{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "spiritual-state",
   "metadata": {},
   "source": [
    "# BDTs Demo Plots\n",
    "\n",
    "Inspired from iris dataset but using $x_1$ and $x_2$ (?)\n",
    "\n",
    "## Get iris data and rename features + target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "vocational-design",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from myst_nb import glue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "worst-equation",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "impossible-choice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "incorporate-audio",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                 columns= iris['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "rocky-rover",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "resident-native",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "statistical-cover",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f4ed8eed7f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Hi90BZnIG1GCnOHFkClS88/KL4r4bs0mvPf2R2huasXo8cMxc9ZpEdVWEkUR9spmWA/UQ6KQwTh5ELTlOXHv1e1vpnYL1q/dFHj6TqGQ47TTJ+Gss2fijDnTkZuXHe8QiRIKk6YEIYoiXK3NsNdUQqZNhyonL+F+gfs9HthrqyFIJNAUFEOeru+3ZV2ogyiKsDaZ0Hq4Ds0Ha9G47ygs9R1Vo3X5RmQNLkD20MKg2kSpxi+K2Lx+O95f9jFqqusweGgZzvjedGTn9P4JMZ/DDfOeWrgaTVBm62CcMhiq7IE5VNXS3IaN67Ziy4bt2L/3EPx+EUMqyjH99MmYMmMCJkwew2E8GvCYNCUAn9sFe201POZ2yNP1UGYl7jdev8cDZ1MDfE47IAiQKlWQqjVQ6AyQ6wwJG3cy8vt8MNe3wXS0Ge1Hm9BW1Yi26kZ47C4AgMaY3rFYbnE2MopzoNAkb72hSHh9Pny3ZhM+fn8Fao/Wo3RQEWaeeRoKS069hpmrxQLL3lp4LU5oSrOQMb4MCsPAXajaarVh57a92Ll1D/bs3B9YzqVsUDFGjx+BEaOHYdjwwRg0pBRZOUb+u6cBg0lTHPk9bjga6+FqbYIgkUKZmR33J+Ui5XO54HPa4Xe74XM54Xe7IFEooS0sZT2nXhJFEU6TDe01zTAdbYapthnt1U0wN7RBPLYYrEqnQVq2AWm5BqTnZkCXZ4RcNTDWHjObLPjqi2/xxWffwNRmwqChpZg2cxIKSwqieh5RFOGobYPtYAN8Djc0pdkwjCmGMjM5/k3GiiiKaG5swf69h3HoQCUqD1WjuqoWHrcHAKBN06CopABFJQUoKMxFTl42snMzkZVthDErAxlGAwwZOkilqf+0IqU+Jk1x4Pd54Wysh7O5ARAEKPQZUOgzknqoy+dywtXSBJ/TAVVOPtS5Bfz2GYLX7YG5tgXtNS0wHW3qSJRqmuG2OQEAUoUM2kwdtFl6aLN0SMvSQ5ulHzAJUieP24PtW3Zj9VfrsW3zTkgkEowYPQyTThvXp2G43hD9fjhq2mA73Aifww1Vrh66EYXQFGcm9b/RaPL7/Wisb0JtTQPqaxvR1NCCluZWtLa0o7WlHS6nK+h4QRCQrkuDIUOPDKMexqwMGDMNyDAakJVtRFZOJnJys5CTl4Xs3KyEX86GBi4mTf2oc96So74Got93PFlKkW9goijC3d4Gd1sz5Do90ooHpcy19ZbP44WloQ3mulaY6lqOJUrNsDWbgGP/4tQZadBm6pGWfTxBUum1AzbZtFis2LltL7Zu3Imtm3bC5XQhNz8Ho8cNx8gxw6Dq5zXVRL8IZ6MJ9spmeNptkKjkSBuUi7TyHCgy0wbsn1MkHA4nzO0WmM0WWMxWWC22wH9P/H+L2Yr2NjO8Xm/gvYIgICvHiMKifBSV5KOwpAAlZYUoKS1E6aBiGDIGTpkISjxMmvqJx2aBvaYKPqcDsjQdlMYsSFK0Mq/XZoWjqR4SuQLppUMgVaVmQUyf1wd7ixnWZhOsje2wNrXD0tAGS30bbK3mQHKkSFNDa0wP6kHSZuogHcDfpkUALU2tOHywCgf2HsbeXQdQXVULiCJy83MwdFg5ho0cjMzsxCjA6bE44DjaCmd9O/xuL6RaJbTFmVAXGqHK1UMygP8sT5UoirDZ7GhvM6O9taOnqqWpDc1NrWhuakFzYwva28yB43X6dJQNKkbZ4BKUDSpGaXkxSgcVoaSsCKokXkeQkgOTphgSRRFeuxXOxnp4LCZIlEqoMnMSpvZSLPncLjgb6uD3eqDOLYAqKzephjZEvwiX1Q5Huw2Odivs7VY4Wi2wt1lgazbD1mKGw2QDjv3zESQSqA1aqA1p0GSkQZ2RDq0xHZpM3YAbWjuR0+VGa3Mrmhtb0djQhPqaJtTW1KO6shZ2mx0AoDfoUFiSj5KyQpQNKomoZEC8iH4R7jYrXA0muJot8DncgAAojGlQZeugMKZDYdRCrtcMiIrj/cXldKGhvgkNdU1oqG9CfW0jGuubUV/bCNuxv0cAkJufjeLSQhSXFqKoJB8FRXkoKMxDXkEOsnIyOexHp2zAJU2iKMJisUS9TYgiRL8Pfo8HfpcTXocdboupY4K0XAGFIQMyzcDq0hf9frjaWuCxmCBIpVDoMyDXpEGiUkMik0OQSqPyeYii2PHyixD9fvh9fog+P/xeH3w+H3xuH3weL/weL7xuD7xODzwuD7wOFzwON9w2J9x2J1w2B1wWJ9xWO1w2J0T/8X8agiBAmaaCIk0NZboaqnQNlOkaqHUaKHVaqNLVECTJ+2crouNpPb/PD58odvz32M9erxderw8ejxdejxdutxsupxtutxtOhxMupwt2mwM2qwM2mw1mixXmdivM7WY4HM7AOaQyKQwZehgzDcjKNCI7NxO5BdnQaJOzmKIoivA53HC32eBps8NjscNndwf2S9VyyNJUkGmUkKoVkKoUkKjkkMilkChkkMilEKRSSKQSCFIJIBU6/j1IOl4D6XdFX4miCJvFjoaGJjTVN6OxoRlNjS1oaW5Dc2NrIDEHOv4N6zN0yMrK6JignmGALkMHnT4d6elapKVpodaqodGooFKroFIpoVAqoFQqIVfIIJPLIJNKIZNJIZVKIZFKIZVKIOnHL4Pp6en8exFnAy5pMpvN0Os5Jk5ERMmlsbER2dksOBpPA66vMj09HSaTKa4xmM1mFBcXo7q6Gjpdaj6ez2tMfql+fQCvMRWk+vUBx69REWZpIOo/Ay5pEgQhYf5h6XS6hIklVniNyS/Vrw/gNaaCVL8+AByaSwDJMzOXiIiIKI6YNBERERFFgElTHCiVSvzmN7+BUpm6NUV4jckv1a8P4DWmglS/PmBgXGOyGHBPzxERERH1BXuaiIiIiCLApImIiIgoAkyaiIiIiCLApImIiIgoAkyaiIiIiCLApImIiIgoAkyaiIiIiCIQ16TphRdewNixYwNrBk2fPh0ff/xxt+9ZtmwZhg8fDpVKhTFjxuCjjz7q1TlFUYTZbAbLUxERUarjPS+64po0FRUV4Y9//CM2btyIDRs2YM6cObjwwguxc+fOkMevWbMGV1xxBW688UZs3rwZCxcuxMKFC7Fjx46Iz2mxWKDX62GxWKJ1GURERAmJ97zoSriK4EajEY8//jhuvPHGLvsuu+wy2Gw2fPDBB4Ft06ZNw/jx4/Hiiy9G1L7ZbIZer4fJZEr5FbGJiGhg4z0vumTxDqCTz+fDsmXLYLPZMH369JDHrF27FnfddVfQtnnz5uG9994L267L5YLL5Qr8bDaboxIvERFRouE9L7biPhF8+/btSEtLg1KpxC233IL//Oc/GDlyZMhj6+vrkZubG7QtNzcX9fX1YdtfsmQJ9Hp94FVcXBzV+ImIiBIF73mxFfekqaKiAlu2bMG6devwk5/8BNdeey127doVtfYXL14Mk8kUeFVXV0etbSIiokTCe15sxX14TqFQYMiQIQCASZMmYf369XjmmWfw0ksvdTk2Ly8PDQ0NQdsaGhqQl5cXtn2lUgmlUhndoImIiBIQ73mxFfek6WR+vz9oPPZE06dPx4oVK3DnnXcGti1fvjzsHCgiIko8Pp8PHo8n3mEkDLlcDqlUGu8wKAJxTZoWL16MBQsWoKSkBBaLBUuXLsXKlSvx6aefAgAWLVqEwsJCLFmyBABwxx13YNasWXjiiSdw3nnn4a233sKGDRvw8ssvx/MyiIgoAqIoor6+Hu3t7fEOJeEYDAbk5eVBEIR4h0LdiGvS1NjYiEWLFqGurg56vR5jx47Fp59+irPPPhsAUFVVBYnk+LSrGTNmYOnSpbj//vtx3333YejQoXjvvfcwevToeF0CERFFqDNhysnJgUajYYKAjkTSbrejsbERAJCfnx/niKg7CVenKdZYs4KIqP/5fD7s27cPOTk5yMzMjHc4CaelpQWNjY0YNmxYVIfqeM+Lrrg/PUdERKmvcw6TRqOJcySJqfNz4VyvxMakiYiI+g2H5ELj55IcmDQRERERRYBJExEREVEEmDQREVFSmD17dlCdvnhLtHgo9pg0ERHRgOF2u+MdAiUxJk1ERJTwrrvuOqxatQrPPPMMBEGAIAg4ePAgbrzxRpSXl0OtVqOiogLPPPNMl/ctXLgQv//971FQUICKigoAwJo1azB+/HioVCpMnjwZ7733HgRBwJYtWwLv3bFjBxYsWIC0tDTk5ubimmuuQXNzc9h4jhw50l8fB8VJwi2jQkREdLJnnnkG+/btw+jRo/Hwww8DADIyMlBUVIRly5YhMzMTa9aswY9+9CPk5+fj0ksvDbx3xYoV0Ol0WL58OYCO2kXnn38+zj33XCxduhSVlZVdhtna29sxZ84c3HTTTXjqqafgcDhw77334tJLL8UXX3wRMp7s7Oz++TAobpg0ERFRwtPr9VAoFNBoNEGLtD/00EOB/y8vL8fatWvxr3/9Kyhp0mq1ePXVV6FQKAAAL774IgRBwCuvvAKVSoWRI0eipqYGN998c+A9zz77LCZMmIA//OEPgW2vvfYaiouLsW/fPgwbNixkPJTamDQREVHSeu655/Daa6+hqqoKDocDbrcb48ePDzpmzJgxgYQJAPbu3YuxY8dCpVIFtk2dOjXoPVu3bsWXX36JtLS0Luc8ePAghg0bFt0LoaTApImIiJLSW2+9hV/84hd44oknMH36dKSnp+Pxxx/HunXrgo7TarW9bttqteL888/Ho48+2mVfMq4P19zYymVUooBJExERJQWFQgGfzxf4efXq1ZgxYwZ++tOfBrYdPHiwx3YqKirw5ptvwuVyQalUAgDWr18fdMzEiRPx73//G2VlZZDJQt8qT44nkdXV1GPQkLJ4h5H0+PQcERElhbKyMqxbtw5HjhxBc3Mzhg4dig0bNuDTTz/Fvn378MADD3RJfkK58sor4ff78aMf/Qi7d+/Gp59+ij/96U8Aji9ncuutt6K1tRVXXHEF1q9fj4MHD+LTTz/F9ddfH0iUTo7H7/fH7uJPkSjGO4LUwKSJiIiSwi9+8QtIpVKMHDkS2dnZmDdvHi6++GJcdtllOO2009DS0hLU6xSOTqfD//73P2zZsgXjx4/Hr3/9azz44IMAEJjnVFBQgNWrV8Pn8+Gcc87BmDFjcOedd8JgMEAikYSMp6qqKnYXf4r8YuImdMlEEMWBlX+azWbo9XqYTCaO7xIR9ROn04nDhw+jvLw8aAJ2ovjnP/+J66+/HiaTCWq1ut/PH6vPp/Oe9+XnX2P2906PWrsDFec0ERHRgPPGG29g0KBBKCwsxNatWwM1mOKRMPUHv489TdHApImIiAac+vp6PPjgg6ivr0d+fj4uueQS/P73v493WDHj9yfHhPVEx6SJiIgGnHvuuQf33HNPvMPoNz7fgJqJEzOcCE5ERJTivF5vvENICUyaiIiIUpzXy+G5aGDSRERElOK8Hk+8Q0gJTJqIiIhSnIdJU1QwaSIiIkpxbjfnNEUDkyYiIqIU53K54h1CSmDSRERElOLcTne8Q0gJTJqIiIh68Nxzz6GsrAwqlQqnnXYavvvuu26PX7ZsGYYPHw6VSoUxY8bgo48+6qdIQ3M42dMUDUyaiIgoqYiiCI/VDFdbCzxWM2K9hOrbb7+Nu+66C7/5zW+wadMmjBs3DvPmzUNjY2PI49esWYMrrrgCN954IzZv3oyFCxdi4cKF2LFjR0zj7I7T7ozbuVMJkyYiIkoablMb2vdsg+XQPtiqD8NyaB/a92yD29QWs3M++eSTuPnmm3H99ddj5MiRePHFF6HRaPDaa6+FPP6ZZ57B/Pnz8ctf/hIjRozAI488gokTJ+LZZ5+NWYw9sdnscTt3KmHSREREScFtaoO18iDEkx6fFz0eWCsPxiRxcrvd2LhxI+bOnRvYJpFIMHfuXKxduzbke9auXRt0PADMmzcv7PH9wc6kKSqYNBERUcITRRG22qpuj7HVVkV9qK65uRk+nw+5ublB23Nzc1FfXx/yPfX19b06vj9Yzba4nTuVMGkiIqKE57VZuvQwnUz0eOC1WfopouRiNlnjHUJKYNJEREQJzx9hRetIj4tUVlYWpFIpGhoagrY3NDQgLy8v5Hvy8vJ6dXx/MJmYTEYDkyYiIkp4Erk8qsdFSqFQYNKkSVixYkVgm9/vx4oVKzB9+vSQ75k+fXrQ8QCwfPnysMf3h7bW9ridO5XI4h0AERFRT2TadAhyebdDdIJcDpk2Pernvuuuu3Dttddi8uTJmDp1Kp5++mnYbDZcf/31AIBFixahsLAQS5YsAQDccccdmDVrFp544gmcd955eOutt7Bhwwa8/PLLUY8tUpwIHh1MmoiIKOEJggBtQQmslQfDHqMtKIEgCFE/92WXXYampiY8+OCDqK+vx/jx4/HJJ58EJntXVVVBIjk+cDNjxgwsXboU999/P+677z4MHToU7733HkaPHh312HrD6XRBpVLGNYZkJ4ixrgqWYMxmM/R6PUwmE3Q6XbzDISIaEJxOJw4fPozy8nKoVKo+t+M2tXU8JXdCj5Mgl0NbUAKFPiMaocZFtD6fk3Xe80YWzcDHX7+NkrKiqLU9ELGniYiIkoZCnwG5zgCvzQK/xwPJsSG5WPQwpZqGuiYmTaeISRMRESUVQRAgT+NIQW/V1jT0fBB1i0/PERERpThBEFBTXRfvMJIekyYiIqIUl52TieojNfEOI+kxaSIiIkpx2TmZOHKo+2VoqGdMmoiIiFJcTl42jhyqjvrafAMNkyYiIqIUl1eQA5vVjob6pniHktSYNBEREaW4guKOde/27zkU50iSG5MmIiKiFGfMNECtUWPf7vAV1alnTJqIiIhSnCBIUFJagN079sU7lKTGpImIiKgbX331Fc4//3wUFBRAEAS89957Pb5n5cqVmDhxIpRKJYYMGYLXX3895nH2pKS8CDu27ol3GEmNSRMRESUVv9+Pxr3VqFq/B417q+H3+2N6PpvNhnHjxuG5556L6PjDhw/jvPPOw1lnnYUtW7bgzjvvxE033YRPP/00pnH2pHxIKWqP1qO1pT2ucSQzLqNCRERJ4+jmA9j89ko42q2BbWpDGiZcNhtFE4bE5JwLFizAggULIj7+xRdfRHl5OZ544gkAwIgRI/DNN9/gqaeewrx582ISYyQGDykFAGzbvBOz586MWxzJjD1NRESUFI5uPoA1L30QlDABgKPdijUvfYCjmw/EKbJga9euxdy5c4O2zZs3D2vXro1TRB0ys40wGPXYsmFHXONIZkyaiIgo4fn9fmx+e2W3x2z+18qYD9VFor6+Hrm5uUHbcnNzYTab4XA44hRVx/pzQ4aVY8O3W+IWQ7Jj0kRERAmveX9Nlx6mkznarGjez/XVulMxYjB2bd8Huz1+yVsyY9JEREQJz2m2RfW4WMrLy0NDQ0PQtoaGBuh0OqjV6jhF1aFi5BB4vV4O0fURkyYiIkp4Kp02qsfF0vTp07FixYqgbcuXL8f06dPjFNFxBUV5MGTosG71xniHkpSYNBERUcLLGloItSGt22PUGWnIGloY9XNbrVZs2bIFW7ZsAdBRUmDLli2oqqoCACxevBiLFi0KHH/LLbfg0KFDuOeee7Bnzx48//zz+Ne//oWf//znUY+ttwRBwPBRQ7Hmq/XxDiUpMWkiIqKEJ5FIMOGy2d0eM+HS2ZBIon9b27BhAyZMmIAJEyYAAO666y5MmDABDz74IACgrq4ukEABQHl5OT788EMsX74c48aNwxNPPIFXX301ruUGTjRqbAX27jqAlua2eIeSdFiniYiIkkLRhCGY8ePvd63TlJGGCZfGrk7T7NmzIYpi2P2hqn3Pnj0bmzdvjkk8p2rU2OEAgDVffYfzL06MRC5ZxDVpWrJkCd59913s2bMHarUaM2bMwKOPPoqKioqw73n99ddx/fXXB21TKpVwOp2xDpeIqF80NbbgaGUNdu/Yj/yiPFSMGIzc/GxIpdJTaiMz24jmxhbs3rEfjfVNGD1uOAqK85GVbYzh1URX0YQhKBg3CM37a+A026DSaZE1tDAmPUypSm/QoXRQMb75ch2Tpl6Ka9K0atUq3HrrrZgyZQq8Xi/uu+8+nHPOOdi1axe02vCT+XQ6Hfbu3Rv4WRCE/giXiCjmao/W4/YbFmP/3kOBbdo0DV7+55MYOWZYRIlTqDamnT4JN/zkSvzsxvvgdLoC20eNHY6nXn4Eefk50b2QGJJIJMipKI53GEltzLgRWLViDbxeL2QyDjpFKq6p+SeffILrrrsOo0aNwrhx4/D666+jqqoKGzd2P6tfEATk5eUFXicXESMiSkY2qw2PPfJsULLTsd2Onyz6JRrrmyNr4+GubVxx7cW446ZfByVMALBz2x68+PTrcDqCt1NqGzdxJCxmK7Zu2hnvUJJKQvVnmkwmAIDR2H1XsdVqRWlpKYqLi3HhhRdi587wf+gulwtmsznoRUSUiFpb2rHys9Uh95lNFlQePhpZG8uD28gryEFDfRMcjtDTGP737mdobeGk4FQQ6T2vbHAJ9AYdvloR36Vdkk3CJE1+vx933nknZs6cidGjR4c9rqKiAq+99href/99vPnmm/D7/ZgxYwaOHg39y2TJkiXQ6/WBV3Exu3SJKDG5nO5ulwGJJLFxubq2ka5LQ0tT+Pd63B643Z7IA6WEFek9TyKRYOyEkfji06/7OcLkljBJ06233oodO3bgrbfe6va46dOnY9GiRRg/fjxmzZqFd999F9nZ2XjppZdCHr948WKYTKbAq7q6OhbhExGdsrR0LfQGXdj9g4eV9dxGWtc2ao/WY+jwQWHfk1eQA40mvpWqKTp6c88bP2k0Kg8fxeGDVWGPoWAJkTTddttt+OCDD/Dll1+iqKioV++Vy+WYMGECDhwIvbq1UqmETqcLehERJaLs3Ezc/subQu6bdsZkZOdkRdTGbb8IbsNmtaOttR0jRg8L+Z67Ft+CnLye26bE15t73ogxw6BQKvDl8m/6McLkFtekSRRF3HbbbfjPf/6DL774AuXl5b1uw+fzYfv27cjPz49BhERE/UcqleKc887CQ4/fi6ycjrmdSqUCl197EX73p8UwZhoiamPe97u2UV1ViydeeBgXXXYe5Ao5ACA3PxuP/eVBzJg1NWbXRIlLqVRg1NgKfPEJh+giFdfnDG+99VYsXboU77//PtLT01FfXw8A0Ov1gUUNFy1ahMLCQixZsgQA8PDDD2PatGkYMmQI2tvb8fjjj6OyshI33RT62xkRUTIxZOiw8JIFmHHGFDgcTigUcmRmG6FUKqLSxuKH7sCPf7YIbrcHGo2aPUwD3PhJo/H6S2+hubEFWTmZ8Q4n4cU1aXrhhRcAdFROPdHf/vY3XHfddQCAqqqqoKJlbW1tuPnmm1FfX4+MjAxMmjQJa9aswciRI/srbCKimBIEAbn52TFpQ6VWoqAo75TaptQxbuIoCAKwcsUa/PCK8+MdTsKL+/BcqFdnwgQAK1euDCpR/9RTT6GyshIulwv19fX48MMPA+sBERERRduSJUswZcoUpKenIycnBwsXLgwqsBzOsmXLMHz4cKhUKowZMwYfffRRP0TbO+m6NAytGIQvP+O8pkgkxERwIiKiSPl8Pqxfuxkfvf851q/dDJ/PF9Pzda5e8e2332L58uXweDw455xzYLPZwr5nzZo1uOKKK3DjjTdi8+bNWLhwIRYuXIgdO3bENNa+GDd5NNZ9sxF2uyPeoSQ8QexuFcIUZDabodfrYTKZ+CQdEVE/cTqdOHz4MMrLy6FSqfrczucff4VHH/ozGuqaAtty87Nx729+hrkLzoxGqD1qampCTk4OVq1ahTPPDH3Oyy67DDabDR988EFg27Rp0zB+/Hi8+OKLXY6P1udzss573nvvfISCgtDDsg31Tbjvzt/jqZcewffm989nmKzY00REdAKLxYqG+qZ+q5DtcDhRXVmD6soauJxcyqQ7n3/8Fe7+yQNBCRMANNY34e6fPIDPP/6qX+KIZPWKtWvXYu7cuUHb5s2bh7VrE68Cd25eNgqK8rDq8zXxDiXhcZU+IiIADrsDhw5U4i9/+it2bt2DnLws/Oi2azBlxsSIHvXvi6rDR/Hma+/g809WQSJIMP+CObjsmoUoLi2MyfmSmc/nw6MP/RmhxkZEERAE4LGH/oKzzpkZ0aLGfRXp6hX19fVd1kXNzc0NPCWeaMZOGIlVX6yF3+8PeviKgvGTISICsHnDDlx5wS1Ys+o7mNrN2L/nEH5520N45S//gMVsjfr5qo4cxQ2X3YG33vgPmhtb0djQjDde+Rd+dNXdqKqsifr5kt2m77Z16WE6kSgC9XWN2PTdtpjGEenqFclm3MRRaGtpx85tPU9wH8iYNBHRgNfY0IxH7vsTQk3x/Off3kFLc3SH6txuD95f9gkaG5q77Kupruuy4C4BTY0tUT2uL3qzekVeXh4aGhqCtjU0NCAvLzHLPQweVgaNVo2vv/w23qEkNCZNRDTgWcxW1FSHHzbZvWNfVM/X3NTS7SPeyz9aieam2N38k1F2hIUXIz2uN/qyesX06dOxYsWKoG3Lly/H9OnTox5fNEilUowcU4HVK9fFO5SExqSJiAY8qbT7X4VqdfSeZgIAqUQCRTcVvlUqJaQyTjk90cSpY5Gbnw1BCL1fEIC8/BxMnDo26ue+9dZb8eabb2Lp0qWB1Svq6+vhcBx/RH/RokVYvHhx4Oc77rgDn3zyCZ544gns2bMHv/3tb7FhwwbcdtttUY8vWkaNrcDObXthNlniHUrCYtJERAOeIUOPsRNCryogV8gxpGJQVM+Xm5+DH1z5/bD7f3jlBcjI0Ef1nMlOKpXi3t/8DAC6JE6dP9/zm9tjMgn8hRdegMlkwuzZs5Gfnx94vf3224FjqqqqUFdXF/h5xowZWLp0KV5++WWMGzcO77zzDt57771uJ4/H28gxFfD7/Vi/dnO8Q0lYTJqIaMAzZOjx20fvgU6fHrRdEAT87onFgYVvo2nGmVND9oqcPvs0jBk/IurnSwVzF5yJJ154BDl5wcvD5Obl4IkXHolZnaa+rF4BAJdccgn27t0Ll8uFHTt24Nxzz41JfNGSlW1Edm4mk6ZusP+XiAgdE2Hf/vAVrPx8DdZ9swHFpYW46PLzUFCUB5VKGfXzFRbl4fdP3od9uw/if+9+CkGQYOGlCzB4aBnXhuvG3AVn4qxzZmLTd9vQ1NiC7JxMTJw6NqZlBgaSYSMGY/3aLfEOI2ExaSIiQkevUmFxPq66/ge45KoLIJfLIISbQBMlhcX5KCzOx8xZUwFBgEIhj+n5UoVUKsWU6VxzNBaGDR+MNavWw2yydOl5JQ7PERF1oVDIY54wBZ1PqWDCRAlhaMUgiKKI7Vt2xzuUhMSkiYiIiAAAOXlZSEvXYvuWXfEOJSExaSIion4zwNaIj1iifC6CIKB0UDErg4fBpImIiGJOLu8YfrTb7XGOJDF1fi6dn1M8lZYVRr2ga6rgRHAiomPMJivaWtrQ0tyGtHQtjFkZyMo2oqmxGS3NbbDbHMjKNsJozIBCJUdzYyuaGlsgCAKyczKRnZMJu90Zso1oxBGK2+0OGYdMHvrXe2NDM1qb22C3O5CZZURmVgbS0rW9/qx6SyqVwmAwoLGxEQCg0Wginjfm8/ng8/ng9foglUggkUohD3N9yUYURdjtdjQ2NsJgMCTEU4BFJQX46P0VnAweQmr8rSMiOkVNDS14/JFn8cn/vghsmzl7Ku6898e465YHUX1sEV1BELDw0gW4bNFFuOHSO2C3dfQQ6DN0eO2tZ/DKs/8IamPQ0FI88/LvUTqouM9xhGvDarHhi8++we/vfwoOe0d16rR0LR75068w/cwp0GjUQccf3HcEP7vpvpOu5Vzc/subep3Y9UXnumudiVMkfD4fzCYLHHZnYJtMLoMx0wBZClVNNxgMCbMuXUFRPoCOvy8TpoyJczSJJXX+xhER9ZHT6cIrzwUnOwBwwQ/m45ZFv0RLU2tgmyiK+M/bHyEtPQ1TZ0wILK57xuxpePO1ZV3aOLS/Ercs+gX+/s6zXYoynswVJo5wbRw+UIn77/pD0LFWiw133fIgln38VwwbMTiwva62ETdecSdaT1h8uONaPkROXhZ+fPuisL1T0SIIAvLz85GTkwOPx9Pj8W6XG6+9uBQfvb+iy77cvCz88c8PIjMrIxah9iu5XJ4QPUydcvOzIAgCjhyqYtJ0EiZNRDTgtTS14t23PgzaZsw0wOl0BSVMJ3r3rQ/w4B9+EUia5sw7Hb+643chj62prkfN0foek6bmEHGEa8Nms+OVZ/8R8lhRFPGPvy7DA3+4CwpFxxp3h/YfCUqYTvTP197BxZedh/zC3G7jixapVBpRktDc1IZ/vPoOPO6uCVZ9TSOaGlpQeKxXhKJHoVDAmGlA1ZGaeIeScDgRnIgGPIfDCbfLHbQtM9uI2ur6sO+xWe2Qyo7f+P1+sUsbJ6rppq1AHPaucYRrw+lw4cjho2GPPbT/CJwOV+DnI4eqwx5rtdjg6ua88eJ0OEMmTJ1qquvC7qNTk5WTyc83BCZNRDTgqTVqqNWqoG1NDS0oKSsM+x6dPh1ud3CicXIbJ+qurcD7tV3jCNeGRqMOGn472YjRw4LaGjKsLOyxeoMuJkvFnCq1WtVtXCWlPX+m1DeZWRmoq2mIdxgJh0kTEQ142TmZuOK6i4O2tbeZIEiEsENWl11zIT78z/LAz599uBILLwu9IGv5kNKIhr5CxRGuDbVGhZt+elXIJ9BkMimuuO5iyE+oMl4+uAR5BTkh277xp1ciOzezx/j6W3ZuJq687gch95UPKUVePw0nDkQZRgMa6priHUbCYdJERAOeQiHH1TdegiuuvRiyE4bcPv9oFZ7/+2MYMXpoYJtcIcf1t1yBs8+djV3bjxcA3LxhGy658gJcvmhhUBvjJ43Gc397FNk5PSclnXFcfu1FEbVRUl6Ep1/5HTKM+sC2rBwjnnv9MRQWB8/1ycnLxitLn+pyLTf85Epc8IP5CTURuZNCocDVN12Cy0J+pn+M6DOlvtEbdGhuak2YopuJQhAH2CdiNpuh1+thMpmg0+niHQ4RJRCH3YnmplaY281Qa9UwZhpgyNCjtaUdba3tcDqc0Bv0yMo2Qq6QoamhBW2t7RAEARlGA3LysuB0uEK20dc4NFo1Mrppw+fzhYwjXA2kUNeiUife0NyJ7HYHWppaYW639PkzHag673nvvfMRCgoiL2mwfu1mvPjM3/HNtg9Yq+kEfHqOiOgYtUaF4tICoLQgaLsx0wBjpqHL8XkFOV2GvMK1EY04QpFKpSHjCCfctSQyjUYNTWkhUBrvSAaOzoKn7W0mJk0n4PAcERERBelMmkztljhHkliYNBEREVEQ9bFq8hazNc6RJBYmTURERBSks1yF1WKLcySJhUkTEVGc2Wx22I+tHdcTl8sNq9kGn88X46hiL5WuJdUoj9XH6lxbkTpwIjgRUZw01jdj43db8e//+x8EQYLLrrkQ4yaODlkzyWyy4PDBKvzjr8vQVN+MGbOm4ryFc1FYnB/2SblEZWo348ih6pS4llQlk0khk8mCFkomJk1ERHHRWN+En93066BaT+tWb8Tk08bh0b88iOzcrMB2q8WGd5b+F0//8eXAts0btuONV97GG/9+DoO7qfadaKwWG/79f/9LiWtJdUqVAg4Hk6YTcXiOiCgOVq1YG5Qwddqwbis2bdgetK2lqRXPPPpKl2MtZiv+8ODTMJuS5wmnlubw17Ikya4l1SlVSiZNJ2HSRETUz9paTVj2z/+G3f/2G+/BZj0+AXfj+m1hKzOvX7sZpnZz1GOMlY3rtoa9lu/WboapLXmuJdUpFHK4nK6eDxxAmDQREfU3Uex28rPP64P/hMTC6+l+onQyLezg9fZwLUiea0l1cjmTppMxaSIi6mf6DB3Ov3he2P0XXX4e0tPTAj9PPm1c2GNHjR0OnS55KjZ3dy2jxw1n9ekEolDI4XS64x1GQmHSRETUzyQSCeafPwdFJV2XSRkyrBzTT58ctC0rx4irrv9hl2MVSgXu//1dMBiTZx22rJzM7q+Fa8olDLlCBifnNAXh03NERHGQV5CD195+Gh+9vwLvv/MJJIKAH1x5Ps5eMAu5+dlBx+r06bj59msw7YxJ+Ovz/0RLUxsmTxuH6358BYpDJF6JLNS1TJk+Htf+6PKku5ZUJ1co4OTwXBBBTKbB8CjoXPHZZDJBp9PFOxwiGuB8Ph/a28wQBCDDaOixTpHZZIHb7UFauhaqYwUIk1UqXUui6rznvffORygoyOvVe59/6m+Qy+V4+Z9PxCi65MOeJiKiOJJKpcjMyoj4+FSa85NK15KKlEoFbLbIKtUPFJzTRERERF2oVEouo3ISJk1ERETUhUKpgCPCNREHCiZNRERE1IVao4LVyp6mE3FOExFRFHk9XjQ1tsBmtUGpUiEzywCNVgOrxYa21na4nC5o07XIyc2CVCpFc2MLzCYrIAAGgw7GrAx4PV40NrbAflIb4YRqI1H09loocajVKtiZNAVh0kREFCWtLe14f9lHeOXZN2G12CCVSjH33Fn42S9vwl8efxWffvAlRFGETp+O3z56DzIy9Xjo3sdx5FA1gI4aTb997B5UHqrCkt/8OaiNu+/7CfIKcoLO53a7sWPrHvz2nseC2nj4T7/C8FFDIJPF91d8a0s73vvXR3j1uTd7vBZKPGqNCm63By6nC0o+3QiAw3NERFHh9Xjx/jsf46klL8Fq6Vg3zufz4dP/fYF7b38Y4yaOCix34nK6oFYrcfOVdwWSHQA4sO8wbrzsDhizjHDYnUFt/OKnv0FLc1vQOasra3HTFT/v0sb1l9yOmur6WF9yt7weL95f9hGe/mPXzyPUtVDi0Wg6egQtZmucI0kcTJqIiKKgqbEFr/zlHyH37di6B3kFuVAoFQCAs8+djf/++1N4Pd4ux7pcbnz52Tc446zTgrZv27wLjfVNx49zuvD6i2+FbeOdpf8Lua+/NDa24JVn3wy57+RrocSkTetImkztljhHkjiYNBERRYHNag/0qIRytKomUI+pbHAx9uw6EPbY3Tv2oWxwSZftB/dXBv7farVj25ZdYdvY9N1W2OP45JPd1v3ncehAZdh9lBgCSZPJHOdIEgeTJiKiKFCplZDJpGH3Z2YbA8Mc7a0m5ORmhT02Nz8bba2mENuPv0epVCDvpOVWTlRQnA/lsZ6teFCplJBKw38euXnhY6fEkJauBdDx95U6MGkiIooCY2YG5n3/rND7sjIgkUgCPS8f/+8LLLx0Qdi2zr1wLlZ88lWXNopLCwM/p6VrcdOtV4dt49qbLo3r5N2ePo+iUq4zl+g6e5ra29jT1IlJExFRFGi0atzxqx9j4pSxQdszs4147m+P4p+vvRPY1tLUCofdidt/eTMkkuO/hmUyKRY/dAeOVtUFDW1lZhvx8ptPdOmdGTZiMO6490dd2vj1736O0kFdh/f6k0arxp2Lf4wJk8cEbe+4lj8hL59PzyU6qVSKtHQtWls4ab8TF+wlIoqi1pY2NNY34/DBKmTnZqKouADZuZlobGhG9ZEatDa3YdDQMmTnZEKhUqC1uQ37dh+ERCLB0OGDkJllhMPhRGN9U1AbufnZIRfztdnsIdtQa1RxuPquWlvag6+lpIAJUz/qvOf9Z9mHKCzM7/X7H/jFHzFn3hm458HbYhBd8mGdJiKiKDJmZsCYmYHho4YGbc8vyEV+QW6X47VaTdCwG9BRH8eYaejSRiharSZkG4nCmGmI+FoodnwmB9CHvyLpujS0NLVGP6AkxeE5IiKiVNfHMSWdPh3NTS3RjSWJMWkiIiKikPSGdDQ3sqepE5MmIiKiVNfHnia9QYfmRvY0dWLSRERElPL6ljUZMvSwWGxwOl1Rjic5xXUi+JIlS/Duu+9iz549UKvVmDFjBh599FFUVFR0+75ly5bhgQcewJEjRzB06FA8+uijOPfcc/spaiJKFi6nC/V1TVi1Yg3qahowbeYkDB89FEqlAker6rD8o5WQSCQ457zZyC/MhdPhws5te7Bh3VYUlxbijLNOQ0amAS1NbV3aGKjFGQOf6edrUFfbgGmnT8LwUb37PJwOFxrqT60N6h+GDD0AoKmhOWEfNuhPcS05MH/+fFx++eWYMmUKvF4v7rvvPuzYsQO7du2CVqsN+Z41a9bgzDPPxJIlS/D9738fS5cuxaOPPopNmzZh9OjRPZ6TJQeIBgan04W1X63HXbc8CJ/PF9heVFKAPzz9a/z46l/AccIyIxdffh6GjxqKPzzwdGDbmd+bjgt+MB/33v5wlzZe/b8nUVDU+0e4k5nT6cKaVd/h7p/8psvn8crSJ1FY3PPn0V0br/7fUygoyotJ7ANV5z3vnZf+hZIxQ3r9/tqaejxw9x/xt2V/xqSp42IQYXKJ6/DcJ598guuuuw6jRo3CuHHj8Prrr6OqqgobN24M+55nnnkG8+fPxy9/+UuMGDECjzzyCCZOnIhnn322HyMnokTX3NiCu38afGMGgKNVtXj9pbdw/sXnBG1/960PoVarkJVjDGy75KoL8Ks7HgnZxpNLXoLNZo/dBSSg5sYW/OLW34b8PJ7640uw23pe6y4abVAf9LF7pLOnqbG+OYrBJK+EmtNkMnWsb2M0GsMes3btWsydOzdo27x587B27dqQx7tcLpjN5qAXEaW+bVt2w+vxhty36vM1mHb6pC7bP/nfF5hzzhkAgMLifFQdPhq2jRUff4W2lvaoxZsMtm3a1e3n0drafkptfP7RqojaoPDC3vP6OKikVqugVCmZNB2TMEmT3+/HnXfeiZkzZ3Y7zFZfX4/c3OACcbm5uaivrw95/JIlS6DX6wOv4uLiqMZNRInJ1M0ioz6fD35/15uIxWwNVNJWq1WBBXbDteH1+sLuT0Xt7eG/dPp8PvjCJENBbbR1/+cSLqGiyIS75/V1Jo4gCMgw6tHUwKQJSKCk6dZbb8WOHTvw1ltvRbXdxYsXw2QyBV7V1dVRbZ+IEtP4yeG/fJWUFaKluWvtmcnTxmPntr0AOoaLKkaGr2JdUlaItGMLmg4U4yeNCruvtLwosMBrt2108+dSWl404D7TaAt7zwvxJSFShgw9Gpk0AUiQpOm2227DBx98gC+//BJFRUXdHpuXl4eGhoagbQ0NDcjLCz15UKlUQqfTBb2IKPXl5mdj5uzTQu778c+uxbI3/xu0zZhpwKTTxmHDt1sAdExYrjpyFNNOnxyyjV89dAeycjKjGnOiyyvIwYxZU0Pu+9VDP4vo88gryO2mjYH3mUZbuHue6Pf3uU1Dho5J0zFxTZpEUcRtt92G//znP/jiiy9QXl7e43umT5+OFStWBG1bvnw5pk+fHqswiSgJGTMz8PBj9+LWu26ATp8OABgxeij++vbTGDdpFMqHlEAqlUImk+K8hXPx93eehUajxtCKQcfeb4BUKsFvH/1liDaewYQpY+J2bfFizMzAI4//KuTnMX5yZJ+HMdPQTRs9PwFNfeQ7xZ6m+qYoBpO84lpy4Kc//SmWLl2K999/P6g2k16vh1qtBgAsWrQIhYWFWLJkCYCOkgOzZs3CH//4R5x33nl466238Ic//IElB4goJJ/Ph+amVvh9fqhUSmRkGgAANpsdFlPHnCW9QReYy9Ta3AaXyw2pTIqsbCMkEknYNgaqaHwe/Ez7R+c97+0n/oHy6eGHV7vz2Ycr8d6yj/Hdnk8hCEKUI0wucS1u+cILLwAAZs+eHbT9b3/7G6677joAQFVVFSSS4x1iM2bMwNKlS3H//ffjvvvuw9ChQ/Hee+9FlDAR0cAjlUpDFk3UajXQarvOnzFmZUTcxkAVjc+Dn2k/8/V9eE6foYPL6YLNakdaeugaigNFXJOmSDq5Vq5c2WXbJZdcgksuuSQGEREREaUe0df3Jz31x4ZRW5rbBnzSlBATwYmIiCh2xFPoaUrXpQHoGLoe6Jg0ERERpTjRe+pJUxsLj8Z3eI6IKF5sVjvMZgsEAHqDPjARvK6mAW63BxKJBAVFuZBKpb1uI5FxAvbAJHr6njR11t9qb+OKGkyaiGhAEUURlYer8ZfHX8UXn34DQQDmff8s/PiO69BQ24jHHn4W+/cegjHTgCuv/wHOv3ge8gtzI2rjJz+/ASVlibsSfFNDC9596wP846/LYDZZMGL0UPzigdswcsywkJPiKXWcypwmqVTaY4X8gYLDc0Q0oBytqsNVF/4Eyz9aFVgK5cP3Psd1l9wOt8eD/XsPAQBaW9rx7J/+iscfea5LjZpwbSz6wa2oPRp6Sad4a21pw4P3PIrnnnwNZpMFALB7x37ceNkd2LJhR5yjo1gT3ae2PI1KrYLVaotSNMmLSRMRDRhejxfvLP1vyG/Mrc1t2PTdNkyeNj5o++cfr0LrCQvzejyebtv49IMv4T+F6sux0lDXhNUr14Xc98ffPIPmxpZ+joj6k999auskKlUKOB2uKEWTvJg0EdGAYTJZsGrFmrD716/djFFjK7ps37VjX+D/zSZrt22s+OQrWC2J9418cze9SZWHj8JqtfdjNNTfxFNcCFkul8PlZNLEpImIBgy5XBZ4EiiUdF0aHHZnl+0Gw/HVA3psQ58OuTzxposaMsKvgCCVSiFLwJgpesRT7GmSyqTwncK8qFTBpImIBgydPh3X3nx52P3zz5+DLz/7JmibWq3C4GFlEbex6KZLodaoTznWaBs7YWTYxGjugjNhNBr6NyDqd6L/1FZNi+OqawmDSRMRDSgTpozB/Au+12X7xZefB4fDiaYT5vbI5DI8/txvkZeXE1EbP7jyfFSMHBL9oKMgKycTf3rut11KKBSVFODOX/0YGm3iJXoUXX6Xp+/v9fm6Lb8xUMR1wd544IK9RNTa0o7ao/VY/tFKSKRSnHPubOQX5sBitmL7lj3YtH4bikryceac6cjNzw75OH5baztqqru2YcjQx+GKIuN0uNDY0IRVn69FbU09pp0+CcNHDeUacCms8573t+uXYMgPZkJpDD+03J37716CsxfMwi8euDXKESYXDmIT0YBjzDTAmGnA6HHDg7YbMvQoLi3EuRd27UU6WYbRgAxj1zYSmUqtRElZEa65iWt3DkQ+uwvoY9LkdLigSWMtLw7PERERDQA+h7tP7xNFETabHbpjC/cOZEyaiIiIBgCvrW8lA5wOF9wuNzKzMqIcUfJh0kRERJTiBIUUXnvfkqaWljYAQF5+Tg9Hpj4mTURERClOUMj63NPU1NAMoONJy4GOE8GJKCXUHq1HS1MrDu4/gpy8bBQV5yOvMAf1tY2oOlKDlqZWDKkYhMzsjLDfmFtb2tBY34wjB6uQlZuJopIC5OZlQxCEU4rN6/Wi9mh9lzjSdWloaWrD/j0HIZFIMHT4IGRmZYSs8+T3+9FQ14Tqyhq0Nrdh0NAyZOdmIiNMfaUTryU7NwuFJfm9vpaubRQgNy/rlD8P6n8SpQw+a9fCrZGoq2mANk2DrBxjlKNKPn1KmqqqqlBZWQm73Y7s7GyMGjUKSqUy2rEREUXkaFUtFt/5O2zduDOwzZiVgWdfW4I/PPAMdmzdHdh+2oyJ+O1j96KwOC+ojYa6Jtx7+8PYtH5bYFtmthEv/uNxDBs+uM+JgsfjwY6te/CzG++Dqd0cFMf9f7gLl557U6AKuUwmxb2//RnOvXBuUNVxn8+H3Tv24SeL7glqY8aZU/Dwn36FnNysoHPW1zXiV7c/0uVaXvrHnzB0+KCIrqW+rhH33vYwNm/Y3uc2KHFIFHJ4210QRbHXf3bVlTUYWsE/c6AXw3NHjhzBvffei9LSUpSXl2PWrFlYsGABJk+eDL1ej7PPPhvLli1LyIUqiSh1tbea8OfHXglKmICOxXNvu2ExrrrhB0Hb163ZhL+9uBS2E9Zac9gdePqPLwYlGQDQ0tSKH1/9CzTUNfU5vtqaBtx63b1ByU5nHP949V+49e4bA9u8Xh9+f/9TqDx8NOjYhvom/Oiqu7u0sear9XjthaVwu48/FeWwO/DMH18KeS0/uvruiK7FYXfg6SUvBSVMQW3U9/3zoPgQlFKIPj/8rt6vQXfkYDVGJVFpjViKKGn62c9+hnHjxuHw4cP43e9+h127dsFkMsHtdqO+vh4fffQRTj/9dDz44IMYO3Ys1q9fH+u4iYgAAK2t7fj841Wh9zW3QRTFLmvF/fedT4Iqf7c0t+GT/30Zto3qqpo+x7d35/6wC/j+951PMWXa+C7b//7K20GLo+7bfTBsG+/+3wdobmwN/ByNa2lpbsOnH3TTRmXfPw+KD4lSDgDw9nKIztRuRmNDM8ZPGhWLsJJORMNzWq0Whw4dQmZmZpd9OTk5mDNnDubMmYPf/OY3+OSTT1BdXY0pU6ZEPVgiopM5HS54veEXEm1pakW6Lg0Ws/X4e5wdj1Cf+HN3i5E21DX3Ob6a6rqw+8Kdt7a6Di6XG0qV8lgb9d224XYfXx7D6ej+Whrre76Wnj6PSNqgxCIoO273XqsTyqzI6y3t2bkfADBxyriYxJVsIkqalixZEnGD8+fP73MwRES9pdGquyRFJyoqKURLU2vQtsxsI9QaVeBnrVaDtHRt2N6cwUNL+xzfyDEVYfdlZhsRaiGriVPHQXPCZPARo4aGbSMrxwi1+oRrSev+WsoHl/QYc0+fRyRtUGIRZFJAJoHH4ujV+3Zs3RN46IBYcoCIklxeQQ6uvfmykPtGjR2O+toGuFzBlZB/fPsi5BfmBn7OzsnEzbdfE7KNMeNHIOcU1mYrKinAoDBJ149uuwbv/+vjoG1KpQI/vPJ8yOTHv9MWl4Zv47a7bwq6oWXnZOLm264OeezYCSORG0GtnWi0QYlHqlH2Kmny+/3YsW0PZn1vegyjSi69TppaWlpw6623YuTIkcjKyoLRaAx6ERH1J5VKifN/MA+33n0D0tK1AACpVIq5C2bhsWcfxOb12wNP/ej06fjF/T/F7HNmQiY7npTI5DJc+MMFuPNXPw5qY975c/DECw+fUiXkwuJ8/PnVP+DMOdO7xDH9zCn4bu2mwLFDhpXjb8v+3OXJvuzcLDz/+mOYffbMoDbue/hOzJl3OiSS47/KZXIZLrzk3JDX8qfnH4roWrpt44XI2qDEI9Mo4DVHnjQd2HsY5nYL5pxzegyjSi6CKIbqHA7v3HPPxYEDB3DjjTciNze3yyOI1157bVQDjLbOFZ9NJhN0Ol28wyGiKHHanaivb4TN6oBarYQhQw9jVgZaW9rQ1mqCy+mGNl2D/IJcKBTykG14PV40NrbAbrVBqVIhM8sAjTY6i5SGi6O5sQUmkwWCIEBvSEdmVvgvn1aLDW0t7XC53NCmaZCTlwWpVBqza/F6vGhsaIbdZo/650H9o/Oe96+/LIXGDjjrWlFy6YyI3vvPv/0b27fsxvJvlwUl5gNZr+s0ff311/jmm28wbhwnhRFR4lBpVCgb1HWujTEzA8bMyHpGZHIZCk4YtoumcHFk5WQiKyey+SJp6dpAz09PonEtMrkMBUV5PR9ISUGmVcLn8MDn9kKq6P727/X6sOHbLVh4yQImTCfo9ScxfPhwOBy9m0hGRERE8SVL63hgwNMeeoL/iXZu3QOzyYLvX3xOrMNKKr1Omp5//nn8+te/xqpVq9DS0gKz2Rz0IiIiosQjS1MCAuBu7TlpWr1qHYYNH4zh3Ty5ORD1enjOYDDAbDZjzpw5Qds7S7N3V9uDiIiI4kOQSCBLU8HVaun2OFO7BVs27sQ9D97WT5Elj14nTVdddRXkcjmWLl0aciI4EVFfOBxO2Kx2KJQK6E6q4B0Lfr8fbqsDgABlupq/y2hAkOs0cDV1Pyr0zcpvIZVJcd5FZ/dTVMmj10nTjh07sHnzZlRUhC/YRkQUKbfbjaojNXjt+X9i66ZdyM3Pxk23Xo2RYypgyIjNE662VjOqvtuLynW7AUHAoJmjUDRxKDQZkVdKJkpGcoMGjprWsJPB/X4/vlqxFvPPnwOdnv8eTtbrpGny5Mmorq5m0kREUbFjyx7cdMWdgaVQqitrsOHbLbjljmtx7Y8uhzYtuo+421stWPnkv2FrNgW2bVn2FQ6t3oEzb7+IiROlNEVGRy+uq8EETXHXpza3bd6F5qZWXL5oYT9Hlhx6PRH89ttvxx133IHXX38dGzduxLZt24JeRESRam5qxW/vfSzk2nEv/fkNtDS3hnhX34l+EdUb9wUlTJ3Mta1o2F0V1fMRJRqpRgGpSg5HXVvI/V9+9g1Gja3A6HEj+jmy5NDrnqbLLutYruCGG24IbBMEgRPBiajXzCYLjhyqDrlPFEXs3rEfJWVFUTuf2+bEkW93h91/ePVOFI4fAoVGGbVzEiUSQRCgyNLBXt0C45TBQXP5GuqbsGPrHjzyxOI4RpjYep00HT58OBZxENEA1NPka6ksdLXrvp8QECThzylIBIDzwSnFqXJ0cBxtgafdFhiuA4CVy1dDp0/HvO+fFcfoEluvk6bS0r6v9k1EdCK9QYfho4Ziz879XfbJZFIMHzkkqudTpqkx+Iwx2Lj0i5D7h8weB4WavUyU2hSZaRDkUlgPN8F4LGlyu91Yveo7/PDKC6BS8d9AOL2e07RkyRK89tprXba/9tprePTRR6MSFBENDMZMAx567B6oNeou+3710B3drsPWV/ljBiGjJKfL9qwhhcgaUhD18xElGkEigSpXD+vBeoj+juVn16/dApvVjkuuOj/O0SW2Xvc0vfTSS1i6dGmX7aNGjcLll1+Oe++9NyqBEdHAMHT4ILzzyV/x/rJPsOHbLcgvysXVN1yC0vJCqDWqqJ9Pk5GG0396AZr21+DQN9sBQcCQWeOQOSgfan1k67oRJTt1YSYcR1vhqGmFpjgTX3/5LU6bOSmqcwhTUa+Tpvr6euTn53fZnp2djbq6uqgERUQDh0wmQ3FpIX7y8+tgtzugVCqgUChiek61IQ0lUyqQP6YcEAC5MrbnI0o0cr0acr0Gpt1HYZJ4sX/PITz27G/iHVbC6/XwXHFxMVavXt1l++rVq1FQwK5tIuobqVSK9PS0mCdMJ5KrFEyYaEASBAGa0iw469qxaeUGpOvSMOec0+MdVsLrdU/TzTffjDvvvBMejyew/tyKFStwzz334O677456gERERBR9qlwDrJp6qI46cO6Fc6HgF4ge9Tpp+uUvf4mWlhb89Kc/hdvtBgCoVCrce++9WLyYtR2IiIiSgSAR4NIpMMiYjZKZU+IdTlLoddIkCAIeffRRPPDAA9i9ezfUajWGDh0KpZKPKBIRESWT3TXVKPWqYd9RA3G+yIWre9DrpKlTWloapkxhZkpEp87j8sBltsFtd0GmlEOZpoYyrWsZAqBjQVFnuw0uqwMQOmovqfVp3RatjEUc8dDa0o62lnY4nS7oDenIys6EinWlqI9Evx8H9h5C0bQpaD5Yi+qN+1AymevKdieipOmWW27B/fffj6Kinh9FfPvtt+H1enHVVVedcnBElPqcZht2ffQdDn61HaLfDwDIHJSP026Yj7QsfdCxXpcbDXuPYsMbyzuSJgAqnQZTr5uH7KGFkMr7/D2wI44Pv8PBr0+IY3A+Tru+axzxUHn4KO657bfYvaOjEKhcIcc1N16CRTddCmNWRpyjo2RUV9sIq8WOUaePg3tvA7Ys+wr5o8ogZyIeVkRPz2VnZ2PUqFE499xz8cILL2D9+vWoqalBS0sLDhw4gP/+97+45557UFJSgqeeegpjxoyJddxElAJ8Hh/2rdiMAyu3BhIVAGg5VIev//IeHCZb0PGWxnasfuG/gYQJAJxmO75+9j1Ym7ouwht5HN6OOFadFMfB0HH0t4a6Jtx85c8DCRMAeNwevPbCUvz3359wzU/qk0P7jkCj1WDI0DIMmT0WbrsT297r+nQ8HRdR0vTII49g3759mDlzJp5//nlMmzYNJSUlyMnJQUVFBRYtWoRDhw7h5ZdfxrfffouxY8fGOm4iSgFOsw37v9wScp+loQ32VnPgZ6/Lg92fbADErseKfhH7v9wCn7dvyYPTbMf+LyKLIx6OHKpCfW1jyH1/fX4pmhpa+jkiSgWHD1dj1NgKSCQSqHRalM8YhYNfbUPT/pp4h5awIq7TlJubi1//+tfYvn07mpubsWnTJqxevRp79+5FW1sb3nnnHcyfPz+WsRJRivG6PPC5vWH3Wxvbg4411TSFPba9ugk+l6fvcXgiiyMeDuw7Enafqd0Mp9PVf8FQSnA5nGiobcTIMcfnMBWNHwJdfia++/tn8Pbx31Kq63VxSwDIyMjAuHHjMG3aNAwZMoSz7YmoT2QKGSQyadj92hPmEkkVMqTnhJ+7o8szQqro25ym3sQRD2WDisPuS0vXQsn6OtRLNUfrIYoiKkYODmwTJAKGnzMJjnYrtr77dRyjS1x9SpqIiKJBqdNi0MzRIfdpM3XQZuoCP8tVCoxYMDV0QwIwbO6EPk8E7ykOzQlxxMOgoWVhJ3tffeMlyM7J7OeIKNnVVNcjXZeOnLzsoO2ajHQMOmM0Dq7ahvpdlXGKLnExaSKiuJEpZBhx7hQUTxoWtD09LwNn/uwiqA1pQdt1eRmYet08yJTywDa5SoEZN5+HtOy+9wbJFDKMWBA+Ds1JcfS3/IIc/PX/nkJRyfGlqgRBwEWXnYtLr74QslN4apAGptraegweVopQ40SF4wYjozQH3/39M7hszn6PLZEJoiiGmFaZusxmM/R6PUwmE3S6+H57JKIObrsTLosDTosdcrUCyjQN1HptyGN9Xh+cJhucJhsgCFDpNVDptZBKww+vxSKOeGhqaEZrSztsNgeysjJgzMpAWnrixEeJp/Oe96+/LEVuYR4AQBRFPPv4q1hwwffw/YvPCfk+l9WB9f/4HHmjSjH9pnM5DecYfj0horhTaFRQaFRIz+253pBUJu0ydBePOOIhOzcL2blZ8Q6Dkpz52MMDJeXhay8q09QYNmc8dn30HarG7UXp1OH9GGHi4vAcERHRANLU2AoAQcO9oeRUFCOnohib/u8LONqt/RFawut10tTQ0IBrrrkGBQUFkMlkkEqlQS8iIiJKXK3NbVCqlMjINPR47NA54yFIJNjw5ucYYLN5Qur18Nx1112HqqoqPPDAA8jPz+c4JxERURJpbWlDXkF2yEngJ5OrFBj2vQnY8d+1qFrPYbpeJ03ffPMNvv76a4wfPz4G4RBRsvJ7fRCkklP6IuV2uCBVyIJ6rUVRhOjzd6mj5D+23IlE0nOHud/vh9/rg0whD9ru9fogCOjSS+5xeyCTy/ilkFJSW5sJBccmhUcia3ABsocVYfO/ViFvVBmUWlUMo0tsvU6aiouLo9ZF99VXX+Hxxx/Hxo0bUVdXh//85z9YuHBh2ONXrlyJs846q8v2uro65OVF/heAiKJDFEXYW82o3XoY9bsrkZZjwKCZo6E1pkOmiqzgosfjgaPFitqtB9F0oAZaYzrKTx8NRZoaHrsTh77aDlurBfljypE/qgwSmRTtR5twaPUOSAQBg84YA11+Zsin3JxmG+xtVhz6Zgcc7