Lesson102-One-Vs-Rest and One-Vs-One Multi-Class Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第102讲 One-Vs-Rest and One-Vs-One Multi-Class Classification\n",
    "\n",
    "## Python学习：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "## Python源文件及数据下载链接: https://github.com/rayc2020/LessonPythonCode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-vs-Rest (OvR)(OvA) One-vs-All\n",
    "\n",
    "One-vs-One (OvO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\python\\Interview-Prepartion-Data-Science-master\\Lesson102-2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F5ZlRUNeimG1Hq1m/3tSvM80tJvGcsOijkxhmK6uzMQomJkQVzzVNHaaxxbQ3m1KniX00EgWx50blMe4qugCLWF/Gi1EiRnrx4sXTp0/v6urCXhwmYpitFiaEEEIIIaMMai0h+wpWTLsTiLu1NoZsRtvTWsigBrHfKDJRGCWxl0TpuquUw77QhJ7IbewazzFuUXJMGEfyglzRSQTJsLfUbmvP3fd0/WU3rrnzAbNipSlBiWUh1bNyrZ3a/ncJSCx8FVX0FbWu665atWry5MlNTU3Y1Na0gC1OCCGEEEJGG9RaQvYVMq/TBOKBaq0NAiQ0ipJAll5tWfwT3YVVyr2yoazL2kcb+0ii7QRtS9HAxoWS2bB10QWXP/yxLz551jnFKdNFa0NPL1HOtBboGHYMitnBpFcX44gaGhqmT5++efNm7MpWaLMChBBCCCFk9EGtJWRfIRNFTSDurbVin7pgqgVRJrVZSGwakhhBy0axWCqKC7IYK3WjKPACX54GhXx71bG0YB+FHKETSHJQMtu2zLzwjw+d8NXHz/lJ69S3TClnAldvsEV5lFFsuztBb5pFDFBl/fr18+bNW716NTKz5x5nTku5JYQQQggZlVBrCdlXyERRE4gzrRXjFO+EhlqtTZ0UQprEMF6oox/IxcMIQQidhdOqqaKsBjFSeKntAS0LUMjEBNBYtJFID2EgJeUpU53t7c+/1nDn+NqJj/vr0Lsjd+k6ofHUbFOktG1QQEIbt1uVQGjVV5uammbNmrVw4ULk4NB0r67WEkIIIYSQUQy1lpB9BRHFigRiuF91tbzgxwSxvDRWryoWaYVgxkXjhXLdsGc6Wgorlnk1C82ylaalw/iQxsCHoNpVXHFaBNSBF9t3z9pVX6hl7MdQ3dA1vhvCbe27fSJ5XJQs7eZ801U0Ti4O7DOVobVhZKRCrD4bBHBsk8i7gWz7Ojbbj2xa4UUNyUGZOOnq6Jw5fUb1vKoQBl5e8pXdhBBCCCFktEOtJWRfIdM8TSDu1tqwrLV2sRT+6Mqjn7Atb+4pLFzw2uVXTTzrf2f9+RpT1wBLhaRCTq1mynJoHFrntFormXDUyJPLlY1x4lJgfE/uwxVBxm7fFyNFFeioGzle7Ir8SpVQPFV9FUFWjxNZJUaOXu6slzRjN3pDo9odRo5ROm7V3HlwWiSwiZZxFMAOkBBCCCGEjHKotYTsK2SapwnE0Nqqmnl15Rf8AIiiZxLoZxHaCNsMfFP0zMwFL5/9yweP/uKscy5KVqw0Qd4xXs74xdiLZOFVxFJ/ohlpXATVCwIvlDVYtBdDW63uSrD6jHKhPGIq8pLYkzfhQoCNKdnhhTEad0sYSODJ9c9yFXEsly6jMgbpS3fo0BHnFpxiadmSpfPmzG3auk12QH1DedOPjIQQQgghhOwDUGsJ2VfINE8TiFVra+tXx/b9N9Y1U62VV+WIgwamo2jmLVty5b9mnP2blX+5KV5TJ84rEhr4UUlWaeVJUFZZ7WXJ9iVAoVREY5BZLzR+7AVwYHtnrVSIPekEquvKXbV2IRfdwWnRaBK48pYgMVbP+CiAdoJIrmQOA3sBsoorwFCxDyNfsWz5jGnTW5qake+7aFkK4HCAlCOEEEIIIaMdai0h+wqZ5mkCset7c+fPW7Vmtdwhi0yoKfw0vQxYtyGonunoCtesC5etMuvWmVyzCXIm127yHcYripQGvjxoCtYpFhvJiq/ni9Yi5PKmtWg6Sib0XONHsSfBBE5cioyDEBgvkAVZvYXXXmAceqaz1bR3mo5O4xWQ7RgXTuuZuBC5UGF5UlXoR+kdunFD/ZpZM2ZuXL9BVmjtmrPcakutJYQQQgjZl6DWErKvkGmeJhD3o7XYozF+ygOMxWzDwDGRK4u4QdH4hY6qeatvuWvNnQ901CyQq5TlFlr4pCzAOjFMNRCzhas2blv/1Itr/n3/lkee9TZvsMvAfhTCZj0/KsWJFyW+b0Kpj9r2Mcxitx1tbS+/0nTT/VvvesSXlWFRZDgtbNZPJOGZKJSfsYn8LRvXTZsyde2ahgCl5AW5cmuuPQQcjSCHQQghhBBCRjvUWkL2FTLN0wTiTGvlHTzGPtMYqNYi6LotDDIK4ghSGhjfMa2dG26+/5WPf+PV406ru/0/prUjDMMiZBNKGpXyUcEasJHLkResmnnBpY9+9MuvffOcjqlTTalDrDiyS7toOZSnQMkFy+jO3m4rwfXN6trFf7rqrbHfnPK57+fenC49JiEGYDx5QlRoTA4jwRCTuGXr5jmzpy9bstR3PX0YMkarS7XUWkIIIYSQfQpqLSH7CpnmaQKxV35vbRKpCNrd2ZqtJuyVvXLLbGyfYtye23jnhMnHf+etz363/q5HTFsuDiMvgqG6JnZc0dAoFwYioCs3zvntX1/43Gmvf+dcp3quiQpy0XLgGS+MXXlzkFyyrForz2HWt+O6ZsP6hVf+7c1jT3/zpB95s+bbd91iAPYaZXlSsjzUCqPatrWxunpeTU1V4Pn6Rh/EulrLi5AJIYQQQvY1qLWE7Cv01bzQ9ZbPrm5cXmf82IQwwrLQAiQ0DafVZdtIbmqNI3fD/KpVd46vv+P+5rk1JlcUg3UjE3uJcQP7ilopHJhofePmV99YcfvdDU880rl5ZWK6ZLU2QJEgCmI/kUcZl+yDkW15qYpG4sK2hmmvLb/tvoYHHnca1spSbRiFIbq2xTBCP469qKaqetqsmV3FAg4qW60VrbVDLVs6KhBCCCGEkNEPtZaQfYVemodNaO2SOdUbV1qtjVKtTUsh1qBmC7eFTsZ+LioWim1ma6PZtsUU8/Km2cA+yDjy4wTGGUJZ5aHGyHE9v2Wb6WgxzRuN6Sq0b+yYU12cs9CtX28830silColsRPJYi+6N3GQJI4fdzrFbaZ5i9nWZIpFE0doEKOQHjCqUpzkguULlk+bNqO5s3Np/epnnnmmtbkFwwv9AMEuKtuX31rskRBCCCGEkFEOtZaQfYVemodN1/fmzK9e3lDnwynlhTxSwv6zNmsT2ES+rMiauJTYy4ux5XsIQeAVA0/eOYvCvn0AMtQTwbFvlRUbDU3gGidvguLaV15968cXzTj7tw33PCZPOY69kjwGWa5d7jJBQS5JjkNZ8vWkfuLJ5cf2/tsE1htILvqB3W5bvXH2mzMbN21py+cv/9u1p5xyyuOPPlbMF9Rs5WHI6bAFPVJCCCGEEDK6odYSsq+QaZ4mEJd8b8bC6sXr6kr2QcYwW1FCq4VpkEuP4ZOJbx9k7CVRFAWJ55liMXZduWjYFkjkGmaIr43lOmETu6EbBsU4QIY8uDiXW3LPI08cf/qTn/hm9RV/Nxs2mrDoxXDfAPpbMkEeTpwEfugFkesFJQQ4swmk7ziUN9vClEtBvG3t1qq3Zq9fuRYivXTZipO+fPJ//dd/XXn5FZs3bpLR6sXSiHgRMiGEEELIvgS1lpB9hUzzNIFYV2tXrqnTJyFLvsqhFrOKil0Ikg1bjCK5gTUK/DiCZ7r2QU5ybTD2w1/hvmKxyJWHQUGDCybuiqIwgr3GG1+c/PqPf//y93655M7xpqPN+AVZqfU9eSstDDYJTRwEfimMpXF0g1bxwwnCkizdStubtzW9+cbU5UtX6Jrw5nWb/nLFXz/7mROefvKpQi4v7/jBKO2arZitxR4HIYQQQggZ5VBrCdlXyDRPE4g9z6uqqa6tr4vlacPlR0NZrYU5wk/tOm2skmndVd6sg91w2q4kyYWBEwWRXLycCi1+woGRncQ+WoD3ivpGsSl5Zs0mM2uJmbM4rKs3bsF48uYeAwXNF0xXwbR3mtYW09mBXvw4CX155DF8GIXQQi7yWlvb58yavXDpMn3EVFiSC57X1ja8+PwLjZs2Iwe6jcB7awkhhBBC9kGotYTsK4i7WtT3EENrq6vn19eukSuJI8gi7DDT2hhC68l7ZiGLEFb7qGQ/Np4Pb/Tt6qxYbBJHsdxkC/0NA3lWVFGWcN0wckyAPVE+lKVbuZzYD+XmW9eJvFIY+nLdMnpDdwXHLK3b9MgzDeMf7Jo2M+nMi0Rbq47cMIhCdNTZ2b5kdtX8aTNKPpowJTQiVzcb3w/hsZ7jqsoiM723lu+tJYQQQgjZl6DWErKv0K/W1lTNb1i9RpZh+2htaLXWSwJ5Yy1cE6oJF4VPRlBZeQSy3BgbBrK8ah/n5IZJmwk7TeDIAq29MzZEDbQbG8c1rpckIslymy5SIVIioabotkx68z/f/OHtJ35lznU3xk3t8gwqebpyktjX9uSLuUUL5y+cOsNpaUNTuaBkzVX+BWHs++m1xz7aLzstYmu1UowQQgghhIx6qLWEDGtgkxpgdLsYrICmQGUTbInsybOXNDOUFVDkxcVivqaqur62TnrQ2qqFVnrlgmMbxA/Ttu0qrQitbTMdXuCFRS8olUK3ZOKCCZ3AF4VWM1aXRsOQYSEJo8SHDyeohrHJ9cnNT7x07xfPuOOYL8y+4jrT0lEsOjJUWwXivXz58qlTp+Y7Oq1J40TIUqxtqvswZYg9QSYKaO/YrCxMCCGEEEJGGdRaQoY1FUI5gJBWrtBajYNAllWB7/tBJK+ZVa0VvdSqqrX4r39EZCWgtTCSa4ljX+6SDT0TBbFXLMJvTezHke/DaK31BjE6heh6UTknNCVH3kUL5ZSLh3OFuGZ5w10T6m6+s+uVt0xbB7qBeGOEcNqGhobZs2dv2bIFXop8HE6/gmrH1oOsWJbAMJCvaUIIIYQQMpqg1hIyrIHIZaiw7ZSsMBwyDhOIpbwjx7MPdIJYVphdsVjcntb2D/JD24iUieWZT2GQ1K8ztetM3QbT0gazRVZoIsdzpZgWtg9MdlDZ8Y0VWmy69uHGshrsuqZUkmcjtzaZYk6eilx2Udjs1KlT16xZo5uIgRxXGRxmmuqD7kIMQ9bVaY0JIYQQQsjog1pLyOgFAiiaKku1kFvfDdRysUfEN5ZXzw5Ya22I5f7X0PiO07B28t/+9fRPf1/zt1vN4tWycmsC3/hu5EkxL4JOO0HYBdE1kdyL6ydREhfkWuUYZhuEcSzv44FyBzbDS+LQ8zzf9zdt2jRz5swFCxZozxgt8hFrWhPW4gVbpB90lwrtDooRQgghhJARDbWWkOFN2SR7B2hdf8FanqzTppuRvKIncuNNazdv29SEivBMvSBZZW9gWmt7Dk3kw0LlZ+AsW/XMub/758e++NipPzZvVZt8MfQKgTw1Cq3L1cgo5xl5QrJ0Js9DlrtrHROXZFz20mg/Nm5kCk5SLJjAhdZCWeHbs2bNWrJkCQaJw9Hrh7MrqHdFa+HAyJcHStliut7bb0lCCCGEEDLSodYSMrwRzywLqg2ZsvYb4HvieeXCCLGXBKXw0Qcf+8d1N65dsw4N+m6AGKbX2dk5IK1Ftj4HKpRnRoXGLRWWrJh26bjHzvjJzN9dZapXQGsjtxCZ0DW+E3lQWQRXrkBGb4FxfeO4ceQ5cqmxHIrcoytXJNtXB8Xp5dOtra3V1dVVVVVdXV2qpiqlihxd2U7Tg62QVd1EfuaxKsPYRKYtQgghhBBCRhvUWkKGN+KZ3Y4qLqgGi/x+g+6yVUQaUcU30Nq7b7vnEx/55DVXXtu0uRkF9FHDoR8NdLVW3mErfYRy5XAcmK5Od8nyzulzC3Pnm21N8Nk4gsaGceIHgVwzjGGgl8hzjeOYXNF0FSDDQew4CCjsB8YJTSlEe9DbApQ3CpcvXz59+nTILboLw9B1Xeh3qVRSL61U3H61Vp0WMrx58+bsMVGaqS0QQgghhJBRBrWWkGGNU3QRSgWnmC9pQLpys2/QvYVc0cm5CIWOYkdz5y3/vPXIQ9/7gSM/eOWf/7J2zbooiGGbnuMPfLUWMQrbVVuYbeAaH8paMr5nnDwcWl7f45es+opJBo4rdXw/v2z12gefWnX3w1tnzzGFLnkyMQRY+hUD9yK5UBlau2zVyrfeequ9vR3dQUrBM888c+ONN27YsMGBGJdVVgZTjjWhadVXyPC0adOuu+46GLIqLmIU0GuSCSGEEELIKINaS8hwoVLPNPY8r251/X/uffCBe/7z0PgJE+5/GGkkdLPf8MD4/zz4wEMISKCw1h1/1/0/O/f8ww46fP93HDD2Qx//yxV/bdraDJ/03WBAWgs19OwTjG0xkVt5wU/gJvDYyI1iz08QAr0/1jcSYtiv65iuwtpHnnv2iz945oTvrvzHXWbLVnknkFzJHLu+55hENsJ4zZq1b7755tatW13XRSeovGjRorPOOuvoo4+++eab8/m89Fw+SwAFEKvNaqyZ69atu+CCC1Dr8ssvb2xs1DJAbycGlau4GuumFsCmgjQhhBBCCBn+UGsJGS6oWWUJxNDaO2678yc//t/rrx6n4e9/+8cN1/79pr/fjLhvuP5v4xDGXXcDAhLIQflx19xw3VXXn/2d7x9+8HsOfNdB7xlzxB8v/tPmjY1w2mK+NFCthdNa87PBXpMcxoEXu14SOCYoyWOQY7Qm1x6LtiZy0XLgmkKp4ZFnnj7xrKeO+faK6241GxtNIYf9fimPXl0TF+OooWHd7Jmz1q9f7/s+lFIPv6qq6rTTTjvwwANvuOGGpqYm9KhLuNK3JTtpAGkAJV67di1k+IADDrj00ks3b96s67daq5evaiYKKJqZFahsnBBCCCGEDFuotYQMFzKJ0gRieN2DDzx09V+vady0pasjJ9cVF93W5rbO9q58V2F7IZfLaQLlEfLthY7mTpjwYQcdfuR73vuTc/935vRZEFc1wIHeWytgr73BFj89k0BKHROW7Dt7EDwUgBXCFlEmgiKGnl+I3MLmeXNrxt1ac/VNm1981bQ1G98xbtHEYVgqoUZj07bX3npz+epV8sJb65l6ElpbW++9997f//73CxYsqLRZAFNFGdVRFVHEWqtQKDzxxBO//vWvX3/9ddRCPgrjZNp6qcpmOVoFZO3rmm02BkIIIYQQMsyh1hIyXMgkShOI4V133XH3X6+8ynN8kUgNMDiN+wuoJcitr+nzkCM3dvPe+Lvu/+B7j/75zy5YULNQnxfl+z5EbsBaq11bZc0WbwO5ihh+mzj2KuW0AAISkE15lU9gnIJp3AZ/NR3tcgVy4MnduZ5v/KjU1jl98pRlq5fn3EKUSB2VVenOmC1bttTW1moawqnOCSotF0eMGFV0L+JSqbRs2bLsdlwpVC6mm1oSVSDMNTU12h0ypWObrmyfEEIIIYQMZ6i1hAwXVLqyBGJo7fh777/u2uvdkhcFceCFCCKWOwiV2BxURPU7brvzt7/+XfW8Grk82D5eGPIG6xuo1soeKSMhLYU0NDCS6iqzWYF0lwuDDTAOmKI8X0re7+Pi2FAMB+iXnCUz5i6fXV0q5gMjN+VqqyDTSyVT1soySONEaUmge9EsEqiCHN2lp1Rb0CrayIoVKy688MILLrhg6dKlWjhrSqsQQgghhJDhD7WWkOFC5lGaQAw9g45Ca0sFR1wRAS4G64R5IdFfyOpKwlaJw8R3g6WLly1ZtDT0o6wMGKjWIhs9W0203WlCXRYh7rkXsSakJP7p+249E8I241iWRRMnCKuqahZNm2u6SoFTCmMvs0qQjRMg3ddmkYC7ogpi3dQySGdOm8mw7sUupPX2XZzeJ5988gMf+MAHP/jBJ554IntOFWKlcjCEEEIIIWTYQq0lZLigYpYlEMO77rtn/Ljrbsh3FfS64iiAf+5Ea6GnWl2SSOibY+0bfeBskDo1upJb9P2B3VuLbNihqB56yMpIlTQHu2QvcuxeREEiFyoX5P5bO2i4rL0TFm7rhsmy+oY3p83saG43vmixvPLHAp9EIYwfo83UVK+a1jRiPUC1UMSKWm5l9aw8YoBMTQAUmzJlyte//vWvfvWrU6dOheTrXuRrgawWIYQQQggZzlBrCRkuZMalCcSQz3vvvu+6a68v5kupPdrV19Qb+waLaq0klEiuQ1YfhuwhH9om+SYulQq7v1qbldG03ezeaxdTIYVwWs+YvInzdo+xD0mW/o1Zvrr+jRmzNmxp8nx9+BMUOF2D1VgTAMPGqdAcXYbVAsVisaOtM/BCHJ3mqM0q+pZa5AA1VT1wJOCr2g6affHFF1944QXXdbELJXWvFiOEEEIIISMCai0hw4VMpTSBWLV23DU3lAoObBA5ukv3Zukdk5WEs2ki29yNJyFjj21Cf/Smci96iU0SxpEPp7YLzKKmcF0rjVu3Nr351pQ1DetQViVTzRO7FNte96aiOYj1KuIZ02be/I9/N67fEnvW5e1zsFAgSqD+sR96sb3lF+2DrK4mFHSKrjGwdNuiZQghhBBCyEiBWkvIcCETKk0ghm6Nv+v+frUWwmZvZd05Wa1eWot4N1/ws2tIr7YjXRfVhxLrZnt7+4wZM+rq6kR0y4uoALs0oSU1rWgOUKFFlfr6+vN/+vNPfvRTt/zz1lxbHmdEnqdlz4w9klhPERIoDFAra1/RBrW1NMui+YQQQgghZKRArSVkuJAJlSYQjwKthTEiXbIvp8UmpBTpWbNm1dTUFItFZKKA2qamNaFI/YpNRVdWkVi2bNmpX/vGu/7r3Zf98fLOli6cEXkNErQ58nEkGmNTl2qlCg4xvSFXkLbKpFmWNIsQQgghhIwcqLWEDBcyp9IE4j2utVmOxkOzWqsXAAMcDlS2urp67ty5juOoxGIvXBdmq6um2SCBVleQ1l3qyaCtre2+++674PxfTJ08DedHbjm2xxjGco9uprXqzKjluwEykAa2gW765hBCCCGEkBEEtZaQ4UImV5pAPNhai/YHW2sBxBKb6AsGu3Tp0qlTp3Z2diI/010kNm7cuGzZslKphDR2aX6WyMAhAG0TcWtrq761SIJtTfvKKqoDR0Fct7q+bkU9jgu7sr2aVjSHEEIIIYSMRKi1hAwXMrnSBOI9q7VZ3WxzCLRWPdP3fcdxVq9ePWXKFLio5qhmI960adN111137rnnzpgxA/moJfXtLk0gR9Nw10xfs71ZQvsCmgOnVa1dOH/R735z0Z8uumTVitVxKE3ZoaXYGoQQQgghZARDrSVkuJApliYQjwKtBXBLdLR+/fo333yzsbERuypF1HXduXPnfvazn33Xu9518803w377+ioSaESbUilFDNRj0YIWU4kFWkZSiXFL3mOPPP7eI9734fd/5Oknn6nU2u5ihBBCCCFkJEOtJWS4kCmWJhCPAq2FOsI2t2zZMmPGjPr6el1rRSZAQkV02bJlZ5111kknnTRhwoRSqdTLTgFyEKOu1sKwtQxy9B5abGqOZiJON2MT+tEbr7355S+e/I2vfLNqbnXWJpBB0GwJIYQQQkY+1FpChguZX2kC8R7R2oysriYQ71mt9X15EDFQpUT7kEZIZkdHx5QpU5YuXYpMbGb+CVAGoOKbb745fvz4bdu2aU66u9wI0FpIdHZ2Ig0weGyK09q31EooH5ck5ECwIaGjrXPi408+89Szxbw8kBl1xYfLVyxjUxOEEEIIIWSEQq0lZLigVpYlEI8grYVhaqyLpRm5XG7q1KmLFy8uFAq6S46h/OIfoJcQw2y3bt2KhO61ewSk0SYQkY0ieO+DDz74wgsvIAdeir1wWpwH1Vqtgl32BypLHPo4caarI5fvKiCRvT4XdXH42ldlj4QQQgghZMRBrSVkuJDJVeZaI0hrVVkzWcUm2odDzp8/f+bMmV1dXcjUBdLMe9U/IbSan41K9pXBpkitBa09+uijJ5xwwplnnrl8+XJ9uBQG7YepnaJlaVwOATXlLbVIaKxHhI50hGgNJaW6RfYRQgghhJARC7WWkOFC5leaQDyCtFaBLqJZxEgXi8WlS5fOmDGjs7MTm+qT2V6A3jWhZpsV0ExFy2uVfD7/73//e8yYMTDbadOmoTr2YtA4D0iL0NpeoiAOvBCnC7EcSCIv+HFdN2u5VJJLkbNlW5VqQgghhBAycqHWEjJcyLxLE4hHltaiQfinPsoYkllbW/vWW2/p7bK6sooyKp+6UqqyWglytFgGNrOSUNP58+f/9Kc/vfrqq5ubm9EI8tEyduFARFOtwfpuEIeJXnss67RxxYGb9OHJQKvreAghhBBCyIiGWkvIcKHbviq0c2RpLWK4YhRF9fX1U6dOXbt2LTJlBbVMNgZF92oahpmlM1AAZLoLd62pqUGzuhfltTukIb1LFy9bvbJWbRanCwnILTxWmjCxF7h+KI9QzuVys2fPRoxsLtUSQgghhIwCqLWEDBfU3LIE4pGltTBMjbds2QJvXLx4MTaz5dDsHlrNkSOpWLDtK7QZWgwx0tm1xBg54kxoMeAVy1b+/ncX/+2a69qbOkwgt9TKdcjlu3ZxbGEciNx63uOPP/6Tn/zk+eefhyRra4QQQgghZERDrSVkuJApliYQjyytRYPwzObm5lmzZsFpkVZrzYQWmyqifUeim/1eEoy92k7WoDqtLsNqy53tXXffec8Hjvrgcccc/9orr+Nc6ZOitOWSW7THJma7cePGH/7wh4cddth55523fv167M2cmRBCCCGEjFCotZjqyj14oR9EgTxgBpuB5+/etJ6Qt0MmV5pAPDy1Vt0SoAVNawJxsVicM2dOVVUVWsYmRBRxJVpxF9EqaBmoD8N7kaP2qzmIsem67tNPPz127NivfOUrCxYsUOPNkLYsSG/btu2Pf/zjkUceecUVV8DANR/ta4IQQgghhIxEqLUGE3oILbRWnVblVmJChpZMwDSBeHhqLYAHVq6s6joqcmbOnFlTU1MqleTCYFtMRlxGC+86WguNADVVJLq6upDGyH3f136Rj8IbN2586KGHILf5fF7r2no9VmI1PXv27DvvvBPuXbmLEEIIIYSMXKi1mOqawPMxrUecrtPq/J6QoSWzLE0gHoZaq2uklQnYLOwRTS1fvnzGjBlbt27VfGRil4y4jObvOlrLymm6JtzZ2fnss89CnmG2OgDEQMeQy+WyF+RqLYBa0pZF01Du9vZ2fYQySoLKMoQQQgghZMRBrU2vOvYcN/QDJHS1FgEzXU52yVCSfd80gXgYam3WiAJvVKWsta/zaWlpQSaaRYx8gPKKLT4wtKKVU7FTx3EmTpx40kkn/eAHP0B32q+WzNRUXRe7VHe1oi2SkpVErGSNEEIIIYSQEQq1Vi5C7uronPj4E2+89rpbgjzEmNPDcnXKmxYiZPDJvm+aQDwMtRaoKyJWh/R9v7GxEU67bt06XQLFLrQMt0Q663Q3QF3tSCkWi1dfffX+++9/7LHHvvbaa8hRcdWSiEWjbae2eIruUjQtjdpnTWEvNvUoCCGEEELIyIVaa9avXXfHbbef+NnPnfLlk5956ulSoahmq3NfkJYjZJDJvmyaQDwMtRa1VBeB5sBpp0yZsmrVKqRhlWgTCbiiGubbAX1l3WETzjxnzpzzzjvvyiuvhEIjR3epoAIUQIx8XZLNkLYsmtbCAGmMU1WcEEIIIYSMXKi15sknJp5x2unHH3vcZz9zwi8v+MWCmvlREMZhevli5ZyYkEEl+7JpAvEw1FoYI+Ksha1bty5YsKCqqipb88xsVn+DNL17oDqwBppe6oxMmO2KFSuQwGblYCoTGb1ysMm1WUIIIYSQ0Qe11ixZtBjhkQkPT3z8iYXzF2zbshVz+tCX9R9MgntNiwkZPLIvmyYQD0OtVWtFDD/M5/Pz5s2bPXs20jBM3YVm1T/fPmhKW1OQo0+EAhh5r10KMjOv7otWqUwodF1CCCGEkBENtVYm8b7rwWyXL12GdDa/ryQtuZfY8Rh2vJeMILLPUROIh0Br51fXrKmr71drIXuZIqJw5oHIBKhbVVU1c+ZM375oB4hfDtofg7RZ7QJojwC7sJnt1cJI6KXFGKTmZAktqWlCCCGEEDI6oNb21tosVJKW3EvseAw73ktGENnnqAnEe1FrKwtXphE7jgOfXLp06Zw5czo7O11Xnq8mrmmxxdPyexBtFqTdVKByq2WwqWmglygjBwPWHJTURnSTEEIIIYSMDqi1w11r0xFUjCHd7km6j4xkss9RE4j3otZmfohEtmarWoi4trZ26tSpmzdvRqbuRTGAvcCWHRS0fUV7BJnWVqZ1edb3/Wx4WkXr2iKEEEIIIWSUQK3FTNl0a215Tl+ptWmxvUQ6iJ7IXN7C1afRRPY5agLxXtRaLZB9zfTWWeQghs1Onjx57dq1KKPX+iK/Eqk8yKQ9ldFMdVpsuq67YsWKiRMnrl+/PsvPflmAlCaEEEIIIaMFai1mwaK1Sxcv6aG1w2Pem87BK1aZVDMA5uiK5qcVyEgm+xw1gXiva236JSsDp922bVvl63z0Ql/sQmEg1YYW/C5kCU1jSBjkNddcc/zxx9999935fF4Hj3wdJNAqhBBCCCFkdECtxeTdBJ4PrV2xbHkqtBqGAToF1/l6L3SmDrRMWoGMZLLPUROI96LW4qulhWGDmkaitbV11qxZixcv1kXa7J7VrOWhJxsnxqDDKJVK69at+/a3v/2Od7zjl7/85YYNG5CJI8VvjZYBtgYhhBBCCBklUGsxHR6RWpuhZdIKZCSTfY6aQDwctDa0L4zFZktLy9KlS6uqqlAL+fqkKJCV3Cvor4CmNYHhQb9vu+22k08++Yknnujq6tIBA/6+EEIIIYSMSqi1mAsPM63NekcspoG5uBhMGCV+bO0jNjAcTNCDRAK2BjZNz9rP0BybiQhtQ1PSIukPMhRkn6MmEO9FrcVetVkkQC6XW7ly5eTJk0ulEkQX+aqImtBm9xY6Ek3oYLC5adOmKVOmwG9tkfQJUlpSCxNCCCGEkFEDtRZT4PTe2mGhtegX03IEJIJEtCU0SWi80PjG5EPJiAODDUiOY+K87BnIND1