make_your_own_2020_governor_map.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make your 2020 Governor Map\n",
    "\n",
    "### Step by Step instructions are provided, this code may seem complicated, but you don't need to understand it to make your own map. Someone with zero coding experience can still make one of our maps!\n",
    "\n",
    "Note: By changing this code, it will not change for other users, so don't worry about that. If you mess something up, you can go to this link to start over with the original code: https://mybinder.org/v2/gh/election-perfection/ElectionMaps/master\n",
    "\n",
    "by https://electionperfection.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Run this Cell, don't change anything inside. This cell will generate ALOT of output, that's normal. It will tell you to move onto Step 2 when it is complete. This cell may take a minute or so to complete.\n",
    "\n",
    "How to run a cell: Option 1: click the cell and click the button that looks like this: '>| Run' at the top. Option 2: hit the black button that's next to the cell. Either one works and can be used interchangeably."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1 is complete, move on to Step 2\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib\n",
    "!pip install numpy\n",
    "!conda install -c conda-forge cartopy --yes\n",
    "!pip install ipywidgets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.io.shapereader as shpreader\n",
    "import numpy as np\n",
    "import matplotlib.gridspec as gridspec\n",
    "import ipywidgets as widgets\n",
    "\n",
    "\n",
    "\n",
    "small_states = ['Vermont','New Hampshire','New Jersey','Delaware','Rhode Island','Massachusetts','Connecticut','Maryland']\n",
    "\n",
    "print('Step 1 is complete, move on to Step 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Run this Cell, don't change anything inside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = {\n",
    "    'Alabama':{'Lat':32.806671,'Lon':-86.791130-.5,'EV':9},\n",
    "    'Alaska':{'Lat':61.370716+1,'Lon':-152.404419,'EV':3},\n",
    "    'Arizona':{'Lat':33.729759,'Lon':-111.431221-1,'EV':11},\n",
    "    'Arkansas':{'Lat':34.969704,'Lon':-92.373123-1,'EV':6},\n",
    "    'California':{'Lat':36.116203,'Lon':-119.681564,'EV':55},\n",
    "    'Colorado':{'Lat':39.059811-.5,'Lon':-105.311104-1,'EV':9},\n",
    "    'Connecticut':{'Lat':41.597782,'Lon':-72.755371,'EV':7},\n",
    "    'Delaware':{'Lat':39.318523,'Lon':-75.507141,'EV':3},\n",
    "    'District of Columbia':{'Lat':38.897438,'Lon':-77.026817,'EV':3},\n",
    "    'Florida':{'Lat':27.766279,'Lon':-81.686783-.5,'EV':29},\n",
    "    'Georgia':{'Lat':33.040619-.5,'Lon':-83.643074-.5,'EV':16},\n",
    "    'Hawaii':{'Lat':21.094318,'Lon':-157.498337+1,'EV':4},\n",
    "    'Idaho':{'Lat':44.240459,'Lon':-114.478828-1,'EV':4},\n",
    "    'Illinois':{'Lat':40.349457-.5,'Lon':-88.986137-1,'EV':20},\n",
    "    'Indiana':{'Lat':39.849426,'Lon':-86.258278-.5,'EV':11},\n",
    "    'Iowa':{'Lat':42.011539,'Lon':-93.210526-1, 'EV':6},\n",
    "    'Kansas':{'Lat':38.526600-0.3,'Lon':-96.726486-2, 'EV':6},\n",
    "    'Kentucky':{'Lat':37.668140-.5,'Lon':-84.670067-1,'EV':8},\n",
    "    'Louisiana':{'Lat':31.169546,'Lon':-91.867805-1.5,'EV':8},\n",
    "    'Maine':{'Lat':44.693947,'Lon':-69.381927-1,'EV':4},\n",
    "    'Maryland':{'Lat':39.063946,'Lon':-76.802101,'EV':10},\n",
    "    'Massachusetts':{'Lat':42.230171,'Lon':-71.530106,'EV':11},\n",
    "    'Michigan':{'Lat':43.326618,'Lon':-84.536095-1,'EV':16},\n",
    "    'Minnesota':{'Lat':45.694454,'Lon':-93.900192-1.5,'EV':10},\n",
    "    'Mississippi':{'Lat':32.741646,'Lon':-89.678696-.5,'EV':6},\n",
    "    'Missouri':{'Lat':38.456085,'Lon':-92.288368-1,'EV':10},\n",
    "    'Montana':{'Lat':46.921925,'Lon':-110.454353,'EV':3},\n",
    "    'Nebraska':{'Lat':41.125370,'Lon':-98.268082-2,'EV':5},\n",
    "    'Nevada':{'Lat':38.313515,'Lon':-117.055374,'EV':6},\n",
    "    'New Hampshire':{'Lat':43.452492,'Lon':-71.563896,'EV':4},\n",
    "    'New Jersey':{'Lat':40.298904,'Lon':-74.521011,'EV':14},\n",
    "    'New Mexico':{'Lat':34.840515-1,'Lon':-106.248482-.5,'EV':5},\n",
    "    'New York':{'Lat':42.165726+1,'Lon':-74.948051-1,'EV':29},\n",
    "    'North Carolina':{'Lat':35.630066-0.3,'Lon':-79.806419,'EV':15},\n",
    "    'North Dakota':{'Lat':47.528912-.3,'Lon':-99.784012-1,'EV':3},\n",
    "    'Ohio':{'Lat':40.388783-.5,'Lon':-82.764915-1,'EV':18},\n",
    "    'Oklahoma':{'Lat':35.565342,'Lon':-96.928917-1.5,'EV':7},\n",
    "    'Oregon':{'Lat':44.572021-1,'Lon':-122.070938,'EV':7},\n",
    "    'Pennsylvania':{'Lat':40.590752,'Lon':-77.209755-1.5,'EV':20},\n",
    "    'Rhode Island':{'Lat':41.680893,'Lon':-71.511780,'EV':4},\n",
    "    'South Carolina':{'Lat':33.856892-0.5,'Lon':-80.945007-0.5,'EV':9},\n",
    "    'South Dakota':{'Lat':44.299782,'Lon':-99.438828-1,'EV':3},\n",
    "    'Tennessee':{'Lat':35.747845-.2,'Lon':-86.692345,'EV':11},\n",
    "    'Texas':{'Lat':31.054487,'Lon':-97.563461-2,'EV':38},\n",
    "    'Utah':{'Lat':40.150032-1.5,'Lon':-111.862434,'EV':6},\n",
    "    'Vermont':{'Lat':44.045876,'Lon':-72.710686,'EV':3},\n",
    "    'Virginia':{'Lat':37.769337-.5,'Lon':-78.169968-.5,'EV':13},\n",
    "    'Washington':{'Lat':47.400902-.3,'Lon':-121.490494,'EV':12},\n",
    "    'West Virginia':{'Lat':38.491226-.2,'Lon':-80.954453-.5,'EV':5},\n",
    "    'Wisconsin':{'Lat':44.268543,'Lon':-89.616508-1,'EV':10},\n",
    "    'Wyoming':{'Lat':42.755966,'Lon':-107.302490-1,'EV':3}}\n",
    "\n",
    "print('Move onto Step 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Run the cell, then Enter your predictions in the dropdown menus.\n",
    "\n",
    "#### Instructions\n",
    "Select Democrat, Republican, or TBD if you wish to leave the state undecided.\n",
    "\n",
    "#### DO NOT RERUN the cell once you make your selections or you will lose them\n",
    "Once you have made your selections proceed to the next cell. \n",
    "To change your predictions later on in this session, you only need to change the dropdown menus here, then rerun the following cells, you never have to rerun this particular cell to adopt the change. \n",
    "\n",
    "Once you leave this page your changes will disapear, so make sure you follow the instructions to save your map at the end in Step 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4904bb9017324a0d95f814675b40bc3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Delaware', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e2d780b066f4879a6feaad0aa2f39e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Indiana', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f1877145bce42509bdf02fae6ef04d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Missouri', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af033761c99e442cbe69ac2a592b25d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Montana', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5615ffe6719941e795ca11e8ffb2e293",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='New Hampshire', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f05b4963dbe452c90393e2a99bfeebe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='North Carolina', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "726995aaa9fc4b40965573202e585e0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='North Dakota', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e65d771cc5b49a9a74c5234cbffa729",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Utah', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82df58ddf8ed4bc9a651193593bd3625",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Vermont', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a04141a18ce74053923e3713bbe0dcf5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Washington', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0df53d8e176465ca116e4c29931e92b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='West Virginia', index=2, options=('Democrat', 'Republican', 'TBD'), value='TBD')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delaware_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Delaware')\n",
    "display(delaware_menu)\n",
    "\n",
    "indiana_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Indiana')\n",
    "display(indiana_menu)\n",
    "\n",
    "missouri_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Missouri')\n",
    "display(missouri_menu)\n",
    "\n",
    "montana_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Montana')\n",
    "display(montana_menu)\n",
    "\n",
    "new_hampshire_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='New Hampshire')\n",
    "display(new_hampshire_menu)\n",
    "\n",
    "north_carolina_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='North Carolina')\n",
    "display(north_carolina_menu)\n",
    "\n",
    "north_dakota_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='North Dakota')\n",
    "display(north_dakota_menu)\n",
    "\n",
    "utah_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Utah')\n",
    "display(utah_menu)\n",
    "\n",
    "vermont_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Vermont')\n",
    "display(vermont_menu)\n",
    "\n",
    "washington_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='Washington')\n",
    "display(washington_menu)\n",
    "\n",
    "west_virginia_menu = widgets.Dropdown(options=['Democrat', 'Republican', 'TBD'],\n",
    "    value='TBD',description='West Virginia')\n",
    "display(west_virginia_menu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Run the cell, don't change anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "states['Alabama']['prediction'] = 'NE'\n",
    "\n",
    "states['Alaska']['prediction'] = 'NE'\n",
    "\n",
    "states['Arizona']['prediction'] = 'NE'\n",
    "\n",
    "states['Arkansas']['prediction'] = 'NE'\n",
    "\n",
    "states['California']['prediction'] = 'NE'\n",
    "\n",
    "states['Colorado']['prediction'] = 'NE'\n",
    "\n",
    "states['Connecticut']['prediction'] = 'NE'\n",
    "\n",
    "states['Delaware']['prediction'] = delaware_menu.value\n",
    "\n",
    "states['Florida']['prediction'] = 'NE'\n",
    "\n",
    "states['Georgia']['prediction'] = 'NE'\n",
    "\n",
    "states['Hawaii']['prediction'] = 'NE'\n",
    "\n",
    "states['Idaho']['prediction'] = 'NE'\n",
    "\n",
    "states['Illinois']['prediction'] = 'NE'\n",
    "\n",
    "states['Indiana']['prediction'] = indiana_menu.value\n",
    "\n",
    "states['Iowa']['prediction'] = 'NE'\n",
    "\n",
    "states['Kansas']['prediction'] = 'NE'\n",
    "\n",
    "states['Kentucky']['prediction'] = 'NE'\n",
    "\n",
    "states['Louisiana']['prediction'] = 'NE'\n",
    "\n",
    "states['Maine']['prediction'] = 'NE'\n",
    "\n",
    "states['Maryland']['prediction'] = 'NE'\n",
    "\n",
    "states['Massachusetts']['prediction'] = 'NE'\n",
    "\n",
    "states['Michigan']['prediction'] = 'NE'\n",
    "\n",
    "states['Minnesota']['prediction'] = 'NE'\n",
    "\n",
    "states['Mississippi']['prediction'] = 'NE'\n",
    "\n",
    "states['Missouri']['prediction'] = missouri_menu.value\n",
    "\n",
    "states['Montana']['prediction'] = montana_menu.value\n",
    "\n",
    "states['Nebraska']['prediction'] = 'NE'\n",
    "\n",
    "states['Nevada']['prediction'] = 'NE'\n",
    "\n",
    "states['New Hampshire']['prediction'] = new_hampshire_menu.value\n",
    "\n",
    "states['New Jersey']['prediction'] = 'NE'\n",
    "\n",
    "states['New Mexico']['prediction'] = 'NE'\n",
    "\n",
    "states['New York']['prediction'] = 'NE'\n",
    "\n",
    "states['North Carolina']['prediction'] = north_carolina_menu.value\n",
    "\n",
    "states['North Dakota']['prediction'] = north_dakota_menu.value\n",
    "\n",
    "states['Ohio']['prediction'] = 'NE'\n",
    "\n",
    "states['Oklahoma']['prediction'] = 'NE'\n",
    "\n",
    "states['Oregon']['prediction'] = 'NE'\n",
    "\n",
    "states['Pennsylvania']['prediction'] = 'NE'\n",
    "\n",
    "states['Rhode Island']['prediction'] = 'NE'\n",
    "\n",
    "states['South Carolina']['prediction'] = 'NE'\n",
    "\n",
    "states['South Dakota']['prediction'] = 'NE'\n",
    "\n",
    "states['Tennessee']['prediction'] = 'NE'\n",
    "\n",
    "states['Texas']['prediction'] = 'NE'\n",
    "\n",
    "states['Utah']['prediction'] = utah_menu.value\n",
    "\n",
    "states['Vermont']['prediction'] = vermont_menu.value\n",
    "\n",
    "states['Virginia']['prediction'] = 'NE'\n",
    "\n",
    "states['Washington']['prediction'] = washington_menu.value\n",
    "\n",
    "states['District of Columbia']['prediction'] = 'NE'\n",
    "\n",
    "states['West Virginia']['prediction'] = west_virginia_menu.value\n",
    "\n",
    "states['Wisconsin']['prediction'] = 'NE'\n",
    "\n",
    "states['Wyoming']['prediction'] = 'NE'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5a: Change the filename if desired\n",
    "\n",
    "In the first line of this cell, change the name of the file your map is saved to.\n",
    "\n",
    "### Step 5b: Run the Cell, and View your Map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-183f0aa540ac>:27: DeprecationWarning: The background_patch property is deprecated. Use GeoAxes.patch instead.\n",
      "  ax.background_patch.set_visible(False)\n",
      "<ipython-input-14-183f0aa540ac>:28: DeprecationWarning: The outline_patch property is deprecated. Use GeoAxes.spines['geo'] or the default Axes properties instead.\n",
      "  ax.outline_patch.set_visible(False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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rhvQ6UOIkqDaID/b14p10PESkmDmeZxLr9YRYwnkKsA3YQ2zflZVAB+BK4Cbg78AXQLeyXkUJzAHn2FNOOegNk7Vr12JM7Sjlem83iT+G2kBqLJ1elwSL+sEXveCrEKz0QFhE6qyDwiWQswjMcmC5MWZH2dtWSqmjThefz9eladOmnX/88cffi8glQMDn8713/vnnp918880et9tN/fr16d27d/KCBQuSH3744We3bdvWidgQFqWUUtWAJp41Q606UOI7v7WBJGggIrYPrj7tIAsLNSb2BvmBWO9mN2ALsJDYvittiY3v/Rh4GsgCXqViE8+PgMG9eh00yV6zZo0Jh1sk+C64AMfHHyPd7N92Ziew4mRYfjJ8nQmL82C1T8SfB741EFkEoSXAMuA7Y8zexMallFLVn9frHTF06FDPDTfc4HrttdfaPvbYY/8DuP/++339+vX7VVkR4cwzz6Ru3br+yy67bJSITNB5n0opVT1o4lkzpNcr4cJCEEs8DTRLhVmnQreHD7GVypnEks4FwDhiiecCYpnXGcC7QB9iPaAXAPcCj1KKLLgUQsD3YA0ePPigZb79dpljTP8jNPyqLrE5o70g9hp6Y72jWz2wvBMs6wSLg/B1IWzwi6RkQ9JKyP0K8pYCy4FVxpjIkYlXKaWqFhERv99/8cCBA122bXPxxRdbAwYM8DmOQ/369Q96XqtWrfD5fMmhUKgt8M2Ri1gppVRZaeJZA6TCqceBu6TlawNZcNooiPwTvIfKWPfN81xGbKhtU2BKrE2uAJ4nNu/y2Hj5XcAcoN+BFSXAIiDN7XZq1ap10KG269evtyum9ZISfhmqOxAgEHs+CmyoC8t7wLLu8L9c+MbAFr9I2s/gWg57v4TCVcBaYB2wW+/kK6VquLY+ny+5devW+5+oW7fuYU8SEc4++2zPa6+9lokmnkopVS1o4lkDWNDz9FKU7wy8BmTGFhk6pDOJJZrHEevFjCetrAD+AnwObOaXsbrPExtuWxGp32dgGrZocdDjwWCQnJy9QI8KaL28bH4ZrjtUiC2xC+QDaxrD8sawrB8sC8JqAz94wURFam8BWQM530LBGmIJ6Tpgm+4/qpSq7jwez4izzz7bU5a12gYMGOCeMWPGKODOxEemlFIq0TTxrOZEJOCGY9qV4hwfMLyEZU8lNpPx4gOeywU+Ac7i1xNEhxDbQTOPg0wcLYdZ4JzWvftBh9GuW7cOny/Vyc31VKPVmj3E+pJPAUZY7E9IDbH+43XHw9rjYe0gWBGCVYWwyQMhl0j6dnCthdByCK8klpCuBX4wxhRWyuUopVQpeDyeUQMHDizxiJ2i2rZti4jUF5FWxpg1iY5NKaVUYmniWf11bgXhpFIMtS0NGzhwRZwXivx8+QHHahPbazPRHGAx2OPPPfegZdauXUs0WsehRmwTJMTmkNYFuu57IvDL8RxgfVNY2xTW9YHvwrAyH9a7YI9XJG0nuDdA/grIXQFmHbF1orYAO3UIr1KqsolIm7S0tNqnnnpqmc63LIuBAwdab7755gXAA4mNTimlVKJp4lnN2XB6n0MsDlRTrADcIub4448/6HisjRs3mnC40VHynk4B2sUfQKwj2xf7MQ/Y0ADWNYB1p8OqfPguApuAn5MgzxZJzQL3dmAzhNdBeCOxpHTfY6suelR2IuJyuVx3ezye+rZtB8Lh8L8KCgo+quy4lKpK3G738IyMDJdllf1e4YABA5Jmz559GZp4KqVUlXeUfEmvudKg3xmlWNG2upoP1G3QwOEQC+Z27dpV/vvfGU4wWBN6PMsjCWgdfwCx90eR90gY2FoXttSFLafE8swf8mFDHmxyYLsLdntFfBHw7gDXVijcCHvXgvMjv05Qd2nvabHqWZZ1+9ixY10bN25k1qxZDYjtBqSUivN6vZdlZGSU6/+vzp07U1BQ0EJEmhpjNicqNqWUUomniWc1JiLiT0rq/teCApPmOHJ2ZQdUgeZAtE2nTofcJuX000/H7RYrtnTSRUcosurIB7SMP/Y7IDl1gJ0B2BKALcfCljPgRwMbwrCpAH6UX3pPAyFI2gvWD2B2QP52CG0HZxewm9hk1d1Ffs4+ChZGMm63u+DSSy91bdmyhbfffvs0ERFN0pWKEZEWgUCgcYcOHcpVj9vtpnfv3s6sWbOGAY8nJjqllFIVQRPP6u0YbNs65rzznJEffGCL45iu4TCvOY6kV3ZkCfY5WLf07XvIMrZtM3x4pnnhhfud/PyLjtBenjWVBdSPP/Z/MRRiW7YWEQLaBuCiVOh2TJH80sBPefBzAexwYvnmHhtyPBBxxXpTPTng2gPWbnB2QP5PENpWJGE9MGnNqkYJ6/4Es3HjxrjdbnckEjkW2FB5ISlVpTRq2LBhnm3b5Z4qMmjQIN/8+fNHo4mnUkpVaZp4Vm+nt23bNnL33Xen3nnnnXz99dfy1FNPOW2WLjWrIxErtbKjS5BdwE6QXr16HbbsySefLB7PO+TnV3xcCmJ5aB6xHV/PKXpAiM09LuZLZSGQ5Yddftjd4Lc55k+RWML6czxhzbJhrxsi7ljC6gqDlQ92HkgEJBz704TAhMEEIRqEglzIz4VoCIgQG2Ncmj/ziCWQ+cQW7zLxRxSwDtN7aRzHEYjtN9i+ffvoZ599dgaaeCq1T0FhYWHp91ApRrdu3YhEIqeISD1jTEWsb6eUUioBNPGsxnw+X8/TTjstBWKr+3Xq1Im//e1v1h9uusk5ftEi551IxOpa2UEmwJdAba836vF4DtuLuXTpUhMKHX+Uz/E80iIC9UpR3sUvK/YW6zAJa66/ZHnjvp/DDgSjECqEoAMhB0