VbkDi9B/pgyOOHHvl0H8O+3PoRUKsEPrzwfw0YMht3mwMrP1+DbrzegpKwQF11+HgqK8qDRqKP90RHFTXurGeMnhq5LFs6QWWOx/o3l2P7eaky+6nsxiizx9Tppevrpp/GrX/0KL730EsrKyk7p5DabDePGjcMNN9yAiy++OOL37d27N6hcQE5OzinFQUR9Y65rxZd/+hfc9uPLeOz/YjOm3bAABeMGdenZCcVa34ZVT/07qI0DX23DlEVnQ5GmxoFV2wAAddsPY9qNC3Dwm+1o2ns0cGz1xv0oGFuOSVfNDUqcXFY7qjfux+a3Vwa21W0/DI9Sgqee/Ru+W7s5sH3jd1vx6J8fxM9//ADMJktg+5uvvYNH//wAZp9zOlQqrvxOyc/r9cFqsSIrx9ir9ynT1CidNgIHv9qOIbPGwlCU3fObUlBEE8EzMjJgNBphNBpx+eWXY+XKlRg8eDDS09MD2ztfvbFgwQL87ne/w0UXXdSr9+Xk5CAvLy/wiqR7noiiy2V1YP2by4OSHQCACHz398/gNNt7bMPabMLGpV+EbGPjP79AerYhsElfkAlHuzUoYepUu+0wWo/UB21zWhzY8q9VQdv0BZnYfeBwUMIEAJdevRBP//GloIQJ6OhJ+/XdS9B87GkjomRnMXf8Hc/M6t39Gugoeqk2aAf0EisR9TQ9/fTTMQ6jd8aPHw+Xy4XRo0fjt7/9LWbOnBn2WJfLBZfr+C9kszm+q5UTpQq3zYnWQ/Uh9/m9PphqmpHWw5ptXqcHrYfDt9F+tBmF4wahZushFIwbhKoN+8K2tf+LLcgZVgy5umNYsHl/TZepBBkjivD6G291ee/QikF4/snXQrbrcXtwYN8hFJXkd3stRImgp3uexdRROiAzu/e1yCRSCcpnjMKuD9ehaX8NsocWnlqwSSiipOnaa6+NdRwRyc/Px4svvojJkyfD5XLh1VdfxezZs7Fu3TpMnDgx5HuWLFmChx56qJ8jJUp94rGJ2OH43N5TbsPrckOu7ZhPJJFK4fOEb9Pn8UIUj7cXapV2QSqBy+nqst3v93Ubh8POpSQoOfR0z+vsacowGvrUfvbQQqRl67Hzw28x+84f9KmNZNbrcS2pVIrGxsYu21taWmJep6miogI//vGPMWnSJMyYMQOvvfYaZsyYgaeeeirsexYvXgyTyRR4VVdXxzRGooFCrlFBmxW+KndGcc9zHmQqRbdtGMvyUPndXgBA88Fa5I8qDXts6WnDIVcfn3eUU1Hc5RhrVTPmfK9rz3RbqwmFxeEfJhk5eljYfUSJpKd7nsVig1qjhiKC+YahCIKA4ikVaNxTjbbqrrlAqut10hTuyTmXywWFIrKnZaJp6tSpOHDgQNj9SqUSOp0u6EVEp06t12LSld9DqGIvQ88aD6Wu5zXR0nMMGH/p7JBtDDpjDOAXIXo7epca9lQhp6IYakPXdrVZOhSMKQ8qEaDUqVE8pSLouKa91Zj7vRnIOWkpkrffeA+3//LmkCUGrrr+h8jISsxlVYhO1tM9z2qxI8PY98WtgY7eJmW6Bvu/3HJK7SSjiJ+e+/Of/wygI8t89dVXkZZ2fNVvn8+Hr776CsOH93/Rqy1btiA/n3MNiOIhc3AB5v7qCmx/fw3aKhugNqRhxIIpyKkohkIT2dNmhuJszPnFpdj5wbdoq2qE2pCGYXMnIntoIZr310CXb4TTbEfW4HxojDqc9YtLcWDlVlR9twcQBJRPH4lBZ46Bxhh8c9AY0jF24UzkDCvC/i82w2m2w1ieh7z8HLz+zl/w1hvv4cP3lkMiCJh19gxMmDwG//e/l/Dnx1/Fzq17kJOXhR/ddg2mzJgAnS4tTPREycVqscJwikmTRCJB/pgyVG/YhwmXzArq4U11ghhh0aXy8nIAQGVlJYqKioKG4hQKBcrKyvDwww/jtNNOi/jkVqs10Es0YcIEPPnkkzjrrLNgNBpRUlKCxYsXo6amBm+88QaAjgnp5eXlGDVqFJxOJ1599VX85S9/wWeffYbvfS+yuhGdKz6bTCb2OhFFicfhgsfphkQmhSpd06c27K1meD0+SCQC0k54as5pscPv9UOulkN+7LF/n9cHt9UBAFCkq3ucGmBrMcPv90OuVkCV1hGfx+1BW5sJAGA0GiCTd3yHtFissFsdkCvkMEawzARRIuu85/3rL0uRW5iHf772DsoHl+CGn1x5Su06LXZ8++rHmHzNXAya2buaT8ks4p6mw4cPAwDOOussvPvuu8jIOPXu6g0bNgQVq7zrrrsAdEw8f/3111FXV4eqqqrAfrfbjbvvvhs1NTXQaDQYO3YsPv/885AFL4mo/8jVylP+tnlyT1GnUEmYVCaF2hB57482s2vbcoW8yzAdAKSnpyE9nT1LlJpsVhsMGafW0wR0/Ls0lOSg6ru9TJq68+WXX0bt5LNnz+62uvjrr78e9PM999yDe+65J2rnJyIiGihEUYTVYoPeEJ1Rlpyhhdj3xRa4rA4o0wZG1fyIkqbOHqBIPPnkk30OhoiIiGLDYXfC5/NDnxGdpClzcAGwYjPqdhxG2bSRUWkz0UWUNG3eHFw9d9OmTfB6vaio6HgyZd++fZBKpZg0aVL0IyQiIqJTZrPaAAAZUUqalFoV0vOMqN3OpCnIiUNyTz75JNLT0/H3v/89MK+pra0N119/Pc4444zYRElEMSGKIhztVvjc3o5J3DoNpPJej9r3ir3NAo/DDQgdc6E03cxN8ro9cFkc8Ht9kCrlgWNDtRGPa4mG9jYTzCYLBEGATq+D3pAe75AoRVktHUsb6aMwp6mTsTQHtdsPw+/3D4glzXr9G+WJJ57AZ599FjQRPCMjA7/73e9wzjnn4O67745qgEQUGy6rA7XbDmH7e6vhNNshlUtRPmMURiyY2qtJ1pHyejwwHW3B5re/ROuRBgBA1uACTLhsNvQFWZDIgn/h2tss2PnBt6hctwd+rw/qjDSMu/h06AuysP4fy4PamHT199ByuB47+ulaosHr9WL/3sP43X1PYPuW3QCAiVPH4teP/ByDh5UNiBsQ9S+rtWMJlWjNaQKAjJJcVK7bg/bqJhhLc6PWbqLq9b9Ks9mMpqamLtubmppgsVhCvIOIEo3f78fRTQew/o3lgYV1fR4fDqzahu9e/xQuS8+L7faWrcmMlU++E0h2gI4q318++Q6sze1BxzrNNqx56UMcXr0Tfm/HEieONiu+/esnaNx/FCc+PyKRSVC79RA2hL0WR9SvJRpqqutw7cW3BhImANj03TYs+sGtqKkOvR4f0amwWe3QaDWQR7EHVpdvhEQmRWOIhbRTUa+TposuugjXX3893n33XRw9ehRHjx7Fv//9b9x44424+OKLYxEjEUWZs92G7e+vDrmvYU817O22qJ7P43Jj34pNIdeO8zrdOLx6J3ye42vF2VrMaD0SOnHY8+kGDD7j+CPO5TNGYe/yjSGP7bgW6ylGH31utxtv/vUdOEOsg2ez2vH+so/h83W/Hh5Rb1ktdhiiNJ+pk0Qqgb4gE037mTSF9OKLL2LBggW48sorUVpaitLSUlx55ZWYP38+nn/++VjESERR5nG64baFX4TWXNsc1fO5rU40H6wNu79pfw3c9uMJxIm9USdztFmDakJJ5LLur6WupZfRxp7FbMV3azeH3f/tNxtgs0a/t48GNqvF2ueFerujK8hE88HabksIpYpeJ00ajQbPP/88WlpasHnzZmzevBmtra14/vnnodX2vNYUEcWfVC4Nuc5ap0jWjevd+WRQddPmyZO2u5uHJJFJgRNil0gk3V5LXyuUx5JCoUBmN+vZZeVk9nlBVaJwbFY7MqI4CbyTviATHrsLloa2qLedaPo801Cr1WLs2LEYO3YskyWiJKNMU6Ng/OCQ++QaJdJzDVE9n0qnwbDvTQi7f+ic8VBoVIGfM4qzIVWEnndRNHEIarcdCvzcuLcaeaPLQh4r1yiRFuVriYZ0XVq3y1gsuulSqNSqsPuJ+sJisZ3yunOhpOd2fAHoroc4VUQ0G+ziiy/G66+/Dp1O1+O8pXfffTcqgRFR7MjVSoz/4ZmwNLbBXHN8+EqmUuDM2y+KyRNnGSU5GDJ7HA6s3Bq0fcSCKUjPMwZtUxnScMbtC/H1X96Dz318HlRGaS6Gzh6PL598J7Dt8JqdmP3zH8DWag5xLQsT9um5UWMrsOhHl+GNl98O2n7r3Tdi8LCy+ARFKUsU/bCarTCEWa7oVMhVCqgz0tBW1YCyaSOi3n4iiShp0uv1ge5vvT76WSoR9T9tpg6zfnYRrE0mtFc1QpOpg6EoG+qMtJg87q7JSMeIBVMw6IwxaNxTDUEiIKeiCIo0NdQnDd1JZVJkludj/oPXoK26CY52C4yledBm6iBCxNzFVwS3ka7u12uJhgyjAT+67Rr84LLzsOG7rZBKJJg4dRyyso1IS2fvPUWXw+GEKIpRWXcuFG2mHu1HozsXMhFFlDT97W9/C/n/RJTc1Po0qPVpyB5S2K/nMxR2XSj3ZFKZFNosPbRZXX/Jh2ujP68lGnT6dOj06SgfUhrvUCjFOawdpTcyYjA8BwDazHTU766KSduJpNdfwV577TUcPnw4FrEQERFRDFjt0a8GfiK1IQ0usx1et6fng5NYr5OmJUuWYMiQISgpKcE111yDV199FQcOHIhFbERERBQFDqsDgiBAr4/NMj3KdHXHedoSry5aNPU6adq/fz+qqqqwZMkSaDQa/OlPf0JFRQWKiopw9dVXxyJGIiIiOgV2uwM6fezm+HXWTuuuZloq6NOnV1hYiKuuugpPPfUUnnnmGVxzzTVoaGjAW2+9Fe34iIiI6BTZbY6YDc0BHXMQAYSs+p9Ker0AzWeffYaVK1di5cqV2Lx5M0aMGIFZs2bhnXfewZlnnhmLGIkoBEe7FZaGNrQcroc2SwdjWR40GemQSE/tm6Tb4YbTZEXzgVrY2yzIGlyAtGw9JHIZXGY76ndVQpAIyBtZCmW6Bj6P91gcdUjL1MM4KB8qnbajjYMntmGAQq2Ao92Ghj1VECQS5A4vgUqvhc/tOeFa9Mgsy4VSp4HTbEfLoTrYWy3IGpyPtBwD1PrELCFAlMjsNgdyCvNi1n7q1wLv0Oukaf78+cjOzsbdd9+Njz76CAaDIQZhEVF3bC0mfPXn94Iq8EoVMsz62cUwluf1OXFyOVxoOViLNS9+EFgoFwAmX/09tFU14eBX2wLbtr37DYbPmwyVToMty74KbM8ZXoyKuZOw+sX/BbWhL8jE5KvnYuVT78DnOb69V20UZeGMn14IjTE28zKIUpXd7oDBEP0aTZ18ro4J4DKVImbnSAS9/s365JNPYubMmXjssccwatQoXHnllXj55Zexb9++WMRHRCdxO1zY/PaqLksW+NxefP3se3CY+j4R02WyYe1LwQmTxpgOCEJQwtRpz6cboEzXQJmmDmwbetZ4rHkpONkBAFNtC/Z9sRklUyr63sbRZmz/75qUf0KHKNocdgf0hth92XAdK2nQ3XJJqaDXSdOdd96Jd999F83Nzfjkk08wY8YMfPLJJxg9ejSKiopiESMRncBtcaB2+6GQ+zxONyz1fV//qbWyIagXCACKJg7FkbW7w76nav1eFB5bkkVjTIe91dKljU41mw8gf3T5KbVRvX4vnGYuZkvUG36/CJ0+dj1N9lYLpAoZ1AYmTV2IoohNmzZh+fLl+PTTT/Hll1/C7/cjOzs72vER0Ul8Xl+3Ewhclr4nFE6Trcs2uVoBlzV8my6LI/DkjFylCHzjDMXv84cMvddt+Pxh9xNRaOkxnA9orm9DRnFOt4tnp4JeJ03nn38+MjMzMXXqVPzzn//EsGHD8Pe//x3Nzc3YvHlzLGIkohPI1YpATZRQ9BFU2w4nc1B+l22mmmZkDS4I+56swflor+lYPsHWYu72/BpjOtzWro8k97YNmVIedj8RhabTxSZp8vv8aD/aiJzhxTFpP5H0OmkaPnw43njjDbS0tGDjxo144okncMEFF3BCOFE/UevTMOai00PuyxtVdkoL1KoNaTCWBz9hU7v1EIonDQuZqMjVSuSNLEPD7koAgNflgbPd2qWNTsPnT8HBr4PnRvW2jfE/PDNhF+ElSmSx6mlqr26E1+lB4bjBMWk/kfQ6aXr88cfx/e9/nwv3EsWJIBFQOG4wpt04H5qMjomdMqUcFWdPwpRr5gZNqO6ttCw9pt0wH4POGA2pvOPhWnVGOkTRj9l3/RDZQ4+t6yZ0POF21t0/hMfjhsZwPA5bu/VYG2MCbWiz9Jh207nIHloEeefTNQKQO6IEc355CXw+X1AbTrMd025cgCGzxwW1MfOW7yNneEmfr49oIEtLi03SVLezEul5GTAUp/4UHUEUxYFSXgEAYDabodfrYTKZoNPFblIcUX9wtFvhdXshkUqg0msDBeZOldvhhNNkh9/nh1QuQ3qOIXA+j9MNoKOHSK3vmPRpbWqHz+PriMOQBrlSDo/LDWe7LdBGWnbHFy23zQm33QUIgEKjhEKjCnstPq8XTrMdfq8fMoU85SeZEkVb5z3vwgkX44U3n4BUGp3fEZ2cZjvWvfYJxl86C0PPGh/VthNRr+s0EVHiiNUwlUKtgkKtCnm+UP1YadmGLtvkSgXkuV1rtii0Kii0ods+mVQmg9bILzdE0RDthAkAqjbshUytQNn0kVFvOxHFZhEaIiIiShhyRfT7SBztVtRtP4Lh50w+Puye4pg0ERERpTi1RhP1Ng9+swPKdDWGzhkf9bYTVUSpp9lsjrhBzhMiIiJKLGqVMqrttVU1onl/DU67fh5kioFTAiSipMlgMPRYsEoURQiCAJ8vdBVfIkpMbpsTXrcHglQC9QlLIDhMNoh+P2RKeWCydm/bCMXn8R4rXilAmaaGVN77eRbRaINoIFFGcfjM5/Vh/xdbkDWkACVTh0et3WQQUdL05ZdfxjoOIupnHqcb5toWbPvPN2iraoRKr8WIBVOQU1GC+p2HsefTDXBZHMgclI8xC2dCl2/s8o0yXBv5o8qh0nUdDrA2m7D3sw2o2rAPgiCgdNoIDJszAdrMyHuoo9EG0UCjjGJPU9V3e+EwWXHG7QtTvgL4ySJKmmbNmhXrOIionzUfqMXXz70XWJLF2tgOe4sFW5atQs3mA4HjGnZXoXFPNWbd9QPkDC06qY0afP3c+0FtrP/7cgw6cwzGLJwJ5Qk9VLYWE7547O2gdeP2r9iMmk0HcNYvL4XW2PNiorbm7tq4hE/aEYWhiFJPk63ZjKr1ezF8/hToCzKj0mYy6fNEcLvdjj179mDbtm1BLyJKfI52Kzb+3xdBa9hJZFIYirODEqZOoihi09IvgpIVR7sVG5d+EXIdvENfbYfrhGP9Pj8Or90dcqFde5sFtVsOoqeScR1t7OqmjUM9tkE0UKkUp540iaKIvSs2QZulw8gFU6MQVfLp9TOITU1NuP766/Hxxx+H3M85TUSJz+1wwd4S/IBHWrYe7Uebwr7HXNcKj8MVGHbzOFywt1rCHt9W3QhdnrHjfHYnjm7aH/bYqvV7UTpteLdzp9x2J46GSOh60wbRQKVQnPrwXO22QzDXtuCsuy8JVOofaHrd03TnnXeivb0d69atg1qtxieffIK///3vGDp0KP773//GIkYiijKJpOs/fb/X1/1TMELHEi6BH0O0cSKZ8vg3W0EigaybOjFSpQyCtPv2otEG0UAlU57awxJumxOHV+9E+cxRx5dTGoB6/Rvmiy++wJNPPonJkydDIpGgtLQUV199NR577DEsWbIkFjESUZQp0lTIHJwftM3aZEJ6njEoMTpR/uhyKE5Y1y5UG50kMikMJ8x3UGpVGDpnQth4hn1vIuTK7ocPem5jQo9tEA1UCvmp/ds4+M0OSGRSjA2zWPhA0eukyWazIScnBwCQkZGBpqaO7vwxY8Zg06ZN0Y2OiGJCqVVjyjVnd1nc9/CanZh45Zwux6v0Woy/ZBYU6uNd/EqtGlOu7tqGIAiYdsN8qPTBpQdyKoqRN7K0S9tFE4fAWJoTUdzdt5EbURtEA5HsFNalNNe3oWFXJcZcOOOUFgRPBb0elKyoqMDevXtRVlaGcePG4aWXXkJZWRlefPFF5OeH/tZJRIlHl2fE3MVXoGF3Fep3VSI914DSqSOgTFcja1ABjny7C7YWMwpGlyO7oijkk2m6/NBtaIzpXeY8qPVaTL3uHJjrWnFo9U5IJALKTx+N9NwMqNIjq1YcjTaIBqJTKUB5ePUOpOcZUT5zdBQjSk6C2MvHTd588014vV5cd9112LhxI+bPn4/W1lYoFAq8/vrruOyyy2IVa1R0rvhsMplYvZzoGNEvhhyW6yxaeypthDy2F+3Gsg2iVNd5z/vbkpdx7kXze/3+9qNN2LLsK8y85fsoHD8kBhEml173NF199dWB/580aRIqKyuxZ88elJSUICsrK6rBEVH/CJfs9CYpiTRh6m27sWyDaKCQSPo2PFe1fi/0hZkoGDc4yhElp17PaXr44Ydhtx+vk6LRaDBx4kRotVo8/PDDUQ2OiIiITp1M1vsSAfY2C1qPNKDi7En8knJMr5Omhx56CFartct2u92Ohx56KCpBERERUfTI+lCOo27HEcg1ShRPGhaDiJJTrz/FcPMItm7dCqPRGJWgiIiIKHok0t4Nz4miiKZ9R1EyuWLAFrIMJeJPIiMjA4IgQBAEDBs2LChx8vl8sFqtuOWWW2ISJFEicdmccFnscLRZodCqoNJroNanxTusbvk8XjhNNtjaLBAgQGNMh0qnhdftSbprIaLe623hV2uTCU6zHUUTOPn7RBEnTU8//TREUcQNN9yAhx56CHq9PrBPoVCgrKwM06dPj0mQRImiY822L1G79WBgmzZLjzNuvRC6/MTsaXU7XDi6cT82v/0lfJ6OZY6kChkmXzUXTrMNW//9deDYtGw9Tr/1wsDyJ0SUGiS9TJraqhogVciQNYCrf4cScdJ07bXXAgDKy8sxc+bMPk0qI0pmXrcHOz9aF5QwAYCt2YRVf34X37vnMmgy0uMUXXiWulZsePPzoG0+txfr/vYJZv7kfMiUcnhdHgAd3y5XPZO410JEfSMRepc0mWpbYSzPg/QUimKmol7PaZo1axYqKytx//3344orrkBjYyMA4OOPP8bOnTujHiBRonCa7TiyZlfIfY42K2zN5pD74snjdGPXx9+F3V/13V4Ujg9+lDhRr4WI+q63N3tbUzur7IfQ66Rp1apVGDNmDNatW4d333038CTd1q1b8Zvf/CbqARIlCp/bC7/XF3a/rSXxEg2f2wtbsynsfluLqctyJx3bE+9aiKjvejM853V74TTboS9g7cWT9Tpp+tWvfoXf/e53WL58ORSK4wsAzpkzB99++21UgyNKJDKlHDJl+KUI0nMz+jGayMhUchiKwv/i0+VnhuxVSsRrIaJT0Is6S05TR2dIWo6+hyMHnl4nTdu3b8dFF13UZXtOTg6am5ujEhRRIlLptRg2d2LIfbp8IzTGxJsDJFPIMXzelJBlQgSJBMWThqF226Gg7bqCzIS8FiLqu95U7HdaHAAATQaXGjtZr5Mmg8GAurq6Lts3b96MwkLOsqfUJZVJMWTWOFScMwmSEyZH5gwrwhm3Xgh1iGGuRJCWbcDpt10YNAynyUjHmT9bCFtrcC9TTkUxzvjpBQl7LUTUNxJEnjS5bU5AAFQ6LoJ9sl4/Anf55Zfj3nvvxbJlyyAIAvx+P1avXo1f/OIXWLRoUSxiJEoYKp0Go74/HYPPHAuP3QWpQgZlugZKrSreoYUlU8qRN7IUZ//qCrisDkAAlGlqqA1pyBxUgLyRpUlzLUTUN73pafLYXVBoVL0uUzAQ9Dpp+sMf/oBbb70VxcXF8Pl8GDlyJHw+H6688krcf//9sYiRKKHIFDKkZSXXWL8gCFBnpEGdEVy4MhmvhYj6oBdzmtx2J5Tp7GUKpddJk0KhwCuvvIIHHngAO3bsgNVqxYQJEzB06NBYxEdERESnqDfDcx6HC6p0dQyjSV59rlBZUlKC4uJiAODqx0RERAmsN7dpt93FJ2jD6NOA5V//+leMHj0aKpUKKpUKo0ePxquvvhrt2IiIiCgaepE1uawOqA1cgzKUXvc0Pfjgg3jyySdx++23B9aaW7t2LX7+85+jqqoKDz/8cNSDJEpFHpcHHocLMoUMCk3Pk68dJhsEQYjoiRav2wO/xwuZStHr1c17asNtd0EQALla2ad2iSgeIkuaRFGEy2xn2ZEwep00vfDCC3jllVdwxRVXBLZdcMEFGDt2LG6//fZeJU1fffUVHn/8cWzcuBF1dXX4z3/+g4ULF3b7npUrV+Kuu+7Czp07UVxcjPvvvx/XXXddby+DKG68Hg9sTWYc/GobWg7XQ52RhmFzJiA91wC1vuu3O2tTO+p3VaHyu90QBAFl00YiZ3hxyAncLpsT5toW7F2+EQ6TDbnDSzDo9FHQZOogkUTWsRyyjTNGQyKVoGF3FQ6t3gFBEDD4zLHIGVbEb6REKcRptsPv83N4LoxeJ00ejweTJ0/usn3SpEnwer29astms2HcuHG44YYbcPHFF/d4/OHDh3HeeefhlltuwT//+U+sWLECN910E/Lz8zFv3rxenZsoXtqrmrDq6Xfh83T8e2mrbEDtloMYe9HpKJ85Esq04z1J1uZ2rH7xA5hqjheObT5QC2NZLqbddG5Q4uRxuHDgyy3Y+cHxyvxtlQ04sHIL5txzGQyFPS+JEK4NY3kudv7v265xlOdh5o+/z8SJKMHpI1yAu3PZJX1BZizDSVq9ntN0zTXX4IUXXuiy/eWXX8ZVV13Vq7YWLFiA3/3udyErjIfy4osvory8HE888QRGjBiB2267DT/84Q/x1FNP9eq8RPFiazVj49IvAgnTiba/vxouizPws9/vR82WQ0GJSqfWIw1o2ns0aJvTbA9Kdjp5XR5sXLoCLpujx/hCtWEsy4W5piV0HIfr0bS/psd2iSi+0rSRFaw117dCma7mF6Ew+vT03F//+ld89tlnmDZtGgBg3bp1qKqqwqJFi3DXXXcFjnvyySejE+Uxa9euxdy5c4O2zZs3D3feeWfY97hcLrhcrsDPZjMXIqX48dhdIZMPABD9IlqrGqDLNwIAnCYbKtftDtvW4bW7kDemDGpdxy/DpgPhk5eWg3Vw21xQart/jDhUG3kjS3F0y4Gw7zmwahvyRpdBwTlORHF3qve89qPNyBpSyKfiw+h10rRjxw5MnNix/tbBgwcBAFlZWcjKysKOHTsCx8XiA6+vr0dubm7QttzcXJjNZjgcDqjVXW8IS5YswUMPPRT1WIj6ROxhv//4AaIodn/8SftFsafGexa6DQGiv5u2o3BeIoqOU7nneZxumOtaMfSs8dENKoX0Omn68ssvYxFHzCxevDio98tsNgfqSxH1N7lGCV2+Eea61i77BEFARtnxLwVqQxqKp1Sg/WhTyLZKTxsRtEZc9pDwaz8ay/Og0PTcExSqjYa9VSgcPzhsD9mgM8awl4koQZzKPa/5YC0giigYUx6r8JJeUi0sk5eXh4aGhqBtDQ0N0Ol0IXuZAECpVEKn0wW9iOJFm6nDpCvnBC3422nEuVOhTDv+91gikaB44tCQT7HoC7OQOyL4F6FKp0XFOV0f0pDKZZh0xZygtsMJ1UbLwTpklOSEjMNQnI2ciqIe2yWi/nEq97zGPdXIGlLA+Uzd6HNF8HiYPn06Pvroo6Bty5cvD9SLIkoGhpJczF18BfZ9vgmtR+qhNqRh2NyJ0BdmQXXSek9p2XqccduFOLr5AKrW74UgCCg9bQQKxg3qUnJAoVFi+DmTkDu8GHs+Ww+nyY6cYUUYOmcCtBGuLxeuDX1+Js6842LUbjmIw2t3QhAEDDpjDPJHl0OTwV+wRMnOYbKhraoRUxadHe9QEpogRmMiRB9ZrVYcONAxwXTChAl48skncdZZZ8FoNKKkpASLFy9GTU0N3njjDQAdJQdGjx6NW2+9FTfccAO++OIL/OxnP8OHH34YcckBs9kMvV4Pk8nEXieKK4/TDbfNCZlSFlRmIBS/3w+nyQYA0ETw6LDb4YLf64NcrYBU1rfvRoE2VApI5R1tiKIIt63jCb9Ieq6IKL4673m7Vm1CZm74siMHv9qG+l2VOP/RmyFTyPsxwuQS156mDRs24Kyzzgr83DkOe+211+L1119HXV0dqqqqAvvLy8vx4Ycf4uc//zmeeeYZFBUV4dVXX2WNJkpKcpUCcpUiomMlEklEyVKnaMwxCtWGIAhMlohSjNflQd2OIxg8aywTph7EtacpHtjTREREA0UkPU2V3+1F5be7cN7vb+B8ph4k1URwIiIiih6v24ujm/ajbMYoJkwRYNJEREQ0QNVsOQiv24MRC6bEO5SkwKSJiIhoAPK6PKjeuA+DTh8NrZHTVSLBpImIiGgAqt64D36vDyMXTI13KEmDSRMREdEA43a4cHTzAQw9azznMvUCkyYiIqIBpnrDPgiCgOHzuq4iQOExaSIiIhpA3HYXarYexNCzxrPuWi8xaSIiIhpAjm7eD0EQMGzuxHiHknSYNBEREQ0QXrcHtVsPYfCZY9nL1AdMmoiIiAaI+p2V8Hm8GDZnQrxDSUpMmoiIiAYAURRRu+0QCicMgcYY+VqWdByTJiIiogHAVNsCe6sFg88YG+9QkhaTJiIiogGgYXcVNMZ05AwrincoSYtJExERUYoTRRHNB2pRMqUCgkSIdzhJi0kTERFRijPXt8LjcKFw/OB4h5LUmDQRERGluPaqRii0KhhL8+IdSlJj0kRERJTiTLUtyKko4tDcKWLSRERElOIcJhuyBhfGO4ykx6SJiIhoADCW5sY7hKTHpImIiGgA0BdmxjuEpMekiYiIKMUJEgFytTLeYSQ9Jk1EREQpTpupi3cIKYFJExERUYrTZHCtuWhg0kRERJTiVDpNvENICUyaiIiIUpwiTR3vEFICkyYiIqIUp9BwEng0MGkiIiJKcXKVIt4hpAQmTURERClOqpTFO4SUwKSJiIgoxSkU0niHkBKYNBEREaU4iZy3+2jgp0hERJTi1Ia0eIeQEpg0ERERpTi1jiUHooFJExERUarz++MdQUpg0kRERJTi/EyaooJJExERUYoTfd54h5ASmDQRERGlOL/HHe8QUgKTJiIiohTnd3viHUJKYNJERESU4nxuR7xDSAlMmoiIiFKc3+2GKIrxDiPpMWkiIiIaAPxuV7xDSHpMmoiIiAYAr90W7xCSHpMmIiKiVCeRwGu3xjuKpMekiYiIKMVJlWp4bUyaTpUs3gFQ/xNFEX6PGz6XE36PBzKVGhKFAhKZPN6hERFRDEiVSvicDoh+HwSJNN7hJC0mTQOMKIrw2m2wHt4P0e8LbJdp0qAtHQSpXBHH6IiIKBakShXgdcPrcECuTYt3OEmLw3MDjN/jhuXwvqCECQC8discDbUQuT4REVHKkRz7Quxzsl7TqWDSNMD4HI6wq12721rg97JqLBFRKhJkcvhdzniHkdSYNA0w3a4/JIphEyoiIkpuEpmca9CdIiZNA4xUrQm7T5DJASknCBIRpSJBKoHf6413GEmNSdMAI1EoIVWpQ+5T5xXyCToiohQlSCRd5rNS7zBpGmCkcjnSyoZArs8IbBOkMmgKS6HQ6SEIQhyjIyKimBEknIJxilhyYACSKpTQFpVBzC/q+AckkUIilzNhIiJKZYIAEVy091QwaRqgJFIp5y8REQ0gAgDmTKeGw3NEREQDgSAAIofnTgWTJiIiooFAImEB41PEpImIiGgAECQSiD4fRJFjdH2VEEnTc889h7KyMqhUKpx22mn47rvvwh77+uuvQxCEoJdKperHaFNb52K+fo+b/7CIiFJIZ0kZFrjsu7hPBH/77bdx11134cUXX8Rpp52Gp59+GvPmzcPevXuRk5MT8j06nQ579+4N/MynvqLD53bB1doMd3srAECZkQVFRiakCi7iS0SU7CTyjqTJ53RCqlDGOZrkFPeepieffBI333wzrr/+eowcORIvvvgiNBoNXnvttbDvEQQBeXl5gVdubm4/RpyafG43LAf3wtlYB7/bBb/bBUdDDSyH9sLn5rcSIqJkJ8jkECRSeO3WeIeStOKaNLndbmzcuBFz584NbJNIJJg7dy7Wrl0b9n1WqxWlpaUoLi7GhRdeiJ07d/ZHuClLFEW421tCdtn63S54LO39HxQREUWVIAiQqjXwmNvjHUrSimvS1NzcDJ/P16WnKDc3F/X19SHfU1FRgddeew3vv/8+3nzzTfj9fsyYMQNHjx4NebzL5YLZbA56UTDR6w0MyYXibmvhekVEREmgp3uePC0dPqeDvU19FPfhud6aPn06Fi1ahPHjx2PWrFl49913kZ2djZdeeink8UuWLIFerw+8iouL+zniJCCgo35H2P2S7vcTEVFC6OmeJ9VoIcgVsNfX8mGfPohr0pSVlQWpVIqGhoag7Q0NDcjLy4uoDblcjgkTJuDAgQMh9y9evBgmkynwqq6uPuW4U41EJocqM/SkewBQZmZ3VBAnIqKE1tM9TxAEKI1Z8FrN3Y4wUGhxTZoUCgUmTZqEFStWBLb5/X6sWLEC06dPj6gNn8+H7du3Iz8/P+R+pVIJnU4X9KKuZOk6SNXartvT0iHXpsUhIiIi6q1I7nkyjRYybTpsNZXwOh1xiDJ5xb3kwF133YVrr70WkydPxtSpU/H000/DZrPh+uuvBwAsWrQIhYWFWLJkCQDg4YcfxrRp0zBkyBC0t7fj8ccfR2VlJW666aZ4XkbSk8oVSC8bDK/dBldrEwABysxsyNQaSOQsOUBElCoEQYAqOxf22mpYDu2DbnAFpErWO4xE3JOmyy67DE1NTXjwwQdRX1+P8ePH45NPPglMDq+qqoJEcrxDrK2tDTfffDPq6+uRkZGBSZMmYc2aNRg5cmS8LiFlSOQKKPQKyNP1ADqqxxIRUeoRJBKo8wrhqDsK88E9SC8bCpmm62gDBRPEATYTzGw2Q6/Xw2QycaiOiIhSWuc9r7HyMAxGY5f9fp8Xjvpa+N0uaIvLoTR0PYaOY1cCERHRACWRyqDJL4JMmwZb1SHYaqq4qG834j48R0RERPEjSCRQZefBo1TD1doEr82CtNLBnOcUAnuaiIiIBjhBEKDQG6ApKIbf54Vp/y642lriHVbCYU9TEvJ7PPD7PIDfD0EqgyCTh62j5HO7Ab8Pfq8XglQCQSKFVKkK2YYgEeD3eCAeq/4tyOSQyOVhF0TuTRxERJT4pEoVtIWlcDY3wFZ9GF6bFZrCYggC+1gAJk1Jx+dywFp5CL4TamsoM3OgzskPrGB9/NiORXdPLGAmVWugLS6HvbYKXqslqA2lMQuWQ3sh+nwAAEEqg7a4DHJtOoSTkqHexEFERMkjMFynUsPV3ASf24n00iFd7gMDEVPHJOLzuGE5tC8oUQEAV0sjnK1NQZP3/F4vnM0NXSq++hx2WI8cgNKQ2aUNd3sLZJrjhSxFnxfWIwfgc7uC23BHHgcRESUfQRCg0Bmgzi+E126D+fA+iH5fvMOKOyZNScTvdMLv8YTc52pqgN97fJ/o9cDV2hy6HbcLkEg6Xie20dIMRYjHTZ3NjcEJmSvyOIiIKHnJ1Bpo8orgczhgrTo84NerY9KURLyu8OXuRb8POCGxEf1+QAzf4+N3uyGRBo/Oin5fyPlLPqc9KGnqTRxERJTcpCoVVNm58Jjb4TG3xzucuGLSlERkSnXYfYJEGtRzJEgkQDcT9yQKBfw+b5c2Qn2LkKrUQdXBZd08hnpyHERElPzkaemQqjVwNNbFO5S44t0tiUiUKghhJlkrs3MgkR3fJ8jkUBozQx4rUSg7eoNO6hFSGrNCrnqtysoNSpokSjUEWbg4coPiICKi1CBP18HnsA/oKRhMmpKIVKGArnxYl4JjSmM2VMac4MRGJoMqK6/LHCWpSo20siFwtQXPd1IYs6AwZMJrswa2CVIp0kqHdCRZJ8cxaBgkIePI5pp1REQpqPNe4HM64xxJ/LDkQJKRqtRIH1TRkemL3ddHkiqVUOcVQZWTD/GkOk1pJYO7tCFIBOiGjTyhTpMMErki5DwnqUoNXYRxEBFR8hOOzYMVT5raMZAwaUpCErk84lpIUoWiV21IFUrgpJ6laMRBRETJrfML9EAuK8NxFCIiIupZZ9LkG7j1mpg0ERERUY8EQYBEroDPNXDnNDFpIiIioohIVWp4zO0Dtsglk6Yk5T+2CK//pLFlv9cDvzd4kp4oivD7fRH/JRf9/gE9Zk1ERKHJdXr4Pe6wK06kOk4ETzI+jwd+twuulkb4vV7I09Kg0BkhCh3ryrnbWv6/vXsPjqo8/wD+PXvO3m+5JwQSwh3kIjdFcCq2UJFhGNApolJB8NJOQbBUq1ItdPwp2FGLxQ6KdQAVxCupxQsC5aJVS0BAGRW5g5AQLskm2fue8/7+WLJkSQILbHKSzfczkxn23bPved5lYZ+85z3PC0iAOT0LBosVkiQhVHkG4ZpqGEwmWDJzYDCZ6lUDBwAtHELE543+Y5CiJQRkqw2yseHF5ERE1LbIZgsUhwu+0qNQ7A4olsaLLqciJk2tiBoOI1R5Gv7Sn2JtkZoqGEwWBE+dQMTnjbWHqzxQ7E6Y0zPhLzsWaw+dOQVbhyKY3elxO1ar4RC8hw8g4quJ78PhhKOgEwxMnIiICIAlKwe+40FUH9gDZ6fuUKw2vUNqNrw814oINRKXMAHR68u1M0Tni3irITStXnFK37HD9S7hRaqr4hKmWHtNdVzBSyIiatskgwHWvPaQDDKq9v+AYMXpNrPGiUlTK9JQ8mJ0uhvc+qRWyFMBo8sd3ygE1IAv9lCLhBE4Xd5oH4HTJ6G14VtMiYgonkFRYMsvgGJzwHv0IKoP7m0TlcKZNLUiDS7OliTgQhm+0Bqs6C1Enb4ELtrHBZ8nIqI2RzIYYM3JgyU3H2rAB8+Pu1Fz9CDUgF/v0JoM1zS1IkaHE+d/FCPeGhid7kY/pEanG6EqT712xWqP/VlSFJjSMuLWPtVlSs+EQeFHhYiI6jPaHVCsNoSrPQhVViBUcRqKwwlLRjaMrrSU2o80dUbSBkiyAlN6ZlxbxFsNxe5ocKG2wWSGbLFC9cevdzJn5UKqkwRJkgRTWkajfRidackZABERpSTJYIDJnQ57YREs2XnQwmHUHDmAyu93wXv0EELVnpQoZcPpg1ZENplgzcmH0eFC4GQZtEgEis0Og9EEZ+fuCJw5iXBlBSABJncGzBlZEJoGoysNEZ8XBqMR1px20STrvJIDsskMZ5ceCJ45FStbYErPgjk9q9H964iIiOqSJAOMTheMThfUUBCRmmqEqj0IVpwCDAYYHS6YXG4oDner/G6RRFtZ8n5WVVUV3G43PB4PXC6X3uFcNjUUhBACkkGGfHbTXFVVIcJhAAKS0QT5bEkBTVUhNDVaAl+58Aa7QmgQZ++skxRjg+uhiIiodaj9zis/fBBpGRm6xCCEiN7l7a1BxOeFdnYbFoPJDKPDBcXhhLGRKyYtDWeaWin5vDICAKJJUp3aS7UMjbQ3RJIMkFrBB5eIiFoHSZIgm8yQTWaY0zMhVBURvw+q34dwtQfBMycBAAajCYrdCcVuh2JzQD5boLklYdJEREREzUaSZRgdThgdTgCAFolADfihBvyI+L0IVZ6OHmgwQLHaoNgcUGz22HIUPTFpIiIiIt0YFAWGOkmU0DSowQDUYABaIIDgmVMInCwDEF02UptAKTY7FKs9bneLpsakiYiIiFoMqXaGqc72LFokHE2iggGogSD8NVXA2bvxDGbLudkouwOy2dJkl/WYNDUzTVWjC62FBhhkGIyNL7bWNA0iHILQogUqJcUIg6IgEg4Cqna2cKUMoShQFAVqMBC7pVMyGCCbLVBVFYjU9mGAJCswGI2N9qFFwhCRaPVvSZEvuHD8UsZCRER0uQyKMfp9ZD87G3V2cXk0iQog4q1GqOLU2YMNMNqdUOwOGB0uyFZb0r6bmDQ1IzUUgr/sJ4QqKwAISIoCa14HmFxp9YpHqqEQQp4KBMpLIdQIIEkwZWTBmpWLQNkxhDyVsT5sBZ0gDAb4jh2JFbmUrTbY23dEJBCAv/TouT7c6bDm5iNw4nh8HLn5gN0F75H98X10KGpwMZ4aCsFX+hPCnouPhYiIKJnqLi43OqNbhcUu651dH+U/cTxatNlggNHphsnphvEKv6P47dZM1HAYNYf3QfWf2/NNRCLw/XQIUocimNIzY4mJpmkIec7Eb84rBCyZOag5ciC+D02DrBhRte/7uK1OVL8PVfv3wFnUJZowne0jVHkGWigIkzs9tmediETgO3YEtvYdYczIhnr8yLk+9v0Ad/erIJst8WM5tDeuCnljYyEiImoO51/WE0JACwYQ8fsQ8Xnh9VQAkGB0uWHJzoXx7KzVpWBF8GaihYJxyU5dvrJj0CLh2GMRDiFQXhp/kMkEEQnX68Oak4/AqfKG94YTGkJVlbEsvFbE54XBZI7uW1eH/8RxmBznfYiEhuCZU3GVXLVQsNFtW84fCxERkR4kSYJsscKcngl7+0LYCzvBnJkF1e9D9f49qD68/5KrlDNpaiaNJUwAICLh6Pqi2seqCqGqcceYHW5EGuhDtlig+moa7Tvi80G2WOvHEwrWW68kIuEGk69wTXXcB+tSxkJERNQSGBQjTO502Dp0hCU7D2FPBfznT1BcrI8mio3OYzBeoBK3JAGGOrM+DWxuGAkGGqxPIVQV0gX6NhiVc5fn6rbLCoQWn5hBkurNPsVirxPfJY2FiIioBZEkCZJsiH5fXeKmKEyamolstTWYDAGAKT0zbtZHkhUYXWlxx6je6uiM0Xl9+E+WwZKV2+h5TWmZCHkq4tokWQYMhnqzWSZ3OrTz2gDAkp0Hg+FcHYxLGQsREVFLIFQVoSoPvMeOwF92HIrdCWtOu0vqg0lTMzEYTXB26l4v2ZBtdlhz8iHVaZeNRtjadYgmJ3UEKs/AWdQ1rg8t4IdkMMCcXT9xsubmQwuH4pIjSZbhKOoWKxQWF0duPmoO74vvI6993CLwSx0LERGRHoQQUAN+BCtOw3f8KGoO70fw1AkYjCY4irrC2anbJRfG5Ia9zai2roQa8EMLh6FYbTAYTY1e7lJDwegmh34fZKMJssUKISuQzhb5UsOhaB+KCZAEhCYQ8UbXNyl2ByAZIEFAi4Tj+tAMMgxqpF4fkqJARMKI+GoASFBsdkiKAoNc/ybLSx0LERE1v5awYW9z0dRIrG5TbekBCBEtOeBwwuhMi5bFuYLvKZYcaEZ160okIlaD4vzbIhWlwcXdAKA00C6bLXF9yABgNDbchyzXm1lqyKWOhYiIKFmEqkYTo1AwmigFg9EbkRBd4qLY7TC502F0OCFb7SxuSURERKlNCAGhRqAFg2cTpCDUUCC6GwUQ29TXnJYBuc6mvtxGhYiIiFKWEAIiHIYais4caWeTpNo7vSVZgWy1wpyeBflsEUuDydysxZSZNBEREVGzEkJECzYHA2c34o3OJMU24TUaIVttMLnSogmSxQapBexvyqSJiIiImlRsDVLAH0uUziVIJsg2O0xpGVCsNshWe4vdw7RlRtXGaJEwtHAY4SoPIAFGpzt6J9olfGjUUBBaJIJwVSUAwORKg6QYAaFBDfgR9tVANpphdLiid8S10A8kERG1fpoager3Qw34oAYC0EJBANGyN4rNEZ1BstmhtOAEqSGtJ9IUpUXC8JcdQ/DMqVibv+wYzJk5sOa2S6hQpBoKwn/iOEIVp2NtgfJSmDNzYDCb4T9+9NzBkgRHUVcodkdcwUoiIqLLVVsTSfV5EfH7YkmSwWSGYnfCmNMu+r3TzGuQko1Jk84iPm9cwlQreLo8Wk/CefGkKeLzxiVMdfuwFxSdrb909k4DIVBzeD/c3a4CzEyaiIjo8ghNQ8TnRcRbg4jfC2gaJEWB0eGCMTcfRoezwe2/WjMmTTrS1Ei9ytx1BU6WQbbZYbhAxVI1HELwdHmjz4cqz8DkSkfwzMk6J45eskukHhMREVGt2hmlcLUHEa8XEBpkixXW7DwYnW7IVlurnkm6GCZNehLi3AxQAzRVvfhmghfrI6JCttRPurQLvIaIiKguoaoIV3sQqvJARMIwmMyw5uTBlJbRpn4BZ9KkI8kgQ3G6o3cRNMDodF10XxxJVqA4nI33YXcg4vfVa1fO29eOiIjofFokgpCnInqTkQBMaekwZ2ZDsTlSekapMUyadCQZDLBkZkcvnZ299TL2nCzDnJ510Q+lQZZhycxBsOJ0g30oThcCp07EtUfvoOMecURE1DChqghWnokmS5IES3YuLJm5bX5/USZNOjOYzHB17QXf8aOI1FQBAIwuN2x5HWAwJbaATjKZ4erSE77Sn8714XTDmtcBaigIg8kMLRSEZJBhysyCJTMHcoJ9ExFR2yGEQNhTiWBl9OYiS3YeLNm5DW7c3hbxXdCZJElQLFY4OnaGUM+Vir/Q4u/zGQwGGKw22As6AaoKSIhuvKsYoVitUKzW6CyUJEEyGllqgIiI6okE/AiePAEtHIIpIwu2vPYJlb1pS5g0tRAGWQGuMJOXjUaggalT2WS+on6JiCh1CU1D8MxJhKs8kK12uIq6ct1rI5g0ERERtVFqMAB/eSlERIUtvwDmzJw2ucA7UUyaiIiI2qBQVSWCp8ohW21wdO7RpkoHXC4mTURERG2IEALB0ycRrqqEOSMbtvwCSAaD3mG1Ci3iXfrHP/6BoqIiWCwWDBkyBFu3br3g8e+88w569uwJi8WCvn374qOPPmqmSImIiFovIQQCJ8sQrqqErX0h7B06MmG6BLq/U2+99RZmz56NuXPn4uuvv8bVV1+NUaNGoby84a1BvvjiC9xxxx245557sGPHDowfPx7jx4/H7t27mzlyIiKi1iM6w1SOSE017IWdYcnM0TukVkcS4mL7dDStIUOG4JprrsGLL74IANA0DQUFBXjggQfw6KOP1jt+4sSJ8Hq9WLNmTaztuuuuQ//+/fHSSy9d9HxVVVVwu93weDxwuVzJGwgREVELU/udV374IGyKjOCpE7B36AhzRrbeobVKus40hUIhbN++HSNHjoy1GQwGjBw5El9++WWDr/nyyy/jjgeAUaNGNXo8ERFRWyc0FcHT5TClZzFhugK6LgQ/deoUVFVFbm5uXHtubi5++OGHBl9TVlbW4PFlZWUNHh8MBhEMBmOPq6qqrjBqIiKilqmx77xgxRkYTUbY8wv0Ci0l6L6mqanNnz8fbrc79lNQwA8MERGlpsa+81S/F5acvItuAk8XpmvSlJWVBVmWceJE/IayJ06cQF5eXoOvycvLu6TjH3vsMXg8ntjP0aNHkxM8ERFRC3Oh7zxzepaOkaUGXZMmk8mEQYMGYcOGDbE2TdOwYcMGDB06tMHXDB06NO54AFi3bl2jx5vNZrhcrrgfIiKiVNTYd57BZIZBYWnGK6X7Ozh79mxMmTIFgwcPxrXXXouFCxfC6/Vi6tSpAIDJkyejffv2mD9/PgBg1qxZGD58OJ577jmMGTMGq1atwrZt27BkyRI9h0FERNRicS+55NA9aZo4cSJOnjyJP//5zygrK0P//v3xySefxBZ7HzlyBIY6hbeGDRuGlStX4vHHH8ecOXPQrVs3FBcXo0+fPnoNgYiIqEXjFinJoXudpubGOk1ERNRWxOo0HTyA7KJOeofT6qX83XNERERtnWy16h1CSmDSRERElOK4pik5mDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJYBJExEREVECmDQRERERJUDRO4DmJoQAAFRVVekcCRERUeKcTickSdI7jDatzSVN1dXVAICCggKdIyEiIkpceXk5srOz9Q6jTZNE7dRLG6FpGo4fP65rxl5VVYWCggIcPXoULpdLlxiaGsfY+qX6+ACOMRWk+viAc2OsrKyE2+2+pNcKIVBdXc1ZqiRpczNNBoMBHTp00DsMAIDL5UrZf+S1OMbWL9XHB3CMqSDVxwfgspIeSZJS/n1pTlwITkRERJQAJk1ERERECWDSpAOz2Yy5c+fCbDbrHUqT4Rhbv1QfH8AxpoJUHx/QNsbYWrS5heBEREREl4MzTUREREQJYNJERERElAAmTUREREQJYNLUjLZs2YKxY8ciPz8fkiShuLhY75CSbv78+bjmmmvgdDqRk5OD8ePHY8+ePXqHlTSLFy9Gv379YjVhhg4dio8//ljvsJrUggULIEkSHnzwQb1DSZp58+ZBkqS4n549e+odVlIdO3YMv/71r5GZmQmr1Yq+ffti27ZteoeVNEVFRfX+DiVJwvTp0/UOLWlUVcUTTzyBTp06wWq1okuXLnjyySfBpcj6aXPFLfXk9Xpx9dVXY9q0abj11lv1DqdJbN68GdOnT8c111yDSCSCOXPm4KabbsJ3330Hu92ud3hXrEOHDliwYAG6desGIQSWL1+OcePGYceOHejdu7fe4SVdSUkJXn75ZfTr10/vUJKud+/eWL9+feyxoqTOf4cVFRW4/vrr8fOf/xwff/wxsrOzsXfvXqSnp+sdWtKUlJRAVdXY4927d+OXv/wlJkyYoGNUyfXMM89g8eLFWL58OXr37o1t27Zh6tSpcLvdmDlzpt7htUmp879EKzB69GiMHj1a7zCa1CeffBL3eNmyZcjJycH27dtxww036BRV8owdOzbu8VNPPYXFixfjq6++SrmkqaamBpMmTcIrr7yC//u//9M7nKRTFAV5eXl6h9EknnnmGRQUFGDp0qWxtk6dOukYUfKdvwfbggUL0KVLFwwfPlyniJLviy++wLhx4zBmzBgA0dm1N998E1u3btU5sraLl+eoSXk8HgBARkaGzpEkn6qqWLVqFbxeL4YOHap3OEk3ffp0jBkzBiNHjtQ7lCaxd+9e5Ofno3Pnzpg0aRKOHDmid0hJ88EHH2Dw4MGYMGECcnJyMGDAALzyyit6h9VkQqEQ3njjDUybNi2l9lcbNmwYNmzYgB9//BEAsGvXLnz++ecp/8t3S8aZJmoymqbhwQcfxPXXX48+ffroHU7SfPvttxg6dCgCgQAcDgdWr16Nq666Su+wkmrVqlX4+uuvUVJSoncoTWLIkCFYtmwZevTogdLSUvzlL3/Bz372M+zevRtOp1Pv8K7YgQMHsHjxYsyePRtz5sxBSUkJZs6cCZPJhClTpugdXtIVFxejsrISd999t96hJNWjjz6Kqqoq9OzZE7IsQ1VVPPXUU5g0aZLeobVZTJqoyUyfPh27d+/G559/rncoSdWjRw/s3LkTHo8H7777LqZMmYLNmzenTOJ09OhRzJo1C+vWrYPFYtE7nCZR9zf1fv36YciQIejYsSPefvtt3HPPPTpGlhyapmHw4MF4+umnAQADBgzA7t278dJLL6Vk0vTqq69i9OjRyM/P1zuUpHr77bexYsUKrFy5Er1798bOnTvx4IMPIj8/PyX/HlsDJk3UJGbMmIE1a9Zgy5Yt6NChg97hJJXJZELXrl0BAIMGDUJJSQleeOEFvPzyyzpHlhzbt29HeXk5Bg4cGGtTVRVbtmzBiy++iGAwCFmWdYww+dLS0tC9e3fs27dP71CSol27dvWS+F69euG9997TKaKmc/jwYaxfvx7vv/++3qEk3cMPP4xHH30Ut99+OwCgb9++OHz4MObPn8+kSSdMmiiphBB44IEHsHr1amzatCnlFp82RNM0BINBvcNImhEjRuDbb7+Na5s6dSp69uyJRx55JOUSJiC66H3//v2466679A4lKa6//vp6pT5+/PFHdOzYUaeIms7SpUuRk5MTWyydSnw+HwyG+KXHsixD0zSdIiImTc2opqYm7jfZgwcPYufOncjIyEBhYaGOkSXP9OnTsXLlSvzrX/+C0+lEWVkZAMDtdsNqteoc3ZV77LHHMHr0aBQWFqK6uhorV67Epk2bsHbtWr1DSxqn01lvDZrdbkdmZmbKrE176KGHMHbsWHTs2BHHjx/H3LlzIcsy7rjjDr1DS4rf//73GDZsGJ5++mncdttt2Lp1K5YsWYIlS5boHVpSaZqGpUuXYsqUKSlVMqLW2LFj8dRTT6GwsBC9e/fGjh078Pzzz2PatGl6h9Z2CWo2GzduFADq/UyZMkXv0JKmofEBEEuXLtU7tKSYNm2a6NixozCZTCI7O1uMGDFCfPrpp3qH1eSGDx8uZs2apXcYSTNx4kTRrl07YTKZRPv27cXEiRPFvn379A4rqf7973+LPn36CLPZLHr27CmWLFmid0hJt3btWgFA7NmzR+9QmkRVVZWYNWuWKCwsFBaLRXTu3Fn86U9/EsFgUO/Q2ixJCJYWJSIiIroY1mkiIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiSmGbNm2CJEmorKxs9BhJklBcXNxsMV3IvHnz0L9//8t67V133YWnn346uQGd5/bbb8dzzz3XpOcgopaLSRNRK7Bs2TKkpaXpHUZSJTNZ27VrFz766CPMnDkzKf015vHHH8dTTz0Fj8fTpOchopaJSRMRtXqLFi3ChAkT4HA4mvQ8ffr0QZcuXfDGG2806XmIqGVi0kTUxG688UbMmDEDM2bMgNvtRlZWFp544gnU3fYxGAzioYceQvv27WG32zFkyBBs2rQJQPQS29SpU+HxeCBJEiRJwrx58wAAr7/+OgYPHgyn04m8vDzceeedKC8vv6J4jx49ittuuw1paWnIyMjAuHHjcOjQodjzd999N8aPH49nn30W7dq1Q2ZmJqZPn45wOBw7prS0FGPGjIHVakWnTp2wcuVKFBUVYeHChQCAoqIiAMAtt9wCSZJij2u9/vrrKCoqgtvtxu23347q6upG41VVFe+++y7Gjh0b1x4MBvHII4+goKAAZrMZXbt2xauvvgrg3GXLtWvXYsCAAbBarfjFL36B8vJyfPzxx+jVqxdcLhfuvPNO+Hy+uH7Hjh2LVatWXeK7SkSpgEkTUTNYvnw5FEXB1q1b8cILL+D555/HP//5z9jzM2bMwJdffolVq1bhm2++wYQJE3DzzTdj7969GDZsGBYuXAiXy4XS0lKUlpbioYceAgCEw2E8+eST2LVrF4qLi3Ho0CHcfffdlx1nOBzGqFGj4HQ68dlnn+G///0vHA4Hbr75ZoRCodhxGzduxP79+7Fx40YsX74cy5Ytw7Jly2LPT548GcePH8emTZvw3nvvYcmSJXHJXElJCQBg6dKlKC0tjT0GgP3796O4uBhr1qzBmjVrsHnzZixYsKDRmL/55ht4PB4MHjw4rn3y5Ml488038fe//x3ff/89Xn755XozUfPmzcOLL76IL774IpYsLly4ECtXrsSHH36ITz/9FIsWLYp7zbXXXoutW7ciGAwm/sYSUWoQRNSkhg8fLnr16iU0TYu1PfLII6JXr15CCCEOHz4sZFkWx44di3vdiBEjxGOPPSaEEGLp0qXC7XZf9FwlJSUCgKiurhZCCLFx40YBQFRUVDT6GgBi9erVQgghXn/9ddGjR4+4WIPBoLBarWLt2rVCCCGmTJkiOnbsKCKRSOyYCRMmiIkTJwohhPj+++8FAFFSUhJ7fu/evQKA+Nvf/tbgeWvNnTtX2Gw2UVVVFWt7+OGHxZAhQxqNf/Xq1UKW5biY9+zZIwCIdevWNfia2vdl/fr1sbb58+cLAGL//v2xtt/85jdi1KhRca/dtWuXACAOHTrUaExElJo400TUDK677jpIkhR7PHToUOzduxeqquLbb7+Fqqro3r07HA5H7Gfz5s3Yv3//Bfvdvn07xo4di8LCQjidTgwfPhwAcOTIkcuKc9euXdi3bx+cTmcsjoyMDAQCgbhYevfuDVmWY4/btWsXm0nas2cPFEXBwIEDY8937doV6enpCcVQVFQEp9PZYN8N8fv9MJvNce/vzp07Icty7P1oTL9+/WJ/zs3Nhc1mQ+fOnePazj+31WoFgHqX7Ygo9Sl6B0DU1tXU1ECWZWzfvj0uEQFwwYXNXq8Xo0aNwqhRo7BixQpkZ2fjyJEjGDVqVNyltEuNZdCgQVixYkW957Kzs2N/NhqNcc9JkgRN0y7rnOe71L6zsrLg8/kQCoVgMpkAnEtsLuVckiQldO4zZ84AiH8/iKhtYNJE1Az+97//xT3+6quv0K1bN8iyjAEDBkBVVZSXl+NnP/tZg683mUxQVTWu7YcffsDp06exYMECFBQUAAC2bdt2RXEOHDgQb731FnJycuByuS6rjx49eiASiWDHjh0YNGgQAGDfvn2oqKiIO85oNNYb0+Worev03Xffxf7ct29faJqGzZs3Y+TIkVd8jrp2796NDh06ICsrK6n9ElHLx8tzRM3gyJEjmD17Nvbs2YM333wTixYtwqxZswAA3bt3x6RJkzB58mS8//77OHjwILZu3Yr58+fjww8/BBC9ZFVTU4MNGzbg1KlT8Pl8KCwshMlkwqJFi3DgwAF88MEHePLJJ68ozkmTJiErKwvjxo3DZ599hoMHD2LTpk2YOXMmfvrpp4T66NmzJ0aOHIn7778fW7duxY4dO3D//ffDarXGXUIrKirChg0bUFZWVi+huhTZ2dkYOHAgPv/887i+p0yZgmnTpqG4uDg2jrfffvuyz1Prs88+w0033XTF/RBR68OkiagZTJ48GX6/H9deey2mT5+OWbNm4f777489v3TpUkyePBl/+MMf0KNHD4wfPx4lJSUoLCwEAAwbNgy//e1vMXHiRGRnZ+Ovf/0rsrOzsWzZMrzzzju46qqrsGDBAjz77LNXFKfNZsOWLVtQWFiIW2+9Fb169cI999yDQCBwSTNPr732GnJzc3HDDTfglltuwX333Qen0wmLxRI75rnnnsO6detQUFCAAQMGXFHc9957b71LiosXL8avfvUr/O53v0PPnj1x3333wev1XtF5AoEAiouLcd99911RP0TUOklC1CkWQ0RJd+ONN6J///6xGkVt0U8//YSCggKsX78eI0aMSHr/fr8fPXr0wFtvvYWhQ4cmvf9aixcvxurVq/Hpp5822TmIqOXimiYiSrr//Oc/qKmpQd++fVFaWoo//vGPKCoqwg033NAk57NarXjttddw6tSpJum/ltForFe3iYjaDiZNRJR04XAYc+bMwYEDB+B0OjFs2DCsWLGi3t1pyXTjjTc2Wd+17r333iY/BxG1XLw8R0RERJQALgQnIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiIiIiSgCTJiIiIqIEMGkiIiIiSsD/AwCs4fLyqUQYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = \"petal length (cm)\", y = \"petal width (cm)\", kind = \"scatter\", hue = 'target', data = df_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "faced-turner",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=iris['data'], columns=iris.feature_names)\n",
