breast_cancer_claasification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/breastcancer-dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 23:03:20) \n",
      "[GCC 7.3.0]\n",
      "NumPy 1.18.5\n",
      "SciPy 1.4.1\n",
      "Scikit-Learn 0.23.1\n"
     ]
    }
   ],
   "source": [
    "import sys; print(\"Python\", sys.version)\n",
    "import numpy; print(\"NumPy\", numpy.__version__)\n",
    "import scipy; print(\"SciPy\", scipy.__version__)\n",
    "import sklearn; print(\"Scikit-Learn\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as s\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn import svm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../input/breastcancer-dataset/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\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",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.drop('Unnamed: 32', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.diagnosis = df.diagnosis.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.3001</td>\n",
       "      <td>0.14710</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>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>0.0869</td>\n",
       "      <td>0.07017</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>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>0.1974</td>\n",
       "      <td>0.12790</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>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>0.2414</td>\n",
       "      <td>0.10520</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>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>0.1980</td>\n",
       "      <td>0.10430</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 × 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",
       "   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",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(labels='diagnosis',axis=1)\n",
    "Y= df['diagnosis']\n",
    "col=X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
       "       '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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Normalization and Feature Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>842302</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>842517</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>84300903</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>84348301</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>84358402</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": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# X = mms.transform(X)\n",
    "\n",
    "df_norm = (X - X.mean()) / (X.max() - X.min())\n",
    "df_norm = pd.concat([df_norm, Y], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X=pd.DataFrame(X)\n",
    "# X.columns=col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.032403</td>\n",
       "      <td>0.182815</td>\n",
       "      <td>-0.301307</td>\n",
       "      <td>0.213053</td>\n",
       "      <td>0.146813</td>\n",
       "      <td>0.198968</td>\n",
       "      <td>0.531437</td>\n",
       "      <td>0.495081</td>\n",
       "      <td>0.487976</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.222474</td>\n",
       "      <td>0.385173</td>\n",
       "      <td>0.279792</td>\n",
       "      <td>0.196998</td>\n",
       "      <td>0.399079</td>\n",
       "      <td>0.351207</td>\n",
       "      <td>0.518192</td>\n",
       "      <td>0.335156</td>\n",
       "      <td>0.229268</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.032403</td>\n",
       "      <td>0.304923</td>\n",
       "      <td>-0.051392</td>\n",
       "      <td>0.282848</td>\n",
       "      <td>0.284671</td>\n",
       "      <td>-0.104905</td>\n",
       "      <td>-0.078833</td>\n",
       "      <td>-0.004450</td>\n",
       "      <td>0.105621</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060427</td>\n",
       "      <td>0.256680</td>\n",
       "      <td>0.264308</td>\n",
       "      <td>-0.056585</td>\n",
       "      <td>-0.065649</td>\n",
       "      <td>-0.024432</td>\n",
       "      <td>0.245339</td>\n",
       "      <td>-0.029717</td>\n",
       "      <td>0.033282</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.059177</td>\n",
       "      <td>0.263274</td>\n",
       "      <td>0.066295</td>\n",
       "      <td>0.262808</td>\n",
       "      <td>0.232497</td>\n",
       "      <td>0.119524</td>\n",
       "      <td>0.170416</td>\n",
       "      <td>0.254453</td>\n",
       "      <td>0.392549</td>\n",
       "      <td>0.129991</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003924</td>\n",
       "      <td>0.225304</td>\n",
       "      <td>0.203602</td>\n",
       "      <td>0.079452</td>\n",
       "      <td>0.165163</td>\n",
       "      <td>0.142341</td>\n",
       "      <td>0.441216</td>\n",
       "      <td>0.140399</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.059229</td>\n",
       "      <td>-0.128132</td>\n",
       "      <td>0.036874</td>\n",
       "      <td>-0.099434</td>\n",
       "      <td>-0.114014</td>\n",
       "      <td>0.416536</td>\n",
       "      <td>0.550761</td>\n",
       "      <td>0.357546</td>\n",
       "      <td>0.279726</td>\n",
       "      <td>0.396657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>-0.041791</td>\n",
       "      <td>-0.076898</td>\n",
       "      <td>0.511335</td>\n",
       "      <td>0.593799</td>\n",
       "      <td>0.331239</td>\n",
       "      <td>0.491044</td>\n",
       "      <td>0.736693</td>\n",
       "      <td>0.584115</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.059241</td>\n",
       "      <td>0.291671</td>\n",
       "      <td>-0.167388</td>\n",
       "      <td>0.298051</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0.035567</td>\n",
       "      <td>0.087292</td>\n",
       "      <td>0.255859</td>\n",
       "      <td>0.275253</td>\n",
       "      <td>-0.001323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.240065</td>\n",
       "      <td>0.223810</td>\n",
       "      <td>0.170669</td>\n",
       "      <td>0.033226</td>\n",
       "      <td>-0.047797</td>\n",
       "      <td>0.102086</td>\n",
       "      <td>0.164583</td>\n",
       "      <td>-0.105806</td>\n",
       "      <td>-0.047001</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0 -0.032403     0.182815     -0.301307        0.213053   0.146813   \n",
       "1 -0.032403     0.304923     -0.051392        0.282848   0.284671   \n",
       "2  0.059177     0.263274      0.066295        0.262808   0.232497   \n",
       "3  0.059229    -0.128132      0.036874       -0.099434  -0.114014   \n",
       "4  0.059241     0.291671     -0.167388        0.298051   0.272369   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0         0.198968          0.531437        0.495081             0.487976   \n",
       "1        -0.104905         -0.078833       -0.004450             0.105621   \n",
       "2         0.119524          0.170416        0.254453             0.392549   \n",
       "3         0.416536          0.550761        0.357546             0.279726   \n",
       "4         0.035567          0.087292        0.255859             0.275253   \n",
       "\n",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0       0.306758  ...      -0.222474         0.385173    0.279792   \n",
       "1       0.000193  ...      -0.060427         0.256680    0.264308   \n",
       "2       0.129991  ...      -0.003924         0.225304    0.203602   \n",
       "3       0.396657  ...       0.021929        -0.041791   -0.076898   \n",
       "4      -0.001323  ...      -0.240065         0.223810    0.170669   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0          0.196998           0.399079         0.351207              0.518192   \n",
       "1         -0.056585          -0.065649        -0.024432              0.245339   \n",
       "2          0.079452           0.165163         0.142341              0.441216   \n",
       "3          0.511335           0.593799         0.331239              0.491044   \n",
       "4          0.033226          -0.047797         0.102086              0.164583   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  diagnosis  \n",
       "0        0.335156                 0.229268          M  \n",
       "1       -0.029717                 0.033282          M  \n",
       "2        0.140399                 0.023837          M  \n",
       "3        0.736693                 0.584115          M  \n",
       "4       -0.105806                -0.047001          M  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "#  Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2e8e7ced0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Explore correlations\n",
    "plt.rcParams['figure.figsize']=(12,8)\n",
    "s.set(font_scale=1.4)\n",
    "s.heatmap(df.drop('diagnosis', axis=1).drop('id',axis=1).corr(), cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=(10,5)\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='radius_mean',data=df, ax=ax1)\n",
    "s.boxplot('diagnosis',y='texture_mean',data=df, ax=ax2)\n",
    "s.boxplot('diagnosis',y='perimeter_mean',data=df, ax=ax3)\n",
    "s.boxplot('diagnosis',y='area_mean',data=df, ax=ax4)\n",
    "s.boxplot('diagnosis',y='smoothness_mean',data=df, ax=ax5)\n",
    "f.tight_layout()\n",
    "\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='compactness_mean',data=df, ax=ax2)\n",
    "s.boxplot('diagnosis',y='concavity_mean',data=df, ax=ax1)\n",
    "s.boxplot('diagnosis',y='concave points_mean',data=df, ax=ax3)\n",
    "s.boxplot('diagnosis',y='symmetry_mean',data=df, ax=ax4)\n",
    "s.boxplot('diagnosis',y='fractal_dimension_mean',data=df, ax=ax5)    \n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd2e2e8a550>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAADCCAYAAABwrARNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXxU1fn48c+dJZPJNlkICZEkLJqwJYACicjuwpIighsqIC7AT6ooWotbbb+trWhbEaEKlohWbC1UoYKCBRdwA9kEZdMQwp4ASchknfX+/ggZmSxkYbaE5/165QVz77lnnntzMs/ce869R1FVVUUIIYTwEo2/AxBCCNG2SaIRQgjhVZJohBBCeJUkGiGEEF4liUYIIYRXSaIRQgjhVZJoPOj9998nNTWVY8eOuZZNnjyZyZMn+zEq71mwYAGpqan+DkP4iLRv0VKSaESbs2XLFlJTU91++vfvz4QJE1i+fDly65hozc5v3++99169ZR5++GFSU1NJS0vzcXT10/k7gLYuOzvb3yF4zQMPPMD06dP9HUaD7rrrLnr37g1ASUkJH3/8Mb/5zW8oLi5mxowZfo6ubZD27T8Gg4HVq1dz8803uy0vKyvjs88+w2AwBMyXKjmj8bKgoCCCgoL8HYZX6HQ6DAaDv8No0FVXXcW4ceMYN24cU6ZM4a233qJjx46sWbPG36G1GdK+/WfYsGFs2bKFgoICt+Uff/wxTqeTgQMH+imyuiTRtNB3333HxIkTSUtLY9iwYbz++uv1fnuo7xp2dnY2EydOJCMjg7S0NMaOHcuKFSvqbKuqKq+99hpDhw6ld+/eTJw4kZ07d9aps+ZUes2aNSxdupQRI0aQlpbG7bffzt69e+vUu23bNqZMmULfvn3p27cv99xzD7t27XIrY7fbefXVVxk5ciTp6elkZGQwceJE1q1b5ypT3zXsPXv2MG3aNDIzM0lPT+faa6/l17/+NRUVFU07sF6k0+mIjIxEr9f7O5SAJ+078Nv3sGHDCAkJ4cMPP3Rbvnr1aoYMGUJkZKTPY2qIXDprgZycHO655x5CQ0N54IEH0Ov1LF++nJCQkCZt/+abbzJkyBBGjx6NoiisX7+eZ555BqfTye233+4qN2/ePBYvXsygQYMYMWIEeXl5zJgxA5PJRHx8fJ1633jjDex2O5MmTcJut5Odnc0vf/lL/ve//7k+XLdu3co999xDfHw8DzzwAE6nk3fffZdJkyaxbNky16WmhQsXsmjRIm655RbS09OpqKhg37597Nq1i1GjRtW7X0VFRdx7771ERkZy//33YzKZOHnyJJ9++ikVFRUXPD6VlZVUVlY26fhFR0c3qVx5eTlFRUUAmM1m1q5dyw8//MDTTz/dpO0vVdK+W0f7NhgM3HDDDaxevZp7770XgFOnTrFlyxZeeuklNm7c2KR6fEIVzfbggw+qPXv2VA8fPuxaVlhYqF511VVqSkqKevToUdfySZMmqZMmTXLbvqKiok6dU6dOVa+//nrX6zNnzqg9e/ZUp02bpjqdTtfy5cuXqykpKW51bt68WU1JSVGvv/56taqqyrV8/fr1akpKivrZZ5+5lo0fP14dMGCAWlhY6FqWn5+v9unTR504caJr2bhx49Tp06df8Di88sorakpKSp3327179wW3u1BdTflpTM3xqP2Tmpqqzp8/v9mxXWqkfVcL9Pa9Zs0a9euvv1ZTUlLUn376SVVVVc3OzlavvPJKtaqqSp0zZ47aq1evZsfqDXJG00wOh4MvvviC4cOHk5SU5FoeHR3N2LFj+ec//9loHUajEQCbzUZ5eTlOp5PMzExeeuklSktLCQ8P5+uvv8Zms3HXXXehKIpr2/Hjx/Piiy/WW++ECRPcrin3798fgKNHjwJw+vRp9uzZw9SpU92+NcXFxTF27FiWL19OSUkJJpOJsLAwfvrpJw4dOkTnzp2bdGzCwsIA+Pzzz+nWrVuzLlHddNNNXHXVVU0u3xT/7//9PzIyMgA4e/Ysn3/+OX/729+IiIhg6tSpHn2vtkLad8MCrX0DZGRk0L59e1avXs3s2bNZvXo1N9xwQ8D1LUmiaaaioiIqKyvrbZxNbbAbNmzg1VdfZf/+/TgcDrd1NX+IJ06cACA5OdltvU6no2PHjvXWm5CQ4PbaZDIB1SOuAI4fPw5Aly5d6mzbtWtXVFXl5MmTmEwmHnroIR588EFGjRrF5ZdfzqBBg8jKyiI9Pb3B/crIyOCGG25g4cKFLF26lAEDBjB8+HB+8YtfEBoaeqFDQmJiIomJiRcs01wpKSluHaJjxoyhtLSUl156ibFjxxITE+PR92sLpH23nvYNoNFoyMrKYvXq1YwdO5a9e/fy+OOPe/x9LpYMBmgm9VyH6Pnfwmqvu5Dt27fz4IMPYjQa+b//+z9ef/11li5d6vqG7XQ6mxxDbVqttlnlL1QmIyODDRs28MILL9C9e3dWrVrFbbfdxuLFixusQ1EUFixYwIoVK7j77rspLi7m2WefZezYsZw5c+aC719eXs7p06eb9HMxMjMzsVgsfP/99xdVT1sl7bv1te+xY8dy/Phxfv/73xMbG+s6iw8kckbTTDExMRiNRnJzc+usy8vLa3T7devWYTAYeOONN9xOb7ds2eJWrubb2+HDh+nUqZNrud1u5/jx43Tr1q3ZsV922WUA9caem5uLoih06NDBtcxkMnHTTTdx0003UVVVxbRp01i4cCH3339/g3/0AOnp6aSnp/Pwww+zceNGpk+fzooVK3jggQca3OaNN95g4cKFTdqPAwcONKlcfex2O0BAjIILRNK+W1/77tmzJ126dGHLli1MnTr1grH7iySaZtJqtQwaNIjPPvuMI0eOuK5jFxUVNen+DK1Wi6Iobt/sSkpK6tzhO3DgQHQ6He+88w5DhgxxfcNcuXIlZrO5RbHHxsbSs2dPVq1axYwZM1zXsQsKCli9ejV9+/Z1XY4oLi4mKirKtW1wcDBdunTh22+/pbKy0nW9+nwlJSVERES4fRvu2bMnQKMxe+sadm01I3Hk0SL1k/bdOtv3E088we7duxk7dqzX3uNiSKJpgVmzZvHll19y5513ctddd6HT6Vi+fDkJCQmNNrjhw4ezdOlS7rnnHsaNG0dJSQnLly+nXbt2bqfNMTEx3HPPPfz973/n/vvvZ8SIERw+fJj//ve/bp20zfXkk09yzz33cPvtt3PrrbcC8K9//Qu73c6cOXNc5caMGUP//v3p1asXUVFRHDhwgP/85z8MGzas3j9CqP6Q+Oc//8l1111HUlISVVVVvP/++2i1WkaOHHnBuLxxDXv79u2uMxiz2cxnn33Gli1byMrKomvXrh59r7ZE2nfraN/nGzp0KEOHDvVa/RdLEk0LpKSk8MYbb/DCCy/w6quvEhMTw5133klMTAxPPfXUBbfNyMjghRdeYPHixfzpT38iPj6eyZMnExERUWfbRx99FKPRyL///W+2bt1Kjx49WLx4Mb///e9bPKqkf//+vPnmm8yfP59XX30VRVHo3bs38+bNo0+fPq5yd999N59++imbN2+mqqqKDh06MG3aNKZNm9Zg3QMGDOD7779n3bp1nD59mrCwMHr06MFvfvMbt7p95Z133uGdd94BQK/Xk5iYyOzZs7nvvvt8HktrIu27foHWvlsTRW1KT5oIGA6Hg4EDB3L99dfz3HPP+TscITxK2nfbJKPOAlhVVVWdZe+//z5nz54lMzPTDxEJ4TnSvi8dcuksgH300UcsX76cYcOGYTKZ+OGHH3j//ffp1q0bN9xwg7/DE+KiSPu+dEiiCWCpqakYjUbefPNNSktLiYqK4tZbb+WRRx5ps0/MFZcOad+XDumjEUII4VXSRyOEEMKr2tyls8LCMpzOS/MkLSoqhOJiuePdkzx1TGNjwz0QTTVp49LGPckXbVzOaNoQnS7wHj3R2skxDSzy+/A8XxxTSTRCCCG8ShKNEEIIr5JEI4QQwqva3GCAQGd3gsVmd7026HXoJN0LIdowSTQ+ZrHZ2bqvwPW6f/c4dAb5NQgh2i75Li2EEMKrJNEIIYTwKkk0QgghvEoSjRBCCK+SRCOEEMKrJNEIIYTwKkk0QgghvEoSjRBCCK+SRCOEEMKrJNH4kdXm4PjpMpwyyakQog2TZ5/4ydlSCx9/exSLzUFqYiQP35qOTqeT56AJIdoc+RjzA1VV+fqHfBQFxlydzI/HzvL2xwdcz0Gr+Tk/6QghRGslZzR+cKakijMlVQzo0Z7RmcloFIU1X+eR2TPe36EJIYTHyRmNHxw6YUarUeiaYAIgKzOZyLAg3tuYiyr9NUKINkYSjY+pqsqx0+XEx4SgP9cBYwjSMm5QZ44UlFJQVOnnCIUQwrMk0fhYkdlCWaWNy9qFui2/umc8ocE69h8p9lNkQgjhHZJofOxIQSkA7SKNACgahXKLHZtT5eq0DhwtKKOs0ubPEIUQwqMk0fjY4fxSNIpCVLgBAIvN4RplNqBHHCqQl1/q3yCFEMKDJNH42JGCUqIiDGg1Sp11MaZgYiKCOSyJRgjRhkii8SGnqnL0VBntTMENlkmOD6OwpIqyCrl8JoRoGyTR+FB+YQVVVgcxERdKNOEAHDklZzVCiLZBEo0P1SSPGJOhwTLhIUFEhOjJL6zwVVhCCOFVkmh8KL+wAoXqZHIhcdEhFBRX4nTKzZtCiNZPEo0PFRRXEhVhQKe98GGPjwnBZndyvLCccovd9WN3+ihQIYTwIHnWmQ/lF1XQPiqk0XJx58rszSsi/0y5a3n/7nHoDPIrE0K0LnJG4yOqqp5LNMZGy4YE64gIDSL3uNkHkQkhhHdJovGRknIrFqujSYkGoJ0pmGOnyrwclRBCeJ8kGh8pKKoeRdbURBMTEYy53EqlReakEUK0bpJofOTkuUQT14Q+GoDoiOoh0IXmKq/FJIQQviCJxkcKiirQaTVEhjd8D835os/d1FlktngzLCGE8DpJND5SUFRJXLQRjVL3GWf10es0xEYaKZIzGiFEKyeJxkdOFlUQH920y2Y1LmsfSmGJJBohROsmicYH7A4nZ85WNjvRJMSEUl5lxyZ3agohWjFJND5QWFKFw6k2O9G0P1e+pFz6aYQQrZckGh9wjThrZqKJi64eCl1SZvV4TEII4SuSaHyg5h6a5p7RxJiMaBRJNEKI1k0SjQ8UFFUQZtQTZtQ3azutRiE8NIiz5ZJohBCtlyQaH8gvqnBdBmsuU2gQJWXSRyOEaL0k0fhAfguGNtcwhRkoq7DhcMrIMyFE6ySJxsuqrHbOlllbnmhCg1ABc7nNs4EJIYSPSKLxsoKiSqDpzzirzRRWPRtnifTTCCFaKUk0XpZ/bsSZKdxAucVOc2dnNoWeSzTSTyOEaKUk0XjZycJyFAWO5JvZuq8AezP7WnRaDWFGvQxxFkK0Wk1KNHl5edx333307duXzMxM/vCHP1BZWdmkN1i1ahWjRo0iLS2NrKwsPvroI7f1hw4d4g9/+ANjxoyhT58+jBgxgt/85jcUFRU1f28C0InCCtqZgtFqW57TTaFBculMCNFqNToBvdlsZsqUKSQkJDB//nyKiop4/vnnKSoqYt68eRfcdt26dcyZM4fp06dzzTXXsGHDBh599FFCQ0MZOnQoAF999RVbt27l9ttvp1u3bpw4cYJXXnmFb7/9lg8++ACDoWmP1Q9UJ8+UEx8delF1mMKCyC+qwNnc625CCBEAGk007777LmazmVWrVhEdHQ2AVqvlV7/6FTNnzuSKK65ocNv58+czatQoHnvsMQAyMzM5dOgQCxYscCWarKws7rrrLpTzHp+fnJzMHXfcweeff87IkSMvagf9ye5wkl9UQY/O0RdVjyksCIdTpchcRXgzb/oUQgh/a/R6zqZNm8jMzHQlGYCRI0cSFBTEpk2bGtzu6NGj5ObmkpWV5bY8KyuL77//3nVpLCoqyi3JAPTo0QOAwsLCpu9JADp9trJFD9OszRRafVZXM7BACCFak0YTzcGDB7n88svdlgUFBZGUlERubm6D29Ws69q1q9vymroutO22bdvcyrZWJ86UA81/xlltNUOcJdEIIVqjRhON2WwmIiKizvKIiAhKSkoa3K5mXe1tTSaT2/raLBYLL774IikpKfTr16+x8AJaTaJp7lObazPotQQHaV0P5xRCiNak0T6ahqiqWueSV31ql1FVtd7lNX77299y9OhR/vWvf6HRNH+kVkxMWLO38ZYzpVbaRxmJigwhPCwYAL1e5/p/7dcXWhdjMnLqbBWxseEXfM/G1ovmC7RjGkht3B8C7ffRFnj7mDaaaCIiIjCbzXWWl5aW1rksdr7zz1zatWvnWl5TV31nSfPmzWP16tUsWrSIbt26NR59PQoLywJmdNaBvCIS24dRUWGhtKx6Smabze76f+3XF1oXZtRxpKCUU6fMDSbp2NhwTp8u9dbuXJI8dUw9+YccSG3c16SNe54v2nijpwxdu3bl4MGDbsusVitHjhyhS5cuDW5Xs652X0xNXbW3ffvtt1m8eDHPPfccgwcPbiysgFdWaePU2Uo6dfDMB4wpNIhKi4OzcuOmEKKVaTTRDBkyhM2bN1NcXOxatn79eqxWq2uIcn0SExPp0qVLnRs016xZQ1pamtsotjVr1vDHP/6RRx99lPHjx7dkPwJO3snqM7fOHeqeubVEzYCAk4XlHqlPCCF8pdFLZxMnTmTZsmXMnDmTmTNnUlhYyNy5cxkzZozbqLCnnnqKVatWsXfvXteyWbNmMXv2bJKSkhg4cCCffPIJX331FYsXL3aV+fbbb3niiSfIyMhgwIABfPfdd6510dHRJCUleWpfferQuUTTKT4cT1zkqBnifLKwgh6dLu6+HCGE8KUm9dG89dZbPPfcczz00EMYDAaysrJ4/PHH3co5nU4cDofbstGjR1NVVcWiRYvIzs4mKSmJv/71r25nQlu2bMFms7F582Zuv/12t+3Hjx/P3LlzL2b//ObQyVLiokMICdZTbrFfdH1GgxajQesayWZ3gsX2c70GfYvHdQghhFc16dOpc+fOZGdnX7DM3Llz600K48ePv+DlsIceeoiHHnqoKWG0Gk5V5eCJEnpd5BMBzqcoCh1iQjl6ugyoTjJb9xW41vfvHuex9xIikAVhAVvVhQvpg7HSuh9f1ZbI12AvOFJQSmmFzeOXuBLbh7F5T8ElO+JICABsVZTl7LxgkbDL+4JeEk2gkGkCvOD73OrH6/TqEuPRehPbh2GxOSgolhs3hRCthyQaL/g+t5Dk+HDXpGWekhhXPVT6cL7cRyCEaD3k0pmHlVXaOHi8hKyrkz1ed1x0CHqdhrz8UtIub9f4BkK0QarDjqPwKGpJPmqVGZxOQIEgI4oxHCU4HHtULGp0EEqQsd46Gu3nkT4ej5JE42Fb9hagqtAvtb3H69ZqFDrGhnGkQM5oxKVHddop2foRxV+sQK00g0aLEhwOGm31+vJCKKiekLFk/0YAFKMJJSIWJSgERR+MEhQMOgM2HNhKCquXhUZWl9H8/HEofTyeJYnGw77cfZKkuDCS4rzz7KBO8eF8sycfhwwIEJcQZ1khlRtepezUQXQdroAuA9BEdkA5l2RqqA4bamUphsgYrOWlOM8WoJadQa004zSfAmslqsOGzWEDh+3nDbV6tPEpaBPTUSTBeJwkGg86UlDK4YJSJl57hdu9M57MCZd3NPHZzuOu+2mEaOscRcep/OjPqDYL7W+ajT0ymfKD39VbVtHqUcKiMXTqg6I3NVhnkK2E0p92gK0KZ+kZnKcP4Ti+F8epg+h7jPDWrlyyJNF40Jffn0SnVehzRTu3e1x6p8R67D1SOkYCcPB4CaHB8usTbc/5/ScO8xnKPnoZUDBlzcJwWSeoqGy0Dq2mOpk0uB5H9cNpg4xoYxLRxiTi7NgT+77Pse1ehzUuGTpleGiPhHxSeUiVzck3P+ST1rUdxmDvTbccYwomJsLAweMlpHf17PBpIQLCuftkVLsV23drUG1W9L1HU1VYgN5yFk37hp8a72K3UJa7t8HVpi496izThMWg7/MLbN+vo/TTbEJujEfbzvODei5FMrzZQ7YfKKC8yk50uAG70+nV97oiMZLc42bX3D5CtDWqqmL/8SvUylL0PYajCYn0yfsqegP6XtejCTJSueFVVGvjZ0+icZJoPOTbfacwGnR0aHdxs2k2RUrHSMwVVkorbI0XFqIVcp7OxVl4GG3nq9CY4n363kpQCGHD7kYtPYXl2xU+fe+2ShKNB1Ra7OzLK6JTfDiaJsw6erFSk6q/3cnUzqItclaYsR/8FiU8Fu1ldS9x+UJwh64Edx+Cbe+nKMe/I8hW4v6DxS9xtVbSR+MB3+Wcwe5Q6RTvmylm46NDCA/RU1BcyRWJvrmkIISvlG9eAQ4bupRrUBQ/fRe2W3BGdoQgI6Vf/BN9+mi3mW3lPpvmkTMaD9i67xSRYUG0iwz2yfspisLlHSPJL6qQfhrRpthP7MN6eDfapD4+65dpiKLVo0vqjWo+hbPomF9jae0k0Vwku8PJvsPFpHVt5/aNx9uu6GiiospOWaX004i2QVVVrNtWooSY/HbJrDZNXAoEh+M4vEO+1F0ESTQX6dBJMxabw9Vv4iuXd6y+GS2/SEbFiLbBcXwPjvwfCUm/HkUbGFf1FY0GXXJf1PJinKcP+TucVksSzUXad7gYhZ8/+H0lPjqE4CCtDAgQbYKqqli2vo8SGo0h5Wp/h+NGE9sZJTQKx9HdclbTQpJoLtL+w8UkxYUT6sWbNOujKApxUUbppxFtguPILpyncwm68saAOZupoSgK2o69UCvO4iyWvpqWkERzEaw2BznHS+iW7J9Oy7joEOmnEa2eqqpYtq1ECY9FnzrI3+HUS9OuMxhCcRz7wd+htEqSaFrI7oQf8oqwO1Q6J5g8+uDMpoqPrr45tED6aUQrZs/bjrPwMIarxrk9qj+QKBoN2oQeqCUFOEvP+DucVkcSTQtZbHY27jyOokBJmcXrj52pjyksCINe+mlE66WqTqzbVqExxaO7PLD6ZmrTxqeAVi9nNS0gieYi5BdV0M4UjF7nn8OoKArto4ycOitnNKL1CMLiusOeHzfiLD5GSJ+RGBxlBNlK0OLwd4j1UnR6tB1ScZ45jEPOappFEk0LVVntnCmpcl2+8pfYyGBKK2yYy61+jUOIJjv3dObSn7ZT/u1KlJBIrA4oy9lJWc5OcNobr8NPtAk9QFGo/OFzf4fSqkiiaaGDx82oKsTH+C7RKBqFcoudcovd1SfUPqp6TvRDJ80+i0MIT3CeykWtNKNN7uvTm50vhmIIQdO+C5afNuOskinVm0oSTQv9ePQsGo1CbKTRZ+9psTnYuq+ArfsKXH1CMRHBaBSFQyck0YjWQ3U6sR/ZhRIajSYmyd/hNIv2sl7gsGHb84m/Q2k1JNG00I9Hz9I+0ohO699DqNVqiDEZyJVEI1oR56kcqCptVWczNTShkegTe2H7YQOqXZ7i3BSSaFqgrNLG8VNlxEf77mzmQtpHGTl6qhSrLTA7UYU4n+qwV5/NhLdDE93R3+G0iDFtBKqlDNuBL/0dSqsgiaYFDhwpRsW3/TMXEhtpxO5Q+enoWX+HIkSjLD9+A5ZydK3wbKaGrn0XNHGXY929DtUpX/AaI4mmBfYeLiZIryHGFBhnNDX9RPvzivwciRAXptqtVOxejxLRHiUywd/htJiiKBh6Z6GWnsb+09f+DifgSaJpgT2HikjpGIlWExjfxowGHbGRRvZJohEBzrbvM9SKklZ9NlNDm9wHTWxnLDv+i+oI3CHZgUASTTOdOlvJqeJKuiVH+TsUN10SItiXVyQP2BQBS7VWYt25Bl2HK9BEdvB3OBdNURQM/cajlp7BdmCTv8MJaJJommnvoeqzhkBLNJ0TIjCXW8mXx9GIAGXdvQ61qpSQq8b6OxSP0XZMQxt3Bdadq1HtctN0QyTRNNMPh4qIiTC4bpQMFF0SIgDIOVbi50iEqMtZUYL1+4/Rde6HPjbZ3+F4jKIoBPWfgFpejHX3Wn+HE7Ak0TSDxebgh0OFPp+2uSniokMIM+r56bgkGhF4rDs/ALsVQ/9b/B2Kx+kSuqPrMgDrztU4Swr8HU5AkkTTDD/kFmG1ObkqNdbfodShURTSLm/HnkPSTyMCi6P4BLa9n6NPHYImMt7f4XiF4eo7QKOj6qu35e+vHpJommHbgVOEBOtIbB/ml/lnGjOgRzzFpRaOFJT5OxQhgHOTmn31NugNBPWf4O9wvEYTGoWh/804jv2APecbf4cTcCTRNJHV5mBXzhk6xISw48fTfpl/pjH9e8ShADt/Ou3vUIQAwH5wC44T+zAMuAWNMcLf4XiMVoNrqoOan9Ar+qGL60LVF2+iKT7k7xADiiSaJtq6/xRVVoer0z0QmcIMdO1o4rufZK4M4X+qtQLL5nfRtOuEvtswf4fjWXaLa1qDmp/y3F1okvuBoqHsf4tQrTJPVA1JNE208bsTtI8y+n3+mcb079aeI6fKyMuXh2wK/6r68m3USjPBg6agaC6NjxrFEIK+21Ac5lNUfvIaqsPm75ACwqXx279IRwpKyTlewjVpHQJutFlt1/TqgCFIy/qtx/wdiriE2XI2Y8/5hqArb0Tbvou/w/EpTWQHQq++DcfR3VSuXyjJBkk0TbLqi0MYDToye8b5O5QGKRqFU0UVqAoM7NWBb/cVUGSu8ndY4hKkLT2O5cu30MV2IqzXkDp9GYE6VbMnBacOxDDobhxHdlG57mXUqkt7gI4kmkbkHCvhu5wzjM5IIiRY7+9wGmSxOdhx4BRb9xUwtE8CWo3Csv/9KEMthU+p1gpK1y5AddjRJF9Jee7uOn0ZgTxVsycF9RhO8ND7cJw8QPn7v8VxKtffIfmNJJoLsNgcLF27j8iwIK7vl+jvcJosxhTMuMGd+S7nDN/syfd3OOISoTrtVK7/G46SU+i7D0dpQ6PMmqtmVFpol3RMWQ+joFLx3+ewffEGurKT1Wd3XDqTpun8HUCgUlWVf234kZOFFTw2sQ+GIC12S+v5JnZD/0R25xTy5tr9tDMZSUmM9HdIog1THXaqPnsdx4qRleAAABadSURBVPE9hA66A7ti8HdI/mW3UJa71/VS12sk9sM7qdr3BVU/bUGb0I2IgbdAROv5AnsxFLUJ11by8vL4wx/+wI4dOzAYDGRlZfGrX/0Ko7Hx532tWrWKRYsWcfz4cZKSkvjlL3/JmDFj3MrYbDZeeeUVVq5cSWlpKWlpaTz99NN079692TtUWFiG8yLuplz1RS7jBnXm/U25fPjNYbomRPDI7X346Js8RmV24o9vbeX02Uo6d4ggzKgn53gJFVU/JyCdVsHu+Pn9FQU8efWqfZSR02cr69QZGRbEFYlRdE+O5HRJFagq+/OKKLc4OHO2kv7d2zNpZDcMeh26885jV32Ry02DL63O2uaIjQ3n9OlSj9TjKRfbxj1NtVVRuX4hjmM/YMi4HY2lBGvRSQB0yX2xH97pVt4QFUvloe/RRMbjyM9BCQ5DNZ9GiYit7stQ7SjGSNSyInA6IMgI1nLQ6DF2Safy8A/gcILTBho96ILAWlldzm6t3kavB0Xneq1ExKKaT4NGe658BUpEe1RzAUpEXHUsx/b+XKfThhIRV73NOTXxKcFhGDunue2DNv5ydMl9se5aS1Dv0Zi69KAkdy/WXWvdnoagiUnGcXgHzqJjoNGiS0xH17kf2sQ0NMYIyj94ntAbn3Q7XpZtKzH0G+/6tz6111m2raw+1g2UP58v2nijZzRms5kpU6aQkJDA/PnzKSoq4vnnn6eoqIh58+ZdcNt169YxZ84cpk+fzjXXXMOGDRt49NFHCQ0NZejQoa5yzz//PKtWreKJJ57gsssuY8mSJUydOpUPPviAuDjfdsB/8FUeZ8usbNp1AoCDJ8xs3VfA2s1HuG5AEqeKq8fG556of/jw+UkGPJtkANf713a2zMrWfQV0T45k7TeHXctvGdaF/3yey5a9pwgzBnHTkC7oDD//2j/4Kk8SjWgxR9Exqj5djLP4OMFD70OfOpjS16e61uuS++I4ssttm4oj57Y1Vz8XTLWWV/9r/vk5YartvGeGnVuP00Zlznb3AJw2sNrcywHYLHDepSlX3U6nq3zNMtVc4IrFVWeteNzKW8up2FXgtg+OI7vQJfetd5vz69Yl90XT8zosX7xJcLdBWA5/70rEiiketSQfy87VaKIS0Jji0YRGY93xXwz9xrv+rU/tddYd/wWalmh8odFE8+6772I2m1m1ahXR0dEAaLVafvWrXzFz5kyuuOKKBredP38+o0aN4rHHHgMgMzOTQ4cOsWDBAleiKSgo4N133+Xpp5/mtttuA6B3795ce+21vPXWW/z617++6J1sqppH7G/adYKRAxL5+NujPntvbzl/AMOn248RpNcyfnBndFrpnhMtp9qtWH9Yj3XbShRDCMZRj6BLTPd3WK1KaMYEdNfcjfN0HvYT+3HkH8BRko9163t1ypb9+wkAKj95DSU4HMUYgd5oRKMPRgmuPpPQleej6IMD8haMRhPNpk2byMzMdCUZgJEjR/LUU0+xadOmBhPN0aNHyc3NZfbs2W7Ls7KyePLJJykqKiI6Opovv/wSh8PhdjktLCyM4cOHs2nTJp8kGovVwdoth1m3pfqr1rC+CcQF+I2ZLdGpQzhrNx9mx4FTXNcvMSAfDioCm7PiLPacb7DuWodaWYKu05UYBk9tU4+X8RWtBgz2UoiKgahroOc1FC59mOhJL+A4W4BaUYzdXETFtv+ij+qAtSQf5+lc1Kqy6knkatVX/M4ToGhAH+xaVvXlP9BEdqg+O4rsgBIWjaL4/ktmo4nm4MGD3HzzzW7LgoKCSEpKIje34eF6Neu6du3qtvzyyy93rY+OjubgwYO0a9eOqKioOuXWrFmD0+lE44W7im12B0dPlbPzp9N8sesE5gob/bu1Z+v+UyTFee56eiAZ3DuBUZl6Pvw6j3fW/8g7638E4B/r9tOpQwSdO0TQISZEznYEUD0jprO8uPryz+lDOE4ewHHyR0BFm9CdoOtmouuQ6u8wW69aAwZqlB/eB+Dq5wFQEntD3k70vbMAUJ0OwjskYT64G2xV2H5Yj7Zzf7BVotqqcBbkAGDL+aa6/6qGNgiNqbpPqib5WGxdUdUI8OLZUJP6aCIi6n5biYiIoKSk4blPatbV3tZkMrmtN5vNhIfX/WA3mUzYbDYqKioICwtrLEwXjabugSopt7JyUy7llTYsdicWi52z5RZUtbqz/srU9gztk0DnDhH8+rWvXZebaiY3CwnW0z7KiE6rCbgJz2qribW+1yHBerp1isbpVCkpt1JQVMGeQ0Xknixl7+FiABQgLCSIcKMevV6DTqMwoEccfa+4dM9+6mtT/uSpeFS7FcuWf6NWlIDDDk4HqtMGDgeqtQLs5w2/VRS0kR0IuuYOdIm9L/i4f53p57aiNRjdXrdlNfuqNRhRtLp6911rqP5b1JliXWXOV7M94FbH+f+6ykbEoI/pCIB69DuCuvZzrbNWVX++hkz8c/UZUOlpnOZT1T+lp1HNp3Ee2oJTVSnYcW4jRQt6A4rOgBIUDBpd9RmSogGNgiY8FsOAW1t0bFo8vFlV1SZlv9plaga5nb+8vnpaeqNhVFRonWUxMfDYpOh6SteV/cwNrv9nDe5a5//nrw9U58dd3+suHQNrGupAFxPT9C86vlBfG2+xGx/wXF3nxDy4yH1BxnUef4+Add6+hnZIbnjfzy0P7ZBc7/J666inLtf2tde5vQ4HOjQWuVc1eo0kIiICs7nuCKvS0tJ6z3Rq1D5zqVFTV822DdVvNpvR6/WEhLS9vhIhhLiUNJpounbtysGDB92WWa1Wjhw5QpcuDQ+LrVlXux+npq6a9V27dqWwsJCzZ8/WKdepUyev9M8IIYTwnUY/xYcMGcLmzZspLi52LVu/fj1Wq9XtXpjaEhMT6dKlCx999JHb8jVr1pCWluYaxTZo0CA0Gg1r1651lSkvL+fTTz9lyJAhzd4hIYQQgaXRPpqJEyeybNkyZs6cycyZMyksLGTu3LmMGTPGNYIM4KmnnmLVqlXs3fvzKIpZs2Yxe/ZskpKSGDhwIJ988glfffUVixcvdpWJi4tj4sSJ/OUvf0Gn05GQkMAbb7wBwN133+3JfRVCCOEHjSaaiIgI3nrrLZ577jkeeugh1yNoHn/8cbdyTqcTh8P98d+jR4+mqqqKRYsWkZ2dTVJSEn/961/rnAk9+eSThISE8PLLL7seQbN06VKfPxVACCGE5zXpWWdCCCFES0lPuxBCCK+SRCOEEMKrJNEIIYTwKkk0Aerw4cM8++yzjBs3jh49evCLX/yi3nIbN25k/PjxpKWlcd111/H222/XWy47O5sRI0aQnp7OhAkT+Oabb7wZfsBZu3YtM2fOZMiQIfTp04cbb7yRFStW1HkChRxP35E27lmB3MYl0QSon376iY0bN5KcnFznwaQ1vvvuO2bOnEn37t35+9//zoQJE/jTn/7Ev/71L7dy2dnZzJs3j7vuuovFixfTqVMnpk+fzv79+32xKwHhzTffJDg4mCeeeILXXnuNoUOH8uyzz7JgwQJXGTmeviVt3LMCuo2rIiA5HA7X/+fMmaNmZWXVKXPfffept9xyi9uyZ555Rr3mmmtc21ssFvWqq65SX3jhBVcZu92ujh49Wp01a5aXog88hYWFdZY988wz6pVXXuk6VnI8fUvauGcFchuXM5oA1dijd6xWK5s3b64zLfYvfvELTp8+zZ49ewDYsWMHpaWlZGVlucpotVpGjx7Npk2bWvzw0tbm/PmUanTv3p2ysjIsFoscTz+QNu5ZgdzGJdG0UkeOHMFms9W55FAzEV3NM+Zqni1X37xAFRUVFBS4Tz17Kdm+fTuXXXYZRqNRjmcAkt/JxQuUNi6JppVqaL6fmtfnz/cTFBREcHCwW7map2vXfpjppWLbtm189NFH3HXXXYAcz0Akv5OLE0htXBJNK9fQnEBNne8nEOcX97b8/Hxmz55N//79mTp1qts6OZ6BR34nzRdobVwSTSvVnPl+LBYLFoul3nI19VwqzGYz06ZNIzIykr/97W9otVpAjmcgkt9JywRiG5dE00olJSWh1+vrzPeTk1M9V/j58/0AdeYUOnjwIKGhoZfUg0urqqqYMWMGpaWlLFmyxG0KcTmegUd+J80XqG1cEk0rFRQURGZmpts8PlA9309sbCw9e/YE4MorryQ8PNxtXiCHw8HatWsZPHjwJXNZwW6388gjj5Cbm8uSJUvq/LHI8Qw88jtpnkBu49rf/e53v2v+Lglvq6ys5JNPPiEnJ4evvvqKM2fOEB8fT05ODkajkYiICBITE1m0aBEnT54kNDSU1atXs3TpUh5//HHS09OB6mGJWq2WRYsWERwcjMViYf78+ezYsYMXX3yRdu3a+XlPfeO3v/0tH374IY888ggxMTHk5+e7fsLCwggKCpLj6WPSxj0rkNu4TBMQoI4dO8a1115b77rnn3+eCRMmANWPk3jppZc4ePAg7du3Z+rUqUyZMqXONtnZ2SxbtowzZ85wxRVX8Pjjj3P11Vd7dR8CyYgRIzh+/Hi96/7xj3+QkZEByPH0JWnjnhXIbVwSjRBCCK+SPhohhBBeJYlGCCGEV0miEUII4VWSaIQQQniVJBohhBBeJYlGCCGEV0mi8YP333+f1NRUjh075u9QhPAKaePifJJoLkGvvvoqGzZs8HcYQniNtPHAIjds+oHD4cButxMUFOSX5zClpaWRlZXF3Llzff7e4tIgbVycT85ofKiyshKofpaQwWBoUw/7U1W1zmPFxaVH2riojySacxYsWEBqaio5OTk89thj9OvXj/79+/P0009TVlbmVvarr75i8uTJ9O3blz59+jB58mR27NjRYH1z5swhIyPDNQd3fdevJ0+ezKhRo8jJyWHKlCn07t2bESNG8MEHHwCwe/du7rjjDnr37s11111X72WBsrIyXnjhBUaMGEGvXr0YNmwYf/nLX7Bara4yqampWK1WVq5cSWpqKqmpqUyePLlZddTU8+yzz7J27VrGjh1LWloaH374YbOOdW5uLk888QT9+/cnIyODP//5zzidTgoLC3n44Yfp168fmZmZLFy4sE4dqqqybNky13tnZGTw2GOPkZ+f71Zu27ZtPPzwwwwfPpxevXoxaNAgnn32WdfcGrVjysvL49lnnyUjI4O+ffsya9YsiouLm7RfgU7aeNPrqKlH2rhn6Lxaeys0e/Zs4uLimD17Nvv27WPFihWcPHmSN954A6h+pPbjjz9ORkYGjzzyCKqq8t5773H33XezbNkyevfu7VbfI488wmWXXcasWbOw2WwXfO+ysjKmTZvGqFGjGDlyJP/+97+ZM2cOWq2W559/nptvvpnRo0fz9ttvM3v2bD799FNiY2OB6nkoJk+ezLFjx7jttttISkpi3759LF26lNzcXF599VUAXnzxRZ5++ml69+7NbbfdBuB6GmtT66ixfft2Pv74YyZNmkS7du1c81k01aOPPkqnTp2YPXs2X375JUuWLMFkMvHhhx/Ss2dPHn30Uf73v/+xYMECunXrxnXXXefa9re//S3/+c9/GDduHHfeeSdnzpxh2bJl7Ny5k1WrVrkmcVq7di0lJSXccsstxMbGsn//flasWMGPP/7Iu+++W29M7du3Z9asWRw+fJhly5ah1+v561//2qx9C2TSxqWN+7yNq0JVVVV95ZVX1JSUFPW+++5TnU6na/nLL7+spqSkqF988YVaXl6u9u/fX50zZ47bthUVFerw4cPVKVOm1KnvwQcfrPNe7733npqSkqIePXrUtWzSpElqSkqKunLlSteyo0ePqqmpqWpqaqr69ddfu5bv2rVLTUlJUbOzs13LXnvtNTU9PV3Nyclxe69ly5apKSkp6tatW13LevXqVWcfmltHSkqKmpqaqu7du7dOPY2pOTZPPvmka5nT6VSvvfZaNTU1VX355Zddy6uqqtT+/furM2bMcC3bvn27mpKSor733ntu9e7bt0/t0aOHunDhQteyioqKOu+/atUqNSUlRd22bVudmB577DG3ss8995zavXt31Ww2N3s/A420cWnj/mrjcumslkmTJrldV6455d64cSNff/01JSUljB07lqKiItdPZWUlAwcOZPv27XW+0d1xxx1Nfu/g4GBuvPFG1+uOHTvSrl07EhIS3B7P3atXL7RaLUeOHHEtW7t2LVdeeSVRUVFusQ0cOBCAzZs3N/r+za2jb9++dO/evcn7V9utt97q+r+iKKSnp6OqKjfffLNrucFgIDU1tc6+hoSEMHToULc427dvT3JyslucRqMRqL4MUVZWRlFREVdeeSUAe/bsqRPTnXfe6fZ6wIABOBwOTpw40eL9DDTSxqWNn88XbVwundWSnJzs9jo6OhqTycSJEyc4dOgQAPfee2+D25eWlhIdHe16nZiY2OT3jouLQ6Nxz/1hYWFu9QFoNBpCQkLcrsHm5eWxf//+BueLKCwsbPT9m1tHUlJSo3VeSEJCgtvrsLAwADp06OC2PDw83HXsa+KsqKhwfTjUdv6H6MmTJ3nxxRfZuHEj5eXlbuVqX8MGuOyyy9xe11yeqD3PemsmbVza+Pl80cYl0dRS3ygZ9dwI8Jp/586d2+C82TUNqUZwcHCT37v2H2ANrVZb73L1vJHpTqeTzMxMZsyYUW/Zpszz3dw6DAZDo3VeSHP2t/a+RkZGMm/evHq3r/mG53Q6uffeeykqKmLGjBl07doVo9GI0+nk/vvvd6uzsZjqK9taSRuXNl4fb7ZxSTS15OXl0alTJ9froqIizGYzCQkJrm9u0dHRDX7T8JekpCTKy8ubFFdDQ06bU4c/JSUl8fXXX5Oenl7nQ+98Bw4cIDc3l7lz5zJ+/HjX8ry8PB9EGbikjUsb9zXpo6ll2bJlbpn97bffBmDIkCEMHjyYiIgIXnvttTpDIaH6D9ZfxowZw/fff88nn3xSZ11VVZXbKbXRaKz3NLk5dfhTVlYWTqezwSGhNb+Hmm9utb+p1YyuulRJG5c27mtyRlNLQUEB06ZNY/jw4ezfv5/ly5dz9dVXM3jwYAB+//vf89hjj3HjjTcyduxYYmNjyc/PZ8uWLRiNRpYsWeKXuO+77z42btzIQw89xI033khaWho2m43c3FzWrVvH66+/Tp8+fYDqjtZvvvmG7Oxs4uPjiY6O5uqrr25WHf7Ur18/Jk+ezNKlS9m/fz9DhgwhODiYY8eOsWHDBm655RamT59Oly5d6NSpEy+88AL5+fmYTCa++OKLOvchXGqkjUsb9zVJNLW89NJLLFq0iJdeeglFUZgwYQJPPfWUa/3o0aOJi4tj8eLFvPXWW1RWVhIbG0vv3r3dRpj4WnBwMG+99RZLlizhww8/ZM2aNYSGhtKxY0emTJniNv7/6aef5ne/+x0LFy6koqKCAQMGcPXVVzerDn975pln6NWrF//85z955ZVXUBSF+Ph4Bg0a5LoXQa/X89prr/HHP/6R7OxstFotgwcPZsmSJVxzzTV+3gP/kTYubdzX5Fln5yxYsICFCxfy5Zdfum4QE6ItkTYu/EX6aIQQQniVXDoTHlNaWkpVVdUFy0RHRzc4lFWIQCdtvGUk0QiP+eMf/8jKlSsvWOaTTz6hY8eOPopICM+SNt4y0kcjPCYnJ4dTp05dsMxVV1110TfBCeEv0sZbRhKNEEIIr5LBAEIIIbxKEo0QQgivkkQjhBDCqyTRCCGE8CpJNEIIIbzq/wNdn0ufMIzvYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"radius_mean\", hist=False, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"texture_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"perimeter_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"area_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"smoothness_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"compactness_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"concavity_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"concave points_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"symmetry_mean\", hist=True, rug=True)\n",
