LR_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9062301b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qvWaUJ+644w71r3AAl2cCP/jgA4iIiIC9e/fChg0bYN26dbBu3TpYunQp3H///UhkEwA87ibBGKv0+g1t1rE+cy3P0lNMsyzop9FoYOXKlbBjxw5Ys2YNbNiwASZNmgSvvvoq7NixA6xWKyiKAhEREfDJJ59UeE65E6AywBHzoap5UhvcddddsHjxYli3bh2MGDECvvjiC2jTpg106tRJPUZRFOjQoQO89tprFZ4jLi4OpZt6PlPeEp6Qn9+VXVvuvffeCjUqAAA6duwIAJcnMI4cOQLffvstrF+/Hr788kt4++234bnnnoO5c+cCwGW9m969e8NXX30FGzduhFdeeQVefvllWLVqVaUaKE0Vqq+EN8QyceLECRgwYAC0adMGXnvtNYiLiwOj0Qhr166F119/vUHswtRUqEpbeC3vNCaTyW2Smbh2vOXboEGD4JZbboGcnBx46qmnoE2bNuDv7w/nzp2DCRMmuNW/ynZArM69bd++HZ544gno3Lmz+h506623Ut2/Bmh+4uppEJNRFQ2oXn31VTRDGBMTo9pGoxGGDRsGw4YNA0VRYPr06bB48WJ49tln3f6yU9v3SVzmap5NeHg4WCwWOHLkiJvv8OHDoNVq1UHqldnivLw8NRQPADwuZezRowf06NEDXnzxRfj000/hnnvugc8//xweeOABSEpKgs2bN0PPnj0bZGX2JceOHUOz78ePHwdFUSAhIQHliUhVlpdWRHx8POzfvx8URUGDpcOHD6v+K9x8880QHR0Ny5cvh169esEPP/wAf//739H5kpKSYN++fTBgwACqsxVAedt0CQ8PBz8/Pzh27Jibr6L2uKLvBwQEgMvlgoEDB1Z6vL+/P4wdOxbGjh0L5eXlcMcdd8CLL74If/vb38BsNgPA5fDs6dOnw/Tp0yEzMxNuuOEGePHFF2ky6n9QfSW84a18rFmzBux2O3zzzTfoL/FyeOSVfD1+/Dg6V3Z2dpX+Uk/UDHXRFlK9vHY85Vt0dDQcPXoUli1bBvfff796fFXCuDwRHx8P33//PRQVFaHVUXL/nZubC99//z3MnTsXnnvuOfXzivp+wjM0P1EzNIhpcH9/fwDAA6qUlBQYOHCg+u+KjoC8JaVWq1X/EitvFeyL+yQuczXPRqfTwaBBg+Drr79GoQYXL16ETz/9FHr16gU2mw0ALg9mAQB+/vln9bji4mK3Webc3Fy3GeXOnTsDAC8XY8aMAZfLBS+88ILbPTmdTspXgf/85z8ovXDhQgAAGDx4MNhsNggLC0N5AgDw9ttvV+taQ4YMgQsXLiD9EafTCQsXLgSr1aouaQa4XN9Hjx4Na9asgY8++gicTicKCwG4nM/nzp2DJUuWuF2rtLQUiouLq3WfjQXK26aLTqeD1NRUWL16NZw+fVr9/NChQ7Bhw4YqfX/UqFHw5Zdfwl9//eXmF7cmlvtqo9EI7dq1A8YYOBwOcLlcbqECEREREBMTU+t9eUOC6ivhDW/l48pf38WxUX5+PixduhR9Z8CAAaDX62HRokXo87feeqs2bpmQqMu20N/fn0K2qkll+VZR/WOMwRtvvFHtaw4ZMgScTieqqy6XS633V6jo2gAACxYsqPa1myI0P1EzNIiVUSkpKQAA8Pe//x3uuusuMBgMMGzYMPXhijzwwAOQk5MD/fv3h9jYWDh16hQsXLgQOnfuXOvbGHbu3Bl0Oh28/PLLkJ+fDyaTCfr37w8RERG1et2GgKc89MS8efNg06ZN0KtXL5g+fTro9XpYvHgx2O12mD9/vnrcoEGDoHnz5jB58mR44oknQKfTwfvvvw/h4eHoZWrZsmXw9ttvw8iRIyEpKQkKCwthyZIlYLPZYMiQIQBwWVdq6tSp8NJLL8HevXth0KBBYDAY4NixY7BixQp44403YPTo0bX0hBoWaWlpMHz4cLj11lvh119/hY8//hjuvvtuNQTjgQcegH/961/wwAMPQJcuXeDnn3+Go0ePVutaU6ZMgcWLF8OECRPgjz/+gISEBFi5ciVs27YNFixYAAEBAej4sWPHwsKFC2H27NnQoUMHt3p/3333wRdffAHTpk2DLVu2QM+ePcHlcsHhw4fhiy++gA0bNkCXLl2q92AaAZS3TZu5c+fC+vXroXfv3jB9+nR1siE5ORn2799f6ff/9a9/wZYtW6B79+7w4IMPQrt27SAnJwd2794NmzdvhpycHAC43HZHRUVBz549ITIyEg4dOgRvvfUWDB06FAICAiAvLw9iY2Nh9OjR0KlTJ7BarbB582bYuXMnvPrqq7X9GBoMVF8Jb3grH2azWf1L/dSpU6GoqAiWLFkCERERcP78efUckZGRMHPmTHj11VfVc+3btw/WrVsHYWFh9f6v7g2dwsLCOmsLU1JSYPny5fD4449D165dwWq1eh27E5zK8q1NmzaQlJQEs2bNgnPnzoHNZoMvv/zymlYbDhs2DHr27AlPP/00pKenQ7t27WDVqlVuk2I2mw1uvvlmmD9/PjgcDmjWrBls3LgR0tLSrvVnNylofqKGqJtN/K6eF154gTVr1oxptVqv2yiuXLmSDRo0iEVERDCj0ciaN2/Opk6dys6fP68ec2XrxJ07d6LvXtkWUdz60NPWiStWrKjw+kuWLGGJiYnqNtj1dRvFuqCiPAQAj9ue7t69m6WmpjKr1cosFgvr168f2759u9txf/zxB+vevbua36+99prbVqS7d+9m48aNY82bN2cmk4lFRESw2267je3atcvtfO+++y5LSUlhfn5+LCAggHXo0IE9+eSTLCMjQz0mPj6eDR06tGYeTAPiynbRBw8eZKNHj2YBAQEsODiYzZgxg5WWlqrHlZSUsMmTJ7PAwEAWEBDAxowZwzIzM6u1nThjjF28eJFNnDiRhYWFMaPRyDp06FDhduWMMaYoCouLi2MAwObNm1fhMeXl5ezll19mycnJzGQyseDgYJaSksLmzp3L8vPz1eO8lc/GBuUtcYWffvqJpaSkMKPRyBITE9k777yjlo8reHt+Fy9eZA8//DCLi4tjBoOBRUVFsQEDBrB3331XPWbx4sXs5ptvZqGhocxkMrGkpCT2xBNPqHlkt9vZE088wTp16sQCAgKYv78/69SpE3v77bdr98c3EKi+Et6oavn45ptvWMeOHZnZbGYJCQns5ZdfZu+//75bWXA6nezZZ59lUVFRzM/Pj/Xv358dOnSIhYaGsmnTptXBL2w6VKUt7NOnD0tOTnb77vjx41l8fLyavjLuFuvs+PHjmb+/f4XXLioqYnfffTcLCgpiAIDORXinKvl28OBBNnDgQGa1WllYWBh78MEH2b59+6qcR3K/zBhj2dnZ7L777mM2m40FBgay++67j+3Zs8ftnGfPnmUjR45kQUFBLDAwkN15550sIyOjSn0DwaH5iWtHw1gV1LAIgiAAYM6cOTB37ly4dOkShIWF1fXtEDUI5S1BNByovhJ1TV5eHgQHB8O8efPc9MAIgiAIoio0CM0ogiAIgiAIgiB8T2lpqdtnV/Rl+vbt69ubIQiCIBoNDUIziiAIgiAIgiAI37N8+XL44IMPYMiQIWC1WmHr1q3w2WefwaBBg6Bnz551fXsEQRBEA4UmowiCIAiCIAiCqJCOHTuCXq+H+fPnQ0FBgSpqPm/evLq+NYIgCKIBQ5pRBEEQBEEQBEEQBEEQhM8gzSiCIAiCIAiCIAiCIAjCZ9BkFEEQBEEQBEEQBEEQBOEzmuRk1AcffAAajQZ27dpV6bF9+/alnUIIopa4UhfT09PVz6jONT769u0L7du3r/S49PR00Gg08MEHH9T+TREE4RGqsw2bOXPmgEajgaysrLq+FcIHUH0lvHEt+V7ROJ2oHZrq/ESDmIzavn07zJkzB/Ly8ur6Vrxy8OBBmDNnDlXYCmgoeUgQRMPh008/VbcXJxoHJSUlMGfOHPjxxx/r+laIWoDqLEE0HKi+EoRnGsq7bX2fn2gwk1Fz586tk8zeuHEjbNy4sUrHHjx4EObOnVtvM7suqcs8JAiiYRAfHw+lpaVw3333Vel4Gig3PkpKSmDu3Lk0GdVAoDpLEA0Hqq8EUXPQ/ETN0CAmo+oSo9EIRqPR6zFlZWWgKIqP7qhxoygKlJWV1fVtXDWMMSgtLa3r2yBqmYZaPhsKGo0GzGYz6HQ6r8cVFxf76I4IgvAG1VmCaDhQfSWIxkFjmp+o95NRc+bMgSeeeAIAAFq0aAEajabS2NXPP/8cUlJSICAgAGw2G3To0AHeeOMNt+Psdjs8/vjjEB4eDv7+/jBy5Ei4dOkSOkaOyfzxxx9Bo9HA559/Dv/4xz+gWbNmYLFY4M0334Q777wTAAD69eun3if9ddd7Hmo0GpgxYwZ88sknkJycDCaTCdavXw8AAHv27IHBgweDzWYDq9UKAwYMgB07dridW6PRuF2zohjnXbt2QWpqKoSFhYGfnx+0aNECJk2ahL6nKAosWLAAkpOTwWw2Q2RkJEydOhVyc3PRcQkJCXDbbbfBhg0boEuXLuDn5weLFy+uicdVr7nyvA8fPgxjxowBm80GoaGhMHPmTHWSxltsukajgTlz5lz1dTMzM2Hy5MkQGRkJZrMZOnXqBMuWLVP9DocDQkJCYOLEiW7fLSgoALPZDLNmzVI/s9vtMHv2bGjZsiWYTCaIi4uDJ598Eux2u9v9eiqfBKewsBAeffRRSEhIAJPJBBEREXDLLbfA7t270XEHDx6Efv36gcVigWbNmsH8+fORv6KyM2HCBLBarXDixAkYMmQIBAQEwD333AN9+/aF7777Dk6dOqW2KQkJCT74tU2Pc+fOweTJkyEmJgZMJhO0aNECHnroISgvLwcAgJMnT8Kdd94JISEhYLFYoEePHvDdd9+hc5SXl8Nzzz0HKSkpEBgYCP7+/tC7d2/YsmWLekx6ejqEh4cDAMDcuXPVfK1Om0F4h+ps0yArK8tjXw0AsHTpUujfvz9ERESAyWSCdu3awaJFi9zOoygKzJkzB2JiYsBisUC/fv3g4MGDkJCQABMmTPDhL2qaUH1tmFSWb7/88gvceeed0Lx5c3Us+thjj7n9cftKHp07dw5GjBgBVqsVwsPDYdasWeByudCxeXl5MGHCBAgMDISgoCAYP358hSt39u/fDxMmTIDExEQwm80QFRUFkyZNguzs7Fp7Ho0Jmp+oOfR1fQOVcccdd8DRo0fhs88+g9dffx3CwsIAANQBq8ymTZtg3LhxMGDAAHj55ZcBAODQoUOwbds2mDlzJjr2//7v/yA4OBhmz54N6enpsGDBApgxYwYsX7680vt64YUXwGg0wqxZs8But8OgQYPgkUcegTfffBOeeeYZaNu2LQCA+n9TprI8/OGHH+CLL76AGTNmQFhYGCQkJMCBAwegd+/eYLPZ4MknnwSDwQCLFy+Gvn37wk8//QTdu3e/qnvIzMyEQYMGQXh4ODz99NMQFBQE6enpsGrVKnTc1KlT4YMPPoCJEyfCI488AmlpafDWW2/Bnj17YNu2bWAwGNRjjxw5AuPGjYOpU6fCgw8+CNddd901PqmGw5gxYyAhIQFeeukl2LFjB7z55puQm5sLH374YY1fq7S0FPr27QvHjx+HGTNmQIsWLWDFihUwYcIEyMvLg5kzZ4LBYICRI0fCqlWrYPHixeivBatXrwa73Q533XUXAFweVA8fPhy2bt0KU6ZMgbZt28Kff/4Jr7/+Ohw9ehRWr16Nrl9R+SQw06ZNg5UrV8KMGTOgXbt2kJ2dDVu3boVDhw7BDTfcAAAAubm5cOutt8Idd9wBY8aMgZUrV8JTTz0FHTp0gMGDB3s9v9PphNTUVOjVqxf8+9//BovFAlFRUZCfnw9nz56F119/HQAArFZrrf/WpkZGRgZ069YN8vLyYMqUKdCmTRs4d+4crFy5EkpKSiA3NxduuukmKCkpgUceeQRCQ0Nh2bJlMHz4cFi5ciWMHDkSAC5PCv/3v/+FcePGwYMPPgiFhYXw3nvvQWpqKvz+++/QuXNnCA8Ph0WLFsFDDz0EI0eOhDvuuAMAADp27FiXj6BRQnW2aVBZX71o0SJITk6G4cOHg16vhzVr1sD06dNBURR4+OGH1fP87W9/g/nz58OwYcMgNTUV9u