EmailSpamDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>for</th>\n",
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       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "<p>5 rows × 3001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   the  to  ect  and  for  of    a  you  hou  in  ...  connevey  jay  valued  \\\n",
       "0    0   0    1    0    0   0    2    0    0   0  ...         0    0       0   \n",
       "1    8  13   24    6    6   2  102    1   27  18  ...         0    0       0   \n",
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       "3    0   5   22    0    5   1   51    2   10   1  ...         0    0       0   \n",
       "4    7   6   17    1    5   2   57    0    9   3  ...         0    0       0   \n",
       "\n",
       "   lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
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       "4    0               0         0         0   1    0           0  \n",
       "\n",
       "[5 rows x 3001 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(r'emails.csv')\n",
    "df=pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5172 entries, 0 to 5171\n",
      "Columns: 3001 entries, the to Prediction\n",
      "dtypes: int64(3001)\n",
      "memory usage: 118.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the</th>\n",
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       "      <th>hou</th>\n",
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       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
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       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.640565</td>\n",
       "      <td>6.188128</td>\n",
       "      <td>5.143852</td>\n",
       "      <td>3.075599</td>\n",
       "      <td>3.124710</td>\n",
       "      <td>2.627030</td>\n",
       "      <td>55.517401</td>\n",
       "      <td>2.466551</td>\n",
       "      <td>2.024362</td>\n",
       "      <td>10.600155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005027</td>\n",
       "      <td>0.012568</td>\n",
       "      <td>0.010634</td>\n",
       "      <td>0.098028</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.914733</td>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.290023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.745009</td>\n",
       "      <td>9.534576</td>\n",
       "      <td>14.101142</td>\n",
       "      <td>6.045970</td>\n",
       "      <td>4.680522</td>\n",
       "      <td>6.229845</td>\n",
       "      <td>87.574172</td>\n",
       "      <td>4.314444</td>\n",
       "      <td>6.967878</td>\n",
       "      <td>19.281892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105788</td>\n",
       "      <td>0.199682</td>\n",
       "      <td>0.116693</td>\n",
       "      <td>0.569532</td>\n",
       "      <td>0.096252</td>\n",
       "      <td>0.138908</td>\n",
       "      <td>0.072145</td>\n",
       "      <td>2.780203</td>\n",
       "      <td>0.098086</td>\n",
       "      <td>0.453817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>210.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 3001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               the           to          ect          and          for  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000   \n",
       "mean      6.640565     6.188128     5.143852     3.075599     3.124710   \n",
       "std      11.745009     9.534576    14.101142     6.045970     4.680522   \n",
       "min       0.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "25%       0.000000     1.000000     1.000000     0.000000     1.000000   \n",
       "50%       3.000000     3.000000     1.000000     1.000000     2.000000   \n",
       "75%       8.000000     7.000000     4.000000     3.000000     4.000000   \n",
       "max     210.000000   132.000000   344.000000    89.000000    47.000000   \n",
       "\n",
       "                of            a          you          hou           in  ...  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000  ...   \n",
       "mean      2.627030    55.517401     2.466551     2.024362    10.600155  ...   \n",
       "std       6.229845    87.574172     4.314444     6.967878    19.281892  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000    12.000000     0.000000     0.000000     1.000000  ...   \n",
       "50%       1.000000    28.000000     1.000000     0.000000     5.000000  ...   \n",
       "75%       2.000000    62.250000     3.000000     1.000000    12.000000  ...   \n",
       "max      77.000000  1898.000000    70.000000   167.000000   223.000000  ...   \n",
       "\n",
       "          connevey          jay       valued          lay  infrastructure  \\\n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000     5172.000000   \n",
       "mean      0.005027     0.012568     0.010634     0.098028        0.004254   \n",
       "std       0.105788     0.199682     0.116693     0.569532        0.096252   \n",
       "min       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "max       4.000000     7.000000     2.000000    12.000000        3.000000   \n",
       "\n",
       "          military     allowing           ff          dry   Prediction  \n",
       "count  5172.000000  5172.000000  5172.000000  5172.000000  5172.000000  \n",
       "mean      0.006574     0.004060     0.914733     0.006961     0.290023  \n",
       "std       0.138908     0.072145     2.780203     0.098086     0.453817  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000     1.000000  \n",
       "max       4.000000     3.000000   114.000000     4.000000     1.000000  \n",
       "\n",
       "[8 rows x 3001 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "the           0\n",
       "to            0\n",
       "ect           0\n",
       "and           0\n",
       "for           0\n",
       "             ..\n",
       "military      0\n",
       "allowing      0\n",
       "ff            0\n",
       "dry           0\n",
       "Prediction    0\n",
       "Length: 3001, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Representation of Null Values'}>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/YMOGDejXr59kdvB5mZiY4IMPPsCoUaO06lq2bImOHTtCqVQiPz8ftra22LJlyzMltH9HEAS89dZbWLRoEQBgwYIFkvqqfJ/lBQUFwcXFBWPGjMGMGTNgYmKCdevWwd7eXrKdt5WVFVavXo3hw4ejXbt2GDx4sKbNjz/+iM6dO2PlypW4dOmS5t8nLy8vmJqaIj4+Hnl5eZLt24mIyIiMuuceEenVk62KU1NTterKyspEd3d30d3dXbP99pUrV8QRI0aIjo6OYp06dcSGDRuKr7/+uvjdd99p9Xnw4EFx/Pjxoo2NjVi3bl1x6NCh4u3btyXPcHV1FXv37q0ztnv37olKpVJs1qyZaGZmJtavX1/09/cXly5dqtl2/LvvvhODgoJEBwcH0czMTHRxcREnTJgg5uTkVLmvJ9uD69p+GeW2nH706JE4ZcoU0d7eXhQEQbINdGxsrPjyyy+Lcrlc9PDwEOPi4nRuFX3x4kWxa9euooWFhQhAs7V0Rdtjr1y5UvTw8BDr1KkjNmjQQHz77bfFO3fuSNp069ZNbNmypVb8urbR1qW0tFScP3++2KRJE7FOnTpi48aNRaVSKdkS/kl/z7I9ePlnubu7a20PLoqP/zkLDAwU5XK52KBBA3HWrFnivn379Lo9+BPnzp0TAYhyuVzr9ymKlf8+y28PLoqiePLkSdHPz0/zz+by5cufuv15cHCwqFAoRHNzc9Hd3V0cOXKkeOLECVEURfHWrVvipEmTRA8PD9HS0lJUKBSin5+f+O2331Z6rEREZFiCKBp5T1ciqtHWr1+PUaNGITU1VbNBBBEREdGLju8oERERERERlVOjE6VVq1bBzc0N5ubm8PPzwy+//GLskIiIiIiIqBaosYnS1q1bERkZiXnz5uHUqVPw9vZGcHCw1mGSRERERERE+lZjE6Xly5dj3LhxGDVqFLy8vLBmzRq89NJLWLdunbFDI6pVRo4cCVEU+X4SERERAajaqq9z584hPDwcbm5uEARB69iKyvZZUlKCSZMmwc7ODnXr1kV4eDjy8vL0OSwtNTJRevjwIU6ePCk5rE8mkyEwMBApKSlGjIyIiIiIqPaq6qqv+/fvo2nTpli8eDEcHR2fuc/p06fjhx9+wLZt23Dw4EHcuHED/fv3N8gYn6iRu97duHEDDRs2xNGjR9GpUydN+XvvvYeDBw/i+PHjRoyOiIiIiKh28vPzQ4cOHbBy5UoAgFqtRuPGjTFlyhTMnDnzqfe6ublh2rRpmDZtWpX6LCwshL29PTZv3ow333wTAHDx4kV4enoiJSUFHTt21P9AUUNnlJ6FSqXC3bt3JRdPNyciIiIierrK/h1tiFVflenz5MmTKC0tlbTx8PCAi4uLQVebmRqs5+dQv359mJiYaK07zMvLq3DKLjo6GvPnz5eUffDBLMyePdtgcRIRERHR8zE3tzB2CBXyGOds7BCqxeCG47X+jp43bx6ioqIkZbdu3UJZWRkaNGggKW/QoAEuXrz4TM+uTJ+5ubkwMzODtbW1Vpvc3Nxnem5l1MgZJTMzM/j6+uLAgQOaMrVajQMHDkiW4v2VUqlEYWGh5JoxY0Z1hUxERERE9I+k6+9opVJp7LCMrkbOKAFAZGQkIiIi0L59e7zyyitYsWIFiouLMWrUKJ3t5XI55HK5pKyk5EF1hEpERERE9I+l6+9oXZ5l1Zc++nR0dMTDhw9RUFAgmVV6nudWRo2cUQKAQYMGYenSpZg7dy7atm2LtLQ0JCQkaE3LERERERGR4T3Lqi999Onr64s6depI2qSnpyMrK+uZn1sZNXZGCQAmT56MyZMnGzsMIiIiIqqFhBo7pWA8f7fqa8SIEWjYsCGio6MBPN6s4fz585qfs7OzkZaWhrp166JZs2aV6lOhUGDMmDGIjIyEra0trKysMGXKFHTq1MlgO94BNTxRIiIiIiKimmPQoEH4888/MXfuXOTm5qJt27aSVV9ZWVmQyf4vw7xx4wZ8fHw0n5cuXYqlS5eiW7duSEpKqlSfAPCf//wHMpkM4eHhUKlUCA4Oxueff27QsdbIc5T0he8oEREREdVsNXnXO88JtWPXuwtf3DB2CDUSJxSJiIiIiIjKYaJERERERERUjtESpezsbAwbNgx2dnawsLBA69atceLECU19VFQUPDw8YGlpCRsbGwQGBuL48ePGCpeIiIiIahmZTKgVF+lmlETpzp076Ny5M+rUqYM9e/bg/PnzWLZsGWxsbDRtmjdvjpUrV+K3337D4cOH4ebmhqCgIPz555/GCJmIiIiIiGoRo2zmMHPmTBw5cgSHDh2q9D13796FQqHA/v370bNnz0rdw80ciIiIiGq2mryZQ8u3Gxo7hGpxbnW2sUOokYwyo7Rr1y60b98eAwYMgIODA3x8fPDll19W2P7hw4dYu3YtFAoFvL29qzFSIiIiIiKqjYxyjtLvv/+O1atXIzIyErNmzUJqaiqmTp0KMzMzREREaNrt3r0bgwcPxv379+Hk5IR9+/ahfv36OvtUqVRQqVSSMlFUQy6XG3QsRERERPRiEvj6Tq1mlBkltVqNdu3aYdGiRfDx8cH48eMxbtw4rFmzRtIuICAAaWlpOHr0KEJCQjBw4EDcvHlTZ5/R0dFQKBSSKyYmpjqGQ0RERERELxijJEpOTk7w8vKSlHl6eiIrK0tSZmlpiWbNmqFjx46IjY2FqakpYmNjdfapVCpRWFgouWbMmGGwMRARERER0YvLKEvvOnfujPT0dEnZpUuX4Orq+tT71Gq11vK6J+RyudYyO27mQEREREREz8IoM0rTp0/HsWPHsGjRImRkZGDz5s1Yu3YtJk2aBAAoLi7GrFmzcOzYMVy7dg0nT57E6NGjkZ2djQEDBhgjZCIiIiIiqkWMMqPUoUMHxMfHQ6lUYsGCBWjSpAlWrFiBoUOHAgBMTExw8eJFbNiwAbdu3YKdnR06dOiAQ4cOoWXLlsYImYiIiIhqGZlRphSopjDKOUrVhUvviIiIiGq2mnyOUpvJteMcpTMreY6SLsyTiYiIiIiIymGiREREREREVI5R3lEiIiIiIqrpBBlPnK3N9D6jlJycjLCwMDg7O0MQBOzYsUNTV1paivfffx+tW7eGpaUlnJ2dMWLECNy4cUOrnx9//BF+fn6wsLCAjY0N+vXrp+9QiYiIiIiIdNJ7olRcXAxvb2+sWrVKq+7+/fs4deoU5syZg1OnTmH79u1IT09Hnz59JO2+//57DB8+HKNGjcLp06dx5MgRvPXWW/oOlYiIiIiISCeD7nonCALi4+OfOhuUmpqKV155BdeuXYOLiwsePXoENzc3zJ8/H2PGjHmu53PXOyIiIqKarSbveuc9tZGxQ6gWpz+9buwQaiSjb+ZQWFgIQRBgbW0NADh16hSys7Mhk8ng4+MDJycnhIaG4uzZs8YNlIiIiIiIag2jbuZQUlKC999/H0OGDIGVlRUA4PfffwcAREVFYfny5XBzc8OyZcvQvXt3XLp0Cba2tjr7UqlUUKlUkjJRVEMulxt2EERERET0QuKBs7Wb0b7+0tJSDBw4EKIoYvXq1ZpytVoNAPjggw8QHh4OX19fxMXFQRAEbNu2rcL+oqOjoVAoJFdMTIzBx0FERERERC8eo8woPUmSrl27hsTERM1sEgA4OTkBALy8vDRlcrkcTZs2RVZWVoV9KpVKREZGSspEUa3nyImIiIiIqDao9hmlJ0nS5cuXsX//ftjZ2UnqfX19IZfLkZ6eLrnn6tWrcHV1rbBfuVwOKysrycVld0RERERE9Cz0PqNUVFSEjIwMzefMzEykpaXB1tYWTk5OePPNN3Hq1Cns3r0bZWVlyM3NBQDY2trCzMwMVlZWmDhxIubNm4fGjRvD1dVVs4RuwIAB+g6XiIiIiEgnge8o1Wp6T5ROnDiBgIAAzecny+EiIiIQFRWFXbt2AQDatm0rue/nn39G9+7dAQAxMTEwNTXF8OHD8eDBA/j5+SExMRE2Njb6DpeIiIiIiEiLQc9RMjaeo0RERERUs9Xkc5TaRdaOc5ROLec5SrpwQpGIiIiIiKgcJkpERERERETlGPXAWSIiIiKimkomCMYOgYxI7zNKycnJCAsLg7OzMwRBwI4dOyT1RUVFmDx5Mho1agQLCwt4eXlhzZo1kjZXrlzBG2+8AXt7e1hZWWHgwIHIy8vTd6hEREREREQ66T1RKi4uhre3N1atWqWzPjIyEgkJCdi0aRMuXLiAadOmYfLkyZrd8IqLixEUFARBEJCYmIgjR47g4cOHCAsLg1rNA2SJiIiIiMjw9L70LjQ0FKGhoRXWHz16FBEREZqtwMePH48vvvgCv/zyC/r06YMjR47g6tWr+PXXX2FlZQUA2LBhA2xsbJCYmIjAwEB9h0xERERERCRR7Zs5+Pv7Y9euXcjOzoYoivj5559x6dIlBAUFAQBUKhUEQYBcLtfcY25uDplMhsOHD1d3uERERERUSwmy2nGRbtX+q/nss8/g5eWFRo0awczMDCEhIVi1ahW6du0KAOjYsSMsLS3x/vvv4/79+yguLsa///1vlJWVIScnp8J+VSoV7t69K7lUKlV1DYuIiIiIiF4gRkmUjh07hl27duHkyZNYtmwZJk2ahP379wMA7O3tsW3bNvzwww+oW7cuFAoFCgoK0K5dO8hkFYcbHR0NhUIhuWJiYqprWERERERE9AIRRFEUDda5ICA+Ph79+vUDADx48AAKhQLx8fHo3bu3pt3YsWNx/fp1JCQkSO6/desWTE1NYW1tDUdHR7z77ruYMWOGzmepVCqtGSRRVEuW8BERERFRzWJubmHsECrUYUZjY4dQLVJj/jB2CDVStZ6jVFpaitLSUq2ZIRMTE5072tWvXx8AkJiYiJs3b6JPnz4V9i2Xy7WSopKSB3qImoiIiIiIahu9J0pFRUXIyMjQfM7MzERaWhpsbW3h4uKCbt26YcaMGbCwsICrqysOHjyIr776CsuXL9fcExcXB09PT9jb2yMlJQXvvPMOpk+fjhYtWug7XCIiIiIinZ7y1gfVAnpfepeUlISAgACt8oiICKxfvx65ublQKpX46aefkJ+fD1dXV4wfPx7Tp0+H8P9PP545cybWr1+P/Px8uLm5YeLEiZL6yuKMEhEREVHNVpOX3vm9XzuW3h1fwqV3uhj0HSVjY6JEREREVLMxUTI+Jkq6cUKRiIiIiIionGrdzIGIiIiI6J+Ch7HWbvz6iYiIiIiIytF7ohQdHY0OHTqgXr16cHBwQL9+/ZCeni5p0717dwiCILkmTpyoqT99+jSGDBmCxo0bw8LCAp6envjkk0/0HSoREREREZFOel96d/DgQUyaNAkdOnTAo0ePMGvWLAQFBeH8+fOwtLTUtBs3bhwWLFig+fzSSy9pfj558iQcHBywadMmNG7cGEePHsX48eNhYmKCyZMn6ztkIiIiIiIiCb0nSgkJCZLP69evh4ODA06ePImuXbtqyl966SU4Ojrq7GP06NGSz02bNkVKSgq2b9/ORImIiIiIiAzO4O8oFRYWAgBsbW0l5V9//TXq16+PVq1aQalU4v79+3/bT/k+iIiIiIgMRSYTasVFuhl01zu1Wo1p06ahc+fOaNWqlab8rbfegqurK5ydnXHmzBm8//77SE9Px/bt23X2c/ToUWzduhU//vhjhc9SqVRQqVSSMlFUQy6X62cwRERERERUaxg0UZo0aRLOnj2Lw4cPS8rHjx+v+bl169ZwcnJCz549ceXKFbi7u0vanj17Fn379sW8efMQFBRU4bOio6Mxf/58SdkHH8zC7Nmz9TASIiIiIiKqTQRRFEVDdDx58mTs3LkTycnJaNKkyVPbFhcXo27dukhISEBwcLCm/Pz58wgICMDYsWPx0UcfPbUPzigRERER/fOYm1sYO4QK+X/gYuwQqsXRj7KMHUKNpPcZJVEUMWXKFMTHxyMpKelvkyQASEtLAwA4OTlpys6dO4cePXogIiLib5MkAJDL5VpJUUnJg6oFT0RERET0/wl8fadW03uiNGnSJGzevBk7d+5EvXr1kJubCwBQKBSwsLDAlStXsHnzZvTq1Qt2dnY4c+YMpk+fjq5du6JNmzYAHi+369GjB4KDgxEZGanpw8TEBPb29voOmYiIiIiISELvS++EClLvuLg4jBw5En/88QeGDRuGs2fPori4GI0bN8Ybb7yB2bNnw8rKCgAQFRWl9b4RALi6uuLq1auVjoUzSkREREQ1W01eetd5du1YenfkQy6908Vg7yjVBEyUiIiIiGo2JkrGx0RJN4Ofo0RERERERPRPY9DtwYmIiIiI/qlknFKo1fj1ExERERERlaP3RGn16tVo06YNrKysYGVlhU6dOmHPnj2a+rVr16J79+6wsrKCIAgoKCjQ6sPNzQ2CIEiuxYsX6ztUIiIiIiIinfSeKDVq1AiLFy/GyZMnceLECfTo0QN9+/bFuXPnAAD3799HSEgIZs2a9dR+FixYgJycHM01ZcoUfYdKRERERESkk97fUQoLC5N8/uijj7B69WocO3YMLVu2xLRp0wAASUlJT+2nXr16cHR01Hd4RERERESVIsh44mxtZtB3lMrKyrBlyxYUFxejU6dOVbp38eLFsLOzg4+PD2JiYvDo0SMDRUlERERERCRlkF3vfvvtN3Tq1AklJSWoW7cu4uPj4eXlVen7p06dinbt2sHW1hZHjx6FUqlETk4Oli9fXuE9KpUKKpVKUiaKasjl8mceBxERERER1U4GmVFq0aIF0tLScPz4cbz99tuIiIjA+fPnK31/ZGQkunfvjjZt2mDixIlYtmwZPvvsM61E6K+io6OhUCgkV0xMjD6GQ0REREREtYwgiqJo6IcEBgbC3d0dX3zxhaYsKSkJAQEBuHPnDqytrZ96/7lz59CqVStcvHgRLVq00NmGM0pERERE/zzm5hbGDqFCXaNcjR1CtUiOumbsEGqkajlwVq1WP3U26O+kpaVBJpPBwcGhwjZyuVwrKSopefDMzyQiIiKi2o0HztZuek+UlEolQkND4eLignv37mHz5s1ISkrC3r17AQC5ubnIzc1FRkYGgMfvM9WrVw8uLi6wtbVFSkoKjh8/joCAANSrVw8pKSmYPn06hg0bBhsbG32HS0REREREpEXvidLNmzcxYsQI5OTkQKFQoE2bNti7dy9ee+01AMCaNWswf/58TfuuXbsCAOLi4jBy5EjI5XJs2bIFUVFRUKlUaNKkCaZPn47IyEh9h0pERERERKRTtbyjZCxcekdERERUs9Xkd5S6L6gd7yglzeU7SrpUyztKRERERET/NALfUarV+PUTERERERGVw0SJiIiIiIioHL0nSqtXr0abNm1gZWUFKysrdOrUCXv27AEAXL16FYIg6Ly2bdum6SM1NRU9e/aEtbU1bGxsEBwcjNOnT+s7VCIiIiIiIp30nig1atQIixcvxsmTJ3HixAn06NEDffv2xblz59C4cWPk5ORIrvnz56Nu3boIDQ0FABQVFSEkJAQuLi44fvw4Dh8+jHr16iE4OBilpaX6DpeIiIiIiEhLtex6Z2tri5iYGIwZM0arzsfHB+3atUNsbCwA4MSJE+jQoQOysrLQuHFjAI/PWmrTpg0uX76MZs2aVfq53PWOiIiIqGarybve9fzIzdghVIsDH1w1dgg1kkHfUSorK8OWLVtQXFyMTp06adWfPHkSaWlpkgSqRYsWsLOzQ2xsLB4+fIgHDx4gNjYWnp6ecHNzM2S4REREREREAAy0Pfhvv/2GTp06oaSkBHXr1kV8fDy8vLy02j1JgPz9/TVl9erVQ1JSEvr164eFCxcCAF5++WXs3bsXpqYVh6tSqaBSqSRloqiGXC7X06iIiIiIiKi2MMiMUosWLZCWlobjx4/j7bffRkREBM6fPy9p8+DBA2zevFlrOd6DBw8wZswYdO7cGceOHcORI0fQqlUr9O7dGw8eVLyULjo6GgqFQnLFxMQYYnhERERERPSCq5Z3lAIDA+Hu7o4vvvhCU7Zx40aMGTMG2dnZsLe315THxsZi1qxZyMnJgUz2OI97+PAhbGxsEBsbi8GDB+t8BmeUiIiIiP55+I6S8fEdJd0MsvSuPLVarZXExMbGok+fPpIkCQDu378PmUwGQRA0ZU8+q9XqCp8hl8u1kiJu5kBEREREz0rgiaO1mt6/fqVSieTkZFy9ehW//fYblEolkpKSMHToUE2bjIwMJCcnY+zYsVr3v/baa7hz5w4mTZqECxcu4Ny5cxg1ahRMTU0REBCg73CJiIiIiIi06H1G6ebNmxgxYgRycnKgUCjQpk0b7N27F6+99pqmzbp169CoUSMEBQVp3e/h4YEffvgB8+fPR6dOnSCTyeDj44OEhAQ4OTnpO1wiIiIiIiIt1fKOkrFw6R0RERFRzVaT31EKjHYzdgjVYr/yqrFDqJGq5R0lIiIiIqJ/GhnfUarV+PUTERERERGVw0SJiIiIiIioHIMnSosXL4YgCJg2bZpWnSiKCA0NhSAI2LFjh6Ru6tSp8PX1hVwuR9u2bQ0dJhERERERkYZBE6XU1FR88cUXaNOmjc76FStWSM5LKm/06NEYNGiQocIjIiIiIiLSyWCbORQVFWHo0KH48ssv8eGHH2rVp6WlYdmyZThx4oTObb8//fRTAMCff/6JM2fOGCpMIiIiIiKdBFnF/0GfXnwGm1GaNGkSevfujcDAQK26+/fv46233sKqVavg6OhoqBCIiIiIiIieiUFmlLZs2YJTp04hNTVVZ/306dPh7++Pvn376u2ZKpUKKpVKUiaKasjlcr09g4iIiIiIage9zyj98ccfeOedd/D111/D3Nxcq37Xrl1ITEzEihUr9Prc6OhoKBQKyRUTE6PXZxARERERUe2g9xmlkydP4ubNm2jXrp2mrKysDMnJyVi5ciXefvttXLlyBdbW1pL7wsPD8eqrryIpKemZnqtUKhEZGSkpE0X1M/VFRERERMQDZ2s3vSdKPXv2xG+//SYpGzVqFDw8PPD++++jfv36mDBhgqS+devW+M9//oOwsLBnfq5cLtdaZldS8uCZ+yMiIiIiotpL74lSvXr10KpVK0mZpaUl7OzsNOW6NnBwcXFBkyZNNJ8zMjJQVFSE3NxcPHjwAGlpaQAALy8vmJmZ6TtsIiIiIiIiDYNtD/68xo4di4MHD2o++/j4AAAyMzPh5uZmpKiIiIiIiKg2qJZE6e/eOxJFscr3EBERERERGUqNnVEiIiIiIjImgefN1mrcy4OIiIiIiKgcJkpERERERETl6D1RioqKgiAIksvDw0NTX1JSgkmTJsHOzg5169ZFeHg48vLydPZ1+/ZtNGrUCIIgoKCgQN+hEhERERER6WSQd5RatmyJ/fv3/99DTP/vMdOnT8ePP/6Ibdu2QaFQYPLkyejfvz+OHDmi1c+YMWPQpk0bZGdnGyJMIiIiIqIKyWR8Sak2M0iiZGpqqvOspMLCQsTGxmLz5s3o0aMHACAuLg6enp44duwYOnbsqGm7evVqFBQUYO7cudizZ48hwiQiIiIiItLJIO8oXb58Gc7OzmjatCmGDh2KrKwsAMDJkydRWlqKwMBATVsPDw+4uLggJSVFU3b+/HksWLAAX331FWQyvkZFRERERETVS+8zSn5+fli/fj1atGiBnJwczJ8/H6+++irOnj2L3NxcmJmZwdraWnJPgwYNkJubCwBQqVQYMmQIYmJi4OLigt9//71Sz1WpVFCpVJIyUVRDLpfrZVxERERERFR76H26JjQ0FAMGDECbNm0QHByM//3vfygoKMC3335bqfuVSiU8PT0xbNiwKj03OjoaCoVCcsXExDzLEIiIiIiIqJYz+Lo2a2trNG/eHBkZGXB0dMTDhw+1drDLy8vTvNOUmJiIbdu2wdTUFKampujZsycAoH79+pg3b16Fz1EqlSgsLJRcM2bMMNi4iIiIiOjFJshqx0W6GWQzh78qKirClStXMHz4cPj6+qJOnTo4cOAAwsPDAQDp6enIyspCp06dAADff/89Hjx4oLk/NTUVo0ePxqFDh+Du7l7hc+RyudYyu5KSBxW0JiIiIiIiqpjeE6V///vfCAsLg6urK27cuIF58+bBxMQEQ4YMgUKhwJgxYxAZGQlbW1tYWVlhypQp6NSpk2bHu/LJ0K1btwAAnp6eWu82ERERERERGYLeE6Xr169jyJAhuH37Nuzt7dGlSxccO3YM9vb2AID//Oc/kMlkCA8Ph0qlQnBwMD7//HN9h0FERERERPTMBFEURWMHYShcekdERERUs5mbWxg7hAr1XVnxax8vkp2Trxg7hBqJr28RERERERGVw0SJiIiIiIioHCZKRERERERE5eg9UYqKioIgCJLLw8ND0iYlJQU9evSApaUlrKys0LVrV82W4ElJSVr3P7lSU1P1HS4REREREZEWg5yj1LJlS+zfv///HmL6f49JSUlBSEgIlEolPvvsM5iamuL06dOQyR7nbP7+/sjJyZH0N2fOHBw4cADt27c3RLhERERERFpkgmDsEMiIDJIomZqawtHRUWfd9OnTMXXqVMycOVNT1qJFC83PZmZmkntLS0uxc+dOTJkyBQL/YSUiIiIiompgkHeULl++DGdnZzRt2hRDhw5FVlYWAODmzZs4fvw4HBwc4O/vjwYNGqBbt244fPhwhX3t2rULt2/fxqhRowwRKhERERERkRa9J0p+fn5Yv349EhISsHr1amRmZuLVV1/FvXv38PvvvwN4/B7TuHHjkJCQgHbt2qFnz564fPmyzv5iY2MRHByMRo0a6TtUIiIiIiIinfS+9C40NFTzc5s2beDn5wdXV1d8++238PT0BABMmDBBM0Pk4+ODAwcOYN26dYiOjpb0df36dezduxfffvvt3z5XpVJBpVJJykRRDblc/rxDIiIiIqJaSCbjax+1mcG3B7e2tkbz5s2RkZEBJycnAICXl5ekjaenp2Z53l/FxcXBzs4Offr0+dvnREdHQ6FQSK6YmBj9DIKIiIiIiGoVgydKRUVFuHLlCpycnODm5gZnZ2ekp6dL2ly6dAmurq6SMlEUERcXhxEjRqBOnTp/+xylUonCwkLJNWPGDL2OhYiIiIiIage9L73797//jbCwMLi6uuLGjRuYN28eTExMMGTIEAiCgBkzZmDevHnw9vZG27ZtsWHDBly8eBHfffedpJ/ExERkZmZi7NixlXquXC7XWmZXUvJAb+MiIiIiIqLaQ++J0vXr1zFkyBDcvn0b9vb26NKlC44dOwZ7e3sAwLRp01BSUoLp06cjPz8f3t7e2LdvH9zd3SX9xMbGwt/fX+uwWiIiIiIiIkMTRFEUjR2EoXBGiYiIiKhmMze3MHYIFRrwRXNjh1Attk24ZOwQaiSDv6NERERERET0T8NEiYiIiIiIqBwmSkREREREROUYJFHKzs7GsGHDYGdnBwsLC7Ru3RonTpzQ1EdFRcHDwwOWlpawsbFBYGAgjh8/LukjPz8fQ4cOhZWVFaytrTFmzBgUFRUZIlwiIiIiIi0yQVYrLtJN77+ZO3fuoHPnzqhTpw727NmD8+fPY9myZbCxsdG0ad68OVauXInffvsNhw8fhpubG4KCgvDnn39q2gwdOhTnzp3Dvn37sHv3biQnJ2P8+PH6DpeIiIiIiEiL3ne9mzlzJo4cOYJDhw5V+p67d+9CoVBg//796NmzJy5cuAAvLy+kpqaiffv2AICEhAT06tUL169fh7Ozc6X65a53RERERDVbTd71btDa2nFMzdbxF40dQo2k9xmlXbt2oX379hgwYAAcHBzg4+ODL7/8ssL2Dx8+xNq1a6FQKODt7Q0ASElJgbW1tSZJAoDAwEDIZDKtJXpERERERET6pvdE6ffff8fq1avx8ssvY+/evXj77bcxdepUbNiwQdJu9+7dqFu3LszNzfGf//wH+/btQ/369QEAubm5cHBwkLQ3NTWFra0tcnNz9R0yERERERGRhN4TJbVajXbt2mHRokXw8fHB+PHjMW7cOKxZs0bSLiAgAGlpaTh69ChCQkIwcOBA3Lx585mfq1KpcPfuXcmlUqmedzhEREREVEvJZEKtuKpq1apVcHNzg7m5Ofz8/PDLL788tf22bdvg4eEBc3NztG7dGv/73/8k9YIg6LxiYmI0bdzc3LTqFy9eXOXYq0LviZKTkxO8vLwkZZ6ensjKypKUWVpaolmzZujYsSNiY2NhamqK2NhYAICjo6NW0vTo0SPk5+fD0dFR53Ojo6OhUCgk119/uURERERE9Hy2bt2KyMhIzJs3D6dOnYK3tzeCg4MrnPA4evQohgwZgjFjxuDXX39Fv3790K9fP5w9e1bTJicnR3KtW7cOgiAgPDxc0teCBQsk7aZMmWLQseo9UercuTPS09MlZZcuXYKrq+tT71Or1ZoZoE6dOqGgoAAnT57U1CcmJkKtVsPPz0/n/UqlEoWFhZJrxowZzzkaIiIiIiJ6Yvny5Rg3bhxGjRoFLy8vrFmzBi+99BLWrVuns/0nn3yCkJAQzJgxA56enli4cCHatWuHlStXato4OjpKrp07dyIgIABNmzaV9FWvXj1JO0tLS4OOVe+J0vTp03Hs2DEsWrQIGRkZ2Lx5M9auXYtJkyYBAIqLizFr1iwcO3YM165dw8mTJzF69GhkZ2djwIABAB7PQIWEhGDcuHH45ZdfcOTIEUyePBmDBw+ucMc7uVwOKysrySWXy/U9PCIiIiKiF0plX2F5+PAhTp48icDAQE2ZTCZDYGAgUlJSdPadkpIiaQ8AwcHBFbbPy8vDjz/+iDFjxmjVLV68GHZ2dvDx8UFMTAwePXpUlWFWmd4TpQ4dOiA+Ph7ffPMNWrVqhYULF2LFihUYOnQoAMDExAQXL15EeHg4mjdvjrCwMNy+fRuHDh1Cy5YtNf18/fXX8PDwQM+ePdGrVy906dIFa9eu1Xe4REREREQ6Gfvdoeq6dL3CEh0drfX7uHXrFsrKytCgQQNJeYMGDSrccC03N7dK7Tds2IB69eqhf//+kvKpU6diy5Yt+PnnnzFhwgQsWrQI7733XlW+ziozNUSnr7/+Ol5//XWddebm5ti+ffvf9mFra4vNmzfrOzQiIiIiIvoLpVKJyMhISZmxVmatW7cOQ4cOhbm5uaT8r/G1adMGZmZmmDBhAqKjow0Wq0ESJSIiIiIi+meQy+WVSjbq168PExMT5OXlScrz8vIq3HDN0dGx0u0PHTqE9PR0bN269W9j8fPzw6NHj3D16lW0aNHib9s/C70vvSMiIiIiohePmZkZfH19ceDAAU2ZWq3GgQMH0KlTJ533dOrUSdIeAPbt26ezfWxsLHx9feHt7f23saSlpUEmk2mdvapPnFEiIiIiIqJKiYyMREREBNq3b49XXnkFK1asQHFxMUaNGgUAGDFiBBo2bKh5x+mdd95Bt27dsGzZMvTu3RtbtmzBiRMntPYeuHv3LrZt24Zly5ZpPTMlJQXHjx9HQEAA6tWrh5SUFEyfPh3Dhg2DjY2NwcZqkBml7OxsDBs2DHZ2drCwsEDr1q1x4sQJTX1eXh5GjhwJZ2dnvPTSSwgJCcHly5clfUyYMAHu7u6wsLCAvb09+vbti4sXLxoiXCIiIiIiLTJBqBVXVQwaNAhLly7F3Llz0bZtW6SlpSEhIUGzYUNWVhZycnI07f39/TW7YHt7e+O7777Djh070KpVK0m/W7ZsgSiKGDJkiNYz5XI5tmzZgm7duqFly5b46KOPMH36dINv9CaIoijqs8M7d+7Ax8cHAQEBePvtt2Fvb4/Lly/D3d0d7u7uEEUR/v7+qFOnDpYtWwYrKyssX74cCQkJOH/+vGY/9LVr18LDwwMuLi7Iz89HVFQU0tLSkJmZCRMTk0rFUlLyQJ9DIyIiIiI9Mze3MHYIFRoe1/LvG70ANo46Z+wQaiS9J0ozZ87EkSNHcOjQIZ31ly5dQosWLXD27FnNduBqtRqOjo5YtGgRxo4dq/O+M2fOwNvbGxkZGXB3d69ULEyUiIiIiGo2JkrGx0RJN70vvdu1axfat2+PAQMGwMHBAT4+Pvjyyy819U8Or/rrln8ymQxyuRyHDx/W2WdxcTHi4uLQpEkTNG7cWN8hExERERERSeg9Ufr999+xevVqvPzyy9i7dy/efvttTJ06FRs2bAAAzXI6pVKJO3fu4OHDh1iyZAmuX78uWc8IAJ9//jnq1q2LunXrYs+ePdi3bx/MzMz0HTIRERERkRaZTFYrLtJN70vvzMzM0L59exw9elRTNnXqVKSmpiIlJQUAcPLkSYwZMwanT5+GiYkJAgMDIZPJIIoi9uzZo7mvsLAQN2/eRE5ODpYuXYrs7GwcOXJE6wAq4PFM1ZPZqidEUW20w7KIiIiI6O/V5KV3ERtaGzuEarEh4jdjh1Aj6T2FdHJygpeXl6TM09MTWVlZms++vr5IS0tDQUEBcnJykJCQgNu3b6Np06aS+xQKBV5++WV07doV3333HS5evIj4+Hidz42OjoZCoZBcMTEx+h4eERERERHVAno/R6lz585IT0+XlF26dAmurq5abRUKBQDg8uXLOHHiBBYuXFhhv6IoQhRFrVmjJ5RKJSIjI8vdo65q+ERERERERPpPlKZPnw5/f38sWrQIAwcOxC+//IK1a9dK9jnftm0b7O3t4eLigt9++w3vvPMO+vXrh6CgIACP33PaunUrgoKCYG9vj+vXr2Px4sWwsLBAr169dD5XLpdrLbPjrndERERERPQs9J4odejQAfHx8VAqlViwYAGaNGmCFStWYOjQoZo2OTk5iIyMRF5eHpycnDBixAjMmTNHU29ubo5Dhw5hxYoVuHPnDho0aICuXbvi6NGjcHBw0HfIRERERERaZLKqHcZKLxa9b+ZQk3BGiYiIiKhmq8mbOYza2MbYIVSLuOFnjB1CjcT9AImIiIiIiMphokRERERERFSO3t9RIiIiIiJ6EcgEvqNUm+l9RsnNzQ2CIGhdkyZNAgB0795dq27ixIla/axfvx5t2rSBubk5HBwcNPcTEREREREZmt5nlFJTU1FWVqb5fPbsWbz22msYMGCApmzcuHFYsGCB5vNLL70k6WP58uVYtmwZYmJi4Ofnh+LiYly9elXfoRIREREREemk90TJ3t5e8nnx4sVwd3dHt27dNGUvvfQSHB0ddd5/584dzJ49Gz/88AN69uypKW/TpnbsOkJERERERMZn0M0cHj58iE2bNmH06NEQ/rLG8+uvv0b9+vXRqlUrKJVK3L9/X1O3b98+qNVqZGdnw9PTE40aNcLAgQPxxx9/GDJUIiIiIiIiDYNu5rBjxw4UFBRg5MiRmrK33noLrq6ucHZ2xpkzZ/D+++8jPT0d27dvBwD8/vvvUKvVWLRoET755BMoFArMnj0br732Gs6cOQMzMzNDhkxEREREBIAHztZ2Bk2UYmNjERoaCmdnZ03Z+PHjNT+3bt0aTk5O6NmzJ65cuQJ3d3eo1WqUlpbi008/RVBQEADgm2++gaOjI37++WcEBwfrfJZKpYJKpZKUiaIacrncACMjIiIiIqIXmcGW3l27dg379+/H2LFjn9rOz88PAJCRkQEAcHJyAgB4eXlp2tjb26N+/frIysqqsJ/o6GgoFArJFRMT87zDICIiIiKiWshgM0pxcXFwcHBA7969n9ouLS0NwP8lSJ07dwYApKeno1GjRgCA/Px83Lp1C66urhX2o1QqERkZKSkTRfWzhk9ERERERLWYQRIltVqNuLg4REREwNT0/x5x5coVbN68Gb169YKdnR3OnDmD6dOno2vXrppd7Zo3b46+ffvinXfewdq1a2FlZQWlUgkPDw8EBARU+Ey5XK61zK6k5IEhhkdEREREtYBMZtB9z6iGM8i3v3//fmRlZWH06NGScjMzM+zfvx9BQUHw8PDAu+++i/DwcPzwww+Sdl999RX8/PzQu3dvdOvWDXXq1EFCQgLq1KljiHCJiIiIiIgkBFEURWMHYSicUSIiIiKq2czNLYwdQoUmbGln7BCqxReDTxk7hBqJ84lERERERETlMFEiIiIiIiIqx6DnKBERERER/VMJAg+crc30PqNUVlaGOXPmoEmTJrCwsIC7uzsWLlyIv74KtX37dgQFBcHOzg6CIGi2CP+rK1eu4I033oC9vT2srKwwcOBA5OXl6TtcIiIiIiIiLXpPlJYsWYLVq1dj5cqVuHDhApYsWYKPP/4Yn332maZNcXExunTpgiVLlujso7i4GEFBQRAEAYmJiThy5AgePnyIsLAwqNU8G4mIiIiIiAxL70vvjh49ir59+2oOmnVzc8M333yDX375RdNm+PDhAICrV6/q7OPIkSO4evUqfv31V1hZWQEANmzYABsbGyQmJiIwMFDfYRMREREREWnofUbJ398fBw4cwKVLlwAAp0+fxuHDhxEaGlrpPlQqFQRBkBwga25uDplMhsOHD+s7ZCIiIiIiLTKZUCsu0k3vM0ozZ87E3bt34eHhARMTE5SVleGjjz7C0KFDK91Hx44dYWlpiffffx+LFi2CKIqYOXMmysrKkJOTo++QiYiIiIiIJPQ+o/Ttt9/i66+/xubNm3Hq1Cls2LABS5cuxYYNGyrdh729PbZt24YffvgBdevWhUKhQEFBAdq1aweZTHfIKpUKd+/elVwqlUpfwyIiIiIiolpE74nSjBkzMHPmTAwePBitW7fG8OHDMX36dERHR1epn6CgIFy5cgU3b97ErVu3sHHjRmRnZ6Np06Y620dHR0OhUEiumJgYfQyJiIiIiIhqGb0vvbt//77WrI+Jickz71ZXv359AEBiYiJu3ryJPn366GynVCoRGRkpKRNF7pBHRERERERVp/dEKSwsDB999BFcXFzQsmVL/Prrr1i+fDlGjx6taZOfn4+srCzcuHEDAJCeng4AcHR0hKOjIwAgLi4Onp6esLe3R0pKCt555x1Mnz4dLVq00PlcuVwu2fwBAEpKHuh7eERERERUS3Cjg9pN74nSZ599hjlz5uBf//oXbt68CWdnZ0yYMAFz587VtNm1axdGjRql+Tx48GAAwLx58xAVFQXgcfKkVCqRn58PNzc3fPDBB5g+fbq+wyUiIiIiItIiiKIoGjsIQ+GMEhEREVHNZm5uYewQKjT5+w7GDqFarAxPNXYINZLeN3MgIiIiIiL6p9P70jsiIiIioheBTOCcQm3Gb5+IiIiIiKgcvSdKZWVlmDNnDpo0aQILCwu4u7tj4cKF+OurUEVFRZg8eTIaNWoECwsLeHl5Yc2aNZr6/Px8TJkyBS1atICFhQVcXFwwdepUFBYW6jtcIiIiIiIiLXpferdkyRKsXr0aGzZsQMuWLXHixAmMGjUKCoUCU6dOBQBERkYiMTERmzZtgpubG3766Sf861//grOzM/r06YMbN27gxo0bWLp0Kby8vHDt2jVMnDgRN27cwHfffafvkImIiIiIiCT0vuvd66+/jgYNGiA2NlZTFh4eDgsLC2zatAkA0KpVKwwaNAhz5szRtPH19UVoaCg+/PBDnf1u27YNw4YNQ3FxMUxNK5ffcdc7IiIiopqtJu96N3W7n7FDqBaf9j9u7BBqJL0vvfP398eBAwdw6dIlAMDp06dx+PBhhIaGStrs2rUL2dnZEEURP//8My5duoSgoKAK+y0sLISVlVWlkyQiIiIiouchkwm14iLd9J51zJw5E3fv3oWHhwdMTExQVlaGjz76CEOHDtW0+eyzzzB+/Hg0atQIpqamkMlk+PLLL9G1a1edfd66dQsLFy7E+PHj9R0uERERERGRFr0nSt9++y2+/vprbN68GS1btkRaWhqmTZsGZ2dnREREAHicKB07dgy7du2Cq6srkpOTMWnSJDg7OyMwMFDS3927d9G7d294eXkhKiqqwueqVCqoVCpJmSiqIZfL9T1EIiIiIiJ6wen9HaXGjRtj5syZmDRpkqbsww8/xKZNm3Dx4kU8ePAACoUC8fHx6N27t6bN2LFjcf36dSQkJGjK7t27h+DgYLz00kvYvXs3zM3NK3xuVFQU5s+fLyn74INZmD17th5HR0RERET6VJPfUZq2o6OxQ6gWK/odM3YINZLeZ5Tu378PmUz66pOJiQnUajUAoLS0FKWlpU9tAzyeSQoODoZcLseuXbuemiQBgFKpRGRkpKRMFNUVtCYiIiIiejqZwPd3ajO9J0phYWH46KOP4OLigpYtW+LXX3/F8uXLMXr0aACAlZUVunXrhhkzZsDCwgKurq44ePAgvvrqKyxfvhzA4yQpKCgI9+/fx6ZNm3D37l3cvXsXAGBvbw8TExOt58rlcq1ldtz1joiIiIiInoXel97du3cPc+bMQXx8PG7evAlnZ2cMGTIEc+fOhZmZGQAgNzcXSqUSP/30E/Lz8+Hq6orx48dj+vTpEAQBSUlJCAgI0Nl/ZmYm3NzcKhULEyUiIiKimq0mL72L3NnJ2CFUi+V9U4wdQo2k90SpJmGiRERERFSzMVEyPiZKuun9HCUiIiIiIqJ/Op7eSkRERESkAw9jrd04o0RERERERFSOQRKle/fuYdq0aXB1dYWFhQX8/f2Rmpqqs+3EiRMhCAJWrFghKXdzc4MgCJJr8eLFhgiXiIiIiIhIwiBL78aOHYuzZ89i48aNcHZ2xqZNmxAYGIjz58+jYcOGmnbx8fE4duwYnJ2ddfazYMECjBs3TvO5Xr16hgiXiIiIiIhIQu+J0oMHD/D9999j586d6Nq1KwAgKioKP/zwA1avXo0PP/wQAJCdnY0pU6Zg79696N27t86+6tWrB0dHR32HSERERET0t2QyvqVSm+n923/06BHKyspgbm4uKbewsMDhw4cBAGq1GsOHD8eMGTPQsmXLCvtavHgx7Ozs4OPjg5iYGDx69Ejf4RIREREREWnR+4xSvXr10KlTJyxcuBCenp5o0KABvvnmG6SkpKBZs2YAgCVLlsDU1BRTp06tsJ+pU6eiXbt2sLW1xdGjR6FUKpGTk4Ply5frO2QiIiIiIiIJg7yjtHHjRowePRoNGzaEiYkJ2rVrhyFDhuDkyZM4efIkPvnkE5w6dQqCUPGWi5GRkZqf27RpAzMzM0yYMAHR0dGQy+Va7VUqFVQqlaRMFNU62xIRERERET2NQRZeuru74+DBgygqKsIff/yBX375BaWlpWjatCkOHTqEmzdvwsXFBaampjA1NcW1a9fw7rvvws3NrcI+/fz88OjRI1y9elVnfXR0NBQKheSKiYkxxPCIiIiIiOgFZ9ADZy0tLWFpaYk7d+5g7969+PjjjxEeHo7AwEBJu+DgYAwfPhyjRo2qsK+0tDTIZDI4ODjorFcqlZJZKODxjBIRERER0bOQPWX1E734DJIo7d27F6IookWLFsjIyMCMGTPg4eGBUaNGoU6dOrCzs5O0r1OnDhwdHdGiRQsAQEpKCo4fP46AgADUq1cPKSkpmD59OoYNGwYbGxudz5TL5VrL7EpKHhhieERERERE9IIzSKJUWFgIpVKJ69evw9bWFuHh4fjoo49Qp06dSt0vl8uxZcsWREVFQaVSoUmTJpg+fbrWjBEREREREZEhCKIoisYOwlA4o0RERERUs5mbWxg7hArN/N+rxg6hWizudcjYIdRIBn1HiYiIiIjon0om4ztKtRmPGyYiIiIiIiqHiRIREREREVE5TJSIiIiIiIjKqXKilJycjLCwMDg7O0MQBOzYsUNSL4oi5s6dCycnJ1hYWCAwMBCXL1+WtMnPz8fQoUNhZWUFa2trjBkzBkVFRZI2e/fuRceOHVGvXj3Y29sjPDy8wsNmiYiIiIiI9KnKiVJxcTG8vb2xatUqnfUff/wxPv30U6xZswbHjx+HpaUlgoODUVJSomkzdOhQnDt3Dvv27cPu3buRnJyM8ePHa+ozMzPRt29f9OjRA2lpadi7dy9u3bqF/v37P8MQiYiIiIiqTiYTasVFuj3X9uCCICA+Ph79+vUD8Hg2ydnZGe+++y7+/e9/A3h8plKDBg2wfv16DB48GBcuXICXlxdSU1PRvn17AEBCQgJ69eqF69evw9nZGd999x2GDBkClUoFmexxLvfDDz+gb9++UKlUlT6PiduDExEREdVsNXl78Nk/dTN2CNXiw6CDxg6hRtLrO0qZmZnIzc1FYGCgpkyhUMDPzw8pKSkAgJSUFFhbW2uSJAAIDAyETCbD8ePHAQC+vr6QyWSIi4tDWVkZCgsLsXHjRgQGBlY6SSIiIiIiInpWek2UcnNzAQANGjSQlDdo0EBTl5ubCwcHB0m9qakpbG1tNW2aNGmCn376CbNmzYJcLoe1tTWuX7+Ob7/9tsJnq1Qq3L17V3KpVCp9Do+IiIiIiGqJGrnrXW5uLsaNG4eIiAikpqbi4MGDMDMzw5tvvomKVgpGR0dDoVBIrpiYmGqOnIiIiIheFDJBVisu0s1Un505OjoCAPLy8uDk5KQpz8vLQ9u2bTVtbt68Kbnv0aNHyM/P19y/atUqKBQKfPzxx5o2mzZtQuPGjXH8+HF07NhR69lKpRKRkZGSMlFU62VcRERERERUu+g1hWzSpAkcHR1x4MABTdndu3dx/PhxdOrUCQDQqVMnFBQU4OTJk5o2iYmJUKvV8PPzAwDcv39fs4nDEyYmJgAAtVp38iOXy2FlZSW55HK5PodHRERERES1RJUTpaKiIqSlpSEtLQ3A4w0c0tLSkJWVBUEQMG3aNHz44YfYtWsXfvvtN4wYMQLOzs6anfE8PT0REhKCcePG4ZdffsGRI0cwefJkDB48GM7OzgCA3r17IzU1FQsWLMDly5dx6tQpjBo1Cq6urvDx8dHb4ImIiIiIiHSpcqJ04sQJ+Pj4aBKWyMhI+Pj4YO7cuQCA9957D1OmTMH48ePRoUMHFBUVISEhAebm5po+vv76a3h4eKBnz57o1asXunTpgrVr12rqe/Togc2bN2PHjh3w8fFBSEgI5HI5EhISYGFRc7eQJCIiIiKiF8NznaNU0/EcJSIiIqKarSafoxR1oIexQ6gWUT0TjR1CjcRtLoiIiIiIiMphokRERERERFQOEyUiIiIiIqJyqpwoJScnIywsDM7OzhAEATt27JDUi6KIuXPnwsnJCRYWFggMDMTly5c19UlJSRAEQeeVmpoKACgpKcHIkSPRunVrmJqaanbMIyIiIiKqLjLBpFZcpFuVE6Xi4mJ4e3tj1apVOus//vhjfPrpp1izZg2OHz8OS0tLBAcHo6SkBADg7++PnJwcyTV27Fg0adIE7du3BwCUlZXBwsICU6dORWBg4HMMj4iIiIiIqOpMq3pDaGgoQkNDddaJoogVK1Zg9uzZ6Nu3LwDgq6++QoMGDbBjxw4MHjwYZmZmcHR01NxTWlqKnTt3YsqUKRAEAQBgaWmJ1atXAwCOHDmCgoKCqoZJRERERET0zPT6jlJmZiZyc3Mls0AKhQJ+fn5ISUnRec+uXbtw+/ZtjBo1Sp+hEBERERERPbMqzyg9TW5uLgCgQYMGkvIGDRpo6sqLjY1FcHAwGjVq9FzPVqlUUKlUkjJRVEMulz9Xv0REREREVPsYdde769evY+/evRgzZsxz9xUdHQ2FQiG5YmJi9BAlEREREdVGMpmsVlykm15nlJ68e5SXlwcnJydNeV5eHtq2bavVPi4uDnZ2dujTp89zP1upVCIyMlJSJorq5+6XiIiIiIhqH72mkE2aNIGjoyMOHDigKbt79y6OHz+OTp06SdqKooi4uDiMGDECderUee5ny+VyWFlZSS4uuyMiIiIiomdR5RmloqIiZGRkaD5nZmYiLS0Ntra2cHFxwbRp0/Dhhx/i5ZdfRpMmTTBnzhw4OztrnYWUmJiIzMxMjB07Vudzzp8/j4cPHyI/Px/37t1DWloaAOicmSIiIiIiItKnKidKJ06cQEBAgObzk+VuERERWL9+Pd577z0UFxdj/PjxKCgoQJcuXZCQkABzc3NJP7GxsfD394eHh4fO5/Tq1QvXrl3TfPbx8QHweCaKiIiIiMjQTHgYa60miC9w5lFS8sDYIRARERHRU5ibWxg7hApFH+xl7BCqhbLb/4wdQo3EbS6IiIiIiIjKYaJERERERERUDhMlIiIiIiKicqqcKCUnJyMsLAzOzs4QBAE7duyQ1G/fvh1BQUGws7ODIAia3er+asKECXB3d4eFhQXs7e3Rt29fXLx4Uefzbt++jUaNGkEQBBQUFFQ1XCIiIiKiZyKTmdSKi3SrcqJUXFwMb29vrFq1qsL6Ll26YMmSJRX24evri7i4OFy4cAF79+6FKIoICgpCWVmZVtsxY8agTZs2VQ2TiIiIiIjomVV5e/DQ0FCEhoZWWD98+HAAwNWrVytsM378eM3Pbm5u+PDDD+Ht7Y2rV6/C3d1dU7d69WoUFBRg7ty52LNnT1VDJSIiIiIieiZVTpT0rbi4GHFxcWjSpAkaN26sKT9//jwWLFiA48eP4/fffzdihEREREREVNsYLVH6/PPPNYfTtmjRAvv27YOZmRkAQKVSYciQIYiJiYGLi0ulEiWVSgWVSiUpE0U15HK5QeInIiIiohebjAfO1mpG2/Vu6NCh+PXXX3Hw4EE0b94cAwcORElJCQBAqVTC09MTw4YNq3R/0dHRUCgUkismJsZQ4RMRERER0QvMaImSQqHAyy+/jK5du+K7777DxYsXER8fDwBITEzEtm3bYGpqClNTU/Ts2RMAUL9+fcybN09nf0qlEoWFhZJrxowZ1TYeIiIiIiJ6cRj9HSUAEEURoihqls59//33ePDggaY+NTUVo0ePxqFDhySbPfyVXC7XWmZXUvJAZ1siIiIiIqKnqXKiVFRUhIyMDM3nzMxMpKWlwdbWFi4uLsjPz0dWVhZu3LgBAEhPTwcAODo6wtHREb///ju2bt2KoKAg2Nvb4/r161i8eDEsLCzQq1cvANBKhm7dugUA8PT0hLW19TMNlIiIiIiIqLKqnCidOHECAQEBms+RkZEAgIiICKxfvx67du3CqFGjNPWDBw8GAMybNw9RUVEwNzfHoUOHsGLFCty5cwcNGjRA165dcfToUTg4ODzveIiIiIiI9EImM9pbKlQDCKIoisYOwlC49I6IiIioZjM3tzB2CBVadrS/sUOoFu/6bzd2CDUS02QiIiIiIqJymCgRERERERGVUyN2vSMiIiIiqmlMeOBsrVblGaXk5GSEhYXB2dkZgiBgx44dkvrt27cjKCgIdnZ2EAQBaWlpWn1cuXIFb7zxBuzt7WFlZYWBAwciLy9PU5+UlARBEHReqampVR4kERERERFRVVQ5USouLoa3tzdWrVpVYX2XLl2wZMmSCuuDgoIgCAISExNx5MgRPHz4EGFhYVCr1QAAf39/5OTkSK6xY8eiSZMmaN++fVVDJiIiIiIiqpIqL70LDQ1FaGhohfXDhw8HAFy9elVn/ZEjR3D16lX8+uuvsLKyAgBs2LABNjY2SExMRGBgIMzMzODo6Ki5p7S0FDt37sSUKVMgCEJVQyYiIiIiIqqSat/MQaVSQRAEyOVyTZm5uTlkMhkOHz6s855du3bh9u3bkvOZiIiIiIiIDKXaN3Po2LEjLC0t8f7772PRokUQRREzZ85EWVkZcnJydN4TGxuL4OBgNGrUqMJ+VSoVVCqVpEwU1ZKEjIiIiIiosmQybuZQm1X7jJK9vT22bduGH374AXXr1oVCoUBBQQHatWun8/Tj69evY+/evRgzZsxT+42OjoZCoZBcMTExhhoGERERERG9wIyyPXhQUBCuXLmCW7duwdTUFNbW1nB0dETTpk212sbFxcHOzg59+vR5ap9KpRKRkZGSMlFU6zVuIiIiIiKqHYx6jlL9+vUBAImJibh586ZWMiSKIuLi4jBixAjUqVPnqX3J5XKtZXYlJQ/0GzAREREREdUKVU6UioqKkJGRofmcmZmJtLQ02NrawsXFBfn5+cjKysKNGzcAAOnp6QAAR0dHzU52cXFx8PT0hL29PVJSUvDOO+9g+vTpaNGiheRZiYmJyMzMxNixY595gEREREREz0ImVPtbKlSDVDlROnHiBAICAjSfnyx3i4iIwPr167Fr1y7J7nSDBw8GAMybNw9RUVEAHidPSqUS+fn5cHNzwwcffIDp06drPSs2Nhb+/v7w8PCoaphERERERETPTBBFUTR2EIbCpXdERERENZu5uYWxQ6jQyl/eMnYI1WLyK5uNHUKNxPlEIiIiIiKicpgoERERERERlWPUXe+IiIiIiGoqHjhbu1V5Rik5ORlhYWFwdnaGIAjYsWOHpq60tBTvv/8+WrduDUtLSzg7O2PEiBGaHfD+6scff4Sfnx8sLCxgY2ODfv36SeqzsrLQu3dvvPTSS3BwcMCMGTPw6NGjKg+QiIiIiIioqqqcKBUXF8Pb2xurVq3Sqrt//z5OnTqFOXPm4NSpU9i+fTvS09O1zkf6/vvvMXz4cIwaNQqnT5/GkSNH8NZb//eyXFlZGXr37o2HDx/i6NGj2LBhA9avX4+5c+c+wxCJiIiIiIiq5rl2vRMEAfHx8VqzQX+VmpqKV155BdeuXYOLiwsePXoENzc3zJ8/H2PGjNF5z549e/D666/jxo0baNCgAQBgzZo1eP/99/Hnn3/CzMysUvFx1zsiIiKimq0m73r3+Ynhxg6hWvyr/UZjh1AjGXwzh8LCQgiCAGtrawDAqVOnkJ2dDZlMBh8fHzg5OSE0NBRnz57V3JOSkoLWrVtrkiQACA4Oxt27d3Hu3DlDh0xERERERLWcQTdzKCkpwfvvv48hQ4bAysoKAPD7778DAKKiorB8+XK4ublh2bJl6N69Oy5dugRbW1vk5uZKkiQAms+5ubk6n6VSqaBSqSRloqiGXC7X97CIiIiIqBYwEbiZQ21msBml0tJSDBw4EKIoYvXq1ZpytVoNAPjggw8QHh4OX19fxMXFQRAEbNu27ZmfFx0dDYVCIbliYmKeexxERERERFT7GGRG6UmSdO3aNSQmJmpmkwDAyckJAODl5aUpk8vlaNq0KbKysgAAjo6O+OWXXyR95uXlaep0USqViIyMlJSJovr5B0NERERERLWO3meUniRJly9fxv79+2FnZyep9/X1hVwuR3p6uuSeq1evwtXVFQDQqVMn/Pbbb7h586amzb59+2BlZSVJsP5KLpfDyspKcnHZHRERERERPYsqzygVFRUhIyND8zkzMxNpaWmwtbWFk5MT3nzzTZw6dQq7d+9GWVmZ5p0iW1tbmJmZwcrKChMnTsS8efPQuHFjuLq6apbIDRgwAAAQFBQELy8vDB8+HB9//DFyc3Mxe/ZsTJo0ickPEREREVULmWDwfc+oBqvyt3/ixAn4+PjAx8cHABAZGQkfHx/MnTsX2dnZ2LVrF65fv462bdvCyclJcx09elTTR0xMDAYPHozhw4ejQ4cOmiV6NjY2AAATExPs3r0bJiYm6NSpE4YNG4YRI0ZgwYIFeho2ERERERE9i1WrVsHNzQ3m5ubw8/PTemWmvG3btsHDwwPm5uZo3bo1/ve//0nqR44cCUEQJFdISIikTX5+PoYOHQorKytYW1tjzJgxKCoq0vvY/uq5zlGq6XiOEhEREVHNVpPPUfry1Ghjh1AtxrVbV+m2W7duxYgRI7BmzRr4+flhxYoV2LZtG9LT0+Hg4KDV/ujRo+jatSuio6Px+uuvY/PmzViyZAlOnTqFVq1aAXicKOXl5SEuLk5zn1wu10yiAEBoaChycnLwxRdfoLS0FKNGjUKHDh2wefPm5xj50zFRIiIiIiKjYaJkfFVJlPz8/NChQwesXLkSwOMdrRs3bowpU6Zg5syZWu0HDRqE4uJi7N69W1PWsWNHtG3bFmvWrAHwOFEqKCjAjh07dD7zwoUL8PLyQmpqKtq3bw8ASEhIQK9evXD9+nU4OztXOv6q4MJLIiIiIqJaTKVS4e7du5Kr/PmkAPDw4UOcPHkSgYGBmjKZTIbAwECkpKTo7DslJUXSHgCCg4O12iclJcHBwQEtWrTA22+/jdu3b0v6sLa21iRJABAYGAiZTIbjx48/05grg4kSEREREZEOMplJrbh0nUcaHR2t9fu4desWysrK0KBBA0l5gwYNNBu4lZebm/u37UNCQvDVV1/hwIEDWLJkCQ4ePIjQ0FCUlZVp+ii/rM/U1BS2trYVPlcfqpwoJScnIywsDM7OzhAEQWuKLCoqCh4eHrC0tISNjQ0CAwMlmd7Vq1cxZswYNGnSBBYWFnB3d8e8efPw8OFDTZv09HQEBASgQYMGMDc3R9OmTTF79myUlpY++0iJiIiIiEiLUqlEYWGh5FIqldX2/MGDB6NPnz5o3bo1+vXrh927dyM1NRVJSUnVFoMuVd4evLi4GN7e3hg9ejT69++vVd+8eXOsXLkSTZs2xYMHD/Cf//wHQUFByMjIgL29PS5evAi1Wo0vvvgCzZo1w9mzZzFu3DgUFxdj6dKlAIA6depgxIgRaNeuHaytrXH69GmMGzcOarUaixYtev5RExERERERgMcbJ1TmCJ769evDxMQEeXl5kvK8vDw4OjrqvMfR0bFK7QGgadOmqF+/PjIyMtCzZ084OjpKzlcFgEePHiE/P/+p/TyvKidKoaGhCA0NrbD+rbfeknxevnw5YmNjcebMGfTs2RMhISGS7f6aNm2K9PR0rF69WpMoNW3aFE2bNtW0cXV1RVJSEg4dOlTVcImIiIiISA/MzMzg6+uLAwcOoF+/fgAeb+Zw4MABTJ48Wec9nTp1woEDBzBt2jRN2b59+9CpU6cKn3P9+nXcvn0bTk5Omj4KCgpw8uRJ+Pr6AgASExOhVqvh5+enn8HpUOVEqSoePnyItWvXQqFQwNvbu8J2hYWFsLW1rbA+IyMDCQkJOmewiIiIiIgMQSaYGDuEGicyMhIRERFo3749XnnlFaxYsQLFxcUYNWoUAGDEiBFo2LCh5h2nd955B926dcOyZcvQu3dvbNmyBSdOnMDatWsBAEVFRZg/fz7Cw8Ph6OiIK1eu4L333kOzZs0QHBwMAPD09ERISAjGjRuHNWvWoLS0FJMnT8bgwYMNtuMdYKBEaffu3Rg8eDDu378PJycn7Nu3D/Xr19fZNiMjA5999plmNumv/P39cerUKahUKowfP/6pB86qVCqt3TlEUV2paUQiIiIiIvp7gwYNwp9//om5c+ciNzcXbdu2RUJCgmbDhqysLMhk/7cNgr+/PzZv3ozZs2dj1qxZePnll7Fjxw7NGUomJiY4c+YMNmzYgIKCAjg7OyMoKAgLFy6U/B3/9ddfY/LkyejZsydkMhnCw8Px6aefGnSsz3WOkiAIiI+P10y9PVFcXIycnBzcunULX375JRITE3H8+HGt3Sqys7PRrVs3dO/eHf/973+1+v/jjz9w7949nD59GjNmzMDUqVPx3nvv6YwlKioK8+fPl5R98MEszJ49+1mHR0REREQGVpPPUYo7PdHYIVSLUd5rjB1CjWSQRKm8l19+GaNHj5bsnnHjxg10794dHTt2xPr16yWZpy6bNm3C+PHjce/ePZiYaE+DckaJiIiI6J+HiZLxMVHSzaDvKD2hVqslSUx2djYCAgLg6+uLuLi4v02SnvRRWloKtVqtM1HStVtHScmD5w+eiIiIiIhqnSonSkVFRcjIyNB8zszMRFpaGmxtbWFnZ4ePPvoIffr0gZOTE27duoVVq1YhOzsbAwYMAPA4SerevTtcXV2xdOlS/Pnnn5q+nmzv9/XXX6NOnTpo3bo15HI5Tpw4AaVSiUGDBqFOnTrPO2YiIiIior9lIuNmDrVZlROlEydOICAgQPM5MjISABAREYE1a9bg4sWL2LBhA27dugU7Ozt06NABhw4dQsuWLQE83g4wIyMDGRkZaNSokaTvJ6sATU1NsWTJEly6dAmiKMLV1RWTJ0/G9OnTn3mgRERERERElfVc7yjVdFx6R0RERFSz1eR3lL76bZKxQ6gWI1qvMnYINdLfvxxERERERERUy1TLZg5ERERERP80MoFzCrUZv30iIiIiIqJyqpwoJScnIywsDM7OzhAEATt27JDUR0VFwcPDA5aWlrCxsUFgYCCOHz8uaXPp0iX07dsX9evXh5WVFbp06YKff/5Z61nr169HmzZtYG5uDgcHB0yaVDvWiRIRERERkXFVOVEqLi6Gt7c3Vq3S/dJX8+bNsXLlSvz22284fPgw3NzcEBQUJNkG/PXXX8ejR4+QmJiIkydPwtvbG6+//jpyc3M1bZYvX44PPvgAM2fOxLlz57B//34EBwc/wxCJiIiIiIiq5rl2vRMEAfHx8ejXr1+Fbe7evQuFQoH9+/ejZ8+euHXrFuzt7ZGcnIxXX30VAHDv3j1YWVlh3759CAwMxJ07d9CwYUP88MMP6Nmz57OGx13viIiIiGq4mrzr3aazU4wdQrUY1uozY4dQIxl0M4eHDx9i7dq1UCgU8Pb2BgDY2dmhRYsW+Oqrr9CuXTvI5XJ88cUXcHBwgK+vL4DHZy2p1WpkZ2fD09MT9+7dg7+/P5YtW4bGjRsbMmQiIiIiIgCAjAfO1moGSZR2796NwYMH4/79+3BycsK+fftQv359AI9nofbv349+/fqhXr16kMlkcHBwQEJCAmxsbAAAv//+O9RqNRYtWoRPPvkECoUCs2fPxmuvvYYzZ87AzMxM65kqlQoqlUpSJopqyOVyQwyRiIiIiIheYAbZ9S4gIABpaWk4evQoQkJCMHDgQNy8eRMAIIoiJk2aBAcHBxw6dAi//PIL+vXrh7CwMOTk5AAA1Go1SktL8emnnyI4OBgdO3bEN998g8uXL+vc9AEAoqOjoVAoJFdMTIwhhkdERERERC84gyRKlpaWaNasGTp27IjY2FiYmpoiNjYWAJCYmIjdu3djy5Yt6Ny5M9q1a4fPP/8cFhYW2LBhAwDAyckJAODl5aXp097eHvXr10dWVpbOZyqVShQWFkquGTNmGGJ4RERERET0gquWA2fVarVmWdz9+/cBADKZNEeTyWRQq9UAgM6dOwMA0tPT0ahRIwBAfn4+bt26BVdXV53PkMvlWsvsuJkDERERET0rmcB3lGqzKs8oFRUVIS0tDWlpaQCAzMxMpKWlISsrC8XFxZg1axaOHTuGa9eu4eTJkxg9ejSys7MxYMAAAECnTp1gY2ODiIgInD59GpcuXcKMGTOQmZmJ3r17A3i8xXjfvn3xzjvv4OjRozh79iwiIiLg4eGBgIAA/Y2eiIiIiIhIhyonSidOnICPjw98fHwAAJGRkfDx8cHcuXNhYmKCixcvIjw8HM2bN0dYWBhu376NQ4cOoWXLlgCA+vXrIyEhAUVFRejRowfat2+Pw4cPY+fOnZqd8QDgq6++gp+fH3r37o1u3bqhTp06SEhIQJ06dfQ0dCIiIiIiIt2e6xylmo5L74iIiIhqtpp8jtKWC+8aO4RqMdhzmbFDqJEMspkDERERERHRP1m1bOZARERERPRPU37zMapd+O0TERERERGVU+VEKTk5GWFhYXB2doYgCNixY0eFbSdOnAhBELBixQqd9SqVCm3btoUgCJpd9J749ttv0bZtW7z00ktwdXXl4bFERERERFRtqpwoFRcXw9vbG6tWrXpqu/j4eBw7dgzOzs4Vtnnvvfd01u/ZswdDhw7FxIkTcfbsWXz++ef4z3/+g5UrV1Y1XCIiIiIioiqr8jtKoaGhCA0NfWqb7OxsTJkyBXv37tWcjVTenj178NNPP+H777/Hnj17JHUbN25Ev379MHHiRABA06ZNoVQqsWTJEkyaNAmCIFQ1bCIiIiKiKjHhgbO1mt7fUVKr1Rg+fDhmzJihOTupvLy8PIwbNw4bN27ESy+9pFWvUqlgbm4uKbOwsMD169dx7do1fYdMREREREQkofdEacmSJTA1NcXUqVN11ouiiJEjR2LixIlo3769zjbBwcHYvn07Dhw4ALVajUuXLmHZssf7u+fk5Oi8R6VS4e7du5JLpVLpZ1BERERERFSr6DVROnnyJD755BOsX7++wuVxn332Ge7duwelUllhP+PGjcPkyZPx+uuvw8zMDB07dsTgwYMfB1zBNo3R0dFQKBSSixtAEBERERHRs9BronTo0CHcvHkTLi4uMDU1hampKa5du4Z3330Xbm5uAIDExESkpKRALpfD1NQUzZo1AwC0b98eERERAABBELBkyRIUFRXh2rVryM3NxSuvvALg8ftKuiiVShQWFkquGTNm6HN4RERERERUS+j1wNnhw4cjMDBQUhYcHIzhw4dj1KhRAIBPP/0UH374oab+xo0bCA4OxtatW+Hn5ye518TEBA0bNgQAfPPNN+jUqRPs7e11Plsul0Mul0vKSkoePPeYiIiIiKh2knEzh1qtyolSUVERMjIyNJ8zMzORlpYGW1tbuLi4wM7OTtK+Tp06cHR0RIsWLQAALi4ukvq6desCANzd3dGoUSMAwK1bt/Ddd9+he/fuKCkpQVxcHLZt24aDBw9WNVwiIiIiIqIqq/LSuxMnTsDHxwc+Pj4AgMjISPj4+GDu3Ll6DWzDhg1o3749OnfujHPnziEpKUmz/I6IiIiIiMiQBFEURWMHYShcekdERERUs5mbWxg7hArFX/7A2CFUizde/sjYIdRIen1HiYiIiIjoRSGT8R2l2kzv5ygRERERERH90zFRIiIiIiIiKqfKiVJycjLCwsLg7OwMQRCwY8cOSf3IkSMhCILkCgkJkbT56KOP4O/vj5deegnW1tZazzh9+jSGDBmCxo0bw8LCAp6envjkk0+qGioREREREdEzqfI7SsXFxfD29sbo0aPRv39/nW1CQkIQFxen+Vz+fKOHDx9iwIAB6NSpE2JjY7XuP3nyJBwcHLBp0yY0btwYR48exfjx42FiYoLJkydXNWQiIiIiIqIqqXKiFBoaitDQ0Ke2kcvlcHR0rLB+/vz5AID169frrB89erTkc9OmTZGSkoLt27czUSIiIiKiaiET+JZKbWaQbz8pKQkODg5o0aIF3n77bdy+ffu5+ywsLIStra0eoiMiIiIiIno6vW8PHhISgv79+6NJkya4cuUKZs2ahdDQUKSkpMDE5Nm2WDx69Ci2bt2KH3/8Uc/REhERERERadN7ojR48GDNz61bt0abNm3g7u6OpKQk9OzZs8r9nT17Fn379sW8efMQFBRUYTuVSgWVSiUpE0W11vtRREREREREf8fgCy+bNm2K+vXrIyMjo8r3nj9/Hj179sT48eMxe/bsp7aNjo6GQqGQXDExMc8aNhERERHVciYyk1pxkW56n1Eq7/r167h9+zacnJyqdN+5c+fQo0cPRERE4KOPPvrb9kqlEpGRkZIyUVRX6ZlERERERETAMyRKRUVFktmhzMxMpKWlwdbWFra2tpg/fz7Cw8Ph6OiIK1eu4L333kOzZs0QHBysuScrKwv5+fnIyspCWVkZ0tLSAADNmjVD3bp1cfbsWfTo0QPBwcGIjIxEbm4uAMDExAT29vY645LL5VrL7EpKHlR1eERERERERBBEURSrckNSUhICAgK0yiMiIrB69Wr069cPv/76KwoKCuDs7IygoCAsXLgQDRo00LQdOXIkNmzYoNXHzz//jO7duyMqKkqzhfhfubq64urVq5WOlYkSERERUc1mbm5h7BAq9L+rC40dQrXo5TbH2CHUSFVOlP5JmCgRERER1WxMlIyPiZJuBn9HiYiIiIjon0gmcKOD2ozHDRMREREREZXDRImIiIiIiKicKidKycnJCAsLg7OzMwRBwI4dOyT1I0eOhCAIkiskJERTn5SUpFX/5EpNTQUAXL16VWf9sWPHnm+0RERERERElVDld5SKi4vh7e2N0aNHo3///jrbhISEIC4uTvP5r9t2+/v7IycnR9J+zpw5OHDgANq3by8p379/P1q2bKn5bGdnV9VwiYiIiIieiUzg4qvarMqJUmhoKEJDQ5/aRi6Xw9HRUWedmZmZpK60tBQ7d+7ElClTIAiCpK2dnV2F/RARERERERmKQdLkpKQkODg4oEWLFnj77bdx+/btCtvu2rULt2/fxqhRo7Tq+vTpAwcHB3Tp0gW7du0yRKhERERERERa9L49eEhICPr3748mTZrgypUrmDVrFkJDQ5GSkgITE+0tFmNjYxEcHIxGjRppyurWrYtly5ahc+fOkMlk+P7779GvXz/s2LEDffr00XfIREREREREEnpPlAYPHqz5uXXr1mjTpg3c3d2RlJSEnj17Stpev34de/fuxbfffispr1+/PiIjIzWfO3TogBs3biAmJqbCREmlUkGlUknKRFEteT+KiIiIiIioMgz+hlrTpk1Rv359ZGRkaNXFxcXBzs6uUrNEfn5+Ovt4Ijo6GgqFQnLFxMQ8V+xEREREVHvJBJNacZFuep9RKu/69eu4ffs2nJycJOWiKCIuLg4jRoxAnTp1/raftLQ0rT7+SqlUSmahHj9D/WxBExERERFRrVblRKmoqEgys5OZmYm0tDTY2trC1tYW8+fPR3h4OBwdHXHlyhW89957aNasGYKDgyX9JCYmIjMzE2PHjtV6xoYNG2BmZgYfHx8AwPbt27Fu3Tr897//rTAuuVyutcyupORBVYdHRERERERU9UTpxIkTCAgI0Hx+MosTERGB1atX48yZM9iwYQMKCgrg7OyMoKAgLFy4UCuJiY2Nhb+/Pzw8PHQ+Z+HChbh27RpMTU3h4eGBrVu34s0336xquERERERERFUmiKIoGjsIQ+GMEhEREVHNZm5uYewQKpR4fbmxQ6gWPRpF/n2jWojHDRMREREREZXDRImIiIiIiKgcJkpERERERETlVDlRSk5ORlhYGJydnSEIAnbs2KHV5sKFC+jTpw8UCgUsLS3RoUMHZGVlaeonTJgAd3d3WFhYwN7eHn379sXFixclfaSmpqJnz56wtraGjY0NgoODcfr06aqPkIiIiIiIqIqqnCgVFxfD29sbq1at0ll/5coVdOnSBR4eHkhKSsKZM2cwZ84cmJuba9r4+voiLi4OFy5cwN69eyGKIoKCglBWVgbg8RbkISEhcHFxwfHjx3H48GHUq1cPwcHBKC0tfcahEhERERFVniCY1IqLdHuuXe8EQUB8fDz69eunKRs8eDDq1KmDjRs3VrqfM2fOwNvbGxkZGXB3d8eJEyc0s1CNGzcGAPz2229o06YNLl++jGbNmlWqX+56R0RERFSz1eRd737O/sTYIVSLgIbvGDuEGkmv7yip1Wr8+OOPaN68OYKDg+Hg4AA/Pz+dy/OeKC4uRlxcHJo0aaJJilq0aAE7OzvExsbi4cOHePDgAWJjY+Hp6Qk3Nzd9hkxERERERKRFr4nSzZs3UVRUhMWLFyMkJAQ//fQT3njjDfTv3x8HDx6UtP38889Rt25d1K1bF3v27MG+fftgZmYGAKhXrx6SkpKwadMmWFhYoG7dukhISMCePXtgalrlM3KJiIiIiIiqRK9L727cuIGGDRtiyJAh2Lx5s6Zdnz59YGlpiW+++UZTVlhYiJs3byInJwdLly5FdnY2jhw5AnNzczx48ADdu3eHh4cHJk+ejLKyMixduhQXL15EamoqLCy0p2hVKhVUKpWkTBTVkMvlzzo8IiIiIjKwmrz07uCNlcYOoVp0c55s7BBqJL3OKNWvXx+mpqbw8vKSlHt6ekp2vQMAhUKBl19+GV27dsV3332HixcvIj4+HgCwefNmXL16FXFxcejQoQM6duyIzZs3IzMzEzt37tT57OjoaCgUCskVExOjz+EREREREVEtodd1bGZmZujQoQPS09Ml5ZcuXYKrq2uF94miCFEUNTNC9+/fh0wmgyAImjZPPqvVap19KJVKREZGlutXd1siIiIiIqKnqXKiVFRUhIyMDM3nzMxMpKWlwdbWFi4uLpgxYwYGDRqErl27IiAgAAkJCfjhhx+QlJQEAPj999+xdetWBAUFwd7eHtevX8fixYthYWGBXr16AQBee+01zJgxA5MmTcKUKVOgVquxePFimJqaIiAgQGdccrlca5kdd70jIiIiIqJnUeWldydOnICPjw98fHwAAJGRkfDx8cHcuXMBAG+88QbWrFmDjz/+GK1bt8Z///tffP/99+jSpQsAwNzcHIcOHUKvXr3QrFkzDBo0CPXq1cPRo0fh4OAAAPDw8MAPP/yAM2fOoFOnTnj11Vdx48YNJCQkwMnJSV9jJyIiIiIi0um5NnOo6TijRERERFSz1eTNHA7dWG3sEKrFq85vGzuEGkmvmzkQERERERG9CJgoERERERERlcNEiYiIiIiIqJwqJ0rJyckICwuDs7MzBEHAjh07JPWCIOi8/nqmUX5+PoYOHQorKytYW1tjzJgxKCoq0tSXlJRg5MiRaN26NUxNTTUH2hIRERERVReZYFIrLtKtyolScXExvL29sWrVKp31OTk5kmvdunUQBAHh4eGaNkOHDsW5c+ewb98+7N69G8nJyRg/frymvqysDBYWFpg6dSoCAwOfYVhERERERETP7rl2vRMEAfHx8U+d8enXrx/u3buHAwcOAAAuXLgALy8vpKamon379gCAhIQE9OrVC9evX4ezs7Pk/pEjR6KgoEBr5qoyuOsdERERUc1Wk3e9O5Kz1tghVIvOTuP/vlEtZNB3lPLy8vDjjz9izJgxmrKUlBRYW1trkiQACAwMhEwmw/Hjxw0ZDhERERERUaUYNFHasGED6tWrh/79+2vKcnNzNQfLPmFqagpbW1vk5uYaMhwiIiIiIqJKMTVk5+vWrcPQoUNhbm5uyMcAAFQqFVQqlaRMFNWQy+UGfzYRERERvXi40UHtZrAZpUOHDiE9PR1jx46VlDs6OuLmzZuSskePHiE/Px+Ojo7P/Lzo6GgoFArJ9ded9oiIiIiIiCrLYDNKsbGx8PX1hbe3t6S8U6dOKCgowMmTJ+Hr6wsASExMhFqthp+f3zM/T6lUIjIyUlImiupn7o+IiIiIiGqvKidKRUVFyMjI0HzOzMxEWloabG1t4eLiAgC4e/cutm3bhmXLlmnd7+npiZCQEIwbNw5r1qxBaWkpJk+ejMGDB0t2vDt//jwePnyI/Px83Lt3D2lpaQCAtm3b6oxLLpdrLbPjrndERERERPQsqrw9eFJSEgICArTKIyIisH79egDA2rVrMW3aNOTk5EChUGi1zc/Px+TJk/HDDz9AJpMhPDwcn376KerWratp4+bmhmvXrmndW5VwmSgRERER1Ww1eXvwY3nrjR1CtejYYKSxQ6iRnuscpZqOiRIRERFRzcZEyfiYKOlm0O3BiYiIiIiI/omYKBEREREREZXDRImIiIiIiKicKidKycnJCAsLg7OzMwRBwI4dOyT1giDovMqfafTjjz/Cz88PFhYWsLGxQb9+/XQ+7/bt22jUqBEEQUBBQUFVwyUiIiIieiYymNSKi3SrcqJUXFwMb29vrFq1Smd9Tk6O5Fq3bh0EQUB4eLimzffff4/hw4dj1KhROH36NI4cOYK33npLZ39jxoxBmzZtqhomERERERHRM6vyOUqhoaEIDQ2tsN7R0VHyeefOnQgICEDTpk0BAI8ePcI777yDmJgYjBkzRtPOy8tLq6/Vq1ejoKAAc+fOxZ49e6oaKhERERER0TMx6DtKeXl5+PHHHyUJ0alTp5CdnQ2ZTAYfHx84OTkhNDQUZ8+eldx7/vx5LFiwAF999RVkMr5KRURERERE1cegGciGDRtQr1499O/fX1P2+++/AwCioqIwe/Zs7N69GzY2NujevTvy8/MBACqVCkOGDEFMTAxcXFwMGSIRERERkU4yQVYrLtLNoL+ZdevWYejQoTA3N9eUqdVqAMAHH3yA8PBw+Pr6Ii4uDoIgYNu2bQAApVIJT09PDBs2rNLPUqlUuHv3ruRSqVT6HRAREREREdUKBkuUDh06hPT0dIwdO1ZS7uTkBED6TpJcLkfTpk2RlZUFAEhMTMS2bdtgamoKU1NT9OzZEwBQv359zJs3T+fzoqOjoVAoJFf5nfaIiIiIiIgqo8qbOVRWbGwsfH194e3tLSn39fWFXC5Heno6unTpAgAoLS3F1atX4erqCuDxrngPHjzQ3JOamorRo0fj0KFDcHd31/k8pVKJyMhISZkoqvU5JCIiIiIiqiWqnCgVFRUhIyND8zkzMxNpaWmwtbXVvE909+5dbNu2DcuWLdO638rKChMnTsS8efPQuHFjuLq6amZ+BgwYAABaydCtW7cAAJ6enrC2ttYZl1wuh1wul5SVlDzQ2ZaIiIiIiOhpqpwonThxAgEBAZrPT2ZxIiIisH79egDAli1bIIoihgwZorOPmJgYmJqaYvjw4Xjw4AH8/PyQmJgIGxubZxgCEREREZH+yQQexlqbCaIoisYOwlA4o0RERERUs5mbWxg7hAqd+nOLsUOoFu3sBxs7hBqJ+wESERERERGVw0SJiIiIiIioHIPtekdERERE9E/Gd5RqtyrPKCUnJyMsLAzOzs4QBAE7duyQ1BcVFWHy5Mlo1KgRLCws4OXlhTVr1mjq8/PzMWXKFLRo0QIWFhZwcXHB1KlTUVhYqGmzfv16CIKg87p58+azj5aIiIiIiKgSqjyjVFxcDG9vb4wePRr9+/fXqo+MjERiYiI2bdoENzc3/PTTT/jXv/4FZ2dn9OnTBzdu3MCNGzewdOlSeHl54dq1a5g4cSJu3LiB7777DgAwaNAghISESPodOXIkSkpK4ODg8IxDJSIiIiIiqpzn2vVOEATEx8ejX79+mrJWrVph0KBBmDNnjqbM19cXoaGh+PDDD3X2s23bNgwbNgzFxcUwNdXO3f788080bNgQsbGxGD58eKXj4653RERERDVbTd71Lu3WNmOHUC3a1h9g7BBqJL1v5uDv749du3YhOzsboiji559/xqVLlxAUFFThPYWFhbCystKZJAHAV199hZdeeglvvvmmvsMlIiIiIiLSovfNHD777DOMHz8ejRo1gqmpKWQyGb788kt07dpVZ/tbt25h4cKFGD9+fIV9xsbG4q233oKFRc39Lw5ERERE9GIRuJlDrWaQROnYsWPYtWsXXF1dkZycjEmTJsHZ2RmBgYGStnfv3kXv3r3h5eWFqKgonf2lpKTgwoUL2Lhx41Ofq1KpoFKpJGWiqIZcLn+u8RARERERUe2j16V3Dx48wKxZs7B8+XKEhYWhTZs2mDx5MgYNGoSlS5dK2t67dw8hISGoV68e4uPjUadOHZ19/ve//0Xbtm3h6+v71GdHR0dDoVBIrpiYGL2NjYiIiIiIag+9ziiVlpaitLQUMpk0/zIxMYFardZ8vnv3LoKDgyGXy7Fr1y6Ym5vr7K+oqAjffvstoqOj//bZSqUSkZGRkjJRVFfQmoiIiIiIqGJVTpSKioqQkZGh+ZyZmYm0tDTY2trCxcUF3bp1w4wZM2BhYQFXV1ccPHgQX331FZYvXw7gcZIUFBSE+/fvY9OmTbh79y7u3r0LALC3t4eJyf+tBd26dSsePXqEYcOG/W1ccrlca5kdd70jIiIiomclA99Rqs2qvD14UlISAgICtMojIiKwfv165ObmQqlU4qeffkJ+fj5cXV0xfvx4TJ8+HYIgVHg/8DjpcnNz03z29/dHkyZN8PXXX1dtVP8fEyUiIiKimq0mbw/+2+2dxg6hWrS262vsEGqk5zpHqaZjokRERERUszFRMj4mSrrp/RwlIiIiIiKifzomSkREREREROXo/RwlIiIiIqIXgYwHztZqnFEiIiIiIiIqp8qJUnJyMsLCwuDs7AxBELBjxw5JfVFRESZPnoxGjRrBwsICXl5eWLNmjaTNlStX8MYbb8De3h5WVlYYOHAg8vLyJG3y8/MxdOhQWFlZwdraGmPGjEFRUVHVR0hERERERFRFVU6UiouL4e3tjVWrVumsj4yMREJCAjZt2oQLFy5g2rRpmDx5Mnbt2qW5PygoCIIgIDExEUeOHMHDhw8RFhYmOZR26NChOHfuHPbt24fdu3cjOTkZ48ePf8ZhEhERERERVd5zbQ8uCALi4+PRr18/TVmrVq0waNAgzJkzR1Pm6+uL0NBQfPjhh/jpp58QGhqKO3fuwMrKCgBQWFgIGxsb/PTTTwgMDMSFCxfg5eWF1NRUtG/fHgCQkJCAXr164fr163B2dq5UfNwenIiIiKhmq8nbg1+4s8fYIVQLT5tQY4dQI+n9HSV/f3/s2rUL2dnZEEURP//8My5duoSgoCAAgEqlgiAIkMvlmnvMzc0hk8lw+PBhAEBKSgqsra01SRIABAYGQiaT4fjx4/oOmYiIiIiISELvidJnn30GLy8vNGrUCGZmZggJCcGqVavQtev/Y+/+46Ks8v6Pv0BkRIFBFARFlLRQVPwVKbqZP0FzW72z29JKLdNsB0sob8Ns09oWCyttt2xbTS0jTJP8UWqWgmuiJYmKGqZlVvxQVwVFGZGZ7x9+nW1kMGZwhZX3s8f1uHfOda5zfY7t/Xj42XOdz+kDQM+ePWnUqBHTpk3j3LlzlJSU8NRTT1FeXk5+fj4ABQUFBAYG2o3r4eGBv78/BQUFDt9rNpspLi62u8xm87WenoiIiIiI1AH/kURp+/btrF69mqysLF555RVMJhOff/45AAEBASxfvpw1a9bg7e2N0Wjk9OnTdOvWDXd318NJSkrCaDTaXcnJyddqWiIiIiIiUodc03OUzp8/z/Tp00lLS2Po0KEAREZGkp2dzZw5cxg4cCAAMTExHD58mBMnTuDh4YGfnx9BQUHcdNNNAAQFBXHs2DG7sS9evMjJkycJCgpy+O7ExEQSEhLs2qxWi8O+IiIiIiIiV3NNE6WysjLKysoqrAzVq1fPrqLdZU2bNgVg06ZNHDt2jD/84Q8AREdHc/r0abKysujevbutj8VioUePHg7fbTAY7PY9gYo5iIiIiIjrdOBs3eZ0onT27FkOHTpk+/3DDz+QnZ2Nv78/oaGh3HHHHUydOhUvLy9atWpFRkYG7777Lq+++qrtmUWLFtG+fXsCAgLIzMzkiSeeID4+nvDwcADat2/P4MGDmTBhAm+99RZlZWXExcVx3333VbninYiIiIiIiKucLg+enp5Ov379KrSPHTuWxYsXU1BQQGJiIp999hknT56kVatWTJw4kfj4eNzc3AB4+umnWbx4MSdPnqR169ZMmjTJ7j5cOnA2Li6ONWvW4O7uzogRI3j99dfx9vaucqxaURIRERGp3WpzefDc05/VdAjXRbhfTE2HUCtV6xyl2k6JkoiIiEjtpkSp5ilRcuya7lESEREREblRuGmPUp12zcuDi4iIiIjIjeuNN96gdevWNGjQgB49evDVV19dtf/y5ctp164dDRo0oFOnTnz66ae2e2VlZUybNo1OnTrRqFEjmjdvzpgxY8jLy7Mbo3Xr1ri5udlds2fP/o/M7zIlSiIiIiIiUiXLli0jISGB5557jm+++YbOnTsTGxtb4Wify7Zt28aoUaMYP348u3btYvjw4QwfPpycnBwAzp07xzfffMOzzz7LN998w8qVK8nNzbVVw/61559/nvz8fNs1efLk/+hcnU6UtmzZwl133UXz5s1xc3Pj448/trtfWFjIuHHjaN68OQ0bNmTw4MF89913dn369u1bISOcNGmS7f6//vUvBg8eTPPmzTEYDLRs2ZK4uDiKi4tdm6WIiIiIiFTbq6++yoQJE3jooYeIiIjgrbfeomHDhrzzzjsO+8+bN4/BgwczdepU2rdvzwsvvEC3bt3429/+BoDRaGTjxo2MHDmS8PBwevbsyd/+9jeysrI4evSo3Vg+Pj4EBQXZrkaNGv1H5+p0olRSUkLnzp154403KtyzWq0MHz6c77//nlWrVrFr1y5atWrFwIEDKSkpses7YcIEu4zw5Zdf/ndQ7u4MGzaM1atXc/DgQRYvXsznn39ul0yJiIiIiEj1mc1miouL7S6z2Vyh34ULF8jKymLgwIG2Nnd3dwYOHEhmZqbDsTMzM+36A8TGxlbaH6CoqAg3Nzf8/Pzs2mfPnk2TJk3o2rUrycnJXLx40YlZOs/pYg5DhgxhyJAhDu999913bN++nZycHDp06ADA/PnzCQoK4oMPPuCRRx6x9W3YsCFBQUEOx2ncuDGPPfaY7XerVq344x//SHJysrPhioiIiIi4xJ26UcwhKSmJWbNm2bU999xzzJw5067txIkTlJeX06xZM7v2Zs2a8e233zocu6CgwGH/goICh/1LS0uZNm0ao0aNwtfX19b++OOP061bN/z9/dm2bRuJiYnk5+fbndV6rV3TPUqXM88GDRr8+wXu7hgMBrZu3WrX9/3336dp06Z07NiRxMREzp07V+m4eXl5rFy5kjvuuONahisiIiIiUuclJiZSVFRkdyUmJl73OMrKyhg5ciRWq5X58+fb3UtISKBv375ERkYyadIkXnnlFf761786XPm6Vq5potSuXTtCQ0NJTEzk1KlTXLhwgZdeeomff/6Z/Px8W7/Ro0ezdOlSNm/eTGJiIu+99x4PPPBAhfFGjRpFw4YNadGiBb6+vixYsKDSd1d1yVBERERERP7NYDDg6+trdxkMhgr9mjZtSr169SgsLLRrLywsrPRLsaCgoCr1v5wk/fjjj2zcuNFuNcmRHj16cPHiRY4cOVKFGbrmmiZK9evXZ+XKlRw8eBB/f38aNmzI5s2bGTJkCO7u/37VxIkTiY2NpVOnTtx///28++67pKWlcfjwYbvxXnvtNb755htWrVrF4cOHSUhIqPTdSUlJGI1Gu0uf6omIiIiIXBuenp50796dL774wtZmsVj44osviI6OdvhMdHS0XX+AjRs32vW/nCR99913fP755zRp0uQ3Y8nOzsbd3Z3AwEAXZ/PbrvmBs927dyc7O5uioiIuXLhAQEAAPXr04NZbb630mR49egBw6NAh2rRpY2u/XNGiXbt2+Pv7c/vtt/Pss88SHBxcYYzExMQKiZTVarlGsxIRERGRusbdTSfpXCkhIYGxY8dy6623cttttzF37lxKSkp46KGHABgzZgwtWrQgKSkJgCeeeII77riDV155haFDh5KamsrOnTt5++23gUtJ0j333MM333zD2rVrKS8vt+1f8vf3x9PTk8zMTHbs2EG/fv3w8fEhMzOT+Ph4HnjgARo3bvwfm+s1T5QuMxqNwKUCDzt37uSFF16otG92djaAwwToMovlUtJT2ed0BoOhwhJhael5Z0IWEREREZGruPfeezl+/Dh/+tOfKCgooEuXLqxfv95WsOHo0aN2X5L16tWLlJQUZsyYwfTp07n55pv5+OOP6dixIwC//PILq1evBqBLly5279q8eTN9+/bFYDCQmprKzJkzMZvNhIWFER8ff9Wvza4FN6vVanXmgbNnz3Lo0CEAunbtyquvvkq/fv3w9/cnNDSU5cuXExAQQGhoKHv37uWJJ56ge/fufPTRRwAcPnyYlJQU7rzzTpo0acKePXuIj48nJCSEjIwMAD799FMKCwuJiorC29ubffv2MXXqVPz9/SsUhbgaJUoiIiIitVuDBl41HUKlvi/eUtMhXBc3+fap6RBqJadXlHbu3Em/fv1svy9ncmPHjmXx4sXk5+eTkJBAYWEhwcHBjBkzhmeffdbW39PTk88//9y2TNeyZUtGjBjBjBkzbH28vLz4xz/+QXx8PGazmZYtW3L33Xfz9NNPV2euIiIiIiIiVeL0itJ/E60oiYiIiNRuWlGqeVpRcuw/tkdJREREROS/mbtb3ThwVhxTKQ8REREREZErKFESERERERG5glOJUlJSElFRUfj4+BAYGMjw4cPJzc2161NaWorJZKJJkyZ4e3szYsSICqfxPv7443Tv3h2DwVChDOBlVquVOXPmcMstt2AwGGjRogUvvviic7MTERERERFxgVN7lDIyMjCZTERFRXHx4kWmT59OTEwM+/fvp1GjRgDEx8fzySefsHz5coxGI3Fxcdx99918+eWXdmM9/PDD7Nixgz179jh81xNPPMFnn33GnDlz6NSpEydPnuTkyZMuTlNERERExDnao1S3Vavq3fHjxwkMDCQjI4M+ffpQVFREQEAAKSkp3HPPPQB8++23tG/fnszMTHr27Gn3/MyZM/n4449tB85eduDAASIjI8nJySE8PNzV8FT1TkRERKSWq81V746e3V7TIVwXod49f7tTHVStPUpFRUUA+Pv7A5CVlUVZWRkDBw609WnXrh2hoaFkZmZWedw1a9Zw0003sXbtWsLCwmjdujWPPPKIVpREREREROS6cLk8uMViYcqUKfTu3ZuOHTsCUFBQgKenJ35+fnZ9mzVrRkFBQZXH/v777/nxxx9Zvnw57777LuXl5cTHx3PPPfewadMmh8+YzWbMZrNdm9VqwWAwODcxERERERGp81xeUTKZTOTk5JCamnot4wEuJWFms5l3332X22+/nb59+7Jw4UI2b95coXjEZUlJSRiNRrsrOTn5mscmIiIiIiI3PpdWlOLi4li7di1btmwhJCTE1h4UFMSFCxc4ffq03apSYWEhQUFBVR4/ODgYDw8PbrnlFltb+/btATh69KjDfUuJiYkkJCTYtVmtliq/U0RERETk19xQMYe6zKkVJavVSlxcHGlpaWzatImwsDC7+927d6d+/fp88cUXtrbc3FyOHj1KdHR0ld/Tu3dvLl68yOHDh21tBw8eBKBVq1YOnzEYDPj6+tpd+uxORERERERc4dSKkslkIiUlhVWrVuHj42Pbd2Q0GvHy8sJoNDJ+/HgSEhLw9/fH19eXyZMnEx0dbVfx7tChQ5w9e5aCggLOnz9vq3oXERGBp6cnAwcOpFu3bjz88MPMnTsXi8WCyWRi0KBBdqtMIiIiIiIi/wlOlQd3c3Nz2L5o0SLGjRsHXDpw9sknn+SDDz7AbDYTGxvLm2++affpXd++fcnIyKgwzg8//EDr1q0ByMvLY/LkyXz22Wc0atSIIUOG8Morr9gq7FWFyoOLiIiI1G61uTz4T2e/rukQrouW3lE1HUKtVK1zlGo7JUoiIiIitVttTpR+KfmmpkO4Llo06lbTIdRK1TpHSURERERE5EakRElEREREROQKSpRERERERESu4FSilJSURFRUFD4+PgQGBjJ8+PAKB8CWlpZiMplo0qQJ3t7ejBgxgsLCQtv9xYsX4+bm5vA6duyYrd8bb7xB+/bt8fLyIjw8nHfffbeaUxUREREREakap4o5DB48mPvuu4+oqCguXrzI9OnTycnJYf/+/TRq1AiAxx57jE8++YTFixdjNBqJi4vD3d2dL7/8EoDz589TVFRkN+64ceMoLS0lPT0dgPnz5zNt2jT+8Y9/EBUVxVdffcWECRNISUnhrrvuqvLkVMxBREREpHarzcUc8s/trukQrovghp1rOoRaqVpV744fP05gYCAZGRn06dOHoqIiAgICSElJ4Z577gHg22+/pX379mRmZtqdpfTrMVq0aMHChQt58MEHAejVqxe9e/cmOTnZ1u/JJ59kx44dbN26tcrxKVESERERqd2UKNU8JUqOVWuP0uWVoctnG2VlZVFWVsbAgQNtfdq1a0doaCiZmZkOx3j33Xdp2LChLbECMJvNNGjQwK6fl5cXX331FWVlZdUJWURERERE5De5nChZLBamTJlC79696dixIwAFBQV4enri5+dn17dZs2YUFBQ4HGfhwoWMHj0aL69//68JsbGxLFiwgKysLKxWKzt37mTBggWUlZVx4sQJh+OYzWaKi4vtLrPZ7Or0RERERESkDnM5UTKZTOTk5JCamuryyzMzMzlw4ADjx4+3a3/22WcZMmQIPXv2pH79+gwbNoyxY8deCtjdcchJSUkYjUa769ef7omIiIiIOMMN9zpxiWMu/cnExcWxdu1aNm/eTEhIiK09KCiICxcucPr0abv+hYWFBAUFVRhnwYIFdOnShe7du9u1e3l58c4773Du3DmOHDnC0aNHad26NT4+PgQEBDiMKTExkaKiIrtr6tSprkxPRERERETqOKcSJavVSlxcHGlpaWzatImwsDC7+927d6d+/fp88cUXtrbc3FyOHj1KdHS0Xd+zZ8/y4YcfVlhN+rX69esTEhJCvXr1SE1N5fe//32lK0oGgwFfX1+7y2AwODM9ERERERERADyc6WwymUhJSWHVqlX4+PjY9h0ZjUa8vLwwGo2MHz+ehIQE/P398fX1ZfLkyURHR1eoeLds2TIuXrzIAw88UOE9Bw8e5KuvvqJHjx6cOnWKV199lZycHJYsWVKNqYqIiIiIiFSNU4nS/PnzAejbt69d+6JFixg3bhwAr732Gu7u7owYMQKz2UxsbCxvvvlmhbEWLlzI3XffXaHwA0B5eTmvvPIKubm51K9fn379+rFt2zZat27tTLgiIiIiIiIuqdY5SrWdzlESERERqd1q8zlKBedyajqE6yKoYceaDqFWUpkLERERERGRKyhREhERERERuYISJRERERERkSs4lSglJSURFRWFj48PgYGBDB8+nNzcXLs+b7/9Nn379sXX1xc3N7cKZyr9mtlspkuXLri5uZGdnW1rT09PZ9iwYQQHB9OoUSO6dOnC+++/79TEREREREREXOVUopSRkYHJZGL79u1s3LiRsrIyYmJiKCkpsfU5d+4cgwcPZvr06b853v/93//RvHnzCu3btm0jMjKSjz76iD179vDQQw8xZswY1q5d60y4IiIiIiIuc8O9TlziWLWq3h0/fpzAwEAyMjLo06eP3b309HT69evHqVOnHJYAX7duHQkJCXz00Ud06NCBXbt20aVLl0rfNXToUJo1a8Y777xT5fhU9U5ERESkdqvNVe8Kz+2v6RCui2YNI2o6hFqpWilkUVERAP7+/k49V1hYyIQJE3jvvfdo2LBhld/l7HtERERERERc4dSBs79msViYMmUKvXv3pmPHqtdet1qtjBs3jkmTJnHrrbdy5MiR33zmww8/5Ouvv+bvf/97pX3MZjNms/mKd1kwGAxVjk1ERERERASqsaJkMpnIyckhNTXVqef++te/cubMGRITE6vUf/PmzTz00EP84x//oEOHDpX2S0pKwmg02l3JyclOxSYiIiIicplbHflHHHNpRSkuLo61a9eyZcsWQkJCnHp206ZNZGZmVljpufXWW7n//vtZsmSJrS0jI4O77rqL1157jTFjxlx13MTERBISEuzarFaLU7GJiIiIiIiAk4mS1Wpl8uTJpKWlkZ6eTlhYmNMvfP311/nzn/9s+52Xl0dsbCzLli2jR48etvb09HR+//vf89JLLzFx4sTfHNdgMFRIvlTMQUREREREXOFUomQymUhJSWHVqlX4+PhQUFAAgNFoxMvrUsWSgoICCgoKOHToEAB79+7Fx8eH0NBQ/P39CQ0NtRvT29sbgDZt2thWpzZv3szvf/97nnjiCUaMGGF7j6enpwo6iIiIiIjIf5xTe5Tmz59PUVERffv2JTg42HYtW7bM1uett96ia9euTJgwAYA+ffrQtWtXVq9eXeX3LFmyhHPnzpGUlGT3nrvvvtuZcEVERERERFxSrXOUajt9eiciIiJSu9Xmc5SOncut6RCui8CG4TUdQq2ko3hFRERERESuoERJRERERETkCkqUREREREREruBUopSUlERUVBQ+Pj4EBgYyfPhwcnP//e3myZMnmTx5MuHh4Xh5eREaGsrjjz9OUVGR3ThHjx5l6NChNGzYkMDAQKZOncrFixdt99PT03Fzc6twXa5+JyIiIiLyn1bTB8HqwNma5VR58IyMDEwmE1FRUVy8eJHp06cTExPD/v37adSoEXl5eeTl5TFnzhwiIiL48ccfmTRpEnl5eaxYsQKA8vJyhg4dSlBQENu2bSM/P58xY8ZQv359/vKXv9i9Lzc3F19fX9vvwMDAazBlERERERGRq6tW1bvjx48TGBhIRkYGffr0cdhn+fLlPPDAA5SUlODh4cG6dev4/e9/T15eHs2aNQMulRSfNm0ax48fx9PTk/T0dPr168epU6fw8/NzNTxVvRMRERGp5Wpz1bvj5w7WdAjXRUDDW2o6hFqpWnuULn9Sd7VDYIuKivD19cXD49LiVWZmJp06dbIlSQCxsbEUFxezb98+u2e7dOlCcHAwgwYN4ssvv6xOqCIiIiIiIlXm1Kd3v2axWJgyZQq9e/emY8eODvucOHGCF154gYkTJ9raCgoK7JIkwPb78h6k4OBg3nrrLW699VbMZjMLFiygb9++7Nixg27dujl8l9lsxmw227VZrRYMBoOrUxQRERERkTrK5UTJZDKRk5PD1q1bHd4vLi5m6NChREREMHPmTKfGDg8PJzz83wdf9erVi8OHD/Paa6/x3nvvOXwmKSmJWbNm2bU988x0ZsyY4dS7RUREREQA3NxUILoucylRiouLY+3atWzZsoWQkJAK98+cOcPgwYPx8fEhLS2N+vXr2+4FBQXx1Vdf2fUvLCy03avMbbfdVmlSBpCYmEhCQoJdm9VqqdJ8REREREREfs2pNNlqtRIXF0daWhqbNm0iLCysQp/i4mJiYmLw9PRk9erVNGjQwO5+dHQ0e/fu5dixY7a2jRs34uvrS0RERKXvzs7OJjg4uNL7BoMBX19fu0uf3YmIiIiIiCucWlEymUykpKSwatUqfHx8bHuKjEYjXl5etiTp3LlzLF26lOLiYoqLiwEICAigXr16xMTEEBERwYMPPsjLL79MQUEBM2bMwGQy2RKbuXPnEhYWRocOHSgtLWXBggVs2rSJzz777BpPX0REREREpCKnyoO7uTk+kGrRokWMGzfOVtbbkR9++IHWrVsD8OOPP/LYY4+Rnp5Oo0aNGDt2LLNnz7ZVxnv55Zd5++23+eWXX2jYsCGRkZH86U9/qnTsyqg8uIiIiEjtVpvLg584f7imQ7gumnq1qekQaqVqnaNU2ylREhEREandlCjVPCVKjqmUh4iIiIiIyBWUKImIiIiIiFxBiZKIiIiIiMgVnEqUkpKSiIqKwsfHh8DAQIYPH05ubq7t/smTJ5k8eTLh4eF4eXkRGhrK448/TlFRUYWxFi9eTGRkJA0aNCAwMBCTyWR3f8OGDfTs2RMfHx8CAgIYMWIER44ccW2WIiIiIiJOcsO9TlzimFN/MhkZGZhMJrZv387GjRspKysjJiaGkpISAPLy8sjLy2POnDnk5OSwePFi1q9fz/jx4+3GefXVV3nmmWd4+umn2bdvH59//jmxsbG2+z/88APDhg2jf//+ZGdns2HDBk6cOMHdd999DaYsIiIiIiJyddWqenf8+HECAwPJyMigT58+DvssX76cBx54gJKSEjw8PDh16hQtWrRgzZo1DBgwwOEzK1asYNSoUZjNZtzdL+Vya9asYdiwYZjNZurXr1+l+FT1TkRERKR2q81V7/51/oeaDuG6aOIVVtMh1ErVWmu7/Emdv7//Vfv4+vrazkjauHEjFouFX375hfbt2xMSEsLIkSP56aefbM90794dd3d3Fi1aRHl5OUVFRbz33nsMHDiwykmSiIiIiIiIq1xOlCwWC1OmTKF379507NjRYZ8TJ07wwgsvMHHiRFvb999/j8Vi4S9/+Qtz585lxYoVnDx5kkGDBnHhwgUAwsLC+Oyzz5g+fToGgwE/Pz9+/vlnPvzww0rjMZvNFBcX211ms9nV6YmIiIhIHedWR/4Rx1xOlEwmEzk5OaSmpjq8X1xczNChQ4mIiGDmzJm2dovFQllZGa+//jqxsbH07NmTDz74gO+++47NmzcDUFBQwIQJExg7dixff/01GRkZeHp6cs8991DZl4JJSUkYjUa7Kzk52dXpiYiIiIhIHebhykNxcXGsXbuWLVu2EBISUuH+mTNnGDx4MD4+PqSlpdl9LhccHAxARESErS0gIICmTZty9OhRAN544w2MRiMvv/yyrc/SpUtp2bIlO3bsoGfPnhXemZiYSEJCgl2b1WpxZXoiIiIiIlLHOZUoWa1WJk+eTFpaGunp6YSFVdz4VVxcTGxsLAaDgdWrV9OgQQO7+7179wYgNzfXlmSdPHmSEydO0KpVKwDOnTtnK+JwWb169YBLK1KOGAwGDAaDXZuKOYiIiIiIiCucqnr3xz/+kZSUFFatWkV4eLit3Wg04uXlRXFxMTExMZw7d460tDQaNWpk6xMQEGBLdoYPH86hQ4d4++238fX1JTExke+//57s7Gzq16/Ppk2bGDhwIDNnzmTUqFGcOXOG6dOn8+2333LgwAG8vKpWHUWJkoiIiEjtVpur3p08f6SmQ7gu/L1a13QItZJTiZKbm+PNXosWLWLcuHGkp6fTr18/h31++OEHWrduDVxadYqPj2flypW4u7tzxx13MG/ePFq2bGnrn5qayssvv8zBgwdp2LAh0dHRvPTSS7Rr167Kk1OiJCIiIlK71e5E6WhNh3Bd+HuF1nQItVK1zlGq7ZQoiYiIiNRuSpRqnhIlx6p1jpKIiIiIiMiNSImSiIiIiIjIFVwqDy4iIiIicqPTYax1m1MrSklJSURFReHj40NgYCDDhw8nNzfXrs+jjz5KmzZt8PLyIiAggGHDhvHtt9/a7v/rX/9i8ODBNG/eHIPBQMuWLYmLi6O4uNjWJz8/n9GjR3PLLbfg7u7OlClTqjdLERERERERJziVKGVkZGAymdi+fTsbN26krKyMmJgYSkpKbH26d+/OokWLOHDgABs2bMBqtRITE0N5efmlF7q7M2zYMFavXs3BgwdZvHgxn3/+OZMmTbKNYTabCQgIYMaMGXTu3PkaTVVERERERKRqqlX17vjx4wQGBpKRkUGfPn0c9tmzZw+dO3fm0KFDtGnTxmGf119/neTkZH766acK9/r27UuXLl2YO3eu0/Gp6p2IiIhI7Vabq96dOl/x76Y3osZeLX+7Ux1UrWIORUVFAPj7+zu8X1JSwqJFiwgLC7M7I+nX8vLyWLlyJXfccUd1QhEREREREblmXE6ULBYLU6ZMoXfv3nTs2NHu3ptvvom3tzfe3t6sW7eOjRs34unpaddn1KhRNGzYkBYtWuDr68uCBQtcDQW49LlecXGx3WU2m6s1poiIiIjUXW6414lLHHP5T8ZkMpGTk0NqamqFe/fffz+7du0iIyODW265hZEjR1JaWmrX57XXXuObb75h1apVHD58mISEBFdDAS4VmjAajXZXcnJytcYUEREREZG6yaU9SnFxcaxatYotW7YQFhZ21b4XLlygcePGLFiwgFGjRjnss3XrVm6//Xby8vIIDg62u1fVPUpms7nCCpLVasFgMPz2hERERESkRtTmPUqnz/9S0yFcF35eLWo6hFrJqXOUrFYrkydPJi0tjfT09N9Mki4/Y7Var/oZnMViAajWp3IGg6FCUqRiDiIiIiIi4gqnEiWTyURKSgqrVq3Cx8eHgoICAIxGI15eXnz//fcsW7aMmJgYAgIC+Pnnn5k9ezZeXl7ceeedAHz66acUFhYSFRWFt7c3+/btY+rUqfTu3ZvWrVvb3pWdnQ3A2bNnOX78ONnZ2Xh6ehIREXFtZi4iIiIiclU6cLYuc+rTOzc3x/9lWbRoEePGjSMvL49HHnmErKwsTp06RbNmzejTpw9/+tOfCA8PB2Dz5s0888wz7N+/H7PZTMuWLbn77rt5+umn8fPzu+q7WrVqxZEjR6o8Oa0oiYiIiNRutfvTu7yaDuG68PNqXtMh1ErVOkeptlOiJCIiIlK7KVGqeUqUHFM9QBERERERkSsoURIREREREbmCU8UcRERERETqCh3GWrc59W8/KSmJqKgofHx8CAwMZPjw4eTm5tr1efTRR2nTpg1eXl4EBAQwbNgwvv32W7s+X3zxBb169cLHx4egoCCmTZvGxYsXHb7z0KFD+Pj42BV6EBERERER+U9yKlHKyMjAZDKxfft2Nm7cSFlZGTExMZSUlNj6dO/enUWLFnHgwAE2bNiA1WolJiaG8vJyAHbv3s2dd97J4MGD2bVrF8uWLWP16tU8/fTTFd5XVlbGqFGjuP3226s5TRERERERkaqrVtW748ePExgYSEZGBn369HHYZ8+ePXTu3JlDhw7Rpk0bpk+fzsaNG/n6669tfdasWcPIkSM5duwYPj4+tvZp06aRl5fHgAEDmDJlCqdPn3YqPlW9ExEREandanPVu6LzBTUdwnVh9Aqq6RBqpWp9eFlUVASAv7+/w/slJSUsWrSIsLAwWrZsCYDZbKZBgwZ2/by8vCgtLSUrK8vWtmnTJpYvX84bb7xRnRBFRERERFziVkf+EcdcTpQsFgtTpkyhd+/edOzY0e7em2++ibe3N97e3qxbt46NGzfi6ekJQGxsLNu2beODDz6gvLycX375heeffx6A/Px8AP71r38xbtw4Fi9ejK+vr6shioiIiIiIuMTlRMlkMpGTk0NqamqFe/fffz+7du0iIyODW265hZEjR1JaWgpATEwMycnJTJo0CYPBwC233MKdd955KRj3S+FMmDCB0aNHV/o5nyNms5ni4mK7y2w2uzo9ERERERGpw1zaoxQXF8eqVavYsmULYWFhV+174cIFGjduzIIFCxg1apSt3Wq1kp+fT+PGjTly5AgRERF89dVXREVF4efnx9mzZ+36WiwW6tWrx9tvv83DDz9c4T0zZ85k1qxZdm3PPDOdGTNmODs9EREREblOavMepeLzhTUdwnXh69WspkOolZw6R8lqtTJ58mTS0tJIT0//zSTp8jNWq7XC6o6bmxvNmzcH4IMPPqBly5Z069YNgMzMTFuVPIBVq1bx0ksvsW3bNlq0aOHwPYmJiSQkJFzxbosz0xMREREREQGcTJRMJhMpKSmsWrUKHx8fCgouVQIxGo14eXnx/fffs2zZMmJiYggICODnn39m9uzZeHl52T6vA0hOTmbw4MG4u7uzcuVKZs+ezYcffki9evUAaN++vd17d+7cibu7e4W9UL9mMBgwGAx2bap6JyIiIiKu04GzdZlT//bnz59PUVERffv2JTg42HYtW7YMgAYNGvDPf/6TO++8k7Zt23Lvvffi4+PDtm3bCAwMtI2zbt06br/9dm699VY++eQTVq1axfDhw6/pxERERERERFxVrXOUajutKImIiIjUbrV7j9Lxmg7huvD1CqjpEGolrSeKiIiIiIhcwak9SiIiIiIidYWb1hTqNP3bFxERERERuYJTiVJSUhJRUVH4+PgQGBjI8OHDyc3NddjXarUyZMgQ3Nzc+Pjjj+3uubm5Vbh+fXDtuHHjHPbp0KGD8zMUERERERFxklOJUkZGBiaTie3bt7Nx40bKysqIiYmhpKSkQt+5c+fi5uZW6ViLFi0iPz/fdv266t28efPs7v3000/4+/vzv//7v86EKyIiIiIi4hKn9iitX7/e7vfixYsJDAwkKyuLPn362Nqzs7N55ZVX2LlzJ8HBwQ7H8vPzIygoyOE9o9GI0Wi0/f744485deoUDz30kDPhioiIiIiIuKRae5SKiooA8Pf3t7WdO3eO0aNH88Ybb1SaCMGlw2ubNm3KbbfdxjvvvMPVqpQvXLiQgQMH0qpVq+qEKyIiIiJSZW515B9xzOWqdxaLhSlTptC7d286duxoa4+Pj6dXr14MGzas0meff/55+vfvT8OGDfnss8/44x//yNmzZ3n88ccr9M3Ly2PdunWkpKS4GqqIiIiIiIhTXE6UTCYTOTk5bN261da2evVqNm3axK5du6767LPPPmv7z127dqWkpITk5GSHidKSJUvw8/Oz28PkiNlsxmw227VZrRYMBkMVZiMiIiIiIvJvLn16FxcXx9q1a9m8eTMhISG29k2bNnH48GH8/Pzw8PDAw+NSHjZixAj69u1b6Xg9evTg559/dpDoWHnnnXd48MEH8fT0vGpMSUlJtr1Nl6/k5GRXpiciIiIiInWcm/Vqm4OuYLVamTx5MmlpaaSnp3PzzTfb3S8oKODEiRN2bZ06dWLevHncddddhIWFORz3xRdf5JVXXuHkyZN27enp6fTr14+9e/fafd7niFaURERERP77NGjgVdMhVOps6emaDuG68G7gV9Mh1EpOfXpnMplISUlh1apV+Pj4UFBQAFyqUufl5UVQUJDDAg6hoaG2JGnNmjUUFhbSs2dPGjRowMaNG/nLX/7CU089VeG5hQsX0qNHj99MkgAMBkOFpKi09Lwz0xMREREREQGcTJTmz58PUOEzukWLFjFu3LgqjVG/fn3eeOMN4uPjsVqttG3blldffZUJEybY9SsqKuKjjz5i3rx5zoQoIiIiIiJSbU59evffRitKIiIiIrWbPr2refr0zrFqnaMkIiIiIiJyI3K5PLiIiIiIyI1Mh7HWbVpREhERERERuYJTiVJSUhJRUVH4+PgQGBjI8OHDyc3NtevTt29f3Nzc7K5JkybZ7u/evZtRo0bRsmVLvLy8aN++vcOCDenp6XTr1g2DwUDbtm1ZvHixazMUERERERFxklOJUkZGBiaTie3bt7Nx40bKysqIiYmhpKTErt+ECRPIz8+3XS+//LLtXlZWFoGBgSxdupR9+/bxzDPPkJiYyN/+9jdbnx9++IGhQ4fSr18/srOzmTJlCo888ggbNmyo5nRFRERERER+W7Wq3h0/fpzAwEAyMjLo06cPcGlFqUuXLsydO7fK45hMJg4cOMCmTZsAmDZtGp988gk5OTm2Pvfddx+nT59m/fr1VR5XVe9EREREarfaXPXuXOmZmg7humjYwKemQ6iVqrVHqaioCAB/f3+79vfff5+mTZvSsWNHEhMTOXfu3G+O8+sxMjMzGThwoF2f2NhYMjMzqxOuiIiIiIhIlbhc9c5isTBlyhR69+5Nx44dbe2jR4+mVatWNG/enD179jBt2jRyc3NZuXKlw3G2bdvGsmXL+OSTT2xtBQUFNGvWzK5fs2bNKC4u5vz583h51d7/5UFERERERP77uZwomUwmcnJy2Lp1q137xIkTbf+5U6dOBAcHM2DAAA4fPkybNm3s+ubk5DBs2DCee+45YmJiXA0FALPZjNlstmuzWi0YDIZqjSsiIiIiInWPS5/excXFsXbtWjZv3kxISMhV+/bo0QOAQ4cO2bXv37+fAQMGMHHiRGbMmGF3LygoiMLCQru2wsJCfH19K11NSkpKwmg02l3JycnOTk1ERERERMS5FSWr1crkyZNJS0sjPT2dsLCw33wmOzsbgODgYFvbvn376N+/P2PHjuXFF1+s8Ex0dDSffvqpXdvGjRuJjo6u9D2JiYkkJCRcEa/lN+MTEREREXFMB87WZU5VvfvjH/9ISkoKq1atIjw83NZuNBrx8vLi8OHDpKSkcOedd9KkSRP27NlDfHw8ISEhZGRkAJc+t+vfvz+xsbF2Kz716tUjICAAuFQevGPHjphMJh5++GE2bdrE448/zieffEJsbGyVJ6eqdyIiIiK1W+2uene2pkO4Lho28K7pEGolpxIlNzfHWfWiRYsYN24cP/30Ew888AA5OTmUlJTQsmVL/ud//ocZM2bg6+sLwMyZM5k1a1aFMVq1asWRI0dsv9PT04mPj2f//v2EhITw7LPPMm7cOKcmp0RJREREpHZTolTzlCg5Vq1zlGo7JUoiIiIitZsSpZqnRMkxl6veiYiIiIjcyNyqd+So/JfTv30REREREZErKFESERERERG5glOJUlJSElFRUfj4+BAYGMjw4cPJzc2169O3b1/c3NzsrkmTJtn1+frrrxkwYAB+fn40btyY2NhYdu/ebbufm5tLv379aNasGQ0aNOCmm25ixowZlJWVVWOqIiIiIiIiVeNUopSRkYHJZGL79u1s3LiRsrIyYmJiKCkpses3YcIE8vPzbdfLL79su3f27FkGDx5MaGgoO3bsYOvWrfj4+BAbG2tLhOrXr8+YMWP47LPPyM3NZe7cufzjH//gueeeuwZTFhERERERuTqnEqX169czbtw4OnToQOfOnVm8eDFHjx4lKyvLrl/Dhg0JCgqyXZdLgwN8++23nDx5kueff57w8HA6dOjAc889R2FhIT/++CMAN910Ew899BCdO3emVatW/OEPf+D+++/nn//85zWYsoiIiIjIb3OrI/8464033qB169Y0aNCAHj168NVXX121//Lly2nXrh0NGjSgU6dOfPrpp3b3rVYrf/rTnwgODsbLy4uBAwfy3Xff2fU5efIk999/P76+vvj5+TF+/HjOnv3PViWs1h6loqIiAPz9/e3a33//fZo2bUrHjh1JTEzk3Llztnvh4eE0adKEhQsXcuHCBc6fP8/ChQtp3749rVu3dvieQ4cOsX79eu64447qhCsiIiIiItWwbNkyEhISeO655/jmm2/o3LkzsbGxHDt2zGH/bdu2MWrUKMaPH8+uXbsYPnw4w4cPJycnx9bn5Zdf5vXXX+ett95ix44dNGrUiNjYWEpLS2197r//fvbt28fGjRtZu3YtW7ZsYeLEif/Rubp8jpLFYuEPf/gDp0+fZuvWrbb2t99+m1atWtG8eXP27NnDtGnTuO2221i5cqWtT05ODsOHD+eHH34A4Oabb2bDhg20atXK7h29evXim2++wWw2M3HiRObPn4+7e9VzO52jJCIiIlK71eZzlOrK3yWd+XfQo0cPoqKi+Nvf/gZcyglatmzJ5MmTefrppyv0v/feeykpKWHt2rW2tp49e9KlSxfeeustrFYrzZs358knn+Spp54CLi3GNGvWjMWLF3Pfffdx4MABIiIi+Prrr7n11luBS1+63Xnnnfz88880b968OtOvlMsrSiaTiZycHFJTU+3aJ06cSGxsLJ06deL+++/n3XffJS0tjcOHDwNw/vx5xo8fT+/evdm+fTtffvklHTt2ZOjQoZw/b/9fxmXLlvHNN9+QkpLCJ598wpw5cyqNx2w2U1xcbHeZzWZXpyciIiIiUidU9e/RFy5cICsri4EDB9ra3N3dGThwIJmZmQ7HzszMtOsPEBsba+v/ww8/UFBQYNfHaDTSo0cPW5/MzEz8/PxsSRLAwIEDcXd3Z8eOHa5P/De4lCjFxcWxdu1aNm/eTEhIyFX79ujRA7j0+RxASkoKR44cYdGiRURFRdGzZ09SUlL44YcfWLVqld2zLVu2JCIiglGjRjF79mxmzpxJeXm5w/ckJSVhNBrtruTkZFemJyIiIiICuNWJy9Hfo5OSkir8aZw4cYLy8nKaNWtm196sWTMKCgoc/gkWFBRctf/l//tbfQIDA+3ue3h44O/vX+l7rwUPZzpbrVYmT55MWloa6enphIWF/eYz2dnZAAQHBwNw7tw53N3dcXP798axy78tFkul41gsFsrKyrBYLNSrV6/C/cTERBISEq6It/LxRERERETE8d+jDQZDDUVTeziVKJlMJlJSUli1ahU+Pj62DM5oNOLl5cXhw4dJSUnhzjvvpEmTJuzZs4f4+Hj69OlDZGQkAIMGDWLq1KmYTCYmT56MxWJh9uzZeHh40K9fP+BSMYj69evTqVMnDAYDO3fuJDExkXvvvZf69es7jM1gMFT4F1pXvisVEREREXGVo79HO9K0aVPq1atHYWGhXXthYSFBQUEOnwkKCrpq/8v/t7Cw0Lawcvl3ly5dbH2uLBZx8eJFTp48Wel7rwWnPr2bP38+RUVF9O3bl+DgYNu1bNkyADw9Pfn888+JiYmhXbt2PPnkk4wYMYI1a9bYxmjXrh1r1qxhz549REdHc/vtt5OXl8f69ettfzgeHh689NJL3HbbbURGRjJr1izi4uJYsGDBNZy6iIiIiIhUlaenJ927d+eLL76wtVksFr744guio6MdPhMdHW3XH2Djxo22/mFhYQQFBdn1KS4uZseOHbY+0dHRnD592u5Iok2bNmGxWGzbfP4TXK56999AK0oiIiIitVvtrnpX+tudbgANGjSoct9ly5YxduxY/v73v3Pbbbcxd+5cPvzwQ7799luaNWvGmDFjaNGihW2P07Zt27jjjjuYPXs2Q4cOJTU1lb/85S988803dOzYEYCXXnqJ2bNns2TJEsLCwnj22WfZs2cP+/fvt8U2ZMgQCgsLeeuttygrK+Ohhx7i1ltvJSUl5dr/gfx/Tn16JyIiIiJSZ9ywywmuu/feezl+/Dh/+tOfKCgooEuXLqxfv95WjOHo0aN2x/n06tWLlJQUZsyYwfTp07n55pv5+OOPbUkSwP/93/9RUlLCxIkTOX36NL/73e9Yv369XQL3/vvvExcXx4ABA3B3d2fEiBG8/vrr/9G5akVJRERERGpMrV5ROl9HVpS8qr6iVJe4fI6SiIiIiIjIjcqpRCkpKYmoqCh8fHwIDAxk+PDh5ObmVuiXmZlJ//79adSoEb6+vvTp06fCYbJw6XCrLl264ObmZisjfpnVamXOnDnccsstGAwGWrRowYsvvujc7ERERERERFzg1B6ljIwMTCYTUVFRXLx4kenTpxMTE8P+/ftp1KgRcClJGjx4MImJifz1r3/Fw8OD3bt3232reNn//d//0bx5c3bv3l3h3hNPPMFnn33GnDlz6NSpEydPnuTkyZMuTlNERERExDluN+4OFamCau1ROn78OIGBgWRkZNCnTx8AevbsyaBBg3jhhReu+uy6detISEjgo48+okOHDuzatctWK/3AgQNERkaSk5NDeHi4q+Fpj5KIiIhILVeb9yiZz9WNv0saGtbefwc1qVp7lIqKigDw9/cH4NixY+zYsYPAwEB69epFs2bNuOOOO9i6davdc4WFhUyYMIH33nuPhg0bVhh3zZo13HTTTaxdu5awsDBat27NI488ohUlERERERG5LlxOlCwWC1OmTKF379628n7ff/89ADNnzmTChAmsX7+ebt26MWDAAL777jvg0t6jcePGMWnSJG699VaHY3///ff8+OOPLF++nHfffZfFixeTlZXFPffc42q4IiIiIiIiVebyOUomk4mcnBy71SKLxQLAo48+ykMPPQRA165d+eKLL3jnnXdISkrir3/9K2fOnCExMbHSsS0WC2azmXfffZdbbrkFgIULF9K9e3dyc3Mdfo5nNpsxm812bVarBYPB4OoURURERESkjnJpRSkuLo61a9eyefNmQkJCbO3BwcEARERE2PVv3749R48eBWDTpk1kZmZiMBjw8PCgbdu2ANx6662MHTvWNo6Hh4ctSbo8BmAb50pJSUkYjUa7Kzk52ZXpiYiIiIhcOnC2LlzikFMrSlarlcmTJ5OWlkZ6ejphYWF291u3bk3z5s0rlAw/ePAgQ4YMAeD111/nz3/+s+1eXl4esbGxLFu2jB49egDQu3dvLl68yOHDh2nTpo1tDIBWrVo5jC0xMZGEhIQr4rU4Mz0RERERERHAyUTJZDKRkpLCqlWr8PHxoaCgAACj0YiXlxdubm5MnTqV5557js6dO9OlSxeWLFnCt99+y4oVKwAIDQ21