0QCARDB1lWON5gp6bVRWqshdFJURgM0kA3SyAQDDKErRAMVkTfHBqI3+nsEI6dQCDoEbKssHl/Nfe+sguXR8ZiMrDqtmnMy08TxlYgEGgIWyEYrIi+OTQQv9PZI0IxBQJBjzjeYNeMLIDLI3PvK7s43mAf4JYJBILBhLAVgsGK6JtDA/E7nT3CsRMIBD2iptWlGVkVl0emts01QC0SCASDEWErBIMV0TeHBuJ3OnuEYycQCHpEaqwFiynYZFhMBlJiLAPUIoFAMBgRtkIwWBF9c2ggfqezR+TYCQSCHjE6KYpVt00LiXkfnRQ1oO0SCdYCweBBlhUUBZ64ZSpHatt4ZcdJmhzuQWErBILRSVGsuWM6e062ICtglGBKZpzomwNAd2P3YJ1vDAWEYycQCHqEwSAxLz+NCcWzqW1zkRLTeyeqr50wkWAtEAwsgfd0epyFA1VtQffjozdNYUZWPFmJYsFF0Hecy1ji9iqs3VYWNGYIzi96Y/fKBQV8cXI6ERGGPplvXKgIx04gEPQYg0EiZ0Q0OSOie31sfzhh4RKsJxTPPqs2CgSCntP1ni4uytUmzOC/H+97fS+bimeLCZmgzziXsUSMGYMDvd9h2YY9JNjMzMpNxmCQzmm+cSEjcuwEAsF5oT9UrkSCtUAwcHS9p2UFcT8K+p1zGUvEmDE4CPc77ChvFMqX54hw7AQCwXmhPwZUkWAtEAwceve0uB8F/c25jCVizBgchPsdfDLCyT5HhGMnEAjOC/0xoKoJ1up5RYK1QHD+6HpPb9h5kqVFeeJ+FPQr5zKWiDFjcDA6KYqVCwqCfofiuXls3FMpnOxzROTYCQSC80J/qFyJBGuBYODoek83OdzkpUbzj+/Opq5d3I+C/uFcxhIxZgwODAaJL05OJ8FmZkd5Iz4Z1u+oYNm8icLJPkeEYycQCM4L/TWgigRrgWBg6O6eHpsi7kdB/3CuY4kYMwYHEREGZuUmk5lgpbbNxYIZGcLJ7gOEYycQCM4bYkAVCIYX4p4WDASi3w0PxO/Y94gcO4FAIBAIBAKBQCAY4gjHTiAQCAQCgUAgEAiGOMKxEwgEAoFAIBAIBIIhjsixEwgEusiywvEGOzWtLlJjhXKYQHAhIuyAoD8R/UsQDtE3zg7h2AkEghBkWWHz/uoQOel5+WnCsAoEFwjCDgj6E9G/BOEQfePsEaGYAoEghOMNds2gArg8Mve+sovjDfYBbplAIDhfCDsg6E9E/xKEQ/SNs0c4dgKBIISaVpdmUFVcHpnaNtcAtci/gldW184HR+spq2tHlpUBa4tAMJxR77XDNW2Dzg4Ihhbd2e3BOM4I+p+ejOWib5w9gyYUU5KkUcCLQCqgAGsVRXlakqSfAf8L1HW+9T5FUTYNTCv7l7u/uYTK+uaQ5zOS4/nDb9ec/wYJLlhSYy1YTIYgw2oxGUiJsQxIe0RYhkBwfgi8174xO2dQ2QHB0OJMdnuwjTOC/qenY7noG2fPYNqx8wLfVxRlEjAT+I4kSZM6X3tKUZRpnY9h6dQBVNY3k/T574Q89Jw9gaA/GZ0UxarbpmEx+U2EanxHJ0UNSHtEWIZAcH4IvNc27DxJ8dy8QWMHBEOLM9ntwTbOCPqfno7lom+cPYNmx05RlCqgqvP/bZIklQAZA9sqgeDCxGCQmJefxoTi2dS2uUiJGVhFqu7CMnJGRA9ImwSC4UjgvVbV4uKlD8tZNCuHgoxY8lJjhDKdoMecyW4PtnFG0P/0dCwXfePsGTSOXSCSJI0GpgMfAZ8DlkiSdBewA/+uXtMANk8guCAwGCRyRkT3meN0LtLFIixDIOh79O7JrvdaVYuLde+Xsal4tlhEEfQYWVawmY0UF+UiK7Bh50mqWlwhdruvxxnB4KKrjUmP6/lYLvrG2TGYQjEBkCQpGtgA3KMoSivwG2AsMA3/jt6TYY5bLEnSDkmSdtTV1em9RSAYMC70/qnG1V+7ejtffv4jrl29nc37q3ssgCLCMvqXC71/XoiEuyezEmyD6l4TfXPoofat29d+yOotpbywvYyFM7PJTrIOO7st+md49GzMgao21twxfdDYl+GIpCiDR1lOkiQTsBH4l6Ioq3ReHw1sVBRlcnfnKSwsVHbs2NE/jexHrl7wFZI+/52Q5xv+9Sz/3vCnAWiRoAf0Oi5gqPbPc6Gsrp1rV28PWaXrzS6AuvInwjJ6heifAl26uydHJ0Wdr3utVycVfXNoEK5vrV88kykZ8UPJbov+eQ6E6wf/+O5sJAkxlp8bYb+wQROKKUmSBKwDSgKdOkmS0jvz7wBuAvYNRPsEAsGZCRdu2Rc5ciIsQyDoO8LdkzWtLu0+E/eaoCd0tfvh+pbT4xMT+AuIcP2grt3FzJzkIWtfziWt5HwwaBw7/Ll0C4G9kiTt6nzuPuDLkiRNw18C4TjwzYFonEAg6J7uZIxFjpxAMLgId096fAqyrAyqiYpg8KJn959fWCjsvWBYjvtDofTSoMmxUxTlfUVRJEVRCgJLGyiKslBRlCmdz18fsHsnEAgGEd3JGIscOYFgcDE6KYqVCwqC7sniuXksf3OvKCMi6DF6dv/+N/eG9C1h7y88huO4PxRKLw2mHTuBQDCEOVO4pZAuFggGDwaDxMh4C4tm5SBJoCjw0oflVLW4RBkRQY/Rs/vlDU4y4i1sEvb+gmY4liwYCqWXhGMnEAj6hDOFXYgcOYFgcJEUFcm698uGVaiU4PwSzu4nRkUKey8YduP+UAgvHTShmAKBYGjTH2EXsqxQVtfOB0frKatr73F5BIHgQqan981wDJUSnF+yEmysXVhIcVEuS+bmDsuSBoK+ZSiP60PBZoodO4FA0Cf0ddhFb5OUB7tSlUBwPgh330xKj6GqJfjeGI6hUoL+R7W1DfYOTjW7WLZhj9bXVi4o4JqJqaIPCXTp6bg+WMfzoWAzhWMnEAj6jJ6EXfTUYIdLUp6gU/tOHSxWbi5hfkEGRgNcnJ3IZTlJRESIwATB8Ee9r+rbO3Tvm8Vzcli9pTRkIjXcQqUE/YvXK/PfsgZ2lDeSlxKjOXXg72vLNuxhSkac6E9DiLNxos7W8erJuD7YlScHu80UMx6BQHDO9DS0QjXY167ezpef/4hrV29n8/5q3fd3l6TcleMNdlZuLuH2wizWvV/G6i2l/O9LO/jHvqohFeYhEJwNsqyw9VAN/9pfTVWLi2/MziE97nTOh8sjo94Gg1HFTTA0kGWFf+yrYvmbe/HJ4HB7dfuano0WDE56Myar7z9a286mvVW8uauSe1/ZfcZjAunJuD4UlCcHM8KxEwgE50RvBobeGGw1STmQcEnKNa0u5hdksHrrkZDVYzEYCIY7FY12jtS08/SWIyx9eRcvbC9j4cxsbcJtMRlQAm5HMfkWnA3HG+ys+vchbQFt2Ya9un1tMAlJCLqnN2OyOtZ/8ZntLPnrZ/x2m/+3T7CZe+x49WRc782iriAU4dgNAHd/cwlXL/hKyOPgoSMD3TSBoNf0ZmDojcHuTZJyaqwFowExGAguSGpaO3h6S/CixuqtR7h5RiYWk4Hl8yfx2qcntfeLybfgbAi3gBbY1wabkISge3obGdN1rFd/+56OtT0Z13uzqCsIReTY9RN3f3MJlfXNuq8dPHSEzxX/KuT5jn1L+rdRAkE/0Ju6Lr2RCu5NkvLopCguzk4c9DLEAkF/YHd7de/BrEQri+fkkJMcRZPDDQxOFTfB0KC7BbT8kTFsKp496IQkBN3TmzE53FgvST0fa3syrqvOX9ccO2GzeoZw7PqJyvpmkj7/Hd3XhAMnGE70ZmDorcHuaZKywSBxWU4SKxcUBCm0icFAcCGQnRilew9WNjuZkBbLxdmJoli04JzpbgFtfGrsoBWTEISnN2NyuLHeINGrsfZM4/pQUJ4czAjHTiAQnBO9GRj602BHRBi4rmAkUzLixGAguKAYkxx6Dz560xRmZMWTlRg16FXcBEOD7hbQxiSLBbShSG8jY85kZ/qyXcJmnR3CsRMIBOeEwSBxzcRU1i+eSVWLi/Q4C/npcWGNfH8abDEYCC5E1MnZ+O/OpqLRjs0cQWpsZJ9PtgTDl57K14sFtOFHbyJjhstO2mCtk9cXCMdOIBCcE7Ks8HZJTY9qzgxnYyoQnE+63ktZCTYO1bQN2tpPgsFLYG06WYG3dleybN7EsH1HLKBduPTFbz/Q84DBXifvXBGOnUAgOCd6Wkh8uBtTgeB8oXcvrVxQwKp/HzrjfSgQBKLWpgsMrSyem8fKzSVMSIsRfUfQpwyGeUBP5yxDFVHuQCAQhNDTguPQvSpm4Hn2VjaLoqMCQS8Idx/qTUyWbdjD/IKMoONFuQ/BmTjeYNecOjgtYf+DayZQ197Ro6LTggsLr1dm94kmNu+rYveJZrxe+cwHdTIYio8P9zp5YsdOIBAE0dsVtXBKWWmxlqDzFBflau9Jj7Nw12XZZCbYOFzbhqL4BSDEzp1A4CfwPkywmbm1MJPxqTFkJthosLv4xuwcNuw8SVWLfzLi8shYRe0nwRnoGgbX4nSzaFYOUqfpVfvUoZo2fvi33SKqQhCE1yvzxu5K7n9jnzY/eOTGydw4NYOIiDPvFYVzqhrtHdrreuGZfRm+2Rsl76GIcOwEAkEQvQ1TCKeK6ZMJOo+s+I1ngs3Mt+bk4PD4+OHfdouwTIFAB/U+TLCZWTgzWysKrYbKvbW7koUzs3npw3KqWlxYTAYuyk7QJiyi3IegK10X7bKTrCy5Mo9175cF9a31OypQlOEXoiY4d/ZXtWhOHfj7yP1v7CMvJZqpoxLOeLyeU5WdZKWy2cVX1n2sOx/o6/DN4V4nr98cO0mSIoEFwOjA6yiK8nB/XVMgEHRPT1a9elNwHMIrZX10rCHoPBt2nqR4bh4ur48Gh5u128qGbYy7QHCuqPfhnZdmaU4dnA6VWzQrR/t33ftlFM/NI8IoiXp1grAcqw9etJtfkMHyN/eF9K1Vt01jxcYD2nPhbL/gwqOqRX9+UN3iYuoo/9/dzTP0nKoVN0xh8Us7ws4H+jonbjipe+rRnzt2bwItwE6gox+vIxAIekBPV73OJkxBTymr63mqWlys31HB/V+cxO6TLbqDw+GaNoBhZWQFgt6gTooMkkR2kpXUWIvuvSJJ/n+zEq0smpXD+h0VzJucJtQKBbrIskJJVWtQX1L7UCAuj0xpbbsW4jucQtQE5056nFU/9SLO30fONM/Qc6rOtJjc28XmnjCclV37UzwlU1GU2xVFeVxRlCfVRz9eTyAQdENPk5bVFTVLZ77O2YQpyLKCosATt0xlaVEu6XF+J2/ZvInkJEdjlNDOr2IxGdhb2cq1q7ezeX91r5P2eyP4IhAMRtRJ0df+8DGfHG/kh9dMwGo26t4rSmdoc0Wjk3Xvl7Fs3sRhE0okOHe62sNj9XZONTsoLsplyVz/w2oy6PYtryxr/x9OIWqCc2diagwP3zBZ6zfZSVZ+fecM7C4fZXXtVDSeeZ6hOlUzc5LJGRGtLQIHYjEZsJqMfHC0Hps5Qvd1seCgT3/u2P1XkqQpiqLs7cdrCASCHtLTVa9zDVPQW7F79KYpzMiKJyvRP0GYkhnH0qI8nt5yOm/oe1eN4x97TrFoVg4Hq1vJiLcyJeN0ofPuwjsGg4SyQHCuHG+ws3JzCXdcks1T7xzW8qB+dl0+P3trf9C98pePy1m5oICMeAsLZmSIXW6BRjjhnaykKB77ZwnlDU4sJgP3fWECD98wmQfePC2E8fiCAqZkxHH52KRhF6ImOHdOtjh55ZNyHr9lKooi4/YqfPvPnwaN9Qk2s7bjC2feXctKsLFyQUFQyY1HbpxM8cufUd7gJDvJyiM3Tg4SbBELDuHpT8duFnC3JEnH8IdiSoCiKEpBP15TIBCEoTchlucSpqC3M3jf63vZVDxbmyDMHZ9K7ohoZmQl0OzwcKS2jX/sOcW8yelaPtHabWWacwZ067j1V12agS6kKrhwkGWFurYOfvT5CXz/1d1aX3Z7FdxeH4vn5CArYJAgPS6SZ++YwaT0ONEfBSGcSXhHFdx59J8H+cPXLualr1+CT1GCHLkxwzBETXDu1LS62FHeQmVzCT+5diLL39wdMtYvnpPD6i2l2jEWk4ER0fq7a7Ks8HZJDav+fYhFs3IwGmBGVgJrth6mvMEJQHmDk2e2HmH94pk4PT6x4HAG+tOx+0I/nlsgEPSS3ipBBTo1NnMEbp+PpKjIMxpUdWcwPc7CzTMyiYwwMCY5iuMN7YB/da6iyaE5S6mxkSxd/5kmBqHnnAHdOm5nG4MvdgEFA40sK5xstnO0zsFnFU1MSovh8Vum4uzwEh9lBkVhf1UrsnJait5iMvDHr10i+qEgBFlWqG/v4JEbJ5MRb+XHr4XWqFs0K4dn3y3F5ZHZcbyJUYk2risYKfrTBcqZFjC7zgWumZTMjdOzaHN5dMfdrERbkDrv0qI8IoxQVtcedA0gqL7ts+/6nUGLycCiWTnsKG/Rzlve4MTjk7WcPBC5+OHoN8dOUZRyAEmSUgARCCsQDDC9CbHUc2pUGexl8yZ269ykxloozI7jphmjWLHxQNDxD711gKVF42hxuGlweHhrdyXL5k1kzR3T2RNGUKW2zaVJb+u9Fhijr1dLr+tg0lPHrb92AQUCFa9XZueJRura3ByuaSPOYqLdLXP/G3tJsJm567LsoHDlwN0Wh9s70M0XDDLC2W21z8Bp4R3w20hnZ3H7KRlxwq5dgHi9Mv8ta2BHeSOygjYmdy018MZnFdw5cwx1jXbuuHQMD/59H9dNzdAdd6tbXFptREWBf+6tIjXWwn2v7w0aa80RUthx39hFAeRMJREEp+k38RRJkq6XJOkIcAz4D3Ac+Gd/XU8gEITSNYEeCEpaDmcQ9Zya1VuPML8gQ1dwJZCsBBvfuTJPc+q6Hn/f63upt3t4YXsZtxdmsXJzCaMTo/ifvBFhE6TDJVerYaSBgi/pcRaKi3J58tapnGh08rU/fMyXn/8oRJTlTGIy3e0CCgTniiwrbNpXxbHadk40Oli7rYxGh4f73/BPfm6ekak5dXD6Hrp5RiYWk0HLVxUIVMLZ7ZtnZGrvCRTeKZ6bx2ufnhR27QJFlhX+sa+KxS/tYPWW0qAxWR0HjzfYeeOzCoompvPNl3ZS/NddfOtPO7m9MItth2opnpsXJLT2+IICzEZJWzx47dOTXDEhRXPq4PRYu+dki1bfNhCLyUBhdmLQeX9+0xQtBy/wHN3NRS5U+jMUcwUwE3hHUZTpkiRdCXylH68nEAgCOJdQwnBOjSqPHRjiKMsKx+rtlDfaiTJHYDUZ+OxEc7fHq/+qYUEHa9r45b8OUjw3Lygf5NGbpmCQIDPe1m0YqbobmX/PbMrq/SFtJdVtvLW7ktsLs3jpw3IADla3YjEZGJ0UdcbwzbMp+yAQ9JSKRjsmo4E4WyRVrR0k2MxB8vPhpOiNBnh8QQHZibaBaLZgkOL1yhyrb+9298NiMvDw9flaqFtgcXth14Y/XUMuDRIhzpI6JgeWGrhz5hi++dJO3fe99GE5i2blMD41ipHxNurbOxiZYENucvDXjyu467JsMhOsuv1SDS/vOu6vum0aoxL8aptRkRGkxUay92Rrt+N1bz73cA/h7E/HzqMoSoMkSQZJkgyKorwrSdKvujtAkqRRwItAKqAAaxVFeVqSpERgPf5i58eB2xRFaerHtgsEQ55zCSUM59SoK73qJEDPeXzkxsnaKly445XOSgTqpONwTRvlDU5tkIizGJmQHsuuEy2UN9iZkhnHNRNTz1h8edeJliBlLTV89K7LslEUggaP5xcWduu49TYnUSDoKV6vzMfHm4LUCIvn5mEwBN83ev0zNyWGJ98+SITRIMKQBIC/P205VEOEwRC2zxQX5VKYncDBqlbGpcWy7v29wq5dQIRTq9ZTsDQa0MZBmzmCmnp72IXaqhYXG/dUklM0jq+s+ygor+7rl4/hd/89xo+/MFG3Xxo6j1fHfaMBisanUGfv4Aurt2vnWrmggKpm51kttF6IufL9WceuWZKkaGA78GdJkp4GzrRn6gW+ryjKJPy7fd+RJGkS8GNgi6IoecCWzr8FAkE3nEsooV4tu+K5eWzcUxk0CejqPCbYzFhMRt7aXRkSorF8/iQ27qnUwn/U56dnJfDqDv/fVS0uXvv0JF4ZFr+0k1X/Psxvt5VxpKadk82ObsNIjzfYdVcf5xdkkJlgCxFmuf/NvaxcUBC2Xp+6C7ipeDYvL76UTcWzh/VgIDh/HKhq0Zw6ON1XvT5Fu2827DzJ0qK8kHvwsU1+uXoRhiRQOVjTitVk5OGN+3Xt7pNvH8RqMuLxyWQlRTFrbLKwaxcY4dSqby3MDHqfGgapjoNun4/U2Mhua2k+dP1kftol1PLpLUdocLiZX5BBXZuLR2+aEtQvn7x1Gnkp0VhMBqpaXKx7v4ysRBuxVhNL/vJZ0LmWbdiDAiF9+9GbppxxQaKn9XuHE/25Y3cD4ATuAe4E4oCHuztAUZQqoKrz/22SJJUAGZ3nuqLzbX8E3gOW9UObBYJhw7mEEgYKrfiVsPyTgnmT04J2ygKdx/Q4CwtnZvPLfx3k9sIs1u+o0FbhCjLjabR3sHx+Pis27tfCfx67aQpjkmw0OdzatW+ekRnihD295QgzshLISowKG1IRzpE1GsDR4Q15rbzBSUa8pdtdwHMp+yAQ6OEPC3Lo9lWXV+at3ZWsuWMGTreXyAgjr3xzJqeaXeytbA0RwehJGJJg+OL1yhysaaWkqo0TTY6gqAdVuCI60sgN0zIYlWgj3mZiakYCEREGYdcuMMKNj+NSY4IULFcuKODynCRtHEyKiuS59w7z8PX5PPD307U0H7lxCiOiTVwzaSb2Dl/YUMs4ixGrKYKntxwOKmeQYDVR3KmGrfbVZ7Ye4f4vTtI9l9Pj49UdJ7X3GySYkRV/1mklw9l29qcqpl2SpGwgT1GUP0qSZAOMPT1ekqTRwHTgIyC10+kDqMYfqtn1/YuBxQBZWVnn2HqBoG8ZiP55rqGEgU5NYIy6em6DQQpyHgMdspc+LOfmGZkYDXDZ2CSqmx00tLt59t1S5hdkYDTA1Mx41n9ynBhrBKtum8bKzSXML8ggK9HKN2bnaNLu4DfEHp+P90vrw6p3hXNkZ2QlEGU26r5mMvrz7Yarge8pwn6eH2RZYc/JJo7Wtev2x/GpMSgFGazYuJ8bpmUwIS2WySPjiY60c8/6XWe1SDPUEX1TH7fbx8flDcgKLH9zH9+YnaPtfgTKxj+/sJD0XCupsZGMShjeuUUDwVDpn+HGx4lpsd0ubo5OiuKKCem88VkFzy8spNnpITnazIlGOz9+7RBNDjfrvqqf1jAhNYZYawSLO/PzAvvlL2+ZSnmDU3tOxeOTdc9VmJ3I2m1lPPtuqTaX6YmA1IWYK99vjp0kSf+Lv7MnAmPx77w9BxT14NhoYANwj6IorZJ0upMpiqJIkqR0PUZRlLXAWoDCwsKQ1wWCgWQg+mdvyht0hxqjrjpeRgPMyUsmwmCgts3Fc1+5iAf/vi9I7CFwcgEwMS2Wp7fsDzHuj98ylSV/+Yx/3TObe68eH5Ifp+5QZCdZqWtz85MAueTiuXms3FzChLQYckZE6zqyKxcUMGtsMgaDFPJa8dw8il/+7IzlG7r7XoZLQrawn/2LLCucaLKzv7KVtg4vr+wIFQxYPn8Sj24qocnh5sHr8pkyMpZJI/0FyC/kfE/RN4ORZYXyBjtH6trx+RRaO2uJ6YlQPHR9PrIiMy0jEbO5x+vqgl4wVPpnOBuSneivK6uEabk6j5iUHsOnFc1BJQvUMXp/ZQv3Xj2OVf8+rL324HX5/PLtgyyYMUp3x6zD68NiMpBgM3PzjEwkCYwSREUaWVqUF1TmZeWCAmIsRtYvnonD7evVeHsh2s7+DMX8DnAJ/h03FEU50lnTrlskSTLhd+r+rCjKa51P10iSlK4oSpUkSelAbX81WiAYTvRFKOHxBjsrN5dwe2EWq7ceYVxKNOlxVh56KzAsYzIpMZG6K2M+GVqd+oVMnW5/iGR1S0dYda5175ex4oYpLH5pR1j1rtFJ/hDNETHmsMZ/Xn4aGYtnsuVgLT4ZzWk8m9p0F2JCtuDskGWFrYdqkIDyRgcuj48mhzsoZM4gQW5KFPcU5dHocJNgM2lOHfTdIo1gaKPanZONdqIsJlZsPBC0UxcoQnFZThJun484i0k4dQJdG5KVYOPtkpozjmMGg4SsEFKyQB2DWzt8bNh5kiVX5jIiOpK0eAsPvLmP8gYnY5KjdOcF8RYTP5k3AYfHF+TE3Xv1OP65t4pFs3KIjDCQlxLNLzb784rV9l06JqnHtu9CtJ39KZ7SoSiKljgjSVIEfqXLsEj+rbl1QImiKKsCXvo78NXO/38VeLOP2zqoKdm/j6sXfEX3cfc3lwx08wRDmK517tQab4HUtLqYX5ChrQR/Y85YzamDTiGSN/bh9MghYg8rFxSwcU8ltsgI3eTrtM6ac+qqcyAuj0x2opX1i2cSYZDC5s8lR0Xyfmk97xyoprqlg+1H6vH6FLISbCGDk8PtY/WWUp59tzQkV6k3XIgJ2YKz41i9nSM17Tjd/lxRdbeuyeEPTX5hexmpsRaaHV5ONDtp7/CRHG0OmXioizRnqkEpGL4cq/fbnfR4m1YnVN2pCxShSI21YDIaeODN/cRazQPdbMEgoasNqWhy6I5jn1Y08cnxhqA5QXclkIwSNDncPPH2YZa9tpdPjjdR3uAEoLLZESJ6Ujw3Dx/Q1uENqdW56t+HmT0uhWffLaXDK/O9V3Zp59IbZ3syh7nQbGd/7tj9R5Kk+wCrJElXA98G3jrDMZ8DFgJ7JUna1fncfcAvgFckSVoElAO39U+TByduxUjS57+j+1rlv549z60RDBd6uuuUGmvBaDgdZunUESJxeWSaHW5e/KCcJ26ZCpI//DI70YbJaOB37x/lwfn5PLTx9C7fQ9fn88yWw+wob2FpUa7uql55o5NRibYQGXj19cLsRErr2nnsn/4dxR/+bXdQ+MZ1BSNDPktfxNtfiAnZgp4jywoVjXZqWjtodXlIjo7E3nnfBO6sSBLMGBWP0Sjx4w17NVGhBTMyBvojCAYJash3dYuL9s4+FCgGFdifshOtREdGsOIfJfzkCxNYNm/isA45E5w9ar/SG8e2HanDEmFk/Y4Kls2byMS0GBSUsCULkqLMPHLjZO5/w6/0a5ROj9ftHT7e2l0ZJJKyfkcFD87Px+2TwzqLEL6WpzrOisgZffpzx+7HQB2wF/gmsAm4v7sDFEV5X1EUSVGUAkVRpnU+NimK0qAoSpGiKHmKolylKEpjP7ZbILgg6Omu0+ikKC7OTtRW3MLtvlnNETQ53IxKtHLt5HTGpkQTEWHgmompLJ+fT2ZiJC99/RJ+d/dFvPj1S2hxeJg5dgTpcRZe2XGSB+ZP0i2v4Pb65Y67rvo9dtMUspOs3LN+V9COovpZlm3Yo/tZupZxOJt4e9VB7PodDOeEbEHPUEMv/7mvmh9t2E1UZASKohBnM2l9Rs1BfWF7GZIBTjTYNadu5YICMRkXAKcX365dvZ3vv7obq9lIcVEusdZgG6zu1MVZzaz4hz9PMyPeesFPcAX6qP1q94lm3XHMJ6OVCrr3lV28vquS77+yJyQiZ/n8SVhNRn7znzJaHB4ev2UqKxdMIX9kHL+4uYDsJCtRZiOL54xl3ftlrNlayrr3y/jSxVlYzAZNkbPr9dUuqzqIXV9Xx1kROaNPf6piysDznQ+BQDDI6Omuk8EgcVlOEo/eNIX7Xt/L89uO8uB1+UE5dg/Oz2fj7hOsXViIw+3jeINdm5y+XVKjCa/EWYwkxVj49p8/C0nAfvnjCn7zlYv4rKIJn+xf1bv36vE88Pd9QTLeRgOMS4khPc7CqWaXtsLX08/SF/H2F2JCtqBnVDTacbllYiKNLLkyj6//4RN/WHGSlYeuz+fBAMnwB6/Lx+2VyU2N4d6rx9HhlcmIt4jJuAA4PXEdlxLNly7J4n9f3KH1pRAbfF0+z757hCaHm0dunMzUzDNLwQsuTNR+lWAzhwjuqOOxyyMTYzGyaFYOY5OjuO/aiVQ2OXni1qkoioKCxGObSrQFqUaHhxX/KNGu8dKiQhbPGcuKjQdIsJlZPCeHrAQbde0dpMVZ+MWmg9S1u7V5ReA4Oik9hsvHJpEWa2F8WmzYcVZEzujTn6qY84EVQHbndST8opax/XVNgUDQc8KFJY6ItlBW105Nq9/xMRr8K8LjUqNZPCeHjHgrHR4fq26dit3tIyU2kppWJ/MmZ2gCJ+rOQ0FGXJDwyqJZOax6Rz8Be937ZYxJspGdaKO2zcWCGRk02Du0+PpApc0lc3OJNBnISY4OWkHsSYhlXwjKXIgJ2YIzI8sKn1Y08/SWwyybNzFoNbm8wcmv3yvVJMPjrSZMRokn3j7IpTkjNBlvEYYpUKlpdTEuJZqlV43jO3/5NKgvPfefUn678CL2nmxhelY8USYj37kyl7Q4C/npcURE9GdAlmAoozpEahjvL2+ZypHatiBRsewkKzEWE3/9uEIbv9Wx/eHr82l2uDWn7t6rx/H7/3dcO7/FZMBqMrFi46fadVZv8du3P37tEg7VtDJnfApv7a5kXGo0G5fM4kSTA5s5gtTYSLISoxid7B+fsxKjwo6zF2Ipg57Qnzl2vwJuBvYqSjghVYFAMFCE23U61tDOkr98RoLNzK2FmWQl2qhpdfHuwRq+evkYJEnSdh1UiotyWbutLCQUcu3Ci4LCJMPtrBkNaHVpVMdLJVxc/97KFiQJrQZe15XH/t5BE8XLBV0pq2vn6S2H+f41E2hzeULqMZY3OPmkvBGryUizI4IX3i9j8ZyxrNlaKnZ9BSGkxVr4/jXjaHP5+MbsHACtP5U3ONlZ3oTVZKTB7ubiiWlMyxbql4IzE+gQVbW4eHRTCXddlh2kTqkuTC2alROS5vDA3/fz+7svZuXNU4iyRGAySpgjJNLjLNxamMm4lBhcHq+WVwen++0nxxt54u3DWqTP6i2HuCZ/pJaf1zVPrrtxVkTO6NOfjt0JYJ9w6gSCwYnerpNBgnlPbyfBZmbhzOwgR+l7V42jw+ujrs3Nihsmc7LJwSs7TtLkcHca8tPOV3qchZtnZNJk9zAxPYYEm1mb3Oo5akUTUpiSERo6pGe4lxblYTMZeW5bGWu3lbF56Wx+f/clNNo7zqrOjUDQF3i9Mscb7Xz98jGU1rYhK/4ckW/NyeG5bWXa6vZlOUk43F6O1raz4gZ/nujYEVFi11cQhNcr097hxafA4do2AN7aXcnCmdm89GE5TQ43M8ckYTD47a0oaSDoKV3H1SaHm7yAnTNzhAF7h6/bxdgPyhq0XbilRXk8d+cMSqrbue/1vSTYzHx3bi7r3i8LCvFcv6MCp0fW5gdVrU7unDmGB97cF5In15MSRCJyRp/+dOx+BGySJOk/QIf6ZJcyBgKBYADpuhr2wdF6XB6Zm2dkhqzSPfXOYZ66bRr3b9mv7eb9aN54shNtJFhNmsOWHmcJcQqXFuXx4gflukV0Vy4o0HXq1PbNy09j/HdnU1rbjtsnc7LJoU2UAapbXZqMsUAwEMiywjsHa4izRuDw+LTda7Xvq6vhj9w4mRaHG0mSuGpSmjYJyU4SfVdwGrfbx8flDVS1dLD8zX0hk+NbCzNJjbXQ3uHFYIBRCRf2DoWgd/Skpp2qVA36i7G+zj9dHpmXP6lgRlYCxxvs/rqKEQYe7izHob5n9dYjrLptGs+9VxoyP1Dz+rqWIFLr09a0usIu1orImVD607H7OdAOWABRSEUg6CGqDHF3xqy/rhdjiaC4KJeMOKvuKl1Jdavubt7jCwp47OYp/OS1vbpO4dNbjrB4Tg6rt5SyfkcFa+6YQbPDTXWL64xiEQaDxNiUaCQJrl29XcTTCwacrvdohBESokx0dPb1rn3/13fMYM0dM3jlk+NMSI/nxmkZYiIiCMHt9nGgupUWlwdAc+ogOB95XGoMJqPEuNRoLXxdIOjN3EF1iFTnaWdFU1BO8Cs7TnLfFyZgd/tCInRURwz8u8W3F2bx1d9/rM0HHr1piu78wen28r9zxmplidTn1X6t5tBbTAbSYi26pQyumZhKRZPjvM2PhiL96diNVBRlcj+eXyAYdpxLXZazcQgDr5dgM/O1z41m7bYy/6pbmFU6PcftRxv2sObL01k8J4fRSVG6Rr0gI45nvjyNI7XtLH9jH00ON0uL8kiOjuzx53p+YSH3v7mX8ganiKcXDAjqPfO7949y1+U51LS6SIo2U1LVRrzVpNv3O7wy5Q12ZuWmMDLBKvqsIAS328e7R2qpb3ezYuMBvjE7J2w+cmpMJFMz4kX4pUCju7kDoDs3CDyma38bEW3GFGHk6X8eDHLYxqVE852/fqrtrnWdDyTYzFjNRt35Q1m9I6gmrorLI2PtUoLIJ6NbymDtwsIgkbYzfcYLkf6UTdokSdI1/Xh+gWDYcbZ1WQLrHX35+Y+4dvV2Nu+vRpa7T3ENvN7NMzJZ9e/DuDyyFjIZVDfu5gI27qkMG3O/p7KF1VtKSYo269aeMUcYONnk5NUd/iRqdTfDF3yqbj/X/760g3uvHs/fvjWTTcWze+zwltW188HResrq2s/4nQgE3XG8wc7v3j/KghlZ/Ohvu7ln/W4+Od7Eqn8fpq69Q7fvH61r51dbjhBrNXNFXsoFO+EQhKekuhVFgRUBIWx6fWnaqHgmp8UKp04QRLi5Q0WjPezcoOsxan9Lj7PwrStytXIa6vnue30vVnME3796vPbero7anZdm8Yt/loTMH5bPn8Rrn55EVvT79cycJP72rZmsXzyTBJuJ+vYOEmzBwX4uj8yO8sZefcYLkf507P4P2CxJklOSpFZJktokSWrtx+sJBEOe7uqydEdPHcKuTk7g9QIdNlUGedGsHH77lRlsKp7NdVPS+f7V48MWDc3rrC3XbHeHGPXiuXk0Odw8veUIN8/IDPpsde0u3bbpDTyq2mZiVCQ5I6J7vCMpDL6gr6hpdXHX5Tk8tPH0pEdW/H3zTx9W8L2rxgX1/e9dNY4/f1SByyPz49f2UNHkGMjmCwYhbrePtg4vB6patT6lt7j26E1TuHx0ElaraSCbKxiEhJs71LR2hJ0bBB4T2N9unpHJwerWkPMl2MzUt3eQHB3J7+++mPu+MJ68lJgghzA11hJUd3bJ3FwWzcqhzeWhqsWl26+L5+YRYYDaNje3r/2QLz//EV/9/cfcdVk26XGnUy0Cc/t6+hkvRPqzQHlMd69LkpSvKMr+/rq+QDAUOdu6LD0p1KkXqvH8wsKg6wX+v6rFxbr3y9gUoE517eR0PilvJCXWoq0sq4b5ibcPsnBmNuYIA+t3VGhSx4riLzb+/WsmaCpbXT9buDCSETHmMIOVS/vc3YVdhHN4e6K4Ndg537mYAj+psRYqm50h/dJiMlDV4uIP/z3Oolk5GA0wITWGFf8oCREFGOp9T9A3qItZJdVtREYYtN0MVYb+pQ/LWTwnh/FpMYyIjmTGqARRn06gS7i5g93tDTs36Fr2QO1vE9JiOFDVFnS+9DgLd12WHZRLt3JBAaMSLKxcUMCyDXu4eUYmJ5scmi0MzJlbNMtfrqOqxcX6HRX8duFF7D7RQodXZv2OCj6XmxQyVgfm5qvXW/XvQ0Gf5Uyf8UK0tQNpIV4awGsLBIMSVYY4cDWrJ3lkqoEOpKtDqOfk3P/mXlYuKMBiMrBh50mWFgWvpK1cUEBWgk07R0SEgUvHJPG5sUm8+LVLKC7yr8a99GE55Q1OVm89Ql1bB9++wi91vGZrKeveL+Nbc3J5YdtRLCYDUWZ/Id3iolyeX1hIVoKNY/X6DpjZaAj5XNlJVmRF4Y1dlfy/ow187Q8fh92FO9sd0MGO2Ik8/6iT8Pq2DrISbUH9MvDeURdErCYjJ5scmlMHQuxHcBpZVtheWktpbTuSBLHWCN7aXRm0m9HkcDM6KYrkaLNw6gTdEm7ukJ0YFXZukJVgY+3CQoqLclkyN5eM+EgKsxNJiooM6Yu3FmaGCEMt27CHWKuZL05O57mvXERWopVXdoTuyK24YTIb91Rqf99emMUDb+4jwiCxcU8ly+ZNxOH26Y7V00fF8/LiS9lUPJsvTk5n2byJvfqMFyL9KZ5yJsTSskDQhbOty9KTQp16Tk55g5OMeAv/+O5sKhrtJNrMTEqPZWdFEz4ZVv37ECajISiXzWCQGJ0cTVWLi9VbSoPO5/LIpMZayEqy8tRt03D7ZIySxNptRzlc287jCwqIthjZdaIFWYEd5Y10+Hx0eGRdo+5w+7TVQJdHJjvJyrevyGXRH08nTxfPzWPl5hImpMWQMyI6aCfLZo4gO8lKeYNTO+9wMPjDeSdyMKI60m98VsFtF4+mrLaN5fMnabvWTQ43NpORJVfm4vLKKAq8+EE59107QVv1FmI/gkBONNmpaeng2fdKmV+QwZSRsdxemKVFOxgNMCEtluhIAxeNShROnaBbws0dAN25QdfyBqoDtvzNvbi9Cnddls3Ln5zui11r1cLpRVKDBJ9WNJE/Mo5bCzMxGOCXt0zlWL0dr+xXw3zo+snsq2zB6ZG10gartx7hxa9dQkpsJD5Zv6xCdlJU0Jh2zcRU1i+eSVWLi/Q4K/npsRgMkihUHsBAOnZiaVkg0OFs6rL0xCEMF6qRYIvkUE0b976yi0WzcrSioirhHIb0OAvFRbmom0Qbdp7EHCGRFmeh2eElOjKClz4sY/7UUXxlZjajEm2MjLewaW91SJ2vMclRum0zGiQtjEOSYFxqDD8KI5Ws1r3pGtL5yI2TeWbrkWGlpNmT0FtB33Gs3i+Y8qVLslnyl0+1RYanbpuGy+PDZDTwy7cPhiwgTEqPY5Monivogiwr1LT6nbrbC7NYvfUI35idw1u7K5lfkIEkgU+GlZtLePLWqcKpE/SIcHMHvbmB3uLg8jf3aWUHXvygnJ9eO5HDtW34ZChvtOuO0SOiLXxa0cybuyqJMkeEjO1xFjO/+U8ZTQ53UEkD9ZrbSut5YXsZa+6YzqrbprFycwnzCzKwmgxclJ1Ag91fBlsds1VnVK2le6LRwcT0WK6ZmCpsbScD6dgJBII+5EwOYbhdPaPhtKxwOMXLrg6DLCscqGoLMuL3Xj2OtDgLt6/9UJv4LrkyT3PE1OT/lz+pCImjf/pL00MKly+fP4md5U24PD7N2VwyN1e3fUYDpMRY9MNN39jH+sUzcXp8w8bgn20upqD3eL0y+6taWDRrLN8L6FvlDU6+98oullyZS15qDIvnjA3KO1112zTGJEdp96VAAH7b+c991RgkmF+Qodm8DTtPhtQHvffqcWcsByMQBBIu97rr3CDc4qCaA1/V4uJgTRtrtvodsfQ4S8gYrc4f7nt9L4tm5fDUO4dDxvYlV+Zq4ejGLusTFpMBpVN4aslfPuNf98zm3qvHaxE6akTO+h0VLJs3kUnpMZpT1/VeUcseCFs7sI6dewCvLRBccITb1fvoWEOIg3Amh0EvJ27Vvw9z71V52u7a+NQYnnj7YNB71AGg66pdi8OtqWhJEhgkaHV6sLt9morW6q1HwravMDtR97Oo53d6fMzMSe6jb3Lg6UnoreDc8XplPilvxOX2ER9nIsFmDsqZc3lk3D6ZPSdb2LinkhfuKsTh9pGbEj0sFhAEfYssK+w52cyhmlYuH5tEnMWoq0ScnWglxmLCFCGRlSjuaUHPUEPG1V0vowEuzk7kspykkF3fcIuDSkAsnaqA3VVcZfqoeLKTooLG3HCLwi7vaWG2wuzEoND0wELnLo9MdUuH5tSpz6kROfe+sos/fu0SXB5Zt5auSEU4Tb/t70uS9DlJkqI6//8VSZJWSZKUrb6uKMrM/rq2QCDQR125m5mTrJULCBRe0ZMiVh2GwHIElc2OkBozCTYzURaTJpryw7/t5vbCrCC5YnV3LRCLycDIBCtNDjfPvlvKC9vLsEQYefEDv8FvCnD6oiONLJ8/KUTg5fKcpJDPEnj+tFjLsKplpzrpm4pna4nlPanpJ+g5Xq/MW3tO8ePX9lDe6GRHeRMPXDeJgoxY7T0Wk4FpmfFs3FPJkivz+NU7h8hNie5RKQ7BhYXXK/P2gWq2HqpFVuCDow2kx1vJTrJq71GFd0bEWpiYHsPc8amiHwl6zPEGOys3l3B7YRbr3i9j9ZZS/velHfxjX1XImBcotqKmVaxcUEBMpJH0OP84mmgzB5VvaXK4mZAWy/+MS9EcKAkoLsplfGqM7tirKKfnEZfnJLGpeDa/v7uQxXNytFw79b32Dn11S9VpdLi9WEyGbiOLBP27Y/cbYKokSVOB7wMvAC8C/9OP1xQIBL0kcPdHlSJeu7AQk1HSQjmAkNy1pUV5vPjBacN8a2FmUHHdwNW2QNnjiWmxIYISM0cnafHxVpOR4pc/C6p5s3rrEZ591y95vOaO6fzju7Opaw+NpdfbyVpzx3QOVLWF7G4NdUfobHIxBT1DlhX+W9bAr7Yc1nKgAkOE67aW0uRws3z+JMwREjdMy8Dp9vL1WWPFrqkgBFlW+KS8kWP19pAcpIdvmMw3X9oZFK7+uTFJogC5oNfUtLqCwnvhtHrllIy4oLFCXRyctHQ2n1Y0c9/re4NsXKvTw3PbygBYPCeHzHgr49NimJIRr42bFY129lS2sHZbGQk2M0uL8jTlTHXRNSPewoIZGUEhoaOTonB2ik7BaccvOSYy7C6ixWQgK9E/vh+qbhWpCN3Qn46dV1EURZKkG4A1iqKskyRpUT9eTyAQ9ILAWPzxqTFsXjqb6lb9xOOyunbdGjNP3DKVtduOcsWEFHJHRIfNf4PTxZqrW5w8dF0+tsgIRsZZmJ6V4Ffa7JwQ17S6eOSGKdz/5l7KG5y6jqbBIDE2JdSh0Qs3VRT44jPbRdiGoEfIssLeymaqWpz84JoJ/LCLWM+KjQd46rZplFS3IssKP31jH/cUjWN6VjxZiSL8UhCMLCscrmlBVpQQufintxxh1a1TNeXBGVkJXC6cOsFZkhprwWjoWZ48+MdLWUFz6tT3rth4gMVzcqhqcWExGbBEGHnqnSM8dftU/zGdc4fDNW3YO7yMS4lm9rgUJAmeuGUqHp9MUnSkFklzvMHOR8caghaKx6fG8Os7ZxAVGUFqTCRZiVHsrGgMyeMrnpvH1oPVvPDVQsob7CRFmbkmP5VxqTF8/9XdQYu1YlHNT386dm2SJP0E+AowR5IkA2Dqx+sJBIJuCHTk0uMsIbtYKxcU8MXJ6SGx+LKsUN5g1x0sTjU7uHNmNg/+fT/fmJ2ju4qWmxJDcVEuBRlxtHV4+clre4OuOT0rQbdAubralxgV2at8pa47WR8crRcKkoIeoQpbfP9Vfz8sLtIX63H7ZK7IG4HD4+V3X71EE0kRCALxC+80c6jGzskmh25fMhgkcpJtxNtMjE6yCadOcNaMTori4oA8NpXudrPCiahkxltZMjcXRYGXPiynyeHGajLyyfEGTjW7ggROHrlxMi9/XE5lcwe3FmYyJjmKA6daGBFjoqSqPWg38NGbpmAzG7lnfZfyC4lRJEVFauU+IiMMjE6OotXRwVdmjuEbASWOlhblkZsS3e1i9IVMf2ro3g50AIsURakGMoFf9uP1BAJBGLoWtH7ts8qQHbhlG/bw37KGoFh8WVbYeqgGl1fWz12Lt/Hg3/drqm5d8/OK5+bx2KYSVm8pZU9li+bUBV7zeINdV81y2YY9JEZFnnO+Uk+KtwsEsqyw71Sz5tQByJ0hQIFYTAaO1rXjlhVm5aUwNkXk0wlCkWWFdw7W0Or08cCb+8L2pWP1dpqdHhQQQimCc8JgkLgsJ4mVCwp08+QDUXPmDZKkP7bHWXlhexnPvusPO3/kxskUv/wZ7x2qDxE4uf+NfSyaNZZvzclh7bYyvrd+N7/acoTK5o6Q3cD7Xt/LgarWkAia4w12RidFsWzeRNa9X8aqfx/mR3/bTVZSND9+Lfh6T285wt7KFnwyQXoBAj/9tmPX6cytCvi7An+OnUAgOM90dZxkRT9cY0d5I5kJVm0n63iDnT0nW3hzV6VuiMTx+tM7eYGqbuNSozlc0x6UHB3umjWtLpxun+5rh2vaAM5pNU4oSArOhLrwIStKUD8MzPHsquQ2fVT8wDVYMOg5Vm/nQFUrGfHWoIWvwL70yI2TGZscRUKUWYTxCvqEiAgD1xWMZEpGXNiaboERMuNSolk+f1JIqRZV6CQw7728wRlWuKSkulX7v/rvnpPNuu/tql0WGEHTNZWiukV/R1FW/Dl+eikZFzr95thJktTG6SLkZvxhmO2KosT11zUvREr27+PqBV8JeT4jOZ4//HbNALRI0B+Eq03T0/frhVvohWv4ZIJCFGtaXciKv2bXSx+W88tbpnKks2DpSx+W85WZWUHnUVXd1n21kGUbggudB0onB17T41M4XNOm+9reylbuWb/rnMROelK8XXBhU9FoR5Z9JEVbQvrz+h0VPHHLVA7WtAWFJY1KsA1wqwWDFZfLS1WLk4w4q6YwGLjwZTRAYXYCoxJsjBHh4II+5kzCWupCb4LNzLzJ6azddjSgXyZyeWd5BPUcHxytp7zBqR0fbu4gdRlS1V1ql0cmPc7CzTMyMRpgXGoM6XGWIEXMEdF+5Wp1znLJ6CQAKpucutczSGAzB7swvZ0nDVf6c8cuRv2/JEkScAMgShz0MW7FSNLnvxPyfOW/nh2A1gj6A738s+4cHb33P7+wMMg4bth5MkTBSi0EumBGhnYumzlCc8iqWlw8uqlEKwyaYDMTExkRcp4H5k/iSE0b931hAuv+3zGtns6UjDh+eu1Efr6pRHvvYzdNYfWWQ1xXkBFynu9dNY4//Pd4n4idnC8FSTGwDD28XpmqFicur8KPX9sTsqty56XZdPh8vLD9tJrhDz8/nuONdhECJAjB4XSz6UAt97/hD0HLTrLy8PX5PPD3/drC10PX5+PxyWSLqAHBeaDruNRg7wipBxeoXL2py1gbWPNuw86TrLhhMsvf3Bcyd7hhWkbQdd/aXckjN07mma1HQtSFVVXtJoebJ2+dxrGGdpb85TMSbGZuLcxkXEoMWUk2nt5yiJ9eO5G69g5kxb9AnBRl7mxXZNBn7M08aThzXgqUK4qiAG9IkvQg8OPzcU2BYLigl3927yu7SPraJYyICRUW0Xv//W/uZeWCAi02vsnhJjoygme+NJ29p/yx6ut3VLBs3sSgEEW3z0digIyxuoPxm69chM1k5Ku//5gEm5klV+aSFmuhosnBM51S8D+9diLf+p9cHnprv2Zo7716HEuL8nB6fBRmJxJjMXJpzgge23yQBJtZK1AeG2lkVKKNBRdlAn5HdLCLnYiBZeghywr/2FdFQ3sHj//rEAk2M7Ki8NB1+URZIrCaDJxsdPLXj8pZc8cMWhxuYiJNPPjWfpoc7pAJkODCxu328WllCxWNdr4xOwfw265n3yvltwsvoqbFRUaClVhLBONTYoVdEPQ74YTJspOs3daDC7Rro5OiWHPHdPacbEFWYHSyVVMGVucO3/qfXKLMhqBSRovnjOXlj8v5/jUT+FEXdeGntxzRIoAkFFZsLCHBZtYWjgMXeAGtTEh2kpWfXZeP0+PDJ/s/n6q8qTdPylg8M6hEw4VAf4Zi3hzwpwEoBET1QIGgl4RTrdpeWs8L28tYc8d0xiRFU9sWvBoXSHmDk6QoE//4rj8c0eNTWP7mXtxehVsLM5mYHsPN00+r+6krfG0uL06PjyizkV/eMhVHh5dGh5sxSTbKOvPrqlpcOD0y93eu4KnUtXdoxlht86p/H+aPnQ5pZpyVnSeayEq0aud59t1S0uMsLJyZzfe61MxLi7Wctx2xs7lOuIFFlFUYnMiywp6TzSzbsIdf3jKVBJuZb83JocHh5kSzE6MEuSnRSAa4NGcEy9/YR1WLiyVzc7UQosG+2CA4f7jdPkpqWoNs4Fu7K1k4M5uXPiznk+NNrNnqr8X51pJZQv1ScF4IJ0z2+7sv5kSjg6VFubyy42RQWGSgsJgsKxyrt+Nyy0j4FypiI40kxfh3yyQJbpiWgdvj468flfPrO2ZwoKqVOXnJtLg87ChvYebYtqBwTDVks7LZweot/nti0Sz/QkjXGnxPvXOYxXNytONvL8zi//78acjiabh50paDtVQ2u3QXWIdrhE1/7thdF/B/L3AcfzhmWCRJ+h0wH6hVFGVy53M/A/4XqOt8232Komzq68YKBIOVwDAIFbVoZ4LNzJGa4BCG3JRofjxvPH8MKB5uMRn4+HgTE9J8zMtPA+D3d1+i5ZxlJdg42ezgk+ONuH0+Gu2eIDnjpUV5/OY/JTQ53Jo0cU1rh9YuvZW/cGIpsqJgkGDjvirue31vSJmEwPAQ9Zintxzh6ompQSuP2UlWVtwwJaS+3dlwplIQPdl5CzewiMn/4EKWFY7WtVNa205UpJGnvzQdS4SBr12ejcPjCykgDQSFKSmdmeNCWVUAp8vBHG9sp9Hu5acB0u5qiNqthZn4Ok2DyyPTYO8gj5juTywQ9AGB41KgY9Vgd/OnD8u5YkIK379mHDWtLl7ZcUKL2lEdupKqVo7UtvHKjpM0OdwUz81DBhraOrRr+GR4blsZTQ43u042MyEtlskZ8RxvsJOdZGV8agzZSdaQcMzl8ydpuXZGQ3dzBv//9eYG6uJpuHmST0Z3gXU4R9j0Z47d187isD8AawhVz3xKUZQnzrlRAsEQRE/VUVXmu3lGJk9vOaIbwhAYw148N4/N+6oAv7HLTozCaABF8T/+X1kdB0618fInFXz/mgkcrWvnG7Nz2LDTv5L39JYjvPj1S0iJiSQz3sbxBjtRZiNr7pjBnpPN5KX4DXdggnU4sRSforCvspXjDf5wpW2HaoPymsIVWD3R5GDl5hIWzcohxmIkxmJi8Us7ztkodzXwxUW5ITuNPdl5CzewiMn/4MHt9rHlUC0nGu2MSoyixekl1hJBjCWCvLRY/u9PO0MWFJ64ZSpA0D0llFUFcLocTFqMmRizif/702dB/Wf11iMsmpVDVoKNJ/99GBA2QXB+UccldY6wfkcF8wsyOF5vZ+lV43h4437KG5xajbnkKDPH6u0hOW8/uXYCEQaJsjo7oxOjaHd68CkE5cWvunUqqXEW6to62FvZwvgR0Xx3bh5PvH2QZfMmhuwcrth4gEWzclj3fhkT0mI5VN0aVigF6DZ09JLRSWHnSXoLrMM5wqbPHTtJkn6kKMrjkiQ9w2lVTA1FUYrDHasoyjZJkkb3dZsEgqFMoKpjeYOdz040a2UEVEMXbpfrl7dM5WB1G58ca+BbV+RysLqVTyuaeeit/Xzp4izN8Vs+fxJbSqq5vTBLi4W3mAw8eF0+LreXBoeH2rYOnB4vB6ra+N37R1lwUVZQ/tzDN0zmlU/KuTRnBEYDXJSdwNO3T2NpQCHSxxcUUFbbzqP/PKgNGAsvG02r08Oyz48nOcbCiBhzkGMFfuMeFRmhrfgtmpXDr97RX7nrrVHuaSmIM+28ibIKgxu328euymYMEiRGR4aE+o5OitL93c0RBl5efCkjoi0YDTA9K14oqwoAv5pqRpyJiqYOHGFKthgN/rD0qhaXsAmC8446Lh2sbmX9joqQXTN10Xf2uBSON9gxGQ38cMNuvnRxFpeNSeTWi7M42WjH3uELGu9/+PnxTMmMZe3Ci2h1eslOtnG4pp07X/hIe8+jN03h5Y/LKW9wUlrbHvb+KJ6bx3PvlfKFKekhpRfuvXocVpMxqC6f3uKpOk/KWDyTLQdrNeVu9b7rupgynCNs+mPHrqTz3x19eM4lkiTd1XnO7yuK0tSH5xYIBj2qquPopCicneIncHpXTG8lK8FmRgJiLEa+OHVkyErWy59UcPOMTJ59t5QVGw/w+C1TQxKcH3prP4vn5Gi5IcvnT2LttqO6ydAPvLmP39w5Iyj+/clbp7F56WyqW12MiLbQ4nDzow17dHcYH7wunyfePojbq4QoZD5y42RiLEZNjXNCWkyQOEFVZ62bszHKPS0FcaZVdlFWYfAiywpbDtVSUt1KRpyVUy1OEmxmrd88veUIT90+Tfd3T4uNpGBUgvbc6OShPegL+g6H24vHK2EzReDxKbr9Z/qoeGyRRv76v5cOqzwewdBAHZfUsMTABeAEmxlJgq/NGsPxersWbrl8/iR8PpkFhaO495VdLLnytAga+Mf7X/7rEE/fPp2l6z8LG+ly3+t7eeq2aRRNtJOZYNO9PwqzE1n+5l7KG5wcrm3nV7dPY+OSWZxocmA0SCzbsJe7Lstm8ZwcbGajbs09daHEYJCYkhFPZbPrjAuswznCps8dO0VR3ur89499dMrfACvw7/6tAJ4Evt71TZIkLQYWA2RlZfXRpQWCvuFs+6decm+g85AWa2F8lxCG9DgLd16axch4K0gwJjmapS+HCxGysmRuLgA+n6y7ghVhMGj/V0MnnB1e3fd+dqI56Drff3UX//jubAqzEvlvWYNfVj5ghzHBZtZi/qtbnNx37ST2VrYgSbC0KI/Mzlphf/xvGT/8/ETNIfxhwK6iGm7R5HCHJH33JDG6q4HXKwXR01X281VWoa8ZzvZTlhUOVLVwosnB2m1l2k7xvVePo7bNxZ8+rKCqxUV5g50Hr8sPWpV+9KYpTEoXpVcHksHaN51OD5IEjU4vn1U0YTMbuffqcaz69+HTUQzX55MYZSY/PY6ICMNAN1nQDwzW/tl1/MtOjGJXwPisipSpqRyBNnHttqPcMC0Du9tHgs3MiOhI3fF+f1WL9rzNbNRUreH0gqtauPzRTSW6C7artxzi7stGk50URYdXZmSclcoWB//350/5xuwcmhxu7G4fa7aWau1Wr3NZTiKX5SQHjes9XWAdzhE2/RGK+RY6IZgqiqJc35vzKYpSE3Du54GNYd63FlgLUFhYGPb6AsFAcDb9s7vk3kDnISsxiknpMWQnRfH0lsO6Ccrq7oSKGgJR2eLUduN+f/fFuitYo5OjQo6zRUaETVQOxOWRqWi0s+9UC8s27NGEUiQJ3V275fMnsWHn6STtymYHv3rnCMVz8wCZn147kR902SlcvfUIi+fkMCEtttuk73A5eF0NfJPDTV5qNP/47mzq2i+Mnbfhaj/VHChLhJFV/z4cVk77Lx+X43D7UBS3NmlQFBiVaBUT8gFmMPZNr1fm/x1roMXp5advnBZL+cm8CSwtyiPRZqayxUl6nIW85CjRh4YxA9U/u1u41Js7rLljOhdnJ2rjduDialebWDw3j2iLkfp2N/cU5ZEebwnJoQ8c79PjLMRYTFp6RKBwkFq4vKrFxYsflLN4Tg6jEmwcb3AQazXxwPx8Dta08+2/nI70efC6fMalRLNh50mWz59EbatLa7eqnm0xGZidm6w7LvdkgXU4R9j0h7V5Av+u2jHACTzf+WgHjvb2ZJIkpQf8eROwrw/aKBAMesIl9x5vsAe9z2CQGJ0czfUFI/nFzQUhuXYrNh7g1sLMoGMsJgMFmfG8uuMk4Hey6ts7KJ6bFxTLXjw3j1PNjqDjLstJ4q3dJ3jkxslB710+fxIb91SGXCcqMoJV/z6Ey+Mvblo8Nw+jBHddlq3b1ptnZGoO28T0WBJsZtbvqOBUcweHa9t0Vw6nj4rX1D4376/mi89sZ8lfP+O328q4+/LRLC3K42B1K3srW5Dl4LFXNfCbimfz8uJL2VQ8m7njUxmbEs3MnGRRhHqIou7UnWpy0ta5w6yXi/rUO4e5/9pJJEeZefGDcp59t5Q1W0tZ934ZSVGRZ7iK4ELD6/XvVERFGjWnDvx96bHNB2nv8FHZ4mR0UhQzRsVhs5oHuMWCoYosK5TVtfPJ8QZ2n2jig6P1lNW14/XKbN5fzbWrt/Pl5z/i2tXb2by/Whvb9OYOS/7yGbGWCB67aUpQ+oaeTVy99Qgj422s3VbGstf2svilnXz7ilyyk6yAf1x/IGC8v3lGphYeGXiOZfMmsnFPJVFmI9+5MpcFF2UiK9Bg72Dd+2X4fAqtLi8PBJRKUlNAvjFnLFUtLtpcHqI6QzC7zjcCC5SfDaoDONzG+f4IxfwPgCRJTyqKUhjw0luSJHWbdydJ0l+BK4BkSZJOAg8CV0iSNA3/LuBx4Jt93WaBYDDS0+RedeXueIOd+rbQGnYuj0xWQHy7ahRPNTm0XbybZ2RSWtvOG7sqg3Ys1u+o4IZpGcBpR+/Hr+3h3qvHE2+NCH7vx/qJ2T/8225uL8zSEplf+rCc//ufHOKsZt22qqEcLo/MzvImFs7MRlYUfqJTGkFtV3bnSltZXbumnKme5y8fl3PDtAxWbyll7bYy3Z27oRpCKdBHlhU+PFaHzRTBiBgLKLC0KBer2ajb5zyy/zk1d3U4heUI+g6vV+bD43X4ZIlWpzdIORhORzRcOiaR0clWoqxiYUBwdqi7bis3l4SMq2sXFnar6Bhu7vDvg7V8eLSOP3ztYtxeOWx+vssjs+dkcFrFg3/fz/MLC/nsRDNeWSYpysy3/sefexd4juBadQrfuWIsTQ4v694vC5p/PHbzFH6xuYTrpmboXt/p9gLwx/+Wc9dl2azddpRFs3IwGmBiWiy2SANZicI+69GfdeyiJEnKURSlDECSpDFAt7+Coihf1nl6XX80TiAY7PQkuTcw5CLBZuan106kuCgXWTkd424xGahudWkhECeaHFhMBv766Ql+cM04RkRHMiImknq7i2/OGcvDAYnJD16Xj8fr44lbC6judMqqWlws27CH9YsvY+OeSuYXZCBJMGd8ClsPVrN24UXsKG8KUqVavdWv0Hmopo0NO09Sb3fTYHeHrc+n/l9N9n7o+vygHb/AQS5wAt5g79B1LjvTBEMGQMHw5FSrHZvJxNF6e1BdsQfmT9INKZJlSIgy88rimdjdPiFyIdDlaH0rta0e7utSqy5QfW/6qHhGRJtIjxWTTsHZo+66LZqVEyJ40ur0dCseFm7ukJVg4ZKi8ZQ3OIi3mXno+nxONTt7nFbxSXkjF2UlsLOiiZ+9dQBzhMSv75yBvcMXVFIhcPz9+Y1T+MvHR0Mic1bdOpXrpmZgNRl0rx9l9rsnTQ43UWYjD87PR1YUYiwmUmMjyUrUt8/Dteh4b+hPx+57wHuSJJUBEpCN2G0TCHpMT5J7VeOvGtQfdBEVUeWN1++o4HtXjafJ3kFuSgwuj4e7Lh9DaW07J5qdtDjdxNnM/HZbqbYqNiEtlufeK2VPpV+YZdGsnKCVaUWR+e7cPO5/Y592zYeuz8fR4WX1ltIuK3dQ2ezghe1lLC3KY2SchV9sPhTipC2fP0nL+VMnTAk2M1mJ/h1HdcdPbWPRhBSmZMRrhttsNOiGlTzeWYtMfW44SBoL9Gl3uvi0vAVrhFFz6sD/uz+88QBr7pjBkoB8jqVFeSRGmzAZDUHqlwJBILKsUNt22qmDYCGqde+XsXz+JBKjzGTFD5+wLsHAoO66dd0N0xvnVfGwEdH+RV+9ucPD101CQQqq/fqLm6dwUXZ8iNLkz2+awuoth4Paozp7OyuaWL3ltJCJo8NHeYOdp26bxvEGuyaOAv52//SNvSyalcOz75Zq53J5ZA5Ut/HC9jJ+eu1EfnZdPj8LLJ10/WRGJVp4fMEUrOYIWhwdmE0GjJLEiJhIbaeurK49yIEDelx0fDg7gP1ZoHyzJEl5wITOpw4qitLR3TECgeA0PUnuVY1/uDj5P37tEgwGmDc5jcw4K/8qqeZIbRv5I+Moq2vX5Inv+8J4KpudXDfVH3b5aqfgyNKiPGaPS0GSYHxqDOlx/oHj1sJM6trdnGh0BMnGP/j3/Tx/VyHZSdawIi5PbznC8wsLaXK4NSdNkiDKbGRcajT3Xj2OlNhIyhvsfPXybAySxI9f26M5gVUtLta97w+pDHTqgLC1pI7Xn85LHC6SxoJQ2p0uSqrtmI0G3GFUXludHhbPyUFWwCBBWqyFVW8f4vFbpg1MowVDguMNrbQ6Pfqh7olWFs/JIc5qIjnGhMXSn2vmggsBddcNTpffCTfOL56Tg9Vk5FhDO2OSo7S5w/jvzqasvp29lS3E2cxaTVn12B+/tpfn7yrE7vLwy1um4vL4SIoyU98eGr1TPDePXSca+MrMMay8eQpxNhN1bR1BTuYvbi7QvT+MXdQ81Mgcl0fm55tKuPeqvKC0jmffO8IzX5pOdlIUHp8PWVH4xh9PO6RP3joNowHuWR/swI1PjelR0fHuhOmGg3PX39YnDxgPWICpkiShKMqL/XxNgWDYoYTR2kqJsXQbJ9/SKcmtKNDh9XKyycnabWX88pap2spaepyFKIuJVe+EFi2NtZqC5Il/Mm8CHT45SNI7MBTJ5ZHZfaKZn10/mf/7086Q8IslV+bi9Mg0Ojq0VcJn3y0lO8nKt6/IZeG6j4POG2FAu1bgTt3/5I1gamZ8iBEOF4Li7cyhErlTwxen08M7Bxv48Wt7cHlklhbl6vaFU83+MMy8lGisEUbWbi/l67PGij4hCEuz08WnFW2caLSHDRubnpXAiJgIEYIp6BPUXbeVm0u0Rc1w43xGnJVfbTlCk8PN+sUzcbh9pMRYONbQziP/OMDXLx9Dh1d/oavR7ubRfx4C4DtX5mplYcalRGuFzX0y7DrRQNHEdBa/tDMo2iFwYfdYfbvu/TExLTYox1+dX3znylwkCdLjbfx+U0mQcvc7B2tZvaVUtz7e91/dxeI5OSEO3K/vnBFWl2B0UpS2Q2czG3vkAA5V+s2xkyTpQfxCKJOATcAXgPcB4dgJBD3gTKtKsqxwrKGdpUV5uDw+XYO671QLq7f4QxsfuWEyL39Sgcsj4wioQxdO0eqJW6aGlBZocLh5M0BgBfwCK2qhc4vJgNMj0+HR3zlLi7Vwf6cCVnaSlVW3TaO0tp3RyVEhBc/VvDz1OVXmGCAr0UaT083c8alBzl248NX8kTF8bmwydreX7B4kXA/nMI3hiNvtY191q+bUAbyyI7Qe4YobJtNg72BqZgK2SCOtTi8rF0zTVrkFgkBkWaGisZ3aNjf3v7GXBJs5JHx8xQ2TGRFjZmSChYw40Y8EfYMWsZMWQ6O9g/WLZ+LyyLywvSxknK9ocmpO0ZaDtby64yS3FmaSlWjjtsJRxFuNJMdEhsnZP/28JIHcuZO2p7JVU6mWJLj7c2P5+h8+CRqjn95yJCjM8pUdJ0PCOpfPn0Rdm4slV+YyJjmKQzVtbN5XxYIZmTQ43MgKlNa28b2iXGrb3bi8MkYJLCYjcLo9gbg8Ml3ErXF5ZKLClGEaEW0JmksVF+WGdQCFY9c9twBTgc8URfmaJEmpwJ/68XoCwbAiXLkDdVXpeIOdJX/5jASbmbsuyw4xqEuL8njxg3Lt2Pvf3KcZ4Xp7R5Ax1zWcKCHP28zGsOIkak2wKLOByAj9hGiLyRiU9K0mh5eGKWPgcHv1zxPhVzesaLQzOvm0IdYLX81KsPHekVr2nGxBVmD3iWamZMaFOIUqwz1MY7jhcnnZfaqZJkdwmJxaN0kV7TFI0O7y8Kt3/JOR2XlJXDkhdQBbLhjMqHZgdJKV+nY3Lo8clOMrSTAh1T/ptpgMjEoY+hNCweCiq1qzLCshC5dqxAz4x0az0RAiYPLoTVOob3Py4HX5PBSYy3bDZGrbnDx28xSO19uxmo0YgGXzxpNoM2OLjKCy2cFfP64gLyVad4yWAobEJoebVqcnKKyyzeWhvcOHokBlkwOrycj8gnQcHp+2E6cKtb268wTlDU5tLqGmfujNAboOxRaTgdSYSN2FXaOBoLmUrOifc7ikaPSnY+dSFEWWJMkrSVIsUAuM6sfrCQTDijOVO1Bfr2pxsXLzIdLjLCy5MpeMBCsmg4GfdwltCIx1/9OHFXzvqnE89Y4/QVrPyEUYpJDnM+NtugXC1y68iCVX5mI1Gahtd/P8+8dCVrZ/fuMUfvn2Qc1wqwNSdqKV9Dirbhvq2jpCdl2WFuXx800lNDncPHrTlBB1rK6D4fH6do7UtAcNIkuL8sgdER3kFKqcyaEWDB6cTg8fVzRS2+bmZKfaa2AfanK4OVjdxrr3y3jkxsk8s/UIP7hmPC99eJwFMzIGsOWCwc7xBjunGltxefziEGrfCiyQ/Js7Z2CQIpmYGjfQzRVcAAQuXNa0uujwyjz4932aIutP5k1gZLyVRoebX94ylcpmB3/8bzn3vb6XxXNyeO9gLY/fMhVFUYg0Gmh0dPDAmyXauPjYzVOQFYKUhO+9ehz3XDWO5Chztw5W4GKyOu+wmAwsnpODQQIZaO3wsWHnSX5+42StIDmcrl2nLjy7PP76ootm+cuJ3Hv1uKD0jx9+fjwR0un5icVkYOWCArISo8hKjArRJfjoWENQu8+krq0yVCN3+sWxkyRJAvZIkhSPvzj5TvwFyj/oj+sNJHd/cwmV9c0hzx88dITPff78t0cwfDhTuYOur1e1uHD7ZH7ymr/em1qTK/DYySPjNHXJv3xczrN3zKC+1cnPb5oSZMyL5+bx3HtHQ4xfOEGKurYOJmfE8a0/+QuZljc4g1a2FQWsJgNur6IdoyZ9lzc6efa9Uh66Pp8H/74/qA3rd1Rw3xcm+pULbWYaOz/Tgov8Bdef3nKYaaPiu3W4alo7QpS6nt5yhBlZCbqOXU/rBwoGFpfLy8E6f/7H8jf36YbJ+QV7TDzzpemYIiQWzszmpQ+Ps2zeRJFTJ+iWZoeb6dkjuHPdR7p965EbpzAyPpJoixGz2TjQzRUMI7pzKNSFy9FJUXxW0cSD8/Opb+8gMdrMyUYn3335s6AFzG/NyeG5bWXICuypbKX4r5+RHmfhgfmTeHhjSdC4WN3iChkrV/37MEuL8oi3mXhwfj4PbTw9Rv/sunxGJ9uYmBaL1WygpsUVVAt0aVEeaXEWEqNMtLt8HK5po8nhpi5Mvd3A3T+XRyYrwcqdl2YxKT2GpUV52N0+DBKkxETyx/8e03LuC7MTuTwnSfuO1IVd9Xs0SFLIXGn9jgrWL56J0+PTFaYbypE7/eLYKYqiSJJ0iaIozcBzkiRtBmIVRdnTH9cbSCrrm0n6/HdCnu/Yt2QAWiMYTpyp3EHX17OTrIxPjcHl0a/3trQoD68ss7QojxExkdS1dXCqyUm0NYK6Vhc/uGYcsRYTI2L8ipQAkgRP3DKVCKNEReeqtZ6zmZFg5WSTE5dHZkxylOY8qrH3FpOBJ26Zyn3XTtRq2VW1uBiTHEVNqwu3V+HX75Wy/n9n4vD4sJmNeHwy8yankZVgw1dSw8lGO4pCSBhoo72jW4fL7vaGDCIJNjNeWeaDo/XYzBG4fT6SoiIZnRTVo/qBgoHF6fRwoKaVtg4PkSYj374iF7dPZvO+Km0xYcaoeEpr20iOjuSRTQdYfft0JmfE8fu7LxkyK6+CgcHt9qEoCh0+mV/eMhVHh5f2Dq82uZwxKp7kaDMuj0JeiljsEfQdPXEo9N7z6ztm8IvNB0MWMBfPyeGuy7JxenyAv0TBXZdlE2ky8O0r/Hlvlc0O2jt8jIyz6DpcmQk27C4fPtnHqlun0tbhxWqO4MX/lnHF+FTcPpm8lGiiLCZtETYqMgKL2cBz75VSNDGNKLORpCgzS4vyaHKEr2GrlkkyGiA93kKT080nx5tItJm13UCLyaA5ZWmxFnwyfFLeGOQEd63x2zXyZ9m8iSGq2oEM5cid/gzF/FSSpIsVRflEUZTj/XgdgWBY0l25A3UlKsFm4s+LLqWkqgWj0cjhmjbNqQpUkcxNieGxzvDFRbNyyBnhlyr++T9LNKfwW3NyeSBgx+yRGyfT5vSgAJEmiSiLiZWbD4Y4jA9dn8+yDXu4bmoGFpOBymZHyHuK5+ZxsnPweGF7mbYbV9nkZM27pVpYpsPj47KxyXi9MvurWjhc00aby8tV41PYc6qFr6z7KCQMdP3imd1+j9mJUUGDiDqwLQqQT1bbs2zeRK6ZmHrG+oGCgcPt9vGvg7U89c4h3XxPtabT4jk5WCKMPLrpgH8Q11FRFQi64vXKbC6pptnuxu72BU0Gv3fVON7aXcmMrHiaHG4uzU4SfUrQp/TEodB7T127/i6YrEB6nBUDMsvmjScjwUq7y8u3//xpkN18a3clS67MIzvJSnmDUzuHxWTgSG2bJsKmhls2Odx876pxWCIMPLb5oFbLsauz5lfC9pGZYMMSYaCx3UVijIVHbpzC/W+cjhL62XX5/G1nRUh+YPHcPN7YVcmXLs7irsuyWbn5EC6PjNPj45LRSWGd4MDvSM23Xjwnh+mj4slOijrj4t5QjtzpT8fuUuBOSZLKATv+IuWKoigF/XhNgWBY0TVfDEJX67KTrCyfn8/BqlbMEf44+8c2H9TqvRXPzeOxgHw7o8F/jsCQi1svGqWFWECn2Mob+/xx+k0OJo6M1YRZAh3Gy8cms2zDbsobnNouodPt440A5UxF8Stn3jAtQ6tds3rrEVbdNk07pxqWaTMb8Xpl3thdGVT4/JEbJzMy1hpGYMXX7Xc4Jjl4Z/PWwsyQcBO1yPC9r+xiU/HsM9YPFAwMXq/MnlPN/Pi1PSyalRO2ptOoBBtxVhN07jhflJ0ofj9Bj9h7qpnS2naAEJn1p945zK/vnEFqTCSjE62iXp2gz+mJQ6H3HptZXxHSIEFVi5O4ztJFXR2wwPFv+Zv7eOq2aXwvwFFaPn8SbS4PS+bmsmHnSZ7e4lfMlvGLoTzWuUsYToQtUAlbddRe/bSUr14+JqieqAGFRbPGatcObJvqHOalxLBkbi5v7a5EQmJvZXNYJ7jrd1TV4mL1llJeXnxpjxyzoRy5059WSWSYCQT9wPEGOys3l7BoVg6REQbyUqJZsXF/kJrU0qI8EqPMVDY7tRpz6XEWbi3MZFJ6rOZggX8HKy1MCMahmjZe2O4XngiMT1dDLPNSorXVPXWX8K7LsllyZR7LA4z50qI8bCYjz20r085dWtuuOZtqCKfHJ7P/VIvm1Kmv3f/GPv686FJdQ5sae9rQhstNCHTUwhUxVwcmdQDt6lALBha328e/D9bg6qzHFG4ikZcSQ32bC6NBwmoykBIbKZw6QY9wubzUtnWQEWflRLNTt3/ZO7xkZMcRZY0coFYKhjM9cSj03tPi6AhRvXzwunyMKDQ4PFqx8XB2U33e5fGxeE4OEQYDk0bG8tx7R7g0ZwRGAzwwfxK/ea+UgzVtxEYayUywaSrX0ZFG3XZXNDlweWQtUmZUgo17rhrPN17cEfLeJwLKGwW2ratz+OB1+fz8Hwe4YkJKWCc43Pc4IrpnjtmZUmEGM/3m2CmKUt5f5xYILmQa7B1hQ9CqWlyamtSWkgq+fWUe4C9TYJAkTVkqsHjznZdmcaIxVFFQjXl3eeSwrxsDlDPV2Hinx0e0JYL7vjCBhKhIYq0RHKpq5bltZUFqWSajpBUoNUogyzKJUZEcrG7VNdZNTne3hvZMuQmqo1ZWp19EVemUQJaQKKtrF7t0gwi328dnlU1UNDpQazaCvpqrBEwcGYslwkCLy8OohME/EAsGB6X1bbR3+DjV4uyspRXav7ISrMRbB/+qvWBo0hOHQu89o0dE89PX9wZFyjz3n1KWfX4CiiTxjdl+hUnQ79fq+BcdGYHZaCA7KYoVG/eHzDWWz5+EIstYI038sFMhW11QVqOFAt+7Zmsp6XEWvjUnB4fHxw/+tptvzM4Jur46d7CdwTmEYAXN7soW6H1HS4vyONbQ3qO6pd2lwgx2RBxBDwinfAlC/VJw/jEbDbohaItm5fDapye5eUYmo5NsXJSVx0Nv+Xfyiotyg8KKAos3j4iO5FdbjugqCq7ZGr7w6M+uy+e5946ytCiPlz+p0HU246wy5Q124mzmILWslQumUNPaoYWEWEz+WjuZcVbq2zt0jXVspInC8YlhDW13uQmjk6K0nbz0OItuLaD1OypYWpTHPet30eRwDxkFrOGO2+2jtL4Nr0/B2Smss3z+JNZuC1VtffC6fCJNBpweL2mx0RRkJojfT9Aj7M4Ompweyhvs2MxGRsREhggu/OLmAiamidIGgv6jpw7F+NQYfn3nDKIiI0iNiaSqxUV5g1OLplE5WHM6P654bh6fHGsIUbdUx7/l8yfxzNYj1LW7ueeqPOYXZITMNVZsPMC6rxZqOerq80+941fPfOq2aTg9PiKM/nq25giJ718zAQPwWECppMAF4W/NyaHB4WZfZStP3T6NX/yzRItACpyHqKg7jN2VLTAYJCalx2jhnoqClhu4qYcCKHqpMEMB4dj1gHDKlyDULwX9i15oYbhQwhiLUTfx+KUPy5GV4PALNZn4iVumEmuNoMnhDipPYJCg1enRdtiaHG7sLo+WWzctM56EqAjmjE8BYNnnJ3Dvq/r17apaXaTHWvnV7dOwmoxYzAYOV7Vru4fq++97fS/J0ZGMTrbqhpSkxUd2W1MmXG5Co72Dg9VtQY7cmjum84/v+gdOm9lIm8uDy5sRVINnqChgDWdkWeGDY/X+QrlNTm1xIjvJyrJ5E6lqdrLuq4U0OzwkRJnx+Pz3xpikGN1SFgKBHrKscLzRQUVjcB9bPn8ST946FUmSSI42k58WLUobCPqd7hyKcJEp41NjdBdEfZ1/qmPy47dM5cm3D2qpHKOTo6hudmglj9xehbsuy+ZUsxOjQT9ss77drft8ZoINp8dHVYuTP31YwYhoM/dcNY7S2jYy4qzarmGgQ3bXZdkhxcqXz59EjCUCAxJJ0Sbd0k2KcrpswSuLZ9Jgd2MzR5AaezpEWs2r68pQEEA5FwwD3QCBQKCPasCvXb2dLz//Edeu3s7m/dWkxFi0UDQVi8nAyHib7k7ezTMyiY40UlyUy5K5/kd6nIUmh5vjDXYk0CSIn323lBe2l5EeZ+XlTypIj7NQXJTLIzdOZlRSFDEWIz4ZHtq4n2anX+Fy5eZDHKxp0zX0B6vbkGX47suf8dBbB9hZ0cSpZhfWSKPu+3eUN+Lxwsj4SJ64ZSorF0zhiVumkp1kYV9lW8h3IcuKdrwaU9/1ezEbDSE7eUv+8hkAl41NZuqoBIwGA6u3lIYUdK9tcyEYOMrq2og0GfD6FE42O/nG7BzS4yyUNzi595VdtLg8/PdoI15ZobSmjQfe3E9khJGsRBF+KegZsqywt7KR9g6ZmlZXUB9b8pfPOFTTRnSkkQlpUSKvTjAgyLJCWV07HxytZ29lCys3B9efu/eVXRgNsOq2aUFh6sVz83jt05PaeVweGZNBYn5BBpIEbp/MY5tKePSfh9hV0cz8ggx+eu1Ent5yhFd2nGRiWqzumGrsrAvX9fkjtW3c+8puntlaypK5udx7zTiqW1ys3VbGstf28sL2MhbOzAZg876qzrE9CpfHR4LNrLVxxcYDJNjMTM6Ixe2TefC6/KDP9eB1+Ww/XKs5gSebXfzfnz/l9rUfMu/p03ODcHOCoSCAci6IHTuBYJASLrRw89LZIaGEy+dPQkLRdZYiI/x5Y4ErYkuL8ogyG8lKsmExG8gfGcsTt0zF7vbSaHeTYIvg/+bkYDQag0RQ1IGiqsVFa2fpBEmCvJQYspOs2oAB8NbuStLirPzob7tJsJmDdhMDc/xU1NXFunYXs3NTON5gp9HegdlooKa1g0PVrSTYzFS1uILCLNWVt3C5CeFWFysa7UiSf6cvnKLYcB8ABjNOpweH24fTrXCkpoW8lBjKG+xaAv+eylayEmw88+4RFs7M5om3D/PoTVO4fIyQoBf0nMoWO81OH9UtLvJSYqhqdmhFnataXIwdEU2CzUysyKsTDAB6O3SBOfXQWVy81cW8/DTylsyitK6deKuJH7+2J2ixMjvJSnuHNygFQg3DVLF3+Ou+VrW4+M17pay4YXLIHGDttqM8fP1kHvh7sEDaix+Ua+1ZsfEAv7+7EKfHpwmsbNh5ktVbj7C0yJ/7/4OAHL3Az6SKFJXV2enwyWzYWRGyw3j//EmkxETik+GLz2wPyvM/WN1KRryV/PTYISuAci4Ix04gGKSECy1UDfj4786mrK6dDq/MiSYHzWGKfk5Kj2FPZUuQcX16yxF+cM04Gu1uSqo6gvJIHr1pCi9sL+PSnBFhZZHXvV9GtMVEaoyZ2nY3LY4OvnNlHg8EKlfNz6fZ4a+tc/OMzKDdxMAcv64DzIIZGYA/Jr66pYMjtW28suMkTQ53iPEPDKkIl5vwyfFG3e8FSeLa1du1sKtHbpwcVGLhQhgABitut49tZfUAyDKkxlmRJHh7fzWHa9tZPn8SLduOUtfeQXHROJKiTLyyeCaT0uOIiBCBKIKe4Xb7OFjVTodHBgUkCeJtJlwembsuy+bpLUdIijIzOkk4dYKBQW+BVx2H1Xy6wEXI8kY7Jxod/OKj8pC89598YSL3rA8911O3TaO21cWadw/yjdk52ni5p7KVBnuHlqcWZTbikxWumJBCjDWCe6/KIynaQlK0mZ+8tldT3755RiaREf5zvLmrMihfrs3lYWpmPF/7wydhP5PFZOBYvZ2Vmw9pc4PN+6qYNzmdHwU4g6tum8aIGLPm1AUuHq/dVsaq26ZxzcRUNg1BAZRzQTh2w5SS/fu4esFXdF/LSI7nD79dc55bJOgNsqzg9Slhd5EMBomxKdEYDfDPfdU8veUICTZziLP0y1sKaHZ6gnbrVOco1mLieIMjpFbTfQHKWnqOpbXToJZUtVKQGcdv/lPGrYWZrN12MFi5auN+fnnLVL9SYZdzqTl+v/3KReysaMInoxUIz0qwhV2h7Gr8u+6oGQyS5ozVtPpXKlN0RBCWz5/Ez/5+uqRCeYOTZzqLnTs9vgtmABiMOJ0eDte3kxRl5kitPTjXcn4+f/24nBUbD/DrO2cQbzVhkGBaVuJAN1swBDlc10Z9uzskn9cgwagEGz+/aQqTM2OIEbt1ggEi3AKvsXP9KnAR8niDnT0nW7QxPbDm7LiUGDo6S8V0PZfD7dXULLsKkvz14wq+fUUuv36vlNsLs1jzbmnQuLx662EeX1BAk8Md4lx13YlbsfEAj98ytVPdWL/kgsVk4N6rx/H7/3dce17NDfzR34Lz+O99ZRfrF8/EYjKELB6rr6tCKX2RUxeunNJgQzh2wxS3Ygwr+FL5r2fPc2sEveVYvZ3739wbovi0ckFB0C6S6iDd94UJZCdH4XT7+P3dF9Pm8rDvVCsRBgM/fGNPyMrY4jk5pMVZqGjSr9VkNEBkhEHXsZyQHsuSv3waZLgNkqR7nvIGe+frobLETQ43CVEmbpyWQW2biwUzMrTBqbsVStX46+2o6YWtrLljOnmp0UHFUEdER1Le4NRWF9XwUY9PZmZO8jn9dkPF+A9G7M4OSmrs1LZ1kBhl4rn/lIYsFjx+y1SK//oZHp/Myx8f51tXjBvgVguGIm63D0en0ENgNMNDb+3ntwsvwuWWGZdqE6UNBANKuHps4zqLdRskMEf4xxd/WoFRW5gFf5+uanGx8uYptHd4dc+VHB1Jgs2sjYUKCkuL8hgZb+VwTTuv7qjg0ZumhChhquNyTWsHP/z8eFqcnrCK3c92OoSltW1hyxSMT43hl7dM5dFNJSH57i63N4xT6mPVbdPClknqK6GUM5VTGkwIx04gGISUN9opb3AGKVUqCiRFmYKMSGqshYz4SEwRRr750k7N4Dxy42Qy4y1hV+jGjojmeL1dt1ZTdpKVy8cm0ebyhqhT+kse7A8y3Ot3VPDA/HxdQ104OoHyunasFlPIrtkTt07FZooIcYDCrVCqDt2E1BjWL57JlIz4EIOq5xQu+ctnbF46m5zkaC0cQ1H8n7NrqEpeSjQzZOWsDfVQMv6DDbfbxz8P1AaFw+rlkjjd/smJxWRgVl6qCJcVnBXHGtsor3foRjM0OzxkxFtJixNOnWBgCVeP7ecBzo/FZGDz0tnEWCJItJn51Tunx+zvXTUOCQWjUSI3JSpkTH/kxsmYjGihx4HXsJiMWrjnR8cadcfl7EQrTo+PCWkxNNr189lVJ1PNo3/t09AyBcVz83h0Uwm3FWbqqmCmxp12cNUFWaMBbOYI0mItJESZgqKP1ON6WpD8THRXTmmwKWwKx04gGIREdYp5VLW4guLo5+Rdor1HlhUUBZZeNZ7/fTF4Je3+N/bx1G3T8Cn64Zw2kxFbnJWEKDMPzJ/Ew5316bKTrHxrTi53/94f/16YHce6rxZS09pBamwkx+raKW9wBrV1fkEGD2/cr1sHDwUqmjtY934JCTaz5qTGRvpj9eeveT/EAQq3QmmQ/OqdFrNB16mD7vMSZ+YkawZYlhVW3DCFxS8Ff2/LNuxhSkbcWRvqoWT8BxOyrLD3VIvm1EH4XJIocwSP3DiZ6iYnY1NjhMMs6DV2ZwctTp8mCgHB0QwjYiLJTbUJwRTBgNM1d1xC4p71u4J2tBJsZj6taOZ4gz0kteKpdw6zeE4Oj766B4vJwMPXT+Kp26ZRUt2KT4Znth7hZ9fna06detzTW47wzJem850rczWBNL1x+WSzU6uTt+KGyWQnWYPmCGppgq4Ldeqi9bjUaI7WtfPSh/4ac6mxkSHO59KiPJ781yHdmrlrt5VpYnBdF4+XFuVpIavnSri5xWAsnSAcO8E5Ea54u8jj6x1dw/fS4kLzwpYW5Wk1WgJ3hr4xO0fX4JRUt/LewdqQYqQPXpdPk72DB9467cz9+s4ZHDjVSlZSlBbHnh5nYe6ENC38wmIy8PObpoQYbqMB3d3FNpeHunY3RgNBYR5Wk4FRiVF8r5ti4l1XKB+9aQpjk6OIs5nISgwf2hjOKdTLxTMZ9cNHz8VQDyXjP1iQZYVdJxtosHvOmEvy8A2TSbBFsHLzUQ7XtrOpePYAtFgwlJFlhV2VrbQ69UO7xo6IJiM+QoRgCgYNgXXtyuraQ3a0bi3M5L7X94adC6hVgVwemZPNLt7cdVRTsL5uagZHqvXLFdndpxU0s5OsPHR9Pg/+PdjhClTCXP6mf0H5e10Uu+OtJp6/q5D739irOaRVLS7WvV/Gkitz8cn+z5CbEsOTbx/kFzcXaHVmAXadaO62Zu7TW47wy1um8pv/lATNQV78oJzpWfF9UtO0p3OLwYBw7ATnRLji7SKPr+eEC9+bNDImKC8sLzVaq8/VdWcoXOmAPZWt8HE5675ayMkmJ1ZzBM32Dh7652mhk/IGJ9/+86csmpXD4YB6dHrJyD99fS+/un2apqxlMRmYmhmvu7u4eE4OCTYTl+UkYjUZg5zUR26c3K0DdM3EVNYuLGRHeSM+GZ7ecphl8yaSPzKu2/y1cCUP9ML1+sNQDyXjP1g41WqntMZJZbND97vLTYnhmS9PJyrSSFSkkdt/+5FQLRWcNUfr2vH4ZGxmo25/GxFtxusTu8CCwYneGDcuJQaXxy9sptenLQFKwTazMSQFYfn8Sbo7bWX19qB5wq/fK+X5uwppdniQICgcFPxj+JHadhbPySEjzkpFk5M1W0tpcri57wsTWDxnLCs6o4P0Qu1/cM047r16PBdnJxIRYWBsit+RDdxNXDI3N0yunVerxRv4Gfpq7O3N3GKgEY7dBUg4xczjRw8zemyoEEG45wEOHjrC5z7f5028oAgXvveP785mVEKUrkxv4M5QVxUr1XF6ZusRwO/cNdo9/Pq9UuYXZJCVYA27MxKY1BxOFbO9w8svb5nKsXo7HV6Zp9853Jl7dyBooBiVYOXFD8r4yswxIWEeJxr1J/ESEmV17SgKQWGSAPe+sou1Cwu15/Xy18KVPNDb4esPQz2UjP9gwO7s4FRTBw/8fR8JNnNIPyqem8eTbx/ksZumsO9UK58bm8TLiy8VqqWCs6a80Y7VZGR/ZYtuVITNHMGY5NiBbqZAoIveGKeGOhokSbdPB5IZb9Pqx8HpmnNr7pgRJIq2fP4k1mwtDTq2vMGJy+Pj6vEp7K1q0c2F6/DKPPuuv0B5oJNVb3eTZDOz5MpcUmIiOdnsDHLqLCYDkzPiuHxMEhVNDm3xNivBFjSm6ukCWEwG6to6QuZBfTn29mZuMdAIx+4CJJxi5u4nl/TqeYCOfUv6vH0XGuHC9+rag/PCAgncGVLj1RfPySEvJQYF2Lj7BPcUjeMnr+/F5ZF5a/cJvnNFHg/8fV9QnRqV7CQrl+UksbO8iWfvmMHDG/cD+gb0WL2Dde+XUTw3j79+erJT5jiSl75+MVUtHZTWtWurdMVz89hzsiXk872y42TIJH5pUR73rN9Fk8PNE7dM1f1OdpQ3Bg1IevlratiKqrD50bEG3d29/jDUQ8n4DzR2Zwe7KltpaHfzjdk5bNh5kvUfV2gKZ2oJjO/OHYdXlskZEcXE9DjxXQrOmhani+jICOraOvDICiOizEFRERkJVsaNEPerYHATGJoJ/qgf1W6+uuNkSDjirYWZgBrJoy+o5vb6WHXrVOxuH8cbHLS5PFoJAzWNwijBsbp2JAkSbWYevj6fBwJCM9UdODWvTsViMlCYncCyDXtpcrh5/JYC0uOsmmOoirw8+Pd9QbnvqnMWWIsuLdbC+LTYEDGZFz8oxxwhseq2aZTVtTM7LzlsLn5ffe+DFeHYCQQDzNmE72Ul2LRQRVmBt3ZXYjUZeXRTCU0Ot2YMp46Kp7bNhclg4M51H+HyhNapyU6y8u0rcvnxa3uYX5DB7pPN/Oy6yTS0O3V3UF76sDxIaCA7yYbPB9Wtbo7WtfPqjpPaKtzqrUe0WnaBn6/J4abV6QmqsRMY1nGkti1seGkg4fLXeqpO2R+GeqgY/4HE5fLyUXkzu040a/134cxsXvrQX6Pu1sJMJqTFUJidjykCMuKiyBYOsuAc8Hpl/nO4gdLadmTFP0nt8ClYTUbsbh8Ao5Ns2KzmAW6pQNA71AXFjHgra7eVhYQjFmYncu/V4zBIkBwdqTu2JtjMfO0Pn/CN2Tmse7+McSnRrLljOjWtHUFzgB9cM55TTU6W/OUzEmz+hZGxI6I51ezkxQ/8AiiP3TSF6lYXS+bmYpRgdHIUFQ12vndVHunxVkpOtSCDFvnjlWVkRaG8wam7eKuqYKtjalZilLZ4mhwVSV17B06PD58MKzeXsGzexD536oYSg8qxkyTpd8B8oFZRlMmdzyUC64HRwHHgNkVRmgaqjQJBX9Pb8D1ZVni7pCbo/SsXFDBtVBzTs+KDdonUnat/7K3SjGWgItXoJBupsRaWv7k3JO7+0ZumkGwx8NuFF9Hs8HC4pj1Een5schTVrR389HV9iXqXR6bF0cFjN03Rdg8DV9iaHG4evWlKSKz+KztO8uhNU7gv4JiVCwpY9e9DQd9FOAdYqFMOXlwuL5sOVAf9tsVz81i/o4KbZ2Sy7v0yMuNtjIyPpLS6jZzUGMaI30xwjpRUt1DZ5Awqb6CGqb2wvYwVN0xmUlrcALdSIDg7DAaJKRlxIXOJn980hdVbDrGjvIXsJCu5KdG6pQYijBKbimdzqtnBmOTJ1LZ2BBU7B/84+sTbh1g8J0eLFlIVMR+/ZSoLLsokymykwe4OCge97wsT+M1/yvjq5dkArHon+Np//fgkCy7KDLt4u+VgLZXNLm1htuviac6IaFJjLUH1cC9Upw4GmWMH/AFYA7wY8NyPgS2KovxCkqQfd/69bADaJhD0C70N39NzWpZt2MOm4tm6xbWPN9gp7bIDpipSPX7LVHaUNzK/ICNEKOW+1/eyeE4Oq7eUsrQoV1PHUrGYDEgGA0+9czjouECJ+uwkK1aziV9tOaztzl0yJpGMOKvmhBokQmL1mxxuZmTFa+EXKTH+WHuT0dAjB1ioUw5O3G4fH5Q3ak4dBPeZCWkx/P7uiznRaGfBbz7EYjII5UvBOSPLCk0Or66k+28XXsSLX7+EyWkxRET0kTa6QDAA6M0lshJsTO+M3LGajHxwtIH1OyqCwjXX76jg8/lp2kLwf8vq+enr+86oshn4XGltG2u2lvKdK3NZ937wffboPw+ytChPN79PjfwBeOymAv780bGgc6vOXncLsyJKJphB5dgpirJNkqTRXZ6+Abii8/9/BN5DOHaCYUZvDFNvnZaaVhev7AgVWHn0psnEWiLIS4nhSK2+3LFqwF/ZcTIkKft7V43D7fXpHqcWE//Z9ZP5vz/5C6cHKmYGOqFqfkBXh00taxD4mXrqAIcLb1XFWS70Fb3zjSwrnGiyU93SQUN7R1jxnjhrBEdq2vjNf8qE8IygzzhW34q9Q7+8gaPDx9RRsSIEUzCk6FoiSR3T9OYS6t+fHG8gOymK787N40Sjg1d2+HPkA+u9GQwSRul0KaBwNWUDsZgMXJSd4B9jw4iuZSdFBeX3BebuXZSVQHWzg19tOcR35+ZR136E8gZnSPqHWJjtGYPKsQtDqqIoVZ3/rwZS9d4kSdJiYDFAVlbWeWqaQNAz+qp/yrKC16dfdDwt1kJZXXuIoU+NtdDkcAfVmYuNNKIg8a0/7STBZuaB6yadUSbZJyusuWM6ZqOBdpeXKEsEZXXtusddPDqB2bmX4PMpZ3RCe7Nj2VMHWC+8NVCcRS/f7kKmP+2nLCt8eKyODq+MyWBkRJgcj4lpsVjNhs5woqm6gjeCC49z7ZvtThcl1XYSbCb98gYxkaJmneCsGYi5Z09zyLsec6rZxbINewLCNCdjM0VwqtlJdYuLA1WtpMZYSIvx2+hwits+n6zdS+rY2mh3s/pL03EHvKZiMRlAUTAZDVo+38KZ2SHhoG6vwv1v7ONPiy5l25E6fDJaWkdvSheEc3ovFIZU3IGiKAqghHltraIohYqiFI4YMeI8t0wg6J6+6p/HG+zc/+Zeiufm+Y0lfqO56tap7D/VxrWrt/Pl5z/i2tXb2by/GllWNCdHrfHywvYyclJi+GlnOFxVi4vfvFvK8vmTgs65tCiPmMgICjJiWTgzm1d3nuBwdTuLX9rJt//yGQ+8uY94q5mlRcFtefC6fH68YS9f/f3HRFkitNdU9Ay06rCpKqDnaoRVZ3FT8Wz+tOhinrhlKh6fwoKLMkmwmbn3lV0cb7Cf0zWGE/1pP08224k0RtDi8PHJ8UaefPtQSJ9ZPn8SBkmh0e5hSkY8l43tm34gGPqcS9+UZYX91e0YJYnPKpr51e3TyE6yAv5+t+KGycTbhtQ0SDDIGIi5Z7gc8u7GtOMNds2pS4+zsOTKXBQFyhvsRBgk7v7DJ3z7z59x57qP2FHRzDNfnq4tCC+ek8MTtxbwzJem8/LH5cRHRbJ4Tg5L5uayaFYOL35Qzk9e24sENLZ36M4J6ts7+PW7pTw4P59bC0Nr5K7eeoSbZ2Ti8sh4fDIT0mJZ936Z5tT1NHpDdXr15kIXCkNhx65GkqR0RVGqJElKB2oHukGCMxOuVh5ARnI8f/jtmvPcouFBTauL8gZn0O6booBBkrgnwNAn2MwcrG7FYjIwOimKayamsnHJLPZXtXK0rp1mhztoRW1PZSt1W0t54papHKxp02SS/dLEU/nR33azaFZOkDGeX5DBT17fS4LNrLXFIEFilFkTQqlpcwWFcGYnWfnZdfn+WnWywph+nLwbDBKjk6LYdaI5RKjjpQ/LRVjHecDrlfnkWDM/feP09/+9q8bxjz2ntPIcAJERBlJiIpk8Mk7kOQn6jIqGdk42ubRFLIvJwAPzJ9Hu8jB1VAKSJDM6UdSsEwwtziaHXD0mPc4Sslu2tCiPBJtZEzxb/qZ/10wtBeKT4cm3D2OOkPj+NRNwun3Iir+GbqDoWUl1GzGRRqaOiuO5r1xEbauL2rYOnvtPKd+YlcM1+Wm0OD1MSo/tNoUjNdbCpWOSzqps0LkKpw2H3b6h4Nj9Hfgq8IvOf98c2OYIekK4WnkAlf969jy3Zvig5o5Vtbi0nLXsJCsT0mKCYte7Gm41TCPCKHG0rp2UmNBwuCaHm5LqtiCpZABFVjSjG/h+9e/AtgDce7W/mL3FZGBfZQuv7jjJD64ZR0a8jUaHm//78+kiqE/eOo0vTO6/kMjjDXZdoY7Fc3J6HNYhOHsOVLdoTh34v/+n3jnMkitzeeLtwyyZm8sL28tYNCuHWblJwqkT9BmyrFDe5NScOvD3v4c3HmDJlbk02t3kpVhFnxMMOXpaIinQSYmK9EfP3DwjdLfs6S2nBc/U51TFS5X0OAu3F2bxo07xk64K2Gpx8l+/V8pvF17Etzpz69Vj2zu8rHm3FJdHZmlRbtjcPXVn7mwFUc5FOO1sQlz7g3N1LgeVRZMk6a/AB8B4SZJOSpK0CL9Dd7UkSUeAqzr/FgguSNSwSjXMITvJyrfm5Gqql4Cu4b73lV18crwRnwwFmXGs3nKYh67PDwqXeOTGyWzcUxl0PYvJQGqcJeh9XV/v+neH93S5BLWmXUqslZLqVq0ejtqu77/avyGR4Yz8uNQYIcrRj8iyQlldO6ea9b9/Nc/OIEHx3Dw27qkkNVY42oK+41i9neoW/f6XEhNJcrSZ0Ulit04w9BidFMWaO6ZTXJTLkrm5LC3KZc0d04PGtK4hiR8erddEUsLtlqn4c/Yjg8Z3vXmFGj6pliO6cnwym5fODsmtv3lGZpAirSrGFjivWHHDZG6alnHOTpTq9AbS0/y8swlx7Wv6IpR0UDl2iqJ8WVGUdEVRTIqiZCqKsk5RlAZFUYoURclTFOUqRVEaB7qdAsFAoeaOrV88k+KiXL5/zQQe2rhfU73sTpVqe2k9X3xmO44OH0/cOo3xqdGsXVjIM1+ezlO3TePt/af47tzQ3KffvV/Kz2+awlu7K4Ny+97aXcnD108Oer9q3DcVz2ZGVrxWxsDZ4UVW9NtV2+aivwhn5CemxQ658IqhgiwrbD1Uwxu7KrGZjbrff1RkBA9fn4/VZGT9jgqWzZsoHG1Bn1LeYMdmDpPjG2thRLRZ7NYJhixur8LabWWs2VrKb7eV4fYGT/y7OimtHT5e/KCcvJQY3XtCHQ4tJgMP3zCZKelxQYvI4RzCiekxrF88ky9MSiM5OpJPK5rZfbI56Bpd5yRVLS5e/KCcX98xg8cXTOH3d1/MDQUj+yQ1o+vid2/y87rb7Ttf9IVzORRCMQUCQQD+QqTxVDa7OFjdqoVNqHl341NjdMMclE7H6sl/H6K4aFxQ3sny+ZP42ufGMj0jntFJURyta8dmjuD5bUfZU9lKo93DQ9dP5nB1K4/fMpWKBjs5I6J59ZMKrT5d0YQUpmTEa4Y5sIyBLTICo6QvndyfIZHhir+PSRZORH9xosmOp3OSYTUbQ8pkLC3KwxxhwBRhJs5q4vd3XzIk8xgEgxdZVogwSlQ1O3T7X7zVRFaSyK8VDE16kkem56Q0Odw8uqkkROnywevySYoy88yXpzEy3sqU9DjMZmOQWrXVFBFUrBz843dJVRs/eHU3j9w4mbq2Dp7ecoQEmznoGtGdC3xdUz92VjTz7LulWgkkvVDJ3oYl9rYucCA9DXHtT/qiBq9w7ASCIUSgkRufGkNmgpW128pIsJm1mjCVTQ5+ftNkfvr6vpBYePCLnnTNO1mx8QD3XpVHbVtHkBxy8dw86trd7ChvITrSyFWT0jhc04az85iqFhfvHq4H4PKxSUHG02CQuGZiKusXz6TV5WZsSnTIJOvJW/u3Ttm5GHlB73E43ZTVOyipbkVW4NVPypk1LlVLwjdIEGU2UtXs5Hf/Pcbv775ECNgI+pzy+naMBon0eCsenxzU/0Yl2kiKMQkbIBiy9GTy39VJ2bDzdC1aVekyK9FGdYuL1VuOaHlym4pnYzYbgeDyQl6vzMoFBSHzA7XG3P1v7OOh6/JDFppjLEZyRkTz1G3TtHHhrd2VLJw5Go9PZsncXAAa7R0hY0Fvc966OoGXjE7q1X0ebiH4fEaT9IVzKRw7gWCIoGfk1twxnTV3TOdITXuQw/Tzm6Zw71V5JERFcqLJoSU4Q/iQivR4Gz/sTIxWn1u91Z9Uve79MhKjIhmdFEVlk5N174eu3Oklbr9dUqO1tzA7jmVfmMjzdxXi8vgYkxR1XiTtzzYJW9A7nE4Pmw7Ucv8bwQqk7x+u4ZbCbOrbO5BlONHk4OVPKrj36vEi/FLQ57hcXj49cVq0JzvJyvL5+bQ43IyIiSQp2kxGnOh3gqFLTyb/XZ2UJoebvNRo/vHd2dS1u5CQuGf9riBVy3A7Q+pYvurfh7QInQlpsTz3Xql2vMsjY+sUaAkUVbvvC+M5Whs8P1k+fxKZCRa++dJpIbW8lGhmyErQfKA3Cpd9IXwyGBaC+8K5FAHmAsEQQc/ILfnLZ6RERwYlJrs8Mj99fS+X5iSRkxyF1WTUct0sJgMT02N1Y+wdbq+uw2c0nFaqCldHb+WCghDD07W9O8pbWLjuYzLirVw9KY3c1Bixaj6M2Fvdqjl1cHphYERsFO+X1nOy0UFJdRtOj48VN0zhi5PTxe8v6HP2VgUrsZY3OFnyl08xRRhx+2QkEP1OMKTpSR5ZYC3Xlxdfyqbi2cwdn8rYFH+92BExkdq8QCXczpA6lpc3OHn23VJWbynl3ld2MXtcStCxJ5scIXODCemxIfOTFRsP0Ob0BT23bMOekDyy3uS89ZXwSV/X1O0ter9bbwVlxI6dQDBECGfkToVRfnN6fFo9usCad795t5RHbpzM/W+cDtVcWpRHUpRZdxUwMHcuXB29jHhLiOFpsHdo74HTNW9E/bjhhywr1LR2hF0YyE+Po7yhnXGp0eSnx4ri44J+I1w/LK1t47KcRMalCCVMwdCmpztL3UWrhNsZMkjwwdH6oHy2cHOP7EQrS+bm8tbuSr41J5fntpXi9iosnpND7oho8kfGUd5g1z3W3uENea7r3KA3YYl9kZs2WDjXKCPh2AVw9zeXUFnfHPL8wUNH+Nznz397BIJAwhm59Dhrt8avyeEOqjNnMRmYMSqeF75ayKkmJ1ZzBFXNDkwRUojD98iNk8lPj9MGDL06ehaTgQUzMoLaKssKp5pdWsimGpa3fkeFqB83zFBVMEfE6C8MTM9KAEVmZk4Sk9JFAXJB/+H1yiRG6/fDqZnxpMRYRP8TDAvOdfLf1TkcEW3hWEM7857eHhLKGG7uUd7oT8tYPn8Sb++vYn5BhraQW5AZx+jkaO29XY9t7MFuYW/CEgeD8MlgQVi4ACrrm0n6/HdCHh0e75kPFgj6mXDhF/npsWHDMsKGbCRHc3lOMoWjExkRY+aqSWlkJ0bxTGdO3ZK5uSyalcMzW49Q0eQ4Yxv0wjDVJGs4HZa34oYpIq9qmHG8wc6eky387v2jOrURpzA22cr/jEulYFSCmFQL+pX9VS2sevsgD14XWqPTapIYkzy0Vu4Fgv4kMOxQkmDJXz7TDWXUG/eL5+bx2qcntdDKKaPiefbdUl7YXsaEtFiyEv3j/Jjk0GOfvHUaeanRZ5xH9CYs8VzKHAw3xI6dQDBE6C78oruwjO5eC1zx++BovRZDH0hgKENPQ0DChUWYjFKvQ/C6Kl1lxlkpqWmlqsVFepyV/PRY4TCcZ9xuH3tOtVDd6iIlJpLMBAurt9Tj8cJvF15Es8NDvM1Egs1EVlLMQDdXcAEgywour49vX5GHw+Pjj1+7GHuHF6PRQLTZyPSsRBH+Kzjv9Fauf6Dao47Z6XEWTWEbTqtVquP+4Zo29la2BgmyuTwy00fF8/LiS0PmBOHmDACbeiBS0tOdycEgfDJYEI6dQDCECGfkujN+PTWMPQ1l6Mn5wp0rNbZ3YRF6SlcP3zCZZ989QnmDU1uNv3FqhnDuzhNut4839pzigTdPh+w+fH0+10xK5u0D9Vr5C1U6WyDob9Rw4K7qwD+7Lp+/7axg+fz8C3KCJxhY+kKp8Xy1JzXWQnaSldsLs4Jq3AWqVapj/j3rd4WM7dmdKtd6hJsz9LVatVDA9iNmQgKBAOjbUIa+Opee0tUDb+5jfkGG9vf9b+xjf1VLr9soODv2nGrRnDro/E3+vp+vfW6sCIMRDAhqOHBX9b2fvbWf7109nvz0uAFuoeBCpK+UGs9He0YnRbHihimaU6e+3lWtUoQ8Dn7Ejp1AIAD6NpShr84VLqRTkoL/rm5xMXVUr5spOAuqw/wmDXY3f/zaJTjcXrISoxiTfGGGwQjOPzWtLmRFvz5nm8srdvMFA8JgU2o8U3tMRumM7RUhj4Mf4dgJBAKNvgxl6ItzhQvpVBSC/k6Lu/CUrwaK9DC/SVqshcLRiQPYMsGFSmqsBaOkr76XLmyDYIAYbEqNZ2pPX6ZjCAYOsYwlEAwjZFmhrK6dD47WU1bXjiwrZz5oEKMX9vHwDZPZuKdS+1stySDoPwL71YhYMw/fMDnkNykYKX4DwcCQlWBjUnosS4uCiyML2yAYSAZb2OKZ2jPY2hvIcJvb9Cdix05w3inZv4+rF3wl5PmM5Hj+8Ns1A9Ci4cFgS9TuC/TCPjLjrIxPjaa6xUVanIV8URutX9HrV7+7+yL+tOhSTVmtYGQcZrNxoJsquACRZYW3S2pYubmEuy8bza/vmEGHV2ZkvLANgoFlsIUtnqk9g629KsNxbtOfCMdOcN5xK0aSPv+dkOcr//XsALRm+BAuMXpC8ewhHTKhF/YxdVSCyKk7T+j1q6//YSebimeL0EvBgBPYPx/aWAKcVmQVTp1goBlsYYtnas9gay8M37lNfyGsnkAwTOguMVogOFtEvxIMZkT/FAiGN+Ie7x0X3I7d3d9cQmV9s+5rBw8d4XOfP7/tEZxGhGieG4MtUVswPBD9SjCYEf1TIBjeiHu8d1xwjl1lfbNuGCBAx74l57k1gkBEiOa5oSY+d41DHwyJz4Khi+hXgsGM6J8CwfBG3OO944Jz7ASCcITbzR0qO4aDNfFZMLQR/UowmBH9UyAY3oh7vHcIx04g6CTcbu5Q2jEcjInPgqGP6FeCwYzonwLB8Ebc4z1HOHaCQU+43DuA40cPM3rsuJDnh8oum0AgEAgEAoFA0BcIx04w6AmXewew+8kluq+9s+r/wjqDwukTCAQCgUAgEAw3hGMnGJZ05wwOpdBKgUAgEAgEAoGgJ0iKogx0G/ocSZLqgPLzeMlkoP48Xk+0oXvOZzvqFUWZ15sDetg/B8t3eTYM5bbD8Gp/f/XP7q45VBnqn2Eotr9X/XMAxvaBZCj+nufCYPy859o/B/ozDeT1L+TPfj6uH7ZvDkvH7nwjSdIORVEKRRsGvg2DqR3nwlD+DEO57SDaP1Su2dcM9c8w1NsvCOZC+z2H4+cd6M80kNe/kD/7QF/fMBAXFQgEAoFAIBAIBAJB3yEcO4FAIBAIBAKBQCAY4gjHrm9YO9ANQLQhkMHSjnNhKH+Godx2EO0fKtfsa4b6Zxjq7RcEc6H9nsPx8w70ZxrI61/In31Ary9y7AQCgUAgEAgEAoFgiCN27AQCgUAgEAgEAoFgiCMcO4FAIBAIBAKBQCAY4gjHTiAQCAQCgUAgEAiGOIPOsZMkyShJ0meSJG3s/HuMJEkfSZJUKknSekmSzGc6x7x58xRAPMTjfDx6jeif4nEeH71G9E/xOI+PXiH6pnic50evEP1TPM7jIyyDzrEDlgIlAX+vBJ5SFCUXaAIWnekE9fUDWWxeIOge0T8FgxnRPwWDFdE3BYMZ0T8Fg4FB5dhJkpQJfBF4ofNvCZgL/K3zLX8EbhyQxgkEAoFAIBAIBALBIGVQOXbAr4AfAXLn30lAs6Io3s6/TwIZegdKkrRYkqQdkiTtqKur6/eGCgS9QfRPwWBG9E/BYEX0TcFgRvRPwWBj0Dh2kiTNB2oVRdl5NscrirJWUZRCRVEKR4wY0cetG77IskJZXTsfHK2nrK4dWe42dFdwloj+KRjMiP7ZO4TdPH8M574p+tHQZzj3z94g+vLgIWKgGxDA54DrJUm6FrAAscDTQLwkSRGdu3aZQOUAtnFYIcsKm/dXc+8ru3B5ZCwmA6tum8a8/DQMBmmgmycQCASDDmE3BX2B6EeC4YLoy4OLQbNjpyjKTxRFyVQUZTTwJWCr8v/Z++74KMr8//fMlmzJJtlsKgmbELIhhRRCQPSEE2JBLxakeSqW0+Pue2JycnqcBTnFhgUPxHIo1t/dCR5WVE4FPfSsEekJJIQkJqT3bC/z+2Mzk52dmTRSNuF5v16+JFtmZmc+z/t5nk95fxjmOgCfA1jS87EbAbw3Rpc44VDZYuYGIgDYnB6s3nEAlS3mMb4yAgICgsAE4U2C4QCxI4KJAmLLgYWA2dj1gTUAVlMUVQ5vzd22Mb6eCYOGThs3EFnYnB40dtnG6IoICAgIAhuENwmGA8SOCCYKiC0HFgIpFZMDwzBfAPii598VAGaP5fVMVESHqKBS0LwBqVLQiNKpxvCqCAgICAIXhDcJhgPEjggmCogtBxbGQ8SOYISQaNBi47IcqBReM2DzohMN2jG+MgICAoLABOFNguEAsSOCiQJiy4GFgIzYEYwOaJrCwowYpBbORWOXDVE6FRINWlLsSkBAQCABwpsEwwFiRwQTBcSWAwtkY3eWg6YpJEUGIykyeKwvhYCAgGBcgPAmwXCA2BHBRAGx5cABScUkICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnEOsrEjICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnEOsrEjICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnEOsrEjICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnEOsrEjICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnEOsrEjICAgICAgICAgICAY5yAbOwICAgICAgICAgICgnGOgNrYURSloijqe4qiDlIUdZSiqAd6Xn+VoqhTFEUd6PkvZ4wvlYCAgICAgICAgICAIGAgH+sL8IMdwAKGYbopilIA+IqiqI973ruLYZh/j+G1ERAQEBAQEBAQEBAQBCQCamPHMAwDoLvnT0XPf8zYXREBAQEBAQEBAQEBAUHgI6BSMQGAoigZRVEHADQC+JRhmO963nqYoqhDFEU9TVFU0NhdIQEBAQEBAQEBAQEBQWAh4DZ2DMO4GYbJARAPYDZFUdMB3A0gFcAsAOEA1vh/j6KolRRFFVMUVdzU1DSal0xA0C+IfRIEMoh9EgQqiG0SBDKIfRIEGgJuY8eCYZh2AJ8DWMgwTB3jhR3AKwBmi3x+K8MweQzD5EVGRo7y1RIQ9A1inwSBDGKfBIEKYpsEgQxinwSBhoDa2FEUFUlRVFjPv9UALgJQSlFUbM9rFICrABwZq2skICAgICAgICAgICAINASUeAqAWACvURQlg3fTuYNhmF0URe2lKCoSAAXgAIDfj+E1DhgeD4PKFjMaOm2IDlEh0aAFTVNjfVkEBAQEAQnCmQSjCWJvBBMdxMbPPgTUxo5hmEMAZoi8vmAMLueM4PEw2H20Hqt3HIDN6YFKQWPjshwszIgZ0UFFBjEBAcF4xFhxpv81EP48OzBS9kZsiCBQEAicKnVdZIyMHAIqFXMiobLFzA0mALA5PVi94wAqW8wjdk52EF+2+Uv8+sXvcNnmL7H7aD08HtIxgoCAILAxFpzpC8KfZxdGwt6IDREEEsaaU8VAxsjIg2zsRggNnTZuMLGwOT1o7LKN2DkDcRATEBAQDARjwZm+IPx5dmEk7I3YEEEgYaw5VQxkjIw8yMZuhBAdooJKwb+9KgWNKJ1qxM4ZiIOYgICAYCAYC870BeHPswsjYW/EhggCCWPNqWIgY2TkEVA1dhMJiQYtNi7LEeQ2Jxq0I3ZOdhD7DpqxHsQEEwc3/W4VapvbBa/HRYTh1b9vGf0LIphQGAvO9AXhz7MLI2FvxIYIAgljzaliIGNk5EE2diMEmqawMCMGqYVz0dhlQ5Ru5AtEA3EQE0wc1Da3w3DJbcLX//PsGFwNwUTDWHCmLwh/nl0YCXsjNkQQSBhrThUDGSMjD7KxG0HQNIWkyGAkRQaP2vlGaxATVSMCAoLhxmhzpv+5R5o/CW8GFobb3sZqIU3sikAK/dn4aNtOIG42JxrIxm6CYTQWRoEqoUtAQEBwJhhJ/iS8eXZgtJ0TxK4Ihoqxsp2xdOCdDSDiKQSDBlE1IiAgIBgcCG8SjASIXREMFcR2JibIxo5g0CCqRgQEBASDA+FNgpEAsSuCoYLYzsQE2dgRDBqBKKFLQEBAEMggvEkwEiB2RTBUENuZmCAbO4JBg1U1YgmBqBoREBAQ9A3CmwQjAWJXBEMFsZ2JCSKeQjBoDETViKh0ERAQEPSiP94knEkwFAxFZZDYGgFwZgqVxIYCF2RjRzAk9KVqRFS6CAgICISQ4k3CmQRngsGoDBJbI/DFUBQqiQ0FNkgqZgDC42FQ0dSNb042o6KpGx4PM9aXNCgQpSUCAoLRxnjmTcKZgY3xbFv+ILZGMFSw4+CLE404Xt8JvUYJgNhQoIFE7AIMQ/GEBFpIvC+lJdK3hICAYLgxXB7kseJSwpmBi8HYVqDNxWIgtkYwFIiNg8IFJrzxbRXqOmwDsqHxMD4mAkZkY0dR1HkAEn2PzzDM6yNxrokGKW9aauHccZP2yCot+U4eRGmJgIBgpDBY3hTDWHIp4czAxUBtKxDnYjEQWyMYCsTGwea9Zbjl/CQ8+3l5vzY0XsbHRMCwp2JSFPUGgCcBnA9gVs9/eQP8roqiqO8pijpIUdRRiqIe6Hl9CkVR31EUVU5R1HaKopTDfd2BgsH2FQnEtAqitERAQDCaGI5+TGPJpYQzAxcDta1AnIvFQGyNYCiQGgcUNTAbGi/jYyJgJCJ2eQDSGYYZShK6HcAChmG6KYpSAPiKoqiPAawG8DTDMG9SFPUCgFsAPD98lzz6kApJD9abFohpFWeitERAQEAghr7SeIYjCjGWXEo4M3Dgb2exoQOzrUCci8VAbO3swXCmPkpx7NzkCFw9I67fY4+X8TERMBIbuyMAYgDUDfaLPZvB7p4/FT3/MQAWALi25/XXAPwV43hj11dImvWm+b8n5QkJ1LSKoSgtERAQEIihvzSewfDmcDnVhhuEM8ceUna25doZWPXPn/q0rbG2n8GA2NrEx0BSHwez8ZPi2FmJ4QPaLI6n8THeMRIbuwgAxyiK+h7eCBwAgGGYKwbyZYqiZAB+BJAM4FkAJwG0Mwzj6vlIDYC4Yb3iUUZ/OfuD8aYZ9Ro8dNV03PfuEW6wPXTVdBj1mtH8SQQEBAQjhv44c6BRiOF0qhFMPEjZ2Ye3z8VH/dgWmYsJAgn9ceZga97ONNJL+HX0MBIbu7+eyZcZhnEDyKEoKgzAOwBSB/I9iqJWAlgJAEaj8UwuYdjg6w2J0qkgo4G6DhusTjfPaxEbqsLVufE40dAFwDsABupNq26z4JmeAlaKAhgGeGZvGXKN+jHzxhHlIyEC0T7FcNPvVqG2uV30vdLjZfjFJaN7PQSjg0CxT3/uMOo1qG6z4ERDV7+cOZAoxHA61UYDhEtH1jZ9769GKUeLuTddjLUxigKazXbMSgjv07YCZS4mNjO6CBTuZME+/xMNXbh1bhJ2/liDug5vPahv6mNlixkbdpdw9goAG3aXIDVGJ2mvbHYE4E2tBDBg+xqOFGBi2wPDsG/sGIb57zAdp52iqM8BnAsgjKIoeU/ULh5ArcjntwLYCgB5eXlj3mRGzBtSlG/C699UYVlePBeSjg1VYcWcBGzeWzYkpaCGThuqWqx49vNy3utjlbdMlI/EEWj2KYXa5nYYLrlN9D37kVWjfDUEo4VAsE8x7njoqul4Zm8ZLs+OGxbO7K/OI5BS1AiXejFStil2f9cWpCPBoIbDxfBs7KUvK/q994EwFxObGX0EAney6K8lgW/qY4vZjuV5Rh6PFi4wodVsl7TXM7WvM+FXYtsDx0ioYs6hKOoHiqK6KYpyUBTlpiiqc4DfjeyJ1IGiKDWAiwCUAPgcwJKej90I4L3hvu7hhphneNOeMlydG48dxTUoyjdBpaBxdW48Nu8tg16jxG3zk3Hr3CQcr+9EdevAlILYvGVfjGXeMlE+IiAgGArEuOO+d4+gICsOO3+sQeECPmeym7xbzk9CaX0nDtd29Ns4OtD4si8QLh1ZiN3f9buOYc3CNCzNG/y8HAi2RWzm7IZUS4Krc+N5qY8eDwMKFGwuN26dm4TYUBX3WYVMelswlvZFbHvgGIlUzC0ArgHwFrwKmTcASBngd2MBvNZTZ0cD2MEwzC6Koo4BeJOiqIcA/ARg2/Bf9vCiL2nYug4bXv+mCq/dPBttFgf0GqXAA51g0MIY3n+YeazylqVC4kT5iICAYCjojzPf+LYKt5yfhLRYnWjkbuu+/qMqgVjnQbh0bCB1f8sbuxEXqh70vBwIc7F/mQf7m4jNnB2QsumsuBB8VDiXs8W+onoWh3vQxx8N+yJ8OHCMSINyhmHKKYqS9dTLvUJR1E8A7h7A9w4BmCHyegWA2cN/pSMHKQUgtglEm8WBSF0QInVBnHfQ1xNxzzuHkTM5rF+DHQvp4r5C4kT5iICAYCjojzPrOmzY9lUFtq88VxC5AwbWlDzQpN4Jl44dpO6v3eXB6Q7roOflQJiLi/KTic2cxZCyaVN0b91cRVO3ZKPxbV9VIDpE3FY8HgYuNzNm9kX4cOAY9lRMAJaeBuIHKIp6nKKoO0boPAENsSagRfkmvL2/hufJSzRokRKlk/REDARs3vKcpAiuTmQkIRYS37C7BIdr29FitmPD4izS/JSAgGBQEOPMh66ajl2Harm/Ny7LQUZsCDYuy4GMxpB4c7T5UgoeD4PDte0ore/kpUOx6UWkkfTIQuz+Fi4wYdehWmTGhw5pXh7rufjz0kasLUgnNnOWYiCcIRX5ktHo01YqW8y4773DXEo8e/wNi7O49M6Kpm58c7IZFU3d/abFj8RvI/BiJCJ2K+DdyK0CcAeAyQAWj8B5xgwDUebx995FBntVMfMS9aBAodVsx+HaDmTEhiAtNkTUEyGnafxQ2QKDNmhYPH/DpSjkTwyxoSoszzNi+dZvYXN6kGBQY+uKPChkFFEuIiAgANA//4hFPIx6DXKNerSY7RxvHq3rxIXTohAXpsbWfRUC3qRBobK5e0Cp7MNx3UM9Zl/pUGx6USBFF8czpJ4he38bOm3QKGSwuVzIS8iG2eGCMVwjOi/TFIWKpu4hPYvhtiXfuTg2VIWF02Oxdd9J3HJ+kne9kRCO85IMxGbOErA2Pe32uahuNUOjlCM6JIh73+NhoFHKBXadYFBjbnIE2iwOHDndgWClDJWtFmh7vm8M13LiQGxKPKv8GhfmjZiNtLBJoGVbBDJGQhWzqkf4JJZhmAeG+/hjCY+HwalmM0rqOlHW2IUdxTVoszgkDdhfAcjl8qC4qk3Q5+aKzEmC3PzCBSb86a0DWJ5nxPbiaqxZmIaL06JR3WYZ0qTQn6LQYCYc/5C4f0pUVYsVK98oxkd9pEQREBCcHRgMb4qpphn1GuyvHjhv3rXzIK6ZZYQpOhgLpkUDwJAX08Pd5JeFlMgBmw7FphcFkkrneIXL5cHXFS0ormqFhwE+OFiLNQvTuGeYFBmMRIMWe483oKyhG5v2lHEOSv++dEX5JpSc7sTLX5/C6oumYVKYasCO15GwJd+52HceZpU5VQqazMNnIY43dAns7OK0aHxS0oANu0tw98JUtFgc8DBASJAMem0QVrz8vUDBvc3iQFG+CaboYCRHBkOloFHXYePZ1+LcOElhk7iVc5AZFzasmzvCh/1j2Dd2FEVdDuBJAEoAUyiKygHw4EAblAcq+vKw9lfXweJoXQc3SQC9qm+mKK9nNm7lHOwpbYTbA85zy072q3ccwNYVeVj5RvGQPCJ99W9KNGgH5W3xLxKXSony7zFFMDFRcvQILlp8veD1uIgwvPr3LWNwRQSBgrHizU17yrByXhKmGIJFFznDwZtDafLLoq90qA2Ls0hT62GCx8PgwyN1WLPzEM/+/Pt1VbaYcaimgxcBrmqxorXbjpXzkuBhvNEJdrF7y/lJWLPzELcRH8gzHwlb8p2LKUp8Hq4ifb/OKkjZ2faVc7B6xwHoNUrYXB7O1gvzk7HxMz6/btrjXXc++3k5x6WJ4VpsWJzFG0tsKuR3p1pEbW9PaSNq222kJcEoYyRq3/4Kr9BJOwAwDHMAwJQROM+ooi8ZWb1GiaYue7+5xXUd4pN5fYcNNE3B4nBj855yPPt5Oa+hJEvYxVWtgsF6uLZ9QLnMfSkKDVZGlg2Jf1Q4F2+uPAf5qdGiMs+Haztx2eYvsfto/bDnWxMEDhyMDIZLbhP8J9XonODswVjypocBqlvFue1wbfuAakH64k2p3zcQCW4pafzkKB02fnocn5Q0EM4cBlS2mLmFKNBrf3denIqmbjt3jxs6bfAwwo1Rp91rW1v29tqX75zM/n8gc/FI2JLvXDzXFCFqUz/93I5fv/gdmYvPEkjZGWu7V+fG4+nPTnCfEbN71rYBQK9RwhSlQ0l9F+rarSjKN6EwPxlbV+Th4rRo0DQlyWduD4bckmCka/YmMkaixs7JMEwHRfF25+P+iUgNliA5jRvOTcCNr/SGsaW8bLGhatGc/ZhQb9pNX6pw7CDxP/9APSJ9KQoNRUbWNyTu8TCiKVFvfFsl8EoSBAZu+t0q0Y1X6fEy/OKS4TmHVCQPING8swVjyZs0BWiUcklP8uY95f1GRfpTYhuqBLeYNH7hAhMe/agEdR02wpnDBKnnc7yhC3f9+yBPgVRGQfCsxV7znZNZxdaBzMUjZUu+6aT+NsWm1LHHInY18SFlZyyPikV2pWw8NlSFG85NwF3/PsjjqbeKa7B1XwWX5ivFZ+wacLAtCUgz8jPDSETsjlIUdS0AGUVRJoqingHw9QicZ1Qh5ZFIitByOfkA38vm73FIi9bhoaumC1TfMmJDAfSt0vW35TkIDpJh1YJkrFqQjNhQ1aA8ImLH3rA4Cy1mOzRKORIMasFvG6iMLOs1/PD2ufj79TPxxJJs0D63ytcrSRAYqG1uF42y2Z2uYTuHVCSPRPPOHowVbxblm5AVH4qY0CAU5ieL8qb/ecXQF29WNHVzx/P/ff1xp2+kZeuKmXi8hzMXz4zn1DEJZ545pOyPYXqf/almM2gKSInWoSifr/iXGR8qeP53L0xFcJAM66+cDo2SHvBcLKXqZ9RrUNHUDavTjaJ8r436Xutg5+GPCufiX789B6/fPBsU1WtTAJmLJwr6imZJ2RmnJtzjrGCx88cagd2zCu5L8+IFPM1mXPjaEmt7O1bOwVNLs/D3FTMRF6bC9XOMSDCoB92SgDQjPzOMRMTudgD3ArAD+BeA/wBYPwLnGVWIeSQeWZSJUI24R7ih04bSemFtxxWZk2CKCkZdhw0RwUGQ0UB1m0VcpUspg9PtwWWZMTh6uosbYCoFjbUF6YgJCcKmz8oG7NXzPbbTzWDte4dR1WLlFkrP7C3j/h6KjKx/LQvrsWmzOIbUa2Qk1OgICAhGD8PNm/UdNsSEqpAWHcITkro4LRof+inBxYdp8ElJA1dLolLQuL8gHXIKeHpPOe+8UvzZH29uXJaDLdfOwKp//iSoO+kPNE0h0aDF4doOQQ3Y9uLqIfdnIrzZi74iCYD32ZfUd+LOtw5Cr1Hi5vMSsHXFTDhcHoSoFYjSee3oo57nr1PJcaKhG4/uLuXNxTqVHD+3WqDXKAdkS76qr5+UNIhG2ViBocHMw6xN+Y+hM52LfUHsa2zBRrM27C5BQVYcZDQwKyEc5yYZIJfTfapHLsyIQXqsDgkGLe555zBsTg/aLA7oVHK8dvNstFkcmBSqQnCQHKkxOthdHsk0zQSDGmqFDN+cbEZ0iNeWG7rsaOyy414fwaH1V07HJJ0KFU3dA7YZ0oz8zDASqpgWeDd29w73sccSUoOlssUsGsbWKGW4+dUfBB6HjwrnIjMuDLXt9bh+23eiYWZ/1Z+Kpm786S2+92L9rmNYOS8JC6fHosPmHBBZs8cGgMs2f8k73n3vHsH2lXNgdbqHJCNb2WLGht0lnAwuAGwvrsbSvHikxoQMepNIQvEEBOMfw8mb2ZP1yJ7cNzdMjeLzpr/X98Fdx/C35Tm8a+wvKtIXb67ecQAf3j4XHw1RgruyxYyNnx4X8Ob6KzOH1J9J6t6ciaLyeIav/VW1mPHTz+2cwA7gffYnGrq4GqRHPj4OlYLGynlJglTdpMhgVDR1cwtioHcuZkVUivJNiJFo8Mxej+/8Lmajm/aU4bWbZyNSN7g2R+yGq7LFjOP1ndBrlFxd1ea9XgGMoczF/ufwt68Ni7Pwq+mxkMvPunbFYwJ2rbU8z8ipoLLP4fKsSaBpSlI9kqYpJEYEwxiuRXZ8GM8R5t8eJilKh4qmblGe1ipluH2BiWtxpVLQ2LoiD4dr+QJENqcHa987gni9muP1gazlokNUSDCoUZAVx/HiBwdrSTPyAWIkVDHzANwDINH3+AzDZA33uUYbYoNFzCO4cVkOHG5xTwcbuu5LHcsfUt4LDwNs3luGrSvyBkXWUsezOt2YkxTR53ddLg+O1nWgrsOG2FA1MmJDIJfTaDHbBURTuMCErPgQzJkSMWgP36lm8VD8tNvn8hZvBAQEgY3h4k1fBcOB8KcUzx2r68TSvHjewn0g/Cl1vKZuG9eQWgq+UQ6NUg6H2w2DNkiSN9VKmseRA42SSN2bM1FUHu/wrUGz9kQoAHDR4yf+c5z3eXZuZf/ta1tSNsDWLW3aU4aL02MGfG1Sx2PASNoT2z6kqtXM9Rljo9NSfRFtTg9mTA7DL1OizuiZi9nXmp2HoNcocX5yxFlhT2ONhk4bCrLieC2m2OeQGRc6oIgWTVOYGhXc71pKKuMiJSoYS/7+De/8h2raJYVYGrvsuHVuEnb+WDOgGmKjXoPbF5gELW6IWvDAMBKpmP8AcBeAwwA8/Xx23GOwHumhiJX0Japic3qgkFGDItT+iril4HC48cGROtzb47FkB9tV2XFQymgB0WzeW4btv50jmHAGsqioajWL3qPqVvOIbexIigkBwehgKLzJYqD8KcVzbg+QlxCGN1eeM6gI21B5UyzKsbYgHWZbG7In68V5c+WcPr8vxaFS90ZMUXmkRDQClUfFbI6mwG30WPiKogB82+prLmY/29RtG/AcNVib8ngYfHyknsvgUSlorL4oBemTQiT7Ij77udeBIaPPPKLWl33F69XjJk0uUG10IIgOUUm2mBruVEWp9OHdR+sF508waFHe2CVqzycaurHtqwqes6Gxy8Zxvv9zqGq1iLa4mTFZLxhb4/lZjhRGInbexDDM+wzDnGIYpor9bwTOEzBgPYKs15bNcxcrYE00aCULuqXIXEpU5e39NVApaET3kfox0OP157X2eBh8faqF29QBvYPtaF0HLA63KNG0WhxDKoLVKuWi90ijHAlfRO/i6bLNXxJpaAKCUcBgeZPFQPkz0eDtuyQmRpVg0PLOOxAMhTcB8SjH+l3H0GFz44fKVlHetDjcfX5fikP7kh33P8dIiGgEOo/625wxXFx45+39Ndx3fG2rr7nY/7MDwWBt6lSzWVCWsfHTE6iXaAlCUb3XuPa9w2csPtGXfY0XUZZAt9H+kGjQYlZC+JBEm4YC/zFT3WZBWc8Gzhd17RaEa5QCIZZ1l2fg7f01nLPh6tx4qBQ0YkJUks+hL8e+L8b7sxwpjMQqeR1FUS8B2AOvgAoAgGGYt0fgXAGLvgpYpdKQpMicPda02+eipL4TJxq6uELooYic9HVtUqhsMWN/dZvoYKvvsMEUrZOomREXSejPsxQdEoSifBNPMKYo34TokKBB/daBYqDpXQQEBCOHgXDTQPmTpin8anos9Boliqta4fZ469fWLEwbUp3RUHgTkI5yUBRgc3lEedPXWTeYDA+xe7NhcRY2fspPNxypReB441H/ZxoZrMKplm5euqavbUmJ6dR12IYkOjZYm5Ja8IZrlKJ2NC1ah1vOT+JFSc7kObDOEjGxn8W5cUM+7mhivNmoP2iawrlJBslm4SONhk4bdhTXoHCBiZdCnhITgnXvH8E1s4x4Ykk2LHYXWi0OtFscvP6iMhrYuCyHU5EVew6sY19sPemL8f4sRwojsbG7GUAqAAV6UzEZAGfVxg4Qry1hXx/sAoHNiZ4SoUV6bAjOm2rgvgdgUIpDfV2bFNgGrmKDLSZUJbnYig4JGlL6kjFcC1N0MFbOS4KHAWgKMEV7PawjAaLCREAQGOiPmwbDn3I5jfOTIxCvV6Oxy4bFuXHcZ4eSwjNY3gT6TqV/e79wgSQVoRwIh0qlTilk9IAdiWeC8cij/s90SoS2TzEc/x6ur9w0m3evR9KmpBa8Lg8jWOgX5ZvwSE9fRPZzZ7qZH25nyVhgPNqoP+RyGpdnTUJmXOiQRJvOBNEhKrRZHHjj2ypO9ImmgCkRGqxZmCZQeP34cB1um58MivL2hbwoPRrpsaH47lSL5HOICVENyLE/EZ7lSGAkNnazGIaZNgLHHXZICYGMBoayQPD9Hpub/N2pFrjcDO7zk+AeSGG8WBG2vzISi+gQFT44WCtYhDy8KBMZsaGSiy0Ag4pO+v7OBdOikRQRPCrENdT6GQKCsxFjyZ3A4PhTjDOjdN7IjH+bgoHyppgQihQ/9SW5X9dhw/bi6j4ViYeS4eF/b4YSaRwKxpJHBzOf9YXB2hb7HBo6bahtsw5qLh6sLUllsuhUciyYFsUt9PuLPJ4J+nKWjAdMlLl+qGtIX5uLCVGhy+bEaR8ep2mqT+eELx+x9Zsbl+Vgsl6LyXotF83WKmVo6LLj0sxYnr1OiwlBemxon89hoI79ifIshxsjsbH7mqKodIZhjo3AsYcNLpcH7x6sFajuXJUdN+QFynAUcQ7kGGLF9L4LhYGoR4odoyjfBFN0MBZMixacM9GgxZqFaVxLAxkN5Br1OG+KgbtfUkQz1EXFUIlrKBjs4omA4GzFcHPnmfLmUDmzKN/Ek4RfveMA4lbOgcXhHjT3slELsUW8f/qe3eXBuvePcOl7axamITMuTPI3DzUF1P8Yo8GlY8Wjg53PBmNzfX32TObiodiS2IJ3SoS35koupwcVeTwTjObcPNw4m+d6X5vTa5S44dwE3qbroaumI0yj4Dm8/NtZ+PNRTIgKbg/w3akWrp9daX0Xbn71B6yan4wtn5eLpkr29RwG6tg/m59lXxiJjd0cAAcoijoFb40dBYDpr90BRVGTAbwOIBre1M2tDMNsoijqrwB+C6Cp56P3MAzz0Zle5NG6DlHVHVNUMLIn6wd9vOHou+ZyefB1RQuKq1rhYbx9O8QIXiyv2FcBy+b0Nl2dEiFN5GLH2LTH2+smKaKXsH0ntfRYHV6+cTaaum0DSgP1nxBnJxoC1qs3HIsnAoKzAcPJnWfKm2fCmZv29HIm+9qe0kZB/7KBcq9UbYc/Dxr1Grx282w0dNphdriQMIj0cibANQHGikcHOp8Bg7O5/uzrTObiwdiSrw0lRwZjiiEYTd29i+ofqloF869YpJqoBp7dc72vzV2dG89t6oBeHl85L4n3Wl/tLGQ0hYM1HbwUYLam1+b0wCbR4Lyh0waGAeQ0hSeXZHPcyDDeqDs7XvpzHpzNz7IvjMTGbmFfb1IUpWcYpk3kLReAPzEMs5+iKB2AHymK+rTnvacZhnlyOC+yTkJFqr7DhuzJgz/eQIs4pbx/Hg+DD4/UCYqSN+wugUGr5JqVsueSKsYHepuupseGSA6KvnrjsfnJ/hNggkGN9VdmQiHznsjl8uDbylbRSQ/AmDQYPxPv/3j2QhIQjBaGkzsHU/wutkGS4sy4MBUv8ibFd8ZwNWJDVVz0jFWPlLqO/vqY+dd2+HNoXkIo7rokDQ2dNpxs6saO4hpOBEuKG4fDaThYjDceHch8xkLK5tKL5sLDYED2xc7JfalRAn3PxQO1Jfb5b9hdgoKsOMhoYFZCOM5JDMe+k004VNMBD+OtX8qMD+VFKMfCdvwRiHL0Z+tc72tzrJ35gh0zABAbqsLVufGgKKDT5sSxug502VxcGvv6Xcfwp4tTufHBfn/NzkM8h1mCQY1rZhkRr9fAYnehzeIATQPvHayFhwFCgmQIUStx578PDslGz9Zn2ReGfWM3gNYGewDkinyvDkBdz7+7KIoqATBiMkuxoWpJIZChYCBFnH2RbGWLWTBAWO9dZYsZX59sRm5CGMx2D47Xd0oW46sU3h5JW/aW47ypBkljl8pNpilw+cm+E2BsqArL84xck9sEgxqFC1Jw77uHBZNeaowOwOCasA8HAmESIyCY6BhO7hxo8bvY2N66Ik+SM4/Xd6G6zcotdqcYgkWvubbdihVzErC9uBrL84x449uqPq+jLyEUlYJGZDD/HvhyaFZcCBbnGnHjK98L0vbYNFCxlMzRVn4bjzw6kPmMhZjN6TVK7K9uxz0+PVr7sq/KFjOe+E8JbptvGvJc3J8tqRUyblO0YXeJoJH9thvzUNbQja37Knjpp8mRwUiM8J5rrFUDx6MtTWT425zUmIkNVWHFnASeva0tSMf276txQWoUEg1a3H1pGsobu0X5W9aTkb/veCP+mJ+C+k4b7vLZuD101XQEK2V45esqLM2Lx8bPjo6ZjU5EjF61ey/6Hc0URSUCmAHgu56XVlEUdYiiqJcpihp8nqQIMmJD8NBV05FgUOO2+ckozE/G89flIi06ZEjH66u3ksfDoLK5G8WVrSit78Stc5MQG6riDJj1ZokNkFCVDGqlHAwAmvIqm7FSs+z52EGnU8lwy/lJ6LI50WZx9FlAmmjQ4qmlwv49GZNC4PEw+OZkM5q67NBrlACAq3Pjsb24Grecn4RVC5Jx58Wp3KaOvdbNe8tQkBWHxi5bnwu2kcJg+j0REBAMDRmxIXhiSRYK85OxakEyivKT8cSSLGTEhg76WP31pPN4GFQ0deOn6jaOO1ctSOZU+aQWFTXtVmzZW46/76vA6TYrbE4XHrpqOo/vCheY8FZxDTbvLcNTS3OwvbiaUxD0vw4WYn3H1hakI1Qlwz2XpuJUSzc8Hoa77hMNXRzf3zpvKh7YdVTAmVfnxnNpoGI9mEabS8cjj7LPxXc+f3p5DmYmhAnqbfxtLjZUhfsL0lHZYubNzVL2FaqSQauU4w/zTfjrB0cFc/H6K6cPaC6W6on3XUUTNi7LwZdlzfhfeTO67U786eJU2Fxu3vW1mZ2CVLpNe8rQ0Ml1mRqTedgX49GWhhssF3xzshkVTd0j3mOtr/P52tzOH2sEPefuuDAFBo0SS/PiuU0d4H1u63cdQ+GFKdi6rwKrdxzEH7cfwNTIYFH+zpkcBpWCxtyUKJxqMYumfHY73FgxJwFymu7XRgd6D0f7XgcqRqbbc9/o805TFBUMYCeAPzIM00lR1PMA1vd8bz2ApwD8RuR7KwGsBACj0djvRcjlNK7InAQZReFuHy/dUL1J7IDxT5eID1Vj7/EGlDV084pUfQusG7tsot67BIMa4cFB+HOPp6MwPxk2pwd1HTae1Oy0aB0na6xS0Fg5L2lABaThWgWK8k0I1yihCZKjzWxHVYsFRW/yC9Bf/6YKOpWM5zFkr8UX7KKKncSGS61ooKkcgSx9O1j7JCAYTQzGPmmaAk3RvEjBU0tzhuSBl+JNo17DS0FbOW8q73yFC0ygaXGOSYsJwYO7vNpdeo0SZocbi1/4BnqNEk8uyUZpQxcYBhz/AoDT7RFIdUv1xrs4LRov3ZiH5i4HKpq7sWVvOdosDtxxYQrW7zqGKYZgHG/oEohiOFxuydQ7Ng1UzFM9FOW3M0l/CzQeHYhtss/F6fYIenv5w9fmls6cjJhQFUrrO/FWT1osOzeLtfdh5+Q7/30Qt85NQlWLlTcXMwzQYrbjsY+P9zsXs9e8dUUeDtW0IzM+FD+3mLEodzLPdh6+KhOb957glDbZ6zPbXaLPyeJwcX+PhmpgX7YWaLY0EujLPkc7Ytnf+diatPSiuWjotKPL5sTTy3NQ1WJGl82NV7+uhFJO4e5L00SfW2ldJ2+D9tjuEqwtSMf6Xcd4XHe6zYKV85IQF6rGz+1W0WPFhapR22FFXoK+Txsd6D0k0eFejMXGThIURSng3dT9g21ozjBMg8/7LwLYJfZdhmG2AtgKAHl5eQPaptd0WLlNHXBmIWCpiWXD4iw0dFgFHgs2pWPbVxWgQIGmhG0B1hZkYNU/93Pf851o6jpsnNRsUb6J29Q9sigTucawfmWeK1vM+KaiBZv3lHOv3TY/Gdu+qhB4AFfOS0JcmIYLpftfCwuVgkZeQvgZtTnwx2AGayBL3w7FPgkIRguDsc/KFjP+9BbfC/+ntw4gLXb4eHPjshykx+qwescB3HJ+ErdwYM+3eW8ZivJNgt5dDy/KxPNflHMbNl+BgLoOG0obuvDSlxUCjogOUeGcKYYBFeFXt1nw/alWbqPJ4unPTuCW85NQ3SouirF1xUzJ1Cd2sS626B2s8tuZLnACjUcHapvVbRZB6qTYfC5lc+wzYOfmDw7W4tFFmTzHr/+crFLQ3FzM/r1qfvKA5+LqNgtX3rBqQTIYBoI5+N53D/PEWNjra7M4RJ+TryT8SKsG9mdrgWZLI4G+7HO0U2EHer5jdXzHU1G+CW/v7633jQsTT7c3+tlNVYsVXTYnp5SeHKXDox+VAADuumQaqlrMkFHia8XaDis27ylHgkGNdZdn4IEPjoraqNRvMtw8m9OekBIyOltTOsdiYyfKcBRFUQC2AShhGGajz+uxPfV3ALAIwJHhupDh9iaJTSxrdh7CpmtmcB49ANj5Yw3qOmyQ0cDqi1Jw7HQHKlvkMIarsb1HblujkOHL8mbe9e38UdjMtijfhHi9Bjv/71xEBCvhcnvFDdweQEZ7/y2lVum/OZMqpp0xOQxOt6ffa3l0USZoisEPla2IDgnCxWnRZyy3PJjBSqRvCQhGHqPBm6t3HMBrN8+GXqNEaowOt85NAtDLnTanB/F6NeLC1Hjt5tmoabNAKZfBYnfgRGM3d2x/ThPjrQ2Ls2DUa0DTFIx6DbpsTpxo6EKXzSXan6+h0wYPI86VMhrQKOWi73kYBuuvnI617/W2iXjwyukw25x456daXJ0bDxkNqBVyeDwMT91wYUYMpt0+F9WtZmh6erRJ4UwXOOOVRwdjl742x4pE2Fxu3HNZGh75qAQyGvj9L5Oh1yrw9xUzYXd6cLKpG8dO90YspObj5Khg7Fg5B2aHG24PUN1q7nMe9r1mGS1uVxTF/9trJzLcdck0PPGf49z5H1+chXaLA3tLG5AQrsWUCO2Iqgb2Z2vj1ZaGC6MdsezrfKw6alOXXVRB9sUb8hCikiMjNhQ/1bQJbLtwgQmdFjvXbFytoKGgKYSqlbA6rAjXKPGoT+ZYrjEMucYwHD3dKei9uPqiFLzyv0oA3s3hC/8tx9YVM2FzejA1Mhgyurd9gv9vYsfrz20WNHXbUd7YjamR3rYI/f32QBLwGUmMyMaOoqjzAZgYhnmFoqhIAMEMw5zqeTtf4mu/ALACwGGKog70vHYPgF9TFJUDbypmJYDfDdd1Drc3Saoou8Pi4Lxw7ADZXlyNtJgQNHfb8fBHJbyJwRQdjFkJ4ei0uXjXxzazfXxJNsobu5AaE4IXvijHicZurL7QhPDgIF5vKTaN0ld1DfCqVR6v7xQ0HJfyrCT0kLDYtbxy0ywAgNPNYK1PY1bfHkJnQmCDIUYifUtAMPIYDd60OT1wut244dwEXtE9G1VpszhQ02bF7f86wOPU35w3BesK0lDXaYeHAaZPChHlLTb9zWjQ4qlPSqGQ0bhwWhTeP3y6z/58Hg8Dl5uR5Mq8hHBEhwRJRlLOnxqJlOhg1Hd4perbbQ7c/3kZL819674K0Qibf3qnVBTuTBeT45VHB2OX7D0SE4koyjdxtUMr3/iRtwl3uty8rJk3vq3CynlJmKzXoLLFgo8P1+HSzFjc/q+fBjwPs8fb+WMN7i9IF/0Nvm0uVAoayVE6PPVJKdYWpOPD273Pyc0wqGjsxp/9It8LM2JGTDWwP1sbr7Y0XBjtiKXU+SKDVVxk9Q8XiJfRfHeqFS996eWeadE6TlOBTTHeW1qPa2YnYNtXpTzb/tueMrRZHFh9UQp3vo3LcrhItTFci+pWM3KNerSaHaAAPLDrGK+euarFiuKqNkyNDMaplm5eH70XV+Rxv0lsvBYuMOHhj47hoSsz+/3tvuPi4rRoVLdZJuRmb9jFUyiKWgdgDYC7e15SAPh/7PsMw7SKfY9hmK8YhqEYhsliGCan57+PGIZZwTBMZs/rV/hE784YYsXLZ+JNEhMCWJoXj/vfFxbM//WK6ahsMXObOva9TXvKcKimA9VtFswy6vHgFfyC/9/PS8ZL+05i855yrN5xAHNTorwGH6YR9JbatKe3MJ8tWGY9bDuKa7A8z8gN3sL8ZMxJMggEVdj74XuvYkNVKMxPxp8unoao4CBE6VRY+UYxqlqswt/Raj6jYtb+xBX8wUrfzkmK4CYWAgKC4cNo8KZKQUMXpBBNYV+aF88tln1fL8iKw8tfnwJ66v+27C3Hox+X4IErMnjXujzPiLXvHYbV6cGJhi5UtVixescBHDot3p/vaF0Hd12VLWbc995hhGuUAuGBDYuzcF6SAcZw6fsjl9PIjAuDKVoHi9ON+DAtnrlmhkCoYPWOAzhc287xplh6p5QIxWA5UwzjkUcHY5fsPbo6VygSsWlPGYIUNBdZZV9/9vMyhPk99zaLA8FBcnTbnHj283LMTYkSFTTpax5mxVfqOmzY9tVJPLwok/cbHrpqOnYdquX+9kaYVdh8zQxolXJQPSqG359qxSMfl0rayEgISwzE1sajLQ0Xhpsrh3o+GQ2uKbkpSlzwhGF6bUZGA0X5Kdj2lZdHt31Vgd+cP7XPNebGT0/gb8tz8FHhXFycFu0t9znZjFPNPfbHMEiJ1mGSXo02i0NwfrcHWLPzEA7VdPA5+L3DeLRnTIiNV5b773vvMDYszpL87b7f2bC7BB8eqcNlm7/Er1/8Dpdt/lJUuGq8YiQidovgVbTcDwAMw5zu6UsXcBhub5JY2kFKlE7UOwKGgdUpXkzP9t6xOFxoMdvxwOUZ0ATJUdNmwQv7ynHNLCPmpkSBooDcyWG459Jpvcf1O1aQnOZC1ycauqDXKKHXKDlv49W58dCpZMidrIebYZAao8Puormo7xTeD7bodn91OzbtOYGCrDiUN3YjL0HPHdP33HKaFkhID7aY9WxP5SAgCDSMBm9uXJYDiwQ/ZsSG4q8fHBXwDUUB18wyoq7DykvdfO6Lcjy+JBuVzWYkRmhxut2Cy7PjEBIkQ4fNzfGjVH++hk47Kpq60dBpg4ym8OvZRnQ7XNAFyfHEkmxY7C4kRWoxY7Ke8wBPixbnUY+Hwd7jDbzeYxmTQkXP69ss/ZFFmaIcKxaFO1s5czB2yd6j0vpO0XvfaXVyjkoW18wy4nhDF+Q0jSeWZKO23QKLww2VnEZChBYbrs6EJkg8DdcYrkZWXAjmpkSJzsNsZGR+SiRC1Qo8d10utEFyROuCEB+mQa5Rz/0mo16DT0oaeIJDuUY9QlWKPlPRRkJY4my1tYFitCOWUuf77lQLbE5vU/LHdpcI0izZthyAN8usodMOvUaBJ5dmo6bNgi6bW7K1AetYuDo3Hm0WByKCg/BFWSPW7zrG2WdabAie/9ybXfbU0mw8vjiLF1n2rTGOC1Nj1YJkLu2+qsWKUI0ct5yfBKNeLXkNVS1WxIWpBOU/7G/3RUFW3IDqcccrRmJj52AYhqEoigEAiqICeoQPV3NDNl8+Uqfk6uSiQ1RcTxqxtBw5TYu+R1NATIgKxVVtAiXNUJUCIWqF4PXaNovosdJidLjpvEQ8/dkJQWpIXYcNX55oxPLZRtzg01tJivBpmoKHATbtOSHoqeN7TPbcpqhg3CHiZZ52+1xMjRrY/T7bUzkICAIRI82bbE2EGKfFhAaJeny1SpkoN77xbRXq2y2Q0xSnMMxGQr45eZpL7bl1bpLo+ZRyGpdt/pK3CDLbnGAY4JGPStBmcWB30Vx8UtLQ78K5utUs6D12f0E6Egxq3kaC9WADXt68553DWDkviSd2JRWFO5s5cyB26Wtz0bpIgQiOSkEjVK3g2UJsqErUtr4o9c6fv+tJ2SzKTxa1oVazA7+encC1u/CdM1mEBMlQ3WrF3e98x7MhY7iW95sqmrpF+9qtv3K6qB1FBqska+GkeicO5n6frbY2UIx2A22x87GRVXYD5K/k2mVzoq7Dhqy4ECyfze+1WZRvwpcnGlGYL96zMTo4SJAeec+lqVgxJxFPftJb/7nu8gxs3lOGP711EKsvNOGVm2bhm4oWuD29CsUqBY3qViu2fVXBS7s3aFXY9tVPkhzNrrPDtUGSv933O1K1rBNFrXUk+tjtoCjq7wDCKIr6LYDPALw4AucJGLDKUJdt/hJLX/gWy7d+izaLE4kGb/GyWGh8SoQW5yYZ8Lhf6Lgo34Ss+FC4PRCEvTfvLcPKeVNFFeIYAGt78vPZYxUuMOFUcze3qWM/v2mPN50pNlSF/7sgWXC8vvrMNHTaUJAVJ5q+sjQvnvc7pMRYSuo7BxXyPptTOQgIJir64k2apiTTijJiQ0X7fwEQ5calefFIjQ0VcNZ97x7B0rzJ3OusGIbvcR9elIl17/N5eP2uY+h2uGFxunHzeQnYuCyHa1XQH482dNoFqXoP7jqGtQUZgt/z9v4a7ns2pwcp0boBp3QRzhSHv82tfuuAoL/hxmU5SIzQ4MEre19fmhcvalv+8/GOYmFvMNY2/XsYbtpThhvOTcCKOQnY9lUFOmxu3P2OMIogtCHxOXjte0dw96VpgvXEqZZuyVo4qd6JgwGxtcAHy6VsbTCr5MqmWVocbsn14KY9Zbjj4mnwMBDYdlG+CVOjtQJbbDY7uE0d+9oDHxzFdecYvRFxuxt/efsQokNU2PZVBbepY3nPl7u3XDsDWqUMTy7JRkiQDHdcmCIYX7sO1Uryodg8Mish/IzT1QMZwxqx61G23A4gFUAngGkA7mcY5tPhPE+goT9lKF+PVmSwiqf4c9n0WGRMCuWpnRnDxcPHNqcHDBjoNUpcnRvPU9k0O9ygKPC8MG98W4XFM+NFjzNjchh+MTUC/zvZLPq+lOciOkQl6e2I7wmhMwzw+jdVWJYXL+pdOdHQhfTYkAnhGQlk3PS7Vahtbhd9r/R4GX5xyeheDwGBL/rjTbFogFGvQXWbBXqNAttXnguH2w2ljIbT7UGnTbyvlzFcw1MzZNOGKAoIVsm5lDjftLhpMcEob+xGU5dNkJJnc3p6MhfKsO3GPJybFCHJ1/48anaIX2O7xcFJhucl6LH2vSOCZulpMSFnrDJ8tsPf5qparHhmbxm2r5wDq9PNzc+1bTbMnByGf//+XNS2WUHTlOhzU8gpgeL1699U4e8rZuKHyrZ+5+HkqGBOaEXKEepvQ33NwXKawsp5SfAw4ObhNosD21eeKzoXS/VOJAhsDLZPJde/LlaHBIOWVx5zx4UpUCtoPLY4E/UdNtw6N4lLgwS8dvVTdRviQtV4/Rt+pO/1b6pwjz5VYItSqsGRwUFchM23VYIxXI3qViuvt6jN6cH0SaGw2N0o2PIVd71PLc3mVGc1Shmcbg8WTo+RvAdS88hETiEe1o1dTwrmRwzDZAKY0Js5X0ipYTZ12XkDzzfPXa9RYmlePFKidEiL0WFKhBZN3XZ0WJ347lQLNEq5KBFH6oKwakEyryHk2oJ0RAQHoaq5m9cDB0CfSpesbLfY+1KeC6Neg5lG8YaSVa1Wrp8P4PVePnTVdJ7K3N0LU9Fld+FEQxcAkMXJCKK2uR2GS24Tfc9+ZNUoXw0BAR9SUYRWs517n+XOpMhguFwefF3RguKqVngY4IODtVh90TRMClPBoA2CXiOuRmmKCkaUzokEgxrXzDIiRK3g8advShzbcoCmKLxVXIPFM8WdU6zQgMPlGXC/Lo+HQUSw+DXW+/RC+8ul0wRpdk8tzRbcE8Kbg4eYdHpBVhzqOmwwRelwqqWbVxs006hHbJgKThcjeG4JBjXMdjdP8ZpN09UoZaL9Ev3/DvGrixvIXJxo0HIRB//P6lQKXrouC6fbLVjIsqluYmsVYluBi6H2qaRpCokRwTCGa5ESHYzKZgvMdhesThesTg+XTuxrG2wkbWaCHgqaQpvFwVvjJRjUMAQHoTA/GR7G69gAgNRoneC1pXnx0ATJ8fSyHDz/hbd2uMvmxrOfl2PVgmTsOlTLC1h8cLAWNpeHq8UDvJz7p7cO4qPCuciarB/wPRNLT53IKcQjUWO3n6KoWQzD/DACxw5I+E/qsaEq3HBuAi9P2bfhrl6jFOQk/2VhKiaHq3Gwp6j+25NNgp5HD1yRgZ+q2wWpPOt3HetpIq7GvZel8donhGuUuOPCFF6Nna9nwr/lgUrhVd0S81x4PAw+KWnAy1+dxNqCdN7iaMPiLGz89Djv820WB1JjdJwHUauUwelhsKWn0epACYmAgGDiQWwzlGBQo7bdhuu38bnz4rRofHikTtBQeuOnx/HnS1LxU1UbchLCBJy5/srpsDpd8DAMfv/LZNR1WEVVC4vyTWAYCGS0dx+pE+3n9Ma3VVApeptB9yciwS7GNuwuEfDxHRem4NWvKwF4F+cWhxtvFddwnvGQIBncHga/eqa3zm8gza8JhPC1OTHp9NUXpeA3503Bo7t7Jd0fvioTVodT8NzWFWTgDz7Nyn3n4qO1ndiwOBNrdnojIx8crMW6ggxejd3GZTm81hhiffGkoggeeARzsP/xWLC1R7nGcMStnIPvKloQE6ZBZbMZN56XAJqiBGsVMiePLfqKyA1HI+7qVivu7Kk3LsxP5tWZsmmQt5yfhG1fVeChqzJx/3tHEKpS8BqJJxjUuO2CZPzm1R8E0b87fWqZ716YCrvbq5rp60xbopTh+f9WIDZUhahgJf5wQTLWvX+UN77aLXbRTIumbjuXvTFUh8Ro1z6OJkZiY3cOgOsoiqoCYIa3ITnDMEzWCJwrIOA7qes1Stx7WRpn2EDvwHvt5tmwOT2ikq2P7S7liuPZxcP2H6rw4oo8/FDVyvWtuTw7TtTL7WGAde8fxZ0Xp3ApPclROjz6UQmUcgr/75Zz4HR7eAMgPlSNx67OQmOXDX9fMRPH67298c5LMogOEF9CqW23c+eZZ4rsabY+DWt2HuJFI9VyGbLiQ7Hqnz9h1fxkbNpzXHBfpMRUBptuQEBAMH7gz5tL8+KRl6DneocBvRyxfeUcgYoZu/g42WSGQkbhhm0/QK9RYuW8JBj1GtR32rDl8zIu+rJ1XwVunZskyp+TwzW4862Dosd/49sqFOWbEK9Xo6bNm5a5NC8e50wJB00B35xsRnSIChenRUumSvpy56tfV3Lcec4UA+555xAAoDA/GSlROui1Crx3oBbPfu6dC7auyMPKN4p518aKqaTGhHCLcMKX/YO1uQ27S/Cni1M5MR3Ae183fnoCK+cl8V67913vvTZolFg1PxkOtwczE/Q47CPLzoKdix/bXYqifBOeXJKN0oYuMAzwr++9Pe9mTA5DQk8Gj8fDcD0VEwxadFjsXG9YqWdY2WLG797YD71GyW3+aQpIj9UhPkzDOVlZu5+VEA6jXgOappARG4qyxm7ud4st6geySSC2NnLoLyJ3pn0q/TeGUmmTabE6bFyWgzazHTedmwiNUo6o0CC8uCIPHTYnQlRyAVc//Zlw/LRYHAIbYxuiK+UUlucZ0WwWfuaBXUfxxJJsqBS0IBjy0pcVeOiq6XhmbxnXO5k4JHoxEhu7CVm50xeRcfnLPa0ATjR2iQ4Ui8PFKRNJTQjsvzfv9XqRO6xOeBjAandxtR59pQaFqZXY9lUl6jpsWLUgGUo5hfVXZgo2dS6XR9CMd/2V0zEnMRw0TXHy3hqlHB7GA5qiUOeXf+0bkt+8pxwJBjXeuGU2uu1u/FTdhtKGLjzxSSnWLEzD7qK5KK0Xvy/ljd2gKH5a5lDTDYYCMkkREIwcpMaXP2/e885heCQ2XlKtCGQ0kBih5S3Q3R6gus0CU5QODhcDiuIvXsT4U6OUiR4/SE5jaV48Jus1mBajg0JG44/beznJt/n0hsVZmBSmglohR0tPOinrVT7R0CXKnasvonHdbCNiwtQob+xGaUMXZBRw96VpiAhWIlwbJLmQ8zDe+ijDzbMRERwkaOw7WgudQOdP/+u7cFoUnG4PyiXmaX8dEZvT27qnxeJAXKgaP7db0dRlh83l6XMudroZ2FxuvPRlBc/ZaQzXclE4fyXVwgUm/OXtQ1izMA2xoSp8d6oFUTpvTV1Ttx1KGc2bh33n4HOTDDhW14U3vjmFwnwTqlos8DBAcVUr7G43FkzzNmT2FWWTWtQ3dEpvEkZzbu4LgW53Q0V/Ebm+0r77uifseycahHYvpYXABhqK8k1456cqFGTFwhStA015FdLFWrD4jp/YUBXiQsVbFDR02nD3pWkoqeuEnKZFP3Oq2YzCBV4hPv9Mi/vePYJbzk/Csz0ZYGIOiYlqI/1hJDZ2E6PDnw/EiOzRRVn41fQYKJUyAL2tAO5557CkJKtBq8QjizJRJSHjzfjcOb1GiRC1gov8sTLKYukavqlB1W0WXJ0bj21fVWB+SiRMUcGct9c3JbSx0y5Q3Vz73hGkRAWjtsPG/dYEgxq/n5fMSyFhzwd4PddxoV7RlH3HG3GioZuXHlK4wIQNu0vwyk2zodcoRX+3w+3BZZu/5E0OA003ONOBGyiT1NmGkqNHcNHi6wWvx0WE4dW/bxmDKyIYCXg8DD4+Uo8/vdU7vp5amoNLp8dwmzuWN/vaeIVLcEdqTAiqW82wOcVT64ryvYqEZoe7T/4sb+gSPX5mfCj+7//9yDseu5hhPc/s4mLNzkNc+lLhAhP2ltbjmtkJPOeZP3dG64IQplWiqsXMa4FQlG9CaowOiRHBkveE3UB8Wd6Ml76sEFzbaEReAp0/xexvw9VZ2PjZcVyeHSd6X/0vW6WgkRwVzPuNz16bi2f2lkn2A1MpaCRFaBEkp7Hz9+fieEM3T7Biw+IsxIepBXMcGyVeveMAL4PnnktTYe2JiPja0u4jdZibEgUZDShlNDbsLsE1s4xo7LQL7Ck5MljUSSB2D5xuBh4P02/mDnvdo72oDnS7OxP0F5GTSvs26jWS9wQAlwp+58WpvGe+80evkqtvKw/fumOW57ZcOwNlDd34v3/sF3zOt80Ve/tZPm6zOERtrKnLjjvfOgSVgsbTPaqV/p9JjNCi02JHiFopek8oiv+3b9RyIttIfxiJdgcfAtjV8/89ACoAfDwC5xk1iBHZ3e8cwv8qmuFy9RpbQ6cNeo0SWqVM0Hrgoaum48FdR9FqtiN7cijWX8mXWC7K75W3jg1V4d7L0tDQ6fXMxYaqOBnlNosDb3zrTenYdE0OivJ7e30ULjDhreIayGjgkUWZCNMoRZswvv1TLU41m0UHSm2HlfdbC7LiBDLNm/eW4bpzjLjh3ARs3VeBNW8fxktfVqDwwhRxufGZk9HYZUN0SJCoXG5Nm4W7NlbauS9yY+ErXf3rF7/DZZu/HLR0s9QkJdXugWB44GBkMFxym+A/KRVPgvGJiqZublENsMXvB1DR1M19huXN2+YnIzhIyJ1F+Sa8+X2VgDPXXZ6B5i4b4sI0SDCocfdlabC53BxnsgsSt4fhFi++/Pnkkiysmp+MN76twitfVwnOu2FxFv7q1+pg054yXJ0bz1277+KC/XfvAn2qaMua684x4uZfJHLcefR0B1d/4nuexi47PB5GVK6blQX33eCJXZsvX/rjbOBPMftb8/YhFGTFYd/xRsEzf3RRJpIjtbzX1hak4+WvTuKW85OwakEybp2bhOe/8LY62F5cjVvOT0JhfjKeXpaD7d9Xc3MxTQPP7C2DxeHmOS5sTg/W7DyE0xJRaNaGfDN4zA63oG3R5r1l+P0Fydj2VQU27ynHddu+w8p5U5EVHypaR9rUZeeiPSzYceFvW2vfO9xny6PRmJv7QqDb3ZnA/xkB3tpjtUKGb042o7LFzKV9v7nyHHxUOBcLM2JQ3Wbh0tpvm++10+P1nahuNaOyxcz1Pnzyk1JeW5c2i8PbFHx+MlYtSMYTS7J5mzXAe39tDo+oXfm2uVp9UQoMPU64q3Pjsb24GmqFTGBjqy9KgUYh43j6sd0lgrH44JXT8dQnpajrdOBkU7fgnvgHQ9ioJYuJbCP9Ydgjdj2KmBwoisoF8IfhPs9oQorIjp7uhE6l4NIcY0K8oimb9pT11nqEaxCslMPFeLA0z8gVhyYY1Hh6WQ6qWi2YGqFFm8WOpXnxkNM0TNHBeOzjEi53mPXyvv5NFV64fibMdheqWy3osDpgdbqxeGY8J6vcZnEgJUqH6XEhkulLcpqGJkhcdTNCG8R7LUguHiJPidah8M2feIPmUE075zn3VTdKitAiJkQFY7gWpuhgTkyFpgCNQoYX9lVwx2A9LgNRmRuOIuIzzVcnICCQxqkWcQdSRbMZTd12AW/6cqPF4UKoRgmLw4EwTQS2fF7G1aZlxIbC7fFgw3/KsWJOAu64cBpONnbhreIabmHNqrpF6oKwLC8eUyI0KMo3IVyrRG27FU99eoLnac6IDcGHt89FU7e3Lc3Jpm7RVge+XmLfxYXvv/UaJWwut+hvT44K5tI5AWE6HMufp5rNsDs9ODfJgIUZMZh2+1yU1HfiREMXz5nHRgDFrq2vvkxnA39K2Z9aQWPh9Fhs3XeSs6mc+DA890UZatvtWDkvCVMjg9HSbUdMSBCWz0pATZsFO3zsK0KrwCOLMtHQ6VWUrGwxY960KMxNicL24mosnTkZHTYn6iTuEVuaIRaJ9bWl2FAVIoKDRI9RWt/Je37rdx3Dk0uyudd852Kr0434UDUv2tNmccAYrsGq+cmwuTzcOqKuw9Zny6PRmJv7QqDb3ZnAPyKXYFDj9gUmLN/6rSDy5PtbWQeZf9ZCgkGLKREaXu/D3Ufq8PiSbNgcLhgNGlS3mDlRu1ULktFmcfCuSaWgYbaLt2oxRenw+OJM1LRb8cr/KgF4224lGrznfHR3qaAe1O1h8Oin3jrUbru3VVdEsNfxHxkchEhdEO5//wiqWqygKK/Cun90fP2V07H9hyru+jYszuJS4BN7VN8nqo30h5FIxeSBYZj9FEWdM9LnGU74pxBE6cSJLCkyGNdv+44ztJduzOMWJ3UdNi6Ngi249i0OrWqx4o4dB7B1xUysfe8Irp2dwEud8F2Y+CoU/VDZxqX6/Lu4Bgunx3LGzi6IPAwDtwfQqcQ3b4kRWrz948949tpcHKxp56TD77x4GlRKmidTOyVCK3oMtUJYk+JhvJ4lf6nu9VdOB8N401UXTIvGFEMwKpq7cbi2Ay/sq+AtrtjJoT+VOWB4yH0gkxQBAcHA4M+dKoVMdHwdOd3B8aMvbwK93LhqfjLuffcItq6YiT//25sOydYUsZE8rwf6hIA3fTmzutWKbV9VYPVFKVx6EZsixB7rgSsyEB6sgMvt7c/UbXehsrlbcO0JBjVSo3VYtSAZMgowaJV4/r8VommWlc3iKfc6lVzAW+znxNJJNyzOwuVZkzA1KhhTIrRIjw3BjMlh+Onndl7fJ980KDG+9MdE5k/WDoPktOj1ZceH4bc9JQq+NsWm1bK2ueXaXNz2z/2i9vX4kmwUvVaMuxemgqYoQZrk9uJqrL8yE6V1HdycCnjn1TaLA01ddty9MBUtFgc8jLc1UVyoCi0WJx66ajoaOm3cxqymzSL6O9z8x+d9v6fFkdgif8PiLPxqeixP5IemgD/5CAexx5Z6hqM1N/eFQLW74YB/3zW1QsZt6gDpTXJ0iApL84TCfPe8cxgvXD8Tah+OWTg9lqtLZrMf7rk0FY98XCqamnl/QbpkSqWMBuQyGm8V1/Ac+togGddv0V+TYdWCZK7cyPc8awvS8f2pJizKNeIPv0yGJkiONoudy7Tw3Ry2me1YW5ABi8MNp5vB5j3HccG0aETqglDTZoEhWIkEg5rnnJsoNtIfhn1jR1HUap8/aQC5AE4P93lGCmJ5uVuunYFHF2Xh7ncO8Qxww+4S3gD6/lSrKJnZXL2f8X+vscuOgqw40TQL38JQGQ1uUvF9n2uoG62FywPc4XPdjyzKxN0LU3nSzYULTGi32DFrioE3YW1cmg0GFJb9/VveZzssdtGaFKvLLRjkHxysxdqCDKzfdZTXtHXL52V4fHE2pvSQ0PGGLi4twHdx5Ts5iDWV9M/RHw5yH8gkRUBA0D/EuPONW2b3W7/RF296owpO0fcjdUGiqY4sb/pz5sZPT3DvsbyZEh2McK0SL39ZAYWMFjTu9eXPBIMaf7ggmSfl/fjiLGz+dQ6sDg/WvneY6/s0Wa/BG99UCiTuH7giAyq/zYbvQopNX/Llz42fHkdmXCjXtD0pMhhGvQYtZgePPx+6ajpmJepx3lTDgPoyTVT+9LXDZ6+dIbC/wgUmUQEJ/4inzelBaV2npH1V9pQzPNqjgMk+szlTvD3mFk6PQYvZDgaUoN4tKiQIr/2vEr/KmsS9l2BQ47b5wmuV08ArX1eJRiy2fF7G+w0qBY2fWy3c97od3tRkwGtna3Ye4myJ3RS4XB5Bv9mHrpoOo14jen9Ha27uC4Fod8MJXyn+b042D2iTnGjQIiVKJ/rZ/dVtODfJwKVI+m/+HvjgKFbNT+bxzqr5yYjUBSFKF4SnPz2BxbnxgrF0x4Up2LC7FOsuT+dlXagUNJ5YkoW0mBCe04rtE2qK0uHm8xIEpTvrdx3Dc9fm4mafFgoPXJGBTcuzUbT9IKcUXLjAhFe+roLRoEVsqAqFb/6Ea2cn8JwrRfkm3HnxNDz5yXGecuZEsZG+MBIRO53Pv13w1trtHIHzDDs8HgaHa9tRWt/JUzBb9c+f8N5t53IRLrcHMNucgjQdqWbfDAPIafH3onUqVLdauNd9UyemReuQFReCC1KjYIrSobq1NzeYnYTqOmzY9lUFXr5xFn7z2g8CT82fL5mGx5dkw+pwQaOU47WvK3DTeUl45CO+zHNpQxe27vOqd12dGw+dSob4MA2C5DS67d0oyjfB7HCDYYDtxdW4ZpZR0DNq5bypaDfbsXLeVIGAitPtBsBP0WAXVzIayE+NQmZcGG9y6K/PyHCQ+0AmKQICgv5R3WrmuBPwLiQ3fFyCG86bwqVfp0br8PBHJbz6jb5407sYFO/NFaXjp6ex3GnUq1GUn4wcYxiO1nZi8cx47nrYRQvLmxuX5eDP/z6EpXnxgjqopz87gaeX5eCJJdmwOFwIUSk4xxn7mT/vPISPCucifpIaTy3NQX2nDXGhKrgZBtfNSUCb2cHjzue+KMfji7N5C6Q2iwMGjQIvrshDt92FSWEqAX+2mu08Hqxus+CZnk0GRXmjjM/sLcMrN83GnKSIAT2vicqfvnZI0xRiQlW8tgNvfFvVZ+N537/ZhSM7JwcHyZAQrkVhfjKmRHjfq+uwwexwc4vOi9OiuObJXTanwGm7aU8Ztvx6BuamRPHeK8iKw7Ofl/EW13tL67FmYRq6HW4wYDhboinA6nAJsmPWFqRj676TCFUpsOLcRGz8rHd+Zvsl+m8IpGwp16iXnHtHY27uC4FodyOFgW6SaZpCWmyI6GfdHkAuo7BhcRZONnWLbv4cbm/02ncDplPJ4XAzONHYjZ37a3DbAhOeuzbXK0Ylp/HMXm87mVazEzanmyfeVN7Yjf+VN3E26W+rD1+ViT9e6E3FZNfaNqcHB3pKetjrWvf+Uby4Is+bRq9RQhMkR227BUo5hcO1nThW1ykaHNm0p8wrQHTNDFid7gltI/4YiY3dMYZh3vJ9gaKopQDekvh8QEDM2+ybDnmq2YrnPi/HrfOmwupwISZUONg+OFiLRxZl8ry+bEpGYb5J0OC0cIEJZocLWXGhXOrE7+clwe5yIzZMA4bx4IbzEnmetLUF6eiyOWF1uLk0p4cXZQISssWG4CBByN3h9gg+y0rXsufXqhQ8z3RRvolLISlcYMKbP1Tjt+dPwcZlOSit74TbA/z3eD1+PXsK/u8f/N4mm/eW4f/dcg6+OdkMmqK4we8bnj9vqnjvvL4wXOQ+kRtVEhCMBjweBvur20XTyfUaOZKjdLA6XNCp5YL6jQ8O1mLD4ixB8/HtxdUoXGBCZXO3aNaAw+3hePO6c4yIDlGhps2Cv+0pQ1xYECaHa7i6EZY7I3VBiA1Voc3iwNqCdLzwRTlXlyzGnyX1nVxa3mNXZ4l+psVsx/7qNjyztwzXzDKiw+oUbMzYhQvgXexrlTKunqTVbEeb1YX7Pyjm0kf9+XP7b+fwztvQaUNVi5WX3gRgUKluE5E/xezwwcvTERGmxEtf9t5XsXSzdZdn4IX/9qZlFi4wodNi5+bEhIhgdFgcvKyYtQXpMNuccDO9UdP02FDuesx28TpLq9ON9Fi+zelUMizPM2J7cTUKsuIQqpLhmtkJWPHy9zxb+uBgLZbnGfH8f7216UX5JsSEqnCq2Yzt33udrgA4pyt7zqc/O4GifBMnwsEqVQ6HLfljNDZegWR3I4nBbJITwjV4dFEm7hZZgy7OjcOMeD0O1rTzyoIAr+2mxYQISmkSDGqsK8jAU0uz0WF1oujN3nYq916Whl9lTeKtadcWpGP799Vo6nZAIaNx3ZxENHXasOaSVKz26xN677uHeSrCu4/U4YLUKE5l3Xez19BlQ5hGiXUf8LMfdh+uQ0Z8GJfy6Qub0ytAZHW6B+zs6g/jpX3CSGzs7oZwEyf2WkBBrNjXt04jNlSNE43dKPzXTwC83mFhikcKLHYnnliSjboOKzImhaDD4sSVOXHosjrxz++reF6x7cXVuDInDnq1AuuvnI42sx0UBbgZ4K5/HxSd5NfvOsa9/tTSbPzz1nNgc3qgDZKL5hP7eme8Gyor0mNDUJSfjB3FvYsNGQXcfF4CLE43rE43Nn4mVD96ckk2Suq7sL24GmsWpnl75O06hmtmGTFZr8GcKeH4oUo8rerbihY8+ckJXiqWWG3dQOE/wGYnDn5jSEBAMDyobDELIl6b93o9piGqIPz29W+4dBz/TdqahWlYYIpE6PUzcaKhC9NidOiwOPDQVZl4+csKnJNk4KUn+mYNPLk0Gz+3Wng8fPfCVESFqATRtfW7jmHlvCTcc1kqJoWp0Wl14t6CdETrguB2M6KebrVChtvmJ4OivB5vMY6lQOGZvWVYnmeE1ekWKMf5pu+pFDQSDVoo5BS6rG7YXG5kxodxqUdSPU5rOqzI9PRmNAxXqhtNU9wCsaHTy8eBulgZCMTs8P4PjqEo38SzuzaLA1EhQXjm1zPQYXUiOkSFquZuLJ05mRMR2V5cjceXZOF4XRfcDHD0dIegiTJrU9OiddxcXN1mgVGvQVWrBQzDt6vYUG8dlMXhRnJkMM+e4sI0ePKTUm5hfcv5SVzEjT3f5r1lePnGWbj7nUNc2m+kLggfH65FQfZkxIWpkWjQotMqnr6cYNCg8M2feKlp6bG6M7YlqQXv2bDxGmkMdJPs8TD4pKQBf9tzgsuEyooPQ327BWsL0mHUa/BJSQM27C4RcPCGxVmYFKrCXRencg792FAVlucZ8Yd/7hddizZ120XHwzPXzEBFs5m34duwWNwp5qsi/PSyHJ7ThHUMtlkciNQF4Xd+zdDXvX8Ujy/JxqMfleD+HkVNfxumKQxb+q9v8Me3L2VabAimRAQWZw7bxo6iqEsBXAYgjqKozT5vhcCbkjmQY0wG8DqAaHj74W1lGGYTRVHhALYDSARQCWAZwzBtw3XtgHSxr4wGNi7LQUZsiEBNyhQdjA9v9w42m8uDv/ao+LBG+fSnx3HHRdNgDNcgLkyF+wsycLCmHUFyGgqawqr5JjR22WB3e2A325ESpcMBH2+K1CRPUd5NWnWrhSt6Zr2Fz+wt465h3eUZ2LzHm4Mv1eOJba4bERyEeL0a//eP/bhVokmwm2FwzhQ9luTGIaFnMZAcGYz91e24898HuRQssQFmFQuR93jCB5uicTb3JyEgCERI8WdKtI7HnXUdNmwvrsbWFXlQyChEh6hg1GvwdUUL1r1/BMvzjNwErlLQeHJpNvQaBWLDpqGiqRs7imuglFNYszAN5Y3dCNcq8cR/SnkTfovFgRaLQ/R65DSNmjYr7vr3IR53hGlkeOCKDE61mPVIWxxubkHDeomf+6Kc49hHFmWiqadOevPeMknupCh+HfGJxm5uAVWYnyzgS/+/j9d3IS0mhFskD1eq20TjUik7tDrdeKu4Bn++ZBoMwUE42dSNJ/9zAm0WB+64MAWxoUGICdOg3dqJadE61LVb8JeFaXC5GTSbHdi6r0Ly2cppWjAXP7woEy1dNngArLs8Aw98cBR6jVJQh8RGCatarKhqMfOUC6Xm/9p2Kx67OgtyGQWDNgjxoWooZDQvM2fripmidgQG3EbS5vSKcHx4+9wzsqWJZkOBiIFskn2DE76CQCvnJSE8WIWadgs27C5BQVYcaBp4Ykk2mrtsyIoPQ6fNiU67CxU+bbB8a/HEbFGquX2nT/pxbKgK151jRIiEkB+b+mxzerMjxByDcWFqNHRaeSnKbDTP6nD1pNWfxGNXZ+Evb/fyelG+Cabo4GFL/2Xvr5goUaDZ+3BG7E4DKAZwBYAffV7vAnDHAI/hAvCnHiVNHYAfKYr6FMBNAPYwDPMYRVF/AfAXAGuG7coh7QH1rf1ivSatZjsUMhoWh1emVaOQcR5XoNco/359LtrMTihoCjVtNl49WlG+CRs/9U4sD16RAYvDBZvTLRgsUoPh6tx4gWf4vnePcLUENAUo5RSUcq+hiRXMbtpThieWZKOssQtWhwvN3b2LIbHznmjoxravKrBhcRYSejxGbg+/ufAHB2slm7aysDk9mDE5DG+uPKdP75NUyHukpZQJCAgGByn+TIsJgVxOi3Inu6k7WteBug4rt6j1rfPttDpxp8+C+a+XZ0AhpyRT5gFw6oNi15MYoeUWwEAvd/xtWQ7sTjevFUtMiAq3+7V0Yb3E5Y3ejdaDu47h3l+l8VKBxM6bHqPD9pVzkBkXJuAv3xpDqQbqb3xbhfOmGjh+E/PiG/WaQacJTTQulZzHp0VhnikCDAMutRHwOjwpMDjdbufNz4ULTHhsdwluX2CCnKb7fLZiNnXvO71pZg8vmo6ifBPi9Rrc5fe5Bz7w2tOJhi4kRmgFoi5i56tssWDte0ewYXEWZhrDcarZLOhXW1rXKSpaVNVq4d0vb+TFNui0Sd/5WaOUC4TkxrMNjVdIOTU8DLB6xwG8/pvZgjTL3/8yGdf1KLsnGNR4ZFEmp+AaHCTr0xZllLh9RvTUPvsGE/QapcAefdeFbB2g/7WbonSwOpxwucFzsLHppWqld8P4m/On4uK0aGTGhaK61QyNUo7okCAYw4cvksbeX7G1dKDZ+7A1KGcY5iDDMK8B+AXDMK/5/Pc2gKgBHqOOYZj9Pf/uAlACIA7AlQBe6/nYawCuGq7rZiHWBHbjshyeoAebttLY5cDyrd/i1y9+h5tf/R4dNidunettXhob6g376jVKNHQ5cOe/D+JEo1mQ784qoNmcHtz//lG0WpwI0yq4wQKAm+R9r4ltTCvlzStt6MKWvV655nvfOYI1C9O86UISnz/e0IXNe8oxSa9BS7cdKgXd53ltTm9z1coWMzweBiU+qmE7f6zhagTYpq0vXD8T7+z/mSeWoFJ4e6vMSYpAokGLyhYzvjnZjIqmbng8TL/NTQfSIJWAgGD0IMWfUyJ6VW59uXP1joP4z9F6fHikDntLG9FmcUBGg/OGbvuqAl02Nx70U0376wdHUdVi4b22eS+/MbeM6nUw+V7P2oJ0nG63iHKH08PgkY9LsXlPOcefh093iH72RA9ndlqdqOuwYet/TyI7PkySO9kFtdXpBk1TAv7y/Q4b0Xx6WQ5WX5TCKR+3WRyiYglJkcEcj35S0sDjzA8OnYbL5bda8sNE41IpO8yYFIrGLgfqO21co/G7LknB7+clodvhFszPm3tEIe579wiSehqWS82LUjbFzrn3vnMECQYtyhrFlThP9MzZD39YwtkR0Pf8z87Dp5rNvDmYxStfVyFer8bKed7funJeEqZEaPHmD9W8z7Epl+z4jNKpuJ58Us3E/efn5Vu/wfI8I7f2YX/XeLWh8QqxxuZsIMDm9KDd4uTWZqsWJOPOi1PxQE/NGpt2ectrxdi8pxwvfVkBnUqBBIMagLgthmuUWH1RisA+OywOJBjUuPuyNNhcvaqsr39ThZXzkrDh6kzccn4SzDYnl058f0E6dh2qFVy7WimDTqUUzAOb95Zh3eUZMOpVXIN2uZzG1KhgzE+NxjlJBiRGBPfrnKho6uatPQdyf6XW0oFk7yNRY7eHoqi1DMPsAACKov4E4BYA6YM5CEVRiQBmAPgOQDTDMHU9b9XDm6rp//mVAFYCgNFoHPRFDzSP2Tcce8O5CQhRK7DSJ3WI9a4uzYvH/T2TRV8pley/5TSNpi4716Rx054ybpJ/9tpclDV2IyFcg8d2exXlpLwlvqpeNqcHzd12PLkkGyFq6VA461mPDVFxaSNvfOsdhEkRwahps/A84r5GXNbYxR23rsPG/fb02BAkR3rD4BaHG0dOC9M8pFI4pkXr+vQij8ceNmdqnwQEI4nR4E+WO1OigrF8tpEnMHLHhSnIjveKTvSXiuY//9qc3pR5gFUy1OD3v0zGC/8t52pN0mJC8PwX5ZifGiXKHaFqYW85KbXOadE6JBjU0AR5p89DtZ34f99UcsJZLHca9RrUd9rw+jdeTmT5yZ+/WJ5/5aZZaLM4EB+mRmO3Hc99US7gTCmIRd7W7DwEvUaJ85MjJBc444FLB2ObUnZY2WLGy1+dxLXnJPI8/0X5JmiUwp6svvbHMAw3J7PPdmpkME63W/H6N1VYlte3wqbN6UGr2Y5zkwyiohXs59osDtA0OLVp1i6euzYXbRYHKpqF83B1q5k3B7NosziglMugVsgwOVzjFcYI13DRm4HOxWLpZX3pEfimAAaSDY0kAmVuF0vPZtejKgUNpZzmRex8U8DFolDrdx3Dlmtzseqf+zlbfOnGPHx/qhVuD/DCvgoA8KZLhqpR3WbF9uJqPHhFBn7/y2ReajDX/3FPOVYtSMa2ryrw+JJsFOYnI9eox8ZPjov2P65qNiM6VMUJ7rGwOT1QK2TIMYYP6V4NJX2Yvb/H6zsDnjNHYmN3AYCtPUqY0fBG3WYP5gAURQXD2yLhjwzDdFI+zWUYhmEoihJsrRmG2QpgKwDk5eX1vfWWwEDymBs6bUiJCsbvL0iGx8NwdRIAPy94amRwvykVjE/KUHJUMI7Xd+LdA7W4ZpbRK7Ftd6HV4kBtmwVdNhfKGrtx1yWpAMMgPFiJmFA153FhJym2PxTgbaarDZLjzn8fFA2Fs+Hsp5bmQEYDHTYnuqwOrJqfDJvLA7cHaOy0cspyvtceGaxCU5cdaqWM1/agzeKASi7Dox+XYPVF05AUGSy54Kto6hbdwD13Xa6kRySpZ7M43nrYDId9EhCMFEaDP1vMdqyan4z0SSH4wz/288b905+dwJ0Xp2BqRP+86T/vevlTh8cXZ6Km3Yon/3MCkcFK/OniVNgcLsSGqVHV3I0bzktEiEohaNNSlG+CzeURnOuDg7WCurvCBSY8+Ukpfv/LZFgdTk5YRUYBabHBeHJJNk40dsHtAZ769ATnkZ49JRwMA3xzshmxoSqBevLv5yXjL28f4mr3tlw7Ax/ePhdN3QNLjZOKvBVXtSJerx4zWfrhwGBtU8wOO6wOFOZPw8qepuQAvxyhL6dnTZu3Jo2dk80OF4KDZAjXKLG2IA2ROiXi9RpBKifbrD7BoIZaIcdf3j4kSLNl52yVgsajizJRVt8FD8DNwTQFVLWYEROq5olXsNeoVcqhVsqwtiBdoMT6yEfHsDzPiCf+U4pXbprNS4ke6Fwsll7Wlx4Be12BZkMjiUCZ22mawsVp0XjpxjzUd9hQ3Wrhov3rLs+Aw+XhiVCZonSceI9OJROtYTt2uhMr5yUhNUaH0vou7K9qx+Y9fPVU383aussz4HQz3LoUEAoR0hRwx4UpeLSn7U1RfjJONHajSaQB+SMfH+eNE1/BvegQ4UZqoKqVQ0lBZ51G6bE6JBi0PP4ONHsf9o0dwzB1FEXthlcJ0wPgLwzDdA/0+xRFKeDd1P2jJ40TABooiortOXYsgMbhvu6BIjZUhV+fk4DVOw5IFlObonQwBCv6rJvwJfSHF2Viw+4SOFwMVsxJECi8WZ0eQQH/E28fhsPFYPWFJqTEhMBsdyFYJedq6lQKr9oca7x1HTYuFG4M1yAmVAWrw43bF5gQopbjxle+5xYVvq0N7r0sjYvi+RrxqZZurPqnt/7knkuncXUnbg84r6J/M9SBThDaIPHoIusROZt62BAQTAR4PAxOt9uw5fNySd60ODwwBCv75M0HrsiAWtHb4JtdxD76UQkA4IZzE6CUU1g4PZbnMb7jwhSog4DCN3+CXqPkeLPb5oJcRqHdYhec6w8XJEMbJONt1lhue+CDo3jxhjzUtNvgYYB3D9QiJToEOrU3SuLL4V5FODd+9cyX3GubrsnhFjEp0To89UkpT9Ri1T9/wkeFcwcs0y0VeXN7+patPxu41ONhUNViRUWzeP+uU81m0brG7cXVuOPCFKjkNK9J/e/nJeO2f/7Em8s/PlzH2ZTV4UZVi7fnrEpB4y8L0zi1P9/erecmGdDUbcd9v/KWSzy46xhPfO2Dg14n79YvTyEuLAgPL8rEvT6LyYeumo67dh5EVYsVCQY1tq7IQ3FVK89O2QU1awNSzpe+UnL9P9uXHsF5Uw0T0obGC6rbLLj1tWKuTnnxzHikRuvwxCeluGFOoiAq9tfLM/DvH6uhUynwt8+E9m93eYVYCvOT8VZxDe65LI2rwWM3f2wWw8p5SVDKKTjdjOTGv7c9CMNt0nYU13DONlY5uCjf24Cc/e5ABPcGE4UbjL37gqYpJEYEwxiuRc7ksIDlzGHf2FEU9Rm8QirTAUwGsI2iqH0Mw9w5gO9SALYBKGEYZqPPW+8DuBHAYz3/f2+4r1sK/h4At4fvjRAjuNL6LpQ1tOOhqzJx37uHuTD2xmU5qGjqxvS4UKjk3ga7GqUc9R1WblJniV+nkiFnsh41bRa0tlp4jR/XvX8Ut5yfhLf318DlAX7//3pTQVk1rg6bG+WN/ImsrsOGzXvK8ey1M/Bzq5W3WfMVIHjzh2psWp6DDpsTYWoFokKCOPVPjVIGl5vBox8f8zY+t7sQG6ZCaV2nwJPT30CRmiCidUH9epGJlDIBwfiBv8CD2Lg3RQXjb58d5xxJ/ryZMSkEtW0WtFkc+PMl0xCpC0JpfTcvPe3jw3V4fHE2/neyGbfOTeIWH09/dgIr5/VuKP1584ErMvDDqRZuszUtWgelnEbRmz/h1rlJPPEnwMttP1S2cguNwgUmvPK/k1j7qwykxujw+m9mw+FyQxekgMvDcAIFrEqcw8VATnsXNYtnxvNaKLDHH0wfsUSDVrIX4OLcuD6/O5G51ONhcKS2HX95+xBunZskancujwf/+r4GK+clISFc422FYXOiICsOr35dCcDbK25atA5mhxvljV28+XjTnjIU5ZsENrW2IB2dVifKfOZh396tADj7Kco3weHyBntsTg+XSdNlc+Jv1+QgWheE+DANZkwOQ0OnDQoZjcc+PoaCrDhQFKBW0Giz2EXnYBndv+T7YFJypaK8vnoEBGMDdsPia2erFiSjqsWKqBCVQLznrx8cxdYVM7lyIvb1zXvLsHFZDtbvOuatdVPIsGJOAvd9X365dnYCatos8DBAdYsF50wJF7Wl5CgdF6Vj9Siuzo2HWkEjNlSFl26YiboOO4KD5Hhw1zFB6mVaTAheuSkPxnAtr70Au0avbDHjeH0nb2xKReHONAU90DlzJFIxtzAM827Pv9spijoXwD0D/O4vAKwAcJiiqAM9r90D74ZuB0VRtwCoArBs+C5XCNZQWsx2nG638SbLp5flcMYg5lFeW5AOhQyYPsmIn1vNePGGPNS1W6FWylHdasY/vqtGm8XBee3eO1CLv/QInLAD8u39Nbjh3ATc9Mr3vONu/74ah2q9hdIUJZ4XzapxPfu5NzwuZrwyipIMlb+93yuAcoPPuTcszsKl6TE43tCFm1/9AfdclorFuUaeR/yhqzJFezz1NVCkJghjuBbGcO2E9iITEExEiKXCABCILPnz5vorp+PnVjN0QQpMCg3CKzfNws+tFgFvrpqfjC2fl2PLtTPgdDO89LTYUBUuzYzFjX682WVz4rWvq7jaPDHeZNUuC//1E1QKmqsb6Wsjyqq4sQvxlfOmYvmL3/IiKvfuPYLLs+O4Td1N5yXy+jsV5Zu4VghDXWQA3oXGr6bHQq9RclEbtt9oIKUIjRY8HgbVrWbsr26H0+2RjAA/fFUmdCo54i5UQ6OU48V9J3GisRtPL8/hbCs2VAWGAf7wz/2S87GY4iXbc1bq+fraz6Y9vTVqrJDF9T3OAG+aZhYmhXh71JXWd6Gm1YwFqTG833J/QbroHJyXEN6vDQwmJfdsiPKOV4htWFg9hlM+rQxY2Jwe1LWLR6/KG7vRZnHg/oJ0dFidAs7cvLcMG5dmo6nLzoto5yXqBVlevlkVhfnJmBqpxaoFyYL04e3F1fjdvKlc5hkLlYLGkdOdvars4RrQNCUapfMNUkg5yMZDCvqZYCRSMd+lKOp8ACaGYV4BoAfw/wb43a8ASLFD/jBdYp/wNRSxpoy+JM2KhXhzkENQWt+J7d9XY/lsI+7oEVjxN152QqAob3F+QVYcHvNrGLk0T9jKYP2uY5wHpc3iAMNAUlyAzXWXSgGlaEr0e1KbxTU7DyFUreAkjRMMWkGzyPvePYznr5+J//PxWPY3UPqbIMbaIzLQfG0CAgLpVJhp0TpRkSV286QNkuPh5579+wABAABJREFUj0qglFP4wwXJuP/9o1g5b6oob0YGe6W0bQ4PnvikdEC8uXJeEm44NwGynqErxZtWh4vjyCkRWhi03rRQKQeebxSvICuOu172ePe9e4SrlVIpaFydG89t6tjPbNpThr8ty8GjizJx9xnWbMjlNM5PjkC8Xo3GLhsW58aNKWeNFX+ydlha34mt+yq4GjrW7lin6pwkA+o6rCh88yfec+2yOUH5CKZICUv4zscWu0tyTt35Y02fUu++nwXE5+C73zmEUI0cUcFBWL3jAJ5Yki3YSD7Yc02+42/D4iycl2To974PdrMW6BGL/jBR53axDUtmfCieWpqDEw3ioh9aiR5zpqhgvLhiJo43dGFSmFrUvrvtLm5Txzokfvv6j9BrlFzZT6hKgYc+OgYAXB9HsbU1G1x4cNcxPHttLm7zcaSwmzV2PcqKQvUn5CPlIJvozomRSMVcByAPwDQArwBQwrux+8Vwn2sk4Gso/guArLgQONweHkm3WRwIDpKjtdsGtwe47pwEnO6wcjnO/pP9+l3H8Mw1M1De1A2zw41QlQwFWXEIDpLj5ZtmodXsgFxi41Va34mlefGI12vw7OdluDw7TnRAzjDquYmMTWWqbrVgUpgap9stUNCUqGePYcDrx+R77v3VbSjIisOzn5ejqcsu+hmny4OPBjlQAnWCIE1XCQgGB6mC9BdvmAmDVokHr5iO+9/vFVmKDVFjT2kdCrInY/HMeEyL1uHJT0pFN0nrdx3Diyvy0GFzYtWCZLg9HlS1WLH7SB1euC4XNE2h0yq+uPYwwKY9ZVzTZkA8gmIM1+C1m2dz/Y88HgbPXz8TP1W3gaa96XiRwUGo77RxUt0spHgzMjgIf9tThsIFJngY8dqTI3WdmDNFPyixFCkECp+OJX+ydsjWcrZZ7Lz03m1fVeCBKzLAMAzufeeIwM5Y52dShDe9NUhO9zkfqxUyNJvtojbFMF6FSo1ChjsvToEuSAFNkBxBCqEsPXtbpGzpwM/tSInSQa9RwuZ0S0ZZ2Cjh3OQIzEoMH/D9DhTbGWmMx7ldKhNC7LW0GB223ZiHLrsLk0LVSI8JAU1TyJgkFP1Yf+V0AAxWX5SCjZ/yMwm6bA6YHTSe/OSEZBpzpC6IW+umxug4ZwNb9qNS0Fg1PxlX5sQhL0HPpXz2pRRvc3pg6+kpGhemRnWrlYvAsembdR1WHK7tgMPt5s7vK/zCBmD6cpBNZHsfiVTMRfC2KdgPAAzDnO5pNj4u4F9UyRpzbKgKv78gmWt14KveMz0uBIdrOgUNFF0e8Ym82+5CVnwoWswOuNwM/vH9CSzPM+I3PU3Oi/LFUyjdHmBKhAavf12JK3PikDEpBA9dNR33vdurxrXu8gxMjdTgo8K5qGox46ef2/HCF+UCMYF1BRl4YV85V6y9YXEW4sJU8DAQlWR2e7wTTmyoCtE68fxknUqORIN2QgyUida4l4BgpCFWkK7XKNHU5UCz2YH3DpzieJNhgBf2leP+ggyBZ1aKNw/WtMPak5EQqlFi2cxY5BjDUVLfhU17yiQXH2wfp9L6LmxdkQcZxSDRIOTNSWEqJBi8Y9vl8uDDI3W8NPy1Bel4/ZtKb6peT6809r3cHmea/7k1QXLUddiw+0gdii5MEf0MTQHfV7YhNcYd0IvLwWAs+dPXDlUKGg6XB9u+4tvec1+U4w+/TBa1s+ToYLR2Ozg11L7m46mRWm/NG00J5uJHF2UiUqeEjE7Czv01WDg9Fus+EapYt1kcWH1RCs6basB5Uw1QK+SSc7Cb8eCGc701TWLXxIpdqBQ0FuXETQhbGm6Mt7ldaiOqlFOcgJ1K4VXSBYCyhm5edPippTm4dHoMJ/oxNUKLsqZur2rmN5W4NDMWKdFeRd+KZjPsLg/XooW1w33HGwXKq94aUjsXhZMSxbK5PNiytxybrskR2Kv/3yq5VxirutWC6BAVTrdbeSnRbMNz9hr+tjwHN/8iUbApnZ0YjqtnjG3GwlhiJDZ2Dt+WBBRFjaukVd8cZd8UnKtz41HR1M15I3wLoFNjZghSbDbvlZZRVilkuOW1Yi4cfcv5SbzUix3FNbi/IJ1ryuibf1yQFYdDtZ04VOsNq7/2m1l4ckk2zA4XtEo5dGoZJoVoUNNugcXhhlohwwWpUYLUjgd2HcWTS7IBytvjiS1GrWzuljz3moVpPelOx7GuIAMP7OqdpB64IgNPfVKKx5fkcH2DxnOaw1BVkwgIzlaI1XcszYvHPe8cxh8u8Bbw+/ImwK+96483kyKDeYsbNprGLj7EUiZ9+zh1WF1Y+UYxPrx9LlwMI+DNyXotV5t1qsXCE3thozkr5yXh0sxYaIJoXoRN1hPR811QFeWbUNduAQBckBqFB3cdxR0Xpghq7DQKGV7YV4E2iwNxK+dMCBGKseRP1g5ZewjTKEVtL1giBS1IRnObOqD/+Zg9boJBjb+vmImGDhsm6dWYk2gAALSanQAgmIPZVgtljV1IitQiPTaUqxt6dFEW7n5HKISz5pJUbNpTBr1G2afSdlG+CTJ64qYcngnG29wutRH1FYOyOT04VNMBgO+Ytzk9+NNbBxCpmw2DRgmry41Ouwv3vXsEeo1SoMJeuMCEf+33Ck55ehxisaEqLJwei637TnJpzKkxIXj5q5O4Oncy3vyhmudIEXOsqRQ0ZDQlurb2tV8ZBS5N+cbzEqBS9LbyEEtRPlbXKfi9m/aU4cPbA3OTPloYiY3dDoqi/g4gjKKo3wL4DYAXR+A8IwLfHGU2lXHrijxYHC443Yyo4QYH8ZvbsuFiu8uN567LxQMfHIXDxWBpXjymRGhR1WKGXqPkws7+Yem6Dhve/N7bmPxgTTtXCH/t7AROpQvwesPBUAhS0AjVqKHXKJASqcMnJQ1cfYFeo8Qf802iRKYNkuGXKVE8ojeGa1HVahace3meES9/dRK3zp2KzXvK4XBVeVUxHS6olXK0W+worupAQ6cNpfVd4yrNQQzjoXEvAUEgQay+IyVKB5vTgykRWvH0R780Gb1GCQVN4bGrM3Gq2YwdxTVQyimsLchAaV0nT+nyp+o2bvEBgKuhWjU/GZPDNTjZ1M31cfKt0Wiz2BEXqsEpl5njzdToEADgarMA8VShjNhQTIvxer5pmsLUKO/iweNhYIoOxsp5SfAw3kyO4CA5uu0uFOYnwxSlQ1WLFa9+XYlV85MRGRwETZAcSjmNv75/lEvr3FPaiNp227jjS3+MJX/62uEb31bhqaVZEtFUGbZcm4tDNe0IktNQ0BTCg4Pg9jC8hsjsfPzctbk4IDIfs/M9RQE0gFmJ4Ujoseu9xxugDZIhTqJGSSmjcFUOP7LgFcKJQZhGjp9+5s/B5U1mzrn8hk/fr4zYEFS2mLF4ZjwYBnj9myrkTA7DsbrxPxcPN8bb3C61EfX4dcxj/xb77KkmM+7670H8ZWEaunrqQcU2Sr71aazoiu/nfBvQr5qfjAd7BILe3l8Dldyb2lnTZsGOYm+7rLUF6Xhn/89YW5COVrOdi2rXddiwt7QeW1fkob7TBrVChhf3ncTvL5iKF744iboOG7rtbrz0ZQWXIWfUC8eQL//7/t6mbhvHzWcjRmJjFwng3wA64a2zux/AhSNwnhGBf1FlZLDXG9vQacfBn9tEPcJOd29zW7Fw8brLM6CUU1w+P/s9moZkzceJxm4w8I5UigKuzImD2icvPzZUhRvOTeApwBXlm1DTZsWG3SWcEltdhw0/t1tFiSxBxHtH0xTmJkfh5zYzghQ02s1OGPXJaLM4cEV2HELVXi/nodpOFP7rJ+5Yt5zvTYPSKGW4+dUfeHnPx+s7kR6rQ2LE6A+0oXosJ7pqEgHBcEOsIJ3xeJ1hte0WUe6s74loAb2c9kefMbfu8gxog2RYJVJI72F6Fd98N3dbPi/H6gtNcHu8EcPUmBC88EU56jpsSDCoUdtuw/Xb+LxZ32lDck9E8Na5SQDEvc8xoUGiPEbTFBZMi0ZiuBYnm7vhdDGoaDbD7fEgShcEuYzi6p6f/OQEd7xbzk/iakeW5sUjLlQ9pnwphqFw6Fjyp68dNnTaYHe5BLZ398JU1HfYecp9RfkmPPXJCbRZHIKGyCcau6FWygDw5+PIYCUWTo/ljv2SwquGOlmvQXWbhYuieBiJFh/ROtHIglIpw9zkSIRrvRvMtBgdKprNSIrUcvXxbOaQSkFzPb58jx1oc7EURjuqON7mdqmNqO8tig1VITVGh7KGLtHPhqgVWJ7nFfVjU9b7qnNTKWgkGrRYfVEKuiWEgeL0avzxQhOSIoMRopLzMhHWFqTD4XTDbHNixblT0Gq24539tVgyMx4r5yVhaqQW3XY3Vr5RzBt/QXIaJxq9ba93/liDuxemosXigIeBqMiLP/+zvzcyODA36aOFkdjYXcQwzBoAn7IvUBT1FIA1I3CuEQFbVJlo0HK5zXqNEvdfno7HPi7h5epvL67G0pmT+1TQeuCDo4Kw+ea9Zbjz4hRsXJaDmlazaP7y+p6GpSxUChpPLslGaUMXtEqZQAFu054yPLkkG3denIra9t4cfLGwd39FpZP1Whz8uQN/9qkxuePCFDz1yXHJfkkbl+XA4fZwIX7f8yUYtJyXe7SI/EyKpMeTatJNv1uF2uZ2weulx8vwi0tG/3rOBCVHj+CixdeLvhcXEYZX/75llK+IYDDwLUj3eBjsPd6A1RelwGx34d0DtQLuXOPT6kVM1VKKO1fNT4aMpjAlQitIk1t9UQpe+V8ltyhXKWg8sSQbT35SivVXZnKLCfZ4bPNbXZACeo0S06J1qGu3iHKy0+0R/+E+v/1EYzfu9GuOvuOHcgEHP3TVdDyzt4zb0PqmRLF8CQgFEkaTg4bKoWPNnzRNca0BGjqt2F5czbM9i9PNqfkBwpYD/g2RH7xyOp76pBTFVR3cOVQKWrQH2H3vHkFsqApy2ru56ra78cHBWsHz37A4q8/NhFxOI1StwHenWnk1ROsuz8AL/+2tj2frrdhxxL42kLlYDKO50RoLIZOxts3BQmojyj5zvUaJG85NwBP/KUVhvkmQEr76ohTQNMXZALsetLvcopui3Mlh+PuKmXhmzwnoghT47S+nAoCgKfmpZjPcHuBIbYcgHXK9iELr2oJ0bN13ElUtVtx5cQq2fF4uGH/bbszDvZel4eGe1gh2t4c7doJBLWijkBYbgqeW5uBPb/WepyjfhFMt3bxed1KYqKnKw7axoyjq/wD8AUASRVGHfN7SAfjfcJ1nNOGb21zXYcPzn5cLZLhZ7zGAHk9E8IDC5janB2FqJXf8BIMaW67Nhd3pBgVAJqNFm9aWNnThpS8r8PiSLNHzsO/fcWEKHl40Hfe+c0TQ6HeuKQKZcWEAgIqmblGjrmwxc5s69thPf3YCq+YnIy5MhY96vKEapQxOtwcLp8dwtXVL84Sb23veOYycyWG8zfJIE/mZFkmPF9Wk2uZ2GC65TfC6/ciqMbiaM4ODkYn+FgCo/c+zo3w1BGeCyhYzVv3zJ6/09dwpuG2+Cfe/x89aeOGLcq5uw9STtukLKe6MCVVxQhUJBjWeuy4XVocboWoF7vr3IUFz27LGLhTlp0Am4aX2MIDT7cYN5/Y24U0wqDnOTIoMxstfncTjS3L6XAyIcc7Tn53ALecncalzWXEhMEXrYNRrYIoKxs+tVm4jyH7nnncOIzs+DMcbxjaV7kw4dKz5k712vUaJ2y6Yioc+KuHu42NXi8+frLKezelBfJgaqxYkg+5pAF7bbhd8vr5DPE3um4pWvPRlBe4vSMdnx+qxPM/IbS5lNJBr1OP8qREApOdgwJspxG7q2GM/8MFRvH7zbHjAcJsSAAJF6v7mYrHnMtobrbESMhlr2xwMpDaigPeZN3XZucyt1/5XicILTd76YbsLrRYHDMFKnGrq7WHHpvHecG4CHrwiA/e/z49a3/vuEbRZHLj3sjQo5TQn6ufrxF+eZ8Qb31Zh8cx4yXTI0np+/TTb1/HZz8thc3lEv1PbZkVcmBoPFKRhUrgWv3291wlX1WLFC/8tx+NLsnG63YJJYRrQFBBvUKMo3wSzw82lIbdZHPioHxsaj+qoA8VwRuz+CeBjAI8C+IvP610Mw7QO43lGDL4TdmyoCk1ddi4tZ+ePNThU2wnD0Xq8evNsNHTaQFMUXtx3kltEbN5Tjtd/M6vfsDn7WnWbhWe0q/65Hyvnec+XlyCussYqvLWbHSjMT4ZGKUNcmKbHe+JBmFrBLSbuvDgFTy/LQUl9J9weYMPuEqxZmMZt6sSM+uK0aFS3WXCioUt04MWEqhAR7E1HEhs0iQYtV1fj/93GLu99Gi0iH29F0gQE4xX+3On2AGWNXVxN3AO7SrBhUQaeXJINt4cBTXu5kxWCAoBnfj1j4NzZyufOP/xjP55cko02ixNtFofg824P8M/vKnHHRakozE+GhwH2HW/E3JQoyGggLTYEYWolL2JY1WLF6h0H8PiSbI47jXrNkHgzSE5zUvu+C47MuDD83GoV/U5169ir941HDmVtsbLFK/0fJKdhignGqzfPQlWLBRqlHFolLTm/sv+uarXyaorE0h2DVXIkGNS4ZpYR8XoNLHYX2iwOrkXCg7uO4ellOXhsdwkKsuIgo4G8hHCut5y/LW25dgamGILR2OXd6Fkc4mlwZocL81Ojea/7b1T6m4vFnt9ob7TGo32NJvydSLMT+T0JkyKDeffwUG0n/vFNFa4/NxEnGrvg9gBv7inDfZelC1LWN+0pQ1G+Cbecn4SMSTqU1nfh48N1XNquXqMUOJw27y3D40uy8ehHJbx+yqJpxlE6xIaquPWxzdnbY5n9jP93qlqtuP/9o1h/5XS43MLNX1WLFafbLfB4wFN5L1xg4qKJLPqzofGmjjoYDNvGjmGYDgAdAH49XMccTfju3tnQtr9a0O4jdbgwIwY3+dS13b0wFRdnuGBzeSCjAIvDJUjhuePCFKgVvRMJm06xeU8ZAPCKr01ROgAMKpvNok1xu2xOZMWFQCGX4b0DtVieZ+Q8zOxx2cE0Wa/BtBgdUqJ1aOrmN6ytaOoWGPWG3SVwur0NIKWkw6tbLZgxWS95H2maQlpsiGRx8mgS+XgrkiYgGI8YKHdSNM1LUfTnTrvTiYcXZeJenz5LYty5/srp2Pipt07Nlzt1Kjn+8d0p0RTKz47WY1HuZNzy2g9cNO7385J5yr6PLMrkiWYAPZsyGYVXbprNRUGGwpuJPeIx/inwffGlRikfNa6UwnjjUNYWN+wuwcp5U3ktiIryTfj4cB0uSI1CWmwInl6eg8c+LuFSGn1VJe+4MAUehsGqBckAvI7dqZHBPDtcW5COYJUMqy9Kwel2G28efuCK3nlYpaDx+OJsWBwuGMO1XIqY/xys1yhR1tDNk7D/+/UzRe9/uEbZ773oby4Ww2hvtMabfY0mpCJKrBOJ3ezFhvLv4aKZk7kWMixONXcLUjTZ9aScBihQeKu4hpe2e8+l07j0ZaA3DfNEQxcnjLJlr9fR4X/swgUmPPlJKVbMSeB60KkUNNJivPa488cawXfWFWTgX99XQa9R4uc2C8I0ChTlJ2NHcQ0vrX5SmIbb1AFC0Rf2c/3Z0ER2KoxEjd24hO+EfXWusNZj896e+jUfg9JrlLA43VyuMLs4CJKBl8/PKlluXTETB37ugN3lQYfVgTaLA1lxIVg+28hbiDx01XT8+8caAMDGpdlw9Xi4t/73JE40dnO5y/5tEtg0DbaNgim6t0jaXyFIzKgLsuK42rm+pMPPm2roU3FoSkTfxcmjReTjrUiagGA8YiDc+fiSbN5krNcoYXd7BNwZrlWIcudz1+Vif3U7GAYI1SiglFNYs3AaQtQKHnf+9fIMHK9rx0s35qG2zQqNUo7Xvq7Ab86fyltEF2TFcZs69jrveeewaFTGV+BiqLypC6LxUeFc0RoOKb6MDgka80XveONQ1hZvOT9J0OT+zR+q8ftfJvNqdNZdnoEOq7efLADc+6s0RAUr8XO7lSd2VpRvwmS9Ci/dkIcWswOynvn4gtQoAEKJ+XXv987DxnCt6Hzpb0tiY+f+948IHBWFC0xweqRrPX3R31zsj9HeaI03+xpN9OdEYu/XU0tz8LflOfjjdu9nrSJR3k67Gzt/rMHTy3Jgc7qhUsjw2O5ep8az1+by0nZjQ1XQqhTY+Bmfx/aW1uMXUw0wRWlh0AbhTxenQKWQIUQtw0s35OH7yla4PeA2c+yGa9tXFVhbkI6mLhtWzU9GlC4I7RYHVs1Phs3l4fqaXjPLCIaBoA0C65CZGhksmU7PRgMHakMT2alANnY98CVZKbUgmka/RHzPO4fx/HUz8cznJwXCJ0o5DZfHm8e/v6oFjy/OgkJG4w6/wXvfu0ew5pJpsDo9WP0WP9zc9G0Vl7ssdZ0yGn0WZns8DFwirRtkPr/PV07ZGK5GdauVkw7vz/D7Kk4eTSIfb0XSBATjEQPiTr/XrzvHKKgduuedw3jmmhlclIWFSkHD3vO3nAYYxoPbLkhGTbtVwL/P/7ccty8w4dbXinm8WdNq5h1T6jqT/aIy/twkthjojze3F1fjlZtmS3qB+6qhGetF73jjUNYWxZ7v0pmTuU0d0OsIXTU/GU9+csKbCvnrGTA7Pdymjv3cmz9Uw6BV8uqRWGXrbrtbch5m+8mJIUrHtyWxa65qscJscwpEhxZOjxnQ/Rjs8xvtjdZ4s6/RRH9OJMBrL3966wA2Ls3GE0uyUdduQaJB2FpGRgFtFgce3HUMD16Rgdvf/Il3jAd3HcUfL0zhXrs6N17gGNm8twzPXpuLv7x9CMvzjFizszezoijfhHi9mucUY7+XGqPDLecnYcveci7ytmpBMhft80W8XsNFvtnvb9rjPS8bhSzKTxbdkOWnRuG8qYYB29BEdiqQjV0P/CdsMcOJC1P3S8Q2pwc//dyGtQUZPInudQUZWLPzEOcheXhRJpq77Wi3OkWPkRihxf/9Y79gYN1yfhInnSx1nclROoRr5JIKlJUtZtz33mGBZ3mmkV/Xx9aErJyXxMkqb7l2BhgG+OZkc58qQlLFyaNN5OOpSJqgb0gpZhK1zLHFQLhzsp7PnZHBQaK81251Cpp4P3jFdJ53edPyGbj//aO4dW6S4BgFWXGcqAp7zL6anvv/rZTR2L5yDqxON2JCvLWC351q4bhObDHAphf58ybrqd64LAdGvaZPkQwpngqERe944lDWFgHh843UidtcZHAQtzgta+yG1SncqBVkxXGbOvZ7rF2VN4pLzCdH6fDSvpPINepR1yF87v5N7aWk203ROm4twC4++7MnXwzm+Y3FRms82ddooj8nEgub04Nj9V7RvMIFJmz7qhzrr5yOtT5CVUmRWtx1yTQ88Z/jKKkX1gE7XAxifM4ntbY9WNOOgqw4QaYYq8gumjasVQicdb62zqbTsw4QqfOyr+8oFqZxblichcy4sEHZ6UR2KpCNXQ98J+ydP9Zg9UUpPInhRxZlIkyj4EmrShGx2wOU1nVyXraUaB2e+qSUi+DZnB7c+85hPHB5BlotTtFjtJgdogZOUcAHB2vx0FWZeGbvCdG0n6c+KcXji7Mlc7QjdUpUtVh5DU4ZBghTK0Slcs+basB5Uw2ICVHhWF0XfvXMl7zjDVZFiBA5wVAgpZhJ1DLHFv7c6c8hjy7KBEUBjy7KxN099XNiPYlUChpqhQyvfl3J8VJqtA5P+HHn0boOyU2k1MKnqsXMNce1OT344GAtHrgiA+v8FOFOtZgRplVgdqJBUjGNXQycaOjC4dpOPP+FsJUBW7+ycp5XmOCTkoYht10hXDlwsLa4YXeJwEEQpRNPbY0OVWHlvCS8/o1X5Y99fSB2darZDINWKbD5Oy5MwUv7TuLSzFher1nf517XYcPr3/TOwSqFTCDnXrjAhGc/L+OcDVE6FYx6zZDtaSAgNhcYEHMizUoIF7VhVlSPdf6borR4/ebZONVihlopR7vFjpe+PIVbzk/ClAh+RI9tt/KXtw9xPMYeV2xtK7Xpq2gWtu0qyjfh5xazYKMZrlHijgtT8M/vq7A8z8hxp1Q0zrfLDDtunrs2F/t/9qbnx4WphmT7E9XWycauB76791azHc3d3ialkcFBqG6z4In/HIdSTuHOi6ehKN+ESaEqxISpEK/X8AyWlYN97OosfFPRAg8DVLeYRVsXaFVy0f42awvSEapSiBo4TQEr5iTize8rcdfFqZDLKGy7MQ8/VbfD6vRge3E1rpllRHRIEE/u2bdBqTE8GioFzTU4ZY89MyGMN9EwDPDK/yqRFR+KOUkRooIrE0VFiICAYGjw93zGhqqQMSkUh2rauTYBC1JjsLe0Ho8vyQYYDyaFqgSL4aJ8E3QqGZbmxXPNxz0MI+BONmNBrJ5NSizCFKXDi1+Wc31AaQo9xflemWyaAjQKGV7++hQunR6Dw7XtKK3v5FQ96zpsPK5j+Y6ta2nqduCJJdk43tAFhgEv7egXUyMIb44SWFtMj9WhrLEbTy/LwcmmbmTHh0KjlIvanIwCL4VMbE6eMTlM1K5cHg8UMgq5xjC8cP1M2JxunG634sUvT/H6MrJRidL6TsSFqZEZF4roEBXaLA5uDgbAE6xgmN5aJavTjTlJve0RiD1NfIhFlIx6jWCz59tyy+b0pgCHqJWgKeCrk83wMMD0SSG4+bwEeADIaQoPXTUdP7dasKO4hmenb3xbhVXzk5Fg0IiKUG3ddxKXZ8eJjgWFjEKn1ckp0SZGaHG63YKoEDWe+6IMb/52Dn5us6K0vgsv7KsAANx9WRqv9npHcY3keX3RZnHgWF0ntuz1ZpItzo2TvI9n0qtuvPa5Ixs7H7C7dwC4ftv3KMo3obrNAg8DLJ4ZD61Shic/OY7fnDcFpztsWPP2YaREBfNaCmwvrsaq+Sb85e1DvMJUsYGgUtC4ZpYRb/7Q298mLSYEz39Rjg6bUCFubUE6zDYnEiM0qG23w2x34bn/lqMwPwWTw9WgKRqhKiOSooJhDNfiu1Mtkg1Kt1w7g6e+tXFZDhLCtYKJxreYdCKrCBEQEAwdvp7PiqZurH3vMP50cSpK6ztx43lJeO3rCixIjcFTn5RieZ4RT316Ar85bwpWzvOmltMUEBwkx89tVk6IQqWg8fx1Qu5kMxbue/cw3vi2CivnJcGo16Cp244uq0MQ9VhbkA6VkkJtux0WhwupMTqkRutAUYDb4908/txqwctfexU1j9V1iS6c6jpsPK7z9ajXddhQ3uhNh/LnebOEZD3hzZEBTVPwMMCppm50O9wwRQWjocuOZ/YeEdicRiFDSV0nZ2M7f/QqA/r2nMuOD8MHB2pFVQXNNiciQ1Rot7rw1/ePos3iwCOLMvHcdTPQZnFymzrfOXjrvgpO3VCQ1hsbit++USywId+6djIPnz0Qiyixm72qFjN++rmd4ybAayt5CeFcVNeXS++5NBVWp4fTdGBtWKfqVd/1OhE8uOvfh6DXKPF4T6qx2wNs/76a68fo7/goyjchTq/G61+fQm27HSvmJAjaEXTbnUiP0fFq6Pzbw9R12LhMBw/TU1P6vTdY4Tv2HrgiA3/7rKzfurgz6VU3nvvcBdTGjqKolwEUAGhkGGZ6z2t/BfBbAE09H7uHYZiPRvI6Gjpt0GuUCFErBER+zSwjWiwObsAcqu3Eg7uOYWlePBLC1Vh/ZSbWvneYlzr04K6jAi/EQ1dlotVsR1JkMNZfOR117TY0ddvx4K5j3CANklE87x3rBS7MT8bSvHhUt1lR1WLFve8c5uo5NizOwgUmr1KXy81INij98Pa5goamQN/F+hNZRYiAgGB40GK2Y3mekTexs15X3/qMF/ZVcLUVyVE61LdbOBU2wMtVf/1AyJ3XzDIiUqfAiytm4qefOzjPcLfdjde/qcKN5yUIeHNpXjyW5sWjqtWKbV9V4PnrZ+L//t+PvFT7126eDbcHXKo5ew2+ym6+XOfvUY8JUWFaTIiAPxPChWIGhDdHFi1mO6fqxz47MZt79KMSAL21bnUdNmwvrsZfr5iOlm47lHIZNn12AodqOxEb2iYalS3MT0ZqtI6bt+955zC2rshDgl4DlYLG1bnCOXj1jgP4qHDugCIyAxHwIfZ09oDd7CUatLA6PVzfTrbW7LwkA6rbLIKobrPZIVBvXb/rGLbdmMezJzbVkm1t4Cty0tTtwN2XpQGMB08uyUZFsxl2l4drCs4GOfztffPeMrz+m9k41Wrut670X99XY/VF03jKn0uUMqyanwyH24O0mBCEa+V4enl2v3VxZ9Krbjz3uQuojR2AVwFsAfC63+tPMwzz5GhdRHSICkvzhKpA63cd41J5/L0Mm/eU45lfz0B9p02QOuSrbMV6ANvMNkSHqHG4pgO5CWFY56PWBXiNXa9ViirEuT3AFIMGr39TidvmJ4OigGnROug1SqzZeQiZcaEAgPveO4xV802i3r2mbhvmJEUgKTKYF25Oj9Xhw9vnoqlbWEx6JipC4zWkTUBAMDgoZbRgYl+/6xi32fL1DrPZAc/8egaiQtQCrqpqsSJEJedFWeL0alQ1W5Bg0OK5L8oF/GhxuAVZByxnPvXpCeg1SphtLtw6NwmAtz/TPe8c5hxdYnwppjTsz2nGcC2M4dphV7ck3Dl4KGU0N39L2dyqBcncZuz1b6rwxJJslDV2YYZRDznF8ER4AG/6V1kjf6HL2lZdh42biwGgtK4D4Vpvqq+YGItvhI1dpFe2mPFDVWufczBA5mECL/oS/xCL6np66vB8e38CwIn6Ll4Ezn+z5S8MVd7YBU+PY8MfDICkiGBRe++wOHH4dAeC5DSXAq9VCutK1yxMgzaIxsp5STBF6VDW2IV//1iDuSlRoCigtL4TF6VHY3pcWL/36Eyi2+M5Mh5QGzuGYfZRFJU41teRaNAiJUon+lBpGpKiKWU9Bi/2Xrxeg876LsyYHIZnPy/DgtQY3Peed+JJMKgFhfxrC9Lx9KfHRXvYbC+uxu/mJQn637EpQ41dNjCMd1HUaRUXZ2G9e4MJNw9VRWg8h7QJCAgGB4tDWv5dih/74s6qFguUMhoxISpUt1nw6EelaLM4cN+v0niCLCqFty9ZkJzijuPLmUtnTkZksBLLZxt5jdJ9eVMqGpIcpcOk0CCOr/ritOFUtyTcOTT426CU4ASLNosDWqUMISo5Ws12bN5TJkg3k7KtvaX1WD4rAU98cpw3fzd2eUUe7r88fdjmYIDMwwS98E/V9HgYVDR1g6Z67ZTdyKXH6pBgUPPESlhbfeenWq42Ln2SjhMPFBPDyo4Pw6GadlGbLq3vlOTx8qZubN5Tzo0btnb5Lz51pecmhSMuTI2Fm7xZE6svShE0TmfLidJjQ/u12zOJbo/nyLhEh5WAwyqKog5RFPUyRVH6kT4ZTVNcEb4vVAoa8WFqGA0aFOWbuPfZHOO3imu4gn7f9+65NBXaIBnSY3Totrtxy9yp2F5czRlMVYsVz33hLexffVEKnlySDYNWieKqDmz/vhobl+WgMD8Zt5yfhO3F1bhtvgmRwSps3XcSt5yfhFULknHrXO97S/PiEaVTITpEhQSDGnKaElyrr3dPKtxc2WKWvDdJkcFctG8gE8Jgz0FAQDB+4Ss5z0KloDHDqOeEKQbCnQkGNZ69Nhdy2iv53tRt5+qd9RolHvqwBDqVHH9fMROPL87EE0uyoVHSePV/lQLOXDlvKn6sbMVdl6SiodOGW+cmITZUxaUJsbxp1Guw/srpvOtbV5CB17+uQLg2iPs9o8GbQzkPgRe+Nig2J68ryMCuQ7UAvHb29xVe4ZO4MA2aOu1wuBhONbowPxl/XzFT0rb+MN+ELZ+X8ebirftOIkSlhFJOobHDNqxzMEDmYQIh2I37ZZu/xB+3H0BRvgkJBjVWzEnAtq8qsH5XCf6yMI2rH/W11UszYwEA0bogHK7pwK6Dp3HL+UlYPDMeMgr427IcPLkkC0X5JrSa7TBF6wQ2zW7CxMZbUb4JWqUMt833ntPucuOGcxOgUtDosnkzLF76sgIxIWrUdfRGyhxuj2Q50UDslo1uS429kfruWCOgInYSeB7AenijvOsBPAXgN/4foihqJYCVAGA0Gs/4pJPD1ALxkocXZSIlUofGbjt0QQpsXTETxVVtMEXp8MhHJVxah2+D2lazA2qlDBaHB6UNXq/0Bwdrce3sBLz6dSX3naoWK8x2FxIjNDjR2IUguVdc5VBtJ9bvOsbVBdx1cSrUShonG7sFnpfCBSYkRwdzhrf+ykysfKMYeo2S84jQFJAeq+MmguEMN0uleYznkPZwYbjtk4BgODFc9unxMKAp4JFFmbjHhzvvuDAFz+49gQ2Ls3Do53Y8viQbTpcbQXIZHhbhzpRoLbrtbjy46yiWzpyMLpsTNqcbO3+sQZvFwUXZWrodiAkNgk6lQEl9J1JjdDjR2M1xJkUBV+bEIS5MhV9lx3HCFP6iKCnROi4djl2kszV6L+wrx/orM3kT+khwmhh/Eu4cmm0mGrRcayK2bu7562eiucuOyhYL/vV9FQqy4qBTyRCvV8Nid/Pm55vOS8SrX1dyaZubrslBuEYpaltmu0t0LmbAjPoczILY0uhhrOZ2/2dMU+A27mxLgA2LM7HyjR+51063WwW2evfCVCgVMmzac1SUGwFv2vJLX1bgySXZePijEjx69XTIKOCJJdmw2F0IUSs4fYjYUBU8DIMHLs9ApC4Ixxu68PHhOiyfbeRKi9hI4T2XpuL5/1Zwm6YpEdqe1h/eSNm+4434zfnCfqUDtdsz6VU3nvvcBfzGjmGYBvbfFEW9CGCXxOe2AtgKAHl5eYzYZwYKj4fBx8fq8a/vvN650h7Fy817TkBOU/jV9FjUtFtwsKYDm/eUY9WCZK6AFeA3qA0OksHhZnhKRIULTPjn91W4Ojee124gVK2AUuGV5tYG0Vx6Jnu8onzvZFFS14UEg5anLuRboFrZYkaiQQuFjOIGtG/NyXlTDUiM8A6I4Qo395XmMZ5D2sOF4bRPAoLhxnDYJ8sBG3aXCNQHVXIate12rNl5CH/MT8Gf/32Qq3ET487Hl2Rj674TogvmN76twua9ZVg5LwmtFgeSo4JR1uh1sOmC5Jxi5rOfl3PeYrmMxn3vHhbwJSuskRYTAsAb1ahqsfL4EgAUMoo3oQ83p0nx57RoHeHOIdqmWknxFKvL6jvRaXdzi8tDtZ1Ys3AaOqwuQUmD7/ysUtAI0yiglNNcPy729QevnA4ZTYmKRWz/7Ry4ZMyozcEsiC2NLsZibhd7xo8syoReo+Q2Y3UdNtS18zfzUSEqwbrRVwyQfY3lRtbO2T55NqcbbRYHSuu68dju49xx2TWwvwIsO54KsmJFNSv+ces5WHd5urdFTk9qZaJByzkG56ZEob7DekZ2eya96sZrn7uAT8WkKCrW589FAI6M9DkrW8xYs/MQzkmKxOodB7B5Tzne3l+Dgqw4nGzqxtG6ThjDtVyjR6nQ89v7axCv1wgMevPeMhRkxUHWc/dVCu8mrrHLhj/8Yz9u/9dPeOCDY4gOUeGppdl44zezseXXMzDXFIFLM2JxwbRION0eUS/GtxWtuPnV7/FVeTOXZ+0L/wExXOHmvtI8xnNIm4CAYGBgOaAgKw6P7i7F5j3l2LK3HJv3lOPlr0/h7svScHl2HGLDVFh9oQnTonWiqZmFC0yobDbzFDSBXu68OjceNqcHSRHBSI0NRm27FU9+cgK3/+sn3PfeEYSq5Hh6WQ7e+M1svLhiJuaZIiCjKMm6v4euygRNAV+WN8LicItyZnQIfxEx3JwmxZ8yGoQ7h4DKFjMO/NyBx3aXcE2VY8M0+PZkE8/eJvcxP7ORg9UXpeCp//x/9s48PKrybNz3mS2Tyb6QhYQEQhIIuyEgWqBK1FKLorJotbgUm/b7RGj5rHxalypWi1qsFLvQuvK1FS0udSlVwf7QitWIyiJbCCQGQhKyZyaTycw5vz8m5zCTOROykfW9rytXkjNnzrwz857neZ/32Q7x76PVWM0GnrtlBr//Xi7P3zKDsSPCcMuK7twqb3D2qQ72fe9iLg1t9L7jJ7cf5meX57BiXiYr5mWSHOXtk+g7/46dtgfMVbWoii/OVlmb/yvnedeyVrOBUw1OVl+SRU5yhN911Vw8vbDJDTuOMD45MuA1YmwWKuqd7DvZwK6j1XxaUsOuo6c5Xm1nenp0WxubUJ7fVRKgIx6+evI5m7dqnuKuo6cprmpClgffPvyA8thJkvRX4CIgXpKkMuB+4CJJkqbhDcU8DvzwXI9DDVlQq2kF60NzybgEHrpqEve8tk/rpzR2RDhj4m0cr3ZQ63DhaNHvYaSWW16Zn8m0UdGYDfCDzbtxtspMSYnk2plp/MinHPf9CyYSH27BYJAoqW6mpNquu4thkODavDQt/KN94mt7Qd5b7uazhXkMVpe2QCDoHO3lpkpylNWv/cGfPijmoasm8fxHxVpfpOWzMwg1G5g4MpKfv7GfK6Z6N746WnCU1zcjK1ae3H7YT27e/uKZ/pyr8rNIi7UxPoi3Iicpkgff/Ipah7f/3dbPAns06S1+eztMJ5j8PNXgFLKzG1Q0OLFZjAEe3/sXTGTrbu98iw8zYzIGN/izEiIomJtBjM3Md89P96vct/rSbFrcIazZupdb52Tozq2vyht4uTCw+MS50sG+713MpaFN++9YlbG+RaFW5WcxZVSk5mV2tsp4ZDlgrgYrBjg9LRrD3Aw2f+xtZbB24SSq7S0kRds4dKrBb17XOlxEWE2kx4Xpz716p99rJEdZufGCdFa/7D9etW3C+qXTmJIaxd6yemodLi1EXw1lzk2LPifzdqgUGBpQhp2iKN/VOfx0X4/DN/Haau64D81VU1PISgjnVL2TpDZ3cmmtg1+9c4jlszOIspk7XFCUt034392QqxmRP7ooM2A35oE39/N/y8/neLWdR/95gCXTR7F24STKah28VOjNO7l3wQQamlu1m02Nsy6Ym8F5o6JJjwvTFeS94W4+WzjJYHVpCwSCztFebqqyQE9+3vPaPrYUzKLVI/ONzDgcLg+JkVbqHC4WTkvBZjGSlaBvjBkkWDnvzCJg+ewMXtldpis3n9zuDdlMj7Nx97fHc9ruQla8i5m4MAvHq+1a6NIDb+xn+ewMbRFhNMDssfHkjY4NWp2wt2RaR/JTyM6ukxhpJTXapi104Yweff6WmdQ3txIXZmHPCf3qfr76eWV+ZkCo2vp3D7cZfRasbSGavrpYXaT2pQ72fe9iLg1t2n/HejL2ye1HePaWGX45wxNGRmkVL1XDJS7MEtBy4I7LxvHEu4eZk53AoumpKArU2lv45T8OsWJeJuD10vnmIr/06df89FvjdedeZWOL34bZkrxUbZ3qO141/HP1S1/w1u1zyBwRTnpcGHe/eia0fv3SaaTFnhtv3WDuXefLgDLsziVd6d+ihkas23aAlfOycLo77kMzdVQMU0edeayirZfdU+8XkRxl5SeXZPPEe2dupIcWTuJkXTOLpqdqJV9b3LJmRB481aD7eqebWlBQdMvVxoeHcOx0E/Z2ZZ7VHnsvFpx/TidmT3rrCASCgU1n5Gd7uan1RQrieWtu9TArI97v+I6DFWzY7s1FmpISGdDuZe3CSViMEmV1zdp1jAY6lJsmg4HKRifNbdEW6rV+ckm21stJPVeS/HudnTfq3OwMt0fIz54R0FMwxsaBcv358OnxGi6fnExFg5NNO4+dVT8HC1WzWYwB+UQPXjmR5Cgrd27d65fr1Bc6WEXMpaFP++84mIz9utrhlzO8Yl5mgEH2u/9XzE0Xpvt5xDLibVrfOIA395xg7cLJ2sadUfLmRqvXVT1w97y+NyDiQc2LBrTXyGrXTkxtyZAWE6qNUW0/kxxlZUvBBTQ6XUiShNVkpLTGTlps73ubh0qBoWFh2HXHvTouMYL7r5ioTeRNOwN3IULNRm8VuHbX8N1NKa938txHx73xwrE2YsMsPPDGfkqqm7VJv6WwlJA2V3RzqyegD0hylLdhukGSkBX8WiU4W880Tk+NtqHgLd/s2yS9s4mmPWleOpgrCAkEguB0Vn4aDBKX5SSSEm2losHJn5efT2NLK41Oj+4ubqjZyK6jp/1kTXpsmHbunhMNVO0o8nrcYm1E2SysfTNQduYkRXJAp3+SKjfHjgjDZDDwl09K/OTmE+8d5tHFU/3G5NvbzGo2dLgz3JvNnoX87D5683Pj9eeRGh3KyvxMZAVtA1XV5+pnXOtw8dxHx7UeXhNHRvJguzlmMpyZV+oC1GiACcmRWogbeOfUfX/fzzM3z2BJXmrA655rHawi5tLQR5W1WwpmUV7vZER4iJ9XGdpkrMWE1WwgxmbhmtxU0mNtLM1L5aXCMm3jwWo+03JA5cnrpvlVsHzoqknMTIthS8EsKhqchIWYCAsxaZ4/Xw+cb8TDhWPjWbP1SwCtkqxRAktb1Xe9VCc1LBPg8g0f6IZqrsrPIisxnHnjEgPmtSwrHDttp6TGTpjFRGJkSKeNwKFS6G9YGHZdca/qKYnVl2Zz1/zxPLLtoN8uxMoXP2fN/JyABU5ajI1Hrp7Mr7cf1oqkTEuN5ni1nXte81cEG3Yc4Xc35PLzv+/H5Va4+/IcHn/noLbrEWOzcOMF6X4x+u1L0TpbZVo9Mo/+8xC1Dhe/uGoyf/3kOIUl9Z3ereuN2GIR5iEQDD06Kz9lWeGdAxV+MmTdoils3nUsYBd37cJJrHzxc20BvX7pNCYkR1DjaAmQnROSI7EYDfz3X3YHyM4nlk7jd/8q4gdzx3ZLbpa29UKymr3tbDZsP6z9r5bf1uNc5GII+dk92s/PGJuFIxVNrNj+ud93v6WwlOtmpBFmMZIUaUWSYN2iyRytsiMrkBEfxm06c+w3153HTy7J5i+flASt0uo7p3aX1GqNmNXnrZmf0yc6WEXMpaFNe1mbHheq1XtQ584jV0+m3tHCI9dM5lS9008WtjeSXthVol3bajZwtKopIHQ+IcJKgU+7mMeXTOVPN+XxybEaUqJCtfN9Ix4sRgN3fTuHY6ftfq9/x2XjuPvb43n4HweDhpGuys8KGqqphthnxPvPcbdb5u195dy5dY/few1mBLZnqHi7h4Vh1xX3qt4iZv27h7nzW+P4w7LpfFZSi0dGE+btFzjqDfePfSe4fV6W3422btEU3XE0Ot1cMTWFrZ+V8fDbB1g2K10rKJCTFBGQJ+Bbiha8N+KJumZNufzstb38/nvTCQ8xEhsW0qnduqESWywQCHqXzspPPRmyZuseNi3L497X97Li4kwSIkIYFWvjf1/Zo0UVqLKmYG4GG7YXcdmEeG6fl621J+hIdpbUONhzoqHbcjMtLowV8zIxSDAyysrzt8zkVMPZvRxCXg4c2s/Pa3ID83c27DjCo4un8sjbB6h1uLhwbDzHqr0VAlVPx8r8TN055nC5+csnJfzPZeO1AkC+120/p5rbeYVfKpjFpJSzh/SKOSXoLO3nSkl1M7/ZcYStP7qAuuZWGprdSBI8++9jfHtysm4+22OLp1JW6yA1JlRrOaNuuq1/97Df6zlbZQpLavy81ocrGpmTGU98mIWTQVoSgDeX+cdb/Of14+8c4oml01hxcSYjwkN07zu7yxNwTA0NdbbKyAp+OkiWFT4qrtaMOt/3qmcE6jFQvN099dwPC8OuK+7VYIuYEREhfNa2E9f+Md/Jpd5wjy6eGqAEiquadMdxqKKJpz8s1nb/Nn9cwpK8VNJibdg7qKqpPt83hll9fHdpLVdNS+m0QhgqscUCgaB36az8DCZDzEaJ52+Zye7SOu5+1VtF0DdUXD1PrSq9YOqoTsvO9FgbyVFWyuudmtxMiQoNmhfdXm4+4tMcHeCqaSkBeX96CHk5cGg/P9tXZQXv/4crGrXv+utaBy2tMvf/fb92bvtQXvD+bzEZuW5GGs2u7uniarurU4syMacEnUVvrkRZzewvb+S+1884E+5fMBFHkHl7qKKRjTuKuPc7OTy+eCoGAyREWPnqZL1fb1Hwzm2PjG6F+J9cks20tOiAfOhV+VkYJWgMsobdX97Axh3ePtDBimS1H4MaKq8+7quDjlfbNeOz/Wu1NwI7or+93b0SPXeOxzgg6EqfGN/KbipWs4EjlU2a4G//mO/kqmhwEmOzIBGoXF4qLOPBKyf6jUPtEaLu/l2Tm0qtw0V4iIlfvXOYr+uadV8zOyGCRxdN5tmbZ7ClsNRvcaLehJWNTjpLsPc92GKLBQJB79JZ+RlMhiRGWvHIcPerZxqE651nNRm47eJMZJ2+YC8VlvHQVZMDZOcvtx1gSV4q4E3mD7OY+PX2I4SYjLqvkZcew+9uyKWgrYy3b55JV2SmkJcDh/bzUy3f7kv7RaHNYgow/rd+Vsa9CybozrHmVg/x4SG611XbFj13y0xdXWyzdG7/XMwpQWfRmysFc8dqRh2cqQKbmRge9H6wmg3UOFpZ8dfPSYgIwSMrOFo9AffB2oWTeHPPCd2wySfeO0yT08PGHUUsn53BinmZLJ+dwQu7ShgZY2NUjE339VUb5Y0vT/DQVZMC9MuU1Ci/Y2pvaPXvKalRfjqoosEZdI3e3ggcyHTUE7qzDAuPXVfcq3oxtg9fPZnH/nkIICBX5FdLpmGQ0IoAjIz2VgcqqmwM2IWodbiIsllYcXEm6XE2FCRO1jn8qmNOHBnBb68/j5+9tp/yeqembHx3QlbOy+IXbx/g4asnMSM9ltWXjmONT0yxmk+wKDel05/RUIktFgw/Duzfx6WLvhdwPCU+muf+sLEfRjS06Kz8DCZD0mJsbNt/SpOFWz8rC5Cj918xESMK973xlW5fsFqHi0irSQvdsYWYOFHnwOVWyEmK5A/LpnOkopHnPjpOeb2TE3UO3epsBgkuzUnE5ZH9wo+6KjOFvBw4tJ+fSZFWxiVF+n03ah6RWgii1eMhOcrqV2isvN6J3dmqO8cmJEfy1pcndOeU6vWdOTqG62akBeQyJUaGdOp9iDkl6Cx6c0VB31Nd1dgS0EtRlXeqhzk9LpQTdU5tHZmXHsWmZXmcanASbTNjkiRWXJxFqMVIjM3it3nhbJVpdrm1KplqqOaSvFRSY2wUn24KeP1fLZnGxJERXDg2joQIbxXb3LQYP/0C8HbbPT0i3IpBgvFJEdiCFERJjLTyxpcnAqrcqjl2g+U+6g3P/bAw7KDz7lW9RYyhrbSrs9W/4s+M9FganW7mP3mmcs+6RVN48dNSXG4lQAk8cOVE/riziPycJBqdbh5sZ6xtKSzl4KlGRseFYTF5J2x5vZNGZysFczOQFW95WrVhZHpcGCaTge9MSibGZqGwpAaP7K2aufrScaTF2Lr0+QyE2GKBoKu4FCNx37ot4PiJfz7VD6MZmnRGfgaTIcer7Rzx2ehSwyYL5maQGh1KSU0zv/9/RVw3I40YmyWo4RdikjAaJO736be0Kj+Lr2vsxEdYefmzr7UFR1OLhze+POFX1ntLYSnzJ80MKjPXzM/x26TrSP4JeTmwaD8/02LDSCmYxfaDlVjaYiUXTU/FIEFNUwt3vLxHC1X7/c4irYjPhJFR7D1RHzDHSqvtZCVF8o+95Ty2eCpHKhv9cu2tZgPJUTacblnT1QYJxsSHkRrdOT0s5pSgs+jNlUanWzek0WoykpUYzlu3e8+1mY1UNLWwcFqKtpbctCxPK4ySHGVl3vgkCjYX6hah8u3RqL7G6Pgw7l0wgU07j/oVGNq0s5hV+Vn8Y2+5XzuF9LhQRseHMzr+jD7R0y/tj43pQP+Mjgtjzfwc1m07oOVzJ0ZaiQ+3MC4xctDcR71RmXPYGHbdQQ3dSI228fDVk7n7VW9vmqc/9E7WfSfrA5JS12zdoyVTq0agJMH0tGhe+Og4+TlJjIoJbJy6YccR1i+dxto3vwq40f76SWlAIRbfnTyTycCFGXEAFJbUsGBKCuvfPYTZaBBVLQXDlmCePBDevHONb9uAigYn7x+s5P4FE3ngTe+CudbhItRs5In3jmgLBN+qZ6rsHJ8Ugckg0djsotkl6xYBWHFxJne/utdPZr7xZWDxqvYyc3ZmPKkxoVQ2OrnmvBSOVTf5bdKdLa9ByMuBTVSombRYm98cWJWfxbMfeXPg1FC1RxdP5XBFI4rizc8LNsc2vu9tvfHw2wcCFrtqBdX0WBsWo1HbMHjkHweQFTqth8WcEnSW9nPF7ZYDKmM+uHASk1MjGRXj3SAYm3Cm0EjmiHDNY+brJfINt9QrRKQWI1Ervz501SSmJEdhsxj55TVTuOW5TwPO9y0wBDAxOZJJKdG9/nnMn5jEuMQIDpxq4HBFI0+8d4Rah6vHFYv7kt7w3AvDrh3BEhenp0dTMDeDlKhQTtQ388KuEhZNT+0wmVot+2o1G7i0YBY/vGgsSZFWjlQ26T6vqLJJW+SYjZLmhg7mqvadpKW1Dm1RoyIqagmGM8E8eSC8eeeCYLJzQnIEF41P4Pc7i7SNrnGJETzcrnCJs/VM1TN1A+2t2+cgSVBjb6HG3qorN51uWSvS0hWZ6bswKq5qYsVfPg/IaxDyc/DhOw9jbBYeXzyVw5WNZCXoz7nDbUUkrGYDU1IjO5xj542K5sKxcSRFWrlsQhJVTf5z63i1XehhQb9gMhm4amoKWQnhnKp3khRlZWJyFCZTYCkNvQ0E1UvkW3woWCGirIQI1i2aTJjFxKSUSHYcqWL1S19w65wM3fMlH3vKajaQFHVu8t0MBglJgjte/nLQ3oO94bkXhl07giUuvnX7HMYnRXLwVINfZUw9l2leeqx2XF3cTPYpd+yR9Z/X4j5TWCAx0hpw43W0k+e746LGOEsSVDW1iHAOgUBwzulIdmYnRFBS3azt2q6Yl6lbeU0VU75eEHURcrRSvzKmWgSgqzLTF7XolSo3wZsLKCoSDj5852F5vZODbYZbsDmnzp/1S6eRGR/e4RxLjwvzmw+qB0RF1cO+Ohi8GxNiHgnONSaTgamjYpg6qmvP8/USgf/6VO9+OHiqUZPlz96c5yf3g1W4TI6ysiQvlbEjwgm3mJBl5ZysS4dCddmeeu6FYdeOYJOiqsnJ/IlJpESHan1v9HJB1i+dxoUZcX47x+0NqzHxga5W3+Tu7iRMq3G5MTaLXznaP31QPKjc0AKBYHDSkezMSY70U/hbPysLSKhXvXtqeFBn5Kaam9zTIhPJUVbdXJKkyMFRSU1wBr15aDUbdPX1ukVTSIm2sig3RZs/PZljiZHegiztG5lnJYSTe44WsgJBT/H1EtXYW8hKCGfN1j26ctq3pYdWYbaDwli/WjKNjBGhAWHR52pd2hs5aoMdYdi1o6NJYTBITE6J0gR/eb2TLYWlbFqWh9ko+SXcB7O21caDIyIsbCmYhcPlISHCitEA56VF+1UEKq5q0m1QqNe8UN1xOXiqIaAc7WByQwsEgsFJR7Kzfd5ArcOlJfS3D2fzTagHf3k3ITniTBEAi5FWj8z8SUlnlY8Gg9Rh01ePrJ9bddmEpD769AS9Rft56Ls4VYv2ZCdGkJMUqXmEQV83q3PsWxOTMBrgP8eqtbkD6OrhtQsn+4Vjqrn3k1OihA4WDFh81625ssKkkVGU1tiJtJp5qWAWdpeHVo/Cva/v1QoGrV86jcTIEN3CWOeNiia97Z44Xm3XjDoIXJf2tCG3Lx1VZw62ph5qCMOuHWdLXOxq/KssK5TW2KloaMHl8VBjb/VrTbB+6TTOHxPnt6DpqEEhEPSx+ROTAhZWMPjc0AKBYPDRkezsSG62D2dTcbtlDlY0cPBUY6d3eoPJzstyEnnnQEXQpq+VjcG9jcHGJxiYdGUTATqeZ+eP8RYlaz+nNl5/Hi63ojufzEZJ6GDBoEQ1sKrtLZz0aX/gK0efvXkmFQ3ejTWXR8Yjw8brz9NylGsdLsYnRfLN7ATtHusoPHJ0XFiPG3L7oqdr0mJsHcr/oYYw7NrRGcOts/Gvsqyw41AFRyqatMpAT39YfFZvWrBclfEr5wAEfSxjhLdXx3B3Qw9Vbv7hCk6crgs4fvDQEb7xrb4fj0Dgy9lkZ1fyBtxumde+PEFpjUMLfYezRyAEk51bCmZ1KDdF+M7QoSubCJ2ZZxCoc/eU1Qc9X8wlwWDEd1Ms2Fr17ZVzGB0XxsFTjVr1S9VI2rZqDqca9NfMHd0THa13u7sR0l7XFFc19fprDGQCy+UItEkxKyOejBHh3bboj1fb2VN2piVCsApDlY1Ov2Md7W509Bic2a20mr1fbVdz9mRZobiqiV1HT1Nc1YQsK2d/kqBPOHG6jrhv3Rbw09Lq7u+hCQRA78nO/eX13PPaPmSlczJTJZh8LK8/t3JTDyFL+4/OzsPOzDO9OdXR+UIHCwYjvgZWR2vVYIaYrBD0fuvonjjbmrY93bk/uvoag50B5bGTJOkZYAFQqSjKpLZjscAWYDRwHFiqKEptf42xK1Q0OAMUQGd28s6249fRYz0pldpRCOhQdFcLBIKBia8h1hXvRzDZmRwVes7kph5Clg4OOjvP2j9mlIKfL3SwYDDS3vgJNr+7U3Wyo3uiKx7u7t4fw82LPtA8ds8B89sd+19gu6IoWcD2tv8HBYmRVk0BwJmKQWfbyetod6Mzu4Hd3TUPthNzvNresw9C0Glu/uEKLl30Pd2fg4eO9Pfwhgxq8/L2Pzf/cEV/D00AmiHWWZmpEkw+TkyOPGdyUw8hSwcHnZlnenNqcmpUh/NJ6GDBYEM1fqDjtarveSqdMZKC3RNd8XB39/44FxEZA5kB5bFTFGWnJEmj2x1eCFzU9vfzwL+ANX03qq7hW90nIcJKbnq0VpGroyqavpxtx683d5Z9GQr9PwY7arilHi37hNHRWwRrXi4al/cP7aui5SRG8NBVk7jntX1albWxI8KZmBzZ4UK5I9l5ruSmHkKWDmzU+ebyePjF1ZP52at7O5xnenMH6PX5JOaNoL/wLTrU0VrV97wYm4UlealkJ0SgKHSrN11XZHN374++lv/9zYAy7IKQqChKedvfp4DE/hxMRwRzE18+OYnctBgcLjdpsWF+JZaD0VGhgZ42LwzGcHNXCwSC/ieY3Lxy8kiyEsI5Ve8kKcrKxOQoTKazB5kEk4/nSm7qIWTpwKX9fEuPC+X335uOyy2TGBmiO8+CzZ3enk9i3gj6i84aP+p5E1bNYXdpHXe/urfHYcOdlc09uT/6Uv73NwMtFLNDFEVRAN1MSUmSCiRJKpQkqbCqqqqPR+YlmJvYI8P5GXFcPD6RsQk9C/E5lww3d3VfMhDmp0AQjP6cn8HkZll9M1NHxfCtSclMHRXTKaNuoCBkae/R23Oz/XwrqW7mR//3GZkJ4f0+z8S8GXwMJd3e2RBig0FCVtCMOuibsGFxf3SOweCxq5AkKVlRlHJJkpKBSr2TFEXZBGwCyMvL65cyUoM9jGK4uav7koEwPwWCYPTn/BzsclMPIUt7j96emwN5vol5M/gYrrq9P+4jcX90jsFg2P0duAn4Zdvv1/t3OMEZCmEUw8ldLRC0Ry2qokdKfDTP/WFjH49o6DMU5KYeQpYOTAb6fBPzRjAY6K/7SNwfZ2dAxbZIkvRXYBcwTpKkMkmSluM16C6VJOkIcEnb/wMS4SYWCAY3alEVvR+95vCCniPkpqAvEfNNIOg54j4auAwoj52iKN8N8lB+nw6kmwg3sUAgEHQNITcFfYmYbwJBzxH30cBlQBl2QwHhJhYIhhc3/3CFrjdPhG52HiE3BX2JmG8CQc8R99HARBh2AoFA0AOC9R4UPfEEAoFAIBD0JcKwEwgEgk4QrLDKwUNH+Ma3zv3rC8+gQCAQCASCjhCGnUAgEHQCtbBKe1r2reiT1xeeQYFAIBAIBB0heXt+Dy0kSaoCSvrwJeOB0334emIMHdOX4zitKMr8rjyhk/NzoHyW3WEwjx2G1vjP1fzs6DUHK4P9PQzG8XdpfvaDbu9PBuP32RMG4vvt6fzs7/fUn68/nN97X7x+0Lk5JA27vkaSpEJFUfLEGPp/DANpHD1hML+HwTx2EOMfLK/Z2wz29zDYxy/wZ7h9n0Px/fb3e+rP1x/O772/X39A9bETCAQCgUAgEAgEAkHXEYadQCAQCAQCgUAgEAxyhGHXO2zq7wEgxuDLQBlHTxjM72Ewjx3E+AfLa/Y2g/09DPbxC/wZbt/nUHy//f2e+vP1h/N779fXFzl2AoFAIBAIBAKBQDDIER47gUAgEAgEAoFAIBjkCMNOIBAIBAKBQCAQCAY5Q9Kwmz9/vgKIH/HTFz9dRsxP8dOHP11GzE/x04c/XULMTfHTxz9dQsxP8dOHP0EZkobd6dMDrcelQHAGMT8FAxkxPwUDFTE3BQMZMT8FA4EhadgJBAKBQCAQCAQCwXBCGHYCgUAgEAgEAoFAMMgRhp1AIBAIBAKBQCAQDHJM/T0AgWCgIssKx6vtVDQ4SYy0MjouDINB6u9h9SviMxEIBALBYEboscGB+J66hzDsBII2fIVIQoSVY9VNrPjL5zhbZaxmA+uXTmP+xKRhK1hkWWHb/lOsfumLAfWZCOEvEAgEgs4gywo7DlWwp6weWQGjBJNTo5g3LlHojT6mI909UNcbgwFh2AkE6AuRVflZxNgslNc7cbbKrH7pC8avnEPGiPD+Hm6/cLzarn0+wID4TITwFwgEAkFnKa2xc6SiiU07i/10feaIcEbHD0/d3h+cTXcPxPXGYEHk2AkE6BstT24/wjW5qdo5zlaZykZnfw2x36locGqfj0p/fybBhP/xanu/jUkgEAgEA5OKhhae3H4kQNdXNLT088iGF2fT3QNxvTFYEIadQEBwISL5OH2sZgMJEdY+HtnAITHSitXsLzL6+zMRwl8gEAgEncXucuvqDIfL3U8jGp6cTXcPxPXGYEEYdgIBwYWIGs2nhgmMjgvrh9ENDEbHhbF+6TTtcxoIn4kQ/gKBQCDoLOmxYbo6Iy12+Or2/uBsunsgrjcGCyLHTjDskWUFgwQPXz2Zu1/d6xfvPSE5ggvHxpEQIYpyGAwS8ycmMX7lHCobnd36THq70Ikq/NvH6QvhLxAIBEOX7uqSMfH6OmNMvNAZfYme7l63aAppMTagd9YbwxVh2AmGNb4JvDE2CwVzM8hOjCAnKZIx8V4hIhKqz2AwSGSMCO9W8vK5KHQihL9AIBAML3qiS4TOGBgYDBKX5SSyaVkehSU1eGRY/+4hzEaD9j32ZL0xnOmXUExJkp6RJKlSkqR9PsemSZL0sSRJX0iSVChJ0sy245IkSRskSSqSJGmPJEm5/TFmwdDEN4G3vN7Jhu1F3PHyl0gSQtD3Mueq0Ikq/GdlxJMxIlx8bwKBQDCE6akuETpjYFBa66BgcyEbthfx1PtFlFQ3i+JnvUB/5dg9B8xvd+xR4AFFUaYB97X9D/BtIKvtpwD4Xd8MUTAcEMU3+g7xWQsEAoGgpwhdMjQQ3+O5oV8MO0VRdgI17Q8DkW1/RwEn2/5eCLygePkYiJYkKblvRioY6ojiG32H+KwFAoFA0FOELhkaiO/x3DCQqmL+GHhMkqSvgceBu9qOpwBf+5xX1nbMD0mSCtpCOAurqqrO9VgFQ4S+qrwk5qeocjWQEfNTMFARc1PQnoGkS8T87D4D6XscSkiKovTPC0vSaOBNRVEmtf2/Afh/iqJslSRpKVCgKMolkiS9CfxSUZQP287bDqxRFKUw2LXz8vKUwsKgDwsEfqjVtbqZSN3l4PzhPD97+FkLuo6Yn4KBTJfmp5ibApU+0iVifp5jxJqg2wT9kAZSVcybgFVtf78M/Knt7xPAKJ/zUtuOCQS9gqi81HeIz1ogEAgEPUXokqGB+B57n4EUinkS+Gbb3/OAI21//x24sa065iygXlGU8v4YoGDwIcsKxVVN7Dp6muKqJmS5fzzUgxHx2QkEAoHgXCL0jCAYYm50j37x2EmS9FfgIiBekqQy4H7gB8CTkiSZACfeCpgAbwOXA0WAA7ilzwcsGJSci75pwwXx2QkEAoHgXCL0jCAYYm50n34x7BRF+W6Qh6brnKsAt53bEQmGIsF63YxfOUe4/c+C+OwE/c0Ny3/Iyaq6gOMjR0Tz56f/0PcDEggEvYrQM4JgiLnRfQZSjp1A0GPURNyKBifNrZ6gPVKEYOiYjvrL9Ndn5/vdJkaKJOuhzsmqOhIX/Djw+Ju/7vOxCASC7tGR3B6IekZw7umMLhdzo/sIw04wZGjvul+Vn4nVbPATDqJHSudQ+8sMlM9OhGUIBALB4OJscnug6RnBuaezulzMje4zkIqnCAQ9or3r/qXCMlblZ4keKd1goPWXCRaWcbza3i/jEQgEAkHHnE1uDzQ9Izj3dFaXi7nRfYTHTjBkaO+6L6938sKuEp6/ZSYKiuiR0gUMBon5E5MYv3LOgOgvI8IyBCoHD3zFxVdeq/uYyL8TCAYOZ5PbA03PCM49ndXlYm50H2HYCYYMeq77WoeLEREhYvHfDQZSfxkRliFQaVUMurl3IPLvBIKBRGfk9kDSM4JzT1d0uZgb3UOEYgoGJXr9TYTrvv85V31nxHcrEAgEA5Ngcl/IbUF7hsKcGOj99YTHTjDo8E2+jbFZWJKXSnZCBDnJkVyWk8jbwnXfL5wtKbonVS1FWIZAIBAMPILJ/ctyEimtdRBjM7Ol4AJaPR5iw0KE3B5m6On9wazLB0MhN2HYCQYdavJtjM3CslnpbNhxJOAGE677vqejvjOj48J6LAxFWIZAIBAMLPTk/rptB2j1yKzZusdP3uemxQ6Yxa/g3NORETRYdflg6K8nQjEFgw41+faa3FTNqANRKbG/6SgpWlS1FAgEgqGHntxfMCVFM+pAyPvhylDU+x2tcwYKwrATDDrU5FtJYsDfYMMJ9XvxRU2KHgzCUCAQCARdQ0/uGw1CNwsGhxHUVTpa5wwUhGEnGHSoybdGiQF/gw0nOkqKHgzCUCAQCARdQ0/uz0iPFfJeMCT1/mAo/tIvOXaSJD0DLAAqFUWZ5HP8duA2wAO8pSjKnW3H7wKWtx1fqSjKP/t+1IKBglpIY0JyBOlxYdz96l6/+O2BdIMNJzoqcKIKw/ax9uK7EggEgsGLntxPi7EJeS8Yknp/MBRy66/iKc8BG4EX1AOSJF0MLASmKorSIklSQtvxCcB1wERgJPCeJEnZiqJ4+nzUggGDwSAxOj6ctNgwpo2KHrA32HAjWIGTwSAMBQKBQNB19OS+kPeCoar3B3oht34x7BRF2SlJ0uh2h/8L+KWiKC1t51S2HV8IvNh2/JgkSUXATGBXX41X0D90pjz+QL/BBGc4199VT9opCAQCgaBzCN0s6CzdnQdCn3efgdTuIBuYI0nSLwAncIeiKJ8CKcDHPueVtR0TDGEGQ68QwcBBzBeBQCA49whZKzjXiDnWMwZS8RQTEAvMAn4KvCRJUqe/QUmSCiRJKpQkqbCqqupcjVHQRwy1Mrlifp5bhtp86WvE/BQMVMTcHFgIWeuPmJ+9j5hjPWMgGXZlwCuKl08AGYgHTgCjfM5LbTvmh6IomxRFyVMUJW/EiBF9MmDBucO3TG5ylJXbLs7k1jkZVDW1IMtKP4+u64j52T1kWaG4qoldR09TXNUU9LsfimWV+xIxPwUDFTE3BxbtZW1ylJXlszM4XNHYoYweqoj52XXOpteFPu8ZAykU8zXgYuB9SZKyAQtwGvg78BdJktbjLZ6SBXzSX4MU9A1qmdwYm4Vls9K1RuR/+qBYuOSHCV0Jx1Dni68yGOxllQUCgWCg4Strk6OsfvpZhMwJzkZn9LrQ5z2jXzx2kiT9FW/xk3GSJJVJkrQceAbIkCRpH/AicFOb924/8BLwFbANuE1UxBz6jI4LY+P15/Gzy3M0pQHCJT+c6Eo4hlpWOT0ulNsuzmRlfiZ/XJZHWoytr4ctEAgEQ5a0GBubluWxMj+Tu4V+HrZ0NpqmPZ3R64OhV9xApr+qYn43yEPfC3L+L4BfnLsRCfqLjiofudwKhysbg7rkRbWtgcO5qGDVUTiGXjuFy3ISafXIrNm6R+weCwQCQRfojAyXZYV3DlRoC/OV+ZlCPw9DelLcpDN6vS/aJAzlqps9MuwkSRoB/AAY7XstRVG+37NhCYYDHQmH0ho7B081kBIVyqr8TF4qLKO83htfLVzyAwtZVthxqII9ZfXIChglmJwaxbxxibqCsrMCtavhGKW1Ds2ogzM7geNXzhGLDIFAIAhCZxfqqrclxmbhmtxUoZ+HCF0xcmRZYe+JOg6eauDWORls/cz73XdW13ZWr5/LdhlDvepmTz12rwMfAO8BIjxS0CWCueQnrJrD7tI6Nu0s1m66VflZvLCrhFqHS7jkBxilNXaOVDQFfF+ZI8IZHe8vlLsiUNVwjPbnBvvuu+LhEwgEAoGXYLq4/UK9osEZkPcu9PPgpis6We/clfOy2PxxCeX1zk7p2q7q9XNBZ+f7YKWnhp1NUZQ1vTISwbAj2EK8oqGFu1/d63fTPbn9CI8vnkpSlJXctJghsasyVKhoaOHJ7UcCvq/ctJgAw64rArWr4Rgi4VogEAi6Tmc3xRIjrSzJSw3Iq3ty+xGevimP5KjQIRXSNhzoik7WO3fDjiMsn53B0x8Wd0rX9kWY5dkY6pvAPS2e8qYkSZf3ykgEww51Ie6L1WzA7nLr3nTOVg+gCKUxwAj2fTlc7oBzu1rGWA3HmJURT8aI8A6/e5FwLRAIBF0nmC5uv1AfHRdG5ohwXRne5HSfVUYLBh5d0cnBzjUa6JKu7YpePxd0dr4PVnrqsVsF3C1JUgvQCkiAoihKZI9HJhiyqPHc1fYWnr15Bp+V1OJ0y7zx5QnWzM8hPTZM1/NS1dRCbnpMP45coEew7ystNiwgdn9ktJWV+ZmoBbS2flZGrcPVKwJ1IOwECvqXgwe+4uIrr9V9bOSIaP789B/6eEQCwcAnNSqUZ2+eQWmNA5vFxPMfFfP92WMDFuoGg8ToeH15nxQ1NBbFw42uRLoEO/eS8QlMSonutK7t78IlAyEc9FzSI8NOUZSI3hqIYHigxmiv23aAa/PS/OL01y2awmU53oIb7W+6VflZZCWGD5kbbygxJl5fSKbH2vzi8dPjQrl9XlZALl5vfq/nMuFaMPBpVQwkLvix7mMn3/x1n45FIBgMuN0yf997knte26fJ5bULJ3HJuATdxfaEpEgeumqS3/kPXTWJiclR/TB6QU/pipGjd+6q/Cwqm1o6/XoDoXDJUN8E7nG7A0mSYvA2DdfMe0VRdvb0uoKhiRqjvXx2RkCc/pqte5icEkXGiHDmT0xi3O1zKK2xY7OYSIwMIS126Nx4Q4lgQrJ9PP6CKSnaYgDO5Ga8dfsc8b0KBAJBP7C/vD5ALt/7+j6yE8OZOiowQsZkMnDV1BSyEsI5Ve8kKcrKxOQoTKZ+aYss6CFdMXIMBokJyREUzM1AVkBR0IrmvN3JwiMDpXDJUN4E7mm7g1vxhmOmAl8As/A2Hp/X45EJhiTV9haWz84gLSbUr1Qu+CevGgwSYxPCGZsw9G66oYiekGwfjy9JaP8nR1m5JjcVSYLT9hbGyMJoFwgEgnNN+zC4anuLbt7UqXonU0fpX8NkMjB1VEzQxwWDi64YOeX1TjZsLwo43tnCI90tXNLf4ZuDid7IsZsBfKwoysWSJI0HHu75sASDkbPdeLKscLLOydMfngnF8y2VO5SSVwXB4/Hbl8v+0wfFAaEYQogLBAJB5+lsg/H2YXCPXD2F9LhQSqqbtfNEzpwgGD2tPt2d5/c0fHO4rSd6atg5FUVxSpKEJEkhiqIclCRpXK+MTDCo0LvxNl5/HmPiwqlsdGKzmHB75IAG0r6lcodS8qogMB7/jS9P8NBVkyitcQSE4fqGYgQT4pflJFJa6+hw42A4CW+BQCCAjvVvjaMFi9GAw+XBZjGxbtsBP9l716t7+N0NufzXn3eLnDnBWelp4RG952+8/jwUBXYdPa3pbkDT5zaLsdvhmwMhp6+v6alhVyZJUjTwGvCuJEm1QElPByUYfLSPm46xWThS0cSKv3yu3UwPXjmR7IRw5mQnILXdT1s/K2NySiRvr5wjFuJ9QF8aP3qx+2kxNnYWVeFslf3CMQFq7C1kjAjXjcFft+0ArT4bA+2F83AU3gKBQADB9e/aN78KKFLmGyWjnmuzmPjNd88j1Gwk2mZmfGKkyJkT6NKZnLyO1hntn58UaeWr