rXw8R/xBCcVmbGYcm9kzo2YRkJQhSmAwB2eeoCcRDoLULFixYvXo1TFV3KZDVzAORDoJgxYoVr7/+eltbm4ygAi2/F0nH0QdruClpVk/S+oQQQgghZORDrcW8eDhpLYwiKJskYriGFxs/hsR4fuhDdCGb0A03jr3IFQUV7R3AHB0F0YItDiUSK0JaU6nWwmkllLV2b56KfY3sc9QE4iHQ2urq6vr6et30fV+XZxEjB5twQpRpaGiYMmVKS0sL7Bd7M6StvU06lJ2Rli6XTzcIIYQQQsiogFqLee4w0lp0C3XV1VMZBVQGjunkTb5D4lLe+J7p6jJBYAK9KHng0/REfBXt+1aQEaduZPMRoM6RxJqpP8hQkH2OmkA8BFpbVVWlWgvUWnWXLtKiQGNj4xtvvLF27Vp1XezKsJVGGCN02IQQQgghZAdQazHPHV5aKwu0sE0YaxImJjB+3lm+eNNTTzY9/HDTfx7c+vAjGyc9a7ZuMZG9LhnB3lyb1t85EFf0IIuxnoldYxDKZquXHGvYa2dgXyb7HDWBeAi0dv78+bW1tdkugHT2mKjsdT7IAb7vY6+SliaEEEIIIWRvQ63FLH7YaS1kRawhsYbbtqX5gQcnf/W0Bcd8cdFHT6w67stVP/xpOK/aeB6cN/Kl5EAcQ7U2iMRs5R5aDbBcaQL/sB/d68LtrrdK9gTZ56gJxEOgtQsXLoTWQmI1R/VVF2Y7OjpmzpwJ78UmQD4KAGli1KGHBtJtQgghhBAycqDWYj47zO6tFb9Qq8Q/x7Rs6rj5treO+kT9fkdv/O/3r9rvQ4s//w0zY6ZxnSiQB0bJY2t3NhfX+boFboQQJCYIDRroDtIE/qHPLOzV87APgo+nMoF4CLS2pqZm9erVmqPuqorb2tq6YMGCefPmua6LfN2LeFSCw68kzSWEEEIIISMEai2mtMNJa9Gv3Nwq1pLEYWgC07nNufuBmrEnbt7vI13v/HDjuz+0+jNfM7Nmm8DxY7mEOESl7U/EdZquyCNhRYUkIIH244pgi6B82WmhMBr21qnY95APoCKBeAi0FuJaW1ur+Sq00Ndisbh48eJZs2aVSiXN111BEEi50YWc056kOwghhBBCyAiBWotZ7TDTWoTIvtpHnh0Vmrbm9tvvn/KRE5cf+NEN+39k1QFHz//MyWYutLbkRJGzfa3VCbqiWqJIhdAu2Vqz7Q7WacWpkcjMVjbSBslgg0+qMoF4CLRWn4SML4ZeY4z8XC4H0Z02bVpLS4uu0Ga7Rh84rr6k+wghhBBCyAiBWouJ7TC7CDkKrLMYg58YRmtX631PTv/6j+d++msLjvvSnGNPqv7hedGCKhMUoZyleCcXIes0XVGrRZv6TCg/Tvww8EMPThuFPtpAUK2FMElJXdq1b/1MmyODSXaeNYF4CLS2pqZm5cqVumm/H2bt2rVvvfVWe3u767pS2q7f6i6piKqjMeCXAAEJt+QgTnDEdpMQQgghhAx/qLUyox1Oj4yKI1lOlRfVygORERcis7Ch9MQr0ROTkseeNI9PdF58wbRsNrJSa/wAppFaSr+oqyDWBNwkiowbiBLZOnBYNIEQyeJtD60Fuoor2MbI4JKdZ00gHhqtbWhogMHqBcZ1dXVTpkxBDnZl5XuAvNER9LueBfwmeL7uSoUWvwgBfhkJIYQQQshwh1ork9fhprVy9TGE1lqLvGG2GJt8yXhF47SZoFPiqOD6RSngigD3rx8W7IKeItaEmpB04EdJ4Mdo0y8kfikJXDFY6U+C2G8SR0koLxmypM2RwSQ7z5pAPARaW11dDZXFdwM5W7ZsmT179vLly+WrYldoQfZgZJRHusfvyKgJ+BWxN5yLx9qlWkmo7hJCCCGEkGEPtVYmtcNNawPj6/IRZMKP0rf4OMbPJcXQOL4pFJNSKXBEaL1IZuRlXekLdqmcZPh+aPzYdOVNZ850dZpS3hQ6JA49zOalS+gTWo3jQEYSWmveS6djHyM7z5pAPDSrtcuWLcPHDWVFYsmSJcjEZlYSTltZUfVv5IZUVsuhxy4cZZyka7Z2F1drCSGEEEJGBNRaTNWHndbGBjqZXowssWdMIHYRRH4Y+q5flFfy2Btk7fqquEZavyfIF5GtJBJXjTZtbn91cvHpl+OnXgyefanzjTeitfXGK2KvNIggVyXLgi21dijJzrMmEA+B1sJjGxoadEk2l8vl83ndla3WaiKrojY4ggOOo78Q4wTYpVr83yD0g1xnF/wWCTlfhBBCCCFkeEOtlRntcNJaBJl9i7bgp5oLzNY+RkqDDy8Vz4Xa2r0D0VoTesYvbXvttZfO/t9XT/z2jE+f+vLxp0w6+3/WP/O0KeXCyJVVOgR0F9gF28S2vp32yZ4lO8+aQDwEWjtjxoza2trMXTPk21JGczStj1YauaFyebYyH794nuPqbyCE9uUXX3p+0nP5rpweNSGEEEIIGc5Qa2UWO6haq0rQl3R3T5AbwSRThQ0xwY4TXxZRA1/WTeXaYHmCsRcbD/NyL7K6u/3W+mqt75piZ+7Jp6Z8+uvVhx7fMOaEqv0//uoJp7Q9+oQptAex46MHNIZm/fRSZGl9O+2TPUt2njWBeAi0dv78+UuWLCmVSoVCoa2trVgsOo6DdD6fRwyQownFKZZGXygVii5+4ohzecTN25r+/KdLvvaVr77w3PN6xgghhBBCyHCGWovZ/QC1tneB7kUtoZxfsV9UJDZJaF+sI5cOi7faiyErwBYcRQPSSRKp00Y2YBO+mwQhVCY0xsUWukVROG65sZ7tybb0W8Y+AspqbVdz9MyzVcd+bdk7Pt7yrmPr3vmJKZ/6Usd/JkBr3cRxTSyXXVqtRU9WpVILErSbfjqzbC+f7BrZedYE4iHQ2rq6utdff3327NmzZs2aN29eTU3NzJkz58yZM3fuXMSaQH5VmXlz5o7KMHf2nDmzZiNRPa9q5vQZv/j5BYeOOeS8c87VM0YIIYQQQoYz1FrM7geitchX9YRVShlZU4VdiIWmT0+VfPyEfIp5okwgi69uHHtWFbElmVJJrVHbiiMTQykRUlNJG88CIvFX1WN7160YDvQEaQ2oKJWAdoGgRm0fgIx+MYbEd4yXyz/19NRPfWX5ISdseNcxS/b75OvHfrnlwUdMsb1kHCfTWgwXjVYKc9omYqvnMgTdUbFL9qZ5ZKDIx1mRQLxntbZfCoVCa2vrFktTU9O2bduQaGxs3LodtOSopKWlZf369Zs3b8ZJ+O1vf/ulL33prrvuSk8TIYQQQggZxlBrRcMGprU95A0puVRYVlPjJPSjKBBdVeFAEXnssPVWeKJjRCzDyJpi+uRV1IKspJbo2SAN419lsKjS2KLdIXPa7Wpt+twfg359VIBw5ztaX3llyhnnzD7264s+cvLcT33lrTN/vOXJJ02pw01c1zYl/+xqbda7kDWLntOzgFCxC1uIye6SKasmEA+B1ip9761Nd+wz4FRrAuehVCpNnDjx8ccf7+zs1ExCCCGEEDKcodaKie261iIbJlHWPet1YhvitKDXxcXihvDIMAw8H5myOAunlXmzCcIY2ojKBjkoF8A7pTVZ2AUopWE7ZPt7hW7KWRiabT8LOFo/3rih6fmX2iY8XvjPI50PPrzt2aejupXGKcRRgLHJenFo4lCWeUVvKlpHZEVcgj1WhO79Gshuk8mkJhAPmdb2RfvqS7p79CLv5jWmqamJTksIIYQQMlKg1oqK7brWQiM8G+B1YhYIVu4w24emdl9jHESmVDKxE4QllNG3X4oloj40NkogmK7VzNSSpSaaUk/eyRhAz/2ohSay0EM40SRcOkZ22WwjNzSeb/I5k+swxU7T2WTatxo3ZzzHBAEaEEm39+7Kw6vEpcpDkih1WtVa23W6E8ehwRYku0MmjZpAPARaW/n+HiQqN/c1fB+/h/glCHASVG6zJVxCCCGEEDKcodaKhw1Ia930UmERSCigiobcuWpMPokKvmsCzxRdUygaL2fCvIl8aCD8ENPlOBQZFGm01yRjyizX82JDhTR1xe323gtbCmOAiQYS0tVYeeNtaEL0qqbtJXL5MXZAg9B1CHX1ZEkqCjFkpAomLEJ2jS8vqRUNtoUDecSUXF/d/awr6UtD+RRJkHzVXVnitTpNdo9MJjWBeBiu1uIrMirRo8OBIw2tBXoeCCGEEELI8Idai/n7gC5ChitC3uxjm0QTrYfaJBy1lMR+oZBfttJ7/i0z6dXgpRc633x1y5y5cXsH+ohgmglswbqlXa1Nb2RFX2hEetwtrRXzROfqtEHZaaGu8pAqdCFya8VbNMjKOGbsQWxXiJGBUmLFqCQPWVanlV2Z1soYbTWE3k6rw5AD0n4Rp4dABo5qVZZAPGRai2ZV7dLtfZXQommeEEIIIYSQkQK1VrRsAI+MSu0uNL68QFYUEHNgK4iBaGFitrTW/vu+OSf/cPkJp1Wd8JU3vvKtmZddG69oMCVXLvFFZXuzqhukWuvY2uXebOMDA+VFPnWR1rplumqKcVlbFfvB5FykCBIEkXXsMi7GgqroGPYqL8WNsCnLyOXDikRzQgS0nC5Nl8eGSrrAjGZgzur5VmjVgQd6CCQlkyhNIB7K1dpeFqd97TtkNqvIt79nDiGEEEIIGbZQa8XSBqK1wGqtLndi3mtdEQl5HBTmweu3LPntFVMO+uSag45ZdsAHpxwx9uXv/MQsrrMiW4qiyIUAJvIWHVkXtWYLM0Qz5Q4H7IRorCKkY9fWMFmX9hAwSDVRNV3koO/AxK4IKdAq0gTMFk5s7/O1r7oV39U7abOWs8ZwAsrnAHvxUwO1djeRz6sigXiIL0Lublxu894XwRmoPPbshBBCCCGEkOEMtRZT1wFdhCyLkwhaBg4nt8gmiX0tbYSGzNq1DX+8av7Bx2zb7+ON+31g3pijp3/vfLN0renIyzOZTCwPoUE1u8opbmkXb0PRR1lMRdjBTFofaZORlsQk3A7Gyqr1WLTiJxIjyAtyJSQIgm5ZVUUiwbGEjol1BVfm8xhS2VZRzK7EJoE8c0qGp/3gXzrWVGPtMUjH6AK5ZDfJPnpNIN6L99YSQgghhBAyUqDWiqQN6JFREFIELYIEVLVkklICOfTlHteNG1Zf+tepYz5W++4P1R541MwjP/Lid84xS+qM65sIrhmKd6IV+CcEEgmVUhvBEAOYi+2oX3QdCWKjifSpNiqZope2QbsMK5tIS/vpq2utOId2UTkWH0piyKorThuWTIyAEUk7ENqycluthRfLWq42Zq91tgNEX9pR1pes5uqhkN0kU1ZNIKbWEkIIIYQQslOotaJiA1qthUnITnidXbKFiPpx5CWBAx2MHbN53aJxN7x43Bemf/T4aR/7xNPHHPfiRX8srqw3YeDFbiEuecYLAi/y3MiTO1qTIETQ7mCI0Mi0/TJqMiB7NKtcM6zaA6+xhcUrkUTKBrHTLA0HlkdCyUOkbPBFaZMIfivv+ikHTzLthcbZHbKQKPtILG0HGuwlkXXeBGnZgQ03MQ6CvZzaLg+jHoqT3UM/1iyBmFpLCCGEEELITqHWiocN8N5au9dqLYBaREnoQ0BMEMUlk29tmfpW4x13F++4q/22f6+/65Z1L78aNbdCLQMj6hsY3w+KcNggdKwGlv04ioMwzoRUUY2RxVb70hGkEWdPsokCeWorKqFWkMhLctECgueHCBBPCek7eUSB9DnHtlN5ClT2fCm9dTbtWI8rGwR2+AHE2/OcUuDAfhH8JIggxWgVbXhoxlaxWm71luwm+HwrE4iptYQQQgghhOwUaq142MC0Vveq+8ntqPBV3ze+J1fzumJ2xQ7T1mLynSbXYgptpr3L+JBR140cJyzGiRM6XYlf8PyclxQ944Yig7E0WPZD7UGCRZ0WMdLQWEmHUQBz9YMolKXiOJEgPpsEnvhz4svCr1xmDFsF1lvVg5Bh306ktovusoBNm9LS6TiyXfKkqTCWl9tC4Eu+geK6buLgwK0YGz+Wt+NSs94O8klVJBBTawkhhBBCCNkp1FqRt91ZrZUCED6YXoAANYXdOVFgTdJevwsDDRx55LGsZyaB78I/TRGK65iSY/I54xeNmwujopSQxU+rtfAU+wCmtAfEPVdr1WogtNBaKQGbdoqms904eVkMDt3YKwah40Ye/FbNVqqgJAaLxkVrraqq0yJHO1V3tUKbPfrY9g8dDn2vKL24RVPoMqWCcfOm2G7CYhQVvdhxE9c+Wap7zGT30A83SyCm1hJCCCGEELJTqLWiYgPV2nIRiB9sDkaKYE1Qr/DFVmhC2KorF/6Wir5cKSzvdg1N3Toza5GZushMWWRmLjRLVph8h7FXJsM/0WIER+5uvxwsumArvUgxeztuYpy1G82ClWZ6jZmzwFQvShYuNduaxHV9N4rRLEYhy6xWltORY1MVVP6Jyab7dJHW3lyrQeQWvlqCKyeOcUtmW6upXm6mLzSzFpsZNfHc+WbzZhN4OHz7HiDbOM4A2V0yZdUEYmotIYQQQgghO4VaKzY2IK2tKKJaawOkNHNaeaoSDEQeygSThbCGceT7rmlqXXfXhGln/nTBl36w/JQfzTn1x4svu7Zr4XxUgJ9AINFmGIm1aKhEZQZaC0OWV/XI2rAJcsWGl96Y+t3zFn/t7Oqvn/XmN7477RcXFd+YIhcIhz7KWq1FUYyirJ32qVT6YKq0G9tTOdl9RJnW5ozrhEWT7wynz175q8vnfvGsRV/+fvXXfjz5rJ+2PPaUaW/F+KPQV9+SYyC7S6asmkBMrSWEEEIIIWSnUGtF5gZ8EXI3styZ6FKtBMmQrcg+oEmDvFfHXsfb3r75z9fNev/xyw792IoxH6458hMLzvxxtHiBiRztEREEEaaSygq2dUOuI5bFXCfw0Visuuz6puTX3jthxjEnLjnwfSvHfLD6iI9NPuFk89wrxnEgpi5aS4LYlIwpGFM0iS9eJEvC5SDPSZbQE3sMEuRQ5GikO8+UOpKpkxd/8YxlB3684YBPLjlo7PSjT+i8+U5TapeXHMnyNI5VhLh3ezsjKj/h2Rpbeh6zkA6me9BadnSSKasmEA+G1sZxHATpqro0h2b0ZKPhOInxBUQi7YQQQgghhJARALVWZvBvQ2sF6xrdyI2wFggbEA1JjNxn29zSefHVy444dt2BR2/Y/33LD/rAym+elVRVyW2r8hpbKWgfTdy/1uKnb1dQg8CTJzf5kcl7DXc8OOcDx657x3ua3/3+tQd/eNFxXzbPvGIKJSeKSrFxosALu6K4U9ZcY6/XcdnBVmz3QQuI9sBsS+3JlDfqT/z2xv/+aNt/f2TTuz668H2f8f95h8m3lMJOKRBAjHZHawG0H9hkHER+KO/Q7U9rkdyN1kcOONmVCcR7VmvxbcTXMt3IQHtQWuzJhBZfV3zBCCGEEEIIGSFQa2USv2e1FqjWAohEGFqRiEKzZVvzxdfXHPmZVft/uH7/Dy046GO1Z5xn5i+VZwlDIvQJUL21NvU7ECTpLllqg3UEsck59XdNmPWhz6874GOb3/2xlft9bMknv26eeM10OiaM0SGqonxZDm172vKuIQdikiIOIgmhtf4bb6w66Tu1//3RTf937Jr9Pznvg5/3bhlv3IK8yxaGhcHFGP1ATpyCGhiorSd3IOsBl4MtUQZlBt78CAInvDKBeI+v1mYthGGoa7bZfdry9wubSAMhhBBCCCEjBGqtzOD3iNZmCSX12tgEvrYVm63NG/9266zPnlb9sS/M/9hJM445ZdH5fwgWrjAoAL+whhKI/pZlRYdh/Q74IqqyC6ps75aNTVdx/RPPv3nSGQs/fsqij3x5zkdPrvnGOWbSZJMrobfAi6UUzFat1mqiJxu7ihxFYhzrxsbL52bMWPD98+d+9EuLPvSlmk98ZfKJ3265e4Ip5GSNVoZlexlA8ynZIiH8ypo7NDrwApdai3gPam124bF8KSvRs1oOkNv0IduEEEIIIYSMEKi1MpXfU1qr6KYC+ZB3yMLZktgUi8UpsxrveKD55jub/n37+rvHr33uxUJzi4dyqcuKF+Jn2lY2EruWad82a/0RTcFo/Ng4ftfC5Q3jH9l823iEdXfev+GxJ+O6OhM5pcQXQUTvcCG7FFx5fbCOTUmz+kP2YkRWuePIy21au+bJifV33LVBwj0N9/2nMLfa+J5okm0cY6vopH+0U5BuAyTRSwiZtcuGUgYtWZuXsJMGRxPZadEE4j2otVrRXhWfNo5Yrv22KosgFx7jfGfLtoQQQgghhIwQqLWY4O9hrVXSTAibmKhx5NnFkckVTVOzaWqS1/B0dcaFHHzTxX7xWdQRvevuXFN2AxGaCeDIckmwSIhchCwLtnnTVTTtnRK6ukyu3YROZIJCXLIrxVZKETx0k5ggiKNAB1aJ7awfZBd6xRA9cekodqN8syk0m3yLad8qryYqOaLOUlQCOkHYbnMWtClr2BZpHxH8XA8fZuv5smabQL3E2WzY/vhGHdlnoQnEe/wiZL2HGXKLlpHAp9DZ3lHI5eVjixMxWyRs2hYnhBBCCCFkBECtlUn8YGhtCrLFV+XqX3vxcCy3xcp7an1IrBuH8rxi7Ldl7HKsWEd3W5KZ/rR2bAK5LthaBwK2UdxD66Fd6ozso5vkRbLywqDIPi3Z8YwbGMc3JXkBLSrEMUI3Oxi85IttSsdIh/JgK3TmGOPEcQFjiX0Pfcr6sl3eQ0EYdP9tlUE76BSnQrF+nmyoX9e8eZv0hbOEA0F7oKy1arZZGMVkH4QmEO9BrcXZzuKskY0bN0548KFXXnq5o60dJ1c+jjiRj4BaSwghhBBCRg7UWlGlQdRaiEQAXSs3ac1N/M9GEETEki9mCH0LY7lkuUIp0moCdqMUQpohWmvNNhDzK5nEgWAmceS5cudtEJhNW5KlK4K5VcnceaaqJlm2NGhqNJGb9Ke1QFutRLOl13QYMdr14dAmiCRId7orfTyV3doxaBFmJQ8+DkMk4jCqW1Z3+UWX/v3acS2NTeJq9sZOOzDRNjSJoP1rGMVkn4ImEO/x1dogCPTMo521a9f+/e9//9AHjz75S19+9OFHYLbdWju6TzQhhBBCCBldUGtFld6m1u6Y7sbwQxc/g1RnJR9ignQoDxHGjkQuQu5fazXZnYEfopJWjn2JciYqynKt9cuc0zXp9Zk/+fX8085ecuoZ80//3vSfXdAyc7o8uDgMVGiVTJP6BftS8U6Xbe04JWBbdF32ZQ1oOtvsCfrSpcKsU2y2NDX//Zq/f/S9Hz72w5966tGJoRPINclyuTTOkZA1mYVRTPZZaALxHr+3FrHruhq/9NJL55133omf/dyXv/iliy/6fU1VNc6vXocsfksIIYQQQsgIgVqLmf4gai2agXlY+bAemNj7SLFtjUQ6wQ/RWuuKJoy2r7VAt7IMERVU98STYYF5E5Ts22nlLTudTuHBZ9447uS5B79/xcFHLTzio68f9/mmZyYZtyR32FaQaVK/YF+qtTpOMSkbcCCaj3/q53p0un67faCyAP0i3dnZ+eADDx039rjD3n3IQf/3gG+e8s0Zb02zt9qmLQLbfI8wisk+C00g3oNaq+c8A+20tLQsWrTo6SefevP1N1atWKl32IrQjvoTTQghhBBCRhfUWpnBD77WwijgmrDW2D6ASR4TVbSxLWSdVm6shc+hpIadI4KDguhATAQt+wG6SGLVWuf+iTM+flLDge9vedeRGw74QNXHTnCeflGeWeUPQGstVlZhO9aL0JseAgIS9lTJsHdx5L6PSqncrl+//off/9Eh+x1ywP/Z/4gDDj90/0MvvfiStm2taNFzyuemIox6ss9CE4j37EXI2Sce2pfWIsbHUVNVvXH9BpzfKAjxi5BegVzxpxVCCCGEEEKGOdRaEaYhWa2FawaRCWF1Jeu0qrXWBgN5yJNobdZtf3LYZ2CyqIlNlJXVzSBG+/I8JhOGchFybvzjUz/xxTVjPrT1nUes2e/988Z+3n/2NdNZGKDWWpsV8RZlRVGMUp1WtRainmlt2Wz7BxKFGD0ihtNic+vWrX/4/R+PHHPk4fsdduSB7/nw+z589213OblS5XtT8TMLo57ss9AE4j2utdoCwCbiUqk0d/ac1StX6QeoS7Xpgi0hhBBCCCEjBGqtCNPQaG35nlq52hhCiLg7X7TWGoXkAPXDsiJmQ8qCBSYiehIlUMYk9iN7WyqUJIDWFpz2R597+bOnzj7yE4sP/Wj1+4597TNfbZ/0mim6sruCTHK2A5pEkGHbdPcA9Kd9EHI22vKAdwi6g9aia5jt/OoF3/nGdw591yGHHXD4Rb+8aMuGRrThlhztYl8j+yA0gXiPPzJKyRrxfX/ZkqV1q2u7rz22y7b75vknhBBCCCEjFGqtzOOH6t5auyRr39AjOWkvcF3P7pLNbmdJg0VL9grYLeVhIaEJvUgeuCxqkmqt45r5yxtvvrtw/c3utf8sjrt18y33Rcvr5GU/YpRis/LDkklOf+gw5PJpKYQkhq55tlK31uqodtASCtsH8OrjoNRsMerXX3j9K5875cff+5/FVUsS38Qe7K37IuR9iuyD0ATiPa61Wl0/d3wEjuPUVFWvXrkqDuWp1Cq3SOz4cySEEEIIIWRYQa0VExsirYXTRmKVyJXmZQdSfmJcWa0VJbWX+trCFdf0VhhjOcPW1xLQFGis5yd2iQ35cqNuJG+vLbqmrd0U86az3eTyEuxbYVVpoDeaAKo6Ikz9IBYrz8e1dwVL+3o8ki2b9rZgO8LyqLaHtGW1Vm+vhdy6rgutLXW5Tz8yacqrU70cOjF+0ZN2rLFnlM+HDaMXOdKKBOI9q7WhfbVPumHBZ1E9r2pdw1o95z0CIYQQQgghIwRqrczgB09rQXdjejVvtlorggqvhdb61mglEovUClJHFRbBkuWXAwTHWk4YQE9kQdWWjWLRWuRBRSM/iFwnLEEHoUCBvLJWdsv1vzDpRF48a++ZldbEb+1Sr7yY1vWN40nwPFkNtu6KH9Kvli/Xsqu15XzEuwBUDbHqmTzdKjaFjpJdp5V01oUcWhlq7dvXWvtJp2TvT0L70NqG+jWhH+DT1yAfwWg+zYQQQgghZLRBrZUZ/KBqrTaVtQoR8dN7ayGE8hwpWaoN4ZP2Dlbshn1oApny4p9IljmtVWp2OjRtDp4nIiIuIjpixU/MBzGU0RpslKQrtFZi0aDc4uuZ2LUXQKOatIsBIYHWO/Lxsjozf4WpWmIWLjbLlpqWVlNyRZxRFO2jbStI1rDEsbrHs0PS8n1Qic1CmluWt32K7Kg1gXjPrtZmdTWBGO3XVFXX19bJl0j/aoA9GhNCCCGEEDJCoNZidj/4WmtbyxoWY03XP0VnIZ89tNbuToPoagTbyLS23Jj+EBEU0dllZDk4kp58q7XoKtXawMjzhx03mr+06tK/Vf/ggmVn/WTpD8+bcf4vWqbPNl0FGWEUJ4HoL8ZjRdyK9C6TjmCXSavtS2RHrQnE1FpCCCGEEEJ2CrUWs/tB1loFDapAiqwibY3UXhucrqbay3khF/KiHpFdXdSFQKYXJnc3gmC9Q914YCOVFtI30EpFtJPYvpIgiorGyZs3pr114jdmjhm7/MAPLz306CnHfG7NwxPlbbci3mEcBYnV7N3QWrJT+tVOai0hhBBCCCE7hlqL2f2g31ub/oCD2HXRVG5lnVS0Vu5xtY7SbbbWaV0JqdYKKI4UQllr7WOcBq61GqRzDYlrQleexlw0pU7z2uR5x5y85uCxTe/+0Pp3Hb7gE5/Z8PATxvGNFxgfQ4/DWB65jJFSa/c4/WontZYQQgghhJAdQ63F7H4QtRbNqIr22ECwaooAtYis1soCavdarvVd67eIxUEQUKCirm1MRBiJAaGNaX3bWuKnWlswpTbz6htzj/3S6kPGbtjvg8vffciMjx+7/tGJ8gQpOK08wZhaO4j0q53UWkIIIYQQQnYMtRaz+6HQ2h5N6oYNUAtobaqv+Ce3sGJDtqEx4o5aH0FWVm2wDdnauj0AUEsbSzdQO5K7e+GscVwwbs5MnTnt1DPnfPRzSz706Zqxx75+8lfrn3zGFB0r2YG8Z1YGF4d2vNTaPUu/2kmtJYQQQgghZMdQazG7H/SLkNUkQ/u8YU8vQMYOq5RqJ14SIchNtomfxPJiHnmOMXbo+m2QlkQ1bQextKDjlNSugrLagjYiwE2hM3Echb68zmdl/ap/37368uvX/vna+iuuWnjDP1pqFhrHlVHGEcpY9Um1VuxogAMgO6Bf7aTWEkIIIYQQsmOotZjdD/ojo7Q9mCSc1rVyK82XfTVOtTaIE8/EjokcE5bkFTxhkJqLOqhdG1UxRixCg23rugNE9AUtaCOiMGg5kkdXyVpx3jVb2syWDrOxxWzeZrY1mULRBCEECOVEtlP7SZ/ALL0PeACkf+R8ViQQU2sJIYQQQgjZKdRazO4HWWu7G5QHF6fLpNiE0zrWTqIoMGEIm/Xyxu0yQU5C4gV+ydY3sQffkCqoZLU2RkBT3R6y66AwBCaR9+FCjxHkMVBoxj6qSvZgcOiuFJlCYHK+KbjGC6G1cjttIm/QtV1K6G4wC4OMKF1ZzEYl2dFpAjG1thIMWN+ZnNF9Niyaxi+UJgghhBBCyD4CtRZT4yHRWvFS8Qb7nCebo6u3MEmRRj/YsilastQsWWIWLYiXLPTqa02+E1VQJ/CtZtgIlfQByGin20N2HRQWgZFFVzTioXNrMdILzoMN2C8bXmRcG9Rp0SG1dpDJjk4TiKm1fcGwIa6ZxGYJBXtBukEIIYQQQvYNqLWYCA+J1soqLebfIQSxe8FWg+eb9s6Njz8z+9zfLT7rl/O/85O5P/5F1VXjwqUrTamkVRGy4mWrtB6ChtLLgXujnfcG2dAAMQH8iyCsob0GWRZw0yCylMjNs74JbYDTUmuHhOzoNIGYWptR6bHQ2uxYkFaCIAhD/KII2FuJZhJCCCGEkNEKtRZT4EG/t1YcsKy1odw1K8u0npUSyQtik3M233Tfy0d/Yd5hn60+5NNTjvrclDPOM1VLTMFBbd9erVw2GFQoB6kP2cQ8vwc6lU83+mAPTa42Fke1QQXJOk0Yy2iyhVsXsbw61zotamVOi6CjGawz1h96XOnGaCQ7Ok0gptZWggHrdxgJTavHIq0FMq1FQstUorsIIYQQQsjog1qLSfEgam13S6nCygJoyZiifXYU5NYJQlNyTGuu5Zpbph907Np3H7f5HccsO+CYqi9818xaaDoLUEpoLQpXaG15hRVzeCT7aO0OsOuwsiors3yxF/VV25CEwF5/HNgrjwPfxml30q94T5Taz3a11hqEkG7vOQap2eFDdnSaQEytrUS+wPbyY8S+RRdpcRRaAJvbW7AFmk8IIYQQQkYf1FrMfwdXazHLlsbKWhua0DUxzFYfiexhth14pqvQesOtsw4/ft3+n9r8zk+sOPiTC752lpmzwOTyUEq0gMK2ufL1wpAaNCptmjjEZF6w3tpNOpfviVaWIaFfsRc0kSqrzdYgfmOfbiUhK6A220Nr9URp6K9DO+g9xmC0OazIjk4TiKm1lXQfexzDZhHrJlQWm0AXaTUTiQzNIYQQQgghoxVqLea/g6u1+h6dVGutVFo5tH5qJdJErulorr35ppc++ulZh31s7sEfnPz+sVO/9wMzZ7bJdyRyO64dDubw4h74kbagGtLXadO5/HZI5UWpPNZsIwvSh03sVGttXl+kzJ4jbXQ7pIVGMtlRaAIxtTYDX2xNZEeBr32pVCoUCpqjvwW6K1uzJYQQQggh+wLUWkyTB321VuQD823V2sq1Vnn+cByEJRMUtrz+0uJLLt/0h8saf39J7R8vWXnrLWZ9g/FLURKG+nwcaQFV8EPcEq3Y64N1UTWJErnAWF4UZLsBmOEjaD6C6K694ljyd6y10oMNOnQkylkqtNZp7SF0V8n2yM608bJ+7CnSRrdDWmgkkx2FJhBTazOywWfK2tXVVVtbO2nSpLVr1+pfc7ArM1tCCCGEELLvQK3FfHlwHxmVNpb+UD+02C5sFJvANbmcaWoxmxrNxk1my1bT1maKBRNAXcukLaQ/ITRyiywa80Vw5Y08suyLhlAnjMRlpZwThG5s13sTExblVlk4MGb+2KwM/aM99bM31VqcN9kLofAhRSZ2QxkTxmNryVuEKkiVt5L+G98u8BZxG7VzEwe+GwbyiqQEBxj6EDMJWqYC7bccttehfi4StltGd/QNe45s5JpAPARaW11dvWbNGtVC3WuX/3u0jF3Z3kEH/WShZ0Yo32nBfuBBZ3vHn/7wx+OPP/6xxx5zHAfHUjnOyjQhhBBCCBndUGtlvjyoWrtjMPOWhdRKwgghXT2zI9EyafmKIHKKbKu1KO7LXbvwvCBAcZisFxovMJ5jnLxxS2K7cMCSPOd4V7V2u1gDzMYhLy0yiRfBabes21LsLMG2Qh+HIHtd19XBq1hq/RStvstYT0GFGE4TR0F7W0tbazPMVuVWTxqwZbpBp9mF09vv0B6RXfzebhnd0TfsObKRawLxEGjtokWLVq9erZ8RNjOhzQoPNeg2C30yMDzflz9h4Hd2xZKlXzn5lP333//yyy/fsmULSuIoAvuXIAxesQ0QQgghhJBRDrVWJst7UWsxEbdrY923Baa9WwvL5uW9JuhpESM3yor/2vVLNWEgLfmBPGB5w3qzts6sqzdrEK83XaWkJAu7e0Zr9XJQ1IXWBklY8jet2TTuqnEvP/dKqcuRHB9jsqOy7Ewsd44cW5IEno8mSrn83bffccctt3a1tWMzCkL9c0C/WpuFNKsf9Ig0bAcdet+w58hGrgnEQ6C18+fPX7lypdpghqhjufBQg26zUJERxhFifL7pty4xzduarr36mpNPPvmRRx7J5XL6SwSkShltgRBCCCGEjG6otTI/3otaCzD5xlwck3LM12EXcDNImgado/dFBwg5tdf8yq2z9o2zdrkRIUiM47bNnDXrqr9W//JXK37165pf/2bOX69tmTffFEU40aqabea3AyTVPxUMWRmOTfu21hv/duNHjvrIaaeePm9mlSzYOoE4rbQvB1h2y90/tWIpaMA67TNPPP2ZY487/lPHTHz0sVxnlw5je6u1WUiz+kGPKD0uBe30aEqH3jfsObLuNIF4CLR23rx569atw2Z26vr91mV1Bx30k4WKDPtVFWScURz6AXIb6te8+uqrbW1tyNf3/aQFytgahBBCCCFklEOtlSnzXtHaXtNupDEdB/Av1TNQWaAvVmsj3/gR3DYI9WrkNBSclscmvvjFU6YdfvTCwz485b1jXzjpq03PvWL8EAKsWqtht2b+on/itJJMIBgw2Kcee/K4sccd/K6DD9nvkEt+d8n6+nUohV04Fj2Kslvu/gmWdtBAZGpmV51x6hmH7H/wmP0PPu3Ub8+bOTfx7Y21dr0aoRIdbdrETuhRsly9J9nos7DnyLrTBOIh0Nqampr6+nrNz9Zss5IAaSXdHmzQTxb6gG+d/fomnuOiAL5gUFnXdXV4SKvZ6oCBrUQIIYQQQkY51FqZPe+t1Vqdeau+gnLurvauWhtAa42nWovt2GaZrlLxwUdnfPoLaw/+0Nb9Prj84I9MPuYL7U+9YLoKxr7vszIMnAr9s5cZr1626oxvnr7ff+136P6HQmuPOuyo8XfdB9eVhdzuw9ojWmtat7b99c9/RUdQ6IP3G3PogYdcccnl2zZttYNKO6mkx2h3mbSyJc1SstFnYc+R9aUJxEOgtQsXLly8eLG0myRbt25tb2/HLrlqwKL5mrBVBx/0k4UKdDywVsTdBeQLJa7r+76W0dEibQfeswlCCCGEEDJKodbK5HgvXoSss3BFZ+F2Np6iZbYHCkBsQuPGxsXU3kBoI0irCVVrH3hsxqdOatjvg5v/68hlB3xo2rEnF1943RTdPai14hL2XC1buPT7Z559xCFH7v+OAyCcH3rvh+6+7a5iZyH0rdlaTeqrtRp2HTkhsWnf2v7HX/3hvYe8b8y7xyAccfARv/3l75obm+zdxdZzurU2QkhHO8DO0FUW+kFbG2CbOwUjrkwgHgKtXbBgwerVq7GJT3PFihVVVVWbNm3CVwQfmS6NSpdlbO1BBp301w8GowPIlmTx1ULQqwawCwnNly9bGWwSQgghhJBRD7VW5tB7997a7bHTeTn2Wq315SnI+GlfWetHVmvzTufjz7702VPmv/eTyw8bW/Whz7z4+a+3vPS6yRd7XYS821qrCiHXS8MuvOiZJ54+/pOfHrP/IYfsd8hlF1+2trZBXFLvgbTsKa3FsS6avfCHZ/7okP1kwfZ73/5e1axqE4jT2oBDylq1QqthgJ2hqyz0g7Y2wDZ3SvZxawLxEGgtPLahoUFvTMXm0qVLJ0+eDNFFWtdstQpA+crNwQJDS4fZG+0dwwCyWb4WIBunrtkCbGbFCCGEEELIqIdaK3Po4am1OwWzduiaLxobxpHcxSqiE8uTiY3jt0yfPfvKq+f/9FeLzj2/5le/nX7lX7bMm4dDFQutcNqdz/z7nA0V1HTDrp5hq721485b7jrqPe8//eunV82okjfZBpBC65l2qH21dqDYNiAxJixGLz3z8mc+ecJxnzj+2YmT3LyHzDiUpdl+tLZPl9mWeLc8XldeR6RZcSgx2klzxJRkMdBK06DTV8N2oLV6PjW9i3TXtQnEqrWQWHyIaoaIN2zYMHXq1IULF3Z2dmqO1Mnua+2DjKnPyHcbdKF/MekGbSPgcG0n2hdGLhuW9C8s+PzLCY01AelFDHRdlxBCCCGEjD6otTJjHtFai6l6kMRRAuUIESONH7KC25kLVtWa2npTV2sa6uN1a+QdtmIN4iZZ2LmP9DkbvYTKNidWuXzRittvvuOlZ15yci5yfFeuQJYlNYvW0sb6NLlLiKWgW7QXm+ZNLQ8/8MhD4yds2WDvqoVZD1BrgyjUDDHbBOfGl7IitFaf5KVB6TGq+g4BqmEZ2BwCra2pqamrq4PyQf9UC5HZ1NRUXV09e/bsjRs3ItN1XV0Ile57Is3taSpbxrdL/l4ThDhcxBgkBqO7gHz3KrRWa2FTExhzrxxCCCGEEDIqodZiOjy8tHYAndu7cVUWA1mz9W2Q21gjyC6m+l5kfM+4RVPKGd+BtGGin9ms1H0bWos8q4yiFmEodgHvam3uKHaWxLZC0VrsV51ArLWkfkaflneMNCK2aWvFpqMth6CNQEHVaW2Zno1qLxp6ZsBsZdN6sqz3eibfXpD2raWjhMb9aS0qZGGP0Wvk2BwCrYW+Qmul3fJqp9LV1bVs2bIZM2bU19drfnaJL9Dyim5q/h4B3yj1VRyk/J0CbeNYs7jcFTqFjdshSFZapWJVFvm6VJst2FYeICGEEEIIGTVQa2WWPHK1VrwGthPLfbVwWiismG0i1yGLshrjhpjIx34SSIad9qvKatD0TugzIBUq5KnWgnQxDLkYhyuLt4Enr7QFqhySSIdbga2y64i+2NbEorWibUGWVdV/rNXaHRXYMhLKZBmOA9UXd8WYw2L4wlMv3nrjbVs2bMVRiOvK+2Nwcvs9RTiQLOwxssFrAvEQaG1VVVVtba3m4ysC8UOsBZBYtWrV5MmTV6xYkcvlXNfF3qwAYkXT2sLbJ+saceD5stqPHuyCrdynrR+0BSNBnHWtFRGr1qrKYi9sXHPUgZEghBBCCCGjDGotJsLDUWt3MgTdvR2tlWy7TIv8wNqlNhU6gRQWO+oRdkJWv4wKlWZX7pRhlJdSM+GCe5TLVORqVnnHLgInUS0JY2h0WhU5Ki3Z3t707EW3ujPUimOzaN7i00759tijPnbnLXcV8/JOVM9Jz52u2fZEj6XiiPYE2fg1gXgItHbBggXQWpVDK63d1+uK9hvT0tICs50zZ05XVxdcEaAwyqAkzjxiLbyn0L+0aBdyiInp6ujc2rhFl23lOmQvXTSWr4EtqZsKNhXY7LZt23TAekSVVy8TQgghhJDRBLVW/GBEay1kEhIr99bKDbVuYjCJtzmQMtkQzXU8V2b22A8Hsd5QGXZCn6GoUEVJhdlqO5XBooJRzqiQwKxYed+ugEMA9ojT3jSNLnYkVxVdaFKDyrCMKDDrVq///QUXffCQ9x/83wed+OnPP/PUc7nOoi7Y6lKwhB7osVQc0Z5APqOKBOKh0drVq1enZ8OCfJxS2KCmcW6LxeKiRYumT5++YcMGVMmqD4bW6vcqJTFOsTTx8SduuH7c6pWrZOUW+8q7/RDf7h5aK98268P5fP6111675pprYOMYpB4FqCxMCCGEEEJGDdRamSUPW63V0IPKHXZBC1N7hCT2TeSZUJ+LLA+SknVbY1yTuNiRoIgs6SbIslJYGXaC9lWR7KW1SEAWEKCY2EaMIWiz5fJSxhpgWRE12PISdg0cLYCaR6Lt3WG7gtencc3okR0br+A/eM9DHzzsA4e987DD3nnIQe8++Nz/+d/62rUoBKd1S56U7m6+fCA76Neio911UCWTLt1EvAOt1Rf8aM6uoA1q3WwT7VdXV0Nrs10Aw1CnhQfCCYHv+47jNDQ0vPLKK+vWrWtvb9clUHVIlKys/jZBQ/bbIkRBuGzJ0u+efsbRR73/jltubWtqllMuX1qrqWUBznqXPMvmzZvPO++8ww8//Morr9TR4ii05PbQFgghhBBCyEiEWivz6D2ntTod367q7AqVQ9DQg8od4hQiqlZrQ3Fa2EgQQV/lHldjinGUj13XBJBdWc4NUUom8DqH18TO5/PaV0VSdU7T1pFTUimraLNnGezuqbVZ2DXkaJMkiEJrdGhT1DqI/B3rZSU9urWDgrgWO0u33njrR9//0QP/64DD9zvssIMOP+1bp69aUacXJ2tpuQ45xR6FDTvuV0e766BKv1p77933DarWzp9fXV9fKyXKT2DSMpkHwhU1x3Xd5ubmN954Y8mSJa2trXDdrHCWePugIV+uZbfEyZJFi79y8ikfOOr9119zba6zy34W6aIrSoaxjC2zax0qNiHq55577pFHHvnrX/+6paUF+XpoGvcLKhJCCCGEkBEKtVZmx7ujtbZAuWxqOImB/Ugs0tEdBhcInjge5uXwnvLiJbbkp6zaIlgJLJNW2yHl45Kwp1DlgAul8ibjxbhsEnJu8zFkaKrYmh5Gdg53eUBZxYrq/aOnAnHoR2vqGs7/6c8PPnDMgfsf9PGPfeKFF14oFEq6YpmO9m2gS4WZeiHuF+zKyminiGGSd995z3XXXl/Ml7JjV5NXre0rtzhZdv3c/s3DImcCdfFTLhmWT0EPDXlFp1BdM6d+zSrYq5aV/HJHFrQDn+5eFC8UClVVVdOnT29sbMRxAW0zrVyR2HVs1ynY1MPXdFdX15NPPgk7Xb1yVfolKZu2LS6Hpq//0WNEAoeJ3+gXX3zxoosumjdvHkaIwrK8bPeDrLp2hE09IRoruktjQgghhBAyzKHWylT3bWstQjrrT+f+3WHQ0cn9rpNW2yEVx7XHULtQMmfQBHRCjUIkKZbrpXufycoB9Q0VVNbVkO7oD2gSYliN53mTJ0/++te//v73v/8f//jHunXrkK9jw4h28aT1Qqv3An3pp9Av2ItamkCMNLT2nrvuveaqazvbu+JQXlcj+Vb7t6e1oPKvGEIkr36VJy3Z40CGHYu838gL3J1qrT3/qrWSxEkDK1aswBlbs2YNTp18aHbYGJ6eUkU/091Ax6EnAYm2trb169ejI81EnJ1bKSDebb8hcRL6ARRXjxf6XVdX5ziOVpFzW3Fk2NQGMeDq6upZs2Zpm2iw8ouqAyCEEEIIIcMcai2mroN2b+2eame0kNlIZg7QBn29rfoSCoAggG9YhZKg5bYTtoMKrYY0qz90GGpf8JyHH3742muvbWho0HEiB8PbbTcDaAFdZDFydqxJ2V5NIIZ6PTD+P3//2z9KBXkXUVYAI+zn6Pq2jRy7Kg7Zk2t3bcC3XXfCmT3P6a210ovuB/oRyJuT0t5sh6qyW7Zseeutt+bOnZvL5ZCJnOxcoWU9h9mAdx1U0XOVgWa1NSR0F74qdrQWdVob9DBlqEm3VGMwiLUFgBqa0AKLFy/+2c9+9tOf/nTRokUqugBl5AwPfPCEEEIIIWSvQK2V2fAAtbY8u++Xyoo7aWefQzUSqGOoOcRhkuvMF/MluX/VmpvdmwWLnsm+YTuohJVVbEegO/UxdNre3g5Vg9sgU80NBXSoAwLVARJoQRO7SFZYE4jh/Hfdevff/npdoaMoR2XvDMUu2F0/R4c9vXrDZpxA9jzH3bBu/bqGtSqBmfLhMKur55XvrS0/9Ku7EWzbYNvRpNbFAOCWOGPV1dUzZ87EedPM7JA13j1QNzvtaBM9IkZONmzdK0MGmdZihDahGu84DoplXzm0oDFqZI3j8CdNmnSkZeLEiYVCQfNRAMU0oTmEEEIIIWQ4Q62VefBAtBZzZ+gX5sp2jp+R1UJeltZAyqgqKEiLcsQGwnb/3Q+88MyLpYIT+lF6rSxOc1/0fGrYBfoRvwoyz9FNfUcrNgEGBoPS0e6G3qCK1spQv0LL0o6Ov9+QfXnKicALx991/z/H/cvJyat0owDCJiPp1X5aS4OCBArakPixX/QeffCRm/7+r7rV9diVtYOj3iWtLSNl7Ao2jgWbiKGCtbW1r7322urVq/UKZI2BLpMOFG1ZQV+VZx5p7K2UWylsbTYOo9C3y/745bSgAGKtjlgOrwxsVntBgaqqqjPPPPOb3/zm3LlzkWmrpiYMKgdDCCGEEEKGLdRamQcPXGsxsa6Y7mt5zUDIWtBALJmfQBsyzXBL3sRHnjzhU589/etnzJo6O/ZUUayQ9iU7pf3t7MuOtRaowKi66PB0qTYbKnbpOCulaMegpJI1ValJAtruN+DQMRCb1gTioOTffcs9N9/w72JnScvomraewAyt1R1A1hoSfuwV3Htuv/v4Tx536SWXrWtYj0wEyKnjyAt+6uvr0YYELZ8ea9YcgqVcS7cwBj11xWJxzZo106ZNW7ZsWWdnJ8aTncBMQftFB9+LzIoVPVJkIgFEOu2CsLYMLd+2ZWsUhBiYXmItS7XlJVmURBp1sYnCWgX5OmyABAY/depUaHk+n0cOyqCA7s0ShBBCCCFkmEOtlZn6ntHaXqGXEowUeh0Fwh4FqgBzADANSMXUydNO+9rpB79rzKH7H/a7X11Uv3INTljgiaX0w54ej3qLmpLmKGpBOkLN2XVQC6BlbROJbNESfiUHjiaxp29AfmCvA0BAwm76RQ9ae8s/bm1v6gidCCdHqtvzgESvgIppwn7vYm0K6cD4xeDOW+4as/8hH/3gx6658to1devQQhTIsm2l1krhLLatwBZt0D5ttgVnBoepaT3StrY2+OGsWbP0PbF66no5ai/0XAGcpQzdlbWAvZqT7VKwicYnT578rxv/uXjhIr0UWUelVbKKaEoTKI8C2SbQLpCDzyirqGhT6QYhhBBCCBneUGtlqv52L0KurKVBdqlVaMmRQ68DQdhzWG2RswFnAJs3b77ot7+H0B5xwJEH/PeBHzzi6Fv/dVu6LNnvOduj48EAEOt4VGmA3dNtRBl9c7aHbUY8DXGpVII0Tpgw4UHLfffd9/DDD0+4/6F+w8MPTHho/INZAjHS4++67+fnXnDWaWc/eN9D/7n3wUf+8yjiB8b/Z8KDDz/4wEO9woT70fjD/7l/woMPYO/DD4x/6KHxE1AF8X13jj/vRz95z5gjDt5vzHsPe98/bvhXS1N74MWeE1ZXz++jtdjAocjHkDktTpYG2V9ebEeM0wit9ezDlvL5/IIFC6ZNm7Z+/XqI4o6dFsiZKjut/UYISGMXElpGG0ExjeUvEBZsrlu37sc//vGR7znitltubW9tw0izWloem9KBRUfYq9ksoa6bpVFe9lWUJ4QQQgghwxlqLabAA3tklK5fZRtZkPUiFbIkiBMvThxjMJPWdV1btjw1zybNw4R0SHoI2bHIjZ3pK2Ey0pK7hVaEkKi3YHPVqlU//P6Pxrz7kMP3e8/+//eA9xx0xF8u/WtzY4uOoR+2l19G1QVkK3I7Ha0cT5k0y5JmWdKs7dCrADatpgkdHR0vvfTSFVdccfHFF//5z3++5JJLkL78T5f1G678s+xCjHDFJZf/5dIrkbjk938685vf/cpJX730D5f99bKrLvn9ny/74+WXXnLZZX+WOAvYRLjikisRLr3kissv/ctlf77yz3+6/PI/XYFTivgPv/3jaaeePmb/Qw476HCEX/3iN+saNuKL6ZYCaG1d3ZpsjVfOcFlroyh91yt2+pFx/FRr9TD1wHGY+Ez1qLEJm125cuXkyZOXLVumtytra/hG9fp2ZUGeXWwz3ZIjxfo74Zqpmqo94iNes2bNqaee+u53v/tvf/tbY2OjFkOZzEV1SPKtSHCkTugH0nUFKK8NAtRCjM10X3lvukEIIYQQQoYx1FqZWA9Qayt2ZoW7tUBXaOEDCF6YuHCDXtNlkM28hwPp9F1lRq0jTlQ2JOxR0JGstdnVNmjPpGee+/zxJx38rjGQ2x+d9ePq2TXpWexhH2V2OB5tM6PvOe8Xe+gpaZYlzbKkWduhVwFsiiSVb+DEMba2thYsLZaujly+vZ+Qa+vqbOlAjNDV2qk5rVtb/nX9TX+/5h/1K9e0N3V0NHd2tnd1tHW2tbQj0Svk2vKd7fmujkKus4i4ow3NFoqdpa7WXMuW1n/dcNORh773vYe97/zzfl49bwHOZODFgSertdvTWkgifuoF0fq19q2QKtmBqxBmMYRz69ats2fPnjNnzrZt22xHfqayWULzeyWioMezo3ul9ZSiF83P5/MPPfTQb37zm+nTp6NT7dqWTX+/sk8Bv+CZXdv93aApgJKKtqzornSDEEIIIYQMY6i1Mpl+W1oLFZA7GjGfDqPQDSNXr1IOTeAnHuJMBDBF1kk2wARaE8MCzPjLTqvP3ZG05iDuj92Y7ltHkFqIoR+ahqH9+8ZbPvTeD3/u2BOfe+p5ryB6EwV2Za8vqCGVZLia0Re0DHB6carlY0nP93axg0pJsyxpliXN2g59CyAHA8AwMAbIdvahp2Ds/QaRrjSRxaET3HPrvf/++y1uHmJmM23QLhQZgLaQBaCNICBh79G95/a7P/z+D537P+fNnT1PG8EAXde3WltX1trsQ7cbVmtFaBNT9ExzZ1xw8S237Vf8aQZj0CPVTE10dHQsWrRoypQp27Zs1WcUV3611GOzHMR1q2sXLVjoFEtZs3qMlacXLSNHP2LtqLOzc8WKFQX7Yp6sPGItL8hByPt+Fi9chN9x9IUCIN1r0RytC9JcS69NQgghhBAyPKHWyvx+97VW1QFOa7UWIYaUJbEfJ16SeLHxMdG3AoKimCL7vq+T/uGFeKK83TQ9tjgJXbluUwzGbmak5e2xpKmBUGksShwmm9c1/u2v19192z1tLe3oThbW0Ha/zaeZvbVWm0Kisn3ZFD3u3rs9tldge/k7pbKiJvAFwOeO4ckmMnY5hH50353jx11zg18McDi+GyAna7ybiirpTsQ4SZrjx0HJH3/XfX/47cVzZs3VTHkul1ygG1RXV/fW2vKG/MEGX2hjWju9516Zcef9z0yeuShfcHEgOCJ1S+mqbJL4elfm5HK52traaVOmrqmrL+TymcFmCcT6rdu8cdOVl19x3jnnojAa0dYAEpUHm6WRABhAZUlNIz/7GgiJKRWK+NU+/6c/+/WvLly1YiWKafW0QJl+MwkhhBBCyIiAWisT3wFpbU/KTpsEYQiHlXWukheXAhFaz5icZzBvVwHQebPOv0HawJDTz8FZrU2VJopxKLEfFLtykeen7wLVUj0dYzfQM6CnApuqH/C0bVua2ls79NU1ZXB+NFiQr0HIhpvu1WaRQIOwR6Q9xy8VHHgg5C3buz22V2B7+bsCKuJ09fAry44/9+wMZzE89t477rvmymu7H4NcBr6J0GOESIrWpjmSQHt2rdJz3MULF61cviK1yjKu65a1Vq48lyCNoICcfCTljTnGrNvUftlVN33ttJ9cPe6O9o4CzBONq6hjzNmw9eQjgQL4ziOBAlBWyGr1vKp8V07HgzjQr5btDsOb8tbkEz79mUMOHjPuuusxpL7nDWjL6Au9ZOpbqdaVliuF8R/6SgyO/b577j3yPUd89MMfeeyRR3W0UqAnfXMIIYQQQshIgVqL+exua61M/dVpkxgT/cgPIj+SRVs3Nm0F01owxVAWu4DOxW1yJ24z2PRzcGWfgQbAaZFu3LDxP/eNX7JgYbpga+lXBgYKWlCvQFqFJLVZ6I19xagtBXWRvxHYYLEFukeSjlj22kEJaBYtIC4UCpOeee7xR5+A2crnU0brDjEYDz56GJcOYKDDwAm5757x1111PUQdx4LWABoRgSyfgW76to0c+7FCHaGRuhKOtA4DjfSjtXLK8LmIgGJD/sSRmG3t/viHX/z9Zf+aMPH1XD7VTh0M0rqZZZZKJW0fOfKBxkkhl6+pqp4+dVrT1m3o3SmWKtds0TV8+7xzzv3iSV948omJnudpU5VogziNGLPmZBKLuPKsdqdt44hx4K+/+trXvvLV737nzFkzZmr5yiqEEEIIIWSkQ62V6e/uaK0UsN6lTisvCZXlsyA2Jc8srW196oX5T7+8tGrJtvau9LWljuNkE3HN2Sv0c3DWZ8R27OpW67amG8fd8KmxH//T736/ad16WVUr83ZGrkKiOqEx7EUbhPzoXsRBBI1SwZJQPsk9xl1ZQFcs0Q48R/fNnD7r1K9944snfenF51+yj+saXhqjg+mL7srKaAzHu/fu+6679vp8VwGymR6LPfAogRGmQTLLoGKFy9urA8oOie85cqRA2o5cIV9dPa+ubrV8gaUDFIPfwirRpmgtvhFebJzIrN/qLV7dtq3DeLJS290IYrVQVXekdS8+02KxiAS+Pwie49auWv3m62+sXrlKPkdUtV+27GFR06ZMffzRxzra2vEd0BYytBck0Cbab25uho0jB5syBnQRp18kgOqpFZdXa7X3Z59+5qUXXkR3aE2xxYVem4QQQgghZMRBrZXp79vXWqiDBzWz67RVS7b85Z8Tv/mja791zo2/uGT8nKrVhUIJpeFutmYqeHuLfg5ODRFRmJS6is89Nem4jx9z4Dv3/9hRH7rj5tuK+YIOGFP/TB52AzUHlc/KdvStPMgRL7K2Vmm25ZPcPe5sl4ZMa0VNIrOmruHCn//qPWOOOPTAw77zrTPnVy+A28iuve0tcK2dfu7ZIDWBGCfnnrvu/fu4f+Q68zhy5KCd9PLjipOgtZTyeUpBFbG7ONGlWv0Lgp5/Pwy8wO+htXad1mqtnnlpyo+MG8pF9ZBbT9by02EAFEBT6swIarZIolnoLXLSp3/BkKMYA9iwbv2MadOr51V1dXRGQYiAMjo8jKpUKGJ4MmiLNFU+D+hLvzlw2jvuuOP555+HMyMfteTedVsWaS0DkLb9po1jhDBbt+QgrWgxkG5X5BBCCCGEkBEHtVam4wPUWp3xY9JsAxwggieIsjjGdIRm/HM1p/x43HHfvfEz3x//6TP+ccPNEzs7XSgAJv/6VKYebljZo4ZBJzUWRaf0EiApiVkwb+FpXzvtPfsffsg7xxz+rsO/9OkvvvziK1oscwagStMvOBXQEBXOTFBVw9KlRRP7oSebERqsPBe7hJU66BNsthzknagy+Hx74ZZ/3nrUYUcduv9hh+x36FFj3nfZxZdt29KkFbMxI6FeN9zI5EoTiHW1dtw1N5QKjlinRcuo3Gp6F8nqagIx2q+pqq6vrbwIGU0j1i+JXY/Ft7y8Exu2olycjELYhLhKQXy8CPaDjvD5wiQxNKQ8FMIn7VltFsls3taycP6iN157s7293X4KosXSpv27DzK6D9BSmXZd96GHHvrUpz51xhlnzJ8/H/nQVy2AL2emvkigEf2UkdAGNYEcufrA/srLwMJIExIIIYQQQsiIhVorM9rd1VpbTCb0IWbt8vRjY5p9c+vjs078/vXHffeWE3786OfOuv3qGx9raSmKGMT2As60kTLaSGUYdHT8Qnmub3UkMG4pmDN93hdP+NIh7zz04P86+LB3Hjb2qI9NfPzJrq4uSIKuhqk57BDxVdVXDWEMCYL9iNlqSNO6NjhAbAuQGTkKq7XSDELsJbm2/F8u+ethBxz+3oOOPOD/7H/Yuw//2f+c37S1GYNHxUyBUEMTw41sYJpADO0cf9f9Q6a1Wb4tlZ5h+TuFDXKW7QmHQrqhnFP5K4U8+SnGV8dEfhS6Xix303pJEspuEzuxCfGFsZ8XPjd8eGHS3trRUL928uTJ69at8/G75zmFUjHAL5FtMNNROwYBm3q9MUz4zjvvHDNmzOc+97k5c+bodxLgKFAMVVBGv59IaDsA+ZVgDBgzDktj/aWTBWdCCCGEEDJiodbKpHZAWos5sJ3xl4uJpkUy78fc2JhcYp6fvvqci+88/rSrjj3tX9887/bHn5mdy4nOomyEErZliEAPtCkNg055/OjNmoBYJjTTHkDr1rZrr7j2g0ccfdD/PfCIA97zm5//FgaihVEuUwXN2QHiDwLKylI2EiqxukILtOtysQGAYZY/grKxWK1FRuxFb7z85re/ftoh+x2633/tf9Knv/D4g48X83IFOFC5BaihieFGNjBNIN67Wmv/atCttQhIB5HvJwHcFR8qJNbzC8YUTZwzST4IC57sMq4xvvxS+EnoyDPVMFhZqpWf+iV3S97aNQ1T3pq8ZMkS/XOD5wWyLpwOsBsMRr4u9isHZZ03b96vfvWra6+9trm5GTlaFzF2OY6j5RHjuLQWkKOqIPB8vfgZIXt9rmwSQgghhJARC7VWZrcD1NqKnUhZrbVz99AJ5NbALTkzafKqv9784mX/ePP+J5