Im9gjvKy+xR54F+TbkucFtQwgRMSJS4Di/6mx1Dth30BAb/W4syxLLspyUlJSCfQe7du2asnjx4j7Ay6V4oZSqyQoKCxOz65PX6+WMM84omDt37nnAPxNSqVJKqYTTxLMa83g8fdq1a/erb79ut5vH/vIX65lnnnF6P/ccl+Xnm6eNSchd5coyH0yjVq1KlExGIhHHsvLEcajW11y9RKzSJZ5lddiE9WCs+KMUWw5tBVqyePFiPB5Pse8lYwzGGNq1a8fSpUsl/rt07NiRJUuWWJZl7d/Ktn379ti23ae0gStVgyWsxxPg7LPPDixZsmQ0mngqpVSVpT1D1ZSIeILB4AmnnHLKb4653W6uv/5668WXXuIFt1tClRBfIn0MzulnnlmiLyijR4+2LesbS0c0HikOsZ7F+pUdSII1Bo5znn766YOWEBEsy2LZsmXYto3L5cLtdrNs2TLcbje2/UsHfZs2bbBtu7HX671LDugqVeooVRCNRhP2b6FHjx6Ew+GuIpKWqDqVUkolliae1Vf7Ro0aRfx+/0ELtG7dmsaNGjl/P4JBJVoU+AbsQYMGlah8kyZNGDRoUNTrPcOJzRdUFetnYtM5A5UdSAW4yvrgg0+jiajJ4/Hw+uuve5s1a3ZbIBB4V0SOFZFS9MAqVeMktMczOTmZjh075ns8nskiUuO3GFNKqepIE89qSkRO79y582H/c+3Xv79Mt6xqu4XDSsAjYlq0aFHicyZOvNvu3v0U4/O1dmI9cqrirAFqagdDlKQkd8K+GDds2JD//Oc//oyMjLNSU1NXWJYV8fv9e9LT01fVqlXrQ5/P94SIXCoiJ4mIrsqsarqE9ngC3H///cmdOnX6nd/vXy0ipyeybqWUUuWnczyrqZSUlH4dO3Y87DL0O3bscBo7TrX9EvsFUKdevSileK+63W4eeeQR+9xzzzWbN98H3F1h8am1QG2HGnkTa0n0hBOaJ/TfjsfjYdKkSd5JkyZRUFDArl270rdv357+008/nfjjjz/2++abb0YvW7aMrKwsd3p6+uq8vLzPI5HIAmAxsKYabSej1OEURKPRhH5u1K9fn6efftr/wQcfHHvvvfd+EggE/hUKhSYYY3IT2Y5SSqmy0cSzGhIRt9fr7VmSjbdXr1olVxyBmCrKHIi27tix1O9T27a59tprZfLkvzih0N01MCmqKjYC9aptj/qhraNly4pbF9rtdtOwYUMaNmxY9OlkgOzsbFauXNl2xYoVbZcsWXLZ8uXLyc3Ntfx+/7/D4fAUY8zaCgtMqSMj4YknxOZeDxo0iG7duvkmT548eu7cueeLyKXGmNmJbksppVTp6BfyakhEhh533HF28+bND1vW7Xab6ryl5Wdg9e5TtsVAMzIyMCbHgoWJDUoV8SPQuIYmnknkV9KGsGlpaZx++ulceeWVPPnkk8lz585Nfuedd/wjRoy40u/3L0tNTZ0rIgNF5KCf4RJzioiMEZEBIpJ8JK9BqcPwu1yuhMyhLk56ejoPP/ywb+rUqfVr1649Izk5+TURqV1R7SmllDo8TTyroeTk5DuuuOKKEn2J7NipEw/6fOYOYmuPVidZwE8gZ511VpnO93g8DBgwMGpZ91SxxEiAS4v8XkhsO5LBwPNA+/jDA5wa//m2IxxjSW0DmtTQzxE/oVDVWRO6cePGjBs3zj137lzvhAkTejZr1mxaIBDY7HK5bhCRFAAR8YpIRiAQeNbv9/9cu3btL84555yHW7du/brb7d6dlpb2ndfrnSIig0QktbKvSR3VTmvTpk3B4YuVT/fu3Zk5c6Y/IyNjqN/vXygiB1+RTymlVIXSobbVjIi0T09Pb1XSZOyWW26xTzvtNB5++GFnytatVm2PJxoVkTzHkUhBgVgieN1up0M4zD+jUavkS/hUvK+A9KSkqNfrLfM8u2HDhtoffnijCYWq0jTEALAcCAM+4EOgSfzY7+IPgGOBOZRh38ojaKcDjavKC5tgqSYnJ6eyg/gNr9fLsGHDGDp0aPKSJUuSX3jhhQcWLFjw57S0tG/dbne7li1b5mdkZKT06tVLWrZsSXz3Fl84HObbb79ts2jRohM///zzq9asWeNLS0vbkJeX90FeXt6HwGfGmOxKvjx1lEhKSurVrVu3lCPRlt/vZ+LEiZ6cnJxm8+fPf1FELjTGVLEbkkopVfNp4lnNJCcn33rppZcmud0l34mhZ8+e9OzZ09qxYwfLly+3k5OTqV27NrVq1aKwsJBt27ZZL7/0UrTNnDl0iUadWYWFVlW4JTwfTOPjjy/XqocdOnQgKcmWUOhFYHSCIkuEQcB7wIXAq8BI4LNKjahs9jpQr4YmnnWtHTuWVXYQByUidOrUiU6dOgW2b9/O8uXLu3bu3Jn09PRiFx3z+Xx07dqVrl27WmPHjk3Ny8tj2bJlrT755JMTXnzxxRv3VXsEL0EdxZKSks5q167dEXu/iQj33Xef96KLLhq4ZcuWW4CHj1TbSimlYmroF8aaSUTq5ufnn3/hhReWqQewXr169OnThy5dutCyZUtq165N/fr1adeuHQ89/LD91ltvkdehA828XucMETq5XE6D5ORoqt9vOrrd0SeAfTPeQsDHwA1AK683epbL5WyPH3OAFcDWcl7vx+B07tatXO9Ry7IYN26c8XrHGthUzogSaQTwH2IDoL8FKm4Rm4oVktgw4ZpoqPXFF8uN41T9hWQbNmxIv379SE9PL/E5lmXx/fffmzfeeCMSCAReBBpVXIRK/UJEPKFQ6MRTTz31iLbr8/m49957AwUFBQ/uG56ulFLqyNEez2rE7XZf079/f6d27YpZH6FJkyb84x//sN566y127dqFbdtW69atSU9PZ+7cudaf33nHmbBjh+WybROKRCQtLc20OuEEJ7NfP/urr76Ktvj8c3wul5Obl2fZtk00GiXZ43Fa5OXRr7DQugI4sRTxrAVrZPv25b6uoUOHyjfffOO8804Xk5d3jxWbX1nZ66y0JbYi7KvA2ZUbSrlEBOofobYEGAdMif/+CJALTIo/niGWBAeJzY29DzipHO31Ixr1Mn36dDIzM8tRT9VijOGzzz7jvvvuC+bk5HwbDAavNcZ8W9lxqaNKh0aNGoX9fn/Jh+4kQCQSYdKkSUGPx/NwJBKpeuPolVKqhhOd5lA9iIjL5/Ntf+GFF+qcdFJ5vkyXz48//khhYSFNmjThwOG+69atIxwO07RpU9LS0sjPz2f58uUsWrTIzP/8c+e7776zk9xuc0xBgdM7P9++jEP3850B0bbXXCU2uo8AACAASURBVGOPHTu23HEXFBTw+OOPOx999DHbt2+zCgv/DlxV7nrLJplYwvQn4C/Ap8AuYonUu0XKHQv8j6o9xzMFWEYs1ormJdYpt4jYa3Jg4pkM3BIv+xpwUzy28vTIPmAaNfqnM3v2m9V2L9yiVq9ezeTJk4OrV6/eHQqFrgVm6lw3dSSJSK1AIPDpqFGjWo8dO9ZT9Nipp57K4MGDeeCBBwAoLCzkrLPO4tRTT+XJJ5/kzTffZOrUqdSv/8vNrj//+c+0bNnysO0aYxg/fnx4wYIFs4PB4DB93yul1JGnPZ7Vx5DmzZsnVWbSCXDMMccc9NiB//l7PB46duxIx44d5ZprrrELCwtZvXq1LFmyxP7ss8+iLyxdaguYRsY4Z0Yi9kigP7+M/04Ddu/enZC43W4348ePt/r27cvvf381hYWDE1Jv+VxB7CpPJZZ8VjcOsaHCR2qorQu4GngUmHyYshcRm0P7CrEEtKx+J9u2/cneuXMndetW5RsAh1ZQUMD9998feffddwsKCwvvKCwsfNoYU64VReNbuQwEPtAv8aokRKSB3++fP2TIkKZjxozxHHjc5/Oxdu1aIpEIXq+XhQsX/irJBBg4cCB//OMfS932P/7xj8IFCxZsDAaDI/X9qpRSlUPneFYTKSkpd1x55ZWVPT60XFwuFyeffDKXXnop//jHP+wvvvySl199VUZOmGBv7tMnelFaGoGkJJr7/dELgXlgDxs2LGHtO47D3Xff7UQilwMNE1Zv2R1D+ZKiyvYzsY+QwBFscwzwMlCSxVc7AqvK2V5DoF30lVdeKWc9lSccDnP99deHZs6cuSASiTQrKCh4IgFJ5/GBQGCRiLwNVIW1yFQVJyI9fD7fd6NHj2522223eeKrLf9G9+7dmTdvHgAzZ85k0KBB5W77k08+4dlnn90bDAb7GWPC5a5QKaVUmWiPZzUgIm1TU1Pb9O3bt7JDSSgRoWXLlrRs2ZLhw4fbxhi2bt3K4sWL7YULF0YLZ8+2vd5iF+gsk08++YSfftoDPJGwOssmt5jnescfRW2s6EDKaRWQZjiiK6GmApcBjxPbiuZQEtWp0UVWrlyaoLqOrJycHH7/+9+HNm7cODPe01PuXk6Px/MHr9d77ymnnOJbuXLlguzs7GCi4lU1j4g0g9S/QuoAx8nz/v73v+dgSSdARkYGTz/9NL169WLNmjUMGzaMJUuW7D8+a9Ysvv766/2/v/TSSxzq/4nVq1dz2223hSKRSIYxprxr3imllCoH7fGsBgKBwK2XXnqppzRbqFRHIkKTJk0477zzeOCBB+whQ4ZE77zzzmgi6jbG8Nhjj5lQ6DpL3/aJsg6oXQlD1v4A/JPYIkKH8jXQJgHtnWqtW7c9Ie/DI2nnzp1cfPHFoQ0bNrwUDAaHJ6qX8/jjj//TtGnT/MnJyaGcnJx/JypeVVOlT4ULz4EtXpcr2Vm4cOEhS5944ols2bKF999/nx49evzm+MCBA5k2bdr+x6GSzl27dnHNNdeE8vPzrzLGLCr3pSillCoX/QZexYlIncLCwgsyMzNrxOImpXH99dfb69evt7/66qty1zV//nx27NhDbKVTlRgbgXqVkHjWBoYTSz4PZjowm9j+qOXVjKys6tWpt3XrVkaMGBHavn37Y6FQ6FpjTJn3hBERKykpaZzX6/3muuuua//KK68EGjVqxPz5813GmLcSGbeqiWw/9LchmWCwj/Xmm28d9iZO7969mTJlSrmG2ebn53P99dcHQ6HQXwsLC18tc0VKKaUSRhPPKs7tdl/dp08fU6dOncoO5YirW7cuN998s7nxxhvNhg0bylXXli1biEaPiepbPpG2AQ0raZGO8cDOA557FGgPnAC8BHxCYhY+qkVBQcERHE5cPhs3bmTEiBHhrKysu8Lh8B/Ls5CKiBwbCAS+2tfLOXr0aMu2bb788ks8Hs8aY8xPiYxd1SwiIlCYGluEDOAu5s2ba0cikUOeN2zYMK699lpatWpVpnYLCgq49dZbI5s2bfosHA7fUaZKlFJKJZx+C6/CRMTlcrnGjR49+nCT2Wqsiy++WNq1a+c8//zz5arH5XJhWdFqkzxUD7uAukfwNS06N7YBECK2jQrxP7cAS4HvgRmUbw/PolyUo8PwiNq4cSOjRo0K5+bm3hiJRKaWtR4REbfbfbnX611+9dVXt3/llVcCzZs333981qxZkdzc3AobZisxJ4nI0XfHrZoTkcYicplI+jTw74bsM+Gb+NG2uFxp0c8///yQdTRs2JBRo0YVe2zWrFlceOGF+x9Ll/56/nUoFOLqq68OffHFF/ODweD55entV0oplVi6uFDVdm7Tpk2TTjnllMqOo1KlpqZSUFD26WnGGHbt2oVIVJfQT6gs4JSjYAh4bRynoMrfpNuXdIZCoRvz8/OfLWs9IlI3EAi8WLt27R6PPvpo4MQTT/xNmdWrVxuv13tzcnLyycFg8GPgc2BTWXtX41uznCwivVJSUs5NSkrq5vV6XQUFBdtFpI0xJq+s16MqloikAr0gcDa4zgF/fehdAOcmQz/gqij49n9OBIP97Rkz3oz269fvN58dxU2r6NKlC126dAFg6NChDB069KCx7NmzhyuvvDK0ZcuWN0Oh0GhjTGECLlEppVSCaOJZhaWkpNxxxRVXpFR2HJVpyZIlfPrpp/bDDz9c5jqmT5/OM8/800QiD+r7PaFyHEiv8glZ+dXHmDCO42BZVfNyE5V02rZ9ttfrfen8888P3HTTTZ6kpKRiy73yyiu+NWvW+JYsWXL5F198ceHXX39tR6PRnSIy2hgzpyRtiUg9ERmZmpp6blJSUrfU1FRz+umn2926dfN17tyZhg0bMmbMmAb/+9//HgDGxc9JA2xjTGI2+FWlJiJuoCt4BkJgKHhaQccInJcC/QU6AHaRN04ev95x5y6++KKtHQqF8PsTtxPPtm3buPzyy0N79ux5KhwO36J7dSqlVNUj+tlcNYnIqSkpKV/OnTvXV9NXsz2Y7Oxszj77bC677DKuueaaMtczbtz4wg8/bOSCF+LP5BL7IlQ1k4jq46QojLfhysoO5AhIYs6cWdStW7eyA/mNgyWdIuIHzrMsKysajX5wqDpEpL7f75/i9XrPf+SRR/z7ephKyhjD3Llzueuuu0IFBQVvBIPBG40xew7SViOfz/dHx3Gu6Nu3r/Ts2dPbuXNnGjRo8Juyu3btYvDgweHc3NxLiY2jXgs8CdygicWREe+NPgPkLKjVF3JPg2Pz4VwfZLjhTA69tVH3KJxkwz/2PxMINI5OnHiznYg9OgHWrl3LFVdcEQqFQndHIpEpCalUKaVUwuk37yoqEAjccskll9T4LVQO5b777os2atQoWp6kE2DlypUW9Ir/thqPpz4+X0MHfih/kEe1sIG0yg7iCDneefPNNys7iN/YvHnzr5JOEXGJyICUlJRpHo9nZ8eOHf9Ru3bt6YFA4IV4IvorIuJNSkq6Iykpaf2QIUMy33nnnVInnfF66N27NzNnzvQPGjQo0+v1rrcs64IDyjQLBALPxtv6/Xvvvef785//7D3nnHOKTToB6tSpw0MPPeRr3rz5c7Vr1943mW8M4AQCgWdF5CgY6n1k7Ztfa1nWmLS0tFlJSUnZPp/v06SkOnfDUz1hixdWp8Ij7thQ2sMtQfCgDS8DK/Y/EwyeY7/xxhsJ2aJo6dKljBo1KpyTk3ONJp1KKVW16dDDKkhEans8nuEXXXRRub9Ubd++ncmTJ7N+/Xocx6FXr16MHz+epUuXcuONN3LMMccQiUTo1asXt9xySyLCT4jPP/+cefPmWe+99165F6/JydlrxVZA/Ryvd4hz4YUXsG7dOhYuvBX4T/mDPWrly9GTeLYr98rKFWHu3Lnk5eXNKCgo+Nrv9//N6/Ve3LhxYyszMzM5IyND6tatS05ODhMnThw+f/783iJynjHm23hyMdzn8z3euXPnwP/93//9avGgskpOTmbixIlJ5557btJtt932r+Tk5KuDweCkQCBwvdfrvTAzM9O6/PLLPaXpOe7Rowc9evRILfpcdnY2Y8aMGbl27domInK+MSZc7uCPUrGVZzlRRPqkpKQMTkpK6pGcnCzdunWzzjjjDH+XLl3YsmULY8eOd/LyMsvQQnegYxResuGB+HN3sWTJCfbevXtJTU091MmH9OmnnzJhwoRQfn7+BYfr1VdKKVX5NPGsggKBwN8HDRpU7mF9xhhuvvlmhg8fzl//+lei0Sj33HMPjz/+OD179qRjx448+eSTRCIRMjMz6du3Lx06dEjQVZRdXl4ed999t/nd734niRja+OCDD/KHP/wBEeHyyy/n2muvtebPn8+33050gkHt9S+7fIH0yg7iCPERieyq7CB+5auvvmL69OkmGo1m1q1bd8j555/vHTx4sN2iRYtflUtJSWHKlCm+t99+u9nkyZO/sG37deAyYwxPPvkkZenhPJyOHTvyzjvvBJ5++uk+//3vf3tkZma6R48e7UpPT8z7JS0tjeeee85/22239V6wYMFCEelrjKlaf0FVmIg0EZFzUlJSzvV6vT18Pp+rW7dusi/RbNy48a/K+3w+8vL2lvGzshDYecANxGa4XPWjc+bMsYcMGVLqGh3H4W9/+1vhv//975xIJDLIGPNl2WJTSil1JGniWcVYlnV+3bp1B0+YMMFb3rrie+0xbNgwAGzb5tZbbyUjI+NXXza9Xi+tW7fmp5+qxpZ8L7zwgmPbtrn22msTMoyue/fu/P3vf+fYY4+lXr16FkCLFi0wJqLbq5RLwVHU45lm5eauquwg9vvTn/7Eu+++S+/evc0dd9zh7ty5szvWcVU8EWHIkCHSvn173wUXXDAqLy8Pj8dDvXqJ2Oe0eElJSdx4443uG2+8sULmC3g8Hh555BHvlClTTpo2bdrXItLTGLOxItqqSUTkdK/XO7tnz5529+7d/V26dOGYY4455DmBQCC+srhD6Wfo3BSFbIF7fvVsKDTMnj79jeiQIUNK9TmflZXFuHHjQt99993KcDg82BizvZQBKaWUqiTa21OFiEjDpKSk56dOnepPxGp/69at46STfr2XYXJyMo0aNWLz5s37n8vOzmbTpk107ty53G2W1/bt23n22WetyZMnJ3TuVpcuXX71JTsQCBCNFmjiWS5H01DbE2XDhp8SMictEXbs2MGQIUOchx56yOrSpQuHSjqLat68OQsXLrQ+/PBDzjzzTF5//fUKjrRi5ebm0r59e3dqampjt9v9YWXHU9XZtt3P5/N9NHXq1JQpU6b4hw0bdtikE2L7IKelpQPvlqK1LOD8KLxkwXwLPAccv5Ply5fbe/YUuwZVsVasWMGQIUNCy5cvfzYYDHbTpFMppaoX7fGsIkREAoHAKyNGjPC1b98+IXUaY4r9QrpvMcglS5Zw/vnns3HjRq688spKX7Fz69atXH311aZdu3bOaaedVqGLhiQnJ5drb1DlENsm4WhJPFuzc2d2lblRkZyczN69e8u0qqvb7aZhw4bcfPPNZGZm8tZbbxW2bdu2oGvXrr527drRpk0bDraNSkWLRqPMnz+fgoIC0tLSfvVISkrCcRxWrlzJ559/7nz00Ue569at8/p8vkW5ubnTHMd5p1KCriYsyxrm8/le/tvf/ubr1KlTqc8fMKB/dPr0v1qOc14J/x08CXxhwTqB4v5vqY/L1Sj60Ucf2ZmZh547aoxh2rRp5qGHHgrl5eWNdhxneqkvQCmlVKXTxLOKcLlcV9evX/+0MWPGJGxYWsuWLfnww193AuTm5rJ9+3aaNm26f47nxo0bGT16NH379qV169aJar5UFi9ezNixY+nUqZN5/PHHK3ylSo/Hg9vtIS/vXWBwRTdXA2URGzBR7hHh1UQXCgpyrTVr1tCqVavKDoa0tDQ2bdpUru1EWrRowYsvvshrr73GwoULWbRokeN2u51wOGw3bdo00rVrV1dGRoa7Q4cOFb5/aTQaZdasWTz22GPB3NzczbZtbwdqR6PRWoWFhal5eXnJlmUZy7Ict9u9w3Gct8Ph8FvAvPz8fF1Y6DBEpI3P53vp+eef9x04CqakMjIy7Pffv8UJBilh4mkBTSk+6YwJhy+yp09/I5qZmXnQz/xIJMKkSZMic+bM2R6JRDKMMatLGbpSSqkqQofaVgEi0tLlck199NFHA4ncPuX0008nEonw9ttvA7Evd4888ghDhgzB5/tlCfxjjz2WK6+8kueeey5hbZfG+vXrue6667jyyit54oknrJJ+yT3ttNMA2LJlC507dyYzM5PzzjuPkSNH8tZbbx32/HHjbjY+36UORMoV/9HpRw6/jUJN4gfOiT711NMlKn3qqady++237/+9sLCQnj17MmbMGADefPNN2rZty+rVv3yHHjZsGFu2bClR/enp6eTk5JS7B7ZNmzZMmjTJNWvWLN/ChQut5557zjV+/HipV68e7777rhkzZozTu3fvwqlTp0bXrVtX3uZ+IxqN8t577zFo0KDce++999tt27YNzcnJOWnPnj199uzZ027v3r3NQqFQuuM4qYWFhcfm5+c3yc3NPSYUCl1vjJmlq9kenojYgUDgv+PGjStz0gmxBaNECi14tYRnnAAcbnj67Xz//Rp7x44dxR7NysoiMzMz+Omnn34QCoVO1aRTKaWqN008K5mIuAKBwBs33HCDt2XLlomum7/85S/Mnj2bc845h8GDB+PxeLjpppt+U3b48OEsXryYH3/8MaExHE4oFOK6664z/fv3N1dddVWZ62natCmvv/46b7/9Ng899BAvvvgiM2bMOOQ5I0aMEJ/PFphZ5naPXluAlHL1uFU/I+wFC5aVaJ6nz+dj7dq1RCKxmxoLFy6kfv36vyrToEEDnnnmmTJFkp6eTjAYTOjQX5fLRevWrRk1ahT//Oc/fQsXLvS8+uqr1llnnSXTpk1zRowYYfr06ZPfvXv3vI4dO0Y7dOjgjBw5Mn/f0P3SyMnJ4a233iIjIyN43333fbNt27Zhubm57Y0xH5liKvT5fH8Wkc1+v/+79PT0WbZt3y4i/UWkdkIuvgZLSkr6vxNOOKHF8OHDy/V+cblc3HjjDcbvv7mEc52bAsHDfMdIx7KaRmfPnl3s0Q8++IAdO3Z8GgwGzzfG5JYuYqWUUlWNDrWtZElJSXeccMIJx48aNapCbgI0bNiQJ5544jfPd+nS5Tcr23788ccVEcIhPfXUU45t2+bee+9N2PDapk2bMmHCBB555JH9K/oWx7IsbNsGKmTRzRpuGZa1XdxuHy6Xx1hWkjHG6xQW+qWgIM2KRusI1AcaAU2AZsBx8Uf5F86qHGcRDO4s8fu0e/fuzJs3jwEDBjBz5kwGDRrEkiVL9h/v1asXixcvZsOGDRy4BcrhZGVlsWPHjgq/cdiiRQsmTZpkT5w40f766695+eWXycrKKmjYsKFJTU11v/nmm55169Zx/PHHH7auH374gU8//ZQPPvhg76pVq7w+n++rvXv33gN8XFyyWVRSUlK7Bx54QI477rh633333YDly5f3Wbx4cWjdunW+QCCQbVnWzNzc3DuMMSXrMj5KxIfY3vnggw/6EjFc+qyzzpKpUx8t4b+BPYD3sHclIpFL7enTX3EuueSS3wQ4c+bMvcFg8IXDvT+UUkpVD5p4ViIR6eD3+2976KGHEvKloDr6+OOPGT16tJ3o62/Tpg0bNmw4bLlotJCja8hoomzmtNO6Ru+/f7KdlZUlWVlZkp2dbWVlZZGVlcXu3budHTt+Mrt2rWTPnj2yd+9eyc3NkUgkgm3buN0eY1keI+LGGI9xHI9xHB8FBX4rGk21IJXYHqG1iM0Rqxd/NIw/GvHbVTIrWoDYokolk5GRwdNPP02vXr1Ys2YNw4YN+1XiKSL87ne/49lnn2Xy5MklrveBBx5g2rRpXHLJJVGgwudDQyzWjh070rFjRw9FXvhIJMLFF1/M6aefHrnxxhu9xSWgS5cuZcKECblZWVlRl8v1bm5u7uvAx/n5+SXuwYpGoy2aNm1KixYtaNGiBeecc44bSHMch02bNtWbMWPGyFdeeSXT5/M9FolE7tfesf2jaV7/wx/+4G3SpElC6ty9ezcuV1IJ91T5CfCVIGEcz6ZND1jbtm2jUaNG+58NBoMsW7bMC8wqa7xKKaWqFk08K1FqauqTN998s6/of7ZHm2AwaM2fP99kZmZKIpPPkt4g93p9wHdA34S1fXRYQ6tWJ1j16tU72F6Qxf5lOo5Dbm4uWVlZkpOTI6FQiNzcXHJzcyn6c3Z2djQ7e4vJyVktOTk5BINBCYWCEg6HJRLJo6AgH9u2sW0XlmVjWbYRsY2IDbiMMS5jjBvH8RCNeiQa9Uo06rdiNxmE2Iq8+dh2vmNZ+cayCo1IoVhWgRGJIhIFohL70xFwMCZKYaGnxG/SE088kS1btvD+++/To0ePYsucffbZPPPMMyUa4r5582auueaaaEFBgfXvf/9bTj755COSdB7KXXfdRd26dc20adNk/PjxzJgx41cLERljmDRpUu5PP/10szHmOWNMyTP3OBER27YbFLfth2VZtGjRgnHjxrlHjhzpnjJlyh/mzp17ncvl+r9oNPqcMaawfFdYfSVqiG1RoVAIx4n9ezh87vm1A41K8B5NRqRFdObMmdYVV1yxP9YFCxbg8/kW5+fn55QvaqWUUlWFJp6Vq+6JJ55Y2TFUqpdffpnLLruMv/71r8XOPS2rVatWcdxxxx223NVX/97cf/8Ese1HouHwN3asl62svsfr7eZYVqoTDjd1GXMK0A04C2hcjnqrHq93c7RZs9JveWNZFqmpqaSmph6u6CHrdhyHSCRCOBwmEokQiUQk/uBQj2AwaACTlJRkxVY2dltutzu+yrGbA38u+sjOzmbChAmlut7evXszZcoUnnvuObKzs39z3OVycdlllx12Ya9NmzaRmZnJOeecw6233ipFFwerTJZlMWbMGLnuuuuSevbsybfffkvR7aA++eQTtm/fvr2sSWfcSV6vN+r3+w85Jr5Ro0Y88sgjvhUrVvjuu+++qRs2bLhdRK4HPjjahmqKyEk+n++PiRpiu0+7du1IS0smFPoTMOkwpd8QeKBESW9e3nn23LkfR6+44or9/+5nz54dysnJebkc4SqllKpiNPGsXAkfYlrdNGnShOHDhzN37twSDt86vC1btjBlyhRGjhx52LIXXHCBlZGRweWX/05WrfoTMLUcLc+gYcMUufrq37vWr19vVq1aFt2w4W3rp59+slwuFy5XSrSgoI7JyzvBBe2AHvFH9duSxO3eQ6KG75WFZVn4/X78/lLPF5X4o1Sys7O54447nJSUFEMphrcOGzaMlJQUWrVqxaJFi4otM3ToUJ5//nmCweBB67nrrruc/v37m4kTJ1Z6L2dxsrOzKSgooOiNtGg0ysMPP5wbDAb/UNakU0TO8Hq9748bN67E46pPPvlkXnnllcDcuXMDkyZNen3Xrl1XAf8pS/vVUXyI7X9vvvnmhA2x3cflcnH77bfJ//3fnSYcvkMOPtzdAbYJ9C9hzS1Zs+bv9qJFi+jSpQuFhYXMmzfPMsbo3qxKKVWDaOJZiYwxtsulfwU///yzY9t2uYaDbd68mczMTPLy8ggEAowcOfKQCwsVFQgEuPTSUdbkyX+LhkJTy/HF/is6dOjgnHvuuTax5MYFsd65n3/+mQ0bNtgbN25k7dq10dWr3+aHH5609u7NFq/Xb0TSopFIY6uwsLUFnYklpG2pqgtPR6O5VmUmnkdSOBxmyJAhNGjQwMyYMaNU74+GDRsyatSoQ5Zxu91ccsklPPjgg8Ue37VrF8uXL7ceeOCB0jR9RG3dupV69epRWFiI4zhYlsX7779Pdnb2euD9stYbCAT+c88996QNHDiwVOeJCL179+aGG24ITJkyZSRHUeK5b4htZmZmQlc93qd3794kJycRDk8HDnZzbwexj7/6Bzl+oKvJzd3A9dePYfjw4dGzzupj27a91RjzQ0KCVkopVSXIUTYCqUpJTU3d/K9//euYE044obJDqTQ7d+4kIyODl156idatW1daHNnZ2fTu3YfCwp3EFrYpqWeBBcBTeDydouPGDbYvueSSEp+dl5fHDz/8wKZNm9i4cSMrV66Krl+/XrZu3Wrl5+fh9SY7jpPuRCJNbcc5SaAL0BtI7NY7pZOPZfn46quvSEpKqsQ4jox58+YxceJEZ86cOZVyFyAUCjFgwABn5MiRjBkzpkreiVi6dCmXX345juNw6623ctFFFzFgwIDgzp07BxtjPi1tfSLiBqK2bectXLjQVdahxVu3buW8887LycvLSy/HUN9qQ0Ra+3y+JTNmzPBV5I2h3r37mF27bpJY4lncZ1E2scXAQpTu/vYGvN6TOOmkk8yyZcueys/Pvz4R8SqllKoatLutEhljrNh2Hkev559/3mnevLlp3bp1pb4QaWlpNGvWzFm/fqqVlPRW1LKyCIdH2i7XZ47H843k5bUnGh0vMKTIWUvxem+gdu06Ztu2EXg8P5doXmlRSUlJnHDCCRS5+bD/dcjOzuaHH36wNm3aZG3cuJHVq9dE16//QLZvjyUfHk+yE43WciKRprYxxwmcSKyXtAtQkdsbLiYQCBwVSSfAjh07cLvdlXaHzu/389xzz1mjRo2iVatW9O9f0uGLR0779u2ZMmUKU6dOpXnz5syYMcNEIpFvjTGfikjDpKSk223bJhgMHnYit4ic6nK5PissLLzG5/MV+Hy+Mv8/1bhxY9LS0uTnn39uB3xd1nqqkRQRIS8vr0IbOemkk5z58yfaHs8L0UhkbTGf3WnEVoFeDHQtRc0tyMsbY5Yv/6sUFBToBstKKVXDaOJZiYwx9tGceBpjeOONN6xHHnmkskMBoG/fs2TDhj/RvXtfzjjjXHvaWe8K7AAAIABJREFUtOnO6tWrrHvu+TNLly513n139P+zd+ZxUZX7H/98z5l9mGEREVDADRQF3HFJxV3LfbcUraulWVp2c0lNq1/ezCxTr5Vm5lWzmylqVmru+5YKuRsqKAoosjP7Oc/vjwEuKijIMIN63q/XvBjOec55vsMwZ87n+W5kszFmNEZAFN1IqTwqRkdHIyIigps27UPRas3lyyo8H4a7uzvCw8MRHh5esIkH7H+3u3fvFhWlLDExUbh58zDdvn2bsrIyiedlUCg0IqAVLRZPsliq8/YemqEAGgNoisfvp3kcvr6+DsvJrez06NEDc+fO5fft24eoqCiX2BASEoJmzZrhyJEjQteuXSvlRaNz585YsGABLl26hJUrV5pyc3PfJiJyc3Nb5+np2TYrK+uRwo+IglUq1b6GDRvqY2Njp/v7+1tQzn5HHTp0kG/YsKE7ngHhyRg7IZfL33n33Xc/i4mJcauoeb76agl/+fJlREePesg1oKYIHOTKJjwBxj4ii2WRFcDO8lkpISEhIVHZkISnC2GMPdPFha5fvw5BENC6dWtXmwIAGDp0KIWHh6Njx448AAwZMoQzmUxQqVTo0aMHN2XKFMTGxtK+ffuYzWYTq1YdTdHR0WS1WqFWg0tLM8HHp7Q5TY8PEcHb2xve3t5o2rQpYM8nLRQjoigiPT0dqampXEpKCpeSkoKbN2+KiYlnxZs3/+DS0u5wOTk5UCiUkMnUImNuosXiSVarPw/UhN1zGgagCYr3nJ5BYGDQMxOjr9VqMXXqVPHdd9/levfuLc6aNcslH9qbN28KnTp1qpSis4CPP/4Y0dHRUKvVGYyx4zKZbHTVqlWb+vj4GJKSkpY87FgiClCr1YemTJni0bhxY+rfv39EzZo1S664VEratGmj3L59e28AxSfRPmXYbLZvb9y48en169cRGBhYuD08PBwjR44srMy8cuVKGAwGNG7cGEuWLMGaNWtARBAEAUOHDsXMmTPvqVB8P3Xq1IHNZiHgbwDFpYvwDLCU0frdAD4WAM1dxjKNZTxYQqJCOHv2bDYA3WMenhMWFlaW/B2J0kBUrvcEjEnviYuQhKdr4Z7l4kInT56Et7e3wHFcpbiZrlatGqpVq3bPNpXqfxVnOY5D06ZN0bRp03sqo/I8j5kzZ2LatGkgqpB6HmWC47hCYdqwYcPCzSjiobTZbEhLS0NKSgp3+/ZtLjU1FcnJyWJi4nkxJWU3d+fOHcrOziKel0EuVzEirWizucNs9uGAi1SnTt9K8Z45i0GDBnH+/v6YNm0aZs2a5RIbMjIyqLLngzdu3BiTJ0/GZ5995k9ENVQq1cKZM2dqX3/9dRNjbH1JxxERabXabWPHjvUaNGgQMcZQrVq1vNq1az+uW74Qd3d3EFGFef8qG4wxQavVrtu6des/xo4dW/iZVygU2LVrF8aMGQNPT8/C8W3atMHGjRsRExODgQMHYu3atQgLC3uo6ATs1726dYOF8+d/5IHiPhNNCThWBssXM2AadenSht+/36ghInrW2uBIVFoeV+CU91iJkpHekyeUZ1f1VAJEUdTs2bMHPXr0gJdXRebkVU4OHTokhIeHPxUC5saNGwgMDHxiwk9lMhl8fX3h6+tbdPM94pQxhoyMDNy+fZtSU1P51NRUpKSkYP36v5ifn5/rFbaTCQwMhMVicdn7a7VaqbJfJ0wmE1auXGkgoqFarXbNyJEjlVeuXIFSqdxhNpuzH3JoC7VaHTRq1CgesHv13377bU1AQEC5/8+eRe1iMBhWbdq0aejYsWMLb7B4nsegQYOwevVqTJw48Z7xU6ZMwciRI9GoUSP8+OOP+PHHH0s1T8uWkdzFi7uZKM4q5n1qyQGfCihV+6FbAKbQ999/jWbNmqFDhw58enp6AwDnSmWIhISEhMQTwRNxk/w0QkT+NptN9p///Efo0qUL+vfvLy5dupRdunTpmblROnHiBPfCCy+42gyHEBcXh9DQ0KfqjSMieHl5oX79+oiKisKQIUMwceJEqFQqobILoIrAy8sLJpPJZfPzPF/hRWPKy5IlSywGg2Enx3E+3t7ezceMGSNbt25dTk5OztcPO06r1U4cPny4umjqQa9evahRo0aOMu2p+myWgmMpKSmKpKSkezYOGzYMv/32G3Jycu7ZXrVqVYwYMQIjRozAa6+9Bnd391JN0rhxY9JoLpVQLTgYQNZDFg4SAawAMEAAQlG3brCtefPmICK0a9eO5ziuW6mMkJCQkJB4YpCEp4sgoh5t27Y1b9++nd+/fz969uzJ7dy5Uxw5ciRr3749Zs+eLRw4cKDS32g+LsnJyTAYDNSuXTtXm+IQEhIShNDQ0KfCe/sozGYzVzRU71lBrVaDiHD37l2XzM9xHLNYypoz5zwuXLiA//73v5a8vLwP5XL5wvnz52uTkpKQlJQkAthR0nFEpLdarQP69esnfR85ACJSarXaTY0aNRLuzzl3c3ND79698cMPPzxw3IsvvghRFNGvX79SzxUREQGLJZ0HitOeCQB0RXbcAbAOwMsC4MeAUHh5fWjr1cvKf/fdAmzcuLYwAqtdu3YqnU43oNSGSEhISEg8EUihti5Cp9MN6dKlixaw3wyMGTMGY8aM4UVRxP79+/HTTz9xM2fOFHNzc7mmTZsKPXr04Nu3b4+qVau62nSHcPLkSVSpUqXS5HeWl4yMDKpbt66rzXAKFouFPDw8XDb/smXL8Pvvv4PjOHAch1mzZmHBggVIS0uDXC6H1WpFq1atMGHCBOj1jqsfQETQ6XS4du0aqlSp4rDzlmV+R0VDfPrpp/D390d0dDQAYOzYsfD19cWHH34IAPjss8/g4+ODTZs2YePGjaU650cffZRntVo/0Gq1n48cOVJVr149LFy40EZEaxhjtpKOI6LBLVu2FL29vR3wyh5EFEWgeGX01EFEGq1Wu61Zs2bNFyxYoFYoFA+MiY6OxpAhQx4QmI9T6M7b2xsajQYWyw4A3e/b6wUgSQYMFoGTBNwkNzdvoUWL+nzv3lPQqVMn8Dxf7D1Iy5YtYTAYWhCRgjFWeVdbJCTKCRH5AvgS9j5oZgCpsJeCvgwgEPamuFkA0hhjXVxl57MEEQkAzsCuka4BiGaMZTp4jlzG2AO1B4hoJYBfGWPriWg5gC8YY+cdOberkVaYXQARyY1GY9Rzzz33wD6O49ChQwd8/fXXtG/fPi4mJgaBgYH8d999J/To0QO9evUSlyxZws6dO1dwQ/VEcuTIkafGQyiKInJycrg6dYprpP70YbFYXJZrGBsbi3379mHdunWIiYnBt99+W5inOnfuXMTExCAmJgYKheKBPDZH4O7uLt64ccPh5y0NVquVdDrH1ERo3LgxYmNjAdj/fzMyMhAfH1+4PzY2Fk2aNCnTOfv166cmog+rVq3a4tVXX5WJoogNGzaYjUbj8ocdp9PpBj3//PPax3gZj0QURXzzzTcGi8WyvSLOX5kgInetVnugffv2LRYuXFis6ATsxZa6d++OmJgYh8wbEREhADGwV7ddCSBaAAIYMBAKhV6sX/8CzZoVTceOHcCRI9v5RYsWomvXrnhYKzEPDw8EBARYADyQi0FECqoMVdwkJMpJ/v/xRgB7GWN1GGMNAEwC0J0x1hjALwAmM8YaS6LTqRjz/+ZhANIBvOEKIxhjY5420QlIHk9X0aZ69eoWb29v1aMGBgUF4f333wcA3mAwYMOGDdxvv/0m5pe+pw4dOghdu3blW7ZsCY2m3AUgncaxY8e4d955x9VmOIT4+HjwPO8SL5izMRgMsNlscJQAKitpaWnw9PREwU11cSG/crkc77zzDnr27IlLly6hXr16Dpvfy8uL3bx502HnKws2m42ysx9Wn6f0NGnSBPPmzQNg//8NDg7GnTt3kJWVBbVajWvXrpXZWzx06FBOr9dr69WrB7lcjhMnTsBqtd4BEFfSMUTEKxSKdi1blq3XY2n59ttvbRcuXLhkMBjez59PDnsT2/OMsZyHH/3kQETeGo3mwAsvvFBr5syZykd5L0eOHFnqAkKPIjIykj96dBGs1rXw9Kxia9YsRNalywR06tQJKpXqsRe3Z8yYoXvjjTdWEVFLxtiFgu1yufx7hULRhIjeYIztcciLkJBwDR0BWBlj3xRsYIzFutAeiQc5AiACAIioDoAlAKoCMAB4lTF2Md9LaQLQEEA1AO8wxn4lopcBNGeMvZl//K8A5jPG9ub//jns/wMZAIYxxu4UnZiI9gJ4lzH2JxH1APAv2Iu1pTHGOhNRJOzecjUAI4BXGGOX8uftA3uz9joANjLGplTA3+axkISnC1AqlX27detW5hV+jUaD6OhoREdHcwBw+PBh/Pe//+U/+ugjITs7mw8LCxOef/55PioqCn5+fo433EHcvXsX6enp1KXL07GAd+jQIQQGBopE9NRHECQlJUGtVj9WWJ4jaNOmDb755hv06tULrVq1Qvfu3dGiRYsHxvE8j5CQEFy7ds2hwrNq1aosNTXVYecrC7169WJjxoyhhQsXolWrVuU6l4+PD2QyGZKTkxEbG4tGjRohNTUVcXFx0Ol0CA4OhlwuL/N5n3/++cLnR44cEY1G4w+PaInR2MvLS6yI/rcnTpzA8uXL80wmU++CUF+tVrtUoVC8mJOTw+t0umTG2La8vLw3HhYKXBnIv7b4wB6/6pn/8ALgyXGcl0ajeXnYsGF+b7/9trwkZ+Dx48cLn3t7e+PEiRMPHVNa3N3d4ePjI2zdupWHA+8pIiMjMXPmTLc5c+bsIaJGjLFUALBarYutVusRd3f333U63fHc3Nxoxth1R80rIeFEwgCcdLUREsVDRDyAzgC+y9+0DMA4xtjfRNQSwFcAOuXvqwkgCnaht4eIHpV7pQVwijH2TyKaBWA2gDdLsKMqgG8BtGeMXSOigpCzi/nbbETUBXZhOjB/X2PYm7GbAVwiosWMMdeEa92HJDxdgEKh6N+uXbtyh5m2adMGbdq0AQA+OTkZq1at4tesWSPMnz+fr1q1KuvWrRs6duxI4eHhDw1rcjanT5+Gl5eXoFAoKo9R5eCvv/566iralsT169eh0+kYivQxdSYajQY//fQTTp06hePHj2Py5Ml4++23ix1bEdWhfXx8uKtXrzr8vKVh9uzZVLt2bUyYMAE9e/YUW7VqxXXr1u2xFwEKwm3j4uIwcuRIpKamIjY2Fjqd7pE9HEtDfHx8niAIFx82huO4rlFRUcpyT3Yfd+/exaRJk4wWi2UoY+wmAMjl8hFVqlQZunHjRpVKpcLly5cDp06dOjwvL+8XAL852obHIX9FPYKIaqvV6lCFQhFqs9lq8jzvo1KpbG5ubla9Xi+6u7vD09OT9/Lyknt6eirr1KmDHj16uMRmmUwGIqqQ61/fvn3p+vXrXj/88MMuIopkjBkYY0d1Ot32ESNGdElISGi3devWMSi+kaiEhITE46AmoljYxeRJADvye0G3AfBzkcW9ot9d6xhjIoC/iegqgPqPmEME8FP+8zWw5yuURCsA+xlj1wCAMZaev90dwH+IKBj2yu1FV4t3McayAICIzgMIAiAJz2cRIgrQaDS+4eHhDj2vn58fpk6diqlTp/IWiwWbNm2iX375RVy/fj0EQaB27doJXbt25du0aQM3N9f2Uj927JhYt27dp8Y7mJCQIAwePPipENGP4tatW/Dw8HCZ8ATs3swWLVqgRYsWCA4Oxi+//PLAGEEQ8Pfff6NWrVoOndvHx4c7deqUy/q1RkdHIzg4GKtXr+Y+/vhj8ZtvvsH777/PNWvWrMznKhCef//9N+rWrQtfX1+sWrUKWq0W/fv3L7etCQkJIoD4h43R6XT9nnvuueKTER8TURTxzjvvGMxm8xJBELYDABE1VKlUS5csWaIpCBNv2LAhXn75Ze0XX3wxES4WnkQUoNVqv9BqtT3DwsKstWvXVgUFBSlq1KiBGjVqwN/fH2q1WgbgkekZzkYul1dovYE333xTnpiYWOfgwYMbiKgXY0zIzc2d9d1333UYPXq0Uq1W16iwySUkKpZzAAa52giJBzAyxhoTkTuAX2HP8VwJIDM/97Y47l98YwBsuPde4WHX74ct3lEJ+/8PwB7GWH8iqglgb5F9RVtiCKhEeu+pufl/UiCiHq1btxYq0gOpUCgwZMgQrFmzhjt06BAtWbIEoijyn376qdC+fXu89NJLwpo1a+CqIimHDh1Cp06dnpriEM9SRduUlBRUqVLFZd7da9euITExsfD3ixcvPhBWbrVasXDhQvj6+jo0zBaw9/LMzc11aVWvVq1aYcmSJdi7dy/XokULbvz48ZgxY4ZQ1lYrTZo0wb59+6DX68HzPNzd3ZGdnY24uDg4on9mSkqKEsCVkvYTEeXm5jZr3rx5uecqSnJyMs6cOUMmk2l6/jw6jUazbebMmer7/x969OgBi8XSnoiqOdSIUkJEbmq1+lOVSnVp2LBhfXfu3Klevny5fvr06Yrhw4cjKioKderUgVqtdoV5pUImk5VLeB4/fhxvvvkmi4qKEnv27Cncv5+I8K9//UtVq1at9hqN5oROp7spl8sP1qpVyyqTySCTySpvXomExMPZDUBJRK8WbCCiFkQU5UKbJPLJ9xhOBPAu7DmU14hoMGD//iKiol+Ug4mIy49aqQ3gEuw9pRrnbw8AEFlkPIf/LTq8BODgQ0w5AiCKiGrlz10QausOoKDoxMuP9SJdgCQ8nQgRkZub29jHye8sD82aNcPnn3+OXbt28X/88QdatGjB//zzz8KAAQPQpUsXNm/ePDG/EEiF25KTk4Pk5GSuV69eFT6XM3jWKtreuXMHFdX2ojQYDAbMmDEDffv2xYABA3D16lWMHz8eADBt2jQMGDAAAwYMgMFgwKJFixw+v1ardcrnpDTIZDLMmDEDW7ZsweHDh+nrr78u091/cHAwMjMzERERcc82nU5XbNGmspCVlQWbzUYA0koawxhjoijypSmKFh4ejvfee6/wd5vNhvbt2+ONN+zFBjdt2oQ5c+YAAPz9/aHRaBiAYCIirVb7Y9euXav27dv3gcUuNzc3dO7cmfE8P6qsr9ERENHE6tWrv7Vlyxb122+/LXd1NMrjkO/xLNVCImMMSUlJ2LJlC2bOnCm0bduWTZw4EXq9Xvzwww+57OxsvrhquwqFAkuXLtXMmjWrycqVK/2PHz8uX7FihduFCxfM4pNc3l3imSY//70/gK5EdIWIzgH4AMAtlxomUQhj7DTsBfKGARgOYDQRxcHure5bZOglAPsAbIU9D9QE4BDs7VjOAJgP4FSR8XkAGhLRSdjzRD96iA13ALwGICZ/7oIQ3XkAPiGiQ7AXHXoiqDSu12cBIuql0+nqd+vWzWU2eHt7Y9KkSZg0aRJvs9nw22+/UUGvPrPZjNDQUKF9+/Zcy5YtqWHDho9VYORhnD59Gh4eHoJGo3liPiQP49KlS5DJZM9ERVvAnjvXuHFjl713DRs2xJo1ax7Y/v333ztlfkEQUNk6Ofj4+GD+/Pnc2LFj0axZM7Rt27ZUx/E8j6NHj96zrUC8AUD16tVL3cPzfvKLUN20WCwP9Y5zHCfYbDbZoyJA1Go14uPjYTKZoFKpcOTIEZRUkIiI0LFjR27Tpk3v8TxvqFatWof333+/xDzSoUOHqvft2zeBiD57RCEkh8MYW5ecnPy+K/vilheZTFZiPrXNZsPly5dx+vRpHDp0SIiLi+MtFguqVKki1KtXj1epVOLo0aPpxRdf5AFgwoQJbNGiRaxfv37c/bnLer0ePXv2BAAcPXoUU6dONZjN5o15eXkuaXUgIeEIGGO3AAwpYd/LzrVGAgDu76/JGOtd5NeSkukPMcYm3Xccg12sPmyO9+/b/nKR5x2KPN8Ku6gtOvYIgJAim97P374S9tDggnGVytMjCU8nQURyjUbz1YwZM7QyWeX4s8tkMvTt2xfVq1enN998E5s3b0ZMTAy/e/duYfXq1WQ0GrkCIRoZGekQIXrixAmxZs2aT42n/fDhwwgMDHRZzp+zyc7OFry8vJ6KRYPHoTIKT8Ae1TB58mRMmjQJkydPZkOGDHGpkTdu3AARPTS/E7A36rZarTKl8tH1hdq2bYv9+/ejW7du2Lp1K55//nmcOnWq2LHDhg1TWSyWgd7e3ooRI0Y89PxNmjSBm5ubR15eXmsAhx9piANhjMXr9foTW7dubeeIvFpXUNTjmZGRgb/++gunTp1ix44dEy9fvsyrVCrm4+MjNmnShF+wYAGaN28OjuN4AJg7dy73xx9/CAXCs3///rR27Vp07NhRnDx58gORMQaDAfPmzTP/9ttveRaLZYQgCFsfMEhCQkJCotJSORTQM4BMJhsXEhLi2a5dO1eb8gDr169Hq1athICAAP6tt97CW2+9xQP2m8eYmBh+586dwqpVq8hoNHINGjQoFKINGjQosxA9ePAgigt5e1I5c+bMM1PRFgAMBgMrbxjmk4wgCC5rJfMohg4dioCAALzzzjvYtm2bOG3aNC4kJOTRB1YA169fh9FoPPOocfkez1Kds0ePHli6dCmioqJw+fJl9O/fv0Th2bBhQ3z66aelamxMRBg6dKhm+fLlL8PJwhMAcnJy5q5cubJx//79XdMct5zIZDKYzWauU6dOLDMzkzw8PITAwECuQ4cO/GeffYaAgABCCWFgw4cPR79+/Xiz2QylUgm5XI7169dTTEwMPvjgA3To0AEqlQrx8fE4ffo0li1bZjAajVtMJtM4xlimk1+qhISExANInumyIQlPJ0BE7iqVas7MmTO1ldFbEhsbK7z++usP3BgEBASgqBC9fv06NmzYwO/YsUP4z3/+Q0ajkWvYsOE9QvRh3lyj0YjExESub9++JY550khISBCGDh36zHgAzWYzScKzcgpPwN5iaefOnTRz5kwMHz4cHTt2FObMmcM7OmT+UVy9etVgsVguPWocz/OmtLQ0TWlCTevVq4ebN2/i999/h6MX8Fq1asV9//33XR160tLzx/Xr1xUpKSnw9fV1kQmPT7Vq1WC1WvHBBx9Qhw4dUJY2WQEBAXBzcxNPnjzJ5bcGg0wmw+DBg2nNmjVi7969rdnZ2aRQKO4AOJCbm7uSMba9ol6LhEQJ5AB43IWhHEcaIlGI9J48oUjC0wmo1eoPOnfuLHd0hU1HYLFYcOfOHb40DekDAwML80MBIDExETExMfz27duFlStXFgrRqKgoLjIykkJDQ+8RomfOnIFOpxM9PT0r7517GanIiraffvop/P39ER0dDQAYO3YsfH198eGHHwIAPvvsM6xduxY//fQTCjxbK1asQFJSEmbNqpi2dpLwrNzCE7AXzPnyyy8pOTkZgwcPpv3796Nz585OteHvv/+2Arj8qHGiKH6zdOnSSZ999lmpyrZ26NABn3/+OVasWIGsrKxy21lA/fr1YTab/YnIq0iPNKegUCgmBQUFCU9qnrifnx+ICIGBgVAoyt4ZJzg4mPbs2SO2adOGA+yRNtOnT89LTU1NNRgMnwCIMZvNTn1PJCSKEhYWpne1DRL3wZj0njyhVO47qKcAIqrJGBs7adKkStd/DQB+++03VKlShVWrVvZuAkFBQZg0aRJ+/vln/uDBg1xMTAyaNGnCb9u2TXz99dfFli1bYtSoUcJ3333Hzp49iyNHjrAaNZ6elmsVXdG2oM9iwVwZGRmIj/9f2lxsbCxee+01zJkzB4wxpKam4ueff8Zbb71VIfYAgMVi4Z5l4Wmz2Sq98CzAz88PNWvWpCtXSuxoUmEkJSUpYa/y91DMZvOne/bsEUprY//+/TFu3Dg4OoRYLpejYcOGJgDPOfTEj4CIWshksg8XLVqkcbZX2lEQEerWrSvs3r37sY7v3bs37du3jwRBwKpVq8QBAwYYz58//7HBYKjHGFvu7IUACQkJCYmK48m4g3qCcXNzWzhq1Ch5SRUYXc3vv//OoqKiHJKjeL8QXb9+PRo1asRv27ZNHDt2LFu+fDnFx8fTSy+9JH7xxRe4ePGiI6Z1GRcuXIBcLoeXl9ejBz8GTZo0KRSe8fHxCA4OhlarRVZWFiwWC65du4YxY8bA29sbv/zyC+bNm4fx48fD3d29QuwRRRFmsxlPcgXO8pLv8ax88fIloNVqKTMz06ntJrKysmC1WglA6qPGMsayRVH814IFCwylObevry9GjBhRbhuLo3Xr1m4KhWJYhZy8GIhIr1arf/noo4/Url6Qy83NLdfxTZo0oVOnTj3W90j16tWRmZlJQ4cOzfvqq69Om0ymxmazeS5jrHTJvxISEhISTwxSqG0FQkQt9Hp913/84x+V9u/8999/s+jo6ApZgKhVqxbeeecdIL+wxPXr15GUlERxcXE4evSo8OOPP/Icx6FKlSq2kJAQWVRUFLp3747S9PWrDBw5cgRBQUEVVtHWx8cHMpkMycnJiI2NRaNGjZCamoq4uDjodDoEBwdDLpdj6tSpePHFFxEUFITevXs/+sSPSVpaGnieh0pVKZ33TuFJCLUtioeHBzIyMpxa/CohIQFqtfr6o1qpFGC1WhceO3ZsyoULFzShoaHFjjl+/PgD21q0aIEWLVoAAPr164d+/fqVw2rg8uXLNsaYU5q05vcXXdWtWzfP7t27O2PKQgwGA7Zt24b9+/fj0qVLtvT0dJnJZEK9evWEhQsX8n5+fmU+Z3h4OLdz504bynhPcenSJXz//fcmi8WiuHLlyhSbzfYNY0zqyykhISHxlFJpBdFTQs06derYKquQSktLQ3Z2Nte8eXOnzBcYGIjAwEC0adOGXn/9dZ4xhoSEBMTFxclOnDghLF26lPvoo49Ir9eL/v7+aNasGffCCy+gQYMGTrGvrDijom1BuG1cXBxGjhyJ1NRUxMbGQqfToXHjxgDsArVly5Zo3759RZqChIQEPIkN7h2JIAjged40a52wAAAgAElEQVTpHk+LxYLs7GxkZ2cjJycH2dnZMJlMMBqNMJlMhc/NZnPh71arFVeuXIHZbHaqUk5ISACAC6UdzxgzyOXy2fPnz//Xd999p60wwx5CVlYWDhw4wKxW63RnzCeXy8d4eXl1nT59+qP7yJSTc+fO4ffff8fJkyfF5ORkZGdnc76+vmKzZs3YuHHjZI0aNYJer8fnn3+OPn36oH///pg2bVqZFlhCQ0ORmZlZqvsJm82G/fv3Y8WKFbmXLl2yiaL4hSAIXzPG0h77RUpISEhIPBFIwrNi+f3s2bOK9PT0CgvHLA/r1q1DcHCwoNFoXFKVlYhQq1Yt1KpVC/369eMBIC8vD2fOnOFiY2PZ0aNHhXXr1vFEBC8vL1tISIisffv26NGjB7Ral9yf3kNiYqLQunXrCv3bFQjPv//+G3Xr1oWvry9WrVoFrVaLon3/iKjCPXE3b96EXq9/ZnqW3s+VK1cQFxeHixcvcuHh4YXb+/Xrh02bNt0ztl+/foUi0Gw2w2q1wmKxMJvNVvCAzWZjgiAg/0GiKFLB8/t+QhRFyOXyog9W8FOhUBT9CaVSCaVSCYVCwYKDg2n79u18amoqHieP+3FISEgQc3Nzi+9zUgI2m23pmTNnZsbGxmoLFlScyfr160W5XP6byWS6VdFzEVEDtVr95eLFizWOjh64e/cutm/fjsOHDyM+Pt6Wnp4uA4CGDRsKHTt25Bo3bkxhYWHQarUPfIbnzJnDDxkyBJMnT2bp6ek0f/78Us8bEBAAm82GxMREBAUFFTsmMTERGzZssK5fv94GID43N3cuY2w9Y8zymC9XQkJCQuIJQxKeFQhjLE+v1/+xY8eO3kOHDnW1OQ+wZ88eoUuXLpVKRGi1WrRq1QqtWrWicePG8YwxJCYmIi4uTvbnn38Ky5cvp48//pjT6/Win58fmjZtyr3wwgsICwtzuq0ZGRlUUYWFCmjSpAn+85//oEaNGuB5Hu7u7sjOzkZ8fDw++OCDCp37fpKTk+Hp6fnM9Cy9n2+//Va4c+cOLwjCPduNRuMDIYZGo9GmUqlIr9dDpVJxKpWKFAoFKRQKksvlUCgU9zwKxKJKpSp8FP1dLpfjvlZMdN/PEklJSRGnT5+O5cuXc85o52Q2m4WyLoIwxsw8z3+4cuXKT7/88kunripZrVasXLnSlJubO6ei5yIitUaj+XXq1Knq8l47DAYDdu7ciYMHD+LixYvC3bt3OYPBQIGBgWKTJk3QrVs3WaNGjRAYGAgiKtUCWaNGjTB8+HDasmVLmRaYOI5D7dq1xZ07d3KjR4++Z9/hw4exaNGinPj4eHAc973RaFwGoL5er59gMBhqENHXjDGpvYGEhITEM4AkPCuYnJyc5Rs2bIgaOnRopSv9fPPmTa5t27aVulAKEaFmzZqoWbMm+vbtW+gVPXfuHHf69Gl29OhRYcOGDTxjDF5eXrbatWvLGjVqhH79+lWoh6egom1FtVIpIDg4GJmZmXjhhRfu2WY0GuHs6rKpqal4Uls+OAKbzcZq1qyJ1atX379LVox3qNJcWz/66COuV69euH79eoneKEfSpk0b+ebNmwcA+LAsx4mieD45OdnpBWV27NgBQRAuMsbK5KUtK0QkUygUv0dGRvr279+/TNddi8WCw4cPY/fu3Th37pxw584dysnJ4Xx8fFijRo3E4cOH82FhYQgJCYFcLi/XYmL79u2xePFi7syZMyjq2X8UTZs2xaZNm9ioUaNIJpOBMYZly5bZli9fnmMymcYC2CmTyUZpNJrdAQEBmuHDh7vt27evxcGDB2ep1ep/m0ym952VYyshISEh4Roqzc3RU8z2K1euyCpbc/AzZ87AZrNR/fr1XW1KmdFqtYiMjERkZCSNHTuWZ4zhxo0biIuLk3311Vfs9IEDtOzf/4aSiOnd3IRqtWvzDSMiqE2bNoiMjHysXnP3c/78eSgUigoXfzzP4+jRo/dsmzPnQcdMcdscTUJCAvLy8rgVK1bA09MTHh4e9/zU6XRPVOGdsiKKIkTxyat78u9//xuRkZFiYGCgU96c5s2bw2g01iciD8ZYZhkOvZuZmenUhbB8cZSbk5PzUUXOQ0S+Wq32l7y8vBZ169a933t9D9nZ2di3bx+OHDmCS5cuCXfv3qXs7GxOr9ez8PBwsU+fPnxYWBhCQ0Oh0WgI+cXbHEWtWrXw2muvCaNGjeI9PDyE8PBwfvTo0YiIiHjocdHR0dxPP/2E5cuXY+TIkZg6darxxIkTV00m0yi1Wj1cFMXvW7VqRWPGjNEUhFM3b95ck5KSYr5y5cpIAAtQikrIEhISEhJPLpLwrGAYY2Y3N7fNW7duHfbKK69UGu/izz//jObNmws8z7skv9ORFDQvDwwMxIrFi8VPAX4sgKuM0V85ObLTcXHsSFycMHv1ai4DIHeZTPSoWpXVbNCAb9qsGTp06ICAgIAyzXn06FEEBQUJcPBNX2UmLy+PmUwm2rFjh5iXlycajUayWCyFD5vNBpVKBZ1Oxzw8PJiXlxfz9vZG1apVOS8vLyoQqXq9Hm5ubnBzc4NOp4NGo3nojXhloWPHjvwnn3zCsrOzSa+vdAEMJaJQKODu7i4SkVOEp0qlQkREhPnkyZNdAKwvw6Fp2dnZTm1mGRcXh+Tk5FwAv1bUHETUQaVSxURHR7vl5uaKZ8+eJeSHSN+4cQN79uzBn3/+iatXr9oyMjL4vLw8qlatGgsLCxP79u3L169fH/Xq1YO7u7vDRWZJvPbaa/yLL76IvXv38jNmzMCBAwcQHBwsvvTSS1zXrl0fqDxutVoxceJEVqdOHfGFF17gBw8enHf79u2DMpnMqFKpDvbt25cbNWqUoqBtjNFoxLJly2yrV6+2AphrNpvnMcZMznhtEhISEhKuQxKeTiAvL2/Fxo0be73yyis6V9tSwJ9//imMHj36qRNN6bdvU3PY786C8x8D7Td5PABkAzhrs3F/JSfjRHKysHXXLlo4bx4nA+Cu1Qo+tWpxoeHhlJ9nWmJrl7/++guhoaGVXy05ELPZLIwePVrWv39/DsXkfxkMBiQlJSEpKYlu3bpFKSkpuHPnDs6fP4/s7GyhQLharVay2WxktVrJarVCEAQolUqo1WpoNBoxX5Ayd3d3eHh4wMPDg9doNFCpVFCr1ff8LGmbSqVyuPe1V69etG7dOnH+/Pn00UcV6iBzKF5eXvjrr7+c+r/apUsX3aVLl/qjbMLzrsFgUDLGnLYQ8csvv5gtFssSxpjw6NFlg4g4pVI5U6fTTZs/f766TZs2+PXXXxETE8O6d+9uy8zMlNlsNgQFBYkRERHo0KGDrF69eqhbty6USqXTRGZJ6HQ6CIKAKlWqiDExMdyKFSu4xYsXC//3f//Hd+rUSRg6dCjftGlTEBGsViuuX79O48aN4wcOHGiWyWTpSqWy3T/+8Q/VoEGDuIKFGsYYtm/fjn/9618Gq9W6w2w2v8kYS3Ll65SQkJCQcB6S8HQOe5KSkuRpaWnw9vZ2tS2w2Wy4ffs237p1a1eb4lCys7ORIwjcwwLC9ADa5D/G5d/YMQAJAP7Ky+Njz55lR8+eFT798UfuNkB6nhfdq1QRg0JDZY2bNEFUVBTq1q2LxMRE4bnnnnvqhPvDMBqN9LDqzBqNBiEhIQgJCSlud4l/K5PJhDt37iA1NRW3b9/m7t69i/T0dGRmZiIzMxNJSUkwmUzMbDaLVqsVNpuN5QtWEgSBbDZb4fOCKrA2mw08zxcW79FoNOzNN9+k3r17P7aoISIIgsAqU8h8aejZsyd++OEHPi8vz2nVoJ977jksWrSoR1mOYYyZ5XK5NS8vT+mMtj2MMezcuVOw2WybHj26bBCRt1arXR8UFNR80aJF6oJ88zZt2iA6Oprq1q0rCw0NRUBAALhKHJ/++++/Cx06dOA9PT3xz3/+E//85z/5S5cuYdGiRdyECROYUqnE4MGD8fLLL5NcLsfChQsRFBQkjhs3LqB79+6Qy//nwP7777/xwQcf5F25ciUlLy/vH4yx/S58aRISEhISLkASns5ByRiTubu7u9oOAPZiGnq9nvn7+z9VHrtdu3YhABBVZWz3QQBq5T/6FvGOGgCcEwTur9u3uT9v3xb279uHZV9+yQOAzM2Nr+iKtpUNi8XyUOH5uKhUKgQEBDwq3LlMHiBRFJGdnY2srCxkZWXh7Nmz9Mknn7AdO3aI7733Hu/v719mOwVBwOXLl/mPP/64zMe6kpCQEHh6egq7du3i+/Tp45Q5/fz8YLFYyhyPrFAocjIyMpwiPC9fvgyz2ZwH4JyjzklEcplMNlatVn/evHlzfsGCBXxR8eXl5YU333zTUdNVKNnZ2Th58iQ/Y8aMe7bXq1cPS5YsIVEU8euvv2Lu3LnMz88Pr7/+Otzd3dGnTx910cWdrKwsLF682Lx582arzWZ7z2azfcMYc3oRKQkJCQkJ1yMJT+fQ0M/PzyCXyytFYtiWLVtYu3btRDxl+YlHjhxBK7sD0yFoALTIf4wu4h29AaCuyYSKrmhb2TCbzVxl7EdbHBzHFYTpAgAiIiLwwgsv0Pjx49GnTx/4+vqKPXv2pD///FO8ffs2FfRTLAj9HThwIEaOHHmPMyq/oBSrXbv2E7dg07VrV37BggUsLy+PunbtWuGRFzzPgzFWZk+eTCbLyMzM9C5rzvXjsHv3bhHARsZYua8ZZKePRqP5d0hIiGdubq4iJCSEFRWdTxJWqxXjx48X69Spw4KCgor9nuA4Dn369MHPP/+M1NRUjBs37p79giBg06ZNbP78+SbG2E8mk2kyYyzNKS9AQkJCQqJSIglP5xAeGhpaaUTexYsXxUGDBlUaexzF5dhY4Z8VLKYJwE0ASpWqUNQ8C4iiCLPZ7PQWLo7Ew8MDa9eu5U0mE9atW8dt2bJFqF69OvXs2ZPLzc0FEUGpVMJqtWLp0qXi9u3bxblz53IFLUg2bdok1KtXr9KGRT6Md999F2q1mtasWSPMnz+fHzRokO29994r8/XfarUWhkAXeJMLHhkZGSwtLU24e/cupaenkyiKHBFRaYUdEbkplUo/Z3yuBEHAzz//bDIYDP8t77mIqIqbm9sWDw+PiOnTp2vbtm2LefPm4ezZs0/s4l56ejrOnDnDbdr06ChkvV5Pt27dEgDwBoMBp06dwtGjR207duwwZWdnx+fm5v6DMXa64q2WkKgYzp49mw3gcWt05ISFhVUKp8PTBBHK9Z4wBuk9cRGS8HQCKpWqWVhYmFObopdE/k0j36JFC1eb4nAybt+mZk6YZweAWjVrPlMVbdPS0sBxXInFlp4kVCoVRo4ciZEjR5b4/g0ePJh7++23xYEDB6JRo0bC+PHj+SNHjnDNmzd3uLczMjISx48fL/x91apVWLhwIfbu3QudzjH1yDiOw4QJEzBhwgT+xo0bGDBggCwqKgo6nQ45OTnIzs4ufGRlZbG7d++KmZmZLDMzkzIzMyknJ4fLy8uDzWaDUqmEXC5nCoWCKZVKUaVSQaPRcHq9nvPw8JDVqVMHrVq1wsWLFwGgKoDbpbFRJpONb9OmDecMb+euXbtgNBqvAdhbnvMQUW21Wr1vwIABPu+8846ioEh4ZGQktm/f/kQuUgBAtWrV0LFjR3HevHn09ddfl/g/b7VacerUKbNOp+MGDhyYd/XqVZVWqz2fl5e3xWaz7QBw0BEeZQkJF1OeC3GlKSr5lCG9J08okvB0AkqlMjI4ONjVZgAA1q9fj5o1a4o6ne6JvSkqjqysrEcWFnIUxwA0DAt74sIty0NCQgJ0Oh1DfhuIpx2FQoGvvvqKy8jIwLx58/ixY8fiiy++oMmTJyM4OBjR0dEVNvfWrVsRFhaGXbt2oV+/fg4/f0BAAMLCwtikSZNIoVCICoWCyeVy5AtI0ul0nLu7O1+jRg00btwYvr6+8Pf3R0BAAKpWrVpQLbigJUiJ15Gff/45+8aNGwEohfAkIrVKpXrvjTfeqPCVDcYYFi9enJubmzu9PKKIiCJVKtX2SZMm6V988cV7/g4tW7ZEeno6mUwmFIRxP0mIoogzZ87Qq6+++tDP+2+//QbG2IXbt2//kpycvB/A4czMTKOTzJSQkJCQeMKQhKcTMJlM9SqL8Ny1a5fYuXPnp0487N69G4GPUVjocbisVttG1K//TH12EhMT4eHh8cwIzwI8PT3xySefIDEx0TZ79mze09OTdu7cWWHC88aNGzAYDHjnnXewfPnyChGeAPD1119T7969BUEQuB9++IGriEq91atXpxs3boQAOPmosTzPj27SpIm8Xr16Drfjfg4ePIg7d+7cQTl6d3Ic11etVq+dN2+epkOHDg/s12g00Gq17Nq1axQaGloOa+00atQIwcHBEAQBtWrVwpw5c6BWq2Gz2dCxY0cMHDgQb7/9drnnKSA3Nxd3796lQYMGlThGEAQsXrw4Ly8vbwmA4wAuS704JSQkJCQexlPl9aqMEFE1IlL4+Pi42hQAwI0bN+i555576sTDoUOHHFpY6GHc5XnuWSssdOvWLVSpUuWZDZlbs2aNbOzYsVS9enXcvHnT4T0fC/j999/x/PPPo1mzZkhISMDdu3crZB6VSoUdO3bwHh4e7Ntvv2WiKDp8joEDB+rc3Nxm0CP61xCRQqFQzH7zzTcrNB3BYrHg0qVLmD9/vsFgMMxgjJX5RRNRfY1Gs1Sn0/24YsWKYkVnAe7u7kJ8fHx5TC5EqVRi/fr12LhxI+RyOdatWwcAOHz4MGrWrInt27fDkRGter0eer2ezZo1CwaDodgxycnJEARB5u3tvUCtVp8AMMphBkhISEhIPJVIwrPiCa9du7bJWQ3RH8bly5dhMpkoLCzM1aY4nPi//hLaOiHn0gIgx2TinrVWKikpKahataqrzXAZHMdh2LBhmDdvHjIyMviSbsbLy7Zt2/D888+D4zh07twZf/zxR4XMU8AHH3zAbd++nQ0ePFi0OwEdR9euXaHT6WoC6PKwcRzHRdevX18VEVG6QHmbzYZbt27hxIkT2Lx5M7799ltkZmYCsOccXr16Ffv27cMPP/yAOXPmWEaPHp3TpUsXQ2RkpPjyyy8bbt26pZTL5f/HcdwbRFT7UfMRkYyIBuj1+mNarfbUSy+99I+NGzeqH3UdrV69On/58mWHK/pmzZrhxo0bAOxh2cOHD4efnx/i4uIcOs/UqVPZqVOn2MyZM4vdX6NGDezdu1e5Z88etz59+jAAT15MsYSEhISEU3mmwgVdARGFh4WFVYov5J9++glNmjQRZDLZU1cUx1mFhQ4A0Lm5wRl9BisTaWlpaNCgwTO/UFWlShX4+fmJGzdu5IYPH+7Qc1+6dAmJiYl47bXXANhFVI0aNfDiiy86dJ6iREREYO/evdy4cePE4cOHs61bt1JBgZzywvM8Jk6cqJ0zZ85nRNSkuHxKIopQqVRfvvvuu26APbcwPT0dd+7cKXykpqayxMREY2JionDr1i15VlaWUqlUWpQymY0JAoxWq2bz5s0sJyfHkpWVpVCpVGaZTCZYrVbeaDQq1Wq16Ovra23ZsqWlYcOGmho1aiAzM7POwYMH5x04cOAzNze3dFEUNxuNxl8BZMC+gMUDkCkUiiiVSjUhKChI9sorr+i6du0KhUJRqtffoEEDiouLc6jwtNlsOHDgANq2bQuTyYRjx45h1qxZyMnJwdatW9G4cWOHzdWzZ0/O19cX48aNw+zZszF79mwUbS9UFJlMxkG6n5CQKDNExACsYYxF5/8uA5AM4BhjrFeRcZsB+DDGWrvG0mcHIhIAnAEgB2AD8B8AXz5OlIyzIKK3ASxjjFXMqrgDkb4oKp6s5ORkKwClqw05fvy4MHz48KdOdGZmZiJHELhwJ8y1C0CtWrWeqYq2AJCZmSl4e3s/U6+5JGrXrs0uX77s8PNu3boV48ePx5gxYwq39ejRA7du3YK/v7/D5ytAJpNh0aJFXOvWreHoyIwePXpgwYIFdXNzczsA2FN0HxF5AIgzmUyYOXNmbnp6ujwnJ0chl8ttSrncyhExwWbjjCaTnDGmrAKYQgBrBED1jEZlTUBZE8ApAK8lJlL9+vWtnTp1orp166oDAwMRGBgIPz8/KBQKFYrxxvXp00cjiiIuXLhQ/cCBA+P27ds3wmg0Mo7jwHEceJ5HvXr1lC+99JKqfv36ZX7trVu3xsaNGx3ymTGbzSjIt2zatCkGDBiA3bt3o0WLFlCr1ejatSuWLl2KKVOmwFELB4Ddu7pq1Sr885//ZN26dRMXLVrEN2jQ4IFxMpmM8IxdEyUkHEQegDAiUjPGjAC6wt61rZD8a2VTALlEVIsxds0Fdj5LGBljjQGAiHwArAXgDmC2qwzKT1mhh4jftwGsASAJz2cdxtimY8eOfZWXlwet1nUdVURRRGpqKt+69dO3WLZ7924EOamw0HEiFh4e7vq4aSeTl5fHvLy8XG1GpSAqKor7/PPP2eTJk8mRnu9t27bhq6++umdbp06dsHXrVowePdph8xRHQkICNBpNiR6tx0Umk2HixInauXPnzgNwTw8nArozAFrAQteuoQlgqQegltms9Deb5X4A/PMf7vbxxV5AQwGM4zisXbtWJ5fLy2Qfx3Fo2LAhGjZsyI0bN86hfd2aNm2KnJwcOOLaX5DjWZStW7fi9OnT6N69OwB7Ze/jx4/D0df40NBQbN68mRYvXkwjRozAtm3bcH/NAsnjKSFRLrYC6AlgPYAXAfwIoF2R/QMBbAGQCmAYgE+cbeCzCmPsNhG9BuAEEX0A+33mXAAdYHcoLWGMLSWiDgA+hP09agwgBnav6VsA1AD6McauEFEQgBWwtxq7A+AVxth1IqoG4BsABekfrwO4Bfv/xh4ArQH0I6JpsH+XqgGsZ4zNJqKJsH9V7iGiNMZYx4r8m5SXZz50rqJhjKWrVKrje/fudakd+/btg1qtZoGBgS61oyI4fPiw0woLXVWrheycHC4uLg55eXnOmLJSYDKZSBKedgYMGEDh4eHs1VdfdUiRoYIentu2bUPt2vemHE6ZMqXCRSdgF556vb5CPkM9e/aEJTe3uZxoFRH1JaIJOqLNvsCK/QByAcUVwG0PoPsGUE4FEA17YmgDAB54eCllDQCVUonbt0vVLtRpKBQKuLm5iVeuXHH4uXNzc3H69Gns2LED27dvx/bt2zF9+nRs3brV4XMBgFwuR40aNUin04ne3t4P7Od5niAJTwmJx+W/AIYRkQpABOxd24pSIEZ/zH8u4UQYY1dh10s+AEYDyGKMtYBdAL5KRLXyhzaCXWiGw/41FsIYiwSwHMCE/DH/BrCKMRYB4AcAi/K3LwKwjzHWCHbv9rn87fXyxzdhjCUCmMEYaw77/0kUEUUwxhbBLlI7VnbRCUhfFE4hJydn+ebNm5v07NnTZYmBmzZtwnPPPScS0VMXDhUfFyf0c1KY10iDQbZ22zZh744dlGMycTqdjtWtU0cMCw/n6tWrR8HBwahVqxbK6nmp7JjNZk4SnnaICHPnzuU6d+6M1NRUVKtWzdUmlZsVK1YIUVFRQAV8jmQyGdRKpZhlNkd3AvrWBRShgGoMAEddELUymXjr1i2uevXqDjqjY/Dw8BDj4+O50hZOKi07d+5EZGTkPfmmnTp1woIFC2CxWEqdh1pazp8/j/nz59OHH35IxXnFZTIZPY3fLRISzoAx9hcR1YRdVP5edF++J6wugIOMMUZENiIKY4yddb6lzzQF65/dAEQQUUGvKXcAwbDXnjzBGEsGACK6AqCgOuAZAAWCsDWAAfnPVwOYl/+8E4CRAMAYEwBkEZEngETG2NEidgzJ98DKAPjBvj77l6NepDOQhKcTYIxt/vPPP7/Nzs6GXu/QaK5Sc+7cOWHy5MlP5Y1B+p071NxJc30A4AOjkQfsV5l9GRm0888/+RMnT7LdGo0tA+DzTCbyqVqVBYeECBEREXxwcDAFBwejevXqDg9ldBYWi4WqVKniajMQHh6OkSNHYvLkyQCAlStXwmAwYPz48fjqq6+wYcMGeHp6Fo5fsWJFhXzmPD09Ub9+fWHt2rX8pEmTHH5+Z/Lnn38iMTGRX7ZsWYXN4ePtzebevIlxQIVcAPWCICYnJ1e6D1dAQIDswoULIsoZXVTgFS+gX79+D/R4dXd3x/79+8szTbGkpKTgtddew5AhQ/D8888XO4bneXAc51i1KyHxbPELgPmwh3AW/bIdCsATwLX8HHw97OG2xZeblnA4+dXPBQC3YRegExhj2+8b0wGAucgmscjvIkrWW4+KNCoMrcv3rL4LoAVjLIOIVuIJrCYuCU8nwBjLcnd3P7h3795Offr0cfr8ubm5SE9P51u1auX0uSuazMxM5DqpsND9KGCvAtAVABgj5OXJACATwPaUFNqbkiI7ceiQGKNWi5mCwFttNtSoXl2sHxrKGjRowAcEBCAgIAA1atSARqNxwSsoHRaLBRaLBR4eHq42BQqFArt27cKYMWPuEZgFREdH4+WXX3aKLU2bNuUuXrzolLkqklmzZgkjR44kLy+vChNueh8fPuXmzUcPfEyqmUz8rVu3GB4elet0wsPDcfDgwSe2/60oipg9e7YYHBxM7777bol/WyICx3HS/YSExOOzAvYQzjP5IqaAFwH0YIwdAQrFxw5IwtMpEFFV2HMv/53vcd4O4HUi2s0YsxJRCO4rBvUIDsO+cLAawHAAB/O374I9r/PL/OiR4goD6GEXoln5nvDnAezN35cDQAcgrSyvzxVIXxROIjs7+7tNmza16NOnj87Zc2/cuBE1atQQ3d3dK51HoLzs2rULQYCorET5yh6wL1EOBQBB4JCbCwC4DmBrQgL3XUIClu3dy7Tu7mJeXh5nMBhIo9HAz89PrFmzJgsODuYCAwOpRo0aqF69Ory8vBxebbQsJCUlQalUQiZz/eWC53kMGqZIvq0AACAASURBVDQIq1evxsSJE11qi0qlIqvV6lIbHheTyYR169bhxx9/FIxGI1/ReaTePj5ItK8YV0jURS1RpIRr1ypdtWmFQoGrV69WKpsehclkwo0bN6DX6/H5558L58+fp19++eWhF6A//vgjx2q1HnGWjRISTxuMsSQAC4tuyw+/DQRwtMi4a0SUTUQtGWP354JKOAY1EcXif+1UVgP4In/fcgA1AZzKrzR7B0C/4k5SAhMBrCCiyfnHvpK//S0Ay4hoNOzfla/D3lanEMZYHBGdhj3/8yqAQ0V2LwOwlYiSK3uep+vvJJ8dtsTFxa3IysqCu7u7Uyf+448/WMeOHSuVJ8BRHD58GK2dVFiovAQCGAvgDCCqW7TglixZwgP23nznz59HbGwsd/HiRRw8eJClpaUJeXl5ZDAYOMYYqlSpwqpXry7WqlWLatasydWoUQP+/v7w8/ODu7t7hQrTIoVnKsX/0LBhwzBw4EC88sorD+xbvXo1fv31VwCAXq/HihUrKswOlUqF69evM4vFQo7OqasITCYTpk2bhnPnzgl3797lfX192YgRI7jevXtXuMfd398f1yvwcxoK4Kfr1yvq9I/FtWvX8OWXiyCX88jMzKwUEQOPwmKx4KWXhotJSTeJMYF0Oh23adMmKi66oICLFy8iMTHRCnuooISERBlgjD2Q6s4Y24v/ebIeSFxnjDWtWKuebRhjJS4W5rczmZ7/KMpe/O89A2OsQ5HnhfsYYwmw53Pef95UAH2LmTLsvnEvl2DXYgCLS7K7MiEJTyfBGMtxd3fft2vXrm4DBgx49AEOJDExkU2YMKHSeAQdyZW//hIGVDIvx6M4A7CQkJDC32UyGSIiIlCkAMk9PfFSU1MRGxtLFy9e5K9evYqzZ88KWVlZzGAw8EajkURRhIeHB/Px8RH9/f1ZYGAg7+fnR76+vqhWrRqqVasGT0/PxxanSUlJ8PT0FFFJ/s5ubm7o3bs3fvjhB6hU96Y3ODPUtn///ti4cSO+/PJLTJkyxSlzlocVK1bg/PnzbPLkyXyzZs1QpUoVpy0k1KhRA8cqMCqhCYCFKSmV6hoXFBQEntczpdJG8fHxaN7cWZnoZUcURSQkJGDTpk1ISroLo/Ez0uvfw8aNG+lRC6WrV6822Wy2RYwxm5PMlZCQkJB4QpGEpxPJD7dtPWDAAKeG26rVanbkyBExMjKyUt2YOYL0O3eomauNKCPxADcwvPRZqdWqVUP37t0L+/XhPgGYlpaGCxcuUHx8PH/t2jWcO3cO+/fvt+Xm5pLRaCSj0cgJggCP/2fvvMOjqNYG/jszm7YbUkio0gQiiIB0BQRUkOYVhIugCCKCYEcUgQtKEf0QxYIIgnoVUAQVREU6KBcQMLTQEyUBDCEhpJGy2TZzvj82iaGnbyLze559dvbMmXPemdkp7zlvCQqSVapU0WvWrEmdOnWUGjVqiFzFNDQ0lJCQkCtG4z179iyhoaHlalZ56NChDBw48LIAK2VJSEgIzz33nHjnnXe08ePHlwul/FqsW7dOGzJkiNK9e/cy77tevXqcL8UZ83ZASmqq0HW93ATwUhSFhg1ryVOn/ix3imdWVhaHDx8mIiJC7ty5Uz9+/Liqqqr08/PD5aosIYXQ0JDrumdcuHCBDRs24HQ6F5SV7AYGHiDXf66o2xqUPMY5qaAYimfZsubIkSPeKSkplGVqivfff18dNmwYPXv2pFGjRmXWb2njycBCRcUFJIIoyZfQ0NBQOnXqRKdO+fNNX3xtp6amXqScRkVF8dtvv+Upp3a7XbHb7fj4+BAUFCQrV66sV6tWTdaoUUMJDw9XAgMD1W3bthEcHExQUBDBwcFYLBaP+Z4GBgbSo0cPvv/+e/r16+cRGQA6d+7M5MmT1dTU1CsGOypPhIaGyi+//JJu3bpRs2bNMu27fv36pIEoLXvtUMDLZCIlJYUr5Zn0FF5eqrDbvTl27JjH/E+dTicnTpzg6NGjHDhwQDtw4ICIj49XAgIC9Jtuuom2bduqr776KrfccouIjIxk2LDhitM5lri4GUpUVNQ1nxk//vij9PLyWm+z2c6V4S4ZGJQpTZs29Uw6AoOrImXpREg3KH0MxbMMkVJmBQQErPnuu+/6jh49usxeQpo0aULPnj3luHHjWLVqlSgPQWJKgvIYWOh6/AX4ClHmaXWCg4Pp0KEDHTp0yF980R/B5XLx119/ER0dLU6fPq2ePXuWv/76C5fLpcfFxcnXX38dh8Mhcj+apmE2mwkICJCBgYF65cqVCQ0NpWrVqmrlypUJCgrKU1KDgoKoXLkyfn5+JbZPjz32GMuWLbuoLL+PJ8CcOXMozdyOFouF+vXr6999950yatSoUuunJPjiiy9M/fv313/44QeeeeaZMh0xyFUGMyilfCqAn5eXHh8fr5QnxVNRENCAo0ePlcnx1jSN6Ohojh49SkREhHbgwAERGxurmM1mWblyZS0sLMw0cuRIunfvjr+//2X3zVtuuQUhpIC9uFz3uIYMGSJGjhzJE088oV5qDaHrOosWLbJmZma+Uxb7ZmBgYGBQ8RFSlisLun88QogGvr6+R9auXetbpUqVMutX13W6du2qPfLII2LUqFEVRlG7Fi+/9BLVNm3SlpQT38OCsBEY5uurbdqzp8LIfDUyMzP566+/iI2NJT4+nnPnzpGcnExycjLp6ema1WqVNpstV1FV7HY7O3fuLNepY4rC/Pnz5fbt2+WyZcvK/XU1Z84cdu/erS1btqzM/38dmzeXe6QUt1y/apGoa7G4xrz+uqmgpsQJCQm8+eabxMTEoOs6Xbp04eWXXyYiIoJFixYxb968vLqTJ0+mS5cuFNZM+b77HtQSEgapvr7vER4eXqIWAlJKzp49y+HDhzl48KC+d+9eoqOjFR8fHxkcHKyFhYWZ2rdvT7du3Qo1C/ziiy9qW7ZUV2EeUNtmseiHAwICmrzyyiuWm266iRo1ahAcHMyOHTt45ZVXTmRmZt4ijRcJAwMDA4MC8M+Y+qpASCmjzWbzwvfee2/0zJkzyyzxq6IozJ49W33qqafo1q0b9evXL6uuS43ow4e1f1cgpRPgTyCgnPlLFhV/f3+aNGlCkyZNrrT6ovPicrlo1aoVFSH6a2G59dZbxapVq3RPy1EQNm/erHXv3t0j9tE+Xl4yweEoNcUzxOEQ8fHx16+IW2kbO3YsAwcOZO7cuWiaxvTp0/nwww/p3LlzicmUmpoJ3IOU73H+/HmqVq1apHaklCQkJBAZGcmRI0fk3r179cjISFXTNAICAvT69euLHj16iB49elC7dm3BNZ7tt99+O2FhYbhcLlRVpU+fPgwdOjTPNzYsLEz99dcv0PU9gNk3Kyv55qys+CemT58+TkoZ6nK5am7YsMFn0aJFmVlZWW8ZSqeBgYGBQUExFE8PkJ2dPWXz5s2PDxkyxPe2224rs35bt25N586d5fjx4+U333yjqGqF0tkuI+X8eaWiBRY6Bnr1+vVvuOvu/PnzeHl5lYtcoCVNlSpVsNls5SLVzLXYt28f586dU4cNG+aR/v38/PQEh6PUZoXrOJ1qbGxsgXwpf//9d7y9vfP8g1VVZfz48fTs2ZO2bduWmEwOh1WFm/HyCtROnDihFkTxdDqd5PphHzlyRD948KCMjo5WAQICArRq1aopUVFRqtPpxGwO0tPTM5QjR44wZswYateufd32fXx8WLFiBQDJyclMmDCBzMxMnn32WZKSkvj+++9RFA1dP4j7FaGWPyT6p6WltQPw9/f/6q233no4IiJCSCmXXaMrAwMDAwODiyj3pmH/RKSU6U6nc/yMGTOyynqweNasWSIxMZGvv/66RDtu1qwZ77zzt6vPokWLmD9/Pnv27OHRRx+9qK7L5aJLly6cP3++yP2lpqaSqWmi6fWrliuOXJJK5UYhLi4Oi8XiaTFKBYfDUW4iqV6LL7/8kq5du2pl7V+cizkoSCRcp05uQrtTuIMQ5U9K9hyw6BrbhgGnT50q0H0tOjr6spl6f39/atSoQWxsLPv372fAgAF5n61btxak2YtITU1FymygLnZ7df7888/L6iQlJbFr1y4WL17Myy+P03r37k27du14/PHH5ccff+w6ffq0cu+996pffPEFmzdv5ptvvlH9/f2lENU1KRPIykpR7PZssrJeYuTIJzl+/HihZAwJCWHq1KksW7YMKSXLly+nX79+VKtWXcIS3IrnMl8w/58QwhcgKytr6tq1axVFUT6XUloLfWAMDAwMDG5Y/nnTDxUETdN+iIyMnGe32y/LRViamEwmZs6cqbz44ovcfffdBRohLwje3t5s2bKFkSNHXhTds3Xr1pw7d464uLi8IC+7d+8mLCyM4vi4bt68mXoVLLAQuFOpDPg7X+cNQ0JCAhaLRaeCna+CEB8fj9lsLtf7FhMTQ3h4uJw+fbrHzByCqldX40+fLnBg26rAHGA0UBAD7abAd/HxBToHUsor+lvmDgS2atXqMh/PwrJjxw6glgSTcDqbq9u379ADAgKU48eP64cOHZanTp1UnU4n3t5Bmt1eXTidt6teXr+ybNkyGjdunGcue/r0aR566CFcLg1FUVCUIGm3/6lC/ufG61itaTz99NN88MEH1K9fv0ABzKxWK2+99Zau67qSnJzMiRMn6NOnD1lZVvn1119LXR+lwD1A50qw9QshxLNSymhVVUdlZ2evLfRBMTAwMDC4oTEUTw/h7e09+r777nP6+vqW+Tno2LEjbdu21f/zn//w5ZdfKiUR8EJVVQYMGMCXX37JCy+8kFeuKArdu3dn/fr1jBgxAoB169bRq1evYvW3a9cuOkCF8i3KTaXSunVFMxAuPufOnSMwMLBCna+CUqlSJWw2W5kpnQ6Hg/j4+LyATufPnycpKYnU1FRSUlJJS8vUFUVh/vw5SkhICFu3bmXChAly0KBBsmvXrh4zCQ6tUoXToFNAv+wqQEdgMfBkAeq3Bs4nJRXoPDRo0IBNmzZdVJaZmUlCQkKJDcZFRkYCDXP2dyCHDg3l0KHzWnb2LSoMAnoBLXE4lLzj4eOzUUtKSsr7HRUVxfjx46Xd3hNd/z733KnwM/AA8BQwFrgF+JCMjFWMGzeOtLQ0unfv7nr99ddN1zJvP3PmDNu2bVMujTbdqdNdyg8/TNIzM3NLvjLDhH6w9F9C+EwF/QMpZYXwazYwMDAwKD8YiqeHUFX1hccee6zkcksUkvfee0/p2rWrvnLlSjlgwIASeRl9+OGH+fe//83w4cMvKu/duzfTp09nxIgROBwOduzYwfjx44vVV/ShQ9qAChZYyFOpVMoDSUlJBAYGelqMUqFNmzZcuHBBmTBhAiaTCV3X0XUdKeVFyy6XC6fTmfPtwunUZO7H5dKx213S4XCvc7k0oWma0HUNXdeElBpSugRouIcwfAF/CYESgiWESKgioIkCoQrs0Hv1ekjfsGGlMmHCBDlu3DgeeuihQinH7dq1Izw8nLi4OJ577jlWrVpVrONUs2ZNCmcIChNxq2dPFKBuY8But5OdnX3dtD133nknH3zwAT/99BN9+vRB0zRmz55N3759SyzlT1paGn/fov5FdnbqdY+/3V6dEydOcNddd/H66zO01atXqw7HPej6t5fco5Np3Lgl9eqdZdOmVmhaS2A8Llcqy5f/zNNPP529evVqv2nTpl2zv9xgTC6Xi5CQEBo0aMCxY8cYOXIkdnu6AqeBukBl4FMfeNEHer8Of0UCxoyngYGBgUGhMBRPD6EoilaWJraX4uvry7Rp05TJkydz1113Ub169WK36e/vzwMPPMDSpUsvMh9u2rQpVquVkydPEhMTQ/PmzYuthKSeP6+0Ka7AZcyfgL+Pj8cSyXuSlJQUQkJCPC1GqWA2m6lX72a5du1JCS2k2+JWkaCInGXcp9xLgA/grbi/vUTOB/et2Az4FeDbP6dNBFc1Wx2nZGcP1rt376drmlPp0aNHae1+galTpw6/FtDMNpebgXbA1wWoawL8fHxkQkKCuPnmm69ZVwjBnDlzeOONN1i4cCG6rtOpUyfGjBnDwYMHCyPiFYmLi2P16i3A6kJd605nc/XIkaPat99+q6xevVax2f4Aal3hmNmoXbsO77zzBnb7FJYv/4aPPx6BrgtCQ0N57LHH/CZNmsSdd95Jw4YNefbZZzlx4oRs1arVRW39+eefCCFSgUqappkeeeQRBg8eTOfOnWnSpIl28OB8FZoB3YDqwG3AM2Z4oz+G4mlgYGBgUEgMxdNDeHl5RcbExFTzZFqT++67j2XLlumvvvqq/PTTT9WSMLkdOnQoAwcO5MEHH7yovGfPnqxfv56YmJhim9mmpqaSqesVLrDQCf45qVQKS1paGmFhYeXWB7K4DBjwb/nBB/NEdvZWxe2d6GlU4GvFZpuiK8pbZepHfjXq1atHUhH8YCcBA4CCJDmxKIoeHx+vXk/xBKhevTofffTRZeVt27a9LLLtm2++WTBhcxgx4llNyoeAewo5yHQXv/02Tv3f/7Zis/0A1LpKvT8JDQ0C3FFqhw17jMceG4rT6QTggQce4IEHHiArK4vVq1czadIksrOzhaIoOBwOBgwYgMvlIiUlBSnlLh8fnwbh4eGNOnTowNtvv827777LX3/9pcIHwONA/3x9/0vAjAeFEE8aqVQMDAwMDArDP/ZFsLxjtVr3RkdHe/yh/dFHHynHjh1T1q4tmcHrwMBAevTowffff39Ree/evfn5558JDw/n7rvvLlYfuYGFSioj5CncgUnyMw2wAC2AJrjnmVrkfFYUsZ9joNe4AVOpAGRkZOjBwcHlPuVIUXnkkUeULl06SB+ftjo4PC1ODirQUgkNDZXlIX9qgwYNSMft5FkYGuO+Bn8uQN0ATZMJCdeLnVt66LrO+PETZVzcBRXmFMGyoTfZ2XZstrnA1Wapj+Dt/V+GD784WrgQ4rI8uRaLhYcffpj//e9/qKrqAvj8889ZsWIFP/zwAy1atMgAlmVnZ3++Zs0aG0CLFi1YvHgxn332GX5+XhIW4p5tB0gCNgG2YKBT4ffPwMDAwOBGxlA8PYTD4TgcGRmZ5Wk5zGYzEydOFDNmzCA5OblE2nzsscdy/Jv+pkGDBvj6+tKuXTvMZvNVtiwYO3fupGMZBBaaDkTgtidrkLMcgXv2pSgcBhl2A6ZSAcjKypL/ZN9WIQRvvPGGEhYWiLd3K63w6lVp8QO3396iXAjj7++PCUgtwraTgTMFqFfDZjPFxcUVoYfiMW/efLp27etq0+Yefd26SGAHUJT/ey00LRsYeZX1FzCZ/sWrr46jRo0aBW41NTUVTdNk48aN7W3a/O2k0LRpU7O/v/8ATdO+2bRpU96MKUBYWBgmkyLgEby8Wmn+/iG6yVSD+vXn6qApQM0i7KCBgYGBwQ3MDTn7Uk44FhUVVS5eCPv06cM333yjTZs2jblz5xbJ/zA8PDxvOTQ0lD179lxWZ+XKlUUXMh8xhw9rAyugn2Q0KANvv93TYniEzMxMdu3aRbVq1bBYLPj7+1/0UdUKdzovw8fHh88++1QZOHCg/Ouv54D5nhYJk+mo3qRJl3JzcP0URSbouriat29uENV6wJF85bdTMFX+Zik5ffq0izJ8tu3evZsFCxYDb5mgPu5wSMUZ073athJFeYQ+fTrQr1/fArfmdDp57LHHrCaTydm7d++LkukOGTJE/fzzz7sDlUwmU8zu3bubdOrknsgUQjBo0MOuiIgI0aFDJ6VlyxdE06ZNycrKUrp3725zOBwFmYQ2MDAwMDDIw1A8PUdkfHy8WdO0cvHSPW/ePLVnz55yy5YtdO3a1dPiXJPU8+eVipaQJDeVSqtWrTwtikew2Wxi37598tChQ7rL5RJOp1PkfONyuTCZTPj6+mI2m6XFYpH+/v6yUqVKBAYGEhgYKIKCghQ/Pz98fX0pyLe3tzcmkwlFKVujDovFwqRJk8TYsa/I7OwPhadvsS7Xncrvv/+ujRw50vM3GcDHy0tPsNvV20qp/cbA3tjYMjPptlqtPPPMKzr8n4DnS7Hfk8DPVK58nFdf/f66tfNz4sQJUlNTZZ8+fcyDBw++6A9pNpsZPXq0z6effvqO1Wr9bPXq1W926tQpL6zvmDEvXPYHXrFihfT29l5rt9szL11nYGBgYGBwLQzF00NIKTMsFkv62bNnK5dU3rjiEBQUxIsvviimTJlCmzZtym3qi+Tk5BIPLHS1t8WSfIu8kVOpgDuf6/z588Vtt912mQKUG+Tk3LlzJCcni/Pnz4vU1FRSU1O5cOECZ8+e5Y8//sBut+sOh0N3OBy4XC5cLpdwuVxomjv1iKZpucu4XC6klAghUFUVRVFQVTX/R5pMJkwm00XLXl5eMucbk8kkvby88PLyEl5eXnLjxo0Ful/27dsXuz1LwMvAnBI/loXjBQ4caKk6nU68vLyK3MqpU6cuGpAaP348RYmU62uxyAS7vchyXI/bgfcSE8tstCE8PByn01vAC6Ws7P4bOMBbb31W6PP4xx9/oKqq7N279xU3fPjhh5XPPvusi6Zp83755RclKiqKRo0aXbW97777LjMzM3NhoYQwMKjACEE6UKmIm2dIWSS7e4NrMF1ML9Y5mSqnGufEQxiKpwfx9vb+IyYm5s7yoHiCOw/nihUrtDfffJO33367XMyQXMqWLVu42R1YqMReLkO43O8sBXcqh5LiRk6lAuB0OkWlSld+RphMJqpWrUrVqteNBpubm6RA6LqOzWbDbrdjs9nylnNyPYrccofDIRwOB3a7nfzfTqcTh8ORt1xQfvzxRzp37sz27f+VUibr8KbqzoNY1GdkcbgVk8ms79u3T7nzzjsLtWWu+fxNN93EgQMHSkQa/8qVxbmUlBJp60q0BVJSU0XuoENpY7fbAbOE0uzsNN7eMezcuRcfH5/Cb336tExPT/cPDAzko48+wmw2u5544om8Z7+fnx/PPPOM77x58162Wq3PPProo3P69u3rNWbMGJ9LB8piYmI4e/asBmwp9m4ZGFQcinPz9sSN/0bAOCcVFEPx9CA2m21/dHT0nV26dPG0KHksWLBA7d27Nzt27OCuu+7ytDiXsWvXLjqUcGAhf6AG7jeprriVzvXAmBLs408goEqVEmyxYnEtxbO0UBQFs9lc7GBWucyYMaNQ9RMTE8WQIaOIj28MNNMh3CPB3KzW/uL112fIH35YJTwd3TawalU17sQJnVIKbBeKeyCjtPPGPv74U1pExHERGmoW7jtIfsYCdYEXc373AGoDn+X8fhl4D4gE8s8svog7Xs/4S9r7jPvv710kpRNg2LBhYufOnQwaNIjbbruNo0ePmmrXrk2DBg0IDg4mICCAAQMGiAULFrTTNG2ipmn11qxZ897atWsfGj9+vF/fvn1JTExk1apVfPvttyiK8l8ppVYkYQwMDAwMbmgMxdOD2Gy2iKioKCt/x6r3OKGhoYwaNYpJkyaxdu1a/P0vfanyLDGHDrkGlcL/dgnwLO5XQoCpuCPZlhTH3alUbsjZTl3XcTqd5e6/VNpUrVqVjRt/UGNiYujb92HFPV5S9hllpPxEnD9fR//ggzly/PhXPBrJvFq1apxyxwkqNTnMXl56QkKCUlqKp8vlYt++AyrM5ty5Z4BBl8Q96gB8h1uR1HGnIEnPt34n0BNYjvtOQ069FcA23HG07wPclrEm0yIGDZpdZHkDAwNZvnx5nun53LlzmT17tkxPTxdOp1M6HA58fX0duq57+/j4TLDZbP2BYUKIubNmzVr03//+t0lqaqqoX78+6enp6xwOx4QiC2NgYGBgcENjKJ6e5VhkZGTBbfjKiJEjR/Lzzz9rb775JjNnzixXylJKUpJaGoGFmgC/XmVdPS6OsFkUDoNsFBZWzFauT1JSErNmzeLIkSN4e3tTs2ZNJkyYgMvlYubMmZw7dw4pJQ888ACjR48uE3PElJSUXP/JUu+rPPLjjz8C1T2jdQKgYLNtUL77ri0NGzaQ/fv391g+VbPZTGIpHwdfKeV3333Hrl27sNlsNG7cmHvvvbfEAk25o3NXlfCUcGf4HXTJPbIj7llPgKO4swTH4zboNwPHcSuYg/hb8dyG+06TCNwPVM1pw4yqpnPrrbcWW+7ca/3555/n+eefFy6Xi71794o1a9bYNm7ciKqqcXa7PS88uZRyrxCidXZ2tnXSpEli1apV6Q6HY54x22lgYGBgUFSMPJ6e5fiZM2f8pCz1lJSF5pNPPlF/+eUXZdu2bZ4WJY/k5GSydF2UVkTM0iQalNtLOZWKlJIxY8bQtm1b1q1bx48//siYMWNITk7m+eefZ8SIEfz888+sWLGCiIgIli9fXqry5BIfH4+vr2+Z9FXeyMzM5IsvvpOwUHhM7wTgNmy2Vcyc+Zb48ccfPXbDORIRobcvxeeODiTZ7erBgwf1X3/9Ve7bt4+pU6fKvn37yqioqBLpY/v27UB36T6fj+NWPvNTE/eY7l+4ZzfbA3cAu4C9QPOcjwIczNlmOfAIEEmnTr1YvPgd7rhjI61afc/SpZ+XSnRmk8nEnXfeyYwZM3x/++033w8//LCuyWSaLoTI7xNQKygoyNqzZ0/++OMPH64+PmdgYFAEhBBSCPFuvt/jhBDTcpanCSHGXVL/lBAitIzFvKEQQhQqYrcQ4m4hhMfSSxVW3ku2fVwIUaY5mQ3F04NIKVMURbGdO3fO06JcRtWqVXn22WfFpEmTuHDhgqfFAWDz5s3UdwcWqlCUVSqV8PBwTCYTAwcOzCtr3Lgxp0+fpmXLlnTo0AFwBxOZPHky//3vf0tVnlzOnTuHxWIpf6MrZcCZM2eQ0iGgPAyX9MBmW8Ybb/wf48aN0zxxXcefOCE7lKIGrgCB3t76k08+qSxdulR88cUXbN++XbRs2VIMHTqUrVu3FrsPtwlv0nXSinbE7c8XRwAAIABJREFUrXTmKp7t8/3ukFPnEdwKpwv4EXgI2Evz5g1p1aoVn332IYsXf3TNCLMlhclk4o477uD222+3AXfnW1WvRo0a2q5duzCbzb9LKa2lLoyBwY2FHehvKJMGHuJx3KOlZYaheHoYHx+f6JiYGE+LcUUee+wxqlevrk2fPr1cmFbtLoXAQmXBacomlcqJEydo0qRJgcpr166N1WolM7P0U/ElJyfj7+9f4c5bSdC4cWOaNLlVh4nl4hqCvths0eLXX8/Sq1dvNmzYUKa9X7Db1ZtKuY+nrFZl4YIFeYqhoii8/vrrTJw4kfHjx7Nw4UI9LS3tmm0kJyezZs0afvrpp8vW1ahRAzhzHeW5A24l8zBuU9s7cc947sStlIJb8fwW2Ix7BtSEyfQV99/fq0D7WRp06dIlwGKx9M5XVK9OnTqmTZs2WdPT08vGRMLA4MbCBXzC3/b5BuWEnJnMrUKIFUKISCHEUpHjsyCE6JlTtgPon28bixDicyHEHiHEASFE35zyx4UQPwoh1gshooQQU/NtM0QIES6EiBBCLBRCqDnlmUKIN4UQB4UQu4UQ1XLKbxZC7MrpY8YlMr+SU35ICDE9p6yeEOK4EOJTIcRRIcRGIYSfEGIA0AZYmtP3peY7pYKheHoYp9O5v7wqngALFy5Ud+7cqWzZ4vno+TGHD7s6VMB0JCfIS6XiEa6VWqIsfDyTk5OpVKnSDal4Arz11usKfKdCnKdFyaE6DschNSPjbSZPnlymFg13duyo/7sUB49eBlaqqu5wOi/7Y/fv35/PPvuMFStWyHvvvZf+/fvrGzZsIDIykgULFsiZM2fqmzdvZvLkyVqPHj2YM2eO9sYbb7Bz586L2lm1ar0Gd1/nwukI/Iw7jY6a852GW/lsn1OnAe5kThNxK6Gz6N69G55Mr3XHHXcAdM/9bTKZ6tepU8e8Y8cORUq5xmOCGRj8s5kHPCqEuFIC9bE5SkGEECKCMp6dMqAl7khxTYD6QEchhC/wKfAA0Amonq/+ZOAXKWVb4B7gHSGEJWddO+BRoAXwkBCijRDiVtwO/x2llC0ALacOgAXYLaW8HXcggCdzyucAH+f0kZDbsRCiOxCW008LoLUQonPO6jBgnpTyNtwPo39LKVfg9v94VErZQkqZXcxjVSAMxdPDWK3WiKioqBI92UlJSbzyyiv06tWLvn378vTTT3Pq1CkAlixZQuvWrcnIyChQWyEhIYwdO1a89tprpKZemu2ybElJSlLbeFSColFWqVQaNGjAsWPHLitv2LAhR48evagsNjYWs9mMxWK5rH5Jk5ycTGBg4A2reN588800aFBfg6HSPQNWXhiNEDW0n376qczOzQcffaTECSGKG6wrl9PAU8BrQGuTSV9Wvbrs8fzzypw5c66oGDZv3pxNmzap27Zto0uXLsobb7yhDx8+XP7yyy96TEyMmDlzphYbGytWrlzJxo0b1ZEjRzJmzBhmzZqlv/vuu/Ts2V87ezZRhYnXeXY2wx3N9s5LygJxJ33Jxe3XCf1Q1TVy0KA+hT4Gw4cP57fffruo7Msvv+SNN94gJSWFli1b8u233xaorbCwMKSUlYUQtQAsFsttycnJQlXVeCnlX4UWzsDA4LpIKdNxB9d/4Qqr389RClrkKCZny1a6G55wKeUZKaUOROCOAtcYOCml/FO6g7R8la9+d2BiziDBVsAXqJOzbpOUMjlHwfseuAt3Fr/WwJ6cbbriVnABHLhHMAH25fQN7pHNZTnLX17Sd3fgALA/R87cqJYnpZQRV2irzDEUT89zLDIy0lFSjV0rwAzAunXraNq0KYWZwRw0aBB169bVXnvtNY/N2lXkwEJllUrljjvuwOl0smLFiryyI0eOUKdOHfbv38+uXbsAsNlsvPXWWwwfPry0RQIgLS2N4OBgT0bW8TiffTZPbdvWJt2KSA+t8JN+Ahia77cLqAL8K1/ZD7hNNhvjVnJ+uG6rNttQdc2atdfxVyw5TCYTtW++WV9XQu0tBJYHBrKuVSvNv0MH+e1334kRI0Zwyy23XHM7f39/xowZw/bt25Vdu3aJb7/9Vv3000/Fli1b1CVLlih169YFYNSoUSxYsIDNmzezaNF3xMU9rboDAgVfRzIVdwqVN/KVLQIuDXA0FrDhjnabIEJDC+/m1atXL9atu/iIrl+/nl69erFx40aaN29+2fqroSgKbdu2dQL35hQ1OHHihO5wOFZcazsDA4Ni8wEwAvcsl0H5wZ5vWePvbCBXe4gL3LOJuYMFdaSUx6+yTW60+8X56jeSUk7LWe+Uf0cfzd/31foXwMx8bTWUUuYG87jafpQ5huLpeQ6fPHnSNzu7ZCY9rxZgpnXr1sTGxmK1WnnuuecK/CKSy8cff6zu3btXWb9+fYnIWVhyAwtVxIQch0GGlUEqFSEEH3zwAbt27aJXr148+OCDzJ8/n6pVq/Lhhx/yySef8MADD9C/f3+aNm3K4MGDS10mgIyMDIKCgm7oe01oaCiff/6JsmvXFmCj6r7vFwYL7qQ+ufeJTUB+b8mDwDjcQWoigZ9yfh+6TrtdOHPmjJqZmcnixYvl7Nnvai6Xq5CyFY6A0FAlqYTMbc1Avbp1tUWLF6vz5s1Tg4KCSqLZi2jdujVfffWV4n5ujwFuLvE+4BlatGhCrVq1Cr1l9+7d2bZtGw6He/wyLi6OxMREWrVqxbp16xg3bhznzp2joEHsOnfuXMnf3/9fAA6H46aoqCjdbrdffxTDwMCgyEgpU3A7fY/wtCwG1yUSuFkIkZvu/ZF86zYAz+fzBW2Zb919QojKOb6UDwK/AVuAAUKIqjn1Kwsh6l6n/9+Ah3OWH81XvgF4Qgjhn9PWTbntXoMMoNJ16pQoRh5PDyOlPB8QEPD7+vXrO/fr16/Y7V0twAzA2rVr6dWrF61bt+bVV18lOTmZgiZZDwoKYsKECWLatGm0adOGoozMF4ddO3fSsQIGFgI4AcrAUk6lkkvVqlV59913r7juiy++KBMZLiUzM1MPDAy8oRXPXPz9/XGP9xVlkrEXsAYYgNvK5hFge8662cAk/laKbgb+A7zDxZY4l9IRh8NX79Chg+Lre5MupUPExMToc+Z8oJRG3tXY2FiOhIfzeglEtk0EFvj66s1rlr7LU7Vq1QAv4DxQrYRbX0mVKluYO3dpkdKmBAUF0bRpU3bs2MG9997LunXr6NmzJ+fOnSMpKYlmzZrRo0cP1q9fz7Bhw67bXrt27dA0rasQQvj6+r6jqupdwO9F2DEDA4PC8S7wnKeFMLg2UkqbEGIUsEYIkQTswB1FDmAG7tnrQznK5yn+Nk3agfuB3BD4Wkq5F0AI8SqwUQihAE7gWdyeJFdjDPC1EGIMsDKfXBtzfEZ35ei9mcAQrj3SvQhYIITIBtqXhZ+n8TJYDsjIyHhn8eLFBXO6LAa55leKotC1a1c2btxYqO379etHWFiYPmnSJL2sc49W1MBCLuB8GaRSKc9YrVZZqVKZDqjl0a5du7zlbdu2cf/99xMfH8/JkycZPnw4AwYMoE+fPkybNq0MpRIUTfF8GHf6DRvumcw78q07ittNJD9tcsqvhS/Z2fGKlGfJzo5VbbY/lfDwYyxdurQI8l2fZcuW0Ra0HiXQVk9vb61hu3a88eabZXJfUFU/vTR8dFX1Lf7zn+cpzjXSq1cvcq1Rcu/z69ato0cP95Hu2bNnga1c6tati7e3ty/QIDs7e2ZmZub9UspyEpXZwOCfhZTSP9/yOSmlOdfUUko5TUo5+5L69aSUSWUs5g1F7jmRUm6VUv4rX/lzUspFOcvrpZSNpZR3SSkn5taTUmZLKUdLKZtJKZvm3x5IlFLen2NOOz1fu9/kmMY2l1K2llLuzi9HzvIKKeXjOcsnpZTtpZRtpZRvXVJvTk7fzXLqREspT0kpm+arMzvff2xljjxGcKEbjHVnz561XxoApihcLcBMVFQUp0+fZtSoUXmj34U1twX4+OOPlSNHjojVq1cXW9bCkJqcrF76Wl0ROA34lUEqlfKM3W6Xnt7/3bt3M3PmTBYsWECNGjV46623GDp0KCtWrOCnn34qM7NjN0VVPJvjHjxdBvS+ZF2uq8j1yq5GblC+ABQlUAYGXim4YvHZ+csvWu8SeO6cBI6B+tqUKYq3d9lk9h08+F8KDCZfEMES4ASq+gd33313sVrp2rUrv//+O8eOHcNut9OkSZM8H/8ePXrwwgsv5D0DrocQgjvvvFMKIe69bmUDAwMDA4NCYCie5QAppeZyuT5cunSprbhtXS3AzKxZs3jmmWfYsGEDGzZs4JdffiExMZGzZwsXIM3f35/XXntNvPnmmyQklOQL2NVJSkqqsIGFTgAWD6ZSKQ84HA7hNjH1DPv27WPatGnMmzcvL1XF+fPnc8wn3VwvGE3JUlTFE6APbt/NRy4pvw13VPT87McdAb4w6CjKeaU0jseGDRs4FxenDiumme1kIeTtfn6yZ69eWv5zWNqMH/8Kt91WS4fRJXg9++NyOYvditlspk2bNkyZMoVevXpx8uRJsrOz2bJlS949f+TIkQUebOzcubPFx8fn9aCgoJ8sFstCk8lUEcf9DAwMDMoFUspFUkrDjBpD8Sw3OJ3OTzZu3Eh6enqx2rlagJk9e/Zw770XD2Dn+gMVll69etG0aVN94sSJZWJyu3nzZhpU0MBCZZVKpTzjdDqFp0xtnU4nL7zwAnPmzKF+/fp55UOHDmXkyJE89dRTLFmypNjXXeEp6nXzBDAFd9Ta/IwDZuKeESXn+/9wZ7YsDJ/j6+vNrbfeWkT5rozL5eL/Jk3S3wZZWFUxv4qeBrzn7S3emj1bzJgxo8xN799/f5biDg5VUplFqiOEhcTExGK31Lt3b6KiovLMai+933fr1q3A9/v777+fDz74oNqECRMeGDRo0ChfX99vc/yPDAxuRIrjClXqblQ3KMY5qaCIsvbVM7g6lSpVWv3ss8/eP2TIkHKfesJqtXLffffpL774onjooYdKVd4Xx4yh7i+/aP+tgD6ez4B+rEsX5aOPPvK0KB6jffv2cunSpSK/4ldWtG3blnbt2lG7dm0mTpx40brExER+++03fvnlF06dOsXKlSspC7PNZs1a4w6NUxhzVn/ccQLysxV3UKHcNF/fA1NxxybwAqYD/Qslm4/PbdpTT92njBw5skSv6Tlz5rD9s8/0Y1Co8DkRwJ0mExtdLjoDw4HDrVppixYv9ti9YPToF7SdO4+o7sjCRQ0apuMOVPQF/v7vsmXLGsxmc8kJWYJIKenbt2/myZMnh0opjei2BgYGBgZFxhjBLEdkZmbOXrJkSVZFGAwwm83MmDFDeeedd0RcXFyp9hV96JB2K6jl/6hcTlmlUinPOJ1O4SkfTyEEs2fP5siRI3z66acXratatSr9+vVj7ty5mEwm/vzzz7KSisKb2l6qdALczd9KJ7iVzMO4I70fprBKJ4DJFKe0b9++xAeSNv/0k/Z8IZVOgEe9vbVKQUH09/HRlwLfm81y1OjRHh2AWrjwQ3Xw4O64c38fLOTWicARFKUbJlN9GjRYzqpVy8qt0gnua+jFF1/0t1gsb+emCDAwMDAwMCgKhuJZvtiWnp6esnfvpb5a5ZN7772X1q1b66+88oqu66WXgz7DalWn4c7Z1xz0EaAtAHbiTtFenokG5fYySqVSHtF1HYfDQWkFqykIfn5+zJs3jzVr1vD9998DsGPHDpxOt29dUlISaWlplJ2/YHF8PEsTHZstU6hqyep1NpuNhMREtU8B6v4F5BqD/gycUlV1+fLlDBoxgueDgggKDaV9+/YlKl9R+M9/JjJ48INAO9wz1/cV8IS+AjSjc2cze/b8xg8/fEX16tWvu5WnSE1NZevWrURERGhZWVlhwFBPy2RgYGBgUHEx8niWI6SU0mQyvfvll1/ObNu2bfkdAs/HnDlzlK5du+rLli2Tjz76aImPhmdnZ3PB4eC333/n/PnzbNu2Tdm/bx/bjh3T0s6fFxdcLiUYZFPQ24PSAkRzoAGet8s1Uqm4g/iYTCZKIydkYQgMDGTBggU8/vjjBAUFsXfvXmbNmpVnWvvSSy+VYW7a8qp4Kuj6cPnUU0+xYsUKUVLH44cffqA26LUvGej8CbgXtxExuD1XZ/v6IhSFKrquZYB49rnnRLVq1cTTTz+tjBo1CrvdftmkW7t27QgPDwfcKXNmzZpF9erV6d69O4MGDQLg0KFDTJs2jW+//RaTqWQee08+OYL169fSs2dPvv76B+FOXZM/BJoLOIA7zc2fCPEGirKS//53Ea1bl79YPZqmER0dzcGDB9mzZ4917969Ii0tzcvHx2y1WrN9wayBdbEQ4lspZbED4RkYGBgY3HgYPp7lDCFEoLe3d8KGDRt8y+5FuHhs376dl156iZUrV1KnTp0SbfvgwYM899xz+vbt2684O+9wONizZw87d+7k6KFD8lx0tHYhM1O1SSnqg94KZDtQm+IOx1KVEshcX0CigZZCsPPQoTLqsfxx4MABnn/+eXbs2OFpUcoNzZq1w50QpOwishYGk+k+rVKlferHH3/MbbcVP5b0O++8w59LlmhVQe0H9APu8fLSjimKqghBX7td+kspVgQHM3v2bJo2bcqiRYv0I4cPM/ejj5TrzcDmKp67d+9m+vTpfPLJJ/j5+TFkyBC+/vprgoKCGDx4MOPHj6ekBoGcTicjRoygdevWvPDCCwwc+KgrMlKq8JOAm4E/UdWBSHkMqIy3t53HHnuEoUMfISgoqERkKC5paWkcPnyYgwcP6r///nvW8ePH/Uwmk0PXVS07O9MMdbOhhzfc7Q3tgdpA+wz4/XEp5feelt/AwMDAoOJhzHiWM6SUF/z9/VesXLnykdEe9mUqKJ06daJDhw7ypZdeksuXL1dKakYB4Pjx41SuXPmqoyPe3t507NiRjh07glunNAGcO3eO//3vf8q+fftYeOyYlhofzwW7XTUBt4DWBkRrUJrinqMoDUPQP8lLpVIhzmNpkJCQgMViKUxCyRuA8jrj6cbl2qSmpr7CsGGPExoaqo8Y8USxAoilpqZyGBSnjw/hwcHyicRE0ap5c7F5zhxOnjzJ+++/L/fv3y8+fftt2rVrB8AzzzxTKDeQ3JQ58+fPz0uZ8/jjj/Pee+/RtGlTbrnllhJTOjVNY9asWXh5efHcc88hhOCbb74yTZ06gx9/bIqUd+PtvZMXX3yaRx9dzOHDhwkLC/OoH6fT6eSPP/7g0KFD7N2713rgwAGRlpbm5etrtlqtdm9NwwfusNntvSzQQUAbwHyFHEgjK0HUKNyRrAwMDAwMDAqFMeNZDhFCtAoKCtq+detWc0n7W5UWLpeLbt266Q8//DBPPfVUifkOT5w4UdN1XX377beL3Zau6/zxxx/s2LGDgxERxEZFaReSksQFl0sJAJqA1i7HXLcp0BjwLUZ/HwFza9fWVq5dWzFOYimwZMkSVq9erX/33XeGP3kOzZrdiTsAUC1Pi3IdDgG/4us7SY4c+YQcNWqUUpTYMq+99hqbNm3i/vvv11577TU1JSWFoKAglHyhhhISEors69iyZUvMZjOff/45jRo1yivXdZ0hQ4aQkpLC8uXLS2SmMSYmhsmTJ6OqKvPmzbvMdzktLY01a9bQvn17PBHFGdz34lOnThEZGcmRI0ece/bsscfExPj5+vrZXC6kzWb1g3pWuM8HOnvDnUA9CjY2lArUsIO9qpSyvLvYGxgYGBiUM4wZz3KIlHJ/QEDAqW3btjW55557PC1OgTCZTHz44YfKiBEj6NSpU4mY6AEcPnxYPPHEEyXSlqIoNG7cmMaNG+cWqeB+Udu/fz+7du1Sdxw6JFf8+acr/cIFNUPXRTWQzUBvB2oT3MroLYBfAfo7BnrN+vVvWKUT3IF7AgMDdYxAZvko3zOef9McaI7N1kl89tndxMfH608++aRy0003FaqVgwcPagMHDhQvvfSSClC5cuXL6hQnwI7JZKJFixasWrXqopQ5iqLw0EMPcfTo0RJROn/99VcmT57Mc889x8MPP3yR4pxLUFAQjz76aLH7KigZGRn88ccfREVFcfjw4ezDhw/rcXFxfj4+PnYwuazWLD8p/VXomZ2Z2c3frWS2AHyKmFg3GOjshM0PAktKbk8MDAwMDG4EDMWznJKRkbF8//79U++5554Ko7g0b96cfv36MXbsWPnTTz8JX9/izBe6zcPOnj2rXJoIvaQxmUy0a9cu18wvz1zXarXy22+/iT179qirjx6VX5w+rWdkZCgZui5CQTZy+5CKZqA0Bm7F/VqWy2GQt95yS6nKXt5JSkoiODjYMLPNhxACKSuC4plLK2y2A+Lnn/vo69f/m+3btxc4WFRycjKnTp1SBw4cWGrS5abMefLJJ/n000958sknL1pXUhlA1q1bxzPPPMPgwYNLpL3CIKUkISGByMhIIiMjZURERFZUVJR64cIFbz8/S7bDoQm73eoH1bOhT7bL1dHiVjBvB0JK+Dn/hD/sHY2heBoYGBgYFBJD8Syn+Pr6Vg0JCakwSmcukyZNYvv27frbb7/NlClTiiX/iRMnMJvN0lOKi9ls5r777uO+++4Dt0Kqglsh3bdvn9i7d68afvw4P5444cpITVXSXS7FF2gIWgvgMKiVzpxh27ZttGrVCn//K7hM/cNJS0ujdu3ahuJ5MRK0CnZMGmC3H1VNphA9PDxcyfGpvi5+fn74+fnJhQsXMmPGjFLb59yUOcOGDSMkJIT+/Qufw/RaxMTEsGPHDl555ZUSbfdKOBwOYmJiiIyM5Pjx486DBw/ao6OjfQHNy8vXYbVme2ua9IEwGwxUMzLa+buVzFsBH0upC8gDwPDWQoiqUsrE0u/PwMDAwOCfgqF4llN8fHzqhYSEeFqMIvHJJ5+o//73v+nWrRsdOnS4bv39+/eTnp5O06ZNL0ppERkZSXBwsE45C85jNpvp1KkTnTp1yi0ygdun7OjRo4SHh6sHDx4k69dfiYuL06ZMmaKkpqYKX19fzGazFhAQQLVq1dT69evTpEkTWrZsmRcQ5Z9Genq6DAkJMcxsL6KimNpejtXaXqxZs0br2LHjda/JJUuWsGDBAtm4cWM5evToUv8PXJoyp6QsJX799VemTJnCuHHjqFKlSom0eeHCBc6cOUNsbCyxsbFER0dnnzx5UouLi/PKyMjw9vMzZ0up6lZrph9UUuHObLjbYrO19HHPYtYBhIdyFFmA+zVYNRC3K7uBgYGBgUGBMBTPcooQokZFVTxr167N008/zfjx41mzZs1lATjyExkZyejRowkICNDS0tLU3r17a5MnT1YdDgfz5s2T7du3L1dK57VQFIVmzZrRrFkzdu/eTUREBEuXLs3zI42Pj+fMmTNqbGwsp06d0qOjo+X//vc/kZiYqACYzWbd399fDw0NVWvVqiUaNGhAo0aNaNasWblJwVBYrFarFhAQYNxn8uE2tdU8LUaRkLKNOH36h+vWe+SRR1xnz55Vp06dKrp3735Z7s2SJDeHJ7h9RdevX5/3+8EHH+TBBx8sctvz589nxYoVzJ07lxYtWhR4O03TSExMzFMs//rrLz06Otp66tQpce7cOR9N04SPj58NFJmdbfPWNIcPhGTDrQ5oKbKympjdCmZzILAgLuVlzONm+GUUhuJpYGBgYFAIjBfCcoqmaVUrquIJMHz4cNavX69NnjyZuXPnqld78VyxYoXeuHFj5csvv1Tj4+N54okn6NOnjwwLC8NisegzZsyoMIpnfiIiIqhVq1ZeKhWTyUTt2rWpXbs27du3h3zBdqSUpKamEhsbq5w5c0aJjY2VMTEx2ubNm/nqq6+UtLQ0YTKZMJvNutls1gMDA5UaNWoodevWzVNMb7rppisGO/E0NpuNgIAAT4tRzqi4M57wBwUJLpSSkiLGjx8vevToUQYylQ5Hjhzh66+/5scff+Ra92KXy0V0dDRHjx4lIiLCtn//fu3MmTNmLy8vp5eXr9Pl0oXNluUjpcnHHRH2Xju08HM6wyxQH2gA1ACUMjCTLSm6A84wIURdKeVpT0tjYGBgYFAxMBTPcorD4QjOb3ZaEfnkk0/Unj17yp9//pkHHnjginXatm2r/Prrrxqg1qhRgzVr1qjvvfceq1ev1pcsWVIhlU6AqKgoGjZsWKC6QggqV65M5cqVuf322yGfPym4TXiTk5M5e/asEh8fr8THx3P69Gnt0KFDrF+/XklOThaapmE2m6Wfn5/u7+9PUFCQWq1aNWrVqkXdunVp2LAh9evXp7gBnwqL3W4XhuJ5KQKomDOecD87djyt2mw2fH19iY2N5f3336dJkybcddddeRGjw8LC1D179uj3339/+RsNuQ6xsbEsWrSIdevW8eqrr+Ln50d0dDQJCQnEx8dz9uxZ+ddff2WfOXNGT0hIMKWlpXn7+fnZhBB6VlaWX3BwsK4oqrDZ+nnbbP293YplfSBQBTxkHlvSeAMPAV8NBmZ6WBgDAwMDgwqCoXiWQ4Qbc2map5UFgYGBTJkyRUydOpXWrVtTs2bNy+pUrlwZl8uVt6OKojBu3DjGjRtX4V5Y8xMbG6v36dOnRBRnRVGoUqUKVapUyVVM4RK/14yMDM6ePSvi4+PVxMREEhMTiYuL0/bt28e6detEamqqYrVa8fHxwdfXV/fx8ZEWi0VWrlxZrVatmqhduzZ16tShYcOG1KtXr8QUVKfTKa5lan0jIoSQoFfQi/tRXK4J2tKlS5Xu3buLyZMn65mZmeLMmTP6J598onp7e+t33HGHEhkZqbdv375CJYn+9ttvmTFjRt7voKAgOXXqVN3pdAo/Pz+7yWTSNU0TWVlZPoqieAcEBNirV6/u7NChA40aNTLfdtttNGnSBLPZbH7ggT76qVNdFBjgwT0qbcJ8wO9WT0thYGBgYFDHiKfTAAAgAElEQVRxMBTPcoiUUloslo8nTJgwfOHChebyaEJZUHr16sVPP/2kv/zyyyxdulS5dF+OHj1K1apVK9QLakFIS0uTdevWLbP+KlWqRKNGjWjUqFH+4ouUU6fTSXJyMklJSUpiYiLnz5/n3LlzxMXFabt372b16tUiJSVFsdlseHl54ePjI729vaWPj49usVhEQECAGhISQrVq1ahZsyahoaGEhYVRq1YtvL29ryiX0+k0ZjyvSEWd8YTs7HfUDz4YzLx58/D29lamTJlC7969VU3T+P3335Vly5Zp7dq1k1OmTCnXzxeHw8GuXbvYtm0bR48e1WNiYkSOM6q0WCzO4OBgR506dUTDhg39atas6Ve9enVq1KhB9erVqVSpElzj+dm4cSN56tRmYHRZ7U4ZowNzrZA+39OSGBiUNkeOHEkHipj7loymTZsaD8ESxks40114FemcmHBmOKWXcU48RLl+MbiRsVqtYw8fPtxp/vz5tz311FOqyVRxT9WcOXOUbt266V988YU+YsSIizTPffv2aU2bNq2wJrVXIysrS6lTp46nxbgILy8vqlevTvXq1S9dddHx1zSNCxcukJKSIlJTU0VycrKSkpJCSkoKCQkJ2okTJ9i9e7c4efKkoqoqdrsdLy8vvL29Ze7H19dXWiwWJTs7W3nvvfeoWrUqwcHBVK5cmdDQUKpWrUq1atUICQmhIv+3i4Jbt6moPp4AjwBdcDpP4nTexbZt22Tv3r2Fqqp06NCBDh06eOx61nWdyMhIYmNjOX/+PMnJyaSkpHDhwgXS09PJzMzUsrOzZXp6upKenq4EBgbKJk2aaN26dVNvvfVW0bBhQ6pUqSIURfHGbU9aKJKSkpg4caIWERGhwpgS3jsBvAS8m/N7NpAJTMv5vQR4G5A5nyeAcSUsQy6bAGs88HspdWBgUJ4oqtJZ3G0NroILr0qSohkOCaRxTjzIjfXGV4GQUjqFEP9aunTp2m+++ab+o48+6jNgwAC1Ivp9ent78/777ytPPfUUd91110WzcrGxseKuu+7yoHQlj9VqJTs7W9SqVcvTohQJVVXzfE6vtBrcs0Vt2rQhPDwcIQQZGRmkpqaKCxcuiLS0NFJTU7lw4QJt2rSRaWlpekJCgoyKihIZGRkiMzNTWK1WYbPZcDqdmEwmvLy8MJlMUlVVaTKZpJeXlzSZTLmmwYqvr69iNpvJSUmDxWLBYrHg7++PxWIhICCASpUq4e/vj6+vb27+SHK3KV9WAxXZxzOXmjmffmhadqmmPEpPTyc6OppTp04RFxfHuXPnuHDhApmZmVitVt1ut+t2ux2r1aqkp6crXl5eBAUFyUqVKsmAgAAZFBQkQ0JCRMOGDdWAgAC1UqVK1KpVi0aNGhEQECAowefgu+++q+3de0HRtDNASd+rfYDvgf9coe11wAfARtznxQZ8WcL952d2JqTPklL+46xVDAwMDAxKD0PxLMdIKWOBZkKIlosWLRr76aefDujYsaM+dOhQS5s2bahIPqCtW7emd+/ecuzYsaxatUr4+PgAcP78eSU3IMk/hQMHDhAUFCS9vLwqzgkqJPHx8fj4+OTNVgYFBV0t5ctFgZIuxeVykZWVhdVqJSsrS2RlZQmr1Zr7O+87V8nIysqSmZmZMjExUdhsNrKzs7HZbMJmswm73Y7L5RIulwtN0/I+uq6jKMqVPlIIcdmyEEKeP39erVKligZcdJ0lJiaq1apVy9Mac9clJCSo1atX13J+SymlkFKS7yNy39FNpmzF5ToE3FG8k1BO8PMreLaPxMREQkNDURQFXdc5deoUe/bs4ciRI8TGxpKenu7KysrCbrcrdrtd2O12oes6OQGz9JCQEJkzWy5uueUWxWKxKBaLRTGbzVSrVo2GDRsSHBwM7v9dmVx/mqaxdetWkpOT2bRps6ppxyh5pRPcj+tRwPvAm5esm4l7BjTXj94XeLIUZAA4BexQQH5dSh0YGBgYGPxDMRTPCoCU8gDwmBDi+W3btg0LDw8fFxAQEDxs2DBLnz59RI6/Ubln6tSpomfPntr777/PxIkTVXD7HVbUHJVX4+DBg9SqVatUZ4E8zZkzZ7BYLJJivtybTCYCAwOvmes1H0WatpRS4nK5cDqdeZ+c3yJfmXC5XDgcDnRd58knn+TNN99Uc5XFXAXy6aefZurUqZeVP//880yaNEnN/a2qKoqiXPQthEBVVcaOfUnabMn/kEGJBhw/vua6+7JgwQIWLlxIjhIpTSaTzMzMVBRFoWbNmnqDBg30Nm3amEJDQ02VK1cmJCQkb9Y9ICAgV8EvT9PWgNu6YezYsfqBA0eFEFU0p/M5BW4uRTmfxZ3bc/wl5UeA1qXX7UV87ATTYimltYw6NDAwMDD4h2AonhUIKeUF4EMhxP+zd97xTZXfH/+ce5M2SXfTXQstFHCADAWZskfZlb0RUBRUBBEFVFD5IoijICICAgqIKIpFoSIiG2QpggOZQm0LHXRkr/v8/kjaX4G2dCXpeN6vV17JHc/znJvcJPdzz3nO+UCn03X88MMPZyYkJHRbuXKl50MPueqio/wIgoCPP/5YHDZsGLp06YJWrVqhJkZq/fPPP2jQoIG7zXAqqamp8PX1rbDwdAVEBLlcDrm89JUszp49WynriyIsLEy6eVOoATclpkOhWImRI+fc9RzQaDRo2rSp7aOPPhL37dtHYWFhVLduXQQEBICIBFRBUVkcOTk5SExMRI8ePfDUU0+xlBSBmUz/CYC3C/5PfQGMBbAMQOk9zZWHCcBKK6BNcMPgHE6Ng4hsAM7Cfj1+BcAYxlgOEUUD+J4x1tiN5tU6iEgNYI9jMQz2eTEZjuVWjDFzOfsdD2AJgBTY8we8zxhbXcb2PzLGUsvQZh/sRaKNAMwAnmCMnS691aUa414AX8CeWGAwgD4AngbwK2NsVFFtuPCshjjm1ewDsE8QhAEvvfTS599//73K1TUay0NMTAwmTJiAmTNn4t137UkygoOD3WxV5fLff//ZWrVqVQOERfFcvXoVubm59MEHHyAkJARhYWGIiIhAZGQkVCqVu82r0ti/vlVer5fAX5DLu9tstuti+/ZdcPLkSfrxxx+Rl5fHdDqdZDAYmNlsJovFQmazWTCb7f/TnTp1glKpRFxcnJvtLz+pqakYN248y8rKwfvvv09EHZnVult0rW5+HkALAI8XWvcAgFMAujh57K0AhN8ZY+edPBCHU1swMMaaAQARfQp7WMPtsfQcF8EYywKQ/3nMB6BljL1TSd1vYYw9Q0QhAP4kou2MsRt3a0REIoDxsIe2lFp4OhjFGDtJRI/DLny7l9XouzAQQCJjbB4AENEUAHGMsSvFNeDCs5ojSVKit7f3nuXLl/ecOXNmmbMwuoOnnnoKu3fvtk2ePFls3749U6lU1fkq/A5ycnJQ1TLaVjZpaWmQyWR06tQpW05ODuXl5ZFOpyODwQCZTAYPDw8ml8uZ4xmenp5QKpXk7e0t+Pj4ICAgAP7+/lCr1QgKCoJarYZarUZwcDBUKlUVSwZUudiFp7OPj8HundLe9tAB0MMufIUiHuJty7EAbr0x5OnZXVKpDGJwcKyk0WiYTCZDdHQ0qdVqwdfXV/T39y8In/b394evry+USmX+n2e1JTc3F8OGDUNeXicmSRsE4GsA49xwogYCGArgE9gz1wL2hEOzAHwP+016E4CPATxXyWMv0QA5iyu5Uw6HY+co7LH0nFKwDx3RCfudPYxARKcYYw8RUVMApwHUZYxdI6JLAJrA/ie51vGcAeBxxti14jpkjKU72tZ1iNuWsIewbC0k4P519NkDwEoADwPYREQGAHMBTGKMxTv27Q7gacbYYyUcx1EALzr2n49CgpqI/gDQ17FfEoBDANrC7p0dwBgzEFEzhx0qAJdg//NpA/udUBsRPQrgHwD1AGwnorWMsfeLMoQLzxqATqebtGXLlgtxcXEeDzzwgLvNKRVr1qwRO3XqhNmzZ9co0QnYS6m4soanOzCZTOjatas0Z86cW8SEJEnQarXIzc2lvLw8ysnJgUajgUajgVarRV5eHrKzs203b97ElStXkJeXV5Dl1mQyFcyxdGS5hUwmY/mPevXqsdWrV1f73yzGGAH7YReHkuNR+HX+w+Z4tjBAJzlEI7MLRz0AI9kjaPKfTQQY8l/DLhzlIJKDSGSCYH8fRVHMt6Pg2f4AAPszYxIxJsFqlRNwkQCvAvvlcgMSEhLQokULl4iunJwcTJo0CYC9XIkoivkJhPDPP/9g7NixePHFFwEA69evh16vx5QpUyrdjuPHj8NgMEKStjmOe1ylj1F6XgCwvNBybwA3AHSD/Vwi/L8orSz2Arhohl3dcjicSsRxY64r7HeUOKVgCL7CVxjibPEpAVAQkS+ADgBOAuhARIcApDPG9ES0HMBnjLFPiWgC7HMhBhbXIRHVg12gXQQwlzF20/H57yGiBxljZxy7Ghlj7R1tJgGY6fBeEoB3iSiYMZYBe/jLurscRy8A35bieBsAGMEYe4KIvgQwCMBG2Ot1PcsY209EbwCYxxh7nohW4lYR2wtAZ8ZYZnEDVPuLOI797olMJnt64sSJa4YOHSobOHCgvF69eu42q0QCAgIgCEKZ5t1VB7RaLQwGA4WHh7vbFKeSk5PDmjZteofwEAQBvr6+8PUtsTZziZ4vi8VSkM02P9Pt2bNnsWbNmupc/LIAuxfwCAsPv+LIpEsQBCJ7dl0ixzLys+yKokgKhUJUKBQOz7E/FIoweHl5QaVSFZSXUalU8Pf3R2BgIAICAm4PeS5XltfWrTtLOt16skd/AcAVmExaoXFj10078vf3x9atWwEAK1asgEqlwvjx4wHYs2Xv2bMHkyZNKhCjzuCjjz6yrV27TjSZXnfaGHdHW+h1KOw3HwrzOG4Nv60ssgG8bAI2mgDrRMaY1QmDcDi1FSURnQYQDXu8/G73mlN9+ApDXCU+jwBoB+BRAAthF3EE4KBjexsA+d7GDbAXVC6KYUTUHvY7w5MdgvMpInoSdj0WDuB+APnCc0tRnTDGGBFtADCaiNY5xh9bzJibiMgL9uuuFqU41iuF5oGeAhBNRH4A/Blj+W/ypwC+KkVfRcKFZw3BarV+TkS/b968edLmzZsfv+eee4QRI0b49OzZs7QZQ11OQEAA+/PPP6ljx47uNqXSOHXqFAIDA2t0KRUA0Gq1kr+/v1NCJ+Vy+R2ZbvV6PWQyWY3IRNWkSWOy2ay0bt26Kn2OfPPNN9DpjII9V0A+G9CgQUObh4dHlQibFUURgwcPxoYNG/Dcc5UdVmpn7969WLt2nWg0HgdQm/J8MNhvck8zANKXgH4GY+ymu63icGoYBsZYM8fF/few3+Vb5mabqgWdsN9V4vMg7N7OugASAbwE+w9kcdEfxV2rbGGMPZO/QEQxAGYCaMkYyyai9bDXwspHV4JN6wB8B3uI01cl3BAcBeB3AIsAfAi7QLbi1vk+hcc0FXptgxOy2NXciVS1EMbYnwaDYbrRaAy6ePHisPfff3/XkCFDqmzK+6ioKOn33393txmVyu+//46oqKga4ZkrCYPBwJzpYSpiPIiiWCOEZ4MGDSgtLc129z3dR0pKCubPX8LsNzajC205gmbNmlYpwTx8+HDs2LEDGo2mUvvNzs7GSy+9bJs162UYje+idonOswBa6oBnzwG5HRnLG89FJ4fjPBxVC54DMJOIalYomBMpLD73wWlOjAMARgO4wBiTANyEfX7DYcf2IwCGO16Pgn2OZGnwhV1c5hJRKICSMu9pABTUTnRkt00F8AqA9SUNwhizOPZrTUT3wV6MuQUAEFELADF3aZ8LIJuIOjhWjQHKr/K58KyBMMasjLEkrVYbn5GRUWUTDj3wwANUljIU1YHz58/X+FIqAGAymQRXetL1en2Fw7KvX7+OZ599Fn369EFcXBwWLVoEi8WCEydOYOrUqQX7LVu2DJMnT0Z+NtbKJjY2Fnl5eVX6t3fGjJckxrozoDmAX2HPLr8VKtVZ6YEHHqhStnt7e6Nfv37YtGlThfsymUw4ePAgnnvuOVu3bt3w00//wWi8AHt2+NqABsBzZuARLXDmJUDTmDF2wt1WcTi1AUfN9t/x/yKGUwqcLT4ZY/86Xh5wPB8CkMMYy3YsPwfgcSI6A7som1bKfn8H8BuAP2FPJHS4hN3XA1hJRKeJKN8LuQlAMmPsr1KMZQDwLuwe1q8BBDpCvJ8GUJos5eMALHEcYzMAb5SiTZHwUNuajVGSJDKZTPD09HS3LXfQvHlz4YcffrDhLnP+qhMpKSm2tm3b1pjjKQ6z2Uyu9nh6eJT/HgpjDNOnT8fQoUPxwQcfwGaz4fXXX8eyZcvw6KOPFuy3atUqnD59GitWrKjQeCURHR0NvV5PWq0W3t7eThmjoly69C8BfxPRTxAEuSSTeTCFQs4kSSuLjIx0t3l3MGbMGAwdOhQDBxabz+GufPbZZ9LSpUsFDw9fSafrJDB2DkBMBb7LDPaEPxdgzyGRCcfNbQnIY0AoAfcIwBMA3O3gYLBPJ5qqByzfAYbnGGPpbjaKw6nxMMa8b1vuV2ixNoVZVAhnhN0yxuYXel2n0OuFsM/1zF/+F3epZcUYW48iPJOMsfHF7B992/LXsAvGwrQHUGwtUMZYp9uW3y202KOYZgXnXOEyMo55n62LGGN+SXYXBReeNRjGGFMoFIarV696N2zY0N3m3EG7du2Qm5srXr16FTUlC2xubm6NOZaSsFgs5O/v77LxHMKz3CGex44dg4eHB+Lj4wHY5wbOmjULvXr1QsuWLQEAn376KQ4ePIiPP/4YzqyJ6+HhgaCgIHbkyBHq0aO43373cvLkocLvtQAA586dw+jRoxEdHe0eo0rAz88PPXv2xDfffFPwGZeF06dPY9myDwSzeTfM5kfL6NE1wZ7o8AKAfxjwuw04LwD/CYAAudxb8vf3lgIDvQQvL09BpVIKSqUSu3evhV1wJkpAonDrNJuisAC4DHv+iRKTd5WDt6zAWymAdjRjrLRhYhwOh1NlcOGcT7dDRKdgv5P5grttKStceNZwBEGYP2rUqDcnTpwonzBhgsxZXpzyoFKp0Lp1a1tCQoLw/vvvV6l5Y+VFp9OJNb2Gp9VqhdlsdmnSKr1eD4VCUW7v06VLl3D//fffss7b2xvh4eFITk7Gb7/9hn///Rdbtmy5PRusU4iNjZVOnjwpVlXheTt6vR7Tpk2TBg4cyNRqdZX06I8dOxabN28uV9s33nhDMpmeEOxJC0sLA7AVwLNMJrMwf38/qW7dILFBg/qyxo0fx8MPPwyHdzi/KOotfPXVV+jWrRsGDhzJbt583AZsdryvJthF7F+w1ws/aQX+FIFUIlIyxjwI+AL20imVwRUA/zMD+k6FQso4HA6n2lFbxCdj7CF321BeuPCs4ej1+neJ6JtPP/101datW9u+/vrrqnbt2rnbrAKmTp0qjhs3DllZWVCr1e42p0Lk5ubCZDKhppdSSUtLg1wud2kpHJ1OxxQKRblvTjDGYC99ded6AKhTpw7y8vJw5MgRuEIMNmnSRDx27Fh+wcUqTXp6OgYNGiTdf//9mD59ultF5+31OY8fP17wOigoCCdOlG06Ym5uLlavXiNdvZos2JP+lZYTACZLMtkVmjZtIo0fP55QxpwJQ4YMAQC8+eZscerUlwB0twF/CUA6CYIX8/b2tUVHB4v3399Q1qLFM2jTpg38/f1pzZo1WLZsIBgbawPeFSuWdJABeEIPSIu46ORwiuWWxC7laMupZGSwaAisxM+kM/YV29b90xtqL1x41gIYY1cAdBcEoc/06dM/W7RoUWCXLiWGo7uM+++/H82bN5deffVVrFixokolLSkrJ0+ehFqtZjKZrMqLiYqQnJwMb29vl4omjUYjeXl5lVv01K9fH7t331oeTavV4vr164iKioJarcaiRYswadIk+Pv7o1WrVhW2uSRiY2Oxfft2G6rBb/C2bdsQEhKCjz76SBCEav0VvQWLxYK4uDiYzZHMbP4ZQGk83dcAzLQBO8R+/boJ8+evqfBc4NatW6Nz51aIjAwTH3qoD1q3bg1vb29CMefGpEmT0LNnT4wePRk3byYx4BuyJ4EqD4kAjmUBxsXlPgAOp4bTuHHjyo5t51QQC5NX4DPhotOd1JyrCM5dkSRph8FgGL5gwQK9szJ2loWUlBS8+OKLtt9++02oiglLysqZM2dQp06dGl9KJS0tDX5+fi4tbaLVapmXl1e527du3RpGoxHbt28HANhsNrzzzjsYMGAAlEq7xyg6OhoJCQmYPXs2zp07Vyl2F4cjs22VDFm9nXPnzqFhw4asJonOfLRaLUymP0V7/W3A7gHMAHAcwJcAFgMYbwPa2IAIBjRE48aXaM+e77Bw4f8qJQGVh4cHli1bipdeegndunUrVcKpqKgo7N37vThkSCuy55f4n2QvuVYWtACe1APacYwx9/8hcDgcDqfGU/OuJDglwhjbbTAYftm4caNb6wiuX7/eNmDAAGRmZtL27dsxd+7cCp+Le/bsQZMmTXD58mUAwObNmzF48OCCR3x8/C3bK5sLFy6gYcOGNdrbCdiFZ0BAgEuFp06no4pkgCUiLF26FD/++CP69OmDvn37wsPDA9Om3Zr1vHHjxnjzzTfx7LPPIjk5uaJmF0udOnVgMBgoJyenTO1uP8dTUlLw8MMPY/DgwRgwYADmzJkDi8VSaXYeOHAAhw4dwmOPPVYtRHJpsFqtuHbtGo4ePeoItZ4sAZ1tQDSzh61GQSbrLfn5zbQ2arRR6tEjTRwxIkb08Mii5cvfxebNnwohISHuPgwIgoDXXnsVGzeuglKZAKCVZJ+vWVrmmwHTD4yxvc6ykcPhcDicwlD+HCdO7YGIGiqVyt937typCAoKcvn4FosF7du3x3vvvYfKnG/6wgsvIDMzE4888sgdc8EAYOnSpUhLS8OiRWWZy1V6Bg4caBsxYoQ4bNgwp/RfVXjttdeg1Wqt7733nsvCRMeOHWtr3bq1WNTnWl3p2bMnmzZtGvXu3bvUbW4/x1NSUvDMM89g27ZtsNlsePLJJxEfH4++fftWio2PPvqoNGXKFBo+fHi1uKHCGINGo0F6evotj//++8/233//4erVq0JWVhYpFArm5eUlCYJIjRo1FOrXr4/77rsPTZs2LXKO9rhx46SwsDC2ePHiKinArVYrnnnmeenw4ZMC8AEDxlPJkfB/AGilAwyxjLHrrrKTw+FwOLWbKj+/iFP5MMbOe3l5fZKQkDBxwYIFzqsbUQwHDx6EQqGQ2rVrV2ked71ej9OnT+OTTz7Bs88+e4fwPHnyJHbt2oUvv/yysoa8g9zcXNT0jLYAkJWVhXvuucel0RJ6vZ58fWvWNJuoqCjp3LlzYmmF593OcVEU0bhxY6SnV175RUEQ0KBBA7eLTqvViuzsbGRmZt7ySEtLs6WmpiIjIwM3b94UcnJyCACUSiVTKBSSSqVi/v7+YkhIiNikSROMHj0aDz/8MHx9fQmlrB+cnJyMs2fPCm+8Ue562U5HJpNh5crlwo8//ohZs6bDZvtKAj4TgKJuLDIAj+sA62wuOjkcDofjSrjwrKXo9fpXdu3aNWb06NGKe++916Vjf/XVV7a2bdtWqudgz549aNeuHaKjo+Hn54e//vqroHxGXl4eXn31VSxcuLBU86fKi06nE2tDDc/c3FzWvHlzlwpPg8GAmiY8BUFgklT6KcFFneOFS9qYTCacPXsWL730UqXZqFAopIyMDKd81owx5OXlISsrq0BIZmVlIT09naWmptpu3LhBWVlZlJ2dLTjquMLT01NSKBRMpVIxX19fMSgoSIyOjkaHDh3AGMOyZctw9OhRiKJYamFZGnx8fKQnnniCZs+eTZ06dSoyQ3JVoEePHmjTpg09/vhk6Z9/GgLYBCDOsTUVwCYGfKwF0i8BlhXus5TD4XA4tREuPGspjLEcuVw++80333x748aNXq66kMrNzcXx48fF/CQvlUVSUhJGjx4NAIiLi0NSUlKB8FywYAH69u2L5s3Lm/nx7mRnZ8NsNiMsLMxpY1QVtFqt5O/v79KQQ6PRSK6sG1oVKeocHz58OJKTkzF48GBcu3YN3bt3R6NGjSptTKVSSRkZGcVuZ4xBp9NBq9UWPHQ6HTQaTcF6jUaD3NxcW05ODmVmZrLMzEzKzs4WtFotRFGEp6cn8/T0lJRKJfPy8qLAwEAxJCRE1rZtW0RHRyM2NhZ16tSBQqEA7pKX4KOPPmJnz56lZs2aVdp7YE/ks1dYuXIl5s6dy3x8fNCpUyf2+OOPC1Xx++7j44OtWz8XN2zYgCVLhoCxfhJwTQeckgGe3wJ5qwAcYIzV+ERoHA6Hw6lacOFZi7FarasuXbo08+eff47p2rWrS8bctm0bgoKCbJGRkZUmXHJycnD8+HFcvHgRRASbzQYiwowZM7B9+3akpKRg4cKFlTVckRw/fhzBwcFMEISq6QqpRAwGA/P393fpmCaTiVw9ZlWiuHN82LBhiIqKwtatW5GRkYEJEyZg79696Ny5c4XHXLt2Lf7991/x1KlTuHz5su3mzZssOzubcnNzSaPRkE6nI4PBAFEUIZfLIZPJmFwuZzKZjHl4eDAPDw9SKBTk5eUleHt7i3l5eTh//rw0c+ZMISYmBvXr18/3YleadzImJobt3buXNWvWrFK9tIIgYMqUKZg0aRJ999132LBhA3799Vfpiy++EESxSk77xJgxY+Dp6YmFC9822WymKQC+ZsxocLddHA6Hw6m9cOFZi2GMWYlo+sqVKz/r2rWr0+MYN2/ezFasWEFLliyp1Cu1H3/8Ef369cO8efMK1o0fPx4nT57EsmXLsH79eshkzj3Vz549i7p160qoxPC+qorJZBLcIDwRGBjo0jGrEsWd4zdu3ChYDvlWJoQAACAASURBVA4OxvPPP481a9ZUWHhKkoQNGzbYQkNDhdzcXAIgqtVqNGrUCMHBwQgLC0NERATCw8OhUhXUvySUkNFGq9WiU6dOQuPGjdGwYcMK2Vcc/fr1E9avX8+mT5/ulP49PDwwaNAgDBgwgLp27crWr18vTZw4sUpmh79y5Qreffddg81m6swYO+ZuezgcDofDqZJ/mByXcjkvL8/pg6xevVpKSEjA8uXL0bFjx0rtOykpCbd7bLt3747ExEQYDAZMnz79lrIqp06dqtTxAXspldjYWKd4O5s0aYIlS5YULK9fvx4rVtinZ61YsQJNmjTBtWvXCrZ/9tlnaNKkCf78809nmAOLxUIBAQFO6bs4zGYzXD1mVaK4c3z16tW3rOvSpQuMRmOFzvHLly+jc+fOkpeXl7Bu3Tpat24dEhIS8Nprr2HKlCkYMmQIOnTogPr16xcWnXfF29sbderUkU6cOFFu2+5GfHw8MjIy6Pp15+bMkclkeO+994SVK1cKFy5ccOpY5UGn0+Hpp5/Wm83m6Vx0cjgcDqeqwD2eHL3JZHL6DYh169YJixcvRqtWrZzR9x3rRo0aVenjlERaWpqta9euTvF2enh4YM+ePZg0aVKR4qtBgwZISkrC5MmTAQC7d+9G/fr1nWEKANeHvVosFjDGyiRyqgmlvlFR3Dl++3lORPj6668rZNR3332H0NBQbN68mSo7jNRisUCpVFZqn4Xx8PBAaGio7cCBA+LQoUOdNg4APPTQQ+jRowebNm0avv76ayrPca1atQo7d+6EIAiOupyv4b777sPy5cvx008/wcPDAwqFAlOmTEGHDh1K1SdjDC+//LIhJydnm8Vi+bjMRnE4HA6H4yS4x5NjMJvNTj8P5HI5q4HCoYC8vDxyVkZbURQxePBgbNiwocjtXbp0wd699hrwycnJ8Pb2dpp30Gq1wmw2w5WJfgwGA+RyOQShZv1cMcZYVTwmuVwOuVzOnDF30dnCEwDat28vJiUl2Zw6iIM333yTAEivvvqqraw1sU+fPo39+/fjyy+/xDfffIPVq1cjLCwMy5cvR2ZmJrZt24Zt27bhgw8+gE6nK3W/69ats504ceJfnU43qYyHw+FwOByOU6l6Vz0cV6M3m81O93zLZDIYDDU3r4VOpxOcWUpl+PDh2LFjBzQazR3bvL29ERYWhgsXLiApKQm9evVymh1paWnw9PR0+pzZwuj1elTVBC41kf3799vatGnjlP8Gi8VCzr4BNXr0aJw5c0Z0xe+NIAjYsGGDePToUWH06NHSpUuXSt02MzMTAQEB8PDwAAAEBATAx8cHX3/9NWbPnl2wPigoqNTfaUmS8OGHH0o6na43Y8xY9iPicDgcDsd5cOHJMVgsFqerCMYYyuoRqC6kp6fDarUiJCTEaWN4e3ujX79+2LRpU5Hbe/XqhaSkJPz88893zAWsTK5duwZvb2+XfpCOzKk18+SpYpjNZly5ckXs3bu3U+Yru8LjGRUVBR8fH6fOJS2MWq3Gnj17KDQ0lIYNG4a33nrLptVq79qubdu2uH79Ovr27YsFCxbgxIkTuHbtGsLDw8tdb/j69esQRVHHGPu3XB1wOBwOh+NEuPDkmAHYxowZk/vmm2+aNm3ahEOHDiE5ORk2W+VFq7Vq1UpKSEhwSfibqzl58iRCQkKcHjY5ZswYbNu2rUjPcadOnfD9999X6KK1NKSmpsLX19elItDX1xeMMRo6dKgtLS3NlUPXOpYsWYLg4GBWr149p/RvtVrJy8vLKX0X5oEHHqDdu3e77PdGoVDgvffeoy+++AJHjx5F3759cbekbSqVClu2bMG8efMQEBCAF198ESdPnqyQHVeuXIGnp+fFCnXC4XA4HI6T4MmFajmMMUZEsadPn278+++/N1SpVA/K5fLGZrO5nslk8g8KCtLHxMSwBg0aqGJiYmR169ZFREQEQkNDIZfLSz3OK6+8Ivbr14+tWrUKTz75pBOPyPW4qpSKn58fevbsiW+++Qbx8fG3bFMoFJg+fTqcGe4L2D0qAQEBLhWearUaO3fuxJIlS9C/f3+MGDECU6ZMwY4dO3DgwAG8+uqrCAoKcqVJlUJViwCYOHGi7cKFC+LSpUudVovWarW6JEnU0KFDae7cuSJjDESuK60bGxuL7du3i4899pjt5ZdfpsWLFws+Pj7F7i+KIlq2bImWLVuiQYMG+Oqrr5CWlgadTofyCPTLly/DYrGcrsgxcDicWyFCHoDiv8glo2EMTi9XV9t4/fXXK/SZzJs3j38mboILTw4YY9cAXAOws/B6IlLeuHGj/o0bNxoeO3askbe394OCIDxgsVgijEajv0qlMgcHB5sjIiKoTp06isjISI/Q0FCEhYUhLCwMQUFBBXMB9Xo9jEYjuTIpjau4dOkSGjRo4JKr27Fjx2Lz5s1FbouLi3P6+Onp6VCr1S5XTIGBgXjrrbfEoUOHYubMmWzDhg0UGhrKFAoFZs6cydavX1/tojfs93xcJ4pK4qmnnpL+++8/Ydu2bVCr1U4bx2q1lktQlZUOHTrAarWy8+fPU6NGjZw+3u2sXr1anDBhgq1nz56YNWsW+vfvf0dyrCtXrkAQhIKbRefOnUN0dDTuu+8+vPXWW5g3bx7kcjkyMjLwyy+/oF+/fiWOefPmTWzatEmv0+l+dtqBcTi1k/IKnIq25RQP/0yqKVx4coqFMWYA8IfjcQtEJGq12jCtVht15cqVqMOHD0cpFIr6np6eDQDUMZvNYSaTycfHx8d8zz33yPV6vXD//fdLw4YNq3FZYtLS0mw9e/Z02nEdP3684HVQUBAKz12bMmVKkW2KKr9RGWRlZaFOnTpuE3nNmzdHUlISabVaBAYGUmpqKvr370/nzp3Dvffe6y6zyoXNZnNpkqbiOHjwIE6fPi1s377dqaITsB+zM0PB8xEEAVFRUdK+ffuERo0auVzdq9VqJCYmiomJiVi8eDFLSUlhU6dOveV7o9fr8dZbb0Gj0UAURdSpUwfz5s2Dt7c3PvjgAwwYMACenp5QKpWYOnVqieNlZ2djzJgx+ps3b34I4AtnHhuHw+FwOOXF/Vc9nGoJY8wGIMXx+KWofYgowGg0nm/QoEGQyWTCggULapzoBACNRkN16tRxtxkuITc3lwUFBbnVu+jh4YHAwEAAQEREBEaPHm0bPXq0GB4ebmvfvr04cuRIREVFudPEUiFJUpXI1rtmzRppwIABCAkJcernKkkSJEmCQqFw5jAF9OzZU9yxY4c0efJkt7mVBwwYgOjoaJo0aRL179//lvPygQcewMaNG4tsN2PGDMyYMaNUY+Tk5GDMmDG6jIyMlQaD4SVW1WK4ORwOh8NxwIUnx2nI5fKZ0dHRfvPnz68SF9jOQqvVOrWUSlVCq9VK/v7+VerDfP7558XBgwfj4MGD4q5du2wDBgwQvby8pIiICNStW1cwGo0wGo0wmUwQRVF69913BWfVOS0LNpvN7d8Ls9mMc+fOCS+//LJLxhJF0WX1WEeOHImPPvpIuHHjBkJDQ10yZlE0bdoUDz/8sDRnzhwsX75cqMzpBrm5uRgzZow+PT19tcFgeJGLTg6Hw+FUZardvChO9cFisaxNSUk5PWjQIN3ff//tbnOcwvXr1yFJEoKDg91tikswGo3M39/f3WbcwT333IMRI0Zg/fr14rFjx7Bs2TKhS5cuJJPJbCEhIbZ7772XtW7dGnl5ebRo0SJ3mwugang8ly1bhsjISMkVYcpms9llohOwZ40NDw+37dmzx2VjFse7774raLVa1r17d3z88ceSXq+vcJ/5ovPGjRtrDAbDDC46OZzqBRHtI6Ket617lYj+IqLTRHSTiK44Xv/kLjtrG0Rkc7znfxDRV0RU7ox4RLSeiAY7Xq8hovtL2LcTEbUttPwUEY0t79iOPpo4juWO84mIoonI4Fj+nYiOEFGjQrbkEtFvRPQPER0gor4VsSUf7vHkOA3G2CUiesRoNI4fO3bs0kGDBnk+99xzHq7Iaukqjh8/jtDQUImIasVNHJPJVCW8hSUhl8vRvHlzNG/enHBbpuEWLVrQ9OnTWXp6Oi1fvhyHDx+2NWnSRHjnnXfIGfMtzWYzPDw8itwmSZJb53hKkoTExERp3rx5giuSHOV7PF1Jjx49xMTERGnkyJFu/X6qVCps27ZNPHz4MBYsWMA+/fRTrFixAs2aNStXf7m5uRg3bpzu+vXr6wwGw/NcdHI41ZLNAIYD2FVoXR8AkxljB4loPYDvGWNb3WFcLcbAGGsGAES0CcBTAN7L30hEomO6WZlgjE26yy6dAGgBHHHsv7KsYxQx5lkA+ceyHoXOJyKKBnCp0LFOBjAHwDhH84OMsb6Obc0AfEtEBsZYhe7m1oqLZY77YIwxi8Wyzmg01v/222+3x8XF6Q8cOOBusyqNP/74A9HR0bXmos9sNlNVF54l0apVK/j6+qJXr15ITU21Pf/882JycjLr1q2b9Pvvvxfsl5ycjK5du0o7d+4soTfgzJkzWLhwId544w3873//w3vvvYcVK1bg/fffR58+fWyPPPIIOnToIC1evBhGo/GWtpIkkTs9nl9++SXkcjl17tzZJeO52uMJAOPHj8fFixeFzMxMl45bHO3atUNSUpLYtWtXLFmyRCpPH3l5eRg/frw+NTX1M4PB8BwXnRxOtWUrgL5E5AkUCIEIAIfcaBPnVg4CiHV4APcS0ecAzhKRSERLiOgEEZ1xiDaQneUOr/UOACH5HTk83A87Xvciol8dnsY9js/+KQDTHR7IDkQ0n4hmOvZvRkS/OMbaRkQBhfpcTETHieg8EXWowLH6AsguagNj7DSANwA8U4H+AXCPJ8dFMMYyAAwhom4zZ878rHXr1n6vvvqqqrqHqF6+fJndf//9teYGjtlsrtYlcYgIa9euJQ8PDwQFBYkAEBcXJ3zyySfSxIkToVarba1atRL37dsn1atXj15//XXWtGlTunz5Mo4cOYL09HRMnjwZN2/exIcffiidO3dOePjhh20eHh7MZrPBZDLBZDIRAIwaNUrs0aMHTp8+LXz88cdSx44dhR49emDu3LlQKBRun+O5du1a25NPPim4ygaH8GQASu1e3bNnD55//nkkJiaiXr16SElJwTPPPINt27aVqr2vry9CQ0NtP//8szh06NAy2ZuZmYm3334bZ86cga+vL+RyOSZMmICuXbsCABYtWoTdu3dj9+7dZRbUs2fPRqdOnYR//vkHZSn3otFo8kXnBoPBMJWLTg6n+sIYyyKi4wB6AUiE3fu5hX+vqwZEJAMQB+AHx6pWABozxq4Q0ZMAchljLR03Dg4T0Y8AmgNoBKAJgFAAfwFYe1u/wQBWA3jU0VcgY+wmEa0EoGWMvePYr2uhZp8BeJYxtp+I3gAwD8Dzjm0yxlgrIurtWN+tDIdZn4hOw15iRgXgkRL2/RXAi2Xou0i48OS4FMbYT0RU/5dffnmjb9++U2fMmKEYMmQIudoTUllcv37d1qdPn1rxPTKbzTCbzajOwhOwZ8ItjEwmw+TJk4UhQ4bg0KFD4g8//GBzCETh1VdftcXFxYkBAQGsYcOGko+PD0aMGCF6enqia9eubMmSJQgLCytRuXXr1g3dunUTTp8+jUWLFkmdO3em+Ph4slqtbg211Wg0Qps2bVyW8dVqtZZZoO3cuRMtWrTADz/8UGzpoLvRuXNncfv27bahQ4eWWmEzxjBt2jQMGDAAb7/9NgAgNTUVe/fuBWAPU96zZw/CwsJw6tQptGzZskw2qVQqtGrVii1atIitXbu2VKHOWq0W48eP16ekpGzS6/VP84tTDqdGkB9umy88J7jXHA4ApUOMAXaP5ycA2gI4zhi74ljfA8CD+fM3AfgBaADgUQCbHaG4qURUVF3l1gAO5PfFGLtZkjFE5AfAnzG237HqUwBfFdrlG8fzKQDRpTvEAgqH2g4DsAr2GyFFmlLGvoukVlwwc6oWjvqgLxLRpwkJCRu3bt0au3DhQq8GDRq427Qyo9Foak1G27S0NHh6elaJ2pPOIDAwEP3790f//v0LBMqCBQvEuXPnQqVSFcwXNZlMkMvlEAShTK7CZs2aYfPmzcKRI0ewaNEiduPGDSE7OxtmsxkZGRno1asXDh8+DF9f30o+sqKRJImKm3/qDGy2sk2J0ev1OH36ND755BM8++yz5RaeEyZMQI8ePcScnByUNjHWsWPHIJfLUdhLGhERgVGjRgGwz+2OjY1Fr169sHPnzjILTwB4++23qXPnzjhz5gyaNm1a4r4WiwWTJk3S//fff5v1ev1kLjo5nBrDtwDeI6IWAJSMsV/dbRDn/+d45uO4OagrvAp2D+Su2/brDeBuv89Uin3KgsnxbEPFdN12ACUVgW8OoMKZQqunm4lTI2CM/aHValtcvHhx1siRI3UJCQmW2+fBVXV0Op1QW2p4/vfff/Dx8alVF7xEhNuTYXl6epZ7riIRoV27dkhMTKSxY8eylStXomXLlujfvz+USiW6d+/usvc3v65maWnSpAlmz55dsGy1WvHoo49i6tSpAOyhqVOnTsWgQYMwYMAAPP3007e0Z4yBiEp9fHv27EG7du0QHR0NPz8//PXXX6W2tTBqtRpBQUG2fG9labh06RLuu+++YrcnJSWhd+/e6Nq1Kw4cOACLxVJmu1QqFWQyGRISEqQzZ86UuO/y5cst//7773G9Xv8EF50cTs2BMaYFsA/2cMzN7rWGUwZ2AXiaiOQAQEQNicgLwAEAwx1zQMMBFJVE4SiAjkQU42gb6FivgT3k9RYYY7kAsgvN3xwDYP/t+1UC7QFcKmoDET0I4FUAH1Z0EC48OW6FMSZZLJYVRqOxwZYtW37s3bu3/ujRo+42q1QkJyeDMQa1Wu1uU1xCSkoKfH19+UVvJSAIAp555hn66aefcOrUKZw6dQobNmxwqTc5LCzMlpSUVOrPU6lU4uLFiwVJko4ePYqQkIK8Cfjwww/Rpk0bfP3110hMTMTzzz9/S3tJklCakNJ8kpKS0KuXPeInLi4OSUlJpW57Ox07dhQTExPLnIUwnwULFmDQoEEYPnw4LBYLDh48iC5dusDb2xtNmjRBeX+zduzYQWq1miZOnIjikq6dPHkSn3/+uU6n0w3nopPDqZFsBtAUwBfuNoRTatbAPn/zVyL6A8DHsHsbtwG4AOAsgI9QhEB05Dx5EsA3RPQ7gC2OTd8BiM9PLnRbs3EAlhDRGdiz1L5RScdRP7+cCoCFAApn3u2QX04FdsH5XEUz2gI81JZTRWCMpQHoKwhCn2nTpq1t2bKl9+zZs1X33HOPu00rlpMnT9aqUirXr19HQEAAv/CtRArftNBoNJDL5WVKvlMRnnzySfHtt9/G+PHjSy1427dvjwMHDqBHjx5ISkpCXFwcfv3VHhmWmZmJtm0LSpDdkTTH4fEs1Tg5OTk4fvw4Ll68CCKCzWYDEWHYsGGlPLpbmThxIuLi4kSNRgMfnztuKN9B/fr1sXv37oLlV155BdnZ2Rg+fDgOHToErVaLxx57DABgNBqhUCjw6KOPltkuf39/vPPOO7Ru3TrMmTMHSUlJt9iXl5eHGTNm6M1m8yjG2I0yD8DhcKo8jLFtKOJ3nzE23vXWcBhj3kWs2we7Zzp/WYK99MicIrooMvMrY6xToddJAJJu234ewIOFVh0stO007HNDS+ozEyXM8bz9fGKM/QtAWcy++2Cft1rp1IoLZk71QZKkHQaDIfrYsWNvx8fH6xMSEiyVUWzdGThKqZSrJEJ1JCMjA2q1mgtPJ6HRaODh4eGy97dfv34AIB05cqTUbXr16oUffvgBJpMJ58+fx4MP/v9/5PDhwzFv3jxMmDABq1atQnp6+i1tyzLH88cff0S/fv3w448/YteuXfjpp58QGRmJGzfKp71CQ0OhVqtLHW77yCOPwGw2Y8uWLQXr8j29SUlJmD9/Pnbt2oVdu3YhKSkJR48ehcFgKJdtAPD444/D19dX+vrrrwvWMcbw2muvGUwm0yabzVZyXR8Oh8PhcKoBXHhyqhyMMYPRaHzdaDQ23LJly3c9evTQf/fdd2Waj+YKLl++jEaNGrmvHoaLycrKQkhICP/NcBKuFp4AIJPJWFkSDDVq1AgpKSnYuXMnOnS4NRLIUaMSgwYNwpUrVzBkyBDcvPn/yfrKEmqblJRUULYkn+7du2P16tX4999/0bVr14LHrl27iunlVuLj48Xly5ez0ghgIsLSpUtx8uRJ9OrVCyNGjMDcuXMxZcoUHD58+BbvpkqlQvPmzbF/f8Wm3DRt2lT4888/C4z77rvv2NGjR2/o9fppFeqYw+FwOJwqAg+15VRZGGMpAAYRUZuFCxd+8umnn9aZN2+eV5MmTdxtGgAgPT3dGh0dXWu+Qzk5OVJgYCAXnk4iLy8PSqWyXGG2OTk5SE5ORmpqKtLS0pCeno6srCxoNBocPFgQrYP27dvj0KH/r02uUCjEyMjIMo3VqVMnvPvuu1i7di1yc3Nv2ebn54c+ffqgT58+mDp1Kk6dOoXu3bsDKFuo7bp1dybWGzVqVEFW2fLw1FNPYcuWLWz79u0UHx9/1/2Dg4OxZMmSO9YPGDDgjnUJCQnltiufNm3a4M033xRfeeUVW6NGjcQPPvjAZDAYBjiygHM4HPdRZNKXMrTlVD78M6mm1JqLZk71hTF2lIgaX7hwYeyECRPe79Kli2LmzJmK4OBgt9pVm0qpAIBOp5MCAgK48HQSGo0GaWlp4tNPPw2LxZL/kKxWK7Narcxms5HNZmNWq5UcNVXJYrEIJpM9k7pKpYKPjw/z9fVlgYGBTK1Wo06dOoRCkS0xMTHSoUOHCpZ9fHzYsmXL2JIlS0r9ucbHx8PHxwcNGzbEiRMnCtYfO3YMDz74IJRKJXQ6HZKTkxEeHl6wvazlVCobR0In4b333kPv3r3h6enpVntup3///vD09ERSUpK4cuVKyWq1fsEYKzndLYfDcTqMwTU1rjilZt68efwzqaZw4cmpFjgmcq8noq/37t37+t69e5964oknPMaOHSu64wKSMVarSqkAgMFgKHUdRE7ZsdlskMvliImJkTw9PQUPDw94eHgIcrkccrkcHh4eBc++vr7w9/cveCiVynxvIuG2JBUvv/xy4UVh1qxZBQs9evSQHnzwwTLdTAgLC8Po0aPvWP/XX39h4cKFEEURjDEMGjQIjRs3LtheFo+nsxgyZAhWr15t27BhA02aNKnK3UTp2bMnHnjgAQwcONBgsVh4iC2Hw+FwahRceHKqFYwxDYAZRLRi7dq1H33++edtX3nlFVWXLl1celF79epVEBECAgJcNqa7MZvNQm06Xlfj7++PoKAg26xZs1w2bzgzM1Ns0aJFqfY9fvz4HetatmyJli1bArAnyHn88ceLbe+Y4+n25FTz5s0Tp0+fjvj4+CpZCmndunUmIlrJGMtzty0cDofD4VQmVe6OL4dTGhhjFzUaTffMzMyBc+bMuTp27FjdhQsXXDb+yZMnER4eLrnbg+NKzGYzcY+n8/Dz84PBYHCpMOvWrRt75ZVXJKvV6vSxqkpysDZt2kAmk7HTp0+725Q7uHnzJhITE5nRaHzX3bZwOBwOh1PZcOHJqdYwxnbr9frYP/74Y87IkSO1b7zxhun2hCfO4M8//0R0dLTbvTeuhAtP5+Ln5wej0ejSOxmLFi0ih9hx+lhlyWrrTBYsWAC1Wo2OHTu625Q7+Pzzz20ymewrR11jDofD4XBqFFx4cqo9jDGrxWJZZjQao3fu3LmxR48ehs2bNzNnenGuXLlSq0qpOJLZwNeXz+d3Fr6+vrBYLC5VZoIgQKlUMlfMk64qwvP777/HggULSCarWjNN9Ho9Nm7caNbpdAvcbQuHw+FwOM6AC09OjYExlqXVaifp9fpHli1bdrxfv366X375xSljpaenW52Z0fbgwYMYNWoUmzFjBpYuXYrvv/8ely9fdlu4YlpaGhQKBaraxXpNws/PD2az2eXKTBAEnDt3zuknVlVILgQAJpMJ9913n7vNuINvv/2WCYJwgDF23t22cDgcDofjDPhVJKfGwRg7S0RtdDrdgGnTpq1s0aKFz+zZs1WVmYH2bqVU1qxZY/rtt9/MMTExyqioKFlERAQiIyMREREBhUJx1/537NgBrVZLMTExtt9++w1JSUmUlZUlWCwWKJVKplQqJZVKxfz9/cXg4GAKDw9HnTp1UK9ePTRo0KDSkx5du3YN3t7eDLdlTOVUHu7weALAsmXLxJEjR6Jjx44FiYKcgbvLqQCA1WqFJEmQy+XuNuUOVq9erddoNPPdbQeHw+FwOM6CC09OjYQxxgB8S0Q/nDhxYubAgQPneHp6IioqytywYUOP2NhYZXR0NKKjoxEZGVmmC1FJkqDT6UoUnt98840pOTl50YEDB5i3t/d9MpmskdVqrWMwGIKVSqUlNDTUHBUVJdSrV08VGRkpRkZGIjIyEuHh4fD09ET79u1x6NAhzJs3Tyxsm16vx/Xr1+nGjRvi9evXkZ6ejtTUVNvFixdx5MgRunnzppCXlwdBEKBQKJinp6ekVCqZr6+voFarhbCwMERFRSEmJgaxsbEIDw+HINw98CE1NRW+vr5ceDoRX19fmM1mWK1Wl3qW09PTAQCZmZlOHacqZLXV6/UQRbFKeF4LwxhDZmamF4A7UwdzOBwOh1ND4MKTU6NhjBkBLCCihRaLJeLvv/9u9PfffzdSKpVNPD09m1osllij0RioVqv1MTExrFGjRqp69erJ6tati+joaKjV6jsuUi9dugSZTAY/P7/ixsT169eVAD5ijOUU3kZEglarDdNqtdGXLl2K3r9/f4y3t/f9oig2sFgsdYxGo1qlUpnDwsIsVqvVLykpCf379y9or1KpUK9ePdSrV69wt7fMNWWMIS8vDxkZGZSRYzBn8wAAIABJREFUkSFmZmYiIyMD169fl9LS0tiZM2coKyuLcnNzyWaz5QtU5unpyZRKJby9vYWAgAAKDg5GaGgoIiIicPr0aQQGBtaqZEquJr9eZ0ZGBsLDw1027ty5c6WpU6dSXFycU9VYVQi1lclkICLk5uYW+/11B1arFYIgSDabrWqk/uVwOBwOxwlw4cmpFTDGJAD/OR57Cm8jIkV6enr99PT0RsePH7/Xx8enGRE9YDAYoolIFhkZaYiNjZU1bNhQFR0dTefOnUN4eLiEYuZIZ2VlQRAE0+2is5AdqY7Hkdu3E5Go0WjCNRpNNIDub7311qy2bdsqgoKCSn2sRAQ/Pz/4+fkhNja28KY77NXr9cjMzKSsrCy6efMmsrOzkZ2djfT0dCk9PZ2dP38e2dnZlJeXJ8THx5faBk758PLyQkpKikuFp1arFXr16uX0capCciGVSoWwsDDbDz/8IA4bNsytthTGYrFAEAT3xyJzOBwOh+NEuPDk1HocXtE/HY9bICL15cuXG12+fLnRvn377lepVM1NJtODZrNZ3a5dO31kZKSlXr168tjYWFVUVBSioqKQlZUFhUKRXE5bbPh/gXxIpVIpZ8+ePXXVqlUqZ1y0q1Qq1KlTB0XMfy1KVNeaLL7uwtvbW0pLS3NZ0rf8MiohISFOH0upVMJqtbo9xrVHjx7i999/bxs2bFiVOZ8dwtP5xVQ5HA6Hw3EjXHhyOCXAGMuC3TN5i3eSiMS8vLzIvLy8+n///Xesh4fHvSqV6kGbzRZrMBgiPD09T1XG+AaD4ZWzZ8/237p1a8MhQ4a4/aKd41x8fX3ZjRs3XDLWTz/9hAULFmDx4sUu8USGhITAaDS6PZO6Wq2GyWRytxm3wIUnh8PhcGoDXHhyOOXA4Zm85njsLbyNiKiyPDuMMTMRPfb222+fbNWqldKZJVw47icyMhKJiYnSyJEjBZVK5bRxrFYr5syZw+bNm4cuXbq45IZGcHAwjEZjlbh5whirEnbkY7VaQURceHI4HA6nRuP2u88cTk2D2am0JCGMsb8kSZr95JNP6tPS0iqrW04V5LXXXhNDQ0PRq1cvyWg03rG9VatWxbZdtGgRunbtWqpar9u2bYO/vz/69u3rMgHm7+8Pq9UKrVbrqiGLxGw2Qy6XV6lEWWazGYIgWNxtB4fD4XA4zoR7PDmcaoDJZFqqUCgUQ4cOnbd27VplgwYN3G2SW8jJycGkSZMA2Mt/iKKIgIAA6HQ6MMawZcsW+Pn5ITc3F8OGDcPatWsRERHhZqtLj5+fH1atWiX069ePffHFFxg/fnyp2kmShD179iAsLAynTp26az3Ob7/9VoqLiwNcWB5HEAT4+Pjgn3/+wUMPPeSqYe/Aw8MD2dnZgruy7JrNZmzbto0lJyezRx55RKhXrx4uX74MIuLJhTicKggR8gD4lLO5hjH4VqY9HOCPP/6o0GfSuHFj/pm4Ce7x5HCqCUajcbFWq31izJgxhpMnT7rbHLfg7++PrVu3YuvWrRg6dCjGjBmDrVu3IikpCUOHDsX7778PAEhISMDgwYOrlejMRxAEDBw4EImJiaUWIsePH0dsbCyGDh2KnTt33nX/q1evUufOnV3++x8YGChdunTJ1cPewsiRI5GXl8dK8z45g1WrVklLly7FH3/8Qa+99pr02GOPYe7cuUySJDURebrFKA6HUxLlFTgVbcspHv6ZVFO48ORwqhEWi2WTwWAY8PTTT+t++uknd5tTpRgzZgzOnDmDDRs24LfffsO4cePcbVK56dWrFyUnJ4tFhdsWRVJSEnr37o2uXbviwIEDsFiKj9o8f/48TCYTNW7cuLLMLTWhoaHS1atXXT5uYbRaLbp37y688847sFpdP63Sz89PiIiIkNavX0979+4Vjh07hsOHD1NsbKwJwECXG8ThcDgcjovgobYcTjXDZrPtJqJHZ8+evUer1foNHDiwSiVKcRdyuRwvvPACnnrqKXz88ceQy+XuNqncREVFITIyUtq4caOQH1pcHBaLBQcPHsSsWbPg5eWFJk2a4OjRo3j00UeL3H/Dhg1o3ry5TSaTubycSGRkJKWmpjqtf71ej3PnzuGff/7B5cuXkZycjIyMDJtGo2EGg0EwGAyC1WqFn58fM5vN9Nlnn0kTJkxw6Q3YunXrIifnjhK/GDNmjM8bb7zxAoAtrrSHw+FwOBxXwYUnh1MNYYz9SkSt//e///2iVCr9e/bs6W6TqgSHDh1CcHAwLl68iLZt27rbnAoxZMgQ2rhxo23SpEklCsRDhw5Bq9XiscceAwAYjUYoFIoihee+ffvwww8/4OOPP3ZLDcvIyEjxwoULDOWYW2q1WnHhwgX8/fffuHTpEpKTk3Hjxg0pNzeXGQwGMhgMgslkgo+PD4KCgqSIiAgpOjpaaN++vRgWFob8R2BgIARBoF9++QXPPvus0L17d0RFRZU4ts1mg0ajgV6vh9FohNFohMlkuuX59tdGoxFarVYyGAxMr9czg8GA2NhYGjx4sKjVau8Qu126dMH8+fObEFEsY+xiWd8fDofD4XCqOlx4cjjVFMbYP0TU6ZVXXjmoVCp9ivNw1RbOnTuHo0ePYtOmTRg7dizi4uIQHBzsbrPKTXx8PH3wwQfikSNHShTRSUlJmD9/Pnr37g3A7vWLi4uDwWCAUqm8Zd+VK1dKI0aMEFq0aOFU24sjODgYubm5Ntzlv8dsNuPAgQPYv38/zp49a8vMzCSNRiOoVCqo1WopIiKCRUVFoUWLFreIyqCgIMhkMsA+jaRET2br1q3xyCOPSDNnzsTgwYOF3NxcZGZmSpmZmVJWVhZlZ2dTXl4eabVaMhqNkMvlkMlkEEWRCYLARFGEKIpMFEXIZDImk8kgk8kgl8vJ09NT9PT0hEKhEFQqFby9vRESEoKvvvqKKZVKmM1mkiQJgvD/Jnp4eCA+Pl7cunXr0wBeqPi7zeFwqjqOpGJnAcgBWAF8CiCBMSYRUScAiQCuFGoykzHG59k4ESJiAN5jjL3gWJ4JwJsxNr+U7ccDWAIgpdDqkQD0AL5njJV5noujzx8ZY6mO5TUOG/8qa1/uhgtPDqcawxj7nYh6zJw586cPP/zQ627ZTGsqjDG8+eabmDVrFsLDwzF+/Hi88847WLx4sbtNKzdeXl4YOXKktHjxYpaYmCgCdm9m165dC/YZPnw4Dh8+jNdee61gnUqlQvPmzbF//3706tXrlj5jYmKE1NRUGwC3eDyDg4Oh1+uL9XZKkoTevXvb0tPTRT8/P9a0aVNpyJAhYpMmTdCoUSN4enoClZib4J133hHGjRtn27hxo83b21vw8/MT1Gq10KxZM4SGhiIiIgL33HMPIiIioFAo8psRypkNWCaT0cqVK/HMM8/cIjrzGTp0qHzr1q2TiGg2Y8xc/iPjcDjVBANjrBkAEFEIgM8B+AGY59h+kDHW113G1VJMAB4jorcYY5nl7GMLY+yZwiuIKLoCNo0H8AeAVABgjJU8B6cKw4Unh1PNYYz9Iopiv6lTp+5Ys2aN8sEHH3S3SS5n69atCA8PL/AMDh8+HImJiThx4sRdS4tUZcaNGyd8/vnn2LVrF3r27IkzZ87csc8TTzxxx7qEhIQi+2vTpg0SEhKE3bt3w2azQZKkgufCry0WC7RaLXJycqTs7GyWk5ODvLw8aDQa0ul0pNfryWQy4cMPPyxTSHNISAiMRmOxwlGr1SI1NVXcunUrGjZsSHCyQFYoFNiyZYvLRPiMGTPQt29f3HvvvUVuj4mJQf369enPP//sB+BrV9nF4XDcD2MsnYieBHCCiOa7255ajBXAqv9j777Do6q2NoC/K3WSSST03kVQEFTAq6IoIEi/SOcTBYELIoJcBLnSFEVA9FIFBGmKCApCLCAi0lRqkF6C1ER6T6ZlJjPr+2MGLiAlITM5Ke/veXiSmXPO3u8QcWZl77M3gH8DGHztAREpDWAWgIIAzgJ4WVUT0tuBiAQDGA3gGQDhACar6jTfsTcBvAjAA+BHAHEAagCYJyJ2AI/7nu+vqnEi0gHAIHh/IbpUVQf62rEAmACgKQA7gH+q6un0ZvU3Uc1S+2gT0V0KCgpqEhkZufCzzz6LqFixotFxyE9mzpypsbGxnu+//z7DBZLFYkHbtm3dbrdbRUSCgoJURCAiCAoKwpXngoKCJDIyMjg6Ohr33HMP8ubNi/z58yNfvnwoWLAgChcujB49erhHjBgRXKtWrTT3f/nyZdStWxdbt2695Tk9e/b0HDx4UObPny8FChTI6EvOdn744QeMHDlyY1JS0uNGZyEiQAQZ+qCseusZEiJiUdWoG567CKASgPvx96m2rVTV2D2psoDdu3dn6GdSpUqV2/5MABQDsBNANQD/gm+qrYh8D2CRqn4mIl0ANFfVFjdc3xl/n2r7OIDC8E219f2CoZCqjvBto/U7gDbw/tyHAnhWVW0ikk9VL4jIGvgKTV8fawD0h3cEdCOA6gAuAlgBYKKqxvqmDDdX1e9FZAyAJFUdcdd/aX7CEU+iHMLj8SwNCQnp2qNHj5krV66M8N3rRtmc3W5HTEyMX6aXRkVFYdmyZX4Z4UtJSQlKb2F4zz33wO124+LFi8ibN+9Nz5k6dWrQv/71L88LL7yAL7/8UvLnz++PuNlG/fr18cEHH1QVkcdUdaPReYgo011bFHGqrQFUNUlEPgfQB97RwiseB9DS9/1cAGNu0cTNptpe+7ABgKoi0tr3OA+ACgCeBTBbVW2+HBfuELUmgDWqetbXxzwAtQHEAnAC+MF33lYA9e/QVqbgPp5EOUhqaup8p9N5aO3atUZHIT/ZunWrp1q1allqy5w9e/YgNTVV7r333nRdJyLIkyeP/vnnn7c979NPPw0CoD/88MNtz8uJwsPD0bdv38ioqKiP5YZPKkSUs4lIOQBuAGeMzkIYD6ArAPNtzrnbkVcB0FtVH/L9KauqK3zPp6fN271HuPR/01rvuKhfZmHhSZTDWCyW92fOnJlsdA7yj/j4+OA6deoYHeM6mzdvRoECBfRmC+TcSYECBTwHD955txAR0cKFC9/0mKoiMTER33//PYYNG+Z48cUXk2bNmqV//fVXuvNkRf/85z8RHR1dCUDDO55MRDmCiBQE8AmAj5X3wRnON9r4NbzF5xXrAbT3ff8CgN/usvmfAPQUkVAAEJH7RMQM71TZLiIS6Xs+n+/8ZADRN2lnE4CnRaSA777RDgCy9MhDlqh+ich/VPWbP//8c0p8fDx4r2f2durUKaSkpODhhx82Osp1OnTogGnTpmHFihVI7x6yhQsX1oSEO6/FkJycHHRlf02Xy4X4+Hhs27YNGzdutGzbti3Y5XI5w8PDNyQlJS1X1WMHDx5sOWXKlBbFihXT5s2bRzVo0CCoVKlSd/X6rrBYLBg5cqRjy5Ytrnr16pnq1q0b+vDDDyM0NDRD7d5JSEgIBg4caB48ePAkEblPVT0B7ZCIjBIhItvxv+1U5gIYe83xp3zHrxihqosyM2Au918A106Z7QNglogMgG9xoVtc105Enrzm8avwrUjrMwNAGQB/+Ga2nAXQQlWXi8hDAOJExAlgGbwLB80B8Mk1iwsBAFT1pIi8BWA1vKOfy1T127t9sZmBiwsR5UDh4eHDGjZs+J/3338/4s5nU1Y1duxYz6+//qpLliwxZPuT21myZAlGjhyJwYMHa4sWLW47JdRms+HAgQOIj4/Hl19+6bFYLJ5atWq58uTJExYdHR1sNpsRFRWFqKgomM1mJCQkYOTIkejUqZN706ZN1gMHDkSYTKYTHo9ntdVq/QXehRiO3jgqICIhAGqbzeaObre7VaFChaRnz55RTZs2TfeU1S1btqB///62lJSUb6xW65TQ0NBGERER7Z1OZ+mGDRt6hgwZEuHb3iUgVBWtW7e2/Pnnn694PJ55AeuIiG4rkIsL0d0J5OJCFFgsPIlyIBEpGB4enrBy5UpTTEyM0XHoLqgqnnzySYwaNQq1a9c2Os5NrVq1CgMHDsTGjRsRHOytjZ1OJzZu3Ij9+/frjh07LPv27cOFCxdMZrP5mKrGJScn74V39T0PgHtCQ0PzhoWF5Q8ODs4XFBQUAyCP0+ksFBQUBKfT+YXT6VwHYIOqXkpPNt+0o9oRERHzXnjhhQJ9+vQJTcstkw6HA+PGjXMuXrzYmpKS8qLH41l6Q7vFzGbz1PLlyz87bdq0yKioqFs1lWFbtmxBr169ztjt9lKqmhKwjojollh4Zj0sPLMvTrUlyoFU9Wx0dPS333zzTZuuXbvyXu5sKD4+Hh6PR2vXrp1l3yDr1q2L0NBQ3bdvn1SqVAk//PADxo0bZ3O5XH+mpKSscjqdcQB2AIi/fPlyamZmU1U3gNUi8tD8+fNX/fXXX+VHjhxput002T179qBfv362y5cvr3Y4HJ1U9fxN2j0hIi0PHjw4o2PHjm1nzZoVmS9fvps1l2E1a9bEgw8+aP7jjz96wrvQBRERUbbFEU+iHEpE6pQvXz42Njb2HqOzUPp9+umnumzZMl2yZEmW/sVB69at3SISfPnyZWtycvIBi8XSR1XvdsGFgBCRSLPZHFuhQoVaU6ZMiYyOvn6NBpfLhenTp6fOmTPH4XK5erjd7vl3WtxDRCQiImJ0TEzMa59//nlkkSJFApI9Pj4eHTt2THI4HCVUlYuGEWUyESTh5gu7pEWyKvge7Ge7d+/O0M+kSpUq/JkYhIUnUQ4lImFhYWGXV6xYYcpteyHmBJs2bUKvXr2wYMECpHfbksw0b948jBs3zp2SktIGQGxWXY1RRIIjIyOn5s+f/4VZs2ZdLRQPHz6MN954w3ry5MntVqu1naoev0NT1zGZTP0jIyOHz5kzJ7JcuXIByd6/f3/7mjVrxjkcjsEB6YCIiCgTZOnfpBPR3VNVp8lk+nX9+vVGR6G78I9//AMFChTwHDp0yOgot/XCCy+gQYMGLrPZ3DKrFp2Ad+qtzWbrcebMmeGtWrWyjRw50jlx4kRPu3btbEePHh1otVqfSm/RCQAOh+Mji8XSu2PHjvY9e/YEIjr69u0bISL/FpEHA9IBERFRJmDhSZSDJScnf71y5Uqr0Tno7ogInE6n0THuaNCgQSaTydQyKCioqdFZbkdV1eFwjElKSqqzYMGCofPmzZvtcDiquVyuyRkpmp1O5yyLxfJ/Xbp0sW3ZssWfkQEAJUqUwLBhw0wRERErrtnXjYiIKFth4UmUg6nqjxs2bAhJTc3UdV3ID5YsWaLnz58Peuihh4yOckdRUVF4++23I6Ojo0cbnSUtVHWzx+MZY7Vau6nqQX+06fF4Ym02W9NXX33Vunr1an80eZ1mzZrJ888/n89sNi/xrdhLRESUrXBVW6IcTFWP33PPPSd27dpV9uGHH/ZLmy6XC19//TU8Hg9MJhNMJhMiIiIQERFx3ePSpUtf3WKD0mfz5s0YOXKkjB07FiVLljQ6TpoUL14cqpqrF2xQ1dUi8sybb765cujQofc0b97crysS9+/fP2z37t01/vzzz9EABvizbSIiokDj4kJEOZzJZBpZtWrVf0+cONGU0T0HVRVDhw5NWbFixW4AW0JCQu4JCgqKFhEzALOqRqpqpMvliilXrpxp/Pjx5kCt9plTpaamomnTptqgQQPp16+f0XHSLDExEW3atDljsVgKG53FaCJSKSIiYv348ePzPvHEE35t+/z583j++edtly5desnj8Xzj18aJiIgCiIUnUQ4nImGRkZHTY2Ji2kybNi2yTJkyd93Wl19+6ZkwYUKCzWarerutHUQkyGQyDQoODh40atSoiDp16tx1n7lNfHw8OnTogN9++w1OpxNHjhzBkSNHcOjQodR9+/bZLBYLGjRoEF2/fn0pXbp0utp2u91ITExE8eLFcbv9LO/GmTNn0KRJkyS73Z7Hrw1nU8HBwY3y5cu3aOnSpZGRkZF+bXvPnj3o3Lmz1eFw/ENVA7OiERERkZ+x8CTKJcLCwnqGhob+96OPPop46qmn0n39li1b8OqrryY7HI6HVTVNS62KyOMRERGxzZs3zzNgwIDw8PDwdPebm1gsFqxevRrvvPMO3G63OygoyBUREXEMwK7k5OQ/VHUfAIfZbG7rdrtbFSxYUJo1axbZoEGD4PLly1/XlqrixIkT2LVrF3bt2pUaFxdnPXjwYISI2EuWLBkyceJEsz+n8SYlJeGZZ55JcblcEVl5ddvMFBUVtaRly5aN33zzzTB/tx0bG6sjR448Ybfbq6jqJX+3T0RE5G8sPIlyERGpZTKZfujevXtUt27dQkTSdgvaX3/9hTZt2thtNlszt9v9Szr7jDGbzfMKFCjw9KRJk8xly5a9q+w5lc1mw9q1a/Hdd99ZNm/eHGoymTYmJSXNAbAOwJFbFXG+BWaeiIiI+D8AbfPkyRPWtGnTiJCQEImLi7Ps3bs3zOPxpISHh/9hsVhWu93ujQDiAFwKCwt7PTg4eOSwYcMimjb1z0K0Ho8HrVq1sp04ceIrm832L1V1+6XhbExECplMpoOfffZZ9AMPPOD39t97772UZcuWrU5OTm7k98aJiIj8jIUnUS4jIiXMZvNPFSpUKN2/f39ztWrVbnu+zWZDmzZtbKdOnRqSkpIy7i77lJCQkB6hoaH/HTx4cETz5s0lrUVvTmS32/Hbb7/hu+++s65fvz7EZDLFJScnz1DV2LsZvRKRIAA1TSZTO4/Hk+p0OjcA2Hy7fSlFpFpkZOT3tWvXLvjOO++YzGZzRl4SAO+I7SuvvGI7ePDgKqvV2kpVs/5eMAEWHBz8cunSpSctXrzYHBLi3/X8bDYbatWq5UpNTTWrqsuvjRMREfkZC0+iXEhEwoKDg18OCwt7v2rVqqZ+/fqZbzYi4/F40Lt3b3tcXNwSm83WMaNTKEWkSmRk5I8DBw4s3rJly1xZea5ZswYDBgxICQsL25GcnPypqi5R1fNGZBERs9ls/sRsNrecOHFiZOXKldN87cmTJzF48GDriRMnPG3atDE3bdo0qHDhwtiyZQu6devm8Xg8D6nqrgDGzxZERKKiotZ379695ssvv+z3ZZ7r1auXfObMmSdUdbe/2yYiIvInFp5EuZiImEJCQnqEhIS8U7NmzbC+fftG3nfffVePT5482TV37ty9Vqv1H6qa4qc+a0RHR69bsWJFREZX2c1uli1bpm+//Xayw+F4VlW3GJ3nipCQkLahoaEze/bsGdG5c+fgoKBbb/Gsqvjmm290zJgxDo/HMyolJWWN2Wzu4XK5nr///vs98fHxQU6ns7Xb7f4xE19CliYiVaKiorZs2LDB5O+2X3vtteS1a9f2VNV5/m6biIjIn1h4EhFEJDIkJKRXSEjIkCeeeCL09ddfjzh8+DAGDRp03rd4ySl/9hcVFbWgdevWz/fv39/vi65kVQsXLvSMGTPmssPhqJ0VR6dEpLTZbP62UqVKFRo2bBjpdrvh8Xjg8Xhw5Xu3243169fb4uPjE6xWa5trX4eIRABoBuCAqm437pVkPSLyYJEiRX7/+eefo292vFq1aqhQocLVxxMmTMCJEycwZ84cTJ48+bZtT58+XadPnz7B4XD827+piYiI/Mu/N5wQUbakqjYAH4rI1N9++63v77//PhBAcEpKynP+LjoBwGq1/nvBggXN2rVrF+bPlVWzqtmzZ7unTp163uFw1FLVg0bnuRlVPSYiNXbt2tUvPj7+AVVNVdVUj8dz5avL4/GkejyeY6o6Q1VTb7jeDuBrg+JndaWLFy/uudXB8PBwLFq06LrnTpw4kaaGK1asKCaT6cmMxSMiIgo8Fp5EdJWqWgCMEJFJAEoGamROVU+aTKYPRo0aNXDKlCn+3eQwC1FVfPzxx64vvvjilN1uf0JV/zI60+34iskxRufIgcqULl06IHsJ1axZE6paWUQeU9WNgeiDiIjIH1h4EtHfqOplAJcD2UdKSsqYuLi4Xps3b4589NFHA9mVIVQVo0ePdsbGxibYbLZaqnrG6ExkjPDw8HtLlix5y/s7U1JS0Lp1awBA8eLFMWHChDS3HRkZiYEDB0aMGjVqhohUVdVbjqwSUfrt3r07CcBNp8mnQXKVKlXu8Wceouzs1itIEBEFkKo6HA7Ha8OHD7e63Tlry0e3240hQ4Y4vv322/02m+1RFp25m8lkqli8ePFbHr8y1XbRokXpKjqvaNq0KYoWLVo6KCioY0ZyEtFN3W3RmdFriXIcFp5EZBhVXXThwoX4xYsX55hVzlwuF/r162f/5Zdftlmt1lqqetHoTGQsVS1XtGjRgLUfFBSEYcOGRYWHh48Xkdy1VDQREWUbLDyJyDCqqhaL5V9jx451WK1Wo+NkmMPhwKuvvmrbtGnT71arta7vnlnK5ZxOZ9FixYoFtI+HHnoITz31lCkiImJIQDsiIiK6Syw8ichQqvoHgNXZedTT6XRi4cKFeO6552y7d+9eZrVaG6uqw+hcZDwREafTGXm7vVFvZdOmTahXr97VP9u3336XmgEDBkSoah8RKXN3aYmIiAKH+3gSkeFEpGZMTMyaVatWRYaGhhodJ81SUlLwzTff6NSpU+1ut/uP5OTkgaq63uhclLVERUXNatmyZcc333wz4P9xT5kyJXXu3Lk/JScnNw10X0S5we7duzP0QblKlSpyq2MiogC+UNUXfY9DAJwEsElVm4pIZwAfAvgLQBSAwwCG832GsiuOeBKR4VR1i9vt3rN8+XKjo6SJ3W7H559/7qlbt67t448/Xn3p0qU6SUlJT/HDAN2M1Wod+vXXX7vPnTsX8L5efvnlkNDQ0DoiUjvgnRFRRlkBVBGRCN/j+gCO33DOV6r6sKpWADAawGIRuT8zQxL5CwtPIsoSkpOTh06ZMsWSlWdh2Gw2zJw501O3bl371KlTf05KSnoyKSmpnqpuNjobZV2qejwoKGj2J58W1OvUAAAgAElEQVR8khLoviIiIjBo0KBIs9k8Q0SCA90fEWXYjwCa+L7vAGD+rU5U1dUApgPongm5iPyOhScRZRUrLl26dPrXX381OsffJCcnY9q0ae66devaZ8yYsdRisfwjOTm5oapuMzobZQ92u314bGysJzExMeB9PffccyhdunSx4ODgLgHvjIgyagGA9iJiAlAVwKY7nP8HgEoBT0UUACw8iShLUFW1Wq3Dpk6dmqVWgj106BAaNWpknzNnzrdWq7V6cnJyc1XdZXQuyl5U9TSAYX369LE6nc6A9iUiGDZsmDk0NPRDEeHm9URZmKruBFAG3tHOZWm45Jb3jBJldSw8iSjLUNWFhw4dsm/ZssXoKACA/fv348UXX7RbrdYeycnJrVR1n9GZKPtKSUn578mTJ9ePGTMm4FNuK1eujGeffTY8IiLi3UD3RUQZ9h2Aj3CbabbXeBgA34soW2LhSURZhqq6HA7Hy3379rWfPHnS0Cy7d+9G586d7TabrZPL5ZpraBjKEXyj+u2+++67SytWrAh4f2+88YbJ7Xb3EJGYgHdGRBkxC8C7d5pNIyJPw3t/56eZkorIz1h4ElGW4vF4lqakpAzr1q2bzWq1GpJh27Zt6Nq1q91ms7VLTU1daEgIypFU9aLdbm86ZMgQe0JCQkD7KlCgAIoUKZIC4N6AdkREGaKqf6nqhFscbici20XkAIBBADj7hrItFp5ElOWkpKT899y5c4v69u1rc7vdmdr3li1b0KNHD5vD4Xje4/F8n6mdU66gqnGpqan/6dWrlzUlJbCzbkuVKiUAygW0EyK6K6oadZPn1qhqU9/3c1S1oKo+pKr3qepzqvp75icl8g8WnkSU5aiq2my2brt27drxwQcfBPx+uCs2btyIV1991Wa325u63e6fMqtfyn1cLteks2fPrn7//fcdgeynfPnykSLCEU8iIjIcC08iypJU1WW1WpvExsae+eqrrzyB7s/tdmPw4MFWh8PRyrdXGlHA+O73fGH58uUXli5dGrB+SpUqFRIVFVU5YB0QERGlEQtPIsqyfPfD1f3oo48s69evD2hfP//8M2w22yEAHOmkTKGqSXa7vcnw4cNthw8fDkgfJUqUQHBwcMWANE6UOyQbdC1RjiOqanQGIqLbEpHakZGRy2NjYyOKFi3q9/ZVFU2bNrUkJCR0UNUf/N4B0W2Ehob2KFSo0NglS5ZERkZG+rXtxMREtG7d+pzVai3o14aJiIjSKcToAA0bNtRz584ZHSPX2bp161ZVrWF0DqK0UNV10dHR361du7Zd+/bt/d7+b7/9hvPnz59D2jbvJvKr1NTU6ZcvX3560KBBLcaNGxch4r/94YsUKQKHw5FXRMJU1em3homIiNLJ8MLz3LlziIuLMzpGruPPDzZEmcFisSxfv359k/bt2/9tFcCM+vjjjy02m22Iqgb8XlKiG6mqikjXDRs2VP/iiy/uffHFF/12G0xoaCjy5MnjuHjxYkkAh/zVLhERUXrxHk8iyi7WxcXFBfv79oAzZ87g4MGDQar6lV8bJkoHVbXbbLZGEydOtG7bts2vbcfExKQCKODXRomIiNKJhScRZRdHUlNTHYmJiX5t1G63IzQ01KKqqX5tmCidVPWw0+ls37t3b7s/b0GJiYkBgPx+a5CIiOgusPAkomxBVTU0NPT3rVu3+rVdp9MJEeG9b5QluN3uZSkpKeN79+5tc7lcfmkzf/78wWDhSUREBmPhSUTZRnJy8rKNGzfa/Nmm0+lEUFAQC0/KMhwOx9AjR45sHTt2rF/+u8yfP38YWHgSEZHBWHgSUbahqus2bdrk1wWAXC4XRCTFn20SZYSquq1W6/PffPPN5RUrVmS4PafT6QHgzngyIiKiu8fCk4iyk30Wi0VOnz7ttwY51ZayIlU9b7fbGw8ZMsR++PDhDLW1a9euFAA7/JOMiIjo7rDwJKJsQ1U9YWFhPy9dutRvo55OpxMAHP5qj8hfVDUuNTW1V9u2bR1vv/12SkJCQrrb8Hg8SEhIiASw0/8JiYiI0o6FJxFlK8nJycM+/fTTFLvd7pf2fIUnp9pSluR0OmenpKSUWbZs2YSWLVtaevXqZdu1a1ear09MTERISMhlVb0UwJhERER3xMKTiLIVVd0lIr8uWrTILxt6ut1uAPDP8qFEAaCqp+12+8CUlJSi69evH9KlSxfrjh1pmzkbHx+P0NDQ3QGOSEREdEcsPP1MVXH69Gn4e5N7Ivqf5OTk/3zyySeOlJSMD1RGR0cDQN4MN0QUYKpqcblc45xOZ7+PPvrImpZr9u3b57Farb8HOhsREdGdsPBMI7fbjSVLluDJJ59E48aNcenS9bOWtmzZgiZNmqBQoUK49957cd9996FPnz54//33MXPmTOzezV84E/mLqm5T1Q2LFy/O8G948ubNC7fbza0mKNvweDyzDxw4kLx58+Y7nrtz505LamrqtkyIRUREdFssPNNo0qRJ6Nu3L1q0aIH8+fPjscceQ2xsLBYuXIgRI0agUaNGePTRRzF//nysX78ew4YNg8lkwpEjR7B06VI888wzmDp1KgYPHoy2bdti9uzZRr8komwtOTn5P5MnT3bs3bs3Q+3ExMQgNTU1xk+xiAJOVV12u73/mDFjLLebXZOYmIg9e/aEAdieeemIiIhuToyeElqjRg2Ni4szNENarFixAkOHDsXMmTMBAF9//TXWrl2LyMhImM1mdOrUCWXLlr3t9atXr0bJkiVRqFAhTJw4EXv27EHRokUz6yVcR0S2qmoNQzon8pOwsLBeISEh71WuXDnstddeM1evXj3dbTidTtSsWdPt8XhC1ej/IRKlkYgEm83mQwMHDixdq1Yt5MuXDyEhIQC8t3wsXLhQP/zwQ4fH4xmckpIyzuC4RNnW7t27kwBE3+XlyVWqVLnHn3mIsjMWnmlksVhQuHBh/PrrrwgLC8twex9++CHy5cuHceOM+TzAwpNyChEJDwoKeslkMr1bunTpqNdeey3qqaeegoikuY3q1as7nU5nQVVNCmBUIr8KDg6uazabP3W5XAWcTmdUZGRkSt68eV3BwcFBZ8+ePW61WluqasamBBDlcrt3787QB+UqVaqk/c2IKIfjVNs0ioqKgsfjubICZobFxMRc/e00Ed09VU1xu92f2my2kvv37//XwIEDDzdt2tSyfPnyNP97LVmyZAqA2oFNSuRfbrd7VVJSUnm73Z7H4/GEWyyWexMTE+sePXq0pdVqrcqik4iIshIWnmnkcrngcrlgMpn80t6xY8cwffp0jB49mivgEvmBqqZ6PJ4FFovl3oSEhPbDhw/f1aBBA+vcuXP13Llzt732lVdeiY6Kihot6RkmJcpCVDVVVU+o6lZV/VlVnUZnIiIiuhYLzzTav38/SpQoka7pe7fTpUsXTJw4EXPmzEG/fv380iYRAeq11GKxVDtz5kzjKVOmLHruueccXbp0sSxfvhwOh+Nv19SvXx/R0dFlADyb6YGJiIiIcoEcXXiqKvbv33/H8z744AOUKlUK9evXx+uvv45vvvkGFy9evO6cVatWoWbNmn7LVq5cOVSvXh2tWrXCihUrOOpJ5Ge+AnRdcnJyW6fTWSAuLq7ne++9t/Gpp55KGTRokCMuLg4ejwcAkJqaio4dO5qjo6NHGxybiIhyCRFREfnvNY/7i8g7vu/fEZH+hoUjCoAce5OhqqJr166YPXs2zp49iwIFCtzy3AMHDqBZs2Z44IEHcPDgQUycOBGdOnXCAw88gCZNmuDNN9/E4cOHUbJkSb/nLFu2LC5evIizZ8+iUKFCfm+fiABVtQL4AsAXIlL8xx9/7Lh69eqeqampRVJTU4M9Ho+Eh4dbAISLSBinKRIRUSZIAdBSREap6u3vCSHKAXLsiGdSUhJmz56NyMhIvPTSS0hOTr7lucnJyShdujSefvppdO3aFZMnT8batWvRvXt3rFu3Do0bN0bFihXx119/+T3nY489hmbNmuGZZ57BhAkT4HK5/N4HEf2Pqh53uVwfWCyWsg6Ho3Rqamoej8cTarPZYmw2WxUWnURElElSAUwH8G+jgxBlhhxbeObJkwdfffUV3G43Dhw4gPXr19/y3FOnTiE6+votmsLDw3Hvvffi5MmTqFixIipUqICEhISAZO3Vqxdee+01fPbZZxg9mjP9iDKDbyruaVW1cf9OIiIyyGQAL4hIHqODEAVaji08AaBt27Ywm80oU6YMdu3addNzTp48iZ07d+JmG8/Hx8cjISEB//d//4d9+/YFZKotAAQFBaF27dr44IMPMG7cOOzbty8g/RARERFR1uHbP/pzAH2MzkIUaDm68ASAChUqICwsDG+//TZSUlL+dvzs2bOIjIxEaGjo347VqlULY8eORatWrTB+/PibFqf+VLRoUbzyyivo1KkTkpK4jz0RERFRLjAeQFcAZqODEAVSji88hw0bBpvNhuDgYJw/f/5vx6tWrYoKFSpg+fLlN73+iSeewOzZs1GsWDE89thjgY6L9u3bo2zZsnj88cdx7NixgPdHRERERMZR1QsAvoa3+CTKsXJ84dm4cWOsWbMGSUlJKFas2E3PGTp0KCZMmIBff/31psfLlCmDTz755LYr4/pLUFAQhgwZgmbNmuGJJ57A6dOnA94nERERERnqvwCu/aAZAu+qt0Q5Ro4vPNPi2Wefxdy5c/HBBx9gzJgxRseBiKBjx45o1KgRatWqhW7dut2yKCYiIiKi7EdVo675/rSqRqrqO76nKgM4ZEgwogDJsft4plf9+vWxbds2lChRAuXKlUP58uXx4IMPIiTEuL+iPn364Omnn8bOnTvRqlUrxMfHI2/evIblISIiIqLAEpFdAA4AWGF0FiJ/4ojnNfLmzYt27dph7969GDVqFB5++GGsWGHcv/mgoCA8/PDD6NSpE+rVq4chQ4YYloWIiIiIAk9VH1TVVqqaanQWIn/KlYWn3W7H6NGjUatWrb8VlrNmzcLcuXORmJgIANi5c6cREf+mV69eiI2NxVdffWV0FCIiIqLcItmga4lynFwz1fbo0aNYtGgRNmzYgN9++w3VqlVDkyZN0KFDB6xatQoAEBwcjCpVqlydXrty5UoULlzYyNhXxcTEoF+/fujevTvatWtndBwiIiKiHK9KlSr3GJ2BKKfI8YVncnIyhgwZgrlz56JBgwaoUaMGunbtilKlSgEAjhw5gurVq6Ns2bK4cOECpk2bhq1bt+L+++9HoUKFDE5/vZ9++glvvfWW0TGIiIiIiIjSJccXngsWLMDvv/+OxYsX33Q7lF69eqF79+6IiIjAsmXLMH78eJhMJgwZMgQiYkDim9uxYwf27NmDb7/91ugoRERERERE6ZKjCs8dO3Zg8+bNePHFF2EymQAAly5dQpUqVW65B2dISMjVqbWNGzdG48aNMy1vWqkqxo8fj3fffRcRERFGxyEiIiIiIkqXHLW40KpVq/DGG2+gbNmy2Lp1Ky5fvox58+ahbNmyRkfLkHXr1sFqtaJz585GRyEiIiIiIkq3HDXiWahQITzxxBOoXbs2OnfujCJFiqBy5cpo2bKl0dHumtvtxsSJE/Hhhx8iODjY6DhERERERETplqNGPOvUqYMNGzZg06ZNCAsLw19//YUWLVpkqXs10+u7775DwYIF0axZM6OjEBERERER3ZUcVXgWK1YMS5YsQfny5bFw4UJUqlQJx44dMzrWXXM4HJg6dSo+/PDDbF08ExERERFR7pajCk8AqFu3LsaOHYty5coBQMALtl9++QUPPvggDh8+DAA4fvw4nn/++avHFy1ahLZt2+Ly5cvpbnv+/Pl49NFH8fjjj/stLxERERERUWbLcYXntUJDQ7Fu3TqcP38+YH0sW7YMjzzyCJYvX/63Y99//z2+/PJLTJs2DXny5ElXu5cvX8acOXPwwQcf+CsqERERERGRIXJ04Tlu3DgULVoULVq0wE8//eT39m02G7Zv347hw4fjxx9/vO7Y8uXLMXPmTEyfPh158+ZNd9szZ85Ey5YtUbFiRX/FJSIiIiIiMkSOWtX2RsWLF8cnn3yCihUrIi4uDs8995xf2//ll19Qq1YtlClTBnny5MHevXuRJ08enDhxAqNGjcLXX399y/1Db+fkyZOIjY3Fnj17/JqXiIiIiIjICDl6xBMALl68iBUrVsBkMvm97R9//BENGzYEADRq1OjqqGfevHlRpEiRux5lnTJlCl555RUULVrUb1mJiIiIiIiMkqNHPFNSUlC/fn2UL18er7zyil/bvnTpEjZv3oyDBw9CROB2uyEiaNeuHSIiIjB16lR06tQJ+fLlQ9OmTdPc7oEDB/D777/js88+82teIiIiIiIio+TownPAgAHImzcvhgwZ4vfVbVesWIFmzZrh7bffvvpc586dcfr0aQBAvnz5MHXqVHTp0gV58+ZFrVq10tTupEmTMGjQINxzzz1+zUtERERERGSUHD3V1mq14uTJkxg5ciTGjx+PU6dO+a3tH3/8EfXq1bvuufr16+PTTz+9+rhEiRKYNGkShg0bhp07d96xzS1btuDIkSPo2bOn33ISEREREREZTVTV0AA1atTQuLi4gLTt8XgQGxuLkydPYuDAgejXrx/atm0bkL4ySlXx4osvYsCAAXjhhRcC3p+IbFXVGgHviIiIiIiIcr0cPeIZFBSEli1b4tlnn4XVasWkSZOMjnRLK1euBAB06NDB4CRERERERET+laMLzytKly6Nzz//HCaTCV9//bVfp9z6g8vlwqRJk/Dhhx8iKChX/EiIiIiIiCgXyRVVjslkQvXq1VGwYEHMmzcPY8aMwbp165CUlHRX7TmdTrjdbr/lW7JkCcqWLYv69ev7rU0iIiIiIqKsIlcUngDwwAMPYOfOnVi5ciU8Hg969eqFd999N03XJiUlYfLkyWjSpAkefPBBVK9eHdOnT/dLLpvNhmnTpmHMmDF+aY+IiIiIiCiryfLbqVy6dAkTJ05E0aJFUapUKdx///0oVarUXbc3d+5c7NmzB8OGDUOLFi1ueo7b7caJEyeQkJCAXbt2Yf78+QgJCcGZM2cQHBwMk8mE4sWL33WGG/PUqVMH1atX90t7REREREREWU2WLzzXrFmDt99+G6VLl0bx4sURHx+PyMhIVKtWDUFBQVBVlC9fHsOGDUPevHlv2obb7YbNZsOoUaOwaNEizJ8/HwUKFLjpuePHj8cXX3yBAgUK4N5770XlypXRrVs3zJkzB/PmzUPVqlX99touXLiAefPmIVCr+hIREREREWUFWb7wbNq0KWbMmIF3330XIoLRo0fDZDLh7NmzEBGICDZs2IAHHngA3bt3x5YtW3Ds2DGYTCaYTCY4nU5s374d4eHhqFSpEmbMmIF8+fLdsr+TJ09i5syZV7c0mTZtGkaMGIEZM2agbNmyfn1tn376KV544QWUK1fOr+0SERERERFlJVm+8AwJCUHXrl3x4osvYs6cOZgyZQoOHz4Mj8eDSpUqoWLFinjkkUdQo0YNbNu2DXXr1kWZMmXgdDrhdDqhqqhatSrCw8Nv24+qIj4+HgkJCfB4PACAixcvYvDgwZg1a9ZNi87z589jyJAhKFGiBKpXr47nnnsOIpKm15WYmIhly5Zh//796f9LISIiIiIiykayfOF5RVhYGLp3747u3bsD8BZ927dvR1xcHH7//Xf8/vvvaN68ORo2bHj1mv379+OPP/6A3W5HiRIlEB0djbFjxyIuLg6lSpVCkSJFEBISAhHB9u3b4XK50L59ezRv3hyAd7QzJCQEO3bswPnz52Gz2WA2m/Hggw8iPDwcCxYsQMGCBVGzZk189NFHKF68OB588ME0vZ7Jkyfj9ddfR8GCBf3/l0VERERERJSFiKoaGqBGjRrqj3scL1y4gBo1aiA8PBxFixa9WjA2adIEiYmJOHLkCI4fP44uXbrgjTfewNGjR3Hw4EEkJSVBVfGPf/wDTz311HUjlufOncPixYuxatUqJCYmIjo6GufPn8fevXtRtWpVxMfHY+PGjbjvvvswevRobNmyBcOHD79j1r179+L111/HwYMHYTabM/za74aIbFXVGoZ0TkREREREuUqOKTwBwGq14sCBA0hISMDZs2fRqlWr6xYcUtWrhaXD4UClSpXgcDjgdruRlJSEtm3bYs6cOQgODr5tPxaLBStXrsTJkyfRs2dPAMCZM2dw3333YdGiRShSpMhtr+/Rowc6dux49VojsPAkIiIiIqLMkqMKz/SYOnUqFi5ciIkTJwIA7HY7evTogV69eqFr16531ea7776Ln3/+GVOmTLnlvZ7r16/Hhx9+iL179yI0NPSu82cUC08iIiIiIsosQUYHMMrFixcRHR2NPXv2YO/evVi7di1EBBcvXrzrNt966y1YrVYsWrTopsc9Hg8mTpyIUaNGGVp0EhERERERZaZss7iQvzVs2BDfffcdRo0aBY/Hg3LlyqFJkyZo3br1XbcZGhqKuXPn4qmnnkKBAgVQu3bt66bt/vjjj4iMjESrVq388RKIiIiIiIiyhVw71TaQvv/+ewwfPhynT59G79690bhxYzidTrRo0QJz587F008/bXRETrUlIiIiIqJMk2tHPAOpWbNmaNasGTZs2IAWLVogX758sFgssNlsKFmypNHxiIiIiIiIMlWuvcczMzz++ONYsGABBg0ahPLly+Oll15CzZo1sW3bNqOjERERERERZRqOeAZYnTp1MGTIEPTv3x+fffYZ8uXLh5dffhlxcXEICeFfPxERERER5Xwc8cwEvXv3xjPPPIMBAwagWbNmEBH89NNPRsciIiIiIiLKFCw8M4GIYPLkybh48SK2bt2KmjVrYtOmTUbHIiIiIiIiyhQsPDNJSEgIOnbsiJ9++gkPPPAActpKvkRERERERLfCwjMTtWvXDr/88gtKlCiBgwcPAgASEhIwe/ZsDB06FJMmTTI4IRERERERkf+x8MxEZcuWRdmyZXHy5EkkJCSgWbNmeOihh7B48WIcPHgQ48aNMzoiERERERGR33FZ1UzWvn17rFmzBm3atEGFChUwdOhQREZG4ty5c/jll1+MjkdEREREROR3HPHMZB06dMDatWvRqFEjtGzZEpGRkQCAmJgYOJ1OnDp1yuCERERERERE/sXCM5MVK1YMM2bMwBtvvIHjx49ffT4kJASPPfYYli1bZmA6IiIiIiIi/2PhaYAWLVrgrbfeQvv27TF69GicOXMGANCgQQPMmjXL4HRERERERET+xcLTIH369MH+/ftRsGBBtG7dGp988gmefPJJHDp0CDt27DA6HhERERERkd+IqhoaoEaNGprb97RMSEhA165dkSdPHgQFBaFOnTro2bNnQPsUka2qWiOgnRAREREREYEjnllCqVKl0KpVKwQFBSE8PBwpKSlGRyIiIiIiIvIbFp5ZxNGjR1GkSBGEhoay8CQiIiIiohyFhWcWceTIERQtWhQmkwlWq9XoOERERERERH7DwjOLiI6OxoULF2A2m3H58mWj4xAREREREfkNC88solWrVli1ahWioqKQlJRkdBwiIiIiIiK/YeGZRdSrVw+HDx+G3W7niCcREREREeUoIUYHIK+wsDA0bNgQO3fuREJCAtxuN4KDg42ORURERERElGEc8cxCkpKSULduXZjNZowdO9boOERERERERH7BwjOLUFVs3rwZDz30EIYPH47Ro0dj7969RsciIiIiIiLKMBaeWcSxY8fg8XhQpEgRlChRAr169UKnTp2MjkVERERERJRhLDyziCJFiqBAgQJYunQpAKBy5co4d+6cwamIiIiIiIgyjoVnFmEymTB//nyMGTMGNpsNixcvxksvvWR0LCIiIiIiogzjqrZZyCOPPIJHHnkEL7/8MlwuFxcYIiIiIiKiHIEjnllM37590bBhQ2zfvh3Fixc3Og4REREREVGGiaoaGqBGjRoaFxdnaIbcSES2qmoNo3MQEREREVHOxxFPIiIiIiIiCijDRzxFZDmAAoaGyJ3OqWpDo0MQEREREVHOZ3jhSURERERERDkbp9oSEWUBIrhPBO+KYKMIzoogWQTbRTBYBOabnP+4CL4TwV8isIvgkAg+FUE5I/ITERHdlsh9EHkXIhshchYiyRDZDpHBEDFfc55ApCNEFkDkIERsEEmAyHcQ+YeBr4AyiCOeRERZgAhGA+gF4DsAGwG4ANQB0BbATgCPqcLuO7chgKUADgGYBeAcgMoAugNIAfCgKo5n9msgyulE5BMAx1X1PT+0VQrAXgB5VNUtImsAfKGqMzLatq/9HwEsUNXP/NEeUYaJ3PF9Dqp2iJgA2AFsh/e97giAogBeAVAMwEtQ/SLzXwBlFAtPIqIsQAQ1APypiss3PD8CwGAAvVXxse+5n+B9sy6minPXnNsNwKcA/q2K8ZkWnshPRORJAGPg/UWKG8A+AH1VdUsG230HwL2q2vE25xwFUBhAqq/vvQA+BzBdVT3p7O8ogG6qujId16zBXRaeaXl9RIYTqQHgT6hevuH5q+9zUP0YIiEAakF17Q3nFQawB95/n0WRzn+XZDxOtSUiygJUEXdj0enzle9rlWueuweAA8DFG8494ftq9XM8ooATkXsA/ABgEoB8AIoDGA7vKH5maaaq0QBKAxgNYCCAmf7uRLwfrIlyF9W4vxWdXte/z6mm/q3o9D5/GsBaAIV8fyibYeFJRJS1lfB9PX3Ncz8BiAbwmQiqiaC4CJ4D8F94R4gWZHJGIn+4DwBUdb6qulXVrqorVHXnlRNEpIuI7BORiyLyk4iUvubYBBFJFJEkEdkqIk/5nm8IYBCAdiJiEZEddwqiqpdV9TsA7QB0EpEqvrbmiHd0BiJSQER+EJFLInJBRH4VkSARmQugFIDvff29KSJlRERFpKuIJABYdc1z1xah5UVks4hcFpFvRZowJ3EAABRjSURBVCSfr69nROSvazOKyFERefZWr09E1ohIN9/3QSIyRESOicgZEflcRPL4jl3J0UlEEkTknIgMTs8PjiiDbvY+d7tznQAuBS4OBQoLTyKiLEoEwQCGwTv178trDo0CMBVAa3jvgfkLwHIAh+G9FzQ5k6MS+cMBAG4R+UxEGolI3msPikgLeAuslgAKAvgVwPxrTtkC4CF4R0u/BLBQREyquhzASABfqWqUqlZLayBV3Qzvv6+nbnL4Dd+xgvBO0R3kvURfBJAA7+hplKqOueaapwHcD+C5W3T5EoAu8N7HlgpgYhoypuX1dfb9qQOgHIAowDt1/xpPAqgIoB6AYSJy/536JsowkVu9z93s3MYAHgXwFVQdgQ9H/sbCk4go6xoP4DEAw1QRf83zbgDHAawE0A3eD+L/BfAsgAUiCM3soEQZpapJ8BY/Cu+9ymdF5Dvx3tcFAD0AjFLVfaqaCm+x9dCVUU9V/UJVz6tqqqr+F0A4vIVURp2At5i9kQveBU9Kq6pLVX/VOy+c8Y6qWlXVfovjc1V1t6paAQwF0Fa8H8wz6gUAY1X1sKpaALwFoP0No63DfaPMOwDsAJDmAp0oA66+z0E1/pZniVQAMBfe9743Mica+RsLTyKiLEgE7wF4DcB0VYy64fAcAF0BtFXFTFUsUUV/AK8DaASgU6aGJfITX1HZWVVLwHu/VzHg6kJZpQFM8E1tvQTgAgCB915QiMgbvmm4l33H8wAo4IdYxX193ehDAAcBrBCRwyLynzS0lZiO48cAhMI/r6GYr71r2w6Bd6T2ilPXfG+Dd1SUKHBErr7PQfXG97lrzysL4Bd4fynVCKpnMycg+RsLTyKiLEYE7wAYAmA2vMvHX3usFLyjF0tVYbvh0oW+r08HOiNRoKnqfnh/yXJlYa1EAD1UNeaaPxGqut53P+dAeLdlyKuqMQAuw1uYAt4PrOkmIjXhLTx/u0m+ZFV9Q1XLAWgGoJ+I1LtDf3fKUfKa70vBO6p6Dt4FwyKvyRUM7xTftLZ7At7C/dq2U5G2e+qI/M+7EvNN3+duOK8MgNXw/iKkPlR3BT4cBQoLTyKiLEQEbwN4G95tHLqp/u0DZXHf15tNvwu54StRtiEilXyjliV8j0sC6ADvfn8A8AmAt0Sksu94HhFp4zsWDW8hdRZAiIgMg3f15ytOAygjImn63CMi94hIU3gX6vpCb/JhV0Saisi9IiIAkuCdAu++pr9yaX3t1+goIg+ISCSAdwEsUlU3vPe/mkSkiYiEwvuBPTwdr28+gH+LSFkRicL/7glNvYuMRBkjct37HG41Rd07jX41vLMX6kN1W6ZlpIBg4UlElEWIYBiAd+C9j+VlVdxsj7J4eD/cthBBzA3HOvu+ZmjPQyKDJAP4B4BNImKFt+DcDd/9XKq6BMAHABaISJLvWCPftT8B+BHeAu0YvNsNXTtt9cpsgPMi8sdtMnwvIsm+awcDGAvg5VucWwHe+6wtADYAmKKqa3zHRgEY4psW3P/OL/2qufCO8p4CYALQB/CusgvgVQAz4L3HzQrvwkZpfX2zfG2vA3AE3r+f3unIReQf3l8KvQPf+9wt9+L0Fp1rAOQF0ACqWzMpIQWQ3Pk+eCIiCjQR9IJ3lckEeBcVufHN+LQqfvad+xG8H8aPwrsIywUAteCdgnsYwCOqSMqc5ERERGkgcsf3Oaj+DJFoeBe4Kgvvvr6bb9Laz759PSkbYeFJRJQFiGAObr8o0FpVPOM7V+BdzbYbgMrwTrk7DmApgHdUwYUXiIgoaxGZgzu8z0H1Gd99nUfu0Fod/G+GAWUTLDyJiIiIiIgooHiPJxEREREREQUUC08iIiIiIiIKKBaeREREREREFFAsPImIiIiIiCigbrvJeMOGDfXcuXOZlYVyoK1bt/6kqg2NzkFERERERMa5beF57tw5xMXFZVYWyoFEpIDRGYiIiIiIyFicaktEREREREQBddsRTyIiCrwQSU1xIyTM6BxE5A/P+L6uydRekxCNaFgytU+i3CIVwc4QTQ03Okd2xxFPIiKDsegkooxi0UkUOCFw833aD1h4EhERERERUUCx8CQiIiIiIqKAYuFJREREREREAcXCk4iIiIiIiAIqS6xqa7fbsXr1asTExKBIkSIoXLgwzGaz0bGIiIiIiIjIDwwvPNeuXYuuXbsiT5488Hg8OH/+PM6ePYvg4GCULVsWW7duRVgYF5IiIiIiIiLKrgwrPC9duoT+/ftj6dKleOutt1C3bt2rx1QVVqsVLVq0wKlTp1CqVCmjYhIREREREVEGGVJ4ulwuNGjQACVLlsTixYsRHR193XERQVRUFAoVKoTTp0+z8CQiIiIiIsrGDCk833//fYgI3nnnHYjILc/Lly8fzpw5k4nJiIiIiIiIyN8MWdW2VKlSOHToEEaMGIGzZ8/e8rx8+fLh9OnTmZiMiIiIiIiI/M2QwrNLly44cOAAihcvjlatWuHjjz+GxWL523khISEsPImIiIiIiLI5w/bxzJcvH8aOHYtt27bBarWiefPmmDdvHlwuF1QVU6dOxYYNG/D8888bFZGIiIiIiIj8wPDtVEqXLo25c+dix44dGDhwIL788kuUKVMGdrsdW7ZsQZEiRYyOSERERERERBlg2IjnjapVq4bly5djzpw5qFevHtatW8eik4iIiIiIKAcwfMTzRnXq1EGdOnWMjkFERERERER+kmVGPImIiIiIiChnYuF5gzNnzuC9997D5s2bjY5CRERERESUI7Dw9Ll48SLeeustVKpUCXFxcWjcuDE+/vhjqKrR0YiIiIiIiLK1LHePZ2ZLSkrC+PHjMWHCBNSpUwcLFixAsWLFkJiYiNdffx0WiwX/+c9/jI5JRERERESUbeXqwvOzzz7DgAED8Nhjj2Hu3LkoVarU1WMlS5bE1KlT8dJLL6F06dLo0KGDgUmJiIiIiIiyr1w91fbSpUswmUwYNGjQdUXnFYULF8akSZPQu3dvrFu3zoCERERERERE2V+uLjxff/11PP3005g5c+Ytz7nvvvswatQotGnTBvv378/EdERERERERDlDri48AWD06NH45ptvcPz48Vue8/jjj6NPnz5o2LAhTp8+nYnpiIiIiIiIsr9cX3gWL14cvXv3xqRJk2573j//+U80adIEjRs3htVqzaR0XlFRUdc9njNnDl577TUAwDvvvIOPPvooU/MQERERERGlR64vPAHgzTffxB9//IGdO3fe9rxXXnkFpUqVQrt27eB2uzMpHRERERERUfbGwhOA2WzGiBEjMGLECCxcuBBr1qzBnj17cPbs2esKTBHBsGHDcPz4cbz//vsGJiYiIiIiIso+cvV2Ktfq1KkTzp07h/j4eGzatAknTpzAqVOncPHiReTPnx+FChVC4cKFUaJECSQmJqJBgwaZls1ut+Ohhx66+vjChQto3rx5pvVPRERERESUESw8fYKDg/Hmm2/+7XmXy4XTp0/jxIkTOHr0KHbt2oXY2Fg89thjt20vOTkZsbGxiImJQeHChVGoUCEUKlQIkZGR6c4WERGB7du3X308Z84cxMXFpbsdIiIiIiIiI7DwvIPQ0FCUKFECJUqUwKOPPoq2bdv+f3v3H1NV/cdx/HW6+NVdLxEm/sA5HJH4j04DLZHWl+6Yrsn6w5zOwPGPszvF8N6gsdba/BVlNXLxo0aCuDnmSpxtzlqU5SwVWiFKo6JfFht5uQiXaih4vv98v+7b8hdxD+d67vOxMdi953zeL2Cwvficy7mt8/x+v1paWnTPPfeor69Pvb29CgaDcrlcSkpKUlJSkqZPn37tbenSpcrLy7P4swEAAACA8UfxtMDnn3+u9957T01NTYqPj7/2uGma+v333xUKhRQKhdTb26tQKKTPPvtMLS0tFE8AAAAAjkTxtEBxcbESExNVU1Mjt9stj8ejyZMn/+39tGnTNHnyZJ0/f17Lli2zOzYAAAAAWMIwTfOGT2ZmZpq8lnD0Pv74Y3V1dWlgYED9/f3q7++/9vHAwIDC4bDC4fC1jy9fvqyzZ88qLS3N7ugRZxjGF6ZpZtqdA4hmhqEb/yIGcIf593/fHx/XqaaMcZ0HxBzT5IdsjNjxtEBOTo5ycnL+0bnBYFCBQECzZs1Sbm6usrKyNHHixAgnBAAAAIDxw308o0hXV5ceeughjYyMqLe3V36/X1OnTlVubq5eeeUVtbW16erVq3bHBAAAAIBRYcczSgwNDWnZsmXasGGD1qxZc+3x/v5+tba26tSpU6qqqtLg4KC8Xq/Kysq0YMECGxMDAAAAwO2heEaJuLg4Xbp0SY899thfHk9ISJDX65XX65UkdXd3q7m5WV6vV6tXr9aOHTs0ZcoUOyIDAAAAwG3hUtso4XK5VFhYqPz8fHV0dNzwuOTkZBUUFOjw4cPq7+/XvHnzVF1drZGRkXFMCwAAAAC3j+IZRWpqarR9+3Zt2rRJjY2NNz02ISFBZWVlqq6uVl1dnRYtWqQTJ06MU1IAAAAAuH0Uzyizbt06tbS0qL6+Xs3Nzbc8Pj09XW+//bays7O1atUq3ez2OAAAAABgB17jGYXmzJmjpqYmrVixQrNnz9bcuXNvevzQ0JA++OADVVRUyDC4xRAAAACA6MKOZ5RavHix9uzZo+LiYvX19V33mCtXrqijo0Pbtm1TRkaG1q1bN84pAQAAAODW2PGMYk8++aS++uorlZSUqLq6Wj09PTp37pza29t1/vx5ff3110pJSVFWVpZ2795td1wAAAAAuC6KZ5QrLy/XypUr9fDDD8vj8WjJkiVaunSpCgsLlZGRobvvvtvuiAAAAABwUxTPKOdyuXTkyBFdvHhRycnJdscBAAAAgFHjNZ53gAkTJlA6AQAAANyxKJ4AAAAAAEtRPAEAAAAAlqJ4AgAAAAAsRfEEAAAAAFiK4ulAoVBIixYtUm5url599VWFQiG7IwEAAACIYRRPByopKdH999+vvLw8ffrpp0pNTZXP59M333xjdzQAAAAAMYj7eDpMV1eXDh06pKNHjyo+Pl6PPvqoLl68qMbGRmVlZenBBx9UIBBQTk6ODMOwOy4AAACAGMCOp8PMnDlTpmlqaGjo2mNJSUkqKirSsWPHlJmZqY0bN2rBggWqq6v7y3EAAAAAYAWKp8O43W6tXLlS77///t+emzRpklavXq1Dhw5p06ZNqqurU0pKinbu3KmBgQEb0gIAAACIBRRPB8rPz79u8fwfwzCUnZ2tqqoq1dTU6PTp00pNTdULL7ygvr6+cUwKAAAAIBZQPB3I6/XqwoULunDhwi2PTUtL065du7Rv3z61t7frvvvuU2VlpUzTHIekAAAAAGIB/1zIgRobG2UYhiZMmHDb56SkpGjbtm368ccf5ff71dbWpsrKylGtAQAAAADXw46nw1RWVqq0tFRvvfWWZsyYMerz58yZo4aGBn377bfyer0KBoMWpAQAAAAQSyieDrJr1y69/PLL2rt3r9LS0v7xOh6PRxUVFZo7d64WL16snp6eCKYEAAAAEGu41NYhdu/erfr6etXV1WnatGljXs/lcqm4uFgul0sFBQU6duyY7rqLv1MAAAAAGD2ahAMMDg6qvLxcr7/+ekRK5//z+XwKBoN67bXXIrouAAAAgNhB8XSA+vp6ZWRkaPbs2RFfOy4uTi+++KLKy8vV2toa8fUBAAAAOB/F0wHOnDmj+fPnW7b+rFmzVFZWpjVr1igcDls2BwAAAIAzUTwd4KmnntI777yjkZERy2YsX75cDzzwgHw+n2UzAAAAADgTxdMBsrKyNHPmTDU3N1s6p6SkRKdOnVJDQ4OlcwAAAAA4C8XTIUpLS9XQ0CDTNC2b4Xa79dJLL8nv9+u7776zbA4AAAAAZ6F4OsTjjz+ucDisL7/80tI56enpKigo0NatWy2dAwAAAMA5KJ4O4XK5FAgEtG/fPkvn/PHHH+ro6NDChQstnQMAAADAOSieDlJYWKj29nb98MMPEV97eHhYBw8eVF5enu699175/f6IzwAAAADgTBRPB3G73fL5fNq/f3/E1jRNUx999JFWrVqlEydO6OjRozpw4IASExMjNgMAAACAs8XZHQCR5fP5lJqaqueff16GYYxprba2NlVUVOjPP/9UZWWlli9fPuY1AQAAAMQeiqfDXL16VR6PZ0wF8aefftKePXt07tw57dixQ+vXr5fL5YpgSgAAAACxhOLpMN3d3Zo+ffo/OvfXX3/V3r179eGHH+qZZ55RU1OT3G53hBMCAAAAiDUUT4fp7u5WUlLSqM75+eefVVtbq+PHj2vDhg3q7OzU1KlTLUoIAAAAINZQPB1mNMXz+++/V21trU6ePKnNmzertrZWU6ZMsTghAAAAgFhD8XSYX3755Za7lV1dXXrzzTfV2tqqp59+Wvv371dCQsI4JQQAAAAQayieDvPbb79pYGBAw8PDiov7+7e3s7NTGzduVGlpqQ4ePCiPx2NDSgAAAACxhPt4OkwgEFB3d7fWrl2r06dP/+W5np4ebdmyRW+88YZKS0spnQAAAADGBTueDpOenq5PPvlE7777rgKBgNLT07V161YlJiaqqKhIRUVFWrt2rd0xAQAAAMQQdjwdyDAMPfHEE+rs7NQjjzyi/Px8rV+/XtnZ2Xr22WftjgcAAAAgxlA8HWzSpEl67rnn1N7ers2bN6uqqkqGYdgdCwAAAECM4VLbGJCcnKwtW7bYHQMAAABAjGLHEwAAAABgKYonAAAAAMBSXGoLADZzafjyiOL+ZXcOAJFw3JapYXkUr0FbZgNONyzXZUrT2PE1BACbDZtxE+3OAOBOF7Y7AOBYFKbI4FJbAAAAAIClDNM0b/ykYbSOYxY4U9A0zRV2hwAAAABgn5sWTwAAAAAAxopLbQEAAAAAlqJ4AgAAAAAsRfEEAAAAAFiK4gkAAAAAsBTFEwAAAABgKYonAAAAAMBS/wGA+7uTmuLl4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = 'my_2020_gov_map'\n",
    "\n",
    "rcount = 19\n",
    "dcount = 20 \n",
    "i_demcount = 0 \n",
    "i_repcount = 0\n",
    "\n",
    "small_y = 9\n",
    "small_y2 = 9.5\n",
    "small_label = 9.1\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "gs = fig.add_gridspec(5, 5)\n",
    "ax = fig.add_subplot(gs[0:-1,0:-1], projection=ccrs.LambertConformal())\n",
    "axak = fig.add_subplot(gs[3,0], projection=ccrs.LambertConformal())\n",
    "axhi = fig.add_subplot(gs[4,0], projection=ccrs.PlateCarree())\n",
    "axsmall = fig.add_subplot(gs[1:4,-1])\n",
    "axcount = fig.add_subplot(gs[4,2:])\n",
    "\n",
    "ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())\n",
    "axak.set_extent([-170, -130, 54, 72], ccrs.Geodetic())\n",
    "axhi.set_extent([-160.8,-154.4,18.6,22.7], ccrs.Geodetic())\n",
    "\n",
    "shapename = 'admin_1_states_provinces_lakes_shp'\n",
    "states_shp = shpreader.natural_earth(resolution='110m',\n",
    "                                     category='cultural', name=shapename)\n",
    "\n",
    "ax.background_patch.set_visible(False)\n",
    "ax.outline_patch.set_visible(False)\n",
    "\n",
    "ax.set_title('2020 Gubernatorial Map')\n",
    "\n",
    "axsmall.set_xlim(0,10)\n",
    "axsmall.set_ylim(0,10)\n",
    "axsmall.axis('off')\n",
    "\n",
    "for astate in shpreader.Reader(states_shp).records():\n",
    "    \n",
    "    edgecolor = 'black'\n",
    "    facecolor = ''\n",
    "    try:\n",
    "        pick = states[ astate.attributes['name'] ]['prediction']\n",
    "    except:\n",
    "        state_dens = 0\n",
    "\n",
    "    if pick == 'Republican':\n",
    "        facecolor = 'red'\n",
    "    elif pick == 'Democrat':\n",
    "        facecolor = 'blue'\n",
    "    elif pick == 'NE':\n",
    "        facecolor = 'lightgray'\n",
    "    elif pick == 'Independent-D' or pick == 'Independent-R':\n",
    "        facecolor = 'purple'\n",
    "    else:\n",
    "        facecolor = 'gray'\n",
    "    \n",
    "    if astate.attributes['name'] == 'Alaska':\n",
    "        axak.add_geometries([astate.geometry], ccrs.PlateCarree(),\n",
    "                      facecolor=facecolor, edgecolor=edgecolor)\n",
    "        axak.text(states[astate.attributes['name']]['Lon'], states[astate.attributes['name']]['Lat'],astate.attributes['name_alt'][0:2], horizontalalignment='left', \n",
    "               transform=ccrs.PlateCarree())\n",
    "    elif astate.attributes['name'] == 'Hawaii':\n",
    "        axhi.add_geometries([astate.geometry], ccrs.PlateCarree(),\n",
    "                      facecolor=facecolor, edgecolor=edgecolor)\n",
    "        axhi.text(states[astate.attributes['name']]['Lon'], states[astate.attributes['name']]['Lat'],astate.attributes['name_alt'][0:2], horizontalalignment='left', \n",
    "               transform=ccrs.PlateCarree())\n",
    "    \n",
    "    # `astate.geometry` is the polygon to plot\n",
    "    ax.add_geometries([astate.geometry], ccrs.PlateCarree(),\n",
    "                      facecolor=facecolor, edgecolor=edgecolor)\n",
    "    if astate.attributes['name'] not in small_states and astate.attributes['name'] not in ['Hawaii','Alaska','Pennsylvania','Kentucky']:\n",
    "        ax.text(states[astate.attributes['name']]['Lon'], states[astate.attributes['name']]['Lat'],astate.attributes['name_alt'][0:2], horizontalalignment='left', \n",
    "               transform=ccrs.PlateCarree())\n",
    "    elif astate.attributes['name'] == 'Pennsylvania':\n",
    "        ax.text(states[astate.attributes['name']]['Lon'], states[astate.attributes['name']]['Lat'],astate.attributes['name_alt'][len(astate.attributes['name_alt'])-2:], horizontalalignment='left', \n",
    "               transform=ccrs.PlateCarree())\n",
    "    elif astate.attributes['name'] == 'Kentucky':\n",
    "        ax.text(states[astate.attributes['name']]['Lon'], states[astate.attributes['name']]['Lat'],'KY', horizontalalignment='left', \n",
    "               transform=ccrs.PlateCarree())\n",
    "    \n",
    "    if astate.attributes['name'] in small_states:\n",
    "        if astate.attributes['name'] == 'Massachusetts':\n",
    "            axsmall.fill_betweenx((small_y,small_y2),1,2,color=facecolor)\n",
    "            axsmall.annotate('MA',(2.5,small_label))\n",
    "        elif astate.attributes['name'] == 'Rhode Island':\n",
    "            axsmall.fill_betweenx((small_y,small_y2),1,2,color=facecolor)\n",
    "            axsmall.annotate('RI',(2.5,small_label))\n",
    "        else:\n",
    "            axsmall.fill_betweenx((small_y,small_y2),1,2,color=facecolor)\n",
    "            axsmall.annotate(astate.attributes['name_alt'][0:2],(2.5,small_label))\n",
    "        small_y -= 1\n",
    "        small_y2 -= 1\n",
    "        small_label -= 1\n",
    "        \n",
    "for astate in shpreader.Reader(states_shp).records():\n",
    "    if states[ astate.attributes['name'] ]['prediction'] == 'Republican' or states[ astate.attributes['name'] ]['prediction'] == 'Independent-R':\n",
    "        rcount += 1\n",
    "    elif states[ astate.attributes['name'] ]['prediction'] == 'Democrat' or states[ astate.attributes['name'] ]['prediction'] == 'Independent-D':\n",
    "        dcount += 1\n",
    "        \n",
    "\n",
    "\n",
    "axcount.fill_betweenx((0,0.5),0,i_demcount,color='purple')\n",
    "axcount.fill_betweenx((0,0.5),i_demcount,dcount,color='blue')\n",
    "axcount.fill_betweenx((0,0.5),50-i_repcount,50,color='purple')\n",
    "axcount.fill_betweenx((0,0.5),50-rcount,50-i_repcount,color='red')\n",
    "\n",
    "axcount.set_xlim(0,50)\n",
    "axcount.vlines(25,0,0.5,color='black')\n",
    "axcount.text(0,.55,str(dcount),color='blue',fontsize=18)\n",
    "axcount.text(45,.55,str(rcount),color='red',fontsize = 18)\n",
    "axcount.axis('off')\n",
    "axcount.set_title('Seat Distribution')     \n",
    "    \n",
    "axsmall.fill_betweenx((9,9.5),8,9,color='red')\n",
    "axsmall.annotate('Republican',(9.5,9.1))\n",
    "axsmall.fill_betweenx((8,8.5),8,9,color='blue')\n",
    "axsmall.annotate('Democrat',(9.5,8.1))\n",
    "axsmall.fill_betweenx((7,7.5),8,9,color='purple')\n",
    "axsmall.annotate('Independent',(9.5,7.1))\n",
    "axsmall.fill_betweenx((6,6.5),8,9,facecolor='red',edgecolor='blue',hatch='r\\\\')\n",
    "axsmall.annotate('Two Party Runoff',(9.5,6.1))\n",
    "\n",
    "axsmall.fill_betweenx((5,5.5),8,9,color='gray')\n",
    "axsmall.annotate('Prediction TBD',(9.5,5.1))\n",
    "axsmall.fill_betweenx((4,4.5),8,9,color='lightgrey')\n",
    "axsmall.annotate('No Election',(9.5,4.1))\n",
    "\n",
    "plt.savefig(filename)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Save your file\n",
    "\n",
    "To get to this page, you first were sent to a Jupyter Home Page, and you clicked on the Senate Map and this page opened in a new tab. Go back to the tab with the Home Page and you will see your new map file, click on it, and download to your device so you can keep your map as long as you wish.\n",
    "\n",
    "If you cannot find the Home Page, right-click on the Jupyter logo and open it in a new tab. You should be able to locate your new file there.\n",
    "\n",
    "You are encouraged to post your map here: https://www.electionperfection.org/forum"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}