8ka+//wnLJiSgtEAM9JjkZH54WbvZsPK/Mxu950bKDl9fUlPq2Je3fbnzyVJeh+IArad7XmSJD0DLAAqFUWZ1O6x/wEeB0YoinJakiQJeBK4HHAANyuKsrsn4xb0Pu3jpq/JTQ1oWv3Uv4oomDuWtW9+5VdRaVxiBGOG6A02kOiK8dNbBqBe7P7ouDDS40IDFhxZCeHkyopuDP6CKSkB3l5f4TwchbdAIBBAcP2rV6RMjZJ56v0ikqOs3HhBOjc9+4mfTggPMVFeLyIfBB2jKIHHOrPO8F0XFFc16VZJv3fBBGJsFsrrncgK3Q7/HOrNyPXo8ZaMJEmzJUm6RVGU/4e3cEpKJ572HDBf51qjgMuAUp/D38ZbdTMLKAB+19MxC3of34aPyVFWxidFcOucDFbMyyS5LVZ/wZQUzaiDM1URT9Q3I8s6EkLQqwQzfo5X2/3OUwXz5Rs+4Lt//A+Xb/iAbftP9dp3NDoujLULJ+tWRT1ebddtHmo0EFQ4Q9cbn6vIskJxVRO7jp6muKpJzEOBQDDoCKZ/xydFEGOz+J2rNpQGWJIXuAG7+qUveOXzE+dE9gsGP2dbH3R2naFS0eBkwZSUgPXA2je/4prcVMAb2bVyXhbpcaHcdnEmK/Mz+eOyPNJibGcd71BvRq5Hjww7SZLuB9YAd7UdMgP/d7bntbVDqNF56AngTsBXiiwEXlC8fAxES5KU3JNxC3ofNW46PS6UZbPS+enfvmTjjiL+9EExy2alkxxlDbo4/+RYTdCbXtB7dNb46apg7ioGg4TZKOmOpaLBiaLA44unsirfuylgNRuYkR7boXDujvA+1wasQCAQ9AXB9O9P//YlN16Qrm2uglcuZiZEsGJeJilRobpyWBWBvS37BYOfs60PurrJmhgZfG2opmmU1zvZcfAUP87P5ukPi9mwvYgfbC7knQMVZ9XX6r2hrg+GWjNyPXqaY3c1cB6wG0BRlJOSJHWrabkkSQuBE4qifClJfm7/FOBrn//L2o6Vd2vEgnOCGjedEm3l2k0fB4R+FMzNYGpqtK473SN3vlSuoPt0thpVb4QuyLJCaY2dioYWHC43sWEhuDwe4sJCGB0XFnQsrR6F7/zmAy0c4+GrJ5ObFk1qtK3DhO3uJHSL8E2BQDAU6Ej/Prndq383bC/SQtxO1jkAqHW4dOWwb4jdUA9bE3SN9usDNVf+cEUjAElBdHtihJXiqia/9A4AgwS5aTG6z1EjgK1mAyvzx1GwubDL+nqoNyPXo6eGnUtRFEWSJAVAkqRumcCSJNmAu/GGYXYLSZIK8IZqkpaW1t3LCHqAwSBhb/HoGgWj48J48r3DrMrP0kI/1ETuLYWlLMrtTATv4GUgzM/OGj89LWcsywo7DlVwpKJJ97teMz+Hy3ISA8byyNWTuff1vX6C++5X9/L2yjmYTIYOhXN3hPdwjL0PxkCYnwKBHmJudg6DQcLh0te/qdGhPL5kCvHhIWzccZjCknqsZgOPLp7Cr5ZM439ePiOHV+Vn8cKuMzXwhnrYWk8ZbvPTd32QHGX1a11kNRt4dNEU7v72eB7+x0Ht2N3fHs/eE/Xc2a74mcUkseIvn5OdEM69Cyb41V9Yv3QaE5IjuHBsHAkR1h7p66HcjFyPnhp2L0mS9Ae84ZE/AL4P/LEb1xkLjAFUb10qsFuSpJnACcC3DWZq2zE/FEXZBGwCyMvLE7FU/USIyaBrFMSFW9hzooGqJhcFczNIi7Vx7LRDW+gPZbc4DIz52Vnjp6fljI9X29lTVs+mncW6SfurX/qCt26fo42losFJq0fhYHm9Xy8l9Xm+Tes7Es5dFd6dMWCHSwuFgTA/BQI9xNzsPMFkWklNM09/WEzB3AzOzxhBYUk9zlaZO/+2h83LZ/LmitmctrcwItzKseomah0u7blDPWytpwy3+ZkWY+OhqyZxz2v7uCY3NSA37s6te1hxcSbLZ2cgSd4CKw1Ot2boqeetfukLCuZm4GyVvWvDHUUUzM3gvFHRpMeFabp2dLxXnys9KKAy3OhpVczHJUm6FGgAxgH3KYrybjeusxdIUP+XJOk4kNdWFfPvwApJkl4EzgfqFUURYZgDDFlWKKm2U+1wsXJeVkB55bo2RVFe72TD9iL+eON05mTFsSg3ZcgulgcCeobJ2Ywf1QCcsGoOFQ0t2F1u0mM7r9grGpyYDAbd3TVJ8v7+qryeGkcLcWEhJEdZmf/kB9w6J8NvJ/Ca3FSMBgg1m5BlpVtzpCPD7GwGrGihIBAIBhOpUaGsXTiJe1/fF9DeQM2d8810cbbKfHDkNGNHhHPFlJEYDBJj4sN4exiFrQnOjiwrfF1rp6K+haqmFkaEW3ju5hmcamjR1fMuj8xT7xdpx4K1K/BNj1PXhi8WnB+wPpFlhWPVTQERX2LTQZ+eeuxQFOVdSZL+o15LkqRYRVH0CqNoSJL0V+AiIF6SpDLgfkVRng5y+tt4Wx0U4W13cEtPxyzoOb4L5uQoK/tPNtLschEfEcqWwlK/3ZothaX8z2XjtedazQYirWbOz4jrx3cw9OmpYfJVeWO3WiMkRVrJSgwPmrthNRuQkDhV38IdL3/JqvxsYmwWrfLVlsJSv9LHm3YWd3rcvmNJiPDuPvv2Umzf6HxCcgRv3T6HqqbARYzIwRMIBAMdVebVN7to9ShEhZp55uYZfFxcjUdG61lnNRsIsxhpavFoz1Vz3Nds3cPklKhORUYIhheyrPBBUSWn6lu4/+/7/UJ2JUnfi5abFsOq/ExeKiyj1uHivFH69RXaq3Or2cCIcGvAmsIgwYq/fE6MzaKtLQ0STEiOEJsOOvTIsJMk6YfAA4ATkAEJb0XLjI6epyjKd8/y+GifvxXgtp6MU9B7qEUxdpfWcfer3nyolfmZfFRUxX9flMX9f98X0I/koasm88JHxQCaQEiMDOnndzL06YlhEuy5KQWzmJwSrQlTPePxTzfl8ct/HAjw3N67YAKbdh5lVX4Wv3j7ALVt3t0ntx/mxgvSaWrxYDDAgwsn8cPNn+m+tsPlCRoSqTeW+xZMIDshnD0nGnC2nr3RuS8iB08gEAwk2i9402JsvHOggnXbDrAyP4vKhhae3H6EVflZhJqNft6NVflZTBwZyb2v7wMI8OYJuTY8kWWFY6ftlNTYCbOYSIwMIS3Wf4OzuUXWjDo4U5RnxcWZAXp+VX4Wd72yl1qHi7ULJ9HscvPU+0dYt2iKn959dNEU6nyK96jPNRkJ0OMPXz1Z62nn6wm8cGycFqopOENPPXZ3AJMURTndG4MRDGzUhfPBUw1++VOyAjdemMEXZXWUVDez+eMSP4+dw+Xm/IwRLJs1hqOnm8hKDCetC6F9gu7RE8Mk2HO3H6zkRJ1TM4T0DMBPjtXozoM4m4WF01J4YZd3BxnQ8u5GRodqQj9Y2Mb2g5VaZTc9Y0xvLA+++RXrl05j7ZtfUV7vPGujc196WkRGIBAIegu9jatNy/JY/dIXLJ+dQUm1Q9PLdpeHrZ+V+cnfF3aVcNOFo1kwJYX02FBKapr9vHlCrg0/9ObUqvwsshLDmTcuEYNB8ubAy4quTna6ZW2eZSeGc7SqyU+/3/v6PtYvncaJuhZSoq1+Ib7V9hYe+cfBgDmamxYToMfvfnWvVtlVRczZ4PTUsDuKNzxSMAxQF863zsnwu8nDQ4zE2ExMT49hZX4msuJtKKkqjOWzM3j6w2I2f38mk1OjRMx+H9ETwyTYc7MSIjh0qoEJyRGMjg/XLX2clRCB1Wzw212zmg08vniqn2CGM81yj1Y1+W0UBGuLoebeHTzVQEp0KJNTorS5FMwYPXiqgWtyU3lldxk5bY174cwcDWbs9rSIjEAgEPQWehtXe8rqcLbKRFiNpETb+PElWaRE23C2elial8pLhWXaIttqNtDodPP0h8X8/nvTuc8nrE7IteGBXohj+zmltsfIiPeG49osJsIsnqCpFeX1Tp7+sJg/3jidwxWwaPqZpuLl9U4OnmrggSsnYjEatBQJgwSy7G234euBs5oN2Fvcuno8OzHCz7sn5mxwemrY3QV81JZj16IeVBRlZQ+vKxiA+C6c0+NCuW5GGhkjwmlxe9h3okG3tH3B3LFs2nmUX1w1menpscKg60N6YpjoPXdVfhYPt4VQZidG4JHBIEkBpY8ff+dgQHjGTy7JJjREv2LqeWkx3P3KXu2YmmvXvgDPtn3lfqWV2+feBTNGPTJEWI3ceEE6d/zty4CiArUOl66xOxz73wgEgoGJ3iba5JRI7v72OGLCQnj8nYNcm5fGT31knNq6QA1731JYysNXT2b22HhRIGWYoeed8w1xVFGLmqibnS6PB6OBgMIl6tzyXmcS5XVeA6/9GjDUbKSqqYWVL37u99x/7C0PuObDV0/GHKSyekJECFvOko4h8NJTw+4PwA5gL94cO8EQJjnKysr8TFJjrNx9eQ6yDK1umaLKJt3S9k/flIfL7eG6GWmMSwoXN2Ef0xPDRGt4WzCLQ6caKatr1kIskqOslNY4+J+XvyTGZtH6z/iWPlbDMI0G+MbYeB775wGsppQAQf7AlROJCDFS63CRHGXlhvPTGBEeQpTNzIs/mEVzqwebxcjKFz9nwZSUgNLKq1/6gnG3z0GS8IZ2XD2Fu17dE6BcHls0lRuf/SRgjhbMzWB8UmRQY1cUEhAIBAMBdeMqxmbhxgvSmT46GrvTw8hoG3f87UuWz84IkI9Pbj/Cr6+dRrjVRJ3dxf/OzyEnOQKTySDk2jBDz+MbLMTRIKFtdsaFhbDuHwdYdsFoCuZmICsQZjGSnRjOT781jooGJzE2C//1590B+vWPy/JodLay+uUvA+bl8tkZvLCrhIK5GeQkRXLgVAMVDU6e3H5YN29v1YtfUOtwsX7pNM4fE+eX5z8cWhJ1hZ4admZFUVb3ykgEAxpZVth/spGPiqq4PT+b000tVDa0kBwViqyg6zpvcrr54wdHuW5mOuMTI/tp5MObnhgmBoPE5JRovq5p9hP81+Sm8uKnZyqfxoVZWJWfRazNos0D3zBMi9HAjy7KYsVfdgdUtRoVG8rHxTU8c1MeB0418tg/DwXE+s9Ij+XeBROwt7i158KZUI8Dpxq4o01x5KVH8dsbcvni6zo8MlqfRI+inyNw3qhovpmd0OutFAQCgaA3SYux8ezNMzhZ30yzy8PXNc2aDna2nmkl44uzVabe0UqrR6bW7mJkTKjIbx+mBEtVyE7wD3G8/4qJjImzkRZjA7zRO9+fPZZ12w6wZPoo0uPCkIDDFWfy6dYtmqx77U9LaoKuDyXJu054ubCMn12eg6xAaowNl1vRNobTYkI5Ud/sl7fnmxMvWhLp01PD7h+SJBUAb+AfitlhuwPB4OPYaTuP/vMAa+bn0OqWqW5y8eT2I9w6JwNjkJK3UTYz9y6YyMTkKEwmQz+OXtBdDAaJnORIbaf4mtxUshPCAiqfrsrP4rS9RXceNLfK1DlcOFvlgKpWqdGTefydw6zMzwzw+qqx/mPivEapvcWjG+pxuKJRe15hST0PvLGfX14zBUDrk3i82q47tvRuGmNCoQgEgr5ClhXeOVDBwVMNxIdZSIoK5Xi1XdPBVrNXv+rJuBP1zczJjGdySpRftUPB8CJYqkJ0mFnzxBkkcLV6WPPKHtbMz2H+xCQALCaJhdNScLplHC43a7bu9bu2zWIKmgZhs+iHVlpNBpKjrAEpEmqI51PvF7FiXiYbdwTm5athoqIlkT49XW1/l7Y8O+Cztp/Cng5KMPAoqbGzYEoKNU1OLCYD6989jLPVWxEp1ub12Pgql4eumkReWixTR8UIo24AI8sKxVVN7Dp6muKqJmTfjqFtjIkPY+P153HjBek8/WExBoNBN+THbJBYOc9/HqxbNIU395wgxGTUjqtYzQasFu/eUrBdPVmB0ho7e8rqefDNrwJCPX5+5SReLiwDvKHCt12cyRVTU6i2e0M71b5Mas6g79h6knwdTKEcr7Z363qCc8/BA19x8ZXX6v7csPyH/T08gSAoqryxWYyEWky43LKfDl45L4s3vjwRIH9X5WcxITmS6emxjI4X6RDDGT0duG7RFH726l42bC9i444iNmwv4pFtB1kwJYXVL33B3hN1lNbYWfGXz7Vzvq5tDtDl5XWOgDXgynlZvLK7DIMkBTy2Kj+L2DAzS/JStdQMOLOWWJLnLcCiOg188S0A11Hl7+FMjzx2iqKM6ehxSZIuVRTl3Z68hqB/UcPNzEYD8WFmkqJtVDa2+IXc/X5nMTdekM5vr8+lxS0zMsrKxJHCSzfQ6azXyWCQGBMXrjX6PnbaritMY8JCqLW3sPn7M3HLitZnyWw08MyHR7l/wUQeePNMJbb7F0zkTzuPatcI1sDUZjEFNfxQFC0/z7ewipqIre5Q93YhFNHjbvDRqhhIXPBj3cdOvvnrPh2LQNAVVHmTHhvGT176ggeumOingzd/XMI1uamkxFj54415NDhbvV4Uk4EZomiZAP2c+2p7CyXVzX7n+Yb1bj9Yyei4ML8CK3rFzUJMRmLDLZrnb3xiBI+9c5DyemfQ1hv3LZhAtC1EV4/mJEXy1x+cT3KUlXFJkUELwImWRPr0NBTzbKwDhGE3SNBrfvqvI5UUVzaRkxxJcnQozlYP9Y5Wv5upvN6phc1dOWUkmYkR/fxOBJ2hM2EM6pw4Unkm3NHlkXWF6fFqB0+9X8SLBedzwdh47fnZCeH88JuZhFuN/PXW86ltdhEeYuaL0jrmjkugqsnF1s/KWH1ptrYL7ZtjlxgZEjTc12zy7joerWoK8CLe/epepo2K1t5LbxZCEQpFIBD0Nnp5u2oUxd3fHkdYiJFb52QQGWoK0MFPf1hMwdwMJMnAr945yIbrzmNySrQw6gQaejowWBsDNZTy7lf38vjiqRysaAS8ht2WwlIeXTyVwxWNKAo889Exnrt5JmmxNk7VO0mJCWX1peNY/+4hxiVG6LY1CDEZ+LKsLkhbpXBtHZkWGxZ0Q9a3eneMzcKSvFSyEyJQFO+9NFzn/rk27IbnpzoI0fPe/PKaKbjcboxGAz/Y/Jl2/J7v5LBm/njWbTvotwgfEx8mvBW9QF8V5Tib18l3Tqh5HL6hP+3bEWz+uMTPuNGbU7+4ehISEv/958/9nrulsJSMEWG8uWI2X9c6sFlMJEaGaIn+k1OjAipq/uSSbH726l5e+P5MLEZDh++ltxE97gQCQW+iJy8fXTQFi8nAnz44ytW5oyho08PpcaE8cOVE7vfpRbcqP4uEyBB+9c5B1szPEUadoENkWcEgwcNXT+buV/cG6GNVpztbZU7WOVAUMBrgvismeJ/39gFKqps13afmq08d5b1+q0emYO5Y3fZHq/KzaJUVXios474FE7Q0C/Wx4zV2LY2ivTGqpo+o66PLchLZtmoOu0vr/N7HcM55P9eGXWDCjmBAoue9+d9X9vDE0mnc93f/4w+9dYC7vz2eFRdnkhARQkKklVCLgemjRMhHT+nLohxn8zr5zomtn5VpbQ3K651sKSzl19dOw+HycLzarvWDW790GgYJdh09jc1iDJhTJdUO3dYYWwpmaQuR9h5fWVYYExeOQYI/LJtOVWMLp+qdPPfRccrrnZTXO/2al+q9l95G9LgTCAS9iZ4OvnPrHu781jitqrD6WEl1M7/9VxG/XjoNt6IQZjFiNhkItxh59uaZQhYJOsR3nRFj84ZQjkuKIDHCyu6SGhZMSWHzx95KlOlxocSEhbD+vX3ammTdNVN46vpcymqbSY6yMjE5KmC+GSSJtW0Gm2/7o+yECFo9Mk/tOEKtw0VceIgWwqmGadY6XLytUwAl2PpoXGKEZtSBKKJyrg07XSRJegZYAFQqijKp7dhjwBWACzgK3KIoSl3bY3cBywEPsFJRlH/2x7iHMnrNT284Pw0FeGzxVE7UOXj+I++NruZTnba7iI8IYXRsKGlxIjG7N+jLKk/BvE5pMTaKq5o4Xt3EY4uncuy0HZdH5r39p/jNdefhdMscqWzkgTe+ArztD5bkpXJZTiJ1zlZe+fwENouRtBgbt87JAM60JgiWK9fc6tGdP3qCfOW8LP78n1LK652a8dYfHjTR404gEPQWehEUMTYL2YkR1DpaA/RwSXUzDc5WjEaJaJuZmWmxWCzGfhq9YDDhu84or3eyYXsRVrOBbavmkBobxvq2x9LjQnn46sksf77Qb02y5pU9Wv87381n9doVDU48sqzbnuj338vF0eqhYO5YXB4ZRVF4ubDMr0k6oBttE2x99NsbckXOuw/dNuwkSTIAsxRF+aiD044HOf4csBF4wefYu8BdiqK4JUlah7fa5hpJkiYA1wETgZHAe5IkZSuK4unu2AWB+HpvkqOs3HzhaJ54zz/f6UdzM/j9zmJqHS4irCYyEyIIsxgYPULk1PUWfVmUQ/U6jbt9DqU1dmwWE0lRIbxzoIJ12w4EtDT4ySXZVDQ6qWxs8etr99T7RSRHWUmLDeOe1/ZqDXTVpqS+oZrBcuWCedb0BPmGHd7mpk9/WKwZb8KDJhAIBjPtIyiSo6zc8o3R3PpCYVA9nBwVyr6T9SRGhAijTtBpgq0zTjU4NT1aY2+htKaZ/xyr0T03JSqUFfMyAVi37QA5SREcONWoeQFvn5ep256otNqOW4YNO86EX66+NJtn/31cM+7UNUH7tJRqe4vuWMJC9NstDNec924bdoqiyJIkPQWc18E51wQ5vlOSpNHtjr3j8+/HwOK2vxcCLyqK0gIckySpCJgJ7Oru2AX+yLKCosBji6dQVNmE2WjQjDrw7ym2JC+VULORFrfMnX/7kudvmdnPox9a9EdRjkMVjZrxpPaTWz47I6AYyRPvHfYWNEmI4O5vjyM52qZ58yJDjNzzmjcU4prcwBLGG3Z450+szcJPLsn22zRQjTO93MJgCmhKSiRvr5zjZ7wJD5pAIBisjI4L48lrp7G/vEGrLKj294JAPZwYaWXfyXqe3H6Et26f08+jFwwmgq0zkiKtmg62WYzc/epev/z65Cgr1+SmYjRAmNXE1s/KqHW4uGv+eCobnRw81eA932TQbU/0xNJpHK1qYuP7RX6PrX/Xu7ZYt+2QX+RQ+2iddYumkB4X6lfJ02o2kBgRInLefehpKOZ2SZIWAa8oitKb+XTfB7a0/Z2C19BTKWs7JugF2oe6pceFsmZ+ju5iWlYgLSaUZpeHktNNrMrPIjEypJ9GPjTp65DC9h4xm8XI8tkZpMWE6s6BEeEhPP7OQa7NS+OnPk1F1y6cpJVEVkslt39uanQoT7x3BItJYv3SaRRVNuGWZSYkez2+erHzE5L1c+eyEiOEAScQCIYULW6Z1784wYIpKdhd7qB6eHxSBJv+31EuGp/A+qXTGBM/PBewgu6ht87YeP15fFXuv8kbY7NgNXn1e629BQXJb1N25bwstu0rx9Hq4aZnP9WOP3z1ZN25a3e5GRGu3+IgMyGcFwvO16Jt9KJ11mzdw6ZleRRsLvRbJ6TFhnVYPXO40VPD7ofAasAjSVIz3iqYiqIokd29oCRJPwPcwJ+7+LwCoAAgLS2tuy8/7PC9eZKjrFybl8ahUw1Be4pZLSae+lcRP71sPFaLQataKOiYzs7Pvg4p9PWIJUdZibCa+fV7R/x26VSsZgOltQ4WTEkJ8Obd+/o+LeY+JtSs+9yMEeF8d2Yao+PDKK3xevq2flbGhWPjkBV0Y+ffun1Ojw3dvqoyOpgR8lMwUBkuc/NoVRO/eveQFgIfTAYbJDh+2s7hyibWXjVJVL/sZwbS/OysrtNbZygKfOc3H/ht8t54QboWfaNG87T3wj26eCp3+niWsxPCiQqyBjh22oHJoJ+OEWk1M2N0LMer7fznWDUGSfLrn6e+ptko8XaQ9ZGI2PHS0wblvZpcJUnSzXiLquT7eABPAKN8TkttO9Z+LJuATQB5eXmiGudZUAVAkU9/smtyU9mw4wgxOuFyq/KzsJmN/Oqdg6zKz2ZSSqTW/FlwdroyP/sypNA3JOOa3FStipVeS4OHr57MY/88xKLpqbo7bmmxNqxmA6mxtoDWBKvys1BQsJoNmhJQjydFWrWiPO2vWdXkjfmfsGoOFQ0t2F1u0ruwmdCXVUYHM0J+CgYqQ3luqnq42t5Cjd3lt2m29bOyoHr4mY+OsX7pNGHUDQAGyvzsqq5rv87YdfS0nw52exS/kMmghc98PMvJUVZ+dFEmD765P2D9cO+CCWzc4c3N11sfJEWFBIx/VX4WL+wqCci9A28FTYE+PTLsJEmSgBuAMYqirJUkaRSQrCjKJ9241nzgTuCbiqI4fB76O/AXSZLW4y2ekgV0+fqCM6gCYN22A/zv/BxtYR9iMmhVkp776DgrLs5kRHgISdFWbBYj9c2tPH3jDK2/iGBg0ZndOr0m9KpHzDeEsrzeqZUonpwSSXZiBAYJah0uQH/HbVSM16Brdnl4YVeJVhFLLWE8MjpUa0AOZ3JGLpuQ1GFuoSwrlNY0U1hSg6zAA2/sZ838nE4ZZ31ZZVQgEAg6i+9CfM23xjEuOZLQdk3HVT2cHhdGVKgJs1Gi0enhmZtmMiZebKwKztCRrlNDGztaG7TXwXqGnJ6ODrOY/DaHD55qoKS6WVs/qGuARmerZqC9sKuExxdPxen2UNXYQlZiOG5PYNSOmlPqW33zWHUTK/7yudio7YCehmL+FpCBecBaoAl4CpjR0ZMkSforcBEQL0lSGXA/3iqYIcC7XnuRjxVF+ZGiKPslSXoJ+ApviOZtoiJmz1AFwJpvjaPe0cLahZPY+P4RshLCtRu0vN7J4+8cxmo28OzNM6ixu8gfl4jJZOjv4Qt06MxuXbBzJo6MoGBuBlkJ/vls5fVOnv6wWOsnI8sK65dOY922A7oNyu/c+iU/zs8mKSqEWoeLp94/UznTajYQbTP7JWCrZZBrHS3kpsUGbb3w1r5y1mzd4/daz3x4lJRoKw6Xp8OQk76sMioQCASdRdXDF4yJZWRMKB7ZQ05yZIAM3vh+EU8snUazy0NaUgTnjxEGnSCQYLquxt7CwVONQdcGvl7jdYumsGbrnrY2G+F+c3HrZ2UBnraHrprMqJhQHl00hTu37kGSvAah1WygvN6prQGsZgPLZ2do46p1uDhwqpGnPyzmhVtmkhAZEjRq57xR0VrunUGC+U9+IDZqz0JPDbvzFUXJlSTpcwBFUWolSbKc7UmKonxX5/DTHZz/C+AX3R+mwJdqewsPXjkBk9Fb9ejHl2Tx08vG80udBftDV03mdFMzE5JjhFE3gOmMZyrYOc/fMpMN270tC9p//+sWTSEtxgb4xOUnRVBjb+HPt57P0comQi0mTtQ5cLkV7np1L3d/ezwPXDmR+/++X7vO/Qsm8nW1nfS40IA2ClkJ4eSm4RfzPyLcitEAu7+u1Yw6dcwbdhxh/dJpXLvp47Pu2vVHldG+QuQOCgSDE1lWON3UwpPXTWNEWAhHT9uRMPOLt78KkMG/uHoy1U1OLhg7gtHxYvEq0CeYrjMbDR168toXz9u0LI8Qk4E7t37pNxdrHS4SIkP44415mAySn86ZnBrN6Dgbp5tcPKAThvnQVZP5zY7D2pjU9kfOVpmdRaf50wfF/HFZXkC7jyV5qSig5dL951i13+PqBnFVU4vQfz701LBrlSTJCCgAkiSNwOvBEwxQZFmhusnFyJhQiivt/Pb6XFplBWerR9d97nC5CTGZRdWtAU5nPFPBznG43NoO27Z95axfOo2DpxrwyLD+3UOYjQbNaFLj8kfHhfHaFye4z8d4U4V1ZZOL/WV1/HFZHqcanCREhvB1tZ0xI8L55TVT+N9X9vg1Ll3/7iEmp0Rp8f6+yuaBKyYGjDnGZkGWFb/m58F27fqjcXlfIHIHBYLBiXrvPvPhUX70zUxaPDJmowFZUXR1cJ3dRWKUVehgQYcE03UOlyfo2sAgobUoAK8uLdhcyG9vyNWdi1WNLfzs1X1aFI+KLCtUO1opqmykYO5YNu08yvLZGRgNMD4pktc+L+WBKyex70Q9za0ymz/25s1ZzQaUtpDPe17fy7pFU1j/7iGWTB9FUpSVr2sc/OzVfdQ6XH5VsmNsFpbNSteMxz99UCz0nw89New2AK8CCZIk/QJv77l7ezwqwTnjeLWd0mo76fFhjIiw8kVZHbICcWFmXfd5wdwMcpIixM0ywOmMZyrYOWmxZxTCnOwEv909QNdoOl5t5+5X9wZ40grmZuCR4f3Dp6m2u7j5G2P44ebP/AqwfP/CMTyy7aCfQVhtbwFo659jYt22AzhbZWztGo8mR1m58YJ0rb+Tn0HZZsS292RdlpMYtIrWYEXkDgoEgxP13v3TTdM5Uetk7VtfsWBKCjlti1Y9HXzRuIRBL7ME55ZgFbWPV9t19f6IcCu7S+u0SpdWs4GfXJKNrCiYjYag4ZTtN4xlWdHSJWJsFm68IJ07vzUeSYKSagdr3/yK8nonO4/UBLQqUHU3QEl1M+mxoay+dFxA6sXmj0v8qmQfPNUQUJlb6L8z9LQq5p8lSfoMyMfb6uAqRVEO9MrIBN1CrzhGaa2DigbvjV7V1MyEkZGU1DRrFRCtZgN3zR/P6kuzteIWakWiMIuRERGiV91ApzOeqWDnjIkPY0y8twfM4YrGoLt7vgKzfZsENSQiNy2GY1VNrJiXqdtg9+5X91IwNyPAIHz25hlcvuEDPwXz3EfHOVHn8IvrX5IXvPm5WmglmCdrKAl8kTsoEAxsfHVxcpQVj0ybl8Rbxt1kMPDbfxVpoekxNotutUBVPgsEZ0OvonYwvW80ELA5+8R7hymYm8Gdf9sTMBdVA6v9hvGx03bNqFs2Kz3gOSPCLdr6IMRkYNuqORw7befzr+s0zx1Aelwobhnd1IvlszN46v0irUp2e0NVPVfoPy89rYq5WVGUZcBBnWOCPkav2fjt87K457V9fh6TlBirZtSB94Z4ZNtBVuVn8bvvTcfZ6sHtUSivczAyJlT0qhsEdKb/3dnOUQXi2Tx/sqxgMxtZmZ+JzWJEate0dFV+Fls/K2NJnn5bBLldmWJnq8xnJbXE2M4oAIfLTcGcMWz64Bg/mptBwdwMZAVSovUbp2cnRgRtajoUd/KGcu6gQDDY8dXF2QnhXDszzW8j9b4FE2hwuv3aG5TXO3lhVwkFczOYkOxtBRwVamZGeqzw1gm6TTC975uvpqLqZ3UuetsQWDl22s7mj0u0kEh1w1iWFQ6UNxBjs3DX5Tl+/eycrTJbCkspmDtWm/tqyOQl4xIwGrw1G2QFPj5axXUz09l5pEp3TJJ0Rr8ZDBKj48KE/uuAnlbDmOj7T1u+3fQeXlPQTdovapdMH6UZdeDNTTpebae6qZVb52SQHHXmJnC2yjS3emh0urGaDIyIsHDJhCTmjUsUSmWQoO7WzcqID9qS4mznqLt7VrNXNLT3/KkLlmv/+DEbthfhcHk0ow7OlCi+JjdVq47li9pgt/0xo0Fi2ax0nv6wmI07ivjDzmLCQrz7Ts98dIy89Fi+MTaO8UmRutfMSYrEYJA6rAxWXNXErqOnKa5qQm5vXQ4yzvY9CQSC/kPVxTE2Cz+6KFNb2CZHWVk+O4NTDU4irSairEY/eVVe72TD9iJkRSE7MYLzx8SJomWCLiHLSoCu09P76uYgeKNubrs4k5X5mYxLjCA5yttbdt22Q1Q0eD1q9y2YwFu3z/HLYztebedknYMbL0j364mssmBKSoATYfVLX/Dx8RoKNheyYXsRf/qgmBvOH81vdhzpcM2wfuk0DJK33576v9B/+nTLYydJ0l3A3UCoJEkNeMMwAVy0NWoU9D3qolYNjUuNsfmFy9184Wg/z4oa7qYmsU5NjeZ3/zrCo4unDSnvhqDz6O3upcXYtHLIEpKWbL31szJMBv9dM3XupcWE0tTi5hdXT+Jnr57xGN9x2ThGx9tYmZ+JrMAbX55gVX42FQ3OgJj5+/6+n+dvmcmIiBDNs6i2XNALJwV9T1Z6XCgn6px87+lPhkyhkc54aAUCQf9QbW9h+ewMrxx0tmp62VcHb9pZzC+unkx6XCgl1c3ac61mAynRoUIHC7qMb4/iBVNSMBpgRnosF2QEbhCom4Prth0IqFSthl3WOlw0Oj089X6R1+hrV2+hosGJwSCx/p3D3DonI0D3Gg36Tc0LS2r8dP1dr+5l+WzvmkKvsfmk5EjsrW7+c6wGm8XE3rI6xiVH8Nbtc6hqOqP/AIqrmoZ9pehuGXaKojwCPCJJ0iOKotzVy2MSdJPESKtfOXnfG+2G89MCPCtPvHeYFRdnsvH9Ih66ahJbPj3O92ePFbsewwDf3jUWoyGgH5wap++rKNoL/59ckk1G/JmQiOQoq1+lKqvZwOpLs71Ny1s9TEmJorHF7ddc9JGrJ5ObHs2nx2t1FYCC4rfAOZtBo5dPsHbhZC1hW73uUAjP1MunEAgE/YssK5ys8/YAjbFZ+Nl3crCaDbo6+Gev7mXTsukU+BSYeuiqSUxMjurndyEYjByvtuvq6nWLpnDFlJG6qRkp0VatdRD4561bTUYtr07NEfUtmhJhNREXHoKzVWbrZ2X85JJsP+dBblqMbsikp13tfGerjNHg9VirlTiNBshMiODNL7/GZjZyV1s+oJruUXLaQXpsGLMy4rXxiErRXnpaFfNnkiR9DxijKMpaSZJGAcmKonzSC2MTdJHRcWF+i9idhyp54MqJ/PZfRaTHhekunEfHhfH0TTOwmCT+99sTh+0Ox3DCt9z292eP5eCpBs17tmZ+TkCoxeqXvmDFxZkBHrUn3jvM6kuytB22a3JTA85Z/+5hLfF5ZX6mVoFLffyuV/fyp5vyiA41dzpmviODRs/wE4VGBi43LP8hJ6vqdB87dKSIxL4djkDQY45X+xeTeOyfB7lr/nhi2xbAvjhbZaoaW3h88VQUFEbF2Jg0MkqEXwq6RUWD0y9vE7xzbM3WPVpLIV8MBiloO4TU6FCeeO8ItQ4XK+dlsaWwlEW5KQC43TIfFVdTXt9MWIiJ9LhQXG4Fq8mg5cMbJGhqaQ0w9u5dMIFNO4/6vZ7VbCAvPVarxPn0h8WsnJfFCx8Vc9vFWfzXn3cHpHsUzM2goqFF6+04XPLrO0NPDbun8PatmwesBZrajs3o4XUF3cRoQOtJEmYxsv1AOSsuzqKoslF34RwZaiI1xkp63PCa+MMR1Ut3vNpOWY2dRdPT/Ha3Vs7LYt22A4xPivDrfRdjs5AYadUV/g0tHrZ+VqaFHQVLfAZvkrTe458cq+FfByu5d8EELR4/PS6UB66cRFFlI41ONxOTIzu92NEz/Poq0Vo0De8aJ6vqSFzwY93H9j76o74djEDQC/iGYZ6sbyYjLgyL2RhUB8fYLLS4ZXKSI0UDckGPSIy0Bg1/DLaRmRChX4hrVKyNJXmpeGTYUljKmvk5GCT49Hg1ZTXNfh60h66ahNPl5qG3DwZcZ1V+lp8H7k87j3L9zHQ/Y2/90mlcmBHH2yvntLU8MtLqkflGZhzbD1bqpnukRIXiUWQth1Bs4J6hp4bd+Yqi5EqS9DmAoii1kiRZemFcgi6gJsseONVAUWUTWz8ro9bh4sGFk/j+N8Zy83OfEmOzBMQur8rPIsRsYFSMCL0c6PTUYGgfprAyP5P17+0PCL9YPjuDykanVmGyudXDz76TQ1mNQ1f4GyS0Xjcr5mXqnpOdGOFtf5AUoZtP4pFhz4kGqnYUsXx2BhFWIyMirPzo//zDk66amtKtnezuNCnvzuctQkEEguGN0+mmyenBaICv65p548sTrMzPZsP2w7jciq4OdssKZpMkqk8LeszouDBmtHm+zraRKcsKpTV2Dp5qCGhtsH7pNGakx5IYaaWy0ck156VwrLqJ+U9+wPLZGTz9oX/kzT2v7eO31+cSY7No7QvUx+wub45ecpSVn185kR9+cywAv752Gser7ThcHiwmSduQVdceFQ1OPLKMzWLsMN1D1bHdqRQ9VDdie2rYtbZVwlQAJEkagdeDJ+gD1Btzd2md1o/EN/H1vtf38fjiqVopZTV2WZJgfGIEZqPE9FGilPJAR89gWLdoCiOjrcSFhegKI1+BZbOYaHF7/MIUgnnPjAYYEW4NeL275o8PCKl4+OrJRIaeaSD+8dEqHrpqMve8dmYu3n/FRH71zkFKqpu1/3///4q8zUjbvHL7TtSzYl4mWz8r00I2f9auv849r+0jKyGcySnRXRbEXS000l0DTYSCCATDE1lWOH66ic/L6jXZperiDdsPs2BKCk+9XxSggyUJshPDSYsdGgtKQf9iMEhckBHHukVT/Jp8t9/IVHXcwVMNbNrpzQVV56VBggnJEZhMBi3ypbiqScuNlyT9tcMXZXUsyUvl5cIyrW2RUYLoUDNr5o8jITKE4qomPwPyJ5dk83JhGZt2FvP0TXmMjArlwKlGP927+tJs1i6cyNe1zWQlRPDTdi0VVB2rt4H78NWTMUhoXj1fhvJGbE8Nuw3Aq0CCJEm/ABYD9/R4VIKz0v7G1PO8PPV+EXaXW1t4q54Vq9nAlh/MYnJq9KCfwMMBPYNhzdY92s5Ze2GkJ7DWLpwUIIz1drfy0mMxGgh4PbXPoa/wz02LJjXaxu++N50jpxoIt5r5zY7DWtjFjNGx3PPaXs1D52yVeeCN/Ty6eCqn6hzEtfPKqXH8Y+L180ErGlo4Udc9QdyVQiPdNdBEKIhAMPzojC42tgUa+OrgF74/kzzRo07Qy5hMBq6YMpLJKVFBNzJVHXfrnAy/taHKhWPj/MKC2+u2YAVRJo2M5MYL/JuU//yKiTjdLkqqHQH3xxPvncnB31Vcg0Ei4Jz17x7m19dOY9PO/dp4ffHVsfMnJjHu9jkcONXA4YpGHvvnIa33Xvt1wlDeiO1Rhq6iKH8G7gQeAcqBqxRFeflsz5Mk6RlJkiolSdrncyxWkqR3JUk60vY7pu24JEnSBkmSiiRJ2iNJUm5PxjxUUCdlMM+L2tCxqrGFlfOy/Pp9rFs0RRh1g4hgBoO6c7b6pS84Xm3XHtMTWGW1Dr/+MFs/K2NVfuC8uDAjjvJ6/ddrbvWGVPzpg2JGx4WRFhtGWZ2Dz0trSY628eCbX1FS3cxT7xexYXsRnx6v8Qu7VK8TYpQ4PyMuwCu3YccR1i6cpBVS8cVqNhAbZtEVxL7vvTfoyEDrCN++QL7jFk1TBYKhy7HTHetiowG//pvqRltiZIjQwYJzQvu+dYBfb7tqe4s2Vzujs9SK67ddnEl4iJFfXD3Zbz6vnJfFm3tOEGE1aUYdeOf/z9/YT6zNcta1qqIEjyT6qryhU+M1GCQkCe54+Us2bC/S1jJ664Tu6vnBQE89dgAVwAdt1wqVJClXUZTdZ3nOc8BG4AWfY/8LbFcU5ZeSJP1v2/9rgG8DWW0/5wO/a/s9rFEnZajZEDT3SQ3JtJgkHl08laLKRqanxfCNsfFCoQwigsWOK209ttt7hfQE1kuFZX7FSWodLrISw7U+MCPCrZiM8GVZHZKE1mdu62dlWp/DzARvrpzqrQPYXVrHpp3FujtparPR9uPOSowIKlQLS2p5ubAsIOb/oasmoaD0iUesO7H60L1cPoFAMHiRZYUD5Q3E2CyMS4zQlRvjkyJ55sOjPLp4KocrGjFIkBYXKnLbBX1CsFSO9LhQ3b5xqs5qn85x17dzOFDewPMflXDbRRlsvD6XPWV1WnGVa/PSOFLRqEX1wJn1gy3EhFHSXw/4rlUXTU/tsD1CR+NV6WzkTHf1/GCgR4adJElrgZuBo7Tl2bX9ntfR8xRF2SlJ0uh2hxcCF7X9/TzwL7yG3ULgBUVRFOBjSZKiJUlKVhSlvCdjH4z43mghJgPpcaGEW0wBi+AHF06izt7Csx95G0yuXTiJMIuBmWNimTU6sFGlYGCjZzCoghC8DbhDzUZ2HT1NYqSV5CirVm3NN9bd45EpmJvBeaOiSY8L08IzxsSHseNQBSdrm7G7PH5zSQ2PvG5GGo+8fYBah4uN15+HR4ZPj9douZ0QKLTf+PKEnzHpK4SVIEafR/aGK72wq4SCuRlMTI4iKSqEiclRlNb6F3BJjrKyJC8Vh8tDcVVTryU+d9dAE03DBYLhg9eoq8diNnDjBek8/s5B3ebKz3x4lPycJE1+Pnz1ZM5LiRFyQdAnBEvleP6WmRyvthNpNXHP5eOJDgshJymSMfFePdfeGFw5L4vXvjjBslnpNLk8FFc24pFBkmDBlBS27Svn2plpPP3hoYD1Q1y4GUUJ092wrWnyrlXL651aJJHvOb+4ejIbth8GzvS501vHqHTWYBvKG7E99dgtBcYqiuLqhbEk+hhrp0BrYZQCfO1zXlnbMT/DTpKkAqAAIC0trReGM7Dw3XWJsVn46bey+d/5Ofxy2wGum5HGY4un4nC5qbG7GBFuwWY28tNvjeNknbdoRVKklZzkKKFM+omezE9fg6GiwUmrR+He1/dSXu8kPS6U2+dlce2mj4mxWViSl0p2QgTPf38GX35dz/p3zxQ7WX1pNlNSo/hmdgKA325ccWUTTS6Pbo7I/y0/n+ZWN7++bhpJkSHsP9nId37zAXdfPp5HF0+lucVNlM3Mzy7P4RdvH9Be77oZaZglWJWfxfikCNJiwzAa4MuyWlAIMPp8jdXyeicbthfx++/lEmE1YzBIfoI4xmYJiOXXyzXsbKGV9udelpPI290w0AZr0/ChLj8Fg5eBODdlWWHHoQpqmlxU2108uf0IMTYLkgSPLZ5Ks8tNelwYX52s50ffzOTrGgerL80mIz6MqanRYnN1CDEQ56cvvh4stVWAJEFVUwt//riEb09OJj3ORnx4CM2tbv5zrBqbxcS6bV5drj7HZIAHF05ib1k9o2PDCDEZuK2toArAbRdnavoczqwffnt9Lne9sheXW+GWC9P5/femU2t3caKuGZvFSJUCtQ6vCeEbSVTZ6MRsNPDLf3zl13C91uFidFwYF4yOw2oNNGE6a7AN5Y3Ynhp2+4BooLLnQzmDoiiKJEnK2c/0e84mYBNAXl5el547GFB3XbITwvnfy8dT09SKoijcc/kEHnr7K63q4Mp5WbS4ZUItBn7xmneH8E835Qmjrp/p6fz0NRhkWeHZm2dS2egk1GzUjDrfMsB6zcD//J8SNlx7Hh8XV9Pqkbnv7/u0eXPvggk43fqhjt7edw6MEszKiNPmYYjJyJ1tFapUT/Efl+WCZKDe0YrRKPH0B94m6HMyR/DOgQqe+fAoi6en8fM39hNjs7D6kiyyEiOwt3goq3X4vbbVbGDviQZ+vOUL1i+dxmU5iYxLjOC3N+QSHmIKWh1L/Yw6W/Gqo3MHm4HWXYa6/BQMXgbi3DxebWdPWT0fFVWxMj+bBxdOIjnSypPbD1FYUq/J1PiIEGrsLaz75yE2LcvjvDThqRtqDMT56YvNYtIieJbNSmdLYSkLpqRw/LSdVZdk8+Cb+3G5FZbkpZIWa6OiwclLhV+zMj+LSKuZphY3FfVOQi0mfrj5TLGz/50/no3fPY89J+qRFYL2sf2qvEHLtX/4H4ewmg08ungqG98v4umb8pg1JpYpN+XhcsukxYYxJt4bBnqirpnSmiYKS+pxuRXWL53GwVMNeGR4cvthFGDhlJEBmyRdMdgG60bs2eipYfcI8HlbEZQW9aCiKFd241oVaoilJEnJnDEWTwCjfM5LbTs2rKhocJKdEM4t3xjDl1/XB5SMfe6j414vx44jPL54KqEWI7UOF+sWTWHW6DihTIYQvsJo19HTOFtlrslN1Yw6CExCTo6ycm1eGtf+8eMAD1l5vZO1b37F40um6oYwlNY42LjDW8ktIdJKjM3CrXPHakYdeF/rvtf3sfH6XFb8pdAvlv+ynERKax2s23aAh66azA9eKNSe55bhv/68Wzt/VX4WL+zyhhCr43O2yqzbdoBWj+xXwtl3/OoY1Dj6rlS8GsrVsQQCQe9T0eAkOTKERdPTKPBZ7N6/YCIudwl7TjSw9s2vKJibQV56jFaYSuhhQV/j8nhYOS8Lp9uj5cL5hgvfNX88Trfs18roJ5dkY3e6+dmr+/x0s9qnztkq88ttB/n996ZrG8ir8vX72DbrGHtOl5vVl2ZztLKJudkjOM+nAqcsK7y1r5w1W/dw65wMrGYDc7IT/HQ0wM9e3UvmiDCmjooJeM9D1WDrLD017J4H1gF76Xn/ur8DNwG/bPv9us/xFZIkvYi3aEr9cMyvs1lM3D4vi70n63VLxq64OJPH3/HemMWn7YyJDxMtDYYAZwsnVOPJfXvLJEdZGZcYwR2XZZMeF8aJOgdj4sPZf7KeW+dkAN4kZN+2GM5WGbNR4oErJ3L/3/f7hW8+++/jgPf66mKlucWtuzu3p6wuIJZ/0sgoGpwu1szPobrJpT1+w/lpfsaos1Xmye1H2LRsOs5WD6fqnSyangqA1WTQjDo1NMTp9nD35Tk8/PYBrcCLGkffldYDok2BQCDoLLKsYLMYyUyIYNkzn/jJrwfe3M8TS6dpm1WyAo1ON1dMGSn0sKBfiAsLYUthKf/9zUwWTEnx07kxNgtx4SHc0W6T9on3DlMwNyNAN6vrBfXY7tJa7Zz3D1byxNJpHDjVgKx4c+xvn5fFb3Yc8QsBNUowKjaU/3u7hIvGJ3DwVCOKAo3OVk7WOxkRHsLmXcdwtsps/cxb9C2Yjj5V72TqKATt6Klh51AUZUNXnyRJ0l/xFkqJlySpDLgfr0H3kiRJy4ESvPl7AG8DlwNFgAO4pYdjHlSoC/vmVheOVk/QcrBJbUUzah0u3LJMfLhFGHWDnM6EE6rx5IdONfiFW/zUJ0TyJ5dk09TSqm0I+Hq71OpV6XGhuD0KJqNEwdwMZMXbqy7E6B/m4GyVyUoIR5KkDqtX+Z7/dY2dWkcrd726V9uBi7FZSIy06s7lT47X8qcPirlvwQTCQ4z89ZNSbp+XpRl1viGnvgnaa+bnaHH0Xal4NZSrYwkEgt7D7ZZ572AFLrcbJIOu/HK6PZouNkiQFGkVeljQb4yOC2PN/BwOnWrAaPDfAF42K53DlY2681huF1TqbJW19QL46/vkKCvzJyXzE5+1yv1XTCTCYuB/LhvHidpmvyizaJuFRbmpPLLtoG6+vK/ne8snpfz0W+N1dXRSlNDRevTUsPtAkqRH8HrVfEMxO2x3oCjKd4M8lK9zrgLc1pNBDlbUBO09ZfXkpcdQXNUUtGRsaa2DJXmpjI4Lw2yUOC9FGHWDnc6ECKrx5BOSI0iPC+N4tT3AC6a3+7ZhxxEK5maQmRDByvxMZo2J4+Nj1X7eYPDOLd9dOqvZQE5SJGaTxENXTeae1/b6VYDbtPOo33uwmg3YQkzc/8Z+bQdODQspa1flUj1fadu8eLDNO/j9C8cQFuLNE2gfcqq+ly0Fs5jcNudlWUFR4PHFUzlS2cj7Byu5aHwC2QkR3l45suJ3bwzl6lgCgaB3kGWFT0tqqGxwkp0Uwekml678OnbazpK8VNLbdPHkkVH9OGrBUOVs0Ty+j49LjGDiyAiOnT6jc1Vdqm62tp/H7ZePvsesZgP3LZjAH9r0vZ5efuCN/azKz2JqahR3/m2P32Prth3U1iTX5KYG9L574M39PLp4Kiv/+jl7TjSw+eNifve96XxeWuvnDZyYLO4tPXpq2J3X9nuWz7GztjsQdI7SGjsl1Q427SzGdkkWZqOBkdGhPHV9Lg++ud+vYMrmj0v4cX4WcWFmkqNCdKsFCQYXnQ0RNBgkRseHkxpt490DFZ3efRs7IpxfvXPQm9icH7w5qOq0s5oNPHz1ZExGiVExYVQ2tmg9axQFtnwSGL+/cl4WP/3bl1ybl6blw6lz9dfbj+iWB9+440yoh81ixNHq4Zf/OKAZhHpjbG71aEaduhkiK96wj1vnZvCrdw5p90t7r+dQro4lEAh6h+PVdlrcHqLDLJxudFFW28xvr8/lgSC6OD7MzJgRYVgsxv4eumCQ096IS4ux8c6BiqDRPMGifS4Zl8C6RVNYs3WPlr6h1xvuJ5dkEx9h0Qw+NS1j7IgwVl+ajVuWMRng+pnpPPHe4YBUEDXscnxSBPXNrbo6OyU6lBXzMrEYg3i+XW7AG0108bhk/uv/zuSyPnL1ZL4zKblH1WW7UjV7sNGj1b+iKBf31kAEgZxuauGxfx4iOyGcuLAQ7n19n98CuNHZSqPTw+aPvcUmTtQ3MzImlIz4yP4euqAX6GyIoCqgTtQ5CAsx6jYX19t9q25qweVWuO3iTFKiQgmzmnRf7/wxsTx1/XkcqmjksX8eotbhYv3SaYxLjODpD/09fPXOVp69ZQa7jlbjkdGMuQ07jvDY4qkcqmhk62dl1Dpc1DpcbP64RDMODRI0NLdqxVCsZgMjo21akZbNH5dw9+U5HX4mpTV2jlQ0+YWdrsrP4roZaazbdihoYZThnmwtEAg6ptreQniIiRN1Tr82LcF0cUpsKKnRwusv6D6yrFBaY2d3aZ3WM9ZqNrBpWV6H0TzBon3eXjmHK6aMZHScja9rvZsR6mbr8tkZGA2QmRDBr945yHUz0iiYm0FKVCiltc08++/j3irrN+ahKLC/vB6A9UunIrf1pW1fnVvtQ5ceF6pVxgS1KFszT39YzK+vnaar09PjwliZn0lmQkRAoba7Xt3L2BFhNLfK2F1u0tuqaapG7dkMtq5UzR6MdMvclSTpe22/V+v99O4QhyeyrFBjbyU7IZw7vjVeM+rgTBELh8vDU+8XUetwsfrSbHKSIjk/PVb0yBkiqCGCVrP3+9QLEVQF1C3PfcLesgYKNn/Ghu1F/OmDYpbNSic9LtS7+xZm8bvO2oWT+OBwFfcumIDRAF/XNfP3L77moasm+Z330FWT8Sgyj/7zIBu2F2kVsVa/9AUmIzxy9WS/86/NS+Oz47Vs2F7EK7vLuCY3lRXzMrl1TgYn6hz86YNibvnGaEbH21iVn0Wtw8VT73vHmxwVyouflmrXWjkvi+On7dq8L6938vDbXs9dsM+koqElIKzjye1HSI2xaZ+Z6vUUCASCzhIRYsTu8gT06tLTxeMSI0Q1akGPUHX7K5+f0Iw68BY8aWhu5dY5GayYl0lyW56Zr14LFu1TY2/h2Gk75fXeHnEPXDlRM+6e/rAYq8nIr945yLV5abz4aSlWk5Ffbz/CU++f0f1VjS38YHMhv/zHIf76SSmhFiOyrPDEtdO48YL0gJDMn726lzXzc/x09sp5Wbyyuwxnq8wj/zjAvQsm+D2+Kj+Lo1XeBujBCrUdrmzipmc/4fvPFfKd33zAtv2ncLtltu0/xeUbPuC7f/wPl2/wHpfbhSwFM3yPV9t78yvsN7rrsVNXlhG9NRDBGWRZYX95HSFmiZu+MYbCkhrdiZ2TFMnvv+ft6RVjM4tedUMMg0HispxEthTMorzeSXKUlYntvmNVQC2fnaGbe7Zp2XQURSEtNow3x8ZzqLIRRQaTAa6ensrBtgpWkSFG5o1P5jdtlTKNBshJiuR3/yricGWTX2uB5Cgry2eP5suv64mwmtj43fNoavEQG2bmeLWdRqebvPQors4dFbCzHWOzsP5db87fy4Vlft665KgQHrpqMlWNLViMBsrqHGTEhfnt5pXXO9lSWMr/LT+fVo8csCNnd+krAUeLW/tfFEYRCARdobm5lcYWD1WNLbryZUJyJH9YlovVZAQU0uPCxAaroEeouv3WOWfy49WCJ3f4FEdTdXOtw8WIcK9e04v2SY8LparRxc4jp7U0hZRoK8/fMoOy2mbiI0JoaG7l7m/n0OqReXTRVO7c+qUWQQNe3Wk1G4mxWQC4Ni+N//q/M+2K1i2aont/FFU2sXx2BqPjbByvdvi1KSqpbqbR2cqq/CxSY2w4WtyctrfQ5HTz1PtFQdsolNY4AgyzLQWzgnoyR7fVIKhocNLcqp/SMVQqYXfLsFMU5Q+SJBmBBkVRnujlMQ1bXC4PhyobOVLZxF/+c5yfzh9PSXUdWQkRuhMboLLBiSkmlJzkeGHUDTFkWekwjh7O7Mz5xrirOFtlGp1ujp22oyCRFGmhtNrBk9uPsCo/C0ALWbz72+M4Wd/MFVNTAHi50BsuuSo/izktHq21wB93HmVJ3iiqmlqoa/YW84kLs/C7/1es7VbnpkWRkxzJf7eV/FbHsvbNr1hxcSbNrTIpUaF8b1YaBknC7vJWew01G7CaTVQ0ODlS2chLhWVYTBL3XzGRB94404KhYO5YfvmPr/j+7LGcP8Z/Vzw9Nkz3XqlxuLS/RWEUgUDQGWRZ4VSDnf0nm4i0mrCajbryJTLUxF2v7OW6GWlkJYaTFivki6Bn+Hrd2hc80SuEFmo2cqy6iTHxYboFwX5x1ST2nmjwS1NYfWk2oxSFY9UOjlU72HmokkW5qThaPTz2zqGAnPkHr5gAwJr544kNMwdEkhVXNeneHy1umVd2l/HglRO19I0pKZHcOncszS43qbGhFFfa/ap5P3L1ZP72o1nEh4cwLinS77345uKrOFtlzavY/nhRZRN7T9RrLZOCGYvB0lwGWx5et3PsFEXxSJL0XUAYdr2Ay+Vh24FThJqMPLn9MD/91jhKqpvZtLOYGJuFVflZfuVgH756MklRIYyKDWWC8NQNSTpTFVPdmQP9aqmHKhrZsL1IC6t88dNSnK0yqTE2TYgmR1kJs5pZ/55/0ZNt+8qJDDX7zbuN15+nm8N24wXprNt2iPXvHubxxVODllBOirRyz+v+TU+3fuY14NJjbX55pOpO5O//XxHP3jyDXcXevL1NO49ybV4a67YdYHxShN8O25j4QIX2qyXTmJQSQW5ajBaPfzYGq0AfiNyw/IecrKrTfezQkSIS+3Y4AkGnkGWFwtLTKLJEi9tDaU0rG98PLPj04MJJSMDDV08mOlREzgh6B1W3+xY3CbaBmxIVyq+3H6HW4eKt2+cwJj6M7IRwnlg6DY+iEGo24JYJSFNQo2c27ijSDCZF9hYju2JqCgYDrLg4E5dH5ry0aCrqW/xaGvhG8gC8VOjtO+cbqfPQVZN58ZPjXJObyrHTTazKz2L7gVMsyk3TcufUtYBvA/S7Xt3L2yvnaIXh1Mil+PAQvjpZT23bZq2K1WwgOSpUdx3kdMuaUaeOs/2aOliay2DMw+tp6cR/S5K0EdgCaMGpZ2t3IAjkUGUjiREhVDa28D+XjcdmNnLHy7u1XYgXdpVQMDeD1OhQkqJCAZlWWWZqasyAn2SC7tGZqphpMTbWLZrC+ncPBSw4VuVn8cKuEu1597y2V2td4PCJW78mNzUgb2TDjiM8vnhqQONSp0vWzWF7bPFU7X97i1tLpm4vYMNDTH5N0tXnllTbA3b/fBuo7yquZsP2Mzt06mN6FULbV7hMi7HxryOVWqXML7+uY3JqFPPGJereO4NZoA9ETlbVkbjgx7qP7X30R307GIGgk3xda8diMNLs8RBpNXPHy96qfL6FJrITIqhqdNLQbMZqkYRRJ+g1fL1umz/2rv/y0mOCtLtq1oyrI5WNfFnmX2zloasmYTMbddcTavqZs1Vm086j/M9l46hs8hpNzS4PsTYLv99ZzIz0WH7eFjmjnu+rowFqHS4amlv9qmXXNDm5+cIMPLLCkaomtn5Wxi8XTeaHmz8LWEe0b4Be2ehkdFxYQOTSY4unsPrSbNa/e9hPR09MjgzY2F05L4uTdQ6/966uqZ+/ZSYKim4l7M5srA9UemrYTWv7/aDPMdHuoJOoXoH6Zhf2llZCzCaaW2Xue32fX1w1eCfihu1FPHndNDZsP8TqS8eRmxorlMgQ5mxVMdVQzfXvHmLBlBRMBvjTTXlU1LcQYjLwi7cP+MXHO1vPtC44bW/Rrh1sF9CjKH7Hk6OsKOifq+awqX3r3vjyhG4rg9Iau7Y7qO72HalsZFSMTfe6khTYCNW3lHJSZGCuXPsKl8dPN+l6GTNHhDM6PlBAD2aBLhAIeoYsK3xda+frmmZa3B5qHa1++Tzl9U5t8fnkddN4btdxnrx2GlPEJqugFwm2SalnuGz+2LuBm5ceRYzNTKtH4bfX51JaY+cPO49xz2v7ePbmGbrrCcWnrsh1M9I4UdscoCv/65sZ1AVpW+DbDkndTPatbL18dgYNFY2MS4zAKHmNv1N1+pvW7RugJ0RYdfXxT/+2h+e/P4MXvj8Tl9tDRIgZR6uH0loHl+Uk8vbKORyuaGTviQY2f1zCoumpAe+91uFiRERIUJ3e2XZTAxHR7qCf8PUKPP/9PE41uCitqfNrEK13E8aHW7hh1mjy0kT1y6HO2Rpn+wo8daGxMj+TTTuLuXVOhm6owrRR0VjNBv7v41Lu+U4OlY0tQXM448LO9LFRk7ZPBGkqXtXUogn2Vz77mv++KJPf/qtI29memhrN/+06zsTUaMA/N8AjE7RZuUFCa4SqjsHXWFy/dBppsR2HSQarlJmbFqNr2A1mgS4QCLqP2gezoqGFtW9+xfLZGTz9YXHQJs7x4RZ+nJ/NpJHRwqgT9Dp6bXhUY6+iwYnTLfPzv++jvN5JXnoU185I56ZnP/Uzyv77orH89l9H2VtWx08uyeaJ9w77Pa5G9QCMirEFROk8uf0Iz9w0Q9tkbX8PzMqIA2DyyCicbllbd6hG55bCUq6bkUZZrYPYtrSiYK2VfBugr186DYMEB081cuucDK19kzquD45Ua43Kb33tM781wfyJSQD8eIt3faTXq+9sufadbTc1EOmRYSdJUiLwMDBSUZRvS5I0AbhAUZSne2V0Q5jj1XbWbTvA2isnYpAM1DS1MC01WgtT23moUjeWPzbMzPmj44RRNww4W+Ps9gZIcpRV83zpCbKV87L47ftHWL9kKiFmA5WNLi2Hs31YwwNXTqSmyanFod9wvjeJOsZmCVAOD189mWibiT8sm059cyuTR0Xz238VsWBKCpIEHhkefHM/C6elkJUQwYp5mZqQToux8at3DwOwduEkvxy7B6+cSJTNQkVDMzecn05Ti5sthaVamAegm2fXHr1KmTE2C25ZZtfR0wE5dINZoAsEgu5zvNrbB/PFT71yJjshjOWzM7AYDTyxdBq/3HZAa0b+4MJJxNqEPhb0Ph3leKvG3ui4ML4qr+f+BRM53dTCqFgbtzz3aYBRVjA3gxsvSKfe6eGDw5WsXzqNsho7qTE2nG6ZpXmpvNRWLC1Y9M7Hx6r518HKgEJmD1w5kUZnK1kJEVQ0NDMi0soTS6dx4FQDHhm2FJZyw/npjAgP4dl/H6Pe2crdl08gymrU1ff2FjdPXjeVxEgr1U0u9p1soKzWgVGCH83N4Pc7i7XevIoCC6akcM9r/ikcvlUw1Y1xtZr27783nRCToVN582fbWB/I9DQU8zngWeBnbf8fxptvJwy7s1Df7OJ/LsvG6fJQ09RCmNXMf/9lt98ifNu+cgrmZpCTFElcuAWrWWJcoojhH04Ea5wtywo2y5lm5DsPVfKdKSM1z5de09FH2kIzL8xsZNqoaE1AAyRFWNh4fS57yurwyPDbfxVxw/npHDnVwDM351HV6NLyPZ/76LhmXOWlx1Babdfi+dPjQlkzfzwbqps1L6JKWqyNh98+QK3Dpe3knWpwaoI6Ny2at1fOoaiyCQk4drqJ0tpmb1nmKCvZiWFYTcYAY7XG3qJ9PnoKsX2lzOQoKzdekM7y5wv9jNPctGjSYvUrig0WgS4QCLpPtb2FjBFhXJuXxo6DpxgdZ9Oq+Knh5HZnK1NHxdAqu7GYjMKoE/Qqncnx1jvnscVTg+bQjYwO5a09Zdw6dyxlNXbCrGZWv/xlwCZqnd2pu6npkWHPiQb4TwmPL56KR1GItVl4cvshCkvqtXvj4bcP4HIrWrrEwmkpmA0Sx6rt/GDuWOLCzVgMBhytbmwWIwVzM5AVb7ujFrfMW3tPcnXuKNb4eB1Xzsvi30VVLJ89lv+5LJuvaxxa3t+i6akdRtdclpPIpmV5FJbU4JHh/r/vY838nIBq2nqcbWN9INNTwy5eUZSXJEm6C0BRFLckSZ6eXFCSpJ8At+LN1dsL3AIkAy8CccBnwDJFUVxBLzLAkWUFe4uHBkcro+LCcLQENj31LWE7IiIEt8fNhCTR0kCgL9TvXTCBTTuP4nIrmqdObTq6cl6WZtRZzQbGJ0VibznTx+XGC9Ixm0ys+MtuPyG5/t3D/OF703F7FMrrmjWBr+aYWM0GLhw7g4f/cVB7Xkl1M4dONeoqh1P1Ti2UYsOOIzyxdBoPvvmVFhJS2djC+WPicLjcfHDktF/FqlX5WYwZEa5b6nlLwaygn8v6pdO4LCfRz1BbkpcaEJp596t7KZibwfikSOZPTBq0Al3QPQ4e+IqLr7w24PjIEdH8+ek/9MOIBP2Bxegt8LSlsJQ183MCcnvWvvkVTyydhtkg0eKGMTqh3AJBT+hMjrfeOWajFDS88WRdM5dNHMmdf/uS5bMztArY6nPv+/t+CuZmEGYxcc93cnjorQN+a4tGZ6sWabPir5/zzE15/GBzYcC9oRY/8d3UfXzxFNb98xDZCeFcOzPNL8S5/Vj/uCzwuht2HGH90ml+1Th/ckm23/OCRdeU1joo8Lke0KV8+WAb6wOdnm412SVJisNrhCFJ0iygvrsXkyQpBVgJ5CmKMgkwAtcB64AnFEXJBGqB5T0cd78gywrFVU18XlqL2SiRFheGosCBUw26uw5psTZSokNJjDQzc8wIsTMoAM6E8S6fncGKeZncOieDTTuPsmBKip+nbvWl2Wy8PpcthaWaUbdyXhbPfHiUxMgQrU1Caowt6Bz8tKSWgs2fEWo28pNLsv1aK6xdOImS001+nrDbLs7EaDDw1PW5pMeFaueunJfFn/9T6ndtWVFYND2V5bMzeGFXCbuKqzlebcfe4tHNiat16CdvO1we7XPRU4iltQ7mT0zi7ZVzeLHgfKaNig66s7n6pS84Xm3XBPqsjHgyRoQLo26I06oYSFzw44CfYG0aBEMPWVZwtnqosbv4n8vGczCITHTLMpJBZtZosdEq6H06yvHu6Jzfvl/E2oWT/HT0qvws4tqaiashi8HCLWUFnnjvMHWOVn53Qy53XOZdP7y6+2sanR4t3z0vPYqqxpYOC6moeCt2OoixWfjRRZmaA6P9GJKjrCyfHbxAi++96GyVeeK9w1yTm8obX57goav837NvdE1nPsuhSE89dquBvwMZkiT9GxgBLO6FMYVKktQK2IByvFU2r297/Hng58Dvevg6fYosK/xj3yme/fdRbvlGBqU13kbRak6d3q5DYoSVaJuZUbER/TVswQCk2t4S0Dj03gUTCA8xartqT71fRHpcKNkJOdxx2XgkCVKirbS4ZcYmhCHLMg9fPZm7X92Lo4P2BIriFYSPbDvIqvwsLQTTIEGrx8PJem/RlBibJaCwyb0LJhARYiTKZuG+1/f5Vei0mg2YjAasJgMjwkP42XdysJoN1NhbdHPinK0y4Vb95sCJbZUxz1b0RP0J1kRVfa+iSIpAMLyQZYXtBytwtnpodLo51eAMKhPjwy1EhIRgsRj7ccSCoUpncryTo6xaGgZ4Wwcdrmxi0sgIXvj+TMrrnUTbzJSctvO7/1fMkrzUgOsF038uj8z+kw2Mjgtj7Zv7A9Yav7h6Mm63W/ca45MitePqGmDLJ6XcdXkOjc5W3TH4FkULVqTI46/WcbZ6G5wvyk0hLcZGblqMbnTNcM2X76kL6CvgVeBToAL4I948u26hKMoJ4HGgFK9BV4839LJOURR322llQEr750qSVCBJUqEkSYVVVVXdHUKvo3rpdpfW8Oy/j7Lm2+NRFGhu9XDrnAxCzQatNLzvrsPPr5hIXbOLTJHXMyTozflpMRoCQhLXvvkVhyvsvPHlCe5dMIG7vz2e+xZM5JFtB7j9r5/z2D8PcqTSzs3PfsoPN+/m5ucKcbjc/GHZdEbFherOwXsXTOCV3WXaazS3enjq/SL+9EExqdE2/nWogq2feRt9LslL1R1TlM1CQ3Mrt8/z9/atWzSZ8rpmNr5fxJpX9nLHy19ypKKJ000uxsSFaeeqWM0GRkaFsm7RlKC7c77N2n2f116Iqzl0vtdZOS+LV3aXYTUbkJAormpClhWGCwNVfgoE53Juqvp5T1ktUaFGyuuaOdXgJCshQlcmPnTVZBRFERs/Ao3enp96+slXz8mywlfljWzaWczGHV59fOMF6fxhWS6HK+3c+MwnrHrxC364+TOaW2VuuTCd8UmRrMrPJDnKys5Dldy/YGJQ/Tc+KZI//6eUgxWNLJiSEqDXf/bqXhKjbPz0W+MCrvHMh0d5Yuk01i+dyqOLpzIy2sri6akUVTaCgjYGtbib1WzgmtxUrSiaWqTIN9rn3gUTeHPPCb/PyGo2kJXoLZpmMhmCRtec7bPsKqq82HX09IBeI/TUY/cC0IC3MiZ4vWqbgSXduZgkSTHAQmAMUAe8DMzvzHMVRdkEbALIy8sbEJ+2mvOzbtsB1i+dyp3fyuFknZPiqiZebqtCdNf88Vw/M52/fHKm0MV5aTEkRVgYHRNGaKi5v9+GoBfo7vzUKwbicHl0PVMRViPX5qXp9rhRyw37Ngd/6K0DrMrPIjsxnP/6Zia/+39n2hPkJEXyu38V+fWjyWyraKko8NS/vI3FKxpaiA410eDU97JV1Dt54r0j3HhBOhuvP48wi4l6ZysmSWLN1r0B4ZYFczPISojQcuJibBaW5KUyLjGCr2ua2bzrmDbGvPRYLsw4kwTdUdETt1tmf3k95fVOkqNCuWRcAm/dPocDpxo4XNHI5o9LqHW4WJWfxY+3fEGtwzWsmpIPRPkpEMC5m5uqfn7t81KWzx5LrcNNZkIED7y5H5db4cYL0rXqmKpeHjsihNToyGEhEwSdo7fnZ7D+deo6wGYxsW7bgQDdueUHs/jh5t1+x5947zAFczP47z/v1kIzrSYDjU4XTyydhtPt4dhpu6b/7l0wgd//y5sfNz4pgsMVjbp6fXdpLaFmo1bczyMr1DhauPnCDNyyTGWDk+d3lfBf38zA7vIE9MV7YVcJWwpL+e0Nudhb3IRZAiOQ4sMsxIRZKK1u4r8vyuT+v5+pxrl24SQkvPewwSBp66RqewsWowGHy6Otl3orX74zRW0GCj017CYpijLB5//3JUn6qgfXuwQ4pihKFYAkSa8A3wCiJUkytXntUoETHVxjwKDmQq3Kz+RolZ37Xj8zMe+4bBxPf3hMC3FbOC2FnKRIIkNNJESYyUqM7u/hC/qZYIJkXKJ+37mR0TbubNeDZsOOI6y4OJPIULNfMRLV4MsYEU641YSswIMLJ9HodFPb5KLF7eFwZRPJUVaW5KUyKsbG1zV2v14y5fVOfvmPg9Q6XNoum9riAOCNL0/Q4Gxl2SzvAsk3pGNlfmbQOP+qJifzJyYxYdUcdpfWaRU3fcet5gy+7ZMEHayKlSwrvPblCS3HwLvzPomrpqYwJj6MCcmRnDcqms+/rvNrriqakgt6mxuW/zBo3p4o1NK3HK+288yHRwN6f61dOIm395zkhV0lLMlLJTU6lJSYUBIizKTFRvX3sAXDAN+iHXrrAF89CF7dWR4kFUF1KjlbZV78tJQVF2dpbQa8Vaxz+O7MNCanRPLzN85sajz2z4P87/ycoKGRdpeHjTuKuOOybLISIzAaDFoPPPU+SosL5d9F1X4byk9uP8Jvr89F9pbmwGQw0OL2EGOzUF7v1KJ9/rBsOlWNLdhCzDzzYTErLs4kKdJKaa2D9e8e1jZfL8tJ5J0DFazbdiAgbFQ1vHqjAEpnitoMFHpq2O2WJGmWoigfA0iSdD5Q2IPrlQKzJEmyAc1Aftv13sebu/cicBPweo9G3UdUNDhZMn0UKTGhVDW08sAVE7GFmPjjzqM8/s4h7rgsm4feOkhzq4fkqFCe/vAoK/OzGRMX2d9DFwwAggmSbavmsG7RFNZs3eMn6I+ftusX4YmzUVTZ5CdcN+w4wqr8LFrcHnaXNPkZfT/91jgMKGy47jzqm1v9es2oCqXW4eJoVRN3XZ7D4YpGqpucAbtq918xkVa3h8ffOcjy2Rl+IR3B8lcMElroZH1zK8er7X7NSTfsOKJV3tLLh9OrYrX3RF1Ar5t7XttHdkI44VYzFQ1OFGDDdv/2DCLfTtDbnKyqI3HBj/Ufe/PXfTqW4U5Fg5PvfyODZ/5dzKOLp9Lc4sYWYuL5j4r5r4uyePK9w2zYXsSvr52GzWIkPUbkugv6Hr11gK8eBK/uTI4KDZo7p7JgSoqmz8FbxXr1S19QMDeD/ScbWDAlhfFJEfy0zUD73b+K/PrNqYZgcVUTU1KjuPvb4/Ao4HLLWmGU5Cgr1+SmUtHgJD7cwutfnNB6P6oVNsNCjByvdvitF3yNVWerzN6yeh5/57D2mCTBPT5jB+/m65aCWax+6YuANUZvG15ny+EfSPTUsJsOfCRJklruLg04JEnSXkBRFGVKVy6mKMp/JEn6G7AbcAOf43VxvwW8KEnSQ23HBnyfPFlWiAkzMWNMFCXVTu7zWRzfv2Aif/2khMRI7404PjGCdf88yI/zs5kxKkZUvxQAcKpeX5CcanDynUnJxNgs7CmrI2NEOOu2HeCKqSkBgj09LhSXW/ELhVAFaFqcjUOnGrXH1Os/9s9DFMzNwCPjV5JYVSgFczOwmoxs/ti7o71xRxEr8zMDrvPAG/u13jrtq2DpNVBflZ9FVmI4aTG2DncoVY9gZ5Ogy4N8jserHdzZZhyvys8clknWAsFwJTnKisvtYVFumhbpoOrn8joHt84dy51/+5Jom5kQk0EUSxH0C8EMCrUCpeqZmpgcGZCKoIY9qhgN+hUx02JsvLDrOHOyE3C0nEmr2HOigc27jmtNzcOs5oDrj4y2YjJIAYVQ9HS32urg30erA9YLvsaq1Wygud1jwfr0VTS0sHx2BmkxoefU8BpMhVh6ath1Kv+tKyiKcj9wf7vDxcDM3n6tc4UsK3xwpJKT9S2MHRGmGXXQtuB9cz+PL56KzWLkoasm41EUHlw4iZmjYkROnQAAt1umxe0JKkhMJgOzM+OxWYwcLK/nvgUTOVrZyL0LJmg7Z6rX7L//vDtAgBbMzUBSvJ6zYOEbwcoip8XaOFXvZEleKuOTIkiOsga9jvoe1LGr55TXO9lSWMrm78+kqcWNzWIiMTKEtNiwDncon/6wGEXpWhJ0sJ3MoqozrRpeKvQWgfH1XIqm5IJg/e1AhE4OZmRZoaTaTqjFxANv7g7Qz39YNp2KeicPXTUJCYXMeOGtE/QPwQyK/PEJXDg2zi9vbEJyBKvysxgREeINYzQbqXW4tOfkJEfqXqvB2cr8Scm6lSn3nGhg3bYDPLRwckCPOTUvfnxShFYIRa/XrG+UTVpMqPaYL+oGsK8x6PtYs0u/EqfFZODpD4u5fZ7+5uyI8N4xvDrK4R9o9MiwUxSl5OxnDT9Kqu3YQoykx4ZS0aDf78OjKESFmnF5ZJIjQ5g4Mlp46gQa+8vr+fkb+wO8Wo9cPVkTJAaDhIJCYmQot7U1F1f7wRgNMDE5MmihlTHxYZTWOjBKgSGR6XGhTGxTAHqC0mYxsbFNSKtCOMSkf25arI21Cyex8f0jAe/lx/nZbYLcyIgIr1FnMEgd7lCqoRzP3zKTGaNjdZOW2xecyUmM4KGrJvnl2K1dOIn1754p4Fte7+SFXSU8f8tMFBTRlFwAnOlvp4cInRy8HK+2YzJKQftxVTa0kB4XhluWmZYSKbx1gn4jmEERFWrWeriCV+9VNLQQa7PwdY2Dlwq9Fa3V9UB2QgQeWeaOy8bx+DuHtGutvjSbsQnh3Pf6Pr/KlL/cdkALoSyYO5amFv0CaSlRobhlhaeuz+VopX6xFd8omxP1zUFTMcYlRvD44qk89s7BgPZIEVaztnGtFlXLTAjnYHkDMTYLBkkK2JxdlZ8V0FuvuwTL4R+Ia4SeeuwEbfhW5WlxuzlV38pvdhzmjsvGa/1G1Dwh7y5CCCEmA1mJoYyJixqQk0PQf5TXOympbtaajUsSKApE20x+cyUuLISSaoefJ0yNu398yRSsZv3ebwkRIVQ3tXgF+oIJPNjm5UuPC+W2i7L4SVtFyru/PZ7TdheyAkYJxiaEB1Tk2lJYysr8rACh+tBVk7C7Wtn4/hGum5FGZkI4f1g2nUanm/hwC/tPNnCXT2EUNdE52A5lZkIEj7x9gFqHi7dXzglq1OkVnLly8kiyEsI5Ve8kKcpKpNVMrcOl5QNIkvf9JUaGMDq+Z2EbepVMxf0tEAwMZFmhxu7EajISbZMC+oHVOlyMjLYSYpJItJkJCw3p3wELhjXtDYoR4VaOVTcx/8kPNB238frzcLkVv0rSqy/NprLRyT/3nWJOdgIKUFnfgsUo8cTSaXgUhYgQEwdONdDkdOv2xlX19MYdRSzNS9XVyyfqm9mwvUhrB5IeF0pJdbPfOWqUjW9oaPuN3pXzsnj8nYPcOjuDH30zkwfe2O9noD345ldYTBIbrz+PyoYWbc3im4P3/Ef+66UXdpVwXlp0j3W673fRG4VYzjXCsOsFfBeTDyzIISEqlN/sOMy1eWlaEqo6+bYUlnLbxVlE2YyYDAijTqCLGj7oa6hZzQYumzBLO0eWFRQFEiJCdAVuac3/Z+/M46Mqr///fmYyk8lkTyALCQmEJOx7RPQrtIJaarGK4FJbbK1+afsrQrWL1brUtcVarNSlpbWLdhFa6kaRqmCrfusGyr4lhAQSs5E9M5nMcu/vj8m9zGTuhAQSMkme9+uVF+Fm5s4zM/ee5znPOedznPz7kL9nzf2bg0VNKhudPPb6Ed2Ze+qGWTjdXgRCV7YCaPcoQfV5910xGbc3WNF58bQsfvTiPpLt1qAG5g1tHZiEIC81FlWFW//6ScBENIuf/euwYaGz0Q7l6oUFulPXXfpDOMGZLavmMX10MtNHn/rsnrxhJsU1wcIx4zMS9MjhmTCYJJElZ4dM0xx8KIrK9sM12KLMqCgcrGoNkWLPTrazo7SO/ylMJztJCplJBp5Ah6K0ro2Vf/kkaI7bU9HM+rdLSbZbQ2rc7ls8mV+9XcJTb/mjb3d9fgLHG5w8/uYR/TG/ubHIsA/tY8um66JiG3dUcPulhax940jQ/aI5ai6Pwt0v7eXxa2dwW8D8d8/iSSTFWHjmy7O468V9eiRO27Qek2onxmLGZILlc3P57bulfHlODhtWzMXp9mGNMrGzrIGls7NRVThU1apnDGmvq9XgNTrd+noJIrcGrr+Rjl0fELiYzM+I59OmDsPGjuu2F/P7r52H2Qx1rW7mF6TJxZ4E6Fn64ENXTWFyZqL+eM2BSLZbQ6JlD3xxMo+/Wew3oh+W87uvnUdFgxObNYomRwf3v3ZIvzbL69v59l8+ZsX8vKBauatnZevGH04JoqyYnxekIKkVZAc6oQArF+Sz9s29/GZ5EXe/vFd3+mIsJpqc7m4LnRdNzmD8rfM43uDAbo0iNtrMeWOSSYmN7jYC1lPlKpNJMDY1LmSCPFsVrcEkiSw5O2Sa5uCjrN7BnopmCtPiiTIJfZEKp2qGfnNjEdkjEpiSkSjLIyQRh9Ecp83bRjVu92/eH1TjdtLh5uVdlfp8DFASpl9d6UmH/v+qZhe//78yHr92Bq0uD2kJNu78x96glEmXR6G4to0V8/PISozheGM7T24v8bdEum6GXu+nne/Zd0uDBFMev3YG/zsvj3Ej4/RedDnJdmpaOvR5NVyrpGMnHbJOvhPp2PUBNS0uku1Wbrskn6O1TiqbnGHVhxqdHsaNjKEoJ1U6dRKg5+mDkzNPLTQCHQitPkwzppXN7eSOsOtGdE9lCyfqnfxiWzFXz8oOqx6lpSNp0b9w4il5I2L1x9gsJiZmGBdkq52TTYOzIyTN4/HrZpxWYepwTWvIZzIjO7nbNMfeKFfVtva9fPFgkkSW9B/honmHi0tIH4DxSPzUtLiwW6KIMoFPVQ3vVWeHjymj4rDZ5PJIEnkYzXFarXx0lMnwmo4O2KCwW82GaZdGKZReJfhcjU43+z5t4am3Svjll2YGOWraczq8Ck+9VcLKBflBG73l9Q7D9EtNJMXlUThY3cJ5Y1K46Q8fBc37l01MZ0tnKmqMJSpIUVN73bEjYqlodLLy4nzcPoWFE9KYmpU0LNfZcjvqLPF6FWxRJr71mTxGp8Ry7yv72LijQl/sBuJfYFopSJPpl5JThIvyVDS3M310Mp+bksn0Lm0wujoQVc0ufxRN4G9T8M5RHrxyin4NJsdZufGCXJ59t5QTTe2G16ZJ+GtMbrukMEjJsuvjbFFmfrZsOrdfWtipVHmUH18xOeg5qxYU8I+PK7BZTCTZrSG7iD997SD3LJ4U9Jw1S6dR7+igtK6NYyeNP5P/ltZz+bp3+NJvPuDyde+wdX81inIqNVRL4ww8b7hdO22C7Pr+ziZ1oz/OKRl8aNG8rj9uj3eghzasSYiJYmp2ArHRFk40OA3v1dQ4K2NTpAqmJDIxmuOmZiey9toZ5I2MNbymx4w4Nf9lJ9kN0y7vWRw8h992SSGpdmvYeb2qycmqBQVh/x7YP89mMdHq8rFhx3EeWzadJ2+YyYr5eUFN1m0Wf+Pzj8oaQub9441O8kbGMTdvBFOzEkPe/0NXTeGx1w+xZuthnnyrhAkZCcPWqQMZsTsrvF6FbYdrSIm1MGZELCfb3Hoz5Wf+XRIiPb9m6TSmjRq+F5vEmDOJ8mQm2gyL/o83tOu7ZN/6bAEbVsylpqWDpBiLnqJg1EPu7i9MZHRyDIVp8bR7fDz5pZlEmQVjR0zlRwECJ6sXFnD/5gM0Ot2sWlDApj2VfHN+PtsPVfGHm86jpqWDo3VtehPzVQsKOFwVmuZRXt9Oq8ujp4PMGZPMPS/v01W4HgvTs2ZHeajRD0xzDCw0b3B0YDGbcLp9lNU7QqJ7/SFfPJgkkSWS4YTXq6AoKmaToMHRAYIQhcCHrprC+HS7VMGURCzh1BkB/u9onWFUrLrJCWjOk2I4tzo6PKy8OJ+R8dEcb2jnn3s+5bLJGay5eipJsVbaXF7K6h26gEmizcLJNje//9p51LV1UFJ7at7X6vq019T0JW5dUMCIeCslNa2kJ9iCWjHcdkkhf/mwnMXTskLGFrgWMnr/Ocl2ZuUkR7xa5blCOna9JLAWym4xg6pystXDweoWFBVe3V3J8rm5PP9+OU9uL2HF/DymjEokPcHK5EzZ0kASSm8bXyqKygGDov9Uu4XaNjcrF+RjFpBstzJmhL/Y+qVdlfr5q5pdeuFyYVocNS3tqCocqGoNyk9/ZMlUVMXHz5ZNB6C4tpXn3ju1w7ZuezGPLpvOc/8t5TuXjmdHWSMxFjPTsv11gD4FNuw4zgNfnGL4/lpdPj233iTy9DQQf55+q+FzfMHzUdj6uTGpsRyqDk3lDBQx6Q/54sEkiSyRDAe0OftkWzvVzW5K6tp0ld+sRAsrL87H5VUwCShIiyM+RkbXJZFNOHXGrCQ7d7+0L0gZcsOO4zx45RSeuG4GMRYzCfaosOuNOzbt5ZZ5eWzeU8l1RTls2HGc64pyuOMfpzZ3H106DYDlv/tQP/bglVOIsZh1gZPX91fx3csmIFBJS7DxaaODK2dkkWCz8LXf+9MsA1szFabF82mTk9svHc/aNw4HvSebxUSMxYyiqEFzd9f3H/h/RVEprWsbtsrU0svoBVotlJYK9sGxek46PNy2cRfrtpXw23dK9Zvh6lnZNDrdZCfZGREfxdSsZOnUSQzpTfogGKduvvDRcXwInnyrhCe3l/Drt0s5UNXa2dvGpfeN0dAKl00mQbPLR11bh+7Uaee868W9HG90cetfP6G4tpV120pCCqU/bXKyYEIGt/xxB4+9foSfvX6Yo7UOYixmhIAHr5zC+bkprFk6Lej9rV54KmXjkSVT+Vtnzx2NjTsqeGTJ1JBUzc17KoMeF84BDpfeWlbvCHqcNkHMzRtB3si4PjH+/XFOiUTSewLnbK8ClU3trH+7VLeR9U4PZpPgt++UMjrZzsR0qYIpGbyMSY3ljkUTefZd/zX+7LulXH9eDmUnHfx06yH2VTWz50STYQpllFmwZdU85o5N5p7Fk1m3vdhQBPAHm/awp6I56Ng9L+/zZ/psL+EfH1dw3thUfvD33dz611189Xcf4lMF/z5US6PDHbTB/NRbJazbVsKhmlZyU+P4wpRM7lg0MWRsq174JKTsIhxd1+lGJRtDHRmx6wWBi8XMRBv5afH8v87G0HBK+fLmi/KYkBHHc1+fw6jkaEYlDK/dAknv6G2Uxyh1c/G0LL3vCwSnKaYn2Hh1d2VIisbDS6YQZRLkpMRworG9W0GVcA1FRyXZ+UFAewSXR+HxN4901t6VsnTmPLYX17H2jcP67tycsSlkJcYwMyeJtHgbJkFIEXaj082snCS9YFpLt7CYTT1Kc5QiJhKJRJuzk+1WPF41ZPPqiW3F/Hr5bJ77+hymZshG5JLBjbaWCFSUTouPRggozIjH6fZxsKqFjTtOhET1Pjc5g7yRcYxJjeW1fVXdCqh19ZFcHoX8kXHYLCZDZc67X9rHY8umgzBeR5iEv/61vMHJZRPT2bBiLtsO1eJT0OvweqouLZWppWPXY7R0Ds2pWz43l5NtHYYXvdkEKbFWJmfEyeamkh7Rm8aXafHBqZuZiTZyUoyVLmtbXcwZk8rqhYU8se2I7lwV5SZTWufgRy/u45Z5ebqqVleDa+uMMmt1eRt2HGfxtCzMJpiUmUC72xf2Hnjoqin4FFU3soH9+LasmsfcvBGA/94yqkvT+skFfiY9dYDDpbcKBKV1bcMuNUMS+ci+eH2Ppli9fG4uzjC2yuVWKBqdSEyMZYBGKZH0HSaTYFxaHOPSgtcSY0bEUXayDbdH4dYFBZxocLJxh782f/XCAto9Xt47epL0BBsT0uODomZGjlggNouJuOgo/vj18yg76QzjDKqYBHz/c+P52b8O643Uc1LsjIyLZv1/jvLesQbWXjuDkfHWoJZK2jl6sjE7FDZ1u7a/6u16JeIcOyFEEvBbYAqgAl8HDgMbgDFAGXCtqqqN52pMWmj3cHULNouJGy/Ipd3jIzbaOFd5xugkZmQlSKdO0ucoisqx+ja9X0uy3a92+Wmn0qVR3rzJJJiVk8T3L5vAoZpWfAqUn3Ty062HdDGVb87PC+kBc/ulhUSbTWQm+lMd46LN3LqgIKi33pNfmmn4uvlp8fz89UPct3jyaY1sbyKWPXWAwzU5/86GXXqTc9k0XBJJyL54fYuiqHh9KtcUZbNhx3Hu7VTh7Wqr0hOscq6WDDp6u/jXavMD58SHl0zBboni06Z2Ghxudp1oxqcozMpN4ufXzODRfx0MyfR54IuTiTKLoJZHqxYUcP/m/Syfm8u4zshd1/ssyiT41X+OsuqSAn79lVnUtbmD1hKrFhRwqKaN2zfuYsOKub3SHQikt5oFkUa49le9Wa9EYtHXE8BWVVUnANOBg8APgW2qqhYA2zr/f05QFJW9FU2s2XqQpBgLv14+m1GJMQjg1/85yuqFwbnKjyyZyoVjUuVEIekXyuodrPzLJzz3nl/85K7LJ/LEtmI27qgIypvPTY3hV1+ZTUltK7tPNDEqIYbEGAu/faeUp94qwRrQ76aq2cWv3i4F4OkbZrHm6qmsvDif3/9fGT/ZeogbL8jlxgtyOek4ZYjBL87yaVN7yD1w3xWT+cmWg5TXt+sTQCB+I2/C6z1lePu6Lk1zFresmsefbj6Px5ZNx+NTWTo7m2S71bDeTiKRDB3K6h08se0ws