    "\n",
    "g = s.FacetGrid(df, col='diagnosis', hue='diagnosis')\n",
    "g.map(s.distplot, \"fractal_dimension_mean\", hist=True, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFSCAYAAADvvinOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeViU5foH8O+ggBIiAhZIYmChppSmyKaIC7IpiuaColmYKAoGYgpZaaCgHiVCEhcwFI+kYiOMMIJ0ElFxCRfUPB5ZjijlMpCDC7LN7w9+8x4GBpyBGWbh/lwX19W8yzPPxOPLPc9yPyyBQCAAIYQQQgghBACgoegKEEIIIYQQokwoQCaEEEIIIaQJCpAJIYQQQghpggJkQgghhBBCmqAAmRBCCCGEkCYoQCaEEEIIIaQJCpCJUiotLYWvry9GjBgBW1tbhIeH4+XLl6+9LyMjAwEBARg7diwGDRqEhISE196zb98+DBo0CH5+frKoOiGEEEJUHAXIROnw+XwsXLgQz58/R0xMDNauXQsOh4OwsLDX3svlclFWVobx48dL9F4PHz5EXFwcDA0NO1ptQgghhKiJ7oquACHNpaSkgM/ng81mw8DAAADQrVs3hISEwN/fH++9916r937//ffQ0Gj83vfzzz+/9r02b94MZ2dn3L9/XzaVJ4QQQojK6/IBcmXlczQ0qPZmghoaLPTp84aiqyEzubm5sLW1ZYJjAHBxcUFYWBhyc3PbDJCFwbEkzp8/j9zcXHC5XAQFBbW7vqrehtSt/agaaj+kI1S9/QDUhhSJ2k/runyA3NAgUPnGoW6Kioowc+ZMkWNaWlowMzNDcXGxTN6jtrYW3333HVasWAEjI6MOlUVtiHQEtR/SEdR+SEdQ+2kdzUEmSofP50NPT6/FcT09PTx9+lQm77Fv3z6wWCz4+PjIpDxCiHpp70JhAGCz2XB1dYWVlRU8PDyQkZHR4pr79+8jODgYY8aMwYgRIzBjxgycOHFC1h+DENJOCu1BLi0tRXh4OAoKCqCtrQ0PDw+EhISgZ8+eEpdx48YNzJo1Cz169MCVK1fkWFuiaAKBACwWq8PllJeXY+fOnYiLi0P37h3/J2BoqNvhMgghykO4ULhfv36IiYlBRUUFIiMjUVFRgejo6Dbv5XK5WLNmDZYsWQIHBwecOnUKwcHBeOONNzBu3DgAwKtXr+Dr6wsACAsLg76+PjgcDoKDg6GtrY1JkybJ/TMSQtqmsAC5Iw8goYaGBqxfvx4GBgZ48eKFnGtMOouenh74fH6L41VVVRg4cGCHy9+6dStGjhyJYcOGMe9TV1eHuro68Pl89OzZE5qamhKXx+M9U+khKg0NFgX5hDTRkYXCMTExcHV1xapVqwAAtra2KCkpQWxsLBMgFxYWorS0FElJSbC1tQUA2Nvb49q1a+BwOBQgK7GOdOyx2WzEx8fjwYMHMDMzw/Lly+Hu7s6c5/F4+Oabb3Dr1i3weDy88cYbGDZsGAICAvDBBx+IlPX48WNs3LgRZ86cAYvFgpOTE8LCwkTW7pCOUdgUC+ED6Mcff4SjoyOmT5+OdevWISMjA//5z38kKuPw4cOoqqpqMV+VqLaBAweiqKhI5FhNTQ3u3bsHCwuLDpdfXFyMM2fOwNramvkpKChAXl4erK2tcfr06Q6/ByFEdbW2UFhLSwu5ubmt3ldWVobi4mJ4eHiIHPfw8EBhYSEqKioANH4hB4BevXqJXNerVy8IBKr7ZVvddTQF6Zo1a+Ds7Iw9e/bAzs4OwcHBIn9vqquroaurixUrVmD37t3YsGEDXrx4gU8++QQlJSXMdXV1dVi8eDHu3LmDzZs3IyIiAleuXIG/vz+1HxlSWA9yRzIVAGB6mrds2YLr16/Lu7qkEzk6OmLnzp2orKxEnz59AADZ2dmoqalhemA6IiIiosWIw6ZNm9CjRw8EBwe/tu0RQtRbexcKC881H+l69913mfMGBgYYOXIk3n33XWzfvh3r16+Hvr4+MjIycOPGDaxcuVLGn4bIirxHFkxNTbF582aR+xwcHGBjY4OTJ09i6dKlAICsrCzcvn0bHA6Hec8333wT3t7eyM3NlcnfSaLAHuSioiLmoSEkTaaCf/zjH/joo4+oIaihuXPnolevXvD398eZM2fAZrMRHh4Od3d3kTYTFhaG999/X+Teu3fvgsvlgsvlAgDu3Lkj8hoArKysYGNjI/Kjp6cHfX192NjY0BAVIV1cexcKC881v7d3794i5zU1NbF//35UVVVh0qRJGDVqFCIiIrBlyxbY2dnJ6mMQGZP3yII4Ojo60NbWZkYdAOD06dOwtLQUCcg/+ugjmJqa0gioDCl0DnJ7MxVcuXIFJ06cAIfDkVf1xOLxniAi4lt8/fV3MDCgndfkRU9PD0lJSYiIiEBAQAAzz2v16tUi1zU0NKC+vl7kWGZmJnbs2MG8ZrPZYLPZAIB///vf8q+8glEbVW/0+1UsSRcKN79GOOwtPF5dXY3AwEDU19djx44d0NXVBZfLRWhoKIyMjDB69GiJ66Rs6wceP36MNWvWYMuWLR1Ooals5D2yINTQ0ICGhgbweDwkJiZCQ0MD06ZNE6lH8w5GYXmySoUqTld7/ihdHuTXPYDq6+uxYcMGLFq0CP379+/w+0nzcNm1KwY3blzH0aMH8dVXX3X4vUnrzM3NkZCQ0OY1UVFRiIqKEjkWEBCAgIAAqd/vwIEDUt+jjJKTf8KNG9eRnPwTAgNXKbo6RMbo99s52rtQuGlPcdPgUFiWsFPo6NGjuHbtGnJzc5nAyM7ODvfu3cP27duRkpIicV2VbZFwTMwOFBQUICZmh8RtVFUWCst7ZEEoMjIS+/fvBwAYGhpi9+7dIvEOn89vMX9dWH7z9Tuy1NWePwoLkNv7ADp8+DAeP36MefPmMfe/evUKQGOj0dLSQo8ePSSuh6QPFx7vCY4fPw6BQAA2+zg+/ni+0nyDUpWHC5EvHu8JTp7MgEAgAJebAR+fRUrTRknH0e+387S1UHjGjBmt3idcRFxcXCzyd0xYlvD83bt38dZbb7WYzjVkyBBmxEsVddU2KquRBaHPPvsMU6dOxePHj3HkyBEsWbIEP/30E4YOHdpqWdLUoylJY4fHjx8jKysTAoEAJ09mYOXKjm+ypewUFiC39wFUXFyMJ0+ewNHRscU5a2trLFy4UC69u8nJPzGBdENDQ5f5BkVUB7VR9Ua/387T3oXC/fv3h4WFBTIyMuDs7Mwc53A4sLKyYgLifv364eHDh+DxeDA0/F8AefPmTZiamsrpU8mfurdReY8sCJmYmMDExAQAMH78eHh5eeGHH37Arl27XlsPcT3cbZG0kzAmZgfq6xsAAPX1DVKNEMibvDoJFbZIz9HREfn5+aisrGSOSfIA8vHxwf79+0V+vLy8oK2tjf3798ttZ7ScnCzU1dUCAOrqanHq1Em5vA8h7UVtVL3R77fzdGShcGBgIDIzMxEdHY0LFy5g06ZNOHv2rMjUL09PT/To0QOff/45uFwu8vLysG7dOuTn56v07p7q3kbbm4K06chCU81HFsTR0NDAkCFD8N///rfNegCNIxOySIUqjrr/bsVRWIDc3gfQgAEDWmQgMDU1Rbdu3WBjY4MBAwbIpb4TJ05G9+6Nm0d0766JSZNc5PI+hLQXtVH1Rr/fziNcKKyjo4OAgABERkbC3d0dmzZtErlO3EJhNzc3REZGgsvlwtfXF3l5edi2bZtIx4+xsTEOHDiAvn37Ijw8HAEBAbh58yY2b94sshhL1ah7G21vx17TkYWmmo8siFNTU4Nr166JzEEeN24c7ty5IxIkX716FQ8ePJBbZi91/92KwxIoMKt0SUkJIiIi8Pvvv4tkKmi6I83atWvxyy+/tJmBIDY2FomJie3aalqaOcgLFsxGTU0NtLS0kZx8WGnmVtEcZMVSlkUy7W2j1H4US9WfQdR+FEtZnj+A+j+D+Hw+pkyZAlNTU/j7+4PH4yEqKgp2dnYiOwCHhYWBzWbj1q1bzLHMzEwEBQXBz88P9vb2yMnJwf79+7Fr1y4mqE1ISEBZWRlGjx4NIyMjPHr0CIcOHcK1a9fw008/YdSoUQAaNwqZOXMm6urqEBwcjPr6eiZryKFDh6Sah6zqzx9Afu1HoVks2pupoLn2Zi6QhqGhEVxc3MHhHIerq7vSNAxChKiNqjf6/RJlp+5ttCMpSN3c3FBdXY34+HgkJCTAzMysxcjCkCFDkJeXh6ysLPD5fLz55psYNmwYjh49isGDBzPXde/eHXv37sXGjRuxevVqZqvpr776SupFepJS99+tOArtQVYG0nz7VtYcgKry7VtdKVsPjrRtlNqPYqn6M4jaj2Ip0/MHoGeQqlH15w8gv/ZDAbKSPVzagx4uiqXqbYjaj2JR+yEdoertB6A2pEjUftooV+YlEkIIIYQQosKUbic9QgjpqNLSUoSHh6OgoICZJxgSEiKyALg1bDYb8fHxePDgAczMzLB8+XK4u7uLXDNo0KAW9+no6LRroTAhhBDlQwEyIUSt8Pl8LFy4EP369UNMTAwqKioQGRmJiooKkZXm4nC5XKxZswZLliyBg4MDTp06heDgYLzxxhst0ictWLAAU6ZMYV5raNCAHCGEqAsKkAkhaiUlJQV8Ph9sNpvJL9qtWzeEhITA398f7733Xqv3xsTEwNXVFatWNe4QZWtri5KSEsTGxrYIkE1MTDB8+HD5fRBCCCEKQ10ehBC1kpubC1tbW5Hk+y4uLtDS0kJubm6r95WVlaG4uBgeHh4ixz08PFBYWIiKigq51ZkQQohyoQCZEKJWioqKRHbjBAAtLS2YmZm12Oq1KeG5gQMHihwXltX83t27d2Po0KEYNWoUAgICcO/ePVlUnxBCiBKgKRaEELXC5/Ohp6fX4rienh6ePn3a6n3Cc83v7d27t8h5AJg+fTqcnJzQt29fFBUVYefOnfD29sbx48dhZGQki49BCCFEgShAJoR0CQKBQKJdpppfI0wV3/T45s2bmf8eNWoURo8ejalTp+LgwYNYuXKlVPWi/K+EEKJ8KEAmhKgVPT098Pn8FserqqpaTJ9oqmlPcdNeYGFZ4nqlhczNzTFkyBDcvHlT6vqqeqJ+2uSBEKKOaA4yIUStDBw4EEVFRSLHampqcO/ePVhYWLR6n/Bc87nGwrLauhf4X08zIYQQ1UcBMiFErTg6OiI/Px+VlZXMsezsbNTU1LRI1dZU//79YWFhgYyMDJHjHA4HVlZWIlkxmisuLsbt27dhZWXV8Q9ACCFE4WiKBSFErcydOxfJycnw9/eHv78/eDweoqKi4O7uLpLdIiwsDGw2G7du3WKOBQYGIigoCGZmZrC3t0dOTg7Onj2LXbt2MdckJCTg3r17sLGxgYGBAYqKihAfH48+ffpg3rx5nfpZCSGEyAcFyIQQtaKnp4ekpCREREQgICCA2Wp69erVItc1NDSgvr5e5Jibmxuqq6sRHx+PhIQEmJmZYdu2bSI9z+bm5sjKygKXy8WzZ8/Qp08fODg44IsvvoChoWGnfEZCCCHyxRJ08Ylzqr5ABqBFMoqm6m2I2o9iUfshHaHq7QegNqRI1H7aKFfmJRJCCCGEEKLCKEAmhBBCCCGkCQqQCSGEEEIIaYICZKKUSktL4evrixEjRsDW1hbh4eF4+fLla+/LyMhAQEAAxo4di0GDBiEhIaHFNSUlJQgPD4e7uzuGDx+OCRMm4Ouvv0ZFRYU8PgohhBBCVAwFyETp8Pl8LFy4EM+fP0dMTAzWrl0LDoeDsLCw197L5XJRVlaG8ePHt3rN2bNncenSJcyZMwe7du1CQEAA8vLy4O3tjVevXsnyoxBCCCFEBVGaN6J0UlJSwOfzwWazmc0ZunXrhpCQEPj7++O9995r9d7vv/8eGhqN3/t+/vlnsdd4eHhg/vz5YLFYzLEBAwbA29sbv/32G1xcXGT4aQghqqi0tBTh4eEoKChgUgWGhISgZ8+er72XzWYjPj4eDx48gJmZGZYvXw53d3fm/LFjxxAaGir2XnNzc3C5XJl9DkJI+1CATJRObm4ubG1tRXYuc3FxQVhYGHJzc9sMkIXBcVv69OnT4tj7778PAODxeO2oMSFEnQhHsfr164eYmBhUVFQgMjISFRUViI6ObvNeLpeLNWvWYMmSJXBwcMCpU6cQHByMN954g8mn7eTk1OIL/N9//w0/P782d3skhHQeCpCJ0ikqKsLMmTNFjmlpacHMzAzFxcVyec/Lly8DgMhOa4SQrqkjo1gxMTFwdXXFqlWrAAC2trYoKSlBbGwsE/waGBi02Lr84MGDAABPT095fCRCiJRoDjJROnw+H3p6ei2O6+np4enTpzJ/v1evXmHLli2wtLTEqFGjZF4+IUS1tDaKpaWlhdzc3FbvKysrQ3FxMTw8PESOe3h4oLCwsM2FwOnp6Rg4cCCGDh3a8Q9ACOkw6kEmKkMgEIjMG5aVb7/9FmVlZTh06JBEUzSaox2gCFEv7R3FEp4bOHCgyHHhyFRxcXGLnmOgMbC+cuUKgoKCOlp1QoiMUIBMlI6enh74fH6L41VVVS3+8HRUdHQ00tPTER8fj8GDB7erDFXfqpO2eSVEVHtHsYTnmt/bu3dvkfPNpaWlgcViYerUqe2tMiFExihAJkpn4MCBKCoqEjlWU1ODe/fuYcaMGTJ7nwMHDmDXrl2IjIzE2LFjZVYuIUQ9STqK1fwagUAg9rhQeno6Ro4cCVNTU6nrRF9uCZEPCpCJ0nF0dMTOnTtRWVnJZJzIzs5GTU2NzFZ4czgcbNy4EcHBwfDy8pJJmYQQ9dDeUaymPcVGRkbMcWFZ4nqlCwsLUVJSgk8//bRddVX1ESyARrGIcqJFekTpzJ07F7169YK/vz/OnDkDNpvN7HzXNMtEWFgYk55N6O7du+ByuUwe0Tt37oi8BoCLFy9i7dq1sLGxwejRo3H16lXm5969e53zIQkhSqutUSwLC4tW7xOeaz5PWViWuHvT0tKgpaUFV1fXjlabECJD1INMlI6enh6SkpIQERGBgIAAJkn/6tWrRa5raGhAfX29yLHMzEzs2LGDec1ms8FmswEA//73vwEAFy5cQG1tLfLz8zFnzhyR+728vBAVFSWPj0UIURHtHcXq378/LCwskJGRAWdnZ+Y4h8OBlZVViwV69fX1yMzMxLhx45jeZ0KIcmAJhJOjuiganiIdpeptiNqPYlH7UT58Ph9TpkyBqakp/P39wePxEBUVBTs7O5GNQsLCwsBms3Hr1i3mWGZmJoKCguDn5wd7e3vk5ORg//792LVrV4vg+syZM1i8eDFiY2MxefLkdtVV1dsPoJ5tSFVQ+2kd9SATQgghTXRkFMvNzQ3V1dWIj49HQkICzMzMsG3bNrE9z+np6dDT04OTk5M8Pw6RIXluQV5SUoLk5GScP38e5eXlMDAwgIODA4KCgkRGHy5cuICFCxe2KN/JyQm7du2SzQcl1INM355IR6l6G6L2o1jUfkhHqHr7AVSnDQlHFvr16wd/f39mC3J7e3uJtiBfuXKlyBbkycnJIiMLycnJOHz4MGbOnInBgwejvLwcP/zwA7S0tJCWlgZtbW0A/wuQIyMjRea16+nptTlHXhxqP62jHmRCCCGEqK3q6mrcuHEDPB4P1tbWYjdrkYS8tyD38PDA/PnzRdIBDhgwAN7e3vjtt9/g4uIiUuZ7770HKyurdn0W8nqUxYIQQgghamn//v0YM2YMFixYgC+++IJZrF1RUYFRo0bhyJEjEpcl7y3I+/Tp0yJXtjBTE4/Hk7ieRDYoQCaEEEKI2jl27Bg2bdqEsWPHYuPGjWg6o9TAwAD29vbIzMyUuLyioiKRVKOA7LYgb83ly5dFrm3Kz88PQ4YMwZgxY7Bx40a8ePFCsg9CJEJTLAghhBCidvbt2wcnJydER0ejsrKyxflhw4YhOTlZ4vI6ewvyV69eYcuWLbC0tMSoUaOY47169cLixYsxatQo9OzZE5cvX8aePXtw+/Zt7N+/X6LdHsnrUYBMCCGEELVTWlqK+fPnt3q+T58+YgNnaclrC/Jvv/0WZWVlOHToEDQ0/jfg//7774tskmVrawtTU1OsXbsW+fn5sLOzk7juqrA4UlEoQCaEEEKI2tHV1RW7ZbhQaWmpVAv2OnML8ujoaKSnpyM+Ph6DBw9+bd1cXFwQGhqKmzdvShUgUxaLNsqVeYmEEEIIIQpma2uLY8eOoaampsW5v/76C0eOHIGjo6PE5XXWFuQHDhzArl27EBERgbFjx0pcPyJbFCATQgghRO188cUXqKysxIwZM3Dw4EGwWCzk5ubiH//4Bzw9PdG9e3csX75c4vIcHR2Rn58vMi1D2i3ImxK3BTmHw8HGjRsRHBwMLy8vieuWmZkJgUBAad9kiKZYEEIIIUTtDBgwAP/85z+xceNGxMXFQSAQYN++fQAae5fXr18PY2NjicubO3cukpOT4e/vL7IFubu7u0iWCXFbkAcGBiIoKAhmZmbMFuRnz54V2fnu4sWLWLt2LWxsbDB69GhcvXqVOWdgYAAzMzMAQEhICAYMGIAhQ4agZ8+euHTpEhITE2Fvbw8bG5t2//8ioihAJoQQQohaGjhwIBITE8Hn81FaWgqBQID+/fu3a7MQeW9BfuHCBdTW1iI/Px9z5swRud/LywtRUVEAGjcI4XA4SExMRG1tLfr164dPP/0Uy5Ytk/ozkdbRVtM0QZ10kKq3IWo/ikXth3SEqrcfoPPbkEAgwMuXL6Gjo9Np76msqP20Ua7MSySEkHYoLy8Hm81GYmIi/vzzTwBAXV0deDwe6urqFFw7QoiqycrKwtatW0WO7d69G8OHD8fIkSOxbNkyvHz5UkG1I8qOAmRCOgmP9wRBQctRUUFbhjYXGRkJZ2dnrF27Flu3bkVpaSkAoLq6Gs7OzlIl8yeEEABITExktnEGgMLCQkRHR+ODDz7A7NmzcebMGezdu1eBNSTKjAJkQjpJcvJPuHHjOpKTf1J0VZTK3r17kZSUhEWLFmHfvn0i28Hq6urC2dkZ2dnZCqwhIUQVlZaWYsiQIczrEydOQF9fHwkJCdiwYQPmzp2LEydOKLCGRJlRgExIJ+DxnuDkyQwIBAJwuRnUi9zEkSNH4OnpidWrV4tNiG9pacn0KBP5o5EOoi5evnyJnj17Mq/z8vIwduxYaGlpAQAGDx6Mv/76S1HVI0qOAmRCOkFy8k/MQoiGhgbqRW6ivLwco0aNavX863bDIrJFIx1EXRgbG6OwsBBAY2/y3bt34eDgwJyvrKyEtra2oqpHlJxCA+TS0lL4+vpixIgRsLW1RXh4uEQT5jds2AA3NzeMGDECI0eOxMcff9wpwyTUs0LaKycnC3V1tQCAurpanDp1UsE1Uh76+vp49OhRq+fv3LmDt956S6oy2/tsAQA2mw1XV1dYWVnBw8OjRXL/5vbt24dBgwbBz89PqjoqIxrpIOpk2rRpOHLkCJYuXYrFixejd+/emDBhAnO+sLAQ5ubmCqwhUWYKC5D5fD4WLlyI58+fIyYmBmvXrgWHw0FYWNhr762uroa3tzd++OEHREdHY/DgwQgODkZ6erpc60w9K6S9Jk6cjO7dNQEA3btrYtIkFwXXSHk4OTnh8OHDIotphG7duoWjR49i0qRJEpfXkWcLl8vFmjVr4OzsjD179sDOzg7BwcE4ffq02OsfPnyIuLg4GBoaSlw/ZUYjHUSd+Pn5wc/PDw8fPoSJiQliY2PRq1cvAMDff/+Ny5cviwTMhDSlsDzIu3fvxo8//ohff/2VSdidnp6OkJAQcDgcvPfee1KVN3fuXOjo6CAxMVGq+yTNAcjjPcGCBbNRU1MDLS1tJCcfhoGBcvxRpDykiiVJG6L207rHjx9j9uzZqK2thZOTE1JTU+Hh4YH6+npkZ2fD1NQUhw8fRu/evSUqryPPFjc3N1haWiImJoY55uvri6dPn+Lo0aMtrg8ODoa2tjbu378PHR0dkV2xJKVMeUg9PSfjxYsXzGsdHR2kpWW1eY+i209Xp0ztp72UpQ3V1tbi6tWrGDx4MBNIqztp2g+P9wQREd/i66+/U5q/X4Aa5kHOzc2Fra2tyG42Li4u0NLSQm5urtTl6evro7a2VpZVFEE9K6QjDA2N4OLiDhaLBVdXd6V6uCha3759kZqaivHjxyM7OxsCgQAcDgdnzpzBtGnTcOjQIYmDY6D9z5aysjIUFxfDw8ND5LiHhwcKCwtb9HCfP38eubm5WLVqlcR1U3Y00qHeaJpg254+fYqFCxfixo0biq6KUupqo+gKC5CLiopE9i4HAC0tLZiZmaG4uPi19wsEAtTV1eHp06dgs9k4e/Ys5s+fL6/q0hxSCf3++++Ii4tDeHg4SkpKAAAvXrzA9evX8ezZMwXXTrF8fBZh2LAP4OOzSNFVUToGBgYIDw/HhQsXcO7cOeTl5eHixYvYuHGj1FvCtvfZIjw3cOBAkePCspreW1tbi++++w4rVqyAkZGRVPVTZj4+i6ChwQIAaGhoUFtVM10twGmPLr65cKu64voEhc5B1tPTa3FcT08PT58+fe39OTk5GDp0KEaPHo2vvvoKX331FVxdXeVRVQDUs/I6NTU1WLFiBXx8fBAbG4t//vOfTPqcbt264fPPP8eBAwcUXEvFMjQ0QnR0HPUev4aBgQGMjIzw5MkT3L17V+r72/tsEZ5rfq+w97rpvfv27QOLxYKPj4/U9VNmNNKhvrpigENkpyuOondXdAWaEwgEYLFYr71u9OjROHr0KKqqqpCbm4vw8HB069YNs2bNkur9JJ23snLlCmRlZaKuDujWTQMrV66AkVHXmKMkidjYWPz222/4+uuvYWdnBzc3N+actrY2XFxc8Ouvv2LZsmUSlVdaWorw8HAUFBRAW1sbHh4eCAkJEclpKU5GRgYyMzNx9epVPHr0CF9++SV8fX1bXFdbW4sffvgBv/zyC6qqqmBlZYWvvvpKJKk86RwpKSkoKCjAli1bmGPr16/Hzz//DAAYNGgQEhMTpe5Jbk7SZ0vza4Q9SsLj5eXl2LlzJ+Li4tC9e8cfocow97IpHx9v/IiAXN0AACAASURBVOtfp7BgwTz07UvPOHUhLsAJDFSf6UFEvsSNoqt7+1FYgKynpyc2t2lVVVWLIc7W7reysgIA2Nvbo7a2FlFRUZgxYwa6desmcT0kn6DeA5Mnu4HDOQ4XF3cIBNp4/LhK4veRJ2VY4MDhcDB79mzMmzcPlZWVLc5bWFggK6vtxT5CwiwE/fr1Q0xMDCoqKhAZGYmKigpER0e3eS+Xy0VZWRnGjx/PBFjiREZGgs1mY+3atTA1NcXevXuxaNEipKWlSZ1STJko6yKKthw+fBgffvgh8zo/Px8pKSmYMmUKLC0tsXPnTsTHx0uUhQJo/7OlaU9x02kTwrKEPctbt27FyJEjMWzYMOZcXV0d6urqwOfz0bNnT2hqakpUV0D5FlklJx/C8+fPceDAPyX6A6gMzx95aO+XdKAxVWB8fDwePHgAMzMzLF++HO7u7mLfY/v27cjPz8erV69gZmaGFStWwMVF9iOUXTHAIbIzceJkZGaeQF1dbZcZRVfYFIuBAweiqKhI5FhNTQ3u3bsHCwsLqcsbOnQonj17JjZVlKzQHNLWPXnypM3eVy0tLZHV8W1JSUkBn8/Hjz/+CEdHR0yfPh3r1q1DRkYG/vOf/7R57/fffw82m43vvvuu1WsePnyIlJQUrFq1CrNnz4aDgwNiY2MBAElJSRLVUVmp4hzDBw8eiMwZzszMhLGxMbZs2YIlS5bAx8cHv/76q8TltffZIjzXfJ6ysKym58+cOQNra2vmp6CgAHl5ebC2tm41JZwqoGH4Rp2RKrCoqAizZ8/G8+fPsWnTJsTHx2PmzJl49eqVXD4TTRMkHdEV1ycoLEB2dHREfn6+SG9jdnY2ampqMG7cOKnLu3TpEnR1ddGnTx9ZVlMEzSFtXd++fVFWVtbq+cLCQrz99tsSldWRDCcaGq9v0nl5eaivrxfp0dHV1cX48ePblUFFWahqcFNTUyPS43ru3Dk4Ojoyv8sBAwa0uZFIc+19tvTv3x8WFhYtNgbhcDiwsrJi2mNERAT2798v8jN48GAMHz4c+/fvx0cffSRxXZVNV5xnKE5HvqTHxMTA1dUVq1atgq2tLdatWyfyJVzo22+/xbvvvos9e/Zg0qRJsLOzw6JFi+Dp6SmXz9QVAxwiO11xfYLCAuS5c+eiV69e8Pf3x5kzZ8BmsxEeHg53d3eR3qSwsDC8//77zOvLly9j2bJlOHbsGPLz83Hq1CmEhobi2LFj8PPzk8mcQCI9FxcX/Pzzz0zmCuB/czZ//fVXpKenix1iFKejGU4kKd/IyKjFl6l3330XpaWlaGho6PB7KIKqBjdvv/02zp07BwC4fv06ysrKMGbMGOb8kydPoKsr+RB+e58tABAYGIjMzExER0fjwoUL2LRpE86ePYuAgADmGisrK9jY2Ij86OnpQV9fHzY2Nh2eK61IlK2nkbxTBZaUlODSpUuYN2+eRF/qZaGzAhxKJae+PDw80bOnDqZMmaboqnQKhQXIenp6SEpKgo6ODgICAhAZGQl3d3ds2rRJ5LqGhgbU19czr42NjaGpqYkffvgBixcvxrfffov79+8jLi4OS5Ys6eyPQf7f8uXL0a9fP8yYMQMhISFgsVjYtWsXZs+ejeXLl2Po0KH4/PPPJSqroxlOJClfXBL43r17o7a2VuKpIMpGVYObuXPngsvlYurUqVi8eDGMjY3h6OjInP/9999bfGFqS3ufLUDjRiGRkZHgcrnw9fVFXl4etm3b1q5RLVVEw/CN5J0q8MqVKwAae3LnzZuHoUOHYsyYMYiOjm7RJmWpM6YJquI0LyEdHR2sWLEC/fv3V3RVlNKJE2l4+fIFOJzjiq5Kp1Bod6u5uTkSEhLavCYqKgpRUVHM67fffhs//PCDvKtGpKSrq4uUlBTs27cPXC4X2tra+P3332FmZoaAgAD4+vpCW1u7Q+8haRYCSYgrp735L5VlgZKHhwd++eUX1NXVoXv37pgyZYpKZCGYP38+tLS0cPr0aeaLVI8ePQA0bgfL4/Hg7e0tVZntebYIeXl5wcvLS6r3U5cUhj4+i3DyZOMUk648DC/vVIFPnjwBAHz99deYM2cOAgMDUVBQgLi4OGhqamLFihUS11Wa50/fvr2QnLxf4uul9fjxY2RlZUIgEODkyYz/z/ak+Dzhz549w59//omnT5+Kfc5bW1sD+F+ATFpqPoXPx2eR2k+zoPkIRGa0tbWxdOlSLF26tEPldDTDSXvL5/P50NTUhI6OjlTlKUsWgo8/no/U1FQAjb2jH388X6JMK8qQhWDWrFliUzTq6+vj2LFjIsdevXqFzMxMjBkzRin++KoT4TA8h3O8y8wzlIasUgUKe4nHjBmDL7/8EgBga2uLiooKJCQkYNmyZRJnY1KW5w8AxMTsQH194xS1+voGxMTsUGgmlKdPnyI8PBxcLldsz7zw9/nHH3/I/L3VTVdME0gBMpGrK1eugM/nw9raWuLAs60sBDNmzOhwnQYOHAgej4e///4b+vr6zPGioiK88847nTYnkLRPVVUVQkNDkZiYSAGyHPj4LEJpaUmX7T0G5J8qUHidra2tyP22trY4cOAAysvLVXKYX9lSyX3zzTc4deoU5s+fj9GjR4sdFSCSUbbfbWegSIDIRFxcHD777DORY35+fpg3bx78/Pzg5uaGBw8eSFSWrDOcNDdmzBhoaGggMzOTOfb8+XP8+uuvInNfVU1y8k9McK+hoaGScwAlRdvByg9l65F/qsD33ntP7P3Cdl1TU9O+iiuYss1hz83NxYIFCxAWFoZJkyZh9OjRYn/I6ynb77YzUIBMZILL5cLS0pJ5nZOTg9OnT2Px4sXYvn07amtrERcXJ1FZHclCcPfuXXC5XHC5XADAnTt3RF4DwFtvvYW5c+fiH//4B44cOYKzZ88iMDAQAPDJJ5+0+//B68h7dXfjN/w6AI0bV6jKIj1CZOnPP//EjRs38Pz583aXIe9UgcOHD4e+vj7Onz8vct358+ehq6sLMzOzdtddkZQtlZyWlhYGDBig0DqoC2X73XYGCpCJTPz5558wNzdnXmdnZ2PAgAFYtWoV3N3dMW/ePOTn50tUVkeyEGRmZmLlypVYuXIlgMYdrZq+FgoNDYW3tze+//57LFu2DNXV1di3b59cd9GT9+rurvgNnxChU6dOYfLkyZgwYQJmzZqF69evAwAqKiowdepUZGdnS1yWvFMFampqIiAgACdPnkRkZCTOnTuH2NhYpKSkwM/PT6qdGJWJsuXKdXFxUenc9spE2X63nYHmIBOZEAgEIsHquXPnMHnyZOa1sbExs3JbEu3NQhAQECDyh6g1mpqaCAkJQUhIiMR16ojOWAFMGQhIV/Xbb78hICAAH374ITw9PbFjxw7mnIGBAUxMTHDs2DE4OztLVJ7wS3pERAQCAgKYraZXr14tcl1rqQKrq6sRHx+PhIQEmJmZiU0V6OPjAxaLhaSkJBw8eBDGxsb48ssvsWjRovb9T1ASyjSH3dfXF8HBwVizZg28vb3Rr18/sYsfDQ3VP9iTBWX63XaGdgXI5eXluHjxIioqKuDm5gYTExPU1dXh6dOn6N27N23W0QWZm5sjJycH8+bNQ25uLh4/foyxY8cy5//6668uvUCiM1YAUwYC0lXFxcXho48+wsGDB1FZWSkSIAONUxqOHDkiVZmdkSpw/vz5mD9/vlT1UnbCOezKwMXFBSwWCzdv3kRaWlqr11EWC8ko0++2M0gdyUZGRiI5ORn19fVgsVgYMmQITExMUF1dDWdnZwQGBqr8N2Aivc8++wyrVq2CtbU1Xr58CQsLCzg4ODDnz58/jyFDhiiwhorVWSuAu9o3fEKAxrUGwnRp4vTt2xc8Hu3s1tUsX75cZrnzSdcjVYC8d+9eJCUlwdfXF2PGjMGnn37KnNPV1YWzszOys7MpQO6C3N3doa+vj9OnT0NXVxfe3t7MSMLff/+NPn36YNq0rrE9pTgTJ04Gh3P8//NuashtfnBX+4ZPCNC4GKutzA/l5eVid88k6k2S6XaEtEaqRXpHjhyBp6cnVq9ejcGDB7c4b2lpidLSUlnVjagYe3t7hIaGIiAgQCQHqL6+Pnbs2CEy/+/FixfYsWMH7t+/r4iqdjoPD08mhZNA0NBl9rKXh169eiEyMrLVVFmk6/noo49aZI4QevbsGVJTU2FjY9PJteqa5J2tpyOePXuGZ8+eKboaREVIFSCXl5dj1KhRrZ7X1dUVm1ydkOZevHiBuLg4lJWVKboqneLEiTRmqI/FYnWZvewlcenSJezfL7r9bXp6OlxcXGBnZ4eIiAg0NDQw57S1teHl5UWbhBDGihUr8O9//xuffvop/vWvfwEAbt26hUOHDsHLywtVVVVYvny5gmvZNcg7W4+0Hjx4gC+//BI2NjawtraGtbU1bGxssGbNGolz85OuSaoAWV9fH48ePWr1/J07d+SaJouol6602UNOTlaTHmQB5ShuYseOHSgoKGBeFxUVITQ0FBoaGhg2bBgOHjzYIoAmpCkrKyvs3bsXf/75J8LCwgAAW7duxYYNG6ChoYG9e/fKZJt60rbm2XoU3YtcXFyMGTNmgMPhYNiwYVi4cCEWLFgAKysrpKenY+bMmSgpKVFoHYnykmoOspOTEw4fPox58+a1mPh+69YtHD16FN7e3jKtICHqYOLEycjMPIG6ulrKUdzM3bt3sXjxYuZ1eno6evTogSNHjkBXVxdr165FamoqrW0gbRo9ejS4XC5u376NkpISCAQC9O/fH8OGDaOFWp0kOfkn1Nc3jvbU19fLJVuPNLZv3w4ASE1NbbFI/Pbt2/jkk0+wfft2xMbGSlxmaWkpwsPDUVBQwKT/CwkJQc+ePV97L5vNRnx8PB48eAAzMzMsX74c7u7uzPmSkhIkJyfj/PnzKC8vh4GBARwcHBAUFMRsMiP0+PFjbNy4EWfOnAGLxYKTkxPCwsJaXEfaT6oe5MDAQHTr1g2enp7Ytm0bWCwWUlNTERQUhNmzZ8PY2BjLli2TV10JUVldcRciSVVVVYmkADxz5gzs7e2hq6sLABg5cmSXmatOOm7w4MFwc3ODu7s7rKysKDjuRDk5Waivb9zNs75e8bt5Xrx4EQsWLBCbQWnw4MHw8fHBhQsXJC6Pz+dj4cKFeP78OWJiYrB27VpwOBxm1KItXC4Xa9asgbOzM/bs2QM7OzsEBwfj9OnTzDVnz57FpUuXMGfOHOzatQsBAQHIy8uDt7c3Xr16xVxXV1eHxYsX486dO9i8eTMiIiJw5coV+Pv7d6mRWXmTqge5b9++SE1NRXR0NLKyGoeMORwOdHV1MW3aNKxatQq9e/eWV10JUVmUo7h1ffv2xd27dwEADx8+xB9//IE5c+Yw5589e0a51f8fj/cEERHf4uuvv6M21MSlS5fwxx9/YOHChcyx9PR07NixA3w+Hx4eHggLC4OGBm0eK08ODo7IzuYyr8eMaX1b7s5QU1PDfNEWp1evXm1mP2kuJSUFfD4fbDab6ant1q0bQkJC4O/v3+bC4ZiYGLi6umLVqsYedVtbW5SUlCA2NpbZRMbDwwPz588X+VI3YMAAeHt747fffoOLS+PIY1ZWFm7fvg0Oh8O855tvvglvb2/k5ua2uR06kZzUTwsDAwOEh4fjwoULOHfuHPLy8nDx4kVs3LiRuvYJaYOPzyIMG/YB9R43M3nyZBw8eJDZtUxLSwsTJkxgzt++fRv9+/dXYA2Vh7ItgFIWNI+diGNpaYm0tDRUV1e3OFdTU4O0tDRYWlpKXF5ubi5sbW1FYh0XFxdoaWm1uaV1WVkZiouL4eHhIXLcw8MDhYWFqKioAAD06dOnxYiHcCvzpnm8T58+DUtLS5GA/KOPPoKpqalIjzTpmA59nTYwMICRkRGePHnC9AARQsQT5iimnj9RAQEBcHFxQVpaGp48eYJNmzYxGSqePXuGrKws2NvbK7iWitdZC6CUOU1Xa+7evYsPP/yQed10HvuePXswbdo0pKamKrCGXcPZs6JBYl6eYoM1Pz8//PHHH5g5cyYOHjyIc+fO4dy5c0hOToaXlxdu376NpUuXSlxeUVER3n33XZFjWlpaMDMzQ3Fxcav3Cc81XygqLKutey9fvixybWv1EF7TVllEOlKNW6akpKCgoABbtmxhjq1fvx4///wzAGDQoEFITEyknmRCiMR0dHSwdevWVs/l5uZKtABG3XXGduXC9xH2UitygZU0JJnHfvIkZY6Rt8bFyBzU1dWhe/fuCl+MPHHiRGzduhVRUVEIDw9nemcFAgGMjIywdetWkdGq1+Hz+SLtTEhPTw9Pnz5t9T7hueb3Cqektnbvq1evsGXLFlhaWoqk2OXz+WI3vtHT00NRUdHrPwiRiFQB8uHDh0W+pefn5yMlJQVTpkyBpaUldu7cifj4eIkmrJOuQSAQ4OXLl9DR0RE53rt3b+zfv79LbT9N80fFCw0Nxdy5c0WeLUIaGhooKSnBoUOHEBkZqYDaKY/O2K68eS+1j88ilWirNI9dOfj4LMLJk40btmhodFOK6WRTpkyBq6srbt68yeQ9NjU1xdChQ2XWJhp3SH39YtDm1wgX1LV277fffouysjIcOnSoxfx5cfdIWo+mDA1bn6Pd1UnVOh48eICZM2cyrzMzM2FsbIwtW7ZAQ0MDVVVVyMzMpAC5C8rKysK1a9ewevVq5tju3bsRFxeHmpoaODk5Yfv27UxPoKamJkaPHq2o6iqEKvbMdYZffvkF9vb2YgNkALh//z7YbHaXD5A7I1VgZ/VSy5pwHnttbS2uX79O89gVxNDQCOPGTUB2NhdOThOU5stV9+7d8eGHH7b6jJGUnp6e2M3Qqqqq2syz3bSnuOkGR8KyxPVKR0dHIz09HfHx8S12Lm6rHuLKaguP94z5N6+qNDRYcgn0pZqDXFNTA01NTeb1uXPn4OjoyHyzGTBgQJsbiRD1lZiYyCw0AIDCwkJER0fjgw8+wOzZs3HmzBns3btXgTVULJo/2n6VlZXQ0tJSdDUUrjNSBYrrpVYFNI+dAI27/ZaXl7d4/bofSQ0cOLDFFIaamhrcu3cPFhYWrd4nPNd8frCwrOb3HjhwALt27UJERATGjh0rUT2Axrn4bdWDSEeqHuS3334b586dw+zZs3H9+nWUlZWJ9Bg+efKkzZQqRH2VlpaKJDw/ceIE9PX1kZCQAC0tLWhqauLEiRMICAhQYC0Vp7FnrjGBfkOD/BLoq0ov9aVLl0Tyj2ZnZ+O///1vi+v4fD4yMjJa9KB0RZ3RO6eqG9pIMo+9R48ezLGGhgb89ddfMDIyoi9fMsTjPcHp078CAH777VcsXry0U3uRJ0yYABaLhWvXrjGjCJJMOfjjjz8kKt/R0RE7d+5EZWUl+vTpA6Dx2VVTU9NmarX+/fvDwsICGRkZcHZ2Zo5zOBxYWVmJrNvicDjYuHEjgoOD4eXlJba8cePG4fjx4ygqKmJ6rq9evYoHDx5QijcZkipAnjt3LsLDwzF16lQ8fPgQxsbGcHR0ZM7//vvvYldWEvX38uVLkYVUeXl5GDt2LPPHZ/DgwTh69KiiqqdwjT1zjQn06+rq5DZ/lMs9AYFAgMzME0o9f/TChQvYsWMHgMa5dFlZWcjKyhJ7rbm5OU3b6iSic0jVY0MbDQ2NFguaKioqMHHiRCQmJsLOzk5BNVM/ip6is2nTJrBYLGakW/haVubOnYvk5GT4+/vD398fPB4PUVFRcHd3F4l9wsLCwGazcevWLeZYYGAggoKCYGZmBnt7e+Tk5ODs2bPYtWsXc83Fixexdu1a2NjYYPTo0bh69SpzzsDAAGZmZgAapxQNGjQIgYGBCA4ORn19PbZs2YIRI0aIxGSkY6QKkOfPnw8tLS2cPn0aQ4cOxeeff858K//777/B4/Foq+kuytjYGIWFhZg1axZKS0tx9+5dfP7558z5yspKaGtrK7CGitUZCfSTk39qEoTXKnUv8meffYa5c+dCIBBg7Nix+OabbzB58mSRa1gsFnr27NligWdX1Rm9c11pQxvacUz2OmMhaVtmzJjR5uuO0tPTQ1JSEpOzXbjVdNORdKDxy0F9fb3IMTc3N1RXVyM+Ph4JCQkwMzPDtm3bRHp8L1y4gNraWuTn54ssMgUALy8vREVFAWicU713715s3LgRq1evZraa/uqrr2jnSBmSegnnrFmzMGvWrBbH9fX1cezYMZFjr169QmZmJsaMGSMyMZ2on2nTpiE2NhaPHj3C3bt30bt3b5FFMoWFhTA3N1dgDdXfqVMnmT/6AoEA2dlcpQ2QdXR0mMA3JycHBgYGlMrtNTqrd87HZxFKS0vUoveYdC5VmaLz6NEj8Pn8do14m5ubIyEhoc1roqKimGC2KS8vr1anTQCNc+klnYbYt29ffP/99xJdS9pHrvtuVlVVITQ0FP/5z3/k+TZECfj5+cHPzw8PHz6EiYkJYmNjmWHNv//+G5cvX5Yq36S66YwE+m+++ZbI67feMpb5e8iDqakpBccS6KwFdLShDWkvH59FEHZgslgshX/JSklJwZdffilybP369Rg3bhymTp2K6dOniywuJ6QpuSeGpGGsrqFbt2744osv8MUXX7Q4p6+vj3PnzimgVspj4sTJyMjgoL6+Dt26ySeB/qNHD0VeP3z4l8zfQ16OHDmCw4cPo6ysTGzSfBaLJTKfrytSld450nUZGhqhXz9T/Pe/pTA1NVX4lyzau4F0BGVOJ3J15coV8Pl8WFtbd+m5pMLFT/X1jV8m5NGzMmmSCzic40yyeGdnV5m/hzxs374de/bswaBBgzB16lQmZygRpY4L6Ih64fGeMJtxPHhwHxUVPIUGybR3A+kICpCJTMTFxeH3339HYmIic8zPzw+5uY1TC958803885//hKmpqaKqqFCdkaLLx2cRTpxIYwJkVQmgUlNTMXHiRCarBRGvKy2gI6opOfkn1Nf/L1uPohcK094NpCPkOgeZdB1cLheWlpbM65ycHJw+fRqLFy/Gtm3bUFtbi7i4OAXWkCirFy9eiE2GT1ry8VmEYcM+UJkvP6qstLQUvr6+GDFiBGxtbREeHo6XL19KdC+bzYarqyusrKzg4eGBjIyMFtcMGjSoxc+IESNk/TE6lbiFwook3LsBALN3w5gxY5jztHcDaQv1IBOZ+PPPP0WyVGRnZ2PAgAFYtaqx96C4uLhFlpOupDNSdCUn/wQWSwNAA1gsDYX33kjqo48+wp07dxRdDZUgXEBHOqZHjx7w8vLCm2++KfY8n8/HwoUL0a9fP8TExKCiogKRkZGoqKhAdHR0m2VzuVysWbMGS5YsgYODA06dOoXg4GC88cYbLTZxWLBgAaZMmcK8FvZsqqo333wL//1vKfNa0QuFae8G0hGq/a+RKA2BQCCS9/HcuXMivYLGxsZ48uSJxOXJu/fm/v37CA4OxpgxYzBixAjMmDEDJ06ckLh+0hKXokvWcnKymOHN+vo6ldkm+JtvvsG//vUvpKam0qJe0i4ff/wxkpOTJc5IoKuri8jISGYXsuZSUlLA5/Px448/wtHREdOnT8e6deuQkZHx2qxMMTExcHV1xapVq2Bra4t169bBwcEBsbGxLa41MTHB8OHDmZ8PPvhAovorK2VbKDx//nyEh4djwIABmDBhAhISElrs3TB16lSF1pEoLwqQiUyYm5sjJycHAJCbm4vHjx+LBMh//fUX9PT0JCpL2Hvz/PlzxMTEYO3ateBwOBItpBD23jg7O2PPnj2ws7NDcHAwTp/+X1q1V69ewdfXFzdv3kRYWBji4uIwePBgBAcH49SpU1J+csl0RoquiRMno3v3xvl2qpTlYNmyZaipqcG6deswYsQIuLi4wN3dXeTHw8ND0dUkSkwgECAiIgKOjo5YunQpMjMzUVNT0+7ycnNzYWtrK7IFsIuLC7S0tJh1FeKUlZWhuLi4RXv18PBAYWGh2qcUa74B0tixToqpSBOzZs3Cjh07EBUVJfKFSLh3g7h9HYh4PN4TBAUtR0UFT9FV6RRSTbF4+vSpVCvMe/XqhcjISLz33ntSV4yols8++wyrVq2CtbU1Xr58CQsLCzg4ODDnz58/jyFDhkhUlrD3hs1mM3+gunXrhpCQEPj7+7fZnpr23gCAra0tSkpKEBsbywxvFhYWorS0FElJSbC1tQUA2Nvb49q1a+BwOJg0aVK7/h+0pTNSdKlqlgNDQ0MYGhrSRjKk3VJTU1FaWgo2mw0Oh4OgoCDo6urC1dUV06ZNg7W1tVTlFRUViWQ/AAAtLS2YmZmhuLi41fuE55r3TAuH8YuLi0WC7t27d2P79u3o2bMn7OzssHr1amY7YUKUTXLyT7hx47rKTN/rKKkC5DFjxmD8+PHw9PTEuHHjRFaHiqOtrd3mrjFEfbi7u0NfXx+nT5+Grq4uvL290b17Y/P6+++/0adPH0ybNk2islrrvQkLC0Nubm6rAbKw9yYoKEjkuIeHB0JDQ1FRUQEDAwNmO2bhRiZCvXr1ktsQf2cEr6qa5eDAgQOKrgJRA++88w6Ti/3y5ctIS0vDyZMnkZqaChMTE3h6esLT0xMWFhavLYvP54sd8dLT0xObp1tIeK75vcKOpab3Tp8+HU5OTujbty+Kioqwc+dOeHt74/jx4yq786y4DZG+/PIrBdWm0blz50RyrDd/xrNYLLmNHKoTHu8JTp7MgEAgAJebAR+fRSrzN6a9pAqQfXx8kJGRgaysLPTu3Rvu7u6YNm0ahg8fLq/6ERVib28Pe3v7Fsf19fWlSuEl796bkSNH4t1338X27duxfv166OvrIyMjAzdu3MDKlSslrqc0Oit4pW2CG5WWliI8PBwFBQXQ1taGh4cHQkJCJNqxj81mIz4+Hg8ePICZmRmWL18Od3d35jyPx8M333yDW7dugcfj4Y033sCwYcMQmXsW5AAAIABJREFUEBCg8nNI1cWoUaMwatQorF69Gt988w0yMzMRHx+PXbt2wcrKCp999hlcXaXPEy5Mofg6za8RBmVNj2/evFmkvqNHj8bUqVNx8OBBqZ5DhobKk4XBw8MDv/zyC+rq6tC9e3dMmTIFffv2ev2NcpKUlISoqCgYGBjgww8/FMm0RKTTWVvdKxOpAuQ1a9bgyy+/RH5+Po4fP460tDSkpKSgf//+mDZtGjw9PdG/f3951ZWogPLycly8eBEVFRVwc3ODiYkJ6urqmOk5wl7ltsi790ZTUxP79+/HsmXLmOkUWlpa2LJlC+zs7CT7oO3QGcGrKmQ5KC8vBwD069dP5PXrCK9/HXlnIKiuroauri5WrFgBU1NT8Pl8JCUl4ZNPPsGxY8dUfqoIj/cEERHf4uuvv1PZHqL8/HykpaUhKysLz549w9ChQ+Hl5QVNTU0cPnwYQUFBuHHjBkJCQsTer6enBz6f3+J4VVVVqwv7ANFnTdNeYGFZba3DMDc3x5AhQ3Dz5k2JPqMQj/eMCVwU7eOP5+P48eMAAA2Nbvj44/l4/LjqtfdpaLDkEujv27cP1tbW2Lt3L7S0tGReflcibh0NBcjNsFgs2NnZwc7ODhs2bMCpU6eQlpaGnTt3YseOHRgxYgSmTZsGNzc3iRdlEfUQGRmJ5ORk1NfXg8ViYciQITAxMUF1dTWcnZ0RGBiIRYsWtbt8WfXeVFdXIzAwEPX19dixYwd0dXXB5XIRGhoKIyMjjB49Wqp6Sfpg79u3F5KT90tVtjqaMGECWCwWrl27Bi0tLeb16/zxxx8SlS/vOeympqYivX8A4ODgABsbG5w8eRJLly6VqJ7KSlXnGd65cwdpaWngcDh4+PAhjIyMMGfOHHh5eYmk8pozZw42bNiAI0eOtBogDxw4EEVFRSLHampqcO/ePcyYMaPVOginbxQXF4sE0sKyXje9Q9WzuCjbNK/KykosXbqUgmMZ6Ipb3XcoD7Jw6NLY2Bja2trIyspCQUEBCgoKsGnTJsycORPBwcGUiLsL2Lt3L5KSkuDr64sxY8bg008/Zc7p6urC2dkZ2dnZEgXI8u69OXr0KK5du4bc3FwmgLKzs8O9e/ewfft2pKSkvP4DN6FMPTjtIa/em9Zs2rQJLBaLWcMgfC0r8p7DLo6Ojg60tbWZ+e2qSlXnGU6bNg137tyBlpYWJk2ahOnTp8PBwaHVvMKjRo3CoUOHWi3P0dERO3fuRGVlJfr06QOgMbd7TU1Ni1zGTfXv3x8WFhbIyMiAs7Mzc5zD4cDKyqrV9gM0BtW3b9+Gn5/f6z6uUvPw8EROTjamTJFszYk8DR06lNn6mnSMqi4C74h2B8ilpaVIS0tDeno67t+/D0NDQ3z66aciw1gHDx7EX3/9hR9//FGWdSZK6MiRI/D09MTq1atRWVnZ4rylpSXy8vIkKkvevTd3797FW2+91eKP1ZAhQ8BmsyWqI2m/5r/Dtn6n7dFZGQgaGhrQ0NAAHo+HxMREaGhoSLwQVVmp6jxDHR0dfPfdd3Bzc5OoQ2bChAlMWkpx5s6di+TkZPj7+8Pf3x88Hg9RUVFwd3cX6Y0OCwsDm83GrVu3mGOBgYEICgqCmZkZ7O3tkZOTg7Nnz2LXrl3MNQkJCbh37x5sbGxgYGCAoqIixMfHo0+fPpg3b147/y8ohxMn0vDy5QtwOMcV3nbWrl0Lf39/ODg4MBmLSPso2+hAZ5AqQK6oqEBGRgbS0tJQWFgITU1NTJw4EevWrcPYsWNFvq2vWbMGRkZGYpOjE/VTXl4OX1/fVs/r6uqK7RUWR969N/369cPDhw/B4/FgaPi/f+Q3b96EqampRHVsD3WY29kZnj17BgDtHnnqjAwEQOOUov37G6fMGBoaYvfu3Sq/BkMV5xnW1NRgzpw5sLCwkLjN9OzZs81/63p6ekhKSkJERAQCAgKY0dLVq1eLXNfQ0CCyQRIAuLm5obq6GvHx8UhISICZmRm2bdsm8uwyNzdHVlYWuFwunj17hj59+sDBwQFffPGFyDNJ1SjbCERsbCx0dXXx6aefYsCAATA1NW0xqsBisbB7924F1VC1dLVF4FIFyI6Ojqirq8Pw4cOxfv16uLu7t0iV1ZSFhUWbQ0pEfejr6+PRo0etnr9z5w7eeusticqSd++Np6cndu/ejc8//xxLlixh5iDn5+djy5Yt7fj0klHVuZ2d4cGDB4iJicHp06dFpsQ4OTkhMDBQJl9cZJmBAGjM/T116lQ8fvwYR44cwZIlS/DTTz9h6NChUtVLmuktjx8/xpo1a7Blyxa5pALz8PAAm81GbW0tNDU1FZ6FQBJaWlr4+uuvsW7dOplmETE3N0dCQkKb10RFRSEqKqrFcS8vrzZTnE6YMAETJkzocB2VjbKNQAhHD01MTFBTU4OSkpIW18hyepe6U4VF4LIkVYDs6+sLLy8vvPPOOxJdP378eIwfP7499SIqxsnJCYcPH8a8efNaPHBu3bqFo0ePwtvbW6Ky5N17Y2xsjAMHDuD7779HeHg4Xrx4gXfeeQebN2+W2xC5svWsKJPi4mJ4e3ujqqoKdnZ2ePfddyEQCFBcXIz09HScPn0ahw4dkjg7RGdlIDAxMYGJiQmAxmedl5cXfvjhB5EvY5KQZg57TMwOFBQUICZmh1wCj6ZZCFgsDYmyEHT2HHZxzM3N8fjxY4XWgSjfCMSvv/6qsPcmqk+qANnc3LzNNF3379/H5cuXMX369A5XjKiWwMBA5OXlwdPTE05OTmCxWEhNTcXhw4eRnZ0NU1NTLFu2TOLy5Nl7AzTON5Y2kOmI5OSfmGC+vr5Obj0rqjiNY/v27QAad0Nrvtvi7du38cknn2D79u0ST9dSRAYCDQ0NDBkyBFevXpWoju3RGV+yVHWeob+/PzZs2ABnZ2cMGjRI0dXpsrpipgOivsQv8W1FaGgorly50ur569evIzQ0tMOVIqqnb9++SE1Nxfjx45GdnQ2BQAAOh4MzZ85g2rRpOHTokFTblKubnJysJgFyPU6dOimX92k6jUNVXLx4EQsWLBC7FfngwYPh4+ODCxcuSFyeo6Mj8vPzRRaLSjuHvSlJMhDU1NTg2rVrcp2D3PglqwFAYxuS1+/Yx2cRhg37QKXmGebn58PQ0BBeXl6YP38+QkNDsX79epGfDRs2KLqaas/HZxE0NBpHEJUl00F9fT3S0tIQFhaGpUuX4vbt2wAaR4YyMjLanBpIujapepBfl6Oxuroa3bp161CFiOoyMDBAeHg4wsPDUVFRgYaGBhgYGLSaaun/2DvvsKiurY2/Q1UgdFCKY8USRWOMiGCwUGWs0WuJqKgoBsVCUJRcE6+gEDsaIklsCMZ+HREQKQZRomKJJVVpinCjwCAD0mW+P+abE4ZhYAbmTIH9e548YZ+9zz5r5DBnnb3XeldX4qOP7JCRkU61R4+WfUa1qoZx1NXVtZpc9d5776Gurk7i+eShQFBQUAA7OzuYmpri9evXOHXqFAoKChASEiLlp5cc/ksWX0bu3bsG2ravVTHOsKk04/3793H//n2RMQwGA1999ZU8zepyKNsOREVFBZYuXYonT55AR0cH1dXVWLx4MQBAV1cX4eHhmDFjBgICAhRqJ0E5adNBLioqEtIRzM3Nxd27d0XGlZeX4/Tp07SqABBUB5KcKUxurvCWf05OtsyvoWwJMpIycOBAxMXFYd68eejWrZtQX11dHeLi4qQqEUt3DPuQIUNw8+ZNJCcng8vlwtzcHMOGDcP58+cxePDgdvwLSIajoxNSUpKo9rhx4lfDO0J29lMEBPhj//5I9Os3oO0TlADBqiBB8SiT0sHu3buRnZ2N77//HsOGDYODgwPVp66uDjc3N2RkZBAHmdAibTrI//3vf/HNN9+AwWCAwWAgKioKUVFRIuN4PB7U1NRoXUEhKDdcLhfx8fEoKChAeXm5yI4Dg8HAjh07FGSdYnn5sqBZ+4XMr6FsCTKS4uvri9WrV2PWrFn49NNPqWS83NxcnDp1Cnl5efjmm2+kmpPOGHYHBwehB21nIyxsG6qq3mLHjv/g8OEYRZsjEUVFRTA2NhZ5wRJQU1MDDocjcblyQucgLS0NXl5ecHJyalGfv0+fPoiLi1OAZQRVoE0H2d3dHf369QOPx8Pnn3+OBQsWYNSoUSLjdHR08P7778Pc3JwWQwnKza1bt7B69Wq8ffsWenp6LerQdmU5nd69++D583yq3aePZIoM0uDs7IbExHi8e9cAdXUNlUmQcXZ2xq5duxAeHo6QkBDqPuHxeDA1NcWuXbs6pSSWtGRmZgi1b968jo0bv5DpNbKzn1L3aX5+HnJzs1ViFdnZ2Rk7d+7E1KlTW+y/du0aPv/8c4nLlRPaz+HDUXjy5BEOH46S+f0pLeXl5WAymWL7eTyeVOFbhK5Fmw6yjY0NVZq1rq4OH330kcqL4RNkT1hYGAwNDXHy5Elat5lVlc2bv8TKlUupdnCw7GMhvby8kZDAXw3h8RqVYotTUqZMmQIPDw/89ttvVEiXlZUVhg4d2qpyTlfC2dkN8fGXKD1nOl6AwsK2CbVVZRW5rfyYhoaGLv2CLi9KS0uQmpoMAEhNvQofn5UKjUO2srLC06dPxfbfvXtXYvlIQtdDquypmTNnEueY0CJ5eXlYvHgxcY7FMGDAQPTu3QcAf/VYFVbl5I2GhgZGjBgBT09PeHp6YsSIEcQ5bgKLNY1yBHk8HqZMkb1md9NdDoC/iqwqiHOAKyoqkJGRQfIi5MDhw1Hg8fhKK42NjTh8WDQcU55MnToV586dE8qbEtwnMTExSElJaVMSlNB1afXpI4g9/uyzz6CmpiZRHCCDwcCqVaskunh+fj5CQkLw4MEDKpEmMDAQ3bt3F3tOZWUljh07hoyMDOTl5UFTUxO2trYICAggzpkCsba2Rk1NjaLNUGo2b/4SAQH+tKweAxCR/VKVJD2Av8J38eJFXL9+HUVFRQD4JcHHjx+PGTNmQFNTU8EWKh7B7oCA+PhLMv/96unpUaW++W3lraL3zTffIDKSr7bBYDCwYcMGkUTMpixcuFBepnVZrl1LEWqnpSUrNMxixYoVePToERYvXow+ffqAwWBg+/btKCsrQ0lJCVxcXMh9QRALg9fK3tTgwYPBYDDw6NEjaGlpSeSAMhgMieK8uFwupkyZAktLS/j5+YHD4SAsLAwODg7Yt2+f2POePn2KpUuXYtasWRg9ejRqa2tx9OhR/Prrrzh//jwVDiIp0lSxUlaUoZIVm83GwYMHceHCBRgaGirUFnmjLPfQtGluqKqqoto6OjqIi0tu8zxF3z/FxcVYtmwZnj59Cn19fVhbW4PH46GwsBBcLhc2NjY4evQozMzMFGYjnUh6/0yd6orq6mqq3b17d1y+nNLKGdIzZYqL0Itut27dEB+f2uo5irp/0tPT8dNPP4HH4+Hs2bOwt7dH7969hcYwGAx0794dtra28PDw6JSSk8ry/QMArq4fC4W7MBgMpKTcaPM8uu+hy5cvIykpCfn5+WhsbASTyYSnpyemTZvW5UNvlOn+aS903T+triA3l86RpZTO6dOnweVywWazqa0vdXV1BAYGws/PT6yja21tjZSUFKFVZnt7e0yaNAmnT5/Gli1bZGYjQXKKi4thaGgId3d3eHh4wNLSUuRhxGAw4OPjoyALOz/y0Fqmg5CQEOTm5mL79u2YMWMGpaX+7t07XLx4EVu3bkVISAgOHDigYEsVi7l5D6EQiB49esr8Gq6uHrh8mU213dwmy/wasmLChAmYMGECAKC+vh7z5s3DiBEjFGtUF0dNTU1IOlFZXkimTp0qNoGTQBCHwgL8MjIyYG9vLxQX5u7ujuDgYGRkZIh1kHV0dESO6erqgslkorS0lDZ7Ca2zZ88e6uczZ860OIY4yPQiD61lOrhx4wYWLlyIWbNmCR1XV1fH7NmzkZ2djbNnzyrIOuXh9etXQu1Xr/6W+TW8vLyRlJSA+vp6aGpqqkyiZ1hYmKJNIACYNMlVSKvb2dlNgdYQCB1DKgc5KCgILBYLjo6OHa6Yl5OTI/JA1NLSApPJRG5urlRzcblcPHv2rNUysgR6SUtLU7QJXR55aC3Tgba2dqsFhqytraGtrS