    "\n",
    "# Renaming columns\n",
    "df['x1'] = df['petal length (cm)']\n",
    "df['x2'] = df['petal width (cm)']\n",
    "\n",
    "df['y'] = iris.target\n",
    "df['class'] = df['y'].replace(to_replace= [0, 1, 2], value = ['A', 'B', 'C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "wooden-circumstances",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "    x1   x2  y class  \n",
       "0  1.4  0.2  0     A  \n",
       "1  1.4  0.2  0     A  \n",
       "2  1.3  0.2  0     A  \n",
       "3  1.5  0.2  0     A  \n",
       "4  1.4  0.2  0     A  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "great-tyler",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "wanted-spider",
   "metadata": {},
   "source": [
    "## Training and Visualizing a Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "least-posting",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "critical-bermuda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Where to save the figures\n",
    "import os\n",
    "PROJECT_ROOT_DIR = \"..\" \n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "proprietary-commissioner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X = df[['x1', 'x2']]\n",
    "y = df[['y']]\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth = 2)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "sharing-indianapolis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.42.4 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"390pt\" height=\"314pt\"\n viewBox=\"0.00 0.00 390.00 314.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 310)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-310 386,-310 386,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"none\" stroke=\"black\" d=\"M218,-306C218,-306 84,-306 84,-306 78,-306 72,-300 72,-294 72,-294 72,-235 72,-235 72,-229 78,-223 84,-223 84,-223 218,-223 218,-223 224,-223 230,-229 230,-235 230,-235 230,-294 230,-294 230,-300 224,-306 218,-306\"/>\n<text text-anchor=\"middle\" x=\"151\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 2.45</text>\n<text text-anchor=\"middle\" x=\"151\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.667</text>\n<text text-anchor=\"middle\" x=\"151\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\n<text text-anchor=\"middle\" x=\"151\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\n<text text-anchor=\"middle\" x=\"151\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = A</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M128,-179.5C128,-179.5 12,-179.5 12,-179.5 6,-179.5 0,-173.5 0,-167.5 0,-167.5 0,-123.5 0,-123.5 0,-117.5 6,-111.5 12,-111.5 12,-111.5 128,-111.5 128,-111.5 134,-111.5 140,-117.5 140,-123.5 140,-123.5 140,-167.5 140,-167.5 140,-173.5 134,-179.5 128,-179.5\"/>\n<text text-anchor=\"middle\" x=\"70\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"70\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\n<text text-anchor=\"middle\" x=\"70\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 0, 0]</text>\n<text text-anchor=\"middle\" x=\"70\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = A</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M122.9,-222.91C115.11,-211.65 106.64,-199.42 98.8,-188.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"101.53,-185.9 92.96,-179.67 95.78,-189.88 101.53,-185.9\"/>\n<text text-anchor=\"middle\" x=\"88.49\" y=\"-200.56\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M295.5,-187C295.5,-187 170.5,-187 170.5,-187 164.5,-187 158.5,-181 158.5,-175 158.5,-175 158.5,-116 158.5,-116 158.5,-110 164.5,-104 170.5,-104 170.5,-104 295.5,-104 295.5,-104 301.5,-104 307.5,-110 307.5,-116 307.5,-116 307.5,-175 307.5,-175 307.5,-181 301.5,-187 295.5,-187\"/>\n<text text-anchor=\"middle\" x=\"233\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x2 &lt;= 1.75</text>\n<text text-anchor=\"middle\" x=\"233\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"233\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100</text>\n<text text-anchor=\"middle\" x=\"233\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 50, 50]</text>\n<text text-anchor=\"middle\" x=\"233\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = B</text>\n</g>\n<!-- 0&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>0&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M179.45,-222.91C185.68,-214.01 192.34,-204.51 198.78,-195.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"201.73,-197.22 204.6,-187.02 196,-193.2 201.73,-197.22\"/>\n<text text-anchor=\"middle\" x=\"208.93\" y=\"-207.94\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M212,-68C212,-68 96,-68 96,-68 90,-68 84,-62 84,-56 84,-56 84,-12 84,-12 84,-6 90,0 96,0 96,0 212,0 212,0 218,0 224,-6 224,-12 224,-12 224,-56 224,-56 224,-62 218,-68 212,-68\"/>\n<text text-anchor=\"middle\" x=\"154\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.168</text>\n<text text-anchor=\"middle\" x=\"154\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 54</text>\n<text text-anchor=\"middle\" x=\"154\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 49, 5]</text>\n<text text-anchor=\"middle\" x=\"154\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = B</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M203.58,-103.73C197.2,-94.88 190.44,-85.51 184.03,-76.63\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"186.71,-74.36 178.02,-68.3 181.04,-78.46 186.71,-74.36\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M370,-68C370,-68 254,-68 254,-68 248,-68 242,-62 242,-56 242,-56 242,-12 242,-12 242,-6 248,0 254,0 254,0 370,0 370,0 376,0 382,-6 382,-12 382,-12 382,-56 382,-56 382,-62 376,-68 370,-68\"/>\n<text text-anchor=\"middle\" x=\"312\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.043</text>\n<text text-anchor=\"middle\" x=\"312\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n<text text-anchor=\"middle\" x=\"312\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 45]</text>\n<text text-anchor=\"middle\" x=\"312\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = C</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M262.42,-103.73C268.8,-94.88 275.56,-85.51 281.97,-76.63\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"284.96,-78.46 287.98,-68.3 279.29,-74.36 284.96,-78.46\"/>\n</g>\n</g>\n</svg>\n",
      "text/plain": [
       "<graphviz.sources.Source at 0x7f4ed8ede340>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "out_f = \"lec04_BDTs_viz_tree.dot\"\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file = os.path.join(IMAGES_PATH, out_f),\n",
    "    feature_names = ['x1', 'x2'],\n",
    "    class_names = ['A', 'B', 'C'],\n",
    "    rounded = True,\n",
    "    filled = False\n",
    ")\n",
    "\n",
    "Source.from_file(os.path.join(IMAGES_PATH, out_f))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "working-explorer",
   "metadata": {},
   "source": [