3KSuGPRRA5WtfKL62aQmxoD+O3QT5ZMJcluHeCRSiS9o6d1ZJqIyHtHT7K3simolGjlxfmoqr+/XJRJsOL5nax944i/Pv/TVvJGxHDljCxMJni0s73Rivl5NDndmIXg5ovyWLkgn5sv8rctKK9vp6ndy2t7K3noquD6+IeumsKbB6qZV5jGgU9bsUaZg9YSWhnT1bOycXkUnG5fr3QHAumtZoHRZ6t9ZqV1bee8Nq+n+gDdEVEROyFEIjAf+BqAqqpuwC2EuBL4bOfD/gj8G7ijv8fj9Sr8c18VNc3tfPsz4/Co8I3ndwbtMLy2t4oV8/PIToohK9nO7Owk2dhU0m9oaQZa4fHKBfn6/zWly3ibmZHxNr75p1PX6kNXTaEoJ4mHl/jbF9i7RJurml08sc1fHxrYVBRgfLq/lvSWeXlBu2BXz8rmkdcOkWy36vn6JgEut5eqZhc2i4lDVS1BO365qTHcsWgi75fW0+ry8D95I/qtrkVTx9x1oom7Alo2aE1RB1NqhkQi6R31jg6umJbFyS6RgXsXT6LN5WH66GQsZshJka1IJIOLntSRdY38fO+ywqBSosAo3OqFBSTbrVQ1u/Ta08mjEkLSITMTbdx1+UQAokx+kbPAPnT/My6VquZYtu6r5MkbZrGnogmfAi98WM7SWTncv9mvA7BqYb5hJo8QWhTdxvljU89IXfpslKn7Ilp2tvRFKmmkRezGAnXA74UQnwghfiuEiAXSVVWt6nxMNZDe3wNRFJX3y+q5Y9MeckfEER9j1XvSwakdhnmFaazbVoLNYpZOnaTfCdcAG06pTDndPr33HJwqXv7Hrk957F+HWb2wgJHx1pBI272LJxmqTtYF1JIGvrZWWK297pPb/QpX9U6PHr3+/X/Lef79cn69fDZ3fX48K+aP4/aNu3js9SN8688f8899Vf26I1ZW79CdOu2zWLe9mGuKsgdNaoZEIuk9AoHT4wuJDDyw+QBun0qT082UTNlXVjL46G7xr9HV+ctNjQ0rbvLENn+0LPBcmmOjkZlo48YLcvn+33dz618/4ddvl3LjBblkJvrXJA9cOYXimlbuenEvBelJrPzLx6zbVsJTb5Vwft5I3akDQlS64VTtnhZdO5ssnjN9bl9Ey86WcGu83qxXIs2xiwJmAc+oqjoTcNAl7VJVVRV/7V0QQogVQogdQogddXV1Zz2QsnoH9W0d3HxRHlaz4GB1S7c7DGNH2qVTJwlLX12fXdMMXt1dyX2LJwc5aPkj4wyv1aykGJbOzua598opqWll8qgEHls2nTVLp7J6YQFRAr58fm7QuR744mRS7FZsFpMuoKL9XRNcCcRmMTEvfwRbVs1jVk4SjU43Vc0uqptcNLt8IZsjd764t1+NZrgJsDA9XjYND6Cv7adE0lec6bVZ19ZBit1qeP+nxUeTEmuVKpiSs2YgbGd6go3c1Bi+fXE+Kxf4f3JTY4IW/13nvsomJ6sWFGA2GStdigDfx2YxkZEYHbTWuKYo21BV9u4vTOzM9CkmOspsqKbZ9f9d1xL+rKKpXD0za0Br33viMPc3Z5tKChGWiglUABWqqn7Q+f+/43fsaoQQmaqqVgkhMoHark9UVXU9sB6gqKjorEMAbR0eEmxWzCYHbR2+sHLvJgE/WTKViemJZ/uSkiFMX12fgXLGB6tbOFLTyl8/LGfF/DwK0+OZmJGAw+01vFaPN7Tz7Lul3LloAmazmRUBacX3LJ7Exp0n+MZn8nnu63M4VufAHh3Fb94+Sl2bm/sWT+L+zQd4/n3/a+Uk2/H6fDx01ZSgNKe1187gvDEpmEwiSO3SHh0VdkLpz5TIcIXUEzMS5E59AH1tPyWSvuJMrk1FUYm1mvEpqrGQQoKNkXGytk5y9gyE7cxJtocImT101RRyku36Y7rOfW0dPl7dXcn3LpvQrdKldq5JGYlMGZWkt07o8CqG8/fB6la9fEMr8dDOoz0+xmIKKf3YsOM4jy6bzpGaVlQVxqTGMGZE6DrgbBUie0MkCK+cTSqpRkQ5dqqqVgshTgghxquqehhYCBzo/Pkq8NPOf1/uz3G0t3uod3j45EQjigon21xh+oBNZWJmHIUjE2Tzcck5QTNyta0uJmYkMGVUAheOSyUjwYZPwW8I4qL52bJpfP/ve0LqylwehXqnm/VvlwbtvD24+QDrrp+J0+3j1r9+EvK8TR+f4Ndfmc3O4434FPjlW8Vcf14Or+09EeRUjh1xygCZTELvSdPicjNzdNI5N5pG6phrr53B2BEyWieRDFXK69vo8Cgk2y0har+rFxaQYDOTkyrrayWDk+ONzpAU47tf2sesnGR9k7Tr3Pfq7kpuXVDAY68fClnL3nfFZFJirXzvskLm5qUyIztJX9NqrROO1rYZzt+qeur3ikan3hZJe41ku5WRcVYev3YGB6tbUFR/ptENc3L5yZaDej3+0llZIe+ztzVvZ+sEhlsvnOvsnt60vzIiohy7Tm4F/iyEsAKlwE34U0Y3CiFuBsqBa/vjhRVFpexkG7sqmoPEFu5cNIEbLxjDc++V6X3AZuUkc35Osux9IzlnhDNyl01M5/WDNUHHf37NDP7+zQsor3dyoKpVb/IJ/vx2o503i1mw6oU9IfVoK+bnMSEjgf8ZN4LRKXZqWlx8blIaHp/KhIx4clJigxw6Da9X4b+l9ewob0BRobbZwSNLpgbdW/1tNPti90sikQweXC4vH59o5kcv7iXZbuVbn8ljxfw8FNUv7pSbYmdaVrK0AZJBS08ENozmvpxkO7NykmlwdPDcTXN4/1gDHV6FdduK9fXBCyvODwlUdG2zFOgQNre7yU2N4ZufyedX/ynB7fW3GMlPj2PD/87Fq6h8WNbAva8cCMoQspj8AmxmE5yXmxIUbdToTbPxPmkTMETWCxHn2KmqugsoMvjTwv58Xe2iKK93hOQR/2TrIVYvLOD7l03A6fYyPiOeqVmy6Fpybgln5DasmBty/Lt/28WWVfPITrbz3b/tDpoEwjUjP9nmNpwsZo5O4jOFabrK5KHqVm76w0fdRsAUReWf+6q4Y1Nw1PAvH5SxYcVc2j2+c2Y0z3b3SyKRDA4UReXD4w26eFRVs4tn/lPKNUXZjE6KYWR8NHkjY2WGjWRQ09OUQaO5T/t/aV0bT/+7pEcZNFqbpa4K2E1ON09sK+aexZN4fV8Vi6dl6bV6hWlxjBkRxwel9SFr6gc3H+DXy2cHOXtGTlhvFCJ74wR2x1BYL0jr1ol2UYQrtm73+DCbBClxVunUSQaEcEZOkyjuery21YXb5wspUk6xW3l4SXCfmVULCqhv6zAUQ8kNcL56qhpVVu/QnTrtceu2F3N+3kjaPb4+61cnkUgkGmX1jhB7WNXsYt22Eiqb2zGbhWxvIBn09IXARrhzmAQhPdy6tlnSFLAdbp/uqE3KSgr6W3WLPwLocHsN1yc1AfdpuHVEbxQiI0H4JFKIuIjdQKFdFF37e4H/Qpo5OpmMBCvjMxLlYlQyIITbpctMjOl2927DjuP6Lpuqwu/+e4w/fG0O65cXsaO8AZ/if8w9iyeFiKF0Lcju6Q5auMeZTcg2A0OUL9/8DT6tawo5fri4pP/700gk+O2O3Wo8h88YnURuil3O35JBT1+kDHY9x8g4G8fq21j0xDshUbRwaw+tvs5IVVOb53NTYg2fW9vaETQeo3VEb2reIkH4JFKQjl0n2mRQ2eQMySO+Z/Ek4mxm6dRJBpRwRm5yZkK3xu+ORRND/pabGktuaizZyTHUtrpYOisLk4Cv/v7DICfwl9uLgwqye2o8wz2uKDdFthkYonxa10T64u+EHN/76DfP/WAkwxK7NYqqpibDOTzeFiWjdZIhQ1+kDAaeo7SujZV/+cQwldFo7aEJq0Goqmbg+mPsiNDnPrp0Gj9/43DQWMKlkvbUgY0U4ZNIYFg7doEKOmaT4M5FE/jdf4/x9QvHBhVbp9qtzMyWxdaSgaU7I9ed8evub4ETw3tHT1Je365LF2sE7qL11HgaPW7N0mlcmJd61vfRuZQ/lkgkgwNFUenw+hgRbyPBFsXtlxTQ0uHDJCA5xiLncImkG7Qsm8xEG1fPytYjcA2ODvJGxunriJoWFx6fyj0v79UVLddeO4NJmfFcOC41ZI0RTsQlymzqkRPWUwd2qAif9AXD1rHTxFLWbD3I4mlZmE0wZVQiN//PWJweH/lp8RyvdzCvYISsqZNEDOGMXHfGr6eGsSfRuJ4az740soGOXFq8P11E21k8E+UriUQytDCaz2flJKMoCodr2pg0SrYkkgwMkbYRGW48WtPz64pyglohFKTFMUtRg9YRiqLy+6/NCZnbjfrQgfEapD+csKEgfNIXDFvH7thJB2u2Hgy5iO+7YjJ//E8pjU43a6+dIZ06ybChp9G43uygna2RNZIwXr2wgGS7VRdJuH3jLsbfOo9xacPbmEskw5WyeuP5/KGrpjBpVLxMwZQMCH0hwX+uxjMmNZYHr5zKiud3BKVj3rFpD1OzEoPm8b5OA5X0LcN2C6u8wcHiaVn6JAD+i/j+V/fzi+tmsGXVPBkFkAwrtCjbllXzeGHF+RFxDxipcD6xrZirZ2Xrj3F5FI43OMKdQiKRDHFqWlyG8/ndL+0jyWaV87hkQOipinQkjMdkEljMQipLDgGGbcQu1hqF2WTcqFlFlbsIkmFJpO2ihVPX7KrAZbcOW1MmGQYcOniAi794neHfRo1M4s/P/vocjyiySE+whZ3Pq1tcTB+gcUmGN73pwxYJ45HKkkODYbMa6ppXnBYfzcTMBHkRS4YUkZbPf7aEm2gCFbhWLywgPSF6gEYokfQ/HtVkqDgK8OnmX5zTsUQiOcl2ZuUkG9qKjEQ5n0sGhkhzlE43nkhWlhxqa5v+ZFg4dkZ5xU/eMBO71S+D/ODmAxF3EUskvSXS8vn7AqOJ5qGrptDQ1sHKBfmYBBSkx8kaGsmwJVw0b7hE8hRF5fWDNbz0yXHDPpyTMxMHeoiSYUqkOUqnG0+kKksOxbVNfzIsHDujvOKVf/mEravnkZsSyx9vmoPT7SUnJZaxIwb+IpZIzoRw+fMTVs2LmNTK3hJOKvl4ozOiJh6JZKAIF80bLpG8QLtX3ezm0WXTcbm95I2MY0Z2klTDlAwYkeYo9WQ8kVaOAUNzbdOfRJxjJ4QwAzuASlVVFwshxgIvAKnATmC5qqru3pwzXF5xdYuLuXkjwkq0SiSDiUjL5+8rjCaaSJt4JBLJwBBo9/ZUtrDqr58A8MKK86VTJxlwIs1RirTx9IShurbpLyLOsQNWAweBhM7/rwEeV1X1BSHEr4CbgWd6c8JIy3OWSPoDeZ1L+psv3/wNPq1rMvzb4eIS0s/tcCQSafckkiGOvMd7R0RtZwkhsoEvAL/t/L8AFgB/73zIH4GrenteLa/YZvG/3YHOc5ZI+gN5nUv6m0/rmkhf/B3DH7fHO9DDkwxDpN2TSIY28h7vHZEWsfsF8AMgvvP/qUCTqqraiqECyOrtSSMtz1ki6Q/kdS6RSAaC7iK5/S3iIu2eRDK0kfd474gYx04IsRioVVV1pxDis2fw/BXACoCcnJyQvw/GvGLJ0OF012dfIa9zyZlwrq5Pybmju9535aXF5OYV9Opv3TloWiTX8G9nKeLSk2tT2j3JQCFt57lB3uM9J2IcO+B/gC8KIS4HbPhr7J4AkoQQUZ1Ru2yg0ujJqqquB9YDFBUVqedmyBJJz5DXpySSkdfn0KO73nd7H/1mr/82UCqb8tqURDLy+pREGhFTY6eq6p2qqmarqjoGuB7Yrqrql4G3gGWdD/sq8PIADVEikUgkEolEIpFIIhKhqpG3wdCZivm9znYHefjbHaQAnwBfUVW14zTPrwPK+3ucAYwATp7D15Nj6J5zOY6Tqqou6s0Tenh9RspneSYM5rHD0Bp/f12f3b3mYGWwv4fBOP5eXZ8DMLcPJIPx+zwbIvH9nu31OdDvaSBffzi/93Px+mGvzYh07AYbQogdqqoWyTEM/BgiaRxnw2B+D4N57CDHP1hes68Z7O9hsI9fEsxw+z6H4vsd6Pc0kK8/nN/7QL9+xKRiSiQSiUQikUgkEonkzJCOnUQikUgkEolEIpEMcqRj1zesH+gBIMcQSKSM42wYzO9hMI8d5PgHy2v2NYP9PQz28UuCGW7f51B8vwP9ngby9Yfzex/Q15c1dhKJRCKRSCQSiUQyyJERO4lEIpFIJBKJRCIZ5EjHTiKRSCQSiUQikUgGOdKxk0gkEolEIpFIJJJBzpB07BYtWqQC8kf+nIufXiOvT/lzDn96jbw+5c85/OkV8tqUP+f4p1fI61P+nMOfsAxJx+7kyYFsNi+RdI+8PiWRjLw+JZGKvDYlkYy8PiWRwJB07CQSiUQikUgkEolkOCEdO4lEIpFIJBKJRCIZ5PSbYyeEGC2EeEsIcUAIsV8Isbrz+I+FEJVCiF2dP5cHPOdOIUSJEOKwEOJzAccXdR4rEUL8sL/GLBneKIpKaV0b7x09SWldG4rSbRqzJEKQ35tEEjnI+1EikfQEaSv6h6h+PLcX+K6qqh8LIeKBnUKINzr/9riqqo8FPlgIMQm4HpgMjALeFEIUdv75KeBSoAL4SAjxiqqqB/px7JJhhqKobN1fze0bd+HyKNgsJtZeO4NJmfFUNbtIT7AxJjUWk0kM9FAHFEVRKat3UNMSGZ9JuO9t0eSMYf9dSSTnGqP78ZElU5mVk0ROirSfEonET0/m7khbbwwW+i1ip6pqlaqqH3f+3gocBLK6ecqVwAuqqnaoqnoMKAHmdP6UqKpaqqqqG3ih87ESSZ9RVu/QDQyAy6Nw+8Zd/OOTSr70mw+4fN07bN1fPax3lDRDfPm6dyLmMwn3vZXVOwZsTBLJcMXofrzrxb3845PKAbcVEokkcjjd3B2J643BwjmpsRNCjAFmAh90HlophNgjhPidECK581gWcCLgaRWdx8Idl0j6jJoWl25gNFweBc2GSIchMp2ocN9bbatrgEYkkQxfurOjA20rusPlcvH222+H/Lhc0o5IJP3B6ebuSFxvDBb6MxUTACFEHLAJ+I6qqi1CiGeAB/H3YXgQ+Dnw9T54nRXACoCcnJyzPZ1kmJGeYMNmMQUZGpvFhBqwOaQZnbyRcb0+/1C4PrszxGfymfQF4b63tHjbgIxnsDIUrk/JwNOdHT1TW3Eurs0PP/yQ7zz9EknZ+fqxpooSfgHMnz+/X15TMjSQtvPMON3cHYnrjcFCv0bshBAW/E7dn1VV/QeAqqo1qqr6VFVVgN/gT7UEqARGBzw9u/NYuONBqKq6XlXVIlVVi0aOHNn3b0YypBmTGsvaa2dgs/hvCZvFxOqFBfzj4wr9MWfjMAyF61MzxIEMtBNl9L2tvXYGY1JjB2xMg5GhcH1KBh6j+3HVAr8dPVNbca6uzaTsfNIKZ+o/gU6eRBIOaTvPjNPN3ZG43hgs9FvETgghgGeBg6qqrg04nqmqalXnf5cA+zp/fwX4ixBiLX7xlALgQ0AABUKIsfgduuuBG/pr3JLhickkWDQ5gwmr5lHb6mJknI1j9W00Ot2AdBjglCHuWuw8kJ9J1+8tLV4WWEskA4V2P46/dR4Hq1s4UtPK8++X0+h0D7itkEgkkcPp5u5IXG8MFvozFfN/gOXAXiHErs5jdwFfEkLMwJ+KWQZ8A0BV1f1CiI3AAfyKmt9WVdUHIIRYCfwLMAO/U1V1fz+OWzJMMZkEeSPj9DD/2BGxbJEOg06kOlFdvzeJRDJwmEyCcWlxjB0Ry6TMBC4clxoxtkIikUQO3c3dkbreGAz0m2Onquq7+KNtXdnSzXMeBh42OL6lu+dJJP2BdBhCkZ+JRCLpCdJWSCSSs0HakDOj38VTJBKJRCKRSAYTPq+H3bt3G/5tzpw52Gyy1kcikUQe0rGTSCQSiUQiCaC1uoxnjrnIKDcHHZdqmRKJJJKRjp1EIpFIJBJJF+Iz80grnDnQw5BIJJIec04alEskEolEIpFIJBKJpP+Qjp1EIpFIJBKJRCKRDHKkYyeRSCQSiUQikUgkgxzp2EkkEolEIpFIJBLJIEeKp0iGPYqiUlbvoKbFRXqCbILZn8jPWiIZWsh7WiKRnCnSfvQ90rGTDGsURWXr/mpu37gLl0fBZjGx9toZLJqcIY1LHyM/a4lkaCHvaYlEcqZI+9E/yFRMybCmrN6hGxUAl0fh9o27KKt3DPDIhh7ys5ZIhhbynpZIJGeKtB/9g3TsJMOamhaXblQ0XB6F2lbXAI1o6CI/a4lkaCHvaYlEcqZI+9E/SMdOMqxJT7BhswTfBjaLibR42wCNaOgiP2uJZGgh72mJRHKmSPvRP0jHTjKsGZMay9prZ+jGRcvxHpMaO8AjG3rIz1oiGVrIe1oikZwp0n70D1I8RTKsMZkEiyZnMGHVPGpbXaTFS1Wm/kJ+1hLJ0ELe0xKJ5EyR9qN/kI6dZNhjMgnyRsaRNzJuoIcy5JGftUQytJD3tEQiOVOk/eh7ZCqmRCKRSCQSiUQikQxypGMnkUgkEolEIpFIJIMc6dhJJBKJRCKRSCQSySBHOnYSiUQikUgkEolEMsiRjp1EIpFIJBKJRCKRDHKkYyeRSCQSiUQikUgkgxzp2EkkEolEIpFIJBLJIKffHDshxGghxFtCiANCiP1CiNWdx1OEEG8IIYo7/03uPC6EEOuEECVCiD1CiFkB5/pq5+OLhRBf7a8xS4YeiqJSWtfGe0dPUlrXhqKoAz2kQYP87CQSibQDEolkIJC258zozwblXuC7qqp+LISIB3YKId4AvgZsU1X1p0KIHwI/BO4APg8UdP6cDzwDnC+ESAHuA4oAtfM8r6iq2tiPY5cMARRFZev+am7fuAuXR8FmMbH22hksmpyBySQGengRjfzsJBKJtAMSiWQgkLbnzOm3iJ2qqlWqqn7c+XsrcBDIAq4E/tj5sD8CV3X+fiXwnOrnfSBJCJEJfA54Q1XVhk5n7g1gUX+NWzJ0KKt36EYBwOVRuH3jLsrqHQM8sshHfnYSiUTaAYlEMhBI23PmnJMaOyHEGGAm8AGQrqpqVeefqoH0zt+zgBMBT6voPBbuuETSLTUtLt0oaLg8CrWtrgEa0eBBfnYSiUTaAYlEMhBI23Pm9LtjJ4SIAzYB31FVtSXwb6qqqvjTK/vidVYIIXYIIXbU1dX1xSklg5z0BBs2S/AlbrOYSIu3nfOxDLbrM5I+O0n/M9iuT8m5IRLsgLw2JZGMvD77h0iwPYOVfnXshBAW/E7dn1VV/Ufn4ZrOFEs6/63tPF4JjA54enbnsXDHg1BVdb2qqkWqqhaNHDmyb9+IZFAyJjWWtdfO0I2DlqM9JjX2nI9lsF2fkfTZSfqfwXZ9Ss4NkWAH5LUpiWTk9dk/RILtGaz0m3iKEEIAzwIHVVVdG/CnV4CvAj/t/PflgOMrhRAv4BdPaVZVtUoI8S/gEU09E7gMuLO/xi0ZOphMgkWTM5iwah61rS7S4m2MSY2Vhbc9QH52EolE2gGJRDIQSNtz5vSnKub/AMuBvUKIXZ3H7sLv0G0UQtwMlAPXdv5tC3A5UAI4gZsAVFVtEEI8CHzU+bgHVFVt6MdxS4YQJpMgb2QceSPjBnoogw752UkkEmkHJBLJQCBtz5nRb46dqqrvAuFc64UGj1eBb4c51++A3/Xd6CRDFUVRKat3UNPiIj1B7vAMJeR3K5GcG+S9JpFI+gtpX/qX/ozYSSTnFNn3ZOgiv1uJ5Nwg7zWJRNJfSPvS/0jHTjKoCdz5sVujWLP1YEjfkwmr5slQfgRwNrt04XrayO9WIjlzjO7JoX6vuVwuPvzww6Bju3fvRlXPSfcniWRY0dXGmARD2r5EAtKxkwxajHZ+Vi0o4Pn3y6lq9vc60fqeSIMxsJztLl13PW3kdyuR9J5w9+TIeOuQvtc+/PBDvvP0SyRl5+vHKj5+h+TC8wZwVBLJ0MPIxjyyZCrJdqu+RoOhZV8iAblFJRm0GO0sr9tezNWzsvXHyL4nkUG4KEBZvaNHz5c9bSSSviXcPWk1m4b8vZaUnU9a4Uz9Jy4ta6CHJJEMOYxszF0v7uWaouygxw01+zLQSMdOMmgJF8Uxd17Vsu9J5NBdxK0nyJ42EknfEu6edLp98l6TSCRnTTgbU5geL+1LPyJTMSWDFi2KE2g4bBYTCyekceG4VNn3JIII9131dJdO9rSRSPqWcPdkeoKN88emyntNIpGcFeFszMSMBLZI+9JvyIidZFCiKComAY8smRqy8zM1K4m5eSPIGxknjcU5RlFUSuvaeO/oSUrr2lAUFeibiJvW00Z+txLJ2WN0T65ZOo2cZLu81yQSyVkTbt4fOyJ2UNuXcOucSEFG7CQRT1dVpZxkO68frOH2jbtItltZMT+PwvR4JmYkMHaE3PkZKE4nkHImETfZ70Yi6T09uW9MJsFlE9NZv7yIHeUN+BRY+8ZhLGaTlB6XSCRnTbh5H6C0rm1QzuuDoV2DdOwkEY3RTbRm6TTWvnEYl0ehqtnFum0l2CwmtqyaFzE31nDkdDLpWhSgp8pXg8GASiSRRm/um+ONTlY8vyMoVUpKj0skkr6i67w/2Of1wdAORqZiSiIao5vojk17WDwtWMWsN0Ickv7hbAVSunK2SpoSyXCkN/dNX9+zEolE0h2DfV4fDDZTOnaSiOZ0ypcaUi534OnrlgSDwYBKJJFGb+4b2UZEIpGcSwb7vD4YbKZ07CQRiaKolNS00urysnphPpmJp24am8VEUW6KlMuNIBRFRVXhsWXT9e8r3PfS08Lj3hrQSC9olkj6E69XYfeJRprbPYY2M8Zi7hdRI4lEMrjpzdx5tvNsT+f1SJ3PB4PNPG2NnRBiJPC/wJjAx6uq+vX+G5ZkOKMoKq/tq+a7fzuVg716YQHPvVdOo9PN2mtncGFeqpTLjRCMcuYfWTKVWTlJ5KQEfy+9ya/XDGjXxxoZUO28a7YeZPG0LMwmOC83hQvyUomKkvtXkqGN16vw0u5K7n5pn6HNfOiqKax64RPK69v7RNRIIpEMDXozJ/dmrg9HT+b1SK7DGww2syfiKS8D7wBvAr7+HY5E4s/B1pw68Ifpn9hWzDNfnkVCjIWZo5N7LcQh6T+McubvenGvoZhNbwqPe2NAy+odrNl6kOuKcli3vThIaOeKaaMiyuhKJH3N/qpm3amDUzbzsWXTibNF8eT2I5TXt+t/OxtRI4lEMnTozZwcbq5fMT+PCRkJPXK8ejKvR7pASaTbzJ5sZdtVVb1DVdWNqqpu0n76fWSSYUu4HOxPTjThdPvkIj3C6E3OfG/z63vaT6umxcXiaVm6U6ed945NewZNUbZEcqZUNRvfV4dqWvnmn3Zyft7IkL8NlpoWiUTSf/TF/K2o9EoA5XTz+mCvwxtoeuLYbRZCXN7vI5FIOgmXg+1TwOWRQeNIoze1cP1VeJyeYMNsQk4GkmFJZmKM4X2lqlJsSiKRhKcv5m/NzvTVXDsYBEoimZ44dqvxO3ftQogWIUSrEKKlvwcmGb6MSY1lzdXTgopTVy0oYPOeSsZGUIGqxE9vion7q/B4TGos5wUI6mjIyUAyHJicmcBDV00JsZn/+LhCik1JJJKwnO38HWhn+mquHQwCJZHMaWvsVFWNPxcDkUg0TCbBF6ZmkhxrZUd5Az4FNuw4zg8+NzFic5qHM72pheuvwmOTSXBBXiprlk7jjk17Tiu2IpEMJaKiTFw1PYvCtDjK6p2U1LXx/PtSbEoikXTPmczf42+dx8HqFo7UtAbZmb6aaweDQEkk0xPxFIQQyUABoLvjqqq+3V+DkkiiokxclD+C7OQYaltdLJ2VJW/sCKY3xcT9VXgcFWXiimmjmJqVKCcDybAjKsrEtNHJTMlKoqzewYXjUoPugUgu9pdIJANHb+fvcWlxjB0Ry6TMhBA7MxBjkgTTk3YHt+BPx8wGdgFzgfeABf06MsmwR97Ykt4irxnJcEfeAxKJpL+RdiZy6UnEbjVwHvC+qqoXCyEmAI/077AkQxlFUSmrd1DT4iI9QUZVIhH5HUkkA4+8DyUSSaQh7VJk0xPHzqWqqksIgRAiWlXVQ0KI8f0+MsmQJJIbT0r8yO9IIhl45H0Ymfi8Hnbv3h1yfM6cOdhsUqhJMrSRdiny6YkqZoUQIgl4CXhDCPEyUH66JwkhfieEqBVC7As49mMhRKUQYlfnz+UBf7tTCFEihDgshPhcwPFFncdKhBA/7M2bk0Qe4RpPflTWQGldG4qiDvAIJeG+o+561CiKSmldG+8dPSm/R4mkD5D3YWTSWl3GM28e4Mev7NN/vvP0S3z44YcDPTSJpN85E7sUiQxlW9kTVcwlnb/+WAjxFpAIbO3Buf8APAk81+X446qqPhZ4QAgxCbgemAyMAt4UQhR2/vkp4FKgAvhICPGKqqoHevD6kggkXOPJd0pO8tt3SuXOTwTQXXNQo3z67nbwAJmyIZGcAae7D7umQ+Uk23n9YI3cST8HxGfmkVY4c6CHIZGcc3q7PohEhnrUsaeqmBcBBaqq/l4IMRLIAo519xxVVd8WQozp4TiuBF5QVbUDOCaEKAHmdP6tRFXV0s5xvND5WOnYDVIyEmysWpiPokKMxYRJCNo9PiZkxJNst3L7xl1MWDVv0BiIoYjWHDTQeHfXoybcDt6k1fM4UNUaZDyfvGEmY1PjqG11kZlow6dAbat0+iSSrgTeh5mJNq6elY3ZBDGWKLxehX8X17KnohlFBbOAiZkJrNl6MOQ+lPZUIpH0FWnxxuuDkXE9T0M2qtGDc7cJHG7NMlRsZU9UMe8DioDxwO8BC/An4H/O8DVXCiFuBHYA31VVtRG/o/h+wGMqOo8BnOhy/PwzfF3JAKMoKnsrm1n/dqm+0F+9sIC/7aig0elm1YICnn+/fFDt/AxFtOagXXezwvWoCbeDV9PSEWQ8k+1WimvaWPmXT0i2W7nxglye2FY8JHfMJJKzRbsP12w9yHVFOazb7r9X1r9dyqNLp9HkdIfY0uvPy2HN1sP6OQbbTrpEIolszCZYvbAgaO5evbAAc08KuzCOlj15w0zcXvWcRdCGQtSxO3ryVSwBvgg4AFRV/RQ406blzwDjgBlAFfDzMzxPCEKIFUKIHUKIHXV1dX11WkkfUlbv4AedzaPBfyM9sa2Yq2dl4/IorNtezDVF2WEjQ4OZwXR9as1Bt6yaxwsrzmfLqnndGlgtshCIzWLC4fYGGc+rZ2Xrk0Hg7zB48/SHCoPp+hwuaPfhuutn6k4d+O+VH2zaw0mHO8SWjk62B52ju0j7YEFem5JIZrhdn1XNLp57r5ybL8pj5YJ8br4oj+feK6e6xdWj5xtFy/ZUNJ/Tur1wa5bBbis1euLYuVVVVQEVQAhxxq3lVVWtUVXVp6qqAvyGU+mWlcDogIdmdx4Ld9zo3OtVVS1SVbVo5MiRZzpEST8SbpdEiFO/F6bHh40MDWYG2/Wp9aiZmzeCvJFx3e6aaZEFzVBqu225KbFBxlMI9O8/8HcNbcdMcu4ZbNfncMFkEjjdPsN7pWutv8ujYDaJkPtwsNtTeW1KIpnhdn2mJ9hodLp56q0SntxewlNvldDodPfYKTJaByrquV0PhFuzDHZbqdGTGruNQohfA0lCiP8Fvo7fKes1QohMVVWrOv+7BNAUM18B/iKEWItfPKUA+BAQQIEQYix+h+564IYzeW3JucUohzpc7Zaqnvp9YkaCTMUbZGiRhQmr5lHb6iIt/lTOfGBKp1mgf/8xFlOv6vgGCtmvR3KuCHethbObE9LjyUy0UdXs0o8VpMWxpct9KK9XiUTSVxiVaqxZOo2cLtkC4TCyZ4FrA42u64G+nIvDrVmGiq3siSrmY0KIS4EW/HV296qq+sbpnieE+CvwWWCEEKICuA/4rBBiBv7oXxnwjc7X2C+E2IhfFMULfFtVVV/neVYC/wLMwO9UVd3fy/coOccY5VCvWTqNsSPsPHjlFO55eZ9+/L4rJvPXD8r1HZOxI4bGjslwQ4vwdc1PDzSeGQk2xmf4BR7irFEhefqPLp1GvaMDICKM7FBXzpJEDkbX2iNLpjIrJ4mcZHvIQmrVggJ+9vohbrwgl+feK6fR6WbttTP06PpQqBORSCSRh8kkuGR8Gr/6ymw+Pt6IT4G1bxzGYjb1aG40cgynZid2W9ffH3NxuDXLUKBHqpiqqr4hhPhAe7wQIkVV1YbTPOdLBoef7ebxDwMPGxzfAmzpyTglkUHXHOpku5VPm9qxmE00OjpYeXE+Lq+CqsKv/lPCo0unMzI+OiIW80OdcxWB6vo6c8akYjIJclJiyUqycd3690m2W7n5ojyEAJOA6uZ2Vr2wK2IcqKGunCWJHIxsZlm9A49PoTK5nUmZ8Tx30xzePXoSnwLPv19OVbOLJ7YV88eb5kj7KZFIzgmKovJ+WQPf/NPOIMXeQ9UtZCXFMDUrEQivcNldhk+4CJqci3tHT1QxvwHcD7gABX96pArk9e/QJIOVwBzqzEQbX7twDI+/eSRot3nTzgo9hUhFlTfnOeBcRaBO9zpazVBVs4un3irRn7dyQb7hJDFQi9WhrpwliRy62szlc3N1wRRNdU4IWLetJOh5Lo8i7adEIjlnlNU72FHeoDt1gbZq/dv+XsTWKMHKv3wSdp0RLloWLoIm5+Le0RPxlO8BU1RVHaOqap6qqmNVVZVOnSQsWg51ZqKNOy+fiMPt5ZZ5eWQm2nT1y6tnZQORWVc1VAm369XXylOne51wilS2KBPL5+by7LulrNtWwnXr32Pr/mqUTpUIRVEprWvjvaMnKa1r04/3F0NdOUsSOQRea1fPyg5RwXzho+NMzExg1cJ8fQME5PUokUjOLTUtLhTVb3uMbNXtG3exp6I55Nixk+HXGaeb2+Vc3Dt6kop5FHD290AkQ4ecZDtPf3kmVc0d/ODvu4MidVoKkRBDT4ko0untrldgOqXdGoXb5yM19vQpX6d7HaMc+9ULCwAMWyBMWDWPMamx57zerbf9/CSSM0FRVMwmeOYrs/nkeCNZiTFB909moo3rinL4xvM7g2zphh3HuWPRRHk9SiSSs6I3JRrpCTbeP1rH2mtn0Ory9Fix93iDg3FpxuuM083tci7uHT1x7O4E/ttZY9ehHVRVdVW/jUoyaFEUlQ/LTxIdZebBzQeCFunrthdz80V5PPtuKRPS49mwYi5Ts5JkXcg5Ipy6ntGul5GxDVxM9qSvXWBq2TVF2TjdPkrr2hiTGhuUYz8yzsax+ragXT6NQMnj0+XYG01OED7X/3QMdeUsycCjKCrbD9dQWudg7Rv+dPW7Pj+eVQvz9cWRLcoUsiu+bnuxtJ8SieSs6WmJhja/1js6+NL5Y7h94y5umZdnuKboapJsFhN2a6i7oSgqeyubDOf28bfOQwj0ufuyielS8beH9MSx+zWwHZ+F40YAAQAASURBVNiLv8ZOIjFEUVSO1jWjqIJGh/FOjtkEqxcWYLOa5KLkHBDo7GQm2nq862WUTqk55qcrWh6TGsuTN8yktLaNrGQ7KlDR4ORHL+6j0enmJ0umkp0SQ2pstC6qMnZELCPjbKx/u9TQ8TxdFNBocnryhpm4vepZRfmGsnKWZOA53uDA41X58wf+hr/RUSby0+M4XN2Gw+3DLGBUUozhte90+6T9lEgkZ8XxBgeHqlu4ZZ6/wmrTzgrDTdN3SmppbfehAsfrHSTbrWzaWcGqBQVB9cCPLp1Gk9OtO3xaRk5GYjSldW26o5aTbOf1gzUcqm4xtG8Hq1v43t92h8zdci4+PT1x7Cyqqt7e7yORDGoUReX9Y3XUt3mpaHTi8vgMd3LOH5vC0do28k/T9Fpy9oRzdv556zzq2rrf9equmXxPi5Z9Knw3wDDfdkkhf/hvGXe+uFeP3K5ZOo0vTMkkKsrE1KzuJY+7izYGOqKaAEt9m5tPm9pJtlupanZJJS1JRKEoKh8fb8LZ4eW6ohzWbS8m2W7lxgtyg9qAPH7dDMNr3241D+DoJRLJYEezQdqGamDJTOAcf7zBwadNHdz/6v6Qxz3/vn9TalpWAgXp8YyKt7HjRAOPLZuOw+2lweEmb2Qs+ypb+e7fgltgrX3jMFdMzzK0b0dqWqUK5hnSE/GU14QQK4QQmUKIFO2n30cmGVRUNDkwCzNOtxezgHhbFA9eOYXVC/2F/jaLiQevnMKa1w5x7ysHqG5xDfSQhzxGUbeVf/kEIWBu3gi955UR4YqVVdU4fTOw+HlvZRN7KppD6uUef/MIV8/KDnIQ79i0h/+W1uP1KpTVOxgZb2XDirn89X/PZ8uqeXp0Tcux18bU1enTHFFNpevZd0u5Y9Nefv12Kcvn5pKZaCMz0cbNF+VxpKZVL9A+14IsEonGiUYHMRYzI+Kj6fD6SLZbuXpWdtB9k2y3UnbSwZql04Js6aoFBXh8MoFGIpH0nK7z3fEGB3e9uDckM+eaomx9jlcUlapml+7UBT7u6lnZVDW7ePbdUsZnxKOq8PrhGj4qa+ThLQe575X9JMRYqGxs15067fl3bNrD4mlZetQvcG5/ZMlU/rajImjsyXYrjQ43H5TWs/1QDUdr5Xwdjp5E7LR+dHcGHJPtDiSA/6Y/0ejgg2ON3PvyPn3Hee3rp9ob3LN4Enkj7PxkyyH2VLZINaNzxNlIBBsVK2s1dl3TN71ehf+W1rOjvMGvlhVlQlEJG/HTHETt2I5yf0vMFc/vCIostrk8vH6gmszEGCZnJnRb76Y5okYqXeu2F7N6YQGqSlDKSF+kakokZ4Lb7dNtZuD9ZTKdum+0TYrA6N09iyfR0u7hhY+O87nJGQP8LiQSyWDBKIPnkSVT9YwWDZdHoTA9Xp/jj510UNvS0e18/uQNM9n/aXBETovoPbj5APdfMTlsaU5Vs0uP+plNsHBCGkl2C9cUZet1xm8frmXZ7Gw+LGsIsoc9na/PVf/eSOG0jp2qqmO7+7sQ4lJVVd/ouyFJBgta4X+02awvULruOLs8Cg9uPsAfbzpPd+qkmtG5oTdiKV0JFA7xq2Ka8fgUFk3JCDKKiqLyz31V3LFpz6nUsWtncKi6JWxRtWbwtWM+Bb0vDvh35opr2oL64Dx01RSump4Vtt5Nc0QrGhx603NA75eYnWzn+50KreC/LvdUNAfV9Ml0D8m5QFFUdlaccurg1AbEz5ZN1+8bo02KBzcfYMX8PK4/L4dj9W2MHTG0FygSiaRvMMrguevFvayYnxfUH9NmMTExI0G3K+UNDqxRwnA+nzs2hUsnpmG3RnHFk++G2LOVF+fz2OtHiLVFGT5/5ugkbBaTHvV76KopTExP4M3DtUHpodqGVjjV7O7m63PVvzeS6Ekq5ulY0wfnkAwyvF6FT4434uzw0RIgeaul2AXi8ii0dXh5YUVwep2kfzld+uLp0IRDLhg3gqlZSaTERlPT4qKs3oHXq1Ba18ZHZQ26Uwd+p+xEg4NxI2P9IjkBr/3jKyYzLTuRDTuOU9Xs0uvuNu+pJDCrzGhz4O6X9rG/qrnbsV42MZ2R8TE8+24pT24v4bfv+NMwc1NjsJhFyHUZLqqoqXCeDpnGKektiqJysKqZhja34bV37KRDv2/C2dKsxBiee6+clX/5pM97UEokkqFJuAyewvT4kDXC2BGn1gix1iiefbeU+66YHPS4+66YzKNbD3Hd+vc5GEYAJSPBRm5qDOkJ0axZOi3o+WuWTuOpt/yCbCsX5HPzRXn8cnsxB6tbQhzQBzcfIMVuPaP5+lz1740kepKKeTrkCn2Y4fUqvLS7krtf8qdePrJkStBujNHOzJjUOMMeJpL+o6/k+rvueOWmxnDrggLufmkft8zLC0kdW/umXwTipgtzWb98Nh1ehZLaNp7YVgz4HTezCQrT4vm0ycmK+eNY//ZR/fXCLWirm11MHx1+nMcbndz54p4gA75uezHrlxeRlRQTcl2aRfeCLL35TIbDLqDk7NAyHD5tbCcr2W547Y0dEUtFo5OVF+czKTPB8DHHG9v11KmepFVLJBJJuAyeJLuFFfPzUFQwCbBGiS7Pi2bhxAw27TzO2mum41MhMSaKsnoHdZ0bVEdr28LYKiePLJlKbYubtW8c1tMtZ+UkYzap7ChvZkd58Ibt8Uan4fwfLup3uvn6bEpSBit9EbGT29TDjANVzdz90j4K0+L46dKp/PjV/Xrx66adFSGRmp9fE7wDJDl3aFG304mldEfXHa/F07K4+6V9QY48EJQ6VtXs4pHXDrPi+Z3EWM0kxFiwRgndqZuYkcD6t4+SGGPlxY9PcPul4/XzaA5XIDaLiazkGI7WtrH9UA0flNZTdjI4ShbOgFvM/nYKXaOXKXYrt19aeEYRzeG4Cyg5O8rqHZTWtpESG80Dm/eHCAbcs3gSj2w5yJqth3nyrRJKalt56KopQY9ZtaCAf3xcof9f1ipLJJKeYJTBs2bpNH704l7WbSvhye0lrNtWEpIJkJMSS0F6HJdNyqCx3cP3/76bG3/3EY9sOaQLk23cUcEDV4baqr/tqEBV4bt/20V5fTtPveV/jW/+aSdNTp/hPC+EMDyutU3o7XwdTghuKNvOvojYSYYZnza7uHxyOlfMyKK62UV5fbte/KrVNv3i2hm0ub0UjIxjarbsVzeY6eowCYGu4Gc1+2vqfrr1YNhI24fHGhgRZ2XF/HF603rNSDe3u/n6ReO4bGI607MTqWnpwOn28syXZ/HjV/dTXt+OzWLiZ8umUV7fHlScvXphAQXpcSwYn47JJMLuSKYn2PToZdaKuWw7VItPgV+9XQrAivl5zBydRG5qbI8jmsNxF1By5iiKSqPDTUaiDYfbG2IzVRUcLo+eovzwkqnMGp3E6GQ7s3KSqWlx4fGp3PPyXv0xslZZIpH0FKMMnnpHB+X17UGP6zqPmUyCBePTGRkXzXXr3w/JiLn5ojw276kkK8mmR/5UFbbuq+KaomwaHMZp55VN7bog2+JpWZhNMD07iT+9VxbSG2/VggLWvVnM56dm8rNl07GaTeSnxfWoxthICG6o286+cOzK+uAckkGCy+VlZJyVCwvS+NafP+aWeXl68etTb/kLcG0WE48tm06S3SKduginJ2pRXR2muGhzSK+texZPItsg3VETR2lp9/LkWyVBk8IT24r58y3n4/YqVDQ5OVDVGqLYlRJrIdluJdYaxeIuxdlPbCtmxfw88kbE+cct4JElU3X55q4G3GQSON2+oEJxgHXbSnhhxfm9csjORphGMrzwehW27Kuiurkdnwpaj8+uNvPRZdNZtTCfadlJpMdbGdt5PWqCQYqi8vuvzTmrtGqJRDJ80TJ4Auc6o3ksymTio7J6UmOjdTvjdPvCKls+eKV/3jXqxxkoCBX4GjnJMfzqP0cNN3xf21vFzRflkZMSQ2WTfxOsqtmlC/Bt6YXAWV+VpAwmepSKKYSYIoS4Vghxo/aj/U1V1av7b3iSSMLZ7uY/R+tQVLj7Jf/i2agHyUNXTWFKVoIeSZFEJlqd2OXr3uFLv/mAy9e9w9b91SEiINqOV25qDN++OJ8xKbGGyqeldW0hqRJrlk5j855KXF7FcFL4z5E6vvSbD/jHJ5WGil0mYWJqVhJ1bcZyy4rqj569W3KS1/dXExsdxdprpvPMl2exeeVFITVvfZWWcbbCNJKhj6KoHK1t4/1j9fz8jcMUpMfzxLZiNu4ItZkPXjkFVVXwKfDg5v3ERltCztcXadUSiUQCfvukbYZ2TaH87t928dGxRm76w4f6miDc3FmYFk+ryxOUhXDX5RP1NUJlkzPE3ml9OOcVpulOHZzasP3shDSeequEp/9dQk6KnUanW39u13m2JyJmw812njZiJ4S4D/gsMAnYAnweeBd4rl9HJoko3G4fe6tbaOvw4vKcWqQH9iDJTYlhdIqdUUk2clNlOlqkE65OrKt8sKY42eHxceeLe4MEUzRcHoV6p4dNOyt4bNl0EP46utwUO7HRZn03ziiaB+EVKneUN5CdHENavHGEzCSgw6tw3yv7uK4oh9UvnGqRsGbptJBdvb5KyxiOu4CSnhMorrPm6ql8/cKxODp3vANtphAwa3QSJbWtNLt8PPtuqdwgkEgk/UqgfUq2W3ls2XSO1LbiU9CjY1qa5e0bd5G1Yi5un8KapdOCWhutWlDAw1sOcm1RdlAWwsoF+fpc3dbh49XdlUFp5xt2HOeHiyZiNhnP+xMzEvjr/55PeoKNnM50dKN5VoqYGdOTVMxlwHTgE1VVbxJCpAN/6t9hSSIJt9vHroom6h0eoqPMRJmCe5poPUie+fJsVBRGJ8tFyWCgN3VifsXJvd0qn9qiTFxTlE20xUTeiDhdMMftVfnZvw7pefPJdivXFGUzOtlObauLzERb2HP6FP843T4f910xmftf3R+UsjF2RCz3vbKPxdOyQnp+3bFpD1OzEkOc1L5yyIzSWiQSRVHZW9nEoeoWvnNJAWkJNlxeJchuagsg7TqekJlAZaOT9cuLuDAvdVgvSiQSSf8SuKlb1eziUE0rT24PLlFweRTibWZuviiPQ9WtnGhs5/2jdTzzldkcrm6l1eXVncCNOyq4Z/EkPfqmCaD5yyjMhumWifYoCtLiDef9wvT4IBX1cPNsTzenhxs9cezaVVVVhBBeIUQCUAt0IzouGUq4XF5e3VfFPZ3NdG0WE/cunsiPr5jMjwMW2Q9dNZWCDDujEmTUYrDQmzqxQCdQS78NLG6+7ZJCYiwmHnntUFDEbFpWor4rKAQ8cf1Mmp1u7n1lf9Cu39Z9Vdy7eBIPBBh/rbD6c5PS2HW0iRc+Os7Ki/NJi48mLcFGst2C26dQXt8eVrjFyEmVDpmkvwjcQS5Mi+O6OTnc9IeP9DYh939xMvcFXPu3X1pIapwVs1C5+6X9va4fkUgkkt5itKnbdS2QmxpDvM3CL948tRl73Xm5HK5qoSA9jrtf2q+3XWl0umlp9+hRuVirmceumc6JBidPbPM/f8X8PHJS7FQ3u3juvXK+5Mth08cn+MmSqfqmsVEfvd6+Dyli1jPHbocQIgn4DbATaAPe689BSSKD9nYPe6tbONHo5JZ5eWzaWUFVs4sHNh/k3sUTeXTZdNrdXmKtUUwZFU920vC9kQYj4dISc5LtlNa1Ue/owGoy0eB0ExsdRW5qDOX17Xoq2Yr5eYxOthNtMWOLMrGqMw0STkXM1i+fTbLdyvK5/kLqmy/K49l3S0OUtVbMzyMr2cb65UXsKG/Ap/jTNe5YNBFVhXaPjyumZ+HyKjz+ZjGNTjcr5udxXm5KUO6+FDORDCTHTjpYs/UgKy/OZ0xqLMW1rSTbrVR1qgc//e8S1l4znVaXl9joKKxRJqqb26lt6QDkokQikfQ/XTd1tTZVgYJodyyaqG/KLp+bG7SRe98Vk7n/i5PZU9mMWfjXEr//v2PUtbm5elY2LR0+zku0caSmlVvm5QHwtx0VNDrd3HxRHo1ON3kjYvn6hWP58wdl/ParRXi8CnZrFOkJ0Wf8PkDO+9ADx05V1f/X+euvhBBbgQRVVff077AkA43L5eW98gZ2nWhCUeHV3ZUsn5urh96jTCZW/fUTv5rb0mnkyJq6QYdRWmJOsp3XD9awZutBXeEq0Jj/6j8llNe30+h0E2Mxs/aNIzQ63Ty2bHqQcc1MtHH1rGwaHR5+9IWJ/Oxf/kheuMjazNFJzMtPAyA7OYbaVhdLZ2WRk2znpd2fsv7t0qBI3vPvl/tFfF7ey5ql01j7xuGQKKKsVZKcSxRFpaSuja9fOJZ6p5tDNa2YBXxzfh6/ertUd+4OVLfy23dK+fEVk2lpcpMaH80z//G33pCLkr7F5XLx4YcfBh3bvXs3qtoXLXxP4fN62L17d8jxOXPmYLPJ71MSWXTd1G10uilIj+Oft/rXAnarWY+GBfanBf98ff+r+1kxP48nt5fomQfLZmfT1uHTyy1GJdoM522zCe5cNIETjU5Gxkfz+SmjKK1tC8r26Wmd3HBsZdATeiKeIoAvA3mqqj4ghMgRQsxRVfXD0z1XMjhRFJXXDlQHhce1tLirZ2Xz7LulZCbG8Ng108hJsTNrdLJMvxykdE1LLK1r4/aNu7j5ojxDY/74tTNoafcQZ4siymzipgtzOT8vlbhoi75zlploC9nh04w6GEfWcgPq3LqOR1Ng1cahRfh8CpTXt5OVZOP3X5tDg6ODDSvm4nT7wrZukEj6i+MNDhJsZko9vqAFzeqFBdx4QS5rth7GZjExZ0wykzNnUN7gwOn2QWuH7E3XT3z44Yd85+mXSMrO149VfPwOyYXn9enrtFaX8cwxFxnlZv1YU0UJvwDmz5/fp68lkZwt4WrNwd+n1t/+KKazYbjxZqwmPunyKKx94wjrl89mxfM7dWfwgS5ql9q8PXlUImUnHUHRwdULC/TMht7UyUkRM2N6kor5NKAAC4AHgFZgE9C3llESMZTUtgYJZQQ2ojSbYPXCAkpqW7l/80H++r/nExXVt7ufkoFD26ULZ8wdHV7ue3V/kABKh1dhYnoMDy+Zyo9e3Gu4w6ddP0b1eY8smRp2MRsuhz4n2c7P3ziCzWIiJTZa1sxJBhSvV6GiqR1FUUNagQT2crp38SQOVbXwyGuHdUduUmY800YnyUVJP5GUnU9a4Uz9/00Vxf3yOvGZeUGvI5Gca3rSl1aj66ZuV4XJ3NQYHrhyCpWNTsPNWDWgq0Cy3Qqgp11azaaw83ZxTathT9ubL8rT+3pqKeljUmNP+35kzXwoPXHszldVdZYQ4hMAVVUbhRDW0z1JCPE7YDFQq6rqlM5jKcAGYAz+xubXdp5PAE8AlwNO4Guqqn7c+ZyvAnd3nvYhVVX/2Iv3J+kl7e0eSuraDG9KswkK0+LxKAqPdu4+pyfINJPBTuBkYLdGdVuzdrzRaZhz/+jSafxrXyVrr51Bq8sT9vqpanaxYcdx1l47g9K6NsaNjGPSqPiwk0+4HPrqFheNTreMcEgGHEVRefNQDbUtLpJjow2vfYAnb5jFr/5dzHcvm6BLeWsLlTEj5KJEIpGcOWcr/d9VYbK8vp2n3irmR5dPClK81CJsz73nz8DJTLRx4wW5erTOZjHxy+tnGs7bdW0dYXvaioAh2iwmMhJsspXBGdITx84jhDADKoAQYiT+CN7p+APwJMH97n4IbFNV9adCiB92/v8O/L3xCjp/zgeeAc7vdATvA4o6X3+nEOIVVVUbe/D6kl7icnl57WAN5fUOw5tyxugkzAJ+/vpRuajuR3qz63a2z+s6GRTlJvLMl2dRXNMaYszvXTyJX24vMYzI/WDTHm6+KI8HNx/g3ismGV4/8wtGEmc10+zy8eDmA3r62T9vnRd2fGNSY3nyhpnsqWhGUcEsYGJmAiPirFw+dZ6McEgGDK9XYX9VM5VN7YyIt9LW4Q27u51st1Bc28ot88ZxXm6KzHKQSCR9SnfS/z2JfHXNjslMtHH9eTlEW0xER5lYe+10KhqcTMpKpMXp0ZuGX1OUHZSl4E+pbA8RY1mzdBoxFjNun3FP21irWf997bUz8CkYvp/xt84LSBc99V7OdN00FOmJY7cOeBFIE0I8jL+v3d3dPwVUVX1bCDGmy+Er8Tc7B/gj8G/8jt2VwHOqqqrA+0KIJCFEZudj31BVtQFACPEGsAj4aw/GLekBiqJyvMFBk9NNo9PDXS/uJdluDUmXe+iqqYyMiyYuOoq7vjBRpg31E2e663amzwucDDITbSyYkMG3/vyxnorxq6/MBlUlxhpFbLSZRqc7bJpmjMXE1bOysZpFiFFfvbCAA58241XgHx9X6DLJLo9CXZuLcWlxhoYZ/H3wAmuW1iydxozs5B4vjqXBl/Q1brePl/d8qreBWbUwn/Vvl5Jst3LbJYU8/uaRoJYG3//7Hhqdbn5+zQx57Ukkkj4nXNlCg6ODQ9WtYdcG2vxoEqf6bGYm2vjm/DycHh/fCIjEPXHdDDLibXi8Co9dM52KRifJMdag1/3y+Tk88tohku1Wvf2BSUBNczuPvHbYn+L5xclBLY9WLyxgXsEIZuacSkn/4Fi94fs5WN3C9/62O+i9XDYxndcP1sjoXifdOnZCCBNwDPgBsBAQwFWqqh48w9dLV1W1qvP3aiC98/cs4ETA4yo6j4U7LukDFEVl++EaSusctHV4Af+No8nZazfleWOSsVnMTMxIICrKxFiZy9xvnGnDzZ4+r6uTEzgZdI3Elde3880/7dT7aimKypql0yitazPccZs8KoFv/fljcq6YzHPvnbp+VBWee6+c7yws4JHXDgXl0msqgOEc00mZ8SHv645Ne0i2W7kof0SvI5LD3eBLzh5FUdl5olF36gAU9ZTt/MN/y/Rrf1JGPA/+86C+kfHdv+1iYqbsUyeRSPqWcGULFrOp20ieNj8m2636huzVs7Kpd7r1DVXwR+JKTzpYvWFXkEPW2O4OcgjTE2y6LdTmeYCVC/wCRuX17Tz175JOJW0f1S3+vnYzc5KYMyaVsnoHHxyrx2491WIp8P0cqWkNeS8bVsyVjcoD6HbLW1VVBXhKVdVDqqo+parqk2fh1HU9t0pnemdfIIRYIYTYIYTYUVdX11enHdIcb3Dg8aqsfeMIiupfnGj1VdpN+dt3/DLc0zqdOsmZ0dPrs7uGm93Rk+dpTs7l697hS7/5gMvXvYPXp+rfeXdNvsFfpPyFKZnMzUvlnsWTgmrxHrpqKsU1/p5d9ugoGp1unnqrhCe3l/DUWyU0Ot3Yo6P0yN63L85n1cJ8frO8iJxke1jHtKalw3BMeyqa2FvZxHtHT1Ja14aiGJuScOctq3d0+3kON6T97Dll9Q6ONzhDrksj2+lV0Z066Nm9LAlGXpuSSCZSrk9N+j9wXl577Qycbl/YeT1wfqzqbBzuV66M1zerNK6elW0oDDU7J5k1S6dh68zYqehMRw+kq9hKeX07h2paufvlfbR7/O0WtJo6bX1y3fr3uHVBAbmpMfo5Hlkylb/tqAh5L5qaptF7HI70JBVzmxBiKfCPTmfsbKgRQmSqqlrVmWpZ23m8Ehgd8LjszmOVnErd1I7/2+jEqqquB9YDFBUV9ZnDOFTxehUqGtvxKqp+Q7y6uzIkBfOexZOIj44iJsYywCMe3PT0+jzThps9eZ6Rk6P1gbtj0x79OV3PMTLu1DmiokycPzaVzEQbz399DtUtLopr2/j564dpdLpZtaCA+jaXYSqmZvCnj07io7IGFBV2lDfQ4fNht0QZGman2xsyptzUGKZlJ7HtUK3eY/GORRNDUktqWlyYhNBllAPPK5tAByPt5+lRFJUTjQ5OtnYwKinmtA1+Vy0ooLrJGXQO2aeu98hrUxLJRMr1GU76vyyMZkJavI16R4eeXQDw9uFafArE2yyYRfB6INzGr09V+cKUTLKSYjjZ2sGxekdIff49iyfx5PaSoNdX1VOiamuWTsPrU0PXJy/tY8OKubR7fKTF2zAJ9Nq+wHNlJsac0bppqNITx+4bwO2AVwjhwp+OqaqqmnAGr/cK8FXgp53/vhxwfKUQ4gX84inNnc7fv4BHhBDJnY+7DLjzDF5XEoCiqGzZV8UPNu3hlnl52CwmNu2sYPncXDbsOK63NZiWnYTP5yM17rQiqJI+4kwbbvbkeUZRPa0P3D9vnUdVs5NxI6YG9S9cvbCAY/VtjB1xqi5NU/FTVFj+uw+DzrluezGrFxaQlhDNivl5KKo/v95uMfO7/x7jJ0umcqyuLaTP16ycZEPDPDrZrrdR0Or+/t9n81nx/I6gBfSarQeZkBEflFrSVcFLc+6GisGXtYPnDkVReaeklprmDu59ZX9Q2pKrc8c51mpm5cX5uLwKqgobdhznzs9P1K9r2adOIpH0J0bS/+HWBjnJdvZWNvPsu6fm4vsWT+ZXb5fwj48r+NZn8oJsXFdHD05t/L5+sIY1Ww+yYv44ntjmb1C+Yn4eOcl26to6yEiI1h2ywL62NouJ/LR4fv76Ib5/2QQ9nfPqWdm6s+nxKczNGwH47XBXMbWp2YlMzkyQjcoDOK1jp6pqfKc6ZQHQ49WQEOKv+KNtI4QQFfjVLX8KbBRC3AyUA9d2PnwL/lYHJfjbHdzU+doNQogHgY86H/eAJqQi6T3aQrDJ6eYHm/bg8ihBfcWef7+ca4qyyUmxMzI+mgRbFO0ewejk4XlzDARn2nCzJ88LF9VLtkdzuMZfXL16YYHukGm1cY1Ot15nF0jX3b5NO/0pEtnJdlRVYVJGAu0eHxaziYomJ1fOyMISZeLZ/zsWks6x7vqZIdHinyyZSnO7h79+UMbaa2dwqLqF/LR4fvD33UHP13rkaWkXXXf9ntjmb4y6blvJkDH4snbw3FJa10Z7h6IX/AemLRWmx3OiwYmqovdn0kR+JmbG88eb5uB0e8lJiQ3aIJFIJJL+prtI3h2d60Dwz5X3b96v18A/859S7v/iZH62bDrODi9OtzdEGGrttTMwm/xzrqaKrdnHwPn2iTf9c/DEjAQOVrfw/PvleobPT7b4a5CLa1vJTY3huqKcoHXAmNRYRsS1kZMSLKam9dJ1uRXKG5xcNjGdLbJROdADx04IcQuwGn8a5C5gLvBf/GIqYVFV9Uth/hTyvM4Uz2+HOc/vgN+dbpyS7tEWgv8+VMXFEzL1mzlQKKUwLY6kWAsWs0BR/cWy01OSh+3NMVCcacPN0z0v3M6dZphdHgWH2xeUMqHRNXXR61WoaHAF7fbddkkhMRYT3+90vDSlwK6OZKB4CvgnlEaHO0iwR1WhrtXF0ZMOdpQ3U9l0gKtnZdPe4TVMBzGbIC3eFrbWcOboJF5Ycf6QMfhnKrIj6T1ut48DVS0IIYKuLW3xcvulhYwdEUtVk5M/3jSHj8oamJadxIV5qURFmchNld+HRCI5N4TL5Oi6Ngg3V2obtVXNLvZUNvPbd07N4ZmJNlbMz2Pm6CRyU2OD1CvDpWqW1Laxp7KFPZUt/PV/z+eqGVlMykxgb6XfwdMyaTbuqOCRq6fyrT/t7JKOuZcV8/OYkJGgi6kZ9dLVNjbl/Hca8ZROVgPnAeWqql4MzASa+3VUkj6nrN7Bvw9VcfXsHHyd4expWf5s2qpm/wL96Mk2nB0+vD6VuWNSGTMibtAvgCWn0HbutqyaxwsrzmfLqnksmpwRUnhsVPgcmLqoKCr/La3nzheDd/v8O3k+br4oj5UL8ilIiyfZHpzGqzlhXc/f4HTrohNPbi/h2XdLaXb59L9rfzvR1G44vqLcFMakxupRya5/HzsiVnf8yuodYcVWBgtnKrIj6R1er8LuT5uIi47CYhb8cNF4MhNP3Qs2iwmvolBc20pKXDTlDW2cNyaFi/JHSLEpiURyTtE28G/6w4e8W1LPS7sq+b+Sk3i9oa2nA+fKzESbLmg2Pj1et3Gv7q4MEkprdLqZkJHAZwrTyBvpXx+mxZ86j9Hc29H52jaLifQEG3kj4yhMj+fZd0uDat8bnW58PuPm5YpKkJiaUS9dKYp2ip7U2LlUVXUJIRBCRKuqekgIMb7fRybpU5rb3RSNGcnXfv/RqXzqKybDB+UcqW1j9cICspNjcLg9TMiIl4uSIYrRzl1gimZgam64XPWyegc7yhtCDHCy3UqszcLaN4NFU7rWt03MSAiqO/r5NTOItoigY1oOPhA0nld3V/LQVVO4+6V9QSlvF+al+mv/DKKST94wkwNV4fv4DEbOVGRH0nMUReXNQzUcO+kIEkW5/dJCfv9/ZTQ63axeWMDoFDsNbR2kxlr51p8/ZsuqeYP2upJIJIOXsnoHa7YeDElnXLN0GldMGxVkl7S50ujxqxYUsGHHca4rymHDh37dhcL0OMam2pmSlaSfR1FUjtW3ce/iSfz67aMhawdt/u+6jgiXPZQQYzGc1zShFU1MrTsFbxmx65ljVyGESAJeAt4QQjTir4+TDAK0BuQer8q9r+wL2uG4/9X9PPvVIhodHpLsFhocLpLsVnIHef2RpHcEGtmqZhcbdhxn/fIiLGYRlMqhpXgcqWmlIC0+pMfMNUXZeo49GNe33XZJIdXN7dx/xWRibVGMSbUzKTMRgK2r51HT0kGLy8PR2jbgVKpwYPpHTrKdWTnJhrn0RvUEqgpf+OU7Qypt8UxFdiQ959hJBweqWoLSiV0ehbVvHOHpG2bhdPtIiIniT+8fY2ZOKsU1rfI7kEgkA0ZNi4vF07JColl3bNrD1KzEoPlOmyuzkmxct/79kLr1R5dN1+vfjtS2cfNFeaQnRAc5h2X1Dh7cfIBvzM/jwSun0O5R+M2NRRysamHSqASyEmOCmo53N0+PSY3leIPDUF14674qVi3MR1XhN8uLOFjVLDc2u6En4ilLOn/9sRDiLSAR2Nqvo5L0CW63j3eO1vl3N9zGIe6TbW7MQuB0+5g8KlkW9w8jAnPxx6fHs3X1PKpbjAuPvV6Ff+6r0outAxW03F6Va4qyyR8ZZ3iNTc5M5Jkvz+Jkawd2m5kfvXgq2vbIkqm6Y9c1qqbt9gWmf2hj6q6WsGtU8r2jJ4fc7t6ZiuxITo/Xq3CgupnyeidZiTGG147L608TPlDVwoTMJHJS7EwelUBOivwOJBLJwJCeYMNs6nk0y2QSYfvcldS2UtXsCorgXTYpjfeOniQz0YZPgaN1rdx9+SSqmttZ8fzOoGwwn+KvuzObBK0uDx8cqw/aKDbKHspJiaUgPY7VCwsYGRfN8UYnW/dV8fmpmUHO3k+unsYvvzSTW//6idzYNKAnETsdVVX/018DkZw9gQv1tLhomjvcON0+KhrbcXl8YXY4oomNNjNlVJJckAxxAq+PjAQbeyubdXXU7tITvV6Fd4+eNFTQ+t3Xiiivb+f+V/frrTO6XmMdPv8kcf7YFO7qbFugneOuF/cyY3QSYKxm+ceb5jAyPvqsnJahmrZ4piI7EmO8XoXDNS0cqGrlnpf9mw+rF+YbXjvRUWaS7RZio81S7VIikUQEY1JjOS83pUfznbYeMAlh+PgL8lIB8Cn+1i23Lihg1Quf4Paq3HhBbpCjde/iSRSmxbGnskXPBlsxP4/DVa14FYLSMx9ZMpXZuUl4fX5Rtq7O3mcL0hgZF01Ni4vPjBjJZwpG8uVnPwhaG9z5jz2sX17U7Wb0cEYWUg0RtKLZy9e9w5d+8wFfePJdmpxeimvbeGJbMRt3+GunAotcH/jiFMxmVTp1w4Cu18eLuyp1pw5OpSceO+kIed5/S+v5+Hij4a5eQ5uH+1/dH1SfF3iN3bd4Mj9//RDrtpVw8x93cF1RTpD4hMuj+AVNTjoMz9+1GemZoKUtBo5L7u5JAvF6FV7aXcm/DtToTh34ldpWLwy+pm+7pJAHNu/H41O5eEI649KkyJREIhl4TCbBBXmprFk6rdv5LnA98PA/DwQJpGiPPy83hatmZDGvIJV118/kl9uLKa9v5+pZ2bpTB/55+oHNB/jmZ/OZlpXAty/O55Z5eRSkxTM6JTYkLfSuF/dy4NNWvvBL/1rk8nXvsHV/NYqioigqrx+s4br177Pi+Y/58rMfUN7gNBRh21HegE+BuXkjdCEXiZ9eRewkkYuR/PmuE00onUWngW0NhIDzxiSTYrcyeVSivCGGIF0lj1U1OCKmXReBuDwKB6tbgqIPx076hVIU1bg5qSOg/UDgNTYhIw4Vwc9fP6TX4Wm5+4HtDmwWEx6fypGaVsPz761s4Tsbdp2V2IlMW5ScjgNVzdz90j5umZcX0tLguffKefqGWdS1dlDX1sEf/ltGVbMLp9s7gCOWSCSSUKKiTFwxbRRTsxLDznfaejHZbmXRlEzWv32Umy/Kw2yCotwUvVWLlhHy3tGT+jweTrjkUHUL3/xsflA5xU+unhp2ndF1U3n8rfNo6/CErGPvenGvXqevYbOY8ClwvMHBuDSZsdIV6dgNEQLlzzMTbVw9K5usxBhibVH6glmTjLdZTJw3ZjYTMxLk4naQEK43TbjHdm1e/diy6SEG1siROlLTyqTMBD29r7zBgaL6ZY+7Kl6tWlBAW4c36Dxa64xfL5/N3ormIHEVCG53oDUhX7ftMFdMywopmr7tkkL+8N+yPhE7OVdpi735niSRgderUNnkItluZXx6fMh90eh0s6uiKWRhYTaZUBRVfr8SiSSi6DrfKYpKaV2bPi/VO0LbBgRutm7pMtd2LWcwWjv4FDjUxWErO+nQH6utS80mKEjzt1TQ1LJdHoX9nzZTUtdm6Ajmj4xj1cJ8FBXMAlLsVn7332PMLxgR9Fg5//qRjt0QYURcNLmpMVwzezTpCTYqGp38Ylsx1ijBfVdM1tPltBznC8akypYGgwQjR23N0mmMSrKRGhtaf2YUvS2uDY6IbdpZYag+9fz75Vw4LlU36rHWKF7dXemXPd5xnJ8tm05xbSs+Bbbuq2LprGzD89z78j7uXTzZcALIT4tn1cJ8inJTiLeZOT9vJD/Zeohku1WPKCdEmxmdYmfp7Gx9vLWtLsakxkas4Tb6ngZ7W4WhjqKo/HNfFc1ONzdekMsf/1vK2mtncKi6Rd/Q+PL5uaTGWYPacdyzeBL3vLyX339tjqxxlEgkEUu49UNuakyP2wYEqjBv2lnBw0um8qPOevlAcZXF07KCzrVxRwX3LJ7E+rePGrZU0BqU2ywmKpvaw2YGRUeZdXXi3NQY7vz8RFYvLCTGatY314ze5yNLpjIrJ2nYiVpJx24I4Gx309zu4bZLxnPsZBtr3zhCo9Ot3zi/+k8Jv14+m5pm/650flosVqt5oIct6SFGjtodm/Zw80V5PPtuKU/eMJOxqXF6IbK2GxfIxh0VPLJkqi5e0uh0k54QzeqFBTjcPlQVnn/fr0ApEJSdbMOngNPt5Yefn8iz7xxl8bQsqpqc5KTYufulfdx8UZ7ukD26bDolnQ6fZqwf2LyfB6+cotcsaRNKZmI0uSkxuH0KZpNJV/HSIsqZiTaWz83lti4KmZmJtiDDnZsaw4NXTg1py3AmdN3py0m2c7zR2SsH0uh7GuxtFYYiiqJy7KSD4w0OYqOjqGluZ+KoRO5+aS9fv3Cs7tSZBXzvsvG0d3j54/+VsWJ+HtlJMWQkxvDYv/wpxoNZWVUikQx9wq0ffv+186hrdfHkl2ZSetKB2+evk290ukmLtwXNiXZrFNnJNv508/mcbOsgJTaKp2+Yxa6KpiBxlV9uLw567UanG7fHxwNXTuEbz+8k2W7l6lnZCAEdXp8uwrJ6YQGAYR/dexZP4pHXDuhRv+uKcvjOhtDNU6P3qaVxTshIMNxgHaoRPunYDXLa2l1sPXCSu1/aG7ITEljPVN3sorKpneRYK7mpciEymAhMs9VwefxSwsl2K8U1baz8yyck263+tgNpcfxw0Xj+GNAYvNHppjA9jn/eOo+6Nhcj42xEmWFneZPu7GkO1MP/PBAiL3zP4km0ujy0uX2Mj7fyz1vncfRkmx5hE8DfdlTorwdQXt9Oo6ODx5ZNJzbarPege/1gDbdv3EVhWhy3Lihg5uikoF26wPQQ7b0+sa2YC/NSWbP1IDdflEe8zUy8zcKK53ecdXSs605fbmoMty4oCGqC3pNzh/ue5OI/cvB6Fbbsq+Lnbxxm8bQszCaYnp1ES7uH68/Lwenx6TvD2v1gizJxpLaNRVMyefzNYr40J4c9lS1DQllVIpEMbYzKdISA1g4vVc0dPP7mkSB7V5AeR06yPST69ejSaVjNfluYNzIWl9tDYVo8DreX7142gRd3nuD683KC1g2PLp1Gk9PNzvJGku1Wls/NDXLaHvjiZL2t0TVF2UF1+mYTLByfRk1rh17SYbQ20DZPw82/Sqe+QNcN1qGcYSMdu0GKoqjUtDg4VOPUnToIFagQwh/Kzk6O4bwxKUNmR2I4EU6uX1XRFaqMjGZgH7g7F03gw2MNZCbGkBJrxSRAVWF0SgwbVsylweHmkxNNvLa3ilvmj6OktpVb5uWxaaffWXtw8wG99UBOsp2KJidtLi/Pvhu8CH5tbxXzCtP8/WsEjEuLQwW9B11pXRtrth5k5cX55I2MpcOrcLSuncevm8FPXztIeX172D48Na0dejrHzRfl8Ys3jQ18b52orjt9i6dl6U5db849VNsqDBXcbh87TzTS0u5h1cLCoFSiR5ZMZdyIOFZt+CRkQ+HpG2Zx80V5ekS7wyv7JklOj8/rYffu3SHH58yZg80mbYLk3KDNS9oaQUuZbHS4cbq9uuLk1bOyaff4iI+2cKimhTVbD+pRshsvyKW1w8uDmw/oNvP+L07G6fZQ2eRCUWHa6CQmj4rnTzefT11rB/G2KJLsFr79l8NcMT2La4pCnbJ7X9nPzRfl6b1qbRaTXqe/9toZTM1OIi6gTq+71NHu1klGG6xDOcNGOnaDELfbR3ljK7WtHmq7iebYLCZMAh68cgrny5q6QUtgfnvXqOzS2dkhRdBwalH62LLpnGh0ogCP/utwkBMWb4ui1eWl3eNjVk4ye0808YVpo/jB33cHG+8OL/VODz5VITsxhtcP1lBe7wiRPH5iWzGPXzsjKIXykSVTWTwlU99MqHd06PV6XXPu7//iZEanxODzGefZp8RaWbfd78ROyIjnlnl5ALrzeabRsa47fT2tO+jJ9yQX/wOPoqiU1zvYX9XCkZpWshJjOF7vINlu1a+bu17cy+PXzTD83k+2deiiU2uWTiMrycbSWVlyk0zSLa3VZTxzzEVG+amyh6aKEn4BzJ8/f8DGJRkeBKYZ/mZ5Efs+beaFj4Ln3dzUGB744mSaXR5ONDjZuKOC9W+Xcs/iSTx45RSizODxgc+n8v/+8nHQfH/fK/t55suzuPeVA/q5spLt3Pvyx0HrjK9fOJZNH1ew/IIxhvbVbILVCwv41b9LWDE/j4K0OCZlJurq3KOTYvSaPjBeG2jKn+HWSUYbrEM5w0Y6doMMl8vL/ppmWtq9fHKiiYK0UBU3zaF76KopjE+PZ2JGgnTqBjGBcv01LS48PpV7Xt5LVbMLc6cDH+iMaOkW0VEmEmKiyLfEcetfQyMRK+b7HaN12/yL1me+Motv/SnUeK9eWMCT20v47TsmHl4ylXXbjvD/PpNvaBS7yhjf9eJexo2MZWpWEsdOOugIiChrTpqWGvJpUzvNTje//295iCDLQ1dNwRolWL2wgIQYC98PcD41463VBmj0NH8+3E5fbyNvsq1C5KGl27Q4O2jp8KdZainLP1g0nhiLmaffKmFPZQuqqhp+7+PS4nhhxfny+5T0mvjMPNIKZw70MCTDDKM0w0eXTWPxtCzdqdPq1W594VQZx+2XFlLb6mL920f58pwc8tPiOVDlb4GkbYRpuDwKn5xo0u3l9eflUNHoDNpw1dYZt8wfh9Vs3Ah9YkYCZfUOPjshjZk5ycwbN0LP7qlsclLv8PDXD8pYe+0MKhocfqczIHKobZ5q8+/4W+dxsNq/iaetC4w2WIdyho107AYRznY3/ympp8Hp1i/s3NQY7ls8mfs3n1K9fHjJVHJTYkiIMVOYnjTQw5acBV2dk/PHpgLw+6/NobbVRUaCjfEZCRyubglKt+iax25klBWVoP/Xt7kNnbWUzlQNl0fhRy/u5WfLpmMJY6R9wU/3p1C2dFDZ5J9ktD5hWn1g17Hes3gSAM+9V965exdPcW0riqJQWuek3eMLiRSu216sF0iPSY3VxTEOVrVQXNvKxh0VunE3yp/vutP36u5KHrpqSkiNXU8ib+eqrYLk9CiKyr7KJtZsPcgjS6Zy8x93GF5z9y2ejPXj45TXO0MUhB+6agrTs5LkxphEIjnnaPN/vaMDq9mE0+3TNymBsBuXRmmGR2vbgsoctCwfI5t42yWFpMZb9ShdYGmHto4InO8zE20kxFgMVbYVFUpqW/nbjlAl7vu/OJmyegex0WYmj0qgrcPL/qoWmpwdfONPH+sCcTdflKe/n8xEm16DN79gJLNykvX3bTIJxqXFMXZELJMyE7hwXGrYDbmhnGEjHbtBQnu7hwM1rRysbtGL+8EvUPGrt0t4dNl0XG4vKXHRpMZaaGp3MyM7eYBHLTkbuivuDXQespPs5KTEMDrFzvEGp2Eeu1GDT5MgyDCnxloNnTV79Ckz4fL4WyfkptpDFsGarHEgfmfTwvc6I2zaMUBXxAoc64ObD+j1oeu2lbBm6VTWbSth1cJ81r9dGtJAWnvezNFJfKYwDSDkM7vtkkIUVeVQdQtZSTFMzUoMMvJGkbacZDuzcpJl5G2Qoigq75TUYhaCVQsLaHF5w6Ys3795P+uXF3HPy3u5/rwcXRDIJGDWaOnUSSSSc482/6/ZejCkbOHJG2bi9qphhT+M0gz9ythTQurVjGzi428eYcX8vKBjWvRNy/AJnO+vnpWtBxu0x2sbrgAxFrOemfPYsum4vD6OnXSSYLPwwdGTnJc3ghXP7zy12XbFZArT4vQxBmYkaerZAJMzjXsx92SDdShn2EjHbhDgbHdTfNJBW4ePrMSYIFEL8Dt3JbWtnJebQofXh8UEY1Li5IJkkNOT4l5FUXWVyWS7le9cUmDo+OSk2IP6cK1eWEBGoo0mh5s1V09lZHw0JkHIjtrqhQVUNDr1c2m7dD96cR/rrp/JyovzGRkXjT06ikZnBzfMyQ1S2Vq1oIDv/X03K+aPo9XlAeC2Swp540AVX5k7Nmx9qPZaGYk2MhNtKCpBjmFX5zO30yBr4iza4hzgLx+Wc+WMLNZtK2H926WGkTujiUBG3gYvlc0ObFFRNLs8jEqMocnpYfXCfGKsZsNrrqbFxTfn5/Ort0sor28/tXs7Qn7/Eonk3KPN/1rZgma3ku1WXG6FI10EzgLXBkZpho1ON60uN898eVZQGU+4mvLAjB7tWGF6PLdfWkiHV+HFj0+wYv44Htx8wLAURAiYOTqJ1nY3VS3uIKG1B6+cQqLNTGy0maVFo7n5jzuCN9te3c+jy6ZzpKZV3wg2mvczEs8ubXKoZthIxy7CaWp3UVzjoLzeGZQa1rW548ycZOzRJpLtgupWNwtGyWjdYKcnxb1dnT+7NYpVC/NR1FOiIjaLiepml65EqSiQHGuhrcPLsZMe6p0emtvdpCfaiLWaWTE/D0X1RyxGJdlY+8YRgKDrzuVRSIyJwmwS3BcQtbvr8xN47qY5vHv0JDEWMz5F5YrpWdS2uLBZzDyxrZg7F03gf+flc6CqOayKlbYjeLiqhdsvLSDOZsFmMRn2uQlMn9DEWbo2QjWZTn1+Q0X5SmJMa7uL94428uaBT7l00ihWddaX2iwm7l08idzUGF0+G/zXWnpCNOu2HeH7l00AARMzEvTifYlEIjnXaPN/V6dp+dxcPQOm61qwpsW/NjBKM3zwysm4PCo/2PSxXsbzwJVTqGx0htVpCMRmMXGkxt+r9tl3S1m1oIA391ezfvlsmtu9rF6Yz1uHalk0JTM41f2Kyfzlw/Igx+2el/19cFc8v5OHrppiuM5xe336fL/9UDVrr52h9xh9dXcl37tsPLHWKN47ejIoFbWntfVDtYcdSMcuomlqd/HJ8RYcLm+I/LomQPHsu6U8vGQqGQlWVNW/sF8wauhcoMOZnhT3asZfM/hdRUU09UmtiPg3NxbR7PTgdPsorXPoab13fX48R+scRJlMFKTFU9nkxOn2kWy3cMfnJtDa4SUj0cbP/3UYgFUL8zvT23xB6oKPvHaIZ74ym7/tqGD53FyefKskKFUz2W7lJ1sP8fNrprNxR6iT9vCSqcRazTx2zXTW/+cozS4P3/xMPj997aD+2Off99ffFXYKAwUuwK1mU0haybrtxTy6bLr+mXV1jiVDA0VROdHgVwuOizbz1QvzQnaCH9h8gKdvmBVUO3LfFZNZt+0I18/JZfKoBD36K5FIJAOFNv/DqWiVUdpk4FrQ41NRFDVISORwTQuldQ5GJcUE2cPy+naeequYR5dNIy3BFiRI8tCVU7BaTEFZPqsWFPDRsXpu6VSujLWaibdFBaVQ/uK6GXrzcG1897+6Xy+v0Ah0WE80GDuWafHRVDW72H6omuvn5AY5qT+92t9Tb/GT7wZt8F42MV3PYDJKUdUYyj3sQDp2EYeiqFQ2O3B2eNlb2cbdL+0NW1dUmB7Hc1+fQ7vbS26SHXuMdYBGLekPelLcmxbvN/7hDP5jy6bz8JaDNDrd/PTqaRyra+OR1w7xs2XT9ZTLzEQbsTYLa98MjnL9+1At6V0M/p2LJmA2C2pbO9hd0YxZwDfn5/Grt0t1566uxRW2fm7lxfm0exRiLGYanW69GWl0lImCtDh+uvWgngq3akEBQqDX8QU2Lj0vNwUFJSSq4nT7DO+VT5va+fbF+Xp/vYyE7lM4hvJu3lDE7fZxoLqZsvp2fviPPbg8CqsWGiu3Hqlt02uSx4yI5WhtKxfmj2R2TjJjpbMvkUgiAG3+X7P11KZmuLRJswlWLSjgnpf3svaaGTS73CTYrNS2+nvMJcVEGYqjlde3U9vSQUu7h591bn5aTIKfbD3Il+bk6Nk7qgofHavnsimZunOolWoEbuweqGoJO75AbBYTtigT3744H7PJxFM3zOKZfxdzft5IzCZ/xoTHp/DLL80kJdYSskH3w3/sCakBvH3jLjasmBu2fGVMaqw+p9ut5iHbww5AFmFFEIqicqKpmWN1Tmpa3EGNx7WdGw2bxUS8LYp4m5kLxo6QTt0QxRolWDE/j5UL8lkxPw9rVPCu07H6NlYvLAjb1DvKLLj90kKe/WoRyfYoHnntEC6PgrPDqz8+fOHzuJDjHV4frS4v698u5cntJfz67VKcHh83XpAL+K/L1Dgro5JiDMczZkQsZhMcqW3l9ksLaXS6eeqtEryKwm0bd+kpctoYspPtIUXT67aVUNXcTqvLx/EGR9BrBO5yatgsJsZ1vq5GSV0bStcigoDPdev+ai5f9w5f+s0HXL7uHbburw77eMnA4mjvYO+nzbR3Tvja9aKoxnaz1eXlB3/3N47eWd7I/ZsPMiHDH6mTSCSSSECLuv3+a3OYMzaZDSvmMi9/hKFNy0+L5/n3y3F7Verb3Dg7FKpbXBxvcPKzfx3CHm3RxdG6PndEXDTtHh+Ha1qpbHJS2+bijs9NYFRiDDNHJ/Hq7kqeequEpbNH65uscEpQ5epZ2fr5wtncadlJQdHH2y4pJDHG4m9E/sYRvv2Xj7mmKIfNeypZt62E2zbuorqlg0e2HOS90oYe1wBqDmbX4zUtrqA5fduh2rBlLkMBGbGLEBRF5URjMx+VhUbpjOqKHlkyldRYMwUjZY+6oUpZvYOVf/kkyADZLCa2dO4qaX9Ptlu56/KJhukMB6padBWrB6+cou+unXR0hKhjBeLyKPhUNeR4ZpJdT/fUHvfEtmJ+tmy6Lh1fXu+kud1jOJ4TDU59PHcumsDqhQVkJ9v1c3Udg9PtNTxPjDWK4ppWBJCTciqaZhTlXLN0GlXN7XraqbbTmD8yzlAcoyeiNZKBR1FUqlra2FneQmVjO+2e4Gitkd18eMlU6lv9DXtdXh95I2PZ0rmbKyOyEokkEuiaMTIrJ0WvHzNqwv2TLQcBuGXeWFZt+CRonvv6hWN5YtsR/veisSFK1vd/cTI//McePUvmietn0Oz0cPvfTpV0PHTVFOwWc5CAmYa2ftB4dXdlSJ+5exZP4tMmJ48um47H68NujaKu1cXDWw4GzbH3vXIqZVPL8Hl02XQExsIpRjWAmYkxho81CxE0p2sOaHdlLkbfw2CZJwbEsRNClAGtgA/wqqpaJIRIATYAY4Ay4FpVVRuFEAJ4ArgccAJfU1X144EYd3/haO/gcG0bDreCoijcuiAfs+lUn7CqZpdeVzQ+I56EaAvZyRZGJ0unbihzOvEU7e9VzS4e2XKQ2y4pDFKk1PrOaM+75+V9ulzxn94/zo8un0hdW0fYJvdG7Q/a3V7DMQE8umw6P3/9EP/vM/n85p1jIYvqB744mUS7lZUL8tm0s4KfbD3EzRfl4XR7aXS4DcfQ4HDzwJVTuPflfUHv65HO9NLVCws43uDQHTQjCeNGh5s7Nu0JmkRe+Og4s3OSqWp2YbdG4fb5SI2NZkxqbI9EayQDi9vtY391M41ON0kxFo7XOylIiw8SRqlqdrFhx3EeXTadktpWZmQn8cJHZXxuShbl9W1cVDBSql5KJJKI4nT1X9r8VtPiwuVV+PEr+6hqdnHHovH87F+HQzZdV8zP45rZo6ludfP24VqeuG4GwiRItFnYfaKJ7102gcomJ20dPgSCe18Jjsrd/dI+1i+fjcVs6tbBsllMXH9eDhs+PB7UMsbh8uD2qdS2ugH/httdl088rZPo8ii43F4anG7uXTyJBwKcxfu/OBm3VwmqAVyzdBqTMxNCHN/VCwv4+ETjaTf9upa5DOY6vIGM2F2squrJgP//ENimqupPhRA/7Pz/HcDngYLOn/OBZzr/HRI0t7v4b0kjZfWOIJn57102njsXTeAnW/2pc41ONzkpsYwbaSc3MZaYGMtAD13Sz3QnnqIoKnarWVfAfPtwLXarmceWTedQTSvj0+N5ZMvBkKbk40bG6SkR7R4f698uJdluNWwcajKpQQY1NzWGrGTjHTGLSdDc7qG8vr2z9cGp+jnNwCfareytbOa375TqSl5mE1Q2tZNqt3L7pYWsfeOUY/rQVVPITo4h2R7Fb28sotHpobi2NahJ6hPbipmVkxy0QO8qYby9viZovJmJNq4ryuHG338YtOu5Ycdx7lg0kUmZxo5u1908ycDgcnnZerCGx988HKKAet/iyUEtC1bMH4fVLDh/bAoClSUzc3j23aN8/aJx5KTI1EtJZOByufjwww9Djs+ZMwebrXu7czbPlUQep8sYMZkEY1JjOVTdypqtB1k8LQuzCcaOiA2bspiRaMPl9nLZ5Aw6fAptDm+QWvCqBQW8uruSnOQYPasn8Bw7yhv596Faw4hfenw0jy6dSmZSDI1ON0dq29hT2aI7VfG2KNISbDQ6Pdg6a+vBOGKmqqfaJZhNYI+O4vE3i7FGCR6/dgYOt5djJ508/e8SvjzH33M00WZmanYSqgrHG51cNjGdLavmUV7v4JMTTTz3XjlLZ2cHvZ626bdhxVzaPT7DHnaDOXMnklIxrwQ+2/n7H4F/43fsrgSeU1VVBd4XQiQJITJVVa0akFH2EV6vQm1bC582KSFNx10ehcdeP8zKi/11VQVp8aTEWkhPsJCdEIfNFklfm6S/CCeekpNsD9pJyk2N4Z7FkzlU1UKS3cqruythepZuQDVsFhOfNrWzfvlsBIL/fX6HHvErrm7h6S/PYteJJnwKPP3vElbMH4fL7eOxa6aTbI/i06YO7npxb0hk8PZLC7l/8wHdeFY2OXVH8am3SnQDX36yDVUNbl46ISOBBzcf0KNvz361CJMQuDw+fvzqfn2B/tBVU0iMsQQ1WYdT6ZoaRqkTuSmxQUa9O2Wx2zfu4p+3zjutaI1kYHC0d7C3qpUf/mNPSH8nl8ffbFyL0BXlJqMCJ+odKEo0OSl2rFE+Hl02Y9Ck1EiGBx9++CHfefolkrLz9WNNFSX8Apg/f36/PVcSefS2zZGmNvn0l2eGjajVtXYwMj6aJ7cc0hU0jea/e1/Zr2f1BJ6jIC0eRYXX91Xx2LLp+BQVa5SJ2GgTJ9s8VDa1U+9w88JHx4N621Y1ORmZYOPbnSUluakx3HfFZKqanCEbufddMZlNO4+zfG5uSLuirfuqOFjdQkFaPEKA26uSnWJndoKNysZ2bvrDRyFRtZoWl/4+jCJ0dyyayNSspLDzwGDO3BkoD0EFXhdCqMCvVVVdD6QHOGvVQHrn71nAiYDnVnQeG7SOXWu7i+oWF582u2lp94bNXXb7FEYn23G43FjMgvPHpMrUy2GEUVqhpuykNeHW1CQf3HzKCbpv8WQ2fXw8xAH73mXj8fgUGh0eLFFCv+amZSXwhelZ/L8/fxx0HT64+QA3X5THz14/zFM3zOKeznTIP/y3TFenvCAvlds37qaq2aUbz79+eJxbLsoL6ocXazWjqvCPj0+lhk7IiOfTJidLZ/uLr80Cokx+tSzNUGuPvfulffzllvMNJy4t8hIudeKyielBjlo4oRmt1rC41h/x3Lp6HtUtLsPdPMm5xetVqGptYc8JJy6v0m1taEltqy66o6gqk7MSmZ6dLL8/SUSTlJ1PWuHMc/5cSWTRmzZHgZxs6wiJqN13xWRGJUVT1+rmRIOTZLs1rN3UjueNiAtKcbz90kIqGp2YBHzlgjFUNjlJi4+mrcPHbQFz7T2LJ+H2qjz2+pGgc2vqxJmJNq4/L4fkGAvZydE0Or1Ba4REWxT/O28cq7u0S1i3vdgwvbK83smh6taQoIgWVQv8HAPLmSZmJFCYHn/aPqU9+R4ilYFy7C5SVbVSCJEGvCGEOBT4R1VV1U6nr8cIIVYAKwBycnL6bqR9iKKo1LY4KD3Zzsk2N0fr2rBGmTAL47D0BXmpgIKqCuZIp25Qc7bXpxpwN4Rrwv38+36nqaqlna9dmEe8LYrbLymgpcNHrNVMaqyVO1/0K62uXpivX3O3zB/H7oqmbo194N81dUqA0cn2oMigoqr8v8/mk5FgIyvJhtvnTxk9VNXC7/97KoUyNzWGJqeHx14/5Xg+vGQqmUnR7Ks0lkxu7fDw6NJp/KCzXk5z3MaO8Dt24VIntqyaxyXj0/jTzedT3eIiPT46aDKA4Mboeytb+M6GXYMmn74viGT76XJ5qWxpo67Vg0kIoqNMQQprXb/H2bnJdHgVkmMsNDjdTM5IHBbf4VAlkq9NiaSvr8+etDnKTLTpZRjgj0g5Ony88NExvfxBVeFX/ynhyhlZumDZqgUFuv0MN/8l2KJ46oaZeHwqx046iDab+Mkbh4IcuBhrFN/ZsDNorn1w8wHDaF9yjIWnb5iJT1WxW6N4YPN+Fk/LCooaTstKoDAtHq9X4ZZ5eWzaWaGvFVwehUPVLUGv9cS2YlZenB82KFLb6mLOmFTWLJ2m19c3Ot3Yosz8dOtBfv+1OaedE3ryPUQqA+LYqapa2flvrRDiRWAOUKOlWAohMoHazodXAqMDnp7deazrOdcD6wGKiooiTpfcL5Di4ERjO6V1bWzcUUGj082diyZgsomQGqf7rphMtFmQGhfD6JQ4uTAZ5JzJ9RkuApWVaDNMJVy9sABVJcTh27SzghsvyOVYvYNb5uUB8NahWv2aa+/whlWJirWa+fbF+WQlxrB6YT4bd5wyuDaLiZoWV1Aj9K6vHWWCzXuq+NZn87npwlwyk+yU1zuYmZMU0pvmRy/uZf3yIjIMdspyU2Oob/PwxLYjerSwKDeFC/NS9XsjXOpES7ubV443cvdL+/R0kK6CLNp70BzkwZRP3xdEqv10trv54HgD8dEWalrdlNa16dfuCx8dD0mvWb2wgMNVLeSnx5MQY2Z6VhpWq3mg34bkLIjUa3MgMKqn2717N6oqN30Hir6+Po0ydXKS7XqJQVq8jWP1bSEqzzkp9s6m48HlCprzp60TnvnKrBDlSm3+u2fxJJ5/r4zzxqbi8vrwKRj2o31s2XTDubYwPV53OF/dXcn3LhuP0+0LUtlctaAAr6KSbLdy9axs4qOjKEiP44GArCNtHq5qdmGzmPAFvxQuj4LLe6oVWNd1y8g4GyaTYFSSLcjR1c7Zk3TKcBlTg2Etfs4dOyFELGBSVbW18/fLgAeAV4CvAj/t/Pflzqe8AqwUQryAXzSlebDV1zW1u/igtBGPV8Xl9lGQHs9tC/N5fFsJP9l6iNULCwB4+oZZnGzrINluxWyGSRmJsp5uGHPspHEE6pkvzzI0qtnJoa0I1m3372wlxFiCNg5WLSjgtb1V/Hr5bGIsZp7+T4mBNPwUTra69Z21QKXNRqebh6+aSm2rC7OAn149Ta970pStNuw4zg8XTWTRlEx+uvUg1xXl6OML1zx6R3kDl0xM01NKku1WbrwglzEjYjlU1YLbq+oTl81yqvUDhE+d8CroTh3QOfkV8+ebz6fF5SHWGkVLh4fF07J0w6+NZzDk0w9VmttdlJ9sB0x8XN5IZpIdi9nEtz6bz8aPjnPljCyykmysXz6bXSea8SqKPzIdF01hRizZSfJ7kwwtjOrpKj5+h+TC8wZwVJK+JlAAzGiDt2tj8Ce2FbNhxQVhI3EayXYrdS0drH/7KDdflEeMxd9jrrimlR9/cQrF1S1cPXs03//7bm6Zlxe2bMERpg3RkZpWPTp4z+JJJMVYKK5t0zeUN+2sYN32Yp64fiY3XpAbsibR5l+t5u/Zd0t5ZMlUntgWnN5ps5iYlBnPr/59NKhWLzc1hh8umsih6hYARsZFB0UGtef2NJ2yqxDbYGEgvIZ04EV/FwOigL+oqrpVCPERsFEIcTNQDlzb+fgt+FsdlOBvd3DTuR/ymdHe7uHTVgduL8RFR1Hh9CsG/uH/Slk6O4dvfSaPe185gMPt47fvlPL0DbNIS7CRlRTNuJEJg2JnQNI/KIrKwSrjlER7dJShUQ1sOh74+JxUOyUGxnX1wgJa2r2caHBy/Xk5vPDRcT0aNiM7ibJ6h16jp53riW3FrL1mOvE2C26fjxaXifVvH+OmCzGM2HmUU4XZgVHGcBFCnwKODh+5qTZ+e2MR9Q5/lOaBV/0CK4HGv6vjpaVOBCqFnZebwsm2jpDPpby+ncqmdj2tc/XC/LOaACR9R3u7h09bHNS0eWh1+VAUlfTEGISA1/dXc6S2jXsWT+LJ7SVcU5TNBLOJ/LQ4vZbTbIZRCZGfLiORdIfP62H37t1Bx3bv3k1i1rigerqmiuJzPTTJOcSoxOCJbcV6zzftmMfnM6xF01oeAVxTlK23MwjcIF0xPw+X28faN4v1HsqbdlZw7+JJQfN0ZqKNa4qyMZsET90wKyjKpr2WpmrZ6HAzKtHGy7sqT9X/XzGZlFgrMRZTSCRQWydofewK0+O4+aI8/vJBGd/8TH5Q7eCqBQWs2XqI68/LYWZOEivm52G3mom3WYLq/tZeO4Mnb5ip9wMeTOmUZ8M5d+xUVS0FphscrwcWGhxXgW+fg6H1KU3tLsrq2nG4vZxscwelX9632K/+c+vCQl216OElU0myW5iQFoc9xjrQw5cMMMdOOiiubTV0ftLjo0MM+I+vmEx2SmgrgtzUGNxeNShtQ3OORqfYOVLTqrc8uHpWth5tizL7d/eMHMUOn8LtnYqa2vnGjozj1r9+EmKo1y+fjcujEB0VPK5NOyu4c9EE6p1uFNUvnJJit/K7/x7jmtlZHKhqDWnC+vz75UHGv6vjZTIJLpuYjsen6Hn1NouJ33/tPMPPsaSuTX/fZpOJx6+bwU9fO6hPQmc6AQzWpqaRQHO7i5JaJ7WtHSRER+H2+vj+3099l/ctnsxfPyzX6zkmZCSwZutBHrpqCmZhIivZxuhk+XlLIhMjZw2M0ylbq8t45piLjPJTqcQyOjf8CFdiENjzzWYxkRRjxWYx85vls/H4VBqdbhJsFqxRQn9M3og4w3ONHRHLs+/6o3ijk06VXTzz7xK95ZGWPdO1ZCg11oqiqhyta2NknJVFUzIN6/+rml3c/+p+XTClu/dks5gQQujOZ11bCb9ePpud5Y34lFMplU9sK+aPN81h3bYSvn1xPr94M9hZ1FSut/RROuVgmdtlnl8f43J5OVTbwvHGdiob2w1DzZokd5PTwz2LJ5Gbaic93sLYVNlwfLjS1WAcb3CwcUeoRO9DV00lJyWWnJRYCldeROlJBzFWM01OD3e9uDfk8fddMTlI7VJzuFbMzyPZbiUrMQaXRwkSRMlMtDEhPZ4RcVZyU2NYPC1LN7iv7q7k2ElH0Pk27DjOdy4pNDTUbR1eclNjmDwqPqjY++3DtXT4lCCH8/ZLC7ln8SR8CiE7lFpK6WOvH0F0ig0ZOV7HG50hzch//vohHrxyiq7qabOYePDKKTz/XlmItPIjS6aSkxJDSmez8t4a7cHc1HQgaW/3cKi2laMnHXrarFHKkWY7V/31E8aNjON37x7l1gUFZCfZyY3QSVYi0TBy1iC8wxafmSejc8OccCUGgY3Bb7+0kJK6Nn7y2kFumJMbpIb90FVTaGjroNnlo7rZv2kZuJFrFjA6OYYFEzJC6pWfe6+cX799lGe+MguzEKx4PlgwRXPUtPTLB6+cwpNvdR+J09YA3Qm43Ld4Mr99+6j+t/L6dqqbT7UvyEy08e2L8xECfKrCXZ8fT0pctOEapLbVxQXjRpx1OuVgmtulY9eHtLW72LK/jopGJ0CIDGvQBe72MmZELDEWE/mpcbLh+DDGyGD86iuzDZt8j021YzIJFEXlSG0bt2/cFdSXRnu82QSFafE0ONyGxm7cyDjuenEPV0zPCkm1uPGCXG7/224K0+JCUiDu/+Jk/vx+edD5Fk/Louykw9BQp8RaeeiqKRysag1y4h6/doaeMqGNae0bR/jnrf6dNaMxZyTYyE2NYUJ6PM/dNIe0hOiQz9Jod3NHeTN3fyGWDSvmUt3sIiPRRoLNwolGZ4gIzV0v7g2q2+stg7mp6UDhaO/gn/tqqWhyhthMo5Sj9s76jvSEaG67dLxsAyMZVHR11qDvHbZwkUGQjcsHG0bqjLddUoiiqqxckI+qgk9R+c6GXay8OD+kfOLul/ax8uJ8nnqrhMxEGw8vmUJtS0dQ0GF0ip0NO46H2N71y2ezo7yRu/6xj2uKsg3n5UBxlnte3hdkr7XjgZE4f9uj0E3rB6+cgjVKsH75bNZtO8Keyhb9HH4htyjdKQ3ckP3tO34nNDZMiYq9j8SzBtPcLh27PqKp3cWRKgf3vrxPr2UKF2q2WUxkJceQmWgmM0FG6YY7Rgbjvlf26cpVgU2+U+OsIc8J7EsTGHlbuSCf8enxhsYu2myivL49pHHnNUXZusGfV5imO3WnxuXfoQs0umYThtHFB744mWanm//P3pnHR1Hef/zzzB7ZI7vJZnOSkEBIQu6EEBAt0EqUog0Ccllb1FZLDxGU6s8TKaK2eKAgVotX1dYKFi8opSpo0YoH9w0JIQkJOTf3bvae3x+bmezszOZccpDn/Xrx0uzuzD47853v83y/z/cIUsj55GbuPKeqpfMHi2pbERMiDilVKRiUN1rwwKw01LVa8cTOU2i02EUeM3/eTb1aicSIYOR01Nd1u1mkROr8evj6qqiHc1PTwcDcbsOJ6jY8+vFxPrfDG6mQI61SjsfnZqKmyYLYMC3VnxSKD/52Bmnj8uGHb3VGtUKG5e8eQpmpnf8MV4wsws+uVUSwxwla1WyFRiHHht3HBfPxIx9KG2RHK5r5XTJ/efHexVmsDk+fWG+8d+K4XUDvvnIpUTqcrWnF+k/PotFix7r5Wfjp5DE4fvGYIJqnwWzDqsJ0T+NxH4fsht2egiy+a5DlM5Lh8C2p2UeG09xODbt+4HS6UdrQgiaLG3WtNmiDZLj7Gk+Fy3a7y+/2+WNzMpERq0OomnrNKNIKo8zUjghdkKCBZ3JUMN+M22S28Tt546N0SDCqBYqek7VX9p7D6sIMrNkh3HX78xceZc0p2BUFycgcpYeLBdbMzkC92QYZQyQV2bgIYQPT/AQDAMDpZvH0ghxUNllgsbvQZLHD6WahlLtE5/E3SRyrbMEf/31KFDrJhTHfOzMFL/23hK9c6esx8/ZuGjRKLMyPQ0qkDizrMeY4A5BhCNJi9JJj6E/BlOHc1HSgcLtZlDeY0WSxo7rFBo3SozddbrbbkKPH5mTCGCzHH3eew9naNuxcPm2QfgWFMrSR2hmkDE98q2TePytNsIM3KSHMszvlb9cqqHOpb7ZLF1nzNcgSjGrEG7V44LrxiA3VoLbFKmqT4FucRaVgkBqt51M4ZAyQExeK2pZ2vPTzPFwwWfi+t40WO7RKOTb/9xympURi/sQ4pEbpYNAq8NynZ/jIo6vGGXH0QhNq2+zQKmUYbdBIjr/JYseW/eWC9gZb9pdjVmZ0QO7BcJrbqWHXB5xON8obWtDU7sK5OotgAbqiIBnBQXKEqRWi3nRr52QiOVKL1Eg9bWNA4fGnMMaFByMpIliU9Ot2s7jYZBW0IVhdmIGX9xbzhT/W3JABtYLB5r1t+Md3ZXjjtkm40GCBSilHk8WGs7VtfPUqnUqGSF0Qjl9sESVGSxmMFxosvNLNGBWColpxTx2jRomX95ag0WLHpp9OEP2+7UcqJXvpcEnRjWYbb9Ry/WcaLXaU1Ft4ow4Qe8w472b6imk4WN6Ehz7o9Pr57u6NDQ98A9Lh3NR0ILDbXThV04ySOgse9Lo3KwqSEaFV4t6Z4/HMJ2cEntr0UXq89LMJUMgYNJht+Okr39PrSqFQRiT++tytX5SLigazaN25oiAZVU2e9CCVgkGMn/XGhNEG/vUEoxq/+1ESnv3ktKBNUYJRjRdvzkNLuwPVLVZoFDLeUOPm8Pe+L8fS6eNEBqDLbcEogxrrF+aAEIJSkxl//boUVc1WHK1sgUrB4PapiQhRy7G/rBn7y5oBAB8drsTS6eOwvqMwyoqCJMnx17XaRJW5AzlHDKe5nVoXvcDtZlFW34YWqwMmiwNgwRt1QOeW8NLpiVDKGChlBC//fCLa7S4Yg5WINwQhUk8T/ClC/CmMseFa3lPnzfl6s6hAyJodJ/DqrfmobrZCJZchSMEgJSoY/7prGsobzB2l/Y8B8OTRPTgrFRaHi89hstjF+U1rtp8Q5MJ5lys+U90Ku8sNjZLB0/85I3oGll2dhKpmK2JCVGi2OkSTzU2T4rHlu3IsnZ6IpIhglDdYBD3k3vi6DPf9eLzAMFs3PxvrPz0juBZSHjOGIXCz4I/lxrVy62EYfzEZEbrOwiiBbkA6nJuaXircbhYldW1otdrgdBOY7S7eqAOEelOtkGFFQTISjBowhECtlMHhcuGDQxcwJzcek8ca8e7SK+h1pVAoIxaGIbxBUdPimTNnpkWhosmCExdbBJE+oWo5xoQH49mFOYjUK2FzukRO1dWzM6BXyfHMghxog2RQK2W47Y3vRW2KykztuPOdg3zBlJgQlSCn/4mdp7AwP44/N9Cp39/65WQ0tzvgZoEGsxWR+iAszI/jq2LHGzXYuLsI989KQ0yIil8LlJna0Wp1YOn0REwYHYqx4VqMj9ZLtnVQygmeW5QLN1ikRun5NVSgrvlwmdupYdcD3G4WFxrNqGu1wWJ3oc3mRLnJAnjlNnFwyaQJRi3Cg5VQyAgMagVSImiBFIo0vVEYXfW3A8titEEDs92JBIMGLAvUtlrBAig1WXgvV1WzFa02JzZ1VKkiBF2WH/7zzRPQYnWhqrkddocLK949xHvvkiKkyydbnZ7XFubH4eEPjsOgUeL2qYnQqWRICNNCzhA8/JN0RHUUQPk/L0MV8IRp5MWHCsoUxxs8Dap74jHzDm/ldiYJAWpbbfi/bUdw/6w0fvcu0A1Ih2tT00uB281if1k9AAbVzTYwDEFNs3SugpsF4gxqROlUsLqcICwDlZKB203wwHUZ/DMxJpxeV4oYq9WK7777TvCaVBsBCmW401WFxvgwLVKj9ShvMEOjlMPpduOB94/C7mRx9zXJWP3xCaREBuO5RblwuNxQyBm0Wuz42WvfCqLLDBqlIH+fw+pwIzZUjWUzkrDtQAWfl/fU/CzcXZDMf8b3mOpmK/RqBY5UNCM4SCZqw7R6dgZ+edVYrNt1CjfmxQn67LVaXXjx82K8u/QKjAn3pKRw66WIYBUYAqRG66BRyhGlD0JcqAbljRZ8e94U0LYEw2Vup4ZdN1itTpyobkFNixVlDRZhqFphut/cpjCtAmONQYjUBw9Ji54ytOipwig1ifvbxYSo8IurElDdYuNLxScY1XhgVhqKatuQEhWMb87VCRKL7S63QPnKiHTO28mqFiRF6vDA+0dx+9REQVPRwuxYnK9v67Js8bgOw6+q2Yr3D1ZgyZQEwQ7gswtz8eP0KMkdy/gw8Y5lTw1gLrzVt4IWFy6ybtcppEbrhryCHq5w7TusDidK69v5prhc+W1/ejM8OAinq1vw0n9LJIvjUCj++O6773D3nz9EaFwS/xrt+0a5HCk1mbFu1yk+nwyAYE4bFxmMseFa7DpRjXW7TqEwOxZZo/S4uyP3fFZmjGAe9m0rs+qj4/yun2Qhs4Z2vPZVCZ8+0Wixo6yhHTLGf/68QsbgN3/ztEtYXpCEzXuFBVDWbD+BZVcnoczUzuf7eadoeEfnSK2Xxnb8f3/bEgyFXnX9HQM17PzgdLpxsqoZFU1WGLUKnK5pFYeq7TgpClVbUZCMWIMambE6hNDiKJQAU9NiFVSg5JqGttldWN9h1MWEqLA4P14UQrntQDkfNjEl0cjL87YDFfjN9ERRuOTq2Rn4x7dlfK87X+8dIdLVMB+fmwmdSoHnF+VCLiO8kr8xL05Uzer37x1G2vJpPTbYemoAc+Gtp6tbRN/JtR0ZitWsLgecTje+KTWhsrEdY41a3qgDOiuwvXhzHu5856BQb4aqERwkw8QEA9YvyhnSDWApQ5PQuCTa941y2WMyi/PJls9IRoPZxs9p3pWzX/y8GOtuzPI7D0u1lRkXEYz1n56RrDT59jdl/Fy6dHoiVHIZtuwvxxNzs9BosYtCPf8wOwN/2nWK/05/EUJWp+fzBeMj+WqZu45X+S2CJkV/2hIMhV51gRgDNewksLTb8W15Ew6VN8LNAmnROr+CWFTbhhUFyRgbroVWKUeoRo606BBagptySYjUqQT97VKjdbjvn0dw9zXJgiqZz3xyWuQNe2pBDs7WtCJYKUOj2c4bclXNVrz+9Xk8MTcLL/98ImpbrKhtteHl/xbjpknxSAjXQKXwyLOvJ06q115ruwOPfHgcT87Lwl+/OM9PDP7COjgDK5AhDlx4q+94ue+UMRiS1ayGO5Z2O74rb8LBDt2pUcokr3+jxc7rTbVSBqWMQMaA6k4KhULpBqWMkXRYblk6hf+Mb7VtrmKmv3nYt61MbKgac3JjwTDAUwtyYHO4UGoS5sJbHW7Ehqjx5461wmtfluDnVyYgPFiJZxbkwGxzotXmgEGrQGF2LP8dwUEyvxWQ1y/KRVZcKLLiQpE5So8xRm2XRdC86YwU6VtbgqHQqy4QY6CGnRdOpxvF9S04XtmGRz7sFKQ/35znN1TN6XYjzqDF+CgNEox66l2mXDLcbhbnTW28Qfbi58VYXpAEg0bp2SH7TOxV81bAZ2tasWlPMe68Ognr3zvC571xBplCRnDHW/sFMr5hdxHe/OUkPHJ9Gl7pCL3gJpTtRyqxenYG1mw/wffae2p+NkaFqrBz+TQwBCg1RYJhgDdumwST2T6g5YK5BHOp78xPCBuS1ayGK243i6oWM/adaxLoztdvy5e8/iq5DEatEufrzXhrnyeUZ+fyadSoo1AolG6w2MUthKwONyx2F/+3b7XtyiYLVhQkw+rw34qL+//HbshEVIgS46P0+P17HiNjRUESX4nb+7gIfRCWTEmAw8ViyZVj8Lt3DgpSIFYUJMPUZhdU8b7nmhQ8dF0qnvz3af61x+Zk4oqxBow2dEZp+CuCJmXkcDtdZ6pb+rzOGAq96gIxhhFv2HEWfnWzFTanCw4Xyy9MgM6Kg8sLkiXbF4wKVSEnVodgGnZJucSUmsxY9s4hgUGWEqWTrELFhRt6JyBzjURlDPi8N++GpOP8FEL5tqQR2w5ewAs/nYAGsx2v3ZqPqmYrmix2OJwuPL0gBxabE4kRWkxMCONbMuw4epEP98xPCMGSK8eInqFnF3ZfLrg/8eZSFUfXzc/GVYlG6oTpB973JFKnQkWTGUFymUh3rv/kDB6bk4lHvVrCrLkhA00WTyGqdbvODOmy0RQKhdIfLkXOlr8WSVH6znWo79z3j+/Kce/M8ahqapesihmmVWLltSnIGR2Cv351Ho9+fBybbp6AHcum4rzJDJYF78jljltVmI41209gcX48tuwvx+9+mMSvLbhInvFROvyuI+we8MwLz312Fi/9PA/Lrk6C1ekGywIvfl6E/ITJgmvTGyOH2+kyaJSi8NGezi9DoVddIMYwog07u92Ffx2vEvRTeuyGDD6JlKPM1A6NQg4ZseHpBTlotzsRFaLC6FAVxoTr6AKRMiBwSs7bIIsJUeGB61L9hhsCEJQDVikYpEVLN+YO0yj97krfPysNmaNC8e15E/53zoRXvxR77nYun8Y/C6Ums6DSpacnTSl+P3M8Ni+ZCJvTjbFGLRIjui4uJBVv/uzCXCQY1ahobEdMiBoZMXq/Oz3DqUTxcMHtZvHv49W8J5cz1vQqhUgO95c1Y8kVLF67dRIqGz19FN/6ugQzM2KQERuCf/zqCppLR6FQLksuVc5WT3qq+et3V9FkganNjldvyUd1ixXVzVZs7EjJ4NYK6bGhyIgLxbGKZiRFBCM4SI6fvvIt396AawDeanWgzNSOjXuK8NyiXJR4FVPj1ilPzc+SXJ+Y2ux45pOzgtd9DbbeGDne6yPv9JBpSeGYNCasR9d7KPSqC8QYRqxh53S68b+SelE/pUc/PsH36OBQKRgEyRmkjwpBk8WB8VE6ZIyiuSCUgUVKyTVa7BgbLh1uWJAaiQmjQ3G6uhUAMH9iHFgWeO2rc3hiXhYe9nJorCpMh5t1455rUvDcZ2cFr6dG6TAh3gCGIYjSq7D9SKXII7ZufrZA8Uh52vaXNWNfSQPm5sb2OKRAKt789+8d5p9RrljL3JzYLo274VCieLhQUtfGG3WA556s/vgE3vrlZEk5VCoUaLM60GZzgmWB265KhDFYiUkJYVSHUiiUy5ZLlbPVU4el79zndrM4WdUq6gEHeHT1g7NSYXW6BWGTCUYt8uJDoVIwAqcy11Cc+13tDhe27r8gWhvEGtSS80Jdq00wVimDrTdGjvf6iBunSsHgxgmxPTaih4IjOBBjGDGGne92eGu7A4cuNEl6EhLDg3kBUSkYPDEvCzq1DEZNEK4aF049y5RBwZ+SS4/Wi15/cl4WQtQKZMSEoL1jMvE2wq5Lj0ZuXCjf64brJ/foxycEHrnNe8/hjds6wyPGGLW4f1YaX2pZxgD5CWGi0EZ/nrbe5rb5C8Vws53//8iHx5EcGYyc0Ya+XlpKN3jrz1arU/Ke1LXasOaGDKz2am3wxNwsjA5Tg2VZRIeoYLG76A4dhUIZEVzKnK2+OCylDM0Nu4vw5i88Trmvz5kELY2sDjce+uAY/nXXNNEag8vjBzxze02LFYXZsZB35NQDnnVAvEEjOvbZhbkIUhDBOlvKYPM2cmparNAoZbC73Cg1mUVzSKB224aCI7i/YxgRhp3UdvjTC3L89tuob7PiuUW5cLEsjFolYvRBiDfSfnSUwaUrT86sjGiMv2saTlW34GxNK57+zxm+D9jMtChBo2/umHGRnn43HG43i/tnpfUsvCNa16U3KVC5bf4MRC5fEOhsfpozug8XldItvvrz9VulC6Jog+T46PAF/GXJRDRZHIjSq5Ado4NGrRzE0VMoFA6X04EjR46IXp88eTJUKlonINAMhZwtb/wZmixYZMWG4kJDu7TTrs0qMLAcLharPjrGh2+uX5SL9Bgdqluk1wRS6xYAkusSX7giaKerW/GLv37vN6R1KOy2DRVGhGEn5aUgBJIhZU/Oy4JBI0eYNggZMTTckjK08OfJYRgCQoB73zsiUMwrtx7Gzo6wj+68P30N7+jPubpDykDk8gU5VAoG0SF0UXKp8NWfDjcrKoKzoiAZBrUCt09NgsXuROaoEIwNH5mTKoUyVGmtLsVL562ILpPxrzVVFON5ANOnTx+0cV2uDIWcLW+6MjQZhiAtRjr/nnufm/fdbhZv3DZZNLePCZdeE/hbM/R0V6qnIa1DYbdtKDAiDDspL8Xm/57DnVcn48XPi/iQsgmjQ/GDxHAolTI/Z6JQhi6BCPsIpGIMxLl8DcRwbRBOVbeg0WIHAD7HLiMmpN/jpUjjK1cv7inCL34wFkunJ8LNelplxBnUNO+YQhkG6GISBU3ce7qLZ7Va8d1333X7OUonQ20XqTtDc2x4zwzRgTaghkIbguHEiDDspLwUZ2vbcMVYA8ZHTUB1sxXRISq6Q0cZ1gy1sI9A4TuJjDFqER+moc/tAOErV0crW/D3b0vxwHXpMLXZ6D2gUIYxPd3F++6773D3nz9EaFxSl5+jCBlKu0jdGZpDzRDluFzXNpeKEWHY+fNSjDZokWAkNDeHclkw1MI+LhVyOYOc0Qb63A4QUnL1y6njkNdRKZVCoQxvfHfx/BEal9Sjz1GGLt0ZmkPJEOUYKWubQDEiDLuh6oWgUAIJlXPKpYDKFYVCkcJfGKfNZgMhBEqlsHDSYIVt0jDS4Q2dg3rHiDDsgKHphaBQAg2Vc8qlgMoVhULxRSqMEwAqDn4Buc6I6OQs/rXBDNukYaTDHzoH9ZwRY9hRKBQKhUKhULpGaifuyJEjYFlxHq1UGGdTRREUoaO6Ddvs705ab46nYaSUkQI17CgUCoVCoQx7miqKBX+31VZC3m5F7VltQF4bLufs7/dUHf0fnvq6BaGfHeNfqy85AUNiNgghXR7r7/WmimIcOeISfO7IkSN4bsun0Bqj+dfMpmrcs/ha5OTkoDt6evyRI0fQVHFecKxHVjK7/Q4KZbhBWO9Ov5cJhJA6AGXdfjBwhAOoH8Dvo2PomoEcRz3LsrN6c0AP5XOoXMu+MJzHDlxe479U8tnVdw5XhvtvGI7j75V8DsLcPpgMx/vZH4bi7+2vfA72bxrM7x/Jv30gvt+vbF6Wht1AQwjZz7JsPh3D4I9hKI2jPwzn3zCcxw7Q8Q+X7ww0w/03DPfxU4SMtPt5Of7ewf5Ng/n9I/m3D/b308ZDFAqFQqFQKBQKhTLMoYYdhUKhUCgUCoVCoQxzqGEXGDYP9gBAx+DNUBlHfxjOv2E4jx2g4x8u3xlohvtvGO7jpwgZaffzcvy9g/2bBvP7R/JvH9Tvpzl2FAqFQqFQKBQKhTLMoTt2FAqFQqFQKBQKhTLMoYYdhUKhUCgUCoVCoQxzqGFHoVAoFAqFQqFQKMOcy9KwmzVrFguA/qP/BuJfr6HySf8N4L9eQ+WT/hvAf72Cyib9N8D/egWVT/pvAP/55bI07OrrB7PZPIXSNVQ+KUMZKp+UoQqVTcpQhsonZShwWRp2FAqFQqFQKBQKhTKSoIYdhUKhUCgUCoVCoQxz5IM9gOGC282i1GRGTYsVUXoVxhi1YBgy2MOiUKhsUoYFVE4pAw2VOQqFMtKghl0PcLtZ7DpRjZVbD8PqcEOlYLB+US5mZUTTSYIyqFDZpAwHqJxSBhoqcxQKZSRCQzF7QKnJzE8OAGB1uLFy62GUmsyDPDLKSIfKJmU4QOWUMtBQmaNQKCMRatj1gJoWKz85cFgdbtS2WgdpRBSKByqblOEAlVPKQENljkKhjEQGzLAjhMwihJwhhBQTQh6QeH86IeQgIcRJCFng8148IeQTQsgpQshJQsiYgRo3AETpVVAphJdKpWAQqVMN5DAoFBFUNinDASqnlIGGyhyFQhmJDIhhRwiRAXgRwHUA0gH8lBCS7vOxcgC3AXhH4hRvAXiaZdk0AJMB1F660YoZY9Ri/aJcfpLgYvXHGLUDOQwKRQSVTcpwgMopZaChMkehUEYiA1U8ZTKAYpZlSwCAEPIugDkATnIfYFm2tOM9QexEhwEoZ1n2047PtQ3QmHkYhmBWRjRSl09DbasVkTpaXYsyNKCySRkOUDmlDDRU5igUykhkoAy7WAAXvP6uAHBFD49NAdBECHkfwFgAnwF4gGVZV2CH2DUMQ5AYEYzEiOCB/FoKpVuobFKGA1ROKQMNlTkKhTLSGA7FU+QApgG4F8AkAInwhGwKIIQsJYTsJ4Tsr6urG9gRUijdQOWTMpSh8kkZqlDZpAxlqHxShhoDZdhVAhjt9Xdcx2s9oQLAYZZlS1iWdQL4EECe74dYlt3Msmw+y7L5ERER/R0vhRJQqHxShjJUPilDFSqblKEMlU/KUGOgDLvvASQTQsYSQpQAbgLwcS+ODSWEcE/MDHjl5lEoFAqFQqFQKBTKSGdADLuOnbZlAP4D4BSArSzLniCEPEYIuQEACCGTCCEVABYC+Ash5ETHsS54wjB3E0KOASAAXhmIcVMoFAqFQqFQKBTKcGCgiqeAZdmdAHb6vPao1/9/D0+IptSxnwLIvqQDpFAoFAqFQqFQKJRhynAonkKhUCgUCoVCoVAolC6ghh2FQqFQKBQKhUKhDHOoYUehUCgUCoVCoVAowxxq2FEoFAqFQqFQKBTKMIcadhQKhUKhUCgUCoUyzKGGHYVCoVAoFAqFQqEMc6hhR6FQKBQKhUKhUCjDHGrYUSgUCoVCoVAoFMowhxp2FAqFQqFQKBQKhTLMoYYdhUKhUCgUCoVCoQxzqGFHoVAoFAqFQqFQKMMcathRKBQKhUKhUCgUyjBHPtgDoFD6gtvNotRkRk2LFVF6FcYYtWAYMtjDGvHQ+0KhDC/oM0vpL1SGKJShAzXsKMMOt5vFrhPVWLn1MKwON1QKBusX5WJWRjSdTAYRel8olOEFfWYp/YXKEIUytKChmJRhR6nJzE8iAGB1uLFy62GUmsyDPLKRDb0vFMrwgj6zlP5CZYhCGVpQw+4S43azKKlrw75z9Sipa4PbzQ72kIY9NS1WfhLhsDrcqG21DtKIKID/+1LTQu8Lpf9QXRp4RroupTLVf0a6DFEoQw0ainkJoSEKl4YovQoqBSOYTFQKBpE61SCOiuLvvjhcLNxulso8pc9QXXppGMm6lMpUYBjJMkShDEXojt0lhIYoXBrGGLVYvygXKoVHfLkJeYxRO8gjG9mMMWqxbn624L4sn5GMVR8dozJP6RdUl14aRrIupTIVGEayDFEoQ5EB27EjhMwCsAGADMCrLMv+yef96QCeB5AN4CaWZf/p874ewEkAH7Isu2xABt1PugpRSIwIFn2eVpbqGQxDMCsjGqnLp6G21YpIHb1WveVSyBrDEIwKVeH2qYkgBGBZ4O1vylDVbPUr8xRKT+ipLqU6tHeMZF1KZSowjGQZolCGIgNi2BFCZABeBHAtgAoA3xNCPmZZ9qTXx8oB3AbgXj+nWQtg76UcZ6DpTYgCDQvpHQxDkBgRTI2FPnApZc2oDcJrX5XQsBxKQOmJLqU6tG+MVF1KZSpwjFQZolCGIr0OxSSEBBFCbiaEPEQIeZT7181hkwEUsyxbwrKsHcC7AOZ4f4Bl2VKWZY8CcPseTAiZCCAKwCe9He9g0psQBRoWQhkoLqWs0bAcyqWgJ3JFdSilN1CZolAolyN92bH7CEAzgAMAbD08JhbABa+/KwBc0ZMDCSEMgGcB/BzANV18bimApQAQHx/fw2FdWnoTotDbsE3K8GIoyeellDUaljM8GUryKUVP5Irq0MuTSyWbVKYogWCo607KyKMvhl0cy7KzAj4S//wOwE6WZSsI8b84ZFl2M4DNAJCfnx/wmsV9jbPvaYgCrSx1eXOp5dOXruT1UssaDcsZfgxV/elNd3JFdejlSX9kszu5ozJF6S8DPbcPZZbd9xAq61sEr8WG67Hp6ScHaUQjk74Ydl8TQrJYlj3Wi2MqAYz2+juu47WecCWAaYSQ3wEIBqAkhLSxLPtAL76/XwxEnD0XFuL7HTSEjdJbupNXKmuUgWSg8pSoXFO8CYTcUZmiUHpOZX0Lgq78mfC1fX8fpNGMXPpi2E0FcBsh5Dw8oZgEAMuybHYXx3wPIJkQMhYeg+4mADf35MtYluWlhBByG4D8gTTqAP9x9qnLpwVsV4KGsFECRXfySmWNMpAMhP4EqA6lCAmE3FGZolAow42+GHbX9fYAlmWdhJBlAP4DT7uD11mWPUEIeQzAfpZlPyaETALwAQADgNmEkDUsy2b0YXwBZ6Di7GkIGyUQ9EReqaxRBoqBzFOick3hCJTcUZmiUCjDiV4bdizLlgEAISQSQI8DzVmW3Qlgp89rj3r9//fwhGh2dY6/Avhrz0cbGGicPWU4QeWVMpSg8kgZDKjcUSiUkUhf2h3cQAgpAnAewH8BlAL4d4DHNaQYDiXc3W4WJXVt2HeuHiV1bXC7R3QO74gmEPJK5YkSKIaD/uSgcn/5MBhyR+WHQqEMNn0JxVwLYAqAz1iWnUAIuRqeVgSXLYMZZ9+TanLDuYlqIKrlUYT0V177Ik+BvI9UJi4vhrr+9P7scNWjHPTZ6eRSyZ2/azzc5YfKDoVyedAXw87BsqyJEMIQQhiWZT8nhDwf6IENNQYjzr6nE8VAFScINMN9IhzK9EdeeytPgbyPVCYuT4ay/uQYrnqUgz47YgItd11d4+EsP1R2KJTLh16HYgJoIoQEA/gSwN8JIRsAmAM7LArgf6FRahJe7q6SxIcyPf19lIGlt/IUyPtIZYISKHorS8NVj3LQZ+fS09U1Hs7yQ2WHQrl86IthNweABcDdAHYBOAdgdgDHROmgpxMFlyTuzXBIEh/OE+HlTG/lKZD3kcoEJVD0VpaGqx7loM/Opaerazyc5YfKDoVy+dBrw45lWTM8zcZ/xLLsmwBeBWAP9MAoPV9oDKfiBN4M54nwcqa38hTI+0hlghIoeitLw1WPctBn59LT1TUezvJDZYdCuXzodY4dIeRXAJYCCAMwDkAsgJcBFAR2aCMbt5sFQ4An52XhoQ+OCeLefSeKodJEtbfJ19xE6BvXPxwmwoFiMBLaeytPgbyPUudaNz8b8QZNf38WZYTAPTMmsw3r5mfj/m1HeySXQ0WP+qM7XUD16aWjJzI12PLTn7mCyg5l2X0PobK+RfBabLgem55+cpBGROkrfSmecieAyQC+BQCWZYs6etpReklPqmsZNEosnZ6IlCgd0qL1GBsurawHu4lqX5KvB3siHOpc6oT2rhYCvZGnQN5HhiGYmRaFzUvysb+sAS43sP7TM1DIGJrITxHhK8PxBg0+OVXDPzMJRjU2L8mHQkZ6tNgdbD3qj57oAqpPA0N/ZGqw5Ke/cwWVHUplfQuCrvyZ8LV9fx+k0VD6Q18MOxvLsnZCPA88IUQOgDZr6SVSinjTzRMw1hiM+jYb/3pVsxUbdxdDpWCwc/m0Iato+1oRbKgupIYCl7LKWqCNxp7ex554lcsbLVj69n5BzsdwqS5HGTikZPjVW/MFz0yZqR1L396PncNcdnqqC6g+7R++MpVgVONPN2YPeZkKxFxBZYdCuTzoS/GU/xJCHgKgJoRcC+A9ANsDO6zLH19FbNAoUVTThp+88CW+LK4fdonMNPk68FzKazoYVdC4RdP1G7/ET1/5Ftdv/BK7TlSLmvhSWaL0BCkdWt18ecoOfSYGBm+ZiglRYXF+PPaVmIb8tafyQaFQOPpi2D0AoA7AMQC/BrATwCOBHNRQxu1mUVLXhn3n6lFS1yZalPYUX0V8Y14cNuwu4l/rSyJzoMbWF2jydeDxd02j9SrJ+9yb+z8YC4GeGpNUli5/AqGrpHTohQZLn2VnMPVnd9Bnou/0VS/emBeHjXuK4GZ7Nx8PhhxR+aBQKBy9DsVkWdYN4JWOfyOKQIavcYqYm0QIAf//2w5UYPmMZGzcU8SHg6ydk4WaFs+iWyp8bbAbjNLk68AjdU033TwBJ6taRdd5ZloUviiqxdGKZrhZQEaArLgQzBgfJXn/feUPuPQLga6MSe/wHypLlzeB0lVSOnTr/k7dadAosTA/DkkRwWDZjoJUfs4/2PqzO+gz0Td6e1+9ZYqbk73nY06mUiJ1kjI1WHJE5YNCoXD0pSpmIYC1ABI6jicAWJZl9QEe26DhLw8okDlPvopYRsBPKFXNVrz9TRmWTk/E5DEGmMwOPufI30ThG0JyY14cTle3IDZUjazYkCFXSZHSPVLXlGWBn7zwpUgG//mbK1FU04bNe0t4OVlRkIykiGCMCRfLZqAWAt3lzHm/r1HKkWBUo8zUzr8vZUxSWRq+9CSHMlB6VEqHNlrsePubMqwoSIZercDaHSd7tMD2N6b0FdPgZjGgVWmloM9E7+DksK7V1itZ85YpwKOfuPm4JzLFyZFBo8SNeXEgBDhT3YL0GJ2kHg4UVD4oFApHX4qnPA/gRgDHWJYdOrEqAaIrj1tPdxy8z8UtcmJCVHC5wTcyHWPUChRxtF6F8dF6/nsbLXakRusRE6LBHW+JF/K+ExM3tpgQFZZMSeB3+zbvLRkwzzNNvg48vtd03znp/Msmi0MQymt1uLFhdxHy4g2SC4pALAS6805Lvf/43Ey8sKcIZab2Lo1J7nePMWpRajLj2/OmQV1YU7qnp7sVPdGjUpUJyxstIgPLnw5ts7lEz0NXC3qpMRk0ShwsbxK1mxmsXTyqX3uGtxzeMS2xS1lzu1mUN5hR02KD2e5EQpgWM9OisHP5NDSYbUiODMb9246iqtmKdkf3MlXTYoVBoxTMwSoFgwSjFvFhl1Z3UfmgUChA3wy7CwCOX45GHdC1N7mn4WvcZMEtCgwaJW65MoGfFLjeXD/JjBEo4vgwrWih/e15/4nb3gqcGxuXF9AXb/hg9Eyj9A4pGUwwqtFidUrKicXu9Huu7hYC3clDdzsvpSYz1u06hdunJqKjiC5e2FOEZxfmAmARpg3qUsaGengcRUhPd+K606NOpxv/Ol7F9wpLMKqx7OpkrProuKQcSOnQszWtvXLCSY1pYX4cb9R19Xu6gurUgcdXDv3JmtPpxmena3C+3iyYm5+cl4W8+FDkxYchLx7Iig1BbasVFrurW5mK0quwMF88Bz/0wTHkjg6lczCFQrnk9KV4yv8B2EkIeZAQspL7F+iBDRZdeZO5MA0uSVlqx4FbjP77eDW/KPAtjGJ1uHH/tqP4usQkKHzBKXTv3ZOeJkVzY5Mx8Dv+ruhpxULK4CIlg2vnZKHcZJaUk/iwvuVYSMnDnjM1OFfrKQpwrrYNjWY77piWiGUzkhAT4pFHb1kzmW1YnB+P174qwaY9xXj1yxIszo/HwbIG1Lbau120DEblTkrf6WlBnq70qNvN4usSE2/UAUBhdixv1HHn9JYD72IVpSYzxhi1SInS9aqYhNSYUiJ1/SowRHXq4OAth1x+nK+sxRs0+KbUhJNVLaK5+aEPjuHfx6vxVXE9vj1vAgBMHmPEGKO2W5kaY9T2WW6ovFAolEDQlx27JwC0AVABUAZ2OINPV97knoSvcbsUy65OliyMwmF1uLG/rAFxBjXGGLV+dyZ6mgvFjS02VM3nWfmOvyt64m2n3sTBR0oGa1qseHNfGe65JgXPfXZW4HkeG943w85fO45l7xwS5PBtO1CBRosdy2ck4+1vytBosfOyppQxIs/1xj1FeGpBTo92Pnoa+kzlcmjQ04iGrvRoSV0b9pc1CM7hT39yzjYp3TkzLapXOaT+8ln7U2DIn04df9c0EDL4eXuXK95y6J2vPmF0KBKMWowxalHeYEZ1sxVuVlq2IoKDRHntPZEphiFIi9H3SW56uuNN9R2FQumKvhh2o1iWzeztQYSQWQA2AJABeJVl2T/5vD8dnvy9bAA3sSz7z47XcwG8BEAPwAXgCZZlt/Rh3D2iO0Oqu/C1mhYrCrNjUdFoESh3KUXP5dwB6FKh9zQXimEIsmJD+lQUo7tFNA2LGzpIyWCjxY6/fl3Khz0yBMiLD+3zvemuHQeXw3f71ES8+HkxNu4pwtLpiUiN1vOy5i90qbTe3GVYHEdPDAUql0OH3hTk8adHa1qsfHl53/suJQf+FsM7l0/rdQ6p75jcbrZfBYb86dRT1S24970jVF4vEb5yyOWr/zAlkr/GNS02XGiwCIqWcagUDMobLX2WqbHhfStM1dPcU6rvKBRKV/TFsNtJCJnJsuwnPT2AECID8CKAawFUAPieEPIxy7InvT5WDuA2APf6HG4BcAvLskWEkFEADhBC/sOybFMfxt4t/S0qEaVXQcYAn5+uxfpFuThd3QKNUoaV16Zg/aeduynLZyRjy/5yzM+L7Tb8szfeuZ6MX8rj190iOpAVQSmBxXsh8+LnxZ3hRv0Iw9QoZVhekAQ36wln8rdrwuXOWR1uTBgdKlg8+ZMpm9OTN6VWyLDvXL3fapoMAZ6clyUqXuG9QKJyOXQIREGeKL0K35yrw6ab83C0ogluFvjmXB1WF2ZgzY4TvBysm5/dbQ4yJyd9zQb393sAoKSurVud7E/+vfP/qLwGnp7IodnuxOena7H8mmSsKkwXVLp8tDAdL+wpFpyzNzLV0+fAdx6O1HXvyKL6jkKhdEdfDLvfAriXEGID4EDP2h1MBlDMsmwJABBC3gUwBwBv2LEsW9rxnmCWZln2rNf/XySE1AKIANDUh7H3iP5Ulxpj1OKqRCPUCpnAq/bEvEy89cvJ+Kq4Hi43sGV/Oe6flQaGAAwhkgo9XBuE7Ucv8vkmPfXOdTV+KY/fuvnZuC49uksvY28rglIGjkBVuCw1mWEy23CxySqQueUzksH48WxzCxyu8pv3d8YbNFg3P1t0rj2nq3HXjGQs3vyNpFx7y6hBo8TS6YlIidIhLVqPseHC76ByObTob2W+eIMGN01OwLJ3DvKysaowHZ+cqMLS6YmIC1UjOkSNqxKNghxkX7mMCFYFZGdDahdPSn+OClXB6FMMSGoH88l5WXj6P2cE30HlNfB0J4djjVpclxWDZe8c4nXM2HAtdEFyFNe1odFiF3y+tzLVk8JUvud6fnEunl2Yi9+/53+nj+o7ykBy7OgRzPvFMtHrJWdPITElTfT6iVNnkHflQIyM0hV9aVCu6+p9QkgGy7InfF6OhaeaJkcFgCt6+92EkMnw5PWd6+2xAwXDEIQHB4nC1h7+4Dh2LJuKubmxqG214sYJsThvasOsDV/CoFFiRUGyoDLX+kW5qGuzCYoIdOed62sPqfu3HYVBo8TMtCj8665pKG8wQ6OUI0ofxB83GA2tRyp9yaHoz4Lae5Fx+9REvPZViUA+Nu4pwj9/cyXijVrBQmRFQTLe2lfmt4jQJ6dqsP7TM7h9aiLUCgYTEwyQywh+kGTkjTruO7zl+ny9sJqmmwWe/s9pvH7rZNF1oHJ5eVHeaMEjHwoLpazdcRJLpydijFGLvPhQQdn4eIMGj8/N5I/hWmoQsH7z28ZF+n9G+lIJ9v5tR/nnxnehnx6jw5u/mAyL3Yn4MC1kDCSNBiqvA4ubBT/fVjVbsXG3J9Jhy9IpcAOiXbz1i3LBEOmUCV+Z6us8fPeWw3jjtknYsWwqLjRaRHMwQPUdZWCxszIEXfkz0euNRx6UfN165MGBGBalG/qyY9cdbwPIC/RJCSExHee+lWVZt8T7SwEsBYD4+PhAf32vqGuzSXrViuvakBKlw+QxRpSazHwhiqpmK97aJ0zwZgjw/qHKbvPevPvknaxq7XMPqf1lDYgNVeNMjfQ5epM/Q5O7xfRUPgcjh8J7keEv5LLN5sTMtChsWToFVc1WxOhVCNUqMCE+1G8RIe6cL37uCWtSKRjsXD6tW6/zxWYLbp6cICgEc881KahqtogW5f1ttE5l1cNQ0Z/+ZGPCaI9BV9VR8IK7T+WNFrywp4h3ArCsp6XGIz9JlzxPeYMZ4yKDJe87gG6fPX/j454bzkERqKIuVD4vjWzWtvrTQTY88a+TsDtZQb5yeowOJR25wb7HlDeY+WI4/Z2H95WYkJ8Qht/+/aDk8T3Rd1RmBpahojspFI5LYdhJaZBKAKO9/o7reK1nJyRED+BfAB5mWfYbqc+wLLsZwGYAyM/PF0XAD6Sy8+dVO1bZgru3HMb6RbmI0CkF73New3eXXoHEiGDsO1fvt4hApE7FGwDrdp1CYXYs0qJ1uPefR0TexORlU9Hc7kB1i2cxHhMiPTaXGyhv6Dp+vyfhfr49qKTC7EbipNOdfHL0N4fC6XTjRFWzx/gKUSMjRg+5vOuuJr6LDCn5YAjp8r76YjLbsKIgGXEGDSw2J9psTthdbpSa2qBXKbG6MA3hOhXO15thd7mx/Ugl73VWyWW8Ucddg+c+O4u3fzlZ9D39CUOlhQg6GSr605/ulDEMfvLCl6L7VNNiRZmpnXcedB4jkzyPRimH281iz5kaHK1ohpsFZATIigvBWGOw5LMX9otJaLI4MCpUjWg/4+NCkjkHBSC9u9NdAY6eOOvSY3Soah45+rMnurO38ul/jm7G4vx4vP1NmUCmrhpnhFYp9yObBB8eroSbBVKjpOfhuF9PgdXuRr3ZhthQDUaF+p+HvavC+ur/7vRdT3TaSJ2DLxU9ndsplIHiUhh2UoL9PYBkQshYeAy6mwDc3JOTEUKUAD4A8BZXKbO3DPQCTsqrxpWD5xT1a7fmY0VBEj4/XYtpKZEgxLPAiAlRoaSuDe0OF/RBMjx8fRrq2mz8AiQtRs8XVHn9q3NYOn0c1u44iTumJUp6AE9WtQgW44/NycSffzYBv/v7IcHYtuwvx/Tk8C53UnqSN+Dbg6onXuyRuJD2R39yKJxONz48UikKS5ubEwu5nPE7oUfpVUgwqrFw4miMNqixdk6moBn0ioJkHChrFIUXr9x6GLFLpyArNlRktDvdLLRKGe77Z2f1v4euS0VRjRnvfHcSi/PjBe89PjcT8QYNAKCp3eHXm15S1yZaiPQ1DJUWIugZA6k/pXTnqsJ0rPpI2Ch83a5TiA1Vod3hwoqCJGzdXwHAU71VxgD6IDlfsMqgUWJhfhziwzRQK2W40GhGUU0b3xaGK5ghI0RS7r4+Z+JD9Z6Yl4VNN08QtP3gdDvQ6Xjr7jmWklff67y8IEnQuoaTz6XTE/nxUP3ZN/nsao5WyglW/SQNJ6tbAXiK96gVMrRYHXi0MB1/2XsOhdmxkDFATlwoqpvb+fu0vCBJ8r6X1FnwwPud8/DaLubhwuxY0fHe+r8rfXe+3n+LDW6nms7BFMrlzaUw7ESwLOskhCwD8B942h28zrLsCULIYwD2syz7MSFkEjwGnAHAbELIGpZlMwAsAjAdgJEQclvHKW9jWfZwT7+/Lwu4/ni1GIYgPUaHpdMTERuqRnlDO97+pgxVzVb++/eVNGD7kUr85odJWLO9s9rb6DANXthThDJTOxKMavzuR0mCBcizC3MBeHZEfjl1HP+71ApG0gN4sald8Lsf/eg4/n77Fdi8JB/7yxoEhVyi9EFQKRgYNErcmBfHG5vRenH8vtT1KTWZRT2ouO/tSVuHS8Fw8072J4fiRFWzKD/pkQ+PIzkyGBkxIaIdt3XzsxGmVUCjkOPughQ82FF9MsGoxvOLc8EQArvTjYomCwxqpeR9LTVZ0GZzYlJ8GD47UyvKwTNolKhq9ixy6812bN5bgtunJor62z3y4XGMNmgQoQtCiFoheQ1OVrXi9+8dCdhChBYi6BkDqT+9daebBVRyBnqVHGWmdv4zMSEqLM6PFxTfWXltCoJkDP646zSsDjc27y3B0wuy8dB1qZDJGEG+1JPzsvDu9+WC3/OXveew5oZMv7so3Oce/uAYti6dwocUO1wsVn10DFXNVqgUDDbdPAEsC4HByel972gLqWvje5399Vhze+0ODoQjYqjrUH/ymb5iGtysdL9ATs6eWZCD0zWtYFnwxvni/His9GpH8dicTCx/9xDKTO3ITwjBXTOSBc4zbz0XJJeeh6uahfPwqo+O491fTRHNwyuvHY/1n3qK68SEqHhHhVrh2Wn2d925e1RU2yopM1wI8lB0Zg11+aJQhhuXwrCzS73IsuxOADt9XnvU6/+/hydE0/e4vwH4W38G1NsFXCC8Wlxo5bIZSYJiFEBn6E5hdixv1HFjeuTD43xvsMLsWKz+WPj+7987jLSYaVDKGJyubuHfYwgRFWBZUZAsGpfV4UZ1ixU/To9GiFqOqmYrZqZHISNGD4Yh2HTzBBTVtAnOMz5aLyhY4O/6GDQKv+GjaoVMUOa7p/ehPwq/v/dxMCac/uSMcQaUN1aHGw1mm+ROKlf0QcZAsDNQZmrH3VsO48Wb8/iwohUFSZL39WxNG+7fVoKXfz5RtGDw7nMHdC5U/eXxfVlcj1e/LMHzi3Ml24N473hLLUR6G4ZKCxH0jIHWn5zujAlRYcmUBBCfqsE35sWJHAPrPz2LpdMTBa/d98+j+NvtV+Dnr30reP2hD44J5BJAh649juUzkvlzc7uFm7xK31sdnpzoa2ND+Wuz8aY8OFwuhAcH4WRVqyBklCsw1Gixe1qQGDR+r43Ude4q7LO7++B7T/qiy/pzLwdKf0pdN4NGiYPlTaJWKd7jrmq24nRNK179slP33Xl1kki2Hv2oc06+IjFC5Dzz1nO9mYcvNrfDoFGiIDUKDpcL8/NiEW/QQCFjsG7XKSzOj+fHsnmvuDCP93Xm7tHTC3IkZUbZEbERKGdWoO7tYO0gUmOScjnTdfKNBISQHxBCtB3//3NCyHpCSAL3PsuyUwI5wEDALeC86WoB58+rVWoyiz7rdrMoqWvDvnP1KKlrg7vDncp957YDFVhVmM5/P7dIff+g/95gqdE6PHDdeATJGcn3a1qsaDDbeSMKAMx2F97aV4bbpyZi2Ywk3D41EW/tK0O7wyX63dF6FT45VYPFm7/Bb/52EIs378Mnp2oAAGONwZIhd96/3d/10ank0AfJRL/3iXlZWP7uIRy/2NLj+8Ap/Os3fomfvvItrt/4JXadqOavb0/ozX28FN/fF7gcip3Lp+HdpVfwOTk9mXTiDGosL0jCshmef1w+pU6l9LuTylWdlHrvSEUT//rW/RVYUZAsKccGjRJtVifumJbIf6/3+Tm4ZsDc8d5wC1arw1MdLis2BEunJ2Ld/CzcPjVRtOPN7QBzcGGo3jL94ZFKlJnEzyYHZ0R7j6k3hVdGCpdCf/rTm97fxxlwFY0WLJ/RKXsyRlpekyM9etNb/ur9FLKS+cx8Msbj0Hj7G6EONVsdvNxxvzsmRCXQDYs370Ntq8efKeXceH5xLv8clzdaJK+NpxKxjH9+Y0JU2HZA/MytKPA8cz25D97Xuq+6rK86dCD1p5R8LsyP440673GfrzfzcqdRyrH9SCUeuyGjW9nqbk7m9Fxv5mG3GwL5GWPUQi5nMCsjGhtvmiAyMP1dd+971GixiebfFQXJOFHZjF0nqvleeb5j6Y0zK5D3tj9zdF8ZrLmdQhko+rJj9xKAHEJIDoDfA3gVwFsAfhjIgQWS3u6C9NSr1ZW3yfs7W60OrChIxvgoHerabKhvs/HnkPKuFdW2Qq2QIS1aJx0axLJwuVlsP1LJl2UGPGW0vb3QKgWDnLhQ/hxcWIkxWImf+XixuV0Q/9XCOn+7Pw/p2Zo2rP+siO8LFB+mQZRehQ2fnUGZqR3bDlSIPOL+7kMgQkb6450czJCVvuSMud0sykztgrBdT/ESNexOt9+dVJYFNErp8CGX16XjKrc+syAHZruTDy8GgCVTEvidPe/dtUaLHZw9qlIwMGqVuOeaFLzzXZlIDrzzlLhxzM2NRV2bTbBrzZ3LdyHiLwz1mQU5WPaPQ5Ke4ED0/xsJBFp/duel577POyKBEGDN7AxoguSQy6T7fnJ68zfTE/Hy3hI0WuyICVFLfjZ3tFAvpkXrO0LmrIIqrs91GP7c5x6fmwmdSoGVW8XtOt78xWTJ382C7VZ3+u4ucflWyVHB2Lp0Cj47XQuljIFGIePbJfTUEdEfXdZXHTqQ+lNKPlMidZLjPlXdgns7wiwTjGrcNSMZerUCK69JRkyoBlqldNGd7uZkTmXISM/m4dWzM7B57znJa8MwBBa7q8fXnbtHMSEqtNvdeOe783wkRlq0Hq3tdjy3uxiNFjt2rZjWryrCQGDv7WCEww/FcFQKJZD0xbBzsizLEkLmANjEsuxrhJDbAz2wQNLbBVxPQ7S8FQQXD3+6ugWxoWpkxYYIvvNQeRN+59V0l2vUvHZOJjZ9XsQnY6dF6/HSF8U4W9uGldck4/G5WXjkw84J/4l5WSita4ObBW6aFA+z1YGl0xOhUcrwxLxMlJksfKGVMI0Sf9tXimcW5IB05MoZg5U4Xd0qyKMDgG0HKlDbau3Rb5f6jLeH1Lsv0Fu/nIz9Zc0APMYB5xHPjtUjOUrn9z4EQuH3J9RuuOVflZrMfGNboHO3YMeyqWAYgu1HKiWNqV3HqzA/Lw4rCpLx7vflgqIAL31RJPiORosdp6pbQQj48GKp0KWNe4qwdHoigoPkCFXL8eYvPVUFw7RKOJwu/H5mKuparHhmQQ6UcgbHLzYLduS87xFDIGpyLrUQ8ReGarY7+f+Xmrz721B7JBBo/Sm1sOKKoVjsLkTpVZiZFoXwYCU+OlwJAoLnd3eG5T50XSr+MDsDf9h+gi+MMtqgQW2rFe9+X445ubFYmB+H1Gg9MmL0ooXsioJkvP11KV65JR/fl3rym176ohgPzkqFyWLn9Wd4cBDqWq14ekEO2u1OJEYEQ8YAF5vbcce0RAAevcnJnsXu7Lfu5K7Hxj1F2NJRmOjb857iLYAn74orwz8tKRyTxoR164jojy7rqw4dSP0pJZ+sH0fW2Rrh3NfQZkNyRDBMbXbc988jfE9Zb10onpOFfRNXXpsCl5vFshlJUClkojDyB2el4mJjO55ekAOtUga9Wo7Htp/E0coWft1ACFDXahMUtOrpdffe4eaqCXs7J5ZdncTr1uoWa7+dWYG8t4MRDj/c5vbhjr8m57Hhemx6+slBGNHlT18Mu1ZCyIMAfg5gOiGEAaAI7LACT28WcD31UHt7ypZMSZCMh0+MCEab1SEKb9y4pwhv3DYJuaNCwDAED/t4a+u+KUOLzQWFzC7o0WRqtSImVMNPQrdcmYAovQqb957DzZMTBDs291yTghKTGQxDMDMtCp+cqsHPXvsWd1+TjFuuTBDlAQQHyVHTYsUrS/LxyEfHUGZql/ztUtcnPkwjvbC2iRc7MgZQdFOCPxAKvz/5asMt/8rfZFVvtmHyGCNWFKRgw+6zfLPwtBg9HttxAoXZsfjjrtNIiQzmK6xy12rtnEzUtRXxcsBVhLM7WX6n2F84cXKkDp+fqsYV48Lx67cP8Odcc0MG/v5NGb+oWV2YjtEGDRblx2Hr/go0Wux4an42zpva+MqDCUY1Ni/Jh0JG/OZD+NuZUSvlgnHRybtvBFJ/+sqqVDGU9YtykTFKhwdmpeEeHyPwyX+fxkPXpeKN2/JRUm8RyOzyGclgGGB8lA4/TIkUFGNJjtShqLYVb+3zOBFMZjsWT47H2h0nYdAoYXO5Bfpz5bUpeON/pWi02PH43Ez86d8nMSM1WnKnudFiR5hGiWcW5KCotpWX5Z7oztEGad3Z7nAJqtYWZsfyjrjtRypx44TYHi3I+6PL+qpDB1p/+sqn282Kxv3kvCz89X/nBXO1SsFg8xI9n+te1WzFv49ViXSh95wsYxy4fWoiMkbpUGayeIr2fHqa/+yzC3OwY9lU1HVE5hyrbBYYeuvmZ6PZ6hCtG179sgTr5mdjVKgKYZogUfVVf9c93qDB43MzUd5gkZQjq9MtuP5S16qkrq3frSL6cm/724e0Lwy3uX2446/JeeW+vw/CaEYGfTHsFsPTquB2lmWrCSHxAJ4O7LAGl556qH1zQfxt7Zv9hFWwLHC2vo036rjXN+4p4kPeNEo5Nn95nve4LZuRhFC7k5+E1u06g5gQFR68Pg3/59M/57nPzmLr0inIjA0VeMmdLhabPi8W7e4AwLpdZ/jJJzZUhTBtkGR5ee/KdWxH5TEpZRkf1qm4OUPU26D0lygdCIXfn1C7wZhw+kNXkxXDEOTFh2JObixfpXXDZ2excOJoxHUsKqelRPILGcAjE6s+Oo4tS6egwWzHoQtNePe7cj6hf8t35XhuUS5cLCv5vRolg4WT4rGvxIQ7piXyOxurPz6Bpxbk4I87T+GWKxNwt9f1XTsnE0oZQUyIStALqszUjqVv78fOLkJlMmL0Ik/66tkZeLUj3Mn7elAuLd09d76y6k9//uuuaXCzrKTujAlVQymTiWSW051jw7V8cQSGELy3vwJ3FyTzO18AcLSyBXV7ivHnm/MQpGBw+5v7Beda/+lZvPmLyZ6F/buHsHDiaL+70/FhGizfcoh3gjw5Lwt58aGColPe1yZ26RTsPl0Ll9vTLLurhWa8QSOqxOjdHqQ7+qPL+qpDB1t/So2bIUCpKVJ0D/eXNQquvZQu9J2TX/3qPFKj0/j519v5GhwkQ1KUDokRwThW0cQbddy57t92FC//fCIOljeKxsIVtHrtK49zeNeKaahu6fq6lzda8MKeItw7M9VvuL2/6x+oVhF9vbeDEQ4/2LJJoVxq+rRjB2ADy7IuQkgKgFQA/wjssAafnniopXJBOLjdgTFGLTRejU1jQlT42RXxiNAFweVmYTJLJ/efrW3lwxnvuSYFbpaF2e5CsFKGlKhgbPrpBJhtTmiC5Hhl7zkU+ytz3NiOzNhQgZfc6nRL767ZXfz/37/tKHZ29J6Tqh7FhVtyxISoRKF+m26eAAAwaBTYsvRK2J0uLHn9O78GsO/1D4TC72uo3VDJv+pp9S5OFrmG9TIGuGJsGAiAfefqEalTITsuBMcqmvkwyqOVLR3hQ4zfnbcGsx1yGeHvdV1b5w6yXEZQ12Tl+95xYXFJkcFotDhwp0Svr6pmK9rtTizMjxPtYq/qqD53//vHBJ/vHIvHAy51LeRyBnNzYpEcGYzqZiui9So0We04W9sGgBZGGWi6eu58F1b+ClbUtloxOkwj0J035sVBrWAQJGdQb7ZLHkcIRI29VxWmY3SYCi//LA/N7Q5eb56tbYNSwYDx08OOBQuL3QW7k0WUXiX5mazYEDz+r5N8Swarw1N5c+fyaQAguRtisbsE4ZVd5R6XN1ok80fz4g090mv91WV90aFDQX9K7UxJ5d555x9nx+oxMT4URCLU1ntOXlWYDo2SwVu/nITaVhuC5DJenublTuUNJn9rA7vTjdgQtV/55ebGncunYfIYI0pNZnx73iQ5B9S0WFFmaseTO0+J5Gjd/GwkhKkxMz0SFrsLpSaz4Pi+5JsF+t4OdDj8UJBNCuVS0hfDbi+AaYQQA4BP4Gk+vhiAeK/1MoZbcBs0CvwwOQIfHa4U9FpSKRhEBKuw50wNqpva8fjcTLywpwg3T07g4+C58LYEo1p0rHfvpOc+85Ty3rSnGAlGNcK0QXj0Y+HORGKERlCynjvP2ZpWpMfoRV5yf549DqvDU33zdHWrpDcvWq/C8oIkvqfStgMV2LK/HFt+NQXtThei9SpR6e9nFuT4XcBJKfWBUvj+jKfBzr/qjTeVC7d1uNyCfDTfcus35sUiwajlc3q+OVeHJ+ZlodxklpSJQxeakGDUIj8hBPvLmvniEglGNVKidKhts8OoZfHer6/EmZpWvl2Hd4sPzuPNvc41iu5qUcN9nssVSTCqUdlkxc9f+87vtZDLGeSMNiBndOf120kn7yGFt97csvRKOFwuKGQySd0FAN+cq8eaGzLw5y+KBeXfucW1lO6M1AWJHEhrd5zEc4ty+bBOTm9G65WYMsaIsgZLl7tmC/PjUNEo/Rm9SiEYA/edUvpz080TMCZMiyaLQ9Dn7u1vyrB0eiImjA5FglErkFV/YdY1LT0PK76Uumyo6k8pRndUDnaznUbb2eomPDEvC//4thTz8+JFufBcqK33nLx2x0msX5SLO985IJCnUSFBSIwI5huF3zEtUVJmovRBOHGxuct5uLs5GPA0JPeWJS6HXcYABamRyIgJwSenavzOIX3NNxsK97a/vYYHe/yDxbL7HkJlfYvo9ROnziDvyoEbB829u3T0xbAjLMtaOgqm/Jll2acIIUcCPbChjNSC+/G5mXj3uzJckRgBGQNMSgiDQg5cbGyH1enGK18V4fczU0Xhko9JLDi8KwRyn+MMqMLsWN6o495bs/0Eti6dwu+c+J7nqnFGTB5j5L3kXBlt3xy7t/Z1fqcnnE6GX/z1e5E3L33FNByrbBblo4yL0KLBYsfYcC2aLQ6RJ7CotnXIxbZ730tuxyklUoe0GD3Ghg+uIdBbb2p5o0XUq867xxLnAZ6bG4vc0aFoMNtQ2WTF+k/P4KZJ8XzenNSi5s8352HNjhN8k96bJifwsqxSeKoHcrsK/nb/EsLU+PPNefjgYDmuTo3pclFj0CiRFq3DshlJkBFg0tgw3OETKrdy62GMv2uaIOxuqC8sRzL+HBUz06JEu82TxoThXF0bPjlZjZ9dMQb3zUwVhOd6L659d+a8m0FzWB2eioi+enPLr6aAYQjOm9pEOvHZhZ27ZimROjwhsSPy5LwsROmD/IQjC/WnQaNEUU2bIG+K07tKOUFevAFBck+FxfP1Zr6YlXfEh+/5Bxupe7rp5gkYawzmxz8UHCq+40wwqrGqMB0NbTaolXJs3H0Wj83J5POBAWGorUouE83Jp/3Ik9vN4kRVM6wOt99K0BkxIahrs4lkznvu724O9t2VXlGQjH8fq4KMAZIigxEcpOh2Dhmu+WbDsUftUKGyvkUy58165MEBHQfNvbt09MmwI4RcCc8OHVcNs9f98IYy3T305Q1mnK5uEVRGe2FPEe4uSMGDHTshCUY1/nBDJsKDg7DyPU+hEwJIVlMrqm3Dn3+WB4vdBRkh+NOuUwA8zVIJ4fp/eSZxf4vmyiYrGs02Qd4btyjn8qy8ww+i9SrMTI9GXZsVEcEqnDe1icpo213SIZs1LTb8n48BwTUIfm9/BW65MgHtDnFe4db9FXhyXpaoaexghsdxE59BoxQl1Q9Eo9Su6K031d/nuYIL3l5+ruw8twPG5YpwRSYAoLLJgohgTwW5Bosd6+bnoK7ViqiQIBwobRLI8imfkCOpxUJZQzse/fgEVhWm47WvzvlteRATosItVwpbKCT7KV9+uqYFZ2qkPdojZaIeSnSlO0tNZqzbdYoP5QWAdbtOITVax+82r//0DAqzY/F9aQPSYvT49fQkPL7zJO6dmSqpO4tr23D71ESkRgeDgGDbgQuYP3G0aFfGOwqCw+pwo7rFCp3JjGXvHIJBo+THxhAgTOupCcYwBGkxejRa7PyOCPcZLo9OKmfHV3/emCcOP96wuwjPLcpFg8WO3/ztgGQe8sabJkg+Kw7fHzQI+BoOUsbrYDyPvnIoY8DP2WoFg2ClHOt2neKdrQaNEi4XKylj6TF6vPrlOUEV6e1HKiXlqbK5HQ3tDpyrbYNKwQgqQXO7aFmxoWAYghnjo5AUEYy8eAMsdidkDINVHx3j5bW7OViqZ+Kmm/OwrGPHMcGoxsprx3c5hwzXfLP+tCwYrKboFMpA0RfD7m4ADwL4gGXZE4SQRACfB3RUg4jUQ89VqjJqgxBv0OBgeZNgt2r5jGQYNXKcN5lxx7REBAfJQEDw278dwB3TEnmj4T4/vb6cbjcOlDXhta9K8OCsVNwxNRFtNqdgcr93ZmfjXalFc5CcwRtfl4mMk2cX5oBlPXlW3ELLO+eAEI9BkBQRjH/d5TH6NEoZ3CwLlxu4d2YKrE63YIFk7ije4g23q8gtXqRCUBotduTFh/Y6PO5Setc4Y6i7AjiDQW+9qVzzWd/Pj4/y7HxtP1IJh4uF0+lGqcmMkz7GGJc3ubwgCRt3e8ItfzM9CWt2nODl6Z5rUtBotou8zAzTKZfbDlSISseHaZR4ueOZWbvjJN90/PapiQiSM0iKDMa6XadQ1WzF8oIk/vxcXhWIn/BhN/D7f/ZtgqcEFn8LpvQYHaqarXC63aJwyntnjkeT2Y6zNa2oaW4Xhao/vzgXi/Pj/epOm9ON174qwe1TE7HjaCV+88MkUU/FLfvLsXT6OL5vGIdK4Wk2zukA7x52HJE6FcaGexa5vhUv183PRl2bDW4WmJkWhZ3Lp6GmxQqdSg6L3YUmiwMPzBqPNzuqcPpzyrW0O/idcinj78TFZnx4uFJQoGPL/nLMyozu8l4MxI6ErzNJavwD/TxK7c7deXWyYM5ePTsDywuSUVzbys/P/kIw5QzBwvx4vp+mSuGp7vve/nLB96oUDIKD5Nhf1oj39nfu1AGeStBJEZ5dNA6GIRgTHoz4ME/Egclsw8abJsBid0GjlMHuckOtkOH+WeP5/PdtBzyy528OPlrRxL9emB2Lkrq2LueQQOSbDcbu13DtUUuhDAS9NuxYlv0vgP8SQjQdf5cAWB7ogQ0WUg+9d6WqV2/Nl+w59PLPJ+Jiiw3bDlRgYX6cIGdkYb7YaOBCPNQKGTQKGb/o/eOu09i4eAKe2HlI8PlnPjmD5xbl4kKDme/hxE0yj83JRHmD2dOA9HgVnlqQg3abE4ZgJax2lyDPzTs+XyqExu5k8cD7R0ULMG6BdP+sNCSEaf2G0XGLF38hKFyVOG7HqLsJobfetd5OMpzx5G/RNZjl8XvjTXW7WclwshUFyXhy5yk0WuxYXZiBv33jadx8/7ajfvM/OE90YXYsb9QBwnxPX1leUZDMhwIDEJWOv+eaFP47rA5PWOb8iXG8wyDBqMbvZ6bibE0rX1TAuxw411/K+7etnp0x5HoSjeQQH38LpqXTE7FxdzE2/XSCSA8+88kZPLMgB6drWpEapROFWzpdrF/dqZLLsGV/Ob8IvzEvji9b7/3ZV2/JR0l9G349fRwe8wo1XlGQDJ1KgWCVgn8OOEeCjAGSI3Wob2sX7Qg/OS8LMSEqPPD+UUFLmJlpUSipb8PhC00COeXaJsj8OCcidEH8a1J6aOv+Ctz34/GiSId4g0ayMEsgdiR6Kse+zqehoEd95bAwOxaPfiROX+CiW7qan2P0alxosGDdf84I3l/98Qm89PM8/PZvBwX6qNRkhkYpw8L8ODjdLDbcNAENZjsvl773QsoI5SqgSu3erihIxthwLRIMmi51N+C5F1v3i+fhJ+dlCeYQhiGCViQAeqy3eiJrl0InjqQetRRKb+m1YdcRhvkagGAA8YSQHAC/Zln2d4Ee3GDQVTib1eFGZaN0/sb+ska8+mUJls9IhtPdWaJ724EKrLw2RfKYtGg9Sk1mvLy3RFD9r90p3R6hpd2B9Z8V4eHr07Ds6iRYnW5MGRsGnUqO5e8ewoOzUmFxuPjcp+UFSQID09szBUC0CDta4cmbu31qouREt3nJRMhlnhAk35BKLldk/sS4bkNQgJ4bbL3xrvWndPOZ6pYhl2vQG29qqU842RijBhcaLXzPLgBYs8PTaoALPdIqZVg9O0Ow6PDO8fC3SHOzEL0WoQvCW1+XYun0ROTFG/CbvwlzVZ777Cyf68eFZb72VQnvMFicH48/7vTs2HGVOr13UauarXhrn6fARGyIGpXN7XA4XUgwSi9wBuO+jfQQH3+6k5OXknqz5Ptna1uxaY9np9j3fX/HjI/SwcWyKMyO5aun+pPXi03tePSjk0gwqvHcolwU1bbB5nTjrX1lGBuuRWq0Dk/Oy8KG3WdFDq2Xfj5RpH8e+uAYlk5PFFTBXLn1MLYsncLrUO/Pr//0LDYvmYhGs130vK0oSMZZn9zjnkQ6xBs0foti9HdHojdyLKpw6sd4Hcjn0VcOu9JjXc3PyZE6vLL3HKaPj5R8v77Vzre6iAlR4VxdG9xuFgwhvAx0NQcnRgRLGqFcrrK/0N3/+/F4qJWeSCLvQlmrCtNFu9L+woe972N/9FZ3snapdOJI6lFLofSWvoRiPg/gxwA+BgCWZY8QQqYHclCDib+HnmU9ZaljQvy/zxlATy/IEXwmRC2d/K5TyQWKm3s9IlgpnSwfJIfV4cYTO0/xoUepUTpYHC48PjcLta02NFjsSIkMxrSUSMSGqAU9xIBOzxQ3Xs5DTYjHQ23QKP1OhPvLGhETosaD7x+H3cnimQU5OFvbCqWMgUYhQ6PFjm0HKvDQdamoN3eG4WXFhQiMOqDnBltvvGv9Kd2cHqMTVIscKrkG3O4m13aiq5LXnAH0/kHPYsW7HQXQ4aAAkBIZjFmZMdiw27PTxnmux0fp8Mwnp3lZUSsYSTn0nY9VCgbVzVYcrWzB0coWfoHuLVuAp78TtwBptTpg0CixcU8R/nb7Ffj9e4cBePJKg4NkeLQwHdU+954LFV03PwvvdYTDrbwmWbTACcR964uXeaSH+PjTnVqlDHdenYSx4dI7/dwug3fZea4tTHqMXvIYpZyBnCGC6qvce75/c03qy0ztuGfrYYGDweFice97h3HPtePxpxuzcKyiGU8vyIHF5kS92YYiP+XqOWPVW8ZbrE5oFHLJzx+taMYzn5zljUuFjOBoZTNfsIrbVfGnP70jHQBPCwV/stbfHYneyLFU7vb4aP2g5mz5ymF3eszf/Hy6uhVHK1swfXyk5PsqhYxvf/D6bZOgksuQEK7B/4pN+N2PPPLudrv9zsGJEcGie6VTyXgjbHSodDsEvVqBsnoLXvvfeTy1IAfFta1wuYEtXn1GrQ5PxeOXfpaHQxea4GY9eYH3z0pDfJjwXvRHb3Una5dKJ46kHrUUSm/pi2EHlmUvECJ4gFyBGc7gM8aoxaabJ6Copg1hGiW0KjnUCgZ/2+fJX1v10XG/RR+Ajl29JgvvPbt5coJkNbU1N2Tg+c/OiF5/fG4mFHIiGVLXaLbxBVXSY3RIiUrB05+cFnmZVxdm4OW9xXyYkHdPMG/PVIJRLTr2oetSEa6TrvLmcgNrtp/AioJkuNwsWHiMwUaLDXanG2tmZyBUq4TF5sST/z4tUJq+9HTx0RvvWn9KN3O5DrmjQ4dcefyeeD1jQjrbT4yP0qGqSbo0e1FtKxZPjsfmvedgdXh6F27a09lP67arxvA5TgpGLIf3zhwPo5fjgZO3f3xXxp8jOVKHh64bD61KIaiy+cTcLKy8Jhmb9hSj0WLHg7NS0WpzoqrZiifnZeFcbRsvNwlGNf50Y7ZkGfzKpnYsmZKAt78pQ4vNhbxQVUDbGvTVyzzSQ3ykdKdeLcPZajNe+0o6nNZbd247UCHQm899dtZvCC4hwB+2nxDoz+1HKiV3xCoaLQA6jbB4gxorCpIQplHiza9LsGBiPB764BiWFyTDxUKQz7d6doZkSwWGQBQq7NkNMUg+d+MignH/rPGIM2jQanVgTIQGShmD+RPjAAC7jlfh9qmJyE8IQavV1S/92d8did7KsW/12fgw7aD2CPOWwwhdEMKDlXjoulTBNb135nhEhwThFz8YIzk/r56dgZf/69GL/uTKez5uMNvxt2/KOnSrMP9++5FKXl/5zsHe9yomRIUIXRAsdgvcLKBVSRucda02jDZoMD9vNELVcr466syMaDjdLJ5ekAODRgaT2Ynf/r0zVHTd/GzMTIsS3Yv+6K3uZO1S6sTh3qOWQrlU9MWwu0AIuQoASwhRAFgB4FRghzW42BysyKj62ZUJfKUv7xBDrgw2541TKRhY7C4kRwYLyif7HhMWrMCUcRH8hM4lxUcEK+F0sdAqZfxOCkMAo0aBxnYn76Hmdj4emJXGt0oAOvIHdpzgvdLcLiKXI/j43EzEGzRgGIK1c7Kw9O3OEvIGjRJmuwuv7Trt13g1aJQI0yqx+uMTMGiU+MVVCQjVBuHJnZ7wke5CTzh6uvjozrvmvbOiUcr99LXq2YJmqJbH9+f1HH/XNBACmMw2XGyyinLaHr4+DU/sPCW6h40WO55ekIMnd3oeW0EYmJzh5S5Eo8TznxXxBU7GhGsRJGfw5M6TApl9eW8xCrNjUddm54sE3T41Ees/E4YRPfzhMTy1IAfzJ8YhOEgGm8uNTR0yyj1n3G4zIcCxiibBblx+QgiWF4xHdYunwM+dP0pEbZsdYdogQaXP/uZz9NXLTEN8xLpzVWE63v6mlN9N5sJpPdEBCjz84TFed1Y1W2G2OnD/j1Ox8r0jomPiQtUYHaZBq9UJBgR2JysIM1PJGYRpFQK9qVXK8NJ/SwRGmPfi/jc/TMLRymbcOzMVRbWtIt21ZvsJQaVBTv8o5R6Z4oy6rnJB77kmBS7Wc85nPjmNmybFw+FmBbLPhSTPTI/Eb//+Tb/0Z29zc32fl/7K8VDQo75yeM81KVhRkAyz3QWGAA6XG0FyGdZ/elZyftaqZFg4cTSsTjdUcgY6VffzsbecAJ0RPFxqg+8cDAjnt1uuTEBti42XwQSjWmRQ3nNNClRyhs9F5do21LbYBPmjL/98oqj1zf3bjiIrNkR0X/pzv7uSNbebhUYpk6xQO9g6cSjIKIVyqeiLYfcbABsAxAKohKdJ+Z2BHNRgUmoy4/fvCRd1G3YXCZpre1dP23BTrqBNwGM3ZCBCH4SaZhsuevVT8j5m2YwkoAZ8Tp63J2/y2Ik4V9vGj0cpY5Ac6Qlp8I23X7vjJNbMzpD0iHlvqFodbqREBuPpBTl48+sS5MUbkBgRDIWMCI71jun3nuiSInV8/tOjhWm8UbdkSgLa7C6s/6wzMd3N9ix5vqeLj668a/76Cb6wp0hQ1GC4h1j483qeqm7Bve8dkWwI/txnZ/HCTRP4whRc+wtuEV1U24olUxKw63gVb8TfmBeHP+46zZ/nvh+noNFiF1QKXF6QhDJTu6h6YEKYGg9dn8bvdvgL5y32yqfyXURv2F0kkolnF+bgrV9OxunqFgTJZbwjwvOsZeKHKXo+4T8uRI1vShuwv6xBEHrU23yOvnqZR3qIj5Tu5CqgcvLChdM+szAbblaOW64cg6c7ClOoFAxSonWob7MLrj93zLIZSahoaufD37i8Xu7cKgWDv/w8D2nRepyqboFS5unC02ixS+YNP/PJGb6wC7f4lJZZT1Gi0QYNghQMEsODkRCmAUM8+rOrXNDyxnYQsLjQ0I53v/eEyrU7XCJd7slhzofFLp1f3Rv92dMdie76Cg5XOZaSQ+8cXwBYeW0K2mydlSV952eGACq5jK9C+cotEwF0PR97V6Tk8NaFqdE6rJmdgfpWGyqaLBgTHiy4V5VN7fjVW52O1jJTO17+bzE2L5mI70obwbKAm2V5HR0TosLi/HjJvM6D5Y091mH90Vv+ZA0QF2fzLsA2XGSJQhmO9KUqZj08PewuS/wt6sx2p5+wCCueW5QLp9sNAoKKJgs0Sjle+m8x7p2ZKgiz4KqtpUTpcKHBItpNW1GQjDNVLWAYAqM2CPVmO+wuNy40mBEfppEclyZIOlyD9SpwoVIwOFvbxheraDDbJJuTei/GuVytG/PiYHe68ND1aajvmBQMGiW/mLljWqJoXD3x/vUmHMKfd01qZ+WRD49jy9IpaHe4LpsQC38e1bM1rV0aUccuNsPNehwIvse63OBlb8v+cmxeko/mdofgc7ogOZ5blItT1S28oZTmJ+epoqkdGmVnfsj4KF23+VRSY26zOgQ5Kb9/7wi2LJ2CME2QqGLiox8fxzMLcrDsH4egUjBYOycTmz4vEoQgc33SeuOZ7asHe6SH+PjTnTKfLqcqBYPz9RZPsZ0fJuKZBTkw253QKuUIDpKjzebqUm9y592wu4g3zFQKT7W/mmYrrM4OGXG6oXS48MJNE9DoI9vcObhcOS6fTeq+O11uMIQI2ihwbRxUCumKupyc64JkyB4dipoWG+6dmYr7/nlEUmdaHW4oZKTHstedrPVkR8LfzvTO5dOGtRx3189TpWAwMSEU351v6FLOOPmK0qtQVm/GpDFh+L60AaUms6CSKYd3jigHNxdzYfCcrEbqVXzeJHevSk3iQkFlpnY4XG4wBJDLGGSO0uPua5I9z4ic8TsH+xuLlA7rr96SkjWpHNCNe4qwZekUUb49hUIJLL1uLE4IiSCEPEQI2UwIeZ3714PjZhFCzhBCigkhD0i8P50QcpAQ4iSELPB571ZCSFHHv1t7O+bewE2s3qgUDBrMdjw+N5N/jwvlcbhYnKtrg16lwNOfnMaf/n0GVocDqwozUNVk4XM0lkxJwGtflWDj7mLc+94RMIQgJkQFq8ON+DA1nlmQA41Chje+LkOUXo2/f1uKpEgdGAI0W10oNZklx1XRaMHyGcmCca2enYEdRyv5v5fPSMb7Byt45aroWGnFGzR49dZ8rJufhRd+OgGZo/T8ebjQpde+KsG97x3Fff88gvaOxfQtVyZAF9RZJMB7XNsOVGBFgXA86+Znw2S2oaSjahgHNyFMSQxHYkRwr5W9vwm83eHq8zmHIpxH1fuaPj7XU0CEQ0o2XG7wbSe8j10+Ixlfnq3F7VMTkRajw9MLcsAQFvFhGsH9l8kY3LP1MDbuLsarX5Zg6fRx2PpdOdbOET4HKwqS8cXpWuhUCrz2VQk27SnGM5+cxurCDNH3vn+w6zGrlHIwBHh8biYem5MOg0YJi90FFtKGoNnu5P9/1UfHUZgdy/+9cU8RCrNjUdtq7ff17o0Huz8yPZzxpzvTovWCa3nPNSn48mwtZmZEw+pwo6i2Fc9/VoSnOwr3XGzsXm8CnZULlxckYf2iXEToFHjxv+fw0n9L4HJ7ik+02V149OMTKG+wSI6Nc4BtO1CBsI4wSuFzlonkKJ1kj8vqFhveuG2SX715//vH8PzuIhwqb8Kf/n0aRbWtkjqT+9s7jNJXf3Khe95cKv1Z22od1nLsTw45A+uJeVmwOV2I0gfh8bmZfuXMoFEiNkQNvUqOF78oQXO7A8FKGcx2F4LkMtF3bD9SiVWF6SKdt+NoJVYUJPP62urwVFYtNZnhdrMoqWvDvnP1COlou+FNglGNNqsLm/eWYP2nZ/Grtw/A7fZ8V3TH+oH7Lt+xrJufLdJhDPH0tA30XOxLV3PzcJIlCmU40pdQzI8AfAngM/SwaAohRAbgRQDXAqgA8D0h5GOWZU96fawcwG0A7vU5NgzAagD5AFgABzqObezD2LtFKixhRUEy4gxqyAiEcfZaJf59vBKFOaNR3WLF43OysOdUFUBkfKx9glGNp+Zn43/nTLhjWiIAzyKCCw157asSVDa1IylSh5f3enqMFdW2Yl7eaNS1WPnKhjEhKsm8N66qmnfozz++LcO9M1NhsTtR3tAuCMGzOtyw2F1wu1lBuewEoxqPXJ+O5xfnwunyNC7//XtHRB6326cmYsPuIryyJB8qBSPqV9dosSPOoMbf77gCNocbTjeLVR8dg93JYmF+HFIidUiL0WNs+OD2shlOSHlUW9sdWJgfBzfrqTZ5zzUpgsbO6+ZnY/2nZ1DVbO3sbWh3YlSIClXN7Vg8OV5Q2GT5jGTsOV2Nx+Zk4tGPjuPGvDj+faAzrG5FgacZOfccpEbpsHnvOdwxfRyKa1v53bYyUzte3luMV2/NR7PFAYYQ/GlXZy4q5wAQFMUozMCzn5zmd9xWFabjtz9MREyIChql9M40V/GQG6NvCHJCmBoxISrJfl+9ud7DacdisPCnOxkGgvymSH0QllyZgPo2O1wuFslROjw+NwMNbXZUNLZjw25Prtqjhen4065T/C4wALzznadfHVfVUq+Ww+UG1u06hTm5sbh5cgLcLMs3dOb0p1RfTS6UE/BEKLz+9Xn88cYsvipmXZsNDW02lJoskovUr4rr8eqXJXhstkdvlpvMGB+j5/Oquc9t2O3Rm9wuitRYHp+biYtNFrTZnIgzqPDyzyfiYHkjXG7g7X3nYdAo+R29QMni5ao/peRwVWE69Co5Hi1MQ4hKjlarCzUtNnx+uhaPFqbjSEWzaH5eOj0Rlc3tUMoYNFrsOFvTyodnAsCqwnSBDl2cH48PDl7AUwtyUFpvxphwLS42WbC6MAMPf3ic132ARy5qWqw4Xd0qmIP/OC8Lda1WxIRq0G53It6owW1vfC85D1/ocFZIydP//TgNiRFqvHJLPqwOF8YYtShrMOPWN75DYXYsZAwwKSEMVyYaIZf32r/fLZerbFEow4G+GHYalmXv7+UxkwEUdzQzByHkXQBzAPCGHcuypR3vuX2O/TGAT1mWbeh4/1MAswD8ow9j7xZuUTf+rmkobzBDxhCcrmrhFxzeiio/IQQL8+P5vnGcN3Dj7rP85+xOFsV1ZlGlrLe/KYOsY8GjVcrwx44m0t4FLv52+2Rs+ukEmG1OhGqVqG224vG5mYjUBYEF8MLus3z4SFq0Hi99UYyjlZ5+bKerW0EIJMuBa5QylDeYcbq6BXdMS4RawSBYKcfjO0/yVTL9hQtxYUenqpr5yeTtbzw5JeMighEkY1DaYEZxbRuSo3R4+j+nYXeyosIFg93LZrjhHe7idrPYcfSiQKYeui4VL9w0AQoZgUGrhJtlsfLa8Vj/6RnMyowRyChXeVBqsfDi50V4flEuHF69GDmsDjfijRo8tes0CrNjQQigkDMozBklOD8nw2Wmdhwsa8KfvyjGg7NS+UqHnANAo5Dx/RgnjTHghd1nBb3B1u44iaXTE+FyAxkxet7o5A3B2Rl41atvk0rhKa3/2A3pSAjXosnsQHiwEqerW7Hi3d5VuKTJ9b3HV3dqlHJog2S4852DgoJGCUY1fvPDJFGBqtzRIVj1sadQRFWzFWUNZlHV3uUzPIYid/+f/c8ZnK1tE+jNldd4dGqELggbb8pBkFyOVpsDwUFybLxpAlqsDgQr5ahttQryo2//wVg0W5w4U9MKwLPrwcm51CJ1fJQOKZHBMFkcePG/57A4Px4HyqRzmwgRGpeczow3aFDXZoNepcC3pY2QESDeqMHG3Z6QYm4H0Du3NFC9ES9X/cnJYcqyqThb2waVnEFxbSs+O1GNmRkxWPaPQ/zvveeaFDS1OyTn5/gwDcxWJ17/+jyWz0jG9+dNmD9xNB4tTIcmSIaKBgteuzUfJyqbYXexkDPATZMTcKHBjH98V87nzT+9IIeXMw5uHv7FX7/nUxuC5AxCNHLUtRE+V1mqtyMnT1v3V/DGpfccHBHs0f+/+dtB3kn2xLws/OPbUtHztG5+NmZnjwq40+pylS0KZTjQF8NuByHkepZld/bimFgAF7z+rgBwRT+Oje3Fd/cahiEYFxkMQoDrN34Jq8ONZTPECvaWqxL5BS3QUfnvg2OCJG2pnY+Nezyx+1eMDcPZ6lZEhahx61UJGBWqQWm9GfMnxuFERRPO1Zn5QiW3XJkgWAj9YXYGfjk1EeUmC1gAp6pb8NsfJeG1r87hpskJeGFPEexOFiuvTeErf3GLIQA4WN4kmMxWFCRj4cTRgpCjrvIFmq0uvH+wgvemu9yeXcy7fbz1N02KR5vNJRnKNJi9bIYzpSYz/s+n4tmT/z6Nv/5iEupb7ThbY4Ld5Qmz+dON2fjFX4UeX9+CFtzrKZHB+MVVY8AF6KwoSMLW/RWCiq+6ILnAQJMqguKdN2pzumF1uPHHXaexoiAZTy/IQVFH36WX95bwix+GJGJGajQqm2yC3WU3C9S1WTEuMhhXjDXgmQU5cLEsFAxBm9WBsx2FhlQKBk8tyIbd6YLDBX7XhJP5lMhgHK1sCZjsUaThdOe4SM+13XeuXmDUAZ4mzFylP6BzV+svSyYK9E1sqIZf4HKf27inCK/fNgnPLcqFSsng+uwY3OGlN/eeqYVRF4TaFhvW7DghWsiuKkxHmEYJvUYOudwTAm+2ORGiUcDucvMVhjkd2253wuZyY3VhOtb47HA/88lpLJ0+Dpv3nkNhdizvEPOnN6uardiyvxxPLcjB2RrPM/Dsp2dR1WzF8oIkbNpTLNCb63adERRm4a5BoOT3ctafXO7ayq2Hcce0RGzaU4yNP50gmq+5nTmp+XlUiAr2YBa/+2ESADdmZsZIthd6fG4mgoPkcLpZVDRaYHe68ZvpiXj96/P45VVj0Wi24bEbMvHox51OqSfnZcHucgsqqkrp067y9hotdrS0OwRzcE1zO1a8e1hgoFY1W/HwB56KxL6/31+lzEBc/8tVtiiUoU5fDLsVAB4ihNgAOAAQACzLsvqAjqyXEEKWAlgKAPHx8f06F1cCutgrJwIQK9h2u1PwNyAuFuCvsMXYcC3O1bVhzY5TeOC68WBZCHY9XlmSj191eGm9q1Vyx/9h+wlB0RWuYfOT87JwfXo08uINuNhkQU2rTRA+qpQTHChrxFMdlei4823YXSSosCkV3sFVtfrjvCw8v/usoJJYglGNtOhUQTgLV030dE2r5DUYzF42A02g5JOTTanr+fU5E97bX4FbrkzgS6n78/hKFbS42OxZgN/jY5y/ta+M300+cbFFIIv+iqDIGIj6O7Y7XHhy5ymRk8J7t8Xb4PQYfECkTgW3mwXLAnaXG2UmM/6ytwQGjZJf1DAEGGPUoNzUjkc+FC5e1mw/gacW5OCPO0/xzaTr2mzDfqERiNYOHIGWT25MkcHinpgyRlpmqjuMfO698/XSct5sceCxHSdx61UJIr3JFfvZvLdEshIm59R47asSPHZDBl78orPf54qCZBg0SlQ1W3kdyxVnSTCq8eqt+fjufIMnPLJjwcydj9PzXYV8ev4/hQ839i7YkRypQ0yIClXNVl5veu5Lz6oM95XhoD/7Ipu+elKlYNBuk56vvVLN+NcSw7WoaLDg0e0eY37TTyfg3o42Lr4y9ciHx3mZWj4jGR8ersRNk+KxcfEEFNeb8fAHx2DQKLF0eiLGhmuhkstwocGMUSEqLMwXGu6++rSreZiTK2/H24qCZL63ns3pwi1XJmDdLs9c72+9cqn6bA4H2QoEgVx7UiiBoC9VMXV9+J5KAKO9/o7reK2nx/7I59gvJMa1GcBmAMjPz2d93+8pXAnodbtO4YFZafxCQ0rBju4oNuFr/GWNCuHLZ6dGS1cHrG62Qq9WICZEBa1Cjif+fUowWXi3SvA3uXOvczkcL35ejIc+OIZx4Vro1AqoFDI8/MFx0Xc/uzBH8nzeFTarmq18eMf4KB0IgIpGC/50Yzae/eS0YNcmwajG736UJKgaxy3WGYZAH9TZywYAX0J6JMXbB0I+Odk8U90iKVMuN0ROAH8e39TozuqW3P1yefXWAjwy8e735fjT/CxUN1lRb7bxJd69kTq/VH/HpEgdFubHITkqGM8vysXxqhZRGwbO4OR22kaFBiHeoOFLZ3O9E9fOycSqj47zuVbrF+XCbHPB7GfxxrJugWf81S9LAhbSNhj0tYm6PwIpn945Q/fOHI9HC9MFPbbS/VRWrWu14cFZqTBZ7HCzQGKEVvJzp6pbcMuVCTBqlHzoJtARIt5RwdVbP3rj/fqjHwv7fXrrUe6znM4qM7Xju/MNfM6e9/lkTOdzxulNrlVMRoweZSYznpyXiQSjFvEGDeQMwbOfnpEMM+WeBaWCwfKCJMSGqCV3zqnu9A8nh+UdBce4udtfw2/fx4W7jwB4Q5/TK93JlHceelZsCB7+4Bg/n3IVMTkj8IFZqUiJ0nWpT7ld3s1L8nGyqgWjQtWobrLggVlpMJltglDiB2elwup0i3rdxoSo0GixY3SYBglGNR9eDHjCjUeSLF0KArX2pFACRZ+yZgkhsYSQqzoqWU4nhEzv5pDvASQTQsYSQpQAbgLwcQ+/7j8AZhJCDIQQA4CZHa9dErgS0IXZsfjTrlN8RUFOwb7884n4x6+uwOYl+ahrtogq/91zTQqqWzzNojftKcbT/zmNP8wWVwd8a18Z1u44iV9NGwudWiFS7lyxCA7v/+f+5iq6cRML9/8VTe34z4lqVDZJV6Zi/ZyvqklYYbPRYodKLsNT/zmNE1WtePLfZ7CvxIT9Zc1wsyyWTk/EshlJ+P3MVKz2WWBt3FOEhflxGB8VjOgQNX89Xv2yxLOjdPMEGm/vhXd1NN+KZRycbG7dL13p8v2DFaKFh7+qmC9/UexZgNyUi9unJuLtb8pg7Qib5OD6JP367QO4//1jeGFPMfQqBRKMasH5fSsJrrkhAzKGYFF+HGJCPEn0qws9+XAbdxdj2TuH4HCzePXLErz4ebHI+Fs2Iwm3T03Ey/8tRlyoFuWNFkHp7GarCzUtVvz99ivwz99M4cuzR+lV/OLNG5WCgUGjlAxpKzWZ+33vBgN/peoH8/f4jqkwOxb3/fMo3v2uHOsX5WJ5gee+vvrlOVGF4eUzkvHJiWpYnW6B7nzsBrHufG+/JxogQq8S6TfvtgXcMd7405tSf3t/lju31PkyYkKw/UilYK547asSqBUylNSb8eS/z0CtlCExIhhyOYPYUDXum5kqkkeul2SCUY2GNjs27/VU1vzLXo/O5J4lmqvUNZwcsvDksDda7Hj7mzIEB8lEFX1XXpsCo0YpOT9v2O25HwB4pyf3GW+kZMrqcKPVKu1k4t7/067TMKiFlTCl9OnNkxOw6qNjaLU6sfwfh/Dkv8/gRFULWq1Ofg5+ekEOLA4X72zlvmvtjpNYmB+Hx+dmYsKoECy7OpmvXPzqlyW4a0ayZMVVCoUyfOn1jh0hZB2AxfAUPuGqYrIA9vo7hmVZJyFkGTwGmQzA6yzLniCEPAZgP8uyHxNCJgH4AIABwGxCyBqWZTNYlm0ghKyFxzgEgMe4QiqXAq5MLyEeLy3nfSUEYDsqEIZpg3D9xi9xx7REfHOuDn9ZMhEHyjwVzNwsiyd2nuITogkBmix2vHFbPv53rkGwQxETokJypA5Vze0ir+ybX5fgsTmZePHzImiVMlEFLu8wN++JRaVgEKSQYcNu//keFxosovPdc00KABaJ4Vq+qpfN6ebHyhUQ4Pozme0ubNrT2dBVagJLidQBIKJ8sA27i/Cvu6YNy52SS0FPd1842fTeFSAE+ME4Ix54/6jAQBJ7fCdif4eMcvf0bG0bnlmQg9e+8oQ1+vaek8rveWzHSTy3KJcP1+SKoKwoSEZihBYsC/zx36dQZmpHglGNtXMz0WSxo77VhuuyYvg8t8omi2B3RkaAMI0Sf/Ta5QM8+XVsxw4MV0jCtwhPXnwYGIZgjFGLC41mrJ6dwedwqRSe/nYuP8VgLlUY0qWmr03UB3JM3AL2aGUL1u44yevD//txGp76z6mO/p8simpb8fY3nmqXz312VqA7G8x2vPnLSfiyyCTa3a1tsYp2IL45V4fbfpCIFQXJ2H2qusd6k/ube9y8Qyg5uHL23udbUZCMmpZ23DszFZVNFoHufGtfGeZPjBPtsDVbHX7D02UM8IfZGfjt3w+KdOabv5iMCF3QsA8hvtRwcmi2u7DtgCcPPN6gRpvNhXa7k68gXdncjh1HLmJmRjTWzM6AViWHQsbgxQ4Dm+vHGROiwit7z2H17AxsO1DeI5lSKRgYg5V+8+MAz301mW2iqtKjwzR4blEuWtodqGuz4a9fl/JzsPc5vOfgmBAVVl6bIilTmaNCMGN8JMobLVj10XGBXD3y4XHkxRuGpQ6kUCjS9CXHbi6A8SzL2npzUEexlZ0+rz3q9f/fwxNmKXXs6wBe7/VI+4B3Dxxfz5ycAcKDg1DVbIVBo4RKzmDxpASU1ZuRNSoExy42I86gQUpkMGZlxggWoI/dkIHtRyr5QgIxISrccmUCn0fnm8+0KD8eEToFlhekCGL0E8I00KsUeHznSb7whHcOxxPzsrB2h2dRu/dMLZ5akI3i2jbB4vn1r8/jpknxeHpBDs7UtIJlwU8em24WJphz14EhwB/nZePv354XvM59TmoCiwlR4VR1i+RkwxXE6IpA5hANZfztvvgWSPAuIc3lN6oUDGZlRGHp9HFYu+Mkth2oEC08fvPDJE8lwCC5oJDOU/OzEKJR4Ml5WQjRyFFWb8HaOZmoaLRg6/4Kv7lQRbVtgkbkT3YYY1weCmeELc6P59t++IYFsSxgc7kFBXxWXpsi+C6VgoFaIUNIh1f7Z1fEd1lIgmEIfpAYgaK6Frxx2yQ0mO2IDVUjc1QIyhstkjLaXRjSUJXBoVhOXGpMUqFfV4w14IrECFxsakdajA4OoxaL8uOgVspExSSkdCfg+a1mmxO/mZ6ENTtOCD6790w15uWNRkRwEF74vIgPi8yJC8VLXxTxenN1YQZe3tuZz3nPNSkd1WRTkBcfigaLHYvy47C1I395cX48tnxXLmgtwxlvJrMdf9krrkDMEIj60CWEaXHkQpPk/ZuYYEC73eUn0oLt0QJ8qMrsQOE9hyvlnt+tUjAorTdjbLgWYyOCUd1shUYpw0+yRwnaxDxamI75eXH4467TAsNty/5yaBUEN18xBht2n/UrU975b6erWnDPNSl457syvsUAV72aGxMhjMh5HKKS41deLTO4z3IG46rCdGz5rhzTx0cK5oPaVqukTCWGe6IezvpxJpSZzL2SkZEuXxTKUKcvhl0JAAWAXhl2wwGuQMMzC3JQ0WjBY7PTYbI4BIUexkfrkRat4wtAcBUr73q3s4Sy944G0JnP8eLNebizY6G7MF9cEGXD7iK+amCTxQ6dSiEZo7+iIBn3zkzFmZpWqDp60MyfGOeZFNRyPjF/fl4c6lpt+Oiwp2w3S4CoEBXu/OE4vPjfc5iTG8t7/AAuZE2BZxfm4vfvHRYslkI0Sryytxi3XjUWBWntIAR87oxUT7LH5mRi5XuHMTsnVnKykTMMdh2vQkyIGhkxelEvnUDnEA1l/O2+1LRY+fej9CrEGzSSJaRTo/S42GTlFwfhwUFYOj0RcoZBUmQw1u06hRCVArf+YAxfSEcfJIPV4cav3tovWXV1VWE6xkcFCyq0AZ57Z3O6eaPy9qmJ/KLGO79NarePa1+gkssAgDcyuffXf3qWL1bBLZKWv3sI989Kw1+W5KGu1e53l2qMUYvyBjMOljfhoY5nhrs+2XGhfSq/PZRlcCiWE483aLB5ST72lzXAzQJnq5tw59XJghYVTy/IRkObAzuOVmJxfjx++WanY+vxuVkCOQQ6dae3TuUcWLUtVqz7zwnRZ9cvysXhC838ebyL8WxeMhHflTaCZYF/dCy4vR0UAPC7H43Dd6UNvDPswetSUd3cjje+9jjdZmXG4PndnYt5hgBpMXqR3lw7JxPtdifWf3oGChmDWRnRADx5pClROpHOXFGQjAe2HcOi/DhJnalWyOB2s7zsSS2wAQxZmR0oODk8XdWCRwsz8NIXRRgVqsJ7By4I8hqlKvo+1qGjvF/buKcITy3IQWWjRVKm/rJkIo5caOb71hVmx+KtfWX46eR4ROuUgsqp3tWrC9KiUdVkERQhUykYXJ95pUiWnpiXBTkDPL0gB29+XYL5HTuKXK6x1eHG1v0XRC1hnl2Yi9IGM5a9c8hvBM+hC01wsSzGGoNR2+qJJHK5gdpWseE2lHUihULx0GPDjhDyAjwhlxYAhwkhu+Fl3LEsuzzwwxs4pBTWk/Oy8O73wn5fK7cextalU3gFL1Wx0t8uVaPFjvWLcsAQgjY/RR6Kaj1NUN/4ugwL8+MkPzPGqIWb9eQo+SrpN38xGSoFgxvz4mCy2PHR4UpRkv7q2Rm4Y2oi3G62w5hTYmF+HJIigxGtUyHOQLCiIBnRISqcrzfjuc+K0Gix455rUtBotvO/Nz8hBC/9LA+HLjQB8OQzjA7TIFqvwu/fO4wyU7tk0ZnHbsjk3/cs6DIxNydWYNx599kDPLkHl2uZen+7Lw4Xy7fb4CbQmWlR2OlTQtrtZhGmVfJFHF7cU4SfZI+C2e7k5fnOq2MFhXTunZmCRzvyIqVkeO2Ok3h+Ua7o3q0qTBeUZed2ih+clQqdWs4XyVHKGEnZTYvRw+1m/cp/WrQe6+ZnQaOU45W951BmasfKrYex5VdTcLBMepcjXBuEXSeqcbqjGqK/HT3f8tvxBk2Xnuee7qQOBkOtnLjbzeKTUzUC/clVz/W+fkW1bX4rVj7y4TE846ewk8PlxnOLc+FwunCmxoxvz9XiB0lRkp89Xd2C2BC15HvN7U6B3uT6fi6d7nFQ3D9rPNpsTlErmHERwbh/Viq0SpkgWuLJeVnIiw9FfJgWFU1mvLIkHxeb26FRyvHm1yWYkRrtaTuz9TDSV0zDyapWrNt1Cr+8aixkxLNQB4Ci2la+wuHW/WJHmbeTgzMQfeerTTdPQGRwEK83tx3whPYPFZkdCKTkkHOA+Mqcv4q+UlUyi2tbMdqgkfy8xebCn78oFumlxHAtzta2Ss7Ba+dkQsawMNvcvE7j7qFKIYdGyeCZhTmoabYiIVyLtTtO8PPlPdekgBDgyX+fhkGjxIqCZIwKVeNcXRv+tq8US6cnIiVKh7RoPWQMMGuDZw7xV2Fz1/EqqBUyLHvnkKSTz9tw43Sid7j0meoWpMfoMCb88pcvCmU40Jsdu/0d/z0AceGTYV8JSGoR95BXTzquNDUhQIvVyVfLkqqS5VuJMKajrLHLzeJkVSu2H6nEox1FV3wng6RIHV7dew435sUhd3SIZDXJU9UtMGqUkmEeFxo8zXqtThfcLPj+St6/a832E1h2dRLeO3ABb9w2CVXNVsEux5PzsgAAD75/TDA+754/MSEq3HJVIg5daIKbBb8o8UwEOXzYlHc+WEpUMEI1Cjz60XFBI+pHPjyO5Mhg5Iw2eK6fmxX12ePyGIZrTlRXSO2+rJufjVUfHRPct5VbD2Pn8mm8Z76mxQoZA3xf2ohHPjwuuFZaJYNQTefi1ldOI4KD/L7HfV9zu0MUJhQeHISHf5KGlnY7kqOCPYsdpwvBQXI+t47btZaS7wsmM+IMGmiUcslqfy43iwuN7dAHyXDXjGScqm6F3eVGY7sdKoU413RVYTqsThffr8rfjh4XqpkYEczv7n189KJod49bwLjdLOpabQLHAlcGf6jI4FAqJy6lPx/s0J/vH6zgGzBnjtLDoFH6lTm9T+VCTne22104V9eGseFa6FQypEZHo6XdLhn6OWlMGMw2p6R81bS08+FynN6cGG+A3eWRgTiDuHfeht1F+PPNeahtaecdbtzCeWy4lpeX/aXC3WLue27Mi8OLnxejpsXGGxhcqB8ArLw2RVBts6rZirf2leHPN+fhcEWTIC+WM9IACK63QaNEUU0blr1zSKQzuTC9oSAnlxopOaxoMPN5dt4GL9BZWImb22UE0CplgnOqFAzSovXQKBnJ+bjUZMaaGzLw5y+KeZlKj9GDEPidg1d9dJyfgzcvyYdCRhATosLJqlb85IUvBfqNM+q4Y73nYe53nKvzpFtMHx/Jtz7auXwar7MA4VwcH6ZGeUM7n9valaPa2zFQ02KVDJdOMGoRH0ZDMimUoUCPDTuWZd8EAELICpZlN3i/RwhZEeiBDTS+4XCcIRcfpsYD140HAeFj8V9VCBPrfRcX249U4sl5WXioIzdOqm/XS18USS5Smyw2LJ4cjw8OXsBog1rkOQ4OkuPPX5xDo8WOx+ek8zl43p7APaercetViSiubQXrZwGVHBmMN26bDJaFqIH1Qx8cwzMLpD3nbhZ8EYv/k2hvEBGsRHhwED8BcpPoa1+V4KkFOahusooaFlsdnh5WOR0NMUpNZn6BxL3PNY29HEszS+2+mMw2yevUYLbhdHUrv3jZ9NMJvFHHfWbjniK8cdskHCxvFMim9/97t7ZQKxhJI8xsdwq+X84AJXVt2LC7CA/OSsWRCy145pMzkgvJ1746xz8D3PsPzkqFw81i5XtHBDLN5ZWunp2Bpz85DbuTFYU3jzVm4d3vy2F3eqqxxhs0qGuzoaXdgYrGdsnfyP2tVsiw71w9H876yamaLnf3xhi1ot0Q7157l6MM9hdOf3o7wAAgWideBK4oSOaLMfneK6Wc4XcVpHTnioJkAMB9/zyCB2elYs0NGXxFXq7tyq/eks5bXlGQDIeLxffnTVh2dTIfwqZSMPjTjdlYeY3n3FJ6r77NhisSjcgeHSq5O+pPZ3FOEe55sjqEJfM9BbSCJXMRLXaXZHuF2tbOgkIcUgty7vtf+6pkxMis9zzO5bEbtEFY/9lx0bO87UAFHrouFWa7SyBjf5idgQSjmndS/WF2BmQyoLrFJjkfv7f/Ahblx+GuGSl45MNOfbe6MB36IBna/ORMcnMwJ0sldW0io5Trk8iFanKvc8ZlTIgKerVCtL7YdbwKjWY7ZIRIzsVLp3ee01seu+ubGKUX997j1gy5o0NHhPOAQhnq9CXH7lYAG3xeu03itWGFdzicb/U9qVj8Dbs9hsZ7EmEzK68dj+vSoxEfpkFti5VfyHLHcjH7AIunF+Sgqrkd4yKC8diOEyjMjuWNoP+T8Bw/tyiXr6gZplXhd+8Iq6et+ug4XrklHxs+O4PZ2bGIDJEO80uO0iExIhj7ztVLKnIZQwTXg2ukm59gQFZsCFZ0LLq9f9ND16VCIZfhlte/E3mtf/PDJLy69xx+NX2c5HiiQzoXHv5yzlKidJdtmW+p3Rep66SQMYLJ31/ftn0lJoRrPbu6z312VpQHWdVk4f9mCJHM98mI1WPZjCSs3XGSD9eN0AXhrhlJsDhcfheS7x+swIzUaEGRgbRoPYLkjEheN+wuwuYlEyFjCB764BjKTO248+ok0bkf/rBz95zLwVu/KBfrdp3CxpsmQKVgJEONHp+bieXvHuIXaZuX5He7uwdAtMDiHAup0frLVgb7Q5RehQSjWhRy9tLPJ+Lxvx0Q3fMVBcmie7V+US5iQlSQM8CzC3MQrJLj12+Lj/3zz/Jwx7REbD9aiZuvSOCLmWiD5HzxHu/PP7MgBwwB1v7rFG69KgGLJsWLcqAfeP8oXlmSj+K6VsnnbpRBjazYUL87Ev50VkKYGiyAV5bkI86gRoJRLag+e2NeHD44VI7f/SiJN1BVCk/LkDCtdN81zkjzfs/fglzGYNBzLwcSbh7ndpXaHS5RJUhvg3dMuBZLfWTsD9tP4C9LJsJsc6LMZEGjxY7gILmopc+G3UXYuHgCZmXGoLbNjs17TwveX7PjJF64aQKcLOs3Osdb3/uTIRkDgcNERoCMUSH8a5xzmPv8lv3lWF6QjO9KG0QG35b95bjlyjGQk875XebjZOlK5sYYtUiJFPfe8zb+KBTK4NLjPnaEkJ8SQrYDGEsI+djr3xcAGi7ZCAcILhyOy0/rSSx+cqQOd1+TjCi9Eq/eko9187Pw9IIcvL3vPD47U4uMKB0cfsqsl5vMOFPdhqLaVoyLCMaW70tRmB3Lh4tYHdJevjabZxdlYX4cDlc0SX7m+9IGzMsbjeiQIH4XT6XorPTJTfRuNyvql8d9JjkyGOvmZyPBqMaSKQl47asSbNxdjKVvH0BLuwMGjVL0vWPCtXyZee61jXuK8KcbsyEjLI5WtvBtHLzH8/jcTGTEhPDn8q5q5j2mtGj9iAn18JZHoPO+WTq8vzEhKtx5dRI0QZ6Qxhgvw9gT0ujJwSBgcfvURCzMj0PmKD3unZmCldemYHy0DrEGNZZOT0SYRom39nlCdLgecm/tK/NUyeww6pZMScDmvSW4972jeGFPMSJ0QZKyR0hn4ZQyUztviN2z9TDMfjzXrVYnqpo7d3K7agLs/XdFowX3z0pDRkwI1i/K5ftVLZ2eiE03T8A/f30l6lptmJ0Ti2UzkmDQKLG/rEGwgPGGW8D4W2BNGB1KiwT4YYxRi7VzskSe/EPljZLXMs6ggUEjx1+WTMRTC7Lx1i8n45rxkThxsRXrPyvCne8cQk2z9H2oa7GBIcBvf5SMXccvdrZg8ePkOF3TCovdBaWcIHd0qN8c6O/LGqBXK7DGp3fe6tkZGG1Qi+67d+9Jf3q0oqkd9287hl+9vR9HK5rx0PVpeOaT03zPO0KAwpzRIqNh9ccnABBRTzNOd/vqB+/efd7fX5AaOaJklrsu3K6S79wdE6Li0wKeXpCD01XSlSKrm6w4Xd2KlCgdxho1cPqZx9udLsnv4d5vszkRG6LC4z5z3oqCZMi8bldXc3F+ggG3XJnA95/7y94SmMx2PHR9KsYYxXl/hdmxKDNZJB1v981MRXqMHq9/fR63T03E8oIkTEk04tmFubxzzJ/MAR4HZFqM3q/upFAog09vduy+BlAFIBzAs16vtwI4GshBDQbe4XBSZYGlvFgA8N7+ctw0OQF3eIX/3HNNCioazPiyhAh2vryPTYwIFoR6PTEvC//4thQvljVDpWDwypJ8yeNiDWosL0jChNGhOFnVIh3OJGOwdsdJPL0gB7/+235+MiMEmJYUjkljwgB4ku/XdTRh9/acr5ufjcZ2OzJHheDFm/Ow4OV9ol1Brnqh9/c2t0svrMobLAjTBuHdpVcgUqdCXIga46M8JaejQ1TIiAkRFE7xV/FvbPjI8DoD/otjlJrMkjsj3iFnXKiR1eFGgjEYxy42IylSh1NVLVj/meeYq1PCceNET+yrJkiORotdEO6jUjAYHabBHdMSMT5Kh2c+EXqjLzRItw9gWfhtk6BVyiSP0ShlCJILw0H9ndv777x4A/LiDZLXKt6gwcdHL4o81gwDv7t73gsYqe9PoGW9/cIwBAoZEd1333xjoKPXppyBmTD8jhynd9Z/eobPedJ3tLnwPbayuZ3ftV07JxONZhue313kt5okQwCVQob7ZqbC5WJFssZ9Tilj8PAHx7GiIFnQCmbj7iKMDc9BgrFzN8K32FZ+QgiemJclCIv3Dte3Oty4//2jeG5RLuxOFm9/U4anOnYS/RmkDWYHrsuMxsz0aNS1iQvkeMt8tF6F8dF6kc7sapfxcoTTBVK6RKoP5os35/VIxkL9yGJEcBDumJboN5xdJvMY989+elaQq/zWvjJMiA/FmPBgXpbW7TrFR1hw43t2YS6CFIzISOPy5B0ut+h7uSJa/pwcPwg24o3bJgvmFQBIi+mUJX8yBwBjw4deRV4KhdJJb3LsygCUAbiSEBIFYFLHW6dYlnX6P3L4wIXDAcLFiFQ5/+UzkvHMJ6fxyPXpfC4QIExuXvr2ASQY1YI8EJXCk0u3btcpwTEPf3AM6xflorLJU3Ftw+4zouPW3JCBv/7vHJKjQnHoQhOyY0Pw8PVpeGLnKf4zq2dnwNqRy9HekSPl3fNsQV4sSk1m1LXaeMXMJVSrFQzSYvR4zKsC15PzsvhCMRxWh6cyJ3eNuM/JGelFnFYph16lwBWJRv71nNEGPqdO6j4MpYp/gwXXdBsA3/og3qDB2jlZWNrR/xAQhpydqm7l89w8Ia5BWLGlBHdfkwyni+X71KVG6/lwtJgQlaigRE5cKJ795DT2dzgavPPnAGDrfnG/vFWF6Wi1OpDgJRscKgUDFqxkyGdRTSsSwoP9ho16dnWzYLU78MJPJ6DMZEZajB5yBrxM+IayltS1SeY8rShIxrr52bh/21F+d8+3EMZQbCUwHJCq7rr9SCV/vb1157mOXE3v+3P/tqNYdnUSGEL4HLuV16YIei/6Gkuck2nJlATsOl4l+flovQpPf3Ka12mP3ZCO5xbl4lR1C9ysZ4xLp4+DvSNKot3hwunqVkEJ+mi9CiV1bXwFVbebxbpdp3D71EToVDLoVAps9Ao9zo4LxcbPzor05qnqFr6YytmaVmw7UIFnF2X7CX8L4isNSvX89JX5+DDtiNeZQKfe5Bw4q2dnYM32E5ItWB7bcUKy6byvjP1lSR5/Hu+59pW955A1OhQxIWo8vzhXUEBq7ZxMtFhsSInWixxnCUY1n/urVcr4SpMqOcO3pGEIQACcrJTeYY7Wq/DWvlKRrsyJC8XRCukKwgzxPKdSRZd8X/PXZ5bOzxTK0KbXOXaEkIUAngHwBTx65wVCyH0sy/4zwGMbNHwXdo0WTwXALb+agt1navkqZQDQbHVIKl0uubnM1I4/f1GMpxbkoLi2FVeMDcOJymbJwhinvSb9/WXNWDYjCK/ekg+T2Y4IXRD++r9zyB1tFLUuWFGQjHaHC2nRetS1WqFTKZBgVGOUQS0wvjbdPIEvt/27Hybx4+YMv3tnpvB99rgxPfTBMcndOZXc08MsY5QOqdF6MAS4973DImN09ewMWOwOROn1vboHQ6ni32Dhr2dQqFohKXNWhws7jlby+ZCTEsIQolZg400T0NzuEBSLWHdjtuD+7zpexTc5916AVzbZPD0UO/JSuMVJo8WOlnYHbxidrWnFpj3FaLTYsenmCZIGXLnJAo1ChhUFyQjTKKENkkOtZLDhsyL87IoEvPNdGf6yZCK+L22EjADPLcpFUW0bkiKD8fpX5zAjNRqP7+wsvPLHednIHOUW9UAE/OerjA7TYFZ6NLJiQ/wuSujCpW9IGcT3z0rDzLQoxOhV+OpcPa87fzo5XvL+jA3X4vcdOclVzVa88T9P+fa0aD1cbhZP7DwlMpbcLHj5fON/pXj11ny0tjs9O4MKBg++f4zXtwaNEiazA49+3Cnnj8/Nwr+OVOIn2aOQYFRjwuhQ/GH7CQDg9WZxXRuOVjTzve1yRofwu+a3T03E85+J+5stnZ6Io5Ut/Fi5EGnvkOJGix31bVZR/7E1N2QgOjSoV9ef6sxOvGUxTKvE0umJiJNoV1BmaofV7sLtUxOREKaGRimXlLEDZU0I1yrxxm2TYDLbARbYduACJo0VzserCtOhV8kRJJOh1NSGsOAgRIcECZ6LBKMad3W0ryjMjkVyZDCsDk++pXe1VABdRu+UN1owLSUSb+0rw5u/mAwWLNQKGY5VNMGoVUrk/qcgMUIbEAcVlTUKZejSl+IpjwCYxLJsLQAQQiIAfAbgsjHs/C3svj1vEhg4d16d1GVIGkeZqZ1f+OYnhODOq5Mlj/Ge9FUKBgfLmxATosbL/y3G7JxYJEeFSrYueHpBDk5Xt+KxHSexMD8OMNuxdk4WpowxCnqesSzwyze/w+L8eFxsbheNwV/eVLxBIzAQl89IhsPthryjKMbYCE84yS+njsOHh8p5YzRUo8AFkxkRoWrEh9Hdjt7ir4/a1l9fKSk/YVqlqNrfioJkuNwsNn1eLCjrHeQTOjQtJVKUhO9tzFkdbj4nhJMBrsXFA9elIiVShwevT0V4cBCOVzTzOXveoUcL8+PwxelaLJ4cj9Venu8VBcmwOV24/QdjYbG5wBBgdJgWL31RjGkpkZI9qKwONx784CiMwUpMTQoXGV3+egNGBAehvNGCMUZtl4sSunDpPV0ZxG12p0B3jg2X3tUNVskFr1U1W7FxdzEen5OOWIMGjRa74Ds5XWt1eHIwGy122Bxu/GmXZ+dkeUGSwIkmVT3ykQ89zqvyRgvW3JCJq8YKQ9UIPGHr3hURX7s1n5dHfzmhvnqTCxsdZdAgJkSF7Ucq8fjcTDz7SRF+f00yNi+ZiEaLAwaNAnIZEBdKdWZfEaRW1LZi4+5ivPDTCdI6Qa/C2n+dQkyICo/PzZSUMZcbqDfbUV9iQnyYBve+dxR3Xp0k0klrd5zEsquTEKkLQrPVhfWfHce/7pomeC7UChmWv3uIdwxwjcP9ydGRiiY8dkMG33fUO4Li7muSsSg/DlH6ID6ss67NhouN7ZARTxEiNwsYNArEhqppODmFMgLoi2HHcEZdByb0ogjLUMLtZv02KJZa2PkuFgnxhKT5a+LMwS0+YkJUmJEajRc/L8LaOZmCBTgXCnfTpHgsL0jC2HAt1AoZXv3yHAqzYwH4z12y2J14/6Cnd01qtB5rd5zElYlGyOWM4DfsO1fP99QxaJSicftbDNe12QSLdC5k77WvSjA+Ws9PFrMyopEarUOD2YYxRg0sdheuSoqgux19xN+uk8PpltwRc7pZUQW4DbuLsGZ2hqj30EPXjRecw59seTsapnSE0nr31VIpGCRFBmNZx04vl7cilbN3xdgwTBgdit/+XVwZ86Wf5aHB4uDDQ71z4rpaPFc3t6O8wSxqjiu1e7SqMB2HLjSi3e5CVlwIZoyPonLZR/zpTn8GcUKY0JCrbLJINktuskj3piMMgxc/F7eI4Ra4CUY1UqN0+OONWXC43Hjo+nT84eMTghy/mBAV4g3SjcvHhmvx1K4zuDLRCKVSJvgN35aYsGF3kcAxUuVT2EVKb1a3WPnQzCvGhvFVX7nQ9bz4UMSFapAXb0CD2QaFzFP11ncuovQMKZlMjAgG2yED/mTuYpMFAKCUE7Q7nKK5+cFZqbA4XIgP00DGENS2WLs0xAAgKkSF5zp2cevarBgXGczLlPc8bHW4sfdMLVYVpvPn9ZWjdocboR27jm7WMwdzrVfKG9r5eZjrIzdjfBTKG8yoabHBYnciPkzLh5lTKJTLn74YdrsIIf8B8I+OvxcD2Bm4IQ0M/sLc0mN0qGoWG3qAeLEo6/AQezdx9sSwB/FeP5WCwaOF6Whud+Ch69P45rd2ZynWL8rF6eoWuNweY+m+mamC8swqBYPVhRlwuF146YsSPFqYLp3o3dSOW65MQKxBjZe/8ITDRenFFaqi9Cp+Ac+F33mPodxklgzfiAtVC3qPeRfn8G5eSnc5AkukTtrQbrY6JHfE7r4mWXKhoQmSi3oPtdhc2Haggj9HcqRO8ru4RdGaGzLwyn/PYXKiUSAfa+dkYu0OYUW/x3acEOWjrChIRl2rDQwjLrBhdbhhcbj4whPcaxv3FOHpBTlQKTortPmOr6KpHfLyJlFzXG+PfZnJDKvXLg43nqSIYJFBSOmevuhO34IL7XYXPjxcKXIYLZw4WsJRlsE7DiqbbFh2dRKiQ1Qob7Dg7W/KoJQT/O5HSXyrA+7+/mZ6IrYdrOCdZv4iFTyh5TK/etNsd4ocIysKkvjzSBXi8S5mxPVz9G4y/dAHx7Bz+TSR843SN6RkkjOeE8I0WL8oF2eqWyRlrjA7lp9rf/fOQaREBvPzolohg83lFvW5++ONWSitN0vKUlqMHmBZ3vHlWy3Sex6OCVFhVmYMNu89h5smxft1+F6bFokxRq2gL2hX8/CYcKrbKJSRSq8NO5Zl7yOE3AhgasdLm1mW/SCww7r0+Atz4/LJuMWKd6lohiG4Znwk3v3VFLRYHbA53Ni8JB+rPjrGFydZUZCMjZ8V8Un1caEayBhAr1bA5XLz33e0sgVrd5zEjXlxSAjzNKcN1Shw71vCPkxrdnh66lQ1W/HSF8VYVZiOzXvPCYpcVDZZYGqzQ6+So9nq8FvoId6gwZUduy5uFnzSNvd9MSEq/PaHiYLk7SAZg9f/V4LNS/JhdbhwtqYVhADzJ8YB8BSW8e1f09VOKKVnuN0szpvaRIb2+kW5SAjTSu6I6VTSfa8qGi1IigzmFzQAEBwkE5wjJkQlWQzFbHXgmQU5aLTY0WpzIDkqGM8uzIGMIZARgpqWdhRmxyJIzmBsuBaVTRa02VwYa1QLPMzcIveZDkPNW+YW5sfB7QbumJbIN9EFPPIfJO+sluhvt6bRYpdsjss5GupabfjD9iOCBtDvfl+OvHgDXfz0gb7oTgDIjNXhrV9MRm2rDaNCVciMDcFd/zgkup8RwUo8syAHhAA6lRxN7Z15zFXNVjzzyVnEhKjw8PVpuPuaZGiV0v3rlk5PxLSUSGzZX87nsN00KR5PzMtEmcnC58uNCdfC1Gb1qzfHGrV4+Po0nK1t5WV0q1f/0qpmK7bsL8dLP8vDiYuenDoZAR7+SRrSovVosNhQZmoXNW9vMNuQ2BHGTvVl/5CSSS4/PDVaj6uTwhGpC0JiRDBK6tqwdb8nwmX17AyEaZV4fG4m5DICg0YpmJvjDBreGcud9w/bT+CVW/JR3WzFUwuyUVzbxstSvFGDc7WtSO7oVegrU243C5b1pDB4nAMyXr+v23UG2bF6vrAP5/BdnB+PFVsOYVVhOv511zQU1baiuLat23mY+z4qWxTKyKIvO3YA8D8Ajv9v77zDrCivBv47d3tnd2kruAsri3QQkaARo6AEE+wIRmOL+fiSLwTUxFgSjS0xRMXEaIotRmMiRmOiBBFjiSaxodI7CMi6tGXZ3u/7/TEzl7n3zt1e7rLn9zz77Nwp77wz75nztvOeAxjgg47LTtcRyczNcXoSOgoG0NDg59WNe9lTUh3U2F504TiOyUyktsHPzoOVbNlfwYGKOq44OS9oFPnWWaPIy04KjNwWldbw+L93BNYx5WWP88zT/rJaALbsryDOBwumDeeHfwseufvbqkL6pydy89kjOXPEAIAgL265mcms2LgvzDTN7fGyqLSG3/xrB/fOHscHOw/R6Iffvr2DotIa5j29kue/dXLAm50TsPp7M4aTHB+L32/w+STiaH5bYin15kppZ3El8//0CZnJ8UGzwaNy0sjNCjczXDi9gIrq+jB32QunF/DK2iK+9gUrDpKz/7ozh/Ojr47k7n9YHlVLqqyBAfdo9kNvbKOotIb504bx8upC5p9RwPw/HWmI33L2CBLiYoLSXTCtgJdXF5KbmcRfVu4JckIAsOdwNT+eNZo7lq4nMzmeK07OC/M26zbzzEiKY/iAVJ64cjLlNXUB5yqOOZKTflPBcesaG8PCQyyYVkB9Y2O7yqi3ymdrdaffb3hn234+P1wbNIt79/ljWPbdU9m4r5wYEe55ZSMAZ4/NaVJvgmUpsXFvOX/9eA/XnzU8Yn5ifDB3Ui479lcwd1Iuz364m0sn5wWtl7v+rOHkZSUzPYLe3Li3PCg/jow+9e4uHr1iEg2NfrKS41mw5JOgPCbG+Vi2YCrZKQmBECVuz7OVtY3U1TXyz837VV+2k6Zk8m+f7KaipoHbXjoyE3bXeWOormugrr6Rhc9+EqQvnbXDD7+5jQXTh3mmu7e0hmc/3M38MwqCZOm6M4fzpw+s/a9eO5VBGcmBMsnJSGRDUXmTdfCawjLuXLqBn88ex4c7DzFr3KCAnpv/p09YtmAqIwam8enByiC9ecvZI0i2vWw6ZQ90WF0cSm+WNUWJdtriFXMOcC893CtmpPVkbqcnNfX+oAbj+qLSoE6dc86Nf13DsgVTGZKdQp+kOOadlk9B/7Swkb67lm4Ia5A7jQSAtATvOF8D0hOYP20YPoG+qYn8X4jnSsfJxV1LN3Df7PHsPlTJpn0VQfd55PJJYSOazvmb9pUD1qhfSVUdh6vqgxwdOOdX1jYGOnWh8YCcCmNncWXADbgzMr1o+UZGDExrlblRR3YQeyJOQ8VpYDicclw2Q/qmMionzXNGbOH0Aq4/s4AROenU1vtJTYplUl4mlz/xQVDZP/DPLTxx1UlBaew+VBXopDk438QlJ+XyWUkV35yaD1iycrCyLtCocdJ1ZPG2l9Z7elM9pk8S96/YxDWn5jNiYPg34lz/+L93cOusUVy7ZBUlVXWBsv/0QAX/+85HYXlsKjhuekJcmJODB9/YyrP/MyVwTmsbKr1ZPlurO3cWV1Je3Rjo1DnHf/S3dSyZN4WzR+ewoaiU8yYMapPeTImgN30Cw/qncf+KTfzk/LF886mVXHNqfmDgw0l/8WtWeJph/dPYvK+8Wb354BtbA16OD1fVc9+KTdwwY4Snp+P95TVMHpLNXeeN5da/rw0bYFh88Xh2HQr+rkIHFFtCb5ZHaFomL5syNBAvEY6EMPj9VSdx9ZMfes70OjPP4wf38Uy3f3oCN88cybUhsvHAP62QF7f+fR1/vOYLbN1fERgMWzB9WJi+bG0dvL+8hn6piUFtkMzkeCrrGpnzyHtBZT/6mDQ27S1rt2yF0ttlTVGinbbM2P2Qo8ArppdzBXf8GgiONTMgPZGymjr6pnp7jnQaMeU1lve3m2aO8Dxv2/4Krjk1n1zbtfJ9KzYB8P0Zw6lr8PPwpROD4sjdfs5oEuw1Ro1+KK6s80w3IdaqfLbsL6emoZFfhTRkV+465Hndlv2Wt07n+VPiY9hz2NvTZ6UdH88rHpBTYRRX1nrOjjhmRy0lkrlXeyulnkKkhorTgXG8BYYyYmAq5TWNzHMFfr77/DGeZV9YUh2UhhPPLrTslq8rYu7k3LCZtQa/8UzXcSrg9nzoyNfnh6vYVVzNw29uY/4079HwvKwkHpgzgd+8tS0wkn39c6sYtXAqnx6q5JazR3Cwsi5g/jSsfyq5mckR3+WhKu9vpqSqzjJ5PVjJxqIytu4vD5hoNddQ6c3y2Vrd6ROhzmWG7lBT72dfWS3jj5WA3ow0+xaqN//w3x1c9oVcBqQnsru4Kiye58LpBQzKTOJwVS2zxg1if3ktmcnxEZ2nxPp87D4UXqaR9OY229Oi2wFHpO/VCd7udpgBVoN816EqzxnrpmagvejN8gjeMnndmcPxG0N5tXcA+AMVtZ77B2UkMX/aMMYPziDWBz+7cByfHjxivnnnuaPZVVxJZrJ3W8Cpi9/eeoCkuJjAjFykoOGhdXByXAyFTchT6Oykl6fXRcs3snD68KDZxLbKVii9XdYUJdrptV4xQ11z90tN5NPiioDTEyfWzFzXKNhvv34ie0oiK1ywG8fTh5GXnczC6cN4zmWOlhjno7bBz+P/3hFwJvG9GcdTUdPAna61Q3edN4b4WCFGfCCG1bsP89g7loKO5LJ5WP9U8rKTaPTDj/62jvlnDOO+FVsC57i9w7mva7R/OqOV888YxjPv7w5r4C+eM4E82313JG9gjitnr9mRJfOm0Boimda0t1LqKTQXJDtSxy8tMZ5vP/N+0PuPFJIjMS54psNZK/T7q05ib1kNnx6s5On3rDAFXqEQ7rt4vGe6jsOVvaU1/ObrJ1Jb3wgGDlTUcrCiNnBNUpzP8/pjs5K5/rnVgBVSxJn5La6o466lG8JM6X58zmj2HK7yXC/n9xtifOJ5n9SE2LCRZ6fx01xDpTfLZ1t05+NXesfiykqJB47ozfx+KRH15tI1hXxvxgj2l9VwyeQ8fvS3I6Z11505nNtmjSQ9KR4MfFZSxf0rNjN3Ui5//XgPt587mitOzovoPCW/bwopCbFhZdoSvfngG5bedJykuL9Xn1jeiJPjY8M8z3o1yB98w5oxamoG2oveLI9wRCaP/+5UNu4ts3RerI97lm8KOGAKLcPBGUksmD4sYELszJYdqKglPSGGw1XBsT/vPHc0fZLjqWts5IF/buWWr4xsti7+5evB8T/bWwcPyU4J6FcnHa/6eNa4QQFZdNJuq2yF0ttlTVGinY7yivlKx2Wp6zHGUo6nF/QPxH1LiosJNEzAHlnbW0ZcjNXx2lNSFRjBu/v8McT44NMDFXzy2eGgRqfbO5rjjOJXl5zA3rIafrJsY8DszO1K2+o8xrBo+WYS43zMP2NYwHFE4eEqTycSi5Zv5KaZIwMdxIEZieRkJAYaRy+vLuQnF4wNeB4MNWdynrGu0TL/czx9xvhg+oj+jM7J4K2t+1k4vYCa+sYWjyY66VbVtW49U3MzVkc7zQXJ9ur4LZhWwHs7igPvzHHWkBQfw93njwlqCP/4nNH89aPPwhoPl5yUy/XPrSY+Vrhp5khuPnsE9RFmW2JEuP6s4Sx+bUtQHpas3B2Ic1dSVcdT35jMv7YcJCHWx4icdH5ywRgefH0rqfGxYc5hbvjy8azec5j4WAmb+R2SncIlJ+WGmdLd8fJ6/nD1ZM+O3c7iSnYXV4Z9M9efNZy4GF/AVMlx2uKO3ddUQ6W3y6dDS3RnZnI85dUNYXpzwbQCauobmtWbC6YVsOqzYm6bNZrVew4HmWs6Ml5Z18CInDRu+/u6IJNIpzyN3wTMyENl/rozh2MwlFTWha3le3l1IYsuGseNL6xpVm9OzO0TeAdOZ3fmL9+xZqGzk7j9nNHNNshr6v0MH5DW6iDSKo+Wzhza13pvCbE+1hWWkpkcT0lVbcDLpd9YZfqNU4byaXFVmMwNSE8gIzmOLXsruPXv64Lq5cLD1RRX1gXMNVtaFyfEWuPeL3y0J0zfeclSTcOROnjeafmccGwf8rKPBBYPdaoVI+Edxkjha9oiW6GorCldzfwbbqHwYFnY/kF903no3p92Q46im7Z6xbwI+KK9q0VeMUVkJvBLIAZ4zBjzs5DjCcBTwIlYs4BzjTE7RSQOeAyYaOf3KWPMPa3NdyhN2Yk7sWbciisnI5GUxDgWe3gNLK+uZ9navVTXN4bZ0P/y9a08MGcCcbE+7nKZWC6cXkBmcjwieK5Zu3XWqEDHrK7RT2Ojn5/PHk9CrGAMQU4unMXV1fVW5ykxzsfuQ1WWi3vbXGjupFz+/P5OlsybQnV9YyBIqtu5RWKcj0l5WSTG+QKOXRbPmcDYQX2CnHlccXJeWIX20KUnYAxU1zd6jrh7uRFviuZmrHoDTYWPcDp+g+ZN4fVN+wOx5S46cTCJcb4gmcpMjuc7ZxwX5O20vqGR04/vzwsfWyEP8rKS2HO4OuA4AOC651bx6OWTqG/0e1bkCbE+kuJimHdaPrE+HxOOzWBDUVnQgn+AvbYsuRvSzvqUUOcwdQ1+XllbxF3njWXe0yuDvqUf/W0tiy8eH2HgoMHzHRZX1lJZ5+dPH3waGKgYlZOOCFzy6HthDayi0hpEmm+o9Gb5bI3uzMlI5PIpeYG1SI5uK6uu59kPdzO070heXFXoqTfvv3g8xkBcrDAgI5Hv2GuLHYcWTtqhpsNu2bMas6ngCvPiDk8zYoAVd7Okso6DlXXcOHNk0HNdf9bxfHVMDmMHZQQFl/bSm07IDcfzorO2CrDNj7dyzwVjudkeXEtPiPGcMRo5ML3Va5V6szw6eMnlzTNHEBcTE7Tv1lmjaGz0BxyQQfBs2S0vruOu88ZErJczk+MZOTCdusZGEmJjmq2L8+3OZklVHclxMSycXsCIgWlkpcR7ypJT9E5c2i8N7x+QB0eu3HozJT6Gn180jh/Ygw952UlB3q+dQavEOF+bZCsUlTWlqyk8WEbCyZeF73/3mW7ITfTTJq+YxpgXROQ153oRyTLGHIp0vojEAA8DZwF7gA9F5CVjzAbXadcAJcaYYSJyCbAIazbwYiDBGDNWRJKBDSLyZ2PMzrbk3aE5O/HQUakLJ4abo921dAMPXTqRz0usdRLfnJrv2egsq67nxyGOAxwTDSAsxpiTtjObd1JeJiB8XlqNEEtZdZ2nk4udxZVcPGkw6YlxPP7vT7l2egHzpw0Lq3Cm5PfF7zdhjZjFcyZwSn52YNTZPUvkduaxaPlmcjISuebUfMYNSuf4gWlsKCrnq796x3PEvS1Kv7kZK8V6R1V1jYF1cjkZiaTEx3DbrFHsLasJyNSFEwcHvF86JMb5mHdaPt887Ti27S+nX1oCD/xza1Ajo6bez4e7DvGXlXs8R6bvWLqeO84dTW52MvvLrADLf/5gd5hnwG0HKoJk+4F/buEXcycE5MntHOb6s4Yzc0xOxLVNvghmlVnJ8Z7vKD7GF5jhc5tDzTstPyhPbqctPqFZme3FjFA4AAA29UlEQVTN8tka3em1HveupRuYd1o+3/rSMH62fCPnjB/kWdYb91prj371tROCYhw65pFeaTvl+NeP93DhxMHE+CAzOY7tByoCeXJkLjHOFyjzX186kd/8awdfm5wb1FAf1CcxKNacl95cdNE4TsnPDip7L8uFlbtKuensJJYtmMqhylo+P1wTaIw7OrNgQGpg1qk19GZ5dPCSy+KqcAdPdy3dwG8um+gpczUNlnXCnpKqiPXywukFpCTEUHa4nqyUhCbr4pT4WBr9/kA97HiZfnbeFxg7qI9nHTwqJ41Tjsv2LMNITrWe/9aUgFwVHq4JOIVxW1HcOHNkm2QrFJU1RYlu2uIV83+BO4AawI/lGdMA+U1cNhnYZozZYafxLHAe4O7YnQfcbm8/DzwkIk7aKSISCyQBdUD4nGwrac5OPHRUKimkMemcv6mojMyUeDLthqVXozPZY+1GTb3lZOKFjyK7647xwT0XjqWorJbbXLb+91wwltvPGc3tLtfhzkj1dWcWUF3XSHysUFhazUNvBHsldGYhmlLOXrNEoR1dZ0Zv2YKp+A1hFeovX9/KH66eTL+0hDYrfQ143jxOuTijy465mTtYeSSTL78hyAmE29U3WPKSFBfDhRMHkxofPjINsL+sltteOiKHP541mt++vS0wM33nuaN54J9bw+7d4Dee38qQvin84PnVfHNqfsQ1WV4OXur9wc/nUFXXGPHZQ/fF+AgENQ4NeO5Fb5XP1uhOx5FE6LmD+yQREyPUNVgF0dRazVD5dQKC1zR4l21SnC9spuWeC8Zyw5eP595XN4fpzJp6P4eq6oiPFWobggcALpo4KCj9ljZqI5mrZaUkBOTl649/EKYz//HdqW1uIPdWeXTwkstIDkuq6ryXEzieXZ9buYcfzDze89pj+iQFnFPlZSdx57ljgkIpuOvimvpG9pXVeNbDTclSpPiabZErZ4372EF9Oqzz1dtlTVGimbY4Pfk+MMYYM8QYk2+MGWqMaapTBzAI+Mz1e4+9z/McY0wDUApkY3XyKoEiYDdwn9fsoIjME5GVIrLywIEDzT6EoyDdeHV8li2YyrPzvsDJ+dme51fbo3gXTxocaHA45zlKvsj2cBV6rU+sDlKx7VAi9PiIAWn0TU0IdOrAUtQ3v7iWXDsA9Pxpw7jm1PxAoOZdh6q5Z/km7jpvDF8YmkVedlIgvdBZCEc5T8nvG1DQOw5U8O72g+w4UIHf1fp1GmvuZ3PSi9TQMxjy+6XqSB6tl8+W4pTLxZMGs2Tlbq45NZ+LThxMcnxsoOyBiPIXunD/4kmDA8dvnjkCnwiP/3sHWw9U8vi/d/DQG9t4+E3LW+XFkwYHOnVOGncsXc/3Zoxg/rRh3Dt7PBnJ8QGnGu57f3aoioXTg7+Vu88fw97DVdTU+z2/pcVzJtA/LSHwnI7sL1m5m6yUBM/3E+k7DxXJxDgf00f05/wJgxjSt30y6/ebiN9RtNIa+WyN7pySn+V57q5D1fzwxXVN6s2lawpZOL0g4PzGwXHyE0knnzQkK2ym5eYX1zI4M5knrz6JBdOP6EzHRG33oSruOHcMaQkx5GQkNmle5ujNyUMsc7f3Py1ulb6EyJ3jAxXBcR+Vlsuml1w668/cOOV966xRYTL314/3AJYZpONcKvTa7S4LhF3F1Tz81lbumz3esy7+6SubGHdsn4j1cGvqYGi7XFXXN2o93El0Vt2uKG2lLaaY24Gqjs5IE0wGGoFjgEzgHRH5pzP752CMeQR4BGDSpEnNtqRaYifuKN0h2Sn8Z/sBz5kCZ8Q3NyuZkqq6wILnvKwU+qcncKiylsS4WL4/43juW3FktPiGLx/P4MxkfvW1E0iI9YUdv+u8Mdz1j41cO73AU1EXHa4hLzvF0xlKTb2fD3aW8Ng7O1h00TgG9UkkK6XpmbPmYtM0Nbqoi6mbp7Xy2VKccklLjCExNiZIPp3ZM2dW2O3kxHGr/du3j3xGNfV+hvdPY9FFY0mJj+WzkiOu2J3Gtzv93KxkT9ncsq+cx97ZwbzT8q01SR7fjeMa/97Z44mPEQoGpJGbmcz6ojIS47aGOfAZ3j8tEJzdy3wpktnkkOyUMG+F1505nMTYIx45nTRG52S0O+huT43x1Br5bI3uLK6sbbPe/P6METz69na+Mi6H684cHjCpTYzzMf+MAv7yYbjnwIXTCzgUISRMaVUdxw+w9PktHnrTb+Cxd3a0aNa2PfoS1AFFa2ipbHrJ5fABafz4nNGBGIpu/XPlKXl2zNlUUhJiuePl9YGO/k8vGIsPw13njQnyjHnXeWNY/NqWoPvuKq6m0ZiA5+pQGf/v9mKuP+v4ZuvhlugOlavoo7PqdkVpK23p2N0M/FdE3gdqnZ3GmAVNXFMIHOv6Pdje53XOHtvsMgPLicqlwHJjTD2wX0T+A0wCdtAOWmpS4yjbzXvL+NuqQu6dPZ6t+8sDziqciiDF9vDXLy2B9MQ49pZW8z9PHWk83HL2CB67chKbi8oZeUwaByvqWPjsJ4HjN88cwfwzhlHT4McYKKuup6i0huSEWE9FPSAjkZ0HKnj6G5N5Z9vBsPwY2wTlxhes4OnNmUy0JDZNJPMLXUzdvfh8QnpieBDuO5au55HLT+SDnSUMH5DKQ187gUOVdSTFx5IQ6+MnyzaELdxPS4rl3hWb+MYpQ+mXdiROk7ujNSQ7mSHZyYFrQmXTJ7BwegH90xO479UtxMcKv7/qJN7dURwmp1v3l3P+hEEBmRo7KCPQEXPMfRdMK+AnyzbywNzxDOmb2qr1HT6fMDG3T1Ag9if/u5P4WAk4EuqflkhuZjIrNu5rd4esN8R4as0am6xka4Y1kt5MjY9l/hnDqGv088Xj+rJ5b1mQ3lw4vYD0xFgGZybx0KUTWbPnMI1+eOjNrcydlMvydUVBjlCKK2oBbzPfQXasw+MHpgbJgzPD4ujMW15cy7IFTZtEtkdfgurMzsBLLgV4bcNez/Iekp2C38Ci5RupazBcPGkww/qnkpYYx+0vWd5V87KTWDxnAtv2V1Db4OfYrCRPC4R+qfE8fuUk3v/0UJiMN/ppUT3cUt2hcqUoSlO0pWP3O+ANYC3WGruW8CFQICJDsTpwl2B12Ny8BFwJvAvMBt4wxhgR2Q1MA54WkRRgCvCLNuQ7jJbYiTvK1lnDdN+KTWFu2BddNI5+afFkpcRz7ZJVXPaFXB56c1uQgv7pK5t4+NITyEqN51BFPT94fk3Q8XuWbwq4WXcaNAB/+O8O7jh3ND92rWO687wxPPXfHazYcDAQM8rtyt7tQtm99qUp2hObRhdTdz+R1pKt+qyUh97YxvdnDA+SyZyMRK44OS/I9faPZ41mX1k1V5w8hCf++yk3nR0cp8npaP36somMH9SH1Z8fDpsxufPc0eRkJLLu8zIGpCXwwNzx9E9LZHBGEvvLa4PcxjvOIkJnekI7Yk5DzG3q15r1HblZKYwYmB7W2HGvOdlxoKJDOmS9JcZTS8sgxgeXnJTrqTcXz5nAiJw0EFhbWMbhqnp++sqmsHVnj14xidKqer5vhzhwcIemSIzz8cjlJ/LI29uJjxXuPHd00NrPO84dzWNvb+fdTw9x/8XjSYmPDZoBbK3ObG85q87sHELlcufBChLjYmg0BOm6O84dTUp8DP9jr5UDePD1bSyYPizI2cqu4mquf25VwMlObp+EsBnAO88dzeLXNlN4uDZMp7pn7jpbppznV7lSejJr16zmgqvnB+1bv3EzE0/upgz1QNrSsYszxlzfmguMMQ0iMh94FSvcwRPGmPUiciew0hjzEvA4VudtG3AIq/MHljfN34vIeixHLb83xqxpQ77bhNsL1dPv7eLCiYPx+eCpqyfTaEyQydaOA1aQ3kgLtlfvKQ1UHl7HRazgvjfNHEl9o59n532BytoGMpLieOobk9lbWkOf5DgefH0LK3eVAlbF8yt7cfShyjo++exwkLtvtxmG328impq114RDF1N3L/3TvMuvtsFuJDcEx6IrKq3hqXd3ce/s8WzeV257bNvGvRePo+hwDedNGMSukPhvedlJ3DhzJBs+LyM7JYF+qUfWuzlOVZ5buZsrTsmnur6RtMR4xg7KAGD5+r0sfm1zwLTyhNxM8vsmc2xmeKMjtCOWl53EI5dPYl+ZJdOtbaj4fMKMkQNYMm8KRaU15GQkMjono1kvhm3pkKkpVDCOnDl68+ezx7PzYCVfGJrFSUOy8PkEv4F7XtnIiIFpnmXw/qeH8EVwAOTozBtnjmTNnlKuO2s4tQ2N5GQk8eTVJ3GgvA4BHnl7O2sKLZ9b3/vLaq4/s4BrTs0nNyuJwsPVrdaZHVHOqjM7n6LSGn7zrx1ccXIe984eT1VtA+lJcfxs+UYWTg93Whap7s7LSuLu88fQLy2BlHjhqasns7csvD5+6l3LpHhwnyR2HaoOmrnrn5bYqXWwg8qV0pOpMzFhoQ1qVt/cTbnpmbSlY/eKiMwDXibYFDNiuAP7+DJgWci+21zbNVihDUKvq/Da31W4la3bTbaXWcWQ7BR+d/lEfOJjwfRhJMfH0NBoqGlwgojGAEfcdYcq8NE56eRkJHJdSNDpJSt3c+nkPBJjLdfxTiXisKu4mur6Rr40vD/V9f6AqYjbDMPLfn/RReP46pgcYmN9asLRg/H7TVjQWmdGzFnLBuEyV1JVx6a95UFus2vq/Nz4whHX8jkZiYF1KM4sVk29n4fe3MbiORO4ddaoQLyuvOwk/u/0YfzAnll55O0dAffdi5ZvZNa4QYhY8n/7S+v4/VWTPTto7lFnx323E9OuLSaSfr+JaGYJ1qx8pPiLrW1U6XcUzID0REqq6oJkLDHOx6xxR0wdczOT+f6M44n1CbecfTw5fZL59GAldY1+Xl5diDGWaYiXzhxj68zQOGX3vGLNEPp88LNXNgflqabezzGZySz+y+pAXM7W6kwt556BI3+Llh+RgbzsJG4+eyRJcb6AvFXVNnCwspa4COFU9hyuDngPvnXWKB55ezvfOm0Ye0urg+rjotIaHnx9Gw9dekJQ7M7FcyaQm5kcJk/u9ZwqU4qidARt6dh9zf5/M1YoAofmPGP2SFqrbA9XNXDjC2sCDQbH/M1x2pCTkRhwRLFk5W5mjRtEjA9GDkznQHkNj//n08AMCBA454F/bmHh9AK+VNAvyFQEWuY+2cvUzMnnqcP6Bl27r6yG5PgY6hr97CyuVFOOKMbvN2woKqWmzk+/tAQWzxnP7kNVGAMDMxIDDdaXVxeGOQLw6vgdqqwPm9n7y8o93HX+GG54/uMwU8Wl80/lkcsnsXLXIYb1Twt06nIyErlw4mA27S1jYHoCl07OCzN9+/xwVUTZckadIdx9d2tNJCOtXRm1cCobisqDvm2Nv9ixtER/7jlcxZ6Sap79cDdzJ+Vygy1Djnnwnz/YxYGKujCdOX5wHwpLqsJ05iNvb2fWuEE8+IblrdCroT5yYHogXufA9ERmjBrIgYrW6Ux3OfdLTSTGZ3nIbKvjHaXj8ZK/G2eOZHj/VP67o5hGQ5C83XnuaH56wWh2FlfjN5ZXzUF9EtlfXsf8acOAI/J1x9L1PP2Nyc3KlyNTXnrolhfXMu+0fEYMTGfm6IFaByuK0m7a0rG7EcuZSZmI3ApMBO7q2GxFD61pqO0srgysIbpw4uDA7AkcCczsrAl5Y9NeFkwfHuTV8icXjPFsAPt81vW52cmMH9ynyYZSJDOMSKZmK3cdYnBmUiAswZDsFDbtLQ8KcNoTvPr1Rvx+wxub97F1X0XQTN11Zw7nD+/u5Bdzx/HU1ZM5WFlLZnI8v//P9oAp5OShWdTUNwbNVCycXkBCbPiI9cWTBrOpqMxTfj4rqeLbz1gdvvnThgU6de44YkBYkGCn0b18/d4mZasjTCQjpbGvrFbjL3YyLdGf+8pq+eXr1no5LwdA888Yxn0rtrRKZzrx82J8eOrLoX1TggYPAI7r33qd6XhN7omeUHsDkeTvw52H2F9eG6aXbntpPQ9fOpFH3l4fsEL41peGBQ3Quuvk+kbTYvmKJE9OHFhnsErrYEVR2kNbOnY/MsY8JyKnYjk1uQ/4DfCFDs1ZFNHShppbcUcKCi12XJ3vnDGcbz/zUVClsqu4yrMB/HN71HlwRhKxsb42zQhEst9v9BPUSO4NXv2OFnYWV7JmT2mYzDizu3vL6oKclTgL+Z01H8sXTg2MKjszDoer6sJm9o7rl4pPvE2UUuJjw/ZdOHFwUAM90rqVHQcr+fVb25qUrY5YdxIpjcq6Bs98OfEXlY6hOf3plEMknTkwPZG87CS+fXoB3/nTxy3Smb+7/EQS43wck5HE2MF92jSDqjrz6MBL/irrGppYC384sH/WuEEBRynOcXednJIQw5T87BbJVyR5cryxOjKl8qQoSntoS4DyRvv/V4FHjTH/AOI7Lks9l9AAqe5t5/eIAWk8cvmkwIiym0gVze7iShZMK6Debx0LDWrakkbKkOwUFl00LpAnp6G/dE1hUCO5qRkSJbrYV1YTUWaGZKcEOnXOvgff2MqFEwcHfu8tqwnI0dC+luvv6no/k4dm8uz/TOFXXzuB+2aP52B5LUWHqzyDSNc1Ngb2OSbGMb7wPHl9C7W2Q5emZKu5gLwtIVIaeVkpnvnqrY5Ougt3OXiVx+6SKr43YwQbPWaNI8n/wfLagM5si74E1ZlHM3lZKRGDlze6ijLSYEOgTm5suXx56SEnKLpb76g8KR3B/Btu4YKr54f9rd+4ufmLlR5NW2bsCkXkd8BZwCIRSaBtHcSjDrc9/wsf7QlzZnH3+WMYMyid3CzL3j509M6paEJH9I6zR/HKahrYcaCiTSZiPp/w1TE5ZCbHs3KXFWtnycrd3DhzZFAjWb369RwGpCdGlJmslPiIM8bOOU6ZNjT4+e+OYlbuOoTfOOvxxgbWnuRkJHLbrFH8bPnGIA+YS1bu5sujJwdkvqi0hiUrd/OzC8cFzaJ4fQvO7GFzstURa9YipQHeZnrqrKBrGdrX0puLlm+MGMz89nNGMbWgX1DIDoisM3P6JFFUWkNcTAx+v2mzSa3qzKOToX1TGD4gLUwv/fic0fz2X9uCzvUq27GDM1i7p7RV8uXooeO/O5WNe8vYsq88EMrFrXdUnpSOoPBgWZh3SVAPk72BtnTs5gAzgfuMMYdFJAe4oWOzFT14uScGPF0WO4p70LwpvL5pP0Ag6LhPYFJeZqBTV1xZy6KLxgWZymWnxHPdmcOD1otcd+ZwDpTXBCqfvOwk7jpvLHEx0upF+rGxPk4d1pfBmUnsL6/hoomDwq5Xz1w9hyHZKYwdnBHWOPn5RePon5bg2Tg4fkBawD284/nvH+uKwkw2NxWVBq4tKq3hN29tY95pxwVCH7jXkgztmxLUacrNTA6SoZKqOgoGpLJ0/qls2lce1KB56NITMAbe3X4wojy3ds1aJJfiXmmoo5POIbQMcjOT2V1S5enmPdDxHpjG54eruG/2eHYcrKS2wR+Qk4IBaZ666fgBaWGmw3ecO5rFKzaxclcpD71peSgcmp3K/vLwezeH6syejVsOk+NjqWtsJDvFWj87+ph0CkuqAmEQDlTU8uamIq6dPpyb7XWcL68uDItbd/s5o7nt71YA87989Fmr6mOfTziufypD+6YwKiedU47LDtM7Kk+KorSHVnfsjDFVwF9dv4uAoo7MVLTg5e568ZwJxMdKwMV76MJmn08YO6gPhYdrwtxkH5OeFJSeE58rLkZoNIYNhaUY4L7Z42n0+4mL8REb42PBs58EZk7mTsptl+v35hrJ6tWv5+DzCdOOH8BxfVMZfUw6e0tr2F9ey/2vbebWWaPCGgcLphVw34pNXH/W8cwYOSDg+c/LZPPeEG+CawrLKH17O/fNHs+mfeX4BOJjjzTMQ2Uqkgzl90sNNGgGpieyoaicr/7qnQ5zEhDpm42Upjo66Xi8yuDu88fwqze2squ42rNMnHJwHJH8+q1tno3a+FgJBK/3CRysqOXNTfu5b/Z4/MbQLy2B++1OHUBmcjxb91VE1NctQXVmz8RLDh3PqjfOHMmMkQMYnJUSVB/PP6OAX7y+JeBk6sS8TPaVVnPv7PHUNjRybGYyP3hhNbuKq9tVHzclUypPiqK0h7bM2PUaIi1inndafpMLm52AyI4b+EY/LH5tM5nJ8UHp7SquZt7TK1kybwpzH3kvbHZl3mn5HJuZTGZyPBdOHExuZhKfl1aTmRxPUWlNpy2q1sZuz8HnEwww7+mPguRn/p8+YfnCqSyxZ48b/QQcp9z4whrGDsogv19q0HoOJ0SBCMT44BdzJ3DtkiONoktOyuUnyzYGxXnziufo5MtLhtz7vdzJt1eePz3o/c0e/92pYV4Plc7BS2/+6G/rAh6Bmyvn0M6bM4Cws7gy0EFzSIzzcc2p+cz/8yeB2bqVu0oDsqw6s/fiJYcPvmF5X73+uVUsWzA1qJ52h2tx4i468vXwm+sAWDB9GHUNhu+cMaxTZUvlSVGUtqIduyZoyj1x6L5Q9+u7S6oCI3kOK3cd8kzPqRRC9w/KSKK6roErTs7zXJ/kXNca1+/K0UckOd1bVoMx8ODr28KOOTLjrOfITI4PClGQGOfj/osnsHzhVPaW1SAI1y5ZFejUhabTkfluT5q7DlV6Ozs4VKkduy4iUrmKBP/2KudInbdldmyvptKtqffTLzWBvOwk5k7K9Vyrpzqz99CUvLidkTj1tBOuxet8h+T4GK2PFUWJatTpSROEerkEq5ERahHhtbDZq1LxG28vXDkZ3vcZ0jeFCcf2CYuH5/ZuqIuqlUhy2j8tscljcGQ9x8WTBofFEPveX1bhNzAlvy/90hICMe+80unofLeVlPhYzzST43UMq6uIVK7GBP/2KuemOvvNpZsY56N/egK3nzsmTJZVZ/Y+mpIXRwZC5a05uTUGrY8VRYlqtGPXBJHcpI8bnNGs+3WvSuW97Qf47ddPZMH0YcyfNoy87CQWz5nA6JwMz/ucNCSLyrrGiKOIuqhagaZDAjQXLsBZzzHh2D5NutjOzUzmkcsnhclua2TP7zfsOFDBu9sPsuNARcDJSntCGYQyID2BhdODwzIsnF7AgPSENqeptA4vmbv7/DEsXVMY+B2pnCM1xgemJ2KMtf544fRhgcGwhdOPuItfPGcCo3IySPQIJaM6s/cRKbzA0jWFARlwy9vbm/ezeM6EIB0XKrfHZiarbCmKEtXoMHYTNOUmfVkzC5tDPVvlZSdxyeQ8vvXHj4IcqswYOaDJoOORXB878fBOyc/WRdW9nOYW2ze3EN/nE4Zkp0R0se33G1Zs3BfmDMhxwNISIjk1mTFyQLPfUmvIzUqhYEBq0BqtggGp5GZpY6ur8JLH3MxkJuZmNlvOXh4BH7r0BDYUlQft++kFYznh2D6IwAm5fVRnKmG45dDyihlDfaOfmWMGBmTFkbdFyzcyc0xOmI47e9TAILl1ZvtUthRFiVa0Y9cMkRYxN7ewObRxkxQXE+QgpabeH+TEItJ9vBo6C6YV8JNlGympqovovELpXTTnZa05efWSs0UXjSM3M9nTCYFbdltCJEdEjvx2lAw7nkLz+6aqR7luxEvmWlLOXp1CYwh4TgVLdm55cS3LFkxlaN9UhvRVnal401KPpoP6JDZZPzvX+/1GZUtRlKhGO3adiLtSeXf7wTY5inAqnuyrJ/POtoMYc8S7IVhrUrQiUdpLJE+ucTE+MpPj2u3kpDMcpURCPcr1bELLr7W684gsn8gHO0vCdKY6t1Dc+HxCVYQlD6GyovWxoijRjnbsuohI5kEtWWjt8wmJcTE89s6OsOuT42M6Jb9K78PLk+v1z61iybyT2yy7Du2Rf6V30xbZ2V1SxUe7Sjx1psqcEkprZEzrYyXamH/DLRQeLAvat37jZiae3E0ZUroVdZ7SRTTnxALCnUv4XXEV6hobWTAt2CnEgmkF1DcGjzIqSluJNKtW39jYbicnXvK/6KJxFFfWhsm6oriJpDtzM5Mj6st9ZTU8t3JPmM786QVj1bmFEkZrZUzrYyWaKDxYRsLJlwX91dTVd3e2lG5CZ+y6iOYcXERyLjFz9EB8PiE7JYElK3dzzan5iFhul5es3M3MMQO7+cmUo4VIo9ZZKQlMzM1q0gFLc4Q6MqhvNNz697XsKq4Ok3VFcRPJGUuoQx+3DA1IT6Skqo6n39sV0Jk+gYm5fVTGlDBaK2NaHyuKEq102YydiMwUkc0isk1EbvI4niAiS+zj74vIENexcSLyroisF5G1ItIjbWmctSNT8vsGHKY4RHIusbO4ErBGFG+cOZLH/72Dh97YxuP/3sGNM0fq6LPSYTQ1q9yU7LYUJ40B6YnMe3olu4qrgXBZV5RQQuVvd0lVs/py8ZwJlFTV8fCb23jsnR2MGJiu3lGViLRGxrQ+VhQlWumSGTsRiQEeBs4C9gAfishLxpgNrtOuAUqMMcNE5BJgETBXRGKBPwKXG2NWi0g2cNTNMTfnXKK5GT9FaS9dJWNd6UhFOTpRfal0Ns3JmMqXoijRSFeZYk4GthljdgCIyLPAeYC7Y3cecLu9/TzwkIgIMANYY4xZDWCMKe6iPHcpLVm8rd7+lM6mK2RMHako7UX1pdLZNCdjKl+KokQjXWWKOQj4zPV7j73P8xxjTANQCmQDwwEjIq+KyMci8oMuyG+X0xLnKopyNKCyrrQXlSGls1EZUxSlJ9ITnKfEAqcCJwFVwOsi8pEx5nX3SSIyD5gHkJub2+WZbC9qOnR009PlsyNRWY8+epp8qgz1HrpLNlXGlJbQ03SncvTTVTN2hcCxrt+D7X2e59jr6jKAYqzZvbeNMQeNMVXAMmBi6A2MMY8YYyYZYyb169evEx6h8+kIBxVKdHI0yGdHorIeXfRE+VQZ6h10p2yqjCnN0RN1p3J001Uduw+BAhEZKiLxwCXASyHnvARcaW/PBt4wxhjgVWCsiCTbHb4vEbw2T1EURVEURVEUpVfTJaaYxpgGEZmP1UmLAZ4wxqwXkTuBlcaYl4DHgadFZBtwCKvzhzGmREQWY3UODbDMGPOPrsi3oiiKoiiKonQ382+4hcKDZWH712/czMSTuyFD3czaNau54Or5Qft2bNlI/vCRYecO6pvOQ/f+tKuy1q102Ro7Y8wyLDNK977bXNs1wMURrv0jVsgDRVEURVEURelVFB4sI+Hky8L216y+uRty0/3UmZiw91Gy+mbPd1T47jNdla1up8sClCuKoiiKoiiKoiidg3bsFEVRFEVRFEVRejg9IdxBp+L3G3YWV7KvrIYB6erOWFGiDf1Gow8tEyUaUblUFKW306s7dn6/Yfn6vVz/3Cpq6v2BAKQzRw/UykBRogD9RqMPLRMlGlG5VBRF6eWmmDuLKwOVAEBNvZ/rn1vFzuLKbs6Zoiig32g0omWiRCMql4qiKL28Y7evrCZQCTjU1PvZX17TTTlSFMWNfqPRh5aJEo2oXCqKovTyjt2A9EQS44JfQWKcj/5pid2UI0VR3Og3Gn1omSjRiMqloihKL19jNyQ7hcVzJoTZ5A/JTunurCmKgn6j0YiWiRKNqFwqRwsaiLzj8QpmDkdn4PJe3bHz+YSZowcyYsFU9pfX0D9NvWgpSjSh32j0oWWiRCMql8rRggYi73i8gpnD0Rm4vFd37MCqDPL7pZLfL7W7s6Ioigf6jUYfWiZKNKJyqShKa/CayduxZSP5w0eGnRtpf7TN+vX6jp2iKIqiKIqidBaRzCujrVPQ2/CayStZfbPn7F6k/R0x6+clH22VDe3YKYqiKIqiKEonEcm88mg0BVRaj5d8tFU2xBjTEXmKKkTkALCrC2/ZFzjYhffTPDRNV+bjoDFmZmsuaKF8Rsu7bAs9Oe9wdOW/s+SzqXv2VHr6M/TE/LdKPruhbu9OemJ5todofN72ymd3P1N33r83P3tX3D+ibB6VHbuuRkRWGmMmaR66Pw/RlI/20JOfoSfnHTT/PeWeHU1Pf4aenn8lmN5Wnkfj83b3M3Xn/Xvzs3f3/Xt1HDtFURRFURRFUZSjAe3YKYqiKIqiKIqi9HC0Y9cxPNLdGUDz4CZa8tEeevIz9OS8g+a/p9yzo+npz9DT868E09vK82h83u5+pu68f29+9m69v66xUxRFURRFURRF6eHojJ2iKIqiKIqiKEoPRzt2rUBEnhCR/SKyzrUvS0ReE5Gt9v/MbsrH7SJSKCKr7L+vdHIejhWRN0Vkg4isF5GF9v4uex9N5KFL30V7iBaZaivRIAdtRUQSReQDEVlt5/0Oe/9QEXlfRLaJyBIRie/uvDaFiMSIyCcistT+3an5F5GdIrLW/rZW2vuiurxb852JxYP2+1sjIhO7L+dHaK3eF5Gb7WfYLCJf7p5cKw4dJYMicqV9/lYRubI7nqUltLZuiPZnFpGZ9re0TURu8jieYOvbbbb+HWLvHyIi1a5v9Leua060dek2+9mlE+5/meveq0TELyIT7GNv2Wk6x/q34/6nicjHItIgIrNDjnmWXwc/v+f9RWSCiLxry+AaEZnrOvakiHzqev4JnfDsja70X3LtHyqdVU8bY/SvhX/AacBEYJ1r38+Bm+ztm4BF3ZSP24Hvd+G7yAEm2ttpwBZgVFe+jyby0KXv4miQqZ4sB+3IuwCp9nYc8D4wBXgOuMTe/1vg292d12ae43rgT8BS+3en5h/YCfQN2RfV5d2a7wz4CvCKLR9TgPe7O/9NPIOnrrO/wdVAAjAU2A7EdPcz9Oa/jpBBIAvYYf/PtLczu/vZIjxvq+qGaH5mIMb+hvKBePvbGhVyzv8Bv7W3LwGW2NtD3GUecs0H9rOK/exnd/T9Q84ZC2x3/X4LmNRBzz8EGAc8Bcx27Y9Yfh38/JHuPxwosLePAYqAPvbvJ93ndvS97WMVEdLttHpaZ+xagTHmbeBQyO7zgD/Y238Azu+mfHQpxpgiY8zH9nY5sBEYRBe+jyby0GOIFplqK9EgB23FWFTYP+PsPwNMA56390dl3h1EZDDwVeAx+7fQPfmP6vJu5Xd2HvCULR/vAX1EJKdLMtoErdT75wHPGmNqjTGfAtuAyZ2WOaVZOkgGvwy8Zow5ZIwpAV4DWhxAuytpQ90Qzc88GdhmjNlhjKkDnrXz68b9XM8D05uZgcoB0o0x7xmrdf8UkfVmR93/a/a1raXZ+xtjdhpj1gD+kGs9y6+jnz/S/Y0xW4wxW+3tz4H9QL8uenZPOrue1o5d+xlgjCmyt/cCA7oxL/PtqeYnpAtNoewp/xOwZjy65X2E5AG66V10ENEkUy0mGuSgtYhlxrgKS9m/hjUyd9gY02CfsofoHiz4BfADjlQo2XR+/g2wQkQ+EpF59r4eUd4hRMrzIOAz13nRLgNeuq6nPUNvpbUy2CPLtYV1QzQ/c0vyEDjH1r+lWPoYYKhY5vL/EpGprvP3NJNmR93fYS7w55B9v7fNBG9toiPanjJoqlw78vmbRUQmY826bXft/omtPx8QkYROuHeiiKwUkfdE5Hx7X6fW09qx60DsUYfucjP6G+A4YALWVPP9XXFTEUkFXgCuNcaUuY911fvwyEO3vIvOoJtlqsVEgxy0BWNMozFmAjAYa2RuRPfmqOWIyCxgvzHmoy6+9anGmInA2cB3ROQ098FoLu9I9MQ82xw1uq6304NlsEl6at3QgRQBucaYE7DN5kUkvaszISJfAKqMMetcuy8zxowFptp/l3d1vroKe4bwaeBqY4wzEHozVp1/Epap6I2dcOs8Y8wk4FLgFyJyXCfcIwjt2LWffY6Zjv1/f3dkwhizz26k+oFH6QKzGxGJw1LYzxhj/mrv7tL34ZWH7ngXHUxUyFRLiQY5aC/GmMPAm8DJWCZAsfahwUBhd+WrGb4InCsiO7HMQ6YBv6ST82+MKbT/7wdexPq+elR520TKcyFwrOu8qJWBJnRdj3mGXk5rZbBHlWsr64ZofuaW5CFwjq1/M4Bi2xy6GMAehNuOte6r0E6nqTTbfX/X8UsIma1z6fJyrHXakdpK7SmDpsq1I58/InZH+h/AD20zXyBgLmyMMbXA7/F+/nbd2/WOd2CtaTwBq1w6rZ7Wjl37eQlwvPxcCfy9OzIRsgbkAmBdpHM76H4CPA5sNMYsdh3qsvcRKQ9d/S46gaiQqZYQDXLQVkSkn4j0sbeTgLOw1oG8CTieraIy7wDGmJuNMYONMUOwKu03jDGX0Yn5F5EUEUlztoEZWN9X1Je3B5Hy/BJwhVhMAUpdpmNRRRO67iXgErE85Q0FCrAcFSjRRWtl8FVghohk2ma3M+x9UUcb6oZofuYPgQKxPBnGY+nbl0LOcT/XbCx9bOx6JgZARPKxvsUd9rOVicgU+11dQWS92eb72/f1AXNwra8TkVgR6WtvxwGziNxWasn9I+FZfp3w/J7Y57+ItX7z+ZBjzgCDYK1x83r+9tw70zHvtN/1F4ENdrl0XjvDdKFnoZ7+hzXaUQTUY9nEXoNlK/s6sBX4J5DVTfl4GlgLrMESupxOzsOpWCYUa4BV9t9XuvJ9NJGHLn0XR4NM9WQ5aEfexwGf2HlfB9xm78/HagRvA/4CJHR3XlvwLKdzxCtmp+XfTnu1/bceawSUaC/v1nxnWB7aHsYaWV9LC7zGdeMzRNR1wA/tZ9hMBG9z+tft5ddqGQS+YX/b27DMyrr92SI8b6vqhmh/ZjvvW+z8OXrvTuBcezvR1rfbbP2bb++/yNaVq4CPgXNcaU7Cqnu2Aw8B0tH3t4+dDrwXkl4K8JFdPuuxrD0ies5twf1PsuW6EmtGan1z5dfBz+95f+DrWN/cKtffBPvYG7asrQP+iO0luwPvfYqd/mr7/zWuNDutnhb7BoqiKIqiKIqiKEoPRU0xFUVRFEVRFEVRejjasVMURVEURVEURenhaMdOURRFURRFURSlh6MdO0VRFEVRFEVRlB6OduwURVEURVEURVF6ONqxUxQlIiLSR0T+r43XThCRr3R0nhRFUXoDTvwzRVGUlqIdu6MAETldRJba2+eKyE3dnSflqKEP0KaOHTABK/5Li7GD06peUjoUEfmWiFzRQWnd0hHpKIqI/E1EPhKR9SIyz95XISL3i8hq4GQR+bqIfCAiq0Tkd65g178RkZX2tXc0c5+dInKPncZKEZkoIq+KyHYR+ZbrvBtE5EMRWeNO0yufrrz+RERWi8h7IjKgw1+SEnWIyBARudT1+yoReag786QcQRtQUUxbGrnGmJeMMT/rrDwpvY6fAcfZDYJ7vSp+EblARF635TVHRLaISC5WAM+59rVzReR2Efm+k7CIrLMriCEisllEnsIKFHpspAZGKPa1m0TkSfu+z4jImSLyHxHZKiKT7fNSROQJu4H0iYic57r+HRH52P47xd5/uoi8JSLP2+k/IyLSaW9Z6TREJNYY81tjzFMdlGSrO3Y686JE4BvGmBOxAjUvEJFsrMDR7xtjxmMFO54LfNEYMwFoBC6zr/2hMWYSMA74koiMa+Zeu+003gGeBGYDUwBHj88ACoDJWINyJ4rIaU3kEzuv79l5fRv4nza+B6VnMQS4tLmTlO5BO3ZRhkcj93GvUTkRmWk3OD8GLnTtD4yc2I3d2a5jFfb/HBF5225wrxORqU3kp8Ju0K8XkX+KyGS7wbtDRM61z4mxz3Ea4v9r70+1G/wfi8jakMb0RhF51E53hYgkdeiLVDqKm4DtdoPgNTwqfmPMi0AR8B3gUeDHxpjdwG3AEmPMBGPMkmbuUwD82hgzGjje6z5NXDsMuB8YYf9dCpwKfJ8jjfAfAm8YYyYDZwD3ikgKsB84yxgzEasB9aAr3ROAa4FRQD7wxWaeQekkXB34Z2zd8byIJIvIiSLyL7FmE14VkRz7/LdE5BcishJY6B5UsI89YOvVjSJykoj81R4IuNt1z7CZEhH5GZBk73sm0nn2/qCZlwjPpTMpvZsFtny8BxyLpfcagRfs49OBE4EPRWSV/TvfPjbHrv8/AUZj6ammeMn+vxar41hujDkA1IpIH2CG/fcJ8DGWLi1oIp8AdcBSe/sjrAa/0k2INYD5D/u7XyfWgGqzOkYs7rWvWSsic5vajzXgO9VO8zp73zEistzWoz935clTF4lIPxF5wdZrH4rIF+39X7LTXSXWIGyaaJu1dRhj9C+K/rAUox+YYv/Osv/HAG9hjc4lAp9hKVcBngOW2uddBTxkbz8JzHalXWH//x7WaJ+TbloT+THA2fb2i8AKIA4YD6yy988DfmRvJwArgaFALJBu7+8LbLPzOwRoACbYx54Dvt7d717/IsrjOnv7PmAnsMr+2wZcYx/LBAqBF1zXBmTR/n078H3X73V2+kOAT137I94nQv62un4/BVxmb+e7ZHSlfT8nzd3ASCADeBqrsbMKqLLPPx14zZXub1RGu10ODdbMBcATwA3Af4F+9r65wBP29ltYAwVhsmcfW2RvLwQ+B3Js3bUHyLZl42Ugzj7v18AV9naFK92mzjPAnGaeayfwbXv7AWANkAb0A/bZ+2cAj2DpTh9WQ/o0+5hTPyTZ8p3tuvc59vbPsfWz/kXPn61j/g0ku+Ty9BD5+i5wj8e1Q7H0Yqb9+0ngqmbkrK+9fRXBenknVv18P/C/Lc2nve3O62zgye5+r735D7gIeNT1O6OFOuYirIHbGGAAVv2Y08T+07HbnC6Z2mHfLxHYBRxrH/PURcCfgFPt7Vxgo739Mkf0fCpWO1LbrK34i0WJRnYZY96zt+fYI7GxWB/UKKzK/VNjzFYAEfkjlqC2lA+BJ0QkDvibMWZVE+fWAcvt7bVArTGmXkTWcmR0bgYwTo7MDmZgdTr3AD8Va7bFDwzCUg7Y+XfuqyN9PQPBamT8zuPYYKwyHiAiPmOM3+OcBoKtBBJd25UtvI8Xta5tv+u3HwI6ToCLjDGb3ReKyO3APiyl7wNqIqTb6EpL6R4+M8b8x97+I9Zs7BjgNbGsZGOwZo4dmpolds9erDfGFAGIyA6sGYlTOTJTAlbHab9HOtObOM8989IU7rykGmPKgXIR8ZpJAauxU4Bl+rZARC6w9zszKcWEz6Sc1YJ8KF1LBlBijKkSkRFYZpGhvA78XUQeMMbsF5EsrEZ5OpbOLLVnQM7G6nC1h1eBu0TkGWNMhYgMAupbmE8lOlgL3C8ii7A6Xu/Yeqk5HXMq8GdjTCOwT0T+BZzUxP4yj3u/bowpBRCRDUAe1gREJF10JjBKjqxwSBeRVOA/wGKxLCL+aozZIyLaZm0F2lCJTioBRGQoljnZScaYEhF5kuDGcHMEGtJirdWLBzDGvG0L7leBJ0VksYm8/qTe2EMUuBrNxhi/iLgbzd81xrzqvlBErsIaFTrR/rB2uvIf2miO3mnt3k05VkMCIlf8h7BmUL4GXAlcjzXr5r4WrJHDWQAiMhFrhMwLz/sYY7wa1i3lVeC7IvJdY4wRkROMMZ9gKfQ9tjxfidU5UKITE/K7HKtT5mnmSPBgQSjuzn/owEAslk77gzHm5mby1NR5NXaDqDlakpewgQ4ROR2rcXSy3eh+iyP61a23dVAiOlkOfEtENgKbscwcgzDGbBCRHwEr7Dq8HviOMeY9EfkE2ITVeP5P6LWtxRizQkRGAu/aje0K4OstyacSHRhjtth161eAu0XkdftQczqmI4g0EBpJF/mwLNPcg6kAPxORf2A9w39E5MvaZm0dusYuuvEalQNLmQ8RkePs31+LcP1OrNFkgHOxpqMRkTysKfhHgceAie3M56vAt+3RFERkuFjrlzKA/fYHcgbWCI7SgzDGFGMp13VYI21/wqr41wLPY3XcbgHeMcb8G6tT9027gfAm1ojcKrFs818AskRkPTAf2BLhnisi3Kc93IUl/2vs+99l7/81cKVY60dG0HRnQOleckXE6cRditXA7OfsE5E4ERndQfd6HZgtIv3ttLNsvQlQ7+i6Zs7rKF4FvmGPZiMig+z76UxKD8YYU2uMOdsYM9IYc74x5nRjzFvGmNSQ85x1yuOMMSc61jzGmKuMMcONMdONMRcaY55s4l5DjDEH7e0njTHzIxz7pTFmrP13sjFme6R82uenutJ53hhzVQe+IqWViMgxWMsJ/gjcS8vbdu9gOTqLEZF+wGnAB03sDx20bQsrsEyNnbxPsP8fZ4xZa4xZhGVdNkLbrK1DR/GiGGPMaq9ROWNMjW2e+Q8RqcL6+Lw+skexzDhWY426OY3W04EbRKQea1SuvW7AH8Oalv5YrKG+A8D5wDPAy3bjfKX9HEoPwxgT6v3qlyG/73SdW47VQXI4KeTcGRFuMybknr/0uI9X3na6r3U3LNzHjDHVwP96XL8Va92qw432/rdwmTa5G0JKt7EZ+I6IPAFsAH6FVUE/KCIZWPXZL4D17b1RpJkSrLUjj2ANEHxsjLmsifM6BJ1JURSlhYzFcgzmx9JF38YaGG2OF7EcPK3Gsoz4gTFmr4hE2l8MNNptyyeBkjbkdQHwsIiswdLdbwPfAq61O1V+LF3+CnAJ2mZtMXJkxlJRFEVRog8RGYK1ZmRMc+cqSm/FboiHmrjfGGpypijK0YvO2CmKEvWIFTfpdY9D021zUUVRlF6NMeaC5s9SFOVoRmfsFABE5H0st69uLjfGrO2O/CiKohwt6EyKoihKx6Ft1shox05RFEVRFEVRFKWHo14xFUVRFEVRFEVRejjasVMURVEURVEURenhaMdOURRFURRFURSlh6MdO0VRFEVRFEVRlB6OduwURVEURVEURVF6OP8PzxKOtG4tXhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['diagnosis','radius_mean', 'texture_mean', 'perimeter_mean',\n",
