3D-Planning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from planning.src.map import ProbabilityMap, BuildingMap, BuildingMapGenConfig\n",
    "from numba import njit\n",
    "from typing import Tuple, List\n",
    "from matplotlib import cm\n",
    "from planning.src.map.util import get_3D_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set_style('darkgrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_big_map():\n",
    "    np.random.seed(15)\n",
    "    size = 50\n",
    "    n_building = 8\n",
    "    config = BuildingMapGenConfig(size, size, size, n_building, 4, 10)\n",
    "    bmap = BuildingMap(config)\n",
    "    return bmap\n",
    "\n",
    "def get_small_map():\n",
    "    np.random.seed(5)\n",
    "    size = 3\n",
    "    n_building = 1\n",
    "    config = BuildingMapGenConfig(size, size, size, n_building, 1, 2)\n",
    "    bmap = BuildingMap(config)\n",
    "    return bmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmap = get_big_map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iwgoGLzNpYWEBa2treO2115BOp21LjMhMigfWHNBphBH824kYy9VkuZKEiIkOIVoE21JrKf5Wi5Z6LA9h3UG8fQtLSziszKSdO3dClmUcOXIEW1tbrsykgYEBO6i3HTKT2vn38ENYwCrhiRhKKQqFgv2aLMsud5I4h7UhRIvAF17tlTC0WrTUGlOjaRouXLiAZDKJc+fO1ZTxEkZAiMmLjyRJrswkwzCwsbGBTCbjykwaHh7GwMBALDOTOnGB78RA3KgRIqZxCNEi4OJXeyUMrRYttVg7LHfQ4cOHMTo62tR910urY4iiwk/sOTOPgHJm0srKCq5du2a/Pzw8jHQ6LRbWBtGJQqwZ9Y14Iiafz7uCfoWI2R4hWgQutqu9EoZWL6JhrB2MMdy8eROLi4t47LHH0NvbW/e+BbUT5Pz5ZSbdunULm5ubdmbS8PAwent7W/KbdOoC32mCsNnHZM2r1j6FiAmOEC0CG687qN6bpNUpz0EtPbquY2pqColEAufPn4/EzdCK+JJ2j2mpF7/MpLm5OTszybLUNKpnkpdOFC2dGIjbapdXEBHj7GB9P4sYIVoEAKJxB3mRJAmGYUQwutoIsohvbGxgamqqbndQLfsWNBZvZlI2m3X1TGpGZlInXgOdKsTiFBPFEzHOchMA7OwkRVHuqw7WQrTc53jdQVE+bUSRclzv/v0WjajdQV6EpSVeEELQ19eHvr4+jI2NgVKKzc1NrK6uNjwzqdMWk1ZbJRqBYRixPiY/EaPruu3acrqTOlnECNFyHxOkFH89xDUQtxHuIN6+hYCIL5IkYWBgAAMDA3ZmkrdnkmWFqSczqROtEp14TO0mxHiBvZaIsd53upM6ScQI0XKfUmvtlTC0WrTwLC2WO+jQoUPYu3dvw/YtLC3thSzLGB4exvDwMIByZtLy8nJdmUligW8P2v2YeCJG13VomoaNjQ0AwMjISEeIGCFa7jPqrb0ShjhkD1n7Z4xhYWEBCwsLOHnyJPr6+hq+byFa2hdeZlImk3FlJg0PD2NoaGjbzKR2XRz8EEIs/jhFTDabtbuxO7NCvc0f2+U3FaLlPqKWUvz10GpLi7WI67qOCxcuQJblhrmD/PYd5vOC+JJMJrFnzx67lYOVmXTjxg1ks1k7M2l4eBjd3d329zpRRArR0l4YhoGuri7XvGeFBjhFjLf5Y1x/YyFa7hMs60oj3UFe4pDyrKoqXn311Ya7g7wIS0tn45eZdPXqVVdmUrNSq5uJEC3thWEYFQ9qhJAKEaOqatXmj3H5zYVo6XCa6Q7y0kpLC2MMS0tL2NjYwNve9raGu4O8CAFRO+123qplJi0sLCCXy2F6etoO7FWU9p9247KARUWni5btjq2aiLEESyKRsN1JrRQx7X/3CHxpRO2VMLRKtOi6josXL4IxhoGBgaYLFgthaamddl4UnZlJO3fuxOzsLHbs2GFnJgHA4OBg3ZlJgujoZNFSSw0ap4ix5hRVVaGqKr7+9a/jxo0b+Af/4B9EPtYgCNHSgVgt0i2F3aqbsRWiZXNzE5OTkzh48CB27dqFN954o6n7t2i1a0wQHyRJqshMWl1dtTOTFEWxrTCiZ1Jr6GTRwnMPhcHZVgAAlpeXXY0fm40QLR2GFWB19epVDAwM2CXNW0EzF27GGBYXF3Hz5k07O8iK4WkVwtIi4MV/KIqCnTt3YufOnQCAYrHo6pnU1dVli5hW9Uy63xCiJThbW1sts14DQrR0FM7aK7Ist7zjb7NSni13ECHElR3UypiaMIJNLEqdS5Cg1VQqVZGZlMlkXJlJVnq1MzNJEB2U0o6INeIRtSDL5XLYt29fZNsLS2f+SvcZvGDbOIiWZogGpzvIeyO12vIgLC2CWuju7sa+ffuwb98+V2bS9PQ0isWinZk0PDyMZDLZ6uF2BIZhdOy5jNrSksvlIm97EgYhWtocv9orra6R0owxLCwsuNxBXlppwRACQgDUnx5cLTPpwoULrp5JnZKZ1AqEeyg4wj0kqBmr0SGv9oosyy1tVgg0TrRY7iAAOHfuXCwnalGnRQBEX9OkWs+kubk5EEJEZlINdLJoifoazOVyQrQIwhGk9kocLC2NWES3trYwMTGBAwcOYP/+/ZFuO0rCHnsnFuwSNP539fZM0jTN1TMp6sykThXFnSxaosaKs2oVQrS0GUFrr8RFtETJ4uIi5ubmcOLECaTT6Ui3HTWtKOMvLC3xo9m/RyKRaGhmUqeK61pqmbQLUf9e2Wy2pfOvEC1tgtV63NkrotrFKEkSDMNo1vAaimEYdrG48+fPx9Id5KVV7iFB/Gjl7+LMTGKMVfRM6uvrs0VMkMykThYtnWppiXoesq6bVhH/2V9g114xDCNwZds4ZA9FwdbWFiYnJzE2NoZ9+/a1zYTZKqtHJ1haOuEYLOK0yBNC0NPTg56eHldmUiaTcWUmWenVvGyaOB1PlHSqaGnE7yUsLYKqOGuvhCnFHwf3UL20kzvISxjRsrS0hPn5eVcAZS0TaCctJp1yLHFe5J2ZSQcOHLAzkzKZDBYXF2EYBgYGBjA8PIzBwUEoitKxi3unHleQvkNhyefzLa0XJERLTHG6g2ppdNjOlpZ2dAd5CSJaKKW4cuUKCoUCjh07hs3NTdy9exdXr15FKpWyn3jDxB50kpVC0FycmUmHDx+2M5OsQneEEKTTaei6HnkabavpVNHSiFgdxlhLz1X7rQb3AX61V8LQrjEt7eoO8rKdaCkUChgfH8fu3bvx8MMPQ9M0dHd3Y9euXQBQEXvgLCiWSqVq2qeg+cTZ0rIdvMyku3fvYnl5Ga+//rqdmTQ8PIx0Ot22xwl0rmiJWlzGYX4RoiVm1OoO8tKO7qFbt27hxo0bbekO8lJNQKysrODy5cs4duwYhoeHuZ/r7u5Gd3c39u7dC8aYXVDs4sWL0HXddiVZZntBPGln0eIlkUhgaGgIq6urOH78uJ2ZtLi42PY9k4RoCU4961IUiNkuJgSpvRKGuLiHrP5D1Y7HMAxcunQJhmFE7g6yxEOzbzKeaGGMYXZ2FisrKzh79qyvxYS3rf7+fvT39+PgwYMwDANra2u2JUaSJDv7Iw6/uaBMJ4kWwO0aiDozqZV02u9kISwtgoYQhTvIS1zcQ9s1DrTcQfv378f+/fsjnzgs0dRs/7tXtGiahsnJSfT29uLs2bN1iVJZljEyMoKRkREAgKqqWF1dxZ07d7C2tobJyUnbbN/d3d2Rk7GgNVhzlBdeZtLW1parZ1J/f78tYuLY56cT75OoA3FbHYQLCNHSUsLWXglDXNxD1USD5Q46fvw4+vv7G7b/VjwdOH/HjY0NTE1N4YEHHsDu3bsj31cymcTu3bvR19eH69ev4/Dhw1hdXcXMzAwKhcK2aayCxtFpT/BBj8cK2k2n03Zm0sbGhu1OMgxDuDibQNQPbK2uhgsI0dIyvO6gRlgY4mDK44knyx2k63rDs4NaLd6spo6PPfZYU252Qgh6e3vR29uL/fv3V6SxUkoxODiI4eFh0ZumCXSaaKk19kOSJAwODmJwcNDOTHK6OK2eScPDw+jv7xfXZUQ0olmiEC33IUFL8ddDXCZKr2jIZrOYmJjAvn37MDY21vBxtkq8GYaBfD6PTCaD8+fPN2US5h2rN41V1/WK3jRWhkhfX19srptOodNES1TH43VxWj2T7t69i5mZmY7KTGolUYsWYWm5z/C6gzoxWt2LU7QsLS3h+vXrDXUHeWmFaMnlcpiYmIAsyzhx4kSsJlxFUbBjxw7s2LEDgNmbJpPJYH5+3n6KskRMV1dXS8YYBwuhgE+jsmx4PZMymQwWFhawtbVlZyYNDw+jp6cnVvdUnGmEaGllCX9AiJamUUsp/k5AkiTouo4LFy5A07SmF4trtnvIKg53/PhxXLhwoam/cy0CLZVKYXR0FKOjo66y7pcvX4aqqnZF1KGhoab+bp1yfwhLS214r0srM2l2dha5XC7SzKROFsmGYUQaxyZEy31CVLVX2hHDMDA5OYkDBw40xR3kpVmWFsYYrl69io2NDZw7d64tA155Zd2tiqhzc3MghNhPu/39/feFpbBehGipn0ZnJnVqjRZABOIKQhJ17ZV2Y2lpCZlMBkePHsW+fftaMoZmWFpUVcX4+DiGhoZw5syZli1SUQs0q/7L0NAQADPuYHV1Fbdv38b09LTdakCY7P1pdcnzqInDAh91ZlIcjqlRNCIQV1haOpRG1F6pdRzN3rdhGLhy5QpUVcWePXtamtffaEvL2toaLly4gIceesj2x9dCFL9Ro3/nRCKBXbt2uVoNZDIZ22TvTK0OWjhP0F7E0XIUJDPJEt+8ZqRCtATHcs21EiFaGoBlXWm1O8iyMjQzfdAKQt27dy/GxsYwPT3d0pTjRokWxhjm5+extLSExx9/vOUFlyya6Z/v7u7Gvn37bJO9lVrtbDVgdQi+X1NY47jI10M7LPC8zKTV1VU73iyRSNjiOp1Ot8Ux1UrUxeWy2azdi6pVCNESIYwxbGxsIJ/PY3BwsOU3gizLTe3Gevv2bczOzuLRRx/FwMAAgNbXSWnE/q3AYkVRcP78+Zb/zhatXBydrQYOHTpkP+1alhhn8737KYW100RLOx6P10LozEza3NxEMpmEruvIZrMd5+YUMS0CX6zaK+vr61hfX7fjAFpJswQDpRSXL19GsVjEuXPnkEgkmj4GP6K2tGxtbWFiYgIHDx5sWZxONeKSCcFrNeBcKHp6euyn3U5uNRCX3yMqOiFGx5uZtLy8jLm5uYrMpFam/UdFI2JaWt3MVoiWOvHWXlEUJRbl84HmCAbLHTQ6Oopjx45VLD6tFi1R7t+yJDWiC3UUT7BxqYLMI5lMuprr5XI5ZDIZu9VAf3+/LWI6jU4SZJTSjiq5TwhBMplEOp3Gww8/7MpMstL+rWtzcHCw7bICRUyLwAWv9kpcuisDZfdQo+C5g7xYvYdaRRQLOaUUV65cQaFQqLAkCcLjbDUwNjZmZ39kMhncvHkTqqoilUqhv7+/7VsNtKM7pRqddjyAO06nWmbSwsJC2/VMijpeR7iH2hi/2iuNFgphaJSVI8wivl2X50ZTr2gpFAoYHx/Hrl27cPTo0VhP2HG2tFTDmf0BlFPlrVYDzsDJdms10GmLfCcGrVY7pnozk+JAlNdfNpsV7qF2Y7vaK5IkNUW0qPkbmLn8ORx66G+gp5efatsI0WK5g/bs2RNoEW/W+ai2/1rPwcrKCi5fvoyjR4/asRlhCbNoddoCVyuSJKG3txeHDh0CYApHZ6uBdoo56LTftNOOBwgnxLbLTEomk7aI6cSAc1ERt80IUnulGZaWzNIf4fqFfwbDMDC+9O+QLaSR6n0MY0c+hINv+VGX1SdK0XLnzh3MzMzg0UcftZ+Kt0OSJDvepxXUYn1gjOH69etYXl7GmTNnal4Yg05Y1rVUr5WkXS0tPJznrqurC3v37sXevXu5MQeWub7ZrQbuRzpRtNQT9+HNTCoUCrYryQo4t67NVmQmRT0fCEtLG2EF225Xe6WRooVSA3NTP4t7S3/l2B9Bf+8WgG9hdvIvsXDll1DQdmNgxzuR6HsShlF/YKPlDsrn8zh//nyomI52C8TVNA2Tk5Po6enB2bNn6zL3WiKi0yb5VuKNOTAMA+vr61hdXY1lq4FO+/3vN/dQWLq6ulyZSblcrqJnkuXqjLuVkEehUGh54UghWrYhbCn+RomWQvYKLr36EaytriGZBBKJ8jgMg4FSoK9XBmAgmbgFlvsUitlPYmI2CSX1AHaPfQAPPvIRKIlwRdDCuoO8tFq0hLE+bG5uYnJyEkeOHMGePXuauu8o6CRLS1Cc9V+AylYDre4O3GmipdOOB2icEHMGnO/fv9+2EnobklpBvVFnJjXqt2q1aBWipQpW7ZUwpfgbsXAsL/w25i7/KxgGRW9v+YLJ5ihUjaK/T0YiUTk2TQP6elRI0mVs3v51fOfmv0BOHUHfwHkcfPAj2Hvg+6vutxZ3kJc4ZA8F2f/i4iLm5uZw8uTJyHy296OIaDVec73zSTefz9tPusPDw01JX+20378T6rR4aVYat9NKePDgQVfW3MLCAiildlf1gYGBuscUdbpzXASrEC0cvLVXwpTij/JHpbSIi9/5IHIbl7nv6zpDuleGLJf3qWmAqlEwxpDuk+GcMxMJgoFEBoWtL2Fu6suYfl2CQQ5jZM978eDxZ9Dbu6u037I7qN6Oxa3OHtouENgwDFy+fBm6ruP8+fORTl7C0tJ6vN2BrVYDU1NTdvpqo1sNxGGijwrrAa6TaJXLy5s1p+u63VX9+vXrdWcmNaIaehyEixAtHni1V1pBfmMcV9/4aRQKmwAAAoAyglzeQCpJkM1RDPRXXpCJBJBQCCgrL2CMEViHsZU1bDHT000BXIO6dg0T3/jP2Mz2INlzDCreigeOPhVJim+c3UPOwngHDhyI/LcWIiJebNdqQFEU25UUVeZHHCb5KOm04wHiE6ejKEqkmUlR9x2Ki2AVosWBX+2VZnN37t9ifvo/gNLygscAEMLQ1yNB0xl6eyRksxSKAqRSnguTEEjE9U8ADLm8gb5eBbx1tFBkGOrPg+F19OB1LF74T7g+MYqBkXfg0MN/Ezt3P1rTsbRatPjt/969e5ienq7L9bUdrbC0CILjTV8tFosVmR+WK6nWhpidtsjHZYGPkrgeU72ZSVH3Hcrlci0vLAcI0QLA7Q4KEmzbKAw9i6uvfxgbq5dKr3gnOwaAQFHM163/JwRQiwYSSYnzndIrhKCnW7E/zxhDNsdAACSTBL09EpzLa1cKSLFbyGU+hdk3Po3JXBIk+RB27/8RPPjI30AiGezibbVo8QoHxhhmZmawtrZWt+sr7L4FwWjVOUulUtxWA9PT0ygWi65WA2Ey6DpJtHSaCAPiK1q8bJeZlE6nbRHT1dUVuXsoDtVwASFaAtVeqXW7YbaVXX8VM2/8HIrFHJzCgxDAMCgMnSGRlMGbzgsFWsomIiVBAug6RUKRwMAAQlzWFfNvgr5eAsNgKBQYNI2hK0XAQCBJgKoyKArQ3W3ezH29KoApbN2ZwncWnkO+MIiewbfi8EMfwejY232PtdWixbl/VVUxMTGBgYEBnD17tuGTrxAttdPqhXG7VgOMMduVVC3eoNN+/04NxG23Y+JlJm1ubrrqF6VSKRBCoGlaJK1HhGiJAY1yB1lpz0GDOmenfhkrt/6E67YBA2RZgiSZUobBFDGKbFpGtrIUfY6MImsbiiVYADBqCRenkGIAI5Akgp6e8nEbBsPmhoG+XgmyJFWIpHyBQtcZBtLrgPEVLFz6Simg9yAGd7wLR0/+HHrT5XThVgfiWsJhfX0dU1NTePDBB21za7P2LWh/eEGT3ngDy5XU29tr32edZpmIS1xDlLSjaPHijNeyMpPm5+exsrKCiYkJMMbsIoy1ZiZtbW0J0dIqwtZeCUtQ0WLom7j2xoexlpl2jg4ExJIbYCCwlIO1/MklwaIbDKkksLVloKtLKruLSv/DWGlysf6PmLKnWKRIpfhWG01lGOhX7P2Zlh4glzeg6wzd3RLSfW6TY083RS5/DcbWdUz81e8hm+tFsucE9h/5AHbv/2stbyC5vr6OlZUVnD59Gj09PU3brxAtnYuiKNi5cyd27jRbaFitBm7cuGGXOh8eHrbnmE6h00QY0BmixYskSejq6sKOHTtw8OBB6LpuB51fv37dFuFhijDGocMzcB+Klka5g5wEKTC3tfqXmHnzf4eqFjzvmIJF12lJhJhxLC6YaTlRZALIBJbljxCgWDSQTEoAqzwuK7YllZLtz1Naim0hQHcXsd1Bzl1JEtDXK5V2S1AoUDAAqRSBRIgrI0mWCPr7cgC+g+Ub38Gl7/1fKCh/B2fOnAl28iLEMAzcuHEDxWIR3/d939f0bsFhRIsQOO0Nr9VAJpOxLXyWFaYdOgNXoxMX+E48JsAdiKsoCnbs2IEdO3YAMF3la2trdhFGKzNpeHjYtympsLQ0mXpqr4RluziOW1f/KRZm/xg8QWJZSRSl8iaiBjUzgyT+uBllSCbNi7RYpNANmNlFScnelze2hRCCdC+BQRlkmdiuJOfYCHGnTXd1lcdWKBi2oPEyO6fhe28WcOyxJd9z0Siy2SwmJiYwMjICXdebLlgAIUTuV5xFxLa2tnDgwAFommZbYgghtohJp9NttWB2oqUl6tTguFDN2p9MJrmZSVZTUl5mUr3NEg3DwNmzZ7Fv3z584QtfQCaTwYc//GHcuHEDhw4dwqc//WkMDW3fdua+EC1ed1Cjbzo/S4um3sO1N34SG2tzpVfK7hyzPgxFIimD67cBICuWtaMcbGuJG0JMx5JFKiUhBbMAnaZRJJOSb2yLGXxbEjTEEioEmkYhSXyRZImrrq6yGDAtPab76eJVFdfndACArnmtSY3lzp07uHbtGo4fPw7GGG7evNnU/VsI0SJgjEGWZaTTabvVgKqqWF1dxa1bt7C5uYmuri47K6kVrQbC0ImipVMtLYZhBO4TVC0z6Rd+4RewZ88e7Nu3r654wH/7b/8tjh07ho2NDQDAc889h/e85z149tln8dxzz+G5557Dxz/+8W2303m/lAcr2LZZggXgi5Z7Cy9i8pvvdAiWMmYyD0EiKdvWDGeNFsuMYa1/zmBbgMEwmClIylsDYJb5TyQIEgnJJUhMgcKgqhS8FGnAtNooimQLFjO2xbEPggrrCmOmy+jq9bJgAQDdULn7iBqrku/i4iLOnTuH/v7+lgoHIVoEvEU+mUxi9+7dOHbsGM6dO4e3vOUtAIDZ2Vm8+uqruHTpEm7fvg1Vbc59ExYhWtqDWi1IVmbS/v37ceLECXz1q1/FRz/6UWQyGXz2s5/FmTNn8NGPfhSf+cxnkMlkAm1zYWEBX/ziF/FzP/dz9muf+9zn8MwzzwAAnnnmGfzpn/5poG11rKXF6w5q5kXpFC2MMSxe+T9wa+7PUL7XHa6X0v94BYklFjSNIqH4TRLmdpyeD0LMNOZczkBfH7+QnBmnQpBMugWJLVAAMM/ExJjZURqMlWURY6CsPFZdZ/jSX2xB9xiZjCaIlmKxiPHxcezYsQOnT5+2J1YhWgRxhhBitxrYv38/KKUVrQYsM30jWw3cz3RiGjcQXXE5RVHwjne8A9/97nfx1FNP4emnn8Yrr7yCl156CZ/4xCfw0Y9+FD/5kz9ZdRu/+Iu/iH/5L/8lNjc37dfu3LmD0dFRAMDo6Cju3r0bbDy1H0p8aXUpflmWzWaLxSXMvP5hbG7cKo3L+oQ5nkLBQFeKXxAOMMWEs5uzVRTOEhi847IDZ/vcheSs+BW/2BZLeFDKIEumOCGEuDxVlguKOF6QiPn63WUNL/1loXT87jHRBouWTCaDS5cu4ejRo3Z1U4tW1om5n0RLpz6t1ktYd4okSRgYGMDAwAAOHz7syvpoVKuB+51OPYeNKi6XSqXwrne9C+9617sCfe8LX/gCdu3ahTNnzuDll1+uexwdJ1riUIrfatJ3c/IpFPOr3M9sZQ309brjQSyXkEGZWYeF434BzJRnAMjlKQhMkZBMSpWpzvZ3iL19U5w406rtTwJWbEtpQJZHSVWtTCZObAsBvjdegK6Xt+YN5zF0jXsO6oUxhhs3buDu3bs4c+YMurq6OONrrXC4H0TLdOYSPvHNjyG3kcNo93689eD34wdP/gh6UrWll3fSOas3BsSb9VEsFu2uwJubm+jt7bVFTK2tBgSdSSNESzqdDv29b33rW/izP/szfOlLX0KhUMDGxgb+1t/6W9i9ezeWlpYwOjqKpaWlwPEyHSNaGl17JQyWe6inK4+eLgUGZcjlZaiqhmyOoVCkLsEClDN5CCn/P6MMBoWji7M726jHkZ68laWQJAZFJmbKswNLzJQDasuChBoMusFcFh33uFiFtcd0WUkwKMP/eCmLzS2Gwwfcl5IVLAwAlEYvWnRdx+TkJLq6unDu3Dnf31tYWhrLV2Y/j0+++YIZU9UNLOEm/nTuU/jstT9GQk3hwaFjePLoe/HWtzwRavHulKffqANXU6mUK2Aym81idXXVbjUwMDBgu5OiqIIqaF+izoqqNXvoYx/7GD72sY8BAF5++WX8xm/8Bv7wD/8Qv/zLv4wXXngBzz77LF544QU89dRTgbbXEaKlGbVXwiDLslk6mUhmRVuJIN1LgV4ZBAaSCQnLGdMc0Z92W1tcsS2kHK+iaWZQreXgsdANBjC4quICZuVaSlkp5dknI4kBkkyQcATb6nq52q5lk3F9hZkBwJk1A1OXC9jcMjfs1QWyDOilWFzDiFa0bG5uYnJyEocPH7Z9on6ImJbGwBjDc9/4p7h0ZwqSzEnP1ymMHg2XixO4PD6B33r1NzBARnB8z2N47yM/hAf2vKUFo24+jfz9CSHo6+tDX1+f3WpgfX0dq6urdsacVUCsWqsBQWcS995Dzz77LJ5++mk8//zzOHDgAF588cVA32t70aLrOjKZDBYXF3Hs2LGWCxbAFC2FQgEElVVnCQH27FKwZ5d56ill2NgwYDCCQpFVuFYA80nNsnZYFpJikaJQNNCf5j9NKYpZ+M2y0hAABmOQCb/aLmAF25qCRdMZVNWsuOusGUMIMHGxiAuXVYztkx3fdR+pU7REaWm5desWbty4gZMnTwZS/UK0RE9ez+Of/+WzuJVZgCSbqfR60QDVKUCARJcCpcs9tUgpCetGBt+69zX8z29+HaQgReJKageaNSdJkmRbWQBA0zSsra3ZrQZSqZTtSnK2GhB0JlF3ea7VPeTkySefxJNPPgkAGBkZwUsvvRR6G20rWpzuIEmSYlUu23ZJEKXCwuEdoiQRdPdIpQJwgKYxZNYMEAIUipVBs4ApLpJJgkTCEWxLGYoaQyopIZujFQXfGACJeKvt8jD3mVCInbXEGEMuz6DrDN/5XgGZNdOs4nzApp4xut6LwNJCKbUbgZ0/fz5wVdF2cQ9ls1kQQmIflzCbuYrf+J//DLlCzn6NSASJbgV60YCckECk8nWjFw0kusz3lJQM273ZzWxX0n+//IfoVfpwuO9BPHn0fTjQe7gFR9YYWlnXJJFIuFoN5PN5rK6uVrQaGB4eDlzPo9PoxIcKi6iD40UZ/zqglELTNNsdpCjKtmXzm4kV00JIotIrw5m/nHNaIkGwe6diZvzALNaWLzBsbjkblZnCwvqn5UpKJQkoZejpthbLYNV2zZRnCkr5lXgB4MZNDZemVdt6AphZShbMowusgF8AoFSv6wbK5/OYmJiwa1uEjY2Is6XFKn63sLAAWZah67pt0o9biuu3Fl7G73znP0LNqyAAlIQCCgpJlqDldCR63SKdEIJEl3ktE5+ZxvpekRVwuTiJy+OTMIoUfUY/Tuw7jR879QGMjRxoyvE1gjgVY+vu7kZ3d3dFq4GLFy9C13UMDAxUbTXQiQt8J2e9RX3t1VsRNyraSrT41V6xJvu4YNdpkStdN7xryO/Ckggx+wF1AcODMnSdYW3dAIOEQpFxarAw8KrXmlYpikTCP7bF7iTtqLar6YBEgFe+V8DSHQPe0AXnuCtiWlyCxqh5wlteXsaVK1fwyCOPBCrx7CXOlhbDMHDx4kUAsHszUUorUlxHRkZabtL/bxP/FV+b/nMApvvHxiAobKpI9SV8KjmbE6dXfBmqAUM1kOxLVnxPTknYyK3i1cw38Z2v/eV95UpqFs5WAwcPHoRhGFhfX7dbDViuJmergTgJsKjoZNESNcViMRYWubYRLdVqrwRpUNhM7PEoyYr3eG2DeNOAteA5j1NRCPr7ZSQT5ntbOdNlk8uZRd4Yp0miuS0gkZBtQVJOfQYqOkk7qu0qCvDaGzks3SkVyuNs18LrHnJZWpge2r/KGMO1a9ewurqKs2fP1nyztHKSrSZa8vk8xsfHsXfvXoyNjcEwDDtwbmRkxK434+0ebJWDHx4eRjJZeX1FjUY1/MpX/gHubFX2j2KUQctp6Eo7xlH62RkYmMG4QboAICckyMny9UA1CoNSKEkZVKNI9CRKmyO2K+lPbnwSn77wB+hV+vDAwEN48uj7QmclNZt2WehlWbavK4DfamBwcBCU0rY5piB0smhpxG8Uh9+9LUTLdrVXWvk0zcMqLkdI5aLC+9FDXQesvJ10r/nF7i4zRqVQYCgUzfgTwFlt14oxML9bUW2Xs/9cnuKLf5HF6C5nsK1n3I6/qUe1SB5LS5jfR1VVTE5OIp1O48yZM207qfiJlpWVFVy+fDmQ9cjbPXhzcxMrKyuYmpoCpdR+Gm5Edsjd7BL++Tf+MTZzG67X9aIBQzOFbCrtucZLh6vndFN4EACUgXqrjnrv4YQECRIM3YBciu9yLo5Up9ALOpJ9SWgo2q6k33r1N9CPIRzbdRzvP/1BjO04GN0JiIB2XeCtVgO7d+8GYwz5fB7Ly8soFot47bXXbPE8NDTUFPHcKDpZtERJnK7jWIuWoLVX4nIyLazickSqtA5wLS0+w6+MSrH8ypVfUGSCvl6Cvl7zRswXGFTNjIlRPXGwlpjhVdulFJi7qeE7rxcdH7b27R23v3vIZYUpxbQEYX19HVNTU3jwwQfras4VB7yihTGGubk53Llzh1sMb7uJgRCC/v5+9Pf329VSV1dX7eyQrq4uqKqKXC6H7u7uuu6LVxe+hf/63X/HdbsqKRlyUjKPjzLoquFyGekF3baUWOluUmkshm6YxRG5flJAVpwlAMzPqAUNTGdI9lS6Wymj2Opax2vr38KrX/8r25V0/uATeN+JH0ZfV+t98O2O1Wpgz549yGQyOHnypN1qYHFxEZRSV2p1nOKwtqNTRYsV7xklcREusRUtcau9EgY7EFfiVGjl3B++h8bRJ1xnQ0VGkoSebobentJCYVgZSQS5PK1IdQbKxe3+56s5LCyVXW3VzrqjCxGMikBc57bptqKFMYaFhQUsLCzg9OnT6Olp/7gFp2gxDANTU1NQFKVqMbwwKIriyg7J5XJ44403MDMzg0KhgP7+foyMjGBoaChwthUAfObSH+PzU5+BVtBNcZtUKlqr2r2dJGILFkYpqG427zR0Ctkb1O0RJZYrSdd0KInKTDuLZFfC/jwzGPSi+RBDKUOyN+GyPlqupE+Nv4BXX/8OPvZ3fjPwcTeKdpq7qmEtWn6tBpaXl3Ht2jUoimK7mvr6+mJ9/J0sWqI8rqgL1dVDLEWLFWzbylL89VDOHqoULTxLix9BY1fJNp+TZYKdI+ZPrWkEjBHkiwybm9SORSmqpjtIVU3BEcww4rS0uAfhtrRUdw85A1LPnz/fVk9q1bBESy6Xw/j4OMbGxrB///6G7a+npwfJZBInT560C41lMhnMzc2BEGIvJFb3ay8GNfCvX/nnuHBr0hQr3eXpgRoUhmYgkVJ8VDYDkSTISbhiVcBKAoYXBM7M/7GFDAGYQUHsydGj2pmVXm2KGGYGcgEgYAYDKdUk2ljcwt2pZYw9+kCIsyfYDr+F0K/VwPz8PLa2ttDb22u7kuKW0h91Aba40IjCcnHIHAJiKFqsgFsgfGfmuJiv7Eh7qfIGJSFUS2DREuo0md2dk0mCgbQEShmW7uj4ky9m7U8kE0ChGGiE9l+V7qHycTLmnz2UzWYxMTGB/fv3Y//+/bH4/aKCEILNzU3Mz8/j0UcfxeDgYNP27S005gysvHz5sr2QDA8Po6urC2uFVfzKV/4BttTNim3Rkhkt0eVwzxAArJzB5vu7EWIKlpJVheoUkuIWJc5MfkuwMIOCyBIqhIvze457yRQsDGtzG7h3adXetyA6gs6vvFYDmUzG1WrASq1udauBTrW0xL0abj3ETrRY5sewKbKWdSOMGbzRcEUL73OcTCHA11oe6TYlyRQwTpyixft5WXK4ghzZSt7kLeduGeW7h+7cuYOZmRkcP34cAwMDnKNoXxhjWF5exubmJt761rc2NVWQ97t7AyutheTy5cuYy13DF+6+CM3T2FIv6jBUA6k0Z+yORpww/3SobOJ4zfl52ILF0A3bfcQbL7GzjsrbYpSBWlYbDms3N5HLlNV2JwngOFDLQ6Gz1cCBAwdcFsD5+XkAsMV1K1oNCNESDCFatqGWgmDxFC2VP3IYqwj3FHBeCzWRcL4ve6w/1U5hMgnkC9amnDEt/u4hb0wLpRRXr17F1tYWzp0719bZBzysZo66rmNsbCwWtQ2cOBeSi/qb+NNrn+Teb0pKgZyUYagGaMnyoSQrY1IA2CLGwoxpIeBKap+A27KQ4Xyn5BqyBYvHlXRn8h42FrPo212OhZKEaImUKBZ4XqsBZzB5KpWyXUnNqEvUqaIl6uPa2toS7qGoiVutFgAgUmUwaZiYFq6ppc57mHHM7ZJHkJtZRabI8K5lijtcwaaqpcUhWorFIiYmJjA8PIzHH3+8456Gs9ksxsfHcejQIQDm8TYTPwubH39598/RNZSEUTSg553i0nLdEMhJGTLMH54xBnVLQ7IvYdZQ8cEVhFuywugaheJX4BBwxbZYsSrubTg+7HAlZWZXsbGYLb9hfSWc31SwDY1wvycSCezatcvOFMzn8xV1iay0/kaI/04VLcLS0gbErZQ/AEhypTL1TW9mle/5pTfXs80glhbZR5gAgJLgv1chWhzjtkTL6uoqLl68iIcfftgO2uskrKfFEydOoL+/H0tLS4Ethq0Sb4QQKEkZSlJGKm26YAobRRBCoBepGexaglEGNatV1mZBKV3eoO7sIPtN8z+nkKEGdRSeqwy4Lf/bjJsp5Sq5Fk5DM3D95QX07+NPpp0miFsN89baaQDd3d3Yt28f9u3bZ9clcrYaGBwcxNDQkG+rgbAI0RIMIVq2oZbJJm6l/AkhAAluTuOtbUENLXV6h+C9Z63O0LxxybK/FcaVdeRIaWKM4s6dO8jlcnj88cdbkkHQyCBtq3rv2tqay93Vir5H4ffpiSUpZeeYzQ0BLa9h41YWzGCARLiCxdqvrMjQiwYYNTN53K4kt13GEixl8eKJhwFQEaiL8tyQXy9g4du3qx6P0CzR0ojaH9Vw1iU6dOgQDMPA2toaVldXcf36dciyXNFqICxCtAQjig7PURFL0VILcXMPSZIERoIr08DrTJjidAFjYipEi6vGivs9p9XfG1vrDOB1LT2MolAoRFafJCxhXSZh0DQNk5OT6O3txZkzZ1z7aGWzxqBsd0oS3QmMPDCI4qYKpUtG9m4eWl5H12AKVLdSh2BfV5bYAcwicyAEStIsJsf7DSRewK1Rstr4BdzOb0DbUp1HYf/lPNvCPRQtrc7O9La4UFUVmUwGt27dwsbGBnp6emwRE7S4IqU0VnGQURF1XRVhaWkAcXMPmSqXE4hLCHSdQVFqzBRqQJ0Xb8Bite7NzjRT74LsTpV2LiQMe/fubdkTjdXmIer9b21tYWJiAocPH8bo6GjF++1oaan2MTkho39f2XpIDQo1p4MZDEbRcF1valar6C/kDrj1eQpkZjNFxVFh1/nb3Z64h81bWQwe2N6KKdxD0RI3q0QymcSePXuwZ88eux7S6uqqXVzR2afLL7U6bscUFVGLsWw2i/7+/si2Vw8dI1riZmmRZdnX0mIYnAydgFaRRuB9IHWV5/csgM7wF2+TRMU1LzDXn63sDdUI8XD79m3Mzs7ixIkTvmbTtrC01BHZLckSUn0J+zjza0XklwugzCy5L3kr4qKU8WNdcKU0Zut6Y4yB6swlWIByvabMzCo2b5kBt35nlTjOdz3HJqik1ZaWahBC0Nvbi97eXuzfvx+U0kCtBjpVtBiGEWngcjabxd69eyPbXj3EUrR0QkyLLMugHEsLAOgGQ8ozoXLXNq4rqPJF35osAbfpzWhyCZMKS4tj+573FGe8iyc2oZWiJcqGmowxXL16FZubmzh37lzV4ljtYWmJbr89Q13oGepCLpNHsjeBrbt5EALIKcUuvy8n5bJlj8ElWMDMmj5azij3LoJPwK3fFMGJfxFEQzst8EFbDRQKhY6rEQWIirhtgSzLTU8vrYYsy9jc0sFb0njrJy8VuV4CV9T17raKNcX5iOs9Dlny+SA6w9KiaRomJibQ398fKF27bS0tYdPyOZ9XUgoGx8oWqOKmCi2nQd3UkOhVHLEs5kbMwHXze/YYGEN+vcgPuHWcVuL4hzumRYiWKImzpWU7vK0GCoUCVldXsbS0hPX1dZcrydvItB2JOqYll8sJ0RI1iqIgl8u1ehgAYKfqra3l8ABHxHv79JhfCrbtUL2LAn7OOw85/0krUpkd71VkFnnTVsvonmqrzSQKS8vm5iYmJyfxwAMPYPfu3YG+0w6WlqCLUKilihfsnZQwfMS8GahBQQ0GvWhAz+tglL/1tblNqFmV+55f8K3zH5IIxI2UdhYtXrq6ujA6Ooq1tTXs27cPkiTZFaJVVbVbDYRtNhoXRMpzk2ln95CqqpicnAQAPPjgcdC7lZ9pRHozvyZL5WNwkG06x+KtdOv8l1d8uQJ4PftQC1m0inrFw9LSEq5fv46TJ0+GetpolWipm3rHvM0YJFmCJANyQkJXOglKzUBeLa+bXaIZw+0372FzKYuBA454Id/+Rvx/dMoCGxfayT0UFEopZFlGb28vt9WA1WzUykrq7+9vi3NgHVdUiIq4DSAOgbhW8bSHHnoIGxsbvk/3da0J9VpaAn3fEYjrOQS3aHG/J7kyi9zvFYvtJ1oopZienkY+n8f58+dDP3G1yj0UytISg2BVSSKQuhW7q/StN+5icynE9eLyCTn+bP2hdRTNKC7XbHhCzK/VwO3btzE9PW23GhgeHkZPT08sxbGo09IGtFK0MMZw/fp13Lt3zy6els1mzRsC5eDU8ud5G6l8iWfd9rs9ao5f2WZjhsH83io3TiwhSYz7OQBQ1da57mpxD6mqivHxcQwPD+Phhx+uaWIKI1qimvgaVkCvIVv1wfciD/AFZ/aQcA9FSifWNAliPfJrNTA7O4tcLmfHwwwNDcWmz1jUokXEtGxDLRNvq+q0WO6gvr4+V/E0SZLMYKgQdTDqIagQ4u6amFVHrW04M3+8wsS5H+oRNM56LxWWFrV9LC3r6+uYmprCQw89hJ07dzZtv1FRd0wL14cYagAhPtw4Wm1FinsQdlg6KabFohaXV5BWA8PDwxgcHIxUOIShEcXlhGiJmFbEtFjuoAcffNBW4c7xaJqGBJEBVpuY4nWp9U1vDrhNv4k8lQIKVvdml6XF/blqlhbXA6/nPa3YWktL0AVkcXER8/PzOHXqVN2BZ20RiFu3Qgm6nxAwn799P8RneXkFk5OTtim/2e0jOm2R79SYlnqOya/VgGWJkWXZvv7S6XTTroeofytN0+z2JK2mo0RLsywtjDHcuHEDd+/e9e2lI8syCoUCCOTK6bUR122d7qFUQkKhUNlTyArwtV5zelm8osW5bW9RuqKaDzbABkAI2dY9RCm1MwfOnTsXiRm8HVKegxLqko3y+q5jW7t27cSRI0eQyWRw5coVqKpqPwUPDQ01/Cm4U357i04TYUD0i7tfq4GFhQVsbm6ip6fHvv56enoi2y+PTvutLGIpWmo52VEWEKuG5Q7q7e2t2kvHFlFEqRAUQY+u7pL9Ib7vFNGVTRIBy4jlnIi97qFqAbx6C2NathMPxWIR4+Pj2LlzJ44dOxZpfEnsLS2BjzXOEyD/eCUi2VVSx8bGYBiGnRVy/fp1u8DY8PAw+vr6GjLJd9LC0YmipdHHxGs1kMlk7FYD/f39toipVqgyLFEeU9x+91iKllpoxkmt5g7yYokWQhIVU2rgMIIQ6c3c4nS84F4/S0vSWbrf/Z7iEC0uS0uFYcs/EFfTCvwdN4FqgnZtbQ0XLlzAww8/bBeeioo43eh+8N1DvAun4UOJHOJ5oHCa6gFTrGYyGczPz9spncPDwxgZGYnEFB63yb5eOtE91EycrQbGxsZAKcXGxgYymQxu3rwJxpidtTQ4OFjXuY76YSlO13JsRUucTOtB3EFe7IVSrlTPXNHCyxQKW5k0yH58tpl0iBZWrf5KlZgWUsXSommtdQ/xrqWbN29iYWEBp0+fboipth0sLfVH3XKI9JB9AlwC3BzbBcGnUimMjo5idHQUjDFsbW1hZWUFU1NTMAzDrs1R6wISl/krKuK0cHUCkiRhcHAQg4ODAABd17G6umq3GkgkErYVplGWwCDouh6rrLH4jCSmqKqKqakp9PT0VHUHebHdQ0pA0RJiTEGL0/F7F/G3mUz4W1rMSrfmi16hosiAblhjcATwejbSakuLy61FKS5evAhKKc6fP9+w2IY4CW8/6s2wib75BIKJHne9fp/Xg++SEIJ0Oo10Oo1Dhw65etXMzMzUXJujkxb5TqzTEicURcHOnTvtjMVCoVBhCbSEdLVWA1FbxOJUDRfoQNES5dOA5Tp4y1veErh0u0XZPVSZt+/X9DAo9aQ3+5FI+FtJXJYWjxhJJgHdMqKwKjEtWuv6QjkDcQuFAsbHx7Fnzx4cOHCgoYtKO1ha6g1p4b5c9ykNec58hEo9Zfy9vWqctTny+TzS6TRGRkaqxiJ0mmWCUtpRxxN3urq6sHfvXuzdu9e2BK6urtoJA4ODg7Y7yWkJibpGy9bWlhAtQahlwreEQr2mLMYY5ubmcOfOnZpdB7Ismze5VClagvYPqj+9OTjJqqKlbGnxWmESCQB5a1xOa4b7c3oLLS3WecxkMrh06RKOHTtmxzU0Y79hPt98OmwRcvYeivBp01mbg1KKzc1NrKys4ObNmwCAoaEhjIyMIJ1O2/vtNNHSacfTTjgtgQcOHLCDyldXVytaDSSTyY7t8AzEWLTUQhSiRdM0TE5Ooru7O5Q7yG8sRKo049UbvxK8+m3wOi/Oh8Vq3Zt5Qbq8cXkr6eqGX+O75nDv3j0UCgWcOXOmaV1c28LSElC0hFuqIlzY6jp9jVlgJUnCwMAABgbMBpBWmfdbt25hc3MT3d3dGB4eRm9vb0ct8p0WiNvOliNvULmz1cDa2hp0XcfNmzcjaTUgREsDqbcqbj3uIC9WRVxCKhdInqWl3qBbHmG26XYPuXdAXO4h9/cUpdymoFqQrtEi0WIYBm7fvg1CCM6fP9/USbezYlqaOLkHKdcf4KvNWpCcZd6daa2zs7PY3NzE9PS0HdAbp4DGsHSapSXqpoKtxHkNbmxsYG5uDpIkVbQasCwxYRDuoYDU2um5FtFiuYNu374dWSaJFfxJpMpMozCWlsDpzXXOJYpSxT3kLBrnzSzytbS4t0FbIFry+TzGx8fR09ODoaGhpj8ltoWlpRGLUISH7L8p/jvOo+FVlG40zrTWHTt24OrVq9ixYwcymQxu3LgBSZLstOpWZoTUQqcF4naa5ciCUopUKsVtNWBlxoVpNSACcRtILaX8NU3D1NQUurq6GvIkzhMtYSbT+noK+WwTlRon4RAtlZVuHenQXveQ5C92nJV0DV0LMOLoWFlZweXLl/HII48gl8tB05q7fyC8gIjiSTaKRbAxy2gjF2cfAdPiBcla5IPUhhkeHo5Nsz0/2tmdwqNTRYs3EHe7VgOKotjxMLxWA7W6hwqFAt75zneiWCxC13V86EMfwq/92q8hk8ngwx/+MG7cuIFDhw7h05/+tN1ROwgdJ1rCWFrW19dx4cIFHDlyBHv27GnImIjEUahc9xB/MmhEejNPtThjWrzxKC5Lizcd2lXDxf2m7CpK1xzR4KypY8Wv5PP5lrhpWmX1CBfTErCVeIu8Q65TEvInjGPDRF5tGGezPWvxGBgYiJ3rohPdQ50oWrY7Lm+rgWKxiNXV1YpWA7qu4+DBgzV3eE6lUvja176Gvr4+aJqGt7/97fihH/ohfPazn8V73vMePPvss3juuefw3HPP4eMf/3jg7cZWtDSy07PTHXTq1KmG9oAgUuW2/bKH6soUClH9lremKXI1S4v/d0mVIF2naDGMxosWXddx4cIFJBKJio7bzWjxwCOogIiydUC4z3NerFvfNUggBjg0555b4R7yUu33cGaEHDx4MLLaMI2i0xb5Tjsei7Apz6lUqqLVwMrKCv7e3/t7WFhYwNjYGB5++GFkMplQWZeEEFvsaJoGTdNACMHnPvc5vPzyywCAZ555Bk8++WRniJZaCOIestxBqVSqKYGZklypUOsOuuUJlBCHwdukMz7Qa2lBFdEiORSYN0jXeWobbWnJ5XIYHx/H2NgY9u/f73qvVQGxrVpk6s4eClH8kDFWuY0oDztQUC7/AiV11GmJgrCWiShqwzQSYWlpD+qp0+KMyfr85z+PQqGAX/mVX8Hdu3fx/ve/H5RSPPnkk3jf+96HJ554YluXpmEYOHPmDGZmZvDRj34Ub33rW3Hnzh2Mjo4CAEZHR3H37t1QY+w40VLN0rK+vo6pqSk88MADDXMHOSGEACS4aOEH3fI2HOglf3iWFmdMi+cUVotpcY630nXkqO/SQEvLvXv3MD09jePHj9tpqO4xxj+LJypCH2vdixDn+w071eE2HIeYlnoW+SC1YYaHh9Hf39+UxVeIlvbAMIxIemcBZoG7vr4+/OiP/ih+7Md+DOvr63j55Zfxmc98Boqi4B3veEfV78uyjDfffBNra2v4iZ/4CUxNTdU9ptiKllqzh4rFysqrjDHMz89jaWmpYX1m/MbDUN++zAVo+3MRpiYLb01zCvNqjRArd1L+s2p9F6pHPukxxjA7O4tMJoNz58753qitdA/FncBxH3FerHwuz+16DzWaKIWyX22Y27dvY3p62q4NMzw8HKg3Wq0I0RJ/DMOI9LicMS0DAwN46qmn8NRTT4XaxuDgIJ588kl8+ctfxu7du7G0tITR0VEsLS1t23zYS0f9YoqiVLiHNE3Dm2++iWw2i/PnzzdNsACmaAGpDMT1exoOnClU51zIdQ/J7slIcVwZzoWtwtLiapLobbTodB0ZkQoHXdfx5ptvQtM0nDlzpuqTRbtYWqIYY6OKy/nTgPMauvcQ/yNxWF8btchbdTmOHj2Kc+fO4ciRI6CUYnp6Gq+++iqmp6exvLwcOpvyfqJTRUvU9WdqTXm+d+8e1tbWAJiuzq9+9as4evQo3v/+9+OFF14AALzwwguhBVBsLS214HUPNSM7qBqSJIFx3EOA2WQw4Tn7QTOFwnRv5sKztHju3WTK0VOo6iriX7rfFdPC9Mhupq2tLUxMTODQoUPYu3fvtp9vB0tLsVjEnTt3MDQ01LSKvUCbPTn7CpX4pjw34/w64xDGxsZAKbVTWtu9Nkwj6VTR0ojeQ7VkDy0tLeGZZ56BYZgPrE8//TR+9Ed/FN/3fd+Hp59+Gs8//zwOHDiAF198MdR2O1K0MMZw8+ZNLC4u4rHHHmtZYRxZlkHB37ehM1dtFCB4ef4w6c3cOBmOy0n2WFqSCSBn9xSqngFhwavTYu8zIkvL3bt3MTMzg+PHj6O/vz/Qd+JuadnY2MDk5CRGRkZw584daJpmxysMDg6GmlgbVsY/duvc9sfYboG4UeGtDaOqatvWhmkkQrQEI5vNIp1Oh/7eyZMn8cYbb1S8PjIygpdeeqnm8cRWtNSa8qxpGsbHx5FIJHD+/PmW1jqQZRmU8Z+avWnFQGMKyQVF8pwmd7XxKnVJq5TulzwxLfWIFsYYZmZmsL6+jrNnz4YKNIuzaFlaWsL169fx2GOPIZFIgBACwzCwurpqp752dXXZT8qNjFeIC34/le8v6BfTEgPREgeSyaQrpbWdasM0kqhjP+JCI2JaahEtjSK2oqUWcrkclpeX8eijj9opVa1ElmUYzMfSEnT9rtMVFNTlJEtey0u5pxCY+71UEihaVfkdG6voWeTKRKU1ixZN0zAxMYF0Oo0zZ86EFrRxdA8xxnD16lVsbm7a4lpVzZMqy7Ir9dXqZTM9PY1isYjBwUGMjIxwS3DHoox/lMHWATbLfP4RtJt6I4mbK8ZbG8YpkK9du4ZEImEL5DjUhmkkndR7yElcYloaRUeIFssdtLCwgL6+vlgIFgAltcu3CDBvfjDqyxTyI+j65RXmru7NHpmTSJRFi/O9aqnStEb30ObmJiYnJ+uKS4qbpUXXdYyPjyOdTuPxxx/fdnw9PT3o6enB/v377Zb0KysrmJ2dRSKRsKtb1mKFCZ49FGKjUZ5qv205XneJY+dnWrzgtkOKsFcg5/N5rK6u2o32+vv7Xa6mToJS2pJ6N40maveQYRixavQZn5F4CHqz67qOqakpuwrq9773vQaPLDjV6sZwNEvw6re81/zSm3kb4LzoFS3OGJeKfkOOq8btHqpmaTFCC4fbt29jdnYWJ0+erKs1epwsLdlsFuPj4zh8+HBN4trbkt4qQDYzM4NCoWBfB0E7Cseyy7MfjPtnFfdQa5+i20G0eOnu7kZ3dzf27t0Lxhg2NjaQyWSwsLCAbDaL2dnZptaGaSSdammJUrTE8RqOrWgJwsbGBqampuwsEsZYbBYnoCxaZDhcLSWCxq/wBUqIQQS1tHg26pyP+E0SWcV71QNxg7uHGGOYnp5GNpvFuXPn6n4aioulZXl5GVeuXMGJEycCBxFvh7cA2eXLl5HNZvHGG29AURSMjIxgeHgYvb29vrV8AtHUeStIkO32W5Fa7B+KwzVXD4QQuzbMoUOH8Nprr6Gvr8+uDdPV1WVfX+0Ya9WpgbiNqIcVJ+ESa9HiX8+Enx0UpxMLOEVLHVVDGxDTwt2N5951B9F63lP4oqXSPeQcRzDRoqoqJiYmMDg4iNOnT0fTtbjFosVq4njv3j2cPXu2YRkbkiTZrqRdu3ahWCxiZWUFN27cQDabRX9/v73IWFaYwNlDDRlxHQT4OVvdMBGI35xUK9bCtWvXLuzatQuMMeTzeaysrNixVgMDAxgeHsbQ0FCs3Al+dKpoATrnuuMR/yvLg9UUT5bllmcHbYcdXElkgAXvPu0krECpbAPAiZPhbNP7UOq0vHjXe2fQrvM9a//Wa+6idNuLFiv198EHHwxdJbEarW6YODk5CUmScPbs2aZOkqlUCnv37sXevXtBKcXGxgZWVlYwPz9vp8UaOue81D3fRSgQQ46FOPbd6ok7bk+o9cAYc127hBBbIFu1YdbX15HJZDA3N+dKu06n07E8D50qWqJ8QFNVNbKWAFHRVqLFCsoMWlSs1UiSBMMwQCBHGpvYCPdQxTZd5fm9sSrM971kErA7KTg+t51ouXXrFm7cuIFTp05FHqneKktLoVBALpfD2NgYDhw40JR9+h2rJEkYHBzE4OAgAHMyWllZQaFQCLjhCAe5DWF/KufHnd9t9YLUaaKl2rFIkoShoSEMDQ0BKNeGWVhYwObmZixrw3SqaImSuGUOATEXLdYEzBjDwsICFhYWAgVlxmWysANxiVIpHgK3fKkvU4hbUZe7H//PeIOGnVYY6kmHTibKosX5jp9ooZTiypUrKBQKOH/+fEPMyq2wtKytreHChQtIpVIYGxtr2n6DCrRkMonR0VH0Z/qB1SYMLALarU5LJxF2gd+uNoyVtt/K2jCdKFqiXvu2traEaAlLWHeQJRTi4FN1iRYPQUvx+2UK8ajHmFDV0uL1bLkCbN07VWT+B3lB0sViERMTExgZGcHRo0cbJjSbbWlZXFzE/Pw8Tp8+jfHx8diI6LYn0E/oOM8xKC7XKb97PcfCqw2ztrZWURumWsB4I+hE0UIpjfT8ZbPZujI3G0HrV/YqbGxshOoxA5hVceMmWoiSCGZoCVWeP2B6M283ga5pZ40Vz5adGUOetxIJZ6aUy3DvEg7r6+uYmprCQw89hJ07dwYZUM00S7RYDevy+TzOnTsHRVGaLpjC7o9vjeBK5ZrHVBc+x+J62WdorS4u10miJcoFXpZlu7YQYLpRrT5JVsC4JWIaWUelU0VLlJarXC4nLC1hWFtbC12jo1ptlGYjy7JpXSCVgUyBM03rjV8J6h+qQrXqvV6vi7PxIvOxtCwsLODmzZs4ffp0U7puN2PhsNpHDA4O4tSpU/Y+W525tB1x7z1Uz25FnZboaOSxdHV12QHj3towjDFbwERdG6YTRUsjmiUK0RICy5QYBlmWY9OO3Q7EJZWBZ7x7JUz3Zn4jxGDj8qvX4cz8cYodr2hx7sZrhXE3XnRbWgzDwMWLF6Fpmm2J6ASsrtMPPPAAdu/e7Xov7paWutOCm6nH/IbqOF7nsbd6QYqzWA1LMztWW7VhDh8+DE3TsLa25qoNE1UfLiFatqfWDs+NpDNWDQdxs7QYhgEicaLl6yn05UPQ9Ga/vaRSgJVM4izPT40q7qEqTRK9PYvm5uZw8OBBHDx4sGOeQO/evYurV6/i5MmT3KZisbe0xPF38Kt864Rw//R8pPXHFsvzWwOtWuATiQR27tyJnTt32rVhMpkMrl69ikKhUFdtmE4VLVEek4hpCUktN3y7iBaevz2UJ6iOjtB+O0olJBQKtGL7uud0UqegqSJavD2LhoaGcOjQoYCDjDeMMczOziKTyeDcuXO+tQzCiJbWFNLjiOdgH4sNfuEtJAYVcTtFtMThWJy1Yfbv3x9JbZhWH1PUNCKmRYiWBqMoSqzcQ2Y0d6UZkytauK/59BSqJ72ZP1w4191q5fkZLW/B6zqqVv5fluLTYqEeDMPA5OQkUqkUzpw5U/XJJvaWlqCfa+rk7nO+vJUMt6HVKc/egmztTByPZbvaML29vbYrKS61YRpNI9xDURb6jIKOEy1xsrRIkmQKDqmr4r0w8Ss8gjZCDJORlEpWq4JbFijORbiy8Jz/NtRilr/jNiKfz+PNN9/E2NgY9u/fv+3nWxHTEqYmTTs9aRKfvxnjX3MkZotsOxN1Km0j8NaGyWazFbVhhoeHMTg4GOtK6vUQtWgR7qGQtLt7yIJIlRkyQQNxfYmyJkuJpEO0VGQFyWXR4iwo5z3VTguSd60utrloWV1dxcWLF/HII4/YT3fbEX9LS/CFvTUugiAmfnfAt4XUYp9WHFwqUdFux0IIQV9fH/r6+nDgwAG7Nkwmk8Hs7CwSiQRUVbWzY9rp2KohREsboihK8NLkTYJIlSljYfztdWUKBd4LkEw4K92635NlAFrpPcebhveDrsJz7reKavuKFqtB55kzZ9DVVWk58yOMaIliYQidPRTWvNeMud3HC8SXJv60eiFqt4W+Gu0etMqrDfP666/btWHS6TRGRkYwNDQUu147YYj6dxIxLU0gTinPFprGqYgb4vv8+JVgK0iYSTPhEC3eSrdmKvP2nZ1dRek8A9eKucBjiQuUUly+fNlO0w77FBN/S0sjFtU6o8xD4nd6W73Ixvl3D0snCTDAbCaqKAqOHz8Oxhg2NzexsrKCxcVFUEoxNDSE4eFhDAwMtPw6CoNhGJGKLtF7KCTt7h5ijEFVVdxd3sKw5+E8VNAtd+Oc1+oM7nVZWqoE2DrfqzzVzpoZ7neKap4z6OYRduJVVRXj4+PYsWMHjh07VtP1GPc6LVJDFqLojtft+nHswZlOH2PR0ikLfScdC+AOLCaEoL+/H/39/Th8+DB0Xcfq6qpdzsCqDTM8PNyUYpj1INxDbYhVxr/VWIXUDMPA/rGHkbvnfr/e+JVGLIPOitkV7iGpbGnxBt9KUlnIOJ/cvcJHV1tnafETan5sbm5iYmKi7jYDYUVE8xcHXnAV/5OMtTbzmTGfvbv0C3G8HI1oubexDoVIGOLU4dmOTlno29095KXa8SiKwq0NMzMzU3dtmEbTCNHCqz/VSuJ1xiMgDu6hbDaLiYkJ7N+/H7lcDkSqVKqhQgnqqL8SZj+JgJYWb6xKMulTlM7zOU1rXaxRGPFw+/ZtzM7O4rHHHqv7KSPofs2KxPVbZRoa09KkoJZg52v7tOjFhQXc3n8bw8PDNZvMx28t4Kd/73lk8zn0EwmP79+Pn3zb2/EDJ09ue+46yTrRSccCBBdhQWrDDA0NYWRkJHBtmEYStbgUlpYm0Gr30J07dzAzM4Pjx49jYGAAd+/eBUPwHz1w0C3nNd48HqYNgKI46694Upmd7iHPfpIKYMsR5r8NTWude8iqmVPtKYQxhpmZGWxsbODcuXORNGuLvXsozlXj4Elh9hUq/Jd37tqJfD6PyclJALAXl/7+/kCLyxcvTOKXXvyUme4rSdgE8I3FBXzjM58C+aMXMDYwiPc+8gieefLd2DM0zBl75yz095OlpRpBa8MMDw+HCtiPiqgtLblcLnYusViLlnaKaaGU4urVq9ja2nJVSJVlGeBaWggoZZA8WUR1Bd3WmUadcIgWWiWV2WtBcVpI29XSous6Jicn0dPTg8cffzyyxSb2gbh1FwdqDe5Tyh9Yb28fDh8+bPewyWQyuHXrFi5fvoy+vj678BjPCvOvvvJl/M5ffYO7XaNYhNyVwnwxj99943t4/nuvoRcEj43uxY+cehwfeuL7Y/+7hyWOxeXqISoR5lcbxgreb3ZtmKhFC6U0di6weI2GQ9ibvxUVcYvFIiYmJjA8PFyx4EmSBAp+9LWuu6vQAvWV5683I8lpWKiodFul/oqiSAAqy/9XxLRoxRAjjBbL0sIjl8thfHwcBw8exN69eyPdb9wtLfVmDzVzWXYdVoAdS46YlkQigd27d2P37t1gjGFrawsrKyuYmpqys0UsK8yzf/Yn+Nzr33Xv2zBAdR1ggNzlrq5KJAk5AN+6OY9v376Ff/LFz2Fvdy9O7d6Dn3rXk9ixY0cdRx0POslqBDTGcrRdbRhFUTAyMoLh4eGG1YaJUrTE9TePvWgJS7MXCavg2MMPP8ydnGRZBmX8EtKm+2T7TKHAIS0hLjDeNhVHh+aKSrcO1eJNZXZZWlzp0O7P6TG0tKysrODy5cu2O69Z+40LfNHCv45achRhd+pyJ/E/QghBOp1GOp3GoUOH7GyRxcVF/M3/9ruYWb5X+R1ZBjEMkKSp7BkDoGv2xc8MCsl6T1GwqBWxuDCHL/zB76GPSDi6cyd+/PFz+Im3vQ0JpX63Y7MR7qHw8GrDZDIZV20Yy5UUVZpy1A0T4yhcOlK0NAPGGObm5nDnzh08/vjjvm3SZVmGwfgxLVwvVtD4lYCH6Zs1w9mmU3x4x+b8ujcQ1+nicokWr6XFUAOMuDF4xQNjDPPz87h9+3bognH17LfRhA/EDTHBxUl7uer48z8iScGeOBVFQXc6jb/3Ry/gVmaF64ylxSIkR/8aQmCbJg1Ng8SJf2KGAUgSsgT43vI9fO8rX8I/+dLnsburC993+DD+9pPvxdGxsUBjbDVxXLzqIWo3ShC6urqwd+9e7N2711Ubxmntq7c2TJQNE+P6sBV70RLHJ1Vd1zE1NYVkMolz585VvcBkWQZj/CcrrzUDaF6mEG8/rkBcj5WEVHEPyT5Bul7hY7RQtDjdQ5RSXLx4EYyxbX+/eonj9euEa2mJ09oUQJy4cZTxD/i7zq9m8IH/8lvYzGW5u6TFIgj8F27ZI1iopoPqGuTu7spTqchYyufw2QtT+JPpK0gZFA/v2IkfPf04Pvz9b0d3Mp6N/YSlJVrC1Ibp7u4OLBijPC5VVVsSTLwdsRctcWNrawsTExM4dOhQoPgHWZZ9Yyk4miVw0C2PemMqnQK9wtLi/K7nyy5LS0UDxfLnaQwsLcViEW+++SZ2796NgwcPNvzpsaMsLXWYWmqNt3J9L4iAcX13+72+cv0afub3fgd+ofsEgOywsDBH2p3f1qWEAqLIgK6DGgaIJIOBQUokQIsqIBFIJXeAqsiYXMtg8utfxa9/+YvY0dOLtx08hP/1HU/i9AMPbDv+ZtFplpZWixYvztowgBln1+raMFZfprjRsaKlETfZ0tISrl+/jhMnTgQuuCNJkmmKRDlY1R4jT8vUEXQbJr2Z6x5yxLR4XTuoYmlxVcv1FACTzbnb3Kah8QfYBCRJwsbGBm7cuIGjR4/afuZGE/eJPlQgbgsMRs5d+kV/+Q1ru/5enx1/E//3n7wICoBRCqZpgCRBSiR8Hx0IIeb+SjcVYwCoAVJS/Nb3CCGAooBYCwxjMHJ5kIRi7sszPxnFIuRUCiuGji/OzuCLszNQdB1vGRrGex89gWeefDf6W5h62g5dnsMQN9HixVsbZmNjAysrK5ibmwMhxM5889aGifI3EqKliVhpz1EpUkoprly5gkKhgPPnz4farjUWmTMF1hV0G6LhIg/zadwjMDz3cEIBtJLgqPqQ64p3cb8rS4CVy0VbKFpyuRxWV1dx5syZptYdMFPb+Za2Ru2v3jL+cV2aghyV8zNSFSvSb37tq/ivL79k/5tIEkjJomIUi5CSSTDr3DgEhn3XWNYWAttEaeg6JFnhnkBaKELqSoE4FkpLKFFKoXBi4nRFwcXle7j87b/Cv//WNzEkyzh34CA+8v3vxPcfO7btuYgSkfLcOiRJwuDgIAYHBwGYbhsrcHxzcxM9PT22KylK4lhYDmgD0VKLcrRK+UchWgqFAsbHx7Fr1y4cPXo09HhkWUaxWASIDDCPETqoVaXOsIOga5gsu7eaSpZFi3OPFZYWx99el5fkcjk1X7QwxjA9PY18Po9HH3206YWS4v502piGiXXil9q8fRFcF36i5f/73/8f/I/XvgPIMiTPHEFV1eUOAmDfgH4Bt9YYJEVxnU2q6WDUANMNyD2VcQlEkoBUCnLpABgDiK6DyRKIJIFqmh38S2SCNTD8xfwN/MX8DSiqioODQ3jfoyfwU0++GyMNLrUu3EPxIZlMutL3rdowly5dQjabxdWrVyOpDZPL5YSlpVlYpfxT3sknJFY67LFjx2pWsVZMC4EcNo6wTMCsVN+Gi9zYmUq8yRaJJAC7XVCV0VcpPOfuWdRc0aJpGiYmJtDf349du3a1ZNINY/mIexl/7lYb7jIKWZzF8RHvoqTpOp7+vf+KizdvujKBqKaBaTogy5BT/NRTxsoBt0zXQXUz/VmSJNP6wjmNUkIBgwKSMhdJpqqu/XKtNglT+PhmJDEGWigA3d24lsvi2muv4D9953+in0h4bHQf3n/mLN5//q2RX+vtvMjziGPRtFpw1obZt28f3njjDQwPD7tqw1iupLC1YYR7qInUWxWXMYbZ2VmsrKzg7NmzdYkfK6YFRIl0gm9EIVPZ43IyY1xYqG14M6JcmUVNtLRks1mMj4/jyJEj2LNnD65cudKSLJ74Zw+1acqzX7CuA+cEvZrL4Uf/4yewvLlZ+TlZNv9zLMrUoJBKF69XlBBFgayYsSlU1wHDAJOkim04HbBSyapivU41HXKCP/0ycDKSdB1M1wHKIPe4XUlEkrBBKb556yb+6vYi/q/PfQZjvWm8+9gxPPPke7A3AreBsLTEHyuN21kbplgs1lwbZmtrS7iHaqHZpfw1TcPk5CR6e3tx9uzZui9seyyk8lRzDy2gKyjUWQkYPOM9VNkhWoIuWF73kKtnEdWbMvndu3cP09PTOHHiBPr7+wFUr4jbSGKfPRRH91AduAJ3Sxff7PIyPvjbv4VcPm9/hhaKZjE4Ss2AWc92LMFCDTPI1gq+tWNbdB1QFHN+cDyxm1YYHUSWfV1JBHAJFmd4GQH/3pYUBaw0TsaYuX/GzNib0lis7zFFwXwxj99/83X83mvfwY8fPIx/+Xc/Wv3EbYMIxI0/vNozqVQKo6OjGB0dtWvDZDKZQLVhao1puXnzJn7qp34Kt2/fhiRJ+Pmf/3n8/b//95HJZPDhD38YN27cwKFDh/DpT3/a7uEUhtiLllqotZT/xsYGpqam7KfzKLBEC1ESVQNYq8ELug2TKRR0DfPew85sIu8mnEG6zgPx9ixyuocYNSItfuSFMYYbN27g3r17rv5PQOssHq0QLeE+H7+JO8jpcpf0d/zD8acsSfjW7DX8/B/+vms+ICiX4qeUmo1GfU6b5LxWS5lDtFCAlEpxv8IohVyqbcEYA1M1gBCQZKKKRcj6LgPVS9ZIWXbvGwSlvCWzI7jlqoLbouOEqioKV6/hblf9Jn4RiBt/tptbnbVhnJWgrdowqVQKIyMjUFUVhw4dqlm0KIqC3/zN38Tjjz+Ozc1NnDlzBu973/vw+7//+3jPe96DZ599Fs899xyee+45fPzjHw+//dDfaANqsbQsLCzg5s2beOyxxyL149ljSVSa4uqxtIQhaO0XbyaJM8bFu5AkE07RUn7Ta8xwbpIyvWGixTAMXLhwAbIscy1k94toAcJVsgzV5bmeyodRE2AXX756Df/pm/+T+x4DAF2vCMRlMCvZSlWuUUlJlC0ujIGpqmmNMQxP1VwCUoqRocWiWUHXui4pq3gYIRIBcQhtZlBQVQVJJX0XWOaJfWEAmKqBqiqKs9fBNA2JCGI3hHso/oSt8uutDZPP57GysoJf+qVfwvXr17F//34cP34c6+vroVqcWJYdAEin0zh27BgWFxfxuc99Di+//DIA4JlnnsGTTz7ZmaKl0e4hwzBw6dIlUEpx7ty5yIOz7EBcUhkXw8taDlN/hUfghou8fXvdQz7l+QFvvyFnfRf3B53zgmVpiZpCoYA333wTe/fuxYEDB7ifaaV7qNkpz+E+z2sNHmYL9STuh8Q3e6jyjezQIxWCxfoUMwxTUPjc6+7YlrKAYYwBlJqF46zPEgKSSpmPBZJUdvU4FnmrBouL0r1llIQT11Kia5C73RVJ7SMtjcXrgiIAkExAv33brDsDQJHrn9M6bZHvtOMB6m9N0N3djf379+Ozn/0sisUi/tE/+kdYWlrCD//wD0OWZbz3ve/FD/7gD+LcuXOB93Pjxg288cYbeOtb34o7d+7YYmZ0dBR3796taZyxFy21YKU8b0cul8PExAT27t2LsbGxhjxJWIG4RKoULUEFCv+1+jKFuGP17Me5nlUXLeU3vUXpXJYWqke+gK+treHChQvbZni1g6Ull8tBluW6m6eFyx6qa1cNYvvxuw/RXUVodedZGInKa8FyVBJJMsWkppvVaT2F4ZwnxXqPGhSAGUsiEcIPuHUugoSAGQYMVYPSzS+FzuBOk2YMYGrR3A6llULHcQwo7Y+qKoii2ONhlCE7MQnZkQItLC2VRN1YMA5EeUypVAq9vb34wAc+gB/6oR/CysoKXnrpJfz2b/82FEXB2bNnt93G1tYWPvjBD+ITn/iEHVsYBR0pWmRZhqpWLxlv+fEeffRRu2hPo8biK1p4sSp1mtbrMeB75yTn5V/Zb6hc4df5Hq3oWeS01tBIRYvl0qvWsNIi7oG4CwsLmJ+ft8/X8PAwduzYgb6+vlCLRejicmGyh1pBiCxnCoJ7g0/AYF32a96iiE5RIjmCYQ2Oq8j5PSswFw6BAwYww/97kGQo3bLtQmKAHQvDiz0jBLbVxnlc1Y7B6U4yCkXkL16quFkVpf7fuNMsE512PEC0zRIBMxDXCpUYGRnB008/jaeffjrQdzVNwwc/+EH8zb/5N/GBD3wAALB7924sLS1hdHQUS0tL2LVrV03jir1oido9xBjDzMwM1tfXK4I1G4EtWkjlosp1DwV8DfAJwKvDmMCbRC28qczEGe/ieL26pcWIxNphVSguFouBXXpxtbQwxuxqy48//jgIIdB1HSsrK5ifn8fW1hb6+/sxMjKC4eHh6GtL1FunpYlU+/monMTanneAMvPC9C70dBtRIntjWzhuHicEZqCrVIpZYYDpOrIsHiifWsuFZG6XgWoawBhYwoyNcQl78B8ozDRprSIV2sLIF6AuLXFP0lpmDZOTk3a9jlqa4HWapaUTRUvUnatrDcRljOFnf/ZncezYMfzDf/gP7dff//7344UXXsCzzz6LF154AU899VRN44q9aKkFP9GiqirGx8cxODiIM2fONOUmtPZBpMqJou7dc2a4oDEtgcSR4wWvkcIdq1L+u6LRomObjNUf02L9hsPDw6EqFBNC6qrdUyvVRIuu6xgfH8fAwAAee+wx6LrpPkskEtizZw/27NkDxpjdd2R+fh6SJGHHjh0YGRlBT09PZZXV0JaWBhT8aRSE+yfAgLV9PwCq8wfIPDEl3qaHflZHWjRFiVeQAGZgrSvoFrBvCkMvxcF4783StS85g20ZAy0WwUouJ1+rDdy1WxilgGT+evrGJgpXZyD7pI/u2zuKI0eO2MUyNU3D4OAgRkZGMDg4GGjx7kTR0qgsxlbRCNEStMeek29961v4gz/4A5w4cQKnTp0CAPz6r/86nn32WTz99NN4/vnnceDAAbz44os1jasjRQsv5dmKfXjooYfsaOlmQqTK8vHcGEjea6HiV4JlCgVbqxxZQZ59OTONXBmn3jotTtFC63MPbW5uYnJyEm95y1tCmxYlSYqVpSWXy2F8fByHDh2yg9No6fw4Jx5CCAYGBjAwMIAjR46gWCxiZWUFs7OzyOfzGBgYwMjICIaGhmqasEIVl2sBQX8y0jdgHomug6qq2aWTUlBVc4kLoPwgwSg1rR6SWTafOM6f04riFCRWdg5j5vcJZ8GXFffvwAzDHEdXiisyoShmFlDSYbXh9Ttyfq+0X219A8WZa1XPTTKRRG9vL3p7e3HgwAEYhoHV1VUsLy9jZmYGXV1dthWmmpu100RLJ1paorTE1mppefvb3+4717700kvc18MQe9FSr3uIMYb5+XksLS3h9OnTTe89Y0GkyjTqhvQUCpopFOi8+ltanN/2CppUEiiq1ueiiWm5c+cOrl27hpMnT9Z0I8XJPbS6uoqLFy/i+PHjdiohYwyMMRhG2Roly3LFxJpKpbB3717s3bsXlFKsr69jZWUF169fRyKRQDqdhqYFrzwcy4XIVSHO7zOVSpkQAiQStkVCX10FYxRGLg+pu4vb+4c4LTC6DkPXQQB/V5KmmUXpQgyVyDKIYhZnYyhZSTQdUippiiZHbRd7O6WxUr1U3I7jqiouLsHIZbcdQNJzLLIsY8eOHdixYwcAU0BnMhlMT0+jWCy6rDCdZo2w6ETR0oiYFlERt0lYokXXdVy4cAGKooRK02oEPEuLFKJoHI96kk35JnECQpzrgSMrqCLA1lmbxf1eMlEWLXB8rhbRYrVUyGQyOHv2bM0xSHEJxF1cXMTNmzdx5swZdHV1me6BkoVFURQ7Rd4wDPs/a/yEENdEK0kShoaG7KqS+XweS0tL2NzcxKuvvoqhoaFtXQDVOiG3DN/UZp/P+9wzJJlCsnRuqGGAUAqaz9vpwl6oYUBxiAcr2Nbevq7zU4xRdjmZFhh3nRfqqKVCULKSWG6n0i5YqfKuF0mpLG4HSlGYm4eRWYXsyMrwE6AJn3YBFj09Pejp6cH+/fthGIYthGdnZ5FIJDAyMmL2TuogF1EnHYtF1O6hfD6/bYJDK2gL0RL2KdnKHnr11Vdx8OBB7Nu3r4GjCwbXPRTi+7xsg6Cqhbsfn52nkkChaG3L39LCqlhhFH6sYGjRous6pqamkEqlcObMmbqejFrZA8iyokxPTyOXy+Hs2bNQFMUlWKyATEuYKIpiLxSWiAFQCuo2P+M9H93d3dizZw9yuRyOHTuGtbU1lwvA6knS5Xqqb6OYFieO35L5XlLlz0iyDMgyCIjZyNAwQHM5GBubkLpSYJpWkWJMSv/DAMAwzBRpTYPU1VXhurFj1whs8WH2JaLce83+nqcwHJjp4vUWenSOqXBzAUZm1TFI79G6SfoE7zrZzBbw8rfewF/7gbN2bxrAXLgymQyKxSJee+012x05ODjY9g0HhWipDmMslpa29r7qfFheXsbGxgbe9ra3RZofXiuSJAFSpZktTPwKj3oKlPptPpmUUCiaqwCtUn/FOUNW1HBx9Cyq1T2Uz+fx5ptvYmxsDPv37w/0nWo0u8ibhVWn54033kA6ncapU6fs39grWHjfBUwRnkgkbPFiuZGsv2VZrrDCOBunMcaQz+exvLyMS5cuQdd1O4YhpHSu82xEt59gI/HLwzGFhZxOQ06nYRSLpkjQdTBVdQsiayuyDLm7XGTOCp6VfCx/tBTv4ixgFyQjySrEYrqNmFlHxlGyP3fhIoirHTtx/OWKerf/TPo9RZS4s7yOn/r7/x6bW1n8+r//E+wY6sLbTj+Ij/zEe3Dk0F7s27cPt27dwpkzZ2wrzI0bN+xrbHh4OHQHYUH0RCla4myJ6ijRYqXC5vN59Pb2xkKwAChdSMFiWvyoK+iWg9+3Uj7eF697yPkvrxZwNVp0fNKyOGxHJpPBpUuXIq2h06pA3GKxiKWlJRw9ehR79+4FAFt0WBND0MnBaV2xrDDOGBhLyHjFGSEEPT09OHDgAA4cOGD3HLl9+zbmM3MRHm30OH8yT0SK+4N1TLCSLJviIpkEenrADANM02Dk89wwZUIImCS5s3lKplAC2A0TK4Nuy0G2VFXNJocltx/gttoQz7aNjQ0Urs8BhgHibDMS4LCrWVpm5m7jf/vl/4xi0TSvEknGyrqGL758EZ/76hvo7U7hyNgIjh7ZgZMnT7nckVZQuNVBuKGp+YJtiTpOJ67CpS2urCCm/UKhgPHxcezcuRNHjx7Ft7/97SaNbnskSQIDx9ISYhuBg245+KV08kgl+VlB3mxhV0E5T0yLu9Gic0dsW2vH/Py8/VRXSz0JP1rhHlpbW8PMzAyGh4ddgsXKbAszwXgnJKcVxnpf13XcvXsXyWTSDsblxcI4e46s3F7Cd2ZeDjSGWHmHIvyU9+Yismz+Z/UK0nXoq2sAo+ZrvPL5lvBgDKSUDcQY86Q3l+47TwAwtQrP+Vht9JUVFOduOnbm2rNzEM6Dsv/yEy3fm5zFL/7q89ANo8JNaGhFKIkUihpwaXYFl2ZX8Nmv/N8YHkjh1COH8Lf++ntx7MGDrqDwjY0NZDIZOzXfsuaFLZAoqI2oLS1xpS1Ey3ZY9QeOHj1aMnnHC1mWwUjwJoy8+JXA5flrzhQySThEi1NfWGOyxlGtNovsWovdlhY/0UIpxaVLl2AYRkOCppsdiHvr1i3Mzc3hoYceQiaTCeQO8uPK6zP45Q/+GnLFLB48dRg//Dfehx/+yPsqztH09DQURcHDDz9s16WxjlnXda4bKY6BuGGny3rn12r2SisjKbHLLJOgr65B6usxRYmmVRaGs2JbXJaSUh8hjnhgjAGS5CpuxxjANDOSXV/JQL215L75iJ9Q4dPFadb61W9O4v/3rz9lXo+u+DQDhq4ikawMwCSSjJW1PL7+nRl87ZWrUCSKBw7swA++63H8xI+8A4ODgxgcHMSRI0egqqotYLa2tpBOp20rTCJAjI0gPFGKlkKh0LJM2+1oa9HCGMP169dx7969yJ/Mo0SWZb6lhRAYBiu5U6pTX1ZQwC8DSCX4lhbArGBulb+h1To7u+r/b29pUVUVb775Jnbu3IlDhw415KmsWZYWxhiuXr2Kra0tnDt3Dtlsti7B8hf/zzfw3P/+7+zzdvnbs7j87f+C3/iF38LI2BDe/sPn8cGffz/uZe5g165droaRTiuMJWCcQb2SJAG0jQJxfX+/kGlFdUCSCUhdXUBXl/l7GoZpKVE1EJ9xMNUsbmeX8y8dh5RMAqwyvZoQgCST0Dc3TcFSOQqfwfFfTibd2/9Pf/hl/MGL3+BvQpKhJBwZVFQHNSgkJQHGKCS5lAVFCAwmY3puFZd//8/xH//bX6C/V8GZE0fwkz/+Azh+7IirQOLm5iZWVlawsLAAALYVJp1ON90KE2crQj1E2Xtoa2tLiJZ64F3UmqZhcnIS3d3dOHfuHPfHiotPTpZlUE5MCwDoht3OxCawK6iOe88v4DfhEC3e+itO0cIci13Vzs6eQXpFy8bGBiYnJxte9K8ZlhZd1zE5OYmenh6cPn3aPreaptkTSpjr8eMf/ff4wh//OQiTIDv6JuhUByUGVm9u4PP/5av4s//8F0imFTz61ofwQx95L973gR9w7ccbC+P8rxETeJRb9D1bTvekr5kk4E5CnQP3eUWpii3pNRsksmIRzDDsG8VZOddVzt8wzOJ0ilJRTA4A1OUVqAuL2x+XzzE6DynlsLT8xxe+gj/67Dccn6OwOn0zMNPq4hgHkRTIEmDoKiQlWbJKlW1T1NAgyQkwAOtZiq+9MoOvvTIDCToO7hvGD77zND70Y+9Ef38/+vv7cfjwYWiahkwmg4WFBWxubqKvr8+2wjS6rYp5zPFYFxpBlKIljjVagDYRLV6syqhHjphqnodVqyUOAWGSJMFg/HLIZoCr+waKutJtmPsz6RQtHjHidPtUyyxyN0l0v+cspX/79m3Mzs7i1KlTdmOuRtFoS0uhULCznawUe8YYUqVF6tVXX0V/f79dgr/adckYw7Mf+hd49Wvfg4IEGBh0pkFnOgymI0lSSBJHCXlCoG0ZeO0vxvHmS5fwsb/z77D3Lbvwzh97Ah/+uz+BwZEB+7NeAbNdZkktNGw58Pv9whpg6qHKwRFZBik9nTLGoK9kStVwVXdsi6aDJBRPaEpJBFAKdek29Nt3yoKmYggOUeF3czuzh0qi5Z994jP48te/694WsVoPqKYVhVQu6oZehKw408FN2WJoRcgKX2RomoHrixv4L5/8Bv7zH30N/X0KHn/0MD70/nfhzMmHsHv3buzevRuMMWxtbWFlZQVTU1OglNop1/39/Q0pANeJheWixtksMW60fkUPyeLiIubm5ratjGqV8o+DaJFlGYzxFwfewz9XtAQNuq1TtDjdzdWygpzvVXZ2Lv/tHXexsAWraeXGxgbOnTvn6+P+9Phr+Hd//nk8tnMUP/fk+3D68FuCH4iHRoqW9fV1TE1N4ZFHHrEzK6yAW1mWceLECTDGsL6+juXlZczNzbmqkjp7CK1l1vHRdz+LW3O3XWNXkABlFCnJDGqkjILCABiBBAmMMCSk0gJiENy6cg+fuvI5/MG/+jQGRwZw4vuP4a///Pvx+NtP2duVJAmyFL86DL7F5Vz/cObc1ymTGnBdEEIgdXdB3mHG2Bm5HIz1dbP6bW8vf5+EoDh3E0YmY//b+V45psUx9ABjSSUS+Nv/8D/g8swCV+R4RQmxBZQBqmtQkpy+aQCUhLufk2GokCQZzDAgO94jkozNHMM3XpvF11+5AlkmOLh3CO/+/pN4+v1Por8/jXQ6jUOHDkHXdWQyGSwtLeHKlSvo7e21XUkpTx2dWunEvkNAtG6vXC4nLC31YhgGLl++DF3Xcf78+W3FSLVOz83G7vTMec/rWgHqm0PDxLTwAn5d7qGgTRKrlPj3Hkshv4k33ngDfX19dldjHv/of3wWf/ydbwIAbs5t4gsvTCOpGXhkx248deos/td3vBuJEFaCRrmHlpaWcOPGDVeLCCt+xBm/QgixAxUB0zKzvLyMq1evIp/PY2hoCNqmgX/8oY9hayMLMIBJgGFoUKQEVKoiKZUnbYlIkCCBgoIyAxJkGMyAhLILSqVFJKUUspkCXvn8G3jl82+AJICxh0fxjh95G/7GL/x1+0m7VvjXahNM766LrImm/lCepPK45J4eyD09MLJZSD09gKbByOXM36p0XeauXAHdynK/79mwz9/8gX7sP3wO925nK4KGGaOguuqKYXFvgtqChVIDUhWBSwiBoqRMFxNRSguoaR22HhgYM2xX0o1bm/jdF7+F5z/9TfR1STj24D585MefxNvOPopdu3Zh165dYIwhm80ik8ng4sWL0HXdrvI8MDBQs7UkytiPTmVra0tYWuohn8/j9ddfx+joKA4cOBDIHxlH0aJAAuBeOHnVPLnzIv/BjPNafRO4ovi7h8y2AyVLS5V0aNfk6Dm+xcU5nD3/bjsF2EteLeKv/7f/hMmFyvohakLG63dv4c2X/wd+7aUvYl9XD9738HH8b+/+a9g/sqPqcUVtaWGM4dq1a1hfX8e5c+d8K9z60dXVhf3792P//v2glOKlz/4lPv53/0P5miUAYYAiJaBTHQpRQJmZ6WFt12AUDAYUyS3eDGZAo0WkOJ3FtaKG+akl/NHUn+APP/4Z7P6hXoz8naAHXflSI+SC62dy/cPPxBAuMLUuQtUp8NkEIUAyCcVqkGgY2Hz1NbBsDpD4FhX334T3p2ff5Z0v3lpGUnJnAxEAIBKURBcYAGrokOXycmDFqlhYgoVSwxS6panA7QouuZWItQeHxYYaUDiuJMYoskUJ351axHen/giE6di/ZwDv+r7jePqpH8CO4QH09fXZ9YXW1tZw9+5dXL161bfK83Z0onso6jiduPYdAtpEtORyORw9ejRUoTFFUWIlWsyn/MqLih90W3v8Shh4+064RIv7PVdMi0O1VKRDu7pDu3fS25PyFSyL62v40d/5N8hsbtivWTKJUQowZnfehSJjUS/i9y98D783+Rr6IePs3gP4qe9/Ej9w/DFOYa/oKuIahoHJyUl0dXXZ1qJ6MoQ++W/+BL/z///DCksJK10HiuS+TRlj0GgRIMRlfbHeM6CjSy5F/jOASgZ0wwDAXJ8nkJDLFDGCYGZ3b1B1tU82Aj8tU7cW9f1+nTdYwK8TWQYrN+tyDMhHtQTZruOYlGQPElISmuau9ksc/28JFgaAGTooo4DHumJe245gcmLtioEahkv0OKFUh+JwP1FDMwU4kSDLbsHNiIKbd7J44cW/xB/96SvoSgInjo7hJ37oCbzz+07ZLlWryrNV7kLTNLvR48DAQFX3TyeKlqiPSYiWOtmxY4ddkCsosiyH/k6jkCTJLPZFZIC5hVQ99Ve4H/OxvvCUOF+0lP+ucPs43UOe7yaTQLFof9L3c4yp4PHy7BX83Cefr+hQbAkWZhgVBb0AgJV6u2xKDF+/PYevf+YFyJ/S8ZaBYfzIo6fxM+/+QaS7eyKriGsF3FpWEvOYaqtwCwD/4uc+ga9+xszmsDKEGGPQqQ5ZkiCRysmXgiIhpcpiCQZIqXarAd0VpAsCSEyGQszFRWVFSJAhQwIhEqje/NYGURMkw6imDTQzwcQS1IFqsIRLH5KVFIjUhaTSbdZh0QoAY2C0cn4kAIisQHIsDQwMVDctL/xrm7gEi52FBDNeRlHcoliSE6bNmZYLJxBmmPMjAF03C9sxAHkVeHViAa9OfBpgf4x9u/rxjrc+gp/88R/Arp3D6OnpwdjYGAzDcPXaSqVSdkaSN3W3E0VL1H2HRCBundRi9oqje4hADjaPBg265b1W50Trcg95AmydTam9Rotkwila/Gu4FNV8xT5/5fMv4g9f/7avqCCSZHbGdWyUqhpASNny4sBIKLi8uYrLr76Mf/3K17BTTuHthx/EEzv34Tx3D8GwAm6PHTtmN5RzCpYwE6FaVPF3f+D/xPVLlW4wU5Qk7B+YMsOsmAoJDMyV/kwIgQzTdcTAIEGGSotQkChnCTFqix+5dMtTRlE0ckixyvMXq3TQAEKT+Y036GHUGKdSGz7f54qW2vfrdqo5KynLIMmesuWR6qBUB9X5DxPWxqwsIUoNEEmuKptswWLoYJRC1wqmcPIcg+t+sQRLqRIvD8OgWLyXxae+8F188vOvojtJ8OhD+/Djf+0JvOedZ2xXEQDbCjMzM4NCoYDBwUEMDw9jaGhIiJYAZLNZjI2NRba9KGkL0VILcRQtIEqklnNu07UQcxtvPVCqWVocG/fGDyvO+6VKd2hdzTn2z/CzL/4+Xro44fgutl9sJMnszOsq2Vv+Hi2qZTEjy7gHHX9y/RI+O3sRv/JXX8bDO/fgp972Tnzg/BOBb3QrPdsv4DbMJHjv1gr+zrt+CavLa+Z4GQXAQEqBtbLHuiIR2bS+QIcCBZQZACvHGRiMQnaYwWyLDWXQmc7NEDKYhi65BwqL4eTtuDDdGUP8v+u/percAi+iPdTXHS4bUi5RR/zK1fkpBh//mcTtoGTVmUlCQhJUTpnpzoYGqmtgJYswg/u+d