finalminiproject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f43f0465",
   "metadata": {},
   "source": [
    "#  Name- Shweta More  ML Batch18\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34348a3a",
   "metadata": {},
   "source": [
    "### MiniProject:Data Visualization of exercise dataset using seaborn dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3981fe",
   "metadata": {},
   "source": [
    "libraries used: numpy,pandas,seaborn,matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642a4bae",
   "metadata": {},
   "source": [
    "exercise dataset: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85a87753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\user\\anaconda3\\lib\\site-packages (0.11.1)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.6.2)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.20.1)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (3.3.4)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.2.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.2.0)\n",
      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce5c0c0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: seaborn\n",
      "Version: 0.11.1\n",
      "Summary: seaborn: statistical data visualization\n",
      "Home-page: https://seaborn.pydata.org\n",
      "Author: Michael Waskom\n",
      "Author-email: mwaskom@nyu.edu\n",
      "License: BSD (3-clause)\n",
      "Location: c:\\users\\user\\anaconda3\\lib\\site-packages\n",
      "Requires: pandas, scipy, matplotlib, numpy\n",
      "Required-by: \n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip show seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cadcea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1acd2cfe",
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1353\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1354\u001b[1;33m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[0;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1254\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1255\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1300\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1301\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1249\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1250\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1009\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1010\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1011\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    949\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 950\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    951\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1417\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1418\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 921\u001b[1;33m         self.sock = self._create_connection(\n\u001b[0m\u001b[0;32m    922\u001b[0m             (self.host,self.port), self.timeout, self.source_address)\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    786\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    788\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    917\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 918\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    919\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-b6f8a68950f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dataset_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mget_dataset_names\u001b[1;34m()\u001b[0m\n\u001b[0;32m    417\u001b[0m     \"\"\"\n\u001b[0;32m    418\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"https://github.com/mwaskom/seaborn-data\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 419\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    420\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    523\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'urllib.Request'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[0;32m    543\u001b[0m                                   '_open', req)\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 502\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1396\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1397\u001b[1;33m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[0;32m   1398\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0;32m   1399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1356\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1357\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1358\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "print(sns.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "165808f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  id     diet  pulse    time  kind\n",
      "0           0   1  low fat     85   1 min  rest\n",
      "1           1   1  low fat     85  15 min  rest\n",
      "2           2   1  low fat     88  30 min  rest\n",
      "3           3   2  low fat     90   1 min  rest\n",
      "4           4   2  low fat     92  15 min  rest\n"
     ]
    }
   ],
   "source": [
    "data=sns.load_dataset('exercise')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad471035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b12c3rEplTlMvLXCNZqH2pqRSm1w2hZLKeCCNdEcb3s+636eqSyG73QPX9bc+IvZHxFhEjI2MjFTcDetV2dPUFQvc5rhQe1PKHhE3rWwKJZXxpJLuaPr9XI73qd/g/qqkGwCKr2eK9mlgbcd2a4BT/XfPlkvZ09Q73ruWbhZqb0pKtcllUiipjCeVdEfT7+dyvE/9pmUeBe4Exouv3+5o/4akLwKrgY3AM4N20upX9jR1riqm7dUyqdYmL2TH5lEmf3jusvf9I78++B2jvaQGclqso+nPx3K8T2VKIQ8A24BVkqaBe5kN6gclfRw4CXwUICKel3QQeAG4CNwVEZe6fmNrlV5usf78jk2tC+bd5HS7/MTUDI8cmbmsSumRIzOM/fJ1tS/A0cu2Kc3w2eTnYzmmNChTLXNHRNwQESsjYk1EfCUi/iIiPhARG4uv5zq2vy8iboyImyLijyrrqdWq6dNUW1yTC3D0sq2nPihnOX7fPP3APKncoFO1Xk77U6iGyE2TC3D0sq2nPihnOdJCDu4d5m7QmTN3gw6QfYAve9qf6sIFqasj3dFLaqDstqnOoNiEutNCnhWyQyo36NSh7Gl3KtUQuakj3dFLaqDstk7vtYeP3DukcoNOHcqedqdSDQHl00dNp5nK/Pw60h29pAbKbtt0FYq9zcG9wwqpayBv2w06dSi77mYqp91l00dNp5nK/vy63vdeUgNlt82pSillTst0SOUGnTqUXXczldPuVNJMZX9+Ku+7tYeP3DukcoNOL8qmHMquu5nKaXcqaaayPz+V970Nmk6ztYWD+zyp3KBTRi8ph15O+1M47S5bXVI2HVWX3N73pjWdZmsTp2XmmZiaYev4ITbseYyt44daN+FRL3pJOeR22l+2uqRsOqouub3vdSn7e9l0mq1N8cNH7h1y+6vf7YhwofbcTvvLVpeUTUfVJbf3vQ69/F42mWZrW/xwcO/Qy5JnKei1+ien0/6UbrrJ6X2vQy+/l03uz7bFj6siLVP2VKnpi2tVS6luv+w+Krudb7opp01phIX08nvZ5P5sW/zI/si9rouKKRhdYDyjLRtPHTXpvulmaW1LIyyk14vO0Mz+bFv8ULTgKG5sbCwmJydr+d5bxw8tGOC+u+f9l7XN/7DD7F/9VBcXTmU8ZfdRL/vSlpbK+5nK57iJfko6EhFj3Z7L/si9l1Oluv7qN1V3W9cCD1VLpSa9Lk19PlJ5P1M5u2pbP7MP7r3Oplf1xa0mT33rWOChDmVPZ1NaCKKsJj8fbUsjLCaVi85t6mf2F1SbXjygybrbpmt+yyp7EazpfVmHJvfR1X4xOXfZH7k3vXhAk6e+uZ12N70v69DkPmpbGsGqlX1wb/rUc2jlz/HTN9/q2l63psfeizKnsymNp6ymx9SmNIJVK/u0TNOnnhcuXhnYF2uvUtNjr1pu44E8x2TtMNCRu6RPA/8SEPDliPg9SdcBfwCsB14GPhYRPx6wn31rumKkrjxxmQqL3E67m96XvShbAZPbPrL26Du4S7qV2cC+BfhL4I8lPVa0PRkR45L2AHuAz1TR2X40XTFSxwIgvd7Mk0ugaHpfltVrBUxO+8jaY5C0zN8EDkfETyPiIvA/gQ8DtwMPFds8BOwYqIcDarpipI4FQJoeU1NSGXcq/bS8DZKWOQbcJ+mXgAvAB4FJ4N0RcRogIk5Lur7biyXtBnYDrFu3boBuLK7pipE6FgBpekxNqWvcvdxEVGbbq3X/WLv0Hdwj4kVJ/xl4Angd+B5wsYfX7wf2w+z0A/32YylNVyNA9QuAtGFMTahj3L2kUJpe79SsFwNVy0TEVyLi1yLifcA54M+BVyXdAFB8PTN4N7srM6NdjtUId2+/iZUrLs/Zr1yhpMdURh37spcUitc7tZQMWi1zfUSckbQO2An8HWADcCcwXnz99sC97KLsUVS21Qjzz3USvkuzrDr2ZS8pFK93aikZ9CamR4qc+5vAXRHxY0njwEFJHwdOAh8dtJPd9DIxfm7VCPc/fpw337o8mr/5ViS7qEgvqt6XvaRQvN6ppWTQtMzfj4ibI+JvRcSTRdtfRMQHImJj8fVcNV293NV80epqHnvVekmhON1SvRQWC0lVstMPXM0XrXKcHbEpvaRQnG6pViqLhaQq2eB+9/abuk6MfzUcReU4O2KTekmhON1SnbatOZqbZIP71XwUlcrsiPdMHK20vr8NmlpYI0dOL9Yr2eAOV+9RVAopqXsmjvK1wyd/9vhSxM8epxrgnUaoVgqf45RlPytkjlK4sHfg6Vd6ak+BpxWoVgqf45QlfeSe42l/GSmkpLpNlrZYewqcRqhWCp/jlCUb3HM87e9F21NSdcyG2TSnEarX9s9xypJNy+R42p+TOmbDbJrTCJaSZIN7jqf9Od3Q8fkdm9h643WXtW298bqkz6p2bB7lI78++rOzjzYvFmKWbHBf6PQ+1dP+uUqMmfMXCN6uxEg1wE9MzfDsydcua3v25GvJjgcWXiwk5TFZvpIN7k2f9ld9lJ1bJUZu44E8x2T5SvaCah2LYJRVR71ztwt1i7W3XY6VJTmOyfKVbHCH6hfBKMu3TS8tx8qSHMdk+Uo2LdMkH8EtLcfKkhzHZPlK+si9KXXMyphbXXiON6jkOCbLl4N7H+qYlfGO96697KaszvZU5XiDSo5jsjw5uPehjlkZm7xAbGb5cXDvQ10X1pq6QGxm+fEF1T74wpqZtd1AwV3Sv5b0vKRjkg5I+nlJ10l6QtKfF1/fVVVn28K3oZtZ2/Ud3CWNAp8CxiLiVmAFsAvYAzwZERuBJ4vHWfFt6GbWdoOmZd4BDEl6B/BO4BRwO/BQ8fxDwI4Bf0br+DZ0M2u7voN7RMwAXwBOAqeB1yLiO8C7I+J0sc1p4Ppur5e0W9KkpMmzZ8/2241G+CYmM2u7QdIy72L2KH0DsBq4VtI/Lfv6iNgfEWMRMTYyMtJvNxqxUFWMb0M3s7YYJC3zG8BLEXE2It4Evgn8XeBVSTcAFF/PDN7NdnG1jJm13SDB/STwtyW9U5KADwAvAo8Cdxbb3Al8e7Auts+OzaPs27mJ0eEhBIwOD7Fv5yZXy5hZa/R9E1NEPC3pD4FngYvAFLAf+AXgoKSPM/sH4KNVdLRtfBu6mbXZQHeoRsS9wL3zmt9g9ijezMwa4jtUzcwy5OBuZpYhB3czsww5uJuZZUgxyAoTVXVCOgv8cF7zKuBHDXSnTrmNyeNpv9zGlNt4YLAx/XJEdL0LtBXBvRtJkxEx1nQ/qpTbmDye9sttTLmNB+obk9MyZmYZcnA3M8tQm4P7/qY7UIPcxuTxtF9uY8ptPFDTmFqbczczs/61+cjdzMz65OBuZpah1gV3SbdJOi7phKQs1l+V9LKko5KekzTZdH/6IekBSWckHetoS3Yx9AXG8zlJM8V+ek7SB5vsYy8krZX03yW9WCxa/+miPeV9tNCYktxPkn5e0jOSvleM5z8U7bXso1bl3CWtAP4M+E1gGvhT4I6IeKHRjg1I0svMLiSe7M0Xkt4HvA58tVgQHUn/BTgXEePFH+J3RcRnmuxnWQuM53PA6xHxhSb71o9iYZwbIuJZSX8VOMLs+sX/nHT30UJj+hgJ7qdi3YtrI+J1SSuB/w18GthJDfuobUfuW4ATEfF/I+IvgYeZXcrPGhYRTwHn5jUnuxj6AuNJVkScjohni///P2YXzhkl7X200JiSFLNeLx6uLP4FNe2jtgX3UeCVjsfTJLwzOwTwHUlHJO1uujMVKrUYemI+Ken7RdommRRGJ0nrgc3A02Syj+aNCRLdT5JWSHqO2eVHn4iI2vZR24K7urS1J2/Uv60R8WvAPwLuKlIC1j6/D9wIvAc4DfxOo73pg6RfAB4B/lVE/KTp/lShy5iS3U8RcSki3gOsAbZIurWun9W24D4NrO14vAY41VBfKhMRp4qvZ4BvMZt+ykFWi6FHxKvFL99bwJdJbD8VedxHgK9HxDeL5qT3Ubcxpb6fACLiPPA/gNuoaR+1Lbj/KbBR0gZJfwXYxeyC28mSdG1xMQhJ1wL/EDi2+KuSkdVi6HO/YIUPk9B+Ki7WfQV4MSK+2PFUsvtooTGlup8kjUgaLv4/BPwG8ANq2ketqpYBKMqafg9YATwQEfc126PBSPobzB6tw+yatd9IcUySDgDbmJ2e9FVm186dAA4C6ygWQ4+IJC5SLjCebcye6gfwMvDbc7nQtpP094D/BRwF3iqa/x2zOepU99FCY7qDBPeTpF9l9oLpCmYPrA9GxH+U9EvUsI9aF9zNzGxwbUvLmJlZBRzczcwy5OBuZpYhB3czsww5uJuZZcjB3cwsQw7uZmYZ+v9daeo1lME0kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.id,data.pulse)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "556cf6b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>pulse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>99.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.124701</td>\n",
       "      <td>8.703932</td>\n",
       "      <td>14.858471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.250000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>90.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.750000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0         id       pulse\n",
       "count   90.000000  90.000000   90.000000\n",
       "mean    44.500000  15.500000   99.700000\n",
       "std     26.124701   8.703932   14.858471\n",
       "min      0.000000   1.000000   80.000000\n",
       "25%     22.250000   8.000000   90.250000\n",
       "50%     44.500000  15.500000   96.000000\n",
       "75%     66.750000  23.000000  103.000000\n",
       "max     89.000000  30.000000  150.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "751e9003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.id,data.pulse)\n",
    "sns.set()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aab87d8",
   "metadata": {},
   "source": [
    "## Seaborn supports various themes that can make styling the plots really easy and save a lot of time. Using the set_style() function of Seaborn we can set any of the themes available on Seaborn library. Here are a few of the popular themes:\n",
    "\n",
    "Darkgrid\n",
    "\n",
    "Whitegrid\n",
    "\n",
    "Dark\n",
    "\n",
    "White\n",
    "\n",
    "Ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be8f573b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.id,data.pulse)\n",
    "sns.set_style(\"whitegrid\")                       #dark,white,ticks\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e54c2a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.id,data.pulse)\n",
    "sns.set_style(\"dark\")                       #dark,white,ticks\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e39f8f1",
   "metadata": {},
   "source": [
    "# Vertical barplot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d6ecfaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>diet</th>\n",
       "      <th>pulse</th>\n",
       "      <th>time</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>88</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>90</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>92</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>no fat</td>\n",
       "      <td>135</td>\n",
       "      <td>15 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>29</td>\n",
       "      <td>no fat</td>\n",
       "      <td>130</td>\n",
       "      <td>30 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>99</td>\n",
       "      <td>1 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>111</td>\n",
       "      <td>15 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>150</td>\n",
       "      <td>30 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  id     diet  pulse    time     kind\n",
       "0            0   1  low fat     85   1 min     rest\n",
       "1            1   1  low fat     85  15 min     rest\n",
       "2            2   1  low fat     88  30 min     rest\n",
       "3            3   2  low fat     90   1 min     rest\n",
       "4            4   2  low fat     92  15 min     rest\n",
       "..         ...  ..      ...    ...     ...      ...\n",
       "85          85  29   no fat    135  15 min  running\n",
       "86          86  29   no fat    130  30 min  running\n",
       "87          87  30   no fat     99   1 min  running\n",
       "88          88  30   no fat    111  15 min  running\n",
       "89          89  30   no fat    150  30 min  running\n",
       "\n",
       "[90 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exercise = sns.load_dataset(\"exercise\")\n",
    "exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae5d57dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'id', 'diet', 'pulse', 'time', 'kind'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(exercise.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88264cb1",
   "metadata": {},
   "source": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86343328",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Unnamed: 0'}>,\n",
       "        <AxesSubplot:title={'center':'id'}>],\n",
       "       [<AxesSubplot:title={'center':'pulse'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e82adee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['id'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61df41ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['diet'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9406a057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['pulse'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a6b39c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['time'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63d658e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['kind'].hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf4d3f9",
   "metadata": {},
   "source": [
    "# Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cac3878",
   "metadata": {},
   "source": [
    "A heatmap is a two-dimensional graphical representation of data where the individual values that are contained in a matrix are represented as colours.\n",
    "\n",
    "In Seaborn, we can make annotated heatmaps which can be tweaked using Matplotlib as per requirement.\n",
    "\n",
    "Now if we get the data of the dataset ‘flights’ and transform it by monthly as shown below, it can give us a lot of information about the data.\n",
    "\n",
    "But this information is in tabular form and can be better displayed by using heatmap as shown below:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09f1ff90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Unnamed: 0  id    diet  pulse    time     kind\n",
      "85          85  29  no fat    135  15 min  running\n",
      "86          86  29  no fat    130  30 min  running\n",
      "87          87  30  no fat     99   1 min  running\n",
      "88          88  30  no fat    111  15 min  running\n",
      "89          89  30  no fat    150  30 min  running\n"
     ]
    }
   ],
   "source": [
    "data=sns.load_dataset('exercise')\n",
    "print(data.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae2da51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>diet</th>\n",
       "      <th>pulse</th>\n",
       "      <th>time</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>88</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>90</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>92</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>no fat</td>\n",