evb3RRJH1RjjzvV1ZHe0/ftVENg446oQgAwLwfQ5LLNtU6YtOweu0vf/arI8ccedrXTl8wZ4EOCU4IVdgVM0SDFQlRI21XE6q4WmD32K7W2n4S3zzyn0c/8ZFPfuQDH7v5hn8XO0tZbyiocXYUww0dYZZAvNe1Vv8qEUcBgtXa2ItiJzTbOorNXY4TJnKdeVKE2SaJg/HgX1NXuLmtVPRx3kNkykj1E0Or4pCJ3nALt2zctHnOnDk1NTWtre1hCEXOvjsp8nUp3wmsBovPbvXq1Rs3btQ0bFbzQaFQWLRoUX19PQ4Km6hViRybHlectDQ1z545q6ujU44aeRieHjEhhBBCCBmZUGtlqj1QrYVMpPvTwqq1QRi52OUnpqNoNm4z6xpNc5dcbosiTkmujQSyLro9tLUhBBN9nfTDEDDnl4tLSx6so2pu9S9+esGLz77k5p0YR2UfPIsyiLWWVu8fexR6B2MW5Am0qhAIahE2SObbwx4CHAZd6N2bZsvmrTdc//dLL7liY8MmSDQGizIYvD3G7qW/YUh2YjWBeFhprQ3GD039+sKDT0yd8OTUdZsdfJ3xtUHBwJev+vot5pEnq+9/ZNqqNZ0is2GEgcofHdCkDXbRF0eiH33c0dY+d/ac6VNnbFi3URby7d999JOyY5DB6Eemj0EGUFlswmb1+VLIwfcTiRUrVvz0pz+96qqrtm7dikytlSHHZtvEL/ujDz/yox/8cOLjTxTzhXRgGCshhBBCCBmxUGtlUrt7WpsWkVSqtQhx4geR3HcoT9OJ5TFRob3gESaFSXXmU5hnp6lKuhsdOnS6r+MJQ4lxNJDbVStWFnJFHQ8kQX5YdPVMDaF/5GjtLZf2cLI3uDhF+yham8ZefZ+QdZu3hR2+nFtpGSfcF2XavLGxrm4NEjAulMGAM8/Z0cj3NtnYNIF472qtfJDYoZcf62ptZBzfvDp51Vn/c/nZ51z+5vRaN5BL6xFC3+CjnjZr6w9+Mu7MH1zx8utLPU++8CqrCBg/WkrTel26/NUBXzZn1YrVs2fOWb2yVh74rH2nf2pJx6MqiwQGjAQy8T3UvfrlzOfzt9xyy3vf+95PfepTr732mt6Lq6BYJZs3bvqfH/34Xe9450/OPW/Tho0itFytJYQQQggZ4VBrZZK9Gxch9yyCeTEszpdLLhMfU25kpE/5hcIh0QOxBaiCJtK8vYoeC7wb1iJpHIbcpiheoQXUCVUk9JpPJHRXP5SbQxx7UeTibJiWLc3PTpw0/a1poRNgM9MbCQOk3541MxswQEblppING4ejiWFFNjxNIB4+WqsBWguPnT634eLLb/3DFbfNqFpT8uUvOMgX43VN1cKWP10x/neX3DZjbr2socpnjRYlwFJDuK88PioMI9dxYbBhFPqB72Lvhoa1M6ZMxa9hZ2enXh2Ab1pms/goAdKaryNEDsaPRKFQQPqll1465phjvvrVr65YsQJf4Ky8HFUFrc0tl/350k+M/fjll16GNLq2hziwM0kIIYQQQoYV1FqZcA9Ia0F/ReAAAVqyWitvsrWTfVmhAphMY5KNGMVEEiRXnMGGocYOowd6LFEiF2LqA2kxMB+yYZ/whBHbUil65WffRjJEkLATlaRRSRQ6ipOefO7Tn/rMz39ywcolKyC6cgLgOfrm0gGCnvvtPBunnGrt3B4Rhprt6psYVmRnVROI97rWIuDr2q21tsuWrmjRyi3L6po7S/YTlivAfXzf/cjkPbO4tnn+ii0dRXvLuTQrZms/E4BvlzwyXH5TZM2+pF0EEOLEFLs658yeOWfOnI0bN+pfTyrNFuh1yF0WZGoZ/SgRNzU1Pf7445BbWC5yVGu1IjY1gRwY7PKly26/9bbVK1chjZA+QYoQQgghhIxYqLWY8A5YazMqiuu1jLISVQ7WCqSdVA8qwvBExlY+kO6Q7tx17PKXHDiq2tOwbP6y079++nsOes9Rhx3118uu6mjuVEuSsxaK7YO0bgXIhNJouvxHgcy4+gE7+g0ZqKsiBHTFbwet7RWy8WgC8XBYrZW/wpQD0vIk5DhyTOIkxouNrPDH8k4fk/hh7MFWi0mcj2RZViqGgf4Zx7aJBoPEuLFxExNEUr6srHI1chRHnucWli9fOn369OXLl+sfUDBC/bCQwCbc9eGHH0aBfF4uV0a+fjcAirmu6ziOXoGMfKDta0kgm1EMCXeKJc9x0y+qfmMJIYQQQsiIhVork9oBaq1M97MClTV62uB2g86w0/rDCDu2PiHdueuUnxcFmUHtxvVbLvrlRUeOOfKA/z7goHcedPwnPz3h/oc727vkZMnzhPRk9DgbaVY5E4lUfnZ40uz57ydkYqNgEyChDQLNHw5kg9EE4r2rtXBGBLm+oDv4UeyGievBN5PAT+CloYl8E8E5Az/I+8ZzjFc0LvbKHxHsd8B+ifBPlmoj49jg2TcbywcELYWIitmaAGbrOMX169fPnj174cKFuVwONdGONGVMqVSaOHHiMcccc/rpp9fW1haLRf00ZdzWb9EOQGFkVjqtosVwQBGG5gcImdZKIIQQQgghIxZqrcxod0dry7N1oMUxga8MFc30KJ/OrStm2xm7qZEDpDyq7SHj1JHs3njUQNJuQrNk/tJvfeXbB73z4EP3P+zgd4059MDDrr96XEdbJ/Zmi7GV6MmBkOgmymTFrN2ky63bQ7utDArahOdoOjMf7UszhwPZYDSBeC9qrTqtWGAPrRWzleeiySurQrFCDAGfSWigsb7vhiYsGa+QeMiLIxPY22vtx4DvUiA2axxYMXwYBXx73TJ2SsDnE7tx4tk3G5vm5maY7cyZM7ds2YJNgI/McZyrr776kEMOOf7446dMmYLPEfkyaIuqrNqsovlaXZFte6QakE43e5QihBBCCCEjDGqtzKnfptYCraE2izgLtpl+yvfL7mnkQNGhbp90qOnEf3fHE9pnHaN2vr0w/o77jxt7/IHvOOjwAw//3ulnyYOj/AB+AfHIVDND80GlwSJzp0Kr6NFVBkV8pqfhaC+9Mvcu2WA0gXj4rdamWhslobyAWbUWHooQwzxlybVkkgJKyBk2Cb4CMF5bUWrJZcu+LO/GSRAbSDCy9DcFrWGvLOdarQWdnZ3z58+fNm1aXV2d67oqsfPmzTv//PP/9re/qe7iWyGDLmPrpWdJ0ZxK5EiRrcGarXzN+ylICCGEEEJGDNRamd3u1r21qf5JKBeWn3aGrAGTdauF5TKyu38yhxyQRm5v4r5jdCA9hlOZ1Z3ba2tXsReX2qNAZXvoLZtbrvjjFYfuf+hnj/ns4xOeCOThuXq7bOSHcsNkJTgi2BL2wlhAEIgAw74A9opIaaNp2CUq13shSLbrVJ7lDA78HA4S2Ug0gRhHPRweGSXymgZ54rd96LfYYaq1odxii7JBaPAh5WPT6RsHm1LCRIG8yAchir0IFe2jkV27bpv3jBfKWq+DihG0Fl7syVXN9pZafEygvr5+xowZCxYsQA7MtlQqLVmypLGx0XEc/UoAGXf50JS+Od3o7xl2ZkFzCCGEEELIiIVaK/Pa3X1kVFmuKmuVK9qkampFme2gJcvlBxcdSI/hVGZpKFOR3FVgqjBbTUNpxFo8eWrURb+86PZ/3b5t01Y0Cs+MosCu+KUlM2AjsE01T2xCRDVfE/rmoYowMNCsJiBI1FrEu6K1EnprrfytQWrIHlmlxSeKbPxsaMw9/uLMR5+dunpdh1xjHMBi7fca5eT5YCiSeLHJeWbewrWPTHzrpdfnN3XIl0CWcZMgwNcnlr9B6B8gdDCbN2+uqqqaPXt2S0uL6q5+Q7BX/2Ah4y4f2s5BQVH18iKtBqQJIYQQQsiIhVork9oBaW3P/Trp12BzkZHurrSCcma5eFz2kfIeddryOmd/6NxdSbN2C1RG59pvim7oYDXdvW/A4BCiRC4lFfFAO+jMM2ExrFtW17SxCV2EvjzaR7VWjrrn4WBThVONpVAolEolZGYrruWBVgZLNvKeIRNjtIkYm2gQYwPa0ds8n3uQbCSaQDwctLbnam1koJ1JJMuw9mssz/0K8QEH2BNE5q2ZDd//6VXf+dGlL721xNEy+JDRHj5pIFclGycwHUVzy51Pfuf7F/3uTzctq2vzjYHC+tKefCUgtDoGvfYY6WKxOH/+/FmzZm3YsEGfdYy9+iKf8mj7B3szylk2VDqtbhJCCCGEkBELtVYmtW9XaxF2oLXlTMycg9g+MycyJXvtJaTEGiaSGlCuF9ICGkAxN7TB3p4YoDXM2G2zA0VHpyGlMqsy7BZwGYwulDe8SCNiNTgyjDg0iR9DfvRdtdBa7AwikZZu5UANq7WwLISujs43Xntz6uRp+S4VmMxR7YntDpbKkVcGi1RMjFtypk+d8fKLr7Q0tWJg4mhyHsuF9jbZSDSBeCi0tnpefd3q7WktzpGsbMpCq7zJR77NctbkRljpXl5IG8WRG4VuGMiVyFULW35/+fhfXnzrW7PqS5G8oza01yejnv0TgnwXnNB0FszDE1//5W+vGffPB9dszPn4vbAH40Uh0mgZsoyvCRIyDlkPlsvRly5dOm3atNra2q6uLv0mYPzl0aYnR+mbk5F+K7KQ+S0hhBBCCBmxUGtlRru7FyFX0F9FmUOLy8pyJRS0EJiiMWu7zAtz1r00e8PChpxr4Ld+JNkIrkz5bQsyZbcmEEbyKhQIYmOHN7mm8eVZG+aubm3x5A5GSGGEHWh8OIlZJVYiBDkJEuzRVVDebYJIrEl3yiOKYcJQJzesmTH32yd/86zTzlq+aAXq+h50qp+DLTfTmzQfaoYWo9gvOY3rN//v//z0lM+f8uZLb8aefDT2Rk7BD0Wz+14UPZRkR6EJxIOttb7nLKia01C7EqdITpQqnvxDQPN+HMEbIyfwi5BXeRyUft0SaCe6RymcMT8qRfIXC1NyTXvOLFmRW7SiraMkt9e6MaRW+lJQUQZtw9ZtbctX1K1btxX6ihJ+ENmnRhmRYfFbeU4yNuVaY3wwaMX217h549Qpb1VXV3d2dqIWXBe/LLJijK+MfW2PJlDYc1z8XmMTackXQ5cWZPTl80AIIYQQQkYH1FqZNO8Bre0PmT1jUg5RKsWYu8MK5q/NXX33q9/91b3fPO+23/5l4huzN2DqD7sKw5y4L8QP03Kdc1sJxEwcDWxozt8+4a0f/2r8t8+98yd//M9jry1u88Vnfccu46KF4TpNF4GwpNt90H26DNhdCMfuxxtXr7/wvF8cecDhR41578UX/qF+9To50h7lBNt8/+3rrjgKEHAyt2zc9JdL/nL0kUcfvt9h/3Pmj1csXCknUdYbpboMw8RebBeZ9xLZgWgC8aBrrestqJrXUCurtciDmmKH3Yu2fZPIo55QEudpTWNr9cqNDc35ki+3PUsJWxhC68WOvbnWvpTWM3Vristr2zqKxkGOFVLbp4B6smZrwSaM1LYkC7macFxotNnSlixa0Vi7rq3oSJvpFfuJgamirOsUobVTpkxZv3FDEFknlodSRTgE9VstuXL5invvvmfj+g2SKX9VkeHqI7i1L0IIIYQQMmqg1sokeBC1FpNpx028wAtNMTZPvlH/nZ/f8+Wzxn/um3efctod1/9rZkuHqEkQyPqrFLbIT4RYnMCPzYyqLT/++b1fOe3+L37rwVO+e/efr39xzbYSbEOuwAwgDiN7mq7HiiBya20n8eOulo5/XXvj0Yd/YL//752HvfuQsR/8+N13jHcKgSzY9jxWOfjtHL7ugvH4nuPkC49NePi4sce94/97x5h3H3LUYe+/7OLLmxtbcfYzrZUXsYpS7zUw2MoE4qHQ2nnzG1avsY2r+iGBZvH9CkRrrVW2tiX3P/z6r/9020MTp3Xk5KFNUkzGkKjWBsb348QNzOJlm6/52z2XXfnvmXOXFTy9MDntFKBH+YytxCJGO8hEnN1MG4TyeOQX36j+w2U33HT7gw3rm1BCLrYHiDHCCN8BB6xYsWLq9GmralcXcnnkw1d1qRZyG3h+S1Pz5Zdedtwxx97493/ku3LIjwJZ1JWFXIttkRBCCCGEjBKotTJXHkStFS2AsoZeZNpK5t4nar505j9O/NY9X/r2Q1/59r1XXDV5w2a5FdV2KNdQ2kQMX8EP1A5iU3TNa1M2nnH2rV869cEvfvOxL5927/l/eHRRXWsgPQRxBB9IHxo8gqgcsF5+jCBrsVZ43JLXtHnb+eecf8QBhx/+7kMO3+/QA/77wD/+/tJtsFB7WirZwbFjl90r8tXe3HLtX686YszhB/z3Ae895H37/df+3/3W99asahCDs6CQG+FT2re0Fu3XVM2vr12jy+U9tdYKqTzzyWzYWPrrtQ+cfOqvr7/xibZOR1ZZUSKOEFRrvdiV22gTM3veqp/87M/f/9GvX31zjhNFvryeNu0UyOjLwGb1edTIR4Op4samrRCPf+T5U0//8a8vumLZygbJ1Qbs76kMCL9RngcT3tq0bdqM6fOra9pb2+xQE1nOld+5eE1d/Wnf+vZ+73r3Ly/4RfO2JuyC9MpN3fL9EWyLhBBCCCFklECtTafLg6S14pyYhPsFNw5dY96qbvzJ7+4/6Vs3fv3Me75x5m33PrCwtVOe/WqFQu7tRLdwFe0f83x9P071krZf/v6hb33vgc997bZv/+DuG+6cvLHd95IgiktRVBwBWqvHY4M9Jz1GK4t38iwh2S9uheMJ4lJX8ZEHJnz2k8eL0/6f/U487sRJz7yQ7yrZFtKKwDbWo7VKdG8oK+Fx5Plvvfb6Fz570qEHHnLwuw4e+8Gx0MVcW97+JcFeBCsmh1O+72mtfWSUnIFMa/UcYNO6LUJXp3n51fn/vPXp16cszeflGdcoLC6awEqDMHGDyMVQ8HVdVbtl/APP3H73IwuX1uM7j/O5Pa3Fpu003YvWhMgUPTNnwarb737oiWde3NbSiWYjDAOF7QXGKJ89E7volNo62hctWDh18pQtmxuxF+6KYtDajrb2u++864zTTn/huecLubxcYo2zhepyrbWgLRBCCCGEkNEBtVam74OqtZjtB7HjG9HatS3BQ5PmX3Tl47/848O33jNz2Wq5ltjOt+G0AYqG9kE92RCgGSiwtSN8aXLdn66edMEfnrj+tslTF2yVB0wlbpzYR0cNf63FAUJJNFjS/DLitdhjV24R5Mgj07a15cZrxh19+Ac+ctRH7rn93kLOQb48VreCflvLSPcmkZhtYrxiacL9D33yo5844uAjrr7s6m2b5G1D8mZda2hoBR43UFHcs2THognEg6+1Tk31nLr6lfKcrkTWzbFDtBY/EKN59GF1v+iYxhavIx/7+KpKXT1p8g2F2UaJ7wQhziW+xLmCac8F0Fx8dYt+wX6c3aAXgATcGKAR+7an9EBwSKiV982Wlq6WzoL+dtgFXTsk22l3YbvOX8wXFtTMnzVj5uqVq9yS/ZLYq5EhunNnz0kXclVopS3tv8eQCCGEEELISIdaK1PeQdXaIJA38oTycs8EZtvmm7otpSW1rdvaw0D6kQWq2JSgtfAFTNihCpAJTLwxBDiFGzmo2BXGqzcXVmzMb85FBft0KS925Ak5w/xeQXsM8DFIUyivqpUX/Mjx9QTjV63V61qlAIQmNmtXr/n1z391+R8vXVu7LrInK6jQWj1wkG73obw3DnwXSoOz1dXa+dfL/nLB//5iYdWixE97QbMwJZSzktZncENIdiyaQDwEWltdk2otPgLVWo3tPwHiGcbQVflg/AQGK6dI/hIhS9wIoTwG2f5xBjVgul5oHF8+Z1seX/5yQxY5gPIwFNHjco4eEj4WtCPfFMT2A0EBdIcvkC0ln5ft3UqvXcWtr617/dXXVq1YGXg+tBYxAvJVdBFQTEva/nsMgBBCCCGEjHSotTLlHUytxQxaZv+Y3wcmKCVhIZI3umKyL/cixl6UwFILsSmK1toXdUIwxFlk/o0A3UK2V4hLnn0LUNEkJbQTFSN5a5BcJmqLDddpOsZmnRZCG3jwI6gFBp3uBBi59SPJqjzxsogamrDkr1y8fMOa9VCcMJBlw93QWr0IGe3KrZWh2bpxy8olq0JH5Dly7c2WVhel/L64WutVV8+rq1ttLyfuobUIEb6hiY9vYGxcx+8Kk0KYlKxSqljisxSnDSJXMuzTiL1IxiRPRYaElt9LrJ0qvcaQgU1pUz4NIzfl2mODoqoV+z7aQUaPS4h1nPInCfsKn9bmlrmz58ycPqOtpVUXZp2iXLWufqulZc22ogVCCCGEEDI6oNbK/H2QtFbBHNpO+uWpsEESi81i6h+K7EEEksSRt4HK0iHm3bZb+YdJuUQIqC7mYHwvkVeI+lAF6xLy5hVZ1UrL2K76R2bxQzuPD0MMSxDxQc+hybcXpr05fXHNEmxCT6AiaQGRKU0KYjXiubaiXJfdHURecBLty2UqSWtuHzGZ3kHGIOfeJtSV0tJ7lWwYmkA8+FobVNcsqqtfr8vlOPU4KbE947G8W0fe7YtY06Fc9y4PeZIPSBDptTk4laLECDImfDVl1T3QoD3uGFRXMIrQJPg1wQcdRKF9JJX+IaP7zxkAo8+CHUn6UOVcLjd37typU6du2rRJX2kr0ls+aqVyExU10asMIYQQQggZWVBrZWo82Fqr2Om3XMopKqVGKrct+uK0idwN2B3UacW4EFBRPNY6nW0D7iH2YG9DFK2VxtPO+mB7FtLtwUcFI71h0kpsUArfenXy108+9Q+//eOqpavVdTEkFBABtj6kZForNlNhtkj3q7Xa446psNksyKjKQZb1drGpwSYbhiYQD4HW1sxfUr9mg2qtXRGVe2XttfGhn3j4giLAMLUmPi/rtAjyVwH5bBAL+jFiVNjGp5SFHjraF1s3Rb/b+h2Qz986LTb11yCtYEFWFvAtQiWNsQsjXLdu3fTp01euXAnL1fIABXAy0w00Z6ugU93MEoQQQgghZCRCrZWp8aBqrSLTZp2cq9MGNoZByB2L8BXboxboFWSS78eJJ4tjmOOHEqQRBK27a1oL0qwK0h19SHe/HWTkNg5M3bL6n/zwfw8/8D0ffv9Hbrvp9vbWDuwSa7VkYglUaRRVUOtuklCwNy1q0RZ2TFq0AjUwbTDNGR5kI9EE4iG4CLlm/uK6+nXy1UKm3a9nHZ/P+saWzdu65Hrw0AS+fOXkBnEpiWCdNtVaqaV058su7ad/7N5u9OMAtsEsCGmFCpCVBQBBRYy6rutqYtOmTW+88cbSpUvb2tpUX23L3U312ywhhBBCCBmhUGtlajwEWpu2Cc0THVWzknsPJYivlPdWBs2Ryb0jIfaSOEyFVoPU3cmTkLGrkr45O0XbGRBQiPSIIrNlw9arL7vmPQcdcej+h/33//uOzx1/4qQnn3NLHg4NviHKoSuoFlSzeiWUz1IaQOVekHa2G9ixpa1Y0vy9TTYSTSAeAq2tnl9TW79aHueFT01OtdzG7Pqman79dTfc88+bH1yxugmbcoeyfOUk2NpIpVcL2BZtEKDDvg34ZmaZO0IOqQcitGlP20EbzprHUaAaEkFgjwKDCMOOjo6amprp06evX79e9+LLphcRaBmAfLv+vN2OCCGEEELIiIBaK1Pjwb0IOfshAU6ga7WwBDcxrk1g006s0zJpeUnKfDywD5TKJUnexI7IcDbnh9PCHwaitf2CaX1GmlVB2tAug0YQi4PZcS5dsOybp3zr4HeNOWS/QxHG7H/INVde29WRw+HBMaRw2VqlVjkI2UY5K00OfEj9Il3uoab2FNl4NIF48LXWqZk/p65+RRQ62R9NYLCOa55+dvZ3zvrdWT/6/RtTl3r6DUUlDYJ+ja3W9s4vf6WRY7/XAwPfhx4NpmR5vUJf8KXCVwsJxIsXL37rrbfWrFlTKpVwMpEJj1WVBfbvKjv69SGEEEIIISMCaq1MjQdZa+0SmE3ZaX6mtV5iH8mTyAIX8tUApICtYu1VdkBnvNjkYbYGZhv5cmmuXTGzdYed1moVcVr8jE3r1rZ/Xvevj31g7EHvPBhae+op33jl+Vf1qcha3h6kNRlbIws9N4SK5OgkO9vpORwirZ1bv2ZlEvvymdmvYRQYzzfLV7bc9cDz9z70Ut36zggfEXbqs87SNnZBa7Mv9YDIWksbTKnMrgwZKqhBEGROixwkNm7c+MYbb9TU1BQKBXzDbVlR38p12uzMEEIIIYSQkQi1VqbGg6m1MDZoKkIPrMnprbG6QiY5qgSJKUbGQQ42wliCaKw8oacowV6KLAKCfHkkzx7Q2h2TNrTLyDtOoRM4GriqPODZbFqz+dfn/+aIg4/8yFEf/c+9D3oFeeGK78r1oq5b2htai8FloYI918HukZ1tTSAefK31qqvn19c3pB+BfBb2RUqxyXlmY0uusb1YwtcXniqPjnLFV+2fVOzXs0Jr9Vym+UOttfi+wWZtUsiEVtMgn89XVVVNmTJl27ZtruuqzSLWhKY1QQghhBBCRiLUWpkavz2t1cm7DX0qWmPrrbVaSm02C1ZrMTV3jCkkvbTW6m8Ue1Fc6qW1gezvVp2+yD6LTuKRsOOECUtASgOydY8NMuoU2a4g7QeZMjobKgpgr7SDsQvy7lltMzLVs2vO+cG5V19xTevWtvTttThaUZH0ImStYhuQkNJzu8eut4UcZDlUsOc62D3kfFckEA++1gZV8xbU1a6VDw2fQhTj2wWCRN7M4xu5Sh7fOft2ZES+PLjbfonsp6xfADRkTyRiaRU5qGq/GH1O8C6h7Uib2lHv7F6hEow8E1qg7/hBK8iE2dbV1U2ePHnt2rVdXV1aoHLBlhBCCCGEjFyotTI1HpDW9twJc7PPJk6DFbny7nKy9+xe8yuCiJ0to0ogVoC5uS+6KVeGhp4UwtRcNQajlZ+h9O2G4o4y/e8HbVOCSmy6KQ+aCuPIg6h4CcwycaG72KGSgs6loLgAugvlwT9w6PIAEaQrFCrGphCbkrwMRkaZHYw9A2WwCwYbeKHvBgtqFm5cv8lz5AJR5O++UWQHgqCdyvAqeh2xZJ+jJhDvca3V6lkjaH/+/IWrVtWmNogTqXec2lfsyFOp5ZPXDzU959qIRf84AtCgBpD+xcSms8y9gz1E+cXRRLFY3Lx585QpUxYvXtzZ2YkceK8WkzKJwRdVDzRN6EETQgghhJBhD7VWJq+Dp7Xpjyz0ybBCVpY0u8aVBLIghpk4ghTAJNtNfE/uo3ViuctWKnhWNbVAxQy+JyohmWZIECfEOMMgjBI05drlOMeYvBtCYUVXYZ2IrdbCaQP7SFvbpequ7VKuMi0mdlU5XYiWceheGV/leKQh+37aTCHeNnJcadB+pes90vJeJjs/mkA8BBchL1q0qK6uDp+RCi0ykdBlT83MwC7d2y+6d/hQeSxI6PE6jgOzhdbW19f75VtwgRyqvbgAJ1ke053ItQbp5QaEEEIIIWTYQ63FbHd3tLa8v1KxejhtN1mF8q4+GaCyHZl/w1cC2KfczSjG1pILa1Y3zVqxden6toIsLEnlyE3kPaK2vLbSk26ntbfmpukgkqc1tRfNorrOucvaFtXnm0viqT6ENIzsE6zsUrC8JNYPZI/ItnSpA5SuxIDt865kVRkZ0r/kY7d2JBsZ2ATSoL0HMtMM3aUJAnqdFsRDoLXV1dW1tbXSLj658sKmeKpF8zO01sgCB4UD0eNS2i2aib0ogwROL34xEMurjsJk25YmbHY/2IwQQgghhAxjqLWYz+6m1tqgqrdDKiv0nwG0nVSMZS5u1RJai4xNW3MPPzvrvIvv+f5Fd/3h74+9OndVwcPOJCrFqXKWdaUn0qCVzO6AsvDU5kI86Y3Vv73isR/98p5fX/HoYy8tbCrJpchh7InZhjLFt/fuYlIv67HZwKTJtCtZwEUS40RA/zYbu23hMmIVZSC0mULoLnhF5lEEVJ4cjQdJaxVsov25c+euXLlSPwgRWauyurcXtlI/+YrWHT5kQ9IEDhDYkQo4EGy6rhuG+MbKget3W37x7NXIjz782MMPPVLMl+xBE0IIIYSQYQ21VmaxA9RaNbrU6yB8mupVVabOkmuXVjWrTFasZ3Z3ViQP6RFBxBam2fMWbjv3V/eccNptH/v6v048++9//teT65s6oyCUC4hRyNqOrd8L0VqESq1Fa5inL1zf/pu/PnnSaTee8PV/ffnMm391+f1VtVuKJvRNKYoCGTKsOUq1NpbH4pb1qaIfHSx6RsBuqwOhXLEsndoCFmsWgmYqmp9uDBDtV067DRUjGvHgtFQmEA/2aq1v3+y6aNGi5uZmXcNssSDRNkC0+vABQ9Jj0bG1WpBATlNTE44X+Zopo29ra9ra3NnetW1LExIb1m288vK/fPWUrz0y4VE9Y4QQQgghZDhDrcXsfkBaCxmDvCGIzEKr1D9VQRFSxRVETu1DZHu3lpZUKyubhpCWlLf++LZNPzZ+aKbPbv7mWbd+6QdPfvrMRz/7/Tv+99IHVq5tkhv/oJCQ1J1pbWVAm13GTFu28fu/uu8L37njlO89dPKZd5z18xsnL6grmsAzhSj2VGsRx6K1nn0oVPcyl0Vsyt5FK62icwRrtnIRs+1Iz0APInsFMthtoVX0vCHIA41sIt0x8sFZqkwgHlStBWEYLl26dPLkyXPmzJk9e/Y8C9JzB47WHT5UV1cjxkHhcLIj0l2aRgEtA6qqqhbULJxfvaB6Xk1N1fzpU2dccP4vxhx0yNnf+76eKEIIIYQQMpyh1mKOP2CttcE6ndptRYAwyJ15sZ8FW6UfRFDKVPYr1cuPJYZWFktmTnXH/1zw8OdOu/8T37jnK+fcd8VNLzU05qLIqqQMpNtSeiIeK2MsL9UiBCbJGbNwQ+dF1zz75e/cfsLX/v2l02+64E/3VK9uLKnWJr5op62amCBOPMitXJ6sd+fKLbU2yP266Vt31W8xAoiwXMEslXcJdKSXgA4I9CU+Vw7adb/HP+LIPkdNIB4krc3+sqCJlpaWrq4uXczs7Ozs2C1khXQ4gSMCGJiuyuqSLEA6Z0EaBy7LtZaWptbO9q7mbS3trR1NW5sv+/PlX/rCl5984ik9UYQQQgghZDhDrRUl2j2txQY0QbTKypUEJGGA8MDEj2I3jrwo9rRaX1RRlJ79omXYsJgt6kMlO/Lm0Wdrf3HpS9/75aN/HPfyq3M2OYGR+wSDUMVOWuiHHkKrITRJPok6jJk0eePvrnjunF88dsnVrzzx4pKuwDjy3GMHWiutyRAwGIhUoGvOelCwXFm/TTwxXr1cGWJVXjNNz0F/yBAtugmbinb3rlq12TBKNMgJkM9hNJCdH00gHmytRQ7SpVJ6BynSWZl+2fHeYcWOv2CVR5qeDWzJdx6/eIlb8p579vlnnnrWKbq2CCGEEEIIGdZQa2UiO2Ctzd7lo0ZnH7Ak02PrfigQRm4oQgsdhH5C9rqbtPNpcUH1zHRHZfDFmdFkYIIIqhn5jp90FE1NXcfMZc3124JO37hQS6lvL4e22LZ7gTGlvWQBIuQaGVl7yaxqSGoWl1auiVtyxsVJgCUnLsYvF06nmi7rr7Hv2YMMXK8Am8WQYLaSsAcoryOyR6GD6Hccewp7oHI2YbNynoIoDPW86/4Rjz2F3QnEe1Zr+wVfyH7RvvYd5HdZfi8l9hw/31WA08qVE4QQQgghZNhDrRUVG9Ajo6zjIcgMWLTCzoPlmlxdz5RrdEPfLtQGsSl5Mba1PfiYOK5IWLdwpvuygD1QtURerhMaWRqNjIPYDQPXmIJ9zWwgY4CNhJ1ORyC336Id1OxLdy+VwfUdeDMGLnrq29thI+MG8pwq67T2wunIPjNKQ5REnosjRJ+BCd3YD0ziiVFiIGESOnZE8PdgsPVS9QO94Gih+xiBDAKjl12pCo5oskPQBOIh0Fo0qB5biXY0KsmOuhfpbx8Ckvil8EK+t5YQQgghZKRArZVZ7AC1thwwA4ZWSIjlSl15YnAA8fOSBPLp2eAkIqYoAnuUSql9QRzsQ5n61VqUtu+MDU0QGj+I5RU78OR8GBQT40YmX4rCAAWTkin68kgnyCpq9kUm6b2cFgFjMYl9eW3oy5YWjWVMer2x1VprthJkh/H9yF7TWYrFq3FcvlwgLfXCsGDiouQlMiY5hEHD2ofVWhxzYJYtWbl08TLoh/Zrz21acoSSfY6aQDw0Wtsv6e59gPR4rc3K77F+jfE1w9ndh04DIYQQQsgIhlorM9fd0VpxRnXaBPpnHw0VBpEbwv0Ss7bZnb1k64zFbbWNSQluCO3zU1HQJc1IHt7Un9YioDQM04QhDDn2vUheJdvU5i6qbZy3ZOPaRrndD42EUeIZvyvoHJDW2h7hgb5IMQKMNHLtXbJyi6osKEuBwGotglUnqDoOL4j8xLQ7ZvmG3PQFmxasbt3SHogCJDDevNVa2LId/6Ch+hFA5BOzelntr37xm1/8/JcwWww5CmIZuJjtYI5gkMkGrwnEQ6C1GZWN74v00Vr9I0+6lxBCCCGEDGOotTKFHZjWKlpGbkCF1uoTj0M3DDxjGpqc+56a/dM/jT/nDw//454Zq9flMDf24STG+H4oTzDWiqIporAStDUNkoFC6ZOpkNraZCa9vOrCSx/90S/v+cs/XphZ1eT6xg9gn74XOwPWWlnqlYVkY4oSYkdupsVwMArRQnQIj9VLkVVrxXvlmcyxmbaw6epbXj73t3df9NfHnn19xZY2WcLFwdkVXNSy7zsaNOQwxfdNri0/7qpx7z3sfe8Zc8Q1V17b2dIhp9AenFWz9GzY0jgJI4bukdsE4qHU2n0dnNfKoKu1I+nrQwghhBCy70KtlSnsgLS2eyd+iDdFVmu9EMGYXGieeHX+d39x0/Hfuur40/79lR/cet+Et9q7YIWiIK7c1ioVZQVWPUwmzj21VoQ3fcEPdkOJ59S0/Oy3D37hO3d89vTbv3zWLVfd9HJjMzo2YSzPbbK+iq2+9KO1CLaLKAxKYViI4xJGLkdhtVZ+oozcHlyhtZGLXmCua7bFf/7HC1866x+f+eYNnz/thvMv/s/0mi3QWYhmkjgwYWl5MDVADjM2Tqfz4D0PHv/x4w/d/9Ax7x7zyY9+avxd9xU7C7q2pveFpuWptWTXwTcFpzYL2SYhhBBCCBn2UGtl5rrrWotsUc1sv4hgBAOME9+NxP3aAnPLhCknnnntCWfe/vkfTPz0abddef3DW5rzsFOpKI97kso71lq5xtjev+obWY19fcaGr33/X8ef8Z/Pfn/isaffft6fH1m5IS9Cg8JoRzwOqb5Iy5VCa4MU1WALCBDa/7+9P3G3LLvr+/5/Icnz/GIn/JJgbCep5HmCndH2jziO2wM8sU1LIIwYjG2o2CYYg34BgyU0u0DCAiQ1GpCExoAk4AqQQQOou9Vz9aRqtbqqu3pSz91V3VV1pzPs4ex8v2utvdZ37b1P3Xur7rTrvl8cnVp77bW+a51Tt3j2p89w54X7hmFtFnKkL8DqZ2VlKxLaN6VT4uu9Z6c/9FOf/I5X3fA3vu/j/9v3/MZ3/cB7PvenT64V+rjceBdrBzeyS3TvdfP8k8//+D/+F9/yH/4nkmnl9mf+gz/zYz/yo08//pSNte5Ryl6ItdgWfVrNP8dwk2c6PNkAAAA41Ii1euW6o1grNxfkHBNr54tistAvK/7Mlx787n/yq3/lH7zzr33vR//2az/w7g/98aX1Qr9qWNKrC1lV4T+GOhxrZYzPK5Jsp3UpefK2+1/4Rz/xwb/+mg/+L6/+4P/+2l9/w69+6cnzpRTU8YVmtxh1clo5z7S6Bdmr378cFEUludWv3MZa7XbvQw6xtq43i2q6UTePvdi8/t998e+89r1/5f9419/83hv+yU9+4it3nt8ofciXtfbjs7Wyzmx19qF3f/B//vb/+c/+R//xn/mP/uz/+O3/w6//2g2XLqzqNzETa7FD/ikV4afX/XPUWzwEAADAoUes1YvXnX221l71pnftar6UkDFdNA9/c/2Gj37l+47/yvf+2Ad+/sQfnn7s5Uur6zrW/YIfX6MsJQwO0yruGltGzBb6a3CfPV9+4jO3/+i/fPdr/sk7XveGj335q49vTHXAfD7Xb3vyF+XbpjsJzYwUkdDrhAzsA7M8tKLS36W7Om2+9NXTr3v9B77/n7z1x/7lr3ziM3c8+ZwEyUa/ithVdC9Bu1p7w+1Gg+pzzzz/0//qdX/+z33bt33rn//Jn/iXzz3zrJyVQOvpmHGKO/cNuSfWAgAAAFsi1kp62EmslX4fa32ylTsfAhv9mGu1kGDXbM6bh5+8eOPJJ79677lvPDFd20xRxKUSbceeAe4FWzktt2lZTSv91bRPvzi5676nbjv55DdOv7K6JtP1l//M5hv6cq1uYHm1bXNbG9LUs2JalBq3L65XD5555taTZ+687/GnX5xtzppZ0ci9fyokY7vnZa/47UiYl/sHH3zwB37gB1772teeOnVKomxZlj7TCjkbJoxN3LlvyD2xFgAAANgSsVYj2c5erc0HSLTwsVa/ClhvpfTM3W+s3Vzotwd3kkdVVRK9wsEg97JvKVXdu52n+itu3RuSi2YylyirC8g5yTT1Qr9/eLdi7TJSX+P6QmOtPLxJ0WzMNLr7Xc0r/TCufzb2J9ZKQ55AybF33HHH7bffri9Zu+dTTmmoJdYCAAAARwyxVtLDzmNt+6ckUI217k3IdTV1vxdnVpSbRT0pm2LezDfKDQmERVFIDPOBxEcvX+EydLz7aGipn4ZtVieVrDGrmolEypm+VCu1JNbO5qt7HGvr2WxSy+Nq6qKsN2blxryeVrqTtUkzc7lKd6JJW5o+6u4Vl+kW8sxUVSVPqdxLW4Kf7/FnibUAAADAUUOs1Vi2k1griVSSm+bYyt3c9wa7w9J/FXDdlLOy2KzqTferXjclgsUoInwyEeG4R+OhG1YtSv0K4no6r6t5vZCVNEP6sFvLrazrTf0NPXsca2X/+v5qCZJVKUEqvEgrD9vdZDOlbFQ/KizRtqzKaZi3B9zTpsFVQqwcSuQT0oiZNnLDxyfu3DfknlgLAAAAbIlYK+nhCmLtzL10qrcQayVzyBl/smwWxbyab5TlarVYr/Vbn5QEku28TqtF9MulJLForUJ/xY/kZ0mW5ayqJcro7mQ9/Y21k314E7JsSJYoy7kkW9l/WUvMlZ3op4iLhR7qEM3isjO5SfZyk/aARjr3SOdzfUo7h7FH+MPRiTv3Dbkn1gIAAABbItZKetjhm5BDeNU3BctNY60GS/dB2EICr7t30VR/R85i7j4b66Y5vn05bnX9JUAu1vqXRcvFdNHo25KFCzYSIPVtz1U52etYW5eV/mIgXVZieVnW8sD1BeNqURaVPna3Gfc1yP6bkPdsL7oD9yZkacdn0jeKQp70MEC4M+MTd+4bck+sBQAAALZErJX0cMVfGeVjp3+91MVa6ZB45YKeDFhICKym7iAlFmHDSZ/mxFBfIuy8Xkh4LiTWVvoBV1lAqtVSeVHPXWze+1drdUFdU2/uWDOsfu2z5O1C36JcyTbchv3IPROfN3vfaXj+UNj24Rd36xtyT6wFAAAAtkSslfSwk1gr/T6+SsYImaIuCv2EZxv/3M39PltJfXJzxzvRVvBF3Ad3JdCGT/C6EOvWjre9Zvbjj/0e2psLWv5sGrOH/IpR6L0mxIfjG3K/67HWv7gtRfyL3p4cdl793hG3qaV/EZc/CwAAAFw9Yq1cd+8w1kocmLpwWzWzmX4fr4t9GvZi1JS2RI5Sv7FYsugOabGwB5cIwjctZ7HWv3aaXkHdS7IZeYhuP17YVbipcNZve4/5Fb3Qda2Ij8g35H4XY63/nq0OCbGxoARdn2nl3je2ye6q7/JnAQAAgKtHrJXr7p2/Whs/PevSxaIp59WmhNxFM3U3fWXVxdqF3JZVWkomtMVlsk8FnRjp+t2wHaSPKyVLxFjrbmELgduJve2xsKwTuq4V8RH5htzv+qu1kl3967Sxjv9VSZ0e39gOt6OthdEAAADAHiDWyoX5tmJtuDxfLOaziZwty3quv/Gm2ZjNp6V+U3Hhfq1svZhIsnVvP5ZYq6/s7viKPt+AJIKwsDqgWBu35G9yl7gxwpzdU2HZazEpxQflG3K/i7E2ThQSXP3XR0tjY2PjlVdeqWv97cqynHRewUu1wr3EO8CfDaMBAACAPUCslQvzHcXayv9en4nkAvcNThIOJApMymZeV2U9qxd6k2H78R7hPS0e2VjbkiciNHq3vRaXvvakZ9U15H53Y62ETN+eTsOvF37iiSduuOGG3/3d37148aLt3+YLtn4/wifYQX5AmAAAAADsAWKtXJtvHWv9pbmQIFE1xbTWl7rOrdUPnn3l9GNrz7/SbBTNVE6UdRWS7dyPltvOyRx/28r+hAVdRX+Fz+ByvtvdNMIPDcF2yY+Mbcj97r4J2efMsiz9S7WPPPLIG9/4xm/91m/9ju/4jo9+9KMXLlyoKn07gB+8HX4/vuwyfkyYAAAAAOwBYq1Gse3H2mpRb9TVpGmefWXxm5++9ad+7iOv+zcf//DH73z+fDMtmqKQ1FvU9eZiMVnUM/36qB2/XCsTSvftUMtmauKNe9xp9SsiK5Z+3faWtK9J67b9N1mFE9g5+QGzDbnf3VdrheRMf7ixsfGFL3zhR37kR77zO7/zuuuu+/mf//n77rvPvwl5+2LNy/BjwgQAAABgDxBr5dp8u7FWrtElScyb5lLVfO5PHvqeH/nFv/s9v/h3X/1Lf+/73vHJ331wfd5My6aopnW9vlhsNPVEP12rCXRHNB+627KZGizjHnda/YrIituKtb4RTmDnYvzzDbnf3Vjr732jKIqLFy8+8cQTEm7vuusuafh3IJft9yFv52dLStWN+9bvRb3s5j4Qnu1TCsfaqe1b8QQAAACwbcRavZK+fKz1ScC98qSxdtY0z15avPcTX73ue97+N7733f/b9/76X3/1u970nq88s96sVpL/ZlWzWteri3q+KN23IbvvfAq1tqUbHQ+B7eyHQHu14s+Jb8j9rr8J2Td8EbmX+nedvOfhRx6VM9pXyw9rex9vjjlyP5++5d5XMF80MylV1fN6Ma/1W5Vni2pal/qx80VVLfRD6O4/eeh8WUUG+K9Sk1vZ1FUoWMv+9MhVBgAAALaPWKsX1zuKtUXTPHex/NCn7/quH3zHX/3uX/5rr37Pd/6j97/jN08+P20uyfV7iLXrzaLQT+IWerku00MtYLn4c+Ibcr8PsfbkyXseaWNtKG5jrW+3R67pUmh7Um76j8KNKuuqmE/15duqkIYf707634ilx/LPQTKt7lv+p+9Q1kfhXs51ydbPAQAAAHaCWKuX0duJtcLHWrkonzXNvWdeecMv/+7f/8e//F3/6F0/9W9//8YHVl8pmk19D26xWGwsFhO5yNdYeyWv1uKIij8nviH3uxtr41zfkHupf++d95x9+FFXPGRYl3AdH1t9b+hyDdcha8tPuMZV+Seht6Kp5s1s0kynzcZ6s7GpPfrbsFymlQkysp2r/5PuuUZhSduhmq8PAAAA7BCxVq+mtxlrvdm8lGv4taK57+wrn7vpkd+78ext37jwyrzZqPSX1dZ12dQzvcmVfq1vwfRCLWC5+HPiG3K/17FWfvLvu/Oex8+EWBvyqX7XmUuinVhrbv5PTaoyVOfUTTlvJpPiwYefWvmjb/72SnXP1zTczjelmC7nR7Zz9VBjrcwKsVZqkGwBAABwZYi1eil9+Vgr5Lo8qqpKkq1ck08XzXrdbDTNxL1+q5nWX+LrFyL7dFBUVeFnhULAcvHnxDfkfh9i7QN33P3U6bP60+pi7dzlTWnoCJ9F5eZfaPUv48ab/2mvdB/6X3OKWXP+4urHPvfFf/Bjn/87P/jy+z7ZnHtBvzitXmgulrm+iI/Ji0Ut/zTq+UL/3eh7GvyKcgMAAAB2ilirl9I7irXu6l6u5PWzsxvFbEMuz90VfsgAPhPolXull+z6C2x39rtAcWTFnxPfkPs9irWeHJZ5rNUXUP1bg+WsjIixVm6SXd3HaLVG7C/0hd2ZxONF0RTT5oVz52/41Gf/ynf/P3/5O5995/ual55tmo3wiXS3XQ23+sKt/JOQFeRWlM1cArH7N6K/IUqOAQAAgJ0i1ur1+5axVrhA4ejlebWo9eLffRGsC7Jyda9X/T7T+goSa92v6iHWYnviz4lvyP3+xdpKv77JZ025hboy3MVXmSeLzZt6Jj/MumytP+/u7QlltdCvQZZYu5g3l165+MdfufP1v3jrz7z1pT/4983G+aqZ6FclS/SV6VJHqtUytKzqaVNsNtPVulivyol+KFdKls1sLv94AAAAgJ0h1uq1+3ZibeRirfwp9xJoC7lAX9QzfROlXOa7DBBirTb1l5foC1R5nAAGxZ8T35D7PY21Qn7yT9159zfPnJVUKfx/mZFbu4/w8yz9kjs3m2paz92LqlVd15JXJazKbVrXs3o+l5gqt5dfbp56rnni6ebl800zWavW9bVc9zKsf2O+/DFfSKbdaJ55pvnm082LzzaXzuu3TLl/VlITAAAA2ClirV67F7P59mNtPkYirFyOu5u+eiW3MFfu/GW8O8K1qR8Ur0as5htyvw+frb33rpOPPfyIFtNk636u/Si5159u6Vzox8Ql0+rvq5I/q6Ys53U1a5r1Rn+ZlWZVScTyZzVv5pNwK2dFM5fRklRDoNX/CqSfoi2aSXPhxWfe/8k7f/wN97zzPdXXv9FsyPi5LDipwn808nTtds8AAADAMsRavXy/8lib3dp8qy9uaeAg1mJHYoTzDbnf61gr9e++6+TZhx/R9yDIj63/jzL2Xgfqj3RZz2b11L15eCqRdVrNps1CYu2aFGkkv/o3J+tvrpXMW1SltOb1QjKt/BOopYB+bLfWD9LWxbxabV589uYf/Znf/O/+1qe/78cufvnmZm1DYq1E3rkupkt6+mjbPQMAAADLEGv1wv3KY61e8JtDPSjdV72GWCv32g1sQ4xwviH3+xBr77r75MNn21gr9eRH2N/LoYySn+aqkCyqt3LeVJO6mi4kxhYzCa4z/R3N9aRp1iXQypTCvSl5Uum3hOuXg+sblqWGjNRAO3f3tcxca1545q6ffstv/43XrPzTn1y76TZ9tXY+Kyo5LSunTXr+EAAAAFiGWKsX7juKteGk/0Ov/tu29uoLXhoEXDTwN+3GNSSErVbo3Q2xmm/I/V7H2uk8j7X+v8S4n1q3kr61eKFvKp4300lzab3ZXG8K/Q3Ni3pel/qa7LSspk0zcR+LlR3q1yhLoJUuH2sL3aW+eFuWmnHnRbMoq3qzuXi++sodFz7xuXOf/1LzxFPNxrQp5tVCv5JKYm3cnue2DAAAACxFrNUr+CuPteEgaDOt3PdPAluIEc435H5fY61+dbfLtO4FU2mW+pnZuX6i9sUXL3z1tvOf+8LmbXdVF87Lvxh9V4IG1kU1db+zdtFsuvckT5p62ui3ScnJed3MKldiUcwbGSUZWY5dYYnKa+vNhQvN+qVmuqFfGeV+t49MJNYCAABgp4i1cvl8RbHWMwe+6V7oin22hXHbh3wVl/ANud+3NyFrMf11VC7Tuj9n+tXH7lu+i9n6yfs+91M/97G/94Nf+ulfmD/2xKKYSvzU8FuH12brciGxdrWp1pp6PSTbel4v5CbDiqZ0WVZftq0qeRi1e0vzVM/ITZKzZNpSzulncX2stfyeAQAAgGWItXJ1fwWxVi7nw4W6u0mj/wqtXJ/LrYzHGLV+vpKeWj9RumviEr4h9/sTax955JGmTLFWfmolq25qrJ3ptzjNJmtfufV3XvvPPvSX/tYX/+GPN18/20wmkko33Auv+j3Ik7qZLSSvziTv6gdwy1pibi0ptZabtPWLk/07lOuF9Ooq80o6KylezjTzLvTUeikZWv8h6YPM+W0DAAAAg4i1cnW/s1irF/56k2tzn2ljsjWTtOUzrV75+z6Mmg1X0tasNp9LLAxduyEu4Rtyv3+xVou5r4zS1KnfSLzpEqf+bM9niwdO3/tL773tX7z+8Xf+RvPwY810symm82JT5uirr7NKUql+1LYuJNnKBH3rcq2/3EdD7nTSbG4287msqS/GarptmqkM04/bNtXcvfarfVJO1lvI45Tp86KRIXJWH7Y+TBnT7j7cUg8AAACONmKt5AN9Yelr993/0IPf0MvkWn+Zp140V/Xlbu71qOxW1WJgjO3Zzm0JrT6kO31st2XCw+7rVdjdm75c2N66p3yaqmr9FTZF+eADX7/1q7dMp1P5IdLvRCpL2bbGPPkhuqJXceXB2Ybc70OsPXlSY60+OveUS79/ZVVuWl+akjAvrTUvnG+efL556plm/Xyz+kLzwjPNSy8001X362z19/JUi3pSFZLyJ00xL/X30Dbrq82FV5rz55rzLzWTzfDsuaQsy0lsnelXS00k41aL+VyeOf/fi6r15uJzzXMvNM+/1GxeXMgYV1/myU35YS6Dawz2nQAAADjCiLVyibyQlHLfPffefdfJi69cmG5ONtc3JNlK40Buy0h8GtarMLLbEuFh93Sn79dtsrG5vrq2dml1NpkWs7m0P/Pbn/6+733NysrKxYsXw8+S/DS5QCsR1x/uyGDs3J9Y6/87ggRaOeHv9VAei74nWKJmXUuSn831q54mLz9/0xcfePM7vv7Wd71w002L1fONhNJyVrnfXLupL9Q2+m7i2Wbz5Def+Phv3fELb3/gve+bnT3bVOV8Wul3I5e6gHu1di6ptV5M5otpWRcapsuqeeX5b/7+755+wy+deduvbT5yuq7Xm3IqVX3SVi7TakKOPQAAADjaiLXuhaPZ/NT9X/vKn/zpPSfv/tp9999/730Sce+9W5LuAdyWuXuJzvTR3cLD2LbO9L27yQ+DvcmPhPxg3HfPvfITcvLOu+Tw373zl//in/8L119/vexK34wrwW4hCU9i3RXyaTM25H6fY63v97E2DDMvXGujKJsLF7/5rvf/0V/8O1/8b//+k+/+aPPiy82iLCpJvPr25c3JTAK9fjny5rT52iN/+o//1ae//W/+0f/x2ks3flUi8eZc37QtA/RTufrOfXkEZVXP5gu5Ffo9ydN58+jjX/vXb/vDb7vu83/pu2e33qOvBvs3+fsXaV2U1eSs+2sjLgAAAI42Ym1TyZX6ojn/0rmzDz/y8Okzcnv0kbNyL4cHclvmzBKd6aO7hYfR8/ASnem7fnvkjOxp4OZ/Ns48dFrGSEN6bnjPe//yt/+ln/iJn5Bd+UwofKyVhv/p2pE4yzfk/mBjrXJv0Zd/I+W8WMi/lOmsOXfh+fd98vb/3w/f/Tf+6Yu/8Vl9c/KinBUh1k42plKkrgr9VbcPP3nH697y+b9+/S0/9M/W77ytqTfms415Xc2qRoZrNnW3qtLP18pNf8ut1H/0iYff+qtf/V++/5a//aPFnaea6ab/TK7GV7ctudNgLC35n7TSXgEAAHBEEWsbuV73V8wL95nJeHhQN58u+sJ2+/Lp47stER52T3f6rt/a1yfjTX4w/GuV+l9A/OuWC/2vIX/0+X//cz/7rx944IGikNjnfnWNRC13Lz3uQeyMPjrTkPt9iLV333VSgno/1vpDoaf8MyA3eVzF5qVvPPj8b//huU//8eYDDzVra3VdzuS5kb8ZmTnVLzXW92AvyubCy8/efPMzn/7si1/4/MaLZ5tmvSknzXQqGbby34fsXrOtSv0aZH3Ds4Td2aRZu3DhjjvPffIPzn32i7Onny2raVnPZNTMpWD9C5IH7W++QnhMAAAAOLqItZpm9XeRuKwymGr2+bZTnemju+1UZ/re3XyWszf/EyINybRy//ijjz3x2OMS4WRX8b3HPtbqPncuzvINuT/YWOvHaOCUJ0SOJHxqei3ratpsrDXra02hv3tWninZh8bO2uXMqX5vVtWUs/l6M1ttNi41k0vF9MVLT5/ZvPGrq3/8p2sPnZFwq+9ALtz3JbuP2BaSiSv3+34Ws2bjQrO62qxtLiYbrmJdNbW09Lu5ZAn/veOypO5SugAAAHDUEWv1yriTW0Kw8e39v+1UZ/robjvVmb5vN/cjYX9Owjdmt1nRkxDok+0ViKVipNy3WKsPygXbeNaTtjyccNbl+fl8Om/mG02x4X4D0HyhXwotz4NspZTA6d5XLCMlrk6riWbQ9c1mXjZrF5/67Oduu/74n/yt7z/97vc1L59rZrOmKBZz/bjuWqO3iRacTerNabXpw2s13dBHKXG5XkiXxlpdRralf06bWm5X+FwDAADgGkKs1Utwn1L8tbu24/2B3HaqM310t53qTN/1m/thCLdev/9pCT8zrj+G2Ln+albpDS/YXoGYJ2O8PESxVvr8A6/11zFfaOYvN/M1DZaVbsTlzHmhX3AssbN2Oyvr2Wy6qSlUIuna5pkPfXLlf/yulb/0t+9669ubl55uZutNMa1nlczaaBarTbXRzDb0w7XltCkmEpvnm4tirsX19Vp9NVhurq7uxFWt5XaFzzUAAACuIcRa4LCIeTLGywOMtXrg+M/W1mWl31QsNwmuVSMTN8v5elNqbHUv5E5d7JTbpFzMZJZGT/f24rkG0WJj8o0vfOmLP/Gzf/BjP37vZz8+u/jNplqvyklRFLpysyiq6bxaLxebc/2yKf0NwFJTErX+NwJ5lO6h6qP173PWOdLS/64gfQAAADjiiLXAYSEZ0jbk/jC8Wqviu/T1d/C0n25dSIitptrSDLtRF+tNPXHJVuJmNS2baSEjqw39AG1ZLWYXXq5Pnym+fmpy7om6Wavrzaqc63dr6VuUN5rnnq2eeay5+MKimOqneSspsSjral5Xfjsh1kpbkqw+9JBstQcAAABHG7EWOCwkP9qG3O9PrH1s8NVad/OHgU+2sqZLsjJ+Ws3m5aQpZs28kO7NRTn379aWAXMJn+6tyZpCG32lt5rrV0yV08ViUi3mmn1lWLFoLm0u7vjaw7/6ga+94x3Pfe73mguX9MO0bpWqqoqFvs240C+S0sAsbccF2nADAADAUUesBQ4LSY62Iff7EGvv3SrWetrpPkusGXUmGVXia63pVF+QLZtiXtbFtHRfHyWhdCbVdFZR1hJ4q0ojsQyr55NivlkUs/ALfiSnyv3F6aVP/cGn/uZ3f+Cv/q93vPEtzfPnm0mp0+VOwrCLsj4ma1J2u5ItuLcfE2sBAACgiLXAYeHzZGzI/T7HWr3J//mz7tYlXRIkC/cy7KIu9dfUTpvpRjPTvFqW+g1PUqp0eXfqflPthn+jskbfUt/DXLqvlXIf0A3vZ74w3fitP/6dv/N9H/uOv3X3m97WvHC+ms11If0NQPKY407cu47dG4818PpMK4cDuwQAAMDRQqwFDovB2Hm4Yq0nJyRPFlP9oqjFrFlb1S8wLgoNm6W+OCvbkri6UVeTpp65Xv3lPzJmJuHWfbOxpF5ZTcbN6+bStLn/7Lnf/OzT7//w2pe/0qyu6ku+shN9fLor3Ync+cNFiLWVj7Xuq6kAAABwxBFrgcNiMHbuT6z1AdLGWh8Z5RbmOO0rrDKobubu+6Gm6809DzZ3nJref7q5sLqoy0kx95Gz1O8zruebG/rdxr7L3aTmbFG5b1GuCv3ArX40t5lMmon0zWrpk+LuVd9C03BV6rLpddpQPd0AAABw1BFrgcNiMHbaWKsvXbZjdiXWzqfhK6P6sVbuY7J1k5S0JKGW2qwX5UTy6do3vnHzz739M685/qWfeevGPQ9ICJ1Ws1Jiqkyb13LzmXSxqGb1fCo3/bW01VpTrGlDomqlv5xWcm81rxblfKFfPSWxVn+fUC2xtpo1VXph1mXaEGvdoezY7QsAAABHGrEWOCz6sTPG2ulaiLV6C2P0i5h8W+NmvLnQl/WELomC+lFVeytmpf0FP3qT/naJAXpWcm3lPt0q2XU2PfXA7/zgP3/3f/c3P/v3f6T+0m3N+uaink2acn1RSJmq0F/WI42yrmaLYq3eXG2mEmgvyQD9zK3E10UzWzTT8OttNeZWGmjlsQsXXuOTEu7sIQAAACCItcBhIUHONuTexlp9kdQnzzBmF2LtfFoMx1q5HyT97pfW6muq8kcxnZ55+LY3//K//8f/6oE3vau55xvN2mpdTdeb+SX9XildU/6YlpXW19dX9duips3sQqmPZyb7kdP+V/fMm4WUde811vcut+JzAgAAACxDrAUOi12LtfZQbv1Y68fUVxRr5y6c6gApWDaTzc0zj2zcd0pu7lfOTqvFfNZUm6W+WVgWlOHr04l+p/Glzea5V5oXX24uvtIsChk2XxSLQrLxwn9z8sTdikUItCK+cusXBwAAAAYRa4HDYndibf/m86W/9z1trJ1N5juOtVJJdiL3Mrwq6mqm4Va/3Ukb9WKu3/O0qGfzsq7099bqC7FyamO6ftv9j/zqx86868PPff5Pm4sXm2paVNNFMa9LfTfyZlNPmloa+sFaR79RmVgLAACAbSDWAofF7sTaLomFfqQkRC3henScHE+n053FWuFXcTeJndP5bN7Ua+VMEmnRLGZVKTdJs/q+Y331dbEmoyT3nl998gOf/ux3/MP/53/6B/e/+deap57XV4qraVXOq6rQNyRLUJYZ+qFd2V2Itj7WhnUBAACAJYi1wGHh02ZsyP1Vx9qQafWrmdr7GGvlfqexVrplvmwknpe2f//wel1N/eu4rpIMkLb0XGjmC2leWH/2I7/3uf/9hz7zV1/90C/+evPC+Wa+WdQTfb231kyrb1nWbz4u3O6UT7bSCCsBAAAASxBrgcMiRjjfkPtdibWSG32grVyGdBN1nK+/01gruyhcuJ0X+pZmfQFYp4S3Jfub3OmAkHgr/XKpjY2Nk6ceu+ETD73rQy98+abm3At1vTlfTCXWNgt9idb92p7SvZ9ZZgeyQ79VAAAA4DKItcBhESOcb8j9VcZaP8bHWrmV5fwqY62o9ffRSlLVz83qsc+vsjd/q/Q9x34jRVVWTT1rFrN6Xk7Wm43N5vzF5tyFZrLZFBuSaeeLmXvXsf5m2vDLafUGAAAA7AyxFjgs4udIY76dz+cffv9H/u2bT6xfWJPEN5tMfeCsNFeaEOhzpL+1/fEdvNKQRCv3EmL9Em1/OZls3Hv3PY8+cjZk2i1jrfT73yarg9woHe/Crcu0cthOlZwsN/1wbLGoq0VZScLVL4ea1YtZ0eg3S1VSx8Vptx+/bd3enorPrX8qAAAAcA0g1gKHRUxcviH3Emvf/54PvO2Nb197ZVUSn76mKtkzRFOfbB0ZHm+9cCgZWOqcOXPmiSeekLnSlnshFcpyfvLOu3ysDS/YpjqDXFhd5C8c+wXl5o6lPxxXGl3lVlX6Au/mYr7ZTCfNbL6YlXUhG9BR8iBcGG4Xla4DEJ95AAAAjBGxFjgsOuFKDmez2W9+8KO/fOLf6ZuQi/BqalEUctZ/UNYH1JAx3dlwH291U86r6ebsQx/88Im3/+Ljjz6hcViKzOZyv7m+cdcddz529lGZVRWl3KRT8q02hvmXavUNxwvNrH7VxK3pY637rT+1fq5WbvptyHWlaVazbtnMZVuujJ/s9pki7h6TJ1CfNH0Ole8EAADAeBFrgcNiMNb+yjt+9d+++cR8YyaxtpYgKakx8J+YVRJE/c39Xh2TcvUF3hBrb3jPr//lb//v/83Pvf7U/Q9IfzkvZORsMr3n5N2PPnJW8u0Lzz3/1JPffP7Z5+T27NPPPL3UN5965omnnpH7eHv6yWeelnt3i51PPPXUk3J79slnnn3yuaeeeubJp7755FNPPP3Uky888dSLjz917vHnX3ri+ee++fzTTz3/9NPPPvPUs8899bTcnnkqLLNHnnvuuWeeeWZ1dVWeYXnqiLUAAADXAGItcFgMxtqPfOA33/H2d25cXJcsWhWlplb3MqP75if98ied5UKsT7b6GqreN/rqrntFdDFvis3y/e/5wLd+y5/7b/7if/uzP/0zjz/8mFSbb05k8L133/PQg9+QHCuNG//0Kzd95cabb7xJGl8dcvMtX/3KrXK76cZbpH3TV78abrF9yy03f/XWm+XwxltvkpEy7Labbr3tptu/8tVbvnzrzV++9cY/veVGKSKjbr/x9ju/ordbbr79K7fe+uXbbv2T27SynN1TN91006233vrYY49NJpP4si0AAABGjVgLHBYaUPOG5K7f/OBH/+kP/+ivvONdH3jv+9/77vfc8J73vu9977vhhhve+953u9t7pf3r79Xb+274dbm9/703fEDu3/O+971b7j/wgfd+8H3vfv97f+WGH/7+fySx9j/9/3zLf/Vt//Vb3vDml1843yyaydq6f7V2Pp2tXrx06cLFzfWN2WQqt7Uhq+trF9clYm+srm34nvW1S/G25m4yxg97ZX1dDtYvbKxeXHt57dJLGxdeWdObtOW2uiqFNmevbG5eXJeRL22untu8eH7joszda5cuXZrP5/55rir9UquyLP0TDgAAgDEi1gKHRT/WSty67abbT7zlF9/6C295079545t+4Y1vedOb3/SmN735zW/+hV94vdze6Lz5jW+Kt7e98a1vf9Pb5F6mvO2Nb5fbW97wtjf+/Jtf9fe+51v+4//sP/uz//lf+C/+4k/8s//rsTOPNnUzWd2864479SujZMH2trjMZ2tlQO1uYYN+ivvArft65NDhz/s/XHehXzOlv/ynqYuiqWbutwLpGP96st756Vphr/kc6/FqLQAAwDWAWAscFjHNxqylryWWiwsvX9y4uH7pwur66prcXnnlldXVVX1ddPWiNLR9yd70pVMZv3pxTe7XLm2uvrz28rmLv/rOX/sL3/Zffeu3/Ll//n/+izNfPy35sZqWs8nc/4KfupSFdK3woVz3nmZ9W7MPrL7bR1f/xU7SlgCs/C+dTd8e5Sa3IVdGSmB1v+OnrN2v9SnKoiknTT2VgCtDCxngZ4WldOW95D9MK434JNs2AAAAxohYCxxqLotpYtwmP8UnTAmL0tSvjJrO33fD+//L//K//pf/108+8MAD/sVYCa2z2ezuu+8+e/Zs+G1BctMpGkarerqQDNpU6/P5rGouTZrnzjVrUw2hUtrdJAvqt1TpeDl2QdfzAzy/Kz3pd1Vru3RjdUw21LT3jNvOgHAaAAAAI0SsBQ61kLq2LU6J04uimE6nH/nIR97whjfcddddpfscaVVVvn9JrC0WzUxulf42nubcavmZ37/1l971O7//x6curlezqnYj9AVYSbZ+lRBK/S01l8ZxHXQQwvI94TQAAABGiFgLXGtsTpOG5Nj5fP71r3/9kUce8Z0+00qjruu7776rF2tlblnr75adz+qpxNrHnjn/c29693f+g3/+lhMffunlzdlcXwvWr1uWUUKm6LuVtRGKuDqOGwAAAADsJWItcC2TjOpD7Gw286/T+kDrT62vr7tY+7Dm0jaOulgrrWLRFNViLnNeurDxyc984Y1v++Bv/+7NF9f0V97qiIXEYxde/YHc+3ltHUdaAAAAwN4i1gLXMsmodV3770mS+/l87r8eyfcMxlofRSXTLvSDtOW8rmZl88L5ydnHL7x4vpBoLGlWvz5Ksu1CWqV+X5QNtKGI+yStvgoMAAAA7C1iLXAtk/Dp+XZZlv7e/+LW+Xx69z13nH30tEunEkTbWCvNuiyrqcTasi70m6MkCS/0Judd16xezJqFZF79SmP3JcZurt4k5ZobAAAAsMeItcC1zAdaEd97bK2vr95z9x2Pnh2ItcJ9d5Qc6Iu90qG/dFa/+7gp66pcTMvFeuVuEm51mH9tdiERd76oNfTWEnqlLAAAALDHiLXANUtfpW1jrW/IffwdrdKYz6eXibX+l/eUpb5vWSYWRbWom3mhv56nasqqmRT1Rr2YLJq5JFgZ6W+aZhfzSm5VITe3FAAAALCHiLXAUdGPuLPZ5O6Tt5995CEba/0oOeu4sCr5Vm6VvmxbNU3ZNFP3Oqz/HT8LibgSa5t6Xhb+G5Glkg6rKzmrtQAAAIC9RKwFjgoJqbYh97PZ7O6Td5595Iz7yqgs1voB+uqry7R1VeitLqd1uV4uXnx5+tIr841ZMyuboimLZjZfSLMpqubc+fqFF4vp3KXfaua+W6qtCAAAAOwBYi1wVMR46Rtyr7H2rpNnH37Ex9rY70Zpw3+Lsn87cS0ZtdbfEfT1R5/+jY/+0Uc+fuPps5v6XclNM2umLuE2Tz41+8Qnb/mND335oTMXNOXqR3G1bIevDwAAAOwKYi1wVMQ86Rtyv81YW5b6Qdlawu2inC2qP/yTW77vB37mtT/0pi98+dHNQmPtWnFxUm9Oq+bGmx794R9+22te8/o//PzXNovFXN+fLDWUVs/5VQAAAICrRKwFjoqYJH1D7l2sveexM4+2sVa/+rjW3+NTy4i6cR+r1Tirv9OnriWjlpOquPP+029624ff9NaP333PS5N5o7/YdrExX0yLRXP//c+/9S2ffMPrP3L7nU9IypXMG2OtJ2t0+C0BAAAAV4xYCxwVMUP6htzPZsW9d9732JnHXZ6VjizWyk1CqU+2cpNQKu1501ya1t84e+6hMy9fWm+K2mXXZiIZViZPNpuzZ9dOn75waVW/NWo7sVb4XQEAAABXhlgLHBUxQPqG3LtYe/9jZ54wsbaUoKpfbtzUetAsSgm3mnQ108rNfwfypNSP0uqnZ6t5UU/Kxbrcz8uZ/8apUqa5b0KWnhBnlwRaz+8KAAAAuDLEWuCoiAHSN+ReYu3dd9lXa6Vf/vCv1mqs1eNm4b4jWd+QLLd5vdDUK019Ybcsq6lk27Ka1Iu5tKWzqMrpXL8AWW8tX9xwC7S3sC+5941tqdsbAAAAjjpiLXBUaJo0DbnPf8GPC4kSMl1UlCjqR+uXRalSGpJ4q4V+wlaTqZxug6mQEfp7ayXWLuqp+wW24QVfja2VC7kSiktXSvvkzOasnlfa0MWlZFHqsFpLxaCrU3289rfQLQX0O5jlRrIFAAAAsRY4KlwSTQ25n80md5+8/ewjD6VY615mjQFS0qx707EkzbaxmMstxE2faeVPDbGLaVnJiNmimhT6e4Ak1hZz/2qsC7huiiwqSxV1M3M3GabfLCV/yIqVJONCFtUxlRy4zs7NIdYCAADAItYCR8VQrM1erfWB1r9a60iWnVWSYyWflpPpfFLW+oLtoikaSbbNVO+ruczV11cb/UpkSaVrs9nFjanMkZKVxlil6TcsqnF3Wuinc8+vFaszF27Dq7m6ugba+J1V7kVgz2/I01M6QG+hCwAAAEcYsRY4KnywjA25N7+3Vl/19IHWj9KXZ/XLjSV1zt3roj5G6ix9fVZfsHWZVt+crN+TXDaLaV2fffK53/rsH338U59/6JFnZ1X8CK7yi0oJybTrs8Ud9z30oU989g++ePOLF6eyeFE2+lKv++5lE1nDTVc0dBvtDQAAACDWAkeFD5axIfdtrH00xFp33p2VVClRtpDbtJrM3cde5/ViVunLsjrApc6F+6Jk/bhtLQFX30p88y1nXvtDr3/1a37uj7/woKThQr8U2b1s6z5PKzdpSLnVSfWuGz78Xa/+wZ/8129+8OzTMkYWk1BdSiH39cuzqtQP6C5inPZvNva3QHYZHg8AAACONmItcFQsibX39GJt5TKtfja2aspJOZWE6T8KW/ivd5JoKRPca6hy87F2fbYxKZt7vvbiG974qf/7Zz5+861PbxbNRrmhkdWl5BRri3p9Vv32yh//5L9+46998BNPPn9BK8vq7uO4MlhX8Tf9jikfbn0ZrRSTrYwPjwcAAABHG7EWOCq2GWvdR1vLajEvm8K/3Cq3SaE3aVT6BVGSaNcWzepisbGoZ3Utg2vplrOrk+aB0xv3nto8f9HH0KJYuM/dtvSF10p/5+1LFzbuf+jRJ557ZVLrt0ZJj4ySm8xalzw8D18oVUgYdh+1JdYCAABgGWItcFRsI9Zqf4i1TSkxdrZw3wI1aR578sKph148+/iFufvEbN2EWFsvNNbKyPlitlk3L682Xz89/9rXy3MXNJdOqjX3LVNt/HR/ypG+bOu/A7nRT+5KW86Uwr1a+8Sza/c98PiZR5+9uCbRWk9VCx9oibUAAAAYQKwFjoolsfbko4+c9W8R9v3+1VH36utCkue0bM5fqH/jw3/wY//8LZ/6ra9cXK3nRagjI3ScfqmUvl350rS+79TqG9/8B6973advvuWljXk9W2zob7uVFOwLu1/z4397bVHW+g3L7vO7unhda3/VrE+az3/x3p/4V2/+t7/0/sefPF8umulM9uOmp5uSzrAPAAAAHG3EWuCo2FGslYipn251r5A+/s3Vt779Q999/U/+0js/LrF2Nteh+u3GhX5llP7S2lpj7WaxuPnWp3/oR/7dq1/99i984VH3YuykkmyrAVmSrQwt9FaV4fuTK/d7hfx3UOmHdOdy9/IrzUc+8cXrX/3Pfur///bTDz/nX8iVYQ6xFgAAAAOItcBR4VNrbMi9xtqTdz569mEba/0fcivc24Ml415abb70J6fe/4F//5WbTq9tNPOyqJuNarFa15vNQn+TrcTaoplP6/rxZ1Y/+ds3f+Q3//SRxy5KHp6Va7X+KiD/e2ldrHW/Fkh69BcIObqcibWSYO+9/7nf+NDv/97KzS+9ONMz7i3RrZhs3Y5DJwAAAI40Yi1wVLi4mhpyvyzW6r27ScicFppsp7PmpZeayVS/26nWtDvRj9cuJhJTfawtm2pal5KEL603L1+U6CvDFmU1kfiaxdp67sKt3Cr9ymVHV5cxtb5dua50rYuXms2pvmd5NtMvqdINJTbWSgMAAABHHbEWOCp8Xo0NuTdvQtao6M/7s/o6qku2ElhLSaYL9xVS7tuJdaBLpO2shY+181p/Fc+saKZzHS9nKv2eZPc+Yz1q34ScYu3CBVP5cyHpd1HPZYL/8mW/oiwvyTYkbEO64y10AQAA4Agj1gJHhQZR05D7NtY+pvFQ86U9W1VVEb4+qnbh1r1qKtl1Xlf6mVj9+if3Iqv+Jtu6WpTFopYJcpOehe+sCikWltWh+pKs3lwc1VHuN9lqNVXqS7vSL9lY1nBF5CarlpKnDalHrAUAAEBErAWOiiWx9p5HH3nCx1q9aQqVs9IqF828KDflXm5lNZWMWtbFrJ7r9x67b4xyn5CV//m3L0uYreblpFrM9Zf1VNO6KmL6VPKHz8Fy03X0hV//Tcj6Mm+t72SWJYpyosFZi0gsLmRNn2LDzWmPiLUAAABQxFrgqPBpNjbkvo214dVaHxL1NVLJnJUOcr+NpyiqeVXPJIS61Fr6l3A1p0quleRZ6duJ5+XMfbXTrKg36oVEU/0C5KrQtzL7VV3y1WQbVm9jrXv518dal2zrmcz14XZRz6tyrkN9FV/I/eluxFoAAAAoYi1wVPg8GRtyL7H25J13PXrmYUmnGmD1hVd9F3FRNZuz5uzjFx57/OLGRF+31Rdqq6nEWs20hi8odK57F7G5qXC6R+a27zLWmyH17S0I0/ZMvoSP+P4GAACAw45YCxwVMbn5htxLrL3HxVrJtD5Gut/K00zL5qt3nH39m9/31rd/6P4Hnp0Vmmzdq6N+WOCreaGrJ5zuCae3LUzbJ8RaAACAMSHWAkdFDIe+IfcSa++9857HHn5EYq1EOB9rp0UzKZrP/eGt3/OaH//BH37dTbc8uL6pWVdibVFN9/MVVAAAAGA7iLXAURGDqG/IvcTa++669/GHH1tUmmnlVjeLWaWv1p4++/wHP/x7H//UH33zmbXC/V4fmVPWhcZZx5cCAAAADhyxFjgqYhb1DbmfzYr77rr/sTOP+y801g/ENvoFTpJjN+fNiy9PX7lYaZf/sid9S66+VOuLXKvkAUahCwAAAIcbsRY4KmJO8w25N7FWv7fYxVp9wbZyn7Atm0Ya80ozrfQWmnD1d/Ncw+Q5cfFeSVuEEwAAADjEiLXAURFDmm/IvcTae++UWPtEXUqWlSgnwbWoFqUczWsNtz7Qymi5yWk//VolT4g8RP31u9VCbr4NAACAw49YCxwVg7H27jbW6jdGaZItq8Xc3WoJtCnTLo5KrPWZllgLAAAwIsRa4Gix4VbfhHzPqUdOPy7JdT6d6VdGLeZNU0i4XTSFS7k9PuZeczd9rIumnFdVoVH+hedeXLu0zu/3AQAAGAViLXC02Fg7n5d33HbyoQcfDulOX5QtJNlKyJV7vdVl/JSpF0ZekzcJsa5x8ZVLn/6tz/zWp377/Esv++cKAAAAhxmxFjhaNJq2jaIoTt5597133/fCcy8++/QzL77w3DNPP/HsM0++8PzT7e1ZvT33/IvPv/Di8y+5mzSusZs+rnMvnn/phXPPPfO8PBXPPfPiz/7fP//X/sr/+smP/5Z/rgAAAHCYEWuBo8XG2rIsH3zg67ffetvdd5287ZZbT95511133n73yTvvufuOu+681d1u19sdd8pNArDcfPvaup2U22233C4P9J6T98r9LTff/n/+2L/4z/+/f+4Hvv+H/XMFAACAw4xYCxxREmv1DcZVfenCxdWLl+R+c31jfXVNbpcuvrJ66YLc1lYv6u2SdMlt3d18e7S37BH5mz6ujbXNzfXJy+curq9OXjl/6ed+9vXf9Xf/3u/9zu+HJwsAAACHGLEWOLok2S7qsphP67LyHy6tS/3eKD3UT5q2N/eJ02vktuQRud/cGz45vLE2/a1PfWbld/9guln4JwoAAACHGbEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBixFoAAAAAwIgRa7GlBz/2M9E7b3zppRvf+bEHw6mr8+DHpJxvhPIDhe3q7nyctcRW563LPhY5GZYd3tf2l1nCLKBPrOvbYdld2AUAAAAwcsRabKkTnWIUDIHznULPx2G+IcNEm0TdSJMO27l+YnsitRKzupa88SXp0LoijI3p0Czl57TrxgqhhLbk3DtvPB0eS1siDTQjtf0xbcaF9ES7r3xqb3PtFsJxe9iuGkaF7UiFUDbU8QuZKXnB0PZLO/as2XB7FDb3zhtv9OPi8gAAAMB4EWuxpRiVfAwKYUx6fSjStGQCWdvQSOUHdE5EYaLtde22oGdW9+u3C4dhaXQ4YZbr7NAJp31SlTN+ih+YbVDOZbnPVHnpwQc1XacC7qyvlG0ubkHOPKgxNFToTFZxhhsiE/3QzpROwXTayc+Ginrg+uNxOyxbHwAAABgrYi22lEenEIZMJIpJLIYobcQB0gihVNgYJSd0oC1vclmrs3pvHXM+7zA7NM0wKr3+6k/Iwk6sFbT9MsospNrF/Hnlj8OgsIpZt/9MZKflwBRo63SmdAqmcU7nrDmX78YUz6sBAAAAY0SsxZay6BTDUEygcuzOt8PkhDZSZurMj9LEVCm0jN7s2OEbbZVYx5zv7DCQAR/7mEu1YZP+3p9J4156qW36+f0qchSnBmZxbcQteFn9dnUvPGvtkE6dqFOwczo/mzbs++Ny7aTu5gEAAIBRItZiS51kZdKRew1RP7DpzodjiYx63MlsgY1RWewyp1O/6qxuOkw6M7PDoWu3dXsV2p6wybaCfvQ07DAWVWl06uit7kb1Ntd56NmhXSNMa+fFOpevEEpIS7r1eOhsONAj92csHjsAAACAMSPW4grF4EU4AgAAAHCAiLW4UvGlwPi64jXk0Ucf9Q/umvfBD34wPGYAAABgnIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWHt4nFs5fkwcXzkXOq6EK2JKhKLHTpzqntm5UydcoXC0G9LuQscVCDV2dV9bajd+dX9ZHVf/N3RADuSvYC+4n/BR/hUAAAAcbfsaa/1Fo9rJhWOaJQ70yrnNMs7eXPvKg93NwlJOnzHZ+GG8VN+13elfzA5/MuJPVVo6/aBtq5b7aTiMT+uBuIK/gsNpl/8FAgAAYD/s96u1O7z6dUkjjXdJ4mCunrshxu1s969/d/ei+qqrnTqxh5f4V7W7bGdXlqn8T1P2tyhd295S9yfigMg2DkOc3PqvYJd+lvb68e7uv8CuPf0HBQAAcHQdXKx1qcDqX+9pdOz2xj5tZMzFbqrdvwKO51yV9ii8R9dbMqnTnfqudCvmZPs47UV1/2zW5+ultXWMP4qjTbXOGTMvFOqtZjp0TDiMjyJfOO8JBh6xZXYneo8s9MgQ30jVOjvrdGTrpjP9zaRzcRvSZbaUsWu4Wq7DzgwnRVzL9oah9lnK9uRP+FHLn0pTUfrsuN5MPyfbmDIP0Jxre+P8OCxfsSPbaDx9+U24rt4QlTrb1dPE7iz7OIw4wlfVw9QSblanasYuYRcx/flW7NFlq/efBFN0+NEAAABgew701Vq5qItXc3qBl18Fuq7+1V7qtbW0HcYON61sE3Ig7dTlj3N6tru7rDdN9+3tbMVMTw8+PSVmQjytfXFBbaTh8WWg2KWzWm6SHWyfQ20NrWZnOGn5VCBrpwGp8hI6qxUyc5hp237UQJl8Z0vWHW5G0nd8ZcXvIw3rjnJ0ehjktqSLub4w2u/TbcEPTc04Ip/tzrZ7bplHpXPb09r2/am3nW3H+fLxfKxlynZGhGbbmU7GZn/FRMcM7DdfYmgTw0PSiPBkmXOxadZZxqyk68SDcysnllU17D7TNlPLzLFD5cC1L1+9MyPtzbYBAACwY4cm1mYnPL3U61/B6kDfPVwr6x2oqkx3uNZdslig4wcuOtO16BVsZbhmnGulqd1dDhQxFTpLxjPdKjkzq7ObeKZTIA7LFuxM7jGDtWnGpjNLa+QnhtfNerMDT7p0oJ5pw0/o6pKS/dTh5g2MdoPdUq6VPVN2rQHmUenI/kNyJbOHkY2zTC3TTOO10dmHHRcPeiu28gLD+1iyCSv1d3eUlWwPhtfJxZIaNU+1f6Um1faqGnafg4sNP6ZTJ9zALarb4tK2Z+0pAAAA7NBhj7X9K73UO1xLz2e0O3aGenFqfJHT96lQ0k5xpzq7CxN87w620tKTvZJprmp3pNqhnV3GReIsUyHbVTqj3XYnwcBq2W5SvW4B3YKbkS3YmdxjBscCXqq/tEZ+YnhdrZrplJJZoSeMdK8ad+fKsZbvzXaLms58tbAbP9Np95dV9l2RnGs7dWb/IYm2pFkhjlNmH+2c7nQ3XsdlE+1mA3++s2IrL5DvY4tNqIEhsdN3mBGedvce74B2jP/HHRa2//0qk1Z3wnAnW8xMjAPawTLQj9uiuimute1JnbnVAwMAAMAShznWuiu9/LIw6xuuNVSoL1xUhtdYjHAiHLW0t1vV9mWLbm8rwwulp0Qf6FB1pzc5jU4VuvPiGVO6NbyaqaXimU6BOCxbsDO5xwzWphmbziytkZ8YXjfrHSDnYw2Z5EKIGFjPn+3U0vJmpfhj6QZnY/3prM9X7K4Vt97ZvOkP9LTrysbFXmHm2OlxvB0b9JcxeuNltHlIaR/b3MRQfxAqp5LGYGeXH9T+Jyu3wqmQarcs0N2nG6uNwQ37rbaRecvqdq6f6tsiKwsAAICdOdSx1l/6mW4dlI6X1NJuW3bJxaJOOB4/LZguTJdcXrqlzQm3M7vMjreSVTSvJfmuVNGN8+3eLmMsT8PN/lOnk85IKz2NWmNwNT/OFMgWiauYdrZgWs7VbIdHdrAbEQ5sO9XoyHd2mXXjdDnolMp73LJicD1drvuX5Sb4Ln867dm3Tf22T/4Mo8zsRE6nNYYeUv/HVMfFMmm+tFJ5U9bUzXbgC+ssu6i0+ytGbo0w3LTTONdpNhFrDw/p/TBnj00OtJ31+eltVcM9tmy54+3DGKxqpL35oa7I8IaVq5aKb1FdZ8ft6kF72rYBAACwY/saa90loSNXcHr917bTmYFL1DRLpPN2SlbLnrvcpaIMSmdjhctNMYOyYVe8lfxkKp8dyYEfF7+NWPlqqYB/ZuJxnOTb+RkZaCq5iUOr2VF2bTdh4IHFnu7T4A/8tFZnP/0d2Z52TGJ2c3Nqmq8GDlP6uwx66wvX11/LS5V0iNmtTjCHreMrZ2ynL2uKdJ+QbEex3XkqO8t2p6WD9u/L7KJzPpsbq5kF/JCBFZN08sSKb+qkgU3IRDPW/DXZIWk3YW3R32HsiVP6+/KrxSI6LBvUrxrY4ltv2MlXUkurd54EcZmhAAAA2IH9frUWR1N60Q/YTf3PEQAAAODIIdZir7mXpMge2G3utU9e5gQAAACxFgAAAAAwZsRaAAAAAMCIEWsBAAAAACNGrAUAAAAAjBixFgAAAAAwYsRaAAAAAMCIEWsBAAAAACNGrAUAAAAAjBixFgAAAAAwYsTaLZw6cUwcXzkXjnfTuZXjJ06F9iC3+OWHbJssthvFfJmhOu7M3jxRl+X/hq7ykXWe6VBTHs1BPSpnqx++XXnol7O7j375z86VLbQ7D3/v/uoP9GcHAADgSCHWbk0udHd4bRqu30W8XE5dqZYUXn5Jbq6Jw5V2Z2rQr5DOdS+q3ZklS+p6/tTy+V4aeVjs7o6kmnvY8jwc/MOUTQz9HUQ7e+jpr7YtqvOzjr212z87u1zvUP3VAwAAYNuOUKw9dWLbV+5ydWuuardKFsPaBJEX8kc+S5xYWVk55Zr98t1uP6Pbdblt+RndEW5XA1fsOtp1uwFhln8IvdFx7KGxqzs6XA9P/g4u97e8893qX2pnwqkTe/l4s393u/3k7m69q68mFfbyuQQAAMCwfY+1Piol7iJQLyfNYX+UudhcMlYOXMNdQqcxIROYjnZeXKKbGuza7ly+GTt8aQ2RZsW9S1d6HHIosdYN6s92+7Xd6QHEXukaWDbyM3oj3LbsLpy0M3e+neUfQ3d09mS2Y/1Qd5CfF2YTfpjjy8Zt+saJ32kHuNNhuCmgBou0u+ydzPpCJbNFGRSOwvB+sbR8mudH+PPSdg2zTbNAX1td5BuJsumpbHwc+Y7CzCW1OrRG53SKtXEBt4I/8mulM0Gq0X2sy54GndEZG4rYhbqbyJltuLnm4fdP5n2+WOdJCodhuK2W7ymb2KsURpn1peum5QXMcpk0GAAAADuyv7FWr+PMJaC/ujOdnWZ7hZm6l48V/eFmFW2GTn9g+uPMQAuYLjtzeOJADe07seI3ZobFUW7PJ05JV9pVogWzijL8eAjB5lEPTW25BfrFXXend2klt43BEu3e7EbNY7EPS4ekEaGpNczgbBk7WQaeiG1HR/vTqUba0dAKqV544TANl1YarS39s9WdnSqaZpjgq2TMwzAzu1XamaZtu7PNx17T1MGuGRvaamcPMHNbbay1a6VF7cbbidrubct0GqaCWFLEDBvYREuX8l1xqqk3tJFusTRcWmFe26d/trqz3Tk/PiyjPbF0fEbbWsFggaydRmR1AAAAsDP7G2s7F3GunV0JpoOseydjM+bM0Nr9A6dTzw5Ip7aooV06UP5Qbk7o2gY3y46VdXUBXV64tULXMn5ob4TrznuXbctX6J/S/tSrW/VH0moLm2ZneGvJYCeVlGZ+yp5LBpeIZfXs0gUCU6GznaxMnBQPBpd2bJ3BdmdfbSU5byrG0baEOUjr5/OWGhjWxtrOhpRZNXuksd/2ZiNapoIYLmLbA5sIZEiv/OCSlyk2UMOUsBsS29iTMgU621lWIA2zK3YmAwAAYPsO/tVabWTShWC8xmsv/rYz1tGeVjhjLiDtWa8/2/SYmenUVjV0lu8JI6VE7NqSe6B2rBQJe3Cn9GTsMlsxU3xv3HjLddveVDnn1hk8oyXSQulIZrTjTbMzPGxfDQ324pTepz71TH9T2RIDK7R9aabOUO0sU6GznXho6nquO1s6Y+sMtrWgmRoeWvgjdMbR7YYTP9Wu344Z3k/QWVWZZ1nPiv5mO4809ad6nZ0HpoJYUqTXzjbhDZbP6rUT1dJiOkPFWaaE3YSIh1ojrdJqK6lwNt/O0gI6LJ7IdtZfBQAAANuw75+tjdeC7dXckou5rLu9+NvO2Owa0pyxF5C2PaBfb+DSc4saer6toZP0ER8f3P0QmZxfSEsJu/tQbqsHYa7mW6479abHY/m5y/aaz0nPg3lGTNMM19bACDs48HPkvrcHGdzfWFxiyQpBb24aHiv05sVDM8QY7lW2zmDbblaESp3eONqWMPrrdwp0SZ3OaZnQeQQ6xneZVbOVst3ocKdTxslGLi+SD1NaNSvY6xCpnn3YWxczo82WOvPioa0dmIKmgGmqZQXSMLtiZzIAAAC2b99jrVzIdS4Qs0s+OUgXgvEab/jycHisOdJWvPg016H9gzRbZauY1YVZ6vI1Oj06eNmF/wC3c7MF7TCH/oENhFbDj+mNcBu5zLbDvNTbH6Ij2i4t164h7YGmGZ56ddrg4CgbYbkTaXnXiksMrhBfjGyHpc/rxvHmMaUaTjrUMfGMHLi2mdhh6wy3dW47ObVdy44IbZmXVpID347rDzwonRGXjbQ3FTLDpRW6Y1Fz0nQK0+/OmHodulw6u7RIbA9soqWlYpcfN7RTN2y42MCTZNdJnU4+Jp7SImfiJHeqLaAHpkKngBk0tKL2h24AAADszMG9Wqvaizi5uAvCNV7skBFxhj+3fGx22ahkgD+pJ9LCbpjZR7uLJJZsv8/UNcOX9So/ZWmN7kDl+/prDXOjwwO02wlXwKHPHFtmX45Z1E1rj2Vct0J3qtPZdDamPWf2qJf8odl+m67QgebAFzGDuw9GxnbWbZn1s6J6NLBC+BlwfEVTwC2ajk/cnJo6NE4Nm0ulfEe2thVLyrhl7WxcVsEsa78ruzfYLm9OhtrudNvOpHkijTDdvUcf23Km+yBsud56aV/5kztY3LbDJjL5M5CG6sh4dJm/+t6TtGx3+YPXpVMp1xEnxlW6s+z/59AC+d6zHh0RZ4aTAAAA2IGD+8ood1VnjpBz17y7/Py4S+d42SxHh/gSOr7QhsNMf0zNX9Th/pkCAADANWpfY62mqhTUOhfE6PGv5uzSc9SNyfl/Yjg8ZJ/yiHu/1weHkvupSj+hh/WHCgAAANe2fX4TsgbbFte/GOCzPD8cY+H/vgL+MxUAAAAOwv5/ZRQAAAAAALuGWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARI9YCAAAAAEaMWAsAAAAAGDFiLQAAAABgxIi1AAAAAIARO9qx9tSJY+L4yrlwfODOrRw3O3JH29/dXj8aV//EqXC0v8ITc0CrOwf58LfvcPxIh7+u/Xy6ruCB+ymH/+8UAAAAWznyr9bKpe1BZ4COq9nR4Xs0u0ejEglkGw78h0A2oH9R8he2v/u4ggfODxUAAMA1gVg7llh76sTgPuW63FyWH75Hs3u2TCD5U3HlrqLOkr+k/bXVD8FeP08H9kN4BQtv+UMFAACAMdjvWKuXkYG/AjUd3mW7Q78c+Ia7IjVjzRWqXOO2LtdrOsRlrorjwDQvq6BT007MkkG+y3xZkSbIKbcNP8Q1zdystK1i5rTso4lnbKfISqtw3o9vd3p85Ywf5xbvzbHS9syw4aIf/3gY4E63w83jU3HjKp4zvb7PDvOr9YZkffFpSLt0o/p1nDDKDek9fD8u6+5uypRKzIywwyWlvVhtq2JmRO9J6D8+MzFO6+/MdLmeJc+TklOmJw1sO32PVHGNzuQ0Pp0Y2uFQb1pKdQsrM8I/Li3SPsL+2azP1xt4ZgAAAHDg9jfW6jWhuQQN14X20tJdNfqDJd3hQrO9aLVnTDuV106zZro49S3pa0vZPQ0yY3VoPDi3ckJaZiHTDLQn7iduMVvZlL5800p1nW7NcGq42bI1tB0OXNMOtuPMQto90DZNs2a3qK2pA1Pb0Zm2djidCnYWtKX6Q9Km21dVO9NdM6+T2P5Uya6Udbv+9si2Ay3XzrPt5aUHmi1bwFRII3VA6LSPIzsRR6cR0oo9ZrprplGWFmmdOKVHYZ47Y2q48+6EkTac76a7Q1/BN1OntNrFbKWWdvnzqaR5EHlxfzpV9D8xabi0OuUBAABwcA7sTcjmetI0Rby6XNJtLjWFDkpHnTleHJ9KGLba0OyMKXV85VT70cGUauPkfqX+4nZlNyEcmf7hptVZyI5Kp7K5/UJ5DTnvj3qPwXbYKsvakZk4WDTMkGZ2yp4LR/l5lRYcPi/ikLyeyKa0B8vq2H77ME1/55mwZewppbNMRyoyXDqb3q3VWat3WqVO+zgy2WrZkGxKe7Csjunv1Il7WDY123l24MV52rjcYxxaorMZb3gn2UazdQaLAAAA4KDte6zVC8VWuD7MLy3j0ZLu/HK3c5mpo9qTeipwXfZcYqvlKxhnKRwAAClXSURBVA5oB/iXbsJUn2rtcl53LZ2s2hXsyvbI9A83rc6W7ah4ql04yR9kXiOblg20HXahZe1s4TCxVzRNCc+jIWfM4HyqngvCgr3S/SGxLxybEZ72D2zRsf0yMZY0/aZbe+MQoUvZqp3jNHywtDZytlZnLVvBrROEzli0ZYu3J9o+f2yKeFqqVydI/doy+0qPecnUdtEkDLInXFeslLEPvLdEdzNePqz/ZMW+rLDqLw8AAIADs7+x1lyNmuvJ3qWlv4Rc0m1aQgelozjHdsfxZvXEVstXHOJHtG9hdXNPhTS29WQn7az3OMJs0z/ctDqr2lHp1JK5rbxGHNwpnXfYksNtabWjzcReUeHntE+qYUqINFVb7eC0YFZ6eEirLTy0m+FOYfttSdNvu9s1gu4m7AZFKjJcujs906kVx9r+VCAVVdLfHuUnVFugf0YMdgrTb4qrbGNDU9MmjaEdamNwZOzsL9HZjJeG2ZL9fXQnD28AAAAAB2RfY21+DTl4Oa2Xj+FycUl355rTVTKFfDuN0ZlZO5XsjMxWzIYabglT49jx4+1rjHrKljKb1ON2WFzQrJw2Lkx/3ly2H7OQmeBOmUcTJ8tBXsgMdCNDBdvtdMbFhQbbabC24vK9oiobYelu2n7TTgu6TrPfgT2ZIfGju3EX2Rw50HbWZ8RJIpXP+nWtdog7aAfZdqDT2sG2vc3Ssa3c/DDLtFMpnW0HDKzg5vl2/4e1O0fbWZ+RSvp12zGmbYdkdEw8IQfSTmO11Z527bZyu992t76vt4QWj33hZyENS5PdON/u/sQM/DMGAADAIbD/XxnlyBWhu3h0l5SxV6VrxcHu1Dk4MFyEhktT3+NP++GdoXGUnE2n4pm0RKLD4irdi9tYrzfVLNyeSoNFKJmGHV+5yWzOnkmrK/MQ7ITucLN+Pl+Yc0O76xZon1FhF+q00wQ58MX8Nwh5+SZkbLtuR6xy7MSKb+rMWCf72zVPhXuHuGeGpOXN+qmz3UJWJzD7yB6+XScfpbP6tTNpeLuf5aWHRltm0yuu2angZ7ttmKHDf01mJTchXyD2ZXVavc7LzBt6HP1HObRD7e8UtgunIvkKneL5RuLRZX5iTIH20QAAAOAQOLCvjDL0WjG//HSWdO+b3tfy7jK5ZD4cl8YH/UTrDtoXwQAAAABgh4i1Q3TpPc+cxFp9DvQp2Ov/gAAAAADgWnbgsdYlyP67+pZ0XzPy9z8eoIN9ov3TQKgFAAAAcOUOw6u1AAAAAABcIWItAAAAAGDEiLUAAAAAgBEj1gIAAAAARoxYCwAAAAAYMWItAAAAAGDEiLUAAAAAgBEj1gIAAAAARuwajrXnVo4fO3Z85Vw4PAJOnTgmdv0huyfyiD2VAAAAAEaDV2uvLZJs9yR+7lVdAAAAALhKRyTWnjqx7VB2buX4iVOhPQb5QyPWAgAAADhi9j3Whne0qpAe/VtnDd+/pNvpFxFpggYwf6QtM7hXuxvV7KL+3OBSRl4qjs6OdGKq48v4YxnmGydOxULufDjK95d2F/pTUWHnBXZ6vtH++n5UYMr4M9IRi5lVh/rihMBuAgAAAAB22/7GWhd+QoKybRuaNA+Fg+Hu4SJpbHz50nalQq5SWrdttrRi7Fq239ZQqW4BbWtf2IBp6pQs9UlHOjq3ciKdETq6PWva2Zz8UEd1d2eb2srWD7Tf96bNSl8Y2HkArpT2+ZrS0kYavoPXyQEAAADgCuxrrDWJSKUwlFKQSKMGu5cU6XQrm63SuaxoduC09dq2OW1POcOlzLAQTbOJ6aC/uPS0A7tx0JxScW6niD1cstKyqS3ptit5g2OzBbI5emaoNgAAAADstn2NtZ3wk7JPHpri0WD30iLujH35MU03hXR8Lo9wKar5gsNLOctKxRJtNA0bS2K3LafMVLOw6K4dt9YpYg9jsSUbHVhfdFfysrG2XrvNti9uW3coBlYAAAAAgF10SF+tvUz30iKBxqnudFsoK9pn621jqcFSYV6Mpr2J3tB0P1Tuu+NlsK0R53aK2MO07NBKy3q1u7/bNNac7j8u7clqDhYDAAAAgN1z6D5bu3X3cJGBDDkYxfoHse3o9BjMhpdKlpaS9vHjKZp2i/q2Dmo7E625rL/tNm1tml3ZmrpqOJWNkgPXtmMtHRxHh+c1jk1F3RPi2+lzwGHc8N/G8HIAAAAAcDX2N9YKH4ackHyEC1KtLbsHi5ixrisea5RK4925wT20solbjF1+WqrkGS7tr1u3m/Vk5NBKoltD2Ydmt27783G+Y/n6Ih+cD41HcuAXdK8uuy7hq6WdhuV918BSAAAAAHB19j3WDpLIM5R4lnRf47ofqwUAAAAALEesPTTOuQ/Udn+vDwAAAADgcg5BrNV3p3rZq5RLuq9d/p28vE8XAAAAAHbicLxaCwAAAADAFSHWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABG7JDG2lMnjonjK+fCceROnDgVjq7W0mV26tzKca20axs7JMLD2oUnaLsO2RPpf0CudjudH9pQVJ5V92iv5tm96gJXY8nf1a7+0OzGv9DO03+VdmNHAAAA2GX7GWvD5XzP8BWnjN6Pa8ddW0av5nft2vkQ2ae/h+SQPZG7vB0p555OeVqvgZ+WZU/Obv7Q7PsP4JYO344AAACOun2OteESWFqmOXx5v2fXjqdO2Lq7tgyxdpdc07H26qtJhcPz7Cx7OLv5Q7PvP4B9nef8EOwIAAAAmf2Nte3FoFwXtlfD7opRj4N4+dheO+qVs5KD0HRD2u4oXWh2T5lL0OyUq2OWFmZoOmM7g4EN56sOr5nv3I1pj/RUGtkWtTpn7aEtMrS5/EHm/E57I+IO5Ex6MJffoRdLpd1k+8oe9kChdMpJ580JVyNfyFaPZ9LOo26Vrjg31NXxcQ/dk3mfr2cW0DHhMAy31fzEtIneznoddn3puml5AbNcJg12lp3360gR1+jtpn0EnfmpuEwb2pafZx9EV2d/Kh9uBpgTWjgeulXC0fGPf9wtbvuS+Dj659Ipe8avno8NO/KdMs01OgPNrrvPBgAAAHbBwXy2Vi/38utGf6SXfO0VoPTGC8Y0Vke0R2mErTFcLbGzxHCRJc0g9ZglOjsLI7S37U5tO1YPtK19plRn48Nns42EgoObc+12RfuATFuacVHtTmv0F7alB5hSWikenFs5oa3LF7JbHR6pNf0Is5AeuHY6a5vBYJVE+/zpONBsJ81IZaQv1tNGGi6tMK/t0z9b3dnunB8fltGeWDputa0VDBbI2mlEVieyBc0810yjl1S3k7V4e5Bvq9fsThtqR6lWNiA1U107VA7MumGSXThNs82hLdhZasmOdJh5ysyZ2BxeEwAAAFfrMMRaw1ww+mZ3pL3CNINTvx1g25GdJQaL5IM6MzLpXL6Y9OuRdpq5cYwZbKJenJ7XEkvPavv4ykr2vurIbLw7Z6CdPUxfV49it52WH/TFSZotToWPk8qkbTzUvCfbVMuMMOfDf/3IZgxODwYeggzvdg0NE7Gwns6WGKgxvF+1vEzGFOhsZ1mBNMyu2Jns5H1x93n3supLJufbigMGpy1rR1s9ADvCjE3/McxMyuYPTsvaQXdVO2RZ8bxOOMhGdKsCAADgyh2SWKsdQXstmLrsSHstaC8cU3+qrX3pyrJlZ4nBItrI2T2otLt2ut1ZPEqbcdKO4uj2NSVT0LObvOzZzhqiv7lsd0vaMiuvGjcWGzkzuqst6x9dKBFS7VaF7PbS6krPtNoR7QA557rsGC/WCoaqeHom34vQzjTM7D2ODH3xuF0gzjIlssdjDrVGd6ODW823s7SADosnsp11V8n74lHevaz6ksn5tjK9acva0fIHYIq3I9rBMjCOM5Oy+amwdoem1uxuIV916Y6yYXqQk1Nmw14sAwAAgKtyCGKtuaa0F4yh6S4P09WfvXRccnXpyzuxx7CzxHCRzqDc8IazHbT9dqgwY8KZ+JpSPr1r6Vk54T+a3J4NdV3bPApbYEk7e8ypP3bbaVvyg01m1xdtfardqlB2Om1KWm13d4QctJHZzhiyrIpnzkZpmLba0v1VunPNaLNSZ148tLUDU9AUME21rEAaZlfsTHbyvqxe6l5WfcnkZWVatndZOxp+ANpqu+0IacuI9OMgTNVsATvNnXFSV5TNEsM76gzLqgfdQgAAANglhyDWpus/7Y7Xgnl322svDNOIzgWjHCy/eNRq5uySItkoObD10hQd1bY7Owvd2tt227bQMsfz15TsTmLbGT6bfXyw7WrH2c25Ae1SS9pppuvtd6dF/IFru1Xig0hciXhCixyPSWOwUKRnY8G4eurNK4dqqbjbkZ2fRl6uipc9Gv+fHNKUuBc/zLe7/11iKF+nEqbTycfEU1rkTJyUbVUPTIVOATNoaEXtD92R7ZOxtpwZuqT6ksmdbdn1XdtOW9aOhh9A6tV10wg3xP44ZFWzBWxhPTIHOVfSnBzeUV7c78uupe2skhwsXRIAAAA7sv+xVq/sgnBR565LVfuVvu13vLqmDAlTTtwcp6Yv/3UjTIWsoOhdOKb1zTcwyyjbn48LHcnAhsPLyi07Y2khKZPvLu27t2vROduW1QOzocHNLTmftU194Xeath42lMa0O3Q97YGlc30VJcOyQf1CXlrRbk/HxDNy4E+01fOVRCqS94vlVYJ8btqmDotHQ09tqGTm+0eWOrKfXzkTp/qB3edkyVbNrPxfSXfvWY+OiDPDSc9MioXMVtLYfvXO5O6abbXO48r2sazdin0yNRXWAWa0P9HuXY9SBTvJLhD7w7Q0TpgNOHYXy3YU+9NsUzNVTOPaDQMAAOCqHcyrtXtILyXNValcRHavUTFMnqoru9BOX86D8en8gzmS8h99eUZInAAAAONyrcVa9wJJ9tIIV6jbdCXPlXu6eYbHjFirP/nmh5gnBAAAYHyuuVdrfdJqcXm6TXpl7/GUHSHpH8vR/m8T6cef/0oDAAAwRtderAUAAAAAHCHEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsfYQOLdy/Jg6vnIu9ByUUyd2fR++5LFjJ06Fjg53ftnJXeFWOPjnFgAAAMDeODqxts2O0Z5Gqb7e+iJlLXf2UEQvCYGdfbTB1NrRk7c0udpHHVdJq6eFt7tc9iSbSYfn6QUAAACw2/Y71p46cRDhIuSjlHM05uwome0Ot48sxrXPxmGOtcLHxfCM5TvfBjeh/3T3HrNfJeuTrm0vZOv19thbDAAAAMA1Yj9jbZtanF94dzhwUaM99dOvc38kJgz5pCJiODEFTeLqZRc/cVmKbfOOL5YlN2Wq9ZZLo6Le4hk3oR2SHbS7iIu4E9lBGucte0CB2ZwfaaZ6qWrnpD3h+dPtir50Wj8tZWeaDajedl3NbKm0idgtXf3dLKMrtqPd6nZqfzkAAAAA14R9frVWwkZKFho0bDbybTsk5RRthbFtM02XljnXjS7aN5CqHJd1DJ3r+/x4N9c1XcuXdgPCKmlAW2x4Ic8Uce20VVvTtntTQjsbM0CH+p3oSFMszkkjfLF233ZM5Ea0Q8w28jPuhGlmE9r6Ub9Xah1fWXEz2/quy+x4UK+0H9vpHt4FAAAAgLE7yFjrIlGKLyFxZEPaEVlne6A5ZeuYoiVMngmHnna6jlQ8DAjD44GuFUeZA9dsa2cHQ9yATDva7iLbUXetgf4tyEgzKU7RVfzqWirt2o6J/PPQMg8xPkHZQdYb9momef1emaZLh8XcLkLXDriyXj5zeBcAAAAAxu5AY206Prdyou3Oh/ijEHQMH0/a/suGFRdnsiF+WuhxB/mKXSdO9TcQprjRbe3sYIgb0K4VavrxdhfZjtKUpXtYwjwQUyxO0XJubW2YQnZM5NeOw9t2tkhLzqRNp0FhQtLvldJhUijrnvj+brbDV7Bzh3cBAAAAYOwOONaGDvtFUtkQzVCaRHrzLB20/GzIMzbQ+GQWOtyBmd499lyNgUyU9S8bFLkBsbbfhj+0q2Y7MFO2LJ9oCVNhoOmGuGJ2rLBjIreldm23j3CQ7TXKhi/beL9XpsVSfpFjx0Wnq8/sK22ls4dluwAAAAAwdvsfazvJYih9tNnEnfOnsolyIO30Cm+aosNSsom026ySrekO7Bx/NvbIoY70JcwW/ADX3faag15Vxw1oe7N17Hh/IhS1U4b3kG0hkD47J7RNt1+lv1fbNrIt+ZphlD9jq+ooOyIcdXY4tJT0mGNfeWg3S9iHGiabRbPl/P56WwIAAAAwQvsca9uoYiJFnmXaxBF0Y4nne01HLKCTl8QgM9xxw2ynmTfcbXfmtpCGySAzR076I7P93voq1DanfE/2HHihVG8Pocts3onj2jdQH185066iY835bHj7lU223rI122P7yOK8gYfb3aMbEjvNFuIw19eddhnZovm8ocXCgwEAAAAwavscaweYj9U6kjh2EGQOse4D20Ptt0iPTZ4199C+LQQAAABg3x1grPXvoe0lsmsi1rpXA/clao49sPkXWPf0qdq/vwwAAAAAB+BAY23/jaC+TxFDAAAAAABbO/g3IQMAAAAAcMWItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAERtxrD114pg4vnLOHJ445Q8GnFs5bkZ7ru+ys5borG1stY2rtGTHoXtoQ1dkjx/FIHkMl19xVzfliu3a87VLlv88ujP7v91d+bnq7t3/49GeA/gx84v3HlDY0z7/yAMAAGCX7GOsDZfI5uJRe9oLzHh2J5fQcjG6k+FDdN0rupjdhbWvzLIdH9iGthQyg/27jV3poUjX8r8I9+MRpm+vXCud6v5s2Zpd5kleXuBKbFXtin8e94zs+OofdiSPz1WTqgf1MJc8oMP3zAMAAGC79vfV2vzKUY+ya/tTJ3Z4/bwLV9zbv5iVkXbg7l7t78CyHe/3hnb0t+X/rrO/benyR/7ciZWVlVOu2S/b675cuUF+QnfAkuVcv3+K5UmNI1z7qpKPq+ALhEfQq5aWPix29efqMDy8JQ/o8D3zAAAA2K59fhOyvXSU9vETJ9LrtXK5aZp6zS+WXX9GfkDoktLxpDvRHp34HVcwVYsLqHQxm0p3LnDteF8lq9DbhpOGpIU721fhpO+XA99wRczYuKF8fqosq6WDNMjPyyd1dB5rGNvdgOeXyLr99OEH20oT4lnpsiNPSax1JfrT3eSse+tyHX5Cb0C/spKH0j4l7lG1A/xD7DxbO+GWC/P9jnrFfHcrrOwXdgfpaQ5SBTM1mycjwsO4qZ3sJoVSnYdvFvClpSMO6Z3M+rJVlXb4o7aC7jHMDNuNddK8rI6cd424h2wXXtyob5vxaWian0+P65utiTgo28ywWNr8DYRKridVBQAAwN7Y78/W6tVhuMpzKVYv++KFo7mCjGO6F4XZdaKczi4pfX9q+YNUyyzQzjODtRm6TbOVVRV2bbPNNMyud5la2m4PtE6+NVs2tO1kNyFNDjN1RL+ZztverB111ojndb3ecv4gHtl2JAWPh9jannRdYZiuJ4WzmolWjNtxtijX5xYYKN4vfZlCbvCSGkPyujnd0NAy2m+e+YGn244wVUxT54UR2pvtJNUR51ZO5FvQmf50KpdmpLrDZ12j15G6wmac2JEeX7/Z232UVvHj/UFnfDqRtc1cNyEcbGcztnKvnQZIK5x1C7QjAQAAsFf2/Suj4kVguOjV67624S//ZER71dk5EPZ6Mj+bLirzZrxwbwfHFZ001szqHDjdni3X1la29ySvlR6TrdmdH+dsOTkbkA5s8WXtli1hz5v+Tom04mBFmahdOl+4s6FrG7S8rX8F5fzI/oB+aenJjiNfYvjcToRNO/1qejb1pic2PafZiNRtmBF5OZVKSrMz15xLBpeInbpAdrrXkVXIi6XdZf1Z8d5+nGx8XDIfL2Ps7DhleO6ONjPc7iwIAACAfbPvsba99ouX1OG6Uv7wV4R6nDNXimFwOMquP9PVpWnbC/d2cH7xmaZpf8YsJGx9tWTtbFgomRcSea1sC9l+7YI6yp3ccnJYNYndsfiydsuuYc+bftMdtxbIKbtzJUPCCD0pTujnaHvrDnNTsoKXKaebCcwU39tfr1t66abcwG3ud1vCPjvPk+tOfelI1g+rZyNStwglnTAiL+fErlMnBs4MPkWp0z0NXuwMfb1BdkDbzoqlrdide7G7s8cgr9MeZeO7j0Z35U4Oz93RZi7XjhMAAACwb/Y/1vrLyxMpb7rLwpX0fUz5VWdOxw5dkgp7ddmOyy7c28Fu/didpmUF+rqnl6w9UCVfUOWDUiVb0w1KR2nOlpMHtqBs8WXtli1hz5v+Tgm74kBFmZiNFsdFd91hbkL2iHZcTjduk1crL20ftuEnD5zwwgb6ujNcndTp5nW3lG9BhoQBqZWNyAa03WZEXs7zfXLfPWFrJHEJndduN63b6s1NHWZwPi/trl9PDe3ey8bHYfn4tAEnThmeu6PNLGt72jNQAAAAAHvmAGKtu9y0l316FWgvQLPLUTmw14xubJhr272rS700PZ5duMeLVbd+OGPb2dWoHJgtiuysiOWEWTs1Y6Y2ZwPboztoC9mafpQtG9pbT9YRdm++bYsva7c6a9hibb90xyHuoB1k21HchaNlVG/YMDc8G7vjcn5Ib0BeWXYeH1HLT0zdQ2O2K6ulT9OSHbULdJ7V0LQjUnfqzVbJBkdu7d7Swp1Iy7tWXCJtwczv/ZQP/NinibYpzO60ZGi6A9ce3r0ydXRQGNUZrzXNwm17eO6ONjPYTh94SCvYBQAAALBnDiLW2utKR6/9eh1B/5JQrzmd9kuDZGqaEMfni5hJ0meGr/hmmBaH5ftxbAnbNsVutttItXoPwjy+tFLqTGubgbFGNjn09uZ2H0gcYDfcaes8lYq51/R8U8/HorqqHaWzuisa9tkKXb6vN3IJt1YcvMNyaaOe+cvI6spBr0B3rmMK7Fh6lsRQoWzFdoB5xPbLjONY3bc98OPdV3AFnaXkxJJHYda3K7glYrn25yIu5fiK3Y54fHzlTKymZ1JtPzFfOavkjy2zSjw9ND71pb9bO7ft7c5dvhk5XNI2c+Jiri9tBwAAAHvjQGLtkacXu1zr7oSLBymZ7Iq8puSTo/NXkr07f4zkb2uXfxoAAAAwYsTag0CsvQIuhe7a0+ZeZUvFjkhMkudQHnPv9/qMD7EWAAAABrF23/l4prgwx/7yP3tj/7lz/0nC4b8NAQAAQBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjdg3E2nMrx4+J4yvnQseVcEVSiVDz2IlTzakT/k9sh3u2eL4AAAAA7J+xxdo2b7ZCfpI0dXWxNiPVtK6sdTVFzVa3m/JCKGzt4kPaZZ2NivQQ9XETawEAAADslzHFWp8TTWKSjnC0q7F21zPydlLe0IM7vLFW2MflUm57QKwFAAAAsJ8OLNaePHnyVa96laSh66677vTp06H3MlzuWxaXTBL1+dCJsdD0hQouiDk6yB+1w/vF0rJxnh/hz0vbN4a2pzOyfje0M3KorzW0hm8qM6nbmY6D9lEF8bG44eFIx8QT3RlW9rjsgS5rKvoSYS9xwuADAAAAAICdO7BYe/3114dcc+yY5NvQu5zLQUsjkCSoFJ9CFotRK+YsbblGHC6tNNq1fBbzOrNTRdv0E0KVAWaSN/BIBroy+Ro6OrRTc7BTJ7az+hsR9rwcuHbqi0/OEFNOF0yV7ZGtn/qH9woAAAAAV+LAYq3mNCP0Lnf5/JPFsyDFKJPAvIFiqUKa52Rl0qR4MLS01Vt8QHc/OscbWCPbX3sw2JlPzIa0zIBTJ/xZHWc3s0Taoze4zuD62UYGdwUAAAAA23Zgsfa6664LeejYseuvvz70LqfxZ3n+6canVjuh7UsFQiqLs1KFTtCKh7au57rt0kN0IVNukCudD9Kugc35g5yeGuzMJ3YeV9COkLPpZKiW1hygY+IM9wDCcLvO4PqhenLZdQAAAADgsg7ys7X+fchX8dnaFMVifDJhy8YrT3vyCJWGpwDWmZcO0xBruDcxO1pOF8nLZHvN1ujszxvszCcuHyLd51ZO9B7E5XfeOZsO7TqD6y/ZCAAAAABciQOLtVdA41AW/iQ0hXjUxqeUmPzgEKPayNYOa99va8anANZJXeZQxqQz7eJp4rBsknI7y3qU32+qpMfxKF8jOyUHrj3YmU3UEb1lhZt5PKXa/pOjDyGWaWWPS4e2R3YdOyj1uxV7ewUAAACAKzGmWKtcfjIkJ6UuiUfxQNqaqXSEmRMClD/juKAVj4+v3JSaMjRNTYEscB3Z2jrAMoOVrxC624NMZ0KoObRGegBm3W5nnKiH8WR/ZR1nertPju8x6wgzJjBDPd/RfUhtnTQuLwwAAAAAOzS2WAsAAAAAgEGsBQAAAACMGLEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBixFoAAAAAwIgRawEAAAAAI0asBQAAAACMGLEWAAAAADBi10CsPbdy/Jg4vnIudFwJVySVCDWPnTjVnDrh/8TucM/nVf5tAQAAAEA0tljb5s1WyJuSlXYxKEk1rStr7UJW9rZXyEe+6PBmv85GxU6C/+7+bQEAAAA42sYUa31KNPlJOvYi1u5OMd1tW8bt/PLBb+jBHersp8m23a7b/PZ3S6wFAAAAsHsOLNaePHnyVa96lSS566677vTp06H3Mi4bDU1Q8vnQidnJ9KUcHOggf9QO7xdLy8Z5foQ/L23fsNvTnraMHxfO2nZrqK/lS+dr+KYyk7qd6TiI2/HiY3HDw5GOiSe6MywdFBfXpczYLbYnLlMYAAAAAHbiwGLt9ddfHxLOsWOSb0Pvci4UmZCUk4zlg5LJVzF3aZ+fKS3XiMOllUa7ls5qdWanirbpJ4Qqy5girh0PvIGuTL6Gjg7t1Bzs1IntLLv7yJ6XA9dOffHJGWLLmRUvs704PFsVAAAAAK7KgcVazWlG6F0uS049Q0FJZ/ggZROYM1AsVUjznKxMmhQPhpbu2mpMdz8yPhhYI9tfezDYmU/MhrTMgFMn/FkdZzezRNpjHuoHd6KD09rZwwEAAACAq3Jgsfa6664LmejYseuvvz70LqcBqZ/KWt341montH1ZtFJxVqqQ5TJzaOt6rnvLjLaNEOdKmzWFdg1szh/k9NRgZz6x87iCdoScTSdDtbTmAB0TZmgzjg1zEzmTPZi0JgAAAABcvYP8bK1/H/JVfLY2RbEYlEzY6se4TroSaXiKWp156XA4jQ33Bv09DNNxeZlsr9kagzWXLGQnLh8i3edWTvQehD8TDnrsWbvZoWXseZE9HAAAAAC4KgcWa6+AhqMs/Ek8CgGqDUopU/nBrp0iWzusfb+tGZ+iVupzzKGMSWfaxdPEDp1ozsRduJ2ZBTzXa8Zns/M1slNy4NqDndlEHdFbVriZx1Oq7T85+rhjmZZ2xnI6tD1y9To7cedDp20DAAAAwNUaU6xVLhMZEqVSl2SleCBtDV46wswJacqfcWJuc46v3JSaMjRN9ZnNlHId2do6IDIjW2GIOxMiYK47x08YWiM9ALNutzNO1MN4sr+yjjO93SfH95h1RBrTTmx73HE6PbDl4ysrrplXBAAAAIArM7ZYCwAAAACAQawFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiBFrAQAAAAAjRqwFAAAAAIwYsRYAAAAAMGLEWgAAAADAiF0DsfbcyvFj4vjKudBxJVyRVCLUPHbiVHPqhP/zQLjFD275K+W3fXV/IwAAAACwLWOLtW3ebIXAJzlqF0OUVNO6stZVFQ2Z1Ll8ITvSS0FWH/GBxNruU72znLq7fyMAAAAAsMyYYq3PWSbiScdexNrdKaZRtd2rtrco2R0eDw4s1gpdO+4729WWZDSxFgAAAMA+OLBYe/LkyVe96lUSlK677rrTp0+H3stwoXZZqjIhyodfJ+Yq0xcquIzm6CB/1A7vF0vLxnl+hD8vbd9YGvp0WnvSDe2NtCN6w13bL23XTRPCse1yYr+b1h7JmDShMyOjo9qnorPFsB2RBth9CHsCAAAAAPbIgcXa66+/PqSfY8ck34be5VxgWprAJGOlvBfiVExh2udnSss14nBppdGuFdOa6MxOFW3TTwhVlmiLO8OPxNQ2Kwp7ZOukfm2FbtNsZdXkQNvZ3HiyLytndmjbqamj7YjORgAAAABgLxxYrHW5MQm9y2UJq2coRKXsZgOZM1AsVUjznKxMmhQPhpZOdGlxmRFBGBjFGXY7dq3Yn+23s3llus6tnPDTdbXOsCE6NTLjs8fcHuQ1syEAAAAAsGcOLNZed911IS4dO3b99deH3uVcwlqaxGyIslmsndD2ZbFLxVmpgomBKh7aup7r3lZ+07mXH6X7iau6pQa2032Y7QZy3YXMyHTKLSHimkN0kNlGO7idm8gJM1Zt62kBAAAAgKt2kJ+t9e9DvorP1kpX6IghyqRDndAb38laaXiKYZ156XA4qQ33dvU302E2rtKhnWnXiv1blnYjZN6pE71h4Uw46slOmx0OPeZOqaEhAAAAALD7DizWXgENTtlrkRKdOjlLh/guP9i141tv47AY8NL4FMNSn2MOZUw60y6eJmZ0Wux3m2lnZgdRVtsOMetng1K/tuxSA9vRTR5Pn6LtPyN2xSSr7FYPR9lu5UDbrkQ4b9sAAAAAsJfGFGuVy0uG5KnUJTkqHkhbs5eOMHNMKgtcOIvHx1duSk0Zmqb6EGdKuY5sbR1gmLHtdMf1m2Nh9hOE8+lEb32v94D6+3BkgDljyoTegU2lQe3MtkePTYk0zTyPK665ZDcAAAAAsGvGFmsBAAAAADCItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACAESPWAgAAAABGjFgLAAAAABgxYi0AAAAAYMSItQAAAACA0Wqa/xe7OqcmjJGJsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\python\\Interview-Prepartion-Data-Science-master\\Lesson102-3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logistic regression for multi-class classification using built-in one-vs-rest\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, n_classes=3, random_state=1)\n",