7qLDEM3RZCPJVeWFcByOzEKQ1cBEN80aQCe9xh0XbWrNjvfI0RCQQO+O7WAb337E3j963/g2k53d7crZmxtbQ2ZTAbXr18HYwyJRALZbNaVudfORB1cLCwtLcBKeY4DdkwL4ZzuoG4f3mshBUqQ1gBOS0tlgK3jux7VkkiUg4xdMS2ez2laAQCwWSjgR57/N5i7d9ezE2v71G1d4eB6v/Q9o1CE1MV/UmOqhmIqiYn1ZfzSn38Wv/zFT+NAdxr/yyMn8bPveh/2DFe2gGCMYXZ2Fmtra3Z6dj3xK1OvXMb/8eO/CrVYfrK1JmUrpsVgZi8Y63XKDBgwkCDJ0qHKduiDRouQJV55eDNmIOF4z2AGDKaBMSAllwIXw6zXjStzExq/6rMNJ9RuedfFNhvws664gm/9LEs+lkqOi5G3X0mSAUmGnOgy46bUHGQQUKpxJwunS8gSW8wny4gQyRTairO9gbOwHexmkc5BKUrKFkQMrBTZb8bVMEqxsXIdVNuqemhWHyTLOrq4uIjl5WXMzs4in89jYGDAtsLEoURGLUSdxp3NZpFO82uLtZq2+IVqdQ+pahWTZxORJMl0DymJiikr8JHxXcl1wZvinNe9t7Ozyz3k+bLsE+/itdZoWh4Xbt/CB5//t8hr/r8PT7AwxszYlioTi+wRLKyoAooMpukVYoYpCua0PP7zq9/Eb7/xCtKQ8Laxw/jpd7wb33/0URiGgampKSSTSZw+fdqOi6lVsHzpha/iN/7hb0E3dJcosZ60ZduFU/oRGFCkKkAMpKTKXjAUFIqULNfVKLmIwAhkzvmTiWxnHzEGMFDoOgU4gbjxqRq6/RhYnTdCmFCnUKekpicN/vvO8FxW8U7pdZelpXzj1fI7minMSTuDyNCKABgMTYU3A9I1ElK6Rwy1lE/AIMspUF111W4x9+F0X0qla5e50vCdlh6zWm85i2lz5Qa0wgYSiXDLmKIoGBwcxMGDB0Epxfr6OjKZjF0/yaoL09vbG5N7YHtETEsHEEv3EK+uSD2WFt5rIYJueVlKzj5IXsHhyjj1ihaZP3l63UPfW9vEP/7t3wT1bpyxClO0F0IIiCVYmPlUR7a5UUkqaQ4olQLP90RLlhkGYAMMX1mcxVc+NQtF03Es2Yff+tv/H7ufTz0Bt//+l5/HZ3/nCwDK4gQM0KnZdE+WKm9FCgMpKVm2IpVM9oSZ+/Y2O5SIZLqOSplcDGaZfss9ZC0O9rmB5Os6iAtu95DDglflWqxzLxF9skb8AnFdl5pf8K3jz5AnZbsr2RIcRFJAJBnM0KCppTozHvcWAFexOl3NQ052c9sVMDD7mnS2EDAfDvwzknLrt2DoZhBdWLeIM6ZFkiQMDQ3ZRSGLxSIymQxu3LiBbDaL/v5+jIyMYGhoKNaNHhshWkT2UJOJm2gpFArIgyLlua4kzmwRtP5KvfErvGlNcce7uvfnfKrzFp5zHJfL0uKw1sx3HcJ3V3T+k3Fp8qOqBoCZmULeSdtTssMSLFTVICUV+E69xBq5Y/yaDqrrkLv59R0Km1t4/cZlHPjV+gQLYwx/7689i4uvTle8R0GhlJ5kdaaBgkKCBBlKaTJ3Xywykc34FwJoUEGoKTsswWMwwx2kC6kkYBg0pkImMhhzj53G5B4JjSvnuXXDiBzfCFene8j5us/B+73sa0ELeD0DkAgBUZJIWcG5hg5q6KXmiu7O17qah5zoKm/dtgoysCqihBD/jKSN5RtQ86uQZHP/PKtiNaoF4qZSKYyOjmJ0dBSMMWxsbGBlZQXz8/O2m2lkZAR9fX2xssI0IqZFuIfqoJaLI04xLffu3cPGxgYOD/WDqu6urUHnD76QCT6GoPO6t+N0KgmUS0j4BEgCkB3qy889NLP7LJKMQKUKGOe3IYSAJB0+b8MAVbWSy4d/sN6+LtYAtrXaJBTICXMcTC9ZbAgBUWRoi7ehLdy2J8NaA243V7fw8+/6Jdy+eadClHgDbhViHjelFAbRIZfK9jPADrKlzLCFTBIplyBh1HQVeRclq1quVZ/FKPUvIpBBKEE3Gt+gLiyByqa4NEujFo+A2/UZcE2jIj6qJUj6s/NJIqz5ibN53ha405UkQykJDEoNFLOroIY5aSSS/E7BEiHlIF2rcm7V4Znv5jbuQs2vmt+z3KohLQyU0kCxK4QQDAwMYGBgAEeOHIGqqshkMpifn8fW1hbS6bQdK9OMRo/ViNrSIsr4t4A4WFoopZienkYul0M6nYas9IB6wjjqFSM8uMkMXPdQ5UveGLqkQ7S4LC2e7xLJYbp3WVrKf6upPhhKD2SYgbZM00Fzef4GATDdqLCEMIOCyJIZCExppWABbKtNEIii2C4nZhhQ5xehL90rDYlxS/IHYfbCHH7hf/nHyGXNbClLlABmeX2FcEQJ1UGIbH/WmaqqWX1dvOZ1ZlpkZDnpeo2CglIDCdl9fmSiQIYCjaqQJQXMCLG4tdii4bLuBRpLnTcS794ME1xbSyxJoBBjH/XmGlL9P1Z1GcFHkmR0p83q1LpWADU0GGoOSrKXWxfGux/DMOs0yaVy/84RrN6ZhpIsx3ax0gUhy+EsDIZh1NTHKJlMYs+ePdizZw8YY9jc3MTKygomJycBwG4x0N/f33QrTNSBuKLLcwSELcPeatGiqirGx8cxPDyMU6dO4ZVXXgEhlcGUQS/uuoVMwM/JHn+VK5XZsZUK0VLFdWRpCOIMDpQkM95EkkAUGdANGLk8aDZnWlso49ZgIbLkCk2hhSJIMlERuMs7r8wo1TLh3NxM01Ccvg6mlovbUcbw3//yr/CBtz8RahL65p+9gn/6M89B0zRTUDgsKjrVKqrYAoBqFCBLCneZoIyazRBLb1rWFbPMf7IitsWMMzBbAICZbiiDGUhICbOgl7O5YhxdKyEFSVMPoe61aJsN+Ma0BNixT8pztPg9/XDc1yBIdqWBrnRpSAxaMWv2ATP8MpIcllaqg1Kz6NzGvaswtAKUhGMOtUVLeEtLva4UQgj6+/vR39+Pw4cPQ9M0rK6u4tatW7h8+TL6+vpsV1IzrDCGYUQac1MoFEK1RmgmbSNawtLKMv5WEbKHHnoIO3futF8nnF4w/JiWYPErYYJurZLw2+G9l92pzNWCZMt/e0v3yxKgG4DMOL8HKR1vQoEykAYG0tDX1iF1dYGpGteNBFISSYpsCp4ShqZB9rlxzZoubsHCDANM0wHDKAsWz/d/5TOfxz958U9xoD+NHzx1Aj/9g+/FyEC/73n4bx//NH7vuU8BIEiU3DUaU6FTDTKUslhwQJmBpFy+NixLiSUEeaJEQQKkdB4oo6CM2uX+KXO7niRIZnYGozBgQIZUTiPlNOyMFUGGF8Bz0jb4xrRs/7rroS60aGnAyfLMS4QQKIkuSCW3kKFrADNKmUmA98eWJAWaVsDW6nWzim8F5ucVpfmixUsikXA1etza2kImk8HU1BQopS4rTCMK20XtHgrrCm8mHStaWlXGf3FxEfPz864iZBZEqvTv8prscv3GdQbdBn0c9V6nbtev/0acw6voWWQaUiBRjmjhbJIoCTM9uStl1rfRddCiam6kymTsFCyMMpCSIqSaDqLIldaYUhxL8dqSbWEh8HTYIQRMVjCXzeO3v/Uq/us3X8GgIuP8A4fw0+99N848/KC5P8bwf37wn+G1r73hEpGEEDN2RDbTB82YEgOEESiEH3BLCIHEpFKFW8lOZbaEiDO2BSiLEgCm24dUupEoM7M0Es50UsZAafALK07yxr1A+30o6MbqHY0P3CcN3v4Z9wMu/UJ83EZ+1hX7JmyCcvN5HuJbJ8tjlJUEgIQjI0mHWtgwA3SVJNT8BjZWZiFJSc73y5H5iZC1VRpdEZcQgnQ6jXQ6jYMHD0LXdayuruL27duYnp5Gd3e33WIgKmtGlIG4ZkfuON3tbtpGtIR1D0XVayYolFJcvnwZqqraXX+9EE6tDd51VnfRON7neF/m7EeqKLXtDPQLaGnxySySeJaWbVYMSZKAZBLEEiSl4FyaL1QPtC2NmxkGiETMei0JxZUiTQtFFMYvQR4sW06qXTKMMTBKsU4l/MXV6/iLq89DMXQ8tHMHNv9qDuprS5BK16kBHRIj0EGRdEy6VkwJCKBTFQpJckSJmZpsW1oCxrZY1h3XeMGgUR0pmV+u34whqLwug9KQOyzARgPdIoHvozpTnqOcZ3wyhvz3UI91JTz8PfjtN3iWklnOIIGuPrPAo5pbw8byLABW1coMAErIOi3NLuOvKAp27tyJnTt3gjGGXC6HlZUVXLp0Cbqu21aYgYGBmscVdUwLUFsCTDNoG9ESZ4rFIsbHx7Fz504cO3bM98cmUmWxnnpTmbnUMXd5LT+uonEV5tuyQHG+462Ca8bJMB/RwoN/AIQQQFEgKwrknm4wSmHk8iCMmW4e3ndKN7JTrDBKoa9tQLt6o/Qh5xc8I6EUVNfMfiuyXFHYTpcVXFjOAEfTwEO9IMsbSC1kkb6WB8tbZffNk2RXuC3FlSglgRFElAD82BYGs56F4uhV5DxflFEkS1VxLTeSJY40qOhjYQISW/D0xXz+dn0mnpNrbTeyz3d8tuU6dI5o4UR2+e8jyFh8Sidwtxj0+HmxLYnu8s5cdV2sHk2SHdMSN0tLNQgh6O3tRW9vLw4cOADDMLC6uoq7d+/i6tWr6Orqsq0w3d3BHyaidA9ZiQdxRYiWOllbW8OFCxdw9OhRu1mXH1zRwvtc3fErnI0GzF/0xtg4723vdpNJoGC7mp2uCP42JG+Ert+4Ak6qRJIg93SDWJVqS5lIrKg6rccVaIu3oa9tOIbgtCZx9pE0+xy5+8KYUF0rlyaXJGDPMIp7hlE4w8Ay6xh8eQm9W+WTqFLVdg15MZg728cSJazU8JAX28IYNeu0ELcosQSLK7aFSKUCdNQsQgdADxX31YqJrPyDOB0kbi3TxHHVme7HzchxeYecwbful7dzD1F7QXdmz9VxbnzaAlQdhPPrges5cLbomiucZlxr25K912QyXABqnBomyrKMHTt2YMcOM+PKssJMT09DVVW70ePg4GBVURKlaMnn87Gthgu0kWiJo/K7efMmFhcX8fjjj2+riiVJAkhlClm99w6/aBzn2Yc3V/DiSbyixeWCcL+XkIFyeJwj5dmzDpZFCz8QN9BrftgPYwRyrxkzRJMJc0HXdNBC0TXwwqVroBubQJ8jvsgZgwL3FGy7aiTJft/CKBYgJf36HKnIL99Cd7eB3i1TiBjMgESIaRWBOSzKjHIVUU5sCyvFolgihIFCgilKvMXkLFHCGCtV2pVc/V2cx2M3oQtlPGmWpSXAfhhfwDSaULNQ4DnLcQQ+7qEgX7Wr61dL+Q9jaOFlBHHDVDj2HE85/iqb5LuznaLFU0HX+1oyZNZMnESLl56eHvT09GBsbAyGYdiNHmdnZ5FMJu2MJG+8ZJTuoa2trYrtx4m2ES210ogeKpRSXLx4EZRSnDt3LtDFYn6G5x4Kvl/efLNtcJ71UgCBwnvNOe94Y1VcXQmcnZ09+6rqHqojaNj3wwwgCbP+itTdZbp4CkVkv/06YKU0O4WKoziNnOhG1+gY9Owa1K1VU9j5/EZyygyio5pmTrKUQUomYeRzKNy4ZhbHg1nF1soGctZrIcRyDZU6PFcUk3OX3Tf/LgXcGlopbsstShijIJBsYWSdDwoKnepIemNbGrDiM166ea23oPMh228bPhtv1mNOnTrA8zbf0uI7UTgXd0o5nw00Y/iMJSg+Ew7P9R14k47yCJwCes45PWxKcZxFixNZljEyMmJb8PP5PDKZDGZmZlAoFDAwMGC3GIgyEDfOJfyBDhctVq2WKDt3FgoFjI+PY8+ePThw4EBgQSTLMhgJJlr8XEFBVQvXExTUUlvFQOMVPopctks44128jRatbXLdQ4HHVeWcbPN9IklmzRdHDRaeqwcwRa6c7AYzdPQP7wWzdmGU+xw5S4oDMFsOlFAzyyjeWrAXEMtCY3ZvNjOHrMBcS2BYJ8gd26LZweT82JZyDIvVZ8igBhKyT38rxpCQEnZlXtNFlaiwilWlFdk4PjEtscpt8BkMt1bQdt/3nU6CWF2YvV8/5xB3ComgtH/lUErXdgC4xhvnZOOy9pbuK8e3UqnOsbRUo7u7G/v27cO+ffvsRo8rKyu4fv06crkcFhcXsWPHDvT09NT1oB7nZolAG4mWekr5RyVaMpkMLl26hGPHjtltzoMiSRIYqc/kFrR/UNAZPdA59aTNOpFJWbRQp6XFmz1UzT1UR0yLLwFUG2GOiZ0SM8WJGgCjMApZJHr63SOx+hzpeilV2rNLxqDeWYJ697arrDBFuSGcRMpPhIahAxKB5BFAgOkycooPS5RQZp7zytiWUm0Wqdw12nIdWW4lK0VaQWm7xEyPJkb09oh6t+jSKcznHeajZiIdSdBt1iuhAqgWXyuT4++ILS3b7a76q/X9Jn4xLfbr94GlpRreRo/f+c53kEgkMDs7i3w+72r0GHb9E6KlhURVFZcxhvn5edy+fRtnzpypKbdelmUw1NeAqp6icVE8R1U0UHT2KXIMztvAuWxpaURMS/3P3DKS6Dn8GLStNRhqHoRnrSjhzR6iugamG1DvLEFfXzVf9AmmtL/D3I3iLFFiPQdX1m0pHSYxQKzy/yUhQgiBznRIju8RUqoB44ht8bqaNKoiISXjOQFUi8fgvhxyoQ+43bq2CfDdI9s9KITUWS6LRBDREsZPF/CjvOBixrOqhsHpHnJ5h6yYlvK13MWpnF2NuGfH1IIkSdi7dy/27t0LSqnd6HFubi50o8c4l/AHhGjZFsMwcOHCBUiShHPnztWs0GVZhsEqLwRCCHSdQVHcF1LQ+iuBJ9wQbhi/D1VYUJzxBo73Kt1DJfdHwJTnuicUbpVXj6XFU+PEUItQuvuQTJsWNEMrguoq5FQPJG9DJgeSkkBhZbksWMyNl7ctmds3A24lVz0W18dLlh8ru8cbcMtA7XgYiUj2pK0ZGgyiIYmk6xD9Ylt0pkFlKnpKxe4CZ6HHAT9XUd0pz41yNgUcl09xOd+//UxRHNES9docuPeSr6ElmMuM+ZwTyulf1FVDH6FOEy1OJEnC4OAgBgcHAZgtZaxO1VtbW+jv77cbPfJK/4uYloio1T1Uj2jJ5/MYHx/Hvn37MDY2VvN2AFO0UPAvBN3gixYvgd1DHMJkanYlgYLd2NGZFeTZG9/QAu8pt8QNX7SEGBgHrp2Jt4h5Tp7SPQCiMej5LTCmQ04kPQG5KciJFKihgxmG7R6Cx/+fu3YVVC26tu1dawghkIlZr4Vf4dZ83RZ3noBbSgwkPKLEHApDQk4ggQQYo1BZEYQRgElIyJxbm5hxM90lNyVjDFq9lsi6lHRYHGKwGburF+5lvM2i7Xfp+7qHQlpaGmZW8uwlhAV0e0uN8+nIvF6d92BXV/w6lceJZDKJ0dFRjI6OgjFmW2EWFhYAwLbCpNNpEEJqFi0/8zM/gy984QvYtWsXpqamAJghFR/+8Idx48YNHDp0CJ/+9Kdtl1attLdjbxvqKeW/srKC119/HUePHq1bsABWhV6+24Fb3T6omzhAIKoffh9LppzFWcqfqnD7OP6mridf/o4CB+KGgnMCOIYRb3p3QulB376H0Tv6FiTTIzCKObOJmwMtvwlCJBBZBikN1ZosDU3D5qVJGNlN/wOGefqssthW6rJEJFuw6IaOUjWWijEbpdiWlNRlB+VSRs3vMPfETYiEJEmBgUEpiS/TPaTbCwh1uJRQOhaFxe+5JdByF2ilbxJ+CzT3SWG7729/LMzP5MTZhFcQNE3fBa7xAvBG5e9eqmxPUIulpZMIJRAJwcDAAI4cOYIzZ87g5MmT6O7uxsLCAt773vfi6aefxmuvvVZTNfmf/umfxpe//GXXa8899xze85734OrVq3jPe96D5557LvR2vXS8aAlraWGM4fr167h27RrOnj1rm9gaORavGACCmEz94XqCQlTedbqIncG31USLt/GecxtVY1p4h8TrIgn4LA6cz/K7ULo3RSm0/CYSvQNQUn1QutMgkgKjmIOW24C6tYpEd7qiXxEAUFWFeusmoFkix/93sVKNeRiMQpEVO2DWrNtiFn7zFoYzD4GY+5LKwbeGQ5SotIiklHKLklITRY2apjPq+Q2o1+cXY5yLtXvZrle0BPy+SpFYUiEtF4Gi4zzyXB6Rl1rgu4fKMR4OAevrYmoM/LkqzBZ4llHedck/lu7ueHYjbhb1FJZLJBLYs2cPHnnkEXzlK1/BL/7iL+LevXv43d/9XTzxxBP41V/9VXz7298OtI6+853vrEhQ+dznPodnnnkGAPDMM8/gT//0T2sapxMhWhzouo7x8XHk83mcPXsWqQgVfHkslTeet+w9UJ8riEeYqSuVdMZ7OPzJHveQ0wvjXfucRSrtHjo8kxJXX4S5LIOdKObZJGU6Et3uwGhCCKREyrRa9A2Bair0fBZUs31l0Dc3kb08VeoaXdq298dyPukSgoScsFO2DWqAMVrK7qnMAiIlm44V22KwsigxmAEKalfTlYgEWVJACIFqFG0XlOu4GYUOM+jWLCpXdkmpRrFK8ZMW4mdIaLJPyGXJyhtIXN+CvGVAuVNAcmYLiaubkJfyIFu6TxxVQIIYWvx0PKfYWjX3Kv+dOl1GXNVSnxjmWVrc80J5HN0RNR1sV6LKhpJlGU888QSOHTuGX//1X8eXvvQlnDhxAs8//zxOnz6NN998M/Q279y5g9HRUQDA6Ogo7t69W/c442cb9qGelOcg5HI5jI+P48CBA9i3b1/ofW2HXTMGEgD34l1PV2YuAYNu/Ui4RIvD0uLRHC53ume8rsJzJVMxzz3E/V39LC1cAprgPWjGFox7W0j0DppWFiKB6ioYNaB0m/5cKZGElDBNRlTToK2vQr21ULkxzw9IUE6nZh4BIxPZtIiQVGVPoiqxLbqhghIKxSe2JSk7BDYzLTwGNQCJIUEqxTeFYfYx8gti5QUL1RP0HQbHfnx6G3s+HzYQJBwkq0GZz4F4LY0qBcnqSK5R4FYRrE8B7VNA0wlA8dk3v6SsY8T877kLrDkDyDhxLFXcQ1yCJSBWbDs6+JYqx79KHyPgabTu7uAPl40oNtpqoizhD5hrYTqdxuDgID70oQ/hQx/6kO3ijgNtI1pqQZZlqKq67efu3buH6elpHD9+HAMDAw0bi6ZpAJErUjYCl+LnETCO1c9lxLuJUwmnaCm/7nUP8WIALZyF5yz47iGeT77KQhrg+0EmakYo1PUVqOv3IA0OQNYI5K60nT3khKpF5K9fA3GmOzv3W+VmZg7fPmMMGlQkJXOSdRaT0w3N7EYNUjF+naqQpQSU0nmxGyUys3puxSRcOvVKyQpjMAOUGVCIafHRqGr/jRAPxIGnrAbNba6jDDKBBl6beJZO876Q1lXIi3mwpAQmE0AzIJW8grRLAilSO3yDbOqQNnVgqQAjrQApGbRPAeuRy9f0NtdwMP3Fufb8gm8r7qX6fhz+PBLsu76CgavjHJZMez8Sd/Q9PcEbC3ZCjRYvUYuWra2tijothJCaxN7u3buxtLSE0dFRLC0tYdeuXXWPr+NFSzX3EGMMs7OzyGQyOHfuXOgiRbWMhUCuuPHqanrIoZ6JBQASDtHCa35od3Z2iRb3B63S/eZ4SpYDbp0WTqyN76RSPeiw2mvE9L34bIJBy65Dy66hsHITcqoXid4BJHoHwQyKwvVZMEOHrPhE1FdxD1nHp1MdOlORkionWIMZUFzF5Jhd9l9nGhIk6Qm6JaU6LcSObXEG+np7EslELnd2phqYtU+ihBIt9VDrs20g71CoyTT4Z6WVIuTbBdNhV3QsogoxBYs3m64E7ZYhbeogmzrk5SKYTEB7FbC0AprazhUc0DJi76xc1t5+2Te+Bdzrn/lEBdVlkQiVPcT7Oq1830eY9YaIaRGiZXuiTHl+//vfjxdeeAHPPvssXnjhBTz11FN1b7NtREvU7iFd1zE5OYnu7m6cOXOm4ReyJEmmgCJK7Q87Qa0qIYJueSSdlpYKCwqgckWL+3Pue8hyDwWPLwpcsj9MFKAsA3ppDFXOh1HMwihmUcjcgix1A4Zuj2nbfXiHUhIViqRAKd1uBjPMeFoigYIfcCsxs6ZLgqRgMA2UMbv8v7PCLWDVbTH/1gwVkiRzehKZlruEtycRLwo8DK2ytNcsWoJtW5nZgqRR/oKalCBvmdcRIwBNEUCWQFQKlpRAcoZbtxoM8oYGqlOkcgZYtwaaVsD6EmBdkutg/I7EJd64ac7871WIlnqtIvWmJgb8sFO0WMfrtwb09gavNN6poiXKY7LcQ2H5yEc+gpdffhnLy8vYv38/fu3Xfg3PPvssnn76aTz//PM4cOAAXnzxxbrH1zaipRb8LC1bW1uYmJjA4cOH7SChpo2FBDzlTXIf8orYOesNVVhQZABa+bsW3rVPkiufivgpzw1Y9fy2Kcmw4omcn6juq+XHEvBSlMvfcMS0gFWU3ZeJ2QbAbKSog1KjlAZtXhve2BZnk8VqPYlUWkRCclhlSrEtlOpQpMpYGNPyFA8/tR/M/UOV/2zUDilD8kahrMwBUMIAiYAYAEtJkHLlOYUwgBQZAAO0RwbRKFiPDGYw03VkbaNHtr9H8gakvAHcLYIpBKQXSHYNQitu+LuBXC/zRIufS8j9o/O3GOJscm6turOH+KaW8t+2Ncl5H5Xf773P3UNRdngGai/j/8lPfpL7+ksvvVTvkFy0lWixYjCCwhMtd+7cwczMDE6ePFmTmqwVWZbN8tFKovImr2PdrtcVxMPlHvLpI2S+x7h/A4DsLPVSg6WFT0D3kA9EkcDsEKc6T1KVy5B51gyDGSAo9wyyrmGJSJCQAkgp3oWpMKhhxqJwYluMip5EpQovjMIghh0r49w3GINcaq5Y2ZPIgETkeAcn+q3b238kxI1Q+pzOkLyeA1S3dVZiBDAASg2QIkAlgNDyFcQAsG6HKNFKVhgJoKVYGKnAv/ZZUkJi3cDA0ANmphctoNgrQ82v+6/824oW559B3E31/faBq+SGwGVpsSOghaWFRyPcQ81cG8PSVqIlLM6KuIwxzMzMYH19veHxKzxs91Cicr9Bb/mgfmc/lxFvceLNi87qvN6YFtkRYOsu3e/+nGteYOFiWkLBm3B9tklk2WEBcW6jSiCty99fxT1kNwiqHJ7t/mFmTIlECGRJ8XydgFGGLtl8arQ6QhNIUEpxUDw3kvmbSkgSBTrVzOJypSBbb2yL1ZMIMOu2SESuSJF2HinHaM/5IOeYuVusFR/riusfdabIg5k1WGY2qw7eaufgzCKioGCEQM5zrm0KgBDIWQMMAE1J5lCpGSPDHNYXwLQkJOQeKOlu9PSOghk6tJ51FIvrHktm9dmhsXVags1W/L36JRds4x7i1KJx7rFPiJZIRUuxWGz6+hiGjhYtVkVcTdMwMTGBdDqNM2fOtOSp0g7EJRzREnzdjfS7AH+9TjhEi+FRLc77vVpMi6u3T2mKkcNYWjjzG2Oc6S1M5c0aJivXteK/avqKFueADZQtJTrTzAWPUSSQggHNZSmRiQIZCiiloISafzOzqaKz1orT9eTsM+TnRvJmMPked9V3q9OUuytCtxYpAsn5bOXay0o5fFVuKKZpkBPmuWRgYBKxRQ3rkkw3EOAK5mWAmVEEwOiWIeXLMTCMMYBRSLICyArkZBe6sBuMGtDYBjbX5ioKOQLeQFz36+4Bc4/C9/iCEUjehsJZB4lySvc7f/90nxAtUYoWQkisz1FbiZZa3EOqquK1117DkSNHsGfPngaObvuxGIYBwlksAguUBlxHvLOZcFwVFQG2jqyg6k0SHfuwHpRCFZzyeyqrA78bsZrVxDcykvMd609PTIuVDeS0ethxKgQo0DwkIkGnKiSi2EJEpzpkIkOyisk5LArlgNtK6xll1Ay4tUJbGMBAYVAKSAaSnLotkVLvz+bn++HW7gi5bc+5kjYNJG8bPoLTLUQZNVz9qaim2oLFHEpZsBi6BhQImKS4LDOMVMbFMAC6lgOlOghJIZHk/D6EQN9YAzQDroJy3JRnx9c8Vijex5oW1RTmlnY9EVnuOuEe4mEYRmSWkTjVY/GjrURLWO7evYtcLocnnnii5V0ry6KlMj0vaJB+vfErvKBb3ozlcg95jCNO4eQs8a97P+fZLxDS0hKYMO6h8uCJ38Jofd8atHPRcmUPVdsnc71uWUQMZrgCbCml0FBEl5UGbce2FKFRDSmpi/uUT5mBhFyepCxRZGUkeQN/zbRoU7PJJFXhRgpM4LksQrEZZJ81Wk6lVR3Krew2m3aIUaunE6Wgulb13Mml6orMYGbRQkkCZBlEliAV3OKdAJDkFAiTIFuChTEYugpJTgBgyC3OQs9tAkm441i44trPVeRzjNxXwyiMYJ/zS63mftb1gGMeo9/5DnMNd6JoiToQFwh3TptNR4oWxhimp6ftKOhWCxbAjGmhlIKQykh3blwKZxv1xq/w4Me0lP+ucA85Num0tFQaHkjFeySUe8gn9qAOnE/JVeNTJMLry+b6TmXpfn4cAQODXNqvjHIjwyItAGDokt1PiYQQUEqRkrohE9l2I0mQIUMuZSPxUqQJGAF0aGbMBJHs+BWDmU+qVnaS242kAohzGXS+ey5QbZNqouKeBvlujvueoeuQFf+pkUgOcWGNy3cIBCSRAtVUEAkgesk9JJnCmbCSNc7QISe7nF+0rTj5u4umYLH3x7Ou+B1rExbowOtb8PuX3zDR7eashajTg+NAlO6hqF1NjaCtfr0gi7Cqqvje974HWZZx+vTp2ChGu4kdx9LC7e8XeLvBxxC0n5HT0lIRYOsULZ4vO5skuprbWZYWTu+hcC6/On9L2SkmnFQTIH4f87rDJP47XHO8GXBrCRadalBpEYwxFGgOCZKyXUkKSSBJUpCYBAM6CCS7qaKFUarJIhEJCZJEQkpCIQp0pqFoFKAzHcTbfAklN5LMNyvXZyKu07zslzTj/Nu5boe8x5V7uq9gAQBZLguWQOfBdsM5XUnlRdfQVEiJpH2NEAASLQsWqqngZ/AQ5JZugGoF98vbpTlXi2nhEnBiqBefLCXuGLctj1Dd+uJHI6wSrSZKoZHL5WpKd24mHWVp2djYwOTkJB588MFIygU3AsKpiMqLVam3QBwPvvGi8jHRef1XdHZ2qBbvvJJMAMVSSrFz/FY6NLdOS5jB1utr9Ykirtis74muYp2pAmUUYAySJFcEzwJly4eVGq2yIpIsZT8RUmqULCfm54grtsW/bgtjDEmpyz4cyqgpJhkgEbliHG7quNjqXvCCqBbrj/DjTKoJkL4BUF0DVYslgeF06Tn+dJxTQy2CyLIZJMvB7UqSSt8pQEr4xxAREFdcjNNqs3VzGkZuE0rvgHtsRvUquN7tOwkahxvGlVPPtORnEeaJRV7/pVpESydaWqI6pq2tLfT0BI8RagUdI1pu3bqFubk5nDp1qkIpxqkOBZEqLwiupSV4qAYXXvxKUIeL7LBIVAuwrdYkkVc8tu46LT6usMBfd9zYbq+P/yrLnFNltYq4fsYZqVyx1qAGDOi2q8cauzVBm+X2TVFLGYXKCtCohi6pFxLnOA1GK+q2aEyFgkS5IaIDq3M0iDlcq/x/nPEt4lclvMiiWqgYIRLkRAr/L3t/Hi/HUZ6L409V98ycfZHOkazFkpD33bItCOAQBxMCJLFMwBgIly0OYblAFn4Eki9JSMgNkNwQEm64JCEXEhbHsmW8YcwSm9gGAzaWZMmyLNlarO0cnUVnn+nuqvf3R3V1V3dXz/ScRYutN5/go5nuruqe6qqn3vd5n9cpVUBECGpVMEagwE94SaIGGBIhoajyN+PgdUJJOuQjRQASAtwt1SlTEXewOnwEwgwJmdZAm8V6wehfs3frzlkld67hoUSkMNwINVVc9fkJWubTezSfEv4LZacUaLGSEqXEzp07Ua1WsX79eripCURrtaQ/P1HGeNb1Zn2HCgOZ4vyVonOGa/QnHR5KZAWlUEuiSKJJ/g/nn6Zl/DMfWg5sZs4yOC3JRJQ6AITZV8fi3hllkiQc7kS8Fq3DQhIos+xCxhkHJEMLb4VjVIFWMEpJ/DsZwi1DCWUQpAIuDVKkOXid59cMEXN+LS8KZx2+LPofy3fF+s8Yg1MqKQ9KBZCBD5JC/VcE1svzUlnxVIzFQkoRablkjg9TmIHQ++KGysWWPo4/uw0l07uSNgtoya0OXSDl0D4tNAFmi4Ke4lc03LjGnJJALWGF9CYBiJTypFkL5svmMzx0GrQssNVqNWzZsgV9fX04//zzrQu31mo5WQaqDbQcr1BQ0U216WkhShZJNLtVT3jO3CHPBrQUBig2N3IekEvsyuoikPr9sJxjpjmbJkHW3Z3DXJAkOGElZpASKhMUoMTLqMoZlFk5W2MIiLKQBElwxAtTLP+vzsmmSHMQza5a68lhOT/GPN8Pd0sASnDKLZBSAjKADHwEM1Phd4AMAvCUUKQGLFIE4Ny1jlfp1ZKEW/O7IMDE3u3ZnULufef83WT2kNWaUcktiG+b6YmdiGt+rz0tzYOW55unZT5By+nw0ALa2NgYtm3bhvPOOw99fX25xzWq9Hw8jXMOMAuKnQMRN8/spFvL7GI5Lr1ZLJeAai3bsXQbbgrsaGvIabFOegXJgc2Eh5K1BYy/CxJxcwVE8o/TQk06LZmI4HIXnqwlBd6YAhmclRHIAC5cCFLEW1sYCQCciAEKBNIDy1koBYWhIhZ7eDg4HLjwsRDZQ/MHIuo6gua1pZz2GQNzy+BuGW5LO/ypcaXPUq6ALMRyEDKcF+W1CTO4bBosAEgI1IYPR4All/OdACq5qMU45ERlD1l3GDmf1eG05GnRGO/sk08+ib6+PvT29qJkFk6z2PMRtMznPZ32tMyz6cn7wIEDeO6557Bu3bqGqNCU8j/RptDwXDgt+VL8sxa+tLTjpDqUAC2pdpPn2b8TDT0txcDUXFeoPA9DfV4H2Y8rKC5ntu1AFUoMRACXl6ykXF0ROt0/IQUcxq18CAmRKIiYDSPFKDSptCvU/c0pEjT/sCGfx2KJFdULDy2Aldq7ILxaCFokguqUSlMPfKM/SWPcAeMSTIMZ/cj17+V7mHh2G8rd/fE5iSsYY5DF3szEOCsoLlfcmgnmWDzFs2w1aj3c4JjBoeT36tNyuYSVK1diaGgI+/fvB+ccixcvxuLFi9He3p4VXnweghageUJynp3sdYeAUwy0SCmxfft2BEGAF7/4xYVcYjo8dDKY4ziQzBYemtt1i4aCigKZ9DttRtZMd3Nm3W4g8d8cEbfgpNnMwzPHSx0AEkZrwn/kXKsODyaR+po6X5KEm96JE8GXAVzmWCdUpfXiQq9XMhSR0YJ16ZpE6TCSDLOSmEVp12liCsjnlBT4rBkrQLItZIVS1/VnzbfEOEepTU3wwveViF/gQ/pJhC98D44ZSmLxIwqq05ja95TqQl5JiiKeljxaT+aLYgBjzvRsy0Wt+Uh5gDnBabF8HX7vOg66urrQ1dWFtWvXwvM8DA8PY+/evZienkZXVxcWL16M3t5euK77vAUt82WnU57n2SYmJtDe3o7Vq1cXRpYnU3jIcRwQioGWpgTiin5Y0HuRfqdLpbDKW35rmfOSnha9MwQYCVBqkS3c17m7WuwdzNRyyeG01JnJ6+1odaqxCv/YQQnn6r8e1VQaLNww20ckxeRYzFUJZBACFwkOlgojsQjMsISo3QzKvDKrnZl1TS26ujVFaGjm82bvI+/4uY0tzrlKh3ZLQEtbROT1Jo5FPJi0+dMT8MdHjR7YOVcm8E1XEG/Y//R4K1irKwcq5LdTyLJt56VWR5yWxA7CPCAGLaaVy2UsW7YMy5Ytg5QS4+PjEYgplUoIggA9PT1zuIfnt01OTp72tMyn9fT0NE0SOtlAC9jcBkRRKX4rf6WgpdNrTY5LfSpIfJ6JA8zHz0lCpEFLYf6KrbdNcFqK1h7K7Uj+cXm7ZGIqaZqFoEJxWwR4mPIsSIIgIg2WWDVXoiZmUOKVJsNIyovCLDFHCYEyrySyiQT5KMxpORH83XqlE4AQCR/nnbPVYZBcfBl34JQdlDt7AO4gmDwGVqqoxZYI3vgIZg7vRanbzsdLclqMfxnhoURHEn2Kj+cFPC0LY3NrJ/ZW1ueXOa4D3/fhOFkvJeccPT096OnpwVlnnYVqtYpt27bhwIED2Lt3L3p6erB48WL09PQ87wTnZmtTU1NYvnz5ie5GXTulQMtszHXdkyY8xDmHoCzJaa6y+3NRvLd691NrgJlNVO+65jppcm5N2QtOAgL1yXK5DTXDc7FhtkQH6/FTGvdOXc9Iq0pkbKQ8+ibfhbEoJOMLD8Qo4rqYFlCAipMUIiSiMLTj1gkjlYwwkgDAVDFG8sGRFJRTdYqKF0880ZIuuc3nOk9sLszm2pybLokyxy3B6VF8FSKCP3kMM4f3Wq7UOC6WDF0meVTW49MFEy3H5MBt+0dzwCLWU3O9alpAzzgkAWTU36WSqp8lhIg2p5zziABvWktLC1paWrB27Vq0tLTg2LFjGB4exjPPPIOWlpaIC9PScjKXtUjafOssTU9Pnybinmg72TwtguzxwiAA0sR3K8F2LvyVghNOepPuFt3IGueZxRTN/nEpgIyjpeBNNTVhWmZYU2q/3stOOf/I1Ciy54Inrs1gBaQ++XC5G/FMiAgBKTInZzwjDKd6QuE52mOjw0J5YST17yCskivgg6GcCCP58ACyTNK2x1/4+S8MupkXbnYTk3xzopTFwk6Mxdyi9NeEHBCS8bRk/87PUJ4/L1RTKrlzIOol050tHByj9Ee55KJSqUBKlZknpYzmeyFEBF5idWkZeWUWLVqERYsWAVCL9fDwMJ566in4vo/e3l4sXrwY3d3dJzUHZr5rBel6fSezvSBAi+d5J7obAHT2kL3WixCEUil339XgM8sCXdTTYi0hkPy3+c5m6B/JM+PjUtnNrqMqQRcl49oXqLm5nG0hk/hLFi8OzXha9On1WAApkBFQkKmwzBiDixIIKjwXH692jekQUZSNBJWNREyAwDKCc/WykQQCVJwmdpXHy9PSDBGXRf9TzE4KjRqy/FXH62JabjFPuzc0y6GyEXGznxWFJ3NVG7dlipmgJXHt6P3kkSdGpzhrYOE4DkqlUgReiCjywui/bdbW1oa2tjaceeaZEEJgdHQUg4OD2LVrF9ra2rB48WIsWrQIlUpxz+TxsIUALac9LfNos3k5TraU5yAIVBg+9Z21i8VRy7xa+jHbKjZrq5TjekOJyTh1nBOBloL1h+ZlW52yPKYwkAQthV1SOTswC1lSVWJWKcguKymvCiHyqGhPiT7VBCVxfSGZ0d2Is5HcyFsjSYIRVxlC1hRplY3kMrcp9/IJjg6dBB1o3mxTVtIRlwd2c7hUOSGh/AhZmoibc6ClB0U+yjcbECrajh3K6Wdi1lwaPDCILT/ZhstecnHiEmnvChFhbGwMtZrK7PJ9P+OF0eY4Dvr6+tDX1wciwtTUFIaHh7F9+3ZIKbFo0SIsXrwYXV1dJ1yocb5By+Tk5GnQMt/WXFXgkyvlGQD27t2LF3WlWQ+AaPTu1vusYNvW6HwBNd48KgigQlo1iyMr7WnRZF6bp4Wo4BTXTPzWdmi9G8nVcDH6lrmm3VVv6yeRhCkUV2Kxxy0QHjh3rLvWgHw43OCiUJzyrG4p6X1xUVKidFxlKmXDSDLhjTnRk25Dq8c9iiznHpq6t7kiomb6lnNszv0lw4055Fvzb+P4Weu0zPVx2OaVol4ec2Nj5SXF9zQxPIXfe80ngLLAmvNX4Vff8kpc/47XoaU19iByzjE+Po6dO3fi8ssvR0tLC4QQkTemHheGMYaOjg50dHRg9erVCIIAIyMjOHToEJ566il0dnZGXphGwnYLYfOdwj09PX06e+hE28nCaZmensb+/fvR29sbTiTJPqXr+AB5AGX2oSDb/FloWq+zHudVR5Cp+1GCdTS3oolWgFU/uyB5cB1PC2fGT5Kzy01dk3Fe6NErCX0XjqWrggRcJwYwCmQQOGANI4EBjNSz1CBEp1MzxhBIP6oaDWTDSJzzbDbSKePBMMJ3iT4fX+DVFNCzYpYccJIAHjl/54WEcpufTz5G8YFi9yAVcxObBSsT15Fxdevoe2LwpIeyV8berQfxpa3/gf/78a+iZ3kXXvzKy/GGm34DS1cvwVNPPYXLL78cra2K3J72wmgQAwBBEMBxHCuZ13VdLFmyBEuWLAERYXJyEkNDQzhw4AAARGTejo6O47IhOM1peR7ayQBaNMFr5cqVaiDPOEBq8bZxReaSFWQFKAU/q3epNBhxebyCkDHrirSnJXz37eGhua2aRePq1vh4/GXT7Zpu6ty+MUQS+kBSrVaCMsJwCmQoDosOIzFiERDR9YV0f03w4QkPjDNrGElYRO1OhSrPDY2xXBLqwi0azTyzBvGh3MvaPUyMmyq49nbM0NJsn4E1rDXXCxTmydg9LbY0aJICZZ7MsmPgGDs0iXv+/b/wva89CFYBzr1iLX7jbeN4zY3XJhZ5kwsDIPK+SCkTpF79fdoL09nZic7OTrzoRS+C53kYGRnB/v37I2VZXV5goWrfnea0nALWbHjoRKY8ExH279+PI0eO4KqrrsLY2BjGxsbA4GQngIJelbmEgooemCkTYPydCfu4xrbX+F0yx4XvulPU02LltDQxAdvGCGf5XzfjsWl0TubaZhdCtVopIkVbjnR9IRYdZ4aRfBGAcxZmcSTbFiRRdjQ/RsKnGhixkCeTJeiqbjEEzM+/vxNl+c6t+b/+rKwZIGALfNo7kMtvMTWAcrKHkuEh8+PGRNyit1OHap49tuC7an0SOeGwSNrf+MyBE4VMBYLoffGkh3LIF6MasPPHe7Dzx1/EZz/wBSxd04+XvW493vK+38TSlUsSTae5MCZ40ZujvJTqcrmMM844A2eccQaICOPj4xgaGsK+ffvgOE7khWlra5s3QC2EmNfwUBAEJx3ZOG2nHGhp1k6Up0VKiSeffBJEhPXr14NzrmT8pQSYk3lb5+RVKWhW/krOsS1loOplj8pUdjYnFeNzkSLp6PAQs4GW47XZL+ppKbpoJp5nnm8/aVHRQ66LHmoxOUBIRbjlGfE9RdQtGZ4SM7tI1RdK6q+UUQlDRxKcOQkPj85GEghQZhXrsnMi/S95i3p+FryNFzG3rJamLW8j1UR4KM+7kgwP5Y05+3X4c0PAYhfUWWehXJAfu6Cnxephtqc8y0gl1xjrxKDS+zl4mJnpiSocXsqMATX3MhzdO4I7/uk+fOufvoOWrhIufMl5+I3/8au45tevThxvI/OmgUwemZcxhu7ubnR3dwMAarUahoeH8eyzz2JmZmbehO3m29NyKthp0LIAVqvVsHnzZpxxxhlYtWpV9CJwzlVfWDHCln0XYvms4PxgJ+La2y5XOKpeOEnU8aAwHn9nqzeUbsfqabH2wbZCzXERqkPELRLqyXQnQcTNPUhl9EACkqHEStbUa4KAy0sgEHzyQESquCFzrPWFNFdFkkQAH0yysJ5Q7OYGUwUS1a3Hk2ogAgjmo4SKhR9yclkCP2pKCzN+Poa5j4t5MXsfrN6TPE5LIvsuC2AIlKxKWsDc54bg7h4BlUug/m6IJT1AT5fSIKjTcxIyq6eUl1FUwFub25CtbTPlOfFFNrScFqKWJFEyylQQCIH0AVLCiwniOhhq4wF+dt9mPP79bfgL/r+x4pyl+KXrXoYb3nM9ehZ3R8fWCyMVEbarVCpYvnw5li9fDillJGz37LPPolwuR14Yzbkpalp3Zj6MiE6JcPHzHrSoVNHj90OMjY1h27ZtOP/887F48eLEdxpAsVKp2DpREKAUBiNNzO0VQ07G1GxIc1pMyf+EdH8up6Wop2VuC5EtI6kup6XArjVjBvio11uXlRBIPyx6yECk1GpZGAYSJI06QQwlxBwYT9YApiYUJ5T+V92nsAvKq6KP8cmDL2uo8LYM0AGUx8Z1XLjhq3/ST1K5Y2P2/Z47xGmGiNsAtBS4jeRvVAAomxwY7ZzwfLCDQ+AHhxBAgi/qAfX3gPq6QSUH6aFiBTKSigEZADYBqMLzVE54SIYiiUlyLqKCoISsLg0Dg8tKIEZxNh0TYFJ5RyLiOgEQwMGnBvGNp76Fr3/2drQuKuOKX7wUb3zPdVj3sksT17V5YUwybz0vTFrYbmZmBsPDw3j66afheV7CC9Mo9COEmHe+zMmeUXjKgZaT+YEeOnQI+/btw7p166w1kiKvTzkrMHc8bquZNsqG0J05aWacViYOMKX7U+Eh3baViDtHgJW328tesw5oScwNZPnLekH7cZQ835M1lHkcJ2YsDgsJodKTbfWFAhkkzpMk4FMASILLylaXNEmgzYlTFpNhJJkJPZ2U71OB6Eny4+N8D3OjtCTepwRfJY+IC/sinvzt7OFNHi7qAQVwyAXjgMscYGRc/f9OQLRVgL5uUH8v0NORqz/EAoF0BQ5raHGugnN5Wk4p0OL4DJ1TJfDQGykRQEhV50sT14nC2l9hf6IyGly9X5JkVk0agC89sBGGH93xGH50x2NgJWD1RSvwqjf8Eq5/16+hvT2e3/O8MBrIaE5lXkZSa2srVq5ciZUrV0IIgWPHjmFoaAi7d+9Ga2tr5IWxcU2EEPPGQQmCYMEIw/NpJ38PTwEjIjz99NOYmprC+vXrc394zWlhzKKKO5d513JuuuhhXhN5dY/KZTtpNe1BMa3ecbo/XM6BiFv34AIP0OTfpDxGeqeWabueJ6Lgb+YwN1cYzkll9AgKFImQVTJKtpw54FJGHhtNQAwoQJmX4afAERCHkQLpwWGliNuiJ2kiauJZHwfiVb2r53kem3p3jjdIa8bTYsZXc/gtOdSrvOtzUuOmBA7JJAIEYFKRvx09vqZm4EzXgP2DINcBLe6C7O8GFvcAZQOl2B6d5wMt6artNCdXbyMZf4DB9RmWDLbCFVp7yEnU8ArIVyR3clTpi9R1YqVoN/JQCgoUuCdklKLJB/ZuPogv/vwr+Nc//Tq6zujAVa+8HG9+32/inIvWJo5t5IUJgiA6xiZsp0EKEUXlBZ588kkIIdDb24u+vr5I2G4+ibinQrozcBq0zNl838fWrVvR1dWFdevW1d1haE4L41lkXHQqbYaXMtvjAKCSB1rSHhTjb7PeUB6npbAi7pzXluyUXj/leRZN5Ox0E1dmiMI0Ch+oSUwVK7S50DlaeGsEJiUkGBE4d+HLGkrm2AkJiGVWhpACLitnspEAwJc1uEzVHIomb1IaMbm72jlhkTmG9hp1hOX+4wRatp/5HgcjfIOcMZQjqpd3fNI7aIaHWORtMBf2aKGWAiVejvrKAgE2MIrywKgaSz1dkH0qlJQNDQHMWtejOCnZztuzeWNjIhMLBBYdcSGFD48YSjzLEeTE4TA34o8REcjIx0yLMpZQRomVUZM1lMPnYb57AOBLX8kXEDB+eAr/9fWH8YOvPQReYVh76Sr8xv/4VbzuLb+SEJmbCxeGMYb29na0t7dj1apVCIIAo6OjOHToEHbu3In29nb4vo+uri77827SJicnT4OWhbDZuB0ZY/OuHAgoZLplyxasXbsWZ5xxRsPjI06LpbJu4duaC6elCTPrICW4KilHifldGqiUS2ojpjqk/lNcXK6JGyi6wM6LTguHrfgLs68f2SYIEEztDNPCcLn1hRgQCB8OL4X8F4vHJpONRBAkEJCPFidL7pMIeTQ8m8l26lkzHJOF60VRS3JUTHASv0DJsJHd09IwS5/ClFjGwHg2LEiSUOGt0XXUeNQjPNQQGpuCMzYFPHMQQcUF7+uF7OsGFncBjqOOSzXPBBWu09hYETcEKoyDSKDkcfQNEhyqQDARjXsNwlyUAEgw5iTWCg3YVRhJIpC+2jhQXOrCkzVUjE2BCfA8WbOCIyJCtVrDM4/ux98/+i/43O99CX2revHSX70KN7739Vj5ohWJ44twYfLIvK7ror+/H/39/ZGw3VNPPYU9e/Zg//79WLRoEfr6+mYtbHcqaLQApyBomY1psDCfoOXo0aN4+umncckllxRGupxzFR7i2UWkSOHC/M8soaBm1nyLN9cELWYoJRMtsc+5ABR1R4MWVhe0FENieaGswqtuUSJuZlExvnNYpJyb/4jt/ZEkICAiLYm0MJyTI+WvhOFKqWvJcHJn0cSd7gMxgRbWCl2TyIEDzh0rf6aQFX3MzV+5qfZNGi5b2BatNvfW7F6RxONNvEs5RNzcWFH8D5e70WGCBAQpLROfvEwoMdIQCr116VCiWwsgDwzAPXgUxBmotwvU36WAb4txrfTuxdr3fLN7mTjcmkT/YAscYpmMOsYYyqwSclSghABCj6Y2QQKAqv0VPRMI1OQMJElULPMyoH4jM1wkQo6QSy4C5qPC4+8YcQzvG8O3/u+9uOtfv4dKexkXvvgc/MY7XotftqRUA3MTtmtra8OaNWtQLpcjYbvJyUl0dXVF5QWK8lROg5aTyDRomY/aEESEvXv34ujRo1i/fj3KFlJtnkVkMJ6trDuXFEF7W/b2i5LkSsbISHuAXRfQen3mpJvmsZjvim7RCloKArGmzMp/mIfwEHdid1MRfgEQuuFVCnKL5bcPyIfL3WiC1WEkbXnCcHo3ma0IrcFRJTpWAyW9cHE4iWykrNnCMgUXnUJHFTQb/yMvQjLvlseVmmP4S+aAkMSYzFn4izRtXj+Btx0jhb4UZd2YXCuZymSLu0ZheETNoUwS2PAY+PAYaMc+oKMd1N+tQkmt5Tk9oUTKc01NNG7AsHSwDUwCHtXAGAenbN9tYVflKfHgMJ4ocaGfiSCOFt4S8cQEREiVUhpI6es5zFHcFyZRQlldmztxuQzpAwxgksObCLD5Bzuw+Qc78JfO/8bStX245rqX403vfT0W9fUkrltP2A6we2H0Z6VSCUuXLsXSpUsjYbvh4WHs378fnPOIJ9Pe3p77zk9OTloTSE42O+VAy2wWs/nSahFCYPv27XBdF1ddddWsPTeMZweGRbojlzib/WxW3YjMxjU1ibjpjZPjxKBFSvO45IVKplpuPU9LI4DRyPIImkU+a9RexiFj/OYF13siQoW3RE0ICqDYKEwBllR9IRVGYvDho4xyFEbSi0ks5a+PN+oLyQASCpSk+6d3p/o6ggIE0gdRFkjZ7+tkjSOdBDGfZiyHr2IOnPS7FFvjQZfITkptUmyaPxGhW/ooO9nQtb6eVlyO+B5hXzg4MDUDNjUDvvcIApeD9fWA+rqBxd1JMm/24pbPjDAZESpVjv6jpTB9m0UeIiKpAAwxVerCsbdDIJR5OQrBShIAGNyQJ1ZO8cSizD5DtZpRDBTijCR1XNlIrKiJGgAZayAZ5vseBnYN4z//9524+X/fgbZFLbjs5RfiDTf9Bq56xbrEsXnCdiaA0UTctE6LKWy3du1a1Go1jIyMYO/evZienkZ3dzcWL16M3t7exLnz5Wn5zne+gw9/+MMQQuCmm27Cxz72sTlf07RTDrTMxuZDyr9arWLz5s1YsWIFzjzzzDldqyhoOV78Fdu84bp1QEui7mA++dYx6hJpK+ppmbMVAUIOt6dD1VubTZxqFL2pJ02XdsNrwTdfenCYo2LxVEpMij5UBpFqMikMB65yndJ8gICC0E3eEl4n7bFJTm5KvK6JKeBkxQbN9qso9loYR0viN0mY+XGk/sqSL6g5hk0nTU6ftCeOICElZepPRdehsEYWkPDc6QMSzRrChqRrjlMcXnIDCXl4CM6REfWoezqUB2ZpD9CWDsFkO+4IozaQD/QMuiCpeSpmP5RGUUBB4t0RFCgSLsuGkUxPTCACgCHyPCbUc9M8Mc1HC8nrNoAkSaJigD4CwZceOBwQRIJEz8AwM1LDI3c9jgfveAQtrS1Yed4yXPuGV+ANv30d2trj52SGkUqlUgReZmZmUK1W1Xzh+3WF7ZYtW4Zly5ZBSomxsTEMDw9jz549KJVK2LZtG6644gpMT0/PGbQIIfCBD3wA3/ve97By5UqsX78e1113HS688MI5Xde0FwRomaun5dixY9i+fTsuuOCCSBBoLsZ4lqFtoxhY05ab4LnYQkE2/optDi/VAy1ODEYk1TvO6I/eldlULW2rgBXF5VmjDJOcdvJAS0YjIycekRttSvYnrQMRgZJwEisb2UVC+hAssMbXRSgMZ15HLS4MAgIcTorIq1zcenHxyFfHEI/4VWDIqIrm2vFytDQUX8t68E4pyxkrifIFVvnfgte0qD0H4SLsU00BDLjRopxHAFdcqACc8bCOVTYUk2bcZsJIAHBsEs6xSTi7D8CvcPD+xaC+HtCiTuu98TAkVJlxsPgoA5cuiCk1HkkSgiRKYUaPraq5GyqO+yKAk6OBFL9LbvjvQHlUwMEks4K7yNtpPDdNpLeFkVjoFVKenVJcVsMASFrDSdQI+7Yewr9tvRlf/rNvoHtpB977qXfitW/8lezz4Rye5+HJJ5/ERRddhEql0pSwXW9vL3p7ewGozfjDDz+MP/zDP8ShQ4dw5pln4qUvfSl+8Rd/cVb6Lz/96U9x9tlnY+1alQb+5je/GXfccce8gpb5Tac5Dna8w0MHDhzAjh07cMUVV8wLYAFyQEvB21oQLTCrpyX+O6OCa3pajO9E+jjH7Kz6zk7EzXYgrWkyL5Z+djxJ5It6U2eNYCYZrmCzpiaKJzz4VIu8KKZpKX8NWALphxMeEqq5Zp8d5qiFBQ4EBfBlXABRy3JzptI/y6wCl7kAI8yIafjUZLHEOY29WYb7CoX+TjHkkhceSqgzhsUB0/fGcv6ReGZJIB5IDy4UZ6rEKijxCjjjCMiHJz2VCpwa9DoEUuKlsDwEV2EkkvCEn0vkJhBKvBTzQyiACPvjkwe3KuAcOAp38y64D2wGf+JZsAODoJma0X2JlhkHS462gEsWjXMw5SnRgMUXNRBDpu9AKOfvuAnPig4NCZkNken3g4dhI+1VEeH7p5+H+cjVe6WK3xKREXoy+6E2LLofpgeoKqZQspR0YeDYc+hZPHT/w9ZnrL3+559/fhTiKZfLaGlpQblcjkTshBDwfR+e5yVAjWktLS34wAc+gHvvvRfvfve7sW7dOtx11134hV/4BVx//fX453/+Z4yMjFj7YbODBw8mIhErV67EwYMHC59fxE57WnJMSomdO3eiVqvVFYybTV/ALOEhi2ehqFelGbOGkC2f1fO0cCPsUy/lOQny8z0t1kUnz9NicRURWa5QJDyUp/yJvJJ94ca3ThP1zJzQ1Y5R81m4FZToHaQvPRAjEGXJsx7FMXluxNaFFIaAVtIkSbQ6agwqYFP0Do4/OGjUtbzKyAtrc30JDc6G+TEsL1NqzOber/kj6peSAE/UVMpz5p0hFV7hSQ+ghARI1b3KNBWFkeISEHpRtoViFD9Ee0QCMDAE8OAi1AySEu7wOMTQKErMAXW0gvp6UPJq6BuswJd+BJzSpkBJJfrbJy+qag7Yq5pzpr0uLHmvPPa2mOHSpHxAEJJ/kxpIkRcn9e5KkvClH1WatlmFt0XgzocPIsW9GRZH4KGK9o7s5lbXtTv//PPR09OTvcc5CNt5noeXvOQleMtb3gIA2LVrF+655x6Mjo4W3rDbAOR8q26/IEBLs5wWz/OwZcsWLFq0COeff/68PnTOOQgWT4vl2OLelyZCQTbQYrmmidGy6rbx3yZQSePChEemnqdlIYg6RU43b8T0tBS9cN6B1p1fMkSkdl5q0g2ED84dqwaLKQynr+NTEO56BVqcLACWJCCZSHAUfPLhwkWAIDGRNjW2TwAPt0iTtCDux4WzPOE4a85z+p1O/DPHJWVcs8TjsSNkAAkBTkp0zea5Y8SiNpXnIAa+6TGsPCAhKBF+pC2Tvq4NPEuSCMgHI4ZSSPBlkzNgkzOohHdT5nENrnqhGLP+VlVWUWZlaygmHSZKCO1JzwrwtYclkXUUEpeFlInK66kzI/Kv9sIwpmuNZcFdCSoNe0ZOwUMVANDRmVwnarUaHn/8cZx33nlWwJK2ZoXt0pyWc845B7/3e7/XsB3TVq5cieeeey7694EDB7B8+fKmrtHITrnw0GysGU/LxMQEHn30UaxevRpnnXXWvKNEx3FALEt2Wgivis2si4BlkXWM0E5aBdeUBZH1wkOJFOPQ02KT8bdxd+ZZCNDaTh5QqRceyiFC5p8A1MRM5EJOmwxJfcp1HLuxA+GjJquJirWACjU55KDEymjhbdGEK2UcRpKQUYqz7nOZlyNegC+9jBu7mB0f1NLYu5L+x0KBlgW631xxuaJpcA2uqZVjkRyvDnfhsrKquwMfPvnRuAE0KAnDKWEoxo1CMV7uGFb8kBJKvAwODp88eLKmQkMiyFF+VqEWrTArSERjUpIEM7LjADMUQxEIECmvbU3OoMzKmVCMCstWcx+dDw9lp5II4wpSi3s6JKQfLAFwDW5LIIPo2ZjeJ4S/gcNViCgQPmTIn0nblJzANE1E/17UHxfc1R6Wc889N+KjNGs6NbpSqaBcLqNUKkXcNiEEdu3aNeeElfXr12PXrl3Ys2cPPM/DzTffjOuuu25O10zbKedpmS2nxfO8hscNDg5i9+7duPTSSxdMZMdxHLunZY5eFZsVDQXNxdNiXi+NCxOYpS6nxW7W+7RO7LOb7BmPc35S+9lC/cvd9KbM5WVDyj9OF9VVnm3XJU6osJZIV8VFKVmVNjo2qcEiQwEtQvLZyVS6s1lvhaiSeQIntdnCFrM+eSEtJ/3IXGwTUR0LSZ0lh1bee5/3WpjvUEbOHwBBwqcahBQoWzSEgDBswp3Eoq7eZ/W/aZE3XaU8kD4Y5yotGQwuXLCw0rKZHQfEoRjNCyFGllBM6OlJPYJA+vCohgprtb5PAfkx+A+9JIASzbPxyxhj4KR0WHSfiCgiLqc9oiZvxhcqiyef/Bu/uwH5UZZWTVYxjmFUoPhsF15yAX7rPW8CoDz/mzdvxjnnnDNvvErTCyOlxAMPPIAdO3bg7LPPntN1XdfFF77wBfzqr/4qhBB497vfjYsuumg+uhy3Ma9XO06mF+6i1ig8RER49tlnMTo6iquuuqopwbhmTYGWYp6WZqxoKKjo5rGup8WcSLLcwdgs4MYeHprrQmJzUzU+jjEeP44sZ9hqlJvVlH8SOczw9ocTYpiynFYdTavmmkDDFz6cHEXbNCgBDI6ChFXLwZGu2hFaHtZJI8ky3/2YD8xS9Bp5mCUv08eaMTQLT4theq4MZKCqO/PsOwDJQjn/OIyiQzEqK41lwkIMLJLNt4ViAhlE4Drmhkh4sgoCEjpD2kzSanxfOhQjUMrRYQFjaGXt0MKKRAh5PFyFfUwdJBZLCOhU55qsoYSk5ICpw2L21RcBOLdvHCWl+hgBJMDUWdKms5xGxAB8xPVOLrj4fNz20NfBGIPneXj88cdx9tlnzxtgSdvDDz+MP/3TP8UPf/hDLFu2bM7Xe93rXofXve5189Azu52SoKVZqxceCoIA27ZtQ6VSwRVXXDHv9YlsfZEW0GKzOXtVbMflzaQpc43HkHl0OZwWQCnp+oE+zAi/1AMtzZj1pgrefGaH3vwKligJk3dQqj9q4opDP2ZBQ1ODxRceJJPWzKK8CVGDH56aEIEYIIHHx2t+gU53dpi99tAJ9bs0THk+TrZg0aGkeJrxj/APFjdeeIxmAQ+LviG43AWBEFAtlLlnKPFylHJrWqRjQgFY+F9OlOB8mGBAvedxCQBf+qhYCKhEQIklw50xIRb2chRMnRjzamSk2qtSuf0EWDI9MYH0IaDCNul3ijTZGLFukpIcUIRbx7I5kUSKhMySfWfh75V5B0OApMsiRNwWxFoqI2IANcxEpyxbvQSbHv5GBFg2b96Ms88+G4sXL8ZC2COPPII/+qM/wt133z0vgOV42AsatMzMzGDz5s1YtWoVVqxYYTlz/o1zDkGW2kOMQUqyZhGlrajWSmFPi+WzpKcl+V0i7JM6t1yOQYt54Ri0FKzy3IxZibxFzssj1WZBhym01bRxDhJKv8KsBWOaIBkREtW/YynxqLhhou+I6sMwPSGClLQ5jz2RCT4MeJRBISDg1KG0UQAgs+6cCChTBDkc7341CSQyHxvvhYmCzYyh3NvOGbPCPDfOHjK9cgwMPCSsEhGqYholVoEgpfVjjhUNBjQ3JLoeZFhYsMW6meLgaNF1ekilOQMqi4hZsph0yAZht3UV9FhDxkZg16EYD0omlyEtdKWATSnyZuhQDCSDy9yczamEw91oWhC6zAFUGDmtmxWL7CmF3bRyte6/+W8zPDsuRxKAZc1Zq/Fn//RRPPbYY+jt7cXg4OCCApZHH30Uv//7v48777wTK1euXJA2FsJOSdDSbHjIcZxMeGhkZAQ7duzARRddVIiJPV+mYoj28FMggHLqXZpT2KegA8I2B6c3PaYOm9kfmeqMWbPIpn3SlKfF6hQqdlNFUmETz7VeeMhcREwebp6bP21c7XZN/QrlJVF1hdITGxBPbmqX5kR8h7g+ESX+DTA4IWlQSqUvoXanaRd2tj6LmCQ4HSlWT+HXawEAgw1AGs6HTBrwSAXon/9uLJgl9FiyoIUxQ185TfzOIVJJm2eGkAk/alPVv5OZZ9pzEMgAZV7KaiUxFfbRgEWQAChZ+DNNVC+xMogkVG1loaoqU5xuK0iAICNwERFwCVFNH5u32SfF7TL5OmbKeDp86jKlJEtcZR4RAQJBFPoiC/k3LptBIFIVidI8tDSoMp+lJ73clOcZmgJxisLGZ597Fu74yX/CcRxMTU3h8ccfR6VSwe7duzE0NIS+vj4sWrQoE+adrW3evBn/83/+T2zatAmrV6+el2seLzslQUuz5rpuwtOyf/9+HDp0CFdeeSVaWgrWXZkn08QnG84XAQGlxotA8QiJZdUv6mlJeXwqZWA6JOAnwj51iiQmpCOkJuI242kpFsqaF09Lo+Nyn29jCyBQNkWkjLh6IIPQ+xGqcRqTMBBPnEyPGKovJS5C5VMe8ghAqgCcXgkyHhsAez9WQ9tVQPeLS2i/zAGv5DyX4IQGjbJGgPxJB5yV9uq8c788LYwPJy97yCLvXy80nEh/NjxrJr42QzqB9FVAkWSianGiLUK00PrSg4PYK5EOJWnBN62cC5CqJJ7wNCiQ7IIjUp+FgEc1NQ8yZiUAC8gwXTu+jubZBAhCUm/8AHSYSrenM4A4kurP0fvEABc6LCQRkIDDCCxXh8Wc2BCDOydnAxpKC9g2KTNyEmM0hBZSoPFFZ70oAiy+72P79u0477zz0N/fDykljh07hqGhITzzzDOoVCro6+tDX18fWltnN+63bduG9773vdi4ceOcibcnwl4QoEWHh6SU2LFjB4QQWL9+/byh1mb7okCLuXVUZi2DU9SDMgfSrdXTkkJVZQO0mC6GdLuOwxFNvpQ9zuZpaSYjzCokl3dgQysWHlIzZ6PrNfC0kMzsXCnFE9CfKdKk0tJIm4SuiZJMz9TpoBlRLYbIhS1B8GVNkR0NHkBtxEPwvRImvu+DlX20X87R/nKg61IOt7f+0xaTBDdDwVlYIgoDAAmUft4P90AH6CT1bOeN1XRBw4wVfR8SeMfuaTHN5SW1kLNS5FXhxKNxZlZ4BpBIm/elB0AV7TNDK6r/sQCczkrTooU2T4PDHAgpIo+N6ouIuFm2oo5RSIh8OHBCjReeeH8SoTCmwkCAEloUFETenMQjJIkAQaRppO8hDlPl1WqixD3rPjPGIk0kG/m3KmdwjIbUeSCsOWsN7n50YwRYNm/ejDVr1qC/X7kOOedYtGhRRMKdmZnB0NAQduzYAc/zsGjRIvT19aGnp6cQH3PHjh246aab8M1vfhPnnXdew+NPRjslQUuzac96QDz66KPo7+/HmjVr5l1/pahxziGECHceKclnS2XX4mnLWa/EXJaO9Pg3PSg2gm10nDHXmPtUHa53csJDxdObbScXc7Vkrp83BDKYpQBoqfM14yqTIdpxhS512xgkyNDtrW4/nswtolSIJ2fNgZE6Dm+Gf8LzOBDtGAPyFalRKuJv5OHxgMmfSkz8hPAcxtB1Xgs6X+ygYz0HbKVIrGvu3MjsjaJuJIHyI0vBBlqUauqCvctzva59UOQScaNm7XHLzJGJB2UXozMtPX5icTUlYuhYlGd1H9OLekSeNYiv1XOWwOMeSkPTcI5OocSdCJToTB3OeaaysuKGxMq5EoHyqpCT4PgFBvnXVH/WOik25VxVxsKJSb6R14PACBBMJMCZ7o8kFu1VtHIuz+HZmABJi+wJZEHSjJxK6LD0L+vDXT+9JaIvbN68GatXr8aSJUusvwMAtLa24swzz8SZZ54JIQRGRkYwMDCAnTt3or29PfLC2DJgn376abzrXe/C1772tXlPQz6edkqClmZtcnISU1NTWLduXYRgT5RFmjHMAdJ1KgoWHJ4Lp8U2D9umufQmP+FBsRBso/McE9AYMXc9p841e8hmtop/eXM3Z/Zdad3r5xxH1j+zh+nTw0mQmABjPEPcUztFczKM00UDKSDDME/am2LjxOgYv5Q+XCeLNjg5YIyj7MRtR6JYUHLrZVZB9WlC9ekAR78GOGcAHVdV0fViF+0XOmBuDmG0agMts4PQmSxgj8P57yXgw+qeSryU584o/vseb7PWvoiBsSZ+Rh/nGVmuY4ZMKA411uNX+KTE1bRpAAxiueRZpWNC4e1I+BevguxphQuA+gDvnADVyUk4o5MoDUyjHISEWOmDM9eatq+LM7phlXIJAY8CMFL8mTzyL4AoTJPWQQJYBgNqArsM07Q9WYt0kNT9qxRuTUBOpjx74Dk8G5WZlxyPmotWldMYwxDKUPe2as0q3PPYrSiVShFgWbVqVV3AkjbHcdDf34/+/n4QESYnJzE0NIQtW7YAAIaGhtDR0YGXvexl2L9/P97+9rfjK1/5Ci699NLCbZyM9rwHLUeOHMGzzz6L1tbWEw5YADOTyUbeyh5/PLwqViCTQi15HpR0u44psWDhtOR5Wqw23zEvQDGMZSPVx7SrJesVy56S3y8NWtRkKKIdmDlpeyKfdKhItXFISHlJBDhzwSSzhjlVOqgHN1yoFCgJ4PJyJr6v+xJlFlGgeDBUQ8nYEc8c8iDuLmPsbh+83UfHlQ7aXiLhtnE47fW9K8ExgtNX9xC7mY91xkHlR2eAjZeQUCo9SbFJbvKQyV0hS9ZP4n7qopb4TxtLnvR/0pLyCjRzxq0pzw5T41AytfgLkmAEQ8dERvwrAhBc+iKIziQg4o4L3t2DoL0VWF1GtToFHJtAy2AVpUk/8tYq4A7IsO5PUp7fQRkOqjQd6cgE0ld91+1TUtI/WaHaB0CqonWKZyOMkFD0Xehxkkxaq6xLCktjhI84KbKXX+9oRk5hAsdU/8Bw5qqVGcCycuVKLF26NHN+UWOMobOzE52dnXjRi14E3/dx33334e///u/x3ve+F1JKvP/978c555wz6zZOFnveghYiwu7duzE+Po7169fjZz/72YnuEoAYtDDmZNa5OXlQ5mC2zUvW05Jg/cV/pT0t3HZUPKdaZfybsoKs27y53uFAYDso323CbJ6czIH5P4oHH04oZ27b7UoSCUJfxDcId9xWUSpWUiTeEOiY/ASb4qgCJeUo3VnrRthIh4o4GaeKBhRAkkCrUZ1cTgHj/y0w/t/AfjaOnkta0LneRceLc8CLzYsokvxGq4XDhQsX7oNnAJMOBNSiG6dsn6yoJcdMT4sJVGzulYKYResSpIm4aY9Gol5QWJAw7YXRPBU97nRWGigkjnMWcdNrl6+GbC+rsURITBwi8OCGhCe3tQNo7UCwDKj5NcixcZRGZtAyVAUkwWUlEAierIHDibgqVTmNCouF70w1aF+qatNp5dxo/BtgLE55Vg8mDdQAFQZyE7WaBMBY+B4mvaD6WSpQpwT4lHKuTEj4z8gpHKOj0D9k75Ie3B0CFiEEtmzZgpUrV+KMM87I9GcuViqV8Ou//utYt24d3vSmN+E973kPDhw4gFe96lXo6urC6173OmzYsAFr166d13aPh52SoKVRDDsIAmzduhUdHR244oorThh/xWaaiAsLKcxmegJJfGY9sFj7BZf8DJBJcFysHBJ9njF5WNRym8keUsqUmQ8tjRa+JJjDE5N6vcvGJ5m71xxwU/d8oBLKiEfx/VD5Mx0SUs2p3aIvPZSi4m+IaxOFnpKYhMgifoKQEj5VQ2CT7IYMd6XmIqYBD0l7NpLLShAk0WqQJs2sDLXgVTC9lTC91cfAl4HyWkLnFUDHeo7Wc7iVUAwAVIN9BkqDxmNllH50BqimRLlMQT5FMF4A7R9bR5q13HGZTXlOhoSMEE8dAGPnw5gHKB+AvZgqJYsAhp/FJHBbr2WkM0QAauteBNnmhl2OPV9SBhCBj1LZnt1CkHAX9wF9DNNnC8ipCfDRKbQMzKDsxSn9NVlNAIB0X00Oi6nxIliQEZNzQ5DvcB6FZvXGgDO7yrSpFYMQnDBiCcKrWZ1def/i/lflDI7haHTXy1cux72Pb0KlUoEQAps3b8by5cvnHbBoO3LkCG688UZ87nOfwzXXXAMA+Ku/+iscPHgQ3/72t/GTn/zkNGg5GWx6ehpbtmzBmjVrEgp/SrxNLrjibSOLPC2lUqEpcS4ApehibpsU6oGWeqm+eeu7LqbIQ1KbVXymiGKelb/QoCPmx47T9IY2nxtRDLW4TpxJoEh7arIVMgizJZLiXumdorlIeyKs42IBvSrdmcNBWEiRapCkdrG5dY7CLbPDVR0WkIJAUaE8WUMlRZrUpENP1pT+RYqYWHsGqD3jY/hWBqcH6FzvoPUKoNRD4OXGg1J4xrM8WkH5kSWgwN5/pYA6H5uSuV6j+PmUINxbuChFr2VF2ianJSlmpgFMHgkcUNlstjCSuahLALUrXgRqtS8fjDsolY2snsBXHgvHReBV4ZRibgrnDnhnD9DZg6klkyg9uR9tM6VQRyYGPWbKM5CdsxRpVgGnMquoAoYIU7A5D8sKxH3iLAa/SkKAoutb6x2Zj5d0yrOf4AKZVqMZ1Nh0OC0wLF9xRgawLFu2bMFUaAcHB3HDDTfgs5/9bARYtK1YsQK/8zu/syDtHg97XoGWoaEh7Ny5ExdffDG6u7sT32mwcKJBi84ewgLWN6pns+WemmqQ9bwS5mnm3Gyq6nKSkBa9kKxZCT31G21kZrpmg8varp9773UuIFNfRhos4STqIAYrKntCWHU0JMnEJKkXFm5Jd1aF65T6qSdrcJgDT6ucpsS99MJWMtRSfVKVo11esu7UPcpyIXQ2UkJV9Bhw7HsCx74HHCr56LzcRedLHHRc5eQ6G6mm/uscbYXzo34onEsJrQttIlxY525z9aoUJKQBieyh+Pw8BF1gcCf4MFkirvo4dI+wcAMXEq/jSshJcTUzjCSkiLgbYA68q9aCKiEYkjJRkV2XlTCNu+qHliIA4w5k4MMpJec/vzqBsaFn0YcSfJnkUgEx/8sUWzSVc+N6QTqMZOrTBABRThFDJb5nHq/VpUkKq/cRzOC3IB6bGvBU5TRGaTAqfnjGsqW45+cxYNmyZQuWLVuG5cuXZ689DzY8PIwbbrgBn/rUp/CqV71qQdo4kXZKghabq3Pfvn0YGBjAVVddhUoli341aCmVioVlFspiTksWtMxpw1iU1mE7rki7dRZu1wW04HBil2KgFjMzipOAtBCRM1Y4+6mJB2dMsImzCqZXJ0NKFm6CxYizyH0tpEzUL0kcBwnGgQpriQixDnNC93VWeVMvLLq6swjDRObEbKaXOilxL0hY00R1doguKyAoiFNWyUHAfGt9JAblsRHwo/ICWq9CkITjc0z+TGLyZxJgPirnA12/4KDjchctq1Ou+b0dKG3ui8NnmVCXhB8qt84PpWX+PS25I6KBh6SuCzDhpAl3AiZoyWlHA0/9rphhQiFFCABY5l0SJEFMKdZKxuCtXwtZMt4hM1QS+HDcnLRpKHKuaVIIkAwgghrGh59VwCJQ4yZd6NAqthh6PdT4J7g8W/xT19oyO6JTnkPWT1ymQN8TY2AkwbkbHW+mPAehVozpPdVZfjVRxSgG9ZWw9IyluHfz7WhtbYkAy9KlSxcMsIyOjuKNb3wjPvGJT+C1r33tgrRxou2UBC2mSSmxfft2MMawfv36XE+KTcr/RJjmtDALEWze+YRWgNI4FNToUunU7EopBi1m6Mg8zpxTrVL+RVnIc/S0MMfuaSmsCWOclaQPpM9n8bEsXldc7oQ7tWS6s5Izp0Rmkdai0Kq5Np0WLQgWhwGkUhsNXd6tTjvS5jAnyWEhpeAJIqWkyygxkTvMhQMXUgYgRnBRgic9cPBEITu9CGoSr/pcwBM1uLyS3OkSUNsBHHnSh8MESksZOl7M0bnegburE3iiA0IKuI59ihKkpeZZ3XBlcZvjNZo5nSxpzszOw61nNul+c9Eu13hEnlUAN7tRkCQShQojbgi010SNSckZqleuAUr2+ZWIEoDFFFPMsvKUcccBcY7JYwei969khCb1dbVatG1u1++Qfq1Nr4eNPAsWpzzrO/RkDSWUDZG9VL2jVMozMV3ROnnpqpzGNCajf/cs6sS9jyvAIqXE1q1bsWTJkgWrczc2NoYbbrgBH/3oR3HdddctSBsng53SoKVarWLLli0444wzsGrVqrqE27SU/4my2NNi0c6Yb09LUe+L7XJ1hNhsnhbbgTbhuUA0UX+oMKel+IrBjAk6Nykoc5L9npKeFss54ffEslkcCW+I8CGZQAnlzP2J9E4RcZqoAh7pXSJHGRVUaRotvC06XocCiAhgKRImU5LmarpXO910f9OcgLL2FFJcI8ZmggJUzBo3euca/kMvpP4AYfQugdG7BDi6Q+VfU5AvDgX4sgaXlRM73bmbzfVlHyCF28sbX6ZOUNRGjqclQ8I3Lhphn6xnpmPUwfJnWhTp1fDsSRIgqIwYiWxZB8Wn1RpCEj75CLiEXH9RtFqkw4UEG8ckJo1zzlW/KXkcEWH44BMJwi6zkPy1dhBI1QtiYeFPW70gs4ghRV6VZEgxPbZjwA8EsgonR89GSQ8kvT9aE8ajaiIk1L+kH/duuR1t7a2QUmLLli3o6+tbsMKEExMTuPHGG/HBD34Qb3jDGxakjZPFTknQwhjD2NgYtm3bhvPPP79QFcy8Ss/H2zQhmFl0AOaidVYcjBT7DABaykDVy36eLpKYlAmxe1r0cbmgpaCjxWrNLFo5qdtFiTosr1Pp842ts9KLsC/qkiRKTgkIvRMx4ZBlJlttETGSu1H8XetoSCnhoxYBFn28rscSwFex90yNFbW7TFd+FhSgJmto5cnietF5ECpdNloUpdKHQRk+/ASJVz0XtXPVGUh6x2vWXcoAPGNh8mQt24k5Atl8s4R8LPye3D7kjBUtOGY645LAwz6eyTwesBdYBEPnkIMVz7SEfU3+ntqDIEkCJCHCsWZXUObgpRbUrloLMrmoRv9E4INxJxEmiu+eIu+IqatDRJBSYPTwdiDkpkS3FAgQxRosQFIcToc4iWTIZ6JMynPkKbGEFIUMMllTcX8FXB6rQ4twnuI5ldY1QNIclvBT9PX34TuPx4Bl69at6Ovrw5lnnmltd642NTWFN7/5zbjpppvwlre8ZUHaOJnslAQtMzMzePLJJ7Fu3Tq0tdkn07SdTKAFAJilSFjhtbcg8EiXUs87N8/KFY6qZ7qylWXBiFlvyM5pAeLK0cXTnmcfC8rdDecQcQszcRPn1KHyGqdw7oIB8KkGojDckpNmGWVokIwWdRO81KvuLKSAT541s0hxXkTEb5Ek4IdhAyaZnXAYXr+Vt+VklGR5NpxxlFlLpKqq6rA4iUXRrHGTLAlBKs3bstO1aW9ogDcf8KT4e5EX7GjikuH4z09zLtqVbHioZTjA8t0VeLKqfie4cC0hRVUEMCmqZCsC6F95VghYsk+ZgCgkRESQwgMYh+OUGmRqEmbGj0TPwfwFyyEHSkolDeBwBywFpPPqBWmPXNozGbdKkackrhekU6CzYzkK3UoBwQKlwUJO4r6qcjrUYVHW0d2Ke3++Ce2d7RFgWbRo0YIBlpmZGbzlLW/Bb/3Wb+Htb3/7grRxstkpCVpaW1vxkpe8pKlMINd1TwpOizZm2bla+Sbz3W4Tx1bMtcP0SqcwhxkZyOO0ALpyNOWEhwpmX9huIG9natkVszz1u4JLX25IyOZp0caVuzyRnSNratJHsnhhGpSY6c5CKsJhycLz0NwEJ3RPK9e4fs5KHMusscKZgzILJcx5yQjDyCirKc2hSQiTCR+cO6FORbqsQMizQbyrD6gGEAMRQ9kCkPR9R9lRIc+Gg4GBR2UFTNMepzqyQfNvRHN/KRuVkcjNJEJynKWk+3sHSli6l4chudiTq4XVOBwwYvZQHotJq7ouFQPgdHSBM4BkACkCSOEboMvsMoMTCskF3gzccqs1JCSlwMjBJ1Bpj73jLPXuqLRsBs5jzpUOKTKocWGrF8TCsgP6eJOEmy51kagXJAMQVNjMWiKDG2MZMpQGYPApwBgGQ1BFWLR4Eb6z+XZ0dndCSoknnngCvb29WLVqVfZ5z4NVq1W87W1vw2/+5m/it3/7txekjZPRTknQwhhrOnX5ZPG0aLOBFiunpaCzxOZVKRoK0juP9AJfLplx63wwksABxvyTrlqtj7OBFttaYIURc91W53AvMtdN1CjKCyOR/WMgsYslZj47JQxXSqh1KsVZSIYSK9mrO0eTp24vme6czSxSgCiqIwMGT1ZRMoojerKKsuHxU2EY1W9P+HB5VjVX9SUrRKe9HiBkFkXOOCBLIEaJ8ITO+NDpqmkqhws3FAETYMQzadYaMDWVPbZgZo1R5RxrIdDmulnrvBXGJqznIEfffgcB/IynSnvetK5OouBhlBETqHCQ1i8Jmw1YCAicErhTAtAK36uq10P4Ko3Z6LsGLKrnyd80CDwcO/xk5rlE7w4pTottLMT1ghRZvCZrKBn1gmwhRf3vQPhg9cYydwGoUGtAPghS+S8tAE89nwrG5AgCeNF99i5ahO88HgOWbdu2obu7G6tXr8ZCmOd5eMc73oHXvOY1eN/73jdPvK5Tw05J0DIbO/lASzarwyY3YcUxBRm7cx3HZUMILAFGRCrsk0NUzYSHItBSMDxUODrUxI0az44lQIfNU9JgR1w35dkAKgi5GASUDJlwbS5zEUiCw92Q/BiAgYXpwnVUc5kbEiu1fkWSm5DcXfLISyJJoCarCd6LaZJUWnaacKgyMigT0gLUxM3AojICEjJSD82vdRTyDEhY5djjhShJtsyEkWxAfMYHOopoATVpuaDZ5im0fJQYJwU8LfWuGb5fi/YSlhxog2QCRE7EEzLHjC+ToTWtoAwCatIDmIBr4dnZgmGO44A7JTilFkgRoDY9ChI+CECpnDOmZICZiYH4Bszf01AI1mNBhVGLkWeF9HM3sZKSWitxvSBl6RIHOvPNkzWUWNkadhqTI5jGeHRsd08X7n38dnT1dEWApaurC2vWrLH2aa7m+z7e/e534xWveAU+9KEPvaAAC3AKg5aYqFXMXNdFrWYh8Z0gC0QxnZaiIaNmCLZFrZIDWtKeFrOZJLhJHsdZvfCQzbK/r3VX3cR9JlKei4Z3cvpEOZ9nGwUcVgozM1hmUTGL1jGoInGqSxJVMY0Sr1jDMKaEeHwbalIWMoh0VmwdauFtICgROZBKa82TM9e6LRIyzMxI9j9NmIx0K5hS/VXhIntIS0DYuQk5ZQUAFfYyuQk2VgsL5oPrYuNxWEpLqBazxzYYl4k05yKDmKXDk4Ql+8tYfKScq2cjKIAna7kANYAfpo6XEZAfpt4DZZTD8Fx9446L1k5ViDbwqxD+DIRfhVtuj8JIUgQYOfQEKh1xxcw8jSNbplw98qyEAOMsGosmebZevSAtzZ+WHgCQKSKpxzIRYUpOYBrj0Xc9PT349uOb0N3bBSLC9u3b0dnZuWCAJQgCvOc978EVV1yBj3zkI/MGWNasWYPOzk44jgPXdfHoo49iZGQEN954I/bu3Ys1a9bglltuQW9v77y0Nxc7sfKwx9FOJk8LEeHg4WOZz20OlOIhnmJt54E9G/4rleygJe1pQSL8AevfgOFpsRZNLLjEzPEdTb7kdTwlud4V8+9i5xNTarWK/Ki9DFqMaiaqqps2n3y0OG2hDot2g4vIO2HLLEoXlpOkyJXaNChhjIWk2YoKERGLMnNkClQmCuiZ/SdAiJD/YHlcMuTHlJyyunfSQmASIqz9YuUmQMm7++TBl7VE/4PQC6WBFWMMHY8PoXXrENi0n+1EzvOZtTWTldTwUJO7YnoA80m5fDpO5+vb66LnsANf+Fbvl75Wi0GkDkIQCajxZYqkuayECm9BC29RYaYcyeK82+LcQaWtF23dy1Bu7UKppRNgDCOHnsjci8ljabT5NAFqIIPoeBGmZSc1hZRmkZaP0xL9pmmAp7PqTK2kGTGVGZPaPFQRsPj5d3YqHZbexT0RYGlvb8eLXvSiuvczWxNC4P3vfz/OO+88/Mmf/Mm8e1juv/9+bN68GY8++igA4NOf/jSuvfZa7Nq1C9deey0+/elPz2t7s7VT1tPSrJ0soOXQoUOYmZnB+WvOgT+a/K5oKuVCeFVsZoIWE4BkH6MJbsg4Li88NIffodlFI+vbTn6fd90iD7QOaDEzQ/J23AH5KPMKGOMRr4VIooQKAgTWSrTaGwIQPKplZPltejBRujN5YMyJCYuhqTRpD5VURptaJHylTJpTQM9Jud51+9YwUsizUSRPAhGLsp3iNNPYe8RD3Rrd90AKxdVhyX6WD03BObgLPdyBt7IT1XN6UD2zHaLHXrBvIcw+XBqM1dm8tIHiDS17toKeoRKE1mHJIZ/yHPKpL3yAkZXUmg4lFb6ttIeOO2A55TpMP5hZ1kBIYRQqzFao1v2XUkAgSPBvtEmLVzJ6PjKAm+uBRATwQFoTRnlxalTFiBxAC5THqqu7C7f96OsJwNLa2rpgBQillPjwhz+MFStW4M///M+PS0jojjvuwAMPPAAAeMc73oFrrrkGn/nMZxa83UZ2yoKWZsNDJxq0EBF2796NiYkJ9Pb2wi1Xkd4XWjktcxibeQRbe/+yn5WM0ZGoI5SmpFCx43Q3rJwW209p5bQ080AsqMU8vy4nxe6RaWbMmU0SAZKC0MuRlSlXk7ESAVPVnUtWnooueAfwhCx/TVbBiKsSASnTRFdN/hUkIKTKKHHIBXFChWVT8NMpomZ9lbwwkhOWHBAkQjqjmxh/ZpiLJ34KyrjlzesGUqASVpoWFKiKvcyBAzd27xNQeW4Clecm0A0gWNSC6XO7UTu3F/6KTvWTsqxUfVM2L/ovWUu8o6aXLnGQ8lAs311B90gpk91lkk894cPJIZ/60kuIpAG6VIMAKM7golRfGt6DLUSWG041D4rng0hAUATg3D5/KY2iOIyqPwNIcWFysqMU8djQQwo9LmpMpdSmWawJ48kaRuhI9EVXVxe+8/NvYVF/L4gITz75JFpaWnDWWWfl3eGcTEqJP/zDP0RXVxf++q//ekHq5zHG8OpXvxqMMfzu7/4u3vOe92BgYCAq6Lhs2TIMDg42uMrxsVMWtDRrJzLlWQiBJ554Aq2trVi3bh22bt0KScWyh4ryV5oxouw1bHNxgohrIdVq70vCYVEvPBQ2avW0HCcumU0EC0B2Rs3tTz2gYz+fGEIPgU6xVHBVUJDQVJFSghhFHA8HMdlQe2FsPA8W/p5lXo7Si4k0EGKZ3ah2oQdSpS0zsLqy/IDBU4EuK8Cs2haRMJmRJeRLT3meCAkNENMkJMq8kuDZcDhweQlVOY0Ka40WL6Vzo6tQqzCGjXzqDM+g65Eq8MgARJuLmbO6MX3JIsCxiVGeADJjgRc5fcTi/Q66hhGnqltM5BCpGVg05tJAwGEulMAfj3lFOe9KfqKTBbSYE4KFRJz4gvRXIpHWr3hLMvJe2qt981BsjsXHE4ved6sidfhvX/gAJ3BKbhAAoEZVTNBI9O+WtjLu+fnGCLDs2LEDlUplQQHLxz/+cTiOg7/7u79bsIK/Dz/8MJYvX47BwUH8yq/8Cs4///wFaWc+7DSnZYGtWq3iZz/7Gfr6+nDeeeepyd9xrKBlIbgqNivq1HBde3gISEVZ6pB0TW9N7GkpqIhr+9Ca2t3EgpPc3ht/1uGk5HWpYEhJh4eIKMxCKKHEy9GiE0gfNVFFOsMmNhUyMXkqfsg/CaQPwQTKoaeEM44Sq6DMK1E9Ix02ME0veprfUublCLB4wrOSWwEtZe6GnADl3dBlAmy1kTjjERgpaT0YC8/GCXe8Js/GYa7qJ0oKyGT6Eqhrpnk2UsATXmJcONMBOp4YxqKv7bTeV2FOVTP03kbRoRxOS9446hh10HuIhc8yyxMC4tCIee9m3SFihBrNIJDJ5xlzneLj0wUO694qSft7mJMpGJdyiI1F/ciSZ1VqfxyCTPOu1L/J4GuFKr0EeCI/AUMBvJLKFIKqXu5LD4JEFBLS0LGjsxN3bb4Fi5csjgBLqVTCWWedtSDhGikl/uzP/gwzMzP4whe+sGCABUBUwHHJkiV4/etfj5/+9KdYunQpDh8+DAA4fPgwlixZsmDtN2OnLGhpdpCcCNAyNjaGxx57DOedd15Uc4KIUC6XsXf/SIOz860pgFIUoVjDQwZoSX3vmqDF9K6kjiubG+t6oMX6cxb/ja3PwxpeyhGVKeg1SYSK0g0kxqTx7JiSLWdg1nHLmQOHOxH3RFBMNpS68ByLL8kZjzKKoolcpkGJSgl2DMIhoBb66TpkQ0FKlt+JPCUUkRlNJVvzNiN1XOj+i0R/NEeCqfQpcBZ7dPwQINl+Pxkq+Lq8FIWNBAWoihkIGVgzkgiqRlHZUdkwHtVQk1VIqbJQmJM3poqNtfxhYju/IWpp2L55hXKNQxhEan2a/o2FDEJvisyMBw1KyqyCFt4Gl6vn41NNATxbaKeJYmh581Hi80TEVQvjZdVuRUra3+y/fk9MT54navkZapAxwEMYGg2vH4UqjfeqzCoo8TKmaRzjcgQIs8U6Ojpwz89uQ/8ZfSAiPPXUU3BdF2efffaCABYiwl/91V/h6NGj+NKXvrSggGVqagoTExPR39/97ndx8cUX47rrrsNXv/pVAMBXv/pVbNiwYcH60Iy9YMJDxxu0HDlyBHv27EmUGiAiCCGwZs0aTEy0wz/c+DrN8FKKmnV9t3xmFkKsLyhnZg8lL2T3tBTktMw5Npa9qKlxk/SgpIm0xvezyBjKkAOZqpgrSSo9Eq6JpwIEGYWJVBgmVp0ljgxxVp2nPBUlI0SgsnJ8CJJocewkVIa0LL8EZ8otbveUMACOAl0hOGEpnk2an6NTRFWpgHxSp0e1WAEXGiB5YKFUum0R4nBQ4U6KZ8PCsEZYQC+0KPTGEIa/Er/qLG0+OS3548VmUgjFKiJkwhg6UwtAVNZBPx8p7SrKbsibcpw4dV2PB4WG8ki0tg8lbPW1kuGhrGdT/Y7xhZ10hWdIMCLwHGl+INY+0iFUxVPJFv0EDGn+cBwLEmHdrrjvE3IUkzQGvacvt5Zx1083YunyfhARdu7cCc45zjnnnAUDLH/zN3+Dffv24d///d+j32ehbGBgAK9//esBqJTqt771rXjNa16D9evX401vehO+/OUvY9WqVdi4ceOC9qOovWBACw9JaQttRIRnnnkGY2NjWL9+PVzXjT4PgiAKD3V3L8VQCrQwxiAEwcndDTZquxhXxe5oyZJWTU+LSIERLckPADJRbyh5XRP46KMKe1oW4ufKk+9NA6kc70oSwKSunRseoijrIdZ8UIs9McoWFURSdVbzQgAGN0zn5GAZN7rK2inBDRcCKiTLr3kqfpiZlOWpaOKjeiwxz4YgUZMzaHWyQomK/MtjOXlKyvL7Vll+BoayUs7VdZcIqtqz7ofBzzF5NkKKsGpxsvSBvrdyyBOSzhx3rHOuGZDjeUgYsx7iwIn4O7osgq6AbBX8YyzMLEMoPQ8QOMq8FPGnbONBe8uQMw9ZvTJ5c2sOyKeo2CMHheUm0le1SfOb4znLU3GAqF5QkAtcbKRbrYY7TVOYxlj4hURrayv+8l//GIcGDsATVUxMTKBUKuHcc89dMMDy+c9/Htu3b8c3vvGNaP1YSFu7di22bNmS+Xzx4sX4wQ9+sODtN2unLGg5GVUANeG2paUFV1xxRbwblMpdy1i8Y87rfxDkbnASVhigFPzMBhASnpa0UJzRR7PqczpjKFFMMeyvUzTleR7Xh8hyOC3ZYws0Xs/TYq5NFteuhEApUbwtiLQjMlkhodsaiKXYRcghMYXZ0qmeTMvyk/I0WLMqoKXMDVInac6BAh52DRAKvTbtIAIEeUBI5LVV5wVTsvwUyvJz4vCplihnoEGJfjdMgOKHtY5sP4skSpQ4ABRQUffAIsAyP1YcSTemaZngpHF4SB+jpeTB1H06vBSKpMlMHSv9t1n3SuvxuKF0vTkX6Uwzl7uQjmNhnuTdbE54yNjFJHi40YSSfGfM/tiyo2LyrAfGudULaXqdzPuSIEVmt3htGFNcIUIQdbS10oq7f7IRK9asiIr0Tk9Po1QqYdeuXejv70d3d/e8hW6ICF/84hfxk5/8BBs3bkSplFfE9IVtpyxoOdmsWq1i8+bNWLFiRVTRMw+wxJZ1VyuPRvI4G0CxWVH6iu1D23GmxycDRkxPSyJjKHkl17JOMlt4yHqDCwBMU5Vtm7c6rpYcMqXylPhwmWtNr1T6E2qCineIIszmiK9jhlpMIKGJraWcbBICHWAfPgAAk5pJREFUJbJ2dOVolXVk2aUzFZJCyMFJpzunawUxAG7IkaFQ+I7VkeV3LLL8gfQVcLKCEolSWg8mKiuQJW4CSpBMq56aZQXyak8V3gPN55DMaTS3L6l3S1CsFBuVO4AicAaUrUEEqGen1I/N8RNn23CD44EmvFL5+sMycVT8cehdMQnDxnjzpW9N31f9DcE606naAVRQ1cnNLlJeGzVW0mFFAPBC0m0ZitDe2tqGu39yC1asWQEiwnPPPYe2tjZcccUVkFJiZGQEhw8fxlNPPYWOjg709fWhr69v1kCDiPDlL38Z999/PzZt2oRyeT6B9vPLToOWebDx8XE88cQTOP/887F4sUqnbAxYADXJJL0ONtqNFYwURCj28FAWGNksyWlJcT7yiiRmPDLZ3aTV01KU05Jn1luqz2mxekoahBBNN3jaJW5CUGZM44yzCFAoj0F+8bb0DlGQUMXsiFll+TWA0KDELKQY31ayHU34lUynwYqEhyPihuSEYZSHp5TxDthk+bVMOhGDm7MjzejBGDybXD0YOCGvR/c/XXcpCWaisgLWHjRheePDMlYbDd/E94lstQRjNfqLg4WhoQAgSvCB4u4RAubHPCICfPLCoFysaGwaZwxEHIIFRliO8uUBbJZLxM1j6Ye/hOkZJN1lGXkhdViRgcOxeCEd5hp6KlUw5qhaRMxNHJc5T/vlKNRhwUD0xra2tOLOR2LAsnv3bgRBgAsuuCAK7/f396O/X3FcJicncfToUTz++OPgnKOvrw/9/f1oa2srHBH493//d9x999244447UKnUEfY7bS8s0MIYg5RyXpnYAwMDeOaZZ3D55ZejvV3F9jXhVi9MuQOXcSCTupd9+ec77FP0s4SnJVNHKP47oZZbh7CrG7HK+Nsm/aZJtxYPVfaiyQNM4wzQKr7MvnBkGkhe3Pp3xDMksnoMWEistOpPgKPMyhFhUUIJgDlcS/8nwzCKl+BGWRiaF5D2zHDE8uWxHoySSXe4Y93tK1DlRKq2Sk9F6cGENONMVlIc7kIhXoLJs6nHS1BViZO8Hs2zCWSQrwfDcn7LgtbU2YVfyIINhhWQSyxe0HUYT3sQMlwhhqjwn4CIQIn5PIlkQhlXh+VkE/Nk3l2Z4SFbmnN6oGVSnsOwIhCWcCCm6g1lvJC1uGK5wVFR6dEyo/YcnYcaptlEtAEpt1Rw6yP/jjNftCLiJ/q+HwGWbPcZOjs70dnZibVr16JWq2FoaAi7d+/GzMwMent70d/fj56entx15+tf/zo2btyIu+++G62tx0/F+VS1Uxa0zIbTojOI5gO0EBGeffZZjI6OYv369ZFbUBNuARRoxwFSurhWWYM5gBabV6XotOka3c9Wdo7/Nr0wMke6X1kYk7d4WuyFEO2TROFsKqtzKxm/Tx5v8G9ygYr5d354KLnWkLXPmihJTHlLsoUIUx4PGB4DKUEQYMxR4RbD9KLkpkJhKpNEws3R34i0VAyAAeiU5qw4VznkSXiyFgIlJQyX9LTk8BJCno2bE64RJDJep0iLBEKVL8h4YIAg9ALp401Ze3XNheIJWMipDU8x0Wa9K4WfW0CqJh3rRRrQxOmkijJDLIFvfh7IAMSkNcNL5qU8297LnLtNZg9pblvs0Uy89yGwysuWY2AJXpbidfmQRJlsOcZUxeYazaDCK5AQUaaQynRTnKpheQQVqHNbWlrw1e9+EavXqvD+s88+i1qthgsvvLDwelOpVLBixQqsWLECQgiMjo5icHAQO3fuRHt7OxYvXgzHcXDGGWcAADZu3Iivfe1ruOeee6Is09NW305Z0DIb06BlrgQnIQS2b98O13VxxRVXRO7+xuGgpDHmZta94mCkoM1hs1fP05JIHa4XHjImJRHkg5YFId1aQ051PCg5RNpcHks9Im7KAvLAUUoQZa3E09B0GMmWwCFIhhk13PhMJIiaGQt3n2Zdl3oeD7C4P0reXREZ7TtcteCZ4ERKCY88a2YUgCiMpC+leTYcLMwCyoKZyGsDFxISnqzCRTlaoINU6rWpT+MLD5w7c09Im+MFkkPG7plLvgvGhkBKKFCd1jbRZRqy1bJBpFLHLeNIheVc6LIMgpT2jdbdISevonWj+zI/F8bfIVBhPP7cvG1iUXaUVlF2mBNtvNJjwgkzp0yvkyQR8XxqcgYlpmp7ObrsBVNjbUqMYxKj0bUrlRZseuibWHvuGgDAM888g5mZGVx00UWzTvpwHCfiuhARpqamsHv3brzvfe8DAFx00UXYsWMH7r//fnR0dMyqjReinbLicrOx+ZDyr9VqePTRR9HT04MLL7xw1oAFAGCpojo3/ooltFSsJ1YzN8F5dYSA+uEhGw4orNPShLhVUdSS+G1s4aH4wLpXtllub7mq++MwDp88eLKGmqglyKymaeJppGJKBBFWt5U51Z3V5K75I1mBN+3xMBd0JxJ485En8Ka5AE6ogquJxDVZQ1XMJLJ/TCMGVEJBuWhHH15fRH0xH5HiLCggx3IFxvQ9OMxBmbdE719VzGQAlXmey9XzZE2NqYWwxqMp7whNNPWpBl/WQtE/aR1HjDGYejqSlLieNrPaN6AFC5UIm5QCPgWwFkND3jjP8bSY2UPR33awxijuS5krkTdd1ZtIwpO1xJhI3APTooUhYBEz4dWzvZ2mSUxEgAVwyg42PfiNCLA8++yzcwYsaWOMoaOjA5dffjl+/OMf40Mf+hB27dqFVatW4Zd+6ZfwwQ9+EPfddx9qtXz13tOm7JT1tMwlPDRbm5iYwNatW2dBuM0xi3iWPbPHFuKZfdinqDmpCb7kAn447yVAS6ph1wECS0YjSHmIB4eWAStTjVmjQwuAqet1PGFk/bMuUTdPp8X4u8TK8MhTWhnaZQ3lytYCWTYZc4e5kXS5LbMozfGI9WBIFWDMSftVdV5smTnK4W/zeERVmBnP8GzUNVOp18ysLlwDZ8pTkvYIac6M6r8mBSueSr3qvIQ4PGByNzThOMHfyH0/C/sUCh5XwJj9HzmOloibpNOdhQxC0UJ136bXLhPOS6QL55PBBQkQk6iwCoRTmvPdmp4WGWUM8ZikbmYPpRozC2gmuUsET3q5qexK1dksrumDAJRRwTQmw1pCqt1KuYL/fPDfcNb5LwIA7NmzB1NTU7j44ovnDbCk7bvf/S6+9KUv4d5770VfXx88z8ODDz6Iu+66Cx//+MfxwAMPoKura0Hafj7YKQtaZmNzAS2Dg4PYvXv37Ai3FvP9AJNjHtqzulxZK0q6LXiu1SxdT1NyKmUDtJhNpBb/chkI1EYHY6MOAHVSIICnf3g2Rs7vLdiBJiYN633akFBO2hNCvoz13DoP0awcaXoOpJmpEp9vTsQqSVOHZwRqQoEL0tL9htnAjAYYQgZRHaG0aRlz7UIXEGBQi4BNAVdn5shwB28rRJiW89cACQTU6hTy82Qt7kv4WExypj1VFQAxxUWJQgAxTyUNShSXwY28OxngskALUWEzQUgRIos5Io1zAxlY9Ub0fdu+A+qnjys/XazOnJ/ybOtsY09LJPaUFvCJ/tLZUT4kCbQ4do4HgVA2xrQWLbSNabO45qQcwwQdi1otlcvY+MOv4ewLVLHDvXv3YmJiYkEBy/33349PfepT+Pa3v42+vj4AQLlcxrXXXotrr712Qdp8vtnp8FADIyLs2bMH+/btw/r16xOAJQiUy5tzu1s6z0YHj+Etl7wXo0ens19aY89FO1vwONtabjksDVpccy2q47AwKUN6YvamS/jh18/FyMEO8KAgpyXvmRYGbLbwUJ3vi2QMpc14SOYixGT8d2VGgYCamE5UdTZNUIAWpzUMfyiiqi4MZysip7rLIi4DA1OF3nIKEYYdjCowB2GdmnQhRX1eXHguLkSo7sHLEZtTgKLiVCIviyrkp35rn2px/aH4gYEzjgB+RPaVYZ0jQL1jiQ1BFAIISb/SDwXDkr+V/rcq7BjzWhRZ9cRUeo+tyLiKnxEPki+aJ2vwRC0XlBAkONd6J2pcxcUUKfPbRSq4UMJrkiiqQp67aSiOWZJEXGS1WdLXUARyFxXeGo25dHHNdO0ll7mpMZ2dX6bkOCZoNOp6uVTBLff/B8696GwAwL59+zA+Po6LL754wer8PPjgg/jEJz6Bu+6666QpPngq2inraTke4SEpJbZv3w7OOa688spoMAshZhcOAvDUo7vw+7/xCVSrNfieJf5eVIJyvmNBBRwdStbfwptJU0NMPicDZo61YtcD52FyEijzCmoF1418nYhsaMz6QBoAoWz2kIWAY2s6cY4ZejCbVgcuOlJC3+EyJCTKvDXh8dAiWSp8k+KGhERYGWYQab0TJ+XxMBchM93YF14m+8Y8L73oaWIuA7dP2kyFJyqGHowmbmqz1XnR/BoOJ6rYrK+fTtFNpGU3EEhTYC1Zd4lAWYG0tB3PbVojd2hCWM04zTwkvEbPUAntMyUIJkPvUzZ9XISquHEdK8SePKkqfgsLoVWPo/Tvlx9Ks9xq3uc2eQMkPWPxvWY9ijrMBzQOa6XHtNY48qiKSYyG/SSUSiV887++gvMvOReAAizHjh3DJZdcsmCA5cc//jE+9rGP4a677sKyZcsWpI0Xip2yoGU21gxo8TwPmzdvxtKlS7F69WoAc+SvAPjuNx/Ap//nPyAQARzmwKtmXxDbZ3NighblBFsjKckPHScGLaZXIT03z0yUAXgAgMkxF1vvexF8zyji5xVVxEXx9GbryZbP6hFxc9vJyR4CEqDFLNZHRFjyXBk9QyVrZo4DB064O3SYaw3VmGEN89qmwFump+E9lTIKuBSlklrDMGBgLFWIMCzsGOvBGAsMUyq+Os2ZMw6H3MQxGlxEejAsDukIKSAQoOJk9TP0fZd5RRVdJCWt7rISmH4WqZ+KhwUTNXC0ZUcJBHByQg4LYlaPRE4YJUFIjY8hAhYNltA7XM5whdKy9gLCqkdi1o6Kr2tkF+UVI5xjRWzAAC1mmnPOeyaltBJnAcXZ0hWezf6zBtwrCaHqNOnPuIObv//vuPCy8wAA+/fvx+joKC699NIFAyyPPvoo/uAP/gB33nknVq5Mk/lOW7N2SoMWPcEWNcdxCoWHJiYm8MQTT+Dcc8+N4o5zBSz/9//7d/zn/7ld9SN8wQYOlXDepdXEcWQpyDYzw9Fi10aalRXFNmlPi5uYxJITa+K8UOh36Jk+PPbTF4FJoMJZzGHw5t9FbxWSs1kd0BI6QSwXr3M94yE5PoMAwCTQM8jRfZTDJz/yOKQXdACZ3WEk8MZccIt7noggWIAyr4QS5gIIU0UBWDNJNK+DmKrFI0hLmLPomglZ/pDXAqYWEkGhxH4m1VaJkmmOjkpVrSlCaAjK7OFOAnGJCmuJ9EV02CpdFJEzB+WwL770oYoDIgolaIvCBvqeDT2YQPoIoMJvQRML7kJYbsaz+XEt1m5qO0boGSrBk15umQYR6rJoT5sIwyP1yhxo0UEwVW2bgcMhHoEbSRKsiRTxfJ0WXQwxLoyYtzlwwaPQqEAAhCq4Mck8JUIIByKsZSWlqt1lghefPAzLI0pPCEDJLeGb3/8KLlp3PgDgueeew8jIyIICls2bN+ODH/wgNm3aFG1+T9vc7JQGLc2a67oNU8qOHj2KXbt24dJLL41y5+dCuCUi/NEbPoWf3f/zzHdf+ItluO1rHXjZNVW85JpJrDnbty6QgZcNy9RNfDGsKFXErvaY/Hdys5bvaeGM4eDWFTj0xIro39F34HCSenrNW1Gg2ujmMw9xFpyWNEcjAFY824KWSaVpYg9h6Iq0tgsq7QwJCY+UfLlLLhhnkbdAh1MiCXOGSNeCMZ6rgMtYOjNHpVLznFRgLVLGUYGuLEyk2mVgEIaKKqALO7aobA0mgZDQm9SHEapYZHgeYyz6W5Mw1X0lF9pA+tZFu1HdJQmC65TgIhScY00Q8YuKJs3a7OEhkPr/voEyukcpJFOXQJAJr5PWrQEoEabTC7cM60BpL16aTK3HSDl81hISPtUgJcHllaZIy/nhIVNQTt9rDhFXb9iMsJaQAQJ44OAopTLmROh5MmtZBeSDICEk4RgGVeYZCK7r4mvf+TIuvuJCAMCBAwcwNDSEyy67bMEAy7Zt2/De974Xt956K84666wFaeOFaC8o0FIvPERE2LdvH44ePYqrrroqKljVnMJt0qYmZvDul34YAwcGEwXn9HV9eDj8bCdue7YTt/1bP5au8PDy1wxj3UuqOOdCP+KG2ObJiTEHba0FJtB5DBk5OWqy6f7t374Ye7YsRolJa2VhWgBPi91s6KzO4ebPm7in/OfMgzj/hUuGlU+3oFyFNV1bi3YhzFKSqZo55kLihP8HqMWkJqtqN4ws2FELepL7oVOFhZQo5SjgSlBcpNDI5FH9TJI2zVRbX3qhN4XBo1pCTdWXXhQ6AtKVmgNIwzOT6Et47xqQBeRDUAACAydmrbOjLVktO9ajscn/u2M+Kj9+BsHKXohl3faKnqExQcBcdChtwyZXhc1UjgX6D5fRNaZDi6qPDLHXSWnTTEdgJR3JiYFKPI/55Kn3lmB9npxxBBIRYVrMg6aNLc05j/DOUl5mXYsrAjChZ5GDA8StasouK2FaTmCaTUYk4JLr4sv3fgmXvfgSAAqwHD16dEE9LDt27MBNN92Em2++Geeee+6CtPFCtVMatMwmPGQDLVJKPPnkkwAwb4Tb53Yfwvtf+VFMTkzF7nYo13ggAhATca2M0AYOlnHLv/Rh05dL6O0LsP4V43jxNZPoO8PiHbLQQqozDC1pPucsvSq2zxrJpgSeg2f++xyMHGmFw9WCpSdKDgaXh3H5ZjwtBTm3OStE5pNMzDzhxMrh1dTpHhcECaA8w7Bkh4dKlUFqvgBsIYxs0TYipSyblyocUIAWHsuUaxIsQan12PQqWLiwuQkF3KTHI8EDYEkFXIp2xcndrZmybZqgAFVZRZlVrBlSKtXWBaIFyAxhZLNaXFaCy0rwZQ0OL8UcBmLR+2mrW6T1YAIRRN6pBPgSEqVdA6jsPgpyOILl3fBX9sBf3g20NY7BcprrImcm1RteBl9GX7cfFegaK4Up8PYp2icvkxKs09PJwlNhjKGEMnyqweVJMjWHA855ooo4gGz64GxMWoSdcuZSJusrO2vPoi9r6ne2pMBPy0mM0XDkWSuVSvibr/wl1r3kUgDAwYMHMTg4iMsuuwyOkw9Y52JPP/003vWud+FrX/saLrzwwgVp44VspzRoadZsoMXzPGzZsgX9/f1YvXp1TEScA3/lkfsewyfe9mkrf0arc7JQrjogX+l1cCexIIwOufjupkX47qZF6OjycfnLx/DSX57BZetnUK5o4l5yOa1OO0BPCs00n1STa+YUIlO7otpkGbseOA/jx1QcWu8ASyhHDXqyCoeVQNUsasmvKWRDLTYr6j5K/dvJUcJLcG/zYQsDQ+sEx/I9LWCBBGNOYterPR71ivgRBEq8ksgKCShAmZetIEF7MlRqJ8vJLEplYTDT4+GBh2nD6ecdgRnjYxVi0JWF7Qs7A0Mbb4dWzJUkIkl9m8ZMHMJQ92Cr1JzOqooF84AgBDM2k5Sqr6QzbaC4F5HnQkiUnhtF6blREIBaXwW0agmCM3tBXW0qlJe6NhfFN0l2Iq71T3UwAUsPVtAxoVKzS2HKtr6ngHy4KEHAtwLHuEYUs4I8X9YSz9P0iHnCC4twGhpB8+JpkVFbxp0m71v3n9S4ECTBEIMwc+yk78HMLpoWUxjDUPSd67j4yp1fwpUvXwcAOHToEAYGBhYUsOzZswdvf/vb8ZWvfAWXXnrpgrTxQrcXFGhJ67RMTk5i69atOOecc9Df3w8gJtzqworNApZvfu52/Mtf/gekpKgaqeZ0pDNEEu5w4edO8MPHAvz3txfhoXs5Kq0S6146ifXXjKKnt4aWBkVBj424WHJGEsjMNhknQQcxsJ+slbHjuxdhakqCGKGE7ITgGVVYaa6cFhsSswKLAu4jE7RkiAWNrTTNcMbuMpCrpaL0MeoW8bN4PMqsrETjQnCS9niIlMBb1GsieNJHuQ5p08wsSoqL2a+ZVMCNK1Nzw2uT8CAZhMhA+ODcsYZq8jKmAvLhk482x668KCHgZgTzWFybKH0P4TMNQu+UiEib8fTnyRmUjwrwoeeAnz8H0dWC2ppeyOW9kH2dDV+a4vOEMa5MfQMinPFcBe1TrpV4qucKQRIgKIJ3qmik5nhoi8jUpOo0pT272gIKIk2bsCugEORlet9ENp/ygteX7k+Hh9L3EN9bgJqsopXbx0RAPmpsOroc5w6+fOcXEoDl8OHDuPzyyxcMsOzfvx9vfetb8eUvfxlXXHHFgrRx2k5x0NIsoDA9LUNDQ3j66adxySWXoLOzE0CScDsbwPJXN/09vn/bD8O+Ge5/AJ6o5bp61c4wrhLtkxfqcziQ8FFhrVFfajMcj/xXFx75ry6QU8MVL/HwC9dM4cqrJ60bI9stBIFaqxsdl7n/RPaL+u+xAz149uGzEQQMFUeHF4JIoKqESiLDBABYbY5kSJvNdlOYFpVpwnqOuug/wOBLHyVWtno8bEBVLw6eiEMfNt0JW5VjAoEk5VZqVnowMcfDFOOqp4Cr3fL6v8mwVkpl1PB4+NKvU6k5HtcAEryZvDRVxhRzp5WV4JMHkCI069CZDeTp90xIEQH/bJkDP64MbZA2AwqUsB9rSwKv8Srath6Gt3kvnNZ2iDMXITizF0GnBQxKsnslGoQxWUgEZxLo2u8pwCJ8yJDEnL6k/h2ccKNDpIizIPVb5/F+JCQqvMXIzIlVkRVASmq0KB0/DjmPfI+kqKAxjxjHkCQrYAFUf1pDT17MXVLZRgH5GJZHlFcXap7/ws1/i/W/eCUA4PDhwwsOWA4ePIg3v/nN+Kd/+iesX79+Qdo4bcpOadDSrOmU53379mFgYCBDuJ1thpBX8/DeX/4o9uzYl/lOE24rekIxXNU6QTMdly8hXACZRJm1IZCq/LrmLkipPBqOqODxH1Xw+I86wTjhsl+YwJUvncaVvziFviUhZ8AyB1SnOSqVYmCgpQxUleQKpiaSoGVg5xI899iaDGnTlM7WC7rp/mfe7Os/6bYzv07h3Mx0SlSR3zkVoiKg73AJiwbKCR5AUrumfs0f/Z1JzCbIMDU0v8qxLz24CY4HEmmq5nla7RRQwlxOjscjfZ7qjybyBgnPTPKpSJTCKsF6MQRCfQzbjjn0eGgypXb/mwDDBCVmaDEgH1KKBiAv67XRoTlF1s3yZogkKrwjIu6qKtIMnLtxBetaAGf3IMq7ByEcBrm8B/7KHgQrFwEtpRBYW3hiFsCdkQYSwLLnWtA6o8CWm5DY1x4PikBG4lzGUUIFNarFc0PoydNkZA1KonvPgDz1bF0qJUipgsT8cFrizsZ/Jt6l+IE4xKN7BqEQd8mXHkZoQNW/gtJh+fLtX8QvXKOAw5EjR3Dw4EGsW7duwQDLkSNHcOONN+Lv//7v8fKXv3xery2EwFVXXYUVK1bg7rvvxsjICG688Ubs3bsXa9aswS233ILe3t55bfNktxeUjD9jDJOTkxgfH08AFiFEIkOoGcBy9OAQ3nzR7yrAQuGkAS2bLRHAj0JAqhOI9CQ0mTItpS5kAF0kDwBcXoomJV+o3WFmkZEMjz/cgS/+bS/ef/1a/PFNZ+L2/+jGyHD2J3ZtdRot+jAAUK4YE5kI3ccSePJHS7D/0TWqei8IlLoHKdXkqRYLRPL0RATpB8U9KHNKby5wXkKGP6fdVBd69wFthwJr1o5pZcescuxHpHGPUjV4EHoYmBMKZvGogq9pZu0eDUg4d0CkOAl5yE3/DpGXh+LFzQZiVH/U+HSNsFYjOXUdFjJLBNSr1KxLBOhn5AkvIgNn74HCStnx8SKU/LeBLv1Mteowhc/PLHPghfwIfQ+q0nFJARbhwwl5Z6Y5gsD3D6Ptx3vQeetjaLtvO9ynDgPTWbK8NWA5Opk44Iz9ZbRMay5KGpTEGxoGHt6zTFTv9mQtqqat78ENgaQvfMgwRTptGuQ5zEWZVyKlWUECgfBDXtPcOS3RvZhvV85lObHonjlX47TeuA7IxzEaigAL4wz/sukLEWAZGBjAgQMHFtTDMjg4iBtuuAGf/exncc0118z79T//+c/jggsuiP796U9/Gtdeey127dqFa6+9Fp/+9Kfnvc2T3U5pT0tzBQp9bN68GZzzqCDWXAm3T/zoSXzkNz8Jrxa6IowMDBlKSNvEvpTctshkiwTSh08eWvNi+Uaxs4D8cKetanUo742IANLuJ1ux+8lW4IvA8hdN4ReumcZLrpnEi87x4ZaykwDn9nh1xViTGSOIgGPPj87CyHM9YUZQcmcYhKGtTJ0ZfQwo2iVlv6SCE+VctDJSNEBmyGI14i0IYPmeClrHOMBNjkeovRJO/Pq6ZhtaPVaHzTSPILqj8LxIPdbILJIkUKOqleMRSeEbnjyT45EuKKgfgfZ0aPImIVkl2KcU4dEMa0nPKpgHJEsE6IVKezxIInfxIFAqdTnkdXAXvvQzGVI6rOVLLwrN2UibLgtBHlj0bijeTzVbOsG4B7OooP5Mm/YgMQLcwQm4gxPAz5+D31WBOHMxxJm9EH0d2Uw1AE7oZeQBsHhHFW3VUqRNoz1Q5jPNhuZYyFMRqMkqWrhd4TdSTGYx6FQeG8X2zgN5IKiwZFH+e1FLjEH7hdNVngWSXBtznAoEGJZH4kwj7uALt/4tXvbKXwCgAMv+/fuxbt06uLZd2jzY0NAQbrjhBnzqU5/Cq171qnm//oEDB3DPPffgT/7kT/B3f/d3AIA77rgDDzzwAADgHe94B6655hp85jOfmfe2T2Y7pUFLUZuamsKWLVtw9tlnY/fu3fMCWB79wRZ8/E2fSuw+tWkXd9mYGDS5ToYThy0tlDMHLUxNQma4wEbQjc5nQE3OwIELGXptTDevJz0c2tOOTXvasen/KS2Yq14xhlf+xjhWrBINPcDTEy60JD9J4Knvn4ep4Y7c3bkDFw5DvNNGPMnn7errmVJrLXKgxT1vJeKm+xyDlnrSLI4PrHimBaWpZPhQLZ7hQiKFIhJyJ9O2ruuTBHkUeqkAp06dJYc5aGPtIAIE+QBYXDTQqN2j789BXPOHgUVtm6EoIN7Fp2v+1KiGCqtknlXUnrHYy3CcarBuHxcMfui1UeqrDA65iRpEWfe/ek6+9KNwU/raZiZJ5KEhrWtTQ6tjZ6mrKsEtiZCUvoYt/Vrfl658nS5zoJ6DRGm8htL2Q8D2Q5CtJXhn9kKu6EVwRlekB0NEcAKGZftbUKlxBNKDE2ZZxUA1lESrw10iAC28LQIaZqXjTDq78bv45MMNRf/SIC9z3pyzhxpvLhJ6T0JGQFiEG740GRlQG7ZRqTwsupbVl275B1z9qpcCUN6PhQYso6OjuOGGG/CJT3wCr33taxekjd/7vd/DZz/7WUxMTESfDQwMRLWLli1bhsHBwQVp+2S25z1oGR4exlNPPYVLL70UnZ2d2L1795z4K9p2PvYMHFmGgzIkBHwoN6ZtwgBUJVIzlu9RDSUWhwjSoETtDjUfoQbGHTDLzla5h5OTc1ze3aj3E9rAwTLu+WY/Nn29A0v6HfzCL03jxddM4ILLZxToSHWdpPpgZqwF37vjHMxMthjpiGkdkmyxs6ifYUplfGFkN1xWBdLsR/asoFl6X3ImZibjph3pYOXOEmS1BmYsdqZJEhBMxOEiiqXIWWpxiNqI0rz1NVTBOx0WTEvaM8RgVZEwg3DfnC4RoEBKtjAehRlJrlUBV4OH1nA86QrTET8pDZCAML2WhxlITmZcpIGVllQnLZhHPBQQs/cnrXSrF9s0uTt+qCoLpdVpRUCeCqWipO6Z2QXn9HMSMoBgApyUsF/S45E8LyYja+5Ssp98xkfL04OQO48ArgOxvAfBmYvgez7O2FtGyVNaKzotPHELjMWpypoDZwBbGxnZ1fo3IgDj3JqFqAELM8od6HuzebPsnscc/k6DrUXyHk1+S/zeutBhLaVjxS1kZE26jc7nDP/nm3+PX3z1ywAowLJv3z5cfvnlCwZYxsbGcMMNN+CjH/0orrvuugVp4+6778aSJUtw5ZVXRp6V06bslAYtjcDG/v37cfjwYVx11VWoVGLX8FwBCwC0d7VF5zpQokceamG2jJKSNs2Mu6vJL16Y6nEjlKta9T2uNUNwUVaS7jl6DYwYKrxFhRcgEoTNqpxGhbVgbJjjvk1l3LepB+2dAi/+5XFc9fIpXPrimYio63KOicFO7P7vc+HXWJZcqXVF6uiQSBJqQjQWZyW9mUZI1tOz1oSgYEPL0Y+gsDMtExxn7qiAewDjberw1EKiq+uakvYIvU4i5HUonlN6ZystO0ldAC8A58xw6SNzXtloT7vOlXs/L35PYFz1XegSAdBcIwV0zKZ4KP1OIUGVgVnDWmZRRGZ4PAQJeFS1hjuJVFXqNPmThf8H2N9vBqYAC1O1l5QeTJLQrP92WRluVI8nUEReZten0YJsUVXksFAjAwcky1UVJlAiNJf0eIS/ryDwUA9GwUEVznWZCg2BkHj/M+MioSpcy9XYESTgpPqpx56pm5M2CfV+xhlnAThzm+O0WPEN2Q9I8HDNlGf13E0RwjjjTN3HsDwSSkkoL8znv/5Z/PJrfxGAKsGyd+9erFu3DqXSXKSM821iYgJvetOb8KEPfQhveMMbFqQNAHj44Ydx55134tvf/jaq1SrGx8fxtre9DUuXLsXhw4exbNkyHD58GEuWLFmwPpys9rwk4mqF22PHjiUAi5QSnZ2deOyxx3Dw4MGGdYjqWXt3NpbMSImqVVgrKmgDJydKR7S5nIlIEXUNwqagQIUNwv9PZ+WUWQUuypBMRBoeabJjkrCJmLAJwoyYgYtsJsXUhIMf3NGNv/mjlbjpdWfhc//fMjz8vU6MPNeNp35wLvwat3oL9ARk6pAIQ8QlIhgmFkOOxV/dhdaHD4OPGr+BBYtYxbysc2njUJD1rLysBgDtow5Wb2sD80IgyKIDwRkPAUsQLp7ZfgoKACgpck06BULdDFHL3Z2KcOJOkE5lkEhhTlv0GaMoNGQSNkVY88dlpTB0WUGJlUAAqmIaQfh92mQoNubyEkq8HIalQrAtfCtnCwiXGBZEuhqB9OFJFWbUIVmbx4OBqRo4UF4S8x6010aDQ4e5EZFWSoma8KzPU12DocQrCeKvCD1JNgDAw2fEwZRUAemxHffHVr3bZa5KJQ65S5Lsz1STmEu8HAEWNS683HGh+ELlRJmCImRkhOFoAsGTtbgeELTeS1ydmzEGJ+QW2YGItWtWSxxqPt8Ex934h8h6h/S7JiExKcdj0i0Y/uE//hav/LVfAqAAy549exYUsExNTeHNb34zfud3fgdvfvObF6QNbX/913+NAwcOYO/evbj55pvxyle+El/72tdw3XXX4atf/SoA4Ktf/So2bNiwoP04Ge2U9rTYzPd9bNmyBb29vbjgggsy/JULL7wQtVoNg4OD2LZtG4gIS5YswZIlS9Da2kCpzbDO3o7MZwkHKGMos5ZIlZKgMon0S6eK3yUzi1Rc241c/5yp2H/CTS1V7Ro3quobx8EDGUAyHxULOU8DGx3n1wCJMSeTel2b4fjxf3Xix//VafAfmDUOngZWCeVV6eXubJ09I+jdOwl27yF4y9swc2EPpi7sAi1J7cptQKYmgPTEVBTI1DnGrKXXOuHgzCdLCEQNPFeoTYTp3cnPGGKhLxvQIyAClWonrFzhmrtk1VIJw4uSFM1We0jMdhX/JaxFk9BS8UCMUMmR30/IwRtEXpOPlOwPQi+TG5JCZchrMRZTBCgZYzuRhQSlmeJQMgRjhrWitFwE8MgPS/MQKhZFXj0GK2GZCJ88kJRwWCnyUtieKSceebLyNXbiKdL0eCiNnTq6S6kxownbkFQ3k6WU0NgREciygdWIoCoFAhaoTVNGcE4YXrCYjAxSCtWccWuoUJCArW6HTSUY4dWzBzdGOFFaOCXT4ZNCij5G5BE4oTQ/Zxyf/edP4VXX/TIARYhdaMAyMzODt7zlLXjb296Gt7/97QvSRhH72Mc+hje96U348pe/jFWrVmHjxo0nrC8nyliD2j3z6Ieff5NSwvdjeVVNuF27di3OOOMMAGhIuK3Vajh69CgGBwcRBAH6+/uxZMkStLfbM3i0ERF+eOePcPs/34tdj+0FeWoBcFKZMenYsoQI67RMW921UWZRxPoXCKDc4A45Uago8yxILXxRnJ18OFB8gTQ3InmemVWQ5KgkJL1TJqSIMhRsrnYAqQUplhUPmJ/hRuhj/L4WVC/swcxFPQhWdgBTNaA9dezYDNCd4vHUPPBKMjxFUoJZCK7j9z4Q/e309UIMjQIASl098MePYfGBMpbsq0TES6V34Ue7WQ4nVz0WQKRJI6GAoSYL6u/yzvOEBzck8aafe/o8vfgzYiGAcKzXVUJrTgg0lDw8QCjxckYSPd1euPfOZBbVI1QHIoiqP6eL0aXfBVUewFcLM/HcsFZUiTryLqj3jOeEteLzhAJTibCsMgq1VWwREF94kKBYW8mw9NgOyIeACEPETuK7tHmyhjKrgBCrEBd5pn7oodL3bFr6PCIJH76ieBHL1dgRJEPdpJiMrEMu+prBKy5Lpg9CASRu+Z382hRKleScKYWPI7sfAgBU2hehNjUS/t2H2pSS23fcVohgBiBg1XPxRlCHtSUJjNKgCoWjBT6r4Q/+8oO4eP356OzsREtLC4aHh7Fu3bpIwmK+rVqt4q1vfSs2bNiA9773vbOmFJy2ps2+FT2VQQsRwfPUCz0yMoIdO3bgkksuQVdXV/R9MxlCvu/j6NGjGBgYgOd56Ovrw5IlS9DR0VH33OHhYdx983147N5tePbxAxAzdVzIiOPuggLUaCYCFCqcgAzwAdSE54Y7x3QxvnoTniZTqpo49Sc88/NGHBUNkPT96EVVL3D2jZeWBleLp7mY2xaRoLuEiUu64Z3bjWB1d1TKlh2bAfUkQQsbmQEtSn6WC1ru+6FSMQXgdnYjmBgDoEDLosdnsOhIueEi4rJSZuHRzy4vFJgWlEtck/yIiKiOR8SLyvN4AGEoMAyXBKTCNbnqsYapWkLM6oWwkTiL3EP63mVYeJGz/KwcQIWOOHOisW0WdgxkkkOTaE9K5aViTnYxl8qLaEvJ1tyWPDKyOQ5Nj4c2671LCclEpLPjkJPM4qNaDlDX4VzX+szNZ6rCyR5ADJy5YLBnawEqa7AU1X6SoQfPDeedOuNCiigcQ9dcCZRTROgc0BJ403DLSQ+vED4GItCyGLWp4fDvGLRwtwIZ1DKgBVBjdJLGMU3j6jzeir/+1z/H6974ahARnnvuOezZsweVSgXlchn9/f3o7+9HS0vjwpdFzfM8vO1tb8Ov/Mqv4EMf+tBpwHJ87fkLWp577jkcOnQIl112WTRg50q4DYIAQ0NDGBgYwMzMDBYvXowlS5agq6srca0DBw7g8OHDuPTSSyPuzBM/2YGb/+FbeOLBp+BNKCBimzwTOyRGmBaTKnvEVLBEfmVdgtoVmloGptnqeOj0TjXh2Scu08Og03L1v+sDJBl6ZxjSMur1FvNABiHvxnIP4S5btruont+DmQt74PWXQIuTuzobaEHVV4qlKRv/3kOqlgGAcksXvOo4mAT6D7Vh0V5Vo4aHYbOsJ8zmTQv3zpJyCZvJZwpICsBC7RMNgvKAgIAS4+PMTSw2eeMC0IRNxwqAbYtWVCKAKB8kGPL42fCFHejo80QYWkx7C/JUg4kINVlFmZdzFnMBAZEgPwcyCEnnPKOzEl9X8VtsoTlNKrYDbh1O5ZnCjlLKiHsR9y/2hAXk52ov6flJ89uIJBxWhmN4PGymlKZLoT5TSmOnnhKz8MC5kwHbQHZcBNesA0qpsS4EuCW8ZQUtgYeBZx4GkAItHX2oTYagxSlDCi8DWgQpHRYXZdQwDc45PvoXf4h3fuitANQmddeuXZGHZWZmBkePHsXRo0cRBAH6+vrQ39+Pzs7OWQMN3/fxrne9Cy996UvxkY985DRgOf72/AMtUkps3boVtVoNl1xySRQrJqKEwu1cTQiB4eFhDAwMYHJyEosWLcKSJUswODiIWq2Giy66KDdOveuJZ/HVz96CbQ/tRHUsJtnZFo6A/Ihc65MPHzUoobb8zCK9EMYZLWqXWn+XHZd/b1Qd2DRf+OCcWyc823maIAjkZ7QkQlfhPWiBskD6GW4AAARljtq5Xahe2IPaeT2gimMHLVMe0J59duP/9SMgFAQst3QhmBzHyh2taBtzMouWJFULyiW1M88DXgAiHoiESGTx2Dg92rTr3/SQJJ5NKqQRwA/J2kCrYxcWM7U/tAihYjvUD2spT40S7uLg4Ia3oNG40PeaDV8kz9PeAk0grSekaHrsTGExtVBLq9aRjEA5j8KReXLwifPCcKdSk7V4RlPjQodgQQwuz3p61DUlwAgcDgiKm8HAC42LmqyG4Zps+nXeeyFIwJM1tPDWXAAczRcwQrZhCDAzH1lBSwBuAeaBNwO3nHz/hF/FwLM/BpD0rpTbF8MLAQzjJZBUGVTL91dCfRpgDEchEKAFbaixGXzm/34K173ldQBiwHL55ZdHG0XTfN/H8PAwjh49isnJSfT09KC/vx+LFi0qvB4EQYCbbroJl112Gf74j//4NGA5Mfb8Ay1BEGDPnj0488wzM4TbuaQz1zMpJY4ePYqnn34aQggsXboUS5cuRU9PT8MXYv/uA/jG5zbhZ9/dgsmRaZVdZN4PeVGKZtQeqTLtAgI+1YrxCmQQueRtO+r8kJBfx/WfXXz0ZyZvIG0RsQ5hMT4ja6TuPZBK0wbLplmbfSGHoXp2F6Yu6oJ3QS+o1ZjMp3ygPTu5T373YUihuFCVli4s/5GP8jSvG9ZSTKNkmrB+Dnm7cykJgrwweygL6Gy6J7Z6PJlnYyxa2gOgXf/1vDY6lGQLa/nkhVllSe9YAB8kFQfG7s1LgeMoRTULiG190d47IhkRWxt58ySpMIwZXlTfEbjlvrU3z+VOBpBY24vIyApM5nFmtBdSQIFIDh55qWzel0R/yAdnrtWbV5XTCaVbQQEC8kEAODl1wrb53ry6XpswNKkyeWKiffDLV0SieFFfRJBJqwaAwK/CLbVkPhvUoMXwrlTaFqE2rfgtYA5AIvK01OQMxmlUhcEAtLJ2/NkXP47r3/prAJSo286dO7Fu3TorYMk8Eylx7NgxHD16FCMjI2hra0N/fz/6+vpyOTBCCLzvfe/D2rVr8clPfvI0YDlx9vwDLYCKOer04IUGLIAiZW3duhVnnnkmli5ditHRUQwODuLYsWPo6urCkiVLsHjx4oYAZuDAUXzj87fhx/f8HONHJsHA4ZOX1PpAaoEOc5AC6YcCsMmfx0aABRDtanWqpM3SIaFkRkv9RUSnqabd5nneHhXW8vN3qJZdvUkMzQU6EAjWdKN6US9mLuiBbOFAS3aCnf7OjxCQh/I0x4qnWlCeZiF/hBXiqCQWcwvhFIgzYaLXzsjKYWBWwKLOU94UU5o+UkXWhTLzPB5SEUhLqcUcsO/O9U7bl15YBdjCtTFBkCGYp8IX+d4XTUbWI9R8hvXDWvnhizx+WFxgL68vBunUACRaPbZemFSHRtMALA+sBuSrZwYXrmUDYAOrWgzSRQk+smKR0bXDzYgOO5mhtiKbGCALomxgVV9PXHsVWMqDnOdpEX4NTin5mwbeNAb3/ARAErSU23rhTY+GR0UuVizd72CEBpS+EVT9td//kw/iPR99FwDg2LFjeOqpp3D55ZfPirdCRJiamsLRo0cxNDQExhj6+vrQ3d0dFR0UQuBDH/oQli5div/1v/7XvHjqT9us7fkLWjRYWWjAMj4+ju3bt+P888/PVNYkIhw7dgyDg4MYGRlBR0cHli5disWLFzcs1nVseBw3/8MmPHD7j3DsgAIw2mwLv6rLEu6G4EFAgrQQVs6OUC+EaY5KHtCJziM/SsUuvogoAJknQW4LawFqQi0a1kp7I9LnEYDailbMXLYItXO6IfrjhWDmvkfgjvo4c0crENgqHFO4+xdwc3a16cXcFJtryPuBFpyjROiPQjBUsoCZQCo9GFtNJ9tvGC+EDojZyd36OP1MdXhIewvqcyN0SCir9pv3GwoKlLw+b7OOt8xvSGbl63xvnvZG6pBQUa+NkCEosZKR63GwVBjXRuRNz0FpbZ16YFUDZ6XiyxMp1TYulQ61kSQ4vGwla9syBzUYFvUE50hAXvtiwEkB9jzQEnhw3ORY8WtTOLr3pwDqgRZ9YaC0L8woggsBgb/8hz/FDe+8HsDcAYvNarUahoaG8K//+q/YtGkTXvayl2F6ehrLly/H5z73udOA5cTb8xO01Go1BEEwa8JtURscHMSzzz6LSy+9FG1tdi6BNiLC+Pg4BgcHMTw8jNbWVixduhR9fX0NpaWnJqZxyz/dgfs3PozBPSOQUmYmq4CCTNFBj2pw4EDAj0AAYCcsatOCZQ7PApL0TlKnXmstiLxnnVC4DD0wZjZIsR1hdiGsF9bSEuT1FkK/X6VST1/YA/Hgk1i6LVSPrUMeZWFYS0IqImx4D5706vCMRBSmyC7mWWK0OicspMjITrg2vDaCREjMRfSb1iOPav0UmyJvLqeCwnpWvAy3QUaLeS0JAozfOm2mBkc6rNXIa2MKrtUrHZG8By/MyikQEkK8mOsUcptlPZIxb0ZKAcbsYT19vzLk5diKZdreQZVqb0+/BmIPGhFBwIcgqQTu4NTN2DI9pIICa0mR4NorE1XQAZXGzC0kZ9vnfnUSR/f9DEAKtLR0w6uOJY5lEnBD0OKihE987o9w428rxdmxsTHs2LFjXgFL2sbHx/EHf/AH2L59O4gI69atw3XXXYdXv/rV6OzsXJA2T1tDe/6BloMHD+Iv/uIvsGHDBlx99dULUmuCiLBv3z6MjIzgkksuaVq8iIgwOTmJwcFBHD16FJVKBUuXLkV/f3/Da9VqHm75p2/h/o0/wqGnB8GkmkA0Ydc0c1esM04C8lSlVCthMUvyTKQt1wUWzFgIk3Vm6i2gPimCaHrhrhfWQpiCWngRMdRG6+6yZ7MQAqiKGZR5pemwlgaHje5BaZcEBt8jvy+B9ADEmVrp39P6W1AcLswtxmeAmYD8cEdefCFUzUiVmluABOsJLwpdNiLy6ntoVDpCkIgE9nT72jtXj4yslI0pWtCTRN46wEoqVWTHEvK0E3kRedxUBpb1stG7IeBHfSsx5W2rlw6uPYHqd+DghleoUVq+y0sqDNUUaMl6YLzqOIb2PQYgCVrcli4E1XElXhcS9ZkA3P1DABg++pe/j3d/+H8AiAHLZZdd1pT4ZzMmpcSf/dmfYXx8HF/60pcAAI899hjuvPNO3Hfffbj66qujKsun7bja8w+0+L6P73//+7jtttvwyCOP4CUveQmuv/56/OIv/uK8CA1JKbFjxw4wxnD++efPi7twamoqAjCu60ZqvI36K4TAXV+5D/f+x39h/45DgJ+KTVtc+QHzQBLwqAoCRd6ZxumpoepuKiSUm54KIBA+HG5XgDVd1MnUzlJE0s0DOgCsu1r9fT2yqi09NepPZmFRi0ggBco56bKCNIE1vg7QOKylMlpYzkJYL3wRGAJ+RTgqCFOvVfZV3jONwBrTxFYZVY0G8sXRglDyfXb6NNl7AJAlDutQG5SsXS5goyDy+Gkibz31WG2CZMhQQVP3EMjACkis54XCaBT56up5wigiypqqwvV+iyCsQaaAZNazU4+/JKWoIzhnqNESEPzKVUijqTwirk2/pTYzhuH9PweQzB5yKx0IapNg3AGFJT+4AJz9Q/jE33wcv/W7bwKgvB9PPvnkggIWIsKnPvUpHDp0CP/2b/9mDeVPTU01FBs9bQtizz/QYloQBPjhD3+IjRs34qGHHsIVV1yBDRs24JWvfGUhlnnafN/H1q1b0dfXh1WrVi1I2GlmZgaDg4MYHBwEYywCMI1coESE797yAO76t+/imc37AZ9ZAUWa8CiZhCeqioCIIHPdPNVVGbL7W3JSbBMgKOJ3yFCN157RASivDWMIOTxJjko9pVMpJQIKmlDjDXf+oeVrX9TgsjIC+AARGJw4G6QuuBBRtpaZUgrUISMTKa0Nyz2k29MlHwSpjBmCrFtg09TUqVc1Ot2fKLsrvZjnZMLo8JOUgJvD20o8t5DIq1N5G+nTsAheJNsOSJUBsP0es0rLhwRIwskp15DmYAkoENpIS4VIQoQAz0rkzSPFSwlJQVwDKGXW95R8OKEnzJYOnbgPS8jTFroMXmUDLT4cC6i3qWfXpkcx/NxmAEmdFqfcDuFNgTulKIvPEQx/+oF3423vUzV9jhdg+exnP4vdu3fjq1/96oJVhT5ts7bnN2gxTQiBhx56CLfddhvuv/9+XHzxxdiwYQNe9apXNeSjAMD09DS2bt2KtWvXHrcqmtVqNSonIKVEf38/li5d2vCFJSI89O2f4Juf24R9Tx6CrMbf2WTafVKiVHrB8cgDmKwvL6/TU/VkZ+yA83ga6julY8Es/I48r40kCSEDuPUWc2PhjVOvOVBkVxvpfCRVZm2KpZonICWFAn7F+B2RUJvMD8FozgxBeZ6Ygkih1yb/mXrSC3km6ibTgma5gCysGp2blWMhawbkg5MDh9uzzkx9GiAkVBMKe8LUOZThB9XzeNRkVRUytHg99cJtpqNrIq8E5T7TNH+pKAdLe6l4mFac/k6XM0jfQyAVJ62oIm8QluTg3G1IVNdaPFn9pfz7qImayuRL68HYQEvgw3GzXhzb71ydGsHIgS0AkqCFu62QwUwsLAfgjz/yPvz2//hNAKqS8vbt2wvxB2drRITPf/7zePzxx/GNb3xjwWoWnbY52QsHtJgmpcQjjzyC2267Dd/73vdwzjnn4PWvfz1e/epXo6MjW/RweHgYTz/9NC6++OITRsDyPC/ywGh1R11OoJH97IHHcev/uQvbf7wLM5PVDLHTxoehMPNFIICAn/iuXgaJKhpXhpPjwuZIghXN12BA3Zo3gCLIRosOqaJuRRcQwFJDKcfDIKSARzWrUJstxVgBEoTpvMX4HemQUL73RYaS7hXYyMierKLMs144DfTy3P42/lKssVP/HgCdlRPXptHXyAWIMlxoc0iwtkVOc04aeW1M7g+RiET0/DBTyhaCUmMx/rwZIq+bU9PJBiw1WAUBktnJ78r7orJ2tJdHhzDTgCVzH2G5A1tZh/r6S/W5PxrIBhRAIgiViBwEv7I+yzOzgZacchkzk0MYPfgEAKDctgie1mbhZUB6cNwKRFDDx//wfbjp7ccXsHzxi1/EQw89hFtuuWXBahadtjnbCxO0mCalxM9//nPceuut+M53voPVq1djw4YNeO1rX4vu7m584QtfwMMPP4z/9//+34Kx1Js1XQ9pcHAQ1WoVfX19WLp0acN6SADw8we34PZ/vhdb/3sHauNBNDGlM4/MRUSnUdaoClABV7MhdgeoBUFpP2SVRdVxJr+DEuJaAanQgW2RaF6/AyCIMPU6X9I9gI8SKgBTiwJBCak1FAeD0tWYDb9DEx3TltEvIeUZg/Lb5GqbmDokJiAporFDJAFbSCj3HgAhfeVdsJCjbWBGk541iKunFaRDKRISnOIKxPW9SD6ISaX5y9OZdpZQUgEir02RV4cw6wO9uFR4tr6WRGDxvgBoKEQI2EuBAMiEJNPHmER1DZgbjQ1PVsFefXW2TQsRV8oA3EIInhkfxOjh7QBSoEVR0+G4Ffz/PvRO/M473ggAmJycxBNPPIFLL710wTgkRIQvf/nLuO+++7Bp06ZZUQdO23Gz06DFNCkltm3bho0bN+Kee+6BlBKVSgVf+cpXsHr16hPdPavpekiDg4OYmprC4sWLsXTp0kw9JJs9u2MfvvG52/Do97egeixIZM4A9t0vhZNiWo23nttfV9aVkJlFtt5uUBd4s2UJpb02RKFWTU56dNSXcKFxLCm/9VLBteoqs9QfylOyjTgh4HByCNtZfocAkYTLS3U9Wno3nFYs1fcxu7ILOWnLIZDJA3qJYplaYyfkRtTzaGnBQhGKFqal6fPGFBGFKeZ2Iq8tXEjhk5IQud4X08OQVeStn36trRmwKmSAgAm0WDxlJtCLiephranQQ5QPShRnTIUydd2yYkBPjY0sUV17+4QlPGQFLTkZRdNjR3DsyA4Adm2Wj/7ee/C777oBwPEBLADwla98BXfccQe+9a1vLRhX5rTNm50GLTabnp7GO97xDnR3d2PlypW499570d3djQ0bNuDXf/3X0dfXt2DaL3MxXQ9pcHAQExMT6O3tjcoJNOrvwT1H8PXP3YqffudxTA6pGid20GJ4YKAWHaXGqxbP9LGAfTeoi9TlpS2bhExJAgGJqEJtPbe/IFXlF6RCOMlQVKDatCw+nvBBTKBiWUBm7/aPs3LS9YcaZuXUUSyt7/bPL7tgE2rToQiytGNelyEWapNEcOuQNc3zJEkrkdfufQkUoVVCVS/PAXqJcUlAgBjA1vciKY+elvw3AXQ9XsisgF7I5amflq+yzhRHJSwQGoaE8stAqMrRSkgyW9qhHtBrVI07XYPIJx885PSUeQWSAfJV6zPn2kTkbJ8BwNSxQxgb2AkAKLV0wze0Wf7wgzfh/TfdqI6bmsLWrVtxySWXFAqBz9a+/vWv4+abb8Zdd921YKGn0zavdhq0pO3IkSN44xvfiHe/+91497vfDUC9wLt378att96Ku+66Cy0tLfiN3/gNbNiwAUuXLj0pAYyUEiMjIxgcHMTY2Bi6u7uxdOlS9Pb2NkzTPnpoGN/8/CY8eOdPMTEwBVON1zZJ6x0qoCTAJVPKsXnPxSRrRm5/Y/KtJ+nuSQ8AwYWTcT/bFmW9QyXKr5WTviddfNBpWENJRpAk7favl5UjiSCkH6bLFnf760ykRpwZm9u/PllTJrpZVJ/GEx6cHH5HPTIyo7CaeM6irENwFKYJc2IRP6IRkVf3tSjQi702dYCeoe2SJPLW03ZJK/KaRN78Z0okEYQk8mYztohYYY9eUaCnQ2kAIF0GvPLF2Wt7NfBy8n0VfhVOKbsBmBo9iLHBpwEAbrkNgTcNxjg+/P534IPvUdWajxdg2bhxI/7t3/4N99xzz4K2c9rm1U6DlrS9853vxDvf+U5cc8011u+1sNxtt92G22+/HZxz/Pqv/zquv/56rFix4qQFMLqcwOjoaFP1kI4Nj+M///F2/ODWhzB2aEplICBbQiCt0JunxtuIAOuTj3JO7Z50No/mFNiyZhLnGYX4FEcFKIW8nNm7/e3nqUyQfMJpJoMkVX+orhBfGILSSrNmmKq+219zf2z6NLaUX4T6NPX4HUZICM3wO2LORUafpk7JDcXvsAM9/XxmA/Q04NZhKkYs8vAV0dnRlgS9jb02eaTr9LuhiepOnYri0f0jrihu3nOjkBBYGHZKpzenMq8k55BXngd0tQOmMJ3ngaeIq1TzwCrZsTMxsh8TR58BAHC3BTKo4kPveyc+/N7fAnD8AMu3vvUt/NM//RPuuecedHd3L1g7p23e7TRoSVu9nZzt2EOHDuG2227Dpk2bUKvVIg/MmjVrTkoAQ0QYGxuLygl0dHRgyZIl6Ovrs4ooSSmxc+dOEBFWn7kaN//jHfjv23+MgWeHwUhNZjYir02NV5CfM+SyE7YKLQRwwqymvPos+lidtgwkFxCte2Kz+p6C7GJuhoTy3f4CDKGaaYrfocISIlO127w2swCwPM6MPq9eSMgEbOpaKKRPoxfzWJ+GRwTsRqrC0bVTQC+/ZEGYIcUqVk9BJlxiAL0onDKPRF5JQvGpeNkK9Kz3Ef7WQgqUcoUIBZKCjsWyzhQ3JYhE/EygFz0f2/2T+v0dnlWctt1/QB4kUag1o/ppzbxyAda3CFjSC1rcrUi3KU8LzVTBWrOelvGjz2JyZB8AgLsVvP+3b8Tvv18p3WpZiYsuumhBszTvuecefO5zn8M999yTqRd32k56Ow1a5suICAMDA7j99tuxadMmjI2N4dd+7dewYcMGnHPOOSctgJmYmMDg4CCGhobQ2tqKJUuWoL+/H67rQgiBJ554At3d3RkQVqt5uONf78V933gAB3cfAfMba64IFoBLFXIJ4CuOB4rwIhQHIR0SqpcO6gkPBImKY8/4ssXwtfeibsmC0F0epfxSXEm4kaqwzoRKP5d6HBUhA1Bu/aEkmIlDQhyMcavOTOLa8AFCuMtOpqHbgFxAvvK+8HKGS6HOy3KGZEiCZYZome3ZpIm8OoW3bhp1SOS1CRHq72fD70iHJ2OgR2G/GtSmsijyCpKgMK05ex6BQsVpG/cngJcpmEmk2iPK1/1J34fO2NJqu40EFSWUYGMZ5Qjo6VTxKM2dM4hFnWD9iyD7uyPvCs3MgFlIrSP7NqNaHQXA8La3vgGf/KPfBXD8AMt3v/td/PVf/zW+/e1vY/HixQvWzmlbMDsNWhbKhoaG8K1vfQu33XYbjh49ite85jW4/vrrccEFF5y0AGZqagoDAwMYGhqC67qYmZnB6tWrceaZZ9Y9VwiBb3/t+7jnK9/Hvm2HAGFX4/XJSxBWJZOhoBhFACZxXQuY0eCCiJSgWs5ipr9gLCaEaiXbRplOnHFr2rJK27Yr7vrSD8Xm7Dv3vPpDqMtRERE4Ckhp5ZTRAsZZ7mKm+5NXiE9dN6vRork/IKCcU4hPL2ZJoKeek8oAygoG6vZC/1GU/usUJvKqgoO2qts2wCpDfgcK8jtU1pkPgMHlbt3Cl6a2S1aorU49KOFHWWnpsGcjIq9kQe5vDCRF/Mw+1eOFCRkoLkvK8wTYxSd1e770wFgWdOv7YIwD3e2Q/b0QvW3gPdmwy/C+zahVR/G7734rPvrhdwFQKuBbtmzBhRdeiK6uLmuf58Puv/9+/Pmf/znuueeeeRMIrVareMUrXhEV6X3jG9+IT37ykxgZGcGNN96IvXv3Ys2aNbjllltOe3Xmx06DluNho6OjuPPOO7Fp0ybs378fr371q3H99dfjkksuOSlLnU9NTWHLli3o6enB5OQkHMeJygk00jAgIvzXpgfxn1/4Fg7sGAR58XcBeZnQiJ60YzXeGsCo7k5QEoEhLnRnCms1kqVXWRrc6inI5UWQEkYr80oOWTPWRNGhCy3p3kitNE4zT3oK8u5fkIBPngIXFhVYm2ciSvkl5UGqR2SNKyzLqJYNY6zuYq5CQgoouUguzPWE2nxZA88RIswjfDMAoBDo5QLWuDaVKUSYd11tvlCcGZtQW16ZAEkSvvTqjI2svH7M/ckneVMYOtIlMxyUUTJAdz3BOV+ql85WDsFOkFa8GUkSFcee8pvW/VGlF5y6ZQuorQLZ342gvws8zGAc2vtzvOMtr8XHfv+3ARw/wPLggw/ij//4j3HPPffgjDPOmLfr6s1eR0cHfN/H1Vdfjc9//vPYtGkTFi1ahI997GP49Kc/jdHRUXzmM5+Zt3ZfwHYatBxvGx8fx913341NmzZh165duPbaa3H99dfjiiuuOCkAzOjoKJ566qkEEU7XQzp69CiIKAIwRTQNfnTfT3HbF+/BUz99BrUZVS7ANGtaNVMaFgTKqPE2IpzmETXzZOlV6ELxFvIWAf0+EBR4ceEaIaH6xQ01J8C2AOdl3dTjqKQL32lvRJQ1UwewmYX40n2ykam1KZVjeyE+231obRdJMpffYRNq056Ces+USMFUGS6anHiiREDeYt5IxC8v2wlQWifpQqHm/evn0oxQmwoJyeiajdSatTdMSvXc8pSO0++Tfq6a19OIw6RKUjauQWSeB1IezSwHTGBEDoLDgVcWqPQvw6uuezn+9jMfAaC8FJs3b8YFF1ywoGTYH//4x/jIRz6Cu+++GytWrFiwdqanp3H11Vfji1/8It7+9rfjgQcewLJly3D48GFcc8012Llz54K1/QKy06DlRNr09DS+/e1v49Zbb8X27dtxzTXX4Prrr8eLX/xiKyl2oW1gYAD79u3DpZdemqv+W6vVonICQoioHlIRjYOHvvMI7v5/38P2h3fBnxLRRG/nHSh3s56sQYCPah3OSHKBMENCjRZBARGGOZICZ/W0VAiAL2pN1R/SnhXbTt48z0xbNomX9bwdmt/ALOJ69XghvvAgIXJ32OlwgRkSaqQCm1+7pz73R4ujpb1hec9UIABJoMzK1to90TNgcVhNEuCGXJ66QDgMCUoISBKJ8FijkBDLEWqrR+SVUihQ2iAdXN+T7nu9dwlQYU/BArBwlBfKdqKwrlUTYc84tEUYpaPwUUMFbahhGu9839vwsc/8IYDjB1geffRRfOhDH8Kdd96JVatWLUgbQghceeWV2L17Nz7wgQ/gM5/5DHp6enDs2LHomN7eXoyOjuZf5LQVtdOg5WSxarWK7373u9i4cSMef/xxXH311bj++uvxspe9bMErjeo07pGREVx66aWF2/M8Lyon4HleVE6gvb29IW/nqZ8/jW9+/lvY/MMnURv3jTCJMvsCFUT8iYC8aPg22gnqydyWtpzO5lGLoA+SLHcRzICZsEYTwBtW+dWgRJ0mEwtz/VTZemnLlvZCjRMpZe4iaBJ5A/JDgMTjkFBdIq/Kkkqn1za6jwXJ2DIXc52xBa31Ugd4hmE/5WHKqijnVZxuLNQmUqAxJvLW5zCF2i7hGDKJvPVKSOi+claspIUuVQFS4cw8r43pRdL/ZuoO6oa2JAlM0QQm6RgAoII23PCeDfj//vajAGLAcv7556Onp8d6jfmwzZs3433vex9uv/12rF27dsHa0Xbs2DG8/vWvxz/+4z/i6quvPg1aFsZOg5aT0TzPww9+8ANs3LgRP/nJT/DSl74UGzZswCte8Yp5rzxKRNi5cyeEELjgggtmHaLyfT8qJzAzMxMVdOzs7GwIYPbu3I+v/91teOwHT2B6pKZ255YFzCzsSDoHSfhQ3MjssKwncKYiCdKazRHIIHLxa4EznSXUKJtFhJlOjkX3ol6WUDO8CA2StOhfEbE5M/yi+ppf80bXWyJQQjCv3n3o8FOjlF9zEZQkwpTfRkJtSm3Zpu2iSKWuPSQUFmnkPPtbqP5kQ2Ka6C3IT6SK590Hkb4OD8FGPS+SDO9ZgrNkGnJdgTfpQxKhlFPlPO1h0b+FtiL6RWmyemMuFqLfLr0RGJWD4HBRxRQA4Fd/7dX4/DcVn6NWq+Hxxx/Heeedt6DE1G3btuGmm27CrbfeinPPPXfB2knbJz/5SbS3t+Nf/uVfToeHFsZOg5aT3Xzfxw9/+EPceuuteOihh3DllVdiw4YN+OVf/uU5F/YSQmDbtm3o6OjA2rVr5y2rSQgRAZjJyUksXrwYS5YsQXd3d8M2Du87gq9/7jY8ct/jmByYSXhgbNWotfseUAt5gACALCQMRhF5NF6YG9X7kVKGvJnsop3mUwTkR9wNTXC19ycVEjKKG9b3IvlAGIJJh5zqL4IBfPLQwluzu3LLjj7mRbDwHhvcRxQSKla7R4MXoHjtHq3tUuaVXKVbW0q2IIEyrzQYHzK6B0EBSkbKb6OQkOKBZL02tva0LgoRchV501lizYSETP2idLZTvewiP7yPdI2tvPs3Q0LH6Cg81NCCdlQxhTe/6wb8+ef/GIACLJs3b8a55567oIBlx44deNe73oWbb74ZF1544YK1AwBHjx5FqVRCT08PZmZm8OpXvxp/9Ed/hB/+8IdYvHhxRMQdGRnBZz/72QXtywvEToOWU8mEEHjwwQdx22234f7778ell16KDRs24FWvelXThb48z8OWLVuwYsUKLF++fIF6rPqsywmMj4+jt7cXS5YsQU9PT0OvzsjgKL7+97fh4Tt/hmOHJhBQ1jNgTa2GB048o8YL1CfAVuUMWnlbboZQAjyEUugsoi/aCbBSShBTC4beAacrYc+mgF1aL0P3qVCl4lQoR1IAByUAFBKS8xZPHyWUrSJ+9e+jZq1UrNtP/x7FRPzi2j3K86T6VFTbRYW1skUa68n9q7FmL9Jo84aZY68egOSMR6Esk8hbL61dndt82YJA+vCohjbHrjYrQs0c/Uh0CjlvMiTUgnZseOdr8cl/+BMAar55/PHHcc4552DRokXWa8yHPf3003j729+Or3/967jkkksWrB1tW7duxTve8Q4IoTY0b3rTm/Cnf/qnGB4expve9Cbs378fq1atwsaNGxf0vl9Adhq0nKomhMAjjzyC2267Dd///vdx7rnn4vWvfz1e/epXN6yIOjU1hSeeeALnnHPOcRVYklJidHQUAwMDUT2kJUuWYNGiRQ0BzPjoJP7j727Bj+95FEN7j0WhkbT2C5B0+yveRg0AwQ/d+DZLu/xlqDvS2OUvQiCSDdXU4yEo4Tgfbp0CdjZAAqiU6jSZ0jyPgSnARgQHHE4BJVtJBEkq28kmcFZPkVfIIJcXkRbxM1Vg64WEBAUhkVfp1JgARhN1baZr9ziMWz0CQJLfoos0cjhgkuUS4BP3kard00hrxuhBgsOUzgRLnCclPPKsJS2sPJ2CIEmPc4EgCrUVLVugw0KcuRnAqkJCHFVMAwCu/qWX40vf+jwcxzlugGXPnj14y1vegq9+9atYt27dgrVz2k6onQYtzweTUuLnP/85Nm7ciPvuuw9r1qzBhg0b8NrXvjajffDkk09ifHx8wZUnGxkR4dixYxgYGMDo6Cg6OzujekiNMqdmpmZw65fuxg/+80EcemYAjkxO/HkgQwubmWq86vM6oZTI5d/MblaVLCjz1tz0WzOU1FyqbAhcQnVdc9HLWzwD8iFkAJeXCxE1dZ90W40EzvS9pHkR9dPB80X8lBcpK1RHobZLvbRlkeLpFNF2UccpmfxY7j8u0lh/fAQRqCia1i5IoCZraLWE59R9ZktaKGCMCBznhYR0tpc6R4Zek/pEb0ECgfRyx0deIVKQ6s8YDcFDFS1oQxXT+LU3vBbv/eN3YXh4GJVKBZOTkzj33HOxdOlSa5/nw/bv348bb7wR//qv/4r167OVqE/b88Ze2KDlO9/5Dj784Q9DCIGbbroJH/vYx050l+ZsUko88cQT2LhxI7797W9j2bJl2LBhA37t134Nd9xxB/7xH/8R3/ve904qVyURYXx8HAMDAxgeHkZ7e3tUD6lRJpPv+/j/t3fm4VHV9/5/nZlJANkTkhA2URE3FNBSpQJaLGIRMhNUrF6V+9DF21699rHV2odbW3+9dbteq62tt2pvtVVQMpMECIKgsoiggCUCguxhJ/sy2WbmnPP9/XHmnEwmZyYDZEgC39dfmmTmfGcInPd8lvd78f+tYMU7qzm68ySKHnt7J/LrOlr4H+sQMe5HcQZgTW+N2KnJ5jxApJmYUcWJHRhpiJcQinIKW0K0eLvEc7Jts7ZsmvrFXVs2RItpcOYitZWrcFyDs3BLyM7EL54LrK5rccMmI1tCkW2qeLlOpkOwU7HP7ol1Mxfh7J6UmK2ttiKxpbUlrHkeu8dBS8uw9YxK/N8P0yHYfN2RxJrHOvXYgpZB3uhZmEhUXaVGVBCiGYCeXMD0u2/hudf/n/G8wSBffPEF/fr1o7GxEUVRyMjISNjjKVGOHTvGXXfdxZ///Ge+9a1vddjzSrok569o0TSN0aNHs2rVKoYNG8aECRNYuHBh0ge3ziZCCHbu3InX6+Vvf/sbqampPPjgg9x5551kZGR09vFsEUJQX19vxQn07NnTykNqb3NK0zRWLPyYJW98wJFdJxGtfeks75dWj0HFIZxh47ggumLmISXmZBs9ABvvcaquGvEDjrZDuXYiINEtkJaWkGoNX5oVmHgDl9YN1nRUidoCsWuvqIQQuo6Ci5QYmTdnanBm/FmoxsSJZXAW789DJyQCOFBIdbT2FzLXe6MrRbrQCOlBnIordosmqiUUGdLY3pYQ2CdZx80g0jVDCCU46G1eS2C09uzs941rtjbAM99bw/snjt2/0CHi70yrcEVdpUIca2nfAVO+PZnXFv8BMD5MbN26lYsvvphBgwYBxiCuaZEQCoUYNGgQGRkZCW0YxuLkyZPceeed/P73v+emm246reeQdCvOX9GyceNGfvOb3/DBBx8A8MwzzwDwy1/+sjOP1eFomsbPfvYzampq+PnPf86SJUtYunQpvXr1Iicnh5ycHLKysjpsc6ijqa+vtwIdXS6X5cabmmo/Q2EihGB14XoWv76Cvf88hAja34ijN5KEIgjqTThwtXHjhXgldmNGJVa1I3IOw5yjMOcJzEpMrE/X5g3/VAZgjXDDECmOnqe2BWJWAWJVCcK5NUBEArQTl8MV1xPFeGzsllB8b5cAzgTt/lURCs/8uHAIBU3RbNe6rTkdemAsVBtZP45EWkLh4Vn7LSH7Fp0QAlUPghI7uyd6hskUSYq1tWV7HEuoahh2/CkJGuCFtBCEh8TtXgfh1f3Wrz2EJlSqKQv/XhpePbff8V3+529PG88bFiwXXXRRzA9HqqpSWVlpbRgOHDiQjIwMBg4cmLDtQllZGbNnz+a///u/ueWWWxJ6jKTbc/6KFq/Xy4oVK3jjjTcA+Mc//sHnn3/OK6+80skn6ziampq47777GDNmDL/5zW9aOb2WlJTg8/koKCjA6XQya9YsPB4PQ4YM6bICprGx0YoTcDgcVgUmlntvJJ+t2sJ7ryxm7+YStOaWX+FYa9QpSmqL94hQ0ZRQO7MmUQm/Ef/gx7txBPVgeH6jbR5SvBaM0RKKvSUU1IOkRBijRTrZxvdEMW+UjjZOtqqutglftN6zcN6NU3GecktI6LGTitsanOkoYbHQXlVLiRCJkWvt8QaLCbeEnI4UW/ER2/vHqOrEen8iX4eR62S0q9pLXDa3ncwkazvzvfjbTrG9Zlo8e8zATCJESlvBYl6vQdRSL2oBcOJkeu40XnzL+NAXCoUoLi5m5MiRCVdzzQH98vJyqqur6dOnjzXfFqs9XFFRwR133MFvf/tbbrvttoSuIzknOH9Fizm0GilaNm3axB//+MdOPlnH8dFHH3Ho0CHmzZsX82eEEBw7dswSMMFgkFmzZuF2u7nwwgu7rIBpbm624gRONQ+p+NPt/PWZBRzYeoRgUxAX8dOoAWtOwTCND7b6XvxPs8HwbEfbaoeZmtuq0hNZ7VBccUMBgYiboEZKuM0R0kPWf0fTEiUgTqna0aw1k+JIsV8X140MnVgtIfOcdrT1dhEJtoQ0o8pjY/cfS1wJIcJ29iF6OOx/T6JbWy0bM/Ht/tXwYLRGeBVdOCOqNnFaSbqxgO1QHLaJ5nar25Yvik7C206mGaFDiR/iGdSDOBQlHBDpIEXpEVG10anRyxEIguE5lhtv+hZ/Xfon4z1QVbZu3cqFF1542inKQgj8fj9lZWVUVlaSmppKRkYGffr0sdxzq6urmT17Nv/5n//JrFmzTus6km7L+Stazpf20KkghKC0tJT8/Hzy8/Px+/3cfvvtuN1uRo0a1WUFTDAYtASMqqrWsJ/d6rdpqNe3b18uuugidm3dy6JXFlO8+iuaa0IoimIrWiIRQqApKkIXcfOQbLcuRAgnLlQMjw1XjE+zQERLqOUTdnsDsAHNGBy1q3acabihQFgtIQGGSVu4XRTL2yWyupOocRwYdv+xMm80oWKkaDsjvqYZQ8wifpigGQho5A/pRlJ2xHsbeysnaNn9txFJNp45ujCsDoUuwsZxdlWbtmIm0io/lteMddaw+Ix+bxNrbSU2yGvGO6Ar1FNNgCZS6UmQZqbdfgt/XPiC8XNhwTJixIgO3RJqbGykvLycRx99lGPHjnHTTTfx+eef88QTTzB79uwOu46k23D+ihZVVRk9ejQfffQRQ4cOZcKECSxYsICrrrqqs4/WZSgvL6ewsJD8/HzKy8uZMWMGOTk5XHHFFV1WwIRCIcrLyyktLSUQCFgCpk+fPqiqyrZt28jKymLYsGFtHnt431HeeTGfTSv/SVNV2zwke5dVLXxT1giJQMSaauxP15pQrRt6tE1+PA8SVTN8VJwxev5nOgBrH24Y+wYY0Jtx4DQs7BNsbbWIJBFzkNfO7h+MakesdWjzrGA/ABtrK0cIYfy5CUdMkWSXQWSarulCs9o80bRq0VkzKmbVJvbvR3QoZXSVLnbIqCCkh+KIPRtXXj2EHl6njukEratUilJrxiuVXkz+7g386b0XjedQVYqLixk+fHhS15r37dvHI488QkNDA6qqMm3aNNxuNzfccMNpx49Iuh3nr2gBeP/99/npT3+KpmnMmzeP+fPnd/aRuixVVVUsWbKE/Px8jhw5wq233kpubi5jxozpsv9gqKraKk5AVVWGDx/OyJEj2xVdpUfLeeclH58t+4K60gYUHDFFS+SNQFdUgmoQh0Np48YLsT6V6+GbfOwB2GgPEjNQz3kKA7DRAY3G88S+eQa0AK4YA7B2xmiWn0g7nihW2wNOSSQZ4ZfGvJArKhco3myH+frtfk/tDADNFppDcbQT62Cstgt0YzaKFiO49ra2zPPazfnEbQm16xLcuiVkhni2F1sAMbaddJVycRyjQWr8nt0w6QbefP9VoEWwDBs2jMGDB9s+f0fQ0NDAnDlzmDdvHvfffz+NjY18+OGHFBYWsnnzZh5//HHuv//+pF1f0mU4v0WL5PSora2lqKiI/Px89u3bx3e+8x08Hg/jx4/vkgKmsbGRL7/8kqysLBobG/H7/aSlpVlxAu0JmJqKGt55KZ9PCj+j5ni9ZfAF9p9eTV8UIUTYeVQLV2JCMZ1s2zrAKlZFRQ+HMNqtyVorrDYOsOb37darTaEUewC2xWvGOEM4h8eRGvdGLoSRdGPOdjhpWe9uT1wZW0L2A7AdZfevh6sjLkdKu8nJmq6BYnijRIukeHEQAb2JHo5e7XoFtXzNyF5KROwZrS3jd8WF02o7xW8JaeHqXeKDvLrQCOpBaigPi12jFXXTdybxl3xj7k/TNIqLixkyZAjZ2dn2h+4AmpqamDNnDvfeey/f//7323xf0zT8fn9SE6MlXQYpWiRnRn19PcuXL8fr9bJz506+/e1v4/F4mDBhQrvOtmeDuro6vvrqK8aMGWM5AOu6TlVVFaWlpdTV1TFgwAAyMzMTWresr61n0Z+XsNq7gfKDVWjouGh947edDSBEQG+C8JpoQg6wCEJaEKFAD5tP7XYtD8s2XtBmODaSyAFYLcomvz1PFMPvRrEVEXY3ZBUVdAWX4or5/tplIpmtlPihkS1rwdFVgrgtGF0QImScKWFxYYiGeNtO8asdsV9H9PxPtAGenfjUwx406IRbQollO5lbcZEVrrbn0akTNTSKOsCYsZk0dSKvFxrblWdLsDQ3N3Pvvffi8Xh48MEHu2xbWnLWkKJF0nE0NTWxcuVKvF4vW7duZfLkyXg8HiZOnNius20yqKysZO/evYwdOzbmVpGu61acQE1NDf369bPWLdsTMM1NAfL+vISPves5saccRRg/bydaIm+g1nCjgCDNbdpFLY9pqRKYQ7wOXDgd8c3NTncAVmAMwKY42vp2RL+GyOcSxkVjVzvMigamG3HiniiaroarCW0HYGO9B6YnijOG3b9dCyZSJLWXnBzZ2oo2joudQaSFK2IuWxFxui0h+7kZpd0tofbmf2r0ClRChigCJkz8Bv/44HXjdWoaX375JYMHD05q2GogEOD+++/n1ltv5eGHH5aCRQJStEiSRSAQ4KOPPsLr9bJp0yYmTpyI2+1m8uTJ7TrbdgQnT57kyJEjjB07tl0jOhMhBLW1tZSWllJVVUWfPn3IyspKKA8pFAqx7O8ref/vH3P462MoodYiLdaNOaA3WeF1kYnD7eUPmQO7ic5EWK2UOGuyqh7EGfMG2H4ooG21w2YdOpJg2O4/VqslZrVDgCvB2Y7IDax4CdDGe6DiVJzoCBuX4PieKPEykaIN8HQza0qE23rtbgkZ0ROmozIYgiU1zvCsEo5pSKQiZl5L0zX8VNFMIy5SUQlyw+TreXPZ/xrPGxYsWVlZDB061P7QHUAoFOJf//VfufHGG/nZz34mBYvERIoWSfIJhUKsWbMGn8/HJ598woQJE3C73dx888306GE/rHgmHD58mIqKCq655prTrvCYeUimX0SvXr3IyspKKA9JCMEH765m6d9WcqD4CKixQxCDImCZnBk2YiqqFoIYRZ7Ya7LG32VHHGddoNWabKS4sNuQMYnnABsvDymgN9ND6YXDYb/pArRYy2OY97mUVJztGK6Zw8z2LaF4Rm26MVdkU+0wz9S2BWO8W+gCZ4yWkGGL7zAqQlEtobjiU1cRCmFX2cRaQubXA3oTKY6eCc//JFK1MVpC1TQKPwAppDL+xrH8fflrxvNqGtu2bSMzMzOpgkVVVb7//e8zfvx4fvnLX0rBIolEihbJ2UVVVdavX4/X62XNmjVcc801eDwebrnlljMOURNCsH//fpqamrjqqqs6bCjYzEMy4wRSU1PJyspKKA9JCMG6oo34Xi1i/9bD6IHW348ULSYqIZzC8ARRCYVvgu2YlAktXB1o20qJ5+0ihDAcYBWXrSGbXYXFbKWIBDdZhCDs6mpUbdodyNUDuJQU2yqB0SKzz+YxTPVcMTxRWsRDiwOsjlNJSdgAD9q2hOy2wVoep1nCo43Y01UUxWW/Eo7eahA7mmiBqYVXwp3tuATHbwkJavQKQgSs37fxE8ax4MP/MwwMdZ0vv/ySjIwMW7uAjkLTNP7t3/6NUaNGtXLxlkjCSNEi6Tw0TWPjxo34fD4+/PBDLr/8cjweD7feequtMVw8dF1n165duFwuRo8endR/7BoaGqw4ATMPKSMjI6Gq0aaP/4n3z0vZuXEfWpOwFS1tUpnD4kURShs3XgivAttsFxlW/xopigvFptoBrT/Nm9tOxk24vRugBhEDsImam+lCoOmhcChg2+eOvUpNeIvHaStKzO0rh1XtiAw3jFft0NEVI6vIzhOlvZaQEiNLyO6904QRaKkIBacjJaZBYGRLSETlTSVaEWs7/9NeS6iaZhpwkoJGiGuvH887K//aSrAMGjSI4cOH2x+6A9A0jf/4j/9g8ODBPP3001KwSOyQokXSNdB1nS+++MKKV7j44otxu93cdttt9OvXL+5jNU1j+/bt9O/fPyEPlo6kqanJcuNVFMWKE0gkD+mrzV/zzos+dmzYQ7CuJSvHfpBXb1WlCIkAuqKH3WFFzJyYlmpHeDhXOFAcSrvVDvMGGJ1iDfHXfVVdJcVhf1NtM08iMDaLhMDlSLHyeOwwV7B1dFShkhJhymfnkNtyJg1NCFub/Fgrzy2ZSOCKMf8TnZxs2OQ7228JRRj+nUpLyMxEcigO21Tq028JCepEldUScpHC1ROualVh2bZtG+np6UkVLLqu8+ijj9KnTx9eeOGFLmmdIOkSSNEi6XqY/1Dm5eWxfPlyhgwZgtvt5vbbb2/jxVBeXs7+/fsZPnx4UvvsiRAIBCwBo+u65cZ7wQUXtPvYvdsPsPClArau3oG/poEU7EVLJAJBs94YvmG2vmm3l3ejCi3cSmn7b0DsUEDjr3689kMbkWTd2O0daU1aRNKpDY4G9RBORbG9kds9TtXVsBOxK65Hi7m5Ba3FHsTfEjIykUBROKVwQ1MExbpZt20JqeFKUuItIRFuO0W3hII0G95AwNXjxrBo7d8twbJ9+3YGDhzIiBEjbJ+/I9B1nSeeeAKAP/zhD1KwSOIhRUt3Zd68eRQVFZGZmcmOHTsAw7X27rvvpqSkhJEjR7Jo0SIGDhzYySc9M4QQ7Ny5E6/XS1FREWlpabjdbmbOnEldXR133nknL7zwAlOnTu3so7YiGAxacQKhUKhVnEB7HNpzmHf/uJhNHxTTUNFs3HCEbtsWCYpmUhWjqqMSQujhVlJ7brSK+f+EKxzGIGkiSdZhl48okRRvbVlDF3q4JdS2qgFtKz4thmvgiFElOLPkZHNO5dRaKaeyJWR+DUTc+Z/WQ9Lh9zliMyxZLaF6amiiPjwIrHH1+DEsWtNasAwYMIALL7zQ9twdga7rPPnkk9TX1/O///u/UrBI2kOKlu7KunXr6NOnDw888IAlWh5//HHS0tJ44oknePbZZ6murua5557r5JN2HEII9u7di9fr5b333qOsrIz77ruPH//4x2RlZXXZHngoFKKiooLS0lKam5sZNGgQWVlZ9OnTp90zlx+v5J2XvHy6dDP+siYU0frnI0WLiaaEcOguBDohgtZcRHvVjpZQwLYViPgtoRApjtQYVZuoik+4JWTs/jiIF/7YegC29Qpy+9UO+wHYdlfC44iLVplIp5CcbAoXxaYlFK9NJ4Qx35TslpATF5ePvQzvun9YgmXHjh3069ePkSNH2j5HRyCE4Le//S0nT57kr3/9a5cwo5R0eaRo6c6UlJQwc+ZMS7RcdtllrFmzhuzsbE6cOMHNN9/M7t27O/mUHc/nn3/Ogw8+yLPPPsv27dspLCzE5XIxa9YsPB4P2dnZXVbAqKpKZWUlpaWlNDY2kpaWRlZWFv369Ws/TqCyjnf/kM+6xZ9TdbgWBYetaAmJACkRw726oqPpqlG9UNrmIQkhCBFsPRAcMcwKcazuI+z+jXgAQ6QoitK+A6zQwl4ibedm4q1Sq3qQFEcP2/erzY084nUoEFe0RSYnC3SUVi2heC7BKma0g12l5XQykczHKq2EWsvsTnyRZIzwxtoSqtUraabRErNXXHU5+RsWtBIsZgp6shBC8Nxzz7F//37eeuutDjOfPHLkCA888AAnT57E4XDwox/9iEceeeScrEKfp0jR0p2JFi0DBgygpqbG+v7AgQOprq7upNMlh+XLl/OrX/2K/Px8q88uhODo0aP4fD4KCgpQVZVZs2aRk5PDhRde2GUFjKZpVFZWUlZWht/vZ+DAgVacQHtnbvA3kvfqEj5c9AkVB6stN16AkAhawYqRmH+vBTohEUQour1giUAXLc66EB0sGFuUhLQQDoejjRuveY5Y1Q6T9lpULSLJEAVmiyiuIV94xsbObyaeJ4ph4pYSY5Xa3iXY/GcynmsxmD41rVfCzeeInSUUryVknxJuzs3Ui9YtocuvuoyCDQut93PHjh306dMn6YLlpZdeori4mAULFnSo2eSJEyc4ceIE1157LX6/n+uuu47CwkLefPPNc7oKfR4hRUt35nwTLQ0NDdx333288cYbpKen2/6MEIKTJ0+Sn59Pfn4+9fX1zJw5E7fbzSWXXNJlBYyZh1RWVkZtbS39+/cnKysroTykQCBI4evvU/TWKsoPVBsbPDFES+TrF4pOk9pgrfzaZdvErnaESHXGEjqtb+RCmF8znr/dakdYgESLi3jVBVVTURxKqxt/5HkjE7Ijv262zBKpdohwS8j0ZWlv/sd8rdGtrfi+OWFr/lgr4bYp4aZLsIIrxuuIbgk5cDLqiotZ/Nl7lmD56quvuOCCC7j44ottn6MjEELw5z//mU8//ZRFixYl7FZ9urjdbh566CEeeuih86IKfR4gRUt35nxtD50K5eXlFBQUkJ+fT2VlJTNmzCAnJ4fLL7+8ywoYIQTV1dWUlZVRXV1N3759ycrKIi0tzbbvH2mqd/nll5P/WhEfez/l8I7joMVfpw3ozfRw9EQTGqoI4sCBpqhx83fM2Q4N1bqpm4Oi7ccPtHzfNoIgxjbTqc7NmNswoKCj4opTebLbdjKvGy8TSUfDYeOuG0/sabpmDAvH8M1pNXQb5TcTz8hOE3q4/WXfEqoTVTSJemsDbNToS1j8+Xs4nU5r2L1nz55ccskltufqCIQQvPHGG6xatQqfz5cUN+xISkpKmDJlCjt27GDEiBHn9Ae68wgpWroz0aLlscceIz093SqBVlVV8fzzz3fyKbsOVVVVLFmyBJ/Px7Fjx7j11lvJzc3tUPfcjsbMQzLjBHr37m3FCZg3nN27dyOEaCPEhBCsWrSGJf+3kgPFh9FDWDMqJqZoiURDxSEMUWIG5pnEuiGrIoSmq7gcPdpk9hiPa9tGEYJwhUBYLR47olepzYpLIiJJCB09bNMW6X0Tv9qhh9117UVSZFusxV1X4FBc4bTu2NUOiBZJitV2irclpOlqeNJGtMkbitWmM9+rBmppFP7w5pfOJZdezJJNi1oJlh49eiS9Evnmm2+yePFiFi9enJCP0ZlQX1/PTTfdxPz585k9e/Y5X4U+j5Cipbtyzz33sGbNGioqKsjKyuKpp57C4/EwZ84cDh8+zIgRI8jLyyMtLa2zj9olqa2tpaioCJ/Px/79+5k2bRoej4dx48Z1aQHj9/utOIGePXsSCoXo169fuy7AQgg+WfYZBa8tZ8/mA1acgJ1oUUXIctcVQhiVF10QIhDzhhzUm0mNeJ7IyoVAxL2RCwxzNoQxA5LoKnW8uZlYN3Jd6Oi6jtPR1kI/8mdatYREKCyS4m8JqXoIRXFYw7yR5zqzKIW21zRdgo0AxcS2hBw4GTlqBEs351mCZdeuXaSkpDBq1KikCpZ33nmHd999l6VLlybkW3QmhEIhZs6cyfTp03n00UcBWYU+h5CiRSKpr6/n/fffx+v1smvXLqZOnYrb7WbChAlddg1T13W2bt0KGBtJqampVpxAInMCX6z9kkV/WsJXn+1BrddbVWAiRUura4aHYHVUgiJo3YCDeoBUR+xBXk