PyCaret2-Regression-Predicting_ENEM_Math_Grades.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the best math grades of the ENEM 2016\n",
    "\n",
    "In this example, we will predict the math score in the ENEM 2016 Brazilian National Exam.\n",
    "The dataset was obtained from INEP, a department from the Brazilian Education Ministry. It contains data from the applicants for the 2016 National High School Exam and can be downloaded [here](https://www.kaggle.com/davispeixoto/codenation-enem2). Inside this dataset there are not only the exam results, but the social and economic context of the applicants. Check here data for description: [Enem 2016 Microdata](https://s3-us-west-1.amazonaws.com/acceleration-assets-highway/data-science/dicionario-de-dados.zip).\n",
    "\n",
    "You will have two datasets - train.csv e test.csv - for using to predict math scores (`NU_NOTA_MT`). But the train file is a good amount of data from ENEM 2016 and contains most of the columns for those who would like to do some EDA.\n",
    "\n",
    "This notebook was created using PyCaret 2.0 by [Simone Perazzoli](https://github.com/simoneperazzoli/). \n",
    "Last updated : 04-08-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install pycaret==2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pycaret\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pycaret.regression import *\n",
    "from scipy.stats import kurtosis, skew\n",
    "\n",
    "pd.set_option('display.max_columns',200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# checking pycaret version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train dataset\n",
    "df_train = pd.read_csv('train.csv')\n",
    "# Test dataset\n",
    "df_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Creating answer dataframe\n",
    "answer = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the registration number:\n",
    "answer['NU_INSCRICAO'] = df_test['NU_INSCRICAO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping the registration number from train and test dataframes:\n",
    "df_train.drop(['NU_INSCRICAO'], axis=1, inplace=True)\n",
    "df_test.drop(['NU_INSCRICAO'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13730, 166), (4576, 46))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking dataframe shape\n",
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#By checking the shape of the datasets we can see that there are more columns in the training data than in the \n",
    "#test data, so we will use only the features that exist in the test dataframe to analyze and determine which \n",
    "#features we should use to make the prediction.\n",
    "\n",
    "cols = list(df_test)\n",
    "cols.append('NU_NOTA_MT')\n",
    "\n",
    "train = df_train[cols]\n",
    "test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>TP_ENSINO</th>\n",
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      ],
      "text/plain": [
       "   CO_UF_RESIDENCIA SG_UF_RESIDENCIA  NU_IDADE TP_SEXO  TP_COR_RACA  \\\n",
       "0                43               RS        24       M            1   \n",
       "1                23               CE        17       F            3   \n",
       "2                23               CE        21       F            3   \n",
       "3                33               RJ        25       F            0   \n",
       "4                13               AM        28       M            2   \n",
       "\n",
       "   TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  TP_ESCOLA  TP_ENSINO  \\\n",
       "0                 1                1                4          1        NaN   \n",
       "1                 1                2                0          2        1.0   \n",
       "2                 1                3                0          1        NaN   \n",
       "3                 1                1                9          1        NaN   \n",
       "4                 1                1                4          1        NaN   \n",
       "\n",
       "   IN_TREINEIRO  TP_DEPENDENCIA_ADM_ESC  IN_BAIXA_VISAO  IN_CEGUEIRA  \\\n",
       "0             0                     NaN               0            0   \n",
       "1             0                     2.0               0            0   \n",
       "2             0                     NaN               0            0   \n",
       "3             0                     NaN               0            0   \n",
       "4             0                     NaN               0            0   \n",
       "\n",
       "   IN_SURDEZ  IN_DISLEXIA  IN_DISCALCULIA  IN_SABATISTA  IN_GESTANTE  \\\n",
       "0          0            0               0             0            0   \n",
       "1          0            0               0             0            0   \n",
       "2          0            0               0             0            0   \n",
       "3          0            0               0             0            0   \n",
       "4          0            0               0             0            0   \n",
       "\n",
       "   IN_IDOSO  TP_PRESENCA_CN  TP_PRESENCA_CH  TP_PRESENCA_LC  \\\n",
       "0         0               1               1               1   \n",
       "1         0               1               1               1   \n",
       "2         0               0               0               0   \n",
       "3         0               0               0               0   \n",
       "4         0               0               0               0   \n",
       "\n",
       "                                CO_PROVA_CN  \\\n",
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       "1  b9b06ce8c319a3df2158ea3d0aef0f7d3eecaed7   \n",
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       "\n",
       "                                CO_PROVA_CH  \\\n",
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       "4  2d22ac1d42e6187f09ee6c578df187a760123ccf   \n",
       "\n",
       "                                CO_PROVA_LC  \\\n",
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       "4  2d22ac1d42e6187f09ee6c578df187a760123ccf   \n",
       "\n",
       "                                CO_PROVA_MT  NU_NOTA_CN  NU_NOTA_CH  \\\n",
       "0  97caab1e1533dba217deb7ef41490f52e459ab01       436.3       495.4   \n",
       "1  97caab1e1533dba217deb7ef41490f52e459ab01       474.5       544.1   \n",
       "2  2d22ac1d42e6187f09ee6c578df187a760123ccf         NaN         NaN   \n",
       "3  2d22ac1d42e6187f09ee6c578df187a760123ccf         NaN         NaN   \n",
       "4  2d22ac1d42e6187f09ee6c578df187a760123ccf         NaN         NaN   \n",
       "\n",
       "   NU_NOTA_LC  TP_LINGUA  TP_STATUS_REDACAO  NU_NOTA_COMP1  NU_NOTA_COMP2  \\\n",
       "0       581.2          1                1.0          120.0          120.0   \n",
       "1       599.0          1                1.0          140.0          120.0   \n",
       "2         NaN          1                NaN            NaN            NaN   \n",
       "3         NaN          0                NaN            NaN            NaN   \n",
       "4         NaN          1                NaN            NaN            NaN   \n",
       "\n",
       "   NU_NOTA_COMP3  NU_NOTA_COMP4  NU_NOTA_COMP5  NU_NOTA_REDACAO Q001 Q002  \\\n",
       "0          120.0           80.0           80.0            520.0    D    D   \n",
       "1          120.0          120.0           80.0            580.0    A    A   \n",
       "2            NaN            NaN            NaN              NaN    D    D   \n",
       "3            NaN            NaN            NaN              NaN    H    E   \n",
       "4            NaN            NaN            NaN              NaN    E    D   \n",
       "\n",
       "  Q006 Q024 Q025 Q026 Q027 Q047  NU_NOTA_MT  \n",
       "0    C    A    A    C    H    A       399.4  \n",
       "1    B    A    A    A  NaN    A       459.8  \n",
       "2    C    A    A    A  NaN    A         NaN  \n",
       "3    E    C    B    C    F    D         NaN  \n",
       "4    C    A    A    B    F    A         NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing training data:\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>CO_UF_RESIDENCIA</th>\n",
       "      <th>SG_UF_RESIDENCIA</th>\n",
       "      <th>NU_IDADE</th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_NACIONALIDADE</th>\n",
       "      <th>TP_ST_CONCLUSAO</th>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>TP_ENSINO</th>\n",
       "      <th>IN_TREINEIRO</th>\n",
       "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
       "      <th>IN_BAIXA_VISAO</th>\n",
       "      <th>IN_CEGUEIRA</th>\n",
       "      <th>IN_SURDEZ</th>\n",
       "      <th>IN_DISLEXIA</th>\n",
       "      <th>IN_DISCALCULIA</th>\n",
       "      <th>IN_SABATISTA</th>\n",
       "      <th>IN_GESTANTE</th>\n",
       "      <th>IN_IDOSO</th>\n",
       "      <th>TP_PRESENCA_CN</th>\n",
       "      <th>TP_PRESENCA_CH</th>\n",
       "      <th>TP_PRESENCA_LC</th>\n",
       "      <th>CO_PROVA_CN</th>\n",
       "      <th>CO_PROVA_CH</th>\n",
       "      <th>CO_PROVA_LC</th>\n",
       "      <th>CO_PROVA_MT</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>TP_LINGUA</th>\n",
       "      <th>TP_STATUS_REDACAO</th>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Q001</th>\n",
       "      <th>Q002</th>\n",
       "      <th>Q006</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "      <th>Q026</th>\n",
       "      <th>Q027</th>\n",
       "      <th>Q047</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>PR</td>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16f84b7b3d2aeaff7d2f01297e6b3d0e25c77bb2</td>\n",
       "      <td>9cd70f1b922e02bd33453b3f607f5a644fb9b1b8</td>\n",
       "      <td>01abbb7f1a90505385f44eec9905f82ca2a42cfd</td>\n",
       "      <td>81d0ee00ef42a7c23eb04496458c03d4c5b9c31a</td>\n",
       "      <td>464.8</td>\n",
       "      <td>443.5</td>\n",
       "      <td>431.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>MA</td>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>c8328ebc6f3238e06076c481bc1b82b8301e7a3f</td>\n",
       "      <td>f48d390ab6a2428e659c37fb8a9d00afde621889</td>\n",
       "      <td>72f80e4b3150c627c7ffc93cfe0fa13a9989b610</td>\n",
       "      <td>577f8968d95046f5eb5cc158608e12fa9ba34c85</td>\n",
       "      <td>391.1</td>\n",
       "      <td>491.1</td>\n",
       "      <td>548.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>E</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>CE</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16f84b7b3d2aeaff7d2f01297e6b3d0e25c77bb2</td>\n",
       "      <td>9cd70f1b922e02bd33453b3f607f5a644fb9b1b8</td>\n",
       "      <td>01af53cd161a420fff1767129c10de560cc264dd</td>\n",
       "      <td>97caab1e1533dba217deb7ef41490f52e459ab01</td>\n",
       "      <td>595.9</td>\n",
       "      <td>622.7</td>\n",
       "      <td>613.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2d22ac1d42e6187f09ee6c578df187a760123ccf</td>\n",
       "      <td>2d22ac1d42e6187f09ee6c578df187a760123ccf</td>\n",
       "      <td>2d22ac1d42e6187f09ee6c578df187a760123ccf</td>\n",
       "      <td>2d22ac1d42e6187f09ee6c578df187a760123ccf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>PR</td>\n",
       "      <td>18</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66b1dad288e13be0992bae01e81f71eca1c6e8a6</td>\n",
       "      <td>942ab3dc020af4cf53740b6b07e9dd7060b24164</td>\n",
       "      <td>5aebe5cad7fabc1545ac7fba07a4e6177f98483c</td>\n",
       "      <td>767a32545304ed293242d528f54d4edb1369f910</td>\n",
       "      <td>592.9</td>\n",
       "      <td>492.6</td>\n",
       "      <td>571.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>D</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CO_UF_RESIDENCIA SG_UF_RESIDENCIA  NU_IDADE TP_SEXO  TP_COR_RACA  \\\n",
       "0                41               PR        22       F            3   \n",
       "1                21               MA        26       F            3   \n",
       "2                23               CE        21       M            1   \n",
       "3                15               PA        27       F            3   \n",
       "4                41               PR        18       M            1   \n",
       "\n",
       "   TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  TP_ESCOLA  TP_ENSINO  \\\n",
       "0                 1                1                5          1        NaN   \n",
       "1                 1                1                8          1        NaN   \n",
       "2                 1                2                0          2        3.0   \n",
       "3                 1                1                8          1        NaN   \n",
       "4                 1                2                0          2        1.0   \n",
       "\n",
       "   IN_TREINEIRO  TP_DEPENDENCIA_ADM_ESC  IN_BAIXA_VISAO  IN_CEGUEIRA  \\\n",
       "0             0                     NaN               0            0   \n",
       "1             0                     NaN               0            0   \n",
       "2             0                     2.0               0            0   \n",
       "3             0                     NaN               0            0   \n",
       "4             0                     2.0               0            0   \n",
       "\n",
       "   IN_SURDEZ  IN_DISLEXIA  IN_DISCALCULIA  IN_SABATISTA  IN_GESTANTE  \\\n",
       "0          0            0               0             0            0   \n",
       "1          0            0               0             0            0   \n",
       "2          0            0               0             0            0   \n",
       "3          0            0               0             0            0   \n",
       "4          0            0               0             0            0   \n",
       "\n",
       "   IN_IDOSO  TP_PRESENCA_CN  TP_PRESENCA_CH  TP_PRESENCA_LC  \\\n",
       "0         0               1               1               1   \n",
       "1         0               1               1               1   \n",
       "2         0               1               1               1   \n",
       "3         0               0               0               0   \n",
       "4         0               1               1               1   \n",
       "\n",
       "                                CO_PROVA_CN  \\\n",
       "0  16f84b7b3d2aeaff7d2f01297e6b3d0e25c77bb2   \n",
       "1  c8328ebc6f3238e06076c481bc1b82b8301e7a3f   \n",
       "2  16f84b7b3d2aeaff7d2f01297e6b3d0e25c77bb2   \n",
       "3  2d22ac1d42e6187f09ee6c578df187a760123ccf   \n",
       "4  66b1dad288e13be0992bae01e81f71eca1c6e8a6   \n",
       "\n",
       "                                CO_PROVA_CH  \\\n",
       "0  9cd70f1b922e02bd33453b3f607f5a644fb9b1b8   \n",
       "1  f48d390ab6a2428e659c37fb8a9d00afde621889   \n",
       "2  9cd70f1b922e02bd33453b3f607f5a644fb9b1b8   \n",
       "3  2d22ac1d42e6187f09ee6c578df187a760123ccf   \n",
       "4  942ab3dc020af4cf53740b6b07e9dd7060b24164   \n",
       "\n",
       "                                CO_PROVA_LC  \\\n",
       "0  01abbb7f1a90505385f44eec9905f82ca2a42cfd   \n",
       "1  72f80e4b3150c627c7ffc93cfe0fa13a9989b610   \n",
       "2  01af53cd161a420fff1767129c10de560cc264dd   \n",
       "3  2d22ac1d42e6187f09ee6c578df187a760123ccf   \n",
       "4  5aebe5cad7fabc1545ac7fba07a4e6177f98483c   \n",
       "\n",
       "                                CO_PROVA_MT  NU_NOTA_CN  NU_NOTA_CH  \\\n",
       "0  81d0ee00ef42a7c23eb04496458c03d4c5b9c31a       464.8       443.5   \n",
       "1  577f8968d95046f5eb5cc158608e12fa9ba34c85       391.1       491.1   \n",
       "2  97caab1e1533dba217deb7ef41490f52e459ab01       595.9       622.7   \n",
       "3  2d22ac1d42e6187f09ee6c578df187a760123ccf         NaN         NaN   \n",
       "4  767a32545304ed293242d528f54d4edb1369f910       592.9       492.6   \n",
       "\n",
       "   NU_NOTA_LC  TP_LINGUA  TP_STATUS_REDACAO  NU_NOTA_COMP1  NU_NOTA_COMP2  \\\n",
       "0       431.8          0                1.0          120.0           80.0   \n",
       "1       548.0          1                1.0          120.0          120.0   \n",
       "2       613.6          0                1.0           80.0           40.0   \n",
       "3         NaN          0                NaN            NaN            NaN   \n",
       "4       571.4          1                1.0          100.0           80.0   \n",
       "\n",
       "   NU_NOTA_COMP3  NU_NOTA_COMP4  NU_NOTA_COMP5  NU_NOTA_REDACAO Q001 Q002  \\\n",
       "0           80.0          100.0           40.0            420.0    B    A   \n",
       "1          120.0          120.0          100.0            580.0    E    B   \n",
       "2           40.0           80.0           80.0            320.0    E    E   \n",
       "3            NaN            NaN            NaN              NaN    H    E   \n",
       "4           60.0           80.0            0.0            320.0    D    H   \n",
       "\n",
       "  Q006 Q024 Q025 Q026 Q027 Q047  \n",
       "0    C    A    A    C    C    A  \n",
       "1    C    B    B    B    F    A  \n",
       "2    D    B    B    A  NaN    A  \n",
       "3    G    B    B    A  NaN    A  \n",
       "4    H    C    B    A  NaN    A  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing test data:\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13730, 47), (4576, 46))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking dataframe shape after transformation\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a funtion to summarize dataframe information\n",
    "\n",
    "def data_summary(df):\n",
    "    '''Summary dataframe information'''\n",
    "\n",
    "    df = pd.DataFrame({'type': df.dtypes,\n",
    "                       'amount': df.isna().sum(),\n",
    "                       'null_values (%)': (df.isna().sum() / df.shape[0]) * 100,\n",
    "                       'unique': df.nunique()})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>null_values (%)</th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO_UF_RESIDENCIA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG_UF_RESIDENCIA</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_IDADE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_SEXO</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_NACIONALIDADE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ST_CONCLUSAO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ENSINO</th>\n",
       "      <td>float64</td>\n",
       "      <td>9448</td>\n",
       "      <td>68.812819</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_TREINEIRO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
       "      <td>float64</td>\n",
       "      <td>9448</td>\n",
       "      <td>68.812819</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_BAIXA_VISAO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_CEGUEIRA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SURDEZ</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_DISLEXIA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_DISCALCULIA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SABATISTA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_GESTANTE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_IDOSO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_CN</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_CH</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_LC</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_CN</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_CH</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_LC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_MT</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <td>float64</td>\n",
       "      <td>3389</td>\n",
       "      <td>24.683176</td>\n",
       "      <td>2692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <td>float64</td>\n",
       "      <td>3389</td>\n",
       "      <td>24.683176</td>\n",
       "      <td>2978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>2774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_LINGUA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_STATUS_REDACAO</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q001</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q002</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q006</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q024</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q025</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q026</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q027</th>\n",
       "      <td>object</td>\n",
       "      <td>7373</td>\n",
       "      <td>53.699927</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q047</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <td>float64</td>\n",
       "      <td>3597</td>\n",
       "      <td>26.198106</td>\n",
       "      <td>3406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           type  amount  null_values (%)  unique\n",
       "CO_UF_RESIDENCIA          int64       0         0.000000      27\n",
       "SG_UF_RESIDENCIA         object       0         0.000000      27\n",
       "NU_IDADE                  int64       0         0.000000      55\n",
       "TP_SEXO                  object       0         0.000000       2\n",
       "TP_COR_RACA               int64       0         0.000000       6\n",
       "TP_NACIONALIDADE          int64       0         0.000000       5\n",
       "TP_ST_CONCLUSAO           int64       0         0.000000       4\n",
       "TP_ANO_CONCLUIU           int64       0         0.000000      11\n",
       "TP_ESCOLA                 int64       0         0.000000       4\n",
       "TP_ENSINO               float64    9448        68.812819       3\n",
       "IN_TREINEIRO              int64       0         0.000000       2\n",
       "TP_DEPENDENCIA_ADM_ESC  float64    9448        68.812819       4\n",
       "IN_BAIXA_VISAO            int64       0         0.000000       2\n",
       "IN_CEGUEIRA               int64       0         0.000000       1\n",
       "IN_SURDEZ                 int64       0         0.000000       2\n",
       "IN_DISLEXIA               int64       0         0.000000       2\n",
       "IN_DISCALCULIA            int64       0         0.000000       2\n",
       "IN_SABATISTA              int64       0         0.000000       2\n",
       "IN_GESTANTE               int64       0         0.000000       2\n",
       "IN_IDOSO                  int64       0         0.000000       2\n",
       "TP_PRESENCA_CN            int64       0         0.000000       3\n",
       "TP_PRESENCA_CH            int64       0         0.000000       3\n",
       "TP_PRESENCA_LC            int64       0         0.000000       3\n",
       "CO_PROVA_CN              object       0         0.000000      10\n",
       "CO_PROVA_CH              object       0         0.000000      10\n",
       "CO_PROVA_LC              object       0         0.000000       9\n",
       "CO_PROVA_MT              object       0         0.000000       9\n",
       "NU_NOTA_CN              float64    3389        24.683176    2692\n",
       "NU_NOTA_CH              float64    3389        24.683176    2978\n",
       "NU_NOTA_LC              float64    3597        26.198106    2774\n",
       "TP_LINGUA                 int64       0         0.000000       2\n",
       "TP_STATUS_REDACAO       float64    3597        26.198106       9\n",
       "NU_NOTA_COMP1           float64    3597        26.198106      15\n",
       "NU_NOTA_COMP2           float64    3597        26.198106      13\n",
       "NU_NOTA_COMP3           float64    3597        26.198106      12\n",
       "NU_NOTA_COMP4           float64    3597        26.198106      14\n",
       "NU_NOTA_COMP5           float64    3597        26.198106      14\n",
       "NU_NOTA_REDACAO         float64    3597        26.198106      53\n",
       "Q001                     object       0         0.000000       8\n",
       "Q002                     object       0         0.000000       8\n",
       "Q006                     object       0         0.000000      17\n",
       "Q024                     object       0         0.000000       5\n",
       "Q025                     object       0         0.000000       2\n",
       "Q026                     object       0         0.000000       3\n",
       "Q027                     object    7373        53.699927      13\n",
       "Q047                     object       0         0.000000       5\n",
       "NU_NOTA_MT              float64    3597        26.198106    3406"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train summary:\n",
    "data_summary(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>null_values (%)</th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO_UF_RESIDENCIA</th>\n",
       "      <td>int64</td>\n",
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       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>SG_UF_RESIDENCIA</th>\n",
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       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>NU_IDADE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_SEXO</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_NACIONALIDADE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ST_CONCLUSAO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ENSINO</th>\n",
       "      <td>float64</td>\n",
       "      <td>3096</td>\n",
       "      <td>67.657343</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_TREINEIRO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
       "      <td>float64</td>\n",
       "      <td>3096</td>\n",
       "      <td>67.657343</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_BAIXA_VISAO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_CEGUEIRA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SURDEZ</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_DISLEXIA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_DISCALCULIA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_SABATISTA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_GESTANTE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN_IDOSO</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_CN</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_CH</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_PRESENCA_LC</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_CN</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_CH</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_LC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO_PROVA_MT</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <td>float64</td>\n",
       "      <td>1134</td>\n",
       "      <td>24.781469</td>\n",
       "      <td>1823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <td>float64</td>\n",
       "      <td>1134</td>\n",
       "      <td>24.781469</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>1839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_LINGUA</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_STATUS_REDACAO</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <td>float64</td>\n",
       "      <td>1199</td>\n",
       "      <td>26.201923</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q001</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q002</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q006</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q024</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q025</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q026</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q027</th>\n",
       "      <td>object</td>\n",
       "      <td>2488</td>\n",
       "      <td>54.370629</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q047</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           type  amount  null_values (%)  unique\n",
       "CO_UF_RESIDENCIA          int64       0         0.000000      27\n",
       "SG_UF_RESIDENCIA         object       0         0.000000      27\n",
       "NU_IDADE                  int64       0         0.000000      46\n",
       "TP_SEXO                  object       0         0.000000       2\n",
       "TP_COR_RACA               int64       0         0.000000       6\n",
       "TP_NACIONALIDADE          int64       0         0.000000       5\n",
       "TP_ST_CONCLUSAO           int64       0         0.000000       4\n",
       "TP_ANO_CONCLUIU           int64       0         0.000000      11\n",
       "TP_ESCOLA                 int64       0         0.000000       3\n",
       "TP_ENSINO               float64    3096        67.657343       3\n",
       "IN_TREINEIRO              int64       0         0.000000       2\n",
       "TP_DEPENDENCIA_ADM_ESC  float64    3096        67.657343       4\n",
       "IN_BAIXA_VISAO            int64       0         0.000000       2\n",
       "IN_CEGUEIRA               int64       0         0.000000       1\n",
       "IN_SURDEZ                 int64       0         0.000000       2\n",
       "IN_DISLEXIA               int64       0         0.000000       1\n",
       "IN_DISCALCULIA            int64       0         0.000000       1\n",
       "IN_SABATISTA              int64       0         0.000000       2\n",
       "IN_GESTANTE               int64       0         0.000000       2\n",
       "IN_IDOSO                  int64       0         0.000000       1\n",
       "TP_PRESENCA_CN            int64       0         0.000000       2\n",
       "TP_PRESENCA_CH            int64       0         0.000000       2\n",
       "TP_PRESENCA_LC            int64       0         0.000000       3\n",
       "CO_PROVA_CN              object       0         0.000000       9\n",
       "CO_PROVA_CH              object       0         0.000000       9\n",
       "CO_PROVA_LC              object       0         0.000000       9\n",
       "CO_PROVA_MT              object       0         0.000000       9\n",
       "NU_NOTA_CN              float64    1134        24.781469    1823\n",
       "NU_NOTA_CH              float64    1134        24.781469    1969\n",
       "NU_NOTA_LC              float64    1199        26.201923    1839\n",
       "TP_LINGUA                 int64       0         0.000000       2\n",
       "TP_STATUS_REDACAO       float64    1199        26.201923       9\n",
       "NU_NOTA_COMP1           float64    1199        26.201923      10\n",
       "NU_NOTA_COMP2           float64    1199        26.201923      10\n",
       "NU_NOTA_COMP3           float64    1199        26.201923      11\n",
       "NU_NOTA_COMP4           float64    1199        26.201923      11\n",
       "NU_NOTA_COMP5           float64    1199        26.201923      11\n",
       "NU_NOTA_REDACAO         float64    1199        26.201923      44\n",
       "Q001                     object       0         0.000000       8\n",
       "Q002                     object       0         0.000000       8\n",
       "Q006                     object       0         0.000000      17\n",
       "Q024                     object       0         0.000000       5\n",
       "Q025                     object       0         0.000000       2\n",
       "Q026                     object       0         0.000000       3\n",
       "Q027                     object    2488        54.370629      13\n",
       "Q047                     object       0         0.000000       5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test summary:\n",
    "data_summary(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing target \"NU_NOTA_MT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution of the variable:\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.distplot(train.NU_NOTA_MT, bins=25)\n",
    "plt.xlabel('Score')\n",
    "plt.title('Distribuition of math scores');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10133.000000\n",
       "mean       482.497928\n",
       "std         99.826323\n",
       "min          0.000000\n",
       "25%        408.900000\n",
       "50%        461.200000\n",
       "75%        537.600000\n",
       "max        952.000000\n",
       "Name: NU_NOTA_MT, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics for target:\n",
    "train['NU_NOTA_MT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis: 1.4225025820577502\n",
      "Asymmetry: 0.9206896733932955\n"
     ]
    }
   ],
   "source": [
    "print(f'Kurtosis: {train.NU_NOTA_MT.kurt()}')\n",
    "print(f'Asymmetry: {train.NU_NOTA_MT.skew()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Kurtosis** is used to identify outliers in the distribution. Here, its value is > 3, which means that the distribution tails tend to be lighter than in normal distribution or, the lack of outliers.  \n",
    "\n",
    "- The positive **asymmetry** means that we have a slightly tail on the right side of the distribution. The data is moderately skewed as our asymmetry value is between 0.5 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning and transforming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to remove irrelevant features\n",
    "def data_cleaning(df):\n",
    "    '''Removing irrelevant features'''\n",
    "\n",
    "    df.drop(['TP_DEPENDENCIA_ADM_ESC',\n",
    "             'TP_ENSINO',\n",
    "             'CO_PROVA_CN',\n",
    "             'CO_PROVA_CH',\n",
    "             'CO_PROVA_LC',\n",
    "             'CO_PROVA_MT',\n",
    "             'SG_UF_RESIDENCIA',\n",
    "             'CO_UF_RESIDENCIA',\n",
    "             'TP_NACIONALIDADE',\n",
    "             'IN_BAIXA_VISAO',\n",
    "             'IN_CEGUEIRA',\n",
    "             'IN_SURDEZ',\n",
    "             'IN_DISLEXIA',\n",
    "             'IN_DISCALCULIA',\n",
    "             'IN_SABATISTA',\n",
    "             'IN_GESTANTE',\n",
    "             'IN_IDOSO',\n",
    "             'TP_ANO_CONCLUIU','TP_PRESENCA_CN',\n",
    "             'TP_LINGUA','TP_PRESENCA_CH',\n",
    "             'IN_TREINEIRO', 'TP_PRESENCA_LC',\n",
    "             'TP_ST_CONCLUSAO',\n",
    "             'TP_STATUS_REDACAO', \n",
    "             'NU_IDADE',\n",
    "             'Q027'], axis=1, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mone/.local/lib/python3.6/site-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((13730, 20), (4576, 19))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning data:\n",
    "data_cleaning(train)\n",
    "data_cleaning(test)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here, the 3 first collumns were dropped due to present missing values >50%. The other ones were dropped after a manual analysis of the [data dictionary](https://s3-us-west-1.amazonaws.com/acceleration-assets-highway/data-science/dicionario-de-dados.zip)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to impute missing values:\n",
    "def data_imputation(df):\n",
    "    '''Imputing values to the missing data'''\n",
    "\n",
    "    df.fillna(df.dtypes.replace({'float64': -100}), inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mone/.local/lib/python3.6/site-packages/pandas/core/generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "data_imputation(train)\n",
    "train.head();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>TP_SEXO</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Q001</th>\n",
       "      <th>Q002</th>\n",
       "      <th>Q006</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "      <th>Q026</th>\n",
       "      <th>Q047</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>464.8</td>\n",
       "      <td>443.5</td>\n",
       "      <td>431.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>391.1</td>\n",
       "      <td>491.1</td>\n",