3bB6mpqbRS2EdQfW2YVJZvK1asgJKSEnjooYcgNDQUfv/9d1i4cCGcPXsWVqxYgc7lcrkgNTUVunfvDv/+979h8+bN8Oqrr0JSUhI89NBDAHA5SuP222+HrVu3wrRp06Bt27bw1Vdfwfjx493ubdOmTXDy5EmYOHEiREVFwYEDB+Ddd9+FAwcOwI4dOyr8Qz/BofmJGoQ1AF555RUGACwtLa3SY2fOnMlsNhtzOp0ej1m6dCkDADZw4ECmKIr6+WOPPcZ0Oh3Ly8tTP+vTpw/r06ePmt6yZQsDAJaYmMhKSkrQeVesWMEAgG3ZsqXKv62p4CkPAYBptVp24MAB9PmIESOY0WhkJ06cUD/LyMhgAQEB7Oabb1Y/mz17NquoGF/J4yvX++qrrxgAsJ07d3q8x19++YUBAPvkk0/Q5+vXr3f7PD4+ngEAW79+faW/vTFx5XkPHz4cfT59+nQGAGzfvn0sLS2NAQBbunSp2/cBgM2ePVtNy/nEmHudW7BgAQMA9vHHH6uflZeXsxtvvJFZrVZWUFDAGGNsw4YNDADYmjVr0DWHDBnCEhMT1fRHH33EtFot++WXX9Bx77zzDgMAtm3bNnS/FZVPAhMYGMgefvhhj/4+ffowAGAffvih+pndbmdRUVFs1KhR6mcVlZ3x48czAGBPP/2023mHDh3K4uPja+Q3EBVz//33M61WW2HbqSgKe/TRRxkAoPpUWFjIWrRowRISEpjL5WKMMeZ0Opndbkffz83NZZGRkWzSpEnqZ5cuXXJrJ4iah+ps46YqfTVjzG0cyxhjqampqM+8cOEC0+v1bMSIEei4OXPmMABg48ePr/kfQCCovjZMKsu3iurfSy+9xDQaDTt16pT62ZU8ev7559Gx119/PUtJSVHTq1evZgDA5s+fr37mdDpZ79693fK9omt/9tlnDADYzz//rH5W0TiduAzNT9QM9T5M72oJCgqC4uJi2LRpU6XHTpkyBc389u7dG1wuF5w6darS744fPx78/Pyu6V6Jy/Tp0wfatWunpl0uF2zcuBFGjBgBiYmJ6ufR0dFw9913w9atW6GgoOCqrhEUFAQAAN9++y04HI4Kj1mxYgUEBgbCLbfcAllZWeq/lJQUsFqtKJwE4PKyzNTU1Ku6j8aC+BdTgMuz+AAAa9eurfFrrV27FqKiomDcuHHqZwaDAR555BEoKiqCn376CQAA+vfvD2FhYegvB7m5ubBp0yYYO3as+tmKFSugbdu20KZNG5TP/fv3BwBwy2e5fBLuBAUFwW+//QYZGRkej7FarXDvvfeqaaPRCN26dYOTJ09W6RpX/vJH+A5FUWD16tUwbNgw6NKli5tfo9HA2rVroVu3btCrVy/1c6vVClOmTIH09HQ4ePAgAADodDp1xaKiKJCTkwNOpxO6dOniFmpC1D5UZ5sGlfXV4jg2Pz8fsrKyoE+fPnDy5EnIz88HAIDvv/8enE4nTJ8+vcJzEbUP1deGSWX5Jta/4uJiyMrKgptuugkYY7Bnzx6346dNm4bSvXv3Rvm7du1a0Ov1KC91Ol2FdVW8dllZGWRlZUGPHj0AAKhPrgVofsIzDXYyKicnBy5cuKD+u9JpTp8+HVq3bg2DBw+G2NhYmDRpkkeNl+bNm6N0cHAwAICbPlBFtGjR4hp/AXEF+VleunQJSkpKKgx7a9u2LSiKAmfOnLmqa/Tp0wdGjRoFc+fOhbCwMLj99tth6dKlSCPo2LFjkJ+fDxERERAeHo7+FRUVQWZmptf7bkq0atUKpZOSkkCr1dbKTg2nTp2CVq1agVaLm6srS0yvNM56vR5GjRoFX3/9tZqvq1atAofDgSajjh07BgcOHHDL49atWwMAUD5Xg/nz58Nff/0FcXFx0K1bN5gzZ47bADg2NtZt2XdwcHCV2lu9Xg+xsbE1es9E5Vy6dAkKCgqgffv2Ho85deqUx7b6iv8Ky5Ytg44dO4LZbIbQ0FAIDw+H7777Tu2/Cd9BdbZpUFlfvW3bNhg4cCD4+/tDUFAQhIeHwzPPPAMAoNbLK3W4ZcuW6FwhISHquJmoXai+Nkwqy7fTp0/DhAkTICQkRNWB6tOnDwCAW79oNpvdQsDk/D116hRER0e7hVNW1Efn5OTAzJkzITIyEvz8/CA8PFwd71KfXH1ofuLqabCTUXfccQdER0er/67EW0ZERMDevXvhm2++geHDh8OWLVtg8ODBFcbLetpNgjFW6fUb2qxjfeZanqWnmGZZ0E+j0cDKlSvh119/hRkzZsC5c+dg0qRJkJKSAkVFRQBw+a/1ERERsGnTpgr/Pf/88zV2340NMR+qmie1wV133QWFhYWwbt06AAD44osvoE2bNtCpUyf1GEVRoEOHDh7zWf7rL+Vz5YwZMwZOnjwJCxcuhJiYGHjllVcgOTlZzQeAa2tvTSaT22Qk0bD4+OOPYcKECZCUlATvvfcerF+/HjZt2gT9+/dvELu9NDaozjZNxP75xIkTMGDAAMjKyoLXXnsNvvvuO9i0aRM89thjAABUL+sRVF8bJt7yzeVywS233ALfffcdPPXUU7B69WrYtGmTKi4v17/KdkCszr0tWbIEpk2bBqtWrYKNGzeqkyNU96sPzU9cPfVewByg4pfbV199Fc0QxsTEqLbRaIRhw4bBsGHDQFEUmD59OixevBieffZZt7/s1PZ9Epe5mmcTHh4OFosFjhw54uY7fPgwaLVaiIuLAwA+W5yXl6eG4gGAx6WMPXr0gB49esCLL74In376Kdxzzz3w+eefwwMPPABJSUmwefNm6NmzZ4OszL7k2LFjaPb9+PHjoCgKJCQkoDwRqcry0oqIj4+H/fv3g6IoaLB0+PBh1X+Fm2++GaKjo2H58uXQq1cv+OGHH+Dvf/87Ol9SUhLs27cPBgwYQHW2BomOjobp06fD9OnTITMzE2644QZ48cUXKxVOvRYo/2qX8PBwsNls8Ndff3k8Jj4+3mNbfcUPALBy5UpITEyEVatWoXybPXs2+h7lqe+gOtv48dZXr1mzBux2O3zzzTfoL/FyqPqVOnz8+HF0ruzs7Cr9pZ6oGai+Nkw85Vt0dDQcPXoUli1bBvfff796fFXCuDwRHx8P33//PRQVFaHVUXIfnZubC99//z3MnTsXnnvuOfXzY8eOVfvaTRGan6gZGsQ0uL+/PwDgl9uUlBQYOHCg+u+Kpou8JaVWq1V34pG3bffFfRKXuZpno9PpYNCgQfD111+jsK+LFy/Cp59+Cr169QKbzQYAlycWAAB+/vln9bji4mJYtmwZOmdubq7bjHLnzp0BgJeLMWPGgMvlghdeeMHtnpxOJ+WrwH/+8x+UXrhwIQAADB48GGw2G4SFhaE8AQB4++23q3WtIUOGwIULF5AWlNPphIULF4LValWXNANcru+jR4+GNWvWwEcffQROpxOF6AFczudz587BkiVL3K5VWloKxcXF1brPporL5XJb0h0REQExMTE+aXNpOXntodVqYcSIEbBmzRrYtWuXm58xBkOGDIHff/8dfv31V/Xz4uJiePfddyEhIUHtm6/8pU9sh3/77Tf0PQAAi8UCANSP1iZUZ5sO3vrqiupkfn4+LF26FH1nwIABoNfrYdGiRejzt956qzZumZCg+towqSzfKqp/jDF44403qn3NIUOGgNPpRHXV5XKp9f4KFV0bAGDBggXVvnZThOYnaoYGsTIqJSUFAAD+/ve/w1133QUGgwGGDRumPlyRBx54AHJycqB///4QGxsLp06dgoULF0Lnzp1rfRvDzp07g06ng5dffhny8/PBZDJB//79ISIiolav2xDwlIeemDdvHmzatAl69eoF06dPB71eD4sXLwa73Q7z589Xjxs0aBA0b94cJk+eDE888QTodDp4//33ITw8HE6fPq0et2zZMnj77bdh5MiRkJSUBIWFhbBkyRKw2WwwZMgQALisKzV16lR46aWXYO/evTBo0CAwGAxw7NgxWLFiBbzxxhswevToWnpCDYu0tDQYPnw43HrrrfDrr7/Cxx9/DHfffbcaDvfAAw/Av/71L3jggQegS5cu8PPPP8PRo0erda0pU6bA4sWLYcKECfDHH39AQkICrFy5ErZt2wYLFiyAgIAAdPzYsWNh4cKFMHv2bOjQoYNbvb/vvvvgiy++gGnTpsGWLVugZ8+e4HK54PDhw/DFF1/Ahg0bKhRrJiqmsLAQYmNjYfTo0dCpUyewWq2wefNm2LlzJ7z66qu1eu2UlBRYvnw5PP7449C1a1ewWq1e2xXi6vnnP/8JGzduhD59+sCUKVOgbdu2cP78eVixYgVs3boVnn76afjss89g8ODB8Mgjj0BISAgsW7YM0tLS4Msvv1RXM952222watUqGDlyJAwdOhTS0tLgnXfegXbt2qmh0gCXl5i3a9cOli9fDq1bt4aQkBBo3769V90q4uqgOtt08NZXm81m9S/1U6dOhaKiIliyZAlERETA+fPn1XNERkbCzJkz4dVXX1XPtW/fPli3bh2EhYXV+7+6N3SovjZMKsu3Nm3aQFJSEsyaNQvOnTsHNpsNvvzyy2tabThs2DDo2bMnPP3005Ceng7t2rWDVatWuU2K2Ww2uPnmm2H+/PngcDigWbNmsHHjRkhLS7vWn92koPmJGqJO9vCrBi+88AJr1qwZ02q1XrdRXLlyJRs0aBCLiIhgRqORNW/enE2dOpWdP39ePebK1onyVtVXtkUUtz70tHXiihUrKrz+kiVLWGJiItPpdPV6G8W6oKI8BACP257u3r2bpaamMqvVyiwWC+vXrx/bvn2723F//PEH6969u5rfr732mttWpLt372bjxo1jzZs3ZyaTiUVERLDbbruN7dq1y+187777LktJSWF+fn4sICCAdejQgT355JMsIyNDPSY+Pp4NHTq0Zh5MA+LKdtEHDx5ko0ePZgEBASw4OJjNmDGDlZaWqseVlJSwyZMns8DAQBYQEMDGjBnDMjMz3bZsr2jLWLnOMcbYxYsX2cSJE1lYWBgzGo2sQ4cOaItaEUVRWFxcHAMANm/evAqPKS8vZy+//DJLTk5mJpOJBQcHs5SUFDZ37lyWn5+vHuetfBKXsdvt7IknnmCdOnViAQEBzN/fn3Xq1Im9/fbb6jF9+vRhycnJbt8dP3482jba07bT/v7+FV67qKiI3X333SwoKIgBAG1BXUucOnWK3X///Sw8PJyZTCaWmJjIHn74YWa32xljjJ04cYKNHj2aBQUFMbPZzLp168a+/fZbdA5FUdg///lPFh8fz0wmE7v++uvZt99+61YGGGNs+/btLCUlhRmNRrc2g7h2qM42fqraV3/zzTesY8eOzGw2s4SEBPbyyy+z999/361fdjqd7Nlnn2VRUVHMz8+P9e/fnx06dIiFhoayadOm1cEvbDpQfW2YVCXfDh48yAYOHMisVisLCwtjDz74INu3b1+V8+hKPRfJzs5m9913H7PZbCwwMJDdd999bM+ePW7nPHv2LBs5ciQLCgpigYGB7M4772QZGRlVGqcTHJqfuHY0jFVBDYsgCAIA5syZA3PnzoVLly5BWFhYXd8OQRAEQRB1QF5eHgQHB8O8efPctBkJgiAIoio0CM0ogiAIgiAIgiB8T2lpqdtnV/Rl+vbt69ubIQiCIBoNDUIziiAIgiAIgiAI37N8+XL44IMPYMiQIWC1WmHr1q3w2WefwaBBg6Bnz551fXsEQRBEA4UmowiCIAiCIAiCqJCOHTuCXq+H+fPnQ0FBgSpqPm/evLq+NYIgCKIBQ5pRBEEQBEEQBEEQBEEQhM8gzSiCIAiCIAiCIAiCIAjCZ9BkFEEQBEEQBEEQBEEQBOEzqq0ZpSgKZGRkQEBAAGg0mpq8J+IqYIxBYWEhxMTEgFZ77XOLlK/1h5rMW8rX+gPV2cYJ5WvjhPK1cUL52nihsVPjhOps44TytXFyNfla7cmojIwMiIuLq+7XiRrmzJkzEBsbe83noXytf9RE3lK+1j+ozjZOKF8bJ5SvjRPK18YLjZ0aJ1RnGyeUr42TquRrtSejAgICAACgFwwBPRiqe5pK0QZYeUJRkE8pLvH8vfatVVtjdyIfO3een6OkFPl0QTaU1gQFqrYz/YzH62n0nh8lc7mkDwTNeG+ztlXQlneCA7bCWjU/rhVf5Wt1uXlHsWofLIpBvly7n2ofy4hAvrDgIpRuE3xRtc+X4jy/M3qXaq+4KRH5mN1+lXdcfWoyb2srX3WhIShd1rG5amd1MCFf3PI01XZezKyxe6gquuAg1c646zrk87/A62jgzyeRz5WdU6P30RjrbMYnbVV7fKsdyLdoe3/V1vo7kC8kmNdnuwO3ocEW3L6nhPL29/mIP5Fv7ImBqu162Ix8rpOnvN57TdEY81VE06ktSrN9h2r8GvpI3G7XRTshU5/zlXVvr9oZvfyRL2Yrr1v6Q7gOuAoKr+m6tYk4ditohftm27EC1Vb+OnpN16nP+UpcGw1h7OSG+C5wFftKnX2yu2onrDiPfM6001U+z4l/d1Ht5uvwO4vh+z1VO4lWh9OKcB75Xacae2dRnW2cNPZ8zZzaHaXF9w3/r3fJh1eZ3Pu6qbbTgutX+OLfqn3emuJq8rXak1FXlr7pwQB6TS1ORmmMwkWlySiNAzyh1fEXYY0ON5BMOKeiwRNVOvF6AKDRCi/UXn6nRuNlMkojL0+r4mQUVKGxZldOUzNLEX2Vr9XFbOX3ZGA4r/R6nldaC34h1UkvwUYr/65eiydN/Kw8L+VnwKQyWKvUYN7WVr7qtHIe8OeuM+HnqhePrYOyJdZtnQmXD72Bdw566TdpavpeG2Gd1Vl4XputuC3U+vFnrbXgtlhn4e2vTpqM0vvjAbFJqPu2ANymGvx5nml0uNzVeP55ohHmq4j8XFkt3JNc9+qinXCjHucrQ+2t1KbphTZNHtfUh+fqAXHspjdIv0nH/xikXOtvqMf5SlwjDWDsVMGFhETVJ2rEei+PZa+m/RT7abHtAHAfB3tEI01Gie8+bnlRjY3cqc42Thp5vnp937iG+9MZ+XmZET+7+vC7ryZfqz0ZVRkaAx78MEe5l4M9/0VAKazeX/BcVt4oZ/UMRj6HP//ra1m41CBKzyxI+ONvSJrnv7Azp9Ojz60RvsbVUE0JfYt4lB5oXaHaWmli6I6Afar9R7NmyPe6sHICAKB/MM/YnYYWyNfRdE61P29+M/K5juFVM00RTUqyap9KDUQ+fbFgSwsXz45NUm1rRgLyWb8U/jqgSCsJq8nRJV1R2niRN3fW07ie5bbhg6jMlNbIF72D34/f6t9r5N7qlCr+Bfb4gh4o/eLg5ap9V0CudPRe1XonD9e9Tm15u/nnH7iuxTY/y21LHvK18sOrYgb68zrb8rPHkW/VqAWq3fFn3PHvFVYzzjgyDvmsw/j1vfZRTQR9HF5Kff0anncjA5d6/N4Xed1QetMZvvIwwopXpfYKO4HS0YY81Y4zHEC+mR8/qNrN52z3eP2mgrZjG5Q+fB8f5/in42PP9eErpXQ3tkc+ywXedxpKcBtgPZGv2ppSXCc0pbwusVK8qlzj54fSzI/fmyMK9xPFzbjPbvM8HlKkAXZGf75Squ1reNUy9c1Eg0boi3VBUn3pzdvT7Hb4tc3lx7+XNt+KfObNN6q25RIeLxumXcDH/szHQOnD8K0FJfLzhO3DAzvNr3zc7TZ2q+ZqL6JhoZNWNN+wga/Q29lZJx/OUcuHplpzk3WNrhXvg3K642eQn8QnYl0m/OP8hmSpdtGDuB/LzOF9nKLg/q95JI7S6BjAIwS2ft8B+cTxu98F/Mfb8L18cYZx/U6oD9BuegRBEARBEARBEARBEITPoMkogiAIgiAIgiAIgiAIwmfUWpgec0p6TqKwnbyU08vyTVEY3NUDLzW3P8uXk69J/hT5hj7KBcPCF/2KfBdm3qTaU4ZuRL5v/zYApf1/5SEFgw7kId87B3qrdsJLeAks2yOEG1zN8lRa1oo4NRaH/FxycSG0EF0x8v1Sypc7WrRYaLyFDS9vLFZ4mIBBg8vjDuE8p0dFIV+zf1EoQEFLngfick8Zh7/nue6CFnjpbvYcHuYTfAiX+6T/O6zaj0RvQr5/nRmC0qeXtVTttKGLkK/fxAdUuygGx1MHHuf111iI63J+C94G4UCUBoqXdsU+lIc2zr51JfKlWnj46s9lFuTraeLP7JvbsVijIoiovngAhzmOsvLlymedOOxn7OwnUPq3KTzET+PCy5cvOHmZzJE2jAjX8Xv7tN2HyNfnP4+pdusp9WO5cl0S+2U2Sj8SysXo1xfjkOk2Jr4U/9HQrcg3IZiH1AVocX0ySmHqGU5ev865cHjKZ+NfV+1nFgxCPldePjQ1zgzGG0YY8nhdtp7Fz9kezNtfvRSKVxrOfbjWARRHcVkDlwnnlc7OzyOHYcswoYmXuljQC+cx53rWYdSX4fu2XOT3c+L+SORLeJb6ZqKe4+U9iN3YSbWPjpZGGkK4TkA6dpXE8voTGYDHxH535vFznoxGPv0evAGQM5aP5aJ/wOOzolh+/fThuO93jeHhQK3/gTcWUYqF+6kBAXOieuib8bwuugGH4nd4jodZniwKRb5EKx8PbH//BuSL2ME3k7j0An4PaG76S7V3Ag5fa2jo2rZS7bQ7w5FP7NdcZlyeTXncDpAUfkoyed9VGom/5wzhkj/6APxcTx3G76OF+3i+Bkt9pd3G+/jyIHz9zBv4+4+m003IF7eeS3AotbBJjSdoZRRBEARBEARBEARBEAThM2gyiiAIgiAIgiAIgiAIgvAZNBlFEARBEARBEARBEARB+Ixa04wCTdXnufQJzVU78y28LfdtcTz29P6gt5CvjPFrnHXieORxz69V7dd7DEW+Zj/xmMz3vkxFviAb1i84M4dr0PzTshb5bunG4ykdq/Dv/SKPa68cLMCx2o57+GN3nj0HhGf6jvoDpfNcPF79cCl+rs1MPNa1TMGaQF0CcdCuwnh5iTAWIF+ikW8pH9LvPPLBv6pw040cUUfEeBpv/V2Y4FlVSdQQCT7qRD6nidcfKevgt5/bqvY4M97a3JqG611gNj/v0J63I19Jb35irSxbJ9yb1oHbAFFvRWvBeglKSSXCKfUBL1oNGoMRuc6M48/vv0/dgXy3vsX1e8R6CACg0/DnoCnEuhXMyc8ZoMMKNZNP91PtvSuwJmCzz3ejtO1hbpta4jrbwcjr/iknLoPFjLe3K3NTkC8unmtWHV2Kfa0n4ranKTAnCmuy7bSHqba/Ftf1Mw6uL5HuwGUsSMvLgwOw/ogBcOUrYVy/L9uJtyZP1HOtv/N3JyNfxNvboalh74TbG9M+Xg/LbfLRHIOkGSVqXYj6TQBYF0q5ihEi0+EyYCj2rAXlNPNjtbgrQP1LaQhu300F/JwKbrpAF8r1tFzZWCOSIOoFsl6ugKjFpJGkOI35Qt2SuvOAk7x91a4LQz5ROTW0Ga7M1rPSGEzQ+LTb8EX0xbxNCMxFLiiM59879Vgn5Iub1/Ta6GviKnS1ji0UtDltuMAEBuF+Qi/oZoZa8DvnkQKu6eSvx338zYFcrzXqIazRGP1InmqfLcdahpkO3hkpfa5HPu1Pe6AhceJeXqfMWdiHpIm1ct5xsywU+8T+13oasC+d11NdGa6zWpekL8WHTlAm9ZXiNczZkp5VPk8XR+HvnR7KNSNj94HPoJVRBEEQBEEQBEEQBEEQhM+gySiCIAiCIAiCIAiCIAjCZ9RemB6Tlmh7WW5Y/C6fE/umzfvIt72Mb134SX4X5HMIsTVawOePNuap9vG730G+/aPLVLulHs/H7SnHj+SCM0i1fyy5DvnOlPGliVa9HfkShPV8T4X/hnxD/3uPavvfChjxOWlxeIO3Jb6NlXBjIUrvKeHbi+eU+yOfVsOfXZgBf09hOJ9dwjzs4SIc7rd43838ezk4FqAVpFXlthsVuutaorS+lNdtOTTDYeFp/4u4vDqEZeDlVs9LSnVS2EjU7zwtfw+keu/w4/5ztzVDPkORsNQ8HdfX8zfy8GCHBccJWrKEtqx1Ar783oPQkLEPwMvqT/Rfotr/aN8B+QxCfECEDtcvlxD2GvoVDsX78yO+dezbvfHWtHm9E1RbH4LzUikrQ+mjT/KthcMsuG0M7MbraRnD+dfTxJewTziGtycuO8fDwtLuxP1EKnSGpka0HofJ6cp5noTqipDvgjNQtV3S37VyXPw8Wg0eC+ikOmvQ8HARsU+XyeuEQxEa9obRVUdr5m2TnwW3Wxo7D+1xWnBbrBMOlcNuRJ9GWvovhsnVFmJonhwKKN6PHF4ooi/F9+m8Lo6fYzuF6RH1G23ndijtCOaDIH0BbgfLg3g9kMdcJqGo5yfgvk8RwngUqWl1mfCxGqfnuub059csD/R4GDgt0jnEsDMv74DE/5DlbZjnd772Hbn0SLQfDqHLLccyCgHC+6kixXn663honk2Px27bC1vx7zH8vZami6qd7cDvYu0sGapd8jd8b9afhMSVMlGPyoYuGb/nm7OEkPJy+WiOWz8qhKLb0qV8FB6lXgpn19l52mGTJA4K8XnswbzzNJzFvsI4QZZECoW3Bwq/SQoJNgjDPGd/LF2h/6H2pCtoZRRBEARBEARBEARBEAThM2gyiiAIgiAIgiAIgiAIgvAZNBlFEARBEARBEARBEARB+Ixa04zS+uHttcUt0B0DcRzi4y0+U+3XsnojX6mLa4GYpOBGUSPIqsNaCgdLuNbUjKI45Mso5dtOhprwFpiRJrxl+GAb39vQLAWMTgw8pNpfS9f4JZ/HnZ4rD0a+ua2+Vu1/t78T+ZS/+FaabrpbTZAilwmlvz/bWrVviTvi8XuRehynHKTD+fxXKc+vH/5qg3zm07zMScWqScJM8vaigs+A57Pz2vE6acnG5ykL5nHKikHWMBF1QvD3vCmlaRQcp41joSU9KSv36YpxW6LpwjPatQWLIhgKBE2bYDPyeVa4qT9odPgumZP/njMDcN7aGX8uX36F2+L2d59V7T5+Z5DvsINfY2bUZuSb4kxWbdclvDdu4G7eT0z99hfkMz+O8+iD3lwlSHcxE/ksWl5nCxScR1oQ8k/SBGQWXroyXcXIp2udxO/76AlorOhaJQqpvchXLIiOFANui+MMvIKfcYQin0XY89jB8DOXdaFcgoBCK9MFj/fZNfkkSud7OK6xoWnB+6rSEpwHAUK7WS7pQok6fBqpEdUK+jCynoQCXnxXMWL0pkEj9hru1xD6Cb3sE2wdPj/T8bPWvuoVQVwbGX2C8Ac63t+5LNLYX0hqHdIaAq3nMQ84uM+F5U9BWy7VH+G0kswfSst1kgn10OUnjbkGcI1Gw+ba05tpNFyFLvBfaVwTNTMCaz1m5QagtKuA6weF/Y7734j1gg6uFvfjrIRrSGn8sQ7VT6O7qvadk35AvnwXPzYlDI8Vj+p5ARLHovWFnM74fd0pDCeNkr6SW30TCDgtjC274DprzBfqpQnnhz2Ja6VGbMDfuzAWv5AGr+WV+txgXHYifhTu0yXfJ7++vhT7SiK4rzQC68rhUlWz0MoogiAIgiAIgiAIgiAIwmfQZBRBEARBEARBEARBEAThM2otTE8pLfXoSx8hL9vn6WA9jtHJKrcKx+G5s4lhW1U7W8FbS55zhKj21jy8Nf2NIXy5/x/58cj3cMQulJ53dqhqF/bGISav9+NLUP/5/mLkO+0Xptpat62s+XK6I9NwSFCrGUKiHm136Uu0AXwx4PbMcOTLPceflxKLF+P3tfGwyUVn+yFfavhBlB4SsF+1/6vvhXwOG1+THLS7qnfddBCXd9uD8TLOdl3SVbtsZSTyFUXxpbuWTGk7U2G5q9vycaG50FSyqtdQIm7DKi0Z9+fth+KH26AAPx6eW2D1vHexHBbYEPC2FPq+QT+h9HEHP9bSBbd3MYZc1T4jhc92E7aJbvPfh5Av/t1fVVvbthXyuQ4dU+3fC5OQ70bbcZR+Z+cq1X6wOa6zYnihHJIrIudeShu+RD1Ch/uQ9LG8/Ma90HjD9PJSIjz6Zm0Zq9q2g7iu73vqbdX+oFxazq3h/b9Og8Pby1z4OXcynlPtR+6djnwvfPieak+N/hH55kMHj/fdmHCE8TGQqxyPgexBvL1jXmOGcV8pVl+d3N4KW8e7pHOK28OL4XQAOPQPAEBr9vy3Tm/hfvLW9Z6ubyjCx5VE8ZAFHLTS8Mgdf6NqX+qBwy9CYvNUO86Gg1XLhQdkd+GHXFDG401ycnEdVAqF+is/fikcElzivuTYF9mM9xEuRQpN0fO+paAUh1IXXeL3E7YD33fI+79CY6QsXHquej4mYlqpLhXyZ6J1eglClU6Jwog0+HtOiySVUMaPlWUUxFcvpz++iKCWAsyIfUWxvE7i4CeiQjTe8hY/21YTPIc9eh7BulPlQLncXJSMWsD77XUZfZFv1HMbVbtUig89uqC9area8VtVr+4zimJxu1WSyMeWxr14nCOGrxoLcf6UhvLzHB