1Hi5QTFxf3JioWanB19ZD5NUxMTOHhwUJ8/CVMnjxF4ZXQpKGwsBCHDh3C7du3weFwEBUVBTs7O3A4HBw4cACzZ8/GsGHDFG1mp8bHZyVSU69S96iPz0pFm4Q///wT58+fp/T5W6JpJca2aE/ulAA2m42oqCgUFhaCyWRi1apV8PT0FBoTGRmJe/fu4fHjx6isrMT58+dha2srNObOnTtYtGiRyPwTJkwQqvpJ6BhSOcg//fQT4uLiYGBgADc3N7BYLNjZ2bUrhofL5baolauvry/2JhbHrl27AIBkoyoQUkGxbUpLSxAa+hW2bNlGi+MhD61lOvD09ER8fDzmzp0rkoxXV1eH+Ph4kYdIV4S/upuI+vo6aGpq0La6q0yV0CQlJycHn376KRobGzF8+HAUFhZSW/3GxsZ49OgR6urqumyhInlhYmIKFxd3pKQkwdXVXeEvWGfOnMHWrVuhpqaGnj174r33OpZ0yuVysWjRIlhaWiIiIoLKneJwOK3mTgFAUlISgoKCsGLFCjg6OiI1NRUBAQHQ1dUVWtw7c+YMmEwmHB0dcfXq1VbnDAsLQ79+/ah2Sz4Vof1I5SD//PPPuHHjBhISEpCQkIBz587BxMQEkydPhqenJ0aOHNlhgwQan5Jy4cIFnD17Ftu3b2+Xk6bo5LbOwpAhQ1oV6k9MTOzyQv2xscfx66+PaVOX8PNbg6Cgf0qmrlq1VubXoAN3d3fcu3cPs2fPxrx589C7d28wGAzk5eVR4Tru7u54/Pix0HnDhw9XhLkKg7+664n4+Evw8GDJxPlITr6CpKQEoWNlZRwAQGiosNqKhwdLaWOSd+3aBV1dXZw9exZqamoilQ6dnJyQlJQk5uyuA90v6QB/Ffnvv/+nFKvHkZGRGDp0KL799luZFDFrb+4UAERERMDDwwOff87/7re3t0deXh4OHjwo5CCnp6dDTU0Nd+7cadNBtrGxEVldJsgOqRxkDQ0NTJw4ERMnTkRdXR1++uknylGOjY2FhYUFPD09ERgY2OZc+vr64HK5IscrKirQv39/iey5fv06vvzyS/j5+WH27NnSfBQKksEpG9oS6m9sbOzS2cKlpSW4ciUePB4PiYnx8PLylvkD6uZN4UprN25cx8iRH8n0GnSwePFi6uf//Oc/QpX0WhojeInuii9b8ljd5XD4uRxGRqqjG3zv3j2sXLkSpqamLZYUtrKywuvXrxVgmXJB90s6wH+R27cvkpa5paWiogKfffaZzCr8tjd3qqCgALm5uVi/fr3QcRaLhc2bN4PD4VBzKktiI6EDSXpaWlpwd3eHu7s7qqqqwGazsWfPHhw5ckQiB7l///7IyRFOKqqrq8OLFy/wySeftHn+w4cPsXbtWkyfPh1r16rGSllnpzUH+NGjR116+yc29jgaGhoAAA0N9bQ8oNLShCXdUlOvqoQOMkmwUhxubpNFVoUDAlYDAPbubVv3XlloaGhoMYFbwJs3bzqcN6PqNH1Jv3KFnpd0ZWPUqFF4/vy5zOZrb+6UoK/54t+AAQOo/vYUsvH19UVZWRm1k79+/fpW/w4I0tEhFYvq6mpcu3YNCQkJuHnzJurq6iQu2+jk5IRDhw6hrKwMRkZGAICUlBTU1dW1mWyXnZ0NX19f2NvbY9u2ba2OJdBHdHQ0Tpw4QbV37NjRYhxWRUUFKioqMH267Ct/qQpNM7sB/ta2rJ1XZ2c3XLmSgIaGemhoaNJSipgOSO6A5MhjBVAVGThwIO7cuYNPP/1UpI/H4yElJQVDhw5VgGXKQ9OX9Pp6el7SlY1///vfWLp0KQYNGoRp06Z1+CWpvblTgr7m5xoYGAj1S8p7770HHx8ffPTRR+jevTvu3buHH374AX/++SdOnDjRpXdrZYnUDnJdXR2uX7+OxMREpKeno7q6GpaWlli0aBFYLBaGDBki0Tzz5s1DbGws/Pz84Ofnh9LSUoSHh8PT05N6qwKA4OBgsNls/P777wCA0tJSLFu2DNra2li8eDF+/fVXaqyWlhbef/99aT8SoZ0YGRlRL0SFhYUwMzMTKeYgEOofNmxYl65Y1PyLWV1d9gqLXl7euHo1EQB/m06VkqwIbVNaWoKrVxPB4/GQlJTYJVYAJWXx4sX4/PPPERkZSSV0NjY2IicnBwcPHsSvv/6KqCjFlj1WNMnJV4TaV68mdnoHuU+fPvjss8/wxRdfYMuWLTAzM2tRnz81tfViOG0hae5U8zGCMDJpHdr3339fyNext7eHlZUVNm3ahNu3b2Ps2LESz6Xo8ExlRmqZt7S0NFRWVsLU1BSzZs0Ci8VqV3Kevr4+oqOjERoaCn9/f0oupXmp0MbGRiHh8ezsbPz9N1//09vbW2islZUVrl27JrUthPYxbdo0TJs2DQC/jOtnn30mkhxD4PP27dtm7UoxI9uPiYkp3N0FSVyexHnqZMTGHkdjYyMAoLHxXZdYAZQUFouFwsJCHDhwAN98ww8NEWiuq6urIygoqMvLgDZ9jvLbjQqyRH4cP34cX3/9NXR0dGBjYwM9vY45g+3NnWq6UmxqakodF8wli/BDd3d3bN68Gb/99ptUDjLJwxKPVA5yeno6PDw8wGKxMGbMmA4Hk/ft2xdHjhxpdUx4eDjCw8Op9pgxY/DXX3916LoE2RMTE6NoE7ocLSkQlJQUQ0tLC8+ePaViSQUoswoBoW3S0pKbxLE3qEyMubxYsWIFpk6diqtXr+L58+dUSWF3d3dYW1sr2jyFI7h3/mnXK8gS+XH48GGMHj0aUVFRMonNbW/ulECKLTc3V8iRFszVVKqNoDxI5eFmZmYiNDQUY8eOJZmWBCHOnz8Pf39/sf1r1qzBxYsX5WiRcjFmjPAb/dixjrRcp7z8DTQ0NET0hAmqj6Ojk1B73LiuvSLaEhYWFvD29sZXX32F//znP1i2bFm7neP8/HwsW7YMI0eOhL29PUJCQlBdXS3RuWw2Gx4eHrC1tQWLxUJiYmKr448dO4ZBgwbB19e3XbZKQvPVUz29jmkCqwJVVVXw9PSUWeKak5MTbt++LaSUIknuVK9evdCvXz+R+yA+Ph62trbtStBrzpUrV8Dj8YjsmwyRWuaNQGiJH3/8ESNGjBDbb25ujpMnT3bZhCx9fQOh9nvvdXxLrbMoEBAIsqa+vr5F6UktLS2JzpdHQQgBr169QmRkJExM6A2JWrs2ENu3b6Xa69dvED+4A8hDa1lS7O3tqfwlWdDe3CmAv0i0fv16MJlMODg4IC0tDZmZmSKV77KyssDhcJCdnQ0AuH37NgoLC2FsbAw7OzsAQGBgIHr37o0hQ4age/fuuHv3Lo4ePQoHBweMGTNGZp+3qyO1x5udnY0TJ07g119/RUVFBRUTJ0AWAe8E1SM/Px9z5swR2z9gwACw2Ww5WqRcZGYKaxTfvHkdGzd+oSBrCKoIuYfE09jYiNjYWKqkcE1NjcgYBoMhsbMkj4IQAr7++mu4urri5cuXEtnWXh4/fijUfvToF4wfP0nm11EmpZWvvvoKy5cvR1RUFGbPni0U/9se2ps7BQCTJ09GTU0NoqKicOTIETCZTOzZs0fknjh48CCysrKo9u7duwEAdnZ2VCijjY0N4uPjcfToUdTX18PS0hJLlizBZ5991qHPRxBGKgf5wYMHWLJkCXR1dTF8+HD8/vvvsLe3R21tLR4+fIgBAwaQWvddFAaD0aJAv4A3b96IfGF0JfgSbPFoaGiAhoaGykiwyYvKykrExMTg9u3b4HA4CA0NxYgRI1BWVobz58/D1dUVffr0UbSZCsXZ2Q2JifF4964B6urkHmpKaGgoTp06hX79+sHDw6PDJYXlURACAG7duoWMjAwkJSWJnCNr5KGTrmxKK66uruDxeIiIiEBERAQ0NDRaVLF4+PChmBlEaU/ulICZM2e2uYsqST6Pr68vreE4BD5SOcj79+9Hz549ce7cOTQ0NMDBwQG+vr4YO3YsHjx4AF9fXwQFBdFlK0GJGTp0KC5fvoylS5dCW1tbqK+mpgaXL1+WSoc0Pz8fISEhePDgAfWWHhgYiO7du7d5LpvNRlRUFAoLC8FkMrFq1SpK+qn5Nfbu3Yvbt2+jtrYWTCYTq1evhru77B0PYQk2dSLB1oRXr15hwYIF+Pvvv9G7d2/k5uaiqqoKAF9K8Pz583j16hX+/e9/K9hSxSK4h969469mknvoH+Li4uDu7o79+/fLZD55FISor6/Htm3bsHr16g6vbEqCo6OTkB47HTHsfKUVfmgLf1VfsavInp6eRBOY0G6kcpCfPHkCPz8/6Ovr482bNwBAhVh8+OGH+Ne//oWIiAiMGzdO9pYSlBpfX1/4+Phg/vz58PX1hY2NDRgMBp4+fYrvvvsOeXl5Er88ySP+LycnB/Pnz4etrS127NgBXV1d/PXXX6itre3Qv4M4TExMMX78JKSkJGHChEkKj81TJnbt2oWKigr897//hampqYhUoLOzM65fv64g65QHcg+JR0NDA/b29jKbTx4FIY4dOwYGgwEvLy9ZmKwU8JVW+OoYDQ31CldaaWkVl0CQFKkcZAaDQf3hC7JCBY4ywN96+PHHH2VoHkFVcHR0RFhYGEJDQ7Fu3TrqOI/Hg56eHrZv3w4nJ6dWZvgHecT/ffXVVxgwYAB++OEHastNGu1Iguy4ceMGFi5ciIEDB7YYptOrVy9K+7yrI9BNragQ1WLtyri5ueHWrVuYN28erdeRVUGIoqIiHDp0CJGRkR1OfpdU//Xnn28ItTMzM2BmJlsHksVigc1mo76+HpqampgyZQrMzDq/WgahcyJ6ORhvAAAgAElEQVTVX6a1tTVV11xLSwvW1tbIzMwEi8UCANy7dw+Ghoayt5KgEsyYMQMuLi7IzMzEixcvwOPx0Lt3bzg6Okol0E53/F9eXh7u3r2LPXv2yE2usLS0BNev84vYpKdfg4/PSrIC+P9UV1eLVGBsSk1NjUgycFektLQEWVm3AAC3b/8MDqeU3EP/z+bNm7F27Vps2rQJs2bNQs+ePVssK2xpaSnRfHQXhNi1axdGjRqFYcOGUX0NDQ1oaGgAl8tF9+7dJZZqlLTQw6RJroiPv0Q5+c7ObigurpDoGpIye/YCXLp0CQDAYKhh9uwFEl1DVoUe7t69CwAYPXq0ULstBOMJhKZI5SA7Ojri6tWr2LBhAxgMBubMmYO9e/eisLAQPB4PWVlZWL58OV22ElQAPT29Dsfw0h3/98svvwDgl2P+9NNP8ejRIxgZGWHWrFlYs2ZNiw/WjkKqoImnT58+ePz4MebOndtif0ZGBgYNGiRnq5SPyMgIaiWSx+MhMjICW7ZsU7BVyoG6ujqYTCZOnjxJOWgt8ccff0g0H90FIXJzc/Hnn3+26JiNHj0akZGRcHFxkchWSWGxpuHyZb6SEI/Hw5Qp02U6P6D4ap4LFy4Eg8HAo0ePoKWlRbXFIXhZkPS+IHQtpHKQV65cCRaLhYaGBmhqamL58uXg8Xi4cuUK1NTUsHr1apJZSUBlZSUqKytbXPWTZAWH7vi/kpISAMCWLVswd+5crFmzBg8ePEBkZCQ0NTWxerVwBTpZQKqgiWfOnDkIDw/H6NGjqTAcBoOBqqoqHDx4ELdv3yaxhABu3EgXamdk/KQYQ5SQrVu34uLFi7Czs8OIESM6XFLYyckJhw4dQllZGYyMjABIXxDC1dWVOt68IERoaCiViCpgx44d6NatGwICAloNI2svCQlxABgAeAAYiI+/RMt3kJeXN/Lz8xSSRHrixAkA/+hdC9oEQnuQykE2MDCgnA2A/xAjciMEAefOncPhw4fx4sULsWM68qYuq/g/gdzcuHHjsHHjRgD8WGUOh4MjR47gs88+k2oVWZKtQWdnZ1y+fJlqu7i40BKbp6XF/5NWpbg/Ly8vPH36FJs2bYKuri4AYP369eByuXj37h0WLlyI6dNlv9qlajQvfNFSIYyuSnJyMj755BNs375dJvPRXRCipWpn+vr60NHRoa3QA1/mTXDP8DrlS7qgkIa4NqFjKFMRGHkglYNcVVWFN2/eiF0FLCoqgpGRkURSXITOxfnz57FlyxY4ODjgk08+wb59++Dt7Q1tbW1cuHAB5ubmEmdr0x3/JxjXPOvd3t4eMTExKCoqQq9evSSyFZAsBrCmpl6kLev4PwCoq+OvUkszt6zi/zrCtm3bMGPGDFy5cgXPnz9HY2MjmEwmPD098dFHHynUNmVBV1cXb9++FWoT+GhpaUklI9kW8igIIW/kIfMGKFehEIJs6Wq/W6kc5LCwMDx58kRsRbRVq1ZhxIgR2Lp1qyxsI6gQ0dHRGDNmDI4ePYqysjLs27cP48ePx9ixY7F06VJ88sknqKiQzGmjO/5P3PalYEWurq5OIjulgVRBa5sPP/wQH374oaLNUFqaO2JdufBOc6ZOnYq0tDR8+umnMpuT7oIQzZGkQERHqK2tadaWvaRlaWkJkpIS/j/0MkHuhUI2b94s9TkMBgM7duygwZrOhbyKwCjTKrVUDnJmZmarDoqLiwsuXrzYYaMIqsfz58+pJCuBMkR9PX/V1MDAAP/617/w448/YvHixW3ORXf83wcffABDQ0PcunUL8+fPp8bdunULenp6YDKZUn76tiFV0MQTFBQEFosFR0dHWhIkOwsffzxBaAXQyWmiAq1RLlxcXJCVlYVly5Zh1qxZsLCwaPFeGj58uAKsUw4yM4Vl3m7elL22eGzs8Sa5FvVyX2m8c+eOyLGamhpwOBwA/GcRj8ejdhWNjY3JjreEyKsIjDKtUkvlIBcXF8Pc3Fxsv5mZGV6/ft1howiqh46ODrUCq6urC3V1dRQXF1P9hoaGEmvZ0h3/p6mpCX9/f4SEhCAsLAzjx4/H/fv3cfr0aaxbt05ieSVp8PLyRlJSAt69479AkCpo//DTTz8hLi4OBgYGcHNzA4vFgp2dHamARZCYhQsXUj///PPPIv1ErUA0N4OOv6/U1KtCSispKUlydXKuXbsm1M7NzcXSpUvh6+uLxYsXU4skHA4Hx48fR1xcHA4fPiw3+1QZeRSBUfQORHOkcpCNjY3x7Nkzsf3Pnj1rUX2A0Pnp27cvnj59CoBf1Wrw4MFgs9mYNm0aGhsbcenSJVhbW0s0lzzi/7y8vMBgMBAdHY2TJ0+iZ8+e2LhxI7y9vdv/j9AKJiamsLS0wvPn+bCyslL41pEy8fPPP+PGjRtISEhAQkICzp07BxMTE0yePBmenp4YOXKkok1UCkiYjnjCwsIUbYLSM3Gii9AOxKRJrq2Mbh8mJqZ4+bKgSVu8vrk8CA0NhYODg4g2vrGxMQICAlBcXIzQ0NA2Q2kI/F3QK1cS0NBQDw0NTVp2QRW9A9EcqRzk8ePH4+zZs5g8ebJIrODDhw9x9uxZqmgIoWvh4uKC6Oho1NbWQltbG5999hn8/f2pLOLq6mp8/fXXEs8nj/i/BQsWYMGCBRLb1BFKS0tQVFQEACgqKiRFHpqgoaGBiRMnYuLEiairq8NPP/1EOcqxsbGwsLCAp6cnAgMDFW2qQuE/oOLR0NAADQ0SptMUaeN9uwLJyVeQlJRAtQUhbwIKCl4gIIAvaenhwYKb2+QOX/N//ytqtS1vfvnlF0qpqCVsbW2xa9cuOVqkugh2QQH8f4l0b5lfQ9E7EM2RqoyYv78/DA0N4eXlhZUrV2LPnj3Yu3cvVq5ciU8//RQGBgZYu3YtXbYSlJhly5YhIyMD2traAPgOc2xsLGbPno158+bhxIkTXVqqKzb2OHg8QaEQfvwWQRQtLS24u7vjwIED+Pnnn7FlyxaUl5eTFR7wH1CC+H41NXUSpkOQCk1NTSou29DQiJZQMlEUK0Woq6uLrKwssf137tyBjo6OHC1SXUxMTNGjR08AQI8ePWlZ4DE37yHUFlxPUUi1gmxmZoYLFy5g9+7dSE1NRXp6OgB+9bTp06cjICCg1ZKxqo4yZVeqAqNGjcKoUaMUbYZSQAqFSE51dTWuXbuGhIQE3Lx5E3V1dejbt69Uc+Tn5yMkJAQPHjygQnQCAwMlSshhs9mIiopCYWEhmEwmVq1aBU9PT6o/Ly8PsbGxuHXrFoqKimBsbAxHR0esX79eqDy6rDExMcX48ZOQkpKECRMmke+gZpSWluLy5ct48eKF2IJCe/bskbNVisPNbbLIqrC/vy9evMjH998fp+X+mTTJVSiMw9nZTebXkIbp06fjyJEj0NPTw6JFi9CnTx8A/O+H6OhoJCcnY+nSpQq1UVXg74IWAgCKil7Ssgv6+vUrofarV5LlLdGFVA4yAJiamiI8PBw8Hg8cDgc8Hg8mJiZdIqFGmbIrlZXKykrcuXOHCiewsrKCnZ1dhytbqTry0iBVVerq6nD9+nUkJiYiPT0d1dXVsLS0xKJFi8BisTBkyBCJ5+JyuVi0aBEsLS0REREBDoeDsLAwcDgc7Nu3r9Vzk5KSEBQUhBUrVsDR0RGpqakICAiArq4uFceemZmJu3fvYu7cuRg8eDCKiopw4MABZGVlIS4ujtpFIciPtLQ0BAQEoLa2Furq6i2uCnaFZ1RbaGpqon9/G9pernx8ViItLRmNjY1QU1ODj89KWq4jKevWrQOHw8G5c+dw/vx56h7g8Xjg8XiYPn26SHwyoWUOH46iquM2Njbi8OEomedAuLi4Iz7+0v8n1arB1dVDpvNLi9QOsgAGgwETk66zgiEvDUBV5vvvv8ehQ4dQU1MjVOWrW7duWLlyJVauVOyXJUE5CQoKQlpaGiorK2FqaopZs2aBxWK1Oznv9OnT4HK5YLPZ1Iquuro6AgMD4efn12oZ34iICHh4eODzz/kvwPb29sjLy8PBgwcpB5nFYmHBggVCDlfv3r0xf/58pKenw92dntjg0tISXL/Oz9JPT78GH5+V5Dvo/wkPD0fPnj2xa9cu2NraEmdYQZiYmMLZ2Q0pKUlwcXFX+P2pqamJsLAwLFmyBOnp6SgsLASPx4O1tTXGjx+PQYMGKdQ+VeKnn1KF2teupcjcQebHOSeivr4OmpoaCg8jk9pB5nK5iI+PR0FBAcrLy0XKnXZW0W15aQCqKj/88AP27t0LOzs7LFiwAH369AGPx0N+fj5+/PFHREREQF1dHcuXL1e0qQqBKBCIJz09HR4eHmCxWBgzZgwVZ9teMjIyYG9vLxTu4O7ujuDgYGRkZIh1kAsKCpCbmyuyosRisbB582ZwOBwYGxtT2txNef/99wHwt/npgnwHiae4uBiBgYFdWudYWfDxWYm///6fwlePmzJw4EAMHDiwzXGVlZXYvn07fHx8Wq3a2hWRR6l7ExNTTJggCCNzVvgLllQO8q1bt7B69Wq8ffsWenp6LUq6ddY3d3loAKoyJ0+ehKOjo0gy1eDBg+Hu7o6lS5fi5MmTXdZBdnZ2w+XL/1SgJAoE/5CZmQkNjXZvZomQk5ODWbNmCR3T0tICk8lEbm6u2PMEfc0fjALt7dzcXLExxvfu3RMaSwfkO0g877//PlUMgqBYTExMsW9fpKLNaBc1NTWUPClxkIVRtvhyeSDVUk1YWBgMDQ3BZrNx7949XLt2TeS/tLQ0umxVKM7ObtDQ4Gf90qUBqMq8efMGkyZNarGPwWDAxcUFb968kbNVygOLNU2oPWVK11X0aI4snWOAv8vV0su7vr6+2OQtAFRf83MNDAyE+ptTW1uLnTt3YuDAgfjoo4/aa3abkO8g8WzatAlnz55tsZIagSANdKyMdgZmzZoj1J49e67Mr9E8jIzDoW9HThKkejLl5eVhw4YNGDx4MF32KC2C2BgAUFOjRwMQUF2ljKFDhyI7O1ts/7NnzzBs2DA5WqRcXLhwVqh9/vwZEmLRhOzsbJw4cQK//vorKioqqGQQAQwGA6mpqWLOlgxBNbW2aD5G8MAUd+5XX32FgoICnDp1ql3hISYmkiWwrl27GsnJV9DQAKirq2Ht2tUwNX1P6uu1hZYW/7FgZib7ueli+PDhCA4OxpIlS2BhYQELCwuR34WgMBCBQJCehIQ4oXZ8/CWZ72ApWxiZVA6ytbU1ampq6LJFqeFXQrPE8+f5sLSkrxKaqiplbNmyBT4+PrC0tMSnn34KXV1dAMDbt29x8uRJpKamdmktW3kkOKgqDx48wJIlS6Crq4vhw4fj999/h729PWpra/Hw4UMMGDBAqpcrfX19cLlckeMVFRWtbps2XSk2NTWljgvmamlVet++fbh8+TKioqLavXBQWlpJPRQENC/yIEBDQxO1tbXQ09PD+vX/fD/IqtADANTV8eUIi4srJBqvpsaQ2Mmni8uXL2PTpk3g8Xiora2lNRacQOiKpKZeFWrTUcRD2cLIpHKQfX19cfDgQcyZMweGhoZ02aSUNNUALCykpxKaKitlBAQEAAD27t2L/fv3U9J/JSUlaGxshKmpqUjyE4PBQEKCqBPQGZFHgoOqsn//fvTs2RPnzp1DQ0MDHBwc4Ovri7Fjx+LBgwfw9fVFUFCQxPP1798fOTk5Qsfq6urw4sULfPLJJ2LP69evHwB+rHFTR1owl6BfQExMDL777juEhYXh448/lti+jtDY+A5qamowN1esgL6ysX//ftjY2CAyMhJWVlaKNodA6HSYm/fA8+f5VJuOIh7Ozm5NZN4YCg8jk8pBLi4uhqGhIdzd3eHh4QFLS8sWt7F8fHxkaqQywK+Exv+Zx+PRssKrbNsL0mBiYgITExORgg5MJlNBFikXXTHBQVKePHkCPz8/6OvrU3HqghCLDz/8EP/6178QERGBcePGSTSfk5MTDh06hLKyMkpxIiUlBXV1dZRUW0v06tUL/fr1Q2JiIlxdXanj8fHxsLW1FUrQi4+Px/bt2xEQEEBLmeOWijwAoEoD7937jcyvqcqUlpZi2bJlxDkmEGhCHkU8WKxpVDI7j8dTeK6OVA5y0ypEZ86caXFMZ3WQ5bH0r2zbC9IQExOjaBOUGh+flUIOsjJJICkaBoNBhS8ICjw0Tejs27cvfvzxR4nnmzdvHmJjY+Hn5wc/Pz+UlpYiPDwcnp6eQioTwcHBYLPZ+P3336lja9aswfr168FkMuHg4IC0tDRkZmbiu+++o8ZkZWVh06ZNGDNmDOzs7PDw4UOqz9jYmLwUKoARI0bg5cuXijaDQOi0yKOIR0JCHBgMBnUNOuKcpUEqB7mzKlRIgrOzG65cSUBDQz1tGeTyuAZBMZSVCUtQvXlTpjLhM3RjbW2N58+fA+DLsVlbWyMzMxMsFgsAX0JNmpAufX19REdHIzQ0FP7+/lSp6Q0bNgiNa2xsxLt374SOTZ48GTU1NYiKisKRI0fAZDKxZ88eoZXnO3fuoL6+Hrdv38bcucKZ3DNnzkR4eLhUn5/Qcb788kssX74cQ4YMwdSpUxVtDoHQ6ZBHEY+0tGQq/JDHa1T4IqFUDnJX3r7y8vLG1asCFQs1Wm4OeVyDbsrKyvDy5UtwudwW42wl3SbvbISFbRNq79jxHxw+TFbdAcDR0RFXr17Fhg0bwGAwMGfOHOzdu5eqepWVlSW1fnbfvn3bTAoNDw9v0ZmdOXNmq2ET/v7+8Pf3l8oeAr34+/ujrq4OGzduxJdffglzc3Ooq6sLjelKOQ+E9mFsbIy0tDSYmZkp2hSlw8TEFB4enoiPvwQPDxYtCzzKtkjYLgHSyspKZGVlobCQn7RmZWUFOzs76OkpNpOZTkxMTOHuLrg5PGm5OeRxDbooKSnB1q1bce3atRYdY0HQ/R9//KEA6xRP0+QGAMjPz1OMIUrIypUrwWKx0NDQAE1NTSxfvhw8Hg9XrlyBmpoaVq9eDV9fX0WbSVBixOVAELoWbDa77UEtMGPGDAD8hamuvBDYFl5e3sjPz6Nt8U7ZFgmldpBjYmKwb98+VFdXCzlC3bt3R0BAABYuXChTA5UJum8OeV2DDjZt2oRbt25h7ty5GDFiRKd+WWoPenp6qKysbNJWHY1ZujEwMKAk1gD+Sp+vry9xigkSQ3IgCAD/OSQtDAaDcpAJikXZFgmlcpDZbDa2b9+O4cOHY/HixZQUUk5ODk6cOIEdO3bAwMAA06ZNa2Mm1UQeJTRVtUzn3bt3sXTpUnz+uWokFdJNcx3bt2/fCvW/fVtJKRLIUsNWFamqqsKbN29gaWnZYn9RURGMjIzQvXt3OVtGUBXKy8uFXrII8qElvW5BvoWRkXBZdnl8z3XlPCl5II86DSzWNKSlpShcwQKQstT08ePHMXLkSPz4449gsVgYPHgwBg8eDBaLhZMnT+KDDz7A0aNH6bK1S1BaWoL161cpvMSitJiYmMDc3FzRZigtzR8WTSXDujphYWHw8/MT279q1Sp8/fXXcrSIoGqMGzcOa9asQWpqKurr6xVtTpeGwylV2PPLysqqXf9JQ35+PpYtW4aRI0fC3t4eISEhqK6uluhcNpsNDw8P2NragsViITExUWRMZGQklixZglGjRmHQoEF48uRJi3MVFxdj3bp1GDVqFD766CMEBgaCw+G0OFYWNK/TQNfvOCEhDtXVVYiPv0TL/NIg1Qpybm4uNm7cCA0N0dM0NDTAYrGwa9cumRnXFVHVSnoLFy7EpUuXMH/+/Bbvj65Gcx3b0tISzJ3L38bT1NTEoUNHFb59pCxkZma2WsDDxcUFFy9elKNFnZNvv41AdvazNsfl5PDHCHY4WmPAABusXr2uw7Z1FC8vLyQmJiI5ORkGBgbw9PTE9OnT8cEHH7R7zvz8fISEhODBgweUEkpgYKBEOxlsNhtRUVEoLCwEk8nEqlWr4OnpSfWXlpbiyy+/xO+//47S0lLo6upi2LBh8Pf3x/Dhw9tts7xpSa+7M2t1c7lcLFq0CJaWloiIiACHw0FYWBg4HA727dvX6rlJSUkICgrCihUr4OjoiNTUVAQEBEBXV1dIJefMmTNgMplU8nJLNDQ0wMfHB/X19fj666/R0NCAXbt2wc/PD6dOnQKDwZDp5wbkU6ehtLQESUkCJzxB4cXSpPJkdHR0UFxcLLa/uLiYbIN2AFWupLdkyRLU19djypQpmDp1Knr27CmSRQ6gy8Z6mZiYwtjYBBxOKSZPnqIyv1d5UFxc3Orug5mZGV6/fi1Hizon2dnP8OzPv8A0bX3F7D1Nfpn42pLKVse9KCmUmW0dJSgoCBs3bsTt27dx6dIlxMXF4fTp0+jVqxemT5+OadOmoVevXhLPR7cjVFNTAz09PaxevRpWVlbgcrmIjo7G4sWL8d///pckG8qQ0tJSnD9/Hr/99hu4XC5VhEgAg8FAdHS0RHOdPn0aXC4XbDab2gVUV1dHYGAg/Pz8YGNjI/bciIgIeHh4UGGI9vb2yMvLw8GDB4Uc5PT0dKipqeHOnTtiHeTk5GT8+eefiI+Pp65pbm6O+fPnIyMjo9WCSO1FHnUaYmOPU9eor29Q+EKhVA6yo6MjTpw4AQcHB4wZM0aoLysrCzExMZg0aZJMDexKqHIlvcLCQiQkJCA/Px8HDx5scUxXT4bo0aMnamtrVC4Bk26MjY3x7Jn4lc1nz55RhUQIHYNpaoWNM9bKZK6d7AiZzCMrGAwGxo4di7Fjx+I///kPUlNTERcXh0OHDuGbb77ByJEjMX36dEyePLnN+4luR8jKykokbMjR0RFjxozB1atXsXIlKSQkC7Kzs+Hl5YWqqir06dMHz549w4ABA1BeXo7Xr1+DyWSiZ0/JSyZnZGTA3t5eKETO3d0dwcHByMjIEHtfFBQUIDc3F+vXrxc6zmKxsHnzZnA4HGrO5tWJW+L69esYOHCg0PU+/PBDWFlZ4fr167Q4yPKQYEtNvSqkg5ySkqQ6DnJgYCDu3bsHb29vDBkyBP369QPAD734448/YG5ujsDAQFoM7QqociW94OBg5Ofnw9/fHyNGjMB77xGVhuZoamqif38bsnrcjPHjx+Ps2bOYPHkyPvzwQ6G+hw8f4uzZs1TREAJBEgQhET179oS2tjaSk5Px4MEDPHjwADt27MCsWbMQEBAgVm1HHo5Qc3R0dKCtrY2GhoZ2fmpCc3bv3g0NDQ0kJCRAV1cXDg4OCA4OxtixYxEfH4+QkBDs3btX4vlycnIwa9YsoWNaWlpgMpnIzc0Ve56gTyBsIEBQ2TM3N1eqvJScnByhqqBN52vNjo4gDwk2c/MeQpKoPXpI/vJCB1I5yBYWFmCz2fj++++Rnp6O5ORkAPy3YW9vb6xYsQJGRka0GKoMlJaWIDT0K2zZsq1LiGRLw6NHj7B8+XKsWrVK0aYQVAx/f39cv34dXl5ecHJygo2NDRgMBp4+fYqMjAyYmppi7VrZrHoSOj/5+fmIi4vD5cuX8fLlS5iYmGDJkiWYOXMmNDU1cfbsWZw8eRJ///03vv322xbnkJcj1NjYiMbGRpSWluLo0aNQU1PD9OmKz97vLNy/fx+LFy9Gr169qPL1ghXKKVOm4P79+9i5cydOnDgh0XxcLrfF3Qd9fX2Ul5eLPU/Q1/xcgfJKa+eKs6OlRSh9fX3k5ORINZekmJiYYvz4SUhJScKECZNo8YFevfpbqP333/+T+TWkQSoHuaqqCtXV1QgKCkJQUJBIf1FREbp169Zp45DpTqBTNpFsaTA3Nyfax4R2YWZmhgsXLmD37t1ITU1Feno6AL529PTp0xEQEEAqWxFahcPhIDExEXFxcXjy5Ak0NTXh7OyMf//73/j444+Ftq2DgoJgamoqNhQMkJ8jFBYWRjlnJiYm+P7776WKlSa0Tn19PXr06AEA6NatGwCgoqKC6h8yZEi7i4s0RVAIqy2ajxE46+1JqmvpHEntaIqJiTTP7XfU/83MZL9LbGlpKfQCamVlRct1JEUqBzksLAxPnjwRe0OtWrUKI0aMwNatW2Vhm1IhjwQ6ebyh0cXy5ctx7NgxzJ49mzjKBKkxNTVFeHg4eDweOBwOeDweTExMaMnGJnQ+nJyc0NDQgA8++ABbt26Fp6dnq2Fe/fr1a5fUoqwdoaVLl2Lq1KkoLi7GuXPnsGLFChw/fhxDhw6V2CZpHBwtLf4jn06nQx7XkBQLCwu8fPkSAN9BNjMzwy+//AJ3d/7u7NOnT6GrqyvxfPr6+uByuSLHKyoqRHYNmtL0BcnU1JQ6LphL2hyL1uyQdq7S0koq96n1cSWUznRqair++itf5j7K//4nvGJcVFSE4uIKMaP/QU2NIaWjLxlSOchdWY5JlRPo5MGbN2/QrVs3uLq6wt3dHRYWFiLJBgwGAz4+PhLNR6fEUnOOHTuG8PBwTJgwAd99951E9hHogcFgwMREdV4MCcrBsmXLMHPmTPTp00ei8RMnTsTEiRPF9svLEbKwsICFhQVl08yZM3HgwAGpvockdXAAoK6OH98sidPRXtpzDbocnDFjxuDatWtUTPjUqVMRHR2NiooKNDY2Ii4uTiSUpjX69+8vEsJQV1eHFy9etOobNc3Xanr/COYS9Etjxx9//CFyPDs7GxMmTJBqLkk5fDiKUgBpbGzE4cNR2LjxC5lew8XFHfHxl/7/RVQNrq4eMp1fWqRykLuyHJM8EuhKS0tw/fo1AEB6+jX4+KxUmVXkPXv2UD+fPn26xTGSOsjy0JoU8OrVK0RGRhKnTMFwuVzEx8ejoKAA5eXlQmXsAf69s2PHDgVZR1B2HB0dkZGRIeQgX758Gd988w24XC5YLBaCg4MlUggAFOMIqampYciQIXj48KFENhLaZsWKFXjy5Alqa2uhra2NdevWobKyEleuXIGamhqmTZuGjRs3Sjyfk5MTDh06hLKyMifZiqUAACAASURBVCrfKiUlBXV1da0qR/Tq1Qv9+vVDYmIiXF1dqePx8fGwtbWVejdj/PjxuHTpEnJycqj77OHDhygsLKRFwQIArl1LEWqnpaXI3EH28vJGUlIC6uvroaGhrvAwU6kc5K4sx+Ts7IbExHi8e9cAdXUNWhLoVHmVWpYlPuWhNSng66+/hqurK7UNR5A/t27dwurVq/H27Vvo6em1+B1CQi0IrREZGQkjIyMsWrQIAN8h3bx5M3r16oVhw4bh5MmTsLa2hre3t0TzKcIRqqurw6NHj0gMsgyxtLQUKmGvpaWFbdu2Ydu2be2ab968eYiNjYWfnx/8/PxQWlqK8PBweHp6CqlKBAcHg81m4/fff6eOrVmzBuvXrweTyYSDgwPS0tKQmZkpsluQlZUFDoeD7OxsAMDt27dRWFgIY2Nj2NnZAQDc3NwwaNAgrFmzBgEBAXj37h127tyJkSNHwsnJqV2frS3evXvXrC17tRUTE1NYWlrh+fN8WFlZK3yBUCoHuSvLMXl5eSMhIQ4AX5+PjjcbVZZ5k7ZcZ2vIS2Lp1q1byMjIQFJSksg5BPkRFhYGQ0NDnDx5EoMHD1a0OQQVJDs7W2h36vLly+jWrRvOnTsHPT09bNq0CRcuXJDYQabbETpy5AgKCgpgZ2cHU1NTvH79GqdOnUJBQQFCQkI6/g9CAABs3rwZ8+bNw4gRI1rsf/z4MU6dOoWwsDCJ5tPX10d0dDRCQ0Ph7+9Phf9t2LBBaFxjY6OIQzl58mTU1NQgKioKR44cAZPJxJ49e0ReuA4ePIisrCyqvXv3bgCAnZ0dYmJiAPArFx8+fBjbt2/Hhg0bwGAwMGHCBHzxxRcqvZhQWlqCoiJ+AaLCwkJwOKWqU0mPyDHRC1/mLR4NDQ3Q0KBnlZpuKisrkZWVhcJC/k1uZWUFOzs7qRL35CGxVF9fj23btmH16tVCsYIE+ZOXl4cNGzYQ55jQbponJ924cQMODg7U986oUaPEViVrCbodoSFDhuDmzZtITk4Gl8uFubk5hg0bhvPnz5O/Axly8eJFODg4iHWQX758CTabLbGDDAB9+/bFkSNHWh0THh6O8PBwkeMzZ87EzJkzWz1X4AS3hZmZGfbv3y/RWFmgrq4udK+rq0vlPkpEbOxxCKLreDyewnfRpfqEXVmOKTb2ONTU1NDY2Ag1NTVafnFNV6kbG+lZpaaTmJgY7Nu3D9XV1UIxpN27d0dAQAAWLlwo0TzykFg6duwYGAwGvLy8JLKJQB/W1taoqalRtBkEFcbMzIzakn716hX++OMPzJ07l+qvrKyEhoZ0D3Q6HSEHBwc4ODhIZQ9B9pSVlUFLS0vRZqgEjo4fIyMjnWqPGyf7UA5l20WX+hWgq8ox8X9x/JibhoYGhf/ilA02m43t27dj+PDhWLx4MbWKm5OTgxMnTmDHjh0wMDDAtGnT2n0NWUksFRUV4dChQ4iMjJT6odkSkmZfdzWJJUnx9fXFwYMHMWfOHBgaGiraHIIK4ubmhpMnT6K+vh6PHz+GlpYWJk2aRPX/+eefJLa3i3D37l3cuXOHaqekpOD58+ci47hcLhITE8mKvYRoa3dr1taW+TXkkeslDe32DrqaHJM8qtzFxh4HIHDuGArfXpCG48ePY+TIkYiJiRFyOgcPHgx3d3csXLgQR48elchBpltiadeuXRg1ahSGDRtG9TU0NKChoQFcLhfdu3eHpqamBJ+aj6QySx2RWPr22whkZ4tPkBWQk8Mfs3DhYonmHTDABqtXr6NFYklSiouLYWhoCHd3d3h4eMDS0rJDEoGEroe/vz9KSkoQFxcHPT097Nixg/oOqKysRHJyMhYsWKBgKwny4M6dO/jmm28A8L83kpOTqaq/zenbty+Cg4PlaZ7KkpmZIdS+efM6LSoWdOd6SYPsg0ikoL1at4mJibhy5QoePnyI169fY+PGjVi2bBmttvLlRwRV7hi0Jek1NvJjfBob36nUKnVubi42btzY4oqshoYGWCwWdu3aJdFcdEss5ebm4s8//8To0aNF5hg9ejQiIyPh4uIika3yIjv7GZ79+ReYpq0nQ76nyRe9ry2pbHPOFyWFMrGtozSVCDxz5kyLY4iDTGgNHR0dsd8vOjo6yMjIoCqpETo3S5cuxbx588Dj8fDxxx9jy5YtVGEQAQwGA927d4eOjo6CrFQ9HB2dkJKSRLXHjaNHTk6ZUJiD3FGt24KCAkycOFHsA1XW8OVHLPH8eT4sLa1oyaxU5RtQR0cHxcXFYvuLi4slLkFOt8RSaGgoqqqqhM7dsWMHunXrhoCAgFZl5BQJ09QKG2fILgl2JztCZnN1BFlKBBLEU1bGQUnJa5n93l+UvISpunhdfGVBTU2t1ap6hM6Fjo4O5fimpaXBxMSEvBypCPLI9ZIGhTnIHdG63b9/P7UFKy8HubS0hFJmKCx8qXD5EWXD0dERJ06cgIODA8aMGSPUl5WVhZiYGKGYwNagW2LJ1tZW5Jr6+vrQ0dERsZ1AP7KUCCQQCAQBNTU1uHr1KqZPn95if1xcHIYOHdpq6B6BjzxCLJQt10thDnJ7tW4BSFwNSZbExh6nhLEbGt7R8mYjjxuQLgIDA3Hv3j14e3tjyJAhQuEMf/zxB8zNzREYGCjRXPLQmiQoH7KQCCSIx8jIGDrvtGS2C7GTHQFtI/K7ISgve/bsQUNDg1gHOTExEcnJyVTMMkE88tjhlkeulzQozEFur9atokhNvUqpIfB4jUhJSZK5g6zKIRYWFhZgs9n4/vvvkZ6eTiVFWFlZwdvbGytWrKDCJSSBbq3J5kiqPUmgB1lJBBIIBIKAR48eYenSpWL7x4wZ0+ZzhsCntra21bYs8PLyxtWrglwvta6bpNderVtFYW7eA8+f51PtHj16yvwa8rgB6aKqqgrV1dUICgpCUFCQSH9RURG6desmcRwyoesgD4lAAoHQ9eByua3GH2tpaSmlv6GMiO5wZ4gZ2X5MTEzh7u6J+PhL8PDwVHgYq0JVLFpCUq1bWSGpvNXr16+E2q9e/S1zrdmff74h1M7MzFAZPduwsDA8efIEbDa7xf5Vq1ZhxIgR2Lp1q3wNIyg9spQIJBAIBAHW1ta4e/euWIm/u3fvwtLSUs5WqSbNwxmbt9tDcvIVJCUlCB0rKSmGlpYWnj17ioCA1dRxDw8W3Nwmd/ia0qAwB7m9WreyRlIN23HjxguFP3z88YR26dm2RtOtZUFbkmuoqTEUqmMLAJmZma1KsLm4uODixYtytIigKshSIpBAIBAETJ06FQcPHsT333+PJUuWUPr2DQ0NOHbsGK5evQo/Pz8FW6mqtO03tYfy8jfQ0NCQqhYBXSjMQW6v1q2iaO7MV1SIOvcdpWdPCxQWvqTaFhaq82ZbXFwMc3Pxkk9mZmZ4/fq1HC0iqAqylAgkEAgEAcuXL8f9+/exd+9eHDlyBH369AGDwUBeXh7Ky8sxduxY+Pr6KtpMlUAgv9a03VHc3CaLrAoLVo337lV84qT85SD+HycnJ9y+fRtlZWXUMUm0bhVFVtYtofbt2z/L/BocTqlQu7S0RObXoAtjY2M8eya+0tuzZ89ajDknEAQSgU3LwwoQSASOGzdOAZYRCARVRlNTE4cPH8aOHTswcuRIVFRUoLy8HCNHjkRYWBiOHDkCLS0tRZupEhgaGjZrS550r6oobAW5I1q32dnZyM7OptpPnz5FUhI//MHDw4MWe1sKf5A1LYVxqArjx4/H2bNnMXnyZHz44YdCfQ8fPsTZs2fBYrEUZB1BmZGlRCCBQCA0hcFg4JNPPlHKnWlVgsPhNGuXihnZeVBoDHJ7tW6vXLkipFvIZrOp5LC//vqLFnstLCzwv//9j2pbWsq+uEFtbU2ztuqoWPj7++P69evw8vKCk5MTbGxswGAw8PTpU2RkZMDU1BRr18quChyh8yBriUACoavx7bcRyM4Wv4MnICeHP6Zp8pM4BgywgZ8f+c4mdF0UqmLRXq1bf39/+Pv702maCDY2g4QcZBubQTK/xs8/3xRqN5dVUWbMzMxw4cIF7N69G6mpqUhPTwcA6OnpYfr06QgICICZmZlijSQoLUZGRmIlAjs7MTHHcP/+PYnGEgeH0BLZ2c/w+LeHYOiqtzqO944fQ/ok/0nr4952XKFAWSgtLcX58+fx22+/gcvlCsXRAvwV5ujoaAVZp8rIT21MUSidzJuycu9ellD77t3bMr+GPMI46MTU1BTh4eHg8XjgcDjg8XgwMTGRq2wfQfWoqqrCmzdvxMotFRUVwcjIqNMm6j1/ni+RcwN0TQeHIBkMXXVoDpWNLGj9b7JVaFIU2dnZ8PLyQlVVFfr06YNnz55hwIABKC8vx+vXr8FkMtGzp+xrGnRGtLS0UFdX16SteJUJuiEOsoQ4O7vh8uV/NH7pKIE4aZKrUAyys7ObzK8hDxgMBkxMFCvwTVAdiIa2bJ0bQLyD86KkEDvZEa2eW17FV+gx0Gk9qfZFSSFsTGW/k0YgyIrdu3dDQ0MDCQkJ0NXVhYODA4KDgzF27FjEx8cjJCQEe/fuVbSZKoG7u6eQD+Th0flzihSmYqFqsFjCRQqmTGm5tntH8PFZSUmnqKmpwcdnpcyvQSAoG5mZmXBxcRHb7+Ligps3b4rtJ0jGgAE2sBk8CNqmeq3+V1H/FhX1b9scZzN4EAYMsFH0xyIQxHL//n3MmzcPvXr1op6tgp3ZKVOmwNPTEzt37lSkiSqDl5c31NX5u1waGhoKLwMtD8gKsoRcuHBWqH3+/Bls3PhFh+ZsqYqMtnY3VFdXwcDAAKGhX1HHFVFFhkCQB3RoaOfn5yMkJAQPHjygEoADAwMlCtNgs9mIiopCYWEhmEwmVq1aBU9PT6ExkZGRuHfvHh4/fozKykqcP38etra2UtkobySNR1YmHVICoSPU19ejR48eAECVnK6o+Gd3ZciQIWJ3rro6Lfkn3bp1x9u3lV3GPyEryBJy7VqKUDstLZmmK/GgpqaGnj1Vp0gIgdARZK2hzeVysWjRIrx9+xYRERHYtGkT4uPjERwc3Oa5SUlJCAoKgqurK3744QeMHTsWAQEBuH79utC4M2fOoL6+Ho6OjhLbRSAQ5IuFhQVevuQX3+rWrRvMzMzwyy+/UP1Pnz6Frq6uosxTORob30FNTQ3m5l0jbpusIEsIHXXIlb2KDEF5KCvjoKTkdZvxo9LwouQlTNXFr9zKC1lraJ8+fRpcLhdsNhvGxsYAAHV1dQQGBsLPzw82NuLDAiIiIuDh4YHPP/8cAGBvb4+8vDwcPHhQqIBReno61NTUcOfOHVy9elWaj0sgqCSqKCU3ZswYXLt2DevXrwfALz0dHR2NiooKNDY2Ii4uDrNmzaLt+qoM8U+Ig0wgEBSMrDW0MzIyYG9vTznHAODu7o7g4GBkZGSIdZALCgqQm5tLPUwFsFgsbN68GRwOh5pTFmVWCQRVIjv7GZ79+ReYpq3XAHhPk78iW1tS2eq4FyWFMrNNHCtWrMCTJ09QW1sLbW1trFu3DpWVlbhy5QrU1NQwbdq0LiktSZAM4iATCCqAkZExdN5pYeMM2a227GRHQNtIT2bztRdZa2jn5OSIrAppaWmByWQiNzdX7HmCvv79+wsdF1T2zM3NFXK6CZ0bOuPY8/LyEBsbi1u3bqGoqAjGxsZwdHTE+vXrlfoeY5payew7SJa7YeKwtLQUko/U0tLCtm3bsG3bNtqvTVB9iIMsIbq6unj79q1Qm0AgyAZZamhzudwWY5b19fVRXl4u9jxBX/NzDQwMhPpljaZm2/rH7UFLSwNmZtJLx2lp8R8L7Tm3syCIY7e0tERERAQ4HA7CwsLA4XCwb9++Vs8VxLGvWLECjo6OSE1NRUBAAHR1dakwnczMTNy9exdz587F4MGDUVRUhAMHDiArKwtxcXHQ1taWx8fs1NTU1GDKlClYtGgRFi1apGhzCCoIcZAlhI4YZAKBIAydGto8Hk8ih7v5GIEsFF0Fb+rr6fkuqatrQHGx9AUf6uoaAEDic9XUGDAxUfxOhCyhO46dxWJhwYIFQvdU7969MX/+fKSnp8PdXfY6+12Nbt26oaKiApqanb+gBYEeiIMsIa6uHkIi2Z1R0oTQPiQtFaxMySudGX19fXC5XJHjFRUVIuETTWm6UmxqakodF8wljZIGQbWhO47dyMhI5Nz3338fAL80MkE2TJgwAdevX8f8+fMVbQpBBSEOsoR4eXkLOchdQSSbIBmSlgomZYLlQ//+/ZGTkyN0rK6uDi9evMAnn3wi9rx+/foB4McaN3WkBXMJ+gmdH0XEsd+7d09oLKHjrFixAuvWrcPatWsxb948MJlMSg+5KaTyK6EliIMsIWVlHKH2mzdlMDYmf1QEPrIsFSyuTDBBMpycnHDo0CGUlZVRK3UpKSmoq6sTkmprTq9evdCvXz8kJibC1dWVOh4fHw9bW1vakqfKy9+A9/adTH/vvLfvRL6zCJIj7zj22tpa7Ny5EwMHDsRHH33UXrMJzRDIQz579gzJyeJrF/zxxx/yMomgQhAHWUKaVo0BgG3bvsTx4z8qyBoCgSCOefPmITY2Fn5+fvDz80NpaSnCw8Ph6ekptDoXHBwMNpuN33//nTq2Zs0arF+/HkwmEw4ODkhLS0NmZia+++47oWtkZWWBw+EgOzsbAHD79m0UFhbC2NgYdnZ28vmgBLlDVxz7V199hYKCApw6dUpqCUETEz0qsVKWNE/y1NLSQC3N1+god+/eRf/+/amX2dWr2w5nIxDEQRxkCXn5sqBZ+4WCLCEQCK2hr6+P6OhohIaGwt/fn5Lo2rBhg9C4xsZGkWTbyZMno6amBlFRUThy5AiYTCb27NkjsvJ88OBBZGVlUe3du3cDAOzs7BATEyOVvQYGhjLdgQD4uxBGRsorF6bsyDOOfd++fbh8+TKioqIwePBgqW0tLa2kEitlSfMkTzqvIatEz0WLFmHnzp2YOnUqAODixYsIDg6Gs7Nzh+cWQHcZ+/r6ehw4cAAXL15ERUUFbG1t8cUXX2DIkCHUmDt37rSozDFhwgSRl3lC+yEOMoFA6HT07dsXR44caXVMeHg4wsPDRY7PnDkTM2fObPVcaZ1ggmohrzj2mJgYfPfddwgLC8PHH3/cbnvLyjgyDdNR1RCd7t27o6qqimoXFhYKtTsK3fJ/ABAWFgY2m41NmzbBysoKhw8fhre3N+Li4tCjRw+hOcPCwoTuKZJILFuIgywhDAaD2iYTtAkEAoHQ+ZBHHHt8fDy2b9+OgICANl/ICJIxePBgHD58GHV1dXjvPf6OzL1799qUZZ0xY4ZE89Mt//fq1SucPn0aX3zxBebMmQMAGDFiBJydnREdHY2NGzcKzWljYwNbW1uJbCdID3GQxZCcfAVJSQlU28DAEG/elFFtQ0NDSq7Lw4NFZN9kDKliRSAQFAXdcexZWVnYtGkTxowZAzs7Ozx8+JDqMzY2BpPJlMpeIyNjvCwvlGmisCqG6HzxxRdYt24dtm/fDoC/kHXmzBmcOfN/7d1rVFTX3QbwZ7ioXAQFjAotGlGIEkBqFESCYMpNdCVoVIwSpVF5vaDRUEVtu1ajrZdlXkOgKFpAU9OoaQUjUaPWC7VZGJNqYkw0gqKGWF9gkCEgYYaZ94NlMuMMw+CZ+zy/tfzAmX32nIFnHf/nzD57H+hyH5FIpHeBbOzp/86fP4+Ojg61/6/c3d0RFxeHiooKjQKZjIsFsp4GD/ZVK5AHDfLV0ZqE4CpWRGROxh7HfuHCBUilUlRWVmLWrFlq+6empmod+kPdCw4Oxscff4x79+6hoaEBM2fORFZWFqKjow3Sv7Gn/6uuroaPj4/GPNnDhw9HeXk55HK52kOcmZmZaGxshLe3N5KTk7Fy5Uq4uroK+oz0ExbIXUhISNa4K/zyy1Px4EEjEhKSsXr1ejMdme3jKlZEZG7GHMeelZWFrKwswcdImhwcHODn5wc/Pz+kpqYiOjoaYWFhBunb2NP/SSQS5dCQx9tJpVK0trbC3d0dffv2xYIFC/Dcc8/BxcUFn332GXbv3o1r167h3Xff5RBQA2GB3AODB/tCKm3HggX/Y+5DsWlcxYqIyLI0NopRX/9/2FqWa5D+7tR/Bx/HpwzSV1c2bdpk1P47GXL6P239qD7/BDz6/6rz/yzg0Y0gPz8/5OTkoLKyEuPHj9f72Hsye0jndIJPMjXf1q1bcf369W7b3bz5aOrMnJzXu20bFBRk1GEnLJB7wNnZGQEBI7hAiJFxFSsiIrI0xp7+r6v+JRIJnJ2ddQ6fSExMxNq1a3H16tUeFcgNDT9ALld03xA/TfWnOv2fvq5cuYob167D38dPZzt3p0efsfn7Bzrb3amvNfg0gY9jgUwWx9pWsXJ21r3E9JPQNkn/jfrabu/eNLU+Orl6unY/3c+d+loE+wYb5cRCRLalf38vuHb0wuqXVhikv61luejd37rOPcae/i8gIAANDQ148OAB+vXrp9Zu6NChPV5ExtL4+/gZND/GxgLZRAoKclFVdaPbdtXVj9p0zpChy/DhI7BkiWHCZg0scRUrAJBKdU8h9CQen6Tf3/9pvSbqb66+BwB4yqf7h0hH+ATB3/9pNDT8wCKZiKgbxp7+Lzo6Gg4ODjh27Bhmz54NAGhpacHp06c1vlV93LFjx6BQKDjtmwGxQDaRqqoben290NfZDQDwY/0POtvdqa812LFZGmtaxcpU9L0Q6ryw+t//zTfm4ZCB6bvIg6JdDgAQ9dJ9IadoMfxFG5G9M/b0fwMHDkRaWhq2bdsGJycn+Pr6ori4GAAwb948Zbvs7GwMGTIEI0eOhIuLCy5evIji4mJERUUhIiLCBL8J+8AC2YSs7esFc7G2VayIhBgyZChCg0fr1bbzG6aAoV3P5NJp+PDu2xCR/kyxjP3atWvh6uqKt99+W7nUdElJidoqeiNGjEB5eTmKi4shlUrh6+uLjIwMLF682Hgf3g6xQCaLw1WsyJ6kp2dgzpz5erXlNwRE5mXsZeydnZ2RnZ2N7OzsLttkZmYiMzNTvwPWA4eAascCmSyOta1iRUREZK2qqm7gy6uXIXLT/cC5ouPREK8rNVd0t7ORIV4skMniWNsqVk1ND/QeQ6oPRUsHGhvFBumLiOyDPucgjmGnrojcHA26VLktYIFMFomrWBER6Uff8eYcw06kPxbIRAJ5evYz+NV3//7aFzQhMqQTJ47h+PGP1LZ1Nc4wKSkFCQnJJjs20h9nuSFLZ40rMbJANhFrDAcR2R+uFEpduWPAxYru1NdihE+QwY6NyNBYIBMR2amEhGTeFSa96DvkQt/Fikb4BHEYhx2xxpUYWSCbiDWGg4iICOAwDrI/1r2wNxERERGRgbFAJiIiIiJSwSEW4CoyRERERPQTFsjgKjJERERE9BMWyP9lilVkOEWO7eIqVkREZI0aG8VcDVYLFsgmwilybNeQIUMRGjy623ZcxYqIiOyVtd0kZIFsIpwix3alp2dgzpz53bbj35aIiCxN//5e+K6p1qirwVrjTUIWyERERERkNNZ4k5DTvBERET2mpqYGr732GsLDwxEZGYkNGzbg4cOHeu1bVlaGpKQkhISEICUlBUePHtVo86c//QkZGRkYM2YMgoKCcOWK7oe/ici0eAcZHKBO1unEiWM4fvwjtW26piJMSkrhssJEepBIJHj11Vfh6+uL3NxciMVibNq0CWKxGNu3b9e57/Hjx7FmzRosWrQIEyZMwKlTp7Bq1Sq4ublh4sSJynYHDhyAv78/JkyYgI8//tjYH4mIeogFMpEN8fLyNvchEFm9/fv3QyKRoKysDF5ej8ZSOjo6Ijs7G0uWLMGIEV2PfczNzUVSUhLeeOMNAEBkZCRu3bqFvLw8tQL57NmzcHBwwIULF1ggE1kgFsgwzQB1IkNLSEjmHWEiI6ioqEBkZKSyOAaAxMRErFu3DhUVFV0WyHfv3sXNmzexcuVKte0pKSlYu3YtxGKxsk8HB45wJMvBqUo1sUAmIiJSUV1djenTp6tt69WrF/z9/XHz5s0u9+t8LSAgQG378OHDla+rFt1ElkDf2SDsbapSFshEREQqJBIJPDw052H18PBAU1NTl/t1vvb4vp6enmqvE1kSa5xhwhRYIP+XOb5e6MlDVnzAiojIvBQKBUQiUbftHm+jUCi0bjcEb293vdv26vXov/wBAwwznNBc70FkCiyQYVlfL/AhKyIi8/Lw8IBEItHY3tzcrDF8QpXqnWIfHx/l9s6+tN2VFqqh4QfI5QqN7bpuwKSnz1PbbsgbMO3tMgBAXZ3+s0I5OIh6VOgTmYJZC+Samhps2LAB//73v9G7d2+kpKQgOzsbLi4u3e5bVlaGnTt3ora2Fv7+/li6dCkmT578RMdhrq8X+JAVkXEY+9wilUrxzjvvoLS0FM3NzQgJCcH69esxcuRIY30kMqGAgABUV1erbWtvb8edO3cwbdq0LvcbNmwYgEdjjVUL6c6+Ol83F96AIdKf2QpkU8wzSUT2xxTnlk2bNqGsrAw5OTnw8/PDn//8Z8yfPx8ffvghBg4caOyPSEYWExODHTt2oLGxEf379wcAnDx5Eu3t7Tr/j/n5z3+OYcOG4ejRo4iPj1duLy8vR0hIiEkf0OMNGCJhzFYgm2KeSSKyP8Y+t9y/fx/79+/H+vXrMXPmTABAWFgYXnjhBezduxerV6828ickY0tLS8O+ffuwZMkSLFmyBA0NDdi8eTMmT56snJECANatW4eysjJ8/fXXym3Lly/HypUr4e/vj6ioKPzjH//Av/71LxQWFqq9x6effgqxWIyqqioAQGVlJWpra+Hl5YVx48aZ5oMSUZfMViCbYp5JIrI/xj63nD9/Hh0dHWrDLtzd3REXF4eKigoWyDbAw8MDe/fuxcaNG5GVlaUcpvPrX/9arZ1cLkdHchGlwwAAEUlJREFUh/pD2cnJyWhra8POnTtRVFQEf39/vPXWWxo3b/Ly8vDpp58qf962bRsAYNy4cfjLX/5ipE9GZDksfaICsxXInGeSdOEYUnpSxj63VFdXw8fHR/nVu2q78vJyyOVyLgJhA55++mkUFRXpbLN582Zs3rxZY3tqaipSU1N17ssimEiTJY2TN+sYZEueZ9LSr2xsmS2MIWV+zMfY5xaJRIK+fTWnsPL09IRUKkVrayvc3YU9ka8tPwAzRJaF5znbZYq/raWPk7e4ad5MPc9kV1PLeHi4KOdz7DRgwAAA0Nju4eHCOR8NyFbHkFrSlbE9MuS5RVs/ne16Sts5SNv5B+A5iCwfz3O2y97+tmYrkC1lnsmu5pCMjIxFZGSs3v30ZM5HQ7O1OSRtYQyppV8Z2zJjn1u66l8ikcDZ2Rmurq49Ol5t56Cenn8A852DbO38Q/rjec528W8LmG2gnK55JnXNFak6z6QqS5lnkoSrrq5We1IcMNwY0s7+uxpDWlNTA7lcLvgzkPkY+9wSEBCAhoYGPHjwQKPd0KFDOf6YyIbV1NTgtddeQ3h4OCIjI7FhwwY8fPhQr33LysqQlJSEkJAQpKSk4OjRoxptpFIp3nrrLURHRyMsLAxz587FN998o9Gurq4Or7/+OsaMGYPnnnsO2dnZEIvFgj8f/cRsZ/KYmBhUVlaisbFRua2n80yqMsc8k2QcljCGlKyXsc8t0dHRcHBwwLFjx5RtWlpacPr0acTExBj40xCRpeh8PqalpQW5ubnIyclBeXk51q1b1+2+nc/HxMfHY/fu3Rg/fjxWrVqFc+fOqbXbtGkT3nvvPSxfvhwFBQVwdnbG/Pnzcf/+fWUbmUyGBQsW4Ntvv8WWLVuwceNGXLp0CUuWLHnioV6kyWxDLEwxzyTZFmsaQ0rmY+xzy8CBA5GWloZt27bByckJvr6+KC4uBgDMm6e+hC8R2Q5LeT7mxIkTuHbtGsrLy5Xv+dRTT2H27NmoqKjgehAGYtYxyMaeZ5Ksky2MIbUmtjaG1BTnlrVr18LV1RVvv/22cprAkpISrqJHZMMs5fmYc+fOITAwUO39fvGLX8DPzw/nzp1jLWQgZp3FwtjzTJJ10jWGdNq0aV3upzqGVLWQ1jWGtF+/fmrtOIbUNhj73OLs7Izs7GxkZ2cLOk4ish6WMse6tud0OtvpOg7qGVYCZHE4hpSIiCyNpTwf01W77o6Desbi5kEm4hhSIiKyFuZ4PqardoZaC4JYIJMF4hhSIiKyNJbyfIyu4zDUWhDWxFjP0bBAJovEMaRERGRJLOX5mICAAK1zI1dVVSE2NvbJPhxpsPsC2cGhZ19HWCJb+AzWzNp//9Z+/NbO2n//1n781s4Wfv/W8hliYmKwY8cONDY2Kh+k6+nzMfHx8crtup6PmT17NoCfno9RfThw4sSJOHz4MKqrq5UF9+XLl1FbW9vjGSys5Xevi7E+g0jBWaWJiIiIdJJIJJgyZQr8/PzUno8ZP348tm/frmyn7fmYY8eOYeXKlcjMzFQ+H/Puu++isLBQrah98803cfjwYeTk5Cifj/nqq6/w4YcfKocAymQyTJ8+HTKZDKtWrUJHRwe2bt0KHx8fvP/++z0eh0zasUAmIiIi0sOtW7ewceNGfP7552rPx7i4uCjb5OTkoLS0FNevX1fbt7S0FDt37kRtbS38/f2xdOlSpKSkqLWRSqXIzc1FaWmp8vmY9evXY9SoUWrt6urq8Ic//AEVFRUQiUSIjY3F+vXruZqwAbFAJiIiIiJSwXmQiYiIiIhUsEAmIiIiIlLBApmIiIiISAULZCIiIiIiFSyQiYiIiIhUsEAmIiIiIlLBApmIiIiISAUL5G7k5eUhKChI+e/ZZ59FYmIi8vPz0d7ebrD+J0yYgI6ODo3XV6xYgaCgIKSnpwt+LzI95oeEYoZICOaHhLDn/DiZ/B2tUJ8+fbB3714AwI8//ohLly4hLy8PLS0tWLNmjeD+nZ2dIZFI8Mknn+D5559Xbv/hhx9w9uxZuLm5CX4PMh/mh4RihkgI5oeEsNf8sEDWg4ODA0aPHq38OSIiArdv38aJEycMFo6oqCiUl5erhePkyZNwdXXFqFGjDHKlRubB/JBQzBAJwfyQEPaaHw6xeEJubm6QyWQG62/q1Kk4efIk2tralNuOHDmCyZMnw8mJ1zG2hvkhoZghEoL5ISHsIT8skPUkk8kgk8nQ0tKC8+fP4/Dhw0hMTDRY/3FxcRCJRDhz5gwAoL6+HpWVlZgyZYrB3oPMh/khoZghEoL5ISHsMT+8rNNDa2srgoOD1bbFxMTgjTfeMNh79O7dGwkJCThy5AiSk5Px0UcfwdfXF+Hh4QZ7DzIP5oeEYoZICOaHhLDX/LBA1kOfPn2wb98+AI+uoqqqqpCbm4ulS5di9+7dEIlEBnmfKVOmIDMzExKJBEeOHEFKSopB+iXzYn5IKGaIhGB+SAh7zQ8LZD04ODggJCRE+XN4eDj69u2LFStW4Ny5c4iNjTXI+0RGRqJfv37YtWsXrly5gi1bthikXzIv5oeEYoZICOaHhLDX/HAM8hMaMWIEAODbb781WJ+Ojo5ITk5GUVERRo4ciYCAAIP1TZaF+SGhmCESgvkhIewhP7yD/ISuX78OAPDy8jJov9OnT0dtbS2Sk5MN2i9ZFuaHhGKGSAjmh4Swh/ywQNaDXC7H5cuXATwaf3Pjxg3k5+djwIABiI+PN+h7PfPMMygoKDBon2RezA8JxQyREMwPCWGv+WGBrIe2tjbMmjULwKOvAAYNGoTnn38eWVlZ8PT0NPPRkaVjfkgoZoiEYH5ICHvNj0ihUCjMfRBERERERJaCD+kREREREalggUxEREREpIIFMhERERGRChbIREREREQqWCATEREREalggUxEREREpMIuCuRDhw4hKCgI3333HQAgPT0d6enpZj4q48jLy0NQUJC5D8OmMD8kBPNDQjFDJATz82TsokAmIiIiItKXXa6kV1RUZO5DMJrFixdj0aJF5j4Mm8b8kBDMDwnFDJEQzI9+7LJA7tWrl7kPwWicnJzg5GSXf1aTYX5ICOaHhGKGSAjmRz82N8Ti8uXLSEtLQ0hICGJjY7Fr1y48vpq2tvE3RUVFSEtLQ0REBEJCQjB16lR88MEHGv0rFArs2LEDEydORFhYGNLS0nDp0iWNPi9cuICgoCCUl5ejpKQEkyZNQkhICGbNmoWvv/5ao9/PPvsMr776KsLDwxEeHo6MjAx88cUXam1kMhkKCgqQmJiI0NBQREREIC0tDcePH1e20Tb+5urVq1i4cCEiIyMRGhqKF154AatXr0Zra6v+v1g7wfwwP0IwP8yPUMwQMyQE82O4/NjUZVpVVRUyMjLg5uaGxYsXw9nZGQcPHoSrq2u3++7ZswcxMTFITk6GSCTCyZMn8Zvf/AZyuRyzZs1Sttu+fTsKCwsRHR2NSZMmoaamBpmZmfD09MSgQYM0+i0uLoZMJsPcuXMhk8lQVFSEpUuX4sSJE3B2dgYAXLx4ERkZGRg0aBAWL14MuVyO/fv3Y+7cudi3bx/CwsIAAPn5+di5cydefvllhIaGorW1Fd988w2++OILJCUlaf1cYrEYv/rVr9CvXz8sWLAAnp6euHfvHk6fPo3W1la9fjf2gvnRxPzoj/nRxPz0DDOkiRnSH/OjSVB+FDZk2bJliuDgYMXt27eV2xoaGhRjxoxRBAYGKu7evatQKBSKuXPnKubOnau2b2trq0Z/8+fPV8THxyt/rq+vVwQHBysWLlyokMvlyu0HDx5UBAYGqvVZWVmpCAwMVMTHxyva2tqU20+ePKkIDAxUnDlzRrktNTVVMW7cOEVDQ4Ny23/+8x/F6NGjFWlpacptL774omLRokU6fwfvvPOOIjAwUOP9vvzyS537EfOjUDA/QjA/zI9QzBAzJATzY9j82MwQi46ODvzzn/9EXFwc/P39ldu9vLwwderUbvd3cXEBAEilUjx48ABisRiRkZG4ffs2mpubAQCffPIJpFIp5syZA5FIpNw3NTUVHh4eWvudNm0aevfurfx57NixAIC7d+8CAOrq6nD16lW89NJL8PLyUrYbOHAgpk6dikuXLqGpqQkA4O7ujhs3buDWrVt6/U469wGAs2fPQiqV6r2fvWF+tGN+9MP8aMf86I8Z0o4Z0g/zo52Q/NjMEAuxWIyHDx/i6aef1nhN27bHnTp1CgUFBbh27Ro6OjrUXmtubkbfvn3x/fffAwCGDBmi9rqTkxN+9rOfae3X19dX7WdPT08AUP7Ba2trAQDDhg3T2DcgIAAKhQL37t2Dp6cnsrKysGzZMiQlJWH48OGIjo5GSkoKQkNDu/xcERERSEhIQH5+PkpKSjBu3DjExcVhypQpcHNz0/UrsSvMj3bMj36YH+2YH/0xQ9oxQ/phfrQTkh+buYOs+O8gdNWrmsdf68rnn3+OZcuWwcXFBb///e+xa9culJSUYP78+QAAuVyu9/s/ztHRsUftdbWJiIjAqVOnsGXLFowcORJlZWWYOXMmCgsLu+xDJBIhLy8PH3zwAebNm4fGxkb87ne/w9SpU1FfX9/tMdgL5kc75kc/zI92zI/+mCHtmCH9MD/aCcmPzRTI3t7ecHFxwc2bNzVeq6mp0bnv8ePH0bt3bxQXF2PGjBmYOHEioqKi0KdPH7V2nVdCt2/fVtsuk8mUV0E95efnBwBaj/vmzZsQiUQYPHiwcpunpydeeuklbNu2DefOncPYsWORn5+vccX3uNDQUKxYsQIHDhzArl27UFtbq/UJVXvF/DA/QjA/zI9QzBAzJATzY/j82EyB7OjoiOjoaJw5cwZ37txRbheLxSgvL+92X5FIpHaV1NTUhL///e9q7aKiouDk5IT33ntP7cqmtLQUEonkiY57wIABCA4ORllZGcRisXL7/fv3ceTIEYSHhyu/kmhsbFTbt0+fPhg2bBja29vx8OFDrf03NTVpXIUFBwcDwBMfsy1ifpgfIZgf5kcoZogZEoL5MXx+bGYMMgAsX74c58+fxyuvvII5c+bAyckJBw8ehK+vr85fRFxcHEpKSpCRkYEXX3wRTU1NOHjwIHx8fFBXV6ds5+3tjYyMDOzevRsLFizApEmTcPv2bRw+fFhtUHxPrV27FhkZGZg1axZmzJgBAHj//fchk8mwZs0aZbvJkydj7NixePbZZ9G/f39cv34df/vb3xAbG6sciP640tJS/PWvf8Uvf/lL+Pv7o62tDYcOHYKjoyMSExOf+JhtEfOjifnRH/OjifnpGWZIEzOkP+ZHk5D82FSBHBgYiOLiYmzZsgUFBQXw9vbGK6+8Am9vb6xbt67L/SIiIrBlyxYUFhbij3/8IwYNGoT09HR4eHho7Ldq1Sq4uLjgwIEDuHjxIkaNGoXCwkK8+eabak9q9sTYsWOxZ88e5ObmoqCgACKRCGFhYdi+fTtGjx6tbDdv3jycPn0alZWVaGtrw+DBg7Fw4UIsXLiwy77HjRuHK1eu4Pjx46irq4O7uztGjRqF3/72t2p9E/OjDfOjP+ZHE/PTM8yQJmZIf8yPJiH5ESn0GSlNOnV0dCAqKgrx8fHYuHGjuQ+HrAzzQ0IwPyQUM0RC2Gp+bGYMsqm0tbVpbDt06BAePHiAyMhIMxwRWRPmh4RgfkgoZoiEsKf82NQQC1M4evQoDh48iNjYWHh6euKrr77CoUOH8MwzzyAhIcHch0cWjvkhIZgfEooZIiHsKT8skHsoKCgILi4u2LNnD5qbm9G/f3/MmDEDr7/+Onr16mXuwyMLx/yQEMwPCcUMkRD2lB+OQSYiIiIiUsExyEREREREKlggExERERGpYIFMRERERKSCBTIRERERkQoWyEREREREKlggExERERGp+H9+QsXfYB06TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.rcParams['figure.figsize']=(10,5)\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='radius_se',data=df, ax=ax1, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='texture_se',data=df, ax=ax2, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='perimeter_se',data=df, ax=ax3, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='area_se',data=df, ax=ax4, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='smoothness_se',data=df, ax=ax5, palette='cubehelix')\n",
    "f.tight_layout()\n",
    "\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='compactness_se',data=df, ax=ax2, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='concavity_se',data=df, ax=ax1, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='concave points_se',data=df, ax=ax3, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='symmetry_se',data=df, ax=ax4, palette='cubehelix')\n",
    "s.boxplot('diagnosis',y='fractal_dimension_se',data=df, ax=ax5, palette='cubehelix')    \n",
    "f.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=(10,5)\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='radius_worst',data=df, ax=ax1, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='texture_worst',data=df, ax=ax2, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='perimeter_worst',data=df, ax=ax3, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='area_worst',data=df, ax=ax4, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='smoothness_worst',data=df, ax=ax5, palette='coolwarm')\n",
    "f.tight_layout()\n",
    "\n",
    "f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1,5)\n",
    "s.boxplot('diagnosis',y='compactness_worst',data=df, ax=ax2, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='concavity_worst',data=df, ax=ax1, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='concave points_worst',data=df, ax=ax3, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='symmetry_worst',data=df, ax=ax4, palette='coolwarm')\n",
    "s.boxplot('diagnosis',y='fractal_dimension_worst',data=df, ax=ax5, palette='coolwarm')    \n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564    M\n",
       "565    M\n",
       "566    M\n",
       "567    M\n",
       "568    B\n",
       "Name: diagnosis, dtype: category\n",
       "Categories (2, object): [B, M]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_norm = df_norm.drop(labels='diagnosis',axis=1)\n",
    "Y_norm= df_norm['diagnosis']\n",
    "col=X_norm.columns\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(Y_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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>842302</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>842517</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>84300903</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>84348301</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>84358402</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>926424</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>926682</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>926954</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>927241</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>92751</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": [
       "           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302        17.99         10.38          122.80     1001.0   \n",
       "1      842517        20.57         17.77          132.90     1326.0   \n",
       "2    84300903        19.69         21.25          130.00     1203.0   \n",
       "3    84348301        11.42         20.38           77.58      386.1   \n",
       "4    84358402        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564    926424        21.56         22.39          142.00     1479.0   \n",
       "565    926682        20.13         28.25          131.20     1261.0   \n",
       "566    926954        16.60         28.08          108.30      858.1   \n",
       "567    927241        20.60         29.33          140.10     1265.0   \n",
       "568     92751         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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_norm=le.transform(Y_norm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "564  1\n",
       "565  1\n",
       "566  1\n",
       "567  1\n",
       "568  0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_norm=pd.DataFrame(Y_norm)\n",
    "Y_norm.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Fitting the ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df_norm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functionalize model fittting\n",
    "\n",
    "def FitModel(X,Y,algo_name,algorithm,gridSearchParams,cv):\n",
    "    np.random.seed(10)\n",
    "    \n",
    "    x_train,x_test,y_train,y_test = train_test_split(X,Y, test_size = 0.2)\n",
    "    \n",
    "    grid = GridSearchCV(\n",
    "        estimator=algorithm,\n",
    "        param_grid=gridSearchParams,\n",
    "        cv=cv, scoring='accuracy', verbose=1, n_jobs=-1)\n",
    "    \n",
    "    \n",
    "    grid_result = grid.fit(x_train, y_train)\n",
    "    best_params = grid_result.best_params_\n",
    "    pred = grid_result.predict(x_test)\n",
    "    cm = confusion_matrix(y_test, pred)\n",
    "   # metrics =grid_result.gr\n",
    "    print(pred)\n",
    "    pickle.dump(grid_result,open(algo_name,'wb'))\n",
    "   \n",
    "    print('Best Params :',best_params)\n",
    "    print('Classification Report :',classification_report(y_test,pred))\n",
    "    print('Accuracy Score : ' + str(accuracy_score(y_test,pred)))\n",
    "    print('Confusion Matrix : \\n', cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "## SVC ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0\n",
      " 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0\n",
      " 0 1 1]\n",
      "Best Params : {'C': 1, 'gamma': 1}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98        75\n",
      "           1       0.93      1.00      0.96        39\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.96      0.98      0.97       114\n",
      "weighted avg       0.98      0.97      0.97       114\n",
      "\n",
      "Accuracy Score : 0.9736842105263158\n",
      "Confusion Matrix : \n",
      " [[72  3]\n",
      " [ 0 39]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed:    2.5s finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:73: 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(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'C': [0.1, 1, 100, 1000],\n",
    "            'gamma': [0.0001, 0.001, 0.005, 0.1, 1, 3, 5]\n",
    "        }\n",
    "FitModel(X_norm,Y_norm,'SVC_norm',SVC(),param,cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.1min finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:765: 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",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0\n",
      " 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0\n",
      " 0 1 1]\n",
      "Best Params : {'n_estimators': 500}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        75\n",
      "           1       0.95      0.97      0.96        39\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.97      0.97      0.97       114\n",
      "weighted avg       0.97      0.97      0.97       114\n",
      "\n",
      "Accuracy Score : 0.9736842105263158\n",
      "Confusion Matrix : \n",
      " [[73  2]\n",
      " [ 1 38]]\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'n_estimators': [100, 500, 1000, 2000],\n",
    "           \n",
    "        }\n",
    "FitModel(X_norm,Y_norm,'Random Forest',RandomForestClassifier(),param,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest: 98.25%\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y, test_size = 0.2)\n",
    "\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=1000)\n",
    "fit = forest.fit(x_train, y_train)\n",
    "accuracy = fit.score(x_test, y_test)\n",
    "predict = fit.predict(x_test)\n",
    "cmatrix = confusion_matrix(y_test, predict)\n",
    "\n",
    "#--------------------------------------------------------------------------------------#\n",
    "# Perform k fold cross-validation\n",
    "\n",
    "\n",
    "print ('Accuracy of Random Forest: %s' % \"{0:.2%}\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "feature id (0.152947)\n",
      "feature radius_mean (0.118457)\n",
      "feature texture_mean (0.118185)\n",
      "feature perimeter_mean (0.107737)\n",
      "feature area_mean (0.082782)\n",
      "feature smoothness_mean (0.054592)\n",
      "feature compactness_mean (0.047692)\n",
      "feature concavity_mean (0.044663)\n",
      "feature concave points_mean (0.039500)\n",
      "feature symmetry_mean (0.038166)\n",
      "feature fractal_dimension_mean (0.033723)\n",
      "feature radius_se (0.017635)\n",
      "feature texture_se (0.016159)\n",
      "feature perimeter_se (0.015938)\n",
      "feature area_se (0.012447)\n",
      "feature smoothness_se (0.011327)\n",
      "feature compactness_se (0.011148)\n",
      "feature concavity_se (0.009726)\n",
      "feature concave points_se (0.009464)\n",
      "feature symmetry_se (0.006852)\n",
      "feature fractal_dimension_se (0.006291)\n",
      "feature radius_worst (0.006028)\n",
      "feature texture_worst (0.005206)\n",
      "feature perimeter_worst (0.004678)\n",
      "feature area_worst (0.004499)\n",
      "feature smoothness_worst (0.004328)\n",
      "feature compactness_worst (0.004295)\n",
      "feature concavity_worst (0.004213)\n",
      "feature concave points_worst (0.004154)\n",
      "feature symmetry_worst (0.004058)\n",
      "feature fractal_dimension_worst (0.003109)\n"
     ]
    }
   ],
   "source": [
    "#%%Feature importances\n",
    "importances = forest.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(X.shape[1]):\n",
    "    print(\"feature %s (%f)\" % (list(X)[f], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5YAAALJCAYAAADPpLzoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeVTV1f7/8SfKlIiAA3ozbzgeFVAOOAKJ4pQpX8d71ZwSNEckzAE1h7gY3tREgTK0G5mlpFfNBufMq1Z8bzhlejNT8+IQKgKmMR34/eGX8+uIJSYK4uuxFmtx9t6f/Xnvba113uz92R+rwsLCQkRERERERET+oEplHYCIiIiIiIg83JRYioiIiIiIyD1RYikiIiIiIiL3RImliIiIiIiI3BMlliIiIiIiInJPrMs6ABGBgoICrl+/jo2NDVZWVmUdjoiIiIhUUIWFheTl5eHg4EClSqW3zqjEUqQcuH79OidOnCjrMERERETkEdGkSRMcHR1LrT8lliLlgI2NDXDzf3BbW9syjqZiOHr0KB4eHmUdRoWguSw9msvSpfksPZrL0qX5LD2ay9JTNJe5ubmcOHHC/P2ztFgVFhYWlmqPInLXcnJyzP+z29nZlXU4IiIiInIfFeabsLKu/EDvmZKSgo+Pz3373qkVS5FyJH31Jqxz88o6DBERERG5j2qNG1rWIZQ6nQorIiIiIiIi90SJpchdio2NxWg0/m6biIgIevXq9YAiEhEREREpW9oKK3KX/vKXvxAQEFDWYYiIiIiIlBtKLEXuUp06dahTp05ZhyEiIiIiUm5oK6zIXbp1K+ypU6cYPnw4LVq0IDAwkDVr1pRhdCIiIiIiD55WLEXuQW5uLqNGjcLa2poFCxZQuXJl4uPjycjIoFq1amUdnoiIiIjIA6HEUuQebNiwgfPnz/Ppp5/SoEEDAIxGI507d1ZiKSIiIiKPDG2FFbkHhw8fpkmTJuakEsDV1fWOp8aKiIiIiFQkSixF7kFaWho1atQoVl6zZs0yiEZEREREpGwosRS5B66urly5cqVY+eXLl8sgGhERERGRsqHEUuQetGjRghMnTnDq1Clz2aVLlzh48GAZRiUiIiIi8mApsRS5B/369aNOnTqMHTuWTz/9lO3btxMSEoKLi0tZhyYiIiIi8sAosRS5B3Z2drz11lvUrl2b6dOn88orrzBw4EB8fX3LOjQRERERkQdGrxsRuUuhoaGEhoaaPzds2JB33323DCMSERERESlbWrEUERERERGRe6IVS5FypPrQPtjZ2ZV1GCIiIiJyHxXmm7CyrlzWYZQqrViKSIWUkpJS1iFUGJrL0qO5LF2az9KjuSxdms/SU1HnsqIllaDEUkRERERERO6REksRERERERG5J0osRaRC8vHxKesQKgzNZenRXJYuzWfp0VzevcL8/LIOQaRc0eE9IuXIlfcSsc7NKeswRERE5A5cx04q6xBEyhWtWIqIiIiIiMg9UWIpdyU5ORmDwcA333xjLgsMDCQyMrIMoxIRERERkbKkrbByz+Li4qhWrVpZhyEiIiIiImVEieUjprCwkNzcXOzs7Eqtz+bNm5daXyIiIiIi8vDRVtgKLiIigl69erFv3z769u2Lp6cnW7Zs4W9/+xtPP/00LVu2pFOnTsycOZOMjAyLa/Pz81mwYAFt27bF29ubKVOmcO3atWL3uHUr7LBhwxgzZoxFm9TUVAwGA1u3bjWX7d69m/79+2M0GvHx8aFv375s3769ROMq6u/DDz8kMjKSNm3a0LZtW+Li4gDYtWsXPXv2xGg0EhISQlpamsX1ubm5xMTEEBgYiIeHB927dycpKcmizeHDhxk3bhz+/v54eXkRFBTEunXrLNoUbQ3+4osvmDJlCkajkYCAAJYuXUpBQUGJxiIiIiIi8rDTiuUjIC0tjblz5zJu3DieeOIJbG1tOXDgAGFhYdSoUYOffvqJhIQERo0axfr1683XLVmyhHfffZfx48fTokULdu7cyd/+9rdSiens2bOEhobSs2dPJk+eTGFhId999x2ZmZl31U9MTAydO3dmyZIl7N27l9jYWG7cuMGXX35JWFgYJpOJ+fPnM2fOHJYvX26+bvLkySQnJzNhwgSaNGnCV199xbx583BwcKBXr14AnDt3DqPRyMCBA7G3t+fw4cNERkaSm5vLkCFDLOKYM2cOPXv2JD4+nn379vH666/j5uZG7969732yRERERETKOSWWj4DMzEyWL1+Ot7e3uezXv+fn59OoUSP69OnDt99+i7u7O5mZmbz33nuMGjWKCRMmAPDUU08xduxYLl68eM8xHTt2jLy8PGbPnk3VqlUB8Pf3v+t+PD09eemllwDw8/Nj+/btrFq1ip07d1KnTh0Azp8/z8KFC8nOzsbe3p7k5GR27NhBQkICAQEBAPj6+pKRkcHSpUvNieUzzzxjvk9hYSGtWrUiPT2dtWvXFkssu3btSnh4uLmv/fv3s23bNiWWIiIiIvJIUGL5CHB2drZIJAE+/PBDEhMTOXPmDDdu3DCXnzlzBnd3d7777jt++eUXunfvbnHd008/ze7du+85JoPBQOXKlZkyZQp/+ctfaN269R86AOjWZNTNzQ1HR0dzUllUVlhYyE8//cSTTz7J/v37cXJyws/Pj/xfvdzY19eXpKQkMjIycHZ2JjMzk9jYWD777DMuXryIyWQCwNbW9o5xNGrUiNOnT9/1eEREREREHkZKLB8BNWvWtPi8Y8cOpk2bxoABAwgLC8PFxYWsrCxGjRpFTk4OAJcuXQKgRo0aFtfe+vmPql+/PsuXLychIYFJk26+YNjPz4/Zs2dTr169EvdzazJqY2Nz2zLAPLb09HQyMzNxd3e/bZ8XLlzA2dmZiIgIDhw4wPjx42nSpAlVq1Zl06ZNrF69ukRx5ObmlngcIiIiIiIPMyWWjwArKyuLz1u3bqVp06bMnz/fXHb06FGLNrVq1QLgypUr1K5d21x+5cqVO97P1taWvLw8i7LbPTvZoUMHOnTowPXr19m/fz8LFizgxRdf5IMPPrjzoO6Bk5MTLi4urFix4rb1bm5u5OTksGfPHqZNm8aIESPMdZs2bbqvsYmIiIiIPIyUWD6CsrOzi23n/Oijjyw+GwwG7O3t2bZtm8XrRH59qutv+dOf/sS+ffsoKCigUqWbBw/v27fvN9s7ODjQrVs3jh8/zjvvvHM3Q/lD/Pz8WLlyJdbW1jRr1uy2ba5du4bJZLKYp5ycnBKfWisiIiIi8ihRYvkI8vX1JTIyktjYWHx8fPjiiy/47LPPLNo4OTkxZMgQVq5ciZ2dnflU2GPHjt2x/x49erBu3TpefvllunfvzjfffMOGDRss2qxdu5YDBw7QoUMHXF1dOX/+POvXr8fPz69Ux3o7vr6+dOnShdGjRxMSEkLTpk3Jycnh1KlTHDlyhJiYGBwdHfH09CQhIQFnZ2dsbW15++23b/t8pYiIiIjIo06J5SNo0KBBpKamsnbtWv7xj3/Qrl07li1bRp8+fSzahYeHk5+fT2JiInl5eXTq1InZs2czceLE3+3fz8+PGTNmsGrVKj788EPatWvHggULGDRokLmNwWDg888/5+9//ztXr16lZs2aFier3m8xMTG89dZbJCUlkZqaioODAw0aNCAoKMjcZvHixcydO5dZs2bh6OjIoEGDsLW1ZeHChQ8kRhERERGRh4VVYWFhYVkHIfKoy8nJ4ejRo/zp8NdY5+aUdTgiIiJyB65jJ/1mXUpKCj4+Pg8wmopLc1l6iuay6Hunh4cHdnZ2pdZ/pVLrSURERERERB5J2gor5U5hYaH5nZG3U6lSJfOhQBVNjSHPlepfjkREROT+KMzPx8paX6VFilTMb+fyUNu4cSPu7u6/+TNz5syyDlEeAikpKWUdQoWhuSw9msvSpfksPZrLu6ekUsSS/o+QcqdTp06sX7/+N+tdXFweYDQiIiIiInInSiyl3HFxcVHyKCIiIiLyENFWWBGpkHSCXOnRXJYezWXp0nyWHs3l3SnMzyvrEETKHa1YipQjP737CtY518s6DBEREfkdj09YVNYhiJQ7WrEUERERERGRe6LE8iG3c+dO3nvvvfvSd2pqKrGxsfz000/3pX8REREREakYlFg+5Hbu3MmaNWvuS9/nzp0jLi6OtLS0+9K/iIiIiIhUDEos5YHKyckp6xBERERERKSUKbF8iEVERLBx40a+//57DAYDBoOBiIgIAI4cOUJwcDBGoxFvb29CQ0O5ePGi+dqJEycSEBBAVlaWuWzPnj0YDAY++ugjkpOTGT58OAADBgww9w+wYcMGDAYD6enpFvEMGzaMMWPGmD/HxsZiNBo5evQogwcPpkWLFqxcuRKAU6dOMXHiRFq3bk3Lli0ZOXIk33//fYnHnpycjMFgYN++fbz44osYjUb8/f354IMPAFi3bh2dO3fGx8eHF154gZ9//tni+p9//pmoqCieeuopPDw8CAoKYufOnRZt9uzZQ3BwMO3bt8doNNK/f/9ibYrm4vjx44wZMwYvLy+6dOly37Yni4iIiIiUR0osH2Ljx48nICCAevXqkZSURFJSEuPHj+fIkSMMGTIEW1tbFi1aRHR0NGfOnCEkJASTyQRAZGQkJpOJl19+GYD09HRmzZrFM888Q1BQEO7u7syZMweA6Ohoc/93Ky8vjxdeeIEePXqwYsUKOnbsSGpqKoMHD+by5ctERUWxZMkS8vLyGD58ONeuXbur/ufOnYubmxtxcXG0b9+e2bNns2jRIjZv3sxLL73EjBkz2LNnDzExMRYxBQcHs337dkJDQ3nzzTcxGo2EhoaSkpJibnfu3DkCAgJ49dVXiY+Pp127dkycOJHPP/+8WBwvvvgirVu35vXXX6d169ZERkby9ddf3/V8iYiIiIg8jPS6kYfYn//8Z6pXr8758+fx8vIyl8+aNYtmzZrxxhtvYGVlBYCHhwfdu3fn448/pnfv3lSvXp358+fz/PPP07lzZz799FMqVarEvHnzAKhatSqNGjUCoHHjxnh6ev6hGPPy8ggLCyMoKMhcFhERgYODA4mJidjb2wPQpk0bunTpwrvvvsv48eNL3H+3bt0IDQ0FwNvbmx07dvDPf/6TXbt2UaVKFQCOHTvG1q1beemllwD46KOPOHr0KBs3bjSvwvr5+XHu3DliY2NJTEwE4NlnnzXfp6CggDZt2vDjjz+ydu1aOnbsaBHHs88+y9ChQwFo3bo1u3fvZtu2bbRq1eouZktERERE5OGkxLKCyc7OJiUlhWnTpplXJwFq165N/fr1+eabb+jduzcAAQEBDBo0iOnTp5OXl8dbb72Fk5NTqcfUqVMni8/79u2jR48eWFtbk5+fD4C9vT1eXl4cOXLkrvr29/c3//7YY49Ru3ZtGjZsaE4qAdzc3Lh8+TK5ubnY2tqyf/9+mjRpQsOGDc33h5vJZVxcnPnzxYsXiYmJ4YsvvuDSpUsUFBQAUL9+/d+Nw8bGBjc3N52mKyIiIiKPDCWWFUxmZiYmk4no6Giio6OL1derV8/ic1BQEGvXrqVRo0b4+vqWejyPPfYYVatWtSi7evUqq1atYtWqVcXaN23a9K76r1atmsVnGxub25YVFhaSl5eHra0t6enpHD9+HHd399v2mZWVRdWqVRk3bhyZmZlMnDiRJ598kipVqpCQkMC3335bojh0UJGIiIiIPCqUWFYwjo6OWFlZMWbMGLp06VKs/tcJUE5ODnPmzMFgMHDy5ElWrVrFiBEj7ngPOzs74OY211/LyMiwWCkEzFtxf83JyYmAgACLraZFirbG3k9OTk4YDAbmz59/2/oqVarw448/cuzYMeLj4y3m8dYxi4iIiIiIEsuH3q0rY1WqVMFoNHLy5EnCw8N/99qFCxfy008/sXnzZtavX8/ixYvx9/enYcOG5r6h+CtC6tSpA8APP/xA7dq1gZsH3Zw+fZrHH3/8jjH7+vpy4sQJmjdvTuXKlUs+2FLi5+fHnj17cHV1Ncd/q6IxF80BwOXLl/nyyy+pUaPGA4lTRERERORhocTyIdewYUPWr1/P5s2bqV+/Pi4uLkyfPp3hw4czadIkevXqhZOTE2lpaSQnJ9OxY0e6dOnC/v37Wb16NQsWLKBu3bqMHz+ePXv2MHXqVJKSkszPCVauXJl169ZRuXJlrK2t8fT0pGXLltStW5dXXnmFyZMnk52dTUJCAs7OziWKOSwsjAEDBjBy5EgGDhxIrVq1uHz5MgcPHqR+/fq3XcksTb179+aDDz5g6NChBAcH06BBA65du8aJEye4dOkSc+fOpUGDBtSpU4dFixZhMpnIzs7m9ddfp2bNmhQWFt7X+EREREREHjZ63chDbsCAAfTo0YP58+czYMAA4uLi8PLyYs2aNeTm5jJr1ixGjRrF0qVLKSgooFGjRmRmZjJjxgy6du1Knz59gJsrcwsXLuTkyZPEx8cDUL16debMmUNKSgpDhw5lwIABAFhbWxMfH4+DgwPh4eHExsYyadKk2x5qczv16tVj3bp11KpVi6ioKIKDg1m4cCGXL1/+w6fP3g1bW1sSExPp2rUrK1asICQkhLlz5/Lvf/+b1q1bm9vExcVhb29PeHg4S5YsYdiwYQQEBNz3+EREREREHjZWhVp+ESlzOTk5HD16lFoHN2Odc72swxEREZHf8fiERb9bn5KSgo+PzwOKpmLTXJaeorks+t7p4eFhPjulNGjFUkRERERERO6JnrGUcqegoMD8zsjbqVSpEpUqVcy/idQeNrNU/3IkIiIipa8wPw8ra5s7NxR5hFTMb+fyUIuPj8fd3f03f4qeARX5PSkpKWUdQoWhuSw9msvSpfksPZrLu6OkUqQ4rVhKufPXv/6Vjh07/ma9q6vrgwtGRERERETuSImllDu1a9f+zfdLioiIiIhI+aOtsCJSIekEudKjuSw9msvSpfksPeVhLgvyc8s6BBG5B1qxFClHTq8aTaWczLIOQ0RE5IFrMvHDsg5BRO6BVixFRERERETkniixrGACAwOJjIx8YPfLysoiNjaWkydPPrB7ioiIiIhI+aKtsBVMXFwc1apVe2D3y8rKIi4ujsaNG9OoUaMHdl8RERERESk/lFhWENnZ2djb29O8efOyDuWeFBYWkpubi52dXVmHIiIiIiIiJaStsA9QREQEvXr1Yu/evQQFBeHp6Um/fv04ePCgRbsPP/yQ3r174+npiZ+fH9HR0eTm/v+T0jZs2IDBYODw4cOMGjUKo9Fo3v5661bYonsmJyfTp08fWrRowcCBA/nhhx/4+eefmT59Oj4+PnTq1In333+/WMxHjhwhODgYo9GIt7c3oaGhXLx4EYDU1FQ6d+4MQFhYGAaDAYPBQGpqKgC5ubnExMQQGBiIh4cH3bt3Jykp6bZzsm/fPvr27Yunpydbtmy541zGxsZiNBr5z3/+w+DBg2nRogVBQUEcOHCAvLw8oqOjadeuHf7+/ixbtozCwkKL60+dOsXEiRNp3bo1LVu2ZOTIkXz//fcWbRITE+nfvz8+Pj60a9eOkJCQYm2K4v/3v/9N3759admyJX369OHf//73HccgIiIiIlJRKLF8wC5dusTcuXMJDg4mJiYGW1tbQkJCuHLlCgCrVq1ixowZtGvXjjfeeIOJEyeyceNGoqKiivUVHh6Ot7c3r7/+OgMGDPjde0ZFRTF69Ghee+01Ll++THh4OFOmTKFOnTosW7YMPz8/Xn75ZY4ePWq+7siRIwwZMgRbW1sWLVpEdHQ0Z86cISQkBJPJhKurK3FxcQBMnjyZpKQkkpKScHV1NZe99957DB8+nISEBLp37868efP4+OOPLeJLS0tj7ty5DBkyhJUrV+Lp6VmiuczLy2Pq1Kn069eP2NhYbGxsmDhxInPnziU7O5vFixfTv39/4uPj2bFjh/m61NRUBg8ezOXLl4mKimLJkiXk5eUxfPhwrl27Zm538eJFhgwZQnx8PNHR0dja2jJo0CDS0tKKzW9kZCQjRoxg2bJl2NjYMGHCBH7++ecSjUNERERE5GGnrbAPWEZGBjExMbRv3x6A1q1bExAQwDvvvMPzzz9PTEwMI0eOZOrUqeZrqlWrxtSpU3n++ed54oknzOV//etfGTt27B3vmZmZyapVqzAYDMDN5yJnzJiBt7c34eHhALRp04bt27ezbds2PDw8AFi4cCHNmjXjjTfewMrKCsC88vjxxx/Tu3dvmjVrBsCTTz6Jl5eX+Z7Jycns2LGDhIQEAgICAPD19SUjI4OlS5fSq1cvi/iWL1+Ot7f3Xc1lXl4eL774Ih07dgTA3t6e4cOHc+nSJVasWAGAn58fu3btYuvWrXTr1g24+Ryqg4MDiYmJ2Nvbm8ffpUsX3n33XcaPHw/cXI0sYjKZ8PPzIyAggE8++YSRI0f+5vy6urrSp08fvvrqK7p06XJXYxIREREReRhpxfIBc3R0NCeVcDNpbNeuHYcOHeLQoUNcv36dZ555hvz8fPNP+/btMZlMHDt2zKKvwMDAEt2zVq1a5qQHwM3NDQB/f39zmY2NDXXr1uXChQvAzWc2U1JSeOaZZzCZTOZYateuTf369fnmm29+95779+/HyckJPz8/i7H4+vpy9uxZMjIyzG2dnZ3vOqkEqFSpEr6+vsXG5efnZ9HOzc3NPC6Affv20blzZ6ytrc1x2dvb4+XlxZEjR8ztDh06RHBwMG3btqV58+Z4enqSnp7O6dOnLfq/dX4bNmwIYN4yLCIiIiJS0WnF8gGrXr16sbIaNWpw5swZ0tPTAejXr99trz1//rzF55o1a5bonk5OThafbWxsgJtJ7q3lRc9yZmZmYjKZiI6OJjo6ulif9erV+917pqenk5mZibu7+23rL1y4gLOz812N41b29vbY2tpaxA8UOxXXxsaGnJwc8+erV6+yatUqVq1aVazPpk2bAjfnOjg4GHd3d+bOnUvt2rWxtbUlLCzM4nlXKD6/RTH9+p4iIiIiIhWZEssHrCh5/LUrV65Qq1Ytc4ISGxvLn/70p2Ltbld2vzg6OmJlZcWYMWNuu53zTq80cXJywsXFxbwl9VZFq4uAeZvtg+Lk5ERAQADPPvtssbqirbF79+7lxo0bxMXFWSSOv15pFRERERGRm5RYPmDXrl3jyy+/NG+HvXbtGl999RVDhw7F29ubKlWqcOHCBfPzgGWlSpUqGI1GTp48aX4O83aKVglvXZ3z8/Nj5cqVWFtbm5/DLC98fX05ceIEzZs3p3Llyrdtk52djZWVFdbW//9/kV27dnH9+vUHFaaIiIiIyENDieUD5uzszKxZswgNDaVatWrmFb0RI0bg6OhIWFgYixYt4uLFi7Rr1w4bGxtSU1PZvXs3c+fOpU6dOg8s1unTpzN8+HAmTZpEr169cHJyIi0tjeTkZDp27EiXLl2oVasW1apVY/PmzTzxxBPY2tpiMBjw9fWlS5cujB49mpCQEJo2bUpOTg6nTp3iyJEjxMTEPLBx3CosLIwBAwYwcuRIBg4cSK1atbh8+TIHDx6kfv36PPvss7Rr1w6AGTNmMGjQIE6fPk1CQsJttzKLiIiIiDzqlFg+YLVq1WLq1Km8+uqr/PjjjzRu3JiVK1eanzN87rnnqFOnDm+//Tbvv/8+lStXpm7dunTo0OGO209Lm5eXF2vWrCE2NpZZs2aRnZ1N7dq1adOmDY0aNQJuHqDzyiuvsGTJEp577jlyc3PZtWsXTzzxBDExMbz11lskJSWRmpqKg4MDDRo0ICgo6IGO41b16tVj3bp1LF26lKioKK5du0atWrXw8vLif/7nfwAwGAwsWLCAuLg4xo4dS+PGjVm8eDHz5s0r09hFRERERMojq8Jb3xwv901ERARHjx4t9h5HkZycHI4ePYrDgaVUysks63BEREQeuCYTPyzrEEpNSkoKPj4+ZR1GhaC5LD1Fc1n0vdPDwwM7O7tS61+vGxEREREREZF7oq2wUu6YTCZ+byG9cuXKD/wk2Qel/vAVpfqXIxERkYdFQX4ulaxt79xQRMolrVg+QAsWLNA22BJ47rnncHd3/82f//3f/y3rEOUhkJKSUtYhVBiay9KjuSxdms/SUx7mUkmlyMNNK5ZS7rz88su/+1qP+vXrP8BoRERERETkTpRYSrnToEGDsg5BRERERETugrbCikiFpBPkSo/msvRoLkvX3cxnQX7ufYxERES0YilSjhx6PwRyM8o6DBGRCqftGJ1xICJyP2nFUkRERERERO6JEksRERERERG5J0ospdRkZ2eXdQgiIiIiIlIGlFjKbR0+fJhx48bh7++Pl5cXQUFBrFu3zlyfnJyMwWBgz549hIeH06pVK8aOHQvAzz//TFRUFE899RQeHh4EBQWxc+dOi/737NlDcHAw7du3x2g00r9//2Jt7iQiIoJevXqRnJxMnz59aNGiBQMHDuSHH37g559/Zvr06fj4+NCpUyfef//9YtcfOXKE4OBgjEYj3t7ehIaGcvHiRYs2r732GkFBQRiNRvz9/Zk0aRIXLlywaDNs2DDGjBnD9u3b6dGjB15eXgwePJgTJ07c1XhERERERB5WOrxHbuvcuXMYjUYGDhyIvb09hw8fJjIyktzcXIYMGWJuN3v2bHr27MmyZcuwsrIiLy+P4OBgLl68SGhoKHXr1mXbtm2EhoayevVq8wl+586dIyAggJEjR1K5cmX279/PxIkTWb58OR07dixxnJcuXSIqKoqxY8diZ2dHdHQ04eHhPP744xgMBpYtW8aWLVt4+eWXadGiBR4eHsDNpHLIkCH4+fmxaNEi8vPziYuLIyQkhM2bN1O5cmUArly5wvPPP4+rqysZGRm88847PPvss2zZsgV7e3tzHMePH2f58uWEhYVhbW3Nq6++SmhoKFu2bKFSJf39RkREREQqNiWWclvPPPOM+ffCwkJatWpFeno6a9eutUgsAwICmD59uvnzhg0bOHr0KBs3bsRgMADg5+fHuXPniI2NJTExEYBnn33WfE1BQQFt2rThxx9/ZO3atXeVWGZmZrJq1SrzvbKyspgxYwbe3t6Eh4cD0KZNG7Zv3862bdvMieXChQtp1qwZb7zxBlZWVl4He9QAACAASURBVAB4eHjQvXt3Pv74Y3r37g3A/PnzzfcymUy0adMGX19f/vWvf9GtWzdzXVZWFhs2bKBmzZrmsgkTJvDdd9/RrFmzEo9HRERERORhpMRSbiszM5PY2Fg+++wzLl68iMlkAsDW1taiXWBgoMXn/fv306RJExo2bEh+fr653M/Pj7i4OPPnixcvEhMTwxdffMGlS5coKCgAoH79+ncVZ61atcxJJYCbmxsA/v7+5jIbGxvq1q1r3sKanZ1NSkoK06ZNM48LoHbt2tSvX59vvvnGnFju2bOHN954g5MnT3Lt2jVz2zNnzljE0bRpU4uksmHDhuZxKrEUERERkYpOiaXcVkREBAcOHGD8+PE0adKEqlWrsmnTJlavXm3RrkaNGhaf09PTOX78OO7u7rftNysri6pVqzJu3DgyMzOZOHEiTz75JFWqVCEhIYFvv/32ruJ0cnKy+GxjYwOAo6NjsfLc3Jsvx87MzMRkMhEdHU10dHSxPuvVqwfc3C47fvx4OnbsyKhRo6hRowbW1tYMHjyYnJycEsVxazsRERERkYpIiaUUk5OTw549e5g2bRojRowwl2/atKlY26JtpEWcnJwwGAwWW0h/rUqVKvz4448cO3aM+Ph4unTpYq7Ly8srpRH8PkdHR6ysrBgzZozF/YtUq1YNgJ07d1K1alWWLVtmfuby6tWrDyxOEREREZGHhRJLKSY3NxeTyWSx7TUnJ4ft27ff8Vo/Pz/27NmDq6srtWvXvm2bolW8olU9gMuXL/Pll18WWwG9H6pUqYLRaOTkyZPm5zBvJzs7G2tra4vDdz766KP7Hp+IiIiIyMNGiaUU4+joiKenJwkJCTg7O2Nra8vbb79d7PnK2+nduzcffPABQ4cOJTg4mAYNGnDt2jVOnDjBpUuXmDt3Lg0aNKBOnTosWrQIk8lEdnY2r7/+OjVr1qSwsPABjBCmT5/O8OHDmTRpEr169cLJyYm0tDSSk5Pp2LEjXbp0wc/Pj3feeYeXX36Z7t2788033/DBBx9YJMQiIiIiIqL3WMpvWLx4MW5ubsyaNYvIyEj8/PwYPHjwHa+ztbUlMTGRrl27smLFCkJCQpg7dy7//ve/ad26tblNXFwc9vb2hIeHs2TJEoYNG0ZAQMD9HpaZl5cXa9asITc3l1mzZjFq1CiWLl1KQUEBjRo1Am6eeDt16lR2797N2LFj2bt3L2+88YYSSxERERGRW1gVPqglIhH5TTk5ORw9epT8I0sgN6OswxERqXDajvm4rEMo11JSUszvmpZ7p/ksPZrL0lM0l0XfOz08PLCzsyu1/rViKSIiIiIiIvdEz1hKuWQymX73eUtra/2nKyIiIiJSXujbuZRLXbt25dy5c79Zv2vXLp544okHGNGD4fXsW6W6JUFERG4qyM+lkvWdD6ETEZE/RomllEtvvPEGubm5v1nv6ur6AKORh5GeySg9msvSo7ksXXczn0oqRUTuLyWWUi4ZDIayDkFEREREREpIh/eIiIiIiIjIPVFiKSIVkrYblp5HcS5N+b+9FV9ERESK01ZYkXJkf9JICvQeS5Ey12XUp2UdgoiIyENFK5YiIiIiIiJyT5RYVkCJiYns2bOnWHlgYCCRkZFlEJGIiIiIiFRkSiwroFWrVt02sRQREREREbkflFiKiIiIiIjIPVFieZd++OEHnn/+edq2bUvLli3p1q0bcXFxAERERNCrVy+Sk5Pp06cPLVq0YODAgfzwww/8/PPPTJ8+HR8fHzp16sT7779frO+dO3fSt29fPD098fX1Zd68eVy/ft2izfnz5wkLC6NVq1a0bNmSYcOGceTIEXN9YGAg586d47333sNgMGAwGNiwYYNFH2vWrCEwMBBvb29GjRrFhQsXzHWpqakYDAY2b95MVFQUbdq0MceSk5Nj0U9aWhrTp0+nXbt2eHp68te//pWvv/7aos3u3bvp378/RqMRHx8f+vbty/bt20tc/3uKYv3www+JjIykTZs2tG3b1vzvsWvXLnr27InRaCQkJIS0tDSL63Nzc4mJiSEwMBAPDw+6d+9OUlKSRZvDhw8zbtw4/P398fLyIigoiHXr1lm0SU5OxmAw8MUXXzBlyhSMRiMBAQEsXbqUgoKCEo1FRERERORhplNh79LYsWOpXr068+fPp2rVqvz3v//lxx9/NNdfunSJqKgoxo4di52dHdHR0YSHh/P4449jMBhYtmwZW7Zs4eWXX6ZFixZ4eHgAN5OgiRMn0r17d1544QVSU1NZvHgxZ86cITExEYCff/6ZoUOHAjB79mwcHBxYuXIlw4cP55///CcNGzYkLi6O559/Hm9vb4KDgwH485//bI5v9+7dnDp1ipdeeonr168THR3NjBkzzPcoEhMTQ4cOHXjttdc4duwYMTExuLq6Mn78eACysrIYPHgwdnZ2zJw5E2dnZ9atW0dwcDCffPIJ9erV4+zZs4SGhtKzZ08mT55MYWEh3333HZmZmQB3rC+pmJgYOnfuzJIlS9i7dy+xsbHcuHGDL7/8krCwMEwmE/Pnz2fOnDksX77cfN3kyZNJTk5mwoQJNGnShK+++op58+bh4OBAr169ADh37hxGo5GBAwdib2/P4cOHiYyMJDc3lyFDhljEMWfOHHr27El8fDz79u3j9ddfx83Njd69e9/VeEREREREHjZKLO9Ceno6Z8+eZcaMGQQGBgLQrl07izaZmZmsWrUKg8EA3EzAZsyYgbe3N+Hh4QC0adOG7du3s23bNnNiGRcXh6enJzExMVhZWQHg5OTEiy++SHJyMm3btmXDhg2cP3+ezZs306RJEwDat29PYGAgK1asYMGCBTRv3hxbW1tq1qyJl5dXsTGYTCbefPNN7OzsALhy5QrR0dFkZWVRrVo1czsPDw/mzZsHgL+/P4cOHWLbtm3mxPKdd94hIyODrVu3UqtWLXO7Xr16sXz5cubPn8+xY8fIy8tj9uzZVK1a1dymyJ3qS8rT05OXXnoJAD8/P7Zv386qVavYuXMnderUAW6u9C5cuJDs7Gzs7e1JTk5mx44dJCQkEBAQAICvry8ZGRksXbrUnFg+88wz5vsUFhbSqlUr0tPTWbt2bbHEsmvXruZ/Y19fX/bv38+2bduUWIqIiIhIhaetsHfBxcWFunXr8tprr7FhwwaLLaRFatWqZU4qAdzc3ADLhMnGxoa6deuar79+/TrHjx+nR48e5qQS4Omnn8ba2tq8vfTrr7+mcePG5qQSwMHBgU6dOhXbgvpbWrdubU4qARo1agTAxYsXLdrdmuA1atTIos3+/ftp27YtLi4u5Ofnk5+fT0FBAe3btzdvzTUYDFSuXJkpU6awa9cusrKyLPq8U31J3Rqrm5sbDRs2NCeVRWWFhYX89NNP5vidnJzw8/Mzx5+fn4+vry9nz54lI+PmuyQzMzOJiooiMDAQd3d33N3dSUxM5MyZM3eM49Y5ExERERGpqLRieResrKx46623iImJ4W9/+xs3btygadOmRERE0L59e+DmKuOv2djYAODo6FisPDc3F4Br165RWFhIzZo1LdpYW1vj7Oxs3hqalZVVrA1AzZo1S7x99Lfiu/X5yV+vXt4aL9xcvT148CDu7u7F7lE01vr167N8+XISEhKYNGkScHNFcfbs2dSrV++O9SV1u1hvV/brcaanp5OZmXnb+AEuXLiAs7MzERERHDhwgPHjx9OkSROqVq3Kpk2bWL16dYni+PWciYiIiIhUVEos71L9+vVZunQp+fn5HDp0iGXLljFu3Dh27979h/t0dHTEysqKK1euWJTn5+eTkZFhTgadnJw4depUsesvX75cLGG835ycnPD39+eFF14oVle5cmXz7x06dKBDhw5cv36d/fv3s2DBAl588UU++OCDEtXfz/hdXFxYsWLFbevd3NzIyclhz549TJs2jREjRpjrNm3adF9jExERERF52Ggr7B9kbW1Nq1atGDduHL/88gvnz5//w305ODjQrFkztmzZYlG+fft28vPzadWqFQA+Pj58//33nDx50tzmxo0b7N6929wGbq6U3boCWdp8fX354YcfaNCgAZ6enhY/zZs3L9bewcGBbt260bt3b4v4S1pf2vz8/Lh69SrW1tbF4vf09OSxxx4jNzcXk8mEra2t+bqcnJwSn1orIiIiIvKo0IrlXfjPf/7DggULeOaZZ6hXrx43btxg5cqVuLq6mp9V/KMmTpzIhAkTmDx5Mn369OG///0vr732Gu3bt6dt27YA9OvXj8TERMaMGcMLL7xAlSpVWLlyJTk5OYwePdrcV4MGDfjyyy/Zt28fTk5OPPHEE7i4uNxTfLcaOXIkn3zyCUOHDmX48OHUrVuXjIwMjh49iq2tLRMnTmTt2rUcOHCADh064Orqyvnz51m/fj1+fn4Ad6y/n3x9fenSpQujR48mJCSEpk2bkpOTw6lTpzhy5AgxMTE4Ojri6elJQkICzs7O2Nra8vbbb1skmiIiIiIiosTyrtSqVQtXV1cSEhJIS0vDwcEBb29voqKiLA7E+SM6d+5MbGws8fHxjB8/HkdHR4KCgpgyZYq5TdWqVVm9ejULFixg3rx55Ofn06JFC1atWkXDhg3N7SZPnsy8efOYNGmS+ZUi/fr1u6f4buXs7ExSUhJLly7ltdde4+rVq7i4uODu7s6wYcOAm4fzfP755/z973/n6tWr1KxZ0+Lk1DvV328xMTG89dZbJCUlkZqaioODAw0aNCAoKMjcZvHixcydO5dZs2bh6OjIoEGDsLW1ZeHChQ8kRhERERGRh4FVYWFhYVkHIfKoy8nJ4ejRo2R+u5iC3IyyDkfkkddl1Kf3pd+UlBR8fHzuS9+PIs1n6dFcli7NZ+nRXJaeorks+t7p4eFxz4tjv6ZnLEVEREREROSeaCuslEuFhYWYTKbfrK9UqRKVKlW8v4v4DXy7VP9yJCJ/jCk/l8rWep5aRESkpCreN3OpEDZu3Ii7u/tv/sycObOsQ5RyLiUlpaxDqDAexblUUikiInJ3tGIp5VKnTp1Yv379b9aX9im3IiIiIiLyxymxlHLJxcVFyaOIiIiIyENCW2FFpELSCXKlp6zn0pSfW6b3FxERkTvTiqVIObJt3QhMeXrdiMiv9R25taxDEBERkTvQiqWIiIiIiIjcEyWWj6isrCxiY2M5efJkWYciIiIiIiIPOSWWj6isrCzi4uKUWIqIiIiIyD1TYikiIiIiIiL3RInlLQ4dOkRISAg+Pj4YjUb69evHzp07AcjIyGDWrFm0b9+eFi1a0L9/f/bu3Wtx/bBhwxgzZgxbtmyhe/futGzZkuDgYC5dukRaWhrjxo3DaDTSvXt3duzYcdtrP/zwQ7p27YqnpyfDhg3jhx9+sGiXmJhI//798fHxoV27doSEhPD999+XeCypqal07twZgLCwMAwGAwaDgdTUVFJTUzEYDGzevJmoqCjatGmDr68v8+bNIycnx6L/tLQ0pk+fTrt27fD09OSvf/0rX3/9tUWb3bt3079/f4xGIz4+PvTt25ft27eXuP73FMX64YcfEhkZSZs2bWjbti1xcXEA7Nq1i549e2I0GgkJCSEtLc3i+tzcXGJiYggMDMTDw4Pu3buTlJRk0ebw4cOMGzcOf39/vLy8CAoKYt26dRZtkpOTMRgMfPHFF0yZMgWj0UhAQABLly6loKCgRGMREREREXmY6VTYX0lJSWHEiBF4eHgQGRmJs7Mzx44d48KFC5hMJkaPHs3Zs2eZPHkyderUYc2aNYwZM4Z//OMftGvXztzP8ePHuXLlClOnTuWXX34hKiqKmTNncu3aNbp06cLQoUN57733CA8P57PPPsPV1dV87bFjxzh79izh4eFYWVkRExPD6NGj2bp1K7a2tgBcvHiRIUOG8Pjjj/PLL7/wwQcfMGjQILZs2WLu6/fG0qFDB+Li4pg4cSKTJ0+mbdu2ALi6upqTr5iYGDp06MBrr73GsWPHiImJwdXVlfHjxwM3t9IOHjwYOzs7Zs6cibOzM+vWrSM4OJhPPvmEevXqcfbsWUJDQ+nZsyeTJ0+msLCQ7777jszMTIA71pdUTEwMnTt3ZsmSJezdu5fY2Fhu3LjBl19+SVhYGCaTifnz5zNnzhyWL19uvm7y5MkkJyczYcIEmjRpwldffcW8efNwcHCgV69eAJw7dw6j0cjAgQOxt7fn8OHDREZGkpuby5AhQyzimDNnDj179iQ+Pp59+/bx+uuv4+bmRu/eve9qPCIiIiIiDxsllr+ycOFC6tWrx+rVq7G2vjk1fn5+wM3VryNHjpCQkEBAQAAATz31FL169SI+Pt4isczKymLjxo3UqFEDgDNnzhAXF8eUKVMYNWoUAM2aNaN9+/Z89tlnDBo0yHzt5cuXeffdd3FzczO369GjBxs3bmTgwIEAREREmNubTCb8/PwICAjgk08+YeTIkXccS1G/AE8++SReXl7F5sLDw4N58+YB4O/vz6FDh9i2bZs5sXznnXfIyMhg69at1KpVy9yuV69eLF++nPnz53Ps2DHy8vKYPXs2VatWNbcpcqf6kvL09OSll14yj3H79u2sWrWKnTt3UqdOHQDOnz/PwoULyc7Oxt7enuTkZHbs2GHx7+nr60tGRgZLly41J5bPPPOM+T6FhYW0atWK9PR01q5dWyyx7Nq1K+Hh4ea+9u/fz7Zt25RYioiIiEiFp62w/+eXX37h8OHD9O3b15yI/drXX3+Ng4ODOQkBqFSpEj169ODgwYOYTCZzedOmTc1JJWBOEn+dNFWvXh0nJycuXLhgcZ/GjRub2xdd27hxYw4dOmQuO3ToEMHBwbRt25bmzZvj6elJeno6p0+fLtFYSuLWBK9Ro0ZcvHjR/Hn//v20bdsWFxcX8vPzyc/Pp6CggPbt23PkyBEADAYDlStXZsqUKezatYusrCyLPu9U/0djdXNzo2HDhuaksqissLCQn376yRy/k5MTfn5+5vjz8/Px9fXl7NmzZGTcfJdkZmYmUVFRBAYG4u7ujru7O4mJiZw5c+au50xEREREpKLSiuX/ycrKoqCggNq1a/9mfc2aNYuV16xZk7y8PG7cuIGjoyMATk5OFm1sbGwAzPW/Ls/NzbUo+3VC+uuyS5cuATdX3oKDg3F3d2fu3LnUrl0bW1tbwsLCzH3daSwlUa1atd+NNT09nYMHD+Lu7l7s2qJx1q9fn+XLl5OQkMCkSZOAmyuKs2fPpl69enesv5dYb1cGmJ8TTU9PJzMz87bxA1y4cAFnZ2ciIiI4cOAA48ePp0mTJlStWpVNmzaxevXqEsVx67+viIiIiEhFpMTy/1SrVo1KlSqZV7Ru5eTkxOXLl4uVX758GRsbG6pUqVIqcVy5cuW2ZUUJ0N69e7lx4wZxcXEWCWzRChvceSylwcnJCX9/f1544YVidZUrVzb/3qFDBzp06MD169fZv38/CxYs4MUXX+SDDz4oUf39jN/FxYUVK1bctt7NzY2cnBz27NnDtGnTGDFihLlu06ZN9zU2EREREZGHjbbC/p/HHnsMo9HIpk2byM/PL1bv4+PD9evX+de//mUuKygoYOvWrRiNRotk6l58//33Ftssz5w5w/fff0/Lli0ByM7OxsrKymKL665du7h+/XqJxwLFV/Dulq+vLz/88AMNGjTA09PT4qd58+bF2js4ONCtWzd69+5923dn3qm+tPn5+XH16lWsra2Lxe/p6cljjz1Gbm4uJpPJfGgS3Jyvkp5aKyIiIiLyqNCK5a9MmTKF4cOHM3z4cIYOHYqLiwv/+c9/sLGxYfDgwbRo0YJp06YxefJkateuzdq1azl9+jRz5swptRhq1KjBuHHjzFtDly5dSu3atenbty+A+ZCgGTNmMGjQIE6fPk1CQgLVq1cv8ViGDh1KrVq1qFatGps3b+aJJ57A1tYWg8FQ4jhHjhzJJ598wtChQxk+fDh169YlIyODo0ePYmtry8SJE1m7di0HDhygQ4cOuLq6cv78edavX28+ROhO9feTr68vXbp0YfTo0YSEhNC0aVNycnI4deoUR44cISYmBkdHRzw9PUlISMDZ2RlbW1vefvtti0RTRERERESUWFrw9vZm9erVxMTEMGvWLAAaNGjAhAkTqFy5MitWrODVV19l8eLF3LhxgyZNmrB8+XLz6zpKg7u7O08//TSLFi0iLS2NFi1aEBkZiZ2dHXDzwJsFCxYQFxfH2LFjady4MYsXLzaf4FqSscDNg4deeeUVlixZwnPPPUdubi67du0qcZzOzs4kJSWxdOlSXnvtNa5evYqLiwvu7u4MGzbMHOvnn3/O3//+d65evUrNmjUtTk69U/39FhMTw1tvvUVSUhKpqak4ODjQoEEDgoKCzG0WL17M3LlzmTVrFo6OjgwaNAhbW1sWLlz4QGIUEREREXkYWBUWFhaWdRBy07Bhw6hSpQpvvvlmWYciD1hOTg5Hjx7l3PGFmPIy7nyByCOk78itZR1CqUlJScHHx6esw6gwNJ+lR3NZujSfpUdzWXqK5rLoe6eHh4d58ao06BlLERERERERuSfaCivlUmFhocW7QW9VqVIlKlWqeH8X6f6Xd0r1L0ciFYEpP5fK1nq2WUREpDxTYlmOvPvuu2UdQrmxceNGZsyY8Zv1ffv2ZcGCBQ8wInnYaOtM6SnruVRSKSIiUv4psZRyqVOnTqxfv/43611cXB5gNCIiIiIi8nuUWEq55OLiouRRREREROQhUfEeUhMRAW2DLUWlPZem/NxS7U9ERETKnlYsRcqRDeuHk6fXjUgFN/y5bWUdgoiIiJQyrViKiIiIiIjIPVFiKaUqMDCQyMhI8+edO3fy3nvvlWFEIiIiIiJyv2krrJSquLg4qlWrZv68c+dOjh49ypAhQ8owKhERERERuZ+UWEqpat68eVmHICIiIiIiD5i2wpahQ4cOERISgo+PD0ajkX79+rFz504AMjIymDVrFu3bt6dFixb079+fvXv3Wlw/bNgwxowZw/bt2+nRowdeXl4MHjyYEydOWLQrLCwkMTGRnj174uHhga+vL+PHj+fy5csAnDp1ismTJ9OxY0datmxJjx49ePPNN8nPzzf30blzZ+bMmVNsDPHx8Xh7e5OTkwNYboWNiIhg48aNfP/99xgMBgwGAxEREezatQuDwcDp06ct+srOzqZVq1bEx8eXaP6Kxr9lyxa6d+9Oy5YtCQ4O5tKlS6SlpTFu3DiMRiPdu3dnx44dxa7/17/+xaBBg2jZsiVt2rRhxowZZGVlWcTzt7/9jaeffpqWLVvSqVMnZs6cSUaG5eE6RWNes2YNgYGBeHt7M2rUKC5cuFCicYiIiIiIPOy0YllGUlJSGDFiBB4eHkRGRuLs7MyxY8e4cOECJpOJ0aNHc/bsWSZPnkydOnVYs2YNY8aM4R//+Aft2rUz93P8+HGWL19OWFgY1tbWvPrqq4SGhrJlyxYqVbr5d4PIyEjWrl3L8OHDmT59OtnZ2ezbt4/MzExq1qzJpUuXePLJJ+nZsydVq1blxIkTxMbGkpmZybRp0wDo2bMnSUlJzJ49GxsbG/P9P/30U7p27YqdnV2xMY4fP5709HROnTrFokWLAKhevTp169aldu3a/POf/2TKlCnm9tu2beP69ev069evxPN4/Phxrly5wtSpU/nll1+Iiopi5syZXLt2jS5dujB06FDee+89wsPD+eyzz3B1dQVubtENDQ2lT58+jBs3jqtXrxITE0N4eDhvvfUWcDOxzMvLIywsjBo1avDTTz+RkJDAqFGjWL9+vUUcu3fv5tSpU7z00ktcv36d6OhoZsyYQWJiYonHIiIiIiLysFJiWUYWLlxIvXr1WL16NdbWN/8Z/Pz8ANi1axdHjhwhISGBgIAAAJ566il69epFfHy8RWKZlZXFhg0bqFmzprlswoQJfPfddzRr1ozTp0+zZs0awsLCGDdunLlNt27dzL+3bduWtm3bAjdXN318fCgoKCA2NpapU6diZWVFUFAQb775Jvv376djx44A/Oc//+HkyZNERETcdox//vOfqV69OufPn8fLy8uirn///qxbt44XXnjBPP7169fj7+/Pn/70pxLPY1ZWFhs3bqRGjRoAnDlzhri4OKZMmcKoUaMAaNasGe3bt+ezzz5j0KBBFBYWEh0dTffu3YmOjjb35ebmxsCBA/n6669p1aoVzs7OFgcR5efn06hRI/r06cO3336Lu7u7uc5kMvHmm2+aE+wrV64QHR1NVlaWxTOnIiIiIiIVkbbCloFffvmFw4cP07dvX3NS9Wtff/01Dg4O5qQSoFKlSvTo0YODBw9iMpnM5U2bNrVIKhs2bAjAxYsXAfjqq68oLCzkL3/5y2/Gk5OTQ2xsLN26dcPT0xN3d3deeeUVrl27Zt4u27hxY5o0acJHH31kvu7jjz+mRo0a+Pr63vUcDBgwgCtXrvD555//P/buPDrHa/3/+DuSoIIMJKmGEkMfJCEDQaIEQQ2psTWUlJgjoVExnpqKOBRpEoeG9mi0NR5OVWvWo6X4tVqNobSoasxTElPm/P6w8nw9YghCJD6vtbKW597TtXeyrFzZ+75vAE6ePMmPP/543zjvpmbNmsakEm4lhwCNGzc2XrOzs8Pa2tp4NPXEiRMkJCTQrl07MjIyjF+urq6ULl2a/fv3G9t++eWXdOrUCQ8PD1xcXOjYsaOxj9vVr1/fZNe2evXqwP99H0REREREijLtWBaA5ORksrKycHR0vGf57clijvLly5Oens6NGzcoU6YMANbW1iZ1co6p5tzzmJiYiIWFxV37yzFr1ixWrFhBcHAwbm5ulClThh9++IG5c+ca+wEICAhg/vz53Lx5k5IlS/LNN9/Qpk0bzM3NH24BACcnJ3x9fVm1ahX+/v785z//wc7OjmbNmj1UP/eaf8763H49LS0NgMuXYngwqwAAIABJREFULwMQEhJy1z5Pnz4NwObNmxk1ahRdu3Zl+PDh2NrakpycTP/+/U3W5X5x3FlPRERERKQoUmJZAMqWLUuxYsU4d+7cXcutra2NO4W3u3jxIpaWlpQqVSrPY9na2pKRkcHFixfvmVxu2LCBbt26MXjwYOO1n376KVe9du3aMWfOHLZt20aFChU4deoU7dq1y3Msd3rzzTd55513OHPmDGvWrKFDhw4m928+KTY2NgBMmDCBOnXq5CrP2QHdsGEDNWvWZNq0acayAwcOPPH4REREREQKGx2FLQAvvPACHh4e/Pe//zV58moOLy8vrl+/znfffWe8lpWVxYYNG/Dw8HioHcKGDRtiZmaW62Ezt0tNTaV48eLGz9nZ2axbty5XPScnJ9zd3fnqq6/4+uuvcXJywtPT877jW1pa3nPXrnnz5tjZ2REeHs65c+ce+hjso6patSoVKlTgr7/+ws3NLdfXSy+9BNx6eM/t6wKYHAUWEREREZFbtGNZQEaOHElgYCCBgYH06tULW1tbDh8+jKWlJT169KBOnTqMGjWKESNG4OjoyLJly/jzzz/v+sqP+6lSpQq9evUiKiqKpKQkfHx8SEtLY8eOHfTq1Ytq1arh4+PD8uXLqVq1KuXLl2fFihUkJSXdtb+AgAAiIiIoXbo0b7755gPHr1atGqtWrWLt2rU4Oztja2tLxYoVAbCwsKBTp07Exsbi5eVF1apVH2puj8rMzIxx48YRFhbGzZs38fPzw8rKijNnzrBjxw4CAwOpW7cuPj4+TJkyhejoaLy8vPjhhx/Ytm3bU4lRRERERKQw0Y5lAfH09OSzzz6jePHijB8/nuDgYNatW8dLL72Eubk5CxcupHnz5syePZuQkBDOnz/PggULjE9vfRjjx48nPDycbdu2MWTIEN577z0uXLhgciS0YcOGTJ8+nTFjxuDk5MTw4cPv2lebNm3Izs7mypUrtG/f/oFjd+3alTZt2jBt2jS6du1KTEyMSXnLli0BntpuZY5WrVqxaNEiTp48yciRIxk8eDCxsbHY2Njg5OQEQPfu3QkKCmLZsmUMHTqUY8eOERUV9VTjFBEREREpDMyys7OzCzoIeX7Nnz+fjz/+mO+//54XXnihoMMpMKmpqRw4cIDfD88kPT2xoMMReaIC+2ws6BAKzN69e/Hy8iroMIoMrWf+0VrmL61n/tFa5p+ctcz5vdPV1fWu76J/VDoKKwXi+PHjnDhxgsWLF/PGG28810mliIiIiEhhp8RSCsTEiRPZt28fvr6+DB06NFf53R5qdLu7vf+zKOjcNS5f/3Ik8izKzEjD3KL4gyuKiIhIoVE0fzuXZ96SJUvuW+7i4nLf8iNHjuRnOFIE6ehM/snvtVRSKSIiUvQosZRn0v1ejyIiIiIiIs8WJZbyTHJzcyvoEEREREREJI/0uhERKZJ0DPbxZWSmFXQIIiIiUkhox1LkGbLkv4Gk6XUj8owI7vX8vhZEREREHo52LEVEREREROSxKLGUPFu9ejUGg4HLly8/VLvffvuN6Ohobt68+YQiExERERGRgqTEUvLMz8+P5cuXU7Zs2Ydq99tvvxETE6PEUkRERESkiNI9lpJndnZ22NnZFXQYIiIiIiLyjClyO5b79u2jX79+eHl54eHhQefOndmyZYuxPDExkfHjx9OoUSPq1KlDly5d+P7770366N27N4MGDWLTpk20adMGd3d3evTowe+//25SLzs7m8WLF9OuXTtcXV3x8fEhODiYixcvAnD8+HFGjBiBn58fdevWpU2bNnz00UdkZGQY+2jRogUTJkzINY958+bh6elJamqqyVivvfYarq6u+Pn5MX/+fLKzs++7HmPGjKF9+/Z8//33BAQE4ObmRufOnfnll19M6mVlZbFgwQJatGiBq6srLVu2ZPHixSZ17jwKm5CQgMFgYO3atUydOhVvb298fHyYNGmSMe7Vq1czduxYABo1aoTBYKB58+YAXL16lQkTJvDqq6/i6urKq6++SmhoqMn65GVue/bsoWPHjtSpU4du3bpx7Ngxrl27xujRo/Hy8qJZs2Z88cUXudrHx8cTFBSEh4cHnp6ehIaGcvbsWZM6c+bMISAgAA8PDxo3bsywYcM4c+aMSZ28/ryIiIiIiBRVRWrHcu/evbz99tu4uroyZcoUbGxsOHTokDERyMzMZMCAAZw8eZIRI0bw4osvsnTpUgYNGsQnn3xCw4YNjX399ttvLFiwgOHDh2NhYcHMmTMJDQ1l/fr1FCt2Kx+fMmUKy5YtIzAwkNGjR5OSksKOHTtISkqifPnyXLhwgcqVK9OuXTtKly7N77//TnR0NElJSYwaNQqAdu3asXz5ct577z0sLS2N43/zzTe0bNmSEiVKADBjxgyWLl3KwIED8fT05ODBg0RHR1OsWDEGDRp033W5cOECEydOJDQ0lLJly7Jw4UL69evH5s2bKVeuHAAzZ87k008/ZeDAgdSvX5+dO3cSERHB9evXGTp06H37j4yMpEmTJsyZM4dDhw4RGRmJg4MDwcHB+Pn5MWTIEObPn8+iRYsoU6YMxYsXByAiIoLvvvuOd999l4oVK3LhwgW2b99OVlZWnr/nFy5cYOrUqQwePJgSJUoQERFBWFgYL730EgaDgaioKNavX8/kyZOpU6cOrq6uwK2k8q233sLX15cPPviAjIwMYmJi6NevH2vXrsXc3ByAS5cuMXDgQBwcHEhMTOTTTz+lZ8+erF+/npIlSz7Uz4uIiIiISFFVpBLLWbNmUalSJT777DMsLG5NzdfX11j+v//9j/j4eGJjY2natCkAr776Ku3bt2fevHkmiWVycjKrV6+mfPnyxmtDhw7lyJEj1KpViz///JOlS5cyfPhwhgwZYqzTqlUr478bNGhAgwYNgFs7jl5eXmRlZREdHU14eDhmZmYEBATw0UcfsXPnTvz8/AA4fPgwR48eZcyYMQD8/fffxMXF8d5779GzZ08AfHx8yM7O5qOPPqJ3796UKlXqnuuSmJhIZGQkjRo1AqB+/fo0bdqUTz/9lBEjRnD58mU+++wz+vbtS1hYGACNGzfm+vXrLFq0iD59+mBlZXXP/l1dXZk0aZKx3b59+9i4cSPBwcHY2dnx8ssvA+Di4mJylDY+Pp727dvTqVMn47W2bdvec5y7SUpKIi4uDoPBANz6vo0dOxZPT0/jXLy9vdm0aRMbN240JpazZs2iVq1azJ8/HzMzM+M8Wrduzbp16+jQoQMA06ZNM46VmZlp3JX97rvvTL7XD/p5EREREREpyorMVsrNmzf59ddf6dSpkzGpvNNPP/2ElZWVMakEKFasGG3atOGXX34hMzPTeL1mzZomSUK1atUAjEcld+/eTXZ2Nm+88cY9Y0pNTSU6OppWrVrh5uaGi4sL06dP5+rVq8bjsjVq1OCVV17hq6++MrZbt24d5cqVw8fHB4AffviB7OxsXnvtNTIyMoxfjRo14tq1a/z555/3XZsyZcoYk0qAsmXL0rBhQ/bt2wfcSvDS09NzJXXt2rXjxo0b/Pbbb/ftv3Hjxiafq1evnutI6d3Url2bNWvWsGjRIg4fPvzAY713Y29vb0wqAapUqZIrJktLS5ycnIw71ykpKezdu5e2bduSmZlpXE9HR0ecnZ3Zv3+/se327dvp3r079erVo3bt2jRs2JCsrCxOnDhhEseDfl5ERERERIqyIrNjmZycTFZWFo6Ojvetc/sv/znKly9Peno6N27coEyZMgBYW1ub1Mk5pppz72BiYiIWFhZ37S/HrFmzWLFiBcHBwbi5uVGmTBl++OEH5s6da+wHICAggPnz53Pz5k1KlizJN998Q5s2bYzHMS9fvkx2drZJcni7M2fO4OLics847vbAnXLlyhmTo6SkJOBWknbnuuTM9X7ufEqspaUlaWlp920D8N5772FjY8PixYuZNWsWjo6O9OvXj7fffvuBbXPc6/uU8328W0xJSUlkZmYSERFBRERErj4rVaoE3Eq4c47z9u/fn3LlymFhYUGPHj1Mvn/3i+POeiIiIiIiRVGRSSzLli1LsWLFOHfu3D3rWFtbG3cKb3fx4kUsLS3ve5z0Tra2tmRkZHDx4sV7JpcbNmygW7duDB482Hjtp59+ylWvXbt2zJkzh23btlGhQgVOnTpFu3btTOI2MzPjiy++MLkPM0fOUdN7udt7Jy9dumRMJG1sbIBb63B7Yp6zVjnl+a1MmTKMGzeOcePGcfToUb744gumT5+Os7MzTZo0eSJj5oxrZmbGoEGD8Pf3z1Wekyhv2bKF0qVLExUVZUzyr1y5Qnp6+hOLTURERESkMCoyR2FfeOEFPDw8+O9//3vPp4p6eXlx/fp1vvvuO+O1rKwsNmzYgIeHhzF5yIuGDRtiZmbGqlWr7lknNTXV+KAauHWf5bp163LVc3Jywt3dna+++oqvv/4aJycnPD09jeU5O5WXL1/Gzc0t19edu2V3unr1Krt27TL5vHv3burWrQuAm5sblpaWrF+/3qTdN998Q6lSpahdu/Z9+3+QnGT4fruY1atXZ9y4cRQrVoxjx4491ngPUqpUKTw8PDh69Ohd17Ny5crArSOzFhYWJg/fuf3IsoiIiIiI3FJkdiwBRo4cSWBgIIGBgfTq1QtbW1sOHz6MpaUlvXr1ws/Pjzp16jBq1ChGjBiBo6Mjy5Yt488//7zrKz/up0qVKvTq1YuoqCiSkpLw8fEhLS2NHTt20KtXL6pVq4aPjw/Lly+natWqlC9fnhUrVhiPnd4pICCAiIgISpcuzZtvvmlS5uzsTO/evRk9ejR9+/bFw8ODzMxM/v77b7Zs2cK///3v+8ZqY2PD+PHjTZ4KCxiPnNrZ2dG7d28++eQTihcvjqenJ7t27WL58uWEhoY+1E7u3eTcb7hkyRJatWpFyZIlMRgMdO/eHX9/f2rUqIGlpSXr1q2jWLFieHt7P9Z4eTF69GgCAwMZNmwY7du3x9ramvPnz7Nnzx78/Pzw9/fH19eXTz/9lMmTJ9O6dWv279/PihUr7rprLCIiIiLyPCtSiaWnpyefffYZkZGRjB8/HoCqVasaX5dhbm7OwoULmTlzJrNnz+bGjRu88sorLFiwwPj01ocxfvx4nJycWLZsGUuWLKFs2bJ4enoaj45OmDCBiRMnMn36dIoXL05AQACtW7cmPDw8V19t2rRh+vTpXLlyhfbt2991rKpVq7Js2TI++ugjSpYsycsvv0yzZs0eGKe9vT3h4eHMnDmTv/76ixo1arBo0SKTI7zh4eGULVuWlStXEhsby4svvmhMZB9X7dq1CQ0NZeXKlXzyySdUqFCBbdu24enpydq1a/n7778pVqwYNWrU4F//+td97xfNL+7u7ixdupTo6GjGjx9PSkoKjo6OeHt7U716dQCaNm1KeHg4S5YsYc2aNdSpU4f58+fnSvxFRERERJ53ZtmP8ihOKTTGjBnDgQMH7noEV54dqampHDhwgF+OziQt/f4PSxJ5WoJ7bQRuvSPYy8urgKMpGrSW+UvrmX+0lvlL65l/tJb5J2ctc37vdHV1pUSJEvnWf5G5x1JEREREREQKRpE6CitFR2Zm5n3fa3mvd5UWdr07xuXrX45EHkdGZhoW5sUfXFFERESee0Xzt3MxmjFjRkGH8EhatmzJqVOn7lm+detWKlas+BQjksJGR2cen5JKERERySsllvJMmj9//n1fT+Lg4PAUoxERERERkftRYinPJIPBUNAhiIiIiIhIHunhPSJSJOkYbN5lZN77dICIiIhIXmjHUuQZMu+rQFIy9LoRebrGdd9Y0CGIiIhIIacdSxEREREREXksSiwlX0RHR/Pzzz8XdBgiIiIiIlIAlFhKvoiJieGXX34p6DBERERERKQAKLGUpyolJaWgQxARERERkXymxPIpOXbsGAMHDqRBgwbUrVuXVq1aERMTw9atWzEYDPz5558m9VNSUqhXrx7z5s0Dbh019fDw4PDhw/To0YM6deoQEBDAzz//THp6OhERETRs2JDGjRsTFRVFdna2sa/HaQtw/PhxQkJCqF+/PnXr1qVv37788ccfxvKcV4PMnDkTg8GAwWBgz549xrLY2Fjmzp1L48aN8fLyyvOcH6R3794MGjSI9evX07p1a+rWrUtQUBAXLlzg/PnzDBkyBA8PD1q3bs3mzZtztf/uu+/o3r07devWxdvbm7Fjx5KcnGwSz/vvv89rr71G3bp1adasGePGjSMx0fThOs2bN2fKlCksXbqU5s2b4+npSf/+/Tlz5kye5iEiIiIiUtgpsXxKBg8eTFJSEtOmTeOjjz5iwIABpKSk4Ofnh6OjI//5z39M6m/cuJHr16/TuXNn47X09HTCw8Pp3Lkz0dHRWFpaEhISwsSJE0lJSWH27Nl06dKFefPm5UqkHrVtQkICPXr04OLFi0ydOpW5c+eSnp5OYGAgV69eBWD58uXArURv+fLlLF++HBcXF2MfcXFxHDlyhPfff585c+Y81Jwf5LfffuPjjz8mPDycqVOncvDgQcaNG8ewYcPw8vIiJiaGatWqERYWxvnz543ttmzZwqBBg3B2diYqKopx48axa9cuwsLCjHVSUlJIT09n+PDhLFy4kBEjRrB//3769++fK45vv/2WjRs38o9//IPJkydz6NAhxo4dm+d5iIiIiIgUZnrdyFNw+fJlTp48ydixY2nevDkADRs2NJZ36dKFlStX8s4772BhcetbsmrVKho3bkyFChWM9dLT03n33Xfx8/MDoGTJkgQGBnLhwgUWLlwIgK+vL1u3bmXDhg20atXqsdvGxMRgZWXF4sWLKVmyJADe3t74+/uzZMkSgoODcXd3B6BChQrGf9+uTJky/Otf/6JYsf/7O0Ze5/wgycnJrFmzhnLlygFw4sQJYmJiGDlypDEBrFWrFo0aNWLbtm10796d7OxsIiIiaN26NREREca+qlSpQrdu3fjpp5+oV68eNjY2TJkyxViekZFB9erV6dixIwcPHjRJnjMzM/noo48oUaIEAJcuXSIiIoLk5GTKli2b5/mIiIiIiBRG2rF8CmxtbXFycmLOnDmsXr061xHJrl27cunSJf73v/8BcPLkSX788UfeeOMNk3rFihXDx8fH+LlKlSrArYTwdlWqVMk1xqO23bFjBy1atMDCwoKMjAwyMjIoWbIk7u7uxMfH52n+fn5+Jknlw8z5QWrWrGlMKm+fV+PGjY3X7OzssLa2Ns7rxIkTJCQk0K5dO+OcMjIycHV1pXTp0uzfv9/Y9ssvv6RTp054eHjg4uJCx44djX3crn79+sakEqB69eoAnD179qHmIyIiIiJSGGnH8ikwMzPj448/JjIykvfff58bN25Qs2ZNxowZQ6NGjXBycsLX15dVq1bh7+/Pf/7zH+zs7GjWrJlJPyVLlqR48eLGz5aWlgC5dsQsLS1JTU3Nl7ZXrlwhLi6OuLi4XPOqWbNmnuZ/e+KXI69zfhBra+tc8cOtXdI7r6elpQG3dpABQkJC7trn6dOnAdi8eTOjRo2ia9euDB8+HFtbW5KTk+nfv3+u9b1XHHfWExEREREpipRYPiXOzs58+OGHZGRksG/fPqKiohgyZAjffvsttra2vPnmm7zzzjucOXOGNWvW0KFDB2NyUpCsra1p2rQpPXv2zFWWczT2QczMzO56vaDmbGNjA8CECROoU6dOrvKcRHjDhg3UrFmTadOmGcsOHDjwxOMTERERESlslFg+ZRYWFtSrV48hQ4bQp08fTp8+ja2tLc2bN8fOzo7w8HDOnTv30EdCnxQfHx9+//13ateujbm5+T3r3W2X9EEKas5Vq1alQoUK/PXXX7z11lv3rJeSkmKyywvw1VdfPenwREREREQKHSWWT8Hhw4eZMWMGbdu2pVKlSty4cYNFixbh4OBgvBfPwsKCTp06ERsbi5eXF1WrVi3gqG8ZPnw4Xbt2pW/fvnTr1g17e3suXrzIL7/8grOzs3Ens2rVqmzZsoV69erxwgsv4OzsTOnSpe/bd0HN2czMjHHjxhEWFsbNmzfx8/PDysqKM2fOsGPHDgIDA6lbty4+Pj5MmTKF6OhovLy8+OGHH9i2bdtTiVFEREREpDDRw3ueAnt7exwcHIiNjWXQoEH84x//wM7OjsWLF5s88KVly5YAz8xuJUClSpVYuXIl9vb2TJ06laCgIGbNmsXFixdxc3Mz1pswYQIAAwYMoGvXrhw8eDBP/RfUnFu1asWiRYs4efIkI0eOZPDgwcTGxmJjY4OTkxMA3bt3JygoiGXLljF06FCOHTtGVFTUU41TRERERKQwMMvOzs4u6CDklvnz5/Pxxx/z/fff88ILLxR0OE/F8zjnu0lNTeXAgQNs/3MmKRmJBR2OPGfGdd943/K9e/fi5eX1lKIp2rSW+UvrmX+0lvlL65l/tJb5J2ctc37vdHV1Ndnkelw6CvsMOH78OCdOnGDx4sW88cYbz0WC9TzOWURERESkqFJi+QyYOHEi+/btw9fXl6FDhxZ0OE/Fg+ackZFx3/YWFkXzR3doQFy+/uVIJC8yMtOwMC/+4IoiIiIi91A0fzsvZJYsWVLQITx1D5qzi4vLfcuPHDmSn+FIEaSjM3mnpFJEREQelxJLeSatWrWqoEMQEREREZE8UmIpz6TbnzgrIiIiIiLPNr1uRERERERERB6LEksRKZKeh/sr0zPTCjoEEREREUBHYUWeKZM2vM11vcdS8iiqy4aCDkFEREQEeAZ2LBcvXkyzZs2oVasWvXv3firjbd++/ZHarl69GoPBwOXLlx8rht69ezNo0KB877cg3DkXERERERF5/hTojuWxY8eIiIhgwIABNG/enLJlyz7xMePi4vDz86Np06ZPfKy88vPzY/ny5U9l/vlt4sSJFCtW4H+fEBERERGRAlSgieWff/4JQLdu3ahUqdJd66SkpFCyZMmnGdZTZ2dnh52dXUGH8UiqV69e0CGIiIiIiEgBK7CtpjFjxjB06FAA/P39MRgMxiOha9asYeLEiTRo0ICAgAAAtm/fTlBQEI0aNcLDw4MuXbqwZcuWXP2eO3eOMWPG4Ovri5ubG61atWL+/PkANG/enFOnTvH5559jMBiMYwJ8+eWX9OzZkwYNGlC/fn169uzJTz/99Njz3LdvH126dMHNzY02bdqwadOmXHXuPAqbkJCAwWDgyy+/ZMqUKXh7e9OgQQNiYmIA2Lp1K+3atcPDw4N+/fpx/vx5k/7S0tKIjIykefPmuLq60rp1a5YvX25SZ8yYMbRv354ff/yRTp06UbduXTp27MiPP/5oUu/bb7+lS5cueHh44OXlRadOnUzmcLejsD/99BM9e/akTp06eHt78+6773LhwgVjec781q5dy9SpU/H29sbHx4dJkyaRmpqa57XNmcOePXvo2LEjderUoVu3bhw7doxr164xevRovLy8aNasGV988UWu9vHx8QQFBeHh4YGnpyehoaGcPXvWpM6cOXMICAjAw8ODxo0bM2zYMM6cOWNSJ2cNNm3aRJs2bXB3d6dHjx78/vvveZ6LiIiIiEhhVmA7lsHBwTg7OzNnzhxiYmKwt7c3HqmcPXs2r776Kh988AGZmZkAnDp1iqZNm9K3b1/Mzc3ZuXMnISEhLFiwAD8/PwCuXLlCt27dyMrKIjQ0lMqVK5OQkMDhw4cBiImJYeDAgXh6ehIUFATAyy+/bOz/9ddfp3LlyqSnp7Nhwwbefvtt/vOf/1CzZs1HmuOlS5cICgqiWrVqzJ07lxs3bjBz5kxSUlJwcXF5YPvIyEhatGjB3Llz+f7774mOjubGjRvs2rWL4cOHk5mZybRp05gwYQILFiwwthsxYgR79uxh6NChvPLKK+zevZtJkyZhZWVF+/btjfUuXLjAlClT6NevH7a2tsTExDB06FC2bdtG6dKlOXnyJKGhobRr144RI0aQnZ3NkSNHSEpKumfMBw4coE+fPnh5eTF37lySk5OZPXs2ffr0YfXq1ZQoUcJkfk2aNGHOnDkcOnSIyMhIHBwcCA4OzvMaX7hwgalTpzJ48GBKlChBREQEYWFhvPTSSxgMBqKioli/fj2TJ0+mTp06uLq6AreSyrfeegtfX18++OADMjIyiImJoV+/fqxduxZzc3Pj93DgwIE4ODiQmJjIp59+Ss+ePVm/fr3JTvpvv/3GggULGD58OBYWFsycOZPQ0FDWr1+vo8IiIiIiUuQVWGL58ssvU7lyZQBq1apFxYoVSUhIAOCVV14hIiLCpH7Pnj2N/87KysLb25u//vqLZcuWGRPLxYsXc/HiRdavX3/Xo7W1a9emePHilC9fHnd3d5Oy25OZrKwsfHx8OHz4MKtWreIf//jHI81x8eLFZGdns2jRIqytrQGoVq0anTt3zlN7Nzc349i+vr5s2rSJuLg4tmzZwosvvgjA6dOnmTVrlvHI8J49e9i8eTOxsbHG+0h9fHxITEzkww8/NEksk5KSiIuLw2AwAODg4EDHjh3ZvXs3/v7+HDp0iPT0dN577z1Kly4NQOPGje8b84IFCyhXrhwLFy6kePHiAFSpUoXu3bvz9ddfm8zd1dWVSZMmGfvdt28fGzdufKjE8s45JCcnM3bsWDw9PQkLCwPA29ubTZs2sXHjRmNiOWvWLGrVqsX8+fMxMzMzxtO6dWvWrVtHhw4dAJg2bZpxrMzMTOPu6nfffUerVq2MZcnJyaxevZry5csbrw0dOpQjR45Qq1atPM9HRERERKQweia3Upo3b57r2tmzZxkzZgxNmjTBxcUFFxcXNm/ezIkTJ4x1du3aRcOGDe95v+b9HDt2jJCQEHx9falVqxYuLi4cPHjQeB/oo9i3bx8NGzY0JpUALi4uVKxYMU/t70ziqlSpQrVq1YxJZc617Oxszp07B8DOnTuxtrbG19eXjIwM45fCyuQOAAAgAElEQVSPjw8nT54kMfH/XmVhb29vTMjgVtILGI+DGgwGzM3NGTlyJFu3biU5OfmBMf/000/4+/sbk0oADw8PnJycch0tvnN+1atXz3UU9UHunEOVKlVy9W1paYmTk5PxCGtKSgp79+6lbdu2ZGZmGtfI0dERZ2dn9u/fb2y7fft2unfvTr169ahduzYNGzYkKyvL5OcOoGbNmiZJ5Z1rKSIiIiJSlD2T77EsV66cyeesrCyGDBlCUlISISEhVK5cmVKlShEbG8vBgweN9RITE6lRo8ZDj3ft2jWCgoKwsbEhPDycihUrUqJECaZNm0Za2qO/gPzChQvGXdnb3Z6A3M+dT4m1tLS86zXAeG/i5cuXSUpKuudR2zNnzmBjYwNgkvACxmQwpy9nZ2cWLFhAbGwsw4YNA27tnL733nv3TN6Tk5PvOr/y5cvnOkJ7t7k87HrfOYec9ShTpsw9+05KSiIzM5OIiIhcO+OAcW7x8fEEBwfj5+dH//79KVeuHBYWFvTo0SPXvaD3iuNh7hkVERERESmsnsnEMudoYo6//vqLQ4cOMW/ePPz9/Y3X09PTTerZ2toad+4exr59+zh79iwLFiwwObZ4/fp1YxL2KOzt7bl06VKu6xcvXnysfu/H2toaW1tbFi5ceNfynB29vGrSpAlNmjTh+vXr7Ny5kxkzZvDuu++yYsWKe45/rzk/K0+QLVOmDGZmZgwaNMjk5ylHTsK7ZcsWSpcuTVRUlPGeyytXruT6uRMRERERed49k0dh75Sz65OzCwS3EpVdu3aZ1GvUqBG7d+/m77//vmdflpaWuXaRUlJSAEyObx4+fJg//vjjseKuW7cuu3fvNtmpO3jwoPFe0ifB19eXK1euYGFhgZubW66vF1544ZH6tbKyolWrVnTo0IGjR4/es56XlxdbtmwxSb5+/fVXTp06Rb169R5p7PxWqlQpPDw8OHr06F3XKGeXOSUlBQsLC5OH73z11VcFFbaIiIiIyDPrmdyxvFPVqlV58cUXjU+JTUlJ4V//+hfly5cnOzvbWK9Pnz6sXbuW3r17ExwczMsvv8zp06c5dOiQ8SE4VatWZdeuXezYsQNra2sqVqyIu7s7pUqVYtKkSQwcOJBLly4RFRVlci/jo+jTpw+ff/45/fv3Z9CgQdy4cYOoqKg8H4V9FD4+Pvj7+zNgwAD69etHzZo1SU1N5fjx48THxxMZGZnnvpYtW8bPP/9MkyZNcHBw4PTp06xatQpfX997thk8eDDdu3dn4MCBBAYGkpSUxOzZs6levTrt2rXLjynmi9GjRxMYGMiwYcNo37491tbWnD9/nj179uDn54e/vz++vr58+umnTJ48mdatW7N//35WrFhh8gcOEREREREpJDuWxYsXJyYmhpIlSxIWFsbcuXPp3bu38amnOWxsbFi6dCne3t7Mnj2bAQMG8NFHH+Hg4GCsM2LECCpUqMCwYcPo2rUr3377LeXLlycqKorExESGDh3KokWLGDt2rMlDYR5F+fLl+fjjj8nMzOSdd95h3rx5vPvuu1StWvWx+n2QyMhIevXqxfLlyxkwYACjR49m8+bNeHt7P1Q/BoOB5ORk/vnPfxIUFERkZCQtW7Zk+vTp92zj6urKv//9b27evMnw4cOZNm0a3t7eLF682ORVIwXN3d2dpUuXkpaWxvjx4+nfvz8ffvghWVlZxiO7TZs2JTw8nG+//ZbBgwfz/fffM3/+fCWWIiIiIiJ3MMu+fctPRApEamoqBw4cYFXCLK5nJD64gQgQ1WXDUxln7969eHl5PZWxijqtZf7SeuYfrWX+0nrmH61l/slZy5zfO11dXfN146dQ7FiKiIiIiIjIs6tQ3GP5LMrKyiIrK+ue5ebm5rmebit5l5mZyf020y0siuaP7qTXPn2mjgzLsy09Mw1L8+IPrigiIiLyhBXN386fgnHjxrFmzZp7lkdERNC5c+enGFHR0rJlS06dOnXP8q1bt1KxYsWnGJEUNs/D0RkllSIiIvKsUGL5iEJCQnjrrbfuWa6k5/HMnz+ftLS0e5bf/kAmEREREREpWEosH1HFihWVPD5Bj/tEXhEREREReXr08B4RERERERF5LEosRaRQScu89xFpERERESkYOgor8gzpszmMxMzkgg7jmba+w2cFHYKIiIiI3EE7lvJM2rNnDwaDgf379xuvNW/enClTphRgVCIiIiIicjfasZRCIyYmhrJlyxZ0GCIiIiIicgcllvJEZGdnk5aWRokSJfKtz9q1a+dbXyIiIiIikn90FFbyxZgxY2jfvj07duygU6dOuLm5sX79et5//31ee+016tatS7NmzRg3bhyJiYkmbTMyMpgxYwYNGjTA09OTkSNHcvXq1Vxj3HkUtnfv3gwaNMikTkJCAgaDgQ0bNhivffvtt3Tp0gUPDw+8vLzo1KkTmzZtyvPcFi1aRKtWrXBzc6NBgwb07t2bY8eOGcvT0tKIjIykefPmuLq60rp1a5YvX57n/kVERERECjvtWEq+OX/+PBMnTmTIkCFUrFiR4sWL8/PPPzN8+HDKlSvHuXPniI2NpX///qxatcrYbu7cuSxZsoTg4GDq1KnDli1beP/99/MlppMnTxIaGkq7du0YMWIE2dnZHDlyhKSkpDy1/+9//8vcuXMZNmwY7u7uXLt2jV9++YVr164Z64wYMYI9e/YwdOhQXnnlFXbv3s2kSZOwsrKiffv2+TIPEREREZFnmRJLyTdJSUksWLAAT09P47Xb/52RkUH16tXp2LEjBw8exMXFhaSkJD7//HP69+/P0KFDAXj11VcZPHgwZ8+efeyYDh06RHp6Ou+99x6lS5cGoHHjxnluHx8fj8FgMNkZbdGihfHfe/bsYfPmzcTGxtK0aVMAfHx8SExM5MMPP1RiKSIiIiLPBR2FlXxjY2NjkkgCfPnll3Tq1AkPDw9cXFzo2LEjACdOnADgyJEj3Lx5k9atW5u0e+211/IlJoPBgLm5OSNHjmTr1q0kJz/cqzxq167NoUOHmD59Oj/++CNpaabvUNy5cyfW1tb4+vqSkZFh/PLx8eHkyZO5jv2KiIiIiBRF2rGUfFO+fHmTz5s3b2bUqFF07dqV4cOHY2trS3JyMv379yc1NRWACxcuAFCuXDmTtnd+flTOzs4sWLCA2NhYhg0bBoCvry/vvfcelSpVemD7zp07c+PGDVasWMGnn36KlZUVr7/+OqNGjaJUqVJcvnyZpKQkXFxc7tr+zJkz2NjY5MtcRERERESeVUosJd+YmZmZfN6wYQM1a9Zk2rRpxmsHDhwwqWNvbw/ApUuXcHR0NF6/dOnSA8crXrw46enpJtfudu9kkyZNaNKkCdevX2fnzp3MmDGDd999lxUrVjxwjGLFihEYGEhgYCDnz59n48aNzJw5k9KlSzNy5Eisra2xtbVl4cKFd21fpUqVB44hIiIiIlLY6SisPDEpKSkUL17c5NpXX31l8tlgMFCyZEk2btxocv32p7reS4UKFTh+/DhZWVnGazt27LhnfSsrK1q1akWHDh04evRoXqZgwsHBgd69e1OnTh1je19fX65cuYKFhQVubm65vl544YWHHkdEREREpLDRjqU8MT4+PkyZMoXo6Gi8vLz44Ycf2LZtm0kda2tr3nrrLRYtWkSJEiWMT4U9dOjQA/tv06YNK1euZPLkybRu3Zr9+/ezevVqkzrLli3j559/pkmTJjg4OHD69GlWrVqFr69vnuYwYcIEypQpg7u7O9bW1vz666/8+uuvjB492jhHf39/BgwYQL9+/ahZsyapqakcP36c+Ph4IiMj87haIiIiIiKFlxJLeWK6d+9OQkICy5Yt45NPPqFhw4ZERUUZH+CTIywsjIyMDBYvXkx6ejrNmjXjvffeIyQk5L79+/r6MnbsWOLi4vjyyy9p2LAhM2bMoHv37sY6BoOB//3vf/zzn//kypUrlC9fnpYtWxIWFpanOXh4eLBy5UpWrVrFzZs3cXJy4t1336V3797GOpGRkXz88ccsX76chIQErKysqFq1KgEBAQ+xWiIiIiIihZdZdnZ2dkEHIfK8S01N5cCBA3xw5mMSMx/uybXPm/UdPstTvb179+Ll5fWEo3k+aC3zj9Yyf2k984/WMn9pPfOP1jL/5Kxlzu+drq6ulChRIt/61z2WIiIiIiIi8lh0FFaeW9nZ2WRmZt6zvFixYhQr9nT/9rK45dx8/ctRUZSWmUZx8+IPrigiIiIiT40SS3lurVmzhrFjx96zvFOnTsyYMeMpRiR5oaRSRERE5NmjxFKeW82aNWPVqlX3LLe1tX2K0YiIiIiIFF5KLOW5ZWtrq+RRRERERCQf6OE9IiIiIiIi8liUWIrIMyktM72gQxARERGRPNJRWJFnSN9Ns0nMvFHQYTwTvuk4taBDEBEREZE80o6l5MmWLVv4/PPPn0jfCQkJREdHc+7cuSfSv4iIiIiIPFlKLCVPtmzZwtKlS59I36dOnSImJobz588/kf5FREREROTJUmIpRVJqampBhyAiIiIi8txQYikPNGbMGNasWcMff/yBwWDAYDAwZswYAOLj4wkKCsLDwwNPT09CQ0M5e/assW1ISAhNmzYlOTnZeG379u0YDAa++uor9uzZQ2BgIABdu3Y19g+wevVqDAYDly9fNomnd+/eDBo0yPg5OjoaDw8PDhw4QI8ePahTpw6LFi0C4Pjx44SEhFC/fn3q1q1L3759+eOPP/I89/T0dGbNmkWzZs1wdXXF19eXAQMGcOnSJWOda9euMXXqVF599VVcXV0JCAhgy5YteR5DRERERKSw08N75IGCg4O5fPkyx48f54MPPgDAzs6O+Ph43nrrLXx9ffnggw/IyMggJiaGfv36sXbtWszNzZkyZQqvv/46kydPZvbs2Vy+fJnx48fTtm1bAgICuHbtGhMmTGDKlClERERQtWrVR4oxPT2dd955h8DAQN555x1Kly5NQkICPXr0wNnZmalTp2Jpacknn3xCYGAgmzZtokyZMg/sNzY2li+++IKRI0dSo0YNEhMT2bVrFykpKcZxg4KCOHv2LKGhoTg5ObFx40ZCQ0P57LPP8PLyeqT5iIiIiIgUJkos5YFefvll7OzsOH36NO7u7sbr48ePp1atWsyfPx8zMzMAXF1dad26NevWraNDhw7Y2dkxbdo0Bg4cSIsWLfjmm28oVqwYkyZNAqB06dJUr14dgBo1auDm5vZIMaanpzN8+HACAgKM18aMGYOVlRWLFy+mZMmSAHh7e+Pv78+SJUsIDg5+YL/x8fE0btyYt956y3itVatWxn9/9dVXHDhwgDVr1hh3Wn19fTl16hTR0dEsXrz4keYjIiIiIlKY6CisPJKUlBT27t1L27ZtyczMJCMjg4yMDBwdHXF2dmb//v3Guk2bNqV79+6MHj2aLVu2EBERgbW1db7H1KxZM5PPO3bsoEWLFlhYWBjjK1myJO7u7sTHx+epz9q1a7N9+3aioqKIj48nMzPTpHznzp288sorVKtWzThGRkYGvr6+eR5DRERERKSw046lPJKkpCQyMzOJiIggIiIiV3mlSpVMPgcEBLBs2TKqV6+Oj49PvsfzwgsvULp0aZNrV65cIS4ujri4uFz1a9asmad+hwwZgrm5OWvWrGHevHnY2NjQo0cPQkNDMTc35/Lly/z222+4uLjctX1ycjJly5Z9+AmJiIiIiBQiSizlkZQpUwYzMzMGDRqEv79/rvLbk6nU1FQmTJiAwWDg6NGjxMXF8fbbbz9wjBIlSgC3jrneLjExkVKlSplcyzmKeztra2uaNm1Kz549c5XlHI19kOLFixMSEkJISAh///03X375JTExMTg6OtKjRw+sra0xGAxMmzbtru3vjFNEREREpChSYil5YmlpafIKj1KlSuHh4cHRo0cJCwu7b9tZs2Zx7tw51q5dy6pVq5g9ezaNGzemWrVqxr4h9ytCXnzxRQCOHTuGo6MjcOudl3/++ScvvfTSA2P28fHh999/p3bt2pibm+d9svdQqVIlQkJCWLVqFceOHQNu3U+5fft2HBwcjDGKiIiIiDxvlFhKnlSrVo1Vq1axdu1anJ2dsbW1ZfTo0QQGBjJs2DDat2+PtbU158+fZ8+ePfj5+eHv78/OnTv57LPPmDFjBk5OTgQHB7N9+3bCw8NZvnw5lpaWVKlSBXNzc1auXIm5uTkWFha4ublRt25dnJycmD59OiNGjCAlJYXY2FhsbGzyFPPw4cPp2rUrffv2pVu3btjb23Px4kV++eUXnJ2d77qTeafg4GBq165N7dq1sbKy4vvvv+fMmTM0atQIgA4dOrBixQp69epFUFAQVatW5erVq/z+++9cuHCBiRMnPta6i4iIiIgUBkosJU+6du1KfHw806ZNIzExkU6dOjFjxgyWLl1KdHQ048ePJyUlBUdHR7y9valevTpJSUmMHTuWli1b0rFjR+DW7uSsWbPo1KkT8+bN45133sHOzo4JEyawaNEi1q1bR0ZGBkeOHMHCwoJ58+YxadIkwsLCeOmllwgPD+ff//53nmKuVKkSK1eu5MMPP2Tq1KlcvXoVe3t73N3def311/PUh5eXF+vXr2fx4sVkZmZSuXJl/vnPf9KiRQvg1lHZxYsXM2/ePBYuXMj58+extrbmlVde4Y033ni0xRYRERERKWTMsrOzsws6CJHnXWpqKgcOHGD26Y0kZt4o6HCeCd90nPpY7ffu3av3iOYTrWX+0VrmL61n/tFa5i+tZ/7RWuafnLXM+b3T1dXV+EyT/KDXjYiIiIiIiMhj0VFYeW5lZWWRlZV1z/JixYpRrNjT/dvLv1u9m69/OSrM0jLTKW5uWdBhiIiIiEgeKLGU59a8efOIiYm5Z3lISAihoaFPMSK5nZJKERERkcJDiaU8t9588038/PzuWe7g4PD0ghERERERKcSUWMpzy9HRUe+eFBERERHJB3p4j4iIiIiIiDwWJZYi8sxJy8wo6BBERERE5CHoKKzIMyRowyISM1MKOowC93Xndws6BBERERF5CNqxlEfWvHlzpkyZ8tTGS05OJjo6mqNHjz61MUVERERE5MG0YymPLCYmhrJlyz618ZKTk4mJiaFGjRpUr179qY0rIiIiIiL3p8RSHlpKSgolS5akdu3aBR3KY8nOziYtLY0SJUoUdCgiIiIiIoWajsIWUWPGjKF9+/Z8//33BAQE4ObmRufOnfnll19M6n355Zd06NABNzc3fH19iYiIIC0tzVi+evVqDAYDv/76K/3798fDw8N4/PXOo7A5Y+7Zs4eOHTtSp04dunXrxrFjx7h27RqjR4/Gy8uLZs2a8cUXX+SKOT4+nqCgIDw8PPD09CQ0NJSzZ88CkJCQQIsWLQAYPnw4BoMBg8FAQkICAGlpaURGRtK8eXNcXV1p3bo1y5cvv+ua7Nixg06dOuHm5sb69esfuJbnzp0jLCwMHx8f3NzcaN68ORMmTDCpc/z4cUJCQqhfvz5169alb9++/PHHHw/sW0RERESkKNCOZRF24cIFJk6cSGhoKGXLlmXhwoX069ePzZs3U65cOeLi4pgxYwa9e/cmPDycv//+m7lz53Lz5s1c906GhYXRtWtX+vXrd98dvgsXLjB16lQGDx5MiRIliIiIICwsjJdeegmDwUBUVBTr169n8uTJ1KlTB1dXV+BWUvnWW2/h6+vLBx98QEZGBjExMfTr14+1a9fi4OBATEwMISEhjBgxggYNGgDg4OAAwIgRI9izZw9Dhw7llVdeYffu3UyaNAkrKyvat29vjO/8+fNMnDiRIUOGULFiRezt7R+4jqNGjeLcuXP84x//oHz58pw5c4a9e/cayxMSEujRowfOzs5MnToVS0tLPvnkEwIDA9m0aRNlypTJ+zdNRERERKQQUmJZhCUmJhIZGUmjRo0AqF+/Pk2bNuXTTz9l4MCBREZG0rdvX8LDw41typYtS3h4OAMHDqRixYrG62+++SaDBw9+4JhJSUnExcVhMBiAW/dFjh07Fk9PT8LCwgDw9vZm06ZNbNy40ZhYzpo1i1q1ajF//nzMzMwAjDuP69ato0OHDtSqVQuAypUr4+7ubhxzz549bN68mdjYWJo2bQqAj48PiYmJfPjhhyaJZVJSEgsWLMDT0zPP6xgfH8+IESNo27at8VqHDh2M/46JicHKyorFixdTsmRJ4xz9/f1ZsmQJwcHBeR5LRERERKQw0lHYIqxMmTLGpBJuJY0NGzZk37597Nu3j+vXr9O2bVsyMjKMX40aNSIzM5NDhw6Z9NW8efM8jWlvb29MKgGqVKkCQOPGjY3XLC0tcXJy4syZM8Ctezb37t1L27ZtyczMNMbi6OiIs7Mz+/fvv++YO3fuxNraGl9fX5O5+Pj4cPLkSRITE411bWxsHiqpBKhduzaffPIJn3/+OSdOnMhVvmPHDlq0aIGFhYVx7JIlS+Lu7k58fPxDjSUiIiIiUhhpx7IIs7Ozy3WtXLlynDhxgsuXLwPQuXPnu7Y9ffq0yefy5cvnaUxra2uTz5aWlgC5joNaWloa7+VMSkoiMzOTiIgIIiIicvVZqVKl+455+fJlkpKScHFxuWv5mTNnsLGxeah53G7u3LlERkYSFRXFlClTqFy5MsOHD6ddu3YAXLlyhbi4OOLi4nK1rVmz5kOPJyIiIiJS2CixLMJyksfbXbp0CXt7e2MCGB0dTYUKFXLVu9u1J6VMmTKYmZkxaNAg/P39c5U/6JUm1tbW2NrasnDhwruW5+yaAsZjtg/DwcGB6dOnk52dzcGDB1m4cCEjR47EYDBQvXp1rK2tadq0KT179szVNudorIiIiIhIUabEsgi7evUqu3btMh6HvXr1Krt376ZXr154enpSqlQpzpw5Q6tWrQo0zlKlSuHh4cHRo0eN92HeTc7uZ2pqqsl1X19fFi1ahIWFhfE+zCfBzMwMV1dXRo4cyYYNGzh+/DjVq1fHx8eH33//ndq1a2Nubv7ExhcREREReVYpsSzCbGxsGD9+vMlTYQHefvttypQpw/Dhw/nggw84e/YsDRs2xNLSkoSEBL799lsmTpzIiy+++NRiHT16NIGBgQwbNoz27dtjbW3N+fPn2bNnD35+fvj7+2Nvb0/ZsmVZu3YtFStWpHjx4hgMBnx8fPD392fAgAH069ePmjVrkpqayvHjx4mPjycyMvKR47p69SpBQUF06NABZ2dnMjMzWbp0KVZWVtStWxe49fqTrl270rdvX7p164a9vT0XL17kl19+wdnZ+a47mSIiIiIiRYkSyyLM3t6e8PBwZs6cyV9//UWNGjVYtGiR8T7DPn368OKLL/Lvf/+bL774AnNzc5ycnGjSpMkDj5/mN3d3d5YuXUp0dDTjx48nJSUFR0dHvL29qV69OgDFihVj+vTpzJ07lz59+pCWlsbWrVupWLEikZGRfPzxxyxfvpyEhASsrKyoWrUqAQEBjxVXiRIlqFmzJp9//jmnT5+mRIkSuLi48PHHH+Po6Ajcugd05cqVfPjhh0ydOpWrV69ib2+Pu7s7r7/++mOvjYiIiIjIs84sOzs7u6CDkPw3ZswYDhw4wLp16wo6FMmD1NRUDhw4wJyE3SRmphR0OAXu687vPnYfe/fuxcvLKx+iEa1l/tFa5i+tZ/7RWuYvrWf+0Vrmn5y1zPm909XV9b7vp39Yet2IiIiIiIiIPBYdhZXnWmZmJvfbtDc3N3+kJ8k+qk9e65+vfzkqrNIyMyhurv+eRERERAoL/eZWRM2YMaOgQygU+vTpw//7f//vnuVxcXE0aNDgKUYkgJJKERERkUJGv73Jc23y5Mlcv379nuXOzs5PMRoRERERkcJJiaU816pWrVrQIYiIiIiIFHp6eI+IiIiIiIg8FiWWIvJEpGVmFHQIIiIiIvKUPNJR2MzMTL7++mt2797N5cuXeeedd6hZsybJycns2LGDevXq4eDgkN+xihR5QRs+JzEjraDDyBdfdxlc0CGIiIiIyFPy0DuWV69epXv37owaNYoNGzawfft2rly5AoCVlRUzZszgs88+y/dARURERERE5Nn00InlBx98wNGjR4mNjWXLli0m7wA0NzenVatWfPfdd/kapIiIiIiIiDy7Hjqx3Lp1K7169aJJkyZ3fXF8lSpVOH36dL4EJ3I3KSkpBR2CiIiIiIjc5qETy6SkJF5++eV7lmdnZ5OWVjTuEZMn79dff2XIkCE0btwYd3d3AgICWLlypbF8z549GAwGtm/fTlhYGPXq1WPw4Fv37l27do2pU6fy6quv4urqSkBAAFu2bDHpf/v27QQFBdGoUSM8PDzo0qVLrjoPcuzYMQYOHEiDBg2oW7curVq1IiYmxqROfHw8QUFBeHh44OnpSWjo/2fvvqOqOtaHj3+pKh1E0ItYsKBSpBgRsGDDIJBEjbEhiXoVUbFg12u5RsVoRBSMiphgSewlxlixxHITrw2J1xa7WGJBQEXRA+f9w5f98wQV0GMQfD5rsRZnz+zZzwwxi4eZPRPBzZs3X3NUhBBCCCGEKFmKvHmPnZ0dZ8+efWn5oUOH5FB5UWjXrl3D3d2dTp06UbZsWY4fP86kSZN48uQJ3bp1U+qNGzeOwMBA5syZg46ODk+fPqVnz57cvHmTiIgI7Ozs2LZtGxERESxbtgxPT0+l/WbNmtGjRw/09PQ4cOAAAwYMYP78+fj5+RUqxr59+2JlZcWUKVMwMTHh6tWrXL58WSlPSUmhW7du+Pr68vXXX6NSqYiLi6NXr15s3LgRPT09rY6ZEEIIIYQQ75oiJ5bBwcEsXLgQf39/atasCaAsiV26dCk7duxg9OjR2o1SlFpt27ZVvler1TRo0IC0tDRWrFihkVg2a9aMkSNHKp/XrVvHiRMnWL9+PY6OjgD4+vpy7do1YmNjSUxMBKBr167KPcOWbjAAACAASURBVLm5uTRs2JDLly+zYsWKQiWWaWlpXLlyhdGjR9OiRQsAGjVqpFFnxowZ1K1bl3nz5in/FpydnWnTpg2bNm3i448/LtqgCCGEEEIIUcIUObHs06cPx48f5/PPP6datWro6OgwZcoU7t27x507d2jVqhXdu3d/G7GKUigjI4PY2Fh27drFzZs3ycnJAcDQ0FCjXl5Sl+fAgQPUrl2bGjVqoFL933mJvr6+GstUb968SUxMDP/5z3+4ffs2ubm5AIWeVbe0tMTOzo7o6GjS09Px9vamUqVKSvnjx485cuQII0aMUGIHsLW1pXr16vz++++SWAohhBBCiFKvyImlgYEB8fHx/PTTT2zduhUdHR1UKhVOTk60bduWjz766IWb+gjxIqNGjeLo0aP069eP2rVrY2JiwoYNG/IdWVO+fHmNz2lpaZw6dQonJ6cXtpuZmYmJiQnh4eFkZGQwYMAAqlatipGREfHx8fzvf/8rVHw6OjosWrSImJgYvvzyS7KysqhTpw6jRo3C29ubjIwMcnJyiIqKIioqKt/99vb2hRwJIYQQQgghSq4iJZbZ2dksXLhQ2WQlODj4bcUl3gPZ2dn88ssvjBgxgs8//1y5vmHDhnx1//rHCnNzcxwdHZkyZcoL2zYyMuLy5cucPHmSuXPn0qpVK6Xs6dOnRYqzevXqzJ49G5VKRXJyMnPmzCE8PJzdu3djamqKjo4OYWFhGs/IY2ZmVqRnCSGEEEIIURIVKbEsU6YM8fHxjBs37m3FI94jT548IScnR2PZa3Z2Ntu3by/wXl9fX3755RdsbGywtbV9YZ3s7Gzg2Sx7njt37vDrr7/mmwEtDH19fRo0aEB4eDhffPEF169fx8nJCXd3d86dO8eQIUOK3KYQQgghhBClQZGXwtatW1djR0whXpepqSkuLi7Ex8djYWGBoaEh3333Xb73K1/k448/ZtWqVYSEhNCzZ08cHBy4f/8+Z8+e5fbt20yYMAEHBwcqVqzI119/TU5ODo8fP+abb77B2toatVpdqBhPnz7NtGnTaNu2Lfb29mRlZZGQkICNjY2yedXIkSMJDQ1l4MCBBAUFYW5uzq1btzh48CB+fn4vnMkUQgghhBCiNClyYhkZGcnAgQNp0KBBoY9rEOJlZs6cyYQJExg7diympqZ07twZQ0NDZsyY8cr7DA0NSUxMZO7cuSxcuJBbt25hbm5O7dq16dixo1InLi6OSZMmMWTIEGxsbPjnP//JmTNn2LNnT6Hiq1ChAjY2NsTHx3Pr1i2MjY3x8PBg8uTJlClTBgA3NzeWL19ObGwsY8eO5fHjx9ja2tKwYUMl+RRCCCGEEKI001EXdurm/+vduzeXL1/m6tWrVKxYkcqVK1O2bFnNRnV0iI+P12qgQpRm2dnZnDhxgujU46SrnhR3OFrxc4e+xfr8I0eOKOeZijcjY6k9MpbaJeOpPTKW2iXjqT0yltqTN5Z5v3c6OzsrEyXaUOQZy/PnzwMoRy5cu3YtXx3ZFVYIIYQQQggh3h9FTix37dr1NuIQoljk5OS88n1Lff0i/xMRQgghhBDivSO/NYv3WuvWrV84655n586dVK5c+W+L59sPu2l1SUJxepKjwlBP/hcjhBBCCPE+KPJvfdevXy9UvX/84x9FDkaIv9u8efN48uTl7zTa2Nj8jdGULpJUCiGEEEK8P4r8m1+LFi0K9Q7lqVOnXisgIf5Ojo6OxR2CEEIIIYQQJV6RE8upU6fmSyxzcnJITU3lxx9/pHz58nTr1k1rAQohhBBCCCGEeLcVObFs3779S8t69+7Np59+ysOHD98oKCFEyfMkJwdDPb3iDkMIIYQQQhQDrb4EZWxsTPv27UlMTCQkJESbTQvxXui1dS3pqqfFHcZr2dTh8+IOQQghhBBCFBNdbTdoYGDAn3/+qe1mhRBCCCGEEEK8o7SaWJ4+fZolS5ZQs2ZNbTYr3lGJiYn88ssv+a63aNGCSZMmFUNEQgghhBBCiOKgtV1h79+/z/379zEyMiIqKkorwYl325IlS/Dz86NZs2bFHYoQQgghhBCiGBU5sWzYsOELE0tzc3OqVKlCUFAQZmZmWglOCCGEEEIIIcS7r8iJ5bRp095GHOIvzp8/z1dffcXx48d5/Pgxtra2fPTRRwwYMIBRo0Zx4sQJxo0bR1RUFBcuXKBu3bpMnToVW1tbvvzyS5KSkjAzM6N379507dpVo+2kpCTmzp3LuXPnMDU1xd/fn+HDh2NsbKzUuX79Ol999RUHDhzg6dOnuLq6Mnz4cFxdXYFnM9fXrl3j+++/5/vvvwcgKipKY9fg5cuXs3DhQtLT0/Hw8ODLL7+kUqVKAKSmptKyZUtmzJhBSkoKGzduRF9fH39/f0aPHk2ZMmWUdm7dusXMmTP55ZdfePjwIXXr1mXEiBE0aNBAqbN7927i4uK4cOECurq6VKlShfDwcPz9/QtVXpCEhARWrVrFjRs3MDIyonbt2kycOJEaNWoA8OTJE7755hs2btzIrVu3sLOzo2fPnnTq1KnQP3MhhBBCCCFKqiInlqNHj6Zz587Ur1//heUpKSksX75clsO+ob59+2JlZcWUKVMwMTHh6tWrXL58WSm/ffs2kydPpm/fvpQpU4aoqCiGDBnCP/7xDxwdHZkzZw5btmzh3//+N66urjg7OwOwc+dOBgwYQJs2bRg8eDCpqanMnDmTS5cukZiYCMCDBw+UXX3HjRuHsbExCQkJhIaGsnbtWmrUqEFcXBx9+vTBw8ODnj17AlClShUlvt27d3PhwgX+9a9/8fDhQ6Kiohg9erTyjDwxMTE0bdqU6OhoTp48SUxMDDY2NvTr1w+AzMxMunTpQpkyZRgzZgwWFhasXr2anj178vPPP2Nvb8+VK1eIiIggMDCQyMhI1Go1Z86cISMjA6DA8oJs2LCBWbNmMXDgQNzc3Hjw4AHHjh3jwYMHSp3IyEgOHjxI//79qV27Nr/99hsTJ07E2NiYoKCgIvzkhRBCCCGEKHmKnFiuX78eHx+flyaWqampbNiwQRLLN5CWlsaVK1cYPXo0LVq0AKBRo0YadTIyMliyZAmOjo7AswRs9OjReHh4MGTIEODZsuXt27ezbds2JbGMi4vDxcWFmJgYZUmzubk5Q4cO5eDBg3h5ebFu3TquX7/Oxo0bqV27NgDe3t60aNGChQsXMm3aNOrVq4ehoSHW1ta4ubnl60NOTg4LFixQZh7v3r1LVFQUmZmZGkulnZ2dmThxIgCNGzcmOTmZbdu2KYnl4sWLSU9PZ+vWrVSoUEGpFxQUxPz585kyZQonT57k6dOnjBs3DhMTE6VOnoLKC5KSkoKjoyNhYWHKtZYtWyrfHzx4kB07dhAfH6+8b+rj40N6ejqzZ8+WxFIIIYQQQpR6Wj9u5N69exgaGmq72feKpaUldnZ2REdHs27dOm7cuJGvToUKFZSkEqBatWqAZsJkYGCAnZ2dcv/Dhw85deoUAQEBGu/Jfvjhh+jr63P48GEADh8+TK1atZSkEp6dUdq8eXOlTkE++OADjeWseTsF37x5U6PeXxO8mjVratQ5cOAAXl5eWFpaolKpUKlU5Obm4u3tTUpKCgCOjo7o6ekxbNgwdu7cSWZmpkabBZUXpF69epw8eZKpU6dy6NAhnjx5olF+4MABzM3N8fX1VWJUqVT4+Phw5coV0tPTi/Q8IYQQQgghSppCzVgeOnSIgwcPKp937NihsSwzT2ZmJps3b6ZOnTrai/A9pKOjw6JFi4iJieHLL78kKyuLOnXqMGrUKLy9vYFns4zPMzAwAMDU1DTf9bxE6P79+6jVaqytrTXq6OvrY2FhoSwNzczMzFcHwNrautDLR18WX3Z2tsb1v2709Hy88Gz29tixYzg5OeV7Rl5fq1evzvz584mPj2fgwIEA+Pr6Mm7cOOzt7QssL0j79u3Jyspi1apVLF68GGNjYz766CNGjBiBkZERaWlpZGRkvDBGgBs3bmBhYVHgc4QQQgghhCipCpVYHjx4kLi4OOBZ0rN9+3a2b9/+wrrVq1dnzJgx2ovwPVW9enVmz56NSqUiOTmZOXPmEB4ezu7du1+7TVNTU3R0dLh7967GdZVKRXp6upIMmpubc+HChXz337lzJ1/C+LaZm5vTuHFjBg8enK9MT09P+b5p06Y0bdqUhw8fcuDAAaZNm8bQoUNZtWpVocpfRVdXl9DQUEJDQ7l16xbbtm1j+vTpmJiYMGzYMMzNzbG0tGThwoUvvD9vNlkIIYQQQojSqlCJZc+ePencuTNqtZomTZowfvz4fLtp6ujoUK5cOYyMjN5KoO8rfX19GjRoQHh4OF988QXXr19/7baMjY2pW7cuW7ZsoUePHsr17du3o1KplF1WPT092bZtG+fOnVOWsGZlZbF7926aN2+u3GdgYJBvBlLbfHx8+PHHH3FwcNDYtfZljI2N8ff359SpUyxevLjI5QWxsbGhe/fubN26lXPnzgHPZj8TEhLQ19enbt26RW5TCCGEEEKIkq5QiaWRkZGSMO7cuRMrKyvKlSv3VgN7n50+fZpp06bRtm1b7O3tycrKIiEhARsbGyXRe10DBgygf//+REZG8sknn3D16lWio6Px9vbGy8sLeLb0MzExkbCwMAYPHoyRkREJCQlkZ2fTu3dvpS0HBwd+/fVX9u/fj7m5OZUrV8bS0vKN4vurHj168PPPPxMSEkJoaCh2dnakp6dz4sQJDA0NGTBgACtWrODo0aM0bdoUGxsbrl+/zpo1a/D19QUosLwg48ePx9TUFDc3N8zNzTl+/DjHjx9n5MiRwLPkt1WrVvTu3ZtevXpRp04dsrOzuXDhAikpKcTExGh1TIQQQgghhHjXFHlXWDs7u7cRh3hOhQoVsLGxIT4+nlu3bmFsbIyHhweTJ0/W2BDndbRs2ZLY2Fjmzp1Lv379MDU1JTg4mGHDhil1TExMWLZsGdOmTWPixImoVCpcXV1ZsmSJcm4jPDtiY+LEiQwcOFA5UuT5cyy1wcLCgpUrVzJ79myio6O5d+8elpaWODk50b17d+DZ5jx79uzhq6++4t69e1hbW9O6dWtld9yCygvi7u7O6tWrWbNmDY8ePcLOzo6hQ4cqz4dnx6YsWrSIlStXkpqairGxMQ4ODgQHB2t1PIQQQgghhHgX6ajVanVRbzp37hxLlizhxIkT3L9/n9zcXM1GdXRISkrSWpBClHbZ2dmcOHGCWalnSFc9Le5wXsumDp8Xdwgajhw5gqenZ3GHUSrIWGqPjKV2yXhqj4yldsl4ao+MpfbkjWXe753Ozs5vPGn1vCIfN3L06FE6dOhAUlISNjY2XL16FXt7e2WJoZGRER988IHWAhRCCCGEEEII8W4r8lLYmJgYKlasyOrVq5Wz+sLCwvD29ubo0aOEhYUp754J8a5Tq9Xk5OS8tFxXVxddXa0f9/pSiz7soNW/HP2dnuTkYPjcTr1CCCGEEOL9UeTE8vfff6dfv36YmZkpB7/nLYX18PCgY8eOzJ49O9/B90K8i9avX8/o0aNfWt6uXTumTZv2N0ZUcklSKYQQQgjx/ipyYqmjo6Mcap+3U2xeggnPzl/84YcftBSeEG9X8+bNWbNmzUvLtb3LrRBCCCGEEKVRkRPLypUrc/nyZQAMDQ2pXLkyBw4cIDAwEIDDhw9jYWGh3SiFeEssLS0leRRCCCGEEOINFTmx9PX1Zdu2bQwfPhwdHR0+++wzoqOjuXbtGmq1mv/+978aZx0KIUo/eb9SCCGEEOL9VuTEsm/fvgQGBqJSqTAwMKB3796o1Wq2bNmCrq4uAwYMICws7G3EKkSp12vLJtJVquIOo8g2fdqpuEMQQgghhBDFqMiJpbm5Oebm5spnHR0dwsLCJJkUL5SZmcnixYsJCAigZs2axR2OEEIIIYQQ4i14o3MUbty4wYkTJ3j48KG24hGlTGZmJnFxcZw7d664QxFCCCGEEEK8Ja+VWCYlJeHv70+LFi3o2LEjKSkpAKSlpREcHMyOHTu0GqQQQgghhBBCiHdXkRPLPXv2EBERgZWVFf3790etVitlVlZWVKpUiXXr1mk1yNIoOTmZXr164enpibu7O+3btycpKQl4dnzL2LFj8fb2xtXVlQ4dOrBv3z6N+7t3705YWBhbtmyhTZs21K9fn549e3L79m1u3bpFeHg47u7utGnTJl+in3fvjz/+SOvWrXFxcaF79+6cP39eo15iYiIdOnTA09OTRo0a0atXL/74449C9yU1NZWWLVsCMGjQIBwdHXF0dCQ1NZXU1FQcHR3ZuHEjkydPpmHDhvj4+DBx4kSys7M12r916xYjR46kUaNGuLi48Nlnn3H48GGNOrt376ZDhw64u7vj6elJu3bt2L59e6HLC5KQkIC/vz8uLi54eXnlG68nT54QExNDixYtcHZ2pk2bNqxcubLQ7QshhBBCCFGSFfkdy7lz5+Lh4cH333/PvXv3iIuL0yh3c3Nj9erVWguwNDpy5Aiff/45zs7OTJo0CQsLC06ePMmNGzfIycmhd+/eXLlyhcjISCpWrMjy5csJCwvj22+/pVGjRko7p06d4u7duwwfPpxHjx4xefJkxowZw/3792nVqhUhISF8//33DBkyhF27dmFjY6Pce/LkSa5cucKQIUPQ0dEhJiaG3r17s3XrVgwNDQG4efMm3bp14x//+AePHj1i1apVdO7cmS1btihtvaovTZs2JS4ujgEDBhAZGYmXlxcANjY23Lp1C4CYmBiaNm1KdHQ0J0+eJCYmBhsbG/r16wc8W0rbpUsXypQpw5gxY7CwsGD16tX07NmTn3/+GXt7e65cuUJERASBgYFERkaiVqs5c+YMGRkZAAWWF2TDhg3MmjWLgQMH4ubmxoMHDzh27BgPHjxQ6kRGRnLw4EH69+9P7dq1+e2335g4cSLGxsYEBQW97n8qQgghhBBClAhFTizPnj3LiBEjXlpeoUIF7t69+0ZBlXYzZszA3t6eZcuWoa//7Efg6+sLwM6dO0lJSSE+Pp5mzZoB0KRJE4KCgpg7d65GYpmZmcn69espX748AJcuXSIuLo5hw4bxz3/+E4C6devi7e3Nrl276Ny5s3LvnTt3WLp0KdWqVVPqBQQEsH79ejp1erbD56hRo5T6OTk5+Pr60qxZM37++Wd69OhRYF/y2gWoWrUqbm5u+cbC2dmZiRMnAtC4cWOSk5PZtm2bklguXryY9PR0tm7dSoUKFZR6QUFBzJ8/nylTpnDy5EmePn3KuHHjMDExUerkKai8ICkpKTg6OmpsUJU3Ewtw8OBBduzYofEz8/HxIT09ndmzZ0tiKYQQQgghSr0iL4U1NDTkyZMnLy2/fv06pqambxRUafbo0SOOHz9Ou3btlETseYcPH8bY2FhJUAB0dXUJCAjg2LFj5OTkKNfr1KmjJJWAkiQ+nzRZWVlhbm7OjRs3NJ5Tq1YtpX7evbVq1SI5OVm5lpycTM+ePfHy8qJevXq4uLiQlpbGxYsXC9WXwvhrglezZk1u3rypfD5w4ABeXl5YWlqiUqlQqVTk5ubi7e2tvNvr6OiInp4ew4YNY+fOnWRmZmq0WVB5QerVq8fJkyeZOnUqhw4dyvff/4EDBzA3N8fX11eJUaVS4ePjw5UrV0hPTy/S84QQQgghhChpipwNeHh4sHnzZmXG6nkPHjxg7dq1ypJHkV9mZia5ubnY2tq+tNza2jrfdWtra54+fUpWVpaSuD9/7AuAgYEBQL7E3sDAIF8y9HxC+vy127dvA8/+QNCzZ0+cnJyYMGECtra2GBoaMmjQIKWtgvpSGGZmZq+MNS0tjWPHjuHk5JTv3rx+Vq9enfnz5xMfH8/AgQOBZ7Om48aNw97evsDygrRv356srCxWrVrF4sWLMTY25qOPPmLEiBEYGRmRlpZGRkbGC2OEZ7snW1hYFG5AhBBCCCGEKIGKnFgOGDCAbt260aNHD4KDg4FnSw0vXbrEt99+y/379+nfv7/WAy0tzMzM0NXV5c8//3xhubm5OXfu3Ml3/c6dOxgYGGBkZKSVOF60XPnu3btKcrRv3z6ysrKIi4vTSGCfn30rqC/aYG5uTuPGjRk8eHC+Mj09PeX7pk2b0rRpUx4+fMiBAweYNm0aQ4cOZdWqVYUqfxVdXV1CQ0MJDQ3l1q1bbNu2jenTp2NiYsKwYcMwNzfH0tKShQsXvvD+52eGhRBCCCGEKI2KvBTWxcWFhIQEbty4wZgxY4Bn79n9+9//RldXl4SEBGrUqKH1QEuLcuXK4e7uzoYNG1CpVPnKPT09efjwIXv37lWu5ebmsnXrVtzd3TWSqTfxxx9/cOnSJeXzpUuX+OOPP6hfvz4Ajx8/RkdHR2OJ686dOzXOLC2oL/B/s6h/3em1sHx8fDh//jwODg64uLhofNWrVy9ffWNjY/z9/fn4449feHZmQeUFsbGxoXv37ri6uir3+/r6cu/ePfT19fPF6OLiQrly5YrecSGEEEIIIUqQQs1YTpw4kQ4dOuDi4gJAw4YN+eGHH7h27Rqpqamo1Wrs7e1xdnZGR0fnrQZcGgwbNkyZAQsJCcHS0pLTp09jYGBAly5dcHV1ZcSIEURGRmJra8uKFSu4ePEi48eP11oM5cuXJzw8XFkaOnv2bGxtbWnXrh2AsknQ6NGj6dy5MxcvXiQ+Ph4rK6tC9yUkJIQKFSpgZmbGxo0bqVy5MoaGhjg6OhY6zh49evDzzz8TEhJCaGgodnZ2pKenc+LECQwNDRkwYAArVqzg6NGjNG3aFBsbG65fv86aNWuUTYQKKi/I+PHjMTU1xc3NDXNzc44fP87x48cZOXIk8Cz5bdWqFb1796ZXr17UqVOH7OxsLly4QEpKCjExMYXurxBCCCGEECVRoRLLFStW4OnpqSSW9+7dw9fXl2+//ZaAgIC3GmBp5OHhwbJly4iJiWHs2LEAODg40L9/f/T09Fi4cCHTp09n5syZZGVlUbt2bebPn6/Vd1ednJz48MMP+frrr7l16xaurq5MmjSJMmXKAM82vJk2bRpxcXH07duXWrVqMXPmTGUH18L0BZ4tI506dSqzZs3iiy++4MmTJ+zcubPQcVpYWLBy5Upmz55NdHQ09+7dw9LSEicnJ7p3767EumfPHr766ivu3buHtbU1rVu3ZsiQIYUqL4i7uzurV69mzZo1PHr0CDs7O4YOHao8H54dm7Jo0SJWrlxJamoqxsbGODg4KMvFhRBCCCGEKM101Gq1uqBKderUYcaMGcovyffu3cPb25vvvvsOb2/vtx6k0K7u3btjZGTEggULijsU8f9lZ2dz4sQJZl29RPpLlhW/yzZ92qm4Q8jnyJEjeHp6FncYpYKMpfbIWGqXjKf2yFhql4yn9shYak/eWOb93uns7KxMKmlDkd+xFEIIIYQQQgghnvd6hw8KUUqo1WqNs0H/SldXF11d+fuLEEIIIYQQr1LoxPLq1avKgfT3798H4MKFCxgbG7+wvqurqxbCE2/D0qVLizuEd8b69esZPXr0S8vbtWvHtGnT/rZ4FgUEaXVJwt/lSU4OhlrasVgIIYQQQpQ8hU4sY2NjiY2N1bg2efLkfPXUajU6OjqcOnXqzaMT4i1r3rw5a9aseWm5paXl3xhNySVJpRBCCCHE+61QiWVUVNTbjkOIYmFpaSnJoxBCCCGEEG+oUIll3tmGQgghhBBCCCHEX8muJEKIQnvyio2OhBBCCCHE+0t2hRXiHfLPLUlkqN7d5G3jpx8VdwhCCCGEEOIdJDOWQgghhBBCCCHeiCSWokRq0aIFkyZNUj4nJSXx/fffF2NEQgghhBBCvL9kKawokeLi4jAzM1M+JyUlceLECbp161aMUQkhhBBCCPF+ksRSlEj16tUr7hCEEEIIIYQQ/58shX0PJCcn06tXLzw9PXF3d6d9+/YkJSUBkJ6eztixY/H29sbV1ZUOHTqwb98+jfu7d+9OWFgY27dvJyAgADc3N7p06cLZs2c16qnVahITEwkMDMTZ2RkfHx/69evHnTt3ALhw4QKRkZH4+flRv359AgICWLBgASqVSmmjZcuWjB8/Pl8f5s6di4eHB9nZ2YDmUthRo0axfv16/vjjDxwdHXF0dGTUqFHs3LkTR0dHLl68qNHW48ePadCgAXPnzi3U+B09epSQkBBl/AIDA1m+fLlGnb1799K5c2fq169Pw4YNGT16NJmZmYVqXwghhBBCiJJOZixLuSNHjvD555/j7OzMpEmTsLCw4OTJk9y4cYOcnBx69+7NlStXiIyMpGLFiixfvpywsDC+/fZbGjVqpLRz6tQp5s+fz6BBg9DX12f69OlERESwZcsWdHWf/X1i0qRJrFixgtDQUEaOHMnjx4/Zv38/GRkZWFtbc/v2bapWrUpgYCAmJiacPXuW2NhYMjIyGDFiBACBgYGsXLmScePGYWBgoDx/8+bNtG7dmjJlyuTrY79+/UhLS+PChQt8/fXXAFhZWWFnZ4etrS1r165l2LBhSv1t27bx8OFD2rdvX+D4PXjwgLCwMNzd3YmOjsbQ0JALFy7w4MEDpU5SUhIRERF88sknhIeHc+/ePWJiYhgyZAiLFi0q4k9MCCGEEEKIkkcSy1JuxowZ2Nvbs2zZMvT1n/24fX19Adi5cycpKSnEx8fTrFkzAJo0aUJQUBBz587VSCwzMzNZt24d1tbWyrX+/ftz5swZ6taty8WLF1m+fDmDBg0iPDxcqePv76987+XlhZeXF/BsdtPT05Pc3FxiY2MZPnw4Ojo6BAcHs2DBAg4cOICfnx8Ap0+f5ty5c4waNeqFfaxSpQpWVlZcv34dNzc3jbIOHTqwevVqBg8erPR/zZo1NG7cmEqVKhU4fhcvXiQzM5OhQ4fi6OgIgLe3t1KuVquJioqiTZs2REVFKderVatGp06dOHz4MA0aHvsnDgAAIABJREFUNCjwOUIIIYQQQpRkshS2FHv06BHHjx+nXbt2SlL1vMOHD2NsbKwklQC6uroEBARw7NgxcnL+7zzFOnXqaCSVNWrUAODmzZsA/Pbbb6jVajp27PjSeLKzs4mNjcXf3x8XFxecnJyYOnUq9+/fV5bL1qpVi9q1a/PTTz8p923atIny5cvj4+NT5DH49NNPuXv3Lnv27AHgypUrHDp06JVxPq9KlSqYmJgwceJENm/ezN27dzXKL126RGpqKoGBgahUKuXL2dkZExMTfv/99yLHLIQQQgghREkjiWUplpmZSW5uLra2ti8tfz5ZzGNtbc3Tp0/JyspSrpmbm2vUyVummvfOY3p6Ovr6+i9sL8+MGTNYuHAh7du3Z8GCBaxevZohQ4ZotAMQHBzMrl27ePToEWq1ms2bNxMQEICenl4he/5/7Ozs8PX1Zc2aNQCsXbsWKysrmjdvXqj7zc3N+e677zAxMWHkyJH4+vrStWtXTp48CUBaWhoAAwYMwMnJSePrwYMHXL9+vcgxCyGEEEIIUdLIUthSzMzMDF1dXf78888Xlpubmyszhc+7c+cOBgYGGBkZFfpZlpaWqFQq7ty589LkcuvWrXTq1Im+ffsq1w4fPpyvXmBgINHR0ezatYtKlSpx7do1AgMDCx3LX3322WcMHjyYGzdusH79ej7++GON9zcL4urqysKFC8nOzua///0vM2fOpE+fPuzduxcLCwsAxo8fj6ura757y5cv/9pxCyGEEEIIUVLIjGUpVq5cOdzd3dmwYYPGzqt5PD09efjwIXv37lWu5ebmsnXrVtzd3Ys0Q9ioUSN0dHSUmcEXyc7OxtDQUPmsVqvZtGlTvnp2dna4ubnx008/8fPPP2NnZ4eHh8crn29gYKAx6/m8Fi1aYGVlxfDhw/nzzz8LvQz2r8qUKUOTJk0ICQnh9u3bZGZm4uDgQKVKlbh8+TIuLi75vv7xj3+81rOEEEIIIYQoSWTGspQbNmwYoaGhhIaGEhISgqWlJadPn8bAwIAuXbrg6urKiBEjiIyMxNbWlhUrVnDx4sUXHvnxKtWqVSMkJIQ5c+aQkZGBj48PT548Yf/+/YSEhFCjRg18fHxYuXIlDg4OWFtbs2rVKjIyMl7YXnBwMFFRUZiYmPDZZ58V+PwaNWqwZs0aNm7cSPXq1bG0tKRy5coA6Ovr065dO+Lj4/H09MTBwaHQ/dqzZw+rV6+mdevWVKpUibS0NL777jucnJyU2coxY8YwZMgQHj16hJ+fH8bGxty4cYP9+/cTGhpK/fr1C/08IYQQQgghSiJJLEs5Dw8Pli1bRkxMDGPHjgXAwcGB/v37o6enx8KFC5k+fTozZ84kKyuL2rVrM3/+fGX31qIYO3YsdnZ2rFixgqVLl2JmZoaHh4fGctEJEyYwdepUDA0NCQ4Opk2bNgwfPjxfWwEBAUydOpV79+4RFBRU4LM//fRTUlJSmDJlCunp6bRr145p06Yp5a1btyY+Pr7Is5VVqlRBT0+P2bNnc/v2bSwsLPDx8WHo0KFKHX9/fxISEpg/fz7Dhg1DrVZTqVIlfHx8sLOzK9LzhBBCCCGEKIl01Gq1uriDEOJtmzdvHosWLWLfvn2UK1euuMPJJzs7mxMnThBz9SYZqpyCbygmGz/9qLhDKLQjR47g6elZ3GGUCjKW2iNjqV0yntojY6ldMp7aI2OpPXljmfd7p7Oz8wvPiH9dMmMpSrULFy5w6dIlEhMT6dix4zuZVAohhBBCCFHSSWIpSrUJEyaQnJyMr68v/fv3z1f+ok2Nnvei8z/fpoSAVlr9y5G2PcnJwfA1jn0RQgghhBClmySWolRbunTpK8udnJxeWX7mzBlthlPiSVIphBBCCCFeRBJL8V571fEoQgghhBBCiMKRxFK811xcXIo7BCGEEEIIIUo83eIOQAhRMjzJeXd3qxVCCCGEEMVLZiyFeIf02fIfMlS5xR3GC234tGVxhyCEEEIIId5RMmMphBBCCCGEEOKNSGIp3jnr1q3D0dGRtLS0It136tQpYmNjefTo0VuKTAghhBBCCPEikliKd46fnx8rV67EzMysSPedOnWKuLg4SSyFEEIIIYT4m8k7luKdY2VlhZWVVXGHIYQQQgghhCgkmbF8TcnJyfTq1QtPT0/c3d1p3749SUlJSnl6ejpjx47F29sbV1dXOnTowL59+zTa6N69O2FhYWzfvp2AgADc3Nzo0qULZ8+e1ainVqtJTEwkMDAQZ2dnfHx86NevH3fu3AHgwoULREZG4ufnR/369QkICGDBggWoVCqljZYtWzJ+/Ph8/Zg7dy4eHh5kZ2drPOvDDz/E2dkZPz8/5s2bh1qtfuV4jBo1iqCgIPbt20dwcDAuLi60b9+eY8eOadTLzc1l/vz5tGzZEmdnZ1q3bk1iYqJGnb8uhU1NTcXR0ZGNGzcyefJkGjZsiI+PDxMnTlTiXrduHaNHjwbA29sbR0dHWrRoAcD9+/cZP348TZo0wdnZmSZNmhAREaExPq9y/vx5+vTpg5eXF/Xr18ff35+4uDiNOikpKfTs2RN3d3c8PDyIiIjg5s2bhWpfCCGEEEKIkk5mLF/DkSNH+Pzzz3F2dmbSpElYWFhw8uRJbty4AUBOTg69e/fmypUrREZGUrFiRZYvX05YWBjffvstjRo1Uto6deoU8+fPZ9CgQejr6zN9+nQiIiLYsmULurrP8v5JkyaxYsUKQkNDGTlyJI8fP2b//v1kZGRgbW3N7du3qVq1KoGBgZiYmHD27FliY2PJyMhgxIgRAAQGBrJy5UrGjRuHgYGB8vzNmzfTunVrypQpA8C0adNYvnw5ffr0wcPDg//973/Exsaiq6tLWFjYK8fl9u3bTJgwgYiICMzMzFi4cCG9evVix44dlC9fHoDp06ezePFi+vTpwwcffMCBAweIiori4cOH9O/f/5Xtx8TE0LRpU6Kjozl58iQxMTHY2NjQr18//Pz8CA8PZ968eSQkJGBqaoqhoSEAUVFR7N27l6FDh1K5cmVu377NL7/8Qm5u4XZf7du3L1ZWVkyZMgUTExOuXr3K5cuXlfKUlBS6deuGr68vX3/9NSqViri4OHr16sXGjRvR09Mr1HOEEEIIIYQoqSSxfA0zZszA3t6eZcuWoa//bAh9fX2V8j179pCSkkJ8fDzNmjUDoEmTJgQFBTF37lyNxDIzM5N169ZhbW2tXOvfvz9nzpyhbt26XLx4keXLlzNo0CDCw8OVOv7+/sr3Xl5eeHl5Ac9mHD09PcnNzSU2Npbhw4ejo6NDcHAwCxYs4MCBA/j5+QFw+vRpzp07x6hRowC4evUqS5YsYdy4cXTt2hUAHx8f1Go1CxYsoHv37hgZGb10XNLT04mJicHb2xuADz74gGbNmrF48WIiIyNJS0tj2bJl9OjRgyFDhgDQuHFjHj58SEJCAl988QXGxsYvbd/Z2ZmJEycq9yUnJ7Nt2zb69euHlZUVVapUAcDJyUljKW1KSgpBQUG0a9dOuda2bduXPud5aWlpXLlyhdGjRyszoM///ODZfw9169Zl3rx56OjoKLG2adOGTZs28fHHHxfqWUIIIYQQQpRUshS2iB49esTx48dp166dklT+1eHDhzE2NlaSSgBdXV0CAgI4duwYOc8dNF+nTh2NpLJGjRoAyjLK3377DbVaTceOHV8aU3Z2NrGxsfj7++Pi4oKTkxNTp07l/v37ynLZWrVqUbt2bX766Sflvk2bNlG+fHl8fHwA+M9//oNarebDDz9EpVIpX97e3jx48ICLFy++cmxMTU2VpBLAzMyMRo0akZycDDxL8J4+fZovqQsMDCQrK4tTp069sv3GjRtrfK5Zs2ahlpvWq1eP9evXk5CQwOnTpwtc1vs8S0tL7OzsiI6OZt26dcqsdJ7Hjx9z5MgR2rZtS05OjjJmtra2VK9end9//73QzxJCCCGEEKKkkhnLIsrMzCQ3NxdbW9tX1nk+WcxjbW3N06dPycrKwtTUFABzc3ONOnnLVPPeHUxPT0dfX/+F7eWZMWMGq1atol+/fri4uGBqasp//vMfZs2apbQDEBwczLx583j06BFly5Zl8+bNBAQEKEs109LSUKvVGsnh827cuIGTk9NL43jRhjvly5fn0qVLAGRkZABQoUKFfOOS19dX+esusQYGBjx58uSV9wCMGzcOCwsLEhMTmTFjBra2tvTq1YvPP/+8wHt1dHRYtGgRMTExfPnll2RlZVGnTh1GjRqFt7c3GRkZ5OTkEBUVRVRUVL777e3tC3yGEEIIIYQQJZ0klkVkZmaGrq4uf/7550vrmJubKzOFz7tz5w4GBgavXE76V5aWlqhUKu7cufPS5HLr1q106tSJvn37KtcOHz6cr15gYCDR0dHs2rWLSpUqce3aNQIDAzXi1tHR4YcfftB4DzNP3lLTl3nRuZN3795VEkkLCwvg2Tg8n5jnjVVeubaZmpoyZswYxowZw7lz5/jhhx+YOnUq1atXp2nTpgXeX716dWbPno1KpSI5OZk5c+YQHh7O7t27MTU1RUdHh7CwMFq1apXv3qIemSKEEEIIIURJJEthi6hcuXK4u7uzYcOGl+4q6unpycOHD9m7d69yLTc3l61bt+Lu7l6kzVwaNWqEjo4Oa9aseWmd7OxsZaMaePae5aZNm/LVs7Ozw83NjZ9++omff/4ZOzs7PDw8lPK8mcq0tDRcXFzyff11dvWv7t+/z6+//qrx+bfffqN+/foAuLi4YGBgwJYtWzTu27x5M0ZGRtSrV++V7RckLxl+1SxmzZo1GTNmDLq6upw/f75I7evr69OgQQPCw8N59OgR169fx8jICHd3d86dO/fCMatateob9UkIIYQQQoiSQGYsX8OwYcMIDQ0lNDSUkJAQLC0tOX36NAYGBoSEhODn54erqysjRowgMjISW1tbVqxYwcWLF1945MerVKtWjZCQEObMmUNGRgY+Pj48efKE/fv3ExISQo0aNfDx8WHlypU4ODhgbW3NqlWrlGWnfxUcHExUVBQmJiZ89tlnGmXVq1ene/fujBw5kh49euDu7k5OTg5Xr14lKSmJ77777pWxWlhYMHbsWI1dYQFlyamVlRXdu3fn22+/xdDQEA8PD3799VdWrlxJREREkWZyXyTv/dSlS5fi7+9P2bJlcXR0pHPnzrRq1YpatWphYGDApk2b0NXVpWHDhgW2efr0aaZNm0bbtm2xt7cnKyuLhIQEbGxsqFmzJgAjR44kNDSUgQMHEhQUhLm5Obdu3eLgwYP4+fm9cCZTCCGEEEKI0kQSy9fg4eHBsmXLiImJYezYsQA4ODgox2Xo6emxcOFCpk+fzsyZM8nKyqJ27drMnz9f2b21KMaOHYudnR0rVqxg6dKlmJmZ4eHhoSwdHT9+PBMmTGDq1KkYGhoSHBxMmzZtGD58eL62AgICmDp1Kvfu3SMoKOiFz3JwcGDFihUsWLCAsmXLUqVKFZo3b15gnBUqVGD48OFMnz6dy5cvU6tWLRISEjSW8A4fPhwzMzNWr15NfHw8FStWVBLZN1WvXj0iIiJYvXo13377LZUqVWLXrl14eHiwceNGrl69iq6uLrVq1eKbb7555fuiz/fJxsaG+Ph4bt26hbGxMR4eHkyePFk5osXNzY3ly5cTGxvL2LFjefz4Mba2tjRs2FBJPoUQQgghhCjNdNRF2SJTiJcYNWoUJ06ceOESXFGw7OxsTpw4wZyrmWSoCne+5t9tw6ctizuEIjly5Aienp7FHUapIGOpPTKW2iXjqT0yltol46k9MpbakzeWeb93Ojs7KxMl2iDvWAohhBBCCCGEeCOyFFa813Jycl55ruXLzip9W+IDfLT6lyNtepKTg2ERNp4SQgghhBDvD0kshVZMmzatuEN4La1bt+batWsvLd+5cyeVK1f+GyN6d0lSKYQQQgghXkYSS/Femzdv3iuPJ7GxsfkboxFCCCGEEKJkksRSvNccHR2LOwQhhBBCCCFKPNm8R4j32JOcd3MHWiGEEEIIUbLIjKUQ75DwrclkqP6+563tUPRzVYUQQgghhPgrmbEUJUpsbCxHjx4t7jCEEEIIIYQQz5HEUpQocXFxHDt2rLjDEEIIIYQQQjxHEktRKj1+/Li4QxBCCCGEEOK9IYllKXP+/Hn69OmDl5cX9evXx9/fn7i4OHbu3ImjoyMXL17UqP/48WMaNGjA3LlzgWdLTd3d3Tl9+jRdunTB1dWV4OBgjh49ytOnT4mKiqJRo0Y0btyYOXPmoFarlbbe5F6ACxcuMGDAAD744APq169Pjx49+OOPP5TyvB1cp0+fjqOjI46Ojhw8eFApi4+PZ9asWTRu3BhPT89C97kgR48eJSQkBE9PT9zd3QkMDGT58uUadfbu3Uvnzp2pX78+DRs2ZPTo0WRmZhaqfSGEEEIIIUo6SSxLmb59+5KRkcGUKVNYsGABvXv35vHjx/j5+WFra8vatWs16m/bto2HDx/Svn175drTp08ZPnw47du3JzY2FgMDAwYMGMCECRN4/PgxM2fOpEOHDsydO5cdO3ZotPe696amptKlSxfu3LnD5MmTmTVrFk+fPiU0NJT79+8DsHLlSgC6d+/OypUrWblyJU5OTkobS5Ys4cyZM3z55ZdER0cXqc8v8+DBA8LCwjAyMiI6OppvvvmGrl278uDBA6VOUlISYWFhVK9enTlz5jBmzBh+/fVXhgwZUmD7QgghhBBClAayK2wpkpaWxpUrVxg9ejQtWrQAoFGjRkp5hw4dWL16NYMHD0Zf/9mPfs2aNTRu3JhKlSop9Z4+fcrQoUPx8/MDoGzZsoSGhnL79m0WLlwIgK+vLzt37mTr1q34+/u/8b1xcXEYGxuTmJhI2bJlAWjYsCGtWrVi6dKl9OvXDzc3NwAqVaqkfP88U1NTvvnmG3R1/+/vJYXt88tcvHiRzMxMhg4dqsyYent7K+VqtZqoqCjatGlDVFSUcr1atWp06tSJw4cP06BBgwKfI4QQQgghREkmM5aliKWlJXZ2dkRHR7Nu3Tpu3LihUf7pp59y9+5d9uzZA8CVK1c4dOgQHTt21Kinq6uLj4+P8rlatWrAs4TwedWqVcv3jNe9d//+/bRs2RJ9fX1UKhUqlYqyZcvi5uZGSkpKofrv5+enkVQWpc8vU6VKFUxMTJg4cSKbN2/m7t27GuWXLl0iNTWVwMBAJW6VSoWzszMmJib8/vvvhXqOEEIIIYQQJZkklqWIjo4OixYtokaNGnz55Zf4+fnx8ccf8+uvvwJgZ2eHr68va9asAWDt2rVYWVnRvHlzjXbKli2LoaGh8tnAwAAAMzMzjXoGBgZkZ2dr5d579+6xZMkSnJycNL52796dL3l9mfLly+e7Vtg+v4y5uTnfffcdJiYmjBw5El9fX7p27crJkyeBZ7PEAAMGDMgX+4MHD7h+/XqhniOEEEIIIURJJkthS5nq1asze/ZsVCoVycnJzJkzh/DwcHbv3o2lpSWfffYZgwcP5saNG6xfv56PP/5YSf6Kk7m5Oc2aNaNr1675yvKWxhZER0fnhdfftM+urq4sXLiQ7Oxs/vvf/zJz5kz69OnD3r17sbCwAGD8+PG4urrmu/dFya4QQgghhBCljSSWpZS+vj4NGjQgPDycL774guvXr2NpaUmLFi2wsrJi+PDh/Pnnn4VeEvq2+fj4cPbsWerVq4eent5L671olrQg2upzmTJlaNKkCX/++Sdjx44lMzMTBwcHKlWqxOXLl+nWrdtrtSuEEEIIIURJJ4llKXL69GmmTZtG27Ztsbe3Jysri4SEBGxsbKhZsybwLOFs164d8fHxeHp64uDgUMxRPzNo0CA+/fRTevToQadOnahQoQJ37tzh2LFjVK9eXZnJdHBwICkpiQYNGlCuXDmqV6+OiYnJK9t+kz7v2bOH1atX07p1aypVqkRaWhrfffcdTk5OymzlmDFjGDJkCI8ePcLPzw9jY2Nu3LjB/v37CQ0NpX79+q8/MEIIIYQQQpQAkliWIhUqVMDGxob4+Hhu3bqFsbExHh4eTJ48mTJlyij1WrduTXx8/DszWwlgb2/P6tWrmT17NpMnT+b+/ftUqFABNzc3PvroI6Xe+PHjmTp1qnKMypIlS/Dy8iqw/dftc5UqVdDT02P27Nncvn0bCwsLfHx8GDp0qFLH39+fhIQE5s+fz7Bhw1Cr1VSqVAkfHx/s7OyK9DwhhBBCCCFKIh31X0+pF6XevHnzWLRoEfv27aNcuXLFHc7f4l3vc3Z2NidOnGBuqooM1d/33LUdCk7KS6ojR47g6elZ3GGUCjKW2iNjqV0yntojY6ldMp7aI2OpPXljmfd7p7Ozs8bk05uSGcv3yIULF7h06RKJiYl07NjxnUywtO197LMQQgghhBB/N0ks3yMTJkwgOTkZX19f+vfvX9zh/C0K6rNK9erpQX19+ScihBBCCCFEQeS35vfI0qVLizuEv11BfXZycnpl+ZkzZ7QZToHmfeim1SUJBXmSk4uhnhxnK4QQQggh3owkluK9tmbNmuIOoVhJUimEEEIIIbRBEkvxXnNxcSnuEIQQQgghhCjxZLpCCCGEEEIIIcQbkcRSiPfQk5zc4g5BCCGEEEKUIrIUVoh3SP+tF8hUvf2/96zq4PjWnyGEEEIIId4fMmMphBBCCCGEEOKNvDeJZWJiIs2bN6du3bp07979b3neL7/88lr3rlu3DkdHR9LS0t4ohu7duxMWFqb1dovDX/sihBBCCCGEeHe8F0thz58/T1RUFL1796ZFixaYmZm99WcuWbIEPz8/mjVr9tafVVh+fn6sXLnyb+m/tk2YMAFd3ffm7yBCCCGEEEKUKO9FYnnx4kUAOnXqhL29/QvrPH78mLJly/6dYf3trKyssLKyKu4wXkvNmjWLOwQhhBBCCCHES5T6KaBRo0bRv39/AFq1aoWjo6OyJHT9+vVMmDABLy8vgoODAfjll1/o2bMn3t7euLu706FDB5KSkvK1++effzJq1Ch8fX1xcXHB39+fefPmAdCiRQuuXbvG999/j6Ojo/JMgB9//JGuXbvi5eXFBx98QNeuXTl8+PAb9zM5OZkOHTrg4uJCQEAA27dvz1fnr0thU1NTcXR05Mcff2TSpEk0bNgQLy8v4uLiANi5cyeBgYG4u7vTq1cvbt26pdHekydPiImJoUWLFjg7O9OmTRtWrlypUWfUqFEEBQVx6NAh2rVrR/369fnkk084dOiQRr3du3fToUMH3N3d8fT0pF27dhp9eNFS2MOHD9O1a1dcXV1p2LAhQ4cO5fbt20p5Xv82btzI5MmTadiwIT4+PkycOJHs7OxCj+358+fp06cPXl5e1K9fH39/f2WM8qSkpNCzZ0/c3d3x8PAgIiKCmzdvFvoZQgghhBBClGSlfsayX79+VK9enejoaOLi4qhQoYKypHLmzJk0adKEr7/+mpycHACuXbtGs2bN6NGjB3p6ehw4cIABAwYwf/58/Pz8ALh37x6dOnUiNzeXiIgIqlatSmpqKqdPnwYgLi6OPn364OHhQc+ePQGoUqWK0v5HH31E1apVefr0KVu3buXzzz9n7dq11KlT57X6ePfuXXr27EmNGjWYNWsWWVlZTJ8+ncePH+Pk5FTg/TExMbRs2ZJZs2axb98+YmNjycrK4tdff2XQoEHk5OQwZcoUxo8fz/z585X7IiMjOXjwIP3796d27dr89ttvTJw4EWNjY4KCgpR6t2/fZtKkSfTq1QtLS0vi4uLo378/u3btwsTEhCtXrhAREUFgYCCRkZGo1WrOnDlDRkbGS2M+ceIEX3zxBZ6ensyaNYvMzExmzpzJF198wbp16yhTpoxG/5o2bUp0dDQnT54kJiYGGxsb+vXrV6jx7du3L1ZWVkyZMgUTExOuXr3K5cuXlfKUlBS6deuGr68vX3/9NSqViri4OHr16sXGjRvR09Mr1HOEEEIIIYQoqUp9YlmlShWqVq0KQN26dalcuTKpqakA1K5dm6ioKI36Xbt2Vb7Pzc2lYcOGXL58mRUrViiJZWJiInfu3GHLli0vXFpbr149DA0Nsba2xs3NTaPs+WQmNzcXHx8fTp8+zZo1a/jXv/71Wn1MTExErVaTkJCAubk5ADVq1KB9+/aFut/FxUV5tq+vL9u3b2fJkiUkJSVRsWJFAK5fv86MGTOUJcMHDx5kx44dxMfHK++R+vj4kJ6ezuzZszUSy4yMDJYsWYKj47MjLmxsbPjkk0/47bffaNWqFSdPnuTp06eMGzcOExMT4P+xd+9xOd//48cfSWUVFR1YMbW40EGXuiKROW8Oc/zNYeScw3IorMzHmLHaiCif9XEYMxvDtDnMYfrgMz7mKucWPg5zqFholaLUVb8/3Hp/XRKhkTzvt1u3W+/X6/V+vZ/vV9xu1/N6vd6vN7Rq1eqRMcfExFCrVi2WLVuGsbExAPXr16d///5s27ZN795dXV2ZNWuW0u+xY8fYuXNnmRLL9PR0Ll++zLRp02jXrh0ALVq00Gszb948GjduzJdffomBgYFyzc6dO7N161Z69Ojx2OsIIYQQQgjxMqv0S2EfpThRuN+1a9cIDQ3Fz88PFxcXXFxc+OWXX7h48aLS5uDBg7Ro0aLU5zUf5fz58wQGBuLr60vjxo1xcXHh999/V54DfRrHjh2jRYsWSlIJ4OLigoODQ5nOfzCJq1+/Pm+++aaSVBaXFRUV8eeffwJw4MABLCws8PX1paCgQPlp2bIlly9fJiMjQznXxsZGSSrhXtILKEtFVSoVhoaGTJkyhbi4OLKysh4bc0JCAh06dFCSSgC1Wo29vX2JpcUP3p+zs3OZl6laWVlhb2/PggUL2LRpE1evXtWrz83N5fDhw3Tp0gWdTqeMg52dHY6Ojpw8ebJM1xFCCCGEEOJlVulnLB+/tB2LAAAgAElEQVSlVq1aeseFhYWMHTuWzMxMAgMDeeONNzA1NWXp0qX8/vvvSruMjAwaNGjwxNfLzs5m+PDhWFpaMnXqVBwcHDAxMWHu3LncvXv3qe/j+vXryqzs/aytrct0/oO7xBoZGT20DFCeTUxPTyczM7PUpbZXr17F0tISQC/hBZRksLgvR0dHYmJiWLp0KRMmTADuzZzOmDGj1OQ9KyvrofdnbW1dYgntw+6lrONtYGDAihUriIyM5NNPP+X27ds0atSI0NBQfHx8yMzMRKfTERYWVmL2G3iqLx+EEEIIIYR42bzSiWXxssVily5dIikpiSVLltChQwelPD8/X6+dlZWVMnP3JI4dO8a1a9eIiYmhcePGSnlOTo6ShD0NGxsbbt68WaL8xo0bz9Tvo1hYWGBlZcWyZcseWl+/fv0n6s/Pzw8/Pz9ycnI4cOAA4eHhTJ48mfXr15d6/dLuubx3kHV0dGTRokUUFBRw7NgxFi9ezNixY9mzZw/Vq1fHwMCA0aNH6/2bKfYyvtpFCCGEEEKIJ/VKL4V9UPEMWvHsHNxLVA4ePKjXzsfHh99++40rV66U2peRkVGJnUdzc3MB9JZvnj59mrNnzz5T3E2bNuW3337Tm6n7/ffflWdJ/w6+vr789ddfVK1aFTc3txI/r7322lP1a2ZmRqdOnejRowfnzp0rtZ2npye7d+/WS/qPHz9OSkoKXl5eT3Xtx6latSpeXl6MHTuWO3fukJqaiqmpKWq1mnPnzj10HB42kyyEEEIIIURl80rPWD7IycmJ2rVrK7vE5ubm8s9//hNra2uKioqUdkOHDmXz5s0MHjyYcePGUa9ePVJTU0lKSlI2wXFycuLgwYPs378fCwsLHBwc8PDwwNTUlFmzZhEQEMDNmzdZvHix3rOMT2Po0KF8++23jBw5ktGjR3P79m0WL15c5qWwT6Nly5Z06NCBUaNGMWLECBo1akReXh4XLlzgxIkTREZGlrmvdevWceTIEfz8/LC1tSU1NZWNGzfi6+tb6jljxoyhf//+BAQE4O/vT2ZmJhERETg7O9O1a9fyuEXgXuIfHh5Oly5dqFu3Lrdv32b58uXY2toqM6MhISH4+/szYcIEunXrhoWFBWlpaRw6dIi33nrroTOZQgghhBBCVCaSWN7H2NiY6OhoZs+eTVBQELa2towcOZIzZ86wd+9epZ2lpSVr164lIiKCiIgIbt++zeuvv06fPn2UNsHBwcyaNYsJEyaQk5NDWFgYvXv3ZvHixXzxxRd88MEH1KtXj2nTprFx40Zu37791HFbW1uzYsUK5syZw6RJk7C3t2fy5Ml89913zzIcjxUZGcmKFSv4/vvvSU5OxszMDCcnJ+WdoGWlUqnYu3cvn3/+OX/99RfW1tZ07NiRoKCgUs9xdXVl5cqVREREMHHiRExMTPDz8yM0NFTvVSPPysbGBltbW5YuXUpaWhpmZmY0a9aMOXPmKNfx8PBg7dq1REVFMX36dHJzc7Gzs8Pb27vcl+UKIYQQQghRERkU3T8VJ4R4IfLy8khMTOTLZFOyCv7+Ferr+6ge3+gld/jwYTw9PV90GJWCjGX5kbEsXzKe5UfGsnzJeJYfGcvyUzyWxZ87XV1dy3VCRp6xFEIIIYQQQgjxTGQpbAVXWFhIYWFhqfWGhoYldrcVZafT6XjUpH3Vqs/3v8iSt53K9Zuj0tzVFWJsKN8rCSGEEEKI8iGJZQX30UcfERsbW2p98bOb4ul07NiRlJSUUuvj4uJwcHB4jhE9H5JUCiGEEEKI8iSJZQUXGBjI+++/X2p9ZUx6nqcvv/ySu3fvllpva2v7HKMRQgghhBDi5SSJZQXn4OAgyePfSKWq/JvYCCGEEEII8XeT9XBCvELydbIJtBBCCCGEKH8yYylEBbJg55/c1v19/y0/7fX639a3EEIIIYR4dcmMpRBCCCGEEEKIZyKJpXhihw4dQqVScfLkSaWsXbt2zJ49+wVGVTFs2rSJLVu2vOgwhBBCCCGEeK5kKawoF9HR0dSoUeNFh/HCxcbGYmpqSvfu3V90KEIIIYQQQjw3kli+goqKirh79y4mJibl1meTJk3Kra+KKDc3l2rVqr3oMIQQQgghhKiQZCnsKyA0NJRu3bqxf/9+evXqhZubG9u3b+fTTz/l7bffpmnTprRt25aPPvqIjIwMvXMLCgoIDw+nefPmNGvWjClTpnDr1q0S13hwKezgwYMZPXq0Xpvk5GRUKhU7duxQyvbs2UOfPn1Qq9V4enrSq1cvdu3aVab7GjJkCMHBwcrxpUuXUKlUDBs2TCnLzMykUaNG7N69WylLSEhg4MCBuLu74+3tzeTJk7l+/XqJOGNjY5k5cybNmzdXZiCPHDnCoEGD8PT0RK1W07VrV9auXavcs1arZe/evahUKlQqFVFRUWW6FyGEEEIIIV5mMmP5ikhLS2PmzJmMHTsWBwcHjI2NOXLkCBMnTqRWrVr8+eefLF26lJEjR7Jx40blvIULF/LNN98wbtw43N3d2b17N59++mm5xHT58mXGjx9P165dCQ4OpqioiDNnzpCZmVmm8zUaDevWrVOO4+PjMTEx4ejRo+Tn52NkZERCQgIAnp6eACQmJjJ06FA8PT1ZuHAhWVlZREREMHToUDZt2qQ3ixsREUHr1q2ZP38+Op2O7OxsRo8ejVqtZsGCBRgbG3PhwgWys7MBmDlzJlOnTqVatWqEhIQAULt27XIZKyGEEEIIISoySSxfEZmZmcTExNCsWTOl7P7fCwoKcHZ2pmfPnvz++++4uLiQmZnJt99+y8iRI/nggw8AaN26NWPGjOHatWvPHFNSUhL5+fnMmDEDc3NzAFq1alXm8zUaDVFRUVy8eJH69esTHx/Pu+++y5YtW0hMTEStVhMfH0+DBg2wsrICICYmhlq1arFs2TKMjY0BqF+/Pv3792fbtm307t1b6b9hw4aEhYUpxydPniQrK4vJkyejUqkA8PHxUeqdnZ0xNzfH1NQUDw+Ppx8YIYQQQgghXjKyFPYVYWlpqZdIAvz000/06tULtVqNi4sLPXv2BODixYsAnDlzhjt37tC5c2e9895+++1yiUmlUmFoaMiUKVOIi4sjKyvric738PDA2NgYrVYL3Jux9PX1pWnTpkqZVqtFo9Eo5yQkJNChQwclqQRQq9XY29srs5vF2rVrp3dcr149zM3NmTVrFj///DM3b958oniFEEIIIYSorCSxfEVYW1vrHf/yyy98+OGHNGnShIULF7J+/XqWL18OQF5eHoDy3GGtWrX0zn3w+Gk5OjoSExNDdnY2EyZMwMfHh4CAAK5cuVKm801MTHB3d0er1ZKamkpKSgoajQZvb2+0Wi3Z2dmcPn1aL7HMysoqMRZwb3weXIL74H1aWFiwcuVKzM3NCQkJwdfXl4EDB5KUlPQUdy+EEEIIIUTlIYnlK8LAwEDveMeOHTRq1Ii5c+fy1ltv0bRpU2W5aDEbGxuAEjNzZZmpMzY2Jj8/X6/sYc9O+vn5sWbNGrRaLQsXLuTcuXNMnjy5TPcE4OXlRXx8PFqtFicnJ6ytrdFoNBw5cgStVotOp9NLLC0sLB4a/40bN7CwsNAre3DMANzd3Vm2bBkJCQksW7aM27dvExAQQGFhYZljFkIIIYQQorKRxPIVlZubq7ccFGDLli16xyqVimrVqrFz50698vt3dS1NnTp1uHDhgl7CtX///lLbm5mZ0alTJ3r06MG5c+fKcgsAeHt7c+3aNX744QclgfTw8KCgoICVK1fi6OioN0Pp6enJ7t279ZLe48ePk5KSgpeXV5mva2JiQuvWrRk0aBDXr19XlvEaGRkpM75CCCGEEEK8KmTznldUy5YtmT17NlFRUXh6evLf//6Xf//733ptLCwseP/991m+fLmy7HT37t1lWvr5zjvvsGHDBj755BM6d+7MyZMn2bRpk16bdevWceTIEfz8/LC1tSU1NZWNGzfi6+tb5vtQq9UYGRmh1Wrp168foL9Etris2JgxY+jfvz8BAQH4+/uTmZlJREQEzs7OdO3a9ZHX2rt3Lxs2bKBjx47UqVOH9PR0Vq5ciYuLC5aWlgA4OTkRGxtLXFwctra22NraYmdnV+b7EUIIIYQQ4mUkieUrqn///iQnJ7Nu3Tq++uorWrRoweLFi5UNfIoFBQVRUFDAqlWryM/Pp23btsyYMYPAwMBH9u/r68u0adNYvXo1P/30Ey1atCA8PJz+/fsrbVQqFXv37uXzzz/nr7/+wtramo4dOxIUFFTm+zA1NcXFxYVjx47pLXnVaDQkJCSUmIV0dXVl5cqVREREMHHiRExMTPDz8yM0NFTvVSMPU69ePQwNDVm0aBHXr1/H0tKSli1b6i3dHTVqFJcvXyY0NJSsrCwCAwMZP358me9HCCGEEEKIl5FBUVFR0YsOQohXXV5eHomJiexKseG27u/7vufTXq//bX1XNIcPH1beXyqejYxl+ZGxLF8ynuVHxrJ8yXiWHxnL8lM8lsWfO11dXR87sfIk5BlLIYQQQgghhBDPRJbCigqpqKgInU5Xan2VKlWoUqXyfS8S3NmuXL85elC+rggjw5K73QohhBBCCPEsJLEUFVJsbCzTpk0rtb5Xr16Eh4c/x4gqB0kqhRBCCCHE30ESS1EhtW3blo0bN5Za/+A7N4UQQgghhBAvjiSWokKysrKS5FEIIYQQQoiXROV7SE0IoadAJxs/CyGEEEKIv5fMWApRgazfcZO75fy6kRG9bcu1PyGEEEIIIR4kM5ZCCCGEEEIIIZ6JJJaVwO7du/n222//lr6Tk5OJiorizz///Fv6r2xWrVrFvn37XnQYQgghhBBCPFeSWFYCu3fvZu3atX9L3ykpKURHR5OWlva39F/ZrF69WhJLIYQQQgjxypHEUjx3eXl5LzqEJ5abm/uiQxBCCCGEEKLCksTyJRcaGkpsbCxnz55FpVKhUqkIDQ0F4MSJEwwfPhy1Wk2zZs0YP348165dU84NDAykTZs2ZGVlKWX79u1DpVKxZcsWDh06hL+/PwB9+/ZV+gfYtGkTKpWK9PR0vXgGDx7M6NGjleOoqCjUajWJiYkMGDAAd3d3li9fDsCFCxcIDAxEo9HQtGlThg0bxtmzZ8t87x06dCAiIkI5/u9//4tKpeIf//iHUnbq1ClUKhWnTp1Synbv3k2vXr1wc3OjZcuWzJo1i5ycHKX+0KFDqFQq9u3bR1BQEF5eXowZMwaAPXv20KdPH9RqNZ6envTq1Ytdu3YB0K5dO1JSUvj222+Vsdq0aVOZ70cIIYQQQoiXlewK+5IbN24c6enpXLhwgfnz5wNQs2ZNTpw4wfvvv4+vry/z58+noKCA6OhoRowYwebNmzE0NGT27Nm8++67fPLJJ0RERJCens706dPp0qUL3bt3Jzs7m48//pjZs2cTFhaGk5PTU8WYn5/PpEmT8Pf3Z9KkSZibm5OcnMyAAQNwdHRkzpw5GBkZ8dVXX+Hv78+uXbuoXr36Y/vVaDTEx8crx/Hx8ZiYmJQoq1GjhpIQx8XFERgYSOfOnZk0aRLJyclERERw8eJFVq1apdf/jBkz6Nq1K4sXL8bAwIDLly8zfvx4unbtSnBwMEVFRZw5c4bMzEwAoqOjCQgIoFmzZgwfPhyAevXqPdWYCSGEEEII8TKRxPIlV69ePWrWrElqaioeHh5K+fTp02ncuDFffvklBgYGALi6utK5c2e2bt1Kjx49qFmzJnPnziUgIID27dvz888/U6VKFWbNmgWAubk5zs7OADRo0AA3N7enijE/P5+JEyfSvXt3pSw0NBQzMzNWrVpFtWrVAPD29qZDhw588803jBs37rH9ajQatmzZwu3btzE1NSU+Pp733nuPb775hj///BM7Ozvi4+Px9PSkSpV7k/PR0dG4ubkRGRmpjIuFhQWTJ0/m0KFDNG/eXOm/TZs2hISEKMc7duwgPz+fGTNmYG5uDkCrVq2U+iZNmmBsbIy1tbXe30IIIYQQQojKTpbCVkK5ubkcPnyYLl26oNPpKCgooKCgADs7OxwdHTl58qTStk2bNvTv35+QkBB2795NWFgYFhYW5R5T27Zt9Y73799P+/btqVq1qhJftWrV8PDw4MSJE2XqU6PRkJ+fz9GjR7l79y7Hjx/n3XffxcHBQZm1TEhIQKPRAJCTk8OpU6d45513lKQS4O2336Zq1aokJCTo9d+uXTu9Y5VKhaGhIVOmTCEuLk5vCbEQQgghhBCvMpmxrIQyMzPR6XSEhYURFhZWor5u3bp6x927d2fdunU4OzvTsmXLco/ntddeU2b4iv3111+sXr2a1atXl2jfqFGjMvVbt25d6tSpg1arxdjYGCMjI1xcXPD29kar1dKoUSPS09OVxPLWrVsUFRVhbW2t10/VqlWxtLRUlrQWq1Wrlt6xo6MjMTExLF26lAkTJgDg6+vLjBkzSoypEEIIIYQQrxJJLCuh6tWrY2BgwOjRo+nQoUOJ+ho1aii/5+Xl8fHHH6NSqTh37hyrV69myJAhj72GiYkJcG+Z6/0yMjIwNTXVK7t/drCYhYUFbdq0YeDAgSXqipfGloWXlxfx8fEYGxvTrFkzDA0N0Wg0LFu2jEaNGmFmZoaLiwvwf+Ny8+ZNvT4KCgrIyMgoMVP7sLj9/Pzw8/MjJyeHAwcOEB4ezuTJk1m/fn2ZYxZCCCGEEKKykcSyEjAyMtJ7hYepqSlqtZpz