       "      <td>548.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>E</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>595.9</td>\n",
       "      <td>622.7</td>\n",
       "      <td>613.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>H</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>592.9</td>\n",
       "      <td>492.6</td>\n",
       "      <td>571.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>D</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TP_SEXO  TP_COR_RACA  TP_ESCOLA  NU_NOTA_CN  NU_NOTA_CH  NU_NOTA_LC  \\\n",
       "0       F            3          1       464.8       443.5       431.8   \n",
       "1       F            3          1       391.1       491.1       548.0   \n",
       "2       M            1          2       595.9       622.7       613.6   \n",
       "3       F            3          1      -100.0      -100.0      -100.0   \n",
       "4       M            1          2       592.9       492.6       571.4   \n",
       "\n",
       "   NU_NOTA_COMP1  NU_NOTA_COMP2  NU_NOTA_COMP3  NU_NOTA_COMP4  NU_NOTA_COMP5  \\\n",
       "0          120.0           80.0           80.0          100.0           40.0   \n",
       "1          120.0          120.0          120.0          120.0          100.0   \n",
       "2           80.0           40.0           40.0           80.0           80.0   \n",
       "3         -100.0         -100.0         -100.0         -100.0         -100.0   \n",
       "4          100.0           80.0           60.0           80.0            0.0   \n",
       "\n",
       "   NU_NOTA_REDACAO Q001 Q002 Q006 Q024 Q025 Q026 Q047  \n",
       "0            420.0    B    A    C    A    A    C    A  \n",
       "1            580.0    E    B    C    B    B    B    A  \n",
       "2            320.0    E    E    D    B    B    A    A  \n",
       "3           -100.0    H    E    G    B    B    A    A  \n",
       "4            320.0    D    H    H    C    B    A    A  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_imputation(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data modeling with PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  1- Seting up parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. It must called before executing any other function and takes two mandatory parameters: dataframe {array-like, sparse matrix} and name of the target column. All other parameters are optional.\n",
    "\n",
    "\n",
    "- **data:** train data.\n",
    "\n",
    "- **target:** target feature.\n",
    "\n",
    "- **remove_multicollinearity:** When set to True, the variables with inter-correlations higher than the threshold defined under the multicollinearity_threshold are dropped. When two features are highly correlated with each other, the feature that is less correlated with the target variable is dropped.\n",
    "\n",
    "- **multicollinearity_threshold:** Threshold used for dropping the correlated features. \n",
    "\n",
    "- **normalize:** When set to True, the feature space is transformed using the normalized_method param. Generally, linear algorithms perform better with normalized data however,  the results may vary and it is advised to run multiple experiments to evaluate the benefit of normalization. \n",
    "\n",
    "- **normalize_method:** Defines the method to be used for normalization. \n",
    "\n",
    "- **transform_target:** When set to True, target variable is transformed using the method defined in transform_target_method param. Target transformation is applied separately from feature transformations.\n",
    "\n",
    "- **session_id:** If None, a random seed is generated and returned in the Information grid. The unique number is then distributed as a seed in all functions used during the experiment. This can be used for later reproducibility of the entire experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_61a46538_d63f_11ea_8162_f83441383182row1_col1 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_61a46538_d63f_11ea_8162_f83441383182row15_col1 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_61a46538_d63f_11ea_8162_f83441383182row28_col1 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_61a46538_d63f_11ea_8162_f83441383182\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row0_col1\" class=\"data row0 col1\" >1991</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row2_col1\" class=\"data row2 col1\" >yeo-johnson</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row3_col1\" class=\"data row3 col1\" >(13730, 20)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row5_col1\" class=\"data row5 col1\" >9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row6_col1\" class=\"data row6 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row10_col1\" class=\"data row10 col1\" >(13730, 20)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row11_col1\" class=\"data row11 col1\" >(9610, 60)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row12_col1\" class=\"data row12 col1\" >(4120, 60)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row16_col1\" class=\"data row16 col1\" >robust</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row28_col1\" class=\"data row28 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row29_col1\" class=\"data row29 col1\" >0.950000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_61a46538_d63f_11ea_8162_f83441383182level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_61a46538_d63f_11ea_8162_f83441383182row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f76b937fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a pipeline to setup the model\n",
    "pipeline = setup(data=train, target='NU_NOTA_MT', \n",
    "                 remove_multicollinearity=True,\n",
    "                 normalize_method='robust',\n",
    "                 multicollinearity_threshold=0.95, \n",
    "                 normalize=True, \n",
    "                 transform_target=True, \n",
    "                 session_id=1991)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 - Comparing regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `compare_models()` function uses all models in the model library and scores them using K-fold Cross Validation. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold (default CV = 10 Folds) of all the available models in model library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "            : ;\n",
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       "            background-color:  lightgrey;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_b4301464_d63f_11ea_8162_f83441383182\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col0\" class=\"data row0 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col1\" class=\"data row0 col1\" >43.3216</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col2\" class=\"data row0 col2\" >4029.5616</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col3\" class=\"data row0 col3\" >63.4558</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col4\" class=\"data row0 col4\" >0.9449</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col5\" class=\"data row0 col5\" >0.1686</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col6\" class=\"data row0 col6\" >0.0862</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row0_col7\" class=\"data row0 col7\" >1.6997</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col0\" class=\"data row1 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col1\" class=\"data row1 col1\" >43.3722</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col2\" class=\"data row1 col2\" >4042.4929</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col3\" class=\"data row1 col3\" >63.5589</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col4\" class=\"data row1 col4\" >0.9447</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col5\" class=\"data row1 col5\" >0.1671</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col6\" class=\"data row1 col6\" >0.0860</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row1_col7\" class=\"data row1 col7\" >1.1562</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col0\" class=\"data row2 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col1\" class=\"data row2 col1\" >43.9795</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col2\" class=\"data row2 col2\" >4145.4626</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col3\" class=\"data row2 col3\" >64.3591</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col4\" class=\"data row2 col4\" >0.9433</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col5\" class=\"data row2 col5\" >0.2114</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col6\" class=\"data row2 col6\" >0.0840</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row2_col7\" class=\"data row2 col7\" >4.0103</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col0\" class=\"data row3 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col1\" class=\"data row3 col1\" >44.3396</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col2\" class=\"data row3 col2\" >4258.4459</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col3\" class=\"data row3 col3\" >65.2399</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col4\" class=\"data row3 col4\" >0.9417</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col5\" class=\"data row3 col5\" >0.1696</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col6\" class=\"data row3 col6\" >0.0927</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row3_col7\" class=\"data row3 col7\" >1.1249</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col1\" class=\"data row4 col1\" >43.9950</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col2\" class=\"data row4 col2\" >4259.0092</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col3\" class=\"data row4 col3\" >65.2418</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col4\" class=\"data row4 col4\" >0.9417</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col5\" class=\"data row4 col5\" >0.2036</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col6\" class=\"data row4 col6\" >0.0892</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row4_col7\" class=\"data row4 col7\" >0.8384</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col0\" class=\"data row5 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col1\" class=\"data row5 col1\" >45.4451</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col2\" class=\"data row5 col2\" >4474.6227</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col3\" class=\"data row5 col3\" >66.8702</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col4\" class=\"data row5 col4\" >0.9388</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col5\" class=\"data row5 col5\" >0.1862</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col6\" class=\"data row5 col6\" >0.0950</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row5_col7\" class=\"data row5 col7\" >1.0607</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col0\" class=\"data row6 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col1\" class=\"data row6 col1\" >49.9354</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col2\" class=\"data row6 col2\" >4923.2115</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col3\" class=\"data row6 col3\" >70.1513</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col4\" class=\"data row6 col4\" >0.9327</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col5\" class=\"data row6 col5\" >0.2652</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col6\" class=\"data row6 col6\" >0.0576</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row6_col7\" class=\"data row6 col7\" >5.0518</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col0\" class=\"data row7 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col1\" class=\"data row7 col1\" >50.4408</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col2\" class=\"data row7 col2\" >5102.6068</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col3\" class=\"data row7 col3\" >71.4191</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col4\" class=\"data row7 col4\" >0.9302</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col5\" class=\"data row7 col5\" >0.3316</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col6\" class=\"data row7 col6\" >0.0684</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row7_col7\" class=\"data row7 col7\" >0.4597</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col0\" class=\"data row8 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col1\" class=\"data row8 col1\" >50.5112</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col2\" class=\"data row8 col2\" >5107.2200</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col3\" class=\"data row8 col3\" >71.4531</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col4\" class=\"data row8 col4\" >0.9301</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col5\" class=\"data row8 col5\" >0.3766</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col6\" class=\"data row8 col6\" >0.0634</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row8_col7\" class=\"data row8 col7\" >4.1116</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col0\" class=\"data row9 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col1\" class=\"data row9 col1\" >50.9306</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col2\" class=\"data row9 col2\" >5112.9920</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col3\" class=\"data row9 col3\" >71.4930</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col4\" class=\"data row9 col4\" >0.9300</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col5\" class=\"data row9 col5\" >0.2772</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col6\" class=\"data row9 col6\" >0.0673</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row9_col7\" class=\"data row9 col7\" >0.0315</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col0\" class=\"data row10 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col1\" class=\"data row10 col1\" >50.9366</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col2\" class=\"data row10 col2\" >5114.1109</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col3\" class=\"data row10 col3\" >71.5008</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col4\" class=\"data row10 col4\" >0.9300</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col5\" class=\"data row10 col5\" >0.2757</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col6\" class=\"data row10 col6\" >0.0674</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row10_col7\" class=\"data row10 col7\" >0.0742</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col0\" class=\"data row11 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col1\" class=\"data row11 col1\" >51.2477</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col2\" class=\"data row11 col2\" >5160.5718</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col3\" class=\"data row11 col3\" >71.8217</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col4\" class=\"data row11 col4\" >0.9294</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col5\" class=\"data row11 col5\" >0.2784</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col6\" class=\"data row11 col6\" >0.0676</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row11_col7\" class=\"data row11 col7\" >0.0114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col0\" class=\"data row12 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col1\" class=\"data row12 col1\" >57.2904</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col2\" class=\"data row12 col2\" >6362.2942</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col3\" class=\"data row12 col3\" >79.6530</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col4\" class=\"data row12 col4\" >0.9129</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col5\" class=\"data row12 col5\" >0.1963</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col6\" class=\"data row12 col6\" >0.1279</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row12_col7\" class=\"data row12 col7\" >0.7479</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col0\" class=\"data row13 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col1\" class=\"data row13 col1\" >68.4823</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col2\" class=\"data row13 col2\" >7590.3098</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col3\" class=\"data row13 col3\" >87.0755</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col4\" class=\"data row13 col4\" >0.8961</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col5\" class=\"data row13 col5\" >0.5244</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col6\" class=\"data row13 col6\" >-0.0036</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row13_col7\" class=\"data row13 col7\" >0.0356</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col0\" class=\"data row14 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col1\" class=\"data row14 col1\" >61.6425</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col2\" class=\"data row14 col2\" >8456.2910</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col3\" class=\"data row14 col3\" >91.9125</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col4\" class=\"data row14 col4\" >0.8843</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col5\" class=\"data row14 col5\" >0.2385</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col6\" class=\"data row14 col6\" >0.1285</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row14_col7\" class=\"data row14 col7\" >0.0831</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col0\" class=\"data row15 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col1\" class=\"data row15 col1\" >62.1216</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col2\" class=\"data row15 col2\" >11308.1117</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col3\" class=\"data row15 col3\" >106.3199</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col4\" class=\"data row15 col4\" >0.8454</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col5\" class=\"data row15 col5\" >0.3329</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col6\" class=\"data row15 col6\" >0.0158</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row15_col7\" class=\"data row15 col7\" >0.0564</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col1\" class=\"data row16 col1\" >101.4546</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col2\" class=\"data row16 col2\" >17137.7881</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col3\" class=\"data row16 col3\" >130.9072</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col4\" class=\"data row16 col4\" >0.7656</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col5\" class=\"data row16 col5\" >0.8277</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col6\" class=\"data row16 col6\" >-0.2622</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row16_col7\" class=\"data row16 col7\" >0.0220</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col0\" class=\"data row17 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col1\" class=\"data row17 col1\" >135.9752</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col2\" class=\"data row17 col2\" >27060.6285</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col3\" class=\"data row17 col3\" >164.4996</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col4\" class=\"data row17 col4\" >0.6300</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col5\" class=\"data row17 col5\" >0.3364</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col6\" class=\"data row17 col6\" >-0.4303</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row17_col7\" class=\"data row17 col7\" >0.0160</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col0\" class=\"data row18 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col1\" class=\"data row18 col1\" >234.8026</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col2\" class=\"data row18 col2\" >73591.9650</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col3\" class=\"data row18 col3\" >271.2742</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col4\" class=\"data row18 col4\" >-0.0062</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col5\" class=\"data row18 col5\" >0.7179</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col6\" class=\"data row18 col6\" >-0.8333</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row18_col7\" class=\"data row18 col7\" >0.0157</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col0\" class=\"data row19 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col1\" class=\"data row19 col1\" >516782043.6764</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col2\" class=\"data row19 col2\" >2566481464996111843328.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col3\" class=\"data row19 col3\" >22656043246.8041</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col4\" class=\"data row19 col4\" >-34828091114500956.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col5\" class=\"data row19 col5\" >0.3362</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col6\" class=\"data row19 col6\" >1439051.0276</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row19_col7\" class=\"data row19 col7\" >0.0350</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col0\" class=\"data row20 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col1\" class=\"data row20 col1\" >10256633745.3887</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col2\" class=\"data row20 col2\" >1010957918896013998817280.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col3\" class=\"data row20 col3\" >449657184787.5454</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col4\" class=\"data row20 col4\" >-13719068301274478592.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col5\" class=\"data row20 col5\" >0.3512</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col6\" class=\"data row20 col6\" >28561015.6825</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row20_col7\" class=\"data row20 col7\" >2.5894</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4301464_d63f_11ea_8162_f83441383182level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col0\" class=\"data row21 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col1\" class=\"data row21 col1\" >4149691457749054.5000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col2\" class=\"data row21 col2\" >165483615650858787290755350403219456.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col3\" class=\"data row21 col3\" >181925048110950880.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col4\" class=\"data row21 col4\" >-2245673121919038750883225534464.0000</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col5\" class=\"data row21 col5\" >1.6141</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col6\" class=\"data row21 col6\" >11555390008799.6914</td>\n",
       "                        <td id=\"T_b4301464_d63f_11ea_8162_f83441383182row21_col7\" class=\"data row21 col7\" >0.0374</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f76c2c41f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "             n_jobs=-1, nthread=None, objective='reg:linear', random_state=1991,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "             silent=None, subsample=1, verbosity=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(fold=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 - Creating a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `create_model()` function creates a model and scores it using K-fold Cross Validation (default = 10 Fold). The output prints a score grid that shows MAE, MSE, RMSE, RMSLE, R2 and MAPE. This function returns a trained model object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col5 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col0\" class=\"data row0 col0\" >42.33</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col1\" class=\"data row0 col1\" >3799.14</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col2\" class=\"data row0 col2\" >61.64</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col3\" class=\"data row0 col3\" >0.95</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col4\" class=\"data row0 col4\" >0.14</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row0_col5\" class=\"data row0 col5\" >0.08</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col0\" class=\"data row1 col0\" >42.70</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col1\" class=\"data row1 col1\" >3919.92</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col2\" class=\"data row1 col2\" >62.61</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col3\" class=\"data row1 col3\" >0.95</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col4\" class=\"data row1 col4\" >0.19</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col0\" class=\"data row2 col0\" >42.90</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col1\" class=\"data row2 col1\" >4076.33</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col2\" class=\"data row2 col2\" >63.85</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col3\" class=\"data row2 col3\" >0.95</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col4\" class=\"data row2 col4\" >0.20</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row2_col5\" class=\"data row2 col5\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col0\" class=\"data row3 col0\" >42.65</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col1\" class=\"data row3 col1\" >3920.33</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col2\" class=\"data row3 col2\" >62.61</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col3\" class=\"data row3 col3\" >0.95</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col4\" class=\"data row3 col4\" >0.13</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row3_col5\" class=\"data row3 col5\" >0.08</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col0\" class=\"data row4 col0\" >46.03</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col1\" class=\"data row4 col1\" >4432.09</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col2\" class=\"data row4 col2\" >66.57</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col3\" class=\"data row4 col3\" >0.94</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col4\" class=\"data row4 col4\" >0.20</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row4_col5\" class=\"data row4 col5\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col0\" class=\"data row5 col0\" >43.32</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col1\" class=\"data row5 col1\" >4029.56</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col2\" class=\"data row5 col2\" >63.46</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col3\" class=\"data row5 col3\" >0.94</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col4\" class=\"data row5 col4\" >0.17</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row5_col5\" class=\"data row5 col5\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182level0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col0\" class=\"data row6 col0\" >1.37</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col1\" class=\"data row6 col1\" >219.67</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col2\" class=\"data row6 col2\" >1.71</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col4\" class=\"data row6 col4\" >0.03</td>\n",
       "                        <td id=\"T_b7b1c0b0_d63f_11ea_8162_f83441383182row6_col5\" class=\"data row6 col5\" >0.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f76addf4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = create_model('xgboost', fold=5, round=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 - Model Tunning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `tune_model()` function tunes the hyperparameters of a model and scores it using K-fold Cross Validation. The output prints the score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold (by default = 10 Folds). This function returns a trained model object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_62a71416_d640_11ea_8162_f83441383182row5_col0 {\n",
       "            background:  yellow;\n",
       "        }    #T_62a71416_d640_11ea_8162_f83441383182row5_col1 {\n",
       "            background:  yellow;\n",
       "        }    #T_62a71416_d640_11ea_8162_f83441383182row5_col2 {\n",
       "            background:  yellow;\n",
       "        }    #T_62a71416_d640_11ea_8162_f83441383182row5_col3 {\n",
       "            background:  yellow;\n",
       "        }    #T_62a71416_d640_11ea_8162_f83441383182row5_col4 {\n",
       "            background:  yellow;\n",
       "        }    #T_62a71416_d640_11ea_8162_f83441383182row5_col5 {\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_62a71416_d640_11ea_8162_f83441383182\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col0\" class=\"data row0 col0\" >45.6315</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col1\" class=\"data row0 col1\" >4401.0212</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col2\" class=\"data row0 col2\" >66.3402</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col3\" class=\"data row0 col3\" >0.9403</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col4\" class=\"data row0 col4\" >0.1628</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row0_col5\" class=\"data row0 col5\" >0.0870</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col0\" class=\"data row1 col0\" >45.2794</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col1\" class=\"data row1 col1\" >4449.0194</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col2\" class=\"data row1 col2\" >66.7010</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col3\" class=\"data row1 col3\" >0.9402</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col4\" class=\"data row1 col4\" >0.2079</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row1_col5\" class=\"data row1 col5\" >0.0867</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col0\" class=\"data row2 col0\" >45.4789</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col1\" class=\"data row2 col1\" >4493.2549</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col2\" class=\"data row2 col2\" >67.0317</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col3\" class=\"data row2 col3\" >0.9394</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col4\" class=\"data row2 col4\" >0.2133</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row2_col5\" class=\"data row2 col5\" >0.0871</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col0\" class=\"data row3 col0\" >45.8112</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col1\" class=\"data row3 col1\" >4512.8476</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col2\" class=\"data row3 col2\" >67.1777</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col3\" class=\"data row3 col3\" >0.9374</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col4\" class=\"data row3 col4\" >0.1372</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row3_col5\" class=\"data row3 col5\" >0.0875</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col0\" class=\"data row4 col0\" >48.7189</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col1\" class=\"data row4 col1\" >4907.9689</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col2\" class=\"data row4 col2\" >70.0569</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col3\" class=\"data row4 col3\" >0.9313</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col4\" class=\"data row4 col4\" >0.2172</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row4_col5\" class=\"data row4 col5\" >0.0952</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col0\" class=\"data row5 col0\" >46.1840</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col1\" class=\"data row5 col1\" >4552.8224</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col2\" class=\"data row5 col2\" >67.4615</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col3\" class=\"data row5 col3\" >0.9377</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col4\" class=\"data row5 col4\" >0.1877</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row5_col5\" class=\"data row5 col5\" >0.0887</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_62a71416_d640_11ea_8162_f83441383182level0_row6\" class=\"row_heading level0 row6\" >SD</th>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col0\" class=\"data row6 col0\" >1.2795</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col1\" class=\"data row6 col1\" >181.7107</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col2\" class=\"data row6 col2\" >1.3294</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col3\" class=\"data row6 col3\" >0.0034</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col4\" class=\"data row6 col4\" >0.0320</td>\n",
       "                        <td id=\"T_62a71416_d640_11ea_8162_f83441383182row6_col5\" class=\"data row6 col5\" >0.0032</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f76b45fdac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = tune_model(model, fold=5)"
   ]
  },
  {
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Extreme Gradient Boosting Regressor</td>\n",
       "      <td>44.9987</td>\n",
       "      <td>4317.789</td>\n",
       "      <td>65.7099</td>\n",
       "      <td>0.9403</td>\n",
       "      <td>0.1652</td>\n",
       "      <td>0    0.089478\n",
       "dtype: float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Model      MAE       MSE     RMSE      R2  \\\n",
       "0  Extreme Gradient Boosting Regressor  44.9987  4317.789  65.7099  0.9403   \n",
       "\n",
       "    RMSLE                          MAPE  \n",
       "0  0.1652  0    0.089478\n",
       "dtype: float64  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking score after cross-validation:\n",
    "predict_model(model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=0.9, gamma=0,\n",
      "             importance_type='gain', learning_rate=0.02, max_delta_step=0,\n",
      "             max_depth=30, min_child_weight=1, missing=None, n_estimators=700,\n",
      "             n_jobs=-1, nthread=None, objective='reg:linear', random_state=1991,\n",
      "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
      "             silent=None, subsample=0.1, verbosity=0)\n"
     ]
    }
   ],
   "source": [
    "# Checking model parameters:\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 - Analysing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals Plot \n",
    "plot_model(model, plot='residuals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prediction Error \n",
    "plot_model(model, plot='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cooks Distance Plot\n",
    "plot_model(model, plot='cooks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Learning Curve\n",
    "plot_model(model, plot='learning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Validation Curve\n",
