{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#6304640862"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exam Instruction EE522 Fall 2022:\n",
    "    \n",
    "1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
    "\n",
    "2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
    "\n",
    "3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
    "\n",
    "4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
    "\n",
    "\n",
    "5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
    "\n",
    "6. You have to submit your code on BE-moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 [50 points]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Information:\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
    "\n",
    "Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\t\n",
    "=========================================\n",
    "Dataset characteristics\n",
    "======================\t\n",
    "hour.csv has the following fields:\n",
    "\t\n",
    "\t- instant: record index\n",
    "\t- dteday : date\n",
    "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "\t- yr : year (0: 2011, 1:2012)\n",
    "\t- mnth : month ( 1 to 12)\n",
    "\t- hr : hour (0 to 23)\n",
    "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "\t- weekday : day of the week\n",
    "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\t+ weathersit : \n",
    "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\t- casual: count of casual users\n",
    "\t- registered: count of registered users\n",
    "\t- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.1 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
    "\n",
    "Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
    "\n",
    "\n",
    "[20 Points]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('hour.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404046</td>\n",
       "      <td>0.866014</td>\n",
       "      <td>0.489164</td>\n",
       "      <td>-0.004775</td>\n",
       "      <td>0.014723</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.003416</td>\n",
       "      <td>-0.014198</td>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>-0.074505</td>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.278379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0.404046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.178056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>0.866014</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.489164</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.120638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>-0.004775</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>0.394071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.014723</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>-0.030927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>-0.003416</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.030284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.014198</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>-0.142426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.404772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>0.400929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.009577</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>-0.322911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.074505</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.093234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.694564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.278379</td>\n",
       "      <td>0.178056</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.394071</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.030284</td>\n",
       "      <td>-0.142426</td>\n",
       "      <td>0.404772</td>\n",
       "      <td>0.400929</td>\n",
       "      <td>-0.322911</td>\n",
       "      <td>0.093234</td>\n",
       "      <td>0.694564</td>\n",
       "      <td>0.972151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instant    season        yr      mnth        hr   holiday  \\\n",
       "instant     1.000000  0.404046  0.866014  0.489164 -0.004775  0.014723   \n",
       "season      0.404046  1.000000 -0.010742  0.830386 -0.006117 -0.009585   \n",
       "yr          0.866014 -0.010742  1.000000 -0.010473 -0.003867  0.006692   \n",
       "mnth        0.489164  0.830386 -0.010473  1.000000 -0.005772  0.018430   \n",
       "hr         -0.004775 -0.006117 -0.003867 -0.005772  1.000000  0.000479   \n",
       "holiday     0.014723 -0.009585  0.006692  0.018430  0.000479  1.000000   \n",
       "weekday     0.001357 -0.002335 -0.004485  0.010400 -0.003498 -0.102088   \n",
       "workingday -0.003416  0.013743 -0.002196 -0.003477  0.002285 -0.252471   \n",
       "weathersit -0.014198 -0.014524 -0.019157  0.005400 -0.020203 -0.017036   \n",
       "temp        0.136178  0.312025  0.040913  0.201691  0.137603 -0.027340   \n",
       "atemp       0.137615  0.319380  0.039222  0.208096  0.133750 -0.030973   \n",
       "hum         0.009577  0.150625 -0.083546  0.164411 -0.276498 -0.010588   \n",
       "windspeed  -0.074505 -0.149773 -0.008740 -0.135386  0.137252  0.003988   \n",
       "casual      0.158295  0.120206  0.142779  0.068457  0.301202  0.031564   \n",
       "registered  0.282046  0.174226  0.253684  0.122273  0.374141 -0.047345   \n",
       "cnt         0.278379  0.178056  0.250495  0.120638  0.394071 -0.030927   \n",
       "\n",
       "             weekday  workingday  weathersit      temp     atemp       hum  \\\n",
       "instant     0.001357   -0.003416   -0.014198  0.136178  0.137615  0.009577   \n",
       "season     -0.002335    0.013743   -0.014524  0.312025  0.319380  0.150625   \n",
       "yr         -0.004485   -0.002196   -0.019157  0.040913  0.039222 -0.083546   \n",
       "mnth        0.010400   -0.003477    0.005400  0.201691  0.208096  0.164411   \n",
       "hr         -0.003498    0.002285   -0.020203  0.137603  0.133750 -0.276498   \n",
       "holiday    -0.102088   -0.252471   -0.017036 -0.027340 -0.030973 -0.010588   \n",
       "weekday     1.000000    0.035955    0.003311 -0.001795 -0.008821 -0.037158   \n",
       "workingday  0.035955    1.000000    0.044672  0.055390  0.054667  0.015688   \n",
       "weathersit  0.003311    0.044672    1.000000 -0.102640 -0.105563  0.418130   \n",
       "temp       -0.001795    0.055390   -0.102640  1.000000  0.987672 -0.069881   \n",
       "atemp      -0.008821    0.054667   -0.105563  0.987672  1.000000 -0.051918   \n",
       "hum        -0.037158    0.015688    0.418130 -0.069881 -0.051918  1.000000   \n",
       "windspeed   0.011502   -0.011830    0.026226 -0.023125 -0.062336 -0.290105   \n",
       "casual      0.032721   -0.300942   -0.152628  0.459616  0.454080 -0.347028   \n",
       "registered  0.021578    0.134326   -0.120966  0.335361  0.332559 -0.273933   \n",
       "cnt         0.026900    0.030284   -0.142426  0.404772  0.400929 -0.322911   \n",
       "\n",
       "            windspeed    casual  registered       cnt  \n",
       "instant     -0.074505  0.158295    0.282046  0.278379  \n",
       "season      -0.149773  0.120206    0.174226  0.178056  \n",
       "yr          -0.008740  0.142779    0.253684  0.250495  \n",
       "mnth        -0.135386  0.068457    0.122273  0.120638  \n",
       "hr           0.137252  0.301202    0.374141  0.394071  \n",
       "holiday      0.003988  0.031564   -0.047345 -0.030927  \n",
       "weekday      0.011502  0.032721    0.021578  0.026900  \n",
       "workingday  -0.011830 -0.300942    0.134326  0.030284  \n",
       "weathersit   0.026226 -0.152628   -0.120966 -0.142426  \n",
       "temp        -0.023125  0.459616    0.335361  0.404772  \n",
       "atemp       -0.062336  0.454080    0.332559  0.400929  \n",
       "hum         -0.290105 -0.347028   -0.273933 -0.322911  \n",
       "windspeed    1.000000  0.090287    0.082321  0.093234  \n",
       "casual       0.090287  1.000000    0.506618  0.694564  \n",
       "registered   0.082321  0.506618    1.000000  0.972151  \n",
       "cnt          0.093234  0.694564    0.972151  1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = df.corr()  \n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vP1ztHXhn6jQsTE3ZfeL4PedbuX0rnk5O9O/UudZzh88G8lyfvgzu2g0Ha2sGde3G6D792HjIR+WyZDIZV04eo8ug4TRt1xEre0eGTZ1FcVEhty8F1ZkhJOAUhiamDHhhMha29rTp0YeWXbpz8bivoszlU8dwbtKcrkNHYWFrT9eho3Bq0ozLJ5VbqosK8vFZ9wdDJ81AR6/2iaGEqAhadO6OSzMvTC2saN21B1pa2pjb2OHdsy+WdvYMnjgVQ2NTrp4+oTLvzQvnKCkuZsT02Vg5ONK8fSe6DB7ORT9fxQ6SqYUVgyZOoU23Xujq3+MElZoahiamSn+VLhz3pVXXHrTt0QcLW3sGvjAFAxMTgs+oznXr4jlKS4oZNm0WVvaONGvXkS6DhnHpRFWukICTGJiYMvCFKVjY2tO2Rx9adu3OxeNHFMuxc3Gn3/Mv0qJTN7S0teuMXlSQz8F/fmfwpJcpyMvD2smlUd53zzbtcG/ZBjMrG8xtbOn13Di0dXVJjIpQWl+2JJ1Tu7cybPps1DU0kMlkXPX3o9PAYTTx7oClvQNDpsykuKiQO5frzn0twB9DE1P6jZ+Eua09rbv3xqtzNy6fqMpt6+JG7zEv0Lxjl3rr8Mj6vxg0aTo6+vp1lqvu+MF9dOs7gJ4Dh2Dn6MTEV+ZgbGbG6aOHVJbX0dVl0qtz6TVoKKYqDniq2/jbz3Tt2x/3pvX3GKiP34F9dO87gF6D5DlfemUOJqZm+N8j5+Q5c+k9aChm9bRArF/VsJx+B/bRqlsP2vbsi4WdPQMnTsXgHtv2rYvybXv4tNkV25B82750vGobCj5zEgMTMwZOnIqFnT1te/alZdceXPSr2oZuXjhLl0HD8WjtjamlNe1698e9ZRulMheOHcbQxIQRL7+Knas7RuaW2Hk2w9RavlPf2dOFc6FR3E1MJU2ay4FLN9DW1KSlk12dr7e9myO5hYUcDblDRk4ewdEJXI9NpEsTV0WZwpJS8oqKFX9OlqZoaWoQEpOgtKzycplSufziknvWNYDvvj306D+QPoOHYu/kzORXX8fEzJyTR+p+z6e9/gZ9hwzDzFL1ScobV69w61owb3+yhJbe7bC0scGjaXOat26jsnxD7Ni6hSHDRzDyudG4uLry1oJ3sLCwYP/e3SrLT572Mq+8OodWbdpi7+DA6LHP06t3H077n1KUuXnjBpZW1kyY+BJ29va0aNWKseMmcPvWrQfOuXPbVoYMG86IipxvLliIhYUFB/aoPqE0edp0Zs6eQ6s2bbC3d+C5ipxnquW8dfMGllZWjJ/4ojxny1aMHTee27cfPKfw3/JUHPzV5O/vj7q6Ot9//z1z5sxh//79nDlzBoDMzEy+//57+vfvz6pVq/jmm2/o10/exahXr16MGTMGBwcH1q9fz/r16+lVcTChpqbGrFmzWLlyJYsWLSIsLIzff/9dab0lJSXs2LGD+fPn8/3335OXl8eqVavqXbYgV1JSwp2wMLq0V25R6tK+Pddu3a5zvoALQQReCOKd115/9KE0NNB2dabghvL6C27cRtfTXeUsxdExUFqGYZ8eoKaGmq4Ohj27UhQZTXmu6rOaRn16UhyfSFF45APFLCkt5W50NJ1rdI/t3LIV18Prbk0MDA4mMCSYBZOnql5uSSnaWlpK03S0tbgVGUFpaWmt8olpaeRJs3Hxqsqhpa2No2czEiLr7iabFBWBa3PlrmCuXq1IiY2mrEy+nsSoCFy8apdJiFTewT+6ZR1NvTvi3MxL5boc3ZsQcT0YaUVrQUzoHYqLCmnSxlupnJtXyzozJ0SF4+TZVGmn3r1FK3Kzs8jOSK/zdapSWlzMqv+9w8oPF7Bj5XKS42IAKCstJTk2Glcv5fdU1WuulBgVgaOHci7XGrkSIyNqLdPNqzUpMVV13VC+m/+hWbuOmFpaUV5WhrFZVVeyx/2+VyovL+fOpSCKiwqxd/Osml5Whs/a3+k8eATmtvaAvOUlX5qNc7UMmtraOHg0JSmq7tzJ0RE4N1PO5OLVitTYmPuuQ7+t6/H07oBTU9Wf15pKS0qIjQzHq0a3L6+27Yis0XJ/v/x9DyHNymL48y881HKgKmcLFTkjHjLnqSPynCPG3TtnZYban/e6t+3EyPBa25CbV41tKCoctxqfSbcWrUiutg2VlZagWeO7U1NLm/iIUMXjsJAr2Ll6sO+vVfz63pvs//kbbp/1RyaTYaqvh6GuDlGpVa2apeXlxGVk4mBuWudrdrAwJSpFuSU0MiUDOzNj1OtoEfF2dSQiOZ2cgiKl6aYGerw5rDdzh/RiTKfWmOqrbsFX5CspISYinJbe7ZSmt/RuR/idun/D63M16Byunk04um8v77wyjQ9en82mP1dTWFDwQMsrKSkhNPQuHWuc8OzYqTM3bzS851B+Xh5GRkaKx63atEGSkc7ZgDPIZDKys7I4edyPLl1rX+JxXzk7d1Ga3uEBchoaVuVs2bo1kowMzgYGPJKcTxx19cf39x/1VL4yJycnpkyZgoODA7169aJNmzaEhIQAkJGRQWlpKT169MDGxgYXFxeGDBmCmZkZOjo66OnpoaGhgZmZmWIawOjRo2nbti02Nja0bt2al19+mYCAAKUWxbKyMl577TWaNm2Km5sbY8eO5fr165SXl99z2YJcllRKWXk55qZmStPNTc3IyJSonCddksFXK1awZNG7GDTwrPn90DAyRE1DgzKpVGl6mVSKhomJynlK0yUkf/8zZmNH4rLmF5x/W4aWkwMpy1apLK+mp4t+5/bknAp44JxZOTnyuquRydzEBEl2tsp50rOy+OafNSye/RoGeqp/1Lu0bs3BM6e5FRmJTCbjdlQk+/39KS0rI6tay3eljOwsAAyMjJWmGxgZky9VnQMgT5qNvrFydn1jY8rLyyioWE+eNFv1cnOqlnst0J+stFR6jhxb57r6T5iEtaMzq/73Dt/Ne4WtP8m7XLrXuD5M39iEvDrqLk+ajb5RzbwmiucaysLGjuFTX2Hca2/x3MzX0NTSYuP3XyJJTSY/NwdZeTn6Kl5zXeuQ56pZXjlXXk7tetQ3Uq7rhghR1PXzimVXdletnvVxvO8AaQnxrFj4OsvffpVj29YzevYbSt1+A332omtgSNtq1xOWFBXK16miPvJqbPPKuaUq5ykvL6PwPurw+tnTZKel0v0e11PWlJsjpby8HONqLcQAxiamZGdlNXg5NSXEROOzYwsz3lqIuobGAy+nUmVOI1NTpenGpqZIHzLnwR1beGV+/TkrMxio2FbvtQ0Z1PhMGtTYtlV9bg2MTJQ+t25erbl04igZKUnIysuJvn2D0ODLSuvNSk/l6unjmFpaMeHNRXh178sV3/3cOXcaA135tpRXqNy1Ma+wGEPduluSDXS0ySuqPY+Gujp6Olq1ypsb6uNiZU5wdLzS9ARJNgcv32Br4BUOXbmJga4O0/p2Rk+79jIq5VR+Nmv8hpuYmpKdWfsSiYZKS0km7PYt4qIjmfv+R0x59TVuXL3Mmp+XP9DysrOzKC8rw8xc+bpHM3NzJBmq9zVqOhcYwJXLlxjxXNV1vC1btebjJUv5aukSBvftxdiRw5DJZHzw8ScPl9NMuT7NzMyRSBqaM5Arly8xskbO/y3+jK+XLmFIv948P2o4MmS8/78Hyyn89zwV1/zV5OrqqvTY3Nyc7IqdODc3N7y9vXnjjTfw9vbG29ubHj16YFLHjnylkJAQdu7cSVxcHPn5+ZSVlVFaWkpmZiYWFd1TtLS0cHSs2tkwNzentLSUvBpnh+7lyJEj+Pr61luus4aMnmX/zX7NajXOTsqQ1ZpW6dPvvmfciBG09mrYWfMHVrNHrJoa1HH9g4aJMZavTCE3MIi88xdR09PFbOworOfNIvnbn2rNZ9i9C2pq6uSdrbubWUOpUaOeZDKoOa3Ckt9/Y2y/AbTy9FT5PMCM58aQkZ3Nq18uBZkMM2MThvfsycZDPqirq+F7NpBv161VlP9hwTsVQWq8hzJZvf3waz0rq1xUtWdqLbfqf0lKEmcO7OLFtz9EQ7Pur64r/n4kRIYx7vX5mJhbEnb9Kmf27yYpOgLn6gO8yGR1VZ2qKHV+Hu7Fwd0TB/eq+nfwaMLaLz/l8kk/ug4ZUbGe2nV5r6pUVb7W9NrhK59oUO4Lfofx37MdTS1tfnn3DcbNfVtlucfxvlcyt7Fl2odLKMrPJzT4Mkc2rOGF+e9hZe9IXNhdbgYF0nXISFYumgdASXER5eUq6qYiRH01Uef738DrTSQpyZw9sJsJb79/z89rQwPIvyfvfzEgb2FY89P3jJs6E0sV1xE9jJrfSQ8zOFtJSQl/Lv+e8dPuM6eK9+p+6qpqG7rXIpVf14AJkziyaS1/L/0I1NQwtbSmdbeeXD8XoDSPrbMbfcZMwMPGkkVTxlP26fvIysvYfemmvEzNHx+12j9HtfLWeKzIrWJGb1dHcgoKCU9W7rEQmVL1OA35weDcIb1o7Wxfz9pVVnedv+ENISuX7wO8uvA99CvGeJg8+3WWffYJ2VmZmNQ42Gyo2t+VDdt8b1wL4cvPFvPG2wvxqjZ4UXRUFL/8tIwpL8+gU+euSDLS+X3Vryz77ls+/OQhRqNX9f3UoJzX+GrpYt6Yv4DmLVoo5fx1xXKmTJ9Bx85dKnKuZPn33/LBx58+eM4nhbjm76E9lQd/mip+SCtb6DQ0NFi6dCl3797l6tWrHDt2jPXr1/P111/j5uamcnmpqaksXbqUwYMHM3nyZIyMjIiIiOD7779X6vqmUeMsZOUXS/XWwfoMHTqUoUOH1lsubmDdo8Y9rUyNjdFQV6/VypeZlVWrNbDSpZBgrl6/xl+bNgHy37by8nK6jRjOe/PeYOzw4Q+VqSwnF1lZGRomymf5NYyMarUGVjIa0IfyomIyt1f1yU/7fS1OP32Njqc7RWHK3dWM+vQg79JVyvPyHzinqZGRvO4qWt4qSaRSzGtkr3T59i2C797h733ynDKZjHKZjJ4zp7No2nTG9O2PrrY2H78ymw+mz0AilWJhasq+UyfQ19XF1NCInu3a08Kj6uClpFR+TUieNFupC2B+bk6tlpLqDFScic/PkaKuroFuxY+9yjK5UkULXGJUBAW5ufzzVdXZS1l5OfERoYQEnGL+j78hk8k4s38Xz70ylyZt5F2TLGztOLN/N9fPy6/Rqb7+mi0A9eWtfO5BqaurY+viSmZqCvqGRqipq6t4zTm1Wh3vmStXnquy/g2MVGXPQV1dAz3Dhg2epa0t77VQWvF+b10hbz29deEsty+dZ/6Pv6GppfVY3vdKGpqamFnZAPLr7ZJjo7h88ihDJ88kNvQ2udJs/HZsUprneuApQP55Nar+ec3JQd/4Xrlrtwzm5+Yo5a5PUnQEBXm5bPi6aqdQVl5OQkQY1wL9WbFxB1patVtZDI2MUVdXR5ql3JKSk51dqzWwoaSZEpLi41i/agXrV62QZ5HJkMlkzJs4hnkfLaZF23b1LOU+ctZoDWyo7Iqc61auYN1K5ZyvvzCGNz9aTItqXQ4rM6j6jN3XNpRTuQ2Z1F0mV1qxDRlWlDXm+dfmU1pSTEFeHoYmpvjv3YFJtevaDE1MsbCTH0zFpku4fTyZ2JvXCD7mw9iF/5OX0dVR6o5poKNdqzWwuryiYgx1lFsG9XW0KSsvp6DGNXvqamq0drYnODq+3oPykrIy0nNyMTfUp6CoSGUZo4r6zq7xnkuzsx74PQcwMTfHzNxCceAHYO8oH8hJkpZ23wd/JiamqGtoIMlQ7h6blSmp1RpY0/WQED58dyEvz5rN6LHPKz23eeN6mnu14MVJ8tGlPTw90dXVY/6813jl1dewtrF5oJyZNVr5MjMzMTOrJ+e1ED569x1efmU2z9XIuaUi58RJk6ty6unx9rzXmTl7zn3nFP57nsqDv/qoqanRvHlzmjdvzosvvsi8efM4c+YMbm5uaGpq1jpYCwsLo7S0lFmzZikO8C5evKhq0fekatlCFS0tLZo3acKFK1cZ2KtqMJWgq1fp36OHynm2/LZa6bH/uXOs3baVf35agdWjGLK4rIzi6Fj0WjUn/+IVxWS9Vs3Ju3RV5Sxq2tpQ832ufFzj/jPa7i5ouzgh2bzjoWJqaWrSzNWVizdvMKDa9QEXb96kb8eOKufZ+IXyEOpnrlzhn4P7WfPpEqxq/LBoampiXfGjeCzoPD2826Guro6Bnp5Sl1GZTIaBsQkxd25i5yI/mVJaUkJCRCh9xtR9fY6dmwfh164oTYu5cxMbZ1c0NORfQ/ZuHsTcuaUYcU9e5hYO7vIRHz3btGf6R65Kyziy8W/MrGzoMmQEGpqaFBcWUl5WpnTGV0NTE31DIwrylLvrRd25qRj9siYHN09O7d1OaUmxoqtj1O2bGJqYYnKPkVXrI5PJSI2Pw9rRGQ1NTWydXYm+c5Nm7Tsp1UtTb9W3t7B38+D0vh2UllRdbxRz+5ZSLnt3D8JClD+70XduYuNSVdf1ad6xKw7Vbjchk8nY8M1nmNvaMeLlV9HQ1Hxs73tdZDIZZRUn57x791eMHlpW0dq357flNG3fiTsXzhF79xa21T6viRFh9Bwzoc5l27p6EHlduQ5j797C2tmlwXXo0bodNh+4Kk07tnktplbWdBo0QuVJTABNLS2c3T25ExJMh25Vt1y5cy2Ydl0e7JodU3MLPv5ReZS/076HuH0tmDnvfqRylMb6VOa8dS2YDt2rct6+Fkz7B7y2yMzcgk+XKef09z3E7ZBgXnuvds7KDNG3b9K8fdX1XdF3btLUW/W2be/uiX+NbTv6jvK2be/mSViI8uc2+vZNbFVsQ5pa2hiZalNWVkpo8CWlHA7uTchMkQ8KV1JWTmZeAeGhd8nIzSM9J4/cwiLcrC1IypQffGqoq+NkYcaJG6HUJSEji6b2yvVQuYzyGgd4zeyt0dfRIiRaeaAXVTTU1bEwMiAmTYJGHQ0rmlpauHh4cjP4Kp16VI1pcCvkKh26qf4NbwjP5l5cCgygsKAA3Yrfm+REeeYH+WxqaWnRtGkzLl+8SN/+AxTTL1+8SK++feucLyT4Kh+9u4jpM19h/Asv1nq+qLCwViOAuob86qkHafGuynmBPv2qbiNy+eJFevepO+e14Kt89N67TJs5k3EvTKyds6gI9RrXq1U+rtXS/BR6mFZmQe6pvObvXu7cucO2bdsIDQ0lNTWVoKAg0tPTcXKSn0WytrYmNTWV8PBwsrOzKSkpwd7envLycvbv309ycjL+/v7s27fvvtetatmNTU1PF21Pd7Q93UFdDS0ba7Q93dG0qT0M9+MwaezzHPQ7xt4jh4mKjeXH1b+RnpHB88PlXeBWrv2buR98oCjv4eqq9GdtaYG6mhoerq4YN7CrbX2yjxzHsGc3DPv0QMvOFvPJE9AwNSHnhHwQIdMJo7F5b76ifEHIDbRdnDAZMwJNGyu0XZywnDWN0gwJxVGxSss26tuLkuQUCu88+C0eKr00ZBg+AWfY73+K6MQElm/aQHpWJmP7yX/cVu3Yxhvffq0o7+HopPRnZWYmrztHJ4wrzrDGJidxODCAuORkbkZG8MmqX4mMT+D1cap3jNXU1GjfbxAXjh0iNPgyaYnxilsNeHWsOig9tP5PDq3/U/G4bc++5GRlcmLnZjKSE7l29jQ3ggLpNGCIokz7voOIDb1NkK8PGclJBPn6EBd6hw79BgGgq6+Plb2j0p+Wtg66BgZY2TuipqaGjp4ejp7NOL1/FzGht8lKT+PauTMUFuSTnyMlJMCf9KREjm3fRG52Fu0qrg87tXcHW376VpGlReeuaGlr47PuL9IS4rl79RLnj/rQaeAQpR+elLgYUuJiKCosoDAvj5S4GNKTqna0Ag7uJfLWdbLSUkmJi+HQhr9JS4inXW/5ejsPGMKN8wFcC/QnIzmR4zs2kZuVRdue8udP79vBthVVt4lo0akrmlraHN7wF2mJ8YQGXyLomA8d+1flatuzH7lZkqq6DvTnxvkAOg2o6nFQVlpKSlwsKXGxlJaUkCfNJiUulszUFJV1be3ghKGZGZKUJDJTU0hPSnhs73tlPcSHh5KdkU5aQjyn9+0kLuwuXh27AvJrBCuzWto7YGnvgLqGBobGprTvP4RLxw4THnKZ9MQEjm76Gy0dHZp3qMrtu2ENvhuq7sfYpmcfcrIyObVrK5LkRG6cPc2toEA69K/KXVZaSmp8LKnx8jrMz5GSGh9LVlpVHVZmqfzT1NZGV98AS3uHe+7ADBg5mnOnThBw/ChJ8XFs//tPsiUSeg2WHyDv3bSOnz77WGmepLhY4qIiyc3JoaiwkLioSOKi5ANMaWhq4uDsovRnZGKCppYWDs4uih3u+zVwVEVOP3nObX//SXamhN4VOfdsWseyJco5EytzSnMobEhO43vnHDhqNDfOBxAS6E9GUiLHK7btyntJ+u/dwdYV1bbtTvJt+9D6im3o6iWCjvrQcUDVNuTdS74NHd+xiYykREIqt6GBVdtQYlQEoVcvkZWeSlz4XXb8+iOychmdB1WdxOjYfzCJURGcO7yfzNQUoq9f5fZZf5p3lR84XQiPoVtTN5rZW2NlbMioDi0pLi3lZlySYhmjOrRiVIeqAW2uRMVjpKfLwDbNsDAyoK2rA21c7AkKi65VN95ujkSnSsjKrz1wSv9WTXG2NMNEXw97MxOe79IWLQ0NrsUk1vFuyw0ZPZbAk8c5fcyXxLhYNv/1O1kSCX2HyHtV7NzwD99/8pHSPAlxscRGRpArlVJYWEBsZASx1QZ06tq7LwZGRvz9y3ISYmMIu32LLWv+oGP3Hg/cojjhxZfwPeyDz4H9xERH8+tPy0nPSGfUGPn14n+uXsU7899QlA++coUPFy1k1JgxDBw8BElGBpKMDLKqXcvYrUdPAs+cZt+e3SQmJHDjWgi//rScJk2bqbx9REOMn/givocPVeVcsZyMjHRGjRkDwF+rf2PR/Dercl69wofvvsOo0WMYOEh1zq7de3A24Az79+wmMTGBG9eusXJFRc5H3O1beDr951r+DAwMuHXrFgcPHiQ3NxcrKysmTpyoGPGzR48enDt3jo8//pi8vDzmz5/PwIEDmT17Nrt27WLjxo00b96cmTNn8t1339WzNmV1Lbsx6TZviuMv3yseW8yahsWsaUgPHSXlqx8fe55BffqQnSNl7ZYtpEsy8XB1YfnSz7Gr6IaQLpGQkHTvH59HLf/CZSSGBpiOGoaGqTHFCUmkLFtJWcWF4ZomJmhZVx0sF96+S/rqtRgPH4TJsIHIiksoiogi5YdfkBVXdddR09XBoEsHsvapHgL7fg3s0pXs3FzW7t9HRnYW7g6O/LhwEXYV3YwysrJISE29r2WWl5ezxfcIsclJaGpo0KG5F398/Cl2Ku7RVanzwGGUFhdzfPtGCvPzsHN1Z/wb7yjd601aoxuLqaUV415fwMldWwgJOIWBiSn9x09StNaA/Pq4kTNeI/DgbgIP7cXU0pqRM1+rdY+/+oya+Rqn9+3kwN+/U5ifh7G5BX1Gj0dTS5vAw/vJk2ZjaefAhHkLFWf6c7OzyEyrqjtdPX0mvvUuR7du4J9vlqCrb0DnAUPpPEC5y/bar5Sv8wi/HoyxuQVzv5RvW4UF+RzZ9A950mx0dPWwcXJh8jsfYu8qH0nWq2MX8nJzOXfkgCLXuLkLquXKJiu9KpeOnj4vvLkIv20b2fDtZ+jqG9Cx/xA6VjuYMrW0YtzcBZzYtYXgMyflt1qYMFmplTM3O4v131RlzwpIJSTgFE5NmvHi21UnX6ozMjVHz8CwUd73PKkUn3V/kp+TjbauHlYOjox7fQFuLVTfWLq6jgOHUlpSzIkdmynKz8PWxZ2xcxcq3eNPmqncPczEwooxc+bjv2cb1yty9x33kuIef5V1uPm7pYrH19P9uR7oj4NnUya89V69ue6ZuUcv8nJzOLxrO9JMCXZOLsz76FNFK0h2ZiZpKcr3bPv166VIqn2Gv3rvbQB+27H/obLcS6cevcjLyeHQru1kZ0qwd3bhjRo502vm/GopGdVyfvGuPOfvOx8sZ6cevYhJTOZctW17/NyqbTtPmkVWWs1t6F2ObdvA+optu9OAoUonR+Tb0EIV21BVC31pSQlnDuwmKz0VbR1d3Fu2YcT0V5Vu+2Ln6s7YOW9yev8uzh7ej4GJGe0GjaBZxcHf+dBotDQ0GOLtha6WJomSbLYGXlG6x5+xvvK9KLPzC9h+9goD2zSjvZsTuYVFHA25o7jHn+I16OvhamXO3gvXVNabsZ4Oozu1Rl9Hm/yiYhIk2aw7FYS0oPCe9d25Z29ypVIObN9KdqYEB2cX3v7kMywr7tObLZGQmpykNM9PSxcrvedLFr4FwN975bcU0tXTY9HSL9n8x2o+X7QAfUND2nXpyvhpL98zy730GzAQaXY2G9etRZKRgaubO19//yO2tvLbaEgyMkhMqDpR53vYh8LCQrZv2cz2LZsV021sbdmyU37pxNDhI8jPz2fvrp2s/vVnDAwN8W7Xnjlz3+BB9RswEKk0m03r/6nK+d0P2FTkzMjIIDGxWs5Dh+Q5t25m+1blnJt37FbkLMjPZ+/uXaxe+QsGBoZ4t2/Pq6/Pe+Ccwn+LWkFBwdPfBvwf9LRc82e9fnX9hZ4AmZ/d34F8YzF+bUZjR6jXrtzGb9FuCE2Np6NjQ9kTcP/NhniYgTwep9KnpOt9M7v7787WGNSfgh5WEakNGxmxsaVk5zR2hAbp46X6NkdPGlfLe18X96R4Wr47LY0bdj1zY0t4XvWtq/4NDrs3PLZ1PU5Px96RIAiCIAiCIAiC8FD+c90+BUEQBEEQBEH4DxIDvjw00fInCIIgCIIgCILwDBAtf4IgCIIgCIIgPPmehguRn3Ci5U8QBEEQBEEQBOEZIFr+BEEQBEEQBEF48qmJdquHJWpQEARBEARBEAThGSBa/gRBEARBEARBeOKpiWv+Hppo+RMEQRAEQRAEQXgGiJY/QRAEQRAEQRCefOI+fw9NtPwJgiAIgiAIgiA8A0TL3xPKev3qxo7QIKnTXmvsCA3i98nHjR2hQYav39rYEepVPvb5xo7QIMWlZY0doUHUn5KzmJoaT8e5wtLy8saO0CBtXewaO0KDPA2fTw31p+OzeSs+pbEjNEheUXFjR2iQeEl2Y0f4T7E0NmjsCA0jRvt8aKIGBUEQBEEQBEEQngGi5U8QBEEQBEEQhCefGO3zoYmWP0EQBEEQBEEQhGeAOPgTBEEQBEEQBEF4Bohun4IgCIIgCIIgPPmegkGonnSi5U8QBEEQBEEQBOEZIFr+BEEQBEEQBEF44qk9Jbd2eZKJGhQEQRAEQRAEQXgGiJY/QRAEQRAEQRCefOKav4cmWv4EQRAEQRAEQRCeAaLlTxAEQRAEQRCEJ5+45u+hiYO//5CdBw+wYedOMiQS3F1cWDDnNdq1alXvfLEJCUx78w1kMhn+e/b++0FV0G3bCrOXxqPbrAmaVpYkf/kDOYeP/Wvrk8lknD+8nxtnT1NYkI+tixv9J0zGws7hnvPFh93l9J5tZCQnYmBiSscBQ2nTs6/i+YykBM4d2k9qfAzSjHS6DB1Ft+GjlZcRHsqVE76kxMWQl53FoMkzaNmlR4NyG/bujsmgfmiYGFOclEzmjr0UhUfVWV7XqxmmI4egZW+LrLSUoohoMncfoDQ1DQCdJh6Yjh6Olo01atralEkk5AYGIfU7VecyZTIZ5w7v53qgP4UF+di5uNP/hclY1lN3cWF38d+zjYykBAxNTOk4cBhtq9UdQGjwJc767CU7PQ0TSyt6jHyeJm3bK5XJzc4iYP8uom5do7iwEBNLKwa8MBWnJs0ACAu+zLVAf1LioinMy0PXwICS4uLHmrO4sIBAn72Eh1whPzcHa0dn+o17CVsXN6XlZKYmc2bfTqJuXaestBRQw9bZhaFTX2lQzlO7t5JekbPTwGF49+qnnPPqJQJ89ihy9hr1PE3adlA8H+TrQ2jIZTJTk9HQ1MTO1YNez43Dyt5RUSbg4G5Cr14iJ1OCuoYmts4u9Br1PKnxsQQdO0xudhaWdg4MnDBJ8R6okpoQx7FtG0mKjkRX3wDvXv3oMfw51Cq68ORmZ3Fi51aS46LJTE2hZZfujJw+u87l3bp4nv1/r8ajVVsmzFtQZ7nG3NbPHdpH0JEDStP0jYx59ctl91w3wK6dO9i8YSMZGem4ubszf8FCvNu1U1k2KjKSH7//jqioKPJyc7G0tGTg4MG8MvtVtLS0AEhPT+eXn37i7t07xMfFMXTYMD5evKTeHPXZuWMHmzZuICNdnnPBwnfumfP7776tltOKQYMHM+vVqpwnT5xgz+5dhN69S3FxMa5ubrw8Yya9+/R5qJwnD/vgu283WZkS7J2ceXHmbJq2UP37WFJczIbfVxITGUFyfBwezb147/NvlMrcuXGNHz79qNa8n//8G3aOTg+VdXDb5nRt6oK+tjYx6ZnsDgohJSunzvJGejo817EVjhamWBoZcjkyjq2BV5TKdPJw5sWe7WvN+/6G/ZSWl99XvtO+hzl+YC/SrEzsHJ14fvoreHq1UFm2pLiYrX+tJj4qkuSEeNybNWf+4i+UygQHnSPQz5f4qChKSoqxdXRiyNjxtO7Y+b5y1eR36ACHdu8kO1OCg7MLk2e9RrOWqt/z4uJi/ln1MzER4STGx9HEqwUfffW9Upk/fvqBgBN+tebV1tHhrx37npicAGf9T3Jo9w6SExLQ09enZVtvXpw5G1Mz8wfOKfx3iIO//4hj/v78uHo17897g7YtW7Lz4EHe/uRjtv3+B7bW1nXOV1JSwsfffE27Vq24cv36Y0ysTF1Pj+LIGHKO+GHz8bv/+vou+R3hysmjDJ48EzNrW4KOHGD3ymVM//hLtHV1Vc6TnZHG3t9X0LJrT4ZOm0VCZDgnt29Cz9CIJt7yHeqS4mKMLSzwbNuesz57VC6npKgQCzsHvDp1w3fj3w3OrN/BG/MXxiLZsovCiEiMevfAet6rJC79lrLMrFrlNS3MsX59JtKTZ0j/ZzNqOtqYjR2F9bzZJC7+CgBZURE5p85QkpCErLgEHQ83zCeNp7y4mNzTZ1XmuOh3mMsnfBkyZSbm1racP3KAXb/+yIxPvkRbV0913aWnsWf1T7Tq2pNh02aREBHGie2b0DM0pKl3RwASo8LxWfs73YePxrNte8JDrnDw7994ccGH2Lm6A1CYn8+25V9j796EMXPmo29oRHZGGvpGRlX1W1yEvZsHOnp6hF69RLs+A2jStsNjzXl08zrSE+MZOvUVDE3NuH3xPDt//ZHp//scI1Mzxbq2Lv8acxs71NTVGfDCZECNqJvX2PHLD7zy6Vd15sxKT2PXb8tp3bUXw6fPJiEiDL9tG9E3NKJpu4qckeEcWLuaHsNH08S7A2HBl9m/5jcmLfwQO1cPAOLC7uDdq5/8oFQGgT572PHLD8z4+Av0DAwBMLe2Y8ALU7CwsqakpISLx33ZsuI7ZGVlDH5pGo4eTbly+jjbVy5j1qdfYWJuUStvUUEB237+HifPZkx/fzEZKUkcWr8GLR1tugwcBkBpaQl6hoZ0HTKCkAB/la9b8frTUjm5exuOnk3vWQ4ad1sHMLO2ZfxbVd9pamr1n7X2O3aUn378kUXvv0/btt7s3rmTd96ez6Zt27G1ta1VXktLi2EjRtC0aTMMjYwIDwvlm6++oqy0jHlvvaXIa2JqytTp09m3p+689+PY0aMs//EH3n3/A9p6e7Nr5w4WzH+LLdt3qMypqaXF8BEjadZMnjMsNJSvv/qS0rJS3nxrPgBXr1yhY8dOzHn9dYyNTfA9cpgP3nuXVat/r/Ogsj4XAk6z9e8/mPzq63h6teTUYR9WfLGEpStWYWFV+/exvLwcLS0t+g8byfUrl8jPy61z2UtXrMLAsOr7x8jY+IEyVurXqgl9WnqwNeAqadIcBrVtzpxB3fl2z3GKSktVzqOprkFeUTEnrofRtalLncsuKinl693KJ1Xv98Dv8tkAdq1bwwuvvIpHMy/OHD3Cb19/zv+W/Yy5pVWt8pV12XvIcG5evUxBfl6tMuG3b9KkZWtGTJyEgaERF8+c5s8fvuWtxZ/XeVBZn/Nn/Nn052qmvfYGTVu05Pihg/zw2cd8vfIPLFW857LycrS0tRk44jlCLl9U+Z5Pmf06L0yfqTTti/ffqfNArbFyht66ye/Lv+elGbPo0KU72VmZrFv9K6t//I4PvvimVvmnjrjm76E9FW2nN27cYNGiRUyYMIGJEyfyzjvvEBMTA8Dt27f54IMPGDduHNOnT2fVqlXk5+cr5r18+TLvv/8+L774Ii+99BKffvopcXFxSsvfsmULM2fOZOzYsUydOpVly6rOypaUlPDnn38ydepUnn/+eRYtWsTNmzcVz1+/fp1Ro0YREhLCO++8w7hx41iwYAHh4eH/cq0o27xnNyMHDWLMsGG4OTvz7ty5WJqbs8vn4D3n++Xvv/F0c2NAr16PKalq+ecvkvHHWnJPBUC57F9dl0wm46q/H50GDqOJdwcs7R0YMmUmxUWF3LkcVOd81wL8MTQxpd/4SZjb2tO6e2+8Onfj8glfRRlbFzd6j3mB5h27oKWtrXI5bi3b0GPU8zRp11HR6tEQxgP6kHvuIrmB5ylNTiVz+x7KpFKMeqtuNdR2dgQNDbL2+lCalk5JfCLZvsfRsrZE3cAAgOLYePIvBVOSlEJphoS8C5cpvHUXXU93lcuUyWRcPeVH50HDaerdEUt7R4ZMeUVed5fqrruQwFMYmpjKW1xs7WnTow8tunTn8vGqurty0g+nJs3pMmQkFrb2dBkyEifPZlw5WbWzcsnvMAbGpgybNgs7V3dMLK1wbtYCC1t7RZkWnbvTddhzxIeHAuDo2eyx5iwpLiYs5DI9nxuHU5PmmFnZ0H34aEytrLkWcFKxnICDe3Bu1oKstFS6Dh5Bu94DaNe7P6NemUtxUSG375UzQJ5zwAtVOVt26c7FajkvnzqGc5PmdB06Cgtbe7oOHYVTk2Zcrlaf4994h9bdemFl74iVgyPDp8+mIDeHxMiq768Wnbvh0qwFplbWWNk7MGD8S5SVlODSvCXePftiaWfP4IlTMTQ25erpEyrz3rxwjpLiYkZMn42VgyPN23eiy+DhXPTzRSaTb++mFlYMmjiFNt16oatvUOdrLysrZd/fq+n93DhMVexsVtfY2zqAuoY6BsYmir/qJyrqsnXzZoaPHMnoMWNxdXNj4bvvYmFpyZ5dO1WWd3RyYsTIUTRp2hQ7Ozt69e7D4CFDCQ4OVpSxs7dn4aJFjBg5CmNjk3ozNMSWzZsYMXIUY8aOxc3NjUXvvoeFpSW7d6rO6eTkxMhRVTl79+nDkKFDCamWc+GiRUx7+WVatmyFk5MTs2a/SvPmzfE/deqBcx47sJfu/QbQe9BQ7B2dmDT7NUzMzDjle0hleR1dXaa+9gZ9Bg/FzKL2yYzqjExMMDEzU/ypa2g8cE6A3l4enLgexvXYRJKzctgScBkdLU3auTvWOU9mXj57L1znYkQs+UUl91x+TmGR0t/9Oumzny59+tFjwGBsHZ2YMHM2JmZmBBw9orK8jq4uL85+nR4DB2NaR12Of3kWg8eMw9WzKVa2dgyfMBEnd3euXax7G63PkX276TlgEP2GDMPByZlpc+ZiambOiUOq94l0dHWZMfct+g0djrmFpcoy+gYGmJqZK/5Sk5JITU6i7+BhT1TO8Lu3MbewZOjo57GytcWzuReDRo4mIvTOA+cU/lue+IO/srIyvvjiC7y8vPj555/54YcfGDVqFOrq6kRHR/Ppp5/SpUsXfvnlFz766CMiIyNZsWKFYv7CwkKee+45li1bxldffYW+vj6ff/45JSXyL8jAwED27NnD66+/zu+//86nn35K06ZVZ5PXrl3LmTNneOutt1ixYgUuLi4sWbIEiUSilHPdunVMnz6dn376CSMjI3788UfFTs2/raSkhDthYXRpr9ylo0v79ly7dbvO+QIuBBF4IYh3Xnv93474RJFmpJMvzca5eUvFNE1tbRw8mpIUVfdBe3J0BM7NWipNc/FqRWpsDGVlqs/IPjIaGmg7O1J4+67S5MLbd9Fxd1U5S1FMHJSVYdijK6ipoaajg2HXjhRFx1KeV/vsK4CWowM67q4UhkWofD65sIA8aTYu1epOS1sbR4+mJEapngcgKSpCaR4AV6+WpFSru6To2mVcvFqRWO09Cb9+FVtXNw7+vZrfPnybDd8s4ar/8VrbWnZGOvk5UuXX9phyysrLkJWXo1nRja2SppYWCRGVZcqJvBGMobEpedJsLh4/wobvlnLn8gV5Ts9mJETW/VlMiorAtVbOVqTERityJkZF4OJVu0xCZN2vv7iwEJlMho6+vsrny0pLuXL6OADN2nVQes7Nq2WdmROiwnHybKp0kOTeohW52VlkZ6TXmUeV0/t2YWJhSetuPest+yRs69np6fz5ySL+XvIBh/75nez0tHuWLykp4e6dO3Tp0lVpeucuXbh+7VqD1hkfF0fQ+XO0a/9gLWUNocjZVTlnly5dG5wzLi6O8+fO0a5d7e6I1eXl52NkXP9BsyqlJSXERITT0lt5HS3btifizsPvDH/x7gLemTmVHxZ/xJ3rDXvddTE31MdYX5fQxFTFtNKyciJTMnC1evjueloaGvxv3GA+GT+EV/p3xcH8/k4ClJaWEBcZgVcbb6Xpzdu0JeoRH1gUFRSgX9H74H6VlpQQHR5G6xrveat27Qm7U/c+0f06dfQwDs4uNHnA1sl/K2cTrxZkZUq4euE8MpmMHGk258+com2HTg+8zCeKmtrj+/uPeuK7febn55OXl0fnzp2xs7MD5GcPAZYtW0avXr0YO3asovzcuXOZP38+WVlZmJqa0qOHcqvI22+/zcSJEwkNDaVly5akpaVhbm5Ou3bt0NTUxNramiZNmgDyA8fDhw/z5ptv0qlTJ8Xyr127ho+PD1OnTlUsd8qUKbRp0waAF198kffff5+MjAwsLVWfmXmUsqRSysrLMa/oTlbJ3NSMC5lXVc6TLsngqxUr+PbjTzCoY0fvvypPmg3Ir72pTt/ImNzsrHvMJ8Wpae15ysvLKMzNxcDE9FFHVdAwNEBNQ4MyqfJ1H2XSXHSbq94pKpNkkvLzaqxmTcf8xedBTY3i+ARSf/2zVlmHrz5Fw9AQNNTJ9jlK7plzKpcpKS4EVNSdsTG5WVl15s+TSnFuprruCnJzMTSRHwQZ1FiugZGx0kFcdnoaIWdO0r7fYDoPGkZqQhwnd2wGoF2fAYpy+RXvcU2PI6e2rh52bh4EHTmIhZ0DBsYm3LkcRFJUBKYV3Xjyc3MoKSoi+Iy8pWzEy3OQJCfhs+4PtLS1MTAyJjcr8x45s3FurrzDoW/c0Jyq6wbgxM7NWDs6Y+/mqTQ94nowB//5nZLiYvQruriZ29jVWL8JeXdu1ZnXyNS8VvnK5+prwasUdesGty9fYOZHSxtUvrG3dVtXdwZPnoGZjS0FuTkE+R5k2/KvmXqP/FlZWZSVlWFmrlxf5ubmXLpw4Z7re/WVmYpr5Z4bM4bX5s5rUM4HUZnTXEXOixfu3WIze+ZM7t69Q3FxMaPHjOX1eXXn3Ll9O2mpqQwbPuKBcubmSCkvL8e4xntmbGrKrWvBD7RMAFMzc6bMmYurZ1PKSks4d+okPy75H+8u/ZqmD9gN0FhP3g25ZotcbkERJvqquyg3VKo0h21nr5AokaKjpUkvL3feGNaLH/efJD1H9cnAmvKkOZSXl2NUoy6NTEy5+5AHvtWd9j1EliSDzr0f7DrPHGnFe15jn8jE1IybIar3ie5Xfl4eFwLPMGHqjAdexr+Vs0nzFsxd9AG//fgdJcVFlJWV0cq7Pa++veiBlyn8tzzxB39GRkYMGDCAxYsX07ZtW9q2bUuPHj2wsrIiPDycpKQkzpw5oyhf2QKQnJyMqakpSUlJbNy4kdDQULKzs5HJZJSXl5OWJj/72qNHD/bv38+sWbNo37497du3p0uXLmhpaZGUlERpaSleXl6K5WtoaNC8efNaXUddXV0V/1f+GGZlZdU6+Dty5Ai+vr7UZ0CXrgzq3fu+6qpmF0IZsjq7FX763feMGzGC1tVe23/VycJcVi6q2rkYPUd+DUztupFR33me2rPI6nji31KjNVmtWoYa1I2NsJgykdygS+RdvIq6rg6mo4ZiNWsaKT/9pjRfyo+/oqajg46bC6ZjR1KankHehcscT05gxd2qa0G/aCM/CVKr7mTUWwe1Pp8yFdNrLVb5tclkMmycXen13DgArJ1cyEpN4cLRQwTs36UoN+a1+apDPKacw6bOwnfzWv78ZBFq6upYO7rQrEMX4sLu8Ms7cxXl7d2bEBd6G0t7R9xbtiE5NpqrZ05gYGRSf05Vr61WTtWvRZWTu7aSEBHGSws/RL3GaGpOTb2Y+dFS8nNzuHTyKLcvXaCw5rUmMpmKUHVGuXcYFfJzc/BZ/xejZs5B10B1t9CbF85yZNM6xePG3tbdWrRWemzr6s7azz7kdtBZGHDvQZ5Ufg7rWffnX31Ffl4+YWFhrPzlZzauX8e0lx9857Qh7ud3p9IXX31Ffn4+YWGh/PLzz2xYt47pM2rnPHHiOL/8vILPv/xKcfL3wYMqP5TJ6s95L7YOjtg6VHXF9GjmRXpaCr77djf44K+9myPju3krHv91vOKkW81NQ632pPsVk5ZJTFrVCaXotAzeGdWPnl7u7L1wn9f7P8Bns6GCg86xd+M6Zsx/B3MV17zdj9rbkAy1erf8hjl76jiy8nJ69BtQf+F6POqcCbExbPzjN0ZPfInW7TqQlSlh29q/WLvqZ+Ys+PfHVPi3qYnRPh/aE3/wB/LWutGjR3P58mWCgoLYsGED//vf/5DJZAwePJjRo0fXmseiom/5559/joWFBfPmzcPCwgINDQ3mzp1LacWF01ZWVqxevZqQkBCCg4NZs2YNW7Zs4ccff1QsqyE/EBrV+vlXllfV7XPo0KEMHTq03uUVJSbVW6aSqbExGurqZGQqd0XNzMqq1RpY6VJIMFevX+OvTZvkWZFfmN1txHDem/cGY4cPb/D6n3RdtPWZ/HbVaIBlFe99njQbo2ojX+Xn5KB/j4v1DYyNyZMqdyXMz81BXV2jzp3RR6UsNw9ZWRkaNfJpGBlSJlU9GIFRnx7IiovJ2lN17UD62k04fr0YHXdXiiKqRgktzZB/dkoSk9AwNsRk5BDyLlymm6UNzY1NFeVKKgYHqF130lqtTNXJ6065xakgV6pUdwbGJrXrNydHqdXGwNhE6fo+AHNbOwoL8pj24WeKaWV1DIzwuHKaWlkzcf77lBQVUVRYgKGJKQf/Xo2lrT39J06hvLSM9V8vxsLWjrjQ2+RJszE2M8fC1o47ly+gpqZeq7VKOadJrZz5Oapy1iiTK0XfqHZXr5O7tnDn8gVeeOs9TC1r73Bp6+igr2+DmbUNts6u3L50gVuXgmjqXdX1Mz9HikEd15LVlbfyuYZIS4wnNzuLrSuqRrar/I79dt5MZn3yJZ5t2jHZsWrAiydtW9fW0cXC1p7MtJQ6y5iamqKhoYEkI0NpemampFYrW002NvJBVtzc3SkvL+ObL79k0pSpaGo++p/6ypwZNXNKMjFXMeiPUk7bqpxlZeV8/eUXTJ6qnPPEieN89umnLP7ss4ca6dPQyBh1dXWkNVr8c7Kza7UGPiz3Js24EHC6weVvxiUTk151HbCmhnyn1khPh6z8AsV0Q10dcgru//q8e5HJIC4jC0ujhnetNDA2Ql1dnZwavRJypVkYmzz8daTBQedY/+tPTJ03/6FG+jQylr/n2TX2iaTZWbVa2R7UqaNH6Ni9J4YNuIa3Lv9WzgM7t+HetBkjnp8AgLObOzq6unz5wSLGT3kZC6uG9bQQ/ruemsNnNzc3xo8fz9dff02rVq04fvw4Hh4exMbGYm9vX+tPR0cHqVRKXFwcEyZMwNvbGycnJ/Lz8ykrK1Natra2Np06dWL27NksW7aM2NhYbt26hZ2dHZqamty6VdWVqaysjDt37uDs7Py4q6BOWlpaNG/ShAtXlLsJBF29SpsWqlv2tvy2mo0rVyn+Xp0yFR0dHTauXNXog788avrq6pha2Sj+zG3t0Tc2IfZu1ftaWlJCYkQYdjW6u1Vn6+pBXKhyt7bYu7ewdnZBQ+NfPo9SVkZxbDy6zZVHN9Rt3pSiyGiVs6hrayOrOXhO5chu9zqhoaaOWsVOmL6mJg76Boo/FwNDDIxNiLmjXHcJkWHYu3nUuUg7Nw+l+gaIuXMLm2p1Z+fqQezdm0plYu/eVOqCaO/ehMyUZKUymakpmFhYYmZlo/izsLWvdfD0OHNW0tLRwdDElML8PGLu3KBJu47yfHb22Lq6UZifV1GfNxWvxdDUjISIUBzc6/4s2rl5EFMr501snF0VOe3dPJTep8rX4uCu/PpP7NzM7UtBvPDWu1jY1t+6oqGpiYamZq33IerOzTozO7h5EhceSmlJcVX52zcxNDHFpI5BC2qyc3HnlY+/YOZHSxV/Tdp44+TZlJkfLcXU0godXb0nelsvLSlBkpqMQbUTKjVpaWnRrHlzLtToOnkx6AKtKy4taAhZuYyysjLK73M0x4ZS5AxSznnhQtD95ZSV18rpd+wYn336KZ8sXkL/AQMfKqemlhYuHp7cqtGN7lbIVTyaN3+oZdcUFxV5X0PpF5WWkpGTp/hLycpBml9IU/uqEzCa6uq4W1sQnSa5x5IejJ2ZCTkFhQ0ur6mphZO7B3euhyhNv3M9BLemD1eXV84Fsv6Xn5gy9y3ade3+UMvS1NLC1bMJN4KV3/MbwVdp0vzheztFhN4lNiqSvoPrP5F/L/9WzuKiolq9Nyof1+yl8lQS1/w9tCf+4C85OZl//vmH27dvk5qayrVr14iOjsbZ2Zlx48YRGhrKypUriYiIIDExkQsXLvDrr78CYGhoiLGxMb6+viQmJnL9+nVWrVql1Ern5+eHr68v0dHRJCcn4+fnh6amJvb29ujq6jJ8+HDWrVvHpUuXiIuL47fffiMrK4vhT1jL2KSxz3PQ7xh7jxwmKjaWH1f/RnpGBs9XXCexcu3fzP3gA0V5D1dXpT9rSwvU1dTwcHXF+CHOZD0oNT1dtD3d0fZ0B3U1tGys0fZ0R9Pm0Z+hUlNTo12fgVw6dpjwkMukJyZwdNPfaOno0LxDF0U53w1r8N2wRvG4Tc8+5GRlcmrXViTJidw4e5pbQYF06D9EUaastJTU+FhS42MpLSkhP0dKanwsWdXO9BcXFSrKyGQyciQSUuNjkUqUz6DXJD3uj2G3Thj26IKmrTVmE8agYWJCzhn5LRlMR4/Aev5rivIFN26h7eSAyYjBaFpZou3kgMW0lyiVZFIcGw+AUd+e6LVqgaaVJZpWlhh274LxwL7kXbhcd931HchFv0OEBV8mPTEe341r0NLWoXnHqro7vP4vDq//S/G4bY++5GRlcnLXFjKSE7l+9jQ3gwLpMKCq7tr3HUhs6B0uHPVBkpzEhaM+xIXepX2/QYoyHfoNIik6kiDfg2SmpRB69SJX/Y/j3at/1evOyyUtIQ6P1t4A3L1ykdCrl/BZu/qx5Yy+fYOom9fJTk8j5s5Ndvz8PWbWtrTsWtXVr9OAoYRevSS/PtDXh2Nb13P7UhClJSVoaevgVS3nofV/cmh91bWabXvKc57YuZmM5ESunT3NjaBAOinlHERs6G2CfH3ISE4iyNeHuNA7dKiW02/bBm6cD2Dky3PQ1TcgT5pNnjSb4iL5DmFRQQEBB3aTFB1BtiSD5JhofNavkXedT0wgJMCf9KREjm3fRG52Fu0q7jN4au8Otvz0rWI9LTp3RUtbG591f5GWEM/dq5c4f9SHTgOHKPWsSImLISUuhqLCAgrz8kiJiyE9KQGQtz5aOTgq/eno6aOto4uVgyMaKlq3GntbP713O/Fhd8nOSCMpOhKfv3+jtKiIFl3uvXP74qRJHDp4kP179xIdFcXyH38gPT2NMc/Luzv/tvJX3pxbNVDX4UOHOOHnR3R0NAkJ8Rw/dozfVq2kb//+aFcbZCc09C6hoXfJy8tDKpUSGnqXqMjIe2a5l5cmTcbn4AH27d1LVFQUy374gfS0NMaOk+dc9euvvPF69Zw+HK/MGR+P37Fj/LZyJf2q5Tx21JfFn3zM3Hlv0K5dOzLS08lITyc7u+5rVeszaNQYAk8e5/QxXxLj49iy5neyMiX0HSz/Hd+18R9+WKx8z77EuFhioyLJlUopKiwkNiqS2Kiqujp2YB9Xg86RkphAQmwMuzb+w9UL5+k3fOQD5wQ4fTuC/q2a0NrZDltTI17s2Z6i0lKuRsYryrzUsz0v1bhnn72ZCfZmJuhoa6Kvo4W9mQk2JlW/5YPbNqOZvTXmhvrYm5kwsXs77M2MORta931iVek34jmCTp3k7PFjJMfHsfOfv8iWZNJzkHzb2L95A798/qnSPEnxccRHR5EnzaGosJD46Cjio6vWeznwDOt+Wc5zk6bg6dUCaVYm0qxM8nLrvrdhfYaOfp4zJ45x6uhhEuJi2fjnb2RJMug/TL5PtH3d33zz8QdK8yTExhATGUFOjpTCwkJiIiOIUTFA1infQ9jYO9C8VcNPcjzOnO06d+FK0DmOHzpIanISobdusvGP33D18FR5+wjh2fPEd/vU0dEhMTGRb775BqlUiqmpKX379mXcuHFoamryzTffsHHjRj788EPKy8uxtbWla8XoY+rq6rz33nv88ccfvPHGG9jZ2fHKK6/w9ddfK5ZvYGDArl27WLt2LaWlpTg5OfHhhx8q7lH08ssvA7BixQpyc3Px8PBgyZIl9Xa/edwG9elDdo6UtVu2kC7JxMPVheVLP8fOxgaAdImEhKTERk5ZN93mTXH8pao7l8WsaVjMmob00FFSvvrxHnM+mI4Dh1JaUsyJHZspys/D1sWdsXMXKt33S5qpfDBmYmHFmDnz8d+zjesBpzAwMaXvuJcU9/0C+Y2qN39XNZjD9XR/rgf64+DZlAlvvQdASmw0u375QVHm/OF9nD+8D6/O3XnRsO6uHvmXg5EY6GMybBDmxsYUJyWRuvJPyiTyLjgaJkZoWVW1ohTeDSd97UaMB/XHeGA/ZCUlFEXFkPrLH8iKK1pg1NUxHTsSTQszKC+nJC2DzL0H6xzwBaDTwGGUlpRwYscmCvPzsHV1Z9y8hUr3pMup0Y3FxNKKsa+9jf/urVwLOIWBsXwY/cp75wHYu3sy4uU5BB7cw9lD+zC1tGbEjDmKe+eBfHj952bPI+DAbs4fOYCRmQXdR4yhbbWbm0deD8Z301rF42sBp7gWcAojM4vHllN+0LSL3KxMdPUN8GzbgZ6jxiq1Gnm2bc+gF6cR5OtDaWkJ186eRk1NDTU1+S0YqueU1hhd2NTSinGvL+Dkri2EVHwW+4+fpLjHH4CDuycjZ7xG4MHdBB7ai6mlNSNnvqa4xx9A8Bl5l7Pt1bY9gG7DnqPHiDGoa6iTnpTA9XNnKMzPQ8/AEFsXN6Yu+h/JsdEEHt5PnjQbSzsHJsxbqGjFy83OIjOtasRCXT19Jr71Lke3buCfb5agq29A5wFD6TxA+cz52q8WKz0Ovx6MsbkFc7988O+AxtzWc7MyObzuDwryctEzNMLO1Z2JCz/CuJ5ukQMHDSY7O5t/1v5NRno67h4e/LD8J8V1bxnp6SQkJCjKa2hosH7dP8THxSGTybC1tWXc+Am8+NJLSst9ecoUpccBZ85ga2fH7n37G1KVtQwaLM+59u81ipzLflqhyJmenk58QtVBi4aGBuv+WVstpx3jJkzgxZcmKcrs3rWLsrIyli/7keXLqt73du3b89vvfzxQzs49e5OXk4PPzm1kZ0qwd3Zh/v+WYFFxD9zszEzSkpVbsld8sYSMap/hpe/Irx/9a7e8G31paQnb1/1NliQDLW1tHJyceet/i2nzkCMqnrwRhpaGBs93aYuejhaxaZn8ceys0j3+TA1qD9L2znP9lB63dLJDkpvPl7uOAqCrrcX4bt4Y6+lQUFxKoiSLlUfOEJeedV/5OnTvSV5ODr57diDNzMTOyZnXP/hYcX1edlYm6TV6Baz+5nMkaVWj3H77/kIAftkmv99kgJ8v5WVl7Fr3N7vWVd371rNFy1o3hG+orr36kJsjZf/2LWRJMnF0ceGdTz/H0lq+T5SVKSE1WXmf6Meln5CeWvWef/K2fKyA9furbmNRkJ/P+TP+jJk4+aGuGf03c/YaMJiCggL8fPaz5e8/0TPQx6t1W158+ZWHziv8N6gVFBT8B9qA/3vu55q/xpQ67bX6Cz0B/D75uLEjNMjwvY/m5sv/piNjn2/sCP8p6k9J15LK65GedIUl//JtVx6RF7q2bewIDfI0fD5vxCXXX+gJsPfSzfoLPQGGtG1af6EngImeXv2FhAZr6/KQgyo9JsmvL3xs67L9bVn9hZ5CT8evuSAIgiAIgiAIgvBQnvhun4IgCIIgCIIgCKiJdquHJWpQEARBEARBEAThGSBa/gRBEARBEARBePKpP/nXIT/pRMufIAiCIAiCIAjCM0C0/AmCIAiCIAiC8OR7CkYgftKJlj9BEARBEARBEIRngGj5EwRBEARBEAThiacmRvt8aKIGBUEQBEEQBEEQngGi5U8QBEEQBEEQhCefGO3zoYmWP0EQBEEQBEEQhGeAaPl7QmV+9l1jR2gQv08+buwIDTLw8y8aO0KDHHoK6lPzKRlpS1396Ti3VV5e3tgRGqS07OnIKZPJGjtCg/iG3G3sCP8ZUWmSxo7QIE3tLBs7QoPoaD4du4ZWxgaNHaFBnpbvpKfGU7IP8iR7OvaOBEEQBEEQBEEQhIfydJzeEQRBEARBEATh2faU9Op5kokaFARBEARBEARBeAaIgz9BEARBEARBEIRngOj2KQiCIAiCIAjCk08M+PLQRMufIAiCIAiCIAjCM0C0/AmCIAiCIAiC8MRTEzd5f2ii5U8QBEEQBEEQBOEZIFr+BEEQBEEQBEF48qk9Pe1WPj4+7N69m8zMTJydnZk9ezYtW7ass/yVK1fYvHkzsbGxaGpq0qJFC2bMmIGDg8MjzfX01KAgCIIgCIIgCMIT7syZM/z555+88MILrFixAi8vL5YsWUJqaqrK8snJyXzxxRe0bNmSn376iS+++IKioiI+++yzR55NHPwJgiAIgiAIgvDkU1N7fH8PYe/evQwYMIAhQ4bg5OTEnDlzMDMz4/DhwyrLR0REUFZWxrRp07C3t8fd3Z0JEyaQlJREdnb2Q2WpSXT7fEoZ9e+N8fBBaJqYUJyYhGTTDopCw+ssr9vKC9OxI9F2sEdWWkpRWASSrbspTZGfgbCcNQ3DXt1qzVdeVETsq283OJdMJuP84f3cOHuawoJ8bF3c6D9hMhZ2926yjg+7y+k928hITsTAxJSOA4bSpmdfxfMZSQmcO7Sf1PgYpBnpdBk6im7DRysvIzyUKyd8SYmLIS87i0GTZ9CyS48GZ28I3batMHtpPLrNmqBpZUnylz+Qc/jYI13HvTRm/VbPcPbQPq4F+lNUkI+tizsDJ07Bsp4McWF3ObV7K+lJCRiamNJp4DC8e/VTKhN69RIBPnvITk/DxNKKXqOep0nbDornr/ofJyTQH6kkHQALWwe6Dh2JR6u2ijJ50mxO79tJWMgVigsLUFNTw9zWnsEvTcPJs1md+dIS4ji2bSPJMZHo6hvQtlc/ug97DrVqPwCxoXc4uWtLxWswo/OgYbTr3V9pOUUFBZw5sIu7Vy9RmJeLkZk5vZ8bT/MOnQFY/fE7SCUZtdavoamJmpraY63P6s77HiTgwG68e/dn4AtTFNMPb1jDzaBA5cJqajh6NH1sOYN8fQgNuUxmajIamprYuXrQ67lxWNk7AlBWVkrAgT1E3bpOVnoq2rp6OHo2o8eo5zEyMyfoyAGlbabf+En1bzPhdzmzZ7tim+nQf0itbeb84f2kxscqtpmuw55TWkZxYSHnDu0l4tpV8nNzsHZwpvfzE7F1cau1vosnj3HW9xA52VlY2zswZOIUXJo2rzNfSnwch7esIyEqAj0DQzr07k/vkWOUPq+VYsPu8s8PX2Jpa8/cz75RTE9NiOfU/l0kxUaTlZ5Gn1Fj6fvcuDrX+agzRt+9zfE928lITqKkuAgTC0va9+xL9yEjFMsoKy0l4PABQs6dQZqZiaWtHQPHTcSz2jZfl15eHrRzc0RXW4tESTZHrt4iPSfvnvM4W5oxsE0zrIwNySks4vzdKK5ExSuV6eTpTHt3J0z09SgoKiE0KZUT10MpKSurtbzuzdzo16oplyJiuRQew7nD+7ke6E9hQT52Lu70f2Fyg7Yh/z3byKjYhjoOHEbbap9FgNDgS5z12avYhnqMfJ4mbdsrni8vL+fcoX3cvniePGkWBsameHXqQrdho1HX0ABg2ZuvqFx/36HDmTT7dcXjU0d88N23m+zMTOydnJk4YzZNWqjuylZSXMzG31cSGxlJUkIcns29WLT06zpfa9jtm/z46UfYOjiy5KeV96yX+hzcu4edW7cgycjAxc2VOW+8Ras2qj83165eZc/O7dy9fYv8vDzsHBwYM/4FhgwfoVTm/QVv1Zr3j3UbcXJxeaicu7ZtQZIhwcXVlVffeLPunMFX2btjO3fv3K7KOW4Cg6vnDL7KBwvm15r393UbcHJ+8JxCw5WUlBAeHs7YsWOVprdr147bt2+rnMfT0xMNDQ2OHj3K4MGDKSoq4vjx4zRp0gQTE5NHmk8c/P2LysrKUFdXV/lj/DD0O3fAfPILZKzfQlFoBEYDemPzzjwSPlxKmSSzVnlNSwts5r+O9NhJ0n//B3VdHcwmjpXP895iADI2bSdzx16l+Ww/XkTh3bD7ynbJ7whXTh5l8OSZmFnbEnTkALtXLmP6x1+iraurcp7sjDT2/r6Cll17MnTaLBIiwzm5fRN6hkY08ZbvAJYUF2NsYYFn2/ac9dmjcjklRYVY2Dng1akbvhv/vq/cDaWup0dxZAw5R/yw+fjdf2Ud99KY9Vvpgt9hLp3wZdiUVzCzseXc4f3s+OUHXvn0K7R19VTOk5Wexq7fltO6ay+GT59NQkQYfts2om9oRNN2HQFIjAznwNrV9Bg+mibeHQgLvsz+Nb8xaeGH2Ll6AMgPpEaPx8zaBlm5jJtBgez741emvv8pVg5OyGQy9v7xK4X5uZQUF9Fj5FjSE+OJvHmdnb8u45VPv8LY3KJWvqKCArb/8j2Ons2Y+v5iJClJHFq/Bi1tbToPHFb1GlYto3W3Xox8eQ7xEaEc27oBfSMjmrXrBMgPQrb/8j26+gaMfmUuRmZm5GRmoqFZ9VU77f3FlJeXKx6fO3KAq/7Had29N2179n2s9VkpMSqCa2dPY+XgqHJ9Ls1aYOfqzuWTx+g3/iXMbWy5csrvseWMC7uDd69+8oMmGQT67GHHLz8w4+Mv0DMwpLS4mNS4GLoOGYm5nQNFhQWc2budfatX0LxjV66cPMqgSTMws7blgu8B9qxazrT/fXHPbWbf7z/TsksPhkydRWJkGCd3bK69zZhb4tGmPecO7VW5HL+t60hPjGfw5JkYmppx59J59qxaztQPP8NUv6rOblw8z5FtGxk+6WWcmzTl4kk/Nv38PfM++xYTC8tayy0qyGfD8m9wadKM2f9bSnpyEvvW/oGWjg7dBw9XKluQl8eev1fj3rwl0izl34eS4iJMLa3wat+JE3t3qHwN/2ZGbV1duvQfjLWDE1ra2sRFhHJww1q0tLXp1G8QACf27uTa+QBGTXsFKzsHwm9eY9uqn5j5wWLsnF3rzNutqRtdmrhy4NINJLl59PTyYFKvjqw+GkBxae2DNAATfT0m9mhPSHQC+y5ex8nCjKHtvMgrKuFuYgoALZ3s6N+qGT5XbhCXnompgT4jO7REU10dnys3lZZnb25COzdHUrJyALjod5jLJ3wZMmUm5ta2nD9ygF2//siMT76scxvKTk9jz+qfaNW1J8OmzSIhIowT2zehZ2hIU++KbSgqHJ+1v9N9+Gg827YnPOQKB//+jRcXfIidq7t83ccOE3zmBEOnvIKlvSPpifEc2bAGDU0tug4dBcCcL5cB4Gkjfz9jIsL49evP6dC9pyLPxcAzbP37TybPfh1PrxacOnKIn79cwpKfVmJhZV0rf3l5OVra2vQbNoLrVy5RkF/3wXdebi5rf15O89ZtyVJxgux++J84zupfVjBvwUJatm7Dwb17+OS9d/l93QasbWxqlb918zqubu6Mf/ElzC0suXwxiJ9/+B5tbW36DRykVHb1P+sxMjJWPDYxNX2onL//+jPz3l5Ii9at8dm3l0/ff4/V/6xXmfP2jRu4uLsz7sVJmFtYcOXiBX7+8Qd5HdfMuXY9hsZGVTlNHjznE+UxjvZ55MgRfH196y03ZMgQhg4dqngslUopLy/HtMZnw9TUlJCQEJXLsLGx4fPPP+ebb75h9erVyGQy3N3dWbJkycO8BJXEwd99OHHiBH/99Rfr1q1DS0tLMf2HH36goKAADw8PAgMDGTt2LNu2bSM1NZWtW7eip6f6S/1BmQwdQG7AOXL95WfjJRu3o9e6JUYDepO1Y1+t8tquzqCpIT+4k8kAyD7gi+2HC1A3NKA8Nw9ZQSFlBYWKeXSauKNlbUX67/80OJdMJuOqvx+dBg5T7CANmTKT3/+3gDuXg2jTo4/K+a4F+GNoYkq/8ZMAMLe1Jzk6kssnfBXLsXVxU5wtv3jMR+Vy3Fq2wa1lGwCOblrb4Nz3I//8RfLPXwTA5qNF/8o66tLY9VuZ4crJY3QZNFyx8z5s6ixWfTif25eCap2NrhQScApDE1MGvDAZAAtbe5KiI7l43FexnMunjuHcpLliR8RiqD2xYXe4fPIYI2fIDwI827RTWm6v58YREnCKxKgIrBycyExNISk6Aks7B1p360mP4aORlZez8oP5oAZXT5+gz5gJtfLduniOkuJihk+bjZa2Nlb2jmQkJ3HpuC+dBgxFTU2N4DMnMTAxY+DEqfJ8dvYkRkdy0e+I4uDv+rkA8nNymLTwI8UBn4mFldK69KvtNMhkMm6cD0BDU5M+Yyagpa3zWOsT5DvpPuv+YOikGZw9vF/l+tQ1NLh+7gxdBg+nTffeANg4uT62nOPfeEdpucOnz+aXRfNIjAzHo7U3Onr6THhTvj1Wtr70nziVjV8v5sqpY3Ssts0MnjyTPz5eyN3LQbSuY5u5HuiPgbEpfRXbjB3JMVFcOXlU5TZz6dihWssoLS4mPOQKI2a+jmMTeYtz12HPEXUjhGuBp3CcMElR9vyxw7Tt3osOveUtosMnTSfi5jUu+h9n4PMTay37WtBZSoqLGDPzNbS0tbF2cCI9KZHzxw7TbdAwpROO+9f9SdtuvQAZty5fVFqOg5sHDm7yOj5zSPV7/29mtHdxw75aK6iZlTW3r1wiNuyu4uDv2vkAegwdSdOKbb9T34FE3b7JuaOHeH7W3DrzdvZ04dzdKMVB24GL13l7ZD9aOtlxtUZLXqX27k7kFhZxNOQOABk5eTiYm9C1qatiOY4WpiRIsrgRmwRAdn4h12MSaeagvKOuo6nJmE5tOHj5Jr28POTf36f86DxouOKgbciUV1j90dvcuRSk1KpcXUigfBvqP6FqG0qOieLycV/Fcq6c9MOpSXO6DBmpKBMXeocrJ48xYsYcQH6A6NHKG4/W3gCYWFji0dqbpOhIxboMjOWtCyZmZgAEXwzCxt6BZi1bK8ocO7CX7v0G0GvQEABemjWHm1cv4+97mOenTK+VX0dXlylz5gEQHxN9z4O/9at+plu/AfLfmXOBdZZriD07tjFo6DCGjZS3xs+dv4DLFy7gs28PM159rVb5F6dMU3o8cvRYrl29SuBp/1oHVaamZg91wKecczsDhw5j6Ej599/rb73N5QtB+Ozfy4zZc2qVnzhlqtLjEaPHEFJHThMz0//OAV8jGTp0qNJB3f26n8afzMxMfv75Z/r370/v3r0pKChg06ZNfPvtt3z55Zeoqz+6K/XENX/3oUePHpSXl3P+/HnFtLy8PM6dO8egQfKNLiUlBX9/f95//31+/vlntLW1H20IDQ20XZ0puKHcbFxw4za6nu4qZymOjoHSMgz79AA1NdR0dTDs2ZWiyGjKc1V/ERv16UlxfCJF4ZEqn1dFmpFOvjQb5+ZV3T80tbVx8GhKUlTdXVKToyNwbqbcZcTFqxWpsTGUlZU2eP3/dU9C/Uoz0smTZuPi1UoxTUtbG0fPZiRE1p0hKSoC1+bKGVy9WpESG63IkBgVgYtX7TIJkREql1leXs6dS0EUFxVi7+YJyLuIAWQkJ+FakVFNXR0NTS109Q3qzJgYGY6jR1O0qm2vbl6tyM3OIjsjvSJfOG418rm1aEVyTNVrCA+5goOHJ37bN7Lyg7dYs/QjAg7uqbOes9JTKSkqwqO1N1raOsDjr8+jW9bR1Lsjzs286lxfQkQoedJsQgL88d38D3k50kZ730HenVImk6Gjr3+PMgUAFObl4tyshWJ61TZT9/KToiNxad5CaZpL85b3tc2Ul5cjKy9HU1P5HKuGljaJ1eqsrLSUxJgoPFq0Virn3qI18RGqe17ER4Tj0qSZ0ufVo2VrcrIyyUpPU0y7ePIYudJseo8c06DMdfk3M1aXFBtNXEQYLk2rPotlpaVoVjvZCqCppUVseGideU0N9DDU0yEyNV0xrbS8nLj0TBwtTOucz9HchMgU5RaniJR07MyMUa/YiYtLz8TG1Ah7c/mBkrGeLk3srYlITleab3j7FtxOSCEmTQJAWnKy/Luz2vagpa2No0dTEu/1WYyKUJoHwNWrJSnVPotJ0bXLuHi1IrHa74KDexPiwu4gSZYftGYkJRIbehu3Gu9ppcKCAi4GnKHXwMGKaaUlJcRGhNOirfJJuBbe7Yi4q7orW0OdOuJDdlYmI8a98FDLAXmXu7C7obTv1FlpevtOnbh180aDl5Ofl4ehoWGt6W/Nmc2k50fzwcL5hFy98lA5w0NDad+xk9L0dh07cftGw3MW5OdhaGRUa/r8Oa8yedwYPlz49kPlfOKoqT++vwdkbGyMuro6mZnKvS2ysrJqtQZW8vHxQVdXlxkzZuDh4UGrVq145513uHHjRp1dRR+UOPi7Dzo6OvTt2xc/Pz/FNH9/f/T19enUSb7xlpaWsnDhQjw9PXFxcUGjoi/9o6JhZIiahgZlUqnS9DKpFI06+gSXpktI/v5nzMaOxGXNLzj/tgwtJwdSlq1SWV5NTxf9zu3JORVwX9nypPILUqu3bFQ+zquRV3k+qcp5ysvLKMzNva8M/2VPQv1WZjCosTwDI2PypXVfkJwnzUbfWPnzqW8sz1BQkSFPmq16uTnKy01LiGfFwtdZ/varHNu2ntGz31B0VzS3tcXQ1AyZrBxNLW3KSksJOupDTpaE8rJyRX5V+Qxq5Kt8XDmPqtdgYGSi9Bqy0lO5e+Ui5WVljJu7kJ6jxhIScJLTe3eqXG/4tWAAWnXtqTT9cdXntUB/stJS6TlS+bqE6ty8WtFliPysdPfho0mOiWT7z99TWlLyWN/36k7s3Iy1o7PioL+mstJSzuzdgZ27/HmV28w9lp8vzX7obUZbVxc7Vw8uHPUhNytTfrLi4nmSoyOUPof5uTnIyssxrFFPhsYm5GZnqVx2rjSr1ue1cv7cimWnxMfhf2APz7/y+kOfMf63MlZa9u6bfPH6y/z5xSd06juQjn0HKJ7zaNmaID9f0pMTkZWXE3HrOrevXqpzvQAGOvITKXmFxUrT84qKMNDVqXs+XR0V8xSjoa6Ovo78APRWfDKnboQxrU9nPhg7iDeH9yE1O4cTN6oORr1dHTEz1Mf/ZtWBcVam/CCw1ufK2LjO7yW49/d3/dtQ1e9Cp0HD8OrUjX+++oSf5r/Kuq8+oWWX7njXuGa50oUz/pSWltCt2nuRmyPvymZcozXJ2MQUaVZWna+hPvEx0RzYvpVZ899RXH/4MKTZ2ZSXl2Fa0YJZydTMjEyJpEHLCDobSPCVywwbVXUdr7mFBW8seIePl37BJ59/iaOTMx8ufJvrIcGPNKeZmTmZmQ3Mee6sPGdFyyGAubkF8xa8w/8++5yPl36Bo5MzH72z4IFzCvdPS0sLT09PgoODlaYHBwfj5aX6RGtRUVGt7+rKx7KKXnuPiuj2eZ+GDBnC22+/TXp6OpaWlhw7dowBAwYoDvIsLCwwq7EhV9fQ/sPd8rLpbXCPCzxrfg7U1BRdOmvSMDHG8pUp5AYGkXf+Imp6upiNHYX1vFkkf/tTrfkMu3dBTU2dvLNB98x4Ij2ZlYvmKR6PnvNWRZSazdwy6mv4rj2LrI4nnh13Lp7n+LYNiseNUb91Zag5n0wmq3dZtZ5VRKj2TK3l1l6OuY0t0z5cQlF+PqHBlzmyYQ0vzH8PK3tHNDQ0GfzSy+z+bTm7f/sJNXV1XJu3wL1lGzJTU+7r41T5ZasUr44y1R/rGxkzZPIM1NXVsXV2pSAvj5M7N9P3+YncuniOo1vWKcrbOrkCYGnvWGs5/3Z9SlKSOHNgFy++/aHSNYkg7wZ7bMt6xePnX38bABevlri1aMUfn75H5M1rj/V9r3Ry11YSIsJ4aeGHtX4oK3OXlpYgk8kY+cpcDkaG19pmZDIZavVtNTXn4f6/kwZPnYnf5nWsWfweaurqWDs607R9Z9LiY+udt/66rZmvcqoapSUl7PrjVwZNmISZiuuwHpWHyVjdjPc+obioiPjIcPx2bcXU0oq23eQnRIa+OJUD69ew6tP3QU0NcytrvLv3JvjsacX8LZ3sGN6+qqV2W+AVpfUp5al3H0q5QGXSys+ks6UZPb08OHL1FgmSbMwN9RnUtjm9W3hy+lY45ob69G3VhI9++Bm/rfJtfbuGBouWfC5fXs36klH/NlTH9qF2jy8nWY3XcffKBW5dOMvw6bOxsHMgLT6Wk7u2YGxhRetuvWqt84yfL96dumCk4qRyrTz3TH9vJSUl/LnsO8ZPm4Glje1DLKk2VXVd73YP3Lx+jW+/WMprb82nmVfV58rR2RlHZ2fFY6+WrUhJTmbXtq20buv9yHLKaMD3E3Dz+nW++2Ipr73ZgJwpSeze/nA5nxRqj/Gav4cxZswYli1bRpMmTWjRogWHDx9GIpEwbJh8HIF169YRGhrKl19+CUDHjh3Zt28fW7ZsoU+fPuTn57NhwwYsLS3x9FR9ovNBiYO/++Tm5oa7uzvHjx+na9euhIeH8847Vdej6NYxgEClhvYfTp6zQOX0spxcZGVlaJgon+XTMDKq1RpYyWhAH8qLisncXjWQR9rva3H66Wt0PN0pClPucmLUpwd5l65Snpd/z4xdzSyZ/NLkqmwVXe7ypNkYmZkrpufn5KBvbFxr/koGxrVbrvJzc1BX10DXwOCeGf7L3Ft7Y+tadT1MY9TvvTIYV8+Qm1Pr7LRyBpNaZ7fzc6RKGVSWyZWib6S886GhqYmZlfz6GlsXN5Jjo7h88ihDJ88EwKWZF2rq6gybMhP3lm3QNzJmw3dL0dDURNegdheee+UDFOuvK5+6ugZ6FV2DDIxN0dDQUDoosbC1o6S4mILcHDzbtMO+YhCT/LxcNv8o/9JvjPpMjIqgIDeXf776RPG8rLyc+IhQgs+c5OUPl6KhJf+JqP6+27m4YWhmRmZaymN93wFO7trCncsXeOGt9zC1rH1Q496iDXYenmSlpDBk2ix0Kr6Pa24zBfXk1jc2qdWiWZBz/99JppbWjH/rXUqKiiguLMDAxJRD//yOcbUBUvQNjVBTV6/VGpaXI63V0lbJ0NiUPGmWcvnKVnljY3Kzs0hLSmDfP3+w758/gIoDNZmMpXOmMfmtd/Foqbq7nyr/RsbqKg9QbRydyJNm439gt+Lgz8DImBfnLaC0pJj83FyMTM3w27UNs2rX0oYlpfKXX1U2jYqdQ0NdbXKqXctuoKNNXlFRna8zr7B2y6C+jjZl5eUUFJcA0KdlE27GJREcnQBAmjQXLQ0NRnRoyZnbEThamGKgo80Xb83h7UnykVPV1dUpqlhvfo60xve3tFarXXUGKloGC3JVbUM1vuNzlD/jp/fuoOOAITTv0AUAK3tHpJIMLhw9VOvgLy4qkpiIcMZOVr4OztBI3pUtu8bAQTnZWRg/4DVw2ZkSkuLjWLdyBetWrgDkn1WZTMZrE0bz5v8W09K7fT1LUWZsYoK6ukatVr6srExMzes+OQ9w49o1Pv3gXabOeIWRo+vuEVGpuVcL/E8cv6989ebMzKzVGljTzevX+PSD95g6YyYjRo+pd13NvFpw+gFzCg+mV69eSKVStm/fjkQiwcXFhcWLF2NtLf++k0gkJCcnK8q3bduWRYsWsWvXLnbv3o22tjbNmjXjs88+q/fY4n6Jg78HMGTIEHbv3o1UKsXLywtHR9Uj5P0rysoojo5Fr1Vz8i9W9eHWa9WcvEtXVc6ipq0N1UYXBKoe1ziDou3ugraLE5LN9x75DUBfQxNTq6qL3GUyGfrGJsTevaUYCKG0pITEiDB6qhhko5KtqweR15Wzx969hbWzCxoaz+5HVFtXV2k0wsaoX1UZDIxNiLlzE7tqGRIiQukzpu5rNezcPAi/pnzNQcydm9g4uyoy2Lt5EHPnlmJ0TXmZWzi4K49MWZNMJlMcnID84NDGyYX4iDBade2JJDWZ5JgoDIxNFYMd1GTv7on/3u2UlhSjqSW/Rin6zk0MTUwVIxnau3kSFqL8GqJv38TWpeo1OHo04dbFc8jKy1GrOADMTElGS1sbPUMj1NTU0KkY1S/06GU0NLXQ1tFplPr0bNOe6R+5Ki3jyMa/MbOyocuQEVjY2SvOSFd/300sLMnNykTPwOCxvu8ndm7mzuULTJz/Hha2drXWVVZWiu/mf5BmpPPC2+9jYGJ6721m9Pi6c7u6E3E9WGnaw3wnaenooKWjQ2F+HjF3btLzuap1a2hqYu/iRuStG7Ts2EUxPfLWDbw6dFK1OBw9PPHbtVXp8xp56wZGpmaYWlpRXlbG60uUh9O/eMqPyFs3mDj3bUxrDEJUn38jY11kMhmlJSW1pmtqaWNsZk5ZaSm3r1xQylFcWkZxqfLJytyCItysLUjKlB8Uaair42RpxvHrd+tcd7wkm2b2yicV3G3kyyivaG7T0lCv1eIvf06+rdxNTCXpmPJgJSM7tEJKHoYmpkTfln/+oWJbjwyj9+i6v7/t3DyIuKb8/R1z5xY21T6Ldq4exN69SaeBVSeWY+/eVOoWXVpcjJqaii5lshr7BsDpY0ewsLbGq4230nRNLS2cPTy5HRJMx2ojgN4KCaZ91+51voZ7MTW3YPHyX5WmnTriw+2QYF5//38qRxCtj5aWFk2aNeXKpYv06lt1W5mrly7So7fqQZ4ArocEs/iD95j88kzGTmjYtYcR4WGYW9QeQbqhOT2bNuXqpUvKOS9fqjfnkg/fZ/LLMxgzvmE5I8PDMXvAnE+cp6hH2IgRIxgxYoTK5xYsqN3I07t3b3r37v1vxxLX/D2I3r17k5mZyaFDhxg8eHD9Mzxi2UeOY9izG4Z9eqBlZ4v55AlomJqQc+IMAKYTRmPzXtU9XgpCbqDt4oTJmBFo2lih7eKE5axplGZIKI5S7n5k1LcXJckpFN65v1s8gLzrQrs+A7l07DDhIZdJT0zg6Ka/0dLRUZxtBPDdsAbfDWsUj9v07ENOViandm1FkpzIjbOnuRUUSIf+QxRlykpLSY2PJTU+ltKSEvJzpKTGx5KVlqIoU1xUqCgjk8nIkUjk9+B6yCGjlV6jni7anu5oe7qDuhpaNtZoe7qjaXN/O1MPtO5Grt/KDO37DeLCsUOEBl8mLTGewxvWoKWtg1e1nbFD6//k0Po/FY/b9uxLTlYmJ3ZuJiM5kWtnT3MjKJBOA6oytO87iNjQ2wT5+pCRnESQrw9xoXfo0K9qBLPT+3YQHx5KdkY6aQnxnN63k7iwu3h17Kooc/fKRVy9WnHjfADHtq5ny/KvMbGwoqgwX3F/Of+9O9i64lvFPC06dUVLW5tD6/8iLTGe0KuXCDrqQ8cBQxQHQN69+pGbJeH4jk1kJCUSEujPjfMBSjtc3r36UZifJy+TkkTUresE+OzFu3d/pa49MpmMa2f9adGpCx36D26U+tTV18fK3lHpT0tbB10DA6zsHVFTU6O4qJBTu7eRFBVBi87dOX/kIJt//AodfQOibt14bO+737YN3DgfwMiX56Crb0CeNJs8aTbFRfJWnfKyMg6s+Y2k6AiGTZsNamrkSbPJz5HStmdfLvsdITzkCumJCRzbtBYtHR2aVd9mNq7Bd2PVNtO6Rx9yszLx370VSXISN86d4daFs7TvV/V9X1ZaSlp8LGnxsZSWlpAnzSYtPpastFRFmZjbN4i+dZ3sjDRi7txi168/YGZtS4suyjvKXQcNI/jsaa6cOUlaUgKHt64nJzuTjn3k11v57d7G+h+/qsrXuTta2jrsXfsHqQlx3L5ykYAjB+haMYqmhqYm1g5OSn8GRsZoaGph7eCkOKFTVlpKcmwMybExlJYUk5udTXJsDJLUZGp61BkBgo4fJTTkKhkpyWSkJHPlzCnOHvWhTdeq+7PGR4Zz+8pFMtNSiQm9w6YV3yGTyegxdGStjNVdCI+hezN3mtlbY2VsyKiOrSguLeVmXJKizKiOrRjVsWrwqiuRcRjp6TCoTXMsjAzwdnWgjYsD50OjFWXCktJo5+ZEC0dbTPT1cLO2oE/LJoQnpyGTySgqKSVNmqv0V1JWRlFpKd59BnDR7xBhwZdJT4zHd6N8W29ebRs6vP4vDq//S/G4bQ/5NnRy1xYykhO5fvY0N4MC6aC0DQ0kNvQOF476IElO4sJRH+JC79K+2jbk3qotF/0OE3kjhOyMdMJCrnD55FE82yi3qpUUFxF0xp+eAwarHKlw0KgxnD11nDN+viTFx7F1zR9kZ0roM1h+8mb3xnUsW/I/pXkS42KJi4okN0dKUWEhcVGRxEXJB5PT1NTEwdlF6c/IxBRNLS0cnF3QfcDR0sdOmIjfkcMcOXiA2JhoVv+ygoz0DIY/NwaAtX+s5oOFVftJ165e5ZP332X4c6PpN3AQkowMJBkZZFVr5dyzYztnz5wmIT6OmKgo1v6xmnMBZxg19vkHyijP+QJ+voc54nNQkVOSnsHwUfL77K7983c+XPh2Vc7gq3z6wXsMf240fQcOQiLJQCLJILvaNZd7d27nbMCZqpx//i7POebBcwr/Lc9us8pD0NfXp2fPngQGBtKzZ8/6Z3jE8i9cRmJogOmoYWiYGlOckETKspWUZci7DmiamKBlXXUwUnj7Lumr12I8fBAmwwYiKy6hKCKKlB9+QVZcdXG7mq4OBl06kLWv9rDlDdVx4FBKS4o5sWMzRfl52Lq4M3buQqXWI2mm8sGYiYUVY+bMx3/PNq4HnMLAxJS+415SDKkOkJudxebvlioeX0/353qgPw6eTZnw1nsApMRGs+uXHxRlzh/ex/nD+/Dq3J1H9ZWn27wpjr98r3hsMWsaFrOmIT10lJSvfnxEa6lbY9Zvpc4Dh1FaXMzx7RspzM/DztWd8W+8o3SfKmmNbiymllaMe30BJ3dtIaQiQ//xkxTD/QM4uHsycsZrBB7cTeChvZhaWjNy5mtK96TLk0rxWfcn+TnZaOvqYeXgyLjXF+DWolW1MlncPB+ArLyc4IBTIAMrBxOGTX1F0YqXJ81S2knX0dPnhTff5di2Daz/Zgm6+gZ0GjCUTgOqDuxMLa0YN3chJ3ZtIfjMSfktDCZMVtzmAcDY3IIX3lzEiZ1bWffVpxgYm9C6Wy+617j5d2zoHTJTUxj58hysnVwarT7ro6amTnpiPDcvnKUwPw8tbR1ysyTIZDIKcnMeW87gMycB2F5t2wPoNuw5eowYQ05WJuEVrSNbfvhCqczAl6bTrt8gTu6s2mbGvL5AaZvJqTG4gomFFaPnvMXpPdu5HuCPgYkJfZ5/UWmbycvOYvP3nyseZ6encePsaRw8mzL+Tfk9QIsKCzh7YA+5WZnoGBjg2bY93UeMqdV62KpTVwpyczjts4/c7Cys7R2Z/Na7mFZ8XnOzspBU+7zq6uszdcEHHNr8D3988Sl6Bvp0GzScboOGcT9ysjL5/fOqHfXLaSe4fPoELk2b8/K7H//rGWWycvx2bSUrIx11DXXMrKwZ+PxExQElyFvHTuzdQWZaGtq6OjRp5c3YV15HV//e3W/PhUahqaHOUO8W6GprkiDJZkvAZaV7/JnoKx9YZOcXsC3wCoPaNK+47UMhR4NvK27zABBwR37Q0qelJ0Z6uhQUFROWlMapm/WfMO00cJj89ezYRGF+Hrau7oybt1BpG6r1WbS0Yuxrb+O/eyvXAk5hYCy/bU/lbR5A3nNhxMtzCDy4h7OH9mFqac2IGXMU9/gD6D9hEoE+ezm+fSP5uTkYGpvQultvutb4brp75SLFhYX06D9Q9Wvo0Yu8HCmHdm4nO1OCvbMLb360GIuKrmzZmRLSkpVPHvzy5WdkVPtsfL5IftD1x64D9dbZg+rTfwA5UilbNqxHIsnA1c2Npd9+h42t/LpCSUYGSQmJivLHjhyiqLCQXdu2smvbVsV0axtb1m2T94QqLS3hr99WkZGehraODi6ubnz2zXd07trtoXNurczp6sZn33yryJmZkUFSYlVOvyOH68z5z9btAJSUlLKmZs6vv6XTQ+QU/lvUCgoKHu0QMs+IxYsXY2lpyZtvvvmvLL+ua/6eNIcnTaq/0BNg4Odf1F/oCeD3ycf1F2pkmo/wXjP/pkd5T5x/U3nNLtnCQ6m8z9+TzlT/0d7/9VkWldawkREbm6XR03ENe3P7f2+QoEfJ6R637XiSPOqRGv8tDub3GGTwCZK25JvHti6rJR88tnU9Tk/H3tETJCcnhzNnzhAcHMxzzz1X/wyCIAiCIAiCIAhPANHt8z69/fbb5OTkMHXqVFxcXBo7jiAIgiAIgiA8G56iAV+eVOLg7z6tWbOm/kKCIAiCIAiCIAhPGHHwJwiCIAiCIAjCk0+0/D00cc2fIAiCIAiCIAjCM0C0/AmCIAiCIAiC8MRTe0pG8n6SiRoUBEEQBEEQBEF4BoiWP0EQBEEQBEEQnnzimr+HJlr+BEEQBEEQBEEQngGi5U8QBEEQBEEQhCefumj5e1ii5U8QBEEQBEEQBOEZIFr+nlDGr81o7AgNMnz91saO0CCHPvm4sSM0yMDPv2jsCPU68eknjR2hQbSemrODT8c5uPLy8saO0CDamk/Hz1oLR5vGjtAg6k/B9TVu1uaNHaFB9l662dgRGsTCyKCxIzRISWlZY0dokHJkjR2hQRzMTRo7QsOoPR2/mU8yUYOCIAiCIAiCIAjPgKfjFKkgCIIgCIIgCM+2p6ZXz5NLtPwJgiAIgiAIgiA8A0TLnyAIgiAIgiAIT76n4DrkJ51o+RMEQRAEQRAEQXgGiIM/QRAEQRAEQRCEZ4Do9ikIgiAIgiAIwhNPTdzq4aGJGhQEQRAEQRAEQXgGiJY/QRAEQRAEQRCefOJWDw9NtPwJgiAIgiAIgiA8A8TB30P68MMPWb16dWPHEARBEARBEIT/NjW1x/f3HyW6fTbQ9evX+eijj9i4cSMmJiaNHYddx/3YdNiHjKxs3BwceHvSFLybNat3vrjkZF5e8gkymYwTv/+l9NxOv2PsPO5HUnoathYWTB81muE9ej5UTsPe3TEZ1A8NE2OKk5LJ3LGXovCoOsvrejXDdOQQtOxtkZWWUhQRTebuA5SmpgGg08QD09HD0bKxRk1bmzKJhNzAIKR+px4qp0wm4/zh/dw4e5rCgnxsXdzoP2EyFnYO95wvPuwup/dsIyM5EQMTUzoOGEqbnn0Vz2ckJXDu0H5S42OQZqTTZegoug0f/VBZG0K3bSvMXhqPbrMmaFpZkvzlD+QcPvZIli2TyTh3eD/XA/0pLMjHzsWd/i9MxrKeuooLu4v/nm1kJCVgaGJKx4HDaFutrgBCgy9x1mcv2elpmFha0WPk8zRp217x/NlD+zh/eL/SPPpGxrz21XLF4+KiQgL27yLi2lUK8nIxNregXa9+aGhqEXTsELnZ2VjZ2zNwwmScmtS9zaQmxHF06waSoiPR1TegXe9+9Bg+GrWKH4Tc7CyO79xCcmwMmanJtOrSg5Evz661nIvHj3Ll9AmkknT0DAxp0rY9/ca+gLau7j3rq9JV/+Nc8DtMbnYWlnYO9J8wCSfPunOnJcRxbNtGkmPkudv26kf3Yc8p5T65ayspcdFkpqbQskt3hk+rnbsmmUzG2UP7uBboT1FBPrYu7gycOKVB7/up3VtJr3jfOw0chnevfkplQq9eIsBnj+J97zXqeZq07aBUByGB/kgl6QBY2DrQdehIPFq1VZQ5vGENN4MClZZrYWvHzE++umdd+W3fVFVXPfvSrVpdyfPf4eSuyvxmdB6knP/GuQAOb1xTa9kLfvoDTS2tWtPPHznImQO7GDLqOWbOfbPObDX5HtzPgZ07yJJk4OjiyvQ5r+PV6v/snXdUVNfahx/a0HvvvdmwY68o9hJLjLHEkpiYnpt70/tNcvOlx/TEGHvvFUUFBRUVFSsooHSQOjD0Mt8fgwPDzCCWWJL9rMVazDl77/M773v2Pmf3jhrDXjibwK7NG0lOSqKiohwnZxdGTXiEweEj2ny9NuvavpWtzXTNeXqhdl0JZ9jRQtfoiY8wJHykMkxxYSFLf/uZq8nJ5GRnMWBIGM+9+p871rl/1w52b95ISXERrh6eTJ/3FIHtO2gMW1NTw9KfvictJZmczAz8gtvxxsefqYWrq61l27o1HIk6QElRIRZW1oyc8AjDxt5ZGR/WKZBQP0+MJQakFxaz9fg58qRlWsObGxsyumt7XG0ssTM349TVDNYfPaM1fIiXK9P7deNSZi5/Rh2/qZ5j+/cSs3s7ZSUlOLi6MXr6LLwCg7WGz81IZ/uKJWSmJmNsakbPwWEMHveISr6qq6sjatsmzhw5TGlJMWYWlvQbOYY+w5qehSN7dxF3IJKSwnxMzMwJ7tKd8KnTMWxj2XkoYheR27cgLSnG2c2dybPn4RfcXmPY2poaVv/+ExlXU8nNysQ3MIiX3vtYJcyZuKMcjtxD5tWr1NbW4OTmzoiJU+jUvWeb9GjXuZv927dQ2qjzkdnz8Atup1Xnmt9/JrNRp09gEC++9181nbGRESo6wydOpuMd6hT8fRCVv4eQyLhjfL1qBf+eOZuQgAA27t/PK199zqpP/oeTrZ3WeLV1dbzz0w90DgjkdFKiyrlNByL5cf1aXp8zj/Y+vlxMTeF/f/6BuYkJ/bt01ZJi65h064zN1IkUrd5IVUoq5gP64vDsU2R/+Bn1xSVq4fVtbXB4Zi6lBw9T8OcqdAwlWE8ci8OzT5L9nuIDTl5dTVnUYWqzcpDX1GLo643N9Mk01NQgO3TktnQCnIzcw6mDexn++FysHZyI27OdTT98xey3P9b6kS4tzGfLL9/Svlc/RsyaT1ZqMgfXrcTYzBz/zooP19qaGixsbfEL6cqRnZtvW9+tomtsTE1qGmV7InF8+993Ne0TkbuJPxBB+Iy52Dg4cWzPdjZ+/yVz3vkYiZGxxjjSgnw2//wNHXr1Y+Ss+WSlXOHAupUYm5kR0Lk7ANlXk9m55Bf6jBqPX0hXkhNOseOPn5j28hs4e/ko07J2cGLqi00fgy1X/oretJb0pIuMnbMAS1s7MpKT2LlsMcjljJj+BG5+AZyK3s/a77/kyfc+xdLGVk1vdWUla779HHe/AJ54/X0K83LYufR3DCSGhDZ+nNTV1mJsZk7v8NGciYnSeN8Xjh/l4Oa1jJwxF3e/AEoK8tm1fDF1tbWMnjXvpra+dDKO/etXMWzaTNx8Azh9aD8bfviKee98goUW3esWfY6bXyAzX3uPorwcdi1bjIFEQs8whe76ulqMzcwIHT6ahJjom2q4wfHI3Zw8EMHIGfOwdnTi6O5trF/0BfPe/USr30sK8tn409d07NWfUbOfJCvlCpFrV2BiZk5Al0a/pyazfcnP9B01Hv/O3bhyJp5ti39i+itv4OzlC4C5tQ0Dxk/G2sEReYOcC3GxbP31e2a+9i72ru6AIj8CDJz4KM6e3pw/FkvSqThKiwpbsdUXuPsFMuM/71KUl8vu5YsxkBjSI2xEk/4fv6ZD7/6MfuIpMlOuELlmOcZm5gQ26gcwkEiY//7/qaSvqeKXfTWFs0eilZrbypHoKJb+/CPznn2BwPbt2btjO5++8yZf/bIYOwcHtfCXL17A3cubsZOnYm1jS0L8SX797msMJBL6DR5yS9dujdjogyz5+UfmP/cCQe07ELFjGx+//QZf/7oYewdHtfBJly7i4eXN+CmPYm1jw5n4k/zyrUJX/8FDAaitrcXCwpIJU6cRuXvnXdEZdziaVb//wsynnyUguB37d+/kqw/f5ZPvf8bWXt1+8oYGDAwMCBs9loT4E1SUl2tM96cvP6OooIAnnn0eR2dXSkuKqampuSOtA9v5MSDYl3VHTpNfKiOsUwDzh/bm8237qamr1xhHX1eXiuoaoi4kE+rv2Wr6NmYmjO7SjtS8wjbpORt3hJ2rljJu5lw8A4KI27+XpV/9jxc/+RIrDd8bVZUVLPn8Y7wCg1n43ifk52SzcfFPSCSG9Bs5Rhlu3U/fUVJUyIQnnsTW0QlZqZTaZrZLOBrDnnWrmDjnKbwCgijKv87mP36hrraGR+Y9fVPd8UdiWL90MdPmLcA3MJhDe3fzw6cf8c5Xi7Cxs1cL39DQgIGBhIHho7hwOp7KCnWfX7l0gcD2nRj76OOYmJlz4nA0v37xP1567yOtlcq26Ny4dDFT5z2Fb2Awh/fu4adPP+Ktr75rRacBA1rRmXzpAv7tOzL60emYmplz4vAhfvviM1547yOtlcqHCl0xaPFO+Vta8I033uDHH39k8eLFPPbYYzz++ONs27aN2tpafvrpJ6ZNm8acOXM4cOAAAHl5eYwdO5bY2FjeeecdJk2axMKFCzl9+rTy/JtvvgnAjBkzGDt2LF9/3dTT0NDQwLJly5g+fTozZsxg8eLFNDQ0/GX3tzpiN6P79mf8oMF4ubjyr5mzsLWyYtOB/a3G+2HdGvzc3RnSQ731Z/eRWMYNHMTwXr1xdXBgWK/ejB84mBW7bv/lazF0ILKjJ5DFHqMu9zrF6zZTX1qK+YC+GsNLPNxAT4+SLTupyy+gNjMbacR+DBzs0DU1BaAmPZOKk2eozcmjrrCI8uPxVF1MwsjPR2OabUEul3M6OpIeYSPx79wNOxdXwmfMpaa6isT4OK3xzsZEY2ZpxeDJ07FxcqFjnwEE9+xN/IEIZRgnT28GTJhKUPdQDCSS29Z4q1QcO0Hhr0uQRcVAg/yupSuXyzkdFUnPYaMI6NwdOxc3wmfMU9jqpHZbJcRGYWZppehNdXKhU9+BtAvtQ/z+JludOhiJu38QoeFjsHVyITR8DO5+gZw6qNpjqauni6mFpfLPxNxc5Xz21WSCe/TGMzAYKzt7Ovbqh4GBBBtHZzr3H4SdswvDp83EzMKK09Ga88yF40eoralmzBNPYe/qRlDXHvQKH8XxyD3I5Qp7WtnZM/zRGXTq0x+jxuezJZkpV3Dx9qVjr75Y2dnjFdSODr36kn0tpU32Pnkggg69+xLSbxC2zi6EPToTUwsrTh86oDH8xRNHqa2pYdSsJ7F3cSOwSw9Ch4/i5P4IpW5LW3vCps6gY2/tulsil8s5dXAfocNGEdClO/YuboycOZ+a6iouteb3GIXfh05t8nv70D6caOb3+Kh9ePgH0WvEWGydXOg1Yizu/oHEN/O7X6cu+LTvhLW9IzaOTvQfNwmJkRHZV5vsWJiTjbmVDT3DRuDuH8jImXMxtbTizGHttqqrrWHkrPmNtupO6LCRnDzQZKuEmIOYWloRNnUGtk4uhPQdSPtefTixf0+L1HQws7RU+WtJdWUFO/78hfDH52BkYtIWsyvZuXkjA4cNZ+jIUbh5eDJ34XNY29iwd+d2jeEnTpvOtNlzCGrfAUdnZ4aPGUvPvv2Iiz18S9e9GTs2bWTQsOGEjRyNm4cn8xY+j7WNLXt3aNb1yLTpPPbE3EZdLoSPGUdo337ExTTpcnByYu7C5xg8PByzFnn7donYupm+Q8IYNHwELu4ezHzqGaysbTigpXJpaGTEEwufZ1D4SGy0NKieP32KiwlneOXdD+jQuSv2jo74BgYR3LHTHWntF+zDwQtXOJ+RQ560jLVHTmNooE8XbzetcYrLK9l28jzxqRlUVGuvfOrq6PBYv27sSUikSKa5QtuS2IiddO07kB6DhuLg4srYmXMwt7Im7oDmkSQJR2Ooralh8pMLcXRzp0OPUAaMGkdMxE5lvrpyPoHki+eY/cpr+HXohLW9A+6+/vg0q0ClJV/G3defLn0HYG3vgG+7DnTuO4CM1OQ26d6/cyu9Bg6h79DhOLm5M3XuU1haW3N4b8u8q8DQyIjHnnyGfmHhGiu1AFOemM/wCZPw8gvAwcmZ0VOm4eHjS8IJ7WXgzTi4cxuhAwcrdU6Z+ySW1tbEtKJz2pPP0DdsOFa26o1aAJOb6bR3cmbUlEdx9/Hh7B3oFPy9+FtW/gCioqIwNjbmyy+/ZPLkyfz222/897//xdXVla+++oqhQ4eyaNEiCgubWr+WL1/O2LFjWbRoEf7+/nz++edUVlZiZ2fHG2+8AcAPP/zAsmXLeOqpp5TxoqOj0dXV5fPPP2fBggVs27aNw4fv7kv2BrV1dSRdu0bPDqrDVXq278C55Cta48WeOUNswhlefnym5nRr65C0aKk2lBhwMTWFurq6Wxeqp4fEw42qS0kqh6suJWHo46UxSnVaBtTXY9a3F+jooGNoiFmv7lRfS6dBS8urgZsrhj5eVF1p28e0JkoLC6goleIR1PTi0ZdIcPUNIOeq9hdN7rUUPAJVW/s8gztwPT2N+vrbsNlDQF5DHeWlUjyb2cpAIsHNN0DlQ7wlOVdTVOIAeAW3J6+ZrXKuqYfxDO5AdgsfSAsK+OXtf/H7e6+xc8nPlBTkq5x39fEn9XwCpUWKvJ12OZGa6ir8O3VRCefdrgOZWj4kslKTcfcLVKmwe7friExagrSwQOt9tsTdL4DrGelkNV5HWlRI8tnT+LYPuUlMqK+rIzf9Gl7BqnndO7i9Mr2WZKcm4+YboKo7uMMt626JtDBf4fdmWgwkEtz8ArVqAYXfvdT83oG89GtKv2dfTcEzWD1MVqrm56mhoYHEk3HUVFfh4u0HKGxVWS6jskLGD6+9wO8fvE7EyiW4+QZoTSf7aoqarbzaqdoqOzVFg/07kpd2TSWP19XW8Mvbr/LTW6+w8advyMtIU7texKo/CezSHc/AW2t5r6utJfXKZTp17aZyvFPXbly+eKHN6VRWVGBqZnZL126N2kZdIV27qxwP6dqNpEsX25xORUUFpmZ3p5KnibraWq6lJNOhxQiW9p27kJx46bbTPRV3FG+/ACK2bubluTN57en5rPj1Z6oqK287TRszEyyMjbiS01Sm1dU3kHq9EE87m9tO9wbhnYMpllVwKjWjTeHr6urIvnYVvw6qFVq/9p1IT76sMU568hU8A4JU8pV/hxDKSoopbiyrL546iZu3L7ERO/ns5YV89dpL7FjxJ9VVVco4Xv5B5KRfI73xu6aksIDE0/EEtijHNeuuJSM1heBOnVWOB3fqTOrlRM2RbpOqykpMTG8vX2nTGdQphKt3WWf1Heh84BBz/u6Yv+2wTw8PD6ZPnw7AhAkT2LBhA/r6+owbNw6AadOmsXHjRhITE/HzU3xAjB8/np49Fb1is2bN4sCBA6SmptK+fXvMG1sgLS0t1eb8ubu7M2PGDABcXV3Zu3cvCQkJDBw48K7fV0lZGfUNDdi00GBjaclJLR8CBSUl/O/PxXz63IuYGmsenhXasSPbD0UzqFsPgr29Sbx2lW3R0dTV11Mik2FnZXVLOvXMTNHR06O+VHWeQn2pDKMgzS/6+qJi8r77Gfv5s7GZ9gjo6FCTmcX1739TC+v6ybvomZmBni7SnXuRHT56S/qaU14qBRRzx5pjYm6BTFrSSrxS3APU4zQ01FMlk2FqaXXbmh5UihsUw47UbGVhgaykRGu88tJSPAI126pSJsPM0oryUimmLdI1NbegoqxU+dvZ00cx3NTRiYqyMuIidrDmq0+Y/dZHGDe+2AZPnk7k2mX88OYr6OrqKVubfVu0yJtaWHAtUapRr6xUioW1TYvwlspzVhqG42iiXY9eVJbLWPHlJyCHhoZ6OoT2YfAjU28at0JWhryhAVNz1bxuYmFJeaLmj+vyUinmWnSX34JutXSlCjtp8o+spFh7vFIpHkGqlR0Ti7b6XdU3+VmZrPryY+rqapEYGjL+yeewd1X0hlTKFOVMr/Ax+HXsjLSokJjtmyiXlmBorLmXrbxUirmVdYvrqtqqvEyKp3kL/S2eW2tHJ0bMmIu9qwe11VXEH9zHqi8/4Yk3P8DawQmAhNhoSvKvM3r2U9wqpaVSGhoasGyh1dLKmnPFp9uURnzcMc6fOc2HX35zy9fXRtkNXdYtdFlbU3L61C3p+uirb++arpaUlZY22s9K5billTUXE87cdrrXc3O5fOkC+gYGPPfaW1SUl7Pit58oKSrkudffuq00zY0MAZBVVqscl1VWY2nStnlu2vB3tifE04Vvd7V9qHdFmcJ2LXuyzSwtSbl4TmMcmbREbZj1jfgyaQk29g4UX79O2uUk9PQNmP7cy1RVVLB9xRJKS4qY/twrAHTq1YcKWRm/f/o+cqChvp7OffoTPnX6TXXLSstoaGjAvMU72NzSitJzCW28+5sTHbGLkqICeg4YdFvxy1vRmXTu7J0LbORQxC5KigrpOeDuf5MKHk7+tpU/Ly8v5f86OjpYWlri6dk0Fl5fXx8zMzNKmn2went7K/+3sVF8QEmlmj8OtV3rRlxt8fbs2UNERITGc80Z3K49Ya1MztWhRYuEXA4tjzXy/i8/MXHwUDo0VnI1MWfcBAqlUp76+EOQy7G2sGRUv36s2LUT3TvaU6XFkEOdG1rV0bUwx3bGo8jiTlJ+4jS6RoZYjR2B/fxZ5H3zk0q8vC+/R8fQEENvT6wmjqGuoJDy4/FtUpR44hj71y5X/h6/4AWFNLVWHrkWiza7HbUoci0nHk4OVsn4oaypd/w9S8U8HjVbybnpPbeM02SqZsfVklV9Vrzbqy4k4eztw+L3X+diXCzdhoQDisVBslOTmbzwJSxtbLl89jSHt20i+2oqHv5Bza6vPc9oFiPXdLRV0i8nErtrG+GPzcLF25fi63lErlvJ4e2bGTDukbYlouEZu5XH60bl91biXDh+hL2rlyp/T3rmZTQlIpfLb+53NUE3kmrud83PRnNsHJ2Y9cb7VFdUcPlMPHuWL2bqi//B3qVpOJybbwD2ru7Yu7rj5O7Jz2//C109Pe3aNN3PTbQ1lWmK464+frj6NJWtLj5+LP30XU5F7Wfo1Mcpysvh8LYNPPbym+jp3/4rV02rhmOaSLxwnkWffcoTTz+LX2DQTcPfqS7F89k2Xd9+9glznnkW/79Alzot7XfzZ7c15PIGdHR0ePpf/8Gkcfj0zKcW8sX7byMtKVarrGuis5crj4Q2jQJYcjCuSVtz5Tpqb9JbwsRQwtTeXVgdG09lTe0tx1cvglovOzWHbzojlzeADjz69PPKIdBjZ87hzy8+RSYtwczSiquJFzm4bRNjZ83D3cePwuu57Fy5lP2b1xPWhsYzUHevvI3PZls4HXeEzSv+ZO6Lr2qcN3pLaCr/7pLOM3FH2bJiKXNe/Bc2d6rzAUFH7PN3x/xtK3/6LV6yOjo6asegeaEEes0+Em4UEHItFZXWrgVonfM3YsQIRoy4+YprFac1t/pYmZujp6tLYYseqaLSUmwsLTTGib90kTNJifyxVbHgiFwup0Eup9/c2bw6azYTBg3BSCLh7XlP8vrsORSVlmJrZcXWqAOYGBlhdRtDcupl5cjr69GzUNWkZ25GfalMYxzzgX2R19RQsnmH8ljBkpW4ffoehj5eVKc0rRJaV1gEQG12DnoWZliOCW9z5c+nY2ecvJoq+vWNw1pb9ppUlJVhYqHZpqDoOSovLVU5ViErQ1dXr81zqR50QiUmBNoYKn/XNuYHdVuVqvXeNEdhK9UGkUpZqYqtTC0s1e1ZVqbWy9gciaERts4uFOdfV+irqSFm+0bGzH1GOczT1smFw9s2cf5YLL2Gj1JJ21SLf80sLNX0ljf2QN7oSWsL0ds20q57KJ0bVzV1cHWntqaaXcuX0G/0+FYrJiZm5ujo6qrpqCgrxcRcswZTDbpv9Jxqi6MJv05dcGlcbAVU80jzHtEKWev+0aZH3e8twsjU71FPXx9re0Xjg5OnN7npV4k/uJcRj8/FWIOtzKysMZAYaq1wabsuNPVsm5pr0q/I48ZmmvO4rq4uTh7eFOfnAYqho5UyGUs+flsZRt7QQGbyZfbt3MGyzdtbnQ9sYWGJrq4uJcVFKsdLS4rVerNaknj+PP979y2mzJzN8DFjWw17q5jf0FWkqktaUqLWG9iSS+fP8em7b/HozNmEjxl3V3W1xNzCAl1dXaQteqhLS0puar/WsLK2wdrGVlnxA3B2UyzkU5if36bK38XMXDIKSpS/9fUUM3HMjY2QVjQNgTQ1MlTrDbwVnKzMsTAxYv7Q3spjN75xPpk+hq92HKSgVH1qhYm5wnZlLRqzy0tLNc5rBTCztNIYXnFOEcfcyhoLaxuVua/2jasGlxQWYmZpxb5Na+nUqw89BioWKHJy96C2uprNf/zK4PGTVL7X1DRYmKOrq0tpi9EoslKpWi/b7XA67ghLv/+GWc++dEcrfZo26ixr8WzKSkuwuAuryp+JO8qy779h5rMvipU+BSr8bef83W1uVPD+yoVc2oKBvj6BXl6cuHBe5fiJCxfo6OevMc6K/37C0g//q/x7cuIkDCUSln74X4b0CFUJq6+vj4ONDXq6uuyLO0bfzl3QvZ2VlerrqUnPxCgoQOWwUVAA1anXNEbRlUiQt1yc5Ia9W2sF09FF5xZa1CVGRljZOyr/bJxcMLGwJD2paShdXW0t2SlXcPbW3lvq5OVLxmXV4XfpSRdx8PBET+/v0a5ioquLi56B8s9DzwBTC0vSElVtlZWqWNhEG87evir2BUhLvIhjM1s5e/mSnqQ6dDk96YJyXpcm6mprKc7LVVbIGurraaivV3lm9fT1MTE3p7JctdHh6qXzuPloTtvVx4+M5CTqapsWT7h26QJmllZYtrKirpq+mmp0WuQfxe+bNyrp6evj5OHFtUuqNrmWeEGlp6k5Lj5+ZKZcVtWdeOu6DY2MsXZwVP7ZOrs0+r1JS11tLVkpl7VqAYXf09T8fgFHDy+l3128fVWeJ0WYi7j6aH+eQNGIdaNSqqevj6O7p4q+ClkZNdVVWrdrcfH2bbRVU09I2qWLKrZy8VHXdi3xAo6eXlrzuFwuJz8rQ/lM+oV05Ym3PmL2Gx8o/5w8vOgzcBCf/fCzxlVBm6NvYICPfwDnTqkOpTx3+hQB7bSvMHjx3Fk+ffdNJj0+g9ET29jLfAsYNOpKOK3a6Hb2dDyBrawoePHcWT55502mPD6T0RMn3XVdLdE3MMDL148LZ1SHyF5IOI1fkPbtCm6Gf3A7SoqKVOb45WVnAWDXxh6Wmrp6CmXlyr88aRmllVX4OzUNz9bX1cXb3oa0gqJWUmqdjIISvtp+kG93Riv/LmXmcu16Id/ujKZYVqExnr6+Pi5e3iRfUG2MTr5wFg+/AI1xPPz8SbucqLJyZ/KFs5hbWWPdOOzcwz+AspJilTl+hXk5AFjZKfJebXWN2reHjq6uWq+oZt0GuPv4knjujMrxxHMJ+ATcWS9z/NEYli76hpkLX6Brrz53lFaTTtWhqInnEvC+Q52njsaybNE3zFj4Al3uUOcDh47uvfv7m/L3vbO7jIODAzo6Opw8eRKpVErlHUzqvlMeCx/JzpjDbIuO4lp2Fl+vXE5BSTETG5fK/nH9Wp777FNleF83d5U/e2trdHV08HVzx6Kx1TI9N4fdsTFk5OZyITWFd378ntTMLJ6ZNOW2dZbuj8asdw/M+oai7+SA9ZQJ6FlaUnZYsSWD1fjROLzYtGRz5fmLSNxdsRw9HH17OyTurtjOeoy6omJq0jMBMB/UD+MO7dC3t0Pf3g6zPqFYhA1qc6+fJnR0dOgyMIyT+3aTnBBPQXYWe1f+gYGhIUHdmirHEcsXE7G8aT+vTv0GUlZSTNTGNRTlZnP+yCGV4Yeg6DG5npnO9cx06mprqSgr5XpmOiWNvQJ/FTrGRkj8fJD4+YCuDgaODkj8fNB3vL05X8p0dXToMiiME5G7uHImnoLsTCJWKJbHD+reZKvdy35n97KmfSRD+g6irKSYgxtXU5ibzbkjh7gQF0u3oU226joojPTLiRzfu5Oi3ByO791JxuUkug4epgwTvXktGVeSkBbkk3Mtle2Lf6S2ppr2oYqXm6GxMW5+gRzetoG0pEuUFORz9shhqioqKC8r5UxMFAU52exbuwKZtIQuAxStylGb17Hq66Z9vNr17I2BxJAdS38nPyuTpNMnORqxg55hI1SGDeVlpJGXkUZNZSWVFTLyMtIoaPwABPDr2IUzMVFcPHGMkoJ8rl48z6Ftm/Dr2LnVXr8bdB8SzvljMSTERlOYk83+dSuRSUuU+8xFb1nPmm+b6e7RCwOJhF3Lfic/O5PLp08St3cn3YeGa9ZdVUlleblCd06W2vWb+73r4GEc37eLy2fiyc/OVG6LENzM77uW/cauZU1zdEP6Kfx+YMMqCnOzOXvkEOfjYumh4vdhpF++RFzETgpzc4iL2EnG5US6NfP7oa3ryUy+jLSwgPysTA5t3UDGlSSCu/cCFHs7mpiZc/5YDHF7d3L+WAxLPn4HgIHjJyvTWPtt03YM7Xr0Qt9Awu7ljbY6c5K4fTvpPqTJViH9BiMrKWrSHxvN+WMx9BjaNHojducWrl48R0nBdfIy0tmz4g/yszKVPjIyMcHexU3lz8DQEDNzczy8vNs0DG30xElERe5l/55dZKan8efPP1BUWMiwUYql81ctWcxHrzdt6XLhbAL/e+ctho0aQ//BQykpKqKkqEitJ+ROGfPIJKL27WX/boWuP35S6Bo+WtHLuPKP3/mgua6EM3zy9psMGzWGfoOHUlxURHFREdIWuq6mJHM1JZnKigpkZWVcTUkmI019EZ22Ej5+IjEHIoneu4fsjHRW/vYzJUVFDB6hGAmwftkSPnvnDZU4WenppKWmUFZaSnVlJWmpKaQ1Wzyo14BBmJmb8/t3X5OVnsaVSxdY+fsvdO/TD4s76FGMuZTKoPZ+tHd3xtHSnKl9ulBTV8/pq5nKMFP7dGFqH9WFT5ytLXC2tsDQwAATQwnO1hY4WCrmQdfW15MnLVP5q6yppbq2jjxpGfWtrAjdN3w0p2OiORF9gOvZWexY+SdlJcX0HBwGQMT61Sz+7CNl+JBe/TCQSNj4+0/kZWZw4eRxDu3cRr/w0U35qlc/TMzM2PT7T+RlZZB2JYkdK5fSoXsoZo2NJkGdu3Ii6gBnjx2hKP86yefPErlpHUEhXVvt9bvB0NHjORZ1kNj9+8jNzGD9n79TUlREv2GKsmfrquV8+9E7KnFyMjPIuJZKeWkp1VVVZFxLJeNaqvL8ydjD/Lnoa8ZPn4lfcHukJcVIS4opl2nfg/FmDB49jriogxxp1Lnhz9+RFhUrdW5btZxFH72rpjPz2lXKS8uorqoi89pVMq81jYyKjz3M0kVfM276DPyC21FaUkzpHeoU/L34e3RP3ANsbW2ZPn06y5cvZ9GiRQwePJiXX375vmgJC+2FVCZjybatFEpL8HF148tXXsW5scWssKSErOvXbynNhoYGVkfsIT03B309PboFBfPr2+/ibH/7lYWK+DMUmZpgOXIYNhYW1OTkcP2H36gvUgxx0LM0x8C+qTeiKimZgiUrsBg2BIuwwchra6m+msb1Rb8iv9GKqKuL1cQx6NtaQ0MDtfmFFG/ZcUcLvgB0DxtBXW0NB9avorqiHCdPHyYufEVlj7/SYtV9kSxt7Zmw4EWiN6/lXEwUppZWDJr0mHKPP1BMcF/1fx8qf58riOZcbDSufgFMeeHONy7WhlFQAG6LPlf+tp0/C9v5syjdtZe8T768o7R7hI2krraWA+tXUlVRjpOXD5OefUVlr7eyFkPULO3smfj0S0RvWsPZmChMLRRbZNzY4w8UvVajn1hA7I7NHNm1FSs7B0bPWaCyx5+spJhdf/5CZbkMYzNznL18eOyVt7CwaXqORs9ZQMy2jWz742eqKsqxsLFj0PjJ6EskHNm1HVlpCfYurkx97hVlD49MKqUkvynPGBmbMO3Ff7N39TKWfPo+RiYm9AwbQc8w1SHbf3ys+lJOPnsGSxs7FjbauO+ocaADh7ZtoqykCGMzc/w6dlZWSG5GcPdQqsplHN29jfJSKXbOrkxe2KS7vLRERbehsQlTn/83+9YuZ9n/3sfIxJQeQ0eoVFYAln76nsrvlHNnsLCx5en/an82eoaNpK6mhv3rVlBVUY6zlw+Tn/uXit9LWwwBtLKzZ9IzL3Nw42oSGvPIkMnTlXv8gaKXdcycp4ndsYnYXVuwsnNgzNynlXv8Ke6zlJ1Lf6OiTIrEyBh7VzcmPfMy3u0UK3Hq6OjSUF+PnoEBh7ZuAMDIxJSx857BrnFOoEwqpaSgpa1eJXLtCpZ/9gFGJqZ0HxJO92YVUys7eyYtfJkDG1dz5vBBxbYVUx5X2eOvurKSvauWUl4mxdDIGAd3D6a9/LrKc3un9Bk4iLKyUjavXkVxURHuXl68/uHH2DsqhsGWFBWSl5OjDB+9L4Lq6iq2b1zP9o3rlcftHRz5fumKu6ar78DByEpL2bh6JcXFRbh7evHmR58odRUXFZGXna0Mf3DfXq26fly2Uvn7P8+q7uMWH3dULcytENp/ILKyMratX4O0qAhXTy9eefcD7Br3IiwpLuZ6bo5KnK8+epfCZu/R915+HoA/t+4CwMjYmH9/+AkrfvuJD/71EiZmZnQN7cWUWXNuS+MNoi8mY6Cvx4SeHTGWGJBRUMzv+4+q7PFnZaq+cNtLowep/G7n5kSRrILPtkTekZ5OoX2okMmI2raJMmkJjq7uzHrldWUvXllJMUXXmxozjUxMmPPvt9i+/A9+fP9NjExN6TtiNH1HjFaGMTQyYs6/32bHiiX89MFbGJmY0q5rD8KnPKYMM2icYtG3yM3rkBYVYmpuTmDnbgyf9GibdHfr04/yslL2bF5HaXExzu4eLHz9HeX8PGlJEQV5uSpxfvzfhxTlN620+r/XFIvP/LB2CwAxkXtoqK9nw9LFbFja1BDs36692obwbUWhs4yIzeuVOp95/W3l/DxpSbGazp//95GKzs8adS5au7lRZwQN9fVsXPoHG5f+oQzn16692obwDyV/kzUV7ic6lZWVd28TMMFdQ9ucvweNsmVr7reENrFrwsT7LaFNhH304BfMB9595+aBHgAMDR6Otq3WWt0fJO73kPe2crcWdPir6e6jfd+2Bwndh8Ce5a3sbfcgseVk27fmuJ/08HW/3xLahJXxna2Aeq9ouKPleu4dA4LuXoPVX0nRj4tvHuguYbNw3j271r1EDPsUCAQCgUAgEAgEgn8AD0fTuEAgEAgEAoFAIPhnI7Z6uGNEz59AIBAIBAKBQCAQ/AMQPX8CgUAgEAgEAoHgwedvvAXDvUJYUCAQCAQCgUAgEAj+AYieP4FAIBAIBAKBQPDAoyPm/N0xoudPIBAIBAKBQCAQCP4BiJ4/gUAgEAgEAoFA8ODzEOw9+qAjev4EAoFAIBAIBAKB4B+A6PkTCAQCgUAgEAgEDz66ot/qThEWFAgEAoFAIBAIBIJ/AKLn7wFlo6z2fktoEw0TH7nfEtqE/kMyRvzAu+/cbwk3ZciHH91vCW0i+v337reENiGXy++3hDah85DkoZq6uvstoU2UlFfebwltouEheDy/3xt7vyW0iRfC+95vCW2ivZvT/ZbQJgwryu+3hDYhf0jKpIcG0fN3xwgLCgQCgUAgEAgEAsE/ANHzJxAIBAKBQCAQCB58HpJRKA8youdPIBAIBAKBQCAQCP4BiJ4/gUAgEAgEAoFA8MCjoyt6/u4U0fMnEAgEAoFAIBAIBP8AROVPIBAIBAKBQCAQCP4BiGGfAoFAIBAIBAKB4MFHR/Rb3SnCggKBQCAQCAQCgUDwD0D0/AkEAoFAIBAIBIIHH7HVwx0jev4EAoFAIBAIBAKB4B+AqPzdAm+88QY///zz/ZYhEAgEAoFAIBD889DVuXd/f1PEsM8HFLlczpFdWzkbG011ZQVOnj6EPToDO2fXVuNlXEkiatMaCnKyMLO0okfYSDr3H6wS5vLpk8Ts3Iy0IB9LO3v6j30E/5BuGtM7FrGDmO2b6DxgCGFTZyiP11RXcXjrRi4nxFNRVgqADjq4+PgxZOrjbdIZvXkthY06u4eNJKTfIFWdZ05yZOcWpc6+Yx7BP6SrShiZtISYbRu5evEsNVVVWNrZM3TqTNz9AwG4ciaehJgoslOvUFdbi56ePs7evvfMlqej95MQG01pUQEAtk6u9BoxBt8OIcow5aVSDm3dwNWL56gsl6Grq2iTcfbyvWe2PLJrK8d2b1OJY2JuwdOffK38XVNdRcy2jSwtzKCsoQF7PT1GGpkzwcSyVX2tYRTSAevHJmMU6I++vR25H39B2e59t5zOzspSNlWUUtRQh4e+hCdNbVoNn5+VQeS6leSmpWJkYkpIv0H0HjkOnWbDSTKuJHJw4w3/W9NzmLr/k06fJHbHZkoKrmNl50C/sY8Q0FlLXtqzg8PbN9JlwBDCHp2pPF5dVcn6774gL+MacrkcfQMDugwMY+CEKa3ew716PncvX8yFuFiVdG2dnJn7zidatd0N+ybERnMhLpbCnGzk8gYc3DzpN2Yibn4ByjCnoveTEBPVpN/ZlR7DRuHdvpNWbXK5nLg92zl/5BBVlRU4eXozePJ0bG+SzzKTkzi8eR2FudmYWlrRbUg4nZrls/NHDnHpxFEKc7ORy+U4uHrQa9R4XH39W01XG1F7drF32yakxcW4uHsw9Yn5+LdrrzFsbU0NK3/9kfTUFHKyMvELDOZfH2r3T/Kli3z53ps4ubrx3tff35a+G0RH7GLf1k1IS4pxdvNgypz5+Adr17nq1x/JuKrQ6RsYzCsfqOq8fOE8W1ctIy87i5rqamzs7ek7dDjDxk28I50Aj/bpwrBOgZgaSriSm89vkUfJKCzRGj7U35PwkCC8HWyQ6OuTUVjCxmNnOJGSoRJudNd2hIcEYW9hhqyqmuPJ6Sw/dIKq2rpb1viw+L0lmzZsYPXK5RQWFuLl7cOLL79MSOcuGsNevZrKV59/zrWrVykvl2FrZ0fYsOHMnf8kBgYGd03T+q1bWLF2LQWFhfh4efHKs8/RpZPmsiH12jX+77tvuZqWhkwmw87OjuGDB/PU7CeUmt7/7H/sjIhQi2tkZMThXbtvW+eGbdtYvn49hUWF+Hh68fIzz9ClY0fNOtPS+Pz7RQqd5eXY2doyfNAgnpw5S8V267dtZf3WreTk5eHo4MCcx6Yzetiw29Yo+HshKn9/IbW1tbddkB2P3M3JAxGMnDEPa0cnju7exvpFXzDv3U+QGBlrjFNSkM/Gn76mY6/+jJr9JFkpV4hcuwITM3MCunQHIDs1me1LfqbvqPH4d+7GlTPxbFv8E9NfeQNnL1+V9LKvpnD2yCHsXd3UrhW1cQ1pSRfxbt+JpJPHaN+rH2djo6mrrWHj918y552PteqUFuSz+edv6NCrHyNnzScr5QoH1q3E2MyMgM6NOq8ms3PJL/QZNR6/kK4kJ5xixx8/Me3lN3D28gGgqqKCtV9/iouPPxMWvIiJmTnSwnxMzM2V16qtqQa5HLlc8Tts2kyuXjx3z2xpbm3DgPGTsXZwRN4g50JcLFt//Z6Zr72Lvas7crmcLb9+j46uDgGdu3M+LgYXbz8KcjIxMjG9Z7YEsHZwYuqL/1H+1mmxolb0prWkJ13kNXM7HPX0OV9bzaKyAix09RhiZKZR383QNTamJjWNsj2ROL7979tK41BVOb/KinjGzJb2BobsrCzjfWkes4sKsbCxVQtfXVnJukVf4O4XyIz/vEtRXi67ly/GQGJIj7ARQKP/f/yaDr37M/qJp8hMuULkmuUYm5kT2Oj/rNRktv/xE31HTyCgczcun4ln2+Ifmf7Km7h4a8pL0di7uqvpWf/dF+SmX6PPqLE4e/kRu3MzJyJ3Y2VvT0jfQRrv+V49nwDSwnwABk58FGdPb84fiyXpVBylf7F9My4nEtS1J66+/hhIJJw8sJcNP3zJ7Dc+wNrBSaHfypqBE6Zgbe+IXK7Qv+P3H5n26tsayy2A+P17OHVwL8Omz8HawYnjEdvZ/OPXzHrrv0iMjDTGkRbms/WX72gf2pfwmfPJTr3CwfWrMDYzx7+xsp+ZnERAlx44+/hhYCDhVNQ+tvz8DdP//S7WDo4a09XGidjDrF3yG9PnP41fcDuiInax6JMPeP/rH7Cxt1cL39DQgIGBhEEjR3P+VDyV5eVa0y6XyViy6GuCOoZQUlR4S7pacjL2MOuW/MZj85/GN6gdhyJ28cPHH/BuazolEgaOGM2F0/FUaNBpaGTEoFFjcPXwQiKRkJJ0iVW//ojE0JCB4aNuW+vEnh0Z170Di3YfIrtYypTeXXhvygieW7xBayWtvZsT59JzWBUTj6yqmgHBvvxn/FDeXbubS1l5APQP8mHWgB78uDeGi5l5OFqa8+yIfhjo6/FjRMwtaXxY/N6S/fv28e3XX/Kvf79Gp5AQNm/cwKsvv8Ty1WtxcnJSC2+gb8DIUaPxDwzA3Myc5CtX+OzTT6ivq2Ph8y/cFU17Dx7gy++/57UXX6Jzx45s2LqVF19/jXVL/sTJUT0/GhgYMHp4OIH+fpibmnE5JYVPvvqS+vp6XljwNACvPvsczz35lEq8+c8/r7VC2Rb2RUXx5U8/8trzLxDSoT0btm/npbfeZO3vi3FycFDXqa/P6GHDCPD1w9zMjCupKXzy9dfU1TfwwpNPArBh+3a+//133nz5ZToEBXMhMZFPvvkaCzMz+vfufdtaHxjEap93jLDgLdLQ0MCyZcuYPn06M2bMYPHixTQ0NAAwb948Vq1axbfffsu0adP48ssvb+sacrmcUwf3ETpsFAFdumPv4sbImfOpqa7i0sk4rfESYqIws7Ri6NTHsXVyoVPfgbQP7cOJ/U0tVfFR+/DwD6LXiLHYOrnQa8RY3P0DiT+o2ttSXVnBzqW/MmL6HAyNTdWulXU1heAevbl6PoGew0czZMrjOHv7Yu/qQU11FYmt6YxV6BwypUlnu9A+xDfTeepgJO7+QYSGj8HWyYXQ8DG4+wVyqpnOk5G7MbWwYuSs+Th7+WBpZ49HYDtsnVyUYYJ79KYwN5tuQ4YDYGlrf09t6depCz7tO2Ft74iNoxP9x01CYmRE9tUUAIqv55FzLYWhUx7nSkI8ocNHM2nhy8gb5HgEBt8zWwLo6uliamGp/GteiQZFJTK4R286SYxx1DNgqJEZQQaGJNVWa9V3MyqOnaDw1yXIomKgQX5baWyplDLUyIwRxua460t42twWaz09zhw+oDH8xRNHqautYeSs+di7uBHYpTuhw0Zy8kAE8sZWgoSYg5haWhE2dQa2Ti6E9B1I+159OLF/jzKd+IN78QgIonej/3uPGIu7f5DGvLTjz18If3wORiYmKufkcjl5mWm4+wfSZ9QEvNt14NEXX0NHR4dLJ+7/8wlQmJONuZUNPcNG4O4fyMiZczG1tPrL7TtmzgK6DgrD0d0TG0dnhk2bhYGhEVcvnleG8Q/pqtDv0KTfwMiQ3Gsparpu2Pt09H66h43Ev3M37FxcGf74XGqqq0iK127vc7HRmFpYMWjydGycnOnQZwDBPXtz6uBeZZgRs54kZMAQHNw8sHZ0YsjUGUgMjUhLPK81XW1Ebt9Kn0FD6T8sHGc3dx6btwBLK2ui9+7SGN7QyIjHFyxkwLARWNuqV8ibs+zH7+g1aAg+AYG3rKsl+3dspfegofQLU+h8dN4CLKytOdSKzulPLaT/sBFYaWg4APD09aNH3wG4uHtg5+hE6IDBtAvpQvKlC3ekdUzX9myKO8uxK2mkF5SwaPchjCUGDAj21Rrnj4NxbD5+luTcAnJLylh39AypeYWE+nsqwwS6OnA55zrRF1PIL5VxPiOHqAvJBDirV9ZuxsPi95asWb2KUaPHMG7CBLy8vXn51X9ja2vHlk0bNYZ3c3dn1Jgx+PsH4OTsTL8BAxgeHk5Cwpm7pmnV+vWMCR/BxDFj8Pb05N8vvICdrS0btm3TGN7d1ZWxI0YQ4OuHs5MTA/v2ZcTQoZw5d04ZxszMDDsbG+VfVnYWWTnZTBg9+vZ1btzImOHDmTBqFN4envz72eews7Fh4/btWnWOGR5OgK8vzo6ODOjdh/AhQzlzvknn7v2RjB85ivDBQ3B1dmb44MFMGDWKZevW3rZOwd8LUfm7RaKjo9HV1eXzzz9nwYIFbNu2jcOHDyvPb9myBTc3N7766itmzZp1W9fIysqivFSKZ3AH5TEDiQQ3v0CyUpO1xsu5moJXkOrwEK/gDuSlX6O+XtGymX01Bc9g9TBZqaofS3tXLyWgc3c8AoM1XsvNx58rZ+IVOoPak52aTH5mBr4dQ3DzDVD5eNSk01NNZ3vy0tOUOnOuqYfxDO5A9tWm+08+dxonL292/PEzP73xEsv/9z6no/crPzABpIUFlJdKlcNA4d7b8gYNDQ0knoyjproKF28/AOrrFGlVlsuUttTR1UVPX5/ctKv3zJYA0oICfnn7X/z+3mvsXPIzJQX5KuddffxJPZ9AfmO6l2qrSK2roZtEc6/kvaBWLie5roauLTR0NTDW6ofsqym4+QZgIJEoj3m164BMWoK0UDF8MDs1Ba9m+Q/AO7gjeWmq/vcKahmmA9ktnquIVX8S2KU7noHt1LRIC/ORNzQgkxZTWlwEwPXMdNDRQaeV+Qb38vmsLJdRWSHjh9de4PcPXidi5RLcfAP+cvu2pL6ujvq6WgxbVKCb6790Mo7a6mqcvTV/0JcWFlBRKsWjmS/0JRJcfQPIaS2fXUvFM0jVf55B7bneLJ+p6a2vo66uFiMNjWetUVdbS3pqMu1COqscDw7pQkpS4i2l1ZKoPbsoLSlh9KSpd5QONOkM1qAz9Q51NifjagqpSYn4t+tw88BacLQ0x9rMhIS0LOWxmrp6LmbmEuiq3rvSGsYSA2RVTQ1elzLz8HKwVVb27MxN6eHrQXxqhrYkNPKw+L0ltbW1XE5KpEdoqMrxHqGhnD93tk1pZGZkEHfsGJ27dL154DZqSrx8mV7du6scD+3enbMX2tYYk5GVxdETJ+jSKURrmM07d+Lj5UVIh9t7Nmtra0m8cpnQbqpTBUK7dePsxbY1dmRkZXHs5Am6Nut9rK2pxbBZ+QtgKDHkQlISdXW3PhT5QUNHV+ee/f1dEcM+bxF3d3dmzFDMfXN1dWXv3r0kJCQwcOBAADp06MCkSZPu6BoFBYoPJFNzC5XjpuYWyEqKtcYrL5Xi0eIDxcTCgoaGeiplMswsrSgvlWpMt6JMqvx9NjaakvzrjJr1pNZrDZkyna2/fU9hbjarv/wEHR0dBk+Zjk+HEK4kxCMrKWlFZykegaoaTMzbqrNU+VtakE/C4YN0HTycnsNGcj0rg4PrVwHQZeBQACpKpY3pm6uldS9sCZCflcmqLz+mrq4WiaEh4598TjkkzcbJCQsbW47t2QGAoYkpx/ftQlZSTHmpFDMrq3tiS2dPH8JnzMXG0YmKsjLiInaw5qtPmP3WRxibKoZ0Dp48nci1y5hzLBa9xngLzGzoaaj5Y/xeUNpQTwNgpaOnctxKV4/yUqnGOOWlUsytrFWOmZpbKs9Z2dlTXibF07yF/zXY1cSihe0tLChv5v+Exrw0erbqUKHmWgDsXd359Z1X0dVV3IeTpzd6unoa49yIdy+ez0pZGQC9wsfg17Ez0qJCYrZvolxagqGxZr/fLfu2JGb7JgwMjfDrqDqPKD8rg5VfNOkfM28hdi6ah3ze8I2JuXqekUm1lwcVpVJMAlQbwm7orZLJMNWg9+jOLUgkhnh31P7xqAlZWSkNDQ2YW6mmaWFlReK5hFtKqzlZadfYsX41r3/yObp62p+ttnJDp0WLe7ewtCKx5PZ13uCNBXOQlUqpr29g9JRpDBg+8rbTsjJVNA6VlFeqHC8pr8TGrO3l14jOwdiamxJ9samBJzbpKubGRnw0bRQ66KCvp0vUhWSWHzp5SxofFr+3RFpSQn19PTY2qvOsbWxsOHnieKtxn35yHpeTkqipqWHs+AkseGbhXdFUIpVS39CAjbVqOWRjbc3x+FOtxp373HMkXblMTW0tE0aP5tn58zWGk8lk7I+OZuG8ebevs7RRp5UGnadPtxp33ksvknTlikLnyFEsnDNXea5X925s27OHwf36ERwQwKUrl9m6Zzd1dXWUSKXY3aSXWPD3R1T+bhEvLy+V3zY2NkilTR9T/v6tT+7fs2cPES0mDOfm5pKUlKT83elGC06LvUzkcvlN9zdROyu/kVSzM2rpNv1flJfD4e0bmfbSG+jpqz4eF08cZd/qZQDU19crK1QTn3mR4ut5HNq8TjEHSK5+DTWdWjSo6mx5K6rDAuVyOY4eXvQfp6hsO7h7kpxwioMbVxOzTTHcZMLTL2pM7F7Y8gY2jk7MeuN9qisquHwmnj3LF9N9aLiywieXN6DTuMjLko/exDOoPV7tOjZd8x7Y0ru96uRyZ28fFr//OhfjYuk2JBxQLA6SnZrMOxYOOOjpc762ij9kxTjq6dNNcv8qgJq4mdnUbSZXP66WwA2bNR3XUTNsk12L8nI4vG0Dj738pjIvlZeWkpWSzPljigVUJi18CYC89GtMXPACFja2ZCRf5uDG1ThomB+ocg8tD9zF53Pqi//BvlkFys03AHtXd+xd3XFy9+Tnt//V6ofk3bLvDeIP7iUhNoqpz/8bQ2PVXl4bR2dmv/EB1ZUVXD5zkr0rlzDpuVexc3El8eQxDqxdoQw7bsHzWvWp+VL9plqolWs8DnA6KpLzsYeY+OwrGGqZr3szWuqRa3JeG6mtreW3rz9n8qy52Dmqz8G6IzTY5W5sw/WvDz+luqqKq1eS2LxiKXYOjoQOHHzziMCAYB8WDOur/P3xpn2N2lS5FZ29/D2ZPbAHX+04SH5p09y6dm5OTOkdwm+RR7mck4+zlQVzh4QyrW8X1sS2/gGviYfG7y3QmKduYuAP/vsJFRXlJF+5wo+LFrFy+TJmzn7iL9PEzV/7fPLuu1RUVHAlJYXvfvmZpWtWM2f642rhdkXuo6G+nlHDht91nXK5hndLS51vvkV5ZSVXUlNY9NtvLFu7liceewyAuY/PoLC4mHkvvQhyOTbW1oweNozl69YpF5R7qBH7/N0xovJ3i+jrq5vsxpw/AENDw1bjjxgxghEjRqgcKy8vp7CwaQJ2TU0NkyZNorxUioV1U2tahaxMrcW6OaYWlmq9HRVlpejq6mFkaqo9jKwUk8aW+eyrKVTKZPz5yTvK8/KGBjJTLnPm8EGeeOND5HI5yz57n55hIzmwYRVGJqZ0GTiU/KwM4vdHoKevr9bjoKrTQk1DpUyTzlKVMBVlqvdvamGpMr8PwKdDJ9KSLjLj9feApmGVzXu5FPf819vyBnr6+ljbKyaYO3l6k5t+lYLcLGa98b4yTH1dHX9+/A6PPPMyXsHtWfXFf3H08KK0sOCe2LIlEkMjbJ1dKM6/DihWlYvZvpExc58hdIui0uqtL+FqXQ2bKkrvW+XPQlcPXaBEXq9yXNpQr+aHG2jzGzT1CJmaa/J/Gbq6ehibteL/sjJlL1d2qiIvLfn4beV5eWNZ0VDbwJw3P6KuthaAzv2H4NuxM6DoBTyxbxcyaYnW+/6rn8/4g3sZ8fhcjM3M0dHVVUnHzMoaA4mhWuNQq9puw743iD+4l8PbNzN54csqCxSp6Hdo0p997Sqno/YxbPoT+HTojJNnU5z6OoW9y0ulmDcrWytvUh6YWFgqRxEo4zTqvWHvG5yOiuTori2MX/AiTp7eWtPUhpm5Bbq6upS2GJlQJpVi0aJXqK1Ii4vIycxg6Q/fsvSHbwHFx7lcLueZqRN4/s33aKdlZcbb0qmhJ/RWuVFZcfX0olRawo71q9tc+TuenM7lnKZh6waNDRXWpsYUljVV3CxNjCmpqFSL35Je/p68OGog3+0+pLbS5/R+XYm5lErkucsApBcUY2igz8Lwvqw7coaGNlbeHha/t8TSygo9PT2VbxiA4uJitd7Aljg2Lrzi7e1DQ30Dn336MY89PkPjd9atYGVpiZ6uLoVFRSrHi0qK1XoDW3JjkRUfLy/qGxr4+IvPmfnoNPRbNHZt2bmTwQMGYGmhvdy4qU6LRp3FqjqLS4qxsbZqNa7jDZ2enjTUN/Dx118xY+pU9PX0MDI05J1/vcobL75EYXExdjY2bN61C1MTE6wsb391bsHfB1H5ewAwNTXFtNkHhFwux9TCkrTECzg3fjzU1daSlXKZgRO0j9l39vYl+azqkIa0xAs4enihp6dwtYu3L2mJF+kZNrJZmIu4+ijmyPh16srsN71U0tiz4g+s7R0JDR+NrbMLNVVVyBsasLCxbdR5ESdPb3R0dWmoryc3/SoDxmtfpt7Z25eUs6otommJF3H08FTqdPbyJT3pgnJ1QID0pAvKuUgALj7+FOflqqRTVlyMpa2d8mP2hi0zk5t6Vu+VLbUhl8tBjlJjc5156dewtLUjL/0aoeFj2L3st3tiy5bU1dZSnJeLu38QAA319TTU16u1Guqi3pp+LzHQ0cFPX8Lpmkr6GTblodO1lbhr8YOLty+Htq6nrrYW/cbVeNMuXcTM0gpLWztFGB9friSo2vVa4gUcPVX9fy3xAj2HjVQJ4+KjsKtfSFee8PRSSWPP8sVYOzgqFt9xdqG6UvHhmZ/V9EFZV1tLeVmpUosm/urn80ajiZ6+Po7unqQlXiCoW09A0XBSU12Fm7/mhSPuln0BTuyPIHbHZiYtfFlli4fW9Tco9UuMjFRW8JTL5ZhYWJKedFFZMaurrSU75Qr9xk/Wmqazlw8p586oHEtPuohDs3wGcOrgXo7t2sb4BS/c9hYP+gYGePj4cfHsGbr16ac8funsGbr2ur2V+qxtbHn3q0Uqx6IjdnEp4QxP/+dNbO1vbd5bc52JCWfo1rtJZ+LZM3QJvbsrCsob5MqGkrZQVVtHbkmZyrFiWQUhni4k5yqmVRjo6RHs6siy6BOtptUn0JvnR/Rn0Z7DHL18Te28ob6+WgWvoS09yS14WPzeEgMDAwICgzhx/DhDhoYpj584HsegwUPanE6DvIH6+nqVxvQ70RQUEEBc/EnCBg1SHj8eH8/g/gPanI78hqb6emhW+btw6RJXUlL417PP3blO/wCOx58ibMBA5fG4U6cY0q9/m9NpkMs16tTX18excZXYfVEH6Rsa+vfo+fs73MN9RljwAURHR4eug4dxfN8uLp+JJz87U7lUenD3pknVu5b9xq5lvyl/h/QbRFlJMQc2rKIwN5uzRw5xPi6WHkPDlWG6DhpG+uVLxEXspDA3h7iInWRcTqTbYMX+L0YmJti7uKn8GUgMMTI1xd7FDR0dHQyNjXHzC+Tw9k34dAjh+N6dHFi3ggtxsdRUV2EgMSSomc7dy35n97Lfm3T2Veg8uHE1hbnZnDtyiAtxsXRT0RlG+uVEju/dSVFuDsf37iTjchJdBzftU9Nt8DByrqUSF7GD4vw8Lp8+weno/XTu3/TCqaooJ6BLd+IPKIb9ZCQnseXX79A3kPzltgQ4tHU9mcmXkRYWkJ+VyaGtG8i4kkRw917KMEmnTpBxJZF2PXsTF7GdNV9/gpt/EIknj90zW0ZvXkvGlSSkBfnkXEtl++Ifqa2ppn1oH4Amn2/bwNmaSnLra4msKuNAVTm972DOn46xERI/HyR+PqCrg4GjAxI/H/Qd275S3gRjS/ZXyYioLCOjroZfZIUU1dcT0k/RS3Bo63rWfvt/yvDtevRC30DC7uW/k5+dyeUzJ4nbt5PuQ8KVw29C+g1GVlLU5P/YaM4fi6HH0KYKdLfBCv8fi9hBYW4OxyJ23DwvGRpiZNKUl4xMTLCwtePSyWMc2bWN1AvnWPPN/2ior6dj76aPlPv1fNZUV2FiZs75YzHE7d3J+WMxLPlYMSpgYGNl6a+y7/F9uzm0dT0jZszF2sEJmVSKTCqlurJCGSZ6S3P9Gcr7aZ5nmqOjo0OXgUOJj9xDcsIpCrKz2LdyCQaGhgR2a4oTsWIxESsWK3937DsQWUkx0ZvWUJSbw/mjh7l4/AhdBzcN+YrfH0Hs9k2ETZ+NlYMj5aVSyktV9baVsLHjORp1gJjIveRkZrD2j9+QFhcp571tXrmUr95/WyVOdkY6GVdTkZWWUVVVRcbVVDKupgKKSryrh6fKn7mFJfoGBrh6eGJkfHtDU4eOadS5X6Fz3R+/IS0qon+jzi0rl/LNB6o6c27oLCujuoVOgIO7d3Au/gTXc7K5npNN7P69RG7fTM/+g25L4w12nLrAxJ6dCPX3xMPOiudH9qeqto5Dl5oW+nlh5ABeGNmU7/oGevPSqIGsOHySixm5WJkYY2VijJlR02IaJ1MzGNYpkL6B3jhYmhHi6cJjfbtyMjWjzb1+N3hY/N6SaY9NZ/fOHWzfuoVrV6/yzVdfUlhQwISJjwDw848/8OJzTfP59uzexYH9kaRdu0ZWVhb7I/fxy08/MmjwECQtFiq5XaZPmcKOiAi27NzJ1bQ0vvh+EfkFBUwaOxaA73/7jWf+9Yoy/K69e4mMiuJaejqZ2dnsizrID7/9zpCBA9U0bd65Aw83N7qG3Np8Xo06J01ix769bNm9i6vpaXz54w8UFBbyyJgxAPyweDEL/9O0DdKuyH1EHormWno6WTk57IuO5sc/FjOk/wClzrTMTHZF7iM9K5MLiYm89fHHpFy7pjIvUPDPRvT8PaD0DBtJXU0N+9etoKqiHGcvHyY/9y+V/d5KWwxpsLKzZ9IzL3Nw42oSYqIwtbRiyOTpyn2/AFx9/Bgz52lid2widtcWrOwcGDP3abU9/m7G2LlPc2jrBlLOJ1BXV0tCbDQABoZGTHp2norOshZDGizt7Jn49EtEb1rD2ZgoTC2sGDx5unJfOgAXHz9GP7GA2B2bObJrK1Z2Doyes0Bl2JeTpzfjnnyWmO2bOLZnO+bWtvQZPYGQZhtFp547w+no/crfR3dtBRQfz/fCluWlpexc+hsVZVIkRsbYu7ox6ZmX8W62cl15aQlRm9YgK5ViIJFQV1NLduoVnLx8mPTsK/fElrKSYnb9+QuV5TKMzcxx9vLhsVfewsKmqfdp9JwFxGzbyBcnjiFraMBBT48ZplaMMVJdTOdWMAoKwG3R58rftvNnYTt/FqW79pL3Sdu2ShlgZEqZvJ61FSUUNdTjqS/hfUtHCht7mWRSKSUF15XhDY1NmPr8q0SuXcHyzz7AyMSU7kPC6d6s4mRlZ8+khS9zYONqzhw+qNhWYcrjyj3oQLH66dg5TxOzYxOxOxX+HzvvabU9/m7G9FfeZOOPX3N091blJu/dh4Sr9NTer+dTR0fRm69nYMChrRsAMDIxZey8Z5SLqvxV9j19aD8N9fVs/+MnlXtvH9qXUbPmN+qXsvPPXykvk2JoZIydqzsTFrygslJyS7oNHUFdbS0HN6yiuqIcJ08fJjzzskoPoVo+s7Vn/IIXOLR5HediojG1tGTgI9OUe/yBYvuKhvp6dv/5q0rc4J69Gf74rX109ejbn/KyMnZtXIe0uAgXD0+ee/NdZU+NtLiYghajHr7/5EMK85v88N9/vwTALxs0L21/N+jetz/lsjJ2b1xHaXERzu6ePNtCZ35LnZ9+SFEznZ/8R6Hzp/UKnQ0N9Wxe8SeF+dfR1dXD3smJCY/Ppv8w1ekSt8rm4+eQ6Ovz1NDemBpJuJKTz4cb9qjs8WdnoTqEN7xzEPp6uswb0ot5Q5oa7M5n5PDuWsWm3uuPnkEul/NYv67YmplSVlnFydQMVh6Ov2WND4vfWzJ02DCkUilLlyyhsLAAbx9fPv/qa5ycnQEoLCggK7NppVU9PT1WLF1KRmYGyOU4OjnxyKTJPDrtsbumafjgIUhLS/ljxXIKiorw9fLim0//h3PjvoMFRYVkZWeraPpz1SoysjKRy+U4OToyZcJ4HpusOvKmvKKCvQcOMH/WrJvOaWwLwwYNQlpaypJVqxQ6Pb34+r8f49w4JLagqJCsnBwVnUvXrCEjK0upc/K4cTz2SNNCgw0N9azauJG0zEz09fToFtKZxd98i4uGPRcfSsScvztGp7Ky8n6O2hJoYfltvDjuB7fasnm/0H1ICouHwZ5DPvzofktoE9Hvv3e/JbSJO1nQ4V5yNz507gU1D8lS5u1cb23j9/vFbW6/eU/5fm/s/ZbQJl4I73vzQA8A7d0ejkqCYYX2Te0fJOQPSZlk6Hjnw4DvBdJNmvdA/CuwfGTsHcXfuXMnmzZtori4GA8PD5588knat2+vNbxcLmfbtm3s3r2bvLw8zM3NGTJkCE888cQd6WiJ6PkTCAQCgUAgEAgEgrvE4cOH+e2333jmmWdo164du3bt4v333+eHH37AwUFzRXvx4sWcOHGCOXPm4OXlRXl5OcXF2rchul1E5U8gEAgEAoFAIBA8+Dwko1C2bNnC0KFDCQ9XTHlYsGAB8fHx7N69m9mzZ6uFz8zMZMeOHSxatAh399a3erpTROVPIBAIBAKBQCAQCO4CtbW1JCcnM3HiRJXjXbp04dKlSxrjxMXF4eTkRHx8PB988AFyuZwOHTowZ84crG5zuxdtiMqfQCAQCAQCgUAgeODRuYdbPezZs4eIiIibhgsPD1fZw7u0tJSGhga1SpuVlRUJCQka08jNzeX69escPnyYl156CR0dHf744w8++ugjPv/887u6TYeo/AkEAoFAIBAIBAJBM0aMGKFSqbtVbmWhNLlcTm1tLa+88gqurq4AvPLKKzz99NNcuXKFwEDNe+veDmKfP4FAIBAIBAKBQPDgo6Nz7/5uEwsLC3R1ddUWaykpKdE6hNPa2ho9PT1lxQ/AxcUFPT098vPzb1uLJkTlTyAQCAQCgUAgEAjuAgYGBvj5+XHmzBmV42fOnCE4OFhjnODgYOrr68lptq9jbm4u9fX1WlcHvV1E5U8gEAgEAoFAIBA8+Ojq3Lu/O2DChAns37+fiIgIMjIy+PXXXykqKmLkyJEALF26lLfeeksZvnPnzvj6+vLtt9+SkpJCSkoK3377LYGBgfj5+d2RlpaIOX8CgUAgEAgEAoFAcJfo378/paWlrFu3jqKiIjw9PXnvvfeUvXhFRUXk5uYqw+vq6vLuu+/y66+/8sYbbyCRSOjcuTPz5s27q4u9gKj8CQQCgUAgEAgEgocBnYdn0OLo0aMZPXq0xnMvv/yy2jEbGxtef/31v1qWGPYpEAgEAoFAIBAIBP8EdCorK+X3W4RAndVHTt9vCW2ipq7+fktoE/p6evdbQpvQu8Mx5veCuvqG+y2hTQx8/4P7LaFNrHz+pfstoU0k5ly/3xLaxIAg7/stoU3UN4hX791CX+/haMc2eEjeQ4b6D8egsOulsvstoU1I9B8Ovz85JPR+S2gTpbv33bNrWYwcds+udS95OEpMgUAgEAgEAoFAIBDcEQ9H845AIBAIBAKBQCD4R3MrG6cLNCN6/gQCgUAgEAgEAoHgH4Do+RMIBAKBQCAQCAQPPg/Rap8PKsKCAoFAIBAIBAKBQPAPQFT+BAKBQCAQCAQCgeAfgBj2KRAIBAKBQCAQCB58HoItsR50RM+fQCAQCAQCgUAgEPwDED1/AoFAIBAIBAKB4MFHbPVwx4ieP4FAIBAIBAKBQCD4B/BAV/7eeOMNfv7559uOf+7cOcaOHYtUKtX4WxOxsbGMHTv2tq8pEAgEAoFAIBAI/gJ0de/d39+Uf9Swz6CgIJYtW4aFhcX9lnLLnIreT9y+3cikJdg5uxI2ZTru/oFaw1/PymDf2hXkXEvFyMSUzv0H03fUOHQau8tl0hIObFhDbsY1iq/n0T60D2NmP6mSxtmjh9m1bLFa2q9+9yv6BhIA5HI5R3dv41xsNFWVFTh7+jBk6uPYObu2ej8ZV5KI3ryWwpwszCyt6B42kpB+g1TCXD5zkiM7tyAtyMfSzp6+Yx7BP6Sr8nxNVSWxO7eQnHCKClkZDm4eDJ70GE6e3irpHNm1hZP7I6irqQF0sHJwYNKCF7F1dtGoLb/RdrlpCtuF9B9Mn5FNtgNIv5zIwY2rKcjJwszSmp7DRtJlwBCVdKorKzm8fSNJp09SVS7D3NqGAeMmE9StJwA/v/0vSosK1a7v2yGEqc+9QnzUfuL27UImlWLv4kLYlMdv6vO9a5Yrfd5lwGD6jhqv4vP9G1aTm55G8fVcOoT2ZcwTT6qlc2L/Xk4dOkBpUQF6+vrI5XLq6+qwd3ElbMrjOPv4a9WQn5VB5LqVTbbrN4jeLWyXcSWRgxvXqNiuc//BKukknT5J7I7NlBRcx8rOgX5jHyGgczeN1zy2ZweHt2+ky4AhhD06U3m8Ut7AUlkxR2sqKGtowF5Pj5FG5kwwsdSqXxNGIR2wfmwyRoH+6NvbkfvxF5Tt3ndLafwVDGrvRzdfd4wMDMgqKmFn/EXyS2Vaw5sZGRLeOQhnawtszEw5m5bFluPn7rquyb1CGNIhADMjCcm5BfxxII7MohKt4Xv4ejCsUyBe9jYY6OuRVVTC5uPniE/NUIYZ2M6XZ4b3U4s7c9FyausbWtUjl8uJ27Od80cOUVVZgZOnN4MnT8f2JmVUZnIShzevozA3G1NLK7oNCadTszKqMCeLY7u3cT0zndLCAkJHjKXXyHEqafzxweuUacjjXu06MubJ59V0Ho/YzoWjh6murMDRw5uBk6ZrLadukJWcRMzW9RTlZmNqYUXXIeF06DtQef7C0cMknjhKUV428gY59m7uhI4cj0uzfLz0wzcoK1bX6RncgbFPvXBPdCafOUn8/gikBddpaKjHys6BkIFhBPfs05RGymVOH9xLfmY65dIShj72hMr5ljqP7d6m4vchUx6/ud+vJHFo81ql37sPHaHm96O7tnE9M03p996jxqumkXyZUwciyMtIo1xawrDH59A+tK/atU4fOsCJyKb3+pDJ03HzC9Cq7W6Vrze4dPIYO5b8gk+HECY985LW656MiuTo3p2N7yJXhk+dgcdN3kV7Vi8l+1oqxqZmdOk/mP6jJyh1pl2+xMHN6yjMy6W2phpLGzs69xtI7+GjlWmcOnyQc8diyM/OQi6X4+TuycDxk/Dw035duVxO/L6dJMbFUF1RgYOHF30nTsPGqfVnMzvlMse2b6A4LwcTC0tCBg2nXe8BGsMmnz7BgVV/4BHcgRFzn1UeX/XJW8iKi9TCuwd1YPyChzMPCf6+/KMqfwYGBlhbW99vGbfMpZNxRK5bxfDHZuLmG8CpQ/tZ98NXzH/3EyxtbNXCV1dWsva7z3H3C2T2a+9RmJfDrmWLMTCUEBo2EoC6ulqMzczoFT6ahJhordc2kEhY8OH/qRy7UfEDOBG5m/gDEYTPmIuNgxPH9mxn4/dfMuedj5EYGWtMU1qQz+afv6FDr36MnDWfrJQrHFi3EmMzMwI6dwcg+2oyO5f8Qp9R4/EL6Upywil2/PET015+A2cvHwD2rlpKQXYmI2bOw8zKmksnjrHh+y+Z/dZHmFsp/Hw6KpJju7fjGdSOkH6DSTl3msT4E1RVVmjUVl1ZybpFn+PmF8jM196j6IbtJBJ6NtqupCCfjT9+Rcfe/RnzxAIyUy6zb81yTMzNCezSA4D6+jrWLfocIxNTxs9biLm1NWXFxejpN2W5Wa+9R0ND04dreamUpf97n6BuPbl4Mo7IdSsJf2wWbn4BnIrez9rvv+TJ9z7V6vM1336Ou18AT7z+PoV5Oexc+jsGEkNChzX6vLYWYzNzeoeP5kxMlMb7v3D8KAc3r2XkjLlUV1YQuX4VRsYmeAV1wNzKirXff8nctz/GQouGdYu+wN0vkBn/eZeivFx2L1+MgcSQHmEjmtnuazr07s/oJ54iM+UKkWuWY2xmTmAXhe+zUpPZ/sdP9B09gYDO3bh8Jp5ti39k+itv4uLtq3LN7KspnD0Sjb2ru5qe32VFnKmp4l/mdjjq6XO+tppFZQVY6OoxxMhM4/1rQtfYmJrUNMr2ROL49r/bHO+vpG+QD70Dvdly/CyFZeUMbOfHrEE9WLTrEDV19Rrj6OvqUlFdQ8ylVLr5qtvrbjCuewdGd23PT3tjyC4uZVJoCG8+MoxXlm6mqrZOY5x2bo6cz8hh7ZHTyKqq6Rfkw7/GDOLDDREkZl9XhquqreXFJZtU4t6s4gcQv38Ppw7uZdj0OVg7OHE8Yjubf/yaWW/9F4mRkcY40sJ8tv7yHe1D+xI+cz7ZqVc4uH4Vxmbm+Dc2QtTW1GBhY4dvp64c3bVFYzrT/vUW8hZ5fPUX/8W/8VlvzqkDEZyJ2sfQx57A2sGJExE72Prz18x44yOtOksLC9j+2yKCe/Zl2Ix55KQmE71hJUZmZviFKHRmJSfh36U7zt5+6EsknImKZNsv3zLt1XewsncEYOorb6qURRWlUtZ+9TF+ne+dTiNTM3oMH4W1gxO6enpcu3COA2uXYWxmjle7jgqbV1dj6+xKUPfeRK76Q+O1bnAyUuH34Y/PxdrBibg929n0w1fMfvvjVv2+5Zdvad+rHyNmzScrNZmD61aq+93WFr+QrhzZuVljOrXVVdg6uxLcozcRKzTrTIyP48D6VYRNm4mbrz+nDx1gww9fMfedv7Z8vUFJwXWiNq/DzVd7ZRPgwolj7F27ghHTZ+PhF8DJqP2sXvQ5T7//Pyxt7DTqXPnNZ3j4BzL3jQ8ozMtl+5+/IjE0pNewUQBIDI3oMWQ4Dq7u6EskZCZfYdfKPzCQGNJ9UBigqCC2694Ld19/DCSGxO3fw+pv/48n3/4YG0cnjVoTovZy7lAkA6fOwsrBkVP7drHrt++Y+u/3tT+bRQXsWfwDgT37MPixOeReSyFm02qMTM3w6dRVNWxhPnE7N+Hk7aeWzsQXXlfJ6xVlpWz69lN8Q7qqhX1Y8tADi5jzd8c88H2aDQ0NLFu2jOnTpzNjxgwWL16sfEnJZDK+/vprpk2bxqRJk3j77bdJS0vTmpamYZ8HDhxg7ty5TJo0iQ8++ICSkhKVODk5Ofz3v/9l5syZTJ48mRdffJHjx48rz69evZpnn32WlvznP//hl19+ucO7V3B8fwQde/elc79B2Dm7MPzRmZhZWHH60AGN4S8cP0ptTQ2jZz+JvasbQV17EDp8FCciI5DL5QBY2doz7NEZdOrdHyMTU+0X19HBzNJK5e8Gcrmc01GR9Bw2ioDO3bFzcSN8xjxqqqtIPBmnNcmE2CjMLK0UrbBOLnTqO5B2oX2I3x+hDHPqYCTu/kGEho/B1smF0PAxuPsFcuqgoseltqaGKwnx9Bs3CXf/IKztHekzajxW9g6cjTmoTOfo7m1Y2tnz6Av/IahrD0bPfgpzaxuSz57WqO3iCYXtRs16EnsXNwK7KGx3cn+T7c4cPoippTVhj87E1tmFkH6DaN+rLyci9yjTOXc0hoqyMh55+kXc/AKwtLXHzS9AWXEFMDG3ULFr6vkEDI2MCe7Wg+ORe+jYux+d+zf6fFqjz6P3a9R94fgRamuqGfPEU0qf9wofxfHIPU0+t7Nn+KMz6NSnP0ammn2emXIFF29fOvbqy/m4I3Tq3Z+ug4ZSdD1HqeHMYc3P3cUTR6mrrWHkrPmNtutO6LCRnDzQZLuEmIOYWloRNnUGtk4uhPQdSPtefTixv8l28Qf34hEQRO8RY7F1cqH3iLG4+wcRf1C1t626soIdf/5C+ONzMDIxUdNzqbaaIUamdJIY46hnwFAjM4IMDEmqrdaoXxsVx05Q+OsSZFEx0CC/pbh/Fb0CPIm5lMqlzDyuS2VsPn4Wib4+HT21txyXVFSy+/QlzlzLorKm9i/RNbJLMFtPnON4cjqZhSX8GBGDscSAvkE+WuMsjT7BtpPnSckrIE9axsa4BFKvF9Hd10M1oBykFVUqfzdDLpdzOno/3cNG4t+5G3Yurgx/fC411VUkxWsvo87FRmNqYcWgydOxcXKmQ58BBPfszamDe5VhnDy96T9hCkHdQzFo1iDWHBMzc0wtLJV/1y6eQ2JkpKxINNeZEB1Jt6Ej8Avphq2zK2HT51BbXcXlU9p1nj+i0Dlw0mPYODrTvnd/gnr04XSzvDJ85nw69R+CvZsH1g5ODJryOBJDI9IuXVCGMW6hM+3SOSSGRvjdQ51u/kH4dOyCtaMzlnYOhAwcip2zK9mpV5RhvNp1pPfoifh17oaOjvbPF4XfI+nRzO/hMxR+T2zF72djojGztGLw5OnYOLnQsdHv8Qea3k1Ont4MmDBV4XeJZr97t+9E37GP4N+lu0qvXHNO7t9Lh159Cek7EFsnF8KmzsDU0vIvL19B0Ti5449f6D/2ESzt7LXaAyAucjed+vSna//B2Dm7MuKxWZhZWhGv5V10/ngstTXVjHtiAQ6u7gR37UHv8NHENXsXOXt6075Hb+xd3LC2c6Bjr774tOtEenKSMp2J8xbSY/AwnDy8sHVyZuT0J5AYGZNy4azG68rlcs4dPkDI4HB8OnXFxsmVQdNmU1tdRfLpE1rv79LRw5hYWtJ3wqNYOzoTHNqPgO69OBsdqRKuob6e/Sv/oMeIcVhoqPQam5ljYmGp/EtPPI/E0AifTg9nHhL8vXngPR8dHY2uri6ff/45CxYsYNu2bRw+fBiAb775hqSkJN5++22+/PJLDA0Nef/996mubtvHXVJSEt988w3h4eF899139OzZk5UrV6qEqaqqolu3bnz00Ud899139OnTh08//ZSMDMWQpGHDhpGZmcnly5eVcTIzM7l06RLDhw+/4/uvr6sjN/0a3sEdVI57B7cnKzVZY5ysq8m4+wWovJh82nVAJi1BWlhwS9evq6nhx7f+xQ9vvMz6H74mN6Opci0tyKe8VIpnUHvlMQOJBDffALKvpmhNM+dqikocAK/g9uSlp1Ffr+gdyLmmHsYzuAPZVxX3LG+oR97QgL6BgUoYfQMDslKSldqrKspx8vBm/fdfsOg/z7Psfx9gZWun1XbZqcm4+arazjtY1XbZV5PxDlbV5t2uA7lp15T6kxNO4errR+S6Ffzw+gss/vBNYnZsVp5viVwu5+yRw7Tv2RtdXT2Fz9u18Hm7DmRq83lqMu5+gaq623W8ZZ+7+wVwPSOd9CtJ5KZfw9HDk+Szp/FtH6LUkJWq2bfZV1PUbOfV4rnLTk3BS+1Z7kheM9tlX03BK6hlmA5kt7j3iFV/EtilO56B7TTqaWdgyPGaSvIb071UW0VqXQ3dJJp7pB8WrE2NMTc2IiWvya919Q2k5Rfhbmt133Q5WJhhbWrC2fRs5bHa+nouZeUR4Nz6B2ZLjCX6lFfXqByT6OuxaO4kfpg3mf+MG4KXvc1N0yktLKCiVIpHs2dEXyLB1TeAnNbKqGupeAapPleeQe253qyMulXkcjkXjsUQ1L0XBhJDdZ1lpbgHNpUr+hIJLj7+5FxN1Zpm7rVUlXsD8AhqR37GNa06G+rrqKut1dhgckPnxWOxBHYPvW865XI5GZcvUZyfh6uv9mHm2lD6PUhVp8LvmstQhc4UPALV3zt34ndN1NfVkZtxTa0s9Ar+68tXgMPbNmFha0uHXupDqVvqzEm/hk+Ld5FPcAcyU65ojJOZmoxHi3eRb/tOlJUUU1KYrzFObvo1MlOv4Okf1KoWxXOrueGyrKiAyrJS3AKClcf0DSQ4efuTl6Y9r+elpeLmH6xyzD2gHfmZaTTUN42iOL57K+Y2tgR07601rRvI5XKSjsfi17Un+i0aCB6WPPQgo6Orc8/+/q488JU/d3d3ZsyYgaurK/3796dTp04kJCSQnZ1NXFwczz33HB06dMDLy4tXXnmFiooKoqKi2pT2tm3bCAkJ4dFHH8XV1ZWRI0fSq1cvlTDe3t6MHDkSLy8vXFxcePTRR/H19eXIkSMA2NnZ0bVrV/bta2p9iYyMxM/PD29v1blnt0OFrAx5QwMmFqrzlEwsLCnXsnBNeakUE3P18DfOtRVbR2dGzZzHpKdfYNzcp9E3MGDF5x9TdD0XAFljWibmqnMoTSwsWr1OeWmpehxzCxoa6qmUyZQ6TVuEMTW3oKKsFACJkTHO3r7E7dlBWUkxDQ0NXDxxlJyrKZSXlgAodSafPYVXcAemPv8qwT1CuZZ0kZL862iivFSKaQtbm7awXXmpVM0fpuaWKvpLCq6TdOoEDfX1TFr4Cv3GTiQh5iCHtmzQeN1rly4gLcync7+BSp+btpibatqKXWWlUg3hLZXn2kq7Hr0YOGEyq77+H/KGBvauXoa9ixuDH5l6Uw2K566lz1rYrkzdr5p8b2Kh4Zkqa7puQmw0JfnX6TfmEa33ssDMFm99CXOKMhmff43XS3J5wtSanoaaP3ofFsyMFB/k5VWqjVzlVTXKc/cDK1NFpbplj5y0olJ5ri0M7xSIjZkphy81fbBlF5fy874jfLH9AIt2H6K2vp4Ppo7Eycq81bRuPDOaypvmz1NLKjQ8yzee0yqZ9nmVrZGedJHSwgKNH9w3yjUTc9X7MTG3oKIVneVlUoxb6DQ2t6ChoUGrzmO7tmJgaIh3hxCN5zOSLlJaVEC7Xv3vuc7qygp+ee15fnr1GXb8togBE6fhGdxRa7par6ft3WRuQXlpaSvxtL+bbtfvmqi88V7X8I77q8vXq5fOkxR/nOGPzb6pTuW7yFz9najtvSKTanqHKjS1/Gb59rUX+PTZOSz+5F26DRxKt4FDtWqJ2roBiaEhARqGUUKzZ9Os5XNmTmWZdp9XlpVqfDblDQ1UlStslpl0kdSEePo/8pjWdJqTdfkSZUWFBPVUn+f5sOQhwd+bB37On5eXl8pvGxsbpFIpGRkZ6OrqEhTU1FJkamqKp6enslfuZmRmZtKjRw+VY0FBQSoVuaqqKlavXs2JEycoKiqivr6empoaFV3h4eF88803zJ8/H319fQ4ePMijjz6q8Zp79uwhIiJC4zmV+/Rrh1+zMfpq7Q9yuYaDTaiNNJHf+nA1Vx8/XH2axraXFBaQdCae3z94Cz0DfaYsfLnxWi0uJtckoKU+1fM35KkcV0tW9R5GzpxPxKol/PbOq+jo6uLg5klgt1CuZ6Y1pqkYHuzi40+PoYo5EY7unpw7GoO0QHMLpCZuDFVpRZoyTPPfJuYWhD8+B11dXZw8vKgsL+fghlUMeuRRtfs/GxuFk6c3ju6elJUUN16jpY3kGq7cnJYGk2vU2hrplxOJ3bWNQROmcHDTWgaMe4Qzh6M4vH0zA8Y9glwub9W16n6Vqx9XfzjV9Le89+bPb1FeDoe3beCxl99UmUPZku2VpVyqreIdCwcc9PQ5X1vFH7JiHPX06SZ5eCqAHT1dGNutqZV45eF4QEOWvseNlH0DvXlyaFMr+GdbG4eBtRCmg06bi5+efh483r873+0+REFZufL4lZx8ruQ05dmknHw+e3ws4SHBLI1uGoZfdPk8Py7+Uvl73ALFoiqanku1Z6wlLeMg13i8rZw/ehhHDy/s3TxIPHmMA2tXKM+NefK5GxdtcU0N5WvrMpsXpmphE6L3c/7IISY887LWOdkXjh3GwcMLe1d3kuLjiFp373RKDI149NV3qK2pJvPyJWK2rsPcxhb3gGBaI/HEMfavXa78fWOBDXVN8ptmk1ux552i8bn8C8vXClkZu5cvZswTC1qf7nGT63Kz/KOt+G6Rzqx/v01tdTWZqckc2LQWKzt7OmloHDm+P4JThw/w+EuvY2iseG7PxcWyc8USZZgRcxdqvLbi9m/ybGp81yqoKpcRtW4ZQ6bPxbCNNrt0PBZ7d0/sXN25cuo4MRtXKc89qHnooUIMV71jHvjKn76Gj7uGhga1j+3m3CwD3aC1NG7wxx9/EB8fz9y5c3FxccHQ0JCvv/6a2tqmOTM9evTA0NCQI0eOYGpqikwmY8AAzStFjRgxghEjRtz0uquPKOakmZiZo6Orq9YaWFFWqta6dgNTC0uN4W+cu10COnflSkJXKmVljJo5j7o6xVCD8lIp5tZNQ7AqykrVWh9V9am3blbKStHV1VPORVPcg2prXUVZmUrLp5W9A4+++Bq11dVUV1ViZmnFjj9+Vk5Ct7ZXTAo3NlNd3MPAwECtItmkTbvtbvSmagzTqP/GtUwtrNDT00O32VLBtk7O1NbUUClTvY/yslKunD3NsMaVKm/4vGXLakVZmVrv3g3MNGgqvw2fR2/bSLvuofQYMpyoLeuxcXRm4ITJ7Fq+hH6jxzf64BaeO9kN2yl0m5prsm9Zo+1MtadTVqZsfc5OTaFSJmPJx28rz8sbGshIvsyZmChe+upn5HI5y8qLed3CgdDGnj5vfQlX62rYVFH6UFX+krLyyCosUf7Wa3ymzIwNKa1s6mUzNZQgq7q1+Yx3QnxqBsm5TUNPDfT0ALA0NaZQ1rSgkoWJEdKKypum19PPg2fD+/NjRIzKSp+akMvlpOYV4myt2npu6eXPoN6hyt/1dYpyumUZ1TIPtsTEwpKKlmVU43Oqbb5sa1SUlZJ67gyDJ08HwKdDZ+zdm0aG1DeWpRVlpao6y0oxNmulLDW3pKJFOVkpK0NXV1dNZ0L0fo7t3sLYp17AscWKyM11Xj2fwMBJCp3e7UNwfPXe6dTR1cXK3gEAe1d3ivNyiY/cfdMPV5+OnXHyUtep/m4qUxtVoKLTQr1nsEJ2+37XhrG297rsry1fs1KSKZeWsG7R58rzN76Dvnh+HnPf/i82js7Kc03vohKVNMvLSrW/iyzVRyU1fX+oxrG2U/jawdWd8lIph7ZvVqv8Hd8fQdTWDUx74VVcmy34FRDSFSP7Jq3Nn00zK9W8bmyufYSAsYYetypZGTq6uhiZmpF7LYWKUik7f/1Wef6GzX577Vmm/OsdrByaFqCplJWSdiGBvhOnAeDZrpOK7gc1Dwn+WTzwlT9teHh40NDQQGJiIh06KMajV1RUkJaWRlhYWJvScHd3JykpSeVYy98XL15kyJAh9O2r6L6vqakhNzcXF5emhRX09PQYOnQokZGRmJiY0KdPH8xaVDhuFz19fZw8vLiaeEG5RQDA1cQLaqt33cDV24+oLeuoq61Rrsx59dIFzCytsLRVn6jcViSGRkgLC3Bw88DawRG5XK5YHCDxonJ7hbraWrJSrzBg/BSt6Th7+5LSYsGVtMSLOHp4oqeneCSdvXxJT7qgXMUMID3pAi4aVtkyMDTEwNCQqopy0hLP07/x2hIjIwwMDbnebJ4iQGFuDmZaKkQuPn5Et7DdtURV27l4+3El4ZRKvGuXLuDk6aXU7+brz8UTR5E3NKDT+LFenJeLgUSCsZnqi+j80Rj09A0I6q74aL3h82uXzhPc3OeXzhOkzec+fhzc3EL3bfi8rqYaHV1dFQ0egcHcaD2+eum81i0XXLx9ObR1PXW1tcq5mGmXLqrazseXKwmqvr+WeAHHZrZz8fblWuIFejauUnojjEtjL7RfSFee8PRSSWPP8sVYOzgSGj4GPX19aqqqqEN9XLsuaKn2P7jU1NVTJFNdnbassgpfR1uyixQfLfq6unja27A3IfGe6aqqraNKWqZyrLi8gk4eLqTmKbYNMNDTJcjFgZUx8a2m1cvfk4Xh/fgxIoa4ZO2LdjXHw86atALVpdX1JIbKDx9o7IG3sCQ9SbWMyk65Qr/xk7Wm7ezlQ8q5MyrH0pMu4tCsjLoVLh4/gp6+PgFdFflZYmSEVbP5dDdGCmQkXcTRw6tJZ2oyfcdN0pquk5cPqWo6L2Hv7qWi83TUPo7v3saYp55X2eKhJYmNOv0bVy2WGBmprD74V+tsiVzeoKzAt4ZGndr8PkH7u8nJy5fUc6rl0534XRt6+vo4uXtxLfECgV2bRh+lJV74S8tXJ09vnnjrI5XzMds3UVVRQdijM7C0VZ2bq6evj7OHF1cvnqddt6ZGlauXLhDUVfO7yM3Hj/2b1qq8i1IvnsfcyhorW+1zfxVbCqn6+ti+3URv38i0515V2+LB0MgYSzvVvG5sbkHW5Us4uHsBCp/nXk0mdLT26QGOnj5cu3BG5VjmlUTs3TzR1dPD3t2Tyf96W+X8iT3bqamsoO/ERzFvsfhL0olj6Onr4xuisI/EyAgzs6bK2YOahx4q/sZz8e4VD23fqYuLC6Ghofzwww9cuHCBa9eu8eWXX2JiYsLAgQNvngAwduxYEhISWL9+PdnZ2URERHD06FG16xw7dozk5GTlNWpqatTSGj58OOfPn+fEiRMMGzbsrtzjDXoODefc0RgSYqIpyMlm37qVyKQldGncuydqy3pWf/OZMny7nr0wkEjYufR38rMySTp9kmN7d9IjLFylVzQvI428jDSqqyqpKi8nLyONgpws5fmYHVtIvXiOkvzr5GWksWv5H+RnZdJlgOK6Ojo6dBkUxonIXVw5E09BdiYRKxRLT9+oyADsXvY7u5f9rvwd0ncQZSXFHNy4msLcbM4dOcSFuFi6DQ1Xhuk6KIz0y4kc37uTotwcju/dScblJLoObrLttUvnuXrhHNKCfNISL7D+u8+xdnCifa+mcfad+gykJP86O/78ldQLZ1nzzWfUVFfRa8RYAKK3rGfNt81s10Nhu13Lfic/O5PLp08St3cn3Yc22a5z/8HISorYv34lhTnZJMRGc/5YjEpFtXP/wVRVlCvC5OVw9eI5YnZuofOAISo+UCz0Ek1w954YNhuG1TNsBGePxnAmJkrh87UrFD5v3EswavM6Vn3d3Oe9MZAYsqOZz49G7KBn2AiNPq+prKSyQqbweXaTz/06duFMTBQXTxyjQ2gfzh45zL61K3H3D2T/htXIpCWE9FP4/9DW9az9tmkbkHY9eqFvIGH38kbbnTlJ3L6ddB/SZLuQfgrbHdiwisLcbM7esN3QJtt1GzyM9MuXOBaxg8LcHI5F7CDjciLdGn1vZGKCvYubyp+BoSFGJqbYu7iho6ODobExHQwM+bO8mLM1leTW1xJZVcaBqnJ63+KcPx1jIyR+Pkj8fEBXBwNHByR+Pug73toiJneTY5fT6BfsS7CrIw6WZkwI7UhNXR3n0poWW5kY2omJoZ1U4jlZmeNkZY6hvj7GEgOcrMyxt7g7DVUAu09fYlz3DvTw9cDN1opnhvejqraO2MSmhQwWDu/HwmZ79vUO8OK5EQNYHXOKS1l5WJoYYWlihKlh00IJk0JD6OTpgoOFGZ721iwY1gcPO2siz16mNXR0dOgycCjxkXtITjhFQXYW+1YuwcDQkMBmH7MRKxYTsaJpT9OOfQciKykmetMainJzOH/0MBePH6Hr4KZFvOrr6sjPTCc/M526ulrKS6XkZ6arzSeWy+VcOHqYgK49tS7jrqOjQ8jAMOL37yHl7CkKc7LYv1qhM6Brk859K/9g38qm5dk79BmITFrM4c1rKcrL4cKxwySeOEKXZuXkqQMRHN2xiSHTZmNl70h5qZTyUinVLba7kcvlXIiLwb9Lj/ui8+S+nWQkXURakE9RXg6nD+4l6eQxArs1zcOvqa4iPyuD/KwM5PIGyoqLyM/KUNsvVeH3ME7u201yQjwF2VnsXfkHBoaGBDX3+/LFRCxv8nunfgMpKykmauMainKzOX/kEBfjYuk2pOndVF9Xx/XMdK5nplNXW0tFWSnXM9Mpyc9T0XkjjFwup6yoSLEfZDOd3YcO5/yxGM7GRlOYm83+9SuRlfy15avE0FCt7DQ0NkFiZIS9i5vGYfShYSNJOHqY0zFRFORkEbF2OWXSYroOUMzPO7B5LSu++lQZvn3PPhhIDNn2569cz8og8dQJjkRsJ7TZu+jEgb1cOXuaorxcivJyOR0TxbF9u+jYbC/EoxE7ObB5LWNnPYmtoxMyaQkyaYnWbZp0dHTo2H8IZw7u5eq50xTlZhG1dikGhob4dWmqYB9c/ScHV/+p/B3cuz/lJSUc2bqO4rwcEuNiuHzyKJ0GKjoRDCSG2Di5qvwZGhljYKg43txmNxZ68Q3p/tDlIU37fAr+vjy0PX8AL730Er/99hsfffQRtbW1BAcH8/7772No2LZFD4KCgnjhhRdYuXIla9asoUOHDkyfPl1li4b58+fz3Xff8frrr2NmZsa4ceM0Vv6cnJzo0KED169fp2PHuzu5Nrh7KJXlMmJ3b6O8VIqdsytTnn1F2donk5ZQ3OyDw8jYhEdf+Dd71yznz/+9j5GJKT2HjqDnUNXhpks+eU/ld/K5M1jY2LLwY8WcmarKCvas/JPyUimGRsY4unvy+L/ewKXZdgU9wkZSV1vLgfUrFStrevkw6dlXVOaTlLXY+NTSzp6JT79E9KY1nI2JwtRCsbR2QLM9pVx8/Bj9xAJid2zmyK6tWNk5MHrOApWtEqorK4nZvhFZSTFGJqb4hXSj39iJKq1gAx95lApZKYknj3Hx+BH0DQzoPXIcnfoohuWWl5aofKwZGpsw9fl/s2/tcpY12q7H0BEqlRMrO3smLXyFAxtXc+bwQcwsrRg65XHlHn8AFja2TH3+VQ5sWMPST97F1MKSjr3706fFJtDplxMpvp7HmCcWqBxv1z2USpmMI7u2Iystwd7FlanPNfe5VEW3kbEJ0178N3tXL2PJp+9jZGJCz7AR9AxT9fkfH7+r6vOzZ7C0sWPhJwqf9x01DnTg0LZNlJUUYWBoRH1tLRmXk6gql6lrKGhpu1eJXLuC5Z99gJGJKd2HhNO9WaVeYbuXNdiuyfeuPv6MnfM0MTs2EbtzC1Z2Doyd97TaHn834zULe5bKSviirABZQwMOenrMMLVijFHri4S0xCgoALdmw6Rs58/Cdv4sSnftJe+TL1uJ+dcRm5iKgZ4uo7q1w1hiQGahlOXRJ1T2+LM0Uf/4eDpcdUhVoKsjJeUVfLND+16ft8K2k+eR6Osxd0gopoaGJOfm88nmfSp7/NlZqA6fG9YpEH09XWYP6snsQU093Rczc/lwg2J+tKmhhCeH9sbKxJiKmhqu5RfxwYY9KiueaqPb0BHU1dZycMMqqivKcfL0aZzz1mQftTLK1p7xC17g0OZ1nIuJxtTSkoGPTFPZoqFcWsKqz5t6UaQF+Zw/cghXvwAmP9+0H2RmchIl+dcJnzm/VZ1dh4RTV1tD9IZVio2fPb0Z//RLreq0sLVj7JPPE7NlnWJ7CktLBkycptz3C+BcTBQN9fVELPtVJW5Qj96ETZ+j/J2VnIQ0/zrDH593X3TWVFcTtWEVMmkx+gYGWDs4Efb4XGVvKcD1jDS2/NCU547v2cbxPdsI7tmH8BlzVa7ZPWwEdbU1HFjf5PeJC19R0Vna4oPX0taeCQteJHrzWs7FRGFqacWgSY+p+F0mLWHV/33YZN+CaM7FRuPqF8CUF/4DQF76NTYu+kIZ5tjurRzbvZX2oX0ZNUvxHAR1C6WyvJyje7Yr3+uTFr78l5evt0r7Hr2oLJcRs2srMmkJ9i5uTHvuVayaf38UqL6LHn/pNXavWsriT97D2MSEXmEjlXsMg2Lqzv5Na5EW5qOrq4e1vQNDJj5Kt8bGTYCT0ZE01Nez6bfvVfR06t2PcS3elzcIGTScutpaYjavoaayAgcPb0Y9+byKz2UlLZ5NGztGzHuWo9s3cPHoYUwtLOkzfqraHn9tISflMtKC6wx+bE6r4R7EPBTUozdMGnXL93xfEPv83TE6lZWVD9soqAeWhQsXMnDgQK2LvdwKN+b8Peho21D6QUO/cT7Sg47eQzCcoa4NG2s/CAx8/4P7LaFNrHz+pfstoU0k5mheIfdBY0DQna+yfC+of0D2jPw7oK/3cAxiMnhI3kOGrSyk9SBxvfTurcD6VyLRfzj8/uSQ0JsHegCQxR67Z9cy69vr5oEeQh6OHP6AU1JSwqFDh8jLy2vTYi4CgUAgEAgEAoFAcK8Rlb+7wMyZM7GwsODZZ5/F0vL2V9MUCAQCgUAgEAgEWhBbPdwxovJ3F9i+ffv9liAQCAQCgUAgEAgErSIqfwKBQCAQCAQCgeCBR+chWBvhQUf0nQoEAoFAIBAIBALBPwDR8ycQCAQCgUAgEAgefMRWD3eM6PkTCAQCgUAgAhk5aAABAABJREFUEAgEgn8AoudPIBAIBAKBQCAQPPjoin6rO0VYUCAQCAQCgUAgEAj+AYieP4FAIBAIBAKBQPDgI+b83TGi508gEAgEAoFAIBAI/gGInr8HlPoG+f2W0CZ0H5IWmIaGhvstoY08+O0xcvnD8WyufP6l+y2hTTy+6Jv7LaFNxH380f2W0Caqauvut4Q28bDko4aHQGdlTe39ltAmjAwejk+uh+S1jp256f2W0CYelu+khwaxz98d8+B/aQoEAoFAIBAIBAKB4I55OJqhBAKBQCAQCAQCwT8aHR3Rb3WnCAsKBAKBQCAQCAQCwT8A0fMnEAgEAoFAIBAIHnzEHMo7RvT8CQQCgUAgEAgEAsE/AFH5EwgEAoFAIBAIBIJ/AGLYp0AgEAgEAoFAIHjwEVs93DGi508gEAgEAoFAIBAI/gGInj+BQCAQCAQCgUDw4CO2erhjhAUFAoFAIBAIBAKB4B/A377yN3bsWGJjY28pzrPPPsuqVav+IkUCgUAgEAgEAoHgltHVuXd/f1PEsM8HkLVr1/LrL78ik5Zg5+zKkMnTcfML0Bo+PyuDyHUryU1LxcjElJB+g+g9chw6zfZCybiSyMGNayjIycLM0pqew0bSuf9g5fmC7Cxid24hLyMNaWE+fUaNp+/oCVqveWzPDg5v30hI/8GYmJlzNjaa6soKnDx9CHt0BnbOrq3eY8aVJKI23dBjRY8wVT0Al0+fJGbnZqQF+Vja2dN/7CP4h3RTno+L2MnlhHiKr+eip6+Ps5cv/cdNwt7FTRkmZscmkk6doKQgH3mDHB0dsHd1Z9TsJ++JRhWbRewgZvsmOg8YQtjUGcrju5cv5kKcagOFs5cPM//zrloap6P3czxyd9OzMWU67n6BWu8hPyuDfWtXND0b/QfTp9mzIZOWcHDjGvIyrlF8PY/2oX0YNevJVu0il8s5smvrffH56ej9JMRGU1pUAICtkyu9RozBt0OIMowme9q6eTL8qZdb1XeDQe396ObrjpGBAVlFJeyMv0h+qUxreDMjQ8I7B+FsbYGNmSln07LYcvxcm651NzEK6YD1Y5MxCvRH396O3I+/oGz3vr/seiejIjkasZMyqRR7F1fCH52Bh7/2ZzEvM4M9q5eSfS0VY1Mzug4YTP/RE5TPYlrSJQ5sXkdhXi61NdVY2tjRpf9Aeg8frZJOdWUlB7eu51L8CSrLZVhY2zB44lTadw/lZFQkRyJ23tOyM+nUCeL27aIkP4+G+nqs7B3pPmQ4HXr1U4Y5Fb2fhJgo5XNr4+SCla09GVcS73keykhO4mRkBHkZ15BJSxgxY66KVoCa6ioOb93I5YR4KspKAdBBBxcfP4ZMfbxNOqM3r6WwUWf3sJGE9BukqvPMSY7s3KLU2XfMI/iHdFWeb2ho4OiurVw6cYzy0hJMLawI7hFK75Hj0dXTU+qM2baRKwmnqCovx8zahva9+xMyMAy5XM7JvTu4eCyG6ooKHD296P/IY9g4ubSqPTvlMrHbNlCcm42JhRVdBg+nfZ8ByvMpCfGcPhCBtCCfhoZ6LO0c6DRgKEE9eivDrPjvm5QVF6ml7duhE1OefUX5+1T0fuL2NZXnYVOm495KHrreWJ7nXFM8r537D6bvKNXy/MCGNeQ2K8/HzFYtz8/ERHH+2BEKcrKQyxtwdPek/9hHcG8ln9yPvH7xZBxHInZSdD2Phvo6bBycCA0bQUif/lqvGx8VybF9u5A16gyb8nirOq9nZRCxZlmjPc3oMmAw/UaNV+pMPH2C04cOkpuRRn1tLXbOLvQZOY6AZs/p6cMHORcXS0F2FnK5HEd3TwaOe6TV9/LJqEiO7t2p1Dl8auv2vJ6las8u/VXtmXjqBPGHDpCXkUZdo85+o8ar6Fz25cekX05US9vO2ZWn3/+f1msL/p6Iyt8Dxp49e/i///s/hkydgZuvP6cPHWDDD18x952PsbCxVQtfXVnJukVf4O4XyIz/vEtRXi67ly/GQGJIj7ARAJQU5LPxx6/p0Ls/o594isyUK0SuWY6xmTmBXboDUFtbjYWtLf6duxKzfXOrGrOvpnD2SDT2ru5cz0ynIDuTkTPmYe3oxNHd21i/6AvmvfsJEiNjjfFLCvLZ+NPXdOzVn1GznyQr5QqRa1dgYmZOQKOe7NRkti/5mb6jxuPfuRtXzsSzbfFPTH/lDZy9fAHFR1nn/oNx8vQGOcTu3Mz6RV8w5+3/YmxqBoCNgzNufgHISorpO/YRspIvk3LuDOu++5z57336l2tUtdkh7F3dNF0Oz8B2jJr9pLIw19NXz5qXTsaxf/0qhk2biZtvAKcP7WfDD18x751PWnk2PsfNL5CZr71HUV4Ou5YtxkAioWfYSADq62oxNjMjdPhoEmKiNWpryfHI3Zw8EHFffG5ubcOA8ZOxdnBE3iDnQlwsW3/9npmvvYu9q7uKPbuMm6b8feNj8Wb0DfKhd6A3W46fpbCsnIHt/Jg1qAeLdh2ipq5eYxx9XV0qqmuIuZRKN193jWHuBbrGxtSkplG2JxLHt//9l17rwoljRKxZwcjHZ+PuF0B81H5Wffc5z7z/Pyxt7dTCV1dWsvKbz/DwD2Temx9QmJvLtj9/xUBiSO/howCQGBnRY8hwHNzcMZBIyEi+wq4Vf2AgMaT7oDAA6uvqWPnNZxiZmDLpqeewsLahtLgIfQMDpaawaTPvadlpZGpK7xFjsXF0RldPj9TzZ9izcgkmZub4NDZKmFtZM3DCFKztHZHL5exdvZTE+DgGTXwUz+D29zQP1VZXY+fiSrvQPuxe9rvGa0VtXENa0kV82nci8eQx2vfqx9nYaOpqa9j4/ZfMeedjrTqlBfls/vkbOvTqx8hZ88lKucKBdSsxNjMjoHOjzqvJ7FzyC31GjccvpCvJCafY8cdPTHv5DZy9fAA4sW83Zw4fYMSMedi5uFGQncme5YvR0zeg14ixAERvWkt60kWGTp+DuY0tOanJRK1bgZGpGRWlUhKiIxk8bTZW9o7E79vJ9l++5bHXPkBiZKRRe2lhATt//56gHn0Imz6HnKvJHN64GiMzM3w7KT6kDU1M6RY2CisHJ3T19Ei7eJaodcsxNjPDM7gjAJNeegN5Q4My3fJSKRu++ZSgrj2Vxy6djCNy3SqGP6Yoz08d2s+6H75i/rufYKnleV373ee4+wUy+7X3KLxRnhtKCG0sz+say/Ne4drL8/TLiQR374mrrz8GBhJOHIhg3aIvmPPWh9g4OKmFv1953djMjH6jxmHn5IKunh5Xzp1h+7LfMTE3x79jZ7XrXjx5jH3rVhL+2CyFzuj9rP3+C55671MsbTTrXP3t/+HuF8gTr39AUV4OO5b+hkRiSOiwkY22SsIzMJiB4yZhZGrGheNH2Pjztzz+ypvKylr65UTadQvFbao/BhJDju/fw5rvPmfeW//FxlGzPfeuXcGI6bPx8AvgZNR+Vi/6nKff/59WnTfsOfeNDyjMy2X7n78iMTSk1zCFPdOuJOIV1I5B4ydjbGrG+bhY1v/0DTP/9ZZS55SnX6S+rk6Zbl1dHb9++AbtuvVUu+aDjo7Y5P2OuafDPk+ePMnUqVOpr1d8RGVnZzN27P+zd9ZxVV5/HH9Tl+5u6TaxuzHnZmzG1Kmb6811by5/S9ft7O7ARAHFxAYFQUC6u/P+/rhw4cK9iNMZ23m/Xrxe3POcc57Pc+L7PKcn8PPPP8v9rFy5knfffReAlJQUFi9ezLRp05g1axZffvklhYWFCnGGhITw9NNP89BDD7Fw4UK2b99OQwuj25rNmzczY8YMrl69CkBRUREff/wxkydPZt68eRw82LanfPv27Tz33HNMmTKFOXPm8P3331NWJhsJqKqqYtq0aW2mlp4/f55Jkya10XsjVq1axcSJE+nSfzDmNnaMmDYLfWNjLhw9rNT/lcgT1NXWMGb2AiztHPDqFkjvkWM4c3g/UqkUgIsRoegbmzBi2izMbezo0n8wfn36EXlonzweW2dXhj70CL49+6IlkajUV11Zwe7lvzF65mNo6+qSm55K75Fj8ewWiKWdA2MeXUBNdRUxZ06pjONiRBgGxiYMnzYTcxs7OvcfjF/vfkQe2i/3czbsIE4e3vQJmoC5jR19gibg6OHF2dDm/Jny7MsE9B2IpZ0DlvYOjJ3zOJVlpWQkXpP78enZh8ToS/QeNY7AoaMImjWfhoYGaqoq74jGpjQLXvE7QTMeQ1tXX+n9NDQ10TcyxsDYBANjE3njtSVnDu/Hv29/ugwYgrmtHSMefhR9IxPOH1FdNmprahg7+/HGstGT3qPGcuZQc9kwNrdkxLRZBPQdiI6+cm0tkUqlnAs9eNfy3L1zN1z9OmNqaY2ZtQ0DJ05GoqNDRlJCm/TUNTSS/2nr3fjZAPp4OhMRk0hMWjY5xWVsO30JiaYmAc6qRwuKKirZez6GC9fTqayp7dB9/gkqTkaS//syysIioEH6j97r5MG9dOk3kO4Dh2Jpa0/Q9NkYGptwJvyQUv9Rp45RW1PNA48txMreEZ8ePekXNI5TIfvkZdHW2QX/Xn2xsnPA1MKKzn364+rXmZT4q/J4Lh4/QnlpCQ8/swgnDy9MLCxx8vDCrpOrXNOdtp3OXr54dOmOuY0tppZW9Bg6Ckt7B9IS4uR+PLp0l5VbK2tMrawpyM5EU0sLLW3tO16HXP06M3DiZLy6Bar8iEpPSsCnZ18Soy/Sa9Q4hk2dia2LG5b2TtRUVxHbns5jMp3Dpjbr9O3dj7MtdJ4LDcHRw5veo8djbmNH79HjcXT34lwLnRlJ13Dz74pbQFeMzS1wC5D9n3k9UcGPT8++2Lt7YWRmgVdgH6ydXchOTuTSkUN0GzYat87dMbe1Z9j0udRWVxF//rRK7ZdPHEHfyJiBDz2CqbUtvn0G4hnYl4thzbocPLxxCeiKqbUNxhaWdB40HHNbezJbvHd0DQzRMzKW/6XERiPR1sG7R0+5n9OH9hPQtz9dBwzBwtaOUQ8/ikE79vzyaZk9HzfncSztHfDuLrPnkSHN5dXE3JKRD8+ic9+B6KiweRPnPUmPISOwcXTG3MaW0dPnINHRIfGy8tkKd6uuu3j74d0tEAtbO8ysrOk9fDTW9o4KflpyOmQfnfsOoNvAoVjY2jP6kdkYGJlwLlx5ekafPk5tTTUT5j6BVWN69hmtqHPUw7PoFzQBOxc3zKysGTj+QWycXIi7eFYezwPznyJw6EhsnDphbmNL0Iy5SHR0SbhySel9T4XspXNjelo0pqeBsQlnVaRn9GlZek6c25ie3XvSt5XO0Q8/Sv+gCdg36hw04SFsnV24eqFZp66+gfz7wsDYhNRrV6mtrqZL/8FK7yv4d3NHG39+fn7U1NQQHx8PQFRUFEZGRly61FxJoqOj8ff3p6CggDfeeANnZ2e+/vprPvroIyorK/noo4/kjbv9+/ezcuVKZs6cyc8//8z8+fPZsmULe/bsaXNvqVTK0qVL2b17N5999hleXrLekG+//ZaMjAw++ugj3n77bQ4fPkxOTo5CWDU1NRYsWMBPP/3EK6+8Qnx8PL/99hsAOjo6DBo0qE2jMSQkhJ49e2Jqatrh9KmtrSUmJoa+ffsquHfy8Sc9MUFpmIykBBzcPBUabJ18/SkrLqI4XzbFKCMxgU4+/grhXHwCyE6+Tn19HTfD/rXL8eoWiLOXL/V1tdTV1ODcIm4tiQQHdy/SW7wIW5OZlEAnb782z5id0qwnIykBZ5+2flSlA0BNVRVSqRRtPT25W3F+LuUlxTj7+FNfV8elY+FIdHSxc3W/YxoPrFuBZ9dAnLx8VN4vPTGen954gT8+eJ19a/6ivHGqVRP1dXVkpVxXko9+Kp8jI/Fam7Lh4qNYNm6WlunZxN3K84aGBmLPnKKmugo7F3eFa+mJ8Wz9/B12f/cJp3esp6qs9IbPZqqvi6GuDgnZzWlTV99Acm4BjuYmNwz/X6G+ro7MlOu4+iqWRVdff9IS4pWGSUu8hpO7l0JZdPPrTGlRIUX5uUrDZKZcJy0hHmdPb7nb1QtncXTzZN+6lXzzyrP88v7rhO/cSk1VlVJNd9p2SqVSkmOvUJidhYOKaV+FuTmUlxRTX18vL7d3024qw8HVg/gLZ2V13duPjMRr5Kal4hbQBQc3zzadLa11OrfR6Ud2SrJcZ+b1tn6cffzJSGp+fntXD1LjYynIygQgPzODlLgYXHwDFPwkRl+krHGKZVZSAvkZqVjYO1JRWoKjp6/cr6aWBFtXD7JaNB5bk52ciEOLMABO3r7kpibLO61bIpVKSYuLpSg3G1tXD6VxSqVSYk4dx7NHL7Qk2kCzPXe5CXuennQNR3fF8urqe2v2vElLXW2t0sbi3azrLZFKpSTFXCY/OxNnj7Z+mnS2LBsALr7+pCUq15meeA3HVjpdfQMoKy5sNz1rqitVNqybtNTV1qJ7M+npc3vTE6C6qgqdFt9CrTkfEYabfxelo8z3POrqd+7vX8odnfapq6uLm5sbUVFReHt7ExUVxfjx49m8eTMFBQXo6ekRHx/P3Llz2bNnDy4uLsydO1ce/qWXXmL69Olcu3YNT09P1q9fz9y5c+nfvz8ANjY2TJkyhT179jB+/Hh5uIaGBr777jtiYmL4/PPPsba2BiA9PZ2zZ8/y+eef4+srM/iLFi3i8ccV58g/8MAD8v+tra2ZO3cuH3/8MYsWLUJdXZ3Ro0fzyiuvkJ+fj7m5OWVlZZw8eZLXX3/9ptKnsLCQ+vp6zM3Nyalsdtc3NCK55IrSMOUlxRiaKDYw9Q2N5ddMLCwpLy3G2VDxhaZnaERDQz2VZWUYGJt0SN/FY+EU5eYwbs4TANQ3ToPTNzRqdX8jyopUj3iWlxTj5N1Kj5GinvKSYqXxVpQWq4z38Oa1WDk4KTQEyktkjagN335OXV0tBkbGTH32ZS5GhN0RjZca06y9dXQuPv54dOmOsbklpYX5HN21lQ3ffs7sNz5AU0sLgIqyUqQNDfK8bdZkTHlsO2XD1ExRn5Fi2bhZyouL5c+pEO8dzPPc9DTWfv0JdXW1SLS1eeDxZxWm0zalZ7WWLuWFBVw6tIfDy39i9JOvKJ1O24SBjuzDrLyqWlF7VQ2Gutoqw/3XkJdFI8WyqG9kTFnMZaVhyouVlMXGvC4vLsbUwkru/u1rz1NRVkpDfT2DJjxIj8HD5dcKc3NJio3Bv1dfpj/3MkV5eexdt4Ky4iLlmu6Q7ayurOCXt16ivq4ONXU1Rjz8KK5+nRXC5aansuarT6itrQFg1PQ5CuX2btlNZQybOoMdf/xIflYG677+FDU1NYZOnYGrfxfiL56lrKioHZ0lOHkpamidZqp1Nnd69Rw5hprqKpZ/+i7qauo0NNTTe/Q4ug4aJvczdMoMQjasZNXHb6He+KE24MFHMLOWjdTrtrqHnoER5cWqtVeUlODgodhJp2tgRENDA1XlZfLyVV1ZycoP36ChrhY1dXUGPjRdoUOsJWlxMZQW5OHTu3ldZVMd0jO6SXtuYtbGf9O1v2PPAY7s3IJEWwePzt3aXLubdR2gqqKCb19/nvraOtTU1RkzYzbuAV1oTbPOVmXKyJjrsSp0lhRj2Kpzvil8WUmR0vQ8ExZCaWEhAb37K40TIHznZiTa2nh07t7mmqr3uL6RMUkqdJYVF2PU5j2uPD3lOkMPUlpYQECrtbxN5GdnkhIXy9SnXlT5HIJ/N3d8zV9AQABRUVFMnTqV6OhoJk6cyMWLF+WjgBoaGnh6erJp0yYuX77M1KlT28SRmZmJtbU1eXl5/PTTT/zyyy/ya/X19fKh8Cb++usv1NXV+frrrzExMZG7p6amoq6ujqdn80JnKysrzMwUK9rFixfZvHkzqampVFRUUF9fT11dHYWFhZibm+Ph4UGnTp04dOgQ06ZNIzw8HAMDA3r0aLvxx759+9i/f38bd4DqatlH548//kinHn1xa1wfIZXKNipRReupO03Pr+DeJoKmNOrY3OmC7ExCt6xD2iDlh1efBcDE0kpp3FKpVMn9Wmlu7SBtikq1Zmk7M9lCt6wnPSGeniOC+OGVZ+TuDzUat8nPLEJDQ5NLx8PZ9dcvsnUl/7DGguxMju7awiMvvtluo8M7sLf8f2tHJ6ydOvHbO6+QGH1RvpZHpagblI02jyAvGx3zf/n0cQ6sWyH/Pfmpxk1T7mKem1nbMPvND6iuqCDuwln2rVrKtBdek2/005Se6QUlmFjbYWrnyM5vFpMRdxlH3+YPhwBnOyb0aB59WHP0rPJ7iuUFSlFmd9TaSazWxUOq4sKc196hpqqa9KRrHNqyARNzSzr3HSC/h76hEeNnz0ddXR1bZxcqy0vZv2G1ak13wHZKtHWY8+ZiaqqrSbl6hdAt6zE2s8DZ25crp0/I65BUKmXUw49yYP1KwrZuwMbZRV5u74bdbMmVyBMcXLcSkL1H9QwNAXjwqRcozMnmyLaNsrWT0rb3aKNThQZFna0fRVHo1XOnuXL6OGPnPI65rT25aSmEblmHkbklAX1lm36cDz9ERuI1Og8ewZXj4TQ0SDmyZS19xj+k7Baye9yoPqtI5JbOEm1tpr38NrXV1aTFx3J85yYMTc1xUDJydeVkBFaOzljYt10P3PZW7etrWxRvbXp35OEDXIgI45EXXkNbV/kaTtl973xdB9DW0eGJdz+hprqKpJjLHNy4FhNzS1xajW63uHOrG0vbuin4Vi5U2bPFnovk8Jb1TFrwtNK1jiCbynv+aCgzXnj9ptKTG6Sn0seSRdTGa8y5SEK2rOehx5/BRIXO80dlU7OVrZ28LxBr/m6ZO9748/f3Jzg4mJSUFCorK3Fzc5M3CI2MjPDx8UFTU5OGhgYCAwOZN29emzhMTEzkDaVnnnkGb2/lUwWa6Nq1K0eOHOHMmTOMGDFC7t66kaiMnJwcPvzwQ0aNGsXMmTMxNDQkISGBL7/8kroWi2dHjRrFjh07mDZtGgcPHmT48OFoKNlkIigoiKCgIKX3qq2tpXfv3kyfPp0cveZKW1FWil6rnqIm9I2MKS9R7NWtKJP1nuo19rbpGyrxU1qKuroGugYdWwuVkZhAbXW1QqXLTU8F4Pf3XuXFb35VGKXSa9XjekPNpSWoq2vI152pei5l6RC6ZR2xZ08z7fnXMDQxwa2FQWta4KypJcHW2QU7Fzf+XPwGOakp2Lq4tYnrdmrMSEqgsqyM5Z++K78ubWggLSGOixFhvPD1L/I0a4mhiSmGpqYU5mbL3fQMDFFTV1eq6abKRmlT2VAepjXunbth12Lzmqb0LC9R7I28k3muoamJqaVs9N7G2YWslCTOhh4gaGZbWwE0rrsxobTVFJmr6dmk5xc1x9s4cmCgq01JZVWzdm0JZa1GA//LNJXFslYjKBWlJW163pvQNzamrFh5WWwdpqkn29rBkfKSYo7s2ib/IDQwNkZDQ1M+ygOy3erqamvlmlr2g98p26mmro6plaxMWjs6kZ+dwcn9u3H29sW9c1f5JibQXIdMLK0Uyu2dtputcQ+Q6ayrqWXl5x/Qa8QYDm9ei46ePt0GDyc3PZWzh/bL1ie3q9OojYbKMmU6Fae2V5QqPv+R7ZsIHD4a7x6yzhxLOwdKCvI5fWAPAX0HUltTQ8SuLYyf9xSWndzxb9yR82TwdhIvnZenj0ELO1VZVtpmNLAlekZGVLTSVVlWirq6Otot1mGrqatj3FhOLewdKczO4tyhvW0afxWlJVy/fJGBDz2i4N6ePW89ytZEe/ZcVZj2iDx8gKM7tzL12Zewa1E+lem8G3UdZOls1livbBydycvKIGLvzjaNP1XpWd6eTiNjypT4b7rWkthzkexc9hsT5j6hsINmS04f2s+RnVt4+LmXsVPxbSFPz5KiDus0MDaWz7hpQlV6xpyLZMdfv/LAYwtV6qyvq+PSyaN0GzC0wxuhCf593PEJrX5+ftTW1rJlyxZ8fX3R0NAgICCAS5cuydf7Abi5uZGSkoKVlRV2dnYKf3p6epiammJubk5mZmab63Z2ipszBAYG8vrrr/PLL79w6FDzolpHR0caGhrkaxBB1tgrKGjeojk+Pp66ujoWLFiAt7c39vb2CtebGDJkCPn5+ezevZuEhASFRmZH0dLSwsfHh5MnTyq4J8dext5VuTGxc3EjLSGOutrmjSaSY65gYGwi752yc3UjudVUkuuxl7F27oSGRsfa/+5dujP37Y+Y+9aH8j9rR2c0NLXoNmiYfGSrrraW9IQ47F3dVcZl6+JG8lVFPcmxl7F2atZj59JWc3LslTbpcHjzWmLOnGLa869ibmOLREcXU0tr+Z+5jR36RsYkt5hS0dDQQElB/j+u0b1zd+a89SGz3/hA/mft1Anv7r2Y/cYHKkcDK8pKKS0qRN/IRO6moamJjVMnrreaanM99rLK57BzdW8sGzUK/luWjRuhraMr36TC1Moac9u26Xmn87w1UqlUYRez1lSXl1FZWtzmo6+mrp6Csgr5X25JGaWVVbhZN6+B0FRXx9nSjNQWjcT/Ohqamtg6dSIxJlrBPfHKZRzclK97cnB1J+XaVYWymHglGkMTU0zMVU9Xk0ql1NU12zZHd08KcrMVdlLMz85ESyJRqulu2U5pQ3OZlKioQ1XlZXI/d7sOyXVaWmNkZo60oQEjM/PGui6LT01dnYb6etIT41V+3DbpTGmj8wrWTs5ynbad3Ei5qmjLUq5eVpiyX1dTg5qa4ieKuro6SGV531BfT0N9Perq6kh0dDC2sMLYwgodPX00tLTQMzQiNS6mOb7aWjITr2GjoqEDYO3sSlq84nb4qXExWDo6K+3MlaPCBl2NPIGGpibuXXsquDfZ89ZT/ZLasef2Lu6kXlO050kxN2fPmzgdso8jO7cw5ZlF7R7xcDfrulI/DVLqa9umc5POpFY6r8dE46BiLaa9qzuprXQmxURjYGyqkJ5Xzpxi57JfGT/ncXxU7Ix5KmQv4Ts3M+3Zl9o94kGu84qizqSYW0/PK2dOseOvX5g49wmVOkG2brqirIyu9/NGL2pqd+7vX8odb/w1rfsLCwsjIEC2ONfb25u8vDyuXr0qdxs3bhwVFRV88cUXXL16laysLC5cuMCPP/5IRUUFANOnT2fr1q1s376dtLQ0kpOTOXz4MJs2bWpz3169evH666/z888/c/iwbPcnBwcHunfvzk8//URsbCyJiYl89913SFosrLWzs6OhoYGdO3eSlZVFeHg4O3bsaBO/vr4+AwYMYOnSpfj5+bVpgHaURx99lB07ZGeo5WdlcGjTGsqKiugyYCgAR3ZsYsN3X8j9+/bsg6aWhL2r/iQ3I424C2c4dTCYwGGj5VMLugwYSllRAYc3ryU/K4NLx8KJPhlBz+HNI5D1dXVkp6aQnZpCXW0t5SXFZKemUJgjG33S0dOT7arZ4k+io4OVoxOXTx0j/uI5cjPS5Ful+7SYyrhn5R/sWfmH/HeXAUMoLSps1nP8CNGnjtFz+Gi5n+5DRpISF8Op/cHkZ2Vyan8wqXGx9Bg6Uu4nZMMqok9GMH7uQnT09CkvKaa8pJiaatmoTXVlJcd2b8OjS3dOHQjmdMhetv/+A6UF+Uh0df9xjcrSTEuijY6+PpZ2DqipqVFTXUXY1g1kJF6jOD+PlLgYtv7yLXqGRnh0Vey5Cxw2muiTEVw8Fk5+ZgaHNq6hrLhIfs5X+PZNrP/uc4WyoSWRsGdlY9k4f4ZTB4IJHN5cNgCyU5PJTk2mpqqSyvJyslOTyctMV1o+1dTU6D50JKcP7iHuwtk7nudHdmwi7Vocxfl55KancWTHZlLjr+IT2AdAIT3LCvPJToonfM0faOsb4OCjuAZLGSfjkhng44aPvTVWxgZM6h1ATV0dUckZcj8P9u7Mg70V47IxMcTGxBBtTU10JVrYmBhiadR2x9Z/EjVdHSTurkjcXUFdDS1rKyTurmha/721QO3RZ+QYLh4/yvmjYeRmprN//SpKiwvla3YObd3Aqm8+k/v379UPLYk2O5b9Tk56KjHnIjm2bxe9RwTJy+LpwweIu3Se/Ows8rOzOB8RxokDewjo07y+psfg4VSWl7F/w2rysjJJuHyJ8J1bCRwyQq7pTtvOE/t2cT32MkV5OeRnZRAZso8rp0/g26t5467w7c3lNi8jDTNrG4rz8zAys7jjdaimuoqctBRy0lKQSqWUFBaQk5ZCSUE+ANq6uji4e3F011Zc/bty+kAwhzeu5vKpY9RUV6El0VaYqr535Z8KR0Z06S/TGbplHflZGUQdP8LlU8fooaBzBClxsZw+EExBVianDwSTGneV7i10uvp3ITJkL4nRFynOzyP+4jnOhh7AvenIhSadOzeTfu0qJfl5xJ4+ztUzJ3EL6EbnQcM5f3g/iZfOk5+ZTuj6FWhpa+PRrfnD+NDaZRxau0z+26/vIMqLC4nYvpHC7EyunIzgauQJugxp1nU2ZA9pcTGU5OdSmJ3JhbCDxJ09iWeP5jSBpo1ejuHeNVDp0RK9ho8m6kQEFyPCycvM4GCjPe/WaM/Dtm9i3bct7HkvmT0PXvEnuelpXD1/hpMHguk5Qrk9r66qpEqJPT91YA9h2zcx9tH5mFlZU1ZcRFlxEVWVFW00wt2r60eDd5B4JZrC3BxyM9M5cWAPUSePEdCnn1KdvUYEcenEUS5EhJGXmc6BDaspLS6ie+Ma0dBtG1mzpPk8O79efdGSaLNrxR/kpKcRez6SE/t3K+i8HHmSnX/9ypBJ03Dy8JKnVWV587mvJw8EE7ptI+MeXYCZlc0N07P3iDFcPHGU840692+QpWf3QbL0PLxtA6tbpKdfY3ruXC5Lz9hzkRzfv6uVzhNsX/oLwx58WKXOJs4dDcXF2xdTy7ZrBQX/He7KOX8BAQHExcXJG3oSiQQvLy/i4+Pl6+/Mzc354osvWLFiBe+//z61tbVYWlrSrVs3tBqnyo0ePRodHR22bt3KypUrkUgkODk5KWz20pKmBuDnn8sM6rBhw3jxxRf58ccfefvttzEyMuKRRx6hqMVidhcXFx5//HG2bNnC6tWr8fb2Zt68eXzxxRdt4h85ciSHDx9m1KhRfzttgoKCKC4u5sdff6O8pBgLW3smP71I3hNVVlxMUV7zbqTaunpMe+4VQjasZtXni9HR0ydw2GgCW7xoTSwsmfz0Ig5vWceFo6Gy7cKnzpSfUyWLt4iV/3tf/rsoIoeLEWE4enjxyItvqNRr5eBEJ28/Dm1cTVVFObadXJny7MsKZ0CVtBopNbGwZPJTiwjdso6LEWHoG5swbMoMhfVt9q7ujH/sSY7t3sqxPdsxsbBi/LwnFc7Pu3A0FICNP3ypEH/fMRPpP24S6hrq5GWmk5GUQF1NDUd3bAZkh7yPmb3gjmi8EWpq6uRlpHH59HGqKyswMDbBydObifOfRrvVOVo+gb2pKi/jxN6d8rIx5emX5GWjvKSIotzWZeNVDm5Yxcr/fYCOnj49hwcpfLgCrPjsfYXfCVEXMDIz58mPv1aqudeIMdTV1NyVPC8vKSF4xR9UlBYj0dHF0t6ByU8twqVx97TW6aljYIS1izv9H56Llrbys71aciw2ES0Ndcb28EVXokVafjGrwiMVzvgz1msbz5OjFRfWe9lbU1Rewbe7w294z9uFjrcnDi3qgvmC2ZgvmE3JngNkf6o8L/8ufj37UFlextE9OygrLsLSzoHpz70iX2NSVlxEYYuyqKOnx8wXX2ffuhX8+cn76Orp0WfkGPo0nqcFsinRh7ZsoDg/F3V1DUwtrRj+0MP0aLHBh7GZOTNffJ2DG9fwx0dvY2BkTNf+gxk47gE0NDWpLC/j2L5dd9R21lZXcXD9SsqKCtHUkmBmbcPYOQvkHRIgmyYdvPx3ykuL0dbRxdzOAZ/APkSfOMqZQ/vuaB3KSr7Oxu+b31/Hg7dzPHg7fr37M+bR+QBMmPckR3ZsJjH6AnV1tVw8JivHWto6TH5mvoLO1oeZG1tY8uCTLxK+dT2XIsLQNzJh6JQZ8jP+QDYrYdzchRzbvY3je3ZgYmHFuMcWKkyPHTZ1BseCt3No42oqykoxMDImoO8g+oyZKPcz7rGFROzcwqE1f1FVUYGhqRm9gibg33igfF1tDUe3rqO6sgIrJxfGP/G8QkOsrEhRu5G5BeMWPMuxHZu4fPwI+sbGDJj0sPyMP1l+V3Nky1rKiorQ1NLCxMqGYdMfw6O74uheRkIcxXk5DJ/5GMrwCewtK68t7PnUZ15qUV5b1SFdPR5+/lUOrF/F8kZ73mt4EL1a2fNlnyra82uN9vzpT2Q24Gz4IRrq69nx588K/vz79G9zIDzcvbpeU13F3rXLG8/xlGBhY8sD8xbi30txN/QmfAP7UFlWxrE9Oykrkel8+NmXFdKzqFV6Tn/hNfavW8myz95HR0+P3iPG0GtEc3qeP3KYhoZ6QjatIWTTGrm7k4c3s15+S5aeYbL03P7nTwp6AvoMYMLcJ1SmZ0SL9Hzk2Vbpmaeoc+aLr7N37QqWftqYniPGyM92BDjbqPPAxtUc2Li6WaenN7Nfflv+uzA3h+tXr/DQguZ9Ee5L/sW7cN4p1CorK//ZA6H+Qxw9epSffvqJ5cuXo6PiENmOsjri3G1S9c/SkXWTgo6jfh8YtfbO0byXSC8oubGne4CZP3x7tyV0iFOffHS3JXSIKiXTwu5F7hfb2XAf6Lyb52veDMo6ju5FtO6TtWD1//B5prcL9ftk+uDUPjeeHXMvUHlN9XEttxtdd9XTxO9n7srI37+NqqoqcnJy2LhxI6NGjbrlhp9AIBAIBAKBQCAQ3G5E4+82sHXrVjZu3Iivry+PPPLIjQMIBAKBQCAQCASCm6LNURmCm0Y0/m4DM2bMYMaMGXdbhkAgEAgEAoFAIBCoRDT+BAKBQCAQCAQCwb2Puhj5u1Xu/d0lBAKBQCAQCAQCgUBwy4iRP4FAIBAIBAKBQHDvoybGrW4VkYICgUAgEAgEAoFA8B9AjPwJBAKBQCAQCASCex+x5u+WESN/AoFAIBAIBAKBQPAfQIz8CQQCgUAgEAgEgnsfcc7fLSNG/gQCgUAgEAgEAoHgP4AY+btHkUqld1tCh9DUuD/6D+rqG+62hA7R0HDv61S7T3rdYjNz7raEDnHqk4/utoQO0fvtd++2hA5x5rNP7raEDqEh1q3cNiSaGndbQoeoqq272xI6xOZTUXdbQod4edzguy2hQ6QXFN9tCf8u7qPdPoODg9m6dSuFhYU4OTnx+OOP4+fnd8NwGRkZvPjii0ilUjZt2nTbdd0/KSgQCAQCgUAgEAgE9zhHjx7ljz/+YNq0aXz33Xf4+PjwwQcfkJPTfsd0bW0tX3zxRYcaiX8X0fgTCAQCgUAgEAgE9zxq6mp37O9W2L59O8OHD2f06NE4OjqycOFCTE1N2bt3b7vhli9fTqdOnejfv/8t3b89RONPIBAIBAKBQCAQCG4DtbW1XLt2jW7duim4d+vWjZiYGJXhIiMjiYyM5IknnvhH9Yk1fwKBQCAQCAQCgeDe5w7uO7Bv3z72799/Q3+jR48mKChI/rukpISGhgZMTEwU/JmYmHDx4kWlcRQUFPDjjz/y5ptvoqend0u6b4Ro/AkEAoFAIBAIBAJBC4KCghQadTfLzWyQ9/XXXzNmzBi8vb3/9v06imj8CQQCgUAgEAgEAsFtwMjICHV1dQoLCxXci4qK2owGNnHp0iWio6NZt26d3K2hoYEHHniAp5566pYaoa0RjT+BQCAQCAQCgUBw76N+729XoqWlhbu7OxcuXGDAgAFy9wsXLtCvXz+lYX788UeF3ydPnmTjxo188803mJub31Z9ovEnEAgEAoFAIBAIBLeJSZMm8c033+Dh4YGvry979+6loKCAMWPGALBixQri4uL45BPZ2bTOzs4K4ePj41FXV2/jfjsQjT+BQCAQCAQCgUBw73MHN3y5FQYOHEhJSQkbN26koKAAZ2dn3n//faysrADZBi9ZWVl3RZto/AkEAoFAIBAIBALBbWTcuHGMGzdO6bVFixa1G3bEiBGMGDHin5B175zzN2HCBI4dO6by+ptvvsmvv/76j+soLi5mwoQJREVF/eP3EggEAoFAIBAIBB1EXe3O/f1LuW9G/t566y00NDTutow7hlQq5fieHVw6Fk51ZQU2zq6MeHgWFrb27YZLjb9K2Nb15GWmY2BsQs8RY+g6cKiCn7jzZ4gI3kZxXi7GFpYMnPAQHl16yK+fDz/ExWPhlBTkAWBuY0+foPG4+XdRes+9a5ZxMSKcoQ89jJZEwqmDeykrLsLC1p4RU2fg6OGlUm9OeioHN6wm83oiOnr6dB04lP5jJ8q3xy0rLuLw5vVkpV6nMCcbv979GD/ncZXxXYk8yc6/fsXNvwtTn1kkT8tjwdvvSlqe2h9M3MWzFOZkoaGpiW0nNwZOnIylnQMA9fV1ROzaRtKVKApzs1FXU0cqbUAqlWLbye2eyvO9q5Zy+ZRiB42mRMLUZ17Gwd1Tpb7c9FRCNq4hK1mWx10GDKHvmIkKWyCnxscSuqXpGUzpNVLxGS4eC+fyqWPkZ2YglTZg5eDMgPEPKtz3XPghLkaEyZ9B09gcmx79MXZ2bzf9pvTpwjB/Twx0JFzLyuOvw6dIKyhS6b+nmxMjO3vRydIMLU0N0guK2HY6irOJqXI/g33deGrUgDZhH/1hFbX1De3qORMWwon9wZQWF2NpZ8/oh2fh1E4dyk5LZd+6FWRcT0RX34Dug4YycNwkefomX43h8LaN5GdnUVtTjbGZBd0GDqbvKMXeyOrKSkJ3bCLmbCSV5WUYmZox9MFp+AX2blfvzaLTxR/T6VPQ8fJA09KCrE++onTvwdt6j5acDj3I8f3BlBYVYWVnT9Ajj+LsqXor7ey0FPasXUF6UgK6+gb0GDyMweMfVLpld3L8VZZ/+TEWNnY88+Hncvfzx8LZsez3Nv7f/mUZWloSpfc9dfggEft2U1ZUhJW9PWOmz6ZTOzqz0lIIXr2ctEadPYcMZ8iEZp1JsVf464uP24R7/pMvsWy0Keciwtn2129t/Lz323KlOu8HjSCzBXfjPVRdWcmRnVu4ev4MleVlGJqaMfiBKfj06KX0vlKplJN7dxJ9/AhVlRXYOLswbOpMzG9g89Pir3Jk2wbyszLQNzYhcHgQnQcMkV/Pz0znxJ6d5KQlU5KfR++gCfQd+4BCHKcP7CHh0jkKs2XvJptOrvSfMBkLu/bv3R4zBvQgqKs3BjraXM3I4ZcDx0jJK1Tp39/RlrlDemJvboK2piY5JWUcuBDL1tOX/raGloTv38PBHVspLirE1sGJqY8twMPHT6nf2poa1v7+M6lJCWSmp+Hm5cNLiz9V8BN3OZoda1eSnZFOTXU1ZpaW9B8+ipETH7wlnWfCQjhxIJiyRps/alr7Nj8nXdHmdxvYyubHxRDayuZ3HdDW5gv+u9z1xl9tbS1aWlo39GdoaHgH1Nw7nA7Zy5nD+xkzaz6m1jac2LuTTT98xfz3PkWio6s0TFFeLlt+WUJAn4GMnfM46QnxhGxYjZ6BIZ7dAgHISLzGrmW/0n/sA3h07UH8hbPsXPoLM156E9tObgAYmpox6IEpmFpZI22QcvnUMXb8/iOPvv4elvaOCveMPRdJ5vUkDIxNyE5LIfbMaUZNfxQHN0/OHTnExp++YcF7n2Js1nanourKSjZ8/yWO7l7Mef198rMz2bNyKVraEnqPkC2IraurRdfAgD6jx3ExIrzdNCvKzSF064Y2DZFTB/bctbRMjY+l68Ch2Di7gBSOBW9j0w9f8dg7H6Orb0BdTQ05qcn0GT2erOQkLhwNxcjMnLq6OvQMDO65PLewtacgO5OBD0zF1tmFy6ePs/mnb5j37icYqcjjjT98haO7F7Nee4+C7Cz2rlqKlkSbniOCmp/h5yX49x3IuLlPkJYQT8j6VegaGOLV+AypcbF4d++FvZsHWhIJZw4fYPNPXzPnzcWYWtnInsHElMGTpmJqaY1UKmX37l0k7tuM95R56JpbKU2/iYH+jOvuxy8HIsgoLGFy7y689dBIXlqxjaraOqVhfB2siU7NZMPx85RVVTPA25WXxw/hw837ic3Ikfurqq3lhWVbFcLeqOF3OfIk+9evZszMOTi6e3I27BBrv/+Spz74H8bmFkrTd823n+Pk4cX8txaTn5XFzuW/oyXRpu+osQBIdHToOWwUVg6OaEkkpF6LZ8/qv9CSaBM4RDalpL6ujjXffo6Onj6Tn3gWI1MzSgoL0OyAbb5Z1HV1qUlMpnRfCNbvvHrb429J9OkT7Fu/inEz5+Lk7kVkWAirv/uCZz78AhMl6VlVWcHKb/6Hs6c3j7/zEflZmWz/6zckEm36jVb8cKosL2fb0l9w9fGjpLDtB66WRJvnP/tG0U1FYyXq9An2rFvJhFmP4eThxenQg6xa8jnPffylSp0rvvoMZ09vnnz3Y/KyMtm69FckEm36BynqfO6jL9A1MJD/1jc0aqNz0edLbqjzftAIEHPmFCEb197x91B9fR0bvv8SHT19HljwNIamppQWFqKpqfoz60zIPs6FHmDUzHmYWtlwat8utv70DXPe+QSJjo7SMMX5uWz/7Tv8+gwgaPYC0hOvEbpxDboGhnh0lXXo1dbUYGRujnuX7hwP3qY0nrRrV+k8YAjWTi6AlBN7drD1p6+Z/daHKvW2x5Q+XXiwVwBLgsNJzy9i+oDufPzIWBb+vpHKmlqlYapqa9l55jLXcwuorq3D18GaZ4MGUl1XR/C5K39LRxNnjh1l47I/mL7gSdy8fTmyfw8/fbKY95b8hJmlZRv/DQ0NaEkkDA4ax+XzZ6koL2/jR1tHhyFjx2Pv1AmJRELC1RjW/v4zEm1tBo8e+7d0Xo48yYENqwmaMQcnd0/OhB1i3Q9f8uQH/8PYrH2bP+/NxeRnZ7Fr+e9ItLXpM7LR5ms32nx7RzQlEtKuxbNnjaLNv59RU7tnJi3et9wwBc+cOcO0adOor68HICMjgwkTJvDzzz/L/axcuZJ3330XgOjoaF5++WUeeughHn30Uf744w9qa5sr/ptvvsnPP//M0qVLmTlzJq+99prS+27evJkZM2Zw9epVebiW0z7nz5/Phg0b+PHHH5k2bRpz585l61bFD6309HTeeOMNHnroIZ588knOnDnD1KlTCQkJkfuJi4vjxRdf5KGHHuKFF14gLi5OIY76+nq+//575s+fz+TJk3niiSfYsmULDQ0N8uedNGlSm7M8Vq5cyXPPPXej5FWKVCrlXOhBeo8ci2e3QCztHBjz6AJqqquIOXNKZbiLEWEYGJswfNpMzG3s6Nx/MH69+xF5aL/cz9mwgzh5eNMnaALmNnb0CZqAo4cXZ0Obe97dO3fD1a8zppbWmFnbMHDiZCQ6OmQkJSjcr7ggj5CNa5g470nUNTRIjb9KQN/+dB0wBAtbO0Y9/CgGRiacP3JYqd7Lp09QW1PDuDmPY2nvgHf3nvQeNZbIkP1IpVIATMwtGfnwLDr3HYiOnr7KZ6+vr2PHX78yaOJkTCyaDbtUKiXy8IG7lpZTnn2ZgL4DsbRzwNLegbFzHqeyrJSMxGsAaOvqMfW5V/Dq3pOYMyfpM3ocExc8Q0l+Hj1HjL3n8ry0qAD/PgPoNSIIRw8vgmY+hr6xMReOKs/jK5EnqKutYczsBVjaOeDVLZDeI8dw5nBzHl+MCEXf2IQR02ZhbmNHl/6D8evTj8hD++TxjH9sId2HjMDa0Rkza1tGPjIbLW0dkq5Ey/14dOkuewYr2TPY9R6ChpaE8qw0lek3ppsPOyKjOH0thbT8In7eH4GuRIv+3q4qw6wIj2TnmWgSsvPILi5ly6mLJOYUEOjmpOhRCsUVVQp/N+Lkwb106TeQ7gOHYmlrT9D02Rgam3Am/JBS/1GnjlFbU80Djy3Eyt4Rnx496Rc0jlMh++Tpa+vsgn+vvljZOWBqYUXnPv1x9etMSvxVeTwXjx+hvLSEh59ZhJOHFyYWljh5eGHXSXU6/F0qTkaS//syysIioEF62+NvyYmDe+nabyA9Bg3D0s6esTPmyNIzLESp/6iTx6mtqebBeU9ibe+Ib49e9B8znhMH98rTs4kdy3+na7+BOLh6KL+5Ghgamyj8qeL4/j106z+IwMHDsLKzZ/zMuRgYm3A6VLnOSyePUVtTw+QFT2Ht4IhfYC8Gjp3AsQN72ujUNzJS0KDeapt0tQ7qvB80Apw+tP+uvIeijkdQXlrK5KdewNHdExNzSxzdPbFVUYekUinnw0PoOWIMHl17YGFnz+hZ86ipriL2rGqbfykiHANjE4ZOmYGZjR0B/Qbh06svZw8323wbZxcGTZqGd2BvtCTKG8kPPb0Ivz4DsLCzx8LOgdGPzld4N90sD/QMYPPJixy/mkRyXiHf7A5DV6LFYF/VMy+uZeVxJCaBlLxCsotLCb18jXNJafg52vwtDS05tHsHfYcMZ8CI0dg6OPLw/IUYmZpy5MAepf61dXSY8cTTDBwZhImSTgIAZzd3evYfhJ2jExbWNvQeNBTfLt24FnP5b+s8FbKXzo0236LR5hsYm3BWhc2PPi2z+RPnNtr87j3pO7qtzffr2RfLRpsf0Kc/rr6dSbl2VWmcgv8eN2z8+fn5UVNTQ3x8PABRUVEYGRlx6VLzsHx0dDT+/v7k5+fzwQcf4Orqynfffcdzzz3HkSNHWLlypUKcYWFhAPzvf//jpZdeUrgmlUpZunQpu3fv5rPPPsPLS/XQ944dO+jUqRPffvstkydPZtmyZcTGxgKyXpxPPvkEDQ0NvvrqK1588UXWrVun0BCtqqriww8/xMbGhiVLljBnzhz++uuvNnrMzMx4/fXX+fnnn3n00UfZtGmTvAHp7++PjY0Nhw83v1gaGhoIDQ1l5MiRN0pepaSnp1NeUoyzj7/cTUsiwcHdi/R2DHNmUgKdvBWnNHTy8Sc75Tr19bJRjIykBJx92vpJT1T8yG/5LLFnTlFTXYWdS7MRb6ivJ3jZb/QbMxELWzukUillRYW4tNAM4OLjp1JzetI1HN09FV5Orr7+lBUXUZyfp/I5lXFkxxaMzS0I6Ks41a44L/eeSUuAmqoqpFIp2np6ijrzm3XWVMkaCQbGxvdWnjc0UF1ZSey50/y5+A32r1lGeWlJu3FlJCXg4KaYx51a5XFGYgKd2pSbALKTm5+hNfV1ddTX1bZJx5ZaC+Mv01Bbg76Ng1I/VkYGmOrrcSklQ+5WW19PTHo2nrZte4bbQ1eiSXl1jYKbRFODH+ZN5qf5U3ht4jA6WZq1G0d9XR2ZKddx9VVMC1dff9IS4pWGSUu8hpO7l0L6uvl1prSokKL8XKVhMlOuk5YQrzD18eqFszi6ebJv3Uq+eeVZfnn/dcJ3bqW+Tnn63w/U1dWRkZyEm19nBXc3vwBSVaRnamI8zh7eCunp3pSeec3peTr0IGUlxQwar3q6V11NDUtee56vX32WNd9/SWbK9XZ1uvsFKLi7+3Um9Vqc0jAp1+Jx9lTMd3f/tjoBfv3wHT5f9DTLvvyERCUfqbU1NXz16vN8+fKzrPr2SzKS2+q8HzSCrA5lpVy/K++huIvncHBz5+CG1fzw+vP8sfgtju7eptKGleTnUVFSjFML+60pkWDv5klmkmqbn3U9AScvRXvu7ONPTkqyynt1hFr5u0l1J6sqbEwMMTPQ41xSc0dbTV09l1Oz8HGw7nA8rtbm+NhbE5WSedMaWlJXW0tK4jV8unRVcPfp0o3Eq7G3FHdLUpMSSLwai0crm91RVNp8n9tr87NSrpPWaNv+Faip3bm/fyk3nPapq6uLm5sbUVFReHt7ExUVxfjx49m8eTMFBQXo6ekRHx/P3LlzCQ4OxszMjKeeegp1dXUcHR2ZM2cOP/30EzNnzkSncRqDtbU18+fPb3OvhoYGvvvuO2JiYvj888+xtm7faHTr1o3x48cDYGdnx65du7h48SLe3t5cuHCB9PR0PvroI/nhiAsWLFAYaQwLC6Ouro4XXngBXV1dnJ2dmTZtGt980zxVR1NTk1mzZsl/W1tbk5CQwJEjRxg1ahQAo0aN4uDBg0yePBmAc+fOUVRUxJAhQ26UvErJy5O9cFpPfdE3NKKsSPX8+fKSYpy8fRXc9IyMaGiop7KsDANjE8pLipXGW1FarOCWm57G2q8/oa6uFom2Ng88/iyW9s0f0ceCt6Ojb0D3wcMAWSNZKpWiZ2Tc6v7GlMcqn75RXlKMoYlZG/9N11qO4LVH0pVoYs6eZp6S6SplJcXyZ2zJnUzLlhzevBYrByeFRpXsfiUA6OjpsXfVUtz8u2JoanZP5bm9ixsxkScYNmUGugaGROzaysbvvsCjSw/KS5Q/syyPTVvdWzGPy0uLcTZs9QyGis/QmohdW9HS1sE9oFurZ0hlzVeyZ1DX1MIlaIrKKZ8m+rKptK1H5IorKjEzUN6oVMaozl6YGehzNKa5AZxRWMKvB4+TnFeArpYWY7r5sHjaGF5fs5OsolKl8VSUlSJtaEC/VR3SNzKmTEXPcnlxMYaminWoKa/Li4sxtWh+9m9fe56KslIa6usZNOFBegweLr9WmJtLUmwM/r36Mv25lynKy2PvuhXUVFcxcuqMDqfFvURzerYq+0bGlF1Rnp5lxUUYmZq38Q8yW2JqaUV2WgrhO7ey4K3FbUaomrCwseOBuU9g7ehETVUVJw/tY+n/FvPU+59hbq04qlFRWkpDQwMGrfLdwNiYhBYj2wo6S4oxbpXvTeFLi4swtbTCwNiECY/Ow97Flfq6Oi6eiGD5V58y77V36OTl06jTlgfnLcTG0YnqqipOHNzHn599wDOLP8Pc2va+0gjNeX433kNFeTkkX83Dt2dfpj79EkX5uRzcsIra6mqGTX5E6X1BZusU7m1oRFlxkcr7lJeU4OjZNkxDQz1VZWXotzMq2h5hW9Zjae+IrYsb5y/c3AiRqb7MXhaVVyi4F5ZXYm54Y1u64pkZGOvpoq6uxrqIc+w9H3NT929NWWkJDQ0NGLVKCyNjE2KLLt5S3ABvLnyMspJi6usbGDf1EQaNGvO34pHbKMO2Nj8pVpWNKsaotc03Um7zv3u92eYPHK9o8wX/bTq05i8gIICoqCimTp1KdHQ0EydO5OLFi/JRQA0NDTw9Pdm+fTteXl4KL0RfX1/q6urIzMzExcUFADc3N6X3+euvv1BXV+frr7/GxMTkhro6deqk8NvMzIyioiIA0tLSMDMzkzf8ADw8PBS0paam0qlTJ3R1m9dTeXu37RnZu3cvBw4cICcnh5qaGurq6uTndAAMHz6cVatWERMTg4+PDyEhIfTp0wejVh8dAPv27WP//v0KbllZWfLprQCdOzf2VLfqdZBKpTfsiWhzVdoUVYsrbeJtG4+ZtQ2z3/yA6ooK4i6cZd+qpUx74TUs7RxIjb/K5VPHmP3GBx24v1SJo0opysW0Q0VZKcEr/2TCvIXo6Otz+fRxrpw+gRT4+sWFTH16kdIb3cm0bCJ0y3rSE+KZ/tKbxJ49xcF1zSPiDz31IgCHN6+juqKCB594/o7rbC/PQTY1NGTjakwtrXFw98TG0Znf3n2VgpzMdiW23iijaWpKe/rkD6Gk8JwNPcDFY2FMe+5VtHUV10KaWdsy583FVFdWcODgQZIP78Jj4kx0za3o7+XC48P7yv1+vuOQ0sRQQ63DxbCXuxMzBwby/d4j5JU2rxGJz8wlPrO5F/ZqZi6fz5zA6C4+rAg/3W6cytJLrZ1K1DrppCouzHntHWqqqklPusahLRswMbekc+NIuVQqRd/QiPGz56Ouro6tswuV5aUc2LiGEVOmK93s5H6hTdrdrE1qTlHqamvZ/PuPjJo2A1NL5Z0KAI5uHji6NU8HdXT35NfFb3Lq0H7GzpjToRtLpdIbVH0lz0Vz+bG0tcPS1k5+2cndk8K8XCL2B8sbVk7unji1WCPt5O7JT++/ycmQA4ybqUTn/aCx7V3/8feQLIisDo2Z9Rjq6urYOHeiqrycQ5vXMvShh7kSeYJ9a1bI/T+w8PnGe7e1fTeqbSr1/s16Gr51AxmJ8Ux78Q2VHRotGeLnzrNBA+W/P9i4T0GGSp0qeG31LnQkmnjbWfPY0F5kFZcSGq185OumaF1euVF57Rgvf/gZ1VVVJMVfZdvqFVhYWdN78NAbB1RBmzJwA5uvolq1ed7Zr75DbXU1aYnXOLx1AyYWlnTu03YjsvuOf/EunHeKDjX+/P39CQ4OJiUlhcrKStzc3OQNQiMjI3x8fNDU1Gx8ESjPlJbuOioWMnft2pUjR45w5syZDp1t0Xr3TzU1NfmHZXtaboajR4/yxx9/MG/ePLy9vdHT0yM4OJiTJ0/K/RgbG9O7d28OHjyIvb09p06dkq+BbE1QUBBBQUEKbuXl5eTn58t/19TUMHnyZMpLFHt4KspK2/QStkTfyLjNCExFaQnq6hro6Our9lNWgl6rnicNTU1MLWUjrzbOLmSlJHE29ABBM+eREhdDWUkxv7zdPGVX2rgGcsuv3/Pi1z8p3L/1SMaN9DZd6wi5GWmUFRex/rsvZTqkUrklrK2pobpxCuXdTEuA0C3riD17mmnPv4aJhRV6BoYKa0Fqq6sB2S5tM195R775wb2S5wC6BoaoqavL4zIwMcXQ1JSSggKlz9zevaG5x1vfUNkzlKKuroGugeIUpLOhBzi6axtTnl6kdC2NhqYmplayZ7DrM5TynExyLkXiPHQcZxNTuZbVPI1Lq9F+GOvrkl/W3GNtpKdDcUWl0udpSS93J54ZPZCf90co7PSpDKlUSmJ2Pramqjeu0mtM39a9/rI6pLwM6BsbU1asqg4phmnqEbZ2cKS8pJgju7bJG38GxsZoaGgqfPhZ2NpTW1NDRVlpm5Hj+wF5erYqW+WlJW1GsJowMDZpk55No/IGRsaUFheRm5HO9mW/s71xN88mm7P4iUeZ+cKruLeaZgqgrq6OnbMrBTltD/TVMzREXUm+l5e0o9PImLISRf9lpc06VeHg6k7U6RMqr6urq2PfyYX8bEWd94NGaM5zZTbxn3wPgazsqKtrKNQhcxtbamtqqCwrxb1zN2Y6OMuvNU2pLi9RHL2vKC1FT0V9l2kykpdJeZiyUgWbfzOEb13P1XORTHnuFYw7OMp5Kj6Zqy02t2qypaYGegqdYCZ6uhSW39iWZhfLZkMk5xZioq/LzAE9bqnxZ2BohLq6OiWtZs2UFhe3GQ38O1g0jt7bO3eipLiI3ZvW/a3GX7ONKlJwL2/H5hsYG1N+kzbfyr6Fzf83NP4Et0yHtszx8/OjtraWLVu24Ovri4aGBgEBAVy6dEm+3g/A0dGR2NhY+WYoAFeuXEFTUxMbmxsv4A0MDOT111/nl19+4dAh5YtdO4qjoyP5+fkKjapr164paHN0dOT69etUVTVP+2o5Atek39PTk/Hjx+Pu7o6dnR1ZWW1fOqNGjSIiIoJ9+/ZhYmJC165dO6xVX18fJycn+Z+bmxv6RsYktxj2r6utJT0hDntX1YunbV3cSL6qOLUlOfYy1k6d0NCQtfPtXNxIjm3t5wr2rspHY5uQSqXyl1XXQcOY8+ZiZr/xAfPe+pB5b32IgbEJ+kbGdGo1BTEp9rJKzfYu7qRei6OutnmtVFLMZQyMTZTubKj0mZ1dmf/Ox3Id89/+CI8u3XD08GL+2x/h4uN319Py8Oa1xJw5xbTnX8XcRjZNSaKji6mlNaaW1hiZmXN8z07U1NXx7tFb/sFxL+U5yBpW1o7OXG9My4qyUkqLCinOz1UZl52LG2kJcdS1WGubHHNFIY/tXNvqux57GWvn5mcAiDy0n6M7tzL5qRfbPVqi1UMgbVwHU1VbR3ZxqfwvraCIwvIKOjs1jzxoaajjbWdFXKbytRNN9PFw5tmggfxyIIJT15I7JMXJwrTdDyENTU1snTqRGKM4jS7xymUc3JRvKuLg6k7KtasKdSjxSjSGJqaYmKv+kJNKpdTVNeeJo7snBbnZ8k4cgPzsTLQkEvQM7s+dljU1NbFzdiHhiuKZrQlXohVG5Vri6OpBcnwstS3SM6EpPS0sMTIx5anF/+PJ9z+V/wUOHo6ZlTVPvv8pjm7Ky6VUKiU7LQUDY9M21+Q6L7fWGYWjinLu5O5BctxVRZ2Xo+Q6VZGVktzuZilNOg1bzby5HzSCrA7ZOHVqM2Xun34PATi4elDYqg4V5GShJZGga2CIto4uJpbW8j8zGzv0jIxJaWG/62pryUiIx9ZFtc236eRGapyivUy5egUrJ2cFe9kRwras4+rZU0x59mXMWk2hbY/KmloyC0vkfyl5hRSUVdCtU/MxEVoaGvg52hCTln1TmtTV1NDSuLXdHDW1tHBydSf24gUF99hLF3D1ur3r3qQNUoX3283QZPOTWk2dTor5Z2x+fd3f03nPoaZ+5/7+pXToyZrW/YWFhREQIFvw7e3tTV5eHlevXpW7jRs3joKCAn755RdSU1OJjIxkxYoVjB8/XuVoX2t69eol31yl5SYqN0vXrl2xt7fn22+/JSkpidjYWP788080NDTkI4KDBw9GQ0OD7777juTkZM6fP8/GjRsV4rGzsyMxMZEzZ86QkZHB+vXriY5uu8ahW7duGBoasm7dOkaMGNGhqROqUFNTo/vQkZw+uIe4C2fJzUiTb4/v0+LMrT0r/2DPyj/kv7sMGEJpUSGHN68lPyuDS8ePEH3qGD2Hj5b76T5kJClxMZzaH0x+Vian9geTGhdLj6HNm9Mc2bGJtGtxFOfnkZuexpEdm0mNv4pPYB9AtqbI0s5BvoOlpb0D6hoaOHv7EnfxHBcjwsnLzODgxjWUFRfRrfG8trDtm1j3bfNZWL69+qAlkRC84k9y09O4ev4MJw8E03PEaIVR2+zUZLJTk6muqqSqvJzs1GTyMtMBkGhryzU0/Wnr6iHR1sHS3gFNLS16Dht119IyZMMqok9GMH7uQnT09CkvKaa8pJiaalmHQ0N9PbuW/kJWciJdBw7l/JFDRB0/SkpcLHtW/HHP5HlNdRVhWzfg5t+F6JMRhG7dwIbvv0RDQ5Pa6mq6DBgqj2fDd18053HPPmhqSdi76k9yM9KIu3CGUweDCRzWnMddBgylrKig+RmOhRN9MoKew5tHyE8f3MuRHZsImiXbEr2suJiy4mKqK5tH7MK3t3yGVDJOhlKWkYyph+rF+HvPxzAx0J+ebk44mJvw1KgBVNXWcSw2Ue7n6VEDeLrFmX19PTvxbNAg1kWcIyY9G2M9HYz1dNDXbl6AP7l3Fzo722FlZICzpSkLR/bDycKUkEvKN8doos/IMVw8fpTzR8PIzUxn//pVlBYXytdqHNq6gVXffCb379+rH1oSbXYs+52c9FRizkVybN8ueo8Ikqfv6cMHiLt0nvzsLPKzszgfEcaJA3sI6NNfHk+PwcOpLC9j/4bV5GVlknD5EuE7txI4ZMRtn/KppquDxN0VibsrqKuhZW2FxN0VTeub22SnI/QdOYYLx45w9kgouRnp7F23ktKiQgKHyNIzZMt6VnzVfJZXQG9Zem7/6zey01O5cjaSiL076TtyDGpqarIOEHtHhT99QyM0NLWwtndEu/E9F7ZzC9eiL1GQm0NmynV2LP+D7PRUAlWsuek3eiznjx3hzJFQcjLSCV67gtKiQno16jyweT3LvvxE7r9z7/5oSSRsXfor2WmpXD57mqN7dtF/1Fh5fh0/sJcr5yLJz84kOz2NA5vXE3P+DL2Hj5LHc3jHFuKjL1KQk01mynW2L/udrLRUeg5pq/N+0AjQa/hook5E3NH3EEC3QUOpqijn4KY15Gdlkngliojd2+k2aJjSOqSmpka3wSM4c3Av1y6eJS8jnQNr/kJLWxvvHs02f/+qpexftbQ5XQcMprSokLAt6ynIyiD6+BGunDpGj2HNNr++ro6ctBRy0lKoq62lorSEnLQUinKbG2KHN67hysljjJnzBNpK3k03y47IKKb27Uo/z044W5iyaPwQKmtqCb/SvHnNS+OH8NL4IfLfE3r40dPdCTtTI+xMjRjV2YuHencm9PLf23G0JcPHP8CJsMNEHDpAZloqG//6g+KCAgY2rs/bvmYF3y5+RyFMZmoKqUmJlJWWUl1VRWpSIqlJze+C0L27iTobSU5mBjmZGRw7dICQXdvoNXAIf5feI8Zw8cRRzkeEkZeZzv4NMpvffZCsfB/etoHVLWy+X6PN37lcZvNjz0VyfL+izY88fID4S+cpyM6ioNHmnzy4h4De/ZVqEPz36HA3UUBAAHFxcfKGnkQiwcvLi/j4eDw9ZT1/5ubmfPDBByxbtoznn38eAwMDBg0axOzZs29KVFMD8PPPZQZ62LBhNxUeZFND3n77bX744QdeeuklrK2tmTdvHp999hmSxl2SdHV1ee+99/j555958cUXcXBwYO7cuXz00UfyeIKCgkhKSuKrr74CoF+/fkyaNEnhuAiQGfIRI0bIG3+3Sq8RY6irqeHQxtVUVZRj28mVKc++rHDeW0lBgUIYEwtLJj+1iNAt67gYEYa+sQnDpsyQn/cGYO/qzvjHnuTY7q0c27MdEwsrxs97Un7eG8im8gSv+IOK0mIkOrpY2jsw+alFuNxgRytrByccXN05tncn5SXFWNjaM/WZl+S9p2XFRRTmNk8V0dHV4+HnX+XA+lUs/98H6Ojp02t4EL2GK06LXfbp+wq/r0VdwMjMnKc/+bpDadl71Fiqq6vvSlpeOBoKwMYfvlSIv++YifQfN4nSokKuXToPyA5aB9i/dhkAplY290yeq6mpk5eRRnZaCtKGBs6GHgCpFHNbO0ZMe7RFHhdTlNecx9q6ekx77hVCNqxm1eeL0dHTJ3DYaAJbNE5NLCyZ/PQiDm9Zx4WjobKjK6bOlJ/xB3D+yCFZQ/mvXxSe3693f8bOXtD4DMUEL/+d8tJitHV0UTc2x23cIxg5qT6uYOeZaCSaGswb1ht9bW2uZeXy6baDCmf8WRgpTqUa2dkLTQ115gzpxZwhzYc3X0nL4sPNsvW8+toSHh/eFxM9XSpqarieW8DizftIyG5/90C/nn2oLC/j6J4dlBUXYWnnwPTnXpGfo9amDunpMfPF19m3bgV/fvI+unp69Bk5hj4jmzcgkDY0cGjLBorzc1FX18DU0orhDz1Mj0HNdtXYzJyZL77OwY1r+OOjtzEwMqZr/8EMHKd4MPTtQMfbE4cW9cF8wWzMF8ymZM8Bsj/tWJ3uKP69+lJRXsaR4O2UFRdhZefAzBdelfeQlxYXUdDig1hHT4/ZL71B8Jrl/P7Ru+jq69Nv1Fj5mYkdpaqigl0r/6SspBhtXT1snZx57LV3cVAxQh7Qqy8VZWWE79pGaXER1vYOPPria/IRsrLiIgpyFHXOeeVNdq9ezq8fvoOOvj79Ro+lX4uzxurr69i/cS0lhQVoSSRY2Tnw6Iuv4tm5eZOkqooKdqxYSllxETqNOue//i4OSkbJ7geNAD6BvaksL7vj7yEjM3Mefu4VDm9Zz7JP30PfyJiAfgPpP2aiUp0AgSOCqKut4fCmtVRXlGPj7MqDT7+kcMZfSWG+Qhhjc0smLXyB8G0biGq0+UMmT5ef8df0jGu/aN4ALSovnKhj4di7ezL1edmmd5ciZO+mLT8q1rneQRPApoMzK1qw+eRFJJqaPDV6AAY6Eq5m5PDu+j0KZ/xZGhkohFFXV+OxIb2wNjakvkFKZlEJy8NOs+cWz/gDCOw/kPKyUvZu2UhJYQG2js4889Z7mDeu1S0uLCS31dThHz/7kIIWZePT114E4JdNOwFoaKhn2+rl5OfmoK6ugaWNDZNmzmHgSMWycjM02fyIFjb/kWdb2fw8xfI688XX2bt2BUs/bbT5I8bIz6SU6Wzg0FZFmz/sQUWbf18j1vzdMmqVlZX/7EFL9xBJSUk8//zzLFmyBHd31dMq/i4///wzmZmZCo3Hv8uqo2dvg6J/Hs1bnJ5xp6i7weHago5zv2z+EXI7Ngy4A4zv5nO3JXSI3m8rX8d8r3Hms09u7OkeQEN8wNw2Wh+zcq/SslPpXmb3uVvbbfNO8fK4wXdbQodIL1C9A/i9xNQ+bdcr34vUlCrfMfufQGJ4fy57uBE3N0H8PuPEiRNoa2tjZ2dHTk4OS5cuxcXFReVuo3+X8vJyrl27xuHDh3n99ddva9wCgUAgEAgEAoFAcDv4Vzf+KisrWb58OXl5eRgYGODv78+CBQtu+8jFxx9/TFxcHKNGjaJnz563NW6BQCAQCAQCgUBw/8w+upf5Vzf+hg0b9rfWC94sn3322Y09CQQCgUAgEAgEAsFd5F/d+BMIBAKBQCAQCAT/Em5hN32BDJGCAoFAIBAIBAKBQPAfQIz8CQQCgUAgEAgEgnsfsebvlhEjfwKBQCAQCAQCgUDwH0CM/AkEAoFAIBAIBIJ7HzHyd8uIkT+BQCAQCAQCgUAg+A8gRv4EAoFAIBAIBALBvY/Y7fOWESkoEAgEAoFAIBAIBP8B1CorK6V3W4SgLcvCI++2hH8VUun9Ucwlmvf+YHxNXd3dlvCv4n7IcwB9bcndltAhAt98+25L6BBrnnvxbkvoEA3c+7bzpW4ed1tCh9iRXnC3JXSIiykZd1tCh9DR0rrbEjpEbV393ZbQIT6aNvpuS+gQ1bW1d+xe2vdJGbtZxMifQCAQCAQCgUAgEPwHuD+6nAUCgUAgEAgEAsF/moZ7fzLCPY8Y+RMIBAKBQCAQCASC/wBi5E8gEAgEAoFAIBDc8zTcJ3s43MuIkT+BQCAQCAQCgUAg+A8gRv4EAoFAIBAIBALBPc/9snv7vYwY+RMIBAKBQCAQCASC/wCi8ScQCAQCgUAgEAgE/wHEtE+BQCAQCAQCgUBwzyNmfd46/4qRv7Vr1/LMM8/clXu/+eab/Prrr3fl3gKBQCAQCAQCgUDQUe67kb8JEybwxhtv0L9//7stBYC33noLDQ0N+e/58+czbtw4Hnroodt6H6lUysm9O4k+foSqygpsnF0YNnUm5rb27YZLi7/KkW0byM/KQN/YhMDhQXQeMER+PT8znRN7dpKTlkxJfh69gybQd+wDKuM7fSCY47u30WXgUIZOnXlP6TyxZwen9u1ScNMzNOKJT75RqvPUvl0KOodOmXFjndeucnTbRrnOHsNGt9F5cu9OctJS5Dr7jJmoEEdNVRUn9mwn4dJ5KspKsbJ3YtBDD5Odcp3zoQcoKy7CwtaeYVNm4ODuqVJLbnoqIRvXkJWciI6ePl0GDKHvmImoqanJ/aTGxxK6ZT15mekYGJvSa+QYug4cKr8efSKCvauXtol70be/o6ml1cb95L7dHN21hc4DhzJ0ygyV2v6p9I0+foSYyBPkZ2UglUqxsneiz9gHsHfzaDfef1JTR/L8r8VvUFqQ3ybuTr4BuAd0JTJk7x3N96vnIjl1cA9Fudk01NdjYmlN4LBR+PcZIPdzLvwQFyPCKCnIA8DKzoFB4yfh2bkbAKdDD3J8fzClRUVY2dkT9MijOHt6q9SdnZbCnrUrSE9KQFffgB6DhzF4/IMKuptIjr/K8i8/xsLGjmc+/Fzufv5YODuW/d7G/9u/LENLS6Ly3n8HnS7+mE6fgo6XB5qWFmR98hWlew/e1nv8HYb4udPDzREdLS3SC4oIPnuF3JIylf4NdLQZ3dUbW1MjzAz0uZSczvbTUbdV01A/D3q4OaKrpUVaQRG7z16+oaagrj7YmhphbqDPxeR0tp2+pODH0siAYf4e2JoaY2agR2h0PKGX4/+2xs3BwazeupX8wgJcnJxY9PjjdPPzv2G4lIx05rz4IlKplLBNm+XuocePs3XvXuISE6iprcXF0ZG50x5mUO/eHdZ0NiyEkwf3UFZcjKWdPSOmzsTJw0ul/5z0VPavX0nm9UR09AzoNmgoA8Y+IK9DsecjOX8klKzUZOpra7GwtaPfmIl4dumuEE91ZSXhOzcTey6SyvIyjEzNGPzAVHwDO64dYHQXb/p4dkJPIiE5r4Atpy6SXVSq0r+hrjYPBAZgb26CpaEBZxJTWH/snIKfnm5OTB/Qo03Y11btoK6h4ab0NTE8wJOebk7oSrRIzS9i55kocopVl09DHW3GdvfFztQYc0N9zl9PY8vJiwp+At2c6O5ij5WxIWpqamQWFnPw0lWScwv/lsaRXbzo7dEJPYkWKXmFbDt1iezi9tNyQqA/9mbGWBgacC4xlQ3Hz6v037WTPTMHBXIlLYtlh0/9LY33CuKoh1vnvmv83Ulqa2vRUvIB3BJDQ8M7ouVMyD7OhR5g1Mx5mFrZcGrfLrb+9A1z3vkEiY6O0jDF+bls/+07/PoMIGj2AtITrxG6cQ26BoZ4dJUZ19qaGozMzXHv0p3jwdva1ZCZlED08aNY2DncszpNrWyY8vyr8t9qasoHt88ekukcOeMxTK1sOL1/F9t+XsLstz9uV+eO377Hr3d/Rj+6gIzEeEI3rW2r08wCt87dObFnu9J4QtavIC8jjVEz52FgYkrsmZNs/uFLGurrGfnIbBzcPDh/5DCbf/qGee9+gpGZeZs4qisr2fjDVzi6ezHrtfcoyM5i76qlaEm06TkiCICivFy2/LwE/74DGTf3CdIS4glZvwpdA0O8ugXK49KSSFjwwRcK8Str+GUkJXDpeHi7+f9Pp2/atat4duuJras7WloSzoUdZPuv3zLj1fcwtbK+K5o6kuePvPw20hYfLuUlxaz76mMMTc04vGktIx559I7mu46+Pn2DJmBmbYu6hgaJ0RfYt2YZegaGuPp3AcDQxJTBk6ZiammNVCol7sxJ1v+0hCfe+Zi8zHT2rV/FuJlzcXL3IjIshNXffcEzH36BiblFG91VlRWs/OZ/OHt68/g7H5Gflcn2v35DItGm3+hxCn4ry8vZtvQXXH38KCls+yGlJdHm+c8UO3Rud8MPQF1Xl5rEZEr3hWD9zqs3DnAH6O/tSl8vF7afvkR+aTmDfd2ZPaQnP+w5Qk1dvdIwmurqVFTXEBGTSA83x9uuaYC3K/28XNh2+hJ5pWUM8fVgzpBefL8n/IaajsYkEOjmpNSPlqYGReWVXEnLZniA6s6QjnDw6BG++eN3XnvqKbr4+rFlTzCLPviA9T/9jI2VlcpwtbW1vPPFF3T18+N8dLTCtXPRUQR27syTj87CyMCQ/eFhvP7pJ/z86acdalReOXOSgxvXMHr6bBzdPTkbfogNP37FE+9/hrFZ2zpUXVnJuu++wNHdi7lvLKYgO5PdK/5AItGm98gxAKTEXcXZy4fBEyejo2/A5dPH2fLrd8x86S15o7K+vo5133+Bjp4+Dz7+DIYmZpQWFaCh2f63TmuG+Xsw2M+d9RHnyCkpZVQXb54c2Z//bQuhuq5OaRhNdQ3Kq2s4HBVHH89OKuOurq3j060HFNz+bsNvkI8bA7xd2XzyAnkl5Qzz92De0D58sztUZfnU0FCnvLqG8CvX6OmuvHy6WptzKSWT5NzL1NbV09/blceG9uaHvUfJLy2/KY1D/NwZ5OvOxmPnyCkpY2RnLx4f2Y8vtx9qJy3VKa+qITQ6nt4endqN38xAj3E9/EjMzrspXYJ/L7c07fPMmTNMmzaN+npZBcrIyGDChAn8/PPPcj8rV67k3XffBSAlJYXFixczbdo0Zs2axZdffklhi5d7XFwc7777LjNmzGDatGm89tprxMbGyq/Pnz8fgP/9739MmDBB/ruJI0eO8PjjjzNt2jQ+/vhjiouLFa6HhITw9NNP89BDD7Fw4UK2b99OQwuDMmHCBIKDg/n000+ZMmUKK1eupK6ujt9++405c+bw4IMP8thjj7F8+XJ5mJbTPt98801ycnJYtmwZEyZMYMKECbeSvHKkUinnw0PoOWIMHl17YGFnz+hZ86ipriL2rOoenEsR4RgYmzB0ygzMbOwI6DcIn159OXt4v9yPjbMLgyZNwzuwN1oS1R9Q1ZUV7Fv5JyNnzEFbT++e1amuoY6+kbH8T09J41ym8xCBLXSOminTebUdnVHHwtE3MmHIlBmY2dji36jzXGjzS8rG2YWBk6bKdCr5IK2rqeHaxXP0nzAZBw8vTCyt6DNmImqoYW5rT5f+gzG3sWPEtFnoGxtz4ehhpVquRJ6grraGMbMXYGnngFe3QHqPHMOZw/vl2yBfjAhF39iEEdNmYW5jR5f+g/Hr04/IQ/taxaaGgbGxwl9rqisr2L38N0bPfExl/t+J9A2a/ThdBg3DysEJU2sbhk2bhURbh+TYaJXx/tOabpTnAHoGhgrl8vqVKCQ6OmSnXMe/T/87nu/OXr54dOmOuY0tppZW9Bg6Ckt7B9IS4uR+PLp0x9WvM6ZW1phZ2zD8oWlItHVIS4znxMG9dO03kB6DhmFpZ8/YGXMwNDbhTFiI8nQ8eZzammoenPck1vaO+PboRf8x4zlxcG+bbbt3LP+drv0G4uCqYjRXDQyNTRT+/gkqTkaS//syysIioOHe6Gnu4+lMREwiMWnZ5BSXse30JSSamgQ426kMU1RRyd7zMVy4nk5lTe1t19TXsxNHYxK4kpZFTnEZW09fRFtTk8430LTn/JV2NWUUFLP/YixRKRnU1iv/SO8o67ZvZ/zw4UwaHYSLoyOvLHwSc1NTtuzd0264H5cvx72TC8P7D2hz7eUnFjJn6lT8PL1wtLNjwfQZeLu5ceTkyQ5pOh2yj859B9Bt4FAsbO0Z/chsDIxMOBeuvO5Hn5bVoQlzn8DK3gHv7j3pM3ocp0L2yevQqIdn0S9oAnYubphZWTNw/IPYOLkQd/GsPJ5Lx49SUVrC1KdexNHdCxMLSxzdvbDr5Noh3U0M8nHncFQcl1IyyCoqZV3EWbS1NOnuqrpzsLC8gm2nLxGZkEJFdftlsbSqWuHv79LP24XwK9e4nJpFdnEpm05eQFtLk66dVM/4KCqvZPfZy5xLSlNZPjceP8/JuOtkFpaQV1rOjsgoqmvr8LS1vGmNA33cCI2OJyolk+yiUtYfO4e2libdXFRrLCyvZEdkFGcSUqmoqVHpT11NjZkDA9l3PoaC0oqb1nYvIpVK79jfv5Vbavz5+flRU1NDfLxsKkZUVBRGRkZcutQ8fSM6Ohp/f38KCgp44403cHZ25uuvv+ajjz6isrKSjz76SN4Aq6ysZOjQoXz++ed8/fXXuLq68sEHH8gbcd98I+vtffbZZ1m5cqX8N0BOTg5Hjx7lrbfe4sMPPyQxMZFVq1bJr+/fv5+VK1cyc+ZMfv75Z+bPn8+WLVvYs0fR+K9bt44ePXrw448/Mm7cOHbt2sXJkyd59dVX+e2333jttddwcFBu3N566y0sLCx45JFHWLlyJStXrryV5JVTkp9HRUkxTt5+cjdNiQR7N08yk66pDJd1PQEnLz8FN2cff3JSkqmvV96bpIqQ9Stx79oDR0+fe1pncV4ef7z7Cn998AZ7lv9GcV6uap1evkp0JqiMO/N6Is7evgpuzt5+N6WzoaEBaUMDmprNg+71dXXU1dbQ0CqOTj7+pCcq15ORlICDm6dCQ7iTrz9lxUUU58t69zISE+jko9gD7eITQHbydQW9dbU1/PbOK/zy9kts+eVbslOT29xv/9rleHULxNnLt8211tzJ9K2vr6OurhYdXf17RtONkEqlXD4ZgWf3XuSmp7bJozuV7y31JMdeoTA7Cwd35VPOGhoaiDp9gprqKuycXchITsLNr7OCHze/AFITlE/LS02Mx9nDW0G3u19nSosKKWpRR0+HHqSspJhB4x9UGg/IOlCWvPY8X7/6LGu+/5LMlOsq/f6bMNXXxVBXh4QWvfd19Q0k5xbgaG5yVzVdU6rJ9K5oak1tbS2x167Ru5vi1Mfe3boTFROrIhREREYSEXmal594osP3qqisxNDA4Ib+6uvqyEy5jotvgIK7i68/aYnK61B64jUc3b0U6pCrbwBlxYXyuq+MmupKdPSa7WPchbM4uHmwf8MqvnvtOX774A2O7Np6U/bMzEAPIz0drmbkyN1q6xtIzM6nk2XbGQs3i5aGBu9MHs17U4KYP6wv9mZtOyQ7gqm+Hka6OsRnNtuYuvoGknLycbK4veVTQ10dTQ2Nm+5gaUrLuBZpWVffQFJ2Hs5WZresa0w3HwrKKzibmHrLcQn+PdxS409XVxc3NzeiomTrB6Kiohg/fjy5ubkUFBRQVVVFfHw8AQEB7NmzBxcXF+bOnYujoyMuLi689NJLxMfHc+2arGHQpUsXhg0bhqOjI46OjixcuBCJRMK5c7I54caNIxIGBgaYmprKfwPU19fz4osv4uLigre3N6NHj1ZohK5fv565c+fSv39/bGxs6NWrF1OmTGnT+Bs4cCCjR4/GxsYGGxsbcnJysLOzw8/PDysrK3x8fBgxYoTS9DA0NERdXR1dXV1MTU0xNb09xqW8RNb41TM0UnDXMzSivKSknXAlSsM0NNRTVaZ6vntroo4foTg3h37jJt3TOm06uTJq5mNMevIFRkyfTXlJMRuWfEZluWIc5aXt6CxVHC1uSUVJ8S3rlOjoYNvJjdMHgikrKpR9VB8LB2RrAVuib2gkT9PWlCvRom9oLL8GsufUV6G3slGvqbUNQbPmMWnh80x47Ek0NbVY+/WnFOZkycNcPBZOUW4OA8Z3bB3rnUzfE8HbkUi0cQnocs9ouhEpV69Qkp+He+duSBsalOTjP5/vIBvN/XbRk3zz/ONs+WUJw6bOxLVVgy43PVXm54XH2b3qLx55ZhEGJqZIGxrQN2qlw8iYsmLlusuKi9A3Mm7jH6CsUXd2WgrhO7cyecHTqKsrfzVZ2NjxwNwneOTZl5jy+LNoammx9H+Lyc/OUur/34SBjjYA5a1GQcqrauTX7jSqNJVVVWN4lzS1pqikhPqGBsxMTBTczUxMyC9Svj4rr6CAz378gQ9eehn9G8x0aGJT8G5y8vMZM3TYDf1WlJWqrEPt1f22/mW/y0qKlIY5ExZCaWEhAb2b90gozMsl5mwkDfV1THvmJQZPnMz5o6GEbdt0Q91NGOnKpsm3HpErrazGUPfW8j2npIz1x8/x1+GTrDoSSV19Pc+NGYSFYfsdfMpo0lKmpHwa3KLO1ozs7EVNXR0xadk3FU6VxtKqagx1lS9H6CietpZ06WTP1lbrFe93xMjfrXPLa/4CAgKIiopi6tSpREdHM3HiRC5evCgfBdTQ0MDT05NNmzZx+fJlpk6d2iaOzMxMPD09KSoqYvXq1URFRVFUVERDQwM1NTXk5rYdvWmNlZUV+vrNxsHc3JyioiIAiouLycvL46effuKXX36R+6mvr2+Tue7u7gq/hw8fznvvvcfChQvp1q0bgYGB9OjRQ+XHye0gODiYnz5YLP/9wMLnAZRsjCCl7VYJirQNIlVxQTkF2Vkc37WVqS++joamYnGJjTzJoQ3No6t3UyfQphfVppMryxa/SdjmdSRGXZC7T1z4nFKdUqkUtRspbR2Gm9c56tF5hKxdwdL3X0NNXR1zG7vGKBTLlFQqbTdaZfrbuCvJi8YLANi7umPv2lzm7VzdWfHZe5wLO8TwaTMpyM7k6M7NTF/0Vpv8byL2zEkOb1gt/32n0vd8WAjRx47w4DMvoa2je09o6gjRJ45i7dRJvtGMUk3/cL4DSLR1mPPmYmqqq0m5eoXQLesxNrNQGOk0s7ZlzpuLqa6sICnqPNv++pWpTfW8dbpJpbSXlG2fqdn21tXWsvn3Hxk1bQamlqrXYDm6eeDYYnMfR3dPfl38JqcO7WfsjDmqb34fEuBsx4QezTMi1hyVTd1r8z3y94rh36Kzsx0TejSPKq85ekaFJjXuuc8mJeVPVd1//+uveGjMGAK8VW9g1JLDx47xw1/L+Pi117BtZw3hDUVJpcqEtvDd2r8KdyD2XCSHt6xn0oKnMW65DlfagL6hIWNnzUddXR1bZxcqy8sI2bSGYZMfUboBU3cXB6b27Sb//eeh4wr3l+u7DWUxObeA5NwC+e/rufm8MmEYA33c2mwK1JouneyZ1LP5O2Bl+Gml/tRQa6P9Vujn5UIvDyf+OnxK5Rq9Jrq5ODC5T3Nn5V+HZdOEW3+LyjT+fZF62hKm9e/O2qNn/pHp3oL7m1tu/Pn7+xMcHExKSgqVlZW4ubnJG4RGRkb4+PigqalJQ0MDgYGBzJs3r00cJo09ckuWLKGoqIgFCxZgZWWFlpYW77zzDnU3qEyAwo6bTTRVpqZppc888wzeNzDmOq02fnB3d+fPP//k3LlzXLp0iSVLluDi4sJHH330txqA+/btY//+/e36qaurY9ADk3FsnD5Z3/j85SXFGJo2TwOoKC1Fr1VPYEv0jdqOuFWUlaKuroGOfsd60TKvJ1BZXsaqz96Xu0kbGkhPiOdiRBiz3vhAPoXxbupUhkRbB3MbOzQ0NZnx2nty9/q6WqU6K8tK24yqtETPyJiKVr2ylaU3r9PEwoopz79KbXU1NVWV6Ogb8OPLT7WJo6KsFD1D5dNdlPUQV5TJ0rDpGfQNlfhp1KtroFyvuro6Nk4uFObKei8zEhOoLCtj2SfvyP005X/UsXCe/vJHXP27YuPcvF7kTqTv+bAQTuzZzgMLX8DG2aVNXHdDU0eoKC0hMeoCQ6fMQFffADV1dSX5eGfyXU1dXb5JjrWjE/nZGZzcv1uh8aehqSn34+bpRfr1RC6eOIqaurp8xK6J8tISDIyU6zYwNmkzKthU5w2MjCktLiI3I53ty35ne+NunlKpFKRSFj/xKDNfeBX3VqOSICuvds6uFOT8+0b+rqZnk55fJP+t0fi+MdDVpqSyeZaAvrakzajBP0VsejZpHdBkcAc13QgTIyM01NUpKCxScC8oKm4zGtjEmUuXOB8dzdJ16wBZO6GhoYF+D0zk1aee5sGgILnfw8eO8cE33/D+S4s6vNOnnoGh0rpfXlrSZnSvCX0jY6V1rulaS2LPRbJz2W9MmPtEm50+9Y1N0NDQUPh+Mbexo7amhoqy0jazBgAup2aRkte8FlFDQxbWUFeboopKubuBjjallbc336VSSM0v6tDIX0xaFql5zaO5mo06DXS0Ka5oUWd0bl/57OflwsjOXiwPO6VQN1RxJTWLlJYa1ZvSUkdBo4GO5JbWOtqYGGKsp8MTI/vJ3Zoa9v+bNYGvd4a2uyPvvcw9sgT7vuaWG39+fn7U1tayZcsWfH190dDQICAggB9//BETExN69JDtiOfm5kZERARWVlYK651aEhMTwxNPPEHPnj0BKCwsVNgQBpA3JG8GU1NTzM3NyczMZNiwG0/JaI2enh4DBgxgwIABDB8+nFdeeYXMzEzs7dsuxr2RvqCgIIJavDhUsSw8Uv6/VCpFz8iYlKtX5B+6dbW1ZCTEM2BS25HUJmw6uZEYpbj1b8rVK1g5OaOh0bGsdwvohvUbnRTcDq5dhomlFT1HjsPcxk5uUO6mTmXU1dZSkJOFg4c3Ji1GE9rV+cAUlfHZdnIlocUI4q3q1NLWRktbm6qKctTU1NpsZJMcexnPrm23vAawc3HjyI5N1NXWynfmTI65goGxibyX187VjfiLiul6PfYy1s6dVOqVSqXkpqdiaS/bFdC9S3fmOndS8LNn5Z+YWFrTc+RYNDQ00dTUUtgt859O33OhBzi5ZycPLHxe5REPEh2dO6qpo1w5fRwNTU08u/dCQ1MTK0dnrsdexqt7T7mfu5HvANIGqbwDR6UfqZSG+gbsnF1IuBKFX4ut4ROuROPb4jla4ujqwcEt66itrZFvipNwJRpDE1NMLCxpqK/nqcX/UwgTGRpC4pUoHn5mESbmyjdRkEqlZKelYO3o3K7u+5GaunoKyhQ3aCitrMLN2pyMAlkjQFNdHWdLMw5cVL127U5ocre2UNDkZGl6xzTdCC0tLbzd3Tl14TzDBzRv3HL6wnmG9uunNMzaH39U+H3k5CmWbdzIsm++xrLFKFrI0aN8+O0S3ntxkdJNYVShoamJrVMnkmKi8enRS+5+PSYar27K65C9qzuh2zZQV1uDZmMdSoqJxsDYVGFk78qZU+xe8Tvj5zyhEHcTjm6eXD59AmlDA2qNDY+CnCy0JBL0DJTvXl5dV0d1qaJtKKmowtPOitTGBo+mujquVubsOtv+5lt/B1tTIzIKVC8daUJZ+SyprMLdxpL0FuWzk5UZ+87H3LKu/t4ujAjwYkXY6Q4f8aAyLW0t5Y1HTXV1XKzMCT57+W9rS80v4qudipsHBXX1QVeixbbTlygou7kdSQX/Lm658de07i8sLIw5c2TTbry9vcnLyyM7O5u5c+cCMG7cOA4cOMAXX3zB5MmTMTY2Jisri4iICObNm4eenh52dnaEhobi6elJVVUVy5cvb9NQtLKy4uLFi/j7+6OlpYVBBxZXA0yfPp3ff/8dfX19AgMDqa+vJyEhgfz8fKVTUZvYvn07pqamuLq6oqGhQXh4OHp6epibK1/UbGVlxeXLlxk6dCiampoK6xL/LmpqanQbPILIA8GYWdtgYmnD6QO70dLWxrtH88fX/lWy89pGPyrbBbXzgMFcPHqYsC3r6dx/EBmJ17hy6hhj5jQvYK+vqyM/KwOQfQxXlJaQk5aCRFsbE0trdPT00Gm15kFTIkFHTx8LO8XG793UCXBk+0Zc/bpgaGZGRWkpp/fvpq66Gt/eii94mc7hRB7Yg5m1LSaW1kQeCEZLWxuvljobz78bPUumM6D/YC4eDSV863oC+g0mI+kaV04fJ2j24wo6C5p01tVSXlJMbloKWto68gZockw0UqkUU2sbinJzidi5CSNzC7KSk7h0LBx7Nw8uHA2lrKiILgNkZ7Md2bGJzOtJPPzCawD49uzD8T072LvqT/oETaAwJ4tTB4PpN6b5zKcuA4ZyPvwQhzevpcuAIaQnxBN9MoLxjz0p13sseDt2Lm6YWllTXVnFubCD5KanMfKR2QBK819LWxsd/bb5fyfS9+yh/RwP3sboR+djYmUt7znX1NJCW1f12py7nefQuNHLiaN4du8lb5h2HzKS/auXYuvsckfz/cS+Xdh2csXEwpL6ujoSoy9x5fQJhk9rPrszfPsm3Py7YGhqRk1VJSfOR3L9agwzn3+F6spKti79BftObji5e3Im/BClRYUEDhkOQMiW9aQnJTLnlbdk6di7H2G7trL9r98YNH4S+VlZROzdyZAJD6GmpoaGpibW9orHEOgbGqGhqaXgHrZzCw6uHphZ21BdWcGpQwfITk9l3Ky2M0puFTVdHbTsG3esVFdDy9oKibsrDaWl1GXfeCnCP8HJuGQG+bqRV1JOflk5g3zdqKmrIyo5Q+7nwd6yEdJtp5qnyNmYyD7qtTU1kUql2JgYUt8gvS09/yfirjPI143ckjLyy2THT9TU1XOphaaHGjVtvQlNGupqWBrJ3u+a6uoY6GhjY2Ko9AP/RkyfNIkPvvkGPw9POvv6snXvHvIKCnhozFgAflqxnCtxcfz0yacAuLXq8IqJv4a6upqC+4Ej4XzwzTc8P28e3fz9yW/sqNbU1MS4A0dA9RoRxM5lv2HXyRUHNw/OHQmltLiI7oNkHdSh2zaScT2RmYveAMCvV18igreza8Uf9B/zAAU5mZzYv5uB45rPyrwceZJdy35j2ORHcPLwoqy4SJaWmpro6svSsvugYZwJO8iBjasJHDKS4vxcju7aSvfBw5VO+VTFkZhrjAjwIqe4jNySUkZ29qa6ro5ziWnN6d54Xt+6iObdRu1MZd9EOhJNpEixMzWmvqFBfqbdqC7eJOcWkFtSho6WFgN93LAzNW5zzl5HOR6bxBB/d/JKysgrLWeovzs1tfVcuJ4u9zOlb1cANp+4IHezNZGNgGprycqnrYkR9Q0N5DSWz4E+rozs7M2mE+fJKy2Xr3+tra+nuvbmNgM7GpPA8ABPckrKyC0pY0SAJ9V19ZxPatb4SH/ZCG7LcxHtTGUadRo12pkaUdcgJae4lNq6+jZnLlbV1KKurtbuWYz3A//mtXh3ittyzl9AQABxcXEEBMjmWkskEry8vIiPj8fTU3Y+j7m5OV988QUrVqzg/fffp7a2FktLS7p16yY/S++FF17gxx9/ZNGiRZiZmTF9+vQ2xzXMnz+fP//8k5CQEMzNzVm6tO0B1coYPXo0Ojo6bN26lZUrVyKRSHBycmL8+PHthtPV1WXr1q1kZmYCyHcgbT09tImZM2fy008/8fjjj1NbW8uuXbuU+rtZAkcEUVdbw+FNa6muKMfG2ZUHn35JYXSjpFDxEGljc0smLXyB8G0biIoIQ9/YhCGTp8vPJwPZRgxrv/hQ/jsqL5yoY+HYu3sy9fnX7iudZUWF7F3xO5XlZegaGGLbyZWHX3oLIzPzNsaix/Ag6mprCd3crHPSU4sUdJYWFiiEMTa35IGFz3Nk20aiIsLRNzZm8EOPKOgsLy5i7ZcfyX8X5+USffwI9u6eTHlOdlZYdVUlx3dto6yoEG19fdy7dKffuEnEnjnFiX27KC8pxsLWnslPL5L36JYVF1OU17wbmLauHtOee4WQDatZ9flidPT0CRw2msDho+V+TCwsmfz0Ig5vWceFo6EYGJswfOpMhTP+qisrObB2BeWlxWjr6GLl6MQji97A9ia3/W7NP5W+FyNCaaivZ+9yxYO+fXr1ZdTM9hsAdzPPQXZGYVFuDqMfXSB38+zek9qqyjue77XVVRxcv5KyokI0tSSYWdswds4CfAL7ND9XSTHBy3+Xlw1rRydmvfAa7v6yD/mK8jKOBG+nrLgIKzsHZr7wqnyErrS4iILc5o0PdPT0mP3SGwSvWc7vH72Lrr4+/UaNpe+ose3mWWuqKirYtfJPykqK0dbVw9bJmcdeexcHV7ebiqcj6Hh74vDDl/Lf5gtmY75gNiV7DpD96de3/X4d4VhsIloa6ozt4YuuRIu0/GJWhUcqnFdmrNf23fTkaMVRKS97a4rKK/h2d/gta4qITURLQ4PxPfzQkWiRnl/EyvDTrTTptgn39OiBCr+97a0pLK9gye4wAAx1dBT8mBvq09PdiaScfJaF3twh1SMHDqK4pJRlGzeQV1CAq7MzS97/QL4+L7+gkPSsm5s6vG3vXurr61nyxx8s+eMPuXt3f39++ex/7YSU4RvYh8qyMo7t2UlZSRGWdg48/OzLLep+EUW5zXVfR1eP6S+8xv51K1n22fvo6OnRe8QYeo1onkl0/shhGhrqCdm0hpBNa+TuTh7ezHpZ1hFjZGbO9OdfI2TzWpZ+8g76RsZ07jeIAWMfuKnnPxwdj5aGBpN7d0FXW4uU3EJ+O3hMYc2bqX7bfH9louLsK39HWwrKyvl4i+z4HF2JFlP7dsNIV5vKmjrSC4r4cd9RhamSN8ORmAS0NDWY0NNfVmfyilgWekqhfJooKZ/PjR2k8NvHwYbCsgq+bBxN6+PRCU0N9TYH0p9NTL3phmrY5WtoaWrwYK/O8rT8I+S4QlqaKEnLRROGKvz2c7SloKyCz7YevKn7C/57qFVWVoom9D1Iy2mfglvnfukpkqiYEn0vUdOBNbiCjnM/5DnI1pbdDwS++fbdltAh1jz34t2W0CEa7r2tW9rwUjcVZ0LeY+xIL7ixp3uAiykZN/Z0D6CjdXMH098talUcJn+v8dG00Tf2dA+QcwfXKloZdWx24f3GP7dlpUAgEAgEAoFAIBAI7hnujy5ngUAgEAgEAoFA8J/mfpnJdS8jRv4EAoFAIBAIBAKB4D+AaPwJBAKBQCAQCAQCwX8AMe1TIBAIBAKBQCAQ3POIWZ+3jhj5EwgEAoFAIBAIBIL/AGLkTyAQCAQCgUAgENzzNIihv1tGjPwJBAKBQCAQCAQCwX8AMfInEAgEAoFAIBAI7nnEUQ+3jhj5EwgEAoFAIBAIBIL/AGLkTyAQCAQCgUAgENzziDV/t44Y+RMIBAKBQCAQCASC/wBqlZWVogl9D3I8LvluS+gQXZxt77aEDrH/4tW7LaFD+DpY320JN6SovPJuS+gQUalZd1tCh9DRuj8mYBjr6dxtCR3iSlrO3ZbQIWb+8O3dltAx1DXutoIbsmXRorstoUMY6GjfbQkdYqa10d2W0CE0XTvdbQkdoqGg8G5L6BDadvfH91xKXtEdu5eThckdu9edRIz8CQQCgUAgEAgEAsF/gPujy1kgEAgEAoFAIBD8pxG7fd46YuRPIBAIBAKBQCAQCP4DiJE/gUAgEAgEAoFAcM8jdvu8dcTIn0AgEAgEAoFAIBD8BxAjfwKBQCAQCAQCgeCeR6z5u3XEyJ9AIBAIBAKBQCAQ/AcQjT+BQCAQCAQCgUAg+A8gpn0KBAKBQCAQCASCex4x6fPWESN/AoFAIBAIBAKBQPAf4F858vfmm2/i7OzMk08+ebel/GOE79/DwR1bKS4qxNbBiamPLcDDx0+p39qaGtb+/jOpSQlkpqfh5uXDS4s/VRn3tZgrLPngLaztHXjvmx9vSeeWzZtYu2o1+fl5uLi68sKil+jarZtSv0mJiXz95RckJSVRXlaGhYUFI0aNYv7jT6ClpQVAXl4eP3z7LVevxpKWmkrQmDG88/4HN60rMvQgx/fvobS4CCs7e0Y/PAtnT2+V/rPTUtm7bgXpSQno6hvQY9AwBo2fhJqaWhu/KfFXWf7VJ1jY2PH04v/J3XPS0wjbuYXMlOsU5eUyeMKDDJk4+aa1t2T/7p3s2ryJooJ8HJw7MWfhU/j4Byj1e/nSRfZs28K1q1epqCjHxtaOsZMeYujooFvSoIywfXs4sHMrxYWF2Dk6MW3uAjx8VZfPNb//TEqirHy6e/nw8oftl8+v338LG3sH3l/S8fIplUo5vX8Xl08cpbqyAmsnFwZPnoG5rV274dKvXSVixyYKsjLQNzKh+7DR+PcfLL9++cRRYiNPUJCdgbRBiqWDI73HPICdq4fcz4oP36S0ML9N3M4+/kx5ehHH9+zg0rFwqisrsHF2ZcTDs7CwtW9XV2r8VcK2ricvMx0DYxN6jhhD14FDFfzEnT9DRPA2ivNyMbawZOCEh/Do0qM5jmtXOROyn+zU65QVFxE0ax7+fQYoxFFTXcXRHVtIiDpPRVkpxmYW9Bo6HA1NLSL27aasqAgre3vGTJ9Np3bqUFZaCsGrl5PWWId6DhnOkAkPyutQUuwV/vri4zbhnv/kSywb0+JcRDjb/vqtjZ/3fluOlpak3fS6EUP83Onh5oiOlhbpBUUEn71CbkmZSv8GOtqM7uqNrakRZgb6XEpOZ/vpqFvS8HfQ6eKP6fQp6Hh5oGlpQdYnX1G69+Adu7/xpHGYTp+MhpkZNdeTyf3hd6ouXVbp32DoQMxmTUPL0Z76ohKKtu6iaP0WxTgfHI/JQ+PRtLGmLjuXglXrKd1/+Ja1DvB2pUsnB3QkmmQWFHPgYix5peXthnE0N2V4gCcWRvqUVVVzMi6ZC9fT5NdnDOiBk6VZm3C5JWUsPXSi3bilUimn9+3i8okjVFVWYOPkwuApMzC/Qd1Pv3aVo9s3ymySscwmBfQfIr8efeKIzCZlNdkkJ/qMVbRJl46GEn08nJICmV0yt7EjcNQ4XPw6t3tvgM0H9rNm9y7yi4pwcXBg0ew5dPX2uWG4lMxM5r71BlKplNDlKxWu1dbVsWzbVvYePUJeYSFmxsbMGD+Bh4PG3DBeVWzavJlVq1eTl5+Pq4sLLy9aRDcV3x+JiYl8/uWXJCUlUVZejqWFBaNGjuSJxx+Xf3+05MKFCyx8+mmcnZ3ZuG7d39aois27d7Fq82byCwpwdXZm0cIn6ebvf8NwKenpzH7uWaRSKeHbtt92XfcC4qiHW+df2fj7t3Pm2FE2LvuD6QuexM3blyP79/DTJ4t5b8lPmFlatvHf0NCAlkTC4KBxXD5/lopy1S+78rIylv+4BK+ALhQVtP1YvRlCDh7g26+/5pXXX6dLl65s3byZl198gTUbNmJjY9PGv5aWFmPGjcPT0wsDQ0Ouxcfxv08/pb6unmeefx6QNRSMTUx4dM4cdmzb9rd0RUeeZN+G1YydMRcnD08iQ0NY8/2XPLP4c4zNLdr4r66sYNWS/+Hs4cXjb39IXlYmO5b9jpa2Nv1GjVXwW1lezra/fsXV24+SokKFa7U11ZhYWOLTvSeHt2/6W9pbcjw8jBW//sz8Z57Hy8+PA7t38dm7b/HNb0uxsLJq4z/uymUcO7kwYco0TM3MuXj2DL9/vwQtiYQBQ4fdsp4mIo8dZcOyP5ix4EncfXwJ27+HHz5dzAftlU8tCUPGjCP63Fkqb1A+l/2wBO+/UT7PHd7PhbCDDJ8+F1MrGyL372bHr0uY9eZHSHR0lIYpyc9j1x8/4NOrPyNnzScz8Rrhm9egY2CAe2MjKv3aVTy6BWLr4o6mRMKFsBB2/vYdj7zyLiaW1gBMe+ktGhoa5PFWlBSz4ZtPcO8ayOmQvZw5vJ8xs+Zjam3Dib072fTDV8x/71MkOrpKdRXl5bLllyUE9BnI2DmPk54QT8iG1egZGOLZLRCAjMRr7Fr2K/3HPoBH1x7EXzjLzqW/MOOlN7Ht5AZAbXU1Fnb2+Pbux96Vfyq9V9iW9SRfvcLkBU9hamnF9asxbFv2O0hh4ux5OHl4cTr0IKuWfM5zH3+JiZI6VFVZwYqvPsPZ05sn3/2YvKxMti79FYlEm/5B4xT8PvfRF+gaGMh/6xsaKVzXkmiz6PMlim632PDr7+1KXy8Xtp++RH5pOYN93Zk9pCc/7DlCTV290jCa6upUVNcQEZNIDzfHW7r/raCuq0tNYjKl+0KwfufVO3pvg2GDsHx+ITnf/ERl1BVMJo3D/osPSZ79JHU5uW386/UOxObd18j9/lfKT51F4uyI9WvPI62ppnjrbgCMHxiLxcLHyP7ye6quXEXHxxPr156nobSM8uOn/7bW3h6d6OnuzJ5zl8kvLae/tysP9+/BHyHHVOaxsZ4OU/t1Iyo5nV1no3EwN2FUF28qa2q4mpEDwNZTF9FQb55EpaGuzvzhfYlNz76hpnOH9nE+7AAjZjyGqZUNp/fvYscvS5j11scqbVJxfi47f/8e3979GTVrARlJ8YRvWouugWErm9QTOxd3NLUkXAg/yI5fv2X6q+/JbZKBiSn9JkzGxNIaqbSB2MgT7Fn6Mw+/8g5Y+6rUfPDEcZasXMGrj82ni7cXWw4cYNH/PmPdV99gY9G27jdRW1fHuz98R1dvH87HXGlz/d0fviMnP583FjyBo60NBcXFVNfU3DANVXHg4EG++uYb3njtNbp26cKmLVt4ftEiNq1fr/L7Y/y4cXh5emJoaEhcfDyffPopdfX1vPDccwp+S0pKeH/xYnoGBpKT27ac3yoHw8P5+tdfef2ZZ+ni58fm3bt58d132PDb79goebc3UVtbyzv/+4xu/v6ci7rzHVGC+4d/XeNvyZIlREdHEx0dTXBwMAB//vkn1dXVLFu2jMuXLyORSOjSpQsLFizA1NRUHq6kpAQ/Pz927NhBTU0NY8aMYfbs2axfv549e/agpqbGAw88wJQpU+T3mzBhAgsXLuTs2bNcunQJY2NjHn30UYYOHapU3+3g0O4d9B0ynAEjRgPw8PyFXL5wjiMH9jBp5pw2/rV1dJjxxNMApCdfb7fxt/qX7+kzZBhIpZw7efyWdK5fu5ax48fzwKQHAXjp1Vc5efIE27Zs5qlnnm3j38HREQfH5o8oW1tbzp09x4ULF5rd7Ox46ZVXAAg99Pd6gk8e3EuXfgPpMUiWR2NnzCHh8iUiww8x4qGH2/i/dOo4tTXVTJr3JFoSCVb2juRlZnDy4F76jhyjMPq3c8UfdOk7EJBy5WykQjz2Lm7Yu8g+uo/u2fm3tLckeNsWBo8cxfAxsgbovKef5eLZSA4E72LGY/Pb+H/wkRkKv0eNn8DlSxc4dezobW38hezaQb8hwxk4UlY+p89fyJXz5wg/sIcHVZTPmQuby2d7jb+VP/+98imVSrkYHkKP4UHyD6QRMx5j6XsvE3fuFP79BisNF308HH0jEwZPng6AmbUt2clJnA89KI9n1KMLFMIMmTqTpOgLJMdcln9o6RoYKviJORWBRFsHty7dWfPpu/QeOVbeaBvz6AJ+fvMFYs6cosuAIUp1XYwIw8DYhOHTZgKyXvvM64lEHtovj+ds2EGcPLzpEzRB5ifIjpT4WM6GHmT8Y7Jy6OrXGdfGnv59q5YqvVd6UgK+vfrh2jizwNTCkt1rlmNsak7gYFm5GT9zLvFRFzkdGsKoKY+0iePSyWPU1tQwecFTaEkkWDs4kpuZzrEDe+g3eqxCHdI3MmrT4GuJmhoYGpuovP536OPpTERMIjFpsg/2bacv8eoDwwlwtuNsQqrSMEUVlew9HwOAr2Pbj8k7RcXJSCpOymyN9Vuv3NF7m057kJK9IZTs3g9A7ne/ote7B8aTxpH/+/I2/o1GDaP8+CmKt8vezXWZWRSs3ojpjKnyxp/h6GEU795H2aFwAMoys9Dx8cR0xtRbavz1dHfiZNx1eaMt+Oxlnhs7GF8HGy5cT1cappuLA2VV1Ry8dBWA/NJy7EyN6eXhLI+nqrZOIYyvgw1amhpcSlYeZxNSqZQLRw7RY/gYuS0ZOWMef777EnFnTynMLmhJ9LEmmySz52Y2jTbp8AF5PKMffVwhzJCps0iMukByTLTcJrkGdFXw03fcg0QdCyMrKQG6qW78rQsOZtygwUwaPhyAVx6bx8lLF9l68ABPT5+hMtxPa9fg7uRENx/fNo2/U5cuEhkVxZZvv8fESFb37SxVN3I6wpp165gwfjwPTpoEwGuvvMKJEyfYvGULzz7zTBv/jo6OOLb6/jh7TvH7o4mPPvmEcePGIZVKOXT41kekW7N221bGjxzJpDGyUc9Xn36ak2fPsCV4N888Nk9luB/++gt3Fxe6BwT8qxt/99NRD8HBwWzdupXCwkKcnJx4/PHH8fNTPgsqKiqKHTt2EBcXR3l5OXZ2dkycOJGRI0fedl3/ujV/TzzxBN7e3owYMYKVK1eycuVKNDU1eeONN3B2dubrr7/mo48+orKyko8++kihN/7y5ctkZ2fz6aef8vTTT7N161YWL15MbW0tn3/+OTNmzGDFihVcu3ZN4Z5r166lV69efP/994wePZolS5YQHx//jzxfXW0tKYnX8OnSVcHdp0s3Eq/G3lLc4fv3UFJUxNiHpt1SPCDrgboaG0vv3n0U3Hv17k3UpUsdiiMtNZVTJ0/QrbvyaRp/h/q6OjKSk3DzVZwa6eobQFqC8jxLS7iGs4cXWpLm0QU3vwBKiwopymvu9YsMPUhZSTGDxk+6bXpVUVdbS2J8HJ2791Bw79y9B3FXVE+5ak1lRQX6LUZZboeulMRr+Copnwm3WD7D9snK57jJN18+S/LzqCgtwdGr2ehqSiTYuXqQmZSoMlzW9UScvBQ/hJy8fclNvU59fZ3SMA31ddTV1qKjp6f0ulQq5f/snXdUVEcXwH8sLL0svfcOCvbee41RY4+J0SQaU03vXZMvJprEaBJTjCbG3rtiwd4FCygCFkBAYFl63/3+WFxY2AWMJmoyv3M4h513Z97dtzPzZubeuRN39BDBbdpTUlBAUX4e3qE17jxSY2M8AoJJS07UmR8g/UoSPiHaLxCf0GZkXq/R68aVJLxD68ukJSfpLVcXHn6BJJ2LIa/a0nrlUhzlpaWEtGilJRcQHkFKYoLOMq4nXsY7SLsNBTSLqNeGAH74+F3+N2M6i2bPJDm+fl2uKC/ny9deYPYrz/H717O5ce3qbX2futhamGFlZkpSZrYmrbJKybUsOZ72sjsq+1+NkREmQQEUnzitlVx84gymzXS7ABoYS1HVseaoysqROjli5KIe7BtIpajKK+rJmIYGgaHhX1LVxtwMS1MTrtys8RaoVCpJycnFvYHf2N1OppUHIDkzGxeZNRIdLv8ALXzcSc7MpqCkrEGd8nOyKc7Pwyukpn8xMjbGzT+I9Kv622jG1WStPABeIeHcTLnWaJ9kYm6h+7pSScLp41SUleFSvUCpi4rKSi5dSaZ9hLZraPvmEZxL0N32AQ6dPs3BM6d5+fEndF6PPnGCUH9/lm3dwtBnn+GRGS/y1W+LKC4t1VtmQ1RUVHDx4kU6tG+vld6hfXvONnFSlJKSwpEjR2jVSrufW7V6NTk5OUx5Qvd3uVMqKiq4ePky7evct32rVpyNi9eb7+DxYxw6foxXpj3zt+gluH0OHDjATz/9xOjRo/nmm28IDQ3lww8/5ObNmzrl4+Pj8fb25s0332T+/PkMHDiQ7777jn379t113f51kz8LCwuMjIwwMTHB1tYWW1tbtm3bhq+vL5MmTcLT0xNfX19efvllLl++rDWRs7CwYNq0aXh6etK9e3f8/f2Ry+U8/vjjuLu7M3DgQJycnDhbZ/LSsWNHBg4ciLu7O2PGjCEiIoKNG+/csqOLwoJ8lEol1nVWvq1tZOQpFH+53LRrV9myahlPvPAykr/4gq2NQqGgqqoKWzvtvRB2dnbIcxp213t6ymR6dOnM6JEjiIiMZNr0+qt0f5XiwgJUSiWW1jZa6ZbWNhTmKXTmKcxXYKFDXn0tD1DvCYzetI4RU55BIvn7m1V+fh5KpRIbma1Wuo3MFkVurp5c2pw6dpTzMWfoM3Bw48JN5Fb9tJLJtNKtZTLy77B+bl61jCkv/rX6WVyQD4C5lbYFztzKmuKCPL35igryMKtjhTKzskapVFJaqHs/2NGtG5CamODbLFLn9ZRLceTLswnr0FWjV11Ll4WVNcX5DeiVn4d5nTppbm2NUllFSbVeRfl5ustt4Pvqoteo8Th5ePHlq8/zwVMTWTRbvR8zKKKFlpyljQ0FebrLLszP09nmAAqq252ljYyhEycz9tmXGPfsSzi4uPLbl7O4eqlmwOPg4srwyVOZ8PzLjJr6HEZSKT9/9iE5mem39Z209DA1AaCoVHuwXlRarrkmqI+hjTUGRoZU5iq00qvkuRjZ2erMU3T8FBZdO2LetiUYGCD1cMd2rNozxMhe/a4oPn4a60F9MQlR708zCQ7EenA/DKRSDGX6LcINYWmqXnQoLtOeeBaXlWNhot9l2MLUmOLS+nkMJRLMjOvvA7O1NMfL0Y5YPZZErXKq26F5nTZqbtlw2y8uyMPcsk4eK3Xb19cnHdmyHqmJCX51+qTsG6n88PpzLHj1Gfau/INBk6fj4Oah996K/HyqlErsbLTbsp2NDTl63p/Zubl89tNCPpz+LBZmut3Yb9y8ydlLl7h87RqfzXiZVyc9wdHYWD75foFeXRri1vjDTsf4I7uR8cfkJ5+kU9euDH/kEVpERvLsMzWTqcTERH76+Wc++fhjDO/COEmn7reecZ13u53Mlpxcuc482fIcZn3zDR+++hoWehYd/00oVap/7O9OWL9+Pb1796Z///54enoydepUzZxEF6NHj2bixImEhYXh4uLCoEGD6NixI4cP35kXni7+dW6fukhKSuLChQuMGjWq3rX09HSCgoIAtdm/doOWyWRYWGivlMlkMvLqDHBCQkLqfT5xQtvl765TZ9VRhapuUpOpqKjgl69nM3LiZByc7677Ut2AKCoV9XSvyyezZlFcVMzly5eZP+9b/liymMcm/T2rbDV6qRrRq+7zvpVqQGVFBWsWfkffUeOxvUNXldul3vPVkaaLixfOM+9/nzFp2rMEBOsP0vGX9ar7vO6gE62oqOCnubN55LGm189j+/fx+w/zNZ+HPHXLzfj2n1e9y7e+i458sdG7OX94Pw8/M0Pnfr1Lp44R9eciDAwMWPvtFzV61WsnjdXHut8ETaXU+j662t9tcjo6irTky0x44RVk9o5cjDnF7nWrSE1Owie4xsKjUjXWB9W5WK3MLX0dXd1wrBV8xysgiNzsLA7u2KK5j1dAEF4BQVoy8z94i6NROxmsw6VYF8293RjausYiuvTAqdrq6FVXoIe6D87AQG9Fy9+0Ham7K66z3sfA0AhlcTGK1Ruwn/woqmovHPniZRja2eK54CvAgKrcXPJ37MZu/CioUuosty5hHi4MaFlTN1cdjlGr+heCxNfPo79itPBxp6CkjMSM7HrXks6c4Mi65ZrPQ5++tY+sXgfTaNuvd72BPikmOkrdJ01/uV6fZOvkwtjX3qespJik2NNE/bmIEc+9Cs4NT7Lrv9dVevvRD+d/x4i+fWkWGKTzOqgH8wbAx8+/gGX15OXVJ57gxc9mkaNQYF9nMbGp6Hiyjfb3s2bOpLioiITLl/l23jwWL1nCE5MmUV5eztvvvsuLL7yAu1vDQcLuBvXf7fqf8ftfzGbk4ME0D2086I7gn6GiooLExESGDx+uld6yZUvi4/VbcOtSUlKCvb393VbvvzH5UyqVtGnThsmT6/tKy2p1KrpWcuqmGRgYaLmK3i7bt29nx44djcoFtGhDy45d6qVbWlkjkUjqBRMpyMurZw1sKvm5ctJTU1iy4BuWLPgGUHfmKpWKZ8c8zLNvf0BY5O25XspkMgwNDetZ+XJz5fVW4+riXD3A9/XzQ6ms4vOZMxn/6ESMjO68uppbWmEgkWgsdrcoKsivZ5m4haW1jKJ8hbZ8dX4La2sK8xRkpaex4beFbPhtIVA9eFep+HjqY0x44TX8w3VH4PyrWFvbIJFIUNRZCcxX5GLTyIvy4vnzfP7+O4ya+Dj9hgy9q3o1WD//4gs8r7p+Lp7/DYvna9fPZ0Y/zPNvf0BYC+36Gdm2HeWWNfWsqlLtDlVckI+VbU16SUE+Zpb6BzoWVjYU5+drpZUUFiCRSDCtszAUG72bo9vWM/TpF3D29tVZnkt1ersBDxHYsq1Gr6L8PKxr6VVcWFDPIqCll7WNpg5q8hTkI5EYavTSKVOYj7mV7nqui4rycg5sXMNDU6YT0kLtYuzg6sbudas4c3g/XQYO0cgW5TfUhmworNOGCqutnvryAHj4BXDuuP6IiRKJBHcfX3IyM5r6lbiUlklaTo0ut4J1WJqZkF9S42ZmYWJMYWnDrnv/Zary8lFVVtWz8hnayupZA2uT88MichYuxtDOlipFHuatWwBQma7eb6kqL+fm/77m5pfzMLKTUZmTi83QAVQVFVOVl6+33NokZmTx656aum9U/RtbmJhouWOamxhTVKY/qEhRaTkWday/5iZSqpRKSuq4pkoMDGjm5Ubs1VSdi11eYc3xrrVwUVWpzl9ckKfVJzXW9s2tbOpZ74sLC7Ta/i1ioqM4unU9D019UdP31MbQyAhZ9YKls5cPmSlXidm3C1ronkTIrK0xlEjIqePFkZufj52ednzywnnOxMfxy5rVgLrvVqpUdJ4wjtcmT+Hh3n1wkMlwtLPTTPwAfNzVEU8zc7Jve/J3a/yRI9d+P+bK5dg3Mv5wca7eE+nnh1Kp5NNZs5j46KNkZ2eTfOUKH3/6KR9/qo5KrFQqUalUtO/UiW/mzKFDhw4NFd003W894zrv9lyFop418BYnY2M4c+4sPy9dCqgnuUqlko6DB/H6s88xfNAgnfkeVP7JLX9NHbP379+fAQNqIqfn56u9oGR16q5MJiM2NrZJ9z5+/DixsbF88cUXt6VzU/hXTv6MjIy0Jmj+/v4cPHgQJyenuzKBqMulS5e0NmReunRJa+NwbQYMGKBVQfRxOOGaznQjqRQvvwAuxsbQutbk8OLZGFq273ibmquR2dnz7lfztNL279hK/NkYpr72NvZ/wZollUoJDgnh+PFj9OrTR5N+4thxevRqejAclVJFVVXVHU24a2NoZISbty/JcecJb1OzHyA57jyhrdvqzOPhH0DUmuVUVpRjVB1VMDnuPFYyW2QOjiirqnjmw8+08pzYF0Vy3HnGTH8JmX39CJd3ipFUil9gEOdOn6Zj15rAAOfOnKZd565688WdO8v/PniXRyZMZPDwEX+LXl5+AcSdjaF1p5r6GX82hlYd/lr9tLWz5/052vUzesdW4mNjmPa67vppamauGdSAesBhbmVNyqU4nL18APX+xBvJiXRu4LgNFx8/ks/FaKVdvxSPo6cPhoY1fcmZfbs4vm0jQ55+Xiucel2SYk9jJJUS2a03xqamqFQqLKxtuHbxAq7Vg7PKigrSkhLo/rD+vY2uvv4kntXea3Xt4gWcvWr0cvP159rFONr1GVhLJg53P/17euqirKpCWVWlteJsZGSEhZW1xr1U893izhHWup3OcrwCAtm5ajkVFeWayJxJF85p2pA+Mq5fazC4i0qlIjP1Oi6e3k3+TuWVVcgLi7XSCkpK8Xe254ZcPag2kkjwdrRjZ+yd7VP9V1NZSVlCIuZtWlK476Am2bxNSwqjDzWcV6mkKlu9MGjVuzsl5+OoUtRxdayqojJLLWPZuzvFh483edRXXllFeWWJVlphaRm+TnZkKNQTSEOJBE97W/ae179XLU2uINBVu3/xdbInQ5FfzyUsyM0Jc2MpsVdv6CxLamKKZS13SZVKhbm1DdcvxeHsVdP2byRdpstDj+gsA3T3SSmX4nDy9Nbuk/bu5Ni2jQyd+kKDfZIWKqVmQUrndzAyItjXj+PnztG7Vn9+/Nw5erbT3faXfjFb6/P+kyf5bf06fv10Jo7Vk96I4GB2HztKcWkp5tVRTq+nq125XRroH/TqKZUSEhLCsWPH6FMdmAbg2PHj9LqNYHxKVc34w8nJieV//ql1ffWaNRw7dozZX3yBm6vrbeupV/fAQI6fPkOfrt1qdD9zhl6dO+vMs+z7H7Q+Rx85wqIVy/nt629w/BusRv8lmjpm10dTPLF0ERcXx5dffsnTTz+t8U68m/wrJ3/Ozs4kJCSQmZmJqakpgwcPZufOnXzxxReMHDkSGxsbMjIyOHjwIJMnT8b8Dn2kDx8+TGBgIM2bN+fQoUPExsby5Zdf3qVvU5/eQ4bx27y5eAcG4R8cyoGd28mTy+naTz3IW790MVcTL/PSBzVnZqWnXKeyspLCggLKSktJqQ5y4enrh6GREe5e2oMnKxsbjKTSeum3w9jx4/n4gw8ICwsnIjKSdWvXkJ2dxcMj1IPt7+d/R9yFC8xb8D0A27ZuxcTYGL+AAKRSIy7GxfP9gvn06NUL41qBIhIS1JHXioqKkEgMSEi4hNRIiq+fX5P06tB3IOt++R53Xz88A4I4Gb2bgrxc2nRXvySi1q7gxpUkHnvlbQCat+tE9KZ1rF+0kG6Dh5GTmcHB7ZvoPnQEBgYGGBoZ4eSuPdm3sLLG0EiqlV5VWUnWDfVekMqKcgrz8si4fg1jUxPsnG7f3Xbw8JF89+X/8A8OJjgsnKitm5Hn5NB3kNoa8+eiX0i6dJH3Ple/fC+cjeV/779LvyFD6dqzN4rqVVGJRPKXrXK66DN0GIvmzcU3IAj/kFD279xOXq6cbtX1c93SxVy5fJmXP6ypnzdSrlNVWUlhfgGlTamf1rdXPw0MDIjs3oeTu7Zi6+yCzNGZk7u2IDUxIahVzSLArqW/AtB3gtpLoFmn7pw9uJcD61YQ3qkb6VcSuXjisFaEz9N7dnB063r6TpiCzNFZY20zkkoxMavpW1QqFReOHSSwZVtNGHcDAwNa9ezLsR2bsXN2xdbJmaPbNyM1NiG01uLE1iU/ATDoMXUUv8guPTizfzd7Vv9JZJcepCUncv7YIYZMmqrJ06pHX5Z//TnHdmwhILIVibGnSUm4yLiX39TIlJeVosi6qdEvP1fOzdTrmJpbYG1nj4mZGR4BwezfuAZbG2tk9g5cuRRPSXERKpWKk/v34hUQxIl9URQocmnXQ92Gdq5eTtqVJJ547R0AItp3Zu+Gtaz95Qd6DBlOdmY6B7ZuoudDIzQvx8M7tyFzcMDZ3YPKyipijxwk/sxJxj77kkbfPRvW4OkfgL2TC2WlJRyN2kFGagpDJ+qPgNcUjiZco1uYP9n5ReQUFtEtzJ/yykrOXasZyA9vrw5yse5YzZ5vF5l6D6mJkREqlQoXmRVVSlWD5wPebQzMTJG6V7uhSQyQOjthHOCHsqCAysy7H4a+Nrkr1+HyziuUxidQcj4Om2GDMLK3I2/DVgDsn56EaWgQaTPUfanExhqrHl0ojjmHgVSK9aC+WPbsQuoLb2jKlHq4YxoWTGncRSRWltiOHo6JrzfXZ311R7qeSLxOp2BfcgqLkRcU0SnEj/LKKuJSa6zGQ6rdgTefUgcaOnMllVZ+XvRuHkTM1TTc7WQ093Zj44n6QUNa+LhzNUtOXnFJvWu6MDAwoEW33pzYtRVbJ3XbP7FzC8YmJgS1rmn7O/9QR+Ht96g6gnOzzuo+af/a5TTr1J30K4nEHz9M/8dqInye3rODI1vW0e9R/X3SoU1r8AlrjpXMjvKyUhJOHSc1MYGhT2kfa1CXcYMH89H87wjz9yciOJh1UVFk58oZ3ke9AL5g2Z/EJSXx3bvvAeDv6aWVPz45GYmBgVZ6v85d+HXtWj79YQFPjhxFQXERcxf/Rq/27evtL2wqE8aN4/0PPyQ8PJzIiAjWrF1LVnY2I0eoFz2/mz+fC3FxfD9fvT1gy9atmJiYEODvj5FUSnx8PPMXLKBXz56a8UeAv/bCma2tLVJj43rpd8r44SP44MvZhAUHERkWztqtW8jOyWHEIPX+/PmLfuXCpQQWfK4+R9jfx0crf/zlBPUzrpP+b+FBiPZpba32gsqtE4NBoVDUswbW5cKFC3z00UdMmDCBQX+T1fZfOfkbPnw4c+fOZfr06ZSXl/Pzzz/zxRdfsHjxYj744AMqKipwdHSkZcuWOg/vvF3Gjx/P4cOHWbhwIdbW1rz44ot/y0z9Fm06d6WosIBta1aSnyvH1dObZ99+X2MBycvNJauOG9R3n32MPKsmwtCs118C4PtVf09gGoA+ffuRl5fHb4t+JSc7Gz9/f76c+zWu1StkOdnZpKXVbIw3NDRkyeLfSE1JUQ+kXFwY+cgoxo4bp1XupEcf1fp88MABXFxdWbuhad+lWdsOlBQWsH/LBgrzFDi5eTDhhdc055MVKhRaz8rU3JyJM95k65+/sfDT9zGzMKdj30F07Ht7h88WKHL58ZN3NJ9PZe3h1P49eAeFMOm1d2+rLIBO3XtQUJDPumV/kiuX4+njw5sfz8Sx2m1FIc8hM70mEEb0rh2UlZWyac0qNq2pOWfQ0cmZ7xb/cdv310fbzl0pKihg65qV5OXKcfPy5rk69TO7bv2c9TE5tZ75p6+9BMCPq+9e/WzVqz+VFeVEr/5Tfci7ty/Dpr2kdZ5WQR1XG2t7B4Y+9TwH16/k3KFoLGxs6DZ8rCakOsC5g/tQVlWxY8lCrbwhbTvSZ3zNXtW0xEvkZd2k3wTtYzja9RlIZXk5u1f+QWlxEa4+fjzy3Cta+3Py67gvyRwcGfnMDPauWUbswX1Y2Mjo9ch4zTEPAO5+AQx5YhqHNq/l0Nb1yBycGDJ5muaMP4CMa1dZ+W2NS8nhLes5vGU94e07M3CiWs+hk6exf8NqVi2cT0lRITJ7B/qOHINUakz0pnUU5Clwdvdg4kuva6x4hXkK5DdrzjkzNTfn8VffYvMfv/HDx+9iamFBp/6D6NS/5sVWVVXJjpV/kp8rVx+p4ubBxJdeIyiixqW3tLiYDYt/oTBPgamZOa5e3kx54z08/AK4Ew5dTEZqKGFQ6zDMjKWk5uTxe/QJrfPfbMzrn7s2rb+2a36wuzOKomK+3hx9R/rcDqYhQXjMq7Gu2D/5GPZPPkb+1p1k3uGEqTEK9+wny9oKu8fGYmhvR/mVq6S98QGVmeq2bGRvi9RN2yJi1b83Ds9MAQMDSi/Ek/rCm5TF17K+GUqQjR6OsZc7qsoqSs6cJWX6K1Rm6I6Q11SOXb6K1FBCv8gQTKVG3MjNZ8WhU1q/sbWZ9m+cV1zKqsNn6B0RREtfT/WxD7GXNMc83MLG3AxvRzs26JgUNkSr3gOorKgges2flBUX4eztx7BnZmj1SYV1+iQbe0ceevoFDlT3SZY2NnQbod0nnT2wF2VVFdsX1++Tbi1sFefnseuPXyjKz8fEzAx7Nw8eevoFrcjDuujbsRN5BQUsWreOHEUufp6ezHnjTVyrz3DNVihIzWz8jMPamJuaMu+dd/nqt0U88e7bWFtY0K1N2waPjmiMfn37kpeXxy+LFpGdnY2/nx/fzJ2rGX9k5+SQWmf8sWjxYlKqxx+uLi6MeuQRxo+tf3TN303f7t3JK8hn0bJlZMtz8ffxZu7Hn+Ba/W7PlstJS9dtYRbcH0ilUgICAoiJiaFLl5r3RExMDJ06ddKb7/z583z88ceMGzeOYcOG/W36GZSUlNz/U+j7mKFDh/Lmm2/SWY85/q+iz+3zfiPS++64Ovzd7Ii9dK9VaBJhHs73WoVGURQ1bWX7XnMupen7wO4lptIHYw1O1+TnfiQu9c4mCf8UE+Z9fa9VaBqSvyeq4d1kzYwZ91qFJvGgRI+d0EjAl/sFIz+fe61Ck1DKmxaB+15j4vZgjOfO/4Pv9mZ3cIbrgQMHmDNnDtOmTSMsLIxt27axa9cu5s+fj5OTE4sXLyYhIYGZM2cC6nP+PvroIwYNGqQVKEYikWDzFy3g+ngwRh0CgUAgEAgEAoFA8ADQtWtX8vPzWblyJXK5HG9vbz744AOcnNReUHK5nIyMmolsVFQUZWVlrFu3jnXr1mnSnZyc+OWXX+6qbmLyJxAIBAKBQCAQCO57HoQ9f7cYPHgwgwfrPkt5Rh2PhRkzZtRL+7sQk787ZNOmTfdaBYFAIBAIBAKBQCBoFMm9VkAgEAgEAoFAIBAIBH8/wvInEAgEAoFAIBAI7nuUD47X532LsPwJBAKBQCAQCAQCwX8AYfkTCAQCgUAgEAgE9z0qhOnvThGWP4FAIBAIBAKBQCD4DyAsfwKBQCAQCAQCgeC+50E66uF+RVj+BAKBQCAQCAQCgeA/gLD8CQQCgUAgEAgEgvsepbD83THC8icQCAQCgUAgEAgE/wGE5e8+RWJwrzVoGhKDB0TRB4QH4XmKM3buLmIV8+6ifFAiwUkM77UGTUNZda81aBSp4YPxLEvKy++1Ck1DIuwCgvsX8cq8c0QLFwgEAoFAIBAIBIL/AMLyJxAIBAKBQCAQCO57RLTPO0dY/gQCgUAgEAgEAoHgP4Cw/AkEAoFAIBAIBIL7HrFP/s4Rlj+BQCAQCAQCgUAg+A8gLH8CgUAgEAgEAoHgvkfs+btzhOVPIBAIBAKBQCAQCP4DiMmfQCAQCAQCgUAgEPwHEG6fAoFAIBAIBAKB4L5HKbw+7xhh+RMIBAKBQCAQCASC/wD/OstfZmYmTz75JHPmzCEwMPBeq/O3sW/7VnZuXEtebi5unl6MnvQkgWHhOmUrystZunAB15OTSE9LJSA4lFc+nqW37MT4OL764G1c3D34YO53d6Tn6lWrWPrH7+RkZ+Pr58eMl1+hRcuWOmWvJCcz+4v/ceXKFYoKC3FwcKRvv348+fTTSKVSAPbu2cO6tWtIuHSJ8vJyfHx9mfTEZLp1735bep3Yu4vDO7ZSkKfAyc2d/mMexTsoRK98ZmoK25YtJu1KEmYWlrTu1otuQx7GwMAAgKuX4tm9biU5GelUlJdhY+9Aqy496NR/sKaMqspKDm7bROyRA+Tn5uLg4kqfkWMIaBbZZL13bNrAhtWrUMhz8PD24Ylp0wlt1lyn7IXYGDavW0PipUsUFxfh4urG4OEj6NV/oEYmNyeHxT/9wJXERNJvpNGtVx+ee/X1Juujj+gdW9m1YS15ilxcPbwY9cSTBIbqr59/LlxAyhV1/fQPDuXlj7TrZ8KF82z4cwmZN9IoLyvDztGRzr370feh4U3WSaVScXzHJi4cOUBZSTHOXr50Hzkee1e3BvOlJV7i4IZVyDNuYGEto1Wv/jTrXFPfEmNOcmr3DvKyb6JUViFzcCKyex9C23WqKSMpgTN7d5KVep2iPAW9x03SXFepVBzZtpFzh6IpLSnG1duPXqMn4ODq3qBeKZcvEb1uBTnpaVjayGjTZyCRXXpoP7eYkxzesp687CxsHBzpPGQEgZGtNNeVSiVHtm4g/sRRivIVWFjLCG3bno4DhyExNASgvKyUgxvXkHwuhuLCAmzsHGjXszeGRlIObt9MoUKBk7s7A8c9hk8DbSgj9Tpb/viN1Oo21LZHb3oMHa5pQ1cuxvHrF5/Wy/fCzNk4Vj+L0wejWffrj/Vk3v/xN6RS4wafV216hgfS2t8TM6mUVLmCzacukJVfqFfe0tSEAS1CcbW1xt7Sgthraaw7flZLxtHakl7NAnG1tcHO0py95y+z98LlJutUF5uHB2M7biSGdnaUX71G1ryFlJ69oF/Hnl2xe3Q0Uk93qhT5KNZuQrF8jXaZw4cgGzEEIxdnKjOzkP++nIIde/6yjreDaWQzbMc9gmlwIEaODmTM/JKCbbv+tvupVCpO7drCxWMHKSsuxsnLh87Dx2Ln0nB7v5GUwNFNq8nNTMfc2obIHv0I69hNc/3SiSNEr1xSL9/kWd9iVP2eOrNnO1fPxaDIysTQyAgnL1/aDRqGzNm1Xr5OwX5E+rhjIjUiPTefqLMXySkoalBHD3sZPZsF4WBlQWFpGccTrxF7NU1LxtjIkC6h/gS7OWMqlVJQUsqB+EQu3bgJQPtAHwJdHbGztKBKqeRGbh4H4hLJbuTeAKt37GDppg3kKBT4engw4/EnaBEa2mi+6+npTHrzdVQqFXuX/KFJP3XhAs9+/GE9+eVzvsbHveF+sCFWrV7N73/8QXZODn6+vrwyYwYt9Yw/kpOT+d/s2Vy5coXCoiIcHRzo17cvTz/1lGb8cer0aeYvWMC1a9coLSvDxcWFhx96iImPPvqXddTH6s2b+H31anLkcvy8vZkxdRotmzVrNN/1tDQee/45VCoV0evW33W97gdEwJc75183+fsvcOLQAVYs+onxT04jIDSMfTu2Mm/WR3w4dz52jo715JVKJVKpMT0GDub86VOUFOnv3IsKC1k0by4hzSNRyHPuSM9dO3cy96svee2NN4ls0YI1q1cx48UXWLZyFS4uLvXkjaRSBg0eQnBwMJZWVlxOSOCzWTOprKrk+RdeBODM6dO0adOWqc88g7W1DTu2b+PN119jwQ8/6p1U1uX8iaNsX/EHg8ZPwiswiBN7o1j67Wye/eh/2Ng71JMvKynm97mf4x0YzFPvfEx2RjobFi1EamJCp36DADA2NaV9r344uXsiNTYmJSmBzb8vQmpsTNuefQHYs341Z48eZOhjU3B0dSfxwllWLPiayW9+gKuXT6N6H4rey6IfFvDkcy8QEt6MHZs3MvPdt5i78BccnZzryV+Kj8PLx5dho8Zga2dHzKmT/PjNXKTGxnTt2RuAiooKrK1teHj0WKK2bWnS82uMk4cOsHLRT4x7chr+IWHs37GV+TM/4v2G6qexMd0HDObCmVMU66ifJqam9Bg0BHcvH4yNjUm6FM+fCxdgbGJC9/6DmqTX6T07iNm3i97jJmHr5MKJHZvZ8MNcHn3rE4xNTXXmyc/JZtNP8wht15m+j04hPTmR6NVLMbW0JCCyNQCmFpa07TcIWycXJIaGXL1wjj0rlmBmaYVPmHpiXlFWhr2rOyFtOhL1569a9zgRtY1Te3bQ/9HJ2Dm5cHT7JtZ89xVPvDcTY1MznXrlZWex7oevadahCwMfe5K0pMvsWbkUM0tLglq0AeDGlUS2LPqRToOGERDZisTY02z+9XvGzngLVx8/9b13bSPmwB4GPDoFBzcPsm+ksv33XzA0ktJhwFAAoteu4PqlOEY++Qy2jk5cvRTPukULQQUPPTYZr8Bgju/dxe9z/8fzn85GpqMNlZYUs/jLz/AOCmHae5+SnZHO2l9+wNjYhM4DBmvJPv/JF5hZWmo+W1hZa12XGpsw439ztdNuY+LXJcSPTsG+rDt+luyCQnqEBfJ4j3Z8uzWa8soqnXmMJBKKy8o5EJ9EG38vnTJSI0MURSXEpWbSu3lQk/XRhWWvbji+MJWbc+ZTci4O2cODcf/iY649No3Km1n15M3bt8HlvdfJ+vYHio6dwtjbE+fXX0BVXkbe2s0A2AwbhMPUJ8ic/S2lcZcwDQ3C+fUXUBYUUnT4+B3p2xQkZmaUJ1+jYHsUzu++9rffL3bfTs7tj6L76MeQOTlzetdWtv70LaNf+1B/e5dns/2X+QS360TPcU+QcTWJg2uXYWphiV9EzaKJkdSYsW9+rJX31sQPID0pgbBO3XD09AYVnNyxiS0/fsvIV9/F1NxCI9cuwJu2AV5sOx2HvLCIjsF+jO7Uip93H6ZCT120MTdlZIeWnL9+gy2nzuNhL6NPRAglZRUkpKsndhIDA0Z1bEVpRQUbT5yloKQMKzMTqmr5y3k62BJzNZWM3HwAuoT6M7pTK37dc6TB57rr8CHmLl7Ea1OeJDI4hDU7dzDjs5ksmzMXF4f6/fstKioreO+bubQIDeVMXJxOmWVfzcG6VtuXWVvrlGsKO3ft4ss5c3jz9ddpERnJqjVreGHGDFYtX65z/CGVShkyeDDBQUFYWVmRcPkyM2fNorKqiheffx4AczMzxoweTUBAAKampsTGxjLr888xNTVl1COP/GVd67IrOpqvfviBN559jsjwcFZv3sxL773Lih8X4uLkpDdfRUUF737+GS2bNeP0uXN3TR/Bv48H0u3z1KlTvPHGG4wdO5Zx48bx/vvvk5KSAsCTTz4JwMsvv8zQoUN56623NPmioqKYPn06I0aMYOrUqaxfvx6lUqm5PnToULZu3cqnn37KyJEjmTp1KmfPniU7O5v333+fRx55hBdeeIHExEStMkeNGsXx48eZOnUqI0aM4O233yYjI+Nv+/5RmzbQqUdvuvbtj6uHJ+OmTMVGZkv0zq065U1MTZkwdTrd+g7A1t6+wbKXLPiWDj164RcUfMd6LvtzKYOHDOXh4cPx9fXl1ddex97BgbWrV+uU9/T0ZMjQoQQGBeHq6kq37t3pP2AAsTExGpmXX32VxyZNIjy8GZ6enjz51NOEhIQQvW9fk/U6umsbkZ260rpbTxxd3Rk0/nGsbGSciN6tU/7sscNUlJfx8ORpOLl7Eta6HZ0HDOHorm2aFSg3b1+ateuIk7sHto5ORHTogn94c65fvlRTztGDdB4whKCIltg6OtG2Rx8Cm7fgiJ7frS6b166hR99+9Bk4GA8vb6ZMfx5bO3t2bt6kU37E2PGMmzSZkPBmOLu60X/IQ7Tv3IVjBw9oZJxcXJg8/Tl69uuPpZVVUx9hg+zevIGOPXrTpY+6fo6ZMhVrW1v2N1A/xz89na59ByCz010/vf0DaNu5G26eXjg4u9C+W0/CIluSGK/fElIblUpFbHQUrXsPICCyNfau7vQZ/wQVZaUknD6mN9/5w9FYWMvoPnIcds6uhHfsSkjbTpzZW2Ox8AgMwa95S2ydXbFxcCKye28cXN25kVxj9fEJa07HwcMJaNEaA4OablelUnFmXxTt+g4iqEUbHNw86P/oFMrLSrl4Ur9esYf2YWkjo9eoCdi7uBHRuTth7TtxavcOjczpvVF4BobQvv8Q7F3caN9/CJ4BwZyupfuNK4n4N2uBf/MW2Ng74N9c/X/61WQtmdC2HfELDcfWwZGWnbshNTbGwcWVNt174eTmzpAJk7C0kXF8b5ROfc8ePURFeTkjn3wGZw9Pwtu0o+ugoRzaubXeKq6FtTVWNjLNn0Si/ZoyMEDrupWNTO9z0kXHIB8OxCcRl5rBzbxC1h6PxcTIiAhv/RYhRXEJW8/EEXM1jZLyCp0yN+R57Ii9yLnrN6io0j1wbyq2o4eTvy2K/M07qLiWQtY3P1Apl2Pz8GCd8tb9elF0+Bh567dQmZ5B8dETyP9Yie34URoZq/69yNu8ncLd0VSmZ1C4Zz95m7ZryfydFB89Qc7CRRTuO/i3b9pRqVScO7CHyJ798YtohZ2LOz3GPk5FWSmJZ07ozRd/5ADmNjZ0fngMts6uhLbvQlCbDpyNrlOvDQwwt7bR+qvNoKdeILhtJ+xc3LFzdafnuEmUFhWQWatdAbT29+LY5askpN8ku6CIbacvYGxkSJh7/cnJLSJ9PCgqLWP3uUvIC4s5e+0GF1LSaRtQsyjRzMsNcxMp647FkibPI7+klDR5HhmKfI3M6iNnOH89neyCIrILithy6gJmJsa428kafLbLtmxmcPcePNy7D74eHrw6eQr2tras3bmzwXzzly4lwMubXh066pWxtbbBXmar+TOUGDZYZkMsXbaMoUOGMPzhh/H19eX1V1/Fwd6e1WvW6JT39PRk6JAhBFWPP7p368aAAQOIqTX+CA0NpX+/fvj7+eHu5saggQPp2KEDZ2rJ3A3+XLeWIX378vDAgfh6efHa9Ok42NmxZsvmBvPN+/VXAnx96d21613V535DpVL9Y3//Vh7IyV9paSkPPfQQc+bMYdasWZibm/PJJ59QUVHBV199BcBHH33EkiVLePvttwHYsWMHS5YsYcKECSxYsIApU6awZs0atm7VHpCuWLGCrl27Mm/ePAICApg9ezbffvstgwYN4ptvvsHOzo5vvvlGK09FRQXLli3jxRdfZPbs2SiVSmbOnPm3VJzKigquJycSFtlCKz00siVJly7eUdn7tm8lX6Fg8MjRd1QOqJ/JpYsXad+hg1Z6+/YdOHf2rJ5c2qSkpHD0yBFatmzVoFxRcTFW1k2buFRVVnLj2hX8w7RdJf3CmpOapNtFKzUpEe/AYKTGNdYF//DmFChyUWTXX4UHSL9+lZSky3gH1bjCVFVWaq0Og3q1+HpiQqN6V1RUkHw5gchWbbTSI1u15lK87lVUXRQXF2NheXcmebq4VT9DddTP5Dusn7VJuZJE8qWLBIY17gYDagtecUE+nsE1rqdGxsa4+QWSfiVZb76Mq8l4BYdppXmFhJGVcpWqqsp68iqVipSEeHKzMnH3b9ztPD8nm6L8PLxDavSSGhvj4R/EjStJevOlX0nSygPgExpO5vVrGr3Sr9aX8Q5txo0rNYtX7n6BpFy+iDwjHYCc9BtcT4jHt1b7cPcLJPl8LHnVngBXLsVRXlpKSAvtdhkQHkGKnrp8PfEy3kHabSigWYTONvTDx+/yvxnTWTR7Jsk6JvcV5eV8+doLzH7lOX7/ejY3rl3VeU9d2FqYYWVmSmJmtiatskrJtSw5nva2TS7nb8XICJOgAIpPnNZKLj5xBtNmul3rDIylqMrLtdJUZeVInRwxclFbCgykUlR1Jq6qsnJMQ4PA8K8Psu9HCuTZlBTk41Gr/zWSGuPiG0jmNf3tKvNaMh6B2s/YMyiMrNRrKGtN6Ksqyvlz5jss/fQttv86n+y0lAb1qSgrQ6VSYWJWY8m3MTfD0tSEqzflmrRKpZKUbAVudja6igHAzdaGq1naXjlXbubgLLNGUu1CHejqSJo8jz4RwUzv35UnenWgU7Cf5roujI0MkRgYUFqhe3ED1Na7S8nJtI/Q3qrQPiKScwmX9OSCQ6dPcfD0KV5+YrJeGYBJb7/J4KlP8dwnH3Hq/PkGZRuioqKCixcv0qF9e630Du3bc7aJFrGUlBSOHDlCq1b6xx8XL13i7NmzDcrcLhUVFVy8fJn2dcps36oVZ+Pi9eY7ePwYh44f45Vpz9w1XQT/Xh5It8/OnTtrfX7ppZcYM2YMCQkJODioXY6srKywta15mS9fvpxJkyZp8rq4uPDII4+wdetWhgwZopHr1asX3av3j40ePZr9+/fTqlUrOlRPYkaOHMnbb79NXl4eNjbqDrqqqoqnnnqKsDD1IPHll1/mqaeeIjY2lhYtWtzV715YkI9SqcRKJtNKt5bJuHgu9i+Xm3btKptXLePNWbM1e33uBIVCQVVVFXZ2dlrpdnZ2nDiu36IB8NTkyVy6dJHy8nKGPTycZ559Vq/s6pUrybp5k4GDdK+I16W4sACVUollnZVaS2sbrsTrftkU5iuwtrWrJ6++loetY40bxpzXnqe4sABlVRXdh46gTY/emmv+4c05FrUDn+BQ7J1cSL54gfgzJ1HVsj7royA/D6VSiY2t9gDVxtYWxZnTenJpc+rYUc7HnOGTOd80LvwXuVU/retYY6xtZFxU/PX6eYu3pj5BYX4eVVVKBo8aS7d+AxvPBBQXqFe8zetYN82trCnMU+jNV1SQpzWABDCzskapVFJaWIhF9fcsKynmtw/foKqyAgOJhO4jx+Mdqnsvpm69tN2bzK2tKVQ0oFd+Pl7BdfJYWaNUVlFSWIiljYyi/Lx6LpMWVtaaewK07TuQ8rJSfpv1HhIDCUplFe37D6ZFt14amZ6PjCdqxRK+fPV5JIaGmkWtoIgWWmVb2tiQFKevDeVho6cNFeQpsHV0wtJGxtCJk3H39aOqspLYIwf57ctZTH79XXyC1b+Bg4srwydPxcXTi7LSUo7s2s7Pn33Isx99hr2O/VR1sTQ1UT+/0jJt/UrLsDbT7Qr4T2NoY42BkSGVuQqt9Cp5LkatW+jMU3T8FI4vTMW8bUuKT8YgdXfDdqx6P6yRvR2VGTcpPn4a68H9KNx/iLKLlzEJDsR6cD8MpFIMZdZU5eT+zd/sn0PTriy167+ZlRXFDbT3koJ8zAK1962aWVmjUiopLSrE3NoGmaMz3UdPxM7Vg4qyUs4f3MuG+bN5ZMa72Djqdsk7vGEl9m4eOHn7adIsTNQLIUVl2nWxuKwcSzMTvTpamBpzLUt7ol9cVo6hRIKZsZSisnJszM3wcrAlPjWTNUdjsDE3o09EMMZGhuzTsw+1V/NgMhUF3JDngZXu76HIL6BKqcTORvv9aWdjw4lzCp15snNz+Wzhj3z+8qtYmOl2Y3ewlfH6k08R5u9PRWUl2/bv57lPP2bB+x/SMixMZ56GaGj8ceyEfssvwOQnn+RidUyB4cOG8ewz9SdTg4YMIbf6Hk9NmcIjI0bcto56dc/PVz9jmfa73k5my/HcMzrzZMtzmPXNN/zv3fewMDe/a7rcryj591rk/ikeyMlfeno6f/zxBwkJCeTl5aFSqVAqlWRlZWkmf7XJy8sjOzub+fPn8/3332vSq6qq6lnnfHx8NP/LqidY3t7e9dJqT/4kEglBQTV7PJycnLCzs+P69et3ffJ3CwO0V/DuxMpYUVHBT3Nn88hjk3Fw1u9u8lcwqLPSqEJVL60un86aRXFxMZcvJzDv22/5ffFiHn/iiXpye/bsZt633/DJzFm4ujY+8GsIlUql9ifTS93vcStVO/2J19+jvKyM1OREotYsR+bgSGTHLgAMGDuRTUt+YcH7b4CBAXaOTrTo1I2Yw/ubrGe9Z6dq/HkCXLxwnm/+N4snnnmWwGD9QTnuGjp/9zsv9pWPP6OstJQrly+x7o/FODg50757z3pyxw/s448f5ms+D3nquVuK1dFLxzOtQ73Lt9parQvGJqaMefU9KsrLSE2I5+CGlVjZ2eNZZ+J46dQxKsrL2LfqD/avWabRq/7vquvGdfWq2wfoSK9XrHY/cen0ceKOH2bQ409h7+pOVup19q5ZhrW9I807ql2HzkTv5kZyIhNeeAWZvSMXY06xe90qUpOTNJMy9f0b+43r193a+jq6uuFYK/iOV0AQudlZHNyxRXMfr4AgvAKCtGTmf/AWR6N2MnjC4/XuGOHtxtDWNdbhpQdO1r51LdUM7r/hRF0lDQx0KK4mf9N2pO6uuM56HwNDI5TFxShWb8B+8qOaxSX54mUY2tniueArwICq3Fzyd+zGbvwoqGp8Aep+5vLp4xxY86fm84DJ09X/1Gu7uhK1aezd6uzjh7OPX63P/qyZO5Pzh/bS+eEx9co7snE1GVeTeOfLb3moc43nxpqjMbV00lJA7+/cVAwMoLisgh0xcaiAzLwCTI2l9GwWpHPy1zM8EA97GX8eONmkdqCr79HXj3743beM6NuPZkH698F6u7nj7VYT2KV5UDDpWVks3bzxL03+NHrW+dyU/n7WzJkUFxWRcPky386bx+IlS3hi0iQtmZ8WLqSkuJhz588zb/583NzcGDyoaXvPm6z7bYyd3v9iNiMHD6Z5E4LuCATwgE7+PvnkE+zt7Xn22Wext7fH0NCQ6dOnU1lZ3w0L0Ozre/bZZwkJaXjga2RU/5HoSvurk63t27ezY8eORuUCW7ahVfWkoTaWVtZIJBLyFdqrtAV5eVjXsQY2lbxcOempKSye/w2L56utQrf8nZ8Z/TDPv/0BYS2aFkzlFjKZDENDQ3JytN1TcuW52OnZ13UL5+rN2L5+flRVKfls5qdMmDhR63fYs2c3H73/Ph989NFtRfo0t7TCQCKhMD9PK72oIL+eNfAWltYyivIV2vLV+S3qbEi/ZQV09vCkKD+P6E1rNZM/Cytrxj47g8qKcooLC7GS2RK1ZgW29vo3yd/CytoGiUSCQi7XSs9TKOpZA+sSf/4cn73/DmMmPk7/IQ81eq87ocH6eZt7s3Rxa3HC3duH/DwFm1ct0zn5i2jTjjGv1qz6VlX3DcUF+VjVskCVFORjZqk/qICFlQ3F+flaaSWFBUgkEkwtagI3GEgkyKp/e0d3T3IzMzgVta3e5M83PBIjqTFt+w/BP6KVRq+i/DwtvYoL8utZ7bT0srbW1MEavfKRSAw1ellY21BUR/figgItK+P+9ato07s/Ia3V7lGObh7ky3M4vnMrzTt2paK8nIOb1jBk8jOEtFAHuHFwdWP3ulWcObyfLgNrvCaK8htqQzYU1mlDhdXWGX15ADz8Ajh3XH8AColEgruPLzmZuvdYX0zLJDWn5r6G1fsHLc1MyC8prdHPxJjCOtbAe0VVXj6qyiqM7LTbtaGtrJ41sDY5PywiZ+FiDO1sqVLkYV5tJaxMzwRAVV7Ozf99zc0v52FkJ6MyJxeboQOoKiqmKi9fb7kPAt5hETjVCppVu71bymq198ICzBrY22xmZU1xgXa7Ki0swEAiwdTCUmceiUSCo4c3+dk36107vHEVSTEnGTptBjcKy1i8r8br5VZdtDA1oaBW3TM3NqaorLxeWbcoKi3HwlQ7wJG5iTFVSqVmP2pRaTlKlbZ9JKegCGMjQ8yMpVr7Vns2CyLE3ZkVh06RV1yi974AMmsrDCUScup4JeTm59WzBt7i5PnznImL45fVqwD12EKpUtF53Bhem/IkD/fpqzNfeEAguw4falAfvXreGn/UeV/myuXY17EG1sXFWR08zc/PD6VSyaezZjHx0Ue1xh/ubupFqoCAAHLkchb+/PNdm/zJrK3Vzzi3ju4KRT1r4C1OxsZw5txZfl66FFBPcpVKJR0HD+L1Z59j+F2emN5r/sVb8f4xHrjJX35+PikpKUybNo2IiAgAEhMTqar2x7/VQGsHcrG1tcXe3p709HR69epVv9A7RKlUcvnyZUKrV11u3ryJXC7H09OznuyAAQMYMGBAo2UevXxNZ7qRVIqXXwBxZ2No3almchh/NoZWDWykbghbO3venzNPKy16x1biY2OY9vrb2OtxZWkIqVRKcEgIx48do3efPpr048eP0bNn038DlUpJVVWV1u8ZtWsXn3z0Ie998CG9evdpIHd9DI2McPP2JTnuPOFtavYDJMedJ7R1W515PPwDiFqznMqKcoyqowomx53HSmaLrIHoZiqVikod+yeMpMZY29pRVVlJ/OnjWnroQyqV4hcYROyZU3TsVjPZPXvmFO0769/cHXfuLJ+9/w6jH32MwcNHNnqfO+VW/bwYG0PrWosXF8/G0LL9X6uf+lApdT9fAFMzc81kDNS/hbmVNSmX4nCuHiRWVlRwIzmRzg/pfy4uPn4kn4vRSrt+KR5HTx8MDfV3nyqVkqrK+roZm5piYGCAuZU1MkcnVCoVFtY2XLsYh4u3r0avtOTLdBumPxCHq68/SWe1XYCuXYzD2ctbo5erjz/XL12gbZ+a/ub6pQu4+QZoPleWl2sFoAH1YBaVur0pq6pQVlVpBV0xMjLCwsqakkLtoxGS4s4R1rqdTn29AgLZuWo5FRXlmsicSRfONdqGMq5fazCgi0qlIjP1Oi6e3jqvl1dWIS8s1korKCklwNlB7d6GOpKnl6MtO2Pv3p7UO6KykrKERMzbtFQHR6nGvE1LCqMbGQwrlVRlqxfcrHp3p+R8HFUK7ckMVVVUVu8Zs+zdneLDxx/40ZSxqalWBE+VSoWZlTVpCfE4efoA6naVcSWR9oP1u+g5e/tx9UKMVlrq5Ys4enjr3Q6hUqmQp6di5+ahlX54w0qSYk4yZNoMZE4uVFRVUVak3ScUlpbh7WinCcRiKJHgYS8juoEjQm7k5hHoqt1mfBztyFTko6z+HdPkCkI9tL147CzNKa+s0pr49aqe+C0/dKpeO9GF1EhKsJ8fx8/F0rtjTX9+/NxZerbT/R5bOvsrrc/7T57gt3Vr+XXmZzg2MBG7fO0KDrayRnXSqadUSkhICMeOHaNP75qtF8eOH6dXz/qLhfpQqlT1xh91USmVVJTrn6zfLlKplJDAQI6fPkOfrjVHjBw7c4ZedbY83WLZ9z9ofY4+coRFK5bz29ff4NhIkD/Bf5MHbvJnaWmJtbU1O3bswMHBgZycHBYtWoRhdccsk8kwNjbm9OnTODk5YWxsjIWFBePGjWPhwoVYWFjQpk0bqqqqSEpKIicnh1Gj7izamaGhIT/99BNPP/00xsbG/Pzzz3h5ef1tLp99hg5j0by5+AYE4R8Syv6d28nLlWv2P61bupgrly/z8oc1Z2bdSLlOVWUlhfkFlJaWklId5MLT1w9DIyPcvbQHT1bWNhhJpfXSb4dx4yfw0QfvExYeTkRkJOvWrCE7K4vhI9WD7QXffUfchQt8V+2Ku23rFoyNTfAPCEBqZER8fDzfz59Pz169MK4OFLFr5w4+fP99XnjxJVq2bElOtjpwg5FUqnHDbYwOfQey7pfvcff1wzMgiJPRuynIy6VNd/VLImrtCm5cSeKxV9TBgpq360T0pnWsX7SQboOHkZOZwcHtm+g+dITGDePY7p3YOjhi76J2P72WcJHDO7fQtkfN5DQ1OZECRS4unt7k58qJ3rQWlUpF5wFDaApDRoxk3uz/ERgUQnB4ODu3bEaek0O/weqQ/Et//ZnEhEt88PlsQH3O32fvv0u/IUPp0rM3udWroBKJBJtaVuIrSeoAICXFxRgYSLiSlIiRkRRP77/22/ceMozf5s3FOzAI/+BQDuzcTp5cTtfq+rl+6WKuJl7mpQ9q6md6ynUqKyspLCigrE79BNi7bTMOTs44V7sGXY47T9SmdXTr17QVTQMDAyK79+Hkrq3YOrsgc3Tm5K4tSE1MCGpVM2jZtVR9DEPfCerABM06defswb0cWLeC8E7dSL+SyMUTh+k38UlNnpO7tuDs5Yu1vSNVVZVcizvHpZNH6TZinEamvKyUvOrAJiqVkoJcOVlpKZiam9OyRx+O79yCnbMLtk7OHNuxGamxCSG1FgW2LfkZgIGPqe8b2bkHMfv3sHfNMiI6d+dGciIXjh1i0KSnNXla9ejDim/+x/GdWwiIaEXi2dOkJFxizIw3NTJ+zSI5EbUNG3sH7F3duZl6nVN7dxLWVn0GoYmZGR4BwRzYuBo7G2tk9g5cuRRPSXERKpWKk/v34hUQxIl9URQocmlXvcd15+rlpF1J4onX3gEgon1n9m5Yy9pffqDHkOFkZ6ZzYOsmej5U04YO79yGzMEBZ3cPKiuriD1ykPgzJxn77EsaffdsWIOnfwD2Ti6UlZZwNGoHGakpDJ3YcCCJ2hxJuEq3MH+y8gvJKSyie1gA5ZVVnL12QyMzor16YXHtsZrgVC4ytcXIxMgIlUqFi8yKKqVKcz6gocQAR2u1dchIIsHS1AQXmZXOCWhj5K5ch8s7r1Aan0DJ+Thshg3CyN6OvA3qAGX2T0/CNDSItBnqPkpiY41Vjy4Ux5zDQCrFelBfLHt2IfWFNzRlSj3cMQ0LpjTuIhIrS2xHD8fE15vrs77SqcPdxsDMFKl7tVuvxACpsxPGAX4oCwqozNQdOOsv38vAgOZde3Fm93ZkTi7YODpxOmobUhMTAlrWLPLtXfYbAD3HTQIgtGNXLhzax+ENKwnt0JXMq0kknDxCr/E19evUzs04efth4+BIeWkp5w/tJSc9jS4jxmtkDq5dxuXTx+k3aSomZuYUV1vpDY2lSE1qJqmnkq7TIcgXeWERuYXFdAjypaKqiri0Gkv2oFbqoE1bT6uDH8VeTaWlryc9mwURezUVdzsZzbzc2HyyZr9tzBW1TO/mwZy+koKNmSmdQ/yIuVITmKZPRDBhHq6sPx5LWUWlZg+ivuNObjFu8BA++m4eYf6BRAQHsy5qJ9lyOcP79gNgwZ9LiUtK5Lv3PgDA30v7aJT45CQkBgZa6cu3bMHVyRFfD08qKyvZfmA/0SdO8NnLrzaoS0NMGDeO9z/8kPDwcCIjIlizdi1Z2dmMrN6f9938+VyIi+P7+ertAVu2bsXExIQAf3+MpFLi4+OZv2ABvXr21Iw/lq9cibubG97Vup+OieGPpUt55C4e8wAwfvgIPvhyNmHBQUSGhbN26xayc3IYUR3bYP6iX7lwKYEFn38OgH+t7UoA8ZcT1M+4Tvq/hX9zFM5/igdu8ieRSHj99ddZuHAhzz33HK6urkyZMoXPPvsMUE/Enn76aZYvX87y5csJCwvjs88+o3///piamrJ27VqWLFmCsbExXl5eWsFe/ipSqZTRo0czZ84csrKyCA4O5q233mrSXqy/QtvOXSkqKGDrmpXk5cpx8/Lmubff11jo8nJzya7jBvXdrI/JyapxS/n0tZcA+HH1xr9FR4C+/fqRl5fHol9/ISc7Gz9/f+Z8/Y1mf152djapaakaeUNDQxb/tojUlBT14MrFlZGjRjF2XM1Lde2aNVRVVTF3zlfMnVMzaGnZqhXf/7iwSXo1a9uBksIC9m/ZQGGeAic3Dya88JrmfLJChQJ5rWdlam7OxBlvsvXP31j46fuYWZjTse8gOvatCTaiUimJWrMcRU42EkMJto5O9BkxRjOhBPXK8571q8jNysLY1ITAZi0YPuUZrXOfGqJz954U5uezZtlScnPleHr78PYns3CsdlPJlcvJvFEzgN27aydlZaVsWrOKTWtWadIdnZxZsGSp5vPrz07Tus+pY0fqydwObTp3paiwgG1rVpKfK8fV05tn69TPrLr187OPtZ75rNdfAuD7Ver6qVRWse6P38jJuolEYoijiwsPT3icrn0bt6LfolWv/lRWlBO9+k/1Ie/evgyb9pKWxaCgjquNtb0DQ596noPrV3LuUDQWNjZ0Gz5Wc8YfQHlZGftW/0lhXi5GUim2Ti70mTCZoFY1VrCbKddYP7+mvh7fvpHj2zcS0rYjAydOUdeNVUspLS7CxcePkc++rHXGX129bBwcGT7tJaLXLufswX1YWMvo+ch4zRl/AG5+AQyeNJVDm9dxeOsGZA5ODH5iquaMP4Beo8ZzaMt6dq/8g+LCAiytbWjesRsdBta4Bw9+YioHN65h1cL5lBQVIrN3oO/IMUilxkRvWkdBngJndw8mvvS6xopXmKdAfjNTU4apuTmPv/oWm//4jR8+fhdTCws69R9Ep1pnNFZVVbJj5Z/k58qRGhvj5ObBxJdeIyiixuW8tLiYDYt/oTBPgamZOa5e3kx54z08/GqsmY1x8GIyUkNDhrQOx9RYSlqOgiXRx7UGvTbm9QNTTO+vbWEPcXcmt6iYuZv3AWBlaqolY29lQdsAL67czGHR3oaDXNWlcM9+sqytsHtsLIb2dpRfuUraGx9QmaluI0b2tkjdtPc5W/XvjcMzU8DAgNIL8aS+8CZl8bWirxpKkI0ejrGXO6rKKkrOnCVl+itUZtR3V/w7MA0JwmPebM1n+ycfw/7Jx8jfupPMv2ECGtmjH5UVFRxct5zykmKcvHwZ9NTzWu29UFGnvds5MGDKsxzZtJq4IwewsLah07DRWmf8lZWWcGD1UooL8jE2NcXB3ZOHnnlFy+007oh6H/eWH7WDa7XsM4jW/WqCkx1PvIaRoSF9IkIwrT7kfdXh01pn/FnVCUSUV1zKmqNn6NUsiBY+HhRWH/tw64w/gILSMlYdOU3PZkE83qM9RaXlnLt+gyOXrtTo4qv2TBrTubVW+YcuJkOptlW/Nn07dSavoJBF69aQk5uLn6cnc958G9fqM1yzFbmkZmbqza+LispK5v2+hCy5HBNjY3w9PZnz5lt0aiTSd0P069uXvLw8flm0iOzsbPz9/Phm7tya8UdODqlpaRp5Q0NDFi1eTEr1+MPVxYVRjzzC+LFjNTLKqirmffcdN9LTMTQ0xMPDg+eefVYzobxb9O3enbyCfBYtW0a2PBd/H2/mfvwJrtXv+my5nLT0G42UIhDox6CkpERMoe+AqKgofvzxR1atWtW48G2gz+3zfiOygbOx7ie2xdwnLl2N0Mzz7gbc+TvILrg9K8a94kLq33fW5t3E2OjBCLNva6E7Ut/9xvnU2xt43ismzv/uXqvQNJR3dmbhP8HG199oXOg+QKl6MALrPOl9+1s97gVGPl6NC90HKOUPRiRdE7c7C5z3T7HnQmLjQneJXuFNX1h8kHggz/kTCAQCgUAgEAgEAsHt8cC5fQoEAoFAIBAIBIL/HmLP350jJn93SJ8+fejT5/YiTgoEAoFAIBAIBALBP41w+xQIBAKBQCAQCASC/wDC8icQCAQCgUAgEAjue5TC6/OOEZY/gUAgEAgEAoFAIPgPICx/AoFAIBAIBAKB4L5HBHy5c4TlTyAQCAQCgUAgEAj+AwjLn0AgEAgEAoFAILjvEZa/O0dY/gQCgUAgEAgEAoHgP4Cw/AkEAoFAIBAIBIL7HqWw/N0xwvInEAgEAoFAIBAIBP8BhOXvPiXppvxeq9AkDCUPxvrBlawH43n6OtndaxUa5budh+61Ck2ib/PAe61Ckygpr7jXKjQJYyPDe61Ck3i55YPxu/8wY8a9VqFJSA3v/9/9oS/+d69VaBKvDB11r1VoElvOXLzXKjQJO0vze61CkygsLbvXKjSJja8+ca9VaBLC8HfnPBgjd4FAIBAIBAKBQCAQ3BHC8icQCAQCgUAgEAjue5QI09+dIix/AoFAIBAIBAKBQPAfQFj+BAKBQCAQCAQCwX2POOfvzhGWP4FAIBAIBAKBQCD4DyAsfwKBQCAQCAQCgeC+R1j+7hxh+RMIBAKBQCAQCASC/wBi8icQCAQCgUAgEAgE/wGE26dAIBAIBAKBQCC471EKr8875j9j+Xvrrbf44Ycf7rUaAoFAIBAIBAKBQHBPEJa/+5AVK1bw448LKcxT4ODqTq9R4/EMCNYrn5WWwq4Vf5BxLRlTcwsiu/ak08CHMDAw0MhcT7jI3jXLyE5Pw9LGlnZ9B9KyWy/N9aqqSo7u2MKFowcpUORi5+xK94dH4RceoXWvwjwF0etXkXzhLOWlJTi5uPLo1OkEhzfXqdvebVvYsWEtilw5bp5ejJ38FEFhzXTKVpSX8/uP87mWnERGagr+IaG8/snnWjIXz5/ly/ffrpf3k2+/x9XDU+8z0kXXUH9a+npgaizlhjyP7WfiyC4oajCPl4MtfSKCcbS2pKC0jKOXrnD6SqqWTNsAL1r5eWJjbkZJWQUJ6TfZcy6BiqqqeuV1CvalZ7MgTiZdZ0dMfKM67966mW3r1qDIlePu5c34KU8THK77eZaXl7P4+++4lpRIemoKAaFhvDXzf/XkKisq2LhyOYf37UEhz8FaZsvAh0fQd+iwRvVpiDGdWtI3IhgLE2MuZ2TxU9QRUnIUeuXbB3rTPzIEXyc7jI2MSMlRsOZoDCeSUrTkBrcKo39kCI7WlhSWlnE88Tq/7z9BaUVlg/qoVCqObtvI+cP7KS0pxsXbl16jJmDv6t5gvtTLl9i/bgU5GTewsJHRpvcAIrr00FzPSU/jyNaN3Ey9Rn5ONu0HDKXjIO1nl5qYwOk9O8hMuUZRnoK+E54gvH1nvXqe3LmZuKMHKSsuxtnbh64jxmHn4tagnjeSEji0cTW5GTcwt5bRsmc/wjt101xPij3FmT07yMvOQqmswsbBiYhuvQlp21Ej88enb1OQK69XtldoM8a/8KpW2uno3RzbtU3TT/UZNR7PQP391M3qfir9qrqfatG1J50H1fRThXkK9qxeTkbKVXJvZhLevhNDHn+qXjllJSXs37iGS2dOUlJUiJWtHd2HPUJo63YNPp9brN6yhT/WriUnV46vlxcznnqKlnraUG2u30jj8ZdeQqVSsW/Vak363sOHWbttGwnJSZRXVODr6cmk0WPo1r59k/RpiC4hfkT6eGBqbES6PI+dsRcb7aM87W3p3TwIB2sLCkvLOJpwjZirNX3U+C6t8XK0q5cvK7+QX3YfaVQnlUrFqV1buHhMXT+dvHzoPHxsk+rn0U2ryc1Mx9zahsge/QjrWFM/L504QvTKJfXyTZ71LUZSKQBn9mzn6rkYFFmZGBoZ4eTlS7tBd9ZP1cY0shm24x7BNDgQI0cHMmZ+ScG2XXet/KYyrnMr+kUGY2lqQkJ6Fj/sOkRKtkKvfMcgHwa0CMHP2R6poSEpOQpWHYnheOJ1jYyhxIBHOrSgV7NA7K3MSZPnsXjfiXrvr9thUo92DGkdjpWpCfFpmXy9JZqrWfX7j1tEervxVJ+OeNrbYio1IjOvgC2n41hx+IxGZkCLEN58uE+9vP0+/Z7yyvrv0Kbwd7yLPh4zkGaervXyXs/O5aXf1t22jhO7tWFQyzAsTU24eCOT77Yd4Fp2rl755l6uTO7ZAU97GSZSI27mFbAtJp7VR2M1MgNbhtKneTDejrZIDAxIzMhmcfRxLqRk3LZ+9xMi4MudIyZ/9xnbt2/niy++oPfoR/HwD+LM/t2snj+HKe/NwtrOvp58WUkJK+fNxiMgmIlvfIA8M52tS35BamxMuz4DAVBkZ7FmwRyad+zKkElTSU1KYNfy3zG3siK4ZVsADmxcy4XjhxkwfhL2rm5ciTvP+oXzmPDquzh7egNQWlzE0i9n4uEfyCPTZ2BmaYWVsgwrG5nO73L84H6W/7qQCU8/Q0BoOPu2beGbTz/k428WYO/oVE9eqVQilUrpNXAI506fpLioUO9z+vibBVhYWmk+W1lbN/kZA3QM8qV9oA+bTp5HXlhEl1B/xndtww87D+p9wdiYmzGmcytir6ax4cQ5PO1tGdAylKKyCi7dyAQg3NOVXs2C2XL6PCnZucgszBnSOhwjiYQtpy9oledmZ0NLXw8yFQVN0vnYgWj+/PlHJk57lqDQMHZv28Kcj99n1nc/6Hyequrn2WfwUGJPnaC4SPeg8fuv/oc8O5tJzz6Ps6s7+YpcysvLm6STPoa3a85DbZoxb9t+buTmMapjSz4YNYDnflmtd5IW7uHCuevp/HnwFIWlZXQL9ef1Yb15f8U24tPUz7driB+PdWvLgp0HiUvNxNnGimcHdEFqZMiCHQcb1Olk1HZO791JvwmTsXVy4dj2TaydP4fH352Jsampzjx5OVms//Ebwjt0YcBjT5KWnMjelUsxs7QisEVrQL1oYW1vT0BkKw5v0f3Srygrxd7VndC2Hdnxx68N6hmzdyex0VH0HPs4MkdnTu3awqYfv2HcGx/p1TM/J5stP39HSNtO9Bn/BOlXEjmwZhmmlpb4R7QCwMTcgtZ9BiFzckFiaMi1uLPsW/k7ZpaWeIeqF29GvvQWKqVSU25Rfh6rv/4M/8jWWveLP3mMqJV/0m/cRDz8gzi9fzcr58/hyfdnYaOnn1rx7Ww8A4J5/I0PyLnVT5kY0766n6qsrMDM0pIO/QcTezBa5/esqqpkxbezMTW3YNiT07GytaUgNxcjo6a9ynYd2M+cnxby+jPPEBkWzpqtW5jx4Ycsn78AF6f6begWFRUVvPvFF7QID+fM+fNa106fP0ebiAimTXwUa0srdkTv441ZM1kwa1aTJpX6aB/oQ9sAb7aevkBOQRGdQ/wY07k1P0UdaqCPMmVUp5acu5bGplPn8bCX0S8yhJLyci7duAnA2mOxGEpqnH4MJRKm9O7Ixeo21hix+3Zybn8U3Uc/hszJmdO7trL1p28Z/dqH+uunPJvtv8wnuF0neo57goyrSRxcuwxTC0v8qusngJHUmLFvfqyV99bEDyA9KYGwTt1w9PQGFZzcsYktP37LSBMbrCSGTdK/ISRmZpQnX6NgexTO7752x+X9FUa0j2BY22Z8s3U/afI8xnZuycejBzL959WUlFfozBPu6cLZazf448ApCkvK6B7uz1vD+/DOsi3Epap/10e7tqFHeADztx8kJUdBK18P3hrehzf+2ETyzZzb1nNc51aM7tiCz9fvJiUnl8e6t+XLx4Yxcd4fevUsKa9g7bGzJGfmUFpRQXMvV14e0pPSigo2nDivJTfh29+18v7Vid/f9S76YsNujGrVOamRhLmPD+fwpSu3rePoji0Y2T6SLzftJTVHwYSurfl8wlAmf79M77MsLa9gw4lzXLmZQ1llJeEeLrw4qDtlFZVsOqUea0R6uxEdl8iFlAzKKioZ0T6Cz8YNYdpPq7iRm3fbegr+Pfxn3D5BPblYsmQJ48eP59FHH+WXX35BWT3QmTJlCmvXrtWSr+sqOmXKFJYtW8bcuXMZPXo0TzzxBAcOHKCwsJAvvviCUaNG8fTTT3P69Om/rOPvv//OQw89RGSXHti7utFnzEQsrGWc2b9Hp3zciSNUlJcz6LGncHTzILhlW9r3G8TJ3Ts0qyMxB/ZiYWNLnzETsXd1I7JLD8I7dOZE1HZNOReOH6Z930H4N2+BzMGJlt164RceoSVzfNc2LG1sGDzpaVx9/JA5OBIa0QI3PRa3XZvW06lnb7r1HYCbhyfjn5qGja0t+3Zs1SlvYmrKxGnP0b3fAGzt6w8ga2NlY4ONra3mT2J4ey/+dgHeHLl0hUs3MsnKL2TTiXMYGxkRrmMl7xat/DwpLC1jZ+xFcgqKiLmayrlrN+gQ5KOR8bCXkSZXcP56OnnFpVzLknPu2g3c7Gy0v6uREQ+3jWDzqQuUVuju3OuyY8M6OvfqQ49+A3Dz9GLi088gs7Vjz7YtOuVNTE2ZNP15evQfiJ29g06Z82dOExcbw8vvf0SzFq1wdHbGPziE0OYROuWbypBW4aw9dpajl69xPVvBvG37MTOW0i3UX2+eX/ceY93xsyRmZJOhKGDlkRiSM3NoH+itkQl2dyIh/SbRcUlk5RdyPiWdfRcSCXJ1bFAflUrFmego2vYZSGCL1ji4udP/0cmUl5Vy8dQxvfnOHozG0kZGz0fGY+fiRvNO3Qht15FTe3ZoZFy8fen28GhC2rRHamyssxzf8Ag6Dx1BYMs2WhZ5XXqe3b+blr364x/RCntXd3qNm0RFWSmXzxzXm+/Ckf1YWNvQdcRYbJ1dCevQlaA2HYndV2O18AgMwbd5C2ydXbBxcCSiW2/sXd1JT07UyJhZWmFubaP5u37xPMYmpvUmf8d376B5x8606NIDB1c3+o2ZiGUD/dSF4+p+avDjT+Ho7kFIK3U/dSKqpp+S2TvSd8yjRHTsiqm5hc5yzh0+SFFBASOfeRHPgCBk9o54BgTh6uOn99nUZtn69Qzp3ZuH+w/A19OTV6dOw97WljXbdPdJt/jut98I8PGld+cu9a698vRUHh81ivCgYDzd3Hhy3HhC/P3Zf/Rok3TSR9sAL44mXOXSjZtkFxSx5dQFjI0MCfNw0Zunpa8HhaVl7Dp7iZyCImKvpnH+ejrtarWh0opKisrKNX8e9jKkRoacvZbWqE4qlYpzB/YQ2bM/fhGtsHNxp8fYx6koKyXxzAm9+eKPHMDcxobOD4/B1tmV0PZdCGrTgbPRUdqCBgZa9c/cWrvfHPTUCwS37YSdizt2ru70HDeJ0qIC4irKGtW9KRQfPUHOwkUU7jt4zzYWPdSmGWuOneVIwlWuZ+fy9ZboRvvOn3cfZc2xs1xOzyJdkc/yQ2dIysimQ6CPRqZHeABrj53lZHIKmdVWolPJKTzcTrfXTmM80iGSPw+eYn98ElduyvlsXRTmxlL6NA/SmychPYs95y9zNUtOhqKAXWcTOJF0nQivulZjFfLCYq2/v8rf9S4qLC1HUVyi+Qt1d8ZEasTu85dvW8fh7SJYcfgMBy8mczVLzuyNezAzltKrWaDePJczstkXl8i17FwyFAXsPn+Zk8kpNPOqGcN8vn43G0+eJykzm1S5gm+37ae4vIK2/rfnJXW/oVKp/rG/fyv/qclfdHQ0EomE2bNnM3XqVDZu3MiBAwduq4yNGzcSFBTE119/TZcuXZg7dy5ffvklbdq04ZtvvqFZs2bMmTPnL1lOKioqiI+Pp2PHjlrpvqHhpNUaoNXmRnIiHv5BWoNO39BmFOYpyMvJVstcScQ3NFy7zLBmZFy7SlWVeuWrqrJCa4UV1KuwqUkJms+XY0/j6uPPhp8X8N3rz/PbrPfYs3WTzgZSWVHBtaREwlu00koPj2xF0sWLjT2KRvn0tRm8MnkiX37wNhfPnb2tvDILMyzNTEi+mV2jr1JJSnYuHvYyvfk87GxIztReIU3KzMbV1hpJ9YA+JTsXZ5mVZrJnbWZKoJsTSRnZWvkGtQojPi2Taw24yNSmsqKCq0mJNGtZ53m2aEnixcbdRfVx+tgRfAOC2LFhHTMmT+SNaU/yx8IfKC0p+ctlOttYYWtpTmytwWR5ZRVxqRkEu+u3rujCzFhKYWnNwC4+NRMfJ3vNZM/ByoK2/l6cSk7RVwSgtowV5+fhFVLTDoyMjXH3DyL9iu62BZBxNQmvYO224x3ajJvXr2nazt2kQJ5NcUE+nkFhNXpKjXH1CyTjarLefJnXkvGolQfAKySMrJRrVOlwN1apVKQmXESRlYmrn+4BhkqlIv7YYYJat9PqX6oqK8m4fhXfUG2rVkP9VNqVRDwDtPspvzDtfqopJMSexsM/gF0r/mDeGy/w00dvc2Dzuib9FhUVFVxMTKR9nTbUvmUrzsXr75MOnjjBwRPHeeXpp5usZ3FJCVaWlk2Wr4uNuRmWpiZcqWWRqVQqScnJxb2BPsrdTqaVByA5MxsXWU0fVZcWPu4kZ2ZTUNL4BKpAnk1JQT4eQaGaNCOpMS6+gWReS9KbL/NaMh6BoVppnkFhZKVeQ1mrflZVlPPnzHdY+ulbbP91PtlpDbfrirIyVCoVlpJ/x1DG2cYKO0tzztRyxSyvrOJCagaht9t3mhhr9Z1SI8N61rPyyipCPZxvW09XW2vsrSy03CDLK6uIvXajwQXUugS4ONDM00XrXQFgbGTE8pceY9XLk/hs/BACXHQvXjbG3/kuqkufiGDOXEklpxG37Lq4yKywt7LQeoeVV1Zx7np6gws9dfF3diDMQ20B1ofUUIKxkWGD30Pw3+A/5fbp6enJo48+CoC7uzs7d+4kNjaW7t27N7mMVq1aMXjwYADGjx/P+vXrcXV1pVcv9f65MWPGsGvXLq5du0ZgoP5VG13k5uZSVVWFvb09WaU16ebWNhRdjNOZpyg/Dytb7f0bFtWrpUX5ecgcHCnKz8M7RHsAa2Flg1JZRUlhIZY2MnxDm3Nyz048g0Kwc3Tm2qU4EmJOoVLVuIApsm9yZv9u2vTqT4f+g7mZep01fywGoNegoVrlFxbko1Qqsa7jEmotkxF3NuZ2HosWMls7Hp06HZ+AIKoqKziyby9fffgOr338GUFNdLGyMDEBoKhUe4JeVFaGpZlutyUAC1MTim5qT9aKysoxlEgwN5FSWFpOXGoGZsZSHuuu3n9kKJFw9loae87XTKJb+Hhga2nOhhPnmqQvQEG++nnayGRa6TYyW+JiY5pcTl1uZmSQEH8BI6mU5954h+KiIv746XsU8hyee/Odv1SmzMIMAEWR9gRSUVSCnaV5k8sZ0CIUeysLouNqJhSHLl3BysyUT8YOwgADjAwl7LuQyO/7TzZYVlG+2sXF3ErbPdjcyprCPEUD+fLxDKqfR6msorSwEAs9Ls9/leL8fADM6uppaU1RA3oW5+fXG1ybWVqjVCopLSrU9AllJSUs+fhNlJUVGEgkdB0xDu9Q3e0mNSGeAnk2oe21rV3FhQWolMp6VplG