       "      <td>135</td>\n",
       "      <td>15 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>29</td>\n",
       "      <td>no fat</td>\n",
       "      <td>130</td>\n",
       "      <td>30 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>99</td>\n",
       "      <td>1 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>111</td>\n",
       "      <td>15 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>30</td>\n",
       "      <td>no fat</td>\n",
       "      <td>150</td>\n",
       "      <td>30 min</td>\n",
       "      <td>running</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  id     diet  pulse    time     kind\n",
       "0            0   1  low fat     85   1 min     rest\n",
       "1            1   1  low fat     85  15 min     rest\n",
       "2            2   1  low fat     88  30 min     rest\n",
       "3            3   2  low fat     90   1 min     rest\n",
       "4            4   2  low fat     92  15 min     rest\n",
       "..         ...  ..      ...    ...     ...      ...\n",
       "85          85  29   no fat    135  15 min  running\n",
       "86          86  29   no fat    130  30 min  running\n",
       "87          87  30   no fat     99   1 min  running\n",
       "88          88  30   no fat    111  15 min  running\n",
       "89          89  30   no fat    150  30 min  running\n",
       "\n",
       "[90 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset('exercise')\n",
    "exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "59b562ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Unnamed: 0        id     pulse\n",
      "Unnamed: 0    1.000000  0.999506  0.688489\n",
      "id            0.999506  1.000000  0.679012\n",
      "pulse         0.688489  0.679012  1.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aFb1UQghhXzZYZufl5cW0adMYNWoUAwcOpF+/fvj5+TF+/HhSU1NxcnJiwYIFjBs3jr59+6JWqxk7diwAkZGRxMbGEhQUxIkTJ5g6darF86mMRqPRUqMLFy7QrFkz0tPTSUlJISgoyKrhuRBCVJcWkZZXSfzu3Py+Nozk3lmcgy4pKeHXX3/ln//8J3DrG1aOHj16T4961242osL7COvpL2wF5Drb2q3r/FN1h+HgHq38Ie6HF/ZPnz6dixcv0rJlS7O7jvIuDiGEkjnCo94WE/SZM2fYvXu3VUtChBBCMWr+ANpyF1q2bEl2drY9YhFCiKpzP3yr940bN+jbty+PPvoobm5upnr5yishhKLdDy/snzBhgj3iEEKIquUACdriFEfHjh158MEHcXJyQqVSmTYhhFAyo7PK6k2pLI6gFyxYwP79+/Hx8THVqVQqmeIQQiibAwwkLSbow4cPk5SUJE8PCiFqFgeY4rCYoH18fLDiYUMhhFCWmp+fLSfoevXq8fzzz9O+fXuzVRyLFy+2aWBCCFEZTg6wDtpigv7Tn/7En/70J3vEIoQQVea+SNDlvSNVCCGUzhFWm90xQbdp08asg6r/vjPV39+fefPm4enpaY/4hBDinjhAfr5zgs7IyChTd/nyZWJjY1mwYAErV660aWBCCFEZjpCgKzRL07BhQyZNmsSZM2dsFY8QQlQJlZP1m1JZnIMuj6ura1XHIYQQVcoRRtAVTtDJycky/yyEUDxnBY+MrXXHBN2jR48yd0Hz8/Np3rw5y5Yts3lgQghRGQ49gt68ebNZ2cnJCbVaTZ06dWwelBBCVJZDL7Nr0qSJPeMQQogqpeSbf9a6p5uEQgihdA4wgJYELYRwTPfFo95CCFETOcDbRiVBCyEckyNMcTjALwFCCFGWrb7UOzExkaCgIPr06cOWLVvKfJ6ens7gwYPp378/EyZMIDc3F4C4uDi6devGgAEDGDBgANHR0RbPJSNoIYRDUtlgjkOn0xEdHc3OnTtxc3Nj+PDhdOrUiVatWpnavPXWW0yZMoXu3bvz9ttvs3HjRqZNm0ZaWhoRERH069fP6vPJCFoI4ZAqMoLOzc0lMzOzzPb76Pd3R48epXPnznh6euLh4UFgYCBJSUlmbQwGAwUFBQDo9XrT1wWmpqYSFxdHcHAwM2bM4Pr16xb7IAlaCOGQnJys32JiYujZs2eZLSYmxuyYWVlZaDQaU1mr1aLT6czaREREMGfOHLp168bRo0cZPnw4ABqNhkmTJrFr1y4aNWrEggULLPZBpjiEEA6pIjMcoaGh5X45iVqtNisbDAazJxSNRqNZ+caNG8yePZtNmzbh5+fHRx99xMyZM/nb3/7GunXrTO3GjRtH7969LffB+i4IIUTNUZEpDrVaTdOmTctsf0zQ3t7eZGdnm8rZ2dlotVpT+aeffsLd3R0/Pz8Ahg0bxrfffkteXh6bNm0ytTMajTg7O1vsgyTou3h/5USmvvJ8dYfh8OQ637uDB1MIDp5MYOCrTJnyNvn5hWXanDlzjr/8ZRYDB/4fISHTSEv7BYDS0lIiI9cTFDSJoKBJLFmyEaPRaO8u2Iwt3gft7+/PsWPHyMnJQa/Xk5ycTEBAgOnz5s2bc+nSJX799VcAvvzyS3x9ffHw8OCDDz7g5MmTAHzyyScygr5XrVs1ZvfWOQwK6ljdoTg0uc6Vk5NznVmz3mHNmlns2fMuPj7eLF++yayNXn+DsWPnMW7cYOLj32HSpGHMmLEcgISEA5w9+28SE9eQkLCab79NIynpSDX0xDZssczOy8uLadOmMWrUKAYOHEi/fv3w8/Nj/PjxpKamUq9ePRYvXszUqVMJDg5mx44dLFq0CGdnZ1atWkVUVBTPPfcc6enphIeHWzyfzEGX49VRfdi0bT+/Xbxc3aE4NLnOlXP48Pf4+j5CixaNARgx4jkGDJhCZORE07zokSPf4+PjTffuHQDo2bMTTZt6AVBaakCvv0FRUTEGg5Hi4hLc3d2qpzM2YKu32QUHBxMcHGxW9/7775t+7t69O927dy+zX4cOHYiLi6vQuSRBl2PavE0A9Azwq95AHJxc58q5dCkbb++GprK3d0Py8wspKNBTt64HAGfPXkSjeZA33lhNRsZZ1Oo6hIePBiAkpCdJSUcICBhNSYmBbt2epEcPx/ltxuHfxVHeS/tv9+WXX1Z5QEII6xgMxnL/fjrdlplKSko4dOgEH3+8iHbtWrNv3ze88sp8Dhz4kA0btlO/vpojRzZz82YRkya9xYcfxjFmTNnVDDWRwz/qvXnzZmJiYujYsSODBw9my5YtbNu2jZEjR5Y7hBdC2E+jRhqysnJMZZ3uCvXq1cXDo5apTqttQMuWPrRr1xqAXr06U1pq4LffLrF37zEGD+6Nm5srDzxQh0GDenD8+Cm798NWnFTWb0p11wTdpEkTmjZtypkzZ5g0aRLe3t5oNBrGjBnDDz/8YKcQhRDl6datPSdPnuHcuYsAbNu2m549O5m1CQh4msxMnWnlRkpKGiqViqZNvXj88Zbs3n0YgOLiEvbv/5Z27drYtxM25AgJ2uo56GPHjtGlSxcADh06ZNUaPiGE7TRo4Mnixf/HlCmLKS4uoVkzb5YseY3U1J+ZM+fWygyN5kHWrZvN/Pkb0Otv4Obmypo1s3B3d2PWrHEsXPgeffu+irOzE126tGPcuJDq7laVcVLV/CWDKqMVCx9Pnz7N66+/TnZ2NkajkSZNmrBs2TJatmxZoZPVbjbingMVlukvbAXkOtvarev8U3WH4eAerfQRnk8+bHXbf/TpVunz2cJdpzjmzp0LwKJFi/D09ESj0aDVaqlTpw7z58+3S4BCCHEvnFRGqzeluusUx7BhwwCYPHmyXYIRQoiqouS5ZWvdNUG3bdsWgI4dHWdtpBDi/uAAy6DlQRUhhGNy+BG0EELUVCoFzy1bSxK0EMIhucgIWgghlEnJqzOsJQlaCOGQZA5aCCEUSlZxCCGEQskIWgghFErmoIUQQqFkFYcQQiiUjKCFEEKhZA5aCCEUShK0EEIolCyzE0IIhXJxkjloIYRQJBlBCyGEQjnCHLQj/CMjhBBlqFRGq7eKSExMJCgoiD59+rBly5Yyn6enpzN48GD69+/PhAkTyM3NBeDixYuMHDmSvn37MnHiRAoKCiyeSxK0EMIhOams36yl0+mIjo7m008/JT4+nu3bt/PLL7+YtXnrrbeYMmUKu3bt4qGHHmLjxo0AzJ8/nxdffJGkpCTatm3L+vXrLfehQj0WQogawqkCW25uLpmZmWW230e/vzt69CidO3fG09MTDw8PAgMDSUpKMmtjMBhMo2O9Xk+tWrUoLi4mJSWFwMBAAEJCQsrsVx6ZgxZCOKSKrOKIiYlh7dq1ZerDwsLMvjQ7KysLjUZjKmu1Wk6dOmW2T0REBGPGjGHRokXUrl2b2NhYrl69St26dXFxuZVyNRoNOp3Och+s7oEQQtQgFZm6CA0NZdCgQWXq1Wq1WdlgMKBS/e/ARqPRrHzjxg1mz57Npk2b8PPz46OPPmLmzJksXLjQrB1QplweSdBCCIfkXIG2arW6TDIuj7e3NydOnDCVs7Oz0Wq1pvJPP/2Eu7s7fn5+AAwbNox33nmH+vXrk5eXR2lpKc7OzmX2uxOZgxZCOCQnldHqzVr+/v4cO3aMnJwc9Ho9ycnJBAQEmD5v3rw5ly5d4tdffwXgyy+/xNfXF1dXVzp06MAXX3wBQHx8vNl+dyIjaCGEQ7LFOmgvLy+mTZvGqFGjKC4uZsiQIfj5+TF+/HimTJmCr68vixcvZurUqRiNRho0aMCiRYsAiIyMJCIigg0bNtCoUSNWrlxp8Xwqo9Fot+chazcbYa9T3Zf0F7YCcp1t7dZ1/qm6w3Bwj1b6CEtP7bW67et+vSt9PluQEbQQwiG5OsAEriRoIYRDkhf2CyGEQjnCuzjsmqB/nyMVtiXX2R4qP0cqbKsiy+yUSkbQQgiHJCPoCioxnLTn6e47Lk7t/vuTrDCwrUdlpYyNVcVvga7ywn4hhFAmGUELIYRCSYIWQgiFkgQthBAK5SzroIUQQpkc4EFCSdBCCMfk4gAZWhK0EMIhyRSHEEIolNwkFEIIhZIELYQQCiUJWgghFEoe9RZCCIVygEUckqCFEI5JpjiEEEKhnCVBCyGEMslXXgkhhELJFIcQQiiUiyRoIYRQJpUkaCGEUCYHyM+SoIUQjslWI+jExEQ2bNhASUkJoaGhjBw50vTZ6dOniYiIMJVzcnKoV68en3/+OXFxcaxYsYIGDRoA8Oc//5lp06bd9VySoIUQDskWD6rodDqio6PZuXMnbm5uDB8+nE6dOtGqVSsAHnvsMRISEgDQ6/W88MILREVFAZCWlkZERAT9+vWz+nyO8LCNEEKUoVIZrd5yc3PJzMwss+Xm5pod8+jRo3Tu3BlPT088PDwIDAwkKSmp3PO/9957PPPMM3To0AGA1NRU4uLiCA4OZsaMGVy/ft1iHyRBCyEckpPK+i0mJoaePXuW2WJiYsyOmZWVhUajMZW1Wi06na7MufPy8oiNjSUsLMxUp9FomDRpErt27aJRo0YsWLDAYh9kikMI4ZAqMgUdGhrKoEGDytSr1WqzssFgQHXb5LbRaDQr/27Xrl306tXLNN8MsG7dOtPP48aNo3fv3hbjkgQthHBIFXlQRa1Wl0nG5fH29ubEiROmcnZ2Nlqttky7ffv2MWHCBFM5Ly+PHTt2MHr0aOBWYnd2drZ4PpniEEI4JFUFNmv5+/tz7NgxcnJy0Ov1JCcnExAQYNbGaDSSnp5O+/btTXUeHh588MEHnDx5EoBPPvlERtBCiPuXLZbZeXl5MW3aNEaNGkVxcTFDhgzBz8+P8ePHM2XKFHx9fcnJycHV1RV3d3fTfs7OzqxatYqoqChu3LhBixYtWLp0qeU+GI1Gu71RpMRw0l6nui+5OLX7708/VWscju9RajcbUd1BODT9ha2VPkbGtc+tbtvG0/qlb/YkI2ghhEOSlyXVUIcO/pNV0Z9SVFTMo62bs/DNV6lb18OszU8/XWDRmx+Sl1+Is5MTkfNf4YknHubatXwWzn+fjIxz1K5di0Ehf2bkS89VU0+U6+DBFFas+JiiomJat27BokVTylzjM2fO8eab75GXV4iTkxMLFvyVtm1bUVpayoIF75GSkgZA9+5P8/rrY8q9Wy6s8/7KiaRnXGDV3/5R3aHYjSP8abnvbhLm5OQyZ/Z6Vr0znX/sfoemTbWsXPGpWRu9/ibjx77JmLH92bFzKa9OHMzM8NUALHl7Ex4etdj1eTSfbnuLr7/6gYMHvquOrihWTs51Zs16hzVrZrFnz7v4+HizfPkmszZ6/Q3Gjp3HuHGDiY9/h0mThjFjxnIAEhIOcPbsv0lMXENCwmq+/TaNpKQj1dCTmq91q8bs3jqHQUEdqzsUu6vIgypKdd8l6KNHTtK2bUuat2gEwPARffjH519z+1T80SMn8WnmRUD3pwB4tkcHVkTfemb+x/SzBA8IwNnZCTc3FwK6tyc5+Rv7d0TBDh/+Hl/fR2jRojEAI0Y8R2LiIbNrfOTI9/j4eNO9+62nrHr27MSqVTMBKC01oNffoKiomKKiYoqLS3B3d7N/RxzAq6P6sGnbfnb+43h1h2J3tljFYW/33RTHfy5dwbvR/xaPe3k1ID9fT0GB3vQr+Llz/6FhQ0/mzt7AmTPneeCBOkyfceuFKH5+rUhM+Ir27VtTVFTC3r3HcXG57y7jXV26lI23d0NT2du7Ifn5hWbX+OzZi2g0D/LGG6vJyDiLWl2H8PDRAISE9CQp6QgBAaMpKTHQrduT9Ohx/40Aq8K0eZsA6BngV72BVANHmBGzagRdVFTEhg0beP3118nPz2ft2rUUFRXZOjabMBoMqMr5N9PJ6X+XoqSklK+/+p4XhvYi9u9vM/Klvrz66mKKiooJnzkKlUrFkJCZTAlbhr+/H66ulhec308MhvKfrjK/xiUcOnSCYcMC2bkzmpde6scrr8ynqKiYtWu3Ur++miNHNvPVVx9x7Vo+H34YZ88uCAfgrLJ+UyqrEvSCBQvQ6/X8+OOPODs7c+HCBd544w1bx2YTjRo1JCv7qqmcpctBXa8OHh61THVa7YM8/HAT/No9AkCPns9gKDXw22868vP1vDbjJRISV7Dxo7kYjUaaNfO2ez+UrFEjDVlZOaayTneFevXq/uEaN6BlSx/atWsNQK9enSktNfDbb5fYu/cYgwf3xs3NlQceqMOgQT04fvyU3fshajZHmOKwKkGnp6fz2muv4eLiQu3atVmyZAkZGRm2js0m/Lu249TJnzl/7j8AbN++lx49njFr0+1P7cn8dxbp6b8CcCLlR1QqFU2baondnszaNdsBuHz5Gjv+vp/n+3WzbycUrlu39pw8eYZz5y4CsG3bbnr27GTWJiDgaTIzdaSl/QJASkraf6+xF48/3pLduw8DUFxcwv7939KuXRv7dkLUeCqV9ZtSWTV5qlKpKCoqMv3aevXq1Rq75KlBg3q8+dZEpk5dSUlxCT4+Xix6O4y0tH8xb+677IxbhkbjyZo14Sxc8AH6wpu4ubmwavUM3N3dGP/KICJmrmFA8HSMRiNhk4fi69uqurulKA0aeLJ48f8xZcpiiotLaNbMmyVLXiM19WfmzLm1MkOjeZB162Yzf/4G9PobuLm5smbNLNzd3Zg1axwLF75H376v4uzsRJcu7Rg3LqS6uyVqmJqZocxZ9SRhfHw8n332GefPn+e5555j3759/PWvf2XIkCEVOpk8SWhb8iShvciThLZWFU8SXixMtLptY4/gSp/PFqwaQQ8cOJC2bdty/PhxSktLeffdd2ndurWtYxNCiHvmCCNoq+agr127RlZWFiNHjqSwsJB169Zx4cIFW8cmhBD3zElltHpTKqsS9PTp0zl9+jTHjh0jOTmZHj16MHv2bFvHJoQQ98wRbhJalaCvX7/O2LFj2bdvHwMHDmTgwIEUFBTYOjYhhLhn980yO4PBQFpaGvv27ePZZ5/l9OnTlJaW2jo2IYS4Z04V2JTKqpuE4eHhLF26lJdffhkfHx+GDh1KRESErWMTQoh7puSpC2tZlaC7dOlCly5dTOXY2FibBSSEEFVBpeixsXXumqDbtGmDSqUy++ba35dNq1QqTp8+bfsIhRDiHqhUDp6ga+rj3EIIoezbf9axaopj7dq15daHhYVVaTBCCFFVyntrZU1T4d8BiouL2b9/P1euXLFFPEIIUUVq/kI7q0bQfxwp//Wvf2XMmDE2CUgIIaqCw89B30lBQQEXL16s6liEEKLKOPwqjt/16NHDbBXH708WCiGEUjnCHLRVCXrjxo0cOnSIb775hsLCQl5++WVeeuklW8cmhBCVYJsRdGJiIhs2bKCkpITQ0FBGjhxp+uz06dNmD/Hl5ORQr149Pv/8cy5evEh4eDhXrlzhoYceYvny5dSpU+eu57IqQb/77rvcvHmToUOHYjAYSEhI4MKFC/LCJCGEYtniS0V0Oh3R0dHs3LkTNzc3hg8fTqdOnWjV6taXdjz22GMkJCQAoNfreeGFF4iKigJg/vz5vPjiizz//POsW7eO9evXEx4eftfzWfVPzMmTJ1m1ahU9evSgV69evPPOOxw5cqQS3RRCCFur+lUcR48epXPnznh6euLh4UFgYCBJSUnltn3vvfd45pln6NChA8XFxaSkpBAYGAhASEjIHfe7nVUj6KZNm3L+/HmaN28OwOXLl/Hy8rK2T0IIYXcVmYPOzc0lNze3TL1arUatVpvKWVlZaDQaU1mr1XLqVNkvNM7LyyM2NpbExFvf6nL16lXq1q2Li8utlKvRaNDpdBbjsipBl5SUMGDAADp06ICLiwvfffcdGo2GUaNGAfDxxx9bcxghhLAbFc5Wt42JiSn3gbywsDAmT55sKhsMBrOpk9tfg3G7Xbt20atXLxo0aHDHdtZMwViVoCdNmmRWljXQQgilq8gcdGhoKIMGDSpTf/voGcDb25sTJ06YytnZ2Wi12jL77du3jwkTJpjK9evXJy8vj9LSUpydne+43x9ZlaA7duxoTTMhhFAQ6xP0H6cy7sTf3581a9aQk5ND7dq1SU5OZuHChWZtjEYj6enptG/f3lTn6upKhw4d+OKLLwgODiY+Pp6AgACL56v5K7mFEKIcKpys3qzl5eXFtGnTGDVqFAMHDqRfv374+fkxfvx4UlNTgVtL61xdXXF3dzfbNzIyktjYWIKCgjhx4gRTp0613Afj7+8PtYMSw0l7neq+5OLU7r8//VStcTi+R6ndbER1B+HQ9Be2VvoYN0tTrG7r7vxMpc9nC/f0qLcQQijdffsuDiGEULr75l0cQghR89wn7+IQQoia5r55WZIQQtQ0tngXh71JghZCOCiZgxZCCEWSm4RCCKFQMsUhhBCKVfNH0HZ9klAIIYT1av4/MUII4aAkQQshhEJJghZCCIWSBC2EEAolCVoIIRRKErQQQiiUJGghhFAoSdBCCKFQkqCFEEKhakSCzszMpEePHmXqW7duXQ3RWM+a+D788EP69u1LYGAgycnJdoiq+qWmpjJ79myzujv9PxZVQ+l/V0T55F0c1ejUqVPs2rWLhIQE8vPzGTZsGB07dsTT07O6Q7MpX19ffH19qzsMIRTPIRL0zp07+frrr7l+/Tq//fYbXbt2JSoqiuPHj/Pee+9Rq1Yt/vWvf9G6dWuWL1+Om5sb0dHRHDt2jOvXr6PVaomOjqZhw4Z07dqVnj17curUKRo2bMjgwYPZvHkzly5d4u2336Zjx46cP3+eqKgorl27Rq1atZg7dy6PP/44mZmZhIeHU1hYSLt27Uzxpaamsnr1at5//32zuL/66it69+6Nu7s77u7udOzYkYMHDzJw4EA7X0H7On78OGvXrmXWrFmmkXSbNm2qOaqa5fjx46xfvx4XFxcyMzPx8/Nj4sSJjBs3jv379wOwZs0aACZPnmza79ixYyxbtgyAevXqsWLFCurXr098fDwxMTEYDAaeeOIJIiMjcXd3t3/HhJkaMcVhje+//57Vq1eza9cuDhw4wJkzZ0z18+bNY/fu3Vy8eJHDhw9z/vx5fv31V7Zt28aePXto1KgRu3btAuDy5csEBAQQHx/PzZs32bdvH59++imTJ08mJiYGgJkzZxIeHk5cXBwLFy5k2rRpACxcuJCQkBASEhJ46qmnTLH5+vqWSc4AWVlZaLVaU1mj0XDp0iWbXSOlmTlzJjNmzCAuLo6mTZtWdzg1zvfff8/s2bNJSkri5s2bHDp0yOI+69evJyoqip07d+Lv78+PP/7Izz//TGxsLNu2bSMhIYEGDRqwceNGO/RAWFIjRtBOTmX/HTEajWbve23fvj1169YFwMfHh+vXrwPwyCOP4O3tDUDLli25fv06zZs3Z+bMmXz22WecPXuWH374gWbNmpmOFRAQAECTJk14+umnAWjcuDG5ubkUFBSQlpbGrFmzTO0LCwu5evUq3377LStWrACgf//+zJkz5679MhgMVvXVEV29epXs7Gy6du0KQEhICDt27KjmqGqWZ555hocffhiAAQMGEBsba3Gfnj17EhYWRq9evejZsyddu3blk08+4fz58wwdOhSA4uJiHn/8cZvGLqxTIxK0Wq0mLy/PrO7KlSvUq1fPVL791zGVSsXvb1Etrz4tLY3p06czevRoAgMDcXJy4va3rrq5uZl+dnZ2NjuvwWDAzc2NhIQEU92lS5dM88a/H0elUllMtt7e3mRnZ5vK2dnZPPTQQ3fdx1Hc/v8Iyl5nYdnt18xoNFJYWGh2TUtKSnBxMf8rPnr0aJ599lkOHDjAsmXLOHXqFB4eHjz33HOmAUVBQQGlpaX26YS4qxoxXKtbty7Nmzdnz549prrt27fTpUuXezpeSkoKHTt2ZMSIEbRo0YKDBw9a/QfygQceoEWLFqYEfeTIEUaOHAmAv7+/aaokOTmZmzdv3vVYAQEBJCcno9frycnJ4ZtvvrnnPtU0np6eNG7cmIMHDwLw+eefV29ANdB3332HTqfDYDAQHx9Pr169uHbtGjk5ORQVFfH111+X2eeFF16goKCA0aNHM3r0aH788Uc6derE3r17uXLlCkajkaioKNN0nqheNWIEDbBs2TKioqJYt24dxcXFtG7dmnnz5t3TsYKCgggLCyM4OBiAtm3bkpmZWeFYPvjgA1xdXYmOjkalUjFv3jzCw8PZvn07bdu2pU6dOsCdbxL6+fnRv39/hgwZQklJCVOmTMHLy+ue+lQTLVu2jFmzZrFq1SqefPLJ6g6nxtFqtbz++uvodDq6du3KSy+9RH5+PkOGDMHb27vclTKvvfYaERERuLi44OHhwZtvvkmLFi0ICwsjNDQUg8HAY489xiuvvFINPRJ/JN+oIkQN9PtKmM2bN1d3KMKGasQUhxBC3I9kBC2EEAolI2ghhFAoSdBCCKFQkqCFEEKhJEELIYRCSYIWQgiFkgQthBAK9f9XF5F3ttOncwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "corr=exercise.corr()\n",
    "print(corr)\n",
    "sns.heatmap(corr,annot=True,linewidths=.5,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b66c69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ftV5kd3c3NTW5DS5yTW/txKnI/aOP9PsQtoOIRJHBsc+hPhDbSb13Rk7zyY6o987gQPQNGnwzx3xM0pJ0JKBuCmalNfgFb4YcrpmRmwkEigLZOlW96Ww+0eoGkxfasrMBt/dYzCvX+ca6AF4cgpqFdFSO0JTQBI8cdPif7dlE+U3t0OhPc+eFfoSUBDQb4Ux+kfHRWGi81j1yEfnd/TbnV4kxBeF+YfOpZR6+8eJQ/bLZpRr1fo22jEGlC9xqUohyHHnvKVu4cCEbN24kHo8jpeTJJ59k2bJl7N+/n+bmZmzb5sEHH2Tt2rX5btq4aB3dOBVluT+xENgVZWgdPeM6rDm6i2pPY+7bA1R7GjkQe3NcxxyISmq9YGi5f0Js8Av2hVUvhZI7vdLkjk0pPv1knIgFVV6Nj53p5Z3z3Xx8mZeULXEcSYAMUvWQTZmwY/B/bwxfvqg1JmmOOATJ5LyH7GgmDmunjeynWFptjLlXVErJuRWSb63z8fGlHr68ysfNZ3n53JNRPvNknNueTdJpT7w4uHLqy3tP2QUXXMCOHTu44YYbME2TpUuXcsstt3D++edzyy23kEqlWLduHVdccUW+mzZ2UqJ39ZFevXxqTl9Wgt7RjT1v5piPaY69yXTf5Kr4H0uNp5HNvU+N65j9YUm9b2q67Gs82WnsKVuqYQFl0hxN4yevpdndny1Z0BG1mV6qc+/2BI7Mrnf5oSXebECgKrEURD4K8zqO5OJpJtt7bTZ3WGgC3jHXzcJSeczZoaPRpcMcj8PcRmjL6Hz6z7HB1w5FHe5+OcE/r3ZhOqpEhjJSQaaEfPKTn+STn/zksG1r1qzhgQceKERzxk0MRJAuY1LLIR2PU1aC1jm+nrLW2F7OnuR6l8dS5W6gLb5/XLl++8KSGu/UBEy6JqjxwsGoZF6pCsqUyUlKjRc7hqqw7xtw6IzZXDnbjVsXxDOSHT1p1taYKrM/x6SmEZM6ppC4HYsSzeJ9C93cs2Oot6zcI5iZ4wlDxxJKg9cQ3LTUiwS2d2eIWgZeffwBlJTQHh8Zzb3ZbxOxNCo0FZQpI6l5uhOgd/XilE3dVBqnLIi5Y/eY90/accKZfspc1SfeeQJ8RgCX5qYv3Umlu25MxxyISNbWT123Qq1X0ByRzFMzmpRJ0jSNOWU6b/Zlb5KP7kvxoSUeXJpga3eGNfUm18x0Yag8spzqx+QX29M81ZKkMaDxuRVe5nozXNmk0xj08uRBi3llGhdNMygRU5NHNoym8as30zzfluGZQ0Pvt6LW4Mr6ifXWVbhHbqv3a/j08fW+KacP1Rk/AVp3H7J06hYyk8EAIhIFa2w3gfZ4M5XuunGXrBiPKk89bfEDY9pXSklrTFKTw6KxI9sjaI6qq5oycVEMdsQM+tJw5Wz3YLFQCRwYsFlQofH3ZxtcWmtTohaTzilH0/jv19M8eTCDI6El4vDlp2N0WSZebFaV2Xxlhc57Z0LVMZZfGgshoF+abI1k/9ax4/RDOAh6EiO7QvuTE68G0OBxeN+CocjMrcMXzvXiUcn+yjGonrIJ0Lr7cKYwKEPXkMEAWm8IZwwrBrQnDlDhrp269gAVrlraEwdYWr76hPv2pUDXwD+BithjVeOFrb0qKFMmpl+afOX5BK0Rh0+d5aE7Lrl+vhtdExgC3ui1cBBYlhqvnAoDts7GtuELd9sym9RfU5L993h+90IILKGhI4dWDAfaLZMvbogPFqGdXqLxT6u9lI7S86ZLmxvmuYbNnARYVWfgTHDGp0s6vHuWxgWNfiJpSZ1PUKFl1OL1yjGpnrIJ0Hr7kSXBKX0PpyQblI1Fe6KZ8ikaujyi3FVNW3z/mPZtjUlqxrBm3GRUewStMXVlU8ZPCHil26E1kr157x9w8JnZ3LGH9qZ4vjXDOXXmsJu7kltuISlzj3xo85vjP1cUg0fbBV/cmOY/ttt02CZCgNAEv9udHrYqwMGww2u9zqjlJaWE5eVw69leGgIac0p1/vl8HzM8k+vVMqTDNDPDIr9FuVABmXJ8KigbLynR+gdwSqawpwwO95T1j2nftvgBKtxTW9etwl1D+xgLyB6KSao8U5uYW+aGcCY7A1NRxkPTBHtDQ0nWTx1MU+rWeL41w8p6k1KPYH1zimlTUGNPyfJrNp9ZMfwXvLLeoGm8KQ+a4A/7LH74WpL9AzZPHszwhQ1xeh0TC429oZGBdUvEPuZwpBubddUOd1/g4a7zXJwRsLK9b4qSJ2r4cpxENI40dHBN4JFuHJygb8xBWVfyEMsrLpzS9pS7auhOjm1NztaopGKKgzJNCCrd2dlNM4NqBqYyNkJkE/sXVQke3p8tFJqy4b5dSW4+y0vKkvhNnZl+8Ks8sqkhYHNI5zc7k3zyLC9pG5qCGotKJN5x5lqFbYM/7IkN2xbPSA5GJCvKHC6bafKj14bPclxeY+A4Frqu4TgS+ZauKyklLiyViK8UhArKxknrC42r0v5EyWAAbf+hE+7nSJveVAdlrhPnnk2G3ygh7SSJWxF8xvGHblvjkiXlUx8oVXoEbTGYObUjycopIiRNXul26Ik5NJTovGuBhwf3JLEcWFxl0BmXvL3BQao8sinV55h8c1OMtAO7+rP5W24dfnipn/FW0dGExG1kh56PZmrZumMX1ul0RF38aV8aU4OPnuFhRkDwesTg+TaLGSUa59YaVORjdqeijIEKysZJ6x/ACfqn/H2coB8tFD7hfv3pbnxGAFOb2irRQojDvWVtzAgsOO6+7fGpLYdxRLl79DpAivJWcQz+eVOSfQM200s0/qLC4OX2NO9e4EHXBJva0iyrMXGkQPW7Tq2+pCT9lrg3ZUMoJSkfZy5qiWbzsTM8fG/LUHL+tKDGjMPPzQEsbpovuGGOHyEkZZrFI22CH742tP8ffBrfuNAzthm2miBkG9gSyg0bTeUdKjmmgrJxEv1hpD8PySZuVzbzNJEE77GvVN3JtilP8j+izFVFd7L1uEGZ7Uj6ktmAaaqVuwVtcQdQa2Aqx9eagH0D2WGszphDb9zhitkeuuIOluNw6Uw38bSFLlV92KlW6RG49WwgdoTXyH6fx8txJBfWSBou8LGly2JaUOfMSo0AR/V8OXJwtmXYMfjZ9viwc3TEHZqjsPQEAyApdB4/JLlne4yMAxdPN/nIIlOVS1FySgVl46T1hXCqyqf+jYTI9pb1D+AcNyhrpcSsmPr2ACVmBV0nyCvrSULABHMK1rx8qwo37BmY8rdRTgFJe+jzmLKhPebQFXfY2Wuha1AX0rhthWfCpQ+U4fqlyb6wJONIZpdo1BrWYO5WuZbh/632ceemOAkrG5D9v9V+yidYKsKFwyK/w5I5AiltpDx2pXyJYLTRaXsMkfjemODHrw/1sD15MENTUOOG6WNbrFxRxkIFZeOkhcLYM6fl5b1kwI8WiuA0HLsGWVeyLW9BWalZccJk/86EpHKKy2EcUeEWdI1S7FFRjhCaIC51gm5BpVfQm8jePB/Yk+IjZ3j4wCI3muNQ45G4VECWEz2OyZeeidOfzP6uXTp8+yI/04zDv18JZwYtfnCJn1AqWxrj6IDM0TSSUsMrnHEtQD6WwKhEs3j3Aje/PGrR86BLMOMEk4U0TfB6z8hg78mDGa6e7sZELZmk5IYKysZJG4jgBPIzV176vCfMK+tKHqLeOz0v7Sl1VdLcv+u4+3TEJWWu/GTllLpgIA0ZR+alZ045uQxIk/t2Z/hzc4K/Xe3n3Qu87B+waB6wObPaJG2D34BKLDVmmSPZGnD2YEAGkLbhd7vSfH6pjjzcJSUllIkMZYcf4I4EZN2Oyc9eT/Nqd4qzaw