    "plot_model(model, plot='vc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n3bp1jI6Osnr1au677z7CMOStb31rrkUjpeRDH/oQlUoFgH//93/n8MMPf1H3pqDgdxXhdjUfcEHBbsqHP/xhli5dyn//7//95R5KwYtAa83JJ5/MlVdeuSDHqKCg4LkpQi0FBTuJP/mTP+G6667b7tBPwa7BjTfeyMqVKwujo6BgOyk8HgUFO5F/+qd/4sEHH+Tv//7vX+6hFGwHzz77LOeddx5XX311XupbUFDwwigMj4KCgoKCgoKdRhFqKSgoKCgoKNhpbLWqxVpLs9kkDEOEEDtzTAUFBQUFBbs0XVG7Wq22Q4UBtdYvSAzwhaCUWlSPZ2ez1RE0m81FywILCgoKCgoKPK961at6lHRfDFpr7vjxPfTXyjtkf/NRSnHIIYe87MbHVo8ehiHgL2oURYuu8+CDD/Y0gtpd+V04z+Icdw9+F84RfjfOszjHXZskSVi/fn0+V+4IjDH018q8+39/k2cndqyI4NKhOp9/vxfb22UNj254JYqiBc2k5rKtz3YnfhfOszjH3YPfhXOE343zLM5x1+elSEXYNNVmbKK1Q/cppHrulXYSL3+wp6CgoKCgoCBHSLnDDQWxA/NQXiyF4VFQUFBQULALIYTa8YaHKDweBQUFBQUFBbsYl112Gffffz9aa9797nfzB3/wB/lnP/7xj/n4xz+OUorjjz+eP/uzP9uuYxSGR0FBQUFBwS7EyxVqufvuu/nVr37FNddcw8TEBGeccUaP4fG3f/u3fPGLX2TZsmWcc845rF27dkGzzudDYXgUFBQUFBTsQgilEGoHGx7PY39HH3103tG5v7+fdruNMQalFBs2bGBgYIA999wTgNWrV/OTn/ykMDwKCgoKCgp+25FCIXewx0M+jxwPpRTVahWAa6+9luOPPx6VGSybNm1ieHg4X3d4eJgNGzZs11gKw6OgoKCgoGBX4iUItfACqlpuvfVWrr32Wr70pS/t2DFkFIZHQUHBbku3B2bR9qGg4Plxxx138LnPfY5//Md/7FFkXbp0KZs3b85/HhsbY+nSpdt1jMLwKCgo2O3QcZuHfngvHWORQYiNE+p9NV69+rUv99AKCp4TIV+Cctrnsb+ZmRkuu+wyrr76agYHB3s+W758OY1Gg6eeeoo99tiD//f//h+XX375do2lMDwKCgp+a5maaeKcpevPcEB/tcx/3H4PVOtELlteioid48Ef/IRDTvq9l2u4BQXPC1/VsmMFv57P/r773e8yMTHBRRddlC875phjWLlyJSeffDKXXnopF198MQCnnnoq+++//3aNpTA8CgoKdlmcc8TakBqLcFCOAgLlH6BT0w0QIOeFUSYnJ3FhGelmlwkETkCrE2Ot3aHdRAsKdjRCvATltOK5v/Nve9vbeNvb3rbVz48++miuueaaFz2WwvAoKNjNSbUh1RoLKCEpRwFCCIy1aG0BR6AUSu1ak7FzjolmTJJqhAActOKEciSznA2HoNfoEIA2IEsRGDvvM0FQrdCamqE+NLDTzqOg4IXycoVadhbCdbOv5hHHMQ8++ODOHk9BQcEOQqmAgeERytWaf8MXApwjTRKa05P0DQzkSZfOOZqNBtOTEy/zqGfpGxqhPjhMJAVRoBAScGAcOK2RSiwwPABSnTD2xBjx1MLunkm7hRgsoXZgR9GC320OOeSQHdborjvvnvOx2xibbO+QfXZZNljha3+5ZoeOd3t5To/HtgZ5//33c9RRR+3wQe1q/C6cZ3GOuwf3338/Rx55JK1OQqoTrBM4BCAIXIcQEBXFcHmERqPD1EQDoVPKfXVGR5cwVKlRH+ijXKtu1/Gtc0y2YlKtCYFaFBCWohcU2jBpyi9v+zHVPfYhUpJSKMlNDOG8/RQGWOdwzuWmh7AGaVOwlsGlQ3SqZWY2TeAyz4fDobTlNa973Xad287md+X7+tt6ji/ly/nu7vEoQi0FBbsRViiemZihRoKSCmSAwxG6lAAHCFILHSeZVGVa/SEmSXn6wYdw2lAeHsBpi407HH7iMVTqted97FaieWa6Q5JqpHA4BxOdhKGoQ39fH0Hw3A++/7z1Tn70uX/CWssffuGjhHO8GllhLAIH9BodWMPmmTYbWpaWdkjh6AsrLNs7ZOrJp8FC2mxy+O8XiaUFuz5CyZdAuXTXCaUWhkdBwW5Aog1PTrSoLtuX6dQxTYB0luGyQ8nuL7o3Oia0ZEtLk1o/casoYviow+k8sQGbpsiSQkUhP7/jfo455fjndXznHJubMYnWCOG8kSAgtYKZxKKaDfr7+7eppzG5cYzvX/4P6DjhsLNPRwbBoqEUEAgBSaqxQlAJJBOthIenTb6GdYLJWGOsZI96ifrQEHvsv8+CMVtrc60PpRTOamya4qxFSIFQURGWKdjp7O7daXcdE6igoGC7eWKihXUOpUQWWBE4oZhIXO4hAO/pmGgbtPMhCwRUQkUUBbiRUZzNEjIFqGqVjb9+8nkdf6qV0E50dqxZhIDUgdYaa+1Wtvb87LqbSDsxAIP77I1wFsvCFLTuknIAvxqP0daysakXrCeEYEZb1F57Uttzj959OEeaprTjhFY7ptnu0Gi2SNotrNE4Z7HGoOM2Oomf1zUoKNhhZMqlO/LPC1EufanZdUZSUFCwXbSSFGvtor4B40Db2cm7pR3xvGoPJQUSQVCOZg0PAOcY37hpm8eO221mJscRSYMRldIfWOS8gfgAj1vEhOilPd1ACIGQkqBawSQp1s2qj/bu1IFzjJYlQVShYxauAt4Am+xYNky0mGol+XKtNXGSYqxFzCnJ7TiFnnN9hACbJouPoaCgYLsoDI+CgpcR5xzaGOyLmNiaiZ3Vspi3GwHgJN1sCG0Weh2sAyfAIpDBbPRVSMngHqPbPHYaNwkxRMIRSUdVGkYCbwXYbDiBcH7fzyFbvuTAfWlNTLHxF+vZvP4xOtMNnIPU+nyO/I+1WJOinaBeUoAjChZ/lFnnSEWAdfDMTCdfHqcaB8jcG+T/KAHxvEvknMXZrVg2BQUvAVKql+TPrkKR41FQ8DLRbCc0Ep29dQuUFNTLIZUwWDQXItWayWabWFuqUcBArYKSkv6SYrzpvLdgwXYOJSUxUAL6S4LJWOQhDOcg1tbnUjhHVC37cIfzb/p7H7jvomM31jI1NUVVOBAirzpxDgKn2UMluMzT4pA4WXnOypb9jj6M6bHNNMcn+fl1NzF84L5URocRUhKbzGOSGR/WQuIAJJHTLKlFzMS6x+vjgEgpqqHyIR/jaMUJ1VJEagzBVgyhQCqvhpp/nsWkCgp2EkVVS0FBwQ6n1e4w3dE4XD75T8eGibEGjdTwmuVD7NFfztefaXfYMN6kY7z7n5am2kjYb7ROKQwJpMI4C6I3yyIU3vOQupC2s1QwCCzOdTNBYEsrYaoVs2qohIkidJxikg4rX/NfFhgyxhjGZjrE2jKA8QfIMjukAKwjcD7fQgufBAoGYTt5uKKZaBLtQxy1KCDKql1+fsOtBKUSUgp+ddudHHjckex75KuROFTms7FAgsTJALQltY5OmrD3QJXUOjZOJ8TGIBCUAsVoLeo5h8m2plqK/Fi2ZUs4l11okEoid3CFQUHBtni5lEt3FoXhUVCwkzHG0IrTXF/DWB+UqIaCWEse3tTgifE2f3TY3izt8xo6T020iG0+FwLQTC1PjTc4cNkQ+41UeXxzg9T6CbNbauokTMaOSAksAk3A/iMl1j/bZFMzZqqjsc5xxPJ+9uwrMWMT9thnCbW+OkKIvOrj0WcnmW5rBqsRQgY+96Hr4siEyRwQoJGCLIzRLYMVPlGz02RTLEiMDw0JBM1YM1AOqZdDGpsn0HFMWKmgopB9Dn4VkQSJzUNFSkBFGNpWYaWgmVqeaVj2GYb9hmrsM1DlV1taSAGlRcp383CPUHgzZjEy3Q/nkFKiosqLvOMFBS+MwuNRUFCwQ0g7HW79xJfoX7EXy489BoLQ51RIL5AVSCgHiuFKxK82N7n90U2cdfhyppptOnrhG7oQ3vhIUk0UBuw7XGXLxCSJiDJ7QNBILIk1GOtopZYDBhRKJxw8rLCjdZwMc2vGWsfo3kvzNyPnHE9tmeFHv97MZMeHMaQQ7DdY5lV7DDBDQJU0TxSbq7bh5g1WSUGj3SG2JaQQOGspk1IRBlrQ7sDKdccynRiCShklYI9DVqLEwn2BoCQsCQIDtFLLllbCYDlAAAORIgqll1hPLdp5b4ySguFaBMBwX5WJ6WmC+eEf56uAZFhCKoUMwm2WABcUFLxwCsOjoGAn4Jzjxo/9AytOOo59XrkvKgpJDXRijQQCpXBAKMlKXQVjMwmtTswzUy2sm5dpkP3Hp1H4yT5QCqylVJLE2r+1Z84IEuMYDVP6SVDWT7bKJliRkoZVLIKOtlQile/bWcvtj26mkZjcU2AdPDrRphIFrBjto6Et9cD6YEt3HQTWzU7WDkcgJA3rDZeu16JsE+8VEZLECuoHreTwviEmNjxNVK0ghoe3ej0FjtwWExAbx5ZWSllJRqqzj7W+UDKTWhqJZaQe5WEdgGqpRLvTQYluuMgigWqthlSFdkfBy0fh8SgoKHhBWGsZb/rEUescoRJ0HnuSQ//odCoDdVSgCKREKZ9Q2uqkCCVxqcZOz7D+zp8RrlpFKOHJyTaNBJScrb0AEL5GlUgJwqwSRQjBpk3PsveKAzDWoa0jEM6HNpxhJLA+9pIXwAiE1Qgd41QJM68k5pHNM0zHekE1ikCwcarNitE+moRoa6lKX85rZInIJD0+ikhKrJDElCgpgRQOgcLICkJYJA6JpN8JguXLmB7bjJASjS8Hnl+eS3YdUuNIjaWvFOaJreVgnuSYgP7IB32W9pV79lEqlYiiCKu1r1oRAhVGhYej4GVHyJcgx2MX0vEoDI+Cgh1EJ459R9V2ykwyWxWhrSNYMkIlCnDWolNDoBRC+OZnOjB02h1MnPDgv9zInhPTdMY2MHjSCbTSQaSSBMKSGhaEW0aqvRNlEsdUSyGJcTQ7CRumY6x1HNAHgRJY52voZz0nAuk02kUIJ3qSSBqLGB35ceZog3SMT/+shBInQSMITILEEiiJDCLaIqJkffGqwvnPJIDEOktXjbSqYO8jDqa5eRLtJNpZSqI3fOOco2NAW6hHvmpFW18K2/WAzKek4JnxqTwlJVKS4YE+X00UhkDh4SjYhZAKobYtuLc9+9xVKAyPgoIXSZIkdFKfVImDSqBQUjA1RxCiXCmhBOjEYowvZlXZpF4KBKqkGJ9o8YoTXoczFgsIESPJKjmsIJQ+gdM5L0I4WglYNlhfMJ5yFDKqJH3lkH2Gq0ghCdIWaZxVlmS6HV2sBSMckZI9Rsxe/RUe2Njomce7oZv+kvLjEBAqQSkQ2Cyh1QRlbFBCGE0qJfVaDZFoVKp9Z1lg7iNwrvSZEFCWIJeNEqeGcQNDkTcccC67FtDQ3uhQ0odJQiWQW0kWlUAgoaNntU5aqUNPTLF0aGDRbRqx5tcTTRodQxgI9u6rsNdAedF1Cwp2NIXHo6CgIO/rkRqbVaF48SklJUmqCbJKDgNZCETSH8F04tdVYYDItgujEBAY41U+hZCoqMTIvntRX5Yys2mcxpZJykMDVEsBk7FBCEGuUpElSo70bb2DbKCUz/nIsKoCOkEILyKWC5Y5RyoUSgaM1CN/bplbYPlIP8v7p3lqqtNjkIQSVi3po6/kQxhSCpyFPBCUuR1cEKIdJFp7dVQpvfFgZZYW6g8130WhBMTaeKNCCKaNI7QO6cBKgUMRBd0QzKwXZGsREil61Vshk3K33miMoqjns+l2yk+fmvAKr9lmY9MdpuM6By3t2+o1LygoeH4UhkfBbot1DmMsSsnnVM3cGp04odGJcc4ipczSDwUS3yvEWeeNB2anz67GZaTmhECMo6okQkpU1iVSSC/45azPrhACgkBRXTZKNDzk31CkYKAkaSYGnbspHAOlkPB5dHvtIoMIGZWwSYdAyUwx1WJlwED/YL4vNU+v4rT/sg93rn+aJ6c6pMYyXA05dK9+VowOoqSgk2q0tiRutlfKfA+J0ZYoihDCoITwuRlARDd80v3boxGEQnj/RWYEpU4gcXQSRyl0hLKrS+CTXZQUKCkzj4zrLgYHqXPoRXsaNSwAACAASURBVJwhzgka7ZjheYbH+i2NLDl31lgRAh4bb7BPTfoyXSGQKtgp+h7OOdLse7agCifDGEOi/TevFAbPKdZWsGvjk0t3rEx/kVxaUPAS4pxjup3QSf2bvXMWYx2j+x7IxokZKmHAUP25tRniOKXVbqMEGKmwFrTzyZChchgtkUrmIRPwb9fSkYVK/GRp8R1ag7KiXC1700X4z828N3ELWCQymNXikFJQLwVsaSVESjFYDtlzoPyCJxdV6UMGETaNEUCpHCBL1W0mUwZKcsKqvbf6eSUKIYLphm88Nx/nvIEVBpJASxqxZibRNIElpYU9G7QTJFYgpEA5h3Ui985YoKMt/eUAhW+I1837EAikhIFqmThJaSdenE0JaCSLJMdkLOgr4xxKOPbtj9DW8WwrRWeS7TjBz59tM1hSjFYUA2VDEEY9MvMvBmst1nlvVveeTLYSxlspifEBqWqkWFKPKAU+R8g5r8Y63dHozKOmREK9FNBfLUJDv6347rQ72PDYhbrTFoZHwW7HVCuhmWgfHnFeGdS7JCTawnRHY1yL0W2EKgCa7Y43OhA4542OqrBIYVEIBBZrBE4GPfFTleVBaGMxeH0M7+4HSYwRisRJIimRWTihW7KirZgNGcwxCISAgZJiSS2iVi6/IG/H7D4EIiojox0/IUWBRCeGNI65+cNX8PiP78cay7JVr+ScK/8ayiWUkmyYalPJJMynUkEtIA9TaSfoWC8XpvyAUcKX8HYNtNhYpmPNYElhrUXbWedGX9lrbpRLEeWS92I452hPTKPnia8BKOGolss0k5TNzZRUGwZKkiWVkK63Y7gS8sRkh9j4EmfjYCoxzCSWvQwsqwtElii8vVhrGZtu0zEWZ30FUz2ULN1jL56ZbtH1p0UKpDNMNjqEgUQJf90n27onZGUcTHVSGrFB443bSEkGKgH1UpFE+9uAlMI/G3bwPncVCsOjYLfCWEsjSek2WXV0Sx1cLicuBLQSgzFmQWihF5sHAoyDirAo4SswPN74cNZkhodfWwqvJJ5ox2ApoqMNSkpKpChnEc6R2AArfFdYISUyyx/JKzfyf+e6+/26u2LbkFKpRJo0+Oxp5/PsLx9FZA+5yQ0b+dJffpSjPngxWkg6qSG1mnZqKCs4fK/+Ho+Rc95Q694W51yPV2i6k/DkRItXDFcYqVfo3g0HTLUTEq2xQlEJFdVSiDGGshK0Mu9JNzlWCEcpkCRO8MREC2stQ+UAZx068zwAhFJwwFCZp2ZSwHtIMucHz7Y1w2WBCi1iO0Mum5sxk1nXXEFmHDloJJaOKIETRIEgwhIFCiG9l8dYh5CCVtLN1+n9UmgLbaPz73dHG+IZH4opjI9dHyHJf4d25D53FQrDo2C3otnR2EWLG+ZmEfjJoxEnDFSfpxy2cwRy1uiYbeXRNT6sf1A4EE6DMZSAer3K09Mxxlo6DmIXIIWjhGYmgYGyyA2KCEHqLGluNGUVKNlR26nFWgjVLvQEyRBCcN9Xvsmm9b/O36xEGLDn75/IXm86laktk5RHhogCibAOKRSNRPPERJtl9ZKvShG+3DdUknaqqYQKMyd8M9VJmY59rk05CnFeicTfWetoO2hpi8AyJVKiIGGkoiiXy0RJQlNrL8QmfVjGABONFmXhaDqfN9LJQhpdI6ASSoyFshIkNmvEh8NmomxN7ahIgXNdwbat5GBYy0Qrpp36/ZdCSaodm1sp1cAbk92KJZXZlimS0BpCbfJwjrNZ6bHsjtKhMk9MF4f39PlQnkVl3jgHPL6lyaOTLZqxpRJKRmolVi2ts6RW2mHfhYKC56IwPAp2K/TiVseiBNkkMd5KeLaRUAok+w5WshJNEEp5b0bmxZiLw6tlxtkDv6QsEQJMmrdQD3DE7SbOeZ2JrmqnRaCdpK0tSTNlqBz4RFThcx5ct9nbnMnEOsfjU22mU8exfbtm75CHvndHT8ihtmJfRo45CqECdKuNGx7K1Fn9LBsIwcZGSktbltYihishHWNJElhSj5hqJXRSg8MxHWvaiWFJNfQ6HIH0+RDCX5vUZiXCXQeXEMSpZRzHcCTApVSEzSZoQct4hVYpBKXQexW0dZljTKDwZc7ezHB5Tg7ZvkU2uRttaM1ME2b2hpQBslTpSTp1zrFxqkWqZ7VdEmPQNu+xlyMhtzQlIOIOlObKtvt/baqRUddz0evxsNZRkiKr3HF0tK+KirXhPzc3GW+lIGAmEbS1ZaKdcPx+IwxVe5NsC14+Fu80/eL3uatQGB4FuxUim3jmBkQWQ0mIQsUPHniUp8YbKKWojQzy8OaQo/YeZEmtRH+1wnSjicyELywid7VPa0dsZ8MeU9oROs3SyMw5isCmKQ5FrC0dbbHWoqSgrASRErQNjLW0TxQEcA5jDKVQUlJ+cky05fHJDsYKxtsp462E4WySiOOE6TjN33j7RpZird0pVQ3amPyNOlCKIAxyV1A0PIhTitLgQJZeM288ApSSpNpgnU8AHW8m9JUDcPDElpiKgom2Zjq1rBgos2KgjBCCQAqiQJIYh8VXrFgg0xnDZEmlQvrmdE7r2VwQAVI4ahiaQiKFr/BxQFtboq43KTNKhPATece4BZVR1UDSF1qEE6Ta+WohazCdBqLanz/oJ5pdowO6XjeBNyzMvC+pZVbjRM/MEEiHI8w7+4I3TG2qsWGQe5dsVk4s8EqtCIGzjnIgqYc+KXeyY4i1oa+kaKU+nDQTayIlWb+lyTGF4bHLIF6CHI8dHbp5MRSGR8FuRagUgfRCVfMKRoBZ8a2BcsC/XPVtho46jENetU/+pmys44ENE5y8chlKKQb66rQ6HZw2dLSgJC2xgY6VebKitv7BHyOZTi394Zy8DCztVNNKDbVQEWZvwqmxJNqxtCwYT730lcRXNJTLIQ9vajCTaISAWFuqUUiQTYoT7ZThakQnThjv5gdkY6kNDLK50WZpf+0lusI+GbLZ7mCszd61fTnrcReewy9v+zEA5eEhLJDONIiGBokG+mfvARBIwbJ6yF4DA2hjeHamw2C9hJKOUHohM+sc5TBgMLUMVWbzEox1SKCkBC09O5Hn+3deFE1Kn5djncjDVl37RwlHgEWj8rfLiXbMcCXMQ1kuS/iNjc08US6vJJE4lpb9kb0TRaCNJQokzjpsGqOyJN5OalnsZVNKCPDnOdeosYC0jtbkFBOPP8mKYw7HCoFTyuugpCkOhzOW1Eoen+ywpBqhpKA/kpkBS/6lEAJKgWSwFKCEzDr3Sh+6c45YGxqxXjjAgpcNH37dfT0eu16wuKDgRVCJFFGoMs0DL3YVCAilIxBQVrDXQJVfXHcTQ4ccxNIlQ7meBsIrYO43WGHjZIvxRodmrKlVKqQyQosAjaJjZ/M8tBWYOTKgLTf7K+UAqRTN1DJQCigFXk9ECkEpUASBhDCkFEqqkSJUXruimRj2Hqhw4HAFiaQWhfnE6hz0l/37wnTHu8znPk8EoI1jvNHqeUvekTTbnTyk1U2B1day4nVHcdQfnUpQigiCAOsc4//+C0ojQwS12QoigaCkFK/bd5gVgxWenmrTHwWEEqI5FSJSeG2OPfuinnPs6qZ0Q1d+BN1/XZYv4ZeoLHdC4r8HIjs+CNS83jQj1YiHNrV5ppEw0U55phEzHXvjb89aQH9JUQ0EfaFkj6qkFsxpoJPl4mibhVOy67NluokSUAsk1UDmIZnutfOGpesRdLPOK7UGy5byn7feycQjjzG94TdMbdhI0mjiHJhUI6QkMZY96hFKkYf4jIX5t14KrzZ7wvIaxy2v8pqlFaJsLNYx6+kp2CUQUrwkf3YVCo9HwW6BtZZWnBKnKc3UEEiZWdU+kVAgmZrczIH77YsQgkd/fD8HrT7BTxLZQ1pJqEcBEkGivdhVOzU0khSHILUOFQR0nMkrLWZ1qrrZAOTLAyGQpQqRjPO8kbkoKZiOfd8WYyyp6Z0tSipg5ahk/ZY2A6UAKSBS0B/49cy8EtGubhbATGxo6zZDtYhquON+zY3WGGuZfzYCSK3lzE9dypH/37k8evs9bG6lDL3+GCpLR3vWDQQctLSGlJLJVoeRauTl2iGvOulOxEIIQikoBRKbXXMf7rJYfJgEAcI5nBOzpoTwFyRQAunsInE3x/yzqIaSFQMRT0zFJMYbDiOVkP2GypRCRSkA4yzWGFQYYV3K/FoW3zPGu1vGGy0SYwm6OUNASUkEzve6cZnmi3N0tPedVELJ3nXFRFsjpGLJMUdx5xe/warffwND++zJxJO/YXLjGEMr9mb4yMNJLLgsjBRK2WNwdItdfFjHUVYCRIDEUg8Vp5b7uenxGUqBZMXgrpk3VLB7UhgeBb/1dNKUdkfTSn1Ts2oY5FOKd2PDYL3Ck49uRuy/AgDdjufoZQCuOymIPHyQC1dlcuCxsZSVROKY65ieG1gpCYeUAqUCRFBiKnEMlrxeR2ocvbaFyK2FBZLe3X+FZMVAidT6pMxaKHl6OmFpnZ7JdNbomF1onGO8GVMeUFtVbnVzJvjng+2qrC7ymbaOLdNtRg/Yl+H9V5Aay29mYhqJxTo/AVcCySuX9DFa92GIZpZjMHd/QoDMzLhAZrk1eBEyAcSdGKMNUSlk41SMdt7w6ysFlAOZ5ToIBiohkZToRM8bscMgSOc4fAUQZMJhe/ZFjDUTHLBHNcQaQyexmeGnUGGIdZCiCEjJ++E672nRTtBIoJ1oL/TVvXbZtQ6lr5CpRYLRvhrTsc7CaQF9pYBms4F2gkjCij84gdLgAOu//0Pat9xBZXSEQ04/idGjDqWVzp6WA2KL10DpsUbJk18c3gPU/cKWAsnr965hVMS+Q9vWtCnYuXQ9ozt6n7sKheFR8FtPu6NBeKGwKOj95ZJCZHLTvctfdfpJKMmswFiWpwC92hlziZRkUysllN49PX+f1jmWDfYRhiGpNmxpdhD48lCZvbknxueIgJ8EVJZUOv+I3fAAQClU1HA9ZZET7YRQKS8FLhZujxDMPD3Goz+4i4ejgKPPOJn+OZ6Hdhwz3THoTMskCiT1ckj0HHoUUioE6aKftVJLNVS50RAqyX6DFbT10vXWOR6Z6DCSlW765FTfZE7P8/aQXRuBIDEOX3Hqr1W5UqYTdwilYyiCZzoQSYeSXqU2NZaRSshAJaTVNhgCAgwyu7MGSYfMIBDQSTTTsaGVaPTkJPssX8LyvhBjM6MnUJSZLZ/2Fob3mHRQlLzvBYcjcZKmVmg0ZdlrUOX3WjgqAkZqJaSUDFZ6kzrT7PtRCwWRleyx5rUcedLriLWhpR371AM2tkzuHepeOQFMx46hsuipHu9+qwWZNybLnxFCsLyvRK2v6D+zq/FShEaKUEtBwYugGad0Ut8yzcemHYm2lILFf7FCKWi2Oz3LDjz+aBQpWIsRgqaRJG5W/jy1DmmtL/2cs5+BkmQmyZINret56AsBHe2IIsFUO8nLOuf6v0Pl4/JC+Am3LwqYSOZWwswaHVJ6FUoBC6pUOtpPtL4jrJizrcNY+LevXsvDN95KUIpIZpr8x7U38rp3/CGvefub2NyICZU/HyUkHW1JtaajDaPVMlG4deNDBUH+xj73ahvr6GhLWFJMdzS/Gm9RjRRDlZC+UkAUSDbNtFm5ZLbawzhy70Sg6BEKk9lZObyR1xNCwNFuNKn3lRitBMhQ0hcp36wu8x6ZrIR1IJIYoTD4BnWOOeEY52gmhrYGIRW1siJdsoT1zzZZOlBlr37vlelWi8weX4CzlCUoqbBW0rIGJ3wPl0hKNk0n7NnXa1DMflcEQklUsHgVSRRISAylMGQoU3kFHwqqG0cjtfn1mG9wdoxjc9tQD0FJmcn7CwLhfMgpu34y0wFxu85cVDCH3T25tDA8Cn5rsNaypdmZza9wkBiLwocVthpOAPScJ7SJW1SVxjmBEQLhYEAZpl2KpZRPecY4AiF68ii6iXsqEyrtDW+4vFGXtW42ASOrhOhOsqUAJJJqJOmrlSmVDL+ZbNHRLi+znPvMEcyf+PAJrUL4/ijWIYRPSuykmnra4g1vO4XV55xO3Ggxtv7XPH7fL7jji9dQfcWBLH/1AYRGI7E4IVAqZEZLptuGZtyiXgoYqUVEW5FlL5fLEMdo061q8TS1pZUa/v3pGdI5RoQAXr2kyquW1Bnpn3Xph0oSSklqrb93cja85ZxDZfk5XUn1QIBJYkwcM3bvv7Fhcoa9TjmZaiWcbbwnvDZGanw1SktLFJl3SmQmWlb2MdHROCF7Ju9QSUYGajzbShiuhlQXGGH+HoXSG52pb66DEwHCZcachJGqWlCt0sU6x9A2BLvK5TI0fU+h+ZuHSqCdL+ue7yTqnpp10Exhc7PFwUuqhHkoS4EzCGuz3BiHlEUJ7a6IkDte4rxQLi0o2A6m2wnauLnzOeAVKEPpBaDmdoTtYp3LJxDnXNYkTWQx71wiin6hmSTMy3Cl8AmTqfHy2oH0/TJaWYJH1yBw2fZKCCpbkaN2olvS6VBCsqS/knsxokCx30idJyeapKml2xfVOUcnMRjnGCwH+SRmM8PHZImJxjoSAwrLnpEhqFSQyv9qR9EA4WGrqPbXAcfYfQ/wylV7+WTMLLFWOYNUAYIABMSpZWwmZvlgZdG3JKUUtWrV53tY6ztpCsGvpyf59eYmsekVXHPAw1vaHL3fkvwebJhqs6WZEAqohoogu29dgy3Rjkpl1gMVCotNOtgkRXdiBpeNUnrF/kw/PUbtgH17xieEn/wjJE9OdXjVcJnU2HxMDpcnHi9Wch0qf69T43BhT9TCfy+yYwh8Umg8121gHSXriIQ3drsG8dw8D2MFwVZCWs45tjRjrMg8EnMSRLtE2fcwNQu37zYgjKRg774S4QL9FEU37GLJjJyCgp1MYXgU/NaQGEtJ+gnEAontPlT90znWhkD2JmU5fHlpNKf1ubOGIAt5ZNWXPqcAh05SHpvWKClYWgvpPvJjY6kEklqoKAeOWM8xgLIjRVJSjfyvlFICM7/sJJvCBqvRgtCJEIIVw3V+tmGcNFO1fHyijRCCfQbKgGa4Euax+Uogs4RUn8PiHAyFBiVcb/trAaVKmaBc5rDTTyKqRF42fE6ARgiI0JRVmJWM+N4e0x3NQGXrfT2kVDDnWHv3l7j78S2ESvV4QoxzTMeGDVNt9uwv829PjdOITZbb4jvvDpUVfaWAVPtja+v7ldQjhRKWAIdONM5a2hNTAETVMgNCYdMEqXonUIGfxFupYbBepdmOSY3BOUcpDAiVZEunvUhyjCeQszotWQHKbHhDgEIQG3Ll2rnEFipCIIQjtd5AyQ0PoK/kr1mzMY1NE1+WIhUqKtMwivFWSkXN3p8FxoeAeqiwxtAys6fQ9ZQJBNVQEMhZj8nckKATXugsiEo7RWiu4IXje7Xs+H3uKhSGR8FvBUmaUBM+PAB+woqko2VlXhY5WImYaicESub6DSDYa7g3eU4IicDnb6TG5e5pa+GpRkozdTQSTb0UUA27ZoVXtiwHgrJSVAOT6S/4slklwErJTDuhv1piqFZm80zHTypznv4qUJSirU/m67e0aMRzX2Udj463Ga4EjFRCBisB5TCkEWvf/dZYsspPStJ7C+Z7KYSAwX32xKQGldXjdCdSX5rqaG6e4JGfPkjqAvY9+VgmEmikhv7ywPOODfeXQ7SDVBtCKfLKIO28wmg7SXnwqSad1BLkAqGOONXYkiS1DoMPXwigmViMdQxEEm0S4kaDzuQMOvGiadZYolqZVqNN37w3d4fv24IQGOO7yc4kxodPyiWUlDjXQgo5q6HR3dY5Eq2RNliQXwK+bLdtHXZr8rgOEgBraRvv0XGZ20RJQX+1TGNmCtIOefqpNdhOA21DrAuYSQz1kqLbenCuJWesTzTuLzlUYml3XWR493wgMuMoy3mZX+Ri/YVHqOLxv6tSSKYXFLzMTLUTXNwm8r3S6f6tsFSkoGkF1VJIrRwxUCtjjKGTaFxWWhinKVHWZEsIgQwiTNJGSYmSXvSpGWumUoEKQpJ2ByEEU+2UalCaLVl0PqmzP4KaktQCH+4wCKzz4l+tJEXgqFdKLOkrM91OSbSvQKiVQypRQJz4qpAwUD1vnNY52ov4z7V1PNNIePUekpG+Wv4m77TLG8rJntfjWbqLVaDQqUFloaBuGa3Whi2PPI7VKY/88Kc89dAjPPit73HSpe/l6VKdmWSSo5YPLqpDMp9QSepRwHScZuOaHUtJScrCsak7YGZDVRLHTKypRd7Qc84bIdY5EuOTVgOd0NoyidUGhM/Z6ExNU45KTI03qA4PZmJiPqSVaEsjMRyypMpUKyaUjoqC30y2eXRzi5VLagxUAmZiixWzKrcCLy++rBpimk10GMw5925SrA9tybkXeN5Fd0C9rFCpYzLWWCdYWvffCWMtLo2Ri2xcEZppoUgctBJ/Tbqhniz4QgnrK1QURCWB1CI3JAX++5wYL5Y3tzoqxzmckERh0aV2V0W+BJLpz3d/69ev58ILL+Sd73wn55xzTs9na9asYY899si7Hl9++eUsW7bsBY+lMDwKdmmsc4xNtdi7Cs6Jecl2AolPAI0CyWQrppNqImcpiRRp/SSlU4VRPpkv1QanysjQ4nTikxiloL9aZTqW9EvH09MdhIDNbU2oJEOVME8mTbSlVFbETmCz3A4QsxODEHS0xbVj+iolBuckEc60Yp6ebPbkkNRLAf3VcvazoK8UMtleWK4aSMGyvjKpMTw9FdNOjRfRcrNGR8cKasp3ypVKkRhHYmdLfxMVIq0hwOTOf2MdLopoPjvO0+sfQ0rJzNiz/PRzX+HoD76HDdMJv9rc4KClz11yWQoUBwxX+M9NljhrTOacvz/7D1Ww1iCF6Ok42/2fybrCeh0W0M53XK0on89QLvcRrghIWi1mntlMWKtRHR6gFWvqS0doa5urmDZiQ0sb9h+qzIaTEFQjhRKCZ1sJj423OXCkSqoTrNUo4XOFjLEMK02oNa5S8fuVIvPgeB2W2HjJdYdDOLmo8REoQSgVYSkTpVOCWuaV0WmCyEXye1E4ykqSaMOWtqGjHfXIV7ZYkzKT+soVnM87Gikr6gFMJi5T3/Vlk9p6gzUIxFxnCc75b61dIH1WsCvxclW1tFot/uZv/obf+73f2+o6X/jCF6jVXlxLhsLwKNil2dJMsmndP6S7xSLdXyGJY6ASMtXRpNoQYSgTI403OoQIwFmsbvPKA/aj026BChAiIiqVvMtfKqQKOCCbW7c0Ysaa3p3/dCNhrJmwT1/AkpJkz3qEQOOcxMxNAJxHaiydROfJpp04YarjDYpAzD4EmolG0qJe9dUeq5b28eMnxnurWJxjxXCNeingifEW7TTlyR/dQ2tyhtFDVhJWq9RGh9iiFZGwlETCVBpghSIQPul2KrFsnEkYrUiWVwWhcOAE1jp0rY9//8n3cXMyLZ/+j1+i4jZSBEy0k+d9v47eZwjrBGONDrG2BEqwR73M0fsM0mw2mOsFMY48TKbw3WQzwQ6kgErg+5/851ib1Fr2rUmWViv077UUGYa0mx0aUy0G9hvJknehEgUEYci+od92bhhF4HVe+kshG6ZiUmMZrkUM2IDpzROoKOwWfpAI740SCDra0cr2o4RAZRZOXTqaPdP67HFqc0q7vdFg0VoTBAFCyAXb+O1mU5VrpYCZdkJLO5paE6eGauCTlEW208Q4nmlq9qoH3nByDilkrpT6/7P35sG2ZXd93+e31trTOeeeO71339jzoO5WS62mEYhJIIwiCMEREJuqxFh2pULFRQXjYKccJzaVqpRt/nNVHJNCKQcbl6tiYydlMKAYAgEkgeahW6gn9fT6zXc80x7WkD/WPsO977amfo1ui/Ptev1e3Xvu3ufsfe5Zv/X7fQfbEllns5oQcASCaJyPJNbNL6OuWeLPHtI05YMf/CAf/OAH39DzLAuPJU40rA+U/nCQ1tQ0SaRNOPVEaWeATBqC9zRBUUuK91F6mIsn0eBsHQ0yREf1hUlmCoMQAsE7vuVin99+dnsmCT2VK+7sGrpp6wAawIgHL9RHGFtTNYrzsD2qMJXjzErGQWmjwZk+3LXRCOPa08ljouxDW12s9zEkrrRkRnH3eod33b3BpHZMGsvH//Ev88Jv/z6bD91P/+47qAZDqoMBm/fdyZUm4YJyPLdb04RIkmxmyazCbhXI0pSu8pjguXL9gM88e41RvnbodXjrqKuGIIbg+arRL1K+7/5NXtqbMCgjT+butYLEaFZUzeVhQ+mgWbQ3J5AZQxNAt+m9Cri0V/K5qyOcj1yN53YC53uGd20V7O0NkSxj/b7TC2LmWLCc7eaMq+rY5ycibeEXuQ5FlvDcH32akCRk/RWUMYhWmF5vdlDVKpKmVu4J0eW0k4BxnqEXXFAgUQLcTeVY8vCksYiDa8OGtaDIlEMWuh4BDzqlUAmTxqN9TbfokSpBdROuD8pZ0R1mPwMHlSc3irEDHzy6lQjrtmSfd5hioSxEOfDOuKJI9IwQvcTJwTfKQMwYgzFf/v3w8z//87z66qs88cQT/NzP/dzXxR1ZvuOWONFYKxKuDkqGljb19VAvANEJrlV6pCrgXYxZn5DPZt6R7KfpaE+iFY2tuV7FULbTOgaR1WVJCFEi0BH4vrv6fO76hEFtuauf0Ms0uVngY3iBELg+qrGtoqWfakzrmeBDNLHaHpRMGkeh2838sZwAYVzVDCrHuHEURnjiQp9umnCql84cS8vGceMLz/Di7/wBSgm7zzzP8PIVeufO0kxKhte36W5tUnrDxEf7+PpI0VA7T9UEvNHslZ4vPHcT13ikOLzz3bj3TsZZB+rwZZUtxyHRmvs3e7d8Xacd7l4p+dLAUTVx2GO0sJIYskSR6GhoZT00zvG5q8O2wxUvmhLh1YHluW7gjq1TmKMkWuLoqLUEidf6aDcqQO0j98c3Nb/6s/+Alz/9AWO6VAAAIABJREFUFE/8lZ/g1P13k610WblwjinTRhForMdohWo7HYkE+joyS1MNGybE0YkI7svIEQ4mDc/uDggIl8XzYE8o9LRQCFgMpddoagotTJqSuza20Erx5JX92Zvn6EuyIUqgSxc7OtY7rBNMKhznBTftHxbKMarssvA4gRC5/Zbpt4Nc+jM/8zN8z/d8D6urq/z0T/80H/rQh/jBH/zBr/k4J0hgs8QSt6JINOtFyuVJYLuKjqIutCFbOiXN8pl0UiR2GipSwsI4JrR/SqeQEOW0V4c1L+2VfP7qkO2DESG0M3dRgKKfKt59xwrvf+s5LvQSijYnZPonqjU8IXgmTWCvtFweNtTO44lqF+tjQTQoLY27NVhtCiEmzQ4rF3ksCN7DQVmzP56POXINlz7y8UPFy5/8y3/L7vMvgg/xGqBmviZHMVX52AA3RzXb44aVM6diBP3e/pyImqXc82M/zLCKbrD3br6+ee4USmvSosupTsL5nuHeLjza89xX1GyaBt0qlkTBy3sVdsEZdnqdjBKuDOtp+OthtGZvIoJSsTt19MO28YFB5TjVSfjkL/0LXv70U4gIr/zRpwjek3ZyZKHFU/tAL9OspIpCC91EWMkNkujWbj/MJNlamAXZHYUPgZf2yxm3xgbNFwaKZ4eaWqdMWuO6ntQUoaYXarbWe4zLpg2R87fayk+vK4FRK55qfKByAY/noHG3kI2nj9d4JLjXfL5LfGNxUtNp3//+97O5uYkxhne/+90888wzX9dxvmKp++STT37Z73/yk5/8uk78ZsOfhdd5Ul9jABpT8LLJCQjKW5JmhA7tvlSE/umzmDQlF43X02m5zBZbgCZAHQJaHP0EdkrHcFwzLhS5ihKAOUkUGtfw/MuvcNf5syzuMwPxA54QrcPjU4ikyd2JZS03NM5xY3804xiUexPuPH/u2NfnQ6CyPo4UjuDKzgEvPP0UAFunT92iLmmGI/7kX/xrkk7Bd/2dn6FzcYugNKuZYnviDi28wXuMim34TmIYNY7O+iqZUdRPfYrJW+6ld/4sb/2LP8LqudNYFxfpLzz/Eox2v6Z79loIAOvn2cwUa4ljypGxotEEfPDQSlyn9+Jon8sHTzdVMyn0FNY2XHnpZV62DSLCxunT9HorrWw0SqdvjCqoKyb7u7z4R5+enf/6F5/jc7/663zrX/4L5P0VCJ7SxbbJNDJeS2hJprTkTB+LFB9ovMM2NQc+Y2O1cziojcjl2RuMsUfu8QDY3615/I5NlISFOiGQSWA8PODpP3mFUecUOslQ0qps2sNLq/wBjdJCbf1MvZBrBc4hRi1YpgU0sSCxLnBQj3n1+T+ZqZy+ETipnzvfSJxEy/TBYMDP/uzP8ou/+IukacrHP/5x3ve+931dx/qKhcejjz5Klh1PQPrkJz/JE0888XWd+M2EPwuv8832GkOIvhCNdbOwtWHjOChdKzdctG1a+DkVd6qriWOv1qSZIUv0LKdl2h1RQJpoLl68gxBsuwtud6vtTrxsVSNTuBBzNJRYBpWl0+1GfkAInF89jQruUB4JTLvnQuPBHOOrYLTm4ce/JabbVhXf+sPfy7P//ncOLRQiQjMa013tYVQkLm52MxpfcVBF06yAkBnNqcIgKkbMdxONlsDdW1v0/uZ/FXftbRosQEigmxr20lOsXDjH1srtiU4flhXVwc4hfsP0nNJ2i073Ul7YnRBa1dLscSGwkiU4H0cUblp6hJib8vBDD1Fkc7M452NBp0Q4mNQkxpAmhtDtUlfNoe7Rtc9/kY/8k3/G9/y1n6RzZguXd1sfl9i50iIYvaASUQqJbmyk2tDpdhgMLdcOSjY6aXxsiCqb6xNH8RpKgDt6mkQdTdCNzYoiUXzL4+/gqWsDKutioWsDph0nKQVeYuBd5fysPV8YhVZC6QM936BER7I1rU9JXVOFSKq+eP/DbPVvz739WvFm+9xZRFVVX3Fj/mbDk08+yS/8wi/w6quvYozhQx/6EN///d/PxYsXee9738u73/1ufuInfoIsy3jkkUe+rjELLDkeS5xwhBAoq4rKOpQoOlmC1prd4YTa+rlkU0W3xpESQmszdrTs0AqUBBwa7ysgFiFFotFq0da67Y74gDIQvG6lk9OyBCobuFou9lPit6z3VDZatwfv8CIYY9jopGgl7I9LGjsV4rajgfaY06/NDxcOOWiKUpy//04e/4s/zKf/z1+f7WC8c1x44lE2L27Nno0Sxfl+Qbe0jBtHplXM7JgdS7ijn7CeqNnOSqt2NLHgaWEUrKRxXPRVKGq/KnTTBG1isUXb2VBt2uuUxbDRSTjby7g6KJnqZEMIdFLNW04VbdeBWZcKYgG2Pao4Z/TMZyB6tSh2RiXXBnFsNZ1YrN1zB9c//zSqjY33Aa4++xIvfOwzvO0//1FOp1FGHWXc877LTHsSBB9i8WSSlCooen7IXlCtJDsWHokKdI1mv4oFTK6FtVxjlMJ6Tz957Z2otEXPZiflyqAkNWpWdHofWM9SRMO1QUVu1GzEOO2M1Wgq60ioUCq+j63zVF4oMRhRjOtjvNeX+IZCvQFZLV+NSe2jjz7Kr/zKr7zm9z/wgQ/wgQ984HU/l2XhscSJhbWW/XE1lwd6x8HYRZXGAukwjjlgYgM9E2WGLsxVDlP3xqR1fIpjlLhTvdBLb2mLK4EEqH00HtUieEnAR+MmFFwt61m66nTlMwJZMlUOxLa5kcCZbL4Q9PKUalLShOipULtoQCbE5xSLo/YIAXqpwk5GeNdAiD2cP/dXf5T73/UYT37oD7FNw8XH38YjP/BdiMCodrgFv5PCqNkueGag2nISxAuN99QW9hvPVpGARPXQ0VTew0ON14m2GNM+OpMGIHeeUVDzEVmAt5/rsVlodkqL8zGv5pHTHfLERHdTmRt/ze+dcHl/zB1H3Gqv7MdCYLEgvf9Hf4SdZ1/EViWqvf6EwIvXDrBXhjx2vk+uFUZH2/3Gx1C8pPX1qLwQiBk6q0XO/u4emfKcSkNrp67QEuiogE4U+02Udp/tJiiB7XGDC5BpQ2YU+rhrLDEH51w/JwA745rGRa7LWjfh4mrOpf259XtoidYLB2AsGUloSFur1aHTjEna7hgsy46TB5GjnkW355gnBcvCY4kTi/1xiVbCqPGzmT+0O/FjfomcD6QKVIBp/wDiAjWdcDsA77lZx8es5scbKc1IisRZeKIVtPwIBWx1DZeHTUvojAtzYYSLKwll09A4IdPCaioE8VzeHcWujAloICEwsNMPfU9qBGyYjXESI6zmhr6q8fYI9yM47nrr/dzz2CN4iYqG6aeUCRVGR+vy2KoXfPA0/vD1MioWObULvDCwdIzCF0CI44kQ5tyG6TW/XRARVJLhqgmmPceaBEJjmWDmhZEIF9cK3pHT7tannQ9o4DV5CZX1WOcwWmOd58MvbpNrRd5GzEtb2K0/+hDv+h//Bi/8u99kfPkqY5PRffxxRm9/J89dH1MYzZl+Ri/VpIma7UCtD+xOGrppTJDrd5L4oe7c7D5kGjLm9y14zzvOrXHjYMzEel7aL7E++m7cnDRcTRVvXQO9KK8NgU5RzK7ZhdWCc/28tUyX2RhPEe3qR7Ul0ZFr5EOYFdSCgMkYuVgMVxIO2YdlxwQrLvGNxTKrZYklvgEIrW9H2YRbeBEBsASSI8XHNHtkGhkfGxzzx0z9LJSruasTmPjD/o1HFRSK2KnQSihrS+nB+cgzMFq4ezVlr4ocgkzBRmHQSpHp6EBJ6ywapbUeHHgVdzLWRxkkgA+xS5IaSNtxTj8XVjKDm5SvcYEcSZZRlRXKW8DHEZNz9IpYQah21xScZULbHZA4uVBtt6BxkVMhWtqvt2TP9nmLRFLm1yqp/UpQeS9yT5pqtkvf7GaErMO4duStDtRNhrEQUtOPqnn35bi6I4ToLPrc9SEPnOnz4Re3eXlvwr3rBSJ6/nMSC8b+g/fz2N/8b9hMNf/soy8hRTIrsl7cnlAkCq0V0hZqhJZYLMJ+1XBhtUPdeCZ1GUc4CyOZRYgINw7GpFp4dqdqu2XTXGRhUHleGSfct6JmzrMvvnyJh966dfi6icxkvdPj5kk0BDPKMKgsIjBpPCtZ20lpzT9C8Iwbd+j5KYkZR0ucLIiSr2o08rUe86RgWXgscSLhrI1psQEOC2Pbf7V8CrPw26kEhnWYeYh7Iut/xoUgKgD6wdIpNPiAk7hgH/2VjDwPjw4K5z0HDUx1FoHobqmMcKGfRkOz1viKwMICF8A1kQvgLLkWGhfD5HyIfAvn4VA/Q2IRMq6hw+S1L5D3jMdjgmtmz13w9DKDdxatW/uoEEcDdWsKuogQYLeynOpmGBHGNrCSxIwICZELIZG1yane7Y1PFxFMp0+9vc313/s9hs99CUGx+vhjbP3A96OUoqwteIvD4BcS2WrnqV2YdUsWYb3Hes/HX93ns9cGuBC4czXjTC9hJTW4EBg1nokNs7dVomB9JbvFW2BYWUrn0RITgBs/pysbpbDO8bkrB+yWFudjgOA71oXEKAKCwc1MvAZBU1rPuAlUzh9WG7U8jv0aiv76rBAblc9/Vddyo5vF4lzglMkIIZAYxWpmcK38XIvQ7SQktWVYWXyIZnZrRUovX2a2LPGni2XhscSJQwgB387TwzQT/Jj5dyA6lhodCXepUVjvDj1yfoy40Drn0K2dtSiF9g0VKpI4mZ+qdp6bY0ui4ox/kUY6Ha1UFhrdmksp1W6lw+y8iBCUxjWOjQREYonhQ8B6ByHhVgHt1CcEJi5QvMYmJQDB1nM319lr9XHBk5YxEDy9PKEZt8FtCzVcqgKizWxsNbHxenaS2CmpXOD8WvcrOhl+rQghJtJeu7HL1f/711Cf/3S0uAcGzzzD6IUXufen/kusczOCjgvS8jkCIxs5PBkerdSse2O9Y1AHRpUloBhVlvtPdbjYj6o8TywY+plCYSld7DbkWpGnCd917yYffnGHPJu/3jyZ/1sW/g+BiXXcHNfAPBvlhs1ZM/E5NZiohlKa2ke+R+OOu+MRLnh2RyW183QTjfkqQ9yUCGf6BZPaRSKpETqJOXamn6eGU7d6uy1xwhDltLf/mCcFy8JjiRMH733LnWCq/7vlMaHdjV8f1ATvueNUr3WNdK9RpgAEDC7yNWbHCaQSXR9VK6cdN55hE3elNgS8i+qIqQJjtL3L3ouvsH7vXcjGKqtZlFkejf0KQBBFpi3TMDnax2QqclKacFxbPj5y4jWFvnWh8t4zsrSZIXHGb2JGbvyF9oB3s+6JAOudhEnVxIAxIDNCVhTsNpNDapomwH4dd89GuO1Fh3eOV/bGXBvWTK5u0zz4KOruB8g/8zGyF59HlGLvE5/k4M+9B3X+IjYovHh8mA/FplescmCbmMnTWN+Gp3mevjkGovz1XG8+Rpg0jtxAR0GRgfOCF0iNIYTAu95ymn4n4Q+euYky8MS9m3TbgLapTb9qRykhxHGGC9FlNQD3rHRBCcMGellrhybSNuACTQis54pLAzmm4AwclI4Pv7SHC4HcKDZ6WzTOkeivLtCtSDVFugx/+2bAkly6xBLfIORpwkFTc2SjDsD1/ZJf/t3nuXZQUTnPmX7Oux44zfseO/Oas1Hxng3jYuaGj9wG5wP7lTAOySzTYiY5bX/Oe9Aq0FQ1H/1Hv8Tlj38OV1XoLOPCO9/B+/72X8N00pYbITPL7khEbaUvRyohIZCJo8HMVAVCHBV8aa9kr4yz+os9w/19Q65j+935GMl+uYKzeUB0pCM6UajgSVyN9hUW0EmG9xbf7rJTFa9pTDHV5FmGkRIlsZsw5U5MPSuKo7OZ1wnvPTeHE64O6sg5aNUkdHtUP/if0rn8AvqLT1E/+yx7n/4MG+cv4FRKcBVemVl3xwgzZZP1nt3SRtdX4kjhzrWcG4MJuVE8vz3AaM2ZXkY3NRQt0Vgkyqs7RgGWuoxkz27H8OPvPE/Tcox8iE6g09o3Jv3G8cVeGf1dHtnI6CSC0jCaKqoCh7KFjBKcF+ogbOSG6+PmkCX2xMZiW+vouGp9YOSF524MecvWCiJCWVWMaw9E3pNJNat5FvlIt5sQsMQ3FOoN4Hjcbnnu68Gy8FjixEEphbQdBuciQ1+1fhYhxHHAL/32s4zKuKsXEYaTht/8zKvkmfCeR87cetDgyZs9RGe4dnHwQeERRhiOOpPKwt8+BHwQPva//h9c+uinoirDGIJzvPLRT/D7/+Sf8+3/9U+ilZopQUQgMSpyTBaON4e0c3ZP5eJrK63jM1cGHNTRcVSAvUnDTpnx8EbGRi5cKx1Dp9iZOE4XhkwWfEBEUZuc1E4Q10SCqDIganb+4BxaK/I2ol1aQqmSBRVQ+7d5AwqPnbGdV3SiYPMUdLs4YPDgo/QefIjuy19CTYbRj0JrBj5jb9TQzw3T0NcQ4j3cKy2DyrWvIRZ9w9ohWjPxHlsHwLNfNjx0qsOpNJ0VhrEjQexkecdzuw1refQ7sS6Wu0qEXEdexhQ6rvTct56xmU7lz/FPoWC7Dm2BEh9r2m/6EGXf0VxMcVDZqGwhsFt5snal2SgMq5mevQcu7Y1ZSdXMV2VKsB1NGp68fMCoCWz1Uh47v3Ys72WJNx+ixfntP+ZJwfJdusSJg4igdeQoTJxnUE/dSSMR8GPP3GA4sTPZIDCzLv/UC7uRub+w4BM8q5kBH7NVQoiLgAf2fULRLlqzz+wwLxJiex38ZMKrH/vMLe1KEeGFP/gYH/r0qy0Bsv25AOPK4n3bz/AO5WqUq2MLhTjOOL26MjvvC3sl49YUbdbhEeHqqOJ6CTeahJHXlDbQSRR14NgxlFMJ4h1B5vkyMC1+hMY6JrWNHRQ0UwWMWrgOPnDbSYfTYmF27VZXCd3urMAD8Eozuet+et/z3fF9oISeESbWc33UsFs5DmpP5Tzbo4pJ4zFaZv4nIUDlA1pp1vOUtU5BP485KFf2q9kYK3Ixp50oYdw4JtaxmplbLqmIkBkVc1CsJ4jCKNhI5uf0ARyBRAKZeCaNo6MDvQQKDbmCfiIYYsHTywwX+zmrheFLexU7o5qrw4ph3dBviw6YdqFiAT69Ro2PJOlEK+7b6NBLFc/cGPDrX7jMzkK2zxJLnFQsOx5LnEhorduwrwpH4PqoYTVXZFqxN7bYMLcuX1xcyzoWKgdVLFYOSsu77t7AaE25b2iCogyR7NeIYTWLBlsHtccRJaV1nDsQiN2OtSJFTwZUBwPMMfEB5d4BH/nMS+RFxnc/uIkSoXKe/XHD3etpdI30tnUFDSgaGp2w2u1Hc6i1LsOqYVjtIcTiKhDHQLSv8+qwYr0TfSOa1u48tMqTo7sHJQovGn+k8Jivp8KwbMgSw2puuDGsSDWk7Y5obAOp0XTS21t4iAi5EQZ1HBv5ojsrHEOIap8gDp8YhsGwIbH7pQLcv9Hh+Z0xlw5KJMCwtvSz5NAsfBrMlyiFFlgvElQbEb+SJYzqGudCy4057ORYutAeS9AqcJSEIQhGCSPr6SmFb2qm1ItZ4dT+o2uEcZjnukyJv0qglwq7VcATybvWRcIrLfF4VDle2ptwz3pn4cwyG90JtNLZ6OGxO2l4ab+ktIH9quE3v3iVezZ7fMed6ydqpr/E14ZDG6HbeMyTgmXhscSJhVKK1SJlVJcoLVwexDZ90UuprEeU4FszpSlOtwoGmbbSlVC5gNFtq1E0ZTDUaNaSuJhogZUEDmxcAFOBqFUQNgvDmX6B65yhv3WK8f6AYmOde777W0l7Xcbbu7z65NP8lfc9iFGKwajEiW6DuyB4C9iocGmfWACUs1R1TWpSvA/sjWtEIhdgaltiWsfQ2gYa59grLeuFYVQ7ausA3UpeW0xb/lOr1hbHEW21CONJxVqmSFXKtVETCbUIRaq5a/32Z3copTjbS9mvHJPGYxeKjunCWmlDx0TZq0hM0a2cx3tYKxLOrmQkEl1t0zTli9vj2RgkhHa0YWLhcbaXoJXioHIMa08nSdhvYEMf5mAQAmMb/+lcwC5ezNlFDDQujvyURFLq9JyHPs7bS18ohQuBVEUp7rQIUBJlrKULrRonkBrBWGlHisLuxHLXWji8+EzPR4w+PKg848azPbGc7mYMa8vuuKFxnlf2xqxmhvs2Y/GSGb0sQt5k0AqOBkLejmOeFCwLjyVONLZ6GYPKMiwtRitq57hzq8f5zYJLN8etg2N8bJFqvuPh04d+XmDm4Fg3DUmSkuCxokmUoNucjlQLGyouQLZxdCSw2skQ8ZTjEUpp3v5D38sLn32ax37iR9BpAkmCXl3l4b/0F3BJivWOSVmRYXnxesWo8dzV6UKmblExiEDTNJSTirLxeO9ZzxMGVRV5K7PnLxSJpmsUlw9K3n5uCyXwws6ExkcHEq3DAiE2oHxN0GYmRb0VgTz6xxNCdFy9q59SByFPDJ30jflYUEpR5BkPbMJT10az16hULLKkJVVa5yhMxm7pKK2P5GKJ/5tYj040vTxBVCwMLw/r9rVHAp0PgdXczNRLq5mmtrFfMLDQ97FLVLmAQ6EQeqni5sQxaByp1tEmf2Hm4n30ajndzaO3jAQyPQ0iDLhWTgvRun/qyTWVXi/eBdUSXL1El9XGeRIVi+DVPGFiHdbH+5Lq2LGxHtI4FeOg7epVbsolEfpZgnOBQd3gfeCFnRFnu/E+TpSikxqyZPlx/2ZBNC683ce8vcd7PVi+E5c40RAR7tvssjtp2BlV7Ezijv4vf999/O7nr/LCtSFCYLOf892PnGZrdW50FQLkiW6tsoW9gyG9Xo+OczRikCNRcgqhEyzogNIqKi/a7ztvedcHfpxzz71CU9V45zBbW+g0Jc0SAoGyAVUUVOMRo/GEp65V/Pjb1ghYji7/0yVNiNkyqVZc7KdsT+whd0kRuGM1pzCaF/dKjFbctdFjvch4YXeMU56AkCkw4jF4vM5pVEJwlkSFBb5AvCiizS1k17hLh6ItOpwPNDYWe+arlHN+NVBKsdIp6KQlSgmlO3wPhLbNLDBs/JzwOqNjxOKjSGJxcGElRQncnFiqxmOU0M8MZ3vzMZFIzLwZ1HEcs9/EzoX20ZU0+Nh9ON1NqGxAK48Rdag1HQT6ucE6RxBYS1PAoUK8V0rHomdUe7ZLOKUCK5nMXlN8Xe3Ypa2kGuu5PIjKnvvWC3pZVLQ0LmbYHJSOiQ2spAojmlTFjse47Uw1zh3KqlnJDduTBnAUiaZyjt2JxXnIjXDPxgpJspTbvhmg3oDC4yQJn5aFxxInHiLCRidlo5NSW8f1YUXtAn/p3fey2U3pJJrnt4cMSzuThAaiCdjj59dmC++4rCm6fSaTMZ2mph55siKLI5gArm4IwZNkyTHziZiBsX7PHQgwqqOHQ25kdvxMKyrrcU3GfVtdfvjb7kMooWkIR0mpIeBEz/xKFLCSJrzrYp9LByWDyiECp7opuTFU1rGSaaxzeO8pEuHt5/psjytuDipMptE6YVDXaJMwaQIbnQKaCu/dPFBNaVKlDxE6p/A+4LznxkHJuLEEH1rJqbDezUnS22etbbRCOU+mI2mzNd6MBUBhaHzAtSMUdeTZOqK81bVZJ6c6GZtFipJYqEwFKPPirpUntp4aIgqhVU4hSDvSil0DF11KFyLmD9836CaACN7reF99jPlzAXYaofKBsfVoYqJxqueqJu/jzysbuNaqWi70c/p5252wnsp6VCtpKJ2lbIT7NmLHYr+sWzdSOOpFZpSgdRztpEp4YaeaXYP9MnBQ7/P2c6ukZll8LPGNxbLwWOJNhdRoLq51bvn6fZs99ic1lw8mWBdYKwx3rPdumW2bNGMlzVgBnv3IJzh17x1tLoJCG01AI8Q2+3FDiukeNnC46Jh+M0sUK0XKmW6fiQt85JUx33tOkyrf0gQBAo0YJl6jrY8Lk8TodY1w11oxWzUnrTFWCIEnzq9Q1U3M+AiBxjrW8oTgI9myCYHBqKTXMyQCw0nV5tPEvI5EQSbzZdwFH9N32zFHAK4PKsZ1tGGfFivDOiBhwkZfoW+Todh6kTCqopfFYkhZphSdXAMKkahCksU7Edq04EAbBAeJCGmaolSgahxVm5GzeP98gPH1GxTeou+5iCjBTUok0WSJQjHniIiSWXFz6MTtNZn6gERdrcaJpvKRMNpJolz53o7HNSXoBFFxRhI7NvFcmYE7einWebpT068QaGxAi6L2URnj/DQKwPL4uRXWuwU3xg5ro7PvVGIOtMVIS0heCFWMz1soG8eLu2MePH04uXeJkwf9BpBLT1IW4LLwWOKbAiLCWidjrXOr6qSxjhujmtDd4Mr+iNO9AqMVd3/Lo3z+t36Pu77t7UhRUGuDD3FXrHQgcR5Z+O3XMt+5JoojRc38Yz7TwrBR/D9fvErjA9cH8N77VtuE27hSiCStqdSUHHtUwxm/ZJQQXCBPNIVRs3NO/26sZbObkieaceM4qEryXsrEKsKCEUBovUhEPFqgtDF7ZHfScHVYM24cEgL3bXTIE4XBUbSW3TZA2Vgm2zfpnTn7em7TDGf7RbSlH1Yz47bcKE53E0DRzRIaH9ibeMKU5xni68hafxGPMGwChsDE1SRGkYiQGxW5Iu0c4vrTL/CHv/jP2X36OcR7iq3T3P9D7+Ft738fiKaynkRBqhSiIbjI2XALlYcKMYtnOu5ZLGu0gqK9h6tpSqo8TTmO170lg0IsWNxUuhwg1YH7NwpqF43QdkqL0tIWQEJhNMM6jlOGleePLx3wzjtWKYyitH42lgptkTRuHEZJ5C4dQ0wUEQ4mzW25f0u8sdBKbslWev3HvL3Hez1YFh5LfFNjUDa8ujtgPVPce3YTCY7dgwG7pWWtk1P1VrF1Q+j2p2EneIQgioAnnfEzA6IUucRME9MuQtPBzmyJCpBo+MSVSP4MIfC2C2s0KiW08koRQTNXYITgEVE435JEw+xQBMBTldiBAAAgAElEQVSIcH4lPVaZ4NuRRGMdvhpz96kVtp2aeYGEI3+sF0ZtUNmVYc2kiefuporaeT59ZcC3n+vSW8lnS6sGUpWwO7GovQOK1ZXDIWch8NLuhBujGhc8K1nCvRsFxZchM3rv+X//xt/j+T/+LP23PECwDXc++gDv/bt/ndxEXs5pnTGpbXsd4/XKjCKffSLHC+WApCV4OqLHSd5u71xd84f/8H9htL1LmhrGuwfUl67whX/961x4+8OceeQBACofQwQ7EiPtVasuqqznoI5n6ppAligyY6icO9yJie8QTGII9XjejTjyulVbikxrF0XsdI2aqNwBZqRWrYROqhnX0ZL98kHFwaimmxkmNhYfU1fa0nlGtSM3mlQJK6/B5fBHn9ASJxJLcukSS7xJEULg8v6I04WZ2QVbH6J6I004qCwbb30LJtfMvaQWuQRC8I55iSGs9PokVcWwZpb8ucglMAo6iWF7FJUWd6/nrBVx925DIFWKRMe2eO0hNyDtclQ5P5PHpu3COZhUjG3gdDch0bHQmObmKRFuDCte3j+gtI4QImdis0hZac2/pq8mtNfD+uikeXPcMIlzmNlLzoyiX6Q0cnjREuL4Ic8Lbjz9DFuPPUqaJOj2k+zJqwNePShnnIiD0nFzVPNtd6y+ZvHxv73/p3j29/8YUYrRtRsQAlc+/DEypfixf/DfATEdtjCKzkyxw6ygElor+3YcVNuA1jGxtvHQ78XO10d/9d8z2N6dFUo6TdF5hrOWL/7G78wKDxFovOBV6zTa3ofMKHLvmVgoPWx1MhKjcWWFDfH6ufbtkWghSRKa1qck+BBD4phfz1kRGAAUncxQjmoaH/1FrD084pl6gdTO47zn5sRSpJqtjsHgeWm/Ybts8EFm3aBHTxccVIfN2qaYckmWONlYFh5LLPEmxaRx5IR50eECYxdHByLT7ALDyENXL7iFtv8J0awrC3Xb6g9Ug13ylXU6hWJvMJplhkx5fiKQJYYi0Qwqx5l+Rj11nZSYoJqFuGt3Idq/x4V0vlAEwHlHKp5TTPAmMNir0FmXbpHNHrRfWp7bKYGAhKiQKW3g0qDm/kTP5KRHBzlNgMqGBeerOTYLg1HSGnEd/qZW4IpOJO42DeMSnr4+5KW9CRudlLRtD5cuOnw+vz3m0bP9W+7L9suv8qU/+hT3vPfdPPSjP4ROU1758McJoxFnv/0dXNkbMW48o9qSa+inOmaRtISGsfXcHNcMas+Zbhq7AtbjWlVLr02XDSGwe+XavDsTAjo1s0prsr13y3OzYbFMiJenY6LSJAQIwaNUQqdTcHMwom6i3Le2AVFQhQlrxhCcjX4gwUfyqdKzI/oQqHxgtcjIsxRC4MakQYhjkuZIxeB84KBqQIRuoiF4Cq25o59xoZ+xM655ZVCTKMVmJyUAqznsTuoFIW/AiHDn2u33Z1liia8Vy8JjiW9qJGa+eNZhXnQAlFXD3t4QlWUUGk6tFtReSERIpjbcix4MIuAsTTkh63Tpdwv2RhNKO5eEhgB7lePhMz08A6w7TFKd7l5TLXTE0cHOVBtKhJtWYySQiSW0AXkg5AK+GeOzZBYIdm1Yz0iw00JJCdQusD1pONs7zHcRiV4RE+vb0upWZctX5J+F6LPx0Zf2eHG3YlhbILCeKda7eZSnhij5PCjtsYd46rf+P97z9/8Od7/nO2ddk4fe8664y2t7AoURQtBsj2tKG1jPDZPGsj2x7EwamhA5HnuV5a7VnNXMMG481gdujirOrRY471k7f5bgfatgiXLeaZHY29q85blNyZqLS79ScojECXAwLtmbuFk+zDQHY3/SkPUSMmXwriY4UMGCtziVMvGe7UlMnB00JRDltIUG5zyJjkVh3TqpNj5wfVQBsJ4nrGSx+AgwE4Of6qRkRrNfh3ngoMB6kdI4jyd2Qi72CzrZ7XWjXeKNgZLjeTqv75gnZ862LDyW+KZFkWj2FmWYC0XHYFxxUFpsEFTjmHjN5etjhmVUJWwUmnvXc9alwYngUAgBIx5nI0FPRLChJYy2EGLr/56NDq/uT9ivHWudaPS1COUbutqxaGpeaM9pgeuDCeHo+iCg8YxGY1Z6PQAq5+cLpLTsgRAwKnZ3pi2NaYGhgFPdjP2q4fr+ZOaiuvjM9krLWh67HkdhnWOns8bTrwxovHBxPSfXitREKemXdsbct9lFtTvz5pgcGYAz3/GtrGf5rOgoipQinZ9TiIuqFhhWkctQWsezz13Cr23gJRp3CVFSemVQsZoZMq2YWM+Ncc0ruxPO9VMe/fM/wOf+3X9gcPVGe5miqkTSjIf//HsPPS+tQBNHcYvwLTEii5IXLu2NGZTN7NoZFcmgo8ZRW8/lg4pebugrhXfRbXfkFQdNw0qq5yqW6fED5EYzcrFQW0kVe5Wntp6dcUVoDdEe2MwX+hcQWumOIjC2YR58x5z/WmjFZq7pFfnSQOxNBP0aBOHXd0w43sf4Tx/Ld+IS37QQEbI0IUxzUlp45xjUc2JeQHAhLmRFqmMg2TByIB5YyyhSTePjIp2Ip9/uHBrnabw/lvRZW8/pXsb2pGFn3LDRSdrSBQjQUe6IT0SIO1/lWEsVCn8MEVBiyB1xMexoxX6wC7wUIUhUYKxlkIrDtUtVog1nVruEEPjMb3wUd2qL9MyZObeFSPjcryxhIeyufboQAl+6PmQXgw+ezLSSXO/oKY02ikTlvLw75u6NLgC9YwiOPgSK8+cIw/HsPN0sJZkqRlooib4o/UxzcxIYlJZR2ZDVFT5J58xNESZNdP9USjGsY1rtzdGETqLYHXve+d//df7kn/5Lrj31NN56tu6/k2/9L36UU/fdjWdqex4wErDTJN+FsdfYxu7DRjfl6qAk9TVbRRoLpxC5KDcnjlET/VLGjeOZm2NE4MJaMbvP/UzNpcNH2mAa6Bm4OnbYIGRG6BlFJoZeaujnKTul52x34Wfbv8f2eD6HtJ2b1TzFLIuONxXUG1B4qGXhscQSfzrYXOmwPxzjnMUINB5GlcU5h3dtY1prGuvaBShKXH07MtmtQ1SjtMSsxgmlV/jRmM4xgXGHzt3L2Jk0TKzn8kFJLzWICJPGcm+x+Ks3/zAQBKMV4mPHYlZ8tKEgU66AAs73E26WDfZQnSCkRnFvT8XWqoT4s4lhWDU8+8pNNh9/DFGx4Bo3ntpF47DKWh7bKji9kmGtjeFlxGLhoKzYl2isliy2bEXYbzyPdMB6RZ0lhBBIjaKTxLHLocKsnWMkRU4zieZm5ojBwLQ4U6o16/KC90Koa0JjwcyNzCRE5ZBrLLU2TGyUnzovfGlnjFLCmTvO8b7/+W8RJhO0d3TW+vHa+kBpY9fJidAERTYdnRBl2JMmjimKROE9rCpLWhSHXtO6TilSx26p2R7VDKpY6I7rhpvDiq2VfHbPtFbHutjXLnCzhBf3q/bYgdXMcPdaDmPHQWXRWqhsoHPEx+2W47WtGJFIlFW3yXtliT89vHEdj5OB5TtyiW96rPY6hBB45plnSTbOUg6GuKBn3hBVY2dMjhDmnQbvAxPr2Vj4hdVK8B4u7ZVcXPV4F2azdtUakQGkRvFgv2Bv0nBjVAHCsHYgwgObXUQ1tMJdYHEDHAgqARc7IlNppW85DZ2imD0+N5qHNzu8tF8yqB0iQj9V3NFPCSog08a7CE3jeLUskSybnU0pRS+V+PqD5i3rGb08wXsYWlASr0VAGDiD9TVGDitlIC6aHlhPYeQURRo9R44b14gIeaKwTSDtFrhp8dcedxq6Nl38cwVJpki14vT50+zuj1BpCmY6iwp0JOC0xofAoLKs5fHfKEGLzKS1UhSken7FRQlJoqIZmXWzMdlUWSRKsd5RkeNBJKuq5BhZs8QgtkwH1joJn3j1gFPdhHMrHc70MjqJjjyL4BAsLphDHQ8fYtfC+sUiTdgrLVeHKqYE11FB86X9isdzM3tO8RoJQ4m+LEbNb1AAOpmZvSeXWOKkYFl4LPFnAiLCcDjgWx58gHDtGjs39xCTUA2GqNOn0e2C3izMN0SETrJgwjX9uoJEK8ZN3JU7F2fr3gd0cCRG088TlBK+/Y51Lu1P2Jk0aBEu9nPWOinDgwNwZTzewhkcmiRJKUMgNDWKqKYovUIXHZQPqOBJ2h9ayTSPbnVbD5BoTuV8wEnkkaj2We83tEXEwotpQ+SUCE3TMJlYOpmh8bEzNEWqFR0TCW9CmNuvtzDt7sy05ZuESF3tJpph2XBQVtFJVITTvZzVImXSlNFMSylqH8mVEEcsM9aLMFtgjRIeOrPCZ13g4MYNzMYmQWmS4NhajcXUqG7Y7CR0U0PVylIPNYzkVtWKarsLmVbz+9veeyVyqJ0Qi8vjF3ER6CZC44X7NgqK1LDVTUiNIsPRo4rjM4Q9wKPx7Z1vnG8dYu0tB90tHSt5ggDruSbViu1RHN2ZtoiyPr73bIgkD6PjGCpPNKd7SxXLmxFaZBZuefuOeVsP97qwLDyW+DMFEeG+Rx/g2X/6b5isn0KMwY9GSJriRVG6lpAZAiuZZiXTx05Fp8RIoyIfwwXapFA41c0xC33NcysZF/rFTNYL0Ov3GRwEtKuZ9sa9aBpMdN0Ug0k0l2/scfDcC2ycPc3Wg6sI0dmyDpDK/MMktmUF78Ns4Y7Jqw5CJExOuwjz4iNu7bXErkfRK7A+KlJmr7NdmI2ORdiktrccYz2PluxN27yoXKCbRPVMaW2bRyP44Lm8P+Z0N+fcasHOuGRYR+JopqIyZMqHmJEn286ED4H1fsF35gnPNCNM5lg/1afbyfAh5r1s5lF2e31UUxK7Bz5Ec63CxFyc1qxldo72TcG0xhDiIt44H5NhQ/TXmL1cFgvFW95cMexvNae0nkQrtAT6lK3rbXxP9LxlBPig2/ca7FeW/crN6sEp90RJoLZw91rWPh8IAntNgMoxajwoRaoURkIcT6WaXpbSy8yx/KMlTj7eGFXLbT3c68Ky8FjizyTe91d/jM/+h49w6foODPYJtqbZPEsg/oKu5oa3bBZ4OWaHG+Y7c4i7YLXwvZlct7ZUjcW1klejY9t8KjlN84KyNqg2jyQoxd6o5sMv7zFpPApYTRQPveNRTq922w+O+aeH8wFRYb5YB6h9IJlu1NvWhFc6WnG7aM9u/bS4AnygaRrC/j5X8oQ1MYcKpCmsC6y1O+9R7XAhnnc1VZwv4hhiZIWtXoYgTFzA+DiEmjqkCIIRuDKY8MDWKmdXe+yMK6Qaob1n7DRFMs8tgfnrbUKg0IosMzzytvsWroIQRLAeShfvS55oBrVDK4US2B5bznUlupG6gJV4vYeDIXZ7j7X770Ja3kqMqY9dHaMCpQukXiiSVlgdPBzzngghymADgSJVJDpKl3s0JHCoc9JVnsxXjCShJCX4qISZ1qp2odLNjKKTysyTRatp0J2A0hSJ4BausALWsoQiX8pm38zQStDh9lYKS47HEku8wWicY1zHLXgn0SRHEjlFhHf8R9/FOxa+5lqyYapVHKXUlqsHkyinXCA2+NaB9Lj0UojqkC9eO2BcOy72U3ITnVGt9YxCxUqR41yUXrogBBRaCaM6RqxvFgmXmorKeq41jrvoLqxbcx6AD0Lt/CzgDeIC2IigfYg24johqIRVCYwbS5IYaCzOt84f3lNeusTjTzzM//6Jy3znXZrN7py96EMMrmtC3IVtFinrucd5R6bj8x6GQFcJWaZbZ1GhtG42nrnl+rSEUBFhNU8YNBqCY9JYUj21lYe56XtcXEMIBOdRavFeTiXECuUhiLCRx3HRqLY0HuqqobKO07kml8CwsWxfu8nnf+XfsLm5yrv/1k/hW8v02oXZcyytBxFq7zEuFo6E1pF0sTgL0Ng4LvEeEqVmg5RU3dojEYkup308iRbW0oQrw4Sb4zp2X4iFnNFCPzUxQwZpiyE5RCZNW7Lq2IY5z+gk9dSX+LqgFZjbXnicDEULLAuPJb4J4JqGz/3a72CdI+0UrD10P3ojmkOJCIOqoZcatFLYpkYQlLm1Da2V0E3nvxKd1LDZzbg+KAk+Gm7VzrMzrnlws3vrEwnRLfIjl/YZ1w3vOLsS8zQgckUEnAvUTbRht963S2s0iirbxNF+nmD3ytnz937OUVh8zqrlLAyrWGDlJnJPAtHe+1oZP2x6qSMzml4qjJuAMWaWFdPPDE+8552Mq5rHLvT56Eu7PHFxlTMrGUqE0noqF9BThQsBrdVslOSIZMxMFGt5HHmMG8+X+8hc5IdopVjpdqkmQ/RM/CttYRL/DZF4WXuPVsekBocA3uL3h5y6cBatNes9z9M3hlHBVNe4ccN+lXIQAjc+9xRX//CP2X7qiySPPRyLCTy74yYmvIbAXmnJjeJsL5JJax9I2lha51wrVY303coGohFsQEto5dmgJYAy4JpZx2NKoN23in1ncDgQuG8jJ0sUN0cNQcNGkdBNFEbpQ8Zlh96ybV2WKdipLWMbx0OFUZzp6yWpdIkTi2XhscSbEt57Pv1vf4vn/vDjDG7u8G0/+WP0VlcgSaiTDIYjTFHEFrUITV1x58ULuLqKCgqr0EmKNl++Jb1apHRTw6hq2BnXdFPNQ1srlI2N4V1t5z14gMAXboyZWM/Ffk5ncWzQjmeCCAeTMhY4CwtKWFBTuBkPIT5gYj3Oh1t2so33bcps7HoMGzDW00sVIPSyeP7KwaCqUUphlKKjPSt5Sr+Ii+pnX9nmoFXcPH7HKkYJL9wcs7VWsJInrBcJk6pmv2ptw32bsLrAGXFtJEmihF4iDOoY7+7C4dlyO905VEBpY0jyDuH5pxmurZO3tvDKaHSa0LjAfuWx2rKeHO52hDaID+DGsy/yG//DL/D+f/i30RunybQi72YMxkP2rm4zbkuWwYuvsP3UFyOP59yZtgOh2Ckjz2KKSbuQr+UGJcJoUqEIaKPRSUJj/YyMrNs2TeSyBMrG453gE0MQjYTYzUJg4BS7jQGtZkOS0gbOdlNOd1JKGw5JfGwIaO9n45bF94wQeGnQMKodAaFyji/eHLEzqXloq4/Wx4fFLXGy8caMWk5OJ2xZeCzxpkDVOEpr22wT4VO//K/4zL/6NQY3d/jh/+m/RRmNcw61uhY/ykMgOMdoUqKqCd31HoiiqZtopiQOW5Ug6it+OButWO1krHYWfDtEoUNN3S582iiujGvK1pSs2y6QiZbWqXK+4zVKGDet3fb0cO1C7nzg6qBaOI9w5aDkLac6sy/5EBg3DufhCzfGILCaaba6KZbIU5h6Y8xTXWPzX0TwCEprtNY8eWmb6+P5jtwFYWwDmysJj53tU/nod9Ff6bBfjZhGsEfDs3g8kbYbsHC9VrKYhEv7+Oj3FTioHHeu39ot2rl0hf/r7/4jLnz3t/O2//h76XRzJM2wDVgVRyevjD3dFU+q2yyWhaKDEHj2dz/C9ouv8lt//x/z3l/4ewSiYqS7uc7o5i5NWSEIxanYDTv1yIM88pP/GbuThkxHt9XdiY1KmgDrRYINcHNsSZRggkNZy3om6CTBBI9tux4LkzhSBVtZYGgDjXcEVaB8hfIWCYF9q9qB+2L3Smbckqk32vT7PsT03FQHQpjbt4vA/sQxqVsTuzlblusjy9ZwwunV3nFv6SVOOPQbQC693SqZ14Nl4bHEiUdZWwatJBPAWce9/8kPUNY1r/7xZ+htroGoeZBYC980JFlKr6PbUDiNSIiOmUoheGxdootjxiavgXFteXW/ZFRbQoi27Gf7Of0i4eVBHeWswc9SawszLzoWMVuo2gXEe48NwqB2XFgt2OymXBvW7P3/7L15kGXXXef5Octd3n3v5VqZlbVKpVJJlizLxsaWF4zdYIbADAE0YLppppme7pmYoIlZ2sM00YS7aWAGPNAz9Aw00eMFDDS2WQaz2Ma4wRt4A1mWLctaS1XKkqqyMiu3t93lLPPHuW/JyiypVCpbsp2/iAqpKvPde8+9953zO7/fdxlURFqRG08jEgjv6ZWWQeV4olMG7xkfAJTWw+F2QlFjUvxEZUHVmiDh74JuYZhKI7ZLd1n9Hm5sa7JYsdbtY13QsxBCMJMqLvYqpGDkUTKkoqZRaPEMbenckCniAzC0rCx+e5NmWaBaR6CxUwXr7/7Tn5Bvdvjyn34YgCOv/mbmT96Ikgpq2m27kdA3A5QxKK13JB1b5y/w6Mc+DcATX3yYB55cZ60MbsSxkswcXKKxtUnZ7eGt4eVv/nFufOVLJlyLYSpRZJGkXznaSQ2yrSs2iRYIEWG1Zm17A+cipmJJrBXhCsMfTaj6OCTTqSS3AqUlgkbdLvIUZUga44lkzVN7r3hGJn11flcnGoHdstCwIyq1Q7BaY3zcZD+G8PsrPcOBqcsE3PbjayKkFKinbFhe2zGfL7GfeOzH8zq89/TLagcuwFmLThPu/AffS35xLVQcYg0IKArIshFtMlQSZK1xUR/TuRocKMAFBVP5FFWPjUHF4xt9tByrmlKD/XLjeHyjT6SaLLUT7lsJW9aLvYqFZrynSqXzYUEcGIsTghhP3wQNESUlrUSSRYp2rDm/vs2S7