0Ybr/RczN2A7mRlQuEg1Sn/ZBm9I28ZSjX8DppTyTpaOGfdyacrq1qIcszpq1pXdv4Aev167oRPxCjJRSdixTprht/a+nUWkIjLx7Jsi+8lmD5+uuvcblcSRcsixYt4s0336SoqCghn6IzQQjB3LlzSUtL46WXXrK+LqvQ5wxStEgkkTQ1NfHBBx/g9XopLi5mypQpuN1uJk6c2GFrmWeKpmlWeJ1prd7Y2Gi58TqdTitOIJG5gZ1bdrPwDwVsW7uLQJ2KhhpTtJgtHrM60qw14HL0ANquUkcLAF1o4QwlJ85wvlFsh9wWEWDMwhizIEZLKF5wpG5lCEFrcRFra0kVIXQ0hK6QmkBLKPwGEBJBq6pilz9kXhNOb26mZUvIzjcndktIFxr1US2hCy+6kKIteaSkpFiCxel0cumllyZVsBQWFvLqq69SVFRE//79k3Ydk/Xr1zN58mSuvvpqS8A9/fTTXH/99bIKfW4gRYtEEotAIMCHH36I1+tl8+bNTJw4EY/Hw6RJkzp0TfNUCIVCFBcXM2zYMLKzs21/prm52RIwgOXGm0iK9oFdh/jb0wvZsWEPTdWBVhUYO7Fg5iQJIVBFCKFo6OjxZ010FaEoOHHarlKrQsUVI4MoqAVxOlwx5mZsog7MoVyhGDf5BERSW7+Z9o3jOAWRZH4vXip17JaQsN2Qinxev6imQdQZ58HBsJHDWPaF1xIsu3fvRlGUpAeLLlu2jN///vcsW7ZMeqJIOgopWiSSRAiFQqxevRqfz8f69euZMGECHo+Hm2++OelrmybBYJDi4mJGjhxJZmZmQo+JzEPSNM0SMImkaB87eJIFL/nYtGIr/vImdIzV6EiiE6kBdEUz0pPRIrZ4DFRdDVdMWl9LIFD1EELopDrthzRbOdISPTejx/FEMeY/tHA4Y/TGT6zWDhgtISXG3Ey8LSFd14znjDM3Yz32DNx1o98Pv6imUfitllBmVgZvLPsTQ4cO4YILLmDPnj0IIbjsssuSKlhWrlzJM888w/vvvx/TnkAiOQ2kaJFIThVVVVm/fj15eXmsXbuWsWPH4vF4uOWWW5K2FdHc3ExxcTGXXnrpad8EzDyksrIygsGgFSfQu3fvdm9gFSerePt/8ti4/J/UHvdbDrDR7rsm5vqyEIIgTUYVRoiY4sB8DBgZSgjD48QcTG3vRm7epKPFhV10gTk3owkVhEJPp30FKtbcjPn8iW8JaTgSnptRcThOzSW4pSVUR6Oos1pCw4YPpeCzd/H76ygtLaWuro6ePXty+eWXJ+TAfLp8/PHHPPXUUyxbtixhcS2RJIgULRLJmaBpGhs3bsTr9fLRRx9xxRVX4PF4mDZtWkLVjERoaGhg+/btXHHFFR02F6CqqiVgmpqaSE9PJysri759+7Z7M6urrmfhH/JZV/AZZUcrcelthYid50pIBHDiQkVFE6GozZq2+TumuNB1HZcjxVa0xMzfCVcknIorZrXDDFg0H3Mqq9RGoKMRGumIEjaxWkLt+c3EagmZxG8J1dAgao2fw8HgYYNZvjWfHj16IIRg3759BINB0tPTKS8vp76+/pQcmBNl3bp1zJ8/n2XLljF48OAOeU6JJAIpWiSSjkLXdbZs2UJeXh4rV67kkksuIScnh+9+97v07dv3tJ7T7/ezY8cOxowZc9rP0R6aplFRUUFZWRn19fVWHlL//v0TiBNowPeXIj5atJ7S/ZVWnICtaAnPvwDhuRcV3dyWiRPiGOldYqY/OxVnuwO5QEsEgaDVKnukYIkmqIVwxlylblt9sXxacOGKM1zcZksoQiQlUkkyh36jr2+0hOqtLKHsIdms+LKglWAJhUJcccUVLe9jlANzv379yMzMjGlgmAgbN27k5z//OUVFRQwdOvS0nkMiaQcpWiSSZKDrOl9++SV5eXmsWLGCoUOHkpOTw+23386AAQMSeo6amhq+/vprrrnmmqR7W5joum7lIdXV1VmfxgcMGJBQnMDi/1vOynfWcnxvKSIUW7SYmMnK5jaSKoLWjdXu5000oaEJtc3KMsRpo4TnZnSh06OD52Z0XUcoIjz0K8J+My2iqL2WULwIglhzMwJoELU0iFpLnGUPGcz7Wwvo1aun5ZQcCAS48sorYwpQOwPDzMxMBg0alPDG3ObNm3nkkUdYsmSJlQkmkSQBKVokkmRj5rp4vV6KiopIT0/H4/Ewc+bMmPMplZWV7N27l3HjxiXdPTQWuq5bcQI1NTX079/f+jTenoDRNI3lCz6i6G8fcmj7MdAUe9ESnYeEYWIX1Jpjzr9EVyU0oYYzgVwoApw2Bm3mtaDFFVjHcMx1nvLcTIuLrfk+KUrbbSZVhAxvG+HA5YjhdGuTSm1sVBkbSfFaQvWilnpREz6Tk4zBg1jxZSG9ehm/L/v376e5uTmuYLF93vp6y8AwJSXFWp+PNXC+detWfvKTn1BYWJjUoEWJBClaJJKzixCCPXv24PV6Wbp0Kb1798btdjNr1iwyMzNRFIW3336bXbt28etf//qsbSa1hxCCmpoaysrKqKqqom/fvmRmZpKent5uO0EIwcf56/H+7xIObT+BCLb+nt0NVRUqTpyAICgCCMVofcRrCZmOtFaooXC2MluDtnMzJgEtgMvRNkfIuKbdKrUgJEIogpiCxDxT5Cr16bSEoO0qtV/U0CT81sp4ZlYmK4oLuaC3MVR84MABGhsbueqqq85oXsX0/ykvL0dRFDIyMtB13RIn27dv54c//CFer5fRo0ef9nUkkgSRokXSNThy5AgPPPAAJ0+exOFw8KMf/YhHHnmEqqoq7r77bkpKShg5ciSLFi06ZzwfhBAcOHAAn89HYWEhqampjBw5kuLiYhYvXkxGRkZnH9EWIQR1dXVWO+GCCy44pXbChg8243u1iK83HUBr0mOIltauvAJhtI5whPOQWv8zZLe2bJjZqaCDS0lJKMnZrI44FRcOnO1EEBgtmpa5GWE55sY6k0m8CIJ4LSEQNIg66kUtDpzoaGRkZvDBl4stwXLw4EHq6+sZM2ZMh24IBQIBjh8/zg9+8ANqa2u5/vrr2bBhAwUFBVx55ZUddh2JJA5StEi6BidOnODEiRNce+21+P1+rrvuOgoLC3nzzTdJS0uz7Lerq6t57rnnOvu4HY4Qgt/97ncsWLCAjIwMVFVl1qxZeDwehg8f3qUTqevr6yktLbXykMw4gUQM+Las2cp7f1zC15v2E6rXrNdpFyVgViaMf58EjaIBMNZ9Y82+6LoOilGpMI3mIu3u482aBPQmKx06kVVqk5AeQhMhejrt55A0oRlVJMV8rsgIAhGzkgRQr9fhF1UAOHEyMGMgHxQX0ruvsal28OBB/H4/Y8aMSWqG1ubNm3nsscfo378/FRUVTJs2jdmzZ/PNb36zy2Z3Sc4JpGiRdE3cbjcPPfQQDz300DkfdCaE4JlnnmHr1q28/fbbpKamcuLECfLz8ykoKKChoYHbb78dt9ud9CTeM6WhocFqJ7hcrnbnISL5ungf775cwNbVX9Hkb8IpokWL/cZPSARw4EQjZLVLoLVgiUYgCGpBUhwuW5Fgl6dktWzCwY7xtoQUlLDfjAj7zaSEX0O8lpAWbpfZW/f7daMlpIUN+9LT01lRXEDf/sZWWUlJCXV1dUkXLAcOHODee+/lrbfeYvz48TQ1NbFq1SoKCgr44osvePbZZ5kxY0bSri85r5GiRdL1KCkpYcqUKezYsYMRI0ac85Hyv/rVrzh27BivvfaabXulrKyMgoIC8vPzqaqqYsaMGbjd7qS7mp4pTU1NlJaWUl5ejsPhsNx4ExksPrj7MAtfKmDLqi9prDLiBGKJlkjbfw2VgN5kzb7ETIiOXKUWGG2ksPldvABIsyVkF1IIsSs3qgih6qqRbWTbEmo7qyME4TVmQYPwUy9qcOJCQyUtPY0PigstwXLo0CFqampaZe4kg0OHDvG9732PN954gwkTJrR9napKIBDoMI8iiSQKKVokXYv6+npuuukm5s+fz+zZsxkwYMA5L1p27tzJ5ZdfntDNpqqqisWLF+Pz+Th+/DjTp08nNzeXK6+8skuX5c08pPLycnRdJyMjg6ysrHbzkBobG1n74ScUf/A1X3y4jYbyZqL/3bLLKjIFhUA3DOoi4gRCegCXYm/wFi+kMFZLyGzv6EKQ4ozh/aIHSIm4plm1aW+VGqBB91MnKgFwkkK/tD58UFxIvwH9ADh8+DDV1dVJFyzHjh3jrrvu4tVXX2XixIlJu45EEgcpWiRdh1AoxMyZM5k+fTqPPvooAJdddtk53x46XWpra1m6dCk+n4+DBw8ybdo03G4348aN69ICJhgMWnlIqqrGzENqaGhg27ZtrYz1qsqqWfByPuuXbKbmmGFZbydaor8mFB1VN6ow5vZONHbVDk1oxpAu2AYbtjzW8HKBtqvU8So3ujB+FkWxHcr167U0i/rw8DEMHDCQFV8W0n9gi2CpqqrimmuuSeqf+YkTJ7jzzjt5+eWXmTJlStKuI5G0gxQtkq6BEIK5c+eSlpbGSy+9ZH39scceIz093RrEraqq4vnnn++8g3ZR/H4/77//Pl6vl927dzN16lTcbjcTJkzo0gImFApZcQKBQIBBgwZZeTU7duzg6quvpk+fPraPra+t591XClnt20BlSY2VhwSxk6KDohknLgKiKdymMb7enm1/y/qxaNPeiW1mBwGtkRRHzzY5Qsbj7FpCwnq+BlGHX1TjIgWVEAMGDGD51gIGpg8AjI27iooKxo4dm9Q/49LSUu644w5eeOEFpk6dmrTrSCQJIEWLpGuwfv16Jk+e3KrE/fTTT3P99dczZ84cDh8+zIgRI8jLyyMtLa2TT9u1aWpqYsWKFfh8PoqLi7nppptwu91MnDjxtC3azwaqqlJRUcGxY8eoqakhOzuboUOHJhTu19wUwPvqUj5c9Akn95aj6Vqb7SOAoN5MqqNlpkaEfWAcONDC1YxI4jvSCtCJ+Z6a7rqAZfXvVFJwKo64fjMAjXoDtaIcABep9B7QixVbCy3BcvToUcrLy5MuWCoqKpg9ezb/9V//xW233Za060gkCSJFi0RyLhMIBFi1ahVer5ctW7YwceJEcnNzufHGGxNaST7b1NXVsXPnTq688kprDsbv95OWlmbFCbQnYEKhEO++UsBa30aOfl2Korfc1KNFC7RsJQkhjDgBoaIpobjVF2gJRxSErf6FA8WhhJ/TfktICEMkKThwKk7bn6nXa2kWDYQwXPj69+vP8q0FpGUY/kRHjx6lrKyMsWPHJlWEVldXM3v2bH71q18xc+bMpF1HIjkFpGiRSM4XgsEgq1evxufz8emnn/LNb34Tj8fDTTfd1CWcd2tra9m1axdjx45tNaBrhvuVlpZSV1fHgAEDrHTiROIEViz8mKI3V1Gy7RghNUgKredL7LaSWpvZBYkmlpOvEIKQHiDF0SPmoK8rImZAFcaQsBMnDlxWSyiFHoQI0K9fP5b/M5/0TCPu4dixY5SWliZdsNTW1nLHHXfw2GOPkZubm7TrSCSniBQtEsn5iKqqfPLJJ+Tl5bF27VrGjx+Px+Nh6tSpnZJ1VF1dze7du9sIlmh0XbfiBKqrq6104vT09HYFjBCCj/LXsfSvK9n7z0NWnECs+Rfz6yKcRC2EQFNUY4soRrVHF5o18yIE6EJFARwOVxvBEk2tVkEj9QCk0IOefVNZsbXAEizHjx/n5MmTSRcsfr+fO++8k4ceeoi77747adeRSE4DKVokkvMdTdPYsGEDXq+Xjz/+mCuvvBK3282tt956VtKlq6qq2LNnzymHQ5rpxKWlpVRVVdGnTx8rTiCRm/rnH/0T75+WsvPzvehNbb9vJ2bMVGodjRAhiDSzizOnogkNIfSY3jEV2glj7iX8fH179+P9rT4yBg8CjO2d48ePM27cuKQKloaGBu666y5+8IMfcN999yXtOhLJaSJFi0QiaUHXdTZv3kxeXh6rVq1i1KhR5OTkcNttt1lrxx1JZWUl+/btY9y4cfToYb8WnAhCCPx+P6WlpVRWVtKrVy8rTiCRPKRtG3fy3h8L2b5+N0G/Gs4hsvF/iRImGiq6rqMSjFN90dFQSQlHAiCMthAYYYsV2glCGHMuAp0+ffqwbEs+WUOM7KmzJViampqYM2cO//Iv/8K8efOSdh2J5AyQokUikdij6zrFxcV4vV6WL1/O8OHDycnJYcaMGQwYMOCMn7+8vJyDBw8ybty4Dp2pEULQ0NBg5SGlpqaSlZWVcB7Snm37WfhSAcXrvqKpKmglLUPsaoqGikM4EegERQAUEf55HR3VyjCKpknUU6NXAKDg4ILevSja7CN7WBYAJ0+e5OjRo4wfPz6pgqW5uZl77rmH2bNn86Mf/ahLOy1LzmukaJFIJO0jhGDHjh14vV6WLVvGoEGD8Hg83H777aSnp5/y85WVlVFSUsL48eOTvsVkl4eUkZGRUGXn8L6jLPh9PptXfklDZTO60G03fqIDHnVFR9NVmvX6mO67DXodAdFEAKM31btXb5ZuzmPIiGzA8Ec5cuQI48aNS6hadLoEAgHuv/9+pk+fzkMPPdRhgmXevHkUFRWRmZnJjh07AM7p1HbJWUGKFolEcmoIIdi9ezder5elS5fSr18/cnJymDVrFhkZGe3e9EpLSzl8+DDjxo0762vXTU1NlhuvoihWoGMiszSlR8t569l32fLRNupKG6LM7NqmUoNh/a+hERTNuHChK8bMimnN34NeBGiiV69eFH2ex9CRQ41rhd+j8ePHJ1WwhEIh5s6dy+TJk3n00Uc7tMKybt06+vTpwwMPPGCJlscff/y8SG2XJA0pWiQSyekjhGD//v34fD4WL15MamoqOTk5uN1uBg8e3OYmuG/fPmpqapJePUiEQCBgCRhN0ywBk8jwcU1FDQtfLuCTJZuoOlKLJuzN7KJXo4Wi06jVU6YfAaAHF+DoJVjyWR7DLzIES1lZGYcOHUq6YFFVlXnz5nHdddfxxBNPJKUlVFJSwsyZMy3RImM5JGeIFC0SSTJpbm5mypQpBAIBVFXlzjvv5Kmnnjony+RCCA4fPozP56OwsBBd15k5cya5ubkMGzaMV155hQ8//JD8/Pwu58wbDAatOIFgMGjlIcWKEIikwd/Iwpd9rC38nPKDVSiipQJj5+dSr9dQrh8DoHfPvuR/9g4XXjwcMOZ8SkpKkl6F0jSNBx98kNGjR/PrX/86aTMs0aLlfAhAlSQVKVokkmRiDoX26dOHUCjEpEmTePnll8nPzz+ny+RCCE6cOIHP56OgoICysjIcDgevvfYaV199dZce9AyFQlRUVFBWVkZTU5OVh9S3b992zx0IBMl/bRmrFq7l+NdlrVpIJn69hgr9GD179mTxZ4taCZaDBw8mfc5H0zQefvhhsrOzefrpp5P6ZyFFi6SDsf1l7dyarURyDqEoivVpPRQKEQqFUBSFxYsXs2bNGgDmzp3LzTfffE6JFkVRGDJkCA8//DCKorBkyRJycnJ48sknqa6uZsaMGXg8HkaPHt3lBExKSgrZ2dlkZ2ejaRoVFRWUlJTQ0NBAeno6mZmZ9O/f3/bcPXqkcs/DudzzcC6hUIiiv69ixT8+5vBXx0EzBYygZ8+eFG541xIsFRUVZ0Ww6LrOo48+Snp6Or/73e/O+nuflZXFiRMnrPaQGY4pkZwJstIikXQgmqZx3XXXsW/fPv793/+d55577rz5xPniiy/yySef8N5771lrzZWVlSxevBifz8fJkyeZPn06ubm5XHHFFV06kVrTNCtOwO/3M3DgQCtOoL2bvxCCle+tYcn/reT48WP8T/7/4+LRIwFDsBw4cKDDV7+j0XWdX/ziFyiKwh/+8Iez8l5HV1pkarvkDJHtIYnkbFFTU0Nubi5//OMfmTRp0jkvWgoKCli4cCHvvPNOzOpBTU0NS5cuxefzUVJSwrRp0/B4PElPLz5TdF2nurqa0tJSamtr6d+/P1lZWQnlIUVimuuNHz8+6YLlySefpKGhgVdfffWsvLf33HMPa9asoaKigqysLJ566ik8Ho9MbZecCVK0SCRnk6eeeorevXvz+uuvn/NbFMFgEIfDkfAGjN/vZ9myZfh8Pnbv3s3UqVPxeDx84xvf6NICRghBdXW1lYfUt29fsrKySEtLiztwXFVVxd69e5MuWIQQ/Pa3v6W0tJQ33nijyw1BSySngBQtEkkyKS8vJyUlhQEDBtDU1MStt97KL37xC9auXSvL5HFobGxk+fLl5Ofns23bNqZMmYLH4+GGG27o0jddMw+prKyMyspKevfuTVZWVps8pLMpWJ577jkOHDjAW2+91aXfO4kkAaRokUiSybZt25g7dy6apqHrOnPmzOHJJ5+ksrJSlskTpLm5mVWrVuH1etmyZQs33ngjubm53HjjjZ3u9RIPMw+prKyMiooKevbsSVZWFi6Xy5phOZO8pUSu//vf/55t27axYMGCLv1eSSQJIkWLRCLpPgSDQVavXo3X62XDhg1cf/31eDwepkyZktSKRUdQX1/PoUOHOHnyJP379yc7O5uMjIyknFsIwZ/+9Cc2btzYaghaIunmSNEikUi6J6qqsm7dOvLy8li3bh3XXnstHo+Hb3/72wnZ8p9tampq+Prrrxk3bhy6rlt5SA6Hw3Lj7YjKixCC119/nQ8//BCfz5fUao5EcpaRokUikXR/NE3j008/xev1snr1aq666ircbjfTpk1LyJY/2UQKlmhB1dzcbMUJCCEsAdOrV69Tvo4QgjfffJOlS5dSWFjYJcWbRHIGSNEikUjOLXRdZ9OmTeTl5bFq1SouvfRSPB4P06dPT8iWv6Opra1l165dtoIlmkAgYMUJqKpqxQn07t07oWu9/fbbLFq0iCVLlnQJsSaRdDBStEgkknMXXdfZunUrXq+XFStWMHz4cNxuNzNmzKB///5Jv74pWMaOHXvKlZNQKER5eTmlpaUEg0EGDRpEVlYWvXv3tjWzW7RoEW+++SbLli1LWORIJN0MKVokEsn5gRCCHTt2kJeXx/vvv09GRgZut5uZM2cmZXOrrq6OnTt3npZgiUZVVasC09TURL9+/aioqGDSpEk4HA4KCgr4y1/+QlFREf369eugVyCRdDmkaJFIJOcfQgi+/vprvF6vdaM3BUxGRsYZZ/L4/X6++uqrDhEs0Wiaxu7du5k/fz779u3jyiuv5ODBg5b3j0RyDiNFi0QiOb8RQrB//368Xi9LliyhR48ezJo1C7fbzeDBg09ZwJiC5Zprrkn6XElRUREvvvgil1xyCdu3b2fy5MnccccdTJkyRfqySM5FpGiRSCQSEyEEhw4dwufzUVhYCMDMmTPxeDwMGzasXQFTX1/P9u3bGTt2bNIFy8cff8xTTz1ltbpCoRBr167F5/Oxfv16Xn75ZaZOnZrUM0gkZxkpWiQSicQOIQTHjx/H5/NRUFBAc3MzM2fOxO12c9FFF7URMOXl5ezbt49rrrkm6YOw69atY/78+SxbtozBgwe3+b6maaiqKj1aJOcaUrRIJJLTR9M0vvGNbzB06FCKioqoqqri7rvvpqSkhJEjR7Jo0SIGDhzY2cc8Y4QQlJWVUVBQQH5+PjU1NcyYMQO3283o0aPZunUr3//+91m1ahWDBg1K6lk2bNjAY489RlFREUOHDk3qtSSSLoYULRKJ5PR58cUX2bJlC3V1dRQVFfH444+TlpZmBUFWV1fz3HPPdfYxO5zKykoKCwvx+XwcPnwYv9/PM888Q05OTlITqTdv3swjjzzCkiVLGDFiRNKuI5F0UaRokUgkp8fRo0eZO3cu8+fP58UXX6SoqIjLLruMNWvWkJ2dzYkTJ7j55pvZvXt3Zx81aezZs4c77riDe+65h88//5zDhw/zne98h9zcXK655poOFTBbt27lJz/5CYWFhVx00UUd9rwSSTfCVrTIkXOJRNIuP/3pT3n++efx+/3W10pLS8nOzgYgOzubsrKyzjpe0tm7dy9z5sxhwYIFXH311YDhzbJs2TJefPFF9uzZwy233ILH4+G66647IwGzfft2fvzjH+Pz+aRgkUiiSF5tUyKRnBMUFRWRmZnJdddd19lH6RRqamq46667eOuttyzBAtCvXz/uuecevF4vn332GTfeeCN/+ctfmDhxIo8//jgbNmxA07RTutbOnTv54Q9/yKJFi7j00ks7+qVIJN0e2R6SSCRx+eUvf8k//vEPXC4Xzc3N1NXVMXv2bDZv3nzetIfKysrIzMxM6Gebm5tZtWoVeXl5fPHFF0yaNInc3Fy+9a1vxfVT2b17N3PnzmXBggWMGTOmo44ukXRX5EyLRCI5M9asWcMLL7xAUVERjz32GOnp6dYgblVVFc8//3xnH7FLEQwG+fjjj/F6vWzcuJEbbrgBt9vNlClTSE1NtX7uwIED3Hvvvfz9739n3LhxnXdgiaTrIEWLRCI5MyJFS2VlJXPmzOHw4cOMGDGCvLy8pOT6nCuoqsratWvJy8tj/fr1XHvttbjdbkaNGsUDDzzAX//6V77xjW909jElkq6CFC0SiUTSFdA0jfXr1+Pz+Xj77bdZsmQJkyZN6uxjSSRdCSlaJBKJpKshhDjj0EaJ5BzE9i+F3B6SSCSSTiSZgmXFihVcdtlljBo1imeffTZp15FIzhay0iKRSCTnIJqmMXr0aFatWsWwYcOYMGECCxcu5Morr+zso0kkiSArLRKJRHK+sGnTJkaNGsXFF19Mamoq3/ve91i8eHFnH0siOSOkaJFIJJJzkGPHjjF8+HDr/4cNG8axY8c68UQSyZkjRYtEIpGcg9i1/uXAr6S7I0WLRCKRnIMMGzaMI0eOWP9/9OhRhgwZ0oknkkjOHClaJBKJ5BxkwoQJ7N27l4MHDxIMBnn33XfJycnp7GNJJGeETHmWSCSScxCXy8Urr7zC9OnT0TSNefPmcdVVV3X2sSSSM0KuPEskkvOakSNH0rdvX5xOJy6Xiy1btlBVVcXdd99NSUkJI0eOZNGiRQwcOLCzjyqRnE/IlWeJRCKxY/Xq1RQXF7NlyxYAnn32WW655Rb27t3LLbfcIo3ZJJIughQtEolEEsXixYuZO3cuAHPnzqWwsLBzDySRSAApWiQSyXmOoijceuutXHfddbz22msAlJaWkp2dDUB2djZlZWWdeUSJRBJGDuJKJJLzmk8//ZQhQ4ZQVlbGtGnTuPzyyzv7SBKJJAay0iKRSM5rTO+SzMxMcnNz2bRpE1lZWZw4cQKAEydOkJmZ2ZlHlEgkYaRokUgk5y0NDQ34/X7rv1euXMmYMWPIycnhrbfeAuCtt97C7XZ35jElEkkYufIskUjOWw4cOEBubi4Aqqpy7733Mn/+fCorK5kzZw6HDx9mxIgR5OXlkZaW1smnlUjOK2xXnqVokUgkEolE0tWQPi0SiUQikUi6L1K0SCQSiUQi6RZI0SKRSCQSiaRbIEWLRCKRSCSSboEULRKJRCKRSLoFUrRIJBKJRCLpFkjRIpFIJBKJpFsgRYtEIpFIJJJugRQtEolEIpFIugVStEgkEolEIukWSNEikUgkEomkWyBFi0QikUgkkm6BFC0SiUQikUi6BVK0SCQSiUQi6RZI0SKRSCQSiaRbIEWLRCKRSCSSboGrne8rZ+UUEolEIpFIJO0gKy0SiUQikUi6BVK0SCQSiUQi6RZI0SKRSCQSiaRbIEWLRCKRSCSSboEULRKJRCKRSLoFUrRIJBKJRCLpFvx/f6EBe7e4ppkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmap.vis_map()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def vis_2d_histogram(bmap: np.ndarray, path_pts: List[Coor]=None, figsize:Tuple[int, int]=(8, 8), title: str=None) -> None:\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    ax = plt.axes(projection='3d')\n",
    "    _x = np.arange(bmap.shape[0])\n",
    "    _y = np.arange(bmap.shape[1])\n",
    "    _xx, _yy = np.meshgrid(_x, _y)\n",
    "    x, y = _xx.ravel(), _yy.ravel()\n",
    "\n",
    "    top = bmap.map.flatten()\n",
    "    bottom = np.zeros_like(top)\n",
    "    width = depth = 1\n",
    "\n",
    "    cmap = cm.get_cmap()\n",
    "    bar_plt = ax.bar3d(x, y, bottom, width, depth, top, shade=True, color=cmap(top / np.max(top)))\n",
    "\n",
    "    # xline, yline, zline = path_pts[:, 0], path_pts[:, 1], path_pts[:, 2]\n",
    "    # ax1.plot3D(xline, yline, zline, 'red')\n",
    "    if path_pts is not None:\n",
    "        edges = np.array([(path_pts[i], path_pts[i+1]) for i in range(len(path_pts)-1)])\n",
    "        for edge in edges:\n",
    "            xline, yline, zline = edge[:, 0], edge[:, 1], edge[:, 2]\n",
    "            ax.plot3D(xline, yline, zline, 'red')\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_zlabel('z')\n",
    "    plt.close()\n",
    "    return f\n",
    "\n",
    "np.random.seed(10)\n",
    "src, target = bmap.gen_random_state()\n",
    "\n",
    "# bmap.free_graph_pos_id_map[src]\n",
    "# bmap.free_graph_pos_id_map[target]\n",
    "# path = nx.shortest_path(bmap.free_graph, bmap.free_graph_pos_id_map[src], bmap.free_graph_pos_id_map[target])\n",
    "# path_pts = np.array([bmap.free_graph_id_pos_map[pt] for pt in path])\n",
    "# bfs_f = vis_2d_histogram(bmap, path_pts)\n",
    "# bfs_f\n",
    "# path = nx.dfs_preorder_nodes(bmap.free_graph, bmap.free_graph_pos_id_map[src], bmap.free_graph_pos_id_map[target])\n",
    "# path_pts = np.array([bmap.free_graph_id_pos_map[pt] for pt in path])\n",
    "# dfs_f = vis_2d_histogram(bmap, path_pts)\n",
    "path = nx.astar_path(bmap.free_graph, bmap.free_graph_pos_id_map[src], bmap.free_graph_pos_id_map[target])\n",
    "path_pts = np.array([bmap.free_graph_id_pos_map[pt] for pt in path])\n",
    "astar_f = vis_2d_histogram(bmap, path_pts)\n",
    "astar_f\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " ...\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 7\u001b[0m in \u001b[0;36m<cell line: 37>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m             queue\u001b[39m.\u001b[39mappend(neighbor)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m solve_bfs(bmap\u001b[39m.\u001b[39;49mmap, src, target)\n",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 7\u001b[0m in \u001b[0;36msolve_bfs\u001b[0;34m(map, src, target)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m     \u001b[39mif\u001b[39;00m (np\u001b[39m.\u001b[39marray(pt) \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39marray(target))\u001b[39m.\u001b[39mall():\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m     \u001b[39mfor\u001b[39;00m neighbor \u001b[39min\u001b[39;00m get_3D_neighbors(\u001b[39mmap\u001b[39;49m, pt):\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m         queue\u001b[39m.\u001b[39mappend(neighbor)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 7\u001b[0m in \u001b[0;36mget_3D_neighbors\u001b[0;34m(pt, shape)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m coor \u001b[39m=\u001b[39m []\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mprint\u001b[39m(pt)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m row, col, height \u001b[39m=\u001b[39m pt\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mif\u001b[39;00m row \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m: \u001b[39m# front\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X55sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     coor\u001b[39m.\u001b[39mappend((row \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m, col, height))\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
     ]
    }
   ],
   "source": [
    "\n",
    "# @njit\n",
    "def get_3D_neighbors(pt: Tuple, shape: np.array) -> np.ndarray:\n",
    "    n_row, n_col, n_height = shape\n",
    "    coor = []\n",
    "    row, col, height = pt\n",
    "    if row > 0: # front\n",
    "        coor.append((row - 1, col, height))\n",
    "    if row < n_row - 1: # back\n",
    "        coor.append((row + 1, col, height))\n",
    "    if col > 0: # left\n",
    "        coor.append((row, col - 1, height))\n",
    "    if col < n_col - 1: # right\n",
    "        coor.append((row, col + 1, height))\n",
    "    if height > 0: # down\n",
    "        coor.append((row, col, height - 1))\n",
    "    if height < n_height - 1: # up\n",
    "        coor.append((row, col, height + 1))\n",
    "    return coor\n",
    "\n",
    "\n",
    "# solave bfs\n",
    "def solve_bfs(map: np.ndarray, src: Tuple, target: Tuple):\n",
    "    queue = [src]\n",
    "    visited = set()\n",
    "    while len(queue) > 0:\n",
    "        pt = queue.pop(0)\n",
    "        if pt in visited:\n",
    "            continue\n",
    "        visited.add(pt)\n",
    "        if (np.array(pt) == np.array(target)).all():\n",
    "            return True\n",
    "        for neighbor in get_3D_neighbors(map, pt):\n",
    "            queue.append(neighbor)\n",
    "    return False\n",
    "\n",
    "solve_bfs(bmap.map, src, target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X56sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m a \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m3\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X56sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m visited \u001b[39m=\u001b[39m \u001b[39mset\u001b[39m()\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X56sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m visited\u001b[39m.\u001b[39;49madd(a)\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "visited = set()\n",
    "visited.add(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1, -1, -1),\n",
       " (-1, -1, 0),\n",
       " (-1, -1, 1),\n",
       " (-1, 0, -1),\n",
       " (-1, 0, 0),\n",
       " (-1, 0, 1),\n",
       " (-1, 1, -1),\n",
       " (-1, 1, 0),\n",
       " (-1, 1, 1),\n",
       " (0, -1, -1),\n",
       " (0, -1, 0),\n",
       " (0, -1, 1),\n",
       " (0, 0, -1),\n",
       " (0, 0, 1),\n",
       " (0, 1, -1),\n",
       " (0, 1, 0),\n",
       " (0, 1, 1),\n",
       " (1, -1, -1),\n",
       " (1, -1, 0),\n",
       " (1, -1, 1),\n",
       " (1, 0, -1),\n",
       " (1, 0, 0),\n",
       " (1, 0, 1),\n",
       " (1, 1, -1),\n",
       " (1, 1, 0),\n",
       " (1, 1, 1)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_3D_neighbors(pt):\n",
    "    for i in range(-1, 2):\n",
    "        for j in range(-1, 2):\n",
    "            for k in range(-1, 2):\n",
    "                if i == 0 and j == 0 and k == 0:\n",
    "                    continue\n",
    "                yield (pt[0] + i, pt[1] + j, pt[2] + k)\n",
    "list(get_3D_neighbors((0, 0, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List, Tuple, Type\n",
    "t = Type[Tuple[int, int]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmap.map > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.615773105863909"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = (2,5,5)\n",
    "b = (9, 2, 5)\n",
    "np.linalg.norm(np.array(a) - np.array(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nx.shortest_path(bmap.free_graph, src, target)\n",
    "# bmap.obstacle_graph.get\n",
    "nodes = bmap.free_graph.nodes\n",
    "n1 = random.choice(nodes)\n",
    "n2 = random.choice(nodes)\n",
    "# list(nodes)[:4]\n",
    "# for n in nodes:\n",
    "#     print(type(n))\n",
    "#     break\n",
    "bmap.free_graph.has_node(n1)\n",
    "# nx.shortest_path(bmap.free_graph, n1, n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "G = nx.path_graph(5)\n",
    "print(nx.shortest_path(G, source=0, target=4))\n",
    "\n",
    "p = nx.shortest_path(G, source=0)  # target not specified\n",
    "# p[3] # shortest path from source=0 to target=3\n",
    "\n",
    "p = nx.shortest_path(G, target=4)  # source not specified\n",
    "# p[1] # shortest path from source=1 to target=4\n",
    "\n",
    "p = nx.shortest_path(G)  # source, target not specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 2, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_xyz.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# The graph to visualize\n",