G9Pb2xuANm3a2FanBg4cSLdu3Xj44YeZO3cuFosFk8nEoEGD7FaZfs1gMFT4zE5V70RERERExBVOJUrz588HoG/fvnbtixYtYty4cQBMmTKF0tJS4uPjOXnyJJ07d2bjxo22laGqcHd3Z82aNUyePJk+ffrQqFEjhgwZwiuvvOJMuCIiIiIiIi6p1jlKtZ1WlERERERqt1p9jtLZuvF3SYN37f13UJOqdY6SiIiIiIjIjUiJkoiIiIiIyBWUKImIiIiIiFzBqUQpKSmJqKgofHx8CAwMZPjw4XalwI8cOYKbm5vDa/ny5bZ+R48eZejQoTRs2JDAwECmTp3KxYsX7d71xhtv0L59e7y8vAgPD+fdd9+t5lRFRERERESqxqmqdxkZGZhMJqKiorh48SLTp08nJiaG/fv306hRI1q2bEl+fr7dM2+//TbJycm2c5TKy8sZOnQoQUFBbNu2jfz8fMaMGUP9+vX5y1/+AlyqrpeYmMg//vEPoqKi+Oqrr5gwYQKNGzfmrrvuukZTFxERERG5ihu35plUQbWq3h0/fpzAwEAyMjLo06ePwz5du3alW7duLFy4EIB169bx+9//nry8PJo1awbAW2+9xbRp0zh+/Dienp706tWL3r17k5ycbBvnySefZMeOHWzdurXK8anqnYiIiEjtVqur3p05V9MhXBcGn4Y1HUKtVK09SkVFRQD4+/s7vJ+VlUV2djbjx4+3tWVmZtKpUydbkgQQGxtLcXEx+/btA8BsNtOgQQO7sby8vPjqq68oKyurTsgiIiIiIiK/yeVEyWKxMGXKFHr37k3Hjh0d9lm4cCHt27enV69etraCggK7JAmw/S4oKAAuJU4LFiwgKysLq9XKzp07WbBgAWVlZZw4ccLVkEVERERERKrEqT1Kv2YymcjJyan0U7jz58+TkpLCs88+6/TYzz77LAUFBfTs2ROr1UqzZs0YO3YsL7/8Mu7ujnM7s9mM2Wy2a7NaLRgMBqffLyIiIiLipi1KdZpLK0pxcXGsXbuWzZs3ExIS4rDPihUrOHfuHGPGjLFrDwoKorCw0K7t8u+goCDg0md277zzDufOnePIkSMcPXqU1q1b4+PjQ0BAgMP3JSUlYTQa7a5f73ESERERERGpKqeKOVitViZPnkxaWhrp6encfPPNlfbt27cvTZs2ZcWKFXbtl4s55OfnExgYCFyqjDd16lSOHTtW6QrQHXfcQYsWLUhJSXF4XytKIiIiIv99anMxhwvFdaOYg6evijk44tSndyaTiZSUFFatWoWPj49tT5HRaMTL69//JT906BBbtmzh008/rTBGTEwMERERPPjgg7z88ssUFBQwY8YMTCaTLak5ePAgX331FT169ODUqVO8+uqr5OTksGTJkkpjMxgMFZIiVb0TERERERFXOLWi5Obm5rB90aJFjBs3zvZ7+vTpLF26lCNHjjjcU/Tjjz/y2GOPkZ6eTqNGjRg7diyzZ8/Gw+NS3nbgwAFGjx5Nbm4u9evXp1+/frz00kuEh4c7NTklSiIiIiK1m1aUap5WlByr1jlKtZ0SJREREZHarVYnSkV1JFEyKlFypFrnKImIiIiIiNyIlCiJiIiIiIhcQYmSiIiIiIjIFZxKlJKSkoiKisLHx4fAwECGDx9Obm6u7f6RI0dwc3NzeC1fvhyAxYsXV9rn2LFjAKxcuZJBgwYREBCAr68v0dHRbNiw4RpOW0RERETkN1itdeMSh5wq5jB48GDuu+8+oqKiuHjxItOnTycnJ4f9+/fTqFEjysvLOX78uN0zb7/9NsnJyeTn5+Pt7c358+cpKiqy6zNu3DhKS0tJT08HYMqUKTRv3px+/frh5+fHokWLmDNnDjt27KBr165VnpyKOYiIiIjUbrW6mMPpkpoO4brw9GtU0yHUStWqenf8+HECAwPJyMigT58+Dvt07dqVbt26sXDhwkrHaNGiBQsXLuTBBx+s9F0dOnTg3nvv5U9/+lOV41OiJCIiIlK7KVGqeUqUHKvWHqXLK0P+/v4O72dlZZGdnc348eMrHePdd9+lYcOG3HPPPZX2sVgsnDlzptL3iIiIiIiIXEsuJ0oWi4UpU6bQu3dvOnbs6LDPwoULad++Pb169ap0nIULFzJ69Gi8vCr/XxPmzJnD2bNnGTlypKvhioiIiIiIVJmHqw+aTCZycnLYunWrw/vnz58nJSWFZ599ttIxMjMzOXDgAO+9916lfVJSUpg1axarVq0iMDCw0n5msxmz2WzXZrVaMBgMvzETEREREREHVOegTnNpRSkuLo61a9eyefNmQkJCHPZZsWIF586dY8yYMZWOs2DBArp06UL37t0d3k9NTeWRRx7hww8/ZODAgVeNKSkpCaPRaHclJydXfVIiIiIiIiL/n1PFHKxWK5MnTyYtLY309HRuvvnmSvv27duXpk2bsmLFCof3z549S3BwMElJScTFxVW4/8EHH/Dwww+TmprKsGHDfjM2rSiJiIiI/Pep1cUcTtWRYg6NVczBEac+vTOZTKSkpLBq1Sp8fHwoKCgAwGg02u0xOnToEFu2bOHTTz+tdKxly5Zx8eJFHnjggQr3UlJSGDt2LPPmzaNHjx6293h5eWE0Gh2OZzAYKiRFqnonIiIiIiKucGpFyc3NzWH7okWLGDdunO339OnTWbp0KUeOHMHd3fHXfb169SIsLIz333+/wr2+ffuSkZFRoX3s2LEsXry4quEqURIRERGp5bSiVPO0ouRYtc5Rqu2UKImIiIjUbkqUap4SJceqdY6SiIiIiIjIjUiJkoiIiIiIyBWUKImIiIiIiFzB5QNnRURERERuZG437lZ+qQKnVpSSkpKIiorCx8eHwMBAhg8fTm5url2fgoICHnzwQYKCgmjUqBHdunXjo48+suvzhz/8gdDQUBo0aEBwcDAPPvggeXl5tvtHjhzBzc2twrV9+/ZqTFVERERERKRqnEqUMjIyMJlMbN++nY0bN1JWVkZMTAwlJf+uCDJmzBhyc3NZvXo1e/fu5e6772bkyJHs2rXL1qdfv358+OGH5Obm8tFHH3H48GHuueeeCu/7/PPPyc/Pt13du3evxlRFRERERESqplrlwY8fP05gYCAZGRn06dMHAG9vb+bPn8+DDz5o69ekSRNeeuklHnnkEYfjrF69muHDh2M2m6lfvz5HjhwhLCyMXbt20aVLF1fDU3lwERERkVquNpcHLzt5tqZDuC7q+3vXdAi1UrWKORQVFQHg7+9va+vVqxfLli3j5MmTWCwWUlNTKS0tpW/fvg7HOHnyJO+//z69evWifv36dvf+8Ic/EBgYyO9+9ztWr15dnVBFRERERJxjrSOXOORyomSxWJgyZQq9e/emY8eOtvYPP/yQsrIymjRpgsFg4NFHHyUtLY22bdvaPT9t2jQaNWpEkyZNOHr0KKtWrbLd8/b25pVXXmH58uV88skn/O53v2P48OFXTZbMZjPFxcV2l9lsdnV6IiIiIiJSh7n86d1jjz3GunXr2Lp1KyEhIbb2yZMn89VXX/GXv/yFpk2b8vHHH/Paa6/xz3/+k06dOtn6nThxgpMnT/Ljjz8ya9YsjEYja9euxc3NzeH7xowZww8//MA///lPh/dnzpzJrFmz7NqeeWY6M2bMcGV6IiIiInId1OpP7/5VRz69a6JP7xxxKVGKi4tj1apVbNmyhbCwMFv74cOHadu2LTk5OXTo0MHWPnDgQNq2bctbb73lcLyff/6Zli1bsm3bNqKjox32eeONN/jzn/9Mfn6+w/tms7nCCpLVasFgMDg7PRERERG5TpQo1TwlSo45dY6S1Wpl8uTJpKWlkZ6ebpckAZw7dw4Ad3f7L/rq1auHxWKpdNzL9672qVx2djbBwcGV3jcYDBWSIhVzEBERERERVziVKJlMJlJSUli1ahU+Pj4UFBQAYDQa8fLyol27drRt25ZHH32UOXPm0KRJEz7++GM2btzI2rVrAdixYwdff/01v/vd72jcuDGHDx/m2WefpU2bNrbVpCVLluDp6UnXrl0BWLlyJe+88w4LFiy4lnMXEREREamcCh3UaU59elfZ/qFFixYxbtw4AL777juefvpptm7dytmzZ2nbti1PPfWUrVz43r17eeKJJ9i9ezclJSUEBwczePBgZsyYQYsWLYBLidJLL73Ejz/+iIeHB+3atWPq1KkOz1q6Gq0oiYiIiNRutfrTuxN15NO7pvr0zpFqnaNU2ylREhEREandlCjVPCVKjlXrHCUREREREZEbkVN7lERERERE6owb98MrqQKtKImIiIiIiFzBqUQpKSmJqKgofHx8CAwMZPjw4eTm5tr1KSgo4MEHHyQoKIhGjRrRrVs3PvroI7s+33zzDYMGDcLPz48mTZowceJEzp61/wb066+/ZsCAAfj5+dG4cWNiY2PZvXu3i9MUERERERGpOqcSpYyMDEwmE9u3b2fjxo2UlZURExNDSUmJrc+YMWPIzc1l9erV7N27l7vvvpuRI0eya9cuAPLy8mwH0O7YsYP169ezb98+W9U8gLNnzzJ48GBCQ0PZsWMHW7duxcfHh9jYWMrKyq7NzEVERERERCpRrap3x48fJzAwkIyMDPr06QOAt7c38+fPt5UDB2jSpAkvvfQSjzzyCG+//TbPPvss+fn5toNp9+7dS2RkJN999x1t27Zl586dREVFcfToUVq2bOmwT1Wo6p2IiIhI7Varq94dP1PTIVwX9QN8ajqEWqlae5SKiooA8Pf3t7X16tWLZcuWcfLkSSwWC6mpqZSWltK3b18AzGYznp6etiQJwMvr0v+DbN26FYDw8HCaNGnCwoULuXDhAufPn2fhwoW0b9+e1q1bVydkEREREZEqcbPWjUscczlRslgsTJkyhd69e9OxY0db+4cffkhZWRlNmjTBYDDw6KOPkpaWZlsF6t+/PwUFBSQnJ3PhwgVOnTrF008/DUB+fj4APj4+pKens3TpUry8vPD29mb9+vWsW7cODw/HhfrMZjPFxcV2l9lsdnV6IiIiIiJSh7mcKJlMJnJyckhNTbVrf/bZZzl9+jSff/45O3fuJCEhgZEjR7J3714AOnTowJIlS3jllVdo2LAhQUFBhIWF0axZM9sq0/nz5xk/fjy9e/dm+/btfPnll3Ts2JGhQ4dy/rzjz+mSkpIwGo12V3JysqvTExERERGROsylPUpxcXGsWrWKLVu2EBYWZms/fPgwbdu2JScnhw4dOtjaLxdveOutt+zGKSwspFGjRri5ueHr60tqair/+7//y8KFC5k+fbrdPqYLFy7QuHFjFi5cyH333VchJrPZXGEFyWq1YDAYnJ2eiIiIiFwntXmP0sVjdWOPkkeg9ig54tSBs1arlcmTJ5OWlkZ6erpdkgRw7tw5ALv9RwD16tXDYrFUGK9Zs2YAvPPOOzRo0IBBgwbZxnF3d8fNzc3W9/JvR+MAGAyGCkmRijmIiIiIiMu0f6dOc+rTO5PJxNKlS0lJScHHx4eCggIKCgpsn8O1a9eOtm3b8uijj/LVV19x+PBhXnnlFTZu3Mjw4cNt4/ztb3/jm2++4eDBg7zxxhvExcWRlJSEn58fAIMGDeLUqVOYTCYOHDjAvn37eOihh/Dw8KBfv37XbPIiIiIiIiKOOPXp3a9XeH5t0aJFtnOQvvvuO55++mm2bt3K2bNnadu2LU899ZRdufAxY8bwySefcPbsWdq1a1fhPsDGjRuZNWsWOTk5uLu707VrV1588UV69uxZ5clpRUlERESkdqvVn94V1pFP75rp0ztHqnWOUm2nRElERESkdlOiVPOUKDlWrXOUREREREREbkROFXMQEREREakzbtjvrqQqtKIkIiIiIiJyBacSpaSkJKKiovDx8SEwMJDhw4eTm5tr1+fw4cP8z//8DwEBAfj6+jJy5EgKCwsrjPXJJ5/Qo0cPvLy8aNy4sV1VPIDHH3+c7t27YzAY6NKli9MTExERERERcZVTiVJGRgYmk4nt27ezceNGysrKiImJoaSkBICSkhJiYmJwc3Nj06ZNfPnll1y4cIG77rrL7vyjjz76iAcffJCHHnqI3bt38+WXXzJ69OgK73v44Ye59957qzlFERERERER51Sr6t3x48cJDAwkIyODPn368NlnnzFkyBBOnTqFr68vAEVFRTRu3JjPPvuMgQMHcvHiRVq3bs2sWbMYP378b75j5syZfPzxx2RnZzsdn6reiYiIiNRutbrqXX5xTYdwXXgE+9Z0CLVStfYoFRUVAeDv7w+A2WzGzc0Ng8Fg69OgQQPc3d3ZunUrAN988w2//PKL7Wyk4OBghgwZQk5OTnVCERERERERuWZcTpQsFgtTpkyhd+/edOzYEYCePXvSqFEjpk2bxrlz5ygpKeGpp56ivLyc/Px8AL7//nvg0krRjBkzWLt2LY0bN6Zv376cPHnS5YmYzWaKi4vtLrPZ7PJ4IiIiIiJSd7mcKJlMJnJyckhNTbW1BQQEsHz5ctasWYO3tzdGo5HTp0/TrVs33N0vveryXqVnnnmGESNG0L17dxYtWoSbmxvLly93eSJJSUkYjUa7Kzk52eXxRERERESk7nLpHKW4uDjWrl3Lli1bCAkJsbsXExPD4cOHOXHiBB4eHvj5+REUFMRNN90EQHBwMAARERG2ZwwGAzfddBNHjx51dR4kJiaSkJBg12a1WirpLSIiIiIiUjmnEiWr1crkyZNJS0sjPT2dsLCwSvs2bdoUgE2bNnHs2DH+8Ic/ANhKfufm5vK73/0OgLKyMo4cOUKrVq1cnQcGg8FubxSomIOIiIiIuM5NB87WaU4lSiaTiZSUFFatWoWPjw8FBQUAGI1GvLwuVSxZtGgR7du3JyAggMzMTJ544gni4+MJDw8HwNfXl0mTJvHcc8/RsmVLWrVqZftE7n//939t7zp06BBnz56loKCA8+fP26reRURE4OnpWe2Ji4iIiIiIVMap8uBubm4O2xctWsS4ceMAePrpp1m8eDEnT56kdevWTJo0ifj4eLtny8rKSExM5L333uP8+fP06NGDuXPn0qFDB1ufvn37kpGRUeFdP/zwA61bt65SvFpREhEREandanN58PK8ulEevF5zlQd3pFrnKNV2SpREREREajclSjVPiZJj1TpHSURERERE5EakRElEREREROQKSpRERERERESu4FSiNH/+fCIjI/H19cXX15fo6GjWrVtnu19aWorJZKJJkyZ4e3szYsQICgsLbfd3797NqFGjaNmyJV5eXrRv35558+ZV+r4vv/wSDw8PunTp4vzMREREREREXORUohQSEsLs2bPJyspi586d9O/fn2HDhrFv3z4A4uPjWbNmDcuXLycjI4O8vDzuvvtu2/NZWVkEBgaydOlS9u3bxzPPPENiYiJ/+9vfKrzr9OnTjBkzhgEDBlRziiIiIiIiIs6pdtU7f39/kpOTueeeewgICCAlJYV77rkHgG+//Zb27duTmZlJz549HT5vMpk4cOAAmzZtsmu/7777uPnmm6lXrx4ff/yx7RwlZ6jqnYiIiEjtVqur3v1SVNMhXBf1WhhrOoRayeU9SuXl5aSmplJSUkJ0dDRZWVmUlZUxcOBAW5927doRGhpKZmZmpeMUFRXh7+9v17Zo0SK+//57nnvuOVfDExERERERcZmHsw/s3buX6OhoSktL8fb2Ji0tjYiICLKzs/H09MTPz8+uf7NmzSgoKHA41rZt21i2bBmffPKJre27777j6aef5p///CceHlUPz2w2Yzab7dqsVgsGg6HqkxMREREREcGFFaXw8HCys7PZsWMHjz32GGPHjmX//v1OvzgnJ4dhw4bx3HPPERMTA1xapRo9ejSzZs3illtucWq8pKQkjEaj3ZWcnOx0XCIiIiIiItXeozRw4EDatGnDvffey4ABAzh16pTdqlKrVq2YMmUK8fHxtrb9+/fTr18/HnnkEV588UVb++nTp2ncuDH16tWztVksFqxWK/Xq1eOzzz6jf//+DuPQipKIiIjIfx/tUap52qPkmNOf3l3JYrFgNpvp3r079evX54svvmDEiBEA5ObmcvToUaKjo2399+3bR//+/Rk7dqxdkgTg6+vL3r177drefPNNNm3axIoVKwgLC6s0DoPBUCEpUjEHEREREXFZtZYT5L+dU4lSYmIiQ4YMITQ0lDNnzpCSkkJ6ejobNmzAaDQyfvx4EhIS8Pf3x9fXl8mTJxMdHW2reJeTk0P//v2JjY0lISHBtnepXr16BAQE4O7uTseOHe3eGRgYSIMGDSq0i4iIiIiI/Kc4lSgdO3aMMWPGkJ+fj9FoJDIykg0bNjBo0CAAXnvtNdzd3RkxYgRms5nY2FjefPNN2/MrVqzg+PHjLF26lKVLl9raW7VqxZEjR67NjERERERERKqp2nuUajN9eiciIiJSu9XqPUo/15E9SiHao+RItfcoiYiIiIjciNxu2OUEqQqXD5wVERERERG5USlREhERERERuYJTidL8+fOJjIzE19cXX19foqOjWbdune1+aWkpJpOJJk2a4O3tzYgRIygsLLQb4+uvv2bAgAH4+fnRuHFjYmNj2b17t+3+zJkzcXNzq3A1atSomlMVERERERGpGqcSpZCQEGbPnk1WVhY7d+6kf//+DBs2jH379gEQHx/PmjVrWL58ORkZGeTl5XH33Xfbnj979iyDBw8mNDSUHTt2sHXrVnx8fIiNjaWsrAyAp556ivz8fLsrIiKC//3f/72G0xYREREREalctave+fv7k5yczD333ENAQAApKSncc889AHz77be0b9+ezMxMevbsyc6dO4mKiuLo0aO0bNkSgL179xIZGcl3331H27ZtK4y/e/duunTpwpYtW7j99tudik1V70RERERqt9pc9c5y9HRNh3BduIf61XQItZLLe5TKy8tJTU2lpKSE6OhosrKyKCsrY+DAgbY+7dq1IzQ0lMzMTADCw8Np0qQJCxcu5MKFC5w/f56FCxfSvn17Wrdu7fA9CxYs4JZbbnE6SRIREREREXGV04nS3r178fb2xmAwMGnSJNLS0oiIiKCgoABPT0/8/Pzs+jdr1oyCggIAfHx8SE9PZ+nSpXh5eeHt7c369etZt24dHh4VK5WXlpby/vvvM378+N+My2w2U1xcbHeZzWZnpyciIiIiIuJ8ohQeHk52djY7duzgscceY+zYsezfv79Kz54/f57x48fTu3dvtm/fzpdffknHjh0ZOnQo589X/EwuLS2NM2fOMHbs2N8cOykpCaPRaHclJyc7Oz0REREREZHq71EaOHAgbdq04d5772XAgAGcOnXKblWpVatWTJkyhfj4eBYuXMj06dPJz8/H3f1SjnbhwgUaN27MwoULue++++zGHjBgAL6+vqSlpf1mHGazucIKktVqwWAwVGd6IiIiIvIfVKv3KP14uqZDuC7cW/nVdAi1UrXPUbJYLJjNZrp37079+vX54osvbPdyc3M5evQo0dHRAJw7dw53d3fc3Nz+HcD//22xWOzG/eGHH9i8eXOVPrsDMBgMtrLlly8lSSIiIiIi4gqnEqXExES2bNnCkSNH2Lt3L4mJiaSnp3P//fdjNBoZP348CQkJbN68maysLB566CGio6Pp2bMnAIMGDeLUqVOYTCYOHDjAvn37eOihh/Dw8KBfv35273rnnXcIDg5myJAh1262IiIiIiIiVVCxgsJVHDt2jDFjxpCfn4/RaCQyMpINGzYwaNAgAF577TXc3d0ZMWIEZrOZ2NhY3nzzTdvz7dq1Y82aNcyaNYvo6Gjc3d3p2rUr69evJzg42NbPYrGwePFixo0bR7169a7RVEVERERERKqm2nuUajOdoyQiIiJSu2mPUs3THiXHnFpREhERERGpMyw37HqCVEG1izmIiIiIiIjcaJQoiYiIiIiIXMGpRGn+/PlERkbaym9HR0ezbt062/23336bvn374uvri5ubG6dPn64wRuvWrXFzc7O7Zs+ebddnz5493H777TRo0ICWLVvy8ssvuzY7ERERERERFzi1RykkJITZs2dz8803Y7VaWbJkCcOGDWPXrl106NCBc+fOMXjwYAYPHkxiYmKl4zz//PNMmDDB9tvHx8f2n4uLi4mJiWHgwIG89dZb7N27l4cffhg/Pz8mTpzowhRFRERERJx3A9c8kypwKlG666677H6/+OKLzJ8/n+3bt9OhQwemTJkCQHp6+lXH8fHxISgoyOG9999/nwsXLvDOO+/g6elJhw4dyM7O5tVXX1WiJCIiIiIi14XLe5TKy8tJTU2lpKSE6Ohop56dPXs2TZo0oWvXriQnJ3Px4kXbvczMTPr06YOnp6etLTY2ltzcXE6dOuVquCIiIiIiIlXmdHnwvXv3Eh0dTWlpKd7e3qSlpREREVHl5x9//HG6deuGv78/27ZtIzExkfz8fF599VUACgoKCAsLs3umWbNmtnuNGzd2OK7ZbMZsNtu1Wa0WDAaDM9MTERERERFxfkUpPDyc7OxsduzYwWOPPcbYsWPZv39/lZ9PSEigb9++REZGMmnSJF555RX++te/VkhynJWUlITRaLS7kpOTqzWmiIiIiIjUTU6vKHl6etK2bVsAunfvztdff828efP4+9//7lIAPXr04OLFixw5coTw8HCCgoIoLCy063P5d2X7mgASExNJSEiwa7NaLS7FJCIiIiKC/ipZp1X7HCWLxVKt1aDs7Gzc3d0JDAwEIDo6mi1btlBWVmbrs3HjRsLDwyv97A7AYDDYypZfvvTZnYiIiIiIuMKpFaXExESGDBlCaGgoZ86cISUlhfT0dDZs2ABc2kNUUFDAoUOHgEv7mXx8fAgNDcXf35/MzEx27NhBv3798PHxITMzk/j4eB544AFbEjR69GhmzZrF+PHjmTZtGjk5OcybN4/XXnvtGk9dRERERETEMacSpWPHjjFmzBjy8/MxGo1ERkayYcMGBg0aBMBbb73FrFmzbP379OkDwKJFixg3bhwGg4HU1FRmzpyJ2WwmLCyM+Ph4u0/mjEYjn332GSaTie7du9O0aVP+9Kc/qTS4iIiIiIhcN27WG/gkrdLS8zUdgoiIiIhcRYMGXjUdQqUuHvxXTYdwXXjc0qSmQ6iVqr1HSURERERE5EajRElEREREROQKSpRERERERESu4FSiNH/+fCIjI23lt6Ojo1m3bh0AJ0+eZPLkyYSHh+Pl5UVoaCiPP/44RUVFdmM8/vjjdO/eHYPBQJcuXSq8Izc3l379+tGsWTMaNGjATTfdxIwZM+zKhYuIiIiIiPwnOVX1LiQkhNmzZ3PzzTdjtVpZsmQJw4YNY9euXVitVvLy8pgzZw4RERH8+OOPTJo0iby8PFasWGE3zsMPP8yOHTvYs2dPhXfUr1+fMWPG0K1bN/z8/Ni9ezcTJkzAYrHwl7/8pXqzFRERERGpqhu35plUQbWr3vn7+5OcnMz48eMr3Fu+fDkPPPAAJSUleHjY52QzZ87k448/Jjs7+zffkZCQwNdff80///lPp2JT1TsRERGR2q1WV73LPVHTIVwXHuFNazqEWsnlPUrl5eWkpqZSUlJCdHS0wz5FRUX4+vpWSJKccejQIdavX88dd9zh8hgiIiIiIiLOcDqD2bt3L9HR0ZSWluLt7U1aWhoREREV+p04cYIXXnjB5YNie/XqxTfffIPZbGbixIk8//zzV+1vNpsxm812bVarBYPB4NL7RURERESk7nJ6RSk8PJzs7Gx27NjBY489xtixY9m/f79dn+LiYoYOHUpERAQzZ850KbBly5bxzTffkJKSwieffMKcOXOu2j8pKQmj0Wh3JScnu/RuERERERGrxVonLnHM6UTJ09OTtm3b0r17d5KSkujcuTPz5s2z3T9z5gyDBw/Gx8eHtLQ06tev71JgLVu2JCIiglGjRjF79mxmzpxJeXl5pf0TExMpKiqyu6ZOnerSu0VEREREpHpOnjzJ/fffj6+vL35+fowfP56zZ89e9ZnS0lJMJhNNmjTB29ubESNGUFhYaLu/e/duRo0aRcuWLfHy8qJ9+/Z2uQhAeno6bm5uFa6CggKn4nd989D/Z7FYbJ+8FRcXExsbi8FgYPXq1TRo0KC6w9veUVZWhsVioV69eg77GAyGCp/ZqZiDiIiIiEjNuP/++8nPz2fjxo2UlZXx0EMPMXHiRFJSUip9Jj4+nk8++YTly5djNBqJi4vj7rvv5ssvvwQgKyuLwMBAli5dSsuWLdm2bRsTJ06kXr16xMXF2Y2Vm5uLr6+v7XdgYKBT8TuVKCUmJjJkyBBCQ0M5c+YMKSkppKens2HDBoqLi4mJieHcuXMsXbqU4uJiiouLAQgICLAlOIcOHeLs2bMUFBRw/vx5W9W7iIgIPD09ef/996lfvz6dOnXCYDCwc+dOEhMTuffee11enRIRERERkevnwIEDrF+/nq+//ppbb70VgL/+9a/ceeedzJkzh+bNm1d4pqioiIULF5KSkkL//v0BWLRoEe3bt2f79u307NmThx9+2O6Zm266iczMTFauXFkhUQoMDMTPz8/lOTiVKB07dowxY8aQn5+P0WgkMjKSDRs2MGjQINLT09mxYwcAbdu2tXvuhx9+oHXr1gA88sgjZGRk2O517drVro+HhwcvvfQSBw8exGq10qpVK+Li4oiPj3d5kiIiIiIi4pijomiOvtZyRmZmJn5+frYkCWDgwIG4u7uzY8cO/ud//qfCM1lZWZSVlTFw4EBbW7t27QgNDSUzM5OePXs6fFdRURH+/v4V2rt06YLZbKZjx47MnDmT3r17OzUHpxKlhQsXVnqvb9++VOVIpvT09Kvev/fee7n33nudCUtERERE5NqrI4UOkpKSmDVrll3bc88953JRNoCCgoIKn7p5eHjg7+9f6V6hgoICPD09K6wCNWvWrNJntm3bxrJly/jkk09sbcHBwbz11lvceuutmM1mFixYQN++fdmxYwfdunWr8hyqvUdJRERERET+eyUmJpKQkGDXVtlq0tNPP81LL7101fEOHDhwzWK7mpycHIYNG8Zzzz1HTEyMrT08PJzw8HDb7169enH48GFee+013nvvvSqPr0RJRERERKQOc+YzuyeffJJx48Zdtc9NN91EUFAQx44ds2u/ePEiJ0+eJCgoyOFzQUFBXLhwgdOnT9utKhUWFlZ4Zv/+/QwYMICJEycyY8aM34z7tttuY+vWrb/Z79eUKImIiIiISJUEBAQQEBDwm/2io6M5ffo0WVlZdO/eHYBNmzZhsVjo0aOHw2e6d+9O/fr1+eKLLxgxYgRwqXLd0aNHiY6OtvXbt28f/fv3Z+zYsbz44otVijs7O5vg4OAq9b3MqXOU5s+fT2RkJL6+vvj6+hIdHc26deuAS3XSJ0+eTHh4OF5eXoSGhvL4449TVFRke37x4sUOa5q7ubnZZZxms5lnnnmGVq1aYTAYaN26Ne+8845TExMRERERqQ6r1Vonrv+E9u3bM3jwYCZMmMBXX33Fl19+SVxcHPfdd5+t4t0vv/xCu3bt+OqrrwAwGo2MHz+ehIQENm/eTFZWFg899BDR0dG2Qg45OTn069ePmJgYEhISKCgooKCggOPHj9vePXfuXFatWsWhQ4fIyclhypQpbNq0CZPJ5NQcnFpRCgkJYfbs2dx8881YrVaWLFnCsGHD2LVrF1arlby8PObMmUNERAQ//vgjkyZNIi8vjxUrVgCXCjUMHjzYbsxx48ZRWlpqt9lr5MiRFBYWsnDhQtq2bUt+fj4Wi8WpiYmIiIiISM15//33iYuLY8CAAbi7uzNixAhef/112/2ysjJyc3M5d+6cre21116z9TWbzcTGxvLmm2/a7q9YsYLjx4+zdOlSli5damtv1aoVR44cAeDChQs8+eST/PLLLzRs2JDIyEg+//xz+vXr51T8btZqppH+/v4kJyczfvz4CveWL1/OAw88QElJCR4eFXOy48eP06JFCxYuXMiDDz4IwPr167nvvvv4/vvvHZb5c4YOnBURERGp3Ro08KrpECp1IaewpkO4Ljw7NqvpEGolpz69+7Xy8nJSU1MpKSmx+2bw14qKivD19XWYJAG8++67NGzYkHvuucfWtnr1am699VZefvllWrRowS233MJTTz3F+fNKekRERERE5PpwupjD3r17iY6OprS0FG9vb9LS0oiIiKjQ78SJE7zwwgtMnDix0rEWLlzI6NGj8fL69/+S8P3337N161YaNGhAWloaJ06c4I9//CP/+te/WLRoUaVjOTooy2q1VOugLBERERERqZucXlEKDw8nOzubHTt28NhjjzF27Fj2799v16e4uJihQ4cSERFR6UFVmZmZHDhwoMInexaLBTc3N95//31uu+027rzzTl599VWWLFly1VWlpKQkjEaj3ZWcnOzs9ERERERELrHUkUscqvYepYEDB9KmTRv+/ve/A3DmzBliY2Np2LAha9eupUGDBg6fGz9+PN988w27du2yax87dixffvklhw4dsrUdOHCAiIgIDh48yM033+xwPK0oiYiIiPz3qdV7lPbUkT1Kkdqj5IjLe5Qus1gstgSluLiYmJgYPD09Wb16daVJ0tmzZ/nwww8dFoDo3bs3eXl5nD171tZ28OBB3N3dCQkJqTQOg8FgK1t++VKSJCIiIiIirnAqUUpMTGTLli0cOXKEvXv3kpiYSHp6Ovfff78tSSopKWHhwoUUFxfb6pqXl5fbjbNs2TIuXrzIAw88UOEdo0ePpkmTJjz00EPs37+fLVu2MHXqVB5++GG7vUwiIiIiIiL/KU4Vczh27BhjxowhPz8fo9FIZGQkGzZsYNCgQaSnp7Njxw4A2rZta/fcDz/8QOvWrW2/Fy5cyN13342fn1+Fd3h7e7Nx40YmT57MrbfeSpMmTRg5ciR//vOfnZ+diIiIiIiL/lOHscp/h2rvUarNdI6SiIiISO1Wm/comXcX1HQI14Whc1BNh1ArVXuPkoiIiIiIyI1GiZKIiIiIiMgVlCiJiIiIiIhcwalEaf78+URGRtrKb0dHR7Nu3Trb/UcffZQ2bdrg5eVFQEAAw4YN49tvv7Xd/9e//sXgwYNp3rw5BoOBli1bEhcXR3Fxsd173njjDdq3b4+Xlxfh4eG8++671ZymiIiIiIiTLNa6cYlDTiVKISEhzJ49m6ysLHbu3En//v0ZNmwY+/btA6B79+4sWrSIAwcOsGHDBqxWKzExMbby4O7u7gwbNozVq1dz8OBBFi9ezOeff86kSZNs75g/fz6JiYnMnDmTffv2MWvWLEwmE2vWrLmG0xYREREREalctave+fv7k5yc7PDw2D179tC5c2cOHTpEmzZtHD7/+uuvk5yczE8//QRAr1696N27N8nJybY+Tz75JDt27GDr1q1OxaaqdyIiIiK1W62uercrv6ZDuC4MXYNrOoRayeU9SuXl5aSmplJSUkJ0dHSF+yUlJSxatIiwsDBatmzpcIy8vDxWrlzJHXfcYWszm800aNDArp+XlxdfffUVZWVlroYrIiIiIiJSZU4nSnv37sXb2xuDwcCkSZNIS0sjIiLCdv/NN9/E29sbb29v1q1bx8aNG/H09LQbY9SoUTRs2JAWLVrg6+vLggULbPdiY2NZsGABWVlZWK1Wdu7cyYIFCygrK+PEiRPVmKqIiIiISNVZLdY6cYljTn96d+HCBY4ePUpRURErVqxgwYIFZGRk2JKloqIijh07Rn5+PnPmzOGXX37hyy+/tFslKigo4PTp0xw8eJDExETuuOMO3nzzTQDOnz+PyWTivffew2q10qxZMx544AFefvllCgoKaNasmcO4zGYzZrPZrs1qtWAwGJz6AxERERGR66c2f3pXmpVX0yFcFw26N6/pEGqlau9RGjhwIG3atOHvf/97hXsXLlygcePGLFiwgFGjRjl8fuvWrdx+++3k5eURHPzv7yPLysooLCwkODiYt99+m2nTpnH69Gnc3R0vgs2cOZNZs2bZtT3zzHRmzJhRjdmJiIiIyH+SEqWap0TJMY/qDmCxWCqs5FxmtVqxWq2V3r/8PFChT/369QkJCQEgNTWV3//+95UmSQCJiYkkJCRc8X5LleYgIiIiIiLya04lSomJiQwZMoTQ0FDOnDlDSkoK6enpbNiwge+//55ly5YRExNDQEAAP//8M7Nnz8bLy4s777wTgE8//ZTCwkKioqLw9vZm3759TJ06ld69e9O6dWsADh48yFdffUWPHj04deoUr776Kjk5OSxZsuSqsRkMhgqf2anqnYiIiIiIuMKpROnYsWOMGTOG/Px8jEYjkZGRbNiwgUGDBpGXl8c///lP5s6dy6lTp2jWrBl9+vRh27ZtBAYGApeq1/3jH/8gPj4es9lMy5Ytufvuu3n66adt7ygvL+eVV14hNzeX+vXr069fP7Zt22ZLpERERERErovq7VCR/3LV3qNUm2lFSURERKR2q9V7lHb+UtMhXBcNbm1R0yHUSi6foyQiIiIiInKjUqIkIiIiIiJyhWpXvRMRERERuRHpMNa6zakVpfnz5xMZGYmvry++vr5ER0ezbt26Cv2sVitDhgzBzc2Njz/+2O7e0aNHGTp0KA0bNiQwMJCpU6dy8eJF2/2VK1cyaNAgAgICbO/YsGGDa7MTERERERFxgVOJUkhICLNnzyYrK4udO3fSv39/hg0bxr59++z6zZ07Fzc3twrPl5eXM3ToUC5cuMC2bdtYsmQJixcv5k9/+pOtz5YtWxg0aBCffvopWVlZ9OvXj7vuuotdu3a5OEURERERERHnVLvqnb+/P8nJyYwfPx6A7Oxsfv/737Nz506Cg4NJS0tj+PDhAKxbt47f//735OXl0axZMwDeeustpk2bxvHjx/H09HT4jg4dOnDvvffaJVRVoap3IiIiIrVbba56d/6rn2s6hOvC67aQmg6hVnK5mEN5eTmpqamUlJQQHR0NwLlz5xg9ejRvvPEGQUFBFZ7JzMykU6dOtiQJIDY2luLi4gqrUpdZLBbOnDmDv7+/q6GKiIiIiIg4xeliDnv37iU6OprS0lK8vb1JS0sjIiICgPj4eHr16sWwYcMcPltQUGCXJAG23wUFBQ6fmTNnDmfPnmXkyJHOhioiIiIi4jpLTQcgNcnpRCk8PJzs7GyKiopYsWIFY8eOJSMjg0OHDrFp06ZrupcoJSWFWbNmsWrVKgIDA6/a12w2Yzab7dqsVgsGg+GaxSMiIiIiInWD05/eeXp60rZtW7p3705SUhKdO3dm3rx5bNq0icOHD+Pn54eHhwceHpdysBEjRtC3b18AgoKCKCwstBvv8u8rP9VLTU3lkUce4cMPP2TgwIG/GVdSUhJGo9HuSk5OdnZ6IiIiIiIi1S/m0L9/f0JDQ5k9ezYnTpywu9epUyfmzZvHXXfdRVhYmK2YQ35+vm2F6O2332bq1KkcO3bMtvrzwQcf8PDDD5OamlrpZ3xX0oqSiIiIyH+fWl3MYXsdKebQU8UcHHHq07vExESGDBlCaGgoZ86cISUlhfT0dDZs2EBQUJDDAg6hoaGEhYUBEBMTQ0REBA8++CAvv/wyBQUFzJgxA5PJZEtoUlJSGDt2LPPmzaNHjx62vUteXl4YjcZKYzMYDBWSIlW9ExERERFXVXM9Qf7LOfXp3bFjxxgzZgzh4eEMGDCAr7/+mg0bNjBo0KAqPV+vXj3Wrl1LvXr1iI6O5oEHHmDMmDE8//zztj5vv/02Fy9exGQyERwcbLueeOIJ52YmIiIiIiLiomp/elebaUVJREREpHarzZ/encv8qaZDuC4aRres6RBqJZfPURIREREREblRKVESERERERG5gtPnKImIiIiI1AmWG3aHilSBUytK8+fPJzIyEl9fX3x9fYmOjmbdunUV+lmtVoYMGYKbmxsff/xxhfuLFy8mMjKSBg0aEBgYiMlkst07cuQIbm5uFa7t27c7PzsREREREREXOLWiFBISwuzZs7n55puxWq0sWbKEYcOGsWvXLjp06GDrN3fuXNzc3ByO8eqrr/LKK6+QnJxMjx49KCkp4ciRIxX6ff7553ZjNmnSxJlQRUREREREXOZUonTXXXfZ/X7xxReZP38+27dvtyU12dnZvPLKK+zcuZPg4GC7/qdOnWLGjBmsWbOGAQMG2NojIyMrvKtJkyYOz2USERERERH5T3O5mEN5eTmpqamUlJQQHR0NwLlz5xg9ejRvvPGGwyRn48aNWCwWfvnlF9q3b09ISAgjR47kp58qll78wx/+QGBgIL/73e9YvXq1q2GKiIiIiLjGYq0blzjkdKK0d+9evL29MRgMTJo0ibS0NCIiIgCIj4+nV69eDBs2zOGz33//PRaLhb/85S/MnTuXFStWcPLkSQYNGsSFCxcA8Pb25pVXXmH58uV88skn/O53v2P48OFKlkRERERE5LpxuupdeHg42dnZFBUVsWLFCsaOHUtGRgaHDh1i06ZN7Nq1q9JnLRYLZWVlvP7668TExADwwQcfEBQUxObNm4mNjaVp06YkJCTYnomKiiIvL4/k5GT+8Ic/VDq22WzGbDbbtVmtFgwGg7NTFBERERGROs7pFSVPT0/atm1L9+7dSUpKonPnzsybN49NmzZx+PBh/Pz88PDwwMPjUg42YsQI+vbtC2Dbs3R5BQogICCApk2bcvTo0Urf2aNHDw4dOnTVuJKSkjAajXZXcnKys9MTERERERGp/jlKFosFs9nMrFmzeOSRR+zuderUiddee81WBKJ3794A5ObmEhISAsDJkyc5ceIErVq1qvQd2dnZFQpDXCkxMdFuJQourSiJiIiIiIg4y6lEKTExkSFDhhAaGsqZM2dISUkhPT2dDRs2EBQU5LCAQ2hoKGFhYQDccsstDBs2jCeeeIK3334bX19fEhMTadeuHf369QNgyZIleHp60rVrVwBWrlzJO++8w4IFC64am8FgqPCZXWnpeWemJyIiIiJiY7Wq0EFd5lSidOzYMcaMGUN+fj5Go5HIyEg2bNjAoEGDqjzGu+++S3x8PEOHDsXd3Z077riD9evXU79+fVufF154gR9//BEPDw/atWvHsmXLuOeee5wJVURERERExGVu1hs4VdaKkoiIiEjt1qCBV02HUKmSLUdqOoTrolGf1jUdQq3k8jlKIiIiIiIiN6pqF3MQEREREbkhqS5YnaYVJRERERERkSs4lSjNnz+fyMhIfH198fX1JTo6mnXr1tnu9+3bFzc3N7tr0qRJtvu7d+9m1KhRtGzZEi8vL9q3b8+8efMqvOf999+nc+fONGzYkODgYB5++GH+9a9/VWOaIiIiIiIiVefUp3chISHMnj2bm2++GavVypIlSxg2bBi7du2iQ4cOAEyYMIHnn3/e9kzDhg1t/zkrK4vAwECWLl1Ky5Yt2bZtGxMnTqRevXrExcUB8OWXXzJmzBjb+Uu//PILkyZNYsKECaxcufJazFlEREREROSqnEqULh8ce9mLL77I/Pnz2b59uy1RatiwocPzlAAefvhhu9833XQTmZmZrFy50pYoZWZm0rp1ax5//HEAwsLCePTRR3nppZecCVVERERERMRlLu9RKi8vJzU1lZKSEqKjo23t77//Pk2bNqVjx44kJiZy7ty5q45TVFSEv7+/7Xd0dDQ//fQTn376KVarlcLCQlasWMGdd97paqgiIiIiIk6zWqx14hLHnK56t3fvXqKjoyktLcXb25u0tDQiIiIAGD16NK1ataJ58+bs2bOHadOmkZubW+knc9u2bWPZsmV88skntrbevXvz/vvvc++991JaWsrFixe56667eOONN1ycooiIiIiIiHOcPnD2woULHD16lKKiIlasWMGCBQvIyMiwJUu/tmnTJgYMGMChQ4do06aN3b2cnBz69evHE088wYwZM2zt+/fvZ+DAgcTHxxMbG0t+fj5Tp04lKiqKhQsXVhqX2WzGbDbbtVmtFgwGgzPTExEREZHrqDYfOHt20w81HcJ14d0/rKZDqJWcTpSuNHDgQNq0acPf//73CvdKSkrw9vZm/fr1xMbG2tr3799Pv379eOSRR3jxxRftnnnwwQcpLS1l+fLltratW7dy++23k5eXR3BwsMM4Zs6cyaxZs+zannlmul0SJiIiIiK1ixKlmqdEybFqHzhrsVgqrORclp2dDWCX3Ozbt4/+/fszduzYCkkSwLlz5/DwsA+rXr16AFwtp0tMTCQhIcGuzWrVKWEiIiIi4qLqrSfIfzmnEqXExESGDBlCaGgoZ86cISUlhfT0dDZs2MDhw4dJSUnhzjvvpEmTJuzZs4f4+Hj69OlDZGQkcOlzu/79+xMbG0tCQgIFBQXApUQoICAAuFRZb8KECcyfP9/26d2UKVO47bbbaN68eaWxGQyGCp/ZlZaed+oPQ0REREREBJxMlI4dO8aYMWPIz8/HaDQSGRnJhg0bGDRoED/99BOff/45c+fOpaSkhJYtWzJixAi7T99WrFjB8ePHWbp0KUuXLrW1t2rViiNHjgAwbtw4zpw5w9/+9jeefPJJ/Pz86N+/v8qDi4iIiIjIdVPtPUq1mVaURERERGq3Wr1H6YvvazqE68J7wE01HUKt5PI5SiIiIiIiIjeqahdzEBERERG5Eekw1rpNK0oiIiIiIiJXcCpRmj9/PpGRkfj6+uLr60t0dDTr1q2z3e/bty9ubm5216RJk+zG+OKLL+jVqxc+Pj4EBQUxbdo0Ll68aLtfWlrKuHHj6NSpEx4eHgwfPrx6MxQREREREXGSU4lSSEgIs2fPJisri507d9K/f3+GDRvGvn37bH0mTJhAfn6+7Xr55Zdt93bv3s2dd97J4MGD2bVrF8uWLWP16tU8/fTTtj7l5eV4eXnx+OOPM3DgwGswRREREREREedUu+qdv78/ycnJjB8/nr59+9KlSxfmzp3rsO/06dPZuHEjX3/9ta1tzZo1jBw5kmPHjuHj42PXf9y4cZw+fZqPP/7YpdhU9U5ERESkdqvNVe/ObDhU0yFcFz6xbWs6hFrJ5T1K5eXlpKamUlJSQnR0tK39/fffp2nTpnTs2JHExETOnTtnu2c2m2nQoIHdOF5eXpSWlpKVleVqKCIiIiIiIteU01Xv9u7dS3R0NKWlpXh7e5OWlkZERAQAo0ePplWrVjRv3pw9e/Ywbdo0cnNzWblyJQCxsbHMnTuXDz74gJEjR1JQUMDzzz8PQH5+/jWcloiIiIiIiOucTpTCw8PJzs6mqKiIFStWMHbsWDIyMoiIiGDixIm2fp06dSI4OJgBAwZw+PBh2rRpQ0xMDMnJyUyaNIkHH3wQg8HAs88+yz//+U/c3atXgM9sNmM2m+3arFYLBoOhWuOKiIiIiEjd43R24unpSdu2benevTtJSUl07tyZefPmOezbo0cPAA4d+vf3nQkJCZw+fZqjR49y4sQJhg0bBsBNN1XvROCkpCSMRqPdlZycXK0xRURERESkbqr2gbMWi6XCSs5l2dnZAAQHB9u1u7m50bx5cwA++OADWrZsSbdu3aoVR2JiIgkJCXZtVqulWmOKiIiISN1VzZpn8l/OqUQpMTGRIUOGEBoaypkzZ0hJSSE9PZ0NGzZw+PBhUlJSuPPOO2nSpAl79uwhPj6ePn36EBkZaRsjOTmZwYMH4+7uzsqVK5k9ezYffvgh9erVs/XZv38/Fy5c4OTJk5w5c8aWcHXp0qXS2AwGQ4XP7FT1TkREREREXOFUonTs2DHGjBlDfn4+RqORyMhINmzYwKBBg/jpp5/4/PPPmTt3LiUlJbRs2ZIRI0YwY8YMuzHWrVvHiy++iNlspnPnzqxatYohQ4bY9bnzzjv58ccfbb+7du0KKKsXEREREZHro9rnKNVmWlESERERqd1q8zlKxeu/q+kQrgvfwTfXdAi1UrX3KImIiIiI3JAsN+x6glRB9Wpyi4iIiIiI3ICUKImIiIiIiFzBqURp/vz5REZG4uvri6+vL9HR0axbt86uT2ZmJv3796dRo0b4+vrSp08fzp//916h1q1b4+bmZnfNnj3b4fsOHTqEj48Pfn5+zs9MRERERETERU7tUQoJCWH27NncfPPNWK1WlixZwrBhw9i1axcdOnQgMzOTwYMHk5iYyF//+lc8PDzYvXs37u72+djzzz/PhAkTbL99fHwqvKusrIxRo0Zx++23s23bNhenJyIiIiIi4jynEqW77rrL7veLL77I/Pnz2b59Ox06dCA+Pp7HH3+cp59+2tYnPDy8wjg+Pj4EBQVd9V0zZsygXbt2DBgwQImSiIiIiFx31nJLTYcgNcjlPUrl5eWkpqZSUlJCdHQ0x44dY8eOHQQGBtKrVy+aNWvGHXfcwdatWys8O3v2bJo0aULXrl1JTk7m4sWLdvc3bdrE8uXLeeONN1wNT0RERERExGVOlwffu3cv0dHRlJaW4u3tTVpaGhEREWzfvh2AmTNnMmfOHLp06cK7777LgAEDyMnJ4eabL9Vnf/zxx+nWrRv+/v5s27aNxMRE8vPzefXVVwH417/+xbhx41i6dCm+vr7XcKoiIiIiIiJV43SiFB4eTnZ2NkVFRaxYsYKxY8eSkZGBxXJpafLRRx/loYceAqBr16588cUXvPPOOyQlJQGQkJBgGysyMhJPT08effRRkpKSMBgMTJgwgdGjR9OnTx+n4jKbzZjNZrs2q9WCwWBwdooiIiIiIlLHOf3pnaenJ23btqV79+4kJSXRuXNn5s2bR3BwMAARERF2/du3b8/Ro0crHa9Hjx5cvHiRI0eOAJc+u5szZw4eHh54eHgwfvx4ioqK8PDw4J133ql0nKSkJIxGo92VnJzs7PRERERERACwWqx14hLHnF5RupLFYsFsNtO6dWuaN29Obm6u3f2DBw8yZMiQSp/Pzs7G3d2dwMBA4FJ58fLyctv9VatW8dJLL7Ft2zZatGhR6TiJiYl2q1VwaUVJRERERETEWU4lSomJiQwZMoTQ0FDOnDlDSkoK6enpbNiwATc3N6ZOncpzzz1H586d6dKlC0uWLOHbb79lxYoVwKUkaMeOHfTr1w8fHx8yMzOJj4/ngQceoHHjxsClFahf27lzJ+7u7nTs2PGqsRkMhgqf2ZWWnq+kt4iIiIiISOWcSpSOHTvGmDFjyM/Px2g0EhkZyYYNGxg0aBAAU6ZMobS0lPj4eE6ePEnnzp3ZuHEjbdq0AS4lM6mpqcycOROz2UxYWBjx8fEVVoJERERERERqkpvVar1hP0zUipKIiIhI7daggVdNh1Cp06sO1HQI14XfsPa/3akOqvYeJRERERGRG5EOnK3bXD5wVkRERERE5EalRElEREREROQKSpRERERERESu4FSiNH/+fCIjI/H19cXX15fo6GjWrVsHwJEjR3Bzc3N4LV++3DbG448/Tvfu3TEYDHTp0qXCO2bOnOlwjEaNGlVvpiIiIiIizrBY6sYlDjlVzCEkJITZs2dz8803Y7VaWbJkCcOGDWPXrl20a9eO/Px8u/5vv/02ycnJFQ6cffjhh9mxYwd79uyp8I6nnnqKSZMm2bUNGDCAqKgoZ0IVERERERFxmVOJ0l133WX3+8UXX2T+/Pls376dDh06EBQUZHc/LS2NkSNH4u3tbWt7/fXXATh+/LjDRMnb29uu/+7du9m/fz9vvfWWM6GKiIiIiIi4zOU9SuXl5aSmplJSUkJ0dHSF+1lZWWRnZzN+/PhqBbhgwQJuueUWbr/99mqNIyIiIiIiUlX/j727j6v5/P8A/jqnckr3EoVCmoo0ZRsxt6XQF8NmI/dhLHe1WRqbm9lq38ztNozcS+6NIWy5GWKpZblr7m9LI0nUKZ3r94efz9dxzknnZGq8no/H5/HQ9bmuz+d9dVLnfa7PdV1676OUnp4OX19fFBYWwsLCAps3b0ajRo006sXGxsLDwwMtW7Y0OLjCwkKsXr0aEyZMeGZdpVIJpVKpViaECgqFwuD7ExERERHRq0nvESU3NzekpaXh6NGjGDlyJAYOHIhTp06p1SkoKEBcXFy5R5M2b96Me/fuYeDAgc+sGxUVBWtra7UjJiamXPcnIiIioleXKBGvxEHa6T2iVKVKFbi6ugIAmjVrhuTkZMyZMwcLFy6U6mzYsAEPHjzAgAEDyhXc4sWL8Z///Ac1a9Z8Zt3IyEiEh4erlQnBVTyIiIiIiEh/eidKT1OpVBqPvMXGxqJbt26wt7c3+LoXL17E3r17sXXr1jLVVygUGo/ZFRYWGHx/IiIiIiJ6demVKEVGRqJz585wdnbGvXv3EBcXh3379mHXrl1SnXPnzuHAgQPYsWOH1mucO3cO+fn5yMrKQkFBAdLS0gAAjRo1QpUqVaR6S5YsgaOjo8bS4kRERERERP80vRKl7OxsDBgwAJmZmbC2toaXlxd27dqFjh07SnWWLFmCOnXqICAgQOs1hg4div3790tfe3t7A3g0glSvXj0Aj0apli1bhkGDBsHIyEjfPhERERERlZvgZqyvNJkQ4qWdwcVH74iIiIgqN1NTs4oOQafba9IqOoQXwq5P04oOoVIyeB8lIiIiIiKilxUTJSIiIiIioqcwUSIiIiIiInqKXonS/Pnz4eXlBSsrK1hZWcHX1xc7d+4EAFy6dAkymUzrsX79eukaycnJ8PPzg42NDWxtbREYGIjjx4+r3efPP/9E69atYWpqCicnJ/z3v/99Dl0lIiIiIio7UaJ6JQ7STq9EqU6dOoiOjkZKSgqOHTuGDh06oHv37jh58iScnJyQmZmpdkydOhUWFhbSEt/5+fno1KkTnJ2dcfToURw8eBCWlpYIDAxEcXExACAvLw8BAQGoW7cuUlJSEBMTgylTpuDHH398/r0nIiIiIiLSotyr3lWrVg0xMTEICQnROOft7Q0fHx/ExsYCAI4dO4Y333wTV65cgZOTEwAgPT0dXl5eOHv2LFxdXTF//nxMnDgRWVlZ0r5KEyZMwJYtW3DmzBm9YuOqd0RERESVW2Ve9e7WqtSKDuGFqN7Pp6JDqJQMnqNUUlKC+Ph43L9/H76+vhrnU1JSkJaWppZAubm5wc7ODrGxsSgqKkJBQQFiY2Ph4eEh7aGUlJSENm3aqG0+GxgYiIyMDNy5c8fQcImIiIiIiMpMrw1ngUcjNkOFnQAA0/dJREFUQL6+vigsLISFhQU2b96MRo0aadR7nAC1bNlSKrO0tMS+ffvwzjvv4MsvvwQAvPbaa9i1axeMjR+FkpWVhfr166tdq2bNmtI5W1tbrXEplUoolUq1MiFUUCgU+naRiIiIiAjghrOvNL1HlNzc3JCWloajR49i5MiRGDhwIE6dOqVWp6CgAHFxcRqP4xUUFCAkJAStWrXCkSNHcOjQIXh6eiIoKAgFBeV7TC4qKgrW1tZqR0xMTLmuSURERERErya9R5SqVKkCV1dXAECzZs2QnJyMOXPmYOHChVKdDRs24MGDBxgwYIBa27i4OFy6dAlJSUmQy+VSma2tLX766Sd88MEHcHBwwM2bN9XaPf7awcFBZ1yRkZEIDw9XKxOCnwIQEREREZH+yr2Pkkql0njkLTY2Ft26dYO9vb1a+YMHDyCXyyGTyf4XwP9/rfr/oU1fX18cOHBAWgUPAPbs2QM3Nzedj90BgEKhkJYtf3zwsTsiIiIiIjKEXolSZGQkDhw4gEuXLiE9PR2RkZHYt28fgoODpTrnzp3DgQMHMHToUI32HTt2xJ07dxAaGorTp0/j5MmTGDx4MIyNjdG+fXsAQN++fVGlShWEhITg5MmTWLt2LebMmaMxWkRERERERPRP0evRu+zsbAwYMACZmZmwtraGl5cXdu3ahY4dO0p1lixZgjp16iAgIECjvbu7O7Zt24apU6fC19cXcrkc3t7eSEhIgKOjIwDA2toau3fvRmhoKJo1a4bq1avjiy++wPDhw8vZVSIiIiKishOqcu2iQ/9y5d5HqTLjPkpERERElVtl3kfp72XJFR3CC2E/6M2KDqFSKvccJSIiIiIiopcNEyUiIiIiIqKn6L08OBERERHRq0CUcKuZV5leI0rz58+Hl5eXtPy2r68vdu7cKZ3PyspC//794eDgAHNzc/j4+GDjxo1ar6VUKtG0aVPIZDKkpaVJ5RkZGWjfvj1q1qwJU1NTuLi4YNKkSWrLhRMREREREf2T9BpRqlOnDqKjo/Haa69BCIHly5eje/fu+OOPP9C4cWMMGDAAubm52Lp1K6pXr464uDj07t0bx44dg7e3t9q1Pv30U9SqVQvHjx9XKzcxMcGAAQPg4+MDGxsbHD9+HMOGDYNKpcLXX39d/h4TERERERE9Q7lXvatWrRpiYmIQEhICCwsLzJ8/H/3795fO29nZ4ZtvvlHbV2nnzp0IDw/Hxo0b0bhxY/zxxx9o2rSpznuEh4cjOTkZv/32m16xcdU7IiIiosqtMq96lx17tKJDeCFqhDSv6BAqJYMXcygpKUF8fDzu378PX19fAEDLli2xdu1a5OTkQKVSIT4+HoWFhWjXrp3U7ubNmxg2bBhWrlyJqlWrPvM+586dQ0JCAtq2bWtoqERERERERHrRezGH9PR0+Pr6orCwEBYWFti8eTMaNWoEAFi3bh3ef/992NnZwdjYGFWrVsXmzZvh6uoKABBCYNCgQRgxYgTeeOMNXLp0Sed9WrZsidTUVCiVSgwfPhzTpk0rNS6lUgmlUqlWJoQKCoVC3y4SEREREUGouJjDq0zvESU3NzekpaXh6NGjGDlyJAYOHIhTp04BAD7//HPk5ubil19+wbFjxxAeHo7evXsjPT0dADBv3jzcu3cPkZGRz7zP2rVrkZqairi4OGzfvh0zZswotX5UVBSsra3VjpiYGH27R0REREREVP45Sv7+/mjQoAE+/fRTuLq64sSJE2jcuLHaeVdXVyxYsADvvPMOtm3bBplMJp0vKSmBkZERgoODsXz5cq33WLVqFYYPH4579+7ByMhIax2OKBERERH9+1TmOUo3FyVVdAgvRM1hvhUdQqVU7n2UVCoVlEolHjx4AACQy9UHqYyMjKD6/2HLuXPnYvr06dK5GzduIDAwEGvXrkXz5ronkalUKhQXF0OlUulMlBQKhUZSxMUciIiIiIjIEHolSpGRkejcuTOcnZ1x7949xMXFYd++fdi1axfc3d3h6uqKDz/8EDNmzICdnR22bNmCPXv24OeffwYAODs7q13PwsICANCgQQPUqVMHALB69WqYmJigSZMmUCgUOHbsGCIjI/H+++/DxMTkefSZiIiIiIioVHrNUcrOzsaAAQPg5uYGPz8/JCcnY9euXejYsSNMTEywY8cO2Nvbo2vXrvDy8sKKFSuwfPlydOnSpcz3MDY2xjfffIO33noLXl5emDp1KkaNGoXFixfr3TkiIiIiIoOVqF6N4x+Sk5OD4OBgWFlZwcbGBiEhIcjPzy+1TWFhIUJDQ2FnZwcLCwv06tULN2/eVKsjk8k0jvj4eLU6+/btg4+PDxQKBVxdXbFs2TK94y/3HKXKjI/eEREREVVulXqO0oJDFR3CC1FzRKt/5LqdO3dGZmYmFi5ciOLiYgwePBhvvvkm4uLidLYZOXIktm/fjmXLlsHa2hqjRo2CXC7HoUP/ey1kMhmWLl2KTp06SWU2NjYwNTUFAFy8eBGenp4YMWIEhg4dil9//RXjxo3D9u3bERgYWOb4mSgRERERUYVholTx/olE6fTp02jUqBGSk5PxxhtvAAASEhLQpUsXXLt2DbVq1dJoc/fuXdjb2yMuLg7vvvsuAODMmTPw8PBAUlISWrRoAeBRorR582a88847Wu8dERGB7du348SJE1LZBx98gNzcXCQkJJS5DwZvOEtERERERP9+SqUSeXl5asfTq0nrKykpCTY2NlKSBDxaDVsul+Po0aNa26SkpKC4uBj+/v5Smbu7O5ydnZGUpL4CYWhoKKpXr4633noLS5YswZNjP0lJSWrXAIDAwECNazwLEyUiIiIiIi2ESrwSh7b9SKOiosr1vcvKykKNGjXUyoyNjVGtWjVkZWXpbFOlShXY2NioldesWVOtzbRp07Bu3Trs2bMHvXr1wkcffYR58+apXadmzZoa18jLy0NBQdmfONMrUZo/fz68vLxgZWUFKysr+Pr6YufOndL58+fPo0ePHrC3t4eVlRV69+6tMfmqLJO6hBCYMWMGGjZsCIVCgdq1a+Orr77SJ1QiIiIiIiqDyMhI3L17V+2IjIzUWnfChAlaF1N48jhz5sw/Gu/nn3+OVq1awdvbGxEREfj0008RExPz3O+j1/LgderUQXR0NF577TUIIbB8+XJ0794df/zxB+rVq4eAgAC8/vrrSExMlDrRtWtXHDlyRNpfKTg4GJmZmdizZ480qWv48OFqk7rGjh2L3bt3Y8aMGWjSpAlycnKQk5PzHLtNRERERESA9v1Idfn4448xaNCgUuu4uLjAwcEB2dnZauUPHz5ETk4OHBwctLZzcHBAUVERcnNz1UaVbt68qbMNADRv3hxffvkllEolFAoFHBwcNAZrbt68CSsrK5iZlX1OnF6JUteuXdW+/uqrrzB//nwcOXIE169fx6VLl/DHH3/AysoKALB8+XLY2toiMTER/v7+OH36NBISEtQmdc2bNw9dunTBjBkzUKtWLZw+fRrz58/HiRMn4ObmBgCoX7++PmESEREREdE/wN7eHvb29s+s5+vri9zcXKSkpKBZs2YAgMTERKhUKjRv3lxrm2bNmsHExAS//vorevXqBQDIyMjAlStX4Ovrq/NeaWlpsLW1lZI9X19f7NixQ63Onj17Sr2GNgbPUSopKUF8fDzu378PX19fKJVKyGQytWzU1NQUcrkcBw8eBFC2SV3btm2Di4sLfv75Z9SvXx/16tXD0KFDOaJERERERPQv4eHhgU6dOmHYsGH4/fffcejQIYwaNQoffPCBtOLd9evX4e7ujt9//x0AYG1tjZCQEISHh2Pv3r1ISUnB4MGD4evrK614t23bNixevBgnTpzAuXPnMH/+fHz99dcYPXq0dO8RI0bgwoUL+PTTT3HmzBn88MMPWLduHcLCwvTqg14jSgCQnp4OX19fFBYWwsLCAps3b0ajRo1gb28Pc3NzRERE4Ouvv4YQAhMmTEBJSQkyMzMBlG1S14ULF3D58mWsX78eK1asQElJCcLCwvDuu+9Kj/Rpo1QqNVbnEEJV5mFEIiIiIqIniX9wM9ZXwerVqzFq1Cj4+flBLpejV69emDt3rnS+uLgYGRkZePDggVQ2a9Ysqa5SqURgYCB++OEH6byJiQm+//57hIWFQQgBV1dXzJw5E8OGDZPq1K9fH9u3b0dYWBjmzJmDOnXqYPHixXrtoQQYkCi5ubkhLS0Nd+/exYYNGzBw4EDs378fjRo1wvr16zFy5EjMnTsXcrkcffr0gY+PjzQ/qSxUKhWUSiVWrFiBhg0bAgBiY2PRrFkzZGRkSI/jPS0qKgpTp05VK5s48TNMmjRJ3y4SEREREVE5VatWrdTNZevVq4ent3Q1NTXF999/j++//15rm06dOqltNKtLu3bt8Mcff+gX8FP0TpSqVKkCV1dXAI+eI0xOTsacOXOwcOFCBAQE4Pz587h16xaMjY1hY2MDBwcHuLi4AECZJnU5OjrC2NhYSpKAR0N3AHDlyhWdiVJkZCTCw8PVyoTgpwBERERERKS/cu+j9HgE6EnVq1eHjY0NEhMTkZ2djW7dugFQn9T12NOTulq1aoWHDx/i/PnzUp2//voLAFC3bl2dcSgUCmnZ8scHH7sjIiIiIiJD6DWiFBkZic6dO8PZ2Rn37t1DXFwc9u3bh127dgEAli5dCg8PD9jb2yMpKQljx45FWFiYNAr05KSuBQsWoLi4WGNSl7+/P3x8fDBkyBDMnj0bKpUKoaGh6Nixo9ooExERERHRP0mo+HTSq0yvRCk7OxsDBgxAZmYmrK2t4eXlhV27dqFjx44AHi3fFxkZiZycHNSrVw8TJ07UWF3iWZO65HI5tm3bhtGjR6NNmzYwNzdH586d8e233z6H7hIRERERET2bTDw9g+olUlhYUNEhEBEREVEpTE3LvgHoi3Zjzr6KDuGFqDW2XUWHUCmVe44SERERERHRy4aJEhERERER0VP0Xh6ciIiIiOiVUPLSzlChMtBrRGn+/Pnw8vKSlt/29fXFzp07pfPnz59Hjx49YG9vDysrK/Tu3Rs3b97UuM727dvRvHlzmJmZwdbWFu+8847a+V9//RUtW7aEpaUlHBwcEBERgYcPHxrWQyIiIiIiIj3plSjVqVMH0dHRSElJwbFjx9ChQwd0794dJ0+exP379xEQEACZTIbExEQcOnQIRUVF6Nq1K1RPLK24ceNG9O/fH4MHD8bx48dx6NAh9O3bVzp//PhxdOnSBZ06dcIff/yBtWvXYuvWrZgwYcLz6zUREREREVEpyr3qXbVq1RATEwMnJyd07twZd+7cgZWVFQDg7t27sLW1xe7du+Hv74+HDx+iXr16mDp1KkJCQrRe77PPPsOePXuQnJwslW3btg29e/dGdnY2LC0tyxwbV70jIiIiqtwq9ap3M/dWdAgvRK3w9hUdQqVk8GIOJSUliI+Px/379+Hr6wulUgmZTAaFQiHVMTU1hVwux8GDBwEAqampuH79OuRyOby9veHo6IjOnTvjxIkTUhulUglTU1O1e5mZmaGwsBApKSmGhktEREREpBehUr0SB2mnd6KUnp4OCwsLKBQKjBgxAps3b0ajRo3QokULmJubIyIiAg8ePMD9+/fxySefoKSkBJmZmQCACxcuAACmTJmCSZMm4eeff4atrS3atWuHnJwcAEBgYCAOHz6MNWvWoKSkBNevX8e0adMAQLqONkqlEnl5eWqHUqnU+xtCRERERESkd6Lk5uaGtLQ0HD16FCNHjsTAgQNx6tQp2NvbY/369di2bRssLCxgbW2N3Nxc+Pj4QC5/dJvHc5UmTpyIXr16oVmzZli6dClkMhnWr18PAAgICEBMTAxGjBgBhUKBhg0bokuXLo+ClesONyoqCtbW1mpHTEyM3t8QIiIiIiIivZcHr1KlClxdXQEAzZo1Q3JyMubMmYOFCxciICAA58+fx61bt2BsbAwbGxs4ODjAxcUFAODo6AgAaNSokXQ9hUIBFxcXXLlyRSoLDw9HWFgYMjMzYWtri0uXLiEyMlK6jjaRkZEIDw9XKxOCQ4lERERERKS/cu+jpFKpNB5xq169OgAgMTER2dnZ6NatG4BHiZVCoUBGRgbefvttAEBxcTEuXbqEunXrql1DJpOhVq1aAIA1a9bAyckJPj4+OuNQKBRq86MALuZARERERESG0StRioyMROfOneHs7Ix79+4hLi4O+/btw65duwAAS5cuhYeHB+zt7ZGUlISxY8ciLCwMbm5uAAArKyuMGDECkydPhpOTE+rWrSs9Hvfee+9J94mJiUGnTp0gl8uxadMmREdHY926dTAyMnpe/SYiIiIiKpUo4dNJrzK9EqXs7GwMGDAAmZmZsLa2hpeXF3bt2oWOHTsCADIyMhAZGYmcnBzUq1cPEydORFhYmNo1YmJiYGxsjP79+6OgoADNmzdHYmIibG1tpTo7d+7EV199BaVSiddffx0//fQTOnfu/By6S0RERERE9Gzl3kepMuOjd0RERESVW2XeR+naN3sqOoQXok5Ex4oOoVIyeB8lIiIiIiKil1W5F3MgIiIiInoZcTPWVxtHlIiIiIiIiJ5SrkQpOjoaMpkM48aNk8oKCwsRGhoKOzs7WFhYoFevXrh586Z0/vbt2+jUqRNq1aoFhUIBJycnjBo1Cnl5eVKdTZs2oWPHjrC3t4eVlRV8fX2llfWIiIiIiIj+aQYnSsnJyVi4cCG8vLzUysPCwrBt2zasX78e+/fvx40bN9CzZ8//3VAuR/fu3bF161b89ddfWLZsGX755ReMGDFCqnPgwAF07NgRO3bsQEpKCtq3b4+uXbvijz/+MDRcIiIiIiKiMjNo1bv8/Hz4+Pjghx9+wPTp09G0aVPMnj0bd+/ehb29PeLi4vDuu+8CAM6cOQMPDw8kJSWhRYsWWq83d+5cxMTE4OrVqzrv2bhxY7z//vv44osvyhwnV70jIiIiqtwq86p3V6NejSeanCIDKzqESsmgEaXQ0FAEBQXB399frTwlJQXFxcVq5e7u7nB2dkZSUpLWa924cQObNm1C27Ztdd5PpVLh3r17qFatmiHhEhERERHpr0S8Ggdppfeqd/Hx8UhNTUVycrLGuaysLFSpUgU2NjZq5TVr1kRWVpZaWZ8+ffDTTz+hoKAAXbt2xeLFi3Xec8aMGcjPz0fv3r111lEqlVAqlWplQqigUCjK0CsiIiIiIqL/0WtE6erVqxg7dixWr14NU1PTct141qxZSE1NxU8//YTz588jPDxca724uDhMnToV69atQ40aNXReLyoqCtbW1mpHTExMuWIkIiIiIqJXk15zlLZs2YIePXrAyMhIKispKYFMJoNcLseuXbvg7++PO3fuqI0q1a1bF+PGjUNYWJjW6x48eBCtW7fGjRs34OjoKJXHx8djyJAhWL9+PYKCgkqNjSNKRERERP8+lXqO0vSEig7hhXCa1KmiQ6iU9Hr0zs/PD+np6WplgwcPhru7OyIiIuDk5AQTExP8+uuv6NWrFwAgIyMDV65cga+vr87rqv5/M68nE501a9ZgyJAhiI+Pf2aSBAAKhUIjKeJiDkRERERkKG44+2rTK1GytLSEp6enWpm5uTns7Oyk8pCQEISHh6NatWqwsrLC6NGj4evrK614t2PHDty8eRNvvvkmLCwscPLkSYwfPx6tWrVCvXr1ADx63G7gwIGYM2cOmjdvLs1vMjMzg7W1dXn7TEREREREVKpybTirzaxZs/Cf//wHvXr1Qps2beDg4IBNmzZJ583MzLBo0SK8/fbb8PDwQFhYGLp164aff/5ZqvPjjz/i4cOHCA0NhaOjo3SMHTv2eYdLRERERESkwaB9lP4t+OgdERERUeVWmecoXZm2o6JDeCGcv+hS0SFUSs99RImIiIiIiOjfTu99lIiIiIiIXgWihIs5vMo4okRERERERPSUciVK0dHRkMlkGDdunFRWWFiI0NBQ2NnZwcLCAr169cLNmzfV2v36669o2bIlLC0t4eDggIiICDx8+FA6f+nSJchkMo3jyJEj5QmXiIiIiIioTAxOlJKTk7Fw4UJ4eXmplYeFhWHbtm1Yv3499u/fjxs3bqBnz57S+ePHj6NLly7o1KkT/vjjD6xduxZbt27FhAkTNO7xyy+/IDMzUzqaNWtmaLhERERERERlZtAcpfz8fAQHB2PRokWYPn26VH737l3ExsYiLi4OHTp0AAAsXboUHh4eOHLkCFq0aIG1a9fCy8sLX3zxBQDA1dUV//3vf9G7d29MnjwZlpaW0vXs7Ozg4OBQnv4RERERERmEG86+2gwaUQoNDUVQUBD8/f3VylNSUlBcXKxW7u7uDmdnZyQlJQEAlEolTE1N1dqZmZmhsLAQKSkpauXdunVDjRo18Pbbb2Pr1q2GhEpERERERKQ3vROl+Ph4pKamIioqSuNcVlYWqlSpAhsbG7XymjVrIisrCwAQGBiIw4cPY82aNSgpKcH169cxbdo0AEBmZiYAwMLCAt9++y3Wr1+P7du34+2338Y777xTarKkVCqRl5endiiVSn27R0REREREpF+idPXqVYwdOxarV6/WGBUqq4CAAMTExGDEiBFQKBRo2LAhunR5tMmVXP4onOrVqyM8PBzNmzfHm2++iejoaPTr1w8xMTE6rxsVFQVra2u1o7T6REREREREuuiVKKWkpCA7Oxs+Pj4wNjaGsbEx9u/fj7lz58LY2Bg1a9ZEUVERcnNz1drdvHlTba5ReHg4cnNzceXKFdy6dQvdu3cHALi4uOi8d/PmzXHu3Dmd5yMjI3H37l21Y/z48fp0j4iIiIiICICeizn4+fkhPT1drWzw4MFwd3dHREQEnJycYGJigl9//RW9evUCAGRkZODKlSvw9fVVayeTyVCrVi0AwJo1a+Dk5AQfHx+d905LS4Ojo6PO8wqFAgqFQq2ssLBAn+4REREREf1PiajoCKgC6ZUoWVpawtPTU63M3NwcdnZ2UnlISAjCw8NRrVo1WFlZYfTo0fD19UWLFi2kNjExMejUqRPkcjk2bdqE6OhorFu3DkZGRgCA5cuXo0qVKvD29gYAbNq0CUuWLMHixYvL1VkiIiIiIqKyMGh58NLMmjULcrkcvXr1glKpRGBgIH744Qe1Ojt37sRXX30FpVKJ119/HT/99BM6d+6sVufLL7/E5cuXYWxsDHd3d6xduxbvvvvu8w6XiIiIiIhIg0wI8dKOKfLROyIiIqLKzdTUrKJD0OnSZz9VdAgvRL2vu1d0CJXScx9RIiIiIiJ6GXDD2VebQRvOEhERERERvcyYKBERERERET2lXIlSdHQ0ZDIZxo0bJ5X9+OOPaNeuHaysrCCTyTT2VAKAevXqQSaTqR3R0dFqdXbt2oUWLVrA0tIS9vb26NWrFy5dulSecImIiIiIiMrE4EQpOTkZCxcuhJeXl1r5gwcP0KlTJ3z22Weltp82bRoyMzOlY/To0dK5ixcvonv37ujQoQPS0tKwa9cu3Lp1Cz179jQ0XCIiIiIiojIzaDGH/Px8BAcHY9GiRZg+fbraucejS/v27Sv1GpaWlnBwcNB6LiUlBSUlJZg+fTrk8ke53CeffILu3bujuLgYJiYmhoRNRERERFRm4iEXc3iVGTSiFBoaiqCgIPj7+xt84+joaNjZ2cHb2xsxMTF4+PChdK5Zs2aQy+VYunQpSkpKcPfuXaxcuRL+/v5MkoiIiIiI6B+n94hSfHw8UlNTkZycbPBNx4wZAx8fH1SrVg2HDx9GZGQkMjMzMXPmTABA/fr1sXv3bvTu3RsffvghSkpK4Ovrix07dui8plKphFKpVCsTQgWFQmFwnERERERE9GrSa0Tp6tWrGDt2LFavXg1TU1ODbxoeHo527drBy8sLI0aMwLfffot58+ZJiU5WVhaGDRuGgQMHIjk5Gfv370eVKlXw7rvvQtf+uFFRUbC2tlY7YmJiDI6RiIiIiIheXTKhK/PQYsuWLejRoweMjIykspKSEshkMsjlciiVSuncvn370L59e9y5cwc2NjalXvfkyZPw9PTEmTNn4Obmhs8//xwJCQlqo1bXrl2Dk5MTkpKS0KJFC41rcESJiIiI6N/H1NSsokPQ6XzY+ooO4YVoMOu9ig6hUtLr0Ts/Pz+kp6erlQ0ePBju7u6IiIhQS6D0kZaWBrlcjho1agB4tHLe40UcHnt8bZWOHZIVCoVGUlRYWGBQPERERERE9GrTK1GytLSEp6enWpm5uTns7Oyk8qysLGRlZeHcuXMAgPT0dFhaWsLZ2RnVqlVDUlISjh49ivbt28PS0hJJSUkICwtDv379YGtrCwAICgrCrFmzMG3aNPTp0wf37t3DZ599hrp168Lb2/t59JuIiIiIiEincm04q82CBQvg7e2NYcOGAQDatGkDb29vbN26FcCjkZ/4+Hi0bdsWjRs3xldffYWwsDD8+OOP0jU6dOiAuLg4bNmyBd7e3ujUqRMUCgUSEhJgZlZ5h2eJiIiIiOjloNccpX8bPnpHREREVLlxjlLF4xwl7QzacJaIiIiI6GXHDWdfbc/90TsiIiIiIqJ/OyZKRERERERETylXohQdHQ2ZTIZx48YBAHJycjB69Gi4ubnBzMwMzs7OGDNmDO7evSu1OX78OPr06QMnJyeYmZnBw8MDc+bMUbtuZmYm+vbti4YNG0Iul0vXJyIiIiIiehEMnqOUnJyMhQsXwsvLSyq7ceMGbty4gRkzZqBRo0a4fPkyRowYgRs3bmDDhg0AgJSUFNSoUQOrVq2Ck5MTDh8+jOHDh8PIyAijRo0C8GjzWHt7e0yaNAmzZs0qZxeJiIiIiPQnSjhH6VVm0Kp3+fn58PHxwQ8//IDp06ejadOmmD17tta669evR79+/XD//n0YG2vPy0JDQ3H69GkkJiZqnGvXrl2p1y8NV70jIiIiqtwq86p3Zz9aU9EhvBCv/dCnokOolAx69C40NBRBQUHw9/d/Zt27d+/CyspKZ5L0uE61atUMCYWIiIiIiOi50/vRu/j4eKSmpiI5OfmZdW/duoUvv/wSw4cP11nn8OHDWLt2LbZv365vKERERERERP8IvRKlq1evYuzYsdizZw9MTU1LrZuXl4egoCA0atQIU6ZM0VrnxIkT6N69OyZPnoyAgAB9QtGgVCqhVCrVyoRQQaFQlOu6RERERET06tHr0buUlBRkZ2fDx8cHxsbGMDY2xv79+zF37lwYGxujpKQEAHDv3j106tQJlpaW2Lx5M0xMTDSuderUKfj5+WH48OGYNGlSuTsSFRUFa2trtSMmJqbc1yUiIiKiV5N4qHolDtJOrxElPz8/pKenq5UNHjwY7u7uiIiIgJGREfLy8hAYGAiFQoGtW7dqHXk6efIkOnTogIEDB+Krr74qXw/+X2RkJMLDw9XKhOALT0RERERE+tMrUbK0tISnp6dambm5Oezs7ODp6Ym8vDwEBATgwYMHWLVqFfLy8pCXlwcAsLe3h5GREU6cOIEOHTogMDAQ4eHhyMrKAgAYGRnB3t5eum5aWhqARyvs/f3330hLS0OVKlXQqFEjrbEpFAqNx+y46h0RERERERnC4H2UtElNTcXRo0cBAK6urmrnLl68iHr16mHDhg34+++/sWrVKqxatUo6X7duXVy6dEn62tvbW/p3SkoK4uLiNOoQERERERH9EwzaR+nfgiNKRERERJVbZd5HKWPIyooO4YVwW9K/okOolAzaR4mIiIiIiOhlxkSJiIiIiIjoKUyUiIiIiIiInlKuRCk6OhoymQzjxo0DAOTk5GD06NFwc3ODmZkZnJ2dMWbMGNy9e1etXXJyMvz8/GBjYwNbW1sEBgbi+PHjWu9x7tw5WFpawsbGpjyhEhERERERlZnBiVJycjIWLlwILy8vqezGjRu4ceMGZsyYgRMnTmDZsmVISEhASEiIVCc/Px+dOnWCs7Mzjh49ioMHD8LS0hKBgYEoLi5Wu0dxcTH69OmD1q1bGxomEREREZFBRIl4JQ7SzqBEKT8/H8HBwVi0aBFsbW2lck9PT2zcuBFdu3ZFgwYN0KFDB3z11VfYtm0bHj58CAA4c+YMcnJyMG3aNLi5uaFx48aYPHkybt68icuXL6vdZ9KkSXB3d0fv3r3L0UUiIiIiIiL9GJQohYaGIigoCP7+/s+se/fuXVhZWcHY+NGWTW5ubrCzs0NsbCyKiopQUFCA2NhYeHh4oF69elK7xMRErF+/Ht9//70hIRIRERERERlM7w1n4+PjkZqaiuTk5GfWvXXrFr788ksMHz5cKrO0tMS+ffvwzjvv4MsvvwQAvPbaa9i1a5eUTN2+fRuDBg3CqlWrYGVlpW+IRERERERE5aJXonT16lWMHTsWe/bsgampaal18/LyEBQUhEaNGmHKlClSeUFBAUJCQtCqVSusWbMGJSUlmDFjBoKCgpCcnAwzMzMMGzYMffv2RZs2bcocm1KphFKpVCsTQgWFQqFPF4mIiIiIAADioaqiQ6AKJBNClHkG15YtW9CjRw8YGRlJZSUlJZDJZJDL5VAqlTAyMsK9e/cQGBiIqlWr4ueff1ZLqmJjY/HZZ58hMzMTcvmjJ/+Kiopga2uL2NhYfPDBB7CxsUF+fr7URggBlUoFIyMj/PjjjxgyZIhGbFOmTMHUqVPVyiZO/AyTJk0q+3eDiIiIiF4oU1Ozig5Bp9PByyo6hBfCY/Wgig6hUtJrRMnPzw/p6elqZYMHD4a7uzsiIiJgZGSEvLw8BAYGQqFQYOvWrRojTw8ePIBcLodMJpPKHn+tUj3K2pOSklBSUiKd/+mnn/DNN9/g8OHDqF27ttbYIiMjER4erlYmBD8FICIiIiIi/emVKFlaWsLT01OtzNzcHHZ2dvD09EReXh4CAgLw4MEDrFq1Cnl5ecjLywMA2Nvbw8jICB07dsT48eMRGhqK0aNHQ6VSITo6GsbGxmjfvj0AwMPDQ+0ex44dg1wu17j3kxQKhcZjdoWFBfp0j4iIiIiICIABizmUJjU1FUePHgUAuLq6qp27ePEi6tWrB3d3d2zbtg1Tp06Fr68v5HI5vL29kZCQAEdHx+cZDhERERERkUH0mqP0b8MRJSIiIqLKrTLPUTr1wZKKDuGFaBSvOf+fDNxHiYiIiIiI6GXGRImIiIiIiOgpTJSIiIiIiIieUq5EKTo6GjKZDOPGjZPKPvzwQzRo0ABmZmawt7dH9+7dcebMGa3tb9++jTp16kAmkyE3N1cqHzRoEGQymcbRuHHj8oRLRERERFRm4qHqlThIO4MTpeTkZCxcuBBeXl5q5c2aNcPSpUtx+vRp7Nq1C0IIBAQEqO2L9FhISIhGewCYM2cOMjMzpePq1auoVq0a3nvvPUPDJSIiIiIiKjODEqX8/HwEBwdj0aJFsLW1VTs3fPhwtGnTBvXq1YOPjw+mT5+Oq1ev4tKlS2r15s+fj9zcXHzyySca17e2toaDg4N0HDt2DHfu3MHgwYMNCZeIiIiIiEgvBiVKoaGhCAoKgr+/f6n17t+/j6VLl6J+/fpwcnKSyk+dOoVp06ZhxYoVkMufHUJsbCz8/f1Rt25dQ8IlIiIiIiLSi96JUnx8PFJTUxEVFaWzzg8//AALCwtYWFhg586d2LNnD6pUqQIAUCqV6NOnD2JiYuDs7PzM+924cQM7d+7E0KFD9Q2ViIiIiIjIIMb6VL569SrGjh2LPXv2wNTUVGe94OBgdOzYEZmZmZgxYwZ69+6NQ4cOwdTUFJGRkfDw8EC/fv3KdM/ly5fDxsYG77zzTqn1lEollEqlWpkQKigUijLdh4iIiIjoSVzo4NWm14hSSkoKsrOz4ePjA2NjYxgbG2P//v2YO3cujI2NpQUbrK2t8dprr6FNmzbYsGEDzpw5g82bNwMAEhMTsX79eqm9n58fAKB69eqYPHmy2v2EEFiyZAn69+8vjUjpEhUVBWtra7UjJiZGn+4REREREREB0HNEyc/PD+np6WplgwcPhru7OyIiImBkZKTRRggBIYQ02rNx40YUFBRI55OTkzFkyBD89ttvaNCggVrb/fv349y5cwgJCXlmbJGRkQgPD3/q3vwUgIiIiIiI9KdXomRpaQlPT0+1MnNzc9jZ2cHT0xMXLlzA2rVrERAQAHt7e1y7dg3R0dEwMzNDly5dAEAjGbp16xYAwMPDAzY2NmrnYmNj0bx5c417aqNQKDQesyssLNBRm4iIiIiISDe9EqVnMTU1xW+//YbZs2fjzp07qFmzJtq0aYPDhw+jRo0ael3r7t272LhxI+bMmfM8QyQiIiIiKhNRwqeTXmUyIYSo6CD+KRxRIiIiIqrcTE3NKjoEndK7LqjoEF6IJttGVHQIlZJB+ygRERERERG9zJgoERERERERPYWJEhERERER0VPKlShFR0dDJpNh3LhxGueEEOjcuTNkMhm2bNmidm7MmDFo1qwZFAoFmjZtqvXaf/75J1q3bg1TU1M4OTnhv//9b3lCJSIiIiLSi3ioeiUO0s7gRCk5ORkLFy6El5eX1vOzZ8+GTCbT2X7IkCF4//33tZ7Ly8tDQEAA6tati5SUFMTExGDKlCn48ccfDQ2XiIiIiIiozAxaHjw/Px/BwcFYtGgRpk+frnE+LS0N3377LY4dOwZHR0eN83PnzgUA/P333/jzzz81zq9evRpFRUVYsmQJqlSpgsaNGyMtLQ0zZ87E8OHDDQmZiIiIiIiozAwaUQoNDUVQUBD8/f01zj148AB9+/bF999/DwcHB4OCSkpKQps2bVClShWpLDAwEBkZGbhz545B1yQiIiIiIiorvUeU4uPjkZqaiuTkZK3nw8LC0LJlS3Tv3t3goLKyslC/fn21spo1a0rnbG1tDb42EREREVFZcP7Oq02vROnq1asYO3Ys9uzZA1NTU43zW7duRWJiIv7444/nFmBZKZVKKJVKtTIhVFAoFC88FiIiIiIi+nfT69G7lJQUZGdnw8fHB8bGxjA2Nsb+/fsxd+5cGBsbY8+ePTh//jxsbGyk8wDQq1cvtGvXrsz3cXBwwM2bN9XKHn+t63G+qKgoWFtbqx0xMTH6dI+IiIiIiAiAniNKfn5+SE9PVysbPHgw3N3dERERgerVq+PDDz9UO9+kSRPMmjULXbt2LfN9fH19MXHiRBQXF8PExAQAsGfPHri5uel87C4yMhLh4eFqZUJwuJSIiIiIiPSnV6JkaWkJT09PtTJzc3PY2dlJ5dpGfJydndXmHJ07dw75+fnIyspCQUEB0tLSAACNGjVClSpV0LdvX0ydOhUhISGIiIjAiRMnMGfOHMyaNUtnbAqFQuMxu8LCAn26R0REREREBMDA5cHLa+jQodi/f7/0tbe3NwDg4sWLqFevHqytrbF7926EhoaiWbNmqF69Or744gsuDU5EREREL4wo4dNJrzKZEEJUdBD/FI4oEREREVVupqZmFR2CTmkd5lZ0CC9E08QxFR1CpWTQPkpEREREREQvMyZKRERERERET6mQOUpERERERJUdN5x9tZVrRCk6OhoymQzjxo3TOCeEQOfOnSGTybBlyxapfNmyZZDJZFqP7OxsAMDBgwfRqlUr2NnZwczMDO7u7qWueEdERERERPQ8GTyilJycjIULF8LLy0vr+dmzZ0Mmk2mUv//+++jUqZNa2aBBg1BYWIgaNWoAeLTk+KhRo+Dl5QVzc3McPHgQH374IczNzbnyHRERERER/eMMSpTy8/MRHByMRYsWYfr06Rrn09LS8O233+LYsWNwdHRUO2dmZgYzs/+tbvL3338jMTERsbGxUpm3t7e0ZDgA1KtXD5s2bcJvv/3GRImIiIiIiP5xBj16FxoaiqCgIPj7+2uce/DgAfr27Yvvv/9e6+azT1uxYgWqVq2Kd999V2edP/74A4cPH0bbtm0NCZeIiIiIiEgveo8oxcfHIzU1FcnJyVrPh4WFoWXLlujevXuZrhcbG4u+ffuqjTI9VqdOHfz99994+PAhpkyZgqFDh+obLhERERGRQbjh7KtNr0Tp6tWrGDt2LPbs2QNTU1ON81u3bkViYiL++OOPMl0vKSkJp0+fxsqVK7We/+2335Cfn48jR45gwoQJcHV1RZ8+fbTWVSqVUCqVamVCqKBQKMoUCxERERER0WN6PXqXkpKC7Oxs+Pj4wNjYGMbGxti/fz/mzp0LY2Nj7NmzB+fPn4eNjY10HgB69eqFdu3aaVxv8eLFaNq0KZo1a6b1fvXr10eTJk0wbNgwhIWFYcqUKTpji4qKgrW1tdoRExOjT/eIiIiIiIgA6Dmi5Ofnh/T0dLWywYMHw93dHREREahevTo+/PBDtfNNmjTBrFmz0LVrV7Xy/Px8rFu3DlFRUWW6t0ql0hgxelJkZCTCw8PVyoTgcCkREREREelPr0TJ0tISnp6eamXm5uaws7OTyrUt4ODs7Iz69eurla1duxYPHz5Ev379NOp///33cHZ2hru7OwDgwIEDmDFjBsaMGaMzNoVCofGYXWFhQdk6RkRERET0FG44+2ozeB+l8oqNjUXPnj1hY2OjcU6lUiEyMhIXL16EsbExGjRogG+++UZjtIqIiIiIiOifIBNCiIoO4p/CESUiIiKiys3UVHPl48oipfmMig7hhWh29JOKDqFSMmgfJSIiIiIiopcZEyUiIiIiIqKnMFEiIiIiItJCPFS9Esc/JScnB8HBwbCysoKNjQ1CQkKQn59fapvCwkKEhobCzs4OFhYW6NWrF27evCmdX7ZsGWQymdYjOzsbALBv3z6t57OysvSKv1yJUnR0NGQyGcaNGyeVtWvXTiOoESNGSOdv376NTp06oVatWlAoFHBycsKoUaOQl5endm2lUomJEyeibt26UCgUqFevHpYsWVKecImIiIiI6AUJDg7GyZMnsWfPHvz88884cOAAhg8fXmqbsLAwbNu2DevXr8f+/ftx48YN9OzZUzr//vvvIzMzU+0IDAxE27ZtUaNGDbVrZWRkqNV7+vyzGLzqXXJyMhYuXAgvLy+Nc8OGDcO0adOkr6tWrSr9Wy6Xo3v37pg+fTrs7e1x7tw5hIaGIicnB3FxcVK93r174+bNm4iNjYWrqysyMzOhUnGJRiIiIiKiyu706dNISEhAcnIy3njjDQDAvHnz0KVLF8yYMQO1atXSaHP37l3ExsYiLi4OHTp0AAAsXboUHh4eOHLkCFq0aAEzMzOYmf1vAZC///4biYmJiI2N1bhejRo1tK6wXVYGJUr5+fkIDg7GokWLMH36dI3zVatW1bqfEgDY2tpi5MiR0td169bFRx99hJiYGKksISEB+/fvx4ULF1CtWjUAQL169QwJlYiIiIiISqFUKqFUKtXKtO1Rqo+kpCTY2NhISRIA+Pv7Qy6X4+jRo+jRo4dGm5SUFBQXF8Pf318qc3d3h7OzM5KSktCiRQuNNitWrEDVqlXx7rvvapxr2rQplEolPD09MWXKFLRq1UqvPhj06F1oaCiCgoLUOvGk1atXo3r16vD09ERkZCQePHig81o3btzApk2b0LZtW6ls69ateOONN/Df//4XtWvXRsOGDfHJJ5+goIDLfRMRERHRiyFKxCtxREVFwdraWu2Iiooq1/cuKytL41E3Y2NjVKtWTedcoaysLFSpUkVjFKhmzZo628TGxqJv375qo0yOjo5YsGABNm7ciI0bN8LJyQnt2rVDamqqXn3Qe0QpPj4eqampSE5O1nq+b9++qFu3LmrVqoU///wTERERyMjIwKZNm9Tq9enTBz/99BMKCgrQtWtXLF68WDp34cIFHDx4EKampti8eTNu3bqFjz76CLdv38bSpUv1DZmIiIiIiHSIjIxEeHi4Wpmu0aQJEybgm2++KfV6p0+ffm6xlSYpKQmnT5/GypUr1crd3Nzg5uYmfd2yZUucP38es2bN0qhbGr0SpatXr2Ls2LHYs2cPTE1NtdZ5coJWkyZN4OjoCD8/P5w/fx4NGjSQzs2aNQuTJ0/GX3/9Jb04P/zwAwBApVJBJpNh9erVsLa2BgDMnDkT7777Ln744Qe1jPExbUOGQqjKNWRIRERERPSy0+cxu48//hiDBg0qtY6LiwscHBykVegee/jwIXJycnRO0XFwcEBRURFyc3PVRpVu3ryptc3ixYvRtGlTNGvW7Jlxv/XWWzh48OAz6z1Jr0fvUlJSkJ2dDR8fHxgbG8PY2Bj79+/H3LlzYWxsjJKSEo02zZs3BwCcO3dOrdzBwQHu7u7o1q0bFi5ciPnz5yMzMxPAo+Gy2rVrS0kSAHh4eEAIgWvXrmmNTduQ4ZPznoiIiIiIqHzs7e3h7u5e6lGlShX4+voiNzcXKSkpUtvExESoVCopP3has2bNYGJigl9//VUqy8jIwJUrV+Dr66tWNz8/H+vWrUNISEiZ4k5LS4Ojo6NefdVrRMnPzw/p6elqZYMHD4a7uzsiIiJgZGSkNSgApQb2eDW7xyNCrVq1wvr165Gfnw8LCwsAwF9//QW5XI46depovYa2IUMhuEoeEREREdGL5uHhgU6dOmHYsGFYsGABiouLMWrUKHzwwQfSinfXr1+Hn58fVqxYgbfeegvW1tYICQlBeHg4qlWrBisrK4wePRq+vr4aCzmsXbsWDx8+RL9+/TTuPXv2bNSvXx+NGzdGYWEhFi9ejMTEROzevVuvPuiVKFlaWsLT01OtzNzcHHZ2dvD09MT58+cRFxeHLl26wM7ODn/++SfCwsLQpk0baRnxHTt24ObNm3jzzTdhYWGBkydPYvz48WjVqpW0sl3fvn3x5ZdfYvDgwZg6dSpu3bqF8ePHY8iQIVofuwO0DxkWFnLxByIiIiIyzD+5GeurYPXq1Rg1ahT8/Pwgl8vRq1cvzJ07VzpfXFyMjIwMtYXfZs2aJdVVKpUIDAyUpuc8KTY2Fj179tS6/HdRURE+/vhjXL9+HVWrVoWXlxd++eUXtG/fXq/4ZUIIoVeLp7Rr1w5NmzbF7NmzcfXqVfTr1w8nTpzA/fv34eTkhB49emDSpEmwsrICAOzduxcTJ07EqVOnoFQq4eTkhJ49e2LChAlqHT1z5gxGjx6NQ4cOwc7ODr1798b06dN1JkraMFEiIiIiqtxMTcv+3u5F+92zfCu//Vu8dSKyokOolMqdKFVmTJSIiIiIKjcmShWPiZJ2Bu2jRERERERE9DLTex8lIiIiIqJXgSjhHKVXGUeUiIiIiIiInlKuRCk6OhoymQzjxo2Tytq1aweZTKZ2jBgxQq3dr7/+ipYtW8LS0hIODg6IiIjAw4cP1eqsW7cOTZs2RdWqVVG3bl3uiURERERERC+MwY/eJScnY+HChdKy308aNmwYpk2bJn1dtWpV6d/Hjx9Hly5dMHHiRKxYsQLXr1/HiBEjUFJSghkzZgAAdu7cieDgYMybNw8BAQE4ffo0hg0bBjMzM4waNcrQkImIiIiIiMrEoBGl/Px8BAcHY9GiRbC1tdU4X7VqVTg4OEjH46XBgUebQ3l5eeGLL76Aq6sr2rZti//+97/4/vvvce/ePQDAypUr8c4772DEiBFwcXFBUFAQIiMj8c033+AlXqSPiIiIiIgqCYMSpdDQUAQFBcHf31/r+dWrV6N69erw9PREZGSk2iZSSqUSpqamavXNzMxQWFiIlJSUUutcu3YNly9fNiRkIiIiIiK9iIeqV+Ig7fROlOLj45GamoqoKO3ryvft2xerVq3C3r17ERkZiZUrV6Jfv37S+cDAQBw+fBhr1qxBSUkJrl+/Lj2ml5mZKdXZtGkTfv31V6hUKvz111/49ttv1eoQERERERH9U/Sao3T16lWMHTsWe/bs0RjxeWz48OHSv5s0aQJHR0f4+fnh/PnzaNCgAQICAhATE4MRI0agf//+UCgU+Pzzz/Hbb79BLn+Utw0bNgznz5/Hf/7zHxQXF8PKygpjx47FlClTpDpPUyqVUCqVamVCqKBQKPTpIhERERERkX4jSikpKcjOzoaPjw+MjY1hbGyM/fv3Y+7cuTA2NkZJSYlGm+bNmwMAzp07J5WFh4cjNzcXV65cwa1bt9C9e3cAgIuLCwBAJpPhm2++QX5+Pi5fvoysrCy89dZbanWeFhUVBWtra7WDK+UREREREZEh9BpR8vPzQ3p6ulrZ4MGD4e7ujoiICBgZGWm0SUtLAwA4OjqqlctkMtSqVQsAsGbNGjg5OcHHx0etjpGREWrXri3V8fX1hb29vdbYIiMjER4erlYmBJ+5JCIiIiLDcP7Oq02vRMnS0hKenp5qZebm5rCzs4OnpyfOnz+PuLg4dOnSBXZ2dvjzzz8RFhaGNm3aqC0jHhMTg06dOkEul2PTpk2Ijo7GunXrpETr1q1b2LBhA9q1a4fCwkIsXboU69evx/79+3XGplAoNB6zKyws0Kd7REREREREAMqxj5I2VapUwS+//ILZs2fj/v37cHJyQq9evTBp0iS1ejt37sRXX30FpVKJ119/HT/99BM6d+6sVmf58uX45JNPIISAr68v9u3bJz1+R0RERERE9E+SiZd4YyKOKBERERFVbqamZhUdgk5J9adWdAgvhO/FyRUdQqVk0D5KREREREREL7Pn+ugdEREREdHLQpRwMYdXGUeUiIiIiIiInsJEiYiIiIiI6CnlSpSio6Mhk8kwbtw4tfKkpCR06NAB5ubmsLKyQps2bVBQoL6wwvbt29G8eXOYmZnB1tYW77zzjnTu+PHj6NOnD5ycnGBmZgYPDw/MmTOnPKESERERERGVmcFzlJKTk7Fw4UK1/ZGAR0lSp06dEBkZiXnz5sHY2BjHjx+HXP6/nGzjxo0YNmwYvv76a3To0AEPHz7EiRMnpPMpKSmoUaMGVq1aBScnJxw+fBjDhw+HkZERRo0aZWjIRERERERlxjlKrzaDlgfPz8+Hj48PfvjhB0yfPh1NmzbF7NmzAQAtWrRAx44d8eWXX2pt+/DhQ9SrVw9Tp05FSEhIme8ZGhqK06dPIzExscxtuDw4ERERUeVWmZcHP1T784oO4YVodV37+/ZXnUGP3oWGhiIoKAj+/v5q5dnZ2Th69Chq1KiBli1bombNmmjbti0OHjwo1UlNTcX169chl8vh7e0NR0dHdO7cWW1ESZu7d++iWrVqhoRLRERERESkF70Tpfj4eKSmpiIqKkrj3IULFwAAU6ZMwbBhw5CQkAAfHx/4+fnh7NmzGnUmTZqEn3/+Gba2tmjXrh1ycnK03vPw4cNYu3Ythg8frjMupVKJvLw8tUOpVOrbPSIiIiIiIv0SpatXr2Ls2LFYvXo1TE1NNc6rVI+e4/zwww8xePBgeHt7Y9asWXBzc8OSJUvU6kycOBG9evVCs2bNsHTpUshkMqxfv17jmidOnED37t0xefJkBAQE6IwtKioK1tbWakdMTIw+3SMiIiIiIgKg52IOKSkpyM7Oho+Pj1RWUlKCAwcO4LvvvkNGRgYAoFGjRmrtPDw8cOXKFQCAo6OjRh2FQgEXFxepzmOnTp2Cn58fhg8fjkmTJpUaW2RkJMLDw9XKhOAEPCIiIiIyjIrvJV9peiVKfn5+SE9PVysbPHgw3N3dERERARcXF9SqVUtKmB7766+/0LlzZwBAs2bNoFAokJGRgbfffhsAUFxcjEuXLqFu3bpSm5MnT6JDhw4YOHAgvvrqq2fGplAooFAo1Mq4mAMRERERERlCr0TJ0tISnp6eamXm5uaws7OTysePH4/Jkyfj9ddfR9OmTbF8+XKcOXMGGzZsAABYWVlhxIgRmDx5MpycnFC3bl3pEbn33nsPwKPH7Tp06IDAwECEh4cjKysLAGBkZAR7e/vy9ZiIiIiIiOgZDN5HSZdx48ahsLAQYWFhyMnJweuvv449e/agQYMGUp2YmBgYGxujf//+KCgoQPPmzZGYmAhbW1sAwIYNG/D3339j1apVWLVqldSubt26uHTp0vMOmYiIiIiISI1B+yj9W/DROyIiIqLKrTLvo7Tf8bOKDuGFaJv5dUWHUCkZtI8SERERERHRy4yJEhERERER0VOYKBERERERET2lXIlSdHQ0ZDIZxo0bBwC4dOkSZDKZ1uPxZrLHjx9Hnz594OTkBDMzM3h4eGDOnDlq1923b5/Wazxe/Y6IiIiIiOifZPCqd8nJyVi4cCG8vLykMicnJ2RmZqrV+/HHHxETEyPto5SSkoIaNWpg1apVcHJywuHDhzF8+HAYGRlh1KhRam0zMjJgZWUlfV2jRg1DwyUiIiIi0ksJN5x9pRmUKOXn5yM4OBiLFi3C9OnTpXIjIyM4ODio1d28eTN69+4NCwsLAMCQIUPUzru4uCApKQmbNm3SSJRq1KgBGxsbQ0IkIiIiIiIymEGP3oWGhiIoKAj+/v6l1ktJSUFaWhpCQkJKrXf37l1Uq1ZNo7xp06ZwdHREx44dcejQIUNCJSIiIiIi0pveI0rx8fFITU1FcnLyM+vGxsbCw8MDLVu21Fnn8OHDWLt2LbZv3y6VOTo6YsGCBXjjjTegVCqxePFitGvXDkePHoWPj4/W6yiVSiiVSrUyIVRQKBRl7BkREREREdEjeo0oXb16FWPHjsXq1athampaat2CggLExcWVOpp04sQJdO/eHZMnT0ZAQIBU7ubmhg8//BDNmjVDy5YtsWTJErRs2RKzZs3Sea2oqChYW1urHTExMfp0j4iIiIiICAAgE0KIslbesmULevToASMjI6mspKQEMpkMcrkcSqVSOrdy5UqEhITg+vXrsLe317jWqVOn0L59ewwdOhRfffXVM+89fvx4HDx4EElJSVrPc0SJiIiI6N/H1NSsokPQ6Zean1Z0CC+E/83/VnQIlZJej975+fkhPT1drWzw4MFwd3dHRESEWgIVGxuLbt26aU2STp48iQ4dOmDgwIFlSpIAIC0tDY6OjjrPKxQKjaSosLCgTNcmIiIiIiJ6kl6JkqWlJTw9PdXKzM3NYWdnp1Z+7tw5HDhwADt27NC4xokTJ9ChQwcEBgYiPDxc2hvJyMhISqpmz56N+vXro3HjxigsLMTixYuRmJiI3bt3691BIiIiIiIifRm8j1JplixZgjp16qjNO3psw4YN+Pvvv7Fq1SqsWrVKKq9bty4uXboEACgqKsLHH3+M69evo2rVqvDy8sIvv/yC9u3b/xPhEhERERERqdFrjtK/DR+9IyIiIqrcKvMcpd01xld0CC9EQDYXQNPGoH2UiIiIiIiIXmZMlIiIiIiIiJ7CRImIiIiIiOgp5UqUoqOjIZPJMG7cOADApUuXIJPJtB7r16+X2iUnJ8PPzw82NjawtbVFYGAgjh8/rnbtXbt2oUWLFrC0tIS9vT169eolLfZARERERET0TzI4UUpOTsbChQvh5eUllTk5OSEzM1PtmDp1KiwsLNC5c2cAQH5+Pjp16gRnZ2ccPXoUBw8ehKWlJQIDA1FcXAwAuHjxIrp3744OHTogLS0Nu3btwq1bt9CzZ89ydpeIiIiIqGxUQvVKHKSdQcuD5+fnIzg4GIsWLcL06dOlciMjIzg4OKjV3bx5M3r37g0LCwsAwJkzZ5CTk4Np06bByckJADB58mR4eXnh8uXLcHV1RUpKCkpKSjB9+nTI5Y9yuU8++QTdu3dHcXExTExMDOosERERERFRWRg0ohQaGoqgoCD4+/uXWi8lJQVpaWkICQmRytzc3GBnZ4fY2FgUFRWhoKAAsbGx8PDwQL169QAAzZo1g1wux9KlS1FSUoK7d+9i5cqV8Pf3Z5JERERERET/OL0Tpfj4eKSmpiIqKuqZdR8nQC1btpTKLC0tsW/fPqxatQpmZmawsLBAQkICdu7cCWPjRwNc9evXx+7du/HZZ59BoVDAxsYG165dw7p163TeS6lUIi8vT+1QKpX6do+IiIiIiEi/ROnq1asYO3YsVq9eDVNT01LrFhQUIC4uTm006XF5SEgIWrVqhSNHjuDQoUPw9PREUFAQCgoebRCblZWFYcOGYeDAgUhOTsb+/ftRpUoVvPvuu9C1P25UVBSsra3VjpgYbp5FRERERIap6LlDnKNUsWRCV+ahxZYtW9CjRw8YGRlJZSUlJZDJZJDL5VAqldK5lStXIiQkBNevX4e9vb1UPzY2Fp999hkyMzOl+UdFRUWwtbVFbGwsPvjgA3z++edISEhAcnKy1O7atWtwcnJCUlISWrRooRGbUqnUGEESQgWFQlHW7hERERHRC2ZqalbRIei0o3pYRYfwQnS5NauiQ6iU9FrMwc/PD+np6WplgwcPhru7OyIiItQSqNjYWHTr1k0tSQKABw8eQC6XQyaTSWWPv1apVGp1nvT42o/rPE2hUGgkRYWFBfp0j4iIiIiICICej95ZWlrC09NT7TA3N4ednR08PT2leufOncOBAwcwdOhQjWt07NgRd+7cQWhoKE6fPo2TJ09i8ODBMDY2Rvv27QEAQUFBSE5OxrRp03D27FmkpqZi8ODBqFu3Lry9vcvZZSIiIiIiotKVa8NZXZYsWYI6deogICBA45y7uzu2bduGP//8E76+vmjdujVu3LiBhIQEODo6AgA6dOiAuLg4bNmyBd7e3ujUqRMUCgUSEhJgZlZ5h2eJiIiIiOjloNccpX8bPnpHREREVLlV5jlKP9uNq+gQXoj/3J5d0SFUSv/IiBIREREREdG/GRMlIiIiIiKipzBRIiIiIiIiekq5EqXo6GjIZDKMGzdOKsvKykL//v3h4OAAc3Nz+Pj4YOPGjWrt6tWrB5lMpnZER0dL5wsLCzFo0CA0adIExsbGeOedd8oTJhERERGR3ip6I1huOFux9NpH6UnJyclYuHAhvLy81MoHDBiA3NxcbN26FdWrV0dcXBx69+6NY8eOqS3tPW3aNAwbNkz62tLSUvp3SUkJzMzMMGbMGI0ki4iIiIiI6J9m0IhSfn4+goODsWjRItja2qqdO3z4MEaPHo233noLLi4umDRpEmxsbJCSkqJWz9LSEg4ODtJhbm4unTM3N8f8+fMxbNgwODg4GBIiERERERGRwQxKlEJDQxEUFAR/f3+Ncy1btsTatWuRk5MDlUqF+Ph4FBYWol27dmr1oqOjYWdnB29vb8TExODhw4cGdYCIiIiIiOh50/vRu/j4eKSmpiI5OVnr+XXr1uH999+HnZ0djI2NUbVqVWzevBmurq5SnTFjxsDHxwfVqlXD4cOHERkZiczMTMycOdPgjiiVSiiVSrUyIVRQKBQGX5OIiIiIiF5NeiVKV69exdixY7Fnzx6YmppqrfP5558jNzcXv/zyC6pXr44tW7agd+/e+O2339CkSRMAQHh4uFTfy8sLVapUwYcffoioqCiDE5uoqChMnTpVrWzixM8wadIkg65HRERERK+2Ei508EqTCSFEWStv2bIFPXr0gJGRkVRWUlICmUwGuVyOjIwMuLq64sSJE2jcuLFUx9/fH66urliwYIHW6548eRKenp44c+YM3Nzc1M4NGjQIubm52LJlS6mxcUSJiIiI6N/H1NSsokPQabPtqIoO4YXocee7ig6hUtJrRMnPzw/p6elqZYMHD4a7uzsiIiLw4MEDAIBcrj71ycjICCqV7ow8LS0NcrkcNWrU0CccNQqFQiMpKiwsMPh6RERERET06tIrUbK0tISnp6dambm5Oezs7ODp6Yni4mK4urriww8/xIwZM2BnZ4ctW7Zgz549+PnnnwEASUlJOHr0KNq3bw9LS0skJSUhLCwM/fr1U1tB79SpUygqKkJOTg7u3buHtLQ0AEDTpk3L12MiIiIiIqJnMHgfJW1MTEywY8cOTJgwAV27dkV+fj5cXV2xfPlydOnSBcCjkZ/4+HhMmTIFSqUS9evXR1hYmNq8JQDo0qULLl++LH39eA8mPZ4UJCIiIiIymIrvO19pes1R+rfho3dERERElVtlnqO00Sa0okN4IXrlfl/RIVRKBu2jRERERERE9DJjokRERERERPQUJkpERERERERPKVeiFB0dDZlMhnHjxkll58+fR48ePWBvbw8rKyv07t0bN2/eVGv31VdfoWXLlqhatSpsbGxKvcft27dRp04dyGQy5ObmlidcIiIiIqIyUwnVK3GQdgYnSsnJyVi4cCG8vLyksvv37yMgIAAymQyJiYk4dOgQioqK0LVrV7V9lIqKivDee+9h5MiRz7xPSEiI2j2IiIiIiIj+aQYlSvn5+QgODsaiRYvU9j46dOgQLl26hGXLlqFJkyZo0qQJli9fjmPHjiExMVGqN3XqVISFhaFJkyal3mf+/PnIzc3FJ598YkiYREREREREBjEoUQoNDUVQUBD8/f3VypVKJWQyGRQKhVRmamoKuVyOgwcP6nWPU6dOYdq0aVixYgXkck6lIiIiIiKiF0fvDWfj4+ORmpqK5ORkjXMtWrSAubk5IiIi8PXXX0MIgQkTJqCkpASZmZllvodSqUSfPn0QExMDZ2dnXLhwoUxtlEqlWpkQKrWkjYiIiIiorDh/59Wm11DN1atXMXbsWKxevRqmpqYa5+3t7bF+/Xps27YNFhYWsLa2Rm5uLnx8fPQaFYqMjISHhwf69etX5jZRUVGwtrZWO2JiYsrcnoiIiIiI6DG9RpRSUlKQnZ0NHx8fqaykpAQHDhzAd999B6VSiYCAAJw/fx63bt2CsbExbGxs4ODgABcXlzLfJzExEenp6diwYQMAQAgBAKhevTomTpyIqVOnarSJjIxEeHi4WpngpwBERERERGQAvRIlPz8/pKenq5UNHjwY7u7uiIiIgJGRkVRevXp1AI+SnuzsbHTr1q3M99m4cSMKCgqkr5OTkzFkyBD89ttvaNCggdY2CoVC4zG7wsICrXWJiIiIiIhKo1eiZGlpCU9PT7Uyc3Nz2NnZSeVLly6Fh4cH7O3tkZSUhLFjxyIsLAxubm5SmytXriAnJwdXrlxBSUkJ0tLSAACurq6wsLDQSIZu3boFAPDw8HjmvktERERERETlpfdiDs+SkZGByMhI5OTkoF69epg4cSLCwsLU6nzxxRdYvny59LW3tzcAYO/evWjXrt3zDomIiIiISG+q/5/+Qa8mmRAv708AH70jIiIiqtxMTc0qOgSd1lgOr+gQXog+936s6BAqJW5QRERERERE9BQmSkRERERERE957nOUiIiIiIheBtxw9tVWrhGl6OhoyGQyjBs3Tio7f/48evToAXt7e1hZWaF37964efOmdH7fvn2QyWRaj+TkZADAlClTtJ43NzcvT7hERERERERlYnCilJycjIULF8LLy0squ3//PgICAiCTyZCYmIhDhw6hqKgIXbt2hUr1KCNv2bIlMjMz1Y6hQ4eifv36eOONNwAAn3zyiUadRo0a4b333itnd4mIiIiIiJ7NoEfv8vPzERwcjEWLFmH69OlS+aFDh3Dp0iX88ccfsLKyAgAsX74ctra2SExMhL+/P6pUqQIHBwepTXFxMX766SeMHj0aMpkMAGBhYQELCwupzvHjx3Hq1CksWLDAoE4SERERERHpw6ARpdDQUAQFBcHf31+tXKlUQiaTQaFQSGWmpqaQy+U4ePCg1mtt3boVt2/fxuDBg3Xeb/HixWjYsCFat25tSLhERERERER60XtEKT4+HqmpqdJ8oie1aNEC5ubmiIiIwNdffw0hBCZMmICSkhJkZmZqvV5sbCwCAwNRp04drecLCwuxevVqTJgwodS4lEollEqlWpkQKrWkjYiIiIiorEq4mMMrTa8RpatXr2Ls2LFYvXo1TE1NNc7b29tj/fr12LZtGywsLGBtbY3c3Fz4+PhALte81bVr17Br1y6EhITovOfmzZtx7949DBw4sNTYoqKiYG1trXbExMTo0z0iIiIiIiIAgEwIIcpaecuWLejRoweMjIykspKSEshkMsjlciiVSuncrVu3YGxsDBsbGzg4OODjjz/G+PHj1a735ZdfYt68ebh+/TpMTEy03tPPzw9WVlbYvHlzqbFxRImIiIjo38fU1KyiQ9BphfmQig7hhRhwf0lFh1Ap6fXonZ+fH9LT09XKBg8eDHd3d0RERKglUNWrVwcAJCYmIjs7G926dVNrJ4TA0qVLMWDAAJ1J0sWLF7F3715s3br1mbEpFAqNpKiwsKBM/SIiIiIiInqSXomSpaUlPD091crMzc1hZ2cnlS9duhQeHh6wt7dHUlISxo4di7CwMLi5uam1S0xMxMWLFzF06FCd91uyZAkcHR3RuXNnfcIkIiIiIio3bjj7ajNoefDSZGRkIDIyEjk5OahXrx4mTpyIsLAwjXqxsbFo2bIl3N3dtV5HpVJh2bJlGDRokNpIFRERERER0T9NrzlK/zZ89I6IiIiocqvMc5SWVR1U0SG8EIMeLKvoEColg/ZRIiIiIiIiepkxUSIiIiIiInrKc5+jRERERET0MlC9vDNUqAz0GlGaMmUKZDKZ2vHkYgyFhYUIDQ2FnZ0dLCws0KtXL9y8eVPrtW7fvo06depAJpMhNzdX7dy+ffvg4+MDhUIBV1dXLFu2TO+OERERERERGUrvR+8aN26MzMxM6Th48KB0LiwsDNu2bcP69euxf/9+3LhxAz179tR6nZCQEHh5eWmUX7x4EUFBQWjfvj3S0tIwbtw4DB06FLt27dI3VCIiIiIiIoPo/eidsbExHBwcNMrv3r2L2NhYxMXFoUOHDgD+t6fSkSNH0KJFC6nu/PnzkZubiy+++AI7d+5Uu86CBQtQv359fPvttwAADw8PHDx4ELNmzUJgYKC+4RIREREREelN7xGls2fPolatWnBxcUFwcDCuXLkCAEhJSUFxcTH8/f2luu7u7nB2dkZSUpJUdurUKUybNg0rVqyAXK55+6SkJLVrAEBgYKDaNYiIiIiI/mkqoXolDtJOr0SpefPmWLZsGRISEjB//nxcvHgRrVu3xr1795CVlYUqVarAxsZGrU3NmjWRlZUFAFAqlejTpw9iYmLg7Oys9R5ZWVmoWbOmxjXy8vJQUKB7XySlUom8vDy1Q6lU6tM9IiIiIiIiAHomSp07d8Z7770HLy8vBAYGYseOHcjNzcW6devK1D4yMhIeHh7o16+fQcGWJioqCtbW1mpHTEzMc78PERERERG9/Mq1j5KNjQ0aNmyIc+fOwcHBAUVFRRor2N28eVOa05SYmIj169fD2NgYxsbG8PPzAwBUr14dkydPBgA4ODhorJR38+ZNWFlZwcxM987NkZGRuHv3rtoxfvz48nSPiIiIiIheUeXaRyk/Px/nz59H//790axZM5iYmODXX39Fr169AAAZGRm4cuUKfH19AQAbN25Ue3wuOTkZQ4YMwW+//YYGDRoAAHx9fbFjxw61++zZs0e6hi4KhQIKhUKtrLBQ96N6REREREREuuiVKH3yySfo2rUr6tatixs3bmDy5MkwMjJCnz59YG1tjZCQEISHh6NatWqwsrLC6NGj4evrK6149zgZeuzWrVsAHq1s93hu04gRI/Ddd9/h008/xZAhQ5CYmIh169Zh+/btz6G7RERERERlw4UOXm16JUrXrl1Dnz59cPv2bdjb2+Ptt9/GkSNHYG9vDwCYNWsW5HI5evXqBaVSicDAQPzwww96BVS/fn1s374dYWFhmDNnDurUqYPFixdzaXAiIiIiInphZEIIUdFB/FP46B0RERFR5WZqqnsOekX7URFc0SG8EMOVqys6hEqpXIs5EBERERERvYzKtZgDEREREdHLSvXyPnhFZcARJSIiIiIioqfolShNmTIFMplM7XB3d5fO//jjj2jXrh2srKwgk8k09lQCgJycHAQHB8PKygo2NjYICQlBfn6+dD4jIwPt27dHzZo1YWpqChcXF0yaNAnFxcWG95KIiIiIiEgPej9617hxY/zyyy//u4Dx/y7x4MEDdOrUCZ06dUJkZKTW9sHBwcjMzMSePXtQXFyMwYMHY/jw4YiLiwMAmJiYYMCAAfDx8YGNjQ2OHz+OYcOGQaVS4euvv9Y3XCIiIiIiIr3pnSgZGxvDwcFB67lx48YBAPbt26f1/OnTp5GQkIDk5GS88cYbAIB58+ahS5cumDFjBmrVqgUXFxe4uLhIberWrYt9+/bht99+0zdUIiIiIiIig+g9R+ns2bNSQhMcHIwrV66UuW1SUhJsbGykJAkA/P39IZfLcfToUa1tzp07h4SEBLRt21bfUImIiIiIDFYiVK/EQdrplSg1b94cy5YtQ0JCAubPn4+LFy+idevWuHfvXpnaZ2VloUaNGmplxsbGqFatGrKystTKW7ZsCVNTU7z22mto3bo1pk2bVuq1lUol8vLy1A6lUqlP94iIiIiIiADomSh17twZ7733Hry8vBAYGIgdO3YgNzcX69ate+6BrV27FqmpqYiLi8P27dsxY8aMUutHRUXB2tpa7YiJiXnucRERERER0cuvXPso2djYoGHDhjh37lyZ6js4OCA7O1ut7OHDh8jJydGY9+Tk5AQAaNSoEUpKSjB8+HB8/PHHMDIy0nrtyMhIhIeHq5UJDiUSEREREZEBypUo5efn4/z58+jfv3+Z6vv6+iI3NxcpKSlo1qwZACAxMREqlQrNmzfX2U6lUqG4uBgqlUpnoqRQKKBQKNTKCgsLytgTIiIiIiJ1Kn7o/krTK1H65JNP0LVrV9StWxc3btzA5MmTYWRkhD59+gB4NAcpKytLGmFKT0+HpaUlnJ2dUa1aNXh4eKBTp04YNmwYFixYgOLiYowaNQoffPABatWqBQBYvXo1TExM0KRJEygUChw7dgyRkZF4//33YWJi8py7T0REREREpEmvROnatWvo06cPbt++DXt7e7z99ts4cuQI7O3tAQALFizA1KlTpfpt2rQBACxduhSDBg0C8CgRGjVqFPz8/CCXy9GrVy/MnTv3fwEZG+Obb77BX3/9BSEE6tati1GjRiEsLKy8fSUiIiIiIioTmRBCVHQQ/xQ+ekdERERUuZmamlV0CDrNM+5d0SG8EKMfPv+F2V4Geu+jRERERERE9LIr12IOREREREQvK9XL++AVlQFHlIiIiIiIiJ6iV6I0ZcoUyGQytcPd3V06/+OPP6Jdu3awsrKCTCZDbm6u1uts374dzZs3h5mZGWxtbfHOO+9o1Fm2bBm8vLxgamqKGjVqIDQ0VK+OERERERERGUrvR+8aN26MX3755X8XMP7fJR48eIBOnTqhU6dOiIyM1Np+48aNGDZsGL7++mt06NABDx8+xIkTJ9TqzJw5E99++y1iYmLQvHlz3L9/H5cuXdI3VCIiIiIiIoPonSgZGxvDwcFB67lx48YBAPbt26f1/MOHDzF27FjExMQgJCREKm/UqJH07zt37mDSpEnYtm0b/Pz8pHIvLy99QyUiIiIiMhg3nH216T1H6ezZs6hVqxZcXFwQHByMK1eulLltamoqrl+/DrlcDm9vbzg6OqJz585qI0p79uyBSqXC9evX4eHhgTp16qB37964evWqvqESEREREVEFycnJQXBwMKysrGBjY4OQkBDk5+eX2qYsU3nKct0///wTrVu3hqmpKZycnPDf//5X7/j1SpSaN2+OZcuWISEhAfPnz8fFixfRunVr3Lt3r0ztL1y4AODRXKdJkybh559/hq2tLdq1a4ecnBypjkqlwtdff43Zs2djw4YNyMnJQceOHVFUVKRn94iIiIiIqCIEBwfj5MmT2LNnD37++WccOHAAw4cPL7XN46k8n332mcHXzcvLQ0BAAOrWrYuUlBTExMRgypQp+PHHH/WKX69H7zp37iz928vLC82bN0fdunWxbt06tUfpdFGpHg1fTpw4Eb169QIALF26FHXq1MH69evx4YcfQqVSobi4GHPnzkVAQAAAYM2aNXBwcMDevXsRGBio9dpKpRJKpVKtTAgVFAqFPl0kIiIiIqJyOn36NBISEpCcnIw33ngDADBv3jx06dIFM2bMQK1atbS2e9ZUnrJcd/Xq1SgqKsKSJUtQpUoVNG7cGGlpaZg5c+YzE7UnlWt5cBsbGzRs2BDnzp0rU31HR0cA6nOSFAoFXFxcpEf4tNWxt7dH9erVS33MLyoqCtbW1mpHTEyM3n0iIiIiInqVKJVK5OXlqR1PD0DoKykpCTY2NlIyAwD+/v6Qy+U4evToP3rdpKQktGnTBlWqVJHqBAYGIiMjA3fu3Cn7zUQ53Lt3T9ja2oo5c+aole/du1cAEHfu3FErv3v3rlAoFGLx4sVSWVFRkahRo4ZYuHChEEKIjIwMAUD88ssvUp3bt28LuVwudu3apTOWwsJCcffuXenIzs4WEydOFIWFhXr1qbCwUEyePFnvduVp+29pVxH3ZB8r1z3Zx+ffriLuyT4+/3YVcU/28fm3q4h7so9UGUyePFkAUDsmT55crmt+9dVXomHDhhrl9vb24ocffnhme135RFmu27FjRzF8+HC18ydPnhQAxKlTp8rcB70SpY8//ljs27dPXLx4URw6dEj4+/uL6tWri+zsbCGEEJmZmeKPP/4QixYtEgDEgQMHxB9//CFu374tXWPs2LGidu3aYteuXeLMmTMiJCRE1KhRQ+Tk5Eh1unfvLho3biwOHTok0tPTxX/+8x/RqFEjUVRUVOZY7969KwCIu3fv6tNFg9tVxD3Zx+ffriLu+SrEyj5Wrnuyj8+/XUXck318/u0q4p7sI1UGTw843L17V2diGxERoZFUPX2cPn36pUiU9JqjdO3aNfTp0we3b9+Gvb093n77bRw5cgT29vYAgAULFmDq1KlS/TZt2gB4NA9p0KBBAICYmBgYGxujf//+KCgoQPPmzZGYmAhbW1up3YoVKxAWFoagoCDI5XK0bdsWCQkJMDEx0SdcIiIiIiJ6BoVCUeZ5/R9//LH0vl4XFxcXODg4IDs7W6384cOHyMnJ0bnVUFmU5boODg64efOmWp3HX+tzb70Spfj4+FLPT5kyBVOmTCm1jomJCWbMmIEZM2borGNlZYXY2FjExsbqEx4REREREf2D7O3tpUGS0vj6+iI3NxcpKSlo1qwZACAxMREqlQrNmzc3+P5lua6vry8mTpyI4uJiaaBlz549cHNzUxuceZZyLeZARERERET0NA8PD3Tq1AnDhg3D77//jkOHDmHUqFH44IMPpBXvrl+/Dnd3d/z+++9Su6ysLKSlpUmLxaWnpyMtLU3aSqgs1+3bty+qVKmCkJAQnDx5EmvXrsWcOXMQHh6uVx9e2kRJoVBg8uTJei8Pbmi7irgn+/j821XEPV+FWNnHynVP9vH5t6uIe7KPz79dRdyTfaSX2erVq+Hu7g4/Pz906dIFb7/9ttpeRsXFxcjIyMCDBw+ksgULFsDb2xvDhg0D8Ggqj7e3N7Zu3Vrm61pbW2P37t24ePEimjVrho8//hhffPGFXkuDA4BMCCEM7TwREREREdHL6KUdUSIiIiIiIjIUEyUiIiIiIqKnMFEiIiIiIiJ6ChMlIiIiIiKipzBRIiIiokrh4cOHWLFihcZGkUREFeGlXfWusLAQpqam//h9cnNzERsbi9OnTwMAGjdujCFDhsDa2vofv3dZFRcXo1OnTliwYAFee+21ig7nXy8vL6/Mda2srJ7bfYuLi+Hu7o6ff/4ZHh4ez+26RKWZNm0aPvnkE1StWlWtvKCgADExMfjiiy8qKDJ1JSUlOHToELy8vGBjY/Ncr11QUAAzMzOd992yZYva34Bu3brByMhI5/UuXLgAFxeX5xrjy6Rq1ao4ffo06tatq1c7FxcXJCcnw87OTq08NzcXPj4+uHDhwvMM85WiUqlw7tw5ZGdnQ6VSqZ1r06ZNBUVF9M97qRIllUqFr776CgsWLMDNmzfx119/wcXFBZ9//jnq1auHkJAQnW07dOiATZs2afyBzcvLwzvvvIPExESNNseOHUNgYCDMzMzw1ltvAQCSk5NRUFCA3bt3w8fHp9R4z58/j6VLl+L8+fOYM2cOatSogZ07d8LZ2RmNGzfW2mby5MkYMmSI3n9A7O3tcfjwYYMSpatXr0Imk6FOnToAgN9//x1xcXFo1KhRmdajLyoqwsWLF9GgQQMYGxtrrWNrawuZTFameB5vOKbNi0hc5XJ5mWMtKSl5bvcFgNq1a+OXX355oYlSQUEBhBDSG+XLly9j8+bNaNSoEQICAtTqPrnHwbN069ZN57k1a9agT58+Ws+NHz8eMTExWs/t3bsX7du3L3MMz1thYSGKiorUynQlyytWrMD777+vsa9IUVER4uPjMWDAAK3t9Hk9ngcjIyNkZmaiRo0aauW3b99GjRo11H7G//zzzzJf18vL67nF+JipqSlOnz6N+vXr6912zJgxmDt3rkb5/fv38Z///Ad79+7VOHfu3DkEBQXh2rVrcHNzAwBkZGTAyckJ27dvR4MGDbTeSy6Xo23btggJCcG7776r14d6JSUlWLZsGX799Vetb1q1/a0qTztt8vLykJiYCDc3N43fRdq+h7qMGTNGa3m7du0QFhaG7t27l/lawKPva1ZWlsbP6s2bN+Hs7AylUqm13fP83lSE0l4PbYqKirT209nZWWv9I0eOoG/fvrh8+TKefssok8l0/p3T9XddJpPB1NQUrq6uGDRoEAYPHvzMmIkqykuVKE2bNg3Lly/HtGnTMGzYMJw4cQIuLi5Yu3YtZs+ejaSkJJ1tdf2Czc7ORu3atVFcXKzRpnXr1nB1dcWiRYukBODhw4cYOnQoLly4gAMHDui83/79+9G5c2e0atUKBw4cwOnTp+Hi4oLo6GgcO3YMGzZs0NquadOmOHHihPRHtlevXmXawC0sLAwKhQLR0dHPrKutn8OHD0f//v2RlZUFNzc3NG7cGGfPnsXo0aN1fqL84MEDjB49GsuXLwcAKXEdPXo0ateujQkTJkh1H9cBHr0Bmz59OgIDA+Hr6wsASEpKwq5du/D5558jLCxM6/30TVwNfVO3f/9+6d+XLl3ChAkTMGjQILVYly9fjqioKAwcOLDU6549exZ79+7V+kdL2/f166+/xl9//YXFixfrTDqfpM8O1DNnztRaHhAQgJ49e2LEiBHIzc2Fu7s7TExMcOvWLcycORMjR46U6srl6k/zymQytT+sT/7RLC2JtLGxwZo1a9C5c2e18rCwMMTHxyMzM1NrO4VCgTp16mDw4MEYOHAgnJycdHf4Kb/99hsWLlyI8+fPY8OGDahduzZWrlyJ+vXr4+2339bZ7sGDB/j000+xbt063L59W+O8rn7qk4A8SZ/X42n379/H/v37ceXKFY2ETtebVrlcjps3b8Le3l6tPDExEe+//z7+/vtvtbqPX/NnfZjwrA8Rfv31V8yaNUv60MPDwwPjxo2Dv7+/zjZvvPEGvvnmG/j5+ZV6bW0aNGiAfv36YerUqVLZ/fv30alTJwCPfj6e1qVLFwghsHr1alSrVg3Ao9evX79+kMvl2L59u9Z7paWlYenSpVizZg2Kiorw/vvvIyQkRPq9VZpRo0Zh2bJlCAoKgqOjo8b3edasWc+1HQD07t0bbdq0wahRo1BQUIDXX38dly5dghAC8fHx6NWrl1S3rEmqTCbTOcKzbt06REZGIiwsDM2aNYO5ubna+aeT7Mcf0LzzzjtYvny52gdjJSUl+PXXX7Fnzx5kZGRovV95vjfLly9H9erVERQUBAD49NNP8eOPP6JRo0ZYs2aNzg81ExISYGFhIf1u+f7777Fo0SI0atQI33//PWxtbXXeU5/X40lnz57FkCFDcPjwYbXyx/9fdf2fbNq0KRo2bIipU6dq/f7o+iBy1qxZ+Oqrr9C5c2fpZ/v3339HQkICwsLCcPHiRaxcuRLz5s2TNhYlqnTES6RBgwbil19+EUIIYWFhIc6fPy+EEOL06dPCxsZGa5vjx4+L48ePC5lMJvbu3St9ffz4cZGamiq+/vprUbduXa1tTU1NxenTpzXKT548KczMzEqNtUWLFuLbb7/ViPXo0aOidu3apbZNTU0Vo0ePFtWrVxc2NjZixIgR4vfffy+1zahRo4SVlZVo1qyZGD58uAgLC1M7SmNjYyPOnDkjhBBizpw5omXLlkIIIXbt2iXq16+vs92YMWNEs2bNxG+//SbMzc2lPm7ZskU0bdpUZ7uePXuKefPmaZTPmzdPdO/eXWe7t99+WwwaNEgUFxdLZcXFxWLgwIGidevWGvVlMpmQy+VCJpNpPR6fk8vlOu/ZoUMHERcXp1G+evVq0bZtW53thBDixx9/FEZGRqJmzZri9ddfF02bNpUOb29vrW3eeecdYWlpKRwdHUVAQIDo0aOH2vG0du3aqR1WVlaiatWqwtvbW3h7ewtzc3NhZWUl2rdvrzNOOzs7ceLECSGEEIsWLRJeXl6ipKRErFu3Tri7u+tst2fPHuHj4yMSEhLE3bt3xd27d0VCQoJ44403xO7du0v93vz888/C2tpa/Pbbb1LZqFGjRK1atbT+n3vs77//FjNnzhSvv/66MDY2FgEBAWLt2rVCqVSWer8NGzYIMzMzMXToUKFQKKSf1Xnz5onOnTuX2vajjz4SHh4e0jWWLFkivvzyS1GnTh2xatUqne1kMpnIzs7WKE9LSxO2trY62xn6eqSmpgoHBwdhZWUljIyMhL29vZDJZMLc3Fzr/2MbGxtha2sr5HK59O/Hh5WVlZDL5eKjjz5Sa3Pp0iXp2Lx5s2jQoIFYsGCB9Dt1wYIF4rXXXhObN2/WGacQQnz//ffC2NhYfPDBB2LOnDlizpw5ok+fPsLExER89913Otvt3LlTNG3aVGzbtk3cuHFD+rl7fJTm3LlzwtHRUcyaNUsIIUReXp7w9fUVrVu3Fvn5+VrbVK1aVfz5558a5WlpacLc3LzU+wnx6PfTxo0bRdeuXYWJiYlo3Lix+Pbbb7X+XDxmZ2cntm/f/sxrP692QghRs2ZNkZaWJoR49LvN1dVV3L9/X/zwww+l/i43lL6/j5+u8+RRpUoV0bBhQ7Ft2zad9yvP96Zhw4bi119/FUIIcfjwYVG1alWxcOFC0bVrV62/kx/z9PSU7vnnn38KhUIhIiMjRYsWLcSgQYNKvaehr0fLli1FmzZtxI4dO8Qff/wh0tLS1A5dqlatKs6ePVtqTNr07NlTzJ8/X6N8wYIFomfPnkIIIebOnSs8PT31vjbRi/JSJUqmpqbi0qVLQgj15OPkyZM6/2g9/uWq6w1z1apVRWxsrNa2NWrUELt27dIoT0hIEDVq1Cg1VnNzc3HhwgWNWC9evCgUCkWZ+ltUVCQ2btwo/vOf/wgTExPRpEkTMXv2bJGbm6tR9+k3zE8epb1JfhzrxYsXhRBCdO3aVURHRwshhLh8+bIwNTXV2c7Z2VkkJSVp9PHs2bPC0tKy1Ptp+6V89uzZUt986Ju4Pvmm7lmHLmZmZuKvv/7SKM/IyHhmsuzs7Cx9L8tq0KBBpR6l+fbbb0XXrl1FTk6OVJaTkyO6d+8uZsyYobOdmZmZuHz5shBCiPfee09MmTJFCCHElStXSu1j48aN1RKdxw4cOFDqG/rHVq9eLWxtbcWxY8fEyJEjRa1atURGRsYz2z2WkpIiRo0aJezs7ISdnZ0YPXq0zjcDTZs2FcuXLxdCqP+spqamipo1a5Z6HycnJ7F3714hhBCWlpbSz+6KFSu0JlmPE2G5XC6aNGkiJa3e3t7Cy8tLWFpaivfee0/n/Qx9Pdq2bSuGDRsmSkpKpD5euXJFtGnTRmzcuFGj/rJly8TSpUuFTCYTc+bMEcuWLZOOuLg4cfjw4VK/L2+++abWN5/bt28XPj4+pbatXbu21g9LvvvuO1GrVi2d7Z5+Y/3k7/bSPvB47Pjx46JatWpizpw5okWLFqJt27Y6kyQhhLC1tRWHDh3SKD948GCpye7TCgsLxcyZM4VCoRAymUwoFArRv39/cePGDY26jo6Oev0/KG87IR79br1y5YoQQoj+/fuLiIgIIcSjvwFP/05++kM4XUd4eLjO+xn6+7hevXri77//1rt/5fnePPn/8dNPPxX9+/cXQghx4sQJUb16dZ3tnvy7OnnyZNGrVy8hxKPfW8/6naPP6/GkqlWrlvpBky7t27cXO3fu1LtdWf6Wnzt3TlStWlXvaxO9KC9VouTj4yNWrlwphFB/szN16lTx9ttva21z6dIlcfHiRSGTyURycrLaL+MbN26Ihw8f6rzf6NGjRZ06dUR8fLy4cuWKuHLlilizZo2oU6eOGDt2bKmx1q5dW/oD+2SsmzZtEi4uLmXqr1KpFPHx8SIgIEAYGxuLNm3aCFdXV2FpaSni4+PLdI2yeOutt0RERIQ4cOCAMDU1ld5sJiUllTr6ZWZmJvXryT6mpaUJKysrne2cnZ21vnGfMWOGcHZ21tmuPImroRo2bCjGjx+vUT5+/HjRsGHDUttaWlpK35MXoVatWtJIxJPS09OFo6OjznZNmjQRc+bMEVeuXBFWVlbSG+Rjx46V+gfd1NRUpKena5QfP3681AT7Sd9//71QKBSiTp06Bn2ief36dTF58mShUCiEubm5MDIyEm+//bbG98HMzEx60/Lkz+r58+ef+cGFubm59Eapdu3a4ujRo0IIIS5cuKD1TcuUKVPElClThEwmE5988on09ZQpU8TXX38t4uLiSh0BM/T1sLa2lkaGra2txalTp4QQQhw5ckS4ubnpbLdv3z5RVFRU6vdAG1NTU+keTzp16tQzX39db7D++uuvUt8I7tu3r9SjLA4fPizMzc1Fhw4dxIMHD0qt279/f9G4cWNx5MgRoVKphEqlEklJScLT01MMHDjwmfdKTk4WI0eOFLa2tqJOnTpi4sSJ4sKFC+LAgQPCz89PvPnmmxptZsyYIT766COhUqnK1J/ythNCiNdee02sXbtW5OfnC3t7e2kEJS0tTdjZ2anVfR6j2C9aeb439vb2IjU1VQjx6EOQFStWCCEeJQCl/aza2tqKkydPCiGEaNWqlVi4cKEQ4tGHpc/6kE2f1+NJb7zxhtYPr7R58umaTZs2iUaNGomlS5eKY8eOqZ07fvy4zms4OTmJmTNnapTPnDlTODk5Sfd5VmJIVJGePcnhX+SLL77AwIEDcf36dahUKmzatAkZGRlYsWIFfv75Z61tHj8//PT8kLKYMWMGZDIZBgwYgIcPHwIATExMMHLkyGfOBfrggw8QERGB9evXQyaTQaVS4dChQ/jkk090TuJ+LCUlRXq+XaFQYMCAAfj+++/h6uoKAJg3bx7GjBmD999/X6PtuXPncP78ebRp0wZmZmZlmkvwzTffoEePHoiJicHAgQPx+uuvA3j0XHhpz9S/8cYb2L59O0aPHg3gf/NTFi9eLM3n0Wbq1KkYOnQo9u3bh+bNmwMAjh49ioSEBCxatEhnu8fP+c+YMQMtW7YEABw6dAjjx4/XujDA81h4YNasWejVqxd27twpxfr777/j7Nmz2LhxY6nXfO+997B7926MGDGizHEAj+bB7du3D+fPn0ffvn1haWmJGzduwMrKChYWFjrb5eXlqc0neezvv//GvXv3dLb74osv0LdvX4SFhcHPz0967Xbv3g1vb2+d7d58802Eh4dj5cqVqFmzJoBHk6rHjx+v9edG13wqe3t7+Pj44IcffpDKdM2nAh6tDvjTTz9hyZIl2LNnD9544w1899136NOnD/7++29MmjQJ7733Hk6dOiW1cXBwwLlz51CvXj21ax08ePCZq5O5uLjg4sWLcHZ2hru7O9atW4e33noL27Zt07r62uTJkwEA9erVw/vvv6/36pyGvh4mJibSHLIaNWrgypUr8PDwgLW1Na5evaqzXdu2baFSqfDXX3/pteKVh4cHoqKisHjxYlSpUgXAo0nkUVFRz5xw3q1bN2zevBnjx49XK//pp5/wn//8p9RY9eHt7a31959CocCNGzfQqlUrqSw1NVWj3ty5czFw4ED4+vrCxMQEwKP/n926dcPs2bN13nfmzJlYunQpMjIy0KVLF6xYsQJdunSRXp/69etj2bJlGj+PwKOfyb1792Lnzp1o3LixdN/HNm3apPWehrYDgHHjxiE4OBgWFhaoW7cu2rVrBwA4cOAAmjRpolb3yUUvZs6cCUtLSyxfvlyac3Pnzh0MHjwYrVu31nk/AFi5ciUWLFiAixcvIikpCXXr1sXs2bNRv359nYs8jBkzBq6urhrz7b777jucO3dO52tSnu9Nx44dMXToUHh7e+Ovv/5Cly5dAAAnT57U+vo99vbbbyM8PBytWrXC77//jrVr1wJ4NJf38cJJuujzejzpm2++waeffoqvv/4aTZo00ejnkwvPNG3aVGOO6ZAhQ6R/PzkXUdfcps8//xwjR47E3r171eYN79ixAwsWLAAA7NmzR+//t0Qv0ku1mAPwaMLttGnTcPz4ceTn58PHxwdffPHFM1eDWrFiRannS0teHjx4gPPnzwN4NCH46WV0tSkqKkJoaCiWLVuGkpISGBsbo6SkBH379sWyZct0Li3bpEkTnDlzBgEBARg2bBi6du2qUffWrVuoUaOG2hua27dvo3fv3ti7dy9kMhnOnj0LFxcXDBkyBLa2tvj222+13k8IgatXr8LW1hYPHz5Um2B66dIlVK1aVWMy+mMHDx5E586d0a9fPyxbtgwffvghTp06hcOHD2P//v1o1qyZzu/P0aNHMXfuXLWJ3GPGjJGSEW2Kioowfvx4LFiwQGvi+vSiF08vPKBLaX8IAODatWv44YcfcObMGSnWESNGaF1I4MkVoe7fv4+ZM2ciKChI6x8tbZPrL1++jE6dOuHKlStQKpXSAhljx46FUqmU/vhoM2DAAPz222/49ttvpT9aR48exfjx49G6dWu1BTWelpWVhczMTLz++uvS9+3333+HlZUV3N3dtbY5e/Ysevbsib/++kv6Xly9ehWvvfYatmzZIiX2j5V1tTqZTKZzFarRo0djzZo1EEKgf//+GDp0KDw9PTX6UqtWLbX/H1FRUVi1ahWWLFmCjh07YseOHbh8+TLCwsLw+eefS8m+NrNmzYKRkRHGjBmDX375BV27doUQAsXFxZg5cybGjh1ban/0XYHqcR/0fT0CAgIwaNAg9O3bF8OGDcOff/6JMWPGYOXKlbhz5w6OHj2qtZ2hK179/vvv0vfi8eT7P//8EzKZDNu2bdNIlp/8v5GXl4cZM2agVatWUiJ45MgRHDp0CB9//DEmTZok1S3PSntPLtzwLI8TXG3OnTun9rvq6Z/tp7322msYMmQIBg0aBEdHR611ioqKsGbNGo0FYZ61QtjSpUu1lhva7rGUlBRcuXIFHTt2lD6Q2b59O2xtbaUPpp5Wu3Zt7N69W2MV1xMnTiAgIAA3btzQ2m7+/Pn44osvMG7cOHz11VfSwkzLli3D8uXLta5A+Ph+W7du1fjbkpqaim7duuHatWta25Xne5Obm4tJkybh6tWrGDlypLT4x+TJk1GlShVMnDhRa7srV67go48+wtWrVzFmzBhpVd6wsDCUlJQ8c/XAY8eO4erVqxqvh42NjVqC/6THvyue/nBAW8Jz+fLlUu//pNJW4T106BC+++47aSENNzc3jB49WufPDFGlU0EjWZWOjY2N2mFubi49K67Ps+b6unz5sti+fbtYu3at1rkuT5s2bZq4du2a3vfp37+/CAwMFFevXlV7tCghIUE0atRIZ7uSkhJhYmJSpti0OXfunBg6dKh48803hYeHhwgODtY6+fl5un//vvjzzz/Fn3/+Ke7fv/+P3ktf9erVK9Oha5GM7t27i379+gmlUqn2Ou7du1e4urqWeu/79++LkSNHCoVCIc3dqFKlihg5cmSp8zASExN1nittYr0QQqhUKrFr1y5pQv7u3bsNerylrB4vrlFYWKizTnFxscZjWCqVSkyfPl36fy+TyYSpqamYNGmS3jFcunRJbNy4sdRHUoR49BjZ22+/rTaXRp/5NPpKTk6WXsubN2+KwMBAYWlpKXx8fEqdyP3666+L9957T5w6dUrcuXNH5Obmqh2lyc/PFwsXLpTmpfz44486f9YM/b/xrEVZnpyzpMvDhw/F/v37xZ07d0rtz9OmTp2q9XfMgwcPxNSpU/W6VmVmaD8tLCyk+XtPSkxMFBYWFjrbeXh4SAt+PPl7Lj09vdRHyxQKhc45MWWd+/syex6PphK9al66ESXAsE9otTl79ixGjhyJ8ePHIzAw8HmGaJDybDjq4OCAXbt24fXXX4elpSWOHz8OFxcXXLhwAV5eXsjPz9fZtnHjxoiNjUWLFi3K2wWdnvcmro8/OXzWIwzPQ25uLn7//XetP3PPeoxSX3Z2djh8+DDc3NzUXsdLly6hUaNGePDgwTOvcf/+fbUR0KeX3n2ara0tfvnlF41PaefMmYPPP/9c62tXXFwMMzMzpKWlaYzolMXdu3dRUlIiLbn8WE5ODoyNjZ/rRr5PKioqwrlz55Cfn49GjRqV+ihjebVq1QrGxsaYMGGC1iV3Hz/iCgA9e/bEsmXLYGVlhZ49e5Z63dIeEzKEubk5jh8//sxRkoryvD75NmQPporcY+rvv/9W+5T+6eXbn2c7Q5eyN3QU28zMDGfOnEHdunXVfs+dPXsWXl5eKCgo0NrO09MTI0aMwKhRo9TK582bh/nz56s9bquNod/Tx1sLXLhwAevXry/z1gKG7KUIvPh9n6KiolCzZk21R+8AYMmSJfj7778RERGhsy03qqV/u5dqjpKhewTo8tprryE6Ohr9+vWTHqt6XoQQ2LBhg849dLS92TExMUFhYaFB97t//77WRwJzcnKeuQ9TdHQ0xo8fj/nz5+v9pvfxH4ILFy5g9uzZOv8Q2NjYPHOu1LNeR5VKhenTp+Pbb7+VEj9LS0t8/PHHmDhxosajdnPnzsXw4cNhamr6zMccdO0xs23bNgQHByM/Px9WVlZqfXg8f+15UqlUWvt/7do1WFpaluka5ubmem32GRMTg86dO+PAgQPSY13ffvstpk2bpnOfGBMTEzg7Oxu84e4HH3yArl274qOPPlIrX7duHbZu3YodO3aU2v7UqVNa9wrSNdds1apV6NmzJ6pWrYpGjRo9M77n8bOTlpaGlJQUnY/KPcna2lr62XqemyeXRfPmzXHu3DmDEqWVK1dKbyAfzzOZNWsWXFxc9NpMtKSkBOnp6ahbt67G/jL6br6ti6enJy5cuKBXoiR0zPE8fvy4RpKvbc7Hk8oy5wN49Lt89OjRWLFihfR3w8jICAMGDMC8efN0PvptaDt9+/mkBQsW4JNPPkHfvn2lvQiNjY0REhKic9No4NEcrbS0NI3XNiEhodQPCcP/r70zj6sxf///6yRHu7QIoU2WUpRsw1hi0FhG9kQUZpA2ET6IksIglD1btjLZBiONJUtISIutlWZsE1mmxai8f3/0O/f3nM65z1Y6Le/n43Eej7rPfZ37uu9zn/u+r/f7ul7X/PmYN28e8vPzYW9vD6CiH9eGDRvE1oxV5dgcP34cU6dOhbOzM+7fv880tf348SOCg4NZr1WVeymuXr0azZs3R0pKCvbs2cPaSxEAvLy8mL5PnTt3lroBOiBfU/adO3fiyJEjQsstLS2ZemtRyJu2S6HUKhQ1lfUtkLdHgDiSk5PFSlnLi6enJ2nSpAkZNmwYmTZtmtQyz6tXrybTpk0T6BUkDQ4ODkwakYaGBsnJySHl5eVk/PjxjCwpG9ra2oTL5RIlJSWioqIi0E9FXFpifHw8UVVVJYMHDyZcLpdJnwgJCRHapqSUAGnSAxYvXkz09fXJtm3bGDWerVu3En19ffK///1PaH1jY2Py9u1b5m9Z0+AIqVAf8vLykivFb8yYMSLlwdeuXUvGjRsn0mbChAlk1qxZhJD/+x7//fdfYm9vL1EevLCwkCxbtoz07t2bmJmZERMTE4GXONauXUsMDQ1Jbm4uWbNmDdHS0iI3btwQaxMREUF+/PFH8u7dO7HriaJZs2YiFdMeP35MdHR0WO2ys7OJtbW1UDoWL62NDT09PaKurk6cnJzIuXPnxKpdElI9544sClTVwevXr8mUKVNIy5YtSaNGjYRS/tiQV/Fq27ZtRE9PjwQFBREVFRXm979v3z4yYMAAsb56eXmRiIgIQkhFWtx3333H9HwSlcrFz5MnT4i7uzuxt7cn9vb2xN3dnVH7E4csPZiq2mNKXvlrQgj5+eefiampKfnjjz8Y386dO0fMzMzI7Nmzq9VOnv0URWFhIXO+iEvz5bF7925iaGhIoqKiiLq6Ojl69CiTGnv06FGxttu2bSOGhobMb9/ExISR/mdD3mNKiPytBarSS1Hevk9JSUlER0eHGBoaMr33WrduTXR1dcm9e/dY7Zo0acK0M+FHkipoVdJ2KZTaQr1KvVNXV5d6hLYylRXQCCF49eoVwsPD0aZNG5w/f7663AQA6Ojo4NChQ4xCjrQ4Ojri0qVL0NDQgJWVlVDaFFvaTXp6OgYNGgRbW1tcvnwZo0aNwsOHD1FQUICEhASYmZmxblNckT8AoWJjHr1798b48eMxf/58gfSJO3fuYMyYMayFtYDwqJeFhQVmzJghdtSrVatW2LFjh9CswenTpzF37ly8ePFC7H7w4P0kpBmlU1dXR1pamkRlNFHo6+vj8uXLQipFaWlpGDx4MN68eSNk8/fff2Po0KEghCAzMxN2dnbIzMyEnp4erl27xiqsAQBOTk64evUqpk6dKjLVS5LowKJFi7Bnzx6Ul5fj/PnzElMxbWxskJWVhdLSUhgZGQmdq6JUxHioq6vj9u3bIo9Nz549WVMMeeImERERMDExwZ07d/Du3Tv4+vpi/fr1rEpbZWVliI2NxdGjR3H69Gmoqalh/PjxcHZ2/mZFx5cvX8ayZcukUqCqDhwcHJCXl4d58+aJ/P7ZZnhEiZ5IM/thYWGB4OBgjB49WuD3n56ejgEDBuDt27esvrZu3RqnTp2CnZ0dTp06BXd3d1y5cgUHDx7E5cuXkZCQINLu+PHjmDRpEuzs7AREIJKSkhAVFYWxY8eybpN/P/mPjaj9PHDgAAghcHNzw6ZNmwSuS1wuF8bGxmKVPauCnp4eYmJiGKUzHleuXMGECRNEKlvKa6fI/Tx8+DBWrlzJpAm3atUKAQEBjOiBJPLz86GqqipV+qy8xxQA1NTU8OjRIxgbGwultVtYWLBmgWhoaCAtLQ0mJiZCadQdO3YUmz3SqlUrxMfHo3379hL3jZ/vv/8e7dq1w+7du6GsXJFQVFZWhpkzZyInJwfXrl0TaWdubo4VK1ZgypQpAssPHjyIFStWICcnR6RdbU/bpVCkoV6l3llYWIi9+Ypj9OjRAv9zOBzo6+vD3t6eVRGuKjRt2lSuh2ttbW2xN3s2OnfujIyMDISHh0NTUxOFhYUYM2YM3N3dWVWXeLAFQpJIS0sTOV3fvHlzsd/T3bt3MWzYMKioqDB57aGhoQgODkZcXBxsbW1F2hUUFIgMkjt27IiCggKJ/u7ZswehoaHIzMwEUHFz8Pb2xsyZM1lthg4dirt378r1XRYWFjKyyfw0btyYtWardevWSElJQVRUFFJTU1FYWIgZM2bA2dkZqqqqYrd3/vx5nDt3jlURiR9R6WSGhoZQU1NDv379cOfOHdy5cwcAe2pZ5d+ULPTo0QO7du1CWFiYwPIdO3aIVUu8desWLl++DD09PSgpKUFJSQl9+/ZFSEgIPD09kZycLNJOWVkZI0aMwIgRI1BcXIyTJ0/iyJEjGDhwIFq3bs08rFUngwcPBgAMGjRIYLmkAOTNmzdYsGABU59QeayLze7GjRu4fv06unbtKpOfubm5Mq3PbydKrrxJkyYoKioSa/v27Vu0aNECAPDHH39g/PjxaN++Pdzc3LB582ZWOz8/PyxZsgSBgYECy1esWAE/Pz+x1042JTVR8K6JJiYm+O6774SCXFFURzsCoEJllSe3z0/z5s3F1ijKYyfPflYXzs7OcHZ2RnFxMQoLC8UOAolC2voiQP5jCsjfWkBbWxuvXr0SSvVMTk6GoaGh2G36+vpi8+bNCA8Plynt7u7duwJBElBx7fPz84OdnR2r3axZs+Dt7Y3S0lKBlEY/Pz/4+vqy2lUlbZdCqS3U+UCJ/4FSlh4BleGvEeL9La18tDysXLkSAQEB2Lt3r8QHXH4kSbjyw18AHhkZiYkTJ7JKlUpCnqJTeW8EPj4+GDlypMhRL29vb9ZRry5duiA8PFzoIT88PFygMF4U/v7+2LhxIzw8PJgR0lu3bsHHxwd5eXlCD148hg8fjoULF+LRo0cizzlxDzxWVlaIjo6Gv7+/wPKoqCjWOpnPnz9DRUVFaGRPGpo1aya2noCf0NBQkcsbNWqEhIQEZkSfw+GwBkri5JQlERQUhMGDByMlJYUJJC5duoSkpCTExcWx2pWXlzO1Wnp6enj58iU6dOgAIyMjpkhbEmpqahg6dCjev3+P58+fM7Oa/LD1fBIFW88nWR7M+Zk+fTry8vKwfPlykTNDbLRp04a1PkYc8tYAyVtnAgAGBgZ49OgRWrZsidjYWGzfvh1AxQMtW+sEAHj16pXIusApU6aIrYkBZO/BBFTs46tXr1jf5xcQknbgQFLtRu/evbFixQpERkYyPbhKSkoQEBAgdnZHVrtPnz4x90wbGxuUlJSwiihU9+xnUFAQnJ2dYWJiAjU1NalabvCIiYnBsWPHRNYoss1iy3tMgYogwsvLC3v37gWHw8HLly9x69YtLFiwAMuXL2e1q0ovRXn7PmlpaSEvL09oQPGvv/4SW+O6cOFCvHv3DnPnzmWOqYqKChYtWoQlS5aw2nl4eMDX1xevX78WeX+UpVaWQlEYisn4qz746w/4pXXlkduNiIgglpaWhMvlEi6XSywtLcnu3bu/id/FxcVk6NChRENDg3Tu3JnpWs57VQeNGzcmL1++JIQQoqSkRN68eSPX58hSa8SPr68v6du3L3n16hXR1NQkmZmZ5MaNG8TU1JSsXLmS1U5FRYU8fvxYaPnDhw/FdiyPj48n6urqpFOnTsTNzY24ubmRTp06EQ0NDXLt2jWx+6inp0eOHDkitPzIkSNi5WjllSMmhJDff/+dKCsrExcXF7J//36yf/9+MnXqVKKsrMxI41ZGU1OTuLi4kLi4OFJeXi728ytz8OBBMm7cuFonmc5GcnIymTx5MrGwsCDdunUjrq6uEmXq+/btyxw7JycnMmzYMHLjxg3i4uJCLC0txdoWFRWRQ4cOEQcHB8LlcomZmRlZtmyZyHNxwIABAi8tLS2ipqbG/H7V1dWJlpYWGThwoNz7z4aGhgZJTk6W2e7ChQtkyJAhJDc3V2bbyMhI8t1335GWLVsyNTShoaHk1KlTrDZVqTNZsWIFadq0KenYsSNp27YtI/e+Z88e0qtXL1Y7BwcHsnfvXqHle/fuJUOGDJFmV0lRURF5/PixVLVYou430tR9VYXU1FTSqlUroqury9Rh6erqEkNDQ5Kens5ql5aWJpMd/z2DbT+/lZS9tbU1UVJSIr179yZbt24l+fn5Utlt3ryZaGhokHnz5hEul0t++eUXMnjwYNK0aVORdao8ZD02/MjbWuC///4jM2fOJMrKyoTD4ZDGjRsTJSUlMmXKFIk1kpVrmqWtcfbw8CCtW7cmUVFRJC8vj+Tl5ZGjR4+S1q1bEy8vL7HbJISQf//9l9y5c4ekpaWJbcHAg+2++K3OGwrlW1Dna5SuXr3K/P3s2TO0adNGaMTx69evyMvLE5tCxjajEB4eDh8fH9YZBXnhNX8dN24cDAwMhEaFxY3GSztiZm1tDVtbWwwcOBCurq7YsmUL68ifuBEseWuNRDXVLSsrg7Ozs9imugYGBjh48KBQk+ALFy7AxcVFZO0OUNHAT1lZGVu3bhVo/jp37lyUlZWJlYfX1tZGUlISzM3NBZZnZGSgR48e+PDhA6ttVTh37hyCg4Px4MEDqKqqwtraGitWrGAd3ealhJ07dw5NmzbFxIkTMWXKFLFpEzxsbGyQnZ0NQgiMjY2FRvfE1QzxIDLUb5WXlyM0NJT1XJUmHVJWLly4gKKiIowZMwZZWVkYMWIEMjIyoKuri6ioKKE0Nx6TJk3C2bNnoaamhgkTJsDZ2Vnq2ouNGzciPj4eBw4cYBTZ3r9/D1dXV3z//fdiU1PkkRW2sLDA4cOHRaa1iaNZs2YoLi5GWVkZ1NTUhL5/tu9D3uafQNXqTI4fP468vDyMHz+ekfnnHWO2mdodO3bA398fEyZMYGrobt++jd9++w0BAQFo1aoVs27lz8jPz4erqytrPaqoWZ6UlBSB/0tLS5GcnIyNGzdi9erVEqXc5aW4uBiHDx8WuM5Jk34ri93Vq1cZCXv++6wo5JmNk8TDhw9x+PBhREVF4e+//8YPP/wAZ2dnjB49mnWGqWPHjlixYgWcnJwE7lX+/v4oKChAeHg46/bkPaY85G0t8NdffyEtLQ2FhYWwsbERugdVJ7I2Za8qkqT7q0uxkkL5ltT5QIkfeXs9ABX5zFu2bIGTk5PA8qNHj8LDw0Pu2ic21NXVceHCBbE9FkSxZcsWLF26FNOnT8euXbvg6uqK7OxsJCUlwd3dHatXr2bW5XWxz87ORkFBATQ1NUU+4HI4HLEPrVUpOgVkvxF4enri5MmTWL9+PVNIn5CQgIULF2Ls2LGsMq9V+f49PDzQuHFjoTSpBQsWoKSkBFu3bhXrc03z77//IiYmBkePHsXly5dhamqKKVOmCKXx8RMQECD2M8UF55GRkfj111+Z+q327dtj4cKFmDp1KquNv78/IiIi4Ovri2XLlmHp0qV49uwZTp06BX9/f9aUvcp8/vxZKMiSJdWnoKCACWDYAjxePcTQoUPFpnaJwtDQEHFxcUIpqOnp6RgyZAhevnwp0o5fVvjgwYN49OgRTE1NER4ejj/++INVVjguLg4bNmzAzp07heoixCGvKEtVRBl4yFpnImlgiu08lzZdWlR6m7OzM54/f45NmzZhwIABOHnyJN68ecO0HBg+fLhUnw1UDID8+uuviI+PF/m+vPtXlV56dZmEhAQcOXIEv/32Gz5//sxaw6mmpobHjx/DyMgIzZs3x59//okuXbogMzMTvXr1wrt376rdN/7WAlVBnAR+dVNcXCzQS08a3+/evcs66FXdvdsolNpEna9R4oew9HooLCxk8o7ZKC0tFTkq361bN2bkpTpp06aNXHnd27Ztw65du+Dk5IT9+/fDz89PYMSMnz59+uD27dsAKh4gMjIyZC6IBWSrNZJUu8HzB2Cv3Vi/fj3Tg0jUqBcbbDE/2/fP7yuHw0FERATi4uKYkejExETk5eVJzBe/evUq1q9fL6DQx2uo+K3Q1NSEq6srXF1d8ejRIzg7OyMgIEBsoCRvzdDGjRuxfPlyzJs3jxGCuHHjBmbPno23b9/Cx8dHpN3hw4exe/duDB8+HCtXroSTkxPMzMxgbW2N27dviw2UiouL4efnh2PHjol8uGELen/99VcsXLhQYJmOjg6+fv0KZ2dnHD16lNVXefn06ZNIVaz8/Hz8+++/rHZBQUHYsWMHXFxcEBUVxSzv06cPgoKCWO0mTpyI4uJi5gFH2pkheUVZ5BVlsLe3x4kTJ6CtrS1QZ/Lp0yeMHj1abFPMkydPCvxfWlqK3NxcKCsrw8zMjPU8r9yPThYuX76M06dPw87ODkpKSjAyMsIPP/wALS0thISEyBQodejQAUlJSazvy7t/Vemld+DAAejp6TH74efnh127dsHCwgJHjx6VOLpfk421K6Ourg5VVVVwuVyxv6kWLVqgoKAARkZGaNu2LW7fvo0uXbogNzdX6P7w+++/w8HBAY0bN5YotCGu1tTHxwezZ8/GqFGjMGXKFKkHW7y9vWFlZYUZM2agvLwc/fv3x82bN6GmpoazZ88KKfDZ2tri0qVLaNasGWxsbMTO6kvKDlBTUxNSFBVHVFQUXFxcMHToUMTFxWHIkCHIyMjAmzdv4OjoKLBudR1XCqW2UC8CJd4DL4fDwfLlywVGR8rLy5GYmChR6Wnq1KnYvn270MP7rl274OzsXO0+b9iwAX5+ftixY4dMo8J5eXnMLIuqqipz05g6dSp69eolkFrAL+awb98+qRuSVkaWolM2VbHKiLvIc7lcbN68GSEhIVKNevF///7+/lJ//5V95amp8bapp6cHPT09PHz4kNXXQ4cOwdXVFWPGjGEe/BMSEjBo0CDs378fkydPZrWtSmra58+f8fvvv+PIkSOIjY2FgYGBUIBQXfC62vN/16NGjYKlpSVWrlzJGijxCniBilnJjx8/AgBGjBghtsgZqCgevnLlCrZv346pU6di69atePHiBXbu3Ck2WP7111+ho6MjkNpVXl6OSZMmIT09XWDd6mgaC1RI9ru6umLDhg2MSmNiYiIWLlwoNvXq6dOnIjvTN23aVGyqp7jGmZKQR5RFXlGG+Ph4oXMaqDh3r1+/LtZPUdeRT58+Yfr06UIPZpW5dOkSowjI/0DP4XCwZ88eVruioiJmIKlZs2bIz89H+/btYWVlxfrgWXlmg/z/thIrV64UO3Nelf1zd3fH2rVrERERIaBeJong4GBGFIOXVr5p0yacPXsWPj4+YmcFarqxNlARoB85cgRHjhzB06dP0b9/fwQEBGDcuHGsNvb29vj9999hY2MDV1dX+Pj4ICYmBnfv3hX6LY4ePRqvX79G8+bNxQptSBLXePXqFdNaYMKECVK3FoiJiWFEec6cOYOcnBw8efIEBw8exNKlS4Uk8H/66ScmNU4WRVH+5wBJqaBs50BwcDBCQ0Ph7u4OTU1NbN68GSYmJvjll1+EVHOr67hSKLUGRRVHVSe8gmoOh0O+++47gSLrIUOGkJ9//llkEbiPjw/z8vDwIJqamsTS0pLMmDGDzJgxg3Tu3JloaWmRefPmVbvP/E1cNTQ0pG7iamJiQu7fv08IIaRbt25kx44dhJCKQu3KdtUl5lCVotOaQN7vvzro2LEj2bhxo9DyDRs2kI4dO4q1Xb58OWnZsiVZv349UVFRIatWrSIzZswgurq6ZPPmzSJtYmNjiYuLC9HS0iI6Ojrk559/JlevXpXK17KyMvLrr7+S7t27EwMDA6nPuSZNmpDMzEyh5RkZGWKbDbZv357cvn2bEEJInz59SEhICCGEkKioKKKvry/W1zZt2jCNRXlCIIRUiAo4ODiw2t25c4doa2uT3377jRBCSGlpKXF0dCSdOnUir169Eli3OprGElJR/D9nzhzSpEkTpsidy+WSOXPmiG2uaWJiQv78809CiGDDyQMHDpBOnTqJ3aY8yCvKIqsoA0/8gMPhkCtXrggIIty/f58EBwcTIyMjufYhNTVVrO3KlSuJkpIS6dGjB/npp5/I6NGjBV7isLOzI7GxsYQQQkaOHEmmTp1K/v77b+Ln50dMTU1F2rCJB7Vt25bcvHmz2vePEEJGjx5NNDU1ScuWLcmQIUOYxqG8Fxuqqqrk+fPnhBBC/Pz8yNSpUwkhhKSnpxM9PT2x26xKY2156NmzJ1FSUiJdu3Ylv/76K/n777+lsisvLxdoxn706FHi4eFBtmzZQv77779v5S4DTxDmxx9/JFwul/W8IaTiuvrXX38RQgiZNWsWI6aQk5NTrU3up0+fTj59+kQIISKb20sjAqGmpsaIwOjo6JDU1FRCCCGPHj0iLVq0qDZfKZTaSL0IlHhMnz5dqIO6OCorV7G9voVyFU/ljO3FxowZMxjFuPDwcObBR1tbm7i5uQmsa2VlRaZNm0b2799POBwOCQsLIwcOHBD5kobnz5+Tc+fOkejo6G8WeFQFWb//6oDL5YoMIjIzM8UGEYQQYmpqSs6ePUsIqXhQzsrKIoRUKDc5OTmJtFFVVSUTJkwgp06dIl++fJHJV3kCM0IIsbS0JKtXrxZavmrVKtK5c2dWu0WLFjF2UVFRRFlZmbRr145wuVyyaNEisb6qq6szD3WGhoYkMTGREFLxEKGuri7W9tKlS0RTU5OcPn2ajBo1ilhYWJDXr1+LtakOCgsLmYBAXIDEIzg4mFhYWJDbt28TTU1Ncv36dXLo0CGir69PtmzZItY2KyuLLF26lEyaNIkZAPnjjz/EKnT16tWLbNiwgRAiGJglJiYSQ0NDsds7dOgQadeuHaNcZWhoSCIiIkSuyx88iFK9UlNTI3v27BG7PTauX79OtLW1Wd9v0aIFiYyMlOuzDx48SPbt20cIIeTu3btET0+PcDgc0qRJExIVFSXSJj4+XuB17do18vjxY4GHdVmQtH+EyK94pq+vzwyyde3alTlOWVlZEn9TampqzPlSE/zvf/8jDx8+rLHtHThwQKSK23///Sf1/ZFHfn4+CQsLI5aWlmKV3dq2bUsuXLhAysrKSJs2bZh7QXp6usRzoKYxNDRkgiMrKytGIfbmzZtES0tLka5RKN+cehUoNQQqj5hFRUUxI2aVH5wTEhJIz549iZ6eHlFSUiJNmzYl2traQi9xswmV+fr1K/n69Wu17U9dx8zMjJnV42f79u2kXbt2Ym3V1NSYYKBFixbk3r17hBBCsrOzRd58SktLyZYtW4RmRqRFnsCMEEJiYmJIo0aNyNChQ0lgYCAJDAwkQ4cOJcrKyuTEiRNSb//WrVtkw4YN5Pfff5e4rpWVFYmPjyeEEDJo0CDi6+vL+CrpoZ4QQk6ePEmUlZWJlZWVVNLCAQEBIkfLi4uLSUBAgER7QiqC49jYWFJcXEwIIRJ/J/LKCss7M6Surk5ycnIIIYKBUm5ursSgnkdRUZHEmelnz56R3NxcwuFwSFJSEnn27BnzevnypVSz0Js3bxZ4bdq0iSxatIi0atVK7Lmqo6PDnNdV4evXr6SoqIjcu3dPqvPn4cOH5Pz58+T06dMCLzbk3b+qMHnyZGJra0tmzJhB1NTUmNnU06dPS5TOd3R0JNHR0d/Er+pkxYoVItsmfPjwgUyaNInVji3j4u3bt1LJWMvSWoDfV3kk8AkhzH278ktHR4e0atWK9OvXT6RM/sCBA8n79++Fln/8+FHsgLCTkxMzyBIYGEj09fXJzJkziZGRkdhZzICAALEvCqUuUK9U7+oqsqp6ff78GampqSJz8EeOHCnSRklJCa9evRLZfVwa9uzZg9DQUEb1zNzcHN7e3pg5c6Zcn1df2L59O7y9veHm5iag0Ld//35s3rwZv/zyC6tthw4dEBkZiZ49e6Jv374YMWIEFi9ejOjoaHh4eOCff/4RsuFXdZIVdXV1PH78GG3btkXLli1x7tw52NraIicnBzY2NkwNkSju3buH0NBQRrCiU6dO8PX1FStRHRISAgMDA7i5uQks37t3L/Lz87Fo0SJW29DQUDRq1Aienp64ePEiRo4cCUIISktLsXHjRnh5eTHrsuXd3759G+3atYOenh6zjC0HvyqKie/evWPk/jkcDjIzM2Fqago3Nzc0a9YMGzZsYLUFZJcVlleuv3Xr1jh27Bi+++47AbuTJ09iwYIFTG1edcEvL81PeXk5EhISRNZn8agsHKOkpAR9fX3Y29tjyZIlrPWWixYtgoaGhsQaODZkvc7l5ORgzJgxSE1NBYfDEZLP5z9vUlNT0blzZygpKcm9f4BgM1ZZ+PDhA5YvX468vDzMmTMHw4YNA1Ah8sLlcsU2I9+zZw8CAwPh6uoqc2NtaamOJs5t2rRBmzZtcOjQIZiamgKoqJVzcXFBixYtcOfOHZF2SkpKePPmDfT19QWWp6SkYODAgWLrRavSWkAeCXyg4vq4evVqODg4MHWRd+7cQWxsLHx8fJCbm4uDBw8iLCwMs2bNEthPXv0QP//88w8MDQ1RWloqcnsFBQX4/PkzWrVqha9fv2LdunW4efMmzM3NsWzZMlaVvsr3h8qiJdK0pKBQFE29EHOoixQVFWHRokUyq3rFxsZi6tSpIm3EFUfm5uaCy+Viw4YNzMOupaUlZsyYIVF9j63HlI+PD/Ly8qq9x1RdYs6cOWjRogU2bNiAY8eOAagIIqKjo/HTTz+JtXV0dMSlS5fQs2dPeHh4YMqUKdizZw/y8vJYBRJ69OiB5ORkuQKl1q1b49WrV2jbti3MzMwQFxcHW1tbJCUlSeyf0a1bNxw6dEim7e3cuRNHjhwRWm5packIhLDBv/+DBw/GkydPcO/ePbRr106om3vTpk1FfsbQoUOl9pWwKGampKRAR0dHrK2Pjw8aN26MvLw8AXGDiRMnYv78+RIDJS6XCwsLC3z69AkXL15Ehw4dxIokpKWliTyuzZs3FyvVLYsoCz/v3r2Dv78/rly5IlLxjO0h0t7eXmTw+eHDBwwcOFBs8Jmbm8v6XmX4H7C/fv2KXbt24eLFi7C2thZ6oGd7wAbku855eXnB2NgYFy9ehImJCRITE1FQUABfX1+sX79eYF0bGxuB45GUlCQQxEvLb7/9hhUrVqBnz56YMmUKJkyYIPFzysrKsGXLFixatIh5IOchqW0AAOZhW9QxqK6i/OoQAkpNTcUvv/yCrl27YsOGDcjIyMDmzZuxcOFCkfvJU4/jcDgYNGiQQFBfXl6O3NxcJqBko1GjRjh27JjMrQX4j+XevXsF3nv+/LnYQOnGjRsICgrC7NmzBZbv3LkTcXFxOH78OKytrbFlyxbMmjULqampzDqPHj3C69evmf/Ly8sRGxsrpGDLD/81UElJCYsXL5a8g6iaaAmFUmtQ6HxWA2bu3LmkU6dOJCYmhqiqqpK9e/eSVatWkdatW5NDhw6x2rVr147MnTtX5pqLpKQkoqOjQwwNDZmi39atWxNdXV1y9+5dsbZ6enpMTjI/R44cIbq6ujL5QWHn5s2bElPToqOjiampKQkLCyM3b94UKJRPSUkR+/lVqRkqKysjMTExZNWqVWTVqlXkxIkTElOomjRpwqR68ZOdnS11qte3hpfCoqSkJJTOoqWlRZSUlMjcuXPFfoaBgQF58OABIUQwpS07O1ts7cf48eNJWFgYIaQixa99+/akcePGRFlZmcTExLDaGRoakoSEBKHtnThxQmzxuChRFg6HI1GUxcHBgZibm5M1a9aQffv2SV1PyeFwyD///CO0/OnTp9VarF5dtabyXOd0dXWZ352WlhZ58uQJIaSiTq5r164C6+ro6DDiJkpKSiKPjbSkp6eTJUuWEBMTE9K4cWPy448/ksOHD4sVW1BXV2cK8us7S5YsYc7xixcvsq63cuVKsnLlSsLhcMiCBQuY/1euXEmCg4PJkSNHvpkIRNeuXQVelpaWRE1NjWhpaREbGxuxturq6qy1sbxrTlZWFlFTUyOEVE/doDz3ADakES2hUGoLdEZJQZw5cwaRkZEYMGAAXF1d8f3336Ndu3YwMjLC4cOHWSXJ37x5g/nz58ucQufj44NRo0Zh9+7dzKhZWVkZZs6cCR8fH1y7do3VtqZ7TNUlkpKS8PXrV/Ts2VNgeWJiIho1aiTyuLHRu3dviSkbkyZNAiAoV81L+ZE0sssvqz1x4kQYGRkx6RNsKZsAkJWVheHDh+Pvv/9Ghw4dAFSk1bVp0wbnzp2DmZmZSLs2bdogISFBKEUoISEBrVq1ElpfkkQ3P2xy3bm5uSgrKxOSZs7MzETjxo2FpPg3bdoEQgjc3NwQEBAgMDvF5XJhbGws8TspKioSKV1fUFAgdqbu2rVrTLrTyZMn8fXrV3z48AEHDhxAUFAQxo4dK9JO3pkhLpeL3bt3w9/fH2lpaSgqKoKNjQ3atWsndv+uX7+OGzduoEuXLmLX48FLheRwOJg+fbrAMSgvL0dqaqpY2WRZuXLlSrV8jjzXufLyciZNTk9PDy9fvkSHDh1gZGSEp0+fCqw7duxY9O/fn5FTtrOzY52ByMnJEeurpaUlgoODERwczDRj9fb2xuzZs1mbsQ4aNAhXr16VqR0FD3FZA7y2HLWFsLAwbN68GU5OTrh37x48PT1x5MgRkecvr7ecsbExJk2aJHFmnUd1tBaoymyLjo4Ozpw5I5R5cObMGWb2p6ioiDk3eX2keCm6/CmGXC4XzZs3FzsbJu89gI2PHz+KTfWmUGoTNFBSEAUFBUwOtZaWFpO+0rdvX8yZM4fVbty4cYiPj5f5wnT37l2BIAkAlJWV4efnJ/FhvqZ7TNUl3N3d4efnJxQovXjxAmvXrkViYqJY+6dPnyIsLEyg9sfDw4O5GVVGlpQkfkpLS/HLL79g+fLlTODSq1cvprmuODw9PWFqaopbt24xN+F3795hypQp8PT0xLlz50TazZo1C97e3igtLYW9vT2Aih43fn5+8PX1FVo/NDRUqn3hcDisDx/Tp0+Hm5ubUKCUmJiIiIgIxMfHCyznNWE1MTHBd999J5SqJQ3ff/89IiMjsWrVKsY/Xh7/wIEDWe0+fvzIHM/Y2FiMHTsWampqGD58uNieWMHBwXB3d0ebNm1QXl4OCwsLlJWVwdnZGcuWLRPrqzy1hh07dkRJSYnYz+WHF2wSQqCpqQlVVVXmPS6Xi169egnUTdQW5LnOde7cGSkpKTAxMUHPnj2xbt06cLlc7Nq1i7m+83/OmDFjkJWVBU9PT8yaNUvu3nb8SNuM1cHBAYsXL0ZaWhq6desGdXV1gffFpXpJapBbHYFSdfT7GTZsGJKSknDgwAGMGzcOJSUlmD9/Pnr16oWAgAD4+fmJtLOwsMCDBw+kHvAKDQ2Fs7MzVFRUxF63xF2rRKGlpYWAgACMHDkSU6dOZV1v+fLlmDNnDq5cucLUKCUlJeGPP/7Ajh07AAB//vkn+vfvDwBMqra8DZnlvQdUDiLJ/+8zdvDgQTg4OMjlC4VS4yh0PqsBI6+qV1FREfnxxx/JtGnTyPr164XUk9ho3rw5uXDhgtDy2NhY0rx5c6HliuwxVZdQV1cXKZubk5NDNDQ0xNrGxMQQZWVl0qtXL+ZY9+7dW2LqlbxoaWmJTIWThJqaGiMNy8+DBw/EppZ9/fqV+Pn5ERUVFSbtQ01NTWa1I1mUFvl7LvGTmZlJmjZtKtVnlJSUkI8fPwq8xJGWlkaaN29Ohg0bRrhcLhk3bhzp1KkTMTAwEKvAZm5uTqKjo0lhYSHR19cnly5dIoRUHFdpUlrz8vLIuXPnyLFjx0Tuc2WWL19O1NXVyeLFixlVtsWLFxMNDQ2yfPlyVrs7d+4Qe3t7Eh8fT96+fSv1sVm5cqVUMum1hXnz5hEtLS3W6xz/NZFHbGwsOX78OCGk4hzr0KED4XA4RE9Pj/k+RcHf20YecnJySFBQELGwsCCNGjUi9vb2JCIignz48IHVRlTKFe8ljbJbZT5+/EgcHR3llmOvDP8xkVcCffDgweTFixdCy8+ePSu230/37t2Z3mv8HD9+nPTo0UOOvZEfaSTiCSHkxo0bZNKkScTGxobY2NiQSZMmMSm5kpBVpVHee0DlnnSmpqakZ8+eZMmSJVU6/ymUmoSq3ikIWVS9+NmzZw9mz54NFRUV6OrqCnVIZ0vZ8PT0xMmTJ7F+/XoBdbaFCxdi7Nix2LRpk8D64kbC+eFwOLh8+bJU69ZHdHV1cfbsWaH0rJs3b2L48OF4//49q62ZmRmcnZ2F0lpWrFiBQ4cOsaqQHTx4EDt27EBubi5u3boFIyMjbNq0CSYmJmIFJKZNm4auXbuyCkWwoaOjg7NnzwqlSyUkJGDkyJFiFaEAoLCwEI8fP4aqqirMzc2lTm+RZ/ajadOmiI+PF1JbunfvHgYMGMA64l5cXAw/Pz+ZxVV4fPjwAVu3bkVKSgoKCwtha2sLd3d3oa71/Gzbtg1eXl7Q0NBA27ZtkZycDCUlJYSFheHEiRNiU8rkOTb6+vrYsmULnJycBJYfPXoUHh4erEIQmZmZmDx5spBCFZEi3bMuUV3XvIKCAjRr1kys6EBV6NWrF5KSkmBtbQ1nZ2c4OTmJLcT/lqSlpWHkyJF49uxZtX5uSUkJvn79ysx6PXv2DKdOnUKnTp0kirRcv34dO3fuRHZ2NmJiYmBoaIiDBw/C2NgY33//vUgbDQ0NpKamCs0C5ubmwtraWui6Ia1CH4fDYRVzETfb0r9/f5GCLVUlJycHjo6OSEtLk6jSyE9V7wEUSp1GkVEa5f949uwZOX78uMSCfAMDA7J69WqRvSLE8d9//xFPT0/C5XKZ0f0mTZoQb29vkY32KNIxadIk0r9/f4GR3Pfv35P+/fuT8ePHi7VVVVUVOROQkZFBVFVVRdps27aN6OnpkaCgIKKqqsrMZu3bt48MGDBA7PZWrVpFtLW1ydixY0lwcLDUs5FTp04llpaW5Pbt28zszq1bt0jnzp3JtGnTxG5TXuSd/RgxYgQZP368QJFxWVkZGTt2LBk2bBirnbziKjxKSkpIYmIiOXPmjNSjtIRUiKycOHGC/Pvvv8yys2fPkhs3brDayHtsmjZtKrJR9NOnT8XOtnXv3p307t2bREVFkStXrgg1WeXHxsaGFBQUEEIqitV5o92iXhT5qI5mrCUlJdXii7SzH7Lyww8/kO3btxNCKq6nBgYGpHXr1kRFRYVs27aN1Y73+505cyZp0qQJc30MCwsjDg4OrHY6Ojrk5s2bQssTEhJE7l91CIhUdbaF13TayclJ6qbTI0aMID/99BPJz88nGhoa5NGjR+T69eukR48e5Nq1a6x2irgHUCi1BRooKZCLFy+SJUuWkBkzZhBXV1eBFxvNmjWrUkPFoqIikpqaSlJTU8UqJFGk4++//yampqakadOmzM1RW1ubdOjQgeTl5Ym1dXBwENkUcO/evWTIkCEibTp16kROnjxJCBFUPEtLS5OYrlX5xsz/MjExYbV7//49GTVqFOFwOITL5RIul0s4HA4ZPXq0yOaF1YG8SosPHz4kurq6xMzMjEnTMTMzI3p6eiQtLY3Vrk2bNuTKlSuEEMH0vcjISLEPWIQQcv78eaapszwpTf/99x958uSJQCNpcch7bHjpY5Xx9fUVq+ynqqrKqLlJYuXKlcx1hV9BTNSLUjVkPW/KyspIYGAgadWqFWnUqBFz7Vi2bBmJiIgQa1vTDXJ1dXWZB/7du3cTa2trUl5eTo4dO0Y6duzIate1a1dy4MABQojg9fH+/fvEwMCA1a4qA16KQN6m07KoNPIj6h6gpKRERo8eLTbds7CwkCxbtoz07t2bmJmZERMTE4EXhVIXoGIOCiIgIACBgYGws7NDy5YtpU7TmDZtGqKjo/G///1Pru2qqanByspKJpvPnz8jLCyMtY9KQ24aZ2hoiNTUVBw+fBgpKSlQVVWFq6srnJycRAoD/P7778zfo0aNwqJFi3Dv3j1GVOH27dv47bffWHub5Obmimzy2qRJExQVFYn1lV8IglRKuRCHtrY2Tp8+jaysLAHRCUlqaVVBXqVFCwsLpKSkMGlwqqqqcHFxwbx588T2Q5JXXAUAPDw8MGHCBPj7+8ukRllcXAwPDw8cOHAAAJCRkQFTU1N4eHjA0NCQtVdJVVQo9+zZg7i4OOZ8S0xMRF5eHlxcXATSifgFDezs7PDXX3+xCozww1MRq/w3pfooKSnBvHnzZD5vVq9ejQMHDmDdunUCYhqdO3fGpk2bMGPGDNZtVhYs4DXInTZtGpYsWVINeyVIcXExI3QRFxeHMWPGQElJCb169cLz589Z7Z4+fSqykXHTpk3x4cMHVrv169ejX79+MDIyYq6vDx48gIGBAQ4ePFi1nfkGLF68GEFBQUzTaR729vYIDw9ntZNFpZEHIQSfPn1CVFQUXrx4IdM9YObMmbh69SqmTp0q03MOhVKboIGSgtixYwf2798vVtlGFOXl5Vi3bh0uXLggc0NFeZkxYwbi4uIwbtw49OjRg17sKqGuro6ff/5ZqnVHjx4ttGzbtm3Ytm2bwDJ3d3ehZoJAhTrbgwcPhBrOxsbGim1SykPa2hZJOfj89TPf4pyritKirq4uRo8eje+++44J6m/cuAGAXdnL1NQUubm5aNu2LTp27Ihjx46hR48eOHPmDLS1tcVuT17J/iVLliAlJQXx8fECTS0HDx6MlStXsj7wynts0tPTYWtrCwBM/Zuenh709PSQnp7OrFf59+3h4QEvLy8sXLgQVlZWQtecyg2ARVFYWCg0wCKp0TVFNIsXL5brvImMjMSuXbswaNAggWtLly5d8OTJE7HblFdtU17atWuHU6dOwdHRERcuXGDqKv/55x+x502LFi2QlZUlJIF+48YNofojfmQd8FI08jadlkWlkQchBO3atcPDhw9hbm4u0wDZ+fPnce7cOfTp00dqGwqltkEDJQXx5csXuXqJpKWlMSNe/A83gHSzA/Jw9uxZ/PHHH/Rix8LBgwexc+dO5OTkMOIKoaGhMDU1FRJXkFeelcf8+fPh7u6Oz58/gxCCO3fu4OjRowgJCUFERIRYW39/f2zcuBEeHh6M+MStW7fg4+ODvLw8AVEJUT0+RFGd5xx/cMbhcBAREcE6+8FGbGwsXFxc8O7dO2bWjP8z2YqVXV1dkZKSgv79+2Px4sUYOXIkwsPDGXEVccgr2X/q1ClER0ejV69eAsfR0tKSVciDhzwzQ/L2G5o4cSIAwM3NjVkmTe+u3NxczJs3D/Hx8fj8+TOzXJIdRTzynjcvXrwQ+ZD79etXlJaWfhNf5cXf3x+TJ0+Gj48PBg0axFyv4uLiRM6o85g1axa8vLywd+9ecDgcvHz5Erdu3cKCBQskSpjLMuClaLS1tfHq1SuhHnXJyclihT2WLVvGZB4EBgZixIgR+P7776Grq4vo6GiRNkpKSjA3N8e7d++E2i5IolmzZmJn8imUugBVvVMQixYtgoaGRq1q1MeGhYUFoqKipBo5bmhs374d/v7+8Pb2RlBQEB4+fAhTU1Ps378fBw4cqLZmmPwcPnwYK1euZB6KDA0NsXLlSrGpM4D8qmc1RXWojpmbm2PIkCEyp8FV5vnz57h37x7atWsn8bwvLi7G+PHjoa+vL3LGha2PipqaGtLT02FqagpNTU2kpKTA1NQUKSkp6NevH2tDxppWpBSX6gRAaHaTR58+fUAIgZeXFwwMDISCal6PF4psyHvedOvWDT4+PpgyZYqAXWBgIP78809cv369hvdEPK9fv8arV6/QpUsXKCkpAQDu3LkDLS0tdOzYUaQNIQTBwcEICQlBcXExgIq05AULFjB9ztiQZcBL0SxYsACJiYn47bff0L59e9y/fx9v3ryBi4sLXFxcZEp7lUal8cyZM1i3bh22b9+Ozp07S/3Zhw4dwunTp3HgwAGRTbkplLoADZQUhJeXFyIjI2FtbV1jKXTycv78eWzZsgU7duxgfShqqFhYWCA4OBijR48WePhIT0/HgAEDJAYfSUlJrLVfos6BkpISEEKgpqaG4uJipKenIyEhARYWFhJlc7W1tZGUlCQ0KpiRkYEePXqIzeGvK2hpaSE5OVnm2Z3IyEhMnDhRSLr8y5cviIqKEjuLJa9kf79+/TB+/Hh4eHhAU1MTqampMDExgYeHBzIzMxEbGyvTPtQ2NDQ0cO/ePalqmyjSI+95c/r0aaamKDAwEAEBAXj69CkiIyNx9uxZ/PDDDzW8J9+OL1++ICsrC4WFhbCwsICGhobY9RUx4FUVvnz5And3d+zfvx/l5eVQVlZmmk7v378fjRo1ErIpLS2FqqoqHjx4IFOwA1TMDBUXF6OsrAxcLlegiTQAVnlwGxsbZGdngxACY2NjoeechlzfTKk70NQ7BZGamoquXbsCqLkUOnmxs7PD58+fYWpqCjU1NaGLXUPuoVAVcYXg4GAsW7YMHTp0EBpxZzsHfvrpJ4wZMwazZ8/Gly9fMGrUKDRu3Bhv377Fxo0bxQoPVKXup64gbxqcq6srhg0bhubNmwss//fff+Hq6io2UFq6dCkCAgKwePFiZuRbGoKDg+Hg4IBHjx6hrKwMmzdvxqNHj3Dz5k1cvXpVJv+/NfL07urevbvUIhAU6ZH3vPnpp59w5swZBAYGQl1dHf7+/rC1tcWZM2fqVZAEAFwuFxYWFlKvHxYWht27d2P06NFYs2YNs9zOzg4LFiz4Fi5WCS6Xi927d8Pf3x9paWkoKiqCjY2N2Pqhxo0bo23btnKlvFbusygtompyKZS6Bg2UFERtG6ESh5OTE168eIHg4GCRKTQNmaqIK2zevBl79+7F9OnTpd7e/fv3GQWqmJgYGBgYIDk5GcePH4e/v79QoFQddT91ifDwcIwfPx7Xr1+XKQ2OVzdTmb///htNmzYVu80vX75g4sSJMgVJQIWiXkpKCkJCQmBlZYW4uDjY2tri1q1bMitTfkv4R9tXr17NPGhpa2tj06ZNrIFSREQEZs+ejRcvXqBz585yiUBQhOnbty8ePHiANWvWyHzefP/99/jzzz9ryNO6Q1UGvBSFPE2nly5div/97384ePCgTLVD06ZNk8tHqnxJqQ/QQIkikZs3b+LWrVvo0qWLol2pdVRFXEFJSUlmgQxZZXMrizJ069YNgLDq2cOHD2Xyo7Zy9OhRxMXFQUVFBfHx8UKzdJUDJRsbG3A4HHA4HAwaNAjKyv93SSwvL0dubq6Aspgo5JHsLy0txS+//ILly5dj9+7dUtspAnlH2/Pz85GdnQ1XV1dmmTQiEBTJmJmZ1frzpi5RVTXRmkYWYR5+wsPDkZWVhVatWsHIyAjq6uoC74tLhSsvL8fJkycZeXALCwv89NNPAtdMCqU+Qs9wikQ6duyIkpISRbtRK5k5cyZUVVWxbNkyFBcXY/LkyTA0NMTmzZsxadIksbY+Pj7YunWrTGkNssrm1qWZy+pA1jQ4XmrIgwcPMHToUIFaBi6XC2NjY4wdO1bsZ8gj2d+4cWMcP368Toi5yDva7ubmBhsbGxw9epTORFczX79+RVZWlsjaRv4+QpKK9PlpyCnUVRnwUgTbt2/H7t27BYR5Ro0aBWtra3h4eLAGSvKmwj18+BCjRo3C69evmVTatWvXQl9fH2fOnBGoedLR0UFGRgb09PQknn8N+Zyj1B1ooESRyJo1a+Dr64vVq1eLTGdqyP1QSkpK4OjoCGdnZwFxhdatW0u0XbBgAYYPHw4zMzNYWFgIHdcTJ04I2cgrm9tQkDUNjpcaYmxsjIkTJ0JFRUXmbcor2T969GicOnWKCXZrK/KOtj9//hy///77N21M3BC5ffs2Jk+ejOfPn0uUwOcfhHn37h2CgoIwdOhQgVmICxcu1ImA/VsiasCrVatWUg14KQJ5m07Lmwo3c+ZMWFpa4u7du2jWrBkA4P3795g+fTp+/vln3Lx5k1k3NDSUyXoIDQ2lAySUOg9VvaNIhPfQWfmCR1NogCFDhjDiCh8+fEDHjh2lFleYN28eIiIiMHDgQJEj7vv27RNpJ49sbkPBx8cH+vr6MqXB8fPlyxeRo/Rt27atDvcECAoKwoYNGzBo0CB069ZNKA2GrZ6qpomIiMDKlSuxYcMGzJgxAxEREcjOzmZG29keJEeOHInp06dLnJGjyEbXrl3Rvn17BAQEoGXLlkLXDbaaurFjx2LgwIGYN2+ewPLw8HBcvHgRp06d+lYu12rKyspw5MgRDB06FAYGBiguLkZhYaGQsEttwsPDA40bNxaarV6wYAFKSkqwdevWat2eqqoq7t69C0tLS4Hl6enp6N69O804odRraKBEkYgkBa6G3A9FT08PV69ehaWlJSIiIhAWFiYgrsDL5xaFpqYmoqKiMHz48Br0uH7j6emJyMhIdOnSRSbZ/czMTLi5uQmMjALfdjCgcrNIfsTJiiuCyr27WrVqhYCAALG9u3bt2oWgoCC4ubmJnIkeNWrUN/W5vqKuro6UlBSZZ+o0NDTw4MEDIbusrCx07doVhYWF1elmnUJNTQ2PHz+u1e0v+IV5ysrKsH//frRt21akME9YWJjIz2BLheNwOFBRUUG7du0wffp0gbpCAOjSpQtCQ0Nhb28vsPzy5cvw8vJCWlqayO01atQIr169Ego63717h+bNmzfoQVZK3YGm3lEk0pADIUnIKq7Aj46Ojswy1hTxyJsGN336dCgrK+Ps2bMiR+m/Bbm5uczfvPGq2pqm4uzszKSXSjvaPnv2bAAQWS/R0Geiq0LPnj2RlZUlc6Ckq6uL06dPw9fXV2D56dOnoaurW50u1jl69OiB5OTkWh0oVYcwj7+/P1avXg0HBwf06NEDQEU2QmxsLNzd3ZGbm4s5c+agrKwMs2bNYuxCQkLg6emJlStXMoHZ7du3ERgYiLVr1+LTp0/Muvyp+Gzj8P/99x+4XK4su0+hKAw6o0SRyLVr18S+z1883NCwtrbGzJkz4ejoiM6dOyM2Nha9e/fGvXv3MHz4cLx+/ZrVdt++fYiNjcW+ffto13IFo66ujnv37tV46qI8Er+Uhkdqairzd3Z2NpYtW4aFCxeKnKljk13fv38/Zs6cCQcHB/Ts2RNAxSxEbGwsdu/eLVObgvrGsWPHsGTJEvj4+IhMg60vUvZjx47FDz/8wAxi8Ni5cyfi4uJw/PhxhIWFYdeuXQKzRPw1n7zBnMqDO/yz71u2bAFQkQq9atUqAZGc8vJyXLt2Dc+ePRMK/iiU2ggNlCgSEVUYzz/y3ZBHhmNiYjB58mSUl5dj0KBBiIuLA1AxAnft2jWcP3+e1ZZ2La89dO/eHaGhoejbt2+NbZNN4jc8PBw+Pj6sylU1DU9CvTKV03UGDhyoAO8aBkpKSoy0uiiklV1PTEzEli1bmJTgTp06wdPTkwmcGips97j6VocrTfpldnY2rK2tBRQtZWmA3b9/fyat+Pnz52jdujUaNWrEvM9TEw0MDGzw5x2lbkBT7ygSef/+vcD/paWlSE5OxvLly7F69WoFeVU7GDduHPr27cuIK/AYNGgQHB0dxdrSruW1h7Vr18LPzw/BwcE1puwor8RvTTNs2DBs374dVlZWTLpOUlISUlNTMX36dDx69AiDBw/GiRMnhJrPXrp0CaGhoQIP5t7e3hg8eHCN70ddhj9Nsyr07NkThw8frpbPqk9U1/Gt7ejo6ODMmTNCSptnzpxhGtAWFRUx6eQ8ZE2/5x3PgQMH4sSJE4xSHoVSF6EzShS5uXr1KubPn4979+4p2hUKpUooQtlRW1sbSUlJMDc3F1iekZGBHj164MOHD9W+TXmYNWsW2rZtKyQhHRQUhOfPn2P37t1YsWIFzp07h7t37zLvb9u2DV5eXhg3bhwzY3b79m3ExMQgNDQU7u7uNbof9YWQkBAYGBjAzc1NYPnevXuRn5+PRYsWsdpK23+pIVFaWoqOHTvi7NmztbK5bHWye/duzJkzBz/++KPAoMcff/yBHTt2YMaMGdiwYQPu3LmD6OhoAdv3799jz549Ag1nXV1dmQCLQqmv0ECJIjdPnjyBnZ1dg1ZLotQPFKHsWNMSv/LStGlT3Lt3T2S6Trdu3fDx40c8efIE3bt3x7///su837p1ayxevFhIjnrr1q0IDg7GixcvasT/+oaxsTGOHDmC7777TmB5YmIiJk2axDo7Ikv/pYaGoaEhLl68WO8DJQBISEhAeHg4nj59CgDo0KEDPDw8hM4nfq5du4aRI0eiadOmTP+me/fu4cOHDzhz5gxrkF1eXo79+/fj0qVLIoPzy5cvV9NeUSjfDpp6R5EIfyExUDHK/urVK6xZswZdu3ZVjFP1AF7dARsN+cGlplGUsuOePXsQFxcnUuKXXw6YTda8JlBRUcHNmzeFAqWbN28yDXq/fv0q1Kz3w4cPGDZsmNDnDRkyROysB0U8r1+/RsuWLYWW6+vr49WrV6x2s2fPhp2dHc6dO1djyo51BXd3d6xduxYRERFQVq7fj0V9+vRBnz59ZLJxd3fHxIkTsX37dqbeqLy8HHPnzoW7uzurPLiXlxf279+P4cOHo3PnzvSco9RJ6vcVgVItdO3aVWQhca9evbB3714FeVX3OXnypMD/vNqvAwcOICAgQEFeNVyuX7+OnTt3IicnB7/99hsMDQ1x8OBBmJiYfBORh/T0dNja2gIQlvjllzZX9MOFh4cHZs+ejXv37qF79+4AKtJ1IiIimMa+Fy5cEBo0GTVqFE6ePImFCxcKLD99+jRGjBhRI77XR9q0aYOEhAShPlwJCQlo1aoVq11mZiZiYmJklhVvCCQlJeHSpUuIi4uDlZWVkOrdiRMnFORZ1fn06RNTY8kv4y0KtlrMrKwsxMTECIgyNGrUCPPnz0dkZCTr50VFReHYsWP48ccf5fCcQqkd0ECJIpHKqRxKSkrQ19cXGkGmyEblwnegQhzC0tIS0dHRYpt5UqqX48ePY+rUqXB2dsb9+/fx33//AQA+fvyI4OBg/PHHH9W+zStXrlT7Z34Lli1bBhMTE4SHh+PgwYMAKtJ1du/ejcmTJwOomK2YM2cOIwsMVNQwrF69GvHx8QI1SgkJCUK9fCjSM2vWLHh7e6O0tJRpAHrp0iX4+fmJPa7y9l9qCGhra2Ps2LGKduOb0KxZM6bpq7a2tsiBF0m1mLa2tnj8+DE6dOggsPzx48cCIkaV4XK59Hyj1HlojRJFKi5dusSaZ0xnlaqXnJwcWFtb09qvGsTGxgY+Pj5wcXGBpqYmUlJSYGpqiuTkZDg4OIjth0X5PyrPcrDB4XCQk5Pzjb2pnxBCsHjxYmzZsgVfvnwBUJEeuWjRIvj7+7PanTx5Uq7+S5S6zdWrV9GnTx8oKyvLXYsZHR0NPz8/eHh4CDSc3bp1K9asWSNQ28V/Hm3YsAE5OTkIDw9X+Mw4hSIvNFCiSCQgIACBgYGws7MTmdteOYWMIj8lJSVYsmQJzp8/zxTbUr49ampqePToEYyNjQUCpZycHFhYWODz58+KdlHhfPnyReRASdu2bRXkUcOmsLAQjx8/hqqqKszNzdGkSROx64vqFcSjoYs58MjPzxcQOdDX11ewR9XP58+fkZqaKvK3PGrUKJE24s4dgL3nlKOjI65cuQIdHR1YWloKBed1OaWR0nCgqXcUiezYsQP79+/H1KlTFe1KvaJZs2YCQSchBP/++y/U1NRw6NAhBXrW8GjRogWysrJgbGwssPzGjRswNTVVjFO1hMzMTLi5ueHmzZsCy+tbM866hoaGBlMzJg0NpVeQPBQVFcHDwwORkZFM8NCoUSO4uLggLCwMampqCvaweoiNjYWLiwvevn0r9J6437K85462trbEfoIUSm2HBkoUiXz58kWsdChFPjZt2iTwP6/2q2fPnrRBXw0za9YseHl5Ye/eveBwOHj58iVu3bqFBQsWCPUPamhMnz4dysrKOHv2rExqaZX7/FSGpuzWLEZGRgCAR48eIS8vj0nbAyoeknnvN0Tmz5+Pq1ev4syZM4wi3I0bN+Dp6QlfX19s375dwR5WDx4eHhg/fjz8/f1hYGAgtZ2kc2fkyJEi7fbt21c1hymUWgBNvaNIZNGiRdDQ0GjwD4yU+gshBMHBwQgJCUFxcTEAoEmTJliwYAFWrVqlYO8Ui7q6Ou7du4eOHTvKZFd5JLm0tBTp6en48OED7O3tadpNDZOTkwNHR0ekpaUJqJjyAt+GPDOop6eHmJgYDBgwQGD5lStXMGHCBOTn5yvGsWpGS0sLycnJMDMzk8muqudOQ0hppNRf6IwSRSKfP3/Grl27cPHiRVhbWwvlGSuyx0t9oLi4WGiUDqDF1TUJh8PB0qVLsXDhQmRlZaGwsBAWFhbQ0NBQtGsKx8LCQmSqjiRE1S5+/foVc+bMkflBjVJ1vLy8YGJigkuXLsHExASJiYkoKCiAr68v1q9fr2j3FEpxcbHIGZbmzZszAyf1gXHjxiE+Pl7m35+8505DSWmk1G/ojBJFIgMHDmR9j8Ph0O7acpKfn4/p06cjNjZW5PsNeYRX0Xz69AmXL19Ghw4dBBSdGiKXL1/GsmXLEBwcLFItja33ChtPnz7FgAEDxDZHpVQ/enp6uHz5MqytrdG0aVPcuXMHHTp0wOXLl+Hr64vk5GRFu6gwBg0aBF1dXURGRjJtL0pKSjBt2jQUFBTg4sWLCvaweiguLsb48eOhr68v8rfs6ekp0k7ec+eXX37BxYsXER4eLpTS+MMPP9SblEZK/YbOKFEkUlf6vdQ1vL298fHjRyQmJmLAgAE4efIk3rx5g6CgIGzYsEHR7jUoJkyYgH79+mHevHkoKSlB9+7dkZubC0IIoqKi6m2PFWkYPHgwgIqHSX7kFXPIzs5GWVlZtflHkY7y8nJoamoCqHjwffnyJTp06AAjI6MGr7C5adMmDBs2DK1bt2b6AqWkpKBJkyaIi4tTsHfVx9GjRxEXFwcVFRXEx8cL1BtyOBzWQEnec+f48eNCKY0//vgjVFVVMWHCBBooUeoENFCiUBTE5cuXcfr0adjZ2UFJSQlGRkb44YcfoKWlhZCQEAwfPlzRLjYYrl27hqVLlwKoSBn7+vUrPnz4gAMHDiAoKKhBB0ryDpTMnz9f4H9CCF69eoVz585h2rRp1eEaRQY6d+6MlJQUmJiYoGfPnli3bh24XC527drV4JUdrayskJmZicOHD+PJkycAACcnJzg7O0NVVVXB3lUfS5cuRUBAABYvXixR8psfec+dhpLSSKnf0NQ7CkVBaGlpITU1FcbGxjAyMsKRI0fQp08f5ObmwtLSkt5IahBVVVVkZGSgTZs2cHFxQatWrbBmzRrk5eXBwsKCNv+Vg8opuzxVR3t7e7i5uUFZmY7T1SQXLlxAUVERxowZg6ysLIwYMQIZGRnQ1dVFdHQ07O3tFe2iwggJCYGBgYGQUuPevXuRn5+PRYsWKciz6kVHRwdJSUky1yjJe+40lJRGSv2G3qkoFAXRoUMHPH36FMbGxujSpQt27twJY2Nj7NixAy1btlS0ew2KNm3a4NatW9DR0UFsbCyioqIAAO/fv2du8A0dWUVHzp07B0II1NXVAQDPnj3DqVOnYGRkRIMkBTB06FDm73bt2uHJkycoKCgQ6ufWENm5cyeOHDkitNzS0hKTJk2qN4HStGnTEB0djf/9738y2cl77jSUlEZK/YberSgUBeHl5cUUtK9YsQLDhg3DoUOHwOVyceDAAQV717Dw9vaGs7MzNDQ00LZtWyan/tq1a7CyslKscwomPz8frq6uOH/+vMj32WqURo8ejTFjxmD27Nn48OEDevXqhcaNG+Pt27fYuHEj5syZ8y3dpkiBjo6Ool2oFbx+/Vrk4JS+vn69Eh0pLy/HunXrcOHChSor2Epz7jSUlEZK/YYGShSKgpgyZQrzt62tLZ4/f44nT56gbdu20NPTU6BnDY+5c+eiR48e+Ouvv/DDDz8w+fumpqYICgpSsHeKxdvbGx8+fJBZdOT+/fsIDQ0FAMTExMDAwADJyck4fvw4/P39aaBEqTW0adMGCQkJMDExEViekJCAVq1aKcir6ictLQ02NjYAgPT0dIH3vsWsIi+lcdasWQLL61tKI6V+QwMlCkWB7NmzB6GhocjMzAQAmJubw9vbGzNnzlSwZw0POzs7WFtbIzc3F2ZmZlBWVqaCGpBfdKS4uJhRyoqLi8OYMWOgpKSEXr164fnz5zW5CxSKWGbNmgVvb2+UlpYy9TaXLl2Cn58ffH19Fexd9VHTCrYNJaWRUr+hgRKFoiD8/f2xceNGeHh4oHfv3gCAW7duwcfHB3l5eQgMDFSwhw2H4uJieHh4MCmPGRkZMDU1hYeHBwwNDbF48WIFe6g4ioqK0Lx5cwBAs2bNkJ+fj/bt28PKygr3799ntWvXrh1OnToFR0dHXLhwAT4+PgCAf/75R+beSxTKt2ThwoV49+4d5s6dy9TgqaioYNGiRViyZImCvau7NJSURkr9Rnp9SAqFUq1s374du3fvRkhICEaNGoVRo0YhJCQEu3btwrZt2xTtXoNiyZIlSElJQXx8vIB4w+DBgxEdHa1AzxQPT3QEACM68uLFC4miI/7+/liwYAGMjY3Rs2dPZjAgLi6OSf+hUGoDHA4Ha9euRX5+Pm7fvo2UlBQUFBTA399f0a7VaXgpjZWpbymNlPoNnVGiUBREaWkp7OzshJZ369aNNuSsYU6dOoXo6Gj06tVLIFff0tIS2dnZCvRM8YgSHTl8+DC4XC7279/Pajdu3Dj07dsXr169YhSvgArJYEdHx2/tNoUiMxoaGujevbui3ag3NJSURkr9hvZRolAUhIeHBxo3biykNLRgwQKUlJRg69atCvKs4aGmpob09HSYmppCU1MTKSkpMDU1RUpKCvr164ePHz8q2sVaQ3FxMRUdoVAoEiGEYPHixdiyZYtQSiOdraPUFWigRKHUIPPnz2f+Lisrw/79+9G2bVv06tULAJCYmIi8vDy4uLggLCxMUW42OPr164fx48fDw8MDmpqaSE1NhYmJCTw8PJCZmYnY2FhFu6gQSktL0bFjR5w9exadOnVStDsUCqUOUlhYiMePH0NVVRXm5uZo0qSJol2iUKSGpt5RKDVIcnKywP/dunUDACa9S09PD3p6enj48GGN+9aQCQ4OhoODAx49eoSysjJs3rwZjx49ws2bN3H16lVFu6cwGjdujM+fPyvaDQqFUoehKY2UugydUaJQKBQAOTk5CAkJQUpKCgoLC2Fra4tFixY1+IazwcHByMjIQEREBJSV6dgahUKhUBoONFCiUCgNmtLSUvzyyy9Yvny5UMNJCuDo6IhLly5BQ0MDVlZWUFdXF3j/xIkTCvKMQqFQKJRvCw2UKBRKg6dp06Z48OABDZRE4OrqKvb9ffv21ZAnFAqFQqHULDSPgkKhNHhGjx6NU6dOMU1RKRWUlZVh4MCBGDJkCFq0aKFodygUCoVCqVFooEShUBo85ubmCAwMREJCArp16yaUXubp6akgzxSLsrIyZs+ejcePHyvaFQqFQqFQahyaekehUBo84lLuOBwOcnJyatCb2sWAAQPg7e2N0aNHK9oVCoVCoVBqFDqjRKFQGjy5ubmKdqHWMnfuXPj6+uLvv/8WOdtmbW2tIM8oFAqFQvm20BklCoVC4YN3SeRwOAr2pHagpKTE+h6Hw0F5eXkNekOhUCgUSs1BZ5QoFAoFwJ49exAaGorMzEwAFXVL3t7emDlzpoI9Uyx0to1CoVAoDRUaKFEolAaPv78/Nm7cCA8PD/Tu3RsAcOvWLfj4+CAvLw+BgYEK9lBxGBkZAQAePXqEvLw8fPnyhXmPw+Ew71MoFAqFUt+gqXcUCqXBo6+vjy1btsDJyUlg+dGjR+Hh4YG3b98qyDPFk5OTA0dHR6SlpYHD4QilJtLUOwqFQqHUV9iTzykUCqWBUFpaCjs7O6Hl3bp1Q1lZmQI8qj14eXnBxMQE//zzD9TU1JCeno5r167Bzs4O8fHxinaPQqFQKJRvBg2UKBRKg2fq1KnYvn270PJdu3bB2dlZAR7VHm7duoXAwEDo6elBSUkJjRo1Qt++fRESEtJg+0tRKBQKpWFAa5QoFAoFFWIOcXFx6NWrFwAgMTEReXl5cHFxwfz585n1Nm7cqCgXFUJ5eTk0NTUBAHp6enj58iU6dOgAIyMjPH36VMHeUSgUCoXy7aCBEoVCafCkp6fD1tYWAJCdnQ2gIijQ09NDeno6s15DlAzv3LkzUlJSYGJigp49e2LdunXgcrnYtWsXTE1NFe0ehUKhUCjfDCrmQKFQKBRWLly4gKKiIowZMwZZWVkYMWIEMjIyoKuri+joaNjb2yvaRQqFQqFQvgk0UKJQKJT/T1ZWFrKzs9GvXz+oqqqCENIgZ5EkUVBQgGbNmtFjQ6FQKJR6DRVzoFAoDZ53795h0KBBaN++PX788Ue8evUKADBjxgz4+voq2Lvah46ODg2SKBQKhVLvoYEShUJp8Pj4+KBx48bIy8uDmpoas3zixImIjY1VoGcUCoVCoVAUBRVzoFAoDZ64uDhcuHABrVu3Flhubm6O58+fK8grCoVCoVAoioTOKFEolAZPUVGRwEwSj4KCAjRp0kQBHlEoFAqFQlE0NFCiUCgNnu+//x6RkZHM/xwOB1+/fsW6deswcOBABXpGoVAoFApFUVDVOwqF0uB5+PAh7O3tYWtri8uXL2PUqFF4+PAhCgoKkJCQADMzM0W7SKFQKBQKpYahNUoUCqVBU1paCk9PT5w5cwZ//vknNDU1UVhYiDFjxsDd3R0tW7ZUtIsUCoVCoVAUAJ1RolAoDR59fX3cvHkT5ubminaFQqFQKBRKLYHWKFEolAbPlClTsGfPHkW7QaFQKBQKpRZBU+8oFEqDp6ysDHv37sXFixfRrVs3qKurC7y/ceNGBXlGoVAoFApFUdBAiUKhNHjS09Nha2sLAMjIyBB4j8PhKMIlCoVCoVAoCobWKFEoFAqFQqFQKBRKJWiNEoVCoVAoFAqFQqFUggZKFAqFQqFQKBQKhVIJGihRKBQKhUKhUCgUSiVooEShUCgUCoVCoVAolaCBEoVCoVAoFAqFQqFUggZKFAqFQqFQKBQKhVIJGihRKBQKhUKhUCgUSiX+H5nh3H9YPmZ1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,15))\n",
    "plt.title(\"Representation of Null Values\")\n",
    "sns.heatmap(df.isnull(),cmap='PiYG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Predicted Class'}, xlabel='Prediction', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Predicted Class\")\n",
    "sns.countplot(data=df,x='Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  1, ...,  0,  0,  0],\n",
       "       [ 8, 13, 24, ...,  0,  1,  0],\n",
       "       [ 0,  0,  1, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [ 0,  0,  1, ...,  0,  0,  0],\n",
       "       [ 2,  7,  1, ...,  0,  1,  0],\n",
       "       [22, 24,  5, ...,  0,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:-1].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape Of X Training Dataset (3620, 3000)\n",
      "Shape Of X Testing Dataset (1552, 3000)\n",
      "Shape Of Y Training Dataset (3620,)\n",
      "Shape Of Y Testing Dataset (1552,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape Of X Training Dataset\",x_train.shape)\n",
    "print(\"Shape Of X Testing Dataset\",x_test.shape)\n",
    "print(\"Shape Of Y Training Dataset\",y_train.shape)\n",
    "print(\"Shape Of Y Testing Dataset\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[\n",
    "        (\"SVM\",SVC()),\n",
    "        (\"LogisticRegression\",LogisticRegression()),\n",
    "        (\"DecisionTreeClassifier\",DecisionTreeClassifier(random_state=0)),\n",
    "        (\"RandomForestClassifier\",RandomForestClassifier(n_estimators=100)),\n",
    "        (\"KNeighborsClassifier\",KNeighborsClassifier(n_neighbors=5,p=2,metric='minkowski')),\n",
    "        (\"NaiveBayes\",GaussianNB())\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name SVM\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.99      0.88      1097\n",
      "           1       0.92      0.36      0.52       455\n",
      "\n",
      "    accuracy                           0.80      1552\n",
      "   macro avg       0.85      0.67      0.70      1552\n",
      "weighted avg       0.83      0.80      0.77      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name SVM\n",
      "SVM Accuracy Score :: 80.3479381443299\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Gyanender\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name LogisticRegression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      1097\n",
      "           1       0.95      0.95      0.95       455\n",
      "\n",
      "    accuracy                           0.97      1552\n",
      "   macro avg       0.96      0.96      0.96      1552\n",
      "weighted avg       0.97      0.97      0.97      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name LogisticRegression\n",
      "LogisticRegression Accuracy Score :: 96.84278350515464\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name DecisionTreeClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.95      1097\n",
      "           1       0.86      0.88      0.87       455\n",
      "\n",
      "    accuracy                           0.92      1552\n",
      "   macro avg       0.91      0.91      0.91      1552\n",
      "weighted avg       0.92      0.92      0.92      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name DecisionTreeClassifier\n",
      "DecisionTreeClassifier Accuracy Score :: 92.33247422680412\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name RandomForestClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      1097\n",
      "           1       0.95      0.95      0.95       455\n",
      "\n",
      "    accuracy                           0.97      1552\n",
      "   macro avg       0.97      0.97      0.97      1552\n",
      "weighted avg       0.97      0.97      0.97      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name RandomForestClassifier\n",
      "RandomForestClassifier Accuracy Score :: 97.16494845360825\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name KNeighborsClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.87      0.90      1097\n",
      "           1       0.73      0.83      0.78       455\n",
      "\n",
      "    accuracy                           0.86      1552\n",
      "   macro avg       0.83      0.85      0.84      1552\n",
      "weighted avg       0.87      0.86      0.86      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name KNeighborsClassifier\n",
      "KNeighborsClassifier Accuracy Score :: 86.08247422680412\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================= CLASSIFICATION REPORT ===============================================\n",
      "Model Name NaiveBayes\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.96      1097\n",
      "           1       0.88      0.96      0.92       455\n",
      "\n",
      "    accuracy                           0.95      1552\n",
      "   macro avg       0.93      0.95      0.94      1552\n",
      "weighted avg       0.95      0.95      0.95      1552\n",
      "\n",
      "============================================= ACCURACY SCORE ===============================================\n",
      "Model Name NaiveBayes\n",
      "NaiveBayes Accuracy Score :: 94.90979381443299\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = {}\n",
    "for name, model in models:\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    report = classification_report(y_test, y_pred)\n",
    "    confusion = confusion_matrix(y_test, y_pred)\n",
    "    score = accuracy_score(y_test, y_pred) * 100\n",
    "\n",
    "    print(\"============================================= CLASSIFICATION REPORT ===============================================\")\n",
    "    print(f\"Model Name {name}\")\n",
    "    print(report)\n",
    "    print(\"============================================= ACCURACY SCORE ===============================================\")\n",
    "    print(f\"Model Name {name}\")\n",
    "    print(f\"{name} Accuracy Score ::\", score)\n",
    "    result[name] = score\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "    plt.xlabel(\"Predicted\")\n",
    "    plt.ylabel(\"True\")\n",
    "    plt.title(f\"Confusion Matrix for {name}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM': 80.3479381443299,\n",
       " 'LogisticRegression': 96.84278350515464,\n",
       " 'DecisionTreeClassifier': 92.33247422680412,\n",
       " 'RandomForestClassifier': 97.16494845360825,\n",
       " 'KNeighborsClassifier': 86.08247422680412,\n",
       " 'NaiveBayes': 94.90979381443299}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_tuples = [(model, score) for model, score in result.items()]\n",
    "df1=pd.DataFrame(result_tuples,columns=['ModelName','Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>ModelName</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM</td>\n",
       "      <td>80.347938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>96.842784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>92.332474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>97.164948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>86.082474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaiveBayes</td>\n",
       "      <td>94.909794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ModelName      Score\n",
       "0                     SVM  80.347938\n",
       "1      LogisticRegression  96.842784\n",
       "2  DecisionTreeClassifier  92.332474\n",
       "3  RandomForestClassifier  97.164948\n",
       "4    KNeighborsClassifier  86.082474\n",
       "5              NaiveBayes  94.909794"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Score')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model Comparisions\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.bar(x=df1['ModelName'], height=df1['Score'])\n",
    "plt.xlabel(\"Model Name\")\n",
    "plt.ylabel(\"Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best Accuracy Score For Email Classification is 97.16 % \n",
      "The Best Model For Email Classification is/are:\n",
      "Model Name: RandomForestClassifier\n",
      "Score: 97.16494845360825\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"The Best Accuracy Score For Email Classification is {round(df1['Score'].max(),2)} % \")\n",
    "print(\"The Best Model For Email Classification is/are:\")\n",
    "best_models = df1[df1['Score'] == df1['Score'].max()]\n",
    "for index, row in best_models.iterrows():\n",
    "    print(f\"Model Name: {row['ModelName']}\\nScore: {row['Score']}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}