    "# define model\n",
    "model = LogisticRegression(multi_class='ovr')\n",
    "# fit model\n",
    "model.fit(X, y)\n",
    "# make predictions\n",
    "yhat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 2, 0, 2, 1, 2, 0, 1, 1, 0, 2, 1, 0, 1, 2, 0, 1, 0, 0, 1,\n",
       "       0, 2, 0, 0, 2, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 1, 2, 2, 1, 0, 1,\n",
       "       0, 2, 0, 0, 0, 2, 0, 1, 2, 0, 2, 2, 1, 2, 1, 0, 2, 2, 2, 1, 0, 1,\n",
       "       1, 1, 0, 2, 0, 2, 2, 1, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 2, 2, 2, 1,\n",
       "       0, 1, 2, 1, 1, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 0, 0, 0, 2, 2, 2, 2,\n",
       "       1, 0, 0, 1, 2, 0, 0, 0, 0, 2, 2, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 1,\n",
       "       0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 2, 0, 0, 1, 1, 1, 2, 1,\n",
       "       0, 0, 2, 2, 1, 0, 2, 1, 2, 2, 1, 0, 2, 0, 2, 1, 0, 0, 0, 0, 0, 1,\n",
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       "       2, 1, 2, 2, 1, 0, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 0, 2, 2,\n",
       "       2, 2, 0, 0, 0, 1, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 0, 1, 1, 0, 1, 2,\n",
       "       1, 2, 0, 2, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 2, 0, 1, 1, 1, 1, 2, 0,\n",
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       "       1, 1, 2, 2, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 1, 0, 2, 1, 0, 1,\n",
       "       2, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1, 2, 1, 1, 2, 2, 0, 2, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 0, 0, 1, 2, 1, 0, 2, 1, 0,\n",
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       "       2, 2, 0, 0, 0, 1, 1, 1, 1, 0, 2, 2, 0, 1, 2, 1, 0, 2, 0, 0, 1, 1,\n",
       "       0, 2, 2, 0, 2, 2, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 2, 1, 0,\n",
       "       0, 2, 1, 1, 2, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 2, 1, 2, 0, 0, 2, 1, 2, 0, 0, 1, 2, 2, 1, 2,\n",
       "       1, 1, 1, 0, 2, 2, 1, 1, 2, 2, 1, 0, 2, 0, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 2, 1, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 2, 0, 1,\n",
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       "       0, 0, 2, 2, 0, 0, 1, 2, 0, 2, 0, 1, 1, 1, 2, 2, 0, 2, 1, 0, 0, 2,\n",
       "       2, 0, 1, 1, 2, 0, 0, 0, 1, 1, 0, 1, 0, 0, 2, 0, 2, 0, 0, 2, 1, 2,\n",
       "       2, 2, 2, 1, 1, 1, 2, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 2, 1, 1, 2, 0, 0, 2, 2, 0, 0, 1, 1, 2, 1, 0, 2,\n",
       "       2, 2, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 0, 0, 1, 0, 1, 0, 1, 1, 2, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 2, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 2, 1, 0, 2, 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 2, 1, 0, 1, 2, 2, 0, 1, 2, 0, 2, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 1, 2, 0, 2, 1, 0, 0, 0, 1, 2, 0, 1,\n",
       "       1, 0, 2, 1, 2, 0, 0, 2, 2, 1, 2, 1, 0, 2, 2, 0, 1, 2, 1, 2, 0, 2,\n",
       "       0, 2, 2, 1, 2, 0, 0, 1, 2, 2, 1, 1, 2, 0, 1, 2, 0, 0, 2, 0, 2, 2,\n",
       "       0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1, 2,\n",
       "       0, 0, 0, 2, 1, 1, 0, 2, 1, 1, 0, 1, 2, 2, 1, 0, 2, 0, 0, 0, 2, 1,\n",
       "       2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 1, 2, 0, 1, 2, 2, 1,\n",
       "       0, 0, 1, 0, 1, 0, 0, 2, 1, 1, 2, 1, 0, 1, 1, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 2, 1, 2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 2, 0, 2, 1, 0, 2, 1, 1, 2, 2, 1, 2, 1, 0, 0, 1, 0, 2, 1, 0,\n",
       "       1, 1, 2, 0, 1, 2, 1, 1, 2, 1])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logistic regression for multi-class classification using a one-vs-rest\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, n_classes=3, random_state=1)\n",
    "# define model\n",
    "model = LogisticRegression()\n",
    "# define the ovr strategy\n",
    "ovr = OneVsRestClassifier(model)\n",
    "# fit model\n",
    "ovr.fit(X, y)\n",
    "# make predictions\n",
    "yhat1= ovr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 2, 0, 2, 1, 2, 0, 1, 1, 0, 2, 1, 0, 1, 2, 0, 1, 0, 0, 1,\n",
       "       0, 2, 0, 0, 2, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 1, 2, 2, 1, 0, 1,\n",
       "       0, 2, 0, 0, 0, 2, 0, 1, 2, 0, 2, 2, 1, 2, 1, 0, 2, 2, 2, 1, 0, 1,\n",
       "       1, 1, 0, 2, 0, 2, 2, 1, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 2, 2, 2, 1,\n",
       "       0, 1, 2, 1, 1, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 0, 0, 0, 2, 2, 2, 2,\n",
       "       1, 0, 0, 1, 2, 0, 0, 0, 0, 2, 2, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 1,\n",
       "       0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 2, 0, 0, 1, 1, 1, 2, 1,\n",
       "       0, 0, 2, 2, 1, 0, 2, 1, 2, 2, 1, 0, 2, 0, 2, 1, 0, 0, 0, 0, 0, 1,\n",
       "       2, 0, 2, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 2, 1, 0, 0, 1, 1, 0,\n",
       "       2, 1, 1, 0, 1, 1, 0, 0, 0, 1, 2, 2, 0, 2, 0, 1, 0, 2, 2, 0, 0, 2,\n",
       "       2, 1, 2, 2, 1, 0, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 0, 2, 2,\n",
       "       2, 2, 0, 0, 0, 1, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 0, 1, 1, 0, 1, 2,\n",
       "       1, 2, 0, 2, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 2, 0, 1, 1, 1, 1, 2, 0,\n",
       "       0, 0, 1, 2, 0, 0, 1, 0, 0, 2, 0, 0, 2, 1, 2, 0, 2, 2, 0, 2, 0, 2,\n",
       "       1, 1, 2, 2, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 1, 0, 2, 1, 0, 1,\n",
       "       2, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1, 2, 1, 1, 2, 2, 0, 2, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 0, 0, 1, 2, 1, 0, 2, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 2, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 2,\n",
       "       2, 2, 0, 0, 0, 1, 1, 1, 1, 0, 2, 2, 0, 1, 2, 1, 0, 2, 0, 0, 1, 1,\n",
       "       0, 2, 2, 0, 2, 2, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 2, 1, 0,\n",
       "       0, 2, 1, 1, 2, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 2, 1, 2, 0, 0, 2, 1, 2, 0, 0, 1, 2, 2, 1, 2,\n",
       "       1, 1, 1, 0, 2, 2, 1, 1, 2, 2, 1, 0, 2, 0, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 2, 1, 0, 0, 0, 0, 1, 2, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 2, 0, 1,\n",
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       "       0, 1, 2, 0, 2, 1, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 0, 2, 2, 0, 2, 0,\n",
       "       0, 2, 0, 0, 2, 0, 1, 0, 1, 2, 0, 0, 1, 0, 0, 2, 2, 2, 0, 1, 1, 0,\n",
       "       0, 0, 2, 2, 0, 0, 1, 2, 0, 2, 0, 1, 1, 1, 2, 2, 0, 2, 1, 0, 0, 2,\n",
       "       2, 0, 1, 1, 2, 0, 0, 0, 1, 1, 0, 1, 0, 0, 2, 0, 2, 0, 0, 2, 1, 2,\n",
       "       2, 2, 2, 1, 1, 1, 2, 0, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 2, 1, 1, 2, 0, 0, 2, 2, 0, 0, 1, 1, 2, 1, 0, 2,\n",
       "       2, 2, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 0, 0, 1, 0, 1, 0, 1, 1, 2, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 2, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 2, 1, 0, 2, 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 2, 1, 0, 1, 2, 2, 0, 1, 2, 0, 2, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 1, 2, 0, 2, 1, 0, 0, 0, 1, 2, 0, 1,\n",
       "       1, 0, 2, 1, 2, 0, 0, 2, 2, 1, 2, 1, 0, 2, 2, 0, 1, 2, 1, 2, 0, 2,\n",
       "       0, 2, 2, 1, 2, 0, 0, 1, 2, 2, 1, 1, 2, 0, 1, 2, 0, 0, 2, 0, 2, 2,\n",
       "       0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1, 2,\n",
       "       0, 0, 0, 2, 1, 1, 0, 2, 1, 1, 0, 1, 2, 2, 1, 0, 2, 0, 0, 0, 2, 1,\n",
       "       2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 1, 2, 0, 1, 2, 2, 1,\n",
       "       0, 0, 1, 0, 1, 0, 0, 2, 1, 1, 2, 1, 0, 1, 1, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 2, 1, 2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 2, 0, 2, 1, 0, 2, 1, 1, 2, 2, 1, 2, 1, 0, 0, 1, 0, 2, 1, 0,\n",
       "       1, 1, 2, 0, 1, 2, 1, 1, 2, 1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM for multi-class classification using built-in one-vs-one\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.svm import SVC\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, n_classes=3, random_state=1)\n",
    "# define model\n",
    "model = SVC(decision_function_shape='ovo')\n",
    "# fit model\n",
    "model.fit(X, y)\n",
    "# make predictions\n",
    "yhat3 = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 2, 0, 2, 2, 0, 0, 0, 0, 1, 2, 2, 1, 2, 2, 2, 2, 0, 1, 0, 2, 1,\n",
       "       0, 0, 2, 2, 0, 0, 0, 1, 1, 0, 0, 2, 0, 1, 2, 0, 2, 1, 2, 0, 0, 1,\n",
       "       0, 0, 2, 2, 1, 2, 2, 1, 2, 0, 1, 2, 2, 0, 2, 1, 0, 0, 2, 0, 0, 0,\n",
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       "       1, 1, 2, 0, 0, 0, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 0, 1, 1, 1, 1, 2,\n",
       "       1, 2, 0, 2, 0, 0, 0, 2, 1, 1, 0, 0, 1, 2, 2, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 2, 1, 2, 0, 2, 1, 0, 0, 1, 0, 0, 2, 1, 1, 0, 2, 2, 0, 0, 0, 2,\n",
       "       1, 0, 2, 2, 1, 1, 0, 1, 0, 2, 2, 0, 0, 0, 1, 2, 1, 1, 1, 2, 0, 1,\n",
       "       2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 2, 0, 1, 2, 2, 2, 2, 0, 1, 0, 1,\n",
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       "       2, 2, 0, 2, 1, 0, 0, 0, 2, 1, 0, 1, 1, 0, 2, 0, 2, 0, 0, 0, 1, 2,\n",
       "       2, 2, 2, 0, 1, 2, 2, 2, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 2, 1, 1, 2, 1, 0, 1, 2, 1, 0, 2, 0, 2, 1, 0, 0,\n",
       "       2, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 2, 0, 0, 1, 0, 1, 0, 1, 2, 2, 1,\n",
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       "       0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 0, 1,\n",
       "       1, 0, 2, 1, 2, 0, 0, 1, 2, 1, 1, 2, 0, 2, 2, 0, 1, 2, 1, 2, 0, 2,\n",
       "       0, 1, 2, 1, 1, 0, 0, 0, 2, 2, 1, 1, 2, 0, 2, 2, 0, 0, 2, 0, 2, 2,\n",
       "       0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 1, 0,\n",
       "       2, 0, 0, 2, 1, 1, 0, 2, 1, 0, 0, 1, 1, 2, 1, 2, 1, 0, 0, 0, 2, 1,\n",
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       "       2, 0, 0, 0, 1, 2, 0, 2, 1, 1, 2, 1, 0, 1, 1, 0, 2, 2, 0, 2, 2, 1,\n",
       "       2, 2, 1, 2, 2, 2, 1, 2, 2, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 2, 1, 1,\n",
       "       0, 2, 2, 0, 2, 1, 1, 2, 1, 1, 1, 2, 0, 2, 1, 0, 0, 1, 0, 2, 1, 0,\n",
       "       1, 1, 2, 0, 2, 2, 1, 0, 2, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM for multi-class classification using one-vs-one\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, n_classes=3, random_state=1)\n",
    "# define model\n",
    "model = SVC()\n",
    "# define ovo strategy\n",
    "ovo = OneVsOneClassifier(model)\n",
    "# fit model\n",
    "ovo.fit(X, y)\n",
    "# make predictions\n",
    "yhat2 = ovo.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 1, 2, 2, 2, 2, 1, 0, 2, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binary classification models like logistic regression and SVM do not support multi-class classification natively and require meta-strategies.\n",
    "\n",
    "The One-vs-Rest strategy splits a multi-class classification into one binary classification problem per class.\n",
    "\n",
    "The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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