584RFBT0yHPnzZvHn3/+yebNm9m4cSMRERG0atWKN998U+kbSr4ipHbt2gCcP38eOzs74N47L//44w9ef/31x8bcsmVL/ve//9GkSRMMDQ3LfrMP8Pb2Zvbs2RQWFipLVzUaDdOmTWP79u2o1WqlfzMzMxo3bsz27dsZNmyY0seuXbsoKCjAy8urzNc1MzOjU6dOnDp1iq+//lopf/BvIYQQQgghxKtAEstK4M0332Tjxo1s3rwZR0dHrKysCAkJwd/fnwkTJtCtWzcsLCxIS0vj0KFDvPXWW3To0IEDBw6wZs0awsPDsbe3Z9y4cezbt4+pU6fy/fffY2RkRP369TE0NGTDhg0YGhpStWpV3NzcaNq0Kfb29nz22WcEBweTm5vL0qVLsbS0LFPMEydOpG/fvgwbNox+/fphY2PDjRs3OHr0KI6Ojg+dyXyY+5+zLN5o5/4lssHBwXrtAwMD+eCDDwgODqZnz55cuXKFBQsW4OPjo7dxz8OsW7eOI0eO4Ofnh62tLampqWzcuBFfX1+ljZOTEwcPHmT//v1YWFjg4OCAlZVVme5FCCGEEEKIl5Vs3lMJ9O3bl3feeYe5c+fSt29foqOj8fDwYO3atdy9e5fp06czcuRIFi1aRGFhIc7OzmRmZjJt2jQ6duxIz549gXuzbfPmzePcuXMsWbIEuPfqko8//pjDhw8zaNAg+vbtC9x7LnHJkiWYmZkRFBREVFQUEyZMwNHRsUwx161blw0bNmBjY8OcOXMYPnw48+bN48aNG0+0+6yjoyM2Nja89tpruLq6KuXFz1U+OAvZvn17oqKiuHDhAuPGjSM6Opru3bsTHR392GupVCqysrL4/PPPGT58OJGRkXTs2JHPPvtMaRMcHEydOnWYMGECffv2Zc+ePWW+FyGEEEIIIV5WBkVFRUUvOgghXnV5eXkkJiaSlFyHu7ryXUgwordtufb3sjh8+DCenp4vOoxKQcay/MhYli8Zz/IjY1m+ZDzLj4xl+Skey+LPna6ursq+KeVBZiyFEEIIIYQQQjwTecZSVEiFhYUUFhaWWl+lShWqVKl834u893atcv3mCKBAV0RVw5I73AohhBBCCFFeJLEUFdKSJUse+dxjYGAg48ePf44RvbwkqRRCCCGEEH83SSxFhfTee+/x1ltvlVpva/tqPjcohBBCCCFERSSJpaiQ7OzslPdjCiGEEEIIISq2yveQmhACAJ1ONnwWQgghhBDPh8xYClGBxG37C11B+fy37P6edbn0I4QQQgghxOPIjKUQQgghhBBCiGciiWUl1a5dO2bPnv3crpeVlUVUVBTnzp17btesiGQchBBCCCHEq0iWwlZS0dHR1KhR47ldLysri+joaBo0aICzs/Nzu25FI+MghBBCCCFeRZJYVjK5ublUq1aNJk2avOhQnklRURF3797FxMTkRYeiyMvLq1DxCCGEEEIIUVHIUtgXIDQ0lG7duvHrr7/SvXt33Nzc6N27N0ePHtVr99NPP9GjRw/c3Nzw9fUlLCyMu3fvKvWbNm1CpVJx/PhxRo4ciVqtVpa/PrgUtviahw4domfPnri7u9OvXz/Onz9PdnY2ISEheHp60rZtW7777rsSMZ84cYLhw4ejVqtp1qwZ48eP59q1awAkJyfTvn17ACZOnIhKpUKlUpGcnAzA3bt3iYyMpF27dri6utK5c2e+//77h47J/v376dWrF25ubmzfvv2R43j16lVUKhW//vqrUrZw4UJUKhWbN29WylatWoW3tzdFRfd2SS0sLCQmJob27dvj6upKx44dWbVqlV7fUVFRqNVqEhMTGTBgAO7u7ixfvhyA5cuX06lTJ9zc3GjevDmDBw/m/Pnzjx0HIYQQQgghKiuZsXxBrl+/zsyZMxk/fjw1atRg2bJljBgxgl9++YVatWqxevVqwsPDGTx4MFOnTuXKlSssXLiQO3fulHh2MigoiL59+zJixIhHzqhdv36dOXPmMGbMGExMTAgLCyMoKIjXX38dlUrF4sWL2b59O5988gnu7u64uroC95LK999/H19fX+bPn09BQQHR0dGMGDGCzZs3Y2trS3R0NIGBgQQHB9O8eXMAbG1tAQgODubQoUN88MEHNGzYkN9++41Zs2ZhZmZGt27dlPjS0tKYOXMmY8eOxcHBARsbm0eOYZ06dXBwcCA+Pp7WrVsDkJCQgImJCfHx8bz77rtKmaenJwYGBgB88cUXfP311wQEBKDRaDhw4ABhYWHk5OTwwQcfKP3n5+czadIk/P39mTRpEubm5vz4448sXLiQCRMm4OHhQXZ2NkePHiU7O5u6des+chyEEEIIIYSorCSxfEEyMjKIjIzEx8cHAI1GQ5s2bZSEJzIykmHDhjF16lTlnBo1ajB16lQCAgJwcHBQyt977z3GjBnz2GtmZmayevVqVCoVcO95wGnTptGsWTOCgoIA8Pb2ZteuXezcuVNJLOfNm0fjxo358ssvleSseOZx69at9OjRg8aNGwPwxhtv4OHhoVzz0KFD/PLLLyxdupQ2bdoA0LJlSzIyMli0aJFeYpmZmUlMTAzNmjUr8zhqNBq0Wi1wb6nqiRMn6NevnzKLWVRUREJCAqNHjwYgPT2dNWvWMGzYMOWeW7VqRU5ODsuXL2fo0KGYmZkB9xLLiRMn0r17d+V6P/zwAyqVSukPUGYpgVLHQQghhBBCiMpMlsK+INWrV1eSSriXNLZo0YJjx45x7NgxcnJy6NKlCwUFBcqPj48POp2OpKQkvb7atWtXpmva2NgoSSVA/fr1gXuJVTEjIyPs7e25evUqcO+ZzcOHD9OlSxd0Op0Si52dHY6Ojpw8efKR1zxw4AAWFhb4+vrq3UvLli25fPkyGRkZSltLS8snSirhXmKZmJjInTt3OHbsGEZGRgwZMoSLFy+SlpbG2bNn+euvv/Dy8gLuzb7m5+fTpUsXvX66du3K7du3OXXqlF5527Zt9Y6bNGlCUlISn332GfHx8XpLk4UQQgghhHhVyYzlC1KzZs0SZbVq1eLixYukp6cD0Lt374eem5qaqndsbW1dpmtaWFjoHRsZGQH3ktwHy4sTpszMTHQ6HWFhYYSFhZXos27duo+8Znp6OpmZmbi4uDy0/urVq1haWj7RfdzP29ub/Px8jh49ypEjR/D09KRu3brY29sTHx9PRkYGZmZmymZGmZmZACWW2RZf+/5E97XXXsPc3FyvXe/evbl9+zbr16/n66+/xszMjHfffZcPP/wQU1PTJ45fCCGEEEKIykASyxekOHm8382bN7GxsVESwKioKOrUqVOi3cPK/i7Vq1fHwMCA0aNH06FDhxL1j3uliYWFBVZWVixbtuyh9cWzpoCyzPZJ1K1bl9q1a6PVajl69Ci+vr7AvYRTq9WSmZmJp6cnhoaGAEoSe+PGDezs7JR+bty4oVdfWjxVqlTB398ff39/0tLS2LlzJ1988QXm5uZMmTLlieMXQgghhBCiMpDE8gW5desWBw8eVJbD3rp1i99++41BgwbRrFkzTE1NuXr1Kp06dXqhcZqamqJWqzl37pzyTOLDFM9+5uXl6ZX7+vqyfPlyqlatqjx/WN68vLw4cOAA//vf/5QYNRoNy5cv59atW/j7+ytt3dzcMDIyYvv27XqzqD///DOmpqZP9JoWW1tbBg8ezI4dOzh37hxQ+jgIIYQQQghRmUli+YJYWloyffp0vV1hAYYMGUL16tWZOHEi8+fP59q1a7Ro0QIjIyOSk5PZs2cPM2fOpHbt2s8t1pCQEPz9/ZkwYQLdunXDwsKCtLQ0Dh06xFtvvUWHDh2wsbGhRo0abN68GQcHB4yNjVGpVLRs2ZIOHTowatQoRowYQaNGjcjLy+PChQucOHGCyMjIZ47P29ubrVu3YmpqqiSL3t7efPTRR8C9JLNYzZo1GTx4MF999RXGxsY0a9aMgwcP8v333zN+/PjHLmf9+OOPqV69Oh4eHlhYWHD8+HGOHz9OSEgIQKnjYGxs/Mz3KYQQQgghREUlieULYmNjw9SpU/niiy+4dOkSDRo0YPny5cqzfkOHDqV27dqsXLmS7777DkNDQ+zt7fHz83vs8tPy5uHhwdq1a4mKimL69Onk5uZiZ2eHt7c3zs7OwL0lop999hkLFy5k6NCh3L17l7i4OBwcHIiMjGTFihV8//33JCcnY2ZmhpOTk95uq8+iOPV4s6kAACAASURBVHH08PBQZgyLl8hmZmYqu9sWmzp1KjVq1GDDhg0sXbqU2rVrExISwrBhwx57LbVazYYNG9i4cSN37tzB3t6eyZMnM3jw4MeOgxBCCCGEEJWVQVHxW+PFcxMaGkpiYiJbt2590aGICiIvL4/ExET+vGSPrqB8vu/p/t6Tb4ZUmRw+fBhPT88XHUalIGNZfmQsy5eMZ/mRsSxfMp7lR8ay/BSPZfHnTldXV0xMTMqtf3ndiBBCCCGEEEKIZyJLYUWFpdPpeNSEuqGh4VPtJFuRte9qVW7fHOl0RRgaVq7xEUIIIYQQFZMkli9AeHj4iw7hpTB06FC0Wm2p9atXr6Z58+bPMaKXiySVQgghhBDieZHEUlRYn3zyCTk5OaXWOzo6PsdohBBCCCGEEKWRxFJUWE5OTi86BCGEEEIIIUQZyOY9QlRChTrZ7FkIIYQQQjw/MmMpRAUSH5tOUf6z/7dsPdimHKIRQgghhBCibGTGUgghhBBCCCHEM5HEUohylJycTFRUFH/++eeLDkUIIYQQQojnRhJLIcpRSkoK0dHRpKWlvehQhBBCCCGEeG4ksRTlKjc390WH8LcoKioiLy/vRYchhBBCCCFEhSSJpSjV8ePHGTt2LK1atcLDw4Pu3buzYcMGpf7QoUOoVCr27dtHUFAQXl5ejBkzBoDs7GzmzJlD69atcXV1pXv37uzevVuv/3379jF8+HB8fHxQq9X06dOnRJtHSUhIQKVSceHCBaVsypQpqFQqDh8+rJTNnTuXHj16KMd5eXl8/vnnSmzdunXjp59+0us7NDSUbt26sX//fnr16oWbmxvbt28nPz+fefPm0bZtW1xdXfH19WXUqFHcvHmTQ4cO4e/vD0Dfvn1RqVSoVKoy348QQgghhBAvK9kVVpQqJSUFtVpNv379qFatGsePH2f27NncvXuX999/X2k3Y8YMunbtyuLFizEwMCA/P5/hw4dz7do1xo8fj729PTt37mT8+PGsWbMGT09Ppf82bdowbNgwDA0NOXDgAIGBgcTExPDWW289Nj53d3dMTEyIj49X3nmZkJCglBVfJyEhAY1Go5w3ZcoU9u3bx8SJE2nYsCE///wzH374IUVFRfTs2VNpl5aWxsyZMxk7diwODg7Y2NiwdOlSvvvuO6ZMmUKDBg3IyMjg4MGD5Obm4uLiwscff8zs2bMJCwuT93AKIYQQQohXhiSWolRdunRRfi8qKsLLy4v09HTWrVunl1i2adOGkJAQ5XjTpk0kJiYSGxurzNj5+vqSkpJCVFQUq1atAmDgwIHKOYWFhXh7e3Pp0iXWrVtXpsTS2NiYpk2botVq6devH1euXOHq1asMGjSI+Ph4xowZw61btzh9+jRjx44F4PTp0+zatYuPP/5YuYfWrVuTlpbG4sWL9RLLzMxMYmJiaNasmVJ24sQJWrVqpXf/nTp1Un53dnYGoEGDBri5uT32HoQQQgghhKgMZCmsKFVmZiZz5syhXbt2uLi44OLiwqpVq7h48aJeu3bt2ukdHzhwgIYNG/Lmm29SUFCg/Pj6+nLixAml3bVr1wgNDcXPz0/p/5dffinR/6NoNBri4+MBiI+Px9nZmbfffpsjR45QUFBAQkIChYWFeHl5AShLZO9PmgG6du1KSkoKV69eVcosLS31kkqAJk2asG/fPhYvXsyJEyfQ6XRljlUIIYQQQojKSmYsRalCQ0M5cuQI48aNo2HDhpibm/Pjjz+yZs0avXa1atXSO05PT+fUqVO4uLg8tN+srCzMzc0ZO3YsmZmZBAYG8sYbb2BqasrSpUv5/fffyxyjRqNhyZIlXLp0ifj4eDQaDU2bNkWn05GYmKgkmzVr1gTuJctVq1bFyspKrx9ra2ulvk6dOnpl9xs7diyGhobExsayZMkSLC0tGTBgAOPHj8fQ0LDMcQshhBBCCFGZSGIpHiovL499+/bx4YcfMmTIEKX8xx9/LNHWwMBA79jCwgKVSsXcuXMf2repqSmXLl0iKSmJJUuW0KFDB6UuPz//ieJUq9UYGRmh1WqJj48nODhYb4lscbJ5f2wFBQVkZGRgaWmplN+4cUOpL+2+4N7y28DAQAIDA7ly5Qo//fQT0dHR2NnZMWDAgCeKXQghhBBCiMpClsKKh7p79y46nQ5jY2OlLC8vj127dj32XF9fX65cuYKtrS1ubm4lfqpWraq8usPIyEg578aNGxw8ePCJ4qxWrRqurq5s3ryZK1euKEteNRoNe/fuJSkpSS+xLN7QZ/v27Xr9/Pzzz9jb2yuzlWVRt25dAgMDqV27NufPn9e7H3k1iRBCCCGEeJXIjKV4qOrVq+Pm5sbSpUuxtLTE2NiYlStX6iWapenRowfr169n0KBBDB8+HCcnJ27dusX//vc/rl+/zsyZM3FycqJ27drMnz8fnU5Hbm4u//znP7G2tqaoqOiJYvX29uZf//oX9evXx9bWFvi/JbKAkmwCNGrUiM6dOxMeHk5ubi7Ozs5s376dX3/9lc8///yx1xo3bhxNmjShSZMmmJmZ8euvv3L16lV8fHwAqF+/PoaGhmzYsAFDQ0OqVq0qm/gIIYQQQohKTxJLUaqIiAhmzpzJ9OnTqV69Ov3798fY2Jh58+Y98jxjY2NWrVrFkiVLWLZsGWlpaVhYWNCwYUP+3//7f0qb6OhoZs+eTVBQELa2towcOZIzZ86wd+/eJ4pTo9Hwr3/9S29msniJ7Ouvv46dnZ1e+3nz5rFw4UJWrFhBRkYGb7zxBl988YXeuy5L4+npyfbt21m1ahU6nY433niDzz//nPbt2wNQs2ZNPv74Y5YvX87WrVspKCjgzJkzT3Q/QgghhBBCvGwMip50ekgIUe7y8vJITEzkztnXKcp/9u97Wg+2KYeoXm6HDx9Wlj6LZyNjWX5kLMuXjGf5kbEsXzKe5UfGsvwUj2Xx505XV1dMTEzKrX95xlIIIYQQQgghxDORpbCiwtLpdI983rJq1cr3z1fTq2a5fHNUqCuiimHJXW2FEEIIIYT4O1S+T+ai0ujYsSMpKSml1sfFxeHg4PAcI3p5SFIphBBCCCGeJ0ksRYX15Zdfcvfu3VLri3eAFUIIIYQQQrxYkliKCkulUr3oEIQQQgghhBBlIJv3CFHJFBbIRs9CCCGEEOL5khlLISqQ02tvYnD32f5bugfIEmEhhBBCCPF8yYylEEIIIYQQQohnIollJbVq1Sr27dtXorxdu3bMnj37BUT06ti0aRNbtmx50WEIIYQQQgjx3EhiWUmtXr36oYml+PvFxsaydevWFx2GEEIIIYQQz40klkKUUW5u7osOQQghhBBCiApJEsuncP78eQICAmjevDlNmzalU6dOREdHAxAaGkq3bt04dOgQPXv2xN3dnX79+nH+/Hmys7MJCQnB09OTtm3b8t1335Xoe/fu3fTq1Qs3NzdatmzJrFmzyMnJ0WuTmprKxIkT8fLyomnTpgwePJgTJ04o9e3atSMlJYVvv/0WlUqFSqVi06ZNen2sXbuWdu3a0axZM0aOHMnVq1eVuuTkZFQqFZs3b2bOnDl4e3srseTl5en1k5aWRkhICC1atMDNzY333nuPhIQEvTZ79uyhT58+qNVqPD096dWrF7t27Spz/aMMGTKE4OBg5fjSpUuoVCqGDRumlGVmZtKoUSN2796tlCUkJDBw4EDc3d3x9vZm8uTJXL9+vcQYxMbGMnPmTJo3b0737t0BOHLkCIMGDcLT0xO1Wk3Xrl1Zu3YtAIMHD0ar1bJ3715l7KOiosp0L0IIIYQQQrysZFfYpzBmzBhq1qzJ3LlzMTc358qVK1y6dEmpv379OnPmzGHMmDGYmJgQFhZGUFAQr7/+OiqVisWLF7N9+3Y++eQT3N3dcXV1BSAuLo7AwEA6d+7MpEmTSE5OJiIigosXL7Jq1SoAsrOzGTRoEAAzZszAzMyM5cuX4+/vzw8//MCbb75JdHQ0AQEBNGvWjOHDhwNQr149Jb49e/Zw4cIF/vGPf5CTk0NYWBjTpk1TrlEsMjISPz8/FixYQFJSEpGRkdja2jJu3DgAsrKyGDBgACYmJnz00UdYWlqyYcMGhg8fzrZt26hbty6XL19m/PjxdO3aleDgYIqKijhz5gyZmZkAj61/HI1Gw7p165Tj+Ph4TExMOHr0KPn5+RgZGSmJrqenJwCJiYkMHToUT09PFi5cSFZWFhEREQwdOpRNmzZhYmKi9BcREUHr1q2ZP38+Op2O7OxsRo8ejVqtZsGCBRgbG3PhwgWys7MBmDlzJlOnTqVatWqEhIQAULt27TLdixBCCCGEEC8rSSyfUHp6OpcvX2batGm0a9cOgBYtWui1yczMZPXq1ahUKuBeAjZt2jSaNWtGUFAQAN7e3uzatYudO3cqiWV0dDRubm5ERkZiYGAAgIWFBZMnT+bQoUM0b96cTZs2kZqayubNm2nYsCEAPj4+tGvXjmXLlhEeHk6TJk0wNjbG2toaDw+PEveg0+n417/+pSRQN2/eJCwsjKysLGrUqKG0c3V1ZdasWQC0atWKY8eOsXPnTiWx/Prrr8nIyGDHjh3Y2Ngo7bp160ZMTAxz584lKSmJ/Px8ZsyYgbm5udKm2OPqH0ej0RAVFcXFixepX78+8fHxvPvuu2zZsoXExETUajXx8fE0aNAAKysrAGJiYqhVqxbLli3D2NgYgPr169O/f3+2bdtG7969lf4bNmxIWFiYcnzy5EmysrKYPHmy8vf18fFR6p2dnTE3N8fU1PShYy+EEEIIIURlJEthn5CVlRX29vYsWLCATZs26S0hLWZjY6MkHXAvaQH9hMnIyAh7e3vl/JycHE6dOsU777yjJJUAb7/9NlWrVlVm3RISEmjQoIGSVAKYmZnRtm3bEktQS6PRaPRm5ZydnQG4du2aXrsHEzxnZ2e9NgcOHKB58+ZYWVlRUFBAQUEBhYWF+Pj4KEtzVSoVhoaGTJkyhbi4OLKysvT6fFz943h4eGBsbIxWqwXuzVj6+vrStGlTpUyr1aLRaJRzEhIS6NChg5JUAqjVauzt7UuMYfGXB8Xq1auHubk5s2bN4ueff+bmzZtPFK8QQgghhBCVkSSWT8jAwIAVK1bw5ptv8umnn/LWW2/Ro0cPDh48qLSxsLDQO8fIyAiA6tWrlyi/e/cuALdu3aKoqAhra2u9NlWrVsXS0lJZGpqVlVWiDYC1tXWZl4+WFt+Dz0/eP3v5YLxwb/Y2Li4OFxcXvZ81a9YoCbOjoyMxMTFkZ2czYcIEfHx8CAgI4MqVK2WqfxwTExPc3d3RarWkpqaSkpKCRqPB29sbrVZLdnY2p0+f1kssn2QMa9WqVWLsVq5cibm5OSEhIfj6+jJw4ECSkpLKFK8QQgghhBCVkSyFfQqOjo4sWrSIgoICjh07xuLFixk7dix79ux56j6rV6+OgYFBiRmwgoICMjIylGTQwsKCCxculDj/xo0bJRLGv5uFhQWtWrVi0qRJJeoMDQ2V3/38/PDz8yMnJ4cDBw4QHh7O5MmTWb9+fZnqH8fLy4sff/wRrVaLk5MT1tbWaDQaVqxYgVarRafT6SWWFhYWD51pvHHjhjJ7W+z+2eNi7u7uLFu2jLy8PLRaLREREQQEBPCf//yHKlXkuxohhBBCCPHqkU/Bz6Bq1ap4eXkxduxY7ty5Q2pq6lP3ZWZmRuPGjdm+fbte+a5duygoKMDLywu4twHN2bNnOXfunNLm9u3b7NmzR2kD92YXH5yBLG8tW7bk/PnzODk54ebmpvfTpEmTEu3NzMzo1KkTPXr00Iu/rPWl8fb25tq1a/zwww9KAunh4UFBQQErV67E0dFRb4bS09OT3bt3k5+fr5QdP36clJQUvTF8HBMTE1q3bs2gQYO4fv26soz3eYy9EEIIIYQQFYnMWD6h06dPEx4eTpcuXahbty63b99m+fLl2NralpjtelKBgYF88MEHBAcH07NnT65cucKCBQvw8fGhefPmAPTu3ZtVq1YxevRoJk2ahKmpKcuXLycvL49Ro0YpfTk5OXHw4EH279+PhYUFDg4OyuY15WXYsGFs27aNQYMG4e/vj729PRkZGSQmJmJsbExgYCDr1q3jyJEj+Pn5YWtrS2pqKhs3bsTX1xfgsfVloVarMTIyQqvV0q9fP0B/iWxxWbExY8bQv39/AgIC8Pf3JzMzk4iICJydnenatesjr7V37142bNhAx44dqVOnDunp6axcuRIXFxcsLS2Be2MfGxtLXFwctra22NraYmdn9yRDK4QQQgghxEtFEssnZGNjg62tLUuXLiUtLQ0zMzOaNWvGnDlz9DbEeRrt27cnKiqKJUuWMG7cOKpXr0737t2ZMmWK0sbc3Jw1a9YQHh7OrFmzKCgowN3dndWrV/Pmm28q7YKDg5k1axYTJkxQXily/26n5cHS0pLvv/+eRYsWsWDBAv766y+srKxwcXFh8ODBwL3Nefbu3cvnn3/OX3/9hbW1NR07dlR2x31cfVmYmpri4uLCsWPH9Ja8ajQaEhISSsxCurq6snLlSiIiIpg4cSImJib4+fkRGhr62L9hvXr1MDQ0ZNGiRVy/fh1LS0tatmzJ5MmTlTajRo3i8uXLhIaGkpWVRWBgIOPHjy/z/QghhBBCCPGyMSgqKip60UEI8arLy8sjMTGRqifrYHD32b7vcQ+wLaeoXm6HDx9W3l0qno2MZfmRsSxfMp7lR8ayfMl4lh8Zy/JTPJbFnztdXV2feWLsfvKMpRBCCCGEEEKIZyJLYUWFVVRUhE6nK7W+SpUqlW4X1kYDaj3zN0eFBUVUqVpyN1shhBBCCCH+LpJYigorNjaWadOmlVrfq1cvwsPDn2NELwdJKoUQQgghxPMmiaWosNq2bcvGjRtLrS/vXW6FEEIIIYQQT0cSS1FhWVlZSfIohBBCCCHES6ByPaAmhKCoQDZ6FkIIIYQQz5fMWApRgVxdfh3DvGf7b1k3uHY5RSOEEEIIIUTZyIylEEIIIYQQQohnIonlKywrK4uoqCjOnTv3okOpVDZt2sSWLVtedBhCCCGEEEI8N5JYvsKysrKIjo6WxLKcxcbGsnXr1hcdhhBCCCGEEM+NJJZClFFubu6LDkEIIYQQQogKSRLLhzh27BgjRozA09MTtVpN79692b17NwAZGRlMnz4dHx8f3N3d6dOnD7/++qve+YMHD2b06NFs376dzp0707RpU4YPH87169dJS0tj7NixqNVqOnfuzC+//PLQc3/66Sc6duyIm5sbgwcP5vz583rtVq1aRZ8+ffD09KRFixaMGDGCs2fPlvlekpOTad++PQATJ05EpVKhUqlITk4mOTkZlUrF5s2bmTNnDt7e3rRs2ZJZs2aRl5en139aWhohISG0aNECNzc33nvvPRISEvTa7Nmzhz59+qBWq/H09KRXr17s2rWrzPWPMmTIEIKDg5XjS5cuoVKpGDZsmFKWmZlJo0aNlL8hQEJCAgMHDsTd3R1vb28mT57M9evXlfriMYiNjWXmzJk0b96c7t27A3DkyBEGDRqkjGnXrl1Zu3at8vfTarXs3btXGdOoqKgy3YsQQgghhBAvK9kV9gGHDx9myJAhuLq6Mnv2bCwtLUlKSuLq1avodDpGjRrF5cuXCQ4Opnbt2qxdu5bRo0fz1Vdf0aJFC6WfU6dOcfPmTaZOncqdO3eYM2cOH330Ebdu3aJDhw4MGjSIb7/9lqCgIP79739ja2urnJuUlMTly5cJCgrCwMCAyMhIRo0axY4dOzA2Ngbg2rVrvP/++7z++uvcuXOH9evX079/f7Zv36709ah78fPzIzo6msDAQIKDg2nevDkAtra2pKWlARAZGYmfnx8LFiwgKSmJyMhIbG1tGTduHHBvKe2AAQMwMTHho48+wtLSkg0bNjB8+HC2bdtG3bp1uXz5MuPHj6dr164EBwdTVFTEmTNnyMzMBHhs/eNoNBrWrVunHMfHx2NiYsLRo0fJz8/HyMhISXQ9PT0BSExMZOjQoXh6erJw4UKysrKIiIhg6NChbNq0CRMTE6W/iIgIWrduzfz589HpdGRnZzN69GjUajULFizA2NiYCxcukJ2dDcDMmTOZOnUq1apVIyQkBIDatWWXViGEEEIIUblJYvmAefPmUbduXdasWUPVqveGx9fXF4C4uDhOnDjB0qVLadOmDQCtW7emW7duLFmyRC+xzMrKIjY2llq1agFw8eJFoqOjmTJlCiNHjgSgcePG+Pj48O9//5v+/fsr5964cYNvvvmG+vXrK+3eeecdYmNj6devHwChoaFKe51Oh6+vL23atGHbtm3KbN2j7qW4X4A33ngDDw+PEmPh6urKrFmzAGjVqhXHjh1j586dSmL59ddfk5GRwY4dO7CxsVHadevWjZiYGObOnUtSUhL5+fnMmDEDc3NzpU2xx9U/jkajISoqiosXL1K/fn3i4+N599132bJlC4mJiajVauLj42nQoAFWVlYAxMTEUKtWLZYtW6Yk6vXr16d///5s27aN3r17K/03bNiQsLAw5fjkyZNkZWUxefJkVCoVAD4+Pkq9s7Mz5ubmmJqaPnRMhRBCCCGEqIxkKex97ty5w/Hjx+nVq5eSiN0vISEBMzMzJakEqFKlCu+88w5Hjx5Fp9Mp5Y0aNVKSSkBJEu9PmmrWrImFhQVXr17Vu06DBg2U9sXnNmjQgGPHjillx44dY/jw4TRv3pwmTZrg5uZGeno6f/zxR5nupSweTPCcnZ25du2acnzgwAGaN2+OlZUVBQUFFBQUUFhYiI+PDydOnABApVJhaGjIlClTiIuLIysrS6/Px9U/joeHB8bGxmi1WuDejKWvry9NmzZVyrRaLRqNRjknISGBDh06KEklgFqtxt7evsQy3nbt2ukd16tXD3Nzc2bNmsXPP//MzZs3nyheIYQQQgghKiNJLO+TlZVFYWEhdnZ2pdZbW1uXKLe2tiY/P5/bt28rZRYWFnptjIyMAKhevXqJ8rt37+qV3Z+Q3l9W/Axgamoqw4cPJz8/n5kzZ/Ldd9+xceNG7O3tlb4edy9lUaNGjUfGmp6eTlxcHC4uLno/a9asUZJlR0dHYmJiyM7OZsKECfj4+BAQEMCVK1fKVP84JiYmuLu7o9VqSU1NJSUlBY1Gg7e3N1qtluzsbE6fPq2XWD7q7/jgEtwH/xYWFhasXLkSc3NzQkJC8PX1ZeDAgSQlJZUpXiGEEEIIISojWQp7nxo1alClShX+/PPPh9ZbWFhw48aNEuU3btzAyMgIU1PTconjYbNgN2/exMXFBYBff/2V27dvEx0drZfAZmRkKL8/7l7Kg4WFBa1atWLSpEkl6gwNDZXf/fz88PPzIycnhwMHDhAeHs7kyZNZv359meofx8vLix9//BGtVouTkxPW1tZoNBpWrFiBVqtFp9PpJZYWFhYPHeMbN27g7OysV2ZgYFCinbu7O8uWLSMvLw+tVktERAQBAQH85z//oUoV+a5GCCGEEEK8euRT8H1ee+011Go1P/74IwUFBSXqPT09ycnJ4T//+Y9SVlhYyI4dO1Cr1XrJ1LM4e/YsFy9eVI4vXrzI2bNnadq0KXDvtRcGBgZ6S1zj4uLIyckp873A/82iPrjTa1m1bNmS8+fP4+TkhJubm95PkyZNSrQ3MzOjU6dO9OjR46HvznxcfWm8vb25du0aP/zwg5JAenh4UFBQwMqVK3F0dNSbofT09GT37t3k5+crZcePHyclJQUvL68yX9fExITWrVszaNAgrl+/rizjNTIyeuoxFUIIIYQQ4mUkM5YPmDJlCv7+/vj7+zNo0CCsrKw4ffo0RkZGDBgwAHd3dz788EOCg4Oxs7Nj3bp1/PHHH3z88cflFkOtWrUYO3YsEyZMAGDRokXY2dnRq1cvAGWToGnTptG/f3/++OMPli5dSs2aNct8L4MGDcLGxoYaNWqwefNmHBwcMDY2VjakKYthw4axbds2Bg0ahL+/P/b29mRkZJCYmIixsTGBgYGsW7eOI0eO4Ofnh62tLampqWzcuFHZROhx9WWhVqsxMjJCq9X+f/buPLyma33g+DeTqDGJEBpjRA4yJ05EEsQQlUauGlpDSZVrSCSlMTS4QrWlqKFEzZXilpSimoq2uKb+DKHUNYTWUENNQYYikWH//shz9nUkCDkZxPt5njyPs/ba66z15rQ771lrr61ubvTwElldmc6wYcPo3bs3Q4YMISQkhNTUVGbNmoW9vT1BQUFPfK+dO3eybt06AgICqFOnDrdv32bFihU4OjpiYWEBgJ2dHRs3bmT79u3UqlWLWrVqFWlJshBCCCGEEGWdJJaP8PDwYPXq1cydO5cJEyYAeYnC8OHDMTExYenSpcyYMYNZs2Zx7949HBwcWLRokfq4DkNwdHSkc+fOfPbZZ9y4cQMXFxemTJmiPgZDo9Hw6aefEhMTw7Bhw2jSpAmzZs1Sd3AtzFggb+OhqVOnMmfOHAYMGMCDBw/Yvn17oftpYWFBXFwcn3/+ObNnz+bOnTtYWlri6OhI//791b7u3LmT6dOnc+fOHaytrQkICOD9998v1PHCqFSpEo6Ojhw9elRvyatWq+XQoUP5ZiGdnJxYsWIFs2bNYsSIEZibm9OmTRuioqL0HjVSkPr162NiYsLnn3/OzZs3sbCwwMfHh1GjRql1dI+kiYqKIi0tjfDwcCIiIgo9HiGEEEIIIV40RoqiKKXdCfE//fv3p1KlSixevLi0uyJKUGZmJsePH6fGfhtMMov2fU+9SHluJuQ9x1X37FJRNBJLw5FYGpbE03AkloYl8TQciaXh6GKp+7vTycnpqZMqz0LusRRCCCGEEEIIUSSyFFaUWYqi6D0b9FHGxsblbhfWOv+sWeRvjpRsBSPT/LvZCiGEEEIIUVwksSxjVq1aVdpdKDM2btzIuHHjHnu8W7dufPrp76dSGwAAIABJREFUpyXYoxeDJJVCCCGEEKKkSWIpyqx27dqxfv36xx63tLQswd4IIYQQQgghHkcSS1FmWVpaSvIohBBCCCHEC6B83aAmhEDJzi3tLgghhBBCiJeMzFgKUYbcXHYe04yifd9Te5SDgXojhBBCCCFE4ciMpRBCCCGEEEKIIpHEUhhc+/btmTJlivp627Zt/Pvf/y7FHpWcU6dOMX/+fO7fv1/aXRFCCCGEEKLESGIpDC4mJoaBAweqr7dt28aaNWtKsUcl59SpU8TExEhiKYQQQgghXipyj6UwuObNm5d2FwwuJyeHnJwcKlSoUNpdEUIIIYQQosyRGctSdvToUQYNGoSnpyfu7u50796dbdu2AZCSksKECRNo1aoVLi4u9OjRgz179uid379/f4YOHcpPP/1EYGAgbm5u9OnThzNnzujVUxSF2NhYgoKCcHJywsfHh7CwMJKTkwE4d+4ckZGR+Pv74+rqSmBgIIsXLyY7O1tto0OHDkRHR+cbw4IFC/Dw8CAzMxPQXwobFRXFxo0b+f3339FoNGg0GqKioti+fTsajYbz58/rtZWRkUGLFi1YsGDBU2O3adMmHB0duXv3rlrWp08fNBoN169fV8vCwsIICwtTXz9LXDdv3kxgYCAuLi4cO3aM9PR0oqOjad26NU5OTrRu3ZqIiAiys7PZsGED48aNA6BVq1ZoNBrat2//1HEIIYQQQgjxopMZy1J0+PBh3nnnHZycnJgyZQoWFhacPHmSq1evkpOTw+DBg7l48SKRkZHUrl2bNWvWMHToUL788ku8vb3Vdk6dOsWiRYsYMWIEpqamzJgxg4iICBISEjA2zvvuYMqUKaxdu5aQkBA++OADMjIy2Lt3L6mpqVhbW3Pz5k0aNGhAUFAQVapU4cyZM8yfP5/U1FTGjh0LQFBQEHFxcUycOBEzMzP1/bds2UJAQADm5ub5xhgWFsbt27c5d+4cn332GQBWVlbY2tpiY2PDt99+y+jRo9X6P/74I3fv3qV79+5PjZ+XlxfZ2dkcOXIEPz8/MjIy+O9//4u5uTkHDx4kODgYRVE4fPgwoaGhAM8U1xMnTnDp0iWGDx+OpaUldevWZdq0aezevZtRo0ZRt25dbt68ya5du8jNzcXf35/Q0FAWLlzIsmXLqFq1qsxwCiGEEEKIl4IklqVo5syZ1KtXj9WrV2Nqmver8PX1BWD79u0cO3aMJUuW0LZtWwBat25Nly5dWLBggV4ClJaWxoYNG7C2tlbLhg8fzunTp2nWrBnnz59nzZo1jBgxQk2wADp16qT+u2XLlrRs2RLIm9309PQkNzeX+fPnM2bMGIyMjAgODmbx4sX88ssv+Pv7A5CUlMQff/xBVFRUgWOsX78+VlZW/PXXX7i5uekd69GjB+vWrWPkyJHq+NevX4+fnx916tR5avxeffVVbG1tSUxMxM/Pj6NHj2Jubk5AQACJiYkEBwdz5swZUlJS0Gq1AOzcubPQcU1JSSEuLg5bW1u17NixY3Tp0oVu3bqpZa+//jqQlzDXr18fAEdHR6ysrJ46BiGEEEIIIcoDWQpbSu7fv89vv/1Gt27d1KTqYYcOHaJy5cpq8gNgbGxMYGAgR44cIScnRy1v2rSpXlLZuHFjAK5duwbA/v37URSFN99887H9yczMZP78+XTq1AlnZ2ccHR2ZOnUq6enp6nLZJk2a4ODgwPfff6+eFx8fT40aNfDx8XnmGPTs2ZNbt26xc+dOAC5evEhiYuIT+/korVbLgQMHAEhMTMTT0xNvb2+9sipVqtC0aVPg2eLq4OCgl1RC3v2jGzduZNmyZSQlJaEoyjOPWwghhBBCiPJGEstSkpaWRm5uLjY2No89/nCyqGNtbU1WVhb37t1Ty6pXr65XR7dMVXfPY0pKCqampgW2pzNz5kyWLl1K9+7dWbx4MevWreP999/XawcgODiYHTt2cP/+fRRFYcuWLQQGBmJiYlLIkf+Pra0tvr6+rF+/HoBvv/0WKysr2rVrV+g2tFotx48f5/79+yQmJqLVatFqtVy4cIGbN2+qyaauf88S14LqTZw4ka5duxIbG0vXrl1p27YtX3311bMOXQghhBBCiHJFEstSUq1aNYyNjfU2mXlY9erV1ZnChyUnJ2NmZkalSpUK/V6WlpZkZ2cX2J7O1q1b6dWrF8OGDcPX1xcXF5cC7w8MCgri/v377NixgyNHjnDlyhWCgoIK3ZdHvfXWW+zevZurV6+yceNGunbtqnf/5tNotVqysrI4ePAgv/32G15eXtja2mJra8vBgwc5dOiQugwWni2uRkZG+epVrVqV8ePHs3fvXn744Qc6duzI1KlT2b179zOOXAghhBBCiPJDEstS8sorr+Du7s6mTZv0dl7V8fT05O7du3oJS25uLlu3bsXd3f2ZZgi9vb0xMjJSZwYLkpmZqZdIKopCfHx8vnq2tra4ubnx/fff88MPP2Bra4uHh8cT39/MzExv1vNh7du3x8rKijFjxnD9+vVnWgYL0KBBA2xsbFi2bBnGxsY4OjoCeQnn2rVrSU5O1kssDRlXe3t7xo8fj7GxMWfPnlXHCvDgwYNnGocQQgghhBAvMtm8pxSNHj2akJAQQkJC6NevH5aWliQlJWFmZkafPn1wcXFh7NixREZGYmNjw9q1azl//nyBj/x4koYNG9KvXz/mzZtHamoqPj4+PHjwgL1799KvXz8aN26Mj48PcXFx2NnZYW1tzTfffENqamqB7QUHBzNt2jSqVKnCW2+99dT3b9y4MevXr2fz5s00atRI3WEVwNTUlG7durFkyRI8PT2xs7N7prFBXhIZHx+Pn5+fer+qVqtlwoQJVKpUSU02Afz9/YsU1969e9OxY0eaNGmCmZkZ8fHxGBsb4+XlpY4VYNWqVXTq1ImKFSui0WieeUxCCCGEEEK8SGTGshR5eHiwevVqKlSowIQJEwgLCyM+Pp5XX30VExMTli5dSvv27Zk1axbh4eHcuHGDRYsWqbu3PosJEyYwZswYduzYQWhoKBMnTuTmzZtYWFgAEB0djbe3N1OnTiUqKgpbW1tGjBhRYFuBgYEoisKdO3fo0qXLU9+7Z8+eBAYG8sknn9CzZ09iYmL0jgcEBAA882yljm5GskWLFmqZLtFzc3PTW1pb1Lh6eHiwefNmRo4cSUREBOfOneOLL75Qk9fmzZsTERFBfHw8vXv31tuFVwghhBBCiPLKSJFtLUUpW7hwIcuXL2fPnj288sorpd2dUpGZmcnx48ex2V8Z04yifd9Te5SDgXr1Yjt8+DCenp6l3Y1yQWJpOBJLw5J4Go7E0rAknoYjsTQcXSx1f3c6OTkV+Bz65yVLYUWpOXfuHBcuXCA2NpY333zzpU0qhRBCCCGEeNFJYilKzaRJkzh69Ci+vr4MHz483/GCNjV6WEHP/3zR1fxnoyJ/c6Rk52JkKqvchRBCCCFEySl/f5mLF8aqVaueePzhTXcKcvr0aUN2p9yQpFIIIYQQQpQ0SSxFmfWkx6MIIYQQQgghyg5JLEWZ5ezsXNpdEEIIIYQQQhSCrJkTopxRsnNLuwtCCCGEEOIlIzOWQpQhyV/+F9PMorVhM7LF0ysJIYQQQghhQDJjKYQQQgghhBCiSCSxFM9kw4YNaDQabt++/UznnTp1ivnz53P//v1i6lnZcPnyZebPn8/169dLuytCCCGEEEKUGEksxTPx9/cnLi6OatWqPdN5p06dIiYmptwnlleuXCEmJoYbN26UdleEEEIIIYQoMXKPpXgmVlZWWFlZlXY3SpyiKDx48ABzc/PS7ooQQgghhBBlTrmcsTx69CiDBg3C09MTd3d3unfvzrZt29TjKSkpTJgwgVatWuHi4kKPHj3Ys2ePXhv9+/dn6NCh/PTTTwQGBuLm5kafPn04c+aMXj1FUYiNjSUoKAgnJyd8fHwICwsjOTkZgHPnzhEZGYm/vz+urq4EBgayePFisrOz1TY6dOhAdHR0vnEsWLAADw8PMjMz9d6rc+fOODk54e/vz8KFC1EU5YnxiIqKokuXLuzZs4fg4GCcnZ3p3r07R44c0auXm5vLokWL6NChA05OTgQEBBAbG6tX59GlsJcvX0aj0bB582Y+/vhjvLy88PHxYfLkyWq/N2zYwLhx4wBo1aoVGo2G9u3bA5Cenk50dDStW7fGycmJ1q1bExERoRefxzl06BAajYZz586pZaNHj0aj0XD48GG17JNPPqFr167q68zMTKZPn66+Z5cuXfjuu+8KjNnevXvp1q0bzs7OJCQkkJWVxcyZM2nXrh1OTk74+voyePBgbt26xYEDBwgJCQGgZ8+eaDQaNBrNU8chhBBCCCHEi67czVgePnyYd955BycnJ6ZMmYKFhQUnT57k6tWrAOTk5DB48GAuXrxIZGQktWvXZs2aNQwdOpQvv/wSb29vta1Tp06xaNEiRowYgampKTNmzCAiIoKEhASMjfNy8ilTprB27VpCQkL44IMPyMjIYO/evaSmpmJtbc3Nmzdp0KABQUFBVKlShTNnzjB//nxSU1MZO3YsAEFBQcTFxTFx4kTMzMzU99+yZQsBAQHqLNmnn37KmjVrGDJkCB4eHpw4cYL58+djbGzM0KFDnxiXmzdvMmnSJCIiIqhWrRpLly5l0KBB/Pzzz9SoUQOAGTNm8NVXXzFkyBC0Wi2//PIL06ZN4+7duwwfPvyJ7c+dO5c2bdowe/ZsTp48ydy5c6lVqxZhYWH4+/sTGhrKwoULWbZsGVWrVqVChQoATJs2jd27dzNq1Cjq1q3LzZs32bVrF7m5T39khouLC+bm5iQmJmJnZwfkJZu6Mk9PT7VMq9Wq540ePZpdu3YxYsQIHBwc2LJlC2PHjkVRFN544w213o0bN5g0aRKhoaHUrVuXmjVrsmTJEr7++mtGjx5NkyZNSElJYd++fWRkZODo6Eh0dDRTpkxh2rRpap+EEEIIIYQo78pdYjlz5kzq1avH6tWrMTXNG56vr696fOfOnRw7dowlS5bQtm1bAFq3bk2XLl1YsGCBXmKZlpbGhg0bsLa2VsuGDx/O6dOnadasGefPn2fNmjWMGDGC0NBQtU6nTp3Uf7ds2ZKWLVsCeTOOnp6e5ObmMn/+fMaMGYORkRHBwcEsXryYX375BX9/fwCSkpL4448/iIqKAuDSpUusXLmSiRMn0rdvXwB8fHxQFIXFixfTv39/KlWq9Ni4pKSkMHfuXFq1agWAVqulbdu2fPXVV0RGRnL79m1Wr17Nu+++y/vvvw+An58fd+/eZdmyZQwYMIDKlSs/tn0nJycmT56snnf06FF+/PFHwsLCsLKyon79+gA4OjrqLaU9duwYXbp0oVu3bmrZ66+//tj3eViFChVwdXXl4MGD9OrVi0uXLnH16lX69etHYmIiw4YNIz09naSkJPX3k5SUxE8//UR0dDRvv/02kPf7v3HjBvPmzdNLLFNTU1m0aBEeHh56/fXz81PPBf3ft729PQBNmjTB2dm5UOMQQgghhBDiRVeulsLev3+f3377jW7duqlJ5aMOHTpE5cqV1aQSwNjYmMDAQI4cOUJOTo5a3rRpU72ksnHjxgBcu3YNgP3796MoCm+++eZj+5SZmcn8+fPp1KkTzs7OODo6MnXqVNLT09Xlsk2aNMHBwYHvv/9ePS8+Pp4aNWrg4+MDwP/93/+hKAqdO3cmOztb/WnVqhV///0358+ff2JsqlatqiaVANWqVcPb25ujR48CeQlTVlZWvqQuKCiIe/fucerUqSe27+fnp/fa3t5ejdOTNG/enI0bN7Js2TKSkpKeuqz3UVqtlsTERAASExOxt7enc+fO/Prrr2RnZ3Po0CFyc3Np0SLv2Y66JbIFjfPKlSvqzDaAhYWFXlKp6++uXbuYN28ex44d0/u8CCGEEEII8bIqVzOWaWlp5ObmYmNj88Q6DyeLOtbW1mRlZXHv3j2qVq0KQPXq1fXq6Jap6u4dTElJwdTUtMD2dGbOnMk333xDWFgYzs7OVK1alf/7v/9jzpw5ajsAwcHBLFy4kPv371OxYkW2bNlCYGAgJiYmANy+fRtFUfSSw4ddvXoVR0fHx/ajoA13atSowYULF4C82TmAmjVr5ouLbqxP8ugusWZmZjx48OCJ5wBMnDgRCwsLYmNjmTlzJjY2NgwaNIh33nnnqedCXmK5YMEC/vzzTxITE9Fqtbi6upKTk8Px48fVZFM3/tTUVExNTbG0tCxwnKmpqdSpU0ev7GGhoaGYmJiwceNGFixYgIWFBX369CEiIkL9XQkhhBBCCPGyKVeJZbVq1TA2Nn7iMwSrV6+uzhQ+LDk5GTMzsycuJ32UpaUl2dnZJCcnPza53Lp1K7169WLYsGFq2aFDh/LVCwoKYvbs2ezYsYM6depw5coVgoKC9PptZGTE119/rXcfpo5uqenjFPTcyVu3bqmJpIWFBZAXh4cTc12sdMcNrWrVqowfP57x48fzxx9/8PXXXzN16lQaNWpEmzZtnnq+u7s7ZmZmHDx4kMTERCIjI/WWyOqSTZ3q1auTnZ1NSkqK3ph043z4ywQjI6N871ehQgXCw8MJDw/n0qVLfPfdd8TExGBjY0OfPn2KEgohhBBCCCFeWOVqKewrr7yCu7s7mzZteuyuop6enty9e5fdu3erZbm5uWzduhV3d/dnmnXy9vbGyMiI9evXP7ZOZmamulEN5N1nGR8fn6+era0tbm5ufP/99/zwww/Y2trqLcPUzVTevn0bZ2fnfD+Pzq4+Kj09nX379um93r9/P66urgA4OztjZmZGQkKC3nlbtmyhUqVKNG/e/IntP40uGX7SLKa9vT3jx4/H2NiYs2fPFqrdihUr4uTkxObNm7l06ZK65FWr1bJz505Onjypl1jqNvQpaJy2trbqbGVh1KtXj/DwcGrXrq3299FZbSGEEEIIIV4G5WrGEvJ2/AwJCSEkJIR+/fphaWlJUlISZmZm9OvXD39/f1xcXBg7diyRkZHY2Niwdu1azp8/X+AjP56kYcOG9OvXj3nz5pGamoqPjw8PHjxg79699OvXj8aNG+Pj40NcXBx2dnZYW1vzzTffqMtOHxUcHMy0adOoUqUKb731lt6xRo0a0b9/fz744APeffdd3N3dycnJ4dKlS2zbto0VK1Y8sa8WFhZMmDBBb1dYQF1yamVlRf/+/fnyyy+pUKECHh4e7Nu3j7i4OCIiIp5pJrcguvtTV61aRadOnahYsSIajYbevXvTsWNHmjRpgpmZGfHx8RgbG+Pl5VXotr28vFi8eDENGzakVq1awP+WyAJqsgl5982+9tprfPrpp2RkZGBvb09CQgJ79uxh+vTpT32vsLAwmjdvTvPmzalcuTJ79uzh6tWrauLfsGFDTExMWLduHSYmJpiamsomPkIIIYQQotwrd4mlh4cHq1evZu7cuUyYMAEAOzs79XEZJiYmLF26lBkzZjBr1izu3buHg4MDixYtUndvfRYTJkzA1taWtWvXsmrVKqpVq4aHh4e6zDI6OppJkyYxdepUKlSoQHBwMK+99hpjxozJ11ZgYCBTp07lzp07dOnSpcD3srOzY+3atSxevJiKFStSv3592rVr99R+1qxZkzFjxjBjxgz+/PNPmjRpwrJly/SW8I4ZM4Zq1aqxbt06lixZQu3atdVEtqiaN29OREQE69at48svv6ROnTrs2LEDDw8PdbbR2NiYJk2a8MUXXzzxftFHabVaFi9erDczqVsi++qrr+a753bmzJnMmTOH5cuXk5KSQoMGDZgxY4besy4fx9PTk4SEBGJjY8nJyaFBgwZMnz6dDh06AHkJenR0NMuWLSM+Pp7s7GxOnz5d6LEIIYQQQgjxIjJSnnUbTvHCiYqK4vjx4wUuwRVlQ2ZmJsePH6f2QQXTIq6itRnZ4umVXgKHDx9Wlz6LopFYGo7E0rAknoYjsTQsiafhSCwNRxdL3d+dTk5OmJubG6z9cnWPpRBCCCGEEEKIklfulsKK8iMnJ+eJz7V83LNKX2TWA52L/M2Rkp2Lkal8ZySEEEIIIUpO+fvLXOTz6aeflnYXnktAQABXrlx57PHt27dTt27dEuzRi0GSSiGEEEIIUdIksRRl1sKFC5/4eBLdDrBCCCGEEEKI0iWJpSizNBpNaXdBCCGEEEIIUQiyZk6IckTJzintLgghhBBCiJeQzFgKUYYkf7UP08znfwKQTcTTn2kqhBBCCCGEocmMpRBCCCGEEEKIIpHEUhjM/Pnz+fXXX0u7G6Xq1KlTzJ8/n/v375d2V4QQQgghhCgxklgKg4mJieHIkSOl3Y1SderUKWJiYiSxFEIIIYQQLxVJLEWJy8jIKO0uPLOcnJwnPvpECCGEEEKIl5kkliXo7NmzDBkyhJYtW+Lq6kqnTp2IiYlh+/btaDQazp8/r1c/IyODFi1asGDBAiBvqam7uztJSUn06dMHFxcXgoOD+fXXX8nKymLatGl4e3vj5+fHvHnzUJT/bQJTlHMBzp07R3h4OFqtFldXV959911+//139bju0SAzZsxAo9Gg0Wg4cOCAemzJkiXMmTMHPz8/PD09Cz3mJ9m0aROOjo7cvXtXLevTpw8ajYbr16+rZWFhYYSFhamvU1JSmDBhAq1atcLFxYUePXqwZ88evbb79+/P0KFD2bx5M4GBgbi4uHDs2DHS09OJjo6mdevWODk50bp1ayIiIsjOzmbDhg2MGzcOgFatWqHRaGjfvv1TxyGEEEIIIcSLThLLEjRs2DBSU1P55JNPWLx4MYMHDyYjIwN/f39sbGz49ttv9er/+OOP3L17l+7du6tlWVlZjBkzhu7duzN//nzMzMwIDw9n0qRJZGRkMGvWLHr06MGCBQv4+eef9dp73nMvX75Mnz59SE5O5uOPP2bOnDlkZWUREhJCeno6AHFxcUBeQhYXF0dcXByOjo5qGytXruT06dN89NFHzJ49+5nG/DheXl5kZ2ery28zMjL473//i7m5OQcPHgRAURQOHz6Ml5cXkDfzOHjwYLZt28bIkSOZP38+NWvWZOjQoezfv1+v/RMnTrBo0SKGDx/OkiVLqFu3LtOmTWPHjh1ERkayYsUKxo0bR6VKlcjNzcXf35/Q0FAAli1bRlxcHDExMU8dhxBCCCGEEC86edxICbl9+zYXL15k3Lhx6iyWt7e3erxHjx6sW7eOkSNHYmqa92tZv349fn5+1KlTR62XlZXFqFGj8Pf3B6BixYqEhIRw8+ZNli5dCoCvry/bt29n69atdOrUqcjnxsTEULlyZWJjY6lYsSKQl9R17NiRVatWERYWhpubGwB16tRR//2wqlWr8sUXX2Bs/L/vMgo75sd59dVXsbW1JTExET8/P44ePYq5uTkBAQEkJiYSHBzMmTNnSElJQavVArBz506OHTvGkiVLaNu2LQCtW7emS5cuLFiwQO93kpKSQlxcHLa2tmrZsWPH6NKlC926dVPLXn/9dQCsrKyoX78+AI6OjlhZWT11DEIIIYQQQpQHMmNZQiwtLbG1tWX27Nls2LCBq1ev6h3v2bMnt27dYufOnQBcvHiRxMRE3nzzTb16xsbG+Pj4qK8bNmwI5CWED2vYsGG+93jec/fu3UuHDh0wNTUlOzub7OxsKlasiJubG8eOHSvU+P39/fWSymcZ85NotVp1yW1iYiKenp54e3vrlVWpUoWmTZsCcOjQISpXrqwmlZAXl8DAQI4cOUJOTo5a7uDgoJdUAjRv3pyNGzeybNkykpKS8i0ZFkIIIYQQ4mUkiWUJMTIyYvny5TRu3JiPPvoIf39/unbtyr59+wCwtbXF19eX9evXA/Dtt99iZWVFu3b6D7yvWLEiFSpUUF+bmZkBUK1aNb16ZmZmZGZmGuTcO3fusHLlShwdHfV+/vOf/+RLXh+nRo0a+coKO+Yn0Wq1HD9+nPv375OYmIhWq0Wr1XLhwgVu3rypJpsmJiYApKWlYW1tna8da2trsrKyuHfvnl7ZoyZOnEjXrl2JjY2la9eutG3blq+++qrQ/RVCCCGEEKI8kqWwJahRo0Z8/vnnZGdnc/ToUebNm0doaCj/+c9/sLS05K233mLkyJFcvXqVjRs30rVrVzX5K03Vq1enbdu29O3bN98x3dLYpzEyMiqwvKhj1mq1ZGVlcfDgQX777TdGjRqFra0ttra2HDx4kEOHDjFgwAC9sSQnJ+drJzk5GTMzMypVqvTEPletWpXx48czfvx4/vjjD77++mumTp1Ko0aNaNOmTaH7LYQQQgghRHkiM5alwNTUlBYtWhAaGsr9+/f566+/AGjfvj1WVlaMGTOG69evP9OS0OLk4+PDmTNnaN68Oc7Ozno/TZo0UesVNEv6NEUdc4MGDbCxsWHZsmUYGxurGwZptVrWrl1LcnKyen8lgKenJ3fv3mX37t1qWW5uLlu3bsXd3V2d2SwMe3t7xo8fj7GxMWfPngX+NwssjyYRQgghhBAvE5mxLCFJSUl8+umnvP7669SrV4979+6xbNkyatWqhb29PZCXcHbr1o0lS5bg6emJnZ1dKfc6z4gRI+jZsyfvvvsuvXr1ombNmiQnJ3PkyBEaNWqkzmTa2dmxbds2WrRowSuvvEKjRo2oUqXKE9s2xJi1Wi3x8fH4+fmpmwBptVomTJhApUqV9Han9ff3x8XFhbFjxxIZGYmNjQ1r167l/PnzREdHP/W9evfuTceOHWnSpAlmZmbEx8djbGys7jrbuHFjAFatWkWnTp2oWLGi+igWIYQQQgghyiuZsSwhNWvWpFatWixZsoShQ4fyr3/9CysrK2JjYzE3N1frBQQEAJSZ2UqAevXqsW7dOmrWrMnHH3/MwIEDmTlzJsnJyTg7O6v1dInZ4MGD6dmzJydOnChU+0Uds25GskWLFmqZLtFzc3PTW1prYmLC0qVLad++PbPAVRzUAAAgAElEQVRmzSI8PJwbN26waNEiWrZs+dT38vDwYPPmzYwcOZKIiAjOnTvHF198oSavzZs3JyIigvj4eHr37q0+fkQIIYQQQojyzEiRbS3LlIULF7J8+XL27NnDK6+8UtrdKREv45gflZmZyfHjx6l9OB3TzOf/T9ImovAbH5V3hw8fxtPTs7S7US5ILA1HYmlYEk/DkVgalsTTcCSWhqOLpe7vTicnJ70JrqKSpbBlxLlz57hw4QKxsbG8+eabL0WC9TKOWQghhBBCiPJIEssyYtKkSRw9ehRfX1+GDx9e2t0pEU8bc3Z29hPP191PWZ5Yv9OqSN8cKdk5GJkWfgMiIYQQQgghDKH8/WX+glq1alVpd6HEPW3MD2+6U5DTp08bsjvlgiSVQgghhBCiNEhiKcqs9evXl3YXhBBCCCGEEIUgiaUosx7ecVYIIYQQQghRdsnjRoR4gSnZOaXdBSGEEEIIIWTGUoiy5Naq7ZhmFj5ZrDU8uBh7I4QQQgghROHIjKUQQgghhBBCiCIpE4llbGws7dq1o1mzZvTv379E3m/Xrl3Pde6GDRvQaDTcvn27SH3o378/Q4cONXi7peHRsbzMLl++zPz587l+/Xppd0UIIYQQQogSU+pLYc+ePcu0adMYPHgw7du3p1q1asX+nitXrsTf35+2bdsW+3sVlr+/P3FxcSUyfkObNGkSxsZl4juKUnflyhViYmLw9/fHxsamtLsjhBBCCCFEiSj1xPL8+fMA9OrVi3r16hVYJyMjg4oVK5Zkt0qclZUVVlZWpd2N52Jvb1/aXSh2iqLw4MEDzM3NS7srQgghhBBClDmlOs0UFRXF8OHDAejYsSMajUZdErpx40YmTZpEy5YtCQ7O26Bk165dDBw4kFatWuHu7k6PHj3Ytm1bvnavX79OVFQUvr6+ODs706lTJxYuXAhA+/btuXLlCv/+97/RaDTqewJ899139O3bl5YtW6LVaunbty+HDh0q8jiPHj1Kjx49cHZ2JjAwkJ9++ilfnUeXwl6+fBmNRsN3333HlClT8PLyomXLlsTExACwfft2goKCcHd3Z9CgQdy4cUOvvQcPHjB37lzat2+Pk5MTr732GnFxcXp1oqKi6NKlC4mJiXTr1g1XV1feeOMNEhMT9er95z//oUePHri7u+Pp6Um3bt30xlDQUthDhw7Rt29fXFxc8PLyYtSoUdy8eVM9rhvf5s2b+fjjj/Hy8sLHx4fJkyeTmZlZqLgeOnQIjUbDuXPn1LLRo0ej0Wg4fPiwWvbJJ5/QtWtX9XVmZibTp0+ndevWODk50aVLF7777rsCY7N37166deuGs7MzCQkJZGVlMXPmTNq1a4eTkxO+vr4MHjyYW7duceDAAUJCQgDo2bOn+vkSQgghhBCivCvVGcuwsDAaNWrE7NmziYmJoWbNmuqSylmzZtG6dWs+++wzcnLydsm8cuUKbdu25d1338XExIRffvmF8PBwFi1ahL+/PwB37tyhV69e5ObmEhERQYMGDbh8+TJJSUkAxMTEMGTIEDw8PBg4cCAA9evXV9v/xz/+QYMGDcjKymLr1q288847fPvttzRt2vS5xnjr1i0GDhxI48aNmTNnDvfu3WPGjBlkZGTg6Oj41PPnzp1Lhw4dmDNnDnv27GH+/Pncu3ePffv2MWLECHJycvjkk0+Ijo5m0aJF6nmRkZEcOHCA4cOH4+DgwP79+5k8eTKVK1emS5cuar2bN28yZcoUBg0ahKWlJTExMQwfPpwdO3ZQpUoVLl68SEREBEFBQURGRqIoCqdPnyY1NfWxfT5+/DgDBgzA09OTOXPmkJaWxqxZsxgwYAAbNmzQm/WbO3cubdq0Yfbs2Zw8eZK5c+dSq1YtwsLCnhobFxcXzM3NSUxMxM7ODshLNnVlnp6eaplWq1XPGz16NLt27WLEiBE4ODiwZcsWxo4di6IovPHGG2q9GzduMGnSJEJDQ6lbty41a9ZkyZIlfP3114wePZomTZqQkpLCvn371N9ndHQ0U6ZMYdq0aWqfhBBCCCGEKO9KNbGsX78+DRo0AKBZs2bUrVuXy5cvA+Dg4MC0adP06vft21f9d25uLl5eXvz555+sXbtWTSxjY2NJTk4mISGhwKW1zZs3p0KFClhbW+Pm5qZ37OFkJjc3Fx8fH5KSkli/fj3/+te/nmuMsbGxKIrCsmXLqF69OgCNGzeme/fuhTrf2dlZfW9fX19++uknVq5cybZt26hduzYAf/31FzNnzlSXDB84cICff/6ZJUuWqPeR+vj4kJKSwueff66XWKamprJy5Up1Zq1WrVq88cYb7N+/n44dO3Ly5EmysrKYOHEiVapUAcDPz++JfV60aBE1atRg6dKlVKhQAYCGDRvSu3dvfvjhB72xOzk5MXnyZLXdo0eP8uOPPxYqsaxQoQKurq4cPHiQXr16cenSJa5evUq/fv1ITExk2LBhpKenk5SURGhoKABJSUn89NNPREdH8/bbbwPQunVrbty4wbx58/QSy9TUVBYtWoSHh4daduzYMfz8/NRzATp16qT+W7csuEmTJjg7Oz91DEIIIYQQQpQHZXbHlfbt2+cru3btGlFRUbRp0wZHR0ccHR35+eefuXDhglpn3759eHt7P/Z+zSc5e/Ys4eHh+Pr60qxZMxwdHTlx4oR6H+jzOHr0KN7e3mpSCeDo6EjdunULdf6jSVzDhg1p3LixmlTqyhRFUXci/eWXX6hevTq+vr5kZ2erPz4+Ply8eJGUlBT13Jo1a+ot12zcuDGQF2sAjUaDiYkJo0ePZvv27aSlpT21z4cOHaJjx45qUgng7u6Ora1tvqXFj47P3t5efe/C0Gq16tLdxMRE7O3t6dy5M7/++ivZ2dkcOnSI3NxcWrRoAaAukX399df12gkKCuLKlStcvXpVLbOwsNBLKiHvi4ldu3Yxb948jh07ps6mCyGEEEII8TIr9c17HqdGjRp6r3NzcwkNDSU1NZXw8HAaNGhApUqVWLJkCSdOnFDrpaSk0KRJk2d+v7///puBAwdiYWHBmDFjqFu3Lubm5nzyySc8ePDgucdx8+ZNdVb2YdbW1oU6/9FdYs3MzAosA9R7E2/fvk1qaupjl9pevXoVCwsLAL2EF1CTQV1bjRo1YtGiRSxZsoT33nsPyJs5nThx4mOT97S0tALHZ21tnW8JbUFjeZZ4a7VaFixYwJ9//kliYiJarRZXV1dycnI4fvy4mmzqNkZKTU3F1NQUS0vLfH3THa9Tp45e2cNCQ0MxMTFh48aNLFiwAAsLC/r06UNERAQmJiaF7rcQQgghhBDlSZlNLI2MjPRe//nnn5w8eZIFCxbQsWNHtTwrK0uvnqWl5XM9Q/Do0aNcu3aNRYsW0axZM7X87t27ahL2PGrWrMmtW7fylScnJxep3SepXr06lpaWLF26tMDjDRs2fKb22rRpQ5s2bbh79y6//PILn376KaNGjeKbb7557Ps/bsyG3kHW3d0dMzMzDh48SGJiIpGRkXpLZHXJ5sN9y87OJiUlRS/+ycnJ6nGdRz+DkJd4h4eHEx4ezqVLl/juu++IiYnBxsaGPn36GHRsQgghhBBCvCjK7FLYR+lm0HSzc5CXDOzbt0+vXqtWrdi/fz+XLl16bFtmZmb5dh7NyMgA0Fu+mZSUxO+//16kfru6urJ//369mboTJ06o95IWB19fX+7cuYOpqSnOzs75fl555ZXnardy5cp06tSJrl278scffzy2nqenJ9u2bdNL+n/77TeuXLmiLkk1lIoVK+Lk5MTmzZu5dOmS2r5Wq2Xnzp2cPHlSL7HUbeiTkJCg186WLVuwtbVVZysLo169eoSHh1O7dm3Onj0L5J89FkIIIYQQ4mVQZmcsH2VnZ0ft2rXVXWIzMjL44osvsLa2RlEUtd6AAQPYvHkz/fv3JywsjPr16/PXX39x8uRJdRMcOzs79u3bx969e6levTp169bFzc2NSpUqMXnyZIYMGcKtW7eYN2+e3r2Mz2PAgAH8+9//5p///CdDhw7l3r17zJs3r9BLYZ+Hj48PHTt2ZPDgwQwaNIimTZuSmZnJuXPnOHbsGHPnzi10W2vXruXXX3+lTZs21KpVi7/++ov169fj6+v72HOGDRtG7969GTJkCCEhIaSmpjJr1izs7e0JCgoyxBD1eHl5sXjxYho2bEitWrWA/y2RBfSS2aZNm/Laa6/x6aefkpGRgb29PQkJCezZs4fp06c/9b3CwsJo3rw5zZs3p3LlyuzZs4erV6/SqlUrIG822MTEhHXr1mFiYqIm90IIIYQQQpRnL0xiWaFCBWJiYpgyZQrvv/8+tWrV4p///CenT59m586daj0LCwvWrFnDrFmzmDVrFvfu3ePVV1+lR48eap3IyEgmT57Me++9x927d5k2bRrdu3dn3rx5zJgxg+HDh1O/fn3GjRvH+vXruXfv3nP329ramuXLl/Pxxx8zcuRIbG1tGTVqFF9//XVRwvFUc+fOZfny5cTFxXH58mUqV66MnZ2d+kzQwtJoNOzcuZPp06dz584drK2tCQgI4P3333/sOU5OTqxYsYJZs2YxYsQIzM3NadOmDVFRUXqPGjEUrVbL4sWL9WYmdUtkX331VWxsbPTqz5w5kzlz5rB8+XJSUlJo0KABM2bM0HvW5eN4enqSkJBAbGwsOTk5NGjQgOnTp9OhQwcArKysiI6OZtmyZcTHx5Odnc3p06cNO2AhhBBCCCHKGCPl4ek+IUSpyMzM5Pjx49Q5ch3TzMLvNFtr+LN9UfAyOXz4sLr0WRSNxNJwJJaGJfE0HImlYUk8DUdiaTi6WOr+7nRycjLopM8Lc4+lEEIIIYQQQoiy6YVZClsW5ebmkpub+9jjJiYmBe4sKgonJyeHJ02om5qWv49vjf4dnumbIyU7ByNTecyJEEIIIYQoXeXvL/MSNH78eDZu3PjY47p7N8XzCQgI4MqVK489vn37durWrVuCPSo+ugT6uZ6ZmpNt4N6UH7I7r+FILA1HYmlYEk/DkVgalsTTcCSWhpOZman+vWnoOyLlHssiuHz5Mnfu3Hns8bp162JpaVmCPSpfTp8+/cRES6PR6D0e5kWWnp7OmTNnSrsbQgghhBDiJeHg4EDVqlUN1p4klkKUAbm5udy9exczMzNZPi2EEEIIIYqNoihkZWVRuXJljI0Nt+WOJJZCCCGEEEIIIYpEdoUVQgghhBBCCFEkklgKIYQQQgghhCgSSSyFEEIIIYQQQhSJJJZCCCGEEEIIIYpEEkshhBBCCCGEEEUiiaUQQgghhBBCiCKRxFIIIYQQQgghRJFIYimEAV24cIFBgwbh7u6Ot7c3H330Effv3y/UuZs2baJz5844OzsTFBTEli1b8tXJyspi1qxZ+Pn54erqSr9+/Th16pShh1FmFGc8z58/z0cffcTrr7+Om5sb7du3Z+LEidy+fbs4hlLqivuz+bAVK1ag0WgYOnSoIbpe5pRELC9cuMB7772Hl5cXrq6uBAcH8+OPPxpyGGVGccfz8uXLREZG4ufnh7u7O927d+eHH34w9DDKhOeN5ZYtW4iIiKB169ZoNBqWL19eYD25BhkunnINMuxn82FyDSrYs8Tyea9Bps80EiHEY6WlpRESEsKrr77K559/zu3bt5k2bRq3b99mzpw5Tzx369atfPDBBwwZMgRfX1+2bdtGZGQklStXpm3btmq9adOmsWnTJqKiorC1tWXZsmUMGDCAzZs3Y2NjU9xDLFHFHc9ffvmFxMREevXqRdOmTfnrr7+YN28eBw8eZPPmzZibm5fEMEtESXw2da5fv86CBQuoUaNGcQ2nVJVELM+ePUufPn1wdnZm6tSpVK5cmdOnT5OZmVncwytxxR3PzMxMBg0aBMD48eOxsLAgPj6eyMhIzM3N6dixY7GPsaQUNZaXLl2iXbt2xMXFPbaeXIMMF0+5Bhn2s6kj16DHK2wsi3QNUoQQBrF48WLF1dVVuXXrllq2efNmxcHBQTlz5swTz+3cubPy3nvv6ZUNHDhQ6dGjh/r62rVrSrNmzZTVq1erZenp6YqXl5cyffp0A42i7CjueN6+fVvJzc3Vq3P48GHFwcFB2bp1qwFGUHYUdywf9v777ytRUVFKv379lCFDhhS982VMScTy7bffVvr06aPk5OQYruNlVHHHMzExUXFwcFD27dunV+/1119XRowYYYARlB1FieXDnzUHBwdl2bJl+erINciw8ZRrkOFi+TC5Bj1eYWNZlGuQLIUVwkB2796Nt7c3VlZWatlrr71GhQoV2L1792PPu3TpEufOnSMoKEivPCgoiP/+97/qspi9e/eSk5PD66+/rtapUqUK7dq1e2L7L6rijqelpSVGRkZ6dZo3bw7ArVu3DDWMMqG4Y6mzb98+du/ezahRoww7gDKkuGN5/vx5EhMT6du3L8bG5f8SXdzxzM7OBqBq1ap69apWrYqiKIYaRpnwvLEECvVZk2uQYeMp1yDDxVJHrkFFj2VRr0Hl/6olRAk5e/Ys9vb2emUVKlSgfv36nDt37rHn6Y41btxYr1zXlu742bNnsba2xtLSMl+9CxcukJubW+QxlCXFHc+CHDp0SK9ueVESsczKymLKlCmEh4djbW1tqK6XOcUdyyNHjgB5fwD07dsXR0dH/Pz8mDNnDjk5OQYbR1lR3PH09PTE3t6e2bNnc+nSJdLT04mLi+P48eP07t3bkEMpdc8by2dpX65BhotnQeQa9PzkGmSYWBb1GiT3WAphIGlpaVSrVi1febVq1UhNTX3sebpjj55bvXp1veNpaWn5vnXX1cvKyuLevXtUqVLluftf1hR3PB+VmZnJjBkzcHBwoEWLFs/b7TKpJGK5YsUKjIyM6NevnyG6XGYVdyyTk5MBmDhxIr169eK9997j119/ZcGCBZiZmREeHm6QcZQVxR1PMzMzVq5cSWhoqHo/ZYUKFZgxYwatWrUyyBjKiueN5bO0L9cgw8XzUXINKhq5BhkmlkW9BkliKUQxUxQl33KXgjxaR7dM6+Hygtopb8u5nsaQ8XzYpEmTuHTpEmvWrHkpliCC4WL5119/sXDhQhYsWICp6ct5WTFULHXfCPv5+TF27FgAvL29uX37NsuXLyc0NBQTExNDdr1MMlQ8MzIyeO+998jJySEmJoYqVaqwdetWxo0bh7W1NV5eXobvfBlT2FgWhlyDDBvPh8k16PnJNchwsSzqNejl+OQKUQKqVatGWlpavvL09PQCv13SedxMmq4t3bmPaz8tLQ0zMzMqVar03H0vi4o7ng+bM2cO33//PfPmzaNp06ZF6XaZVNyxnDlzJp6enjg5OZGWlkZaWhrZ2dlkZ2eTlpZGVlaWoYZS6oo7lrp63t7eevW8vb25d+8ef/311/N3vgwq7niuX7+e3377jaVLlxIQEECrVq348MMP8fDwYPbs2YYaRpnwvLEsavtyDSo6uQYVjVyDDBfLol6DJLEUwkAaN27M2bNn9coePHjAxYsXsbOze+x5umOPro3XtaU73rhxY27dukVKSkq+eg0bNix333AWdzx1Vq1axeLFi/n4449p3bq1Ibpe5hR3LM+dO8eePXvQarXqz6+//srevXvRarXs2rXLkMMpVcUdyyZNmhR4vm5W6MGDB8/X8TKquOP5xx9/YGNjo7fRBUCzZs24ePFikftfljxvLJ+lfbkGGS6eOnINKnos5RpkuFgW9RpUvv4vIEQpatOmDfv37+fOnTtq2c8//8yDBw8KfN6fTr169bCzs8v3YO/4+HicnZ3VP4j8/PwwNjYmISFBrXP37l127NhBmzZtDDya0lfc8dSVffLJJ0RGRtKtWzfDD6KMKO5Yfvzxx6xcuVLvp2nTpri5ubFy5Uo8PDyKZ2CloLhj6ebmhoWFBfv27dOrt2/fPqpUqUL9+vUNOJrSV9zxfPXVV7l+/Xq+XTZPnDiBra2tAUdS+p43loUl1yDDxhPkGmSoWMo1yHCxLPI16JkfUCKEKFBqaqrSunVrpXfv3sru3buVjRs3Ki1btlRGjhypV2/cuHFKs2bN9Mq2bNmiaDQaZfbs2cr+/fuVTz75RNFoNMrOnTv16n344YeKh4eH8s033yh79+5VBg4cqHh5eSnXrl0r9vGVtOKO54EDBxRHR0clJCREOXLkiN7Pn3/+WSJjLCkl8dl8VHl9hlhJxHLVqlWKg4ODMnXqVOWXX35R5s2bpzRr1kxZvHhxsY+vpBV3PK9evap4enoq3bp1UxISEpQ9e/YoEyZMUBwcHJRNmzaVyBhLSlFi+fvvvysJCQlKQkKC4uDgoIwdO1Z9/TC5BhkunnINMuxn81FyDXr+WBblGiSJpRAGdO7cOWXgwIGKq6ur4uXlpXz44YfKvXv39Op88MEHioODQ75zN2zYoHTq1ElxdHRUAgMDlfj4+Hx1Hjx4oMycOVPx8fFRnJ2dlb59+yonTpwotvGUtuKM57x58xQHB4cCfz744INiHVdpKO7P5qPK60VdUUomlqtXr1YCAgIUR0dHpUOHDsqKFSuKYyhlQnHH8+TJk8qQIUMUHx8fxc3NTXnjjTeUjRs3Ftt4StPzxvJJ/z98mFyDDBdPuQYZ9rP5KLkGFS2Wz3sNMlKUl2w7LyGEEEIIIYQQBiX3WAohhBBCCCGEKBJJLIUQQgghhBBCFIkklkIIIYQQQgghikQSSyGEEEIIIYQQRSKJpRBCCCGEEEKIIpHEUgghhBBCCCFEkUhiKYQQQrwkDhw4gEaj4cCBA891fvv27YmKijJwr4QQQpQHpqXdASGEEEI8v5SUFL766iv+85//cPHiRR48eEDNmjVxdnYmODiYjh07YmRkVKJ96t+/Pzdv3mTr1q0l+r6G9N1333Hnzh0GDBhQ2l0RQogXgpGiKEppd0IIIYQQz+7kyZMMGTKElJQUOnfujJubGxUrVuTatWvs2rWLY8eOER0dzdtvvw1Abm4uWVlZmJmZYWz87IuWHjx4gJGREWZmZk+sVx4Sy0GDBnH+/Hl27NhR2l0RQogXgsxYCiGEEC+g9PR0QkNDURSFDRs24ODgoHc8PDyc/fv3k56erpYZGxtjbm7+3O9ZoUKF5z73RXH//n1eeeWV0u6GEEK8cOQeSyGEEOIFtHbtWq5du0ZUVFS+pFLH29ubgIAA9XVB91hGRUXh7OxMcnIyI0eOxMPDA61Wy8SJE8nMzNRrryj3WGo0GqKjo9m2bRvBwcG4uLjQs2dPTpw4AcCmTZvo3Lkzzs7O9OrVi7Nnz+qdr+vnlStXGDJkCO7u7vj4+DBjxgyysrL06ubm5rJkyRJee+01nJycaN26NR999JFekg15M6udO3cmKSmJkJAQ3Nzc+PDDD+nfvz979+7lypUraDQa9Udn+fLl9O7dm5YtW6pLjtetW5dvzO3bt2fQoEEcO3aMPn364OLiQtu2bYmNjc1X98GDB3zxxRd07twZJycnfHx8CA0N5ffff1frKIrC6tWrCQ4OxtnZmZYtWzJq1CiuXbv2zL8PIYQwNJmxFEIIIV5AO3bsoGLFirz22mtFbktRFAYOHIhGo2HMmDEcO3aMb775BktLSyIjIw3Q2zxHjx5l165d9O3bFxMTE5YuXcqQIUN4//33Wbp0KW+99RaZmZksWbKEyMhIvvvuu3z9/Oc//0mzZs0YPXo0Bw4cYPny5aSnp/PRRx+p9SZPnkxcXBzt27cnJCSEM2fO8PXXX/Pbb7+xZs0avaW86enpDBo0iICAAIKCgqhatSrVq1cnNTWVGzduMG7cuHzjiI2NpU2bNgQGBmJkZMTPP//Mv/71L3Jzc+nVq5de3cuXLzNs2DC6detGcHAwCQkJTJs2DXt7e/z8/IC8RDg0NJS9e/fy2muv0a9fP+7fv8+BAwc4ceIETZo0AWDSpEmsX7+erl270rdvX5KTk1m9ejVHjhxh06ZNVKtWzWC/KyGEeFaSWAohhBAvoLNnz9KwYcN8y1Pv3btHRkaG+trMzIyqVas+sa2srCzatWvH+++/D0CfPn1ITU3lm2++MWhiee7cObZs2UL9+vUBsLa25oMPPmDGjBn8+OOPWFpaqn3+7LPPOH78OE5OTnr91Gq1TJkyBYC3336bDz74gHXr1vHuu+9iZ2fHmTNniIuL4x//+AczZ85Uz7Wzs2Pq1Kls2rSJN998Uy1PTk5mwoQJhISE6PW1Zs2a/P3333Tt2jXfOH766Se95bIhISG8++67LF++PF9ieeHCBVasWIGPjw8APXv2xN/fn2+++UZNLDdt2sTevXsZOXIkoaGh6rmDBw9GtxXGr7/+SlxcHNOmTaN79+5qnYCAAHr06MGqVasYPnz4U38HQghRXGQprBBCCPEC+vvvv6lcuXK+8s8++4xWrVqpP2FhYYVqr2/fvnqvvby8uHPnDn///bdB+gvQsmVLNakEcHFxAfKWjOqSSgBXV1cALl68mK+N/v37673u168fiqKwe/duAHbu3Ankbb7zsD59+lClShX1uI6pqWm+ZPBpdEllVlYWKSkp3L59G29vb/788898y20bNmyoJpWQd5+qq6srly9fVst+/PFHqlWrlq/PgLqjb0JCApUqVaJt27bcvn1b/alVqxYNGjRg//79zzQGIYQwNJmxFEIIIV5AVapU4e7du/nK+/XrR8eOHQEYP358odoyMzPDxsZGr0y3rDI1NZUqVaoUsbd56tSpo/daN5Nau3btAsvT0tL+v737CWn6j+M4/vom5ghKD2MMWQ7nJVCblJiIZnnRGOSioIJ2sENeuoSXDh4URBIKQWIkhAuVYJCXWTBBIy+BSHUIYWH4J7RDQSCTwInb7xDu51dXzLZf+xnPx+37/X62z+fLTq+9v9/P23TeMAw5nU7TudLSUknS58+fJf149NQwDLlcLtO4w4cP6/jx41pdXTWdt9ls+xWZvR4AAAOuSURBVN7QaHJyUn6/X5FIRFtbW6Zr0WjUVCEuLi7e8/nCwkJ9+PAhefzp06eU1eedlpaW9P37d1NI3elPt5QBgN0IlgAAHEBlZWWam5tTLBYzBRKXy5UMVRaLJa3v+lUoyWZXsp+1OMnLy/vP597+vt33ut9Q+ebNG92+fVunT59Wd3e3bDab8vPzNT09rSdPnigej5vGp9PWJdW6dovH4yoqKlJ/f3/K6+xkCyDXCJYAABxATU1Nevv2rcLhsC5evJjr5fwRiURCy8vLyc1sJGlxcVHSv5VBh8OhRCKhhYUFnThxIjkuFotpZWVFtbW1ac31s6AXDodVUFCgoaEhUyjdudPufjmdTr17927PnwQ7lZSU6PXr1zp58mTWKsgAkE28YwkAwAF07do12e123bt3z9SSYqdsV/z+D0ZGRkzHo6OjkqSGhgZJUmNjoyTtaekRDAa1vr6uc+fOpTXPkSNH9jyKK/2orhqGYapMrq2taWxsLN1b2KO5uVlra2sp25Bs/4Yej0fxeFwPHz5MOebbt2+/PT8AZAMVSwAADqCjR4/K7/ervb1dly5dUktLi6qqqmSxWPTlyxe9evVKS0tLqqqqyvVSsyY/P1+zs7Pq6OjQqVOnNDMzo4mJCV2+fFllZWWSfvTLvHr1qoLBoKLRqOrq6jQ/P69gMKjKykp5vd605qqoqNDExIR6enrkdrt16NAheTwenT9/XoFAQG1tbWptbU3unmu1WvX169ffuq/W1laFQiE9ePBAc3Nzqqmp0cbGhmZmZnThwgV5vV5VV1fL5/MpEAgoEono7NmzslgsWllZ0eTkpK5cuaJbt2791vwAkA0ESwAADqjy8nKFQiENDw/r5cuXmpqa0ubmpqxWq9xut9rb29XU1JTrZWaNYRh6/Pixuru7df/+fRUUFOjmzZvJNinburq65HA49OzZM01PT6uoqEjXr1/XnTt3TD0sf+XGjRv6+PGjxsfHNTo6qkQiIY/HozNnzqivr0+Dg4Pq7e2V3W6Xz+fTsWPH0t4sabe8vDwNDg7q0aNHev78uaamplRYWCi3221qt9LZ2amKigo9ffpUAwMDMgxDdrtd9fX1yQ2bACBXjMTf+JwMAAD4q9y9e1cvXrzQ+/fvc70UAEAKvGMJAAAAAMgIwRIAAAAAkBGCJQAAAAAgI7xjCQAAAADICBVLAAAAAEBGCJYAAAAAgIwQLAEAAAAAGSFYAgAAAAAyQrAEAAAAAGSEYAkAAAAAyMg/TG8Hm+dPILgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.DataFrame({'Feature':list(X),\n",
    "                        'Gini importance':importances[indices]})\n",
    "plt.rcParams['figure.figsize']=(12,12)\n",
    "s.set_style('whitegrid')\n",
    "ax = s.barplot(x='Gini importance', y='Feature', data=feat_imp)\n",
    "ax.set(xlabel='Gini Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0\n",
      " 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0\n",
      " 0 1 1]\n",
      "Best Params : {'n_estimators': 500}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        75\n",
      "           1       0.95      0.97      0.96        39\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.97      0.97      0.97       114\n",
      "weighted avg       0.97      0.97      0.97       114\n",
      "\n",
      "Accuracy Score : 0.9736842105263158\n",
      "Confusion Matrix : \n",
      " [[73  2]\n",
      " [ 1 38]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    4.6s finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:73: 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(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'n_estimators': [100, 500, 1000, 2000],\n",
    "           \n",
    "        }\n",
    "FitModel(X_norm,Y_norm,'XGBoost_norm',XGBClassifier(),param,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# BALANCING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.032403</td>\n",
       "      <td>0.182815</td>\n",
       "      <td>-0.301307</td>\n",
       "      <td>0.213053</td>\n",
       "      <td>0.146813</td>\n",
       "      <td>0.198968</td>\n",
       "      <td>0.531437</td>\n",
       "      <td>0.495081</td>\n",
       "      <td>0.487976</td>\n",
       "      <td>0.306758</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.222474</td>\n",
       "      <td>0.385173</td>\n",
       "      <td>0.279792</td>\n",
       "      <td>0.196998</td>\n",
       "      <td>0.399079</td>\n",
       "      <td>0.351207</td>\n",
       "      <td>0.518192</td>\n",
       "      <td>0.335156</td>\n",
       "      <td>0.229268</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.032403</td>\n",
       "      <td>0.304923</td>\n",
       "      <td>-0.051392</td>\n",
       "      <td>0.282848</td>\n",
       "      <td>0.284671</td>\n",
       "      <td>-0.104905</td>\n",
       "      <td>-0.078833</td>\n",
       "      <td>-0.004450</td>\n",
       "      <td>0.105621</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060427</td>\n",
       "      <td>0.256680</td>\n",
       "      <td>0.264308</td>\n",
       "      <td>-0.056585</td>\n",
       "      <td>-0.065649</td>\n",
       "      <td>-0.024432</td>\n",
       "      <td>0.245339</td>\n",
       "      <td>-0.029717</td>\n",
       "      <td>0.033282</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.059177</td>\n",
       "      <td>0.263274</td>\n",
       "      <td>0.066295</td>\n",
       "      <td>0.262808</td>\n",
       "      <td>0.232497</td>\n",
       "      <td>0.119524</td>\n",
       "      <td>0.170416</td>\n",
       "      <td>0.254453</td>\n",
       "      <td>0.392549</td>\n",
       "      <td>0.129991</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003924</td>\n",
       "      <td>0.225304</td>\n",
       "      <td>0.203602</td>\n",
       "      <td>0.079452</td>\n",
       "      <td>0.165163</td>\n",
       "      <td>0.142341</td>\n",
       "      <td>0.441216</td>\n",
       "      <td>0.140399</td>\n",
       "      <td>0.023837</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.059229</td>\n",
       "      <td>-0.128132</td>\n",
       "      <td>0.036874</td>\n",
       "      <td>-0.099434</td>\n",
       "      <td>-0.114014</td>\n",
       "      <td>0.416536</td>\n",
       "      <td>0.550761</td>\n",
       "      <td>0.357546</td>\n",
       "      <td>0.279726</td>\n",
       "      <td>0.396657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>-0.041791</td>\n",
       "      <td>-0.076898</td>\n",
       "      <td>0.511335</td>\n",
       "      <td>0.593799</td>\n",
       "      <td>0.331239</td>\n",
       "      <td>0.491044</td>\n",
       "      <td>0.736693</td>\n",
       "      <td>0.584115</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.059241</td>\n",
       "      <td>0.291671</td>\n",
       "      <td>-0.167388</td>\n",
       "      <td>0.298051</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0.035567</td>\n",
       "      <td>0.087292</td>\n",
       "      <td>0.255859</td>\n",
       "      <td>0.275253</td>\n",
       "      <td>-0.001323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.240065</td>\n",
       "      <td>0.223810</td>\n",
       "      <td>0.170669</td>\n",
       "      <td>0.033226</td>\n",
       "      <td>-0.047797</td>\n",
       "      <td>0.102086</td>\n",
       "      <td>0.164583</td>\n",
       "      <td>-0.105806</td>\n",
       "      <td>-0.047001</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0 -0.032403     0.182815     -0.301307        0.213053   0.146813   \n",
       "1 -0.032403     0.304923     -0.051392        0.282848   0.284671   \n",
       "2  0.059177     0.263274      0.066295        0.262808   0.232497   \n",
       "3  0.059229    -0.128132      0.036874       -0.099434  -0.114014   \n",
       "4  0.059241     0.291671     -0.167388        0.298051   0.272369   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0         0.198968          0.531437        0.495081             0.487976   \n",
       "1        -0.104905         -0.078833       -0.004450             0.105621   \n",
       "2         0.119524          0.170416        0.254453             0.392549   \n",
       "3         0.416536          0.550761        0.357546             0.279726   \n",
       "4         0.035567          0.087292        0.255859             0.275253   \n",
       "\n",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0       0.306758  ...      -0.222474         0.385173    0.279792   \n",
       "1       0.000193  ...      -0.060427         0.256680    0.264308   \n",
       "2       0.129991  ...      -0.003924         0.225304    0.203602   \n",
       "3       0.396657  ...       0.021929        -0.041791   -0.076898   \n",
       "4      -0.001323  ...      -0.240065         0.223810    0.170669   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0          0.196998           0.399079         0.351207              0.518192   \n",
       "1         -0.056585          -0.065649        -0.024432              0.245339   \n",
       "2          0.079452           0.165163         0.142341              0.441216   \n",
       "3          0.511335           0.593799         0.331239              0.491044   \n",
       "4          0.033226          -0.047797         0.102086              0.164583   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  diagnosis  \n",
       "0        0.335156                 0.229268          M  \n",
       "1       -0.029717                 0.033282          M  \n",
       "2        0.140399                 0.023837          M  \n",
       "3        0.736693                 0.584115          M  \n",
       "4       -0.105806                -0.047001          M  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm =SMOTE(random_state=42)\n",
    "X_res , Y_res = sm.fit_resample(X_norm,Y_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       " 0   -0.032403     0.182815     -0.301307        0.213053   0.146813   \n",
       " 1   -0.032403     0.304923     -0.051392        0.282848   0.284671   \n",
       " 2    0.059177     0.263274      0.066295        0.262808   0.232497   \n",
       " 3    0.059229    -0.128132      0.036874       -0.099434  -0.114014   \n",
       " 4    0.059241     0.291671     -0.167388        0.298051   0.272369   \n",
       " ..        ...          ...           ...             ...        ...   \n",
       " 709 -0.032359    -0.012007     -0.061861       -0.007334  -0.025348   \n",
       " 710 -0.032353     0.195926      0.013262        0.197582   0.164595   \n",
       " 711  0.042521     0.165906      0.075031        0.164219   0.136778   \n",
       " 712 -0.033110     0.047753     -0.011298        0.051928   0.026466   \n",
       " 713 -0.032897    -0.028316      0.069606       -0.035608  -0.033205   \n",
       " \n",
       "      smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       " 0           0.198968          0.531437        0.495081             0.487976   \n",
       " 1          -0.104905         -0.078833       -0.004450             0.105621   \n",
       " 2           0.119524          0.170416        0.254453             0.392549   \n",
       " 3           0.416536          0.550761        0.357546             0.279726   \n",
       " 4           0.035567          0.087292        0.255859             0.275253   \n",
       " ..               ...               ...             ...                  ...   \n",
       " 709         0.113216          0.078381        0.021472             0.023358   \n",
       " 710         0.128751          0.097484        0.182285             0.253878   \n",
       " 711         0.117655          0.022346        0.164023             0.253756   \n",
       " 712         0.116693          0.058793        0.110797             0.128327   \n",
       " 713        -0.042346         -0.124850       -0.061887            -0.014072   \n",
       " \n",
       "      symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       " 0         0.306758  ...      0.324113      -0.222474         0.385173   \n",
       " 1         0.000193  ...      0.310239      -0.060427         0.256680   \n",
       " 2         0.129991  ...      0.259723      -0.003924         0.225304   \n",
       " 3         0.396657  ...     -0.048353       0.021929        -0.041791   \n",
       " 4        -0.001323  ...      0.223081      -0.240065         0.223810   \n",
       " ..             ...  ...           ...            ...              ...   \n",
       " 709       0.052913  ...      0.008773      -0.076060         0.007341   \n",
       " 710       0.080415  ...      0.177476      -0.012425         0.157113   \n",
       " 711      -0.012301  ...      0.166550       0.182555         0.177612   \n",
       " 712       0.057787  ...      0.084362      -0.014003         0.069789   \n",
       " 713      -0.094765  ...     -0.049647       0.077165        -0.057323   \n",
       " \n",
       "      area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       " 0      0.279792          0.196998           0.399079         0.351207   \n",
       " 1      0.264308         -0.056585          -0.065649        -0.024432   \n",
       " 2      0.203602          0.079452           0.165163         0.142341   \n",
       " 3     -0.076898          0.511335           0.593799         0.331239   \n",
       " 4      0.170669          0.033226          -0.047797         0.102086   \n",
       " ..          ...               ...                ...              ...   \n",
       " 709   -0.010879          0.125867           0.075487         0.043525   \n",
       " 710    0.130319          0.044264          -0.011723         0.039488   \n",
       " 711    0.115549          0.052680          -0.039450         0.094024   \n",
       " 712    0.050186          0.141964           0.003753         0.075360   \n",
       " 713   -0.049041         -0.051298          -0.121970        -0.065884   \n",
       " \n",
       "      concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       " 0                0.518192        0.335156                 0.229268  \n",
       " 1                0.245339       -0.029717                 0.033282  \n",
       " 2                0.441216        0.140399                 0.023837  \n",
       " 3                0.491044        0.736693                 0.584115  \n",
       " 4                0.164583       -0.105806                -0.047001  \n",
       " ..                    ...             ...                      ...  \n",
       " 709              0.083481        0.049774                -0.009425  \n",
       " 710              0.094692        0.029159                -0.030981  \n",
       " 711              0.233841       -0.056883                -0.052138  \n",
       " 712              0.116056       -0.002740                -0.025512  \n",
       " 713             -0.010152       -0.120626                -0.100275  \n",
       " \n",
       " [714 rows x 31 columns],\n",
       "      0\n",
       " 0    1\n",
       " 1    1\n",
       " 2    1\n",
       " 3    1\n",
       " 4    1\n",
       " ..  ..\n",
       " 709  1\n",
       " 710  1\n",
       " 711  1\n",
       " 712  1\n",
       " 713  1\n",
       " \n",
       " [714 rows x 1 columns])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.fit_sample(X_norm,Y_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.2min finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:765: 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",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 1 1\n",
      " 1 0 0 0 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0 0\n",
      " 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0]\n",
      "Best Params : {'n_estimators': 100}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        68\n",
      "           1       0.97      1.00      0.99        75\n",
      "\n",
      "    accuracy                           0.99       143\n",
      "   macro avg       0.99      0.99      0.99       143\n",
      "weighted avg       0.99      0.99      0.99       143\n",
      "\n",
      "Accuracy Score : 0.986013986013986\n",
      "Confusion Matrix : \n",
      " [[66  2]\n",
      " [ 0 75]]\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'n_estimators': [100, 500, 1000, 2000],\n",
    "           }\n",
    "FitModel(X_res,Y_res,'Random Forest',RandomForestClassifier(),param,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 1 1\n",
      " 1 0 0 0 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0 0\n",
      " 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0]\n",
      "Best Params : {'C': 1, 'gamma': 1}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        68\n",
      "           1       0.97      1.00      0.99        75\n",
      "\n",
      "    accuracy                           0.99       143\n",
      "   macro avg       0.99      0.99      0.99       143\n",
      "weighted avg       0.99      0.99      0.99       143\n",
      "\n",
      "Accuracy Score : 0.986013986013986\n",
      "Confusion Matrix : \n",
      " [[66  2]\n",
      " [ 0 75]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed:    1.8s finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:73: 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(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'C': [0.1, 1, 100, 1000],\n",
    "            'gamma': [0.0001, 0.001, 0.005, 0.1, 1, 3, 5]\n",
    "        }\n",
    "FitModel(X_res,Y_res,'SVC',SVC(),param,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
      "[1 1 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 1 1\n",
      " 1 0 0 0 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 0 0\n",
      " 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0]\n",
      "Best Params : {'n_estimators': 100}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99        68\n",
      "           1       0.99      1.00      0.99        75\n",
      "\n",
      "    accuracy                           0.99       143\n",
      "   macro avg       0.99      0.99      0.99       143\n",
      "weighted avg       0.99      0.99      0.99       143\n",
      "\n",
      "Accuracy Score : 0.993006993006993\n",
      "Confusion Matrix : \n",
      " [[67  1]\n",
      " [ 0 75]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:    9.6s finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:73: 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(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "param ={\n",
    "            'n_estimators': [100, 500, 1000, 2000],\n",
    "           \n",
    "        }\n",
    "FitModel(X_res,Y_res,'XGBoost',XGBClassifier(),param,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp.index = feat_imp.Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_to_keep = feat_imp.iloc[1:15].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.indexes.base.Index,\n",
       " Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
       "        'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "        'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "        'radius_se', 'texture_se', 'perimeter_se', 'area_se'],\n",
       "       dtype='object', name='Feature'))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "type(feat_to_keep),feat_to_keep\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.1min finished\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py:765: 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",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 0 0 1 0 1 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 1 1 1 1\n",
      " 1 0 0 0 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
      " 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0]\n",
      "Best Params : {'n_estimators': 100}\n",
      "Classification Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97        68\n",
      "           1       0.96      0.99      0.97        75\n",
      "\n",
      "    accuracy                           0.97       143\n",
      "   macro avg       0.97      0.97      0.97       143\n",
      "weighted avg       0.97      0.97      0.97       143\n",
      "\n",
      "Accuracy Score : 0.972027972027972\n",
      "Confusion Matrix : \n",
      " [[65  3]\n",
      " [ 1 74]]\n"
     ]
    }
   ],
   "source": [
    "X_res= pd.DataFrame(X_res)\n",
    "Y_res = pd.DataFrame(Y_res)\n",
    "X_res.columns = X_norm.columns\n",
    "\n",
    "param ={\n",
    "            'n_estimators': [100, 500, 1000, 2000],\n",
    "           \n",
    "        }\n",
    "FitModel(X_res[feat_to_keep],Y_res,'Random Forest',RandomForestClassifier(),param,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Reloading the Saved Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open(\"XGBoost_norm\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pred1 = loaded_model.predict(x_test)\n",
    "loaded_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
       "       '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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train1,x_test1,y_train1,y_test1 = train_test_split(X_res,Y_res, test_size = 0.2)\n",
    "    \n",
    "train=pd.concat([y_train1,x_train1],axis=1)\n",
    "train.to_csv('./train.csv',index=False,header=False)\n",
    "    \n",
    "\n",
    "y_train.to_csv('./Y-train.csv')\n",
    "\n",
    "    \n",
    "test=pd.concat([y_test1,x_test1],axis=1)\n",
    "test.to_csv('./test.csv',index=False,header=False)\n",
    "    \n",
    "y_test.to_csv('./Y-test.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}