bO15qjmZsiN4bGNnNx6VL24hyRAsF1YDmQOaz2dYYVFwHQcqjqTl2Kcx1pLYXfKo79wLibSCkcALSpZa0S4AOBsR5L1gQEtSJUiUZJ2shNo6+qkC8K1ya112koRTbXwtNjuDlg/v8qRUzeOcAjry0+Ee2U99//xX9C49VZmbvKYKjyrIdW242NEP8cIgfSC2Fdsn13mw7/09tH549e+lpXtIJYmhcA4x+rAcXB+niMnjnL7y29nq1+h1FglFQESwY0zKav9Eu9lrTQqSJSYAPRCL5liu1fRiBKkD5WfyS6I9Z5znYrHt0oGxtGKFCfnGyzUpm290iClJJ4AyQ4hqKrGkWg56tCNksYZ7YlFOFdwq/EcyeCh7d2gwVDZ8nQLQ7e0ZLFiep/p8jUT6iuQeOy3WvZjP64yrHOUtXR15VztGqtAhtbBN7/5v0eWJYPHl8PMPejjm1ko05clG8tP4udaTLVTRKRRkcaLod5FYCc4t3fi4b3now+tcP/ZVZAS2UgRQnLqQJP5ZoL3ns599xGbivulIEsjDh0+wZMD2C4ty1s503ET1JifYYcgUg+d0lJYSGuVT4EYKWs6EVQoj0812D6zSu49vdVLqKNH6JYOWRuT+RoMKQTgPBv9iplU044VxnsK46kcZLEflVqt9zhkYOcoiak114+3JJFWXA7flFKACX4oiZYcaUdsFRUNBc1EBobMxPU4x0jG3TtP3EhRckKgSwiiqTYXzixz+KYbAEhbLaQI4lelcRw8uoD3YlexqFN5nsglER5MibOWfLVPZ622uI9jGne8kCSNMdZSFBW+ThJXreX2xTZbg4ohVGSIXx0WDJQQnJpN6ZSOgWHH+Z0P6qCCgAkpjCfRAXArhy0Q4L7VPmc3c6jxHv3KsTYIYOOZVJNbTzbRGfHU3jtRkLkvXX1RE+dOJLTj8BbJoXw+kGnBbORZL7nsuYUW2RcvdEatmXaiuXWhSaz3cR/78dzGPux5P57XIRBsFZbKWZpx2GHr+k+ATihs3KB96ymmbj5B+6YbiIoc190mkoKlG49A2qDo5Vw6cw7nfA2KdOAttszJu51x2X4i/uJjn+fLKx1Us0nUbATgoLU8tNbDWMfgns9xYLrB1NwUrZk2utFgdvk0L5hJOZDFbBWOc90C53xQE3V+5ChbOU/hwk66sj60NIYVmfpShACdJjSWDiKiiDiNaeCCpkRN661vEh5IIsGxmZTFVkwzVswkmsWmZiZVKCFHeAwtIdNQFBXHmnBD5jnSVLVOx967IinrKokPehmNKOL0pT7vv/8iD6z2WNkuWO2WgQUzLE95cPlgJLZ2ebiJKsnt3/EtYUGXQbNC106tYyaPx1jHdhFAmr4G6jrriE+c4NSbvidc5/QUzUOL2Mqw9sgZNk+fpX/hIr2VVS4+9ChfePf7qKzdNcohtiLRoU2S1rTVSSSGsT6IsdVVovVBVWui1IBZoDSWs5sFl2dMzsPpjQGqbpukWgK+phYH0a/FpqzF2Bi9j0F8zTMTh/ug6kId4VKIZKhoyYn313tPphW9yo3eKSGgU1Q8fKm/57O41jDGst3P2eoN2O4N6OU5ZVlSVRXuMrPB/bj6GHq1XO8/VxMPPfQQb3jDG/id3/mdXT/75Cc/yQ/+4A/ywz/8w/zar/3aNY/vaSse991331P+/O67777mk38txTfCOJ+XY0wy4tmDzDaiXYuFFAItPZEM7ZRhvbt5+BCpMZRFFQCnUmKzJsJD2emgZ9o478LWHCjKgvXTD7G62R0de+vcBTaO3EGj7rkPt43GOjqDkkfuf5gXzU/V6ppDpU1HdvAAcWRZmskAQVlWdHp9Go10RI3NrePido+ylvqMI41uNPDDgvsIPQgIgUxilk4eQ3mHsY65VLGZh0qEp2bEOMdNMxlZpEaL1kj3wTPhXhqOXZYlyhtmtUVGArBUQuHqnfSY27EznKvFtwpD13hOX+px+lKPRqQojWEqjfmeFx4E76jKgnz5CRZPHN/zWN778Tu30GbxVS/mzMc+i0Cx9qUHOLJ0EK8T6ryDfhWM/ORw1VUKlcR4Y5i+8w7m7/kiC9/ycvz8NOvLFzBlBUDZ64Xz9Xr89W++lx94/V2U03NEl7GEAuM5tGa0ljS9JbehejPEcAwpz54AxH1iu2SuockiifOCs5sF3aKilUT13RYMG0/d0lJaz+FWTKwETR3Ez4T3DMocbxNipVjKAivIuCD41ooEwtn6Xd55DwUwF0PVgEtlqHlk0rHSz7G77Y3pdKB37hEU15AUCIlutBBC4qoCbyuWjh4nijSRCp423noGZUVVlZiq4vyFC2MzxivE83LeeY7jK6Nc+vTH6/f7/NzP/RyvetWr9vz5z//8z/OOd7yDgwcP8ooVjfUAACAASURBVKM/+qN853d+JzfffPMzvpanTTzuuOMOkiu4cN5999287GUve8Yn/VqLb4RxPl/HeH57wKCo9vzSeKgFp8SOf1NSotMY6zxVUSIA5wSy2WSwuUajnY1aH8HgTHJgbpZjN90y6uU/pB/gvBHoURkCQKC1opXGiK5DoEYb2yiJyeamyeZnsY6R4mkjDS2ZdhoxqCwX+zlOSJI0I0knBiMESgbDMnzYCQtESGZSTZTE2MGAwfkVjt50nEakWB+YGhcCi82EhSwOqctQM0LUIlyCCSVQQIBWCulcoOzVY9C+okQhhy2TicsLOZIYCWf1K0cr0bQbEf3SBYExHbFdBb+ZGw9M4b1nefVSDWDYY9Kzbsc797KXvYzu+iaf+ZO/pLm4QCNLqZzA+HFyJwVIP3FlSiGUonHsCC/9ubcAcKEzoBwU7ABVCkH56KMkUcylz3ye+W97PUrK8eTuwy4zJBWCyoGWilkd3Hs7JXgv6s94pA1iY8bBSrcKcvLGBzquCG2sIQbHEXRJlAj6HqL+rxdB3dR5UFHCWuFYqk3pplMxAn6I+plaYQC3g4Y9fJ6LTUWrlbE0k1FZR2d5Y8977rzntkOHaSfPDO8R6OQVzg8ZNOEikihU0kZFLRHo0XGkEVIyPz9PFF35XM/XeedqoiiKp92YX2t8Rei0V5F4xHHM2972Nt72trft+tny8jLT09McOnQIgNe97nV86lOf+sokHvuxH1/NcM7R6fZG5ezgm3XlL8xwkYSJ+bjeucZJjNIKayxSCkx/wPnT57HWMX14YYRDCAu0pzQBbKmkwDVbqB44c9ks70FrSctViLSNN5a4kdBemCNKE4SUSHYalAkB3dIxmyUkXUPpHJdvRsUEtXZUXakMpihIswRvDPmlDaRStNOYmUxw3Dq6pSXVklQrVA1+HZ23vmBR77v3vI/DDKy+B8obrIhG5nKO0Aqqas7o8F6biXaK9R7hh0BXeHJrwMFWBN4xf+tNDLa7SCVxE0BWby3TczO7Lqc1N8OdP/BGSmMxNgiGqTr5SISj8OM2iauv0ntIpCBTgiQSzCQZdmmWCxc3qIzD9/tk2xvc9vfuovmm76Y506YdS7ZLixISiQiYGbkTgGcBnKRX2SAgpsB4gXWBJu0I+CMpgqR6K5EsNDXdwtCvHF6EikXAzHokkFtPqgVTUaBP4z1KQEMJNvslD+WWW+aSkRrtsLOikgRfKXyVM9SpDdiQcE+Nl4iRf5AgjTS52V1piJUii57ZtO+8Z2NQ7khqEYLKWAYCmtFkQ8qP8lxvQ4VOyqenrO/H8yO01mi99/uxurrK3Nzc6O9zc3MsLy9f23mu6VP7sR/XMUpjeWC1y6V+SV6WtGPFiekU4zwbhWNgPQ0VfClasRrv6K90wImdotaKSARqrU5jZo8d5t4PfgxhK171z94EhD6+RXKpm4cysnPkUUKqKoy1O4oFEACZ8vhxkrkUKQWtZhpaLlKG3jvjbgn1lfYrQ95xJBqEDbvcoZ28d565lkJLxaV+GVxkvSff2qZ3cZXsxFGE01SDnOkjB0fVn0gFtdDJaxuqmQ6vI+QLfo/dr0c4M0Z51Z0d6SxGqLpKFBadwgaa8uR90FKwOTB0Csvh6QQtJee2coz1HGxFo3MqIGu36He72LxASIHJC5rtJlPzs3s+vlYSsW5swPLUTznBo2TEoDJUNlSUhqBRLaEZx7j6GrVSvOgFx7n95CHWHnyUzSe6zLz+lcRZFsaRNuhUQN1wGLZOCuPJapbvcKylC+/fsN+T6uCz44FECboeptLJaVRwci7jkfU+/dLW5oGBCdWvQusn02JXBU8ImI8FD2wa1gaKhSzCexcYWTJQxa0QGCSRN6PkQwiPcZKO1xyoBeOEECxNJTx2qb/jsXvgYCt+xiX8XmFq9+PwOTeROJfWkapwbwZlUMitO4RoGRLRQV6SqPAeSq2ROr7iufYjhJRc94rH84jUsp947MdzG8Y6Pn5mne28wrqg0LlVWPqVZ7ZRtw4Q5NZR2KCQOdPQowm1soH9sUNyyXu8CABELcDUE74zBjPIuflbX8Fn3/WHPPyJv+Poa+5i4MBbRTPxSOGx3hPHMZGytBJBXtl64oVIKRIpKJFsF5YjB9pB56JewKu64jABC6G0lqrWbYi1QslAc5Q4vIOZJFRJpHDMNRRbuSVKImhn2KJNZSyR8MzdcISklY18Ti4P5/yorA8B16EQdTtGjCce7xEmR0yWZcIPsEIiZIRSgrksWKr38ooLnXxCByT435y51OMNtxygFWuk9LxwqcVap+DQ1M7WrJSCRqtNNDNLdhWUzkasmfEJnfqdEARRssV2Gyk1q9s9ihqMG2lJK1IjCffKh0lNSomIIhZOHKVx/DhehIqLjiOECEZ3ubV4HzBCjVhRSUmi3Q4GztB1WIkhXmNcnZIK5jKNmQAGCyGYSiNevNRmc1DSLR3NRCER3Huhw1ZesZjtbl0775A4ZhPYruCQUlxcX+fgwSWkUkEHxXlUFGNdhHAG4R1eCPpeMZXGNCfaJ4faKUoIVjrFyH9ooZnsejZXE6P3ybngWwTjUgwhsVrrWaZrYRJPrd9iA5BZyoDPkQBlRSxzoqy1L+n+FPFctVqeKhYXF1lbWxv9fWVlhcXFxWs61n7isR/PaTx0qct2XtXAy6E/h6R0wWk1Hi6YNeagU1laCURS1XoYAMOSdt1T9x5rXdDdMAYnQ7ulu7oxmi+PvvQO7vvY5xAve2XNDnFsFCXzqaxpipJWJNkqLK3av8UTFgglBFpJVJawVViacThmZR1IhZ7QMfP4Gi8wlNYiLDCpQqBBBPnxAAQMi95iK6ZXOSLdZnZumoYG4RxlPec7QqHiciJOYT1bhaEZBZyAdZ5BFXahkQq6AApLJBWVEwgRTOpUfTCHROqUJFYkE335ZhpxRMLFTjFiYcw0FC87No1SApeX5Gsb6KJiau0ST0YlR48f2nFtUoR7d7XRTCKyWGOcC+2QOms6caDFTBaxvLpJljV2snsISYIb/ZugSpuoWgvEEzAVG7lhqzAj8G1eWvrGM5dq1nNoxzUol903eRJNJAi7+iGNdfI3lZQcaqfktvb4MRYlBE90Cm6eiRFKBtBpXd1ydXIshEApRZSkXFi5yJGjxwCoXDk6uZACRDTC4LS0YCrbXUVYbCUstp55onF5ZLFmfVCG66wLe6NxCkFugkT8gVShxaRzc21E6D0eicUjhKTwHgY9kmb7WV/b12soIa670uiz1QU5evQo3W6Xc+fOsbS0xEc+8hF++Zd/+ZqOtZ94XOeoioKP/D/v4uzf3kuVFyzechOv+sd/nyMvesFzfWnPy9gYVKNd9LC64XyoYpQ2JB4C8KIWUnKegRGoKLAPcgvdyjGThEoCPlQ5+saxUTmEs5jKkpcGGm1iL4jzPiqOGQwKEHJUtm5GkEUCiSDWktvnUx5cL9gobNiRCkkiQ8//SBahBJQOdG3DgZAoarKMDC0U6wMIsRGHcSjq3TgT4E8YJQpSBBnwIzPZjvtUGcN2v6By4yRHCTlajKxzXBqEknh1GYDElI7FhkRpgXUCrxRrecSUhASH9uClpiKizEs2tiuaacUNs9koYYqUZDqRGOcpjAtJoZaYvKDz5AXsIJjaRc2M+973YdQbXs2h28egM+/HRnNXG0IIoj2wAVrKUYdoyHi5UngETipSHYz6lBAkWtBOJBe65WhslfN0K8u0DNgFL8e790g4rJcj3EUYD7RiOca6XHkUARArBbOp5ORsFlRd61aU9x5TFuAd1sNa7nnB1O4x7xrmGBO8J273eoZWkkhC3+ykfCsBiQr4JQRIEfAql1+3YMioEHjv8DVzKN6Dwr4fIaT4CiiXXsXx7rvvPt761rfyxBNPoLXmQx/6EN/2bd/G0aNH+Y7v+A5+5md+hje/+c0AvPGNb+TEiRPXdC37icd1DO89f/iT/ztPfOGB0WJ69m/v5f4PfYzFUzeilOLAzTdw1z/6fg6/8Jbn+GqfHzHZ6w6tgQCGLK0nkg7jZT2ZidEkFonQI4+UJDcWIaCh5Y4JuLSOXuVR3mMDQjUwG7I2ssxZXz7P9IkbGE7nkYT5hh4taM57pBTcOpfyZLfCEXbBK91gtlZaP5KqLl0wE5MTi4GxjrwKDIcIT1nUxxACrQIOYSiXLsS4PQKw1iv5wJdXQQTWzpHphFccm6WdaHqVwzqHlhpjLcZ41gcV63lFIwpthOgySMfQSwZCUrWVV+ChQuNkkJN3Fi70KjYKg3UO2RGcWe9z84EmR6caVMaEClPN5JBCBll452kuHqB3cQ07CO2b+Ztv5JNvfw/f9Qs/CVKghQQhaVyjXbtzjn6eY41BKU2WJjhbjRdjH94dT12B8GOVWlk/23YcxjlsmbVjRTSdcG67rN/D0BpoRYJY75zylQjvU+WhX1pO1/iNhpYcaMYstZNdbQPvPcKPn207Urz6+CzJ0NTGu9DqEqCjmLLIOd/3zDQiEunZ6Oc7jielxBq7G9jkQV5BJ+V6RqZVAOoS3i0lBLEaesyEe1dfzs5LFIG9Y5yvdUfC99TaIbpmP55Pcccdd/Dbv/3bV/z5y1/+ct773vc+6/PsJx7XMU5/+h7O3fvADk38S2fO0d/cZu3042FC1Jq/ecfv8fIf+V6+59/+z2RT39jlxiNTKU9u5QGHUHPXBWGBGBhPrIKqqKjtxiMlyDR4gvpnokEhd+36bO3YitQIZfFDLQEBXQPLX3qYl77lJ0e/34oE42ZIEPvyNcLyQCopun3665scabUws22Wt3MWmqEdUVmPc44slqEN4gPbIRLhOvrDigiB3VA6yIQlUmOgLIRFKq8sZzYHpHHY9SopWOkZPnlmncV2wlrfYJwjjSRHpxKmsoT/fHqduTQii6IRUHKSYqwVo1aWVgpfGqZigRBqlPh1KstmYcbOsSJQSh9c7TGbSGKlyI2lCia2OMBZF+TnhSCdmaaIYkQU0Wq26G53sZUlSpMgMy8c3bxkOpPPqLdfVhW97nYwWENiqwHbZR/X20ZNz5CXbtRyEIQkQcshvVOgRa1QKyZ+qf5vQ0tasaJbDtkfATh8+doupSQCYjx/c2aLQWkRUtArYaVbsNorufPQ1Kgi4r0nUYLc1YBKEd6tSI+fihMS6R0CjxOC9UohI8mhZlTrYThENG6fpJGmZ91Y34Xhgw4MlqsN7/0IMK2kCNW3qyiZDNtSxgUWy/ATg8qhZEj4rAc5wsOMUVfOQWlCRacgMIISudMkbz92RnhnvvoVj69W7Cce1zGe/OLOpKMc5GxfWMUU5Y7fK7a7fORX3slnfuv/45U/9gO86f98CxvnznP373+AL/35Ryh6OSdf8zK++1//j6TN7PLTfF3FsekGy5t9zm3lI2qkEGFSFIJQtZChEqKFYK6hRgujqvEgoZTLDgpr+HnoSYs4xuU5zliqvKC/eolv/Tdvpmg0Rp9N9EQpvT5ONRigdITwkHe6VNsdTK9P4h1LzTa9wtJMNLLeAeJDolG5kGlUDkovQFwuZg2l8SSTst11rPYremWQhs8iPXKZHRjH2a08tFeAbuF4YG3AHQc1J+aaPLzWY6EdEwSwxowCgFasglT7+RU2nrhI8+SNRDMt3AR4bT0PGANJrd9Rb12tcyxv5Zyca+J8oJhqLt/VCuJ2C1uzFbyU3PFP/iGVF0QETIL0oQrUK0rajUkBk6eOXreDcoax/ZxEOc/i7BQzDcVy5ajtTZAMqZ3j90hKQSTGpagxniZEogRdQqLbjEO9bVSJ2jlE7rvQY1Da8Xfch3u81iuoqpKpLKkhIYLKC4R3NTDVY9m90Lpa7wNBrfPi2Co9Le9oxJKpmTF1UUpJM43Jy1CRAlBKksb6KRO50jjufXKLrUHFfBZxar5RA58FxnikkiRR9LTJRxppKhMYRaVxxDqwv4wLeJZY2NB28mDqZzFkz3j8KDn0BDYXXjD1le4RfQ2HEqEte12PeZ2P92xiP/G4jlHlBevLT+IqS3N+hu2VtV1Jx2T0Nzb5+K//DkLAY5/6PI/fcx+2DJiHM5+5h7/93T/mx37z33Hbt7+GQafLlz7wEUxZcdt3fAvTS9eGJn6+hRCCw62Ei92Ksi7lSjGmG5bWsdbLuW2hRUOFnWugUYYS9rDEbuud1rBjEUtBXoM1K+vRjcDQiFtNjh9bJFKC890KW3+gtJ6mDkuDA1xR4qwjShRVUeKqClcWyDih3NikPTvDVm6prCNWIlBYXS1RXbMeLGIkPObZmXxUFgYm7MYdnhiPE5JzmzkeQSvVgUXhgxdIgI2EZCyV4GtV1nvvOc9dtx+jGU9xZr1PQ0GWxkwnijRSZEqSFH3ufuf7aC4t0l5aoLywQiE8ut0eLTh2smwwrHrUz2eod6IltY9ILeSlAtUTpfATzf98q0M61aIUmkbNhAmgX6ieRsVyMqxzyImkYzLSSNEpKrQYq6xGapwt+Pqu40OpX9bviJaCREuKelBBrt4RSUk7mTBtG5I3JjANa71qhClx3mNqICvA2c2Su9opOtZMpQmPrnVYz01o2QlBQ0tSrZFajlqGQzXZwHxyIXnG0zeORItdCYWUkiytkzvvxw62V4i1XsH771+hUxikEHzLDVOsdAvmsphEh4EYY/DGECmB1PEVzRLTOAoGeZ11qjSjLF19Tz2+MkSmwhpJHA8pveG5yPoeXw7nGNirBxp/I4aQ17/icSU7hOci9hOP6xDee/7y/3o7H/+P72Zj+TzOWDbOPTmS077yB8EUBR//9d9FRhJbVSNlSDz01jZ4zz9/C6/40e/nw7/0Hyn7A4SUtA8e4Lt+6sd53Y//V1/5wX0VoqjcCGwJQZzKuPECJbzjzGafwnikhFRJbppJiSM9mnh7lQu73XpRmI6DkuRQECxMs55GJElqU7Ybp2NW+1X4PeNxcc2OcR5vbQ10deSDgnI9mJA5YxCVRLuw860cNZjTMx354Gpaj+OpvuZSDNtJjpVOzlwWgRC00ygAB8UwaRkvbgJoSYGvqmDKJhXR3Awf+70PcvxbXsHJ+XlSLYLxm/NkWnJ0vs37f+G3OXLXN6GTsGhFWQMdRbg8R9ZVn1QHme46cxpdp/cQFTmrj61jrcNPTeOz8BktBKZW+7TGYPKcfGsbVxlmjx7D+cC0SfVEb+AZUASfigUjR7fH1wwfH2Tz97jrppZhza1Di2DElyhJ6QIYcioJSV5eQTv2o6R38ttragE16kSwmhAQ9TUjaC13nGxFPNkpWOtXdaoZNFu6ZcBnHG3HTDzacGzrxokOHusEhfUMel32io1ezkqnZGCCmFoWKY7NNmjEO6nKf/3YOt3SBl2PVkwjUjjv2RpUoU1Yq5A6PM46nBug48YVk48sicgfepS//Pe/wcI330ljboZb3/jtgU4uBFYqypo2LhAUxoLwo8R1MjqFxVhLI2tijEXvG9ftCCUE7jonCtcbrPpsYj/xuIp49EKXBz/4Oaak567bltg+d56l207RnJ0G4KGPfpq/eefv01lZBcA/E3MkH0r6stzZ7/d4TFmy8uBp/vQt/278+8KyuXyeP/yXv8DS7ae49fWvvC5jfC7COc8HvrzCer9gurG3qJDzjoYOlt6tWNVsF8eFXsXxaYESEluDC7dLh/cOhSRSMJ0I+kbVjqKCVEMzUlDvgGMlOdpORsDObmkpjafqDzCVodPp00gizNb2RF/dIZWqBax2JpZD4B0w8hQJlZnLxx0YAGc2B5TOs5BFqHry1jIIgEkx0fLwYeeohaCwoWg/mqa9J7vtBfSQHG9EDMmdkQzKmsuPniFbmBslHQBlt0c2N4PUatSiWsgiNgs7cl8Nh3aIbpf//Iu/zGv+yZtoHZgjrneyvtFACIjTlMoYOmsbuMqQtlu0FuYQUu4cd135USp4yVgXwLtP5R+hhMQLGUCYl0XpHFONmH4ZQJiOcYJ2OcLR2ADAbcaKSAfg8lZRkhvPifkWZ9f7ddvJ08ktUw3FULPD+VodVMBSO2ZzUOK82PHktZQcnY4x1rHeK1jv7bS1FQBCMCgdncKSxQEwba2nso68XpknExKPJ+9u7xp3Z1Dx+EZety7CdfQqx6NrPU4eaIbqngrsnQtbY4DqpC+NdQ7vbF0RCn8KH949aUqkalzxmbzg217N5rnz3P0HH+Dm174cURSQNdG1NYAzjpJAX94ysN4vOTKV7kjGt0vLwBjyfpejS4sUgy6F1DQajX1tj2+Q2E88niLKyvK/vetj3P+O36J1fhl9/hx/5ixx1mDxpmO85Pu/k+/6V/+chz76Kfobm5SDHFsbUz3TcM6N0Onee/yVSpH1zFR2+3zibe/+mk48PvrIGvdd2EbWO/0AXhtC0sJQU604MhUzNSGOFCvFXEOy3q840EqRfqzloKVCESojSmnS6DLsR31cWzc/Aq0x/GMrVnRKy+Of/zKf/Y3fwxYlN774Bdz2Xa9HR1FYcL0nbmU4IQl7xXFUzlMXXcA7EBItPJUXo8XQ+9CyOLtVYAlS34mSQfCMYF3eKe3Iq2V4hgAEFDgX6KHCO6SzuH6fKm1yw1zwRmHHblpQyIjG/ByT4b1nsLFJNj+HGuIglOKW+ZSzmyUDEzRQpuKIj/7L/4Pi0gaPfPzTvPyHvpskktjuBt31S8iDS0QqJAftpcVdAF8pIKnfaec9Skm284oLnZKhIV6iBMdmM/Qeu2wpJTpq4MoeO6oweLZ6A+amLW3t2K5CUbownkiOZcqH71DlHJ0yUK8j6Wo11iDidvtBTWFcSIAEDBzkPUMrlkQ1TUnWrbOT8026ecWZzXJ0H6UQ3DSf4WrtmfW+CXoul71vobXig/BZFfA9eVlhfV0Ary/WC4mU0NJ7L8CrvZzdM4OnXzkeW+8FczrvAyBWC2wt+X+hW3LLfIaUQ3+jmlXDMDH2VBY6pqLhFc2nwI688h//AC/9oe/m0mOPk8016UcZpbVMUaClY+iCnMWSSz3Lly92WWglSBGS+15puXMhGuGJBOBdxWAAzWZzz3N+o4XkqZPyazrmdRYkezaxn3g8Rbzzg/dy+jd+m+zsI0RrKwhj8AjKTpeVBx/l0+/6Q+KsQZkXbF9cxxtzzedSSo130E+rDRDikY9/9prP91yH954HVzujL9fDqz0OtCKascbjaccRN8w2WNnq01CSfmVHlukA1HTaeiM9KuE7H9Q7RQ2wDG2LsDc01vNkp6BynoVMk2bxZdLm0NSSk3e9mPt+931sXVyjM6jY2s6ZO3oQ8pzO2WUWX3IHG8aPEqXhIjqwkMjgsaKkRMiwMCUi7JiN9xjgfLcKWBZC1cWLcWIipWQoLlnaoJmR6lqPwnlmWjGpkggCeLErHLa0RPFO2e5hJK3mnslwvt3FA7OtowFQCUihuHE2RSNoZzEPfPCvwFgWTt7AS974emw+oDsY4AmeDi3lOdszHMwUjqEB2vgcjbrF4pwj0Zrt0tIrhzKf4We5cZxd73NyYW92V6vdpNsXmCIHH+jJKkrZ3lzGHzzATCJIlKdbefpOUhmLisYS75X1XOxVo/dmqHGiZZDf//TyOuv9kqVW2JVbgmfLUIgsqgXsQuFJ8E1HpzkyXbC8He7pwVZCEoWkyQOFsSOc0iSINfxb0JgRdaI8pQzbRmOH7y4ha41lABPvJXNRXu4dRMDneMQIp+OBQWW4bbHFFy50sS5oyZzbzjk+k5IqRknH8PehBuN6z/agZCuvRol/O9asdAs6NespixWHp1IWTt1IWVSkXtAWRX1MWedQoap4+6zmoU4AL3tCW+uOacVUvDPRFAics1jnnrHey9djBMD6dT4mPG8YzPuJxx6R93p88c8+wl/++YO0zp1F9roIM+ZEegSmMvTWN7n/Lz5O0es/q6QDJWgfWmD7/CrsKuBfOYatmbI/YPX0WaYPL9Haw3jr+Rjew6DcuXdb61asUeG85/vumOGWxRbLG13ObJcBpAc0Y8VSK0YKgYba12R8jMl2lR1JhXtWuiVfXutT2rAje3zTc6gV85KDTYZ6554g2d5IIv7Ln/sXPHjvwyy98BbSLB1pdjRuOE6z2WBjK0cIT6zH4EtBAFkOzjyOmp4ibmbEkQKtAxvEe4TUzGQxg8qyXRiKIUWSoHGg6qqG8WFXHLQ/wkLZjGrn2ImxTs1OkW/1xwnYZTdZSEU+KJj246xghCdQmksDw0waYZ2jW9gRmyjv5My/5lV8zwtvp6lBmoLexUuh4jM7Q9xuoyPN4VZgNkzFgtwGsK8UkEjQSuCdpxQR81nKhV6Hy8siQggq5+jnBVm6t8pmK8sguzK7q6GDyVrfGLpWYWwN8vUe6YeVrd2hleRCL6dysNIrmGtEJEpiHEQqVAZkXY4I/wm+KAvNmDSKKCZo0sPbr5Wsi12hVjVKBrwnjSRaCJQKwNF2axbX6dG3HoNCEzRHlAwJ0ja75eW1ErBjqglOxqHKEYTltnNTL+CCk3Mp57cLzmzkfLJXsj6oeMOJ6XqW8Tv2OILat6jWOrEetqqSsxv9Hb/XrywrnZxWojiQKJqxqh2Dd99ljePGdko0HzPTTKmqijLvXeFJ+h16Nt/IIeXV2dg/o2OGUu/zIvYTj4kwZcm7f+Lf8IU//TBmUBLrDJcPkKbg8sZxUBysOHv3fSHpCJrQ13RepSJ+/H3/L3/+i7/Oo5/6HP31TarLBIT2iv7WFr/4yu/DVgYdR8TNBje+4iW88ad/guR5TsOVUjCbRaz2drN+YiU5NpNy/8o2hfHoyI+orp3SIrolh9sxmw8/ysEX3bbn8Se78NZ5vrzWD/gFMdbreKJb0k4Ux2dbGGNIFcQ6bG/14hwv+vZXUhiHswHXgQhurd3C0EoU3cIghGAmCT37ysOR6YzzieZP/tef51v/6fdxPnckx2/AVRapFY3pNtHBRdCKfuUwzgejrUiNpOGVDKqFK92SqSS4H2ZzRwAAIABJREFUzg71KYb3wY5aUoJp6en3C9pTTUZbmloATQk49ca/x8rd99Jot0GAThN0I6Gam+d8p6RbeaZjgVZBdn5EDxaCdHaaxJdEMkOIIJwWtVtIrRFCECsFIlQxGlqM8grrQ8WmXxqe7OWsdosaZLp7MvU+gA2zq2fZhrErjbfV8IETS0lmDbkXFFay2IxIsyYrxdYuhdFhG0ZLSekshfU80SnC+yfhcGtqlHRMfkqKwD5KowBcnjyeroG2sRJkccJmXlFZi3PBPn4+1XVbS7LQDslsKRSRhmicTiIEdApDvsf0vNCM6RZ9/GX3UUlBFik2ByYwtYY4Ji1ZbCdYD5sDQ790bBeGuXR3a6uGE+0YU24cg8qO1YVHRVkfEhrrOdrS3DY1Psau58SYWiufQr9DfAXaC1+rMcnsu27H3AeXPj/jfT/9S3zuDz4AzoXF3ObovA/isl2TAJzHFCWmLPF72E8/k5g5ssSxb7qD/+73/gMAD33s0/zqd/3XmKrCPcWxi+0eZz7zeXQSkTSbTB1a4PQn7+b9P/t/8/ff+lPP6pq+GvHSozP8xYMXd0xWzntesNgmjTQXOjlxpAO7YYLe2a0cpt+nv9XDOI9iZ6VjcvrzznG+U1BZWzNnxOhJSgQrvYo7j6Z0ut2xFogPSQQiSKeXYmfpt5sbLvQr5lPJUjsOvXmgiWCrl7N+6Div+emf4MIn/oZq7iC6DAmKs5bepQ0aztE+ehiBorQuLBR+OPF7vA1iU6fmUjaKoI8wxBnAcGfqQ+vPWpTwrJ1+jPRFtweJeVlrK9TCYYmOuPm1r+DJTol2FpTiUmEZ9MJ1beWGqSgaTXQ7XCyFpHKCWAri6Tbe1MlbzaAIFSeBsS60vnzw2DEutLaWt0sqD85VpK14zzVHAMIbBnmBVJJIqasCGcq4gR0YvHcUlSMPPQc2Co/QitXcI4oemVYYHyoDwwQkVpJeaRlUdtSOG2qxtOOhN8zui1VAqiVKhkXZ++F7WU8L9adOzDeprCU3nlQFb5+A22HH2DZKy1RU40nqGBjH49sFco9lfDpLWCoNnX6OwJK7QGduJ5rKesxQOI+AKXE+JB9HplMOTXnmswgrxqq5l0c1wgGEjVa/qv2NhzipGkckRFB4Nc6y3Km4sRnR2IOY4hB4IcnqVqBSCiE13lU76lAeDyre87n74ZdSiKsSO9uP53/sJx7A/R/+BJ/8zT/gnt//AM4ODQkEsXIYrZGmwovQU/eAqCcv/ww0Ca4UQivmbjhEOchJajfQW173Sl70vW/gnj/886s6hikqnO1gjSFpZpz9uy+wdX6F6UMHn/Jzw7Lmc/VlfvGRaRBwz/Im6/2KZqJ4wWKb156cJ68Cw0QIai6jQ2pFoCc6LmwNKG6+lUu9itk0QquhhPnO3qgQYF1IOoa6H7qm+3mClsbKZpeWCknPcKof1reGQDnPuK0j8DSFoaFiclP3pP2wSeZZSBWDEzfwd//hXRz79iNjinR9Qd4GxcpUKxBheTHeY4wf7cRNKUIFRkm8d7XQ1PCnoRc/fP+csUQIHBItIY3Gk7epk4BEherKxdwzWavXArJYouWk+qsYjx+wSuNxSB0RKVBa1foiYcypCnezMEFWfOjlspEbvJBBa8MHvQxZYy9kbd87BGgKqemWNpjYRYYsSZ621y+VgqxNlfe51B1gPGyXMNdKa/YTtUInOBd2kJOYzVh6Ts2lI2bIhW4ZxuQ9kQg6HTuYZj7ow6S1n09DSQbG7fz+eE8jHvrbKKKJxXgv068DzZTPP7nFQqaJpKBvHOu5DWJ52u5yIi6LgikGTKe2BpkavBRsuJCQCyHwzjHY2KKsDDRbyDgi1ZKlVk2nlpLce1L8juSjdCIAomWgwBpjKGuVUzHx7oWXw4/MEJUUPNF3nJpS4CdbxR4nY5qxpqwMyjq0VqSNBnk+nj89AqEi0j1abcaUeGNqA0mBUBKl905Qvp5CCZ4J8/zqjnl9D/es4hs+8fj8H/8Ff/RTv8jKw2cmVJTCV0dZi1caKxVOCKKq2EPK6BqinnSDE6XmzGfu5Wdf9F9wy7fexT/41Z8laWb8t+/+Vf7oX72Vv3n7e+murj/tIZ2xFN0+q4+c5dDtp1g9vXzFxGP53i/x4V9+O2f/9gsIJTj5mm/m5D964/UY2TOOFx+e5sWHp3dNsFoFeXTjPCrSWOfr9kKQVjdpEyEEa4OKVqyQQqJkkJ+W3o9Ao9ob5uJx79h7aq+R0A6Y1orSgh0CURn3unMbFoG8XlyySJJpRTMSLKUxpZPYeiEdX3moOERxg9e+5X9h5UsPhH8e4iqkJDswG3Kp4TZyct2q/2u8o7LBBC7RciQWJXz9OYbj8ZS9Ps0TN6LFmDZZWcd9F3us9Ssq52lGAZswKcYlgHaiUCKU93esi2K4ww2+LAMEkfAjjQchQqUpVQJDMJ8rnON8tyQ3blebwhnLF/704xw8epCZkzeikyBWFSUxrWj8mw4oK48SJVk67r2EXa+rvzfjMUipWO55zvVCYnEgC5Rr58a3VsqQGA21N6xzlFUAisd1JjKTSFIleGRjgBMRrUgxsCFpcz60WIKarBwJfy1kirVBSF593dpqR4pWfPVT/FI75cRcxZn1AcYH5YZjUwmH2ikmV/SLEq0ksQ5TtRl0EYQXeCTx7wyzUkCSsH5xg875FZyplUTFJaKZaaaOLdXg1oBVMki63qN3VBNUMDEUAoWjcG6I590V3o0rY1IIciJ8nCBsNdqgORnh0XVlCKR0RNaSJTFZ1sQ5xxfu/QJ3vvjOPRMJa6qgbcS40uGMBV8ik2fYl/saC/EVaLXsC4g9RxFwGSU6jkfKl3/7nj/h4sNnJ5KOnaGtQVsDUqKThLiZUg4GmH5xTdeg4ojpQwcpuj0Gm9s4a9A6ob++xef/6EN0Vtf5iT97J957splpDt9xC521dc5/8aGrGl/R65P3ehy6/dSev3PxkTO840f+Jy499jimrPDWceH+R/jM7/wRN9//YRZvPH5N43q2cXnVJZKSA1nE+U7BUCI9KHl6slhR2PHCu1UYFrOoXjjDoiy8Q1kDwjEdSQ42FBf6YbGplZ2JpODYdBrMz3yYlIcLemUcj3cMlR3iSzz9yjKTOo7Uc54VQwT/Tl1SJQRGeKJWk0MvvZMn7vki8fQUeGjOtEFpXK2WmejQmhhCECIhiPXYybasgX5KhuuWQ1kh53HWUvR6pFNtZuebXOhWTNV6Hfdc6HKpX40msIHxnNkYMLe5xiIlxaCAxUXk0cM0IjlR4dj5HHRdVg+sFUc00fbCg6kzFOPcqFVUXx5ShKN6PJtnz9E5v4K59/MUr301ydISXsDSiaNUOiX2YxyPEFBYxxCl5KoCWxWBYi5AKr2jGmKGmUD93ozfgnFCI2XwbXEu4GOsF7UxnEPX/ixZpDk11+DUfBMENGVopbhanExJSRTHDIoSCG67B5uSygYfkkgJjBfET+GdYqylqELFKYk0WiluXWhzw0zGE9s5mRY0I4EUkk6dE5RVQLEKaxEu4MmsFxgkEo8SDukNB1TMZ/760zRvOB4YKnWSQX/AwVQwVX9HQvIdPINMjekZi6bVKqMieMAMTMl8FjEwQf6duvAWK8FmbkbfwVas6VqJ8zFKSDIdXH6HNHdPuPeF80hhaCQR3cKip+dZ7RQ0Ek070TvmAWfsrvcxeA5ZnLVXFDr7eggVHuD1PabbTzy+qjFMMO57/0fYXlklm53htm9/Nd/0g2/k9Cc/d3UtExcKy7YymPzKMuhPF9OHFzl460089ql7QIqg+qfGk+XDn/gsH/m1dxGlCX/3nj8hbmYhKVIKruY6pSDOGiNxs8vjL3/lHVx6bJlqsDNxMoOCf33ydZy465t4w7/4p7zsB7/7msd4PUIIwakDLZyHjUGFMwGl34r0DuS7EIJEemYih/IV0obJzlQVPooZUkXvnNekkWC555EIWonm2FQa/DkcdCuYjhm1M84P7CjpGJ0LwXZuWUgCA2EkK85uUJ0MW23ObFecFxmDP/8EC/NTzN94hKTZoDU/h6oxERlBMtv4oMo6xAvEErwL+grB6CwsOEJAGmuiWLOBYqVnMI9vESlJOw6pyfrA7G6hGUs3bnBcQpLGyFaKG/RR8ZjK6hmOeSy6Zn3w5xBUuIAuYdiO8YQKQEiKAsBz4M2IfSMImjOmrLj9FS+keep7iRopkdZBT0KE6sR2YcligZZqhLMBcFWFKfJR2wtCiX7pwOyoSnawlbBSg5SHC92eCA0/xD2MWwzeM3JdbScKvOCTy9soAQcampvn0lABUAqtdcAoiEBtHp5DKxkYVoQEsZXuLYbXywvyyo7GlleWJFK00oQ0UpyYbTAoql3vkhACYy3KGzxQ+GiU9Jr6ScSuZOXcCnpznbWVVdqnbkIlKVWnywte8xLadRtjeO4AhA3Jx9BV2ePReERt+BYpQaolA2Npx7pOkEOlq1M5tgpDKw6slqlEjvAznbxkc5iIOU9hLUoI5hsRUkCvqNgYVIEi32jRKQ2dwtBLNEtT6fg576FYEp6tqHWPvn4TDynE2IbgOh7z+RLfEInHZ//T+/jrt79n9EJ3Vy/x2d/9YwadLnn3StSu3WGKAluZa2avIAXVoGD10bPk253RP9vL/Fze+z/829EMoSIdGAQ8Pc1WSMn00iKHbzs1ArJdvvic/vQ9VIMrMGacZ/me+3j3j7+Fopfz6h/7gWc6wusarTTm9oNtOoOK9X5JbgzdytGvxr0NiWehoYh8GaTOCV8wHWmKssTWyUffanQccWMcFudkwvp8iLFYLwVNFSzVwznGbqMQchLnPVuFJ8tEUPYc1aHH+2sPbA5KPvTgGpv9EmtSVpqHUQ+f5dSHPso/fM+vjozGhkWWREliGO0qIzWkpUoULqAyakomztGIFPee7/DY5vhZVtZxqVdy8kC2q3WF93ghKJEjhgF4dJYyIaVW/6ofL0Y+/H29l3OoHWNFwDcMqxvKO5RSpHXLIoskZzaDYNdwojN5zlxTM33kCCrL0EqSRvX9F6F95H0QANPx8BnWwmMmVBeGX7lhCyfWGm8qRBQz00yYTgZs5YZuaWlFgkxY+l6GikwN0xwWRjqlIdVqJPZWOU+kQgKVm4CncR6e7FV4a7hlNqIsPQUCrzRbhSPTIQGLlQh6Lt4Ta0XW2Fv1s6hMcLW9rKWVlxYtq9oLZZgU7V4gvPdEcUonL0ZJB6M3TlCIBKl63Py6V/Lwhz/Bo2/7LSpreflP/DfMTDXDu3pZl18LSW5tDYoKKfrQX0WLQOmcayjObJV0S0crDlif7dzSryyJlpxaaNEe6aYEnRyJINWC3IbqkiMkG6v9isOtiFlZklpDQ0CBpO8lHkmvNGzn1UjFWIiAb9p1L/Bf9xiPr/f4un96zjm++P6/2vVlFlJy/4f/GvdMlEb9swOUCinJuz3WHjn71L/o3IheYY2hGgyurGQ6EUdfchtCS87c+2Xe+c9+ig/++99ka2un30NvfeMpjyG1xlaGv/qVdzwvOPWNOGJxOuOGuSabpSev3Hji9p7ZVNNWtl68xterpERFEZdKxXIecakKwFRdL6g7sjgRGC7WQ9fAVsXEXsvXi8Vk5aM+h59ULp18vzyfO7fFdmGQMgiERUmCuOVWoh96U+jqjWgCjBec+v+1DB4igREgqDxsDSydwoIJu95OYTgzmXS4AIbcKh2nL+XoWrLc+zFoVkhJLCYooL3eSML88qUuQF48zoX+/EJDjUzvnPf1Z0RtwDY+ppaCE7MNWpGiGSlasWQhFtwQO2SW4WAsAscYTwOMqh9Qy6V7T2UqrKuTARcAsiO8Tu3nI4TgzsPTHJ5KyaQn8zkJFVOUJBiEt0GiXQTHX28NC5livqGZTxVzqSJVEus8W6UdPdNYjqs9wyaUMCXWOpY7FWe3Sx7ZLDizWdAvSrYGV9byySuzJ4tECCiqMI6nolB67xmUhkqMxcqGTQyJQwlHOtXEO8eRl94R3tdmi4Mnbwh4nStslko7kezUFbXSQW6hcAEge6SdhMpHZSmMZ64RIYTktoU2J2az0f1aH1R4F5LW0o5N7ATQiBTGeS5sDxCmIBaOCEdTVszSx+Pw3rPdL7D1HCsnRRUn7oOU6uu62gFjAbHr+ec6d26eVXzdVzyKTpftC6t7/mzt0bPIaLdIz1cqvLFUZvAMPwR7F/N3RjY7zeYTFzFlSWNujrWHH2P1odOc+/yX+JFf+1lata5HY2oKeOKKx1FRhAC2zq9QDXLi7Mq+DV/NaCQRdx2d4aG1Ht2yAh8kzo+0NMIVu/1xpETjaWuPsWFCjxS0I4VSkkFVYxLq2yokSOeRBHXRSIGphiX5cSVDCcF8Ev4lkZ7CGUy9TCslazVTeMWxaU4tNPnM45s8dnYNqcP2+uAtJyh7fWLVRGpN6QR2yOyo++95ndsKAec7ReilizDprvYFc6liM68wNdLfOk/hxolMt3IcbkQMKvv/s/fe0ZZld33nZ+99ws0vVr1KnbvVUieFloQFii1ZZizApFmAESZ6gGHAxpq1ANlj0jJmEAzGTAC0YLDFMGoLaUAghARCslBsqVtqdanVuSuHl++76YQd5o+9z7n3VVVHVYtSWb9er6vq3XvPPWefsH/79/sGz7rAMzvAsSymk6OzDoyhRCAiUVN2hagwJgaslxdP1HQMphuYtmVmQwnBQjNipZv6lxYOcfhRRRsvrtUR08pFdZyxmDoNe4CuIC8NxvqKyNYooyh9u22h40+AkNPJR0rJ9Xs6TPo52gQlVgldDI/0czZyP6HOJZJblxIK4dC+gUUiBbG1nNouplUfPAYot9P2DUC/FPQLV2NRBF44bSOD+fRJRASf5PatJlcpJUp59dVdWAdrfbIqLL7R4cKeW1KC54qQJC1FdGgPp7XFCIlVirLQ/jyHtt5shllow2IzMMXCuCvp0MYnsuPSoqRPHtPIJwH9ccHRfo6ScGCuUV+34Gnmsyqus/OckgKcoLCOjUKwtyawCCI0LQrGpGjnGEwyeq0mKoo95mSG1SIjhYou3Mq6nEKeh/a+SNu8ROKyTzw2jp/m1P0Pkw+GCCFpznXYe8O1geIopjPPJRsulByfYj+lpMgzlq69ChU0FoQQbDxyhLve9X7u+AHfNjlw642cvPdLT7iZ3r5lds6so6J4l7HYpRDNNOaFB6fKrM451vsDnN29Yq8R8NaDAlfaya572AENhWezOBvaDH6lZoylzEtaKqI0dpcuAkA3ETU91jrPkNnINS3pmO944zRtvWz7fCPmlVcvcubEGmY05vUvv55rDiyysDxXA1ZF5RPigihY+Cr/4DdsZeUUSxJaP5uZIZUiVB78e2eZNVL4FSZAYQylhYaUHGxGLJUa5+D+ETw2sYzXT9JIE563p8WL9ndBSITwFFmTl2Sl5uxEc3ChhTqnPlqtuH2y4qsDLuAoEiVpSt+n/8LqiLMkHAhYCtGMOB+B4cGfPknyiU9hDFsTy05/gDGuzr/745Kmsiys7F402GKCOScB/cxqwdDIcIYFvcTbzrdjQYr1yYdwlM6ykDhGutqbgN/Y3a1iUPqDFW6aoCEEpXsiRIIPpYTH6pxz2NbBJDecHQ85NNegFYCp/roLyY30GjAimNU5PJOqSXVtThsvnYUOy9qglpeZjCYMs4IiikiEPO8eiCNZt/yw06RPSVgba7TzbCgvFOfft9BKODny2I5YCB5ZH9DPDODIjCOdYSi5eq/Cn8JhhMRUsgT1zghiYWq6srGQ5QWtZoMoSnAqnmHeXDqT53MZSordNPyLEF8TEPsKxekHH+X/eNMPkvUHnoYlYOfsBtlowhUvuokDN9/Aw393afudCKmeltvteMO3UE597jDd/SssXXtl+Lzk7P0PY63lb37993j8E/dMgQXn5DJJp401jslWn3o2uYSjorgWRpKc0wu2gSniVHLeYQh8N0thUYoAmPTjoaRgdGaNL77n/QzOrqHm5rnija9l6YZrWGpGRJFiaCyDSUktNQqkaQo4nPX010qMrJ0obr5hP+zrcnDPHAshOfGlcq9imYcJeXtiaKcKGVagO7m54IPWWkcjjZFCYkLyQdgTzzDwAMhWErHcimlGknai0I8/ihCCw0PH4aHXNxlrSWY1WxPNQxsZC63Yj4OAtMjYOHGWxWsOUVhL4wLG2s7hHWTxANnqkmpIgzUOkKwNNVuZQcqc/d2GB2Fe4Hw6B5Hyx5HEinGu+aOPHcOWJd9wwzzLnZjSOh5bnfDh+87wW9ddvevcmhnfHPAJ2sjsftxWEicTbeikkd8PJ3BSEM9Ubxw+KehE/pqYBLxIZnVgw+8eCW+7JjHGoC7QBmglMYX2KqbVR0vj2BgXfPrEDtp5psjVCy1ec+0SKfDIAydZeeFtDCY5OEvFnfKVN3/V4kQ9g4f0lPbSHK962y9grGV9XFA4SWFsMIfz15Y2hkY8WzGaORbnMRqj0tEKarSF9q28SApiKegkkvtXB2SzVhIOtLE1fsq4mbG0jtJ624NevLuG6+9HQRxBI3zfbAJZtYH+W4rngNRySQ3hZZl4ZIMhKo7405/5VSb9AXEjpcwLrPZI/2I0ptFt831/8DZ+6ZY3/n3v7pOGCCX2mupgq8z/Cfq21jE4eYbB6bO0lxdpLsxRZhm/cNPrWX/0mP+McyAFUZqgyxKMJW43WbjyADunVwGYbO/wxz/6Vt789l/9yh7wU0SuDVlpacS+/NtqtTFOsLO9RcvPIui8wKZNxiIhuoDLZ5VTCaEw1YodgRNQjDMGx09y9CMfxwXDr4277+G1//JH6L7m6zzIUUnSSLI5yrABrOmAUenPRyQ95XeU+55JO41ZbC8gpKCVemfZCvAIXhBtc6KZaMegNHRjRTeR5/W3pwcgyLTh6sUmj22O68m2ojUutxNcwGFEMmK+GbOv1yDd8yKO3HOYR3YmaKcYq4QoyNMvtVNybdHG+9to65gQsXLlPuJmylahWVGewji7otXGksZRjScRAhSObjp9tJQOdnLN6Z2cXhphbYzGhspGNWn5VouxjnYj8uqlQvPwyQHjQnPfiSG9ZkShLVlpGY/G3Htkkxdfu1R/j4xTmIzqfXt8p4JKzlw/oY1V3TpuZkJ0swm58AJj+1qKUkSUYb1Y0ZrP7TA5HHONyHukPIHTbq+RMMq9j4pz8PjWhC+uDqgsi4yFB1cHdFPF7YcWMMY/r4SuwOciOLp6XMc045xJPsRUoE1KRa8R+8TW+eTCOd8Ka8W1Csj5FQrh2VVzjYgozH6N2CcfmYY9raCSKm2tWXJ2kHFsOwMHN+7p0Eqj2lwPB6PA5mnHgrai/u7qe3MR01GztZuvxeUcl1Xi8cjHPsOn/vO7WX3kKFEcc/Tu+6juuLiR4myMsxYhJWmrRWdxngM3P49HNrY8W+UrFZUf9NOIWjJ9VmxqZpX7xB90jNa3KCc5973rfQxWN2C2cmIdQikWD67Q3ruAnpSM17eYbPVreu+9f/E3vPkZHtpzFcZaVgd5ECNydfk3lV4fQHYWyKxmePQYo7VNui+6DSf98vlcFI/WJVJFM4mbH1iBp2+mvS4HXnILJ++61w93UfK5//dPufbVL6d6JCopmGsk7Iy9uVk1+ftWi9eTmGsIxqUhLw2tWJAMh8gVb2ohsCjnAXyD3FAEyoV1MCo0S82EphKMit0sBxfOfSeN2ddV7O8mPLA2YlxY2omimUT+0gqA0ShgOwrjaMSC4cEr2TlzsmZvNCLJQnMqf15aL4RlESAj4nZCN5EMSsnpiaOjNK3YT27bhSVREZ3UT3hpJIiFryTN6i+0Q/KnLdx1fIeD3RQXiSDMVWl3+E+UFrJC04gVzTjaha/YCeBNGRgU5pzEO4oinEqx5QTnxK6Uo2rGnRo7FlNHLAWltlipUMHGONWWlUYJUtJpxBzqNSizCYXzQFuAZiwZ5KYuWzsczjo6aUQ7fXK8WBRFzAUhsE8c2eTeMwN6acShVoxzsDbOGRSW+1eH3H5oYfo5ZyhmakS+cSRBGE/rVh4/VOGRnLA0lMeozKdJUHH1aqSJkjQiUScEs+JgIvwO50hjuUtpVQBp5Mt468Ocvd0GzcSPwtqw4IHVUZ043ntqh/1zDVY6CY1YMSoN1nln2j1p1WaZNupGJJR4l96wdiBSEq01X1odcWRzjJBwaK7JLfvn6mTocg7FxafTfk1A7DmIk4cf5P3/7n+nzLw+hS4KyizDFJqk1fBVAikRUlJmOV/84Ef56blbKfMc8zQYIxc1rBcSM6V+5hiTZ/J2a3HOMngCcG05GrP56DE2Hz226/cCv4IqRk9tVPeVivVh4c2qAi7HOstYg1befl0JgYwk89deTXzFlWxMDLEyxEoSC2+ABtOHM7i6klQXhOsVpKXR6yLCilwI2Hj0GP0Tp+kdOlDvU6wk7TQOueAUiFrhHZqR90l5/Q3LFKMxW/1NTFkSBQwOAlLhao0SFZAI3UgQCcFSQzLIBcXsytr5Vk4r9g/9Zhxx60qXM8PC+2qISoYcmpEk145BYUhUwSMbYx5aH4Ko2DN+X1UYm2YsWWhECAGldkwCGjFWkm4s6ReOoYFhBX5F4Qysjkt6jdgnCrpkHFhBiYBWBDcsxBzrx2xPNMutiCQ4v5Zuuur2bS6fVBTGMso1vWbCCw7Nce+RrRoYKRBICXNNxYuuWTzvOilkwoPbI65oRxzsRGxtTzE6Ai8U92DfcnVH0IgT4kiFCRMWG4pmJJhYuGGvTxAHwjEel3islaCT+mttUjpKa4mkpNuU7O+mOOfOk3mflJpH1keU2lDBVDqpYifLuWVvh14a1S2JvZ2E04OcU4PdGjtKCVLjkw9bN1Sq/1x9v4Jvdw21mFFP9QPXDBUOYx2lcaFLaMHJ88oL0lkEktL6BDKSlcgYbE40Skma8bSFdWRzQgVPEnjRu1OmRZaGAAAgAElEQVT9jNM7Oa+6ZoG97aCn4yyFiFklooVnE46JMagAma3uK0GpNcNJTkMYCq05NSg5ujnmyOaYb7p5/2WffHgs7kVOPC6hIbtsEo/Pvfv9ddJRRWtxjv7JVYw2qADaKrMcneVYY6b11qeBodjVlLwIYUqNkAJnLuJGLxDlcPyMP2OtRUhBZ2n+qd/8FQhjXe2Q6azdpauhLaRhAnXhAdxLJIkSnBlbisILgS2kCoEliiLGm9uknRbEMYjdZXGrvSTT1uPHEcGDBRxSSWQcz5gQ++8SKmhaGIuTMvTKfY1ZV5V+IYg7bdJOm52zGywcXPHnHhDGsNCOyV0FWMSDLIVf9V3Vi1mfGMZBtr3ZUN6/hdB1wycQvVSFdoWvIqRqCsSblJYsthzbHntWSCthlE/qFdC4NByca7DYiL2VPX51m2pLJKqxlqTKK3QSyvYe1+GrFOPSIpxlWFDXO8b4JGW5kfCqK9q84lCHfZ00ON/6/e8XFaCUXZN2GYTgvufV13Js7TDb46I+HiUFr3pem+hctCvwwPqI1QmcnWhSJQge9TMtHS9x3tcRHSRSh8Q18scVK0VcWi9QJSWJUghR7ioNNJOIxaav0jQi6SnToQpX6Us451gb5pzayQKd0T9AdKhw7W2ltBJVFRjIjSWRgusXW+xtJZzeGSNTzyiTcUJsJ0TOBV0SvNAXDpK0TnwLA+u5ADl7TfvvNdaPm3G+lSZxSGv8luqWlwNj2BhrNktPiVXCJ0r72xEWQSNWXDnfqCtVhApd/W1C1B4u1lr6WclKJ6WfFyTKm+sZBFY1du0h4DV05FS7I00i9sWKPe2Ydx1eJzNwsj/h8OkdXnTwwgKJl0tIycUXu3hup5pnFJdN4tEP2ITZWL7mSvLhGJ3lQISzFlOWqDiU2cMVL5XyiciTxcU+ac4DEZ9tpJ02+TMQP3tG4RxSRbz+p3/4udn+M4xK5wKYajjUr02BWHUOJwSxhKWGZDOH0jmckvQ3t7jp2iuZiyWPP3yM9t4lL9ZQLwUEJi/YevQIW0eOB/VA/2V7bryOuf17w7tcKPVbJH4VXaHzbK0r7ZOiaGYS6F15kP6xk6w+coy5A3uJUsWYCOMk6YxJmwtiXwqLiCT7O+E1qRgVhkFhQDuSyM+rBAyFEN5vpFoMauPVJQvjWB0VFAHEetVik0mh2ZyUHJpr0WvEaOvYzg1zqQrYC+edeYO+gg4T67ksDyU9iLc0lqGVOBFaAUHnQzvYKi2LzQSlpG9RhG0o4YGGQ+3bAtXtUE2k1lqu3tvlV77vJfz5Z45zemtCrxnzxhcfYPvkw09wrUz/nht/cRSFJlE+U4ql5Ialpl9rCFGDGWex1s1YUBYFaaNBGkfESnrl1plV9rh0dFPp8RzOBSrs1FV3bZizMSq82RcgpCBGEAkvPZ5GMxgLAa1I1olVK1GUxhH3lvjCyU1KY1mMYX/LofBmcEoK0qRBpBqo2IOJT6wPcJwrE1ZfsbXJIYQ5TQDOIGbo22fHmlMjb/ZXjeWgsBTactV8AxkqYEr4JMZaX+Wpqsa7gLfCs8e0s7RiFe5Tz8bKtPEmiTN7uDa2LLUFiCm1GyFQSvCmGxd49/2bWCc42R9f/onHcwGovYRKHpdN4tFZWuDsOb+TUnLw1udz3Stfyvbx08TNBne/633orMCUM8JhF+N8PJuKyJdB5S0mGUKpi+KQe6F43U/9IHf81A8+J9t+OmHLHFsWOGvQTmIt03KumA53JY5Tt08grMglrdgbyo1Kw/owR1gYZQWD0pIuzPlKmCgRkcdFCGuJE8UDf/m3QAWrcTTnurzix74v/G4qlZ3ajMwqnIhqpc9p9mDPg/EIYOGqg2TaMjLT09+Y0cjwxzfV9pBB3wJ8FWBQmPB3iw1A0Mw4TvYnHOi1QlncT/xrk5IgwBp69JJx6ZOl5+/rUmivgBoryUQ7JmXJpNTsbcekkSJVvqSvtQmdqPNvFIFfzUvhE4d6kRaoOwIorYQooCzO2YTf3ykXxTq/YK+8UQAWOin/7HXX7/rc3U8gRXPFXIP10awSsPdOcc7RSyO+4Zol2knE4ZNbSOfl8y9A8MJZU1c9FtspW6Pcj3kAbjYTRbeZXlBBUxvLuDAY53Y9FlIJYyNQIrQ6xEwiUJ0kfKshKw25iChKw9lBwVFn+aKSNCNJZvw1MtfIuH4RWiYiDUaKT/Yos9b7GnWdIk4lzoldFGnnHFu5JQs9oapKZIH1iWG5rdleXef05w6zdMO1HoQ6P8dKJ+F4fxKShel4zjViOml03tj669ovKJT01+oj60Ms8PyoE97jk9Fu4hOWdhLVx9ZUoj43X4uvzrhsEo9bv/kNPP7pz59XuZjbt5dv/vl/SZR4TYr7P/BRBtkGzrp6ErkYmWCly7CLI/Yclraeq4Sjik/8wZ18yy//K5LGV94F0pY5OhsjBIw1DEyYlLyAZJjsAAetWNRI/hoqKqZldYSntDqgb9oc2xoz34hIu/4BZ4xlVBQgI1baCctzB/mxP/pNPvtf3sfWiTP09i3x8u/+FgohGIdJ3zmvMRGj2JzkzDW9s6cLD10lvPR0olT9EK72TQX2RiQvnHdWE7C2Aus0KYZSO7ZyzcmRY3+vQS8W9JIpL38wKUk+/nfc94UvQZJw6BteztzLbqcMokuxFOEB7yf6ioLbOge8iRDkxjEsDL1g2OWAEkkrcgwK7ycDUzEiv8IN4l/OeWVYKXdd/o7dgmHnHqtkhhGjfCLTCKt46xwbo5xRYZBAI5bs6TzxNXnNYptHN8bs5LvB4omSvPKaJVqJf+TduNLjzNagxj7MRiSrVpZnu0RKsafXwgRdl1jJJ530CmN9qyiopYIjFv6IKyqxsb6iVOukTosExErSz8qw374qYJygn3s+S9Vi2skND65PuG2lTYFvRVo7SyieDUGq/DFtZYZYCtKG8iJxNdxzqmMC/rwuN2OP53CwMyr43O++g5P3HOa27/sOlm+8jnI45ro9i5heytqoDMmjZzW9+EAXJSW6rlaCxYakUlAaixSKQVaynRvSWNZMGIdv9ewUvgoHcPu+JlfOe3+l0WgEUtBIve/P5RbqMq94qF/4hV/4hQu9YIxhdXWVvXv3Ej3BiT19+jQHDhy44Gtf6Vg4tJ/mXI/1x45x4vBDrD92jGw4Yv7APkxZcuiFL0AIwWOfuIcTX3hg91P/IoiICRFKpc75p+eFNnnpnPcnDwFlXrB54jQv/tZ/9BX/epOPcc5SGseOVl6FNJQUqp64EtBLBO3EtyEm2nB6WKCE3OX2Wk/4QlBa339O5BT/IKU3ADMIRoX1gNBGwlUvvpnnvfrlXPWSW4iSmK1RSW58QhGEujFISgvbuWE+USi88mlhPd4kqYTGQvk7FDYwhpoBKThfUbD6felgWwu6rSbv/dIGuYFeAld0/f1oncNqQ3bsBK3Icfi9H2J06iynP3k30YH9NA/sq71EgDCRe4EzKZyXJQ9jWsmYC+FXwfONqRKUEiL07r0nSQWJquiZCA+4LIzzbBIxVWKt2mDN4Lg7C4SsjmFQGHqxwApBGiuaSUwr9d4lRzbHYAyx8uPnnGN7nFPKGBV7xsYsNkQIL9meaUtuLJEULLVivuHqadIBHk+SKrFLJVRAXTECQRRFuxIMKYV3sn2KB/j2OGdzMlUi89sNrsW2aiUR9n1KKVbCf4exjlz7QTbWy+OXJpjRhc9UURhLrxHRjLymS2mpwcU1rgWfELaTiMw4P+FLQTNSaIJbs/DX85lRRTCHA52YVqzCuRRIo+ktzrH+6BGO/d1nGJ5dx+Q517/sFq5bmWNfJ6WbKK5fanH7wR6xEuQz9+I0/KJvog1r/TEfO9Kn1/IaLQe66fR+EL4ClihfFbHAZq7ZyQ2J8veX1oYkif9ehMWezhz5bLcpWvMI6ZPvi/bjLEz6F3V/n21cVqni/ltu4Og9h9k+cRrhvCbDgx/6GP3TZ+ifWeMbf+bHycbji5JonBvV6nD+igNkOwOK4Rhbea5U4UDGEd09S+y76XpO3PslRmubF31fvqwIS1UpJY99/B6G65t0ls9nDzxX4ZzDao11jsKJXcTMRAkqsL4S0E48DTLXhgc2M5pRcA1xnhYKEKnQL1aCRDkPYlSBumk8iFMynQRK6xjlJWnkaISJylhLYWY8YgDCzZzEEWOjGc1YTkvCJBOwKXGkZqiJgrb0FZNROU1Gdo0BVdXD0U1jFlsxrUigrWU+VeTGURjrv2NtHVsW9JYXuealt/DYXfehIsnWPfeycNvNu+SlBdCIJEst/6DeGJd+hRwwGnGwh5+OuKvxFlXi0YklWnnQYWnBSsEw1wxKiw4TWkMJ9raTkIgH+Xnnze6kcIiQdTkHmfaU3swKLA5bmjpB2BwXJMIzR6qc3p9TRa/TZVwaMm3Y22mQzOAFpJTcfuipgdGNRiX6ZmZyIX8DCKGecSm/GqcT/QwTjOesm6qfFtZ/V+Z8oiaMIw6fUUyVKmcJG+NA89/VxpoJIQSjwrDQjGmlCYXOKK3Ht1TAYCkF7STC4lhpR2xWfcFQhXJS+ftGeFDxqHR0gk7OzNHhoojutVdz3Stfzufe+V7W73+QueUeceJ1YxZbMUvtaRIgBQxzXavoTscJ+plGOMfnTg+98bbzar/ro5KVbjL7tRTGsT4qODmYAozXJoZD3ZhD3YSiKEkvMZXlLzek2IVnvijh7NNWcXjO47Jpkjnn+L3v/HG2jp32E01gOkwGI1YffJwHPvRxtk6f5ZGP3vWclJzidpPFK/ajYkU5yXyZNljeCyXrpV9vZQ9XvORmTF7g9AU0lP++IzzcrTFsPH6Mt73qv+cd/8PPceLe+78yX+9cqChMzcB2v07N6Cidf5gfH5ZkpWVY2NoQzeFvsgoX4pwvbUfSVymC75c/XDG9EUrrGOaajVFGf+JZUqWxFyxgQWj/zOynFPjvUf7JEanghTHTbxHCl9AlU5nxmSOsgauD3JAIx05W8JrrlrllpUMvVeHzvjLhJlloNTm6S/N45oRj7TOfY7S6dj52wTkO9lJedsU8jahymvU6H1lIHrxapQsONFWVydXVjVgJnPVMiWGuWR9mZOMMnRcYbRgVltUArEyUZ1NsTQo+d2rASDsm1jExjp3SkOnAZAjVEWcd64Mc5xzjQqNmQLcijJ3EK5sSzm/Vlng2kSSJVwcOM7G/HhRJmuCsRk9G6PEAPRlh9fnf45xjZ1JwdmfCmZ0Jq4MJxjoybSmNX3gYB2NtGeWenaSNIdfeRwd8u3Cx6SXjtbV1QpyVhu1KuwRq2/lzv78Ze9DxzqTwRoBKspAq5lLFUlMxF3AWzoF1gvlGRDf2zK9YiV16HYc6iR/fcxhDAuEBsc0Gveuu8r+Tkpu+6Q3eQTtUER1BNTi0mWIM52r4lcYxyDT3r/l2SX9SEIUk76HNCacGOWUw7smN5cwg41h/mnRUcXJQkpd+PC+3qAwDL/bPpRKXTcXjwQ9/ks0jJ2sqlnOuVukcbW1z+ksP86n//O7AcLl4FQ8hJQtXHcAUJcONbXSeY4LjrQ2v15LnDrZPnGb7xOmL9v3PZVjr2D55hi/+5YfZOb3KD77jf6M1/9yiyWvvCWdJEJzH26m6WQGdJwTkgXGgnccnVKqZvq3u6wdViXtX6zRQVqxjpvdeQ1RZG+RsZZor55pTFcZzonSO9XHOfDMOgLjZUrybKXefe5zQiDyGpbCOJDycrQNtHJkxxFJ6C3ZtMXjRMCmpfTMEkFdtFCnYOnW29jlxuqR9+gTxVYdqSfZECfa00+DlEnHTSpf7zw6YlB40aZwDJzjYjrwTrZgapDn8xDcqLaOiZH9ssS5iY72PmVmaWa2RUcREJoyCEFiCl94+MJeSaYcQUyCUxVFoM61YhD7ETpbXic/sua/+lE4gncMKjxV4tiGlpNFoeDXR4MsjpcRpjc5Gns2Dr4hpo4mSBjKpHc7oTwpGha7PubbOY4qcq8XurHXk1p/vXupZMs46BrmGRNJLI3AOi639eqTwLcD5JmxNSqTw5yaeKZFV8vitSFLY0CbDeaGvsD8GX3Hx53d63NV1Fp1TcmunEVfMWYbZdDKXgtA29G0hOT+PFPDqf/UjzF91yNPOq2veee8bfwc6ruolKOGZOKcnltw4RrlmbVKQWUeaRLz24Bx5aViblCAEj2xkPLqRBYdmn+Duae+u3Hk8i+X0uOS61lceh/a1+PLiKROPw4cPP+nrd99990XbmS8nvvjBD1OWJZ6manfLiTvonzjLn/3rX58umZ9BiFjiygs/3Jy1bB09VZdZz12lPx2flUs2rKWYTLDWcuyLD/LuX/s/uek7LrLEvJDIKKJ/9BQnPvZZnLW88Sd/gG6viZKOBobMRTNU0wrxPltlEPVrp4clpXW0Q2+6xGsn2NDzVjMQAwtgvTZCLEE4R6Z92hEJ3wY5urbD+mMP0F7cQ6N9ftKVTTLGueHE9oQDvQaNeKrjoK1DRrONi90TqZR+JSsBYxxr44JGHNWaFgLf5ukbn2jFSjAqoammqP+000ZnOdsnznDqvof8kApBs93k5td8HUm3yTDXXtBM+UQty/L6vl0UMROVog0kruDq5TliYhASi6jpjzaUvEttWWlHJEYzOn4WLdLdxivCV1CMNgxLX0nIBcTCkMbnP26kEBTaC70ZY9nOvFLFTn8HJSy9uYVd77faYLXGFAX3/ekHuOobX4eTcPqRC4vkPds4sH8faaNJ3eMxBViN3t7i+NmNcKySzp5959XEhYpJlGA78+NuHXQSyXIrqfEZTsBSW1GG1fpY+0RFSj/eqYL5NEbJBOsajLOS9e0+m6MxWkREKqIbw0oSs7EzqKtmvU4ThyIS0/vC4jBu2qxxeJl4YaY09SopETgWGzFziZdFl7KiwPrPOQFnjp7iqpfexqGXvbDWo5mymaaXg3Suxhd1E8/IObyRo5Tk0FyT5dJwZpgxl8aIUJXp55oiMFYEsDny4nTj0pIotas6o6TEWLjnyFkWIkfabAEWrEEXJZPhdtDl+eqLXQuki7bRi7y9LyOeMvG45ZZbggHW+XH33Xdz++23X/Sdejaxv9njC7//bnLrQlXjAvEsV0bOWGQUPeFFXCUXT+kg+1UYTls0BeQli43WRTvfzjm2xgVFWWC2tlm4vsk1z78BV2Q4YzBOYIUkVdJXIvCgOyG90VcFZrQO9rRjjIPtTOOAsyONcyXWOVbaCQtNGSifUyxOtQ/GOtqJJNOu9vFwECiLkCQpK9e+gEPzTTZHGZPSVJpUNOOI5W6DjWKDndww3hzz/L2eLWMDeNC6C+M4AEojqBb62jhf0QgTN/jESFtfiZgEF9tYCjZywd5GUNJcWebU4Yf5yO/9l7rMreKY29787TTnOjgHvUZMEcr3AkGcSp5/y62sDnJKa5l3PolZbCX0mt6/pyxLhqVvPVnnmTCNCDpBdVVLxbEP/x3NV7/Wg6lDSOVNDT1VuSrvOgJxdRfTRVT/l4Kzw4J+PqUgK2L2d5Nan0UIMEWBKUqcgzP33s8X7vxzHv3Ip/j23/xfOHQRn0MbgzHC6N1PfhkhnWa+1WJ5/yGkipgUviVXgfeqcM6xtTWhtD5BVAIWm3HtQkyYVKyDRqTQ1pIFgTiHPxe9NJlqWQC9JGKulXJTHNFp+ARGScGp/hgdsobCGNLIt1pmx9qrj/p2TCy9NT0ESrYT7OTaa4sEgS/PaPL4KBeSTodDG8vauGR57yJ7v/87iNMEGXkps0pPRwAy2C4KsdttaKhBKUUa9r2bRqECBA0Vcd2i4oHVIUZbxlnJIAxUYTxQWFuFUIEFha/GjbWl1e6QNuLas1FJ6PYky3v3Mt9KLyg0dzEiz/OnXJg/2xCIi+4may+hzOOyabUcuOkGrn75C3nkY59B58VFBZBKFZE0G+i88Nv+byyctYy3+rSXF576zU8ztsYFZZET49hSKWWn6SfrZg8nDF1nkCJCS7/KUQAyMDIQ5MZPyEIIYqXY2/Y96FODPIBjBd1YcmJYstyKdzEBgLCC946dpbHkXrAUqJgmvgWiQrtBCMFiu8GZ/pCxcSRAXpac2LHe5VRoMusYl14bo1rpFcbSCIyn2T3QQROiwqv4lbG/ZoXwx6mtq5VCbXjd+5N4imJhQAjFlXe8kleIiJP3PUjUSLn+v3stbnEP2tiAkfBsHuOm7q2n+lndYnLOV1zODPIw4UhKoRiUJdp6sSoPvlUeixH2c3Rmjc2//CB7/uEdXlOm1ESdFlJKIhzN2GNF/HwwbUVUxy3wk4Qxlu3c1mMPviJwalDQkNBKvfmazrzR49ajRzj6yc9y67e8geH6Fvf+P+/h0M/9TxflurTWsjkughAcpErQCyJzlghtNRECYy1b44KdbKrXEUtIY+VxRM7TuEvrSIRAiqraMK0OVNhVa0U9LuAFxOoqXvV+4XVYcmPpMpVmj6SsMQ7j0niF3tmsY2ZMC2MZl64GEY9K6zVPQlXHOctcK0W6kk4jYlJadNAGMc7x2OaIZqK44cXPJ1GKcndR2Suw6hJhDe1I1owtIfyab2ygHatp1Qef7Ihwv4LgeXu7bI0L7j0zqNk/o8LQiD1rx5opZqsRSZSKKIyvsMnYu8wZ6481RTApNN3mVx/w9LmoeFxCEI/LJ/EA+NH3/A7v+JGf5fPv+SuK8eRJ3ysqaeOn0QqJmk1kpLDjzP+pL3Ew02xt/2KEAxkpmgsXB99hnSMrDQ0Ja0PNxMlQGvYT7I5ViMywt2WQ1mBVcD8NK6CSgEcQofwbet/zDa97XRivIyCAjWzMAxsTblhsEkuvRGrChC7wlZNMu1mDT7+PTP1MDs03GWcZx3emSac2gLZorYmiiG4U0QUy42id4xU20RaBI5F+rWYslFbUIFiFZxPkpmr/sGsiqtaN1WRdWIt0vupi8JWcG+54BVe85hVBY8QyLi2r45K5NKKTeEdZ6xz9zEuRp1HkqbSzfX/g9E5eq2oaggx4hbsJe5Bpy0Q7rv++72bzkSPos2cYO4mIIjrtFpQFi70W0cyTzhhLhvAiZFXbDLDGcnaocTOJ2ex5ODvRXN+IefQjn+LkPfeR7Qy58Y6v54Xf8g9xQaekmGRkeUnjKUzanixOPnKUs8fPYpaXkXNz9eQ41paJcaw0FQiBEQohJUe3RgwzzaS0ATcuKAzkxiCFd2JNIkkC4KbCXjVQduYgh0XJ5lizU3gZ2rk9bWphk5n+nCejeEZTpCSuzGlTkDlwQtaVqciJXbe+w1/z1nkW0cmhB++OipBUCq9U6ixkw4INKVgsLAfakZe9zw07eclKp8FiK6awkER+AWBC0jopDaeGJbk2KByxEiw3FftaCokgCw4Vvoqyu+0opQfY6lDBXGwl3LLSwTiP39qZlCwlhsWmYL3w1YAkUjSiqvbhGTxtaRlZX30yFohAf5W2uqXYzW66KHEJJR6XDasFIE5Tfugdv8mrfuyfPvlZkwKVxKTdNs25LvJJOM1CCtJOk1ve9LoLYjguybjIu5i0muy78TryneFF2Z61vuesnWNipyu/KoSAkZUY7XDOeG0C/4pPTqqWWZi5HV5bQiB8SboR1yvGOCQzezoNhv0NGpHyvWHnV0ZCTFsh1k1/qvPcDO8/sXOBSpcQdNJo101krENKhRD+oRhLwYFeg6uXeqRJjEZiQwVCCa/YnsRTnQyfFJ3rSLu7RWEtFFbQEpZETE+2DJPSuPSiUKWBzcywMSnr1o83DxO1Nfu5oZ3HlXg68u5ll8VPBMPCe7a09yzR2b9Cq90iWl9j7a/+muzez9N1mrjbDpoS/rOFtRzZmvjqUfVdxnFyJ+fkIKvfWOc4/nTX+z3Z3uGhD3+CQy96AZ09iz7pCIPTmu9RPsvFgHOOj7/zLzhx7AxFo0EZp1MfEnxrLtOWnYDxknHKsCgZZZpB7sdZhATYOhdaU14qvNpGURpmeVFVdUAbmJSGDzy8yf1rI/q5ZqL9NvJAd6raF9P99cmHGW6Rj3bQxtKWjsSVxML7wFQJa50uOp/8Kim8X0p4NVKCRLjpeQqVttI6NsYl65lhLok40El53nKb5Y5v/0hgWHg12zLs78lBQR5uKCMEpYUzY81GSHQrtlml0CrC2IpQEZFCkESSduy1Sva0E9JIcm1H8PqDETcsNEgiyd6mYrHhmTpzqTf1E0LQihxN5egpn7zNnr+vxaUXl1XFo4prX/ESPvWf3s1oY/v8F4X3cCmzjHw4BiFIWk2SVpOdM+f4vQhBc67H/DWH+IE//A2ElNz3F3/LeHN7+kQ9p6x5ycTF2icBZZZx4r4HWHvs+EXZpJKewqfNE6ksemyD1hqbF9CUkFTl0sqcyn92UlqcqNwzxXmHrSTs5CVprBj3tzl44CDzLUkSKQaTnLzU4CxF5dhZf4vX/tjfa1AUxRMPpxAkEjJb/dNfED1RIGyO1I7J9pCRipmfX2C+mTApNP1JXgPlXJhofAncEAtLBOQWtgt3Xm9WCEAqjo00G5OC5Yaim8gwpr6K4/1ABL1E0U0VhTHs5I7CeqZNph2xdDU8Y/YBrd3uFbl1wQvH+vFG+ARpbZiT7F2h2+sQz3VptxKuf/ltdPbtRVvHIDfk1hELL9620EwZ5oYkkuTa8ujmqPb48N4o52PJGpEkiSNe/u1v5PB7/oo91111HlYkabc8oNUY753yDOJLf/0xbLuNkBKbNlDBgG/XbS0EE+09XBYaKSd3JmTaYpwlmTVkEz6RyY3H5JwdZSw2Y8DjKObTuB5XEwRnjm2NSeKgrDspaSeKQa7ppr5lE89kHj4Jk9g8Y6MUGNkkQgSQsmKx6VgdaSKlaUayriBmup6GUQqW2wnaOtbHJaW1oZU4HU9jvSni1kQT4djOLd3UX0el9SDj49sZ+7oxnSRmkGlyY2euIc9WUgg2M0sqBY/vlOztNAJwW9SJmHVuF3Nkr0sAACAASURBVFPHV2C8WNhKbFhMDCMt2Ta+4iSFX6iU1rca59KIduRoxX57sYTYTu+ZxoyGiDsnob+U4++z1fIrv/Ir3HvvvQgheOtb38ptt91Wv3bHHXewb9+++j779V//dVZWVp7xvlwWiUeZZRhtaHTaANz2zW/grj/6Mx7+u7uYbPd3MVyWrr6CH//T3wPgE3/4LraOneKql76QN7zlR3jfL/0W//X/+iOK4RgZKdqL83T3LHLlHV+HlJLv/u1fREWKRz9+D6uPPIbTfikupNjNornMwhmLMQV/+x9+n8WrDvC6//GffVnbE0LQSmPKrKxFpmYzJZ17DQgjDUc2J2yMdtg73+aqQ8tAUCEF1ocFhbG0ksjLTwfti04jOBEbx+ZEo43lnuObLO+/kv4oY9N5a/D9cw2cEEHPwFcIZm/OWEAsLPYpCoOV8yj4h1tqMiSFV8GszONcycb6Oo25RbppxCgvaylpL54EEZbl1KJq6KVjPobHh64WUhP4Xv92CbkVtNOYiQNbwqQoiKXCWsvO2hZxJDl07X5GpfHATevpld5a3tOPnfF6IOdU9XdNvJbpQ1s7x8NrIx7fGNfqmr1GxK3793Dj9VfQa0RhH70B29rYU8tbScSw0KEVFDAzacTZ7SFOGzTl7sTD+QnmYCdGSkHa6/INP/JdqCSu7zUHJK0G3f17n3UV+cTDR1i49abQcg3btb4M56+FKR6hUi0V+InvgroIgoBZkOTBnG+5mfgKgXXMNyOEkGijOTPMOdLPaQZVvG4a0U4Ua2PNqLS0Ysn+TuwVZkOFTkWC/sSbOHrm1nS8SmtZbkVBXl7UoFyYVs1KbTEI5hLBvmbKeqY5O9Jo6zUzwN+f1jnWx5rj/cwrohrHFb2UxXZMoiRL7ZhGSLoKay84oTsHuYH/eso7ZLdTVYOTq8RICEksdq+TpBAYY+hFXmxnaP0FOnM6UCKw1aw367MYVHg5BqwStb/Lw6s7jAqf6jQjyYH5Br3GpY37kM8BuPTp9Fruuusujh49yp133smjjz7KW9/6Vu68885d73n7299Ou93+svbkqzrx2Dp5hr/9rT/goQ9/knF/QHthjpd+9zfx6h99M2/6+X/BX7/tdzn1xYfYObuOlJJ/8P3fwbf9+5/BWsud/+IXue8vPoQtNcVowqt+7Ht407/5SZw2fOI//QmT/oBiNGHfa5/HdW98JY98/LN09y7x5t/99xx+/4f54Nt+jzMPPOrt7YF8MsHk5bS2+dWWh8ze1bPVHEJJNOiRvP+Xf/vLTjzAG0ht65h2VDAsvRurcA6jNbosSYXhpFE8MhYgYjb7JVtsc+uBOV8S1h74hhBMSoOS3pp9WBoi5cvIZ4YeQDnf8Oqf84lACl9XLoTjvpPbZE7ygqUm7ViSS1dXYSIlSCPBuLQsJk+CHXBQzLSRG5GkITIPxpxZASoh6AjHye0+O40W3VgyyDRCenM1AfQiG1QuRTgFglQ59jXgVOhGxErQTSSl9ZPBsDR+hSoEwsGn//jdmI98BKc1N7/xVVz9w9/FDinVKnQXjiKsqqzzVRcVxEqUmLr9Vpexn2gdp/oZD66OauCgw5f37znR50AvxVoPaCWsznuJYqeYmaRnykrdVsJ6ljJ58GEe/6u/4vZf/Fk/lsInLoe6CY04oiw1kVK88JvuYGNtk3xniNGaRqdNc2HOT3pCPGOlUQCdZfXfxWSCabZRceT7WUGE0DnfckuUYpyXtCOJFB4QGqBGNRunQh1oY4lCWwLhBbkMsDEJzKtB7s9xGOB2rLyeB/58jApbm9Ltace0koheI2JjlIMgSJnPHIioZO8dO4X1ujWiAizvrih0lKCXer37biRZhWBlL2sK7bicaopk2vKS/V3PdqnOXSIQTmDwY+OcDknZTOtPQCuCWxcTvrCR81i/4DVXNneBi6uExVk3dScW0FTTOp++kDmh8JOzwS8wtgpLXhr2tSXtNKHZaiGA+88MmJQmgFdhVBoeXR/zvD3QvoTVTv++Kh6f/OQnecMb3gDAddddR7/fZzgc0ul0Luq+fNUmHqYs+ZO3/Dse/dhnGG/3KbOcNWM5cte9/M1vvJ1/9LM/wQ+84zd57JP3MN7c5nmv/Qc0e10AfunWN3L2gcdqieIv/tUq/+bqV/FTH/oj7vrjPyPbGSGlxJQl99z5Pu798w/RnusCjv03PY9b3vQ6vu7N30pzfo5Thx9ECMGBW27koY/exb3veT/5aIJKIlScMDi7HozrZjIR3+ysV/uXhNZH3dIXAbRJjcSfTZRHW/2LUrIUQtBtNfwkNyoZlQaNINseUJw9w3EXcXprQmu+h4ojrDEc6Q9Y6Da5opcyKS0fP7rJ3naCBc5sT1jqpHTSiKKQRBJWx4a9zYirF1usNPwavoIupEpww0LCx44PWG9G7GkntSR7ZR5Yubx6CqBvfdRjVR+IZ0IoIWjGESvtGJllKCHYPnmWz77rA2weO03aaXLDq1/Gwde8kkFhaEYJnUbMJPTKJZZEVCkIu5gJ3RiapS9B72srNJJ+rukHGqRnVTgoCprZhI2sII0kzjnK0mBj6tbUOU0b3w13AuOsB7dahxSKduKPOSttwC34SfbY1vmgbYHHfhzbmtBZ6RDJSjF2apKmrSWWkrLClghQQiKRJNubmMmYl1+1yCgrarlxh5/QjXWIQtNMY7rzc6SdNsYYIqXqU5HG0bO6JnvzXcYbmzQX5pFlCeMROlJEcVRLu7djRSfxYy6cQ0hBUwkK7f1G/HXit1dROvuZri8VM1OhAa9IClBoy6gsaSaKVqJmsA/+GiyzkpFzLHUS5loNdia5xwIp8YQQNiW8Bk1dSKwqNjMJSDvxf1o8g6aTSAa5pz8bIWrlURt0ba6Zb+5KOmIlaETeMXptVHrauZIU2tTP1Ko6t6chacQSjeTaPXM4oz1+yPmWX7VfIvSF8tLwzs+e4AVLMbce6JAkvrphz0k+rBNThV8rODEoeHwroxVLXnIw5gUdyZmdCdlM0lGFsZbTOznX77m0E4+LDS69QP52Xqyvr3PzzTfX/15cXGRtbW1X4vHzP//znDx5kttvv523vOUtz+q++6pNPD7/Z3/N0bu/wLi/QznO/AM0HP/2qTU+8Gu/Q3tpnpd/zz/Z9bm//Y9/yNkHp0kH+FbJpD/gt+74Xpy1yMD7NkWJLnLKLMOWJU4bNo6c5PBffpi006LR7fCyf/otfOfb/jUAt3/nP+Y73/ZzOGNJWk0AvvjBj3LnT/5b1h8/EXRABGm7SdxuMNro10qrX+kQ0lcxrJlBADpH2uvQWpxj6+ipC1bm5NMwyXq6ESlFq9ngQByjrW8FfPC3384J1SS65RYAxlv9+v2thTm2JppD3YSxNsw3E9ZGJZujDClgvOUTvBvmIl7SyFgsBUt795GmIrjB+hVo1eJQUnDNfIPCBP0I5zg7KhgUBuMglYLFZsTeObh6ucdjqzu7KIQAiYAD3ZhhKYgjST8vWQTWHzvBn/38f2TSnwJyj3/uS7zg8dNc9/1vZmtScLDXZK6ZkGvN2s74vPERAu/hoWC+GdOLHIkSDDVsjP3KWQjoxBGxFFjpePH3fhtr11/Bo3/y5zz26c/zgjfdAftafnsXOAcutLlGuWFiLMPCsDYWvHBfh2aSoKRha1xSXSa5theENSkpmJSW0sJgWKCtbw6VxqEiVe9roiQmVAqMNSwpy/iqK7jijq+v91ApuctTQgiBNl4FtJHGKCVYXevTm58nApI4JoqeGbajipd9zz/hfb/2u+x7xUuJ0hQ52MFpDd02veVFGpEC4XEbslqFOsdSK6Iwhvs3xoE5FAUxNMvZUU6hDVHk2UTGBVFDKVkb5AxzTaQEq8Oc4cQQy5J9Xa++Wbv+OnB5jkii2qHWWp/kxpWM7hPchjVoEz/OVZumtIaNUcHeZrNOJAGunks4NSjZCfTZqtUhpERby0JzmnQo4c3lKln4iTEoIVloRowKQa49ZqStBCttSTeNcDiummsw34w529dTcK21gXbuSyCT0vLee09x38kBi0mXqzJDK1I0haW0cldBtgIdG+sp4qujAiFgoh2fObHNcjthlOtdz/nZ8anahF+LJ49zyRQ/9VM/xate9Srm5ub4iZ/4CT7wgQ/wjd/4jc94u1+1rJat46cohuMgKORqhkN1VffPrPH5/++D533u4//3nRecOKvkQ8yUa01Z1v3kcjymnGT+C5xDZwWT/g4fe/udfPR3/pjPvPO9fOg//D6H3/dhX6oNcfMbX82/ve+DvP6nf5i5AyscuOVGrnrZbfT2LvvS8HPFkjkXDg+76CPOeXZI1UZBCG9gt7zA4hUHQJyPW3HWsXTNFRd1NyOlaKYJ3WaDuXaDQzddt2vScTN/STptFF7lMVaS65Za3H5ojq+/eqnuGTutOThcpRHHrCzPMT52AjEj/CbE1LQLPI7Du6o6Tg28q6gOk2xmHKdHBaf7Y7KioHS23kb1U1rfamlE/iaVUjGxik+/8y+Y9Ef4WkT4EZIvvf+jjLa2KY1jdZgxKg3NJGG+me5a1Ql8qyKJFQiJsYY0UBFLY1Fh1duIJGkkpxgNpVh83nXc8J3fTL4z5PCffhB0WV8O1XhWa3DrYCfXfO7UDse2J+TGy84PS09r9L3x4E4qPYtHCL+yVjLQ/sJEuNCMKbSpKYwOx0NrA+4/M9iVqFTiV9r4FXJ7aYFX/NB3eZuDJ7hOZl+Jo4gzp07SbTZoNRvPOukASFtN/vH//M/RR48zOrNKsmcPvSsO0F1axOFN2rLS1GJe1SNGCBFo0r66caw/4UtrQx7aHLGdaYalZVIGholzSOXNDAeTgnGu+dLxLRoCkkiwMcwZFWWgVUtiCWY4ZLy6TrPZoJ1EaGMwVmOsxzbMnMRdzz4dWnA4WydK1nkBrvVxQW4g15VGjAvAYcGhXsLzFxsc7Cbs6ybn6d4QjjkJBotFBQo2jse3JqyNChZTwfVzihctJdy4mDAfWkc4mBSaR1Z3KI03gNTWM7AmpQd2F8byyMYIIz3w+ONHBwwmJWeGDmstLanBeSq6RhIH00fn4MROXmM4CPt2z4ntWq7+QnGh47uUQjxHP08Ve/fuZX19vf736uoqe/bsqf/9rd/6rSwtLRFFEa9+9at56KGHntXxfdVWPDylzj6hpoYrNUfv/sJ5vxdPZvl3zpkx5XTCcpWmsKteKzFlCUz4k7f8Mq3FOUyp6a0sc8+f/CXf9r/+rJ/AgShJ+PZf/VmSZoOH/vaTfv+XFxn3B/RPncU9kaKqEOy/6XpWHzmKeYbCZUm76UFwmRdTE1IgoygkT9QJj3MOFUf1Aqq7dxlnLZ09iwzOrOGs1/Bw1tFamOMtH3nnM9qPZxov/e5v5u6f+Q2mTQe/q0mnSdLrstT2KpxppGjElry0tBsRN670+MKJLZ4nx+xLHM1OG5RfqRXDMWncrZucIuBJKupgoiST0rBTmF1JqfNTLse3JyiXIJDnXSNCeCxEN3Y11iNTCWcePn7BSTQvSo7/3ae54ZveyKQ0rPYnjKKMndKQAvOxx1rIUFIHQafdopAF49IyNt5Er5NEtCNFsas3HiZHa+leeZDWyh4e+sinyGTErT/0vV5lcga3Ya0XDXvvF06xk2kiKVmZa/DCQz02x0UNKpxt+1y/3GZ9WGDxOg5OeBzBQitmXy+h1FMhsNODnE8e26HQlpVuQjeod/rVskELSdxqsndPj2ZwpH2iatozMbgy1jLKPahYCEEjVvX2LxSNTptX/vPv5ujmyFvGBzyMwzN8ukHX5Ny7NDcOJQS59RPouec7Lw0r7ZheI/b3Ymk4uT7i6PqY0tj/n703j7PsKuu9v2utPZ2x5qquntKdpDOTEcIMkSkgBBRBwuBF5RX0wkfF+YqK3tf3Xi9XvSqvIi8qqCCDEAxEGcQwhJB5Tmci6XmqueqMe1pr3T/WPudU9ZB0kk7ovJfn80l3+tSpfc5ee++1nvU8vwGdG1557iRCSMqhB8K1l9JMQxxTnR6nGjq9inacIhH4CtIcIs9pzKzdtzgvnbGyY8QIYL6TsRTnpBq0db+/YyUhUs4kbiiSlFBI6RJwJ5NvGSsHzLQTfCVZ6jo9GAGutYgtzA0t8+3MMXLinFAYakP+GgV51esdSYGy0jFobM/wziFCtLWkuWHnUpcw8JgaiVhsJHzqjnkuO63GxqGQajmgHnrMaYtnwfNc1W/3Skw7W+0c7QYkN4aaDys9prZYPZ9YRssnb5sFCnDpCQZ52ONIPV74whfy4Q9/mCuvvJLt27czOTnZb7M0m01++Zd/mY985CMEQcAtt9zC5Zdf/oS+y0mfeFhrufua/+D7376JtNNlctsWnvtTb+TiN76Gr/73v6I5f2xb+eV9h9h3z/1sOO+s/oR22fv+E5969385YoJzC+sweZI4sNjjYKmknW5fsKw5u8D8jj2Uhod43e//Etd99J/Yd/f9CCHYeP7ZvOg9b+fgfQ9htObF734baRzzmff9Hll3IPPe0wuZOutUahNj1KcmWNi1j5WZObLOQPPgaOGVQkyundeBlPhlCdYweeZpHLjnwV4Dec3vGOOQaFG9ysLOfbSXl7HaIHwPKSRTZ2zh7Mtfwk986LefEIDv8YQfRbz6V9/NddfdSVwqA4KgUiaaGKVSGGzpQlF0KPLJfE2aG8pDIRUdEtiAJaGpSwmFomN7Zo5yrbymEuUmaSd6tGE4cNoUxg6KQqsmq0QbFrsZVih6nTEhXKm3qLzjK4UvoastJc9De2Gfhto7nLbuvgor5QLMJ8gtLMSaJDfM5JZFZZksQcUXpEAYhAR+QCk3zMSuxy9xVZtMOBBsrp2JnB/4jvqbpA6HMDFO59AsjYd3kuQZEieJ3VNtvXnXIjfvWUbgdoAWmFnpcqe1nLuujhJOmM153bie/2g54Nmbhnhovk07yfGkYLIaceHGOt/bsURunKLkwUbMruXYmZ1Jwd6lDltVtS/X3YtISc6eqvfvK18p0jxf+3xaS3CcGI5cG5baiVPcLDKmJNdk2lB/FAXLZpyRFloXx6vVE3kSi0vO+pUQ3B+eEIRKUg8HqrmVyOd5Z04wMdzim/fNsmO+wyeu38NFm4fYNFph81iJJDcoAcNbNtBemueUkTLGOOquKNocnnAut86zx1W7cmNpJLqoSImiWuOSaSUl0rjqjMbZAVgMqRV0W5bpKkRC0cpdNa3sS7aOlAg8yUInZX8jYazkMxz5fbdnsBxspc7zBVAYFrqaIV+wecglUD1wdccIctz3NLikf/UQx7nmgdl2X68nCn2symhnhq8+2AAsLzx9nEtPq+IJS6o17Y5lvp3SWrVZ6P2phAPf+p5ivAyLXVeFs7h7caISMlk7uY3lflDg0osvvphzzz2XK6+8EiEEH/zgB7nqqquo1Wq88pWv5CUveQlvectbCMOQc8455wm1WeAZkHh840//hts//xU6yytgYe+d97Hjhjt4y198kLf+5R/yF5f/VN8N9vDQacaHnv/jjGxaz+v/669SGq5z86euRvoeOnYW0kI6l8OgFOGXo7UaHU8oLEm7y62f+zLN2XnaC0v9n9z/je86ZszH/ojyUN2921oaB+e44e8/T3thCZ3m+OWIF7/7bbz6v/xn8iShNFTn1s9ew11Xf52lfQfZ8b3bjwlINZlmfOsmXvoL7+Ch624mqld4zQfex7/81ofYd8d9SCWPqBJZbfBKIRZBY36hnxe7BEwzsnk9b/7j33kSY/L4YnxiiEt/5FIW44xmkqOtpRZ4DEfKCSsVodKUkoRqFGAR+GKYA82UQ3HObENwhiruC2NpzswzunEd4HZDsbbMdTX1Sonhsk8rdd4c1rLq/AeJxX2zbc6aLK4Z9Mu+XsF8wLpEIlJOXGniWWfzwMO7UL6HF/hOV0BAaXyELS993gDNby0IQegptIWWNjSbltGSZKwSEGeGKLLMdlwLyFcOnNibcHuLmlO1FAzVK8ixYeJGE5mnnPO6l3HuW38cE5WIU41Uijh1TJhH5tuF5kcBQyyOMd9KKHuKg0tdds42iUIPJQS1yAMBE9WAU0bKTFZDQt9RPUNf8U+37me+lfYnuB5QGWsZrYRM1wJWEk07dfdf1ZdcsGF4jfZGFLhWjpOUd3iDwPcIjmIwd7ToGeKtTVKcdHY58I7p2zFIVIpbJs85cNvdCCEILzmPKAz6M3c308S5YaLksXuZgt2yFkAKMF4O1+xalRRoIzh1sspdu5eYb6ak2nLjI0sMlz06eegqWBb8PIe45eTrs9XPq8CTElk42c4mrgVh7SqvH2tZSTRLcYaxhUeRNWtgIakGVYCo9zRytgx7VAOBJ6CRGoS0TFdDpioBUaDIDcy3YnwpGQoVe1diZto5AovHAPez2E3ZWjG0jIf0QmID5rAqszawvxUTSMeieXCuRWZddSS3lkY3RRfjpXCeSM/eOkKmNXOdzLktF+DUUEmXzNpeC8ziS8nmIZdYVAPFSKRYSTRSekzWQvwn0Zp7uqJnHnki43hXtV/7tV9b8++zzjqr///vfOc7eec73/mkv8tJnXgs7N7H9X/7WVYOzfbxBo3ZOTqLy9zwic9z+W/+Aqc890J2XHfLMY9hjWV5/yx/87ZfosdfWz3ph5UKfimis9ygsX+G4788x/pA90dncYWFPfuJKg7Yt7T3IMsHZsjTlO1f+w5bnv0s3vXpD1OfHOc1v/1eznvNZXz/OzeBFJzzqpew7szTAAhK7gF6zpVXcMlPvpbuSpNfm7j4mB9v8pzpc7bx8ve/i5e//13917Nu3J90pKcc06Y4Vb8cMf3cC9h//e1HFOMEsPvWezCFY+TTEcMlBwxTUjAS+X13VKCvJO0JHCtm1cI/UilRDX0enGux1EnZEXic6n7UX/yXU0tuJImFeskBAuuRjycFi52837vGVYex1jIcKRZbkOSa0FP09raOUQKRhFs//SX23HQHWZxQ37KRM3/iCmbvfYDF7+9ABgHWWoJqhQvf/Q6U7/fHtuS53WGcawIl6OZuQu1kmjFchaST5hhjyS1045xGnPcxHYESICTGWIZLjmJZ37COyc3rOeNDH6CTF6BI62Su01xTDRSNxJJkrlzvlCsHyUfkSe7Zs+QwJKGHUJpQKZa7GfWSx1glZH09OuJ+eOGpo/zb9kN9SoXA4gkYqwZcvKFGZmCkJBkt+W5XH6kjjiGEICroy0+EPdVzez08jIU4y6mqo1c9AiX7SeDOb17PvZ/8Au25RbBQmRrjkp9+E5tf/DwONFO6+UD1NfIUrXSgFlpAwEA4SnAvMRQ4LIyUAgycv3mYa++bRRvLlskKU+NVuoY+nqabW9qqWhgjyqL9U5wLDlskoQCPWnSRYHQzzYEioXHpgGNc9ZTqe8mH21G7RNvgfFqEcHihpJMy13GmhKGSTNclW0crTNci7j6wwj2zHUZLCl86ECrCVTAiJTiz7qjUkU1pmAAjjrLIC+fRMtfJWO7m5NYpvUpPcmAlZ7mbYYwlsTBeDXnluVPUI5+dSx3SwkzPiRDCqPToZpZm6triI6HHlpGIsq8cIBpLKBXDJUHpSYCQfxgnNk7qxOOmf/yiW6yTtM8IUZ5i5eAcD157A3de/Q123XzHox4jS1KIk0E+IYR7YouWQ9xsURqqYtLsxNJaraW7uEJUKbNycJb5XXvd8S0kzTY7b7qLP33ZW3nt7/4iy/sOMrp5A8952xuojo+y+5a7uO2f/5VNF5/H5Gmn9A8ppSzopapvDHV4qDDkrX/9h0e8Pn32aTz0rRtddUg4F9FebLn0QsLpcfZqfcREL6Qgj1MWd+9jfOvmEzQ4jx0bhss04pSDy4MersX1PgWCii+RRXtA2N6k6lowEyWP+UbMgs6Q2x9heKzG5LbNLCSGxMg1mDxtnQLnhqGIM8Zg53JMJ9NuoRCOTTJVdn4nt+9vcPp4lUA5rIe1zrHzxj/7GDu+cxNKuW+3uGc/SWa45L0/Q/fQLAs79hDUq5z2o6/AL0Vk2lDyJLVQ0nMoKfmSbq5ZTnJ6EvA9eXELVEOPJE9ZinNy7RIGlxgJrDWOzmgsXgEArAa9cncPy+PAqmnSRfsBtUAxUvZY7mbILCXLcsr1at/grpPmjNdd0ttNNF0cwHKpmXDBRYdZ1RvDI7MNpiuKl50+xi37GrS6Kb6SnDJa4vXnTlLyJAeWYteysg6j4omQWvnY98ATYU8NIKCP73jV0MOTgj0P7OTmv/p7hNaoYpGKl5a5/i8+QWd8HWpqql/JMdY9k+PlgLm2Y/JI4bQlTMEOUQXuptf+8JTACKiXfM5cV2PTeJmN45V+qwYcaFfgKgUHGjEbhyJ85ZgY2joshiv8OcBqZsCTjlo6285x4rJO18Xi/Fv6FYH+WPTGy51DbqEQ/2Sk5DMSWUYjD78Ak7Y7XXxPce5UhVRrWqlju9SKykvJE2wou4TAPTuC0GZ0UUfgoox17cjJMsRxxnlTPsOBw3/sK1t0XuNgM0NKuPyMCTaPlmklui/77sanqPQp9xxdUK0UrSg3L2hr6c1wvQT2GWF30QvxxO7/xzrmyRIndeJx4N4HSVrtNeBLozV5lvHQt25A5/qx2yKHYzWsHQBFi58v7Tl4Ar/1IDorDYY2TDG/c+9aAKm1pO0Oh+5/mE+957cd+FNJquOjlIZqBKVSn7a65XkX8vo/+BW6jRYPX3cL0+dtY9OF57LzpjuPeu7vueojLO87hOd5VMdH+69PbNvK5LatzD28y7F1rEsqyiNDXPben2LFhwe++O+YHqC2+LkXhXihT1h9ckp1TyQCpVCexKR6jbW8FHaASl81mwocQ6fsSeeWaqErPaaedT4L2iM7xpNnrBN0Wl8POT/0aMQZ3dxQ8SW+kmTaLexTtYgb9iwzHHkMRx4L3ZQNK3Ps+u4tSCkL4KbTHNj37RuIKhHnvvUnof9WNgAAIABJREFU2PzS5/fVVNOCshp4krzAqvQYE5HnytjLcY6SkrlWSqAE49WIyWrIQnvgmqqNRSnBVNVVD3paBRZIclf+92SvKmN7A4QIA0SSYjM4dywk0QFjpTrG83lwtsNCJ2OqEjJ+lB64sRYpBVlu8AvA5e65Bt/escBix4EMq4HkdWeOMFn1KQcBwyUfsNwx03E6GMXOfTnJuWcuxyCYHi733VafbASeJEvyIyZtKcWjAkx3LrS4bf8KO676Gs124qo9vupTV/M045GvXsu2d75tTTsOQEnJumpAI9EkWmMMBL6i7heKm6tuVXDXe8NomY1j7pmKM01WXCNXZei5qTjTOSEEYRDQyWLaSU7kCWJtya1LYL1iallONHGu6fkErQ5Lr4I3aKn1flL2nHeRss71tupLIk+6c18Vea7xPcXFG4a451CTyFNMK4MvBmwvis/JrMOcxNJVLku+7OsWJZkToytJwXMmPVpF+62daR5YzCj7iqGSu2Z3HGqykGpnsCig7EtcLc32TyzVhrGSR7dwru0lWca4dk1uLYFgDWPxZA8pTryOx8lE5DnpEg+d53z3Y59mxw13cPc11x7J+CgSB314QnESRnt+iYe/c/Ox32AsWScmqJTAWOZ37HGgzh69VSl237Gdu67+d1pzi8TNNlbrI+XZhUD6ipH1U3zy//pNklYHPwrZeME5/Py/fJSoUuHSt72Bh751I6XhGu35JYzWhLUKG591Jhf+2OXcedddbLzgHA5t/75rwwjR3/FtOP8sahNjT+FIHT2ynnjE6sQCCuqgLV6xg0m02OwaIfCjkMiXnPaal7vFcvV27yiRG0sj1gTKURp7VQ1wi0FmLNXQI5JwRs2iRIbJBel999H7SoPLYrGex+w9D7D+x1ImqiXHsJDSMUcqbhLOClaNc/N2HzZZ8amHilBAKzfsXIpZjDUv2jrGlpES1++YZ7gUIITgjPFSoYPgPttXomA6GFLj+t12FZZdFpUu4/sonfPqZ03TzUxfpfKsqSozrYzRKORAo3vUuoFbO9xPDq10+NqDc8Ta9BOfbma5/WCHt14wRVR4kCx0nNhZHwRYLAy5gf2NhGqgGK6WjvOuePSoRT6ZNv2SvC1wIrXQ51gsgaVOwk17V9AW8qbTXdHWtS2Er9yuX0C+0lz7WYFiKFTEuaaVwGhZYq1Poi2BgomyYiktvG9W/V5PEZTiu3lSYLSTZPfU6pQAakWyJKVksasZDhygVQiBtAqhwFfOgyfJHO5IygHeRBdiYEoKQjX4Lr3rGPmSaqAKXQx3j+RW9J2FjbUsdJzoV+AJxiJLtVLmkk2jzDS6pElGwMBrwN0Ggv2xZG8MSqT4UuBLwXDJI88tzVwzEgXUA8OQ59qdK3HO/QuZaw1KQTWU5NbJvs83E6aHSljrFFVrgRoAJYXboBicT0uiXdtH9hL6os3oCfG4fXx+kPGDApc+XXHSJR5f+r0/Ycf1txG3O6TtI0WV+nHsiuozKoSSWK1J2t1+q6f3t9YanaYcWm649kjhw9GrdCjfQ/k+fmFyt7R/xk0q0lEXdt18J3/5unfxq9/8DKV6jZ/8X7/Ld//mc+y/9wGklGy66Bxe8vPvQBXuvD/9D3/Kx970CyztO4TVGul7jJ2ykbf91ZGtm6cjSr7DAISFqZgSgwU+LxZsKZwrq8H90BrDcu765JtrIeVAOp0O4XZAx0o+urlhdqHDuqrHSDTQH+iVwJPcMurlvHFr0NdH2DYMe87bwm2f6ZVyWUM9VbUaE+NDgJPXVrgqBMVikxeZijEWT4EvJUhLyXO7ulJgqfmKTpqRpBnl0EdJyVcfmKUeKZ617pRBBxH6SUjgSTpZoURqHWivN2aeFJjAYzgKCH1FFHiMQF92ffOIE+TaudAm8I/cIfrS0Yu7ac58K8H3JPHqzUHxWc3UEPlOQGvlCJry6nHXaGvJCyXSJxI9/Q+30AhGK6GrIhTf69FApQC371sp1F8F4bpJGnZwPTNt8JVCArWN6/EL1st0LSxM/SzG+qRlw76VmGZm8JVgOPLQCM4a8djfyukUznsB0JXO8dZa8D3BRDlkLnY4Hl+680hyg281k9WQLNccaMQYowHVF3NTslfwE4TKJQvLsasc9Aa6f39Yw4ayABU4gLZwUuehJ/usHIlTU/WKRKuTaXY30oJSLQr6bM5mK5mslVg3VCZOfNLWEso43yUhBLMJLGSK4VCipKCRaDqpYSXR+BLGK86TxysqMJXAQ1tYSU1fb0ZK6Sg4WLpZz8hO9BlZoTe4nhNlz93fepBw5cYWVUuFUhI/DE986+IpjKcCXHoy1XtOqsTjwPaH2HnDHSAEMw/tfPQ3G3typXBPNKwl7caPjS+xFNLr9JMu4XuEtQqloRrzj+yhWHuxWmNyjRcF7L/7AWZ37mF0wzqMNrz8/T/bN9M7PNZt28pv3Xw1t/3zvzK/Yy/jWzdxyZt/FD/6wVDPPCWphp6jExqnu+HgOQ58WbOFcZeUCGvQuWa5m9LJBdtGS9RCD1+5BAacMmI3t0d8jikYKsZa9qyk/TJzT/o7yS0lH8bQSLt2R7r5nFM59fkX8MgNdxWvu8TCas0pL7iEejmgkTjGjBTux9pY/AKj0UPq+IXugi1wG+BaXYGnMAhmGy1qpTKvP3eKpbajMfaqfkLgStHFjSFxSVZuLUr2WBeDna5E0Mmg5Ns+5XG1oJK2ho3DETOtpD9ZS2DdUISRsNzNCryCYqpeIugkLLRX6cwIWOqmTFR8BySUcgAUPWz4e8lSXiRfjyeacc53d8zTSXOkdDLe56+vs2G4QinwOJ4airUOmDha8hkveWy+8rVce8ttdBaW+hRogNrUGBf8xGtYsbaQ1x9M46GShEqyaSjiYCul4rtrORQ6R9UNNR9jILbuuqQFmNdYqASSWuTjt1Menm+zmLl5oBIoRkLBrsW2a7FZ14pbTlx7ZTVGoxeOEr1qgMWg9YAxdDPJZCSohv6afZv0BEYbkjQjER5RkVwdaGWF50txPwpBaiw7FrtM1tzoRqFPaoY5tNzEI0MKSdN6bB7xXNVEwLqqZTnO2dtIqYY+vpKOti4lWKcu6u5D0W+pGuuqdVI4TRVjHX7JFucjcF5KEyWfsZKPMU6+3lMKa52qauBJakpSLUfPqKTj/4Q4qRKPXbfcBUB7YZk8SR7j3TxJ2utTEAVg9fGEzvNjC4gdK3ofYQx5ktJaWMIYvQYwinB0YmMs/+PSN7g2jTFEtSqXvv0N/Pgf/RZh+cip2Q9DnveONz6+7/MUxkQlwJOCwMtJck1cCDUpLG0NcZZh4hhlDMHoCNWqTxVXRraWAnjnjhV6Ems0qS2M0Ypx7FUDMmOJFCSpplR2zAopnB16xVrUqh2ZwZVwkZLnvv0K9t77CGmj2Z/gtl72fLa9/tXEKw1odfDrNQhDrAGtlDOjs71KxSrQH6IPpO1VUKwQGCN5ZL5F5CvedNE0eeaqCL32Sv8IBaajlTkfl/KqnaHt/+GOvW8l5ZSRoyWVlueePsGuuSb3H2hisGwZLZEK5+PRA+kFniTIBfVSwGInHdz6FmYbCdvGy0gE62s+e5ouWToceTBWcuPsP87+u7HwtQcOMVzyGKsE/eNuP9QkkIKJ+qOgVldFmmlOG4n6rrrURvnx//EbXPexz7DvngfxpWDjJefy/He9hbHpYQ61sj7gUggH2O2Z69VDj0biTCOHI4/MCBqpu6d8IVjpZtRCDwrmiicpEhjBQ3MdtHVGb9Y6afpDJoJmAgVGQgmXCGWFAVwrcYnBUDTQOPGVoBZ6lHxnZBcXyXiu3aLtS0nKwDYe3J1T8iBD0S1spVqpppMd5jprXfLfPDTLv37yn/CjgOmXvJBDw5MF9sglXKeNqoHGDS5hGSn5ZNriq8KXBoiRBDYhEIKyLxkOJY1CgVSbnl8S+FIVrU9Bmmsm6xFbhqN+O8VaWOxmLMWaocj2n/dWqpmoqWdk0tGrHp3oY54scVIlHpXRYbe7TbNnZhvlcbZ/pO8NwJxPILQxBEKQpxlSHrldNNpgs5i8GyOUxAsC4pUm3/3YZ1Cex5v/9Hef8Gc/XSGEYLQc9JUGrbW02h2SAlcgRUBSDelmgwfL2h76w1UygL4jZhgogmICzbQl09BOc2baKc+arFAugHW9HDLTTlo6kgLhh5AlQM+ky6mG1qcnueL/+xA7vv5t0k6XU557McOnbubA3fcTdTtsfvZ5GCCVBUrFGBZTTaIttUBSCh1Ft6c82gtDz2zNVT+scO6avgLfU4xFCovDivSArb22Q6EJd8ywONfRbmYorWqpWAtRQffdMlFjy0SN/Uttkiyn3XXMsh5cxiVlilRbyoFHO3H3sq8EZ03XiMLIKeNaw7lTNe6fbdHNtEscpWBdNWSyGhD4HupRWiFHi31dQX3CQ0m55pHzleS+2RYvPc7EQxvNumpId5W2zfCm9VzxB7/C9tk2p46EjFYCJC4hmKz4FEWJATuqyCAlgqqyREFYYHh64wxK5Nx6oMHZY2UqJddqKPuKsoTv3XI/7VKNsFzue6j0rmEzyxmJgr4TsAVmml3uPdRmoZtiraAeKc6ZrDptlYrPaoXKsq+oeo5JNRQqtJAI26soODG6yBdI4aGUxbeCTBvaqasyqF7ZpHhmFh7eRdbpcuC6WzBJwnXfvovaFa9j7PxzGK84ufXVXjKrb8F6qEjN6lcEM13BWAlCCedPRXxvf5fMCEbLLolzKqN+nxUUeJJubliJcyLPOf4udzNCaal6hut2L1MpQL3DZZ/zpoeP6z442UKIEw8GPYnyjpMr8TjvNZdx4z9eRXthGWuMm2xPIIhUeuqYEusnJI7zuwolGd28gaU9B57Ux9k0J5iqQLtNMDJEe3F5jSlSr33zgp95E2e89FJKIzWas0vc97XvcMcXvsKP/OJPM77lxHqvPNWRJTGJHuyKDBZMsUBbh4TXWpMa40BlSmCsA+T51jh6qQAjBbFwHhL7GwnP2eCopG4n2ystuz56NzVILJ7wEKyqxAmBtIZYC2QYcubrL+//nrGw8xvf4eLLX8zDs03KtSq+l+MpSSfTLHViaqWIMHCGYOKwDqzDLFgUmtDmdIsJ21iIc9eW6OSaWuAjlcQaJ20+u7iCiiqEniTODGnByDliHI2l2U2554YbEcvLTJ51GpuefT4WuQZr0Ulz9q50mSj7fUbK6ggKIK7VBgmMl32eu2WEU8aH1rxvOoCpeondC23iLGOk5BN4itD3KD8K2+RY4ZXrhQ18cSlWjZu1Lik/XqZM5Etya/uYEHDX8IzxMvUC79OT35L9lHbtZ7rkzyC9gNS6ilxv0bZAqt04HerkbAxCRssBu794DV/64lfhuc/HP+1UqtNTRCNDrnVI4dhqnR5I7xwzbblxb8PZ1wtXKmslmlv3reBJwYahyJnS9cZJCspKYbAEkkISffC9I0/0222mqLYFnsNFBVKhVJGAG8HKoUNk3Zh0do7y6Vs5773vIiqXSHPNQwtt7jvUoBYMYys+iFWgb3qVD1joZFRDg6+c9kyMT7sLWhsaiccpoxWUknQzy1iWoa11gGhjqQSKjUMRkYBU57SzHF8KRkNRMHAU20YCHlpKqQaK9bVozT3yTIpVNcwTesyTJU6qxEP5Pj/6gfdx1W/8ETMP7XALZ5Ftn4h4SpOOxxHWGpb2HxpgNp5EjJ6ynpGN0zQOznLwvu/TXWlgcsegkJ7Hi3/uLTzripf1x3B4/SQv+Jk3oQKfQ/c9/IxLPDqpXuvjArSynE7qgHraOOR+v3+tLXnaZqIasqrrgBIWaWHewkVTpX7ScXgo4aSfu9oSegJvDZzTYpAcyhRW5wyFCln4bRhrGRob5u6O5I5D84TeIiMVn0Y3J80Npw55vOnSLXQzw4F2zlhFEHlef1sicL0ghcETzkCsLSUNq/qAUYzogx+jKKSZZMQa6lIyWhLo0NJK8r4C6OrY++BObv7jvyKfnaVaCjHasO7cbbzlTz6w5n33HmrQzgyTQhQCZ6a/+8S6XdlE1eesiUmUlIxXQ3zv6NOKFIKt4873YSBY9sSmw3CVkunaPbSruOxf7rJ59LEp4EoKjHEMlVQ7wK9rf7mpP18z99j+wq2LNpg2rnKkCiE2xEDHYnVrSwnBOZM1utpy3nSd+772bW78h88jEIwN1ymfdgpBKXJpTQHktNaZIXrCSesD7Fzo0M20E+/qVS6KVuFdBxtM10MCJfsQuKAQLZMMcBNC9HxABvfFmrvaurZkog17F7oYa5mshIwMV+kcmsXLurzkA7+0SvzN59JqxI75NvfNtNg64s5jdYJjraWbOxyGFOCrwnwQS6otS7E7p83DJYZCB3ptp6692sndfRZ6roVT9aDi+YDoV/ri3DnrTpQ9DnQMZ45XuWTjWr2ZZ1L8kE77NMemC8/lPV/4CP/r5W9jYdc+8iQlbrQe+xefSWHAHEPm/XhDSPfQnXP5i3nJu9/OZ3/x92nOLjC8YR0I2HDemey67R7OfNlz1yZuRTvo7Fe9iOlztz258/gBxOqkwxjLvYfayKLPXo/8I54urTWJ8rDGYgvBMRi0CtZXFUjVn8hzY/FWK2aKgtpmBXOJIcoUFc+BQ7VQLOMTepbRSBF6biJNtGU21hBFzBifWtkJcs00BtWSHW3LN3auMB8bzhgtEXmKOE2pRU7IStheF37gRjoeGDpdGC1FBEoWEtOgc01mYmJtKYc+nrBuwRQwVPJZ7mZOO0SJwtMj57a//ATd2XmUdD1w31csPrSDb/753/Ha3/nF/vdsprlT0cw01cDpPWTa9XGstYS+4rSxqjM5exxxPAmH0Zpu3KWZWjTCaUyEHtUoYChdphWV+rv1wYGhGiqEPT7clFKqD14OlKSAH2CsJc6cIZ8Sq8vUjpoaSEGiDZ0CS6MLZVk40tCud8+FnmLzcMhyJ+HWL11LllvqG6e46AUXsMeP+riLXtVNAmMlr88oETiKrypwDZ1UE/myX5bvFrb2gRqAdteOTyGs1aOZMugOH16s7WaaG3Yvoou9396lLhM+VHbt5II3vuaIFpdEsGW0zAOzbQ40UzYNhf0xM8YJns133Jw330nppBopLIFSCCnYUAuZrvrFfeGexcBTBdZB42qOrrLkSQF28CxLISj7qkis4Iqzhhg/QfTsH8ZTEydd4gEQ1aq84KffzK2fvQakYOcNdxwf2PT/oOi1oO686mss7TvE7Pd3IT1F2omZPGMrb/hvv8Fn3vc7lIbqa1x2e09rbXKMr376X3n7b73nB/Dtn3g4O3U3Cd9z0CUdFpcwpMbgF74QfadWLKIwwBoKDis3WsfWMNDn/nvCgTN7OgY9SmWcO/dZrcokQKgUSkKnnTJVUc7zBFeuVkowVRbwqsvYXCq5hcha9i512b53kcVOSi4VjyzGjFRCDrZStoxEGJzL53jJ0TctThl1sKBZpssKqQatoNVJmIdFeR5xbvo/yK3TH5lpdVhOcySC5s7dNHfswkpJPfD6ixTA7pvvXiNVrnBjtK+RMl11GiPaimKRg3PX1TlRUvpJponTjFacIE1OICxx4QQiBCTGEmc5c60uQ+s3EhpBnDu6JbhFtuxLxks+vhKP8WkulJQYpejxi3rYiqQ37v3DrD1eKfSpK8Wu5WVKnlgzhoeHKP4YjhSpNlgMaaOBFHDK8y9heGwIzwj2x5ZOQYWt+Ip1tYCSr9DWFoJxMFb22L/i2m09kCaAVIKpWlgkI2qg2tmvKxTvw9FOHdvJFEmIWPNdc2s52EwcAHrVeRyKNROTGwlK0RFQNiEcvmbTUIkHZltsrIcFgNsJic13ncfKQtf5L/XCWs1Q5DFV8QrclSHyJVLIvsGgryR50V61gBIeDhO19ju4Z1lhrANBy5Npi/84Q/AUgEtPombLDyzx2HnTndz9pX+nNb9Ebd04F//Ea9h4/tmAu1lPee6FLO49wNLeA5hLz2fXzXc9bmv4ExVCKfxS6PwyChfakyX23XEf++5wIlYIQVSrUpsa4yv/z4fxoxJZN0b6/hHtqm6jxY1//FEuesULOefZ5z2l39Fay02f/CIPXnsDQ+un+NHfeS9B6YntSCpRSNbuYoBYG8pr+vxrKxquWuF2hD1a5Jq1BPe6WvW6LPQ1Uu0m5UacsRg7h08hnGKktdDWmpovGIoUnoAc0AWqH5yI2fDoEFobtwMXgolqyNaxCg/ONPn2rmWGSs651FOKpVgzXfX72BXn6WExWg8YL0ISKUiLKWT1eTj/Diep3QP1SeFe18aybbzM9tkOs+2ExZlFhIBa6BF5a5OGNI4xed73kxkphyTN2Il9NTOkyBzF0Vief8rwMZMOo3Ns7na4wvNpppZdyx0ssL4aMVkLB9dAG1Y6SeEi60zHKp6lY9wi0r++1hSATUGOwlOCqrCUPafv0GNzeFIet6W4EAK/ELHqmeVpASutjHLBJDl8AVBSOBVPpRDyyE5wTzdl9a/VAtVXwVVSMLRhiuU9BxiankR6ipq1nFNxJnFISeQptDFkhZjXUOjOb7ISsH2mdQT1WEnBtvEymXZOxw4gLQr9i8F37GEtjIXECJSGaNUqYIGFdsp9M62+zowoqOBCKZaHx9271lCjBycqpeWc8XKRdDjsxVwnJtUuqer5DPXfL6ARZ1z7yBLt1Gmv1AKP08dLnDFRRRaVn16S3dP9WOpmlNRgjK21dHJYTCC2KStxzkjJZ7w6uM+eSfHDVstTEHd/+Rtc++cf72McZh7awa6b7uQ1v/0+KmPDfP1DH2V+5x7AWbU//51vorO4zPyOveRF8nFCfVUeIyyWtPUoYmYnS1hL3Giy724nEBYN1dh39wOc8ryLsL2qhwCEZMeNd6E7Xf7ld/+Ec77y8eP+iIU9+/n2Rz7JA9deT6lW4/w3vJKXvudteMHRzbc6jSZ/etmVzD+yGyEl1hhu/IfPc+WH/4ALf+zyx32KnudRK4U0uymrbwGBcxlWhdiTADc/Sof78Lwjkw6LGwudZk7zwlPIYhHS2jDbzVhMtGtrQH9hopjIG6lhLOxVOgZJh7VuwvekoBr4/d9TUhD6kjOnqix0NdJzGg6VQJFbWEwMUeFbX1EpnnCCUqtZCh0HWeRomLmkKI04jZABndFY1wa4/Kwp4kzTOX2MT199FXGzRdzqIKUkKDta7eTpW/tJB8DpYxWaaU4nyfuVIbCcMVlhuHz0SV0nXXSW9r/1/TMNHlzOXJInBDsW2mwcLnHJdJ04y+nEOal1mJbcQkW6K6VXJR3W9hg+RSWm0FhBCnwhGQ5V0TYa4DyON4QQeIfhUkI/I9Mab01iNVAatYXo2VDo0+ym+AUgt7dgVHyHrRAUrRopSLXp//yCN76a/bdvp3loto930WlGLJRjoxXAaHDnkzRaWOnagM/ZUOOBhS4LnawwMfQ5Z7LMZMVDGE0zSTDWsUtaBkqeM3sD+riQOHfJdDd3WItAOQ2NHQsdvvrAPNmq/bG1A2xLx4todlJq1VL/YeotaJk2bKpHjFZ8J9qnDZmEzfUQbS3t1EMJONhKi/Nyv9jJNE2dExQJYyfX3H3QOfNuHSmtqa5ECsJSCd9KFlsxZc/d5Y0U2hoCTyCEw7jMt1M8JQvZ/mdWHL6xOFHHPFniaU88jDHc/E9XHwGszJOU733in4mbLdrzS31NirTd4bqPfpLF3fvxwsCVn7tPc9vl8eps/IAjWWmS5zl+GHLPv30bg2DzhWfjhQFZN+bhG+/iu3/7zyCgPbtw3MddOTjDn7z0Spb3H8QUY7Lj+tt46Jvf4+e/8NGjlgY//o7395MOACElSavDP//KH3L+61/5hMr0YRAQBgHQWLOzjTOD19N97kchQFQA0XpPn8DJK4PFFqDjXBvyRhsDBEN1mqnBK3Z7R4c3C9q57Wsx9IimhRQCoewZuQ1+33PIOl506jDf27NC2Zdsqod99H1W7BQ962HzhJaGzEoiZQl9DyMUPfarA+311FsdLXiosLAffEO32Pf8WiJfEQ1VGN66ids+fTU6caaBfqnE+NaNXPq21685w0rocenGYXYvd2kXOJENQxFD0dETTZNn6CzpLyqLccaDizEWQdpTUpWCPUsd6r5ksuz1Zbx1kdSsvny95AkOvwaDNxkr0MaBbJ0mCwyVj/79jjem6xEzzbhoVbjrqoQgLCoJPSfbrSMltmeaOC+SRuGSosBziWgPd5Dnmtl2yqZhl+CtO+d0fuwvfp+Ze+7HJgmxF7GkweDAu41UU1aCyWpAY7HJ4p4DTJy2kflccLCVUA091tdD1tdD6oFcMz4ToSWRoIuEIreWxUZMJ3EeQCuJw4ZM1UJCT9DNBJ3U0kxS/vp7e5ltJpw6VaMeev1r4TAvGm1hMTfUhKsUrpbB932P0pDnZOqtRWhNPQpQ0mmPRJ5kKPIYLsU8MNdFCqdC6kwPJacMRxgsBxoJ2sL35ztM1yLaBR6k4kum6uVCFyTgvgMrdHJN2fOohD7lYK3bsRCCZpw9MxOPPgD4xB7zZImnPfGY37GHxT37B9bgqwZj1y13EdWq+OHhk4Ygz3ICzyNPjgHK7G11TkYPFyko1WvEzdbjFwt7giGMRWcZe269h5033EF1fJTRU9Yz98geus22e5NSbL7g7OM+5t/9p19hcdfeNa/lScK9//pNHrz2Bs56+QuO+J29d2w/qjlTe36Z7/3tZ3jRz73t8Z3YqtgyVmH/codS0eu2QCfOKEf+GiVOay2tDFIjKHuFNLQFck3JW3u/xM0WWZygajXamaYcOIlqVuUzq3ulqR7sTkSR5PSEwVbHoF0iGC15fcGpsVLgRKMYJBGREixngnbi/CrALaZSGqarBi9Qa46pLUgMge88Kw5XdDHWEniyz255+Oa72XfPg1SnJugurrjWiudhPY+NF517xDgHnmJbwUZ5rLC5awQZrWk329zdANdY1WyGAAAgAElEQVSPAF84q/PcOBbCwUbMeLnG4SlFbgVK2OL9a0eyx5DQq8bY4mimnSwjs1ALFfPthKFS8LipuibP0d0uXqXChuEy3SQl1/oIaq6QAq9ov10wXWe2ldBMcowxVAOH5WinGl+JPvhUW8v357tsGw3xBExsmGJ8w5RzlV3JsL2WBu5eSrQhTjPGKoqVMOTuhZT52BntaQuLnYyVOOc5G6r9RSqQlqoC36Q0CTEIOknObbuWaSUZ520aRknHWtHWumuDuy8PLKeIAle00EqIM03ZV9RCxRkTFSarISVfMVLyaHVz6iWvXwFzYzEYn9AX+DLoP4dCgbWSdqrZVI/Yv5LQygzGOmfmc8bLTNcdduTUkTIPzndY6KS00pxmaikpOHWsRhT55Nrw599+hEfm2wgBp09U2DZeY7jkMXGYuWF+Mq4HP4wfQOKxay8Lu/aRdROEFIS1KiMb1qF8D2tM35hsdQTlEkEUkqcpHKvFYi1D69cRNxokzZOsLWIs3ZUG0nPsiqdDcXV+1z723fsQXhCgtaY1v0hrftH9sDdDaM0DX/wqv3vdjUxeeh4XfOJ8PP/Yu4OHvnnjEa9Za9Fpxtf+518fNfHQ2bESRcvCk3QFPn/jENoadi20+33oyFeU7cBSvvgwBwzFkFvpMBdpxuLDu4lO34QQEp3nxI0W8UoT4Xk0uqnbxfpqlYwzHF7Bt/SYNm5XbIqWgKNcWtSqhVMX30kIV225aF2VZg85WvzlS0GSF9Zu0qMV58UZuC+wHOeOyWApNDWcsNOBRsLeZsL5U1WMNf0Jv+dxUZKDXfodV38dm2vKI8OURwYCS8YabvzMNVz2rp98wtfEWljef5DlhRX2hSO0Ayfk5TACwl0D4xaEvGiv9Fo4osBTdIwkEIZQGGcUtrrsX5zvmpqWNdw5E/epr6GSnDFWRgi3yz6eqprJc/b902dYufMu8labYHyUsRe/iMnLXwU4o7U+u0QIx8bo670IpmoRUzXItaYTp04mH5cQ9aLieyRovH4W6q5rIzXk1kmoe2KAVBJC0MgsI9UIOTHC7GziEoyijyiEYLmbsXs5YctIhMIQkiNwFTLI6VqfL903wwMHW0wNhQgg9FVfn6Tn36MEbB2N6HnUzK3EpBUfSh6Xn7mB0bLvWo7SgUh9JYu2jSm8hfp3QIEZGVgE9EDcoRIkBYZqXTXg4cUYX8JYKWCqGhTPrCDwJOdMlrl9f86BppOLbwho71/hJaeN8+XtB3lkvtUf/+VOhpKWlTinkbTwC4bbcNkneAYZw60O9yyc+GOeLPG0Jh4z39/JN//s40DBU9eWztIKeZwwdeapbL7oPBb3usXIaE17cQWpJOXhIc58xQvZ/m/fPPqBi5u7vbB08rJfLE6l9Gm6+u2FJcdmUa7naVnV2lqV+HRXGsTNJnM79vAHN9/L723/d/yj4DUeueE27KPojiTHoDyPbJhibsfeI173Ap8Xvfutj+OMjh4XbRrhok0ja5gY359tMttO+hba4BasuUbM1EiV6aEyabPNFz74x1z8Mz/J9NnbyNOkzxSS1QpLcY5X9OVLnuofv5dUgGMWVf1ezuAon9DDgojCI8WZhvUk2UueIs4tjdSQGYknBom0kk4+vZM5e3LsoNXQq4i0UsNSJ4WidZQXOg6RJwmERBbLVl4kOUqIwhtjALjsLK0cdSylkDTmjr/1drR45Ibb8QOPBb9KonwCAdmqc/CkRBuDMZZ66DHXyQiUROJaMAKLtoLlXFGSBmXzQihLEGtoZ6ZfaYCCqmk01VA5fQsl2VSPnNOqtSy1Ykqhohw+Oshw98f/nuVbbgPhqgmd2QW6X7gahGTq8leSa+1kwYXzqDmWOJlAFN4/azcXDk/khN+0tmTWYYP8IkHtYXIG8OgBw8YIwVwq+jgLi7uXJK51t9RN2TasUMYJXugiodvVSNnfTXhorkM70xxYjlloZ5w+GdB/Lop9kK8Ek7WI6XrIzvk2IPCF5R2XbGSkHPaTQiiqb4I+LVf2MigK4zeOzp9w1T6fRpoTKkktUKyvl1hXCUiNXXMcJSUXrh8iChR7V2KS3LLQTth+qMGDs+01lfJ2mnPf/gb75zu044xq5HP2piHOmq6zcdMzk1YrrEWc4A3qiT7ek4mnVdbtts9dQ9LuMLppGi9wTAshBGmni/IUL/3Fd9KaW2D3bXez5/btLO09wMLu/cw89AjnX/EKfuyPfhN5eEWkd/9JBwg8RjP+5Imn6eI72XnX8H60hKEHQBBCML9zH1f9+n8/6tu2/9u3HhUWfe6rX0pjdr4PlOvFK379PSh/bX5rjeGsV7yIsU3rj/t8HitWT0Rbxyukxtmxt9OcVpqz0MlYbrfZMlzGk5LyUI2NF5zNbR//HA9963t0lxvkWc7SvoPs3jNDiuwzXOJco43Bk86YKuq5sGHI8pxWNwOTEZATCIM1hkacM9fJ2d9KWeg4KqFE0M4MS7G7Hu0sL8rNttBiEKs6hZZWepicvnVKrT1dD10kHeB+txZ5dHPjAJAFCFKIwft6Y1RfN3nUMTRGP2FBOW0MWa65+dNfZnHvAbrKR1iLJ9zi2hPE6mlOlALFUOgx00pdn984imiiHc4lsYIVLVGlKhPDdXwMowHUPUuW52S5Ruc5gdSU/YDJSsCWoRJnjZWJPJeUpdp563QTTfwojLh0cYmVO+7GQsGsMf1Ecd83r3MibZ5HGPiEvv+oiqhKSdRhrBqBq2TFuWWxo9nX0SxnhlhDI+Oorry922C1aqkb58G/DO7aBhh8k7vJXAisUCwmsKvl8BPVUHH6VJXnbB1zAnzGCYlR4FeUoFD+hJedPlZQyS2vOmuKWuivSTp6X8ZV+gyZtv32ItBvFcEqMPaqcdAW6qHPxRvqvPG8KbYNh5SDnheLO3EpnNT7RNUnVJKpStgXbfv+XHvNca21HFjocPVNe7lv7zIHl7o8fKjJl2/eyxdu3XtU1d5nRFjz1Px3ksTTWvFY2ncIAL8Use7s04sKRYofRQjl8ZHX/ixJJybrOsqqVIra5DhD05Pc9tlr+NlP/TnX/81nOXDvg2sPbEH5rvSZD9BQT+ep/f8iBHDvV77JW/78g2tet9aiwqCo1hx9XG/+zJfY/vXvMHXGqbzgZ97MthdfCsClV76esFzi6//zo6wcmCWslnnWFS/jDf/3rz9l5+FJybM3DnH/bIvlToYRMBz5lJptqquErl7xqz/HF3/zj3jg6q/z4DX/gdGG0Y3rOO8Dv8pq0nSkJNM1HykcPTDRhlZu0AYyIRG5ZSm1VAJD6Htk1hQgUVc+X0kNndziSTd22jpAqkDQzXKwlvFKMBB2Eg6Rf7iRrsXtFI5G2GgmOUoKOrlBCE3kOQyCMZZYG5YTzUjV9b+f+9Yr2HH9rWSHUcOHN67n0je+6jHHV2vNUjuhnel+C0IVPaSFmUW+87HPc+ovT+BNjCOt272SW4TyENI5r14wVWUlsyilmO/kDEeKUEJdaVTRRkisQmtDN81IjQOmlzxJqX8JXeV0V6NLJQoJfdcCkNbRofskJAFJnhMGPkcD2LV37cKmiatC9EpLxe9li4vMLrfZOF5/zHHpRTkMoJX0q1VKOjflvSsJG+slBJDmFlW4AxsrGQoUM52ckjf4fkrAaOQWzvGyz8NLcT+hkUKgjROrk1JigFq5hJDOzTgQlrPGNYudjB+/aD1zrazP6uoUJjKpMVghyLQTRav4ksiX/LcrzmHnXIvTp6oodaSexOp/1QJHEQ59h4XKDATKvcc5265VR82NZSXRjFRcWyfNNX6gqK96LmWR6CRFhm2tpZvk3HOwQTXwGA4V++zA2+jgXAdtLc0kR6aDasvdu5bYOd/ijKnjv3YnS7iKx4lNFE6misfTmniUhwc3gBCC6vgo4AB9O264zWny61Uuq9aBuKJahbTT5fbP/xv/+Zq/4w+f9WriRrN/LOV7eFEECMJqhbjVcmj9H8ZxhZPALhoGq272bqPJTf9wFTf+41XMPLzrUdk9M/c/AkqxuGs/y/sOcd7rfoSFnU55dvrs03jvNX9HeahefIYlT1O8IHjKkNa10OfSTSN9/w1fSW6b27nmPSMb1vHOT/wx937lWyztPcDIpvWc9vIXstBJaSSu795MNZNV3zFFhGuFHGy5ikWvQtHRBl8KujmUfKgFHpGCZqrJjCn68qLwvLDoAqcU+hJjXYsgyXMC5aSiPWGZbydUwsFC2WvfeEoeUcq21rJnOWayHhEJQTcvFiTRawJBs5vRiFPqUcD6bVu44r++n+v/7nPMPLgDz/NYf8HZvPpXfvaYtOheGK05sNylmTrDOG1cW0EqQdkTXPr+93Dfp77AzI23UbnkIvedh+qocpmw5qOUZNtoxKkTdW7dt+x0GqSToh9TeV8lVGAJhKabWbSK+o6sq+Xfew7ESW6pMCjIKeGkwgOv1+5ybq+x7lIvOYDk6iht3AhKodMB5VwVi5o3XCfwJM12l0opPC68iFKS9UNl7ti/TG6cEFk7NZR9tYYGnWmL8tx9FSrpRMIEeELiKcFo5PxdupnB8zymaxEHm0kfYJvkmtBTrGQwp0OU9RCFp0+gJCMlwVhJMdPSjFclK93MgV4TjZSCpEg4eu2l5SSnFnisHw5ZP+wEwBpJfsQerp9Q4SqAgVL91p4ne5U2lxRm2umxygK71EgNqYF21qESeCx3U6bCoBAGG1RWdKF2qo1lvpNigKV2xkrHuQOXA0Un1WS5IUl1cc+snUuSVJPnJ88u/4cxiKc18Tj3NZex86Y7+zsKay1Jq83iapDh6qxMQGep4f5XCOJGk/HNG/iNG6/iz17xdjpLKyjf708Gp73w2Wy68Gy+/ZFP0kmO3sf+YRwlipnEYjn3VZcB0Jxb4HO/9PvsvfM+FvcdOmJ3fNTQmuVDs3QbTWYeegS/FNFdbnHXlyR3fflafu5z/y+3f/Ya7v+P79FZWqa+boJnvfZlPPstV/Qnqju+8BXu/4/r6S43GNk0zSVvfi1bnnPBEz61o5lEWWt5YLbJzsUOzSRn7KzzedHll7HYSdmxFDNW9hmKJJmx1AKvX4kIlCQ1xjnj9ha/onohhcNVaGOIrSTyBGVP0cxcSpdm2u0uC/VGYwzlwGNdNaQaKCJP8L/Ze/N4y66yzvu7hj2c8c731lypVCYyMIWAECSIYAIoIg0KLygOraK8Dv1BWxtt5LXBbhWQlteBxrEb0KCiIAjIPCUQUyFDkVSGmuc7D2fY01rr/WPtc+65dasqVUklBF6ffCpV955z9l57OHs963l+QzNSSCGohRHXbtbsnWuTWJ9wSCHY1Ix50lSDfbMtFrsZzlkWk4LpVoZUksUkpxb2ZKZXH8K5cWQOji+nNEsa7GXPeiqXPvMpJJ0EpSRhfG5CSyvdrJ909NglCFFWcQWTV15K5Vd+nt1/dTP5iWmiLZupxCFxNUQHknogiMt2aS3UZN0cJWBYmzXS5P6oBRVRkOSGbuEZFsC6UvtiahmtrzIYlBAE2mNkTEkxKhwUmaGbdxivRdQH6MDx5ATx5ZfTvnu3V8ssrzfGUr/26R7bgyPJ84fFivSiEgVct3WYXUcWwTpGYt0v+/dG78pjKaxjsZ2y77591DdOcc1UlWYYUlg/8XcKP/6dY1Um6wHznZwTrYyh2CvJWucZUqlxhEqUgmGOSMmyguYxMLWopwEjmO7k/gyLVb8XIQTtzJDkBZVAg3MEUtIp23diYNzOOSph+TsBGkGOISnAWkkUSITzOJSlpTYP7jlAkhVc+oxrAMiMY8+hBb5+aJ5asMDLrppioh4hnGf/9ByAW6nBup5Mv09gLJ4Zdu3mJnvnujykFsmt7RMbe2OMA8XG4XNzKX7ixWPRGnniJGGPa+Jx2fOexXf/zGu4/W//mbmDR1k8ehLwzIe8k6CiACGlV2vsPdjL1aEzlg1XXALApiddyn/6zAf5yG/+Pvu/9g3yNKM5NcHmay7nGa95GUIpvvLeD7Iyt+A//zBtl57vyeOFv3jMQspVtNi5Rv88O0a3beKVf/Cb5GnKH7zwtZy890GMMeeHmzGWdKVN1kn8dkt8ydLRE7zlkhtoTE2Qd7pYa+ksLLFw+Di2MDzrdT/El/7k/dx+88f6DrvLJ2Y4ctceXvpbv8glz3nGeQzi7PHlfXPceWyJpPCr0MVuzt7ZFk/d2OzTL7WUaOnKlZgg1n4y77EU+qdYDNJXPSK/MB6HEQeC+dTRzgoOL2cMh4rjrbTPNHGtjJl2yjM2NgiVQg/4X1QCxdUbmnSSnH+48wRfvH+WdlpQDRUvvmaKn3m+99k5vNgmdyssJt5TZaaVMlaL+6qlXjHSf4dOhegIIajUzg18d2Spy9GlLnlhCJUiVKs6A9VA0CiTJlDUp0ao/uzr+OSv/D9c94s/hZwYQoaa4QC0VORZxsziMsOhpKa0N+CTaxUtoZd8ODQFtSiiGniX4aKwZOV9NZ9YLL4FVpgejsRX8NLCMtfJ+8Z2NS2px36VHSjZ9wIBmHr9j9H6i/9Ncf8eyFLkUJP6tdcy/OIXD5DA7Hm53i62U5qhYrZb4IRvbVSc6ot+CmAlKVg5cowT39jNve99P+HwMIee+2ye9mOvoBmH5Tn1A1ACtjRXmSk9TRElBJVAkhtffdLKz8DWOQIhkNIXK6VYNW7rgV/9X70ahk+wVjJLpLx6bigdufTJa59p4aASiDX3qxBQCXUJ8BV0Z+fotFP0UJNWN2V6JQMBe765j4su2850O+PYSsIVU02Mc3xh3wI7RmKu2tj016RsBU23PTbn+FLSr0KBd7n9yWdt5zXXVaFb8MX7Z7xVQHkkoZJ818WjjNQenZ7LtyzO9zl+rtt8gsTjTqe97tUv48qbbuCPX/YfqY+PYAtD2mqTrrQp0hw12Id1ENYqFHnO1GUXc9VNN/S3s/FJl7DpystozS70Kx73f+Fr3PqXf0c8MsTEpRehopDFYydx1lAuAwCfaCBkf1J0p+obfzuGEARxiMkLz555mAjrVXQYYo2hOtxk/CmX84t//16U1rz1Sd/Lyfv3rT6PHkE4Y+gvQcpttOcWac8t+h+kYOnYNLXRYe7+2Ge5+vtfwN0f+2w/6eiFyTJu/+BHL1jicXIlYffJFQrj2DpU8StjQOKolqu8wrhS+dI/6DxF1tuceyMx/5B39NQ0/aQeSokSEqFKqiiC0VgzWtEESrBnplO+twQMSkGSG/YudLlqqoHD9cW/HL4n+7++cpAvPzDrJwoh6OaG9996iGMLCW/9oWvYOlxj63CNVuqVQe/ae4RWphFi7eSopWDilIdwYSztrKAoy+GBklQjvW5i3TfXYu9cuxRFkxTCT3CRlmg5mHSUU6SUDI80+b73vJ0TbUNSOFwCSzmMhY7hUNBKC6Kw1Hko3BlvMyEg0BIdBCSFIZCSKJS4HI4spTy4mHHxUEytrIYUxuNqjHMcWkr610kIr5CZO8tUPWaxkxIoRTXSxIGm0qgz9uM/Tr7cotpZItwwhSyrG4OAyVPB02eKLC8QwjFe1SWDyfUBr5GWKOETt5XDx0jm53noM18mCAOKVovpT/wr99WrXPeal5X79i7D04UhK3xFoBZ6hd0esym3oMt2Te8r58r71n8VRf8RZx2oQcrUqee7pMoGeNp3IARIryqrhFdl1Wsqiaumc41Q43AsLCyjxsd95aGny+Sg2+4y3c5ZSQ21KOifz1qsOd7Kqc932ThU6X+3rHMcWuiyf77T35cSPrkKy8rZT9ywk6VOzqG5NklhCZVk21iVn/6ebz8TzH48FmDQ/7+CS3txzz9/lrkDR0iWVrxVvSj7tdbL7AaV2EujO4fUipP372P5+DQ3/9Jv84rf/TWiWpUTex7ioa/eDnnO8okZWovLtBZXwDmWp+dYOV6nyHKvDRIGBJWYrN3xFRAhqI40/STYe5A8gbLB8w0hPd3QGXtOmImhTVNM7tzO1JN28qp3/VeiWpVdu3ahg4Cv/vXfMf3QgbMCSc85znZOrcNhac8tcPD2e/jGhz/JzN6D5IlvIUghieoV6uOjzOw9uIYu+2ji0EKXtDBMNaJ+0tGLHkCyW/geeG/CsYC1Ain9+R2LFSfbBYX1AFPjHFUtqYWw1M04+JkvcezrdyBxbLrqUq5+5feT5H61LMWqtLoPwUJS9PiWvqRdruwemG5x56HFfjtj9SOCL98/w1yry1jpwlmPvP5KLVvENYZYTov+6VdKMlYNWOgWLCQFo5WA2OUsZ6U1eXn/FHlBYR3D1VXsjbGWw4vd/p2QW9sXPCusYzhW6xxiBeCEl7dPTEGPJVlYONl1VAPFcBSUtExBvcS6eMcbsWY7UkiM0P0JMSlsWXoXHGsbNtVCNg/F5ekRJCXgcq6TkQ1oaPRaCUuJZSgyRCrAOEcrydFSev2XMMCONNDjQ/1r7yXOS/o/giwrkJFY1846Ndq5Z5lIKdjeDDjZKejklrQwhAoqQcDS4jJH/+0b7P38LSwdPVFeWk+onfnqrYz8+MuJAsWR5ZSFJMey6iezkuYIEfSl0BeTgolqsKaq5YClxHh10sLTVa2DusyJq45jXU/VFeWbjfXU8Vh5sKoxrpS5t1SVT+gKJ9FKnnKVVr1plACTZLgB/FaWeUK1w7Pt8jQjVBqjHd3c9O93qQX3nFjmOTvGyKwjN4aP7j7OsaV0zX1hHGyuR4yWifTm0Spv/5Gn8LndJ5leThhvRHzv1RuIgm9PDQ/4d3Apu3fvPuvru3btOu+dfvZ/fYDFYydPC1Z0zhDUh2hu20i60qF1Ygab57RnF/jiH/01t/7vv+fl7/89TtxxH0v7D9CenSdJfIKx2jx1dJdbvacN1njqHeXEAoBWa6og3+6hAg0CD5A7y3HpaszO77+ByasuZcuzn8ruPff1X9u1axefetf71jahH8Nw1oKSLBw5ziff+T5m9x2CYoD6qySVk3NMXnMpd9xxxwXZ5+FDh3BF7B/YA+coDDxALlYKgaObeZaIll40KTVe+VTgBZdGY8lS6phLixJb4Dg+kzJ38AiJqlNs3UH705/m5N172Hfrnez49Tet1tfXnYiyz24MaV7024sPHlugk5nT+hLlzvG+T36DF+1YxRwEQcDoxBRHk9SDMp0HWYoiY9kVGOOZOZ0kZSQOCKTEFzccJi9oJ749duTAHFlSrjCDmJYexh+5Bw5mhSEoW0rhGTxR3ADVd00IOJlYRqqyT8uUUpBYTYBDYugtxR2SQkX9iSmQgiBUpfy75LnbGuQGGJTJNqUy7WlAhb38rZ0bIukocl/GnzlxnM7yAkhJdXiCydEh7/viHMpmSGegrCrMF5IEbxSjXUqyOEf7NBYOQgds2bYdVYJctza94IsQIKWvTNx9y+3c8X/+oX98vfEJBHZxmcw4kiJjoZv1kw7XA9cK7xAclRooy6XQ3FAoEUKRFYbFxLCUGqqBr94F0jN+xkKLQFAAM4l34nVAICWhdMx1ixJ3JKkrQ0Vaevoi1nkKrVN6AE/hyIwjcGWyrvWapGx2bmm1JSMlUqsy0fPA6vlO1u+GF8Zx9913+QSqMcqVGxvMd3K6menjwGqx4poN1XXPhAlgogZY2H33idPcfP8eT5R42MTj6quvJjoDoGrXrl1ce+2157yz+cPH+OT/+GNO7PrmGRkSzjgmtm9h6oqd3P63H/Vtg4EHWLbS5iM/+p8Z3zjJ/JGT5IVdszjv/3OgkuFK+XBbqoYKIahUq+SVNlmn+5hPsI91OGsxeY6OQrTWSK08xuKUJ39Qjbnpv7yRl/7mL6zbxq5du4hnW3ROzD2+BnzG4qSlWqmsTToAjKU7t8Do2Nh53Wdnil27dvF9113FB+44vAbwUNGehrmQFExWJYHyTAtjHZHsAfDoP3wdXmsiMZZ2YUkKw0oGeatUPlWK8KKL4IUvpP2JTzK3/xATn/8i8pnrlV2BkuIrCAJNoAKEkDjn2DZpse7waSXnhbVs3bSRa6/d0S9XJ2nK1w4uYIQkKk3xjHW0rGQ48hbrUni57F5Bvtc1D7RiqF7DCsnE6DCNEnzZSjJmDy70fUp65f28sATK9er268IBrcyU51ewoR4SKn9c3cL2/Vn6xyMkSzZEY1BYnBMkSIZQBKdkbL0Ki0F4fNbgTgWIMyV40K+cCaWphSFSCsaHh2heejHttGAxyelYQ004IpcgrE+Ees2gujJkhSCXisJVqU1WuWwoRgbrn5HH5le8nwm9hKMctwOtNFdddzVf17JfARclS8cB8eQ4K5khs5bcruq7CDHQArSOihb95M8Yhy0MB1ZSCitKQTrvkZMUBSspXDMWlHaGgi1VCEPJbEes0R4xzrGUFgRC0NA93Vj/mhQQuoKVAqRUWOfIjW/DJDjqSKqBd+LN8pzpuRWOHFsoE1dH2KgRh7p/HaqhQoqQuXaOcb5FGW++lGs2Nrn76AJDsuCmJ03y0EybduZBspdPNagGimu3PfpnwqOJNE0fdmH+iOPfWy0XJpxz/PNb/oC5A4fPLKONn0QP3bGbEw/up0hOL/qTLreZTo5gBrzOBxfp65oEDuyAGJMTjuEtG6iMDnP8nj19x9vzilN3IsW3VDvEGUveSYiaDVT5gDdp3jfjk2HAD779V3nhL//Uus/e+ZFP86FffisrR06uM+9bE71G7gU+TqkUR+6+74yvzx1Yr3z6SKMeaa7Z0OTIclLabQt0yQQoLCykBcORL+3HarV37ViVPAdICsNMx3itDutQWpIut33LC/+ZYPs2RKWC6nZYfuABdt70Qh6c7fRbDw7PPBiJJSO1gFoU9Fe1Qkiu2zHKaC1kejld02pxQD1UvOypm1nupqS5xVjDTCcjsQ6lVufd3Hqdh+W0oBEplFwFhRYOwoFtgv+eDhbS40BRKxVgV1exDicg1orUOpT1tMzecXvzL8dMO6eiBduH4wFBLUFTDYAS8Tnt1jUAACAASURBVPmxsX7VnAkJeAVWKaEowZzrtOtKjZTBk+LwOVDhoKoVi0W+ZuUt8ElLI1R9im2kJTIQHFnocGCxTWF9RaEmLRfVVhlLvXWSEFBTlkXrQaKdArrdhNppEo/ResxcKyEQvjLUY2sI6asU49s2svOZT+OhW/3KvScdL3Fse8FzSa0HddqyPbx6qKVsuxReEr4scFY11EPBRVpzqO16pwlwjMWaWqTRwvg+WHk+soK+s+6p90FWOIRee/58VUagrWMld9x1bIlDCwndvKAZBzxra5OdYzWioQa3fOhTVC69lMsu2USlEuKEJA1i4kAy2zV9WwOtJI1YM9NKaSWGlcS77qZ5gZZ+fFdv8lT8wroSPPro265P6PgOTzweN1m3h758G9MP7GPx2EmP6zhLmCyn2wMhnjEc6hR62uorZwkBKggY37GVN9/2EV78ll88vcf4ucSatvsTQyEv63QY3rKJ4U1TjGzdyNTlF3Px9c/gqS97Ed/zf79+3fuP3fsQH/y5N9M6PuMxNWc6F0L4dk4PTXkBo8iyswJiD9913zmD+s4lnrNjjEvGqn1wpBICJQWBEjRjTRwq3x8un7Ql/IKei4Zznp0xUdXsHKkQBX612APG9h/QOkCM+DZFGARcMV7lmVsaVAM/WUgnOLmcceWGEYarMUopAq0Jg4BAa5RS/PdXXEM9VCXGwO88Bl7/3IuxztFJc4z136e0cB40PXCq+owM1wOu+tVzD7wnsUhs/5JKfLLRC60U9VB7EKjrldv932nJdOgUphTssoB3l61on7RtqIUDSUdvTOV5LH/OjGUpLfrntgeKFLhS1XTttXfO0ckNC12/38Ho+YkMxZpKsHrefIsCJqpB3623R2XNjOXAYguc6LOL6r2q0Oraph9qoKTigJUzrKPiMGDzaINKFNEqBKmTILVvIZXDfvFvvJGn3vhcao0q0jkak2Nc/tr/wLabvhcp/HgCVSZiA6wOa62nX+NlypuhYDyWICRaKcYiX5kKlWBjPWS8FtKMFCKIsVKvqRKdJqfz9/pAFXk1bey9x/LFffMcXEpBCeJIs5waPnbvNPvnO8T1Gjf95Ct45tN2MjXeZHi4wdj4MNtGKgxVQoZj3VdLdc4fwzePLRMGkh2jNU4udxjpGSgO3NC6ZCCOf7uyVc41/l259NGHNYYv/un7OXzXfWTtC2PgZo3zbJXzDB2GBJWYvV+5HVMUvPjXf557PvZ5Dtx6nliVwYe7VgxtnGDp2PTj5j57pnDGMHvgIM3JcRpT44QVr1b51FfciNLrL/e/vP0PV9sy/dnnlOqNAKUVOgxKKfYLPOiHqaC4wjCz7xCTO7dfsF0+fcsIW4ZTZlopS90cpfxKeHDSPTXX8S09gRNeBny8GiDwqP/MOiojQ3TnF+gD7tIENzuHNYaLn3MtWkk2NmNGayGHFlKGKgFXbmieFaR46cYh/umXvpu/+NJe9hxbYbIR8arv2s6O8SrzrWTNe6PyIe0XtGVpvEyWgpL62J8unU+jVmsPBoPEIPoJ/eB5iJTEyl5CIHHWy5yvJIZ6rEiNJTUeXNiMNFpKdgxHa9gP/ZZDbz4v/7eSDdrArSZLhQWlBElWEEYKKyS9mmaoFSPS0ckd1tm+RoYQgqqWdLFM1UKS3NI1FglUtaAarp6H3FiUkix2Cwq7dhVdlK21HjV3sMB56u36cIvvWiWkW9g1XxsDLHRyMiN4yht/ks2vew0rK23CoaZvtdGrHnnWUJIb74RcglorgeSyYe11WxDkzpGz+rlaJFHKJ1u6BEr7KpLEqACJQzhDIKDbw8INDLCXqImBu2bgVQ4s5axkqxRogSAOFeC47dAiO8eqfhyNWrk9X30DbxZXDSSBCsgKy2w7p8BXfC4Zr7NxKObQnDeBG4kUyxl9oLCWgrwwjFTPTU/l2zacO7Mh6qPZ5hMkHpfE4x//y+/yjX/4pJ+0LlCYs7UEzhJFmlGkGclKmz99xc9SadRZOnoCXYkpknTdxQnrVayx4Jx//TQRVWKe8Zof5PPv/kuMeQRtmwsZDnQQUqQ58wePsuPZT+d5P/Marrrp+ad9++LhE37iK7nzDneaREBgjSVPMsJahax9DmJiFzh+7/pXUB1qElYqPP1VL+H6//gjDE1NPKptTtYjJusRs+2UpW62JgHoeYucGqLEEOjS7hzhja+Ot1J0JaY2MU57Zg6EIHvgQWyactkLr+eKG5+HEF7QKVKS79pRX1cJOFOEWvGGF1y25nedNO+blvVirKqZ6XhL9t7ve/inkdKN1Kt9+l7Pqe0L6QzGra94aSXIyupGr1rQqwQ9tNhlvBIwUtGESpADVW1ZKmxZgXGlDLjot5j6+ysnxV4L69QprleZUaVolH/D6viUFNRC70oalvRnh6ebKixR4GXAC+MnXCEEhbF0ehVXB+TWJ0uhF+DqXfElIxizgnIu7YfD0XW6/0slYKiivLBaktLNDJ3C0ahGbBuulNoZXtAszc2aeywzzkvpCwjjiKEwJB0QQeu9taIVFzUjVnJLUlhqoWfgaG3KzqfD0Eu8/NCsg1j7al5vwveYc+vlB0RE6HKmYsNK2ycFfayc8IlKIUpjutJ/uXcGUiPYM5+tKX72TlEUKE6uZGTGrHGGXdNawwOFC+topwWhFsy0Ci4Zr/Oqp27GOW+mlxsvt9+MRF/J1OErZGlhqX6HFz2+k+MxTzy6Ky0+9+6/8E6pT6BwxnBs9/2YNKezuEQQhtg8X9cGylqdUphrffYplKQ2OsLU5Tu4/YMfQcehx69c6MzyVDzJw7w+vmMLcaMOQFSrcuWNN5zxo80N4xy526+Sz4jvKCdhZy2ZtYhA4y7w9ayOD9OZPXN7rXVyjtb0PEIIju95kHs/9QV++uY/Ymjj1KPe91g1RACtkqGipaAWBxRpSmq9xXxf0M45r3shVmfRyXqAAOaSArFhnPpwg/z++4maAZf83n9h81OuXLvDC0AL1j1TLTzIsJX5iX4o0iwlhXetxbNaRqsBtUjR629YnNdCGByHs7g8oVkN1u1rohpydDkF4asEttTwcM5RUZKTbS8OFWvPhNgzn3ghKylRacF4LSQOVp1z/Xlc2+F0iPIzYm0G4gYqEeV4fUPHT7hJYelmngnkpKSdWZaSglooaUSSoUAQx7os4glcIImtY7FboJRvm/hJWlLT0DWOTuEn2mOJZJtyRGIVitsuNF2neoQ5tDDMJApt2wQCggAaGuY6bT51cpnv2TlOHGiaUcCiLSXES3ZGoAQqc4RaEWoPOq05P7F2C1viXxzDsWY41kzWPLMlMZA5SWosofC+04OpwZonlfD4GaW803JmfBVFSUkQVYgiwb6DJxirR17HprwXjHNkRrNoJLVS0t4CuZMc69o1t3C/RYZXKnUC5hPHcOwIpE/Oeo/EksBNUhS0M4eW4JwgCCSvf+Y239LDtzK1cmSFZ9H0kg6ASCv2z7dpRPoUPZHvnBDOItyF7WlfaHruo4nHPPH40C//tydc0tGLpWPTRM06RVZgsuLME+8pJS+pFTuvf0Z/MsrTjGS5RaVZO6M9/KOKh8tjBl4Pq5V+0gEwt+8QJ+/f21d9PTVu/LU3sPsTn8eeAci7Lqw9N9bLwyVLp0Sy1KIyNnx2bI9zCCVxxnLw9t3c+tf/wE2//vPnvpMzDVUIxmoRo9Wwb3svhGARR9bNBtw8/WEVtudxsfr5yUbIRD3AOdgxvgF57Q5aSeqrEqfEIMDzkUaofe8/zQ2z3YKkWNVwaUQKLQWTVY11kFp/C4sSf4H1D/NQepVJn1QaPMPYsdTueEVMwJWl/ql6wEw7xzmYqAVUA4kWsG0oopsbHP597czQTikN1/xZa5VeGj3Jc4+RoT/JhMozL1Ljgbo9nQgBVEMv2NZzmvWfdywnhW9fOMdcp+D2o8tcOVWnHgdsqIdEPWE45WnAPdCmwVOCG6Fivpszl2R+tY9gKFQ0IkVufZUgR/LgiqGhfEJnhSSxAuMsndxwZDlFK8m1kx4I6ztcvlowUdFkheNrhxZ4/s4JlJKM1qK++62WkuFKwN6i3ccGyXJyj5Qs2SKW8apmUz2kV5RUQlKTIApLisa7mKyqC3vxNP/vtHBUAj+2wW+scZ4u7kXuHA/Mtlk6sux1R8qEeksz4sZLRgBNx3n2Sm+7lcBSDRXLabGq5Fu2o3Jr2diMvVha4VCBTzaVED65tWCcZbQaI5Xh4NETbN+0gSs3NNl9fIncOrYMVdjYCMmNIR04nl51R5XMqgPzbS6ZaDyq79ETNpyFC5x4PJEwHo95unjk7m9euI0NItS1Oq1Ne6AEtVAxXgu4emON4cqZcyuT5STLLb96Oo/WjS0Myydn+z/3JuK42aAyfGGcEKVWXsX1LFb04BHyOo4QUhLVqmw5dXUtxWnpmL3Y8pQrvTHYBViFr4nzLPrYvDgHQDE463zbp5vw5ff9LQ9++bYLBjwVJci0N4EPVSPi0vtEitWERClFchqAtPf0oK+kGwe6BOmVD2fnJ7g4uDD5/nA1pl2W33tjFuU4urljrpNzsp35KoUD4wS59ZOQFo5YWJw1COdFwaQOMQ4vNGT9qtcYS1oU1ENJpGBrM2Ak1sTaC0lVAk+VnappKtp/97YPxzRCSZYVHPzoJ/nqW9/BV97xXvbfcgd5mqFwfWAh+DaQx646MutFpTJjGY4VAkk7t6zyiaCdGTp5z31WeH+c3HDP8RWGItVPOpSgDxC15Z/erbKUFhxaznAOhiJNPZDMdDMWkoJGqGhGiooStNKcVg6F0DjppeLbac7+pZTcwUisCEr8RE+Po3cvTNUDFrpZX55cCH/tG3FIJdR9tdHeZ6TwSZGUnuI9GgdsqAWrCRerf8dK4JwlDENqlSqF9RTvPtHP+QSjkxVr8DO911qZYaFTkBeOVmrJjPUpTLmNAwtd/nXvAiNBWR2TAl0Wo7SSjMSaZuxBx4PeOVpIvuuiEYSAbmHJTI995EpTRZ/81kLF5ZMNxuwyy2nGF/fOsNDNaKU5dx5d5HMPzdGIQ0wPVC1KIb+yGiaE1x/5jo0eyvpC/3mCxGNe8ag0G/3S4qOOgW24EncxGFpCNZCEWnLRWIV6pBmrhew+tsJc5/RVF/sIcSezBw7TnBxDSEkQR4TVCrXREepjI+y95Y6+HPuakAIhJEEckLWT9a+XEVQr6ECDEDhrSdvd028P3+75vl/9WfZ//U7ybrKOlTJ+8TYmL91xxn1948OfxORmFbvxcNdJsGYyvaBxDlWSwWrLwqGj/NXr38T1P/kqXvwbv3BBlE37+3GOJMsJlEQLv2qW0lueaym57dA8w0L0jbicc3Qyy0Qj7m9DK0UtFmSFZ3wIIYkCdcHGqdTqpNwvs7vS2RPHibbhyHKXSAm2DVeZqIeeKuugKQsqgeizsVLjsDIYhHj6/wuQ5WQUaV/mhnICwhKWoNVeBaeioAuMVzRff9sfMH/PvQgpWQCOfOU2xi/Zzg/8/psJ9Op5CpVkQ02zklnywhFpwUhF084tShgvBe7hIgigm6/eA77ikaKEl0QXg/fPKafZ9j8DJ1oZW4dCGgPXY6oelBbxfnEolWD7SJVQ6VUQpYLJRoWKVuxZSBgOPa32dEwvBYzEId3clFota8P0KmcWVtElol91GKwK9cY9+AVpJRkbhyoIk6GcxSB9glWel9w4Di0nbGpENEKv4ZIbaOWmtKy31JF0C09Z7t9FzrP0ji5nFNYxpAVdJyiE12/JjWNjI/LtMiFYSQusdUzUQi4eqxEq1R9lK7eo3FEPvD5OFHijOpzDGEMuPV18EA9SCRSFsXzzxArDlQDjBvyi+lUvn+R8x8a3sOLxO7/zO9x1110IIXjzm9/Mk5/85P5rt9xyC+9617tQSvG85z2PN77xjY9oKI/5lXv6K1+KjC4QCkgMrN5PM/FtHYrYPBxz5YZ6/4supWT72LkZYZ1PmCRj9sARAPIkZdPVl9GZW8A5x9SlF617v9QKpTXxUJ2tT7+G63705Ui9XnlJV2J0GPQrEEII5Flg87YwPOvHX8n/9cf/jcbE2JrVdVitcMPPve6sE50Y+MKf04ToeGySjnLb5/V252jNLvCVP/8Qe285fwXds8VSN6WT5Rhjy0nBD07hJ4PLJxssZ5ajKykz7YyT7YJ6JWSqufZek1ISh5pKFBKH+ryTDmsMuz/xBb74J+/n7o99xivwDm5feGxEbkohJ+tFmFz/OglSAw/Mtrl/pk0oPZ6hIwIMEqEChA5JZbQGuOmco5tbWpmlnfvSfNQTCPEzB4HvLawbTygFst1mac8Da6ptUgrmHjzAPR/513X3kAcRKrQAqSQHFlLmO/maSs5yWpBb22/hjMSSDfWAF+wc4yeesZEfuHwUM3CtTqWK9oZunaMRKZqnXI9YS+qRKu3Y/e8CtT5hEAKalZBLRitIIc9426YWKoHkoekV7juxRH5KlawygFwV5X/OeR+ctDAcWOjQSs1ptRadg1v2z/PVfXMsdAraWc7++Y4XQMu8GuiD8x06uWGmU3Cy7f/MJ4UHCuNt7dtZAWvqSauRG8tiWpS3hQfCGueBqN0CGnHARC3iopEKl03W2T4y4ATbW2D3WjSiTE77bRNLXhTEYxuonEbaXCvJTDtl01C8nlPjPCi5Hn/7SqI/UeO2227j4MGD3Hzzzbz97W/n7W9/+5rX3/a2t/Ge97yHv/mbv+GrX/0qDz300CPaz2Ne8bjhDa9l9798jt0f+9y614SU56eSeQamAcBQrNgxWTstBbIRneMNKsRpE5ozxcr0HGknIV1c9ni4QBIcO4kOAqRWa4Cq/t+GdLnFkbvuQ4chI1s3s3DkeGliB66w2DzHDRjlCaVoTE6weOT46QfhHO9+wat50xdv5sf+6vfZ9aGPsXj0BLXREa794ZfSnBwHYHrvAT7+23/Iifv3EVUrfO+bfponv+R7eOoPvogPjw2zeHz6cVUsvRDhjCVttXDWcO+nv8wl118YI7l2mpHnZs2EIvDL/m6W09CK0WrIM7eOMNv2q8LxakBwmkTy0cTS8ZN84t1/CZUqJknY9fcf5/a//Rgv/+//mdGtmwCYqIe05rprZtheVXUpWa3mCSGY72Tk1hJKgUORC02l5luDZqlN0P+8o12UuJByrnDC28w76xBSoHCn784JDxrNlpbW64k4X6E7uftB+GGLcR7k6Jwvx7fSAiMVc8sZBxY7dHPDdVuGqGg/uXdzr+nx4Gyby8cqhANJgZaSi0arZMaQ2hIzUWo+9MbpWTL+GdII1ToWjRAC4Twg15RCYgOHteZ+cMBwrMkKSyu3NMP17YylzL+xWzJpbj+yyEUjFTYO+Qk60opaGLBcXqceI8da7/VydCVlohowWnE0I72G/bOU5GwZreEEfOyBOQ4vdlnpFoSBYudYhSsm60zVAjqFb5klRc+crmwJOkctFMRaUQ10H0MjBo4v1op6oOgWjsWsZDWxSm0VCCpakhT+59RYqtJrp/ROurG+Uhb2ROOEp58bJ0gzgxOKauiZTYVZ++wVwjFUCRmv5cx3ct/SEd6JuBIqRr+DKbXfKq+WW2+9lRe+8IUA7Ny5k6WlJVqtFvV6ncOHDzM0NMTGjRsBuOGGG7j11lu55JLT4wfPFo954iGE4I0f/XP+540/yv2fu9X3yANNkee+LFqCBR9t5MathXaX4WmR57jKPJ+VfKAoktRTbHtPgwJsbkmKFaRSSKU82n5gu7bwycf9n/2q/4UQ6CgkiEKSVhtbGJKVFlIqGpOjPPvHX8mDX7yNxWMnzqh3sXxilj/7kV/g17/2Tzz3p1697vWvvf/D/P1/ehudpeW+p82DX/o6Ub2GLQpMXlw4qvN5gkofdTjI2l1m9x66YJtc7GRrMAg96qgVrBGzEkIwUb8wD78j9+zhvs/eghBw1Y3PY+qyi/nGnqNsfPWrfDukyNl0w3PZ9+F/5tPvfB8/8u7fAmBDs8JcO2M5MassF+fdTPvMiHIfhcWvurWng0YDbTmpJNZZrzpqV5OOwXBAbiEcZKMMzuz4IUgEiye99H6vKLJaxncIrUsGC7iiYCm1SK1BKpK04P65NgLHRcMxI4EjUpbUOFDeKG5TPWK4sp59I4WfSNuJB2/Kct8O1uAQwCt+uhIS2R99eShKell5gTdOS8uJVZfYiz67xvnkJisE86mlNkBfXczwFQJWcTcABxe6bGhW+j9vaHqDu5UkJzfeY2Yl9S0y62DPXIenTtV8m0LJfoL20HxCqBX3HFtmtp3hgEZF8+ztI2xqRugSrzSGv2dPtDIyA1oIAuWoaMVwpMms4KLRmL1znbWAZ+fYOV6lEiraBiqh9zaKA00rT8qWlCidbH2lLSnvNy191UsICBQ0yhvGOEcsBbmjb3WhS6B1PdQsdlefQQ6olVioLcNVGnHOSlJweK7NQiennRbsrQR8184xhirfgbxaZy/8c/QcEpnZ2Vmuuuqq/s+jo6PMzMxQr9eZmZlhdHR0zWuHDz8yVenHRcdDCMHP/9P7ePcLX8vs/sPkSYo1FqklUbVKnqZkne6jSkA6uafRNU8DJj2+fGY8RT+U9E/bc7zYYoDzv4bnX5bCrTGegfFwyYxzqwkMgJTUx4YZ3bqJynCTK7/vecwdOMLx+x6kM790+qFrxfSD+1k4eoKRzRvWvJanKR//7T+ku9LqVzSc8wjDZGnl3A72fOJcvywXOEHR4YW5lY11tDPD0Cll3J6SozlD8vdIw1rLl/7iQ4SBZPPVF1MkGfd8/LPcO9NBT07S7/4HGrllMzte/lLu+oM/YXl2gXZYwVrHzvE6S92MkysZzhqOL2csp3nfMbQXFb0qFx5JRxCvtoWGKyEzy22q2rcaBgGN/fa6gMJ4h12kbwsIYXFOrtlRYR2mul6nRGyYQl55Ffq7n8numQ7bmhHVUJE5h8gNYaA42UrRAq6cqLG56i3QA+kZNo0AOtayeTgi1h734ROfHrjTu9niDEKUKYVblVYfvHKhFBSOErwoyvPsP9NTFU0LS6ewJSBS4ApH1zhGIomSkqLwvWoZKLLCcqLryooKfWXUU5c81jmOL3fZVFY9hBBsaFaYrId8ae8sB5eSNeuLdmb56pFlNtRCtHPMdjIemu3yvEvHaXVzZtsZILhyqs7W4QojlQCE9/nFeiWAaiC5YrxCJ/f4jEAJKkHZUnKWi0arnqa+nJIVlkog2T5a5crJGsu5QyiNcN6PZbrlq2YrWUFVq1I8TRCo8pxaWEgKRmLFcKhoRKV5pfN05szCfCcnNR73Mhwr2oVBCk8MyMvnqhRw7ZYhZlsJx5ZSlBTsn14hc44kMxjrmF5J+ddvnuQFT5pkrPYdVv34FiUe6z7yGLXVHzevlrBa4ac/9Ed86U8/wOE772X2wGHS5RbDmzego5CZvQfpLCwhlaI+MUYQeZzD8XsfPOcLsOdEi2s2N312jgej7ZvrcGD+YRIPASoMMd1zSFDKOJe2xCNKpKylyAuCSszSiWk+9T/+lOf81A9zbPcD5N2MvDsg3iVAaY3SGpsXLB2fXpd4fPMTX2Bleg5XePEiex7J1WMaF3gMo9s3X5DtdPOClcxQCxV6jc6En5TOhrc530iTlL237WLDjk3oEgcVxjHbnvMMFkfHSfPVKgYAUqKmJhm77mk8NN8hrPsX59op9Ujx5M1D3PGNb7B9Ygd3HlvyvXXn2R0Sx5ZGhJaOSDmGqhXkYKtCKcYbNU4stZGARXLqrOnK/v5yJyfWgjyQ1BRIYbAlq8NYx2zqGLv8Yp70ipew5x//xffkn3QF+vrrqY6PwvgEJ9s5c92cqyZqDMVhvyKhJGxoREzEok9ay40tdT8kIYZjHdjaDL3mRzlI1x+jIwokFa1KbYkB4Gb5l3UOiyDWXsTKJ2h+5V30FhRlewJWgbM9AbJWBuOxpGsNWU8BVkBcQjacgJXszDd4NzN0c9M33fOXVlGPQ/L5rm8BDYAonYP7Zzo8NL2CVrJUzMUrvgrBtuEKm4Zij5UYuGYG/wzsadNoKUqjPkhyS1r4ikQcKC4Zr7NzvF4mk551IqWkVUCW5iwnOTPtrATESqTwGCCvMSKJlOqPOTeOhbSgMI5qpBHOn/OZdkYnM6U6sGA5KTDOMVbRLCaGSCsEhlALstTwu597iFbqf795OOLi0Sqx0oRasdTOsDiS3HD7gXluvGrjGc/3t2c8Fs/ph5+PJicnmZ1dZWxOT08zMTFx2tdOnjzJ5OTkIxrJ45Z4AIxs2cgPvu1XfE/XGD79zvfxwOdvJW13mLx0Bxc94xpe8pZfIq7XsMb37T/whjezcPDYOW2/nVu+fmCRqaZ34lzq5syfgc3SC6FKnElxnlojUl54SdsyksVl9n71dpCSmb2HWDh2gqf90I3s//qd3Pfpr/jMVXg1Rh1FIATxcJPNT75i3bZMUeBKY6ULjJF+woTQik1Xrz/2RxJz7QRrHfOdnNFq0E8+nPOsia31+OwbKOPoQpuVtOgXdqJAs32k0qfaJmmKKTJkYQji1VKxc5AEMTpQJQ32lL53EHD5D754jVaLFJ5iOt1KMUhiJdjSiFksBdEQ3tvimVvrJAYMClGamnn2TkFhLZFWbB1rstLuspisZ1H1sAWdwmMOEmNIA8VQIMiKgk5hacYh4zGkVvDMn/gP7Hzes9j/+VuYuexK9NgoYW0VgGic4MBixlM3hH0fkp3DFabbOfGqVU75Xtdvc3mHW1Pqggx6p/rEqKI9U0UIL662+jV15crb/9QDWXoNDkFqLJ3c+jab8y3SHsOkdxl6+Aikb121ckNFi75C6XQ7BQSNWPexEKvX1rGSFuTOcWgpIdZeQn/7iK84PHnTEA/Mtmhnpp804SAzhrFayHIjZinJWewWJLlhpOJ1Y8br4TrKbO96+XELOrnl6ErGUCjpFo4oUNQCRWZ9YiJ657HMHrLC6Fc5uAAAIABJREFUESj8PSwEx1bS/vkS1kvch8rSSgtGKxUoz5UQXpW0GgR0Cy82d/9Mm/tn23Ryr/8xWgm4aDjuJyCt1LK1GeGcoxkH3Dfd4c/vONav2GXGct+JDGvhiqkGUgqqkaaV5CCgkxkKY79jxcQez7j++ut5z3vew6tf/Wq++c1vMjk5Sb3unzVbtmyh1Wpx5MgRNmzYwOc//3ne8Y53PKL9PK6JRy+EECituenXfo7nveG1nNizl7Ftm9aoUEqluPqm5/MDb/1lPvAzv4ExxXqMQw8MKgVCKlzhjaZOLJ+bGNZ3v+G13PF3/0J3cfn8S0oXqgR1NkCrcwjnWDh4lF0f+jivetdvMrptM1//Px9GSInSqv/5p73iJoJwfa/zmpe8gOpIkyJJHjs2yvnGBXbybYyP8ZSXvfCCbKsorJeYNo4TLe+uqoWgU1iSwrBj4uF1Wo4stOlkZnWljMdW7J9rs3OiQSfNOL6UkBQGd/FlBDZDzs9g08y3CyiFtAJN1mcdCHSgCSoRoxNDCO3L2qZMKpUQLHRzTFCjlfoegLH+WGpakqU5H7hzmpXcUxPHayHXbhliqhbQU5zM8gLXzYgDRaS9amRvzhZ4I6+Jmma+a3BYJqoxQ9WIvdNLHG/nHqgYQSglFWeJbE59+ySN172c20526OR+YlcS4nKSWMn9qrd3rrSSNCJVtkrK5MP5FXvv39bBkZWCbU2Ilf8O2JIN0jWOOFidhKUUKOddYcG/LzWWVla6NgtPmW1WAhY6GaHMiIWjWzhaAl8yQKzJ2q3zCU1hHcdXUiSOSqCY62QUFrYNx5geRsatrjOXStppWpRjzQ0L3ZzFbs4lY1Uqoea7d4zxxX2zLCcF1lqElDQizWg1YGTbCE/Z2KQSKLLccGS5UybHXio+M5ZYy/5we/gacKxk/tx3C0tFS8ZD7UXKrMWVqnmDqYuDvlPvYjcnN6vAVp93+eNQwld/tZIEcm2VrKEU955sc9fxZRqVgNGaZlMjYijyJnUrSUGoJIEWOOsYrsXEoeYT9+337b413yrBgfkOl0zUvCLuKRisC0mlf0LEYwAuPZc56+lPfzpXXXUVr371qxFC8Fu/9Vt8+MMfptFo8KIXvYi3vvWtvOlNbwLgJS95CTt27HhEQ/mWJB6DUR1qcvGznnbG15/9+ldx2wc+yv6v3UHWTXz7olwdqTBAas3Itk3UR4c5cs8e8m6CLYqHLVPJQLHhip0kK+2y/XB+E2FtYpT2zPyjTkCk9iDU0/rAOEdRUjrNoWPc+5mv8Lr3/g6j2zZy+99+jM7CEtWRJtf+8Pfz0v/6i6fdflit8Pyf/zH+5W1/SNY9vdfM4x49k4eBVeQjTYpUJeKnb/5/T2uA90iiGWuOLGcIPPixnfulZ26t9+14mLDWrwJPp0xaWMtyu8v+xYSsbH05J+gQUh/bSHT8sFdRrQeclAInJKHWFGlOEGqE8HTPqPRdUdJRWLFqLy4grNQ43MrYP98hLQxeC8av9HsCVQg4sZLy6QemufHScaYaqwmrwNuRe6feiKXEY0UasaIe+yrJtoFjmmslFE7SjAKMg+l2QaQEw7IgFCACjcbhrCPpJOg4ojCQ4oi1WKO74UGgPvlIjKFSnu9e9UIKr3CqFURSspILWpnBOD8JxqGiLi3J/CJho4YMfEsi1BJRWI8vSHI/yZXiV4W1LCcZ9ViTFTl15SdbLQQzXddXsl2DDynbbbNJTlT6jMwnBVoKdoxUUFKWyYY/17K8twX+2IqyBdubxE+sJAyFinpkmKxF/MhTNvOp+6c5sdhivBkRKN822jpUYeuwB6bunW2xb67LzvFKH3tkhE+q4hIY6/AYlMI6ptt+MWYcJYbFUVjfcimcQFgPyO01rlT5byEE7TJJG6yarp4Pf4y9+2rwagoEhxa7RIEi1JLhSDMUr35PlZQsdXKGqwFKCuJQM9/JCbSkGWtWkrVV6LSwzLdzJhvRGnxdo6LXJiLfCXE6dPej3ua5PWN/5Vd+Zc3PV1yxWk2+7rrruPnmmx/1UL7licfDhRCCH373W/jIb76Th770dbqtFgKojY/xw//zLez/2jc4vGs3AOM7trJw+Bh5N/EZe5KdMTEY37GNQ7t2Y/LszEnKYDVizT0guPaVL+G2D/4TyeIjBGj2kPGFWecPc2o4a8jaHe78x0/x8rf/Ki/5jV/gJb/xC+e8qxt/7Q1sfvIVfPS33sX8waNYa8haHX9+vlXRRyzSl0E/7yROQH14iEuf98wLNqzReoWR5ZT5pCApvGaHsR7guKn58G2WUwGdQClyldMtDHOdrI/kB0AqhDW0nKQ6Mky9VkFpxYSxTDtNYSGq+Am/EgjqA9TwQAik9NNBUdIWo0rE4eOLvsxf7kMpQZ5ZkL3yvf99ahwPzrXXJB6Dx5EUhqmh6rrXBmO2lXolVCW9oqz1jrcLRjAZOqSUNEMHRY41hiIvEFojrAdhDlU0Apht+/MzXg0w1rFkPHtGD8x01jla1k9ifoylhxCCWMGev/lH9n72K3TnFqgMNdlx/bV89xt/DKUVc5lloZuTGUc99KJvFS0BL08+t9IlYFAMWVIPLEsZuBKo2pPrjpRgKXMMx76d0M78vTJRCZBrXIDLEB4HMl4JmOv2qLP9lzAWVrKCeqRpJRmj9ZiXPmkDn7/9HoaGJ5DC4142NOL+yr6wXm30eMswEmvGa6HHoFiPvQhKgxQjHMYJRiuauU7hWUvGspAU6LLt1y167ry+zaSl4CkTVQ4tZd5wr+xgnG4aDJV3Pha98k4foOIXh93CEmqPK2mcRkQtUJLlbs5irJnPVmhnhksm6mwftSx2c3YfW/Y0YuGvd1RWs9KyHVUJJNdfMn7We/TbMvq9tgu8zSdIPOETD4CRbZs4+cA+siRFaY1UiqzV5rPv/DN++u/+iD9/zS8x/eAB8k4XoSTxcIP6+BhFkjKz9xA4i5Q9nwiHVIqXvOUX+ew733fmiyslz3jND3DHzR/vy6n70qUgalQxWUbeOXcwaj+E8FTbQFGk2bm3HISgM7fELX/xIbY/48nUx0YYv3jbw38Oz7C56qYbuPrFz+//rsgy/uk33sFn3vW+/hikUuclHX9BwvkqgZQCa879i6ECjY5Csm7C4Tu/ydanXvXwHzqX7UrJtuGYcCVlOfN2ZIGQjFU1UyO1s37WS6KvZTJ0i4I9Mx3auUfuF9aX5XcMx4Q91VMpEc6R1xroSOKEoFqL2eIkrcyUk4LXfXBl47+3wpPOry2906tjKTF0sp4Las/S3QM/V8/u6r862enLuVlh6eQ5jUqIOovkfl661gq8x4ixjsWTc1SbNZaSAl217D80Q+voPKI5jLUWjW8DhVozHCm+cHCxv6rWUjBZC5isR3RywXAoUMJXGRInQciBVbdACs+s2H3zR7jnbz+KkBIpBclKi3v/5fOYLONJP/uTtHIvnBWUvjBzXY+/qQaqL+XtsCUexJ+fZijR0tIufEVBK0mkBInxBxxphVaSTc0K062MOPQCaIGWA1LhvuoghKAResGzmU7OsdYqdVTJVSaNsb5lEmnFkMy5dsvwac97pBUj1YATKykLSYFxbTY3K0Ra+XaOtWgl6OmRhkr65KNrAEdaWILQV2ZCJQGPhTHA9dtGaFRDFlPLfCdjtBKwdBrMD0CtFP/qNRUtXma/W1i6uafMFtYhhDttVcKV99qxVkagBHGgqIWKldQxWg25fKrOvSf84m60GjISB3S6OSY37Bir8ZxLxs56f37bxmOiXPrvicd5xUfe/HssHj3h9T+SlCzxyjwHbruL977iDSzPzNGencfakjW/6HCFZcMVO0lbHVamPRJXKEEQxjzr9a/guh/5AT7+1nefeafWMr//CJNX7GBu/xFcWbauDNXZ8exrOfbNBx9RQqq0QgWe8ia19pLtg6uFU6N8SUiBKQr+9ffey/DmDQgl2fikS7jx197A+I7TJyCfedef8dW//BCtmXnCWpXLv+fZvPZP347SGh2GvPL338zJ+/dx/+duocjSb52HkHWIUBPGMVm7c9a3nuqF46xl4fDxC5Z4AAzXq1SigKVOhi21C5qVsA8MBZ9kLHZz5joe6T8UChCeDtjDI1jnOLjQJTN+JemBooJu5g3GdoyUdNYyKRBhhKsEpcW598YYikt/lRLLocVa3xK/Re8C6hCMVE7pB5WtC+d8MtJbi/Zs76rh2oe2KysL1jlmuzmzhxd5yqahEgh4yqadW20plKtSrQTtNGPmoTn+7ZOfQT7tWmQlRgHx/CxywwaU9j36y4Yj7jjZITWuv5I3Do6vZERKMlWPmEn8OCsaGtFaHILo/+3Y95mveJQopb4GXujswS//G5OvegVho1EmSGUzwQlWMts3rpMCujlE4WoXUElBrPyfxdQSBJLEDOwYj62pBoqJWsB4xTMuwLcOU+NYTs3Ae/2fqVqAEoLlzLCYFMSB8iZqgxf1YWKkEnLxaI2scCx0c5ZSy+J0i8la6HU8pFwzUEepTSJy79Jb6m5k1lILNb2U2jlHFGmml7r820MzhIFm52SNkYpmobu29VHVgvF6WGqy+PZptzB0c0u31D4ZqQQcX0kIdUgnM2WVaZVtlOX+vsys5f9j773j7brKO+/vWmu302+/6pIlF1lyNx3bNNMhgCmhJECAQAKTNm8+Sd4Eksm8JBOGlMmbhBIIyaQ4EDIQCKFjwEAwuPeiYkmWdHV1++m7rLXmj7XPOfeq25bBSvx8PlflnnP2XrucvZ71PL/iKYU2lpGi73RBUs1EJeTeqQalyOPKLaM895xxgtPkdfRE/PjijLiCe2+8AwHErbZrS/SXPJa9N95xxLvdDd+cXWDhoUNMnnsWtVXjpHHMZa99KU/76WuY2LKRtNslLJc5Uey96Q42Pflixn7iXDacvZnicI2tz7+ST//y7zpzuUfAahFKIZRECIlI0r5/i1M6zY5BwRVIT6ICH50kZEmK8h04a9e/38yf/8Q7eNa73sQVP/t6/HDAZf/KBz/Kl3//Q/3zkcUJt3z6izRmZnnP5/6q/763/u8/5INXvJbZvfvxlEfS6WB+DG7COklPScQsixMndqUNQgoKtQrnPfcZp308oe8zUTtapApcheaB2SbTjTgHwVmaoUegJIV8BZ04kQmW4oz+elAIBxwVzqRLGze5xZnBE44JMtXMMNat8NuZQQqRm6iJvJpi8+pFXqUiB/jhKLDjpYjL1la59WCjDy4MlMD3JJ4Uy4TRHG5i65ibcpYvhjJjaSeaVAs8CXcfanD5+mOvvH0lsNrmI3MbKY4O051fZOm+HUSFEqXLLgVr8XyfkdEaACVfsnMxoZtpxJGeGwIWY03oa6x1K2WdOJDm+mqAsYJWYvLJC9JWm/bcvDuO/Gz3Chdxvc3inv1MXHh+3nkaNELSZSwZIaBrnHhamA9HCYERgm5m6BiBNH0s+0CJVUDRk4QyIPSWVZmEoOC5hLIea3zrqgNugpasLgeMGUui3T0S5D0lKUW/lXSiqBV8jC0Q+Yq5VsJSN6EUeEyWQxpxCta1Y/xlTA+Lw8ksdTPiTDBehBVZFOArxXs/dTs37Z5zHkPA5EiBdz53MxtrEUtxhrWWcugxXvRz/RRL0RccbKQ0Yk1mnRaOwVIp+HmCY5luxFRCr++IHCeGJNMk2lLyg5xN5M7dZDmgmzkDxNdevJqL1tYYq5x+64vHa4icWHB6twmPF27jGVGjkp6HTjOMNsdCNx07rGVxahqjNUm7Q3Nmnp3fuZEvvf/PuPWzX8aPIjZcvh0VHHtyAYe/WDxwCGsML/qt/8KO63/Ih3/iHey+4Rbm9j7kqiCnEF4hwisWEJ4iqpaprVuNF4VEQ1V6Ikc6Sfuqrr1jdMwVj6BYxKROkCwsF9FZxkO33s2he3cyfc9OPvvr/4P/dv7V3PO17/T3+f1P/NNRJ0lIwe7v3cLsnoHaXGl4iN++8ytc8jPXMLl1848l6Xg4kXa6xM02WRyTdLr4UcTC8eTkT3NYa6kvLXHvwTn2L7RdOd3Yfrk+y8vk4CatLHeFXR5KDoTItLXEqWZVyeOsoZDxos9o0aMaSqabCYuxZradsmuhSyfT/QdRb+rsJR3LQwg4Z6xApER/hZ9owyWrykwUPTAGqw0VT3D5RJFq5OeiaK4GkuZKnQtdN+lnxtJOM5Ls6CRbCMFI0QdsH/wphcAvFigPVyiNDrH0re/QvPEmPAFDm9YRepJV5YDV5SA/V0c/CAWiD5DsAWKVFHRTzVw7I/IEtSjX6gGCUoFouHbMa+ZFIavP3shopBgvejmGo5e0if7Ye/n+QgqNDJJMO18SK1hMyLEPA6aIxFV3lHBtLE+JPnulRyu1WELlKlfDRZd0CCn7iYsvBSVfMl70c7dZSyk8/vPoyBguBmwZLXH5+iGevWWcDUPFPoU48CTlQOX7d+fYEzASeZw7UmRDrUAp8FZMANbCF364j+/vmHGMllx99NBcmw9/fRe1yOOs4QKbR4pM5JgScJYUvufRzSyLccahVsxSnNFMDIvdlErBY0PVGdVNLXWYayXMNxP2zzV5aK6NDVRfKbY3kF6FZrTgc+54if31mNnW4wQc/6MIax6bn8dJnBEVj20vehZ7brzdfTMeRsKm44Rd372pD0vf9d0b2RdF7L7hVkymeebbf5J7vno9iw8dOubnrbV06k1m7t3Fr00+ibjRcr8/1UqHEPiFiOENq9FxStxs0Wk00VlGaWQIFQR0lxro1FEmTeaSCyFVDrjUWGNI4xjpKQq1CtVV40zft4u42XKlcylIk5T5vQf4i5e9jbdd+6ec//wrmdt3AJNmuVfFAHFureVz7/0j3vZ3f9J/cEgpufSt1/DZL3/v1E/ujzi8YoGsPRBPs9aiwgC/EPK1D36Un/rYBx5zSt3i4hJzsWExAZu3RyxuAumV53VOs4w8QaB8Cr4c6DnkGYMQUPYVowUnrlQJ1bIWgAPcra+F7F7okgFaW3bOdzin5lELBEb6HEUiACSGQFhGQsHTxgLunmnSDSImih4TcYNxqTHDTqxLdlqI6UXaBYlfLuFLV+lY6Ga0UpsLdrlkqqAE7SQm8I5ecY6XI6y1LHQytHGr1dCTTGzbzOYP/T7N6RmMUhwmZL6b0ko1QkCgAkqByitCK6Pgyz7w0bWtegmCoJUOsCC+AqsNJak576rLuf0zXwHpWh3gqosve98vsG7VoFpTDRTzHScSVw4cBTQz1tGM3adoZpasmxEVFKkmN7qz+FKSSJdM9oTkeveAzDMSQ65mirtHlHStFWENA0u4/q4QuKqRwBKSkXZSOi2LRhCWh/JqzPHva5EzcJCwbqjAUjdF0ZP2z6nWEiIh0caQGAd+VXnbLfIUqXE+LhOlgDv3La7Yn8grY7OLXW7Zt8CTNw0ks7W11EJF6Cm0Mbn4Xtb/TH7VMAZkIBmpOCBxnGi0hHUTZYq+dPoensT0qnm9r4u1VKOBkd9MM2a0GDzm3/PHRzxWQo+PD2O9MyLxePFvvofv//Wnmd2d+3E83AuSPxCdxXyLuT37+er//Ai/8cPP81Mf+wB//qK3HPtz1pJ0Oszcs9uVuKVwVZdTDeVaKFG1grVQmhzj8AMP0plfpLvYQEiBF4ZYbXJ8CnhhwOj6tZQnR1nYN0Wn0cQvhKy/eDtxo+FApkuNXIXUrhAx04nhY695N36xQBanOBEkVtbRBey/9S6+8oEP86LfePeyQ7Us7D81oTZw1RN7muXDTxQ2y1ZUu5TnMCH16Vmm7t3Foft3sXrrwzcrOtXQacJiohlQQ6CoHNugmjNNYm1pxA6hIfPEZEM1Yud8p59wYJ0r6LljRYYjL9dHWNmLBwfUDDxJJ9Y5oh/2NjK2VBWeTPGUIpQOf9aDPs80Ouw/MMeDX72epW9eT5qmFDZtovKy55GtHs+X9a1+kpRZS2N+kclqGSFgIdbEOk84tGWypCj7Ek9AlqZMLaRMVEuoZY7GUkomq0VGy04sSgpoxppuXg0cWT3OrVMN5jpxn8mz0M2oxxlDoSLyFJ1Ur5zshOj7yCyfAo/84hclhCJFCLjiZ16NjWN2XP9DGgsNVLHIU9/5JtZfflE/gQKXIIxECmsNCstUs+v0KJSik2gnTuVLMgSZdQwRTS4DT0bJk6T2iDXQsZKDPp7EYrShEEiUciUTayyDBtzAY6YHo/EspFnKxGiNmUabieqJQc3Lz1s5ULRjiU71Cn3DHqi2V0XqVW4iX1JWHltGi8SpoX1EIiiEK9FrYzm01O0fp8Apnk5WIhJtaMepw+r024ru82VPofKKFfkVDAOVV8ec909PiyRSknqSkWjwJVQCj0ro0cjHFGfOJNA/jerBj9swDth+erf5+DlvZ0TiIaXkV775j7z/khf3qw5G60d0XYQQmCzj8K591KdnWXXeZvxidFyGSpZrX7j2mHh4yODM0WBL46M0pmeZ37mXpLm8auK8XMJKCZNpdJqx8UkX9UGMo5vW9gbN1f/17XzlDz7kmDC9MRyn8pIuqwwcFRaWDs1w1xe/xWWvfSkTWzYCsP+G2zDpiVtHy92Ef5RJBzDAfxzx3dFpRmepTrfefEz3HycJqXFl8kBBksHGoUJftlsK54USKulW5fnEtH6oQOBJDjbivkPoaMFnohSRaJ1//tjRA6kCREowXAzoWEEoINEOozDkg8Jy3Z4Gs+2UmQcOkK3ehHj5COq6r1PfuZtv/n938rRfegejm9f3J39jLX4YugpNZvC9gQ+NAFaVFJXAVQPivMOppODgQoOJWsVpY/QmISHwleovpgpW0EmdrsViJ2WuM9A16d01WY6nWFfxOdiwuV6KpRgo1pdChBpUlJYnH0XfJSpKQEHpPu5CSMFVP/9GnvKWa5jef5j7qDK5foTYWEperr2RYzmEEvjKox5blloOszDke9QKTkdiPtVUI8XeQ0sUogCVC/PVu5o6mlqoMDbXT8lbQccLk1lKvsgrOJZu6q6bzD1opBTLKib9w8TzfNJMo7PUJXSnqFORakNmHJtESWdYFylHAa53XQKrc40IJdw9Zo2lHmeMFAKGSwGH6ytbGkIIfA/OW10hz50cSyfyCXyPwHfMGF82Vjwio5xh5MtlyU5+Ta21WCFynRTBeDkiUDCUeX3sjTseSz0xDgcnBvL1T8SZHWcExgNgdMNaXvUHv0F5fISgWBik1KcYywWqrLEu+djxIKMb11EeG0EeA7UPvTJrfyOnvkMh+onK+ssvwisWMFo7ddHeQ8Ra0m4XrEV6iuH1q1cwJ3oxumkd5z3n6bz4vb/Aqq1bUL738CovR0R7oc7+2+/lnq9+Jx+G5ZaP/dNJRbxU4OMVIqTvHfGkPEGIwd8qilDho3SSzIfoLVNpLQ7VWH/Jtke33ZOEUooeIbVkNZNlr69voHNNCshbADlYsncLrCqHXLa6ytPX1zhnpIi1ggONLl1tc7r+4Lz3IAjaWha7TokXC7XIRwnYt9jmazvm+fy9s3x5xwJ3zsZ0UstIQdGaXUAnmeuRlyvoZ1yBLwVxknLbtZ/FknuTIKitW8PkORtZv3ktpVAReIrVJY+yL3OaqSTVNjdRy9tIuVDZrtkGexfa1DvHBgOHnqQQeCgs891scLsve48QjgHx0GLMbCMhTlI6SUaSaFZXPEqBS8p1fgICCcMFRcFXdDV0Nbmq6wDsKaUgLEasPXs9Nr/P+pWO/NqoHg5FCEqeY8tYnCNxN08Y7zvU4Ps7Zjg0PU+7m9BOM1qpdhTRzDDXyYhzymjau4bWgY5bSUa9m5FmJqdXG6SUZMYw1zUsxppWZqknlqXYfV7i8BfWWqabCbdNN7nzcIt2ZvGE8w861Vjequhdr/7aeYVam0UbB0AF+liVl1+27uhutrVsWVPlyRuGiTyPgu8zVgoZLQ6+g76nWFUJUcJVV5RwGBhfCgJP5ddI9JlVy5/fvetSiUKUJ+lmhnaiWepmTLdShHD3QiX8DygUdpywxjwmP4+XOGMSD4Cr3vlGfueer3Ppa17yiJQqrTaYzOEm4kaLv3j527j2Pe9j2wuuIqpU8IoRYtl2hVJ9865TDRX4hOUSQSEiLBWJqhV2ffsGDt15PyoMKE2MUl09OXCuNS7puPgVL+Bl/+1Xjtqe9D2e8sZXIITg3Kueyhs/8vusPv+8h5cEHXUiLN1Gkxv/4bMAfOwn38PMPbtO6D3jFyKG1kywZts5rL1wK8o7RRCcHfyt4y46Pj2iZcJziaLn+1z5c29ckYg8FhGEEVHOPLBGU/McaFDmP1iD0AZhLW5ott9a6SEBBFCRGWXfkmrLbNt5bri32dywzL13tp3Sg4ZEyjFSds+2uHl/g4V2mgMtU76zt849M21GCz5ZkvZxAwKwY+OIoSGGx4aZ3rmXu758Pe2FJcoTo4SlIuXAcxoI+bPcV4LRgqLo5XTgvN7gVrjuPUoIlropmbYcbsZ0kqOrZEIIhgoBoe/AjcdLaBdzufAeNfT8iSqTlZC7Djc5ezhiTTmgkK/Whwp+DuJ1Y0m1pYWXy48PtjmQXlc0llFZj1yn9Cop40Wv//9WoumkGV+69zB3znaJhoepVIqUA0UtVJR8mbcHLM3EXbdA9VoGmulWwkwrZbad8lA9ZqaV5Cwkp4Cb9HXAXaVE4/ylVH79f3iwyff213lwMWbXYpfr97d4YL7LYifpq52eLEJvsICyOevGWJeAKSmOAjt3M6f3UYvc9/ktV23mzVedxVglRAonPrd+TZmrLprkloMNUmNZO1SgVliJtRBCsHWyykjBgY1VDkwNVA80urJppnptR+ES1dB3FbRqIWJ1rUhsHdZG5pWiUqhYP/Sfh9WC0Y/Nz+MkzohWy/IoDdUY3bAGqRSak9Mvl8eRD8Ck1eG7f/mPXPaaF7HlmZczdc9OknaH+vQMQkj8MHDaIXFyStmi9D1XjQF6dWdrLVG6+ECbAAAgAElEQVSpgE4STJr0Wy1+IUJrjUk1L//v/5Vnv/vNANRWjXHnv11Hc26B2qoJLr3mRZz11Ev6+9h3y53M7dn78Ns+x4hD9+3mi+//Mx745vdP+l6R6wKMn72Ry17zUr7wO3/snIPhaKZRj50DKyiyUp4+gTKhJIVqhWe/58087aeuOS3bPOH+hGC8WuLQUpsu7rpK6zANJknciheLKrjrbxF5u2VQzzDG4ElYE1n2aIiNJUax73ALayy1yKOTGid3biyVYoC24EUKbT0enO8cVWiy1nLfbJdVZR/lef3JBgApMWGErwTbXngVu26/n6AQMH7hVqLQwwiBsIMSuBDC2agHkljbfnlcMLjVUm2YasaMlSMEgsVOQiEokGrDHVNLdFPt2DZSUgsUE6WA3QvdFeVzcK2edqwZLQasqRX6Yw48D2MVB+YajI1UkWpgf5AaC3ZwmxkECQplNdiBm21bOzbKdCtjshzSo7324E5a6341KsxbAekywpwCto4XuXhdjdgMqiY9d9dGnDHTSjh/tEjoy3ybgqHIp5XovnbHUidDthOKq4aI8yyyj7kSjoKcWoe12bfY5UAjXtlKELBjISYKAuqxxgIl32NNLVpBkz3yPh0uhBxudkH03AksU/UEkQN1e1yizBiWOilnj5URQpBqTTfJeNWT1vLiS1bzwGyLnbNNosBHCkE9zvjBvgV8JVg/dLSiracUzzl7nJsemmeunTlXZGGRQh4Fqu0lkEVpKIc+hWXaHL6nOHe8TKObOTPAQFEOPP5zgErzsOb0m5Dax0+d4YxLPACm799NWCqSPgwb+2NFT6r85k9/kbOvfArrLtlGddUYN/3j59FphsxBdF4hRCcpJtNOb+OI0qeQkqhWIWkdja0QQvDsX3gLe2++k7jZXrHykkISVkIuf+1L+r/b+rxnsvV5zzxqO516g2/86Sf47sc+SXN24VEdN9B7AvKDa/+F9mLjpNRg5SuKIzVe96e/Q2V8lDv+9WvMPLjPYWCOyH+kknhhgDXmlLQ5+tGvEBw7pFKufC1d9ef1f/a7DK/70dlhR2HAxnGfb37xGxz2fTZecDbCWIpjI6gg6JeStckNtYzJSap5iVlIIi8ENLUkZSEVtGPNt3YvLMshXdWjnRg2jVmGigEzrTQXvNKEy4B11vFyWUpTDhw2FIertOcX+8mdbTbRhw8jPI/L3/o6nr9lI3G7Q7VWdqJiCFJAalfqF3kvvuhJluKUoqeOym8PtzMOLMbE6QJPXj+CNpZb9i/yg4cW6KbaTYjWEvmKTUMRZ3sFzhsrsGO+SyftGbPBRNFneilmrBweVY0QCLpWMX1ojuJQLZfMFivuDYnDyppcJ8slXK6NtHMpw1hLYmDvUoyUMBy6BCHTBm0tgRoALAf5smC6HrN+uMCLtk4wHEkOtfQKT0MpHOsm8gJ8TyIRZD16rnB28k7DApSSNDPZN/ID+gsYIWTfssh53KSsKTlH2/l4wGiQAg63EtZ5ilZqaHQz6t2U8ycrx3VjrRYD2mnGwXqX2WaMAO6fabPUzVhTDRgu+GQWupmlm2muPi8g1Zpm14nhdRJNagzjRR9vosyd0y0CJYk8Zxm8Y7bF+qEi1jojwSTH2xUChacUF6yqMd9OiDPNTCshyQbaM9ZaIk8yFCrKUcD8of2Mbhg/Kqlw1Q+fKqdOL34izpw4IxMPLwyorhqnOTt/ejZoLPtuvIPq6gk2P+1SnvrTr+bfP/EpdJqhcovtsBix4UkX84YPv59rf+43md9zgE69QVQuMbJhLQhBY26OxX1TA1yItVzwkudw4YuezRXveD3X/eknVgiECSk5//lXUhk/sdfAzO59fORV72Tqnh0n9XU5ZgjhDPWUQqepw4cYQ7fRorVQP2lmLaRk3cXbWHXeZrww4O/f+Rsc3rmHiS0bmXlwP1k3xsv1UHSa4oWBM747AqxqT5RVuDe4VoGUxxBSc58XUiCU4rznPuNHmnT0QgjB9mdcyqf+n9/j4Ob1bH/BVegswwsC/FKRoFSkGEhasem/34GIIVTgclmPoaIg6RjuOtTAmOWsCCco5nAHKRPl0K3ste6blbmkxrpJLEuQxnDrHftJpUd17SSN6TnSdhtz261UJ8fY+rpXUD5rIx1tqVVLCHIcQN6WMUBmHZvCYmkmhm4GrSRlOPLwcgv4uU7KnYfbdNKM3XMp541X0IHHDx5aINGmr/vQO4YDjZjAU4wUPC6aKNJMLK0kY7jgUQw8Di7FRJ486q6QAhIE9cQict8RdUQC1GteNTOBlhJfgodHK7OMlgvUsy4FXyGk5IG5LluHA8qB7NNeU20JlKSbWTINCMtsM2HHTIs3XL6O0YKHkhYlLJ3MVbMkgkC5JEZI2a86OKC16I99uODTSLRrm3k+i+0U4XlO50O41kGvjSYESJOxdUhhrEIA7cxy/0LMUuLG6kmnB9JK3f3UTjVT9S7rh4/vo7OqVqRWCLktW+CGvYtkOej0QD1mupkS+QqL5dK1Qygp6HRc8tC79/LiEkORRyXwWIozEm0o+C6xstay1En6yqPgsCilwKcU+XhK0ugmWGNppZo4Tz4KviRUEk9J1g2XmN7d/M9VyTjV0BorfvTutD+qOCMTj01PuZiZXfuorhqnPj1zauwWpeAEZX6dZbTnFzh49wNuZa0NSc4OUYHPBa94Pm/+xAeJyiWe90tv43sf/ySdJcekCCslnv7WV3PpNS/m63/8Me77+vdQnsfT3/Y6Ln3VCwF45ft/FS/wuemTn6c1v0RYLrH9BVfwk3/230869K984MNM37/bueg+3BCgIif3bTKN1cZNXIJTrkaUx0bAwsYnX8w3/uSvuPtL36I1t5iXrAVCCaTn4RciOkt1JwmPa4csj2MlE0dFfi2dUuuxx+eHAZe88gWnNPbHIka3bOKlf/I73PnpL3DXdTcwsmE1G596GZObN5HlpXR36A6O6gunhqnkYOXsSUkltLnXycoqRo9d4nzeBIF05fHhgk8j1hidobsxNktJuzHpnXcT3XsH8sILMb5g4/ZzqHRbDF2wgdqmdXS0Yy0UA+UAptZitCuBL2cbgMMh3He4TVdbCr5kz2KCr6CTGhqxZrGd0k0dbmHvQodawXfiYiKnEPdgDEArMX0syxxw9miBsXKY4xUsZ48WaKVHHr9zk8VaFufrjAxXyHKFVzfCQWFMWMiQ1LUktIIAARJKvmWi5NNKLdoYVpcU3UxTCmQOLnXJViPO+P6+OmO1iMP1mOEo4Oev2IQnBVmm2bOUEGvTH5+2hkKgGCv6tPTgWiohyPoNDPoJnWM6KRpaoDPtcAz5MXrSVW0KCgJpsMqBKi2OHXX+SMgPproYrKs8SCj7kmbqxtM8CQMNXAXi6WeN8ZSNo9w33eCH+xZoxBmZcRWfc8YrXLVltH9slpXMJneSBUORlyuWQifVBEo4IPCRNGgErSQl9CWhpwjLBTypsI0ORX9wepQQrD+J+eB/+jAGTnvicXo392jijEw8rvjZNzCzcy/WGsJq2aH5tSZeOr5TbFAqkJyAcqmTlCzwmdm5h7jdWWHeppOUqXt3cMcXvsH9X/8ezbkFDu/eR33qMCbTVCfH2HPTHVz4sufxgl99Fy/41Xcdvf0sY/PTLmXV1i1sfvplDK1ddUwGy5GRJQk7rv/BI6YPR8ND6Ni1Q3rVkoeLbg5KEesv284l17yQ37/0pdSnZ90Dx9i+/kicNMEadJzQzTKUl/dkHwkWxToMjNF6ZbJiASl45jtez8j6NQ9vm6cprLWkmUbUhtj4029wExNgPEWinfS5wDEshF1J/rH555v5ZKu1YLTo84C1/d6+WLafQLnVrRKCCyfLbKiF3PxQnb27D9OenQcs9tAhgm9fB2i8b8/g79/NBc++HCGckqfWmjSfPAc1FTfxdjJDqJzGggWaieFAI8EI1/9v5FWb6WbKUid1E2N+KU2up9BKs3414Mjoeb4oXCXEkxJtbZ+CvHGkxIGZOrHw6JnpOmEwSZZlGAT1bsZowaedaGwOWuzZt/ewmo6xYrH9qoOgqCStJKPoC4Yjn7lmzJROqC6r4Hxr1zw37Gtw9bZJCqEzJOslOIfbGXHmZPkHIViIDdXQYHIhrh5tVRmBzstIaV7OUEIwXnJy90uxppUYlBR9WqivBDXfJSyRJ0lz8TkBhEqwqizJrKIa+AQS4uVt2iPvSe3aM/4ymnMvlBRsX11l++oqrThjsZsyVgpWAFGlEOhlD5iB+ip9Nd7evqqRR5LpY1cqLHQTTTn3DBoqBlQjj+lGN3cGVoyUwieqHCcLa8A+fsCgpzvOyMRDeR6v/sPfYs+Nt7Pv5ruIKiUuedUL+eAVr2X/bXcf9X6vEBFG0QkTD4Ck2XYPGrNs2QZgYequHXzp9/6cytgIB+9+gNa8U/hTvsfM7n187QMf5et/9HHGNq3jDR9+P9uuvrL/8d0/uJVrf+63OLxzDybT+FHAJde8iDd//H+e0hfQLndtPQkO4sgIyiU63VyLxBxhPS/lKQGY2vN14kaLj1zzc8zvn0JYsFIclcDEzXY+XkOmc/aKynUcTwFU6qiAroUghCAoRA7Yi6uAlIZqXP2rP8vVv/z2Uzr20xnNOGVqKUYKgzWWuW6aW6Q7hkUnMxxqdFlbCfuTfN8qnLyVYdzvih40M8uuhTbj5YBzxkq0koy5dkKm82RFCFdN0IY15dCpT0rFS88f45u3/5D77rgbNTuDnTlMoi0xTl9EBQEmZwsAJD3xPOuqFpVgAD4VONEzzzrqZSPuiU65RGSmETPdjEkyZyk/Vg7wlPM79ZVkVTmkow1+nlAYa/vy3ODYCl5ujlcNPcYqEc04Jc2MU08VMBH5LCXOnc0K6azeZ5Z4YOcBLtq2vg9mXepq99201nmABB5rKyGeEgTS4Sly2BJCQK3gUQ49lLBEvsT3JGlmmGumfUzIcDHkaWcFeGqAy+mdu25uCtkH6+ZfG20czXmoGJAZhzMRwh1rog2JdlWWUiAZiTx8lf8+c5LVc03NaDlgTdkprAqdYa0735VA0c1FsgSwqRoilcf+epdzxkp0sh62zDKcU1nTTNNOsn41VCaS0FdEwbEf7aXQo3RMe3pFmmnni5MvuiJP0kg0U40Yg2NXjZVCxorhcdcSK12QXUgpWf1EheNhhTUay+lNPCw8bnisZ2TiAe4GP+spl7Cw/xD/+r4/4u/f9ZvHFc7K4pjGiYCoQqA8hU6zgTDWMb5YC3sPEJaKdBbrfbBb2hmI7dhMM7NrHx96+Tt412c+zIUvfi5Jp8sn3vRLzO890K8Tx42MG/72M3hhyJs+9P4THufsnv1EtbIDEj6CHl117Sras/OOxnrk50+W9OQP3E69zr3f+K6rmPSAcqfCThGuLVKoVohbbZJm2x3HcZInkfciSuPDbLz8QrI4YeOTLmLthecxunEdZz31kn4b50cZ3VSzZ77NUOjAhIc6KdVA9WW98SHRmmbqevc907blZ9cwoDcqKRx1M7OUDJwzXqadapLMMN3ocmCpQ7UY4OVmYcXArR7d5Ch4ytVP58A//QtpmpH1ybjQTTNWP/lipHBMDGsHGhPgKgTtTPetzC253LtwNMtSoJxqp9HcM9Vmz0KbUAkqhYBCppmudxkvh5RDn+2rKvieZLQS8uBCh4V24lgUDJ5t4/nkGHmSbZMVx3QpDIwMAUbLBeb3T7NvdpYdCwlfvXk3u/fPIqXkqz/cxRVP2sL52zchpcxlyEFYQSPOaEeKVT1jRDtI2Bz1xeExfCURAkZLAXOthG5q0MbSRVIpRZRzUVBjLf/+wCxXbJ0gUiL3B+7prPS26S6i50mUlFhr+kK2Eqd9MhQKJks+FscAstb5+di80hOnKWtDRWTc88gKyKxzIM7y7XTze+HLOxdpZfC0jVXaqTNaAxgthoyXArQxtGLXjuwtYCyWbpqhVC7qdooR+gpjfQypAwELyDQ0EsOWkSIIKAbOnLISeXieJI2zoxZOFkt4HE2kJ+KJ6MUZm3gAXPtf3sd3PnIt9kSTYG8Zc5zXvDAiqpWpTI6huwlxq83SgeN5t0BnqeGSE8GxMRc5duIf3/3bXLD7Odz4yc+zsO/gYCwAQuAPVbnn1vu461Cd8XLAxDHKj5/8hd/mB3/3L2RJ8oiVQud2PoiQwjm65tWE3hgkcMJ6hx38Pb/nwMPfuYUsTonbHQduzZO7Y0UPkOsFPs98+0/yqt/7tYe/v8coDjViip6gkxoONRNGisuSjjwCpSjlE9PKXn9ueZ+v8MElBbsW24wUAsCpV5ZDD0IYLvpUCz6z7dRNesvus+k772P3AzsZWjXG5W99LTf9zT9jTIwSLikpPely4ic/jWaS4UlodDWV0OsrbAIsdjWZdswCX4l+cU8Jl5hYC9XAY/9Sh3XDBYby5GGyFpJmloVmwnPOGXPVBAkFJXjWpmEemG3y4ELH0X+Fo6lGyqm7bhwp9mXWDy512D3fcqqrkcf542VqaydIOoa/vvbrZNr02WQGy3S9w5pWzEilkLMi8lMrBHPtjLFisEw5dPC3L12CZ7BYK/CUZHU1pBFrDjTTPIOhn5VJIfBCn099fy9vfOYmQg+6XZt3Cpe1ICQMB8q1kXqfk/Rt7Xvv7InKWZxQWm90z1hTIFLLpMVx8uBgaWeWuVZKZiwPLSXcOuUS9YNLMW96UsSG4QKRLwmVIjOGLE9sjlU1TVL9sBIPIQTF0Cf0FYUkY7adMt1uo63DikjpdIeqoXPANdaSZm4M/aTHuqQj8J5IPB51GMNJns6PIMQTFY9HG91mk+9/4p9OnHTA8dsSwjlXjm5ezzlXPRUVBqy7dDthscA/vft9NGfmjvpIZWIUL/SXN+KPvWkpaM0tsO+Wu9jzg9swZvDlRAiitWuQUQRBwKGlNoebMTNRm1WBxUZFsJbr3v+/uP4j/9A/hl4bAsAvRpTHRmgv1IkbJ24fdWbmCcolwmoZnWZkna4DbVp72jQ1ThRWa9CG0ngNk2YY03YAV7FSWEpIgR9FnH3Fk3jeL73tMR/Xw4k40wRY9tdjlBREUh7zkeDLntqmq/n3DF2VECjhZvhmrvPg9YCdR80ZglrkMd9J+/PioYUm03/zd8g0YW73Pm7dc5Da+tU869ffzcyOXSSdmMLFF1M+ezOpNix2EoyFA/NNpDWct2bEtTbyZKmVarqpZrjoO8xEH1/ixr5noc1Y2clng8NzeEriBRAMRRR85e4fY5ltOe2JLSMltk+U2b/YIfAESkjmOwk3PbTASDliqKC4Y2qJew83GCl4TJRCIiXYO99ipOTzrVsezNk99IsL4BRjO90EWS2wvBhprKWrDUtx1hcWC6RESvByPZJeCqixfUtwT0mMlX1qZy8ybSkXPBY6CXONLp4n8aWhqwHh0kcpYLIgCUWGwBIKSSoERrgKB5Y+xNRpA1o6qaabuhthOHBKqWbZ8Yn8R1qYb2XMd1L2LCR8a8+Sez3Xz9gz3+K8iRICSLUm7WikOBrP0YuTMsiOE0pKSlFAKQoYL4fsXehQj5367FDB56zhomsxC0GtGNBJshws7MwBj9fieSIeXjgq/ulutTx+cDVn7F3yjf/1CdLuI7dJDopFCkMVrnz3mxk7a/2K1y559Yv4bm/Sz0MoSXXVOMr38KOIpNM5Zsug93CXvkdnsc76S7djtRk8SEtF8DywhnRhkeve9kvYLKN81kaiLKG+Yxft+UUaU9NgnEfBoErh/qiunuDNf/UB/v8XvCV3sT1xZpw0W6TtDmG1jPS9h6etcRrCCpjYupnmwqKrvAiB53uMblrHm/7yD7j7K9/GpBlrLziPy17zYvwo+pGO72ThKcF8y7mvRp7Ek85n5MhQEkTaJcGJa/XYDal2csUjBQ9fCfY3EqcYCogjHgZC5O2B/P+BgvPr05Rf9uzceM4y/cCDfOejn+R7f/bXvOTjH0RbQTczLHYzOplB51CesVqRXfc+yHd27WPb1o0Mj1TxfY9MW0SOv/CXVW4CJbDW+WuUIy8Hh7p2TC98JZhtdRkvBWBd4qStdaqpeExUAoyxdDPDeClkKPL52n2HeOn21dw/22Qo8thQi1Ycdzsxfflzd94Gj8j5uTpsXtWfrHtjstZS9JSj6KbOz2Ss6ONLmOtktFONFCJPcpwQW4bD4iy3r9e5WqyxrgpRCBT3HWxwwfoqk9WIZqJzHQoYDQwV5bQrXBLkKM5CAsqn2c1Y6KYuOfUlzUSvWJuU8i6hJwey5ssv/6fvnuXBxfgo2nCoJIcbMQcWO9w30yTVlvW1AueOF/GVPAqkbq1FikdfdSgGHudPVo77upKScvTYKgb/pw2rTz+49HEE6D1p4nHXXXed8PWbb775tA3m4cTU1KGTgiylpyiODJF2un1zuV54UcDaS7YdlXQAlMZHVvw/HKpQXTuJHCrRmJqhsnEVi7v3O0zIEboa1rq1RnntJEuRYN/+/SteN2lGfOgw+B5BrYpJUoRSHP7Gt0kbDafuaXLUGrg+cn/j7o/67Bx/++73otM017U4efJhjaG7WD/xCXuMIml3OHDPLlQUMrZxjcOJCOgs1rn2l3+X8uoxLn3Ha9m3cy9fvvI1rLrkfJ70c69/TMbySO7XVPrEpXEQkk6m8YRCSEjsYAKRAiJhaMTQSjPKgUIJN1HGuZ9H6GuWupq5rmY4En31xuUhcHTKQDktjKevG0I8tIjJ0nyFbpk8ZxNPfv3L+PL/+DCHb7+X0vatzkckdYBIL3djlUKyevN6Duzcy3337WF43RrOXTfK6qECKjfwOrJU32w2Ca0hSc3AUM4MpLidK7J7LfScc203M0w1U/YudQmkYFMtYqTgM99OkBg8m/KlG+8mjmqM1sK8ojH48iaZ5rz1w3zrll39hKwXU4fmEdogsX31XJMn5OVAgtVoK1hoJ6RpTIIiWUZRrscZrUSzeaRAO7F59cFlMabHBskj05rpuTpbCy1KwQhKCoai3ngMFVKyXMm0d8a0hUxnSCTTrZg4c6t/FYs8KRhUNVqxpVTsgS/zP/NWV2wcEDjTzhBO4No1Q5HHpeuHmagE7JjtsNDO2DPf5s6pBndORVy9pUrhCLdWbTQL04f6jLPHe/y45pDHdRgHRj6tcbrpuY8iTpp4XHDBBYRheMzXbr75Zi6//PLTPqhTiQu3bePWj/8zOjm290dxZIjamklnZW8sabvN3IMP9ZOVpN1h6cBh9t52DxPPeCrSaIKuUxZtzsz3qwtBMaK6ZpILrr6K1/3J+3jwhluY23eQdZdsY2HvQT73vj9k3y139XEkUnkMrZ3kpb/6Li6++GK++Pb34hcLfeBrT9tCt9okWZoD1kC3W+55KCX2JJmp7aZ0D8+7pKPXXz0GbfXIdsbDZcSctrBQPzhFUCzSPTxPoVZlYd8B0k6X1tQscw88yI4vfKv/9qmb7uKOv/sc7/z0h7n45c87bcN4NPfrXVNLHKx3Aafx4PkehTzJ7GEaQ5MyY5zgXDcbTNzkdNI4l9W+cNz5f0y3s9ybZdDvt1jGCx7P3TzMzrkOw5FkwfRAve7iCSFYe8G5+FFIZ3aWkoV2avCU7O8zyt1mTeSz5pyzMNYyEnmUoqCfdBgLrk7Rk0cXzHcsN+1ZoGEMa0bdatdaSDJD6CtKkYeUTghKCQfyvG+2TZbfZwKY7zY5d6TAqnLAUlczXC5hKxWmW7mo1xH3t5SCKy/ZzDdu2cuu/bMD3QxjmBypcPW21TQ1LMVOLaMc+IwWPOY7jgCaZppi6NPVOSNk+faFYKadEXqJY/jkvz4S/GuxzNa7jFYrPPWyTUe5oCosQti+4+vyzwssn7nrMIfbzsMmUJLJSsBIpCCvqkw3Ey4YLzBWiDgiT8BaqKcwWSty7+H2gNUyHPGscyYIPZljUGBDWKQYKO6dbrB/qcP1+ySvuXgNwrq7RylJ5HtsWrPqpPf04yF+nHPIo404jk+6MH8ijh1nbKslKBS44h2v49sf/oejJtza+tU87e0/yfQ9O7CZZmjjWvbddCdzux/qvyfrxOhymc6ms2mWqkjPQ2UZ/qH93P5/vuRwFdIpfpYmR3nuL74VIQSbn345m5/uviirz9vCthdcyf677ufz7/0jWvOLbHryRVz40uey9bnP4NbPfpnm7Dxe4COVRCcpslQiazYhy0ApjM6w6cCP4lQ0NrwoJIsTpOcd23RNQFQpE7fasJyK+1gkHb0H9HHxLq5VZOIU46W02h0a03OYLENIic4ysiOPQYCOE/7ytT/PX3QfYOreHTx4w21UJkbZ9sKrHpFB4KON8yertFJNO9EcbGk2lQ0oD4tEYvBNSqYNKT5Yp2vRp2KuCOfJgpBMFmG2rUlzHY+CElQCSS3HEV20qoTUGuV5pFlGcWSIqFpF+R7WaJ7+ttex5smXoIUTKdM4bYhS4PXFvIaMJY5Sbpuqs2WkQGYtS92McqjwpcRgSa0DsM4sNLn2B3t4cKZFtRgwXIqIQr9fmSkHis1DBeJcWwTgoXq3n3TAYDJ+cLHLmkqY4x0sT1pf4ys7EzLjwJTWWlLjbk8pLKmxvO3Vz+BL37uXnftm0Nqwcc0or3/ONsrFkLKAVTbos4MAqiG0MsOmSpE4Mzww26RHBV5+2j0laCQmV0m1eUXIYT+wDjCqtWXdSJHt64c43MoIlKToS6KcWWT6dYtBe6yXCN4/12F/Pelf61gb9i50sCNFnr9lmNl2ymQlpBx41LWgAg7zg/tK1lM4FMO6oYjzJ0vcOdWk6AkuWlNzTr+e887prRtGSyHFoE0z1hxYiqnHhg3DxeMCTZ+IMy+sNtjT3Gqx4nGCLOUMTjwA3vAX72fDky/i2nf+Fjod4BYmzj0Lk2as2n4uczv30pqdZ98Pbl3x2WCoRnt2nr2f/xKld/w05dFh4jTj4EKXTr2FUJKwUuKSV76Qre+4hvHNG0zpfRkAACAASURBVI47jnUXnMe7/+Uvj/q9CoIBv14pRCiIFxbdUzzv158UHHuMSDtdhJDHnez9QkTcbB87iVEyFx/gUSUiQamATlJ0nkCs0BrJQyzT+hB5WX95knHcJCufOXSc8L5zn01zZp603UVFPqvPP4c3ffT3WH/x9kc++EcQ1mi2jhRoJE75sREnqLhDLXATUsdKZrPAlcitK8f7ctkkZS0Yw0KaU1WEwZOSiZJTpeyzZARMPbCbm/73p9l3z25e8ms/6xKOkWFKo8P9icVY2P6SZ1McH6aVaNqppJMZyjnlsa9AKSD0PZ68bsiBy4QzW1vopFQCj4KvyIwDle6fb7FnzmmxNOOMuw4scvnmMYqhj7WWTqbZPd/mvNFijolg4ABr+7c1Fic6Ndd238mi71EpBGybqDDd6LC2EhE7zDEy94eJcyfSl1y58rpWi867FdtjB/V3RynwGClK4sww00r6fi3WsCLjUwyE3HpfGU+Ch6RYUivKH9Y6JkxqLEtxhhQegZJYJIkVyCPK38ZYbj/U6beteiGE4OBih7lOxbFAgHai2dfUlDwo5U/e+djSysiTBrhswxCrqwERli2jRaqRt4JKmxdiGS4EzLdb+J5csc8n4j9ImMcC4/FE4nFa4vDOPdz5r9dRWzPB0tThPmhy/233cPD2+8jimPL4CF4hOgpQ6VcrCGDh7vtYeHAfzcNzbhJVknUvuRr2P8Rzf/FneMqbXsktt9xySuNZOHCIr3zgI5gs46lvvoZtz7+C0vAQrflFgIHLbb4UtmnWx3I8nNBp5lg5vk+hViHtxhitEUqx/tLtdBtNDt29w4EbheyrL1qbL+8gp+c+8syjPD7K6KZ11A/NkLTaLB6cPgpnspICLJCPgGY3s2NP/99ZkrDnxtv565/6Fd5725f6tMvHOrTWdJIUcAJZqbCoIMATAYdSTZoNVDMF1ul4GHAOpPRX2W0tXAk/nyAya+lqy9AyZ87G4Vm+8jt/TGepSSfV3Hv9jVz6kmdRXDuJ8BybRCBI44Tqlk0Y4XAO40WfmXbmRnBEQmpx7Qy9rK0DThW1ECh8JakoeM72tTy4lHD9vdPIwGey5ioJcTNBSUEp9Eh1xsFGzPai38dH9FpNy4XJHBDU0IoNl20YwVrLtskq40WfqaUuBoHKPVh6BmLHyoW1sVg5eGX5HSaES8ASbRgt+NS7GbHWeFIwnIt3ZcY46/dcyGx59NpLQuYaKb3kA5ELnAlaqSHI26NtI9GpppAbzCUapruC4XLIXNzhSJ0/Y2G6ETNeCpy6qoBWoulmkrmVfR66OTZksZuSWMkLNpfo4oDAAQbTo63mmUcjdmDn4YLPaPEJgOd/uLCPgY39KShl/6jijE48vv2hv6M9v0hxuMbCQwf7v+8u1lGB8ydpzsxTWzPJkdLdIp+0dKdLPLeAXyoxunEtIxvXcuWLr2TDCQyYjhUffe27ufML12GyFKkUN177Oba98Fk89xd/hn97/5+ik8yZs/XGYC02O7amxQlDCFc90MZN5ELgFwYskM7CEmsuOJfZHXuO0hkRQmAy7SzrPdDxI2e3zO/Zz+L+KVQQEBSj44Nbc36kCnySZuvY7zlWHC8nMpape3Zy33X/zrbnX3mcN53eSNLMmWLFmk42oEanxuJJSeC7ik5mHHhxInIJR2pEzkoQ1BPRF5vqNQP6qYqx/eTwjk//G52lBgbHfrj9q99DG3jBu9+IFRLhKXSWEYyOgnCr+UKonLaDdbLny8OBFwWJNgxFHp3M6XiAS0akEHiCvtPpKy5eQznw+PqOWYbKwWCUAlqJkyvbOac5eySiVggYLfrMtpKBMSJubiz5iuFCwHBR0ElSklRTCD3GKwUQkvl2MvB1EYIAB4oU1uleLOXsEGs8VleOdrCVuBaN1RpPgPQEG2ohM62E0VKAtZZd822WOm7MpUAyVg4p5kle7xr2jfcE/YpJ7ygsTpVWG/cjpaJp4VArA5z7rTawZqjEmqEiD8232DnXcePJ7/tq4KGEYyr9YO8iQkqG80QE3L4zbWjEKVJKFruOCdNJDIgYI3yUkhSUoZMLrTRaMVP1LsXA49lnj3L/4QaJ1pQCj22rqkcBdJ+IMy/6JpCnc5s/FoDfseOMvUPTbpeDd90PQHGoyvKurtVmYA1uDHGzjXcE7UvHjoqrikUq69YwvG41oxvXIYSgGh2djx16YDff/atPcsPff4ZOfaUnzGf+3w9w+2e/gsncRG60Jm61ufOL12GM5mc/9RdsfvplfQfXRxM9+WuntrpynNYYsiRxT9EjHj7G5OfEWtcieRRJR3+bmSZtd2jNLiwb4FEDxi8UnO/KIxRBOzKsMdz8z/92WrZ1KmGMMzuLlyUdMLgWVrjSvS8Fq4qSkq+ohYrRSDISSkqeJPSW3Z/9P93vkmXnpX5wetCvwOlL3P71f2f/Pbs4vH+aZmYRfuAqDPnnu6l7pFSCgSLpkfWsVqpppYZK6BN4zlm1FHh4AoJlBmzDxZAXbZ9kbS0i8pTzFpEDGWyLayP9461T+EqyfaJEJVRYa7BYjDUIARuHIjI7qGgYLJ3EJXBKOMZOJ9Wk+bErKSn4ik6q2TnXYqaVMN2IuX+myd1f/y43fewfuPvT/4rJNKEUhJ4gkJLAk5R8iScEkafYOFyg6EnuOtRkrplgcl2VeqzZt9Ch74Wbtzb6RQT6ObL7N5ZQSaqhRyl0eieREkyUfKqhR2Lc+fdyBopAsGGkxFjBI1ROwGxV2efcYY/5VsyBeuIce5c61Lsp7UTTSQ3NbsZsy2FfkkQzW+8yu9TmcFszrAwloRHgsD/KkrXa3PqtGzlvrMhrLl5FK9Z0Uid13+hmfG/3HPXusQH3T8QT8XiJM7bicaSCuPK9FfgBk6RoHLZCJwlB0WEShHBSx+niEmGtylkveBZnPfNJDqdgLbXIpxb5y/Zjue1vPsO379rV//8P/+FzPPs9b+ailz2PpNPlhr/557xUvHLW1UnKrf/nS1z9K+9g63OfyZ+/7G3c89XrMcdR7zylEK40jBnsy2TaqZtqg/J9uktNlO+vwL30ZBSl52EeSaXlVOPI3MKY40rZnyhEFGBP8AC94W/+mc5Skzd//ANEPd3rxyiEgG62Eilql71mIfe4MEQq1/Mgbwvkk5lyBY9+q0AdUzwMolqlfxdlvYTEGPbdcR8XblzbB2v2EqA4s6S5jkfB682c/Z4B4JxH59opZU8Sp05EK9POIM4TrvIBg+9TOQp45WVruX+ug9YDt1yb7zszlgOLXR5a6DBWCbhoVYWpepd6YvCVZG05wPccnXi5wqsxhnaScdP+RQIl+9LygZIMFXzizLDQSfomcl7SYeHDH2Xfjl1I6dpMay+9gOLZGxEM2mxCCAqe0xMRQnCoFdNKs2Urfzf+1Fhm2wmrKxHWin7LWwiHHeklaz0YqcQlJ/Wuc2Y1WMqyx1gaHJev3Laxgs2jRe45VGey5POcs2pIKZgoKHbVDcOVkLl24nROrKGb6f5zLPCgWnTqYldvqDLcrpNYxWi1iPU85loxndvvZudffYrhVodXvuJydiWGwHMAWCd7b2knmjsOLHHFlvGjb64n4swJ8xi0Wk6zINmjiTM28QgKEau3n8uB2+8Bjk48wE38mpS000V6HlG5yNqLtoG1FEeG2PiMy9nyltfT1a40Wot81g8XVqxqb/vcV9n3vVsIlc/8vgNOnEcp/vYdv86aC86mWBuitbC0bCYa7N8as8LL5aynXcLdX/rWIz5mPwrxwsCxWpKUtN1FepK008Xa/8vee0dbdtV3np+9T7r55VS5SlUqiaoSCgiUACEwtDEmGTWIYGDsYYyn3ZjRwpa9Zmy3ZzzYuBlj3LPssRsMthtjy3bTBFsig4QklFGukirXC/XyzSftveePfe6976mCqkoqJEH91qrw7jv35HP2b/9+32CTrP514yweniJpt49pf0jXfZrbZrbLK2m3IpvBZYnYaZf7noHlcqpxsqSjs/rHbv0+n3n3f+RX/8d/PavAOtd1EUJlPjOrt2Mg8/UQGVbBikt1WC2dUcwRvcTUk4KOwFgnKenEBT/7Gvbffi+JSu1gli1279e+y9q3/hxrO622TB+kljnItlON77oUPclsMyHnOiCs78dsM6IcOBRzVmFTKWNN6xyRsUQsTbRz72oDm/rztBLN/qWw+7kdjAUL7YiXTJRteyljW6yp5FhjDC7QthkKcRRz8JE9LO09yPB5G9h22U4ebzaZa0bkXYfBgo8jLEajGib230gxlncYyEnUQ/toCoOQDtt/5pVsufpSBidG7H39NHyPyKpOysBymBxDh8XuEu1YMVSwgM122qPXOkKQZlUQPzOec0WPmdSpbjWTjnhYL6SwZoEYKJU9dg5UmChZdlMkDDGCPh/6czk29uVYbic0Ek3ec6iFCY0oZftwib7AY/0lPkxNYeKYRmSoLzcRUhA329z26c/hBz5uPqCWK1FwnO51BnAQVHKCWtuaxp2K+/W5eIHGucTjhRuv+pV386Wb/ojWco3CQB9Rs338AU/YHny+v583/NaH2XjZLvJ9ZbwT6JOsjCe+eQfL+44Q1erHzOb3HJ234l1aZyOQsGyTLIyBse1buj+v3XUBQblAWG304OknGaD9YoHX/+avcO8Xv0LYaBLVm6RRhNIax3NRKiWNejeTTlOmH38KtLUvd3wP07Gu1+aElY6ngxG7A40UGHPyfTzOypCuYw3lzmJ0HGAPP/Aoe757F9tfc+VZ25bveVSClGbSU6DtiGAZbQdwsOBBpQVSGtJOBSz7gi+gqk2mStqrSAQuGZbA6n6M7jifXR94J9/+7D+hmzXbNRsZYeidv8B8AuVYEaaWCtrJK40x3X0YKvoIDE8thdQjq97ZF7is78uh6VVZUmUlzz1XHDNISwGVwGHzYInHZhoIIQh8mbE7YLSSY6KcgwwQ2pMrsS2VioTFdsJdf/IZDv3g7ozdZPjR+VvI/fvrCdasIVSa6XpI0XOQQhAqjecIdg15DOfsdtI1w6z72C/hlit4pWIGlJYZcJUexYUVx4TpSteLzjNG9/HEdSS1MGEhtJTggmcN34SAohAEjuihZQ2kWuOvSHKUtgnOynd4Dz5mGM9LRoKsYirAN4rQOJRzPp1HKe9JEqWZrMasKflsWl8h7ztW3CzS1F1JEtkVCIy1GADGX/oSFh9/kk0vuxiTyzPsie41iVNDM2sFFgMnS37hqbkGdx1cRGnYNJTnyo0D+M8DHf1cnF6c0aTvmdYpTuM9fpbjRX0HTlywlfd99o+594tf5olv/QCVKqJ6gzROeiwWKfHzASPnbaQw0MfDX/k2u372Nae8jb0/uJuoWj8Wu5DFyqqC0QZDhgMwVkvjbZ+4qfv77dddxbpLdnDgrgdtEmBsZUF6Vq1UrZzlOxLhOPzw7/477VqdxtGFXgJwHLGwTugV7B0jICgWiJpttD7F9orJzK2EpdwGpQJBpURtavaUEhDhOPjFIq7vopSmvVw9sUnfGYZ0HGQGhtSp4uiefWc18QAoFfIMG8FCK7ZMC6y4oCsFroOdIbvWM8S6wmqUsjbjBoi0QBkIMlyBIyBwyFxOTZaDGqrtlFsKGzG/8hFyB/dx+bYR5sY3AdmsXBuUshbxMiuvOwJyK8omvuswnPfpD3reJ7Ey2FNm8RUa2z4qeMe/sYWUCDRbhgr88OAy6wbzJBkoNec6GOBIPaYv565qMWojcKUmnJsnPzJo2VvGJopzTzyF+bO/YNvHfx/sbtFIVPY9w6ayz1CuA8iVlDZtwPVcpITQuBkI1B63MCZz5rWhtG0BhalmrOhxpCozFksGpM2WG8hJjjYiQpuLU48FlcBlJO/gOk5PrTXD7ghsq0rSE+sbyLu00wTVKYAZe+3yLgx5TwN0o/GkQzPVxKllysRa04o1oyWP4YKP79jzoI21nh8YG2H+4JS9ztm+CyFAa9Zf/BJe/1sfJpa98wH2XvIdwVI2EXGk4B8ePMyBxbZtQQEzjZAn5hq899L1FM95qryw42xUPMS5isdzFqXBfq791V/kml9+F597/43UZuaozy2yfGTatg2EYGTrRoKiZalUJ6dPed3tWp3q1Kz94VTHTm0wEoqD/fz6t/+ewXVrur/Kl0tc9f53UJ+Zozo9lwHcBMJ1GNq0Dp0qZp/cTxrFCCGIm03m9tSP3cYpViB0qmjXm5nT4WlGhgnJQAX0rRmltVi1DAxHkLTCVYsLKSmPDfPOT/8uSRix/TVXEbdDPv6ynyesHucYzjCEtLNex7dgYcf3KI0MPWfrP1kMFHOUcz7VMCHVmjhJSRNr+OY6NhnsGIJ5jsBIh4WFGospRLhIzyVVksG8Q+A6XSOx7BSTKsP+hSYCOH+ij+Ftr2DX2gp3Ha5RCjyklCy1UxwpKLiSnOegtEJpQ+K5TFVjEmVn/F18SAekqjNQp1JEtTqq0WQ57zO4eaK7WHcYN3YA3z+9TCAEO9eUaSQa3VHnlRZUGaaa+VbKaLGHiepUPxyVYsbGyV91JbpWJXzsCYQxRJNTqHaIk1/tx1MOXDb2eYSxwpHguxKhsyQDcIUhxkEYm9gLKVkJF1fC2tK7DjQizYb+HNP1iFaicLCVjo39PkkGvMk5EGbq6WGiqEvDYLH3OuxUVXT2r8AgjLGaIAjGCi7VWBEp61Lb78L6vD6GsWiAUEHgOBT9FQla3jBdj0i0QghpH1GR6b84ksJAmcZ8T/MHKfFyOd7+n3+bKE4QSh/DxHcdiScUbW14dHqJfYttm7iIXtVnsZXwb4/N8I6L1x3nDj8X5+LHEy/6xKMTru/ztj+6iW//yWd46o77MMbg53NUxoa7SQdYKfVTjcbC8mmrZHr5HOMXbqXQV6a1ku2RxVUfuJ7hLRu4869vZuqxJ/HzOXb+7LUkUcR3Pv353oAu5XNTans265CCJIyJapYGK1wX13etfobjQEf8TMDYhefxzj/9PS587dXdr//1+/83i7s5SYXmtHfJdfDzuW5SOb79PC5603XPybpPJVxHMlS0LTqVpnz+w/87r//4TcSJLctjskFTCJJ2xGQtJGqGRI0mCEFlw1qUybG2IrtmYXFmb14LEw4stHnbSycYyFv2SSnncvWmAQ5XI5t0CYHSUIuVpccZwYOTNUb6ckhhPV76czYpiVLV1YqLUo0nBfUjUyS1hlWNbUCydhjPdZCOrWJEqaYaKlINGkmtGWMEjJQCUm3wHetB0sG7ROmKip8BVxrCVLE7dqmPTJC/pABCkLvkUupf/Rp6YQnVaq9KPAywfbiEJzWuvyIJOg4AVyOQGSZlJdNGYmXcVQx9OdmVRy+5FlOztuIz3YhZCHv76wpIDYAg1nJVy8lo03Wa7W3b3soaawY5nJcUHcNsK6bPA08eCzFXCBLhrqpIgU0GxkoBRxsR46Wgu6HU9ZFRu6t504H17r/jPq5531tJU4s16pjmCdMD/rYTRS1MuHeyxmKYYrReRXPuXKSpxpmba56LH1Ocq3i8eGJow1qu/5PfoVWtcfNH/hPzB46sAh0abbjwddec0roWD08x/egeyuPDVI/On7LCaNIOmX1yP30TozzwpVtxA4+pR/ZQHB7kojddh+v7nP+qV3D+q16x6nuffM27aC70EpVTSTqk5+IV8pTGR1nYvfeU9u+0QpvVrZvMjhtjcPMBRknLnDGwdHCKp267e1Xicejeh5HZYGkHyWe3O0JKW7oH/FzAhkt38vZP/Bau//wIKD3xrR+QZCXyTsvN9xx8R6LjhFoCMpcjHwS4paLdd6WtKmaYMlRw8YVlNLRizb65Jmv7c/TnrWR6ObCGZ4ErWd+fY7K2GnDbTg0lX1Ip+rQTRd6zy5tsxPQdSVurbhli9sgMS4ePkt+wHlyXpF5n3/5pzls/jJvLoYVgOVRoIFaKONXkfJfBvMdsM2Ig09NIOxp4xiYiYH92hCEv4KGlhFZqiKuZKaExOJUyxeuuJR+1uPqijexdbNNKrPbE1uEi5w0VWa41cIXu5cqiN/B22ypCZFUFgTAak1VELIbDJl6xMlR8h8V2ymDOMJqTpDo5VtcGeiDfFTdnx+hxxZsjo87azwqu9WuJFSRGMJL3uGemwctHPAKpu75JCsHuZcPEwPFBnq4UuBm+pLNBncTUqnX2fvcu4ijGETD32B6SYpnbH9zLNUMDDA4PdBMcne16I04JU03OdQgcmWF4wMN0WUv29IkuS+lcvHDDaH1GqtYnXeeLySTuxRiFvgpv+8Rv8a//139h8qEn0ColXymz42ev5fJ3v2XVslpr5vYeJCgW6F8zRhKGfOV3P8XBe36EUspKj5/miBnVm8w1D/L9P/879t/5AEExj9aae77wJd70Ox9h7UUXAvYF145T7vqbf+bwA2dgNiQlhaEBCkMDLJz+t88geslD2mHrZKyNqNHk1k/8fzx6y/e48HXXsPalF6K1FStTSYJKe/32YyJbByd6LoTAL+SRjuSyd/48L3vnz1EZG2HNjvOfV5nouNmmcegIYRiTz/kZs8JWYrRSRJlgnHA9fM+3LbRsEGinBh87cxcZiPD84TyO46AQlPxOu8QuX/AkOUeSdGa4GQ1TaauK2ow1SisCV1KLEvpyHq5jfT4AWoni4PQCeD61qZnuMRyo2qrC1o3jtKWLMtZ07fBcsyusFSeKwYLfVQrtYFJcKRnIu0ij8TLcSqhgMUwhiY+pcrnj47z0V97HttEK20Yrx5zPXC5Hu5UiV9QajDEoITOvFFb8bVjpytJLPuy6nKyalGhpXQKMoOQKFqOeFos21oiuHoLyNEpn7B5Wk9SEoNvy6QCxC76DTDWpMgSuYKzg8a3JmC1lSdm312M6sm2StfSSmKc/AY4QXWp1++gcyfIyzcUqd/39V0jjmJGtGyldcgl9W7d075tUaYx0UBkeS2etMbBquOWci6hFtnVjDP4qDI7p6r2cixdwaP3sqtUnWucZRJIk3HTTTUxNTeE4Dh//+MdZv361q/uOHTu49NJLuz9/7nOfwzmJsvRPZOIBUBkd5l2f/j0WDk1SnTpqGSXF1Wqkj97yXW77q39g6pEncHyPLVdeiuO5TD2yh6jRQgB9E6PM7z9sVUdPJ4xBJylH9+xlwyU7kVLSXFjm6//5r/jA5z9JdWGJ3fc9Dp7L9/7sc2hlabr6NLJcR0jOe+0rOXTnvQjPtRLsZzOOlzes+CwNQw4/8CjNxWXu/PzNRM02cRjhBD5atU/cbTlJTmJ/b5COxMvneNvHf4PS0MCZH8NzGBe87mpu+6u/5/B9D7P1qkvx5IpyvQClNNL1MI7sHp+UDkbTtTLvQGlKgUspsCqXqRDEClSaYQqEsEqdK9gU2hhcR+I7Aimg4Lt0GLHTjYi855CXloliwayCwlA/rfnFbufL2BWx59H9TN3+Qza/7U3MLEdE8Yp70FhlzaIrcF0na/FAwZWsKQckOtvXbBZdDmySUBgewi8Vreonws7etGbHpZtOeD5zvguUqDWa9vnBECuJ41oMTUd2vFcH6bVbRFY2ECZTIzUa3wXHdUm1xhGGwUCwGAkamcR9OzXEygI9apFmsZWwc6xMOXCQ2VZWJh1kPzuZ3KnvCCJlZ6azDz5CftM2nqqlDOZ9Rgp5thQk0lj2kHTEMbd4qg1R5qhr6nXaM0eZ3XuIe//lVlqzCyTz81QffISLL78csODl/csRs7rFpsECvmsZLVH2bsqETUmU6WqTrNyowSaLb90xccJrcC5eIHE2Wi1nuL6vfvWrVCoVPvnJT3L77bfzyU9+kk996lOrlimVSvzt3/7tKa/zJzbx6MTQhrUMbVh7zOdP3X4Pf/3+G2kv23KwlJLq1CxRo2m9UJ6tDgUGISVxM0QlqZUpBxYOHObOz/8TdS0ojg5j4pi42bJaCoGPboentG2/UqI0Nsz4rguYvPdB+taNs7z/yKntXGd6+FyHsdopHX8VmQEojVK4QUDSXgFIPdEU8ASRhBFv/cMXTtIBkK+Uuewdb+SHn/pLkvb72HjFpfiDFYwwtFSm2uk4WXuod6BSCgaD1QORoZcMKA2elHhZBynWmjCxyqkd0GPOkZQ9SSNSxApyrp01K20oBy7VKKEdC/IlF9eVSAnF4QHiMEJFETK1GBEDBJUSE2v6ibRNljr7swrfoDSvWFPmaDPJNEOcFftuMq0SO+gFrjVuU57fq9p4Ftz64FSNvN9iMO+xdbjUrTB0Iue7+P1lFhot6mGnvGbwVEreEWhp8AXguCBEloeJ7knUQuBJgZGwbbCAIwRtDC4pLort/YIjTcOBuiJMU/KuICckC0oTKsNDR+u8bssgUhnaygJKhZDZ9TEETo9+LLFuwEmiefALX4Jcnmt/72PkfavsooylJYsMINqVMzMWP7LYtjLpzcRw26c+y/R9D9FeWGLpwGFMJtMvAp+pf/smL/utjzAwMUYMLEUpjZkaw3kfKcBxJDKj1aZK8+S8rVYFnoNS2joJG6jkHN6yc4Lh8mpg77k4FyeLO++8k7e+9a0AXHXVVfz2b//2s17nT3zicbyI2yGffd9HaS0sd99ZSmmSpeqzSzhWgCg75mzGGFTaSzwAHvjKt7j4l27oal0UR4dpzMyBEDi+d3yr+xUhAx+TKlrzS8w/uZ/yxBhLB46cGohTCLzAJwnPMsBMWOl41/ctoFeAX8gR1lvoNO1qOyCtedwzHXNhoI+Xvvn1Z3efzyCu/qV3MrR5HY9/43Yeuf9HXPqRDzETQxIrhn3ISU3b2EFaeK713fAkriO7VM1OBbTTKnCFxskSVyMkrnSQaIq+pBFrxoseQTbbFUKwyRHMt1ILbsVWBYbyHoM5FykEngMLrZREOOTHR0njBB1GxAsLCKUJCjnWXLKd+VZqB7AVVbdizsVoQ6sVkhoYyDvo9up8URs72/YdgZSSsWLAvqWmNV7LJuppqQAAIABJREFUHrDhgkdfYCmgU7U2h5ZbHFhu8Zotw6uSGMiAm+UiraRBrDKqspAorGBZJKCIQjoSgbW6VwbiVJNoSx0ueNIOyqKjhuER49JWmrEKTDZqXDqao5Cdx9QEHK7G7KvGHKqGjBbtoI4R3daS7wiKnuwmZbb1pDk016R2YJKgXKCfiJiCZcNojRAOAqvT0mmpGAO1KCXR1t22LRRRGKG1veYohZCS/EAfju9RqJToHx+hoW0ba3qxxfcenOaKrYNcft6Qlb/Pe6TAw9N14qziQaJ4+YZ+3rB9zN5/z2Nb8lycXhitMc9xxeNMyQrz8/MMDg4CdDF7cRzjr8DWxXHMjTfeyOTkJG94wxv44Ac/eNJ1/lQlHkop/uE//C73/9PXaHQYJ0JkswVzSoP2yZYR0lrVZ0B5MOD4Ll6uJ1SWRDHh5FH7Q7aqXe96C/O796Gi6JReDh3Ev4oS9n//Lvtia7ZXK5CeKIwhyQCjIgNqPleMk9XbsTuqkpT+NWNUxkf48Jf+kn/62B9w52dvtrPHUgGdKFSa0j5J4iE9FyklCweOMLB2/Lnf12cZF1x3NRdcdzWPHZnnS3sWWGxZE7GC53DhSJ7zBgqkjkR4PoHrIKS1gbcy3z0tCCE6ImTguA5mRVtBCMH6voBmvNovJu9JCp5DxXeYa6e0YsXaik/Okd3lYm0IPAffEbSMizDgOg6O7yGNYWJ8AG0EFd8hqQQcXWzhu5LRco7Agf6cZKhYoZmkRNp6wjRiTWosgLEepyy0E9uaDFzGSz79OY/lMEUIwXkDOfKe0y209ec8pushC+2ER4/WuGi80n2hdUIIQSXv02gntm2w4nMDtJWhiEJjqCtodchTWPBn51yu6HwhM4322Ei2DeZsZSRbrytgc79PPVGEqSJFEEgrm+5J+2y7Uq4AvNoEZ7qhaR6dA2Bk4xpEGJEvlmjEisCTpAa8rG+jspKWwbKjdGz1VJSGyuZNzD70BE6pSG5wgNLoII7rYYzh5e/8OfrLeSbnWhjg/j3zxInme4/Pc8eeBXau70M6Di0J5VJgW25CIBFsHS6dSzhejGH0cy4gZlHnJ4+bb76Zm2++edVnP/rRj1av5jjjxW/8xm/w5je/GSEE733ve3nZy17Grl27Tridn5rEQynF75x/LYsHJlcPtMZYy+njSIkfG9n3OgNFVkbHEZhUYbTCKRTQrTaW+a/pGx/tPvjZxIn53U/RnFsk398HwOCm9Vxz44d48O/+heqRqeNuedWxZBRVL59n4cn9+MW8rRokp2gOJUWXG7gSMPech7G99uXJGeb3Hea/vufX+Pf/z+8w/9RBokYLgLm9B0mqMcJ1MCdQO3Vcl8JQP2t2bDs7+/kcRJQqvvr4HI0k03LI/FMemGnSV8gxUQlQGHuuNTSVnYf7bm8kc7Bgxw61Vay43bwMIJl3JZHqYBx6+APflQzlPfISip7bHYSNsS2BnGuYKAfsXw4RnofBIIOA/pzDQN6z7BApGSx4FP0y64qW7us5HeQvlHzBYrXNfFvTl/dIUsNcu5N02MF+OUxZbCdMlANKgWXtBK60s/lsxi8FjJcDfNdhMOfQihKkFHiOg7+iMlhwJXUyyujTBs/UdPQ1NI6xbYwOWcORMjOnO/Y6eVJAR/nXGLQx3ftfKUMRmKtGrK3kkZkS8UI7phS4FH3ZvSaxNsw3Y9CK6R/cTX/eJZ45ihBQCdzudRMIjiy3ufPAErONmLwruXC8zM41FZTJ5PIFbH3bG5n+4f3UJ6fp27jOgnOBiYt3sPnVV5IiGMw7HFoKma+GGSDZVlLuemoBpW3L69qXraVcDHCF9eQ5tNhm63DpDO7oc/F8hlH6xI7fZ7pO+czru/7667n++utXfXbTTTcxNzfHBRdcQJIkVqriaUzCG264ofv/K664gj179pw08fipEfP/l499nMUDkzbBePoLyZhTu8jd8VkgPZe+9WvIVUqQWuAcBlSG1xBCMHbBeVz5/l9geMsG1l2yg1d9+D24QYDr+zz+5W9gVmSgYzsv4LW/fyPFocFTOh4VRoTVKmk7pDW3aI3YTjV/SHvOvc95Vn2cqM3M0Vpa5sF/upX/Y+u1mXR0Z1SVVrsko+iuDJEBSvMDFV5+w1soZInaCzHuObRMK1E9ZmZnvDbw1FwdXxp8nRIIjTGayVrI3TMt9i3FLEea5UjTiDs+IE+3G7RhVTtX/Lzyd9hql+eIrrJqpx3QWaLoWaqlJwVeVh3zpeSxow1+dKTKI9N1dh9tMugLCp6D50ie/rCMFHz2LYU8erRBrA3VKLUYAugqlDYTxXwr5lC1jeuIjHlhdU6U7vihSCTQihUdH5Q4TUlWyPo3oo67LE9jlmmKbiZdLqDsGtYUYDwHQwG2TXXCK2UVXKXr4rgurufhuC6NdkK1FRPFKTffPcmffP1JDsxb/ZrZVsrDs00OVdsstRPmmjGTtZh2aliemWffLd9huZ0wU4uZd/I04hRfWh+cI8tt/tt9Uzw202ChmXCkGvH1J+a49YlZCxjO7he/WOCq3/9NNr3+Wiqb1jO0/Twueu/b+JmP/1am6WEYKHidQ8iSInteO5LvYFtSOdfBzZR95SlNqFacnRWJ2Ll4/qKTeDzXf84krr76am655RYAvvOd7/CKV6yWgti3bx833ngjxhjSNOX+++9n27aTTxJ/aioeD331m72qRpc4f4ZhDEYblg8egayHbAFovXaNkwvwczne9Lu/3v3a8tRRvvNnn6d/YpT93/w+zaNzbP2ZV5Eb6KMxM8sTX/6GXeepxouJjy+tuZlOU3Z/4w7e81f/N/f947+StEMKg30Yra3leT6PkILy2Ahxq03fxAiXv+stvPp/fd/zfQQnjUZk3VDTbGA1nVZTmtJOXDAa12iWE8mhWog2tlT/5HKIUxVMlAM2lOzjeLwXf+eWtcyFnjtt507u4DtMpkuR+csdA2CtBC7VMMV1JCZVHF5u05dzGS17eI6VGa8lhkFtjlsFdIRgrORzwUiJI9XQynlnLYtUmSwxEsy1ElzZA2J2zok21mPGCDso1mJNtZ1QChx7/pSmU/SoR71j0sYgM6M9i/no3f5ddVNp2xb2STzOgGssDybVgrl2QjOxiZDnCArSRYgkU4aFpWbMP947xX987RaasaKVavYuhlbYzRGWsTazwKGvf4eFRkjfrh2c98F3c890k+FaSl/OJfAc7t6/QJhpvVilYntdHpqss6YvTy3WJEqT8yQT5QIv/ZX340noy7mrr7MQeNKhlWo2buhHZeey2UpYWGha8GjRZ6C0YiZq4MKxU6t2KKVYasWkWlPwHHzX/jlnNHcu3vjGN3LHHXdwww034Ps+f/iHfwjAX/7lX3L55ZdzySWXMD4+zjve8Q6klFx33XVcdNFFJ13nT03i0X0Dm2x2eCpAzJOtTusuDeF4Oh9Jq0VQWk3f7ZsYZXTLBhYPTzEeBMw+todDP7jH/lKIVQDUzmdnBX/xPMTKOXwaxzx6y/cZO38zUb3R/dyC8WyP4FUffg+XvP3f4Xjei6JHPVr27TUUdobeBS1HEYGnEUkCUjDVSFblizLDLMy3YozWbO1zcbS2TIXOKGWsaNdSmHDXkSpKQ1/gsGWwQCVwMcbqSjgp1KOIgk+XDroylDEM5BzWlDzLxlCaxXZKarKZt7AUbQ0sxzBwHPJDmGouHCll1RBWJUBWTdRqS/hSsrE/hyMEUnZEq+zBmGw99TBloOhRjxWxNgzmHYToAU0jZf1uarFBZaqpw3mHvC+yNkmGn1iRaEgymXNtcFaqhRqDVjY5PFRLCFUPKxOmhhCDm2hSpfj5ncOEiebew3V+dKRGqeQTtu3yM41eO1Pli+Q3b2b0Pe9i82uv4dGjTQbzFr/RTDTNRLNvoWXbXaKjG2K1QsJEcdfBZdYN2ndEO9VUwwZbB4us7z/WvNJzJI9MLzFVDSmXAqqZ+mix6GEoUltuc9GWIZwsURDApRv6GDsFBstSM2SuHlpdEGAZQdF3GCz6FAL/RfH8/aTFWTGJO8OxpKPd8fT40Ic+1P3/xz72sdNa509F4qHSlFxf6eSOqVKccgXBzgSfAchpYHr3Pn5/1xvIVUpsv+5Krvmfb+CK9/8Ct37iL/CBtRddwMKBIzTml3AD6yQbGtPT4/hxJR3PsgB0KmHbOmQiC1CfnSco5lct051dCWguVp83RdIziYvX9HPvoWVm66HVbuhgARoNFv/H1xB/9JskjkeYxt0qgCetloNB0Iitk+y+mmFDyUWGMThOxvgQHFhu88R8q2vUtthOaczUuWS8hO+6HK4m3H1oiTBRvHRNhU2DeZwVTsnaaIvZ8GQ3KXGloOxLwlgRhdYfyEiJ8jyaSlPRchXdVWvDYqToL3gIbNtlupF0y/ydR0gKGCp4DOSthobEVrviDjbFwFzLDv5518nUUA2N2DBY6G3PcSR7F1p4mQhaMzHUY8W2PhdrDyPQGpbbKWkmpOZ7dpb+4JEmgavZOJjHk4LAlSTKsJhVLzruxnZ/bLIUI7hoTbm7/R3jRWbbiuVGQiNO8RyZuQvb5Mb3PIKdOym5krlmwuRSi63DI105+U4yFia6e4wmA5nGSmciZ72qhjJwuNpm2/CxyYI2hidmm9m6BMWiTxSlpMrQXwn4wNWb2Lmun/0LTaQQXDhWYnRF0qG05shyGwOs68vhZuJOxhh2z9bpy3l0pgcGq4TqSIHvuXgnEYI6F2cnzgrG4zle37OJn/jEY3lqhs+8+yMcfSZJ8dNoW1j2ii0Xn2zAbhydJ240ka7L0qFJph99khv+4g/Y9qpX8NBXvkncarP2pRdy5QeuJ6o3mHx4NwfveYgnv//DH3Ol48eQeXQiO89BqUTfxAize/Yfu4hSL2gg6fFCSsG7L13PrbuPcmChhdKGNf05Lt22nUNqjlw+wHTyyew7rrSti7ayfXUB1GPN3bMhjrDiYEKIrgfHyraFMoZGrHlwpkGUahaaCe3EVuEenKyTcyVDRR9X9kr1vmOxHZ2qQ9IK0dLF992uyqDWmiRNacYukS8pCKs5kShDNVIEnaqcgL68y2jJY7YRdys3Alt9WVf22TPX4KHpBo6ArUNFijmHwJW2daGgz7dusFLY44yV7g6IShtmGwmOI2klunsc7URxuAEXDrhEqWa6EXfFy5QxfPHeaYb6izx+tA4IBvIOW4fy9BV8jrZSrtw8tArsDZBkvi5Ot15iw3MEl6wtccu+Ou0wReY9+3tjr+I15w3xg/1LSAH7FqwXj/c0T5axcsD++RaOFF2TPYQg8BxKOZd2oi3AGNsmasUpI6UcS60UQdq95gXXpZ2pk3bulVwGYjVALVFsGiywaXB1lRVg70KDI8ttOmW4/Yst+gKPS9b1cWipdYI7WtCKlZWZP5d4/NjjXOLxIo9b//DPOfLgYxilcfI5VDt85i+dLLJWgJCSQl+J1mL1pIsnYYwbGNr1JlOP7eEz7/wPGK3x8zn8fI6wWufRf/0Ob//ETdz3D19Fp89evOyM4jQqPs86hGD3N29nYd9B0lRRGurvVjuMMazZdQHbnuZl82KIUs7lF166tmvi1ZnRViqvRwpJ3tWUfUkrNd25ZZqdcj+bTTcShdYCnQ34noRm5oKad3sDZqoNqbaVj/lG3GVSgG1R3H1omV1rKqypBF12S8n1soKTwBWC+uwcYs0623YUKgMtSnxPEiYpRvokQjLfTsg7gsD3aKe6O6MHwfmDBfoCh4VWijKGku8wUQpoximVwEMbQ6rgkaMNwiRlqBSwpj9nMQuOiyN77RopwMs0PWYbkcUb+C6eYwG5jhBI34qGzbYUJlWrfEfuP1zDD3wenq5Z6iuw2Fbcc6SBNoZNI0WmGyGDeX8VfsWVwgqnidX3fyFwKfoO123p54GZlnWTVQrPkVyxvp/zhsscXo7YO1cjTAypNrRi1bWcFwJ2rOmjlSiO1iISbVVMC77LResqGTPHECa97UoBnuPQn4davUW5kFVgjGG85DNZO1Z/x3cEniv49lPz9OVczhsq0J+31cJaO+HIUriK4iOFoBYl7J6t23ura/S38vhtO+tcm+X5Ca31aalYn+o6XyjxE5143PPFr3D3F75Mu954TgZV6TpI1yVXKjK4cS2tpeozJh5gpdMdz6M5v0TcaDG6bXP3t0IIZvfs46v/6U+Jmi1amZLqjzU6kpk/JkyJF/gYY5jZvRchJPWZOXKVEqNbN3HB667iNb/2wRfECy9RisVmTKos7bUUeOT9Z35khLCqpWATKc/3LP7DCLYO5dk9H5IojTICk/mc+JmWTMcevZMsdFoXStvlZYcBomxyo5S1q5dS4EiBytoZjVjzw0PLXLLOujFLLC7EE4LAsQmR9DyEUaQ4T0OhanyV4MmCBYMqCLUh8KyiqtYaR2azYAGjxYCRot+9jTreIYEnWVvJcXg5tBTagTyulCTaIDO8RCNWTJQCBvMuhaDXWpMrzp+AbrlfZSflSMNwdLFJQRoSbXh8usnhasT4YGGVy2ynlieAaivhgck6r9w8SLBCw8MCNwVFR+KgkUJQDBwGCtblZL4ZMVULCbNzG6WK7+1fpOg7XLN5kP0LDVsUMIaDi20uHC93KcC+K3nVtlH2zTc4kOmjTPTlbLKjYfNgDk9KqmHK/sUWgwWfR6arTNUikjRhtGzYMV7GkYLL1/cz9ejRVZfKc6zeSV/eugcvtBJqUY3L1/VTClyenG8ey+LLjnmmHrFzvMzXd9cYKfqrC5/CoI0412Y5F2clfmITj8XDU3z/L/7OynT3cG3PKrx8nvWX7shoioLm0jJeIW+prCcIozRKaVS9gZ/PkSsfizIXUrJ4yOp3nA6gKFcpkUaxtZ5/LkJAUC7h5QIas2fPdi6JY8iUU4WUaKWojA/jBT5XvP8d1vb+eY56GLPQiFaIRUErSukv+PQVjgX/nSy6Qm1CMJQPuHyNy5F6zFI7oXVgkuXbfsDkcovCpg2UX/Nqqw8DGZNDUA5c6lFKp9AQKp3lhwbPoVuqD1xJS+ku2NnpgFO1pr20zFERMz5UpuQH1k02l0NgiJOUBJvUSGn/FEt5lDbW8A1DagRhqEi1YaTokyiN9RrrKEoIhMhAntp0xbIsswVKeQ+3o6+B6EB9aCaKqXqIIWDzCr2JkVLAgcUWrUR16bo6S0IQAkdaIbCvP3zU0ocNrB8pnvAR7yREUgoen6uza7xC54UgEIzkHTaUnG5loIOO0QYePNpaTWMWglTDbQeWOG+kwnkjJfYttIgSw8HFJgbDhoECpawKVYtikIItI6XueyhwBZetrXRB1+PlgE2DOeYaCQeWQ5s8SZfZVsLs3kWu3TLAJesqNBLFnQeWaCUKT0oGiz67VuBSwLbF9i+12DVeITnJrNkYqOR8cp7LE7MNzhsqdL19alFKOXBfEBOAn8o4G1IH5yoeZz9+9KWvAz3qoXkOKh5CgF/Is/3aK8iVSxQG+vjaH3yamUeefOYva0Pcap9wUB3bvoWph3ej4iRzdH1mwzfrdipxcwFpHD/7qo6BuNFCZcqmZy1W7KfRGpUYlo/M4Bfy3PP3X+Z1v/5LZ3f7JwhjDPONmHqUEqdJZlne087QQC1MKAZeVychUZrppTqp1vQVcgwWc6te1lJKpJBoo7PkA3zXYctAnju+/h0e+ut/yFozhvm77mFtpY/+yy62CZmxrrI5VzJQ8FDK0EoUGEPBlwzlfUqBw+RySDW0YEB0inQ9UqMZKvroRpPqkWl0mvLo1CzxlnGWpWHjtg0YPyBMNNVGSJDP4QSurbZIiZLSMm1WCJJIYQXMZpsJBsOakp/hUhLun6ozlzE+Bgs+a/sCXCmYyqjDXmZm52WKqjqrrGkNdaXQxLysi70wxHHCUMGjuZweQ+sVxmJq+gpWOj5MFKkxuI6gr+hxaLGNEdlsI2NqaGPI+w6OhMVWwmIzZqJiE8iRokfg2Ovbmd937tCpZkIzVvjOsbTSaphYJhYw0Zcj0YYokczUY2bqEZWcy0RfDs91unoadtJik8Khgk+UaptQCUEBSaJhrpE9f6LDwIOHZxpcvKbMG7aP8caXTLDYipmstZltHP9ZbWVGfznfIWof/10ihW0Hvn7bMLftW+DOg8tUci4512HLUJ4N/XmUUqtcRo0xRKkiVbY0l/NkF5dzLp67OIfxeJFG3LIS4o7roBIByGedQSZhxJPf+yGLBw5z091fJigW+Off+INTX4GB5ZlZCkP9OG7v1But2fH6V7Lxsp18/8//G5XxUZYOP4OCaVbydl0XMJRHh5nYcT57vvUDgJ4XymmEEAI38ElPJfFY2ZaRAu/pJnCnE8YQNposHppi+cjMMy9/lmK6FlJtJ9b5dcUgqOklH0ob2klK2fGZrzV4ar5pwYlCMFmL6Ms12bFmsEtrBBgoBSw2MvO/rGzRrjV49ItfyphRdv0536V62w/IrVuLPz5mh3sDrURT8lw2DAUstlMSbbEUrpQUfYeN/Xl+sH+J5TCxWhEGhos+mwYLzDz2FCrpXc/Hn5rmqcf3Iwb3YDZt4ZUbKnbgDdzubN8YCFNFznVJtMa+r0RX8LbgSoyxYmeJ0nzrqSWa2UBnMExWQ47WIwbzDkqDIKMYZ7gXrU1GgBVZS8mw1Eq4c/8iV27sp9oMMRj6PXAqLtMt3cFzdvcBoBi4JKnVwUg1LNRDxvMwfuRJjsoCYs2a7nGv7c+zc10fh6q2mrBvscVIyWdNJcCVGYU41eSlwbNlFRqpYbKeWlG2FY9Soqz+R+AI/vFHUyyFCakxjGVS9WPlHKnWzNYjCr5HnAmnGezxu1kVSwqB70hCpbv6JkXfYZrEapF0DhqYbyU4WaVHClsRqkUpR+vxcasSHZDrhaNl7jyweMwyxhgmyj6OFEgJrz5vqPv5ymW1Mb1kzBhq7ZhEqR4VObFYmILvHftAnYszDqPPQuJxruJx9mNix/k8/LVv4xcKaG1Iw+iEZdhTDRUnqCRh5vG9fGzsMq796AeJm22CcpEkjNCnUKVoL9XYf9cD+IU84xduxS/kuejNP8P5116JEIK1O7fz1d//NA/88y2oND1ueaw0MoRXCFCpIt9XYXDjWi6+/o0UhwZQYZO9dzx4RhUeW31IrEnYM7WmVmJBtCGJIqRnBy99BhUTow3tao3W0jNhZp59GGOIEutOKhEUApc4VSzUQ9pKU/Akged22wGdQ83GToQQKK3Zu9AiNSu0LISgGqYcmKty3ljPRdeRkpFKgVaYEKkUKWDvt25DhxFyxewfQO3dS+Nb32Hkve+yrQEElZzLtsE8zUQzWgpQxiCxg5gFaTq8/+UbeOJonYd3P8WWjWtoKU11bmlV0hEZwQHyLPePIIzARCm37lvmtduGOL/co3S2E0XOk92ZuefSZdh0gLM6eynunmvRiNLuOdAZYDKKFWv6c1y8vgDC4iKascJAFxDaaQl1Btj9S012jORW0dTLgUdbpdRjjcpyOa01sTLU4pQdmwY4NFvnkckawW3f4PDu+zFhyAACvWY9wVveyshIP9e8ZB34AQN5nwena4yUfBpRiiTobk8bwXRkS9zKWJbJZRMlJmsRtVjjCFhuxbQSnV1Xq79RztnXqO84+K5krOxT8l22DRXZs9BExOD4kjSKUXGMFwRsHrSVz6jbNrMPnMhOyEoBOM8RDOZdVJqSCo2QEsd12TiQ5+BSi2qYkiqbnAWuJO85rKnY9Qeuw66JCo/M1NAaNIY4NQwXPbaNVux9S889eWXSYbJWXyfacWqTDgRxas9R4AhacYrv9NRSz8WzD6PPgo7HC0hw8ic28dj5s9fy0Fe+SW12nvr8wnN3ETsUvFbIN/7gz+0PGTZCFgRRq30KCYggjWIW9h3m17/z96zdcX73N+MXbOXKX3w7e2+/h7DeJGmFto3SFR0Ar2CxIpfe8GbWX7azu0+O61CbybAZnYrEaWJbtNLWMVbKk+ueHHNEdlA6kd/KKa0jw3uczTDGsNCIiJIEMkXMehhb740wpehJBnIuSttBWIoe0LHjrZH3HI4uNYiVPmYmKYRg6QSlbde1ypM5T/aumRQIDWC62hL+kQNct7HCYmhFsFxHEqcZq0PYgbsWW0aHFNhePAVeMl6hPWm4aP0Au2drzD8N+zNNQLsVQZKAa2fciTLcc7jKcNHHkdJiM7CJhsVo0BUL6xyfK8BxrO7McpisOgdK26TuvNFSr+pjIJASGQiW2zGdm3ll7qq0YaTo9SinK2IwJ4mVppVAajTtRFGPFIeW2hgp6R8osvP+HxI8dBftySm8gT78IAdTh/D+5jOYtaNMHXk5W975VvoLHq/cNEjRs1WOjty40hqDoOw7WNF18IQgRrJtpMBjR1u0kpSBgo8XpSy1YlzHoZ0oHCkoBi6uI1EGpmoR24ddHCnYPlxk/2KbaHERYWCoELB1NGCg5BNrnSWdncTVttbyrswk64VlRwlB2RNESpP3HTCGNEkQjhWbE6bXEEtSjTQwvkLDY7Bggb8PT9e6OJn9jm0BXra+395fSXpM0uFIuarNEitNog2NKOk4RNDEJkY516HkvHh0d87F8xs/sYmHdBze+ae/yx9fc/3Z720ZiBpN8pUyru8RP1PiYQzSdUmimMe/cfuqxAPggtddw/gFWzl430N4+QBtrJx4J5FwfI91l+1k3aU7rJ5I9gZvzM4yf2CqqzOQIfEQ8tSzXZn5oySt02ubdFs0Z8iKkY5kePP6M/ru6UQ9jAmTpOsP0gF+tjJZ64GcNVhzJBjd7YyAgZwjyLmSeiuikSqU6bzus2MApDBdM7heFcDwjd2zPH60QTNOKXgOG7ddhJf774QZyNYYy4TRxjD8kvNpK0EpcBjI+yhhcQ2Hqy3CRNNKUoqei+fYCkMjTLlj3xxXbRkBbKKwc6IfXa3yrTvvozAyRBLFLIkScd36j8hcDt+HeGE2AAAgAElEQVSVKG2oRylzzYSRkh2gOtWNRBsK3rFlfIFNEsM4JMjGpY5iqRAwXA66s9+V1QtXCPKOw3KUZi2XHos7jBVF//hYASkEJc8OjkdqEXONmCjtiXINFn1aTzyCbjYBQ7K03E3sQmNwwhYLwwPs8iSVQKC0YLltK0GJ0gSuxJUOqdYWIGsEnoC+vEO1rZgo5yg4Dnvmm6Qa1pd9HprWJNlGwkRTyfUEyVpJL3kWQjCQcxmmgRd4OELRn++3iY5ZfW61geVWQiXwu+xXT1qAriNMhq/Q5FwrYjZbbTFa8hkqGhZbCQooeJIo0RxYaLJpqAhg9VSm6hnbqlfZuvPAIhsH8gyXLOU6VQqTtQ0dR+K7q4cHYwyNMEXTqf7ZOkmsDI/N1Ng6UmaweHrA63Nx/NBKd6uKz+U6XyjxE5t4ALhBwNSju59xOeE4tr3wbEIbVKqQrnvyKkNmUmeUQnguhx98BIB2rc53/9+/4fCDj6GTlP71E4T1BgsHJxFSopSm2F+mWa0zvHUjXi7gR/98CxMv2cbw9s3c/4Uvc/TR3Ri94jiyBOR0cgGdKqJ6cxXv/1TijPEdgFvIs+6lF+AHAZWxkTNez6lEmKiuLHsnVCYgVfDECsEtO9vXBqS0SUeHJWIw9AUuMzLNZKYztIIQOFLiOpKHpqu4UrKmEvDDg0vcd6TaVb5MtOHJlqHvta8l/bdbstm29TqprJvgJe/5BXzXoa/gU1hB3/UbkmoroT/X66dLAYHnsNCM2D/fALc369y1fQO3Ts9y319/EeM48O5fRPoufrlIce0EjutYJU2tmam3GS35BJ6kL3BJtaEaJQSuh0t23NmNJLNZ8N7llLGizxNzLZQWaHr6JQKOEdMSWEzD0WqYtXBEBjq1HjEF3zmG0R1neIr5ZswX7p2irxwwUgrQOqWUz/Wk25tNjvcECyFIowQyKXYpBHnfoZ0qZhsx9USxZSCPIy3TRCK7Am3tRCNFJs3uSDb095R255oxh2sxeV92vWI6sVLt1RgoORovnwOlrHfPcpV8fx8BgqVYghCk2mQJmeyea4NNOnxHkBpoZxohxhhibUHKlQwAPZD3WG6ntFJFwXfYO9/oJh5PzTeP+zgb4NGZOq/eGuC7VqFUG1viN8f5wlLbyv13ko4eXNZQDVP2zjdJlGG0HJxjwzzLOIfxeJGG1prP/fLHSMMTUE2FICgXSKME6UhUIk4Jo3GycH2PwkCFpB2enBligQJopRjetAGVptz80f+Tub0Huw9sa6lKZXyEV//q+0jimC1XXEqQz/E726/jiVu+z/CWDfjFAgfueoDCQIXG/BJxvdntwz/r+DGJmEnPZdPLdln2h+twydv/3Vnd3okOyxa8BanWBBmcTghbxej4oXSxGMb284fyLnMtO++1QEGHzHWd2BgaacLe+Sa37ZsnSg2eIykHbjbACtpXvJK3v+5lPHHrd/FGRhnZeT5DWzbi+y5CJzw5F3eltYcKHv2BQ2aYmu2GIVFWfrsUuMw1Q4oD4zw6XUVK2DxY5MY/uZHPblrHo9+6g1argTuykfLaMau+mx2j5zoshwpjNJXAwxhDGKfUGm0Gch5K2ME/1ZZ94wiF79oB71A9Zm1/gcPLbVSmhtaOFQVX0c6caws5l3wmex6lCldKRgq2nbAQKoQUbBkocNHaAVphlDEmINHaCqUpw/2TNatumirmGhFDvl7VAgrGRinWl1laXu5ez8719go5xi6+sPvB4/NNFlpJd4CvhikvHSuRC5wu894V1v3VdSQq1fiuhBXaXRsqOVKsA60UWcIqrKz84IrEUBtDEsagUjrVsdZilbQdZXR4QdWz+iZhaihkGisdGXfhiC6FOFX2vHfE4xArkhwhGCx40IJWqjJgssEkMSVHPU2TtRfpisEoVYo4ikmNBQPHqSZFMF7J4zoOjViD0SsSSrtvi+2UUFntlkaUWEzKucrHs4pzrJYXYTzx7Tv4uw/dxPy+QydeyBiEdHA9O1NNwmMVAU8npOfy0W9/gcdu/T5f+u0/PnHVQxuMNKgkRWvFU7ffzV+969dYnpzGz62m2raWqjQXl3ntR/4nAL73539rlU0FzO87SK5SJigWWNh7wGp5CIHjuqfGSjnFEJlD5XNCsRUghGVECOng5XwG1q9BCMnQpnVc/u63sPkVFz/77ZwkPFeSxqsfQJklGGGqWQyh4Dkr/EcEqdIZyG616+lEySdwJfOtBIEV8HIktJMO08I6fW4YLDLfjFlqxiy1Ykp+nsvWV8g5El0cZtf/8gGE0XSsVWqRYim0PXeNnQ1PVq0zapxqGpmU9krVToS1RG8ZgY5SUmNYbC0zUQn45Y/eAB+9gelqm/9y+75jqllBhimYrScMFXwa7ZRamNJfDIiVQmLlzDvf08ZwYL5JNdKMFQNSYLQcMNeIOTzX4PsPTXPtznHygQvGVpn6Cpb6O7PU4k3b+qgEFgMhpKCSD+jL9G0cKai1IlKtiFPNQivl3sNVds/YjEspSISmnRpWqlesf8N1tBamUWMj1I/OdZMAv1Rg3RWXsv0tb8AYmK7HzLd6TBAhBKkxRFpT4thWj8yYJAbr99Jx4B0s+yRCMFWP7F2RrW+05LM2o+ka7LE3FJQjCyrtRNwOUWFIKVEM79jBUpgQJop2GOG7Lm723IWpIXBBGIEvRVahIrsOq/dVCCgHDs0kZXO/h/r/2XvzeEuvss73u9Y77vnMU81zZSQkJGESEuaxASU0irSIA2Ir2oof23ZC7227r3ar94LXgW4x4IQIXEGBIGKYIaESMleSSs116sxnz/ud1lr3j/Xufc5JVSoVqEDAej6f86k6Z+/9jmu/61nP8xtay5gs5fKKYsqBhxuaw20GSafWhs1DBXpJSqMTk+aVDoHAk9YrRxrDkaUWlULASNHjjpN1aqFNnjFW76MZKzuW8+pfN1WMnHElL8YTiicBXPpUcjP/nks8siThr972K3SW65xRt31UxK02frFAoVohjRK0/uYnVzfwqYyP4vgej4fmNMbgBh6Te3dglObEHfeSRTETe3dsKFEKIVg5dmrw+13/8M8IR9rMVUDUbBE1WoPXpSvJ0sy2jrQ652EIeX70YpMpatunka7D0qFjj/v+x96h4Loffi3Lh08QtdrUpifZ98Jn8/y3/zAqzSgOVb8t5dlK6BGnCo3ZcH0skM/QihWnRcpI6BB4EscoVpfalIcqOI5je+2sTVqjoUvJd1nqpgP30T5TQeSr5rGyT9rpEH/9SxRnT/KCX/wxyjnrQEpJmlMW3Xy53U41WohcxCqvxeSMGQcxMBpbH2HeNhmgTozFrsw2Y6bKIcXAY7pW4Dk7RzhwskGqTF6lERQ965nSSBSrkQIEQeDSUYbFRkyUKDZVA0LXIdWG2VbMffNtNlUDioGklRgcIZisBHzy6ydJUs0X71vgaTuHGa2ECAHHFtu4Ep65tWRX7cZWMlDgiRjpuJQLAY7jMFwpYozhNz5+H81oYxUyTRVCOGT5k8sYQ8FzuPH7b6S5bYgDf/ePnLrnIN1Gm8LUOFf8yE1se8FzkdJWmw4ur3mWrA+VV458R65JwhsQwuSVMEPoeWSupRELrZkoS7YMhzRjmySNFTx8TxBleXsz387YaI3lI4eYHHEH4nBuIcQZGmJBe7idhOGCx7bNBT5+4AgT5QKiGKKMTXjrvQwHQz2CpaTFTLWAm+uhnFHdFCCNYXOQ2eeE0ZQ8Qc+TXDosaKUpi5HtH24eKjBecFhsx+vATCLHbYCPddIt+w6n6l22D4WUfIeFToLsyg2iaoFn24iuvEAV14vxPR3fc4nHbX/9D7QWLW9dSgetH7t9YvJVXNRqIxwB38Kivjo1xgff8Ztr1Yb+d+9RlY+hzTOUJ4YpVtfWa47n0lleJWq2KdQ2qhCG1TU1xzRO8AsFkm737GDR/GF3PniVgZrmecTqidPses41pN2Ixuz8eX6qvyOQ0ipCPvNNr2P79U9j9dQcY9u3EJTONLR6ssNzHMbKIa1ebFfxxlZBaoGk4AjmOhlRkrKoFBWdsnJyFpUqprdMMjI1ihbW3VWINUv7QNjSfKQs46Ivcw72ed5ZXGbxve+jd/e9XP8z/4Hu3Dxpo8Hwji0I3wL7dD5QnLx9kmtfbdCtUMZQDuwqGGH7//33elIMJL0fPTSOrHS5bLoGwJahIifrEa1ccMwVYHKMQZi3TwYfN7DQSjjZ6HFwqUPBdUiVIcoUZd8hdB08IcFkIATdKGOlaX1BuknGF+6bHzB14lTxymdMDzxEVC6bngqH5VSyVO/RWWiz3I7pJZprt40wUw1pRu0z7qFKFZcOKTZPVSh4DpdMVvAcycwLns3+FzwblWVIxxkIlWljcKVNKNTJFpkxOGzEZbQSRS10cyl6i2Lo3+elrm2TuI4gcCQGgTaSwAXfgWqw1lpxpGDvRJFmlBFnmnacsdDq8vDULu5YaeDrjJoD20aGWO1ItNDQi1jppmyqBly6fZJDp1aoBhpfSrIcY5LmbLNWCgu9LtuHfEYKPqxPPoyttu0e9sDEYKyvjiMEY0WHVqK5dNjl/oZmy0iZPWNlOrmxoP18v21jfzKdg6U1jIQu9SjjsvEiB0636STKAtGloOg71uk4cEDYNuTF+Nbi3zzG49577z3n6wcOHLhgB3Mh4r6vHUClVu1QOBIeB7ah0tSCN7/FVkJjbok0u4fi6BCF8WG6fVrr+klAQGtxiW6jQXlqnKBqwV+iYCef5kqd7FFUeGf75OAah1sm0V+/G7dYQCepHUjCYlNk4D0h+usToa0apWjW6xSnxzCuQ9LuEJ3Lo0asPcD6qzKvXKQ9FPDAI4cAmDv4wHnv/0LG2car53m4nkccRZz83NfBSCauu4aFdcye0ycXcMOA6lAF6YhB6yXLZ/mq72xsfZB/0ZOE0dljFC7bweHmKqN7dyGkQ9qLqR89xcjenUgEFQ/8ASjTSpNr1kzk7PYMp5sxE+WAYuANkhuBoRXbh4o2hiTbOOgXl1scmD2Uvw6eKqG1wJWOZSgY8F0LhO2HECCNIPBkrvxp5c0lgqGCy1UzQxQ824ZwBaTGIORaQmsMG66HEOQS4pY1ggDleEhhPWowgpIvKQy7rHZjPv/QadzOIlEnI133vBQYXnTpBGEpYKHepCgV8ekjyLNCSzeGAYw7gcyrRv2jc4RtwaTaMBy6VHNdjkQbHlnqEiUJ49UiJNAVhsCVCOkiJWRKbFj5x72I2xdXONbWPLDQoRtnJAYmygFeWCQ1hkhKlhdjNtdCHEeAsA7EJ+tdRk2HJEu5azal5DlM1QoWYNwHROctpOP1hIonN1QJ0zQjimLq7SYmcPA9d4MOR9mFXVXJlrKgqRI6nQ4yCJDiUQ+dfD/GWAM71xGUXAdtLNj38rGQB08t0Yol1WoJiSYQQKpoxl1WmsuczM7fxuGpNoc8FeLfPMbj8ssvJwjODhQ6cOAA11xzzQU/qG8lKu2Mgx+6BZ1L/UZKPba2hBQ4rmdXj56HVuoJTd7rI+1GpK0u/sQYm/fvYblUYvmRjRgTISRGKdKuoj07T210P15or61joDRcG1QysjSlMj5C/NBJSlu2su8Fz+HSP9rH7x64n9VTc7jF3BJba0qbhpi+fC/3ffLWb1kk7bGiUqkwc/k+rnzNi/nM7/0Zx+64h6hx5mrULQToNLO0vFw91fE9Xv0b7+C667+zjrPnM17nPvVlTj54lJFrrqY0M40T+qA1WavD/NwKulBktACafELWBpOkHPzopzh58AgmCJh6/nMJdu2ggKZUkNR2zODu3cK1P/QavHIJlSmSXkxzYRnV7TJUK+IOAHuGsieox31dj7WJw3UFeyfKxJmhk2QIrEBUNXTJtKKZKOJU4Ti5OyoGT8KmWo2KP4yQLsViyCWp4dMHFzjZ6OHm4M/NtRDfXSeNnf9byyW04/yhFbiS52wfIXTXALgTZUk7yQgcn7FKwFw9GiRk/SgXPIwUgwKgELYKZvIj7YcUUA48JocrtIKQd15b418OLjDXiCj6DpdtKtNTho7SOK5DbByS4gTP2zlOKXhs9cw4U3zjVIOJTHG6HeeKquDm4E1HChqxoh0rKrmey7FGTKYNw4XQVu2w9F2jrdKH0NDViknXzU3+BKnv82Czw60PLRPltNpMG+YbMdO1kGrBo1KwuiyLnYShgofnWDZUYmBiepo4jJg/smzVY70zqwd9XEo7VVRzYz0FxDjEqUNtdJyySECrM1rNrufgOy6GAgqrmrr2nj60lvy4NdXAHTC9AEJX0k0E1+6eYahcpBmldBOFNhY8XQs9Am+G842n4hxyvhHH8eMuzL/Z0FojLnCF4qnkTuu8613vetfZXlBKsbCwwMTERC7LfWacPn2amZnzH2TfjhjZtokHPvslGifnQAibUGTK6irItVUDAhzPww18vMDnslfcwOn7DiEdmQMg1wP3xOPTSwVkUUxjbpH20qo1WXvUNiyoy/aMdaZwPJdCrYIxhs1XXsKPf/A9DG2Z5tiBe5h/4BCLjxzj2Nfv5u6PfYaFQ8e4+vtfxtU3vYLlo6eIWx3CSok9z7uOt3/svTRnFzn0hduftHLaD/zhb/CyX/opJvfsQKUZj3zpAEbpjYmaEKANXqnA6PbNeGHI5P6dvOX9f8B1b/x3T8pxPZE4n/E6/+BhTj3wCHtvejVDw2XKYUCx6OOXi+D5FAJ/0JIQQLfR5B9+4f/g8Bdvo3N6jtaxE5z83FcQWnHVM6+gVgopl4u4tSrCdXF9H50nY/5QlVgphnLTOav6CQVX5mJNdrQYY3CloBa4BK6DJy21N1aaVNkWSdl3acbKCo5Jiy0JcuGpsbAv5KVIs5RSocCl0zWEUpRcwcyQxQz0c4V+smNXuIaS79KOM5TWXL91mFrO2hgUtoTAk5Jy4LB1JOArh1YGXh5gcF3BZTuGaaSGzVWfgitpZdBMrLOu79gvpBB9XIygmygyrbluxyjXbR/lhn0T7BgLObLaQ+Sy60rbdkCcKeIsY8twiUY34dDpFoEnB5P2A/NN7jzVoN6L8R2HyVz91eTUX09KnBy4qYEoMzRiRZLTrB0BBd/JRbMs46OvQTJR8qyxWn62CPCl5N65Jp1EoXN8Th9oOlUN1tpZ+X1N1FrrbLoSsneiwjdmG2itGauEG5LPvlotwEw1pBQGZAb+9c4T/OUt9/Op2w7zia+fYPdEgZGKP6D6dlNbJflfty3xhcMdHEcyPVzM5esf/Wyzv4eOrXg9+hXXEbTijKFSSOg5VEKPaugNRNSeSDwV55DzjfOZI7/ZbQYH70TE62wWLsCP9gLifU+/oMf7zcb3HMZDCMHbP/KnfPAdv8XDn/sqSa/H6M6tjO/cytLRk8wffIQ0ipGOxPE9vDDgJb/8dl7wjrfQXX0Lhz7/NbIkw/Fcq2zo2jJnqVamPD5KbfMUKs5onp5n/tCRtRKozidhDXHzzEqAvfkaIaQFiGpNt9FkRGxi2zVX8NJf/ilczyPpdDl621056yY3IosT7vj7T3DJi57DNTe9krd+4A/O2HzUbqNVduHotI+KP3/jO1j8rZ8HAyNbZ9j9vOs4fvvdrJyYXWtt9cWJ4oSgXOS3HviXC34cT3Zc84ZXkuzaR7ngW5M4AASB6+BUCgPBrH6r5et/8SHqJ2cH524AiWHxnz+L/+ZXE5YC8CuUEHR6MXGcYRx3AB4NPI8kl7vuTxJKG0ZCl6EQVmOrWBo4a2V1IaxJWug6RJkiyumeE2WPlU4Ewvbcqy4MbyhWCutUG/UoFUtct2ucTGvuPL5CohmsbA2WOdH3kSn6LvsnKzR6KcMF96w5uJQCrQy+6/K2F+zkCw8ucmK1RyfOePqeMUoFn0wpbnl4lctmqgSeY6sIxhD0HDZV/A2TVug5xKkeCF4BzLdjhLRsniTHmsSZRiCI4iafuXOe40sd4lQReA5P3z7Cq67dDFpxyViBXqpxpMX5FFyHU60152qLlbH9l0d/e5qxouApAleug0MYyp4FU1pQsU20pITJss/rnzbDvXMt7j3dohFZ1lOcaeq9lNFKv1q09p2JU0UgYKUdUe8kvGTvBJ+87yRKmUE1bE1F1+I2NtWKeK7DF+89xce/dAilra9QL9P84acP84PXb+alV47TiBRfO9riMw83mW1avMp9p1q84qqYl105RaL77a/+yVuMiyvlmRcDkIgNLRywq+kotZWPwHXw3Is4j4vx2PE9l3gABKUi/+F//18AGxQke80WCw8fpdtocueHP0mxVuWl//ntFHKg58996v3MP3SYuz/+Gfxyiae/7qWE5RIf/+P3sWffPi558XMHlLgDH/4k7//RdyKlJIli1Hm0aPrAcek4OGHA01/3Un7g9/4LxaHa4D1f+l8fPCsTRyvFV97/Ya656ZUb/h73enzqv/0xp+5/aN1D48KHTlNu+d0/YetVlxE12zTmFonbHYuN6Sdf+d4d32PxkWOsHD/FyNZNT8rxPFnhl8tM7t1mPVS0HlQ2DOBKie/Sp40gEMzf/9AAZGryioXnCFSccPCWz3P1G16ZP8ahWAiIFGR5CV44Dqkx9GsDax4m+SRjoOA6g9X4o++sI2yCkGpjbeo9l5LUVColfJXgy7ONBYHK9LrfYNdokdOdmE6sc50Oq3YbG0HRs8BTg2Cq7G8Qx3p0RFlGpqFc8Lnh0kkQcPeJBp99YIFtE2WKocu28RLLPYWfGEZLttIQZYqFTjLwF8nyKoi7Lpm152tbd0mmWeqkxNlam+BIM+LOB5fZM1NjqGarCvcvtth5YplLZmqkuRGb1lD2RQ7ctS0LYWzSpdY5wfV3q4E40xxa6TBRCqj4FjfRSxXjBXfd/bLVTIlAuoKZaoAysHusxBcOL3NkuQc5S8SOJ0NxXTXBAU43Y46tRghsS+OSYYdS0aedWu+kfuVDCJiuBoPJ/TN3HKOX5l44+fXopZp3f+YwxeEhPvtwnYdP9xdDa9fz1vsWeO6+McqBk48vgXCg5EocIelmZwfIGRjI4VtH54h2nG3A04e5m12iNK4jGSv6BN735HTzpIRR6lsXtTzLNp8q8T0/EtY/uArVCtuuuQKAS17wnLO+f3LvTl78iz+54W/bb7ieKx/VhzRan7cx3MYDsiyWoFLi+393LenQWnPre27m+B33ksUJbu72qNLMmjVJSa/RIo3jQfJzy+/9Kf/0W/83aS/6tiCWk06PYwfuscqm66MveAQ4oY90HHSasXri9Hdd4qGNwXWdgRV7P2mA3No9Z57oXFp6UOkwdsLMmwYAdhvrWE1CgO+7xJmlOgvHoRelNGNNLVjnh4JNQuqRIvC8DXmodTE1dNIMgbBqmwIOrXQZLwV4RtKO7GuugKKE0DEbqxT5/zOlWGpFpNpQCzyqvhm0eobLIY8sd2n0UpQxlnJr9ICyuz76WJdmN3+w5QdsDFwyU+GrDy+xUO+xeaxIKXSxOETL+KiGrm2tZHpwbvY6Cp6+pbZhP7vGyjy83KHRy4hTNbiwBphbaTM9XqBQzkv+riRwJI/UY4arMSMFb3Cwq70Uz5X0UkXe5bAMH5knWcbeR1cKVjoJrdhq53RTyz1y83u7d6ywlgzmfRZDXjgw/TEjuXbLMEeWe5R8l7GyjxCCSuARetbbxcWw1LaCZv1KQqwMh9qS12wvsNrLmG1FOTtHsHO4wJ6JNfbbYqNHK84G30EApTQ9Azd/+Sixcq3h4KOqFJ0k5Z4Tqzx375pasBTkBnkKkbee1jdP+vc61ZpGL2GhFQN6kIjovIXV1tZLSEhLj2nHGeMln5HSRq2ii3H2MFo/CSZxTx2Mx/d84nGhImq1iTtdqpPjdFcb3PZX/98TTjqs7XxAaXSYV73r5ykN2wfryXsP8u5X/CjN2YVBVpr2ojXNjjwOf/kAv33ZSxjaPM0z3/IDfPzX/+eTbqq2PnSaET/OOasooRdZMbMPvO1XuPzlN/Cy//zTlEeHz/m5p0q48swyMtgHsiNs4oEhf8gbpi7dy+qJ02hjBcT6uYZXDNn3kufRihWpslUNV2JprI6D0TZ5qUcZKz2YKbvUAgdH2ArAqVbKbCvh8snKYKaXCBo9q2Y6VPQHlMduaqW0u0nGcOgNhJyUFqQGFIai1PQ924LAPvybvZRUryUl1svD4jqU0uzPJ7f+KvrIcoso1WdOYsb6xaT5WO0nZJbKKdk3WSaVEi/cSGLtJGqg/eBIaPasBb0BQt9lrLJxkioFPrtGShxZ6jJIOgwsLzVZWOmyZ9sY1YI3qAR4rsQIwdGViIktPn2eRb9l0i9w2NaSvUeBa6shoevw+YNLLHQiAt/FERZLUi34jJR9Ko5DnFmfl370z0wbaERq0Maphi57xgrsGSszXPZJtTVgMzlltx1nKKNxH8UuMQg+8/Aiep2nizGGVpSxdaQ0wLDoXLyr/640pwQbY6jViiysJAONkvX3zSAorZPkF3krJ8kU7cxWgqpBv7+TJ9XCOtm6UrLaieglGaXAtRgCNhZrpRSkecUDYKmTMFQMzvr9uhiPiieBTns2p/PvVFxMPB4nWovLfPUPb+bWU4tE9SZju7bgeD6n7jmIGwZk56t4KuHaN72GLVddxlWvfQnjO7cOXnr3y95C49TcGR959MAzStPqxtT2X8IdRxfZ+R9/gpWvfp3lr9x2/ifUF1VbI1GsOXVd4Fg6fJyvvf8jNGYXeOtf/uF3hX+DEIJa6NKN00G93RF9ECI40sknDDtBXffWm5h74BBzj5wAKfEdgZFw7ZtfRxoUBpMxBnqJ4bYTTfaPF3B7PRaWm4ixcYwUnGilHG0kkK8yO3kS0UnVYMJItKYeZ1YttZP0NwvGUAlcAtfNG0B27W2EQGnoCivt3Wz2+OLxFvcvnaDkO/zg1ZsYKjzaCMz+u9KJqRlDwXORUpIpZUGYvm0zZDmmQWCNzLrJxjPY3q4AACAASURBVITUk4JY2cmql2qCgpWUP7zUxhWScugyXPJs9cd1KHoO4xUPL28rpdpQ7yZnSG8/bdMwv3fzrYxMjyIdh/nZJVr1DpUtmyiH3jp20FoFqpVYpVfPkaRKI6Wgk2xsNxkM7STjy4cbdBNtK1GdhMB1iOoxnTQDY5lFO6cr7BotsdhN8R2raLoe49NOFQZjTfiMba98364xtg0XKHmSexbadBKNydtpVtNEnvH90MYw10qYKK+nOQtWuwm3n1jl+3aO8bE7T9LSDq3MTv4SjSfseY8Ol5ieqpFlTU4sdsiEbWGNDoeM1EKKoUtHCB5e6bJ7OMQREoyhk5lB0tRKFKEr88TbnqHKW7qOEAyF3kCx4NHaQv1in9L2Ghig3ksYKV6UU3+8MMo8CXTaJ6cN/83ExcTjHGGM4SO//N859vnbUZ0IlaYsPnKcNI7xS8UnVLrySyXe+v4zQaH3fOKzZ006zhbF7VvY/ws/g1dZExUbuuISwqkJTn7k44/9QSEIh6pk3a7FohgxOPbalmkQ0Dh++hyf54lDR4xBJSm9VodHvnSAh279Kjuuv4rb//bjLB89QVgtc9VrX8rE7u1PcMNPfkxUiyyuNohzIy4hrFZFH9g3mB6Ebd+95vd/jc/93S3MPnSUqdEql73iRqb27aCTWhVQMGilObzYJOr1uP94l8LqEgLwlSGcngKj80oFZMoKP1VDD4MgyqxfSy9RVHwrNd5JrR9HwZWEnhwwM9ZHv+ydpZqTnYTPH2nQy+w+2oniRL2LpMBsO7EGYEDZd9hc9Sl6Lq16F2MEbm6Q1hcsKz66Vy9sOT6fqwDrcYIQrHZiVjoxm2tVlNFoDZFR9NqZdYOtWpDs5mpgVS/z7fmOoNGNGS76Z6j5bvEUdx88Plg5C8D0eta7xaJ7EcIKrmXGUM4rUSXfwRhJogwLnTQHa9p9LrUTyzbKGUHz9YhUacqBldLPJXNIM02rm7JSzDjViDnR7FELPDZVfEq+S5Rposx6qQSuVfP0HUFdSha7GT2PgaV8v+piJ/b1V89GL1VnrQ4IIZhrxnz0wAn+6FMP4klBpVyg3UtIMoURDrumCjzzmu2kRuCXPYa6Po1OythYyNRECSkEI5WARqKoL3VZ6qbsGylQ9h0G6vj5IfUyPaAfV32bILkyv+7GnuvZAO39M+qDlAHMU2fRfTG+g3Ex8ThHPHTrV3nkSwforTRwpCRqdwaVgV58/gI5AOWxYRYPH99Q6Xjw1q/yD7/x++e9jc2vfdWGpAPsmnPqRTdw+lP/gup2179gcQRS4hVDLn3Rc5i99yEa84tk3RiDZmTbVn70r/+A//dVbz3nfoNy6Uxcx3mEEMLqlvR6HPv63Xz+T/+a5tzCYCK5/5bP88L/9ONc8Yobn/C2n+wYqpToRjFa96sept/Bp/8Q7a9xXUdwxetfzs4Udo8WqRVDvnpsiSTVVAKXJMs4vtxmpZOC1iwcOkrhyCHKW2ZIVupUN03hOpJmYtsyqTbMVAsWTLnuea6MrWcEOR6hFroErq2G6HzGXpun7AcTZZkUxxsR1aJPIddt6CSK+VbEw4tdVroJtZLHeDlgtWdoxRn7x4oDrY5Ma46tdhkr+RsYHHaA2cThuXsm+Of75jZgY8Bw+aYhlhPFyXoPAM+x7SFtoNFLGS167BsNGCp46z7FIAE5We+yZbi04d78+Kuu4Vff+2miQtWyv4RANOuIeALhFfNEMa9EAJuqAYFjzdVipTlSj2mnejCpd5PMtlqMoRllZPk90Aa6iQV2GgxpZttSmbJiaY04JfAcZlsxp1sxEyWf6UqI0rYNpzTUQkkv0wSOxZQcWrEV0sB1BmlGpqHgOfRSvTbhA75jZ/dUG7xHJ5UYPvTVE3RjRbXgUQg9wtBDa3v19u2ZIgw86o0Iz3WQFY9dIyE7piogBdWCP8hzhBDUo4zlXkaiLKOonxiRY5cybVBGs9wzVH3JSNG3zr1YZ+azrUv0WbKMvjjbxTh3aKXXTHku5DafInFxFJwjlg4fp9doIoCo1Tkr2+R8QyUZf/WTv8INP/sjbH76ZfzJa36C5SMnbDJznlHevu2sf3cKIaPPvIaFz35h7Y8mB9ZKwdXf/1Le9Gf/na//zcc4fNtd3PXRTxO1WqyenOX3n3uTNZ57nHBD/7Gdfh8rjB3sSilO3Hk/rfnFDatXnSm++N6/4ZIXPQfX98+xoW9/aGNASKTD4AmssT4Y60Ng0Egc6VIJYKmT0ogyyr7H7Qt1tIZUWbaBkQ5ZkqGjiLl/+RxgE9KdNz6bWGmKnosXClK9HtK6xsoKXEkzSvFdj0rgbujZ9+ertbWlIFWKROlc30NSDV20sRPaw/MtvnakTj1KrXqoMQyXPK7ZNoxxJadaCbuGC4Ntp1oTutY2PUk1aZzguC6h71It+jiO5GVXznB4vsnR5Q6+43DtznECz+UTB+ctriK/lK40g+O8bLJKrRAA5lFpnY16L2WyavEJ/dh96U5eWu3wjRNLtMtDOCpjePEUV+0b4vjYHqJUUfBsBWiqHLBrtEgvx62caCZEyoJYTb6zVNl9L7cTS6vFKrlGuTCWoC/kpsEINo0VCV1JnFn8Qv/ar/RSGt2UTBvKgUOUKq7dbM0A23nbR0qJL+F4vctsvUecGaoFl/3jRUaKHkudFGNgpOgx7GkOrmbWoVis3WtjDEOhx1KjhxBrOAptDDI3tJtb6VKtBPiuQ6Js5euyLUOEoRVMtMnR2nixSZlBZgrXkYPKT5LlEvLCVmqUhpWewpAwUvQH961fXevL1BtyB13yqpIx1Nbhby7GucOYJwFc+hQqN11MPM4RYzu3opLUfvG+FW0MIUjaHRpzC3zxvX9LfXbeOufmomLn28vT2WPIuktJ1u7iFwsgrYS6MYawWuZZb3k9r/+9XwXgWW+5iS/+7w+SdLtobch6vfM69rQbobV6wi2XPvC1u1znwIf+iaHNU4xsmUau83LoLNd58NavctlLnnf+G/42RB8GY7CYC6ENSInG5JqlNpQ21DOZMxLs36yol6DgShqRsiv8yBpxGWDr9hkeKhVJO11qu3cC5JMSTJc9q2KpNO3EDOTCjbGr5NDVHF/pcPlMba21YiyQr0/n7UeqDI04o5NluZKoQAg7KUqEXfUPKJqC1U7CvacaXLV1eKC62Q8JtKKEhfseRAtJ2ukxf++DJCvLvPSX3jaoxO2crLJzsrrhs4Er6eatISHWmBtKG0ZKvj329S2s/j3ATojHV7rsHt9Y6Xvav7uBV1SGue+Tt6LTjF0/+9PMBxVEs4fnSkLXYbzkUfZdZH6dHEcyVvTI2glSOAMBtkwb5psRp1Z7g4m4VvKI8lZH/5JeOlNl82iJMHQH7SWlNb1UE3qSKNPUfJdWkvHgYptMGUIpuH7bEACt2FJo7zrZ4MhKZwC0bUYpS+2ENz5tmn0T1cF9bbVa7BwNOLTUJtWGwLET+EjJ51nbhvF9h05s2SPKKJvU5SZ3/bZdnxa+d7zEqy+d4F+PrqLMxq+x7U5ZrZjQsclpfyD1t6OUJk7t34QUtBLNcMECpB1hr+18O6KTaBJttWyGChZz4wtBteAyXf32ezN9t8a/ecn0f8ux94ZnUh4fYfn47Le0Hb9YACHorjTwwpC5g4/Ynn6mnpCMbePeB5h4/pk04GRxmd37tvLWr30YYwxzBx8harXZctWlGyoJS0ePc/r+h0mjCJWcJyPHmDOYM31nW+E5mPT8WDU6y1g5epLW3CITe3ZQGh06v/1/h6I/4fRXckYrK/6WAzXBPrC7WV5uFjnjIFHE+ZN9UzUk1RGNSIPjItOEmk4YdmHmmc/g9IG72H2TVXT1pGCk6FmDLQEF6SDQdFI90LUQRrC16rGj5hKv54cIcITO7wt4LmQKTjYj2okiWycCIrAGbiud2GIi1oVAsNROUDoXj8pDG/AEiF6XiS3TKM+W9Ed3buXk7Xdy65/8FS//pbc95rXcO1nmK0dWcqbL2ip7uOhx1aYazShlpROfwdPNtKWVhkrT6Fpju1Qb4kwjJ3Zw3MD2H3o9U9UCq52E1YU20nGYqQQbSvp9/RyBnSBLvkNFSnpxxkNLHQyw2LL7F8YwXQ3xhguMl30W6zGdVHHZ5iozw0UEhiRf2bvSJk+h5yAFGAHl0GWkFFANPb52dIWvHFvlrtMtLpsuM1Er0o4zjq12N7B7BJBkmttPNnjh3nHWX4ido0VGii7LnZSi7zBRCbh00iYnu2cq3HloxS5gAIQhyazQ2dbpCq6E8ZLHDTuHmakGpBomSx6nWslgD30MxnDoUQ2sSE0/n9XGjolEKRbaCRqLk3GlBS2v9pKBTL0xhrLn4AvJSmRbNs0oo+oJhOtQDp9aFc2nehh14cGg5qkj43Ex8ThXCCF4xa/9LH/7n34bdb7slbNE0u3h+h6O5xJ3uqgk/abEXI79zYcpbJqhsmfnoAKTtjuc+NBHmRgqDzQ+pi/ZfdbPn7rrIGkvPv+k4zGiXwI836RjfaRJwuIjR/EK+/CLBUpjw+y74Znf0vE8GeG5DiudaCBi1W9HGMRgXrDgwJyOaiw1NlF60BrxXYf94yVaUUYvVQRJhGpphHYYufJSZl79UspTVkOhHDh4jtgw+VrQKIBASoPQBlcYFBL3jFWrrc+4rsCXcHilSzvJiJRdkYp1Im+JsnocnhSkot+ht/vOlF3FjhU9lAaNpuhKCq5kpScohkVcKWwi4kimrryE0vgYdx+aRReLeFKyeSiktm6iefn+KZbaCQ8ttAfl/Urg8txdY8TKMFQMON3s4ebYFUO/WmPHl+/Aajelk6qByJrrSIQ2nGjErEYZowWPTGmktEwSe9XyipXpl66t1HrRc6wmhxRsHSrwjdkGo2UrZT9VDXFziu/uiTK+I1nqxPiOzP1qJK62+BBlDI4jLcrBGKSBw0sdmpFl0bjC4LsOqdZ0Eo3ShtlGNNCAWQubOCy1z/6MqYUel0xWGF7HBql3E0ZHi2xuxswudvuDAN+VbJks8drLJxkvWfVdA8RK0U4VW2tFEg1L3dQyroSl1G6pFc5g1fSvX6psspfmFaKiZ6/xeLlEPdZobRVmu4nGdQRDobMhrWpEGQvtlGrhIpvlYti4mHg8Tjzzzd/P8RMn+OyvnslIOe8wBpWmqDTDr5S+aQU5Hcfc/1//ByPXPp3yrp1krRZz//p5xrdtIvaswJg38dhf7l3PecaT2+c7H1quNqhM0V5cYXz3Np774298yuE7ANqx4o65DjuGQsYKLiCRiDNOL3St0VaUqQ1JB/TnecFw0WdcCrQpkI4OEStNsHna6nlgH/CulAPMxoAhIHJWDXYSTjPFcL6SlxiyDetmy4ZxkNy92CXKDKHrkKjU6kDktE6tDaMln0rgkmnr8xLl4l0CC/6zLQpLG04yTdGBelfjOVZYTWGTLsd1kWFIYdcOmkoyv2oxBycbPS6brLJ5qGCTNQEv3j/BZVNVjtd7lHyHXWMlHClYbsdsGiowXS3y4GJ7MIT6l9nPrehXI4udWI9zkAJ8V9COFWXfwc8BpM1YMVLwbHKgtaX1Clu9auW4jTTHPYSew56xMredqLNztEjBc5C51bvMlVJroUsjUrnaqXURVlj6qMz5PNoYjix1iBKV65hAqmGlGZMqw/bRkqXNnmWs9Rk4obt2DmLdeRZ8l6HCxu/IP953GmNg345hNk2VWVjuISXMjJf491fNMF72N+B/QsdhuRsx30nZN1rg8vEixxsxBvCkxLOefbh5QtU/LoAk1Sw3oj6WmI7vUC37OQZE5eBme93BVkWUylAa6lHG/fMtfvJZT+0K51MtjNYXHAx6UUDsuyw2Xf80RnZuYeXwiW96G0YbonaHE7fffc73Sd9FP05FYuX2O1m5/U6EFJRGhqlNjlMeH6E0cu4vd3lshOEtMyw+fPSJHv75hV4HjHiMcAIP1/cZ2jLND/7xf2Vi19kBs9/pONnskSjNQys95gKHfSOhrX70sQjGJghCCnwpaKtHr2LtJNWf8KSwwmSOgFrgUvN9tBSYXADM5IA/hZ1QrbS23V4vU7nQlxj0Ddy8LaBZu9xRnPKNL3+D5UNHCacmGHvmtVQCl16qSbRGGCj6DhXPYf90hbtPNZGQr2ABDE/fUsN1HO6cbYKUzK92ePUlEwAb6LqaXDa7UERqgy9BdzPAoDPDAwstQldw/1wTpTQT1ZBawePp5Y0TaKz0QBBt71iJY/WebaVg2xZbagXmmj0SZTb4tpCft8gnwyjV1AKHlV5Gogxz7YTRgosjBL4jyIxV3LRy52uJlhC22hS6VudDAxhDN1EUvP7K3d7rku9QzOnMxmgakSJJFaebMYvtmG6icKQgcKzYXKatGJs2hpOrEdtGSmwZLvDQQjsHEOfnYQxKa3YMF9g2XGS5Zx1f0SkjJZ/JSjgQZOskGbONLgfnWsic9VIMPXZuti2PLdWA4YKXK4kalDGcbia0kgxtDL1EcXS1R8GVlALblopSTaQ0BpuY9hMfrQ2NTsKxxbbVCJGCVjflyw/MUy54bLpx9wa6r5dXq4QA6bi0kpR2ohktBoyVL1Y7nkiYvmPkhd7mUyQuJh7nEX65yPT+3USrLeKebZU84UEhBNJxHtfTpTo+RmNu8XGrIkJKwkqJsZ1bMNpw+ctvtIZ2jxNv/H/exR+/7m3nL3z2BEO6rsWEnO36CIEXhgxNT3LFK1/4lE06YCPcoBkruqmm4FkQXp/0aQCpDNpokkyTKYOVr7DusSKnYTpCDjAGRU8yWvTWKKNY8GUvVfRUf8vWcM0KUwq6OcLUCIERBt+RpHkLpd+daa82+OR/+Z80jxy35oJaM/uPt7DnHW+jOjmB1trSKvMe/pUzNcqByyMLHbpplst5h4yWQysyVfLJtGEOuwJ25ZmkSW1AC4FwBFmqrONrzrQ9Otfknx+YI84sw6Hku1y3bZjLpiobzOAMZoDbHi4FDJeCQcXHMjaUFe96jMjyCxnkbIkg1aTaVog6iWK6EoA2GG3wHIeiJ3PJbzPAMkghqOZsj6Ln4Lt9pooZeLoErrV8t/OsQEiHgqt5cK5LomxlCPJWlTGEjqAd2+M2xvDgQotLZ6oMFz0um6lx72yDNK+Q2XEjuWSqQui7bMrVRA8cf5Dp6nbAVhY+d2iJb5yqc3i5hxS2cmWMvT8mT1a3j9iWSZ/S+8Bil1acDZLiRqJYjdJcvdXw8FKPK6fKA0yTyqnW/VioR3z0S8cGd36+GeE4gkLgkKoMV64BxV0p8HLdG/Ikb6zkMxR6KL0mq34xHj+0MogLjvG4mHh8V4V0HS5/+Q0sPnIMM6chDDFaE3d7mMczh8tXrm7gk57HZF+fnXtc5ohfKuAVQjZfsZ/q9ASXv/wGrnvTa8/rXC596fN51ltezzc+egtpt0eWpk+cJnuO0GnGtmdfRfPEAvXZuTUktYCgXKRQqzCyYzPXvP5lF2yfT0ZsGy7w8FIHjV0pr0YZgeNZuqIxKGMn8MVeympP2coGgAbPMWvmcsYwWnSsQqhZY3X0bdn7D3vfkXSSFJlLaSMEcaopeQ7a2JZOLbAgRk8KAtdiNZJcbOwzf/DntI8eHzgaIyW90/Mc/csPse8X/yNO7rSrlVUdzaRkohqyebhIqg3NXko5cCn6Ft8Qeg6hKwk21UiUBYL2PUz6w7PPlDHGsNRNB0JRzV7KvbNNHCkIXQlIuoniC4eWmCz7jFfCQfXEd5wNsuPAhsqR5zi5mZu2VZb1rSxjNUdqoceO0SKPLHcpeJoC9tpUgxzvkR+wzvE4Rd+x/it5dFOF6zpUQxdv3eQohUBKQZKmTJc8dO7lQp6QnG5EZLlpXV+AQ+S007ZSZPnY17nI1uceXuSarcOMlX1u2DPGkaUO9TglyTQPHG/w6fsXuHrb6FnH49994xR3zTZJlEYBytjrXCl4kLcAy4GbtzusBsmpVrQh6ejLqAshUAYcrBDd/Qsdrt9cpZcamqmt2jhC0Isz/un24wP13fGRIjt3DFPK8TRHl3psHyviSkmWu+gOiFZ5hcTkwOhHljrsXecvczHOHabPXb7Q23yKxMXE4zzj+T/9ZnSWccdHPsXKkZM4vse+p+1nePMUt777/Y/9QQPScywd9XwouY/zlsLIEFuu3M/Its28+b3/Dem6T0iKXAjBG9/9W+x89tXc84+fpbPSQGcZcw8fpru4ikrTJ0SZPVvM3XuIa9/4aq55w6v4yvs/zKm7HiDp9gjLJbZdeyXP+bF/z9jOp261A6DgueybKHPvXBOtDSOBpJuZwUPYz30olrsZ2mgCz4FcdCpRgiA3zhopeAyHzoCW6w5693Y//fI/wmo8PLzUBWNY6SacbkS8aP84mYbJkjNYlSY5ZsERgiRTPHRkgbkD99rVudxYbCpMjWPSDOG51hVXCoy0xzO/0kY7DkIICq6g4Nk2iMEqonqOZKjo0c40I8pQyBOEzFgdDCntBDbbStaptMLxle4Au6INONLg5l2iB+fbzFQDjAYjBcNF73HH7yXjVW49vMhQ6OXtKzvRLndTHCHYOVIg9FxKvkMrTi0YGEHoOCTrHrZKaxxHDhRLbUKgOdmIKPiSauDSW3ceYGmkjgTXdSyaw5FIad1XDyq7nVipgT+K/YwhztZM0jJt37fSSfnXhxaZKHtctqlGqeDRjBVH55oUHMnsytnp7V87usRCO2HXeMlqCmWahWbMfCuiG/cIXIda0UcCDy11uXSyTMl3acVqw7VN1q14k0wNtFFWopTTrZh2ojnditkzbgXbUq1ZaScYA6PDITu2DtlKirFeOl2lObYasT3XewFr/qeMFZ1z7CBFaUM7eQpRKi7GdzwuJh7nGVJKXvjzP8bzfupNtBZXKI8O4xcLqDTla+//KL1G66yfG9o0iVKazuLK4+6jT1M9V4xsngJgYs82HM974ieCBQU+680/wLPe/AODv336gx/mrvf8LXGrRZZkdFbqpL2ItBehsuwJJSNxs83p+x6mPDbMW2+2yqxRq41WauDG+90QO4cK+BjSNCVRVi2y/xhPtaGbaQqexHctDRFsYhIlipLrMlF0KOfuq9owoOGux2WsI8mAgPFKQL2X8qUjqwitOdGoMlPxB5oWjhQDzQ4EVEKfsYWTJCdO4ExM4Do+ayPI4I+NYrIM47qQTzQi37nnuRxd6jA9WqLgOwMwoW0t2EqLFHai+8TBJa6YKlENPdqJQinFpqEimdI048xqiGBX1H0NEE3u9msssyFWmq+frOM6gmdtHWI49KkEjz+GPc/hyk1DfPnIMl5OW21GKZOVAjfuHsPP2yzT1ZClTmql2QfXdq1FpIwVtJPCqo72UsVsKyHVhooneXC+hdZQK7rUQh/PEQSeIJUuicodeoXVTHGkzH1hrEKp1sZK3ffvsdKkiaIXpQSBh5eDglOlWe1mHJxrMRS6XFWTPHjcbrPonSmupbXmvsUOxcAZiHP5rmTTcEiiNIvtmNOrPfYFLnFm3XO/crzOs7dtNGXMtPWiWRsZ0EktU0Vpw0fvnUdISTlw2DlaQhtN6LtcsnWIuw+vMLVBR8UwXA4QQtCKMk6t9tgyUqDesVReTV9MzO7HlYJe/BgaRBfjrGGUWXMxvJDb/Cbjtttu4+d+7uf4nd/5HW688cYzXv/Yxz7GzTffjJSSN7zhDdx0003n3N7FptsTDC8MGdkyY7U5AMfzeOE7fwLH9xBSIh0Hkf/sf/H38Rv3fprRLZvOq8x1Nr+D9eH4PqWRIYpDVa7/4dddkPPpx+ju7bz1A7/PVa97GZuu2E9xuEZ1YoyxXdtwgycODEvimOWjJwe/h5Xyd03SYYyh3o1ZbEcYY4i1INZr1Mz+M8Fg6YtrqYM1QvM9ByMgNpJ6pIgzZbUqugnNnJ0BG3M5uyq0E8dwweO6bcNoZViJLCgwUXqgYdGveFhGgmDL912P40jiY8fpzM4h4wipMoS29FLpunb8rWPLGCBKFc04A60ZDjwKrkMht5M3QC/NmG/FLLUTHphv8vd3z/HlY3WGij4ztaLV93Akk2V/MAlLYamyJq+KpEqzmFvLx5kmU5p7Tzf58D1z9HKhu/OJyXLIay6f4dLpIXaOVdimVnjR3slB0gEW57FnvEicKk63YppRiiNtIiXoa1bAai/j8GqP2WZsr3sv5c4TDY6t9DhR73HvbIuvHV2m3UtxpFUoXWzHNjET/daJYXMtJNMbKx1pqkkzzWo9YmGxQ7OZUF/t5ZRT64VTDR2yzFBxBVtHrDR+NXDZPVk+47xPNaKBIigbRppgvBKAsUlFfxSGvkMnNfzL4VXm2/a616OUKNMbOFBaQyfOaPZSGt2EE6s9TtV7LLRiljsxeReNVz1zC9fsHaMYelazxHeYGStRKng2uZNw/+kmiTKcavXItN5Q2BVAZtan7BfjfMJoi/O4kD/fLKHx+PHjvO997+Pqq68+6+vdbpc/+qM/4i/+4i/4wAc+wM0330y9Xj/nNi8mHhcgXvVr7+BH/uJ/ML57G2GtQm1mghf+wlv5uVveT7FW5UXv/DGE42xQ7Dx7nPshXJkYZd8Lns1Nf/DrjG7bfOFOII/R7Vt41W/+PD/xwfdw3Q+9hqHNU9SmxhnaNInj+2v8Otj4/7NEWCyy/bqnXfBj/HZEJ07pxCnNOKOXaVJtE41YmQHVEMBzBI8WggJLAe23Z7WBdmLIjKXMptrSXvv9drBJx2pPbWiRbKqGbBsrUfQkjUSxfk92QrdvzozBdR2+7x0/aqsizRb1Bw+xctd91O97kPrtBzC5JH4f1d7XnTi22EJi2D9epuw760zTDHGqWO2mJJliuZMghZ3aHphv89BiG1fo/PgNbj6t+I5tz+wYs7RUY6yIVJxjIDwpqBVsa2Wlm3D7scZg0j5XHL39Lv7+nf8nf/b6t/OFd/4mjU/eQijPfIoaYziy3KUZ6+MPxQAAIABJREFUZySZ5lQroZtYhow1a5O004wj9d4gCVHGqpamWg+ORQgrXnZwsQMG5lsxDy52mG1EVkck/1zBk7iuoOQ7DBc9PMeylrqNmGYzQeX3KM00RSmYqPgMFTxkroA7UvJpJIKh0GWiEvK6q8/8Tkc5yLyPF1rfBvIc22pzhaAbpQSuM5A6BwuK7mWKJKdLh57VHMm0xQYJY9tnoeuwZ7LCnvEiO0aKrPRSTjUi6r2UONVce+kElapPqeQxVAnw1mFytIbt4yVONKz8++lWTJJXlQA6qWKunbBzbKPfzsU4d/SVSy/0zzcT4+PjvOc976FSOTtG56677uKKK66gUqkQhiFXX301d9xxxzm3ebHVcoHiuh98Ddf94GvO+tozbnoVn/jtd7Ny/BRxu3v+IJ91c3t5fJTn/dSbeMWv/swFONrHj6te+xIOfeE2VJoxvHmapBuRdntI12F0x1aiVpuFh46cFbciPZdLXvxchqYnvy3HeqHCGGsS1ugl9NaJVYl1iMq+dDX0y/hnhhR9ToRdHZd8ZyDNDbbE7UqbCKTaSpf36Z0IgcRWUjaNlTDASi+jnVitin4oY3ByBokj4Dk/9cNE3Yhv/P0nqB8+juu5lGamiI4eZ+7WLzB64/PxPAtwTbTixHIX6bpsKgW5FDcorUBYiuZyN6GZZBa7IGDTSIGlZkyUKR6cb3HduIdwPBItqfcyVrsptYJtOfmuwzVbhzlwfJVmZGmwBc/iRQbaEkIw1445Ue9SDjxqgcuXjqzw8EIbIeCy6QpXbRpi7q77+Piv/z5ZbIHZ3ZU6S4ePM3T5bp7xjGdsuO6NXsp8KwYsriBThmONyOJXcj2U+RwE6wiBwiZwUaqRUtDLNAVXWkYSVqTrwYU2J5p23/fMtZhrx1R8x07qSrN7rMxU2edUPeKzc22iXooExso+i+0kx7mY3PfF9tWMNkxUfHaOl+gkhpdfNc2LLpkcqICuj6G85dNPOvrjxAiIU1tdeO1V09x5bJWT9S7VgsdEJRiAcKPENvZ8R7ClVqDVy1iOIE50zrwSaKMHVSHy2lAnUXSTjNlGj8NLdrs7J8r4+fVRxtDsJDQ6CSfn2kyOFpgeL9FKFI24h+/Y9mKmDcOhw0ytcMa5XYzHDv0k0GnRhm/GKadQOPe9W1paYmRkZPD7yMgIi4uL5/zMxcTj2xRv+rPf4c/f9PMk3d6GkpdwHMhXv0GpyOiOzXhBwMl7HsBkGuFIqlPj7Hne9Tz/7T/8bTveTVfs58Xv/Em+evOHWT56ktEdm9FpRnl0FL8cMrV/Nw9//mscve0bZHE6SECEI/m+n/ghXvYrP/1tO9YLEcYYTjcj2nFCScCQZ+il0NV2Ylf5JK+UoWdUrhI5KOCv1SNyVkNJCuq9lGrg5MZoYsCwsHgNSDNDR/X1JOxq2WD3MduOiDKFwlZQ7l/qsL0WUg1cHAmdRFH0nNzV1aY5L/mFt/Lcn/1RPvT2X6U9O5c7isLiRz7GI3MNxm94Hm4hpJFoVE7l3DtRwncduklGpKyjqEaz3LVaElbTwSZC00Mh9V5KM9G8754VJkoeN26rMBwI7piNON2wRmAaWO6kDJUCtDGUfYkQG4urykA3teBEQY96L+POk6sstpL/n733DLL0Ou87f+e8+ebOafIMBjMIBAaBIAEmMJOSLXslygpe7Za4plQra0trqnZLsqo2lj9Y3rVrXa510SuJJa7WsmhZWllhFShRoigKAAEiEGGAyT3dM51vvm865+yH897b3cAQBIkBCRn3j0JNuuENt+95zvP8A9rAV680eWKxya39AdEDD5A71kI+ef4s+fo6V7difv1LLzA7N8V7jk/hOpJuankkUghcRxDn2kqVc+hnGZELJlfcvVCjkyo6g5RLO4Mh9QVjsDwJYR1U23HOWi+lFnk4AhqRS+i6CAEzgUs3zpku+ZR9j+PTDo8HDvEgw5WCt986zXQ1wABXNnu4kUtq7KdkuuJzbLpEKwONoBy6xEpzo57AdNlnpuxzvZNCIUnVxvJJdvop9xys8/hyk81eStqxDquXA5fb5qtUIw/fkYWkWzAVeQRScOt0ic1+Ssl3EAg6Sc75rQFbg4w4UwSu7cxoA5HvstqMmSl5LFR8Oqkm15r1VsJ2K+al5SZJprm83mXyWoeH7pzHHal/wBXwnmPTN/eHdYw3DJ///Of5/Oc/v+/vfvqnf5p3v/vdr/k1Xsv4dFx4fIdw/J338j+98AV+6+f+KX/xr38NrRRuYFUEKknxSyEP/vjf4/aPvpfTH3wXm5eWeeq3/4i402Pm+GHOfP9H8b4NrsXrwW0ffg+nPvguNs5fxgt8Jg8t7fv39XOX+ON/9hnO/ulXiLtdZo4f4aM/91+/KWPuvxlacUacpCyEQ8mmoOrZDsdqn5GLZ24AZW28PWm7A/meQtIY6xmxk2u0sS6Ow6At3xE2ARRbYISBQxJb/49he1xrw3J7QDu1oxcXa7JlkFxsJWBspkjFl9y3YFufQ1MzAZQ8wfzbTnP26rVRp8YApb/+EtWH3k4rCHEERL7DgYmIhVpUOGcWQW0CurEq1DP2xISxHhFr3dS29qV1O73cjPkPg5y/d7KGk6V0tEs/T/ddiyOTZbpxRlbM/g226EhzzVKjZG3btb1ODxye5KtXdlhtJQgpePZah/njc0x8/CP0r11nOxe07ryfWAmMUrywPWBFNXn6WouHjk6yVA8Lcy8bM5+qnCS3KhWB4Voz4cRUidPTJXJt2O5nTIQ+yzsDuokada9MUUjOVANqkfVciXzHFotajzxDKoE7yrtxpOSe45P8yZPX+L77DrDQiIrzNdx5sEEYOFzvZoAhzjWxMsjCS6STKh670uSeA3VmK+G+z6UQgjtmq1zcXityUooAwUSz00ssF6mTjQrXTBnaccb5zR73H54ouCECISQ7/ZQD9ZCy71gZdvEetcDlzvkKj6+06CS2M+M6kkGas9aOOTZT4b7FClHosTPI+crZTZ66sEWW7zq7CCFY2+pzZb3H8fkKoStxHMldCzUONMbhcN8qzJBMdjPxGjoon/jEJ74pMfTlmJ2dZXNzc/Tn9fV17r777ld9zrjw+A7C9X0+8b/9Au/+1A/x+f/2f+X6C+cRjuTA207z9z/zT6hM7barpo8c5AM/88nv4tFaSCmZu+XoDf9t9sQRfvRf/xOa19ZJe32mjx1C/g01CRpkiunAFh2mWK0vtlLW+nnh6iip+lahslTzKBcKhExpYgW9NCc3RS7KkNWP3UFXfOuAKYWg4skRMbSb5kxEHlmhDMm15lJzMBqr9JKcoFiY01xb/w7HPv9YIxp5hThC4KJHk7mHfvwTbLx4ga2XLpArez6O6/C2ozMEkw1LKh3k1khLaXx3b94uxLna515pgEFiuQLDoDwDIKxa5emNAT/4wHH+7yeu0k3yogiyfI4fOrPEpa0+f/bSBlmREdNPc07OljlUyDD35q+cmKmw0koAw0wtYIBDSRraQZU1nSMcD+kJ8jihj4tIFVOVgK9caXJ8qoTvCOJM40pJPXRHXRujDTOhw3zJ41orJtaGXFki7ENHJvnSxW3i1EbCe9Iqco7MlKgGHoNM226VgMwYgkKyYfYcO8DJxQYLtZCJkm+LSWk7BtWS7UpVA01zkO2TtQ6RKsNz1zuES5JquF9i/O+eWuHepRrz1WDUFbvWSenGOV+9vGMfVPhzSAGh69DPFFvdlEa5sE4XhrlqSNmzXB0pxe6xC9uRO9SIOLvR41DN468uNXlurYvStluy00343jtmCT2Hlc0eWb5nDElBcJWSjWafuw83uGOhxrHJMpVgvMR8O9Dq5vt43PTXK3DXXXfxC7/wC7TbbRzH4YknnuDnf/7nX/U540/FdwHzt57gp3//s9/tw7hpaCzMfrcP4XVD6By3mKUb4Ox2wmovG41QernCcySH68Go6ABLWvQciByPfq5J85yuMiN+TjtRhdW2g1cw7oamZKHjEOc587UQCofQ3FgPjHrgcsdsBV8K1nopO3FOoiyh8XA9YrY0LGbs3Nbu9G3B45dCPv6Lv8Bjv/tFrj53DjcKWPrgw1Cvo7GmXL6rSTNNO86oR96oNa605bk4UtquQVGEpGo3g0QbA9qei5SSNh4HJ0p8+r3H+csL22z2ExaqAfcdmiDyXA42Sty1VOfxKzuk2tp+z1ftzn4vWVdgC4GSZw3HqpF1E93o5yxngly41iJdWzWZcCSDVJPnGiHhxc0eB2oBJc8Zva4rBBLDi6sdXCm5e7FOO9Wjog3gyESJ+UrA2Y0u3TSnFnikxuAX99lzhE0LLeTCsmgvGWPNxyqBMyL/HpmpEHoSrXcXZYN9bMWTtOOR6z3DcD5LGTKsthOeXblCP86JpODOsjUhO7NY2+eVAXC4EcLRCb52pTk6V2Osj4zrSGv7nuVM4GMwRHJYJAlypfEdSV+rfVLukidZqAZcacac2+hZC3RjC+atfsZvP73Gj719idCTo8+3FIy8bYbcmdmKz9sW/mYo2N6seCPktN/u633xi1/kl37pl7hw4QLPPvssn/vc5/jlX/5lPvOZz3D//fdz5swZPv3pT/PJT34SIQQ/9VM/9Q2JqEOMC48xxgAC1+ZvGGx3Ya2fF9+thTV6sVCV3Bt3dAJXEOcgtcKVgmzUg7a708P1ANeTo13ycBEPPcm1dowyBdFPQyAFRydKI2XAXCVgrhLgSMGVnT5LtQBpFDZZpDjKYtEZjjI8z2PyXQ9SfvCdgDU880RBHZQ2ByVzrMTz4lYfIwTlwGGzm9It3ECFsERR19jsEsReh5AhDREqhcW37zq8/+TMDa9PLfR4+OQsxhj+6vL2aAQ07AwNf6+0Ic0UgWdlrNc6CXFayDGFZdNgIBcStzjvRGlCIdnpJfSSnKVGyPHJiPVWwrOXmqwX5NCpim+vvbvfEh9sWNzbFmp4jrB+I6ttVEEwTpSm5LlW8SNsF0NpQy7sWGOwk7NUjyj7NmBudHEKuNJ2IlzpclDAejtlbZDiufY1HSnY6Kb0s5yVZowB1lsxj3YHVBe2OVi/8Yh1sRoQuJJeql5W5NgCYCLyWG0NaA1yHCF45lqXA/WAh09M4TsCYxwSpQsTO8vjufdAjX/7xCpSWiLrMFAvVrZD99RKmzOHG6xs9pFCFGog+95SwJH5Ku88PHnD4x3jW4C++Rbn4ttseLzvfe/jfe973yv+/lOf+tTo9x/96Ef56Edfuxv138y++Bhj3GRUgmDUet5O1Gh3SKFcGdqcS/nyJYsROVRjCISi6pp9BCvLoWAkxdzbRhdAWKhkruz0SXPFbJH8ue89hPXsmIh8NnopGsmugbn1zOjmhs3Y0EoNg1xTdNg5VPM5PhmyUPWZiRxKroMj4eh0ibsP1vnhew8yU/LZ6mYMcoUxgkGmyNRQYmrNs8RuI8een7C8lQcO7TerejUIITjUKBWW8fYaWQmoIM4VX7m0jXAdosCOSTpxDsJmg4ySaaFYFO0F7MYpz662uboz4OJGl8cu7vC1lQ6brWRUdABsdVPWu+lIirwXBtuJutIc8NvPrLHb6RbWElzbotSXshhpWALqoMg9acUZ692UdpzuVlJYz7bi9rLdS9nspIAhj3OeOLeJMYZukpMqXShQdv04VODzJ1+//gpL+SF8R/COo40Rr2X4f5prpio+3VSz2U2LT4jtLp3f6vOFc1uALZZrgUM1cKgHLgvVgLDIDQKKMD1betvsF8HFnZh7jk1y58E6mdK0EkVuNEoYjh2ocu+RiRuqc8YYYy/GhccYYwCO47CT2NUiciW+I0brx3A02s80SfbKbYMUjAyzcg0LkTWHMvta4NomuN7gvQNHcr09oJ3kTEYeJc/+WJphM17sLni+KxHGhpwNlCRWklRBK4VOar1GBrmmmypCz2Gp5o84JmDb8BVfkGaK5eaAjW7KemfAh07N8v13zjMdeqMMlOXtARc3e1xrJXSSnGro7PGTsB4QHzk1y2LjW5NKHmxEHJ6IRsWVEHaBfvJqk2utAeXQGRVnmdIkRfS67+35ujK2+HAkrDZjskLNgrCKiuevtZmqBq/wCfmzFzZpx/tD54aFTDvOeWGjy+2LNaZKHq60nwBHFtLQXNskX22VHd0k58BUmUroE7i229FONM3C9E0bMwoVbA0yurF1ABYIliYi1pt9vnZh26bDZortTlEkCev1IhFcaQ5GY7CXI1WGuVqJMwfr6MKHxP6qeejIBK1Btm+kFGcKg+DK9oCdvo1GSHKrjmknmTWnyzXTZZ9U6VEHbVh85NpQCl3O7Qz40L1LHD5Uxyu5+GWPci2gp7jpwWZvVWil35D/3ywYj1rGeMsjyxU73R6+hHZm0MLlYCMiUYbWIGczzkYLbjPJqRXZK2Ard5sNolksO8SpjzAZx6uCbmZo5+AJmHQycpxd89A9HY3cGNqpLhY6Sxb1pEAbURQddoctBESOYdrPSZVECzBakRtDN9FkCNJil+4IqPuSsm+PdZhie72T8PxGn/myy3TJpdsfECeSfpxxoRljHEEvzWkOspFPST9VRL7dFVdDFwksNSLecWSCU7O113yd01zx1Ss7aAN3H6gzWwl4bNk6HK40B5zb7BMF3ogvMbzGShtcaYm7+VBCJAUlV9BPbSdiGDpneS/2nggpuPvQBE9e2cGVglvnK8zXAoTWDHJVdBLsSKGb5Cy3Y2qRz1TJoxG5OELQTxWrnYR+qsmUoptqTFE+ytH77oYCdlPFRi9FGk0j8jk2WSI3mp1uOjqhYYdsthpybbvPRNVjs5ePCgQM3LpQoxI6hK7DpWbCYtUvTNnMyGZ/e2BtyA9Olrm01afVzzg8GfHeE1PMF90LsMXbsCuy3k0oeQ5fPL+F0oa1booGfujueXqpNYU7NVvmhfVuUXQXaiRjmKoElAOPZqzY7vW41o4pR/7o/saZ5rOPXObOA3WW6mMly+uBKQjRNxU3+/VeB8aFxxhveex0B5QdQ1NJMmOHK6Er0UZTjzwyremkmnpog8h6mS64HoZUG7JcMxFK69fhuqhcI7Si7ElKLoBgO1Zs5jFzldBGzBe7YQX0s5xRnjyw1c+p+u5oMbULqV0Eao4iU4JUpyAkgbQL4Gwo2EkUvVxQD3dlnkOragGkec4z6z3unA6ZKe+2ww0gjWJnkNLqW3ttz5FW2VK0XAapInQFVc/nzoUatdBlurxf+nkjPHV1hz98fh0NTJR9fE8iDHx9rcOxyYg7Fxu8tNVltZ2Qa7urFsUBC1N0iIyNm1dKkyQ5gSeJpOQn3nGEX3nsMu24OEcBEqtq8aQtoH7ifcd47NI2rrR8morv0ij7rLZjzm31acc5QkCmbdflcCPiQC0YKXeiwitluTmgkzp00ny36BzOUIprnOTKqnqEwAjBX5zfAmOshfzLrkuuNGutmH6mKDsOm2SAdRU9OlsZBbhVQpdOqnlpe8DxiZCK75Iq67Oy3EkR0nJETkxHtGKfj5yasTk7QODZeyiFpJsoPMeOjfqp4oX1LlJKUmVQWvPs9S63TJVBWD7OO49M8PhKm61eiu9KpisBx2bLo8/j11dbN+QqZrnm1x+7yqc/ePKbfjbG+MbQ2rwBHI9x4THGGG8KKG3whSLXtp3uFGoFRxiqvh1jOJWAWa0JPctKHHpY2MRNuxMveQ7H6j5V30F7Ps00p9+znhYXWhlnmxllV3LfAYfQtR2MaugBhsBxODFVIs4Ma72EQaa52BwwV/apBA4YgdKKUGfE0qGZSowAYwSOhIoDgVT4EmqesaqGAsYYYmW9QrZ7CQ1fMlv2XrEQGuBgzefF9R4SUSxoBWHVGLQRSCE5MBFSC13qoctU6RvP8nNt+FdfPMdfXthiuuxz21KdTGtEbsc9kSe5tDNgqR7x4OEpLm72iDxJnmsybQufUfFRqHWSVDFZ8ZmvBRxvBMRKc8t0lZ1eRqotAbaXZLxtvsJ9hyYo+w6PL+8Q+g5eIUMeKMP2Vo+VTloQVm3CrhSQ5DkzZUuqHZJ1wX4m5qoBna0+Fd8tlD2QKVUQSS3ZJM70iNwpC77FC+td7lmq2zTfYUUFXNzo0s8U7W7K8xe2OHR4krNrHQ5MRqOiI3AFJd8lyRWD3PDcxgDP2TXnEljiahg4nLltvig0rLmcNoZDjYjn17pkajdJ2JUQBR6BI2n1U3xHUil7/PXlJk+vdjg9W0YKwUorZqkRcaSwOg+93XTkemAl4DcyYBZC0h4Hwr1uvB6L82+I8ahljDHeHNCFPDLTe5QaglG733NACAdlpPVmwCabbvQzXGn5GVIKYmU430q5aybEk5Ja4PPkesz5rcHItbObaR672uLYZMRSLRhxQIQQRfKpZtr4PHGtbY3LuglHKi4PLIS2tZ461jxL7O7wlYY+gHDQJifyrRRUYHdN28UiJIRACclkORil3BZDgtE5131nRNjcW5kMzakcaZUSi7WQo5PlG8bZK23468ubfO7Rq2z3M4QjcT2J0gpHWOMqWYyOfFfy1LUWty/UuXOhzoXNPpe3++x0UybKPp4jCzWRYW29y4FaxHtPzlAOXVxp+Ql3LFZ5caNrvUn6KT9yZpE75qtIKUZdn15mo9oppNK5EUXyriD0HDKlybWhEflg7K5976kpA46x5lxDnom9Hlbd4hSdj718kn6mWG4NWGnF9FLFiYkS1cBhkCkubvR49Pw26zt96gHM1x3qKuXhE1P0lCb0rJQ5KHhGviNJi8yWXJs9n1P7vpORS+g5+I4kcO110QZOz5Zp9jPOb/ctZ0QKaqHH0amIbqpYqgdMlQOeXmlTCX2SXPGlizu0+hmbG11Cz2WuEXH6SGM0BrpzocbbD07y3EqLq9v9VzjSGqNZGtujj/FNMC48xnhLw5XW99MV+3kXAEopm1WiDcJ1rUxWmyKMy7pVGiFGzqW51qz3FYsVSW7g5GTEpZ1k34LUyzRXWjHHp0r7VJdC2Nf2HMF8NWClnSARbCaKrUHORODSVZAbkJhRFocQkBWSf4Vgq5/Ty60DqSuKhbtwLHOlwGhoZzA5VGjuUWlUHDgxVeKrV1t7jqw4PmM4MVPm46fmR+qSvVjvxDx9rc25jS6PXNqhl+YjPktrkHF2vcfp+SoCa1zlFx2IvGgnn1mq04kz/vz8JqvNmM1OQj3yuHupzvfdPsfjF7eZrIc4xfHqUQqq4Htum+O3nlrlgUMNTs5WkIVKRhRciHIRtGfMbqEZunbM4EvoZ6Z4LUM/zUlTbe3GC0v8LNestwecXW5yx9HJfQVXbgwqM9Qil8VaiC58Sh5Z6yCFJfE+enGHP35yFZ1ra7wlwGjNw6em+d67FkecjfYgoxoFCFfiSonWtqNmVVW7UuPhxjXyJPMVn0rgkhRGcMPPhMAazxkhWGqE5EXXQwpBK1ZWaSPsWK4WOrTjnNCVxFKyttKmO8hoi5TNnT4vLTc5faTBpx6+hXcenQLgR99+kGevtYlfRrauRT4/cv+hG/6sjfHaYdTNH7XcdF+Q14Fx4THGWxpCCJAujsnwxJCcacjjhExZpUpvfYtMKUoHFpHStpxnSgHNpFBHjDSmgl5miiA2qIQ+9y5VeXK1Y23Pje0a3D5btkTSQjWAsPbkQ7OmiueM0mKNgXPNjDAQBJ7EGIMQtvAIXZtHkuaal9oDIs9BIRCFe6wnbRCYX4TAmX6f1AtIlM37ENItUmcZKTfefrDOSitmtZOMPDakgBMzFX7kzIFR0WH9HezrP7na4s/Pb5Ipw6XtPv00Z2/bxGA5IuudlPlaMCpIpBBc68ScW+9yYrbCe0/McM+BCZZ3+kS+w+GJCCklSaY4NFu1KbfsPte+tqHkuxybKHGwEeI7YuQqOmS3SGEIHUG8x+LbGGtFP8jU6Hg6iWY9Tyj7DttdqzAZEn4vrvfYbNm4eGHMyH3WGKiFDhOhzTaJM4UQLncu1Li6MyBOFXmuyNLdgsFow/edWeQ9J6dHnx0DVCPPKly0RAvbpcq1HflI4OvX2pR829noJxkbzT4nyoL7Fyow2SB1JIEr8aQkUYrV9sCm0GKza4YwgONIepmm089455HJEbfms396DqUMnhCFURykSc6FS9s8cHhXNn1oosynP3CSzz16mZXmACkEhyYifvI9x8dupTcBRoO+yYXCDQKdv2sYf0LGeMujXinR7fUpOTkGQ6/TJ+72SQYpyhj67S7GGLLl6zjz8wSutIRPdg2woMhf8RxaGVCMMo5OlDhYD1luxbhSMl3yUMZ2UUaeIGa/9XZejASGDpfXY8MUGtcxI8Jprg07A8Ugs3LTRy630EJwZDLi8GS5OD5R5MQIHAxSK0S7RTw5gTLSGooJ9nVPpBB8z22z/PXlJpv9jLLvcOdCjXcemcKRgkGa002yEe9CG8NOL8ERkn6RjSL2LPzGWCKl4zmWREuA59huRJwp+pniX37xHPccavBjDxymGrrctrBfKdOKM7vgi12JMuwWFtpoDk+VbMCdAVk8RBfcjeGjrfOoDfHrpmqfayrYQuRLF3Z41/EpygUhNNea682E33tilWOLu8c1fGbkCSYjH0cKHAPKkWTKMFX2uXOhyh8+dY04yRGug4tdTFSWc3qxVnRO9h9D2behkcqAxqC0vY/PrLT52pUmjpQ4aYK6fp3GgTme7rqc9FJK7Tb5/DwTk1UizxaWg0yN/GdeDlPwma51E+bqlvCslGajGeM6ApUZxB4SR6en+Ce/+yy1eomSJ/nYbXOcOdjgzMEGudKFJHrsznCzYLR+7Snm38JrvlkwLjzGGAOolEtorblwbpXf+8n/DuE4LN13N0fe/y6gWPQGfZzCjqnsSSLPLwoAawBVDRwCzyFn+IMlCqmn5Ohkmfl6iZ1+zNWdAU7gIPfsy2WxCGVKs9FLR8oOXZAURRHMVvKcwpDMjPoJK80BCiubvLDVZ7IcUPUdjLS7WKUmcBAHAAAgAElEQVQUriPwajUkVvnRM5K6sEeQaTMiDjpSUBIO9x+sM8jBdeDEdJmNbsJXLm8zXXKZqQRFJ8SOTRqRx+1zFR5dbiL3qGgeOFDlbXMlaqFDL9Ust1NiIXGkjV3f6qVoBKWSx5cvbrNYC/nQbfOvuDfDED0BmJet1doYyr7D8xsdpiOPhVqI58uiaDD4hbt9J8lJlcGVglackeZ6xM2guHbdOMcNXP7FH5zlvmNTVCOXa82YZ5dbaGO4vxEyNCYdnmPJc0fFjhQQuBJHQC+BmZJPlmnuOTbFTCUgU5rnV9tc3eoVQXOvLAg00EtyFHC04XOhmfLMtQ7Pr3Zs50IKdJaSlBusbKa0B12Sps8Pn66g1zfoCY9ukjFZDVF6l3cyVfKKwEJBK05JcivfTnPNixs9pISa75IpS+wd7CUiCoFf8ljrZ6SeNUD7N1+5xHuPT/Pwydl9ZOYxbg60Mje94zEetYwxxpsQQghaW02SVhcn8Ln4l48ydedpKjN2ro0x+NJQC13s5s7uwOuBY+W2gTvqUkjAkyCKlUpj6MQpnYFioDShtku0nQzYcUhzoLjcjHfl9gXvoFRkhgwyO44JXOukmSnDSxs9rjbj0c44yzUb3YRqI0AlGY7nIV0HVZQ5GIPnCoyxnBKn4HeY4cKOPf5q4JBqRa6sm+fXrraYqQTMlP1RhwR2E2wbJZdG6NIIPXpJzrsOVnjX4ZodSRhohA5zlRJXOoon1gYjmawxVoVTL3v8x+euc3yuyrGp/QHxUloZqNIKaUSh6LHFiCckh+oR11oJK62ExWL3PvToSJX16Di/2cd3BL0kZ7uTUip5lAKXTBl6aUaibCFXLvlMTZX4q5c2UdqakkkpOTBfxRT3wRaFBSm3OEZtDL04p5/k6GK04bqSH3jHQSZKu14XJ+YrfPXCDqvNPo2ohnlZ8SEwXOukzFc8ItfhpY1eYVdv70vJg4WlaSZrlu/S6qWcu7LN//VEi08cbXNtR9NYmCaTKZMln1acc6AeEfm7+UITkcv2IOVaJyXONBt5QqZt+vJULSDuZ3TYLYvcwKUUeTRq4egoNfCXF7d4z4npcadjjG8Z37Tw+PrXv/6q//7444/ftIN5M+OtcJ5v9XM0gCnX8acmUJ0uJsv48v/5q5z+2PuZOnoQEQRMei6x0gQISnscQSPPkBSqF1eK0TihsKrEaMNmq8va5iYmmmBnkFH2XFxH4Aj7RS6lRDoCXSR/lj2Xo3WPjViP+AFxrm3yaC8lzhWrrZhaybNBZkaQ5Dm+NDgqQyuDkJIodMmM1a+4rrSkWKzzpZX27p7/EEIIyp5kp5+x2U7I85yqXyrGQ7uPDBxr4iWMwVEpZUcxVXa5e6E8IutKaVU7vis5iODpDUE6TKSVglRpfNeh2Rnw619+ng8vvsxW3g9xK1N2fCXdEQs4zxT5znWeWU1JBjk9fH7v+XXuXqwxVwnwPcmVnZhHrjTp9VOOz1RYaFjHVKW0TeaVkDmSwLHXYT1XHFioMdEI2WnGCAFLMxWCwPpnaHYLjrJrxxPGGNZbCVmuKJX8UbiakIIw8skNuMNrIQQfv3Oetc0moSdQSBxhOw/GGK51Bqy1+zywME0jsD4cCEHkO/STnFvn6rjGdsqUNlQijzO3zvLnj17if/mDS7z7IxPcMa3otRS+K1moBVQCZ7TZHRZNkyWfzb4dmSW5HvFcDi5UuXhxiyzPR/cvdAKOLFYLsusu8hx+44tf4+TrzIN7K3zvfKswb4CPx003JHsd+KaFxx133EEQ3Dik6PHHH+fee++96Qf1ZsNb4TzH52hxtTlg7Xs/zIu//lu4UtDrxzz9738fIwTzP/wJDjxAYde9X/fhSoEni9m53L9wmmJmH/g+BxeWuN5LUUbTyxWBkdZhUkDJd7htpkIrzmgmOUrDZqwBa4XtOM6ode46glYnY6Lij97r1HTIoVpALXCQQHuQotKMWuQQa6tyyIvRjcGQKgEojCNHtuEC21ERoiieHIlrXKYqUaEmMft2uFJar4k4U6TSp1x2KJU081UfsMVX2XetT4ayluMlT5InCr8IgkMJhDDUyiEq8Fg6eYSF2n5JZrOf0o6zkYdFp9Xk1kMLhIdsKF2rss6/feIq3czw5csty1kw1v0xdC0h+NJml7l6iO85aGVTbV1XUvIc4tx6cASOBG1D6aanSlRCb8Sr8aXtGhSNKDIN0oWzV1tUQ49SZE3fDLDZS2inCteR5Fg5btmX3D5fIfIcbl+oMMzYafYzMgTrvYSLHYXv+zyznWCkYCJw6KaaqbLPbCUg8iTZHiXJUFZ78ug0q52Uai0qVDCCTBk8x9rPDwsfpTWZtu8rjWGnlxL5uxb1Xujz4D0HWDy/xVpzQK3sUZupUavdwCzOGA4ePsy9x6df9Wfq1fA3+XsnSZJvujH/dvFG+HjcdF+Q14HxqGWMMfZgqR7y9r//d6hMVFn94pfJrq6TVevU3/0Q9bffZ5Un0jpa7tUJ5Bo6g7Tw5JCUfBvpbswePkbRLj/YiLi83Rt5Y2gNTuHZEOeKdmIVEMaAwv4qHYknDam2O/Na4LCMjX0HOD4RcnIyKhY+gXQEC/UIpQJ6mcYY+zyjh/0KSzxtxpp+lu4WSoXT5nzFs9Hu2J378CtrkBvK/v6iqxK4VHyPI5MZW/0U3xGUAnffSAZs3kxnuMC7dhEfkmqNhnrkjzJvXo5GyacWeQxShSMFz14+S3j8wOjfHz45w9dWWjx3vY3jSBwhaXditja7CKUJAhe3FPDUlSZHp8tUXEmaZmi8UdGhlEZltlhwHUN3kJLnhslqgBAwU/FJMkWcKyqBhyrGbX/1/BpZpnnPmUVcV7LZT9nuZcwWi7XG3sfj02X7uRHWbVQZe9993+Vcu48xgtsWqpzb7LHRz/nTi01cbTBKkSrD8ZkSnpTkReEhijEdxlCKfLzAI1VDZ5ZdDJlEUkCsCndYYf0+uklOnGsmSt5IgjxQ8M8/+c6RPPcXv/AinXR/twNs8fv2cRLtGwKjbz7HY+xcOsYYb1IIITg6WUZ+7P3c/7fejxCGl7YGLLdiepk1mqpHLl5BTFTa7AuI08bKW3upohR4GAOeI0Zx6UIIuv2Yuu+QFdWIBoS2BY0GJkv2eanStIp0VoxhJnLxpWStr8B1ONAo0YqtS+Qtk6ENkBuehz0ZHNcpqhdrNrb3q0cbQyvJR+dSXAAbCOc7ZNoQuQ5zFZ9L23102SfJNQJG7quWd+AyV4s4NlNhtTVguTkg0VBy9u+whIBOahhu2HNtw+6EAM+VuFLQCH0Wv4EBlRSC8jeQagoh+EcPn+Dx5SZfeHGdc8vbvPD8GnpY2BiN53uYQxNcWG2xubLDg29bZH6uBkKOOklCwETgkGeajrE5Pv1BxvG5MjuDlCvNAdpYkupM2efuhQpVX3Jhu8+zKy2iyBsVcWmu8NyhXBkaobun6Ni9ExXfwRWCXFpi6dpGj4ubPTsO0oYgcCmXfK63Ym5brJHu8UgZ3bM4o1oL2ejEHJkuj9xPB5mi5NuMlzjXNmG5kMouNwdIKUhzxSCVlIprazBc3Oxw67ydoXzs9By/+dQqaq/zjDGcWWqMipMxbi6M4uaPWl5ZO37XMC48xhjjBphrRKw3e5R8h1MzZU7NlBlkmn5mcIUctdspJKX9RI1stgXFIl/8OVWGUFg9zCBJ7WMk+EaQF9/lythd/16CZ+DKggho8z82BwZHWqKh0tabQgcuvmPHBcOyw2BGLX+wxmL9TFlCZGFAoY2hk9oU1VTZILpRg0IINnoZ19sxz693UcYwUw4o+Q7TJZ841yQKPCmZLHvM1aLRYjtXDbneSdhKNTKQhIV5gAH6OURRyK3TgsvNPpvd1JJbsZ0fRwrefXzqhgZlrwWiMMuqRx7nL27tl94KSZZmNLd7KKNJ45j7Ts2yHSuSTGPyXQmzIyShgPOXdpiqBYhaSH8iolPksAw7OTu9hEHic9t8mS8/u0apFnBooYbj2q5Ga5Dhu5bbEjkCKQtfEIGtMIvTFMUHKU4Vf/DVq1xe7wK2GIsCl51OQq2a0+omLDYiotCj10+5uNVnq5OQKk2nn1KpBxgElzb7HJ6MCDyHnX5K5Flvj2wPa/nKdo9uokbFcKo0JcBzLAeoEu4uDXcs1pkqe/zu19doxRmh5/DuY5PcdWDX12OMMb4VjAuPMca4AULXYbZeZq0TI431pnAdh0NVn06c2SIiVSRZblUHiJEaxQCJUsRdO5s3BpqDjKVGRJxkuxapwgbF7eeL7JI3h0TVobkXwvpPhK51JJ2IPFQ/QwpItSZy7PhCCmG5ChhSZTi3HdNKcqQQ1AOHmZLPROSQazti0BrSYTgbtqi5Pkh5ab3LVi9DGcNGJ2GtE/ODZ5ao+B6BK5gq+za/Zg8cKTgyWeLydp+1WOMIG5RXDjyOzlcAOFUoZrd6KY9e3qYV51QCl3sPNm7Y7dBas95NaRVprI3olVkzQzxzrcO55R3rncH+Do8xgrWNDqFJcB3JC2s9Jqo35q8tb/ZIMsVGM8Z35ajogMI8LVU0IpeXNgcs+IYkTXnmxQ0m6iHVko90JFJI1tsxWZYzFQUcmbDmZLO1EKWV7ZIZQzdRLO8MOL/aYbMdF2mwMEgUSa7xfYdeP0eH8Mi5TW47UGdls896OybXhiRTaCHoDnJcx6EVZ7y0oSn5toh4ab3LfD1gtrDLX23FbBRFX2b0SBodeo5NNQ5dFl+WLrtQL/EPHjr6Da76GDcb2ph9jsc3A+Imv97rwbjwGGOMb4DQczg8WX7F3wfFYtvuJ3x9p2fNpl72Q32tnfLo5SautO6Tt87VODVfZ7Mz2Pe4giZQZImAJwXJnhZrXlQcoiCYZtqQp4rIc3AFnJ6JuNxMWO/lHKpZj4yoMDjTBlaubfH0v/k8cauLPz/L7Ec+QHBgmpmKR9mTdFKFlBAIQZIba25m7IJ4YLLEYsMuVOudhK1eypNXW/zn9x+6YU7LEJMln0bosdFLUNowUbKR7i/HdCXg47cvvOo90Frz4kbXKmeKt+ymCl2bR2uNfJmUsx3n1uK+8NvIc4WUEoMhyxTJICGIoF6LMEKw0hwwGXqFP4dVqVxe6/LspSZSWjLmeiuhut3n8GSJuUZI6DhkWpNmmpWdHo9cXONHP3wS33FZbw04v9qiMVFCI7i2M6A9yGiEHv0k46O3zZDmlsDz7FqX1XbKVj+lnyp2ugn9RBUFrMEveziOtBwbbRgoRZQ7PHmlSaubIuVuweq59t5nqSIPrB9HP7OSbolhkGqiuuCx5XYx7gGhTKGospbvsiACf+zU7Kve3zHeeChjbmj89rowLjzGGONvPmqlgLVuymHPZmsMvyh6Sc5XLm4zyIb8AsNkyS8WQDHy+gCKrgdoI3CcoYuo9djQZki+NJgkodXsIHwfWS5bdYknmasEzJZ92olCOlB27a5VA9evbvCbP/mPyQax5Xg8ZWg/8hjOz/5DDt53K40icr2X5QghkcISDzNlPUIEtoOx1Ijoxjn9TLHSjm3K6TdZl6QUzFVvoIT4FrHWSfYVHWBJktoLWeskLLysQ1LyJEcPNHjy+esYFHmu0YVjo8oVvmOLub/94AmiiRJPrbb50vNrmFRTDl1WtvsMEjsM10bgOxLPhamKz+Hp3WA8XzqEnkPkVvjgLVO4wwLoYIOHtOZzX1nmesemE2Ngs5PwFy9scHG9yyfffYRn13ustBNSbchVIWl1BFlBxAkqHrLgaQyluUIKmv2M0LfqJqPFiD8khaDsO1R8yV0LVa73Usu/8R3mqgEPHZ5ACGjGGec3+jjCKpwEljQ7XfaphR4/fGaJaugzxncX2tx8vy/x5qk7xoXHGGO8Hnzg1Dy/8fgytdAhcCTLOwMeX2lT9Z2R7LQWebzvFis5jAKHQZKjzS6nwvIKNNL2P+xi59iiQ2U5rQtX0IPYOplqgwgCvIOLaDcY7frroWslnsaQFwXL5//7f0o2iPfwRgRZq83yb/y/bN/xj1ioBByu+zQTh16q2FaWB5IWBNIhpIDpasCV7T6+tM6c3ym04pxvtPluxTkLL/OQODJpo+BPHZ/hmRfXCH2HJLN5KSrPmZsM+Mh9J/iHH7+Dc5s9lpsDmq2UOMkxhQ08hVpEANWSJc4u1sJ9XYBhjs5CI7Lf6AaCwMVxJBUh+J63zfNLf3kFbTRxnKOUIRWCF1ba/NKXLhFUAjQUFu4Gpex9nG5EbLWTUdExxPC9FdDrZwgHoJDMSogCiV8Yp906WeK26QqTFY/Jsk9uDKHvIgR85OQMl2f6vLDe4+zmgIrvEvoOqYJukr+ZzC3f0ngjOh7jUcsYY/wnglro8V89dIzzGz1WWwNqpYBEwZVtO1I5MlXm7961OFIMLDWqrOy0SVKNLlQCjoCNgcIROfXIY5grkuWax//Vr+AvLFA9Yefr0hGYLCVdvcbhO09Y4upwjRqti4at58/SXVlFFLyPvbEg3XPnbeS6AFdK5soOlIvI9URhfJdBtp8CP1xo7zlY37cA/+aTK/z+c9dpx1Ydc8tMhZ99/4mbtmt+rV+Vxhj+5MUNzq53SXPF1FyNM4HL5kYXX8KdBxs8fMcitx+aJCjGPiemyzx0fJo/+usrALuJtnsKj0rgkcQp1bgLjqZcKxMUi7jAhq0Zo6mUdv1UBIKT8zVOzJZ4drmF0YapeoDvOSRJjpLSjlsYeqrshgWGgVMUFbsYHpMxVmbZ78W4novjSpuFIwRGGTJXcfvsBEpZovMglYgK9AY5tZKVIvczRT3yuWVGsBXvVx15juTidp8zS+OOxxhvLMaFxxhj3AQcnylzfMbyQR4+OUtrkOEIsU8dMMTSxJ6wMWNTSBeynF99bJmlakCj5CIQdHoxW197Bv3I4yx86GHKJ47hhAHJxhbXHnuCtx3/FKVSHaFtSm4G+BIcBLo/wJeC1OwuWkOymicENc/2V4YKEkcKyoHHziAn9CTVwKVTpO8aoJ/kvP3QBO87MTM69n//5FV+7atXR6PjTBmevNrkH//ec/zi375ztMC/HtQCh0Gqbtj1qO6R1j5yeYdnr1v+QilwiXxDPahy15FJfvwdR27IWRBC8KGTM3xmssSFQkkisBWFwF6zB6opW5/9LOZHf4DKyWMEImL/rAwcx9nz+mJEDX7o5Cznr3dYWKwTeHIkpQWzL07ejJ4pQArSQU6l7JMp+z6icEI1w3BBxyHP1CirxwhBLgXCQG+QjeTBrV5C5NmqdKuX2tfDdlieWGnTHmTUIm90HNMV/4YeKmN856F4A0YtN/flXhfGhccYY7wBqO/5Qn81DBesyHP5ntvm+dOXNnhuvYfBcM+Uh0wTpDGs/eEX4A+/gOM6ZFnOPX/ng9Yqfc+3iUNhFCUFh+67k7mDc1xftXH1Q/VKrjRqdolnVjscniwReA6z1YBS4HPS90dW7PXIo+S79FJL1vwv7z/EkenK6L2MMfx/z629gq8mhGR5J+aRK9u85/gMrxcLtZBWnBNnet9oijxhobYr5zy32dtvsy4EnuvQTjQXNrss1CNcKfBdh61ewkYvZbbiM1kK+G8+fCs/9xtPEQ8zUYrzOzRTxvyHz+FlMdtbHebrNXDcUZtCGYErDMYR7PQznl5u0s80tdDltoUqC/WAo4v10dhEMOxeSAIPK+N92fnO1EIOnykTFRbtG+2EK1u9UeGRJZm1aVcGlWV2/JbnhNWISiXgwmafB06A59gR02e/8BIP3T7PLQcaeI6gNch5dr1rM4G0sUVm6DJbDahHHrOVG6t8xvjOQr8BoxY5HrWMMcYYL8eBRsSP3X+IZt/uTiseXD6yxM7lFUYTf6OpzU1xy7vuxSvtJ2+6EnIjiJUlPz74yR/kz/75r9DrD2gmmn6SMYiq9O99L7/8lWUahZLhoeMz/NiDR3AcePuhBs+vdekkOYEDC9WQO+YrIyXPEIOsMDe7AZTWPLHcvCmFh5SSU7MVVlsx7SQHYVNUr6+fR8rFPcdzY3ek9Vaf/+NPXkIrzV3HpqiWPBv7riHJFfPVkPfdOsP//P138Et/foGV7QGuIzi5WOOd6hqPJoLl+z+CGwccNwK/sIHH2FFYt6+YqPj80dfX6MapNQfThqeWm7zn1Cxh4JLlu06jQxizPx7OdSSTFZ9K6FpVUa7xXMniZGQNvda6xN0BKlUF2bToJhlLmt3Y6aG1oRx6XNroIYEvv7DB8maPppBUrrbxXUkvUUgpqJV8jDTUQo9b52xBWfVdDnwD87YxvrNQbwC5VL556o5x4THGGG82NIo0043OgNu+7yP85b/8LFrZcYMUgiN33Uo0NYEQuwTEXXaBoZvbP0294wE+/C+O8Pzv/DHPXVhjNQ1I738n+DbWvjnIcR3Bs6s29l0KQcn3uPfgNzeG8l2J54hR+34vBILZys3jCUgpOTCx31di7WWPmSz59Fv7pcqb7ZjnrjSZLvk8fNcCgeeMOj+eY51SV1sDvnxxmw/cucj771ignygcCV9dafHM55/n8un7UEGISlI2d7oEpQjXkWSpItV29BErRZplhXcKOI41kHv6apPId1HKjFJoBYCx3JpLm23CwKVe9tFCUQp2fVh8zyHLNbkyTJUDXhzsoDKNEAJjbH7PbvNcgFGkeUbcM/zaX1xEa0Ot7HP6+BRe5BErTapscaa0odVLKYcuEmv/P132OT1bKUIAx/hu443oeDjjjscYY4zxalhp9dnqJhx6+CHeGwY8/7tfoHN9g/JknVs/8BDVuZlRsQAFQbH4sysLoiiCxuIc9//Ej3L1mTXiFzb2PX64xMSZXeD8b6aR3QNXCm6fr/Lo5Z19BRAYaqHLe068/m7Ht4J7DtS51h7s2yVe2ejhuw63HqgTes4ob2Z4rURhmrXSjsmUxnMk5YKTU/ElKwdvIV87X3ioGK5e3eTI8SXiRI1GJq6E5c1+oYbZfXNXCtr9lAOVAM+V9OMcpa3VehS4zJQ9VjZ79Ac59bKPI21K7YjvIWyOS64NoS8phx55mkFx3GbPr0bnhL6kUo6YqofcslQnzzXlkmezVwY5q51k3/XSxtAZZHzo1hluf7k0aIwx3mCMC48xxniTwRSpoSDYHihm334vBx68D4A01yzWIoROyXJt010xuxkgSELXmpCpIlJdKU2j5OM7cpTuOoQvJQuN8NvK3PjZD5zk5/7js1zc7KON9QCvhi7/xQMHOdAofdPn30wcnSrz8dPzPH61yVYvJXAlJVfihy610iudTof8UEcKBllOWhQeQ1zcGtAUEU61imq3AfjaM1foao/DS5NUIo9BmnPxWoetJLMdFMf6aZjiP9+RzJR91noptaKL5TqCsufw0dNz3DpV4neevk6/8E7RRTExVMzIQlmTZZrD0xHtyOHKWgfkkCysMUox3yjjlkLq5YBThxp4rgMFVcOTgh96xyH+2Z+eJ3lZOumJqdK46HiT4o0YtbyZpNLjwmOMMd5keLmyoJNqSId/MqwPchYrAZDiFsmxxhi6mSFH4Ts2GVZhLEGxl3NkssSxuQpnV9ujADnfEVQDhw+envu2jjNwHf73v/s2XrjW4o/OrrNQD/n47fOU/ddGrL3ZODZd5tj0rtPszs6AF653yJR+hX36sN2jDVR87xXOqnGmkI5ATUyRCQfZ74HRPPr8Nb58dpvFyRIbrRjPkdxzetomCiuDljAsBA82Qm6ZKRF4ku1+Rq5t8q9Whn/3xFVqkcsHbp/jseUmmx3rWlqNXIa2+cYUNvZK8z/8rTtQBn7niWW++MxV4jjj5FTAf/bgLYTlEmc3Oqz20n1kXyHg6FSJY7M1/sePneJXH7nMtU6KLwVnDtb5xL0H34C7MMbNwLjwGGOMMb6jEGIofX3l6EMbazDmuB5COug8wwGkI/Eiyf/zyIscmJkkdK0M1XGsbLYauHz/mUW+Ug04u9YFbTgwEfGxO+Z522vgdLwaTi3UOfUm3Dk/eGKaF9c6vLTSYrZeQsq9EfH22qaZ5q6l+iuC6eZrIaHv0Cj77FBH1epoDOUkp70TI4x1HM2V4uRkxJNrXRs3jx1/lAOHd52YoKkMU5WAqUqA1oblnX6ROGzt3Q3QCD1ypWn2C7fRwJJ+tdYsVny+54EDzNUjQs/hZz5ymp/5yOlXnOsDx6f4oxfWObvRJdcaV0qOT5f42GkbjDNbj/jZD596g6/4GDcLbwTH42Znv7wejAuPMcZ4k8GRctd842XFh9JmJHmUUiL9XfmjBI64MQMNm+2ETGmmIo/Jko8rBcYIHjoxzftOzbJYD5mIfP5TzuR4x4lpdgYZX3juOs8vb3PrUgPfcyyBsvgSvu9gg1Nz1Vc8913Hpnj8yg4YQ+BJ2oMMbQynD0+wcq3DdmswUrC8tNLmB+6e54WtAf1UMV3x+PCpaUqhT18LuknO5VZCN9O0B/uVQJe2+xybiigHDpvdlG6S01UpJ6bLvP9wnYlGncB1Cb7JKEwIwUdOz/GBW2cZZIrIlbjOtz4+G2OM7wTGhccYY7wJcWKqzNmNTmFPbhfKVBuWGuGrFgsVR/Oek7Ns9VOSXFNyBK0kZ1D4YFQDl9lKYHkAbwF87M4FPnTbHBc2OgSOoK8NxgiOT5cJXuUaeI7kHzx4lN977joXox4GODEd0UkVxhE0aj6u2+F6K+aZa12mJ5vcd3iC+XrAXD1ES0GsBVJALXS5xRH8+YUdlDb7lCP9VHF2vceDx6Y4MhGR5JqleoQjYHV1lUoYELjyNReIrhT7jNXG+JuJ8ahljDHG+I6jFHjctdjg0k6fXpLjui6npkr4r6FgEEIwXd7thExUXuXBbwG4juTk/Lc+CqpHHj9S8CCMMfz/7d19TNblo8fx9/0INw+CIkbCqd9QHD+TVhBJpoDSzNN6FIijiD1s4jzlrDCktMAnaq3+6GG5GVmNQBZtas0Dy2jMsVqypBW5ZukGh1fvWC4AAAqHSURBVB5OP/EQCMLN/b2/5w/sPj9E0BBu6Mfntd0bXny/1/eB4ffDdV3f6/rw6//m1JkerDYLIdNdPJRwDT+f6eG7tk5O/aOX/zn3D+6+6Voiwm0E2K0Da+14Tew2K0FBTqJDA/ils2/IcQzTJCzAwdzIwS0v5zraCZzzt1Fds/y1GYx9V4sxZIj1xFHwEJmkrFYrsRFTPDVMIi1nz/PPnReNbZ1Md9m5/YZZzHQ5iAmxEx4cgMNu8a3j4rWaOGw2LBZYEDWN4z91cfF8pTNcThL/Ldyv1yKT23isTjuZZsNXJ6CIyGVYLBZ6+71DHgb/e97Did+6aety4whygdWK5cIYHcuFDwx0gcye7mLF/GsIC7TTb3gxTZOYMBf/kRhzYQ0XkQEDXS3mGH8m+qr+n1o8RESuwMxgJ2d7zmHarL5xGqZp4jW9xIQNTF/fZ4DTYfG9vmu1WHyzpNptNpKvm05STDi/dvUSYLcSEay1UWTyO3bsGJs2baK0tJSlS5cO+f4NN9xAYmKi79/vvvsuNtvw3cIKHiIiV+DR2/5GyX+doMttDMyfYkK/4eXv14Ty96hQ+g0Tj2nS028QaB8IG4F2GzarFYfD7mv9sFotzNaaKDKCydTV0trayjvvvDMoWFwsJCSE8vLyK65TXS0iIlfAabfx/L/PZ+ncSGa4nERPCyT75hj+c8kcZoUGEh7kIMRpx26zExTgJDzIhSswgMCAgSnRRa7U2HezjH6wamRkJG+88QahoUNfOx8ttXiIiFyhQIeNlTdFs/Km6EHlNqudQIf+O5Wx4b3wGes6R8PlunzrnNvtpqCggJ9++ok777yTRx55ZMTt9ZsiIiIyiUzUzKXV1dVUV1cPKtu4cSNLliwZcb/CwkLuvfdeLBYLa9as4ZZbbiEhIWHY7RU8REREhOzsbLKzs//0fqtWrfJ9nZKSwsmTJ0cMHup4FBERmUT+mLl0rD/j4fTp0xQUFGCaJh6Ph+PHjxMXFzfiPmrxEBERmUSuZjDoSHWORn19PW+//TanT5/mu+++o7y8nH379rF3716Sk5O5+eabiYqKIisrC6vVyrJly7jxxhtHrFPBQ0REZBLxMg6v045yv/T0dNLT04eU5+fn+75++umn/1SdCh4iIiKTyGRq8RgPGuMhIiIifqMWDxERkUlkPAaDaq0WERERuaSJmsfDX4YNHuaFk3S73SNW0NfXN7ZnNElNhevUNf5rmArXCFPjOnWNk9cfz0ZzHB7ogZERY95CERgZMbYVXgWLOcxd6+rq4uTJk/4+HxERkb+MefPmjdk6Jh6Ph+bmZgzDGJP6Lmaz2ViwYAF2+8R2dgwbPLxeL93d3TgcDt+qiiIiIjLQ0tHf309wcDDWMVwE0OPxjGvwmOjQASMEDxEREZGxptdpRURExG8UPERERMRvFDxERETEbxQ8RERExG8UPERERMRvFDxERETEbxQ8RERExG8UPERERMRvFDxkSmtrayM1NXVIeWpqKm1tbcPut3v3bpqbm6/6+GfPnmXdunVkZWWRmZnJqVOnAGhpaSEvL4/c3FzWrFlDS0vLkH1bWlpYvnw5JSUlozr2oUOHrubURURGRcFDZBS2bt3KggULrrqel156ieTkZD788EMef/xxPv30UwB27tzJqlWrqKioYO3atWzfvn3Ivk1NTcyfP39UwcMwDN58882rPX0RkT9NU6bLlNbW1sbq1as5evTooPLU1FQqKytxOBxs3rwZgN7eXnJycsjKyiIvL48NGzZgs9nYu3cvUVFR/Pjjj9jtdsrKynC5XOzZs4eamhpmzpxJfHw8v/32Gy+//LLvGKZpkpycTF1dHWFhYb7y/v5+kpOTaWxsxOFwYBgGSUlJHDt2DKfTCQy0dqxfv57Ozk6WL1/Os88+y44dO2hpaaG7u5u7776bRx99lJ6eHrZs2UJHRwfd3d2sWLGC/Px8tmzZwuHDh7n11lvZsWPHoHvw+uuv4/F4ePLJJ0lMTCQrKwuv18u2bdsoLy+npqYGwzCIjY2luLgYwzAoKCigs7MTj8fD0qVL2bBhw3j/6ETkL2riV4sRmcRqamqIjY1l+/bt9PX1UV1dPWSbr7/+mk8++YSIiAjy8vJoaGggLi6OqqoqamtrsdvtPPzww1x77bWD9mtvb8disXDw4EGOHDmCy+WiqKiIkJAQgoODcTgcwMDCTtOmTePMmTPMnj0bgOuvv578/Hw+//xzSkpKKCsrY9asWezatQvDMHjwwQdZtGgRwcHBZGRkcP/99+N2u7nttttYvXo1Gzdu5IsvvmDfvn0jdin19PSQlpbG7bffzjfffMORI0eoqKjAYrFQWlpKdXU1UVFReDweKisr8Xq9lJeX4/V6x3ThLBH516HgITIMi8XCkiVLqKyspKioiLS0NHJycoZsN2fOHCIiIgCIjo6mo6OD77//noSEBFwuFwAZGRmcOHFiyL5dXV3MmzePhx56iIMHD7J161ZeffXVIduZpjniKtFffvklv/76K42NjQC43W5aW1tZvHgxX331FVVVVTgcDvr6+ujo6Ljie2CaJomJib5jtLa2snbtWmAglNjtdu666y5ee+01Nm3aRFpaGtnZ2QodIjIsBQ+Z0oKDg+ns7Bz0YDcMg46ODkJDQ4mOjubw4cM0NjZSW1vLe++9R1VV1aA6bDbbkHov/ov/Ug/iGTNm4HQ6WbhwIQB33HEHJSUlRERE0NPTg9vtxul00t/fz7lz53zh5lKcTiePPfYYK1asGFS+Z88e3G43+/fvx2Kx+I71zy4ONP39/YPK/mh5cTqdLFu2jOeff35IHYcOHaKpqYm6ujoyMzM5cOAAgYGBw56viExd+rNEprTp06eTkJDARx995CurqqoiJSWFadOm8fHHH/Ptt9+yaNEiiouL+eWXX/B4PJetNzY2lubmZtxuNx6Ph88++2zINlarlbS0NOrr6wE4fvw4cXFx2O12UlJSqK2tBQa6exYuXOgb33EpSUlJ1NTUAAOh54UXXqCjo4P29nbmzJmDxWKhrq6O3t5e3G43VqvVdx0hISH8/vvvnD9/HsMwfK0mF0tMTOTo0aN0d3cDUFFRQVNTEw0NDdTX15OUlERhYSFBQUG0t7df9h6JyNSkFg+Z8l555RV2795NdXU1pmkSExPDiy++CMDcuXMpLi7G6XRimibr1q3Dbr/8r018fDwZGRlkZmYye/Zs4uPj6ezsHLLdc889R2FhIW+99RZWq5WdO3cCsG3bNp555hn279+P0+mktLR0xOPl5ubyww8/kJOTg2EYpKenEx4eTmZmJk899RQNDQ1kZGRwzz33sHnzZj744ANmzpzJypUref/993nggQfIzMzkuuuuY/78+Zc8RkJCArm5ueTl5REQEMCsWbNYuXIlZ8+epaioiLKyMmw2G4sXLyY6Ovqy90hEpia91SIyDjweDwcOHOC+++7D6XSya9cuIiMjWb9+/USfmojIhFKLh8g4sNvt/Pzzz2RnZxMSEkJYWBhPPPHERJ+WiMiEU4uHiIiI+I0Gl4qIiIjfKHiIiIiI3yh4iIiIiN8oeIiIiIjfKHiIiIiI3yh4iIiIiN/8H/6VQm8zw+wjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Manifold Learning\n",
    "plot_model(model, plot='manifold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "plot_model(model, plot='feature')\n"
   ]
  },
  {
   "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>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base_score</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booster</th>\n",
       "      <td>gbtree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colsample_bylevel</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colsample_bynode</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colsample_bytree</th>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>importance_type</th>\n",
       "      <td>gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_rate</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_delta_step</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_depth</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_child_weight</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators</th>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_jobs</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nthread</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective</th>\n",
       "      <td>reg:linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_state</th>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_alpha</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_lambda</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale_pos_weight</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silent</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsample</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verbosity</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Parameters\n",
       "base_score                0.5\n",
       "booster                gbtree\n",
       "colsample_bylevel           1\n",
       "colsample_bynode            1\n",
       "colsample_bytree          0.9\n",
       "gamma                       0\n",
       "importance_type          gain\n",
       "learning_rate            0.02\n",
       "max_delta_step              0\n",
       "max_depth                  30\n",
       "min_child_weight            1\n",
       "missing                  None\n",
       "n_estimators              700\n",
       "n_jobs                     -1\n",
       "nthread                  None\n",
       "objective          reg:linear\n",
       "random_state             1991\n",
       "reg_alpha                   0\n",
       "reg_lambda                  1\n",
       "scale_pos_weight            1\n",
       "seed                     None\n",
       "silent                   None\n",
       "subsample                 0.1\n",
       "verbosity                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model Hyperparameter\n",
    "plot_model(model, plot='parameter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 - Predicting math scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `predict_model()` is used to predict new data using a trained estimator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_COMP1</th>\n",
       "      <th>NU_NOTA_COMP2</th>\n",
       "      <th>NU_NOTA_COMP3</th>\n",
       "      <th>NU_NOTA_COMP4</th>\n",
       "      <th>NU_NOTA_COMP5</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Q001</th>\n",
       "      <th>Q002</th>\n",
       "      <th>Q006</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "      <th>Q026</th>\n",
       "      <th>Q047</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>464.8</td>\n",
       "      <td>443.5</td>\n",
       "      <td>431.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>450.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>391.1</td>\n",
       "      <td>491.1</td>\n",
       "      <td>548.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>E</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>499.760010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>595.9</td>\n",
       "      <td>622.7</td>\n",
       "      <td>613.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>619.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>H</td>\n",
       "      <td>E</td>\n",
       "      <td>G</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>-97.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>592.9</td>\n",
       "      <td>492.6</td>\n",
       "      <td>571.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>D</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>605.760010</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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4571</th>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>398.3</td>\n",
       "      <td>558.2</td>\n",
       "      <td>511.6</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>421.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>427.6</td>\n",
       "      <td>579.7</td>\n",
       "      <td>471.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>431.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4573</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>639.2</td>\n",
       "      <td>643.8</td>\n",
       "      <td>604.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>660.619995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4574</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>427.1</td>\n",
       "      <td>467.9</td>\n",
       "      <td>540.2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>471.299988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4575</th>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>-98.129997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4576 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     TP_SEXO  TP_COR_RACA  TP_ESCOLA  NU_NOTA_CN  NU_NOTA_CH  NU_NOTA_LC  \\\n",
       "0          F            3          1       464.8       443.5       431.8   \n",
       "1          F            3          1       391.1       491.1       548.0   \n",
       "2          M            1          2       595.9       622.7       613.6   \n",
       "3          F            3          1      -100.0      -100.0      -100.0   \n",
       "4          M            1          2       592.9       492.6       571.4   \n",
       "...      ...          ...        ...         ...         ...         ...   \n",
       "4571       F            1          2       398.3       558.2       511.6   \n",
       "4572       M            2          2       427.6       579.7       471.1   \n",
       "4573       M            1          1       639.2       643.8       604.9   \n",
       "4574       M            2          1       427.1       467.9       540.2   \n",
       "4575       M            1          1      -100.0      -100.0      -100.0   \n",
       "\n",
       "      NU_NOTA_COMP1  NU_NOTA_COMP2  NU_NOTA_COMP3  NU_NOTA_COMP4  \\\n",
       "0             120.0           80.0           80.0          100.0   \n",
       "1             120.0          120.0          120.0          120.0   \n",
       "2              80.0           40.0           40.0           80.0   \n",
       "3            -100.0         -100.0         -100.0         -100.0   \n",
       "4             100.0           80.0           60.0           80.0   \n",
       "...             ...            ...            ...            ...   \n",
       "4571          120.0          120.0          120.0          100.0   \n",
       "4572          100.0          100.0          100.0          120.0   \n",
       "4573          160.0          140.0          120.0          140.0   \n",
       "4574          140.0           80.0           80.0          140.0   \n",
       "4575         -100.0         -100.0         -100.0         -100.0   \n",
       "\n",
       "      NU_NOTA_COMP5  NU_NOTA_REDACAO Q001 Q002 Q006 Q024 Q025 Q026 Q047  \\\n",
       "0              40.0            420.0    B    A    C    A    A    C    A   \n",
       "1             100.0            580.0    E    B    C    B    B    B    A   \n",
       "2              80.0            320.0    E    E    D    B    B    A    A   \n",
       "3            -100.0           -100.0    H    E    G    B    B    A    A   \n",
       "4               0.0            320.0    D    H    H    C    B    A    A   \n",
       "...             ...              ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4571           40.0            500.0    E    E    D    A    B    A    A   \n",
       "4572          100.0            520.0    C    C    C    A    A    A    A   \n",
       "4573           80.0            640.0    D    F    D    B    B    A    D   \n",
       "4574           80.0            520.0    C    E    C    A    A    A    A   \n",
       "4575         -100.0           -100.0    C    C    A    B    B    B    A   \n",
       "\n",
       "           Label  \n",
       "0     450.679993  \n",
       "1     499.760010  \n",
       "2     619.380005  \n",
       "3     -97.839996  \n",
       "4     605.760010  \n",
       "...          ...  \n",
       "4571  421.130005  \n",
       "4572  431.380005  \n",
       "4573  660.619995  \n",
       "4574  471.299988  \n",
       "4575  -98.129997  \n",
       "\n",
       "[4576 rows x 20 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = predict_model(model, data=test, round=2)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 - Finalize Model\n",
    "\n",
    "- `finalize_model()` function fits the estimator onto the complete dataset passed during the setup() stage. The purpose of this function is to prepare for final model deployment after experimentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = finalize_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 - Save Model\n",
    "\n",
    "- `save_model()` function saves the transformation pipeline and trained model object into the current active directory as a pickle file for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(model, 'xgboost_model_04082020')"
   ]
  }
 ],
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