Loan default prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FINAL PROJECT - loan Default Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-->The dataset below is downloaded from Kaggle website and is established by a german professor Dr. Hans Hofmann. \n",
    "\n",
    "-->It is composed of 17 inputs that characterize 1000 bank customers having applied for a loan and a target which represents the credit risk ( bad or good ) .\n",
    "\n",
    "-->Good risk means that the customer has fully paid back his loan in time whereas bad risk means that either the customer didn't at all pay it back or paid a proportion or paid it fully but not in time .\n",
    "\n",
    "-->The categorical data in this dataset has been encoded as well as the attribute names that have been replaced by indices , which makes it not suitable for data visualization . We're going to replace the  categorical data by its original values and the column indices by the original headers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A11</td>\n",
       "      <td>6</td>\n",
       "      <td>A34</td>\n",
       "      <td>A43</td>\n",
       "      <td>1169</td>\n",
       "      <td>A65</td>\n",
       "      <td>A75</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A121</td>\n",
       "      <td>67</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>A192</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A12</td>\n",
       "      <td>48</td>\n",
       "      <td>A32</td>\n",
       "      <td>A43</td>\n",
       "      <td>5951</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>A92</td>\n",
       "      <td>A101</td>\n",
       "      <td>2</td>\n",
       "      <td>A121</td>\n",
       "      <td>22</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A14</td>\n",
       "      <td>12</td>\n",
       "      <td>A34</td>\n",
       "      <td>A46</td>\n",
       "      <td>2096</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>3</td>\n",
       "      <td>A121</td>\n",
       "      <td>49</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A172</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A11</td>\n",
       "      <td>42</td>\n",
       "      <td>A32</td>\n",
       "      <td>A42</td>\n",
       "      <td>7882</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>A93</td>\n",
       "      <td>A103</td>\n",
       "      <td>4</td>\n",
       "      <td>A122</td>\n",
       "      <td>45</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A11</td>\n",
       "      <td>24</td>\n",
       "      <td>A33</td>\n",
       "      <td>A40</td>\n",
       "      <td>4870</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A124</td>\n",
       "      <td>53</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1    2    3     4    5    6    7     8   9     10  11    12    13  14  \\\n",
       "0  A11   6  A34  A43  1169  A65  A75  A93  A101   4  A121  67  A143  A152   2   \n",
       "1  A12  48  A32  A43  5951  A61  A73  A92  A101   2  A121  22  A143  A152   1   \n",
       "2  A14  12  A34  A46  2096  A61  A74  A93  A101   3  A121  49  A143  A152   1   \n",
       "3  A11  42  A32  A42  7882  A61  A74  A93  A103   4  A122  45  A143  A153   1   \n",
       "4  A11  24  A33  A40  4870  A61  A73  A93  A101   4  A124  53  A143  A153   2   \n",
       "\n",
       "     15    16    17  18  \n",
       "0  A173  A192  A201   1  \n",
       "1  A173  A191  A201   2  \n",
       "2  A172  A191  A201   1  \n",
       "3  A173  A191  A201   1  \n",
       "4  A173  A191  A201   2  "
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_original=pd.read_csv('GermanData(3).csv',header=None)\n",
    "df_original.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the original dataset to a readable one with meaningful labels \n",
    "df_original.columns=['check_account','Duration','credit_hist','purpose','amount','sav_account','employ_since',\n",
    "                     'status_sex','other_debtors','residence_since','property','Age','other_install','Housing','num_credits','Job'\n",
    "                     ,'telephone','Foreign_worker','credit_risk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "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>check_account</th>\n",
       "      <th>Duration</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>sav_account</th>\n",
       "      <th>employ_since</th>\n",
       "      <th>status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>property</th>\n",
       "      <th>Age</th>\n",
       "      <th>other_install</th>\n",
       "      <th>Housing</th>\n",
       "      <th>num_credits</th>\n",
       "      <th>Job</th>\n",
       "      <th>telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>credit_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A11</td>\n",
       "      <td>6</td>\n",
       "      <td>A34</td>\n",
       "      <td>A43</td>\n",
       "      <td>1169</td>\n",
       "      <td>A65</td>\n",
       "      <td>A75</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A121</td>\n",
       "      <td>67</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>A192</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A12</td>\n",
       "      <td>48</td>\n",
       "      <td>A32</td>\n",
       "      <td>A43</td>\n",
       "      <td>5951</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>A92</td>\n",
       "      <td>A101</td>\n",
       "      <td>2</td>\n",
       "      <td>A121</td>\n",
       "      <td>22</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A14</td>\n",
       "      <td>12</td>\n",
       "      <td>A34</td>\n",
       "      <td>A46</td>\n",
       "      <td>2096</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>3</td>\n",
       "      <td>A121</td>\n",
       "      <td>49</td>\n",
       "      <td>A143</td>\n",
       "      <td>A152</td>\n",
       "      <td>1</td>\n",
       "      <td>A172</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A11</td>\n",
       "      <td>42</td>\n",
       "      <td>A32</td>\n",
       "      <td>A42</td>\n",
       "      <td>7882</td>\n",
       "      <td>A61</td>\n",
       "      <td>A74</td>\n",
       "      <td>A93</td>\n",
       "      <td>A103</td>\n",
       "      <td>4</td>\n",
       "      <td>A122</td>\n",
       "      <td>45</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>1</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A11</td>\n",
       "      <td>24</td>\n",
       "      <td>A33</td>\n",
       "      <td>A40</td>\n",
       "      <td>4870</td>\n",
       "      <td>A61</td>\n",
       "      <td>A73</td>\n",
       "      <td>A93</td>\n",
       "      <td>A101</td>\n",
       "      <td>4</td>\n",
       "      <td>A124</td>\n",
       "      <td>53</td>\n",
       "      <td>A143</td>\n",
       "      <td>A153</td>\n",
       "      <td>2</td>\n",
       "      <td>A173</td>\n",
       "      <td>A191</td>\n",
       "      <td>A201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  check_account  Duration credit_hist purpose  amount sav_account  \\\n",
       "0           A11         6         A34     A43    1169         A65   \n",
       "1           A12        48         A32     A43    5951         A61   \n",
       "2           A14        12         A34     A46    2096         A61   \n",
       "3           A11        42         A32     A42    7882         A61   \n",
       "4           A11        24         A33     A40    4870         A61   \n",
       "\n",
       "  employ_since status_sex other_debtors  residence_since property  Age  \\\n",
       "0          A75        A93          A101                4     A121   67   \n",
       "1          A73        A92          A101                2     A121   22   \n",
       "2          A74        A93          A101                3     A121   49   \n",
       "3          A74        A93          A103                4     A122   45   \n",
       "4          A73        A93          A101                4     A124   53   \n",
       "\n",
       "  other_install Housing  num_credits   Job telephone Foreign_worker  \\\n",
       "0          A143    A152            2  A173      A192           A201   \n",
       "1          A143    A152            1  A173      A191           A201   \n",
       "2          A143    A152            1  A172      A191           A201   \n",
       "3          A143    A153            1  A173      A191           A201   \n",
       "4          A143    A153            2  A173      A191           A201   \n",
       "\n",
       "   credit_risk  \n",
       "0            1  \n",
       "1            2  \n",
       "2            1  \n",
       "3            1  \n",
       "4            2  "
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "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>check_account</th>\n",
       "      <th>Duration</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>sav_account</th>\n",
       "      <th>employ_since</th>\n",
       "      <th>status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>property</th>\n",
       "      <th>Age</th>\n",
       "      <th>other_install</th>\n",
       "      <th>Housing</th>\n",
       "      <th>num_credits</th>\n",
       "      <th>Job</th>\n",
       "      <th>telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>credit_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>... &lt;    0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical account</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>1169</td>\n",
       "      <td>no savings account</td>\n",
       "      <td>.. &gt;= 7 years</td>\n",
       "      <td>male:single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 &lt;= ... &lt;  200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>5951</td>\n",
       "      <td>... &lt;  100 DM</td>\n",
       "      <td>1  &lt;= ... &lt; 4 years</td>\n",
       "      <td>female:divorced/separated/married</td>\n",
       "      <td>none</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>12</td>\n",
       "      <td>critical account</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>... &lt;  100 DM</td>\n",
       "      <td>4  &lt;= ... &lt; 7 years</td>\n",
       "      <td>male:single</td>\n",
       "      <td>none</td>\n",
       "      <td>3</td>\n",
       "      <td>real estate</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unemployed - resident</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>... &lt;    0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882</td>\n",
       "      <td>... &lt;  100 DM</td>\n",
       "      <td>4  &lt;= ... &lt; 7 years</td>\n",
       "      <td>male:single</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>4</td>\n",
       "      <td>life insurance</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>... &lt;    0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>delay in paying off in the past</td>\n",
       "      <td>car(new)</td>\n",
       "      <td>4870</td>\n",
       "      <td>... &lt;  100 DM</td>\n",
       "      <td>1  &lt;= ... &lt; 4 years</td>\n",
       "      <td>male:single</td>\n",
       "      <td>none</td>\n",
       "      <td>4</td>\n",
       "      <td>No property</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         check_account  Duration                                credit_hist  \\\n",
       "0        ... <    0 DM         6                           critical account   \n",
       "1   0 <= ... <  200 DM        48   existing credits paid back duly till now   \n",
       "2  no checking account        12                           critical account   \n",
       "3        ... <    0 DM        42   existing credits paid back duly till now   \n",
       "4        ... <    0 DM        24            delay in paying off in the past   \n",
       "\n",
       "               purpose  amount         sav_account         employ_since  \\\n",
       "0     radio/television    1169  no savings account        .. >= 7 years   \n",
       "1     radio/television    5951       ... <  100 DM  1  <= ... < 4 years   \n",
       "2            education    2096       ... <  100 DM  4  <= ... < 7 years   \n",
       "3  furniture/equipment    7882       ... <  100 DM  4  <= ... < 7 years   \n",
       "4             car(new)    4870       ... <  100 DM  1  <= ... < 4 years   \n",
       "\n",
       "                          status_sex other_debtors  residence_since  \\\n",
       "0                        male:single          none                4   \n",
       "1  female:divorced/separated/married          none                2   \n",
       "2                        male:single          none                3   \n",
       "3                        male:single     guarantor                4   \n",
       "4                        male:single          none                4   \n",
       "\n",
       "         property  Age other_install   Housing  num_credits  \\\n",
       "0     real estate   67          none       own            2   \n",
       "1     real estate   22          none       own            1   \n",
       "2     real estate   49          none       own            1   \n",
       "3  life insurance   45          none  for free            1   \n",
       "4     No property   53          none  for free            2   \n",
       "\n",
       "                           Job telephone Foreign_worker credit_risk  \n",
       "0  skilled employee / official       yes            yes        Good  \n",
       "1  skilled employee / official        no            yes         Bad  \n",
       "2        unemployed - resident        no            yes        Good  \n",
       "3  skilled employee / official        no            yes        Good  \n",
       "4  skilled employee / official        no            yes         Bad  "
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing the encoded values by their original values \n",
    "values_dict={'check_account':\n",
    "             {'A11' :     '... <    0 DM',\n",
    "             'A12' : '0 <= ... <  200 DM',\n",
    "             'A13' :      '... >= 200 DM' ,    \n",
    "               'A14' : 'no checking account'} , \n",
    "             'credit_hist':     \n",
    "            {'A30' : 'no credits taken/ all credits paid back duly',\n",
    "              'A31' : 'all credits at this bank paid back duly',\n",
    "    'A32' :' existing credits paid back duly till now',\n",
    "              'A33': 'delay in paying off in the past',\n",
    "     'A34' : 'critical account'},\n",
    "            'purpose':\n",
    "                  { 'A40' : 'car(new)',\n",
    "     'A41' : 'car(used)',\n",
    "     'A42' : 'furniture/equipment',\n",
    "             'A43' : 'radio/television',    \n",
    "             'A44' : 'domestic appliances',\n",
    "             'A45' : 'repairs',\n",
    "             'A46' : 'education',\n",
    "             'A47' : 'vacation',\n",
    "             'A48': 'retraining',\n",
    "            'A49' : 'business',\n",
    "            'A410' : 'others' } ,\n",
    "             'sav_account':\n",
    "            {'A61' :  '... <  100 DM',\n",
    "    'A62' :   '100 <= ... <  500 DM',\n",
    "    'A63' :   '500 <= ... < 1000 DM',\n",
    "    'A64' :          '.. >= 1000 DM',\n",
    "              'A65' :   'no savings account'} ,\n",
    "             'employ_since':\n",
    "             {'A71' :'unemployed',\n",
    "    'A72' :       '... < 1 year',\n",
    "    'A73' : '1  <= ... < 4 years', \n",
    "    'A74' :'4  <= ... < 7 years',\n",
    "    'A75' :       '.. >= 7 years'} , \n",
    "             'status_sex':\n",
    "             {'A91' : 'male:divorced/separated',\n",
    "              'A92' : 'female:divorced/separated/married',\n",
    "              'A93' : 'male:single',\n",
    "               'A94' : 'male:married/widowed',\n",
    "              'A95' : 'female:single'} ,\n",
    "             'other_debtors':\n",
    "             {'A101':'none',\n",
    "              'A102':'co-applicant',\n",
    "              'A103':'guarantor'},\n",
    "             'property':\n",
    "             {'A121':'real estate',\n",
    "             'A122':'life insurance',\n",
    "              'A123': 'car or other',\n",
    "             'A124' :'No property'} ,\n",
    "             'other_install':\n",
    "             {'A141' : 'bank',     \n",
    "            'A142' : 'stores',\n",
    "             'A143' : 'none'},\n",
    "             'Housing':\n",
    "                {'A151' : 'rent',\n",
    "                'A152' : 'own',\n",
    "                'A153' : 'for free'},\n",
    "              'Job':{'A171' : 'unemployed- non-resident',\n",
    "    'A172' : 'unemployed - resident',\n",
    "   'A173' : 'skilled employee / official',\n",
    "    'A174' : 'highly-qualified employee'} , 'telephone':{'A191' : 'no','A192' : 'yes'},\n",
    "                  'Foreign_worker':{'A201' : 'yes','A202' : 'no'}}\n",
    "             \n",
    "df_transf=df_original.replace(to_replace=values_dict,regex=True)\n",
    "df_final=df_transf.replace(to_replace={'credit_risk':{1:'Good',2:'Bad'}},regex=False) #credit_risk should be replaced \n",
    "                                                                                    # alone because it's numerical\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "check_account      0\n",
       "Duration           0\n",
       "credit_hist        0\n",
       "purpose            0\n",
       "amount             0\n",
       "sav_account        0\n",
       "employ_since       0\n",
       "status_sex         0\n",
       "other_debtors      0\n",
       "residence_since    0\n",
       "property           0\n",
       "Age                0\n",
       "other_install      0\n",
       "Housing            0\n",
       "num_credits        0\n",
       "Job                0\n",
       "telephone          0\n",
       "Foreign_worker     0\n",
       "credit_risk        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data cleaning \n",
    "df_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset has no missing values --> No cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> The dataset is now suitable for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "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>Duration</th>\n",
       "      <th>amount</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>Age</th>\n",
       "      <th>num_credits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.903000</td>\n",
       "      <td>3271.258000</td>\n",
       "      <td>2.845000</td>\n",
       "      <td>35.546000</td>\n",
       "      <td>1.407000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.058814</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>1.103718</td>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.577654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1365.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2319.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>3972.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration        amount  residence_since          Age  num_credits\n",
       "count  1000.000000   1000.000000      1000.000000  1000.000000  1000.000000\n",
       "mean     20.903000   3271.258000         2.845000    35.546000     1.407000\n",
       "std      12.058814   2822.736876         1.103718    11.375469     0.577654\n",
       "min       4.000000    250.000000         1.000000    19.000000     1.000000\n",
       "25%      12.000000   1365.500000         2.000000    27.000000     1.000000\n",
       "50%      18.000000   2319.500000         3.000000    33.000000     1.000000\n",
       "75%      24.000000   3972.250000         4.000000    42.000000     2.000000\n",
       "max      72.000000  18424.000000         4.000000    75.000000     4.000000"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# According to the summary table , the average age of the borrowers is around 36 , the average credit amount borrowed is 3271 for 21 months in average . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's analyze the distribution of the features and the target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28092afcb88>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the distribution of the target \n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Distribution of the risk')\n",
    "plt.xlabel('type of risk')\n",
    "plt.ylabel('number of borrowers')\n",
    "vc=df_final['credit_risk'].value_counts()\n",
    "vc.plot.bar(rot=45)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Around 70 % of the borrowers represent a good risk whereas 30% represent a bad risk . Unbalanced ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the features\n",
    "# Step 1 : Separate visualization\n",
    "# visualize the distribution of the numeric features \n",
    "numeric=df_final[['Duration','amount','Age','num_credits']]\n",
    "ax=numeric.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,6))\n",
    "categorical=['Duration','amount','Age','num_credits']\n",
    "for i in range(1,5):\n",
    "    plt.subplot(1,4,i)\n",
    "    plt.title('Distribution of'+' '+categorical[i-1])\n",
    "    df_final[categorical[i-1]].plot.box()\n",
    "    "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The plots above show us that :\n",
    "# Most of the borrowers are aged in their thirties \n",
    "# Most of the credits applied for last for around 20 months\n",
    "# Most of the  credits applied for are less than 3000\n",
    "# Most of the borrowers in the dataset have only 1 or 2 existing credits at the bank . \n",
    "# The box plots show the positively skewed distribution of the features( which is also confirmed by the bar plots )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the distribution of the categorical features \n",
    "plt.figure(figsize = (20,6))\n",
    "import matplotlib.pyplot as plt\n",
    "categorical=['sav_account','credit_hist','purpose']\n",
    "for i in range(1,4):\n",
    "    plt.subplot(1,3,i)\n",
    "    plt.title('Distribution of'+' '+categorical[i-1])\n",
    "    vc=df_final[categorical[i-1]].value_counts()\n",
    "    vc.plot.bar()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The plots above show us that most of the borrowers have a savings accounts under 100 DM , with a good credit history \n",
    "# and having loans for radio/television or a new car purposes . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,6))\n",
    "categorical=['Housing','Job','property']\n",
    "for i in range(1,4):\n",
    "    plt.subplot(1,3,i)\n",
    "    plt.title('Distribution of'+' '+categorical[i-1])\n",
    "    vc=df_final[categorical[i-1]].value_counts()\n",
    "    vc.plot.bar()\n",
    "    "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The plots above show us that most of the borrowers are skilled employees , have their own houses and\n",
    "# a car or other property which explains why the majority represents a good risk (70%) . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x280917bf108>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2 : correlated visualization\n",
    "# visualize the distribution of credit amount by status_sex \n",
    "import seaborn as sns \n",
    "g=sns.FacetGrid(df_final,col='status_sex')\n",
    "g.map(plt.hist,'amount')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# For ALL the credit amounts , the dominant category applying for a loan is male-single , followed by the\n",
    "# female-divorced/separated/married . According to these plots , no female singles have applied for a loan in the\n",
    "# dataset . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28091815388>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the distribution of credit history by age \n",
    "import seaborn as sns \n",
    "g=sns.FacetGrid(df_final,col='credit_hist')\n",
    "g.map(plt.hist,'Age')\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# The majority of the borrowers who have the worst credit history (critical account ) are aged in their thirties , \n",
    "#same applies for those who have a good credit history( existing credits paid back duly till now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28091a0f2c8>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3 : visualize the impact of the features on the target \n",
    "plt.figure(figsize = (15,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('credit_risk=Good')\n",
    "vc1=df_final.loc[df_final['credit_risk']=='Good']['Job'].value_counts()\n",
    "vc1.plot.bar(rot=45,color='red')\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('credit_risk=Bad')\n",
    "vc2=df_final.loc[df_final['credit_risk']=='Bad']['Job'].value_counts()\n",
    "vc2.plot.bar(rot=45)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Surprisingly ,the majority of the borrowers who represent a bad credit risk are skilled employees and half of the\n",
    "# highly qualified employees who represent a good credit risk actually represent a bad risk . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "features=['Housing','status_sex','other_debtors']\n",
    "colours=['green','blue','red']\n",
    "for i in range(1,4):\n",
    "    plt.figure(figsize = (12,5))\n",
    "    plt.subplot(i,2,1)\n",
    "    plt.title('credit_risk=Good')\n",
    "    vc1=df_final.loc[df_final['credit_risk']=='Good'][features[i-1]].value_counts()\n",
    "    vc1.plot.bar(rot=90,color=colours[i-1])\n",
    "    plt.subplot(i,2,2)\n",
    "    plt.title('credit_risk=Bad')\n",
    "    vc2=df_final.loc[df_final['credit_risk']=='Bad'][features[i-1]].value_counts()\n",
    "    vc2.plot.bar(rot=90,color=colours[i-1])\n",
    "    blue_patch = mpatches.Patch(color=colours[i-1], label=features[i-1])\n",
    "    plt.legend(handles=[blue_patch])\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# According to the plots above, the probability of a bad risk is the highest when the applicants are :\n",
    "# single males \n",
    "# who own their houses \n",
    "# don't have any debtors with them . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we're done with the visualization , we're going to encode the categorical data in order to be able to apply the ML prediction model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "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>check_account</th>\n",
       "      <th>Duration</th>\n",
       "      <th>credit_hist</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>sav_account</th>\n",
       "      <th>employ_since</th>\n",
       "      <th>status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>property</th>\n",
       "      <th>Age</th>\n",
       "      <th>other_install</th>\n",
       "      <th>Housing</th>\n",
       "      <th>num_credits</th>\n",
       "      <th>Job</th>\n",
       "      <th>telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>credit_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5951</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2096</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7882</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4870</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   check_account  Duration  credit_hist  purpose  amount  sav_account  \\\n",
       "0              0         6            2        7    1169            4   \n",
       "1              2        48            0        7    5951            1   \n",
       "2              3        12            2        5    2096            1   \n",
       "3              0        42            0        6    7882            1   \n",
       "4              0        24            3        1    4870            1   \n",
       "\n",
       "   employ_since  status_sex  other_debtors  residence_since  property  Age  \\\n",
       "0             0           3              2                3         3   67   \n",
       "1             2           0              2                1         3   22   \n",
       "2             3           3              2                2         3   49   \n",
       "3             3           3              1                3         2   45   \n",
       "4             2           3              2                3         0   53   \n",
       "\n",
       "   other_install  Housing  num_credits  Job  telephone  Foreign_worker  \\\n",
       "0              1        1            2    1          1               1   \n",
       "1              1        1            1    1          0               1   \n",
       "2              1        1            1    2          0               1   \n",
       "3              1        0            1    1          0               1   \n",
       "4              1        0            2    1          0               1   \n",
       "\n",
       "   credit_risk  \n",
       "0            0  \n",
       "1            1  \n",
       "2            0  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data encoding \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "categorical=['check_account','credit_hist','purpose','sav_account','employ_since',\n",
    "              'status_sex','other_debtors','residence_since','property','other_install','Housing',\n",
    "             'Job','telephone','Foreign_worker']\n",
    "for cat in categorical:\n",
    "    df_final[cat]=encoder.fit_transform(df_final[cat])\n",
    "cleanup_nums={'credit_risk':{'Good':0 ,'Bad':1}}\n",
    "df_final.replace(cleanup_nums,inplace=True)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that all our data is numerical , we will select the most relevant features that we're going to use in the ML prediction model , using the correlation map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAMnCAYAAAB/YbCYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd1yV5f/48dfFGWwEWQIqTnCPnJWZ2vhUWlnZTtMcWVmZ7Syz0nY2LCuzoaWZmaWZG/eeiCAyZanIUhmHdeD+/XGORw6IglLI7/t+Ph4+HnKu+9z3m+u61/u+rvtCaZqGEEIIIYQQQogrn0N9ByCEEEIIIYQQomYkgRNCCCGEEEKIBkISOCGEEEIIIYRoICSBE0IIIYQQQogGQhI4IYQQQgghhGggJIETQgghhBBCiAZCEjghhBBCCCGEqCWl1A9KqQylVGQ15Uop9YVSKl4pFaGUuqoutisJnBBCCCGEEELU3k/ALRcovxVoa/03Dvi6LjYqCZwQQgghhBBC1JKmaZuBnAsscicwT7PYCXgqpQIud7uSwAkhhBBCCCFE3QsCUiv8nGb97LLoL3cFQgghhBBCCHG54vr9T6vvGCoK2bbmcSxDH8+arWna7FqsQp3ns8v+HSWBE0IIIYQQQohKrMlabRK2ytKAZhV+bgocv6ygkCGUQgghhBBCCPFvWAaMsM5G2Rc4o2naictdqfTACSGEEEIIIeqfalh9S0qpX4EBgI9SKg14EzAAaJr2DbACuA2IB0zAqLrYriRwQgghhBBCCFFLmqY9eJFyDXiqrrfbsNJcIYQQQgghhPg/THrghBBCCCGEEPVPnW/SRlGZ9MAJIYQQQgghRAMhCZwQQgghhBBCNBAyhFIIIYQQQghR75SDDKGsCemBE0IIIYQQQogGQhI4IYQQQgghhGggZAilEEIIIYQQov41sD/kXV+kloQQQgghhBCigZAETgghhBBCCCEaCBlCKYQQQgghhKh/8oe8a0R64IQQQgghhBCigZAETgghhBBCCCEaCBlCKYQQQgghhKh/8oe8a0R64IQQQgghhBCigZAETgghhBBCCCEaCBlCKYQQQgghhKh3SmahrBHpgRNCCCGEEEKIBkISOCGEEEIIIYRoIGQIpRBCCCGEEKL+OUjfUk1ILQkhhBBCCCFEAyE9cEIIIYQQQoj6J5OY1Ij0wAkhhBBCCCFEAyEJnBBCCCGEEEI0EDKEUgghhBBCCFH/ZAhljUgPnBBCCCGEEEI0EJLACSGEEEIIIUQDIUMo60lcv/9p9R3DWVumvVPfIdi08G1c3yHYZOUV1HcIV6wuwYH1HYLNztik+g7BxsPFqb5DsOPh7FzfIdjEncio7xBsvN1d6zsEG3WFDRcqLy+v7xBsCktK6zsEG39P9/oOwSYpI6e+Q7DT2t+nvkOwKTaX1XcINj9u2FnfIdhZ8vyoK+tkUw0lfweuRqSWhBBCCCGEEKKBkAROCCGEEEIIIRoIGUIphBBCCCGEqH8yhLJGpJaEEEIIIYQQooGQBE4IIYQQQgghGggZQimEEEIIIYSof1fYzLxXKumBE0IIIYQQQogGQhI4IYQQQgghhGggZAilEEIIIYQQot4pGUJZI9IDJ4QQQgghhBANhCRwQgghhBBCCNFAyBBKIYQQQgghRP1zkCGUNSEJXAMV+Ml0HEPacHrxX5ya++u/tp30lCTWLZyHpml0u24gna/pX2WZHSuWkRAZjk6v57YRY2nk48uu1cuJ2b8HB50O/2YtuPGB4SilMOXnsXbBXEz5uTg46Lh/4ss1jmXH+rVsXbsKUNw/ZjzNW7exlZWWlPDzV5+Tk5VJYx9fhj/1LAajkcSYaP74aQ4OOh2de/bh5qH32L5TkJfHG0+O5v4x4+lz/aBa1cv+LRvZu3EdCsXg4aMIbNHKLpa/fviGM9lZNPL2Yehj4zEYjewKW82O1SvQtHKe+2imbfk9G9ayf/MGdHo9A4feS+uOnRtsLJWFrVzB6uXLUEox7pmJtA4JtZVFRx7i608+4nhaGt/MX4iPnx8AR6Ii+WHWl+h0Onpdcy13P/DQJW8/PfkoaxbOAw269R9Il/Psv9tXLCX+UDh6vYHbHh2Lp48vaQmxrPr5B3IyTjJ++sd4eHkD8PcP35CbkwVARloKQ0aNp23Xq2oUy77NG9i9YS0Kxe0jRhPU0r6d/pgzizNZWTTy8eGeMU9iMBpZsWAeaYnxlJaU0LJdB257aAQAG5ctIXLPLpRSdOl7Ldfddnut6mXH+rVsW7cKheLeMeNp3sr+WPpl1uecysrEy8eXR560HEtR+/eyfOEvGIxGvHx8GfH0JHQ6HauXLCJ853aUUvS4tj833HFXrWIByEhNYsPv80HT6HTtADr27Vdlmd2rl3M06iA6vZ6bHh5NI28fonZuZdfKpbhb2+fWkY/j5unFwc3rObBhDZqmMWrqB7WKpS6Pp01//0n0vt0AdO57LdfeMqSWsWxgz4Z1KKUYMvyxKrH8+f3XtljuGv0EBqORhV/O4ExONlp5Ob1vuJmrrhsIwLGjCaz5fQHlZWUEtWzNLQ8Mr1UsB7ZsZO+mMAAGP1K1Xpb+8A2nc7LxbOzNndZ62R22mh1rVqJp5Uz88Avb8jkZJ1kx/ydKS4rx8GrMPeMm1CoWqPtju7S4mLW/zeN0ViZaeTl3P/Eczq6uNYpl18YwdqxbjVKKu0eNo1mr1nZ1s/CbmZzKysLLx4cHxj+NwWikpLiYJT/OJjsjg/Lycka/8CoaGj98/L7tu0djopn23Txc3NxqXT9Q98dVbe3csI7t61aDUtz72DiaVTrPLPj6C9s1+6EnnsFgNDJv5gxOZWUCcCz5KMMnTKJzz95sXbuSnevXWe4z7nuI0M5dax3Pnk1h7Axbg1KKoSPH0rSlfTstmv0lp7My8fTx5b5xEyzH09efczw5CScXF9w8GjFi4ksAZJ9M5695cygpKsbT25sHn5xY63jOauXnzZhBfUAp1kbEsCEq3q48wMuDp2+5DnNZGXoHB2aH7SAp8xShgX6Mv/EaArzceer7P8jON11yDKLhkSGUDVTG+5+SNWvOv76ddQvnMeSx8Tz0/GT2rV9DUUGBXXl2+nGSYw7zyEtT6Dfkbjb++RsAbbv1ZMSrb/HIS1MoyDtD8pHDAIT99gvXDBnKg5Neq1XyVpCfx4Z//ua5t99n1MQXWPT9t3blOzaso0lQU16Y/iH+QUHs2LAOgEVzvmX0pJd5ftqHxEVGcPL4Mdt3Vi1ZROt2HWpdJ4UF+excu5LHXp3KsPFP888vP9qVH9i6EZ+AQMZMfhufJoEc2LoRgI49+/D0u5/YLZufe4Y9G9YxZvLbDJ/0KmsWzae8vLxBxlJZfl4uy5csZvpnM5k0+Q2+++Izu/LmLVrywVffENrBvg2+++IzXpwylfe++IrI8AMcS0255BjWLJzHHaOf4OEXJrM3bDWFlfffE8dJPnKYES+/Sb/b72bjEsv+6xPQlBGvTCWowo0YwO2PjefhF17ngYmv4OjsSssOnWoUR2FBPttXr2Ds5Le474ln+PvnH+zK92/ZgG9AEI9PmYZvQBD7t2wA4Ob7HmTc62/z1Nvvk5YYz8m0VIoLC9m7aT1PvvUu49+czq71aygpKqpxnZjy89i04m8mvvU+jz77Ar9XOpZ2Wo+lSdM+xD8wiJ3WY2n5wp8Z88JrTJr2ITqdjiMHD1BUaGJH2FpefO8Tnn/3Y7auWUFxLWI5a8Pv87llxDiGPfsK4RvXUmSyb6ec9BOkxkZz/6TJ9L1tKNuW/m4r63j1ddw78RXunfiK7SazTbceDH99Wq3jqMvjqbiwkP1bNjBuynTGvjGNPevXUlJc87opLMhnx5qVjH7NEsvySvvMga0b8Q0IYuzr7+ATcC6WG4c9yONTpjP6tbfYuPQPSktKMJtLWbNoPg89/QKjX51a6+StsCCfnetWMuqVNxn2+NOsmP+TXXn41k34BAQx5rW38A4IJHzrJgA69OzDhOkfV1nfP7/8wJ2jxjHq5SmXlLxB3R/bW5f/SbsefXlo0ms8/MLrNU7eTPn5bFm5nAlTp/PI05NY8uN3duW7N4bhF9iUZ95+D9/AIHZvtCTBq35fSLer+/HUlHd4eup0XNzccHVz5+mp03l66nTueORRQjp1ueTkDer+uKoNU34+m1b+zTNT32XE05NY/MNsu/JdG8PwD2rKc+98gF9gELus9TLi6Uk8+9Z7PPX62zi7uNKuSzfyzpxm29pVPPfOBzzx2pss/eUnysvKah3P1lX/8MSUaTz41HP8Ndf+/mnPpvX4BQbx1NT38A0IYs+m9bayu0aO5ckp023JG8CfP83m3jFP8sQb71xW8gYwZlAfPluxmSm/rWRw9w64Ohrtyk+ezuO1X/9hyqJVLNh2gGF9LMlratYpXv11ObEnMi9r+1cc5XBl/btC1WlkSqmflFLDrpT11Ael1Gv/xXbMmVn//jZKSyktLsbTxw+dXk/TtqGcSE60WyYlJprW1idhzULakZGWCkBj/ya2ZXQ6PQ46B8rLy8k6nsaetStZ8PE09m9cV+NYkmJjaNOhI3qDAR//JhQXFVJaWmorj408ROeevQHo0rMPcYcjASg0FdDY19Kz07xNW2IjIwDIycwg91QOwa3b1rZaSEuIJzikHXq9Hi9fP0qKizBXiOXokcOEdusBQGj3HiTFRAPg1sgTnd6+0/t0Zia+gUHo9HocnZ0xOjqSk5HeIGOpLPbwYTp06YLBYMA/IJCiwkJKS0ps5a5ubji7uFT5nqkgH1/r/tMmtB2R4QcuafuV999mbUM5kWS//ybHRtO6czcAmoe0IyPNkiw6ubhgdHKqdt3xEQdo0b4DeoOx2mUqSomPo0Voe/R6A439/Ckpsm+nxOgo2ne3tFP77j04esTSTnq9AYAysxmDoyMeXl7ojUY8vBpbbsxLSjAYjTjodTWsFTgaF0Pr9tUfS3FRh+jUw3Isde7Zh3jrsRTQLJhCUz6aplFkMuHm0QiD0ZFGjRtTUlJCaUkxBqMjOl3NY4Fz7dTIxxedXk9QmxBOJh+1WyYt7ggtO3YBoGmbUDKPpdrKondtZ9GMd9m+fAma9YGDq0cjdLraDzCpy+NJbzTi4ellaye90YhDLWJKTYijRWg7yz7j6181lugoWyztuvUkyfqQzKdJAAA6vQ6lHFBKkRofi9HJiUVff8737021xV27eml/rl6KKtdLFKHdrrLG0oOk2AucZ7IyKS0uYcX8n/jhvbeI2rOrVrHAv3NsJx2JIjHqIPM/nsbmZX/UOJbk+FhateuAXm/A28+f4kp1E384ko49egHQqUdvEqIt7RQXeZDo8P3MnDqZlYsWVFnv3s0b6XndgBrHUdm/cVzVRlJcDK3bVTzPFFU5z9jqpWdv23nmrMj9ewjt3BWD0Uh2RgZNmjZHp9fj5OyCo6MjWSdrd21KiY+lZYV2KikqrHQOjqRDd0s8Ha/qxdEjUbayZb/8yFdTXyV8x1bAcv9QUlzMX3PnMOvtyUTs2l67yqlAr3PA0aAnIzcfc3k5h4+dpE0TH7tlyjXN9n8XRwPJWacAMJWUUlRqvuRti4btyk0tG67/JIH7LxQW5ONY4Qbb0dmFwvx8u2WKCgpwcjn3pLLyiT4lJpqCM6dp1rYdprxcMo+l0nPQzdw/8RUO795B1olj1ERBfj4urueeRDq7uGLKy7P9bMrPsz2pdHZ1pcBa5ubhQdrRRMylpRw5GI4p3/L5P4sWcMuw+2u07cpMBfl2T2ednF0oLDhXL4X5+Thb68TZxbVKnVXU2L8J6SnJFBWayM3JIT01+YLLX8mxVJaXl4ubm7vtZxc3N/Lyci/6PfdGnhyNj6O0tJSD+/aSl3vx75xPYUE+ThX3XxdXCgvy7JYpKsjHqUL91bTHMXLXNjr2vrZWsdi1k4sLpgp1a8rPx9m6fzu5uNr2U4Blc7/no0lP4eHphaOzCzqdjtCu3Znx4rN88sLT9B54oy3RqwlTfr7dU33nStsrqOZY6n39IL56ZwpvP/M4DjodwW3aotPp6HhVT95+5nHemjCOa2+6Bb2h5rGApQ0cnSudZwoqnWdMBTie5zzTukt3RrzxLsMmvkJeTjZH9u6s1bYrq8vjSafT0bZrdz5/+Vk+e/lZeg24Ab2+5glcYX4+Ti7n2qlyLBVjdXK1358ANi37ky5XX4veYCD31CnSU5K594lnuPfxp/nrh2/QKtwUXjSWgny787yjS6V6qXAdqLz/VpZ7+hQnUo5y60OP8tDEF9nw16Iq7V2zeOr22M48lkpwu4489Pxkso8fIyHyYI1iMeXn2Y5dAGcXFwryK16bzrWTs6srBfmW89nxlGRCOnVhwpvTSE9LJTp8v+07ZWVlHN6/l869+9QohvOp7+Oq4jUZLPViyq90zbbWm7OLq12dAeypkMD6NgngWFIihSYTp7OzOZacVGX5i8ZTkFfp2HatEo+z29nj6Vw8tz88imenfcSoF15j/dI/yD6ZTu6pHI4nHeXOEaN57IXJrF68sMrxV1PuTo4UFJ97sFlQXIy7s2OV5Vr5efPeg4MZO6gv4Uk1u28S/3+7rHfglFIjgBcADYgAyoD+SqlJQBPgJU3TFluXfRG4D3AE/tQ07c3zrUPTtOGVtvEO0Ax4TNO0KmdgpdQU4HbAGdgOPK5pmqaUagN8A/ha47pX07QEpdRLwHCgHFipadorSqlu1mVdgATrtk4ppTYCL2iatlcp5QPs1TSthVJqJHCHdfnW1t/nJaXU+4CzUiociNI07eHLqd/6sm/DWmL278bL15/iwkLb58WFhXYXRLCc6IpM58ZdK4dzzwQy0lLY9Oci7pkwCaUUTi6uuHl64dcsGLA8Fc08loZPQNBFY3J1c8NUYYhMoakAF/eKyYE7poJ8vPGn0FSAq/XC8fATz/DHT3PQAJ8mTWjk5c2x5CRAEdC0eW2q5dy2XN04VuF3Lio02V/A3dxsdVJkMl1wKI6LmxuD7rqX+Z9+gKtHI5o0C8bdq3GDjKUyd3cPCiolKe7uHhf93oQXXuKHWV+iAU0CAmns43PR71S0d/0ay/7rV3n/ta8bACdXN7v918Hh4s+0ikwFZKSl0DykXY1jcnZ1o7BiO5lMdjc3Lm5uFJoK8KJqG97x6GgGPzKS+Z9/RGxEON7+TYjcs4sXZ3yFppUze9qbdOjRm0aNvWsUi4ubm91ws0JTAS4VEm3XSsfS2Th//fZLXvrgU7x8fPn12y/Zv30LQS1aEb5zO2/N+h6tvJzPprxC19598fS+eJuFb1pH3IG9ePr6UVJU6TzjUuk84+JKcWHV80zF5UJ69CY5OpL2va+pUT2cT10eT1npxzm8ZxeTPv4STdP4/t03aX9Vbzwa1+yYsmzrXDtVjsXF9Vx5scmEc4X96cDWTZxMS+E+69AuF1c3mrcNxcnZBSdnF1zc3CnIy8XNo1HNYnG1j6XYVKleKlwHikwmu4dtlbm4uuHftDke1nNLk+YtyD6ZTtMK70dV5988tp1cXGndsQtKKVp27ExmWiqtO138PSsXN3cKTRWPJxOulR5cnS0vNBXg4mopc3Vzp123q1BK0a5rd44nJ9He2osZc/AALUJCcXRyvuj2K7tSjisXN/dK5xmT3XmmYnlRpTJTQT7Hk5NoYx2i7uruzm33PcTs99/G3dOToOAWNKrltcnF1d3uFZCiwoLzx+NrOcfb2snDw1Ye0rkrx1OS8A9qRpPmwbZzblBwS7LSj9O8TUiN47m1W3uuDgkm/XQeLhWGTLoajeQXFldZPjEjm1d//Yc2TXwYO6gvLy9YXqvfv0GRSUxq5JJ74JRSHYHJwCBN07oCz1qLAoB+wBDgfeuyNwNtgd5AN6CHUqr/BdZxdhsfAn7AqPMlb1ZfaprWS9O0TliSuLNvic8HvrKu9xrghFLqVmAo0Mf6+YfWZecBL2ua1gU4BLxZgyroBtwPdAbuV0o10zTtFaBQ07Ru50velFLjlFJ7lVJ7F6an1WAT9aPHwJt46PnJ3DpiDAajkdycLMrKzByLjyWwhf17A81C2pEYZRmWmJYQi1/TZgCcyjjJyrlzuGPsU7aTpN5gwNPHl9ycbMAyQYqXn3+NYmoREkrCkSjKzGZyMjNwdHLGUOFJf9uOnYjctxeAyH17aWudfCOweTBPT3mHJ1+dgikvj45X9SQlIY6Tx48x8+032L15A2v/WkJSXEyN66dp6zYkxx6hzGzmdHYWRkcnu16HlqEdiI2wPE2NjdhPi4u8Z9exV19Gv/YWgx8ZhcHoWKOb3ysxlspCOnTg8KEIzGYzmSfTcXJ2xmC8+JDD5i1bMfWjGbz+7gfk5eXSo0/fWm2356CbefiF17ltxFj0RiNnsrMoM5tJi48loNL+2zykHYnWJ+2W/ffiSX303l20u6q33cOKi/5ObdqSHBttaaesTIxOldqpXUdirE/gY8L306q9pZ3ODjnV6XQYHZ0wGo2gaThav28wOqLX62v1DlzLthc+ltp06ETUfsuxFLX/3LHk4OBgu0l282hkSc41DUdny/eNjo7oDYYavwPX7fobuXfiK9z08GPojUZyc7IpKzNzPCGOJhUmyABo2jaUJOt55nhiHL5BlvNMxRv01JhovPwCalwP51OXx5OmgaOzs7WdjJa6qcU7cM1atz0XS1ZmlVhatOtAzEHL8OKYgwdoaY0let8eInZsYdj4p21JS9PWbclKP05ZWRnFhYUU5ObW6t2qpq3bkBIXc65enKrGEhtxwFovB2gRWn29NPZvQmlJMcWFhZSVlZF5PK3G55l/89huHtqeE9YhhunJR2t8bQpuE8LRI4cpM5s5lZVpOzbPat2hE9H79wEQvX8fbTp0tH2emmCZrCI1Id429BVg75ZN9LjE4ZNXynHVom0oCdZ6sZxnnKqeZw5YzzMH9tK2wvvEB7ZvpVvfa+yS7m59r+XZt9/nnlHjMDg62l6NqKnmbUI4GhNtayejk7NdO7Vq35Ej4ZZ2OhK+j1btLe10ttfSbC4lKfYIvk0C8WnShNLiYoqs+/DJY6l4+dQunpXh0UxZtIpZa7ZRUmrGx90VnYOiXZA/cen2r8gYKgxLLyguodgswyYFqNoMo7D7olJPA000TZtc4bOfgLWaps23/pynaZq7UupjYBhw2rqoG/Aelh4su3VUWE93YJemaeMuEsc9wEvWdTUGZgJfAdGapjWttOwnwBFN076r8Fkj4JCmac2tP7cGftc07aqL9MBdq2naWOt3VgLTNU3bqpTK1zTtolfGuH7/u7SKt/J7aSJOnTugDAZKEpM48dpbl7yuLdPeqbbsRFIiYYt+QdM0uvYbQJdrrwfg7+9ncfvoJwHY9s9fHI2KQKfTc+uIMXj6+rH4y0/ITj9u68Hpc/NgWnfuxsnUZMJ++5mysjKCQzvQf+i9dttr4Vv9U7XtYWvYtm41oLhv9OM46ByIPhjOzUPvoaS4mJ+/+oxT2dl4eXszYsJzGIxG1i37k0N7Le9Y3HTnPXSyjrk/a/nC+fgGBJx3FsqsvIIqn521b/N69m1aj0Jx2yMjcXDQkRAVQb/b7rDMDjdnFrmncvDwasxd1hkFI3fvYM+GdaTGx9CsTSg33H0fzduGsvjbL8nNycbgaOS2h0fhXeH9wZqoj1i6BAfWKLZ1K5az5p/lKKUYM+FZdDod4fv2cPcDD3EsNYVvPv2EhNgYglu2ov+NN3HrnXexdNFCdm/fBsBdDzxEz75XX3AbO2OTqi07kZTI2t9+Bg269htA136W/XfpnFncOcay/25d/ieJ1v33tkfH4uXrR/bJE6yZ/xPpKUfxCWxKx97XcNWAGwH45aN3uPnBkbaHFRV5uFT/3tzeTWHs2RiGQjFk+CgcdDriD0XQf8idlJYUs3j2LHJzsvFo7M2wcU9hMBr55fOPMOXlUV5WRnBoO261Tj6x6rf5JB6OREOjdftO3PLAI+fdpofz+Z/gbw9bw/aw1SgUwx6zHEtHDoZzk/VY+uWrz2wzCg63Hkv7t29h3dIl6A0GXFxdGTnxJZycnVn6y0/ERkagoRHSsQtDh4867zbjTmRUWzcnk4+y8Y8FltnyrulPx6stMwqu/Olbbh35OAC7Vi4jKfoQOp2emx4eRSMfP7Yt+4OUmMM46Bzw8mvCDQ8+ik6nJ3b/Hg5t28iJxHgCWrXh6sFDCWx17n1Xb/fqe87q8nhas2iB5V0aTaNl+47cfF/VQRlKVf+0ed+m9ezdFIZSisEPj8JB50B8ZATXDbbsM0vmfG3bZ+62xvL22EfwCQiyved13/hn8GjszYFtm9izfi3lZWX0HzKUDj3PPzyvuqGG+zdvYN9my8QOtz1ctV7++v5rzpzKoZFXY4ZaZ8SM3L2DvRvXkRofS7M2IQy6y1Iv8ZERbFy6mLKyMrr3u57eg24+7zYLS0rP+znU/bF9JjuLFfPmUGYuxcuvCbcNH233kMbf0/18YQCwc/06dq63zG5418gxOOh0xEaEM+iOuykpKebXWTM5k5NNo8bePPSkZbbFnMwMfvv2K0pLS/BtEsj9jz+Fg4MDxUWFfPD8M0z+4ptq3ydNysipNpaK6vq4qk5r//Mn4DvWr2VH2BpQimGjxuLgoONIRDg33nk3JcXFzP/6C05nZ+Hp7WOb7RbgsymvcN/o8QQGt7Cta97MGZzOzsJgdGTYqLH4Bpz/GlRsrn5yk90b1rFrw1qUUtw5YgwOOgdiDx1k4O13UVpSzG/ffMmZnCwaNfbhfutsobPfm0pJURFlZWVc1e96rrPOJBsbEc6aJb9RZjbT6/pBXHPTrVW29+OGmg09be3vzeiBllko1x2KZX1kHAATb+vPZys206NlU4b27kx5ueW28ceNu0nKzCHAy4NxN1xNa39vUrJOseVIIqsPVv8wesnzoxpE19bRYSMu6/64rrVcPO+KrLfLSeCeAfw0TXu9wmc/AcsrDJvM1zTNzZo4xWqa9u3F1lFhPWYsSdxNmqad92yllHICkoGemqalKqWmWotmAIfPk8Cd/XxOhc8ulMCtA17TNG23UqopsLVCAtdT07QJ1u8sBz7WNG3jf5XA1aULJXD/tWgTc2IAACAASURBVAslcP+1CyVw/9fVNIH7L1wogfuvXSiBqw/VJXD14UIJ3H/tQgncf+1CCVx9uJyZZ+vahRK4/9qFErj/Wk0TuP9KdQlcfbhQAvdfq2kC919pKAlc0r2PXjH3xwAtfp97Rdbb5UxiEgbcp5TyBlBKXejOezXwmFLKzbpskFLK7yLrWIVlCOY/Sqnqzpxn75ayrOseBqBpWi6QppQaal2vo1LKBVhjjcPl7PY0TTsDnFJKXWdd13Bgk/X/SUAP6/9rOitmqVKqdm/xCyGEEEIIIUQNXPIkJpqmRSmlpgOblFJlQLVzfWuatkYp1R7YYX3amA88Us06Rlb43u/W5G2ZUuo2TdMKK633tFLqOyzvrSUBeyoUDwe+VUq9DZRimcRklXXCkr1KqRJgBZZZIx8FvrEmdonA2TFAHwOLlFLDgfXUzGwgQim1v6FOYiKEEEIIIYS4Ml3yEEpxeWQI5fnJEMqGQYZQnp8MoayeDKE8PxlCWT0ZQnl+MoSyejKEsnoNZgjlfaOumPtjgBaLfrwi603+DpwQQgghhBBCNBCX9Xfg/ktKqT+BlpU+flnTtNX1EY8QQgghhBBC/NcaTAKnadpd9R2DEEIIIYQQ4l8if8i7RmQIpRBCCCGEEEI0EJLACSGEEEIIIUQD0WCGUAohhBBCCCH+/6UcpG+pJqSWhBBCCCGEEKKBkAROCCGEEEIIIRoIGUIphBBCCCGEqH9KZqGsCemBE0IIIYQQQogGQhI4IYQQQgghhGggZAhlPdky7Z36DsHmutffqO8QbE5+/219h2CTkJ5V3yHYeTznRH2HYLPbxam+Q7Bxd75yYvFydanvEOykZp+u7xBs/D3d6zsEmwVb9tZ3CDa3dOtQ3yHYcXY01HcINnEnMus7BBt1BQ3rikpNr+8Q7Fwd2rK+Q7ApLjXXdwg2Hw0fWt8hNExX0LF2JZMeOCGEEEIIIYRoIKQHTgghhBBCCFH/5O/A1YjUkhBCCCGEEEI0EJLACSGEEEIIIUQDIUMohRBCCCGEEPXuSpow6EomPXBCCCGEEEII0UBIAieEEEIIIYQQDYQMoRRCCCGEEELUPwcZQlkT0gMnhBBCCCGEEA2EJHBCCCGEEEII0UDIEEohhBBCCCFE/VPSt1QTUktCCCGEEEII0UBIAieEEEIIIYQQDUSDHEKplCoDDgEGwAzMBT7TNK28jtY/Elijadpx689zgBmaph2ui/VfTHpKEusWzkPTNLpdN5DO1/SvssyOFctIiAxHp9dz24ixNPLxZdfq5cTs34ODTod/sxbc+MBwlFKY8vNYu2AupvxcHBx03D/x5TqNN/CT6TiGtOH04r84NffXOl13dbaGrWHz6hUoFA89/iTBrdvayuKjo5g36wtOHj/Ge9/+SGMfXwC+/mAap7KzKC8vZ8CtQ+h3w811Eot/I3du7hICQHjycQ6lnLArN+p1PHBNd7zdXVhzMJaotHQAgho34pau7Wjs5sw3a3eQV1R82bH8c+ggf4XvRyl4/sZbCG0SYFf+885t7EpKpLxc47Frr6NncEsm/7WYjLw8yrVy7u7ek8Gdu15WDLs2hLF93WqUgnsee5xmrVrbykpLSvj165mcysrEy8eXB594GoPRyC8zP+VUViYAx5KP8siE52jashU/fzGD8vJyNK2cu0eNpXmFdq6JfZs3sGfDWlCK20eMJqhFK7tYlsyZxensLDy9fbh7zJMYjEYWzPyE3JxsysvL6XPD/+jRfyAAy+bO4djRBMrLy+l36+10vbrfZdXTWdvXr2XrmlWg4IExT9C8dRtbWcKRw8z/eiYZJ47zzqzv8fLxuaxtnUg+ypoFc9HQ6N5/EF2vvb7KMtv++Yv4iHB0Bj1DRj6Op48v5tISlv80m9ycbDwaezNk5Dj0BiOlxcWs/nUuZ7IyKS8vZ9hTz4EGi2d9altfWkIsE2d8jZtT42rj2rd5A7s3rEVhbaeW9u30x5xZnMnKopGPD/dY22nFgnmkJcZTWlJCy3YduO2hEbbvmPLz+GjSBO4YMZru/aqePy9FSz9vRg/sg1KKtRExbDwcb1ce4OnBhFuuw1xWhs7Bge/CdpCcdeqytnkyJYl1v/0MaHTtN5BOV19XZZmdK5eREHkQvV7PLSPG0Mjbl+g9OzmwaS1KOWB0cmLIY0/i6OxMaUkxYb/9wpnsTLTycoY+/ixOrq4XjCF86yb2bwoDpbj14ZEEBLe0lZlLS1j242zOZGfRyNuHO0ZZ9ovTWZks++EbzGYzbbt057ohQwH4+aPplJeXUVJUxNX/G0ynvtcCEB95kO0r/0bTNEK6XsXV/xtcq3oK9PJgyFUdQCn2JKRw4Ogxu3KjXseoAb3x9XDj731RHEw+biu7rl0r2gb4oJRiQ2Q8iRnZtdo2QHryUdb8OhcN6H7dQLqc97haSsKhA+j0BgaPHIenjy9p8bGs/OV7ck6e5Inpn+DR2BuAfRvWsmfdKjRN44l3Z9Q6nrOaentyb9+uKGBbzFF2xafYlbcP8uO27h0wl5VRYi5j3ua9FBSX0CU4kDt6dMDbzZXn5i295O1XtHH1SsJWLAcUoyY8Q6u2IbaymKhIvvvsE9KPpfH53Pl4+/oBUFxUxI9ffUFmejrl5WU8P3Uabu7udRLPlrWr2bDqHxSK4U9MoEWbc9eWuMNR/DjzU04eP8ZH38+z3UOcNf2lSTQJDGL0xOfrJJa1K5az6m9LPT8x8XnahLazlf0+/2e2bdqATqejTUgo4yc+j1KKeXO+JWzVSoKaNuXdz76skziuGPKHvGukQSZwQKGmad0AlFJ+wAKgEfBmTVeglNJpmlZWTfFIIBI4DqBp2pjLiraW1i2cx5DHxuPu2Zif359K26497C6y2enHSY45zCMvTSE19ggb//yNO8dOoG23nvT53xAA/po9k+Qjh2nRviNhv/3CNUOG4hvY9F+JN+P9T3Hu2R293+XdXNZUQX4eYX8vZfJHn3EqO5s5n37Iqx+cu8gFNm/Bax9+xhfvTLH73t3DR+EfGERpSQlTJoyjz3UDMBiNlx3PzV1CWLYvirzCYh69vidxJzIpKjXbykvLyvljVwTdWwbZfS8rN595m/dy79WXlzCdlVtUyKJ9u5kz/DEy83J5a/lSvn1kpK18R0I8+cXFfPnAcLvvje8/kGaNvSk2m3n4+2+4sX1HHPWXdmow5eezeeXfPDf9I87kZPPzzE+ZOO0DW/mujWH4BwUx4tnnWfX7QnZtDKPfzbfyyNPPAWAuLWX6s08Q2qUb5tJSRk56CfdGnqSnpvDb7Fk8+877NY6lsCCfHWtWMH7qu+Tm5PD7tzN5/I1ptvL9WzbgGxjE/U9OJOzP39m/ZQN9bvgfN9/7ED5NAigtKeHzVyfRpe+1ZJ9M5+SxVJ6Y+h7FhYXMfP2FOkngCvLz2PDPMl5+fwanc7L58bOPefG9j23lgc2Ceen9GXw1feplbwtgzYK53DnmSdy9GvPju1MI6dYDZ1c3W3nWiWMkHYni0VenkhIbzYY/fuWux5/h4LbNeDcJZOjYCWz5ewkHt22mx4Ab2fL3Ejr06kurjl3stjP8pTcAOJYYz+ali+22UVlhQT7bV6/gibcs7bTom5mMn1KpnQKCeKByO933IHq9AYDZ06ZwMi0V/6bNANi47E+CQ0LrpM7OGj2wD1+s3ExOvol3HxzCnoQUCopLbOUnz+QxeeE/AHRqFsCwvl35ZPnGy9rmut9+ZvCo8bh7evHLh2/RpstV570WPPziG6TGHWHzn4u4fcxThHTvSftefQHY+vcfRO3axlUDbmT7P38R2qM3LTt0rtH2Cwvy2b1uFaNff4fcUzn89d0sRr021VYevnUTPk0CuXvcBDYt+4PwrZvoOfAmwhb/yvVD7yU4pB0/fzSd9j164RMQxEPPvYxOr6e40MS3b75Cp77XYsrLZc+61Tz83CvoLvG8M+SqDvy+8yC5hUWMv/EaotNO2p2DzWXlzN+6j95tgu2+FxLgi5NRzw8bdl/Sds9a8+tc7hhtOa5+eu9N2nbriXOFdso6cZzkmChGvGI9rpYs5K5xT+MT2JRHX3mLRTM/tltfaI/edLtuILOnvHRZcd3btyvzNu3htKmQF4YMJCLlBIUlpbby9NN5fL5iM+bycq5r14oBHdvwz/7DJKRn8f7S9Uy+68bL2v5Z+Xl5rPxrCdO/mEVOVhZffjCdtyskHc1atGDaF1/xweuv2n1v8c9zufr6gXTt2atO4jirIC+PNcv+5M0ZMzmVncU3H7/PGx9/bisPCg5myoyZzJg6ucp3D+zaibOLS53FkpeXy7LFi5jx7fdkZ2by8bSpfDxrtq38mv7Xc+/Dlmv2u1Mmc3DfXrr17MWQofdw021DmPnhe3UWi2hYGvwQSk3TMoBxwARlMVIpZTszKKWWK6UGWP+fr5R6Wym1C7haKTVFKbVHKRWplJpt/f4woCcwXykVrpRyVkptVEr1tK7jQaXUIet3PqiwnXyl1HSl1EGl1E6llP+l/D7m0lJKi4vx9PFDp9fTtG0oJ5IT7ZZJiYmmtbWXpFlIOzLSUgFo7N/EtoxOp8dB50B5eTlZx9PYs3YlCz6exv6N6y4lrAvHnJlV5+u8kMSYI7Tt2Am9wYBvkyYUFxVSWnruRsrF1RUnZ+cq3/MPtCRQOp0O5eAAdfCQR+egMOh1nDEVUa5ppGafIcDTw24ZTdPsbvTOKjaXUVpW3TOE2os6foxuTZtj0OkI9PSisLSEEvO5m5iwI4cpMZuZsPBn3lr+F/nFRQA0sz711Ts44KDUZVVLclwMrdp1RG8w4O3fhJKiIsyl524Y4qMi6djDcjHu1LMXCYej7H+H/XsI6dwVg9GIs6sr7o08AdDp9TjodLWKJTUhjuDQ9uj1Bhr7+VeJJTE6inbdegDQvnsPjsZEA+Bj7bXU6XUoB4VSCg8vL/R6PWVmM8VFhRdMSGojKTaGNu0t9eXj34TioiJKK8ToXM2+fCnMpaWUlBTj6Ws5tzRvG8qJJPtzS3JMNG06dwegeUh7TqZantinxBymbderAGjb9SpSY48AcDQ6koTIg/z84TtsWrq4yjYjd26jU98LJ7op8XG0uEg7te9eoZ2OWNrpbPJWZjZjcHTEw8sLgNNZmeSdPkXTlq2pK3qdA44GPRm5+ZjLy4k+dpI2TewfWJVrmu3/zkYDyZmX1/tmLi2ltKQYTx9fy7WgTdVrQWrsEVp37gZAs7btyDhmaa+KiVBpSQk+1nNf8pEojh6OYOGMd9n695KLxnAsMZ7mIe3Q6fV4+fpRUmzfNskx0bTtatlfQrpeRYp1v0hPSSY4xNKL0LZrd5JjjtjFVVJcbHugGBdxAGdXNxZ+8RHzZ7xHxrHUWtWTzsEBo17PqYJCyso1kjJzaOrtabdMuaaRX1T1HNypWQB6Bx2PDezNsL5dcTTUPoE0l5ZSUnzuuGrWNpQTSQl2y6TEHLY7rjKsx5WTiwtGJ6cq63TzaHTJyexZegcHHPU6svNNlJVrxJ/MItjHy26ZUwWFmMstA5fM5eWUW/9fUFyCuaxOBjQBEH/kMO07dUFvMOAXEEBRYSGlJRWv2W44OVdNiiIP7OPg3t289fyzLJr7Y53FkxATTWjHztZ7iADrebdyPFXPu+Xl5YQtX8qNQ+6os1hiDkfRsWs3DAYDTQIDq9RNULPmtv8b9Hp01utgYx8fHKSn6v+0Bp/AAWialojld/G7yKKuQKSmaX00TdsKfKlpWi9N0zoBzsAQTdMWA3uBhzVN66ZpWuHZLyulAoEPgEFAN6CXUmpohXXv1DStK7AZGHspv0thQT6OFZ7uODq7UJifb7dMUUEBTi7nnu5p5fYn2pSYaArOnKZZ23aY8nLJPJZKz0E3c//EVzi8ewdZJ+yHlzQ0Bfl5uFa4gXZ2caUgL6/G3//n94X07j8Ag+Hye9+cjQa7J73FpaU4Gw2Xvd5LkVtUiHuFmwFXR0dyi2y7L1n5eSil+PKB4XQICGLejm1235+7Yys3te+I8TJuHAry83FxO7dvOrm4UJB/rm1M+Xm25MfZxRVTvn277d28kZ7X2Q8/Ki8rY/H333Lz3ffWKhZTfr7dU3BHZxdMBeeOpcKCfFssTi6uFFaKZeOyJXTt2w+9wYCzqxve/gHMePEZZk5+gYF33lOrWKpTkJ+Hi1vFfdkFUy325dooLMi3u0lydHE9z7kl366H5+yo9MIK5xwnZxcKCywxZh5LpUW7jjzy4utkHU8jIfKg7bvlZWXER+wntHvPi8ZVsZ2cXFwwVYjL0o7n2qniPrNs7vd8NOkpPDy9cLT+bmF//l5n7XOWu5MjpgoPYQqKi3FzcqyyXCs/b6Y/MJgxN/TlYPLlnWeLKreXswtFBedpr2quBRHbNvHjO6+RFheDT4Algcs6nkZwaAfuf+5VstOPcTQq4oIxFFa61jg6O1N4gWPobLtVfJuh4v5SXl7OT++/xTdTXiLUmpTnnT5FTkY6DzzzIjcMe4jlc+fUoHbOcTEaKKyQVBaVmnGp4TnYw9kRDY0fNuwmLfs017evfdJfWKkNnM5zzbbU47m2rHzN/je4OhrtetsKS0pxdTr/Nc/dyZHrO7Riy5Gj/0os+Xl5uLqfO8+5uLqRX4PzXErSUTp2686Ujz8jLTmJ8D276iwe+/Nuze4htq5bQ49r+9XJyJ2z8nJz7YaFuri6kZebW2W5iAP7yMnOplO37nW27SuVclBX1L8rVUMdQnk+NanlMuCPCj8PVEq9BLgAjYEo4O8LfL8XsFHTtEwApdR8oD/wF1ACLLcutw+4qUqASo3D0lvIiEmvcP3td9nK9m1YS8z+3Xj5+lNceO6mu7iwsMo7Ck6urhSZTOfW63AuD89IS2HTn4u4Z8IklFI4ubji5umFXzPL0JHmIe3IPJZmu6A3RK5u7vY34qYCXGs4Ln77+rUcS0li3AuvXnzhC+jRsimhQX6cyjfZDTd0NOjtbib+Sx5OzuQVFdl+LiguxsPp3FNED2dn+lrfR+vbqjWfrlttK1sReZDErEzevuPuy4rB1c2NlIIC289FJhOubhUuTm7uFFrLC00mu4uoqSCf48lJtO7QyW6dv82eRccevQjt0q1Wsbi4udkdJ8WFJlwqJv6ubhSaCvACigpNdr1q+7du5GRaKvc/ORGwvKOTeyqH5z+ZSZHJxOxpbxDSpTt6w+Ul664V6gOsdVJH73ictWf9ao7s3Y2Xnz/Fhfb1UfXc4kZxxXOLdTpnZ1dXikwFgC9FhSaczt60u7rRulNXlFK06tiVjNQUWneyjA5IjIogqHXb8/YwVGRph3PbLKq0X7i4Vd9Odzw6msGPjGT+5x8RGxGOp7c3oPALqpvh4rd0a8/VbYM5cToPF8dzN20ujkbyz/POamJGNpMX/kObJj6MHtSXVxcsr7LMxezfuJbY/Xvw9LVvr5Jq2svSLhYVrwVdrr2eLtdez641/7B77QoG3P0ATi6utOzQBaUULdt3JuNYKi0rDX+tyNnVleIK6y8utO99drZt35fiQpMtEVcVpgGv2GYODg6MfOVNTPl5zHn7dTr0uhpnVzdatO+ITq+nSfNgTHlVb17Pp2/bYDo2a0JOngkng/052FRSs3OwqaSUuBOWd29jT2Ra3qOrob3r13Bk3/mOq6o99M6urhQVnv+aXdf6t29F9xZBZOYW4FQhkXU2GuweQpzlZNAzelAfft124Lz7dF1wc3enoOJDmYL8Gr3L5ubuTrdevVFK0bVnL5ITE+nWq0+dxGOyO+9e/B6ipKSE7RvDePGd94mNOnTZMZzl7uFRpW7cPexH8RyNj+Onb7/mzfc/Rkmvm7D6/6IHTinVCktyloFlUpOKv1fFu4eis++9KaWcgFnAME3TOgPfVVr2vJu6QFmpptnG0JRxnuRY07TZmqb11DStZ8XkDaDHwJt46PnJ3DpiDAajkdycLMrKzByLjyWwhf1TwWYh7Ui0PjlNS4jFz/rex6mMk6ycO4c7xj6Fi/WmWW8w4OnjS26O5cXs9JQkvPwuaXTnFaNVaDvioqMwm81kZ2bg6ORco960Azu3s2vTBsY89xIOl3kB3Xc0jQVb97My/AjmsjI8nB1xUIqmjT05capmNyB1rWNgEBHHUjGXlZGeewZng9GuN61782COpFsmWDly4jhNrUPONsfFsOZwFG8OGXrZQzKC24aSeCSaMrOZnMxMjE5OdklOmw4dOXxgLwCHD+y1S9bCt2+la99r7Npm6bwf8fDyov+tQ2odS7PWbUmOtcRyOqtqLC3bdST24H4AYg/up2U7y83b4X27Obh9K/eOf9oWi6ZZbsIcHHQ4OjlTZjbbhhtdjhYhocRHR1nrKwNHJycMl5kUVtZr0P8Y/tIbDBk5DoPRkTPZWZSZzaTGxRBYaZhhcEh7EiLDAUiLj8XfOnyneUh7Eg5ZPk84FE7zkPaW5UPb24ZhnkhOtDu31GT4JEDzNhdvp5hwSzvFhO+nVXtLO50dZqTT6TA6OmE0Gjl2NJGsE8f44YNpHNi2hc0rlpKaEFf7SrNaFR7Nm7+v4pu12yguNePj7orOQdE+0J/4dPuh44YKQ3wLikooqdAzXxtXDbiJBya9xi3DR6M3OtquBWkJsQQEV7oWtA219aIdS4jDL8jSXuYKw8GcnF1sPQbNQtqTnmzpZUlPOYqX74UHrQS1akNKXAxlZjNnsrMwOtq3TXBoe+IiLPtFXEQ4waGW/cK/WXNS42MBiD90kOYh7Skzm209T0ZHR/QGA3qDgeDQDqQnJwFwJicbR6eaDRneGZfM9+t38eeeQ5Say2jk4oSDUrTwbUxa9ukareNoRjZBjRtZftfGjcjON13kG+f0HHQzj7z4OoMfHYvBaDx3XMXHEFDpuGoe2p6EQ5be6bT4WPwqDIura5ujE/l85RYWbNtPibkML1dnHJSitb83SZWG9Rp0Doy9oS9rImIue8jvhbRp14GYyEOYzWayMk7i5Oxco16sDl26kRATA0BibAxNAuvmwXPr0PbERkXa4rGcdy8cT2b6CUz5BcyYOpnffviOQ/v3snHVisuOJbRDR6IiDmI2m8k4mV6lbo6npfLp+9N5eeo7NPL0vMCaxP81Sqswbr+hUErla5rmZv2/LzAf2KFp2ptKqX7Ah0A/IAhLr9odmqZtrPQ9TyAGaAHogJ3AYk3Tpiql/sYy6+QG67IbgReAY9blegCngNXATE3TllZa9zAswzFHVvc7/LBxd7UVfyIpkbBFv6BpGl37DbDNaPX397O4ffSTgGWmuKNREeh0em4dMQZPXz8Wf/kJ2enHcfeyzPbW5+bBtO7cjZOpyYT99jNlZWUEh3ag/1D7oWjXvf7GxSv9AvxemohT5w4og4GSxCROvPbWJa/r5Pff1mi5LWtXs2XtShSKB8c+gYNOx+Hw/dxy972kH0vjl29mkhwfT1BwMH36D2Tgbbfz5H130qRpM5ysNwhjn38ZL+/qJ17ZEp1QbVlFTTzduamzZUatg8nHibDOQnlHj44s22d5x2tY3y74uLthLisjNfs0qw/G0NjVmZu7tqOJpztZuQVEpaVzIKn6YVeP55yotuysvyPCWXbwAErBczf8D52DA7uTEnmkzzWUmM28t2o5GXm56B10TBl8J95ubgya8T7B3j44Wy9gU28fip+7xwW3s/sCL5XvXL+WHWFrUQruHjUWBwcdMRHh3HDn3ZQUF/Pr119wOjsbT29vHnryWdvF6osprzBs9HgCg1sAkJIQx6eTX6KV9abQzcODUc+/UmV7Bed5t+WsvZvC2LvRMoPekEdG4aDTER8ZQf/Bd1JaUswf383iTE42jRp7c8/YpzAYjUwd8wi+gYEYHS3Pc+574lncPb1Y8t0ssjPSMZeW0v3a/lxznpnyGrvV/uX2bevWsG3dalBw/+jHcdDpiA4/wM13DePksTR+nT2LlIR4ApsH06v/AK6/peYz9KVWupE9npTI2l/noWGZ4bZbvwEA/PXdlwwdOwGALX8vITEyAp1ez+CRY/Hy9ae0pITlP31L3qkc3L0ac/uox9EbjJzJzuSfuXMwl5bS2L8Jg0eMQTk4UFJUxOypL/Pk9Bm2dxfdqhm+BZZ22rMxDIViyHBrOx2KoP8QSzstnj3LNgPmsHGWdvrl848w5eVRXlZGcGg7bq00Oc+6P37D2z/gvLNQLtiyt8Z1eFZrf29GDbDMQhl2KJb1UZbE8Nlb+/P5ys1c1bIpQ3t1tr0L99PG3SRl5lx0vbd0q77XJz05kbBF8wGNLtdeT+drLNeC5T98zZDHngBg+4q/OBp1CJ1ezy2PjMbT14/tK/4i5Yhl0mQnV1duGT4GJxdXzmRnsXr+D5SVluLl58//Hn6sSm+Qs6P9A4QDWzZwYPMGUIpbHhyBg05HYtQhrrn1dkpLSlj2wzfknsrBw6sxd44ej95g5FTGSZb9NJsys5k2nbvS//a7yT2Vw5JvZ6IcHCgrLaX3Df+zzUK5beUy4g4eoKysjJvue5jmbS0T0BxOTb9o/QEEeTVi8FXtQSn2JaSy72gaYJnE4/edlsRp+HU98GvkTqm5jOSsUyzdG4nOwYG7enWikYszZVo5i3cePO+7cgAhgdUnuyeSElm7cB4a0K3fALpaj6ul333FnWOfAmDr8j9JjDyIg17P4EfH4eXrR3b6CVYv+JH05KP4BDajY59r6DHgRqL37uLA5jDSEuJo2rot/e8YRtM252Zt3FNpNsnqNPf25B7rLJTbY5PYGZcMwKPX92Tupr3c0KktN3UJsT10PHI8g9UHY2jt782t3dvTyq8xiRk5bIlOtJu5s7IxN1590Vg2rFrB+pX/AIqRT05Ap9MRsX8fd9z3AMfTUvn+i085GhdLsxYtuXbQjdx8+51knkxn9oyPKSktISCoKeOee+GiD1+La/jgZNOalWxabbmH0nq00QAAIABJREFUePjxJ9HpdEQe2MfgYfdzIi2NubM+Jyk+jqbBLbh6wCBuGHzuvbfoiHC2rw+76CyUPu41e1d6zT9/s3r5sv/H3n1HR1G1Dxz/3mxJ2TTSSUIILQm9gyJVUUFBELGAgHQVxYKKYu+v5dWfHcWGgoIiiErvvYYklJAEAqRQQwgtvc3vj90s2SSETVCS+D6fcziH3ZndeXLn3pl55t65C8CDj09Bp9MRvXMHQ4eP4JWpT3E0JRkfX/NMmHcNG0GXbjfw1/x5rF+9ktTkJJo0C2PyM89R/wojD5r41asT3XepEx+vVYlJgxkf18pyq6sJXNmfEZiFOeEqVub+5dmYn1HbB/gDr5ZN4Czf8yZwH5AEpALJlgTuLuBtIAe4HlgKPK1pWqRSajgwDXNv3BJN06ZavutvS+CutatN4P5O9iZw14K9Cdy1Yk8Cd61UlsBda5UlcNdadRK4f1LZBK4mVZbAXWvVSeD+KZUlcDWhbAJXk+xN4K6FyhK4a83eBO5asSeBu1bsTeCuBXsTuGtFErjqqa0JXJ18Bk7TtMtOR2cZxnj/ZZa5lnn9IvBiBevNx/ZZud6llv2M+WcLLvvdlolQyk/JJoQQQgghhKjYP/iM6L+JlJIQQgghhBBC1BGSwAkhhBBCCCFEHVEnh1AKIYQQQggh/l3+yZ/Z+DeRUhJCCCGEEEKIOkISOCGEEEIIIYSoI2QIpRBCCCGEEKLmqVo5a3+tIz1wQgghhBBCCFFHSAInhBBCCCGEEHWEDKEUQgghhBBC1DwZQmkX6YETQgghhBBCiDpCEjghhBBCCCGEqCNkCKUQQgghhBCi5skPedtFErgaEurrVdMhWJ369quaDsHKf9yDNR2CVd4TU2o6BBuHeveu6RCsjiYdq+kQaiV/D7eaDsFGQC2KJ6+wqKZDsOrTKqymQ7BqHhxQ0yHYOHQqvaZDsArwdK/pEKyG5F6o6RCsChsF13QINlLTz9V0CFaZubk1HYJVwvG0mg7BRhO/jjUdwr+WUqof8DGgA77RNO2dMstDgB8AT8s6z2matuRqtilprhBCCCGEEEJUkVJKB3wO9AdaAMOUUi3KrPYi8Kumae2B+4Avrna70gMnhBBCCCGEqHGq7s1C2QVI1DTtMIBSai4wCNhfah0NKBlS4AEcv9qNSgInhBBCCCGEEFUXBKSWen0U6FpmnVeBFUqpyYAJ6Hu1G5UhlEIIIYQQQghRhlJqolIqstS/iWVXqeBjWpnXw4CZmqYFA7cBs5RSV5WDSQ+cEEIIIYQQoubVsiGUmqbNAGZUsspRoEGp18GUHyI5Duhn+b6tSiknwAeo9kw30gMnhBBCCCGEEFW3E2imlGqklDJinqTkzzLrpAA3ASilmgNOwOmr2agkcEIIIYQQQghRRZqmFQKPAsuBOMyzTcYqpV5XSt1hWe0pYIJSajcwBxitaVrZYZZVIkMohRBCCCGEEDXPoXYNobSH5TfdlpR57+VS/98P3PB3blN64IQQQgghhBCijpAeOCGEEEIIIUTNu7rJGf9nSCkJIYQQQgghRB0hCZwQQgghhBBC1BEyhFIIIYQQQghR41QdnMSkJkgCVwttXbOSTSuXAYp7xz9ESJOm1mUF+fnM+vxjMtJP4+Xjy8hHHsdgNHI4IY75M7/BQaejdaeu3DL4Lutnsi5e5KVJ47h3/EN07XVjtePatHoFG5YvQaEY/uAkGjZpZl2WGBfLj198wqnjx/jPV9/j5eMLwPR33+TsmXSKi4vp3X8A3W+6pdrbt0fgB2/hGNaUc78t5OwPc/7RbZWo7+lG/3bNUQp2HT5KTLLt7zca9TpG9uiEr5uJJTFx7Ek5YbO8d4smtAkJ5JNlG686lg0rl7F26WJQilEPP0qjpmHWZQf2x/Ldp//HqWNH+e+3s/D29bX57JvPPIl/UBATnnj6qmI4lZLEql9mARptu/eh1fU9yq2zbemfHNq3G71eT79R4/Hw9iVu5zai169EKQeMTk4MGDsJR2dnFs/8iosZZwBIO5rCbaMfpGmb9nUultJqQ1vatnYVW1YtB6W4e+xEGjS2Pc78PP0T63Fm+MOPYTAaiY2OZMkvP6E3GPHy8WXEo09y4dxZfvzkA4qLi9E0jaFjJhBS6u+xx871q9m2egVKKQaPnkBwoyY2sfw64zPOpZ/G08eXeyY+isFoZO70jzmenISTiwuu7h6MemIq2ZkXmfnhO9bPJh2I59Uvf8DF1dXuWNJSk1k/7yc0NFp160WL67qXj3f5IpJi96DT6+l7/1jcvX3Yv20TO5b+iZuXNwC3PjARV896JMXuYduShej1Bty8vLl5xDgcdLoqlQ/AplUrWL98CUrB/RMfoWFT2zrzw+cfc+r4Md6ZMdNaZ754p6TOFNGn/0C69616nTmRfIQVP/+Ahkb7njfS9oZe5dbZvHghiXti0Bn0DBj9IJ4+vhQW5LNo5gwuZJzB3cubAaMnojcYSdwbw4Y/fkNvMODu5c0dYx/GQadj85I/SIjaCUDLLtfT9ZbbK40rLTWZDfN/Bk2jZbeeNO9afj9FrlhM0n7zfrpp2BjcvX0A2LVqCanxsRRrGl1uHUhwWHNWzv6Gi2czADhzLJW+I8bRqFW7KpfXX9u28vuWTQBMvfteIhqE2Cz7euliAry8AHjzgbH4eXry3Hdfk3buHEXFxQzt3pOB111f5e2WOJmSxOq5P6JZjnmtu/Ust87WpX9yeK95f/UfOQEPH1/idm4lat0qlFIYnZ0ZaDnmRa9bReSa5WhaMRPf+KDacVWnjefn5bHwh6/JSDtFcXExo6dMq1JbBojetI5d61ajlKL//WMIDG10absF+fz53VecP5OOh7cPd4x9EIPByNn0NP749iuKCgto1rY9PQfcCUDk2lVEb1qLTqen9+ChNG7RmvSTx1n4zXR0ej3FRUXcPnIcASENqxRjddvY0cQDLJn1LRmnTjLp7Q9xtxx7xP+Gf+0QSqXUOqVUJ8v/lyilPC3/Jl3hc72VUosus+wbpVSLSj47WikVeDVxZ2VeZO3iv3jy9XcY88TT/PrtVzbLt65dRUBQME+/9R7+QUFsXbsKgF+/+YpxU57lqTff4+C+PZw6fsz6mWULfqVJxGXDtjuu1X/9wdS33mf8lGf5ecZ0m+WBIaE8/95HNAlvbvP+kJFjeP69j3j27f+y+JefKcjPv6o4riTtnf8j/Ytv/tFtlNW/XXMW7NjLzHU76dq0IU4G2/sihUXF/LIlmm2JyeU+a3I04u1q+lviyLp4keV//M4L737Iw89MY9b0z22WBzdsyKsfflphXYjevhVnF5e/JY5Vv8zi9jEPcd+Tz7Nr7Qpys7Jslp85eZzkhP3c/8xLdBtwJxt+/xWAsPadGP70Swx76gX8Q0KJ3b4ZgNtHP8h9U57n7sem4uTiQmjzlnUylhK1oS1lZ2ayfulfPPbq24yaPIXfvpths3z7utX4BwXz5Bvv4hcYxPZ1qwFYPPcnxj01jSffeBcHnY74PdE4OTkzdsqzPPnGuwx78FHmz6xa+8vOzGTTssU8/PKbDHvkSRb+YPv5nevX4BcYxCOv/gff+kHsXL/GuuzO0ROY9PJbjHpiKgAurm5MevktJr38FgOGP0Czlm2qfMG3ft5P3DJqAnc99iy7168iN9u2zmScPMHRA3HcPeV5ut42iM1//mZd1uL6Htz1+LPc9fizuHrWA2Db4t+5bdwkhj45DQcHHSnxsVWKB8x1ZtVfC3n27feZMOVZfprxhc3ywJBQXnj/43J15q5RY3jh/Y947j8fsOjX6tWZFT//wKDxkxj5zEvsXL2cnKxMm+XpJ46RFB/LA9Nepecdd7F2vvnG2e7NG/AOCGTUs6/gHRDI7s0bAFi/cB53Pfw4o559BZ1Oz+H9e8nLzWH3pnWMfv51Rk97jaj1q8nPy600rg3zf+aWkeO5c/JU9mxYXW4/nT11gqMH4xj6xDS69LuDrX+Z91Py/r3k5+Qw+NFnGDJ5KsFh5jK7ecR4hkyeyqCHp2B0dqFBeNXb9oXsLOauX8tXjz3JG6PG8P5vv5Zb547ruzHj8SnMeHwKfp6eAEwaMIjvpjzDjMen8O3ypeQVFFR52yVWz/2R28c+xLApL1z2mJeSsJ/7p77MDQOGsH7hLwCEte/M/c+8xPCnX8S/QSix281JaFiHzox95T/Vjgeq38ZXzJ9L2+tu4KEX32DSy29VuS3nZGWyfeUyRj/3CkMmPsrSn2faLI/ZtB6f+oGMff41vAMCidm0HoBV8+bQ586hjHvhdY7ExXL6xDEyL5wnct0qxk57jfuffI6V8+ZQXFxMPV9/xr3wOmOee4U+d97Dhr8WVLl8qtvGfAKDGT3tNYJK3XwT/zvqVAKnlKpWj6GmabdpmnYO8AQqTeCu8D3jLb/lcDmjgatK4JIOJNC0RUv0BgM+/gHk5eZQUOpgfmDfXlp36gJAm05dObh/HwA52Vl4+foBENK0GQf27QEg43QaF85m2Nzhr47DCfE0a9kKvcGAb0BJXJcuBlxMJpycnct9zj8wCACdTodycIB/uGe88HT6P7uBMnQOCqNex7nsHIo0jeT0swR5edisU6xpZOZVfOHUq3kTNiUc/ltiSUyII7xVa/QGA34B9cnNzbG5YHMxuVa4j4qLi1m56A/6Dhx01TEUFhRQkJ+Hp48vOr2e4KbhnEi2/ftSD8TTpLX5znaDZhGkHUsBQKe/1LwL8vPxsdSdEof2RhMS3hK9wVjnYimtNrSlpIMJNIkofZzJtTnOHIzdS8uOnQFo1akLiZbjTP0GIWRnZaFpGrnZ2bi5e+BsMuHmYb4Q1ev16KrYu5SSeIBGES3Q6w14+/mTn5tDYalYDsfto0V7cywtO3TmSKkE6M/Z3/P5q9OI2bqp3PdGbVpPh+7l72RXpqTOeFjqTGCTME4lH7FZ51hiPKEt2wIQ1DSc9GOp1mXxO7Yw7//eZuuiBWjFxQB41Q8iLzsbTdPIy83G2c2tSjGBuc6EWetM/erVGaWqXGcKCwrIz8/D09cPnV5PSLNwTiTZtqHkhDiatjb3QoeENedUqrkNpSTsp1nbDgA0a9uB1APxAPgGBpNrKY/cnGxc3NwxGIy4edajMD+fgoJ89AYjOt3lT/dFhQUU5ufh7m3eT/UbNyMtpcx+OphAaIs2gGU/HT8KwMHonRQWFrDws/dZOetr8nKybT6XFLubBmHN0RsMVSssYF9SEu2bNMWg1xPk40NOXh75ZZKxJTu2M+7//sv0RX9SbKkjIX7mc7dep8NBqWo37UvHPPP+alDBMS/lQByNW5nrb4NmEaQdNdff0se8wvw8fOoHA2By96h0X9ijum08cd8eEnZH88XrL7B8XtVH1Bw9nEjDsAj0ej31fP3Iz8212W5S/H7CLHU0vF0HkhPiADiZkkxDS2If1qY9yQlxnEs/jW9gMDq9HkdnZ4yOjmSknbzUtoC83Bz8S/W42uNq2piTiwtGJ6cql0ut5+BQu/7VUrVuCKVSahTwNKABe4AiIANoD0QppV4GPgVaY47/VU3T/lBKOQPfAy0w/xK6c6nvTAI6Ae8ATZRSMcBKTdOeuUwYrkqp34BWwC5ghKZpmlJqnSW2aOBby3dqwHdAquX1T0qpHOB6TdNyqvr3Z2Vm4mK6dJfJ2cVE9sWLeFiGXGRnXrTehXI2mci6eNEcsLs7R48cJiC4AfG7Y2jT2ZzkLf71Z/oNvZddm65ueF5W5kVMZeLKungRTzu77BfPm0uXnr0xVOOCtzZzMRrJLSi0vs4tKMDZaN+J38vVBaNex6nzmVde2Q5ZFy9icr10cejiYiIz8yL1rrCPNq5aQeduPTAYr37f5GZl4uR8qSfP0dmF3DJ3E3OzMq29E4D1Qhdgz+b17FqzHL3BWG4I1f4dW+jQ++Y6GUtptaEtlT6OmGNwITvzIh71Sh1nLDE6u5jIyjQfZ7r0upHpb72Ck7MLQaGhNkMli4uKmPftV9wy5J6qxZJ1EWfTpV5oJ2cT2ZkXcS8Vi7Oll9rJdCmWgfePweTuTnbmRb5882UaNG6Kt38AAEVFReyPjqT/fSOqFEtudiaOV6wzWZgsCStcqjON27Qnoks3AFbN/pb4yG0079KN5l268cf0/8Po5IRPUAP8QxpRVVkXy+6vqtWZRfPm0LVnnyrXmZyybcjFRE7mFdqQVmz5bBZOLpb95uxCTpZ5v7Xu1oM5H72Lo5Mz/g1CCAxtDECT1u2Y/uJTaJpG99sHmxOKvIp7onKyMjGW20+2PU252VmYPC7dTCtJlrIunMPJZGLwo8+wZ8Nqdq1cQrc7hlrXS4jcRpueN9lZQrYuZGfhVmokg6uzMxeys/GxxNGrTRtu69IVgNdm/8jSyJ3cbnkN8P2KZdzasRPGaiSPYC4Xm/rr4lKuNyc3KwvXCuovwJ7N64hcbTnm3TqgWjFUpLpt/ERqMrcMvY+BI8Yw6+P3iY+JIqJdB7u3m5OZiVPp7VrKw81SX3OyLi13KlW3S+pwyfuZ58/h5RfAydQkcnOyyc/N5WRKMjmWOnc86TBLZn/P+TPp3PvolCqVzdW0MfG/rVYlcEqplsALwA2apqUrpbyAD4EwoK+maUVKqbeBNZqmjVVKeQI7lFKrgAeBbE3T2iil2gBRFWziOaCVpmlXGtjeHmgJHAc2Y/719NK3eNsBQZqmtbLE7alp2jml1KPA05qmRV7m75sITAR48pU3GHD3feXWMbm6kl3qRJSTnYVLqTu2Lq5uZGdl4o0/OdlZmCwn9fsffoz5M79BA3wCAvCo582x5CRAUT+4aneEKmKybLd0XCY77yRvWbOSYylJTHx62lXHUVt0aRJCi2B/MjKzcSx159LJYCAn377hL71bNGFtbOLfFpPJzY3sUgf+7OwsXF0r30f5+flsWbuaqW++Q0Ls3mpvO2rdSg5E7cTT19/mjnZ+TrbNCRTAyeRqM9xJlbrD1eaGXrS5oRfbVyxmx8ol9B5ibiO52VmcPpZKg2YRdSqWitSGtuTi6ma9+DDHkI2Lq1uFy3NLLZv71ec8/Z8PqOfjy9wZnxO9dRPtrzc/ezR3xue07NiJiDZVe27IxeRmc/Gdm5NVcSy+5rJ3MZmXmdzdrcvDWrfleEqSNYE7sCeahs3CcXQq3ytVkd3rV5MYE4mHrx95uZfuu+XnZuNksh225WQy2dSrkjpTkqwAhHXsSnLcPpp36caauT9y79Mv4VbPizVzf+Rg9E6aWXob7GVycyt3XrC3zmxes5Jjyck8+Iz9dWbnmuXER+6gnp9tG8q7TBvKyy5VHpbfcHI2mSxty5fcnEvluPTHbxn7whu4e3mzZNa3xEVuwy+4IfG7dvDIfz5C0zRmvfc6Ye07gdF2/+3ZsJrE3bvw9PEjv3Tbzs3B0aVMXC4m8nIu7UuHUvupYfPWAIQ0b8XG+Zd6dvKyszlz/ChBTcKoDncXE5ml4srMycG9VELnXirGWzp2YlvcfmsCt2j7Ng4dP85bo8dWebtRa1eSELUDTz9/m/qbl5ODc9lyuUz9BWhzQ2/a3NCb7csXsWPFYnrfNazKsVSkum3cxdWN8LYdUEoR3qY9J1KSqpTAObu6knvk0t+am52Nc+mbZyZXci11NzcnG2fL9ZQq9TtkuTnmz7i4utJn8N3M+fh9TO4eBIQ0tCaCgaGNGf/iGxw9nMiS2d8z8eW3rhjb39HGxP+22lYLbgR+0zQtHUDTtAzL+/M0TSuy/P8W4DlLL9o6wAkIAXoCsy2f24O59666dmiadlQz3+aIAULLLD8MNFZKfaqU6gdcsOdLNU2boWlaJ03TOlWUvAGEhoVzKD6WosJCMk6n4ejkjKHU3bhmLVuxb5c5P9y3K5JmLc0nosCQhkx++Q0mTXuZ7IsXadmhEymHDnLq+DE+ff0ldmxYy8qFC0g6mFC1krBoHB7BwbhYCgsLOWON68p3c6O3bWH7+rWMf3Kq9QT6b7DjUAoz1+/kz12xFBQV4eHshINShHh7cizjvF3fUc/kwu3tWzCie0dcnYz0b1u9hKBE0/DmHNi/j8LCQtLTTuHk5HzFXrXTJ0+QnZXJf195gbnfzmDvrkjWLltc5W136H0z9015nn4jx6E3OnIhI52iokKOHjpA/YZNbNZt0CycI7Hm5nns0EH8gsw3GApLDQlzcnaxiT1h1w7C2ne2udioC7FUpDa0pdBm4RyK31/qOONkc5xp2qIVsdHm40xsdCTNWrQCzBfBJT1zru4e1jvlC3/8Dvd6XvTqP7DKsYQ0DeNIQhxFhYWcTT+N0cnZZvha4+YtiY/ZBUB8zC4aW547LOlZKCwsIOlAPL4Bl0avV3X4ZNteN3HX48/Sd/gYDAYjFzPOUFRUyPFDB/FvaNtjFtQ0nOT95psdJw4n4hPUAMDmAiv1QBz1/MzJpHJQOFou4p1dXcv1FNmjcXgEBy1t+0xa1evMhClVqzOdb7yVkVNfYsDoiRiMjpw/k05RYSGpBxMIbGTbhhqGNefQvhgAjiYesA4hCwlrzqG95vcP7Y0hxDIkTTk4WJNdF7eSGwUajpb9bjAa0RsMFFTwDFybnjcxZPJUbhw2Gr3R8dJ+Olx+PwU2DSM5zrKfjiTiE2geEhjUNJy0lCQA0lKT8LA8egBwMGYnTdp2qHbbbhUaSsyhQxQWFXEyIwNnR0eb3rSLpepI5IEEGvr5A7Buz26W79rJ66NGV6ttd+hzM8OeeoH+I8djMBgvHfMSD1A/1HZ/hTSL4LD1mHcAv2Bz/S19zHN0cUFvdKxyHJdT3TbepHkrjh423+RMPZyIT0D9Km03uHFTUg4mUFRYyLkz6RidnGy2GxrenIN7ogE4uCeahpZnSAMahJBiuVZK3BNjfb9Fp66Mee4V+g9/AIPRiKe3j81QZicXF7tHsvwdbexfS6na9a+WUpqm1XQMVkqpxwA/TdNeLPXeTGCRpmm/WV7vAoZrmpZQ5rMLgY81TVtreR0FTNQ0LbLUEEpXy3e1qiSG3ph70QZYXn8GRGqaNrNkCKXlO12BWzE/93ba0iNoXX6lv3VNbOJlC37L6hVsXrUcUNwz7kEcdA7E7Y7hlsF3kZ+Xx6zPP+LsmTPU8/Zm1KNPYjAaWfXn7+yN3A7AzYPuolVH2zu8i+b+hG/9+hXOQmnU29cRu3HlcjauXIpCMWyCeeaw/TFR9BtyNyePHWX2l5+SnJhIUMOGdO3Zhz63DWTSPYMICG6Ak+VO+ISnnqWeZSawiviPe9CuWC7Hb+oTOLVugTIYyD+cxInnX6v2d/30hH1DIQLrudOvbQRKQdSRY0QnmSeQGdKlNQt2mC8ghnVrj5+7KwVFRaSkn2NRtO2jlI/163HFWShv73Dlh+rXL1/KuuVLQClGPvgIOp2OvdG7GDD0Xk4cTWXm559wJPEAwQ1D6db7JvoOuMP62f17Yti8ZpVds1DuTjp22WUnkw+z+tefAI02N/SidTfzhfSi76YzYOzDAGxZspAjsXvR6fX0GzEOT18/tixZSEq8uVycTCb6jRxvvdCb88Fb9L1vFL6Wi2V7XetYIoL87YrrWrQlgOzLPHsJ5tlut65eAUoxdMwEHBx0xO+Joe+gIeTn5fHT9E84dyYdT28fRkwyz3YbvXUTq/5YgMFoxNnFxOjHn+bU8aN88MIzNLZc5Li6ezDuqefKbS+vsKjceyV2rF3F9rUrUUoxaNR4HHQOHNi7mz4D76QgP49fvvyM8xnpeHj5cO9DkzEYjcz4z6vk5+ZSVFREh+696NHPPOQrLzeH/059nOf+b/pln8dLSc+o8H2AUylH2PDbHDQ0Wl7fk5aWmUuX/zCDWx+YaI532Z8k79+Hg15P3+Gj8fDxY8tf80lN2I+Dg456/gH0uW8UOp2eg9E7iVq1DJ3BgKOzC7eOnojR8dJzK+1Cgy8bS2kbVy5jw4plKAXDJkxCp9MRG7OL/kPu4eSxo8ya/inJiQcJahjKdb3Mdebhu++wqTMTn37uinXm0Cnb54iPJx1m5RzzrIbtevShXffeACz8+jMGT3jUHNtfCzi8zzzb4+2jJ1DP15+C/HwWzfyKi2czcKvnxcAxD6I3GImL3MbWZYvQG4w4ubgweMKjGJ2cWDt/LkkJ+0HTaBjeghuHDuN81uWfQjiVksTGBXNA02hxfQ9aXGfeTyt+nMEto8z7aefyv0iO24dOp+PGYWPw8PGlqLCANXN/IPNsBg46HX1HjMfkbh7iuOCTd+g5dIQ12StttIN9E8D8sXULf2w1T3r09NB70Dk4sD0+jlF9b+Hzv/5gR0I8OgcHGvr788J996PX6ejx1BOE+vvj7GhOmkpmp7ycXw2Xn8zjRPJh1vwyGw3N0qNmPub99e0XDBxnngJgy+KFHIndg4NeT/+R483HvMULSbY8e+ZkcqX/KPMxL37XdnZvXMuxQwcJatKM7gOHlOuh9PO4cm9wddp4xuk0fvv6cwoKCvANqM/QCY9cMcHNzLVN/KM2rCVqwxqUUvQb/gAOOh2HY/dyQ/+BFOTn88d3X1pnSh007iEMBiMZaaf48/uvKCospGnrdvS6YwgAC77+nAsZZzAYHek3/AG8/QM4sDuKTUv+tMZ167BR1A8JBaCgyL6hjtVtY2dOnmDZT99xMvkIvkENaNmlGx37XH5o/6geHWtvNlLK8ederT2JCRD4zqu1stxqWwLXEvgd8/NjZ0oNoSydwL0NuAOTLc+ltdc0LVopNQVooWnaeKVUK8w9Z9eVSeA0IErTtMvO8WpPAgckAfmapl1QSrUDZmqa1k4p9RfwYUkSWZnKErhrzd4E7lq42gTu72RvAnet2JPAXSuVJXD/y+xN4K6VyhK4a62yBO5aqyyBu9bsTeCulbIJXE2qLIG71uxN4K6FyhK4mmBPAnetlE3gapK9Cdy1Iglc9dTWBK72XLkDmqbFKqXeJLGdAAAgAElEQVTeAtYrpYowTxZS1hvAR8AeZZ76JwkYAEwHvldK7cGcvO2o4PvPKKU2K6X2AUsrmcTkSoIs2yq5FVTycMFM4MurmcRECCGEEEKI/0WqFg9brE1qVQIHoGnaD8APlSzPwTxhSUXvV/hgmaZpoaX+P/wK21+H+dm6ktePlvp/71KrlnuSVtO0+cD8yr5fCCGEEEIIIarr3zOrhBBCCCGEEEL8y9W6HrhrRSnVGphV5u08TdO6VrS+EEIIIYQQ4h/0L5qx/J/0P5vAaZq2F/PvuQkhhBBCCCFEnSBprhBCCCGEEELUEf+zPXBCCCGEEEKIWkRmobSL9MAJIYQQQgghRB0hPXBCCCGEEEKImic9cHaRHjghhBBCCCGEqCMkgRNCCCGEEEKIOkKGUAohhBBCCCFqnJLfgbOLlJIQQgghhBBC1BHSA1dD0i9m1XQIVodOptd0CFZ5T0yp6RCs7v/ow5oOwcYXEx6u6RCs2oYG1XQIVnkFhTUdglXkoZSaDsGGXld77tEZdLqaDsGqWYBvTYdgte3AkZoOwUZYoH9Nh2Dl4exU0yFYfXc6o6ZDsArxcqnpEGzsTjpa0yFYdW0WWtMhWO1LOV7TIYh/MUnghBBCCCGEEDVPZqG0S+25PSuEEEIIIYQQolKSwAkhhBBCCCFEHSFDKIUQQgghhBA1z0GGUNpDeuCEEEIIIYQQoo6QBE4IIYQQQggh6ggZQimEEEIIIYSoeTILpV2kB04IIYQQQggh6ghJ4IQQQgghhBCijpAhlEIIIYQQQogapxykb8keUkpCCCGEEEIIUUdIAieEEEIIIYQQdYQMoRRCCCGEEELUPCV9S/aQBK4Witq4jsh1q1Aobh85hsDQxtZlBfn5LPzuS86fScfD24fBYx/CYDSyffVyti5fgqYV8+T7n1rX37l2JVEb1qLT6+kz+G6atGxd7bj8Pdy4pU0YADHJx9mbcsJmuVGv475u7fF2c2HF7gPEHj0JQJCXB/3aRuDl6syXK7dyMTev2jGUqO/pRv92zVEKdh0+Skzy8XKxjOzRCV83E0ti4thTJtbeLZrQJiSQT5ZtvOpYKhP4wVs4hjXl3G8LOfvDnH90W2UFe3kwpGsbALYeSGbnoRSb5eGBfvRrF0FhUTH5hYX8tGkX2XkFV73dk8lHWDH3R9CgXc8+tOnWs9w6W5b8QeLeGPR6A7c9MAFPH1+OHjrAslnfkZF2iofe+i/u9bwBOLR3Nxv+/A29wYC7lw8DxzyIg05nVyynUpJYO282mgZtbuhFy+t7lFtn+7K/OBK7G53ewC0jxuLh7XspzkW/E7dzK+Neew+AXz/6D0WFhej0enwCg7nxnpHVKSJOpyazacEcNKDF9T2I6HJDuXV2rVxMyv69OOj19Bk2GncvHy6kn2bNnO9RSoFS3HT/WFw9vaoVQ2lpqcls+O1nNE2j1Q09ad61e7l1dq5YTHLsHnR6PTcNH4O7t48lziWkJMSiFWt07jeQBmHNq7z9v3s/zf/0fYqKCgE4kXSY4c+8jG9QA7ti2bZ2FVtWLQeluHvsRBo0bmpdVpCfz8/TPyEj/TRePr4Mf/gxDEYjP376IWfTTwNwLPkIIx+dQutOXdi0cinb1qxCp9dz2z3DCW/dtsplUyL9aAqbf58LmkbEdT0I79Kt3DrRq5aQErcPnV5Pr3tH4eblQ/bFC6yb8z1FhYW41vOi590j0OkN1Y4DYOf61WxbvQKlFINHTyC4URPrsoL8fH6d8Rnn0k/j6ePLPRMfxWA0kp+Xx8IfviYj7RTFxcWMnjINF1fXascQuX4N29euBGDQA+PLxTDv6885dyYdT28f7p7wCAaj0bp8+hsv4hNQn7snPGJ97/SJ43zw7OM8+MLrNAqvWh0+nZrMxgU/o2nQ8vqeRHStuD0n79+DTm+gz32jcff2IWbtCpL2xQBw8WwGjdu054bB97Llj3mkpRyhsCCfwCbhdBt0d5XKZcfaVSjgjgfGE9TI9vph/tdfWMvlrgmTbMrlqzdewjugPkMnTALg23de53jSEbrdejs33Tm0SmVSkQBPN25tGwFA9JGj5c7LRr2O4Td0wMfNxLLd8exLNV9DXN+sIRFB/hRrGifPXWD57oRqbX/b2lVsXrkMpRR3j3uwXNv+6YuPOZt+mno+vtw/6XEMRiOxUZEs/uUnDAYD9Xx8GTl5ChfOneWHj/+LVlyMpmncNWYiDZs2q1Isp48ms2nBXECj+XU9iaigPUetWkLy/r3o9Hp63/eA+Rxw5jRr58wEpVDAjfePw9WzHvs2r2PvhlVoxRrDX3irWuUj6hZJc2uZnKxMtq1cythprzL0ocksnv29zfLoTevwqR/I+BdexycgkOhN6wBo2akrk9/+wGbdzAvn2bl2FeNfeJ2RU6ax4tefKC4urnZst7QJ489dsfy0KYpOjYNxMtjm/wVFxczfvoedh1Jt3k+/kMmPGyI5dvZCtbddVv92zVmwYy8z1+2ka9OG5WIpLCrmly3RbEtMLvdZk6MRb1fT3xZLZdLe+T/Sv/jmmmyrrCFd2zB74y4+X76Jns0b42y0vWg7df4iny3bxOfLN7H/6Cl6NW96mW+qmhVzf+SOcQ9z/9MvELl6OTlZWTbLz5w4TnL8fkY9+wrdBw5h3YJfAPCpH8yo514lqHETm/U3/PkbQx56nJFTX0an03Fk/z67Y1k7bzb9H3iQe554juj1q8jNto0l4+RxUg/Ecd9TL3L9bYPZ9Mc867KsC+c5m3ay3HcOGPcI9zwxrdrJG8CmBXO4acQ4Bj3yNHs3rCGvTFxnT53g2MEE7nz8OTr3u4PtixYAsG/zOiK6dmfQo88Q3vl69m5cU+0YStvw28/cPGo8Qx6byu71q8uX06kTHD0Qx9Anp9Gl/x1s+es3AJL27yUvN4c7H32GIY9NrVbyBn//frpr8jPc88Q0bh/zMJ4+fnYnb9mZmaxf+hePvfo2oyZP4bfvZtgs375uNf5BwTz5xrv4BQaxfd1qAEZNnsLjr/2HR158HWcXExFt2nHx/Dk2r1zGk2+8y8PPv8Ifs2dSXFRUneIBYPPvc+kzfCwDJj3Fvk3l68y5Uyc5npjAoMlT6XjLALYv/h2AmNVLCevcjYGTnqKef30ORG6rdgxgLqNNyxbz8MtvMuyRJ1n4g+3xbef6NfgFBvHIq//Bt34QO9eb6+iK+XNpe90NPPTiG0x6+a2rSt6yszLZtGIxD774OsMmPcGfP35rszxyw1r8AoOY9PJb+NYPInLDWuuy/VGRODm7lPvO1b/Po3FEy2rFs3HBz/QdMZ7Bjz7Dno3l24+5Pccz5PFpdO53B9sWzQegXZ9bGDx5KoMnT6WefwBN2nUCoOvtdzJ48lSGTnmRtJQjZJw4Zlcc2VmZbFmxhIkvvsa9kx4vVy67NqzFNzCIh15+E5/6gewqVS5xUZE4OjvbrD90wiP0H1b941xZt7aN4I+de5m1IZLOTUMqvIaYt203O8rcbIw/fprv1+3gh/U7MTkaCfWt+k2r7MxM1i35i8df+w+jHnuKeWXa9ra1q8xt+8338A8KZtvaVQAsmjub8U9P48k330On1xO/OxonJ2fGPfUcT775HsMemsyCmV9XOZ5NC+Zy0/3juGPS0+zbuPoy54B47nzsWTrdOtB6DojdvI6Irjcw6JGnCevcjX2Wc0DjNh24Z+qrVY6jVnJQtetfLfU/ncAppWpdD+TRQ4k0DItAr9dTz9eP/LxcCgsu9Yocid9PeLuOAIS370hSQhwArh6e6PS2f86506fxDQxCp9fj6OyM0dGRjAouSO2hc1AY9DrOZ+dSrGmknjlPfU93m3U0TSMrL7/cZ/MKiyi4iguXimIx6nWcy86hSNNITj9LkJeHzTrFmkZmBbEA9GrehE0Jh/+2eCpTeDr9mmynLJ2DA0a9nozMbIqKNQ6fOkOIj6fNOueyciiyJPSFxcUUa9VP7ksUFhRQkJeHp48fOr2eBs3COZFkW9bJB+Jo0rodACFhEaQdNZ+snVxcMDo5lftOn8AgcrOz0DSNvJxsXNzcqhSLh48vOr2eoCZhnEw+YrNO6sF4GrUy94oENwvn9NFLNx+2Lf2TLrcOKPOtisXfTWfex++SkrDfrjjKKiosoCA/D3dvc1z1GzcjLSXJZp3jiQk0bGHuLQ9sEkb6MXNcXgGB5OdkA5CXnYWzq20brFY8BeZ4PCzxBDZpRlqZcjp2MIHQlube3KCm4aQfOwpAYvROigoK+P2z91nx49fkWWKrin9mP5nFR24jvGNXu2NJOphAk4iW6A0GfPwDyMvNpaDU8fdg7F5aduwMQKtOXUgsczNhX9ROwlu3xWA0ciYtjYDgEHR6PU7OLjg6OpJ+qnrH36LCAgrz83D39rHWmdOptjenjh9KoEFzc52p3ySMjOPmfXT+dBq+DRoC4BfSiOOJ1eu9KJGSeIBGES3Q6w14+/mTn5tjc446HLePFu3NZdSyQ2eOxMcCkLhvDwm7o/ni9RdYPu/qRiOkJh6gUbg5Bi8/f/Jyc8vF0Ly9ORlq0aETR+LNbbW4uJitK5fS7eZ+tn/ToYO4enri4eVd5ViKCgsoyMuvtD0fu0x7LpGTeZELZ9IJCDXfvCo5lxcVFaI3GnHxsD12X05q4kFCw5tbyyW/XLnEEtHefP3QvFy5LOP6m/vbfJ+Hd9XL43J0DgqDTse5kmuI9HME1rPvGuJs1qXjSlGxRrGmVXn7SQcTaNq8Ram2nVOubbfq2AWAVh27kBhnrrf1G4SQnZWJpmnkZGfh6u6Bs8mEm2Wf6PV6u0eEWP+GMu05oHEz0lKTbNY5fugAIaXqzBlLe64XEEheTg5gOQdYzocubu7odLXuklb8g+p8AqeUClVKxSulflBK7VFK/aaUclFKJSmlfCzrdFJKrbP8/1Wl1Ayl1ArgR6XUaKXUH0qpZUqpBKXUK6W+e4pSap/l3xOW90xKqcVKqd2W9++1vN9RKbVeKbVLKbVcKVW/On9PdlYmzqZLvUNOzi7kZGVaX+dkZuLsYl7u7GIiJzOz3HeU8PIP4GRKMrk52VzIyOBkanKl61fG2Wggt6DQ+jqvoKBcj8614mI02sSSW4VYvFxdMOp1nDpfvXKoK0yORnLyL52ccvILcHE0Vriuq5MjPSIasznhSIXLqyInKxMnl0t3tx1dTORkXbRZJzcrE6dSdfxKvcKtr+vOLx+/x4yXn8FBp6N+qSHFlcnNysSxdCzOLuRmZZZZJ8vmbnxJEns27SQFebnlem4Gjn+E+556gX6jxrN67o/k5+bYFUvZbTqW2qbR2Znc7DJxZWfZxK5Z4goOa87+rRv45b1Xid2ygebXlR/qWFU52Zll4nEp14OQm51ls181yz7LOn8O5aC489Fn8A9tTOTKJVXe/j+xn0rE7dxKROfr7I4lO/OiTc+Qs4sL2ZkXbZebXC3LTGRl2tbtnRvW0alHbwB8A+pzLOkwOdnZnDtzhmPJSeXWt1duVhbG0vvIyblcGeVl29arkn3kVT+QVEsSlRK3t9yd/qrKzrpY5hxlKldGzpYRDk6mS2V0IjWZpi1b8/BLb3LqWCrxMVHVjyGzzHmy3H7KxLnUfipZtmvjWlp1vg69wfZYuHrhPPoMHFKtWMzt+VLPlaOzC3kV7ptL8WplEpCDu7bT1JL0ltg4/2dmvzENF3dPHJ1se8YuJ6fU3w0l5ZJZ4fLS5RK1cR2tOndFb/jnzunORiN5pRIm83m74nPS5TT0qYerk5GU9LNV3n5W5kWbsin994O5zpS0fWeTiayL5hFDXXvdyBdvvsIbjz+ETqe3GSpZXFTEr99+ya133VulWMq2Z/Mxz7Zdlq8zlnNAs+bEbd3Ar++/xv6tG4ioYLi7+N/wb0nXw4FxmqZtVkp9B0y6wvodge6apuUopUYDXYBWQDawUym1GNCAMUBXQAHblVLrgcbAcU3TbgdQSnkopQzAp8AgTdNOW5K6t4CxVf1DXEyuHMu+dLcpNyfb9qDj6kquZXludrbNSazcd7m6cuOdd/PT/72Lyd2DgAYNcatXtaEHHRsFEx7kx9nMbBxL9fA5GvTkFFz981JV0aVJCC2C/ckoE4uTwWCTrFSmd4smrI1N/KdCrHHdIxrRtmEQ6RcybYanOBkNFT7f5mjQM6Z3Z37dGkNmbsU9lvaIXLOChKgd1PPzt94dBMgrU38BnEyX6jCAwxV+82Xp7O8Y/fzruHt5s2z2d8RFbqd5p8v3qkSvW8XBmJ14+paJJTcbJxfb9uJkMtn0GjlYHp7eungh3QaUv6BzdjXf7XSr541vUAPOnU7Dz9K7cSV7N67h8O5duPv42SR++bk5OJaNy8VkE7sqiWvRfLrcNpjGbTpwMGo72xf/Ts+h99u1/bJ2b1jNoZhdePj42ZRBfs5l4skuFY9lnzm6mAix9Po0bN6KDfPt71n5J/cTwJmTx9EbjHj6+Nkdk4urm82Q35zsbFxc3SpcnltmWXZWJseTk2jaohUAJjc3brtnODPeeR03T0+CGobiUcXj775NazmyJwp3b1/ycyqvM44uJvJzL5VRyT5qf9NtbPp9DoumR+MVGIyLu329OZfjYnKzudjMzcmquIx8zYm/i8nN+n542w4opQhv054TKUlEtOtQvRhcXW1uMpTdFy6uruYYfS3nSVdXCvLzid68kXHPvmQduQIQFx1JcKOmmOzs2S+xd+MaDsVE4uFbpj1X0H4cXWzrr1K2w7IO7NpO35Hjbd7rcddwbhh8L8u+n05K/D4atmhzxZicKyyXMtcPpcrFxVIuMZs3MKZMufxdOjVuQPMgPzKycnA0lLmGsPO8DeDn7kqflk35ZWtMteIwubqRk126bZett642bdtkWTZnxuc8886H1PPxZc5XnxG1ZRMdunW3LmvZoTMRbdrZFcO+jWs4tCcKj7LngJyccsc8RxeTdbQFXDoHbFu0gM79B1nOATvYsWQhPe4aXpWiqPXKtg9RsTrfA2eRqmnaZsv/ZwNXuiXxp6ZppW+dr9Q07YzlvQWWz3cHftc0LUvTtEzL+z2AvUBfpdS7Sqkemqadx5xAtgJWKqVigBeB4LIbVUpNVEpFKqUiVy38rcLAgps0JflAPEWFhZw7k47R0cnmrlij8BYc2GO+c3lgTxShES0q/UNbdr6Occ+/xu0jxmAwOuJpmXjAXruOHOXnTVEsjYmnsKgId2dHHJQi2MuTE3/jM2322HEohZnrd/LnrlgKiorwcHbCQSlCvD05lnHeru+oZ3Lh9vYtGNG9I65ORvpbHqj+t9gUf4TPl2/il60xFBQV4WlyxkEpGvt5lbtradA5MLZPV1buPVCtO5qldbrxFu5/+kVuGzUBvdHI+TPpFBUWcjTxAPVDbZ9pCwmL4PC+3QAcPXQAv+CQSr/bwcHBenJzdnMr1/NQVvvefbnniWnccv9YDEYjFzLOUFRUyPFDBwko03sX3CyCI/v3AnD88EF8g809OefPnGb1Lz8y/7P/knXhHGt+nW0Zwmk+bOTn5pB+/ChuVRhy1brHjQx69Bn63PcAeoORi2fNcZ08nIh/SCObdQObhpMaZ47r5JFEfEp6mDQNp5I76K7uV9Wb0rbnTQx5bCo3DR+NwejIRUs5nTh8EP+GtvEENQ0j2RLPicOJ+ASZD2/BzcKtw8XSUpLwqEKy9E/tpxJx27fQvPP1VSqT0GbhHIrfT1FhIRmn03B0csJQ6vjbtEUrYqMjAYiNjqSZJVkDiN6yiXbXdbO5IdHuuht4/PV3uGvMRAyOjnj52l8+AK2692HgpKfode8o9EYjmWczKC4q4uSRRPxCQm3Wrd8kjFTLsK+TRw7hFWjeR0ZnZ24cPpYBD09BbzDQuG31kqYSIU3DOJIQR1FhIWfTT2N0crY5RzVu3pL4mF0AxMfsonFz83NlTZq34uhh882z1MOJ+ARUa5AKAA2ahnEkId4ag6OTU/kYdlti2L2LxhEtyTidRk52Ft//9y2WzPmRA3ti2L52JceTkzgct49v3n2dg/t2s/inmZw9nXbFGFr3uJHBk6fS577R6I2X2vOJIwfxK9N+ApuEkRJnHm57onR7Bs6lnUQphaevv/W9kmGPDjodBqOxXI/h5culGUmWcjmXfhpjuXJpQcJu8/VDwu4oGkW05OzpNHKys/nhv2+zdM4sDu6JYYfl+a+/Q+ThVGZt3MXiqP0UFBXjbjlvN/Cux/Gz9p63nRnQsSW/79hbpaSvtNBm4RyKK922nW3adrOWrYmNsrTtqEjrjRgHBwdrr7ubu4e11+73H7/Dw7MevW8baHcMrXrcyKBHnqb3vaNszwEVtOfAJmGkxJvrzMkjh/AOLLmkLH0OcLvqHnVRd6myXfl1jVIqFFivaVpDy+sbgclAa6CbpmlpSqnuwJuapvVWSr0KZGqa9l/L+qOBPpqmPWB5/TpwBnOvm5emaS9b3n8DOK1p2idKKS/gNuAhYAXwOzBD0zS7rxZ+3bb7sgW/a8Madq1fg0Jx24jRODjoOBS7h+633UFBfj6/f/MFF85m4F7PizvHm2eR2rdjKzvXriI1MYEGTcO5acg9hDQL57evPuNCxhkMjkZuu38M3v4B5bZ36KR9z2kFeLpxc2vzLJS7k49bZ5C6o2NL/txlvnAYel0bfNxcKSwqIvXMOZbvTsDL5MwtbSMI8HQj/UIWsUdPEp1U8UPZeYWFFb5fVmA9d/q1jUApiDpyzPp9Q7q0ZsEO84XesG7t8XN3paCoiJT0cyyKtn1m6bF+PSqdhfL+jz60K5bK+E19AqfWLVAGA/mHkzjx/GvV/q4vJjxcpfUbeHtyZxdz78i2g8nsSDQ/azaiR0dmb9xF75ZNualVM06eM5+QEo6nsWrvAbu+u21o0GWXnUg6zMpfZoEGbbv3pm33XgD88c0XDBpv7hzftOh3DsfuQafTc9sDE6jn68eZUydY8dNMTqYcwScwmJZdutGhd1/iIrezfcVi9AYDjs4uDBr/iM2zcnkFl68zJ5OPsO63n9A0aN2tJ60sM2Iu+f5LbhvzkLlslv5B0v69OOj03DJibLkem29fmcq4196jqKiQOe+/gd5gpLioiI433Vru+Sp7Ly7SUpLY/PtcNKD5dd2tsz6umvU1fUdOACByxSJS4/aZZ6G89wHcfXzJOHGM9fNm4+DgQHFRET3vGYl3/cvvC73Ovnt0p1KS2Dh/Dpqm0fL6HrSwzAK5/IcZ3PrARAB2LPuLlLh9OOh03DR8DB4+vhQVFLB67g9kns3AQafj5pHjMbl7VLgNQyXPifyd+wnMQ9S+f/05hj31Es4VTJbRyO/yiffWNSvZunoFKMXQMRNwcNARvyeGvoOGkJ+Xx0/TP7HO4jfCMlMdwEcvP8c94x4isGGo9bt+/PRDzp1Jx2B0ZOiYCfjWDyy3vb0px8u9V5HTqUlsWfgraBrhXbtbZzpcM/tbbhwxDoColYtJjY/FQaej172jcPf25djBeKJWLkYpRVCzCNr3va3S7YQF+le6HGDH2lVsX7sSpRSDRo3HQefAgb276TPwTgry8/jly884n5GOh5cP9z40GYPRSMbpNH77+nMKCgrwDajP0AmPXLH3vbLh1TvXrWbHOnOicceocegcdBzYt5veAwZTkJ/HrzM+53zGGTy8vK0zYZY4tH8fUZvX28xCCfDLl5/SpU/fCmehTDqdcdlY0lKS2PT7HDQNWlzXwzq0eeWsr7m5pD0v/4uU+H046PT0uW80Hj7mWVR3LFmIk6sbbXreZP2+Zd99QW5WJsXFRdRv1Izr77CdATLEt16l5bJz3WoUMHDUWBwcdBzct5telnL5bcbnnM/IwMPLi6EVlEv05g3WWSjnfz2d5IPmhNA/uAGjpjxX4TZjy8wmeTn1Pd25pW04ADFJx9htmT16cKdWLIw0Jyv3XN8OX3cTBYXFpJ45x9KYOO69vh3ebiYu5uQCsPVgMomXuW7p2iz0stvfunoFWyyzp941ZiI6nY74PdH0HXSXuW1/8fGltv3IExiMRqK2bGL1n/PRG4w4m0yMefwZTh47ygcvPE1jSz1xc/dg3NPTym1vXyVtOy01ic2//wJoNO/a3ToUctXsb+g7wtwbu2vFIlLiY9HpdOZZKL19yTh5nA3zZqEcdBQXFdHr7hF41Q/iUEwk+7du4FTSIfxDm9C53yACGtnePH3y9l51omsr7d2PalVi4vfsE7Wy3P4tCdwRzMnaVqXU10A80B/4QNO0pUqp/wPaV5LAvY25By0H2I556GMxMBO4DssQSmAkcArI0DQtVyk1GBgN3APsB0ZaYjAAYZqmxV4u7soSuGvN3gTuWrA3gbsW/o4E7u9U1QTun1RZAnetVZbAXWvVvTv8T7E3gbsWKkvgrrXKErhrzd4E7lqxJ4G7Vq5m1uS/W2UJ3LVWWQJXE+xN4K6FyhK4a62yBK4m1JkE7r1Pas31MYDf1MdqZbn9W56BiwMeUEp9BRwEpgM7gG+VUs9jTr4qswmYBTQFftY0LRJAKTXT8j0A32iaFq2UuhV4XylVDBQAD2ualq+UGgp8opTywFyuHwGXTeCEEEIIIYQQoqr+LQlcsaZpD5V5byMQVnZFTdNereDzaZqmPVrBuh8CH5Z5bzmwvIJ1Y4Dyv1gshBBCCCGEEH+Tf0sCJ4QQQgghhKjLavGPZ9cmdT6B0zQtCfPza9X9/EzMz7oJIYQQQgghRK1We55wF0IIIYQQQghRqTrfAyeEEEIIIYT4F5Af8raL9MAJIYQQQgghRB0hCZwQQgghhBBC1BEyhFIIIYQQQghR45TMQmkX6YETQgghhBBCiDpCEjghhBBCCCGEqCNkCKUQQgghhBCi5inpW7KHlJIQQgghhBBC1BGSwAkhhBBCCCFEHSFDKAUPZpyo6RCsDvXuXdMhWH0x4eGaDsHGpK+n13QIVpxh0csAACAASURBVMlffl7TIVgVFhfVdAhWro6ONR2Cjf1HT9Z0CFbebqaaDsHqy5WbazoEq/t7dKrpEGycuZhZ0yFYJZ3OqOkQrFoEB9R0CFa/bo6u6RBsfD7h7poOwWpT3OGaDsGqZ8umNR1C3SQ/5G0X6YETQgghhBBCiDpCeuCEEEIIIYQQNU9+B84u0gMnhBBCCCGEEHWEJHBCCCGEEEIIUUfIEEohhBBCCCFEjVMO0rdkDyklIYQQQgghhKgjJIETQgghhBBCiDpChlAKIYQQQgghap6SviV7SCkJIYQQQgghRB0hCZwQQgghhBBC1BEyhFIIIYQQQghR8+SHvO0iPXBCCCGEEEIIUUdID1wtFLVxHZHrVqFQ3D5yDIGhja3LCvLzWfjdl5w/k46Htw+Dxz6EwWhk++rlbF2+BE0r5sn3P7Wuv3PtSqI2rEWn19Nn8N00adm62nEt3rubhTFRKAVP9e1H+P+zd9/hURT/A8ffcy3JpZMCJKEGSOi9iAiIiogoiA0RkC4gKKKiolKs2L5WsCsKKiBIr0no0gIklFRISEKoKZRcLu2S/f1xx3GXhHAkSMDfvJ7Hx+R2b/fDzuzMfnZmN7Vq2y2fv/sf9qQkU1KiMPLOu+hQrwFvLF/CuZwcSpQSBrbtwIMtW1d6/7a2ha1n87o1IATDxk+kQaMm1mWJsTH8/NVnnD2Zzic/zcfHz8/uu+++8iI1AwMZM/nlGxKLraAangzs3AqAXYmpRCal2S0PCfCnT5tQTMUlFJpM/L5jP8aCohseR3kCPn0PpyaNuLBkOed//fNf28+uTWH8E74egeDx0eOo27CRdVlRYSEL5n7B+cwMvH39GDLhBbQ6HTEH9rF64QK0Oh3evn4MmzQFtVrNhr8XE717J0II2t/ZnXsefuS6YtmzOYKd4RsQAh4d+Sx1GgbbxfLnN19ZY3lq/CS0Oh0LvvqM85kZAJxMPc6QiS/SokMnAHJzcnhn0lgeHTmWjt3vrvQx2hGxkW0b1iIQDH52AvWCG1uXHYuL4be5X3L21Ek++O4Xavia6++mtasIX7kMpaSED76fV+l9X3Y2LYVNi+ejKNCqW09a3HFXmXX2rF9F8pFo1Bot9w8dhaePH/H7dhO1NRwhBE7OLvQdMR4nFxeOxxzkn1XL0Gi1uNfw4YFhY1Cp1Q7Fsn/bZiI3h4EQPDRsFIGl2ry/f5zLhaxMvHx8GTh6Alqdjj+++pRL2VmUlJTQ+Z77aW8pj/Clizh25CBqjYZ+Q0dSu279Sh+jhv4+jO7VGYQg7FACm2OO2S2v7e3BpD53YSouRqNS8X3ELlIyzhMS4M+4e7tS29ud535aSpbBWKn9V+a4rPvzN04kHcNUVEiD0GY88NQwAH6aPYuS4mIKC/Lp9sDDtL6jW6WPC8CZ1ONsXPgbKNCm+9206tq9zDo7167g2OFoNBotfZ8Zg5evH7vXryb+QCQqtYpadetz36BhCFG1O+61vdx5oE1ThID9yelEp56yW67TqBl6Vwf83F1ZGx3HobTTdst7NgumVd0Avly/vVL7r0yfvfDr/3HpfDZKSQkde/Wm3V09Afj14/c4lZrMHff1pWf/RysVz2X1/WowvGdHBBBx5Cjb4pLtlreqW5vHurTGVFxCvsnE3A07MOQXUsvLnTH3dAHg+LlsFmzfX6U4ANasXMHypUsRQvDSq68R0rSpddmCeb+wOSIctVpDSGgoU159DSEEa1auYMnChWg0Glq2bs3zL92YPjty6yb2bNoIQjDgmdEENbDvF/76fg4XsjLw8vHj8bHPmcvr2y85nZqCs16Pm7sHQydPvSGxAGzduJ5Na1cjhOCZCZNo0NjmeibmCD99+RlnTqbzv18WWK9nvv3kQ1KTk9C7uuLu6cnkN2fesHik24McgbuBhBADhBDNqrKNvFwDu8PWMfL1mTw2bhJrFvxitzxqxxZ8awcw+o238a0VQNSOLQA079CZSe9/areu4dJFIjeHM/qNtxk65XU2Lv6dkpKSSsV1KT+Pxfv3MnfwMGb2G8D/wjfYLd+VdAxDQQFfDxrK3MHD6FCvAQDjut/ND0NHMHfwM/yyczsFJlOl9m8rNyeHDSuW8caH/2P8K68z/5s5dsuD6tVj5v++Iji0bFFE7dmFi15f5RiuZmDnVizYvp85G3bQvWlDXHRau+VnL+bw9fodzNmwg9j0s/Ro2ugqW7rxzs3+jMy5P/6r+zAacti6dhWTZ83mmRde5q+fvrNbvntzOLUCg5jy7kfUDAhk9+ZwAFYvnM/ol6cx5d2PUKvVxB+MIj/PyK6IMF754FNeev8TdmxcS0F+/nXEYmDbulVMmvkeQydNYenP39st37MlgpqBgbzwzmz8AwLZsyUCgCGTXmTSrPcZ/+YsXPSuhLRqY/1O+LIlNAhpSlXkGnKIWLWCqe99zOgpr/LH99/YLQ+oW59pH31OcKn9tL+jG29/bf9vqIpNi+fTd/g4nnzxdaK2hJFvzLVbnnXmFGkJsTz18lt0fXAA25f/BUDjNh146qU3GTTlDfzr1Cd27z8A/LPqbx4eM5FBL72BWq0mNf6IQ3Hk5RrYtXEto9+YxRPjnmf1/J/tlh/Yvhm/gECefetdfGsHcmD7ZgB6Pz6YcTPeZ8wbb7N5xVKKCgs5lXqc9OSjjJvxPo+Pe57VpdrP6zW6V2c+X7uN6YvW8WDbZrg66eyWn72Qw7Q/1zB98Xr++CeKxzqbb1CdyDzP63+uJvF0RqX3Xdnjct/jTzH2zbeZMGs2J5KOcTb9BADPvDyNMW+8zajXZ7Jx8R+VjuuyjQt/4+FR43n65TfYF7GBvNxS9ef0KVLjYxn26gy6PTSQLX8vAqBJ2w4MnzaLYa/OIPfSRVLjY6ocywNtmvL33sPM2xJJ50b1cNba35s2FZewaGcUu4+llvmuq5MOHzfXSu+7sn32fY89xdi33mXk6zPZutJcfwEGjBrH/U8OrXQ8tob37MicDf/w9tIw+rQJLVN/T56/yNtLN/L20o1EHU/ngTbmNmdwt/b8+U8U7ywNQ6dR07Ju7fI277BLly6x+M8/mPvjT8x87z3+99Fsu+U97u7FT/N/5/t5v5Kdnc2+vXsB+PHbb5nz40/88Nt84uPiSElOLm/z18VoMPDPhjWMe+sdBk+YzIpf7fvEfds24RcQyIQZ7+MXEMC+bZusywY8M5rxb717Q5M3Q04OG1b8zVsff8aEV6fx6zdf2y0Pql+fWZ9/TaOmZa9nnpkwibc+/uw/l7wJIW6p/25VMoG7sQYAVUrg0pOOUa9JKBqNBm8/fwoL8jEVXRmhOR4fS0ib9gCEtG1PSkIcAG6eXqg19p3WhYwM/AICUWs0OLm4oHNyIvvcmUrFFXPqJG2C6qJVqwnw8iavqJBCm2QsIj6WQpOJiQvnM2v1cgwF5gvtOjV8ANCoVKiE4EacCscS4ghp0RKNVot/rdrk5+dZOz8Avasbzi4uZb5XUlJC2OoV3PtQ/xsQRVlqlQqdRkO2wUhxiULy2Szq+nrZrXMhN49iSxJtKimhRKlcQl0ZpozMf30fx48mENy0ORqtFt+atSjIz6PIpv4ejTlMi/bm0ayWHTpzLNZ8kV+7Tj3yjAYURSHfaMTNwxOtzgnPGjUoLCykqLAArc4JtYMjOgCpRxNoGGqOxadmLQrz7c+lYzFHaN6+IwAtOnQkKdb+QjLmQCRNWrZGqzNf9GRnZHDxQjZ1g6uWdCcnxNO4eQs0Wi1+tS4fI9v661pu/fX09kajuTGTJkxFRRQVFuLp64daoyEwuAlnUuwvjtIT42nYwpyQBDUOJSPdPJps284UFRbgWzsQAJ/ageTnGVEUhYI8Iy5uHg7FciLpKPVCmqLRaKnhX7NMOSXHxRBqafOatm3PcUub52uZAaDWqBEqc0ebeeY0AfXNd9O9fHw5n3HOblvXQ6NW4aTVcO6SAVNJCbEnz9Kolq/dOiWKYv1Z76QlNfM8AMbCIvKLqnazqrLHRaMx3zQqNpnQOTnh4e1t93lhQQH+QUFVis1UVERRQQFevv6oNRrqNA7hdKn6k5oYR3BL882Puk1COWepPzVq1rKuo9ZoUKkcP6fLo1YJdBo1F4x5FCsKqZnnCazhabdOiaJgKCgs9/s9mgazI6HyiUFl+2wfa/3VIITKeqHoaekzq+py/c24ZKC4pIT4k+doWNN+21k5RkzFlv6ouMRan2t7uZN8NguApLNZNAuqWaVYYg4fpk3bdmi1WgICg8gz5lFo02fXqVfP+rNWq7G28/Ua1MeYm0tRURFFRUW4ubtXKQ6AtKREGticVwWlyispNoZm7ToA0KxdR5LjY63LVi34hbmzphG9a0eV47DuLz6OkBatrNczBXmOXc8ALPhuLrOmvMCuLZtvWDzS7eM/M4VSCLEcqAM4A18oivK9EMIAzAHuBc4D04CPgLrAZEVRVgohnIFvgA6ACZiiKMpmIcRwoIOiKBMt218NfKIoyhbLdr8A+gF5QH8gGHgY6CGEeBN4VFGUpOv9dxhzDbi4Xrkb6OyiJy/XgLuXuRPOMxhw0ZuXu+hdyTMYrrqtGjVrcSYtlfw8I4V5+Zw5kVrh+hW5lJ+Hu7Oz9XdXJycu5efh62ZuUDMNOXi4uPD1oKH8tT+S33b9w4Se91jX/3XXDu5r2hzdDbgAzc3JwdXtSkOu17tiMOTgfY2Ob3v4Rjp2vct6QX6juTrpyCu80hHkFRahdyp/X27OTtwV2pBvw/75V2KpLkaDAb2bm/V3F70rRkMOnt41APPo0+XlLq6u5ObkANCpRy/mvDMdZ72ewHoNqNfIPKWwebsOvP38syglJfR5bBAarRZH5RoM6G3urDvr9eTaxGI05ODi6mYXp61927bQ/YF+1t/X//UnvQc+QdTOyk2zuhJXDq6u9scoNycHrxt04eaI/FwDTi5XRqKd9OZ2xm4dYy6unlduQCg2NxsO/7OVA5s3otFq6dT7QQCadb6Tv7/+BJ2zC35BdahlGYW/FqPBvs1zctFjzDXgcbnNyzVYy8lZ70peqXLasvJvWnfphkarpWZQHXZtXIvJVETGyZNcys6yaz+vh7uzE7k2F/25BQW4uziVWa+hvw9j7umCr7srH63cVGZ5ZVXluKz87Sfi9u+lUYvW1nIuKSnmx/dnce7kCe5/YnCVYsvLNeCst60/ruTl2pdLfq4BN5vjXnr2R2pCLIaLF6jTJLRKseh1OrtkOb+oqMzMh6up4aZHp1Fz9mLl+kWoep+9ddXftOxy53W1bY5wd3bCaFN/jYWFuDuXrb8Annpn7m8dwvvLzLMQTmRdoHX9AKKOn6R1vQC786AyLl26iLvHlRs6rm5uXLp4Ed9Sjzcc2BdJZmYmbdubE94+fR/kmaeeRKdz4p7evcusXxl5hivnDYCLix6jIQePy/1Cbql+wdJHPTR4OK4eHhgNOXz33gzqNGyEj83NiMoy5OTgattnurpiyMnB26fi/mDwmHF4eHpiuHSJ9157mYYhIdSsHVDleKTbx38mgQNGKoqSLYRwASKFEEsBV2CLoiivCiGWAe8C92EeJfsVWAk8B6AoSkshRCiwUQjRpPxdWLkCuxVFeUMI8REwRlGUd4UQK4HViqIsqew/Qu/qxknjlecl8vOM9o2Nmxv5luX5RqNdx1FmW25u9HrkcX7/7ENcPTypVace7pZG6np5OLuQYzN9LbegAA/nK3eFPFxc6GJ5vqhLw2A+s5liufbIQZIzM3j74YGV2ndpru7uGG06QaMxFze3iu/MFRYWsnNzBFPfnU1CzOEbEsdl3UIb0LpeIJmXDHZTd5x12nKfb3PSahjRsyOLd0VjyK9ax3ir0bu52U2lyjPmorcpG1c3d4y5BnyoaVlmrtt/fvc1Uz/8DG9fP/787msO7NxOYP2GRO/eyay5P6GUlPD59Ndo3akLXj6+ZfZbHlc3N9JsYsk3Gu0Tfzd3a6x5RqNd4mnMNXAqNYXgZi0AOJWaghCCWkF1KnFUSsdlPgaX5Rlzcb0Bd5YdEbUljMSofXj5+VOQd6WdKczLs2tnwJwU2K4jbP64ass7e9Dyzh7s3biGyLB19Bj4JOF/zmPw1Bl41PAh7I95JBzYS0i7TteMSW/TpgEU5BnR27Z5rm7kGXPxpmx7eGDHFs6mn+DJCZMBqBlYh9Z3dOPn2e/gU7Mm/oF1cPVwbCTwsgfaNOWOJvU4cyHH7gaMq06HIa+gzPrJ57J4/c81NKrly5heXXj1j9XXtb+rqcpxeXjYKB58ejh/fPkxRw9FE9KmHSqVmrFvvo0xJ4c5M16lZeeuOOuvb+rgvk0bSTiwF2//mhTk5dnFVqb+uNrHr1JdqT/n0tPYsmwxjz/3UqWnKHUKrkuzoJpkG4w42dwYdNZq7W6kVaRns+AyzzVer6r02VE7tnIu/QSPj3+hSjHY6t0qhM6N65rrr83NSr1OhyG/bP110WmZ3Lc7P0Ts4VKeuY9fsH0/w3t05IE2TTl7MYfzlXyG8zIPD09ycq4k+LkGAx6e9qOkxxIT+ebLL/n4iy8RQpCbm8uP333LomUrcNHrmfriC8QcOUzzFpV/jh/M5ZFnM108L89o10fpXS39gp99v3C5HdG7udO4ZWtOpaXckATOrdT1TF5urkMjjZePn5uHBy3btSctOem/k8DdwtMWbyX/pSmUzwshDgK7MY/ENQYKgfWW5YeBrYqiFFl+rm/5vBswH0BRlHggFbhWAlcIXO6l99tsq0JCiLFCiH1CiH3hy8vP8YKCG5GaGE+xycSFrEx0Ts52d+YahDQj8dABABIPHaB+Oc952WresQujps3iwSEj0OqcHL74LbOdgEAOnTyBqbiYM5cu4qLV2Y2mta1bj/gz5gfD40+fIsgybWfb0QQ2xsYwo98AVDfopGwU0pTE2COYTCYyz53F2dnlmqNqGWdOY8w18MmMN1j40/cc3r+PzevX3JB4dsQfZ86GHSzaFU1RcTFeri6ohKChfw3SLFOqLtOqVYy8uzNhhxPLLPsvaNA4hKT4GIpNJrIzzuHk7ILWpv42ataCmAP7AIg5sI/GlpfqqFQq60WPm4cnuQYDKApOLubv65yc0Gi11/UMXL3GISTHx1liyUDnbH8uNWrWnNgocyyxUfusyRpA9M4dtO7S1XrReSI5iXOnTvLNuzPYt20Lm1YuI/VoYqWOUcOQUI7GxWAymciyHqN/Z1S4tLY97+PJF1/n/iGj0Op0XMrOorjYxMmkRGrZvHgBzNMmj8ccAuBk0lH8guoCYLKZ7ums11vPPSFU1oRA7+5Ofqlnoq6mTnBjUhPN5XQhs2w5NQhtTuJBS5t38AANLG1e7P69HNy5g8fHTbJLDrrc24exb77NnX0eoladutc9RW9ddBzTF69n7sZ/KCwy4evuilolCA2sydEz9tOQtTZTenMLCm/IM76XVfa4XJ5+pVar0To5o3XSUWwyWUfAtE5OaLQ6NJWocx169ebpl9+k77AxaHQ6LmZlUmwykX4skdr1g+3WrdsklOQjBwFIT0rE31J/ss+dYc2vPzBgzET0VbhxsTcpjXlbI1m5P4ai4mI8XZxRCUFdHy9OZl90aBvernoebNuMId3a4+as44HW1z8aWNk+O+5AJId27+DRZ+3rb1VtPJTAO0vD+CFiNwUmEz7uetQqQUiAP0mWaZGXadVqpvTrwfLIIySdvVK3sw1G/rdmK+8vC8dJo2Fv0okqxdS8ZUsORUVhKirizOnTuOhd0Nn02SfS0nhv5gzenv0hXpZrB5VKoNVocdHrUavVeHh4kHPpUpXiAKgb3ISUBPN5dT4zA6dS5dWwaXPio80vbYmP3k/Dps0BrDf7TKYiUhLj8btByVJwaFMSYq5czzi5XPt6BsxJMJinMyfGHKF2YNWmRUu3H6HYzOG/XQkhemIeXeutKIpRCLEFmIl5NMzNss5MwKAoyieW3w2KorhZpl5+qSjKJsvn2zGPyrUCuiqKMsHyeTjw7uUplDbbfQzopyjKcCHEPBwcgVu8++BVD/z+bZvYv3UTAkHfIcNRqdQkxRyiW9+HKSosZNmPc7l0PhsP7xo8Ynnz2JG9u4jcHM6JYwnUaRTCPQOfoG7jEJZ89zWXsrPQOuno+/SIcu8Y3Rvr2IjUqkPRrDwYhRDw4j33o1ap2JuSzJDOXSk0mfhg/WrO5VxCo1Iz/cH++Li50et/s6nn44uL5WJh5kMD8He/+h3xpJ49HYpl64Z1bNmwFoRg6LPPoVarORy1n36PPcnp9BPMm/Mlx48lElSvPl173sO9/R62fjf2UDT/bAq/5lso/9i+z6FYbNXx8eKRTuakZPfRVPYeMz/3MeSu9izYvp+ezRtxT4vGnLlgvhuZcOoc4YcdSwQm/PDNtVeqgP/UyTi3bIbQailMTuH0tFmV3lbqt3OuumxnxEZ2RmxAIHhs5LOo1CriD0Zz34BHKSwoYMGcz7mQnYVXDR+GTnwRrU7HgZ3bCV/xNxqtFr2rK8MnT8XZxYUVC+aReOQQCgpNmrdiwNARZfZnKim+aiy7N4WxKyIMIWDgiDGoVGoSDkVzT/+BFBYU8Oc3X3IhKwsvHx8GW96ICfDl9Nd4bNQ4AurVL7PNdYv/wLdW7XLfQunmVP4UpdK2h21ge9g6BIKnxoxHpVYTG32APgMf58zJdBZ8+xWpx44RWK8enbvfzd19HyJyxza2blhDUlwcwU2bMmDwMBpZLi6uJjb96s+8nkk9zua/FqAolhE1y1sE1/zyLQ+OGAfArrUrSIk9hFqtoffQUXj5+rNr7QrSEszPhTjrXbl/6Cic9a4kHNjLvrC1qLVanFz0PDhiPDqbadc+7lcf7dm3NYJ9WyJACPoNGYFKrebYkUN0f7A/RYUFLP1hLhezs/Cs4cOjY8xvhZs5egh+AQHonMz7eGL8C3jW8OHnD9+mpLgEvZsbDz8zBrdSd/oBfnfw3A6u6cOou81voQw/nMimI0cBmNy3O5+v3Ub7BkEM6NSSkhJzc/7Llr2kZGRT29uDsffcQXBNH9Iyz7M9PpkNBxPK3cfTd3W4ocfl9y8+xmjIobi4mPpNQukzaCgXs7NYNPdzhEpFcVERd/Tue9W3UObkOXaT5HRKMmGL5oMCrbv1pHW3HgCs+HEu/UdPAGDH6mUkx5jrT99nxuDt58/irz4h68wp65S1zr0fpFGrtuXuIyUj26FYArw96NM6FCHgwPGTRKWcBGBgp5b8vdfcvz3VtS3+Hm4UFReTlnmB1VGxdtt4vs9dFb6FslnQ1UdbKtNnvzN2KL61A63nyOPPPo9HjRos//lb0o4mUmwqwj+oDk+/UPaFGcv2HHLouDT0r8GwHua3UG6OOcaWWPNTHc/d3405G3bwYLtm9O/QnBNZFwA4nHaa5ZFH6BpSn17NzdPYt8cnszW24qdB5ox5/JqxrFq+jJXLliGE4MVXpqLWqNm7ezdDnhnOS89PJDUlBX9/87N2Tz/zDHfe1Z2//vyDdWtWo9FoqVO3LtNmzLzmc9A74q79POPeLeHs3RwOQtB/2ChUKjVHD0fT86FHKCosYPF3X1vPqyeeNb+d+IcPZlFQkE+JyUS7bj3o1qffNfcT6FO27SnPlg3r2LxuDcLyVm2VWs3hA/t46PFBnE4/wc9ffcHxY4nUqdeArnf34r6H+vPBtKkU5OVhMpnods999Blw7RlO7esH3hZDW1nfz7ulEhOfscNvyeP2X0ng+gOjFUV5yDINMhrog2MJ3BSguaIooyxTJ8Mwj8B1xPy8XDcgEIgBHr5GAvcVcEBRlGu++qyiBO5mczSBuxkcTeBuhsokcP+mqiZwN1JFCdzNVlECd7M5msDdLBUlcDdbRQnczeZoAnczVJTAVQdHE7ibwdEE7maoKIG72RxN4G4WRxK4m8WRBO5mcTSBu1lumwTux99umetjAJ/Rw27J4/ZfmUK5HtAIIQ4B72CeRumouYBaCHEYWAQMVxSlAPgHOI55uuUnwAEHtrUQeEUIESWECL7m2pIkSZIkSZIkSdfhP/ESE0vC9UA5i9xs1plZ6jtulv/nA8PL2aYCPH2V/dludwmwxPLzP1TxzwhIkiRJkiRJ0v9L8iUmDvmvjMBJkiRJkiRJkiT958kETpIkSZIkSZIk6Tbxn5hCKUmSJEmSJEnS7a2yfxvy/xs5AidJkiRJkiRJknSbkAmcJEmSJEmSJEnSbUJOoZQkSZIkSZIkqfqp5NiSI+RRkiRJkiRJkiRJuk3IBE6SJEmSJEmSJOk2IadQSpIkSZIkSZJU/eRbKB0iR+AkSZIkSZIkSZJuEzKBkyRJkiRJkiRJuk3IKZSSJEmSJEmSJFU/+RZKhwhFUao7hv+X4k9n3DIHPiUju7pDsErPvFDdIVhp1LdWIxLk413dIVjVG/dcdYdgtWnmzOoOwaqBv091h2CnwFRc3SFYuTppqzsEq5gTZ6o7BKuGtXyrOwQ7GRdzqjsEq5Jb6Pqk6BY6l5y0t9a99wBvz+oOwep8rrG6Q7BKOHWuukOwM/3R3rfFw2XZ8xfdOic+UGPok9c8bkKIPsAXgBr4UVGU2eWs8wQwE1CAg4qiDK5KXLdWKyBJkiRJkiRJknQbEEKogTnAfUA6ECmEWKkoSqzNOo2B14E7FUU5L4Twr+p+ZQInSZIkSZIkSVK1E6rbYqDQVifgmKIoyQBCiIVAfyDWZp0xwBxFUc4DKIpS5eHZW2uOmCRJkiRJkiRJ0u0hEDhh83u65TNbTYAmQoh/hBC7LVMuq0SOwEmSJEmSJEmSJJUihBgLjLX56HtFUb63XaWcr5V+jk8DNAZ6AkHAdiFEC0VRKv3iB5nASZIkSZIkSZJU/W6xP+RtSda+r2CVdKCOze9BwKly1tmtKEoRcFwIkYA5oYusbFxyCqUkSZIkSZIkSdL1iwQaCyEaCCF0wCBgZal1lgN3AHY7sAAAIABJREFUAwghfDFPqUyuyk5lAidJkiRJkiRJknSdFEUxAROBDUAcsFhRlBghxNtCiIctq20AsoQQscBm4BVFUbKqsl85hVKSJEmSJEmSpOonbr+xJUVR1gJrS3023eZnBZhi+e+GuP2OkiRJkiRJkiRJ0v9TcgROkiRJkiRJkqRqdxv+HbhqIUfgJEmSJEmSJEmSbhMygZMkSZIkSZIkSbpNyCmUkiRJkiRJkiRVv1vs78DdqmQCd4MJIaYpivL+jdhWxLq1bFi9EiEEY5+fTHCTEOuyuCOH+ebTjzmVns63vy/E198fgPiYI/w892vUajUdu97JwEGDqxTDns0R7AzfgBDw6MhnqdMw2LqsqLCQP7/5ivOZGXj7+vHU+ElodToWfPUZ5zMzADiZepwhE18kqEFD5n/5P0pKSlCUEgaOGEPd4MbXFcvZtBTCF80HFFp3u5sWd9xVZp3d61aSdOQgGo2GPsNG4+njR1zkbqK2hiGECp2zM/1GTsDJxYU1874jJ9v8Ftdz6Wn0Hf4sjVq1dSiWM6nH2bjwN1CgTfe7adW1e5l1dq5dwbHD0Wg0Wvo+MwYvXz/SkxJZP/9nss+dZdx7n+Dh7QNA0uGDbFu5BI1Wi0cNXx4a8SwqtdqhWHZtCuOf8PUIBI+PHkfdho2sy4oKC1kw9wtrGQ2Z8AJanY6YA/tYvXABWp0Ob18/hk2aglqtZsPfi4nevRMhBO3v7M49Dz/iUAzXK+DT93Bq0ogLS5Zz/tc//5V9gLnObP5rAYoCre7sQfNy6sye9as4HnMQtUZL7yEj8fTxsy7buXoZcZG7GDXrIwDWzfuOnAvZFBUUENrxDtr3ut/hWHZvDmdn+AYQgsdHjqVOqXL645svyc7MoIavH4PHP49Wp+O3r/5ndy4NnTiFlh06sXHZXxzcsxMwl1OvhwZU8ghB5NYIdkdsRAjBgOFjCGpgf44v/v5rLmRm4OXrxxNjJ6LV6SgsKGD5rz+Qfe4sJSUlDJ/yOno3t0rHsGtTGDvC1gOCJ0ePo26w/bGZP+cL67EZ+py5DicnxLF03o+o1GpaduhM7wGPWr+Tm5PDWxNG8eTocXTu0eu6Yjl3IpVtS/8ARaF51+407dytzDr7Nq4hJfYQao2Ge54agYePLwD7w9dyIj6GEkWh0/0PEdSkKeG//0TmyRPoXPS4uLnxwIgJDseyd0sEuyM2AIKBI8YSVKr9XfTtV5zPysTbx5cnx02yls2yX74nO+McJSUljHjJXDYLv/mSuOgDNGvbnifHTbquY3LZjW730pOOsumvP1CpVTRq1ZYu9/dzPJa0FMIX/oaiKLS5625alhPLrrUrSToSjVqjoe+wMXj6+rFnw2oSDkSiUqupWac+9w4aihCCbSuWELN7B97+tRj04mvXdVxudDsTs2s70dsiUKnVBDRoRI9Hn3L8uNzgMlr187dcys4EzP1kvxHjaNy63XUdH6hc+xcTtY+1i35Ho9VRw9ePIRNfRO1gv1ieqO1b2Lc1AoAHh4wgoH5DuxhW/PwtF7Kz8KrhQ/+R49DqdOyN2MCujetQlBImf/Sldf15H71DSXEJhQX5dO3Tj1Zd7qx0XLW83HmgdSgIwYHj6RxMtf8b0DqNmiHd2uPr7sq66HgOnzgNQPOgWnQMroOiQIHJxN97D1FoKq50HNLtRU6hvPGm3YiNGHIusfrvJbz3+VdMeeMtfvjyc7vldes34MM53xLSrJnd5z98+TmvTJ/JB1/O4Uh0FCdPpFU6BqPBwLZ1q5g08z2GTprC0p/t/xD9ni0R1AwM5IV3ZuMfEMieLeaGccikF5k0633GvzkLF70rIa3a4OTswvApU3nhndkMenYiy+b9dN3xhC+az4MjxjHoxWns37yR/Nxcu+VZZ06RmhDL06+8Rdd+j7Bt2WIAmrTtwOCX3+Kpl96gZt36xOz5B4AHhz/LoCnTePz5qTjr9dRv2tzhWDYu/I2HR43n6ZffYF/EBvJKx3L6FKnxsQx7dQbdHhrIlr8XAeBbO4hhr80k0OZCDGDbyiUMHPcCQ6dOR61Wczz2iENxGA05bF27ismzZvPMCy/z10/f2S3fvTmcWoFBTHn3I2oGBLJ7czgAqxfOZ/TL05jy7keo1WriD0aRn2dkV0QYr3zwKS+9/wk7Nq6lID/f4WNyPc7N/ozMuT/+K9u2tfmvBTzwzLM8Mfk1oraGk2+0L6fsM6c4kRjHoJfe5I6+A9ix4i/rstxLFzl/7ozd+r2HjOKJya/z1MtvcXDbJgrz8xyKw2gwsHXdKp6f+T7DJk1hSbnnUhAvvvOh3bk0bNIUXpj1Ac+9+TYueldCW7UhP8/I7k1hvPTeJ0x572N2hK2rdDkZDQZ2rF/D+Onv8tRzL7L8V/syidy6Cf+AQJ6b+QF+tQOJ3LoJgI1LF9K6y52Me/MdJkx/r0rJW64hh81rVvHi27MZMfllFpeqw7ssdfjl9z6iZmAguyx1ePGP3zFqyqu89O5HHD1yiLOnTlq/s/7vxQSH2reNjtq29A96Dx3NI5OmcmhbRJk6c/7sadKPxvHY5Nfp1Odhdq1aAkBq7GEK8/IYMPEVBk6aSlCTptbvdH/0aQZOmnpdyZvRYGD7utVMmPEeT0+awrJ5P9gtj9wagX9gEJNmfYBfQCCRlgvTDUsW0uaObox/6x2em3GlbPo8+TRDn3+pUsfkshvd7oUt/I3+Y55jyCtvkZYQR9bZ0w7HEr7wN/qNHMfgl95g/6ar9wdDpk6nW7+BbFlmjqVxmw4Me30WQ6ZOJzfnIqnxsQC063kvT02pXPd9o9uZXWuX8/gLr/HUy29x9kQKWWfsL+grcqPL6KGR43j65TcZNPk1nFxcadCshcOxXFbZ9m/Nwt8Z9dLrvPjOh6jUauIPRV33vi/LyzWwO3wdI16bwWPPTmLt7/Pslkfv2Ipv7UBGT5uFT+0AondsBaBZh85MfO+TMtsbMuV1Rr4+g+GvvkX4kqrdhHygdSjLIo/w69ZIOgXXxVlrP7ZSVFzCol3R7Dlmfz0Xd/Is87ZG8uu2SM5cuESrugFVikO6vdy0BE4I4SqEWCOEOCiEOCKEeFIIMV0IEWn5/Xth1lQIsdfme/WFEIcq2G6ZbVg+bySECLfs74AQItjy+VQhxGHL57Mtn7URQuwWQhwSQiwTQnhbPt8ihOhg+dlXCJFi+Xm4EOJvIcR6IcRRIcRHls9nAy5CiGghxO9VOV6JsbE0a9UKrVZLzdoB5OflUVRYaF3u6uaGi15f5nvGXAN+NWsB0CgklCPRlW/wUo8m0DC0ORqtFp+atSjMz8dUVGRdfizmCM3bdwSgRYeOJMXG2H0/5kAkTVq2RqvT4eLqirunFwBqjcbh0aXLTEVFFBUW4OXrh1qjIahRCKdT7f+I/YnEeIJbtgGgTuNQzp1Ms+7vsqLCQnwDAu2+l3Q4irohzdFodY7HUlCAl68/ao2GOo1DOJ1iH0tqYpw1lrpNQjmXbo7FWa9H5+xcZpu+AYHkG3NRFIWCPCN6d3eHYjl+NIHgpuYy8q1Zi4L8PIpsyuhozGFatO8EQMsOnTlmSQxr16lHntGAoijkG424eXii1TnhWaMGhYWFFBUWoNU5VeluZ0VMGZn/ynbt9mEpJ09LnQkMbsKZ1ON265w4Gk+DFq0BCGocQkb6Ceuy3etW0qnUiMDlumQqKsLduwYanZNDsaQcTSA41Lac8suU05VzqZO1nC47ciCSEMu5pNU54eldgyJrOekqXU5pxxJpENoMjUaLj39NCvPz7M7x5LgjNGtrjqt5u44cjzef48eOHCLhYBRz336DDX9V7eIlJTGBRs2uXocTjxymZQdzHW7VoTNHLccmz5hLDT/zzIO6jRqTeMTcTWRnnOPS+WzqXecIP0CxqQhTYQEePuY6U7thY86l2deZk0cTqN+sFQCBjULIPJUOwNGoSEymIpZ//TFh83+gIM9o/c6O5YtY+sVsjh7Yi6NSjyXSsOmVsikop/1t1s5SNu07kRRnTkSOHjlI/MEDzJn1BusX/2Fd36uGz3UeDXv/RrtXkJeHp2X0slb9hqQlxFUqlqDGZfuDtIQ4gluaz+06TUI5Zzm3a1j6RwC1WoNKbb4McvP0QlRi6ta/0c5416xNYUE+xSYTxaZinFzK9vUVxXIjy+iyY4eiqN+0mcP9pK3Ktn+169TFmJtr7afcPTyve9+XpScdo16Tpmg0Grz9/MtczxyPjyGkjXlkMbRNe1ISzXXRzdPL7hriMo3ls6KCAvwD61Q6LrVKoNWouWDMo0RRSMs6T4C3/b9TURRyCwrLfLdEUaw/a9VqMi4ZKh3HLUWobq3/blE3M7I+wClFUVoritICWA98rShKR8vvLkA/RVHiAJ0Q4vLY9pPA4gq2W2Ybls9/B+YoitIa6AqcFkI8AAwAOls+/8iy7m/Aq4qitAIOAzMc+Pe0scTWEnhSCFFHUZTXgDxFUdooivK0A9u4qpycS7i5XbmI17u5kZNz6Zrfc/f04vixoxQVFXFw/z5yLl37O1eTazCgd3O1/u6s15NryLH+bjTk4OJqvrvronfFaLMMYN+2LXS4q4fdZyXFxSz56Tt6D3z8umLJzzXgbNOJObnoyc81lF1HfyVepaTE+vOhf7byyzvTSD+agG9t+wQudu9OmnW6w+FY8nINONskz056V/Jy7f/t+bkGnF2vxFJiE0t5WnbpxqIvPuL76a+gUqupbTO1oyJGg8Fu9KN0OeQacqzLXVxdyc0xL+vUoxdz3pnO28+bp2rWa9QYtVpN83YdePv5Z5k1cSx33tcHjVbrUBy3ovxcA076a9WZXLt6VaKYy+n8uTMUFeTjV07HvOrHr/lpxisEBjdBpXKsCTXalAOAi15vV05GQw56m3Mpt9S5FLltCx3u6gmAWq2mWbsOvPPCON55fhx33lv5cjLm5uBiU0+dXVzLxOViaQOcXa/EdfpEKo2at2T8W+9y9uQJ4qMPVGr/YGlnXEvV4ZxSx6acOuzm4UH68WRMRUXEH4y2xr1m8R/0eezJSsWSl2tAV6adsR/ByDfm2tWry+d27qUL5mmoE1+hZr2G7A8z/03XO/s/wRMvvcmDoyeyP3wtFy1TYq/Ftn0Fc/trVza5BvSWsnNxdcVo6R9Op6XSqHkrJkx/l7PpJ4irQtnY+jfaPb2bO2dPpFJsMpESd6TM+VlRLKXP7TxDOef2VfoDMCd4uRcvUKdxqEP7vJp/o51p2qkrCz6Yzi+zXiUwuDFulpuf1/JvlNFlR/b8Q/NOlZsmWNn2r1OPXnzz3gzefWE8ao36uh+7sJVX6vrASa8nz6ac8mzqi3M51zOllZSU8NMHM5nz5iuEtu1Q6bhcdDryi0zW3/MLTbjoHG/P29QP5Nl776Cur/d/J4GTHHIzn4E7DHwihPgQWK0oynYhxKNCiKmAHqgBxACrMCdsTwCzMSdJFfXGd5fehhBiCxCoKMoyAEVR8gGEEPcCvyiKYrR8ni2E8AS8FEXZatner8BfXFuEoigXLduNBeoBJyr6ghBiLDAWYNZHn/DEkGFXXdfd3YNcmw7JaDDg7u5xzaAmvjyVn+d+jQLUqh1ADV9fB/4p5XN1cyPN5uIl32jE1S6pdLdOz8gzGu0aaGOugVOpKQSXmm6x6Pu5NG/fkZBWbRyK4cCWMBIPROLlV9PujnZhntGuEwJwdnWzm7oibC6uW93Zg1Z39mDPxjXsDVtLz4GDLP+mXDJOnnCoE9+3aSMJB/bi7V+TgrwrU+cK8ox2F1pXYrkS77Uu9Nct+Jnh097Go4YP6xf8TNy+PTTt0PmaMend3OymyOQZc9HblJGrmzvGXAM+1LQsM8f553dfM/XDz/D29ePP777mwM7tBNZvSPTuncya+xNKSQmfT3+N1p264OVT+TpUHaK2hHM0+nKdsSmnfKNdBw7mpMS2Xqksd9t2rVlO134Dy93+Q6MnUlRYwOLPPiCkfSd8St0QKI/tuQKXz5fyz6X8Ussun0uNLOfSuVMnid69kxlzfkQpKeGLGa/TqmMXvHyuf4RF7+pul6Dk5+WWH5ef+VzRu7pbPw9p3Q4hBCGt2nI6LYXQNtf/XAyY2xlj6Trsbh+DbR12tdThp8c/z9J5P6IAvrVq4entw8nUFEBQO6judcVwaFsExw7ux8vXn0LbdiY/D6fSdUbvalevLp/bznpX6jVtCUDdpi3YvtQ8MuliOZ7Orm7UCWlO5skTePr6cS2l60zpeqF3dSPPeLnOXCk3vZs7oW0sZdO6LadTU2haybKBf7fde2DYKCL++h0U8PL1x83Lu8L1928OM8dS+tzOyyunP3C1i8W2PziXnsbWZYt5dOKUSo26wb/XzhTm57FrzXJGzJiN1smZFd99wemU5Apv6v2bZQTm+nUuPY26TSqX7Fa2/Vv43Rxe/uBTvH39WPj9HKJ27aDtHWWfSXWES6nrgwKj/bFxsakv+Uaj3U2l8qhUKka9PhOjIYfvZk2jRac77JLna+kYXIemgTXJNhhxspky6aTVkG8zMngt0SkniU45Sdcm9enapD7hR446/F3p9nbTRuAURUkE2mNO5D4QQkwH5gKPKYrSEvgBuDx+vwh4QgjRxPxVpdwaKYRwvso2rtYiC0C5yrLymLhyjErPLSiw+bkYB5JhRVG+VxSlg6IoHSpK3gCaNGtG7OFDmEwmMs6ewdnFBa3u2lMX6jZoyMyP/8eb739ITs4l2nfucs3vXE29xiEkx8dRbDKRnZGBztnZ7k5/o2bNiY3aB0Bs1D67ZC165w5ad+lq1zms+O0XPLy96f6A4w+qt+t5H4OmTKPP0FFodE5cys6kuNhEelIitevZz9Wv0ziE4zHmaVQnk47iH2i+iDMVXZl64OyitzuOCfv30qRtR7vO/Wo69OrN0y+/Sd9hY9DodFzMyqTYZCL9WCK169vHUrdJKMlHDgKQnpSI/zUuKFUqlbXTd3F3d/hOdIPGISTFx1jK6BxOzi5o7cqoBTEHzGUUc2AfjZu3tO7vcufl5uFpvlmgKDi5mL+vc3JCo9X+a8/A/Zva9ryXJya/Tu+nR6LV6biUnUVxsYlTSUepVeoiKKhxKMdjDwNwKvkofkHmO+EXszKIWPQbS7/+hNxLF9i0eAGKolBsMt8p1Wi0aLQ6h6cT1W8cQlJ8rE05OZctJ8u5FBO1j8Y251LUzh20sTmXFEUxtwd25eTYs3il1W3UhOMJ5nP8fGYGOmcXu3O8YdPmxEfvByA+ej8NLc+JBjdtQXryMQBOJB/Dt1btSu0foH6Tiutw4+YtOLLffGyO7L9ShwPq1mPS9HeY8Pp0jDk5NG/XgbSko5w9dZKv3n6Lvds2E7b8b1KOJlwzhlbd72HgpKn0emo4Gp0TOZfrTPJRatZrYLduQKMmpMaZ68zp48fwDQgCzNMpz6WlAHDuRAqelumdBZaLwmKTidPHj+HlX9Oh41KvUROOJ8Ray8apVPsb3KwFcVHmsomL2k9wM3PZNGrWghNJN6Zs4N9t9/wCghj0wqs8PvEl8nMNBFumGV5N+7vvY/BLb/DAsNGWc9vcH5w8lkhAqVjqNAkl2dIfmGMxn9vnz51l3a8/8vCY5+ySiOv1b7UzQqhQazRonZzN/YKLnoJSz9SV9m+WEUDcvj2EtuvkUD9Znsq2fyqVyppImfupikfFKhIU3Ii0owkUm0xcyMoscz1TP7QZiZZn7BIPRVE/5OrP0BabTNaRS63O3AZf7yyIyKQT/LZtH6sPxGIyFePh4oxKCOr6enEy+6JD21DblEd+URFFxY6Npt7yVOLW+u8WJRTlevKZKuxIiAAgW1GUfCHEAGA4cAdQH1ADu4EliqLMtKwfCcQDhxVF+egq2/QCEsrbhhBiNzBbUZTlQggny/LuwHTgXkVRjEKIGpZRuIPARMuo4EzAU1GUF4UQPwL7FUX5RggxGZisKEp9IcRwoIOiKBMtcawGPlEUZYsQ4jzgryhKhbdQ4k9nXPPAh69dzcY1qxFCMHriC6jVaqL3RzJw0GBOnkjj288+JSkxgXoNGtL93vt4oP8jrFi8kL07zS/peGTQYDp0ufbUwJSM7Ksu270pjF0RYQgBA0eMQaVSk3Aomnv6D6SwoIA/v/mSC1lZePn4MNjyhkOAL6e/xmOjxhFQrz4AaUlH+eyNqTQMMT/Y7+bhwYiXyr7pKz3zwlVjOZOaTMTi3wGFVnf2oGVX8/TM1T9/Q7+R4wHYuXY5x2MOo9Zo6DNkFF5+/uxcu5w0y4Pqzq6u9Bk62pos/fnpe9w7aFi5U+U06qt3VqdTkglbNB8UaN2tJ627mWNZ8eNc+o82v6hgx+plJMccQq3W0PeZMXj7+ZN19jQbf5/HmbTj+AYE0bxTV9r1vJe4fXvYs3ENGq0WJxc9/Uc/V+Z5hCCf8u9O74zYyM6IDQgEj418FpVaRfzBaO4b8CiFBQUsmPO59c1aQye+iFan48DO7YSv+BuNVove1ZXhk6fi7OLCigXzSDxyCAWFJs1bMWDoiHL3WW/cc1c9No7wnzoZ55bNEFothckpnJ42q9Lb2jRz5lWXnUk9zpYlv6Mo0LJrd1pY3si29pdv6TtiHAC7160gJfYwKrWG3kNG4uXrb7eNn2ZMZdSsjyguNrH0q48Bcwce0q4T7Uq9hbKB/9VHwXZtCmNXxEYQgscs51L8oWjutZxLv3/zJReyMvHy8bW+LRTg8+mv8YTNuQSw8vdfORpzGEVRaNy8Jf2HDC93nwUOvJFs7+Zw9mwOQwhB/2GjUalVJB4+yN0PPUJRYQGLvv2ai9mZeNa48qbD7IxzLPlhDkVFRfjVqs1jY5675p18V6erX+DsjNjIP+Hmty0+Mcpch+MORtPbUofnz/mc81lZePv4MMxSh8NXLuPwvj0A3Nf/UVpYnqG5bPXC3/GrXbvct1DGnDhT5rPLzqalsP3vP0FRaHbHXTTrYn6j4Mbfvqf3sLEARG5YRWrcEdRqNb2eGoGnrx/FpiI2LfwVw/lsVGo19w4ZjauHJyu++R9FBQWUFBcT0qELrXvca7e/hrWuPsK9Z3M4ezZtBASPDB+NSm1uf3s9PJDCwgIWffOV9dweZHlzX3bGORZ/PwdTYSG+tQN4Yqy5bNYuXEB89AEuXThPzcAgRr7yBk7lPPOUcfHqF8k3ut3bG7aWowfNF82d73+QRi3tZ2aUVHB9cjolmQjLjZXW3XrS6k5zLKt+mstDo8yx/LNmOcctsTwwbDRefv4s+fpTss6cwt27hnm/vR8kuGUb9m8OIy5yN1lnTlGzTj3uHzICb78ryXZRBefSjWxnAKK2hBG7dydqtRovv5r0HjLK7vxy0l79fvGNLiOABR+/Q++nhluT4NJKP7NVnsq0f1G7dhC+4m/zc/R6V4a/8DJOLi4V7ud8rvGqyw5s28z+beYXMfV9ejgqlZqkmEN06/swRYWFLP/pGy6ez8bTuwYDRo1Hq9NxZO8u9m0J58SxROo0akKvR57Ay9ePv775AqFSUWwy0fnePuW+hTLh1LlrHheA2l4e9GkdAkIQdTydaMtbKB/p2JJlkebEf1DXtvh5uFJkKiEt6zxro+K4K7QhDfzN9TivsIiV+2MosJmOWdr0R3vfutmIjQtLV96cxMRBXo8+fEset5uZwN0PfAyUAEXAeMzPow0CUjBPP0y1SeBetqzfQFGUlAq2+2552xBCNAa+A3wt+3tcUZRkIcRrwDCgEFirKMo0IUQb4FvM0zCTgRGKopwXQoRins5pADYBQxxI4D4EHgYOVPQcnCMJ3M1SUQJ3s1WUwN1sFSVw1eFqCVx1qGoCdyNVlMDdbBUlcNXBkQTuZqkogbvZKkrgbraKErjqUFECd7NVlMDdbBUlcDdbRQlcdXAkgbtZKkrgbjZHE7ibRSZwlXOrJnA3rRVQFGUDsKHUx/uAN6+y/idA2Xe3ll3vzfK2YZl2WebWq6IoszE/W2f7WTRQZq6hoijxQCubj960fD4PmGezXj+bn18FXr1W3JIkSZIkSZIkXVHZ51L/v7m1hhgkSZIkSZIkSZKkq7q1xuErIIRYBjQo9fGrlpE9SZIkSZIkSZKk/7zbJoFTFOWR6o5BkiRJkiRJkqR/yS385sdbiZxCKUmSJEmSJEmSdJuQCZwkSZIkSZIkSdJt4raZQilJkiRJkiRJ0n9YJf9g/P838ihJkiRJkiRJkiTdJuQInCRJkiRJkiRJ1U/IsSVHyKMkSZIkSZIkSZJ0m5AJnCRJkiRJkiRJ0m1CTqGUJEmSJEmSJKnaCSH/Dpwj5AicJEmSJEmSJEnSbUKOwFWT3Ykp1R2ClbuLc3WHcEsqKDJVdwh2TCXF1R2C1aaZM6s7BKtet1AsG6dPr+4Q7Dhpbp0mPulsZnWHYNWxUd3qDsFqa8zR6g7Bzt0tGld3CFaFRbdOm3f2Yk51h2Dlob+1+uwsQ251h2BVVFxS3SFYBdfyre4QpP+wW6d3lyRJkiRJkiTp/y+VnELpCDmFUpIkSZIkSZIk6TYhEzhJkiRJkiRJkqTbhJxCKUmSJEmSJElS9ZNvoXSIHIGTJEmSJEmSJEm6TcgETpIkSZIkSZIk6TYhp1BKkiRJkiRJklT9hBxbcoQ8SpIkSZIkSZIkSbcJmcBJkiRJkiRJkiTdJuQUSkmSJEmSJEmSqp2Qf8jbIXIETpIkSZIkSZIk6TYhEzhJkiRJkiRJkqTbhJxCKUmSJEmSJElS9ZN/yNshMoG7BZ1JPc7Ghb+BAm26302rrt3LrLNz7QqOHY5Go9HS95kxePn6kZ6UyPr5P5N97izj3vsED28fAFb9/C2XsjMBOJeeRr8R42jcup1DsezftpnIzWEgBA8NG0Vg/YbWZUWFhfz941wuZGXi5ePLwNET0Op0/PHVp1zKzqKkpITO99xP++53A7Dy1x85eTyJkpISuj3wEK3RInOfAAAgAElEQVTv6HZdx+VsWgrhi+YDCq273U2LO+4qs87udStJOnIQjUZDn2Gj8fTxIy5yN1FbwxBChc7ZmX4jJ+Dk4sKaed+Rk51lPS59hz9Lo1ZtHY5l818LUBRodWcPmpcTy571qzgecxC1RkvvISPx9PGzLtu5ehlxkbsYNesjABZ//gHFJhNqjQbfgCB6PTH0uo7Nns0R7AzfgBDw6MhnqdMw2LqsqLCQP7/5ivOZGXj7+vHU+ElodToWfPUZ5zMzADiZepwhE1+kRYdOAOTm5PDOpLE8OnIsHS3l56gbfWzWzfuOnAvZFBUUENrxDtr3uv+64rmWgE/fw6lJIy4sWc75X/+8odu2de5EKlv/+h0FhRZde9CsS9n6H7lhNSkxh1BrNNz79Eg8fHyJ3b2DvetW4l7DfD7f/8xY3Ly8rd9Z8sVsvPxqcu/gEdcVz40+n9b++j0Z6Wk4uehxcXen/5hJ13eALAK8PXiofXMEgr1JaRw4nm63XKdRM+ruzvh5uLFyfwzRKSety7o3bUjjWn6ohCAi5ijJZ7Oue//7t21m7+YwBJY2r4F9m7f0x7lczMzE09eXRy1t3to/fiM9+RhFhYU0CG1G38HDANiy8m+ORO5BCEGrLndyV9+HKnVMAAK9PenfsQUAe46lsj/Z/rg4adSM7tUFf093lkceJspyXGq46XnijjYoigIKLNwZxcW8/Ove/54tEewK34AQgoEjxpZpYxZ++xXn/4+9+46L4ugfOP4ZrlAtKKBiwQaIvccWSzTGJEbTE40x9phEY6Lp1bQnxZQnPZreNFFj771XRECkgzQVaTbq3cH8/rjzvKN5KBH8PfN+vXwl3M7tfpnd/c7OzuySlYWnlxcPTzfnGENREct+XkB2RgYlJSVMfu5lJJKfPv7A+t0TMVG8+/1vuHl4VCme4J3bOLh9MwCjH5tCs1b28Sz5/mtr2/TA1KfQ6fXW5d++8xpejZvwwNSnAFjxy/ekJcZTIku4+fZRdOtX9lyozJmUJLYt/t2c8wYMLvdcOrhhNYkRoWi0Om57dLJdztu7ZhnRh/cz+a15GA0GVnz3X0xGA7K4hL53jqZVhy4Ox1Jdx29+7kX++O8863eT42J47ZsfcXW/8n46umcHR3ZsRQjB7Y9MxLdlq8sxGA2s+mk+57OzqNfQi1GTHken03M2K4OVP86n2GTEv0s3Bo68h/zcXP7+6hPrd1PjY3n+8/m4unsQfyyMvetXIaUkoGsP+t125xXjOp18gk0Lf0Ui6TbwFrr0H1SmzN61K4gPD0Wj0zJywuPU9/LGZDSw5pcFXMjJpm6DhoycMA2tTk9aQixbFv+Jk5MG/y7d6Dui8vM7dM9OQnZuBSG4/ZEJNPG7XC8mo4FVPy+4XC8Tzds4l5XJqp++w2Qy4d+5GzePvBuAVT99R/yxMPw7d+OuidMAKDaZWL7gKy6eP0exycTQ+x+mVVDHK9aLcmO6YTpwQohcKWXVMnzVt7FPStnv39yGIzb99RujJj9BnfoN+PX9N/Hv0gNXd3fr8uzTp0iOjmT8i2+SEhvNjmV/c/e0GXg1acb4l+ay5KuP7dZ316TpgPnknv/687Rq79gJXZCXy/5N65g+9z9cyMlhyfwvefz1d63LQ3Zvx9u3KQ89+Qxbly8hZPd2bhp6G8MfGItX4yYYDQY+f3k2nfv0J/tMOmdOpvLE3PcpKijgy9eeq3IHbsvfv3PnxOnUqe/JHx+9RdvO3XGxrZf0UyTHRPLI86+TGhfNruWLuWvKUwR060lQrz4A7Fn9D8cP7qX74GHcOeFxa738OPcFWgZ1cDiW7Uv+4PbHHsejvieLPn6HNl264+J2OZac9FOkxkbx8JzXSIuLYc/KJdw56UkA8i6c52xGepl1jpz8FHU8G1SpTgDyc3PZtX41z743j/M52fz+5Wc88+6H1uUHd2ylUdOmjJ81hw1L/uLgjq0MGH4742Y+C4DJaOS9WU8Q2Lmr9Ttbli+lVWBQlWOB6q+b4eMmo9FqKSku5pd3XqFTv4HoXVyvKrbyZHzwGa49u6H18aq2dZZn55I/GT5+Kh71PVn8ybu07tytVL2cJi02igdmv8LJ+Bj2rlrK7RPN5277vjfTu5yLgxMRoVddF9V9PgEMfehRmrUNvKp4LrmrRwcW7w/lQkEhT9zan8i0dAqNJutyU3EJv+8+wk1tW9h9L6CJNy46HT9uP3jV2y7Iy2XfxnU88ZY55y3+7kumv1Eq5zVpysOlc96DY9BqdQAsePcNzqSlUr+hF8E7tzF73udICZ+9+Aw33XIreheXq4ptdK+OLNobwoWCQmbcdjORaWcoMBity43FJfy6K5i+/n523+vr35LD8SkcOZFGj9bN6BfYivWhUVXadn5uLrvXr+GZ9z7ifE4Of3z5GbPeudwJO7RjKz6+zXj06TlsWPoXh3Zspf/w29mw5C+69h1Auy72N8Zmzn0PgOT4WNb/vbDKnbf8vFz2bFrLjLc+4EJODn99+zlPvvkf6/LgXdvx8W3K2KeeZfOyxQTv2k7fYeYbP5Ehwbi4ulnLpqcmk56Wyoy3P6SwoID/vjK7yh24bYt/544J0/Go78nCeW/TtlTOy04/RUpMJGOee520uGh2r1jCyMnl5zwnjRPDH5lIvYbe5Ode5K+P33W4A1edx2+jZs2Z9trbAKQmxLF56d8Odd4K8nI5uHkDU15/l4tnc1j2/ddMfuUt6/LQPTvxauLLfY/PZMfKfwjds5NeQ25ly5JFDLnnfvwCgvh13rsE9eiNd5OmTHzpTQDSEuPZvnwJru4e5F28wMGtG3hk9stotY5fxm5a+CujpzxJHc8G/PyfNwjo2sPud8o6fZKk6OM89vJcUmKj2P7PIu55/GnC9u6iYWNf7p46g92rlxG2dxc9Bg9j06LfuO+JZ6jX0Iu/Pv+IgK49adi4SYX1cmjLBia/9g4Xzuaw4vtvmPjKXPt6aezLvdNmsHOVuV56DrmVrUsXMejuB/ALaMfv894jqEcvvJo0ZfA9D9K530CO7d9jXUdCRDg6ZxcmvjyXc1mZLP32c6bYXLMp/7+oZ+Bs1IbOm8loxFhURH0vHzRaLc39AzmdlGhXJjk2ijadzBfbLQLakZGWAoCLm1ulFwfx4UdpGdQerU5fYRlbqQlx+AUGodXqaODTCENhISbj5QuGxKjjtOvaA4Cgbj04EWO+KPCyJDCNVoNwEgghqOvpiVarpdhkoqiwwKGGwJbJaMRoKKK+lzcarZZmbQM5nWxfL6mx0dZ6ae7fjoyTKZY4Lid4o8GAl29Tu+8lHDtKi8AODtfLpX1UzxJL0zYBpCefsI8lLppWHc2NbjP/QDLTUq3LDqxfRe/bRpZaq2DtT9+y5PMPSYmJdCiOS5LjYmjdrgNanY6GjRqX2U/xxyPo0KMXAB179iIh8rjd94+HHCagUxfrHeqczEzOn8uhRZu2VYoD/p26ubT/TEYjdTwboNU7VzmuSmPOzKrW9ZW7Dcvxe6lefNsEcKZUvZyMj6al5UKtadtAsk5erpfoQ/tY8tl/2L9mGbKkBABZUkL4rm10vvmWq46nus+n7UsXsfDjd4kOPlDlmAA0Tk7otRrO5hVQXCJJysyhecP6dmVKpCS3sKjMdzu1aIJO48TkITfxYJ8uOOuqfn8yJT6OllfIeUHdbHJetDnnXbr4LTaZ0Dk7m/OdXk9dzwYYDQZMBgM6vR4nrabKMYGlXjSX6+VERjbNGjhWL2fOX8RFb47PTa8nr5wyV5IcH0vrdu3RanU09GlEUekcE2mTY3r0JiHKnMPiIsKICg3hy7mvsn7xwjLrDd61g543D65yPKnxsbQKbG/dT6XjSYyKIKhbTwDad+/JiWhzPCUlJezfvJ5+t46wlq3r2cCubapqZ9J8Lhnsc16pNjstNprW1pzXjkxLmw1wYP1Kbhp+OedpNFrr6JxWq0NUYTpZdR6/to7u3UW3/mVnApUnLTEev4B2aLVaPL19ysSQFB1JgGUGUGDX7iRbrhvSU5LxCzDfNAzo3M36+SXh+/fQ2XLTNy7sKK7uHiz6fB6/f/I+GTa5siImoxGDoYj63uZrqxblXVvFRNG2k/lmQ4uAIM6kmvdTSkykddaSf5fupMZGA1BUkE+9huYbf01atia5krb7ZGI8LQLaoblUL0X29ZIcE4W/5UZHQJfupFi2Ya6XdpZtdyM5xvx53XJu9nr6NKLYZERKSUFeLu51612xXmolJ6fa9a+W+lciE0KME0IcEkKECiHmCyE0QohcIcSHQogjQogtQojeQogdQohEIcQoy/cmCCFWCiE2CCFihBBvlrNuIYSYJ4SIEEIcE0I8ZPn8dyHEaJtyf15abznr6GATX7gQwt/yea7lv4MtsS0VQkRb1iUsy3oJIfYJIcIs66hj+f3mCSEOW9b3+NXWXUFeLi5ul+8OOru5U5B30a5MYV6u3Z3yEssF3ZVEHNxLh979HY4lPzfXbuTP2dWN/Lxcu1gvdcRc3NwpyLWPc8eqZXTpMwCtToeruwcNGzXh0+ef5stXn2PI6PscjgMsv7PNXVNnVzcKbWKxlrG56ylt6iV8705+fucV0uJi8Gpi34GLPLSP9r37VikWZ7crxZJnF2+JNMdyNiMdY1Eh3k2b25W/a8pTPDznVUaMn8LWv37DUFjgcDx5ubm4eVz+vV3c3Miz2Rf5uRet+8nVzZ38UvvJfBF1eSrJhiWLGH7vgw5v39a/UTcAq3/4ih/ffJ6mbQJwqsUJtSKF+bk4X/H4zbOru0vHb+vO3Rj32nvcN+slLuZkWztHUYf20qZLD7Q6XdXj+RfOp8H3jeHRl+ZyzxPPcHDjGs5lZlQ5LjdnHYWGy6NthQYjbs6O3Vip6+pCiYQftx8kNfscg9u3ufKXSjHnNPtzKT/3cr2Yc+LlnGd7Lq369UfmzX6KuvU9cXZ1Q6PRENilG58+P4tPnptJ7yHDrBfKVeXurKPA5mKvwGDE3dmxdcWlZ9LH349n7xhEH38/DiWkXPlLpdjmEADXMjnmcr25uruTl3sBgFMpyQR07MyMN98lPS2VqNAQ63eKi4uJDAmmU++briKe8vZT6XjK5rwju7fTsVcfu5t1ru4eeDVuwkfPzeC/r8xm6OgHqhRLYV6pc9vNjYLS51J+Hs6255JdzivCu5n9aPIl25f8Sa/hdzgcS3Uev5cUFxcTffQI7Xv2ciyGXPvrE5dS9VFgc/1ivm4wL7tUJ6U/vxRDbFgI7bqbY7h47iw5Z9IZM+t5bn1gLKt/+f7KcZXOeaW2AWWvrS7FVJCXZ82FLq5u1msyV486nElNpthkIikygsK8vEq2n2eXT51dXcvUi/2+KadebLZdHk9vH4wGA9+8MoeFn33AwLvuqbCscuOr9imUQogg4CGgv5TSKIT4BngEcAd2SClfFEIsB94FbgXaA78Cqyyr6A10BPKBw0KItVLKYJtN3At0BboAXpYyu4AfgGeBlUKIekA/4LEKwpwOfC6l/FMIoQfKuy3aDegAnAL2Av2FEIeAv4GHpJSHhRB1gQJgMnBeStlLCOEM7BVCbJJS2t1iF0JMA6YBPDbnZQbbnFzB2zYRE3IIT59GFBVcvngvKsgvM1rl4u5BYX6+9WdHLmgL8/PISEuhheVOjiPcPOy3U1SQj5ttI+7uQUF+Hp5AYak4Q/bs4ExaKg89+QwA8RFhXDibw5xPvqQwP58F775OQOduV7z4DNmxmdiQw9T3bkRRweVYDAX5dokWLtXL5QQqbOqlc/9BdO4/iIOb1nJo8zoG3/uwtV4yT6bS3P/K9XJ0xxbiQi/FYrOPCvPtErM5Fne7eJ2EOZb9a1fQb+S9Zdbt6lEHgDqeDfFu2pxzmRn4NPcrU6487h4epNg0HIX5+bhb1gfg5lGHAsvygvx8uzvM+Xm5nEpOoo1lWu2p5CSEEDRuVrYTVZl/s24A7poyA6OhiMWfvU9gj940LNUJr63Cdm4lPjSYet4+FNl0yg2F+biUOa/t6+XS8WtbfwE9biI5KgL/br2IOXyA0U/O5lRinMPx/Jvnk5vlmHN198AvqCMZJ1Oo7+3jUFx9/f3o2LwJ2bl5uOgvN0sueh35RQaH1lFgMBB72txpjD2dyV09HJ8SfYk5p12ul8JS54ubR8U5b9Rjk7lz3AT+/HweseGhNGzUmIjDB3n+06+RsoQF775J+x69qWd5ltER/QJa0qlFE7Iv5uFikytd9DrybaZPVuaObkFsDIsmIjWdrn6+jOjajhWHIxyOASw5xOZYKCiTYzysywvy83BzNy9z96hDu67dEULQrks3TiUnEdTVPJIRE3aUlgGBOF/FFGBz22Sf89xKxVOYlwfe5mWuHh4YDQaO7t3N5BdfJ8lmdCfuWBjnz2bz4qdfU5ifz7dvv0Zglyu3TUd3bCb2aDD1vX1KnUtlZ5i4uJU6ty05b9/aFfQfWf5F9v51K3F2c6NjX8dGvqB6j992lv0UdyyMFv4BDu8nVw8PCk/Yx+Ba6rrh0nVFYYF538DlOrF+bvOdhIhwmrfxx9kyw8jV3YNWQR3RarU0buFH3sULFcZzeNtGooMvXVvZX8+Ul/OK8svuJ1d3d8vx5k1hweXcfef4qWxZ/Acgqe/tg0d9+1Fxu3pxd6fI5pgtKnWcuFrzrbflus/9ivVSWtjeXdRr0JCHZs7hXFYmi7/6lGlz36+wfG1VlVHn/2X/xq3soUAPzB2rUMvPrQEDsMFS5hiwU0pptPx/S5vvb5ZSZkspC4BlQOkHpQYAi6SUxVLKM8BOoJeUcifQVgjhA4wB/pFSmijffuAVIcSLgJ9lW6UdklKmSfPtj1BLjIHAaSnlYQAp5QXLNoYD4y2/70GgIeBfeoVSygVSyp5Syp6DS90Z6XnLcB557jXuGD8VrV7P+ewsik0m0uJjadLS/k5yi4B2JEaEAZCWEItPBXfvbEUFH6Rd9952F2FX0ryNP8mxURSbTJzLykTv4mLXqLVq14HYMPMd1diwEFq1aw9A5JFDhO3bwwPTZ1o7l1KaE5iTkwZnF1eKTSaHRg67D76Vh2e/wohHJ6PVO3MhJ4viYhNpCbE08bOvl+b+gZw4Hg7AyYQ4fJqa68VkvHwB6OLqZvcwe8yRQwR06+VQvXQbPIwHn3mZ4Y9MQqfXcyEnm+JiE6cS4mhs83IXME+TORF5DIBTiXF4WzpE57Mz2fr3b/zz1cfkXTjHtsV/IKW0dnoMhQVknUqzvrDCEX7+gSRGm/dTTmbZ/dS2fQcij5rvgUQeDbZ21gBC9+2hS59+1v2UmphAxqmTfPvumwTv2sG2VctJjout0bopNplPY61Wh1and3iqa23QZdBQ7pv1IsPGTkSn03PRpl4a2TzADuZpk8mWejmdGI+XZSTS9oIiNTYKT5/GXMjOpKggn1XzP2fviiWkREUQsW/XFeP5N8+nSxfUxSYTJxPiaODT2OF62h+XzPfbDrDs0DEMpmLqubngJAR+Xp6kZp9zaB2JZ3Ks0wqbNqhH9sWK74ZXpEXbK+e8GMsoUkxoCK2DzDnPaDDXiUajQe/sgl6vBylxtnxfp3dGq9ViKKzay0P2xSYxf8t+lh4Mx1hcTH03V5yEoJV3A4frRSDIs3SCcwsNuOmrfv74tQ3gRHQkxSYTZ7Myrb/XJW3adyQq5AgAUSFHaNu+g/Xz1IR4AFIT4q3T6wGCd++kx1VMnwRo3jaAEzHRFcbTOqgD0WHmeKLDjtC6XQdyMjMoyM/j54/fY92i34gND+Xg9s1IJK7uHta2yeRg29Rt8K089OzL3DZusl3OO5kQW37OszmXLo24nc/KYOvfv/PPVx+Te96c88DcOTyXkc4gy41GR1Xr8WsRWoXpkwDNWrclJS7GHEN2VpkYWgYGERd+FIC48KP4WZ61bty8BSlxMQDEh4daPwcI37/bOn0SoGW79py2TEE/n51lN2JYWq9bbuPRF15n5IRp6PTO1mur1LgYfFvZ5zy/gCASIkIBSIuPpVFz835qERBEwjHz5wnHQmlhmerp3bQZY559iQdnPk9BXq51ynl5mtrUy/nsLPTO9vXiFxhEXHiopV4u//6NmrcgNd7cBscfC7Nuu3wS1zrmGxkubu5VzjfKjUVIKat3hULMBHyllC+X+tz6EhIhxFwgV0r5se0yIcQEYIiU8jHL528D2VLKz23K/BcIl1L+ZCnzO7BESrnK0iEzAA8Dk6SU9g/72MfTBrgTeAaYIqXcZrONwcBzUsqRlrJfAcFACPCNlHJAqXX9AyyQUm50tJ5+2Xm4woo/nZTI5r9/BwldBgymywDz9LaVP3zD6CnmB5/3rFlO4vFwNBotdzw2FU9vH7LPnGbTn7+QnnICL99mdOjdz/pygT/mvcPwMRPwKWdkpY5rxc/NBe/cSvAO81uTRo6biJNGQ3xEOAPvHI3RUMQ/33/D+Zxs6jVoyH2WN33NnTIOb19f9M7m9T74xCzq1Pdk2fffkJ2RjslopFv/geW+Nepsbn6Zzy5JT05k6+I/AUnn/oPo1M9cL2t++paRk54AYN+6FZw4fgyNVsuIcZOp7+3DvnUrSLE8A+Hi7s6IR6dYRzUWffIewx4eX+60veJKGvH05BPsWPonUkKnfgPpaHlT6Lqfv+MOy4snDqxfSVLkMZw0WoaPm0R9L/vRiB/ffIHJb31EcbGJRfPeQavTU1JcTI+htxHYo+y0Ij+fil9wcmDbZvZv3YwQcO/EqTg5aYgJD2Xo6HsxFBWx6NsvOJedTf2GDRn75CzrRfcXb7zE/ZOn4+vXssw61y9eiFfjJuW+hTI5I+e61c0/X5rfhFZsMhHYvTfdS72F8pa5cyuMxRE+LzyDS6f2CJ0OQ2ISp20euK+qTW+8UeGyMykn2LV0ERJJh74DrW/n3PjrAm57zPwWsUMbVpEcGYGTVsuwsROo5+XDvtX/kBoTiZOTBs9GjRny8Hg0msujVGlx0UQf3l/uWyidK3nAv7rPpyVffIShqIiS4mLa9+5Hj1uG220v4Yxjzxo2bVCPkd3bIxAcTkyxvm3xob5d+Xu/+QJn/MCeNKpXB4OpmOTMHFYER6BxcuLe3p2o5+ZKSUkJiw+ElftMGECvthXf9AreuZXDO7YiEIx81JLzjoUzcKQ55y1d8I31bXT3TzPnvD8+n0f+xYuUFBfjF9iO2x82v0V2w99/khgZgUTSJqgjIx4eV2Z7B2KTHKqXZg3qMaqn+ebLofgUghPNz/2M6deNRfvMF8QTBvWiUb06GIuLScrMYdmhYzSq58G9vbtQIkvQODnxz8FwzpyveBrWkI5l7j2a49y2hQPbNiGE4J4JU3DSaIgND+WWUfdiMBSx6JsvrW3B2CefRqfXk5OZwd/zv8ZoNODd2JeHHn8KJycnigoL+HDO07z6xXdoNBU/F2gwFle47PCOrRzasQWAUeMno3HSEBsRxuCRd5tH6xd8bY3nwWkz7G7cJURGELJ3Jw9MfYqSkhKWLPia7DOnMZmMdO8/mAEjyrZNldVZevIJ65t3O/UfRCdLzlv783fcacl5+9etJCnS3GYPf3RyOTnveSa/NY/8ixf47qWnadK6LU5O5rp5YNaLdjNtvOrajxzZqs7jt6iwgM9fnsOcj7+sdD8VGe3vlYfs2k7Irm0IIRgx9jGcNBoSjx+j/+13YTQYWPnTd9YYRk+ejk6nJyfjDKt+nk+xyUTbTl0ZNOpeSwyFfPv688z84L92MexZt4rYsBBKiosZ/tA4WvibX55kLK643T6VlMjmRb8hkXS9eQhdBwwGYMX3X3H31BkA7F69jMQI85uA75wwFU/vRhgNBtb8Mp+LZ3Oo49mAuyY+jlan5+CmtcSFmc+9PrfdWeYt1hon+5Gko7u3c3TXdhCCEWPGW+uln6VeVv30HRfO5lDXswGjJ09Hq9NzNuMMq35ZYKmXLgy8y1wv25b9TcKxMHLPn8OrSVMeenoOAMvmm2c5mQwG+o4YSQebx0Me6d/9hhjayt22q3o7JtfI45aBtbLe/o0OXHtgJeYplBlCiAZAHeC4gx24/2CeQlmAeTRrkpQy2KbMvcDjwB1AA8wdq5uklOlCiEbAISBdSlnhxHohRGvghJRSWjqESVLK/zrQgVsIRHN5CmUdS5yTLPE8YJk2GgCclFJWeAu4sg7c9VZZB+56q6wDd71V1oGrCZV14K63yjpw19u1duCqU2UduJpQWQfuenO0A3c9VNaBu94c7cBdLxV14GpCZR24662yDtz1VlkHriaU7sDVpMo6cNdb6Q5cTbthOnA79tSa62MAj8EDamW9VXvrLqWMFEK8BmwS5sm7RuCpKqxiD/A70BZYWOr5N4DlQF8gDJDAC1LKdMu2zwghooAVV9jGQ8A4IYQRSAfediQwKaXB8tKUL4UQrpg7b8MwP3/XEgixvOwkE7jbkXUqiqIoiqIoiqI46l+5PSul/Bvzyz5sedgsn1uqvO1TmRlSyhnlrNPD8l8JPG/5Z0cI4Yb52bNK/xqvlPJ9oMyTnTbb2AHssPl8hs3/Hwb6lLPaVyz/FEVRFEVRFEVR/hW1Z37NNRJCDAN+Aj6VUp6v6XgURVEURVEURakC9RZKh9SqDpyU8hfgl6v87hbA7sEGIcRtwIelip6QUqo/jqEoiqIoiqIoyg2nVnXgqpvlrZAOvxlSURRFURRFURSlNvt/3YFTFEVRFEVRFOUGUcve3llb/Rt/yFtRFEVRFEVRFEX5F6gOnKIoiqIoiqIoyg1CTaFUFEVRFEVRFKXGmf+EtHIlqpYURVEURVEURVFuEKoDpyiKoiiKoiiKcoNQUygVRVEURVEURal56g95O0SNwCmKoiiKoiiKotwg1AhcDanr5lLTIVh5urvVdAhWjerVqekQrIITUmo6BDsezs41HYJVK5+GNR2C1aY33qjpEKyGv/12TYdgZ/e77+661TQAACAASURBVNR0CFbdWjWr6RCsEs9k13QIVuMH9a7pEOwcSzlV0yFYbQ2PqekQrMYPvqmmQ7AKSzpZ0yHYCfT1qekQrBLOZNV0CFb5RYaaDkH5f0x14BRFURRFURRFqXnqD3k7RE2hVBRFURRFURRFuUGoEThFURRFURRFUWqe+jtwDlG1pCiKoiiKoiiKcoNQHThFURRFURRFUZQbhJpCqSiKoiiKoihKjRPqJSYOUSNwiqIoiqIoiqIoNwjVgVMURVEURVEURblBqCmUiqIoiqIoiqLUPKGmUDpCjcApiqIoiqIoiqLcIFQHTlEURVEURVEU5QahplAqiqIoiqIoilLz1BRKh6gROEVRFEVRFEVRlBtErR+BE0I8AyyQUuZXR7kbwZFd2zm0fTMCwV3jJ9O0VWvrMqPBwD8/fMP5rCzqeXlx35Qn0en1rFv4G2mJ8RgNBlq1a88dY8cDsGPVMiIOH0QIQec+/bn5jruqJcZ92zazZ9MGEPDwlCdo0aatdVlCdCR/fvslGadP8c43P+Lp5VUt27S1Z+smdm1ch0Aw9vEn8Wvjb10WH3Wc3775gjOnTvL+/J9p4OUNwLcfvsvZ7CxKSkoYfPtIBgwdXi2xZKYms2fZIiTQvu/NtOvdv0yZI5vXkhJ5DCetliFjJlC3gRcXsjLZtuhnhBAgBEMfmYRH/QbXFMvV1Mu2davZsmo5sqSE9xf8ck3bBziwfQv7tmwEIXhg0jSat758bBgNBhZ++wU5WZk08PJm7BNPo9Pr+e3LTzmblQnAyeQTPDpjNp169mbT8iWEHdwHCHr0H8gtd91dpVgyUpPZueRPJJKO/QbRvs+AMmUOb1xD0vFwNFotwx6ZRN2GXkQe2MOh9auo06AhALc9Ng2P+p7W7yz9/APqezdi2NiJV1FDlfP95D2cA9pybukKzv66qNrXf0l6ShJb/voNKSVdbx5Cp34Dy5TZv24VCRGhaLRa7hg/lXpe3hzcuIaYkMM4aTQ0at6SYQ8/islo5J9vPsVkMFJSUkz/kffQpmOXq4orZPcOgndsQSC489GJ+La0z38rfvqO89lZ1Gvoxd2TpqPT6/nrq0+5cDYHWVJCr1uG0/3mwVdbLWSlpbBv+V9IKQnqczMBvfuVKXN0yzpSoyJw0moZ9NB46jTwIv/iBXYu+plikwkPzwbc/MA4NFqd9Turv/6Yel4+DHxo/FXFtW3DerasXQVCMGXGLFoHBFqXRR8/xvzPPuZ02km+/n0hDb19AAg5eIC/fv0JvU6PVyMfZr74ChpN1Zr9o3t2cGTHVoQQ3P7IRHxbtrIuMxoNrPppvnV/jJr0ODqdnrNZGaz8cT7FJiP+XboxcOQ9AORknGHDwl8xFBVRt0ED7p36FADxx8LYu34VUkoCuvag3213VinGFl6ejB3QHRDsikpgX8wJu+UdmjVmVK+OmIpLMJhM/LD1AHlFBkb17EivNs25UFAEwMertyOlrNK2S9u7dRO7N64HIRgz7YlSOTiSP7415+D3vvvJmoO3r1vNltUrkCUl/Gf+z9e0/YzUZHb9sxCkpEO/gQTdVDbnBW9aS1KkOecNHTORug3NbfSRLetIjT5OiZT0vu0umgUEsfmPH7h4NgeA7JOpDBs3mVYduzoUS3W2BXs2r+fAti3mXPTgWAI7VT2/ZKQmsX3JnyAlHfsPpkM57cGhjWs4cTwMjVbLrY9Mpl7Dy9cv+9cuJ/rwASbO/RCAhPCjHNq4Bo1WQ+cBQ2jXq2+VYwJzztm7/C+QknZ9biawgpyTEhWBplTO2WGTcwaWyjnK/283wgjcM4BbNZar1Qryctm3cR1TX32LB594mtW//2S3PGT3drybNOXxN97Fu0lTQnZvB2D4g2OY9trbPPX2B6QlxnMmLZWiggKCd27jybf+w/Q33+Pgtk0YCguvOca83ItsX7uK2e98wKRnnufvH76zW+7b3I8XPviUVgHtrnlbFW1/6+qVvPDePKbMfpGFC761336Llrzy0X9pExhk9/m9j07klY/+y4v/+Zi1fy/EaDBUSzx7li1i6LjJjH7qOY7t2kZRfp7d8rNnTnMyLoZ7Zr1ErxGjOLhmGQARe3fQ7qYBjJ7xPIG9+nJs97ZriuNq66VH3wG8/dWCa9r2Jfm5uexcv5qn5/6H8TNns/Qn+/Ue3LGVRk2b8ew7H+Lj25SDO7YCMH7mbGa99T5PvfY2rm7utOvclcKCfA5s28yc9z5m9nvz2LN5PUVVPH53LvmT4eOnct/TLxK2cwuFpfZNTvpp0mKjeGD2K9x0x2j2rlpqXda+783cN+tF7pv1ol3n7UREKHoX16pWjcMyPviMrG9++NfWf8mWv35j5KTpjJ3zKke2baIwz75ustNPkRwTybgX3mDAyHvZsfxvAPy79mT8y28x7oU3yLt4nuToSJw0Ttw+bjLjXnid+2fMYdviP64qpoK8XA5sXs+kl+dy//SZrP3D/mL26J4deDXxZcqrb+PV2Jeje3YAcOv9Y5j2+rtMenkuO1f9c03n9r7lfzF47CRGPjmHiD1lz+dzZ9I5FR/DqJkv0GP4SA6tXQ5A2Nb1BPTqx8gn51C/URPigg9Yv5McGY7exeWqY8q9eJF1y5fy1qdfMOvl1/jx6y/sljf3a8V/vvyWgPbt7T7/65cfee7Nt3n386/QaLSEBQdXabsFebkc3LyBCS+9yb3TZrB+4S92y0P37MSriS+TXnmLho19Cd2zE4AtSxYx5J77mfzq25yIOk7m6ZMArPvjZ+6aMJUJL75u7bzlXbzAwa0beGT2y0x48Y0qd94Axg7ozg9bDzBv1TaGdvTHTW9/EXv63AXmrdzGvFXbCEs+xbDOlzu/a0MimbfKvOxaO295uRfZtmYlz733EZNnv8Bf35fOwX689OFntA60bxt79BvAW1/Ov6ZtX7Lrn4UMf3QK98x8gfBdW8vkvLNnTpMWF8X9z7xM7xGj2L/anPOSI49hKCjg7hnPc+/MF2gWYG4nbh03hXtnvsDoJ2ajd3WjeWAHh+Kozrbg4vlz7N28gWff+ZAnXnmTlX/8QklxcZXrZvuSPxkxfhr3z3qJ0B2by20PUmOjeGj2q/S54272rlxiXZZ34TxnM85Yf5YlJexZsZj7Zj7HfU+/QOjOrRTmX934wd7lfzHEgZwz2pJzDlpyTqgl59z15Bw8GzUh1ibn3MiEk1Ot+ldb1arIhBDuQoi1QogwIUSEEOJNwBfYLoTYbinzrRAiWAhxXAjxluWzp8spl2uz3vuFEL9Y/v8By7rDhBC7KomlgxDikBAiVAgRLoTwt3w+zubz+UIIjRDCTwgRJ4TwEkI4CSF2CyGuangnJT6OloFBaLU6Gvg0wlBYiMlotC5PjDpOULceAAR168GJ6CgAtJa7LsUmEzpnZ+p6eqLV66nr2QCjwYDJYECn1+Ok1VxNWHaSYmNoG9QBrU6HV6PGFBUWYrSJ0dXdHRfXf+8CNzEmGv8OHdHqdHg3bkxRYQFG4+ULNrcKtt/ItykAGo3GfFJWwzTrYpMRo6GIug290Wi1NGntT0ZKkl2ZU/Ex+LXvBIBvmwCyTqYC0KCxL4YCc8Ivys/D1aPuNcVytfVSz9MTrbZ6BuOT4mJo067iYyPu+DE69OgFQMeevYmPjLD7fkTIYQI7dUGn16PTO1PPcvwaDUXo9Ho0GsePX5PRvG/qeZn3jW+bAM4k29+ZPxkfTcsO5ju5TdsGWvcNQPShfSz57D/sX7MMWVICmBvt8F3b6HzzLVWrmCowZWb9a+u2bsNoxFhURH0vHzRaLc38AzmdnGhXJiUmijaWu9zNA9qRkWY5bhs1tpbRaLQ4aZzQaLTUs4wmaLU6EFfXtKQlxOMX0A6tVountw+GIvv8dyI6ksCu5vwX2K0HSTHm/NewcRNzPFotQjiZR7WvwuXz2QuNVkvj1v5kpibblTmVEEOLIPP53KRNADmn0gA4n5mBV3M/AHxatOJUfAxgPmYi9+6gff/BVxUTQFxUJEGdOqPT6WjUxJfCggK7Tqq7hweurmXvXzb3a0l+bi5SSvLz8qhbv36VtpuWWGp/lGqPkqIjCejSHYDArt1JtuyP9JRk/CwdgIDO3UiOieJcViZGQxHrF/7Kzx+8RWTwQfPvFnYUV3cPFn0+j98/eZ8Mm3PQEVonJ/RaLVkX8yguKSEuPZOWPg3tyuTk5mOynMPFxSWUWP4fYETXIF4cPZShHf25VidiY/Bvb8nBjRpTWFjoUA6uW796cnCxyYipTHtUKufFxdCyfWfAkvMsx2/c0cOYTEZWfDWPzb9/T1GBfWck6XgYzQOC0OocG+GpzrYgOyODxs1aoNFqcXF1w9nZmawz6VWqm0s571J70LRt2fYgLS6aVh3MddOsbSCZNsfioQ2r6DX88s2FgrxcXD3qoHdxRaPR4unTiDOlcqgjLu8zL+s+Ky/nNK8g53iXk3OU/w21qgMHjABOSSm7SCk7Av8FTgFDpJRDLGVelVL2BDoDg4QQnaWUX5RTriJvALdJKbsAoyopNx34XErZFegJpAkhgoCHgP6Wz4uBR6SUycCHwHfAHCBSSrmp6r++JSm4u1t/dnFzIz/X2hclPzcXV3cPyzJ38nMvWpet+vVH5s1+irr1PXF2dUOj0RDYpRufPj+LT56bSe8hw6wdvWuRl3sRNw8P68+ubm7kX7xYyTeqV17uRdzdbbfvTl4Vtr92yV/0HjgYnU5/zbEU5uXhbHPhpHd1pTA/175Mfh7ObpfLSGm+eGgWEETk/l38/dFcju/bRVA50zmq4lrrpTrkl3ds2Byj+bkXcbPE6OrmTl6ufXyHd+2gp2X6m0ajoX33nrwzazrvPD2d/sNGOHzxAFCYn2u3b5xd3SjMK7Vv8krtG8uFXevO3Rj32nvcN+slLuZkE225sxl1aC9tuvSoUhy1UUFert3v7ezqRkFu2bpxcbuci6TNRS+YO3h558/R3N9+NGHL4t+5aXjVR1EA8kvnP1c3Cmz2WUFuLq6WmFzd3MvEvHP1Mjr16X/V+6f0+ezs4lrmmCnKty9zqTPg2cSXtOjjAKRGHbPeRY8N3k+rTt2uaWpT7sULeNSpY/3Zzd2d3IsXrvi9QcNH8M5Lz/H0hHFotFraBlZtVkRBbi4updoju/2Rd3m5i83+uJTjbD+/eO4s6SlJjBgznjGznmf7iiUU5Jk/zzmTzphZz3PrA2NZ/cv3VYrR3UVPgeFyxyC/yIiHS/m5va6rM7d09GfH8XgAth6LZe6SDXy6ZgddWjbFv4l3lbZdWt5F+/zndp1zcEFeLvoyOc9+NKd0e3Tp+M27cA4hBHfPeJ5Gfq05snmd3fdigg8Q0LOPw7FUZ1vg3bgJJ5MSKcjP51x2NieTk8qUv5LCvLLtQUHp9iA/D+dyct7ZjHQMRUV4N21++ffxqENB3kVyz52lqKCAU4lxZerasbjy7PaZ3oGccymuBk18SbXknBSbnKP8b6htz8AdAz4WQnwIrJFS7i7nTuqDQohpmGNvArQHwquwjb3AL0KIxcCySsrtB14VQjQDlkkp44QQQ4EewGFLXK5ABoCU8gchxAOYO36OTRAvh6u7BwU2w/CF+fn2DYKHBwX5eXgChQX51s4cwKjHJnPnuAn8+fk8YsNDadioMRGHD/L8p18jZQkL3n2T9j16U6+B/d3JqnL3qEOBTaIqyM/Hzebi4t/m7lGHfNuLiPw83B3c/r5tmzmZksS0516+phiO7d5GYtgR6nr5YCgssH5uKCywawDAfAFTVHC5jLCMTuxf8w+977ib1p27ExdykINrlzPw/keuOqZrqZfq4lbeseFRp9zlhaWW5eflcio5ibbtOwKQceokoQf28ebXPyBLSvj8zZfp3KsP9RtWfvyG7dxKfGgw9bx9KLLbN/m42JwvAC7u7nZ3mi9Nl7DtuAT0uInkqAj8u/Ui5vABRj85m1OJcQ7XSW1yZPtmYkIO4endyO6YLCoosLtQB3Pd2E4Jsp1KkpGWws7li7lvxmy70a69a1fg4uZO5/5ln6dzhJu7Bydt81+pHOfq4WGNqTA/366zd3TPTjLSUnngiVlV3u7xPds5ER5C3YbeGAoqP5+d3dwpKrwco5OlXroNvYO9yxdx4tujNPRthlvd+piMRuJDDnH71KdJPxFf5bgu8ahTlzzbG3l5eXjUufKI/fzPPubDr+fj5dOI+Z99zL6d2+k36Er3OC9z9fCg8IR9e2S3P9xt9kdBPq6WtkrYjMBe2oeu7h74NGtBXU/zc76Nm7ck+0w6ru4etArqiFarpXELP/Ic6JgCDOngT882zck4fxFXmymTrnodeYVlp9C66LRMHz6A33Yd5mKh+Zm3vCJzOWNxMSEn0mjp7Unc6UyHtl8e9zp1yLfJf/nXKQeH79pKfNgR6nv5WGd1gGPtkZNNzvOzjPK0COrI7n8uP39blJ9P9qk0mrYJcDim6mwL3OvU4Y4Hx7Lgg7epU78+Tf1aUs/TsefFQ3duIe5oMPW97dvqooICuzwPl+qmbM47sG4lfe+8x66sEIKhYyaw4bcF6PTONPRthnsVRrgjqphzDIVl4+o29A72LF/Emm+P0sCSc/5fqMXTFmuTWlVLUspYzB2kY8D7Qog3bJcLIVoBzwFDpZSdgbVARQ8W2E5mt5aRUk4HXgOaA6FCiHKvBqWUCzGP0BUAG4UQt2CedPerlLKr5V+glHKuJTY3oJnl6x7lrVMIMc0y/TN40/Kl5RWhRVt/kmOjKDaZOJeVid7Fxe5ucqt2HYgJDQEgJjSE1kHmZx4uTafRaDTonV3Q6/UgJc6W7+v0zmi12mp5Bq5lQCDxUccpNpnIyczA2cUF3XUckWgd2I64qOOYTCayMzNwdnF1aDTt6IF9HNy5nSnPvmBttK5Wp5tvYfSM5xny8GNodXouns2muNhEemI8jVq0sivr2zaQ1KhjAKSfiMfr0l08Ka0dClePutd89+xq66U6tfQPJCE6ssJjo237jhw/an4O5/jRYPwtDTTA0X176Nqnn3XfSClxcXVFp9Ohd3ZGq9PZdcgq0mXQUO6b9SLDxk5Ep9NzMce8b04lxNHIz37fNG0bSHKked+cTry8b4psOhGpsVF4+jTmQnYmRQX5rJr/OXtXLCElKoKIfRXOwq6Vegy5lbFzXuX28VPQ6fVcyMmiuNjEyfhYfFu2sSvbPKAdicfN98bSEmLxaWaum7MZZ1j/6w+MmvqU3UXXke2bOZuRzpD7xlx1fM3atCU5Ntqc/7Kz0DuXyn+B7YkNN+e/2PAQWrYz57+okMOEH9jDfY/PvKpzu8OAIYx8cg4DHxqPVq8n92wOJcXFpJ+Ix6dFS7uyTdoEkBplvut95kQCDXzNaV/v6mp+juWJ2Wh1Olp16c7FnCwMBfls/PErDq1ZRlpMJNEH9lQ5Pv+g9kRHHMNkMpF55oz5vNBf+dx2cnKydiDq1q9P7gXHOkeXNGvdlpS4mMv7o1R71DIwiLjwowDEhR/Fz/J8bePmLUiJM0/nig8PxS8wiAaNGmMsKqKooIDi4mIyT6VRv6EXLdu157RlKtv57Cy7kYbKbD8ex7xV2/h152EMJhMNPNzQOAn8G3tzIiPbrqxOo+Gp225mXUgkJzJyrJ/bdvwCfX1IP3dto2WtAgKJj7ycg11cXK5LDu48cCj3znyBW8ZMQKt3vpzzEsvmPN+2ASRb2qPTJ+Lxshy/TdsGWqf/Z6QmUc/yIhyAuNDDtOnSvUrPA1VnWwDQtU9/Zr39AfdNnIbO2ZkGNvFVpuugYTzwzEvc+sgktHo9F2zag8Y2L0gCaOYfSJIl551KjLOOuJ3PymT74t9Z/vUn5F04x44lf5rLtw3k/qdf5PYJj2MsKqKxn/36KtNxwBDuenIOg6qYc9JL5ZxbbHJOa8t0ZuV/g7jWh3arkxDCF8iRUhYKIe4GJgBtgFFSyhNCiC7Ab0A3wBvzyNuLUspfhBDHLpWzrCseuAuIAZYAF6WUE4QQbaSUCZYyR4GJUsrQcmJpDZyQUkohxH+BJGATsBLzFMoMIUQDoI6UMlkI8SVwGkgGxkgpR1b2uy47fKzCig/euZXDO7YiEIx8dCJOGg3xx8IZOHI0RkMRSxd8w4WcbOo2aMj9055Cp9fzx+fzyL94kZLiYvwC23H7w48CsOHvP0mMjEAiaRPUkREPjyuzPU/3qr/7Ze+WTezdshEEPDT5cZw0GqJCjzL8nvs5czKNRQu+ISUhHt8WfvQaOJhBIxybUqVz8Bmn3Zs3snvzegSCMVOfwEmjITI0hBH3PkD6yTT++O5LkuPjaernx00DhzDkjrt48sHRNG7WHBfLCyimznkRz4YVvyEzOCHFoVgyUpLYu/wvJBDUZ4D1rV9bfv+eYY9ONa9r0xrrW+uGPPQYdb28yTl9kp1L/sDJyYmS4mIGPvgoDZs0rXA7Pdu0+Ffq5fCeXezcuJaEqCjaBAVx99jxtA2q/EH1/KKKXxKxf9tm9m/dBEJw/8SpODlpiA4PZdjoezEUFfHnt19wLjuL+g29GPfkLOuF6H/feIkHJ0/H16+ldV2r/vyVuOPHkFLi36ETo8dNKLO9uPSK75ifSTnBrqWLkEg69B1Ih743A7Dx1wXc9tg0wPxsQ3Kked8MGzuBel4+7Fv9D6kxkTg5afBs1JghD4+3e3tfWlw00Yf3l3kL5fC336603hzh88IzuHRqj9DpMCQmcfqVt656XbvffafCZaeTEtm6+A+klHQZMJjO/QcBsPrHb7hr8pOAeUTtxPFwNBott4+fQn1vH5Z+9QnZ6aeoY7kDftPwO2ns14qvX5iJb2t/nDTmi66Hn33Z7gLMw8XZoZiP7NrGkZ3bEAjuGDcBJycNCcfDGXDHKIwGA8t/+IYLZ3Oo69mAeyxv4X1n2qN4NWlqfVHIA48/Td0GFd+hT8k6W+GyzNQk9q9YjJSSdjcNIPAm81tlt/3xI7eMmwxAyOa1pEUfx0mjYeBD46nb0JuTcdEc3bwWIQS+/u3oNuwOu/Weio8h/sjBMm+hHNHV/qVCFdm6fi1b160BIZj01NNoNBrCjgRz90NjOJWayoIvPiUxNoYWLVszYOgwRoy6m307t7Py70Xo9HrcPTx45tU3yn1WztaxlFN2P4fs2k7Irm0IIRgx9jGcNBoSjx+j/+13YTQYWPnTd9b2aPTk6eh0enIyzrDq5/kUm0y07dSVQaPuBSDheDg7Vv5DSbGJrv0H0esW86Pie9atIjYshJLiYoY/NI4W/uaXjGwNd+yZHj9vT8b0N7+FcndUAnstb6GcMrQPP2w9wPAugdzetT2nzp4HIDItnbUhkUwcchON69UBAbGnMvjnYMWTecYPvsmhWPZs2Wh5Q7Pg4SnTLW1jCLdZcvDC774iOSEeX0sOHnz7SIL37mLXhnUkREfRpl0Qo8aOp21Q+wq3EZZ0ssJlZ1KS2L1sEUhJ+743076POedt+m0Bw8ebc97hjatJjopAo9Fwy5iJ1PPypthkZNtfv5J7NgcnjYZh46bgXrceAMu++ICB94+zdvZKC/QtvzNVnW3Bb19+yrnsLHR6Z+6fOBXvJr7lbjPhTMXPEJ9JPsEOyxs6O/YbSIe+5pkC63+Zz+0THgfg4PpVJEUdQ6PRcusjE6nnZf+7/Tz3RetbKHevWMyZ5BM4OTnRb9R9ZTpwlbWTtjJTk9i3YjFISeBNA2hXQc5JteScQTY5J8SSc5qWk3NKmzNy8A3xB9YKQiu+Pq4Jrl071cp6q20duNuAeUAJYASeAPoCTwGnpZRDLC8juQlIBIqAVZYO3MxS5e7H/FxaKhABeFg6cMsAf8yjaVuBZ2Q5lSCEeBkYZ4kjHRgrpcwRQjwEvIx59NJo2aazZVv9pZTFlm2sllJW+D7gyjpw19vVdOD+LY524K4HRztw14sjHbjrxdGG6XqorAN3vVVHB646VdaBu94c7cBdD5V14K43Rztw10vpDlxNcrQDdz042oG7HirrwNWEijpwNaGyDtz1VpvaSbiBOnBhEbXm+hjAtUvHWllvteoZOCnlRmBjqY+DgS9tykyo4Ltfliq3FCgzT1FKea+DsbwPvF/O538Df5fzlT42ZRzahqIoiqIoiqIoSlXUqmfgFEVRFEVRFEVRlIrVqhG4mmCZtvlhqY9PSCnvKa+8oiiKoiiKoij/AqdaOWOx1vmf78BVMG1TURRFURRFURSl1vmf78ApiqIoiqIoilLzbP+OpFIxVUuKoiiKoiiKoig3CNWBUxRFURRFURRFuUGoKZSKoiiKoiiKotQ8oV5i4gg1AqcoiqIoiqIoinKDUB04RVEURVEURVGUG4SaQqkoiqIoiqIoSs1TfwfOIWoETlEURVEURVEU5QahOnCKoiiKoiiKoig3CDWFUlEURVEURVGUmqfeQukQ1YGrIXVdXWs6BKvU7HM1HYJV43p1ajoEK62mdg1QR6al13QIVk0869V0CFbO2tqTxna/+05Nh2Dn5tder+kQrELmfVjTIVjVpmMm5lRGTYdgx93ZuaZDsOraqllNh2CVfu5CTYdg5e6ir+kQ7BQaTTUdglVQs8Y1HYJVQnpWTYeg/D9Wu65QFUVRFEVRFEVRbhBCiBFCiBghRLwQ4qVKyt0vhJBCiJ7Xus3acxtSURRFURRFUZT/WULcWGNLQggN8DVwK5AGHBZCrJJSRpYqVwd4GjhYHdu9sWpJURRFURRFURSldugNxEspE6WUBuAvYHQ55d4BPgIKq2OjqgOnKIqiKIqiKIpSihBimhAi2ObftFJFmgKpNj+nWT6zXUc3oLmUck11xaWmUCqKoiiKoiiKUvNq2R/yllIuABZUUqS8gKV1oXlO6GfAhOqMS43AKYqiKIqiKIqiVF0a0Nzm52bAKZuf6wAdgR1CiCSgD7DqWl9kojpwiqIoiqIoiqIoVXcY8BdCtBJCc1x9TAAAIABJREFU6IGHgVWXFkopz0spvaSULaWULYEDwCgpZfC1bFRNoVQURVEURVEUpeY53VhjS1JKkxBiBrAR0AA/SSmPCyHeBoKllKsqX8PVUR04RVEURVEURVGUqyClXAesK/XZGxWUHVwd21QdOEVRFEVRFEVRapwQteslJrXVjTVOqSiKoiiKoiiK8j9MdeAURVEURVEURVFuEGoKZS20f9tm9m7ZgEDwwJTptGjd1rrMaDDwxzefczYrE08vb8Y9OQudXs/xkGDW/PUHOr0eTy9vxs+cjUajYeOyxYQe2IcQgh79BzJ01D1X3P7p5BNsWvgrEkm3gbfQpf+gMmX2rl1BfHgoGp2WkRMep76XNyajgTW/LOBCTjZ1GzRk5IRpaHV6jEVFbFz0K+ezMikpKeH+p54FCUu/+cy6vrSEWJ759FuoV6fCuA5s38K+LRtBCB6YNI3mpepl4bdfkJOVSQMvb8Y+8bS5Xo4Gs+7vP9Hq9DTw8mbcjGe5cO4sv33xCSUlJUgpuX/iVFq08Xd095SRkZrMrqULkVLSsf9Agm4aUKbM4U1rST4ejkarZejYidRt6AXAkc3rSIk5jiyR9BpxF80Dgq4qhjMpSWxb/DtSQucBg+nY9+YyZQ5uWE1iRCgarY7bHp1MvYbeRAcf4OjOLQghcHZx5Y6JT+Ds6sqJ42HsXb0crU5HnQYNuX38VJw0mirHdXjnVg5s3YQQgrsnTKVZqzbWZUaDgcULvuJcVib1vbx5cNoMdHo9hqIiVvz6PTkZZygpKWHC7Jdx8/C46nrZ8vfvgKTLgCHl1suB9atIiAhDq9UyYvwU6jX0JurwAY7u3IwQTuhdXBg56UmcXV1Z9+sCMtNScHZ1w7VOHUZPnVmleNJTktjy129IKel68xA69RtYpsz+datIiAhFo9Vyx/ip1PPy5uDGNcSEHMZJo6FR85YMe/hRTEYj/3zzKSaDkZKSYvqPvIc2HbtcVT1VxPeT93AOaMu5pSs4++uial23raO7dxC8cysAd46biG/L1tZlRoOBlT99x7mcbOo3aMjoSdPR6fUc2rqR/ZvWI2UJz3z0hbX8Lx+9Q0lxCYaiQvqNGEnnPv2rFEtGajK7/lkIUtKhX/nnc/CmtSRFWs7nMTbn85Z1pP4fe/cdHkW1PnD8e7Yl2U1vQEKHEELvXXqToqgoinRQERv2rmC56lXvtdIEFEVUEASkSE3oLYQOIZAQEiBAElqSTdvk/P7YZbObRoJAzP2dz/PwPGRndubNOTNnzjvnzCT6CPlS0q7fYKo3CCM3J5sti3/hWmoy+VIyYPxTuBpNZYpl7+Zw9oSvAyEYPGo8wYXKZcnsaVxJTcHbz5/7J0xCbzCw4OvPuXYplfz8fNr36kfrrj0AWD5vNmdPxZKfn0+XuwfTvGPR36s0ezZtZNfGtSAEQ0ZPKHIuL5r1LVdSk/H2C+DBx59CbzDw64yvSDodj6vRiLuHJyMnv8KV1BR+mfYFMj+ffCm5d9R4p7a8rG51u3twy0YORKxHSsmodz4qVyxRWyKIjFiPQDBwZNHjd+ncGVxNTcHLz58htuN314Y17FizCinzef7Tr+3rb/rzD47t3Q1A0w6d6dx/ULliuZVtjBCCzct+58jOrfgEVuXh518rVyy36pgBWDjza6IP7COsRWsefPypcsVR2PYN69iydjVCCB5+7Elq1is4/mKjjzJ/2tdcTDrLB9Pn4OMfAEDEqhVsWLEUmZ/PBzPm/q39g7WeNvz6I9J2bSq2nlYvJ+6QtZ9198iCeorZtweNRkuVmrXpNcxaT4e2byYqYj1arZageiH0HDr8b8dYYSrZS0wqinbKlCkVHcP/S3EXL00p7nNzehoLv5vOCx98SsPmLVkw42s69eprX759/RoMLi6MeuYFkhJOk5x0jlr1Q/j+v58w4aU36TV4CIf27MJgcMHT25tfZ37LG59/TcdefZn/7X/p2LMvOp1z3p6Wle3085LpX3LvhEm07dWflT/OJqxNe/QGg315StJZ9oavY+Qr7+DtH8D2VcsIa9OefVvC0RsM3DPuSZLPneHShfME1a7LpqWLCGneim5DHqR5527oDQb0BgPNO3ejeedu+FcL5mpqCi279sTd1aXY8jKnp7NwznSef/8TQpu14NeZ39CxZx/78h0b1mJwcWHk08+TlHia5PNJ1KwXwg9ffMqEl16n56AhHIrchd7FQECVajRv35Fudw+mdkgoi+bMdNrWdanpGTeuSGD13On0GTWB5t16s/HXedRv2RadvqC8Ll1I4tCWjTzw3Kt4+vmzd/0q6rdoQ/zRQ1xJvkDfkRMIa98ZL7+AUvejLaVR+/O7rxkwZiKtevRh3YLvCW3dzimG1PPn2L9pA8NeeAMvP392r1lJg1Zt8Qmsakv4unLpfBJXUi5SrXZdVs6dzj2PPUPrXv2JO7QPnUGPT2BV+/Y83FxvWC7m9HT++H4WT039iJCmLfh99jTade9tX75r4zoMLgYefvI5zicmkHI+iRr16rN64c80atWW/g8Op223Xk7HXnEyCh2/jpbN+pqBYyfSukdf1vw8l4at2qMzOJfLvk3reeTFN60Xx79WENqqHb5VqtovqpcuJHEl2VouJw7spePd99Bp4H00bN2+yP5uNHd/6cyvGDRuIm169uOvn+bQsHXReKIi1jP8pTfx9gtgx19/0rB1O4wennToP5hmnbtxaMdm3EzueAcEUKtBGO363E1Ii9Ysn/U1rXv0ddpfrY3hpcZzI5n7DpJ7NgmNyUjWgcN/a1tJfYueYwCZGems+GkO4998j/qNm7H8h1m0siUdAFGbNqI3uHD/Y5O4cDaRSxfOE1ynHl5+/nTuP4jdG9bQoc/d9vWbtO9M6249adK+E799+1869h1QZJ8Z2Tklxrn6++n0HTmBZl17E/5b0fP58oUkDm3dyP3Pvoqnrz9RtvP59NFDXLl4gT6289nTdj7vWvkHtZs0p/2A+whr39lpW1DyuZSZkc7yebN5/J0PqN+4GUvnzqRNt5725ZER69G7uPDgE89w4UwiqReSqF63PtVq1aHroCG06NyVX7/9grY9epN87ixRWyN44u0PaN6hC7988x869RtY7H6La2fM6eks/WEWk6b8iwZNmhc5l3eHr0NvcOHhJ5/l/BnbuVy3Pocjd9F7yFD6PDCM5rZEWiJp2rYDnfsNpFb9UJb+8B1tu/cqNpar5sxiP4db3+56+PjSslc/Dm0Jp3m33kX2Zyrh2pSZkc6KH+cw4a33qd+kGcu+n0nrrgX1tHfTBvQGAw889hQXbfUUXKce3n7+dL57MLs2rLEfo9mZmSyf9x1Pvf8prbr25I/Z02nVtQfaQtfsHEteieVya9uYQPyqBdG4XSdOHtxHkxKSfqNL0Tb6Vh4zANXr1qd+o6YkJZymcet2Jf7+LvrSxyUy0tP4Zda3vPLRZ4S1aMlP076kc+9+9uVubka69O3PySOHadmxM262my2+AQH0vfcBIlb9Sc9B95a6j+sup5tLXLZs5lcMHDeR1j368df84uvJ8dq0c82fhLYqVE/bN+Pm7o63fyB/TP+CUa9NpWX33uxcvZygOvUweng67bNVneCpZQq8guVduTqlomNwpPX2+keW221Nc4UQ3kKISQ4/dxdCrLid+7TtJ/0Gy2sLIYrtjQghJgshjLcnshs7deI49cIao9Pr8a9SleysTHJzc+3LTxw5RBNb49W0TXtOHrX+GtVq1CLTnI6UkiyzGXdPL/QGF7x8fcnJySE3Jxu9wQXtDUZRLLm55ORk4x0QiFano2ZIKEnxcU7rnD5+jPpNWwJQs0EYFxITAEg4fpSQ5q0ACGneisSYaOvvdOwwsYcP8NO/32fTst+L7PPwzm006VD63eD4E8ep19CxXLKKlEvj1m0BaNKmnUO51MSckWEvFw9PL9xMJjy8vAHQ6XQ3LJPS5OXmkpuTjZdfAFqdjqB6IVw8fcppnbMnjlO7cTMAguuHknL2DAAn9+0hLzeXP775lLU/fkd2ZsmNfWksubnk5uTg5W+NIbheA84XqrMzMdHUtY3OVA9pSPIZa505dgxyc7LxrxYMgF+1YLIyzUgpyc404+bufCEoi4STMdRp2AidTo9fYBVysjKxONRZ3LHDNGpprbPGrdpyKvoIACcPH+T4gX1Me+9N1iy6+VEfi61uvG3lUr1+KEmnncslMSaaek1bAFAjpCEXzxZXLjn4BwXbfw7//RcWfPYB0ZE7yx9Pdjbe/tZzq3pI0XgSjh+jXlNrPdVo0JCLZxIB8K1SkDxrtTo0Wg1arQ4v291hnU4P4tY355bklFu+zcLOxJ6kVoMwdDodPgGB5GRlOR0np6KPENrC2q40bNGa+JhjALh7eRfp2AL2G1S52dkEBtcosrw0eZZcLDnZeNrO52p1Q7iYUMz53MjhfD5nPZ9P7NuDxZLL0m8+Zd1PBedzYswxTh87zJKv/82uVUvLHEti7AlqhYah0+nxDaxSpFzijh2hYYvWAIS1bM2p49Zy8a9aDQCtTovQCIQQePr4oNPpyLNYyM7KxM1UvhHthNgY6jjEkp3tHEvs0SM0amX9m7SNWrUlLvqofdmf879n2tQ32L9jKwBuRhPutvZXq9Pd1Mj+7Wh3jZ5eaLXln5RkPX4bFhy/2YWP36OE2uoptGVr4o+XfPzqDAY8vX3IzcnBkpODzmBAU46YbnUbcz3Om3mpxK08ZgC8fP3KHUNxTsUcJ6RRk4K+RGYWubkFN3TcTCZc3dyKfM/T26fY9uZmFFybrPVUo5hrU0LMMfs1u0ZICfWk0yFsN1z8qlYjJzuLPIuFvDwLLmUc5Vcqr9s9hdIbmARMuxUbE0LopJSWW7GtUkwG5gNl7k0LIbRSypJviZWDOT3dabqYm9GEOT0NLx9fwHr36PpyN5OJjLQ0ANp168m377+Dq9FIcK061KpvnRLYuFUb3nv2CWR+Pv2HPoxOry91/5kZ6bi6FeSvLkYTmenO+XBWRjru3j72n6XMt303wz41yNXNSGaGNbbks4l0vecBej80giUzviT28AH7VK/8vDxOHoyix/3DblAuaYXKxehULub0NIy2Tomb0URGekG5TP/wXVzdjATXru00VTI/L49Fc2bS9/6HSt13aTLN6bg4lJfBzUiW2XnkLsucgbuXl/1nmW8tr4yrV3A1mbjv6Zc5sHkDketW0fmeoeWOISvDOQYXo5HMjEJ1Zs7AZOs0QUGdARzatomo8LXo9Hra9bXelW/UvjNLvvkMg6sbAdVrULVWnXLHZc5Iw81UcBFxdbMey54OdebmbjteTAV1lpR4mr5DH2bwiLH89OWnRO+PoqGtA18eWYWPZTcjWYXLpfCxnF9QLge3bWLvxjXo9Aba28ql+wOPYHT3IDMjnYVffEzVWnXxDggsUzyZGem4GJ3jKXpuZeDu7VBPDvGAtfOVcfUKNUIaOn2+fuFP9hgrm8yMdKcphdePXw9bvTi1K7b2sDT5+fl8/8l7JJ89Q++hj5Q7FkORY6bo+WxyOJ/zr5/P16zn85CnX+bg5g3sXbeKTvcMJfXcWdr1v5cuQ4bx1w/TOX3sELXCmt4wFnN6utP54+JmxJyRjqe9XNLtiZir0URmoXKJWL6E5h26oNPr0ep0+FWpxn9efpac7CzuGz+xfOWSnu6U9Lm5GZ3P5Yw0+3I3owmz7bo0ePgYTJ6emNPTmPnhu9SoWx8/Wwc0Pz+PP374jp73lr/N+ye0u9eZM9ILtXOFjt/0dPsojlsx11NHWq2WkOYt+fLV55BS0v2e+4vMmCnN7Wxjyut2HDO3gjmtoK8A4GYyYk5Lx8vX95bt40Yyy3LNzsiw3+iA4usp3aGeGrXvzLwP30Kr09OwdTun71Y66i2UZXJLEzghxAvAONuPs4EOQD0hxH5gHbAScBdC/A40AfYCI6SUUgjRGvgP4A6kAGOklElCiAhgO9AZ6182/7yY/dYBFth+n78KLXsZeAhwAf6QUr5rW6QTQswDWgIxwChgAhAEhAshUqSUPYQQjwBvAAJYKaV81bbddFu8/YAXhRCDgHsAC7BWSvnSzZSh0d2dTIcOQ6Y5A6N7wXNhJncPzBnp+FHFtszaEP0y8xte+eS/+PgH8MvMb4javoXg2nXZv3M7U6fNQebn88U7r9G8XQe8bc8BONqzcQ3RkbvxCaziNBKUnWnG1eR8J8fV5E62uWAdYbvz72Yy2S6iAWRlmnG93rkwuVOvSXOEENRt3JyLiQn2BC7uyEGC64VgcC19Sp610+xYLmancnFcnuWw7NeZ3/LSR5/j4x/Ar7O+Zd+OrbS0TQP5dda3NG7dhobNWpS67+Ic2LyB2P178fIPdCqvnMzMIne+XI0msh2mAl2/Y+ZiNFHT1pGrFdaEzYvLN9q0L2IdMfsi8Q4oGkPhO+yuRpPTOsJhtKZp52407dyN3WtXsmfdarrdP4z1v/zA8FfexdPXj3ULfuB41G5CW5U8baU4RpOHU+c3KzOj+DoLsHa2jCYP++ehzVshhCC0WUuSEuLLlcBFRawjJmoP3gFVCpVL8ceyY8dPOEwfa9a5G806d2PX2pXsXreK7vc/bI/fzeROrbAmXDybcMMEbm/4Oo5H7cYnoArZmQXHQXZmZjHxmMhyPLcc4rl4JoFNfyzkgadfcLojvm3lUlyNJpp1LvoMRWXgVqgOss1m546fQ5lkmc1Ona/iaDQaxr8+xdoBnPoGTdp1xNVY+qSKg5s3cPLAXrz9A8lxPGaySjifHepRY6sjV6PJnpjVDGvCFtv57GoyUSusCUIIajZsQuq5M2VK4Izu7k7HQnamuVDH051McwY+QFamc5lFbY3gwplEhk2aDMDJwwe4dvkSL37+NVlmM7M+eJsGzVre8IaefV/u1n1dl5lZqP01FZzL1rbZGovJ09P2u3gQ0rQ55xLi7Z3xxbNnENayNQ2alv25zX9Cu1uY0eTOWYd6KlwXbg71mGU2OyV7haWcP8fRPbt44bNvkFIy51/vEtaqHZ43SC5udxtzM27HMXMrGD2sfSh7XBlmjB4394x1eUXZ6sk7sArZWc715Fb4+DUVumYXqqfNSxdy/1PWesrOymTbiiVMmPpv9C6uLJn+X5JOxVLN4ZlD5X/PLZtzY0vAxgLtsSZujwGfALFSyhZSypdtq7bEOsrVCKgLdBZC6IGvgaFSytbAXOBDh817Sym7SSmLJG82XwLTpZRtgfMOMfUFQoB2QAugtRDiei8nFJglpWwGXAMmSSm/As4BPWzJW5Dtd+hp+35bIcQQ2/dNwGEpZXvgKHAf0Ni2vQ9KKKPHhRCRQojIlYt+LfYXqRMSSmz0EfIsFi4lX8TF1Q29w0W2fqMmHImKBOBIVCQhja0XIo1GY79ouHt6kZGeDlLi4mb9vsHFBZ1eT3ZWVrH7bduzHyNfeZtBYx5Hb3DhamoKeRYLiSeOE1SoEajVIIzYw/sBOHMyhio1agLW6ZSxh6yfxx7aT03bCzlqhYbZp2EmnY7DJ7CKfVtlmT4JUDsklNjoow7l4lq0XPbZymVfJCGNmtjLxehULta7fEt/nIunjy/d7h58w30Xp3nXXtz/7Cv0Gj4GvcGFtEup5OVZSIo7QZVCo1XB9Rtw+tgh6+8fdxL/4OoAVA8J5WJCPAAXE+Lx8i/bSM51Lbv3Ydjzr9NvxHj0BgPXbDGcjY2hqsND9NZ9NeTUkYMAnI09QUB1a51ZHKaOuBqN9ufNhNDYRz2MHh5FRiHKomb9Bpw6fow8i4XLKckYXN2cOox1wxoTvX8vANH791I3rDEA9cKacCbuJACJcSftU8LKqlX3Pjz8whv0HzkencGFa5dSyMuzcCY2hmq1nI/lGiGhTuUSGFxMubgVlMv1RCPPYuFs7Al8A2/csWjdow/DX3yTu0dNsNWTNZ6zJ2MIql0ongYNibPFcyY2hsDq1imAly9eYPW82dzz2FNOnaC94eu4fPE8PR4o30jTP0n1evVJOHGcPIuFK6kpGFxdnY6T2g0bEXNwHwAxB/dRO7RRidvKs1jsI2J6g7XNK0uS0qxrL+5/5hV6PjIGncP5fK6Y8znI8Xw+dRL/IOv5HFzf4XxOjMfLltgH1w/lYqLt83Kc5zXqhXA6xnr+XElJLlIudRo2JuZAFAAxB6Ko09BaLkf37ubA9q08OPEZe3IppTUR1mi0uLi6OZVTWdSs14B4h3PZxcW1TOfy9ZtqFksu8THRBFQLAmDFzz/g4e1DlxKewyvJP6HdLax6vfqcjokuOH4LlU2d0EbEHLTV08Eoajcs+fiVElzcrO2k3mCwXrOzi79mO7qdbczNutXHzK1Sp0EoJ4859CXcXNHrS3/O+lZp1aMPj7z4JnePnIBeX1BPZ07GUK1QPdUMKains4Xr6cfZDJ5QUE9CCLRaHXoXVzQa67W78Ii08r9HSClvzYaEeA7wu/6Xx4UQ7wPJwONSyia2z7oDb0op+9h+ng5sA/ZjHWW7PglYCyRJKfvaRuDelVJuKmXfqUBVKWWuEMITOCeldBdCfAYMBa7YVnUHPgI2AJullDVt3+8JPCulHCKEiAfaSClThBD3Ag9IKUfZ1huPNUl7QQhhAVyklHlCCB3W0cRIrKOMK6SUJT8lD6w/fLLEgt++YS3bN6xBIBg67gk0Wg3RB/bTZ8gD5GRnM//bL+xvZBv59PPoDQaitm9h/bIl6PR6jCYTYya/gqubG8vm/0DM4YNIJA0aN2PIyLFF9nfu8lXnn+PjWPeL9e1ILe7qQYsu3QFY+t03DHnsaQC2/LmEuMPWt3sNHPMYPgFVyM3JYcUPM0m7fAkPH18Gj30Cnd7A1dRkVs6bjSU3F98qVRk4agJCoyEnK4tZU15l0of/sT8HUbWUt1Du2LiOHRusb7QaOvYxNBot0Qf30/ve+8nJzubn6V/Z38h2/e2c+3ZsZf2yJegNBtyMJsY89xIXzp3h8zdfpm6oNcF09/Ri/ItF364Vk3SxtCq0u5AQz5bFvyClpHHHu2hke9Phmnmz6Df6cQB2//UnCccOo9Fq6TV8LF7+AeTl5rLh13mkX76ERqulz8gJmDy9StyPoZSpNOdPnyJ80XyktI2o2d5otfL7GQwca50utWPVMuub87Q6+o4cj7d/IDtWLSPhuPXZA1ejiX4jx+NqNHE8ajeR61ah1etxcTMycOyTTqOk1XxKjtPR7vD17ApfhxCCe0dNQKPVEHPoAD0G30duTja/zfiGq5dS8PL1Z9jEZ9AbDFxKvsjv331Lbm4uAVWrMfSxp+wd0WLL/8q1Usoljg0LfwYkzTp3o2kn6xtVV8ydzqBxTwKwfdVSTh05hFano/+I8XgHBLJ91VISbM9kuJpM9B85AVejiUVf/Zuc7Gzy8/Jo1K4TrXs6vzREoyn9rnVSfBwbFs5HSknzLt1pZnvD659zpjF4vPVx4W0rl3LqiLWe7h41Ae+AQH7/5nNSz5/Dwzb9qH3fgVStVYdvX3mGoLoh9udVHn7+daeyuuutt0uN50YCX5mMa9NGCL2enLh4kt64+We5oz79pORlm8PZu3kjAAMeHYNGoyX2yEG6DLjH+ha/OdO5evkSXj6+DBn/JHqDgcO7dxAZsZ7EkzHUqN+Anvc9hLd/AIumf4nQaMizWGjfu3+xb6G8eLXkaWwXEuLZsuQXkJJGHe+iUQfr+bz2x1n0HWU9n/es+ZPTxw6j1Wrp+YjtfLbkstHhfO49wno+X7uUQviv88iz5OLlX4WeD492upse5FvyuRS5aQORERtACAaNGItGq+Xk4YN0HXgvuTnZLP5uGlcvpeLl68cDj1nf4jdlwggCgoIwuFjP14eefA4Pbx+WfDeN1IvnseTm0rJz1xJfYqIv4Zm03RHr2R2+HoTg3lHj0Wi0nDi0n+62c3nhzG/ssTz0hPVc/u6jqWRnZ5FvsdCqSze69B9EYtxJvnn3NWo3sE79cnzTYGEJKZdKr6db2O6e2LeHw9s2cf5ULFXr1KPDgCFUc3g7ZmAp16a9mzeyd9NGBIIBI4oev3/Mnsa1y5fw9PHlPtvbQg/v3sGe8PUknjxOjfqh9Lr/IWqGhLJ24QLr88BSUiesMX0ferTI/tJLeXHTrWxj6jVtwd7wdRzbs5PU8+eoUqMW/UaMxSegitM+/UsYwbpVxwzAXwt/Jnp/FGlXrxAYVJ2xL75e7MwdT+ONX7C1bf0atq5bgxCChyY8gVaj5eiBffS7bygXzp5hwcxvSYg9SVDNWrTr2p1udw9i77YtbF6zitjoY9RrGMY9w0dSr5RkHCD2fMnPECedjmPjb/ORSJp1Lr6ettvqSaPTcfdIWz19+zmXzp/Dw9taT+3s9bSWIzu3odVq8QmsQv9RjxW5bo7v0a5SzE3MSThzaxKTW8RQs/o/stxuZQI3GfAtQwL3kpRykO3nb7AmPXuxjoZ1LGa7EbbvRJay71SgipTSUiiB+xyIkVLOLLR+bWCTlLKW7eeewDNSyvsKJXBDgPtLSODSpZTuDtt0AXoBDwPVpZQ9KUVpCdydVjiBq0ilJXB3WlkTuDultATuTitrAncnlJbA3Wk3SuDutL+bwN1KpSVwd1ppCdydVloCVxFKSuAqQmkJ3J1WWgJ3p5WWwFWEkhK4ilCWBO5OKS2Bqwgqgbs5/9QE7la+tmwzMEQIYRRCmLBOKdwGlKXVOw4ECCE6Aggh9EKIxuXY9zasiROA4+2qNcA4IYS7bbvBQojr8yVqXt8f8Ahw/XVHaQ4x7wK6CSH8hRBa23pFRgJt2/eSUq7COj20/A9VKYqiKIqiKIqi3MAtu6UvpYwSQvwA7LZ9NFtKuVcIsc32yv7VWKcXFvfdHCHEUOArIYSXLa4vgCNl3P1zwALbNM7FDttdK4QIA3bYHshNB0YAecAxYLQQYiZwAphu+9osYLUQIsn2HNwpSl8LAAAgAElEQVTrQDjWl5isklIuK2b/HsAyIYSrbb3nyxi3oiiKoiiKoiiA+IfNZPmnuqVzsqSU/8H6ZkbHzwr/OfgIh2VPO/x/P1DkNWpSyu5l2O8pwHH65ccOy77E+pKTwoqdvCyl/BrrC1Wu/7wA6xsuC6/n7vD/JKwvSlEURVEURVEURbltbusf8lYURVEURVEURVFunX/OWxHKQAjxJvBgoY8XSSk/LG59RVEURVEURVEqCaHGlsqiUiVwtkRNJWuKoiiKoiiKovy/pNJcRVEURVEURVGUSqJSjcApiqIoiqIoivI/Sqi3UJaFGoFTFEVRFEVRFEWpJFQCpyiKoiiKoiiKUkmoKZSKoiiKoiiKolQ89Ye8y0SNwCmKoiiKoiiKolQSagROURRFURRFUZQKJ9TfgSsTVUqKoiiKoiiKoiiVhBqBqyAnki5WdAh2Vbw9KjoEu2xLXkWHYKfXais6BCd+HqaKDsHO5KKv6BDsYi+kVHQIdi3rVK/oEJxEffpJRYdg1+rlVys6BLsfn51c0SHYPdyldUWH4OSPXQcqOgQ7DzfXig7Bztf9n9P+ZubkVnQITtxdDRUdgt01c1ZFh2DnalBdbOX2UUeXoiiKoiiKoigVT73EpEzUFEpFURRFURRFUZRKQiVwiqIoiqIoiqIolYSaQqkoiqIoiqIoSoXLdHWp6BCc/HPeEuFMjcApiqIoiqIoiqJUEiqBUxRFURRFURRFqSRUAqcoiqIoiqIoilJJqAROURRFURRFURSlklAJnKIoiqIoiqIoSiWhEjhFURRFURRFUZRKQiVwiqIoiqIoiqIolYRK4BRFURRFURRFUSoJlcApiqIoiqIoiqJUErqKDkAp6mJiPOGLfgYpadK5O407dCmyzu41Kzh15ABanY4+j47Hy8+fIzu3smv1Mjx8/AC4e8wTuHv7cGDzRvaFr0VKydgpn5Qrlr2bw9kdvg6BYPCo8QTXqWtflpuTw+LZ07iakoKXvz8PTJiE3mBg1YIfORN3ktycHOo0bMSA4aPs3zGnp/HpC09zz6jxtOzStVyx7Nm0gZ0b1iKEYMiYx6hep55TLAtnfcOVlGS8/QN46PGn0RsM/Dr9S86djsfVaMTd04tRk1/BnJ7GD//52P7d+JhopsyYh9HdvcyxXEiIJ3zRfKSEZp270bjjXUXW2fXXn7Y60tN3xDi8/ALsy7av+INje3Ywfuq/AVj89afk5VkASIqPY/jL7xAQXKPM8ezdHM6e8HUgbPVU27melsyexpXUFLz9/LnfVk8Lvv6ca5dSyc/Pp32vfrTu2gOA9Yt/4+Rh67E1aOQ4qtWsXeY4AHZsXMfWdX8BgmETJlKzXn2nWH769ksupSTj6x/AyKeeQ28wEHf8GIt/mI1Gq6Vpm/b0HfKA/TsZaWm8PWk8wyZMpH23nuWKxVGQjyeDWzdGINgdm0DUqTNOyw06LeN7tCfA053le4+wP/6sfVnXsLqEVA1AIwQbjpwg7kLqTccBELUlgsiI9QgEA0eOJahQfS2dO4OrqSl4+fkzZNxE67H8zX+4dvkSMj+ftj370uqu7je9/31bIojctAGAgSOK7n/Z3BlcuZSKt68f99r2v3vDGnasXY2U+Uz+91f29X/49/vk5+WTk51Fp/6DaNah803HVZqgzz/EpUF9rvy+lMvzfrkt+yismrcnA1s1QgCRcYnsczgmwHrMjO7algBPd1ZGHeVAwjkAWtYOpkej+lwxZwKwaNcB0jKz/1Ysq5YvY/kfixFCMPnl1wgNC7Mv+3ne90Rs2IBOp6VBwzAmv/wqQgienzSRXEsuAEcPH+a7efOpFxJy0zFcSIhn48KfrO1el+40KaHdizu8H61OT7+R4/HyCyA6cif7Nq1HCIGLqxsDxj6Ji5sb5+JOErH4FzRaDfWatqRtnwFljuX86VOs/WUeEmh5Vw+ade5WZJ1tK5cRe2gfWp2egWMex9s/gDMnY1g9fw6XLlzgyQ8/x9PXer1cMuMr0q5Yz69W3XoVu72y2LFxHdvW/4VA8OCEidSs69z+zZ/2JZdTkvHxD2DEpIL2b8m82Wg01vavj0P7V163ulw2LFrAufhYLDk51GwQRq8Hh5erLG7VteCtiePwDQgEIKx5C+4e+nC5y2bPpo3s2rgWhGDI6AlF+hCLZn3LldRkvP0CePDxp6zt7oyvSLreh/DwZOTkV+zfMaen8fHkJ7l3zARad+lerlj2b91E1KYNIAR3PzqGarXq2JdZcnNY/v0s+zXgnrGPo9MbuJKSzPK5M7BYLIQ0a8ldg4YA8NOnH5Kfn0dOVhYd+w2kyW1qg5V/lts6AieEmCiEGFXM57WFEIdv575vhhBilRDCu6LjCF/0M/1HPc7Q515jf8Q6sswZTssvnU8iMeYYw154kw4DhrBt2SL7ssYd7+LBya/x4OTXcPf2AaB+i9aMfOuDcseRmZHO9jWreOzNqTz05LP8+dNcp+VRW8IJqBbME+98QEC1YKK2hAPQ96FHePyt93jqvY85E3eSC2cS7d+JWP4HtRqEljsWc3o6W/9ayZPvfMAjTz3P0nmznZbv2bSRwKBgnpryEQHVgtmzaaN92X1jHmPSOx8yytbwGt09mPTOh0x650MGDR9NSONm5UreAMIXzefu0U/w0OTX2LdpfTF1dI7EmGM8/OJbdBwwhK0OdZRx7SqXL553Wv+BZ17mocmvM3Dsk3j7B5YrecvMSGfH2lVMeHMqD018lhXF1VNQME+8/QH+jvX04HAmvvsvHnvzPcKXLSY3J4dzp09xJu4EE9/9Fw9OfJYV878vV7lkpKcRvvJPnn/vY8ZOfomFc2Y6Ld8Rvp6qwdV56cN/UyU4mB3h6wFYOHsm4194lRc/+DcnDh/kwrmCjvJfSxZSr2GjcsVRnMGtG7Nwx36+27iDTg1q46p3vn9lycvnpy172Xb8lNPnDaoF4KrXMyd8F99t3Pm3k7fMjHR2rlvNuNenMHTiM6wsVMb7tkbgXy2ICW++h3/VIPZtjQCgz9BHePztDxj3+hQ2LbfW103vf/1qxr72LkOfeIZVP//gtHz/1k34VwtmwhtT8asWxP6tmwBo1KY9T3/4WZHtjXjhdca9/i5jXn2b9b/fvsTq4sf/JWXa7BuveAsNbNWI33cdYG7ELjqE1Cr2mPllexQ7TsQX+e7eU2eYG7GbuRG7/3bydu3aNX7/9Re+njWHt9//F1986nwjrmuPnnz343ymz53HpdRU9u7ZDcB/p83gm1lzmPqvTwiuXv1vJW8AGxf+xIAxExn2/OvsK+balHr+HAnHj/LIS2/TaeAQtiy1tnshLdrwyItv8fALbxJYozZHd2+zbW8+g8ZP4uEX3iQxJppLF84X2WdJ1v4yj3vGT2LES2+xZ+NaMjOcY0lJOsfp40cY9doU7rrnfsKX/AqAf1B1Rr82lWCHxAqg+30PMfq1KYx4+S22rVyGJbf855c5PY1Nq/5k8tSPGf3cSywq1P7ttLV/L3zwb6oEBbPT1v4tmjOTcc+/ygsf/JsTR5zbv/K6HeUy8uW3Gfvm+5yLjyX5rPONr5Lc6muBRqPhhfc/5oX3P76p5M2cns62NSuZ+Pb7DJ80mWWF+hCRmzcSEBTMpHf/RUBQEJGbC/oQQ0ZP4Mm3P3BK3gA2LltM7dCG5Y4lMyOd3ev/YvSr73DfY0/x18/znJbv37oJ/6pBjH19Cn5Vq9nb4A2//0K3IQ8y7o2pxB87QkqStWyGP/8qo199h1GvvMXGJb+VOx6lcipXAiesyvwdKeUMKeWP5Q+rYkgpB0gpr1RkDJbcXHKzs/HyD0Cr0xFcvwEXTjt3KM+ciKZO42YAVK8fSvLZggTp2K7tLPzPv9i+YgkyPx8Ak6cXWm35B1sTTp6gdmgYOp0e38Aq5GRlYcnNtS+PO3aEsJatAQhr2ZpT0ccA0On0AORZLOhdXPD0sSaSV1KSSbty2emuV9ljiaFOw0bodHr8AquQk5VZKJbDNGrZFoDGrdpyKvqIfdny+d/z7ZTX2b9ja5HtRm3dRKsu5bvTWqSO6jXgfKE6SjwRTZ0mzQGoHhJKskMSu3P1ctr1G1TstqMjdxLaun254kmMPUGtG9RTwxYO9XTcWk/+VasBoNVpERqBEIKU80kE1bbWj7efP5eTLzpt60biY45Tv1FjdHo9/lWqkp2VSa7D92MOH6Jpm3YANGvTnhNHrfdxMs0Z9rurNeuHEHP4IACXki9y7fIlatX7ex1PrUaDQaflckYmefmS+ORL1PBzvleTLyXpWUU72k1rVkOv1TC+R3se6tAcF/3fm7hwJvYktRo0RKfT4RMQSE62c32dij5KqK2+Qlu2Jt5WX372+tIhhAYhxN/Yf1jB/rMK7/8IoS1aAdCwRWviY6z7d/fyRqsr+rvrbJ/lZmcTWI4bD+VlSU65bdsujlajwaDVcsV2zJxOvkywb3HHTPEd/Ra1g5nQoz29moRwczVV4OjhQzRr2RK9Xk9QcDCZmWZyHBL4GjVr2f+v1+vRarVO31/312p69e3/t2Kw5OaSm5Pj3O7FxzmtcyYmmrr2dq8hyWcSAJyOm9ycbPyrBQOQk2W2j/RUqVWbMyeOlTmWnOxsvAMC0ep01AgJJSk+1mmdhONHqd+0JQA1G4RxMdEai6vRiMHVtcg2fatUBUCj1dnOrfLX2qkTx6kXVnL7d+LIIZq0trZ/Tdu056St/ctybP/qhXDC1v6V1+0ol+t1l2exYDC42G8M38itvhZIKfnvO6/x9Xtvk3gqlvJKiI2hjsN1MrtQuxt79AiNWrUBoFGrtsRFH7Uv+3P+90yb+oZTH+JySjLXrlymeh3nhLcszsadpGaDhmhLuAacPn6MkObWOmrQvBUJMdEAnE84Ta0G1oQxpHlLTh+3fn69jnKyswkIql7ueJTK6YbJmG207JgQYhoQBYwUQuwQQkQJIRYJIdxt630shDgqhDgohPjM9tkUIcRLtv+3FkIcEELsAJ5y2L5WCPGpEGKP7btP2D7vLoSIEEL8LoSIFkL8LGw9FiFEWyHEdtv2dgshPEraTgm/UzUhxGYhxH4hxGEhxF22z+OFEP4Ov/N3QogjQoi1Qgg32zr1hRDrbfuOEkLUs33+ssO+p95UbQBZGem4uBntP7u4GcnMSHdex5yBi9Fk//l6olavWUtGvf0vhk5+jbRLqURH7rzZMADrXSI3U8F+XI1GzOkFsZjT03EzuduWmTCnp9mXLZ83h09feApPbx/777Phj0X0uPfmpoaYM9KcY3Fz3p85PQ03d+tyV5OJDNuywY+O5bkPPmXsS2+wcdliUh3u8Obl5XF0XyRN2pYvYcrKSMfF6FxHWYXrKCMDV4d6zJfWOrp88Ty52VkljrAd27ODhm07lCseaz0UlI2LmxGzQzzWeiyop0yHcgOIWL6E5h26oNPrqVK9Bqeij2Cx5JJ0Op5rl1KLHH+lyUhPx2gqGM10M5owpznX0/XRTjeTiQzbMndPT86cisOSm0v0gf32ul25cAH9hw4r8/5LYnTRk5Vjsf+clZOL0cVQpu96urmSL2FO+C4SU6/QvVH5b0A4Mhc+rwqd45np6bjZzm83o4nMdOfy3/TnEpp26IxOr7+p/WdmpOPq0H64GAvtPyPDvrzweV2c/Px85nw0hW/fepmGLdvcVEz/REaDniyHTlVWbi5GQ9nK/NjZC3z112bmROzC2+hGs1pBfyuWa1ev4uHpaf/Z3d2Da1evFlkvKnIPqSkptGjV2unztatX0af/3X8rhiLXJmMZrk22dg/g0LZNzPvgTc6ePI6fLYFzNXlw8UwCeRYLCdFHiowWlaTwMezqZixynliP44J4r18nb2T7qmU0atfxps4vc3q602wOt0LnT0YJ7Z/J05Mz8db27/jB/Tc850pyu8plzYJ5THvjedy9vHF1cytTLLf6WvDqJ//h+fc+5r6RY/n+i6IzAW4k06G/AuDmZnTuQ2Sk2Zc7xjp4+Bie/eBTxrz4OuHLl9j7EOuW/EavIUPLHQc4t7EALm5uhdrgwn0r6zLH88l63bDGmJ+fzw8fT2XGO68Q2tL53Ff+d5X1VnIoMBZ4B1gC9JZSZgghXgVeEEJ8A9wHNJRSyhKmIX4PPCOl3CSE+NTh8/HAVSllWyGEC7BNCLHWtqwl0Bg4B2wDOgshdgO/AcOklHuEEJ5AZknbkVI6D41YDQfWSCk/FEJoAWMx64QAj0gpHxNCLAQeAOYDPwMfSyn/EEK4AhohRF/b+u2w3rZbLoToKqXc7LhBIcTjwOMAw597hS4D77Uv279pPSf2ReIdEEhOVqb98+zMTKcTHawndHamuWC7Go398+satG7H6WOHCWvXqZhfrWzcTO5kmgv2k2U2O12cjO7uZJoz8AGyMs1OjeM9o8czcMQYfv7yU2IO7sfbzw8QBAbf3N0ho8mDLIeLe1ZmBkZ3D4dYPKwX/wBrJ8Josi4z2To9RncPGjRtzrmEePxsd1pjDu6jVkgoLq5luyDti1jPif178A6oQnamQx1lmYvWkcm5jjS2gesdK5fSadD9xW4/9fw5dHoD3v6BZYrnOqO7O1kO9ZSdaXa+cJpKrqeorRFcOJPIsEmTAagSXIPmHbsw9+P38atShcDgGvYyLAuTuztmh3rKNGdg9HCuJ3NGOn5UIdOcgcl2PD365LMs/mE2EvCvWhUvHz/Ono4HBNWq1yxXeTjqGFKLJjWqkZqegauhoLlzNegxZ5dtilRmTg4xSRcBiElKZnDrxjcdD4DR5M5Zx/OqUJ24OdRnltnslOzt27qJi2cSefDJ5256/24md6epb9nmQvs3mZz273gsFUej0TD+9SmY09OYOfUNmrTr6NRBrGza169J4+pVSU034+rQiXfR68nMKdtodFZuwc2CgwlJhFT158Dpczcdk6eXF+kOnd/09DQ8vbyc1jl5IoaZ33zNJ//90ml0Nv5UHC4uLgRXv7m2d1/EOmJs1ybHNi0nM9PpuIFirk0OE3aadu5G087d2L12JXvWrabb/cPo++hYNi35FSklXv6BuHuV/gRD5Ma1RO/djU9gFaf9ZBcTi5vJRFYx18nSHNq+heSziQx57Okbrlsco7u7UxKaaXa+TpkKtX/Xr6fDJz7LknmzQYJflap42UYly+p2l0u/4aPpM2wEi6d/QeyRg9Rv2uKG37mV1wIAd0/r8V69Tl0MLq5kpKdhcijbG3Gz9Vfs8WSanfsQpoI+RKZDX8exDxFi60PkZGcjEFS5yRkHbiYT2Y5tcKF6KmijA8jOLLgGOJ5PjtcNjUbDmNfexZyexuz33qJR28rdBitlU9bpkKellDuBDkAjrMnRfmA0UAu4BmQBs4UQ9wNmxy8LIbwAbynlJttHPzks7guMsm1vF+CHNRkC2C2lPCOttx32A7WxJpNJUso9AFLKa1JKyw22U9geYKwQYgrQVEpZ3O2uU1LK/bb/7wVqCyE8gGAp5R+2fWdJKc22ffcF9mEdpWxY3L6llLOklG2klG0ckzeAFt168+Dk1+jz6Dh0BgPXLqWSl2fhXOwJqjq8YACsU/Lij1inFZyLO2EfzXHsxCceP4ZPYLUSfv2yqVk/hNMxx8izWLiSkozB1dXprmSdho05vj8KgOP7o6gbZn1O6fqzOVqtFoOLKwaDgbOn4khJOsvcTz5g37YtbF61jMTYE+WIpQGnjltjuZySjMHVzSmWumGNid6/F4Do/XupG2btZF+/q2Wx5BIfE01A1YI74eWdPtmye28emvw6fR8dh/6GddSQU0cPAbY6qm6to6upyWz47UcWf/MZGdeusHHhfPt3ju3aTljbjmWO57oa9W5cTzEHrPUUcyCKOrbnyY7u3c2B7Vt5cOIzaBwu4h169+fxt96jc//BVK1RE43GeTpWaWo3CCU2+gh5FguXki/i4uqG3iGWkMZNOLw3EoDDeyMJadwUgKCatXjmnfeZ9Po7mNPSaNyqDQmxJ7hw7ixfv/c2uzeHs27pEuJPHC9X2ew4cZrvNu5kye5D5Fjy8DK6ohGCWv4+JKaWbbZ03IVLVLdNnQv29SI1rWyjBCWpXq8+p2OirfWVmoLBpVB9hTYi5qCtvg5GUdtWX8ei9nBw51YeeMK5vm5m/wknjhfsv9DxUrthI2IO7rPtfx+1Q0t+/jDPYiHfdgdfb3BBp9ff9MjgP8WukwnMjdjNssjD5OQ5HzNnLpXtmHF8Vq5uFV9S/uYx06hJUw7u348lN5fzSUm4uRkxGApGkM8kJvDR1ClM/ehjvH2cp7itWbmCvneX/eUghbXs3odhz79OvxHjndq9s7Exxbd7tmvT2dgTBNhuvjg+T+ZqNKK3xe4fVJ0Hnn6J+558nqyMDPujASVp07MvI15+i4GjH0NvMHA1NYU8i4XEk8epVmhqfs3QMGIPHQDgzMkYAmuUfiMoZn8kR3Zv557xk8qU1BSnTkjp7V/9Rk04EmVt/45EObd/T7/9PhNffwdzehqNyjmSfTvL5XrdabRa9C4u9rq7kVt5Lci1Td8FuJKaQmZG+g1vLBVWs14D4h36EC6F2t2S+xDWc9feh6gWxJlTsSQnneW7j98jatsmNq1YRsLJmDLHEly3oA2+Wsw1oFZoGCcOWrufJw7up1ao9YVFVWrUJNG2n5OHDlCzQRh5Fot9FNXg8r/RBitlI6SUpa8gRG1ghZSyiRBiMDBcSvlIMeu5AL2Ah4HqUsqetgQpHZgNHJBS1rKt2wxYYNvmYmCWlHJNoe11B16SUg6y/fwNEIk1QZompexSaP1it1PK7xUEDASeBT6VUv4ohIgH2gDu139n27ov2T77D3BUSlm90LY+B2KklM5P6ZZi+rrtJRb8hdOniFi8wPoWyk5dadzR+rbG1T/M5O4x1pmhu1YvJ/7YIbRaHX0eHYuXfyDbli8m4fhRNFoNPoFV6fXIaLRaHTFRezi0LYKkuJNUq1ufjgOHEFS3IL+s4l3yXazITRvYE7EBgWDQyLFotFpOHjpI10H3kpuTze+zpnHtUiqevn4Mtb21af6Xn2JOSyM/L49aoQ25++GRTttcv/g3/KpUK/YtlPpSntXbHb6eXeHrEEJw76gJaLQaYg4doMfg+8jNyea3Gd9w9VIKXr7+DJv4DHqDgVkfTSEnK4u8vDxadenGXf2tz55lZ2Xy2SvP8dp/pxd5XuS6c6V01s6fPkXE7z8jJTTt1JUmnay/y6rvZzBg7EQAdq5eRvzRQ2i0OvqOGFdkZG3Ou6/Y30IppeT7917jkRffxq2EF6r4e5Z8wYrctIHICOsbrQaNsNXT4YN0HWitp8XfTePqpVS8fP144DFrPU2ZMIKAoCAMLtbnHh568jm8fP2Y+8l75OflY3R3557Rj+Fe6E4/gI+p5FHL7RvWsm39GkDw0Pgn0Gg1HDuwn75DHiAnO5ufvv2Cy6mp+Pj5Merp59EbDKxf/geHIncB0OfeB2jSuq3TNlf8+jMB1aoV+xbKdQfLltQF+3oxqFUjBII9cQnsjbM+jD+sYwt+22G9WI7q2oYqXh7kWPI4nXyJpZGH0Wo03N+uKV5GN/Lz81m480Cxz8oBtKxTtlGOvZs3snfTRgSCASPGoNFoiT1ykC4D7iE3J4c/Zk/j2uVLePr4cp/traHvPz4S/2rB9udUHnziWTx9fUvdT0nte9TmcPbaHtIf8GjR/S+dM52rly/h5ePLkPFPojcYOLx7B5ER60k8GUON+g3oed9DePsHsGj6lwiNhjyLhfa9+5f4FspWL79aprIpSeArk3Ft2gih15MTF0/SGzc9U50fn51cpvWCfDwZ0NL6Fsq9p87Y31w6tH1zft9l7QQ/2qU1gZ7u5OblcTrlMn/uPULvJiHUreJPvpSkpGWwPPIw+SXUxbMDupcplhXLlrJi6RKEEDz30itotVr27NrJ8FFjeOW5ZzgdH09AFWsbM3zkaDrd1RUpJY/cfy8zv/8RL++yvZ/rD9vvVZzzp0/Z377btHM3mtravZXfz2Cgrd3bsWoZ8UcPotXq6DtyPN7+gexYtYyE49ZnilyNJvqNHI+r0UTkhr+Is90saNNngP35uetKe940KT6Odb/+iARadOlOc9tbAJd99y33PmZ9SmPrij+IO3wAjU7HwNGP4xMQSOr5JNYs+J7zp0/hH1SDxu070bp7bz59ehx+VQvaw3snTMLDp+D8Kvz8Y0m2b1jL9g1rEAiGjrO2f9EH9tPH1v7N//YL+xteR9ravw0O7V/vYtq/ws6Wcm261eWyePoXZKank59noXr9UHoOLdL9K/I8sWNZ3IprwaWUZGZ89D4GV1dkfh73DB9FaNPmxe6zpGdSAXZHrGd3+HoQgntHjUej0XLi0H662/oQC2d+Y79OPvSEtQ/x3UdTyc7OIt9ioVWXbnTp7/z8+trff8WvatVi30KZlpVVYiz7toSzb3M4CEH/R0ah0WqJO3KITncPJjcnh+VzZ9ivAfeOn4hOb+DyxQss/2EWeRYL9Zs2p+vg+7l2+RJLZn5tbYNzc2nXq1+Jb6F8tHOrv/s47h2RlpZWemJyh3l4ePwjy628CVwA1tGonlLKk0III1Ad6xRHo5TyohDCFzgppfS9nsBJKT8TQhwEJkkptwohPgEG2rb5ODAAeFBKmSuEaACcBdpSfAK3AIimYAqlB9YplOOK246UssitTyFELdsyixBiMlBbSjn5RgmclHKKEGIn1imUS21JqxboArwP9JJSpgshgoFcKeXFksq1tATuTistgbvTSkvg7rTSEriKUFoCd6eVlsDdaWVN4O6EsiZwd8qN2vc76e8mcLdSWRO4O6GsCdydUloCd6f93RcG3UplTeDuhNISuIpQUgJXEUpL4O600hK4iqASuJvzT03gytU6SimThRBjgF9syQvAW0AasMz2TJgAni/m62OBuUIIM+A4SjYb69TIKCcyLYYAACAASURBVNtLSpKBIaXEkCOEGAZ8bXuxSCbQu5zb6Q68LITIxTpCWORPHZRiJDBTCPEekIs1YVwrhAgDdtiePUgHRgAlJnCKoiiKoiiKoijldcMETkoZDzRx+Hkj1tGxwtoV890pDv/fCziOeU+xfZ4PvGH75yjC9u/69592+P8erM/jFVbcdoqQUs4D5hXzeW3bf1Nw/p0/c/j/CaDIPC4p5ZfAlzfat6IoiqIoiqIoys26rX/IW1EURVEURVEURbl1/jkTzG8DIURTnN94CZAtpSzfH/5SFEVRFEVRFEX5B/ifTuCklIeAG//BEkVRFEVRFEVRlEpATaFUFEVRFEVRFEWpJFQCpyiKoiiKoiiKUkmoBE5RFEVRFEVRFKWSUAmcoiiKoiiKoihKJaESOEVRFEVRFEVRlEpCJXCKoiiKoiiKoiiVxP/0nxFQFEVRFEVRFKVyyNXqKzqESkGNwCmKoiiKoiiKolQSagSugvh5mCo6BLsFWyIrOgS7Hk0aVHQIdiFVAyo6BCcz1m2r6BDsejQOqegQ7NrWr1nRIdjFXUit6BCcuOj+OU38j89OrugQ7EZ99UVFh2C3wMO9okNw0rhGtYoOwa57gFdFh2C3KPZMRYdgN7hNk4oOwcn7i/6q6BDsfNyNFR2CXV5+fkWHoPwP++dc3RVFURRFURRF+X9LyoqOoHJQUygVRVEURVEURVEqCZXAKYqiKIqiKIqiVBJqCqWiKIqiKIqiKBUuX82hLBM1AqcoiqIoiqIoilJJqBE4RVEURVEURVEqnFQjcGWiRuAURVEURVEURVEqCZXAKYqiKIqiKIqiVBJqCqWiKIqiKIqiKBVOTaEsGzUCpyiKoiiKoiiKUkmoBE5RFEVRFEVRFKWSUFMoFUVRFEVRFEWpcOrvwJWNGoFTFEVRFEVRFEWpJNQI3E0QQtQGOkkpF9yO7UdtiSAyYj0CwcCRYwmqXde+LDcnh6VzZ3A1NQUvP3+GjJuI3mBg14Y17FizCinzef7Tr+3rb/rzD47t3Q1A0w6d6dx/0C2JsU6gH+N7tEcIwbqDx4k4etJpeTVvT57ufxeWvDy0Gg3fbdjB6ZTLf2ufFxNPs2nRz0gkTTp1o1GHLkXW2bNmBfFHDqLV6ej96Dg8/fw5unMru1cvx8PXD4B+ox/H3duH+CMH2blqKTqdHg9fP/qMGI9Gqy1TLDvD17N9/RoQggfHPU6NuvXty3Jzclgw/SsupSTj6x/A8CefRW8w8OPX/+FySjIAZ0+fYuTTL9C0TTu2rlvNzo3r0ep0DHhoOKFNm/+tcqob6MeEnu3BVjfhRwrVjY8nz9jqRqfRMGvDDuKTLxMaFMjE3p2o5uPBU3MWk5puvqn9X0w8zebFC0BKGnfqSlj7ovUUuXYl8Uet9dTrkbF4+vkDsHf9KhKjj5AvJe36DaZ6gzDW/zyHlLOJGNyMuLm7c/fYSWWOZe/mcHaHr0MgGDxqPMF1nM+lxbOncTUlBS9/fx6YMAm9wcCqBT9yJu4kuTk51GnYiAHDRwEQsXwJh/fsQghBsw6duWvA4HKVS8qZBLb/8StSSsI63EWDdp2KrLNv/SoSjx1Go9PRbdgoPHz9MaddY9Mv35NnseDu48tdD45Aq9Pbv/Pnt5/h5R9I12GjyhXPra6n3Jxstiz+hWupyeRLyYDxT+FqNJUrJrC2HQNbNUIAkXGJ7Is/67TcoNMyumtbAjzdWRl1lAMJ54D/Y+++w6K42oePfw9bgKVXFVRQVMSOvRvUGDVGTTeJMfaYanozvT150qNJjMkTExOjxq6x995FRFGaIkUFBFRY2hbm/WPXZVdQAY3o7z2f68oV2Tmz5+bMzJm555wZIDI0mKgWTbhQVAzAgr2HKSgurXb9VRH01Sc4N2vChYVLOT9r7r9SB8C59FS2L56DokDLbr1p3qVHhTIH168k9VgsKrWGqBGj8fTzJ2bzOk4djQGg4HwejdtE0mP4w+xatoDstBRMRgNBYeF0H/ZglWPZvWk9OzesQSB4cPwkGl7W583+8TvO55zDxz+AkU9PRqPVEhd9gBXzZqPRavHxD2DUcy+hUqlYu3g+MXt2IYSgQ4/e9Bt673W105LVq1i4fDkIwZTJL9AiPNy2bNXGDcxZvAgn4YS7mxufv/se7m5uvPXpJyScSMbDzQ0fb2+++fDjatV5aMcWDm7ZiBCCQY+NISi0UXl7GA0snznDdp4eOvZJNBot53OyWfbrDMwmI03bRtJ7yL0U6fX8/f1XtnXTkxN59bsZaJxdWPzz9+gvnMdsNtHv/kdo3KJVtWJcuXwZSxctQgjBy6+/QXhEhG3Z7N9/Y/PGDahUasKbN+el199ACMHK5ctYOG8earWa1m3b8vzLr1Srzqqo7+vFfV3aALA7MZX9J9IclocHBTKwXXNM5jIMJhN/7ThIUanxhsZQ19uDQW2bgxBEp2RwOPWMw3KtWsXInh3w93BjdUw8R9LPAtCyfl06hTVAUaDUZGLxvlgMJvN1xVLP25PBkZZtc/BkBjGpFfu8Ub074e/hxqpDx4hNO+uwPKpFE1qH1GPq6u3XFYd0e/k/m8AJIVSKolzfUVX596qBUOBR4IYncMWFevasX83Edz+h4HweC2dMY8LbH9mWH9qxBf96QTw46Xk2L13IoR1b6Nx3AC07dqFjn35MffNFW9nS4mKit29m8mffoigK0958iU5R/dE6u1x3nOOiujB19Tby9EV8+sgQ9p9Io7DUYFuedbGAKfNWAtCqQT0e6NqWr1Zsua46ty74iwGjJuDu7cP8rz6mcZtIhwvEvMyzZCQe58GX3uJ0cgI7ly9k0JhJALTo1ovOAx0vuPesXMLg8c/g6evP+j9/JS0+jtCWba4ZR5Fez9bV//DyJ19yIS+XP6d9zYsff25bvnfLRuoE1+eJya+wesFc9m7ZSM8Bgxj13EsAmIxGPpo8ieZt2lFw8QI716/hlU+/wmg0MPX9Kbzy6ZdVTiQrM75vF75dZdk2nz06hH3Jl22bCwW8Nddu23Rpy5crtpCec543567grXv717hugG2L5jDg8fG4efmw8JtPaNTacTudzzpLRtJxHnjhTU4nJ7D7n4XcNXoSqceOYCguZvizr1b4zt73P0ZQWNNqxVFcqGfX2lU89cGn5OflMf+naUx6t/wiLXr7ZgLqBTPi6RfYuGQB0ds306XfXQx46BHU1gTp54/fJSsjHW8/fw5s3cRLX3yHosA3r79Al753onWp+rG0a8k87nh0LG5e3iyb+hkhrdribNcuF7IyOZOcwNDnXuPsiUT2rVxCv8cncHjjapp16k5YZCdiNq0h6cAemnftBUDqsdhqxWDvRm+n/WuW0ySyIw2bV+8i83J3t2/Bwr2HKSguYWK/bhw/nUWJ0WRbbjKXMXdXNJ3CGlZY92BKBluPn7iu+qsi+7NvcO0YiTrQ/1+tZ/viOfQfadlGi779lNDW7Spso9NJ8dw3+U3OnEhkz4pFDHjiSdpFDaBd1AAAVsz4lrB2HQHocve9qNSW0/7SaZ+Td/Y0vvWCrxlHkb6Arav+4ZX/fMWFvFxmTf2Klz/5wrZ8z+YN1A2uz5gXXmXV/Dns2byBXncNZsW8Pxn/yhT8AgP5Y9rXxB8+RFhEC3ZvXM+7U39CAT6ePImeAwbhXMP9+GJBPn8tXMicn2aQfe4cb3zyEbN/mG5b3r93Hwb3s/Rp0379H8vXruHR++4H4K3JL9ChTfVvmBUX6tm7fg3j3/mYgvN5LP7lB8a99YFtecyOrfjXC+L+J59jy7JFxOzYSqeoO9mwYC5R9z5ASLMIZn3xMREdOhNQL5gxb7wHQMbJZDYvWYCrmzsJMQfROjsz9q0POJ+TzcLp39G4xSdVjjE/P5/5c+fwvz9mcy47iw/ensKM32bZlveJ6svI0WMAmPLaqxzYt49OXbrwv59+4q+Fi9DpdDw9fhynTp4ktHHjK1VTI/d1acPs7Qe5WFTMC4P7cDT9LMWG8gQt62IB36/ZgbmsjB7hjegT0YTVMcdvaAyD2jZnyf6j5BeXMC6qCwlnsh36GaO5jL93x9CxcQOH9Y6fziIuIxOAO1qE0aZhEAdOpl9XLIMjI1i0N5aC4hLG9+tK/JmKfd68ndF0rKTPc3PW4udR/RtltzI5g7JqbssplEKIUCFEvBBilhAiVgixUAihE0KcEkK8K4TYATwohGgnhNhjLbNECOFjXX+LEOJbIcQuIcRRIURn6+duQoiZQoj9QohDQohh1s9HCyEWCCH+AdYBnwG9hBAxQogXhRDbhRDt7OLbKYS4diZQiYwTyYQ0a45arcYnIBBDaQkmY3nHlhJ/jPB2HQAIj+zAqQRLp+bu5W07MV+i1mrx9PbBaDBgMhhQa7U4qa4/Z1ernHDWqMnO12MqK+P46Sya1HW8kLGfw+yq1ZB67vpG30xGI0ZDKV7+AajUaoLCmpGVmuJQ5nRyPKEtLSfj4Cbh5Jwu71Tj9+1iwTefsnvFYpSyMgB86wVTWlSEoiiUlhTh6uFRpVhOJSUQ1rwlao0G/zp1KS0pwWi3jZLijtCyQycAWnXsTPKxow7rH43eT3jrtmi0WnKzs6lbvyEqtRoXVx3Ozs7kZGVWv4GsLt82x66xbXTOGtvIaJHB6HDSqAmzyYjJUIqnn2U71WvclOy0y7ZTUgKhLSyHR3CTcHLOZACQdGg/JpORpd9/wfo/f6G0uHwEcMfSv1n03WckRe+rcixpyUmEhkegVmvwDayDocTxWDp5PI6ISMuxFBHZgZR4y7F0KXkzm0xonJ3x9PGxHEs+vrZjSaPV4qSuepJtNln2X08/f1RqNXUbN+VceqpDmTMnEmgY0RqAemHNyLO2y8Vz2fg3CAEgsGEjziQnAKCUlXFs5xZa9LijynHYx3Ojt1N64nFSjx9l8bTP2btqabVjAlA5OaFVqbhQWIy5TCH13HmCfb0dypQpCvoSQ6XrtwsNZnxUF/q1aoqoUQRVYzqX8y9+u4XZZMRYarhsG51yKHM6OYGQFpZ9JiismUOfB1CsLyA/N4e6oWEAtnOE2WxCrdWi83Js2ytJSUogLMK+zyuu0Oe16tAZgNYdu9j6vHoNQigu0qMoCiVFRbh7eqHROuPl64vBYMBoKEWjdUZ1HTesYo8do0ObNmg1GuoHBVFUXIzBUL5/aDXlo9UlJSU0aVQ+Uvb599/z+LNPs3rjxmrVmXHysvP0ZX3LqfhjNGvbHoDwdu1JtZ6nM9NSCWlmGWlp1ibS9rntd9m9gzbdLCPhvoF1MRmNKIpCsb4QNw+vasUYd+QI7SLbo9FoCAquT3GRY7s0CAmx/VujUdu2QUijUIoKCzEajRiNRtyreF6sKpWTE1q1mjx9EeYyhZNZuTT0d9wPLce/5TxtKiujTCm7wTEINGoVF4qKKVMU0nLPE+Tj2L6Kojjc+LzE/vypUak4l6+/YbGYFYW0nPME+zrGUqYo6CuJBaBPizC2x//7N62kW8/tPAIXDoxTFGWnEGImcGleVYmiKD0BhBCxwHOKomwVQnwIvAe8YC3npihKdyFEb2Am0AqYAmxSFGWsEMIb2CeE2GAt3w1ooyhKnhDiDuAVRVGGWOvJA0YDLwghmgHOiqLE1uSXKirU4+pWfjfFxVVHcaEeD28fAIr1elytd2BddW4U66/ceahUKpq2jeS71yejKAp3DL0Ptfr6N7mHizNFdp1JYWkp7i7OFco1DvRjXN+u+Hu68eXyTddVZ0mRHmdXne1nZ1cdJYWOv3tJYSFudhcklxK1xm0iaW6drrZh9q/EH9hDROfuRHTuzrLp36B1ccE/uAF1GjaiKor0Bejc3W0/u+p0FOkL8PLxLV/u5m5d5kahvsBh/f3bttBnkGUqa0Ddepw+dZLioiJKi4s5nXqqQvnq8HBxdjjpFJaW4uFa+baZ0K8r/h5ufH6d28ZecaEebYXtVOhQpqSoEDev8hNUmXU7FeZfwMXNjeHPvkrsto0cXL+K7kMfoMewh3B196CkUM/SH74ksGEjvPwDqhSLw7Gk01Gk1+PpYzmWivR6XK3byUXnRpFduy+f9SvHDu6jaeu2OLvqcHJyIrxtJF+/OhmlrIyo4ffbEr2qKCksdNx/XVwr7L+lRYW4eZbvv5faxadeEBnxcXj1DCT9+BFKiyztmXhgN41aRzpMp6yqf2M75Z45TeeBw+g5/GHW/D6d1ONHCLEmpFWl02oosbsQLjEa0Wmr9vsdP51lm3p0X6c2tAkJqjAt6nZi2WdcbT87u+oovcY+c/nfT0o6uJcmkZ0cPtu+aA4njxyiQXhLnF1cqYoivf6yPs/Noc8rtOsTXd3cKCywHEud+/Tlh4/exUWnIzikESFNLKPoLdt35MPnn0QpK2PgAyNQa6q/D19yMT8fT7skw8PNnYv5+QT4l9+4WrRiBX8umI+zszPjHnsMgFeffgYfb28u5Ocz7sXJtIpoToOga49GguU87HJZ3+Jwni4sX+5id55W7BIRF50b+osXbD+bzWYSD0fT7/4RAPgEBGIyGvn+rZcoKSrikckVZyZcTX7+RTw8PW0/u7m7k3/xIv4Bjn1n9IH95OTkENnBcjNr4OC7eeKRh9Fqnek3YECF8tfLzVnrMNpWbDCic9ZWWtbdxZlezRvz0/qdNzQGV63W4WZlicGEaxX7GbDcKOrSpCEmcxk7E1KuvcJV6LRaSgyXx1J5e1zO112HVq0m6+L1JZHS7el2TuDSFUW5dFTPBp63/vtvACGEF+CtKMpW6+ezgAV2688FUBRlmxDC05qwDQCGCiEuTfp2AS6NWa9XFCXvCrEsAN4RQrwKjAV+r6yQEGIiMBFgwutv03/4AxXK6NzcOV1UPvJQUlxku8gEcHV3p8S6vKSoyOEC9XI5mWc4tn8vL335PYqi8Oun7xHRvjOevr5XXOdqBraLoFvTEM5eKHDocHXOWvQlFZ81OZmdy5R5K2lS159xfbvy5pwV1a7z8NaNJMccwCsgkNKSYtvnhpIiXOzaBcDFzc1h1EY4WQaY7accNevQhdTjR4no3J1N8/7g4VfewcPHl03z/iDp0H6aXnaxUxmduwfFdhe7xUVF6Nw9Kl1ectmyokI9Z1JP0cT6LIObhweDH3qUnz/7EA9vb4JDQm0XRdUxqF0E3ZqFkHnZtnHTatFX8hzQyexc3pxr2TYT+nbl9RpsG3ux2zaSfPgg3v6BGOy2gaGk2GGaIFi2R2lx+bZ0sttOly74G0a0Yvsiy7NFrtb2c3Fzp0F4S3JOp1cpgXN1c6fY/lgqKnK4CNW5u1NcVIgPFY+zoU+M4+6Ro/nruy9IjI3Br05dju7fy6tf/4CilPHzx+/RokNnvKzPVV5J3I7NpMRG4+kXgKHYfv+t2C7OOjdKS8rjvdQukf0Gs3PJXFKmH8IvqD46T29MRiPJ0fsYNOF5MlMcn3G8mn9zO7m4uRES0QohBA2btyL3TEaVE7guTRrSsn5dcvVFuNhdzDtrNA4Xe1djf0EWm3aWpnX9b8sE7sj2TZyw9nkG+z6v+Ar7jH2fJxzHHRMP7qX/4+MdPut1/6P0GP4wa36bTlr8UUJaXHuyiM7d/bI+r9ChX3Nz96CoUI8fdazLLMfS3Bnf89p/v8HHP4C5M74netd2gkMbE7NnFx/8+CtKWRnfvvsGbTt3xduvZtNRvTw9ybe7kVlQqMfLLnEBuH/IEO4fMoRf5/zFzLlzeeWpp/HxtiS+3p6edOvYiYTk5ConcK7u7pSkOPYtDudpN7vzdHERrtb2EKJ80tPlfc6Jo7E0CGtqm0oas3Mrnr5+jHjuZc7nZPP3tK+Z9MFnVYoPwNPTi4KC8ptShXo9nl6OIzvJiYlMnzqVL76bihCCwsJC/jfjJ/5esgxXnY7XXpxM3NEjtGxVvRsxlenZvBFtQ4LJydfjoim/9HTRaip9vs1Zo2bMHZ2YvzvmiiPu1dUprAERwXXI0xfhbBeDs0btcOPoWmJOnSbm1Gm6Nwule7NQNhxNqnYsncMa0qJ+XfL0hQ7t4axRV7nPi2rRhE1x1a/7Vif/kHfV3JZTKK0u38KXfi68vGA11hfA/YqitLP+11BRlEtzHK74vYqiFAHrgWHAQ1zh2ThFUX5WFKWjoigdK0veAOqHNSE1MR6zycSF3By0zi4OdycbhbcgMTYagMTYaEKbt7jyL6iAs6srao0GjVaLWqOhtLTkiuWvZU3Mcd5bsIaf1u+k1GjC38MNlZMgIqgOyZmOU4o0dlNiCksMGGo4Na9tn37cP/l1+j86Bo1GS0FeLmaziTMnkqgT4jhiFtwknNRjRwA4ezIZ/2DL3PVSu4v49MTj+ATWBUA4CZx1llEIV3f3CiMQVxLaNJwT8ccwm0zkncvG2cUFjd02atKiFXGHDgAQd+gATe0ePD+0awftuna3XQwDtOvag8kffsb9YyaicXbGNyCwyu1zyeqY47w7fw0/rtuJwW7bNA+uQ9LVtk2pgVLT9U2bBGjTux/3PfcafR8ZjVrrXL6dTlbcTkFNmpF63LqdUpLxD6oPWLbfpSli2emn8LK2w6XtZzaZOJuSjHdgnSrF1LBJU1ITj1uOpZxzaF0uO5aatyQhxnIsJcRE0zjCciwZrdOMVCoVWmcXtFotKArO1vU1WmfUajWGkmsfSy17RjHk6Zfp/fAo1Fot+vN5lJnNZKYkE9gw1KFsvbBmpB+PAyAr5QS+1nbRuroS9ehYhjz1EmqNhkZt21OQl4OhuIi1v37PvhWLyUg4RvyeHdeM59/cTsFNwslOt36edgov/6rvx3uT05i5ZR/LDhzFYDbjpXPBSQhC/H3IyLtw7S8Ah4ugxnV8ySmo6qng1tK6V1+GP/caUSNGo9ZqKThv2UZnU5IIvHwbhTUj7bhluuLZlPI+D+BCdiZCCLwDyo+XS9P8nFQq6zmhanf6GzUN50R8nF2f51qxz4u29nnRB2ja0nLB7+TkZEtS3D29KNTrLceSq2V9rbOz5bxUhWPpStq0aMGhI7EYTSbOZGWic3W1HLNWpaXlN7A83N1xdbbMSMi3JjcGo5FDR44Q2sDxWaerqd+4CWlJCeXn6cv6ltDwCJJiDwGQFHuIkHDLtMm6DRqSlmSZAp0cG2P7HCB293bb9EkAFGxJsqvOvUr9jb2WrVsTe+gQJqORzLNncdU5tkt6WhqfvP8eH372X7ytsxKcnAQatQZXnQ6VSoWnpycF+fnVqvdKdsSn8MPaHfy9Owaj2Yy3mytOQtA40Je0y15wplE5MTaqC+uPJFZYdj32n0jnj20HWBF9DJPJjKerpZ9p6O/N6byLVfoOld25u8RoxGiu2fTOfSfS+H3rPpYfjMNoNuNli8WH01Xs83zcddzdvgUje3XAw8WZQe0irr2S9H+GuB0zXetbIFOwvAlytxDiFyAeeA7oqChKjrXcYeBZRVG2CyHeB7wURXlRCLEFiFcUZZIQoicwXVGU1kKITwFPLNMuFSFEpKIoh4QQo63f+6z1ezsAXyuK0scupg7AP8B2RVEevtbvMH/P4Ss2/MFtmzi4dRMCweCRo3FyUnEiLpaeg4diNBhY8r8fyT+fh6ePL/da35x3dN9u9m/eQHpyAg2ahNPvvodo2DScdfPnkBIfB4pCo4iWDHjosYqx7Iq+ZptfLqyOH2PusLyFcuORRNtdoMmDevPd6m20b1Sf4Z1a2+aL/75lH6fOXWkAs1xUq2ZXXJaVlsK2hXNRUGjZrTctu1le5LB21s/c9cREAPatWU7qMctb/Po/Ohov/0B2/bOI9IRjODmp8KlTl6gRo1Cp1CQd2k/0hjWoNBqcXXXcNXqiwwtemta98ijP7k3r2b1xHQjBA2Mm4OSkIj42hv7D7sNQWspf06dyITcHbz9/2xvZAL599w0eGjeJoJBQ23f9Me1rLuTmoNE688CYCQTUC6q0zqpOIwmrY3lDKEKw4Ugim6x3B18Y3JtvV22jQ6P6DO/cmrIyy7b5zbpt6vl4MrFfN8Lq+JGWc57t8SdZezih0jqiWl75hSJZaafYvnguKAotuvWihfWFG+v++JkBoyzbaf/af0g9fhSVSkXfR8bg5R+A2WRk07xZ6M/n4aRSWV7e4OnFsulfYywtpcxsJrxjV9r2cXzJSrDflZ8NObB1I/u3bEQgGPL4GJxUKpKPxNJ7yDCMhlIW/vwj+Xm5ePr68cDEZ9Botcz+7guKCgooM5sJCW/OoBGPA7Dm7784eewoCgphEa0YOGJkhfpOZuVeMZZz6afYvXQ+iqLQvEtPwq1vFNw0+1f6jhwHQPT6lWTEx+GkUtH74VF4+gVwOimeQ+tXIoQgqGlzIvsPdvjeM8kJJB/cW+lbKJ2vMmX6Rm+n/LwcNs+bhdlkxMu/Dn1HPGEbBQfIzq/a1OAgH08GR1reQnkwJYPoFMuzdw90acvCvYcBeKxnBwI93TGazaTmnOefg3H0b9WUxnX8KVMUcgoKWX7g6BX/ntCoqd9WKZYrCXztBVxat0BoNBhOnuKs3QssqmvVlLevuCw77RQ7lsxFUaBF115EWN+8u/7PX7jz8QkAHFj7D2nxR3FSqYkaMdo2Or1v1VJc3D1o07uf7fvWzPyRkkI9ZWVm6jVqSrehFW8ktmxQr9JYdm1cx66NaxEIHhj7JE4qJ+IPx3Dn8PsxlJYy+4dvuZCXi7evH48/+yIarZboXdvZsGwxao0GnZsbo194DRdXV5bN/p3Eo7EoKDRr2Ybhj4+ptM47Aqr23NfilStYtGIFCMGbz09GpVKx+8B+xj7yKNNn/c7eg5bk0svDk4/eeBNPDw8mvPwSRcXFmEwm7hkwgJEPXP2NnAtOZDj8HL1tM9HbNiGEYOCjT+CkUnEy7gg9Bt2D0WBg2cyfbH3LsHGT0Gi05GVnsfy3GZhNJpq0bkefofcBUFpSwvR3XuW5z761PYtmKC1h1XfEQwAAIABJREFU4U/TKC0uwlhaSvdB99CqczcA7qrihfo/S5ewfMkShBC8+OprqNQq9u3Zw8gnRvPy88+SeuoUgdYbYo898QQ9evVmwdw5rF65ArVaQ4OGDXnrvfev+YziRwvWVCmeSxr4eXNvZ0uSvycplX3JlrdQjuzVgdnbD3JHyyb0a9WUzAuWPiPhTDYbjiRW6bt93HXXLoTlzY8D24aDEBxKySDGOlp/b6fWLNlvuXE1onskAZ5uGE1lpOWeZ9Wh4/Rq3phGgZZZMsUGI8sPxlF6hRvUl57ju5YgH09b8hWdkmF78+79nduwaJ/laZxHe7QnwNrnpeWcZ0X0MYfveH5Qr2u+hfKDBwf+m48G3zCnzxfcUolJsI/HLdlut3MCtwrYBnQHkoDHgWM4JnDtgJ8AHXASGKMoynlrArcb6IMlYRurKMo+IYQr8K31OwVwSlGUIZUkcBpgDeAP/K4oyjfWz+OBFxRFuWZvdrUE7marSQL3b7laAnezXS2Bqw03+jmA63G1BO5mu1oCd7NdLYGrDVdL4G62qiZwN8P1JnA30tUSuNpwpQSuNlQ1gbsZLk/galNVE7ibpboJ3L+pqgnczVDVBO5muV0SuPTz+bfM9TFAAx/PW7Ldbp2ze/WVKYoy6bLPQu1/UBQlBuh6hfUXKYry5mXli4EnLy+oKMrv2D3XpiiKEehnX0YIEYRlSuq6KkUvSZIkSZIkSZJUTbfzM3C3DCHEKGAvMEVRbvD7biVJkiRJkiRJkqxuyxE4RVFOYXntf03Xv+OGBWP5vj+AP27kd0qSJEmSJEnS/09ux0e7aoMcgZMkSZIkSZIkSbpN3JYjcJIkSZIkSZIk/d9ypbcHS47kCJwkSZIkSZIkSdJtQiZwkiRJkiRJkiRJtwk5hVKSJEmSJEmSpFpXVianUFaFHIGTJEmSJEmSJEm6TcgETpIkSZIkSZIk6TYhp1BKkiRJkiRJklTr5Esoq0aOwEmSJEmSJEmSJN0mZAInSZIkSZIkSZJ0m5BTKGuJEKK2Q7AZ2K5FbYdgE1G/bm2HYLMnMaW2Q3DwWK+OtR2CjYtWU9sh2GyNS6rtEGxG9elc2yE4SDiTXdsh2Izo2aG2Q7CZ4+Fe2yHYDP7k49oOwcGm99+v7RBsZqzfUdsh2Lw+/M7aDsFm+f4jtR2Cg0dvoXNTcua52g7B5kzexdoO4bakyDmUVSJH4CRJkiRJkiRJkm4TMoGTJEmSJEmSJEmqASHEQCFEghAiWQjxRiXLXxJCHBNCxAohNgohQq63TjmFUpIkSZIkSZKkWlfG7TWFUgihAn4A7gQygP1CiOWKohyzK3YI6KgoSpEQ4ingc+Dh66lXjsBJkiRJkiRJkiRVX2cgWVGUk4qiGIB5wDD7AoqibFYUpcj64x6g/vVWKhM4SZIkSZIkSZKk6gsG0u1+zrB+diXjgNXXW6mcQilJkiRJkiRJUq271d5CKYSYCEy0++hnRVF+ti9SyWqV/hJCiJFAR6DP9cYlEzhJkiRJkiRJkqTLWJO1n69SJANoYPdzfeDM5YWEEP2BKUAfRVFKrzcuOYVSkiRJkiRJkiSp+vYDTYUQjYQQWmAEsNy+gBAiEpgBDFUU5Yb8gVY5AidJkiRJkiRJUq271aZQXouiKCYhxLPAWkAFzFQUJU4I8SFwQFGU5cAXgDuwQAgBkKYoytDrqVcmcJIkSZIkSZIkSTWgKMoqYNVln71r9+/+N7pOOYVSkiRJkiRJkiTpNiFH4CRJkiRJkiRJqnVlt9cMylojR+AkSZIkSZIkSZJuE3IE7hYUvX0z+zdvQAjBkMfHEhTa2LbMaDCw5NfpXMzNwcvPn3vHPYVGq2Xe919zMS8XpayMzv0G0L5XFACnU06wbsEcysxmghuFMXDE49WKJSvtFBv+/hNQaNszilbdelUos2f1ck4cPYxarWbgqPF4+QVwfP8eDm1djxBOaF1cGDL2aZxdXTEaStn492wu5p5DKStj+JOTcXFzq3Yb7diwjq1rVyEEPDbxGUKaNLUtSz4ex6wfviPrzGk++/l3fP0DAPjxs485n5tDWZmZqEH30LP/gGrXW5mcjDR2LpkHikLzrr0I79y9QplDG1aRdvwoKrWaPg+PwsPXn6KCfLbM/Q2zyYS7jy+9HxyJSq2pdv0Ht21m/+b1IAT3jBpH8GX7y+L//ciF3By8/fy5b/zTaLRaVs/9g/QTyZiMBho1b8GgR0YB8OtnH1BmNmMoLaHnoKG07daz2vHs27KRPRvXAoL7xkykfuMwh3j+/mka53Nz8PHz5+FJz6HRajGUlrLkt5/JO5dNWVkZY15+E527O/OmT+V4TDQtIjvw8KTnqh2LvWAfL4Z1agXA3uRUDp7McFjurFYxvm9XAr08WLr/CIdOnQbA113HQ93aWR6sVmDerkNcLC65rlg2rVnNhpXLQQjGPzuZxs3Cbcvi444w45svOZtxmh/+nINfQCAA0Xv3MG/WTLQaLf51Annu9bdQqWrehddkv5kz7Svy83IpKyujS7+76NDb0s8sn/U/TqecoKysjJ6D7qnRfnPJquXLWL5kEUIIXnj1DcIjImzL/pr1G1s2bkStVtGseQQvvPo6QghefHoSRpMRgGNHj/LLrNmENW16pSqu6lx6KtsXz0FRoGW33jTv0qNCmYPrV5J6LBaVWkPUiNF4+vkTs3kdp47GAFBwPo/GbSLpMfxhdi1bQHZaCiajgaCwcLoPe7BGcV1N0Fef4NysCRcWLuX8rLk3/PsvyUo7xeYFs1EUaNOjDy0rOR/sXfMPKXGHUak1DBg5Fi+/ANuyXSuWcHz/bsZ98LntM7PZxKyPptCiSw+6DrquZ/oBaBToy9ioLggEG44ksuVYssPyet6ePHNXT0zmMlQqwf827iE15/x113vJ1nVr2Lx6BQjBE089R6OmzWzLEo8d5dep35B1OoOvZs7GL8DSNj99+V/SUk6g07nh4eXF5Lffr3H9makprJv3ByjQrncUbbr3rlBm16plJB+JQa3WMPiJCXj7B5BxIpE1f84kLzuLSZ98iaePHwDG0lLW//0HF3Is5+z7nnoR1xqcs7etX8Pm1StBCEY99SyNmti3Sxwzp1na5ctf/7S1y4yv/kvayZO4urnh6eXF81Peq3a9h3Zs4eCWjQghGPTYGIJCG9mWGY0Gls+cYbueGjr2STQaLedzsln26wzMJiNN20bSe8i9AORlZ7FmziwMpaV4+vpy34RnSD5ymM1LF6DWaNBonblv4jPo3D2uGdeNvm6I27mFo9s2oigKI976uNrtdCu53V5iUltkAncFQoh7gcVAhKIo8Ter3uJCPbvXrebJ9z4h/3weC3+axsR3yg/GQzu2EFAvmIeemsympQs4tGMLnfsOoP8Dj+Bftx5Gg4Fpb71E6y49EE6CdfP/4tHnX8XZ1bVG8Wz4+0/uHjMJD28fZn/+AU3atHdIuHIzz5CacIzHXn2H9KR4ti2Zzz3jn6FZZEciOnUFYMc/i4jbu5P2d/Rn18qlhHfoTKMWrWvcRoX6Ajb8s5S3v/yO87k5/PL157z1+Te25UENQ5nyxXd89+E7DuvdP2oMdYKCMRoMvPPsRLr0vgONVlvjOC7ZuWQeUY+Oxc3Lm6VTPyO0VVucdeVtdCErkzPJCQx77jXOnkhk78ol9H98AjEbV9OsU3eaRHYiZtMaEg/sIaJrxQuiq7HsL6uY9P6n5OflsWDGNJ6021+it28mICiYh59+gY1LFhC9fTNd+t3FnQ8+gtqaLP788btkZaRTp34DnnjlLdRqDSXFRUx765VqX4gX6fVsX72CyZ98zsW8POZ8/w3PffiZbfn+rRsJDK7PyOdfZu3CeezfupHudw5i7cJ5tOvWk/C2kQ7fN/Dhx+jYO4qD27dUK47KDOvUirk7o8kvLuHZu3pxLCOLYoPRttxoLmPWtgN0axrisF63pqHsT07jYEoGHRrXp3t4I1bHHK9xHPqCAlYtWch/vv+JvJxzTP3sEz757gfb8gYhjfh02nT+M+UNh/Xm/f4rr7z/EYF16jLtv59y+MAB2nfpWqMYarrfDHjwUVs/892bL9Gmaw9yszLJOp3OU+//h9LiYqa9Xf395pL8/HwWzpvLjFl/ci47m4/emcL0mb/blveO6stjT4wB4J3XX+Xg/n107NyFb378CYDcnBxeePrJGidvANsXz6H/yPG4efmw6NtPCW3dDhe74/l81llOJ8Vz3+Q3OXMikT0rFjHgiSdpFzWAdlGWm0IrZnxLWLuOAHS5+15Uasupdum0z8k7exrfesE1jq8y2Z99g2vHSNSB/jf0ey+3ecFsBj3xJO7ePsz98iPC2rZ3aJu8zDOkJx5nxMtvk5GUwI5lC7h77NMAFOZf5Hx2ZoXvjN2+Bd86dW9YjGOjujBt9Xby9EV8MuJu9p9Io7DUYFuedbGAt/+2vGugVYO63N+lLV+v3HJD6i4sKGDdssV88O0P5OXmMP3z//De11Nty+uHhPLBN9/z5btvVVj3iaeeI7xVzc+Ll6yb9wdDxz2Fh7cvs/7zHk3bdnBIuHLPniE1/hijXn+PtMR4tiz+m+ETn8W/Xn1GvfE+C77/0uH7dqxYQvMOXWnc8jrO2QUFrF22hA+++Z683Bx++uIz3v3qO9vy+iEhvP/1NL58b0qFdUc99WyN26W4UM/e9WsY/87HFJzPY/EvPzDurQ9sy2N2bMW/XhD3P/kcW5YtImbHVjpF3cmGBXOJuvcBQppFMOuLj4no0JmAesGsmv0bw8Y+iYe3j+07/IOCGfPGe6g1GvZtWseedavoe9/D14ztRl83NGrTnoiuvZj/3+onudLtSU6hvLJHgB1Y/p7DTZN+IonQ8Oao1Rp8A+pgKC3BZCy/yEw5Hkd4uw4ANG/XkVPxxwDwr1sPAJVahRBOCCFIT05E6+LC/Onf8et/3udUQvUuOk1GI0ZDKd7+AajUauo3Ceds6knHeBPjCWvdDoAGTZuTfTrNGkf5vQGjwYB/kOWCJTU+jpRjscz7+lN2/LO4WvFccjIhnmYtW6HWaAioW4/SkmKMxvITtM7NDZdKEtY61hhUKhVCCBA1qt6B2WTEZCjF088flVpNvcZNOZee6lDmzIkEGkRYTkD1wpqRd8Yy8nPxXDYBDSzJQmDDRpxJTqh2/eknkggJj7DsL4F1MJQ47i8nj8fR3Lq/RER2IMW6D1xK3swmE1pnZzx9fBw+N5SWEli/frXjSU1OpHFEC9RqDX6BdSi9LJ7kuKO0aN8JgJYdOnPiuGX/TTp6mPjD0fzwwRTWzJ9jK+/t61ftGCqjcnJCq1JxvrAYc5lCSnYu9X29HcqUKQr6kop/WzPrYgEuWku76LRaCispUx1Jx48R0boNGo2GOvWCKCkuxmgo33/d3N1xddVVWK9BSChFej2KolBUWIint3eFMlVV0/3GoZ9xEggh8PTxQa1WYzaZKC0pxtXNvcZxHTt6hDaRkWg0GoKCgykuLsJg1zYNGpYn1xqNBpVK5bD++jWr6TdgYI3rN5uMGEsNePoF2I7n7LRTDmVOJycQYr0BFRTWjJzT6Q7Li/UF5OfmUDfUMvJ8qS80m02otVp0XjXfbldiOpdzw7+zQh1GI8bSUrys54PgsGZkpqY4lElPiqdRq7YA1G8azrmM8rbZs3o5ne8a4lDeUFLCqWOxNLEmu9dLrXLCRa0hO1+PqayM+DNZhNVxTGrL7O7uu2q1pObk3ZC6AZITjhPeqg1qjYbAuvUoKXE8tnVu7pWemwBm//wjH748md1bN9e4/kvbyNs/EJVaTYOm4Zw95XjOTk08bjtnN2zWnOwMyznbRadD6+JS4TtPxcdxMu4wf335MduWL6pRXJZ2aV2jdvnrl+l8+Mpk9tSgXTJOJhPSrDlqtRqfgMAK/dyp+GM0a9segPB27Um19nOZaamENLOM/DdrE0lqwnEu5JzDaChl9ZxZ/PbZBxw7sBcAbz9/1BrL+UGlUuPk5NgnVebfuG7QeXjipLp23dL/HTKBq4QQwh3oAYzDmsAJIZyEED8KIeKEECuEEKuEEA9Yl3UQQmwVQhwUQqwVQtSrad3Fej0uuvILIBdXHcWFetvPRYV62900FzcdRXq9w/pbly+hTbceqDUa8s+fJzMtlQefep4Hn3yOpTN/qtbQdEmhHhe7C0lnVx0lhfqKZezuGillZbZ/x+7cym8fvUVGUgL+1jvOOWcyCAlvwcMvvklu5mlS4mKrHM8lhQUF6NzL28hV50ZhQUGV11+xYC5dekeh0Vz/6FtJYSFauzbSurhWaKPSokKc7cpcaiPfekGkx8cBkHb8CKVFhdWuv0ivd7i76uyqo8iu/uJCve2C2kXnRrG+vJ2W//ErX778DB7ePrb4ysrM/Pzxu0x98yVbolW9eAocLuBddDqK7OosKtSjs8br6uZGUUE+AGfTUmnSsg1Pv/sxWRnpHI+JrnbdV+PmrKHY7sRdbDDi5ly16apJmefo2jSEFwf3oWvTEPadSLuuWPQF+bh7lE+x0bm5obe2w9X0GTCQj954hedHj0SlVtMkvHmNY7ie/QZgy/LFtO3aE7VGg6ubO3516vH1q88zbcorRA27v8Zx5V+8iIenp+1nd3cP8i9erFAu+sB+cnNyaNe+g8Pn61av4s6Bg2pcf0lhocNsBWdXHaWVHs92fd5lfWrSwb00iXQ8drYvmsPsj95E5+mNs0vNZkPUtpJCPc66a50PCh3OGWWKpa87n52JsbSEgOAGDuUPbFhN+6gBN+JeGgAeLs4Oo22FpQY8XJ0rlGsc6McnDw9mfN8uHE49c4Nqt4yuu9mdmyzH9rXPTY9NmMRHU6fz0vsf8c/8uWSfrVlMxYV6XOy3kc6N4kLH+ksK9Q6zaMrsztmVOXc6nZDmLXn05SnknjnNiaOHqx1XYUEBbnbTCnU6N/T6a7fLo+Mn8eF3P/LSex/xz/x51W6XYr3j7+qic7yeKrZrC0s/Z1mmKGV261g+L7hwnsy0Uwx8ZBSPTH6VzUsXOHyX/uIF9m1cQ8e+d14zrtq+brjVKYpyS/13q5IJXOWGA2sURUkE8oQQ7YH7gFCgNTAe6AYghNAA04AHFEXpAMwEPqnsS4UQE4UQB4QQBzYsXVhpxa7u7pTYHZAlxUUOF8Q6t/LlpUVFuNqdLA7t2EpWRhpRwx+0lW3YNBwXVx2evn7o3D0orMKFYvSW9bYRstLiItvnhuKiCs+rubg5xiucynepNj36MOadT2nWvhP71lumrLjo3GjUog1CCBpFtCb7srvXVeHm4UFRYXmdxUWFuHlce845wM5N6zmdmsrQR0ZWu157R3ds5p8fv+LAmuUYiottnxtKih2mQYDlJGooKW/HS20U2W8w2WkprJj+NWVlZeg8q39nXufuTklR+XeXFhehs9tfXN3cKbZun8v3paGjxvHK1z9SpM8nKdby7I6Tk4qJb3/IS59PZcs/Sxy2bdXi8aDYbtuUFBU5PA+gs4+nqNC2TOfuQfN27RFCEN42krOpp6pV75V0bxbKk/27MaBNOC6a8oTNRauhyG765NUMjoxg7eF4vlm1lfWxCQxsV/PECcDdw5NCvf1NmULcPTyvsobFjG++5L8/zGDarL/w8PBg13Xcqb+e/SZ6xxayMtLpe6+ln0k+epj883m8/NU0Xvz8O9YtmONwl7s6PL28HC549foCPL28HMokJyUy4/tpfPCf/2L9g6gAnEo5ibOzM8E1GDk+sn0TS6d9zr7VSzGU2B3PxZUfz/b9on0MAIkH99Kso+PU1l73P8rj73xGSaGetPij1Y6vNh3asoH53/6HXSuXUGrX15WWFDncvANwcXNsGydh6et2r1xK10HDHMoWFeSTnZFKSESr645xYNvmvP/AQB7uFonO7saMTqutdFT9ZHYuU/5exRfLNzMuqst113+Ju4eHw40Qy7F97XOTh3Ufd/fwpFVkB1JPnqhWvQc2reOvLz9m+/KFjtvosmMXLp2z7baR09UvA110boS1tJ6zW7Z2GFWtKjcPD4ebzUVFhbhX4Tkxh3Zp357UkyevsYYj18v6uZIix/ZwtWuLkuLy6ykhytvkUv/n6uZOYP2GePr44uKqo26DUHKzMm1l5v/wDfc8MQF3T8f+yt6tct0g/d8gE7jKPQLMs/57nvXnnsACRVHKFEXJBC5dPYUDrYD1QogY4G2g0isIRVF+VhSlo6IoHfsPf6DSihuENSU1MR6zycSFnHNonV1sw/MAoc1bkHD4EAAJhw/RqHkLAI4f3E/s7u08MOk5W4dcP6wpOZlnMJvNlBYXU5if7zBydSXt77iTES+9xcDHx6HWOpOfl4PZbCLjRCL1QsIcyjZoGm4bRTt9IonA4IYAmOymNLq46mzPmjVoFmGbdpOZloKP9QUN1dE4vDlJx45iMpnIzc7G2cW1SqNph/bsYu/WzUx46bVrnrSupVXPKO55+mX6PDwKtVaL/nweZWYzmSnJBDYMdShbL6wZ6cctd8wyU07gG2TZPbSurvR9dCxDnnoJtUZDY+tUjuqw7C/Hy/cXF8f9pVHzliQetoxmJR6Otu0vl6avqFQqNM4uaJy1mE0m291YjbMzao0WdTVHKUOaNCMl4Rhmk4nzOedwviyesBatOH7oIADHDx0krEVLAJq0aEX6CcvLBtJPJtum6l2vXYmnmLFhNwv3xmI0m/HWueIkBI0CfEnPvVCl7xAI2119fYkB3XU+N9k0ogXxR49gMpk4l5WFi6trlZ7FdHJyst2o8PT2Rp9/7ZsxV1LT/ebYwX0c3rWDB+36GUWxjKY6OalwdnF12I+qq0Wr1sTGxGAyGsk8exZXVx1au7bJSE/jPx+8zwf/+QxvHx+HddeuXMGAQYNrVG/rXn0Z/txrRI0YjVqrpeB8LmazibMpSQSGNHIoGxTWjLTjliTsbEoy/najSheyMxFC4B1Qx/bZpWTWSaVCo63+MVXbIu/oz0MvvMmAx8ai0WrJz7O0zZkTSdS1e/ENQP2mzUk5dgSAMyeTCKhvaZuLuefY+PcfLPr+SwrzL7Bp/mzOnU6nWF/Aou+/5MCmtRzbt5MTRw7VKMY1h+N5f+Eaftqwi1KTCX8PN1ROgubBgSRnOk4v1dhNMyssNVBqNNeozso0CY8gIc5ybsrJzsLFpWrH9qUbOiajkcRjR6kXXL2bEB37DuCxV95m8KgJqLVaLubmYDaZyEhOpF6o4zm7YbPmnLSOomWcSCSwfsOrfnfD8AjOXjpnp6bgE1jnquUr0yQ8gsRj19sucdSr5s2Z+o2bkJaUYOnncnMq9HOh4REkxVr2uaTYQ4SEW6ZN1m3QkLQky9TE5NgYQsIj8K1TF2NpKaXFxZjNZs6dycDbz9/yYq5pX9FryHDqh1392dtb5bpB+r9B3MrDg7VBCOEHZADZgAKorP9fAsQoivKbtdxiYA6QAPysKEq36tSzYG/sFRv+4NZNHNhqeWvS3Y+NwUnlRPLRWHrdPQyjoZTF/5tOfl4unr5+trfDfThhJP71gm1z2B+a9Dyevn4c2rmV/ZvWU2Y203vIcFp0rHi38WJhcYXPLslMPcnG+X8BCm169KF19z4ArJg5nSFjnwJg16qlpMQdQaVWM3DkOLwDAtm1ailp1ufzXNzcGPj4eFx0blzMzWHtXzMxG434BNbhrsfGOozaRdSv2sPs29evYdu6NQgBj0x4GpVKRVzMQQbd9xCZpzP4c/o0UpOTCA4JpWufKKIG38NTDw6lbv0GuFinME185Q18/K780P+exJQrLrN3Lv0Uu5bOB0UhvEtP21vrNs3+lb4jxwEQvX4l6fFxOKlU9Hl4FJ5+AZxOiid6/UqEEAQ3bU5k/6tffIYGVv482IGtGzmwZSMIwZCRY3BSqUg+Gktv6/6y6JcfuZiXi5evH/dPeAaNVstf331Bkb4As9lMaLPmDBzxOBfzcvn7x28RTk6YjUa6DRh8xZdRXHomrDJ7N29g76Z1gODe0eNxUqlIiI2h79D7MBhK+Xv6NC7k5eLt68eIp55Ho9WSdy6b+T//gMlgwL9eEA9NfAYnJydWzZtNfEw0+RfOUye4PmNfnYLzZc9pbI1Lumq7XVLf14uhHS13+/clp3HgpOVO8iPdI5m7y3ISH92nE3W8PDCazZw6l8fifUeo4+XOfZ3bUqaUoXJyYtHeWLIuVj79Z1SfzlWKZePqlWxcZXlT3dhnnkelUnH44AGGP/wIZ9LT+Xnq15xMTKBhaGN69uvPwKHD2bV1M8v+notGq8XN3Z0Xprxb6bNy9hLOZF9xWU32m/fHjyQgKAits7WfeWoyHt4+LP7lR3KzMzEZjUT26E33u+6uUF/vFk2q1DYrli1lxdLFCCGY/MprqFQq9u/dw6OjRvPa5OdIPXWKgDqWGz+PPv4E3Xv1RlEUHrlvGDN++wOvKjwbOGf7gSsuy047xY4lc1EUaNG1FxFdLcfA+j9/4c7HJ1jabu0/pMUfxUmlJmrEaLysb7rdt2opLu4etOndz/Z9a2b+SEmhnrIyM/UaNaXbUMebd4M/uf43xgW+9gIurVsgNBoMJ09x1u5FDdW16f33r7gsMzWFLQv/QlGgdffetLK+4XDVbz8xeMwkAPasXsapY0dwUqkZMHIs3v6ON+l+fe81h7dQAsTt3k7BhfMV3kK54Uj1nwtuXMePMXd0RiDYeDSRzXGWG0PPD+zF1DXbad+oPsM6trI9Czdr635Onbv2c3CvD7/21DiALWtXs2WN9W2Lk57FSaXiaPQBhjw4grMZ6fz2/XekJCfSIKQR3aP60n/IMD576zVKSooxm0z06HsnA4ffd9U6jqZdeSrh2VMnWf/3n6BA25530Lan5Zy97H8/Mmy85YUyO1Ys4WRcLCqVmsFPTMAnIJDcrLOs++t3MtNS8A+qT8vO3Wl/R38u5uaw6o//YTYVYi+6AAAgAElEQVQZ8Qmsy+DHxzmcswFaNrj2Dbeta1ezZe0qEILHn3wGlUrFkUMHGfLAw5zNSOf3H6aSkpxI/ZBQut/Rj/5DhvLfKa/btUt/7hp29XYBSM485/Bz9LbNRG/bhBCCgY8+gZNKxcm4I/QYdA9Gg4FlM3+yXU8NGzcJjUZLXnYWy3+bgdlkoknrdvQZaqn3RFwsW5Ytosxsol2PPnTqO4Cdq/9hx6rlBFqT7rCWbeh9j+WtlWfyKk7/vuRGXzecPHyQ47u3kXnqBHVDw+hw11DqNnJM3l8ecseNmq38r4pNz7ylEpM2Dereku0mE7jLCCGeBNorivKk3WdbgQ1AF2AoEAAcByYCy4FjwOOKouy2TqlspihK3NXquVoCd7NdLYG72aqawN0MVU3gbpYrJXC14WoJ3M1W1QTuZqhqAnezXC2Bu9mqmsDdDFdL4G62G5HA3UhXS+ButpokcP+WqiZwN8PVErjaUJUE7ma5PIGrTVdL4GqDTOBq5lZN4OQUyooewTLaZm8REIRlZO4oMAPYC1xUFMUAPAD8VwhxGIgBKv5BD0mSJEmSJEmSpOsk/w7cZRRFuaOSz6aC5e2UiqLordMs9wFHrMtjgIp/LVOSJEmSJEmSpCopkzMDq0QmcNWzQgjhDWiBj6wvM5EkSZIkSZIkSbopZAJXDZWNzkmSJEmSJEmSJN0sMoGTJEmSJEmSJKnWyZcrVo18iYkkSZIkSZIkSdJtQiZwkiRJkiRJkiRJtwk5hVKSJEmSJEmSpFon30JZNXIETpIkSZIkSZIk6TYhEzhJkiRJkiRJkqTbhJxCKUmSJEmSJElSrZMzKKtGjsBJkiRJkiRJkiTdJuQInCRJkiRJkiRJtU7+HbiqkSNwkiRJkiRJkiRJtwkhM93a8ffumFum4U1lZbUdgo257JZpFnzcdLUdgoPcAn1th3BLquPtUdsh2FwoLK7tEBy4OTvXdgg2mRfyazsEm8Z1/Gs7BJsTmedqOwQHfd9/v7ZDsIn56ovaDsGmoLiktkOw0apVtR2CAyFEbYdgo1HdOm1zLv/WOmc/M6DHrbOhrmL/yYxb50IQ6NS4/i3ZbnIKpSRJkiRJkiRJtU7+HbiqkVMoJUmSJEmSJEmSbhMygZMkSZIkSZIkSbpNyCmUkiRJkiRJkiTVOvlujqqRI3CSJEmSJEmSJEm3CZnASZIkSZIkSZIk3SbkFEpJkiRJkiRJkmqdnEFZNXIETpIkSZIkSZIk6TYhEzhJkiRJkiRJkqTbhJxCKUmSJEmSJElSrZN/yLtq5AicJEmSJEmSJEnSbUImcJIkSZIkSZIkSbcJOYXyFnRo+xYObN0IwN0jxxAU2ti2zGgwsGzmT1zIy8Xb149hYyeh0WrZt3Etu9etRlHKeOHzqbbyedlZrPrrd4yGUjx9fLl/4rPXrD9mx1ait24EIRj02GjqhTSyLTMZDSz/7Wcu5ubg5efP0DETUWu0XMg5x/KZP2EymWjaJpJeQ4YD8OcXn1BWZsZQUkK3u+6mVdceACQfPcyu1f+gKArN2ran2113VxrL2dQU1s2ZhYJCZO++tO3Rp0KZnSuXkhwbg0qjZsjoJ/H2D8BkNLDi95/Jz8vF09ePIaMtcSYfiWHbsoWoNRo8ff0YOvYpnFQqdq5aRkL0fgBadu5GlwGVx1OZ/Vs3smfjOoQQDB89gfqNwmzLjAYD83/+ngs55/D2D+Chic+i0WoxlJaydNYv5GVnUVZWxuiX3kTn7l7lOq8mMzWFdfP+AAXa9Y6iTffeFcrsWrWM5CMxqNUaBj8xAW//APasWUF89H6cVE7UbRjKnSNGIYS4afVnnEhkzZ8zycvOYtInX+Lp4wdAxokk/h979x0W1bE3cPw7bKE3BeyKIkVs2HvvXdNj1NjTvOnJTWJuem6Sm3KTaBI1atQkamLvsfcKdqWDNAVBwAIssAvn/WOXZZcmoBG973yeJ09gz9kzP+bMzDlzZs64Z9VybFQ2NG/Tjq5DRlY6luP7dnN013aEEDw0ZSaNmlmfm5Xz5pB57RruHh488ew/zOdm7S8LSE9NpbCwkGmvv42CwuKvPjd/91JEGJ/8vOy25+z0oX2c3LcbIQTDnppCfe/iuqTX57Nx8fziujT1GTQaLZnXUtmwaD4FBj2+bdvRe+Q4wFiX/1q+lPy8PFxq1eKhGS8AEH3+LIe3bTTWpaAOdC+nLpUUvH8Px/fsACEY+/T0UuV21YIfuJ6ehlttTx6d+QIarZaV874nOT4OOwcHnJxdmPjym1xPv8aKH79FKSykUFEYM2kajZo1r1QMRa4mxLHnz19RFGjTsy+tuvUqtc/xvzYRe+EMKrWGIROn4Vrbk/CQY5zevwshBLZ29gyf8hy29vZciY1m35oV2Khs8Gndjk6Dhlc6lqN7dnJ4118IBI9Of5bGFn+LPj+f3378jsxrabh7eDLh+ZfQaLVcPBXC5pW/odFqcffwZNI/XkWlUrF97Z+cOXYEIQQdevRmwOhxVc6Xvat+M+ZLjz60LCdfLl08i0qtYfCEqbjW9jRvO7J5HWHBR5n24X/MnxUUGFj68WwCu/Sg67DRVYqnMup//Sm2fs25vno9mUtX3PXjFzl1cC/Be43nfuTEqaWuk+sW/WSuW+OmPWcsv3O/4UZGOkphIZ0HDKZ9r34AfDBtPA19fAEI6tGbjn0GVCmWlIQ4dq1chqIoBPXqR+sy2ryjWzcSc+EMKrWa4ZNm4OrhyfHtm4k4FYyNSkWdRt4MfGIiQgg2L57Hzcx09Ll5BHbpRqeBw24bQ3L8Jf5avhQUhfa9+9O2Z+lr5aEt64k6dwa1Ws2oKcXXyk2/LOBGRjqutWozynRNL7L0i4+oVacuoybPJDk+jr+WL8FG2GCjsmHk5Bm4e9Yplc7dvIf4/s0Xca3tAUCzwNb0GmWsQ5W9hyjPqYP7CNm3C4FgxMTS91nrF88zxzjWdJ+1cu433MzMQCkspFP/wbTv1bdKaVpKTYxn/6rfUVBo1b0PgV17ltonePtm4i6eQ6VWM/CpqbjU9iD02CFObNuIcy3j9XHI0zNxcnM3f2f1d5/j5lmHgeOnVDu2mib/Ie/KUX3wwQc1HcP/SxeTUj4o63Nddhabf13EtNkf0bxlGzYuWUD73v3M20/t34NGa8tDM57n6uVEMq6m0KCpD661PegxdCQndm+n66Dixn71/DmMnfoMXQYOJbBD5zJjsZxvrMvOYttvvzDlnQ9pFtiazUsW0s6ikTp1wJj+2OnPkWpKv35TH7YsW0T3YaPpN/ZRDmxYQ4NmPjg4u9CqS3fa9e5Hqy7dWP3Td3QZNIycWzfZs+YPnnzpDdr16kej5n7m45est2t/+o4x05+n04ChbFm2kBYdu6DRFl9criVf5uTenUx88z3cPDw5snUDLTp24fTBvWi0WkZPfY60K0nGOL2bsW7BHB55/mW6DBpO1NlTqLVaHF1d2fbrIqa//zlBPfuyecl82vbsg5O9/W3PY05WFut+WcALH36Gb+sgVi/8kc59B5q3H9+zE62tlieee4mUxASupSTTyKc52/78ncD2nRj66Hg69Rlg9TeVR5eff9t9ANbO/57R056jY/8hbFu2kICOXa2On558hVP7djHhjXdx9fDk6LZNBHTojIOzC92GjaJtz76cO3IAe0cn3Dy9KpXm3Uhfa2dPUK9+JEaF49++E7b2DgCs+elbxj3zDzoPHMaRrRuo690MBydn8/Gc7GzLjCMnK4s1i+bz4sef498miD/m/0DX/sXn5tjuHWi0tjz1wkskJyZwLfkKjX182bLyd1p26MTwx8fTpa/x3Gi1tnTpO4AufQdQt2EjMtNS6TZwcKk0c/UG88+67Cy2/LqYqbM/wqdlazYt/dl8wwhFdUnLuOnPk3o5yVyXNy9dSM/ho+g37jH2meqSo7MLa+bPZfSUmXQZOIQWprqcfesmu9es5MmX3qR9b+u6BKBVl/2MLicri/VLFvD8B//Gr1XbUuX2xN6daLS2PPHci6Qkmcpts+ZcCDnOwLGPMOjhx2lrehijoNC6U1d6DBlBk+b+rF/yM536lr4BzsrNKzMWgE0/z2H45Gdp328QO5f/gn+HzlY3kekpVzizfzePv/oOrrU9OLF9C37tO+HuVdfU4etNRkoy16+lUs+7GRvmf8+oGbPo0H8Ix//aRJ0mTbG36Gy7OzmUky+3+PPnn3j1ky8JaNuO5fPm0H1A8Xk+sms7WltbJv3jVZIT4klLvkKT5r788t8vmP76bAaMGsv54ONotba4uLmxcv4PvPP1HLoNGMxvP/yXbv0Hoy5xTjKzcsrNl80L5zLs6Wdo13cQO1cswa+9db5kpFzh7IE9PPbK27jW9iR4x2b82nUCIPvmDcKDj5J14zrt+w0yf+fsgT3k63JwcqtFQ1//Umk23bev3HgqQ3f6HPrLydg4OpB79sIdHStlSOk6Bsa6tWnpImb862N8WrVh/eL5dOjT37z95P7daLS2PDxzltV1sm5jb3qPGEPb7r3588dv6dh3ICqVilMH9/LCx1/Svlc/qxt5S/kGQ5mfA6yf/z0jpz5Lx/5D+OvXRQR06IJaa11+T+3bxfjXZ+NW25OjfxW3uV2HjqJNjz6cP1rc5vq0DqJtz7606dGbrUsX0Kp7L9Rqjfl4KpvSk6dW//QdY6c/T+eBQ9m8dCGBnUpfK0P27OTpfxqvlYe3biDQdK1Ua7WMmfYcaZeTyEhNMedB5NlTpF1ORGtrh39QB4SAoB596NBvIA7OzgTv3kFAh05WD/ru9j3Eid3bmfnBZwT17EMT/xYAFd5DlJU3Jemys9i8bBHT3/2Y5q3asOGX+XToXbL8aHl4xgukJiWSfjWZBk19qNfYm14jxtC2ey9W/VRcfsqTk1f+NXvroh8ZPGkGQX0GsmfFEnzbdypRt5M5f3APD7/8Fi61PQjZuRXfdh1JS0rA1dOLQROmEti1J1q74vuUSxfOkJ58GY2tLc1atyuVZmefxh/eNnPuAwnp1z+o6RgsNfZwuy/zrUanUAoh3IQQz1v83lcIsflvSOdZIcSkanzPKr7b7Jtl+r+3EKLaV62kmGia+LVArVbj7ulFfm4uBr3evP1S+EX8g9oDEBDUgbjIMACcXN1QlbgxuH4tDX1ePlt/X8Lizz7kYvDx26Z/OTaaxn4BqIrSz7NOPz4iDN+2xobBr217EiLDAUhJiKeJXwAAvm3bER9h/Lwopvy8PDzrNwQg6txp7B2dWPn9l/z+zWekXk4sMxaDXk9+fh5unl6o1Goa+/qTHBdrtU98RBjNTQ1VY78WXE1MACAhIhTftu1N8bQn0RSnZ/2G5ObkoCgKubocHJxd0Gi0OLu5Y8jPR6/PR63RolJVbnA6ITqSpgGBqNUaanvVIT9XZ5VfsWEXCDTdULVs34lL4RcBiL5wjoizp/nxo9lsX3X3nlIb9Hr0eXm4eRjzrFFZeRYZhk/rIAAa+wWQmmTMs1p16pr3UanV2NiUf2H6O9K3c3BAa2dX6ph5Op35CWxd72YkRIRVKpb46EiaWZybvBJ1KTr0Ai07GM9Nqw6diQkLBSDqwlnCzpxizgez2fbn8lLHDTmwj46VePKaFBtNE7+AcutyXHgofqYy6h/UnnjT32WsS8YbFb827YiPCDPW5fw8ti1fyi+ff0hoiLEuR5011qUV333Jr1+XX5dKSoiJpKl/C9RqDbW86pBXop7HhF4ksH1HAALbdyI2PNS8bdNvv/Djh+9w5ughAOwdHHFydQNM5aaCG5qyGPR69Pn5uHp4olKraeDjR0qJMpMUGU6zVm0BaOgbQJqpzFi2efr8PDzqNQAgPzcHF9MT6jpNvEmKqlyZuRQVgU+Llqg1Gjzq1CUvV4feIl+iLp6nlanz3LpjF6JDjU19vUZN0OVkGduVnBycXFzRaG1xrVWL/Px89Pl5aLS2Fd7slZkveXnW+RJ/yWqfxKhwmprzxZ+0pOLzf2zbRjqXGK3Oz80lLvQczYM6VjqOqjKkXfvbjl0kMSYKb/8AY/n1rFPqOnUp7CL+QR0ACAjqSJyp/HrUrQeASq1CCBtzxyPrxnUWfvoey7/7ksy01CrFUrLNa+jrT3K8dflNiAjDp7XxPDXyCyDVdJ6s2lyVGhuVjSk+tfnYLu610WjLfkhVMgZ3i2vllUtlXCvbGK+VTfxbcNVUh+LCQ/FtU3ytjDddK5XCQkL27KRjv+JOtJOrG7amB5vGul76FvJu30OgKCz9z8f8/s1npCTEAZW/hyiP8T7Lom0uWX7CQ83lx79dB+JMbXNtc/lRW5WfqjK2ecV1u76PH1dL1O3L0eF4tzSWmQbN/blm8TeGnzjCqv/+m6Ob16IUFgLG83XuwB7a9OqP9P9DTb8D5wZUqoNUGUKIMu+6FUWZpyjKsmoc8q7GVxm67CzsHBzNv9s6OKDLzrLYnm3ebufgSE7WrXKPdfN6JskJlxg2/mnGv/wGe9f/aXWsstPPtk7f3r5E+lnYOzpZpG/cpiiF5n3s7B3QZRvjKiwsZMnnHzLvvTfxb2dsEG9dzyQjNYUnXnyDAY+MZ/PSheXnhX3xk3JbB0d0Wdbx52ZnYedYHG9RHFb5ZBFP6+69WPHtF8x793VUKhX1vZtho1Lh0zqIn959jZ9mv0a73v1LdYbLk5N9C3uL9O3src9JTtYt7J1McTg6km3alpwYT/OWrXnuX59w9XIi4WdOVSq92zGWnxJ5lm1dRkrmWWFhodX2+IhQsm5cp5HpYnqv0y/JwcmZq4nxFBgMxIVdIPc2ZbhITtYtc1kFsHdwMOe/cXuW+dzZOzqSnXUTgCsJ8fi1asOs9z8hJSmRMItzU1BQQOipEFp37nLb9HVZ1n+nXam6XLzdzqJsW9Ul0+e3rmeSkhDH0Ccn8eRLb7B3/Sp02cbPM66m8ORLbzDo0fFsWvJzpfJGl5VlnTf2DtblNrs47+wdHMm5Zdw2avxkXvzkSya/9jZ7N64l/WqK+TuFhQWsW/Iz/cc8UqkYiuRmZ5lHW6F0mweQm5ONrUPpeg5w/vB+ln4ym8vREdQ2deDsHJ1JTUqgwGAgIfwiuuzsSsWSk5VlNS3WvkQbm511y7zd3tGRbFO+dO7Tnx8+fo+PXnwGG5WKJs19UalUtGzfkY9efIYPZ82kx6ChqDUaKis3Owtby7pk71Cq7OdmZ1u1kYWmfMlMTUGfl4tng0ZW+4fs2kb7foOp3m3n/UOXlYWdQ/F5MrbxxXmTk11ct+0cHczXqSL7N66jTbce5vPx2jc/Mn32R3TqN4h1i36qWixlnKfS1ynr66pSos1LiAgj+8Z1GvkWt7nr53/P/HdfpUFzP2xuM6pkzI+S7a51DLrsLOzLiCE3O7s4rxwc0JnK+9kjBwho36nMMpufl8vetX/SbWjp6ex3+x5i2rsf8/Sb/2LAI+NZt2AuUPl7iPJYlo/i9CxizCrOK/sy7jv2b1pL6649qlSfLeXmlGjzyqnbluWq6Hw1a9OOCe9+ysMvvcWtjHTCQ44BEHbiMD5tO1Q7pvtJoaLcV//dr+7pO3BCiFeBqaZfFwJdAR8hxBlgJ7AFcBJCrAZaASeBCYqiKEKIDsA3gBNwDZisKEqyEGIfcAToAWwEvi4j3Q+ALEVRvjLtfxzoh7GDNk1RlINCiJbAL4AWY8f2YeDjEvF9CGwA3AEN8K6iKBvuXg6BvaMTuTnFNxt5OTnWN1qOjuTmGKfc5Obk4OBY/js4Do5O1GnYGBf3WgDUbexN+tUUGlbwfoq9oyN5lunrdCXSL4rPkzxdjrkRFKL4ApOrK47ZxsaGyW+9T07WLRZ+9C6Bnbph7+iEd4uWqNRq6jZuQs6tm1YxBO/ZTnjICdy96pCnK55elKfLsbohBrBzdCIvp3ifojiM+WSMM1eXg50pnm3LFjF19se41KrN1l8XERZyDK+GTQg/eYIXPvsWRVH49T8f4deuI+6OZU+zss5jZ3Itbg5zddlW0/scnJyNN4+exptQB0dn8+f+bdsjhMC/TTuSE+IIMI2sVkfInh1EnCrKM5358zyddfkBY57lWuSZ5c1BalIC+9b9yaMvvFalp4t3K/2yDJs0jd2rfgcF3Dy8rOb7V8TByRmdRVnW5eTgaHVunMzbdRbnxtHJmYAg47kJaNuOK/FxtDCdm4izp/H288fW7vbTa+2dnMi9VPx35paqy8X5kKvLMU/xK6su2Ts64WVZlxsZ67K9oxNNW7RCbapL2SXqUkWxWeWNLse63DoWl1tdTo650+Lo4mLKO2d8W7flSkIctU2jCGsWzqNFuw74mUYabuf0vp1Eng7BzdPLqp7nl2hzwHijZ7mPZR617tGH1j36cGLHFoJ3bqPPQ48z+Kkp7F+7EkVRcPXwMo8Q3o6Dk5NVZ0+XY12fHZ2cycnOojZ1TNuMca6YP5c3v/gv7h6erJg/l1NHDtLAuxlnjh3hwx8XoRQW8u17b9G2c1fcTKPJ5efLLqLOBOPmWaIu5eZY3RiD8aGQZb7YmPLl6Jb1dB/5kNW+ObdukpoUT/eR47h49GCl8uN+Ze9kfZ3MLdHOOFhcR/Nycqymz54+tJ+rSQk89vzL5s8cnY3l2rdNEJuWVa4zcHLvTmObV/I86XRlXKccrdo8UaLN3b/uTx6e9apVmzv2mRfR5+ex/MtPadGxKx71G5SKIXj3dsJOGttdy+NbXpuL2Ds6kasrHYOdxbWyqL026PO5cOww4195i4SoCKvjFBgMrJ03hx7DR5tn1Vinc3fvIRxM56Zu4yZobO3MHcCK7iFux8HRicsW+VWy/BjLV/F9lmVenj60n9SkRB597qUqpQlwdv9uos+E4OrpRV5ucZnJzy2+RylSsm6bz5dFG+DXoQvxYRfwbdeJiOBjjHn+Va7ERlU5LunBdM9G4EwdsClAF4wdtxnAF0CMoihBiqK8Ydq1HfAyEAg0A3oIITTAHOARRVE6AIuBTy0O76YoSh9FUUp13sqhVhSlsymd902fPQt8pyhKENARSALeKhFfLjBOUZT2GDuAX4sq3OUKIWYKIUKEECG71q8pc5+GPs1JiIqgwGDgevo1tHZ2Vk9UvAMCiTx3GoDIc6fx9g8sN71adeqiz88jT6ejoKCAtCtJt715aNCsOP0b6dfQ2lqn38S/BVHnzgAQde6MeU56nUaNSYyOBIyLKjT2a0GBwWB+aqS1tUWt0aDWaGjiH0hKfBwANzLSS90Md+o/hIlv/ouRk2ei0dpyI/0aBQYDiVER1LdYaAGgiV8LYi4Y40mKjqROo8aAcTplzHnj5zHnz9DYNCVN2NiYG0AHZ9NNKgq2dvaoNRo0Wi1qjQZ9Xm6F+VSkcXM/LkWEUWAwkHktDa3pOEWatWhJ+JmTAISfOUmzFi0B8GnRiqTYaAASY6PNU3uqq2P/wTz1+rsMnzQDtVZrzrOk6EjqeVvnWWO/AGIvnAUgKSYSr4bGPMtITWHL0p8ZO2MWDs7OpdL4u9Mvj2f9hjzx0j95dNZr5GZn4dOqch2EJs39uBQeaj43tiXqkk9gK8JOGc9N2KmTNA9saf48McZ0bmKsz03Iwf10qOSL6w2b3aYu+7cgylSXo86dNteluo0am2+aok11rFaduujzStdl74BAkk1Tb26kX7N6qluRxj5+xFmUW9sS9by8clvUuTEY9MRFhuNZrz4Am39fgrObOz2rsJBAu76DePyVtxkyYRoarZabGekUFBi4HBNJ3RLvITX0DeDSxXMAXI6JwtNUZgz64ndM7BwczO/8eNRvyMOzXmfcc6+Qm51N05ZtKhVTU19/YsIvUmAwkJGWiq2dPRqLfGke2IqLp0IAuHgqBN+WrQHjQ4iiG0AnF1eys7JAUbC1N36/qP3Ly719u9Ku70Aee/ltBj811SpfrsRElZ0voecBuBIbhWdD44jbjfQ0dv+xjDVzvyL75nX2/PkbaZcT0WXdYs3crwjZs53QE4eJOX+6Uvlyv2nk40t8ZLixbl1LK3Wd8g4IJOKs8W+LOHuapgHG62TYyWDOHT3II8/+w/zgKC9XR2FhAWCcxlfUYbidDv0GMf612QybNN10nq4Zy290JPVLtHmN/AKINZVfY5tnPE+ZqVfZtnQho2e8YH5QoCgKBab37dRqDWqt8b+ydBowhElv/otRk2eisa34WtnYrwXRpmtiYnQkdUx1qIl/8efR543tTWZaGrk5Oaz8/kt2r15O7IVznD6wF6WwkPULf8S/XUcC2ncqM6a7eQ9h0OvNdfxmZga5OcbRvdvdQ9xOQ5/mxeWnjBib+gcSec448yLy3Cm8i8rPqWDOHTvEw8/847YPHsvSts8AHn7pnwwcPwWNRssti7pdx2KhFzBOm4w31e3k2Gg8TKPplg+sEyPDcPeqy830NPJ0OWyc/x2H168iIewCF44cqHJ80oNF3KvVXoQQLwG1FUV5z/T7x0AaMFNRlFamz/oCsxVFGWT6/SfgMHAG4yhb0aRuFZCsKMpg04ja+4qi7K8g7Q+wHoGbrSjKYSFEHeCwoijNhRDjgdnAMmCtoihRQghvYLNFfBrgv0BvoBDwB5oqipIihMhSFMWp5HfK88fRM+Vm/KkDezl5YA8Aw5+ajI2NipiL5+g5fLRxdaRFP3EjMwNX91qMNa2udeHEUUL27SIxOpJGzf3oP+4xGvv6E33hHPs2rKagoIB2PfvQuX/pl8INJaZznD64l9MH9oIQDH1yEjYqFbEXz9N92Cj0+flsXDyPm5kZuLjXYsy0Z1FrtGSmXmXjkgUUGAw0b92W3qMe4mZmBmvnz0HY2FCg19N5wBDzKpSHt20k6uxpCgoKGPTYUzQ2vUxfUGidLVfiYtm5YhkKxtW9gnr2BWD9z3MZO8O4oubBTWuJvWBcqWmEaVUsfX4+m5fM51ZmBs7utRg15RnUGi1hIcc4+tdm1Botdg4OjE1n+VsAACAASURBVJ0xC62dHXvXrCQuIhQUhSb+gfR/5MlKjcABnNi7i+N7dyKEYMyk6diobIg8f5Z+o8ahz8/jj3lzuZFxDddaHjxuWukwIy2V1T//gF6vx7NuPR6Z8cJtLwjptyo3dTA5Lpadf/wKCrTt2de8GtmGhT8yZrpxRvChzeuIvXgOlUrN8Kdn4O7pxZ9zviI95Yp5lKfL4BHmdyaqorrpp19NZsfvS0hJuIRH/Ya07Nyd9n0HcmLnVqJMN2Ndhoyguen9uSJ13MrvbB7bs4tje4wrhI6bPB0blYrIc2foP/oh8vPzWPHjHPPqa+Off9F8bv6Y/wN6fT6edevz+DPGc5OXq+OL115k9vfzyn2X6Xq2zur3Uwf2curAHoQQDB3/tLku9TDVpQ2L55lXSh0z7Vk0Gi0ZqVfZ+Mt8U10Kos9o40hKzMVz7NuwhsICA0E9+tDJVJcPbd1I5NlTFBYUMPjxCea6BOBoW/67Myf27eLE3l0gBGMmTcPGRkXU+TP0NZXbP+fPNefNY88Yy+3Pn31IXl4uhQYD7Xv2oefQkSTGRjP3/bfwNk25LVqdsqSU6+U/JU+Jv2RebbF1jz7mVfy2/DKPEVOeBeDo1g3EhRrLzOCJ03Dz8OLo1g0kRBjfb7JzcGTIxGnYOTgSsvsvYk2d446DhpvfnyvSrE75D7KO7N7Bkd3bEQgemfoMNiobws+eYdDYh8nPy+O3H741rwI8cdYraLRaTh05yK4Na1FrNDg4OjL55Texs7dnw29LiLxwDgUFv5ZtGDux9MpwMSlpFebLvtW/G/Ole29amfJl6y/zGG7Kl2PbNhAXeh4blZrBE6bi5mG98NCi99+0WoUS4OLRg9y6nlnmKpT973BhM683X8audSBCoyE/No7kd6r//v+Zr78sd9vJ/XsI2W9c4XXEU1OwUdkQfeEcvUaMQZ+fx9qFP5nr1kPTn0ej1fLRjAl41Gtgftf2sWdf5GZmBht+WYDW3h4BjJg4lXqNvUuld0tXfuc7OS6W3X/+hqIopsVHjG3epkU/Mmqasc07vGU9l0xt3rBJ03Hz9GL13K9JT7mCs0Wb6x3Yij++/QKAAr2BgI5d6DRwqFV6WnXp9udKXCzbVywDRaFdr34EmR40rVswl3Gm1acPbFpLzAVjDKOmFF8rN/0y33xNHz31GavFNOLCQzl/7BCjJs8kNOQ4GxfPMy9y4tWgEUOfmlxqtsbduoe4kZHOH99/hcbWFqWwkH4PPU5T08Ok8u4hNJV8z/TkgT2c3L8HgWD4hNL3WesW/miOcZyp/Hw8c6JV+Xn0mRdxqVWr3DTSbpZ/zb6acIkDq1egoNCyW2/zCrPbly5gyNMzATjx10biQy9go1YzcPxkXD28OLJpDYkRodjYqHCvU5d+T0yyemc/KSqc8OCjZa5C+cLgHg/E7Ol9YbH31bzFvi2a3Zf5di87cC8DtSrRgXtdUZSRpt/nAiEYp1IuUBSlWxnH3Wf6TkgFaX+AdQfudUVRQoQQHkCIoijepv18gBEYR+amY+wwWnbgJgPDME7r1Ash4oC+iqLE3c0O3L1WsgNXk0p24GpSZTtw90plO3D/31TUgbvXSnbgalpFHbh7raIO3L1WUQfuXquoA1cT7rQDdzdV1IG71yrqwN1rZXXgalJ1F/P4O1S2A3cvVNSBqwmyA1c992sH7l4uYnIAGCuEcBBCOALjMI6uVebuKwLwFEJ0A+NImOmdtbtGCNEMiFUU5XuM79K1AW6ViM8VSDV13voBTe5mDJIkSZIkSZL0/5WiKPfVf/ere7aIiaIop4QQS4ATpo8WKopyUghx2LTs/jaMi5iU9d18IcQjwPdCCFeMcX8LXLyLIT4OTBBC6IEU4CNFUTJKxPcFsEkIEYJxWmf4XUxfkiRJkiRJkiSpQvd0FUpFUb7BuJKk5WfjS+y2z2LbLIufz2B896zkMftWIt0PytpfUZRrgLfp58+Az8r4bsn4Sk3jNO3nZPp/HMYVNCVJkiRJkiRJku6qe9qBkyRJkiRJkiRJKsv9PG3xfvI/1YETQswGHi3x8SpFUT4ta39JkiRJkiRJkqQHyf9UB87UUZOdNUmSJEmSJEmS/if9T3XgJEmSJEmSJEl6MN1H/5rUfe1e/jMCkiRJkiRJkiRJ0h2QHThJkiRJkiRJkqQHhJxCKUmSJEmSJElSjZOrUFaOHIGTJEmSJEmSJEl6QMgOnCRJkiRJkiRJ0gNCTqGUJEmSJEmSJKnGySmUlSNH4CRJkiRJkiRJkh4QcgSuhujy9TUdgllUclpNh2BW182lpkMwc7W3q+kQrMSlZdR0CGaNPdxrOgSzfH1BTYdgtvtcRE2HYCWoacOaDsHM+T6qT309XWs6BLP5Ow/VdAhWan39ZU2HYBb02hs1HYLZrvffr+kQzJrV8ajpEKwcj4qr6RDMHG21NR2CmZerc02HIP0Pkx04SZIkSZIkSZJqXCFyCmVlyCmUkiRJkiRJkiRJDwjZgZMkSZIkSZIkSXpAyCmUkiRJkiRJkiTVOLkKZeXIEThJkiRJkiRJkqQHhByBkyRJkiRJkiSpxhXKAbhKkSNwkiRJkiRJkiRJDwjZgZMkSZIkSZIkSXpAyCmUkiRJkiRJkiTVuEI5h7JS5AicJEmSJEmSJEnSA0J24CRJkiRJkiRJkh4QcgqlJEmSJEmSJEk1Tv47cJUjR+AkSZIkSZIkSZIeEA/0CJwQIktRFCeL3ycDHRVFmXUX09gKjFcU5frdOubtpMRfYsfKZaBAUO9+tOneu9Q+R7ZuIPr8GdRqDcOfnoGbhydJMZH89etiMlKv8uynX+HiXhsAfV4eO/9YxvVraSiFhTz03CvYOzpWOa767i6MbB8IQhAck8DpS5ettmvVKqb07YynixObTl7kbPwV87ZeAc3wreeBEIK9F6KJTU2vcvqpifEcWLMcFIWW3XvTokvPUvuE7NhCXOg5VGo1A56cgkttDwBO7tpKYvhFChWFzkNG0dCvBTt/W8itzAwA0i8nMnDCNJq2CqpULCH793B8704Axjw9nYZNfczb9Pn5rPr5B66nX8OttgePzngBjVZr3v7Tx+/iUbcej854wfxZWvIVvv7nSzwz+yOa+reoct5YqufmzLCgFggBJ2OTOGNxHsB4nib26oinsyNbz4RxLiHZanvfQB/aNK7P938drFb6KQlx7Fq5DEVRCOrVj9ZllN+jWzcSc+EMKrWa4ZNm4OrhyfHtm4k4FYyNSkWdRt4MfGIiQggObFjNxWOHcPeqyxOvvFWlWO7meVq/5GeSYqMpVArpNWw07br3qk72ANDYw53xPdsDggNhMRyJuGS1vWXDuozu1ApDQSH5BgMLdx8jOy+f0R1b0cmnETd1eQB8tWlvtZ9WpibGc2D1chRFoVWPsutT8I4txF801afxFvVp51YSIi6iFCp0GjqKRn4tOHdwD2f37UJRFCa991mVYkmJv8SOFUtRgHa9+tGmR59S+xzesoGY86dRqTWMmDzT2OZFR7Ltt0VkXL3Kc59+jUstY5u3dt733LqegVJYSPs+A8o8XmWs27aV1Rs3ghDMfullAv39zdu27t7F8rVrsBE2ODk68p/33sfJ0ZF3/v0pETHRODs64u7mxn8/+qRaaVekqVctpvbrgkCw63wk+0KjrbbXc3PhhSE9MRQUolIJFu4+Rvy1zDtK89TBvQTv3YUQgpETp1Lfu5l5mz4/n3WLfuJG+jVca3swbtpzaLRaVs79hhsZ6SiFhXQeMJj2vfoB8MG08TT08QUgqEdvOvYZcEexlaf+159i69ec66vXk7l0xd+SBkBqYhx7V/0OikKrHn1p2bV0XTqxfTOXLp5FpVYz6KlpuJrqEsDRLesIDz7GlA++ACDm3GlObN+MSq2iTc9+BHTqVq24juzeycEd2xBC8MSM52js09y8LSY8lN9+nENq8mU++WkR7h6eAOzbupndm9ejFBbyybzF1Ur3duq4OjGwtR8CwZn4y1xITLHarlWpeKxbELWdHdh5PpLQpKt3Jd273eYVWfPdF7h51mHA+MmVjuXUwX2E7NuFQDBi4pRS9Wn94nnm+jR26rNotFqO797O0e1bUZRCXvlyTnHMe3dy6sBeVGo1/cY+ik/L1tXIHelB80B34O4FRVGG3+s0d6xcxuhpz+HsVouln72Pb9sOVh2u9OQrxIeHMumf75MQGc6+tX8wduYsPOo1ZNJbH7Bq7ldWxzu0eR0BHbrS7A4r9cj2gaw6dpabulyeHdidsKSr5OoN5u2GgkJ+P3SSzs2bWH3Pr54ndlo1i/eeuKP0D6xZzuCJ03F0dWf1fz+laet22DkU50vm1WSSosJ45OW3uRwdwdFNqxky+VniQ8+Tr9MxdtYbVscbNGE6AAUGA799OptG/i0rFUdOdhaHdmxh1oefczMjg5U/fcfz7//bvD3kwF686jdg/AuvsHPtn4Qc2Eu3gUMACD0Vgp29Q6lj7l63imYBlUv/doYFtWDtifPc0uUyvX9Xwq+kljpPfxw5TUefRqW+62irpbZT1Tv3lnatXMbIqc/i7FaLXz//AN+2HbCzLL8pV4iPCGXCm++RGBnOvnV/MGbGLHyDOtJlyEgA1i+YQ3x4KN4tWtK+70DadO/NX79V7Wbibp6nlMR4UpISmfXRF+TqdHz7zqt31IEb37M9C3cfIzNbxzvjBnLmUhI5+Xrz9uTrN/lywx4MhYX0bdmcgW382RB8HoAtp0I5FhVf7bSLHFi9nEGTpuPk6s6qb0rXp4yrySRFhvHIK8b6dGTTaoZOfpa40PPk5eoYV6I+NW/bgZbde/P7p/+qciw7Vixl9LTncXavxZLP3sc3qKNVm3ct+QrxEReZ9NYHJESGsXftSsbN/Ace9Rvy9Fsf8ucc6zav77jHqFWnLgZ9Pj+//xaBnbuh1mhLJluhG7du8vvq1SyfN5/UtDTe+vRjfvvhJ/P2gb37MHzAQADmLFrIxu1/Mf6hhwF456WX6dCmbZXzobKm9uvCnG0HycjK4dMnRhAck0B2Xr55+9Ubt3j3j60AtGpUl4e7tOWbLfuqnZ4uO4ujO7bxzPufcjMzg9Xz5jDzX8Ud09OH9uFZrwGPPfcSe9av4vShfXTuP5iBjzyJR9166PPzmfPOq7Tu0gONVouLey2mv/NhteOprNTP/4t9x3aovTxuv/Md2Lvqd4ZOmomTmzsrv/oYnzYl6lJKMomRYTz+6mySoiM4vGEVw6c+B0D2zRtkphZ3UJTCQg6t/5Mn33wPlUbDqv9+jnfLttg5lL5uVCQ76xZ7tmzgrS/+y/WMdBZ/+yVvfva1eXv9Rk345xff8MMn71t9r333HvQaPJT3Z82sTlZUysDWfmw+FcotXR4Te3UkKuUaeRbXKH1hIeuCzxPk3eCupnu32zyASxfOorWzr1Icuuwsju3cxsz3PuVWZgar589hxrsfm7efPrQPj3r1efTZF9m7frW5PrXs2IWOfQbw/duvmPfNunmD4L27eOa9TzHo9Sz+/EOeef/f2Ng8uBPs5BTKynlwz/BtCCGaCCF2CyHOmf7f2PT5EiHEIxb7ZZn+X08IcUAIcUYIcUEI0cv0eZwQwkMI4S2ECBNC/CyEuCiE2CGEsDft08mUzlEhxJdCiAvVjdug16PPy8PNwwuVWk0jX3+S42Kt9omPDMOntXGkqLFfAKlJCQDYOTigtbMrdcy48IvEXjzL7199woGNa6oVl8rGBq1aTWa2joJChbi0DBrWdrPap1BRyMrNL/XdVo3qobZRMbVfZx7p2hZbTdWfGxQY9Bjy83Cp7YlKraZeM19SE6xHLS5HReAd2AaABs39uXYlCYCo08EYDHrWz/2Snb/+TJ4ux+p7cRfP0sivBWqNplKxJEZH0tQ/ELVaQy2vOuTl5mLQF998x4ZdoEW7jgAEtu/IpfBQAAoLCzm6cxvdBw21Ol5CTBRObm64mkYP7oTKRqBVq7ieo6NAUYi/lkmDWq5W+xQqCll5pc8TQJ8WPhyKiC1zW2WULL8Nff1Jjrc+XkJEGD6tjTe3jfwCSE1KBKBWnbrFf4dKjY3K2Dw5ubohhKhyLHfzPLm410KtVlNgMJCXq8PByYnqUpvq0rVb2RQUFhKVkoa3l/W5z8jKwVBYCEBBQSGFpp8Bhga14J9jBjCglW+1YyjQ69Hn5+Fqqk/1fXxJjS+jPrW0qE+XjfUp+nQwBXo96+Z+yY5lxfXJwcUVlarqddug15Ofl4ebp2WbF2O1T0JEKM1btwOgsV8LUhMrbvOKypKNSm0qO1UvP+dCQ+nQpg1ajYaG9euTo9ORn19cb7QW7UVubi7NmzY1//6fuXOZOOt5tu3eXeV0b0etssFOrSH1ZhaGwkLCr1zFp451B6XQ4gbIXqsl/lrGHaWZGBOFt3+AsS551iE/z7ouXQq7iH9QBwACgjoSZ6pLHnXrAaBSqxDCxlyPs25cZ+Gn77H8uy/JTEu9o9gqYki79rcd25yGqc1z9TDWpQbN/bhaoi4lRYXT1FSXGjb3J+1yonnbib820mnwCPPvuuws7J2c0drZo1Kpcfeqw9X4qrfJlyIj8A1shVqjwaNOXfJ0uej1xeXX3tERO/vSHQ8XN3dU6r/v2b7KRqBRqbiRk0uhopCUcZ16bi5W+yiKYvVA4m74O9o8pbCQcwf30MY0slxZSTHRNPELQK1W4+7pVbo+hYea65N/uw7ERYQBxmthyXNzPS0Nz/oNUKnV2Nrbo7W1JSPVekRT+t/0oHfg7E0drjNCiDPARxbb5gLLFEVpA/wOfH+bY40HtiuKEgS0Bc6UsY8v8IOiKC2B68DDps9/AZ5VFKUbUFD9P8fYeFs+abN1cESXfctqn9zsLKsRDcubu7KkXU6kSUBLxr82m/Qrl4m5cLbKcTloNegsGphcvQEHbeU6PC72tigoLN57gqT06/Rp4XP7L5Wgy85CazEiYmvvQG52ttU+uTnZ2FrkXVG+ZN+8jhCCsbPeoE6TZpzcudXqexEhx/Dr2LXSseRkZVmNDtg5OJCTdavEduMNvr2Do3nbyYN7adWpa6mRgN3rV9Fv1EOVTr8iDlqt1Whbrl6PfSXPUy0nB7RqFVdvZFU7fV12ltU5sLV3QJdlfbzc7Gyrp55KifKbEBFG9o3rNPINqHYccHfPk72jEx516/Gf12fx7TuvMmDMo9WOy9FOi85itC0nT4+TXdmjQy72tvRv5cu+i8YpcrvPR/LBqr/4ZvM+2no3wLeeZ7Vi0OVkYWtRn7T2DuTmlK5Plm1R0XnKvnEdYSMYN+sN6ng3I6REfapyLNlZVuXBrowyo8suO5bbObJ1g2n0rXJ1wNKNmzdxcXY2/+7s6MSNmzet9lmzeTNjn55EyNmz5g7cG8+/wB8LfmbOvz9n4fLfSLxiPdX8Tjnb2Vrd3Gbn5eNsb1tqv2Zetfn08eFM79/Fajp7deiysrBzKH5oYWfvgC67+BzlZBfXNTtHB3JKnL/9G9fRplsP83l47ZsfmT77Izr1G8S6RT/xIMvNtq5LtiXyBoquTaXbvMzUFPLz8vBsUDwbwt7JGV32LbKuZ5Kn03ElNqrUta4ycm7dwsGx+JzZOzqQc6v6bfvdYq/RWI225ekNlb5G3Ym/o80LO3EEn7btUVWxfbGsL1C6PumysrA3lRd7B8dS7aGlWnXqkpIQT64uh5sZGaQkxle4v/S/40GfQqkzdbiA4nfgTL92A4ruin8F/nObYwUDi4UQGmC9oihldeAuWXx+EvAWQrgBzoqiHDF9vhwYWVYCQoiZwEyAp197m76jxpm3hezZQcSpE7h71SFPpzN/nqfLMd9kFrFzdCI3p3gU6XZD5XYOjvi0bIMQgqYtW5OWlIhPq8pN7+nq24SWjeqScSsHO4uRM1uN2mrKV0Vy8vVEJacBEJmcZnyPrpLOHdhN9NmTuHl4kW8xcpafq7O6IILx77TMu6J8sXNwpEkL4/TRxi1acXBN8bsQeTk5pF9JooGPX6VjcnBysmr4c3NycHBytt6enQ2exm32Tk7o8/M5ffgg0/75L/PTNICw0yE0bNocR4sbxero7NOYwIZ1yMjKwdbiCZ2dRmPVWahI30Af9l6Mvv2OZTi5d6ex/HqWLL86q4cNAHaOjlblV1iU39SkBPav+5OHZ71arVE3S3fzPEWdP8uNzHT++c0P5Obk8NNH7+Lftl2VOgb9WvrS0acRqTduWd2w2Gs1ZJcxcm2nUfPs4J4sOxDMrVzjO29FN+76ggJOXUrC29PdXLcq4+yB3cScOYmrh5fVSHS+rpz6lFN8LovOk62DI41N9alJi1YcWFO9d4tC9uwg/GRRm1ccS55OV6rNs3d0JFdXdpkpz/kjB0m7nMjYGdV7JdrVxYWbFjdCt7KzcHWxHil4eORIHh45kkXLf2fxihW8/tzzuLsZZya4ubjQrWMnIqKjaVT/zqeCDW0bQFdfb1Ku38TBtrj8OGi1ZJnKh6XY1HRm/7GV5nU8mNa/C2+v2FLttO1L1qUS1yUHx+Lteaa6VOT0of1cTUrgsedfNn/m6GzMR982QWxatrDacdWkM/t3EXU6BDdPL/JzS7R5ZV6bSpffY1s30G3EOKt9hRAMeHIyfy1bgEZrS+36DXF0s57tUhkOzs7kWHYKsnNwcK7+zIE71d67Af71vcjM1lnNwrFVq8mt5DWqOv6uNs+g1xMRcowxz73CldioKsXk4OjEZYtrYMn6ZKxvxu25OTkVrlng4ORE/3GP8vt/v8DRxZW6jZrg7F6rSvHcbwqRUygr40EfgauKohJhwPR3C+MdohZAUZQDQG/gMvCrEGJSGcewvEoWYOwAV/ouU1GUBYqidFQUpaNl5w2gY//BPPX6uwyfNAO1VsuN9GsUGAwkRUdSz9t6xKqxXwCxplG0pJhIvBo2rjDdxv4tSDZNFUiJv4S7V53KhsyxqHgW7TnOuuDz6A0FuDrYYSME3p61SEqv3Loul1LTzdP4GtRyJT0r5zbfKNam9wAe+seb9H9yMmqtLbcy0ikoMHAlNoo6TZpa7Vu/uR/xYcb3hJIvReNRv6Exzeb+pCbEAcaXzV09vczfiToTjE/b9pW6ISzSqLkflyLCKTAYyLyWhq2dndWNfLMWLQk/exKA8LMnaRbQkoy0VHQ52fzy1adsXbGMyHNnOL53J1fi44gNu8DCLz4i6sJZtvy+pFpTik7EJLBkfzAbT15EX1CAq73xPDWu7cbljBuVOoa7owMj2gUyoWcHnOy0DGtb+RGwDv0GMf612QybNB2NVsvNjGsUFBi4HB1J/RLlt5FfALEXzwFF5df49Dkz9Srbli5k9IwXrDpa1XU3z5OCgr2jEzY2Kmzt7DEYDLcd+S5p78Uovty4h6X7g8k3GKjl5IDKRuBb15NLJRb10ahUvDCkF1tPhXIptXj6m2XHz7++FynXrUfnb6dt7wE89OKbDBg/GY1FfUouoz41sKxPsdF4NDDWp4a+FvUpIQ5XDy+qo2P/wUx4411GPD0DjUWblxgdQb2mJdo8/xbEnDe1edGReDWquM2LPBPCxRNHGD3t+SrVbUttAgM5ff4ceoOBK1dTcLC3R2uxyE1eXvHlwNnJCXtb4yjYzVvGc5Kv13P6/Hm8G5V+17Q6/jobzger/2LeriPkGQx4ODuishEENPAiOsV6qqBGpTL/nJ2XT57+jiaG0MjHl/hIY126fi0Nra11XfIOCCTi7GkAIs6epmmA8SFd2Mlgzh09yCPP/sP8QC0vV0dhoTGelIR4HJxdeBAF9RnIoy+/xaCnpqLWarlZdG2KiaKuxYIUYKwzcaY270pslHnE7ca1NPb++Svrfvia7JvX2bfqd+P+zf155MV/MmzyM+jz8qjbxPp4ldHUz5/osFAKDAYy0lKxtbdDU8X3QO+mU3GXWXHkNH+dDUdfUICzvS02QtCgtitXrt+8/QGq6e9q826mp5Gvy2HT/O85vGE1CeEXuHjkQKViaujTvLg+pV8rVZ+a+gcSee4UAJHnTuEdUPFD75adujLtnQ8ZMWEKGq0tbrX/3nc+pfuDeJBfFqxoFUohxEZglaIov5o+H6MoyjghxLsYR8z+KYQYC6xTFEUIIZoAlxVFMQghXga8FUV5WQgRh3FUzwnYrChKK1NarwNOiqJ8YHrnbbqiKMeEEP8GRhftV54l+4PLzfjkuFh2/vErKNC2Z1/a9jSuoLZh4Y+Mmf48YFyYJPbiOVQqNcOfnoG7pxfpV5PZ8fsSUhIu4VG/IS07d6d934HcSL/G1mULKTDocfeqy/CJ06xuair7BL+Buysj2rcAITgZk8jJS8b54Y92bcuqY8abq4m9OuDl6ozeUED8tUw2hFxAZWPDuE6tcHWwp0ApZPWxs2W+KwdQ1638i/nVhDgOrl0BikJgt14EdjUuIrFj2QIGTzK+cB28fRPxYRdQqVT0f3IKrh6eFBj07Fm5lKzMDGxUKgZOmI6ji7FDufb7z+n9yARzZ89SYw/3cmMJ3rebE/t2ATB60jRUNioiL5yl78ix6PPz+HPBD9zISMe1Vm0emznLanXDmNALnDq832oVSoA/5s2hc7+B5a5CeTouqdx4LNV3d2Fo2wCEgFOXLnM6zjiF66HOrVl7wnhxerJ7O7xcnNAXFJBw7TqbT4daHePFob0qXIWyorxJjotl95+/oSgKbXv2Na8AuGnRj4yaZiy/h7es55Kp/A6bNB03Ty9Wz/2a9JQr5qeHXQaPwKd1ECf37iQs+BjpKVeo06gJQyZMwd2z+CFERYuu3K3zVFhYyKoFP5B+NRmDQU/7Hn3pOXREqfQ2hZwvNxZLTTzdebKHcRXKg2ExHDatQjl9QFcW7j7G4Lb+DAsK5EqmsfMdmpTCllOhTOnXhbquziAg8koqa46fqzCdoKaly3WRqwlxHFyzAkVRaNmtF4HdjPVp53vwMgAAIABJREFU+9IFDHnaWJ9O/LWJhLAL2KhUDBhvqk96Pbst6tOgicb6FHU6mAuH95NyKYa6TX3oOnws9ZoVr3rnbF/6XbUiyXGx7Fy5DAUI6tmXtj37ArDh5x8YY6onhzavI/bCWWzUakY8PdPY5qUks335L6TEX8KjfiNadulOh74D+XLWVGrXrY/W1pjmmOnPWz2Vfsq/SckQyrR2y2bWbN4MQvD2iy+hUqk4GhLM1CfH89PSJRw/GQKAq7MLH7/1Ni7Ozsx47VVydDoMBgOjBg9mwiMVT7d9cnnVR8aa1anNlL6dEQh2X4g0j5wX1dv2TRsypmMr87twS/cHE5dWuffgHuvevszPT+7fQ8j+3QghGPHUFGxUNkRfOEevEWPQ5+exduFP3MxIx6VWbR6a/jwarZaPZkzAo14D83uKjz37IjczM9jwywK09vYIYMTEqdRr7F1mmkGvlV40oiq83nwZu9aBCI2G/Ng4ku9g4ZRd779f7rar8ZfYZ1ohuVX33rTsZlx5d9uS+Qyb/AwAx7dtJC7sPCqVmkFPTSn14OOXD/5pXoXy4Po/uRp/CRsbG7qPfrhUB65Fw7pUxuFd2zm0cztCCB6b/gwqGxWhZ08zZNwjXL2cxPL5P5AQE039xk3o3LsvfYaN5OThgxzYvpWY8DB8AlowevxEfG7TgTgeFVepeIrUdXVmQGtfBIKz8Vc4n2hcCXlk+0A2nzJeix7u3AYPZ0f0BQUkZdxgx7mISh3b0bb8TurdbvOKJEWFExF8rNQqlF6u5T+IPHlgDyf370EgGD5hMjY2KmIunqPn8NHGVV0X/sjNzAxc3GsxzlSfLpw4SvDeXSRGR9CouT8DHnqMxr7+rJ4/l5sZ6WhstQx/agq165RdPh7r2vbOprXcI+tCLtxXHZNxHVvdl/n2v9yB8wYWAx5AGjBFUZQEIUQdYAPGUbjdwD8URXESQjwNvAHogSxgkqIolyrZgesC/AxkA/uA3oqi9Kgo9oo6cPdaVaZg/d0q6sDdaxV1UmpCZTtw98L9lDd3umrm3VTZDty9UlEH7l6rqAN3r1W2A3cvVKcD93cqrwNXE+60A3c3VdSBu9cq24G7V6ragfs7VdSBu9cq6sDVhAelA7c2+Px9c38M8FCn1vdlvj3Q78BZdt5Mvy8Blph+jgP6l/Gdq4DlihVvmz5fCiwtY39v04/XgFYWn1uuW33RtFgKQoi3gJAq/imSJEmSJEmSJEm39UB34O4jI4QQb2PMz3hgcs2GI0mSJEmSJEnS/yLZgbsLFEX5A/ijpuOQJEmSJEmSpAfVA/xm1z31/2kVSkmSJEmSJEmSpAea7MBJkiRJkiRJkiQ9IOQUSkmSJEmSJEmSalyhnENZKXIETpIkSZIkSZIk6QEhR+AkSZIkSZIkSapxD/K/T30vyRE4SZIkSZIkSZKkB4TswEmSJEmSJEmSJD0g5BRKSZIkSZIkSZJqnJxCWTlyBE6SJEmSJEmSJOkBITtwkiRJkiRJkiRJDwghhyprxraz4fdNxqfeyKrpEMweyr1Z0yGYLS7Q1HQIVuq5u9R0CGaZWTk1HYKZEKKmQzALaFCnpkOwknL9/qlPtZwcazoEs7Sbt2o6BDPfel41HYKVc/GXazoEszy9oaZDMBv44Yc1HYLZma+/rOkQrDjZaWs6BLPr2bqaDsHsZk5uTYdg5ZmB3e6fi2UFVhw5fd/cHwM82b3dfZlvcgROkiRJkiRJkiTpASE7cJIkSZIkSZIkSdUghBgqhIgQQkQLId4qY7utEOIP0/bjQgjvO01TrkIpSZIkSZIkSVKNK3zAXu0SQqiAH4BBQBIQLITYqChKqMVu04BMRVGaCyGeAL4AHr+TdOUInCRJkiRJkiRJUtV1BqIVRYlVFCUfWAmMKbHPGGCp6efVwABxhy/wyw6cJEmSJEmSJElS1TUAEi1+TzJ9VuY+iqIYgBtA7TtJVE6hlCRJkiRJkiSpxt1vq+MLIWYCMy0+WqAoygLLXcr4Wsk/ojL7VInswEmSJEmSJEmSJJVg6qwtqGCXJKCRxe8NgSvl7JMkhFADrkDGncQlp1BKkiRJkiRJkiRVXTDgK4RoKoTQAk8AG0vssxF42vTzI8Ae5Q6HGuUInCRJkiRJkiRJNa7w/ppBeVuKohiEELOA7YAKWKwoykUhxEdAiKIoG4FFwK9CiGiMI29P3Gm6sgMnSZIkSZIkSZJUDYqibAW2lvjsPYufc4FH72aacgqlJEmSJEmSJEnSA0KOwEmSJEmSJEmSVOPut1Uo71dyBE6SJEmSJEmSJOkBIUfg7kPH9+3m6K7tCCF4aMpMGjXzMW/T5+ezct4cMq9dw93Dgyee/QcarZb8vDzW/rKA9NRUCgsLmfb62ygoLP7qc/N3L0WE8cnPy3Bwcqp0LCnxl9ixYikK0K5XP9r06FNqn8NbNhBz/jQqtYYRk2fi5uFJUnQk235bRMbVqzz36de41DL+e4Un9+4keNdfKIrCc//+ptp5tOnYUdYdOQTAm48+TkCjxlbbft62hbq1agHwydNT8XJz463FP5N6/ToFhYU80rM3o7p2q3b6aYn/x959h0dR7Y8ff0+2pBdIgYTQSaN3pIMUpYoISu9NRURQxI7tXu9Vr11BARUREaT3mtA7gQDpPYGEkARI22R3k/n9sctmNwU3AQl8f+f1PDwPu2d255Mz58zMZ86Z2SSObFyDLEOLrr0I7NK93DLn9u0gKTwMhVJF3zFTcHH34ELwXhIvXwAg92Y2TVq3o/uI5zi+ZT0ZyQnodVp8mgbQ7amqTZU+fySEsyH7kZAYMnEqPo2amMp0Wi2bVy7ldlYmru4ejJg2B5Vazdpv/0fOzWzkkhI6PT6Q9j37APDrpx9zLSmergMG0+epZ6pcN9eTEwlevxpZhtbde9Oia89yy5zavY2EKxdRKFUMnDANV3dPU9nx7ZuIOHOC6e//F4ArJ45w4fABbBQKfBo3o/czY6sUy8F1vxli6dGHlpXEEn/5AgqliicmTreI5dj2jUSeOcH09z811OPSL9HrtMjFJXQd8hSNW7SpStWUfu+BvRzZswskibGznqdhUz9TWWxEOKt/+Jrr167y8dKV1PYwxBO8cxv7t21GLinhX8t+rtZ676hOezl1YA8n9uxElkt45dNvTMsf2raJiHOnAWj1WHe6Pzn0nmI7cXAfx/bvRkJi9Iw5NGjSzCK21d9/xc3MG9Ty8GTCCy+jUquJj4pg46/LsbFR0KpjFwaMqFq7DT0awrmQA0iSxKDxU/Fp1Lh0nTotW1cuM9XH8GmzUanU3MzMYMuKZRTrdfi1aUevoU9TkJfHn99+bvpsSmw0r321DJWtHRt//Ja8WzcpLtbT75mxNGneskoxHtq7m+Bd20GSmPz8SzT28zeVRYdfZsXXX3D9aiqfr1yNu6ehzSz97D8kJ8Th4OCIs6srL7+9pErrLCs9OZH9a1chyzJte/alVbde5ZY5sXMrcZcvoFAqGTxpJq4enpzas52o82ewUSioU78R/cdMRJIktq9cSs7NLHSFRTTv0pVO/QdZHUtGSiLB638HWaZl9z60eKxHuWVO79lu3M8oGTB+Oq7uHqVx7thE5JmTTF3yHwDiwkI5vWc7CqWC1j36Etip+seHivh8/jG2/s249ddmbv76x339bnPnjwRzJng/kiQxdOK0cn1704ofTG356enPm44Ft7OzkEtK6NxvIO179gXgakIce9evoaS4mHqNm/LkmIlViuV+ncs4ODmReT2dTT8vR1tUiKu7OxPmvmJVDPerbwOcDd5P6NFgFAolfUaMoknzVsSHX+bQ1g0AFGk0SJLE7CX//tu47vdxUpOXy4E/f0OTl4uNjQ3PvPSaVfXzMBIjcNYRCZwVJEmaAnSUZXmuJElzgAJZllcZ398ry3LZ33uotoK8PI7s2s78j//L7exsVn/zBS9/WJqEnQ45gJePLxPnLWT3X2s5HXKA7gMHsXv9Wtp27UFgm3YW3/fSko8BSIqNZtefa6qUvAHs/eNXhk9/Aedatfnl3+/h17Yj9o6OpvLMtGskRV1h0uIlJEdHELxxLU/PegkPH18mL36fdd98ZvF9AR0607ZnX358d1FVq8YkpyCftYeC+WXhIjJu3eLd335hxSuvWiwzvGs3Zjw52OK9F4Y+RQMvL4p0Op7714cM7NARW5WqWjEc2biG/hNm4Ohaiw1f/otGrdpi51BaLzevp3E1JpKRL7/BtbhoTm7fwMDJs2nbdyBt+w4EYPuyL2natiMAXYY8jUJp6I6bv/kv2WlXqe1dz6pYNPl5nNy3i1nvfkzuzWz+WvYNM9/+0FQeejQED28fRs+ZR/Dmvwg9GkLnxwcyYNRY3Ot6o9Nq+fathbTq0g2VWs2I6XOIu3KJnOysatVN8PrVDJo8Gye3Wvzx2Yc0bdPeom6y06+REh3BmIVvkxoTxdEt6xky7QUA8nNuczMj3eL7TuzczKS3PkZtZ8e6L/9NVvo13Ov6WBXLwXW/MXjKHJzcarHm0w9oViaWrPRrJEeFM/bVd0iNieTI5vUMnV5xLDYKGwaOn4qruycFebms/eyjaiVw+Xm5HNy+hTf++yU3s7NY+cV/ef2T0osZPg0asvg/X/DNR+9afK5Dtx70HDiId1+cWeV1mqtue2nRsQsde/fj6zdKT5yKNBrOHwnm5U++RJZlvnljAZ369kdta1et2Arycjm0cxuv/vtzbmVn8evXn7Pw409N5SeD91O3ni9T57/GznVrOBm8n55PDGb9imXMfO1Nant68f3H79G682PU8bG+/5zat5sZ73xE7s1sNv70HdPffN9UfuHoITy8fXhm9kuEbNnAhaOH6NR3APvX/0Hfp0fR0D+IXz/9iKAOnfH0rsfUxe8BkBofS/Cm9dg7OhF14RxqW1umvfk+NzMz+OuHr2jS/GOr6yU/N5e9Wzby/pffkZ2VyQ///Tfv/e9rU7lvw0a8/8W3fPbum+U+O/n5lwho2crqdd3N/rWrGDptDs5utfntkyX4temAnaNlf0qKCmfCondJiY4kZNOfPDVzLn5tO9LlCUNiv/nHb0iKDKdRUAsGTZqBQqmkpLiY5Utep3WPPtja2VsVS/D633ly0iyc3Gqx9rMPadq6XZn9TBop0RE8t+AtUmOjOLZlPYOnPQ/c6dvXTcvKJSUc3byOsYveRaFSsf6LT2jUog12Dg73o9oAyPjkC+w7tkPp5fH3C1eTJj+PE3t3Mfu9j8m5mc1fS79h1jsfmcpDj4bg6V2PZ59/mYOb15v6dv9RY/EwHgu+eXMBrbp0R7KR2Lvud8bNew1be+u2ibn7fS6zYeWPjJkzF9datatUH/erb9s7OnE2ZD8z3/kIvU7HL//9kJnvtKBJ85amizFHd261Ovm438fJ4L/W8Nigp/Cwcr8nPPr+v51CKUmSojqfk2V5qSzLq4wvpwDWnUlaKSk2miaBzVEqVbh71aGosBC9Tmcqjw2/TIsOnQBo2aEzcRHhAMRcvkjEhfN8s+Qtdq1bU+57zx4OoaNxhMVaep0ObVERbp5eKJRK6vsFkJYYZ7FMclQ4zVoZdrQN/IPISEkGwM7BAbVd+ZM4JxdXU6JSXZcTE2nXtBkqpZJ6Hh5oiorQmtURwM7Tp5j+xWf8sH0rJSUlhvi8vABQKhTYSBJSNddfrNehK9Li4u6JQqnEu4kfGcmJFstcjY2iYXPDSZNPU38yr6ZYlGvycsnJyqRuI8MVyTt1UlysR6lW4+DqZnU8qXGxNPQPRKlUUsvTC22RZZtJiAwnoG0HAALadSAxKgIA97repnVLkg2SZKgRV+NoaXXodTp0RUW4ehjqpl5Tf9KTEiyWSYmJpHFLQ+Lj6xfAjdTSujm5ayudn7AcwalVxxttUSHFej3F+mJs7a07qdLrdOi0WstYEuMtlkmNjqSJKZZAbqQmm8WyhS4DS2NRKJSmK6BKpcpUX1WVEB2FX/OWKFUqPOvUpbCwEJ1Oayp3cHTEroITJhe3Wijvse9A9duLk6tbub6rVKtxcauFTqtFr9WiVKuxUVQ/xoSYKJoGtUCpUuFRpy5FhRp0ZrHFXLlEyw6dAWjVsQux4ZcBKCzIp7anoX83aOpHzOUwq9eZGl+mPsrscxMjw/Fv0x6AgLbtSTLWR3pyEg39gwDwb93O9P4dYSeO0rqrYVSotldd9DodsiyjycvH0dm1SvUSGxVBQMvWKFUqvOp6U1ioQac1bzNOFbYZgNU/fs8HC1/mxKHgKq2zrDt9283DcDzw9QsgLcmyPyVHRdC0laE/1fcPJMPYt2vXqWtaRqFQYqMwnHrcaU96nQ6XWu6o1LZVisXUt5v5c73MfiY1JpLGLVoD4NssgBtm++DTu7fSaeAQ02tNfh72Ts6o7exRKJTU8qrD9TJ/273S38i8r99XkZS4GBoFBKJUqqjtWad83464YurbgW07khhpOH/wMB0LFKZjQUpstOGi2Q9fseLfS0z7AWvdz3OZ7BsZaIuK2PTzcr5Z8hYXTx63Kob72bdvZd7A08cXhVKJrb09altbssskUZdOHqPVY+Vn45R1v4+TJSUlZF1L5dyB3fz5xb+5cPiAVfUjPNoeaAInSVIjSZIiJEn6SZKkK5Ik7ZUkyV6SpBBJkjoal/GQJCnR+P8pkiRtliRpmyRJCZIkzZUkaYEkSaGSJJ2UJKnSSzGSJDWTJGm/JEkXJUk6L0lSU0mS+kiSFCxJ0hrgknG5CZIknZYk6YIkScvuJHaSJE2VJClakqRDQHez710iSdKrkiSNAjoCvxs/ay9J0ieSJIVLkhQmSdJnFcX1dwrycrF3LB0ls3dwID8v16w8zzQCZu/oSH5eDgDXkpPwb9maue99RHpqChEXzps+U1xcTPj5s7Tq3KVKsWjy8yyuCNnZO6DJyyuzTL7FVUrZmCz9k3IK8nE2W6eTvT05BQWm171bt2b92+/x48sLSMvOZtfZMxaf/3nvbp7o0BF1NUffCvPzLa5I2to7UJRvWS9FBfnY2pfWXdmrcjHnTtGsXSeL945sWMPqD9/AwcXN6qvQAAX5eRajonb2DmjM4tHk5WFv3I72Do7ltuGhbRtp9Vh3lNWsD3OF+XnYmm0bW3sHCsvUTWF+PnZmSViJbGgzNzPS0RUV4lmvvsXyQZ27sfrf7/Lz+69Tr6kfTlYmt4X5eRbJnq2DZb2A4cTf1sF8O5nHUoSnbwMqErz+dzoNHFxh2d/Jz821GAl3cHAkPzf3Lp+4v+61vZhTKBT4tWnHV6+/zJevv0ynPv3uKcksyMuzqBt7B0cKzPZ/+XmldWfvWFpvji4upCbGo9fpiAq7YPGZv6PJy7MYRbIr0040+aXldmb1caetlH0fDPvc6IvnCWxv6OO1PL3Q63R8++YCfv/iE3oNH2l1fAB5ubk4mrcZR0fyrGgz42fO4cOvf2DBkg/Ztu4PMtKqP1lEU0HfLts2CvPzLY4ZZY8HyVER5N++RX2/QNN7m5d9zbK3F1CvmT82NtadkpTr2/ZW9O2S0r6tLSqy2M/YOzmjyc8l79ZNijQarsXHUJifb1UsDxNNXh52DqXtpGzfNu/7do4OFJQ9FmzdROuuhmNBzs2bpCcnMfr5eYye/RKbVy6t0tS2+3kuc/tmNlcT4hkxZTozF73FrvV/lIu90vq4T327tldd0lMSKdQUkHMzm/TkJDRmbeR6SjJ2Dg64uf/9COv9Pk4W5OaQeS2V9n0HMOql14g8c5Ks9Ps2MeyBK5Hlh+rfw6omplD6AWNlWZ4pSdI64O9uVmgJtAPsgFjgdVmW20mS9AUwCfiyks/9Dnwiy/ImSZLsMCSr9YHOQEtZlhMkSQoCngO6y7KskyTpe2C8JEn7gPeBDsBtIBgINf9yWZb/Mv5w36uyLJ81JpNPA4GyLMuSJJU7y5QkaRYwC+Clt99n0KhnywXt4OSMpqB0p6ApKMDRydms3MlUrinIx8HRUObo5Exg2/ZIkkRgm3ZcS0okqK3hylLUxVAa+QdYnRScPbiXyHOnqeVVhyJNaWJUpNFY7JDBsOMtNFtGsvIAfC9cHBzJM1tnnkaDi9nO0MXsoD2wQ0dORoQzxJi8bj91krhr1/h4yrQqr/fSkYPEXTiLq6cX2kKN6X2tRmNxogBg6+BoUXdlR2uiz52i/8QZFu/1fGYc3Uc8x+6ffyA58jINm7e2Ki4HRyeumiWwhZoCywOnkxOFxvLCggKLk/fQo4fISE1h9PMvW7WuyoSG7CfmwhncPOtQpCmtm6LCAosTOgA7R8u6sZEMbebEjs10G2p5Yqst1HBix2amvvcJKls7tiz7irTEeLzN7usoH8s+okPP4ubpZbEebQXt167cdjLEcnzHZrob73so68TOLdg6ONCya/n7f6zh6OxMgdmBv6AgH0dn57t84v66l/ZSVmb6NcLPnGLBZ98iyzIr/vUeQe0741Lb+mlOFrE5OVmcFGkK8nEw2/85OjlTkJ+HO3WMZYa4x82Zx8Zfl4MM7nXqVmkU2d7JicIEs/ooKFMfjmb1oSnA3rjOO23F9L7ZZ+Iuh1G/qR+2xlkIF44dwqW2O2NeWsjNzAz+/OZ/zHm/dDrZ33FyNvzddxTk5+NkRZtxdnU1ft6Flu06kBQfh5d31SaNnAveR9T509Qq27c1GouTYzD07cKCio8HGanJHNq0jmfmLrDYH46YPQ+dtog1n35MUMfH7joF7MKh/cQY+7b5PrhIoym/nynbt42xnNy5ha5DLPu2JEn0GzuF3at+RKW2xd3HF0c362dBPCwMfbe0/5Rtlw6OpeVFBaVtGQzHguupyTz7wnzTsg38ArCzd8DO3gEHJ2fyc3NwcrFu9Ph+nsu07NQZ7wYNcTP263oNG3Mj/RoNm/lzN/ezbzs4OdF3xGj++OpTHF1cqdugIc5utUzLXTxxxDTiXpl/6jhp5+CIo2st0wXH+v6BZF5NtfpWA+HRVBNTKBNkWb5g/P85oNHfLB8sy3KuLMs3MCRT24zvX6rss5IkOQP1ZFneBIYf0JNl+U5POC3L8p2x6n4YkrQzkiRdML5uAnQBQmRZviHLshb404q/KwcoBJZLkjQSKCi7gCzLP8qy3FGW5Y4VJW8ADZv5kxAZTrFez83MG9ja2VmMjDRt3pKI8+cAiDh/jmbNW5jeT4mLBSAlLtY0JQLg7JFDdKjC9MmOjw9kwmtvM2TyTFRqNbezMinW60mJjcK7cVOLZRsEBBF36SIAqbHReNWveMTifmrZqBEX4uLQFxeTnp2Nva2txWhartkJxNnoKBp61QEgJOwie86d4YNJU6y+0muuVc/HGfHSIvqOmYJSrSb3ZhbFxXrSEmLwatjYYlmfpv4kRximd6UlxOJhdrXsVkY6kiTh5lnH9N6daR02CgUqtRqlSm11XL5Nm5EUHUmxXs+trEzUtpZtpnFAc6LDDCOy0WHnaRTYHICI82cIO3mUZ2a/VK36MNeuT3+enf8GA8dPQ6VWk5NtqJtrcTHULZNs+foFkhB+CYBr8TF4+hrq5nbWDQ78uYoN335Gfs4tDq5bjSTZoFAqUdnaYWNjg529A0UFd78y3q7PAJ575Q2emDDdIparcdEVx3LFMN3ualyM6QB4OzODA3/+xoZvPyPvtiEWMCSHtzLS6T1yTLXrqrF/ALHhV9Dr9WTdyMDOzg5VFbb3vapue6mILIOtvT1KlcrYblUUFRVWO7bGfgHERV6hWK8n+0YGtnb2qMxia9a8JVfOnwXgyvmz+LUwTlNu0JC573zInDfepSAvl+btOlq9Tt8mzUiOiSqtjzL73EYBQcSEGa7fxYSF0jDAMLWqbv0GJMdEARAbdsH0PkBY2ZM5GVMiau/ghLawanXULCCIqCuX0ev1ZGZcx87OHpX679tMvnFEQa/TER1+Ge96vlVaL0CHvgMYt/AtBk2aYexPmYb+FBuNTyPL40F9/0Dijf0pNS4aL2PfvplxnV2/Lmf4zBdN9SDLMsV6PWCYkqxUG/7dTdve/Rk9fzEDxk9D+bf7mQASjbFci48xjVjczrxB8Lrf2PTd5+Tn3CJk/e+G5ZsFMGre6wyaMhtdURF1G1Z+kehhVb+pX2nfzrxRrm83CmxO1EVDW466GErjO8eCc2cIO3GEUXNKjwW+Tf3ITL9GcXExRRoN+Tk5VbqH/n6ey3jW9UanLaJQU0BxcTHXr6ZQ28Prb2O43327eccuTF38HoPGTUalVptG20pKSog4d5rmnR67azz/1HFSqVLh6uFJ7k3DfevXkxNx8/z7+hEebdKDfNqLJEmNgO2yLLc0vn4VcAJ6AG/KsnxakiRf4Kgsy43MHx5iXD7R+DqzbFmZ9bgA4bIs+5Z5vw+GEbOhxtcvAT6yLL9RZrkRwNOyLE82vp4H+BsfYrIEyJNl+TNJkkKM33fWuJwthiRwDOAry/LjldXFrouRlVb8yYP7OXlwL5Ik8fSUGdgoFESHXeDx4SPRaov44/tvuJ2dhWttd8a9MA+VWk32jQz+XPYdOp0Wz7o+PDf7RWxsbCgq1PCfhfN46+ulKBQV3/aXcbvyqQhpifHsW7sKGWjbow9tevQBYMtP3/HUzBcBOLp9E/GXL2KjVDJk8ixqeXqRlZ7GnjU/k56UgIdPfVp06UaHPv2JOHuK0MMHSI2LwbepH72Gj8LX7CrayMKcSmMxt+XEcbacOAbAq6OeRWFjw6nICCb1H8h327ZwOioShY0NDevU4a0x41EqFPRcOJ9Gdepgb2u4z+LO0ykrs7K48pOJjOREjm76A1mG5o/1JMj4BLR9v/3EgImGh0yc3bON5MjL2CiU9B0zBVfj0wRP79yMnZMzrXv1M33f7pXfU5ifR0lJMd6N/eg6fFS5dXrXcqk0nnOHD3Lu0EEkJAZPmIKNjYK4K2H0GDzc8OSx5d+TczMbl1q1eXrGC6hz6RqzAAAgAElEQVTUaj6cNREP73qmexVHz56HS+3abF65lOSYaIr1Orx86zP+5fIPnLmZV+76hEl6UgIhf/2OLEOrbr1oaXxS3c6flzJ46hzAcH9ZYvglbBRKBk6YhluZg/GK9xaZnq4VGrKP8NPHUSgUuHnWYeCE6RYJ593uRUtPSjA96atV996mp+bt+HkpQ4yxnNi5hcTwMBQKJQMnTq8glteY/v6nFOTmsHTxPLybNMPGxtCXRr/8ukUsgfXqYI2j+/dwdO9ukCTGzJiDjUJBxIXzPDFyNOlXU1mz9FuS4mLxadiQLr360mfQUM4eO8zh3TuJi4ygaWAQw8dNollQ5ckVQPqtivtTddrL5dMnOBO8n5TYKOo3C6DfyGdp4BfA3nVrSIi8ArJM46AWDHx2fIXrrO1U+UieueMH9nL8wB4kJEZNm42NwobIixcYMOIZtEVFrP7uS25lZ+FW252Jc19BpVZzYOsmLp09BUD/p56hZYdOd13HjRzL6YfnDwdz/vBBJEniyXGTsVEoiL9yie6DhqHTatmycik52Vm41HbnqelzUKnUZGdcZ+vPyyjW62nWqi29jdMiiwoL+eGd13jpky9N+1xtUSF/Lf2GIk0BuqIiug0aRsvOhqcc+nlbd6IVsmcXIbt3gCQxac5cbBQKLp8/y9DRY0hLTeHnb78iITaa+g0b063v4/Qf+hSfvLmIwkINxXo93R8fwJMj/n7qZljS1UrL0hLjObBuNbIs06ZHH9NTibet+J5hxof/HNuxmYQrhv40aNIM3Dy9+Ovbz8lKv4az8QEUXQYOoVHzlvz5peEJkMU6PYEdu9Cp/5MW6yvS6SuN5XpSAiEb1hieQtmtFy2MI+K7flnGoCmzATi1ayuJEZdQKJQMGD8V1zJ9++clr5ueQnlk8zquJyVgY2NDt+HPlEvg+r//PvfCa9F87Fo1R1Kp0MYnkvZm9b/vwuefVlp27tBBzh4yPHVxyPip2ChsiL0cRs8hT6HTFrFx+Q+mtjzS2Lc/mDnB4ljw7Jx5uNR2J/TYIc4c3EdJcTG9ho6geceKb8Nwsqv4YsL9PJeJCrvA7vVrKS7W07lPP3oMrPiJpbfyNRav72ff3vjTd+RkZ6FS2/LkuMm4G+/tjA+/zJmDe3lu7gKLdecUVH6h5n4fJzNSkgj+y/DE0AYBQXQfVn5y2+z+Xat76/8DtSL49EM1b3F6384PZb09LAmcL3BOluUfJEmaD8y/lwTOuOxJDFMoNxsTKwWG6ZPmCVxzYAuGKZQZxmmQzoAWOAm0xzCydhC4WEECtw34nyzLwZIkOQEOZt8TK8typfOI7pbAPWh3S+AeNGsTuAfhbglcTbhbAveg3S2Be9Cq+zCRf4K1CdyDUlkCVxOsTeAehLIJXE2yNoF7UO6WwD1od0vgHrR7TeDup7slcDWhsgSuJpRN4GrS3RK4miASuOp5WBO4h+UplJ8Bz0uSdBy4X8/YnQjMkyQpDDgO1C27gCzL4cDbwF7jcvsAb1mW04AlwAlgP3C+7GeNfgGWGqdfOgPbjd9zCLDuR0oEQRAEQRAEQRCs9EAfYiLLciKGh5LceW3+pEbzJza8bSz/BUOSdGf5Rmb/tyirYF0xQNkpjPFASJnl/qSCe9xkWf4ZKPdrubIsLzH7/wZgg1lx58riEQRBEARBEAShcuKHvK3zsIzACYIgCIIgCIIgCH+jJn5G4L6SJOk7zH6nzegr4wiaIAiCIAiCIAjC/xmPfAIny/KLNR2DIAiCIAiCIAj35mH+8eyHiZhCKQiCIAiCIAiC8IgQCZwgCIIgCIIgCMIj4pGfQikIgiAIgiAIwqNPPIXSOmIEThAEQRAEQRAE4REhEjhBEARBEARBEIRHhJhCKQiCIAiCIAhCjRNPobSOGIETBEEQBEEQBEF4RIgEThAEQRAEQRAE4REhplDWkMSM7JoOweRKSnpNh2Cib+xb0yGYNKjtUNMhWFh3LLSmQzB5om1gTYdg4uJgV9MhmFxMvFrTIVhwtFPXdAgmGq2upkMwGdaxZU2HYLL1zKWaDsGCWqmo6RBMmtTxqOkQTC58/mlNh2DSduFrNR2ChZn9h9V0CCaDO7So6RBMajs+XOcQjwoxhdI6YgROEARBEARBEAThESFG4ARBEARBEARBqHHid+CsI0bgBEEQBEEQBEEQHhEigRMEQRAEQRAEQXhEiCmUgiAIgiAIgiDUODGD0jpiBE4QBEEQBEEQBOERIRI4QRAEQRAEQRCER4SYQikIgiAIgiAIQo0TvwNnHTECJwiCIAiCIAiC8IgQCZwgCIIgCIIgCMIjQkyhFARBEARBEAShxokf8raOGIETBEEQBEEQBEF4RIgEThAEQRAEQRAE4REhplA+5DJSEgle/zvIMi2796HFYz3KLXN6z3YSrlxEoVQyYPx0XN09uHLyKKd2bcG5ljsAg6bMxsmt1j3F4uvuxujH2iABx6ISOBWbbFEeVM+Lwe2aoy8uRqsvZtXhs+QXaWnd0IfhHZrj7uTIK6u2VHv96cmJHFi7ChmZNj360qpbr3LLnNi1lfhLF1ColAyaOBNXD08izpzgfMh+JElCbW/PsGkvYGtvT2jIfs4e3IMslzDrw8+rFMvZQwc5HbwfCRg+eQb1Gjcxlem0Wjb89D23sjJxc/fgmZkvoFKrTeXLPnwH97rejJr5AgArPvmAa4kJdHtiCP2eHlW9yjHTyLM2U/p0QgIOXI7hcES8RXnrBt6MeqwN+uISCvV6vt9zlLxCLXXdnJnZ7zEAEjKyWX3kXLXWn56UwN61q0CGtr360rqC7XR85xZiL11AqVQxePJM3Dw8SY2LZvdvK8nOuM6cjz/Dxdh2t61cSk52JgAZqckMnToHvzbtrYrl3OFgTgfvQ0Ji2KTp5bfT8u+5nZmJq4cHz8wwbKeda1aRGh+LTqulcWBzBo+bREFeLqu//NT02aSYKN7+fgX2jk5W10tGShKHN6wBWaZFt14EdSnfl8/u3UFieBgKpZJ+Y6fi4u5h+Dv27yQl8golskznJ4bh6x/EvtXLyb2ZDUDW1RT6T5hO45ZtrY4nPTmR/WtXIcsybXtW0p92biXu8gUUSiWDJxn606k924k6fwYbhYI69RvRf8xEJEni8Ja/uHLyKLW86jLmlcVWxwHGNvPHr8hAu559ad29d7llju3YQtylUBRKFUOmzDK0mdhodq1eQfb16zz/8ee41Da0mQPr13AtMQ69VksD/yD6jR5XpXju2LF1C5s3bECSJBa+vpiAoCBT2epffib4wH4UCiUBgYEseH0xkiSxY+sW/lq7FqVSSas2bZi38NVqrRvuf1/SFRWx789V3Mq8gVxSwsjnX8He0fGuMaQlJbB7za8gy7Tv9ThtepTfNkd3bCYm7AJKpZJhU2fj5uGJXqdl288/cjs7C9fa7gybOgulqnQ/+Ot/PqB2nboMmzKLtKREdq/5BRvJBhuFDUOnzKSWZx2r6+n4gX0c2bsLSZIYM/N5GjRtZiqLiwxn9fffkJF2lY9+WEEtD08AQnZu58D2zcglJXy0dKXV66rI+SPBnAk2HGOGTpyGTyPL/cymFT9wOysTV3cPnp7+PCq1mrXf/o/b2VnIJSV07jeQ9j37AnA1IY6969dQUlxMvcZNeXLMxHuKrTI+n3+MrX8zbv21mZu//vGPrMOcn7cn8wb1QpJg+7kr7L4QaVHu6+7G4hH90BeXoLCx4csdIcRdz8K7lguLR/RDlg1T6/61aR83cvLva2x1XJ3o19IfCbiYdI3LqekW5WqFgtGPtcHd2YH9l2IIv3r9ntd5PTmR/X/+BsbzmZZde5Zb5uSurcRdvohSqeTJSTNwdfck4sxJQg/tQ5JsUNvZMdR4PhOy4Q/SkuLRa3XU9wugzzNj7znGmiKmUFpHjMBZSZKkvLuU9ZEkafs/sd7g9b/z5KRZjHp5MRdC9lFYYLnjyk5PIyU6gucWvMVjg0dwbMt6U1mLrj0ZPX8xo+cvvufkDWD0Y21YdegMX+06TJ/mzbBXqyzK02/l8tXOw3y16wiXU9Lp08JwEI1Lz+STLQe5VaC5p/UfWLuKIdPmMHbBW5wL3kthvmVdZKVfIzkqnPGL3qX70JEc2vwnAP7tOjH+tXcY9+rb1KnfiCunjhreb9+Jae/9u8pxFOTncXzvTma9/T7PvfAyW1etsCg/dzgYT596zHn3Izy8fTh3ONhUFnH+LLb29hbLj5r5IoPG3r+D9JQ+nfhuzzE+2LCPJ9sG4mirtii/evM2H2zYywcb9hKakMqgtoaT0nE9OvDHsVA+3LAPtVJBqwbe1Vr/3rWrGD79eca/+hZnD+xBU3Y7pV0jKTKcSa+/R49hIwnZaNhOHt6+TFq8hHpNmlosP2zaHMa/+jZj5i/G1t6Rxs1bWhWHJj+P43t2MvOt93n2+Xls+83yJO38kWA8vesx+92P8PSux/kjhu008NmxzHr7A1784BNS42O5npqCg5Mzs97+gFlvf8CgsRNp2rxVlZI3gMMb1jBw4gyefmkRYYcPlOvLN6+nkRoTwaj5b9D5yeGc2PYXAEnhl9BqNIyY+xojX1qEr79hew2YMIORLy3iqecXoLZ3oH5AiyrFs3/tKoZOm8O4hW9x7mDF/SkpKpwJi96lx9CRhGwybCe/th2Z9Mb7TFj0Lvm5t0mKDAegfZ/+jF3wZpViuGPvH78yfPoLTHj1bc4c3FuuzWSmXSMp6gqTFi+h5/CRBG9cC4CHjy+TF79PvSbNLJbv8/SzTHztHaa+9SHXEuO4cTW1yjHl5OSw7o81fL98BUs+/pj//fcTi/LefR9nxW+/8+Mvv5Kdnc3Z06cBWL50Kd8tX8FPq34jMiKCxPj4ir7eKve7Lx3dvonADo8xbsGbjH/17b9N3gB2r/mVETNeYNKidzh9YA+afMtDYWbaVRIjrjD1jSX0Gv4MB/4yJAMXjx3G3duHKYvfw72uDxePHTZ9JvrieYv9oLObG+Pmv87kxe/y2BNDOLR5g9V1lJ+Xy8EdW1j40X+Y9sprrF3+g0W5T/2GvP6f/9HEP9Di/fbdurPk66VWr6cymvw8TuzdxfQ3lzBqzktsL7OfCT0agqd3PWa+/SEe3j6EHg0BoP+oscx+92Omv/k+IVs2oNNq0et17F33O+NeepXpbyz5x5I3gIxPviDz++X/2PeXNW9QLz7euJf5v2xiZJfWONnZWpSn3bzN3BUbmP/LJlYGn2Rir04AjOjUip3nI5j/yyb2XIzk6c6t73ts/Vr6s+N8OH8cD6VDE19sVZZjG7qSEjafvcy5+KrvRyqz/8/fGDJ1DmNeebPS85mkqHDGv/YO3YY+zeFN6wDwb9eRca++w9iFb1GnQSOunDoGQM+nRjN2wVtMXLyEtKR4Mq/dv1iFh5NI4B5iep0OXVERrh6eKJRK6jXz53pSgsUyqTGRNG5h2KH5NgvgxtUUU1nEqeOs+9+/OL59I3JJyT3ForSxwVapICuvgOISmdjrmTT0sEwKb+Zr0BvXoy8pocT4//wiLfrie1u/XqdDpy3CzcMLhVJJ/WYBpCVZnhglR0fQpGUbAOr7BZKRaqgLhbJ0Z6zXFuHh7QuAo4srCkXVB6FTYmNoFBCEUqmitlcdtIWF6HU6U3l8xBUC23UAIKh9RxKMJ7glJSWc2LebrgMGWXyfq7t7lWOojFJhg61KyY2cPIpLSoi8mkGTOpbfn5VbYNoe+uIS02+ueLs5E389C4C461k097X+Cvgdd9qsaTv5BZCWaLmdkqIjaNrKMFLUwD+QjFTDSK6dgwNqO7tKvzs2LJRGQc0truLfTbIV2ynoznZq14GEyAgAlErDhYlivR6VrS0utSzbeeixw7TrXn4k5G6K9Tr02iJc3A192buJHxnJln35akwUjZob+nK9ZgGmA3BM6Bn0eh2bv/2Ufb/9RJGmwOJziVcuUt8/CKXK8oLK3ZTdTr5+FfSnqAiatjL2J//S/lS7Tl3TMgqFEhuF4TDi5OqGJElWx2Aei7aoCDdP8zYTVyaWcJq1agdAA/8gMlLu3mbu9PlivR612rZaF7CuXLpE23btUalU+NTzRVOgQavVmsrrN2xo+r9KpUShUADQsHEjCvLz0el06HQ6nJydq7xu+Gf6UmLkFeKvXOT3zz7i8Na/T5LuxFDLuG0a+AVwLaFMDFERNGtt2DYNA4K4bowhMTIcv9aGkXK/Nu1JijaMuMglJZw9uI+OfQeavsPJ1c2U0CmUpW3KGgnRUfg1b4lSpcKjTl2KNIXodKXbyd7REbsyF80AXNxqWRwbqislLoZGAYGG/YxnHbRFlvuZhIgrBLQ17GcC23Yk0Xg88KhruECmUCqQJBskSSIlNhq1nR3rfviKFf9eQmJUxD3HVxn9jcx/7LvLUilssFcrSb+Vi764hLCkNALreVksU1xSOuriaKsm7rohvoSMbJzsDPt8Z3s7buXf24XgshQ2EiqFDbc1hZTIMilZt/B2s+yzsiyTX6St5BuqrvR8xnA88K3gfCYlOtLUt+v7BZJx1dCvzNusTqvFw6eexfvFxXpUalucXO/9or3wcBMJXBVIBp9KknRZkqRLkiQ9Z1bsIknSJkmSwiVJWipJ0j3XbWF+Hrb2DqbXtvYO5a5+FhbkY+tQehX1TqLWtHU7Jr3zL0bNX0xudhaRZ0/eUyyOtmo02tKDkkarw9Gu4hNpZztbejdvwpHIhArLq0NTti4cKqiL/HzsKqgLgLBjIaz84A1SYqJMO7xqx5KXZzH6YufgQEFeXoXl9g6OFOTlAnD+SAgtO3Wp0ol2VTnb2VJgdqAp0GpxLnOl8w5XBzueaBPAvrBoAFKybtGmkQ8AbRr6lLtCag1Nfh52DubbyRFNfq7FMoX5ediZXfkvsfLiwuVTx2jRuXuVYjEfYSi7nQrMtpOd2XYC2PrrCj5d8CIubrUs2l1xcTGRoedo3rGT1XHciUVdpi+XveJq6Muly5gugOTcQpIkRsx9jToNm3Bu306Lz0WdPYl/x8eqHI/5umztHdDkWd+fwJDg5d++RX0/y5GNqjK0GbPtVEEsmvx8i3ZlzQWpPWt+5fs3X8HJ1a3CE/i/k5NzG2cXF9NrRycncm7fLrfc+bNnyMzMpF0Hw0n6k4OHMHnsczz71HDatGuHh6dnldcN/0xfunE1hYaBLRi38C2yrl0l7vLFu8eQV1EMZbdNHvYVtJPC/HxT/7NzcEBj7F8Xjx8msH2nCveD2qJCgjeuo+uTQ+8al7mC3FwczPbH9o4OFORWOmHmvjPUkdnxoMxxusBsP2TnaLkPAji0dROtu3ZHqVKRc/Mm6clJjH5+HqNnv8TmlUv/T0wnc3GwJ6+w9LiUV1iEi335Cwz+3p58N30ULw/uzdk4wwWjc/EpDOvYkhXPj2FYhxbsOB9+X2OzV6ko0ulNr4t0euz/wWM0GPttueNB2f1v3l3OZw7x84dvkhoThYd36fnM/j9X8dM7r1pcEHkUlcjyQ/XvYSXugauakUBboA3gAZyRJOnOvJDOQHMgCdhtXPYv8w9LkjQLmAUw7uVF9BjyVIUruXBoPzGhZ3Hz9EJbWHq1qUijsejQYDjxNL8iL9nYmN6/w79DZ5IiLhPUuVuV/+BeQU1o16geN3LysTObMmmvVlkkCqZ4VEqmP96FP46FkldYVOX1lXU+eB9R50/j5lWHojJ1YV+2LhwrrguA1t370Lp7H07t2c7pvTvuaX64vZOTxfS3woICHJycLMvz88GztEyn1XLh2GGmvv7OP3JVdWDrALr4NSD9Vi4OZvfbOajVFW4He7WK+YN78dOBU+RoCgFYfeQcU3p3YlDbIK7fzuVmXkG5z1Xm7MG9RJ0/TS2vOhRpzLdTQbmphnaOThQWlH63jc3fX+soLMgnIzWZBv7WJwv2jk5ozNZTdjs5ODmhKcinFlBYJs7hk6czZMIUfv/qU6LDLhDY1jCSEHPpIg38/LG1s+7gGHb4ALEXz+Hm4YXWrG1qCzUWF17gTl8urTsbs77cMKgVAA2CWnJkQ+n9KkUFBWRdS6VeU3+r4jln7E+1PMtuJ41FIgCG/mS+ncz7U0ZqMoc2reOZuQuqNeoGhjYTee5OmyldT5FGU67N2Ds6UlhJ367ME+MmM+C5CWz44UviroTRrJX19wcCuLi4kptbmjDl5+Xh4upqsUxsdDQ/fP01n371NZIkkZ+fz/JlS/lz0xbsHRxY9MrLXLl8iRYtW1m93n+yL9k5ONK0RWskSaJxi1bcSE2hqXHWgrkzB/YQYdw25t9viMGyndg7OlW4bQztJx/wNMWu12m5fPIY415ZTHJMlMX3FOv1bFz6Dd0HD8fTx/euf4c5B2dnCsxOfjX5BTg4V216870odzwos50cHEvLiwoKsDfbB4UePcT11GSefWG+adkGfgHY2TtgZ++Ag5Mz+bk5OLlYtrtHxdOdW9G7eTOuZt+ymMrvZKcmV1P+uBSddoMXV/xFYD0vXh7cm+d/Ws/sAd1YcfAkRyLiebylHzP6PcZXOw+X+2xVtWtUjwBvT27layymTNqqlGjMErr76XzIPqLPn8HN03Kfp9UUVLD/tWxXluczvWndvTen9u7g9L6d9Bk5BoD+z03i8VHj2fLj1ySEXzLNSBL+bxIjcFXTA/hDluViWZavA4eAO5fiT8uyHC/LcjHwh3FZC7Is/yjLckdZljtWlrwBtO3dn9HzFzNg/DSUajU52VkUF+u5FhdDXbObowF8/QJIvBIGwLX4GDzr1QewOOimREVQy6t69zMdjojnq11HWHPsPFp9MbUc7bGRJJrWcSfxxk2LZVUKG2b2e4y9YVEklSmrrvZ9BzB24VsMmjgDlUpNTnYmxcV6UmOj8W5keX9HA79A4o11cTUuGi9fQ13ozabT2Do4oFRXfWTJXP1mfiRGRVKs13Mr8wZqOzuLq8lNgpoTdfE8AFEXz9M4sAU3b2SgKSjg18/+xa4/fiMm7AKng/ffUxzm9oZF8eGGffx04CRFej3uzg4obCQCfLyIM06LvEOlULBgaG82n7lsmqYCkJ1XwP92HOJfm/Zjq1RyOi6l7Goq1fHxgYx/9W0GT5qJUq3mdlYmxfpKtpN/IPHGK/+pcdF4+Tb42++POHuKwPadrTpxN62nmR9J0RGVbqfGgS2IumDcThfO0ySoOWCYlgKgUChQ29qhNkuIL1Rx+mTrXv0Y+dIiHh87BaXaltw7fTk+hjoNG1ss69PMn6SISwCkJcTiYTyJrdcsgIzkRMDwUCNXz9KpRzEXztC0TXur66VD3wGMW/gWgybNQKUu7U9XY6PxKbOd6vuX9qdUs/50M+M6u35dzvCZL+LgVL3pgWBoMxNee5shk2eiMmszKbFReDcu02YCgoi7ZGwzsdF41b97m7nT520UClS2thYPEbJWi1atCAsNRa/TkZ6Whr2DvUVbSElO5uMl7/HBJ//BzTjN1sZGQqVUYe/ggEKhwMXFhdycnCqt95/sSw0CgkgzTsNPT0qgllfF06Q79XuCSYveYdiUWahsbUu3TUwUPmW3jX8QsZcuGOokNpo6xhgaBpS+H3vpAg0Dgrh54waFBQWs/fpTDvy1hvjLYYQeDkYuKWHz8u8JaNeRwPZVG91u7B9AbEQ4xXo92TcysLW3Q2XlNOv7oX5TP5KizY4Htpb7mUaBzYm6GApA1MVQGgca9jMR584QduIIo+a8ZEq8fZv6kZl+jeLiYoo0GvJzciwuOj1qNp2+xPxfNvHp1mAKdXq8XJ1Q2NjQqoEPEWUeBKJWKkz/zyvUmkbFJOB2geEi4618TYUjd9URmniVtScusDssCl1xCc72tthIEr61XUm7VbU+a632fQYwZsGbPDlxOkq1ben5TFw03g3L7H/9Akgwnc/E4FXP0K/Mz2fs7B1M+zaLfZ7aFmU19nnCo0X6vzA8/yAYH2KyHAiTZXml8b3fgPVADvC+LMu9je9PA1rJsvxKZd/3w77jVlX89aQEQoxPrmvZrRctuhpOHnf9soxBU2YDcGrXVhIjLqFQKBkwfiquHl4c27qB5KhwbBQ21PKqS7+xkyu93+tKSnqF75fVwN2NZ4xPoTwencjJmCQAJvfuyK+HztKvpR8DWvuTdtOw84u8lsGei1E0rePOoHZBNPGqTXxGNkci4rmYdK3CdbRrXPmV17SkeA7+uRoZ2TiiZnga2rYV3zNsuuGJjsd3bCbhShg2SiWDJs7AzdOL4zs2kxR5BTBc1Ro0aQZ2Do5EnjvFxSPBXI2LoV5TP3oMG2kxklHb2aF8EEZnQg5wJuQAEjBs0jRsbBTEXL5I76Ej0GmL+OvH77idnY1r7dqMmjXX4gQyLvwyoccOm55CueGnH0iKMZwA1PGtz6QFFT/Bb92x0ErjMdfEqzaTehueQhl8JZaQcMP9RC8+0YPv9hxlSPvmPNWxBSlZtwC4lJzG5jOX6RbQiMdb+AFwJDKeQ+Fxla2CJ9pWPhKWlhjPvj9/Axna9OhjemrdluXf89QMw998dPsm4q+EoVAoGTx5JrU8vci6nsbe338hPTkBDx9fWnTuRvs+/QFY/emHDBw7xZREmHNxqPyAfvbQne0kMXTiVGwUCmIvhdFr6FPG7fQ9OdlZuNR2Z9SsF1Gp1az+6lMKcnMpKS6mYUAgg4wPEigq1PDVGwtZ+Nk3pvudyrqaVX6K3R3XkxM5svEPkGWad+1J88cMTx3bu+pHBk6aBcCZPdtIiriMQqHg8bFTcfXwpFiv4+DaX8m7mY2NQkH/CTNwNF6R3/j1J/QaNcGU7JVV2TRnMGynA+tWI8sybXpU3J+OGfuTQqFk0CRDf/rr28/JSr+Gc63aAHQZOISmrdpyLngfEWdOkpV+jTr1G/LEhKkWTxJU3CXJTEuMZ9/aVchA2x59aNOjDwBbfvqOp2a+CBjbzOWL2CiVDJk8y00JL/EAACAASURBVNBm0tPYs+Zn0pMS8PCpT4su3ejQpz8bfvgSTV4eJcV6fJsF8PgoyxH3YR2texDOts2b2LppE5Ik8cpri1AoFZw+eZIJk6ewcN5ckhIT8TImQeMnT6Z7z16s/2MNu3ZsR6lUUb9BA958b0ml7QVg65lLd6+X+9iXbmdlsnPVcor1Omp51WXwxOnlkn9lmfvPriXGs+ePVSDLtOvZl7Y9+wCw6cdveXrWXAAOb9tI3GVDDMOmGp4gqdNq2fbzMnJuZuNSqzbDp822uH81MTKcSyePMmzKLMLPnmLryqWmpzd61avPk+On4Otu3X08x/bv4ei+PUiSxLMzZqOwURB+MZQnnh7F9auprFn2Hclxsfg0aEjnXn3oPWgo544d4fCencRFRtA0MIjh4ybS1JhcVeRGTuXTMs8dOsjZQweQJIkh46dio7Ah9nIYPYcY9jMbl/9g2s+MND7t9oOZE/Dwrme6V/HZOfNwqe1O6LFDnDm4j5LiYnoNHUHzjl3Kra/twtesqpe78Vo0H7tWzZFUKrTxiaS9+X61v2tm/2F/u0yAjxdzn+yJJMGO8+HsCjXMRHlr5AA+3riPx/waMrZHe0qM98J9t+cosemZNPKszcJhfSgukVHY2PC/7cEkZGRXup7BHar2MCeAuq7OPN7SDwkIS77GJeM50dB2QWw3xjmyUys8nB3RFRdzNfs2ey9F/+331nas/BwiPSmeA+t+B2Rad+9Nq26Gvr195Q8MnfY8AMd3bibhyiUUSiVPTphuOJ/ZuZlk432Udo6OPDnRcD6zednXFObnUVxcjG8zf3o//Vy5dc54vEv1pks8YJ9tD3moEpNXh/Z5KOtNJHBWMiZwk4DZwGCgNnAW6AIEArsonUK5C/hRluVK7xK3NoF7EKxN4B6EuyVwD9rdEriaYG0C9yDcLYF70O6WwD1od0vgasLdErgH7W4J3INmbQL3INwtgasJZRO4mmRtAvcg3C2Be9DuRwJ3P1mTwD0o1Ung/il3S+BqgkjgqudhTeDEPXBWkCRJCRQBm4CuwEVABhbJspwuSVIgcAL4BGgFHDYuKwiCIAiCIAiCFR7mB4c8TEQCZ50WQJxsGK58zfjPRJblECDkwYclCIIgCIIgCML/Tx6euRIPKUmS5mB4KMnbNR2LIAiCIAiCIAj/fxMjcH9DluWlwNKajkMQBEEQBEEQ/i8Tz+awjhiBEwRBEARBEARBeESIBE4QBEEQBEEQBOERIaZQCoIgCIIgCIJQ4+78FqBwd2IEThAEQRAEQRAE4REhEjhBEARBEARBEIRHhJhCKQiCIAiCIAhCjRNPobSOGIETBEEQBEEQBEF4RIgEThAEQRAEQRAE4REhplAKgiAIgiAIglDjSsQUSquIEThBEARBEARBEIRHhBiBqyFN63jUdAgmXQMa13QIJimZt2o6BJOLiak1HYKF72aOrukQTM7GJtd0CCZZefk1HYJJgI9XTYdgoVCnr+kQTJzs1DUdgsmH63fXdAgm43p2rOkQLESnZdR0CCanYhJrOgST1g19ajoEk5n9h9V0CBZ+2r+tpkMwufrc4JoOwSQ27UZNhyD8HyYSOEEQBEEQBEEQapyYQGkdMYVSEARBEARBEAThESESOEEQBEEQBEEQhEeEmEIpCIIgCIIgCEKNEz/kbR0xAicIgiAIgiAIgvCIEAmcIAiCIAiCIAjCI0JMoRQEQRAEQRAEocaJH/K2jhiBEwRBEARBEARBeESIEThBEARBEARBEGqceIiJdcQInCAIgiAIgiAIwiNCJHCCIAiCIAiCIAiPCDGFUhAEQRAEQRCEGiceYmIdkcA9hE4G7+f4/j0gSYyeNov6TZqZynRaLWt++JrszBvU9vBk3PPzUKnVrPrmf9zMvAHA1aQEJs5dQKuOnTm6bxcnD+5HoVQy+NlxBLRqU+24Qvbs4sDO7YDE1LnzaOLnbyqLunKZn778nPSrqXz16++4e3oBUFRYyM/ffc2N9HRKSopZuOQjnJydqx3DHWcOHeDkgb1IksSIKTPxbdzUVKbTaln347fcyryBm4cnz86ai0qtRltUxOZffyI74zolJSVMWfAGDk5O9xxLXTdnnmgTCEBoQiphyWkW5WqlgnHd2+Ph7Mjui5FcTkkHoKtfQwLr1aFElkm/lcOei1H3HMuOrVvYvGEDkiSx8PXFBAQFmcpW//IzwQf2o1AoCQgMZMHri5EkiR1bt/DX2rUolUpatWnDvIWv3nMcd1SnLV8JPcvOP39HqVJT28OTCXNfQaFQWL3O0KMhnAs5gCRJDBo/FZ9GjUvXqdOydeUybmdl4uruwfBps1Gp1NzMzGDLimUU63X4tWlHr6FPU5CXx5/ffm76bEpsNK99tQx7RydiL13k2K6tyLKMf9sOdHtiyD9SF/9Uvz5z6CCnDu4FSWLE5Bnl+s/6H7/jVtYN3Nw9GT3rRVRqNWuXfk1aUiJ2Dg44Obswcf4iANYt+4bIi6EEte3A6FkvVimOO04c3MfRfbsBiedmzKFBU8u6+e27r0x1M/HFl1Gp1cRHRbDhl+XYKBS06tiFgSOeAeDtOdOobdz/BLVpy6BRY6oVU1m+tV0Z2aW1Id7oJM7EJVuUB/h48WTbQPTFJWj1en4/eo6CIt19WTfA4X27Cd61AySJSc/PpXGz0v1vdPgVVn7zBdevpvLZit9w9/QEYNnn/yE5Ph57R0dcXF2Z99Z7VV7vhaOHOH/oAEgSg8ZPwbthaX/S67Rs/fnH0v40dRZKlZpbmTfYunIper0ev9bt6Dl0BABfL5qHq7sHAE2at6LnsKcBiL18keO7thn6U5v2dLWiP1WmjqsT/Vv5IyFxIemqaX97h1qh4NmubXF3dmDfpWjCU69Xe113nAo5wIn9e5AkiZFTZ1G/iWV/Wrv0G25mZlLLw4Mxc14yHY82/vwjWRkZlJSUMP1Vw/Eo83o6m35ejraoEFd3dybMfaXacfl5ezJvUC8kCbafu8LuC5EW5b7ubiwe0Q99cQkKGxu+3BFC3PUsvGu5sPj/sXfncVFV/+PHX4eZYRs2ZRFBBRVB3PdKzbXFSnPJNs0tTcs028v8VFr2qT7tu7aZpalpueQu7pg7IKIgKgLuCogyCzAw9/fHjMMMoAxIgr/veT4ePhzm3Ln3zZl77r3ve849DOqLolieR/rv0g1cvKKvchwVCfnkPdwiI8hdsoxLcxf8a9u56p+NG9i+fg1CCB576hmH483xlMPM+/YrLpw9zczvfqJOgKUtbVm9ko0rl6GYzcyc9fMNx3A+M52YRb8BCm2796bVHXeWWWbXmhUcTzqAWq2m38hx+PoHkrx3F/FbNyCEC67u7vR/ciJuHh6YCgvYuGgel7MvopjNDJowBXet9objlGqvWpnACSH8gGGKonxbwXI6RVGqdAUuhEgHOimKklWVz/9bDDodW9f8zUvvfUxuTja/ffUpL8z8n61895aN1AttwKgpL7Nm8QJ2b9lI93vuY+TkFwEoMpl4d8rTNG/TjrzLuezYsJaX//sJJlMhX06fxsv//RiXSlwMX6XLy2PNsr9478tvycnK4usP3+Odz7+2lTcMD2fml9/w4X+mOnxuyW9zuaNnb9p26lzFGinLoNMRu3YVk9/9kMs5OSz49nMmTX/fVr536yaCQkIZPulF1v+5iL1bN9H17n6s/3MhbW/vRlSb9tUWC8C9bZuzfO9BrhgLGNO7C6lnL5JvKrKVm4rNLN51gI5NGjh8LuXMRXYezQBgSJfWhAfWJf1iTpXjuHLlCn8s+J0ff53HxQvnmfGfacyeM9dW3rN3H54YPQaAaa++wr49e+h82238OGsW85f8iaenJxPHjSU9LY3wJk2qHMdVVd2XVy2cz7iXp1I3MIjfvv6MlMR4Wrbv5NQ2jXoduzesZdybM8m7lMNfP3zD2Ddm2MoTYrcSUD+EhyZMZsvyP0mI3Urn3ncTs3gBvQcPJSwymrkfzSS6YxcC64cy5nXLBe+ptGNsXroYD60X+rwr7N64luEvTkWtdu4QWpvatUGnY8e6VUx65wOuWNvPs3btZ9+2TQSGhDJs0gts+GsR+7Zt4o67+gEwaNQ4Gjdv4bC+ex8eRsc7exMXu9Wp7Zem1+WxedXfvPrBJ+TmZPPLF5/w8n8/spXv3BxDcGgDnnzhFVb98Ts7N8fQ4977+ePH2Yx/9Q3qBgbxzcy3advlduqFhOLi4sKL735QpViuZ8htbZi3fT+XDUaev78nSSfPYiwsSdDOX87j67WxFJvNdItqTM/oCNYkJFfLtvV5eaxbvpQZn31NTnYWsz76gLc++cJW3iAsjOmffsXHb08r89mRz0wiqlXrKm3XqNexJ2YtY//zLlcu5bDsh28Z88Z0W3lC7FYCgkMYMn4SW1dY2lOn3nezcckCeg56mLDI5vz20XtEd+xMQP1QhIsLo157y2Ebhrwr7I1Zx/AXXkflZHu6nrtaR7Iy7jB5xgJG3NmJo+eyKLA/HpvNLN17kHbhoTe8LbC0p+1rVvL8e//jck4O8776jCl2+9+eLRsJCmnAiOdeYu2ShezZspFu99zH2sULaXdHd5q3dTwf/fnz9zz29CR869S94dieu68H7/21nqw8Pd+OG0psygl0+QW28rOXLjPppz8BaN84lBE9OjN98VoGdW7N6rhk1h1IoV+75gzu0obvY3becDzXcuGDz/Do1B51UMC/to2r9Lo8Nq1azusffkZuTjY/f/4Rr75fcqMupGEYr334Kd/MdLzZ0aFrN+68px9vTxpfLXHELPqNB8Y8jbdfHeb9bwYRbTo4JFzZ586QceQww195k5NHU9i29A8GjHuWyPadiO58OwCxf//Jod076NDrLv5ZtYyojl1o3KJqbV269dTWZ+D8gIk1HURNSD96hKbNW6LWaAioF0xBfj4mU8lFwtFDB2nZ0ZIMterUhWOHkxw+nxS3l6jWbdG4upJ94QLBDRqhUqtx9/DEzc2NrPOOdyOddSzlMNGt2qDWaAiqX598oxFTYaGt3FPrhbuHZ5nPJcXv58C+Pcx4aQp/zJ1TpW2XlnkslcbNW6BWa/APqkdhvpEiuzpKS06iRXtLHbXs0JkTKYcsv0NSIkcOxPPtO9NYt7h67vKpXAQalYpcQz5mReFkVi4hdXwcllEUBX1BYZnPXtIbbK+LzcoNDxs4dPAg7dp3QKPREBLaAKPBSKHdd9QwLMz2WqNR23q1whqHY9DrMZlMmEymaukhharvy/UbNsKg16MoCvkGA94+vk5v81TaMcIim6NWq6kTGERhfr7DvpGecpjIth0AiGrXgYwjlgvsc5kZhEVaeisj27S3vX9V4s5Y2tzR3RL3gXg8tF4s+OIjfvvkfS6cPvmv1cVV1dmuM4+n0jgqGrVaQ92gehQUONbR8cOHaNHBkjC36NCZtJTDtrK/583h2xlvkLAz1vaeb11/p7ddnvTUI0S0sK8bo0PdpCYdpHWnLgC06XQbR611YzTobT1tjSKakZqUCFja22dvvc5X77zJyRPHbyi2q1QuLriq1eToDBSbFdLOZ9MowM9hmVy9kWKzGYAisxmzYq6WbQMcO5JMVKvWluNvcH3y88s7/nqU+9n5P3zHOy9PYdfWzZXe7um0YzSKbI7qansqta9kHEmmmTUBiWzbgcxUSw+PpT1ZRiU0a9uejCPWnh9FYe7/3mX+p+9zLjMdgKOJlva08MuPmP+pc+3pWq4ejy9bj8encnKp7+fc8biqMo6l0sTufFRQ6phz7HBSSdvu2IXjyZb2dDTpAMkJcXw1fRpr/vgdgJyLFygsKGDpnB/5avo0Duz6p8pxaVQueLiqOZebR1GxmcSMszQPDXJYpthccs7Rurly/LzlfvaJCzl4ubsC4O3hTq7eWOU4nFF08ebdRz+ReoRmLVqVHG+M+ZhMJfuDh1Zbblvy8atTLTcYwHJDzlRYgF9AICq1mgYRUZzNSHNY5mRqCk1btwOgYbPmXDht6fG3j8FUWEhAiOVGREbKIU4cTmThp/8l9u+/qiXOmmLp+a09/2qr2prAfQA0FUIkCCE+EkK8IoTYK4RIFELMKO8D5S0jhAgXQqQIIeZa318ihLDPMiYLIeKEEAeFEM2tn6krhFhmXX6XEKKN9f3pQoifhRBbhBBpQojn7Lb9hBBijzXe2UKIyndxWRl0eQ7D+jw8PTHo8hzLtV7WMi16uzKAvdu20OnOXgAEBtfndHoaRoOB3OxsTmekl1neWbq8PLTeJXF5ar3Q5VW8rsz0E7Rs1563Pv6cUxnpJOzdXaXt2zPo8/Cwu1Pl7qEtU0ceXpZyd21JHZ09mUFEy9Y88+ZMzp8+SUpC3A3H4uHqSoHdyTrfZMLD1bVS6wgLqIOXuyuZWZduKJYrVy7j7VNysaL18uLK5ctllovbt5esrCzad+wIQL/7H2DU44/yyMAHadu+PQHW4Vc3qqr7cpeeffjuvbeZOeUZVGoVjZo2c3qbRp3O4S6mu6cnRr2upFxfUu7uqcWos5Qpdhfb9u8DFBcXk3ogjuYdLBdhebmXyDl/jsenvMLdDw/j719++Nfq4qrqbNdGnQ4PrV0sHqVi0efZyj08tRis7XzAsNE8N/MjRr80lc0r/iK7ijeDStPrdLbfvfQ2wbHuPLRa9NYyLx8fTp1Io8hkIuVAgu13eO3DT3nhnQ8YPGIMcz7/uFpi1Lq5OvS2GQtNeLqV38693N24s3kTdhw5US3bBksPnNar5MaKp6cWnRPf+bBxT/POF9/y4tvv8vcfC7lw9kyltmvU63H3LGlPbh4eZdrT1X3F3VOLobz25OGJUW+Jdex/3mXUq2/Sd+gwln5vGcGRl3uJnAvneOy5V+g7dBgr5/5YqRjteWg0Dr1tBaYiPFw1VV6fMwy6PMf25Onp0B4NOp3tfOWh1aLXXQHgTGYGka3aMOntmZw7dZLkhDguX8rh9Ik0Bo0ey1OvTmPN4gW2Oq0sH08PdPkliYkuvwAfD/cyy0XWD+SbsUOZcn9P9h23JM/7004yoFMrfnrmMQZ0bMmquMNlPnerMuTlOR5vtJ4Y8qpWx1WVr9c53PB28/AkX68ru4xd21PMJW0qccdW5rz7BqeOHiGgviWByzpzirCoFjz6wlSyz53mxKHEf/m3kGparRxCCbwOtFIUpZ0Q4h5gKNAFEMAKIUQPRVG2XV3Yukyz0ssAmUAUMFZRlB1CiJ+x9OxdPatnKYrSQQgxEXgZGAfMAOIVRRkkhOgD/Aq0sy7fHOgNeANHhBDfARHAo0A3RVFMQohvgeHWz1Wap5c3Rn3JWHOjwYCn/Ynbrjy/VJlBr+NMRjoRLVoBoPX25v5HhvH9B+/g7edHaFh4lYdleHl7o7c7kRj0Oqd6ary8vWnXuQtCCNp26kxGWhrtOt9WpRiu8tR6k29XR/lGffl1FAj5Bj2eWm/b+1FtOyCEIKpNe85mptO8XYcqxdCpSUOiQ4PI0Rtx05Q0IzeN2uFCryJBPl70bhnBop0JVYrDno+PL3l2F756nQ4fX8feq2OpqXz35Zd89MWXCCHQ6/X8OHsWi5Yux8PTk1dfmMKhpIO0rOKQK3tV3ZcXzv6Gl9//hDoBgSz8/hvid8bS3tr7VREPLy/yT5T0bOYbDI4XV1ov8g2W8nyjAQ9rYiBEyb2sfKPjZ44nJdKwaTPc3N1t62gc3Qq1Wk1wozD0eVf+tbqA6m/XHl5eGA12sRhLxaItaT+WOC11obXeHPD08qZZ67acyUzHv16w09u9Fq2XFwaHutHj6e1YNwa9Dn/qYTTo0VrjGf7Mc/z5y48oQEBwML51LD2BXtYe2waNm+Dq5o5e55j8VEb35o1pGxZK1hUd7nbt3N1VU+7zbW4aNWN6deaPnQkOF883Suvt7XAhbzDo8XLid/K2tn8vbx9adehARloaQfVDnN6uh1ZLgd2+UmA0ltOe9EAgBUaDLVG5Vnvy9LbsQ8GNwtC4udsSwPDolqis7cngRHsqrUN4KFEhQVwqfTxWq8mvxPG4Kjy9vB3bk8HgmGzbtTej3flI6+VN83aW81Hztu05k5FOq85dqN8oDD9rr3ZoWGMunjtDmN3zjhUZ3KU1PVtEcDonF63dTQYvd1fyjAVllk89e5Fnf1pC89Agptzfk2d+WMyEu7vy06ZdbE9Oo0+rZozreztfrN5W5rO3Ik9vy/HkKqPegKf3jT8L74y4LRtIjduLX2A9Cowl56lCo6HM82rutrZlIVxK2lSbbj1p060nu9evYs+G1fQa8hjunloat2iDEILG0a25cPokjVu2+fd/KanG1NYeOHv3WP/FA3FYkqjSt+Svt8xJRVF2WF/PA+yvBK/2M+8Hwq2vuwO/ASiKsgnwF0JcvQpepShKgfW5uQtAPaAv0BHYK4RIsP5c7gNEQojxQoh9Qoh9q5csKveXDW8WxfGUwxQXFZFz8QJu7u5oNCV3ECNatOJQ/D4ADsXvo5n1og4g/p9Y2t3eFRe7ht7u9m5MeecDHhozHo2bm23IUWVFNG/BkaSDFBUVkXXhPO4eHmic6Glq0aYdx49YJudISz1CcMiNP3fQKCKSE0eSKS4q4lLWRVzdPVDb1VGT6JakJOwHICVhP02iWwLQNLoVp9KOAXAy7RgBwfWrHMO+tJP8tn0/q+IOYyo24+PhjosQNPSvw5lLZXu9ylNH60H/ji1ZuudgpZK+a2nZujWJ8fEUmUycO3sWD08PXO2+o5OZmbw3/W3e+eBD/OrUAcDFRaBRa/Dw9ESlUuHj40PelcpfQJWnqvuyi4uL7Q6pl49vpXqXGjSJIPPoEYqLisjNzsLV3d1h3wiPiuZoYjxgGboVFmUZNhncsBGZRy376bHEBNv7AIk7t9uGTwKEN2/B2QxL78rl7Czcyhk6XF11AdXfrhs1jSTdrv24ubk71X6uJphFRSbSU1MIrEQicD3hkVEcTzlkVzceDnXTrGUrkvZb6iZp/z6atbTcXAhpFMbkt95l4tS3MOTl0bJDJ8swYOvQwtzsLIx6x969yopNOcE362JZtDMBU3ExfloPXISgSVDdMj3mGpULT/a+jQ0HU2+4N720iKhoUg8nlRx/3Z07/