0+vMg1qYr+byUdyTXTDWp9Xh45kGZOqc61s80TFnR1HMn04Mg0iQUVOqb68Cg5pIKy8UimwLanbCHyt5J+LyJ0/PG53lQHC0uW56U9pWYlfamO4+7TlZCU5iko0zVB0AW9SahTNaWUo0hN46fb0mxoyd7QQymH5gGLnb0WDQGdPzeneM9CL0I6qMz+3BFC0BkbGaS0Rh0cjOP+quPC4CvPJeiMZ49/5lCGvSGbb13gwSsthIAe26Q9LgmagnqPg+s4Q5XH4sPiomq4qNaFkA6OM7Zh0yXlgoUVOjsPr5Fa4hK8e54LVA+rkkMqKBsHLRxFBvyMWg56Cjh+H8aB45fF6Et15m34MmiWE8r04kgbTYyeXN+ZkNSMd177JJS7soFgnU/dWZUhIVvn6ZahZXx6EpJwSvL2WW7SNpxRbbCvP8Pb6g3VS5ZDjiNZVW/w+93pYduvneNCc+zjlv7qTDAYkB3RFnXoSsJMD+xOmNz+TDbJHuDq2S4+PE/HNYGhQykB+/jteasSYfGVlW4OxSDjwDS/GHW5JkWZDFXRfxzEQBgnHzMvD5N+L1o4cszXU3aSpB3Hf4K6YbliaAY+PUAo3XvMfboS5G34EqDULehKqCRbZTihCQJHfQ7/sCvJuQ0mzx1K86s3EhwIWXxggQvDUblAuTbb53D7Kh+1Po1St+DmZR7OqeS4vVFCgKaPvB0JwGMIkhh8Z3NiMCAD+NO+NC3J/D6M+aTFfJ/FkoClAjJlSqiesnHQBqJIf56y2AHp9yEisezVbJTeuf50FyVmBULkL7YucVXQl+o85rJOPUlJWR7KYQy2x4RuFZQph1maTndKYJjwrgUefnZ4ttxASvLEgRSfXu6jzLDxCQehhp2mhCEdVpY7nLnOgy0hIGycEyTs9zkmv9qZ5G0zXTxxYKiX7d3zXVQZNmFHp32UYdH+pISjLsm2ptGT1rAk1Lglpgq6lZOMCsrGQQtHkN7xLs42CaYBuo5IJJG+ke/bm+qkxJWH8hxHKTHK6U11MJelI16zHMlAOhso5UuZW9CVVEGZAmFp8MNX07zQbvHl1X7aoxY3n+UjmnbwGILehEMsI2nU1RI6U01KcB9eMmks1SKaI5Ln2yzWNbn4xDIvsYwkYArOq9fQZYagLjiz2mBr9/CaYA0BDQ4PX8YxuHenxUP7s8PWS6p0blvhoUz1aCknERWUjYMIRXAqy/L6no7fhxiIjhqU9aU6CRj5bY/fLKUv3TXqa30pCJrZBPx8KXOpWmVKNsF8U5fDC+3Zm3ZrxGZBhcmDe1McDNt4dMH7F3sQKoGsKB25ZGxoSbOhBQwtu1TShYdn8KQcwUeWevnhK3H2hGz8puBzK7zUmUMB9s4ByUP7h3rZtvfYPHrQ4v2zQKo/u3KSUEHZOGjhKHZTfV7fU/q9aJEoTv3Iqv19qS4CRmle2xM0SulNjj4DsycpKcvPxNRBJS5Bb1JdcU93hqHxfOtQj8hTLWlW1pvMKdO5sMlF2pI815JmbV0ex9aVMZsZhCqvoOdwKoLlwF8u9RAUFmk0frojw1MtcS6e4eL8Jhc1XsE5VRLNyu6vaYI3+kYOVT7fZvGuWS4MVUdMOUmooGwcRCSK9Odx+BKQXg9iYPRk/95UO0Ezv8OXQbOcfdHto77Wk5CU5DHJH7K1yvpSIKVE5GlWrFJ8pIRFVQZburI9ZS1hhzllkuW1Bls7M8wp07ntHA9B1FBWMQoKm79b7ef5NouOmM2yGpOzqzWkTNOd0fnzweyQ5NH5Zj+41E+9kQ22HEcyr3zkjPCza3UM1TuqnEQmFJQ98sgjPPDAA3R0dKDrOnV1dVx66aVcf/31OW5eEXEcRDyJPM6SR1NBej1o4eior/Wlu2jwzcpre0rMcvpTow9f9qagJM89ZW5dYGgQyeT/vZXCi2NwMJ5d1qvOL5lbrrOnP3ujbo/arKw3+MJZJum0jZQqICtWHRmD29ZHKfMIKjwaL7TFWVln8oVlOpljJKUd2S6FoD1jUO6BVfUGmw4PYU8v0bh6lqnqiCknlXEHZd/73vfYtGkTN9xwA3V1dUgp6erq4ve//z27d+/mi1/84lS0s+BELI70uEDLbxUR6fOg9Y9e1T+U7iGY55yygFlGKDN6SYzuhKTEzH9vVakLepP576VTCiuBwXe3pnmx3eKsGoN1013MKzdY25SNzlsjNrYE23YG111UilNrzEGSnU3Zn8wG1RvbMty81EWtRzKzVOPAwFCP15wynVp3tmTG6xGdv38uhgTWNrm49RwfDX5Bo1fiVYG4cpIZd1D2xz/+kT/+8Y+4XMO7Ja666iquvfbaUzcoC0eRvvyXjZc+L6K5beR2KQmluwmaZXltj0/3k7TjZJwUpjY8P6c7KZlbUoigTNCThFkleX9rpYAOxuHFw70iXXGHaErSVKLx1ME0Arh4uhvbdtRi0SeBEnPkw26NT8MlJB5p8ZVVXh7cl+GlTouVdQZXzzJxywxxYfDdVxKDk2mfbknzdEuab1zowyutEedUlGI37m4fTdNGfeq0bRvTHFsthCeffJIbbriBK6+8kq997WsAbNy4kWuvvZbLL7+cu+++e7zNmnJaJJb3oUs43FMWjY3YnrBjgMCl5bdNQmgEzTL60z0jXutNUpDeqqAJvSl14z3dhI4qGt8WdTB0wf27ktT4NKq8Gg/sTnBGha6CsiJjC43WjMmbcYMBaSIEVHgFK2qH+gg0AR9d6sWrZXvNKkSGj8wTfOcCNx+eK6g4XOYi5QwtMn+0SEb9zZWT07h7yq6//no+8IEPcM0111BXV4cQgq6uLv74xz/yjne844THt7S08Pd///f85je/obKyko985CNs2LCBv//7v+eee+6hvr6em2++mQ0bNrBu3boJ/VBTQURiSF/+Z25JrxcRjY8oIBtK9xA0ywuS3B40ygile6jxNA7b3peSea1RdkTAzA5fKqcHIUDXNcq9GpoYqoP1s9fjfHqFj1KXQEMyI2BSohL7i0pa6Pxmn81vd2UT932m4M4LffQkbCq9Gn95phdLZsth/HJ7gjPO9xIkg6YJNE1gWsOXRirRbc5vNHnuqJm3uoBp/qH6ZYpyMhl3UHbzzTezcOFCnnjiCTZu3IiUkvr6ej7zmc9w8cUXn/D4xx9/nKuuuoq6ujoA7r77bpqbm5kxYwZNTU0AXHvttTzyyCNFFZRpkSjSk/+eMgwdTGNEAdns0GV+y2EcETBKCaW7h22zHUkkkw2Q8q3EJehRQdlpISH62RF+ic1dGzi//uv81XIfv30zSVfMYXmtScKSnFWpUaJyiYpSS0Lw211DXZzxjOTfXk7ysaUeHj+QGLZv0CXQhSQkW3mx80kORHZzXt1lzA8sx+UEANAch48vcaGL7ALmdX6Nv17hpdaVUWuaKielCc2+XLdu3YQDpubmZkzT5FOf+hTt7e1cdNFFzJs3j+rqoTpcNTU1dHZ2Tuj8U0WEozilhUlakj7v4Z66o4OyHvx6YdrjM4OE3jJ8GUpDwMhv4dgjSkzYN/pcCOUUIjWL+w/+lCdb7gdgdV2cJw6YnFtvUuHReK0rQyIDmirXX7T6Rnl42j9g0+AXzC7V2HdUMv/NZ3oQWhdff+kWQqns5KLNnRt43/ybuaz6gxxZualMZPj8Up2/XOLCpWWT+1WxWOVklfc6ZbZts3nzZu655x58Ph9/9Vd/hcfjGTYMV4w1p0Qkhqwffb3Hqeb4PIhoDGqrBreF0j34zcIEZX69hP7U8J6yvpSktEAlKYIuQX9KXYVPdQNOB+tbHhj8d29iLyvqzsSlCQZSDqsbXLRHLDwCtYxSkar1jUxjXlptUKLZfHWVh50Dks6YZHGlxkyvw97YrsGA7Ig/7P05Z1SeS52YP5jfLByHEhxw1J9eObnlt74DUFVVxZo1a6ioqMDj8fC2t72NjRs30t09dJPv7u6mpqYwAdCxaNF4QRL9AaTHjRaND9vWn+7GbxQmKAuYpfSn3xqUZYOjQigxoT9VkLdW8sgWw2fTbe7+KYbo4bc7E6w/mObBPUmunuXCUItQF60Gt80tK7y4Dt95GgManz3Lg+HYlAqLVWU2102TzPVYGNLBkukR55DA1p4XiIruEa8pysku70HZxRdfzLPPPks4HMa2bZ555hmuuOIK9u/fT3NzM7Zt8+CDD7J27dp8N+24RKxwQRkeD+ItBWT7090EChWUjZJT1peUBAq0PoTPgJQNaVs9I5+KNA2SWghNaJxbN5Q2sSe0mdbEN/jGxRb/cp6Xb1/goUYfeRNXiochHS6pdfjhpX6+d7Ev+zfThv/NpJSg2/SI/XQmWilxlQ17/eKma3iu7fFjFrFWlJPZhG+j69evH5HYP9q2t1q2bBkf//jH+eAHP0gmk+H888/nAx/4ALNnz+aWW24hlUqxbt06rrjiiok2LfdS6ezsR7MwUYf0uhGR4WUxQukelpSeW5D2BIxSBtLDhxT6kpJAAQrHQnYx6qAr21tWm/9ScsoUSooQz3Q+yB/3/4LPnvUP1PgauH7Oh9kd2sa0wCzKPFU4ci9NrqVq3Opk4UgqtAy4GPVvFtE6eaT5V7hNN483/57PLf8nXu58lq54GwsrltEZb6Ur0Zb3Go2Kkg8TjjJ++ctfjgjARts2mne/+928+93vHrZtzZo1PPDAA8c4orBENJ5Nsi9Qnpv0etD6h098CKf7CjZ86TdLCGf6h+X+9aYKW1E/O4QpqfUVVy6iMnFCCF4NPctv9vwXAF3xVsrdldy/9xc0BmayufMZVtZdjJAj1zxUTj5pLUrI7uD+PT/nQHg3Z1SdQ8pOcu8b3+fyGTfQEWvhj/vuJWOn+eQZt1Oq1akZlsopZ8JB2Y9+9KMxbTsVaNHCFI49Qno9aJGhnDJHOkSsfvxGYUpiuDQ3mtBI2DF8RnZqem8KGv0FaQ4AQVPQp/LKTimOluKJlvsG/70rtA0kXDnrvdiOxVnVq9nR8wrlDTWql+wklxIR/nfvv9EYnMFLnU8D0BScDUBbrJknW/7I+Q2XcXHTO2jyzqVcawQn79k3ijLlxh2UtbWNXPLnaA0NDRNuTLES0TjSm//CsUdIrxsRGwrKYlYYl+bF0AqUxEV2DcyBTO9gUBZKSYKjLJWSL/7DPWXKqUMTOtXeOg5G9gCwqX09719wM+2xQ2zpeo45pYt53/xPE6BKxWQnM2HTnNrO8x1PcJX3fZS6yhlI97Ox7XFuXHQL61seJGHF8OlBFgVWZmuUqT+4cooa91396quvRgiBlJJkMonP58MwDMLhMJWVlTz77LNT0c6Cyi5GXsCgzONBJJKDVf0H0r0EClQ49oiAUcJAupd67wwAQqnsckcFa48p6FNB2SnB0dL02W0IG86tW8frvZtJ20kkkkebf8fNS2/n3TM/hYkHYZuotcZPbm3WHg6EdwHwbOujXDv7Q9y78/vsCe2gJbKPd8y+kfPrrsBvVyPVklnKKW7cQdmWLVsA+OpXv8qqVau4+uqrAfjzn//ME088kdvWFYlsNf/CBWXoGrgMRDyB9PsYyBQun+wI3+GgDLLV/KNWtreqUIJqqaVTQlrE+NOh/+Hh5v/jomnXUOOt411zb8KWNprQSNtJQqk+5rhK1JqWpwBNE7za+Rx+VwCBIJwO8VzbY3xo4WcBiVv30Bzew6/3/Cfvm/1ZvLKs0E1WlCk14fGmbdu2DQZkAJdeeik7d+7MSaOKjYjEC7PE0lEG18AEBtK9+PVgQdvj0wMMZPqAoWr+WgEL/gZVrbJTQmtqNw83/x8Afcku3IaX5vBufrvrv/n9np+RspMYwlC9Y6cIGwtTN3m69WE+uPAzBF1lHAjv5pWu5yhzV/LT7d/hzy33s7H9cfZGXy90cxVlyk04KHMch02bNg3+++mnny66Kvy5IqKxguaUwfC8smwuV4GDMiM4WKsslJKUFKia/xEBU6icslNAZ/Lg4H9v630ZQzPpTnRww7ybuGrW+9ja/RIN3lmDldyVk4vQJBHRRVh0gGZzILUVj+GjK97GH/f9grWNV/DueX/JNbM/wH9u/TryqOSxfeEdaAVYxk1R8mnCmeJ33HEHn//85zFNEyklUkq+//3v57JtRUPEEgWdfQngeNyDPWWhdM9ggn2h+I0gvakOAPrTFKxG2RFBEwZU3dCTXomrfPC/HWnzePN9XDP7g7RFm/HpQT59xt9Tpc1UQdlJKC1iPN11P/ft/Sm2Y/GhhbfwYudTtMWaee/8T9CT6CRpJVhcuYKWgX3YcvgKDnNKlqgha+WUN+Gg7JxzzmH9+vXs2pVN0FywYAGGUbjZgFNJK3CiPwBuN1psKCg7kmBfKH6jlL2R7UB21mOggPlkkM1ni1rZ/LZCLIquTFxGxGlL76En2UHAHeTipmt4quVPSCRpJ4XlZLhi2gcxrcDgA6By8nCERae1j65UC7/e/f8Nbn8z9BopO04kHeKeN/6DUlc5huai0lPLyupL2dS5nv3hNwG4sOFKZvtVgWDl1DfhKCoWi/Htb3+bvXv38t3vfpd/+qd/4m//9m/x+wtYrGoqWDZkLHAXdnxOeoaq+oczfcwNnlHQ9viNIOFMNtE/lMoudVRImhD4jWxvWUVhOzWVcXA0i8fbf8Uf9v0MTeh8fvnX6Iq1874FN+NIm4F0P33JLhzHVsHYSUgIwfbYs/xm938xt3zJsNe2dm/i3fP+kgPh3QAMpPvR0FhasYoSWcdtS++mJ9OKoZlU6I3ozujXYKnZpIniIoBwVCFh5eQ24Zyyr33tawSDQXp7e3G73USjUb761a/msm1FQcQT2XyyAufLSe/Q8GUhq/kf4TdKiGRCQLaaf7DAw5eQHcIMpdWN+2QSctq5f9/PgexwZX+yi5X1F7EntJ2XO58laJbiOBKDAvdUKxMSF/38bMd3GEj3U+WtG/Za0k4ggVvP+joLys/k7JoL+crKH1BrZIvGmo6fen0+1WLWMQOyfnGIn+z5Z7784of4yd6v0S8OTfWPpChTasL9G2+88QZ33nknGzZswOv18q1vfYtrrrkml20rCtnCsYXvepFez2CifzjTX/CgzKv7SDkJMk6a/qRgVkkxBGVCzcA8ycSt8LBk7pboflqjB7CcDKXuCp44eB9/c9Y30GwVlJ2MLJkikhkAQEPQGJhJa/QAAEFXGUvLV1HBdBYvWQMIpC1GLJ1kaUm6MweJZgao8U6jjHqkhJQW5jtbvkRnvBWATR1P0hzexf9b/kPcTmGvj4oyURMOyjRteCebbdsjtp0KCl049gjpcaNF46TtFLa0cGvegrZHCA2/kV0DM5SuKHiiP6iq/icb3QCP8FHtrac70Q7AEwf/wBUz38OyqlVYtsMH5txCmWhQQ5cnqYBWwarai9nUuZ4/7Pkf3j7z3VzYeAX1vuk0+eYTcKoBkPbo9460iPGHg/81uNyWS3Pzt2ffTZOxhJ5022BAdkRH/BA96TYaC/zQqigTNeEo6txzz+Vf//VfSSaTPPPMM9xyyy2sWrUql20rCkWR5M/h4ct4gnCmj4BRWhTlR7JDmH2Hl1gqdGsYzClTiltGxNmReI5/23EbkUyIa2Z/kCWVKzA0k0UVZ9Hgn4FPL2GBZxWl1KuA7CQWdfpZ13QV5zW8DUMz2dG7hfklZ7LYe/5gQHY8Hel9w9Y/TTsp/mv7nWS0GG599BEMt17YB1ZFmYwJB2W33XYbPp+PYDDI3XffzYIFC/jSl76Uy7YVBRGNFzzJHwDTBMsmkugueI2yI3xGkHCmn4F0Yav5HxEwBX2qqn9REwK2hp/j3169ndd7X6In0c6+gTcxNBdXz3o/XsPHm/2vo0380qQUmKUn6HL2MaC18p87/pFvbv4iXfE2rpz1Xs6tW0ede/aYi/8OpPtGbOuIt5CScSr0Bt4+4z3DXrtixnup0E+99ZeV08eEhy9N0+Qzn/kMn/nMZ3LZnqIjorGi6ClDCKTXQyLcXfB8siN8eoCeZC8O4C6Ce2jAhJaYCsqKWVqL8fu9Px78d1eiDUdaLKlcQTQTZkH5Mlqjzfj1MlX+4CQU1br4z23/xK7Q67x3/ifZHdoGwJ7QDvaEdgCwrOI8arU5YzpfjbdxxLbFFWfjE6Vojotrp32UsyrPozPRQq23iSbvfDSnCJ4QFWWCJhyUbdq0iR/96EcMDAwM2/7b3/520o0qJiIax6mpLHQzgGyyfybcj88sbOHYI7x6gK5kH0GTohhODZjZ8hxK8RICdG3osvPkwQd4z/xP8Ie9PyeaDuM3g3zmzH8gSM2wCQBKcUuIATpSe3ml51l2hbLLIR3rmiAY+7WiWp/JXy39Cj/d8W2SdpyZwfl8dOHfoB2ejelyAsxxr2COe0X2AOc4J1OUk8CkKvrfeOONTJ8+PZftKToiFkeO8rRWCNLjwo704q0ujqDMbwTpSfYWRT4ZZIcvQyl1VS5GmiaI0YfE4pKma/nfnT8AIJIZ4A97/4fPnfXPuKSPgFGKV5apPLKTiKUl+fW+7yOEZM/AG4PbWyL7WFixjJ19rw1uO7tmLZVGw2DwJLUMMRnCJTy4ZXDEsKYmDVYE38a8VctIOXFK9WoMx5ePH0tRCmLCQVllZSUf/vCHc9mWoqTFEsUxfEl2BqaMRvDVF0dQ5jOC7I3sK4qZl5DtKVOJ/sXHFkleCq3n3je/xzWzPkjCjvEXiz7Lrv5tlLjKqPdPZyDVxyLPInDUqOXJptdq5bn2R5lZMp/ZpQvpiLUAsLHtca6d/SFW1l7Em/2vc2blKpaUrkR3sgn6YdHO/+76D17pfo5KTy2fWPJlZnvOAmd4LoSUEKCagED1hCmnvAlnAl1yySXce++9HDx4kLa2tsH/nWoGi8cWA48bLZ4smpwyv1FC1Oor+BJLR3h1yDiQttVtvZgcSu/iv7bfRdyK0pvqpN7fxEP7fk1XvJXXujfxRt+r6OLUXKLtdJBxsk9CB8K7mFO6iGpv/eBrbdEWzqm4hJvn/SMrS6/AJysAcPQM9+7+d17pfg6A3mQn33zlNrqcffn/ARSliEz4Stjf3893vvMdvN6h6cdCCF555ZWcNKwopDPgSCiSNT2l24272yqq2Zdxq5+GIhlNEEJk88rSUKNmxRcFTRPs6H958N+vdD5HiVnGeY1vw2f40YXOrv7tlLlOXB5BKU7VrkYa/DNoizXzy50/4LIZ76LKW8vs4GJqzZkYeEjICC7hA5ntVY85fWzp3jjsPI602d63GW9lkKCsLcSPoigFN+FoY/369Tz77LNUVVXlsj1FRcQSSJ+n4EssHSG9brwJib9IgjK/ESTthIpm+BIOL7WUktR4i6dNpzPHkZS5hybK9Kd66E12UeefxrOtj1HhqeGG2R+jRpulxi1PUm6nhFuX3cWjLb/m9d4XkVKyrPw8SmQ9IdnKoy2/ZmvPJpZXn8dl095DqazHFB7K3JWEUr0jzrdzYAvnllxRgJ9EUQpvUjllFRUVuWxL0SmWav5HSI8bX0rHrxdHUObWvDhYeI0UUBxdUwFTqLyyIiA1m36nlbgVocxdQb2/ifbDuUabOtbziaVf5v+d/T1Mxwe2oQKyk1ypbOC90/+aG6YnMPEgHY2UHubuLV+mPXYQgMcO/o43+7fypWXfxeuU8peLv8h3tvzd4CzbM6tW0hLdR6Wrlnh5L12JQ/iNkuxi5LIIakUqSh5MOCibP38+H/zgB7n44otxuYa+MDfddFNOGlYMiqWa/xFpl6A07aZfK442CSEQlGCIfoolKFNLLRWe1CxeDD3KT7d/i4CrhBsXfY6zay7A7ypBSgchNNpjLSwNnp8NyJRTgyMw8A3G192p1sGA7IjmyG560q3U6/NZ6F/F36y4k+bIbkzN5FD0AM+0PsJtZ3+D21/4MHErCsA753yUy+rej+EUxzVGUabShK+IyWSSWbNmceDAgQm/+Te+8Q36+/u566672LhxI3feeSepVIorr7ySW2+9dcLnzRVRRDMvAaJGisqMuyhqgh3hOEE0rR8ojiraPrXUUsH12W38dPu3cHDIOBkSVhyP4ePBff9LykpwfuPlNAVmI2QRVBxWpoypjd67ZR5+qJS2xjTvPLb3vswTB+/DbXj52JLb2Nz59GBABnDf3p9xZuUaGvWFeWm3ohTShIOyO++8c1Jv/Pzzz3Pfffdx0UUXkUwmuf3227nnnnuor6/n5ptvZsOGDaxbt25S7zFZIlYkSywdFhNxqqWGlrFxTL3QzUFKie0EgP5CN2VQwBT0qZ6ygurPdOEcrl2QsGIk7TgvtD/JxU3X4tLcvNL1LBfUX6l6yU5xVcY0Lmh4O8+2PTq47ZKm67LLIB0ubdGW3Mcb/a/yjjk3knHS2NLmqUN/GnGu/mQXjX4VlCmnvglfFbds2cKPfvQj4vE4Ukocx+HQoUM89dRTJzw2FApx991386lPfYqdO3eydetWZsyYQVNTEwDXXnstjzzySOGDsmJZYumwmB0l4bIx4hnSpYUPylIOSIKk5cj16QolYEJzRAVlheQz/RjCwJIWAL/f/TPeO//jaEInZaX42KIvUavPVXlkpzjdcfPemZ9mZc3FNEd2MTO4gBm+xYPLIEWNDu5746c0h3fTHN4NwPkNlzOrZAH7w28OO1e1tzh64hVlqk14/OCOO+5g+fLlRKNRrr32WgKBAJdffvmYjv3qV7/KrbfeSklJtt5WV1cX1dVDU+Jramro7OycaNNyRkSLK6csboVJuiRmPFPopgAQzYAh/MStIuopMwT9aqmlvHOERZu9k42hB0jbSd634FN49GytFJfuwtBcLCxfwSVV76NOm6+GLk8THlnGQu8arqj9KAu8q/HIEmKilxb7dda3PoAjh1eD3dT+JO+Z/4nBWmeGZvKxxV+k2ji1V45RlCMm3FMmhOCTn/wk/f39zJ49m2uvvZZ3vetdJzzuN7/5DfX19axZs4bf//73ADiOMyxPSkpZFHlTWiyBXURBWcyKkHYJzETxBGWmFiBmjZzWXijZqv6qCyafhICd8Rf4t1f/HwCfX/EvbOnayJWz3oMuDJJ2gh29rzC/5CzVOXaaOrJsVkIL8R+v386K2vN5tPl3vGfeX7JvYOfQfkCpXslXV/yI/kwXXt1PmVaHdAp/P1CUfJhwUOb3+wGYPn06u3fv5uyzz0bTTvz0+9BDD9Hd3c11113HwMAA8Xic1tZWdH1oOK67u5uampqJNi1nsuteFk9QFrXCWG4PZrw4MtmjGXBrQWLW3kI3ZZBaain/klqEn79x9+C/u2KHOKPqHAZSvRyM7GVu2RLq/NNwCY8asjyNWVqK3dEt7AvvZHbZQkpd5Wzpep4bF93Ca92b8Bp+Lpv+Tqr1WeAI6vTsSIpUSyspp5EJjyGceeaZfP7zn2f16tX85Cc/4a677sIYQ+X7n/70pzz44IPcf//9fO5zn+OSSy7hv//7v9m/fz/Nzc3Yts2DDz7I2rVrJ9q03JAyu8SSx1PYdhwlmgnjuM0iGr6UeLQgcbt4cso8OlgOpNRSS3njkCaSDg3+e1PHU8QzETZ3PosmdJ5ufZgZwfkEUVX7T0tC0kcLexIv05VoBeDZ1se4ZvYH2R3axv/u/CFpJ8Wc0sXUG/MGq/4ryulowj1lt99+O6+99hqzZs3i9ttvZ+PGjXz729+e0Lncbjd33XUXt9xyC6lUinXr1nHFFQWu6Jw+HPiYxTNDLGFHcNwuzFhxdAVFMxKvXsJAEeWUCSEIuqA/BXVFsvzTqUzoDrrQWNPwNp5pfQSAvQNvYAiDT535/0hl0lR56igXjUgVJ592hIC9qS38fOfdzC8/gwb/DAzNJGnH+cOen/POuR8l4Cphpn8BVcZ0DOfYD8GaBrbIoDmm+iwpp6xJ5ZRVV1ezY8cOqqurue6660gkEuM6xw033MANN9wAwJo1a3jggQcm2pycyw5dFk8vGUDMCiO8Hsze4ugpC2ckfjNIRzxU6KYMEzQhlJbU+dQT91RxhMXB9HYe3P0L3jb9eup807h0+nVs7niaSm8d5ze+nbSdZL5nVfYAdRM9LSVFmB9t/zppO0WJq5xHm3/LRxb/NRsOPURXvI1Qqoc1NZfjt6sHy2SMJiw6ebb9T2zp2ciK6vO5oPYqtT6mckqacFD23e9+l5/85CdUVg6tayeE4M9//nNOGlZoWixRVEGZlJK4FUX4fZhFEgRFMjDd70YiSTsJXFpxVNwOmIIBNQNzSrVZu/j65s8BsKTybDyGjx1tr7C6/hL6Uz083fow7533iQK3Uim0lIzTl+wGwG8GkVLys+3/xoqa85hftpS19VdnA7LjSBthvv/aHewP7wKgObyb13te5Nal38K0/VP+MyhKPk04KLv//vt57LHHqK09NZ9Wim3dy5STRBM6eD0YRTL7MpYBn6nh1UuJW324XI2FbhIAAUMttTSVdF1j46GhgqBbe16k1jeNS5quI+0kqfBUU+HpxaMVxxqtSuEERDmLK1awo+8Vfrf7J7xjzl9gaCYV7hpmBhZSQdPgzMzR2CLJrsiWwYDsiD0DOwaXa1KUU8mEE/3r6+tP2YAMirFGWQSv7sdyG9mSGEWQVBHNgE8Hj15C3CqeZH+fWv9yiklMfWili+29L9MQaGJL13P8Ztd/88d9/8vc0jOo0WcWrolKUdAcNx9deBsLy5eRcdI83vx76n1NCDTWH/oDu5MvkRHxYx7fkTlAV6Jt1Nd0UTz5voqSKxP+VK9Zs4ZvfvObXHrppXiOmqG4ZMmSnDSs0EQsjiymJZasMF7DhzQ0pBDZpZZchbsoZYdTwWNky2IU0wzM7FJLhW7FqUcISIgBHCzmlS3hcc3EcrK9tr9844fcds43uWnRlzDxEKASx1GB8ekkrUWIOxH8WimmMzSsWCYb+esl/0rE7kXT4Aev/yP7wtnaZI8c/A03LbqN8yveMernpS/Vyd7QDpZVr+K17k2D21fVXUKl0XjcPDRFORlN+K5+pPDrI488MrjtVMopE5FYUdUoi1kR3Fp2OqHlMTDjGVIFDMoSNhgCDE3g1gLEiqinLGBAe0wFBDmlOexKvMyPtn+dUlc5lzZdzwcXfpr2WAu2Y9EUnM3ByB4WeFdiWQ6Oyuw/bQgBrfZOfvjaP9EZb6XBP4NPnfEV6vV5gx36uu2mjAZ2xTcNBmRH/Gr3f3LWqgvwUj7i3BXuGl7qfJq3z3gX8+adQVvsINODc1hZfSm6XTzXZ0XJlQnf1Z988slctqPoaLE4dnlJoZsxKG6F8ejZRHrLY2DGMqTKCpdYH82AN7uEHW69uKr6B01BSFX1z6k+5xDf3vK3ONImaJZiaAYvtj9Fe+wgujA4EN7NpU3vGFyIXDl9ROjhX1+5jVgmAkBbrJlvv/olPn/W1yjVawjIoUT+I+uhHi3tpJDH+NzUmbN437xP8ZvdP8LQXcwsmcfV0z9IwC58cXFFmQoTDsp6enr4v//7P0Kh0LDtd9xxx2TbVBSyOWXFM/syag0M9pTZbqPgVf0jGfAd/vR49GBRBWUBE0IqpyynOhIHcaQNwKHoftJOioBZwmUzbkBKSUeshXJXDc7Ie65yiutLdwwGZEcMpPrY3vcyT7U8xJdX/Bslsg6ARu9svIafhBUb3Peqme/HT8Wofau69HBJ9XtYXnUBsUyEKncdPllRDCm1ijIlJhyU3XbbbXi9XhYvXlwU61TmmiiykhjRzAABsxQgm+xf4Kr+0YzEd3hlLLdeQm/qQEHbc7QjSy0VyxqqpwK3MbxXdsOhP3FJ0zt4sWMDGSfDtTM/xCzPUlWP7DQUMEvQ0Ib1khrCQBMaPcl2dg68wsqSqwAoE/V85Zzv8/DBX9ES3cNFje9gecXa465tKaRBBU1UmICjPmLKqW3CQVlnZycPP/xwLttSPKREJJJIT/Ek+ketCFXuegBst174nrJ0dkkjoOiWWnLpAkODmJUN0JSJ0QT4YlFEfxijRmNN/aU8357NGT0Y3ovlZPjE4tvxyjKwdXW3PE2Vaw18YMGnuffN7w1uu2bOh9jY9gQA3Yk2tDKB40ikhCoxiw/P/lscLDTHhXxLgr8mwBePIfoHwOslWVpCRtNRlNPBhIOyhoYG4vE4Pt+pt5aNiCeyyyvpxXMhiFlh3PrhRH+3gSta2KAsnJF4jOzTbbYkRvEstQRQYmaXWlJB2cQIAcGOTpyf3w+2jf7XK7Edmw8s+CsyThqX7mbfwC7OqrogG5Appy3hGFxQ+Q4WrF5Gc/RNElaclzo2cCi6H4DpJXNHDDdKWyAwkUdF8oahgQRfZyfyZ3+ATHYs3HveWYgLziWtqxIYyqlvwp/ympoarr/+elauXDmsJMapkFMmonGkr3iGLiGb6O81stPMbY+B2R07wRFTK5KRVLiPBGVBEvYAjrSzBW6LQHb9S0lTQA1fToQnncb57WNgZ/PItL4oZ1SdwxMH76Mt2sxZNWtYVLEc4RTH31spLF26qTNmszOzhd5kJy2RfZS7q3j7zHejSZ3jdaNqSAL9/fDiVrSGGpyNrw4GZABy46u4l8wjXVU19T+IohTYhIOyxsZGGhuLo4J7roloca17aUublJ3ErR01+7LAOWWRNEw73EmqCQOX5iVpD+AzKgrariOytcrUeNpEGek0hKOD/27Yl+b3VeuZHpzD2bUX8Ebfqxi4CMgqNWqpZNkG80rPZMOhu7h85rtIWgmePvQwn19213ET8wOhEPJHvwZHQkUZsm9gxD4iEgMVlCmngQkHZZ/97Gdz2Y6iImJxnCKr5u/WvWiHk9aLIdE/YoHXHOqF8uilxKy+4gnKjOzwpTJ+QoBm6MjaSmRndlZt8Lk9fOqSq9g33UVXppd3z/4kja6Rw1LK6a3RWMhfnfFV9kfewOXzcFnjeyiV9cfcX9MEvHkgG5ABsrMHMa0Oeahj+I4VxVOeSFGm0riDshtvvHHYjDZd1ykrK2PdunVcf/31uWxbwWjRGBRRUBazBvAZgcF/224dLWMjbAepT3ilrMm1KQ3eoz49Xr2UmNVLNXML0p63Uj1l46cJ8A8MIA4cQmuqhTXLsJ99BdkTAreLalctldFqBuqy5Q1UF5kyghTUaHOoKZ1z+N/H311DogmJffjfzvY9GNdejJ1OI7v6wGWiX3QujlaY65yi5Nu4g7K/+Iu/GPZvx3Ho7e3lnnvuob+/n5tuuilnjSsUEYkVWY2yCB79qAkVQmC7DYx4hkww/8FjxpFYEtxHXSfdepCYXTy1yoIuaFNV/ccl0NeH/K/fIG0Hed0l0NmLNrsJzloEto21Yy/69GP3eijKePk6uxE+L+ga2A44EuvBDRgfuhrZ3gO2hf3SNkRFGQRLC91cRZly4w7K3v72t4+6/dprr+XGG288ZYIyp7Z48hdimQG8un/YNstj4IqlCxKUhQ8Xjj26x9SjBYllevLelmMJmqj1L8fB0AW88Fr2xgjQ1YfUNUTQh2zpQFSWYSyajaV6LJQcMYWE9S9i9fRhXL0O51AnWBba7CbsJzchD3UO7itLAsc5k6KcOnJ2hS0tLT1lCnVq0TiyiIYvo9YALm148c5CJvtH0uB/Szjv0UuIWsUUlAn61fDlmGkCiAzN6LVf2YFWVY698VVkNIG9dRe2I0mWqd4KJTeElBCLw0AU64H1yJZ2ZCgMPg+ye6jEjrbuHFLlZYVrqKLkUc4Kv0gpsaxTY40VEY0VVUmMSCaEzxjZU2bGClOrLJKWw/LJIJvo35ncOfoBBVDiglBKVfUfC1OA4Tjoi+dg7W3JbkylsZ7chPG+K7P5Pl4P8WAQS6ieMiU3MkLDe8EK5G8fAxgKxBJJ9AuWg6YhdB179jTSqniscpoYd1D21rUuj2y75557OOuss3LQpAKTsviWWLIGKHNVD9tmu3VcBQrKwhlGBGVevYRYEfWUuXUBAhL20BqdynBCCIL9ffDUi4jSAE55Ccbbz8d+dSfoGvryRcjeENEzFuE4aqFxJbe8qSS4DPRr1uFseh08LvSzFmK/8Fo2nwxACORnP1TYhipKHo37drV69WqEEMjDc+GFEFRUVLB27Vpuv/32nDcw30QiWXTV/KOj5JRlPCZmgar6D6QlXmN475Pn8OzLYlJqQm8SfCodZVT+aBj537+FjIWYNwOtqR7r8Y1oMxrAcbCefw3j8vNQHY1KrrkEmM9sRu45CAtmIhproMSP7AsNBWSAuOw8EgH1BVZOH+MOynbuLJ4hqqkgIjGkz3viHfMoZoXxGsMvTJbHwN8VPcYRUyucllR63hqUlZAsuqr+2bIYqqr/6PSuXpzDldOdlnbErEb0lUuB7MOWqKnANgxsW/WSKbljaODv6sbavA0k6OUlWM8/A4A2bwbGlRciXSZObRWJ8jIs1PdXOX2oBJG3EJEYThHlk1nSIu2k8GjD22R5C5jonxk5JKgJHZceIG4XzxqYJSb0JlWy/7EM+/In09lK6uEo9pObsNa/iOMySTXUFqp5yilGE1ASChHYfzDbQ2aaICXOvkPoF50LbhfOvhbsSIzU3JmEKyvVQuTKaacg2Tbf+973ePjhhwFYt24dX/rSl9i4cSN33nknqVSKK6+8kltvvbUQTUMrshplR8phvDVZ3fKYhcspS4PfHPn06tXLiGa6CRjFUU4kcHj4UhnOY2UwEgnwexEVpYPL2jibt6O981L0Wz5EWtNJuj1HCq0ryqR4HBtPKIT937+Di8/FfnUn+nlnYa9/EWfnfujoQb9kJXLBbKJ+P6pzVjld5T0o27hxI88++yz33XcfQgg+/vGP8+CDD/Ktb32Le+65h/r6em6++WY2bNjAunXr8t08RDgKRdRTFrFCeI3giO2OqSEc0NI2jit/T5NSSqIW+EZ5y2xV/+JJ9i9xCXpUT9kgISDY34/8v4egPwwfugpt+SKQEuIJRGUZdns3ztyZxA2Xqtiv5EQwFoGHnobqisEF7oklkK2dGFddiOwdgKAPOW8mA14/qIBMOY3lffiyurqaL3/5y7hcLkzTZM6cORw4cIAZM2bQ1NSEYRhce+21PPLII/luGgAiEsUpopmXkUwI31uS/AEQgozPyHtvWdwGQ4Cpj9ZTVkLU6s5re46nxKWGL4/myaSR//tgNiADZCoD6TT2xlext+/Ffv419LoqbLWgpZIDvnSS0p5uxB+egO4+ODyD135pG8bl5+E0t2M9/Az2vkPIpgbCAbW+paLkvads3rx5g/994MABHn74Yf7iL/6C6uqhkg81NTV0dnaOdviU08IRrMqygrz3aKKZEB7DN+prGa+JK5oiWZ6/iQkDafCZo7/m0UuJZArzdxtNqUvQk1SP3UcY0RgMHDU5JBzDaW5Hv3BF9t+ROHYqQ9LlVr1kyqT4Min0Xz2EWDgb52B2cXFRU5FNLBuIYm3YjH7eWTC9nkxlOVGPb3BGv6KczgqW6L97924+9rGP8aUvfYmmpqZhOVOFLPiphYtr9uVAph+vPvqUcMtj5L0sxkAaAscI5b16GZFMV17bczylLuhVSy0N0g0dXEMRtf30ZvRlC5DhGM6egzhNdaTOmK/yyJRJMR0b96F2ONQJ0RiUZdMv7I2vYlx7MdriOWj1VTjTG4jW1RJze1VApiiHFSQoe/nll/noRz/KF77wBd75zndSV1dHd/fQsFd3dzc1NTX5b5iU2ZIY/uIJyiKZfnzHDMpMXJH8Rh0DaXnMYqxevYyIVTxBmc+ApAUp+/S94AsB/mSCYGsbZDIYl6xisPBYMo3sDyPOWULiQ9cRmTeblOEqbIOVk5IhHYIDIUoOthDo6BycPGJv2Ymx9hzQdWRvCOv+J5Ez6km/+wrCDfVYanalogyT9+HL9vZ2PvOZz3D33XezZs0aAJYtW8b+/ftpbm5m2rRpPPjgg7zrXe/Kd9Mgmc52r5vHGJ8rgHAmxKzAolFfs7wG7nwHZSmJzxi9F9NnFFdQpglBqRt6ktA4Slre6SAQDsNPfgfxJOIDV2G3tGO8/XykZSNMHXvvITRHkjmNA1dlcnQk/m07cR7cABWlODMb0WoqQNeyy3VteAnjbauRXjdObTXJslLSUlVjUpTR5D0o+/GPf0wqleKuu+4a3Pb+97+fu+66i1tuuYVUKsW6deu44oor8t00tHAE6R89f6tQopl+fKPMvgSwvCa+nnhe29OfGlk49givXkbCChVVAdkyl6A3KWn0n34FKA0hEU+/hIxn64LI3n70WdNw9h3C2XcI0ViDvnSeKs6pTJgQUNLfj/WnpwGQyRRawIv1/KsY116E/dJ2ZP8ATiiMfcY5RF3FM4lKUYpR3oOyO+64gzvuuGPU1x544IE8t2Y4EY7gFNHQZcZJHy4cO3qbMt5CDF/CjODoN3FNGLj1ADGrl6BZgOHnUZS6oCtxevYCGY4NrUM9l87W3WjzpiNjcfRzFiO7+rAPdZJaMLeArVROZv54DLm3JVtWBbI9sj4v2A7WH5/K5o/NnoazfKEKyBRlDNRSzUfRBqLIIqpRFs704zdKjjnpIeM7XEBWSvK1QOFAGvzH+dT49Aoimc6iCcqCJnSfhkGZECA0HW3BTOyNrwIgO3qQQT/6hefgJJLIeTNJVVWSVnk9ygTpXb1gWeBxZdM/AOvPL6BfuhrKgjiOxK6tIhYoASnRNIGUqMR+RTkGFZQdRQuFi2rmZSTTj/8YQ5cA0tBwDA0jYWEdq05FDllSErfAf5y38hnlhK0OGlg65e0Zi3K3oPM0C8oCsSja3mZEWQlUlKItnoPzxl4wDERdJTKTIbxgLuq+qEyWJiXWptcxrrgAa8Nm6A8jgn7EjHpClVWDnzFdOvj7+uDN/eD1IOfMIFoSVJ9BRXkLFZQdRYTCODWVhW7GoIFM3zHzyY7I+Fy4I6m8BGXhdHZGo3acXjmfXlZUtcrK3LAzVOhW5E8gHkP8+LfIWAJxzhJEYy0ylUZ/2xpwHJwde9FnNCKEUL0VyqRJvxcMHevhZ9GXL4KAD1FdTqZ0eMAV6OzG+envhurfuTcR/OR7CQeOf31TlNONmgJzFG2guBL9B9I9xyyHcUTGZ+IK52eBx/4UBE9QMcFrVBBKt+alPWNR7hKn1fCl3toBsQQAsrULZyCCtmg2sjcE8ST6isXYmoajipEpOWBrGsbqZegrlyITSYSp4xxsIyOGnvdNJM6TLwwvSJxKo72xB2+mMOv3KkqxUkHZUbSBCE6geIKy/nQPfvP4S49kvCbucH6S/fuSksAJOuT8RgUDmba8tGcsSl3ZPLjMaRKEiNTQTU62d6P5vdibtiIPdmBv34vV2kWmiHqDlZNbqrwMO2Njb9mBbO/Gem4LctkiUkflKQokIjXKNSqWxNx7oGCFwhWlGKnhyyOSKbAdcBdP8cyBdC/TffOOu0/GZ+IeyE9PWV9a4j9GjbIj/Hol4UxHXtozFromKHFBTwLqT+FaZYaQGFYGUV6SrQ9lZ5eXsh5+FuN9V+AE/EjDIFVSQlpTz2JKbqQ1A2fVMnyLZyMSKezyEuIuz7BesYzQ8F2wAvs3jw1tFAJRWQovbMVcOI+0KsuiKIAKygZp/WGcEn/eZjGOxUCmj4BZetx9Mn4Tf3csL+3pS0iqvcf//fiMcuJWH7bMoIviKMJb4RZ0JCT1p2itsmAsitjwEvLAIeSVF2JcexHOjr3IaAJt8Wycjm4yq6YRL5LaccqpQxPg6w3B48/h9ITQz16Cd/li4u6hWewGIAdiGNdfiv3SNoTHhXbGPOyNW2B6A44mQC1RqyiAGr4cpIUGkIHi6UpJWnGktHEfo0bZERm/K2/Dl70pKHEdP7DRhIHPKC+qZP8KN3TET83hS6+Vhl88gHxtZ7ZGFAJ78/Zsxf7yEuxNW9HKS5GOuuspuReMhJE//i1y3yEIR5HrN2FsfIWjO9Q1x0a++gYynkBMqwUhsB5Yj4wl4LzlWOqjqSiDVE/ZYVr/ALKo8sm6CRhlJ+y4y3hNjEQGYTlIY+pibCkloTEk+gP4jSpC6UOUuaZNWXvGo9wtaDtFgzIjFIbD6wySsSASRWusRZSXIB0HvbEGO5YkZZqqN0LJqUA6hdjbApY9bLt88XXc56/AOlwsNq0ZuFcuxX7oafTzlyPmNCFmT4M5TQwEg8MnACjKaU4FZYdpvSGcYPH0lIXS3QRd5SfeUROHe8uSJCumLqiMWdmRXY9+4iHAgFFFKHNoytoyXpUeeCN0al75dU1w9C3RfvVN9GXzsZ7bAtEEYs0yrFXLUB1lSq54MmlcLW1oofDomWBeN/KovEUpJZmFczDjSexnXga3C+2qtcRLSlWdMkV5CxWUHab1hrDrqgvdjEF96W78+thq+KQDLjyhqQ3KepLZmYxj4Tcq6UsdnLK2jFelR9AWO7WiEo9jY0QiCEBbOg/n9d0AyNZOnGm16B+5nrTLRcrtPpLzryiTpgtwbd4K2/cgG2uzdclqKpBdfUP7XLOOqOnm6IgrYbpJrzkb94olSE2QMt14Egnc8TCO30vC7VEBmqKggrIsKdH6Qjglx68Jlk89qXYq3WNbqijtd+EJJaa2PUk55qAsaNSyP/b8lLZnPCrc0JvMlsUwtZM/2T8Yj8FvH8mua/nhd4DHjXHFBchoHBHw4XT3g+MQN91qyFLJKU8ygXzmFQC05YuwH38efe3ZcNZCSKYQ0+qIzWgatTCx7UjiphtNQLCtHedXD2dr5/m9lLz/KsK1tYBUwZlyWlOJ/oBIHC4pUUTlMPpSXQSNijHtmwm48PbGp7Q9XQlJ8ARJ/kcEzRpC6ZYpbc94GJqg3A0dU/srygtDSMRTmwYXGnd6Q2j11VjPvoy9eRvWc6+gTavFViUGlKlk28ieENqKRdhPv4z9xAs4A1HSdTUnLG/hTSRw7n0Q4oevu7EEzr1/pLS9neBr2wmG+tFUoplymlI9ZYDW3YdTGiyachiOtAmluykxy8a0fyroInhoYErb1JWQzAyO7ffj1ctIOwlSdhT3CVYkyJcar6AlKmkKFMffeKJcmQxy5/6hDa1d2IkE+orF4HJBKoW9r4X0ormFa6Ryykp6vPjXnoNcvwnnlR2I6fXoV12InNFIoqSEtDjxc74WjkI685YTp6GlA/nE86AJAp94D+EKVeRYOf2onjJA6+nPBmVFIpTuxWcEMLSx1flKB914Qkmmst+/K5GdxTgWQmiUmHX0pvafeOc8qfJAc+TkH8sTmoZoGMp9dF7diVZfA34vzqEOnPJSrMsuIKVqkilTwJaQPvsMxPuuRCyaDfNnYs2dwUBp2ZgCMgAZ8GYLHB/NOOrz6kjYsBlT3Z2U05DqKQO0zh5kEQVlPak2ylxVY97fMXVsl447nCJV6jnxAeOUtCRJmxMusXS0UrOe3vR+GnxLc96eiaj1Cg5ETt4hEV86hdHdixbwIpYvwursHRz+kV19aKvOJH7OMmzbUTk5ypRKmi6Ss2dizJ+N48hxr6Nqe9y433Ex9h+ezD5IagLj0tXYL20b2ikSUz0GymlJBWWA3tlD5oz5hW7GoK5kK0FjDOUwjpIq9eDtiU9JUNaRgHI3aOMY3g2adXQld+e8LRNV6xM81X5y9pT5kwm0e+5H9vQjL1sDGQt91Zlg6AhdxznYDlJiqSqcSh5N5PPmsdKYDz2F092Pcfl5SE2gNdRgPfAUsm8oBUM7c/7hyQInd7qBooyXehhxnOzwZfnxF/7Op85EC+Xj6CkDSAXd+HqiU9Ketrikwj2+Y8rMRrqTu6akPRNR5YGBFMStk6sbSQgwDhyCnv7shq4+0HWwbJzN27Hf2Is2f4ZK7FeKno6Dt6MLuX0vsqsP69HnsNe/hNPVh75qKaKhGlFVjv621Ujp4BRJjq+i5NNpH5RpvSGkzwNmcazTKKWkM3mIMtf4aqalSt34O6dmDczWmEOlZ3wXyFJXI33pZmxpTUmbxksTgjof7AufbEGZgK7ewX872/Yggj7s13dlK6P7vFiv7MCqLCtcIxXlOIQAfypJSWcXsqNn+IvJFKTSWJu2IirLENPrsF/ahpwzXS2/pJyWTvvhS629C6dyfEOFUylqDSClg98YX89dstxL7daObI5Gjp8wD8XgwrrxndPUPPiMCvpSB6j2FMdMwAa/YPeA5IyxVRopOCFA0zS0xpqhcmO2jbVhM8Y163AsG+lyYdVUkjDH2ZWpKHkghKCkrxfnD39GNtUhqsqzI5JHPRs5bV3IG69DdvWC7SAvPIeoP6CWX1JOS6d9T5l+qB2norTQzRjUlthPhbt23HGV5TWRupbzxcnjliSazuaUjVeFazodyR05bc9kNPoFO/tPjsdvfzJBcNtOfH9+BulI9HXnZuvoaRrarEZkf5jknBmEGxtUQKYUpUA8SknzQeRvH4NoHFwm9svbMa5aC4eXtNPmTofLzifq9hJumkZ45nSivoCarKKctk77njK9pYP0yjML3YxBh2J7qXDXTejYeIWXQHs4p8n+B6NQ7R1fkv8R5a6ZHIq/xtKyd+SsPZMxIyB4rMVBSpkdFixSXiuN9uuHkK1diIpSxIxG7J370NcsA13H2bkfbWZjtj5BEf8cyunH41gY8UR2Tdaf3odYuRTncD6kKAsiB6JY619EX74IvB7kglmEPVO3PJyinGxO654yEU+gRWPIsuJJ8m+J7aHG3TChY5MVPoKHwjltz/6wQ80488mOqHbPoTX+6qhLrhRCmVtgaNnh2GJm9oUGK/bLgShyIIJ+9mJwHIgn0JcvxE4kyby11pOiFFAwFsX8xf2I//gF2pv7EeEosi+EqM0WgbXWv4hx6Sr0s5eAx4UzdzrRkuK59ipKMSiqq/of//hHrrrqKi6//HLuvffeKX8//cAh7Noq0Irj1xCzIoQz/ZSPcc3LEcdX+yg5NJDTIrJ7wpIG/8SCMr+RnUHaX0RLLs0OCrb2FvcQpmYdNTnCtpFdfTgtHdgvbcPetgdr6y7sBXNwivvHUE4jbunA/U8MPUwc/nA6r+3K9vCWBiCexHr0OZyqMmIrlxMur1CzhhXlLYpm+LKzs5O7776b3//+97hcLt7//vezatUq5s6duiRxY/cBnLrxlZ6YSgeib1DrbUIfY2Xst8r4XSAE3t44iSr/pNsTTksG0tnhy4kQQlDrWciB2AtUuKdPuj25MKdU8FK3w9UzirfivfC6EeUlyP5sr6ezeTvaRSsxLlhBBoFVEiSpFc1XV1Ewk0lobh/aICV43ZBIYT30DPrKpYjGGqy6amL+AHZxdJ4rStEpji4iYOPGjaxevZqysjJ8Ph9vf/vbeeSRR6buDW0bY+9B7MaJ5W9NhV3h16j3TiJ4EYJoXYCyfX05ac8bIUmTf2L5ZEfUeRezJ7IhJ+3JhbmlgjdDklimiO8Klo2++kz0c89ATK9Dv/BshJA4pkm0rFwFZErR0XQBZUOrothPv4zxtjVoK5ci6qtwaitJNDUS8amATFGOp2iCsq6uLqqrh2pz1dTU0NnZOWXvp+9rwSkNIn0T7AbKsaQdpyW2m0bf7EmdJ9IQpHJ3b06GMF/rdZgxxkXIj6XGs4C+9EEiman7W46HRxfMDgo2dRXv2J+tacj+CM6+QwiPB/vVndn/X+iGKcox2A4Ya88ZWtMylc6uNHH2YhI3vpPIgrkkTVdhG6koJ4GiCcocxxk2I26qZ8iZr+7Amtk4ZecfrzcGXqbOOwOXNrnyBslyL8J2CHRMrrp/T1LSm4RpE8wnO0IXBtO8Z7E99NCkzpNLy6oEj7YUcVAW8OP0hw/3lNVjnL8c+1AHGZ+apaYUp7THg9XZi/G2NeiXrcG44gLIWNimi4wtVYkLRRmjognK6urq6O7uHvx3d3c3NTUTS3g/EdEXwjjUjj1j2pScf7wcabO5dz1zg2dM/mRCEJpZTu2Wtkmd5tl2h/mloGuTD4xnB8/n9YEHyDjJSZ8rFxaUCrqTkl2h4gzM4h4vXLoae3cz9jMvY3f24ly6hpQo3jw45fSWQeCctwJ7IIr9zMtY2/cgLzybeCBQ6KYpykmlaIKy8847j+eff56+vj4SiQSPPfYYa9eunZL3cq9/gcz82WAWR27O6/2b8Og+qiZYCuOtBmaUEeiM4O+ITOj4nqRke79kcXluPh4lZh0Vrpls6ftNTs43WbomuLBO4+dv2kVTruNoUkK4tIzke67C/tyNxK9cR9Q7+YkbijKVYh4vsUvOw/7sjaQ/9A7CFZU4xff1UpSiVjRBWW1tLbfeeisf/vCHuf7667nmmms488zcF3U13tiD3t6NtXByuVu5MpDu5dmuB1lWfn7O6oBKQ6N7cQ0zn9yLlhlfJpIlJb/dZ7OsUuAxcjd8fEbZNWzp/w09yb05O+dkrKgWDGQkDx8s3kyttNCIGy4sqcoGKCcHW0LcdKleXUWZICGLsatgAnp7ozgneCzTdx/A8+CTpC5ahawoy0/DjqM/3cPvmn/I7MAZzCvJwdDl0aSkdmsnWtpi7xULsD0n7hVM25Lf7LNJ2nBRgzapWZejaYm/wo7QQ1w37RtUeebk9NwT0ZuU/HinzccW6lzUcOKbSHV18JivjeXzpyiToT5/SiEd7/On5M4pE5T198dGvyilM2iHOjC37EBraSdzwdk41YVbkdp2bHpSbewceIWtfc+zpGwlc0uWTs2bSUn19k6CbRE6l9UTml1BqswzfGkeKelLwa6Q5Jl2m1ofnFero09R50xL/BVe67+PhSWXsaD0bdR45qMLc2rebAw64pL/22MxIyi4arrOonINvzn6D19Zeez8mGN+/hQlR9TnTymk433+lNw5ZYKyY0n+zTcH/1tUl4OrcAEAQHe8nUhmAMjOTNTF1Oe11UQ9mE52pPr+pS1smtUz+Fo0I2mLDiW8e/Iw6uAwgCQ7qcPgHAL6N09wxNTKOHBgYGgY85kPVRT12piKoijKqemUD8oURVEURVFOBkWT6K8oiqIoinI6U0GZoiiKoihKEVBBmaIoiqIoShFQQZmiKIqiKEoRUEGZoiiKoihKEVBBmaIoiqIoShFQQZmiKIqiKEoRUEGZoiiKoihKEZj6cvJ5otZ+U6aaWntQKST1+VMKSa19mR+qp0xRFEVRFKUIqKBMURRFURSlCKigTDnpCTH0P0VRTn6n43f5dPyZlZEKklP23e9+l0cffRQhBO9+97u56aab2LhxI3feeSepVIorr7ySW2+9tRBNU04iMXTakhopGywpaI1YlLk1FpULKkSm0M1TFGWcEujsjQoOhB1mlWjMDki82Cc+UEBb2uSNfgeXDovKNar1DPIkSLOzhcbBhM7Ofpsqr2BBqaBEWIVullIgeQ/KXnzxRV544QUeeOABLMviqquuYs2aNdx+++3cc8891NfXc/PNN7NhwwbWrVuX7+YpJ4m00PnVLgu/W6fKq/H9V2Icuf5WegXfvNCnAjNFOYnYQuMXb9o8tD89uO3aOS4+Ml9Dd5zjHnsgaXLbhhj24YtA0CX49jof1VpxXwOEELzcr3HnptjgttmlGv+42kMAFZidjvI+fLly5Ur+53/+B8Mw6O3txbZtwuEwM2bMoKmpCcMwuPbaa3nkkUfy3TTlJNKW1Cj16nTGHB7dn+LoB+LehGRn//Ev4oqiFJfOjD4sIAP449403Wn9+AfqGr/cmRoMyAAiacmr3XbRDwnG0Pnhq4lh2/YNOByIFqhBSsEVZPjSNE3+/d//nZ/85CdcccUVdHV1UV1dPfh6TU0NnZ2dhWiacpJI2RJDE3gMQTg1cowimpYIwUkxfJFrIhZHa+tCG4ggUmmwbdA0pMeNUxrAqatBBv15bVMo3cOB6Jv0pTqI21EcaaMLA68eoMpTx0z/QkpcFXltU09Csjcs6UpK4hlwkBhC4Deh1iuYVyoocRX5Xf0UkrJG/7KmbHncO5WDoDc58tj+pEQIgSzii4AtBZH0yPYl7eJtszK1Clan7HOf+xyf+MQn+NSnPsWBAwcQRz3SSCmH/VtRjmYYGtMCgkebszkj66a7+PXO5ODrAlhUoSPl6dVbprV14V7/PHpHN3ZVBTLgA9PIZhBLIJPBiCXQe/qwq8pJX7wau6lhStu0O/wavz/4I9ri+2nwzqLELMele9EQODik7CThnl7aEgeY7p/Pu6Z/ilnBRVPapld7HH6x26YzLmkKCEpd4NZBE2BLSdKC/jS0RCULywQfnq8zq0TNico1IQS6LrBtBymhzguNAY3W6ND3dnpQo9YDHCdG0R2bG+a5+NeXhvc4nVtn4Di5G740DA3HkSPqwR1r+1gENYt3zHVx3+6hHkKXBjMCGowll0455eQ9KNu7dy/pdJpFixbh9Xq5/PLLeeSRR9D1oS7q7u5uampq8t00pcil0dif0IhYAp1sMBZNS9I2fGCRhycPpil3Cz621MM0t3XcC/mpxnx1B66nXiBz5iLSa5aDfpwhH8dBP9iG5/ePkl6zgszKZVPSpsfbfs0jrfdyYe21XDvtJnRx7DZZjsUbA5v5951f4obpN3Nh7TVT0qZf7bV49KDD25s0FpVraMd5+Ms4kld6JF/dbPHJRToX1p9gGE0Zs7A0eLHLYXOHzco6g3OqBSVY/MMaL7/eleaVLotz6kzeM8/EI48fWEkJKyrg1rO9/OrNFD5DcNMZHmZ4cnMNSKKzfQCebLGYXaqxrtGkSssc3i54siXD7FKNtY3m+HPYHMk7ZxkEXIJH9meYFtD46BluaoyTY5KCknt5D8oOHTrEv//7v/PLX/4SgD//+c+8//3v55vf/CbNzc1MmzaNBx98kHe96135bppSxIQQvNQjSDoQtxx+9UaSWEZyZrXBWTUG5zXoXDfLiy4dDCdzWgVk+p5mXBteJPW285HBwIkP0DTsmdNwqitx/3kjMujHWjQ3p23a3PMkj7f/ivfP+mtKzPIT7m9oBkvLVzPNN4ffNH+fMlcVS8tX57RNj7XYPHnI4ZOLdQLmiXviTU2wqkYwMyD4rzdsKtyCJRWqx2yy0kLn7i1pXu3KJrJvas9wbp3BF8/KBjufXqyTXGTiETaMsafLg826asHqag+akDm7BmgaPHFI8uPXsz3xG1vhwb1p/u0iH8+22fz364lh2+9e56NsnBOMgsLiXU1wVZMHl3DQHBWQnc7yHpStW7eOrVu3cv3116PrOpdffjlXX301FRUV3HLLLaRSKdatW8cVV1yR76YpRSyJzobWDD5DMKtMJ5bJXrW2dlts7bZ4aJ/Gf6zzYMjTrMs/lcbz0HrS560YW0B2FOn3krrgbNyPPo09oxHp8+akSZFMiHv33807mz4xpoDsaOXuaq5qvJGf772Lfz7rXrxGbnLfehKS/9ll87GFYwvIjlbrE1w3U+O7r1v8xwUmbl2lVkxGZ0obDMiOeKnDoivtptG0wXHw4Iw7qJJS4iK3PeQDjsG9O+LDtoVSkn0R+MWO5IjtzRFJWcn430dK8OS47crJqSA5Zbfccgu33HLLsG1r1qzhgQceKERzlJOAPPx/jpW0K0/Tq5lr06vYtdU4NZUTOl5WlGFPb8T17GZSl1+YkzY9eOjnLChZTq23aULHT/PPYbp/Po+2/ZLrp388J226d4/FuTWCGu/EAqoFZRpbeiQPHbR556xTZsnggjjWd7UYE/IFo8dJx74OKcrkqL54peg5mo5pwAXTTM6sMdEFeN9yX/zwYs/YikyeStIZXC9vw1oyuaHHzOK5mNt2QSI16SbFMmGe736UcysvmdR5Vla9jac6/kDKTp545xPoS0pe7JKcVzu5y926Bo0HDjhYauHvSalzZ9MOjnZOrUGt59i/V0fTSGnG1Ja40AQpYWRnfBwW1Gw+uMg9bLcyt2B2ieCDiz3Dtpe6BTOD2WM1TZDSDBxN3WKV8VGPfErRkkKwP2nQnQBLSmIZSdAFpW6NW8/181pXhp645IpZJotKZFE+aU8lY+de7MrycQ9bjuD1YNfXYG57k8y5Z07qVC/0PM7swCICZumkzlPuqqbBN5OXe5/ivJrJpTI80WpzRrnAa0zujl7vE5S7YXO3ZHWtGsKcKJe0+ZvlLl7oNHipw2JVvcGqWg3TGVksVQhBS9rgnjdSNIcdrphpcuk0nWCOC6v2Oib37c7wUkeSs2sN3jXPpFJkkI7kskader+Xx5ozzC3TuaTJoIwMb2vQqfN5ebw5w5wj20WGCAZPttg8sj/JtKDGRxa7aXJZp931SZkYFZQpRas1bfBqt01nzKE16rC9x8LUYEGFQVOJxl8uceF2LCzr9MzFMF9/E3vWtJycy5o5LSdB2fPdj3Bu5cU5adPC0rN5vvuRSQVlUkqeanO4dkZuZk4uq9RY32qzepK9bqe7UmFxRb3g6mnm4ZIYowdZ3bbBbRtipA53gv98e4r+pIub5gvIUY9lShh87cUEBwaypTge3p9me4/FN8734JEWXmzOLYM1VSaO4wyW2RjabhwuiZEBTXDfHmuwxEVHzGFrt8X3LvZTrRf36gJKcVBXFqVo7exz0DVBQ1Bne0/2op1xYFuPxcP70uwJOVjW6VWLbFAima1H1pCb0jFOXRVaKIwIT7yUeF+qi55kG03++Tlp0+zAIg5EdxLLhCd8jkMxSFowLUe1cheVC7b2yWxBU2VSpJRYln3cHqSDUWcwIDviT/vShJ3c9Sd0JBkMyAbfN+LQ8ZaRc8uyR61FZlnO4PaIY/DAnuGrEqTt7M+hKGOhgjKlaPnMbHqHBEab8HY6z4Iz9h3ErqkCI0c3J03Drq/B2Ns84VO83v88swKLjluPbDxMzU2Tfy7bB16c8Dle6rKZXyZyVozaZwgafPB6nwrK8sGtjfy7+UyBLnL3+3cd4zriGuW9T0QXEv8os3tP52uVMj4qKFOKjqXpRISLOeUGoaRDW8TmyjnDk21X1RvUe07fp09jXwtOXVVOz2nXVaNPIijbFtrEdP+CHLYIpvvn83r/CxM+/pUeyZyS3N4QZ5dobOk+fT97+eBoGgPSZFpQY3bp8NvUJ8/0EBS5m9RTY9pcPds1bNvbZ5qUuLMJ++Phx+bmZcMnAMwo0Zg5ybRP5fShcsqUotJhu3iz1yGesemOO1R4NZZUaegaLK40aIvYzCrVmF8C7mPkoZwO9OZWUmtX5vScTm01rle2Z4smjbNnyZEOu8Ovsbrq8py2abp/Hve3/HhCx2YcyZ4ByfUzc/vsObtE8PDB02ymbx71S5MfvZbihbYktT6NL6/20xmz6IpJFlVqzPQ6E1rS6Fh06fCheTqr6n3sDTlU+DTe6LG4+Yk4HzvDw4U1EhdjC8KllCyp0LltpZ+9IYsyt8aZ1Tp+cXoVtFYmTgVlStGIC4NfvJHEYwjqAzr37c6WaHDr2SGL287xct50ecyk4NOFGIiAZSFLcvv4LX0epNuF1tOHUz2+umftiQO4dd+kZ12+VYWrlrSTojfVSaW7dlzH7h2QVHnBM8lZl2/V4IPuJEQzctyFaJXjszWN/+/VFJvas9/xzrjDF9ZH+OElflaXW9nv/hQEN15szgwK+pOCf98c50jK4Pe2JGi4wMci/9iCMkfT+PHrKZ45lKHEJUhYEsuB71/qp95Qif7KianhS6Vo9CRhVplBOAVbOocCr5QN/UnJ+pbMuIcTTkV6aydOVcW4e7PGwq4qRz/UOe7j9ka20eCbmfP2CCFo8M5kb2TbuI/dGZJM8+f+d6Rrgka/4M2Q6vrItbCtDwZkRzgSWmLOlJeUsDWN+/dmeOscjs1d1pivO1FH59lD2eArnJZkDi9McEgl+itjpIIypWj4TUFfwkHg0BAY+dGcWaLndNjiZKW3duBUlk3JuWVFGVrb+IOyfZEd1HmmT0GLoNY7nQPRN8Z93JshZ0qCMoBGH+weUDfaXPNokspRVl0ocU39w5guHWaXjLzuNAX0MQeELiGp8Y08Rz7ar5waVFCmFJyl6XQ7JlLTmFmqs7LRzfwKg+BRF7Jqr8bqurFfHE9lWnsXTkXZlJzbqShDb+sa93EHojsnvKzSidR5mtgX2THu4/aGJY1TFJQ1+AW7VE/ZpCWEQUvaoM/JTrX2YfH5FV6O/qtdOM2kyTv1v2vpSG6Y78J31JB0Y0BjWZU25gXCvVj89dneoxcFYFW9QZVXoyVtkBAqY0g5PvUJUQpqQJqsP2hjSQtNaLzeleGiGSZVPp2vnOenP+ng1gWzApISVE4GUqJ39ZFevXxKTu+Ul6CFBsCywRhbaYuMk6YreYhqd8OUtKnGM43W+D4c6aCJsT1HRjOSSAYqPSfedyIa/IJHWlRP2WR0WC7+4fk4nXEHU4O/OsvL2hrJkqDN9y/10xpzKHUJpnklnjwtoebX4ZPLvAykJEJAtVfgHkf5DSlhkT/DDy7x0xJzCLo0LAc+/USUtAM1Po1/OM9HvZ4+8cmU05IKypSCEQL+3GJhGhpuIfjRawkAXu3O5pTMKtW5a40L12me2H80EQojXSa4XSfeeSJ0HRn0Z5P966rHdEh74gDl7hoMzZySJnkNP27dS0+qnRpP45iOORCR1PlAm6LFEstc2VzHgbSkVA1NjVtG6HznlQSd8Wxgm3Hg319JMO8SP9PMDPVGhvrczhk5ISHgsYMW9+4YvgbsV1Z7WTGetkioNTLUlkJrRuOzT8YGX+qKO3xnc4J/WePC5agZvMpIavhSKRhb09nem11G6a1VuwH2D9hEbPURPZre1YtTXjKl7+GUlqB19415/0OxfVS766ewRVDtaaA1vm/M+zdHJDWeqQuWhBDU+bLvo4xf1NHY3T/yS38kSCsES+hsbB35ALitx57wBKPOxMjPx56QTdRS1zVldOqToRSMLh1ml2lU+zRco4yUNfg1/Lq66R1N6+5DlgSn9D2c0gB6V8+Y92+N76VinOUqxqvSXUdrbOxB2f6IQ80oCeO5VOMVHIyqz+dE+DRJU3Dk7afSU7hbkoHD8tqRF6L5FROfYFTpHvkZbAyo65pybAX5Bnzve9/j6quv5uqrr+ab3/wmAH/3d3/H5ZdfznXXXcd1113H448/XoimKXmQERrtlsn+pMHaJjcZW9KXcLhu3lDVfq8Bt53rxaOGLofRuvtwSqe2PLgsDY6rp6w1vo/KKe4pq3TVjqunrCUKNd4pbBBQ7RGqp2yCTGw+d7ZvWFL9hxa7afSMf0hPCOiTJjuiBi1pk8xb8g6jGLwZN9ibNEgeJ9FeOpKrZphMO2rm97l1BkvKJx7cN7htblw8dF07Um/xdC58rRxf3nPKNm7cyLPPPst9992HEIKPf/zjPP7442zbto1f/OIX1NTkZoFlpTil0PnTQQfTcAiYgv9+LcLls9wsrjQo8wgubDRJ25J6L5Rr1phnPZ0utJ4+rJljy6uaKKckiNk79hIU7YmDrK56+xS2CCrctbza/9yY9pVS0hqTVHun9pmz2gsbO9QHdNyE4JlujZ9ti3HDfDe6JphfrrPAb6E74x++3JM0uf2ZGOnD8dx1c118YK6OW9p0Oya3Pxun5/Aw4pJKnS+d7aZEjB4UVWoZvnmBh7YEmEJQ63EmFUCZOFw3XWNNvZ9IWlLrE5QJdV1Tji3vQVl1dTVf/vKXcbmyicpz5syhra2NtrY2br/9djo7O7nsssv47Gc/i6ap0dVTzcGEwNA1OmMOj3RmiFvwh90p/nC4ev+/rvOxyJe9CKoL11tIiRYKI4NT3FMW8CFicchYYB7/EpG2U0Qy/ZS6Kqa0TRWuGnqSbWOagRnOZAuO+qf46lblEbQXMAfqZNVj6fzHK9mq+b/YngTA1OCHl/qp1Mb3+0wJg7s3JwYDMoD796RZ2+hjjk9w/+7MYEAGsL3XZnu/ZM1xPq5eaTHnyKzdHFyDdOlQbzjUH/48quuacjx5j3rmzZvHWWedBcCBAwd4+OGHufDCC1m9ejVf//rX+fWvf83mzZv57W9/m++mKXkQSkkcCVVejbZRqlyHkuqKdSxiIIJ0uU4YKE2apiGDAbT+gRPu2pU8RJmrGk2MrXzGRLl0Dx7DT3/6xDXUWmOSGm82GX8qBU1IOdnyG8rYDaQZUTU/42Qr4I9XwhG0jnId6UtKLDS2944cDt07MPHEfUWZagXritq9ezcf+9jH+NKXvsTs2bP5/ve/T01NDV6vlxtvvJENGzYUqmnKFKrzadhScihisbhyZHBR51e9o8ei9Q/kfL3LY3GC/jEFZZ3JQ5S7xlY6Y7LKXdV0Jg6dcL/2uKRiCmdeHiGEoNoDbTEVlI1HlUcMyyUDCLrEhGrKBXSHpdUjryP1fg1D2qybNvK1M6sMtTKIUrQKcgd8+eWX+ehHP8oXvvAF3vnOd/Lmm2/y6KOPDr4upcQwVAm1U4WNoC1jsCvpwhaCOr/GGVUmb5/lYlZptofFa8AXz/VSb6oE2GPR+gZwAv68vJcM+ND6Qifcryt5iFLX+BYvn6gyVxVdyZYT7tcWk5SPMuttKlS4BW1xdYN/q5A0eTVssDmUXa3j6F7LMs3iH8/zUX44cK70Cv7hPB+lx8jzOh7DsbnlLPfI64grm7d1yTSd8xuz9fN0Ae9f6GZuCRzKmLzQr7MjZhAfRy/vgDR4NWzwUkinyzanvDdWOf3kPfJpb2/nM5/5DHfffTdr1qwBskHY17/+dVavXo3P5+NXv/oV73znO/PdNGUKaJpgU59O3IJ4xua+3UlMTXDVHBczggZ/u8pHxpYEDUclwJ6A1hdCBqZ4SuFhMuBH9I2hpyzRQpmZn6Cs1KygK9l6wv3aYpKmQH5uluVu6FBB2TC9jsmXj0qu9+jw7Yv8NBjZFTmklMzzZviPi7yEM1BiQEBkJvzdr9Yy3HWem740ePVs0HfkXCVY/M2ZOh9eZGIIQYVp8WoI/nHjUEHX5TUGty134eP4QWG/NPm7Z4cK3rp0+Pa6bLFbRcmVvAdlP/7xj0mlUtx1112D297//vfzyU9+kg984ANYlsXll1/ONddck++mKVMg7Og8eyiNRNAQ1OlNSEDyk63ZBN9Lp5vcskRDOlIFZCcg+gdw6vMzO9kJ+DA6uk+4X1fyEMvKL8hDi7I9ZQeib55wv/a4ZFllfgYByt1CDV8eRQh4pdsellyftOF3u9N87vD3HLLJ7n6swckYk/3uuxyLumOcS3McavRsIJVwDP79lcSw17d0WRyMu1joO/7PtbXHGVbcNm3DL99Mc9uZOmICs0YVZTR5D8ruuOMO7rjjjlFf+9CHPpTn1ihTLe0IvKZGb8KhPznywtUSdXCEgcjT2nYnMy0Uxpo3Ky/vJQN+tFD4hPv1pNopy9PwZalZRU9q/Qn3605me7DyocIt2NGvgrIjhBB0xEb5nkeK43uekWLUyUQnmqwhhKBrlJm2rREHG0OtV6jkjMqqVqaMEIJyl4PjSOaUGcwpG3npunqWC02tAXdiUqINRJGB4zzO5/Lt/F5ELAH2sf82lpMhkgkRNMvz0qZSVwW9qU7kcbpVopns7F5fnu6S5W7oGmUpndOV40hW1o3yPZ89tu+5ENmUh+PNjtQ0wURTuYKazdqm4Wu06gKaAse/FTqOZPkoEwqunePClKqXTMkdFZQpOScEdFgmW6MGr/XrXDbLTY1PIJB8dKmXco/Aa8DHlno4p0qoYcsxEIkk6Bq4pmbR7xE0DenzIMLRY+7Sl+4iaJShT3E5jCM8ug9d6EStY+e6dSUk5e6pL4dxRIkLohlIv7XGw2lsts/hi+cOfc9vOsPNuWP4nvdLk21Rk8c7NX68GzYPGMQY+mylRTYx/3/2StZ36YTk+L8LwnH46CIXb5/pwtBgRonGXWv91Bonzgub6bP5u1U+Kr3Zn+vGJW5W13DchwRFGS/V66rk3KGMyYudNn1Jh4MDDlu7Lc6sNrhwmsGZVQaXTAugSxs/FlI9ZY6JCEXyNvPyCBnwow1EsMtLR329N9WRt5mXR5SalfSmOgiaZaO+3pUgbzMvATQhKHNnh0wb8/vnKVomDudXCpZf5MUBAljIE1TFj2Dw2z0ZuhOSl9qzAdIDe7I5p59arGPisKEDfvBqfPCYxqDGXed5CJwgQf+tykSGTy0S/MVCPy4kLpkZU5FYQzqsKpecuc6LDQSEjVS9/EqOqZ4yJac0TfBUSwZDE9T4dLZ2Zy+YW7stvr8lyd+sj5LI2Pikmmk5HtpABOnPz8zLIxy/FzEQOebrvckOSvI0dHlEiauC3lTHMV/vSkhKXXlsENkgsFsNYQ4jpcQrLfxj/J4fiEJdQB8MyI7488EMXRmdsDT46bbhCfqtEYfmY3fkHp8jCcgMrnEuoSSlxHPk51K1zpQpoIIyJaeEEMQy2RlQx7oYW6pzbNxEOIL0TaC65mT4vGih4wRlqQ78xui9aFMlaJSNISjLb+2oUpfKK5ssy8kujTX6axIHMWwppaOPU5RTiQrKlJzRNIEUgktnmBgaRNIODW9JoF1WbVDtVjew8dIGIkhfnnvKfB608LGDsp5Ue957yoLmWIKyPDYICJqCbrU82KRMDwhCSYeZpcPzExeU69R6oFSzuG7u8D+s3xQ0BUcG4JquIQx1a1NOTiqnTMmJXsdk/wAkbIkuJFVeQZXXYG65waudGd7ss1nTaHLZNB3TUcUWx0uEIjgN+alRdoT0+xBtx15rsi/VSZNvXh5bBCVmOfujO4/5encSVlTlt6eszA2dqoDspFQbGS6e7mJhpcHrXRY7ei3OrTe44v9n784D46rKxo9/z11mz740bZPuK21pS1ta1gIKslVZXEAFcUH0RV/xRRABxeXlBZVXFPUV9aeCiAsiKqAUELBAC4UWKN33Pc2+zT53Ob8/pkk7TdomaZKZJOfzD8y9M3dOmpt7n3vOc54zxsDrWrRIk5NKJV5TY2W1xYQCjSsneyjRDis6K2BX0uTxLSkiluSKSR6m57mYqO40ZfBQQZlywqLC4PebU+iaoCKo88j6OIYGc0eYTCjQ+Og0DwHhYLiOSuzvJa0tjDNp7IB+pwz40Y43+/IoCff9Jc8sOuai5I0JScEA1ShrV+CBTc0D+51DTYNjcsfLUarydaYV60wp1in2QpFuk8Tg3tcTbG52KPIJZpWZmJqk2HAzUiT2Jk1u/ne0Yxh0TZ3NN04LMDdfXXOUwUMFZcoJ2x+FMQUGtVGX53clgXSux5sHLN48kF4ceHGp050JTspRaOEo7gDnlMmADxGJppMDjygxIaWkNdWYhaCskOZU1ysNpBxJ3KajSvxAKfAIGrsojKx0356wJGJJNjbabGxMJ997NDi9IkhzUrK5OZ1Q1pyQvLw3BcBF40yqDo5oaprgzVq7U17aY5uTzF6oaiEqg4caeFdOmK6lZ5Q7rsTsomSVodbsPTGWDZYFvgHuAjIMMAxELN5pV9huwaN5MbWBTeAK6CGSTpyUk+y0rzGR7rXSBniR6AIPNCXBVdOJe62rWrG6BgJ51EKyh/+epQSP3vl9Xj1dH1FRBgsVlCm9JjRBQjMoC2hUt9n4TMFF4zMDh5ApmFqkTrMTIcLRdJL/AAcbAG4wgAhHO21vTtaTbxYPeHuE0MgzC2mxGjrta0wOfJI/gKkJvDqEVapkt9maTkwYiIMB17g8GBHIvE5cN9NPnrAZ4XE5fVRm9+eZo03KvYd6J6WAuSMMvEc8FF493avWpVQGFTV8qfRKGIO36yUp10UXMK7QoNgn0DXBrQuDrK2zKA9qnF6hU6ZbqibZCdDCkQGfedlOBnyIcAQqyjK2N6fqCZkDWw6jXZ5ZSHOynnLf6IztDQlJ/gCXw2hX4DmYz5al7x80BOxImDy4JsH+iMsFY00um2BSoFncflqQ1w9YHIi4nDLCYFaxhpQOJg6fm+XhzNEma+odZpfpzCoWmG56mLMVk79utXizJsYX5gWoDrvELZezK03G+uxuFYZVlFyhgjKl5zTBMzsdAh4NQ9P4v3diSNK9YiNDGhdPMLlxhsBxJK6rArITJcJRpH+Aa5QdJP0+tLZop2WkW1J1WQvKQkZBl8n+jQnIG6BVqI5U4BE0JGBCfna+f7CotU2++nK0o77Y37aliNtw+WSTm18KEzQFRT6NV/ameM9Yk89P18F1ycPmtGI4s0zDOWw9VqlpPLI+xQt70t2U//tGjJFBjXvODFBASgVkyqCjxpWUHotJna0tLuvqbaKW23Hdi1iSrc0OD61L0mbruKridZ/QwpHsBWU+b7qn7AhNyTqCenYikKCRT0uq8/BlfUKSl6WeqjwTmlStsuPaG3E7FXx9fneK3WGJ7UJrUrKr1cGR8K/dFm1u5nik42R+uM3ReXFP5rjxgajL9jaV2K8MTiooU3rMEKALSb5XdJlcW+rXMIW6QfUV0RZB+gc4yf+go5XFaM5iT1nQyKepy54ySX6WespCpqBRBWXHFehi1k+BVxDsYnu+R2Ac5zpiapKCLtY67ep7FGUwyEpQ9pOf/IRLLrmESy65hO9973sArFixgiVLlnDBBRdw//33Z6NZynEIDaJaA3F2s2SSxuRig5QjGX1Y1X5NwGdP9mGoKeh9RrRlMafM7+s60T9VT2iAl1hqFzILaU52DsqaEmQtpyzPAw1JFZQdz9ggnFSS2ft141w/40KSGUds/8JcPyHNIqY30KLtpYGdtGnVJLVwx5yXIA7/MTfzb2NWmc4YtTi8MkgNeE7ZihUrePXVV/nrX/+KEILPfOYzPP3009x333088sgjjBw5khtuuIFly5axePHigW6echSWFuedln9jSwtDN1h54KfMKb2WkcGpzCj105wAy5FMKhCMNFVybV/SIjGcrPWU+dC6CMpaU43ZC8qMgi6HL5uSMms5ZfkmbG/LzncPJkFsvjbfy44wtKYkY/M0Kr02mpTcNt/LzjC0HNw+whdheeOzpGSCv257mJgdYULBdN5T9X5GBSZQaUxDSskphQ4/OjfI7rBLoVcwPgSBHi40rii5YsCDsrKyMm677TY8nvTc9YkTJ7Jr1y7Gjh1LVVUVAEuWLGHp0qUqKMsh+xKbidgtNCcbebPmZRoTtbzbsBwNDVPz8N+LHqZAVqTfrAKyPiUiMaQ/iz1l0Vin7a1WUxYT/fNptRoztjlS0mZlL9E/zyNoSarSC90RwubkvPZXTsf1IoTNrMO2b4m9Q0omeHTTTzs+u6N1I17dS5G3lA9P+A8CsgRdSsZ4LMaUDOAPoSj9ZMCHLydPnsycOXMA2LVrF8888wxCCMrKDk25Ly8vp7a2dqCbphyFELCjbSOWaxEy82lMHPrduLgk3QSNiQNZbOEQJiUiFs9aThkeExwHUoeSqZNOHNu18GrZCRSDRgFhqwX3sCW7WlPpSv76UQqN9rd8E5o717NVeknTBOubVpF0Ep32bWx6h1GhsTSl1D1CGXqylui/detWPvWpT3HrrbdSVVWFyKjOLDNeK9klJVQEKjE1D6508eqdZwLme4qy0LKhT8Ti4DFA72KphAFpgEAG/Onllg5qX14pW3+jhmbg1XxE7NaObc0JSX4WCse28xuQdNJLPSknznUlVXmTulwxojwwipZE9obPFaU/ZSUoW716Nddddx0333wzl19+ORUVFdTXH1rPrr6+nvLy8mw0TTmckIS1WmrlVkYER9KSbCLpJLh80nUIDt2QPzL5Bor1UVls6NCVzaHLdtLvQ4scGsJssbJ/QwyZhbSmDg1hNiUhz8zeg5wmBHke1VvWW7aWoJFd7HPXs89eT5teTVVoPG2pFhZWnNvxPkMzuWT81YzPn06hGJnFFitK/xjwnLIDBw5w4403cv/993PaaacBMHv2bHbu3Mnu3buprKzk6aef5sorrxzopimHEQI2x9+gxWogbkd5tfo5JhRMY3rRHPK9Rdx26g+IpSKUekdRqlehuVnsphjCRCSKm6UaZe2k34c4LChrTTUSNLJbJTVk5NOaaqQqOAmA5qQklKV8snbpIUzJiIDq5e+JuGhmWc3fiNhh/r33aa456Yvc/84viVhtnD/2Cs6pvJRzKi8hbsco8BTj1woo1kaCVBWdlKFnwIOyX/3qVySTSe69996ObVdddRX33nsvX/ziF0kmkyxevJgLL7xwoJumHKaNOl6vfYG4HaPYV8rutq3sbtvKS3ufAuCGmXcwP/+CdLV+NWLTb7RILHv5ZAdJnzdj+LIl1UDAyDvGJ/pfwMjPWP+yMSEJZnl9kpApaFI9ZT22JfIOPjPAE9sfYnHlJSzd9ThhKz00/dzuv/Dc7r/wzVN/wUT/gkMfUtccZYga8MvYnXfeyZ133tnlvieffHKAW6McTZvVREWwkh2tG9neuqnT/k3Nb3Nq4YWdKmwrfUtEYuDNflCmdQrKQllsEQSMEK2ppo7XTUmyVs2/XehgT5nSfZom2Nayjjxveji8zF/Bsn3/6PS+pkQto4JTB7p5ijLgVP+v0qUCs4QDkb1oQmdy4Umd9p9UPF8FZANAhLNXzb+d9HszCsi2WtkfvgzqeRm1ypqzWKOsXdAUKijrIdeVTC2a3TF5qDa2nzF5Ezu9r8RXMdBNU5SsUEGZ0kEICIs69rsbaHPrWTDyHOaUnUaJfwSTC2d0vG9hxXlMzZubxZYOH+lE/yznlPkyc8paUg2Esh2UGfm0ZgRl2U30h3SNtEYVlB1TXGtme/IttiRWEhZ1aBpUhiaQsBNcMv4qVtb8m/PHXk6hN110TBM6V039HIWe0iy3XFEGRpazMJRc0iB381bDMsJWGw3xGra3bOS9Yy6jMm8810z/T2zHxqMFKNFHo7vZ7b0ZLrRIDGdsDvSUHVZAti3VRCAHgrKWwwrItqSyn+ifZ8LOcHbbkMvCoo4frLmF/ZFdAITMfG6d/33uffNmJhfNZMGIxdw09zv49CBfW3A/a+pfx5Y2K6r/xfLq5/nyrO8SkmXH/hJFGeRUUKYAIDSX53b/mSJfCUXeUpbu+jMAf976/wAo9pXxrXm/xuvmgxq1HDAimgOJ/n4fWjTe8brNas6JnrLwwZwyKSVtKbIelIVMVdX/aISAja2rOgIygFGhsTy94w/E7Ahr6l9nTf3rAHxl7vd5Yc/fWH7guYxjbG57m3l5FwxksxVlwKnhSwUAR1g0xGtoTjZgualO+5sS9SRl5+V2lH7UXs3fl93hS7yedEV/x8F2LZJuDL+e3RWfg2Y+bXYLUkoiFnh0MLNUzb9dnpleWUDpTAjBvvCOjG2l/gr2RXZ2em/UbmVXeEun7dXR3WhZ/h0rSn9TQZkCgCF9zCpdyMjgGLy6D+2IU2NWyQJCojhLrRumkilAgJnlDm0h0mUxonHarGYCeh5CZPfS4dG8CCDhxGhOZT/JHyBoQtgCx1V5ZUdyXcnMklMztm1uWpNRGLZdqW8kZ428qNP2k4pOwVX/tsoQp4KyYS6qNbLLfoctiZVMKJyCkAIpJR+f/gUKvOkgbHrRXK6Z+l+qQOwAE9EYMpDlXrKD2gvItlmNWVuI/Egho4A2q4mWHEjyB9CFIGBAm3X89w4njkiy116PJZN8aPL1eHQfmtCZW3Ymp1dcwHmV70dDw6v7uGbaf1JhTmBh2fkZ26+acgMJN8o+ewOudtg6rCLMtuRqXm7+C9uSq0kKldQ3XKxcuZJLL700Y9vatWv5z//8zx4dp6mpialTc6fcisopG8ZiWhNP7XkYXWgU+kr485b/x5SiWZxW8R4q88Zz54IHwNXJEyVoKrF/wGnReNZnXrZrT/Zv8zdnvXBsu6CRfzAoG5X1fLJ2eSa0JCVF3uwHiblA0wSrWpfxi3V3AzAyWMXVUz/PjKL5FDIKXI2rxt3EJWM+jiZ08ijFdcEkwFXjbuJ9Yz7M+uY3eW73X6iJ7QPgP2bdxdy892CLJE/t+Q3P7f1Lx/ddMOaDXFF1g3qAHKZmzZrFAw88kO1mnBAVlA1j+2PbKA+MJGqF+efOPwGwpXktW5rXAnDbKfczwXuKqp6dJSIaQ/pyIxiWXi9aNEZrfhMBPTeCsoCRR6vVRHOKrFfzb5dnClpUXlmHCI38btOPOl4fiO7l4Q33c9spP6TQW5ne6OjkMQLkEXOIHJ3GZC2/3fijjGM+svlHTFswj4jTnBGQATy353HOGbWEUjGuf34gJSetWrWKr3zlK1x11VU8/fTTPP3009x2222EQiE2b95MTU0NU6dO5bvf/S7BYJDnnnuO+++/H7/fz8yZM7Pd/Axq+HIYS7oJXOng1X3ErWin/Qk3kYVWKe1EJIb05cgTv89zcPiyKetJ/u0CRh6tqUaak5JADgxfQjqvTBWQPcSRNnG787Ul6ca7eHdnSafzNShqhXGwutx3tM8oQ9frr7/O1772NR588EHmzs2sn7lu3Tp+9atf8c9//pP9+/ezdOlSGhoauP322/nxj3/ME088wejRo7PU8q6poGyYMgyNUcHxRK0Ie8M7mV9xVsZ+j+ZllH9sllqnwMGesiwvsdSuff3LllQDQTO75TDaBfT0UktNidxI9Id0j13LMF3/Uoj0dUUI0HUNTROERAlnjnpfxvs8uo+R3by2jAqMw9QyH0wWj76YIMWUekZREajM2FcRqKLUO+rEfhBl0KipqeFzn/sc733ve5k2bVqn/WeddRYejwfTNJkyZQqtra2sXr2aKVOmMGnSJAA+8pGPDHSzjylHOv2VgZIQbexLbiYei2HqJpMLZzA2fxKWkyJo5vNW3XJGB8dy1eT/oEhUIqV66s8WLRzFDfiz3Qzg4PqX9U20phqpDHReBicb0sOXjTSnYHxuxIkHFyUffn8zEdHAupbXqItXM6nwJFbXvUqeWcSZoy7gjNEX4DX8vFnzb0aHxvXo2lIsKrlzwU/445afUR3bzeJRl3DuqMvB1fCSz3/N+R5/2/kb1jW+ycySBVw2/jq8To6cDEq/03WdX/ziF/zHf/wHF154Yaf9vsPKCQkhOs65w889w8itMCi3WqP0L03y7wNP4PcEMDST3737EwDmlp/O1KKTuXjsh7li3PWY0o/meFRAlmUiGoPiwmw3Azg0+7LVamKKMSfbzQEgaOSxP7aD1qQkZOZGp3/IhOro8Pq7sbQYv9p4D1ta3uWqqZ/jh2/fCcDpo87n4Y33s7n5XSpD41k48jzyzSLKjDHIbpa2kBJGalP50ozvYZHAJ/ORhyWeFcrRXDfhayQnRPESBFfvjx9RyVFlZWWccsopfPWrX+XWW2/l9ttvP+5nFixYwJ133smmTZuYNm0aTzzxxAC0tPtUUDaMtMk6auL7iLa1UeqvwJE2AKtqX2ZV7cusL13NF6bdg+vkRn7OcCei8axX828nfV60WJyw1UwwR2ZfBow82qxmWnOgmn+70DAcvmyw9rO+aRXzR5zN8urnO7ZX5U1gxcHX+yI7OwrFzik5g3JtQo++Q3M8ePF0PefI1fGieseGs8svv5xnn32We++9F10/dmBeXFzMfffdx1e+8hVM02TBggUD1MruUUHZsCLRDhb9dHE67XWwkUhABWW5QERzoJr/QdLnRcTihK0WglleYqld0MgnbDUTsSGQI1eykCloSQ2vpZbkwTmTmhBIefyfXarp3EofWLhwIU8//XTH6wcffDBj/7333nvU14sXL2bx4sUdr7vTwzZQenUp+9vf/nbM/ZdddtlxjxGJRLjqqqt48MEHqays5Gtf+xqrV6/G70/n0HzhC1/g/PPP703zlC5I3SJPL6DQW0Jl3viOqv3uYZPQLxn7UaSTG8NAw56UiHgid2ZfmgY4Dprt4tFyI1AM6CHCdgsBQ6KL3HiQCA3DpZbKPJVMLpzFmvqVfHDyp9nZthmAmug+JhRMZ0frxo73ziyZT4kxqqP2haYJLC2GEALhGggnR6JrRcmSXv0FLF26FID6+np27NjBokWLMAyDlStXMn369OMGZWvWrOHOO+9k165dHdvWrVvH7373O8rLy3vTJOUoHJFiT3I9bXYjEijzV+DRveiazudm38Hq2lex3BTvq/oQY30z1GLjuSKeBEOH43TFDxghcHweRrqliBwJgAzNxBBeQmYYyI0lwPwGJBywXJn1tTgHiukE+dxJ32BVw0s0xmu5cfY3WF79PNKVfPKkL/NW3XI2Na9hevFcTh1xLoabfvCOikZeq3uGl/c/Q0WwijNGnU+xp4JKcxpC5sh5rygDrFdBWXs34Wc/+1nuv/9+xowZA0B1dTVf//rXj/v5xx57jLvuuotbb70VgHg8TnV1Nbfffju1tbWcf/75fOELX0DTVK/NidqT2sCe6FbaUi2sqH6exkQdASPEpMKTmFxwMjdM/zrSMnEcVwVkOUTLoSWW2llejXI7N4Kfdl4tH4wWciUo04QgeLC3rDS3fn39Kk+W856yqxAauA6cMu29WFqM76z6HDE7wqjQWF7Y+3dW1y3n1lk/xMTP03t+ywt7/wZATWwfm5rW8P6JH0cvNBil586yN4oykE4o6jlw4EBHQAYwatQoampqjvu5u+++m/nz53e8bmhoYNGiRfzP//wPjz32GKtWreLxxx8/kaYppGsFvVz9Tyw3hd8I0JioAyBmR3i34Q3+sv3/cSC2Jx2QKTklXc0/t+7qSQ+UObmR5N/OEHl49eZsNyNDngmtw7AshutKHFsiZfq/1bFd7I/uojnZwPrG1bQmm9jdtoUGq5qwbOClfU9lfD7hxABY27gSbZj0MirKkU4oKCsrK+OBBx5g79697N27l+9///tUVVX1+DhVVVX89Kc/pby8HL/fzzXXXMOyZctOpGkKAJICTxGa0BGi869aQ8PQcmTampIhl5ZYapcwHYqtULabkUETeZh6U7abkSFkCJqHWV5ZV44s+npou4kuDLx654cOTWgEzTxVjkcZtk4oKLv33nvZsmULl112GZdffjnV1dXcc889PT7O5s2befbZZzteSylzrqDbYGPpMSKigdNGnoft2jTG65hRMi/jPReN+wiF2sgstVA5FhGNI705kuR/UMywKEjlVu+dlEF00ZLtZmQIHlyUfLgrMUZzxsjMav7nVX6AYn0UIUq4esrnM/aNDo0jbkeZUbQAV7OIag2k9Ag5ksKoKAOiV5HPl770JX70ox/x6U9/GkgPWwJs27aNa6+9lqeeeupYH+9ESsn//M//sGjRIgKBAH/605+4/PLLe9O0YU8IwT57AzWRPSScGPvDuxidN46q0Hhmly1kYcW5NMRrmFJ4MmN9JyFUscWclIs9ZWE9QUHSSyzbDTmM44Ygx3rKgsbwm4HZFcP18eHxN7JgxGJ2h7cwPm8a4wIz0FwPLpIZRfO5ae5/s6NtEwWeYkr85fj1IIZu8P+2/jerapdR6q/gMzO+ygTPHKRU0ZkyML72ta/xhS98ISvrYvYqKLv++usBupXU3x3Tpk3js5/9LFdffTW2bXPBBRdw6aWX9smxh5tWDvDKgX/Slmwmz1vIS3vTAXLQzMOn+/nynHs5vfD9uK5ElQvKXVo4ihvKjYW/27UZMcZFinMqKEu5IVwas92MDCFT0JRQf1wAflnASf4zmBk8M+Oa42op/r7zYfK9hSyvfh5NaMTsKFOKZqGj81b9cgDq4wf47uqbuXvhQ5SIMcf4JkXpOytXruTGG2/Mynf3KiibOXMmAKeeeuoJffmLL77Y8f8f+9jH+NjHPnZCx1OgNr6PEn85pu5hRfW/OrZHrTBRK8z2tg2UF+TG2oXK0YloHFmaGzMK2zVrEWYkSrPdjAwpOw/HsyPbzcgQNGH/MFtq6XjcI5ZVispmbGnxdv1rNCcbOrZPLpzB41t/lflZ6VAT301JQAVlQ5G9egP2P1+G5jYoyse4+GyMeSed8HFXrlzJ97//fVzXZfTo0QQCAbZu3YrjOFx//fVceumlbNq0iW984xvYto3X6+Wee+7hueeeo66ujs9+9rM8+uijFBUV9cFP2X2q5sQQEzTzcFybcLKVMn/nfLECT27d6JWuiWgsZ5ZYatesh/Elst2KTAknj5TMvdmXw22ppZ4yhY+kk+x0jYpaYQq9JZ3eHzQLBqppygCyV2/AfmxpOiADaG7Dfmwp9uoNfXL8Xbt28fDDDzN27FhmzJjBE088waOPPsqDDz7I3r17efjhh/nkJz/JE088wYc//GHeeecdPvvZz1JeXs4vfvGLAQ/IQC2zNGRYepSI20zADGA7FpWhcUwvmc2e8DZs1wJgQv40xgamqWHLQUDE4pBjOWWNohlvInfKp7hSEnfySDq5FZQFh+FSS8eT0sI0W3UEPXm40sWScc4cfQHhVCsbm94m6aSj/S3Na/nkSf/FD9++s2M5pvnlZzHSO17VURyC7H++DJadudGysf/5cp/0lo0fP568vDxWrFhBIpHgL3/5CwCxWIytW7eyePFivv3tb/PKK69w3nnnce65557wd54oFZQNAS1iP2/XvULSSRCx22hLtTC7dCEF3hK+Mu+7tCabCBkFVPon43cHPvJXekhKRDyJ9OZOUJZykySEjZASzXJwzexPEInY4NNCJJw2pHS7LPuSDXnDcKmlY2lkFz98+3amFc+hxFeOxOXpHX9gRGA0l4y/ipvn3UtLohGfHmRMYAp+Wch3Fv6a2vgegmYho70T8Lq5VR9P6SPtPWTd3d5DvoO1Hl3X5fvf/z4zZswA0rVRCwoKME2TuXPn8tJLL/HQQw/x73//m//+7//uk+/urdy4iim9JnWbp3Y9QsRuw28GeW73X3j9wAv8fO3/8L1VN/N69YvMzTuPyd5TVUA2SIh4Ir3WpJ47f55RO4zPDGL7DIyYle3mABCxwG8YGJqPhBPOdnM6+HRIHlxqabiz9Ti/2HAPjYk6KoKV+I0Af932MJabYl9kJz9few9PbH2IWflnMs13GgG3BCF1yrUJzAqewwTPHLwyP9s/htJfio7yuz3a9l5atGgRf/jDHwCoq6vj/e9/PwcOHOCmm25i7dq1XHXVVXzpS19iw4b0sKmu6ziO06dt6K7cueorvZKQYVqTjexu20pbqvMwzur6V0kSzULLlN5K55PlVj2wuB3GrwewvQZmPEeCshT4dfDp+cSc3CmLoQlxsKp/tluSfTG3lZ2tmyj1V7A/sqtjmPJwm5rfIe7mTlCtDBzj4rPTD6CHM4309j70hS98gUQiwaWXXsonPvEJbrnlFsaMGcPnPvc5fvazn3H55Zfz/e9/n29+85sAnHPOOXz2s59l7969fdqO7lDDl4OcVwTwGUEKfcWEzM5PF1MKZ+ER/iy0TOktEYnnXI2yiB3GpwdwvAZmrvSU2RKfAZqWR8xuosQ7PttN6hAyoTklKfUP79paPi1ERaCS5kQ9IwKju6zyXxWagE8EVa7rMNSeN9Yfsy8XLlzIwoULAQiFQtx3332d3jNt2rSOPLPD3XHHHdxxxx0n3IbeUEHZIJXSojTbB7CcFBeO/yDbW9YTtto4pfx03qpbAUCBp4gPTrwe4aillAaTXCwcG7Pb8Gr+dE9ZLDcSpiJWuqcMPY9oDvWUQbpWmZqBCR4nxGdn3sH3Vv8XSTtOghiLKy9m2b5/AuA3gnxmxm0YbuCox3BEkmb3AHE3TNyOUuodTZEYhZDZz2tUTpwx76Q+CcKGChWUDUJxrZl/Vf8ZTWiEPPk8uf1Rzh97GRPypzG7dCHvG/shpIQK71gCbufp5Upuy8WgLGqH8WoBHK+eMz1l4ZTEbwhcESJm59YMzJAJLSnV9QNQZczg7oUPU584QIG/kLZUE3PLTydmRWhNNqOJowdXlhbj33V/BeHyly2/xsVFQ+Nzs+5kTv654KoMHGVoUUHZILQjso48bwEJO8Zftv6ahBPniW0PAelFxu9e9HC6+rWaQj4oaZEo0pdb615G7FZ8uj89fBnNjZ6ysCUp9QlskUfUzq2q/kFD1SprJ6UkjxHkeUewN7mOe1Z9OWP/q8HnuH3u/2E6nXvLapI7EULyxNaHcA9e0FxcfrH+Hu5ZdBIFqLV7laFFPWYMMkII9kV2kXKSaEIn4cQz9ru4tFm5NZSj9IyIxJC+3Er0j1ht+PQAdi71lFngN8Cn515QFjIFjWpR8k6aUw2dtu2P7iTpdj0ZqSXVgCtdbJlZy8p2LcJdTGxSlMFOBWWDjJSSCQXT8eo+Ek6cAk9mmQtT81DirchS65S+kA7Kcmv4MuYcDMp8BmY0N4KyiA1+Q+DT8nMwKINmtf5lJ2W+zgs8n1Q0l4DougRCmW8UrnTw6Zm9aAEjRKGnvF/aqCjZpIKyQcIWcWrcLeyy3yHoCSAlFHlL+eCUz1DkTa9HGDLzuWnO/5DPiCy3VjkRubjEUvTw2Zc5UhIjerCnzKvnEcuxqv4hU9CSG6O8A8ISMfZa61gTfZEd9mo2J19jTexFWrR9Ge8bYY7lMzO+ikdP9wSPDo7nE9NvRnO7Pt/LjbGMDIzl6mmfJ99TCKQnMH15zj3kUdavP5OiZIPKKRsEpLB5peEpTMNAFwZ/2PQzRoXGcOao91Hmq+CW+d/Dth3yjRKCshQp1RP6YKZF4zlVp0xKSdyOHBy+1DDiFkgJInvlHixXYrvg1QDyiedYon96/cvh8XfoahbPHfgjf9/xcMe2C8d9iM1N7xKx2rh57ncplmMB0FwPCwsvZvrCeSScGIVGOaYbPOqxNelhTuhcIqKRKfNPxnIsCoxSArJIXeeUIUn1lA0CzfIATcka3q17gw2Nb5FwYuxo3cRvN/6Ie1fdzKbGdxmpTyHglqgL1WDnOJBKgSd3Ev2TThxdGBiagTQ0pC7Qk9mpdt0uYkHASOdYerUgSTeKc0TeUTalZ18yLP4em5xqntzx24xtz+1+glNHnkN9/AAbm9/K2CddyJMjKNPGHzMg63i/FATdUkrkOCq09FJxw+CfVRlg+/bt47zzzgPgtttu44knnsjYX1tby/XXX9/v7VBB2SAQt6PkeQrRNI36eE2n/Qdie9C04V2kcqgQ0YOFY3Po9xlx2vAbh26eti/7BWQjB4cuAYTQ8OmhnOot8xy8siayG7sOiLgd6Vg8vJ0rHVyZ/uEbEjXoObRkmJJblu37J599/hKueHIen33+ko4adrlmxIgR/PKXv+z371HDlzlOCEGpdyR723YQMIKMy5/MjtaNGe85pfRMHEc9Og4FIhJD+nNrBYao3YZfPzwoMzFjKRLF2Wtn+LCgDMCnFxB1mgiZuZFnJIQg3wNNSRg9xK+ypd5RFHiLaU0emvVd6q8gkkovKj29+BQc5/j1edKj4aKjd9EwNGxb1fUZypbt+yc/W/PfHctv1ccP8LM16QXBF1de3OvjLlmyhB/+8IdMnDiRm2++mVAoxLe+9S3efvttfvazn1FeXs7WrVtpaGhg6tSp/OAHP+jyOPF4nE996lNceumlLF68mGuvvZYXX3yR2267jVAoxPr166mtreXGG2/kyiuvJBwOc+utt7Jnzx6qqqqoqanhJz/5CZWVld1ue9YeXyKRCJdeein79qUTQVesWMGSJUu44IILuP/++7PVrJyhI8lvaaFw714q9ka4csSHmV48l4CRx/vGXolH9xEy8/nMjK8y1j8j281V+oiIRnMvyf9gOYx2uVAWI2zJdDX/g3x67s3AzDOheRjklQVkEbfO/QFTCmcBMLVwFh+YeA1v1izj0zNvYdxxrk9CQF64jbx31pP3xtsUtDVTtG8foReWU7huIwVRtS7mUPXoxp92Wg816SR4dONPT+i4ixcv5rXXXgNgy5YtvPVWegj9lVde4eSTT8Y0Tf70pz/x/PPPEw6HWbZsWadjWJbFF77wBd73vvfxsY99rNP+mpoafv/73/Ozn/2M733vewD89Kc/Zfz48fzjH//gxhtvZMuWLT1ue1ae4dasWcOdd97Jrl27AEgkEtx+++088sgjjBw5khtuuIFly5axePHibDQvJwTr6tFr6pGxOO7abVQ1tVA2awzxkyYhRy/iojFXoUsvflmI6w79C/9woeXgupdRpw2vfqhXLBcKyIZTEt9hQZlXyyOWY0FZyBTDIiiTEsrEeL488z4SMkpADxJxW5i1YCEhdwTucXrJQuE2+OWfkckUFOWjOQ72S2907BdlReR//P20+Y6+FJMyODV0kY5zrO3dtXjxYh566CEWLVrEpEmT2LFjB42Njbz88ss88MADRKNRHn30UXbs2MGuXbuIxWKdjvGjH/0ITdP4yU9+0uV3nHHGGQghmDJlCi0tLQAsX768Y43NWbNmMWXKlB63PSs9ZY899hh33XUX5eXpOjPvvvsuY8eOpaqqCsMwWLJkCUuXLs1G03KCiYQVbyNbIwiPB9nQDK7Et2Y3RX94jeIfP0tpvACvU6ACsiFGhCOQY0FZJNWKTzt0Q8yFoKwtJQkYh/LuvHqISM4FZdA8jKr6646PoFuCsHzkORUE7DJc99gBmRACsXU3JNPnkz57Ks6KdzLeI+ub0Wvr+6vZShaV+ruuqXm07d01d+5cNm3axIoVKzj11FNZsGABS5cuxbZtNm7cyFe+8hV8Ph9XXHEFCxYs6HJCziWXXMLixYt54IEHuvwOrzd9nRaHzULXdf2EJ/dkJSi7++67mT9/fsfruro6ysoO5YKUl5dTW1ubjablBCFlehaeZXX9C7ZsxHEudsrgJCK5N3wZsVvxG4cNX/oMPNnuKbMgYB56nS4g27lafDaFTEHTMOgpOxFCAInDIldDB6vzLFppDYMZE8PQx6bfiFfPLP/j1X18bPqNJ3RcwzA4+eSTeeSRRzj11FNZtGgRDz74YMew5kUXXcSVV15Jfn4+K1euxHE6n1/Tp0/nlltu4amnnmLjxo1dfEtnp512Gk899RQAmzdvZuvWrRlBW3fkxJQY13UzGi6l7PEPMlTomgCPgZgzHVF+cDFxb2Z5BLFoNklfbiWDK31DC+fgEkt2Kz49t2Zfhi0I6IeuEbmaU9akqvofk+tKmDoeDv4q3U270GZPzXyT1wMVJQPfOKXfLa68mM/PvpMy/0gEgjL/SD4/+84TSvLvOPbixcTjcSZOnMipp55KY2Mj55xzDh/60If4xz/+wZIlS/jSl77EKaec0pHbfqTCwkJuvvlm7rzzzuP2+gLceOON7NmzhyVLlvDAAw9QWlqKr4fX85yYF1RRUUF9/aHu6fr6+o6hzeFCCAi1taI3tCAcG4lAmgbC58X4wHm4a7cgG1sQp5xEasYUbHWtH5LSsy9zKyiL2m2dg7Is95Sll1g69NqvF+RkULa5JdutyG2m6yAsG/2K83FWr4eUhT53GqKkAPfdLYjSIrSz59GaXwBqcGBIWlx5cZ8EYUe67LLLuOyyywDIz89nw4YNHfvae7OO9OKLLwJw7733dmy7/PLLufzyy4+6H9K9YgAvvPAC1113HfPmzaO6upqPf/zjFBVlLoV4PDkRlM2ePZudO3eye/duKisrefrpp7nyyiuz3awBFYxG0d/agBuOIrwe3FXrwetBmzoOKkqRS84jpWmkdEPlkQ1hIhpFBnItKAsfURIju0stuVISt48siZGDPWUeQXNSRRLHEti7H/f3/8DVNLTJYyAviGxswdm4A33BTFzXJVZQgMrWUAaDCRMmcNddd+G6Lpqm8e1vfxtN69mAZE4EZV6vl3vvvZcvfvGLJJNJFi9ezIUXXpjtZg0ora4BQgE0vw+nfeZRMoX77hZ4dwtiRCmJytGgArKhy3UR8WROzb5MuUlAYopDQ+iuoSFc0FIOrkc/+of7ScQCnwGayBy+TDituNJBEwPfpq7kD7NE/57yIHGXvZl+4bq4m3cBIPKC0BrB+cfLAJhjK0n2sLdBUbJh1qxZnVYC6KmsBmXtXYGQTpB78skns9iaLBMaQhwsnKhrdHo0zKEK70r/ENE40mtCD5+s+lPYasGvhzKXuRQCy58ewkx6Bj63sTUFwSPiLk3oeLQgcaeFoJEb+Uc+HWwJCVviM9TfbycCMLq4BWmZ1z+hrn3KMJI7V/9hyhAQcG208iLc1jBuYwv6gpmZbyouwC4tzk4DlQEjIlFkILcmcETsVgJG5/UJbb+ZtRmYR868bOc3Cojk0AzM9qr+jaq3rIMQ4HMdfI4FQqCfsyDzDaaBKAhBLF1QVJw0kVR+XhZaqijZkRPDl8NVKBHD2LUPbAfhuunZlgEfUkr0Jecgd1fDqHKcqROIeXIrz0jpe+lyGDkWlFmZMy/bZTPZv+2Iav7t/HrhwbIYUzvvzJKCg1X9RwdVb48hXYK79yNfeA399LmgCdymNozL34O75wDC60GbNh63NYKYPRUmVmGPqyKZI8PRijIQVFCWJR7pov17JYSCiPwg9sH8CfKCiJJCxHsWEp0zA8eRJ1yMThkctHBu1ig7fImldrY3e7XK2lISfxfDgT49n4iVW0VG8zyCBlUWA4BAQwPu759GP3MuzvY9CNvB3bAdFxAjSpB5AZzT5hKpqECbPgXXdVGXPmW4UcOXWeKJxdAMA7emHnfvYUtKhKPIXfuRL76BkCogG05EWySnkvwBwlZzxszLdo7PwBPOzrhcS1IS7OJx0qvl5dTwJbTXKst2K7JP0wRs2pl+4fehlZfgbtzesV/WNiK37UUcqCddO1sFZEruqa2t5frrr+/X71BBWZZIXU+v9abriGAXQ1ZF+ahr0vCitUVyLqcsHZSFOm23/FnsKbPoMijz64WErbqBb9Ax5JmqpwzSBcHJT59HQtchlYKu6vF5u0gWVJQcMWLECH75y1/263eo4csBpgvwJxNoGoixo9I1TGw7vd5h+3IjpgGLZmM76mI+nIi2CLI8N2YOtgtbrYwLdpVTZmJGspTon4KA2Xn40m8UUJPY0MUnsiffAzvaht/fsS7Al0xi2BYu4Ph8aONH4wT9uNv2QFkR+pmn4Dy34tCHxo7CUhOahp3ndib4+Ttx6qIu5UGNG+b4uWD8iedQr1y5ku9///u4rsvkyZOprKzki1/8IgDnnXcev/3tb3njjTd45ZVXaG1tZe/evZxxxhl885vfZOXKlfz85z/H5/Oxfft2pk6dyn333UddXR3XXnstL774IrfddhuhUIj169dTW1vLjTfeyJVXXkk4HObWW29lz549VFVVUVNTw09+8hMqKyu71W4VlA0gn5XCs3kbwnHRTBN3VzVMGoMoKcS46iJkSxg0gTNqBOH8AlT//fCihXNv9mXYaiVgdO4ps7PUUyalpM2C0FF6ynJt/csCj6BxmPWUeR0L7+Yd6KkU9surMc5biHh7E05dI/p7FoHfm86l9RgYH78U2RJGFoRIVYwgbniO/wXKkPHczgTffT1K8uDSk7VRl+++HgXok8Bs165dvPTSSzz00ENHfc/bb7/N008/ja7rXHjhhVx99dUd25955hnKy8v58Ic/zKuvvsqUKVMyPltTU8Pvf/97tmzZwrXXXsuVV17JT3/6U8aPH8/PfvYz1q5dy0c+8pEetVkFZQPIs3sfuuMibQf7+dfSC++u3ZJehLcoH/uTVxLXD3bfq4BseJEy50pi2NIm5caOOvtSTzoIx0XqA5cFEXNAB0y9i54yvZCI3ZBTa+cWeKBxmC1K7q2uRU9Z2M+8ijZjEs67W5D70nmzzjOvpLvRPnd1+sEToCqLjVWy6ufvxDsCsnZJJ729L4Ky8ePHk5d37JIqc+fOJRRKP3hWVVXR2toKwOTJk6moqABg4sSJHdsPd8YZZyCEYMqUKbS0tACwfPly7rvvPiBdTPbIQO54VE7ZADEMDdZuTeeRuTIdkLWzHahvRg9Hs9dAJbviyXTRTDN3npMiVit+PZRROb+DEOmyGAM8hNmWguBROlNMzYcmDBJu24C26VhCJkQtsIbJShyaJmD7HuTB65sYWZYu7XM4RyKaOt/glOGnLtr1+llH295T7YuBdxRmP8iyDi0T5/Uemlx1+PuOtv1w7e85/CFQ1/UTmqCngrIB4roSqkYgTBNhdFF3x2Pm3Mw7ZeBobWHcUOfSE9kUtpoJGEd/yrQCJp4BDspaUl0PXbYL6sU5VRZDay8gO0xmYEopYUTpoWtcNA4FnYe/ZVeTm5RhpzzYdQhytO29VVRUxLZt2wB49913qa/vv2vEaaed1rHg+ebNm9m6dWuPeu5VUNbPdCSheIz85iaMyeOQiQROSxv6opMz3qdd9h7iOVY4VBk4Ipx7My/brOYu88na2X4TT2Rgy2K0piSBYwRlAaOIsF07cA3qhkIPQ3oGpheXgnArRU2NFNTUIsaMxI3G0KaNx3l7I8bZ8zOWDhPzTiJVXJi9Bis544Y5frxH9FF49fT2vnTxxRfT0tLCxRdfzCOPPMJJJ53Up8c/3I033siePXtYsmQJDzzwAKWlpR09dt0h5BAphNXYGEn3RuUQQ7oEt+1ES1npBP5X3kKbOQkxuhx8HnAlMmnhFBcSy8/DUTFyTisrO3qv0Ymef+aqtei792PNn9XrY/S11+ufozFZy8lFi7rcX7qhjkRRgAPzRw9Ym5budUjYMLu067+Vd5r+QlXwFGYXXT5gbTqeJ3Y4nDVS49zRJ1aZvj/Pv97yOxaedzehaeJQnqzXg37dZUhAiydwEYiQH7ctivT7SBUVktRzZ5he6Z5jnX8nor9mX2bL3//+dyorK5k3bx7V1dV8/OMf51//+le60kI3qL+MfuQPhxF1jbhNrZC0kI0tOMtWdewXn7yctnFjs9hCJVdoLW3IQG5diFpTjQS6qFHWzvKbeNoGdlyuOSkp8x19KMBvFNJmHRjAFh1fngfq4rn1wNhXfPUNSNfF/tfKdG4sQDKF89DfcP/jo0SLDyvxUlCUnUYqOe2C8b5BHYQdacKECdx11124roumaXz729/udkAGKijrX20R8HgQRQU4b6zttFs0t8HB2R3K8CZa2nBzrEZZq9XIuOC0o+63AyaBxtgAtgiakzA+7+hBWcAoojG5cwBbdHyFHkHtEA3KZEs4PXHJPnIKXQotGgNfbg3JK0p/mzVrFk888USvP6/Gy/pTfghSKWRzK9qYzsGXLCrIQqOUXKS1hpHB3Er0b7WaCZjHSfQf4KWWWlPpGY1HE9SLCVs1R39DFhR5oTae7Vb0D1GYB5pIl/U5nNeDq5L5FaXHcqqn7JprrqGpqQnDSDfr29/+NrNnz85yq3pGExCIRTDiiXRya0kBWmE+aAK3oQWa09P1xZmnkCxT1auVNK01t2ZfulISsVoI6vlHfU/H7EspYQDqglmuJOWC/5iJ/iW0WbmV6F/kFdTH+2aKf65JlJXiOVCPcf7p2P86mFPmMREfuZC4P0D7WnGaBnnhMFpDEwiBDAWwgkGiXhW4KcrhciYok1J2VN9tD8oGo1BDA/r+WmQskV5OJJlCO3UWorAA86qLcFM2jt9HLBTCFqqjUgGSKXAc8OZONfOo3YpH82FoR/9blLqG49ExoymsUP+Xc2lOpnvJuqybdpBXC+HIFEknireLorfZUOBJt91xJbqWG0Vt+0pcN3HnzsQXDmOMqUBaDnZBHlF/gMPnHRQ0NWE//HfcWDoHUYwsxZwxibzpkwiH+ieBXFEGo5yJCnbs2AHApz71Kd7//vfzu9/9Lsst6jmfa8Mrq3HrmhAeE7m3BlnXhPP0MuzfPon94kqi5WWE8/JVQKZ00JpbkXmhAelt6q6WVAN5ZuFx35cKefC2DcwQZlMS8o+zXrUQgqBRmlPJ/oYmyPNA/RCtVZZEozWvgObiUlpGjCDiywzIvDq4r7wFsUP/APJAA0gQb65lABeEUJSclzN/Dm1tbZx22mn89Kc/5aGHHuKPf/wjy5cvz3azekR3bITjIFvakLHOSSSyuh7dtrv4pDKcaS1tOTV0CdBiNRA8RuHYdlbAxNs6MNFGU0Ies3Bsu6BRQqu1v/8b1APFXkFNbGgm+x+PYVnImi7WJG2LQGMzeo6VMlKUbMqZccK5c+cyd+7cjtcf/OAHWbZsGWeccUYWW9UzKY8XPS+Elh9EdFHeQMyZhmWaHXkWigIHe8pyLChrTtYTNI6eT9bOCnjwDVBQ1pCU5HmO35sY1ItpSVUf930DqdjLsA3Kkh4voZmTcP79ZsZ2UVoEZcXYmgYqMFMUIIeCslWrVmFZFqeddhqQzjEbLLllwXgMMxxGOA5i3nTcPTW4loV+7qk4y9+GlIWYMQln/iwcde1RjiAaW5Ch3Mh/ateUrKPUd/xyLVbQxN80MFMLG+KSSQXdCMrMUppTewagRd1X5BXsHyJBmSFd/E1NaPXNiNJChOtCWwSZtHBHlhErKspIz7BtF3fONLSmVty1W8Aw0BedjCvAnj4x54p+K0o25UzUEw6HeeCBB/jjH/+IZVn89a9/5Vvf+la2m3VcoVgU4611uLEkmqFjv/Eu2rQJiOkToKwY/eQpOEIj7vdj5c5osZJDtKYW7KkTst2MDC2pesaHjl6jrF0q6KFwZ/MAtCidLJ/fjZ6ykFFObXzjALSo+0p8sKll8AcfmiYIbN6JfPw5tIvOQm7ZjbNlF/LAobUEg1dfTNv4cRmLMrf5Q/guPRff4gUIwPF4iHm82OROHqWi5IKcCcrOPfdc1qxZw2WXXYbrunz0ox/NGM7MVdru/YhgAM3vw3n+NZDgbtwBG9MTF/j81YQL1ewi5ei0plZk/tEr5w80V0parEZC3Uj0t0IevK3Jfi+LYbmSsAV53bhihYwyWnIsp6zUJ6iODv6yGL5EAvn0Mgj6kZEoIuDPCMgA3Kf+je/zVxM3MmcTJ6RGInT8IXFFGc5yJigDuOmmm7jpppuy3YxuE0JAOIpsnz7U1TKiKWtgG6UMLokkwraR/txZZiRiN+MRXjza8Ut0uKaO69HwRFKk8vqvLEZTIr1ckdaNkhJ+vQDLTZB0IniPsUzUQCr2pmePWq7EHMRlMYTjQDKJKC5ERhPg6+J3HoujOW6O3V0UZXBQ42m9JMTBG8TEqnRw1hZBlB9RDDYviFOkngyVo9MamnEL8nOqHEZjopZ8T/cLGyfzvPia+zevrD4hKepmGTchBPlmBU2p3f3app4wNEGRB2oGdlWqPpfy+xGzpiAbW9BGlSF0nSNrWoh5M0n6jv+QIQToutZx6huGhmme2KLtijLYqWeZXgikEph1jQjLAtOEwjxEfgC9sgJ3/TbcnfsR40Yhzz+DmNn/RTWVwUtvaMLNz60k/8ZUTbdqlLWzQh78TXHaxnT/Mz1VG5Pk96C2bp45gqbkLkb6Z/Rbm3qqzC/YE5FUhXInAO8pC4HnvaejBwM4W3ahzZqCcdl7cF5fg2xuQzvlJOyFs7GPkz4XSCYwdu6FHXsRk8agjyjBfXsTMpVCmzWFaMUIUqqWozIMqaCsh7zSwVj2BiLoR+SHsJ96Bjwm2slTEJUViIvPwtUNUrqhEvuV49LqGpEFuZVzWJeopsDsWU+ZvzHajy2CmrhkhL/7wUyeUU5Dckc/tqjnSn2wJ+xyRsXgvi5EvX70807Dm0phmwaa42JMHIsrJTHTc9wZ5l7XwfjHS8jNuwCQazbDlLFgGLgbtuOu3kDw2veTGj26/38YRckxg/vqkAWe1ja0UCBdqX/dtvTGlIW7aj3O317AfXcLcdOrAjKlW7S6xvTwZQ6pT1STb5Z0+/3JfC/+xv4dl6uNpwuwdle+OZL65LZ+bFHPjQgIdoQH/wxMAMeFmOEhJTUSmkFEN4kZxw/IIH0NbQ/I2rlbdqNVHSrB4ryyGr82NP6tFKUnVOTQQ9KV6dV1BeA6nd9gd7FNUboiJXpdI24O5R260qE5WUuhpwdBWZ4Xf3Oi3wqAJp30zMuCHmQCFHhG0ZjcmVGWIdsq/ILdQyQoOxFSdj0LNeN35bgIVS5DGYZUUNZNQoAHF1GYh9vYgijMR585ufObpk5QxRCVbhFtEaSmQQ7NvGxM1hI08zG14ywyeRhp6lgBA38/JfvXxNKzF4+1EPmRfFo+QmiE7dp+aVNvlPigzYJwavhdHzRNpFM/BMjCfKgambFfjBmJrGvqeK2fPofY4K8goig9pnLKusHrWHira9CTqXTgNbEKNC296sAHzsNdvw18XuTpc4kUF6lllJRu0Q/U4ZYUZrsZGWoTeynylPX4c4lCP4G6CPGSvl8uan9UUtzD+TJCCIo8Y6hLbCbfPP7KBANBE4LRAdjeJplTOnx6gQLJBMaGrbC7Gn3OtPTY56kzkWMqcPfWIKaNR5tQhfvym2gTq9AWnkz8iKBNUYYLFZQdhxDg3bEnHZClLOyX3kjXHisIoVVVIGdMJvmxJdgO2I6rAjKl2/T9tbjFhdluRob9sV29C8oKfIRqIzROL+/zNu2JuJT2IMm/XaFZSU18I5PyFvd5m3prVFCwtdVlTunwGKQwBBgrViNffxdjyTnQ1HroGlqYh1ZejHQlbSXFGB+6CCEhYakuMmX4Gh5XhhPgcR1Yswn3QH0656G9GGxrBHfdNtzHn0WPxNIBmaL0gLbvAG5pUbabkaE6toMSb897lhJFfkIHwv3QItgbgXJfz4OyYu9YquNr+6FFvVcZFGxoHj5Pbt54HPnGWijMQ9Y0ZF5DW8K4W3YjX1qJL5EgmXJVQKYMeyooOw4pBNIwEB4zXST2SKaJVPV0lJ6y7XSSf0nuBGUJJ0ab1Uxhr4YvfXjCSfSE3adtak1KLBcKelCjrF2JZxwNye3YbqpP23QixuYJtrRKnByagNCvNAGGkZ4Apa6hinJc6i/hKIQAn2Pjs1IYi05GFOUjUylEcUHG+7QLzyThVQVilZ7R99XgFhWAmTsZBHtj2yjzjkTvzQ1SE8RLAuRVt/Vpm7aHJRUBur6ZH4eheSkwR3Egvq5P23QiQqYgz4RdbUMzKPNpLgWJKCEnhRDgekz0C06HSAxRmKeuoYpyHLlzR8ghOpLQgVq0tgggcffVoY0sQ4zKQ6+sQDa2QDiGO2kM8fJSNdtS6TF9x17cEaXZbkaGneGNlPkqe/35WGmAgj0ttEzofuHZ49na6jIy0Puk+DLfZHZH36AqeEqftelETcgXvNXgMrFgaD0TF0bbkC+8jrthB9rESgpPm4P9j5eR5cUYV7wXtzWMNm40etVIZEOzuoYqShdUUNaFQEsLYtd+3OZWZGsUuXMfLkB+CLwe5DXvJ+YPqAuJ0mvGtl1Y82ZmuxkdpJTsCG/gzBEX9/oY0fIQo9/YC1L2yVqerpRsa4UPjO39sSp801nT/FfOLP/cCbenr0wpEKyolXxoYrZb0nf8uMhnXsXdml5vVJ88FvvRp8GVuE2tuJt3Ik6bQ3hkBQ4CKivTPWnqGqooGYbWo1ofEQ0tYOiIsmLkzn2HdrRFoL4JrbZRXUyUXtMamhHxZE7lk9XE96BrOvlG73u5UnkepKYRrIv0SZt2tEGeCUFP74OyIs8YEm4bTcncWZx8XJ6gOippSAyda4gnEukIyACk7WQWE5YgX3sHbyKBlOmHAHUNVZTOVFDWFZ8HoeuQtCDQubCn9Kn8B6X3jLWbcMaO6pPepL6yrmUlVYFJJ9YkIWgbnU/JpoY+adNbjQ7j80/s30gIjUr/HDa2PdsnbeoLhiY4qUiwrHrorP4hTROC/o7XQtc7vyk/hGuowRlFOZacCsqeeuopLr74Yi644AIeffTRrLXDGlGK0xJGOg7G2fMz9okZk0jlWMFPZRCxbcx3N2FPGJPtlnRIOgk2t73NuND0Ez5WW1UBxVsb0FInNgszaku2tMCkEwzKAMaGFrGh9ZmcmoV5SqnGs3tdnCHSWxTxBzEuPrvjtWxqRYw7bEFxAeID55Ewur9ShKIMRznz2FJbW8v999/PE088gcfj4aqrrmLhwoVMmjRpwNsSN71w5vz0wrmOjfGJDyAjMWRekGRZKUldXViU3jHf2YhbXIgsyMt2Uzq83fQyI/xjCBqhEz6WHTCJlocoX1tDzbzeTxp49YDL+DzwGScelOWbIyg0K1nf+g9mF11+wsfrC5UhQciEV2pczhnVRa/SIOO6LtFJ4whe/0FkQzMi4EOcMRea2iCeQJYWES0oYLhUAlGU3sqZnrIVK1awaNEiCgsLCQQCvO9972Pp0qVZa0/c9NBaWkrLiAqaR42iZcokWkeOVE96Sq+JaAzPq6uwZk3NdlM6tFktrGp8iRkFC/rsmI1TS6l45wCecLJXn29ISFbVS2aX9N3laXrBhaxsfJiY3dxnxzxR7xmt89stDlFraEQqKTSaS8tomTaF5jFjaPUFaRs1kraJ4wkXFKYT/BVFOaacCcrq6uooKztUtLK8vJza2txZTFhRToht4/vb8zgTqpBFBcd//wCw3CRP7v0VU/LnkGf2XZuskIemiSVMXLoFzepZ3lTClvxhm8PcUkHQ7LubeKFnNGMDC3im+ls5M4w5Nk8wpUBw/7s29hAZxlQU5cTkTFDmum5GgUgpZa8KRipKrtEam/E/+iQIgTVrWrabA0B9opo/7HyAoJHPtPy+r+HVPKkYK2Ay9a/r8TdEu/WZ/VHJLzY6lPlgWmHf/+2fVHARmtB5Yu+XaUru6fPj98aFVRpRG7612qYmpgIzRRnucianrKKiglWrVnW8rq+vp7y8+4sba5oK4JTsOdr55/v1n9EP1OOMGYU9dTx6U3aHz1zp8o99vyNsNTPWP4ZRgfGkavcd/4O9sKcCKvbazHhsLZF8nZcuL0F28e9ku5I/bnOI2TAmJCj2C7b1zzKaFJincyD+Mr/b9QkKzamcU/6z/vmiHjijQuPVGpfPv2Ixp0TwrVN7vqaUuv4pytCQM0HZ6aefzo9//GOamprw+/0899xzfOc73+n254uKgv3YOkU5tqOdf0nTgJFlGEiMTdsHuFWdSSk5NVyOYMTBkhx1/fp9SeBAvkAAq5vX4HbRN+9KiWE6FJoQdmBja782CQCdCUQswav9++N3m6ZrjM2XmB6dkpKeT7hQ1z9FGRqElLkzH+app57i5z//OZZl8cEPfpDrr78+201SFEVRFEUZEDkVlCmKoiiKogxXOZPoryiKoiiKMpypoExRFEVRFCUHqKBMURRFURQlB6igTFEURVEUJQeooExRFEVRFCUHqKBMURRFURQlB6igTFEURVEUJQfkTEX/E9XYGMFVi/oq/aisLO+o+9T5p/Q3df4p2XSs80/pO6qnTFEURVEUJQeooExRFEVRFCUHqKBMGRKkptEqTSIYaJrIdnOUYcYSOs3SJKkNmYwQRVGyQF1BlEGvDYM/bLR5bneCgCG4YbaPRaUSQ7rZbpoyDNQ4Hn6wOs7WZocxeRo3L/Az1mOhVhVWFKWnVE+ZMqgJTbB0t8PSXSlcCRFL8r+r4uyOq1Nb6X9xYXDXihhbmx0A9oRd7nglRqtUz7uKovScunMpg1ocned2pzpt39LsItQoptLP6pNQF8vskY1Yktp4lhqkKMqgpoIyZVDz4DI2X++0vSwg1PCR0u9ChsDo4iqaZ6ongmPR6hoJ/vhhvM8sy3ZTFCWnqKBMGdQ01+WTM7x4D4vLphTpTMlXN0Wl/xXrNp+b7c/Yds1JXspMJ0stGhy8/1qOPXkcxo496Huqs90cRckZKvFBGfSqPDY/PS/I3ojEq8OYEASlne1mKcOBlJwzQjLl3CC1cZdSn8Zon4OuJpkclQhH0GvqSS2aA5qG+dZ6nDGjst0sRckJKihTBj0pJSWaRUl++4asNkcZZnTpUuVxqfIAOOr8Ow5j2x6cUeWg69hjRuH/57/BcUDvnIagKMONGr5UFEVRBoy+ax9OeWn6hd+HGwygHajPbqMUJUeooExRFEUZMPr+Gtyy4o7Xblkxxr4DWWyRouQOFZQpiqIoA0LE4ohkCpkX7NjmlhSi7avJYqsUJXeooExRFEUZEFptA25xIYcXEXSLC9Fr1PClooAKyhRFUZQBotU14hbmZWyTeUFEIgmJZJZapSi5QwVliqIoyoDQaxtw8zODMoTALcxHr2/KTqMUJYeooEzJKSlNJ4KJ1NSpqQwwTRARJklhqCW6+onW0IwsyOu03c0PoTWooExRVJ0yJScIAdsSJj95K87esMviSpOPT/dQJKxsN00ZBtoweHyLzTM7o5T6Nb4418/0kI1Qa3X1HSnRGltwuwjKZF4IraE5C41SlNyiuiOUnFBnm3zt5Si72lwcCS/utfj52iSO6jFT+psm+Ms2m6e2p7BdqIm63PlqlOqUembtSyIcRZoGeMxO+2R+EK2xZeAbpSg5Rt3xlJywPyqxjliZZuUBm1ZHVflW+ldU6izdlcrYJoE9EbVUUl/SGluQBaEu97l5IbTm1gFukaLkHhWUKTkh2PnhmXyPwCPU8JHSv0wB5f7Ol8KQqRLL+pLW1IKb13VQJoMBRDgCrgqEleFNBWVKTqgKSBaNyhwu+sIpfvKEk6UWKcOFx7X5wlw/2mEx2LRinfFdxw9KL2mNzchQsOudho70ehHh6MA2SlFyTL8mTUQiEa666ioefPBBKisrO7b/7ne/49lnn+WRRx4BYOPGjdxxxx1Eo1Hmz5/Pt771LQxD5XMMJz7p8MVZHt4/wUNrSjI6pFHlc4i6OpoGfungCkFManiFxHBVsKb0nalBm5+cF2RP2CVoCsaHwKdJotLAL1x06ZLAwAGCwkaqDp0e0xpbcMaMOup+GQqgtYZxupgIoCjDRb/1lK1Zs4arr76aXbt2ZWzftm0bv/jFLzK23XLLLXzjG9/g2WefRUrJY4891l/NUnJYAJvpQZtFRQ5lpsvz1YL/eCnOfy6LszVhcv9ah089H+PbqyyqbVOVLVD6jpSMNCwWFjnMDNmEHY27V1t86vkYv9vmsrrN5KZXEnz2hTiP7xZEUbmOPaU1teDmH737UQYDiNbwALZIUXJPvwVljz32GHfddRfl5eUd21KpFN/4xjf4z//8z45t+/fvJ5FIMGfOHACuuOIKli5d2l/NUgaJd1vg/96J05qUzCk3efCdOK/ss0g5sLbe5rZXYrS4XSSiKcoJimDwtVeivF1nk3KgImTwnRVRaqIuUUvyuw1Jlh2QaJp6Kug2x0FEY8hQ4KhvkQEfmgrKlGGu34Kyu+++m/nz52ds+9///V+uvPJKqqqqOrbV1dVRVlbW8bqsrIza2tr+apYyCGiGxj92HKpPNipPZ3tL5nBlOCU5EFOTAJS+VxOHlmT63MrzCOpinccqn9yeIq56y7pNa2lDBgNwjBI3MuBXPWXKsDdgif7Lly/nwIEDXHnllRnbXddFHDYOJaXMeK0MQ246p6ydlGB2caYGDHWeKH3Pf9h5lbAlwS5mYVYENUzUQ0F3ieMMXQK4AT9aW2SAWqQouWnAgrKnn36arVu38oEPfIA777yTdevWcdNNN1FRUUF9fX3H+xoaGjKGPJXhx3UlSyaa+A/O9Vi2J8UVU3wZ7zl/rMlIn8q2VvreCI/DByZ6ALDc9EPByOChS6Uu4LqTvGhqskm3aY0tR595eZAM+NJlMRRlGBuwKY733HNPx/+vXLmSn/zkJ/zwhz8EwOv1snr1aubNm8ff//53zj777IFqlpKjRhkWPzonyM6wiyZgYoHOqSMCHIhJSnyCsQGJKdVNUel7hnS5apLOaaMCNCQkFQHBuZV+drdJEo5kbJ7GSNNCdZR1n9bQjMw7TlDm96FFYgPUIkXJTTlRd+K+++7jzjvvJBKJMGPGDK699tpsN0nJMimhTLcoK2zf4lDkgwm+Y3xIUfqID4epAZh6WF56UX77/zkqIOshrbEZe/qkY7/J6wHbBssGMyduTYoy4Pr9zH/xxRc7bVu4cCELFy7seD1t2jQef/zx/m6KoiiKMtCkRG9sIXWcnDKESCf7R6LIooKBaZui5Bj1OKIoiqL0GxGLp//H5+20rz5RzZN7f03CiXFexZXM8fvQIlEcFZQpw5QKypRBRwhIiPSp65M2Ug0lKQNM0wRRqWMIiakS/o9Ja2jGLcznyGrPlpvkr3t+ybSCUyj0lPJSzV+ZZLwHTySepZYqSvapoEwZVFJCY3WT4JH1CQCumeFlfrHEVOveKAMkhsGy/S5/2RqnwKPx6Vk+poVsNPV00CWtrhG3oPPQ5dtNr1LoKWF8aBoAC0rPY8e+7UwOTx/oJipKzlALkiuDyuawxvfeiHMg6nIg6vK9N+JsCqvTWBkYmiZ4tcblF+8maIxLdrQ63PFqlH1J9Xx7NFptA7IgP2ObKx3ealzGtIJTOrZV+KvA52NHzRsD3URFyRnqbqYMGoah8ewuq9P2pbssDEOdykr/S6Dzt22pTts3NDlqLdaj0GvqcYsyg7Jdkc34jSBFnrKM7cUFY2lt2ktzsh5FGY7UnUwZNKSUjAh0UV09IJBq6EgZADqSYl/nczDfI1RuY1dsG625NZ1TdpiNraupDHQukaH7g1S6I3hq328GqoWKklNUUKYMGo4jee+YQ5X+AfwGvHesieOoO6LS/3TX4ZMzfRy+FnmpXzC9SF1Ku6LX1OMW5INx6I/WkQ47IhuoDEzo9H7Ha1Bm5/NW4zLq4vsGsqmKkhNUIoQyYIQQJISOgUTv5Yy1UabFD88JsrXVRUqYWqhRbliql0LpFqlpJKWGTzjg9u6kmei3eODcIFtbXAImTC7QKBKdh9UV0PccwC0tyti2P7aDkFFAwOic/O94dTwJlznFZ/H0vt/yqcm3D1RTFSUnqKBMGRBxdF6rg8c3J8jzCj45w8eUYM9nrEkJ5bpFeXH7FkcFZMpxCQEHLA+/3ZhgS7PD4iqT9483KaAXwZSE0abF6I50KFUS42j0nXtxxlVmbNsR3kCFf0yX77e9BkbCYm7Rmfx6+z00Jmso8VYMRFMVJSeoPnel3wkheKMBfvxWetbkliaH21+JslfNWFMGSIs0ufWVKK9V2zTGJU9sSfHg2iSupme7aUNXIol+oA5nRGnG5l2RjYzwVXX5EXlwwk5A+phRuIAXD/yl35upKLlEBWVKv7OExl+2JjO2SeDdBgehpqwpA2BfVBJOZXapvl5t02Sr86+/mJu244wsz1jHMmZHaLOaKPGWH/Vzts/AiFucXHQ6K+qXYrtqaFgZPlRQpvQ7TUCRp/Oplmee2KxJV9dIaQaapm6syrH5urjSeTQwevlQIITA0nTV03Y0UmK+uRZ7QmaP2L7YNsp8o9HE0W89jsfAjNsUecoo9pSzvkXVLVOGDxWUKf1OcxyuneHNmLFW6BXMKund6ScF7EqafGeVzZdfSfBMtSCm0iOVYxgdgLnlmefIdTN9FOl2j48VQ+eFOo0vv5LkG2+k2JowkarHN4O+bRc4Du7IzB6xPdGtlHpHHvOzjlfHSKR7xyblzWJ147L+aqai5Bx1J1MGxES/zY/ODbKxycFvCk4q0iju5Yy1WsvkK8uitFfBeHBNgpTr4/2jUUn/Spd80ua/5nrY2mZSHZFMKtQYH3CRPZyBKYRgZV06PxJgfwRueznKj84NUmmqYTYApMS77A3smVM6rXe5J7qNU4rPOubHbY+OEU8HyxPzZvLHXT9GSqlSHZRhQQVlysCQkkrToqqiPXDq/Yy1HW0uR5Yle3xzkveM9hOg5z0fyvAQwmZuPpxSALKXa6Um0Xl8SyJjmythfaND1Uj1UADpGZfYDk5l5qzJhBMjbDVTeEQV/yO5po6RSP8dF3hKMDWT6vhORndR10xRhho1fKkMqL64aXn1riuqG+pBWumGEzkHdSHJ93Q+0YKmqujfznxnI86ksZ16yapjuyjxjkA/Rj4ZgGPqGPFDvY6VgYlsbVvTL21VlFyjgjJl0JmYLxgRyDx1Pzvbh1eqXjKlf7VX9D883Cj0Ck4qVpdSAGwbY+de7KrOeWP74zso9hy/5pjjzQzKKvxj2RZe16fNVJRcpYYvlUGnUFjcc6afDc0uTQnJrBKdMT5b9VQoA2JywOaH5wZZ1+gQMgUzinufHznU6PtqcAvywOfttG9/dAfjQycd9xiOR8fbeqiEToV/DO82L+/TdipKrlJBmTIoFQmLM4rTIyRSuunCZ4oyAISUjPFYjB114vmRQ42+9wBuWXGn7a50qUvsY37Jucc9hmMemn0JUOKtoClZR9KJ49X9fdpeRck1qs9dyRlS03A0HSHSs9xsTQft2Keo6h1T+kr7OScPnnNCS78WR6mDp869zvTqWtySwk7bG5M1+PQgXt133GO4nkOJ/gC60CnxVrA/trMvm6ooOUn1lClZJ4VgR9zg0Y1J2lKSz5zsY2+byz93phgd0rhqqocqj31ChWYV5ViiGLxeK3l6R4qqkMaHp/t4fnuSt+uSnDbS5MKxplp0vBu02gasmVM7ba+J7zlmFf/DOR4dI5mZH1riq6A6vpMJeccf/lSUwUwFZUrW7Usa3LIsigSq8jReq7Z5cls6p2RXq8ObNRY/OTdIma5uikrf0zR4fo/Dw+vT59yccoN7Xo9SHUmXzdjblmRdg803F3gwpRqqPKp4ApGykMHOQ4zV8d3HLYXRzvHo6Ekn3RV5cAZnkaecatVTpgwDavhSySohBG/X2x0pYWdWenhuZ+Y6mSkHdod7V1dKUY4nIg3+vCXV8brQp3UEZO02NDrUpVTNlWPRG5txC/I7lcKAdE9ZkWdEt44jdQ2EQLMP/Q6KPeUciO/us7YqSq5SQZmSZZKAceg0TDoSv9n5ol7gEziarta5VPqcBvgPGzPo6hTTBAQNgXsw51HpTDS1IvODnbY70qY5WUuRp7Tbx7K9mXllxd5y6uL7+qSdipLLVFCmZJWUMLtUI3gwEHtxd4oPTD6UDOzV4ab5AV7aa3PLihR/3ytoU6PuSh/yY3PDyYeG3LY1O8wbcegcGxnUuH1RkAfXJrnt9RSvNekkhFqI/EhaUwtuqHNQ1pCsIWQWYmjd/7t1TB39sLyyArOE5lQ9jqpFqAxx6u6mZF25YXH/4gDvNDiEU5IF5TrTzw7wTp3D7BEm310ZpTmRHuDc2eqws9XkCzN1NFcNaSonTkqYW+Ty/cVB3qq1KQ9ozCj1sLvNYWuzyykVJne8HOlY2uu7b8S5eb6fs0rVDMzDac1tuEX5nbbXJfb1qJcMOs/ANDSToJFPc7KeUt+xFzRXlMFMBWVK1kkJZbrFBRXpHDPXTYEB08YL1ofdjoCs3Ut7LT46zUOppoIypW8Y0mWSz2XKeIGUDlI6lBXAwmLBM9V2p7VW/7gpycKzvCrx/zBaSxtOVeeK/bXxfRR4Snp0LMfsPAOz0FNKXWK/CsqUIU0NXyo5Q0pw3UN3P9eV6F2coYYGXSx/qSgnzHVlRu+X48gu11oNGEJdPI8g2iLIYKDT9rr4PgrMnvWUOR4NPZEZlOWbRTQma06ojYqS69R1RclplQGYWpyZv3PVVC/FusotUQbGjCKNvCMWIb9upg/dVb1kHWwHkUgifZnFYaWUNCSrKexhT5lrZA5fAuSZRTQkD5xwUxUll6nhSyWnBaTN7fO9vNsk2dnqMKfcYHJIItUNURkgZYbF/54d4K16h5ak5NQKg7E+Wy3tdRgRjiIDvk5TV8N2C7ow8fVweSTX1DoNX+abRTQkqk+4rYqSy1RQpuS8fGFzZgmcXSZwXdVDpgys9pzHC0e25zxaKiA7ghaJIAOdA6+GxAEKe5jkDwcLyHbRU7Y1/G6v26gog4EavlQGnNA1hNHzU+/wfDNF6Q1NEwhdR/Si2NiROY/KISIcRfo7r2vZkDxAvqeox8dzu0j0zzeLaErW9rqNijIY9GtQFolEuPTSS9m3L130709/+hOXXnopS5Ys4Wtf+xqpVLqK9saNG7niiit43/vexx133IFtq96QoUgKwY6kyXffcfjmmzZrIwa2UM8FysBocE0e3i756usplh4QhNVAQZ8RkVjXQVmimjyj50GZY3bOKQsZBbRZTbhSzbpWhq5u3xETiQSbN29GSkk8Hj/u+9esWcPVV1/Nrl27ANi5cye/+tWv+OMf/8iTTz6J67r8/ve/B+CWW27hG9/4Bs8++yxSSh577LHe/TRKTtubNPjKv6O8Vm3xTp3NN5bH2BBWQZnS/8IYfO3VGE9sSbG5yeHBNQl+t9lCaur86wtaWwTp83Ta3pCsId8s7vHxHI+WUTwW0rXKfHqQsNXc63YqSq7r1hXpnXfe4b3vfS833HADtbW1nHPOObz11lvH/Mxjjz3GXXfdRXl5OQAej4e77rqLUCiEEIIpU6ZQXV3N/v37SSQSzJkzB4ArrriCpUuXnthPpeQcTRO8dsDulIrz