+ymZXT35W9dkDg1bbm+hyL7JtUvZhLXtyKjpL6PIyWLXit+pKCuj18ixDefNz6dKqcSuThuyk8k4oacNZcvlfPbdPD5/620szJtWd1dt2czNnBwG9uzVuLAeLE3Vk+TiMu0+qrisHEtTE735LEyNKb5ZP4+hRIKZsZSiOuXZWprj5WjHmqMxTdKruEBdP80tteunmZUVxQ3Uz5KCfMwCQ+rksUZVXT/NrW2QOTrTffRE7Fw9qCgr5fzBvWyYP5tHZryLjQ63doDDG1Zi7+ZBSPmdu3zeD9ha6u4784pKsLPSbQ3XxaCWodhbmrP3Qk3feeZKKg+1bcb5lHRuyPOI9HGnY5CP3kWBhrjVj+cWaVvkcouKcbBqfNFj1cuTsDE3w1BiwOLoE2w8WbMl4nq2gi827CEpMxszYymPdIjkuykjmfL9ctLkt+eq+He+i2rjamtNM09XPlsXpfN6Q9Q8S20d1c+y8d986QsTNc/yjwMn2XJadx8MMKlHe0rKKziScPW29byfUP6LLXL/FP+pyZ+Pj4/WZzs7O/Lybq8zqV2GmZkZJiYmWmmy6oHF7ZZbm3puYSoVt9M/37LE1c5TN3tda13vUePZvnQRv378NhgYIHNwonnHLpw7clArj4uXL90fHgWAs6c3BkX57Nm2pd7kT9+NVSpVg25vjeHi7oGLu4fms39wKNlZmezYsFbv5M/RxorXhvXWfF5xSO2WW7/7MNCVWAdtgVvf5Nbj9HKwpUuoP9vPxJEmz8PO0py+kSF0Cwtgf1widpbm9GgWyO/Rx/9iB6b97FSouK3KUQeVSomBgQHTXnkdcwv1i2bi09P58sN3yVPkYiOzbbSMbqF+TO1bE7xk5tpd1brV0fw21OwQ6M3j3dsyZ/NesvJrVlLDPFwY1TGSn6KOkJCehavMmsm92jO2c0uWH6oJGqBIvMD8xV9rPg+b+kK1DvVaQr22UZf6WVS3/4X0cPHEUaJW1OxtGfzks+qi694SVf3EutS7rqqXbGxiwuhX3qGirIzUyxc5vHEVVrb2eASF1M1M3NGDOHl64+Cu20Wo/u0a1lHvc7wNVCoVFlbWDHz0CSQSCS7ePpQWFbF79Z/0HDGmaX2LjudkoEfxD776khEDB9I8pP7z0cWeQ4eY9+siPn39dVwb2ENYlzAPFwa0rJm8rzocU63ZX3hG9VueXtkWPu4UlJSRmKHb+pp05gSH1y3XfB4webruIlUN30d9tU7fVef3d/bxw7mW+66zjz9r5s7k/KG9dH54TL3yjmxcTcbVJB6a/gqGvzS8j/Z+pXuYP9P71yyufLx6h25BA4Mmt5eOQT480bM9szfuISu/Zt/8T1FHeW5AF76bMhKA9Nx8os4lNOimeYs+zYN4ZWgPzec3l24G6quk/o0b1/P5X9dgZmxMmIczU/t2Ij03n11nLwEQl5pBXGpNQJILKRn8PG0sI9pHMG9bw55a/+S7qDZ9I4KRFxY36oEC0KtZIC8OqjE4vLv81raNOmMLA4Mmtf5XlqzHVCol1MOZKb06qF1AzyXUk3u4bXMGtQrjzaWbKNazj1Dw3+E/NfnTFRTg1p4/XYMGXe5ShnX2lhkYGGil3SpHWStoQm22b9/Ojh26O3ilUj0Q/+qrrwjq1IOAFm0A9WqruZWNzjwW1jYaq8YtNKuz1Xl0yhTmI5EYYlbtmmRuZc2IaS9SWVFOSVERljYyotevwsahxt3C0kaGvau2b76rhyfyLRvr6WVpZY1EIiFfodBKL8jLq2cNvFP8AoM5fnC/3uvygkL21PLDN5SofyNLU2MKSmpMrBYmxhSV6XeHKCotw6LOqru5iTFVSqVmU3b38EAupKQTc1XtZpKVX4jU0JDBrcM5EJ+Eh70MCxNjnu7TSVOGRCLBy8GWVr4eHL98Rec73spa/TzzFNrRv/IVinrWwNtBZmuHrZ29ZuIHaKKm5mRlNWnydzzxOgnpWZrP0ur2YGthpuUCY2NuhqK4cXfSDoHevDioO99u218v0uf4Lq04GJ9MVPXL7Xp2LiZSI6b378zKwzGaCbWVVwB9u9S4T1dVqt0C61rKiwsKMG8gWJCFtTVF1dY4TZ7CAiQSQ0wtmr4Srw+/5i0Y7eFVT8/ignwsa+lZUlhQzxpYG3Nra43VsHYeiUSCiUXNSryBRIKNg3pS4uDuSW5mBqd3b6s3+SsuyOfqhVi6jqhvTTO3tMJAItHZ71hY334/pS+PLixtZEgkhkhqufnZu7hSUV5OSWFBPctubWTW1hhKJMhzFVrpckVePWvgLU6ePcuZ8+f5ZdkyQD08UyqVdBr2EK89M53hAwZoZPccOsSHc+bwwcszbjvSZ2JGFr/uqXk+RtXfz8LERMsd09zEuJ71rjZFpeU6+iipVh91C4mBAc283Ii9mqp3b4tXWHOt/ZRa9VNWt35a1ct/CzMra4oLtH//0sICDCQSTC10W4okEgmOHt7kZ9+sd+3wxlUkxZxk6LQZWNs3zWp8P3I88ToJN2qCRBkZqX93mYWZVlRXG3PTetYrXXQM8uHlIT2Yu2WfVqRPgPySUmati0JqaIiVmQnywmIe796WzLzGA44dunRFE+gEavp4O0tzrQmmzMIMeWHjemZUBzm7cjMHO0tzJvVop5n81UWpUnHpxk087GSNlvtPvotuYSSR0DM8gF1nE5q0oHsk4apWcKUaHc21Jpcyc7Mm/ea3nuXVLDm2FmZM7Nam3uTv4bbNmdSjHe8s36IJ/PQgIwx/d85/avLXEDY2NsjlNS595eXlpKam4ufXtEACTWXAgAEMqDVgqMuECRMIDg7Gv3riB3D14gWCan2ujZtfANHrV1JZUY6R1Fgjb2kjw6Y6yIebbwCXY7WD0FyNv4CLtw+GhtpVwEhqjJXMmKqqShJiThLSqiZ8urtfILmZ2iGCM2+k6Yw0aSSV4u0fQFzsGdp0qlnZjIs9Q6uOnerJ3wkpV5KR1XF9rU2VUlXPPaWwpAxfJ3vSc9UDUEOJBE8HW3af0/0CAkiV5xHspv1d/ZzVZdzq9KWGknoDKfU19YTz0o2bpO86pHV9SOtmyAuLOXwpmeaeun38jaRSfPwDuBBzhnadu2rSL8SeoU1H3UcGNIXA0DBOHDpIaUkJpmZqF5nMG+qJq4MeV6u6lFZUal5At8gtLCbS201jUZAaGhLq7sySaP1BIUB99MXzA7oyb/sBna4pJkZG9V6wSlV9y42hsQkyx5q9LCqVSh3A5FIcLt6+gHof5Y2ky3SptmTrwsXHn+RzZ7TSrl+Kw8nLu17b+SsYm5pqJmMaPa2sSUmIx8nLR6NnenIiHYeO0FuOs7cfV87HaqWlJMTj6Oldb8FKC5VKM6CvzaUTRzA0MiKgRdt61wyNjHDx8uHKxQuE1Dpe4crFCwS31N1PufsGsK9OP3UlXrufagoefoFcOHEElVKJQfUESX4zA6mxMWaW+icfAFKplJCAAI7FnKF3l5o+6XjMGXp20t0n/fndd1qf9x89xqKVK1k05ysca+kddeAAH389l/dfmqEzKExjlFdWUV6pPdArLC3D18mODEWtPsrelr3n66/q3yJNriDQVbvd+jrZk6HIr9dugtycMDeWEnu1gT1CJqaY1wq+o1KpMLOyJi0hHidPH0BdPzOuJNJ+cMP18+qFGK201MsXcfTw1husS6VSIU9Pxc7NQyv98IaVJMWcZMi0Gcicmr4n6n6kpLyi3qRcXlhMSx93rb4z3MOFRXv1B3wC6Bziy0uDuvP11mgOX7qqV66iqgp5YTGGEgM6Bftw8GLj0SlLyivquVzmFBTRxt9TM5kwNjIkwtuNH3Ye0lWEXgwMDDA2atht18/ZnqTMxvcG/5Pvolu0C/TGysxUp7VNF7p+85yCIlr5eWomrlJDQ5p5ufJzE45eqY2BgYFmMnmLke0jeKx7O95dvuWBP+JBcPcQk79qIiIiiIqKon379lhbW7Ny5UoqdQyK/m4mTpzIO++8Q29Tazz8Aok5sJfCPAUtuvYEIHr9KtKvJTP2xTcACGvbgcNb17N1yc90HPgQuZkZHNu5hU6DhmmskC269uRMdBS7Vy2lRZeepCZf5vzRgwydPE1z3xtXkihU5OLk6UWBIpdDW9ajUqpo13egRqZNr34s/XImR7ZtJKR1ezJTr7F76yaGT3hM53fpO/Rhfvl2Dj4BQQSEhhG9YyuKXDk9+g0CYM0fv3HlcgKvfjSrRo+U61RWVlKYn09ZaSnXr6iDXHj5qifhuzZtwMHJCTdPLyorKzm6fy9njh/lmdfrn/3XEMcTr9E5xI+cgiLkhcV0DvGjvLKSCynpGpmhbdRupJtOqkNQn05OoY2/J30jQjh9JQVPexkR3u6sO1YTcOZyehbtA31Iz83XuH12Dw8kMSMLlUpFWUUlWRXaR1hUVFVRWlGhtYKqi/7DhrPw66/wCwwiMDSMvdu3opDL6TlA/TxXLVlE8uUE3vjkM02etOvXqaysoCA/n7KSEq4lq4MyePupI5116NaDjSuW8fO3cxk+bgLFRYUs/flH2nTqgvUdWBQ3n77AyPaRpMrzSM/N45EOLSitqGR/fE1QiBcGqs/4+nab2mrbOdiXFwd1Z3H0ceJSMpCZqyejlcoqCqv3Z55MTmFo63ASM7K5nKF2+xzXuRUnk1MaXHU1MDCgZfc+nNi5BTtnF2SOLhzfuRmpiQkhrWusNDt+/0X9rCdOASCiS3diD+xh35rlRHTuxo3kROKOHWLg4zXBP6oqK8nJUA+gKysqKC7I52bqdYxNaiag5WWlKLLUgySVSkWBXM7N1OuYmltgUsuqb2BgQES33pyK2oatkws2jk6cjtqG1MSEwJY1E63dfy4CoPf4JwAI79iN84f2cXD9SsI7diX9ShKXThyhz6NTNHlORW3F2csXa3sHqioruRZ/noRTR+kyXNu6pw70coiAFm30hu5v17s/m35biJu3H+7+gZyp7qdaVvdT+9avIv1qMuNequ6n2nXg0Nb1bFn8M50GPoT8ZgZHd26h8+BhWl4XmSnXACgrLcHAwIDMlGsYGhnhUH0WY8tuPTkVHcWuVUtp3b0PefJsDm5eT8tuvZrk8jnu4Yf5cM4cwgODiAgLY+22rWTL5YwYqG5D8xf/RlxCAvNnqvskf28frfzxlxORSAy00nfuj+bDOXN4YfJkWjZrRk6u2jpvZGSETQPWsMY4kXidTsG+5BQWIy8oolOInyZYxS2GtFbv5d5cHdr9zJVUWvl50bt5EDFX03C3k9Hc242NOvYXt/Bx52qWnLwmWEBuYWBgQPOuvTizezuyOvUzoGXNQsHeZb8B0HPcJABCO3blwqF9HN6wktAOXcm8mkTCySP0Gj9Zk+fUzs04efth4+BIeWkp577xO6IAAPXmSURBVA/tJSc9jS4jxmtkDq5dxuXTx+k3aSomZuYUV1uTS1RKzAzuPOiLgZkpUvdq7xaJAVJnJ4wD/FAWFFCZmdVw5rvExpPnGd2xBanyPNLkeYzu1IKS8gqtvvOlwWq3wa+3qI9E6Rrqx4zBPVi09xgXUjI0e90qq5Sa4B5Bro7YW1mQnJmDvZU54zq3wsDAgLW13l+3w+qjsTzarQ3Xs3NJzVEwsVsbSsorNF4ZAG8NV5/Xd2sv3PB2EWQo8rlefX5dpLcbYzq11Nr//nj3tsSlZpIqV2BhYsyI9hH4O9szd8u+v6Tn3/UuukXfiGDOXbvRJAuqPtYdP8u4Lq1Jyc4lTZ7H+C6tKC2v0PJYeu0h9R7i2RvVEZWHtWlGhqJAc15hhLcrj3RooRmvAIzq0IJJPdvxv/W7Sc1RYFtdL8oqq+rtJ36QEHv+7hwx+atm1KhR3Lx5k08//RRTU1NGjx6tZQn8pxgwYAB5eXl898OPFOXn4eDqziPTX9asjhflKzSDSAATM3NGP/8au1b8zpLPP8TU3IK2vQfQtneNdVHm4MjI6S+zZ80yYg7sxdJGRu9REzRn/IF60Hpg01oU2TcxNjHFLzyCwY8/rRVy3dXHj+FTn2f/xjUc3rYRazt7ho17lJ4DBuv8Lu26dKOooIAtq1eQlyvHzcubF9/5EPvqvTB5ublkZWivRH3z6Yfk1Pp+H7+i3qv181r1HoPKygpWLv4VhTwHqbEx7p5evPDOB0S0rm+haIgjCVcwMpQwoEUYpsZGpMnzWHbwlFY0NJvqDv8WecUlrDh0mr4RIdXHPpSyMyZec8YfwMGL6slq9/AArMxMKSkr53J6Fvsu3H7457q079qdwoICNq5aTp5cjru3Dy+//xEOTuoJhiI3l5sZ6Vp55nzyPjk3a57nBzOeB+C3DerQ9qZmZrz28Sz++Ol7PnrlJcwtLWnVvgOjHnvijnRdd1x9dMbTvTtiYWrM5fQsPl69XetcJQdrbbfJ/i1CMDKUMKVXB6b06qBJP5+SzvsrtgGw6kgMKpWKcV1aYW9pQUFJKSeTU1h64FSjOrXpM4DKinL2rPqTsuIiXLz9GD79Za0JTn6udqREG3tHHp76ItHrVnDu4D4sbGT0GDlOc8YfqA8n//OLmrPJzmVHc+5QNO4BQYx64XUAMq9fZc28LzUyR7dt4Oi2DYS260SPMdqLJy169qOyopwDa5dRVlKMk5cvQ55+QUvPQoV232Rt78DgJ5/j0IZVXDi8HwsbG7o8PEZzxh+oIyPuX/MnhQoFRlIpMicXeo17gsBW2m3nRlICedk36T1Bfx0IbdOekqJCDm3bqOmnRj1b008V5inIrdWOTc3MGfPCa+xc/ju/VfdT7XoPoF1vbS+IRbM+0PqceC4Gazt7ps/8Sv097ewZ8/yr7FmznEWz3sfC2obmnbrSeeBDenWtTd+u3cjLL2DRyhVky+X4eXsz94MPNfvzcuS5pGXc3ur4um3bqKqqYu5PPzH3p5806a2aNeP7zz6/rbJqc+zyVaSGEvpFhmAqNeJGbj4rDmn3UdZ1AlTlFZey6vAZekcE0dJXfTTNrthL9Vy9bMzN8Ha0u62gU7eI7NGPyooKDq5bTnl1/Rz01PMN1087BwZMeZYjm1YTd+QAFtY2dBo2WuuMv7LSEg6sXkpxQT7GpqY4uHvy0DOvaCzgAHFH1IPzLT9+o1V+lbkNEywad1FvDNOQIDzmzdZ8tn/yMeyffIz8rTvJnPXVHZffFNYeO4uJkRFT+3bC0tSYhBtZfLByu5a1yNFa21V2QItQjAwlPNWnI0/1qXF3P3c9nXeWqfeUSY0MmdC1NS4yK0rLKzmZnMLcLdENuhE3xLJDpzGRGvHSoO5YmZkQl5rJa79v0NLT2UZ78cNQYsDTfTriIrOmSqnkRm4eC6MOs7HWhMXS1IRXhvbAztKCorIyLqdn88KidVxM+2vuin/Xu+jW92vu5cqczfv+km63WHkkBhOpEc8N6IqVmQkX027y1p+btZ6lk432by6RSJjSuwMuNlbVzzKfX/cc1SwEAQxtE47U0JB3R/bTyrsz9iJfbtp7RzoLHmwMSkpKxBT6PmTpoTONC90HBP3FEMz/NPsv6g+Tfz/RKzzgXqvQKLM36z6A+36jb/Pbi7Z7ryjTc9Dw/YaNuf5IuPcTwz0ejD7phwtX77UKTaKuG9n9yENf/O9eq9AkXhmq38X8fiK/1l74+5nbidh5L3lQjlbY+OqdLfr+Uyw/EvOP3Wtsxxb/2L3+Sf4dh+MIBAKBQCAQCAQCgaBBhNunQCAQCAQCgUAguO/RF51Y0HSE5U8gEAgEAoFAIBAI/gOIyZ9AIBAIBAKBQCAQ/AcQbp8CgUAgEAgEAoHgvkf5AHl9btmyhbVr15Kbm4uXlxdPPfUU4eHheuWvXr3KDz/8wOXLl7G0tGTAgAGMHTu2SUcZ3Q7C8icQCAQCgUAgEAgEd4kDBw7w008/MXr0aL755htCQ0P58MMPuXlT97ElxcXFvPfee8hkMubMmcPTTz/NunXrWL9+/V3XTUz+BAKBQCAQCAQCwX2PSqX6x/7uhPXr19O7d2/69++Pp6cnU6dOxdbWlm3btumU37dvH2VlZcyYMQNvb286d+7MyJEjWb9+/V0PciMmfwKBQCAQCAQCgUBwF6ioqCAxMZGWLVtqpbds2ZL4+HideS5evEh4eDgmJiZa8nK5nMzMzLuqn9jzJxAIBAKBQCAQCO57/smjHrZv386OHTsalevfvz8DBgzQfM7Pz0epVCKTybTkZDIZsbGxOsvIzc3FwcGhnjyAQqHAxcXl9pRvADH5EwgEAoFAIBAIBIJaDBgwQGtSd7vcbqCWux3YRR9i8icQCAQCgUAgEAjue5QPwCHv1tbWSCQScnNztdIVCkU9a+AtbG1tdcoDevP8VcSeP4FAIBAIBAKBQCC4C0ilUgICAoiJidFKj4mJITQ0VGeekJAQLly4QHl5uZa8nZ0dzs7Od1U/Yfm7T8nMK7jXKjSJuNS7uwn17yLI1aFxofuA9Scv3GsVGuWF/p3vtQpNIjEz516r0CRMpQ9GN1xaUXmvVWgSG9Lk91qFJmFpatK40H1ASa2ByP3KK0NH3WsVmsRXm1bdaxWaxMW5X95rFZqEUqm81yo0CYlE2FnuJg+C5Q/g4YcfZs6cOQQGBhIWFsa2bduQy+UMHDgQgMWLF5OQkMDMmTMB6N69O8uWLePrr79mzJgxpKWlsXr1asaNG3fX3UEfjFGHQCAQCAQCgUAgEDwAdO3alfz8fFauXIlcLsfb25sPPvgAJycnAORyORkZGRp5CwsLPvnkE3744QdmzJiBpaUlw4cP5+GHH77ruonJn0AgEAgEAoFAILjv+Sejfd4pgwcPZvDgwTqvzZgxo16aj48Pn3/++d+tltjzJxAIBAKBQCAQCAT/BYTlTyAQCAQCgUAgENz3KB8cw999i7D8CQQCgUAgEAgEAsF/AGH5EwgEAoFAIBAIBPc9D9Kev/sVYfkTCAQCgUAgEAgEgv8AYvInEAgEAoFAIBAIBP8BhNunQCAQCAQCgUAguO8Rbp93zj9i+cvMzGTo0KFcvnz5jso5dOgQQ4cOvUta/b2MGjWKqKioe62GQCAQCAQCgUAgEAD/kOXPwcGBJUuWYG1t/U/c7l9L11B/Wvi4Y2os5YY8jx0x8WQXFDWYx8vBlt7Ng3G0tqCgtIyjCVc5cyVVc31C1zZ4O9rVy5eVX8hPUYc19+0a6q91vbC0jG+3RjdJ736RIXQI8sbc2Jhr2bmsPRZLpqJAr7yVmQkPtWmGh70MBytLTiWnsPzQaS2Ztv5ejO3Sql7eN37fSKVSqZWmUqk4vHUD5w5FU1pSjKu3H71GT8DB1b1BvVMuXyJ63Qpy0tOwtJHRps9AIrv00JJJiDnJ4S3rycvOwsbBkc5DRhAYWaOXUqnkyNYNxJ84SlG+AgtrGaFt29Nx4DAkhoYAzHl+is77+7bpSOTAEfXS+0QE0z7AGzNjKddzctlw/ByZeQ0/z8GtwnG3s8HBypLTV1JYdSRGr3ykjzvju7QmPjWD3/Ydb+AJ6Wff9q3s3LiWvNxc3Dy9GD3pSQLDwnXKVpSXs3ThAq4nJ5GelkpAcCivfDxLb9mJ8XF89cHbuLh78MHc75qs05n9ezgRtY3CPAUOru70emQ8HgFBeuWz0lKIWrmUjGvJmJpbENmlBx0HPoSBgYFGJuXyRfauWU52ehqWNra06zuQFl176iwv/uRRNi/6Eb9mkYx85iW99z0dvZtju2r07DNqPJ6BwXrlb6alsGvFH6RfVevZomtPOg+q0bMwT8Ge1cvJSLlK7s1Mwtt3YsjjT2mVEXNwH+ePHiY7PQ2VSomzpzddh47As4Hno1KpOLptI+cP76e0pBgXb196jZqAfSPtKvXyJfavW0FOxg0sbGS06T2AiFrtKic9jSNbN3Iz9Rr5Odm0HzCUjoOGaZVxfOdWks6eJjczA0MjI1x8/Og8dCQObvXvfWpfFEd3baUwLw9HN3f6jJqAVyPPc8fyJdXP05KW3XrSZdAwzfO8eOYEZ/bvJSPlGlUVFTi4utFp4EMERWr3R2UlJURvXM3F0ycoKSrE2taO7sNGEdamvd7neXz7Ji4cqX6eXr50f2R8o88zLfESB9avRF79PFv16k/zzjXP8/yR/Vw8cQR5xg1UShWOHl50GDQMN79AjczZA3s5fziafHkOAPYubrTpNxjf8Aid9+wU7EekjzsmUiPSc/OJOnuRnEbeRR72Mno2C8LByoLC0jKOJ14j9mqaloyxkSFdQv0JdnPGVCqloKSUA/GJXLpxE4D2gT4EujpiZ2lBlVLJjdw8DsQlNnjf2ozr3Ip+kcFYmpqQkJ7FD7sOkZKt0CvfMciHAS1C8HO2R2poSEqOglVHYjieeF0jYygx4JEOLejVLBB7K3PS5Hks3neC07Xes3cb08hm2I57BNPgQIwcHciY+SUF23b9bfery6Fd29m3dSMFilyc3T0Z9ugk/ELC9Mqnp1xj3eJfuJ6UiLmlJR169aXvw49o9aWHdm3j0K7tyLOysLV3oPewEbTp2uO29DoctYPorRspyFPg7O7BQxMm4Rsc2oBe11m/5BdSktV6te/Zlz7DRmrpdebwQfZt3UB2RjomZmYEhjdnyNjHsJLJADi2N4pTh/aTmZaCSqXCzduX/iPG4Bscove+9+L5xR47zN7N68nOzKCqqgpHZ1e6DhhC22496t/wPkcpLH93zD8y+TM0NMTW1vafuNW/lg5BPrQL8GbzqfPIC4vpEuLHuC6t+XHXIcorq3TmsTE3Y3SnVpy9lsbGk+fwtJfRv0UoxWXlmpfpmqMxGEpqDMCGEglP9elEfGqGVlk5BUX8sf+E5nNTze49mwXSPdyf5QfPkJVfQN/IEKb27cT/1u2mrLJSZx4jiSFFZeXsOXeZDkHeessuq6jks7XaL7y6Ez+ApKPRJB3eQ/9HJ2Pn5MLR7ZtY891XPPHeTIxNzXSWnZedxbofvqZZhy4MfOxJ0pIus2flUswsLQlq0QaAG1cS2bLoRzoNGkZAZCsSY0+z+dfvGTvjLVx9/AA4sWsbMQf2MODRKTi4eZB9I5Xtv/+CoZGUDgPUVuypM+do7nstOxfFjVSOrliEe1hkPb26hwXQLdSflYfPkJVfSJ+IIJ7s3ZHZG3frrQdGEgnFZeXsu5BI+0D9zxPAztKcwS3DSM7MaVCuIU4cOsCKRT8x/slpBISGsW/HVubN+ogP587HztGxnrxSqUQqNabHwMGcP32KkiL9g8iiwkIWzZtLSPNIFPKm63jx1DH2rPqTPmMn4uEfyJn9e1g9fw6T35uJtZ19PfmykhJWzvsSz4BgHn39feSZGWz7/Rekxia07TMAAEV2FmsWzKVZx64MnvQ0qUmXiVr+O2aWVgS3bKNVniL7JvvWrcTDX/9kCiD+5DGiVv5Jv3ET8fAP4vT+3aycP4cn35+FjR49V3w7G8+AYB5/4wNyMtPZuuQXpCbGtO8zEIDKygrMLC3p0H8wsQd1L9hcT7hIaJt2uPsHIpUac2LPDlbO+5In3vkYOycXnXlORm3n9N6d9JswGVsnF45t38Ta+XN4/N2ZGJua6syTl5PF+h+/IbxDFwY89iRpyYnsXbkUM0srAlu0BtSLAdb29gREtuLwlnU6y0lNvERElx44e/kCKo5s3cDa+V/x2NsfY25cs5gVd/Iou1Yupf+4x/AMCOJU9G5WfPclT3/wGTZ2Djqf57JvvsAzIJhJb36EPDOdzYt/wtjYhPZ9B1Y/q0t4B4fS/aGRmFpYcuH4Ydb88A0TXn5bM6msqqpk2bdfYGpuwfCnnsVKZkeBQo6hkVTn9wE4vXs7Z/btpM/4J7B1cuH4jk1s+H4uj779aYPPc+PCbwlr35l+jz7JjSuXiV71J2aWVgREqp9nWuIlAlu2xc03ACOpMTHRu9jww9eMe+19ZI7OAFjKbOk0dCQyR2dUKiUXTxxh6y8LGPPqu1g4OGnds12AN20DvNh2Og55YREdg/0Y3akVP+8+TIXed5EpIzu05Pz1G2w5dR4Pexl9IkIoKasgIV39LpIYGDCqYytKKyrYeOIsBSVlWJmZUFXrUC9PB1tirqaSkZsPQJdQf0Z3asWuc4kUlpbpfbYAI9pHMKxtM77Zup80eR5jO7fk49EDmf7zakrKK3TmCfd04ey1G/xx4BSFJWV0D/fnreF9eGfZFuJSMwF4tGsbeoQHMH/7QVJyFLTy9eCt4X14449NJN/86/1oQ0jMzChPvkbB9iic333tb7mHPmKOHmLDH4sYMelJfINCORy1g59nz+K1/83F1qF+/15aXMzCzz/BNziUFz/+nKz0G6xY+B3GJib0GPQQoJ60bVm+lFFTpuEVEMj1pMus/uUHzCwsCW/Vpl6ZuvU6zMalvzH8sSn4BIVwZPdOfvlyFq98Nhdbh/ptvbSkmJ+++AS/4FBe+OgztV4/LcDYxITuA9Xv5qsJF1n+4zwGj5tIs9btKMhTsG7xLyz74VuefvN9AJIuxhHZvhM+gcFITUw4sH0zP8+eyUuffoGji+t98/zMLa3oM+wRnNzckRgaEn/mFKt+XoCltTWhLeovpAv+3fxlt8+TJ08yevRoqqrUnf2NGzcYOnQoCxYs0MgsWbKE9957r57b57lz5xg6dCixsbG88sorjBw5khkzZpCYqL2Ct2fPHiZPnszIkSP56KOPUCgUWtezsrL49NNPGTduHCNHjmTatGns378fqHE13bdvH6+//jojRoxg2rRpnD6tbUG6fv06H330EaNHj+bRRx9l9uzZ5ObmaslERUUxffp0RowYwdSpU1m/fj3KWpOMGzdu8NZbb2nucfz4X7OWNES7AG+OJFzh0o2bZOUXsunkeYyNjAj3rN+53KKVrweFpaXsjFWvysZcTePc9Ru0D/TRyJRWVFJUVq7583SQ8X/2zjssiquLwy+99yq9N0HsvffeuyYaS3o0ml5NNflSTLEk0cRu7L1i7x1EEQsoKqCgtIWl77L7/bG4sOyCiEnU5L7P4/M4M+fO/PYyc2fOPfeea2RowPlbmj2yCoVSw66wmpdlVdqH+rM/LpG45DukS6SsPBqNiZEhjfw8qi2TU1DIptNxnLmeTGFJzdeRFpdo/KuKUqnkxumjNO/Wm6CGTXF086DH2ImUlhRz5eypas97/thBLG1sVZEMVzcatOlAWIvWRO+LUtvEHNiLZ2AILXr0xcHVjRY9+uIZEEzMgQqH9M6Na/iHN8Q/oiE2Do74R6j+n3YzSW1jYW2j/mdqaU1aQjyW9k44emtGWwHahvpxID6Riylp3M2Vsvr4OVV9+tZUn0VsOXuR6KQUCktKq7XT19NjVNsm7Dp/hez8mnvxa2Lv1s207tiFdt16UM/Dk1ETX8DG1o5Du3fotDcxNWXMCy/TvltP7By0HZzKLJ33My07dsYvqPrIjS7O7ttNeMs2RLbpgIOrG12Hj8XCxobYI/t12l86cwK5rJRez07Cyc2D4EZNadGtF2f3R6k7Ps4fPYCFjS1dh4/FwdWNyDYdqN+yNWf27dI4V1mZnG0Lf6Ndv8HY6Hi5V+b0vigiWrWhYduOONZzo/uIZ7C0tuXcYd0640+fQFZaSp9xk3Fy9yCkcTNadO/Nmb0VOm0dnOg2YiwNWrXD1NxC53n6T3iRJh274urpjYNrPXqMGoexqSlJ8XE67ZVKJecO7aVZ114ENmyCo5s7PcZOUD1X0dU/VxeOHsLSxpZOQ0dj7+pGROv2hDZvRfT+iufK1duX9gOHE9K0BUbGxjrPM/jladRv2RZHN3fVM/3MRIrypdxJ0nyHnN67iwat2tKoXScc67nTY+SzWFrbEnNId31ePH0cWWkJ/cY/j3N5fbbs0YdTe3ep67P7iLG07tkPN19/7J1daNd3EK5eviScj674ncePUCjNY9hLr+MZEIytoxOeAcG4lXcK6arP2MP7aNKlFwGRTXCo50630ar6TKihPi8eO4SFtS0dhozG3rUe4a3aE9K8Fef271bb9HhmMpHtOuPk4YWdiysdh43F2MSUW5cvqm38IhriExaBrZMzds6utOozCCNTE9JvXNe6ZhN/L04l3iQh7R6Z0gJ2xsRjbGhAmLvuTgKASB8PCopL2Bd3lez8Qi7cukN8ShrNArzUNuFebpibGLHx1HluZ+eSV1TM7exc0iV5apt1J85xMTmNTGkBmdICtkfHY2ZiTKi7S7XXvk//puGsP3WBEwk3Sc7M4cfthzAzNqJ9qHY7e5/f951k/akLJKZlkCbJY9Wxc1xPz6RlpXdox/oBbDh1gbNJKdzNlbIz9jLRSSkMbB7xQE11pfDkGbLmLyL/4NF/fMXrQzu30qxdR1p26oaLuweDxk3E2taWE/t267SPOX6E0pISRr34KvU8vWjQvCWd+g7k8M5t6mcq+tghWnTqQqPWbXFwdqFRq7a06NSNA9s21VrXkV3baNq2Ay06dcXF3YOBz07AytaOk/t16zp3/CiyklJGPP8qrh5eRDRrSac+Aziyq0LXrWsJ2Ng70L5nX+ydnPEOCKJNt54kX6+YwjT6pSm06dYTdx9fnOu5MXj8ZEzMTEm4EPtE1V9g/QjCmzbH2c0dRxdX2vXsQz1Pb25cvVzrOn5SUCr/uX//Vurs/NWvX5/S0lINh87a2poLFy6obS5evEh4eHi151iyZAnjxo3jxx9/xMrKiu+//159M1+9epUff/yRHj168PPPP9O8eXNWrFihUf6XX36hpKSEmTNnMnfuXCZPnoyFhebHzeLFi+nXrx8//fQTjRo14ssvvyQrS9Ubl52dzbvvvou3tzfff/89n3/+OUVFRXz++edq5y4qKoqlS5cyZswY5s2bx8SJE1m/fj07dqg+ZBUKBTNnzkSpVPLtt98yZcoUVq5ciUxWO+eoNtiam2FpasKNSr2IcoWClKwc3O1tqy3n7mDLjSoRnKS7WdSzs0a/0nCByjT08eB6eibSIk1HytbCjNd6teflHu0Y2CwCW3PdEbPK2FuaY21uSkJ5lBFAXqYg6W4WPjqGmj4sRgYGfDCkOx8N7cHEzi1xt7fRsimUZFNSIMU7pGLIoZGxMR7+QdzR8WFzn7Qb1zXKAPiE1udu8i3KylQRy7Sb2jbeoeHcuVHxAeruF0hK4hWy09MAyEq7Q3LCZXzDdH8YyEtLuB1/Hu/GzbWO2VuaY21mSmJaRoV9mYKke1l4Oz56ffZoGEpOfiExSSl1PodcJiM56RphkQ019odGNuL61SuPpO/grh3kSST0GTL8ocqVyeWkp9zEJ1SzLfIJDed2ku574M6N63j4B2k4Hz5h4eTnSsjNylTZJF3XOqdvaAR3b91U3yMAR7ZswNrBgfCWbR+sM/kmvlrnrM/tJN1D227fuIZngKZOvyo660KZXI5cJqvWWczLyqQwLxevSve/obEx7v5BpN2ofhhe+s3reAVrPzP3Kj1XdUFWXIxSqcSkkt4yuZy05Jtaz5pvWDipSbrnn99OuoZnQHCV+owgPzenxvosLSnSqKuE2Gg8/AOJWr2Mn95+jd8+eZfDWzdU+xsr6rNi6JehsTFu/kGk3ay+nUq/maRRBsArpD73UqqvT0WZ6m9rUs3fVqFQkBBzGllJCa6+mo6RTfm76Oa9bPU+uUJBSqYENx3t733c7Gy4maH5LrpxLwsX24p3UWA9J25n59K1QTAv92jHc51b0jrYr9p3FaiGierr6T0w6udiY4W9pbnGlIdSeRnxqemEujvXUFIbMxNjjesZGRpojboolZcR6vFgh/RpQy6XcftGEkERmqNSgiIiuZl4VWeZW9eu4hscipGxSSX7huTlZJOdofo2kMvkGBlpdvQYGRuTcv0aZdWMENLUJef2TR26whvUoCsB3+AQjWc9KCKSvJwccjJV71ifwBCkkhwunTuLUqmkQJrH+ZPHCYlsVK2W+22nmYX28/Wk1J9SqSTx4gXupd/BN6T6YbGCfy91dv7MzMzw9/cnLk7VMxwXF0ffvn3JyMggOzub4uJiEhMTiYiovvdr7NixNGjQAE9PT0aOHElqaqraMduyZQuRkZGMGDECd3d3evXqRcuWLTXKZ2RkEBYWhq+vL66urjRp0oQmTZpo2PTq1Yt27drh6enJ5MmTcXR0VDtuO3bswNfXl/Hjx+Pp6Ymvry/Tp08nMTFRHYVctWoV48ePp02bNri6utK8eXOGDh2qPkdsbCwpKSlMnz4df39/wsLCmDRpkjoi+ldgYap6qAuKNaM2BcWlWJrq7hkHsDAxpqBEu4yBvj5mJtrDj+wtzfF2sif2puZchdvZuWyLvsiqYzHsiInHwtSEZzs2x8y4+iFMANZmqqFKVSNy+UUlWJuZ6CpSa+7lSVl9PIZF+0+x/PBZZGVlvNqrHY5Wmg1uSYFqLpy5leZ8U3Nrawrycqs9f0FennYZK2sUijKK8vPLbXKxqGJjYWVNobSip7pZt16ENmvF4pkf8ePU51ky8yPqt2hNw/addV439eI5yuRyvBpoD3WxMlXVWX6Rdn1aPWJ9BtZzItLbjY2nLzzYuAbypXkoFAr1fIj7WNvaklclcv8w3L51k21rVzJx6nT1XMnaUpQvRalQaP09LayqvwcK8nJ12NuojwEUSLX//lXvkRuXL3I1+jTdR417oM7C+zqtNT+iza1tKMitSae2fWWddeHwlvUYm5gS2ED3R879c+t6Rgry8nQVKS9X/XNVXF5ndeHg+lU4uXtSr5Kzcr8+LarMNbewtqnx765tr9rOz5PoLHP24F6kOTlEtGij3peTmcHl6DMoyuQMf2U6HfoP4dyRAxzcuFbnOQql1dSnpTWFNfwdC6W5mFs+XH2e2L4JIxMT/MI1P0Az76Ty69uvMu/NlziwZjm9J7yMo5vmiAILk/J3UYlmG1RYUoqFafVtkIWpsdb7q7Ck/F1U/h6xMTcj2M0ZfT191p+M5djlJBr6uNM+LKDa83aOCOauRKqexlAddpaqzkpJQZHG/tyCImwtzWssW5nejUJxsDTnQHxFB8e5G6n0bxaOu70NekBDH3daBflgb1H78z4tFEilKBQKLG1sNfZb2tgiraZ9l0okWNlotlH3t6W5qjLBEZGcPrSf5OvXUCqVpCRd4/TBfZSVySmQVj+fvUKX6r1jWaXttLSxVV9DS1euRNve2latGcA7MIjRL09l5S+zeW/CaD59ZRJKlIx4/tVqtexatwoTE1PCdAxXfdz1V1RYwPsTx/LO+JH88f1XDHxmAqGRT9+QT4VS+Y/9+7fySHP+IiIiiIuLY9iwYVy8eJH+/ftz/vx5dRTQwMCAoKAgtUNXFR8fH/X/7e1VkQuJRIKjoyOpqak0a9ZMwz4kJIQ9eyqG1N0fZhodHU1kZCStWrUiICBAq8x99PX1CQoKIiVFFdm4fv068fHxDBs2TEtbWloaLi4uZGZmMnfuXH755Rf1sbKyMnWEMjU1FXt7e5ydK3oPg4OD0deveyJVfX193uxf4RysOX4OACVVbkQ9qu7RoupxdSeqjoINfTyQFhVzLV2zhzvpbsV2Bipn8OUe7YjwciM1q+LDpLGvB0NbNVRv/77vRDUiHqz7QdzKyOFWRsXw3JsZWbzRrxOG926xc85P6v3Nhz+numTV3mMllSpDN1XL3G8HNPZrnVbzl12NOc2l08fpPW4yDvXcyUhN5sD6lVg7OBHRqp3WNW/GnKJecH1MLCxp6OPO4BYVH2iLDpzSeQ29R6xPcxNjhrdqxMpj0dXOfXlY9Khad3VXKJPJWPDDtwx9dgKOLtUPLXugJq2/p7LGW0CXvdZ+rRPc/516FOZL2bnsD/qOf6HaCJrO61bdoVTq2FmThEd7us7s303s0YOMnPo2Jmaqj+b408fZtWKJ2mbAC1PKr639+2t+qmrQ+4DnsToObVjNnaREhr/+bjXtbtWHVKm9T8NaR1uhaz9wJeYM+9evYuCkl7FxqDSvSKnAwsqK3mMnoq+vTz1vX4oK8tm7dgWdh4wk/vQJdq5YpDbv9/xrurWifHC9aLVt1ddn7KG9XDx+mIEvT9ea72zn7MrItz6mpKiQ6+dj2PvnImbMmk3fbq3VNutPxqplaWrgke87PT0oLJERFXsJJXA3V4qpsRGdwoM4GK8dqe1UPxAPB1v+PHJW6yOtQ5g/L/eoiLR/ti6qavGKi9ZSd6sgH57r1IJvt+wnI6/CsV6w9ySv9mzLnIlDAFQJcOIS6BpR8/zep5mHb6OqtKVV9ncbNBRproQ5n30ASiWWNrY0adeBg9s2o/cQ31La73mljjaqBvuKhx2Au7dT2bx8EV0GDCE4IpI8SQ7bVy9n/aL5jHxB2wE8GrWDUwf2MvmdjzA1q975f1z1Z2JqxvQvv6WkpJjE+Di2rliCvaMTgeG6EzsJ/r08kvMXHh7O9u3bSU5OpqioCH9/f7VDaG1tTWhoKIaG1V/CoFIP/v2b+P4HVm0+Frt3707jxo05e/YssbGxvPXWWwwbNozRo0fXSr9CoaBp06ZMmDBB65itrS0l5b2br7zyioYTWZmH/ajdtWsXUVHVvIjKMTExwTawPi4BqmveT8hiaWqiMRzTwkS7N7UyBSWlWJpoRgbNTYwpUyi0PvL19fSI8HIj9mbqA3+TrKyMTGk+9lV6TONT0rmVeUC9bWig0m1lZoKksKLHterv+CtQKiElS0KXzl24XqlzV1E+9KkgLxcru4qhkYXSPK2oTWUsdEQGi/Lz0Nc3wLR8OIcqgqAZ5SiUSjV67w9vWkvTLj0IaaLK8Ofk5kFedhand+/Qcv7upSYjSUslrLMqscSl1HSNTHQV9WlKbmFxhVZTE61o4MPgamuFtbkpk7q0Uu+7/zzOHN2XWdsOkJlXuzmAllbW6OvrkyfRnDcrzc3Fuko0sLbk5mSTlprCkrk/sWSuyrFXKpUolUpeGj6Q196fQVjD6ofhmFlaoaevr/X3LMyXakXN7qMrOlSYr/pb3//7WljpsJFK0dc3wMzSgtvXr1GQK2HN7G/Vx+8/W9+9NpEJH36BvUvFnF3z6nRK87Cwfgid5ZHn6srUxJn9uzmyZQPDXp2uMT8toEEjxnhUJAu6P5RI+7mSYl5DVmfVc1XlmcmXajxXD8OhDau4GnOGoa+9qTWfsrr6LJDmaUX3KvTZkK/D/v6xylyJOcOWRb/Rb/zzWpk+LWxsMTAw0HBGHVzdkJWWUpgvJTCyETZunupjZXJVe1worVKf+VKtaKDGb7SyUUcNK5fRVZ+xh/Zycscm+r8wFVdvX61zGRgaYuuk6sR08fLhbspN1v65nCx5xfvg/rvIwtREY0SHubH2KJPKFBSXqkewqMtUeRcVFJeiUCo0/MosaQHGhgaYGRtpvLM6hQcR4u7C6mPR5BZqRvMATl9LJuFORbIgQ0OVblsLM40M2TbmplrRQF20CvJhet+O/LD9oEamT4C8omJmbtyLkYEBVmYmZOcXMq5DsxozMD+tWFhZoa+vrxVNy8/LxapKNOs+VjpGfeSXj2S4H3kzMjZhxPOvMHTCC6p3hZ0tJ/fvxcTUDAsrq1rosq5WV9XonlqXjqjg/Wf//m85sHUjnn4BdOyjSqxSz8sbYxNTfvnyY3oOHYltpQ6fo1E72LV+FRPfeB8vf93R6sddf/r6+jiWJ6Fx9/bl3u3b7Nuy4alz/rQCIYKH5pGcv/r16yOTyVi/fj1hYWEYGBgQERHBnDlzsLW11RqC+TB4enpy9armGOiq26BaRqJnz5707NmTdevWsXXrVg3n7+rVq0RGqqInSqWSxMREWrdW9WT6+/tz9OhRnJ2ddTqp5ubmODg4kJaWRufOuofpeXp6kp2dTUZGBk7lmQwTEhI0EsJU5r7WBzFrx2FyKr2U8otL8HV2IK08y5mBvj6eDnbsv5hQ7TluZ0kIctOcz3D/HFV7SoPdnDE3MdJKva0LA319HKwsuJWRrbG/RC6nRKo5vjyvsJggN2dSsiSAKvOkn7MDW6PjH3idh6WenQ1p2blYVMrip1QqMbGw4taVS+oPHrlMxu2kRNoP0I74qs/l68/1C+c09t26cgkXL28MDFT3Sj0ff5KvxquzPwIkX43Hzbei4ZeXlqKnp9lzqa+vD0rt+yPu2CHMbe1w8lWlYC+Vl5FVJfFKXlExga5OpFaqT18ne3acu1Ttb3kQKZkSZm09oLGvR8MQzIyN2HQ6jpz8wlqfy9DICC+/AC5diKVJ64qe98sXYmncslUNJavHzt6Bj2fN1th3KGoHl8/H8uLb7+PgVPOcHQNDQ1w9fbh5JZ7gxhWjCW5diSeooe42ys3Xn8Ob1yKXyTA0Ug1Lu3X5EpY2tuoIj5ufP4nnNe+Rm1ficfH2wcDAEFdvX8Z/8LnG8aNbN1BcWEjXEWOxcdB0VgwMDXH18uHGlXhCmlTM+bxxJV4re+h93H0DOLhpDXJZKYblcz5uXI7X0FlbTu/dxZFtGxn2ynStJR5MTM3UmSFB9VyZW9uQfFXzubpzPZG2A6t/rlx9/EmK06yz5KuXcK70XNWWg+tXkhBzmqGvvaXhRN/HwNCQel4+3Lh8kdBK9Xnz8kWCGzXTsgdw9wvgwMbVVerzIpY2dhr1eensKbYtmU/fcc9rnPs+nv5BxJ8+gVKhUPe8Z99Lx8jYWOWU6umpHS3QrE9VBtNK9dl/aLV14OrjR1JcrMa+lKuXcPbUrM9zB3ZzaucW+r0wRWOJhxpRKijIz9dyjvKLS/B2slcnYjHQ18fDwZZDOqJz97mTk0tgPc373cfJnruSinfR7WwJoR6akX17S3NK5WUajl/ncsdv1bFosqtpm4pKZVodnNn5hTTycVePbDEyMKC+hyuLDtScoK1NiC+v9+7AjzsOcfzqzWrtZGVlZOcXYqCvR+tgH45euVHjeZ9GDA2NcPf1I+HiBSJbVESEEy5eoEGzljrLeAcEs33VcmSlper5dYkXz2NtZ499lbbbwNAQ2/KkX7EnjxHWqEmtRlEZGhri7qPS1aB5xXsm8WIcEc10L63iHRDEjtUrqui6gLWdnTrrZmlpidb1729X/oQ6vHMbuzesZsIb79W4xMOTVn9KpQJ5LeZUCv59PNIi7/fn/R08eFA9ty8kJITMzEyuXr1a43y/B3E/G+jatWu5c+cOUVFRnDhxQsNm/vz5REdHk56eTlJSEjExMXh6emrY7Ny5k2PHjpGamsqCBQu4d+8evXv3BqBPnz4UFhbyzTffcPXqVdLT04mNjWXOnDkUFqpeKqNGjWLDhg1s2rSJ1NRUbt26xf79+1m7VjV3o2HDhri7u/PDDz+QlJTElStX+P333zWimn8Fp6/dolWQL8FuzjhZW9KvSX1K5XLiU9Iq6qxJOP2aVCSLiLmRipWZKV0bBONgZUGkjzsNvN04lXhT6/wNfT24eS9bI0J3n87hQXg52mFjboabnQ2DW0RiZGDAhVt3Hqj78OXrdA4PJMKrHq62Voxs25gSuZxzSRXzCke1bcyoKmv2udnZ4GZng4mxIeYmRrjZ2eBiU9GD1T0ymGA3Z+wtzXGzs2FE60a42VlzPEHzhaunp4dv87ac2buDxNhoMu+kErVclbI/pNJ6WzuX/s7Opb+rtyPbdEQqyeHA+pVkpd8h7vhh4k8do0mXHmqbxh27kpxwhdO7t5Odnsbp3dtJSbhK407d1DZ+4ZGc2buTpIvnyc3KJPF8DNEHdhPQQPP3ykpLuHz2FN4NW9Q4TOXo5SQ61g+gvmc9XGysGN66EaXyMo1EBsNbN2J4a81IWD07a+rZWWNiZIS5iTH17KxxtrFUXbusjLu5Uo1/RaUySmRy7uZKNVKt14au/QZw4uB+ju7dTVpqCqsXLiA3J5v23VURzY0rljDrkw81ytxJSSblRhL5eVKKi4tJuZFEyg1VRlQDQ0Pcvbw1/llZ22BoZIS7lzemZg9OPtS0S3cunjzKhWOHyEq/w761K8iXSIhsq1qT7/Dmtaz+6Ru1fVizlhgaGbNz2e9k3EklIfYsp/Zsp2nnHuq/T2TbTuRLstm/7k+y0u9w4dghLp48SrMuqs4AYxMTnNw8NP6ZmJljbGqKk5sHBjo6nJp36UHciaOcP3qIzLQ77FmzgvxcCY3K1w48uGktK3/8X4XO5i0xMjZm+5LfybidytVzZzm5ezvNuvbQuI/uptzibsotSoqLKC4o4G7KLTLTKjp6Tu3ewcFNa+n9zETsnV3Iz5WQnyuhuEj3x7Wenh6NOnTl7J6dXDsfTead2+xesRAjExN1lBsgatkfRC37Q73doG0HpJIcDq5fRXb6HS4eP8ylU8do0rniuSqTy7mXmsy91GTkMhmF0jxVVDzjrtpm/5oVXDp5jF7jnsfE3IKCvFwK8nIpLamIiAM079qTCyeOEHv0IJlpt9m9ejnSXAmNy+fcHti4hhU/fK22r9+8FUbGJmxdsoB7t1O5cu4MJ6K20aJrT3V9xp85yZaFv9Jx4HC8AoPVdVVUUDEUsHH7zhQV5rN7zXKy0tNIir/Aka0baNyhi87nW09Pj4btuxC9dxfXzseQlXabvX8uwtjEhKBK9bl7+R/sXl5Rn+FtOpCfm8PhDavITk8j/sQRLp8+TqPO3dU2MfujOL5tA11GjcPWyUVdVyWV/rbHtq7n9vUE8rIyybyTyvGtG0i9lqBx7ftEX09Wr7fnaGVBr0ZhyMrKuHS7Ynmg3o3r07txRWKf8zdTsTQ1pVN4EPaW5kR4uRHu5caZSlG02BupmBoZ0SUiGDtLc3yc7GkT4kfsjYoEVF0bBBPu5ca26IuUyORYmBhjYWKMqdGDOw62nL3IkJaRtArywcvRjql92lNUKuPw5YqEOq/36cDrfTqot9uF+vFG304sPXSG+JR0bC3MsLVQJb25T1A9J1oF+eBiY0WYhwufDFPdKxtOPdr86ZrQMzPFOMAP4wA/0NfDyMUZ4wA/DF1qzib8V9ChVz/OHj7IqQN7uXs7lU1LF5KXk0PLLqp7bsfqFfw68xO1faPWbTE2MWH1/DmkpSQTd+Yk+7duon2vvupnISPtDtFHD5GRnkby9USWz5lFemoyvYbXbhQXQLuefYk+cpBTB/eph2vmSbJp2Vn1Pt655k/mf/2Z2r5hq7YYmRizZsE80lOTiTtzigPbNtOuZ4WusEZNiY85y4l9u8m6d5ebCVfYvHwR7j6+6uUjDm7fws41Kxg26SWcXN2QSiRIJRKKCnW3nY+r/vZuXk/CxQtk3bvL3dupHNyxhehjh2ncRnv6yZPO/ZE//8S/fyuPvM5fREQECQkJakfP2NiY4OBgEhMTCQqq+5j3kJAQpkyZwooVK1i1ahXh4eGMHj2a3377TW2jVCr57bffyMzMxMzMjMjISCZO1Fwwe9y4cWzatInr16/j7OzM+++/j2P5Q+vg4MA333zDkiVLmDFjBjKZDCcnJxo1aoRReW9/jx49MDU1ZcOGDSxduhRjY2O8vLzo27cvoOoF+uCDD5g9ezZvvPEGTk5OTJw4ke+++67Ov10XJxNuYmRgQI+GoZgaGXInO5dVx2I0soxZm2uuBZVbWMSa4zF0bRBMY19P8otL2H3+itbkeFtzM3yc7NlUTbIPazMTBjSLwNzEmMKS0vJFbE+RV1Ss074yBy4mYmRgwOAWkZiZGJGckcP8Pcc11viz1TEx/o3+mgtl1/esR3Z+IV+uV6VDNjU2YmirhlibmVBUKudOtoS5u47oXLDXv2UHbE2N2L92BcWFBbj6+DHkFc05L9IczSimjaMTg158nUMbVnHh6EEsrFXp6e+v8Qfg5hdAn/EvcGzbRo7v2IytozN9nntBvcYfQOdhozm2fRP71iynMF+KpbUNEa3a07JXf43rXY05g6y0BK+GuiM89zl06RpGhgYMbB6BmbERKZk5/L7vhMZ9YGuh7Qy93qejxnaYhyvZ+YX8b9PeGq9XF5q1aUeBVMqO9WvIzcnGzcubV9//WB2hy83JIfOu5jqSc2Z+RlZGxX35xVuvA/Dbui1/iaaQJi0oKijgxK6tFOTl4ljPnSEvT1NHc/Jzc5FkVlzfxMyc4a+9yd7Vy1n2v08xNbegaeceNK3k/Ns6OjHk5WnsX7+S2CMHsLSxpcuwMdVG6WpDaNMWFBXkc2znFrXOYa9Mr6RTQk6lejI1M2fElLfYvWoZi7/+BFNzC5p36UnzLpqjCxbNnKGxfS0uFmt7B17+8nsAog/tQ1FWxubf52nYhbdso7Ug/H2adu2JXFbK/rV/UlJYgKu3H4Nenq6xJl1ejuZ8bxsHJwa+MJVDG1cTd/QgFja2dBwySr3G3/3f+Oc3FR9pcZmHiDt2CPeAIIZNeRuAC0dVker1c77XOH+Lnv3oUinyGNa0JUX5+RzbsYX8PAlObh6MePUNjfqUVKnPUVPfJmrlUhZ9NQNTc3NadO1F80rR/XOH96NQlLF37Qr2rq3IQO0VGMLYN94HwNregVFT3mbvuj/548sPsbC2oUHr9rStslh9ZRp36YlcJuPQelV9unj7MeClaRr1mV+1nXJwov/zUziyaQ1xxw5haWND+8Ej1Wv8gWoBd0VZGbuWzNcoG9KsFd3GqKY8FOblsmf5HxTk5WFiZoaDmwf9n5+Cd2g4RaWawzlPX7uFoYEBXRuEYFq+yPva4zEaa/xZmVV9FxWz/uQ5OocH0dDHg/zyZR/ur/EHqsRga0/E0Ck8iHEdW1BQXEpc8h1OXK3o0Gvkq+rcHdFGM2JvZ2nJymOayzhVZcOpC5gYGvJCt9ZYmhqTcCeDGWt2aUQInawtNcr0bBiKoYE+k7u2YnLXiohSXHIaH6zcDqiyfY5p1wRXWyuKS+WcTUrhh+2HahwG+6iYhgThUWk4ucOkZ3GY9Cx5O3Zzd+b3NZR8dBq2bEOBVMrezevJk+Tg6uHFxLfex748WpYnySHrXkVHjZm5Bc+/+xEbFv/OTx+/g5m5BR1691OvpQeqKTiHdm4lI+0OBgaG+IfV59WPv9SKbNWsqzWF+VL2b9lQrsuTCW+8p47iaesyZ/LbH7Fp6R/8POM9zMwtaN+rL+179lXbNG3XkZKiIo7v3cW2lUsxNTPHP7Q+vUeOVduc2BdFWVkZK+b+qKGnSdsOjHj+lSem/kqLi9mwaD6S7GyMjI1xdnNj1Auv0ajSCB3Bfwe9oqKif6Vre/fuXSZNmsSsWbMIDKzlMJcniFk7Dj9uCbUiS1r7IYGPk6B6DzcE7nGRdC/7wUaPmV6RD7fG3uPi2iMsVP9PYqBft0Qn/zTFsqdjeJB5NWsDPmnoWpf0SaSq8/ckcuTKzcctoVZ8v1V3ttcnjSs//LWd138X1U2vedJ4lASA/yTdIp6Ob+Wfdh39x641tee/0zl+Ou5IgUAgEAgEAoFAIBA8Eo887FMgEAgEAoFAIBAI/m7+zXPx/in+tc6fi4sLW7dufdwyBAKBQCAQCAQCgeCJQAz7FAgEAoFAIBAIBIL/AP/ayJ9AIBAIBAKBQCD491B1nWrBwyMifwKBQCAQCAQCgUDwH0BE/gQCgUAgEAgEAsETj0j48uiIyJ9AIBAIBAKBQCAQ/AcQkT+BQCAQCAQCgUDwxCMCf4+OiPwJBAKBQCAQCAQCwX8AEfkTCAQCgUAgEAgETzwi2+ejIyJ/AoFAIBAIBAKBQPAfQET+nlA6hPo9bgm1oqCk9HFLqBUmhk/Hre5gZfG4JTyQ+h6uj1tCrUjJyn3cEmqFnt7jVlA71p2Ke9wSakWwm9PjllArPorwfdwSaof+k99HvP3clcctoVZc+eG7xy2hVoRMe/NxS6gV9l9+9Lgl1IqS6POPW0LtiAh83Apqhcj2+eg8+a26QCAQCAQCgUAgEAgemacjHCIQCAQCgUAgEAj+04jA36MjIn8CgUAgEAgEAoFA8B9ARP4EAoFAIBAIBALBE48CEfp7VETkTyAQCAQCgUAgEAj+A4jIn0AgEAgEAoFAIHjiEdk+Hx0R+RMIBAKBQCAQCASC/wAi8icQCAQCgUAgEAieeBQi8vfIiMifQCAQCAQCgUAgEPwHEM7f38DevXsZNmzY45YhEAgEAoFAIBAIBGrEsM+nlP07trFr0wYkOdm4e3oxauLzBNUP12krKy1l6S9zuJV0nbTUFAJCwnjny6+17OQyGVvXruLEwQNIsrOwtrWjx8DBdOvbv846D0ftZN/WTeRJcqjn4cngcRMJCA2rVueq338l9UYS6bdT8QsOYeqMLzRsYk+d4NjeKFJv3EAmK8XVw5Meg4YS0bR5nTUCHNy1najNG8jNycHN04sRz00mMKx+tTqX/zaX5KQk0m6nEBASypuffVXtuRMvx/P9x+/j6u7BJz/OrbWmk/t2c3TnVqQSCc7uHvQZ/Sw+waHV2qenJLN1+SJSk65hZmFJ805d6dR/MHp6emobuVzOwS0biD1+hDxJDpbWNrTt1ZfW3XqpbY7v3sGp/XuRZGVgbmlFaKOm9Bg+GhNT01prr8qGdetYuWIZWVlZ+Pj6MXXaNCIbNtJpe+NGErO+/ZabN25QUJCPg6MjXbt1Z8KkyRgZGdVZw9mDezmxezv5ubk4ubnTffhYvAKDq7W/dzuFXSuXcOdmEmYWljRq14l2fQaq6/NWwmUObFxD1t10ZKUl2Ng70rBtB1p176M+R8yRA8SdPErGndsolUpcPb3pMGAIXgHVX/fswb2ciNqOtFxnjxE167ybqqmzcfsqOq9eZn8VnY3aaeq8dPYUx6O2k33vLooyOfbOrrTo2pPI1u1qXb/VMbptE3o2DMHS1ISrd+7xy+5jJGfmVGsf7lmP8R2b4e5gi4mhIffy8tkde4UNpy88spb79IgMoWWQD+bGxtzKzGb9qfPclUirtbcyM2FA0wjcHWxxsrLkbFIyq47FaNg08/diVNsmWmXfXrYZuULx0BrX7Y5ixbatZEkk+Hp4MO3ZcTQMqf75v09yWhrj338XpVLJgcVLNY7J5HIWbdzAziOHyczJwd7GhtF9+zGiZ69qzlYLnVFRrNi6uULnuOdoGFpLne++rdK5dLl6f3R8PK989omW/apZP+Lj7l5nnQDjOzanb5P6WJmacPn2XX7cfoibGdnV2kd6uzG5ays8HewwNTLkbq6U7TGXWH38nNqmZ8MQ3h3YVats9y9+oVRe9tAaj+3ZxcEdW5BKcnBx92TA2PH4heh+ZwKkpdxi45I/SL5+DXNLS1p27ka3gUM12v1je3ZybM8usjMysHNwpMuAwTRt1/GhtT0sppHh2I0aimlwIIZOjqR/+R3SnXv+9uveZ/3B/fwZFUVWrgRfN3emjhhJw8AgnbZpmZkMef8drf2zprxOy/AIADIlEmavW8PVW7dIvXeXni1b8eFzEx9Zp3lkOJZNG2FgYY4sK5u8g0cpvZ1WYxmLRg0wjwzH0NoaRXExhZeuID16UuOcFg0jMLSxpixPivRUNEWXrz6y1icBMerz0RHO31PI6aOHWfnHfMa+8DKBoWEc2LmdHz6fwRezf8HByVnLXqFQYGRsTOfefYmLPkthQYHO8/72/TdkZ2Uy7uXXcKnnRm5uDrKS0jrrjD5+lPVL/mD4xOfxDw7lyO5d/PLV53ww62fsHZ106zQyon2P3sSfi6aoUFvntcvxBNaPoM+I0VhYWnHmyGEWfPc/psz4vFqn8kGcOXaEVQsXMGbySwSEhnFw1w5+/vITPvlxbo312alXH+JizurUeZ+C/HwW/fwDIRGRSLKzaq3pwqnjbP9zCf2fmYB3UAin9u1myayvmTrze2wdHLXsi4sKWfTtl/gEh/LyjJlkpN1h/R+/YGxsQttefdV2a375GUl2FgPHT8bBxZX8vFxkpRV/4/MnjrJrzZ8Meu55fIJCyM64x8aFvyGXlTJ44ou11l+ZfXv28NMP3/PGW+/QIDKSjevX8ea011m2cjWurq5a9kaGRvTq3YfA4CCsLK24lpjI/76aSZlczsuvTamThvgzJ9m9ejk9R4/DKyCIswf3sXL2t7z4ydfY2GvXZ0lRESt+/B9egcFMeO9Tsu6ms3XxfIxNTGjZrTcAxiamNOvcHWd3TwyNjUm9lsiOFQsxMjahaUfVx+CthMuENW2Jp38gRsYmnNq3i5U/fcPkD7/E3kX7t8efOUnUquX0GjMOz4Agog/u48+fv+WlT77GRsffvbLOie9/SlZ6OlsWz8fI2IRW3ct1mpbr9PDEyNiYlGuJ7FiuqdPM0pK2vfvj6OqGvoEBiXGxbF36O+ZWVgRGNKxTnQMMbRnJoOYR/LD9ELezJIxq25gvRvbmhflrKCqV6SxTLJOx5Ww8NzOyKZHJCfNw4dWe7SiRy9kec6nOWu7TOTyQDvUDWHU0hnt5UrpHhvBitzZ8vXEvJXK5zjKG+gYUlJSyPy6BlkE+1Z67RCZn5obdGvvq4vjtOXGcH5Yu4a3nJhIZEsz63buZ9vVXrPxuFq6O2vfBfWRyOR/N/omGIaGcu6xdVx/N/ol7WVm8O+l5POu5kp2bS0lp3dv4PceP8cOSRbw1cRKRwSGs3x3FtK++ZOWsH3DV0cZX6JTx0U8/0DA0lHOXdP9NV34/C2tLS/W2rbV1nXUCjGrTmOGtGvL1pn2kZOXwbIdmfPfsAJ6Zvbzae7GoVMaGUxdIuptFsUxGhFc9pvftRLFMxuYzFzXsxvy8TKNsXRy/2JPH2Lx8EYPHT8I3KJTje6P4/duZvPW/H7DTUZ/FhYXM//pzfINDmfrZ12Sk3WH1/DkYm5jQsbeq0/b43ii2r1rBsIkv4hUQSPL1RNb98StmFpbUb9z0oTU+DPpmZpQm3UK6ay8uH771t16rKnvPnObHVat4c8wYIgMC2XDwAG/8/CMrPvkcVweHasvNmjqNQA9P9ba1hYX6/zK5HBtLS57p1YvNhw//JTpNgwKw6diW3P2HKb2dhnlkOPaD+pGx5E/KpPk6y1h3aIOpnw95h48jy8xCz9gYA8sKneYN6mPdrjW5ew5Qmn4XI1dnbLt1QlFSQknSzb9Et+Dp5ql0/pRKJZs2bWLnzp1kZGRgY2NDp06dGDduHIsXL+bkyZNkZGRga2tL27ZtGTNmDMbGxgBkZGTw22+/ER8fT2lpKU5OTowePZr27dtz9+5dJk2axKxZswgMDFRfr1+/frz77ru0adMG4IHX+LuJ2ryRNp270qF7TwDGPP8ScediOLBrB0OfGa9lb2JqyrMvvQpA6q2bOp2/i+diuHQhlq9//R0raxsAHF1cHknnge1baNGhE226dAdg2ITJXD5/jqO7d9F/9DM6dY6c/BIAt5Nv6nSqho6fpLHde9gI4s+d5cKZU3V2/vZs3UTrTl1o160HAKMmvUD8uWgORe1k8NhxOnWOfeEVQFWfNTl/S+f9TKtOXVAqlcScOFZrTceittO4TQeadewCQL9nniPx4nlO7d9Dj2GjtOzPnziKrLSUoZNfxsjYGBcPTzLSbnM0ajttevZBT0+PxIvnuXYpjje++QkLK9WHlF0V5/bWtQQ8/QNp1Ka9+njDNu2JP3uq1tqrsmrln/Tu05f+AwcCMO3Ntzh18iSbNqznxZdf0bL38PTEw7Pi5etarx7nYqI5fz62zhpO7d1Jg9btaNyuEwA9Rz3L9UsXiD60j86DRmjZXzx9DFlpCf3Hv4CRsTHO7p5kpt3m1N5dtOjaCz09Pep5+1LP21ddxs7RmSvnzpJ87araqRo08WWN8/YaPZ6rsdFcj7+g0/k7uWcnkVV1xl/g7KF9dBmsrTPulErngOcq6UxX6WzZ7QE6Eyt0+oZoRrlbdOnBheNHSE68+kjO34BmEaw7eZ7jV28AMGvbQVZMeYYOYQHsir2ss8y19EyupWeqt+/mSmkd7Et9T9e/xPlrHxrA/rgELiTfAWDl0Wg+HdGbxn4enEi4qbNMTkEhG8sjjw28a44+SYtLHlnjyu3b6dO+AwO7qJ7/N5+bwMkL59mwZzcvjxpdbbm5f64gwMuLRqFhWs7fqQvnORMXx/off1Y7Um46OrceTuc2+nToyMAuqvvozQkTOXk+lg27d/Py6DHV61yxggAvbxqFhVXr/NlZ2zyyw1eZoS0j+fNoNIcvXwfgq4172fTWRLpGBLE1Ol5nmYS0DBLSMtTb6RIp7UL9aeDlpuH8gZLs/MJH1nho51aatetIy07dABg0biJXL5zjxL7d9B6hXZ8xx49QWlLCqBdfxcjYhHqeXty7k8rhndvo0Ksfenp6RB87RItOXWjUui0ADs4upCRd58C2TX+781d48gyFJ88A4PL+m3/rtaqyas9uerduzYB2HQCYPmoMJ+MvsvHQQV4aPKTacjYWljjY2Og8Vs/RkekjVc/fgejov0SnZZOGFF66QmGc6jnIO3AEUx8vzCPDNSJ59zGws8WiYQQZy1Yjz64YQSHPqGgzzcOCKYyLp+hqIgBluXkUurhg2azRv8L5E0s9PDpP5Zy/pUuXsnr1aoYNG8bcuXN59913cSzvDTU1NWXKlCnMmzePl156icOHD7NmzRp12V9++YWSkhJmzpzJ3LlzmTx5MhaVenZqw4Ou8Xcil8m4df0a9asMmavfsBHXruj+mKoN506dwCcgkN2bN/HGxGd596XJrFjwK8VFRXXTKZeRknSd0AYNNfaHNIjkRsKVOuvURUlREeYWlg821IFcJiP5+jXCIjXrM6xhI65frXt9gmooaa4khz5Dhj+cJrmcOzdvEBDeQGN/QP0GJF9L0Fkm+Voi3kEhGFXqgAgMj0QqySEnU/XxcinmLB6+/hyL2s7/pr3MrHdeZ9vyxZQUF6vL+ASGkJZ8k+RrqpeGJCuTK+eiCW6ge4jmg5DJZCRcvUKzFi009jdr0YKLcbUbxpeaksKpkydp2KhxnTSUyeWkJd/EL0xzWLRfaDip1xN1XzPpGl4BwRr16V+/AVJJDpKsDJ1l0pNvkpqUiHdgSI1a5DIZpubabU61OsP+Wp1pyTdJva66X3ShVCq5cTmerLtpNf6WB+Fqa4W9pTkxN1LV+0rlZcSnpBPqUfuOJT8XB0LdXYhLrnkYVG2wtzTH2tyUq3fuqffJyhQk3c3Cx6n6aEBtMTIw4MMhPfh4aE8mdm6Fu73uj8iakMnlXL2RRIsGms9/i4gGxCXofv4BjsXEcPRcDNPHPafz+KEzZwj192flju30e+Ulhk6byveLF1FY6fl/OJ0yriYl0aJBpKbOBpHEJVQ/vOxYTDRHY6KZ/tyEGs8//v136fPCZF79/FOiL16s0fZB1LOzxsHKgjPXU9T7SuVlnL91h/qe9Wp9ngBXR8I9XTl/67bGfmNDQ1a9/ixrp4/nq9F9CXCtPjpbHXK5jNs3kgiK0KzPoIhIbibqrs9b167iGxyKkbFJJfuG5OVkk52husflMjlGRpod00bGxqRcv0ZZNZHupx2ZXM7V5Fu0qDJ1o3lYfeKuX6ux7Pu/zKX3G6/zwv++Yn/02b9TJujrY+TiRMnNFI3dJbdSMHbT7hwEMPP3pSw3DxMfL5wnjMV54jPY9uiCvplZhZGBAcoyzcizUi7H2NUF9J/Kz37BX8xTF/krKipi8+bNTJ48mW7dVL1jbm5uhISoPlJGjhyptnVxcWH48OFs3LiRsWPHAqrIX+vWrfH1VfWE6xp29iAedI2/E6k0D4VCgbWtncZ+G1tbLj1CZCTjbjqJly9hZGTEy++8T1FBASsW/IokO5tX3nn/oc9XkCdFoVBgZWOrsd/Kxpartfzorw2Ho3Ygyc6iefsOdSqff78+q+i0trHlsuR8nXWl3rrJ1jWreO+rb9E3MHiosoXlmiyr9D5a2thw/VKczjL5uRKs7R207O8fs3dyJufePW4lXMXA0IjRr06juLCQrcsXkSfJZvSr0wFo0LI1hflSfv/qE5SAoqyMhq3b0WN49dGGmsiVSCgrK8Pe3l5jv729PWfPnK6x7IuTJ5Jw9SqlpaX0GzCQF156uUb76ijMl6JUKLCw0qxPC2sbblzR3eOfn5uLtZ19FXtVFKIgNxc7x4qIyU/vTKEwX4qirIx2fQfRpEOXarUc3LwOYxMTgiK1HVm1TmttnfmXdessyM3FqqpOK906f3y7Qmf7fto6iwsL+fGdKZTJ5Ojp69Nr9LMEVPkQfRjsLMwBkBRoRkRyCopwsDJ/YPklr4zGxtwMfX09Vh6NYee5R+uMAbA2U81brRqdkxaVYGNe9zmtAPfy8ll1PIY72bmYGBnSPtSf13q157st+8mUVj86oCqSvDzKFArsqzz/9jY2nLmo+/nPzMnhqwXz+Xr6dCwqfwRW4s69e1y4ehVjQyO+mjad/IICvl+8WFV22vTa/1C1Tmn1OuMk1euc/xtfT3+zWp2Odra8PWkyYf7+yORydh4+zKtffMa8jz+hUVjdRnfYW6rutxyte7EQR6sHdxyunT4eG3MzDPT1WHLoDFvOVjyPyZkSvtm8n+t3MzEzNmJoy0jmTBzCxF9WcTs7t9YaC6TS8nbfVmO/pY0t0mr+7lKJBJsq7b5V+d9DmivBwdmF4IhITh/aT3jTFnj6+ZN64zqnD+6jrExOgVSKtZ2drlM/1UjyVfemXZXIsb21NWd1DIcGMDMx4dWhw2kQEICBvgFHz8fy8fxfKX1uIj1btvpbdOqbmaKnr4+iUPO+LCssxMTcQ2cZAxtrDKytMAsORBK1DwDr9m2wH9iHzJXrACi5mYJ5eCjFiUnI7t7DyMUJ84gw9AwM0DczRVHw6FHqx4lY6uHReeqcv5SUFGQyGZGRuj9Kjh07xubNm0lLS6O4uBiFQoGi0pyLfv36MW/ePKKjo4mMjKRVq1YEBAQ8lIYHXeOfQK/KtlKJxgTvh0WpUKKnp8fz09/GvDwSOmbyS8z69CNyJTnY2NbxBVFFk1Kpva+uxJ46wablS3hu6hvYP+Lwpap19yhNi0wmY8Gsbxj67HM46hjaV2tNVbZVQx2qrzvd9hVHlEoF6MGIF1/D1Fz1MdTvmedY/N1X5OdKsLSx5caVSxzYsoF+z07E0y+ArHvpbF+xhH0b19J18MNFMDW0ad0Hygfer59+MZPCwgKuJSYyb/ZsVixbyjPjxv9lGlAq0auhPqseqqhOzQPPvvUhspISUpOusX/DamwdnWjQsq3W6U7viyLmyH7GvP4uJtV8+OrSqXyATq2fVc2BcW9/SGlxCbdvXGPf+tXYOjjRoFWFThNTU57/6EtKS4q5cTmePWv+xNbBCd9Q3YmPqtKxfgCv9qxIEPPJml3l+mvWWx1vL9+KqbEhIW4uPNepOem5Ug5c1B0BrY7Gvh4Ma1URtf5933HVf+qoqSZuZWRzq1LykJsZWbzZrzPtQv3VQ0Yfhod5Zj6ZO4fB3boRXk0yC1B9MOkBn702Bcvy5//N555j6lczyZJIcLC1fWiNunVW/y76ZM7PDO7WnfCg6nV6u7nj7VYxtDYiKJi0jAxWbNtSa+eva0QQb/TrqN5+d8U2tTYN7ehRm9b+tYXrMTM2JszDhRe6tSYtJ489F1TRuEup6VxKTVfbxqek8/uLIxncogGzdx6plV5NTVVQKmtq9qt9d93f323QUKS5EuZ89gEolVja2NKkXQcObtuM3r88CqTrGaquMm2trBjdvYd6O9THB0m+lBVRu/4256969Kq/K/X00DM0JGfnHsokqs6FnJ17cJkwFiNXF2Tpd5GeOoOBhTmOIweDnh6KwkIKL13BqlljUAjHSfAUOn81jfW9cuUK33zzDaNGjaJx48ZYWlpy6tQpFi5cqLbp3r07jRs35uzZs8TGxvLWW28xbNgwRo8erW4oKl9DXmVYRG2uURO7du0iKirqgXYhTZrTtI12pj0rK2v09fXJlWhmy8vLlWBdx5c3gI29PXb2DmrHD8CtfNJzdkbGQzt/FtZW6OvrI62iMz9PgnU14+kfhthTJ1g650eeeWXqI2X6tKymPqWPUJ+5OdmkpaawZO5PLJn7E6C6p5RKJS8OG8BrH8ygfsPqhzCal2uS5mr2Ghfk5WlFA9W/w8ZWp73qmKqMla0d1nb2ascPwKme6iNLkpWFpY0tezaspkHL1jTr0BkAV08vZCUlbFw4n04DhmDwkFFMG1tbDAwMyMrSTHaTk5OjFQ2sikv5nFNfXz8UZQr+99WXjBozFkPDh2u2zC2t0NPXJz9PorG/QJqnjuZVxdLGhoIq9VkoVdVn1TL3o2vO7p4U5OVyeOtGLefv9L4oDm5ex8gpb+Lu61+zzlxNnYU16LSwsSH/IXW6eFTSWcn509PXx95ZVeeunt5kpt/h6M4ttXb+TiXe0hhOaVR+r9hZmmtEvmzNzcgpePBw8ru5quybtzJysLUwY0zbJg/t/MWnpJOcuV+9bWCg+ti1MjNBUlihwdLUBGnRo8/Vq4xSCSlZEhytHm5aga21NQb6+mRJJBr7c/LysLfW/fyfjb/IucuX+GP9uvJrK1EolbQZM4q3JkxkYJeuONra4mRvr3b8AHX2zLtZmQ/t/NlaW1WjM1crGqjWefEi5y5d4o91azV1jhrBWxMnMbBrN53l6gcEsuf4Q8yZvnqDy7fvqrfv34v2luZk5FUk0bC1MCM7/8H3Ynp5Jtgb97KwtzRnfMfmauevKgqlkqt37uFhb1trvQAWVuXvzCrPf35ertYImvtY2dqSV6X+77cHluX3ipGxCSOef4WhE15AmpuLtZ0tJ/fvxcTUDAsrq4fS+LRga6m6N7OrtI05Uin2DzGPtL6vH9sf4r57WBRFxSgVCvTNNUdCGJibaUUD1WUKClGWlakdP4AySS7KsjIMrCyRpd8FeRmS3fuR7D2IvrkZioJCzCPCUJSUoqjjVJ4nCTHn79F56pw/T09PjIyMOH/+PG5ubhrHLl++jIODg8awzHv37lU9BY6OjvTs2ZOePXuybt06tm7dyujRo7Epf2Hl5FQ4AklJSXW6RnXcv+6DiK40T6YyhkZGePsHEB97jmaVnMNL58/RpFWbWuuoSkBIKGePHaW4qAjT8qhE+h3VvAZdGS8fhKGhEZ5+/lyJO0+jSrquxJ2nYfNH60WLOXGM5XN/ZuwrU2jUsvUjncvQyAgv/wAun4+laeuKD+FL52NpXMdz29o7MOOHORr7Du7azuXzsbz0zgcPrE9DQ0PcfHy5Fn+BiOYt1fuvxV+gftMWOst4BQQStWYlstJS9fyva/EXsLK1U2eJ8woM4uKZk5QUF6uXbci6q5pHZVs+Z1ZWUop+ld5gPX19lHWMhRoZGREUHMKZ06fp3KUiHfqZ06fo2Klzrc+jUCooKyurU4TdwNCQel4+3Lh0kbAmFfV343I8IdUkPPDwC2DfhtXIZaUYls+XSbp0EStbO2wdqs9iqFQqKZNrZg48uWcnh7auZ+Srb9a4xMN9nUmXLxJW6e+cdOnv0SmvolPLRqGkTFb7OUFFpTKtrInZ+YU08nEnsTxphpGBAfU9Xflj/8MlENLX08PI4OGjFCVyOSVSzd+QV1hMkJszKVkSAAz19fFzdmBr9KPNK9NFPTtr7mTnPVQZI0NDgn39OB0XR5dKEYfTcXF0aq67o2vFN99qbB8+e5bFmzay8IsvcSofFtwgOJh9p05SWFyMefnzn5ymev5rysxZvU4jgv38OB13ni6tKuu8QKfmutupFd9+X0XnGRZv3MDCL7/CqYbOoMRbN3C0s621tqJSmdaQyyxpAU39PdUdFMaGBjTwduPX3Q/3ca+np4exYc2dYH4uDly/m1mjTVUMDY1w9/Uj4eIFIltUvHsSLl6gQbOWOst4BwSzfdVyjXY/8eJ5rO3stUbDGBgaYlue5TL25DHCGjXRauv/LRgZGhLs5c3py5fo3LSZev+ZS5fo2Fh7OZbqSExJwfEv6KyuFoUC2d0MTLw9KU68rt5t4u1JUaXtypTeSUPPwAADG2vKclVti4GNNXoGBpRJqyxXo1CgyFd1vJmFBFJ84+bf8jMETx9PnfNnbm5O//79WbJkCUZGRtSvXx+pVMq1a9dwd3cnKyuLgwcPEhISQkxMDIerpOOdP38+TZo0wd3dncLCQmJiYvAszyxoYmJCcHAw69evx9XVlcLCQpYsWaJRvjbX+LvpMWAQC378Hr+gYAJCQjkYtRNJdjYde6hSu69btpgbCQm89flMdZnbKcmUyWTk5+VRXFxEcpKqYfHyU0UhWrbvyNY1q1g4+wcGjBxDYUEBK/+YT9PWbeocAevUpz/L5vyEt38gfsEhHN0bRW52Dm3Ls2pu+XMZt64n8tpHn6nLpKWmUCaXU5AnpaS4mNSbqiyBHj6qOZrRx46wdO5PDBo7joDQMPLKI3YGhoZYWNatF7Nbv4Es/HkWPoGBBISEcShqJ7k52XTorlr7asPyJdy8lsD0T75Ul7mTkkyZXE6+NI+S4mJSbqg6CTx9/TA0NMTdy1vjGlY2thgaGWntr442Pfqwbv5cPPwC8A4M5vSBPUglOTTvpHKgotauJDXpGhPf+QiAyJZt2b9pPet//4VO/QeTmZ7G4e1b6DxgiDqiHdmyLQe3bGDD77/QedBQigsL2bZiCeFNW6h7iUMaNuZY1A7cffzx8A8g+246ezesISSy8UNH/e4zctRoPv90BmFhYUQ0iGTTxg1kZWYycNBgAH6dN5fLl+L5ac48AHbt3IGxsTH+/gEYGhlx5fIlfvtlHh07da5zRt0WXXuxedGvuPn64+kfSPTh/Uhzc2jcXjXvbf/G1dy5kcTY6e8BUL95aw5v28SWxfNp23sA2XfTOR61lfZ9B6nr88z+3dg6OuHgokoYcSvxCif37KBphwon90TUdg5sXsvACS+pltYo79U3NDbG1Ex73lvLbr3YtPBXVf0HBBJzSKXz/vy8fRtWc+dmEs+U6wwv17l50Xza9RlA1t10ju3S1Hm6is7kxCuc2L1DnekT4Mj2zbj7+mPn5IxcLuNa3HniTh6j5yjtrLwPw+YzcYxo3YjULAm3s3MZ0aYxRaUyDl2qSLowvW9HQJUJFKBfk/qk50q5Xe6chXvWY3CLBn9Jpk+Aw5ev0TUimHu5+WTkSenWIIQSuZyYpIoOt/vr9a08WpHRz81O9YyYGhuiRImbnQ1lCoU6Qtk9MoRbGdlk5OVjamREu1B/3OxsWH/y4ecOj+rTh0/nziHM358GwcFs3LuXzJxsBpVHxuat/JNL168z50PV8+/v6aVR/nJSEvp6ehr7u7dpy8ING/ji13lMGjIMaWEBPyxZTOcWLaqN1D1YZ18+nTObMP/Acp27yczOZlA3VYbneX+u4NL1a8z5aIZKp1dVnddVOivtX7V9O/WcnfD18EQul7PryGEOnTnDV9MfLVvkupPnGdu+KcmZOaRmSXimfVOKSmXsjatIovPeINUz8dXGvQAMat6AdEmeel3KSG83RrRuxOYzFXPwxnVoxqXUu6RmS7AwMWZwiwb4uzjww/aDD62xQ69+rPxlNl5+AfgEhXBi327ycnJoWZ4xe8fqFSRfT+TF9z8BoFHrtuzZuJbV8+fQZcBQMtPvsH/rJroNHqZ+/jPS7pB8PRGvgCCKCvI5vHMr6anJjHzh1YfW97DomZli5F7eSa+vh5GLM8YBfiikUuR3dSek+qsY2a07ny38nTAfXxoEBLDx0EEycyUM7KDKD/DLhvVcupnE7OmqJSh2HD+GoYEBQV5e6Onpc+xCLOsP7uflwUM1zpuQkgxAQXER+np6JKQkY2RgiG+VYERtyY+Oxa5XV2Tpdym9k455g/roW1hQeF41r9SqbUuMXV3IWrcZUCWDKb17D9senck9cBQAm05tKU1LR5au6tgwsLXBuJ4LpWl30Tc1wbJxQ4wcHJDs2lcnjU8aIvD36Dx1zh/As88+i4WFBatWrSIrKwtbW1s6depE7969GTx4MAsWLKC0tJRGjRoxZswYfvnlF3VZpVLJb7/9RmZmJmZmZkRGRjJxYsUinVOnTmX27NlMnz6devXq8dJLL/Huu++qjzdv3vyB1/i7ad62Pfl5eWxds4rcnGzcvbx5/aNPcXRW9fTlZmdzL10zM96Pn80gK6MiQvnJdNV6aQs3bQfA1MyMNz/7kj/n/8rnb07D3NKSRi1aMvTZ8XXW2aR1WwqkUqI2riUvJ4d6nl689O6H6h7JXEkOmXfTNcr8+vXnZGdUvBT+944qEcHs1RsBOLo3CkVZGeuXLGT9koqhtgFh9bUWhK8tzdq0o0Cax451a8jNycbNy5vX3p+Bw/36zMkmI11T5+wvP9Woz8/fnArA/PVb66ShKg1atKYwP5+DWzYgzZXg4u7Js9PfVUfxpJIcsu9VDGsyNTfnubc+YOuyhcz75H1MLSxo07MPbXpWLORtYmrKc299yLbli/jl0w8wNbcgrHEzjaUjOvZXzRHYu3ENudlZWFhZEdywCd2HaC8zUFu6dOtGbm4uSxYtIisrE18/f76d9QOu9VTOSFZmJrdTK7LnGRgYsHzJElJSU0CpxMXVlcFDhjJipPYSF7WlfrOWFBXkc3THZvJzJTi5eTDy1TfVaybm50rIyaz4e5qamTPm9XfY+ecS/pg5AzNzc1p27UWLrhWLYSsUCvZtWE1uVgb6+gbYOTnTedAImrSviGiePbQXRVkZGxZoRoIbtGpL//EvVKvzSCWdo16rorPSfWdqrtK5a+USfv+yXGe3XrTsVqFTqVCwb72mzi6DNXWWlhSz88/F5OVkY2hkjKNrPQZMeIHwR4zSrzt5HmNDQ17q0RZLU2Ou3rnHR6t2aEQInaw1E27o6+vxXMfmuNhYUaZQkibJY/HB0+z4i5y//RcTMTIwYEiLSMxMjEjOyOG3Pcc01vizs9Cek/lmf81IdbhnPbLzC/hivWpdPzNjI4a1aoS1mQlFpXJuZ0uYs+tIjQvaV0e3Vq3JlUpZtHEjWZIc/Dw9mfXOu9RzUj3/mRIJqXfvPuAsmpibmjL7gw/5fvEinvvwfawtLGjftFmNS0c8UGfrNuRK81m0cT1ZOeU6332/ks6ch9Ypk8uZvWwpGdnZmBgb4+vpyax336N1HbP93mflsRhMjAx5vXcHrMxMuJR6l7eWbda4F11sNDsQDfT1eL5rK1xtrSlTKLiTk8v8vcfZcrYiSmxpasIb/Tpib2lBQUkJiWmZTFm0kSu3az8i6D4NW7ahQCpl7+b15ElycPXwYuJb76vXxc2T5JBVqd03M7fg+Xc/YsPi3/np43cwM7egQ+9+dOjVT22jUCg4tHMrGWl3MDAwxD+sPq9+/OUjz5OvDaYhQXjMrohKO0x6FodJz5K3Yzd3Z35fQ8lHp2uz5uQW5LN4xzaycnPxc3Pnu9emUq+8Lc3KlXA7Q9MBXbxjG+lZWejr6+Pl4sL7457Tmu83/vNPNbaPXjiPq4MDG776pk46ixOukWtmimWLphhYWCDLyiJ741Z1FM/AwhwDG82hqtmbtmPTqR2OIwahlMspuZVK3qGj6uN6+vpYNmmIgZ0tKBSUpNwmY9V6yvKqRAYF/1n0ioqKhA/9BFLdsM8njYJHWAT+n8TkIeeJPS4yHiIr4OOiQ4jf45ZQK3adrz7d/JPEX5T/6G9n5fHYxy2hVgS7PfwQxsfBRxG+DzZ6EngKhgYO2vTPjr6pK2/0qVtW6n+akGn/7Jp8dcX+y48et4RaURJd98zh/yT2L098sNETwAerd/1j1/pyxIOnaT2NPPmtukAgEAgEAoFAIBAIHpmnIxwiEAgEAoFAIBAI/tPUNQGdoAIR+RMIBAKBQCAQCASC/wAi8icQCAQCgUAgEAieeBQi3ecjIyJ/AoFAIBAIBAKBQPAfQDh/AoFAIBAIBAKBQPAfQAz7FAgEAoFAIBAIBE88YtTnoyMifwKBQCAQCAQCgUDwH0BE/gQCgUAgEAgEAsETj1KE/h4ZEfkTCAQCgUAgEAgEgv8AIvInEAgEAoFAIBAInnjEUg+Pjoj8CQQCgUAgEAgEAsF/ABH5e0LxcbR/3BJqRWp27uOWUCucrC0et4RaIZOXPW4JD8SksOBxS6gV9/LyH7eEWuFo9XTcm2/06fC4JdSKvRcTH7eEWmHo5/O4JfxrsLc0f9wSaoVCoXjcEmqF/ZcfPW4JtSL7g88ft4RaoWdi8rgl1Ar7lyc+bgm1Qsz5e3RE5E8gEAgEAoFAIBAI/gOIyJ9AIBAIBAKBQCB44lGIwN8jIyJ/AoFAIBAIBAKBQPAfQET+BAKBQCAQCAQCwRPPv3HOn0wmY+HChRw6dIjS0lIiIyN56aWXcHR0rLZMVFQU+/fvJzk5GaVSiZ+fH2PGjKF+/foPvJ6I/AkEAoFAIBAIBALBY2DBggUcP36ct956i6+//prCwkI+++wzysqqTwIYFxdHu3bt+OKLL/juu+9wd3dnxowZ3Llz54HXE86fQCAQCAQCgUAgeOJRKpX/2L9/goKCAvbs2cNzzz1Ho0aNCAgIYPr06dy8eZPz589XW+7NN9+kb9+++Pv74+Hhwcsvv4yZmRnR0dEPvKZw/gQCgUAgEAgEAoHgH+batWvI5XIaNWqk3ufk5ISHhweXL1+u9XnkcjkymQxLS8sH2oo5fwKBQCAQCAQCgeCJR/Evm/OXk5ODvr4+1tbWGvvt7OzIycmp9XmWLVuGqakpLVq0eKCtcP4EAoFAIBAIBAKBoBK7du0iKirqgXY9evSgZ8+eGvuWLVvGmjVraiw3c+bMao8plUr09PRqpXPLli3s2rWLL774AnNz8wfaP/HO3w8//EBeXh4zZsx43FJqRW5uLmPHjmXmzJlEREQ8bjkCgUAgEAgEAoHgIenZs6eWU1db+vfvT8eOHWu0cXJyQqFQoFAoyMvLw8bGRn1MIpHUKnPnli1bWL58OTNmzCAoKKhW2p545+/555+v9aTLvXv38ttvv7F27dq/WdXjZ/OG9axeuYKsrCx8fHx5ZerrNIhsqNM2NiaGdWtWceXyJQry83H38GDIsBH06ttPw27f7ihW/bmC1JRkzC0saNK0GS++8hr2Dg511rl3x1Z2bFhHbk427l7ejJn0IsH1w3XalpaWsnjez9y6fo07qSkEhobx/sxvNWzm//gdR/fv1SprbGLC72s311nntk0bWbdqJdlZWXj7+vDCq1MIbxCp0/bCuXNsXLeGq5cvUVhQQD13dwYOHU6P3n00bN6ZNkWr7Pwly/H09q6zzsNRO9i7dRO5khzqeXgydNxEAkJ1Nw6y0lJW/v4LKTeSSL+din9wCK/P+FLDJvbUCY7s3UXqjRvIZKW4enjSc9AwGjRtXmeNAGs3b2L56tVkZmXh5+PD9FdepVGDBjptk27e5Juff+LGrVvk5+fj6OhI906deH7ceIyMjAD45H9fs11H75upqSlHduyslSalUkn0nu1cOXWUksJCnL18aDNoJPaubjWWu3M9gZNb15FzNw1zaxsiO3YnrFV7nbbXzp1h/58L8QoNp+eEV9T7/5z5Afk52Vr2niHhPPP62xr7og/u5eSeHeTn5uLk5k7XYWPwCgyuVt+92ylErVpK2s0kTM0tadS+E217D1D3GF45d4Zzhw+QnnKLMpkMx3putO7Vn6DIxupznDtygLhTx8i8cxulUomLpzcd+g/GM6D661blUNQO9mzeUH5vejHsuUkE1nBv/jl/Hik3rpN2OxX/4FCmf6rZA5oQf5HNfy7l7p3blJaUYO/kRJsu3enWf1CtNVVHl4ggmvl7YWZsREqWhC1n47iXm1+tvZWpCb0bh+FmZ4ODlQXnbqay/qTmRPym/l409nXH2cYKPT090nJy2XPhKrcyaj9spzJr161j2fLlqmfI15c3pk3TmBNSmaSkJP737bfcuHGD/IICnBwd6d6tG89Pnqx+hioTGxvLCy+/jLe3N2tWrqyTvr9LZ3RMDHPnzePWrVsUl5Tg6urKwP79eWbs2EfSCTCidSO6NQjGwsSYxPQMFuw9QUqWpFr7FoHe9IgMwdfZHmNDQ1KyJKw/GcuZ6ylqm89G9CLcs55W2eTMHF5fvLFGPcf3RnFoxxakuRJc3D3oP2Y8vsGh1dqnpSSzaekfpCRdw9zSkhadutF1wBCN6MC540c5uGMzmelpmJiZEVg/gr4jn8XK1haAUwf2En3sMHdvp6BUKnHz9qXH4BH4BofUqLUy6w/u58+oKLJyJfi6uTN1xEgaBur+6EzLzGTI++9o7Z815XVahqs6yDMlEmavW8PVW7dIvXeXni1b8eFzE2ut51ExjQzHbtRQTIMDMXRyJP3L75Du3POPXd+6fy/shg/GwMGO0pvJZM77neK4S9XaW3Zog93oYRh5uFOWm0vupu1I1lTca85vT8W6RxetcoqiYpL6Dv9bfsM/ydMy6NPGxkbDmauOgIAADA0NOXfunNpZzMzMJDU1ldDQ6tsDgE2bNrFixQpmzJhRK0fxPn+b8yeTyXS+dB4WCwuLv0DNwyOXyzE0fDJ94wP79jLnpx+Y+sZbRDSIZPPG9bz75nQWLfsTF1dXLfv4ixfw9fNn5Ogx2Ds4cub0Kb7/9n8YGxvTpXsPAC5eOM9XX3zGC6+8Stt2HcjJzubHWd/y5Wcz+P6nOXXSefLIIVYs+JVnX3yVoLD67Nuxje8+/ZCv5s7H0clZy16pUGBkbEzXPv05H32GwgLtj7Gxk19i+LgJGvu+eOeNah3K2nBo/z5+nf0Tr0ybTv2IBmzbtJGP3n6L35Ysw9nFRcv+UnwcPr5+DB05CnsHR6LPnOLn777F2NiYTl27adj+ungpVlYV47htyl/AdSH6+FHWLvmDkRNfwD84lMO7dzL3q8/5aNZs7B2dtOwVCgVGRsZ06NGb+HPRFBUWaNkkXo4nuH4D+o0Yg7mlFWeOHGL+d1/z+ozPq3UqH8TuA/v5fs4c3pn6Og0jIli3eTNT332HNYsW46qjPo2MjOjTvQfBgQFYWViScP06M2d9T1lZGVNeeBGAN195lVcnP69RbtJrr1XrUOri/MHdxB3eS4fhz2Lr7ELMnh3sWPAzw9/6BGNTU51l8rIz2fXHXIKbt6bTqOdIv3mdoxtWYmphiV+Dxpq2WRmc2r4BV98ArfMMmvIuSoVCvV0ozWPDT1/hH6l5jktnT7JnzQp6jHoWz4Agog/tY/Wc73h+xlfY2Guv91NSVMTKn77BMyCY8e9+SvbdNLYtWYCxsQktuvUCIDnhKt7BoXToPwRTC0viTx9n/a8/MWb6+2qnMjnhCmFNWuAxPBAjYxNO79vFqp+/ZeIHX2Dvot2mVOXssSOsWbSAUZNexD8kjMNRO5j75ad8/MNc7J2quTeNjenQsw/x56IpLNC+N01MTenYuy/uXj4YGxtz/epl/pw/D2MTEzr06P1ATdXRPtSftiF+rDsZS2ZeAZ3DA5nQqSWzth2gVK47rbaBgT4FJaUcunSNZgFeOm38XBy4kJzGrYx4ZPIy2oT48VynFszeeYQsqfbvq4nde/bw3axZvPv22zSMjGTt+vVMmTaNtatW4aqjjTcyMqJvnz4EBwVhZWVFQmIiX86cibysjKmvvaZhm5eXx4xPP6VZ06bcy8h4KF3/hE5zMzNGDB9OQEAApqamnD9/nplff42pqSnDhg6ts9ZBzSPo3zSc2TsPcycnl2GtGjFjWE9e/WMdxTK5zjL1PVyJS07jz6PR5BeX0D7Un7cHdOHj1Tu5fPsuAN9s3oehvkHFbzTU54dxgzh+9UaNemJPHmfLisUMenYiPkEhnNi3mz++m8kbX/2AnY61vYqLClnwzef4BYcy5dOvyEi7w+oF5c9DL1VH7s2EK6z6bTZ9Rj1DeJPmSHMlbFzyByt//Znn3/0YgOtXLhHZojU+gcEYmZhwZNc2fv/2S17/4hucXLWd2KrsPXOaH1et4s0xY4gMCGTDwQO88fOPrPjkc1xr6CieNXUagR6e6m3rSt92MrkcG0tLnunVi82HDz9Qw1+NvpkZpUm3kO7ai8uHb/2j17bs2BanVyaT8dOvFF28hE3/3rh9NYPkCa8gv5epZW/evDEuH7xJxpz5FJ6JwdjLE+fpr6AsKSV383YAMucuIGvBEo1yHj//j6IL8f/IbxI8HBYWFnTr1o1FixZha2uLlZUVf/zxBz4+PkRGVgQhPvjgA4KCghg3bhwAGzZsYNmyZUyfPh13d3f1/EBjY+MH+k5/mXfz3nvv4enpiYmJCfv378fZ2Zlp06axaNEi4uPjMTY2JjIykkmTJmFnZwdAWVkZCxcuZN++fQB06dIFmUxGSkoKX331FaA97PPixYssXryYW7duoa+vj4eHB1OmTCEvL4+ffvoJgH79VA3hqFGjGD16NDKZjBUrVnDw4EHy8/Px9PTkmWeeoXFj1YdXXFwc77//PjNmzODPP//kxo0bvPfeezRr1owNGzawa9cusrOzqVevHkOGDKFTp07q352QkMC8efNITk7G09OTsX9B7+SDWLtqJT1696Fv/wEATJn2BmdOnWTLpg1MfvFlLfsxz47X2B4waDCxMdEcPnRQ7fzFX7yIo5Mzw0aMAqCemxuDhgxj9o+z6qxz1+YNtO3SjU49VB+hz77wMnExZ9m/Y5uWAweqj73nXlZFy1Ju3tDp/JlbWGBe6aZOuBTPvfQ0XphW9wZ749rVdOvZi159+wPw8tRpRJ8+zfbNG3nu+Re17EeOfVZju++AQVw4d45jhw9pOX+2tnaP5PBVZt/2zbTs0Jk2XboDMHzC81w6f44ju3cxYPQzWvYmpqaMmvwSALeTb+l0/oaNn6Sx3WfYSOLPRXP+zKk6O39/rl1L3x49GdS3LwBvTZnCiTOnWbdlC69Onqxl7+nujqe7u3q7nqsrMedjiY2LU++ztLSkcv6q8xfjuJ12h0/fe69WmpRKJXFH9hPZqYfaaes4chzLPn2ba+fOENaqnc5yl08cwdzGhjYDRwBg51KPe8k3uHBor4bzpygrY9+KhTTr2Z871xIoLtS8d80srTS2r5w5jrGJKX4NmmjsP713Fw1ataVRO1Ub02PksyTFxxFzaD+dBmn32F48fRxZaQn9xj+PkbExzu4eZKbf4dTeXTTv2hM9PT26j9Bsk9r1HcS1uPMknI9WO38DJr6kYdNz9HgSzsdw/dKFWjl/+7ZtplXHLrTtqmpPRkx8gfjYGA7v3sHAMeO07E1MTRn9vKqtun3rpk7nz9s/AG//Ckfa0cWV2FMnuHY5/pGcv9Yhvhy6dI34lHQA1p6M5YPB3Wno487pa8k6y0gKitgWrfpwCvfS/YG85vg5je3NZ+II83AhqJ4TJx7S+VuxciX9+vZl0MCBALz95pucOHGCdevX8+orr2jZe3p64ulZ8WFdr149omNiiI2N1bL9/Msv6dOnD0qlkn379z+Urn9CZ2hoqEZvt7ubGwcOHuRcbOwjOX99G9dnw6kLnEy8BcDsnYdZ9PJo2of6s/vCVZ1lFh44pbG95kQsTfw8aRHorXb+8otLNWzah/phYmTIvouJNeo5smsbTdt2oEWnrgAMfHYCV+NiObl/N72Gj9ayP3f8KLKSUkY8/ypGxsa4enhx785tjuzaRvuefdHT0+PWtQRs7B1o31PV9to7OdOmW082L1uoPs/olzRHpAweP5n4mDMkXIitlfO3as9uerduzYB2HQCYPmoMJ+MvsvHQQV4aPKTacjYWljhUEwWp5+jI9JGq33ygFmnq/2oKT56h8OQZAFzef/Mfvbbt0AHkRe0jb8duADLnzMe8WWNs+vUm64+lWvZWXTtRcOI0eVtUI17kaXfJWbkO25GD1c6foqAQCgrVZUzrh2LkVo+7X/3wD/yiv59/4yLvkyZNwsDAgG+++YaSkhIiIyOZNm0aBgYVHUvp6ekai75v374duVzON998o3Guzp07M23atBqv95eGtg4ePEiPHj34+uuvKSgo4N1336V79+5MmDABuVzOsmXL+Pzzz/nuu+/Q19dnw4YN7Nu3j9deew1vb2927NjBoUOH8PPz03n+srIyvvjiC7p168Ybb7yBXC7n+vXr6OvrExISwuTJk1m6dCkLFiwAVEPCAH766SfS09N58803cXR05OzZs3z++efMmjULX19f9fkXL17MhAkTcHNzw8zMjGXLlnH8+HFefPFF3N3duXLlCnPmzMHS0pJmzZpRXFzMZ599Rnh4ONOmTSMrK0t97b8LmUxGQsJVho/SfDk0bdac+Itx1ZTSprCgACfniuhbeIMG/DH/V44fPUKrNm3Jy83lwL69tGjZqk465TIZN68l0nug5ssgvFFjEq/UPnXtgzi4eyfuXt4EhobVqbxMJiPxagJDyp3e+zRu1oxL8RdrfZ7CggIcdUQ4prwwGZmsFC8fH0Y9M47IRo11lH4wcrmMlKTrdO07UGN/aIOGJCVcqdM5q6O4qAhziwenCtaFTCbjSkICY4eP0NjfomlTLtSyPlNu3+bEmTO0a9W6WpuN27fj5+NDZHjtIr7S7EyKpHl4BFV8VBoaGePqG8jdW9erdf7u3krCI1Bz2IVnUBgJZ0+iKCtDv7xhPr1zM1b2DgQ1bcWdawk1alEqlVw9fYyAxs0xNDZW7y+Ty0lLvkmLbpqOjW9YOKlJuj8kbyddwzMgGKNK5/ELi+DwlvXkZmViqyMiDFBaUoSpefU9g2VyOXKZDLMabO4jl8lITrpG1/4DNfaHRjYi6epfd2+m3LhO0tUr9Bk+6sHG1WBnYY61mSmJaRURL3mZghv3svBytKvW+asLBvr6GBoYUFQqe6hyMpmMK1eu8MyYMRr7W7ZowYW42rXxKSkpnDhxgvbtNYcnr123jqysLL6eOZPfFy6spvTj11mZK1evcuHCBSbr6DiqLS42VthZmnP+1m31vlJ5GZdS0wl2d67W+dOFmbER+cUl1R7v2iCYczdSa4z2yuVybt9MokNvzakXQeENuJmoW8utawn4BodoPOtBEZFErV9NTmYG9k7O+ASGsGvtSi6dO0towyYU5ks5f/I4IZG6h+FCpWe9FqOsZHI5V5NvMbq80/g+zcPqE3f9Wo1l3/9lLiVyGZ7OLozo2o3OTZo+8Hr/egwNMQkKIGfNJo3dhdHnMK2vexiunpERyiptirK0FCNnJwxdnJHfvadVxrpPd0pu3KL40l/7rSD46zA2NuaFF17ghRdeqNbmjz/+qHH7YfhLnT8XFxcmTlSN016+fDm+vr6MHz9efXz69OmMGjWKa9euERQUxNatWxkyZAht2rQBYPLkycTExFR7/sLCQgoKCmjevDn16ql6qCr3Ipqbm6Onp6eOLAKkpaVx+PBhfv/9d5zLnZ2+ffsSGxvLzp07efnlikjZqFGj1NHA4uJiNm/ezGeffaYeR+vq6kpiYiLbt2+nWbNmHDx4ELlcztSpUzEzM8Pb25vhw4cza1bdo2UPIjdXgqKsDDt7e439dvb2RJ89W6tznDh2lJjos/z8y2/qffXDI/jwk8+Y+dknlJSUUFZWRpNmzXn3w4/qpFOal4dCocDa1k5jv42tHfHnz1VT6uEoLCjg9LEjDHvmuTqfIy83F4WiDFs7TZ22dnbkRGvP0dLFqePHiI2J5vs589T77B0ceHXaGwSFhCKXy9i3O4r3pr/O/378mYhq5mbWRH6eFIVCgZWNrcZ+Kxtb8uKqXwT0YTkUtQNJdibN23esU3lJbi5lCgX2VerT3s6O09HVP9sAE159lauJCZTKZAzs04dXJk3SaZefn8++Q4d4eWLt54QUSvMAMLfUTKVsZmVFYa6k2nJF0jzMAjVfwmZW1igVCooL8jG3tiH16iWSzkczZNr7tdJyO+Ey0uwsQpq30dSYL0WpUGBRJd2zhbUNN6/oHq5TkJeLVZW6vl8+P0+i0/k7e3Av0pwcIlq00Tp2n0Nb1mFsYkJggwd3VuRLy5/1KvemtY0tVySPfm++98Jz5OflUlamoM+wkbTv3qvO57IyMwHQ+njPLy7B2lz30N+60q1BMKVyOZdT7z5UOYlEQllZGfZV2nh7e3tOnTlTY9kJkyZx5epVSktLGTRgAK+8VBHRvXbtGgt+/51FCxdq9CbXlb9L53169+1LTvk1Jk+cyNDBg+us1dbCTKW5oEjzNxQUYW/54Mx49+nZMBQHKwsOXdLt6NSzsybcsx5fbdSel16ZgvJnxtJaMxJmaWOLNF634yzNlWBjp1nXlta2qmMSCfZOzngHBjH65ams/GU2MlkpirIyAsMbMOL5V6vVsmvdKkxMTAlr/GBnTJIvpUyhwK5KG2Vvbc3Zy7rnqJmZmPDq0OE0CAjAQN+Ao+dj+Xj+r5Q+N5Gedexc/rdgYGONnoEBZTkSjf1lORIMGuvOOVB49hyOr0zCrElDimLOY+ReD9uhAwEwdLDTcv70LcyxbN+GrIXL/o6f8Fj4ty318Dj4S50/f39/9f+vX79OfHw8w4YN07JLS0tTj0+tnJlGT0+PwMBAMjO1xzkDWFlZ0aVLF2bMmEFkZCSRkZG0adMGJx0Rl8o6lEolr1QZgiKTyWhQZb5QYGCg+v/JycmUlpYyY8YMjcnUcrkcl/J5SykpKfj4+GBmZqY+HhJS+0nTj0LV9K9KJdQmI+zFC+f58tMZvPr6dELDKob13bxxg9k/zmLs+Odo1rwl2VmZ/DZvDrO++R/vfVT3TKvaOpXoUbvUtQ/i+MF9KBUK2nTSntj8sGil01VSK53xcRf43xef8eKUqQRXij56eHnh4VUxNyi0fjh309NZv3pVnZy/Cp1VZD5EKuAHce7UcTYuX8yEqW/ioGNO5sOgsz4fIHPmxx9TWFhI4vXr/PzbryxZtZLnRo/Rstuxdw+KsjJ6d+te7bl27t3Dwu++V2/3nFDeyVNVg1LXTk2q3geVh5wUF+RzcM1SOo+egEktomQAl08fw8nTG0d3z2ostP7INWrUuk+V1ewHrsScYf/6VQyc9DI2DtrzigBO74vi3JEDjJ76DiaV2rYHUvVZR1mrNulBvPHZV5QUF3Mj8Sobly/B0dmFFh06PbggEOnjzsBmFVmXlx46rdNOD72/NItA62Bfmgd6sXD/KUrkuueTPQhdt+qDnvWZX35JYUEBCYmJ/Dx7NkuWLuW58eMpLS3l/Q8/ZOqUKbi71Zzg6HHqrMyC+fMpKiwk7uJFZs+di5ubG3161264b/tQP17oVtG58eWGPWptGtof4v5sGejNuA7NmLXtABl5uqN63RoEk51fSHRSis7jVdFuJ2tuz7WPqR92AO7eTmXz8kV0GTCE4IhI8iQ5bF+9nPWL5jPyBW0H8GjUDk4d2Mvkdz7C1Kz2TrCu93p1bZStlZVGpDDUxwdJvpQVUbv+885fBVp3ZrXtUd72KIzcXKn3+QfoGRqiKChEsmErDuNHoyxTaNlbde0IBvpI9xz4y1ULnl7+UufPtFLSBIVCQdOmTZkwQXtul62trfoD6mE/XF9//XUGDBhAdHQ0p06dYtmyZXzwwQfqiF1V7n8cz5o1S6u308TEpNrt+/o++ugjLefyURLB1HbNkLYdOtGxS1et/TY2tugbGJCdlaWxX5KTrRUNrErc+fO899Z0xk+azIBBmr2ofy5fSkhoGCNHq+YH+QcEYGpqxtRXXmTi8y/qTHxSE1bW1ujr65NbJcNhXq5EKxpYVw7u3kXT1m2xtLJ6sHE1WNvYoK9vQE62pk6JJAdb+5p1XrxwgY/ffYtnnptI3wEPzkAYEhrGof376qTT0toKfX198iQSjf35ebla0cC6cO7UcZbM+ZFnX3n9kTJ92trYYKCvT1aV+syW5GhFA6viWh6Z9/PxoUyh4MvvvuWZESMxrPLcbtq+nU7t22NTpfe5Mu1bt2HItIrnuaz8A7xQmoelbcVzUpQvxayG+8fMyppCaa7GvuJ8KXr6+phaWJJ+8zqFeblsn/+T+vj9tmPBO68w7I2PsHWumDNXlJ/HrfjztBk0Uuta5pZW6OnrU5Cneb0CaZ5WNPA+FtY25Ouwv3+sMldizrBl0W/0G/+8RqbPypzeF8XhLesZ8dobuPn667SpiqWVdfm9qZnVUpqbqxUNrAuO5XMO3b19yMuVsG3tylo7f5dT00nJrNBlaKCv0mxqQm5hsXq/halxjUP5HobWwb50axDM4oOnSK0hk2R12NraYmBgoPUM5WRn4/CANv5+QiU/Pz8UCgVfzJzJM2PHkpmZSdKNG3z2xRd89sUXgOodrVQqadG6NT/NmkXLli0fu87K79b7TmpAQABZ2dnM//33Wjt/p68lk1BpaK9ReRtiZ2GmMRzTxtwMSWGRVvmqtAz0ZmrvDvy887BGps/KGOrr06l+AHsuJDwwMmFR/sxIq4w6yM/L1YoG3sfKxlan/f1jAAe2bsTTL4COfVTz1+t5eWNsYsovX35Mz6Ejsa3U4XM0age71q9i4hvv4+WvnaRKF7aWVhjo65Odq9nm5Eil2NfQHlelvq8f248fq7X9v5Wy3DyUZWUYVHk3GtjZaEUDK5O1YAlZfyzDwN6WMkke5o1VgQydQz57d6fg8HEU0uqzGT9t/Bvn/P3T/G3pLP39/Tl69CjOzs7VOkt2dnYkJCSoI3BKpZLExESNYZu68PX1xdfXl6FDhzJjxgz27dtH48aNMTQ0RKHQ7Pnw8/NDqVSSk5OjFemrCU9PT4yMjMjIyNDItlPVZt++fRQXF6sd36tXa547UNs1Q7KkhTr3GxkZERQUTPSZM3TsXBHxij5zhnY1rCdyPvYc77/1JuMmTGTocO0Pz5LiYi3nWL/8Q6kuD5qhkRE+AYFcjD1H87YV8zkuxp6jWavqh5vVlusJV0m+kcSYSdWPj64NRkZGBAYHEXP2DO06VnxQnjt7hjbtO1RbLu58LDPefZsx4ycwaFjtUidfv5ZY52UzDA2N8PTz50pcLI0r1d+VuPM0bP5ovafRJ46ybO7PPPPKFBq3rH6eXW0wMjIiJCiIU9Fn6VrpfjwdHU2ndtXP66mKUqmgrKwMRVkZVLov4y9fJvH6dd54pfphTAAW5ubYOFZEL5VKJWZW1txOuIyzpw+gmquWfuMaLfpUP5zMxduPm/GxGvtSE6/g5OGNvoEBTp7eDH3jQ43jZ3ZtpbSokDaDRmBVJUPn1TMnMTA0xD9Se4iVgaEh9bx8uHH5IqFNKhzwm5cvEtyomU597n4BHNi4GrmsFEMj1VygG5cvYmljpxHZu3T2FNuWzKfvuOc1zl2ZU3t3cnjrBka8+sZDLfFgaGSEl18AV87H0qRVW/X+KxdiadTir+3ZVyqUyGW1n0NXKi8jO1+zLc0rKibA1Ynb2aoPWEN9fXyc7dl17tHnIrcJ8aVrRDBLDp6u8xIPRkZGhISEcOrUKbp2qWjjT50+TedOtXN6QTU0qqysDIVCgbOzM6v+/FPj+Lr16zl16hTffvMNbvUenOjjn9BZHUqFAllpabXHq1Isk5MukWrsy8kvJNLbjWvpqpFFRgYGhLq7sPRQzUNUWwf78lrPdszedYQTCTertWse6I2VmSn74mqe8wuqDmR3Hz8SLl6gQaX2O/FiHBHNWugs4x0QxI7VK5CVlqrn/SVevIC1nR125cO7S0tL0NfX1yh3f7vya/zwzm3s3rCaCW+891BLPBgZGhLs5c3py5fo3LSiTTpz6RIdGzepoaQmiSkpONYiBf6/HrmckoRrmDdpSMHhCmdYtX2i5rIKBWWZqo4Xy07tKYq/TJlE0yk3CQ7EJMCPzHm//+XSBU83f5vz16dPH3bv3s0333zDkCFDsLGxIT09naNHjzJhwgTMzc3p168fGzZswN3dHU9PT3bt2kVOTo7WHIL7pKens2vXLlq0aIGDgwPp6encvHmT3uW9gS4uLpSWlnLu3Dn8/PwwMTHB3d2djh078uOPPzJx4kT8/f2RSqXExcXh6upK69a6P3bNzc0ZNGgQCxcuRKlUUr9+fYqLi7l69Sp6enr07NmTDh06sGzZMn766SdGjhxJdnY2a9as+buqVM2wkaP46vNPCQkLIzyiAVs3bSQzK5N+A1XRpwW/zuPK5UvqJRpiY2J4/+036D9oMF2791BHDfX19dVz3Vq1acv3//uKzRs30Kx5C7KzMpn7808EBgXrXD6iNvQcMJjffvgWv6AgAkPrc2DXdiTZWXTupVoPb82ShSQlJvDuF1+ry9xOvoVcLkcqzaO4uJhbSdcB8PbTjEIcjNqBi5s7IeG1d+irY9CwEXw38wuCQ0IJi4hgx5bNZGVm0bs8gcWi+b9y9cplvp6liu5cOHeOj997m74DBtKpa7eK+jTQx7Y8qrlx7RpcXF3x9vVFLpOzf08UJ44e4cPPvqizzi59BrBkzo94+wfhHxzCkb1RSLKzadtNNaRm85/LuHk9gakffa4uk5aaglwuoyAvj5LiYlJuJgHg6aNKqnT22BGWzP2RwWPHExBan9zy6I2hoSEWlnWLqI4eNowZX31F/ZBQIsPDWb91CxmZmQwpz8I7Z8EC4q9c5pfvVXNjd+zejbGxMQF+fhgaGnI54SpzF/xO5w4dMK6U3ABg4/ZteHl40LiaDpnq0NPTI6JdZ87t24Wtsys2Ts7E7N2JkYkJAZUcqwMrFwPQadR4AEJbtSP+2EGOb15DaMt23L15nYSzJ+g8WjWiwcjYBHtXd41rmZiaoVSUae2/n+jFP7JptUtLNO/aky2LfsPNxw8P/0BiDh9AmiuhcfvOKn0b13DnZhJjpr0LQP3mrTi6fRNblyygTa8BZN9L40TUNtr1GaQeVRF/5iRbF/1G5yEj8QoMJr88emBgaIhZeWKfk7u3c3DzOvo/9yL2zq5qG0Nj41oNB+vSdwCLZ/+Ad2AQ/sGhHNm9i9zsbNqVz8/btGIJN68l8vqMivs/LSUZuVxOvlSqujdvlN+bvqp788DObTg6u+DipqrHxEsX2bt1I+271z3TJ8DxKzfoGB5AZl4+mdICOoUHUCorI/ZmRTKQoa0aArDuRKx6Xz1bVWTDxMgQpVJJPVtryhQK7uWpetTbhfrRrUEIa0+cI1NagKWpKvIsKyujpJqlBKpjzKhRfPzJJ9SvX5/IBg1Yv2GD6hkqn/c2Z+5c4i9d4pe5cwHYvmMHJiYmBPj7Y2hkxOXLl5k7bx6dO3VSP0MB/pptqJ2dHUbGxlr7H7fOVWvW4O7mhnf5sPmY2FiWr1jB0EfI9AmwLSaeIS0iSc3OJS0nl6EtG1Isk3P48nW1zZReqg6qn3eqlhtoE+zL1N4dWHLoNJdS0rE1Vw2DlivKtLJ8dmsQTNytO9zN1XQ6q6Ndz76s/m02nn4B+AQGc/LAHvIk2bTsrMoYvXPNn6QkXVMv0dCwVVv2bFrLmgXz6DJgMBlpaRzYtpmug4aqn/WwRk1Zt/A3TuzbTVBEJFJJDltWLMHdx1e9fMTB7VuIWreSkS++hpOrG9Ly0SSGxsaYmT/4WR/ZrTufLfydMB9fGgQEsPHQQTJzJQzsoOos/WXDei7dTGL2dFUG7h3Hj2FoYECQlxd6evocuxDL+oP7eXmw5t8zIUWVbKmguAh9PT0SUpIxMjDE9y8epqwLPTNTjNzLr6Ovh5GLM8YBfiikUuR3H205lAchWbcZl3enUXI1gaKLl7Hp1xNDB3tyt6qyeTpMfBaTkEDuvKXKv6BvbYVlh7YUnY9Dz8gI655dsOzQhts65pxb9+1Baeptis7XPnnd04BCBP4emb/N+XNwcOCbb75hyZIlzJgxA5lMhpOTE40aNVKv/zd48GAkEol6iYauXbvSsmVLJFWGtt3HxMSEO3fu8PXXX5OXl4etrS0dO3ZkyBBVRsnQ0FB69erFt99+i1QqVS/1MHXqVNasWcOiRYvIysrC0tKSoKCgB0YCx44di62tLRs3bmTevHmYm5vj5+fH4PIXm5mZGR9//DHz5s3j9ddfx8PDg/Hjx/P555/XeN5HpVOXruTl5rJ8ySKys7Lw8fXjq2+/x7U8TXN2VhZ3bld8yETt3E5xcTFrVv7JmpUVvb8urq6sXKdaGLRn7z4UFhayaf06fp3zMxaWljRs1JgXXq45wlITLdt1IF+ax5Y1K5Fk5+Dh7c0bH3+Oo7NqyI8kJ5t76Xc0ynz/2Udk3qsYuvDR66q5mku37FLvKyos5OSRQwwcMeYvme/WoXMXpHl5rFy2lOzsLHx8ffnsf9+ond7srCzSblfo3LNrByXFxaxfvYr1q1ep9zu7uLJk9VpAlZ3z91/mkZWZgbGJCd4+vnz69Tc0f4Q5Dk1at6VAmseujWvIy8mhnqcXL7/7kXp+Xq4km8y76Rpl5n39GdmV1vL6+p3pAMxdvQmAo3t3oSgrY92SP1i3pCJzVGBYfa0F4WtL906dyc3LY+HyZWRmZ+Pv48OPX31NvfL6zMzO4vadivo0MDBg8Z9/knI7FaVSiauLC8MGDmDUUM35wgWFhezev59Jzz5bp797ZMfuyGUyjm5cRWlRIc5evvSe/JqGI5Yv0RzCZm3vSM+Jr3Bi6zounTiChbUNrQcM11rjrzakXU8gN/MenUZVn6AorGlLivLzObZjC/l5EpzcPBjx6hvqKF5+rgRJRsXzYWpmzqipbxO1cimLvpqBqbk5Lbr2onnXitEF5w7vR6EoY+/aFexdu0K93yswhLFvqD4Yog/uQ1FWxqbf52roiWjZln7jNddX1EXTNu0oyJeyc/0a8nKyqefpzSvvf1xxb+bkkFHl3pzz1WdkV/otM99+HYBf1m4BQKEoY+PyxWRl3ENf3wAnV1cGjhlHu24PHjlRE4cvX8fI0IB+zcIxMzYiNVPCogOnNNb4u/+RX5nXemtGrkM9XMnJL+TbLarlEloG+mBooM+otpoRkOikFK0F4R9E927dyM3N5Y9Fi8jMzMTfz4+ffvhBnegsMyuL1EptvIGBAYuWLCElRbVwdz1XV4YNHcrokdqjPP5K/g6dirIyZs+Zw520NAwMDPDw8ODVV15RO5R1ZePpOIwNDXm+SyssTI1JTMvgs3W7NNb4c7TWnLfbo2EIhgb6TOzckomdK4bFXkxJ4+PVO9XbLjZWRHjVY9a2g7XW07BlawrzpezfsoE8SQ6uHp5MeOM9dRQvT5JD1r2KZEFm5uZMfvsjNi39g59nvIeZuQXte/VVL+sA0LRdR0qKiji+dxfbVi7F1Mwc/9D69B5ZsdzLiX1RlJWVsWLujxp6mrTtwIjntZfnqErXZs3JLchn8Y5tZOXm4ufmznevTaVeeRuVlSvhdpX1Ixfv2EZ6Vhb6+vp4ubjw/rjntOb7jf/8U43toxfO4+rgwIavNNPY/x2YhgThMftb9bbDpGdxmPQseTt2c3fm9zWUfHTyDx5F39oKuzHDcbK3p+TmLe689xnye6o6NHCww8hNswPeunsnHF8YD+hRfOkKt6d/QMlVzYzQemZmWHVqR/ay1X+rfsHTiV5RUdET5UNPnTqVsLCwGtOd/heobtjnk0Zqdu6DjZ4AnKxrl4zjcZN0N+vBRo+Z5k5Px3CdBdFPR1prR6un4970dLB93BJqxd4HrK/2pPBe77YPNhLUinG/b3jcEmrFuHa1Hxr5OGlbXLvo5eMm+4O/t6P9r0KvSn6JJxWP7X//yLW/gskL1v1j11ow+dFGHDyp/G2Rv9pw7949YmJiCA8Pp6ysjKioKG7evMmrr9Y92iQQCAQCgUAgEAgEAm0eq/Onp6fH/v37WbRoEUqlEk9PT2bMmKGx5IJAIBAIBAKBQCAQiGyfj85jdf6cnJz45pu/fzy3QCAQCAQCgUAgEPzXeazOn0AgEAgEAoFAIBDUhgetoyl4MPoPNhEIBAKBQCAQCAQCwdOOcP4EAoFAIBAIBAKB4D+AGPYpEAgEAoFAIBAInnjEqM9HR0T+BAKBQCAQCAQCgeA/gIj8CQQCgUAgEAgEgiceJSL096iIyJ9AIBAIBAKBQCAQ/AcQkT+BQCAQCAQCgUDwxCOWenh0RORPIBAIBAKBQCAQCP4DiMjfE4pS9Gz8pTwt9al4CsayK+Xyxy2hVhgbGjxuCbVCX0/vcUuoFbezcx+3hFohk5c9bgm1QpGd87gl/GvILy553BJqhb7+09HfXhJ9/nFLqBV6JiaPW0KtUJY8Hffn08LT8j33JPN0tEQCgUAgEAgEAoFAIHgkRORPIBAIBAKBQCAQPPEoRODvkRGRP4FAIBAIBAKBQCD4DyAifwKBQCAQCAQCgeCJR8z5e3RE5E8gEAgEAoFAIBAI/gOIyJ9AIBAIBAKBQCB44hGRv0dHRP4EAoFAIBAIBAKB4D+AiPwJBAKBQCAQCASCJx6FiPw9MiLyJxAIBAKBQCAQCAT/AYTzJxAIBAKBQCAQCAT/AcSwz7+QuLg43n//fZYvX46Njc3feq3NG9ezZuWfZGVl4ePjy8tTptIgsqFO29hzMaxfs4orly5TUJCPm7sHQ4aPoFefvhp2+/bsZvWfK0hNScbcwoLGTZry4iuvYe/gUGede3dsZceGdeTmZOPu5c2YSS8SXD9cp21paSmL5/3MrevXuJOaQmBoGO/P/FbL7vihA+zYsJb027cxMzenfmRDRk6YjK2dfZ11btu0kfWrV5KdlY23jw/Pv/oa4Q0iddpeiD3HprVruHrlMoUFBdRzd2fgkGF0791Hw+bdaVO1yv62ZBmeXt511nk4aif7tm4iT5JDPQ9PBo+bSEBomE5bWWkpq37/ldQbSaTfTsUvOISpM77QsIk9dYJje6NIvXEDmawUVw9PegwaSkTT5nXWCLBuyxaWrV1LVnYWft4+THvpJRpFROi0Tbp1i2/nzObGrVvkFxTg6OBA944dmfzMsxgZGant1m7ZzNrNm0m7excXZ2eeGzWaPt261VqTUqnkdNRW4k8coaSoEBcvXzoMGY1DPbcay92+dpWjm9eSnX4HC2tbGnfuQXibDurj12LPEr0vitzMeygUZdg6OhPZoSuhzVtXnON6AucO7CYjNZmCXAldRo3XOF6Zswf3cmL3dvJzc3Fyc6f78LF4BQZXq+/e7RR2rVzCnZtJmFlY0qhdJ9r1GYienh4AV2LOEH14P3dTbiGXyXCs50bb3gMIimysPsfS778kOeGK1rkd67nz4idf11g/f5fuWwmXObBxDVl305GVlmBj70jDth1o1b1PteesLd0ig2kR6IO5sRHJmTlsPHWBu7nSau2tzEzo1zQcd3sbHK0siUlKYfXxc9XaN/RxZ0z7plxKTWfR/lOPrBdg3batLFu3jqzsbPy8vZn2wos0CtfdllYm+fZtnn3tVZRKJYc2bvpLtDytOp9p35TejcKwNDXhyp27zNl5hFuZOdXaR3jVY0Knlng62GJiZMi9XCk7Yy+z7uR5tU2vRqF0jQjG28kOfT09rqVnsuTQaeJT0h+o59ieXRzcsQWpJAcXd08GjB2PX4ju9hwgLeUWG5f8QfL1a5hbWtKycze6DRyqfmZU59zJsT27yM7IwM7BkS4DBtO0XUf18fOnjnNg2yYy76ZTVlaGk0s92vXsS7P2HbUvWA3mkeFYNm2EgYU5sqxs8g4epfR2Wo1lLBo1wDwyHENraxTFxRReuoL06EmNc1o0jMDQxpqyPCnSU9EUXb5aa026sO7fC7vhgzFwsKP0ZjKZ836nOO5StfaWHdpgN3oYRh7ulOXmkrtpO5I1G9XHnd+einWPLlrlFEXFJPUd/khaa4NpZDh2o4ZiGhyIoZMj6V9+h3Tnnr/9uk8aYtTnoyOcv6eQA/v2MvenH5k6/U3CG0SyZeMG3nvrDRYuW4GLi6uWfXxcHL5+/owYNRZ7RwfOnjrFrG//h7GxMV26dQfg4oULfP3FZ7zw8qu0adeenJxsfvr+O2Z+9gnf/TS7TjpPHjnEigW/8uyLrxIUVp99O7bx3acf8tXc+Tg6OWvZKxUKjIyN6dqnP+ejz1BYkK9lk3Apnt9++JZRz02iSYvW5EpyWPLrHH79/hve/aJ2H6lVObR/H7/N+ZlXXp9OWEQE2zdv4uN33ubXxUtxdnHRsr988SLefn4MGTkaewcHYs6c5ufvv8PI2JhOXTWdkV8XLcXS2kq9bWNjWyeNANHHj7J+yR8Mn/g8/sGhHNm9i1+++pwPZv2MvaOTlr1CocDIyIj2PXoTfy6aosICLZtrl+MJrB9BnxGjsbC04syRwyz47n9MmfF5tU7lg9hz8CDf/zKPd16bQmR4fdZt3crrH7zP6t//wNVZ++9uZGhIn27dCPIPwMrSksSk68z84QfkZQqmTJ4MwLqtW5nz+++8P20a4SGhxF+5wswff8Da0pJ2rVrVSlfM/ihiD+6hy6jx2Dm7ciZqG5t//YGx732OsampzjJ5WZlsXTCb0OZt6DZ2ImlJ1zi0bgWmlpYERDYBwNTCkmbde2Pn7Iq+gQE34+PYv3opZpZW+ISpHF5ZSQkO9dwJadqKvX8urFZj/JmT7F69nJ6jx+EVEMTZg/tYOftbXvzka2zsHbXsS4qKWPHj//AKDGbCe5+SdTedrYvnY2xiQstuvQG4lXgFn5AwOg4YipmFJRdPHWPtLz/yzBsfqJ2zYS9OpUwuV59XLpcz/7P3CGtSu06Av0O3sYkpzTp3x9ndE0NjY1KvJbJjxUKMjE1o2rFrrXTpomP9ANqHBbDmWAz38vLp1iCYyd1a8+2mfZRUqoPKGOrrU1BcyoGLibQI9Knx/PaW5vRpUp+ku5l11liVPYcO8f2vv/LOK68SWb8+67Zt4/WPPmT1b/N1PlP3kclkfPj1VzQKDycmLu4v0/M06hzeqiFDWkTy3dYDpGZJGNOuCV+P6ceEX1ZSVCrTWaa4VMbmM3HcuJdFiVxOfQ9XpvbuQIlMztboeAAivd04dOka8f9v777joq7/AI6/jiUCsreCA8SJC/feM7c5cmtamalZliOtTLPSTJypSIqlpmmpP1cpUqZZDkRQVHCiKCBbNnf3++Pi5LhDceT3C36ej0ePh3zve/Du4L73fX/G+x1zj5y8fAY0q8eiYa/w5vodxCanFhvPuZPH2f39dwwY8zpVfWpx4vAhAhZ/zowvv8HOwPU8OzOTdV98RtUatZg6/wsS7sby47qVmJUrR/uefQA4cfgQ+7b9wKvj38TTuzq3rkbx04ZvKW9pRZ1GjQGwsKpA576DcHaviJGxMZGhZ9gRsBora2tqNWik93OLMvfxxqZ9a1KD/yD3zl0s6tfFvn9vEjZtQZmu/5kNYN2uFebVqpD2xwny7ieiMDPD2MpS+7hFvTpYt2lJ6m9Hyb0Xh6mrM7ZdOqDKySHn2o3HxmSIVfvWOL09gQT/b8mKuIhNn564L/qYW+PeJj9e/71p0bQRLnPeJ2HlOjJPncXM0wPn6W+jzskldfc+AO6vWk/i+k06z6u0/Euyzl94qhiflFH58uReu0n6wcO4fDTjhfxMoWwSyz6LUKvV/Pzzz0ycOJH+/fszZswYNm3aRFxcHL179+b48ePMnTuXgQMHMmnSJEJDNaO/cXFxzJ49G4ARI0bQu3dvvvnmm/8kxp9+3Ea3Hj3p1acvlatU4Z13p+Pg4MDen382eP7wUaMZN+EN6tarh7t7Rfr0H0Cbtu049nuI9pyLFyJwdHJi0JChuLm7U7tOXfoPHERkZPGjZI9zcPcuWnfqQoduPajo4cmoNyZha2dP8P7/GTy/nLk5YydNoUP3ntg76N8wAkRfjsTewZHufQfg5OqKd81adHmlL1cNzFqU1M87ttO5ew+6v9Ibz8pVeGvKNOwd7Nm35xeD5w8ZMZLR4ydQx9cXN3d3evXtR8s2bTn+x+9659rY2WJv76D9z9jY+KnjPLpvD83adaBVp664VvLg1XETsLGz489fDxo8v5y5OUMnvEWrzl2xLWb2dtCY1+nabyBVvH1wcnWj56tD8KhWjfOnnn62YsvOnbzStSv9evakqmdlZrw9GUd7e3bu3WvwfI+KFXmlazd8vLxwc3GhbYuWdOvYiXMRD28CDxw5TN8ePenWoSMV3dzo2qED/Xr2JGj7jyWKSa1WE/b7Yfw6dce7vh8ObhXp/NpY8nKyuXK2+P/XiBO/Y2ltS7uBw7B3caNOizbUbNKS0KMPR1srVa9JNd+G2Lm4YePoTP12nXB0q0jstSjtOVVq+9KiV3+8G/ihUBR/2f378AHqtWxDozYdcHSrSPdho7CyseXM70cMx/fPcfJyc+gz5g2cK3pQq1ETWnTrxd+HD2rLYXcbMpJW3XtTsaoX9s4utO09ALfKVbl87oz2+5S3tMLKxlb7X0z0ZfJycqhfaIbzUf6LuN0qV6VOkxY4uVfCztEZ3+atqFa7Hrein202oE0tL45GRBF+6y5xKelsO36WcqYmNKxasdjnJGdksftUOKevxpCZm1vseUYKBcPbNOZgaCRJ6ZnPFGdhW37exStdutCvRw+qenoyY9IkzXtqn+FraYEVgYF4V61KpzZtnlsspTXO/k3r8eOJUP68dI0bCUks3hNMeTNTOtatXuxzou7dJ+RiNDfvJ3MvJZ0jEVGcvhZDXU837Tlf/HKEPacjuBp3n9tJKSw/8AeZuXk08fJ4ZDy/H9hLkzbtad6hCy4VK9F/9HisbW3568ivBs8/e+IYuTk5DHtzMm4entRr2pwOr/TjjwP/075nzhz/nWYdOtGwZWscnF1o2KI1zTp04ej/ftF+n+p1fKnbuCnO7hVxdHGlTfdeuHlU5vrlyBK9jlZ+Dci8eInM8IvkJyWTdvQYqowMLOobnt01trPFsoEvSbv3k331OsrUNPIT7pNz/ab2HIvaNcgMv0DW5SiUqWlkX44m8/xFrJo0LFFMhtgO6kvaoSOk7f+VvFu3ub9yHfmJydj07mnw/AqdO5Dx1z+k7TlA/t04Mv8+TfLWn7AdOkB7jiojE2VyivY/U3c3TN3dSNtn+Hf2vGWePEXiuu94EPInqF7e6S+VWv3C/iurRPJXRFBQED/++COvvvoqq1atYubMmTg6PkxENm/eTO/evVmxYgXVq1dn8eLFZGVl4ejoyKxZswBYtWoVQUFBTJw48bnHl5eXx5Url2nctJnOcb8mTbkQUfIR08yMDKysHs5I1fH1JSkxkRPH/0StVpOaksLRI4dp1rxksypF5eflcSM6Ct8iI4l1GzYi6lLJPmQMqV6rNinJSYT+cxK1Wk16Wionj4VQ36/JU32/vLw8oq9coVFj3ec3bNyEyIiIEn+frMwMrCpU0Ds+9Y2JDB/Yj1nTpxEWevapYgTIz88j5tpVatVroHO8Zr36XH+GxNeQnKwsLCytnuq5eXl5XIq6QjM/P53jzfz8OH+xZKOjMXfucPL0KRrVq/fw++bmUc7MTOe8cmbluHD5MvnFzNYUlpZ4n8z0NDxq1NEeMzEzw71ade5ev1bs8+7duIZnDd0ZUM+atUmIuYFSqf9z1Wo1MVciSU6Io6JX8TeVhijz87l76wbVauveRFWrVZfbV6MMPuf2tWg8vWtgWui18apTj/SUZFISE4r9WTnZ2ZhbWBT7eOifIXjVrY+N/eOXfL+ouO/dusHta1FUrl7zsTEVx97KAmsLc67ExmuP5StVXI+7T2Xnp182XqBHw1okZWRy5lrMM3+vApr3VBTNGuleS5s1asT5i8VfS//852+O//M377351nOL5VHkHKerbQUcKljq/F5y85WE37pL7Ur6q2WK4+XiSO1Krpy/GVvsOabGRpiZGPMgO6fYc/Lz87hz/Ro+vrpbC3x863MjyvDgxs3oy1StUQtTs3KFzm9AWnISSQmav+f8vHxMTXWvk6ZmZsRcjdaZ2S+gVquJijhP/L1YqtasVWy8WkZGmLo4kXND9+8752YMZu6GX8fyXlVRpqZRroonzuNG4Dx+JLbdOmFUvvzDk4yNUSuVurHl52Pm6gJGT3GbamJCOR9vMk+f0zmceSYU8zqGrx8KU1PURWaA1bm5mDo7YeJieNbauldXcq7fJPvi8/0MFoT/mlj2WUhWVha7d+9mwoQJdPl3L5G7uzs1a9YkLi4OgL59+9K0qWYp1KhRowgODubatWvUqVOHCv/e/NvY2Pxne/5SU1NQKZXY2dnpHLezs+ds0ukSfY+/jh/n7JnTLF+9VnusTl1f5nz8KYvmf0JOTg5KpRK/Jk34cM7cp4ozPS0NlUqFta1unDa2dlwIK36vzONUr1mbSe/PZM3XX5GXq4mzboNGTJz2/lN9v7TUVM0+LQOv57mzZ4p5lq6//zrBubNnWLJilfaYvb0Db7/7Hj41apKfn0fwr78y+713+eIbf3yL2Zv5KBlp6ahUKioUWTZawcaWy+Hnn/j7FeePQ/tJSUqkaduSzfgUlZKWilKlwr7I793ezo5/Qh/9ex8/bSqXo6LIzcujX4+eTBo7TvtY88Z+7Dl4kA6tW1PLx4fIqCvsPniA/Px8UlJTcXzMvtTM9DQALIok6BYVrHmQmlLs8zLSU6nko3tTVL6CNSqViuwHD7D89/eRk5XJxk8+RJmfh8LIiHYDX6NyLcN7HIuN8UE6apUKywq61w5LaxuuXzKcOD9ITcW6yF5XS2trTeypqdg56t+0nD76G+nJSfg2b23weybG3eXWlUu8+tY0WcTt/+EUMh+ko1IqafNKf/za6e+5KakK5TU3zkVvzNOzc7CxKG/oKSXm4+ZE/SoV+eZ/Ic/0fYpKSUsz/J6yteOfZMPvqftJiXzu78+XH83F8hFJ/vMk5zjtrTTfOzkjS+d4ckYmjhUsDT1Fxw9TRmJjUR5jIwXfHzvNvrPFr4gZ074ZWbl5/HXlRrHnZKRrrudWRa7nVja2pBcziJuekqI3GFPh3/uM9NQUHJxdqOFbn39+D6Zu42Z4VPPi9vWr/BNyBKUyn4z0dKz//ZzLyszgs3feID8/DyMjI/qPfp1a9R+/5NOovDkKIyNUmbqz2srMTMpZVDL4HGMba4ytK1C+RnVSDmlWAli3bYV9v17c3/oTADk3YrCoW4vsqGvkxcVj6uKEhW9tFMbGGJU3R5XxZLPoxjbWKIyNUSan6MaZnIJxI8N7+TNPh+L49uuU92tA1tkwTCu6YTuoHwAmDnbkx8XrnG9kaYFV21YkBm5+otiEZyeavD87kfwVEhMTQ15eHvXrG744AFStWlX7b3t7zc1Lamrx6/r/M4U2eGuo9Q8ZEHH+PJ/P/5jJU9+lZu2HMxo3rl9npf83jBg9lsZNm5GUeJ+1q1fxzeIvmfnRvGcIUzcotVqNghIEWow7t27y/bo19B0yDN+GfqQkJ/HjdwF8t3o5b7z79Gvg9eKkZHFeCA/nqwXzefOdqdQotEeukqcnlTw9tV/XqlOXuLi77Nq+7amSv0KB6sap1j/2tM79/Re/fL+JsVPfw97Answnof9757Gv5+ez55CRlUXUtausWL+eoB9/ZMywYQCMGz6CxORkxk+bCmo19nZ29OrShc3bt2NkYGT44JEjrC207PqVCZMLItONy0Cs+v8vRQ4UfPAUesCsnDlD3p9LXm4Ot69E8ufu7VSwd8DDpwSj6Xo/r8gPfNx7Rv/0YgKHyLOnOLxzGwMmvI1tMUurQ4+FYGVjS3XfBiWOWfPj/pu4R834iLycHG5fiyZ414/YOjpRr5jEtaiGVSsxsPnD63lg8Ml/f5buzYMCxTNVEbAoZ8bgVo3Ycux0sfvHnpXBa1Qxf7vzvlrMwF698K315H9/z0oOcXasW52pPR8OYH20bZ82mqKxluS3/l7QL5ibmlKrkgvjOzbXLAENv6J3Xr8mvvRsVJuZP+wlswR/B3qviuZCWfz5eq+t7vEu/QeRnprCyvlzQK3GysYWvzbtCPnfbhSFrpPlzMszfeFicnKyiboQzt4fNmHv6ET1uvV4Oo94HRUKFCYmJB/4DWWK5l4p+cBvuIwbgamrC3n34kj/+xTGlhY4Dh0ACgWqzEwyL16iQpNGz7i8sehzFfqH/pW27xCm7q64fTYHhYkJqoxMUnbtxWHMa6iVKr3zK3RuD8ZGpP929BniEwRpiOSvkJKMJhTes1VwwX2SUYiDBw9y6NChx57Xql172nfUL2pgY2OLkbExyUlJOseTk5Oxe0y1y/DzYcye8R5jxk+gT/8BOo9t/T6ImrVqM+S14QB4eXtjXr48095+i3ET3jBY+ORRKlhbY2RkRGqybpxpqSl6s4FPYu9PP1LNpwa9BrwKgGfVapQzN2fhzPcZNGIMDk76G+UfxdrGBiMj/dczJTlZbzawqAvh55k38wNGjh1Hr779HvuzatSqzR/BhvdAPY6ldQWMjIxIT9GtTPcgLQXr5zDLfO7vvwhauYyRb099pkqfttY2GBsZkVjk956ckoy9ne0jn+vyb0GIapUro1KqWPjNUkYMHoyJsTHm5cox9733mTV1GonJyTja2/Pz/v1YWlhga+D/v02LFgwxfrh0tWDJU2Z6GhUKvU+y0tMob2VdbEyWFWzITEvTOZb1IB0jIyPMLR/OGCiMjLD9N2F2quhBctw9zhw+8ETJn4VVBRRGRjxIS9E5npGepp0VK8rKxoaMIoNPBbOcRZ8TefYUuwO/pe/YN3QqfRamzM/n/MljNGzdAaMS7k/9r+MumAV0ruhBRloqf+z9ucTJ38WYe9wqVM3R5N8b4ArlzUnNzH4Yj7kZ6Y9Ypvc4rrYVsLEwZ2KXhxVcCz4fvhjRm6/3HCUhzXAxjMextbYu5j2VojfLVuB02DlCw88T8MMPgOZ+V6VS0aJXTz54ezL9exre9/Qs5BTnX1ducOlOnPZr03//lu0sLUhIe1j4ytaiPClFZgMNuZeiqQR7IyEJO8vyjGzbWC/569fElzHtmzJn2z4ux8Yb+jZalhX+vZ4XWXXwIC1Vb3VHgQq2tqSlFDn/3/eQlbXmGmhqVo4hE99m0Lg3SE9NxdrOlpPBhylnXh7LQqsejIyMcHTV7FusWLkq8XfucGTPrscmf6qsbNQqFUZFZmmNLcrrzQZqn5ORiVqp1CZ+AMqUVNRKJcYVrMi7Fwf5SlJ+DSblcAhGFuVRZWRi4VsbVU4uqqzH/36KUqamab5/kc9wYzsbvdnAwhLXbyJxw2aM7W1RpqRh0UjzehSd9QOw7tmVjD9OoCqmyI3w31GXaMhGeBSR/BXi4eGBqakpYWFhuLs/uvy7ISYmmpdTpdIfJSrQvXt3unfv/tjvdT9NvzIjgKmpKT4+NThz6h/adeioPX7m1Cnatmtf7Pc7fy6U2R/MYNS4cQwcPETv8ZycHL0ZlIKvn+aNZmJqShXv6kScC6Vp67ba4xHnQmnSotUTf78Cuc85TlNTU7x9fAg9fZo27Ttoj4eeOU2rRyx9DA87xyezPmT4mLH0G1SyEs/XoqOxe8q2GSYmpnhU8+JSeBgNC71+l8LDaND06fZlFjj713G+X7WcEW9PoWFzw+0HSsrU1JSa1X3458xZOhd6/f4+e5aOrUtezEGlVqNUKlEplVAoCTExMcHl3wT/t5CjtGrWzODMn6WFhTYZA80AjUUFa2IuX8TFswqg2Zcaey2aVn0GFhuHa5VqXAs/p3Ps1uVInDyqYGxc/OVTrVahzH+yGSBjExPcPKtw/WIEtf0e7um9HnmBmv9W6iuqUjVvjuz6kfy8XEz+3etz7WIEFWztsHV4OBBy8fTf7Nm4lj5j3qDWIyp4Xj53hswHD2hQwkIv/3XcRanV6id6XXPy88lJ193rlJaZjY+bE7cTUwBNQljV2YF9Z56+Yl9MYgpL9gTrHOveoBblzUz5+Z/zJD0wfD0vCc17qjr/nA2lc5uH19K/Q0Pp2MrwtXTrmm91vv79r7/47sdtbFzmj9MztO4pLXFm5ebpzcAmpmfQqJoHV+5q9pSaGhtT19ONgCN/PdH3VigU2mSywMBm9RjVrikfbdtXohYPJiamVKxajSsR56nf7OE190rEeeo1aW7wOZW9a7Bv2/fk5eZq98pGRYRhbWevt1LD2MREW+Tr3Mnj1G7oZ/A6WUCtVpVo7zQqFXlxCZSr7EF21FXt4XKVPcgq9HVhubF3URgbY2xjjTJVM8CjXZaZXqS9ikqF6t/3Svma1cm+fuPxMRmSn0/OlWgs/BqQ8cdx7WHN14/5fatUKO9rBjCsOrQl60KkTuIKUK5Gdcp5V+P+6oCni08QJCaSv0IsLCzo06cPmzZtwtTUlDp16pCenk50dDR+RQpYGOLs7IxCoeD06dM0bdoUMzMzypd/tn0khgwaMpQvFsynRq3a1PWtx97dP5OYeJ/e/foBEPDtGi5FXtS2aDgXepY5H7xPn34D6NylG0mJiYAmaSqY3WreshVLv/qCPT/vonGzZiTdT2T1imVU96lhsH1ESXTvO4C13yymmo8P1WvV4ejBfaQkJdKxh6ZP1/ZNgVyLuqLTouHOrZvk5+eTnp5GdnY2N69pPlAqV/MCoGHTZgSu9OfI/v/h28iPlKQkfgj4lipe3gbbR5RE/1cH8/WihfjUqkXtunXZv2c3SfcT6dm7LwDfrV/LlchIFi1dBmgS6Y9nfcgrffvRvnMXkpI0r6exkTE2trYA/PLTdpxd3ahcpQr5efkEH/6Vv/48xpxPP3uqGAE69OrD5pX+VPaqTrUaNfnz8CFSk5Jp3aUbAHu2bObm1SjemTtf+5y7t2NQ5ueTkZZOTnY2t29cB6BSFc3y5TPHjxG0yp/+I0bjXas2af/OLBqbmGBppV/ApiReGziQj7/6kto1a1C/Th12/e9/3E9MZMArmr6SqzZs4MLlS6z+StPDcf/h3zAzM8O7SlVMTU25eOUKqwM30LFNW8z+vcm5efs2Fy5FUrdWLdLTH7Bl506u3rjBxzM+KFFMCoWC+u06c/q3/di5uGLr5MLp3/ZhWq4cPo0eJiy//aBpw9BluGa/Yd2W7Tj/51GO/fwjdVq25e71aC6dOkHXka9rn3P6t324eFbF2sEJpTKfmxfDuXz6JG0HDNOek5uTTep9zU2nWq0iPTmJhDsxmFtYYFtor0yzzj3Y/d23uFf1wsOrOmf+CCY9NZlGbTX73IJ//pHY69cYMV1TXKpO05b88b9f2LNxHa179iUp7h4nDu2l7Sv9tTNPF079xe7AtXQeNAzP6jW0exyNTUwoX6Swz9ljR6laszZ2T/he+i/iPhX8K7aOTji4aGYobkZd4uRv+2nc7unbPAAci7xKJ18f4tMekJD2gM6+PuTkKwm9fkd7ztBWmpnRbccfFmlyt9PMSJqbmqBWq3G3syZfpSY+NZ28fCVxKbo3stm5eRgZKfSOP43X+g/g4yWLqV3Dh/q167Br/z7Ne+rf3qKrvgvkwuUrrP5Ccy31qlJF5/mRUVcwUij0jj9vco7z53/OM6y1HzH3k7mTlMprrRuRnZtHcMTDokQz+mgGVBf/m8j3bVyXeynpxPw7UFCvshuDmjdg7+mHxcBebd6AMR2a8uUvR7idmIKdpeYzPydfSWZO8ZVh2/XozdY1K/Cs5k0Vn5r8deRX0pKTad5J035p/48/cOtqFG/O/gSAhi1b89vPO/hx3Uo69R3E/XuxBO/9hS4DXtW+ZxLuxnLrahSe3j5kZTzgjwN7uXf7FkPfmKz9uYd378TTqzoOzi7k5+URGXaWM8f/oN+ocXoxGvLgzDnsenQm714cubH3sKhXByNLSzLDNIMnFVo3x8zVhcSfdmteh5sx5MbFY9utI6lH/wTApkNrcu/eI++eZkbN2NYGMzcXcu/GYWReDqtGDTB1cCDl4NOtlAFI+Wk3LjPfJefyFbIiIrHp3R0TB3tS9x4AwGH8KMrVrE7sDE1dAyPrCli1a01WWDgKU1Osu3fCql0r7rw7W+97W7/Sjdzbd8gKK3lRuOdBUd4c04r/TkwYKTB1ccbMuxqq9HTy44ov8FXWvMSFTp8bkfwVMWrUKCwtLdm2bRuJiYnY2trSoUOHxz8RcHBw4LXXXmPz5s2sWLGCDh068O677z73GDt06kxaWio/BG0kKTGRKlWrseirJbj8u4wjMTGR2NiHNzKH9u8nOzub7du2sH3bFu1xF1dXtuzYBUD3nr3Iyszkl107+XbVCiwtrWjQqBET33r7qeNs3qYdD9LT2LN9KylJyVSqXJn35n2Go7NmCWlKchLx93Srpn09fy734x8usZg7TfPzg/Zo2hm06dSVrKwsDu/bw9bA9ZS3tKCWb32Gjhn/1HG269iJ9LQ0tm0OIikpkSpVqvLpF1/i4qpJepMTE7kb+zDOwwcPkJOdzc4ft7Hzx23a484urmzcth2AvLx8NqxZTeL9BMzKlaNylap8uuhLmjxl9VQAv5atyUhP59DPO0hLTsbNw5O3Zn6kHfVNTUnmfpzuqPO3X3xGUsLDD4UvP5wOwIofNW1B/jx8CJVSyc5Ngezc9LD/nHftOnoN4UuqS/v2pKal8d2WLdxPSsKrchW+WbAQt3+XDt9PSuTO3YcNgY2Njdm0bRsxd+6gVqtxdXFhUJ8+DBvwcEZOpVKyZedObt6+jYmxMX71G7BhmT/uriUfmGjUsRv5ebn8/tMWTZP3ylXp++Y0nR5/6UWWrFk7ONJ7wjv8+ct2wo//jqWNDW37D9X2+APNbHTIT1t4kJqMiakpds6udB4+Dp9GD2fY4mNu8suqr7Vf/3NwD/8c3EPNJi3weH2S9nidJs3JynjAn/t38yA1BSf3Sgyd/L52f96D1BSS7z98f5iXt2D4tA85sGUTGz7/mPIWFjTv3INmnXtozznzRzAqlZJft3/Pr9u/1x739KnJqPfmaL9OTojnxuWLDHj9yd/z/0XcKpWKI7t+JDUxASMjY+ycnOnYfwh+bTvq/fwnEXIhGlMTY/o3rUf5cqbcSkhm/eETOj3+bC31B+3e7a37OVDHw42kB5ks2vXfN1nu0q4dqelpfLd1K/eTkvGqUplv5n9W6D2VxJ27xVegfFHkHOf2v85RztSEyd3bUKF8OS7diWfWlv/pzBA62+gOhhgZGTG+U3NcbSqgVKmITU4jMPgk/ys0S9y7cR1MjY35aGBXnef+GnaJJXuL3w/WoHkrMtLTObx7J2kpybhW8mT8jNnanq1pKckkxj9culrewpKJM+eya2MA/vM+pLyFJe169qZdj97ac1QqFb8f2EvC3ViMjU3wql2HyfMW6swM5mZns+u7daQkJWFqZoazuzvD3niHhi1LtpQ6+0o0qeXNsWrWGGNLS/ISE0n6ea92Fs/Y0gJjG92l20m/7MOmQxsch/RHnZ9Pzs3bpP3+p/ZxhZERVn4NMLazBZWKnJg7JGzbiTLt6QdOHoT8iZF1BeyGD8bJ3p6cGzeJnTWf/HjN56Gxgx2mRSqUWnftgOMbYwAF2RcvcWf6HHIu61YsVpQvT4UObUjaXLI2Q8+TeU0fKq1YrP3a4fVROLw+irT9vxL3+dePeKYg6FJkZWWJHFqGilv2KTd3ktMef5IMOFZ4MRXvntXV+ESpQ3isZnZP1wbiRQu6cF3qEErE9hmrTAq6zt+6+/iTZOCj1k9bXEMo6tUthnudys073UqWYEnN79gfUodQIpn7Hl8/QQ7UOU+/n/hF8ji8W+oQSqTv1xtf2M/a/d6YF/azXiTR508QBEEQBEEQBOElIJZ9CoIgCIIgCIIge6LP37MTM3+CIAiCIAiCIAgvATHzJwiCIAiCIAiC7KnEzN8zEzN/giAIgiAIgiAILwGR/AmCIAiCIAiCILwExLJPQRAEQRAEQRBkT6z6fHZi5k8QBEEQBEEQBOElIGb+BEEQBEEQBEGQPVHw5dmJmT9BEARBEARBEISXgJj5EwRBEARBEARB9kST92cnZv4EQRAEQRAEQRBeAoqsrCyRQr8kDh48SPfu3aUO47FEnM9XaYizNMQIIs7nTcT5fIk4n5/SECOIOJ83EafwMhAzfy+RQ4cOSR1CiYg4n6/SEGdpiBFEnM+biPP5EnE+P6UhRhBxPm8iTuFlIJI/QRAEQRAEQRCEl4BI/gRBEARBEARBEF4CIvkTBEEQBEEQBEF4CYjkTxAEQRAEQRAE4SUgkj9BEARBEARBEISXgEj+BEEQBEEQBEEQXgIi+RMEQRAEQRAEQXgJiORPEARBEARBEAThJSCSv5dIt27dpA6hREScz1dpiLM0xAgizudNxPl8iTifn9IQI4g4nzcRp/AyUGRlZamlDkIQBEEQBEEQBEH4b4mZP0EQBEEQBEEQhJeASP4EQRAEQRAEQRBeAiL5EwRBEARBEARBeAmI5E8QSiA7OxuVSiV1GIJgUGpqqtQhCIIgCIJQCoiCL4JsJCYmkpqaqpdkeXt7SxSRhlKpZODAgSxfvhxPT09JYykr8vPz+frrrxk1ahRubm5Sh1Pq9e/fn6ZNm9KlSxf8/PxQKBRSh2TQlClT6Nq1K+3bt8fKykrqcAzq27cvmzZtwtbWVud4WloaI0eOZPfu3dIEZkB0dDR79uwhJiYGgEqVKtG3b1/Jr5mGhIWFaeP08PCgfv36EkdUegQHB5f43I4dO/6HkQhS8ff3Z8KECVhYWOgcz87OZu3atUydOlWiyITSyETqAIT/TnBwMG3atMHU1FTneF5eHseOHZPNh8TVq1f5+uuvuXPnDmq17liEQqGQ/GbL2NgYZ2dn8vPzJY2jLDExMSE0NJTRo0dLHcpjrV+/nq5du1K5cmWpQynWvHnzOHz4MIsWLaJChQp07tyZTp06yS6xbty4MTt37uS7776jefPmdO3aVXZJQNFrUIG8vDxMTOTzkRkSEsI333xDvXr18PPzA+Dy5cu89957TJs2jQ4dOkgcoca9e/dYtGgRN27cwN7eHoCkpCSqVKnCrFmzcHV1lThCXXl5edy8eZPU1FS9v4XGjRtLEtO3336r83VeXh5KpVI7yKNWqzE2NsbU1FTSz/XBgweX+Nzt27f/h5E8WkRERInPrVu37n8YSckFBwczevRoveQvJyeH4OBgkfwJT0Q+n2TCc+fv70+jRo30RrCzsrLw9/eXTfK3cuVKnJyceOedd7C3t5flrMWQIUPYtGkT06dPx8bGRupwHuvYsWOEhYWRkpKidwMzd+5ciaLS1bJlS06cOMGAAQOkDuWRoqKi+N///oeXlxddu3albdu2eh/AUmvYsCENGzbkwYMH/P777xw+fJgdO3ZQt25dunTpQsuWLTEzM5M6TEaNGsXIkSM5c+YMhw8f5tNPP8XOzk6brDo7O0sW2y+//AJoBpwOHDhA+fLltY+pVCouXLhApUqVJIpO3+bNmxk+fLjeDfeOHTv4/vvvZZP8rVixgvLly7N+/Xrt7zc+Pp5ly5axYsUKFi5cKHGED4WGhrJ06VKDy6ilHIgsnCidOnWKLVu2MGHCBGrUqAFokv4NGzYwZMgQSeIr8MYbb0j680tq9uzZKBQK7Wdj4SS68NeA5IPP6enpqNVq1Go1Dx48wNjYWPuYSqXi1KlTevd4gvA4YtlnGdanTx82b96sl6xcvXqVjz76iK1bt0oUma5Bgwbh7+9PxYoVpQ6lWJMnTyYuLo78/HwcHR0xNzfXeXzFihUSRaYvMDCQPXv24OvrazCZnjZtmjSBFbF161Z2795N3bp18fb21ntN+/XrJ01gBty+fZvDhw9z9OhRMjMzadGiBV26dMHX11fq0Iq1b98+NmzYQH5+PpaWlnTr1o0hQ4boJDVSS09P5+DBg2zduhWlUkn9+vXp27evdibrRRo/fjwACQkJODg4YGT0cEu8qakpzs7ODB8+XHvDLbVBgwaxfPly3N3ddY7HxsbyzjvvsHPnToki0zVw4ECWLFlC1apVdY5fu3aNGTNmyCZO0CQvderUYejQodja2updO4uuopHCW2+9xdSpU6lZs6bO8UuXLrFs2TK9WUJBX1pamvbfV65cITAwkMGDB2tf00uXLrF9+3bGjh1LkyZNpAoT0NzHPW5A/LXXXpM88RdKFzHzVwZNnjwZhUKBQqFg1qxZOiNFSqWShIQESW6uilOlShWSk5Nlnfy1atVK6hBK7OjRo8yYMUP2MR8+fBhLS0uuX7/O9evXdR5TKBSySv4qVarEmDFjGDVqFKdPn+bw4cPMmzcPJycnunTpQvfu3alQoYLUYZKUlMSRI0c4cuQIiYmJtGnThq5du5KUlMSOHTuIjo5mwYIFUocJaG6wDh8+zLFjx7C3t6dz584kJSXxxRdf0LVrVyZMmPBC49mwYQOgmRWYPXu2bPckFvD19SU8PFwv+QsPD5fNUjUAJycncnJy9I7n5ubi6OgoQUTFS05OZvDgwZLOQD9OfHw85cqV0zterlw5EhISJIio9LG2ttb++/vvv2fChAk0bNhQe8zV1RUbGxs2btwoefJXMDM+Z84cZs2apXNdMjU1xcnJCQcHB6nCE0opkfyVQQU3/Tdv3qRx48Y6I/0mJiY4OzvTsmVLqcLTM3LkSDZu3MiIESOoXLmy3r4aOdxUX716la5du9K4cWOdGQE5UqlUeqPsclRws12aKJVKMjMzycjIQKVS4eTkxNGjR9m+fTtvv/027du3lySuEydO8Ntvv3Hu3Dk8PT3p3bs3HTp00FmeWrlyZcn3haSkpBAcHMzhw4e5d+8eTZs2ZebMmTo3Xq1atWLBggUvPPkr8Pnnn0vyc59U48aNCQoKIjo6Wmf5319//cWwYcM4ceKE9lwpr/fjxo1j3bp1TJw4kerVqwOapdQBAQHa2Va5aNKkCZGRkbLbh1iYj48P69at4/3339fe9CcmJhIQECCbWWnQ7Evcvn07f/zxBwkJCSiVSp3HpV5OWSAmJsbgIISDgwO3b9+WICJdBatLAgICcHR0lP39h1A6iGWfZdiRI0do06aNLPb6PEqfPn20/y68vEGtVsui4AvAkiVLOHnyJJaWlnTq1InOnTvrjbjLRVBQECYmJrz22mtSh/JYxe1NVCgUfPTRRxJGpisqKorffvuNY8eOUa5cOTp27EjXrl21N4l79uxhx44dbN68WZL4hgwZQtu2benWrVuxlR5zcnLYtWsXw4YNe8HRPdS/f3/c3Nzo0qULHTt2NLh/NjMzkwULFrzQJGzt2rWMHj0ac3Nz1q5d+8hz5bKvqfB181GkvoYOHjyYvLw8VCqVzt4qIyMjvWWUUhYBAcjIyGDJkiW4u7sbHIiUwz75u3fvsnDhQm7fvq2T/FWsWJE5c+bI5nNp48aNHDt2jEGDBhEQEMDIkSOJi4vj2LFjDB8+nB49ekgdIgDvvvsubm5uTJ06VTujmpOTg7+/P3fv3uWbb76ROMKHsrOzuX79usG9/HIa0BfkTyR/L4kHDx7oXSzkMKMGmmVKjyKXfVWZmZmEhIRw+PBhoqOjqV27Nl27dqVVq1YGl+G8SIVvWNVqNSEhIXh6elKlShWdZb8gn5vX0rI3cfLkydy5c4eGDRvSrVs3GjdurPeapqamMnLkSPbs2SNJjNnZ2Xp7JuXowoUL1KlTR+ow9BRe6jl79uxiz1MoFLIqUFIaHDlypMTndurU6T+M5PGOHTvGsmXLyMvLo1y5cnrXJKmT0wJqtZrQ0FDtzJSHhwcNGjSQVbG08ePHM2nSJPz8/Bg8eDD+/v64ubmxf/9+wsLCmDVrltQhApo9f5999hn5+flUqVIF0KyaMjIyYt68efj4+Egb4L/OnTvH4sWLSU9P13tM6gEeofQRyV8ZFh8fz6pVqwgPD9dZciGnGbXS6ubNm/z6668cPHgQExMT2rRpQ9++ffHw8JAknkfdsBYll2VtI0eO5M0335T93sRt27bRpUuXUrOvIjk5mby8PJ1jct7DJAhyMXbsWNq0acNrr71WKgZT5GzgwIGsWbMGZ2dnRo0axbx58/D29ubevXtMmTJFNok0aAbPQkJCuH37Nmq1Gk9PT9q1ayerv4FJkyZRvXp1Ro0aVWo+iwT5Env+yjB/f38ePHjAlClTZH+xSE5OZt++fcTExKBQKPD09KRHjx7Y2dlJHZqexMRE/v77b06dOoWxsTGtWrXi/v37vPPOO4waNUqS1gVySeieRGnZmzh06FCpQ3isjIwM1q1bx59//mmwH6WcBnoOHz7M77//TkJCgl6sAQEBEkX1aLGxsTg6OspuCf3Vq1cJDw83uAxs7NixEkVlWHp6usE4PT09JYpIX0ZGBj169JDVTb8h+/btY9++fcTFxbFq1SpcXV3ZsWMHrq6utGnTRurwAE2hn6SkJJydnXFzc+Ps2bN4e3tz6dIlyVfKFGVubk737t2lDuOR4uPjmTt3ruzv5YTSQSR/ZdiVK1dYsmSJrJtTA1y8eJFPPvkEW1tb7Yb1kJAQfvnlF+bPn69X0loK+fn5/P3339qiGtWqVWPgwIG0bdtWW1Dn2LFjrFq1SvK+df7+/kyYMEGvF112djZr166VvOhHgW7duhESElIq9ibeuXOH48ePG0xY5PB6BgYGcv36debMmcPnn3/O1KlTSUxMZM+ePbIqqrFr1y527NhB9+7duXDhAj179uTu3btcuHCB/v37Sx0eoNkzW7FiRTp16oRarWbevHmEhYVhYWHBJ598IovrEcDOnTvZtGkTTk5OeoNkclr+d/XqVfz9/bl58ybwcOWJHFegtGzZknPnzuHm5iZ1KMXavXs3u3btYuDAgWzatEl73MHBgX379skm+WvRogVhYWHUrFmTPn36sHjxYg4dOkRSUpJs3usFTp8+rU2mP/30U5ycnDh06BCurq7Ur19f6vAAqFWrFnfu3JH136ZQeojkrwxzcXHRW/4lR4GBgbRt25ZJkyZpK1mpVCpWr17Nhg0bWLx4scQRappTA7Rr147Ro0cbnLFq2LAhlpaWLzo0PcHBwYwePVov+cvJySE4OFjSZMXQ3sRz587Jem/iqVOnWLRoEdWqVePq1atUr16du3fvkpeXJ5v9a2fOnGHGjBnUqVMHIyMjvLy8aNOmDXZ2dhw8eFA2S2sPHTrE5MmTadWqFfv27eOVV17B1dWVbdu2ER8fL3V4gGbg6YMPPgA0r+u1a9dYsmQJISEhBAUFyWaWfffu3bz11luyKZxRnOXLl+Pg4MCECRMM9s6TE1dXVzZv3syFCxeoUqWKXsEXObSfOXDgAJMnT6ZJkyZ8//332uNeXl7cunVLwsh0jR49WvvvVq1a4eDgwKVLl3B3d6dp06YSRqYrJCSE1atX06VLF86fP6/dIqNSqdi5c6dskr8ePXoQGBhIYmKiwc/L4gp9CYIhIvkrwyZMmMCmTZt46623ZFMBzJDr168zbdo0nRLGRkZG9O3bVzZFP15//XVat279yGVfVlZWkrYvSE9PR61Wo1arefDggc6Hg0ql4tSpU9ja2koWH6Ad/S9QrVo1AL2S2nK6Qfzhhx8YNmwYr776KoMHD2b69OnY29uzdOlS2cwCZWRk4OTkBIClpaW2KEDNmjVZsWKFlKHpuH//vrbcv5mZGZmZmQC0bduW9957j3feeUfK8ABNO4qC0u+nT5+mdevW+Pj4YGVlxfTp0yWO7iG1Wi2bG9NHiY2N5cMPP5T1Z1CB3377jfLlyxMZGUlkZKTOY3LpPZqQkGBwNY+JiYnBfopyUbNmTdlcLwvbuXMnkydPpm3btvz222/a4zVr1mTLli0SRqbriy++AGDVqlV6j8ltBl2QP5H8lWELFy4kLy+Pt956CxMTE72RIrlsuLawsCAuLo5KlSrpHI+Li5PFTBrIo8T34wwfPhyFQoFCoeDtt982eI7USyzlMmvyJO7cuaNdSmVsbExOTg5mZmYMHTqUTz/9VBY3hK6ursTFxeHs7EylSpX4448/8PHx4a+//pJNVV8AOzs70tLScHZ2xsnJiUuXLlGtWjXu3r0rm4S/QoUKxMfH4+joSGhoqHbWX6lU6u1Xk1KPHj04fPiwNj65ql27Nrdv3y4VyV9p6D3q4uLC1atX9Yo4nT59Wlb7Jwv3mTRELq0JYmNjDSal5ubm2sEpOZDrfmihdBLJXxkml2Vzj9O2bVuWL1/O2LFjqVmzJgqFgosXL7Jp0ybatm0rdXilRkEJ+jlz5jBr1iysrKy0j5mamuLk5CQ2iz+F8uXLk5ubC2iSl9jYWCpXroxSqSQjI0Pi6DQ6derE9evX8fX1ZdCgQcyfP599+/ahVqsla5ZuSL169fjnn3/w9vama9euBAQE8Oeff3L16lVat24tdXiA5qZ0yZIlVKxYkfT0dPz8/ADNCgU57bcZNmwYn3zyCVOmTDHYk04Oe1EBpkyZwvLly7l37x6VK1fWG4SsW7euRJGVTgMGDODbb78lJycHtVrNpUuXOHr0KDt37pTN7xwezlQVVTDII5eZKgcHB+7cuaOXTF+4cEHbx1UODh48iJOTk94y7wMHDpCYmMiIESMkikwojUTyV4ZJ3TOppMaMGYNarcbf3x+VSgVoZlh69Oihs29AeLSCfogBAQE4OjrqLKMVnp6Pjw8XL17E09OTJk2aEBgYyI0bN/jrr7+0BYqkVnj2sX79+qxZs4bo6Gjc3d21vavkYPLkydrZsx49emBlZcXFixdp2bKlbKrtvf7667i4uBAfH8+YMWO0lR+TkpJktb9u8+bN2uJTDx48kM3MaVGxsbFcv36d0NBQvcfktlyt8J5kQ+QwoNq5c2eUSiVBQUHk5OSwdOlSHBwcmDhxomyKvQB6PU+VSiVXr17lu+++Y+TIkRJFpa9bt26sW7dOu+Q8ISGBCxcu8N1330m+Uqawo0ePMnPmTL3jXl5e7NixQyR/whMRff5eEqWh91d2djb37t1DrVbj5uYm+3Lbcpadnc3169cNllaXy3Kb0uLevXtkZWVRtWpVsrOzCQwMJDIyEnd3d8aPHy+795Hw9PLz89m8eTO9evWS/e916NChvP3227K64TfkzTffpHr16gwaNMhgwRdra2uJItNXtF9qfn4+t2/fRqVS4eXlpV1dIRepqamo1WrJ93I/icjISFavXi2rvchBQUHs3r1be49kampK//79ZZVQDRgwgNWrV+vNRt67d49Jkyaxa9cuiSITSiMx81eGlabeX6BZY1+lShVycnK0N9dyvwGTo3PnzrF48WJt0Y/C5DbSXhoU/rA1Nzdn0qRJEkbz0NatW0t87rBhw/7DSB4tIiKixOdKvQTQxMSE/fv307NnT0njKAkzMzNtwSQ5u3//Ph9//LGslswWx9Ce5NzcXJYvXy6byr4FoqKiuHfvHk2aNAE0A36mpqZ6y2rlxtLSknv37kkdBqCZjQwNDaVfv34MHjyYmJgY1Go1Hh4e2hZOcuHk5GRwKWpERIS2QJUglJRI/sqw0tL765tvvsHHx4devXqRl5fH+++/z82bNzExMWH27Nk0btxY6hBLlXXr1tG4cWNGjRol9viVYcePH9f5Oj4+npycHOzt7QHNMsVy5crh4uIiafI3e/ZsbV83eLjnp+jXII8BqYYNG3L+/Hm6dOkidSiP1LdvX227B7ku+QRo0KAB0dHRpSL5M8TMzIzBgwfz8ccfy2LZb3JyMgsWLCAqKgqFQsHatWtxdXUlICAAMzMzJk6cKHWIAERHR+sdS0pKYufOnbIZtDA2Nubzzz9nzZo1WFtbaysRy1H37t0JCAggPz+fevXqARAWFkZQUBADBw6UODqhtBHJXxlWWnp/hYaG0rt3bwD++ecfMjMzCQoK4rfffmPr1q0i+XtC8fHxzJ07VyR+z6BPnz4lvqGWKmFZuXKl9t+HDx8mODiYadOmaWfL4+Pj8ff3p3379pLEV6BwL7IrV64QGBjI4MGDtRX2Ll26xPbt2xk7dqxUIeqoX78+QUFB3LhxAy8vL73l53JZNn3hwgUuXLjA6dOn8fDw0Cv4MnfuXIki0+Xn56fdJ2uoP5lcXs9HSU1NJSsrS+owAM2ebjs7O7Zs2cK4ceO0x1u3bv3YPYsv0vTp03UGfQrUqFFDVoVpqlatyt27d3FxcZE6lEfq378/aWlprFu3TruSy8TEhN69e4vkT3hiIvkrw0pL768HDx5o9yycOXOGli1bYmtrS9u2bdmxY4e0wZVCtWrV4s6dO6V2pF0OPvzwQ+2/U1JS+OGHH2jRooVOwnLy5EnZFATYunUrH330kc4yaWdnZ8aPH8+CBQskncUqvKfr+++/Z8KECTRs2FB7zNXVFRsbGzZu3KhdwialghvovXv36j0mp2XT1tbWtGjRQuowHmvNmjUABq/lcno9AX755Redr9VqNUlJSfz++++yGYQ8f/48CxYs0KnmDJr3UUJCgkRR6SvamkChUGBjY/PIXrlSGDZsGBs2bOC1117D29tbb7BHTq1yRo8ezZAhQ7h16xaALJenCqWDSP7KsNLU++vmzZvY2dkRGhqq7VGXnZ0t+/0LctSjRw8CAwNJTEw0ONLu7e0tUWSlR+FZ8c8++4zRo0fTrVs37bEuXbrg4+PDyZMn6dWrlxQh6khJSTHY4Dk3N5e0tDQJIjIsJibG4P4UBwcHbt++LUFE+opWKZSradOmSR1CiZSW1xP0E34jIyNsbGzo3LkzgwYNkigqXTk5OXqzvABpaWmySqxKy379+fPnA7Bo0SKd1R5qtVp2gxOg2Xfu4+MjdRhCKSeSvzKstPT+6ty5M1999RX29vYYGRlRv359AC5fvqzX+F14vIL+SqtWrdJ7TI4fZnJ3/vx5g3tkfX19Wb9+vQQR6WvYsCErV65k8uTJ2n0rUVFRrFq1igYNGkgbXCGenp5s3bqVqVOnUq5cOUBzM7tt2zZZNaguTQoX/jA3Ny81hT/kqDQ0ea9Tpw5Hjhxh1KhR2mNKpZKffvpJuxdMDo4dO4alpSWNGjUCNKsTDh06hKenJ9OmTdPuTZaa3Cq4CsKLIFo9vETi4+Nl2fsLNMUrEhISaN26tXZm4MiRI1haWtK8eXOJoytd4uPjH/l4aRmRlYvx48fTvXt3Xn31VZ3jO3bs4ODBg7K4YUxNTeWbb77h7Nmz2v6OarWahg0bMm3aNNmUgr9y5QqfffYZ+fn52mvQzZs3MTIyYt68ebIY0Var1ezfv599+/YRFxfHqlWrcHV1ZceOHbi6usqmtUJxhT9Wrlwpq8IfpeX1LHDs2DHCwsIMtsmRwz7KW7duMWvWLKpVq0ZERARNmjTh1q1bZGRk8NVXX8lmuf+kSZN4/fXXadSoEdHR0XzwwQcMHz6cs2fPYmtry4wZM6QOURBeWmLmrwwLDg6mTZs2mJqaApqbfmdnZ/Ly8ggODqZjx44SR/iQoeIzpaVJvdw4OzuTnJxMZGQkqampqFQq7WMKhaJUlLGXk+HDh+Pv7094eLh2z9/ly5c5d+4cU6ZMkTg6DRsbGz755BNiY2N1ypVXrFhR6tB0+Pj4sH79ekJCQrh9+zZqtZr27dvTrl072fT13LNnD7t27WLgwIFs2rRJe9zBwYF9+/bJJlkpLYU/SsvrCZoK2Xv27MHX1xd7e3tZVlH19PRk5cqV7Nu3DxMTE3Jzc2nVqhW9evWSzWwaaAYhC1bunDx5kubNmzNw4EAaNmzIxx9/LHF0um7cuMHBgwe5e/cuU6dOxd7enr/++gtnZ2e8vLykDk8QnjuR/JVh/v7+NGrUSG/UPysrC39/f1klf0qlkitXrpCQkKDXk1BOcZYGR48eZcWKFajVaqysrHRuYETy9+Q6duxIxYoV2bt3L3///Teg2Wj/1VdfUaNGDYmj07hy5Qo+Pj64u7vj7u6u89jRo0fp0KGDRJHpMzc3p3v37lKHUawDBw4wefJkmjRpolOp1MvLS1toQQ5KS+GP0vJ6gua9MmPGDNlUwi6OnZ2drBqQG2JmZqatkBoWFkbnzp0BsLCwIDMzU8rQdJw9e5YFCxbg5+fH+fPnyc3NBTTN048cOcJHH30kcYSC8PyJ5K8MK9iwXFRCQgIWFhYSRGRYTEwMn332GXFxcYBmk71SqcTY2BhTU1OR/D2hzZs3M2DAAIYNGyb2/TwnNWrUkE2iZ8j8+fNZtGgRHh4eOseDg4NZvXq1bJK/4ODgYh8zMzPDzc1N8pH2hIQEKleurHfcxMTEYFEdqZSWwh+l5fUEUKlUVK1aVeowHispKYn9+/cTExMDaAajevToIav2PrVr12bDhg3Url2b6OhoZs6cCUBsbKy2Crkc/PDDD4wfP55evXoxePBg7XFfX1+96q+CUFaI5K8Mmjx5MgqFAoVCwaxZs3QSAKVSSUJCAn5+fhJGqCsgIABvb2+WL1/OqFGj8Pf3JyMjgzVr1sh+dFOOMjMz6dy5s0j8nkF6erq2Im5Bi5TiyKFybr9+/Zg3bx5fffWV9saqIPH74IMPJI7uoW+//Za8vDyUSqVOs/eCv1WlUkm1atX49NNPsbGxkSRGFxcXrl69qrc39vTp07IqSlNaCn+UltcToFu3boSEhMimhYshoaGhLFiwACcnJ+0e2T///JOff/6ZOXPmaAusSO3NN99k9erVHD9+nEmTJmkT0zNnzui0epHarVu3DLbxsLKyeuy1XxBKK5H8lUEFS1Zu3rxJ48aNdfrAmJiY4OzsLKvGulFRUSxatAhzc3MUCgVKpRJvb2/GjBnDunXrZNWTsDRo3749p06donfv3lKHUmqNGDGCTZs2YWtry/Dhww3OoMupFPigQYNITU1l7ty5fPnll5w+fZrVq1czc+ZMWfTOK/DBBx+wbds2Xn/9dZ2qpBs2bGDIkCE4ODiwbNkyAgICeO+99ySJccCAAXz77bfk5OSgVqu5dOkSR48eZefOnbJqTj127FhmzZpFVFQUeXl5BAYG6hT+kAu5v56F90eq1WpCQkI4d+6cwTY5b7zxxosOT8+6devo2rUrEydO1LkurVu3jvXr12v7KkrN0dGRefPm6R2XU6Vx0CR5iYmJek3er169arAtjSCUBSL5K4OGDRsGaAp/tG3bVlvwRa7UarW27LuNjQ2JiYlUqlQJR0dHYmNjJY6u9Bk/fjwLFy4kLCzM4A1Mwd+HULwFCxZoZ/RKSynw8ePHk56ezvvvv09ycjKzZs2STWPqAhs2bGDatGk6S2hr1qzJ+PHj8ff3Z82aNYwfP56lS5dKFmPnzp1RKpUEBQWRk5PD0qVLcXBwYOLEibIqTmJubs7y5cs5ePCgXuGPovumpST31/PmzZs6X1erVg1Ar++kXIq/xMfH88orr+jF06tXLw4dOiRRVKVXu3bt+O677/jwww8Bzex5eHg4gYGB2n2KglDWiOSvDGvcuDGZmZna5VM3btzg2LFjeHp60q5dO4mje6hy5cpcv34dV1dXqlevzs6dOzEyMuLXX3/VK14hPN7Bgwc5e/Ys1tbW3L17V6/gi0j+Hs/X19fgv+XkxIkTescaN25MWFgYbdu2JTc3V3uOXGb64+PjtQM9hZUrV07bosTFxYUHDx686NB0dOvWjW7dupGamoparZZNq4zCJkyYwKZNmxg+fLjO8bS0NMaOHSuLGekCcn49P//8c6lDeCLe3t7cuHFDr5LvjRs3JN8vW1heXh7bt2/njz/+ICEhAaVSqfO4XP4+R4wYwbJlyxg/fjxqtZpJkyYB0LZtW509gIJQlojkrwz78ssv6dChA126dCE1NZWZM2dib2/P//73P5KSkujfv7/UIQIwePBgsrOzARg5ciTz589nzpw5WFtba0fjhJL78ccfGTduHP369ZM6lFLrSfZ6SLXn74svvij2scOHD3P48GEA2SxNBahevTobNmxg+vTp2NnZAZp+dYGBgdr9S7GxsbJZbiXVvsOSKK6gV0GTd7mYM2cOs2bNwsrKSuf1zMzMZOHChaVmZl0uevbsSUBAALGxsdrWM5cuXeLAgQOMHj2a6Oho7bne3t5ShckPP/zAsWPHGDRoEAEBAYwdO5a4uDiOHTumN2AhJRMTE95//31GjBjB1atXUalUeHl5iYFnoUwTyV8ZduPGDe3yqhMnTuDm5sY333zDyZMn+e6772ST/BXeoO7q6srq1atJT0/Xa1MglIxKpaJZs2ZSh1GqFbfPrzCp9/zt2bNHkp/7LKZMmcKCBQsYN26ctidZUlIS7u7uzJkzB9AkL1KOuKenp7N582bCwsL0+mQCbN++XaLINAr2qCkUCjZt2qQzk6pSqbhy5Yp26aIchIeHG1yGmpuby4ULFySIqHT7+uuvAU1V5+IeA+kHfY4dO8akSZPw8/Pju+++o1mzZri5ueHh4cG5c+fo0aOHZLEVtnXrVvr374+rqyuurq7a4zk5OezatUuslBHKJJH8lWE5OTnaxsnnzp3TJgReXl7cv39fytAMSk1N5d69e1SrVk0WFRRLq06dOhESEiI+tJ6BmI34b7i7u7Nq1SpCQ0O1e6oqVapEw4YNtcl2ixYtpAyR5cuXc+3aNbp16ybLRt8Fe9TUajW3b9/WafdgYmKCl5cXAwYMkCo8rcIzUDdu3NDpR6hSqQgNDZVVa4LSIiAgQOoQSiQlJUXbesbc3JyMjAxAM9i7ceNGCSPTtW3bNnr06KG9VyqQk5PDtm3bxOeoUCaJ5K8Mc3d356+//qJly5aEhoZqbwhSUlKwtLSUOLqHMjMzWb58OSdOnEChULB27VpcXV1ZtWoVdnZ2si67LUc5OTn89ttvhIaGyrZindzJdZ9fcdRqNfv372ffvn3ExcWxatUqXF1d2bFjB66urrIorFFAoVDQqFEj2ZSkLyosLIzPPvtMtn0dC/aoLVu2jIkTJ8qqZ2th06dP17YcMlT10czMTFyLnkLRlhly5eTkRFJSEs7Ozri5uXH27Fm8vb25dOmSwX2/Uilu+fS1a9d0BiwEoSwRyV8ZNmzYMBYvXkxgYCD16tXT3sycPXtWVsuCNm7cSFJSEsuWLdPZ49ekSRM2b94skr8ndPv2bdlXrCtt8vLyCAkJ4datWygUCm3RJLnsrdqzZw+7du1i4MCBbNq0SXvcwcGBffv2ySr5u3z5crFLKuWQDNja2urNAsjRtGnTpA7hkQICAlCr1UyYMIGvv/5aZ7+fiYkJNjY2ohfpUzh27BiWlpbawZOtW7dy6NAhPD09mTZtmnY5tdRatGhBWFgYNWvWpE+fPixevJhDhw7Jpt5AwdJyhUKh135CpVKRl5dH9+7dpQhNEP5zIvkrw1q2bElgYCBJSUlUrVpVe7xBgwayqf4H8M8//zB79myqVaumk5x4eHgQFxcnYWSlU2mrXid3t27d4uOPPyYrK4vKlSsD8Ouvv7JlyxY+/fRT7dImKR04cIDJkyfTpEkTvv/+e+1xLy8vbt26JWFkunbt2sXGjRtxc3PTW1Ipl4GJkSNH8sMPP/Duu+/q9EgVnkzBDNWePXtQKpVcuXKFhIQEvf1/HTt2lCK8Umvr1q28/vrrgGZp7Y4dOxg+fDhnz55lw4YNzJgxQ+IINUaPHq39d6tWrXB0dCQyMhJ3d3eaNm0qYWQab7zxBmq1muXLlzNixAid1VAmJia4uLhoC+oIQlkjkr8yzs7OTltVr4DcljM9ePAAa2trveNZWVkYGRlJEJEgPLR+/Xq8vLyYPn26doldZmYmX3/9NevXr2f+/PkSRwgJCQnaxLQwExMTcnJyJIjIsL179zJx4kReeeUVqUPRMXnyZJ3kMy4ujhEjRuDs7Kyzpw5gxYoVLzq8Uu327dvMnz9fO5BnZGSEUqnE2NgYU1NTkfw9ofj4eCpVqgTAyZMnad68OQMHDqRhw4Z8/PHHEkf3UFBQEE5OTtrCLjVq1KBGjRocOHCA77//nhEjRkgaX6dOnQBNa5natWuLWWjhpSKSvzLu2LFjhIWFkZKSglqt1nls7ty5EkWlq3r16vz999/07dtX5/jBgwfFyJsguYsXL7J06VKdvVUWFhaMHDmS999/X8LIHnJxceHq1at6+4FOnz6Np6enRFHpy8zMlF3jedDMTAj/jfXr1+Pt7c3y5csZNWoU/v7+ZGRksGbNGskTgNLIzMyMrKwsQLM/taARuYWFBZmZmVKGpuPo0aPMnDlT77iXlxc7duyQze/exsaGu3fvahPq0NBQgoOD8fT0ZMCAASIpFMokkfyVYYGBgezZswdfX19ZVq0rMGrUKD7++GNu3bqFUqlk9+7d3Lp1i8uXLz+yl5kgvAhmZmbaSnWFZWRkYGZmJkFE+gYMGMC3335LTk4OarWaS5cucfToUXbu3MnUqVOlDk+rbdu2nDlzhl69ekkdig5R0e+/ExUVxaJFizA3N0ehUKBUKvH29mbMmDGsW7dOzKQ+odq1a7NhwwZq165NdHS0NsGKjY3FyclJ4ugeSk1NNdgn09rampSUlBcfUDGWL19Onz59qFSpEvfv32fBggX4+vqyb98+MjMzdZavCkJZIZK/Muzo0aPMmDFD9qPatWrVYvHixezatQtXV1fCwsLw8vJiyZIlVKlSRerwhJdc06ZNWblyJZMnT9Yumb506RKrVq2Sxd4VgM6dO6NUKgkKCiInJ4elS5fi4ODAxIkTZVXsxdHRkS1bthAZGUmVKlX0llT269dPmsAKef3111m6dKneUvQHDx4wbdq0UlNqXy7UarW2uqONjQ2JiYlUqlQJR0dHYmNjJY6u9HnzzTdZvXo1x48fZ9KkSdp2GWfOnKFhw4YSR/eQk5MTFy5c0OmdBxAREYGjo6NEUem7ffs2Xl5eAPz555/UqFGDTz75hPPnz+Pv7y+SP6FMEslfGaZSqXQKvcjVrVu3MDY25t133wU01UiPHj3KqVOn8PDwEMsuBElNmDCBZcuWMXPmTO0eVJVKRbNmzbSFF+SgW7dudOvWjdTUVNRqNba2tlKHpOfXX3/F3NycyMhIIiMjdR5TKBSySP7i4+P1qpCCpuJrYmKiBBGVbpUrV+b69eu4urpSvXp1du7ciZGREb/++ivu7u5Sh1fqODo6GmydUbRipdS6d+9OQEAA+fn51KtXD9AsUw0KCmLgwIESR/eQSqXSDkKFhYVpl6W7urrKaoZSEJ4nkfyVYd26dSMkJET2rRKKLrv4/PPPqVu3rlh2IciClZUVH330EbGxsdy+fRu1Wo2Hh4csb1yjoqK4d+8eTZo0ASA7OxtTU1PZDKBs2LBB6hCKdeLECe2/T58+rbPHU6VSERYWhouLixShlWqDBw8mOzsb0FRSnT9/PnPmzMHa2lqntY9QtvTv35+0tDTWrVunrfBqYmJC7969ZZX8eXp6cuDAAZo0acL58+e19xtJSUkGC9EJQlmgyMrKUj/+NKE0WrNmDb///juenp6ybvY9dOhQvv76aypWrMgvv/zCP//8w+eff65ddiHnG0bh5SD3wknJycksWLCAqKgoFAoFa9euxdXVlZUrV2JmZsbEiROlDlH2+vTpA2hmIIv+jo2NjXFxcWHcuHGyWepbmqWnp2NlZSXbfehy06dPnxK/Vrt37/6Po3ky2dnZ2nYzHh4esmufEhERwcKFC8nMzKRjx47aPdKbNm3izp07zJ49W+IIBeH5EzN/ZVhMTEyxzb7lRCy7EOSsNBROCggIwM7Oji1btjBu3Djt8datW7N27VoJI4O1a9cyevRozM3NHxuLlANSe/bsAWD8+PEsXbrUYLEK4fmoUKGC1CGUKoVnSFNSUvjhhx9o0aKFthr2pUuXOHnypCxX+Zibm+Pj4yN1GMWqW7cu33//PVlZWVhZWWmPd+/eXbtXVRDKGpH8lWGlpdm3WHYhyFlpKJx0/vx5FixYoHPzApoBlISEBImi0rh586Z22dfNmzcljaUkxEoDQW4KX3s+++wzRo8eTbdu3bTHunTpgo+PDydPnpRdJd3SwNjYWO/aKZZ4C2WZSP7KmM8++4z33nsPCwsLPvvss2LPUygUfPTRRy8wsuKNGTOGhQsX8vPPP9OxY0dthc+///6b6tWrSxuc8NIrDYWTcnJy9CpnAqSlpUnejqLwIFRpGZBKT0/nzJkzJCQkaBPXAqIthCCl8+fPM378eL3jvr6+rF+/XoKISp+S3ieBPJb1C8LzJpK/MqbwcprSsrRGLLsQ5Kw0FE6qU6cOR44cYdSoUdpjSqWSn376SVtpTw6uXLlS7BKwo0eP0qFDhxcckb5Lly4xf/58TE1NSU1NxcHBgaSkJExNTXFxcRHJnyApa2trjh8/zquvvqpz/Pjx42KpcgmVxvskQXieRMEXQRCEIgrvTVOr1YSEhMi6cNKtW7eYNWsW1apVIyIigiZNmnDr1i0yMjL46quvcHNzkzpEAEaMGMGiRYvw8PDQOR4cHMzq1av56aefJIrsoQ8//JBq1aoxceJEhgwZwvLlyylXrhxLliyhS5cutG/fXuoQhZdYcHAw/v7+1K9fX7vn7/Lly5w7d44pU6bQqVMniSMUBEHuxMyfIAhCEUX3phVXOEkuxV88PT1ZuXIl+/fvx8TEhNzcXFq1akWvXr2wt7eXOjytfv36MW/ePL766iucnJyAh4nfBx98IHF0Gjdu3GDKlCkoFAqMjIzIy8vD1dWV0aNHs2TJEpH8CZLq2LEjFStWZO/evfz999+ApormV199RY0aNSSOThCE0kAkf4IgCEWUlr1pBebNm4evry9+fn4MHTpUNn39iho0aBCpqanMnTuXL7/8ktOnT7N69Wpmzpyp7U0otcJ7J21tbYmPj9eWqE9KSpIwMkHQqFGjhkj0npPZs2cXO4hnZmaGm5sbHTt2xNvb+wVHJgj/HZH8CYIglHLe3t6cOnWKLVu2YGpqSs2aNfH19cXX15fq1avLKhkcP3486enpvP/++yQnJzNr1ixtexc58PLyIioqiooVK2r3I6ekpBASEqItRiUIUktMTCQ1NRWVSqVzXCQpT6ZSpUr88ccf2NnZaQvMRUVFkZycTPPmzbl48SL79+/n008/pX79+hJHKwjPh9jzJwiCUEbk5OQQGRlJeHg44eHhREVFYWpqyvbt2yWL6cSJE3rHVCoVGzZsoGHDhjqJX8uWLV9kaAZFRUWRlZVFvXr1SE1NZenSpURGRlKxYkWmTp0qEkBBUlevXuXrr7/mzp07qNW6t28KhUJ2Td7lLiAgALVazYQJE3SOF7R8GT9+POvWrePKlSssWbJEihAF4bkTyZ8gCEIZkZycTEREBGFhYYSHh3P//n1q1Kgh6TLWPn36lOg8ceMqCI/37rvvYm1tzdChQ7G3t9dbsujs7CxRZKXTa6+9xpIlS3B3d9c5fufOHWbMmMGWLVu4efMmH3zwAT/++KNEUQrC8yWWfQqCIJRya9asITw8nPj4eHx8fKhbty6TJ0+mZs2amJqaShrbnj17JP35TysqKop79+7RpEkTzM3Nyc7OxtTUVFZLaIWXT0xMDP7+/lSsWFHqUMoEtVrNzZs39ZK/W7duaWdWTUxMZFPcSxCeB5H8CYIglHIHDhzAxsaGQYMG4efnh7e3t+xuVvLz8/nwww959913qVSpktThFCs5OZkFCxYQFRWFQqFg7dq1uLq6EhAQgJmZGRMnTpQ6ROElVqVKFZKTk0Xy95x07NiRFStWcPfuXZ09fz/99JO2bUZERASVK1eWMkxBeK5E8icIglDKffvtt4SHhxMREcGhQ4fIysqidu3a1KtXj7p168qiCISJiQlxcXGyS0qLCggIwM7Oji1btjBu3Djt8datW+v0fxQEKYwcOZKNGzcyYsQIKleurFOdFkTT8ic1btw4bG1t2b17N8nJyQDY2dkxYMAA+vfvD0DDhg3x8/OTMkxBeK7Enj9BEIQyJiYmhp07dxISEoJarZbNXrrAwEAAnaRKbkaOHMmCBQuoXLkygwcPZvny5bi6unLv3j0mT54si0b0wsur8B7awgMparVa7Jt9RpmZmQBYWFhIHIkg/LfEzJ8gCEIpp1KpiI6O5vz584SHhxMZGUleXh7e3t74+vpKHZ5WdnY2v//+O+fOncPb25ty5crpPP7GG29IFNlDOTk5erMpAGlpaZiZmUkQkSA8tHDhQqlDKJMK7/EFxB5foUwTyZ8gCEIpN3ToUPLy8vDy8qJu3br06dOHOnXqYG5uLnVoOm7fvo2XlxcA9+7d03lMLstB69Spw5EjRxg1apT2mFKp5KeffqJevXoSRiYIyGowpywQe3yFl5FI/gRBEEq5Dz/8UJbJXlFStpwoqbFjxzJr1iyioqLIy8sjMDCQW7dukZGRwVdffSV1eMJLKDo6mmrVqmFkZER0dPQjz5XD/t7SROzxFV5GIvkTBEEo5UpbMYLc3Fzu3r0LgJubm6yWU3p6erJy5Ur279+PiYkJubm5tGrVil69emFvby91eMJLaPr06QQFBWFra8v06dNRKBR6Dd5B9Mp8GufPn2fBggVYWVnpHHd1dSUhIUGiqAThvyWSP0EQBOGFyM/PJygoiH379pGfn49arcbU1JRXXnmFkSNHGtxr96LNmzcPX19f/Pz8GDp0qNjzI0guICAAGxsb7b+F50fs8RVeRtJ/0gqCIAgvhY0bN/LHH38wadIkateuDcCFCxcICgpCpVIxfvx4iSPULJs7deoUW7ZswdTUlJo1a+Lr64uvry/Vq1cXyaDwwjk7O2v/vXLlSvH3+ByJPb7Cy0i0ehAEQRBeiJEjRzJ16lQaN26sc/zUqVOsWLGCoKAgiSLTl5OTQ2RkJOHh4YSHhxMVFYWpqSnbt2+XOjThJRYUFERERIT271EMTjybmJgYZs6cSbVq1YiIiKBJkyY6e3zd3NykDlEQnjsx8ycIgiC8EJmZmbi6uuodd3V1JSMjQ4KIipeZmUl6ejqpqamkpqZiZGQkimkIkiuYoSo8OFF4ploMTpRcfn4+y5YtY+7cuZw5c0bs8RVeGiL5EwRBEF6IqlWrsnfvXt566y2d43v37qVq1aoSRaVrzZo1hIeHEx8fj4+PD3Xr1mXy5MnUrFkTU1NTqcMTBEAMTjwPJiYmxMXFUaFCBYYPHy51OILwwohln4IgCMILERERwaeffoq9vT01a9YE4PLlyyQlJfHxxx9Tp04diSOEPn36YGNjQ69evfDz88Pb21s2PQgFwdDghK+vrxiceEqBgYEAOm0eBKGsE8mfIAiC8MIkJiayf/9+YmJiAPDw8KBnz544ODhIHJlGbGws4eHhREREEBERQVZWFrVr16ZevXrUrVtXzK4IkhKDE8/X6tWr+f3333FxccHb25ty5crpPP7GG29IFJkg/HdE8icIgiC8EAVtFEpTcYqYmBh27txJSEgIarVa9FETJCUGJ56v2bNnF/uYQqFg4cKFLzAaQXgxRPInCIIgvBCloVKhSqUiOjqa8+fPEx4eTmRkJHl5eXh5eeHr68vo0aOlDlEQtMTghCAIT0okf4IgCMILJec2CoMHD9YmewX7qerUqYO5ubnUoQmCGJwQBOGZiWqfgiAIwgsl50qFH374oUj2BNkaOnSozuBEnz59xN+rIAhPRMz8CYIgCC+EqFQoCM/mzJkzItkTBOGZiORPEARBeCFEpUJBEARBkJZI/gRBEIQXQlQqFARBEARpieRPEARBkISoVCgIgiAIL5Yo+CIIgiC8EMVVKvT29sbX11fq8ARBEAShzBMzf4IgCMILIdooCIIgCIK0RPInCIIgvBCiUqEgCIIgSEskf4IgCIIgCIIgCC8BI6kDEARBEARBEARBEP57IvkTBEEQBEEQBEF4CYjkTxAEQRAEQRAE4SUgkj9BEARBEARBEISXgEj+BEEQBEEQBEEQXgL/B3p+vGqx0BExAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)\n",
    "plt.savefig(\"correlationheatmap.jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "    log_cnt  \n",
       "0  2.772589  \n",
       "1  3.688879  \n",
       "2  3.465736  \n",
       "3  2.564949  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(df[\"cnt\"]) #ln(price)\n",
    "df[\"log_cnt\"] = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"weathersit\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"weathersit\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"holiday\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter3.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"season\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter4.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204.869272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>175.165493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111.579281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cnt\n",
       "weathersit            \n",
       "1           204.869272\n",
       "2           175.165493\n",
       "3           111.579281\n",
       "4            74.333333"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weathersit')[['cnt']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.114569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208.344069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>236.016237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.868856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cnt\n",
       "season            \n",
       "1       111.114569\n",
       "2       208.344069\n",
       "3       236.016237\n",
       "4       198.868856"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('season')[['cnt']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190.42858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156.87000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cnt\n",
       "holiday           \n",
       "0        190.42858\n",
       "1        156.87000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('holiday')[['cnt']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt',\n",
       " 'log_cnt']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model1:\n",
    "\n",
    "X=df[['atemp',\n",
    " 'weathersit']]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 5-fold CV on the train data: 1.367802004486845\n",
      "RMSE (Model1:Linear) on 5-fold CV on the test data: 1.3678160765058405\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 1.0759753227999354\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 1.0761084736013986\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_5cv_model1 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 5-fold CV on the train data: 1.367802004486845\n",
      "RMSE (Model1:Linear) on 5-fold CV on the test data: 1.3678160765058405\n"
     ]
    }
   ],
   "source": [
    "# Summary of model1:\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 1.0759753227999354\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 1.0761084736013986\n"
     ]
    }
   ],
   "source": [
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on the RMSE, model1 with information such as atemp and weathersit performs the well as RMSE train data < RMSE test data\n",
    "#...same as MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.3 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last excercise, using all features which are\n",
    "- holiday : weather day is holiday or not \n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df=pd.get_dummies(df,columns=['holiday', 'workingday','','GATE'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt',\n",
       " 'log_cnt']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "      <td>4.779123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>4.488636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>4.499810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "      <td>4.110874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "      <td>3.891820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt   log_cnt  \n",
       "0              13   16  2.772589  \n",
       "1              32   40  3.688879  \n",
       "2              27   32  3.465736  \n",
       "3              10   13  2.564949  \n",
       "4               1    1  0.000000  \n",
       "...           ...  ...       ...  \n",
       "17374         108  119  4.779123  \n",
       "17375          81   89  4.488636  \n",
       "17376          83   90  4.499810  \n",
       "17377          48   61  4.110874  \n",
       "17378          37   49  3.891820  \n",
       "\n",
       "[17379 rows x 18 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model 2: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['holiday',\n",
    " 'weekday',\n",
    " 'workingday',\n",
    " 'weathersit',\n",
    " 'temp',\n",
    " 'atemp',\n",
    " 'hum',\n",
    " 'windspeed']]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=10, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV: 1.4962228699389952\n"
     ]
    }
   ],
   "source": [
    "err = 0\n",
    "for train,test in kf.split(X):\n",
    "    lasso=linear_model.Lasso(alpha=12)\n",
    "    reg2=lasso.fit(X[train],y[train])\n",
    "    y_pred =reg2.predict(X[test])\n",
    "    e = y[test]-y_pred\n",
    "    err += np.sqrt(np.mean(e*e))     # compute rmse for the estimation\n",
    "rmse_10cv = err/10              #average the rmse\n",
    "print('RMSE on 10-fold CV: {}'.format(rmse_10cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV: 1.3062620893771508\n"
     ]
    }
   ],
   "source": [
    "err = 0\n",
    "for train,test in kf.split(X):\n",
    "    ridge=linear_model.Ridge(alpha=2)\n",
    "    reg3=ridge.fit(X[train],y[train])\n",
    "    y_pred =reg3.predict(X[test])\n",
    "    e = y[test]-y_pred\n",
    "    err += np.sqrt(np.mean(e*e))     # compute rmse for the estimation\n",
    "rmse_10cv = err/10              #average the rmse\n",
    "print('RMSE on 10-fold CV: {}'.format(rmse_10cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV: 2.7212330922146766\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err += np.sqrt(np.mean(e*e))     # compute rmse for the estimation rmse test data\n",
    "rmse_10cv = err/10              #average the rmse\n",
    "print('RMSE on 10-fold CV: {}'.format(rmse_10cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.4 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2 features was better because using all features without regularization the data would provide a problem of overfitting\n",
    "#...in the model which is not good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.5 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The umemployment rate in Apr is 14.8%\n",
      "The unemployment rate in May is 13.3%\n",
      "The unemployment rate in June is 11.1%\n",
      "The unemployment rate in July is 10.2%\n"
     ]
    }
   ],
   "source": [
    "if unemp < 10:\n",
    "    print(\"The umemployment rate in Apr is 14.8%\")\n",
    "    print(\"The unemployment rate in May is 13.3%\")\n",
    "    print(\"The unemployment rate in June is 11.1%\")\n",
    "    print(\"The unemployment rate in July is 10.2%\")\n",
    "    \n",
    "elif unemp < 9:\n",
    "    print(\"Nothing\")\n",
    "elif unemp < 5:\n",
    "    print(\"nothing\")\n",
    "else:\n",
    "    print(\"nothingg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2550"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sum(range(0, 101, 2))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n",
      "20\n",
      "22\n",
      "24\n",
      "26\n",
      "28\n",
      "30\n",
      "32\n",
      "34\n",
      "36\n",
      "38\n",
      "40\n",
      "42\n",
      "44\n",
      "46\n",
      "48\n",
      "50\n",
      "52\n",
      "54\n",
      "56\n",
      "58\n",
      "60\n",
      "62\n",
      "64\n",
      "66\n",
      "68\n",
      "70\n",
      "72\n",
      "74\n",
      "76\n",
      "78\n",
      "80\n",
      "82\n",
      "84\n",
      "86\n",
      "88\n",
      "90\n",
      "92\n",
      "94\n",
      "96\n",
      "98\n",
      "100\n",
      "The Sum of Even Numbers from 1 to 100 = 2550\n"
     ]
    }
   ],
   "source": [
    "max = 100\n",
    "total = 0\n",
    "\n",
    "for num in range(1, max+1):\n",
    "    if(num % 2 == 0):\n",
    "        print(\"{0}\".format(num))\n",
    "        total = total + num\n",
    "\n",
    "print(\"The Sum of Even Numbers from 1 to {0} = {1}\".format(num, total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.70805020110221"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(1)+ math.log(3) + math.log(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from pandas_datareader import data\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_list = {'TSLA': 'TESLA'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(ticker_list,\n",
    "          start=dt.datetime(2010, 6, 29),\n",
    "          end=dt.datetime(2022, 9, 30)):\n",
    "   \n",
    "    ticker = pd.DataFrame()\n",
    "\n",
    "    for tick in ticker_list:\n",
    "        prices = data.DataReader(tick, 'yahoo', start, end)\n",
    "        closing_prices = prices['Close']\n",
    "        ticker[tick] = closing_prices\n",
    "\n",
    "    return ticker\n",
    "\n",
    "ticker = read_data(ticker_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSLA    16554.454618\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = ticker.iloc[0]    #Get the first set of prices as a Series\n",
    "p2 = ticker.iloc[-1]   #Get the last set of prices as a Series\n",
    "price_change = (p2 - p1) / p1 * 100\n",
    "price_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSLA    16554.454618\n",
       "Name: 2022-09-30 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change = ticker.pct_change(periods=len(ticker)-1, axis='rows')*100\n",
    "price_change = change.iloc[-1]\n",
    "price_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_change.sort_values(inplace=True)\n",
    "price_change = price_change.rename(index=ticker_list)\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax.set_xlabel('stock', fontsize=12)\n",
    "ax.set_ylabel('percentage change in price', fontsize=12)\n",
    "price_change.plot(kind='bar', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(ticker_list,\n",
    "          start=dt.datetime(2010, 6, 29),\n",
    "          end=dt.datetime(2022, 9, 30)):\n",
    "    \n",
    "    ticker = pd.DataFrame()\n",
    "\n",
    "    for tick in ticker_list:\n",
    "        prices = data.DataReader(tick, 'yahoo', start, end)\n",
    "        closing_prices = prices['Close']\n",
    "        ticker[tick] = closing_prices\n",
    "\n",
    "    return ticker\n",
    "\n",
    "ticker = read_data(ticker_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'closing_prices' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [104]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mclosing_prices\u001b[49m\u001b[38;5;241m.\u001b[39msort_values(inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      2\u001b[0m closing_prices \u001b[38;5;241m=\u001b[39m closing_prices\u001b[38;5;241m.\u001b[39mrename(index\u001b[38;5;241m=\u001b[39mticker_list)\n\u001b[1;32m      3\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'closing_prices' is not defined"
     ]
    }
   ],
   "source": [
    "closing_prices.sort_values(inplace=True)\n",
    "closing_prices = closing_prices.rename(index=ticker_list)\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "ax.set_xlabel('stock', fontsize=12)\n",
    "ax.set_ylabel('closing_prices', fontsize=12)\n",
    "closing_prices.plot(kind='bar', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'price_change'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [102]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprice_change\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m     44\u001b[0m     )\n\u001b[1;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py:3598\u001b[0m, in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3595\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3596\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3598\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_CountPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3599\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3600\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mci\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3601\u001b[0m \u001b[43m    \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3602\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\n\u001b[1;32m   3603\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3605\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py:1584\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1579\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[1;32m   1580\u001b[0m              estimator, ci, n_boot, units, seed,\n\u001b[1;32m   1581\u001b[0m              orient, color, palette, saturation, errcolor,\n\u001b[1;32m   1582\u001b[0m              errwidth, capsize, dodge):\n\u001b[1;32m   1583\u001b[0m     \u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1584\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1585\u001b[0m \u001b[43m                             \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[1;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, ci, n_boot, seed)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py:153\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    152\u001b[0m         err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret input \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(var)\n\u001b[0;32m--> 153\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m    155\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[1;32m    156\u001b[0m orient \u001b[38;5;241m=\u001b[39m infer_orient(\n\u001b[1;32m    157\u001b[0m     x, y, orient, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequire_numeric\n\u001b[1;32m    158\u001b[0m )\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'price_change'"
     ]
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"price_change\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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