2/CPluPcznFeKtOch3qYz3XsH/h+dRzpXgnO30OM8DheGyE9Gdv9NsfPhm5MtvwdvfcqtU14U+tiQCPwsK0yMIgiAIgiAIgiAIgiAaBTQZRRAEQRAEQRAEQRAEQfgMmowiCIIgCIIgCIIgCIIgfEataUZ50zt6esAalC4UtuIOMxQiX5qGay+NCf0d+f7f0TtVW7sYaws9+NKXqh1pwue8YOdxl83MecgXq8cRtOff5DomyjisPeWXzY+duv8+5FvW6QPV/qG4DfKll/PftPDWZcj3JuBjmyKu9nyrcYUVIZ/Gwp+5VYe3uRzkx7dmf/69BORbdT+OW57SjuvRmPwlTRMhJl9f5lWIo0mgmHGctKGY50FxAH6u/cMOq/aiR7EWTcKrPC8dNvw9vXBOWTPEZeZ5IG9RqnFIexAL33X44+bNksnz+cRofP0PruPtxeQ/seaRroxrdpQH4e9h5YuGx2xJSw3AT7VGxe9Dnn+m8zj3r69bjXz5Co9tD+6a6fF6rgD8xHThvN1WALfTnU1nUXrgdq4n1AL2I58eeBlRmPw3Fl5GYkKxxsr+s3x75NEwEJ8zBWskNFYKEjz/TUpXwOtQ2J92j8eVM89DCZeUH6E63Ka3NvAyod26F/li9bxc7bbXpmJB/aW4GRd4YnasH6QTui6H/HikbahFRM0ohwvnj6gZoZF2Ghd9suaMfKyoKSXrSXnDZfKimyJgysXnzE/kN1enmlEajar9otHzvpM58DjDOSBFtb//6D3k+7zwlGrvKMJaeg+G/qLa/74wCPkCDby+DAnE7ffqXH695zt9j3zPXRig2g+Hb0G+tUXtUTpQx69xq/9R5JudMVi1n4zagHxzz93G7y0Bt99JnXifcamfDflGzOPtxU2PTUO+gOU7oKFSHoYrjEbQ5tKasE9r4ZW5zIT1GnVlvO3VSHpSLt6dg9Mia9fK4yyeVvCQDxxWz/XXm7RtaTg/J2lGVYOa0g0W8lpskwAAmEsoa0waT4unkL8ntGcBx/DYbaCVjys3X8LvtB8N4fpJ8/x7AwCAlpUDFEO9QJKtBo2d94/luGkCP0FWVa4z/pn8ubbbfi/y2Sxlqn3kOG5fO1x3RrV/2N0O+bp2xNqlX23g2oKde+G2eONH3Bd7JgP5zEH83cweJL0nXeBlrjhG0pUTxuuuS5egJqGVUQRBEARBEARBEARBEITPoMkogiAIgiAIgiAIgiAIwmfUXpiehKvfDapt036FfMftfAvtFia89Kut/3nV/rUYbwteYuchM3mpeDnjCeGcWeV40bZL2AJRDuk44vBD6Us3cL+2XFqy9idfFu5vwkuwRQK0ZSh9tjxUtY3S2nbddTwU0HUEb23eVLiUwrf5HRiJl3P/pPDnU+DEIT8GDc+PoI/wdsBZY1qjtEkjLJ+XVsImxPMyaDdEV/GuGy9ySJ0YKlcegOtEiBCC0zwMhzkVtuAhUfJ2otbzvJ5pXJ6XJrvkbb+NuP6K3y0Nw82b08zTfhn4PMfsUarNpK2sdaV8ibw9AZe5hhimZx/cVbVb/9wR+YLW8boXdBxvK5v2MLdNbfCa5JMO/ozGNsdbDn/Xp59q63PxOZ0tY1Q7/V68Tnv4tMdQWjHzJeSn59wEmN3giSLGw8ueSVqLfE+tn6zah02RyNc15rRq40XOjYviVp77rqQVvD7rijyH6TkYDmcW+7zD9hjkM2vk65WCJ+aeT1Xtl2I2It/b149UbbbngMdzNHQK4oUxiPSoxOGDVsoeMbTGHoJ9phyx/cPtnRgmLbfFciieiBy2J57XZcLttL6E+6Sig8Ik5PA+MYTPVIB99uCqhffVOozBld8uh+aJjH1rnWo/cxG3w81N2ardzIT70Z1l8ardKeAM8iUYedyIToNDbrRCfmwtw21duJGH2ZxxBiFfnAFvPX7ByWUu1hZdh3xt/C+otgI4P24J4aE7UXocLn2snPe/hS48Bl+v4X3LmOfWI9+6FXwsDYqXwlkPCY/D+ZpXyH+3yYTlQhRFkB/Q4DA9cUd3lxSfKkRUgiMRl0VHEK54tqN8fOTCQz5w2vizlUMBmYGXK40D+8pCayjMrKlQ3bA8KeTS7TxC2q1NEr/r5fre2jLNkTSU3lHKpVb0WtwOXRDal1OPdgIAAJe9DOCVzzyev7bRRfKwtfJA7LOc5fXEcCNuC63v8tj4nAex/EBUMD+2rAjH97UM4u10fjFu78ZHb1ftZ7a2wL6obSi9N4G/U50uwIGwIUPO8esPxflaspz/psJWuN007BHexaSoTSVe6DcoTI8gCIIgCIIgCIIgCIJoqNBkFEEQBEEQBEEQBEEQBOEzaDKKIAiCIAiCIAiCIAiC8Bk+04w6OZJrjBQoOEYy12FR7eZGHPtaovDgZasOay+90eFz1S5Mxuotq7K6qHYXWzryNTPwWO19Jc2R77v8zij97pjFqv3gyqnIl92ex10uu2458p10hKl2GcP6KuJvMmvx/svHJvOtExOfbJqaUeXCFtXRRqwtEGjmZcBPh5/d6mLPGzrnZOF9r78p5mXOz4TPE2rm2jVZRQ1Lh6A2cJmxtoAuj8eOl0bi+vp7Id+GWjsA61kUzopTbWOBrEXC07pyHKiscfA0M+D5c5fR83y6vhSfpyiW/47Yf25Hvte78q2ttTFYmEW8N6atJ7ok18C5PrzZ1x/EbVPwMv5cCsf2QL67k/mW4vkKfkZxep4Pb/w2EPmSBM2AzB44rj3id16/00dHIN/1XfBWtSKZ6xNRWtSFkru1QC3vb/7x8iTkcwmSI1Ov24p8N1mOqfYz0M3jvTR0hnb807Pzd8GXfJ3HwwySmFCJwnVNbJLQkdz/6zSe6/C2M1wzITgWfy+zB9dhCN/j8RQNHnHLaF0pbn9KI3nbpC/GPkcQr3dau6R3affcjnnT7NM6Rdu7npTOLtqejy0Lw/fi5F0zBJz2vNV4Q0MfF4vSe4v4mHVQ0F/Il+nkZduswfpB5Yy3b7IeabaTj4H+3x93Ip9lP68/bz36G/J1MvL0pKP3IN/NEXgcOjec67Ndv/Mu5HNu4+JksZOxvkqCkWuMXJB0qSyC4FkH01nk+6MsQbWHWfFz+nDKE6od/g7WCa2XCPo8peW47w0N5OPOeBvWk/rrItcu1ZZ6bi+lVyQQZbs0hdLrXiAe9yp67tdITYCoBaVxSZpRemF8ZMBfdIbjaxC1RHW1pq71ux6wC52WVY/FDDMcfAyo65J32SjxrEfpCwp683GGy4Sfh5nL90FxGRZTu+6ZE6p96q8k5FOCeR3OK7Ag38hWXOP04EdtkW9hYH/Vjt6O2/5ZrXGbDie5xmtMr/PIVezgY7CJsVhr6u/txqq2rJGrxZdEFMXz6/nv8nxcdaCVUQRBEARBEARBEARBEITPoMkogiAIgiAIgiAIgiAIwmf4LEzvlh77VVsMUwPA4VZZTrwFYomwx6i8ffTXuTeo9omicOQbEHZYtd850hv5ii7ypcz9Ox9EvhaWLJTeWNBetd+7cxHytdLzrRyX5qUgX7yJn0dcsggAoAh7sRa4cHhhYgoObWqKuPz4skE5z8VnF2YoRL4n/7hDtVvAfuTTZcuhknwJo1PBc7JRZn7e/AIvaxabCLoyHH+hK+X11RGAl3h+t5tvUd0adiKfKVcIKXEL2/CypazB85y5/D2tkNaXYp8px/M1irP5MtrrkjIkL9/rVaM0/K2KnUE8P4/d/y7yzRvdRrUt2o3I93N2K9U2h+1FvksuIcwiCYdZZLTkS6ANxfj5KfsOqbbjbhwWeCgTbz8etJyH2lq/3oF8gYtwCJcnCvBOufDmmPdUu70xG/kidVU7Z0PnOsuFyg8CgIu9Qjz6dCBvI8/TQboS5JPD9LzhPC6EV9+IfQ5rww+ZrQpCVBYY86RQPKuwrboUJseMPA8MUv+nL+HfUyRpBG9tcW2gkyI08pJ5n2vKxf2/sZD/Jpd030zoJnRtWyGf69AxqBO0/P6f+ekb5PqpiIdnfJ6Jw4AnRPKwCjGEDQDArOH9b4oRP59Rxwer9uM3bEa+hca+qn2oHNfJSX+bpdoXe+L8/2FRFErvm8nDDe9K/AMfa+WhvLuL4pFvbAhvZzpYTiNfntB85Ehj4ih9nmr/WNIS+XY/x8fkqe90hnqPEJKsKLj8tgu+qNoHcvAzLy3h70HMiPNHLPdSRCfYQ/ixpixpLF2Ix1UOQeVCDtXR2oX7Nlc9dNYUULfhV00GjdQXVjf0Tj6PN4RraOKbIddAf972HCvF8gtZQihxjK0AAACcOjscuZr7rGF0Zfy3GORwdx6ZBuW5uG2683oeq7Yvow3yHcnig83oX3Hn/MbnPLzZaMV5deHXGH4vOIIPzD/ifjzgHK+oGQdwmKA5h1/z1WgcTq0XDlUiy5GvLJS/CzEfLleilVEEQRAEQRAEQRAEQRCEz6DJKIIgCIIgCIIgCIIgCMJn0GQUQRAEQRAEQRAEQRAE4TNqTTNK6++P0pfKeBxiqas58vUP5vpObYx4e8Kj5Vw35M+SOOTrak1T7QQz1vsYa+NaUP/5cwi+NyFGc//iDsj3Yyccv/nh8LdVe3lOd+S7wXpKtRXAcabJRq47E6orQr5sA4+ZLRa0iwAAihw8NjxI2grYeQZrsTRWnIKkSJYYyA4ABi2Pg3046ATyffdWX4/n1JXh/Mlz8fJot+NqkGnn1zRkFyOfJMvRJNA5sEaAYuTPS4nGewkH/epZD0bnZZdfUetJ0UnbkDs8axR4m02X9Z2Y1nM8fOB+Hovdqcs55NsvakY1ggLQuhVvm/aX4/y7P4jHwC/Mwlp7WaW8Tf+rHD/bSEGOwigJToQu4Vtu68JC8c0Eco3AxCfx1tzsxk4offwhHtve9hesqSFSoJilT7g+SsJ3pcjz61CuQRKgxb4NxTHQFIg05FXpuJmPrUDpLBdvG/0lXZsyxuuTvI37+XKcBuDl8cQrWBgq8nde93X3N42/nelsWDfTKWgoapye2zBJXhG0Fl4PFV31hnpye6cIp5Gv5zLJ9yZs+S616YZCQcsmF7fvxhDeJpXbcP+vcXkuA0ZBQtIRhr9XVyWHdeeao8fKsR6oOGbsKY1lzgnbnxdK7dmfhXxcWOjE48f9adx34jusIXLHuK2qbZcyrySCPyFDLm7bHX44fZ2Naxv18T+MfO9n8fqbsR6P83tPOara/zqD6/nkmF9U+2AZ1p+5zsTfCZKMmch3V1p/IZUD9R2NgVcghwPnQZLlkmrvycTPQCnm33NrAYQPZI0XMa1IW9aDnBS3eJfbGa3nNoiJGlKSTyt+T4/bIOYkPdYao7oaUQCgMfE2hNk9a3xpDFjrmTn4eCy3Mx7XtTVawBNFQpvl/F8BdfpSnKgCzN/+rtrNvvV8nKYrni94a/VY1WY34TywnON14VInXPZNudzWShqNtjSe1jqwz+mH65c9gLch5YFSvRT6XIc/9sV/x8duhoxc5GN5fF7FlZcPvqJpjO4IgiAIgiAIgiAIgiCIegFNRhEEQRAEQRAEQRAEQRA+o/bC9EKCUbpkAF9eWyKFy7w3ZKRq59yLQ9pe7Pi1avcMwNvzPvYL366wa+s05DtSwsM4ptyxAd+Lwpcb7u2KQ+HaG/Ayxe0lfIvgRL9L4ImOfqc9+l5JT0XpCz/wa0bsxrFLgSf5UmPnWRwu1FRgBiGMMhcvV25u5UsKN5biUFDt1r0ez+mXicucS5iHdZbh7TLPFAapdkgxDt1pipSF4uW5fud5GIVGms62nfa89Fpfypdz2234iy6jsHWwofpz5OKSVzksTzHIR3PC/uK/KUSPQzMVYftsjRQxqG/Bt692pp2ChsDDzX9QbYe0PPpgOV9u3cyEl+8uacO3G5eXYecrPJ7nkWZ4S/EJb01V7Y4d0pHvQrEQjvJLMvLF/XsXSlsOdOEJnRQjJNDZlCl9wkN29JcKkae1mW83HqLFIYu9LTx05oseg/Apd+z3eP2GhhhG/rvdcyzt/bYslF5fEqLaIVIoulGI7zIbcL9pk8Ih8xWeHtIP5/mJ16SwTgF5+/HGgiYkCKXF8BptDm7TRHWC/Otw4xQeyst66R84fFoYAqHQOwAArRD34xb6J4TnyiF8+hJvoSJSyLRwXnMmPpHJxMtgUQL+TV6GYGAQqnZxMxy+FuDlzmqTnGTeTmaU4zFxmdAhbc9ORL5psT+q9tEiHJK8fW1H1Q7fjyvBjHm87V0V1Rn57EJGHyjHIci7nlyo2skfzkC+J2Z/itL9/Xib+Z/cG5AvxMb7zjvGb0O+x37m4/XYb3HB+tv4MNXuGIGlOrpH8Xa4TOrEn45Zp9p/T+QhM6DYAfArQb1Am8hDF+VxZ4CO9z+h/iXIl2cP4gm56xOqFpPqsjheqSwSSqzPcpugCGNyXSk+keLi7YVGwe2TUwhF1CS3Qj6275D3GyJ8AnNUrSMVw/Jk9GW4fT/r5OOBC2U47Dw5gNfv9POX+3elBI+96its558obRbkiMrHXYd8xjweXq2THp3WKciSGKT3FKF+a+UweelYFMYndb8uoXmR1G7QONiZ7nnuwpfQyiiCIAiCIAiCIAiCIAjCZ9BkFEEQBEEQBEEQBEEQBOEzaDKKIAiCIAiCIAiCIAiC8Bm1phnlVe9I2obS8jXXibB8hYMkn5p3n2rvn/gm8s3+U9B+ymyNfHEbeZDmH1E4NluMjb6UgmMwgw/iWz3o4lvzGouwfkHe/Tzucle3ZcjXYdmjqt3i7zvwven4Vtby9qaKuH2mLMjDGsG+8lVBKB45pVibpkcYFwKYc3g48oXAUfCEMR+XObOG61IwaTva7DweYBtsKKj8fhs5+mJc7uRtukUsp/nzkuSVUPyz3o7zQ1/M64HTHzdLOgc/k+sq9KR0Zfi+tQ7PzZ3up32q/Wch1imzhwmx347qb6FbX4jQ8XbrnDMI+XqbuS7QBxd7It+6ZH5s/r09kO+Xl/+j2hO3T0S+lp9wPYB9k/B23z2TuQ7gr5FhyJc7LgWl35iyWLWf7DUK+V7O5noUnyy9BfmifuP6G4Vd8Dbpo61c/+Sn0kDkm7Z+kmrHB0nbz0Pj4QYjLw+T025HPq25UD5cJdvF28k4QzbyKYJASaGC9YqKFaznk+Xi9fSmgOPId+Si3IoINPyqWCF5XaNR2pjHn6XDin+0KOsW3QZrpcVY+bbMJ0rCkc8ezNtwnbSbt6gXI+tyKUZBH8ZV9QxwmXCfIX5XZ8d57HRy0YyULlgnNH0PH+eVROFzBp7k5agsGPcTdaUZZSjmv3NtBtbEGx23W7VbBmAxrDMOrpV2f/CvyKcbyZ/X+i5tkS9R0MvL3BOJz9mbl4eLdqzj8uwOrln1x72vIt8rWbitf2Ld3ao9vu/PyHfhFL/vzUZ8b5O7blXtb6PbI9/ydh+qtk6Dy9VeO9e3KnThtuT3kiTVVqy8bRd1jOoT5ZG8JDIHLqM5Tn/5cBVdCT/WZZHaRC8/len5s3QZ8XOVtZ/E9yIpC5AWlHhOAACNMH7Wh2E9QJdQl/Pb4P41YB8QDRhdOO9Tzg7FY+1ZZ/i7WZkLv3/rBCEz09HL9dllr5/1FQAAtIKIkyK9C7VO4HYZFnMTdRFlvTakPSv9dFFPStaoddOFEoZSWundRNSFdDuP3rPmKvq9TPoiq71BF62MIgiCIAiCIAiCIAiCIHwGTUYRBEEQBEEQBEEQBEEQPqPWwvSuajmX4iX8TDjN5lK82LqgI19fHhmZj3xnEnh4l78f3qrd4eLL0AzSVqQ5WrwHYmxHHsZxKh0vdY+z8CWph8qlLYgzhfNKz4IpQlorLe3zsn1mU0Gxed5q1CWsdwz3x9uJewtilLeqDdKVVHwgADjy+NpHdva8x+OaCnI4hsMmLruVwkz/wmEVIk4hVEPreQd5tzBApq36El5F+K5Wum+dEBqobxGP7y3tlGpflNqZ4kheeCyXcClzhgvhDvVwK+mKyHTx3/fO2b7It8YihPbk4rC5MDOvM6Y83N5d9+V01Y7aJoXk/MrDlGNm4y3Md2fE8oTUZWQNwtv9/jNtqGpHPokP/uEQD3GI7oHbhexk7vOXtpEfeu9U1S6OwsF3bX/jbT/zw6FljSlg+oyLt6kHL+Bt5BPjA+XDK0SR1qEbNJ7bcJ20ZjxPWE/eziS1twqupyIuk0dXgyYnGY8JonpyyYPs9TiE2CE0VU+02IJ8H5y7SbXFcDEAHKbHpBX73rZ419mrt0xfDvcT00yP24vSbB6KFdciF/n2tuG230V8ztIwXgaLY7APt2S+w/88H8+l/xWBfI5Y/uAzpBDhXjYuORCuw/UlxT9dtT/L7IZ8swrvVO1Rt+LwPoOQsadLg5FvePv9qm3T4lDmWwP3o/SW1jwk2i4VkHHdeVv/w3ksnfHe9ptVu2/nQ8jX1sjH69+X4gIpSiq0kTL9/ZJeqq1x8uekcXkJ761DygP589IYcC+S7eD9lNxG6kt4HVGkkFcmZIFG6phQ3ZZDhaQ66RbKI/rENsHguQ0ICsBhellZvIGyB+L7rqvQ2SaPRhpPe3v/Ft5PT83Bbc2w23hdv8uyB/l2FLZU7Tw7Dq3VCgO9qF8vv8M7nXbAAfr1CDlUTUBbxMu7+Txuw53CzzbgKQgUmsek7HCJofBSVZND8dAppdt0+nt5b9J6WYck/t5aDMuToZVRBEEQBEEQBEEQBEEQhM+gySiCIAiCIAiCIAiCIAjCZ9BkFEEQBEEQBEEQBEEQBOEzak8zyhtyzKpGmBOT4lcdgcK27pIuxT3X/67aLUx4a1x/LdeTcklzbmUK17wJ1WN9kZAbcLpQ4fHzca3zkO/zXB5Dq5WCO73pWWh0PA6XuaR4XfHZaKS5Qm+xvY0IXR4vlqEtcLBtiJ6nDx3D+hmtIcPjOQPTsRaXQwim1/lJzzWXlw+lDOvWNEUUo1ZK8zJq8Zeej5cyKm63bTuNBQtcZqFOSBpRLuH6lW0njvSmpGP1gt5JcVus32ESNKNKHXgrWqG5AK0DB2a7/LnTy2apdYouGOuDzNx8r2q3+X9/Id/pEq4LZRuAVR2Oz7letVu/cw75Wn17WrW/ObcT+bpHzFTtyCG/IZ9mZBfVLgvG+R76X6xDoNHzdiHr3q7IFxwoBOj/9ify+cXwdvr83Xgf+xbj+P7SgXrcHZb27aTa5l31VtHgmnEI/ao9B+s7KPI24gI64D6tJFgg9rlR+jzkC2K4foltcaLes9aUTElM/dSFuVbi5/2O0mE/cx3LTCPu85wW3qYdt0ciX0YB17OzgGdkXSixtXfXoLkK/T7hvPoSz+22y4T7F78z/IvtbsZ9+qpwrh9kKMAabyXR3E76O26DfKd8gTFm8vGkMcHo8bhAA+5Hg7S8He6++RHk2z7gDdVOG/Jf5Gvx7YP8nDqs37P+fDvVfippHfLN+In3CdMH/oR8479/FKVHp+xS7SJpoOun4/lzKQf3H//Xa7NqPx5yEvkSV3Ltvhn9NyFfe/MZ1f62oBPyDQnk7fchG/99rqo3I3WHFpfKYAPP8zKnQT66Ssj6b9pyXl+dJsXrsaI8jKw/I7566cpwfWWxvJzFBuQhX1Z6iGqXhVa97SBqES86QErv61E650n+vvVo0jfIV6Lwuv/PL+5EPq2D53XHWw8jX7wpS7X9TmYDAIBTweOyeoU33aQsrmmocWC9TUEK060+uUzVqwvSFAjqj936WCEp9/FX04/7CloZRRAEQRAEQRAEQRAEQfgMmowiCIIgCIIgCIIgCIIgfAZNRhEEQRAEQRAEQRAEQRA+o/Y0o2RdKBEpBlMjxC8yOVZZz491MHy7diEQ8s+SWOQrdvJ4Vj8d1gsSv2fS4uDyQqcZpaNN+apdouCY/zAD1wPIU/D3XFh6wzPedKBY09CIkrGeErSFumMtBVHvK7n1WeRzgGf03/+B0hecgfwaASXIl2fyrO3QFJE1nERibAX4WMHWR2ENE1GjwFCM650itAG6Ms/lXluJZpSoE6VIcdGmPN64FEfitkRUvriUb0U+Y5BwnlPIBRqlrtRIqo4rLw+ldSW8fikluOwfe6OHaie1x7pQLf8f1wA5/GgM8j2TynVuOi7FGict1/B6Kkt5lFv5vfw29z/I98ksrOv1eTeuCRL6OdaTErXdSkZ2R76zqTzfByUdRb7jm5urdvFS/Juyh/Jztj4gNeh5+dBYSPHS3mm86CUE6XjZMQKus2WCLlSeghWLzjhCUTrZxMuZRVt1HShjdHHlBzVAmBPXkuxpXItC93wu8pWe4bpQYt8IAFBawls1WTNKJ0h0yNoxIk6p2IvaF7pKZD7E8zotuC3WCfp9ih77/DO4r0zSF+vQirclra7PRL7Do/gY0OmsJ6JBDn4fgf64jiiCAEhr/4vI18HI8zkoBJfz/v99UrUNN+DyMKH7NtXenpOIfO1Dzqv28kvdkC8skvfjLfR4LDuw40GU/vpoB9Uefd1e5NufzzXNBl+Hv3eyNFy1O7x2K/KxVnz0dn8g1vzbV87L+MAArHEYIxRC/XnhWdRXDRqxqOfjsn2jP9clXOdoh3xaYXAr6kABACjCO5LGJfkMohAUvhXFiNt2nV14D/Py+qaRqlZoUFHFBwKAroiXcUdA/R8rXRPiO683nSH53djbsdW5NmBdYgD3PkUkc8ZNqt3lvn3I11HP9cD++9pw5Av976+qHQ+/gif6jsN6zmI/5TyZfvl/5u3trf7iyhU0o6RsNHiuFgDiMMdNz0m8gOST3sXEY+U6ayjiN2QPwT5tIc/X+qK8SSujCIIgCIIgCIIgCIIgCJ9Bk1EEQRAEQRAEQRAEQRCEz6i9ML2rgLk8h+VovKwXFbeEDtaXeDzOIO1PLKblML1QA14SrRPWpQdLvhJhW1txm0sAAJfJy9JLORbRE7WxnLMBEHiS50mMn+dwmBmxP6D069DW47Hy9vZBOr5ldKAfDgXMD8FhnQTGKWzF7ZL2GhVT5S2jkU/cetQtvK2GthoVQ/MUI743YyFfClzQHDd9GgMPVXKdxUEt4hbyup3yHq31v05q9DgcwJzJn0vxaBzSxoTtn4vKcfhWyMUc1TZlBSHfP9eNUO2W/8BLtlkQD4mVw5LN+Tz9cjauv6kBOFzj2DN8++DEp/E1ji/g4YUpXY4hX8lHrVU7/sYc5Is38/R/U8OQ75Uuq1R7UZvRyKc7fwEaCx1en67arV/ZjnzaFvEev6f1ssDbIMRyROlxG27T4vbWrOH1MsflObymz9QpKB1/qczDkY0LZT/fGlu/4Ubki7mdh3cFSmMg45+8HRNDeQAAHEIkshCJ8b9juS2H8IlhetLQyW37aHHYJZ9HDNsrC8bttC2d978bLiUj3+sJK1X7wQcfRT5D+i6ob+R046HGI2O/Rz6TEHu17kJ75LveL12193T9HPkST0/lid14XDPu+p2qvTq9I/KZdfx6nyVuQL6N4f6q3W3XPcg3MO4ISh/ovVS1vy/FfeVXxzupdsdAHOYtyloc2o3r+XsPv6Pa+dLYoFDhsaJr8zoh38zwn1XbdY6HIboaQNiPMQdXil0lLVS7qBS/T4ijI3mbeHEIxrSSBIqTf9MUgiu6PReHY7JisQJ7HtdoHXisFmbh70VtAnC46QFHS9V2BNaXgKDaR2PC+QfCO663kLlqI70bervG2b/dhNK3jt6h2qdKcDxXxqgg1Q496zkUzxtb81qhdJL/JQ9H1lO8hF9q/Xm7aQ/F5duUxSumRg63E6q+W78pnqaS1wsxZFZSEQJjgfi+Ja07Kqt/Ycy0MoogCIIgCIIgCIIgCILwGTQZRRAEQRAEQRAEQRAEQfgMmowiCIIgCIIgCIIgCIIgfEbtaUbVkL4R03uOM1YEPSmHJEqgq+KGhfL3tF7u2yEHdwoYpP1OXWZvW3tWcQ6wiWhEyfgfyVLtu0NwnPKWIr7l7cGyZsini+T6DK6LeNtnZzusfRJn4FoDkZZC5DtvtAHhGXEH8VIH1iPyF+zc6/C+4H65vE46zdLWs140ozSCLpNcq+XvMaQZJZ1TkHyTNau0gQHC9aTtkUMbtoYYc+D7N+fw3y5vFdt62m+qXb4J15lDL/H6FrQDP7+Hxq1T7dfevwX5Wi7lAfOGbKy7J27r3t7vDPK9l3UzSid8xzWCTryCtXOaJ3MNuF1/YI0CfT9+zff2Y72EoW34VuFdEk8hX7EQhH+hG9bXaLYFGg0xkk6UiOvseY8+s6B5Uw64Pts0XJMgT8HtQKYrAKVbG3hbfcThue01r/ndo6+pEL4I94ftH+Bjie6WE8i3+jivdyWROH9EDQt5WMOqODxx+56u4uMqQry+PRi3QcZdvL7KukOf5XdRbcPG+qcRBQCga9MSdLrLmjG2k1yn54v069FxbyZzLaioWKyrFqHj+ko7yvCDXnHbQtWO12NtpEKhX3u9/RfI18nIzznlTCry/fIL16x68ravke+VNbej9DfxHVT7b53WI9/abotU+6QzEPlaGfhvvHcZ1gM86eDt6zmXFfkCtPwZTgrdhnwXBN1WUSeHsVrQ5akBdGU8f5ytsMbb1qwk1bafxc9A7H20khyWOB6T66B4rJ9J+uIF3A6Le9NrFGlc5eU9LD2H6wylhh/E32vMyx28aPoyezU1eWpJJ7hsWDfVHn3XT8i3M4eP81wDsOaXqPGp0eN2qKraV5GmApT+9EBX1U6EvVU6R31Fo+MVTuOouu6trPvm0SefUi4Ogl8cSwPguqeVi6O2/lXM+ndHBEEQBEEQBEEQBEEQRKOFJqMIgiAIgiAIgiAIgiAIn1F7YXo1hdHzeja7sE5cJ61702qqtrzRJO9P7AWztD7W7hKuL62fUyzSXo4CzOXZRwC4jp1U7SgdXl8ohlVGGvDS9tz+iapt+wyH6TmsuKiL24n3CDqJfCVOHp5T/zbArHtcBr4cNL/QH/nEVHkgXjYqrtZVDHgenInbiUuhY4qX8A+tVJV0Zbwd0JXiNkEMDZSXukNEqMdrWGw8PEzR4XAtQ3H9r8viEm0AgOybeNhe0jLcbomhrqcv4G1+W8XxJdy5W+KQ7z9fDlHtmD+l5+4vbo2LtwK3bebb1r/iug/5Oj2zF6VL/s7ru9/3OPTr1Lkw1R7V+zfkizDygvfZyS7I99fTfPvzsw/gQrHzIG9PAupn1EeNIG5DLYcXiCGeC3Nx2GZ3y3HVLlHwVtaZCg8zKWM4lFfGJMRsfZHTTfKWgUe04v7I9b8e1gabPu2h2mdGBiNfeYC4tbS09bcQzuwtZEAGbSMvtcteVAzcwnXM2fx+HAG4vbdH8bIzLwKHcw24d7Jq6+EP5NOaedssh5DUynbqHnAdPg4azeUyL/6ysvJkdNw5J8+vbCk0LVDooAIBt0tC9wuXXPjBGoVxb7gOh0Q7hDFqM3Me8j1462bVnhKYgXxRI5eh9He5nYR7wfXujPA78lx4bLBD8MnjZZfwpPylmJKT9kjV3s1wIbMr3tuW+gYTw2qycZt5Y3KaaqcXNEc+8TXFxaRwHJ3ndx3x8QRZSpGvyIS/py8RzivVV43TcwiSxcT7iEhDHv6ecAl9USNb++AlhC7vfiwj4H+B12E5vFgMf7uqd0MxpE+6F60Fj7PCn+TvOGVynblLKFxSPyq2qUqZ1BdXsf8NNUjSDBfNHo5seDAxNFPuD/lrpHtorbFiGwBQpyFVdbf3HdSvSsXRW9i8K0IYK5zL8HygD2lkrQNBEARBEARBEARBEARRn6HJKIIgCIIgCIIgCIIgCMJn0GQUQRAEQRAEQRAEQRAE4TNqTzPK2xaVV7F9pcmfxyM7pCBIRQiYdEmiBKIWlF0SMxD1pBQpKNMgBWXKMfEi8jVFNH7V1LCQn41IDW3z2ZD4uhDrLMQac1Q7SIe3xi0N5/khbxDuMuO88hc0o8L1hchXYOcxzTiqv2kiyTSAsJsy2IvlgGeOE0tGQHEUr7+GIs9lWa5WYrWXty/VKJLmkZ1/WV/m+RryduJlMXybY/8zkoZJC35OnQMLrDCdoMvi8Wp1i92GH6g5jWeoITcXHxzIn4P+tBTbf99Z/r0xsficl/g1zNlYnyXvMV6/Ahbh7b6d3VupdvT/O458w4N3o/SOd2/gCSyPA7qLnsvhH/lc66igCGtNKW15YW75jxzkA6OgkZCdh1yNSaGoqttQ50gVupPwyE86ipDPX+tZiCjDifMgTs/LTk65RTrai2ZUE9WJEol+dbtqH4abkK/0Ot7+WaU2TRzWuHB2gOJFg0br4ueRZMJAI8kyif2GvLW0YvTcWuYlcU2T1sseQr4WP/zq8XtumiZ1RP64bqAzXm47gyecVj/vF4Dbt2TjBdXeIWXC4N/47/6/9j8iX08/fp7TTtwQinpL8vj0gFC3Rgdi3Zp0J9dM/KQQ6ycapIwdGrxPtWU9uMP2GNU2a8qRL0fQjJLHXHkufm/HnbgNuM3Gr+cv3UvqylmqnQQ7oL7jsIr7rWPfkSKujSVrNGGtNs/1060OCtkTaMT146yXcRbTS+OqUuF+JD3eolLeEChSmdMK2927TPX4/UWrA9D87wGI/YpWFgLiPl3rJORKe5GXW0c5ftZKINdNCtmIT4n07OT3P/H6TOpTvbwPHnm5A0ovjOa6b8/Nn4h8YRdroU0VfkeIHo8NLOcbzxoYbaDwpinVC12Z0FdKMl2iTpQev8biF4lKHpVWaGLdtB+ZZ5+2nJe5+jKKajylgiAIgiAIgiAIgiAIgqj30GQUQRAEQRAEQRAEQRAE4TNqL0xPxlv4mRcsZr4OzeVl7kwOt0Pn0OHlwg4ve8Vrpf0RFWHNnPYq9kA2muW94z1QzefSVPgwrTtKv9J2pWqXS2GbDrw7MqIkHB8rhmoGSVsgn7nIl763hPSq3mqjxWnC9Y5peZk1nPUcHhX2J14z7n+yQLUVM163qhi97EMqbEPuMkjLwKUty7VlfNGpuBQVAEBbyuukEGkAAACanHx+365myJfegzeTTjO+T41wP/V1k2l7IH5m+uvzVFv7fgHyMRsPxXL6S8uOQ0P4OaXt2IOP8WdrTs9GvsID0aoduf0g8ikt41T7j12tkO9cGxzSF7qELyc/ujQF+aCc/8YU/3TkWrecb7Oc9PJ25Cu/tatq598QiXwBabxd0BjD8PUuZkKjoYpbNH+Z1gmlT5fy8jAsdC/yiWE3B0pwfRoaiCvf9jIeDnps2XXIFwZCCIGXkAkCoLA1HnMYcni7JSkVoBA7OSxPDCGQhzwuIXxHDqf2PjzC7YUi7DIv3xsT23u/qo+56gs5A8pA+7/i/34iH68cLsftywkHD4e713YG+Sb3+sjLFXjmdTbJMR4icv3IF2wcYxmjv6jaETopvv4qcDEe9q3T4AJy3pmu2tF6ebDG+4xMFx6PjT54r2qfPYyfoaGkYY2fDUVCeQ7A+XPgUpRqG3G37FZHEEL1FcPiAACcVs9tpLZcSgvDJZd0PY2Xa4hccOI+WxHaC3NW/c0rjU4Hmv+F6TGxX/HSx7iOnkBpg6GtasvyFflFvI8rmo3DqZvPFcYkcugdq1ofJ45jAACeHPAtSs/8/S7VTnrXc1jeVSGHDQrogoJU26A5j3yWC/U4XLMixHZMyg9XeJBqG/NweydOO5TjagEasauWqgWanqisyohDN+kFROuo2AaAein5QyujCIIgCIIgCIIgCIIgCJ9Bk1EEQRAEQRAEQRAEQRCEz6DJKIIgCIIgCIIgCIIgCMJn1J5mVA3FJJoMPJBZBzhGVdRwMmmxPkyBk8folig4ftciB0sLKHKQpiCMUOSStjoXKGb4GonhPAbeLeqXtC6qTHYa3rpY247nuU7Kq9Joz8+1uBk+VtwiWCfphDEXzdGKyLHIGoU/L2NB1XUANHbhmZfgvb51OiH4Wef5+Vemy6Qp5edlXs7DLNK+5IEB/PLFOMDa4s/vm+mw1oXSADSjorbmoHRWGa9TrihJg+sU1w7p2jUL+Xa+Ea/aId/ja+Q9xLfqNj2PNUf8LvAyUtalJfKZ96SpdstH8X0e/W8XlA58hGtqtJ6ItZ90QTwo/7mn7kK+FvO5RsLZv2G9hsA03p5YLuB+wWXiZTJjYADyxe6FxkMV+6PoEYdQ+qxgLwKcryL6aKzz8uW/JqB06I+8LoYtrSE9i0aERs+HaWgbcAmtBfs0mYLWnZ/n8zM3zSaxP9R49Mnf09o9Hyvj9PPcb4j6OIqp4WlGtXhgP+g1l3uDJ4JuVT93tYlHx5WF8fHks0n4YWJNL3x+rVBdZalUl/C9wJP42YWsP8aPy8K6floL13jTBOA+jpWUorRG1DnVSnnuEq6p4OsrxYIWlKyV6uV9wQ94H9FKsBsixgI+tmCSrpDDwjPaID0O8fVGVyrprwkHa2TJISP/IMGK8/yIMwkfK5QzuVzpBW0ujdRdOJx8DCG/WzkCeRmI3Fn/dGquwBzlwOSHd5VEjzys2pe+bo18+QW8ft1+O+7j/vqyjWorfx1GPnFcAwq+P00oH8cVPYrzNl/QbAQAaDWJn7emWlSNMGaX+yWNmTdEOklMMOA0Hvs3aPS87CtGnD8OsRJJRcsgSP3J7bv4vlVZkUR9pazzpvDrM/kaFs9av3UFvXUTBEEQBEEQBEEQBEEQPoMmowiCIAiCIAiCIAiCIAifUXthet64iiW6ZeV8zZpZ2p/QICz/K3LhsJtQA18SnOnAIRZaYe2bvITQIK1BNQnXNGs8L5E3a/C9WQ18KWK+fLD4+6XtbymED2M7htcXGoWgR7k8MLPnBah+mbiMiU9dzjudkfJARF6WLaIv8uwL2HcRf+AUTqSX1o26ambxsBiap5HPWS7kc3Yevnwu35JaDFkAACgrbcWPMze8+XtNegZKG9oGqXZ5EG43zXk8ROPCvDDkG/z8ftX+eU8K8kW8zp/ZmVQcznzbnTykLs+B44WCDDwEZG8KLhO6PNw9BZz1XBA1/jw0sOX7uNyJ35o/+X3ke27+RNX2P4/bCKeV9z1hf3lu+5sKGhMuK8xexeX2Zvy9oB04bSrg9VSue0qJsJ7dy1bSBIBe6rcYCnfDxypiuJ3UpOHwO89jMy/DIQDwvgW80yKMwexeQr219Te0pyq48oTR3479yCe2klFQ+3gb1aB6Jtq1RT3cWtwXnO0ntG8GHNJWms9LhEF+RZKGSx6Rmkj/CP4elGDGYff6YnxseZDQJsihoUJdl0P4HIW8cQnS4ZMyK//i2Vtwf97qa6g36Fs0B7328u84NSZG/bzchsupS2i35BDi+KRM1b498k/k+zKtk2qfLwtEvpSPD6j2z7NvRL7SEJ4RxmJ8LxkDeY2em/AV8j3/7WiUTirbwRNX8f7tFfndVTxlsE215fcr41k+1m4Qb1pexh2K0XNsq76M2zppqCSoCHmv21LWSNMVKC0pFaG0HKbv8uNj2/ryRlNf7oMgCIIgCIIgCIIgCIJoAtBkFEEQBEEQBEEQBEEQBOEzaDKKIAiCIAiCIAiCIAiC8Bl1oxl1FTGqBSeDVHtbXCvks+m53ohDCrxUhC2Jm5ukrc0dXBdFJ2+NKwsoeEG8ZrEkymDW8ThZN80o8fd7kUsgAGI2ZKJ0+aP8mftLEccxcXh7UxE5ZldEKwXiOu1VDdBvGjgsuJA6rDxtzvayfXfaqVq7p9pEKcOFxSVsXSw/C5cg7oDVbuoPmpAglPY/x4PZDZmFyMfOnldtV4cI5PsrN1q1A9Nw3dP9uFu1Y3/E1996rLtqXxwpPds8vsVs/GBcliYN2oLSQ0ZxzZV7Wz+GfM3+xXWpHAOxnlX6DP47Zn2ENRn0XNoAjKdxP2EU2mnmh9v3BqF1UMNUWSNKQm4HXKZmKG3bf4n7JL0ajYGXD+bAGisEpjwHa7UJO0S76Tu5rELZ9jIKVEzSduIuzwMWpq/6uE6LdKKqfg2tmf9GpawM+TR6/kPkrcYJoj4QsZu/F2QC3l7d3I2PX41Zwcgn6naK4y8AgGLhJSboOB7LZrfjY9lHg9OR7xNp7Ob05+dRpLrsEm5VeO36383xY5/edBdytXm3gB92AY/P61Mfmt0jCnTGy21L31F/qJ8fKwhHxzmF90NRexgA4IaQM6p9qRzrFLcM4XpdBi3+5YNt+1T76wkdkC/wY36ec6k4b+/vzsc867Px95Jm7QCP1JBeG3N5zkHFxHsfl7TmRVMsF6B6jpfnVRIljAul8qDoeX1y4aqOdKK8afLK0xHyeUQkCWX0XTfNKougReZ20brR86OVUQRBEARBEARBEARBEITPoMkogiAIgiAIgiAIgiAIwmfUTZievLWkiLRETLF6XsN2tixItcsV/FPy9DxoJt+Bl6+LiOF0AAAGKWRLLyyp9JOOFclxWVF69/k41W4GB+TDOUp9Wqxa/3AdOY7SJUI4pJx30f58STAOPgJw+OMyJ26dGyVtR2s9KO2D3cSRtx4Vl3ya86u53brWB6GQ3uqWfH0vxyplvG0xlOD2yZpb/0OHWE4eSl8YE6vaYfukdnNgpGpHrziGfOfGh6n20Gd/Rb6VXXuqtiYR16frY/l5Wkjta4eAc6p9pmsI8mU68FL3X0paq/bU+75DvmOjeShesfMI8v0naqNqj104C/mKmvP7yeuCN1gvieBlJOg4zmfjISCqiVauMl6WhXsLBSAwzVrgrdsv/CWG2Urhxf683OuK8d8kxdC8qwm98wYOy8MocpyAEKkZHF2AXNoo/puU9NM1cWsE4TNMa3eqdtxa7Lv0zXWqnTsMhyuX5/ExqdYfh6AyIaz1QgzuzzWX+J7u7bbfi3zOlvj6GoXXdb9MabxcyH06qf3uMZp3hqe7476/mqNDnxP42e+g11wOLfuh+U3q5yG9LqDjBkTzsUWEAbdNuU5/1e7mfxL5AoJ5aFqGE4dgrivopNpjk3Yj3+TXd6n29rIY5OtsylDtaQ/PQD4t5EJtozEIYdF23E+XRfH3b1nCxnnxEjQWnGZeT+TwcjFMTu7jxDGQHIonDpHl0DtZ1kd8N5PD7RXhPNKwGxRj/VuHVP/uiCAIgiAIgiAIgiAIgmi00GQUQRAEQRAEQRAEQRAE4TNoMoogCIIgCIIgCIIgCILwGXWjGSVrRHjRkJp50ybVHmLF2ksbitqp9kB/LOKRL8SpBmrxvoaXFB7PqpOimv01OEgzUAjaNEu3+Zuda4x0N+HY4nbXf6Daz0JX5KMtiKtPtqDN1d6It4rtYOMx1NulDSu1Dlzm/DV8HjZfwRlrzqqbrS3rK5E/4RhvTRHXM2BWC/JVWeGlrrXSvF1f8gX/wetrwJbD+NjQINWsr+o2rgKsbfDno2+r9mlnEfI11/P6deuSbsh3d1K6aj8VivWkXrh/r2rrNL7/G4crKE21nVJOmDS8jMr6Z7fetFe1F47ejnziebosmIl8MRuqe6dNEEmfzS8b97mu42kej63zdqIeUNUxwsV9kSjNYspU2ynpZhqz+HOWtSZEZM0XURdDU8ltied109PQ8T7WFYDz2GXh7UfpqSDki8o57/l6NJYi6jviu470HhQ+nOsR5b6D+95ObU+p9oVirKWYlcf77ODQPOQrc/BKWJzvh3zNu+C6dPo812y8JRW/a2kF0Zk1u65Hvq423n6f7jEQ+WDHftUU33sA6m99jZu33aNvBxhUW9+sA/I5moer9vpmuL3NT+TtbXFz3N4FNc9TbZeCx06rtFxPqvAQ1tRs+Tx/ttrivR7vudbwoudYFsp/76/5SdipyIq+DZe81jy/XCZcnw1Cn+ewSu+UWp7WOL1paFf9XrRSduiL+HkVA/Y5/ao4RpfnZrzoe14rtDKKIAiCIAiCIAiCIAiC8Bk0GUUQBEEQBEEQBEEQBEH4jLoJ05PxsvRr/b18y/AVrW5FvqJoPpf2ZrMhyKcIWxIrFi8bjOoln066l1K+3FBXgufuzFk8bczD3wtM50tQjbAT+err8tSGwOL0m1U7qdVK5PvkUBfVbgH7kc8/E+fzSScv+n+UJSBf2B98W9SGsjVtbeIIt6I0i7GptjGjQD6c00hCbqwX+H2XpSQinzmj4S057rp7jGovaPc58u21CyFtDryH8w+Te6j2N8/jJernLwbxhLSyVye0sRotrlHOMr5+mJVJ5UVui4WlzeDw/HcUeat6MQyo9Xy8BH998+6qvSb1L+QrF2L6wv6U99glEFexnFtDUdC1QuJTv6K0PqG5ahe3xSF8lzoL9U6qdi68E7dH5NA7d4RQBKkjNebxOhqyDReIgMM5/F4O4XDghtmDEMT/ENtFL21m6E5cKbMS/FW7sFSqoIyfJ+t8IPYJfWZQGA7JzyryR2m9kdcum74U+bZfwuMekVf389C8pIIy5BPrK/MS1tUQcZ7LQGmNkPaXjpXT1SFcStf1u4m399jAj3eodsbHvribuiF2Cy/v5UF4OsVQyMu7opfC1LWe+06NIvSbLu+DJaYTQvGMeNyrcfLvlttwexK4l0uvuNVK8b3Nh+9stDKKIAiCIAiCIAiCIAiC8Bk0GUUQBEEQBEEQBEEQBEH4jGqH6bH/LSl1guOqFN+vFo2L74TndGBJeJedz6UpZfgmUJievEZc5CrC9DRlUviHcH1XOf6e08GXMGpZ7YV4OOHyuVkNqdz7Kl+ri7OYl4fiQpx3Somwe5D0zJ0OvHy4SPhuqR0vN3UKZU6pxbyrjJrM22vJV8WJnx0TdkvTuvBOlS7xeTGpbrGGuUwblR0n/g1iWXFVsazUdZ11lXiuQyXCUnq5DoFQDsR6CACglArPSFqBrNF5DtNT7Px6NRWmJ7fTit7zbxLvu6QQ563YpMvtR0Vtel3na93iJUxPagfkZ+ms521Gg81XRRw7SeEzYr2To6mZOHbyEm5ZSZieuGMeOPCxLjtPOx3X3qZWhwabr0Sl1Jexk3c8t5mucqmNFPpblyQXopTxNlMplV7phD5T7PcBAFwuXPEVYRxsL5LGz8L1UV8PAIpJGBd4Gw966yOqCNXZxklDzVeXOCZ24LqnEd4VFKnsMzlEV/ye8AwqDdMTQnQVjecwPacD13Wvfaw4BrvG8dfV5KuGVTP3z549C3FxcdX5KlELnDlzBmJjY6/5PJSv9Y+ayFvK1/oH1dnGCeVr44TytXFC+dp4obFT44TqbOOE8rVxUpV8rfZklKIokJGRAQEBAaDxMstH1C6MMSgsLISYmBjQaq896pLytf5Qk3lL+Vp/oDrbOKF8bZxQvjZOKF8bLzR2apxQnW2cUL42Tq4mX6s9GUUQBEEQBEEQBEEQBEEQVwsJmBMEQRAEQRAEQRAEQRA+gyajCIIgCIIgCIIgCIIgCJ9Bk1EEQRAEQRAEQRAEQRCEz6DJKIIgCIIgCIIgCIIgCMJn0GQUQRAEQRAEQRAEQRAE4TNoMoogCIIgCIIgCIIgCILwGTQZRRAEQRAEQRAEQRAEQfgMmowiCIIgCIIgCIIgCIIgfAZNRhEEQRAEQRAEQRAEQRA+gyajCIIgCIIgCIIgCIIgCJ9Bk1EEQRAEQRAEQRAEQRCEz6DJKIIgCIIgCIIgCIIgCMJn/H/bTdKHYs8b5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x150 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mxnet as mx\n",
    "from mxnet import nd,np,gluon,npx,autograd,init\n",
    "from mxnet.gluon import nn\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_inline import backend_inline\n",
    "from IPython import display\n",
    "\n",
    "npx.set_np()\n",
    "\n",
    "# num_inputs = 784\n",
    "# num_outputs = 10\n",
    "batch_size = 256\n",
    "num_epochs = 10\n",
    "lr = 0.11#learning rate\n",
    "net = nn.Sequential()\n",
    "net.add(nn.Dense(10))\n",
    "net.initialize(init.Normal(sigma=0.01))\n",
    "\n",
    "def get_dataloader_workers():  #@save\n",
    "    \"\"\"在非Windows的平台上，使用4个进程来读取数据\"\"\"\n",
    "    return 0 if sys.platform.startswith('win') else 10\n",
    "\n",
    "def load_data_fashion_mnist(batch_size, resize=None):  #@save\n",
    "    \"\"\"下载Fashion-MNIST数据集，然后将其加载到内存中\"\"\"\n",
    "    dataset = gluon.data.vision\n",
    "    trans = [dataset.transforms.ToTensor()]\n",
    "    if resize:\n",
    "        trans.insert(0, dataset.transforms.Resize(resize))\n",
    "    trans = dataset.transforms.Compose(trans)\n",
    "    mnist_train = dataset.FashionMNIST(train=True).transform_first(trans)\n",
    "    mnist_test = dataset.FashionMNIST(train=False).transform_first(trans)\n",
    "    return (gluon.data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                                  num_workers=get_dataloader_workers()),\n",
    "            gluon.data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
    "                                  num_workers=get_dataloader_workers()))\n",
    "\n",
    "\n",
    "#@save\n",
    "class Accumulator: \n",
    "    \"\"\"在n个变量上累加\"\"\"\n",
    "    def __init__(self, n):\n",
    "        self.data = [0.0] * n\n",
    "\n",
    "    def add(self, *args):\n",
    "        self.data = [a + float(b) for a, b in zip(self.data, args)]\n",
    "\n",
    "    def reset(self):\n",
    "        self.data = [0.0] * len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx]\n",
    "    \n",
    "def accuracy(y_hat, y):  #@save\n",
    "    \"\"\"计算预测正确的数量\"\"\"\n",
    "    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n",
    "        y_hat = y_hat.argmax(axis=1)\n",
    "    cmp = y_hat.astype(y.dtype) == y\n",
    "    return float(cmp.astype(y.dtype).sum())\n",
    "\n",
    "# W = np.random.normal(0, 0.01, (num_inputs, num_outputs))\n",
    "# b = np.zeros(num_outputs)\n",
    "# W.attach_grad()\n",
    "# b.attach_grad()\n",
    "\n",
    "def use_svg_display():  #@save\n",
    "    \"\"\"使用svg格式在Jupyter中显示绘图\"\"\"\n",
    "    backend_inline.set_matplotlib_formats('svg')\n",
    "\n",
    "# def net(X):\n",
    "#     return softmax(np.dot(X.reshape((-1, W.shape[0])), W) + b)\n",
    "\n",
    "# def sgd(params, lr, batch_size):  # @save\n",
    "#     \"\"\"小批量随机梯度下降\"\"\"\n",
    "#     '''\n",
    "#     在机器学习中，.sgd 函数通常指的是随机梯度下降（Stochastic Gradient Descent）的缩写，它是一种用于优化模型参数的算法。\n",
    "#     随机梯度下降是梯度下降算法的一种变体，用于在大规模数据集上训练模型时减少计算成本。\n",
    "#     '''\n",
    "#     for param in params:\n",
    "#         param[:] = param - lr * param.grad / batch_size\n",
    "\n",
    "    \n",
    "# def softmax(X):\n",
    "#     X_exp = np.exp(X)\n",
    "#     partition = X_exp.sum(1, keepdims=True)\n",
    "#     return X_exp / partition  # 这里应用了广播机制\n",
    "\n",
    "def evaluate_accuracy(net, data_iter):  #@save\n",
    "    \"\"\"计算在指定数据集上模型的精度\"\"\"\n",