l696VZkMpF6+BD1GzSo4BOOQu3a0+XsLFxL7SthUdEcTbTcfDpq127qNWzEyWOpABw7eIBGkdEUmUwUWYeqXbmUQ77B0rsXFtWCcxnpAFzOycbNvfyhoNcTl36aBf/Es/ZACqbiYrw93HARglB/X87kVs/x7FrCIiI5YW3bl7Iu4lbqmNO0RSuS4yztKTluPxEtWtreP3ncej46bjkfBQbXx1RYQL7RQHFxMedPn6RuQOXO2Uv3HOT5X5by0YrN5JuKCPL1QuXiQutGISSfdpywxVVdMlhIl19o670UwGVDPmAZGlxez92tqnFkFMeS7Y7FHu5oNJUbNVNVHXrdzWMvvkG/EWNRu7pxJSeL4uIiTh1PpX5YU4dlGzaLsvWinT5+lKDQRgC2NgSW3u2rx4GGkdGcs56XzmWeoE4Vr/VqA0VRatW/2krUxuCEEOHASkVRWgkhPgFSFUWZXc5yOkVRvK61jHU9WxVFCbP+3AeYrCjKYPtJTIQQnYCPFUXpZU3ChiiKkmb9zEmgJfAioFMU5WPr+0lAf2AAEKIoiuPsHRVYn5h6zYrfuWkDOzdaZogbOuYpXFxUpCQmcNfAIRQWFDD/uy/Jzc7Czz+AJyZOsTXgz996nUfGPk1IWLhtXb9+9Sm52VloXN0YOuapci+6gvycuzu9ee1qNq1ZBQhGT5yESqUiMW4/Dz7yGGdOneSnLz/jxNFUGoY3plufu7hnwEAunj/H959+TKGpkPqhDRj/wssOF6KlnczKdSqWPZtj2L15A0IIBo4ch4vKhdSDB+g9YDCmwgIWzfqayzlZ+NYN4FHrrF85Fy+w5IdvMJlMBAbXZ+hTz143lgPpp5yKpb6fD/e0jQIgIf00BzIsw5QGdWrFsn2W53UeuaMdgT5aTEVmTmbnsiYhmUfvaIe/t5Y8o+VEufNoBsfOXftZgIn9ys5SVdrfy5ayYulShBC88MqrqNQq9uzaxROjRvPSc5PISE8nKKgeAMNHjaLbnT1YvOB31qxaiVqtoWGjRrzx9vQKZ33cdyzzuuVXVWVfjt8ZS8zyv9C4uuLhqWX0lJdxu8bzPQDZOsfZ0eK2bSZu2yaEEPQbNgoXlYq0Qwfpdt8ATIWFLP95FldysvGp68/AsU+j0biSc+E8K+bMprioiIjW7ej54BDAMovqd2++wuQPPneok9jVK0g9EIe5uJh7Hn2CRs0s37+/17Vn/brZ7RpwmEzH3p4tMezZHANCMHDkWFxcVBw9mEAva/v5Y/bXXM7JxreuP49MsLSfH96fQUFBPuaiIjp070n3fv0BWPvHfFIS4si7nEtQSAPGvDQVV/eyF3xXn6kpzz8b17MjZh0geGTsBFxULiQfSOCeQQ9RWFDAb998zqXsbOr4+zNy0gtoXF2JWbGUg/ssQ7LvHvgQrTp2JifrIrPefxdXd3cUczEPDhtZ7gydf+9LKvNeRRr6+zG4iyV53HU0gz3WNvDEnR2Zt30/vVpG0LdVM87lWm44HDlzgZiDqRWud9idzk3Qs3XdGrasWw1CMGLCs6hUKg7G76f/0Ec5e+okv3zzJSeOpdIgLJyuvfpyV/8H+XDaa+TnGykuKqJbn7u4d+CQCreTevaCw8/x2zcTv20zCEG/x0fa2lNXa3ta8fMsrlzKwadOXQaOfRq1xpVLF86z4pfvre2pLT0GDOFyTjaLvvwYjZsbitlM7yGP0th6c2DHmhUcPRBPcXExdz8y3NaeTmU7dz6wF+zrTd/WzRAIDmSc4eBJy6zA/Tu0YKV1KOBDXdoQ4K3FVFzMqZzLrE+seAbgNmHXvmGxa1MMuzZZZkUePHocLioVqYkJrb0mAgAAIABJREFU9HlwCIWFBSz49itbexo28Tnb+WjR7G8wmQoJDA7h0QmW89GRxATWLl5IcXERXXr1pfs995XZ3odLY5yqi6iQICb1uxMhYFXcYdbEW57tnTbkbt77awO3Nwvj8e4dMFufhftmXSzHzmURHliXlwb0otisoHJx4dOVmzlx4doTbP0Q87dT8VxL0KvP4966BUKjoTAtnbNvlPukjFNO//BdhcvsiFlH7AbLrKGPjJuAykXF4QPx3Dt4KOdPn+L32d+QefwYIY3C6NKjFz3v68/+HdvZtm41x1OSado8mgeHjaBpqQmdSjt29uI1y85lpLHxj/mAQptuPWndtScAK3/+jv5PPgPAP6uXceLQQVRqNf2eGItfYBD/rF5GpvW5ZHetln4jxuHuqeVydhbr5v9MsclEnaB63Dv8SYdeO4BxfW4TFVZOLTBt4epalZi899j9tbLeamsC5w/EKYoSZh0e+S7QV1EUnRAiFDApinLBLoErdxnAEzgBdFUUZacQ4gcgRVGUT66TwH0JXFQU5V0hRC/gM0VR2gshplN+AucJLMcyhPKCEKIu4K0oSsb1fsfrJXA3m7MJ3M3gbAJ3MzibwN0sziRwN4uzCdzNUDqBq0nXS+BqwrUSuJpwvQTuZqtKAvdvcTaBu1lKJ3A1qSoJ3L/legnczeZsAnez3GgCV52cSeBuluslcDVBJnBVU1sTuFr5DJyiKNlCiB3WJGkN8DuwUwgBoAOewDKE8ery64UQ0eUsUwwkA6OEELOBo0BFrXs6MEcIkQgYgFEVxHpYCPEfYL2wDPw3Ac8C103gJEmSJEmSJEkqIf+Qt3NqZQIHoCjKsFJvfVHOMl52r78ovYx1CKVZUZSny/lsuN3rfUAv6+scYGA5y08v9XMru9eLgPIfapMkSZIkSZIkSaomt8IkJpIkSZIkSZIkSRK1uAeuOiiKkg60qmg5SZIkSZIkSZJqlhxC6RzZAydJkiRJkiRJknSLkAmcJEmSJEmSJEnSLeL/6yGUkiRJkiRJkiTdGmrjnzerjWQPnCRJkiRJkiRJ0i1C9sBJkiRJkiRJklTjZA+cc2QPnCRJkiRJkiRJ0i1CJnCSJEmSJEmSJEm3CDmEUpIkSZIkSZKkGmeWIyidInvgJEmSJEmSJEmSbhEygZMkSZIkSZIkSbpFyCGUNaSgqLimQ7ApMBXVdAg2uvz8mg7B5rZm4TUdgoPY5LSaDsHGWFhY0yHYmIrNNR2CzfHzWTUdgoPoBsE1HYLNFUPtadt1vDxrOgSbY+cu1nQIDjQqVU2HYKN1c63pEGxy9caaDsHm/o4tazoEB6cfvb+mQ7AJfeqZmg7BxvTd1zUdwi1JzkLpHNkDJ0mSJEmSJEmSdIuQCZwkSZIkSZIkSVI1EkLUFUJsEEIctf5fp5xl2gkhdgohDgkhEoUQjzqzbpnASZIkSZIkSZJU4xRFqVX/btDrwEZFUZoBG60/l2YARiqK0hLoB3wuhPCraMUygZMkSZIkSZIkSapeA4G51tdzgUGlF1AUJVVRlKPW12eAC0BgRSuWCZwkSZIkSZIkSVL1qqcoylkA6/9B11tYCNEFcAWOV7RiOQulJEmSJEmSJEk1zlzLZqEUQowHxtu99b2iKN/blccA5U35PK2S26kP/AaMUhSlwum1ZQInSZIkSZIkSZJUijVZ+/465Xddq0wIcV4IUV9RlLPWBO3CNZbzAVYB/1EUZZczcckhlJIkSZIkSZIkSdVrBTDK+noUsLz0AkIIV2Ap8KuiKIudXbFM4CRJkiRJkiRJqnE1PetkNc9C+QFwtxDiKHC39WeEEJ2EED9al3kE6AGMFkIkWP+1q2jFcgilJEmSJEmSJElSNVIUJRvoW877+4Bx1tfzgHmVXbfsgZMkSZIkSZIkSbpFyB44SZIkSZIkSZJqnLl2TUJZa8keOEmSJEmSJEmSpFuE7IGrhfZu3ciujesRQjBo9FM0aNzUVmYqLOSP778mN+sifgGBPDJ+EhpXVxZ+9wVnMtJx9/TEy8eXkc+/CkD2+XMs+/VHCvML8PP35/GJz1c5ru0b1rF57SoEghHPTCI8opmt7OjhQ8z56jPOnznNRz/9St0Axz8i/96rLxIcEsrY51+q1DbjY7ewf8tGhBDcN3wMIeGNS+rCVMiKn2dzOTsLX/8AHnxyAhqNK5eyLrD8p9kUF5lo1rY9PfoPBmDf5hjiYzejUqnpNWgoTVq0JuvcGZb9+B0qtRpzcTEPjBhLcKOwCuPatTmGHRvWIoTg4bETaNgkoiSuwkLmf/sFl7IuUicgkOETp6BxdeVQ3D5WLZqPRqOhTkAgIya/yJXcS8z94mMUsxlFUXhozHjC7Oq1svZu3cTuTetBCAaNGldm31n8/TfkZl/Ezz+Qh8c/a9l3Zn3J2av7jrcPI6z7TlXFb9/Cvq0bAXjgiTGEhDdxiGH5z7PIzcnGr64/A598Go2rK3s2rmPn+jUoipnn//elbflf/vcu5mIzhQX5dO3Xnza3d6tULGczTrD+97koKLTv0Ye23XqWWWbHqmUcS0xApVHTf/QE/AICKTIVsvKX77mSk41PXX/6jx6PWuPKqeOpxPwxHxcXFc3atueOfgOcjuXCyXQ2L54PikKrbr1oeXv3MsvsWbeSE4cOoFKruXv4WHz9A2xlO1ctJWXvLsZM/xCA44nx7Fm3EpVaRZvuvWne+Y5K1c1V/2zcwPb1axBC8NhTz9Coacm+fDzlMPO+/YoLZ08z87ufqGNt11tWr2TjymUoZjMzZ/1cpe3aq+791qDL44Pnn2Hg6HF07N6rynEF+3lzX9vmIARxJ05xIOOMQ7mrWsUT3TsS4K1lTUIKB0+eBaBlg2A6N22IokBBURF/7UmksKjYqW1W5zEv58J51v4+l8KCAnzq1mXIU89y7OABNi9bjFqjQePqxpDxz+Lp5V2peonbvoV9W2IQCB4YUbaNL/t5li3GQdY2vvDrT7lyKQfFbKZzn3vocGevSm2ztAsnM9i25HcURaFVtx5E31a2Pe1dv4qMQ4mo1Gr6DhuDj7U97d+wmswjh1DMCp37DaBhZLTtM39+8SF+gfXoO2z0dbf/b5+b0g4nsXXFnwAUGI0IIZgw/f0q11c9Xy/6topEAAcyzpB06pxDuatKxcO3t8Xf25OYg0c5fPp8lbdVntpwnHFGyCfv4RYZQe6SZVyau+Bf205tvYaQbh2V6oETQhTbzZCSIIQIr44ghBD/VMd6qpsQopcQYuXN3KZBpyN27SqeeWsmjz/7Asvm/uhQvnfrJoJCQnl2+vsE1g9l79ZNtrLBo59i4lvv2ZI3gKW/fM/D4ybyzJvv3lDyps/LY/2KpbzxwSc8/crr/Dbra4fy0LAw3vr0K5o2jy7z2fjdu/Dw9Kz0No16Hbs3rGX0628zZPwk1vz+i0N5QuxWAuqH8OQbM/APDiEhdisAMYsX0HvwUMZOe4cTyYe4ePY0uiuX2bclhienzmD4C6+zYfECzGYzdQLrMXbaO4x5/W16D36EbX//VWFcBp2OLav/ZsqM9xn53Ess/tnxz4Ps2hxDvdAGvDDzf9QLbcCuzTEArFw4j3EvT+WFmf9DpVaTciAed3cPxr70Oi/M/B+PPz2Zv375odL1ZB/XjnWrePrNdxk28XmWl9p39m3bRGBIKBPf/i+BISHs21ay7wwaNY5n3px5w8mbUa9jV8waxrz+NkMnTGb1/F8cyi3fWSjj3piBf/2S76xFp9uY9N7HZdb3xItTeXLq24x+7U1illT+ZLr+97kMHDeREa+8yd6N6zDqdQ7lWWdPk55yiFFTp9PjwYfY/KdlGwd2bMM/OISRr72Nf3AIB3Zss6xvwa8MHj+Zka+9RcaRZLLPnXU6ls2L59Nv5HiGTnmdhC0byDfoHcpzzp3lZGoyj744jdvvH8SO5SWzCeuvXObShZILKsVsJnbZHzw0+WUeeu5VErZuJN9gqHT96HV5bFq1nJdmfsiTL7zCwh+/cygPaRjGax9+SpPI5g7vd+jajelfzqr09srzb+y3m5b/SXiUY8xVcV/b5izdm8TcrXvp0rQR7hrHe56mYjOLdiaw+1imw/vJp8/zy9a9zN22l3O5V2jTKMSp7VXnMQ9g9bw5DBj9FKNfe5MhTz0LQEBIKGNef5sxr79NZLsO7Fq/ulJ1YtTr2LVhDU9Onc7Qpyezat4ch/L42C0E1A9h3LR3CAgOIT52CwB3D32c8W/O5Mmp09m64k9MhYWV2m5p25b8zt0jxzHkuVc5sHVj2fZ0/iynUpMZ+sJUutz3IP/8vQSA9MMHKcg3MnjSKwx57lWH5O1E0gFc3T2cqoN/+9zUpEUr2/fU6rautOh8+w3VV99WkayKO8yCf+Lp2KQBbqX3ZbOZZfuS2J926oa2U57acJxx1oUPPiPr2x8rXvAG1NZriNrCrJhr1b/aqrJDKI2KorSz+5fuzIeEENft6VMUpWsl4/jXVRTzv/X5zGOpNG7eArVag39QPQrzjRSZTLbytOQkWrTvDEDLDp05kXLIVrZi3hy+mT6VhJ2xAORcvEBhQQHL5v7It+9MI3F31fPk40eSiWrZGrVGQ2BwfQry8zGZSk7Anlov3D3KnvjMZjMbVy7nrv4PVnqbp9KOERbZHLVaTZ3AIArz8x3qIj3lMJFtOwAQ1a4DGUeSATiXmUGY9aQc2aY9GUeSyc26SGBIA1RqNW4eHri6uZFz4RwqlQohBAAF+UbqNWxUYVzpR48QEd0CtUZDQL1gCvKNmOziOnroIK06dgGgVccuHEu2fEf1GzbCoNehKApGgx4vH188tFq8ff0AUKvVuKhUla6nqzKPp9I4Khq1WkPdoHoUFDjW1/HDh2jRoRMALTp0Ji3lsK3s73lz+HbGG7Z9p6pOHT9GWGT0Nb+zEymHiGpn+c6at+tIeqrlO/Py9UOlLttk1Nb3TAUFBIU2rFQsRSYThYUF+AUGoVKradQsirPpaQ7LZBxJJqJ1ewAaRUZz/qTlQjzzyGGaWfetZm07cDI1BYACo8HWK1Y/vAkZRw7jjCKTCVNBAb4BgajUakIjIjmfccJhmVNHU2jcsg0ADSKiuHj6pK1sz9oVdL7nAdvPRr0ODy9vXN09UKnU1Amqx/kMx9/NGSdSj9CsRauSfdno2K49tNpy27WPX51yv6+qqO799lLWRa7kXqJB45I72lWhchFo1CpyDUbMikJm9iVC6vg6LKMoCvqCsomI2W7qaY1KxcUrujLLlKe6j3mmwgLW/D6XOR/M4PC+3QD4+Qeg1mgsv6NKjYtL5Y45ljZuF2NB6TZ+mKh2HS0xtu9IujVG/+D6lm2q1QjhYjvuVkWxyYSpsABff0t7CmnajAul2tPpo0cIt7an0Igosk5bEpNj8XspNplY+vVHrP/1BwqMlhsfitlM4vZNtLmzd8V1cBPOTfYO7tpB60qOPrCnchFoVC5cNuZjVhROZudS38+x1/Va+3J1qA3HGWcVXcz617dRW68hpFvLDbcMIYQ78B3QCSgCXlQUZbMQYjTwAOAOaIE+QohXsPy9AzdgqaIob1vXoVMUxUsI4QJ8DfQETmBJMH9WFGWJECIdmAsMADTAw4qipFwjpoPAncBlIAt4QVGUX4UQv1nXEetkzO/YrbMzlr/E/hBwHvgKaI2lDqcrirK8vN+5svVp0OfhodXafnb30GLQ5eFTp66lXJeHh5el3F2rRa/LA2DA8DFofXww6PKYNfMtGjaJIO9yLmfST/DKx1/h5u7BV2+/TkTLNnh6eVU2LHR5eQ6f8/DUos/Lw6+u/3U/Fxuzno7duqNxda30No06He72deHpiVGvw9uvjqVcX1Lu7qnFqLNcJCl2d0zcPbXoLudSNyiYcyfTyTcaKMzP51xmBka95Y7tmfQ0Vs+bw+XsLB6d9GKFcel1eXhoHevCoMvD1/Yd6Wx15aHVos+7AsBtPfvw7cy3cff0JDSsscMwB3NxMX/8NIt7H3q00vV0lVGnc4zLw9Nx39GXxO3hqcWQZ913ho227Tuz33ubhk0i8K8XXLUY9DrcPUu+M7cy35neVu5urbfrMZvNzPnwHS6ePsVdQx+vfCweJT2/bnb7yFX5eh1e1tigZN9xiNPDE6PeEqeHlzfnT2YQUD+U9MNJtiSvIvl6HW72sXh4lukNzDfo0VpPxGC5oAS4dOEchQUFBNolsB5e3hj1eehyL6Fxc+dM2lHCW7RxKhZ7hrw8PO33Ga0nhjwdvnXrVnpdVVXd++2GvxbRd9BQDuzccUNxebi6km8qsv2cX1iEh6vG6c+3Cw/ltohGFBWb2XHkRMUfoHqPeXm5lziXmc6z732Cq7s7P733Fo2jW9rqUnc5lz0b1zLi5WlO/04ABr2u1DmqVIw6HR7WtuNRTpvb+vdftL69my2JrAqjwbE9uXp4lumByzfo8fItSbivtif95VzctVoGT3qFA9s2sm/Daro9OJTkPf/QtG0HVE7EdbPOTQDnT2bi7umJn91w6sry0GgosNuXC0xFeNxA/VdWbTjO1Ca19RqitrjxP732f0NlEzgPIUSC9fUJRVEGA88CKIrSWgjRHFgvhIi0LnMH0EZRlBwhxD1AM6ALIIAVQogeiqJss1v/ECAcS2IUBCQD9gOfsxRF6SCEmAi8jPVvKJRjB9ANyADSsCRzvwK3A89UIuZeAEKIrlgStoGKomQKIf4LbFIU5UkhhB+wRwgRU/rzpYMSQowHxgNMnDadfkMeKRO4p9abfLuDd75R7/B8gqeXt+XgHmg5QXlqLWVaHx9beWTrtpzJTKdeaEOCG4Xha02yQsMak3XuDI0iIqksL29vDHZxGQ16tN7Xf26isLCQf7Zs5JV3PyD10MFKb9PDy4v8EyXDwvINBseDntbLNmws32jAw3rAs9wHoOR9rReeXl70HvQwC774CK2PL8GNwmwn25DwJoz7z7ucSjvG6nlzGP/We9eNS+vljdHgWBeO35GX7QScbzCgtZYt+P4bXvngU+oEBLJg9tfE/RNLh67dbWUtO3SmeZsK/3bjNXl4eTnGZTQ4xqUt2XeMBoPtBGG/7zSz7jtVTeAs30lJDAVlvjNtyXdmMDic1Mvj4uLC2KnTLRfpM96gVZc7cK9gOO7eTetI2beHOkH1bHfXwdJ7Zn/RBeCu9aLAbujh1X3HEqceCCTfaMDdGucDI58i5o95gIJfYBBefn5cT8LWGI7G78MvMIjCfKNdLEaHRBcsF3T28QoXSyy7Vi/njgcGOywrhKDv46NZ++v3aFzd8A9pgLaCWMrj6e2NwS6RNOoNeHpX/gbPjajO/bawoACBoF4le2vtdW7akOjQeuToDA7DzNw0avLt7pJXJCH9NAnpp+kaGU7XyHBiko5W+JnqPOZ5aL0IatDIlggHNwwn+/w5GjSJIN9o4I9vPmPAqKfw8nHsVayIp9aL03Zt5ur2HH4HuzZun+zFx27lwqmTPPzMlEpt86oD2zZyPGE/vgFBDm2l0GjErbz2ZChpc1fbk5unlkbRrQEIi27Ftj8XUGQycWTfLgY+8wJn0m7u93S9cxPAgZ3baXNH2ef7nNE+PJSo+oHk6o1l9mWjXUL3b6sNx5napLZeQ0i3lhsZQnn1iqI78BuAtUcsA7iaIWywS2Tusf6LB+KA5lgSOnvdgcWKopgVRTkHbC5VfvUBpf1YEr1r2Y7lr5r3wNLT1loIEQrkKIqiq0TMANFYet4GKIpy9UGHe4DXrcnsFiw9bo2u8XkbRVG+VxSlk6IoncpL3gAaRURy4kgyxUVFXMq6iKu7h8OdyibRLUlJ2A9ASsJ+mkS3BLDdzS8qMpGemkJgcAgBwcGYCgrINxopLi7m/OmT1AkIuk61XVvTqGhSDyVRVFRE1oXzuLm7o9Fcv1ft4rmzGHR6Pp0+jUU//8DBuH1sWev88xYNmkSQefQIxUVF5GZn4eru7lAX4VHRHE2MB+BoYjxhUZahKcENG5F59AgAxxITbO+36HQbY15/m/uGjULj6oqff4DDMA53T0+negrDm0VxPPkwxUVF5Fy8gJu7Bxq7uJq1bM2huH0AHIrbR0SLVoAlGbmasHj7+Np6n5b++jO+fnXodb/zE2KUp1HTSNLt9h03N3cn9x3LicK279R37nmd8jRoWsF31rwFqdbvLDUxnvCoFtdcV3FREWbrXXONqxtqjcapu/ad+9zLiFffpP/o8Whc3bicnUVxUREnjx4hxG5yDICwyGiOJ1nuSZ06lmobQtsoMprjBy3vHz+YQCPrsKfA0AY8/sLrPDL5FYx6HU1bX/9k2a7nXTz8/OvcPfxJ1K6uXMnJpri4iDPHjxJsN/EDQINmUaQfSgTgTNpRW4/b5ayLbP7jN5Z+8wn6K7lsWTzfsnxEFEOfe437Rk/AVFBAcJjj+pzRODKKY/b7skfF7bq6Ved+e+rEcS6ePc0PH7xD3I6tbF25nMxjqZWKZ+/xk/y6bR8r4w5TVFSMj4c7LkLQKMCP0zmXnVqHysXuQt1kwlTs3HMU1XnMq1vPcvwvsB7/L545ZTnmFRay6KtPuLP/IBo0rfxkBw2aRpCRmlISY6nvq3FUC1IT4wBITYwjvLmljSfH7SVxVywPTZiMi0tlLz0s2vboy5DnXqXvsNFoXN3Is7ans2lHqRfW2GHZ0IhIMpItNw7Pph0jILSBJf5mUVzITAfgQmY6vgFBXMm+SKHRwN+zv2TH8iVkpiRx6J9tXMvNODeBZQRC8v49VX7+LT79NAt3JrA28QimYjPeHm64CEGDur6czb1SpXVWRW04ztQmtfUaQrq1CKUSfZVXhzqWem8Z8KWiKJusP2/H0sPVAeikKMok6/ufAKmKosy+1nqFEF8ACYqizLG+/xfwu90Qyk6KomQJIToBHyuK0usacTYEFmFJzKYBXwAxQENFUV6uRMy9gJlYErS3FUVZZX1/PzBMUZQjpbY72v7z1/N3XPI1K37P5hh2b96AEIKBI8fhonIh9eABeg8YjKmwgEWzvuZyTha+dQN49OnJaFxd+f796RTm51NcXEyH7j25s19/AFITE1j/1yKKi4ro3LMPXe++r8z2gnyduxO2df0atq5bg0AwfMJEVCoVSfH7eWDoo5w9dYq5335B+rGjNAgL545efej7QMlzb8mJCfyzaWOFs1CmnXccfx63bTNx2zYhhKDfsFG4qFSkHTpIt/sG2GY0vDpT4MCxT6PRuJJz4Twr5symuKiIiNbt6PngEAD++uEbruRko3F1o9+wUfjXCyb1QByxq1fYLijufXwk9RuFAxBwnTuEOzeu5x/rTKEPjRmPSqUiJTGeuwY+RGFBAfO//YLc7Cz8/AN44tnn0bi6EvdPLBtX/Ila44qHVsuYKa9w7vQpPpn2Mk2sJ3JvH1/Gvjy13G0aCyu++79nSwx7NseAEAwcORYXFxVHDybQy7rv/DH7ay7nZONb159HJlj2nR/en0FBQT7moiI6dO9Jd+u+cz3G60xAELdtM/utE03cP3w0Li4qjh9KpPv9D1pmqPvpOy5fysG3Tl0GjX0GjasrSXt2sm9LDCePpdIwIpI+gx/BLyCQxd99gXBxobioiNvu6lfuLJQF15nd70x6GhsW/IqCQrs7e9POOiPhsh++ZtBTlma6/e+/SEuyzFT3wOinqBNYD1NhISt/mU3epRy869RlwJgJqDWu7F6/iqMHLBdmt9/7ABFt2jtsT59fcM1YzmecYMufv1tmoezag5Z39ABgzS+zuW/0BAB2r1lBevJBVCo1dw8fg2+pGy5zpr9mm4Vy+7I/OJ9xAhcXF7o++FC5CVx0g4p7UnfErCN2wzqEEDwybgIqFxWHD8Rz7+ChnD99it9nf0Pm8WOENAqjS49e9LyvP/t3bGfbutUcT0mmafNoHhw2gqbNr52MA1wx5F+z7N/Yb9cvWYh/cHC5s1AmpDs3UUN9Px/6tY0CIYg/cYoE6yyUgzu3ZuleS3LwWNf2BPpoMRWZycy+xOr4ZO5s3oTGQZaeL2OhiRX7DzkMYbPXrL7jjL3Vecw7fiiRLcv/xFxcRLtuPenc5x52rPmb2NUrCLImNE1btqHHgJIeXpUTydX+bZvYv3UTAsH9T5Rt40t//JYrl3LwqVOXweMmonF15d3xIwioH4qruzsAD094Dp8KhtBduHztIdbnM9PZ/ucCFEWh5R130uKOOwFYN/d77h01HoA9a/8mMzkJF5WKvsPG4BsQSLHJxMaFc9FdysFFpeLuEePQ2vVCnjqawpG9u8rMQunv7djD92+fmwDSDiexd9P6MkP7T2bnXrfeyhPs602fVs0QQGLmGQ6etDxn1799NCvjLc/oDencmgBvLabiYk7nXGb9QedufnSJqHjm5pt1nAl96hmnYr6WoFefx711C4RGQ2FaOmffmFHldaV/9/U1y2riGuLu1s2q/uDpTfT0D4tr1SDKWU89XCvrrToSuBeBloqijLUOQ9yApTfrcRyToXuAd4G+iqLorD1iJkVRLtglcA8Do4AHgUAsQyjHVzaBs24vFbiiKEonIcRrwCRgkvVZNWdj7oVlqOZYYD0wRVGULdYhlD7AZEVRFCFEe0VR4qsrgbvZnE3gbobSCVxNul4CVxOcSeBuluslcDfb9RK4m+16CVxNcCaBu1mul8DdbM4mcDdD6QSupjmTwN0s10vgbrbSCVxNqkoC929yJoG7WW40gatO10vgaoJM4KqmtiZw1XGk/hZQWScOWQSMVhSlzFWMoijrgd+BndZllwClH6L6EzgFJAGzgd1YJiKpit3A1dtH24FQLJOXOB2zXeznsUye8o0Q4jYsiagGSBRCJFl/liRJkiRJkiRJ+ldVahKT0r1v1vfygdHlvP8L8Eup977AMpyx3PUqimIWQrxs7aHzB/YAB61l4XbL7wN6VRDrCLvX/2CXrDobs6IoW7A844Y83cPIAAAgAElEQVT1+beWdotPqOjzkiRJkiRJkiQ5xyynoXTKzf0DG85ZaZ3Z0RV41zqZiSRJkiRJkiRJ0v95tS6Bu95zbaUJIcYApecj3qEoyrPVGpQkSZIkSZIkSVItUOsSuMqwzlY5p6bjkCRJkiRJkiTpxlRmcsX/y2rPdFOSJEmSJEmSJEnSdckETpIkSZIkSZIk6RZxSw+hlCRJkiRJkiTp/w9yCKVzZA+cJEmSJEmSJEnSLUImcJIkSZIkSZIkSbcIOYRSkiRJkiRJkqQaZ5YjKJ0ie+AkSZIkSZIkSZJuETKBkyRJkiRJkiRJukXIIZQ1ZM7mXTUdgs1HIwbVdAg2R85cqOkQbJIyz9R0CA56tIyo6RBsVu0/VNMh2DQNDqjpEGwMBYU1HYKD4+eyajoEG3fX2nO6KTabazoEmzM5l2s6BAfurpqaDsEmyNe7pkOwuaQz1HQINnW1njUdgoNjZy/WdAg2pu++rukQbMKfmVTTITiKXVfTEThFzkLpHNkDJ0mSJEmSJEmSdIuoPbdEJUmSJEmSJEn6P8uM7IFzhuyBkyRJkiRJkiRJukXIBE6SJEmSJEmSJOkWIYdQSpIkSZIkSZJU4+QkJs6RPXCSJEmSJEmSJEm3CJnASZIkSZIkSZIk3SLkEEpJkiRJkiRJkmqc2SyHUDpD9sBJkiRJkiRJkiTdImQCJ0mSJEmSJEmSdIuQQyglSZIkSZIkSapxchZK58geOEmSJEmSJEmSpFuETOAkSZIkSZIkSZJuEXIIZS3XJMifcX1uAyHYkHiEzYeOOZTXr+PD5H53UlRcjNrFhe837iT94iWiQoJ4+q6u1K/jzbM//Um2znDDsWxYvZK1fy8H4JnnXyIiqrmtbPH839ixdTMqlYqIyCiefv4lhBD8+uNsNq5dQ2iDBvz3869vOIarzmacYP3vc1FQaN+jD2279SyzzI5VyziWmIBKo6b/6An4BQRy6lgqq3/7iZzz55j430/xqetfpe1fPJVB7F8LAYXo23vQvEvXMsvExawm4/BBVGo1vR4bhU/dAK5kX2Tzgl9ACATQZ/hYvPzqkLRjCwe3xaCYFYZNe69KMQFsXb+WTatXIoRg1MTJNG4WaStLPZTET19+xrnTp/h0zjz8AwMBmPXxh2SkHcdTq8Xb15fn/zO9ytsvLdjPm/vaNgchiDtxigMZZxzKXdUqnujekQBvLWsSUjh48iwALRsE07lpQxQFCoqK+GtPIoVFxU5tMyF2K3FbN4IQ3Dd8NPXDGtvKikyFrJjzPZezs/D1D+DBMeNRa1zJzbrIip9nUVRURLM27bmz/yAAVvw8i2MHD9CsTXsGjBkPQHFREUu//5q8y7kUFxXRd+hjNI5uVal6yTqVyY6lC0FRaH77nUSVs//Ex6wmMzkJlVpNz0dH4l03AEPeFbYsmENxURFederS4+EnUKk1ldr2Vecy09m48FcUFNp2703rrj3KLLNzzQrSDlra0H0jnsI3IJDd61aSGr8XFxcV9RqF0/fREQghOPjPNuK2xKBSqQhp2ow+Q4c5HUt1fme/ffQeZnMxhfn53HHvA7S6vVuV6gegvp8P97ePBmB/2ikSMk47lLuqVYzs0ZkAby2r4w+TmHnWobx3iwhah9XnyzXbq7T96t5PDu3YQtK2jSiKwmNvzKx0PBdOZrB18XwUFFp17UmL27uXWWbvupWkH0pEpVZz1/An8fEP4PCuWPasWYG39Xh776jxePnVsX1myRcf4BdYj7uGjXE6lrjtW9i3JQaB4IERYwgJb2IrMxUWsuznWbZ9ZtCTT6NxdWX3xnXsXLcaRTHzwkdflcS8eQNx2zajUqvpPehhmrZsXal6OZ+ZzubF81AUaNOtJy3vuLPMMrvX/s2JQwdQqTXc88ST+PoH2sr+WbmU5L07GTvjfwAYdXlsXPQbRl0eLi4uPDT5lUrFErPoN7C261blxLJrzQqOJx1ArVbTb+Q4fP0DSd67i/itGxDCBVd3d/o/ORE3Dw+2/LmAsxlpFBWaaNgsil4PPV7puqnOeEyFBWxcNI/L2RdRzGYGTZiCu1brVCy7NsewY8NahBA8PHYCDZtE2MpMhYXM//YLLmVdpE5AIMMnTkHj6sqhuH2sWjQfjUZDnYBARkx+kSu5l5j7xccoZjOKovDQmPGERTSrVL04I+ST93CLjCB3yTIuzV1Q7euvreQklM75/7oHTgixRQjRyfp6tRDCz/pvYhXW9U8F5bqqxnk94/rcxuert/HWojU80L4FWjdXh/LzuXm8sWAVb/2xlt93xDP0trYAnMy6xNQFK0k9e7Fa4sjLu8KKJX/wwZff8sqbM5j1xacO5V179OTz73/mk+9+4NKlSxzYvw+A/oMe4oMvv6mWGOyt/30uA8dNZMQrb7J34zqMesfqzzp7mvSUQ4yaOp0eDz7E5j8tB7+AkAaMnjqDULsDd1XE/rWQvsPH8uDEl0navpECg96h/NL5s5w+msLg516j070D2L3yLwAO7dhC89u6MfDZl4ns3JWk7ZsAaNKmA4+8Ov2GYtLl5bFu+V+8+dFnTHztDeZ+55gwNwgPZ8bnXxMR3aLMZ0dNnMybH31WrckbwH1tm7N0bxJzt+6lS9NGuGsc7xmZis0s2pnA7mOZDu8nnz7PL1v3MnfbXs7lXqFNoxCntmfU69gTs5ZRr73F4KeeZe38uQ7lCbFbCQgOYczU6fgH1ychdisAG5csoOegh3nyjRmkJx8i66zlQr3X4EcYMmGywzqOJyWicXNnzNTpDH1mChuXLKxUnQDsWLqQ3sOepP/El0iK3VRm/8k9f44zx44wcPKrdLynP7tXLbXEv3ENkZ27MmDiS9SpV5/Ufbsqve2rNi78lQeefJrHX5zG/s3rydc7xpB97gyZRw4z/NW36NZ/CFuXLQKgWbtOjHh9BsNffQv9lctkHjls+Z1WLuXxF9/gidfe5nzGCbLPni6zzfJU93c27IXXGPXaW4x89T9s+mtRlesH4P720fy5O5FftuzhtmZl99+iYjMLd8Sx62hGmc9q3Vzx93buwvJaqns/adymAw/fwHFm6+L53DPyKR567jUObI0hv1Q8OefOcio1mYdffIPb7h/IjhVLbGUt7riTh6a8xkNTXnNI3k4kJeDq7lGpOIx6Hbs2rOHJqdMZ+vRkVs2b41AeH7uFgPohjJv2DgHBIcTHbgGgZafbmPzfTxyW1V25zN7NMYyb9g4jXpzK+j/mYzabKxXP5sXzuG/UBB55/nXiy62XM5xMTeaxl/7DHfcPInb5YluZ/splLl0457i+Jb9z+30DeXjKa5VK3gBiFv3GA2Oe5rEX3rhmu844cpjhr7xJ1/7/r73zDrOqut7w+02BGYYuTRREEbAr2EvsGrtRY4zG2DXRJGqMGqNJLIkxmpjEmNhLbPFnN7bYUeyogBQBFakqIlhowwwzrN8f+9zhzmVmGIycfS6s93l4Zs659875OHefc/bae+1vHcKwh+4FYODgrTjq7N9w5C8uoGfffox74xUAvnXw4Rx51gX88LyL+GTqh8z+eEZUPa8+/jCDttyGI848j++fdX6rg7eF8+fzwhOPcsbFl3HM6b/gvltuaPT660Ofpedaa/Pz319Bz7XW5vWhzwLw2P/dyUln/4qf//4KSsvKmPDOSCoqKjnxF+fx899fwZE//hkP/uvGFTonrWXWH//K7GtuWil/2yl+ii6Ak/S1Zg3NbD8z+xLoDLQ6gJNUmnx+2aHPlUxZaQlty8uYNXc+dUuW8O5Hn7J+r26N3rMkb7Fnu7blTJ39BQALaxezaHHdN6Zl4rvj2HjzLSgvL6dX794sqq5mcW1tw+tr9enb8Ht5WRmlpaUAdO3WjRLpG9MBULd4MbW1NXTu3oPSsjL6DhjEJ1M+bPSeqRPHs/6mgwHoO3BDPp0eAoSKdu1oU1HxPx2/vm4xdbU1dFyjG6VlZfRabwCzpk9p9J6PJ71H343CKG7v/gOZkzz0uvTqTU11NQA1CxdQ2aEDAO06dKS09H+bEJ80YTyDNtmMsvJyevRak5qC76hdVXsqKpvuKN15/TVcfNYZvPbC0P9JQz6lJaK8rJQvF1azxIxpc76gd5dOjd5jZiyoqV3ms/ntury0lM/mtm585KMPP6DvwA0oLSujS/ce1NYsom7x4obXp04cz4DNQ7sYuPkQpr03AYCZ06ayzsAwozxg88FMnRj2d+zSdZljdOnRk/q6xZgZ1QvmU9Wx0zLvaYnC9rPmegP4bHrjAODjSRPps2FoP2v2H8jnSfv56rNZdO+zDgA9+q7Lxx9MXKFj56hbvJjFtTV07hauoT7rD+KTqY2voWnvjWe9TcKAUJ8BGzBrxnQAuvbs1fCe0rIyVBIeI2v0WpPamkXU19VRX19H23at61h9099ZaVm4jmpraujee+0VOzF55LffejOmzf6Ctbo2/q6XmDG/ifYLsMtG/XlpwqSvffyV0U7adehISXJvXlFybaZTt+6UlpXRu/9APp06udF7PvpgAv02Dm1mrfUHMfuj6Q2vTRj+Kvf99Q+89tiDWBIg2ZIljB72PJt9a/cV0jJj0gesM3ADypppM5MnvMugLbYEYNDgLZkycTwA7Tt1bmgfOb787DO6916L0rIy2lZW0qZtWz4vCKhaom7xYhbXLD0va/UfyMyC8zL9/Qmsm1xLaw8YxGczlp6X1//7CNt8+4CG7SVLljDn4xm8/dyT3PPXyxg17LkV01JbQ+dEy9pNXNfT35tA/023AJLr+qPwbMw/L4tra+nWe61G++vr6yhv05b2nbrQWlaGnqkTxjH53dH831/+wMuPPthqLVPen8j6G25EWXk53Xr2omZRNYvz2sz748awyZbbALDJltvwwfhxAKzZpy8LF8wP9/uFC2jfsROVVVV06NQZgLKysq99TS2Pus9mr5S/66waZDKFUtIxwNmAAaOBeuBzYDAwQtJvgauBTQn/h4vM7D+SKoFbgY2A8UBl3t+cAmwF/BHoL2kU8IyZLTO8JWlX4ELgE2ALYCNJ882svaQ1gXuAjsmxTzWzl/I+2w14FPi9mT3+v5yHDhVtG3VuF9TU0KGy7TLvW6/HGpy8x3Z061DFFY88/78cslnmzZ1L+yTYgBAMzJs7l67dGgeUo0e+zedz5rDJFoNXig4Io68Vle0attu2q6J6fuMO/qIF8xuN8pqt2IhqSyxasIA2+cevbLfMqGLNwgW069h5meOvPWBDHr/hKia88TL1dXUceuavvjFd8+fNo6p9+4btyqoq5s+bR5c1Wk4TPerkH9OxUyfmz53LpeedzXqDBtFzzdbNeLVEZZs2jQYRFtXWUdmm9el+W/Rbi23X70td/RJemTh5+R8AqhcsoCIvcGhbWUn1gvl0SNpC9YL5VFaFc1TRroqFSbvJbx8Vle2YP/fLZo/RpXsPFtfWcs35v2BR9QK+f/qKjZAXtp82FZUsKphBXqb9JB3ermv2ZvqEcXTaqQfTxo9ZZkamtVQvmE/bRtdQu2VmsRctWED7TstqyDFt4njmf/UlfQaEIGqjbXfktkt/TWlZORtsuU2jz7as5Zv9zpYsWcLtV/yOzz6ewR6Hfb9VGpqiXZs2LKotbL9tWvjEUrq2b0ebsjI+/errJ2ZkoZ000rOwoM1UtltGz6IFC6hqos2st9nghjTzZ++8mQlvvc6G2+zA+OGv0H/zLSkrX7E04IUL5lOZN/NSUdmucZuZP5/KpE1VNvF8yKdrz17MnDaVRdULqa1exMzpU1t8fyGLFsynbbvln5f862FJ0na/mDWTxTWL6L5Wn6X/t3lzmf3xDPY55mS69urNfVddTp+BG7JGr+XfkxcVPhub1FLwbMy7rke/8iJvP/8UZeVt2Hbv/Rv2P3vP7Xzwzgj6bbgJbZsZBExLz+yPZ7DjAYew22FH8chN/2DyuNGsu/Fmy9WyYP68hvsIhHaxcP48OiWDdAvnz6dd8vysrKpiwby5AGy7y+5c8/sLqWjXjrXWWbdRquSS+nruvfk6vn3YEa0+J87ycRfK1pG5AE7SxsAFwI5mNltSV+AvwEBgTzOrl/QH4HkzO0FSZ2C4pGeBHwELzWwzSZsBI5o4xHnAJma2xXKkbJO8r7DneBTwlJldmszONdydJPUEHgF+bWbPNPF/OwU4BWCL7x7Dutvt2uSB991iQ7YfuA4zv5xHu7yUyao2bZhfXbPM+z+cNYdf3f046/fqxsm7b8cv//3Ycv5rK06Hjh1ZkPdQW7hgPh06dmz0nskfvM+/rr+WC//4Z/QNz7oBvPn8U0x4azhdevSkpnrpmr6a6oXLpFFUVLWnZuHS90j/+2Tz2JeeZ9LoEXTq1oPaRdUN+2urqxt1QCEElbXVyx7/9cceZOt9D2a9zYbw/ojhDH/iYb51WOvXCrVE+w4dGjq3EDrG+UF3c3TsFGYV2nfsyKZDtmTah5P+pwBu6/592HCtnnw+fyFt81LO2paXsShvxHN5jJryEaOmfMQOA/uxw8B+PDv2/eV+prKqqlFntaa6uvFDu6p9kt7UnZrqhQ2dwPz2sah6YaPPFPLOK8Po1HUNjvjZL/hy9mfc+4+/cMpFly1X29iXhzJ59Ag6rtGd2uq89rOoepnZqrbtqqhdlNd+klmuwXvsx8sP3c1j146ka++1G3XeW8OIoc8wccRwOvfoSU1eG66prm7o8OaoqKpqdJ3lNADMmjGNYQ/fy6E/OQtJ1Cyq5pXHHuSki6+gvG0FD177Vz6ZPIk11+2/XE3f9HdWUlLCceddyML587jpkl+z0dbbU5HXwV4e2/Tvy0Zr9+Lz+QsapUy2LS+jurZ17Xe3jdbn+XHLb69NkYV2ks87Lz7HB6PeolP3Ho3aTO2ihVQUXCfNtZn8++PALbdl6vixDBi8NRPffJ2DTzuLjz9csXPVrqo9H+Xd3wuv2cr27VmUvL5o4cJGwd4yf6t9e3Y/5HDu+uvlVHXsRK8+69ChiZn3Qka+8Czvj3qTzt17NmRVANQsWrjM86DwvJQkbfe1xx9mhwMObfzedlVUdepC97VDVkufgRsw+6MZLQZwI154hvdG5LQsPU5tM8/G/BTP/Ot6sx13YbMdd+GNpx9n+DNPsOuhYQBkzyOOYffv/oD/3PB3Jr87pmFmPoaeinZVrLvRZkhi3Q03ZdZH01sVwFW170B13nGqFy6gXfu8Qen27alOBmIXLVxIVfLa3Tf8k3P++Be6dOvO3df/gxGvvsyQHXZqeG3jIVuzwWbL6046zjdPFlModwfuN7PZAGb2ebL/PjPLuRjsDZyXzKK9AFQAfYGdgTuTz40mzN59XYY3EbwBvAkcL+kiYFMzm5fsLweeA85tKnhLNN1gZluZ2VbNBW8A/x01nt/e+yTXPP0KtYvr6NahitISscFaPXl/ZuMp9fK8qfsFNbXU1H1zaZP5DNpoY8aNfoe6ujpmfTqTispKyvNGoz+eMZ2//vFSfnnR7+jU+et3Flpi692/zQ/P/Q0HHHcK5W3a8tWc2dTX1TH9/Yn0LugorjNwQyaNHQXAjA/eo2deiufXZZNv7c7BPzmbXY84hrLyNsz7Yg719XXMnPwBPfr2a/Te3v0HMm3CWABmTp7EGg2pXNbQ6als3+EbGRnP0X+DDZk4bix1dXXMnvUpbQu+o+bIBeZ1ixfz3rixrLnW1087A3hz0nRuH/YWj414l7q6ejpWVlAi0bdbZz76/KtW/Y3SvIf4osWLWVzfuhnUtdZbn2nvT6S+ro6v5symTduKRqP76wzakPdHh3bx/uhRrDMoGFT07NOX6R+8B8AHY96h78ANWziKNaS+VrSronbRolZp22Sn3TjwtF+wyxHHUNamDfO/+Jwl9fVNtp81+w9kepLCM3PyJLom7adNZSW7H3UCB5x6FmXl5ay3+ZBWHTvHkN324shfXMC+PzyJ8vI2zP18NvX1dcz44D3W7Nf4Guo7YAM+HBduoR9Neo8ea4dZgi9mfcp/b7+JA0/6SUMHSBKlpWWUt62gpKSEinZVy6wDao5v8jurr6trGMFv07YtZeXlKzy7M3zSNP714nAeeXsci+vr6dTQfrvw0efNz8zm06V9O/YfshFHf2tLOlS0Zd8tWmpPjclCO8ln81324LAzfsmeRx1PeXkb5n0e7nsfT3qfnnlmMxDSJqe+OwaATz78gG7JzFL+YNr098bTpUcv5s75jJrqhTxy/VW88vB9TBs/lrGvDmuVprX7r8/U9yZQX1fHl020mXUHbcR7o8P47XujR9Bvg2XX/eaz8dbbceL5F7P/0cdT3qYtndfo1uL7AQbvuiffO/NX7P2DEyhv04a5eeelV56hCsDaAzZgcnJePv7wfbon19JXcz7juXtu54F//JkFc7/k+XvvpKy8nE7dujPvizlAMAHp3L1Hi1qG7LoX3z/rfPb54YmUtWm79Lqe9B5rrtP4uu4zYBCTG67r9+mxVng21i1emu1TUdmu4dmR219SWkp5m7aUteKZsjL19Bm4YUOK6sxpk+mynHOTo9+AQUwa/y71dXV8/tks2lZUUp7XZgZsvCnjRoS1++NGvMX6GwVjqpKSEtolz+wOHTuxcH7o8j10+y106tyFXfc7sFXHd5xvGmVtqlLS6UAPM/t13r5/AY+Z2f3J9tvAUWY2seCzDwNXmdnQZHsEcIqZvZWXQtk++VvN2sYlKZRnm9kBefvmm1n75PfewP7A6cCfzOx2SQuA+4GPzOz85f0/D73y1lad+P491+DE3YIL5bNj3uP5ZBbizP125m9PDGPLddfmO9tsypLEtufWF4Yz5bPPWbNLR07ZY3v691yDabO/4KUJH/LUO02vl/nTD7/TGik8/fijPPXYIwD86IyzKC0tZeSbw/nuUUdz4bm/YMa0qXRLnA0PO/JottlhRx594D5efO4Zpk+dQv8BA/nZOee1GCC8MvHDZl/L5+MpH/LM3cFBb4tv7cYWO+0KwMM3/oPvnPxTAF569EE+HBvc0PY/7mS6dO/JnJmf8ORdtzBz6mS6r9WHjbfZgS1326vJY8xpYd3VrOlTeOWhewBjw213YoNtw4jcs3fexJ5HnwTA208/xrQJ4ygtLQ0ulGt05/OZHzPsvjtQSSlL6uvZ5fCj6brmWkwa9RbvvjaMT6dMome//my9z8H0KghKd954+cYrLzz1X4b+93EkccypP6WktJQxI97iwMO/zyczpnPL1Vcx+YP36LPOuuyw2+7sdeDBXHb+udRUV1NXV8dOe+zFPt85dLnHefztcct9DwQXv302HwQSIyfPYFTiQnnI1pvy0JuhQ/P9HQbTvWMVi+uWMG3OFzwxcjzf2mA91u0RRsGraxfzyNvjqGlmTWf/gnWhI18ayshhQ0FinyOPoaS0lA/HjWGHfQ9kcW0tj9xyHXO/+JyOXbpy8Ik/pqy8DV/M+pRH/nUD9XV1rL/p5ux8YDgHzz94D5PGvMP8r76k25prccTpvwDgweuvZtHChdTV1rL9Pgew8TbbAzDzi7mtOi+fTZ/Cqw/fC2YM2nYnNtg2OCU+f+fN7H70iQCMeOZxpk8YR0lpKbsccQwd1+jOR+9PYMQz4ftda8AGDN5zvxaP07mq+ZmnT6Z+yPP33IlhbLbjrmyWOLk+evM1HHhiWCb86uMPM3ncaErKytj3hyfRuXsP7v/nlXw+82M6dA7fzzZ770//Tbfg7aFPM+71VygtLaVLj57sc8zJlOQF4hVtmk/4+Ka+s7lffM6D11+NSkqoX7yYbfb4dpMulO99PKvF85ajd5eODcHXiMkzGDklGKUcts1mPDA8dDqP2nEI3Tu2Z3F9PdNmf8FjI95t9DdO3/dbLbpQdqxsfk3uN91OPnznbca/NoyZUybRq19/tvz2QcvcZypaSHP+dNpkht1/N4ax8fY7N7gtPnXbDXz72ODSOvzJR5j67lhKysrY86jj6NStB68++gDTJ75LSUkpXXr2YrfvH9Noze+M9ycw4c3XlnGh7N6x+Znwt4c9z9svPo8Q+x19HCUlpUwaN5qd9juIxbW1PHTTNQ1t5pCTTqO8TRvGDn+NN4c+y/QPJtJn/UHscej36DtgEPdf/w/mfj6H8rZt2O8Hx7NG3jrPHF+04OQ8c+pkXrj/Lsxg0x12ZpPE0fWJW69jv+N/DMDr//0PU94dQ0lpGXsffQKduzUOPG6+8NwGF8pZ06cy9P5/s6S+nr6DNmTHAw9r9N78Aa5ltXzIc/feBRib7bgLm+4QruvHbrmWA044FYBXn3iYyeOCQ/I+R59I5+49ePWJh5k2IbTdiqoq9vnhSVS0q+Lh6//OogXzqa+vZ+31B7LLISuWLvhN6/lqzmyeuusW6hcvpkuPnnz7Byc0mrVbp3vzs6evPfc0rz73NJI47PhTKC0tZcLokex58GHU1tRw1zVX8eWc2XReoxtH/+RMytu0YcSrL/PcIw9QVt6Gyqoqjj/jHGZ+NIMrLzib9ZLBpA4dO3Hi2csuh+h36k9X6FwV0uPcM6nYdCNUXk7th1P45PyL/6e/N+Dlp7751KiVwBF/uy1Tgck9Zx6byfOWxQBuY+AhYHszm5OXQpkfwP2BsAbtZ2Zmkgab2UhJZwEbmdlJkjYBRgHbFQRwBowws3Va0LArzQRwktYhBGl1ks4E+pnZmYkLZSfgPsLs3R9b+n+2NoBLg9YGcGnQ2gAuDVoK4GLQmgAuLVobwKVBYQAXk9YGcGnRUgCXNi0FcGnT2gAuDVoK4GLQUgCXNi0FcGnTUgCXNi0FcKs7LQVwafO/BnDfNB7AfT2yGsBl7i5gZuOAS4EXJb1DCN4K+R0hZXG0pLHJNsC1QHtJo4FzgeFN/P05wCuSxkr609eQuCswStJI4DDgqry/XQ98H9jt65QqcBzHcRzHcRzHaYnsDInmYWa3Abe18Ho1wbCkqf1N2o6ZWb+83wKZ3oAAACAASURBVFt0jTCzFwhr6/L3tW9JW97rtcC3W/r7juM4juM4juM0ZknGMgOzSuZm4BzHcRzHcRzHcZymyeQMXFpI2hS4o2B3jZltG0OP4ziO4ziO46yuZM2bI6us1gGcmY0hFOp2HMdxHMdxHMfJPJ5C6TiO4ziO4ziOUySs1jNwjuM4juM4juNkgyWeQdkqfAbOcRzHcRzHcRynSPAAznEcx3Ecx3Ecp0jwFErHcRzHcRzHcaLjLpStw2fgHMdxHMdxHMdxigQP4BzHcRzHcRzHcYoET6F0HMdxHMdxHCc6nkLZOnwGznEcx3Ecx3Ecp0iQR7rFjaRTzOyG2DrAtbRElvS4lqbJkhbIlh7X0jSupXmypMe1NE2WtEC29LiWeHznTzdnKjB5+JwTFVtDU/gMXPFzSmwBebiW5smSHtfSNFnSAtnS41qaxrU0T5b0uJamyZIWyJYe1xKJJWaZ+pdVPIBzHMdxHMdxHMcpEjyAcxzHcRzHcRzHKRLchbL4yVJetGtpnizpcS1NkyUtkC09rqVpXEvzZEmPa2maLGmBbOlxLZHIctpilnATE8dxHMdxHMdxonPA5TdmKjB57Jcnu4mJ4ziO4ziO4ziO8/XxFErHcRzHcRzHcaLjmYGtwwM4Z5VBUlszq1nevtUNSeua2eTl7XMcx3FWbSQdCuwEGPCymT0UWZLjOF8DT6EsQiTd0Zp9KWnpLul8STdIuiX3L4YW4LVW7lvpSOop6WZJ/022N5J0YgwtwANN7Ls/dRVkq+3mHX8dSXsmv1dK6hBBw55N7Ds2bR0Fx79CUkdJ5ZKekzRb0tER9ewk6fjk9+6S1o2kY5MYx22KjN1nnBbIwvUk6Rrgx8AYYCzwI0n/TFNDgR5JOlrSb5PtvpK2iaSlVNKzMY7dFJL2bWLfj2NoSRuzbP3LKj4DV5xsnL8hqRTYMpKW/wAvAc8C9TEESOoFrAVUShoM5BacdgTaxdAE/Au4Fbgg2X4PuAe4OS0BkjYgtJVOyahrjo5ARVo6CshS20XSyYQiqV2B/sDawHXAHilL+a2kw4CzgfbATUANcFvKOvLZ28zOlXQIMAM4HBgK3Jm2EEkXAlsBgwjXVXmiY8e0tQDXSWpDuMb/bWZfRtCQ419Evs8ASBpDmNFZ5iXAzGyzNPVAs5q+At4Cfm9mc1KWlIXraRdgE0ty1CTdRgjmYnENsATYHbgEmEcYcNw6bSFmVi9poaROZvZV2sdvgt9IqjGz5wEk/RLYlfB8chwP4IoJSb8CzicEKnNzu4Fa4tnMtjOzX0Y6do5vA8cROt9XsjSAm0c4XzHoZmb3Jt8ZZlYnKe0AdxBwANAZODBv/zzg5DSFZLTtAvwE2AZ4A8DM3pfUI4KOXYBfAKOS7d+a2d0RdORTnvzcD7jbzD6XoplxHQIMBkYAmNnHMWZKk2PvJGkAcALwlqThwK1m9kwEOVm4z0C4z2SN/xIGFf+dbH8/+TmXEPge2MRnViZZuJ4mAn2Bqcl2H2B02iLy2NbMhkgaCWBmXySDI7FYBIyR9AywILfTzE6PoOUg4DFJ5wD7ABsk+xwH8ACuqDCzy4DLJF1mZr+KrSfhMUn7mdkTsQSY2W3AbZIOM7Om0gVjsEDSGiQjwJK2I4z+poaZ/Qf4j6TtzSxKKmmeliy2XYAaM6vNdaQkldH0TMLKpguwLTCJMBCxjiTlRsoj8YikCUA1cJqk7oQOTgxqzcwk5a6nqkg6gIZA/9eE2Zy/A4MVGtH5ZvZgilKi32cAzGzq8t+VOjuaWf4M7RhJr5jZjpFSgR+NdT1JepTQRjoB45NBBwiDV6+moaEZFidZGLn2250wIxeLx5N/0TGz2ZIOImQ3vQ18N/LzIDW8Dlzr8ACuCDGzX0laC1iHvO/QzIZFkHMGcL6kWmDxUinWMYKWtSV1JMww3QgMAc4zs6cjaDkLeAToL+kVoDvw3Qg6AD6QdD7Qj8bt5YS0hWSs7QK8mJybSkl7AacBj0bQ8TrwRzO7RVIlcDnwCrBDBC1IKiGchyuAubn0IuDgGHqAeyVdD3RO0l5PIFzjqSNpM+B4YH/gGeBAMxshqTdhzW2aAVwm7jOS5tFyCmWM50F7Sdua2RsAydqq9slrdRH0XEi4rvOvp7RmVP6c0nFWlL8DDwE9JF1KaLu/jiXGzG5L7r99zWxiDA1515KSn22A9YDvJmN6Ma4lJ4N4Ie8iRNIfCekg77J03ZmZ2Wo9vS7pHTPbXNK3CalxvyGkNg2JpKeMkMYoYKKZLV7OR1aWjlcJ6xTfJm+dYozZyqy13SRQORHYm/A9PQXclPZIp6S+ZjatYN/OEQNbJL1mZtvHOn4hSYDd8D1FSllE0jBC8Hi/mVUXvPZDM0vVlCcr95msIWlr4BZC0CZC6uRJwDhgfzO7N2U9IwqfRU3tS0FHT5auMRtuZrPSPH4TejYgrDkW8JyZjY+o5UBCsNvGzNaVtAVwyeret0qbff5wfaYCkyfP/1EmC3n7DFxxcggwKCv2+Mk0/87J5gtm9lgsKcnP/QiB2zuKtGhH0uHAk2Y2Lkm1GiLp92Y2IoKcLKxTzJGptgtUAreY2Y3QYKpSCSxMWcf0JK1rPTO7RFJf4qUr5ng6MVZ5MHbqjoLj5Eu5oE3BLbSfmU2JIOfBwiBN0hlmdlWE4O3Qgl0DJX0FjInZMU/WkTYYJRUOTqSBmb0JbCqpE2GwOt9sJrXgTRky2ZL0PeBPwAuJjqslnWNmUVyJE94nBNdl0PRgVopcREgrfQHAzEYpntvtjsAoM1uQPBuGAH+LeG5SwyeWWocHcMXJh4QF0dE7wcmMytbAXcmuMyTtZGbnRZDztqSngXWBXyUmB7Hy6X9jZvdJ2olgsvJn4FrCOqe0ib5OMY/MtN2E54A9gfnJdiXwNOmnLmbGjS2Ps4AqoF5SNXHT4e6j8XdSn+yLcX6OAf5WsO844Kr0pXAisD3BzRCCS93rhEDukggB5UEEI6newCxCqvR4CtxnU9LSFjiMJHU8N5ZnZpekLCXfZOsveftjmGxdAGydC+6TNWfPEq+szM8IqaWfEq7pXNpg6q6lCXVm9lXBuG+saOJaYHNJmwPnEpxl7yAYXjmOB3BFykJglKTnyOsIR3JK2g/YwsyWQIMt8UggRgB3IrAF8KGZLUwW9x8fQQcsTQ/cH7jWzP4j6aJIWnLrFGsI6xRjdsSz1HYBKswsF7xhZvMlxSg9kTU3NswsistjM5SZWW1uIzGeSfX8SDoSOApYT9IjeS91ANK2pM+xBNjQzD6FhvS43EDRMEKHL01+B2wHPGtmgyXtBhyZsoYc/yEYurxNxAGjjJlslRTMzM4hbj3gMwgZGbGun0LGSjoKKFVwmj2deCYvdYlx08HAVWZ2syLXBnWyhQdwxckjyb+s0Bn4PPm9U9oHl7SBmU0gBG8QOlhpyyjko8R0YU/g8mQ0OMqDMmMd8ay13QWShuRSWyVtSXCJS5usubGR6MhKevRnkg4ys0cSXQcDs1PW8CrwCdCNMMuUYx7xrNj75YK3hFnAQAsW9THWwi02szmSSiSVmNlQSZdH0AGwtpntE+nYDUg62szuBPpJOqvwdTP7SxMfW1k8KekpIFei5PuEcguxmE4E19QW+BlhlrKGUH7iacKgRAzmKZQHORrYOXk+lC/nM6sE7kLZOjyAK0KSEb2scBkwUtJQwszOzkDaNvFnEYoxX9nEa0ZIS0ub7xFqt/zZzL6UtCZwTgQdSNq5qf0xDDIy1nYBzgTuk/Rxsr0mcEQEHZlyY4PMpUf/GLhL0j8I95nphFTG1DCzqZJmAAvM7MU0j90CL0l6jJBOCiFlcJhCmYUYBca/lNSeMPt3l6RZxHF8BHhV0qZmFrNQNYQ0ZFjqgBkNMzsnWTe5I+E6us7MHo4o6UPgBUmP0zgjI82gNp8eZnYBIYgDGsxw3oyg5QjCjP+JZjYzWRf9pwg6nIziLpRFiKTJNJGXbWbrRZBDEpxsTXggvGFmM2PoyBpJ7vq3ks2XzOydSDrybfErCIu03zaz1APbrLVdAEnlLHXxmxDRLTQzbmyJntE0To8uBUaaWaz1KSTBgcxsXkQNjwA/NLPoMweJSdOhwE7JrjnAmmb2k0h6qggz2CXADwgZGXea2ectfnDlaHkXWB+YTAgOcqnj0dpvLNS4zENhesoiQv3JC8zsuZR1XdjUfjO7OE0dOSSNIJQF+SjZ3hn4p5ltGkPP6spev782U4HJM78+NXpKV1P4DFxxslXe7xXA4UDXNAXk0hYl5SyQZyQ/e0vqHcltEUk7sGy9s9sj6DgDOJmlNaHulHSDmV2dthYzO7BAWx9Cfa8YRG+7TbA1S9vMYEmptxlJ/YHJZvZPSbsCe0n6pMA5LwZR06NzZMiQAkKHd4ykZ4AFuZ0x1nEma2QmEda8fY8QrMRcZ/XbxPF2CXAbQJJCGcMFd98Ix1wGSX9v6fU02k1LafTJwMwmhJn2TVa2lnxygVpiOGb565Ej8SPgYYVyAkOAPxDW+aeGpJfNbCctW1sx5tr1VPGJpdbhAVwR0sSC379Jehn4bYoyMpe2KOkOoD8wirwaY0DqARzBUGVbM1uQaLucUOQ39QCuCWaQ8oM6R0babgMZajMPAFtJWh+4iVBE+9+k3HkooKn06LRd83JkwpAi4fHkXzQkDSSsXzqSMOt2D2FmcreYuoC9WDZY27eJfSsNSR3NbC5hbWIWeDv5uSOwEeG7gjB49XaTn0gRM6sH3pGU+rNJ0iYEo52uyfZs4BgzG5e2FgilJySdTlj7tgjYy8w+S1nDTsnPLK1ddzKIB3BFSN6sF4RUla0ITmipYWanJL/ua2aN6lVJqmjiI2mwFbCRZWP4RuQVzWapRXL6QsKDOXdOSghmL7HSOaO33QKy0maWmFldsj7lKjO7OudIGQszu1vSCyxNj/5lxPToTBhSQFjHKakS6GtmEyPJmAC8REj3+gBA0s8jaUHSqcBpQP8k9TZHB+CVlOX8GziAEBwZje+7BqSarp1b9yvpOGC3XIq2pOsIgUImMLPrIxz2BuAsMxsKkGQf3EjKZVySZQb5z4B2hAGjm5OMjFQLeUsqAUabWZSBVqc48ACuOMmf9aoDphDSZ2LwKiHVYHn70mAs0IvgFBebW4E3JD1E6EAcTKjjEoO38n6vA+42s7Q7VTmy1HYhO21mcWJTfwyQS3mN6jgm6Tkz24M819C8fWmTFUMKkvSqPwNtgHUlbQFcknIn7zDCDNxQSU8C/0ekAaKEfxPcDC+jcQmZeWmvfzOzA5KfUQowt0BvQkCbOx/tk32rM1W54A3AzF5I1lGmzZ8jHLNZzGyJpHcUt6h5NKIPpxYJHsAVIRlIk0FSL2AtoFLSYJZ2HjoSRq/S1JIbPesAvCtpOI0drVIdPUuO+Zdk9iJnLnC8mUWZUUlmDNoAA5NdsWYNMtF2C+hGNtrM8QSnxUvNbLKkdYE7U9YANMygtwO6SepC42s7VodzJ+C4xAQntiHFRQQjoBcIIkYl31dqmNlDwENJZ/c7wM+BnpKuBR4ys1RndhJDl68k/RqYaWY1yWzKZpJuj7GWU9KOwCgzWyDpaMKg4t8idoj/yNKUZAgFmS+KpCUrfCjpNyytV3g0YR1nqpjZi8lawKfMbM+0j98MawLjkmdT/lrb1PszTjbxAK4IkdQJuJCl9ZleJIwAp+mK9m3gOGBtIN/ydx7pr5PJ1OhZASIs6I82Op50pG4jzHYJ6CPp2BhlBDLSdvO5KNJxG2Fm7xKKxua2JxM6fABIesDMDktJzo8I5RV6A/lmRHOBf6akoZBMGFIk1JnZV2pcazLKmHGyxvYugmV/V8K6qvOIl5qXv5bzZsLsbay1nNcCmyduwOcmeu4gBE6pY2a3SvovwXAG4Dx3bOYE4GKC2ZcI5SeOjyHEzOolLZTUKQsOs4Tz4jjN4gFccXILIfUrl3r2Q0LK3qFpCUjy+m+TdJiZxXQ9w1pZk0nSa2a2/crWkxzrt4TO1AOEB9Otku4zs9+ncfwCrgT2zq3XSQwQ7ga2jKAletvNp7VtJwOktm7HzK4CrpL0sxiuqflk0JACYKyko4BSSQMIgferkTWRpCpen/yLRf5azr9FXstZl7h0HkxYV3qzpGMjacmVfNgTWM/MLpHUV9I2ZjY8lqbYmNkX5A1cZYAsOcy2+GxKsz+TNl7Iu3V4AFec9C8Yjb9Y0qgYQszsAUn7AxsTbOFz+2PYey+PNM1VjgQG5wxeFIoijwBiBHDl+WYLZvaeQu2zGGSi7RahVXOMJ9pNks4ipC8awTTjukLTopVMpgwpEn5GKPRbQxgIeQr4XQQdWSRLaznnSfoVIS1v5yRFLua60msI2Ri7A5cQBiUeIJgErZYkg4lns2zpn9RdrBOiO8yuALHM4pyM4AFccVItaSczexkacv2rYwhJnLTaAbsR7M+/C2R1RDHNTvAUwg0219ltSyiWGoO3JOXShyAU2I1lX52JtutWza3iNkInMzcLdyShDR2eloAsGlKY2ULggqQ0iFnEouIZJDNrOYEjgKOAE81spqS+wJ8iaYFQVmZIbkbSzL5I1iavztwHXEfoO9Qv570rnabWi+dcQzOIT1Ot5ngAV5ycSkhfzBXW/YKwHi0GO5jZZpJGm9nFkq5kafHq1ZkawgLkZwg32r2Al5UUdU05JeNU4CeEVJXcOoNrUjx+oZastN1cAe0Z+aYLQBTTheUQYw3lIDPbPG97qKRY5ScyY0ghaWtCKnCHZPsr4AQzi17TKzbLW8uZspaZ5K3PTtpKjJqgORYns4AGIKk7YUZudabOzK6NLSJHltaLr87Er+pTHHgAV4SY2SjC4uyOyfbciHJyM0wLJfUmFJXNzGh5AWl2gh9K/uV4IcVjN8LMaggdmb8s770paMlS24VsmS4AkDg/9jGz/HpaqRVCzmOkpO3M7PVE17akX9MrR5YMKW4GTjOzlwAk7URYxxnDETNTJIH2RcA6hP5FLiU59VTXgvToNoT0yflm1qn5T61U/k54JvSQdCkhW+XXkbREJTHcAXhU0mmE85LvApxq6Yk8srRefHnELBviZAAP4IoQSX8ArsjNEiQdvl+YWYyHwaOSOhNSU0YQHpg3RtCBpMvN7Jct7PthSjpKgb3M7Og0jrc8JB1AWKNT2KlKfZ1XxtouLDVdOISIpgtJyYmDCN/PKOAzSS+a2VkAadrCSxpDuI7LgWMkTUu21wHeTUtHAVkypJiXC94AzOzlJFhwQnD7c0KKdtSUuML0aEnfIZR/iIKZ3SXpbWAPwj34O2Y2PpaeyBSuaT0n77VYa1shQ+vFs9KficELF/3Ug9NWIJ+qLD4kjTSzwQX7RphZqsWzJZUA25nZq8l2W6AilgVvU+cgSe1MfWRc0lPAgWZWm/axm9DyAcHlcYxFvuCz0nbzjv0G8DeCKcWBybqdsWa2Sco6RprZYEknEWbfLozYdtdp6XUzm5qWlhySXgSeJKyx2hn4jJBSuWkELX8lrPu9m9DZPIKQCvwAgJmNaP7TqzaS3jCzbZf/zjhIet3Mtotw3BJgdNr3FWfFkHQL4ZrOXy9eZmaplzbIUn/GySY+A1eclEpqm6TGIamSYJKRKma2JFnztn2yXUNeGkRaSDoVOA1YT1J+2lkH4qV8TQFekfQIje2IY6QxTgfGxg7eEjLRdvPIiulCmaQ1CeUVLohw/AbyA7QkPXCAhRpW3UjWfUUgS4YUWyQ/LyzYvwOh8xfLQS8LDJX0J8I66PyUuNSDWoVSBjlKgK2IV69viaR3JPWNsW4zq0iqIDy7Yzrd5hN9vXhG+zNOBvEZuCJE0rmEdKtbCTe9E4BHzOyKCFouBkYDD8YKEBJDjC7AZYQitjnmxcqll1TYuQPAzFIvzpmYLvyOUDQ7v1OVejCZpbbbGpRSAW1JhwO/AV42s9MkrQf8KY1jt6DpQkKnd5CZDUzWuN5nZjvG0tQcWoVrIhUTkoY2sdti2MJLujVvs44wqHajmc1KW0ui53lCyYDhNB7UOyiGniwg6V6C021u0OxIoIuZpeZ0W6Bnd+D1xGk2ClnszzjZxAO4IkXSPoSioAKeNrOnIumYB1QRHpCLiLC+Skmx37yF0Y1Y3W96kp4G5gNjyHM9ixFMJnoy0XZbQ1Mpn6sLCvX5BgMjcucgqyk8aX5PSQfrQkIqJ4SBkUtipY47xYGkJg13bDkFm1dlJL1T4HTb5L4U9dwObEcwY3sp+feyhYLjaWnw/ozTKjyFsghJ0rxeMLMnk+1KSf3MbEraWgoXikcic8V+k5HoZUZHYoxEA13NbO8Ix12GLLXdVpLKCFcyW9BUezkhjeM3Q21iHJKzPa+KqGV5pDkSeQswlpDuCsFM4FbCOtPVEklHm9mdCoXflyHSbP/ahBqGOxLax8vAGWY2I20tCdOAT3LpgUn6eM9IWrJClpxuMbNjEh29CS6h/wR6k25fOXP9GSebeABXnNxHWG+Roz7Zt3XaQiTt3NR+S7FuimWw2C9wdt7vFcBhhFnKGDwrae80nQxbIDNtN2M8lvd7BXAI8HEkLTnulXQ90FnSyYR01ygOsxmjf0Fq68XJbOXqTC64z8KAXo5bCZ3hXDre0cm+vSLp8XvfsmzLUqdbgL7A+JwTbtqz/Qo1Jr8FbArMBv5BmIVLjYz2Z5wM4imURYikUWa2RcG+KGkHkh7N26wg2DS/neZMk6QWHQyz4gqX2MKnXrcqL821BlhM3DICmWm7rSFWCmXiWvdspBnbfB17AXsT2sxTZvZMTD3NkXIK5WvAOWb2crK9I/BnX4O3fCT9yswuS+lYTd1rltmXFsV270uDrDneSpoNTAKuA4bGyAwplv6MEx+fgStOPpN0kJk9AqBQG2l2DCFmdmD+tqQ+QNqGFFcmPysIpgvvEDqcmwFvEByuUqUgfz3ngNYrbR0Q0lwTPQMI5ygmmWm7yfEPAJ4wsyXNvCVGAW0I31XfSMduIAnYMhG0JZ29AWb2bJJ+VmZmufpradZE+jFwe7IWDkIJgVg16YqNwwnmDGkwO5lRuTvZPpKwtikWmbr3ZQEzm9qU062ZTY6kp5ukjQnrWy+VNACYaGZp3l8y159xsokHcMXJj4G7JP2DcGFPB46JK6mBGUCqtW7MbDcASf8HnGJmY5LtTWicypgmufx1WOqAdmIMIQq1xc4A1iYUid4OeJVQUDZtstZ2vw9cJekB4FYrKKybVtppMkuanw4xk0jBYxNaGhFp5vZk4BSgK9Cf0JavI2nDZjY2JR0lBFfOzSV1TI49N41jryKkWaD3BEIK3F8J7fnVZF8s8u99EJ6Vq2wx5taQ73RLSG9tQ3CkjOJ0m1zTfYF1gH5AJ/KMv9Igo/0ZJ4N4CmURI6k94Tuct9w3rzwNV7O0s1dCqJE0xcyOjqAlMykzyQxBYX2ba2PUt0nWE2xNsEfeQtIGwMVmdkTaWvI0RW+7eVo6Ekbnjyd8V7cCd2dBW0wkXUIIJO8gdLx/QBgdj1GuZBQhPfuNPEfMMRankPcwM2ty7a/TMmqiOPHqRnP3PknHmtltkWRFIWtOtwp1115O/g2LaHiTqf6Mk018Bq5IkbQ/sDFQIYVBTTO7JIKUt/J+ryN0fGO5SI2XdBNhBM8Ii9bHt/yRlcZtwFzg78n2kYSOcIz6NovMbJEkFIpoT5A0KIIOIFNtN3fsuckMXCVwJsFA5BxJfzezq9PQIOk5M9tjeftS5ttmtm3e9rWS3iD9FGmAGjOrzbUXSWVEKsoMPCPpbOAeGtfzcnvv5bPSZ+AKBhWXwcxOX9kaWsLM5jfz0hmE58bqRKacbpcXOEq62sx+lpKcLPVnnAziAVwRIuk6oB2wG3ATwe52eAwtZnabpO7J75/F0JDH8cCphAchwDDg2khaBhUsTh8q6Z1IWmZI6gw8TOh8fkEkh8Mstd1Ez4GEtKr+hAB7GzObJakd4WG5UgM4SRWE89FNUheWdnA7EuyrY1Iv6QfA/xE6EEcSnPNi8KKk84HKxFjlNODR5XxmZXEC4XycVrB/tbb3llQKnG5mf23hbfelICV/UPFiQs2+YiDN9NKsUGxOt2mmdmapP+NkEE+hLEJyKQZ5P9sDD6ZZ60thKPxC4KeEB08JYQbu6pizKUnqYl8zmxhLQ6LjX8B1BfVtjjWzwk5f2rp2IeT1P2lmtRGOH73tFui5HbipqbIXkvYws+dW8vHPIMz69QY+Ymknbi5wo5n9o7nPrmwk9QOuYmkdrVeAMyM5s5UQ1pA2OGISvrfUH2DNpEdfZ2bVaWvJGpJeMLNdY+vIEctF9uuwuqaXFovTLaT/HWWlP+NkEw/gihBJb5jZtpJeJxSPnQOMNbMBKWr4ObAfYZHt5GTfeoQRoieXMwq7sjQdBPwJaGNm60raArjEzA6KoGU8YWF2o/o2hAXRFivHPzZZaLtNaOrJ0lpMw81sVgQNP0srXfObQulawlcRUoHrk+1SoK2ZLUzj+AVa7iUE2Hclu44EOpvZ95r/1OqBpEsJA0SF6aVRrM+LKSgqpmBzdSXN9pSl/oyTTTyFsjh5LEmJ+xMwgjAKnHbawTHAXmbWYINsZh8q2DY/TXD+SpsLCUYHLyR6RiWzCDHYJ9Jxs04W2m4Dkg4H/kxoMwKulnSOmd2fpg4zuzpxGduIvFIPZnZ7mjpWkDQt4Z8D9gRy64cqCfeZHZr9xMojS+nRWSP3feRnYRgQtZ5hkRBr7XjqtOB0G61GaStJM801S/0ZJ4N4AFeEmNnvkl8fkPQYUGFmX+Vel7RXCmkI5fnBW562zySVr+RjN0edmX2VMzqIiaVcgLRYyEjbzefXwNa5WbdkPeezQKoBXGKnvSshgHsC2JfghJblAC7NC60i3/zBzOYn6xRjMFLSdgXp0atN57slchboMSkIDtpJypV5iBocJANXxxDs6Rv6XjlTFTP7aQxd5c1hIAAADe5JREFUMTCzDrE1fE2uSvFYmenPONnEA7gix8xqgJqC3Zez8ovvtrR+KvW1VQljJR0FlCoU4DydUPvHySAR224+JQUpk3MI6znT5rvA5sBIMzs+Seu8KYKOFSHN/PsFkobkUvEkbQnEWnO2LXCMpEbp0QrlOlbb9GhoSEf+A9DbzPaVtBGwvZndnJaGDAcHTwCvA2NIubZYllGGCnlLGgicQ6gDlx9k7578/FeKcrw/47SIB3CrJmkM2WyeN7JZeOyKJvanwc+ACwhBwb8JRge/j6TF+XqkPdz4pKSngLuT7SMIHa20qTazJZLqFOrSzSL7roZpfldnAvdJyrmnrkn4rmLg6dHN8y9CHcULku33COvhUgvgMkyFmZ0VW0SWUMYKeRNcUq8jpPXHctzN4f0Zp0U8gFs1Wekj42ZWurKPsSIkpgYXm9k5LO08OMVHqq5KZnaOpMMIHQYBN5jZQ2lqSHgrSbG6EXibsNYrZnmFrFjCA2BmbyoUoB9E+J4mmNnitI5foMXTo5unm5ndK+lXAGZWJyl2Rzgr3JFY5T9GXubBal4/8BCSQt4AZvaxpJgzqHVmFt2q3/szTmvwAM5ZJTCz+iStynFWCDN7AHggsoZceYnrJD0JdDSz0RH11Es6mBbMiMzsDylKguAU2o/w3BosKesmL6sjCyStQTIQI2k74KuWP7LaUEswb7qApQNVRvZn2lcmmSrkDTwq6TTgISIG2d6fcVqDB3CrCJLaJmuKAKbE1BKRkZIeIcwM5FtYPxhPkrOCTEnjIFlzQZP0nJntQTj4lMJ9kXhF0j/IgCW8pDsIxdZHsTS1yci2ycvqyFnAI0B/Sa8A3QnrO51wbtZvyvxrNSZrhbyPTX6ek7cvVpDt/RmnRTyAK0Ik3WJmJ+Rttwf+A+Q6gIfG0haZrgQTinzLagP8hpcRErv1e4B7zGxS4etptd2sGB1IqgDaAd0kdWHpurKOhOLeMcmSJfxWwEYxCnc7rcfMRkjahaWprhNjpbpmkHFA6nULM053guPvXEKb+S2hXEgUzGzdWMduAu/POC3iAVxx8pGka83s1KTT9zhxR62yQglwhpl9CZCcmyvjSnIKOIhgPnGvpCWEYO5eM5vW8sdWHpKGADsRHo4vm9nIFA//I4JBR2/C2jclOuYB/0hRxzJkwRI+j7FAL+CT2EKc5bINS1Ndh3iqawP1wChJQ2mcnnd6PEnR2cvMfkme87CkK4FfxhAjqakBxK+AMQVuxWng/RmnReQDmsWJpMuBTsCWwB+TdTyrNZJGmtng5e1zskFijfwb4AexTHEk/ZZQkDo3qvkd4D4zS9XtK9HxNzObK+k3wBDgdzHSFfM0RbeEz9MyFNiCYOyS3/k9KG0tTvM0l+q6mgcpAEg6tqn9ZnZb2lpiI+lU4DRCamJ+JkYH4BUzOzqSrseB7YGhya5dCaUfBgKXmNkdKWrx/ozTIh7AFREFo0MidH6HA0+C50Yn6Xm7mtkXyXZX4EUz2zSuMicfSf2A7xFm4uoJ6ZRRRhYljQcGm9miZLsSGGFmG6asY7SZbZbURPoDYaT1fDPbNk0dBZr+S2IJb2abSyoj1KlL/XpK0vKWwcxeTFuL0zzJ9eSprk6LSOoEdAEuA87Le2leTFdOSY8CJ5nZp8l2T+Ba4CRgmJltkqIW7884LeIplMXFgQXbI4HyZL/nRodO76uS7iecj+8Bl8aV5OQj6Q1Cm70PONzMPowsaQqhbuGiZLstjUeE0yI3W7E/cJ2Z/UfSRRF05JMZS3gze1HSOoSCv89KagdkqpSJA3iqa7NImkwTxklmttq5UJrZV4TUxCNjaymgXy54S5gFDDSzzyWlvZbT+zNOi3gAV0SY2fGxNWQZM7td0luERb8CDjWzdyPLchpzrJlNiC0ijxpgnKRnCA/JvYCXJf0dUl2f8lHixrYncLmktoQ1EDHJjCV84lB3CmFhf39gLULB3ZgunU5CMnNhhBS4dyV5quuybJX3ewUhdbtrJC1O07wk6TGW1rg8DBiWlDf4Mk0h3p9xloenUBYhkm6jicWt+c6UjpNVJO0PbEzoxABgZpc0/4mVqqXJdSk50lqfkswo7UNYLP++pDWBTc3s6TSO34ymIcDVwCaEmZXuwHdj1KeTNIpgjvFGbg2IpDGeTpQNmktxzeGprk0j6WUz2ym2DicgSYSgbUdC0PQy8ICnBDtZxGfgipPNcsEbgJl9IckXtjqZR9J1BNv83YCbCDWihsfSkxUDATNbSF4KtJl9QuQ0tIxZwteYWW3oX0GyHs87VRkhF6BJujxxFWwgMdxa7QO4ZEAkRwlhRi4T5UycQBKo3Z/8c5xM4wFccVIiqUvB4lb/Lp1iYIfErGO0mV2cWEZHW7sp6QDgd8A6hGsoSiHvDJMVS/gXJZ0PVErai+Bg92gEHU7L7MWyFvD7NrFvdeRKlg461BHW3x4eTY3TQG4mVNI8Gg8M+fPAySze6S9OfHGrU6zkzEIWSuoNfA7ELJ76N+BQQuqiz+jk0ZwlPBAjgDsPOBEYQ6id9wRhBtfJAHm28P0l5afYdgBejaMqc+xLSM/rx9K+1/eBKOnjzlJyaaxm5jOiTtHga+CKlKQmU25x63O+uNUpBpIaZ1cTzCf+SQgIbjSz30bSMxTYw8yWxDh+lnFLeKe1FNjC/xHYOXnpZTMbGU1YhpD0JMEIYwRLB0SIVULFaZqklMsAM7tVUjegg5lNjq3LcQrxGbjipSuwILnJdJe0rt9knCJgAlBvZg8kgxBDgIcj6jkXeELSizR2zftLPEmZIbolvKQxtLDWzcw2S1GO0ww5W3hJrwN3EtKiBdwm6UYzuzqqwGywtpntE1uE0zySLiSsTRxEqIHZhtCed4ypy3GawgO4IqSJm0w5fpNxioPfmNl9ySjnXoR04GuBWAWrLwXmExwx20TSkCkyZgl/QPLzJ8nPO5KfPwAWpqjDaR0nAtuZ2QJoMDB5jTDrvrrzqqRNzWxMbCFOsxwCDCbMkmJmH0vytEonk3gAV5z4TcYpVrJWsLqrme0d8fhZ5M+xBeQws6kAknY0s/wBqvMkvYKvH8oaIi89MPldkbRkjZ2A45KC3jUsNcjwWeTsUGtmJilX+7IqtiDHaQ4P4IoTv8k4xUrWClY/K2nvmPXWskZGLeGrJO1kZi8nOnYA/L6XPW4F3pD0ULL9HeDmiHqyxL6xBTjL5d7k+dRZ0snACcCNkTU5TpO4iUkRIulsYAAhBe0ywk3m377OwMk6WStYndhGtwNqgcW4bXQDkkaY2ZCCfaNjzBhI2hK4BeiU7PoSOMHMRqStxWmZpN7ZToRraZibmDjFRFKmZG9C+33KzJ6JLMlxmsQDuCLFbzKO878jqYSwnmpdM7tEUl9gTTN7I7K0aORbwgMf5L3UAXjVzH4QRRggqSPhufVVLA2O46x6SCol9KX2jK3FcVqDB3CO46y2SLoWWALsbmYbSuoCPG1mW0eWFo0sWcJLOtrM7pR0VlOvu1uo4zjfFJIeAX7oA0ROMeBr4IoQSYcClwM9CDNwnvblOF+Pbc1siKSRAGb2haTV2o0yY5bwuXVubtLkOM7KZhEwRtIzwILcTjM7PZ4kx2kan4ErQiR9ABxoZuNja3GcYkbSG8AOwJtJINedMAM3OLK06EgaDWyfZwlfBbzmrnmO46yKSDq2qf1mdlvaWhxnefgMXHHyqQdvjvON8HfgIaCHpEuB7wK/jispM2TGEl7SFcDvgWrgSWBz4EwzuzOGHsdxVj3M7LYkA2NgsmuimS2OqclxmsNn4IqIJHUSYBegF/AwjQvsPhhDl+MUM5I2APYgBCfP+eBIIFl3diwhwIVgCf8vM/tbBC2jzGwLSYckOn4ODDWzzdPW4jjOqomkXYHbgCmE50Ef4FgzGxZRluM0iQdwRYSkW5NfjWVHws3MTkhZkuM4qzBZsYSXNM7MNpZ0I/CAmT0p6R0P4BzH+aaQ9DZwlJlNTLYHAneb2ZZxlTnOsngKZRFhZscDSLoNOMPMvky2uwBXxtTmOM6qR1JnLQu11h6VNIGQQnlaslZxUWRNjuOsWpTngjcAM3tPUnlMQY7THD4DV4RIGllostDUPsdxnFWFZKBqrpnVJ4YqHcxsZmxdjuOsGki6hZDhdEey6wdAWW7w3HGyRElsAc7XoiTpzAAgqSs+m+o4ziqKpHbAT4Brk129ga3iKXIcZxXkVGAccDpwBvAu8OOoihynGXwGrgiRdAzwK+B+wmjR94BLzeyOFj/oOI5ThEi6B3gbOMbMNpFUSShpsEVkaY7jFDmS+prZtNg6HGdF8Bm4IsTMbgcOAz4FPgMO9eDNcZxVmP5mdgWwGMDMqolU0sBxnFWOh3O/SHogphDHaS2edlekmNm7hOl9x3GcVZ3aZNbNACT1J6+EiuM4zv9A/mDQetFUOM4K4AGc4ziOk1kkCbiOUMC7j6S7gB2B42LqchxnlcGa+d1xMouvgXMcx3EyTVKfaW9gO8Jo+etmNjuuKsdxVgUk1QMLCPeWSmBh7iVCjd2OsbQ5TnP4DJzjOI6TdV4H1jOzx2MLcRxn1cLMSmNrcJwVxWfgHMdxnEwj6V1gIDCVpSPlZmabRRXmOI7jOBHwAM5xHMfJNJLWaWq/mU1NW4vjOI7jxMYDOMdxHMdxHMdxnCLB68A5juM4juM4juMUCR7AOY7jOI7jOI7jFAkewDmO4ziO4ziO4xQJHsA5juM4juM4juMUCR7AOY7jOI7jOI7jFAn/DwyqC/O5UMIzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Features selection\n",