    "       'area_mean', 'smoothness_mean']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "equivalent-logging",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "copyrighted-plasma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['radius_mean', 'texture_mean','smoothness_mean']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "right-guitar",
   "metadata": {},
   "source": [
    "# Label Encoding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "configured-smell",
   "metadata": {},
   "source": [
    "It is used for concerting categorical data into numerical form.\n",
    "Here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "unnecessary-pride",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_Y = LabelEncoder()\n",
    "df.iloc[:,0]=labelencoder_Y.fit_transform(df.iloc[:,0].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "endangered-illness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "564    1\n",
       "565    1\n",
       "566    1\n",
       "567    1\n",
       "568    0\n",
       "Name: diagnosis, Length: 569, dtype: int32"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "global-hacker",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= df.drop(\"diagnosis\", axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "operational-badge",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[[\"diagnosis\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "still-details",
   "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>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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "detected-provider",
   "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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diagnosis\n",
       "0            1\n",
       "1            1\n",
       "2            1\n",
       "3            1\n",
       "4            1\n",
       "..         ...\n",
       "564          1\n",
       "565          1\n",
       "566          1\n",
       "567          1\n",
       "568          0\n",
       "\n",
       "[569 rows x 1 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "violent-welcome",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "confidential-prescription",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30,random_state =0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "regional-efficiency",
   "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>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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>11.490</td>\n",
       "      <td>14.59</td>\n",
       "      <td>73.99</td>\n",
       "      <td>404.9</td>\n",
       "      <td>0.10460</td>\n",
       "      <td>0.08228</td>\n",
       "      <td>0.05308</td>\n",
       "      <td>0.01969</td>\n",
       "      <td>0.1779</td>\n",
       "      <td>0.06574</td>\n",
       "      <td>...</td>\n",
       "      <td>12.400</td>\n",
       "      <td>21.90</td>\n",
       "      <td>82.04</td>\n",
       "      <td>467.6</td>\n",
       "      <td>0.13520</td>\n",
       "      <td>0.20100</td>\n",
       "      <td>0.25960</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.2941</td>\n",
       "      <td>0.09180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>10.490</td>\n",
       "      <td>18.61</td>\n",
       "      <td>66.86</td>\n",
       "      <td>334.3</td>\n",
       "      <td>0.10680</td>\n",
       "      <td>0.06678</td>\n",
       "      <td>0.02297</td>\n",
       "      <td>0.01780</td>\n",
       "      <td>0.1482</td>\n",
       "      <td>0.06600</td>\n",
       "      <td>...</td>\n",
       "      <td>11.060</td>\n",
       "      <td>24.54</td>\n",
       "      <td>70.76</td>\n",
       "      <td>375.4</td>\n",
       "      <td>0.14130</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.08423</td>\n",
       "      <td>0.06528</td>\n",
       "      <td>0.2213</td>\n",
       "      <td>0.07842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>12.250</td>\n",
       "      <td>17.94</td>\n",
       "      <td>78.27</td>\n",
       "      <td>460.3</td>\n",
       "      <td>0.08654</td>\n",
       "      <td>0.06679</td>\n",
       "      <td>0.03885</td>\n",
       "      <td>0.02331</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.06228</td>\n",
       "      <td>...</td>\n",
       "      <td>13.590</td>\n",
       "      <td>25.22</td>\n",
       "      <td>86.60</td>\n",
       "      <td>564.2</td>\n",
       "      <td>0.12170</td>\n",
       "      <td>0.17880</td>\n",
       "      <td>0.19430</td>\n",
       "      <td>0.08211</td>\n",
       "      <td>0.3113</td>\n",
       "      <td>0.08132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>18.310</td>\n",
       "      <td>18.58</td>\n",
       "      <td>118.60</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>0.08588</td>\n",
       "      <td>0.08468</td>\n",
       "      <td>0.08169</td>\n",
       "      <td>0.05814</td>\n",
       "      <td>0.1621</td>\n",
       "      <td>0.05425</td>\n",
       "      <td>...</td>\n",
       "      <td>21.310</td>\n",
       "      <td>26.36</td>\n",
       "      <td>139.20</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>0.12340</td>\n",
       "      <td>0.24450</td>\n",
       "      <td>0.35380</td>\n",
       "      <td>0.15710</td>\n",
       "      <td>0.3206</td>\n",
       "      <td>0.06938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6.981</td>\n",
       "      <td>13.43</td>\n",
       "      <td>43.79</td>\n",
       "      <td>143.5</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.07568</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>0.07818</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930</td>\n",
       "      <td>19.54</td>\n",
       "      <td>50.41</td>\n",
       "      <td>185.2</td>\n",
       "      <td>0.15840</td>\n",
       "      <td>0.12020</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2932</td>\n",
       "      <td>0.09382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>18.810</td>\n",
       "      <td>19.98</td>\n",
       "      <td>120.90</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>0.08923</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>0.08020</td>\n",
       "      <td>0.05843</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>0.04996</td>\n",
       "      <td>...</td>\n",
       "      <td>19.960</td>\n",
       "      <td>24.30</td>\n",
       "      <td>129.00</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0.12430</td>\n",
       "      <td>0.11600</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.12940</td>\n",
       "      <td>0.2567</td>\n",
       "      <td>0.05737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.460</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>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>15.090</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>9.436</td>\n",
       "      <td>18.32</td>\n",
       "      <td>59.82</td>\n",
       "      <td>278.6</td>\n",
       "      <td>0.10090</td>\n",
       "      <td>0.05956</td>\n",
       "      <td>0.02710</td>\n",
       "      <td>0.01406</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>0.06959</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020</td>\n",
       "      <td>25.02</td>\n",
       "      <td>75.79</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.13330</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.11440</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.08136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>9.720</td>\n",
       "      <td>18.22</td>\n",
       "      <td>60.73</td>\n",
       "      <td>288.1</td>\n",
       "      <td>0.06950</td>\n",
       "      <td>0.02344</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1653</td>\n",
       "      <td>0.06447</td>\n",
       "      <td>...</td>\n",
       "      <td>9.968</td>\n",
       "      <td>20.83</td>\n",
       "      <td>62.25</td>\n",
       "      <td>303.8</td>\n",
       "      <td>0.07117</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1909</td>\n",
       "      <td>0.06559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.04105</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>12.480</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "478       11.490         14.59           73.99      404.9          0.10460   \n",
       "303       10.490         18.61           66.86      334.3          0.10680   \n",
       "155       12.250         17.94           78.27      460.3          0.08654   \n",
       "186       18.310         18.58          118.60     1041.0          0.08588   \n",
       "101        6.981         13.43           43.79      143.5          0.11700   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "277       18.810         19.98          120.90     1102.0          0.08923   \n",
       "9         12.460         24.04           83.97      475.9          0.11860   \n",
       "359        9.436         18.32           59.82      278.6          0.10090   \n",
       "192        9.720         18.22           60.73      288.1          0.06950   \n",
       "559       11.510         23.93           74.52      403.5          0.09261   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "478           0.08228         0.05308              0.01969         0.1779   \n",
       "303           0.06678         0.02297              0.01780         0.1482   \n",
       "155           0.06679         0.03885              0.02331         0.1970   \n",
       "186           0.08468         0.08169              0.05814         0.1621   \n",
       "101           0.07568         0.00000              0.00000         0.1930   \n",
       "..                ...             ...                  ...            ...   \n",
       "277           0.05884         0.08020              0.05843         0.1550   \n",
       "9             0.23960         0.22730              0.08543         0.2030   \n",
       "359           0.05956         0.02710              0.01406         0.1506   \n",
       "192           0.02344         0.00000              0.00000         0.1653   \n",
       "559           0.10210         0.11120              0.04105         0.1388   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "478                 0.06574  ...        12.400          21.90   \n",
       "303                 0.06600  ...        11.060          24.54   \n",
       "155                 0.06228  ...        13.590          25.22   \n",
       "186                 0.05425  ...        21.310          26.36   \n",
       "101                 0.07818  ...         7.930          19.54   \n",
       "..                      ...  ...           ...            ...   \n",
       "277                 0.04996  ...        19.960          24.30   \n",
       "9                   0.08243  ...        15.090          40.68   \n",
       "359                 0.06959  ...        12.020          25.02   \n",
       "192                 0.06447  ...         9.968          20.83   \n",
       "559                 0.06570  ...        12.480          37.16   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "478            82.04       467.6           0.13520            0.20100   \n",
       "303            70.76       375.4           0.14130            0.10440   \n",
       "155            86.60       564.2           0.12170            0.17880   \n",
       "186           139.20      1410.0           0.12340            0.24450   \n",
       "101            50.41       185.2           0.15840            0.12020   \n",
       "..               ...         ...               ...                ...   \n",
       "277           129.00      1236.0           0.12430            0.11600   \n",
       "9              97.65       711.4           0.18530            1.05800   \n",
       "359            75.79       439.6           0.13330            0.10490   \n",
       "192            62.25       303.8           0.07117            0.02729   \n",
       "559            82.28       474.2           0.12980            0.25170   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "478          0.25960               0.07431          0.2941   \n",
       "303          0.08423               0.06528          0.2213   \n",
       "155          0.19430               0.08211          0.3113   \n",
       "186          0.35380               0.15710          0.3206   \n",
       "101          0.00000               0.00000          0.2932   \n",
       "..               ...                   ...             ...   \n",
       "277          0.22100               0.12940          0.2567   \n",
       "9            1.10500               0.22100          0.4366   \n",
       "359          0.11440               0.05052          0.2454   \n",
       "192          0.00000               0.00000          0.1909   \n",
       "559          0.36300               0.09653          0.2112   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "478                  0.09180  \n",
       "303                  0.07842  \n",
       "155                  0.08132  \n",
       "186                  0.06938  \n",
       "101                  0.09382  \n",
       "..                       ...  \n",
       "277                  0.05737  \n",
       "9                    0.20750  \n",
       "359                  0.08136  \n",
       "192                  0.06559  \n",
       "559                  0.08732  \n",
       "\n",
       "[398 rows x 30 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "exterior-joyce",
   "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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diagnosis\n",
       "478          0\n",
       "303          0\n",
       "155          0\n",
       "186          1\n",
       "101          0\n",
       "..         ...\n",
       "277          1\n",
       "9            1\n",
       "359          0\n",
       "192          0\n",
       "559          0\n",
       "\n",
       "[398 rows x 1 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "weekly-consolidation",
   "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>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>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>13.400</td>\n",
       "      <td>20.52</td>\n",
       "      <td>88.64</td>\n",
       "      <td>556.7</td>\n",
       "      <td>0.11060</td>\n",
       "      <td>0.14690</td>\n",
       "      <td>0.14450</td>\n",
       "      <td>0.08172</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>0.07325</td>\n",
       "      <td>...</td>\n",
       "      <td>16.410</td>\n",
       "      <td>29.66</td>\n",
       "      <td>113.30</td>\n",
       "      <td>844.4</td>\n",
       "      <td>0.15740</td>\n",
       "      <td>0.38560</td>\n",
       "      <td>0.51060</td>\n",
       "      <td>0.20510</td>\n",
       "      <td>0.3585</td>\n",
       "      <td>0.11090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>13.210</td>\n",
       "      <td>25.25</td>\n",
       "      <td>84.10</td>\n",
       "      <td>537.9</td>\n",
       "      <td>0.08791</td>\n",
       "      <td>0.05205</td>\n",
       "      <td>0.02772</td>\n",
       "      <td>0.02068</td>\n",
       "      <td>0.1619</td>\n",
       "      <td>0.05584</td>\n",
       "      <td>...</td>\n",
       "      <td>14.350</td>\n",
       "      <td>34.23</td>\n",
       "      <td>91.29</td>\n",
       "      <td>632.9</td>\n",
       "      <td>0.12890</td>\n",
       "      <td>0.10630</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.06005</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>0.06788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>14.020</td>\n",
       "      <td>15.66</td>\n",
       "      <td>89.59</td>\n",
       "      <td>606.5</td>\n",
       "      <td>0.07966</td>\n",
       "      <td>0.05581</td>\n",
       "      <td>0.02087</td>\n",
       "      <td>0.02652</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>0.05586</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>19.31</td>\n",
       "      <td>96.53</td>\n",
       "      <td>688.9</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.10170</td>\n",
       "      <td>0.06260</td>\n",
       "      <td>0.08216</td>\n",
       "      <td>0.2136</td>\n",
       "      <td>0.06710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>14.260</td>\n",
       "      <td>18.17</td>\n",
       "      <td>91.22</td>\n",
       "      <td>633.1</td>\n",
       "      <td>0.06576</td>\n",
       "      <td>0.05220</td>\n",
       "      <td>0.02475</td>\n",
       "      <td>0.01374</td>\n",
       "      <td>0.1635</td>\n",
       "      <td>0.05586</td>\n",
       "      <td>...</td>\n",
       "      <td>16.220</td>\n",
       "      <td>25.26</td>\n",
       "      <td>105.80</td>\n",
       "      <td>819.7</td>\n",
       "      <td>0.09445</td>\n",
       "      <td>0.21670</td>\n",
       "      <td>0.15650</td>\n",
       "      <td>0.07530</td>\n",
       "      <td>0.2636</td>\n",
       "      <td>0.07676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>13.030</td>\n",
       "      <td>18.42</td>\n",
       "      <td>82.61</td>\n",
       "      <td>523.8</td>\n",
       "      <td>0.08983</td>\n",
       "      <td>0.03766</td>\n",
       "      <td>0.02562</td>\n",
       "      <td>0.02923</td>\n",
       "      <td>0.1467</td>\n",
       "      <td>0.05863</td>\n",
       "      <td>...</td>\n",
       "      <td>13.300</td>\n",
       "      <td>22.81</td>\n",
       "      <td>84.46</td>\n",
       "      <td>545.9</td>\n",
       "      <td>0.09701</td>\n",
       "      <td>0.04619</td>\n",
       "      <td>0.04833</td>\n",
       "      <td>0.05013</td>\n",
       "      <td>0.1987</td>\n",
       "      <td>0.06169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.710</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>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>...</td>\n",
       "      <td>17.060</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.16540</td>\n",
       "      <td>0.36820</td>\n",
       "      <td>0.26780</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>17.990</td>\n",
       "      <td>20.66</td>\n",
       "      <td>117.80</td>\n",
       "      <td>991.7</td>\n",
       "      <td>0.10360</td>\n",
       "      <td>0.13040</td>\n",
       "      <td>0.12010</td>\n",
       "      <td>0.08824</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>0.06069</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080</td>\n",
       "      <td>25.41</td>\n",
       "      <td>138.10</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>0.14820</td>\n",
       "      <td>0.37350</td>\n",
       "      <td>0.33010</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.3060</td>\n",
       "      <td>0.08503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>13.710</td>\n",
       "      <td>18.68</td>\n",
       "      <td>88.73</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.09916</td>\n",
       "      <td>0.10700</td>\n",
       "      <td>0.05385</td>\n",
       "      <td>0.03783</td>\n",
       "      <td>0.1714</td>\n",
       "      <td>0.06843</td>\n",
       "      <td>...</td>\n",
       "      <td>15.110</td>\n",
       "      <td>25.63</td>\n",
       "      <td>99.43</td>\n",
       "      <td>701.9</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.25660</td>\n",
       "      <td>0.19350</td>\n",
       "      <td>0.12840</td>\n",
       "      <td>0.2849</td>\n",
       "      <td>0.09031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>13.300</td>\n",
       "      <td>21.57</td>\n",
       "      <td>85.24</td>\n",
       "      <td>546.1</td>\n",
       "      <td>0.08582</td>\n",
       "      <td>0.06373</td>\n",
       "      <td>0.03344</td>\n",
       "      <td>0.02424</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.05696</td>\n",
       "      <td>...</td>\n",
       "      <td>14.200</td>\n",
       "      <td>29.20</td>\n",
       "      <td>92.94</td>\n",
       "      <td>621.2</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.16670</td>\n",
       "      <td>0.12120</td>\n",
       "      <td>0.05614</td>\n",
       "      <td>0.2637</td>\n",
       "      <td>0.06658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.09240</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.03996</td>\n",
       "      <td>0.01282</td>\n",
       "      <td>0.1692</td>\n",
       "      <td>0.06576</td>\n",
       "      <td>...</td>\n",
       "      <td>9.845</td>\n",
       "      <td>25.05</td>\n",
       "      <td>62.86</td>\n",
       "      <td>295.8</td>\n",
       "      <td>0.11030</td>\n",
       "      <td>0.08298</td>\n",
       "      <td>0.07993</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.07393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "512       13.400         20.52           88.64      556.7          0.11060   \n",
       "457       13.210         25.25           84.10      537.9          0.08791   \n",
       "439       14.020         15.66           89.59      606.5          0.07966   \n",
       "298       14.260         18.17           91.22      633.1          0.06576   \n",
       "37        13.030         18.42           82.61      523.8          0.08983   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "7         13.710         20.83           90.20      577.9          0.11890   \n",
       "408       17.990         20.66          117.80      991.7          0.10360   \n",
       "523       13.710         18.68           88.73      571.0          0.09916   \n",
       "361       13.300         21.57           85.24      546.1          0.08582   \n",
       "553        9.333         21.94           59.01      264.0          0.09240   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "512           0.14690         0.14450              0.08172         0.2116   \n",
       "457           0.05205         0.02772              0.02068         0.1619   \n",
       "439           0.05581         0.02087              0.02652         0.1589   \n",
       "298           0.05220         0.02475              0.01374         0.1635   \n",
       "37            0.03766         0.02562              0.02923         0.1467   \n",
       "..                ...             ...                  ...            ...   \n",
       "7             0.16450         0.09366              0.05985         0.2196   \n",
       "408           0.13040         0.12010              0.08824         0.1992   \n",
       "523           0.10700         0.05385              0.03783         0.1714   \n",
       "361           0.06373         0.03344              0.02424         0.1815   \n",
       "553           0.05605         0.03996              0.01282         0.1692   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "512                 0.07325  ...        16.410          29.66   \n",
       "457                 0.05584  ...        14.350          34.23   \n",
       "439                 0.05586  ...        14.910          19.31   \n",
       "298                 0.05586  ...        16.220          25.26   \n",
       "37                  0.05863  ...        13.300          22.81   \n",
       "..                      ...  ...           ...            ...   \n",
       "7                   0.07451  ...        17.060          28.14   \n",
       "408                 0.06069  ...        21.080          25.41   \n",
       "523                 0.06843  ...        15.110          25.63   \n",
       "361                 0.05696  ...        14.200          29.20   \n",
       "553                 0.06576  ...         9.845          25.05   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "512           113.30       844.4           0.15740            0.38560   \n",
       "457            91.29       632.9           0.12890            0.10630   \n",
       "439            96.53       688.9           0.10340            0.10170   \n",
       "298           105.80       819.7           0.09445            0.21670   \n",
       "37             84.46       545.9           0.09701            0.04619   \n",
       "..               ...         ...               ...                ...   \n",
       "7             110.60       897.0           0.16540            0.36820   \n",
       "408           138.10      1349.0           0.14820            0.37350   \n",
       "523            99.43       701.9           0.14250            0.25660   \n",
       "361            92.94       621.2           0.11400            0.16670   \n",
       "553            62.86       295.8           0.11030            0.08298   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "512          0.51060               0.20510          0.3585   \n",
       "457          0.13900               0.06005          0.2444   \n",
       "439          0.06260               0.08216          0.2136   \n",
       "298          0.15650               0.07530          0.2636   \n",
       "37           0.04833               0.05013          0.1987   \n",
       "..               ...                   ...             ...   \n",
       "7            0.26780               0.15560          0.3196   \n",
       "408          0.33010               0.19740          0.3060   \n",
       "523          0.19350               0.12840          0.2849   \n",
       "361          0.12120               0.05614          0.2637   \n",
       "553          0.07993               0.02564          0.2435   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "512                  0.11090  \n",
       "457                  0.06788  \n",
       "439                  0.06710  \n",
       "298                  0.07676  \n",
       "37                   0.06169  \n",
       "..                       ...  \n",
       "7                    0.11510  \n",
       "408                  0.08503  \n",
       "523                  0.09031  \n",
       "361                  0.06658  \n",
       "553                  0.07393  \n",
       "\n",
       "[171 rows x 30 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "criminal-ordering",
   "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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diagnosis\n",
       "512          1\n",
       "457          0\n",
       "439          0\n",
       "298          0\n",
       "37           0\n",
       "..         ...\n",
       "7            1\n",
       "408          1\n",
       "523          0\n",
       "361          0\n",
       "553          0\n",
       "\n",
       "[171 rows x 1 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "established-texture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    357\n",
       "1    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "requested-converter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.74998027, -1.09978744, -0.74158608, ..., -0.6235968 ,\n",
       "         0.07754241,  0.45062841],\n",
       "       [-1.02821446, -0.1392617 , -1.02980434, ..., -0.7612376 ,\n",
       "        -1.07145262, -0.29541379],\n",
       "       [-0.53852228, -0.29934933, -0.56857428, ..., -0.50470441,\n",
       "         0.34900827, -0.13371556],\n",
       "       ...,\n",
       "       [-1.3214733 , -0.20855336, -1.3143845 , ..., -0.98621857,\n",
       "        -0.69108476, -0.13148524],\n",
       "       [-1.24245479, -0.23244704, -1.27759928, ..., -1.7562754 ,\n",
       "        -1.55125275, -1.01078909],\n",
       "       [-0.74441558,  1.13188181, -0.72016173, ..., -0.28490593,\n",
       "        -1.2308599 ,  0.20083251]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "sc=StandardScaler() \n",
    "X_train=sc.fit_transform(x_train)\n",
    "X_test=sc.fit_transform(x_test)\n",
    "X_train  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "lovely-rogers",
   "metadata": {},
   "outputs": [],
   "source": [
    "# support vector classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "twelve-occupation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier=SVC() \n",
    "classifier.fit(X_train,y_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "administrative-colonial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=classifier.predict(X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "rural-present",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[104   2]\n",
      " [  4  61]]\n",
      "0.9649122807017544\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.96      0.98      0.97       106\\n           1       0.97      0.94      0.95        65\\n\\n    accuracy                           0.96       171\\n   macro avg       0.97      0.96      0.96       171\\nweighted avg       0.96      0.96      0.96       171\\n'"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm)\n",
    "print(accuracy_score(y_pred,y_test) )\n",
    "classification_report(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "rolled-stand",
   "metadata": {},
   "outputs": [],
   "source": [
    "# logistic regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "enormous-credit",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  sklearn.linear_model import LogisticRegression \n",
    "lg_classifier=LogisticRegression(random_state=0)\n",
    "lg_classifier.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "seasonal-lending",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lg_classifier.predict(X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "gothic-consortium",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[104   2]\n",
      " [  4  61]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm) \n",
    "accuracy_score(y_pred,y_test) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "heated-january",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0     0.9630    0.9811    0.9720       106\\n           1     0.9683    0.9385    0.9531        65\\n\\n    accuracy                         0.9649       171\\n   macro avg     0.9656    0.9598    0.9625       171\\nweighted avg     0.9650    0.9649    0.9648       171\\n'"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_pred,y_test, digits=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "celtic-canberra",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KN_classifier=KNeighborsClassifier(n_neighbors=5,metric=\"minkowski\",p=2) \n",
    "KN_classifier.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "alternate-right",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=KN_classifier.predict(X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "competent-closing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[105   6]\n",
      " [  3  57]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm) \n",
    "accuracy_score(y_pred,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "tough-organ",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "pleased-atlantic",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB \n",
    "NB_classifier=GaussianNB()\n",
    "NB_classifier.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "applicable-ranking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=NB_classifier.predict(X_test) \n",
    "y_pred  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "short-ozone",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[99  6]\n",
      " [ 9 57]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9122807017543859"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm) \n",
    "accuracy_score(y_pred,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "funded-complexity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DEcision Tree classifier \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "competent-sphere",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "D_classifier=DecisionTreeClassifier(criterion=\"entropy\",random_state=0) \n",
    "D_classifier.fit(X_train,y_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "architectural-intent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred =D_classifier.predict(X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "charged-place",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100   5]\n",
      " [  8  58]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9239766081871345"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm) \n",
    "accuracy_score(y_pred,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "chubby-words",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "communist-interval",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:05:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.97      0.97      0.97       108\\n           1       0.95      0.95      0.95        63\\n\\n    accuracy                           0.96       171\\n   macro avg       0.96      0.96      0.96       171\\nweighted avg       0.96      0.96      0.96       171\\n'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_classifier = XGBClassifier()\n",
    "xgb_classifier.fit(X_train, y_train)\n",
    "y_pred_xgb = xgb_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_xgb)\n",
    "classification_report(y_test, y_pred_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "religious-suffering",
   "metadata": {},
   "outputs": [],
   "source": [
    "#random forest classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "upset-packing",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-140-9ff825d7cc60>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rc_classifier.fit(X_train,y_train)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=20, random_state=0)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "rc_classifier=RandomForestClassifier(n_estimators=20,criterion=\"entropy\",random_state=0)\n",
    "rc_classifier.fit(X_train,y_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "hydraulic-rebate",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rc_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "circular-nightlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "through-tackle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rc_classifier.predict(X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "fourth-canadian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[104   2]\n",
      " [  4  61]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass normalize=.0% as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
    "cm=confusion_matrix(y_pred,y_test) \n",
    "print(cm) \n",
    "accuracy_score(y_pred,y_test, \".0%\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "tamil-jimmy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[104,   2],\n",
       "       [  4,  61]], dtype=int64)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcm = confusion_matrix(y_pred,y_test)\n",
    "dcm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "quarterly-combine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dcm,annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "renewable-citizenship",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:593: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.95      , 0.9125    , 0.95      , 0.97468354, 0.96202532])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score \n",
    "cross_validation=cross_val_score(estimator=rc_classifier,X=X_train,y=y_train) \n",
    "cross_validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "worse-portsmouth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation mean accuracy 0.9498417721518987\n"
     ]
    }
   ],
   "source": [
    "print(\"cross validation mean accuracy\",cross_validation.mean()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desirable-karen",
   "metadata": {},
   "source": [
    "# Accuracy:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "natural-visitor",
   "metadata": {},
   "source": [
    "Support Vector Machine    :  0.9766081871345029\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tender-indianapolis",
   "metadata": {},
   "source": [
    "Logistic Regression       :  0.9649122807017544\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-appreciation",
   "metadata": {},
   "source": [
    "Naive bayes Classifier    :  0.9122807017543859\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "raised-ensemble",
   "metadata": {},
   "source": [
    "Descision Tree Classifier :  0.9239766081871345\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-investment",
   "metadata": {},
   "source": [
    "Random Forest Classifier  :  0.9649122807017544\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organic-playback",
   "metadata": {},
   "source": [
    "# Accuracy of model using Support Vector Machine is maximum while, accuracy of model using Naive Bayes Classifier was least."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vulnerable-conditions",
   "metadata": {},
   "source": [
    "cross validation mean accuracy : 0.9498417721518987"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "recent-manchester",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}