    "    metric = Accumulator(2)  # 正确预测数、预测总数\n",
    "    for X, y in data_iter:\n",
    "        metric.add(accuracy(net(X), y),  y.size)\n",
    "    return metric[0] / metric[1]\n",
    "\n",
    "# def cross_entropy(y_hat, y): # 交叉熵\n",
    "#     return - np.log(y_hat[range(len(y_hat)), y])\n",
    "\n",
    "def data_iter(batch_size, features, labels):# 这些样本是随机读取的，没有特定的顺序\n",
    "    num_examples = len(features)\n",
    "    indices = list(range(num_examples))\n",
    "    random.shuffle(indices)\n",
    "    for i in range(0, num_examples, batch_size):\n",
    "        batch_indices = np.array(\n",
    "            indices[i: min(i + batch_size, num_examples)])\n",
    "        yield features[batch_indices], labels[batch_indices]\n",
    "\n",
    "#@save\n",
    "def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):\n",
    "    \"\"\"设置matplotlib的轴\"\"\"\n",
    "    axes.set_xlabel(xlabel)\n",
    "    axes.set_ylabel(ylabel)\n",
    "    axes.set_xscale(xscale)\n",
    "    axes.set_yscale(yscale)\n",
    "    axes.set_xlim(xlim)\n",
    "    axes.set_ylim(ylim)\n",
    "    if legend:\n",
    "        axes.legend(legend)\n",
    "    axes.grid()\n",
    "# evaluate_accuracy(net, test_iter)\n",
    "\n",
    "# def updater(batch_size):\n",
    "#     return sgd([W, b], lr, batch_size)\n",
    "\n",
    "updater = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})\n",
    "\n",
    "def train_epoch(net, train_iter, loss, updater):  #@save\n",
    "    \"\"\"训练模型一个迭代周期（定义见第3章）\"\"\"\n",
    "    metric = Accumulator(3)# 训练损失总和、训练准确度总和、样本数\n",
    "    if isinstance(updater, gluon.Trainer):\n",
    "        updater = updater.step\n",
    "    for X, y in train_iter:\n",
    "        # 计算梯度并更新参数\n",
    "        with autograd.record():\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "        l.backward()\n",
    "        updater(X.shape[0])\n",
    "        metric.add(float(l.sum()), accuracy(y_hat, y), y.shape[0])# 返回训练损失和训练精度\n",
    "    return metric[0] / metric[2], metric[1] / metric[2]\n",
    "\n",
    "\n",
    "class Animator:  #@save\n",
    "    \"\"\"在动画中绘制数据\"\"\"\n",
    "    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n",
    "                 ylim=None, xscale='linear', yscale='linear',\n",
    "                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n",
    "                 figsize=(3.5, 2.5)):\n",
    "        # 增量地绘制多条线\n",
    "        if legend is None:\n",
    "            legend = []\n",
    "        use_svg_display()\n",
    "        self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)\n",
    "        if nrows * ncols == 1:\n",
    "            self.axes = [self.axes, ]\n",
    "        # 使用lambda函数捕获参数\n",
    "        self.config_axes = lambda:  set_axes(\n",
    "            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n",
    "        self.X, self.Y, self.fmts = None, None, fmts\n",
    "\n",
    "    def add(self, x, y):\n",
    "        # 向图表中添加多个数据点\n",
    "        if not hasattr(y, \"__len__\"):\n",
    "            y = [y]\n",
    "        n = len(y)\n",
    "        if not hasattr(x, \"__len__\"):\n",
    "            x = [x] * n\n",
    "        if not self.X:\n",
    "            self.X = [[] for _ in range(n)]\n",
    "        if not self.Y:\n",
    "            self.Y = [[] for _ in range(n)]\n",
    "        for i, (a, b) in enumerate(zip(x, y)):\n",
    "            if a is not None and b is not None:\n",
    "                self.X[i].append(a)\n",
    "                self.Y[i].append(b)\n",
    "        self.axes[0].cla()\n",
    "        for x, y, fmt in zip(self.X, self.Y, self.fmts):\n",
    "            self.axes[0].plot(x, y, fmt)\n",
    "        self.config_axes()\n",
    "        display.display(self.fig)\n",
    "        display.clear_output(wait=True)\n",
    "    \n",
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "\n",
    "def use_svg_display():  #@save\n",
    "    \"\"\"使用svg格式在Jupyter中显示绘图\"\"\"\n",
    "    backend_inline.set_matplotlib_formats('svg')\n",
    "\n",
    "def train(net, train_iter, test_iter, loss, num_epochs, updater):  #@save\n",
    "    \"\"\"训练模型（定义见第3章）\"\"\"\n",
    "#     animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.2, 0.9],\n",
    "#                         legend=['train loss', 'train acc', 'test acc'])\n",
    "    for epoch in range(num_epochs):\n",
    "        train_metrics = train_epoch(net, train_iter, loss, updater)\n",
    "        test_acc = evaluate_accuracy(net, test_iter)\n",
    "#         animator.add(epoch + 1, train_metrics + (test_acc,))\n",
    "    train_loss, train_acc = train_metrics\n",
    "#     assert train_loss < 0.5, train_loss\n",
    "#     assert train_acc <= 1 and train_acc > 0.7, train_acc\n",
    "#     assert test_acc <= 1 and test_acc > 0.7, test_acc\n",
    "\n",
    "def get_fashion_mnist_labels(labels):  #@save\n",
    "    \"\"\"返回Fashion-MNIST数据集的文本标签\"\"\"\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]\n",
    "\n",
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  #@save\n",
    "    \"\"\"绘制图像列表\"\"\"\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    axes = axes.flatten()\n",
    "    for i, (ax, img) in enumerate(zip(axes, imgs)):\n",
    "        ax.imshow(img.asnumpy())\n",
    "        ax.axes.get_xaxis().set_visible(False)\n",
    "        ax.axes.get_yaxis().set_visible(False)\n",
    "        if titles:\n",
    "            ax.set_title(titles[i])\n",
    "    return axes\n",
    "\n",
    "\n",
    "train_iter, test_iter =  load_data_fashion_mnist(batch_size) \n",
    "train(net, train_iter, test_iter, loss, num_epochs, updater)\n",
    "\n",
    "\n",
    "def predict(net, test_iter, n=10):  #@save\n",
    "    \"\"\"预测标签（定义见第3章）\"\"\"\n",
    "    for X, y in test_iter:\n",
    "        break\n",
    "    trues = get_fashion_mnist_labels(y)\n",
    "    preds = get_fashion_mnist_labels(net(X).argmax(axis=1))\n",
    "    titles = [true +'\\n' + pred for true, pred in zip(trues, preds)]\n",
    "    show_images(\n",
    "        X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])\n",
    "\n",
    "predict(net, test_iter)"
   ]
  },
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