    "def plot_correlation_map( df ):\n",
    "\n",
    "    corr = df.corr()\n",
    "\n",
    "    s , ax = plt.subplots( figsize =( 15, 15 ) )\n",
    "\n",
    "    cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "\n",
    "    s = sns.heatmap(\n",
    "\n",
    "        corr, \n",
    "\n",
    "        cmap = cmap,\n",
    "\n",
    "        square=True, \n",
    "\n",
    "        cbar_kws={ 'shrink' : .9 }, \n",
    "\n",
    "        ax=ax, \n",
    "\n",
    "        annot = True, \n",
    "\n",
    "        annot_kws = { 'fontsize' : 9 }\n",
    "\n",
    "        )\n",
    "plot_correlation_map(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're going to retain the features that have a correlation coefficient with the target > 0,1 . These features are : \n",
    "# check_account(0.31)\n",
    "# Duration(0.21 in absolute)\n",
    "# amount (0.15 in absolute)\n",
    "# sav_account (0.13)\n",
    "# property (0.14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML model prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem of this project is to classify a new applicant as a good risk or a bad risk . Since it's a classification\n",
    "problem  , the classification models that we're going to use and compare are : \n",
    "\n",
    "-->Logistic regression \n",
    "\n",
    "-->KNN \n",
    "\n",
    "-->Random forest "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "What makes this problem different is that the False Positives and False negatives imply different costs :\n",
    "If an applicant is a good credit risk but was predicted as bad then this would cost the bank 1 unit . \n",
    "If an applicant is a bad credit risk but was predicted as good then this would cost the bank 5 units . \n",
    "This rule is expected since losing a good risk applicant implies the loss of only the interests on the loan , whereas approving of a bad risk applicant implies the loss of the amount of the loan + the interests . \n",
    "Therefore , the accuracy isn't the only criteria to evaluate the performance of the model . We will also need to calculate the total cost . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression evaluation structure : \n",
    "# Here's how we're going to proceed --> We're going to evaluate the performance of the model using three criteria : \n",
    "# The accuracy \n",
    "# The confusion Matrix ( from which we'll extract the precision and the recall ) \n",
    "# The total cost (since the False positives and the False negatives imply different costs in this problem )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy=0.72\n"
     ]
    }
   ],
   "source": [
    "# applying the logistic regression model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "x=df_final[[\"Duration\", \"amount\", \"check_account\", \"sav_account\",\"property\"]]\n",
    "y=df_final[\"credit_risk\"]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.35,random_state=40)\n",
    "\n",
    "logreg = LogisticRegression()   \n",
    "logreg.fit(x_train, y_train)  \n",
    "y_pred  = logreg.predict(x_test)   \n",
    "print(\"Accuracy={:.2f}\".format(logreg.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>223</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0   1\n",
       "Actual            \n",
       "0          223  29\n",
       "1           68  30"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix ( 1= Bad , 0 = Good)\n",
    "confusion_matrix=pd.crosstab(y_test,y_pred,rownames=['Actual'],colnames=['Predicted'])\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    " Interpretation : \n",
    "30 applicants predicted as bad and they are bad ( true positives) \n",
    "223 applicants predicted as good and they are good ( true negatives ) \n",
    "68 applicants predicted as good when they are bad (False negatives ) \n",
    "29 applicants predicted as bad when they are good ( False positives )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall is : 0.30612244897959184\n",
      "precision is : 0.5084745762711864\n"
     ]
    }
   ],
   "source": [
    "# Precision and recall \n",
    "recall=30/(30+68)\n",
    "print('recall is :',recall)\n",
    "precision=30/(30+29)\n",
    "print('precision is :',precision)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Interpretation : \n",
    "30.6% of the applicants that represented a bad risk are correctly predicted .\n",
    "50.8% of the applicants that were predicted as bad are actually bad ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_cost is : 369\n"
     ]
    }
   ],
   "source": [
    "#Total cost \n",
    "# 1 FN costs 5 units , 1 FP costs 1 unit \n",
    "Total_cost=68*5+29*1\n",
    "print('total_cost is :',Total_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The logistic regression built-in model that we used from the scikit learn library takes a threshhold = 0.5 , by default . \n",
    "# However , the threshold = 0,5 isn't the optimal threshold . Since we want to reduce the total_cost and since we know\n",
    "# that the FN imply a much higher cost than the FP , we would want to correctly predict the max of the bad applicants as\n",
    "# bad . \n",
    "# For that , we're going to define a function that returns a dataframe composed of the recall , precision , total_cost and \n",
    "#accuracy for all the thresholds ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're going to construct a dataframe containing all the possible metrics for different thresholds to detremine the optimal one . \n",
    "def threshold_summary():\n",
    "    lst1=[]\n",
    "    lst2=[]\n",
    "    lst3=[]\n",
    "    lst4=[]\n",
    "    lst5=[]\n",
    "    index_lst=[]\n",
    "    \n",
    "    for i in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]:  \n",
    "        y_pred_new_threshold = (logreg.predict_proba(x_test)[:,1]>=i).astype(int)\n",
    "        confusion_matrix=pd.crosstab(y_test,y_pred_new_threshold,rownames=['Actual'],colnames=['Predicted'])\n",
    "        recall=round((confusion_matrix.iloc[1,1])/(confusion_matrix.iloc[1,1]+confusion_matrix.iloc[1,0]),2)\n",
    "        precision=round((confusion_matrix.iloc[1,1])/(confusion_matrix.iloc[1,1]+confusion_matrix.iloc[0,1]),2)\n",
    "        total_cost=confusion_matrix.iloc[0,1]*1+confusion_matrix.iloc[1,0]*5\n",
    "        accuracy=round((confusion_matrix.iloc[0,0]+confusion_matrix.iloc[1,1])/confusion_matrix.sum().sum(),2)\n",
    "        f1_score=round(2*(recall*precision)/(recall+precision),2)\n",
    "        lst1.append(recall)\n",
    "        lst2.append(precision)\n",
    "        lst3.append(total_cost)\n",
    "        lst4.append(accuracy)\n",
    "        lst5.append(f1_score)\n",
    "        index_lst.append('threshold'+' '+str(i))\n",
    "        \n",
    "        \n",
    "    summary=pd.DataFrame({'recall':lst1,'precision':lst2,'total_cost':lst3,'accuracy':lst4,'f1_score':lst5},index=index_lst)\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "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>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>threshold 0.1</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.30</td>\n",
       "      <td>239</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.38</td>\n",
       "      <td>213</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.3</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.45</td>\n",
       "      <td>230</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.4</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.49</td>\n",
       "      <td>281</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.51</td>\n",
       "      <td>369</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.6</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.56</td>\n",
       "      <td>427</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.7</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.64</td>\n",
       "      <td>459</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold 0.8</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.00</td>\n",
       "      <td>470</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               recall  precision  total_cost  accuracy  f1_score\n",
       "threshold 0.1    0.97       0.30         239      0.35      0.46\n",
       "threshold 0.2    0.85       0.38         213      0.56      0.53\n",
       "threshold 0.3    0.70       0.45         230      0.67      0.55\n",
       "threshold 0.4    0.54       0.49         281      0.71      0.51\n",
       "threshold 0.5    0.31       0.51         369      0.72      0.39\n",
       "threshold 0.6    0.15       0.56         427      0.73      0.24\n",
       "threshold 0.7    0.07       0.64         459      0.73      0.13\n",
       "threshold 0.8    0.04       1.00         470      0.73      0.08"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "# According to the table , the total_cost is minmized when the threshold is 0.2 --> Total cost is 213.\n",
    "#In this case , 84% of the applicants representing a bad risk are correctly predicted as bad , 37 % of the applicants \n",
    "#that we predicted to be bad are actually bad and 56% of applicants are correctly predicted ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min cost for the logistic regression is 213 with an accuracy = 56% for a threshold = 0.2\n",
    "\n",
    "The best accuracy for the logistic regression is 73% with a total_cost of 427 for a threshold = 0.6\n",
    "\n",
    "The best f1_score for the logistic regression is 55% with a total_cost of 230 and an accuracy of 67% for a threshold = 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're going to construct a dataframe containing the total_cost and accuracy for each number of neighbors to determine\n",
    "# the optimal one .\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "x=df_final[[\"Duration\", \"amount\", \"check_account\", \"sav_account\",\"property\"]]\n",
    "y=df_final[\"credit_risk\"]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.35,random_state=40)\n",
    "lst_accuracy=[]\n",
    "lst_cost=[]\n",
    "lst_f1_score=[]\n",
    "lst_index=[]\n",
    "for i in range(1,31):\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x_train,y_train)\n",
    "    y_pred=knn.predict(x_test)\n",
    "    lst_index.append('for'+' '+'n_neighbors'+' '+str(i))\n",
    "    lst_accuracy.append(round(accuracy_score(y_pred,y_test),2))\n",
    "    confusion_matrix=pd.crosstab(y_test,y_pred,rownames=['Actual'],colnames=['Predicted'])\n",
    "    total_cost=confusion_matrix.iloc[0,1]*1+confusion_matrix.iloc[1,0]*5\n",
    "    lst_f1_score.append(round(f1_score(y_test, y_pred),2))\n",
    "    lst_cost.append(total_cost)\n",
    "summary_knn=pd.DataFrame({'total_cost':lst_cost,'accuracy':lst_accuracy,'f1_score':lst_f1_score},index=lst_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "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>total_cost</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 3</th>\n",
       "      <td>419</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 1</th>\n",
       "      <td>424</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 9</th>\n",
       "      <td>432</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 7</th>\n",
       "      <td>433</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 8</th>\n",
       "      <td>433</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 27</th>\n",
       "      <td>441</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 19</th>\n",
       "      <td>443</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 13</th>\n",
       "      <td>443</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 5</th>\n",
       "      <td>445</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 17</th>\n",
       "      <td>446</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 15</th>\n",
       "      <td>446</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 12</th>\n",
       "      <td>448</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 18</th>\n",
       "      <td>448</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 10</th>\n",
       "      <td>449</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 6</th>\n",
       "      <td>450</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 28</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 11</th>\n",
       "      <td>451</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 30</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 29</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 23</th>\n",
       "      <td>452</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 2</th>\n",
       "      <td>452</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 25</th>\n",
       "      <td>453</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 16</th>\n",
       "      <td>453</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 4</th>\n",
       "      <td>454</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 14</th>\n",
       "      <td>458</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 26</th>\n",
       "      <td>463</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 24</th>\n",
       "      <td>466</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 20</th>\n",
       "      <td>470</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 21</th>\n",
       "      <td>470</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 22</th>\n",
       "      <td>473</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_cost  accuracy  f1_score\n",
       "for n_neighbors 3          419      0.61      0.29\n",
       "for n_neighbors 1          424      0.57      0.28\n",
       "for n_neighbors 9          432      0.69      0.24\n",
       "for n_neighbors 7          433      0.68      0.24\n",
       "for n_neighbors 8          433      0.71      0.23\n",
       "for n_neighbors 27         441      0.72      0.20\n",
       "for n_neighbors 19         443      0.72      0.20\n",
       "for n_neighbors 13         443      0.69      0.21\n",
       "for n_neighbors 5          445      0.64      0.22\n",
       "for n_neighbors 17         446      0.71      0.19\n",
       "for n_neighbors 15         446      0.70      0.20\n",
       "for n_neighbors 12         448      0.70      0.19\n",
       "for n_neighbors 18         448      0.73      0.17\n",
       "for n_neighbors 10         449      0.70      0.19\n",
       "for n_neighbors 6          450      0.69      0.19\n",
       "for n_neighbors 28         451      0.73      0.16\n",
       "for n_neighbors 11         451      0.67      0.20\n",
       "for n_neighbors 30         451      0.73      0.16\n",
       "for n_neighbors 29         451      0.73      0.16\n",
       "for n_neighbors 23         452      0.70      0.17\n",
       "for n_neighbors 2          452      0.68      0.19\n",
       "for n_neighbors 25         453      0.71      0.17\n",
       "for n_neighbors 16         453      0.72      0.16\n",
       "for n_neighbors 4          454      0.70      0.17\n",
       "for n_neighbors 14         458      0.71      0.15\n",
       "for n_neighbors 26         463      0.72      0.12\n",
       "for n_neighbors 24         466      0.71      0.12\n",
       "for n_neighbors 20         470      0.71      0.11\n",
       "for n_neighbors 21         470      0.70      0.12\n",
       "for n_neighbors 22         473      0.70      0.10"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_knn.sort_values(by='total_cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "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>total_cost</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 30</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 18</th>\n",
       "      <td>448</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 29</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 28</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 27</th>\n",
       "      <td>441</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 26</th>\n",
       "      <td>463</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 19</th>\n",
       "      <td>443</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 16</th>\n",
       "      <td>453</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 20</th>\n",
       "      <td>470</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 14</th>\n",
       "      <td>458</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 25</th>\n",
       "      <td>453</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 17</th>\n",
       "      <td>446</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 8</th>\n",
       "      <td>433</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 24</th>\n",
       "      <td>466</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 23</th>\n",
       "      <td>452</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 4</th>\n",
       "      <td>454</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 12</th>\n",
       "      <td>448</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 15</th>\n",
       "      <td>446</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 10</th>\n",
       "      <td>449</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 21</th>\n",
       "      <td>470</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 22</th>\n",
       "      <td>473</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 6</th>\n",
       "      <td>450</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 9</th>\n",
       "      <td>432</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 13</th>\n",
       "      <td>443</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 7</th>\n",
       "      <td>433</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 2</th>\n",
       "      <td>452</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 11</th>\n",
       "      <td>451</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 5</th>\n",
       "      <td>445</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 3</th>\n",
       "      <td>419</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 1</th>\n",
       "      <td>424</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_cost  accuracy  f1_score\n",
       "for n_neighbors 30         451      0.73      0.16\n",
       "for n_neighbors 18         448      0.73      0.17\n",
       "for n_neighbors 29         451      0.73      0.16\n",
       "for n_neighbors 28         451      0.73      0.16\n",
       "for n_neighbors 27         441      0.72      0.20\n",
       "for n_neighbors 26         463      0.72      0.12\n",
       "for n_neighbors 19         443      0.72      0.20\n",
       "for n_neighbors 16         453      0.72      0.16\n",
       "for n_neighbors 20         470      0.71      0.11\n",
       "for n_neighbors 14         458      0.71      0.15\n",
       "for n_neighbors 25         453      0.71      0.17\n",
       "for n_neighbors 17         446      0.71      0.19\n",
       "for n_neighbors 8          433      0.71      0.23\n",
       "for n_neighbors 24         466      0.71      0.12\n",
       "for n_neighbors 23         452      0.70      0.17\n",
       "for n_neighbors 4          454      0.70      0.17\n",
       "for n_neighbors 12         448      0.70      0.19\n",
       "for n_neighbors 15         446      0.70      0.20\n",
       "for n_neighbors 10         449      0.70      0.19\n",
       "for n_neighbors 21         470      0.70      0.12\n",
       "for n_neighbors 22         473      0.70      0.10\n",
       "for n_neighbors 6          450      0.69      0.19\n",
       "for n_neighbors 9          432      0.69      0.24\n",
       "for n_neighbors 13         443      0.69      0.21\n",
       "for n_neighbors 7          433      0.68      0.24\n",
       "for n_neighbors 2          452      0.68      0.19\n",
       "for n_neighbors 11         451      0.67      0.20\n",
       "for n_neighbors 5          445      0.64      0.22\n",
       "for n_neighbors 3          419      0.61      0.29\n",
       "for n_neighbors 1          424      0.57      0.28"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_knn.sort_values(by='accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "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>total_cost</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 3</th>\n",
       "      <td>419</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 1</th>\n",
       "      <td>424</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 7</th>\n",
       "      <td>433</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 9</th>\n",
       "      <td>432</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 8</th>\n",
       "      <td>433</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 5</th>\n",
       "      <td>445</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 13</th>\n",
       "      <td>443</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 15</th>\n",
       "      <td>446</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 27</th>\n",
       "      <td>441</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 19</th>\n",
       "      <td>443</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 11</th>\n",
       "      <td>451</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 17</th>\n",
       "      <td>446</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 2</th>\n",
       "      <td>452</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 12</th>\n",
       "      <td>448</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 10</th>\n",
       "      <td>449</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 6</th>\n",
       "      <td>450</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 23</th>\n",
       "      <td>452</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 18</th>\n",
       "      <td>448</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 4</th>\n",
       "      <td>454</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 25</th>\n",
       "      <td>453</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 29</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 28</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 16</th>\n",
       "      <td>453</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 30</th>\n",
       "      <td>451</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 14</th>\n",
       "      <td>458</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 24</th>\n",
       "      <td>466</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 26</th>\n",
       "      <td>463</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 21</th>\n",
       "      <td>470</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 20</th>\n",
       "      <td>470</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_neighbors 22</th>\n",
       "      <td>473</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_cost  accuracy  f1_score\n",
       "for n_neighbors 3          419      0.61      0.29\n",
       "for n_neighbors 1          424      0.57      0.28\n",
       "for n_neighbors 7          433      0.68      0.24\n",
       "for n_neighbors 9          432      0.69      0.24\n",
       "for n_neighbors 8          433      0.71      0.23\n",
       "for n_neighbors 5          445      0.64      0.22\n",
       "for n_neighbors 13         443      0.69      0.21\n",
       "for n_neighbors 15         446      0.70      0.20\n",
       "for n_neighbors 27         441      0.72      0.20\n",
       "for n_neighbors 19         443      0.72      0.20\n",
       "for n_neighbors 11         451      0.67      0.20\n",
       "for n_neighbors 17         446      0.71      0.19\n",
       "for n_neighbors 2          452      0.68      0.19\n",
       "for n_neighbors 12         448      0.70      0.19\n",
       "for n_neighbors 10         449      0.70      0.19\n",
       "for n_neighbors 6          450      0.69      0.19\n",
       "for n_neighbors 23         452      0.70      0.17\n",
       "for n_neighbors 18         448      0.73      0.17\n",
       "for n_neighbors 4          454      0.70      0.17\n",
       "for n_neighbors 25         453      0.71      0.17\n",
       "for n_neighbors 29         451      0.73      0.16\n",
       "for n_neighbors 28         451      0.73      0.16\n",
       "for n_neighbors 16         453      0.72      0.16\n",
       "for n_neighbors 30         451      0.73      0.16\n",
       "for n_neighbors 14         458      0.71      0.15\n",
       "for n_neighbors 24         466      0.71      0.12\n",
       "for n_neighbors 26         463      0.72      0.12\n",
       "for n_neighbors 21         470      0.70      0.12\n",
       "for n_neighbors 20         470      0.71      0.11\n",
       "for n_neighbors 22         473      0.70      0.10"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_knn.sort_values(by='f1_score',ascending=False)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "It is clear that the Logistics regression is a better model than the KNN classifier since the minimum cost of KNN is 419 with an accuracy of 57% whereas the minimum cost of the logistics regression is 213 with almost the same accuracy ( 56 %)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min cost of KNN is 419 with an accuracy of 57 % for n_neighbors=3\n",
    "\n",
    "The best accuracy of knn is 73% with a total_cost= 448 for n_neighbors =18\n",
    "\n",
    "The best f1_score of KNN (29%) corresponds to the min cost ( 419 ) for n_neighbors=3 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We're going to construct a dataframe that contains the accuracy , total_cost and f1_score to determine the optimal number of trees . \n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn import metrics \n",
    "x=df_final[[\"Duration\", \"amount\", \"check_account\", \"sav_account\",\"property\"]]\n",
    "y=df_final[\"credit_risk\"]\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.35,random_state=40)\n",
    " \n",
    "lst_accuracy=[]\n",
    "lst_cost=[]\n",
    "lst_f1_score=[]\n",
    "lst_index=[]\n",
    "for i in range(1,20):\n",
    "    clf=RandomForestClassifier(n_estimators=i)\n",
    "    clf.fit(x_train, y_train) \n",
    "    y_pred=clf.predict(x_test)\n",
    "    lst_index.append('for'+' '+'n_estimators'+' '+str(i))\n",
    "    lst_accuracy.append(round(accuracy_score(y_pred,y_test),2))\n",
    "    confusion_matrix=pd.crosstab(y_test,y_pred,rownames=['Actual'],colnames=['Predicted'])\n",
    "    total_cost=confusion_matrix.iloc[0,1]*1+confusion_matrix.iloc[1,0]*5\n",
    "    lst_cost.append(total_cost)\n",
    "    lst_f1_score.append(round(metrics.f1_score(y_test, y_pred),2))\n",
    "summary_random_forest=pd.DataFrame({'total_cost':lst_cost,'accuracy':lst_accuracy,'f1_score':lst_f1_score},index=lst_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "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>total_cost</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>for n_estimators 13</th>\n",
       "      <td>256</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 7</th>\n",
       "      <td>283</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 11</th>\n",
       "      <td>290</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 17</th>\n",
       "      <td>291</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 19</th>\n",
       "      <td>294</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 9</th>\n",
       "      <td>294</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 5</th>\n",
       "      <td>300</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 14</th>\n",
       "      <td>304</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 16</th>\n",
       "      <td>308</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 15</th>\n",
       "      <td>309</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 8</th>\n",
       "      <td>309</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 12</th>\n",
       "      <td>316</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 18</th>\n",
       "      <td>318</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 1</th>\n",
       "      <td>334</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 10</th>\n",
       "      <td>339</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 3</th>\n",
       "      <td>341</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 4</th>\n",
       "      <td>345</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 6</th>\n",
       "      <td>352</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for n_estimators 2</th>\n",
       "      <td>379</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     total_cost  accuracy  f1_score\n",
       "for n_estimators 13         256      0.74      0.55\n",
       "for n_estimators 7          283      0.73      0.52\n",
       "for n_estimators 11         290      0.73      0.51\n",
       "for n_estimators 17         291      0.73      0.51\n",
       "for n_estimators 19         294      0.72      0.50\n",
       "for n_estimators 9          294      0.72      0.50\n",
       "for n_estimators 5          300      0.71      0.49\n",
       "for n_estimators 14         304      0.73      0.49\n",
       "for n_estimators 16         308      0.75      0.49\n",
       "for n_estimators 15         309      0.72      0.48\n",
       "for n_estimators 8          309      0.75      0.49\n",
       "for n_estimators 12         316      0.71      0.47\n",
       "for n_estimators 18         318      0.70      0.46\n",
       "for n_estimators 1          334      0.67      0.43\n",
       "for n_estimators 10         339      0.72      0.43\n",
       "for n_estimators 3          341      0.67      0.42\n",
       "for n_estimators 4          345      0.72      0.43\n",
       "for n_estimators 6          352      0.71      0.41\n",
       "for n_estimators 2          379      0.73      0.36"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_random_forest.sort_values(by='total_cost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min total cost of the Random Forest model is 256 with an accuracy of 74% fo a number of trees = 13\n",
    "\n",
    "The best accuracy of the Random forest model is 75% for a number of trees = 16 \n",
    "\n",
    "The best f1_score of the Random Forest model is 55% for a number of trees = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now , let's compare all three models in a plot : \n",
    "accuracies = [0.73,0.75,0.73]\n",
    "total_costs = [419,256,213]\n",
    "f1_scores=[0.29,0.55,0.55]\n",
    "models=['KNN','RANDOM FOREST','LOGISTIC']\n",
    "plt.figure(figsize = (15,5))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title('accuracy')\n",
    "plt.bar(models,accuracies)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title('total_cost')\n",
    "plt.bar(models,total_costs,color='red')\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.title('f1_score')\n",
    "plt.bar(models,f1_scores,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we take the accuracy as the evaluation criteria then the RANDOM FOREST ( for n_trees = 13) is the best model .\n",
    "\n",
    "\n",
    "If we take the total_cost as the evaluation criteria the LOGISTIC REGRESSION ( for a threshold = 0.2 ) is the best model . \n",
    "\n",
    "If we take the f1_score as the evaluation criteria then LOGISTIC and RANDOM FOREST are the best and this case ,the choice would on whether we prioritize the total cost over the accuracy or not . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}