    "G = nx.cycle_graph(20)\n",
    "\n",
    "# 3d spring layout\n",
    "pos = nx.spring_layout(G, dim=3, seed=779)\n",
    "# Extract node and edge positions from the layout\n",
    "node_xyz = np.array([pos[v] for v in sorted(G)])\n",
    "edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()])\n",
    "\n",
    "# Create the 3D figure\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "\n",
    "# Plot the nodes - alpha is scaled by \"depth\" automatically\n",
    "ax.scatter(*node_xyz.T, s=100, ec=\"w\")\n",
    "\n",
    "# Plot the edges\n",
    "for vizedge in edge_xyz:\n",
    "    ax.plot(*vizedge.T, color=\"tab:gray\")\n",
    "\n",
    "\n",
    "def _format_axes(ax):\n",
    "    \"\"\"Visualization options for the 3D axes.\"\"\"\n",
    "    # Turn gridlines off\n",
    "    ax.grid(False)\n",
    "    # Suppress tick labels\n",
    "    for dim in (ax.xaxis, ax.yaxis, ax.zaxis):\n",
    "        dim.set_ticks([])\n",
    "    # Set axes labels\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"y\")\n",
    "    ax.set_zlabel(\"z\")\n",
    "\n",
    "\n",
    "_format_axes(ax)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 18\u001b[0m in \u001b[0;36m<cell line: 22>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m fig \u001b[39m=\u001b[39m go\u001b[39m.\u001b[39mFigure(data\u001b[39m=\u001b[39m[go\u001b[39m.\u001b[39mMesh3d(x\u001b[39m=\u001b[39m(\u001b[39m70\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mrandn(N)),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m                    y\u001b[39m=\u001b[39m(\u001b[39m55\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mrandn(N)),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m                    z\u001b[39m=\u001b[39m(\u001b[39m40\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mrandn(N)),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m                    opacity\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m                    color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mrgba(244,22,100,0.6)\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m                   )])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m fig\u001b[39m.\u001b[39mupdate_layout(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     scene \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m         xaxis \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(nticks\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, \u001b[39mrange\u001b[39m\u001b[39m=\u001b[39m[\u001b[39m-\u001b[39m\u001b[39m100\u001b[39m,\u001b[39m100\u001b[39m],),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     width\u001b[39m=\u001b[39m\u001b[39m700\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     margin\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(r\u001b[39m=\u001b[39m\u001b[39m20\u001b[39m, l\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, b\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, t\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m))\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X31sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m fig\u001b[39m.\u001b[39;49mshow()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/AI/lib/python3.10/site-packages/plotly/basedatatypes.py:3398\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3365\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   3366\u001b[0m \u001b[39mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[1;32m   3367\u001b[0m \u001b[39mspecified by the renderer argument\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3394\u001b[0m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   3395\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   3396\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpio\u001b[39;00m\n\u001b[0;32m-> 3398\u001b[0m \u001b[39mreturn\u001b[39;00m pio\u001b[39m.\u001b[39;49mshow(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/AI/lib/python3.10/site-packages/plotly/io/_renderers.py:396\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    392\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    393\u001b[0m         )\n\u001b[1;32m    395\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nbformat \u001b[39mor\u001b[39;00m LooseVersion(nbformat\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m LooseVersion(\u001b[39m\"\u001b[39m\u001b[39m4.2.0\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 396\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    397\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    398\u001b[0m         )\n\u001b[1;32m    400\u001b[0m     ipython_display\u001b[39m.\u001b[39mdisplay(bundle, raw\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    402\u001b[0m \u001b[39m# external renderers\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "\n",
    "N = 70\n",
    "\n",
    "fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)),\n",
    "                   y=(55*np.random.randn(N)),\n",
    "                   z=(40*np.random.randn(N)),\n",
    "                   opacity=0.5,\n",
    "                   color='rgba(244,22,100,0.6)'\n",
    "                  )])\n",
    "\n",
    "fig.update_layout(\n",
    "    scene = dict(\n",
    "        xaxis = dict(nticks=4, range=[-100,100],),\n",
    "                     yaxis = dict(nticks=4, range=[-50,100],),\n",
    "                     zaxis = dict(nticks=4, range=[-100,100],),),\n",
    "    width=700,\n",
    "    margin=dict(r=20, l=10, b=10, t=10))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([[1,2,3], [4,5,6]])\n",
    "data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function BaseContext._insert_global.<locals>.on_disposal at 0x10f2bb2e0>\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/huakun/opt/anaconda3/envs/AI/lib/python3.10/site-packages/numba/core/typing/context.py\", line 474, in on_disposal\n",
      "    def on_disposal(wr, pop=self._globals.pop):\n",
      "KeyboardInterrupt: \n"
     ]
    }
   ],
   "source": [
    "# @njit\n",
    "def build_graph(map):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    return G\n",
    "\n",
    "m = np.random.randn(100, 100, 100)\n",
    "m[m < 0.3] = 0\n",
    "m[m > 0.3] = 1\n",
    "graph = build_graph(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a 3D BFS\n",
    "@njit\n",
    "def bfs_3d(start, goal, map):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    # BFS\n",
    "    path = nx.shortest_path(G, start, goal)\n",
    "    return np.array(path).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a 3D A*\n",
    "def astar_3d(start, goal, map):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    # A*\n",
    "    path = nx.astar_path(G, start, goal)\n",
    "    return np.array(path).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a 3D DFS\n",
    "def dfs_3d(start, goal, map):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    # DFS\n",
    "    path = nx.dfs_preorder_nodes(G, start, goal)\n",
    "    return np.array(list(path)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3D RRT Algorithm\n",
    "def rrt_3d(start, goal, map, max_iter=1000, goal_sample_rate=0.05, max_step=1.0):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    G.add_node(start)\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    # RRT\n",
    "    path = []\n",
    "    for i in range(max_iter):\n",
    "        if np.random.rand() > goal_sample_rate:\n",
    "            rnd = np.random.rand(3) * np.array(map.shape)\n",
    "        else:\n",
    "            rnd = goal\n",
    "        nearest_node = min(G.nodes, key=lambda x: np.linalg.norm(np.array(x) - rnd))\n",
    "        new_node = nearest_node + (rnd - nearest_node) / np.linalg.norm(rnd - nearest_node) * max_step\n",
    "        if np.linalg.norm(new_node - nearest_node) > max_step:\n",
    "            continue\n",
    "        if map[int(new_node[0]), int(new_node[1]), int(new_node[2])] == 1:\n",
    "            continue\n",
    "        G.add_node(tuple(new_node))\n",
    "        G.add_edge(tuple(new_node), nearest_node)\n",
    "        if np.linalg.norm(new_node - goal) <= max_step:\n",
    "            path = nx.shortest_path(G, start, goal)\n",
    "            break\n",
    "    return np.array(path).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3D RRT* Algorithm\n",
    "def rrt_star_3d(start, goal, map, max_iter=1000, goal_sample_rate=0.05, max_step=1.0):\n",
    "    # build a 3D graph\n",
    "    G = nx.Graph()\n",
    "    G.add_node(start)\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    G.add_node((i, j, k))\n",
    "    for i in range(map.shape[0]):\n",
    "        for j in range(map.shape[1]):\n",
    "            for k in range(map.shape[2]):\n",
    "                if map[i, j, k] == 0:\n",
    "                    for di in range(-1, 2):\n",
    "                        for dj in range(-1, 2):\n",
    "                            for dk in range(-1, 2):\n",
    "                                if (i + di, j + dj, k + dk) in G.nodes:\n",
    "                                    G.add_edge((i, j, k), (i + di, j + dj, k + dk))\n",
    "    # RRT*\n",
    "    path = []\n",
    "    for i in range(max_iter):\n",
    "        if np.random.rand() > goal_sample_rate:\n",
    "            rnd = np.random.rand(3) * np.array(map.shape)\n",
    "        else:\n",
    "            rnd = goal\n",
    "        nearest_node = min(G.nodes, key=lambda x: np.linalg.norm(np.array(x) - rnd))\n",
    "        new_node = nearest_node + (rnd - nearest_node) / np.linalg.norm(rnd - nearest_node) * max_step\n",
    "        if np.linalg.norm(new_node - nearest_node) > max_step:\n",
    "            continue\n",
    "        if map[int(new_node[0]), int(new_node[1]), int(new_node[2])] == 1:\n",
    "            continue\n",
    "        G.add_node(tuple(new_node))\n",
    "        G.add_edge(tuple(new_node), nearest_node)\n",
    "        near_nodes = [node for node in G.nodes if np.linalg.norm(np.array(node) - new_node) <= max_step]\n",
    "        cost = [np.linalg.norm(np.array(node) - new_node) for node in near_nodes]\n",
    "        min_cost = min(cost)\n",
    "        min_cost_node = near_nodes[cost.index(min_cost)]\n",
    "        G.add_edge(tuple(new_node), min_cost_node)\n",
    "        for node in near_nodes:\n",
    "            if np.linalg.norm(np.array(node) - new_node) <= max_step:\n",
    "                if nx.shortest_path_length(G, source=tuple(new_node), target=node) < nx.shortest_path_length(G, source=tuple(new_node), target=node):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_3d(df, x, y, z, title):\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.scatter(df[x], df[y], df[z], c='b', marker='o', alpha=0.5)\n",
    "    ax.set_xlabel(x)\n",
    "    ax.set_ylabel(y)\n",
    "    ax.set_zlabel(z)\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# plot voxel grid\n",
    "def plot_voxel_grid(voxel_grid: np.ndarray, path: np.ndarray, title: str):\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    x, y, z = np.nonzero(voxel_grid)\n",
    "    ax.scatter(x, y, z, c='b', marker=',', alpha=0.01)\n",
    "    ax.plot(path[0], path[1], path[2], c='r', marker='^', alpha=1)\n",
    "    # ax.voxels(voxel_grid, edgecolor='k')\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "shape = [100] * 3\n",
    "m = ProbabilityMap(shape, 0.0)\n",
    "m.map[0:90, 10:20, 10:80] = 1\n",
    "list(range(0, 100, 5)) * 3\n",
    "x = [20 for i in range(100)]\n",
    "y = [i for i in range(100)]\n",
    "z = [50 for i in range(100)]\n",
    "\n",
    "path = np.array([x, y, z])\n",
    "plot_voxel_grid(m.map, path, 'Voxel Grid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "\n",
    "def randrange(n, vmin, vmax):\n",
    "    \"\"\"\n",
    "    Helper function to make an array of random numbers having shape (n, )\n",
    "    with each number distributed Uniform(vmin, vmax).\n",
    "    \"\"\"\n",
    "    return (vmax - vmin)*np.random.rand(n) + vmin\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "n = 100\n",
    "\n",
    "# For each set of style and range settings, plot n random points in the box\n",
    "# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].\n",
    "for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:\n",
    "    xs = randrange(n, 23, 32)\n",
    "    ys = randrange(n, 0, 100)\n",
    "    zs = randrange(n, zlow, zhigh)\n",
    "    ax.scatter(xs, ys, zs, marker=m)\n",
    "\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'plotly.express' has no attribute 'bar_3d'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 30\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m z \u001b[39m=\u001b[39m [[\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m],\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m      [\u001b[39m4\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m0\u001b[39m],\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m      [\u001b[39m3\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m4\u001b[39m],\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m      [\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m, \u001b[39m0\u001b[39m]]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m# Create figure\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m fig \u001b[39m=\u001b[39m px\u001b[39m.\u001b[39;49mbar_3d(x\u001b[39m=\u001b[39mx, y\u001b[39m=\u001b[39my, z\u001b[39m=\u001b[39mz)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39m# Show figure\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X16sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m fig\u001b[39m.\u001b[39mshow()\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'plotly.express' has no attribute 'bar_3d'"
     ]
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Data\n",
    "x = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "y = [\"W\", \"X\", \"Y\", \"Z\"]\n",
    "z = [[0, 1, 2, 3, 4],\n",
    "     [4, 3, 2, 1, 0],\n",
    "     [3, 2, 1, 0, 4],\n",
    "     [1, 2, 3, 4, 0]]\n",
    "\n",
    "# Create figure\n",
    "fig = px.bar_3d(x=x, y=y, z=z)\n",
    "\n",
    "# Show figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb Cell 31\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m camera \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m     up\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(x\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, z\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     center\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(x\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, z\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     eye\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(x\u001b[39m=\u001b[39m\u001b[39m1.25\u001b[39m, y\u001b[39m=\u001b[39m\u001b[39m1.25\u001b[39m, z\u001b[39m=\u001b[39m\u001b[39m1.25\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m fig\u001b[39m.\u001b[39mupdate_layout(scene_camera\u001b[39m=\u001b[39mcamera, title\u001b[39m=\u001b[39mname)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/huakun/Documents/UT/CSC2630/CSC2630-Project/3D-Planning.ipynb#X64sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m fig\u001b[39m.\u001b[39;49mshow()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/AI/lib/python3.10/site-packages/plotly/basedatatypes.py:3398\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3365\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   3366\u001b[0m \u001b[39mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[1;32m   3367\u001b[0m \u001b[39mspecified by the renderer argument\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3394\u001b[0m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   3395\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   3396\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpio\u001b[39;00m\n\u001b[0;32m-> 3398\u001b[0m \u001b[39mreturn\u001b[39;00m pio\u001b[39m.\u001b[39;49mshow(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/AI/lib/python3.10/site-packages/plotly/io/_renderers.py:396\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    392\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    393\u001b[0m         )\n\u001b[1;32m    395\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nbformat \u001b[39mor\u001b[39;00m LooseVersion(nbformat\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m LooseVersion(\u001b[39m\"\u001b[39m\u001b[39m4.2.0\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 396\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    397\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    398\u001b[0m         )\n\u001b[1;32m    400\u001b[0m     ipython_display\u001b[39m.\u001b[39mdisplay(bundle, raw\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    402\u001b[0m \u001b[39m# external renderers\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @njit\n",
    "# def manhattan_distance(coor1, coor2):\n",
    "#     return sum([abs(coor1[i] - coor2[i]) for i in range(len(coor1))])\n",
    "\n",
    "@njit\n",
    "def e_dist(coor1, coor2):\n",
    "    return sum([(coor1[i] - coor2[i]) ** 2 for i in range(len(coor1))]) ** 0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 1000000000\n",
    "c1 = np.random.randn(size) * 100\n",
    "c2 = np.random.randn(size) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4471979.733124693"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(c1 - c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4471979.733124803"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_dist(c1, c2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 'write spec')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = []\n",
    "heapq.heappush(h, (5, 'write code'))\n",
    "heapq.heappush(h, (7, 'release product'))\n",
    "heapq.heappush(h, (1, 'write spec'))\n",
    "heapq.heappush(h, (3, 'create tests'))\n",
    "heapq.heappop(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 'release product')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heapq.heappop(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "d = defaultdict(lambda : 0)\n",
    "\n",
    "d['a'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [3,6,1,67,23,7,89,21]\n",
    "np.argmin([3,6,1,67,23,7,89,21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set([1,2,3,4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16214d330>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = np.zeros((10,10))\n",
    "a = np.array([9, 9])\n",
    "b = np.array([1, 1])\n",
    "max_radius = 3\n",
    "\n",
    "direction = b - a\n",
    "length = np.linalg.norm(direction)\n",
    "offset = direction / length * min(length, max_radius)\n",
    "c = np.array([a[0] + offset[0], a[1] + offset[1]])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.scatter(a[0], a[1], c='r')\n",
    "ax.scatter(b[0], b[1], c='g')\n",
    "ax.set_xlim([0, 10])\n",
    "ax.set_ylim([0, 10])\n",
    "ax.scatter(c[0], c[1], c='b')\n",
    "ax.plot([a[0], c[0]], [a[1], c[1]], c='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x162210610>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = np.zeros((10,10,10))\n",
    "a = np.array([9, 9, 9])\n",
    "b = np.array([1, 1, 1])\n",
    "max_radius = 3\n",
    "\n",
    "direction = b - a\n",
    "length = np.linalg.norm(direction)\n",
    "offset = direction / length * min(length, max_radius)\n",
    "c = np.array([a[0] + offset[0], a[1] + offset[1], a[2] + offset[2]])\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(a[0], a[1], a[2], c='r')\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(8, 8))\n",
    "# ax.scatter(a[0], a[1], c='r')\n",
    "# ax.scatter(b[0], b[1], c='g')\n",
    "# ax.set_xlim([0, 10])\n",
    "# ax.set_ylim([0, 10])\n",
    "# ax.scatter(c[0], c[1], c='b')\n",
    "# ax.plot([a[0], c[0]], [a[1], c[1]], c='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class A:\n",
    "    def __init__(self):\n",
    "        self.n_step = 0\n",
    "\n",
    "\n",
    "    def h(self):\n",
    "        for self.n_step in range(10):\n",
    "            pass\n",
    "\n",
    "a = A()\n",
    "a.h()\n",
    "a.n_step\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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