+JYUXda9UJQ+tC8KDfHMs+/5XRZNtqrM3xdEpPPwpZSS5lQ9Bb0evuw8mSfPKKQ5VdfrdipKruvW3fB73/seDz30EIWFhVRUVPC9732Pu++++5ifufvuu5k/f37H69GjR3PGGWcA0NTUxKOPPsp73vMe6urqKCsr63hfWVkZtbUqb2Ao8nRx//Oqe6IyALqqa6cJUOXu+oaIxJA+b8a2iN2KIUw8mvconzo6x9TRU53TWEJmAc2phl63U1FyXbeCskQiwaRJkzpeL168GMfpXUmC2tpaPvGJT3DllVeycOFCXNfNSLqVUvYqCVfJba4rWVRh4DnsjBPAR6Z6wVE5Ikr/qgzAhILMJ4APTfVSZKj81b4govFOPWVNqbpeJflDuiSGZrtwxMSKkFFAiwrKlCGsW5muhmHQ2traESzt2LGjV1+2fft2PvOZz3DNNdfwqU99CoCKigrq6+s73tPQ0NAx5KkMLaNMi/vPDbKi2iZuS84abap6T8qACGDz9YU+3mpw2dLkcGqFzvQCEKreXZ/QYvFOPWXNyTryjMLeHVAIXFNHTzk4vkO3qYCRR0tSBWXK0NWtoOzzn/88H//4x2loaOC//uu/WL58Od/+9rd79EWRSIRPf/rT3HTTTVx22WUd20ePHo3X62X16tXMmzePv//975x99tk9OrYyOEgJowyLD48TgKr3pAysQmHxnnI4v0LrdU+/0gXHgVQKPJmJ/k3JOoJG5wXKu33Yg2UxDg/KQkaByilThrRuBWXnnnsuEyZMYPny5biuy4033sjEiRN79EWPP/44DQ0N/OY3v+E3v/kNAOeddx5f+tKXuO+++7jzzjuJRCLMmDGDa6+9tuc/iTJoHF7rSQjQdA2kxDly1WdF6WNSgnPYcLmupx8QHDWE3msinkB6vZ2q+Ten6hjlH9/r4zoevdMMzJCRz57oll4fU1FyXbcL9Wiaxkc/+lFeeuklli5dyrXXXkteXt5xP/fiiy8CcN1113Hdddd1+Z5p06bx+OOPd7cpyhARw2BNk+TZnRaTizTOH2NSrlvZbpYyHAjYnTT5+7YUCQfeP9HD5KCDrmpg9ZiIxsHXOZm/OdXA1Py5vT6uY2roR8zADJr5tFqNvT6mouS6biX6f+Mb3+CXv/wl27dv5+tf/zr79u3j9ttv7++2KUOZJli61+F7b8RZU2/z+JYUt74So0Wa2W6ZMgzsTZn817+jvLTX4rVqi6+9EmVLVE0F7g3RRT6ZK13CVvMJDV92VdU/aBTQpuqUKUNYt4KydevW8c1vfpPnn3+eyy+/nHvuuYf9+/f3d9uUISzs6vxpUzJjW2tSsjuihjCV/qVpghXV9pET+/jz5iRC1czrMRGNI72Z+WQRuxWf7sfQer9qgmt0LiAb0IPE7Si2q3rUlaGpW1cgKSWaprF8+XIWLVoEpMtkKMqJ0I5SO0pR+ltX55mKx3pHxDoHZS2pBkJmwQkd1+mip0wIjaCZr3rLlCGrW5ehMWPGcP3117Nv3z5OPfVUbr75ZqZOndrfbVOGsALN5uMnZdY1KvULxgSz1CBl2HBdyekjDYwjrn4fmuJFqoT/HhPxBNKTmXbQmmokoJ9YUOaanXvKIJ3s35pSeWXK0NStvuV77rmH559/nnnz5mGaJvPnz88oa6EoPeW68J5RglGhAMv2WUws0Dl9pE6BUMMSSv8b7bH44TlBXtxrEbPh/DEm4/yqZl5viGisU6J/i9VA0Aid0HHdLhYlBwga+bRZTSd0bEXJVccMytavX9/x/5MmTaK1tZXW1lZOPvlkduzYwYwZM/q9gcrQ5cNhbj4sOFnHdWW6bpmiDAQJo02L6ya3l8RQNfN6S8QSuHmZAVhrqrFXC5EfzjE1PJFkp+0BI08FZcqQdcyg7Itf/OJR9wkheOGFF/q8QcrQYBgaspu1x2xbDRkpfUc/mBzWndpj6fNTRWMnQsQSXeSUNTLKP+6EjuuYOlqqc5Ffvx5Sw5fKkHXMoKy9xpiidJclNDaHNZ7bbVEWELy3ymSUaSHVfU/pZ1IIdiUMlu60EAIuHG8y1msj1MnXr7R450T/sNVM4ATKYUD78GXnoCxghGhRQZkyRHUrp6y9Av+RPvnJT/ZpY5TBTQh4u1njnpWxjm3P7Ejxw3OC/V4U1jA0HEci1Q142NqZMPjKv6Md/V7P7Upx/7lBxnr6f1hciHQP3bA8B+PJjKDMkTYJJ4ZfD5zQYR1TQ091nVO2N7r1hI6tKLmqW0HZli2HlrVIpVK8+eabnHbaaf3WKGVwSmLw2/WZpVLiNmxucSkv6Z/vjGLwbpPk1Wqbk4p1ThtpUKwmCww7hqHx9IZUxkCkBJ7dZfH56Vq/LqMUxuCteskbB2zmlOucWq5TIDoHE0OSlIhEEg4LysJWK349hCZOrMZI+4LkRwrqeaokhjJkdXv25eFqa2u54447+qVByuCkaQJNE11n5xyl4+BQ74Lbu+FNTfDENpsntqYAWLHf4pldGt89w0dQDpObooKmCXRdw6XzDfx4p9WJrn1pC51frrN4ZV/6QWBFtcWyEp2vn+rF6w6DczCRAtMA7VAA1mY1ETSPvwTf8bimlq5TJmX6YnFQwAgRVkGZMkT1qtzyiBEjVEV/BUhfK2tsk5f32DQnUlw13ccP3jw0fOnTYWqRBkfcMNukwep6yepam/kjDE4pE+TTs5tYk23w923RjG37wy77ojD1xEZOlEGiwTV5dZ/NrlaL9473smzvoV5SAVw4zkzPrDySgH0pkxe2W8RtyfljTcb7bbQePh00WFpHQNZufaPDgbhgXOflIIcc0UU+WZvVTEA/sXIYAFLXkJpAs11c89ASWOmSGCooU4amHueUSSlZu3YtJSX9NB6lZEVve63qHZP/WhYjZqU/1JCAry0K8tLuFBVBwfljTUYYdsYxU5rOj99Ksao2HYQt329xaoXBV+aYmLJzb8exdNXUYZbRMyT05vxrlSa3vRKjMZH+wJ6wyzfOCPLq3hSagEvGexjj7br22P6UyU0vRWmfHPzsrhTfPTvIFH/Phr6H+7km4olO616GU834+yAoA3A8OnrSzgjKPJoPVzoknThe3d8n36MouaJHOWUtLS0UFRUxevRovvrVr/Zrw5SBExUNrGtZybqmVZxcspAZhacScLtXY2hri9sRkAG8ecCiOuzw43P9GI6D43SeeVmX0DoCsnZv1NjUpbyMNrsflBUZDpdO8PDk9lTHtpFBjSrVSzaoxEQT61pXsrbxDWYWz2dm0SKC7vEf+nZHZEdABrCz1eH7r0f5zYVBgjjYdte1xzRNsOKAzZHVWv68Ocmd84weVfUv87icNsrgtepD5/PUIp0KnxwWEZuIJ5GeI3vKmvDrfbM0h2Pq6EkH67AYTwhB0MgnbLWooEwZcroVlH3mM5/h1ltvZePGjQCccsopfPSjH+3XhikDw9Ji/HrTvaxtfBOAlTUvMr/8bD49+Q5013ecT3d930m5kLIlwu365uYe5W7l9nDoSLguH5qkM6nQz0v7LGaW6JxTaRBEJfoPFo6e4JEtP2B13StA+vybVXIqn5/2LQz32NF1V2eLQ/rc83LswOrIxciBTkFadxiuw+dmejil3GRFtcX8EQanj9TxyeFxDop4AryZSyy12c2MCUzuk+O7Xax/CYeq+pf6RvbJ9yhKrujW9Jjbb7+dD3/4w6xZs4Z33nmH973vfSrRf4hosPZ3BGTtVtW9TKNd3a3PTyrQ8OmZ2645yYvvGMOQFV7JyWWZzwOzywxGeHt+VwzhcHaZw3cWmHxwLGrm5SDTaO3vCMjarW18gwbr+DmrY0OCAm/myuJXTfOSrx27t9V1JaePMjotSv7BXq59mS9szq9w+c6pJpdWSgqH0TnY1bqX4VQLAePEE/2hvaesc1AWMEIqr0wZkrrVUxaPx/nIRz7S8fqaa67hscce67dGKQNHyq5vQt2ttTTCsPnBOUGe2WVxIOpyyXgP0wuOXavJIx3+a66X5TUGKw/YnDbK4LQRGp5ezpiUEiyrZ7loSm442nniHuW8PFyhsPjeWQH+tddmW7PDBeNMZheBdI9/LlR5LH5wTpCndqSI2/D+CR4mBZ1eDzlKKYflOSjiCaSZGZRF7Bb8Rt8MX6ZnYHZVQFYttaQMTd0KyiZMmMBbb73FKaecAqRzzCorK/u1YcrAKPOMZlLBTLa1ruvYNrNkPiXmKI4zAgSkb0YjDYvrp2oIoWHb3VvUuUBYXDJKsKTKwHUkUpWwGJZKPKM5qfgUNjS91bFtcuEsyryVR07Y7VK5bnHNRAHCwLF7EBRJGOuxuGmmjpSotS97ScTiGTXKkk4CF4lHHD/1oTvco/SU+XXVU6YMTd0Kyqqrq7nmmmuYOnUqhmGwYcMGysrKWLJkCQBPPfVUvzZS6T+mG+LzM77Bm/Uv8U7jCuaVnc28ksUYbs8SaHtT50lKiWOrO+FwZjh+PjPtdlY1/pu36l9hTsnpLCg7D9Ppfk/LiaxfqdZdPTEilsAtOzQpKGK3EDTyDi8rdkIcQ+uygGzAUOtfKkNTt4Kyr3zlK/3dDiWL8uQI3lt+NedXXI3rdH/oUlH6QkiWcV7ph3lP+YeRbjrnSxkcjswpC1stfVYOA8D1aBiJLhL99Tz2xbb32fcoSq7oVlB26qmn9nc7lCxzXdmt4cqeEiJd6b8/l7lRBr/+Ov8gXf/MdXu5aoRyTCKeyCgeG7Fb+6wcBqQT/T2RVKftKqdMGap6VdFfUbqj0TVZWetQHXE5fZTB5JCL2Y0EbkXpCwmhs6VN8HqNzfh8jfnlOkXDaGbkQDhy3cuI1XrCC5Ef7mjrX6aDspY++x5FyRUqKBsi2quhDwQhONj71XXXg64LGm2DW16J0XywuOc/dqT4ynw/Z5YKNTw6hGgH60oM9JDjsXq/NE0gBLywV/L/1sY7to8Oadx7ho9QD5fzUo5OJDKLx7Zazfj6tKfsKMOXRp5a/1IZklRQNsjFRSvbIu+wofktJhXMYFr+PIKy/5bACos61re8ye7wFk4uWciEwCy8Ml2TKI7OpjbB6lqb6aWyIyBr96t1CU45x49f3RQHPSGg1tnO6rpXsF2L+WVnU2FMRshulT7stYRoY3t0DeuaVjE+fxonFSwgJEsPNiq9nuXyapupJQaPbDhiXdSIy+4ozOi7mGF4s+x0FV7jUKHCiNXM6MCEPvuKo82+9Gp+HGmTcpJ49GGwyKgybKigbBCTusXfdv0/Xtr3JAAv7P0bM4rn8x/Tv43pdv/OI0Q69+t4vR0JrYUfvnsbe8PbMTSTDU2rOWvkRZw/4mokOn/f4fKnzUkAyoN6p8+nelkGSoh0z4dKAM8dte52vvnmDdhuejjwHzt/z9dP/T9G69N7fUxdP3rvKwCaw9L9j/Lc7scpC4xkefWzVIYm8KWZ38Xr5rE7aXLzv9PrWV4zQ6OriZUn0pmszsNMHeteHjbVsi9zysIW7IpplMUd1jdLJuWDV09/V3qppXReWamuqvorQ4cKygaxemd3R0DWbn3TKuqtvYzSp3XrGG2ihrXNr1MT3cuc0tMZ65+BcZTllQ4kdrE3vJ3FlZcwIjCK/ZHd2Fg0sR9pT+DxLYd6JnQN/AbED3vIvXq6lyB2twMzIaDONnmz1qEp6XLaSJNxfhtdDX9mlaYJXt//QkdABuDisnTPn7h+0rdwe7heUVw0szWyhk3N7zCl8GSm5M/pcu3VFreWsNXCFZM/xf7ILspHjsSRDrXJPYz3zeIfO6yOpZJer07x3nEent15KEk83yMYG+p5rQYhoPbgediSdFmkzkOgfYmlzHUvI1bbCQdlKRder3HYE4FRHsF5ls3yAw5/3wlnjhScWaGhCUHQKFBLLSlDjgrKBqmE1sLW5rVd7nNcBzp3VHUSEfXc+9Z/0pCoBeDZPY/z6Rlf5bTCS7rsDXCkw7Ti2TiuxWNbftmxfVPTO3x08o8y1g7886YEn54dYGODxf5IutL/vBJ6NAOu3jG5+eUY4VT6Q3/ZkuLbZwSYFVLDn9kkhCDuRDptj9sxED0LVFwtyZ92/JQVB54H4F97/8qC8sV8csrtGE7mw4EmQBc6j235Rce28flTmV+2GCEgdljNu63NDtNLDD45y8+K/SkmF+osmWBS0ItE/zrH5OZlMSJW+viPb0nxnTOCzAwN70kDR868dKRNyo3jPYFE/7gNz+x1CBlw9igNU2joEt43StDiCF6rddnc4nD1JP1gT5nKK1OGlv5NAFH6TXViJ5ua1jC9eG7G9lHBMZR7u7fawr749o6ArN0ft/wfMdH5QicEFPtLmFd+Jsurn8/Yt6HpbRD7OLfqUL2i1qTkTxsSfGaGyb0LPZxV6uDvTon2w2xsdjsCsna/WZfE0roRcSr9RkrJzNL5nbafNvI8kD3riWp0qjsCsnZv1i2j0drX6b0uLi/veyZj2862zSSdOLbtsmRiZq/Nk9uSnFyq8d1FHj4zVVCu9y6I2tDkdgRk7R5en8AZ5uehiCczapRF7TA+PYDWy8qxtgtL9zmUeGFmsYapCRACy6PjSdrkewTnV2oUeeEXGx10EaJVlcVQhhjVUzZIOa7NypqXuGLSJxmTP5HNTe8yvmAK51d9EK9b0O1jHMlyUh3rYR6ea9Yk93PPqi/zmVm3IrsYgIzbTVw3fSyTinRe2msxq0Tn4vEmPsfq9eo1XS0lmLAlkj4qF670ihCC/ZE9XDfjy7xZ8zKOtFkw4mx2t21nQWHPftt2F+cgpHtUNS0zz9GVLm4Xxcycg2tdTgrY/M9ZQf68OYkm4MNTvYzx2sgeDqceToh0LuSREo7EpVsd0kNWunDs4eUwTqxw7PIaB78Okws0xGGBnWXomCmbRMCDJgTzynS8msumlgCVgYYT+hkUJdeonrJBalRgPPmeQp7Y9htW1bxCqX8EBWYJpWJMt48xOjgB3xFDDZeM/ygBigmLOt5ofYa/7v8ZO1JvsaXtHVpTTbyy/xlmlMzL+MyIQCUjvGPIFzaXjpZ87zQPn5gsKNVObHhnerGGccQZevV0L145/BZ+ziWO43JS0Sn8dsMDAHh1H3/Y/CAnlyzkKDHWUaXXXp3R8dqj+/j0zFvYHl7L36t/wR5rLY5I54WZupfZZYsyPl/iG0HIkw+ALiXTAxZ3zTf4+jyDKX4L7QTzvqSEGSUa+hHPAVdN8+IZ5uehiCfgsJ6yiN2K3+hdULY7DDVxmFGUGZAB2KaG54jIeGaJRoknj38fqCOulmpThhAhh0jRqMbGyJCcFaVp6bpeXfUcNLGH5/Y9zo7WDZw58kLml5xLoItyGIcf43BCQJ27k6V7/sjeyA7Oq/wAswvPAuHyvTU3sT+yCwC/EeT9Ez7On7b8HIArJl1H1AqzqfldTio6hXNHX0ahHNXnP7crJXuSJo9tSdEYd7lssofZReDL0s2wrCzvqPuG6vkHYBhapzUiXWGzJ7Wep3Y+guVaXDr+Y0z0zUaXmeUJjjxnuxIWtbxS80/ebljO5ROv4zcb/pfW5KFhqS/NuZtZwbNocPfwWt2zJJ0EG5veYUzeRMblT6YqMJlxntl99wMf0X7n4Hn454Pn4RWTPZxcCN4eDsefqFw7/7zPvwqOgz19EgBvNS5jX2w7c4vP6tFxUi78ZYfDySUaxd7OveAz3tnH5hkjOVBVlLF9X2wN7zZtpMJzN7fNNXo9bKp0z7HOP6Xv9OvwZSQS4aqrruLBBx+ksrKSFStWcM8995BMJrnooov48pe/DMDGjRu54447iEajzJ8/n29961sYxvAeWdWRBJuaYNtutMoKaA3jNrXBxCoS5WWkNJ1ixvDRMV/GERaa6+1UlNUVKfZbW3m3cSVF3lJOKpxPIYeCJymhTIzn2gm34WKjuSZSwrbE6o6ADKDAU8SY/InowsCRNk9se4hSfwWfmXErk3zzcPvw3hTDYHMbbGy0mVykM71QcstsHRcDzXXUUjkDKGgl8VTX4O7cjygrRo6vpDWQ7gnRpME4czZfnD4LkODoGfVOYqKJrZE17A5vYUrByYwLzsDn5nf5PXlyBJdUfJKLRn2MdeHXMgKy0aFxROwWnql9iNGh8eyP7GJn62YmFExjZ+tmNjS+xTfm/aLPl2iKYLCxRXZMGJiaf+g8FM7w7iFrJ+IJ3LxDMy3DVmuvkvzXNLoUeekyIIP2nrLOXbB+PZ+Qp4mGhOQP2xw+Nnl43zOUoaHfzuI1a9Zw5513smvXLgASiQS33347jzzyCCNHjuSGG25g2bJlLF68mFtuuYX//u//Zs6cOdx+++089thjfPSjH+2vpg0Kobp63F//Bf2sebj/WIZsaEnveHkV/vefiz1rOq4rcV2BwNMpz0sI2BJ/ix+8/dWObQWeIr4+/0HyZUXGe6UjEJgdRzi81IFAcNH4j/Dw+h/xiZNuYlXty0SsNi4YeyWVnmndDsi602Piahq/32zzjx2HyhicPsrgppNNTOkcNTetO8dWuk8IgamD+cYG7BdeP7R9RAl5H1tC2Os/9GancwZESovyq033sLbxjYNbfs9FYz/CZZWfRbhdX3KkBOF4sNxDv3tN6Fww9gp+ve6+jvP7ykmfYlz+FN6o+TcnFZ/CeyqvwO8WdXnMjuP08PxICZ3/ezfFa9XtgUCKSyd4+ORUDe0YJ/xwOw9FLA4lh/7tw3Yz+WbnUibHErVgU7PkzIqjZ9LYRjrR/0g+PZ+408xHJur8YoPDpHyXhSNURo4yuPXbGfzYY49x1113UV5eDsC7777L2LFjqaqqwjAMlixZwtKlS9m/fz+JRII5c+YAcMUVV7B06dL+albO81tJCurqkC+8DhKE33coIDvIfXY5vkTimMexRIw/bvm/jG2tqWZ2hjcetw2jAuMIHMwNmVI0i3fqXqMuvp/frP9fbNem2FdGU7wenzh+/kgEg1WtOn/YCWvCBvFjpEbXW3pGQAawotqmJtn1aRrG4I2W9LHXRo59bOX4NCT5rS3krXqHYF09zsurMvbL2kaMuuMnVten9h4WkKU9u/vPtLg1x/3smOBkDC2dpzSjZB4rD/w744HjL9t+TYGnlDtm/5wPVX2RInn0mcYpNDbFDP6wE5Y36bTK7j2D1iS1wwKytKd3pKi3uj6/Yui8E05/z6pWnYgcHuehiCczFyO3el44dk2TS2UIfMbRhx5to+ueMp+eT9xuJmhIPjxR56frbfZHh09QrAxN/dZTdvfdd2e8rquro6ysrON1eXk5tbW1nbaXlZVRW5tZpmG48LoO5tKX091cyXRl/C5T/iwbcZyFvV0c4na00/ake+xgDqCACu5c8FOe2f0HTN3DgejedFuQbGh6C4Bi74iOQt7t/z0y783SdP5vTSrjBveBSR6unaShddF+6yi9DF1tTwqdH76d4q3aQ8f+8FQvV40XCDXG2Suh5mbkL/4MrgvXLAG7841Q2sfvGj28t6udi5ueaXmcx8AybRzfWPB/PLXzd5QGytncRS2+pngdeqEno1fq8PMuPWsYXj4g+Ok7sY73jM/X+M5pPoLHWebLPsp5aLuy03RLV9P44xaHp7Yf+pnPGG3wpVnp3t2h7Mg6ZRG7Db/R/aAsasP2VslZI499Uthm1z1lujAxNB8JJ0xlqIBzR2nc87bF9xeZ+I8R5ClKLhuwvl7XdTNm1UgpD5Zb6Hr7cORpbUOu24a7ZTfarCmHdhxRNVssmk3S5+dYfOSzZPzHMrYZwmB83vEr/UsJpYzj2vG38cFxN/DeMZd1es/CEefhupJq2+Qf1RprIiavNRv8aRdsjhlUOx7WtXbucXhyW4oGu+vTrtwrmVGaederzNMY2cWPWh3XMgIygMe3JGl0VF5Jbxi6gNfeSQdkgLPtiHMQIOBDlh9/XdURvirK/JlV1meWLKDYPH7ldSmhQpvCZ6d8k8srb+CScZlpDALB7LLTOgKwFmnyapPOY7thZ9Jgv+3hH9UaayMmv14Xz/jszjaXPZ2fUzqp8ENlKPMcnVGqU+7pHKw1WHpGQAawfL/NgaP07g4lRwZlUbutRyUx1je5jAoeWjrpaCxTx9vFouQAfr2AqNMIwPwywUi/4IF1dtcPs4oyCAzYHayiooL6+vqO1/X19ZSXl3fa3tDQ0DHkmevSsxqP0pvVG+29EMkUcl8t+gWn427dg3H5e3A3bEfWNsK8GVjTJ3G8WeCuK1lQfD7eGQGe3fNnSnwj+MD4T1Cmjev+ApRSY13LSl45sJSPTvsPXj/wEqZmctnET1BpTmVfyuTL/45y7lgPK6pt1jekL5xFpwT45YoIHz2pczQlAesoBZ48rsPNp/h4bo/NimqLU8oNLh1v4pedS2t01XvmyoO9GUP/fgiApmm4bt9kuAuA6KEgRr72LvrVFyNKCnHXb0NUlKKdMZfWvLzjJtX73SJumfO/PLfvcTY2v8WC8nM4u+ISdKf7C0dLRwM0yv2juGrq53j9wIv4jQCLRr6HoB4CmR6+vuv1OHva0g0q8gf4+dsRLBdumO3vur5YN/65AtLmm6f5eXqnxVt1NqePMrlgjIHZzfPwWNuHDMdJ96QeLImRcpOAxBSeY3/uoJQLm1skpx8jl6xduqes6/I6Pr2AqN1AqXcCQgguHqvx600OT+x0uHKCekBTBp8BO2tnz57Nzp072b17N5WVlTz99NNceeWVjB49Gq/Xy+rVq5k3bx5///vfOfvssweqWb3it1J4mprRojFkbSOytBC7ajTR4/ReHY+W50eWFCIbW3A3bIf9dRhLFiNbI8ip4xHnnkoklI/TzQu+V+ZxasGFzJt9LhpmOim7B/eKiFbH7zY/QFuqhS3N7zKr9FQc18ZxXAzNwz93WlguVObpPHdwjcFRIY0dLQ4pB9qSkoqgRk300J1wZnuPw1HaUSQsrhovuHK8FxMX6XZ9MR4VgDK/Rn380LHnjTAo9cg+n4mXa7yuQ6CuHrlzH4QC6VmRoa5nNnaX5UgCp81BbtvTsc19eyPa6XPQyouRPi9Jv5/uxoCFjOYjY/4Te2wSU/q6/bkMusufN/2CLc3rOLlsAUknwW83/Iirpnyec0o+yO4wHQHZhEKddfV2OuAHPAYsrvLw4p5DvVhBU1Dk7V7EXqJZXDdZ8LHJxz4Py7ySk0p0NjQeigBHhw727g7huEwkDuaTHRzVCB8sHNvdQY7NLS6lPgh0Y5jRMnW8XeSUQTqvLGo3drw2NcFVk3R+udFhTMhlQfkweUJThowBC8q8Xi/33nsvX/ziF0kmkyxevJgLL7wQgPvuu48777yTSCTCjBkzuPbaaweqWT1mShfztbcRPk/GzDStvITANR8g5ul+b8DhgskE7u+ewlh0Mu7+unRP2UVnYv/5WUikbyxSCIKf+SBtpaU9OLJAO6yHQgjRrZ49V0uxI7I+vZ4hkHQSrKp9GYCzRl2EENBycAmkww/nMwTRg0vSPLktwWdODrCl2WZLk80Zo03ed5Qeh8NJV2Jw9NmWACFs/udMP3/dbvFuvc3ZlSYXVBkYR7l5DhWaBoGde7H/eGi5IVkQouATl9EaPME6QvlBjIvOxNm8CwrzEKVF2L/+a8duvXIE/qsvJW50rzdEugJD+HEPO0GE6MH6p8IlnGoj4cR4o2ZZx+aI1YIQoiMAA/Afdt4BtCWh2K9xxRQfb9akGB3SWTDSJGa50L3md+s89LgOt8zz8czudO/u3HKD908wCRznHB/sRCwB3kPXlYjd2jE56HhcCRuaJbOKuxcwWaaOJ9l1fp5Py8sIygAKPIKPTNR5YJ3Nt+YbTMhXgZkyePR7UPbiiy92/P9pp53Gk08+2ek906ZN4/HHH+/vpvQJX1sbmq7hLH87c0ddI0Z9I4zuXRFVva4BWd+M/Y+X0U6dif6ehcjquo6ADEjfzV5+E/ODF2XckLqSEhH2JDaxo20jo4PjGRUcw462TTQnG5haOJvRnslobue7kxDQJPfTkNzP37Y/zNmjL+KFvX/v2O/RfYwOTMS204uMr9hvkbAlxT5BU0Kys8XhfeO9vLw3he3Cg+/EmFSo85UFAcZ5raP2OPRGqWZx/VSBNdWLFwd3iAdkAAErhfOv1zK2idYIWnUdTO59UGboAvetDWglhWjjRiNGlWP/KXOdSfbVYjY0E68YcdzjxUQzO6Pr2R/dycSCGRR5S9jaso64E2Va4RxG6JMQsuubZZuoYUvbGry6nzNHn8/Otk0Z+ycWnoSUkjEhgd9IL2K9qdHmM7MDvHkgfQ68ecBiRFBjXYPNrFKD2pjLr96N8+Nze79Y9tEUCouPThB8cIIXzzF61YYSEYsjfUcusdS9JP/9UTAEFHYzOLZMvcvZl5DuKQtbdZ22V4UEl4zR+O/VNvcsNBkRGJ55ysrgowbde8p2wDQg1cWFt4vZaj06LiCK8hHBAPazy9GnjOv0NhmJI1wJx1r/UZO8VPsEf9n2KyBdgf/RLT+mIV6DJnRCZh6fPulWTgqc0anXoknu5durPs8l469ib3gH04vncun4q3mrbjkl/goum3gtxaISKSVTQg53nR7g8c1JPn2yn7drLTY0OmjS5VtnBPnDpgS2Ax+d7mWUaSOPMezaox6Uw7kSE2eoj1h2EI6DjCc7bZddnY89oAkwqkZi/+U5cFz08087yuzL45/jlhbjka3/y+q6VwC4eurn+em7j2K5KXRhkLB/xu0LHmCMMavTZ8Oijnvf+k8aErUsGHE2Jb4RfHjK9bx24AV8eoDTR72Xhlgtwi8o1W3uWxziD5uS7GpzKPBIvn56kD9tTOBKyZKJHsYVaDy3M8XEQp0bZwco0axjnme9PQ/lwfNwCI9YZjhy3cuw1dLtwrHrmx2qQqLbE7ocq5YKowAAOdVJREFUQ0NzXITjIvXMQN6n///27jtAqups/Pj33Hunb++Fpfe+FAEBiaCgAgqosUQsiYnJm5iiJtbE/LDEqG80Jr4m+iZ5U0w0McbeuwIiWOhdOsv2MrMzO3Pn3vP7Y2CWZfuyDTifv3buzJ09d/fMzDPPOec5yRTXNV3mZ3SaRm0Ufrra5BdTHKS7VWCm9H4qKGsnMyUJUVyGNn4Y9mdHvRk4HdiZra9Ma4oQoKclETV0tPHDsVZ8AeEIIruJLZNmTMAUosX5KhVyL8/t/HN903Q3ZaFDzOl7AenubKrD5ZSGiwgmlOOx6n+HELCxYg21pp/SUBF9Ewfzxp5/k+rKYFT6BECQZfSLB1eGtBmfaDN2ihOk5PR0DRMDJxbSthl9mhMJ6C1tiCjgQMTBxnILpy4Yla6Rpbf8wXkqC3l9JEwdh3XU0Dm6hsjLbP6kFvjqQuj7iwCJvXU3WLHw1v5yP9qIQbG5jUe4XdgZLRdqBSg198UDMo/hI2KHWTDwcsJWiIgVJs2dyXsHXuTKgSPgmNWye2q3UlYXK4mzvmwN5w+6ghe/fJJxmVOIWGGe3Pxbbp/8aHz1ZZ4R4YaxOlEMXNjYtsX4aU6QoNkRBvURnNvHFSuNbDffrywh2FtnsLHcIt0tGJGqkSJO/oxXR4lgqMHKS38bM2V+E0pDMCq1HQGSEJhOA1c4tin50Tx6Cv5o40zZEVOyNKIW3P6JybLJDrI8KjBTejcVlLVTne7AmHM6xsFD6F4P9uYvIT8LZk4i4Evo0NfshJoaov94BWPBLBBaPAtnrdmIsfArWJ9vAdOEMyYR6tenxV8R0QLsqN6AJesDIVtaTM2dTUmwiLf31g9FVg+oYGHeN8COLYUUQuA3qwF4f98rXDLsOvoGBrGtcj1O3cM5BZfgamKrHO2obWeOzha0VP089vtgd9jBje/VYh0+KcEh+O9ZXrL05j8QO5xVOwlEozbW2OHoho69YQd4XOizJuFPT2/3AgdvuA7tby8gSyvQBhU0mGsod+5DO/M09LTkWGCWn4WcNZmgx9vqBPawXV8bzKE5KEgYwB82PBjvWxoa1475CRZR9GPegsJWfR29OivIl9VbOH/g11h+8E08RgI3TXiAPMfgBtcqbBsH9YeO7o/24QxWSzQNPq/SWbayvl5Gnk/j3ulukkXzXyhO5X4oakMNSvX4zSpyPX1bPW9bVawMhqG1LziKOGO1yhoFZUZyozllx5qeq6FrcMsqkzsmqDlmSu+memcHBLw+/EMHEZk5Ce2r5yD65sGBYnyBmjavPjrCoYG+cRtU1hB9/h3s4jK0cbH6ULKolOgrHyLSkhBXLMQ/ZCARveU4uji8h7WlHzMybcJRv8PFgKShrC9rWGX95V3/oNIuit+2bcnY9Kmxn7H5x9bH2Fuzg2tH/YTL+v2IFJnfvotrRs3hSvyrKnT+sSUcD8gAAqZkTYnV5NCGLQR7wg5eKdJYWaFTJR2d0p4TTdDlxu7fBzF2KKJwOJHERKIdGL81SsqhNLbPpL3nIPqw/g3ut979BHvUYCLf/CrBhXPwJya3KQhJcCST5o5l7kzbpCRUFA/IINa3PjzwOrpo3Jf7JgxucHxN8Qc4NRc/Lfw9N41+iIHOCc1u1dQeQkCp7eD9Mp31AQe/W9uwqPLBWptd/qbPlUKwL+Lg1SKNj8p1KuxTrx+K2uAxhWOr8bQy0d+WsLVaUpDQ/o8d06HjaqIshltLJGLVEm2iYPHRpmZrzO2jceeaKO8cOLmL+ionNpUp6yBbglFRhfXEM/GCm8Ltwvetiwm0YxWct6ICWVETuyHBXvEF+qzJ6NMLYxmK3EzkzEn4XZ42fSBG7DCriz/g4qHXkuXNZWP5Zxzw72JizszG14Adq75+1HtknmMwt058mH/tfIKgGeDcfpeS5xiCtJqONtubLQhg8P8+ruPLapvT8x1UhRufXB2WTT7vJr/OT5fXZzNyvBq/mOE55YaZfAeLkH95vj4jmZqE7+ol7S7JopmR+hxS1MLeuR9j3nSsL7YgdR0xZyrB1FSiou2lVISAYDjIwoFfY1P5Z5SHSqg1G0c3/kgVlrQaZcoytX7cMfk3PLPjD5TXHeLsvhcyOW0OItryqub29sPiqIMb3g9Sa0ouGeHGH2kc1YabmQO5PWhwywe18T9Jqlvw4Ewvadqp0w9FbQg7o36fy7ZssbSvFjw6JDraP4RoOowmC8gKoeHRUwhES0lxtvylcXSaRoZb8M+dFqtLbL450mh2E3RF6SkqKGsnXziEfrA4ti/l2i3Iowsw1YXRtu9BFI5pU9kJp7SxX3wPY/xw7LVb48et91cjrl5EZMYkLF3HlI3nkFWLInbWbCAUrWVw8miyjYFga6R7Mkl0pvCvbf9Lnq8fI9Mn4DUSKfAMIc2VSUW4vlDv0JQxpDnyOHp0R9gGA5yF3DT6YSQ2hu1u8sPOFoJ9YYMtFRYpLsGwlLbNwdkdgC+rY3+zz4tNLh3hYWtFw8rrU3ONRispI8Lg8XUNsxmHgjY7ayQTk1v9tSc0IcBXG0Dbfwjh8yBf+bDhAypr0A+VQv/Wh48aPG+iDww9vsjE3rYb0lPg6kWEhUFENJ/RiK3S3c/2mnXY0mZYyjiC0QA7qzcyIHkoL+z8GxmebAqSBpHlyUMgGuxheV7/SzFsV+NYT2rk6yP5wchfEpUmDult8bVUIw221UhKQ5JhKTr9PBZ6K1uQCSFYdciKl9BYsT/CWf1dvLijfgGFoUG/RA2OGfq0NJ3/21jXoN2VdZJNlTYzOjal9IR09OpLS0YJ20HcrUz031ppke/rWBDU3FZLAF4jtU1BGUCOV3DdSJ33D9p8/yOT8/pqLOink+RUwZnSO6igrB28ZhjtHy8ji0rRRg5CBoKNH1QbavO3ds22EbUhrPXbMOafgfXZZrAstNPHE8rOJKwZTWYoqkURd6/5DtWRytjzoHHb5EdIdWTx8Be3cdGQb/BFyUr2+nfiNXzMybsQr5XBzRMe4oXdf2VL5RdMyj6Ds/MvwrDcTbfNir3hNnUZQsBGv87PlrdvDg5A+KixylAUtlVGuWq0h3f2hPEYgqtGuennjjb6xVGgJtK4NXXWyT+pxxfwI/73GWSwDm16IXYw1PhBkZaHb5oSrQtjLJiFtXYbVNWgjRwUm1RtSSKOloeYyuQe/t8n36bOCpLoSGbhwK/x963/A4BTc3HN6BvZULaaDWVryPUW8JNJD/Lv7X8kEK1hQf/LGZs8gxZjJ8vAwGgQyB2rFoNlq8LsrK4PnH4y2cPp6S3X4hMCKo4q7b/PbzMhBy4d4ea9vRFyfRpXjXKRY5iN+qElBdVNZHdrzaazuycrEaxDumPZyyNZMq2FIL7WhOIQjGjPBP+jmE6tyeFLiG215Dfbvl+yQxOc1UdnQobkgyKb73xoclqW4Ct5OqNSRbvnuylKZ1JBWRsJAc5DpVhFsUyTvXMf+qxJWPuPeTMY1h+kJNFfg7b/EFLTsPtkU5uQ2OgNW+oCbdo4rFc+JHqwFG3EQNA07LzsWEDWjK3Vn8cDMogNQ76+5xkm5czgYO1e/rTxvxmRNp6xmZMpCxbjFUkgIZUCrhp0M6asw4kHaR/zJipsSuzd7KzZiFNzMThpNMmycd21OmE0OQfnSz8UtlJYvm+ChlMnvgXO8v0meV7Bf890o0mJbjf+IATwEWXJECd/2lCfzdBFbJPpY7MZJxMhQNu5FxmM/b3tzV+iH1mhe4SmIXPqV1/6QsFYNjdUB/k51KamYDVRQkWmphB96jW0gX1gUAHWxh2I0UMIu1wtLhrQNMHqQ+9SZ8W+lEzPn8vLu5+K3x+xw/x+3b38dPKjXD7gR/G+dtOYsdjSimW/WiiP4hcl7ApsxG/WMDBxBDmOgU3OI9sdoEFABvC7tXWMne1pcdNx25bMyHfw3I76QPb57WGWTfeyqL8bBzZaM/3QhcWSIS5++3l9YCyAkek6spUM3clEC4biQZk/Wo3HaHnKxvZqmxxv+yf4H2E6DNyh5oKylHYFZUekuQWLBuicZUrWlkv+sDlKeRhGpwrGZWiMShUUJAi0U3QvZqVnqKCsjXwBf3xSNBDbn/JACfpZU7G/2IJ0OhFnTSOYnk5CZSXyiX9hRy3QNITHRcI1S/An1kcsuiZwfbEVWVyO/pXJ2Ou2ISuq0eZOJ5Dc/HicEBCI1DQ6PjFnOoeC++K3N1d8weaKL0h1ZWBhonM4I2bpOPA1mX84GN3GXau/G1+5mehI5o5Jj5FKw2EBS9JktqAtWassI8qDs3z8eWOYfX6beQMcnN1Hx2G1nGGTEubkazg0N8/vjJDp0bhmtIs8R9MfnicLIQTCH4xfoqyoBsuKfSFYtw2SExBnTyeQFOtbvlAQ8ad/I/1B0DWIRkm4ejHVOTmNnlu6nDgWzcZasxG5Yy/6yEEwfACt7eIlhKAiXBa/nexMpbaJPlkZKaHAGF0/981yokGL2a+AKOWXn/+QktCB+LEfT3iQIa7JjR7b1JyvWlMSla1/iPb3RFk23ctfNoapsySXDncxNEHiamXFsJSS07OAQg//3hYmxS24epSbPs7G2d2TlmXFMrOH65T5zUq8LWxELiVsq5aMbmMF/6aYDp3EmiYyxMSGL6vNoibva4sEh2B6jmB6joY/ItlZI/ms1Oa5XZLaKAxPEYxL1xifISjwtb2+mqJ0hArK2kAIgbZtd+zF6DDAjAUQ9sYdaGnJRK+9GEtohNFw6SA+XouUoJ81NRZFRS1EZTVGclJ8lZw7GES+sRxpRiHBizZ8AITCWJpoMqtxhJQwPLWwwRydvomD2VT+Gf2SBqOhYR+V5ji3/yW4ScJu7RNDt3lux/81KKXhN6vZWPkJM9MWN8jy+USUxUOc/G1Tw6xV/ybm4DRuv6TAYXLbBIOI1PAQRcq2Fd31YTE/XzA7340D2Ww242QipUQryGmQuLJWrUdfPAf7ukswhUbkqJUaelEJ2qTRsYDMjCK8bqxV63Cdn0lYNNwFXi8uI/rvN9GG9Ucb0g9r85ewfjvub19GyGh+RaFl2UzLPov39r8IxCZbT8+fx/v7X44/xtAcpLjasx1YzJ7arQ0CMoAnt/6GOwp/h2E1nLPU19cw6wpw7gAnyVq01fIgupSMSYjyi2mxWnpOabV56NGDxVnZgpnZbjQhY1t7neT98GgiVBdbeanV73vpNpqf5H8oFMsmtrWCf1MiTgN3ExP9AbxGGiV12zr+5EdJdArGZwjGH+66/ohkt1+yocLmud0SQ8D0HI3Z+ToFCSo4UzqfCsraQAjAX0t03eG5X19sRVZWo48cjHXaWIKHl/ALAe6SMmSVH/3MyVirN0B1IP4kvq8voTorK3bTsuLBHYEg9pqNsZ8njmq1PbnGYG6e+BD/3P57/GYVFw3+Bm/tfZYdVRu5atQPeX//q9REKjkj/1ymZZ6D3YYMliRK5VHZjyOqI5WN9suUNswr0HHpbl7cGSHLG8ta5bYja6XZNu7WQ8VGbFviamFo6mQjhMCqronN/fp0I4RNtPHDkaWVmMN1IscE8LrXRfTTjVB1eMWjAOOC2QjbBr1hUEbEBCmxt+w66gk0NGkBLZd56OsawY/G/4J/7XgcXRgkOZKZ1+9C1hR/SIYnhxn5cwmZQdBbfJpGQlZto2M1kUoszEZvVpmHs67/tzHM3hqbc/o7mFugQ0vFio9hHM6MtbcfSilxcgplx44iAkGkp36lb41ZgbeFlZdbqizy21HBvymmU8dV1/TwpVdPo8Y81OHnbkmiUzAmXTAmPfY/PxSCDRU2d3xiku8TLB6gMylTZc+UzqPqlLWFbaMP7RcLzJ5/F+F2og8fgD2wgICrfqK8K2pi//uNw/WeRH1ABrEPvzeW4zw870S6nGjH1IXC7cROS2m1OULqDHQW8uOxv+bnE/7AcO8UZhUs4GDtXv66+TdkeLIZm3EaI1Mm4rJameQVb57grL6LGh0flz41Xj39aAlEOb+P5JEz3Px8soOBrlMrW9BdbFtCbjbR15cjstIRA/tgrVoH2RmxnR2OVROoD8gAZCyzZuixxzqtKEnFxSRt2Iye6G0UqIkp46hztlx+AkCXLkZ6T+f28Y8xM3M+pm3yeelKxmdOw+dI5K+bf0uGq237wIZFgJ3hz1he+RyZnmy0YzJ65/T7Km678ZD+kazr7RMMfjPLzcX9JYmtLDRRjp+orZ9PBlAdqcCrNz2nLGLB/gDkH+fekxGXgbu5oMxIozZaji27dm6pEIJcr+DsPjo3jNUZnSb409YoN66MsqHi1JlPqHQtlSlrg4TyCqx3VsWyZGs2IovLEcMHEM7PbjDkodk2VAewDxQjcpvY9sZfi2bbuIREf+1DRL88NJ8Xe/setJwM5NzTCXpbr5h+hG65MISLrXWf8NGB1/nq0G/ywf5X2e//kiWDv06+a2ibqrxrmuDzmvfZVP4ZiwdfxUcH3sBteLhkyHXkOYY025541koFY11K+twY58zA+mQ9MmKiTxmDzElvcrhNhhqvwpSBIMK00J06ns/WY78V26IpmpyAcfFcoh+vhYoaxKTRRMePwGrDnCyIDaXrlgchYHL2LCws1hz6gAxPDl8fdSM+I6nVNRi2ZvLq/r/yyp7YQoEcbx++O+5nvLr7n1TUlTC370VMyzinxaHFI1nX1ubCKZ1D1AaRnvqgzG9W4k1svI8pwI4aSYYbnPrxBWWmQ8cwLYRtI7WGuQRdGLj1JALRUpIcjedOdgVdE4xNF4xOE2yokDy0LsqIVMG1ww1SVO0z5TiooKwVDiS8vRK5p4hoUVmsbIChYzschPWGQzxhpwvf1HHYH32G0T8/NpHiqA8KcXohEYcDX1k5cv02rPXbELkZ6KMGIStqsA1Hu5fUR7Ra/rz5V5SGithetYGJ2TOxpYVH86Hbrng9qd2BLdhSMiBxOOmiLyCp4iC7/FtI8aTxly0PU2v6SXamMiF7BmGrDqdwo52iVfN7CyFA7NhL9I0VaCMHoTkMrE82IIrKcCyZh3lM0C3ysxr1O33yaAIeD54aP/Y7R+3qUB0g+s/X0b53OWG3m4jDgd2BL/x1mp/frv05Lt3NxOwZVIXLeWL9L/nR+F8w1H0aADXiELsDmwlbYQYljcS0I+wJbCfTmx0PyAAOBffz2Nq7+MW0v+ARSbH5kCra6lU0fy24jgrKolV4m6jmLyVsqbIZktQJQYoQRJyxArLHbrUEkGBkUBU50G1B2RGaiAVnw1ME7x20+cFyk/8abTAlSw1CKR2jgrJWaNKODQkBREzsL7YAIFKTG821siREp4zDcDpjtccWn4X18bpYpmJ6IebIIbEPmEh9Gl4WlWEVHZ7Ldfbp7W5fVEaoOVweI2DWxCdbj02fAsTqSS1b/R1C0dhcHZfu5meTf4chHNy15jsEzBouGvINgmbsGqsjlby7LzaB+7Ss2e1uj9K5hBDgr23Q9wCo9iNsCcfMKQtkpJOw9Hyst1YiAyH0yaOxxg4jGrVjcxiPjbpsGzsQpM6X2O69M4+wpIk/UkWljHJo7/748bAVWy1XI4q459PvURkuw6E5uWLE9/jzxoexsbl46LWNni8qo/jNGpKN/A7MOlS6mvAH4sOXYasOS1q4tMa7SZSFIWLHSk90hogrVhajqaDMZ6RTFdlPX9/ETvld7eXUBXMLdIanSB7fFGV9ucbVw3RV80xpNxXOt8IWGtppYxvfMSC/yQKVQaebwLRCIhefS+3QQYSvXIT57UsJTBhDyBF7M9GSfeA75k2sIAczseW945riJYW5fS9qcEwTOnneAWiaYOWhN+MBGcTeRD8peYfPyz8iYMbKGGws/5RJ2Q23YXJqLvI8/drdHqVzuM0wSQcPkrhxK/qAPo0fMHV8k3PKTDQq8/Oxli6Cb11M4PSJ+N2xVYvRRB/0yW54QoIXK/X4tkTwksrsgkUNjhnCIM/bH4DN1Z/HF5FMzJ7Bu/tejK8QrgpXkOMraHBulieXDGfb5qMp3U/4a5He2FzaGrOCBCO5yT1/N1dY9PF1Xp2vWFDWdJFkn5FOZWRfk/d1p76Jgm+P1NlZI/nZ6miTBa8VpSUqU9YCTRN4N+9AHipFP2Mi1hdbEW4n2rwZBNKa31PFtqHOOOrbnK5zZMKLLxzCfuYNjLOnYW/YgX2wBG34AKwzJjcqWdAW0hbMybsQQ3Pwzr7nyPDkcNnQ75Gp9UfTBKWhg43O8ZtV1Jr1ixA2V3zBksHXkOhMYXXxB+T7BnDZ0O+QKvq0absopXO5rCjO1z9Ert+OBGTfnNjcr/dWQ8REnDGZ8KB+LQ5112oGaAZH71Ru6jqes6Zhr9mA/eV+tLwstCljCTuO823AFpxbcDkew8u7+18g29uHy4Z8l3StLwJBdbg8/tBERzJbw+vit9/d+wJfG/E9Nld8wabyzxiVNpElg65tcmK/0jtogSDW4Tll1WY5viYKx0Ys2BOAmbmdlymKOA08zRSQTXBkcTC4rsn7upvHEFw2WOOt/TY/+djkzkkOco9zoYNy6lBBWQvcdSHsl9+HcASSE9BHDkSGTWyng2gHvv0JAY6iEqz9xUQPFKMN7oc+fjh2oJaou/UVb83x2mnMz7ma2TlLMIQL3XKBhGjUZkbeuaw89HaDx49Ln4pb9/HWvmfjx57d8SdumvAAi/t+EwduNNvZaQFZVGjsq9PY55ekuQUDEiS+k7gK//FyVlUj12+P35Z7DxEtq0J8+1IihkHY6Nj/xlNbS/QvLyD6ZKGPH44srSD695dwXXcpkdTU42qzz05nQe43OCv34gZ9UCIZlTaJZ/hfAL4o/ZipubN5dfc/gdhQ5V82/ZpfnP5nlg66ESc+hN3+LydtEcRgVwDK6yR9EgR9PTbGKVSFv7PEMmWxTH91pBxvE0HZtmqbTA+4jnOC/9EiTr3ZTFmikUVVZH+T9/UETcSGM1NcNreuMrlzonF49xFFaZkKylqgRa1YQAZQHcD6OPZNTIwc3KHnS6ipgdLD2yNJsLfvge17wO1Cm2cf13/DtsFF4/IXAzyj+c6Yn/LMjv/FljZLBn+dwd5CNHS+P+4unt7+OyJWmMUDr6a/ZxROu/l6Qx2haYJVpYIH19TvEzop2+CGQieeNhaNPeVEmsgGBOuwwxHqXJ4Ob7AoDs8pk3sPYe2tr+skwuEWzmo72UwfzDWGcFPh/fx926PURv2MSCvEZyTxxt5nSHam87Vh3yONPtBFwRhAWOg8ui7CioP1fe77EzzMzjp19qvsFLYdKx7riQ1fVkbKYqtsj36IhM2VklHHUcG/KRGXgae2ueHLDGqtcqJ2BEM7jiq1ney0LA2vAXeuiXLHBIOhKSowU1qmgrIWhL1ePCMHITftrD/oMLAz2p9V0DSBWLsFkeTj2J2LxbRxhJ0uWlrTb2pBSsy91FpVVNaVkeJMp693OB675bYYtocJiWcxeuI0JBKXnYjEpszeA1Jy3ZjbSXFkkGjntLgfYUdV2zqPrW24Pcqa4ij7g06GNJ4brAB2WjKaxwWho4KlfrkdmnN4NM3lROZmIg/v3wqAz4N27PzGZvi1YvbUbqEmUkmury8FrhEYVuvnatJgqHsKdxSOxSaKm0SyXQUMSx2DrjlI03NAdl1ABnAgJBoEZACPrw0xYY6XZFXbrM1EIIh0O+FwWYqqSCl9vIMaPOZAbewt7ngq+Dcl7HKQUeNv8j5N6CQYmVRG9pLp7tiX5q4yOk3DocHdn0W5rdBgeKoKzJTmqaCsBSYC57wZ6Ile5NqtkJ2BOHcmAV9Cu79ea5qA8iqsLbswFn4Fa9U6pL8WffxwzEljsFoIiKRm8m7xv3EZbp7c8tv48aEpY/juyLvxyJQWf7eU4LBiGTCJ5IC1hbtXfy++pVKmJ5ebC39NEtktPU2HmFJQaza+tpD6HGxW0O0h8Zol8PpHyP3FiJGDsGd1bM7h0axwBMfEkdg792F/uR+Rl4U+bijRUBha3k+aoF7Gnzbfz4byNfFjV4+6gTPSFsdWdrbBkQBud3Q99675QbzYZ663Lz8e/ysSZBO1/TpJU/2tzoqtDmzvrgOnMlETQPrqt7uqipQzPHlCg8dsrLToe5wV/JsSdht4gk1nygCSHDmUh3f1uqAMYFiKxuIBcO/nKmOmtEz1jGZ4I3Ukl5XiLC5DG9wX/RsXYl06n5rUtA7N54lGbZgwMjaP59UPEbmZ6BNGYA8bSK3R8lfKCquIQLSal3f9o8HxbVXrOVC3o13tkLrJv3Y83mCPy9JQETtqumaSbLJuMS2vYezv0iHfpya+NkdKsDxu9DlTMC4/D/200USdx592iCYmEv1sMzJUh37aaITTIPrWSuyU1nd9OBDa2SAgA/jn1scps9u34s3Sw/xj26MNqq8XBfeyK7C5Xc/TXnk+geeYr6CFWQapDjV22R5ajT8+n8yWFn6zEp9e338qw1BeF/t7d7aw24G3haAs0ZFDWbh974fdaUiyxgX9Ne7+LMqXNWouo9I0lSlrgjdSh+OjT6EuTHTt1vhxx9zT0SeNbXPF82PV5eXgWXwW9tsfY+/ch3bWVOoym1/FGT/PCpLgSKImXNnovlA0CO1YIxDFpDRU1Oh4daTi2FHVTqHbNt8a7SLFpfHevggDknWuG+cmQ4+quTzN8EVNjNc/JLqh/gPGPW861uRxmG3Yx7Q5dZqO4+Jz4N2PsVatRxTkIq5aTNDlbnVXhqPLqhwRjAYw7fbNR4vKCOV1JY2O+82qdj1Pe6VpJvef4ePx9WF2VEaZme/g0mHO2GbiSptp1YF4OYxqsxKP7sPQ6j9G1pXHsmR6F+wFaTp09KiNblpYjsbpzWRHHvtqP+3039uZhqVozO8Lyz6NcvdkB33UpubKMVRQ1gSjpBwtNYnoax81OG6/tRLP8EGx4ct20gS4amuRbhf6ZedhJ3ipcXnblHXLcOaxt2Ynk3Nmsbnic+YPuAxLWrG92BL6tGubI4dwMqfgAp7a9rsGx4cmj+2yIClFmHxruOCKYd7YFlO2qQKyFjjLyxsEZADW2x/jHdqf6oS27WV6hCbA5/cjisvAYWBlZxBeMBvn3AhRh4MIWpv6T56vP2f1XUSmJxfTjuA2PJTUFpHmyIF2xDVumcjZBUv4144nGhwfmDi8XdfVXlJCH4fJzyY5CEsnPmGDCsjaTVRWx4cvK8MlJDrq57QGTNgbgDPyuijQEII6jwNvbRh/irfR3SmOfD4PP42UsldvED4qTSNsw51rTH4xxUGWp/e2Vel+KihrSl0E2dR+M0eq8XdggWJCcQnyT88ibRkrBpGTgefyhbEsRStcdhIXDrqWnf4NTM2dze/W3UPYqgPgNde/uH3Co6TQerFNTROsqXyHsrpDzOt/ER/uf40EZxJfG/Y9ch2Du3YPS1viVftktklT+1cStRBNrcpsRUJFBfKJZ5BWbLhQS03CddViaj2NP9RakqxlU1FXylt7n4sfu27MrTgsX7uq7ksJYzOmUBUpY/nBN0lypXJe/6+S6Ejr8I4C7WHYVuxNT/XDDtGqqokOiBX7rYgUk+hIid+3rsKmTwI4u7CKfcjjxFcbaTIoc+vJCKHhjxZ3+3ZL7TUhQyMchZ+tNrlvikPtl6nEqTllTZCZqYeDr2NWlmWmEU1qPkvmtKMklpWStONLkopLcFkm7miE5EA1vPxew9WVh8owikubfa5jpcoCJqWezZpDH8YDMoDqcAXrKle26ZuhX5by5y0P89be51hb8jFnFixgVPpEEo10tcdlLyIzU8HdcA6ZyM/CbGbul9OKklRSQtL2L0kqLcVpxeYLOpDwziqwjqoJV1mDvrdxQeHWFNXt4rOS5Q2O/XXzbwhQ1q7nsfQw/7flV2ws/5TZBQsZnjqWv295jF2BTe1uk9L9tMoaZELsW2lZXRGJRgoAtSbsrJYMSOzaj5Sw24EvUNfkfUIIUp19Ka7b0uT9vc20HI1RqYKfrzGbXAylnJpUpqwJweRkEgb3xchMxfpkA/JAMWJIP+Tc6dTpTQcvOhL355uQbyyPfwl3L5qDLK1E65dLtDrQ+KRg028uzRG24FCw8cTq4uB+tIyGn71NMWU4PjfoUHA/L++KbQQ9IX2m6gm9iD8xkeQrL8B65UPkoVK0If3QzppGjda47xnSxr3qc+T79ZPw3bOnYk8ZH9u3tbK60TlaVXWjfVtbE4w27r/BaICIHaY9ObeojFAeKqYqXM7BXXvjx2silbTriZTuZ1mI2mB8+LIsXMTI5EkAfF4Wy5J1ZrHYptR5HCTUNP++mersy8HgBoYkfqVL29FZvpKnEbJsln0aZdlko8v/fkrvpzJlTbAQVGdmERrYH/nVc5A/vIrQorPxe5vPkvkCfuRbKxoc0+rCaB4n1jsfo48e0vAEATK79Un+Ddg6cwoWNzo8MXMmVhsmgCdqGYxOn9zgmFNzke0paOYMpSfYNlSmZ2BdsRDx/SsILz6bymbmknkCgQYBGYB8dxXuQABd09DGDGl0jsjNbPcK4hxvX5xawxUl4zKmkqRntOt5PCQy55h9MgH6d/GcMuX4aZU1sYBM17ClpDxcTLIzneoI7PJLBnZDxfqQ10FiC0FZumsAB0Nru7wdnUUIwTkFGj4D7v0sitkFtSKVE4sKylpQp+n4DRcBhwuzhT+V2zLR9h9qVPxVWhYIgTxUDh4X2qRR4DAQackYl80n2M6tbaSEMUnTuGzof+E1EkhxpfPt0XfQ1zWyTefrtourh93E9Ny5GJqDAUnDuG3yI6SK/Ha1Q+ketcKgxuWlTjbf95qsxi8lWjiCsGJz+PRp48HlhOQEjHNmIKsDaFr7Xvqp5HHrpF/TP2kohuZgRt48lg79EZrVvlIdtg0zsxawcMAVuHQ3mZ5cbiz8JbnGoNZPVnqUVl6JfXj6RnWkDJfmxqm5WFlsMTBJdOlcsiOCXleLQVmqsx+Vkf2ErSZGJnopTQguGKBhS7j/iyhRFZid0tSgVSdwlldCURkiPQVZXlV/h2EgyysRORlYH3yKyMlAn14I4QiRnCws2v8m5pJJzM64hGlZ8xDouO2kdmU9kmQOVw28ha8O/A5O4UG3PGrT8ROY5nFhJfrAf1TJipREhMdFncOFw6Fjr9+DPnUshE2iH6zB+Oo87KYWsrRASsjXR/CTsY9gyhAekju8LZJXpnF+3rWclXcRhnDitH1qNe4JQCutQB4Oykrq9pPqymRvAKojMCate4bdQl4HnmAEYdlIvfEXC10YpLsGsD/4BYMSZ3RLmzqDLgQXDtR4eqfNf6+LctNYA70bglyl91GZss5QF8ZavQF9eiFiYB/QBKJvDgzqgywchT5jAtqQvsiScuwd+7DHjyDobn3VZXOkBLeVgstK7FhAZet47DT0NmyRo/RuVp2JMWcKol9urN/1z8f4ymTsugiWZWONHII2ciDWyrVYm3ZinDuDYHZWh3+fYXnw2GnHvU+ltAUeOxWHpQKyE4VWXIadHBtGLwrtIdHIYMUhi5EpAq2bSlBITSPkcZLUQrYs0zWE3bWruqU9ncnQBF8dpFEVhgfWqqHMU1WPBGXPP/888+fPZ/78+fzyl78EYMWKFSxcuJC5c+fy0EMP9USzOkxLjb1RRV98F+F0oH/lNMTQ/tQmJlGTlkZwcH+sxWfDDVcTvuJ8alJS1QeR0imspASi73yC8Hlj/c7jIvr+mvgqYb/HR+0ZUxDf+xp862Kqhw8hrKkEudJ+enEZ9uH3ugPBXRQHs0hzQYanez9GahNdJFcGm70/xzOSXYEVJ+QIgEMTXDJIoyYCv/g8Svg4ikUrJ6ZuD8pCoRD33HMPf/3rX3n++edZs2YN77zzDrfddhv/8z//wyuvvMKGDRt4//33u7tpHeKrC2G9+B7GwlmIzDTsrbuRZRVYY4bF56GFhU5Ad+J3uI97/0JFOVrE4cBYPAcZqMV6dxUyWIdxwWxMo36lpmlJalwe/A437Ry1VJSYUBgRDCGTEjFtk5K6g5SGsnpkc+1AoovU8ubnjCUaWRjCyaG6rt26q6sYhwMzW8LPVkfxR1Rgdirp9leUZVnYtk0oFCIajRKNRklISKBfv34UFBRgGAYLFy7ktdde6+6mdYi+9wDsOUj05Q8QBTnos09DpqUQ9qihQaXruf0Bon97AZGYgD57KsLnIfqXF3AHTpyJzkrvpx84hJ2eCppgU9VOonY649NdOHpg3pM/0UN6WfP9WwhBvnc8W2ve7MZWdS5dEywZoJHlgZtXmRysVYHZqaLbg7KEhAR+8IMfcO655zJr1izy8/MpKSkhMzMz/pisrCyKi4u7u2ntpmkCKmpiN8wo9qebsN5eBRt2oKmUhNINRF0YLIm9cQfW2x9jb9oJto0IN79xs6K0l7HnAHZmGn5T8uaBTaQ5C3qsCr0/2U1yZQjNav49tq9vIttq3iZqn7ivA00I5hXoTM7UuGWVyZpS9ZlyKuj2oGzLli38+9//5t133+XDDz9E0zR2797doCJ9b9+77AjbljCwT+M7Jo/G1NW8HaXrWclJkHBM1dXkBKzkxJ5pkHJS0r/cRyAjgz9uiWLo6xmQOKDH2mIZOoFEFxkl/mYf4zMySHX2PaGzZUdMztK4ZJDOoxui/HFzlIiaZ3ZS6/ag7KOPPmLatGmkp6fjdDpZsmQJq1atorS0fsuh0tJSsrI6vkKsO4Uy0xEXzY1tyaTriDMmYY4cEgvYFKWLhVwuxFWLoM/hvf765SGWXkDQaF/9MEVpjqisAX8tj5YkkuXdi1MDXzuLBne2inQfefsqW3zM4MSvsLriSSwZ7aZWdZ1+iYLvjNLZ5Zf8aIXJpkqVNTtZdXs6Z/jw4TzwwAMEg0E8Hg/vvPMO48aN48UXX2TPnj306dOHl156iQsvvLC7m9YhptCJDh2MZ0ABSEnY6UJ9kVG6i5RQk5SM64rzMUyTqMOhFpMonUZKycHlmyj35jAqTafS/JAcz4geH8koy05i7Gd7WTupL7KZQsiZ7sF49BTWVf6HwrSLu7mFnc9rxEpmbKyUPPBFlJGpgq8NMcjz9f5RJaXtuj0omzFjBps2bWLJkiU4HA7GjBnD9ddfz/Tp07n++usJh8PMmjWLc845p7ub1mFSyvrMhArIlB4QFjphpwrGlM6zs9rmHxtD3L55EzWTp5LlLWJryTYmpX+tp5tG0Oci5HHSZ08F+wY0n7Ubm7KID0p+S1/fJNJdPTfk2lmEEIxOEwxNFqwoltz8scmYdMGCfjojUkSPB8vK8RPyRCzm0oTy8oAaMlS6VGZm8/O0VP9Tulp39L9aU7K61Oat/Tb7Azb/79By8uwge8cM472SX5PjHkWOZ8Rx/57OkFJRy9DNh3hjwRhMV/P5hb21n7Kl5nUW9XmANFe/bmxh16uzJJ+VSj4tsxHA9ByNyZkag5MFRievjG2p/ymd56QJyiora9WHotKl0tOb35Be9T+lq3Vm/zMtSVUESkOSoqBkl99mS6Vkb0AyMFEwxRvi7G2rSSo+xCeF6XwaeIEURz4Dfaf3qmzMgO3FJFbXseb0gVSn+Zp93J7a1ayveoFxqUsYmXwOSY7cXnUdx0tKyYFayaZKyc4am7I66J8oGJgk6Jegke0VZLghxSVIcNChHRha6n9K5zlpgjJFURSlXtSWXPDvSqrDbXuLd2owMFXnl+/9GZ8Z2+h+V0I1dXrsZ43mg56eNKw61q57CnexNaX5Sv+2LMOiKH7bKSaR7nygy9vXE0JRye5qC7Od6wFm93OybKbKiPUkFZQpiqIoiqL0AmpDckVRFEVRlF5ABWWKoiiKoii9gArKFEVRFEVRegEVlCmKoiiKovQCKihTFEVRFEXpBVRQpiiKoiiK0guooExRFEVRFKUXUEGZoiiKoihKL6CCsha8+OKLnHfeecydO5cnn3yyp5vTJZYuXcr8+fO54IILuOCCC1i7di0rVqxg4cKFzJ07l4ceeqinm9gpAoEACxYsYP/+/QDNXuPmzZtZsmQJ8+bN4/bbbycajXZbG0+F/tZRv/3tb5k/fz7z58/n/vvvB5r/HyonlhPhtdmZjr3eW2+9lblz58bfg998803g5LlepZ2k0qRDhw7JM888U1ZWVsra2lq5cOFCuX379p5uVqeybVvOmDFDmqYZPxYKheSsWbPk3r17pWma8utf/7p87733erCVx++LL76QCxYskKNGjZL79u1r8Rrnz58vP//8cymllLfeeqt88sknu6WNp0J/66jly5fLSy65RIbDYRmJROSVV14pX3zxxZOun56KToTXZmc69nqllHLBggWyuLi40WNPhutV2k9lypqxYsUKpk6dSkpKCl6vl3nz5vHaa6/1dLM61ZdffgnA17/+dc4//3z+9re/sW7dOvr160dBQQGGYbBw4cIT/rr/+c9/cuedd5KVlQXQ7DUeOHCAuro6xo8fD8CSJUu67dpPhf7WUZmZmdxyyy04nU4cDgeDBg1i9+7dJ10/PRWdCK/NznTs9YZCIQ4ePMhtt93GwoULeeSRR7Bt+6S5XqX9jJ5uQG9VUlJCZmZm/HZWVhbr1q3rwRZ1vpqaGqZNm8ZPf/pTTNPkyiuv5Nprr2103cXFxT3YyuN3zz33NLjd1P+2uLi40fHMzMxuu/ZTob911JAhQ+I/7969m1dffZUrrrjipOunp6IT4bXZmY693rKyMqZOncqdd95JYmIi1113Hc888wxDhgw5Ka5XaT8VlDXDtm2EEPHbUsoGt08GhYWFFBYWxm9fdNFFPPLII0ycODF+7GS87ub+tz35Pz8V+tvx2r59O9dddx0/+clP0HWd3bt3x+9Tf6+TQ298bXalgoICHn300fjtpUuX8txzzzFo0KCT8nqV1qnhy2bk5ORQWloav11aWhpPOZ8s1qxZw8qVK+O3pZTk5+ef9Nfd3P/22ONlZWXddu2nQn87Hp9++ilXX301N954I4sXL1Z/r5NUb3xtdqWtW7fy+uuvx29LKTEM46S9XqV1Kihrxumnn87KlSupqKggFArxxhtvcMYZZ/R0szqV3+/n/vvvJxwOEwgE+M9//sMNN9zArl272LNnD5Zl8dJLL5101z1u3LgmrzE/Px+Xy8Wnn34KwPPPP99t134q9LeOKioq4rvf/S4PPvgg8+fPB5r/Hyontt742uxKUkruvfdeqqurMU2Tp59+mrPPPvukvV6ldWr4shnZ2dn86Ec/4sorr8Q0TS666CLGjh3b083qVGeeeSZr165l0aJF2LbN5ZdfTmFhIffddx/XX3894XCYWbNmcc455/R0UzuVy+Vq9hoffPBB7rjjDgKBAKNGjeLKK6/sljadCv2to/7whz8QDoe577774scuvfTSk76fnop642uzKw0fPpxvfetbXHbZZUSjUebOncuCBQuAk/N6ldYJKaXs6UYoiqIoiqKc6tTwpaIoiqIoSi+ggjJFURRFUZReQAVliqIoiqIovYAKyhRFURRFUXoBFZQpiqIoiqL0Aioo6yHDhg2joqKiwbHXXnuNpUuX9lCL2q+wsJD9+/e3+rjf//73nHPOOZx99tn85je/QS34PfWsX7+e73//+42OX3fddTz77LM90CLlVDV79mzWr1/f081QlCapoEzpUu+//z6vvvoqzz77LC+99BKrVq3i1Vdf7elmKd1szJgxPPLIIz3dDEVRlF5NFY/tpX7zm99w4MABSktLOXDgANnZ2TzwwANkZWUxe/ZsFi9ezMqVKykqKuKCCy7ghz/8IbZtc++997J27Vpqa2uRUnL33XczceJEbrnlFtxuN9u2baO8vJzZs2eTkpLCu+++S2lpKXfffTfTpk0jEonw4IMPsnr1aizLYuTIkdxxxx0kJCSwZs0a7rrrLoQQjBkzBtu24+395je/yaWXXsqcOXMaXMebb77JggUL8Hq9ACxZsoQXXniB8847r1v/nkrPWrVqFXfddRd/+MMfuOWWWygpKSEvL4/y8vKebppyAlu1ahUPPvggeXl5fPnll7jdbu677z6eeOIJhgwZwje+8Q0Abrnllga3AWpra7n11lvZs2cPmqYxatQoli1bhqZpvPPOOzz22GOYponb7ebmm29usE+wonQVlSnrxdasWcOvf/1rXnvtNTweD0899VT8vmAwyN///neeeuop/vjHP7Jv3z7Wrl1LSUkJTz/9NK+88gqLFy/miSeeiJ+zadMm/vznP/O3v/2NP/7xj3i9Xp566imuvPLK+OMef/xxdF3n2Wef5YUXXiArK4sHH3yQSCTCD37wA2655Raee+45pkyZQl1dXfy5n3jiiUYBGcS2yMnNzY3fzsnJobi4uCv+XMoJYNmyZYwbN46XX36ZO+64g127dvV0k5QT3IYNG1i6dCkvvvgiS5Ys4cc//nGbznvzzTepra3l+eef55lnngFg37597N69m4ceeojHH3+c5557jrvuuovrr7+eYDDYlZehKIDKlPUYIUSjY7Zto2n1cfJpp51GQkICACNHjqS6ujp+35EAKDs7m/T0dKqrqyksLCQ5OZmnnnqKffv2sWrVKnw+X/ycM888E4fDQWZmJl6vl5kzZwLQt29fqqqqAHjvvffw+/2sWLECANM0SU9PZ9u2bRiGwbRp0wBYsGABP/vZz1q9Tillg2uVUja4RuXUsmLFCm6++WYA+vXrx5QpU3q4RcqJbvjw4UyaNAmACy+8kGXLlrVp8+6JEyfy0EMPsXTpUk4//XSuuuoq+vXrx5NPPklJSQlXX311/LFCCPbu3cvw4cO76jIUBVBBWY9JTU2lqqqKtLS0+LHy8nJSUlLit91ud/xnIUSDCfIul6vRfe+99x733HMP11xzDXPmzGHgwIG88MIL8cc5nc4GbTCMxv9+27a57bbbmDVrFhBL8YfDYQ4ePNhogn5T5x8rNzeXkpKS+O2SkhJycnJaPU85OR3bj9vShxSlJbquNzqWmpraoJ+ZptnoMQUFBbz55pusWrWKjz/+mGuuuYZly5Zh2zbTpk3j4Ycfjj+2qKioTYGeohwvlbLoIWeccQZ//etf4/Oyqqur+c9//hMPhjpi+fLlnHnmmVx++eWMHj2at956C8uy2vUcM2bM4MknnyQSiWDbNj/96U/51a9+xbBhw5BS8v777wPw9ttvN8jcNWfOnDm88MILBINBIpEIzz77LGeddVaHrk858c2cOZOnn34agIMHD7Jq1aoebpFyotuyZQtbtmwB4Omnn6awsJDU1FQ2bNgAQHFxMZ988kmj8/7+979z6623MmPGDH784x8zY8YMNm3axLRp01i+fDk7d+4EYouVzj///AbTNRSlq6ivqT3k9ttv57777mPBggXxb3oXXHABixcv7vBzXnrppdx4440sXLiQaDTK9OnTeeONNxpMyG/Nf/3Xf/HLX/6SxYsXY1kWI0aM4JZbbsHhcPDoo4/y85//nF/96leMGDGC9PT0+HnNTfSfPXs227Zt4+KLL8Y0TebMmcOiRYs6fI3Kie3OO+/k1ltv5dxzzyUnJ0cNBynHLSMjg4cffpgDBw6QlpbG/fffj67r3HTTTcybN48+ffowderURuctWrSITz75hPPOOw+Px0Nubi5Lly4lOTmZZcuWccMNNyClxDAMHnvssQZTQRSlqwipikYpiqIoJ6Ajq3pfeumlnm6KonQKNXypKIqiKIrSC6hMmaIoiqIoSi+gMmWKoiiKoii9gArKFEVRFEVRegEVlCmKoiiKovQCKihTFEVRFEXpBVRQpiiKoiiK0guooExRFEVRFKUX+P/0gEHvnlQFmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 616.475x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.set_style(\"ticks\")\n",
    "sns.pairplot(data=data,hue = 'kind',diag_kind = \"kde\",kind = \"scatter\",palette = \"husl\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c9ef39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}