    "## Decision Tree Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eleven-ivory",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/claire/.local/lib/python3.8/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/papermill.record/image/png": 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ohzy7E+vpOG/wxrH9ZczKiOvoGgoxAAAAH0ncn6idKTslSTtTdipxf2K5Yz0d5w3eOLa/jFkZcR1dQyEGAADgA4ZhaMq6KQq2BEuSgi3BmrJuSpHdAldjPR3nDd44tr+MWRlxHV1HIQYAAJBn+5bt2rJ+i7Zv2e7xsfO7BDbDJkmyGbZiuwWuxno6zhu8cWx/GbMy4jq6jkIMAAAgzz+G/ENDrhiifwz5h0fHPb9LkK+oboGrsZ6O8wZvHNtfxqyMuI7uoRADAADwsvO7BPmK6ha4GuvpOG/wxrH9ZczKiOvoHgoxAACAPMMeHKYHn3hQwx4c5rEx87sEFlmK/Nwii7Nb4Gqs3W73aJw3OhXunHegjVkZcR3dV8XsBAAAACqKUWNHeXxMq82q5IxkGSpmWXkZSslIkdVmlSSXYrOsWR6Ns9qsCq0SWoazK5475+3qsf1lzMqI6+g+i0FZWi4ZGRmKiIhQemqKwsPDzU4HgDdZcyRJ9Zu30dEjyaobG6OVh5Yr2hJtcmIAKrrk9GSdOHWi2M+jw6IVGx7rVqyn47zBG8f2lzErI66jlJmRqdaRrZWenl5qbUAhVk4UYkAlQiEGAABK4E4hxj1iAAAAAOBj3CMGAACQZ2DngTqWekx1Y+rqy21fmp0OgABGIQYAAJDnWOoxpSalmp0GgEqAQgwAACBP3Zi6Bf4XALyFe8QAAADyfLntS207uI1piW7YuH+j+s7sq437N1boMVEyrrnvUYgBAACgTAzD0KS1k7T3+F5NWjvJIw/r9caYKBnX3BwUYgAAACiTxP2J2pmyU5K0M2WnEvcnVsgxUTKuuTkoxAAAAOA2wzA0Zd0UBVuCJUnBlmBNWTelXN0Ub4yJknHNzUMhBgAAkGf6K9P1yvhXNP2V6WanUuHld1Fshk2SZDNs5e6meGNMlIxrbh4KMQAAgDwfvf2R3p70tj56+yOzU6nQzu+i5CtPN8UbY6JkXHNzUYgBAADALed3UfKVp5vijTFRMq65uXiOGAAAQJ6Zn86UNceqkNAQs1OpsPK7KBZZZKhwx8Qii6asm6KeTXvKYrGYNiZKxjU3H4UYAABAnk4JncxOocKz2qxKzkgu8h/vkmTIUEpGiqw2q0KrhJo2JkrGNTcfhRgAAABcFlolVMvuXqYTp04UGxMdFu3WP969MSZKxjU3H4UYAAAA3BIXEae4iLgKPyZKxjU3F4UYAABAnn179uns2bOqUqWKmjVvZnY6AAIYhRgAAECe2668TalJqYqJj9G2g9vMTgdAAGP5egAAAADwMTpiAAAAeQbdOkjpaemKiIwwOxUAAY6OGAAAQJ4nJz+pye9N1pOTnzQ7Fa/YuH+j+s7sq437N3os1tNx7vDGmIGE61OxUYgBAABUAoZhaNLaSdp7fK8mrZ0kwyj6+VHuxHo6zlvnUxlxfSo+CjEAAIBKIHF/onam7JQk7UzZqcT9ieWO9XScO7wxZiDh+lR8FGIAAAABzjAMTVk3RcGWYElSsCVYU9ZNKbJL4mqsp+O8dT6VEdfHP1CIAQAA5LljwB265tJrdMeAO8xOxaPyuyM2wyZJshm2YrskrsZ6Os5b51MZcX38A4UYAABAnj0/79HuH3drz897zE7FY87vjuQrqkviaqyn47x1PpUR18d/UIgBAADkqRpSVSEhIaoaUtXsVDzm/O5IvqK6JK7GejrOW+dTGXF9/AeFGAAAQJ5Nezdp7+m92rR3k9mpeER+d8QiS5GfW2RxdklcjbXb7R6Nc6dD4875VEZcH/9CIQYAABCgrDarkjOSZaiYZeVlKCUjRVab1eXYLGuWR+OsNqtXzqcy4vr4F4tBSVwuGRkZioiIUHpqisLDw81OB4A3WXMkSfWbt9HRI8mqGxujlYeWK9oSbXJiAFC85PRknTh1otjPo8OiFRse61asp+Pc4Y0xAwnXx1yZGZlqHdla6enppdYGVXyUEwAAAEwQFxGnuIg4j8Z6Os4d3hgzkHB9/AeFGAAAQJ55s+bpVPYp1QiroaH3DTU7HQABjEIMAAAgzxsvvaHUpFTFxMdQiAHwKhbrAAAAAAAfoyMGAACQZ8LbE3Tm9BlVq17N7FQABDgKMQAAgDz9ru1ndgoAKomAmpo4ceJEXXbZZapVq5bq1aunwYMHa8+ePaXut3DhQrVo0ULVqlVT27ZttXz5ch9kCwAA/MHG/RvVd2Zfbdy/0adx7sbC/5n5ffNnzfcCqhDbsGGDRo4cqa1bt2r16tXKzc3VVVddpezs7GL32bx5s2677Tbdfffd+vHHHzV48GANHjxYu3fv9mHmAACgIjIMQ5PWTtLe43s1ae0kFff4VU/HuRsL/2fm982fNXMEVCG2YsUKjRgxQq1bt1b79u01d+5cHTx4UNu3by92nzfeeENXX321HnvsMbVs2VIvvviiLrnkEk2fPt2HmQMAgIog7USaThw7obQTaZKkxP2J2pmyU5K0M2WnEvcnFrmfp+PcjYX/M/P75s+aOQKqEDtfenq6JCkqKqrYmC1btqhfv4Lzwfv3768tW7YUGZ+Tk6OMjIwCLwAAEBj6d+yvjjEd1b9jfxmGoSnrpijYEixJCrYEa8q6KYW6BZ6OczcW/s/M75s/a+YJ2ELMbrdr9OjR6t69u9q0aVNsXGpqqurXr19gW/369ZWamlpk/MSJExUREeF8NWzY0KN5AwCAiiG/S2AzbJIkm2Erslvg6Th3Y+H/zPy++bNmnoAtxEaOHKndu3drwYIFHh133LhxSk9Pd74OHTrk0fEBAIB5Lu93ufpd20+X97u8QJcg3/ndgvO7CeWNczcW/s/M75s/a+YKyEJs1KhRWrZsmdatW6cGDRqUGBsTE6MjR44U2HbkyBHFxMQUGR8aGqrw8PACLwAAEBimzp6q2Utn6/onry/QJch3frfg/G5CeePcjYX/M/P75s+auQKqEDMMQ6NGjdLixYu1du1aNWnSpNR9EhIStGbNmgLbVq9erYSEBG+lCQAAKrD8LoFFliI/t8iiKeumyG63ezTOMAyXj02nIjCY+X3zZ818AfVA55EjR2r+/PlaunSpatWq5bzPKyIiQtWrV5ckDRs2TPHx8Zo4caIk6eGHH1avXr00depUDRw4UAsWLND333+vWbNmmXYeAADAPFabVckZyTJUzBL0MpSSkaIsa5ZH46w2qyS5HBtaJbQMZ4eKxNU/a974vs08NhwsRgCVuRZL0RX9nDlzNGLECElS7969dcEFF2ju3LnOzxcuXKinnnpKv//+uy666CJNnjxZ11xzjUvHzMjIUEREhNJTU5imCAQ6a44kqX7zNjp6JFl1Y2O08tByRVuiTU4MgKclpyfrxKkTxX4eHRat2PBYj8e5c2wEBjO/b/6seV5mRqZaR7ZWenp6qbVBQBViZqAQAyoRCjEg4P3z9n/qz+N/Kio6Sm9+8qbZ6QDwM+4UYgE1NREAAKA8tiZuVWpSqmLii160CwA8JaAW6wAAAAAAf0BHDAAAIM/an9fKMIxi7zsHAE+hEAMAAMhTs1ZNs1MAUEkwNREAAMADNu7fqL4z+2rj/o0lxs3cNFPNJjTTzE0zfX5slI5rCV+hEAMAACgnwzA0ae0k7T2+V5PWTir2Ibh2u13TNk5Tri1X0zZOk91u99mxUTquJXyJQgwAACDPV59/pc8/+Vxfff6VW/sl7k/UzpSdkqSdKTuVuD+xyLgZm2bodO5pSdLp3NOasWlG+RJ249goHdcSvkQhBgAAkOfZ0c9q9PDRenb0sy7vYxiGpqybomBLsCQp2BKsKeumFOqm2O12Td80vcC26Zuml6sr5uqxUTquJXyNQgwAAKAc8rsoNsMmSbIZtiK7Ked2w/KVtyvm6rFROq4lfI1CDAAAIM9jLzymF996UY+98JhL8ed3UfKd300pqhuWr6xdMVePjdJxLWEGCjEAAIrRrWk33dL3FrPTgA/dPOJmDX9wuG4ecbNL8ed3UfKd300pqhuWr6xdMVePjdJxLWEGCjEAgE9tWb9FjYIbOV8XVL1Abeq0Ub92/fTIiEe0fsV6n/72+f033tfCuQt9dryiZGdl6/UXXtddg+5S50ad1Si4EQWgH8jvolhU9MOfLbJoyropstlsxXbD8rnbFXP12HRySse1hFl4oDMAwBSDbh2kPgP6yDAMZWdla9+efVq1dJX+/fG/1eOKHpr52UxF1I7weh6z35itBhc0cLkD4g1/Hv9Trz//uurWr6u2l7TV8SPHTcsFrrParErOSJahov+BbshQSkaK0k6n6UzumRLHOpN7RlnWLIVXC/fosa02q0KrhLo0ZmXFtYRZKMQAAKZoc0kb3Xj7jQW2PTP1GU14YoLee/09PfT3h/TR8o9Mys636sXW07d/fKvYBrGSpBbhLUzOCK4IrRKqZXcv04lTJ4qNiQ6LVnTNaC25c4l+T/u92LgmUU1cLsLcOTaFQ+m4ljALhRgAoMIIDg7W068+rR3f7dD6leu17Ztt6tyjsyQpIz1D0ydO11eff6WUQymqGV5TPa7oocdeekyNmzZ2jrFw7kI9evejmrdynr7b9J0Wzl2oY6nH1LR5U40aO0rX33q9M7ZRcCNJ0uE/Djv/W5I27dukhhc0dL7f++tevfSvl/Ttxm8VFBSkHv166MW3XlS9mHoeOe/Q0FBnEQZzdW7UWalJqYqJj9G2g9tKjY+LiFNcRFypcR0bdFTHBh09kaLbx0bpuJYwA4UYAKDCufXOW/XdN99p7fK16tyjszLSM3RDjxuUfDBZt9x5iy5udbGOph7VxzM/1qCEQVq2bZkaNG5QYIyJ4ybqdPZp3fGPOyQ5CrRRQ0cp50yOcxritA+n6YVHX1BUdJRGjRvl3LdO3TrO/05NStWQvkPUf3B/jZ80Xv/d+V/NmzVPWRlZmrdynjMuNzdXmemZLp9jVHRUma4NACAwUIgBACqcFu0cU/MO/O+AJGnqs1N1cP9BLd28VK3at3LG3Tz8Zl3V/iq99txrem3OawXGSDueppU7Vio8wjHd6/b7b1f/Dv31wr9e0HVDrlO16tV04+036tVnXlV0/ehC0yTz/b73d834vxm67pbrnNuCgoL00cyPtG/PPjVr3kyS9P2m7zXkiiEun+NB20GXY+E7bTq2UVzDOAplAF5HIQYAqHBqhdeSJGVmZMowDC2Zv0RdLu+imPgY/Xn8T2dcjbAa6tiloxJXF15a+vYHbncWYZIUHhGuofcP1eQnJ2vL+i3qM6CPS7nUj6tfoAiTpG59uumjmR/pwG8HnIVYy/YtC3TI4J9mL51tdgoAKgkKMQBAhZOZ4ZjiVyu8lk4cO6G0E2lKXJ2oDvU7FBkfFFT4aSwXtbio0LaLW14sSTp4wPVuVKOmjQpti6wTKUlKO5Hm3FY7srYu73e5y+MCACo3CjEAQIXz685fJUlNmzd1PrunxxU99I/H/+HzXIKDg4v/8JzVrq1Wq07+edLlcT210Adcl3QwWH8eL/4RqlHRdsU3shX7OQB4Eg90BgBUOAvmLJAk9b2mr+rUraPw2uHKyszS5f0uL/Z1vt9+/a3Qtv/993+SpEZN/upyWSxFP8TVXds3b9el8Ze6/IJvJR0MVu+WsRp4WYzjNfojDVza2PG/edt6t4xV0sHChffMTTPVbEIzzdw00yO5bNy/UX1n9tXG/Rs9Mp63uHre3jgfT49p5jX3l+8bvkdHDABQYdhsNk0cO1HfffOd+g7oq8u6XyZJuuHvN+jDtz/Ul4u+1MCbBhba7/jR44quF11g2yfvfKI7HrjDeZ9YRnqG5r07T+G1w9W1V1dnXI2aNdzqZBWHe8Qqtj+PBynnTH7RbZd6PS9VsTr+d9O/JDk+f/mJp1W1SpoiIiP0wpsvyG63a9rGacq15Wraxmm6P+H+IqfCusowDE1aO0l7j+/VpLWT1KNJD4/9MsCTXD1vb5yPp8c085r7y/cNc1CIAQBMsfuH3fr8k88lSdmZ2dr3v31atXSVDv9xWD2v7Kk3573pjH3spcf0/ebv9eCtD+ram69Vx64dFRISosN/HNa6r9ap7SVtC62aGBkdqUEJg5xL1S+cu1BJB5M0edZkVa9R3RnXsUtHfTr7U736zKu6sMWFCgoKUr/r+qlGWA23zqe894jNnTFXGSczJElnc88q6Y8kvfmy4xq0bNdSV153ZZnHxnl6TJRCTjn+O+SU4/03T0qStqz/SieOpigmPkYvvPmCZmyaodO5pyVJp3NPa8amGXro8ofKfOjE/YnambJTkrQzZacS9yeqV7Ne5TsfL3D1vL1xPp4e08xr7i/fN8xBIQYAMMXSBUu1dMFSBQUFKaxmmGIaxKhLzy6acOsE9b66d4HY8Ihwfb7xc816bZaWLVymVf9ZpSpVqii2Qawu7X6pbrv7tkLjj5s4Ttu+2aaP3v5Ix48cV5OLm+jNj9/U4L8PLhD3+EuPK/3PdH349ofKOJkhwzC0ad8mtwux8po1dZYO/3HY+f7Q74f06jOvSpJuGnYThZjH2KWeExz391nk+N+eE6Rvxun8Ozbsdrumb5peYNv0TdM1svvIMnXFDMPQlHVTFGwJls2wKdgSrCnrpqhn054Vqkvi6nl743w8PaaZ19xfvm+Yh0IMAOBTCb0TyvQMreo1quvhpx7Ww0897FJ8lSpV9Ohzj+rR5x4tMS66XrTeXfRukZ9t3r+5yO1lPYeSFHcseNi53TDJUYyd0xWbPGuJLmqZo6DgoAJdoXzl6Yqd2x2RJJthq5BdElfP2xvn4+kxzbzm/vJ9wzws1gEAACqJc7ph58rvismumPhGuuDCC9SgcYNCXaF80zdNl91ud+vI53ZHzpXfJclfHdRsRXXD8p173t44H0+PaeY195fvG+aiEAMAAJVDfjfs/Flh53bF8hTVFcqX3x1yR353xGYUXB7/3C5JReDqeXvjfDw9ppnX3F++b5iLQgwAAAQ8u72Ybli+vK6Y3W4vsSuUz52uWH53xFKoAnSwyFIhuiSunrfNZvP4+Xj6Gpl5zf3l+4b5KMQAAAHl5hE366DtoBJ6J5idCiqQarUzpCpnCnfD8lkkVTmjPXu+1sovV+r0/4ruCuU7k3tGWdYsl45ttVmVnJEso5gq0JChlIwUWW1Wl8bzlixrls7knikx5kzuGaWdTvP4+Xj6Gpl5zf3l+4b5LAbleLlkZGQoIiJC6akpCg8PNzsdAN5kzZEk1W/eRkePJKtubIxWHlquaEt0KTsCqAhWbv9Je5L/KPbzlnEX6Mkb7lFqUqrqxNTRMyueKTa2SVQTdYjv4PKxk9OTdeLUiWI/jw6LVmx4rMvjecuPh3/U72m/F/t5/nl743w8PaaZ19xfvm94XmZGplpHtlZ6enqptQGrJgKAq0JC84oxx++vjh85qisbXq3gvLkFQUVMMmjTsY1mL51dYNtdg+7S7h93l3q4ex+5V/c+cq/zfVZmlvq27utSqu8vfl/tOrVzvv962dca/+D4UvcLqxmmdb+sK7Dt5cdf1tIFS0vdt+81ffXKO68U2Daw80AdSz1W6r7jXxlfYFn5fXv26bYrCy9JX5Qvvv1C9WPrO9/PmzVPb7z0Rqn7Nb24qRZ8vaDAtn/e/k9tTdxa6r633XObHnnmkQLbOjfq7FK+b3z0RoFu3Zb1W/TwMNdWgtx2cFuB96+/8Lr+7/3/K3W/rj276s1P3iyw7dZ+t2r///aXuu/DTz2sofcNdb4/knJE13W5zqV8/2/1/6lZ82bO90vmL9GEsRNK3a9uTF19ue3LAtvGPjBWa5evLXXfQbcO0pOTnyywrU+rPsrOyi513wlv/5Vb1eCqamZtpntuuKfU/SRp7c9rVbNWTef7915/T++9/l6p+/nrz4i4iDjFRcRJ8t3PiLZ/b+t8z8+IovEzwvs/I/pd28/5fuf2nYV+RrizkA+FGAC4qWreIliG3a4TKUdLjI1rGFdo25/H/1RqUmqpx8nMyCzw3jAMl/aTpFxrboH3Z06fcWnfc/8hmS89Ld2lfU/+ebLQtmOpx1za9/SpgtPAzp496/K52m0F/0/vVPYpl/atFVGr0DaXv5v0zELbXM3XmmMt9N7VfYvKw5V9/zz+Z6Ftx4649t2cyj5V4L3dZnc537NnzxZ4f/rU6TKf68k/T7q0b3paeqFtR5KPKCuz9GmEZ06f0b2P3KvMjEzVCq+lXGuuy/meP8EoM8O174afEfyMOB8/Iyr2z4hzufMzoigUYgBQRkFBQYqsEyMjyCZLkOHsjJ2rZnRNHTeOF9pWL75e6ePXCiqwb7ayXdpPkrKrZhfY11rN6tK+NWrWKJRv1dpVXdq3WlS1QvtGxkTKrtJ/O3i2xtkC+2YGZ7p8rieDTqqqUdX53h5md2nfiHoRhfINiw5zad/g8OBC+7qa7+mQ0wX2PR1y2uV9zz9mcHiwS/uGRYcV2jeiXoTqpZe+rz3MXmDfk0EnXc43MzizwL5na5x1ad/ImMhC+VaLqubSvlVrVy20b3RctGpklf6Abms1q24YfYPz/S/bf3H5XP/Un8oxcpzvg2oFubQvPyP4GXE+fkZU7J8RBf7OVS38d85ut+t4yvHzdy0S94iVE/eIAZWMNUcNWrVRUnKy6tWP06oNv+rAyWDlRiSrfu3g0vcHEBB+3LxVuTlWVQ0NUcduXc1OB0AFkZ2ZqWsubs89YgDgTflTFJvUr6IDR+L0Z+HZDgAC1IsP/EsnjiWrdp04PTV1b7Fx4bWlujH8zhuoLE5lV3c5lkIMADygSX1+nALu2pq8RpO2jdETnV9T17grzE7HLcHBjnXwT56w6F8jiv+HV0iooaVrchUb76vMAJgpK9P1fw/wHDEAAOBzhmHorR+e1oH0X/XWD0/73cNt77j7n/rbbU9JGlNinDXHopNpxT28DEBlRiEGAAB8bkvyav18Yrsk6ecT27UlebXJGbnnjntG66a/P6PSCjEAKA6FGAAA8CnDMDTjx+cUZHHcaBlkCdaMH5/zu64YAJQHhRgAAPCp/G6Y3bBJkuyGzS+7YgBQHhRiAADAZ87vhuWjKwagsqEQAwAAPnN+Nyyfv3XFbr66k+4bGiepndmpAPBTFGIAAMAn8rthlmL++WFRkN90xdLT05SZcVzSn2anAsBP8eAbAADgE7l2q1KyD8mQvcjPDdmVmn1YuXarQoJDfZyde+IaNFZwcKhSk2NVUt0YEmqodmTFLywB+B6FGAAA8ImQ4FDNv3az0s4cLzYmqlrdCl+ESdLcheskSSlJ0sm03GLjakcaPMwZQJEoxAAAgM/EhDVUTFhDs9PwmNh4KTaejhcA93GPGAAAAAD4GIUYAAAAAPgYUxMBAADc9M4bLykrM101a0XogYefMjsdAH6IQgwAAPiMY3ELS7Gfl2VxC2+MWZrPF8zW0dQk1YuJd6kQMyNHABUbhRgAAPCJlCRp0BVVZc0pviAJCTW0dE2uy0WJN8b0NH/IsaLZmrxGk7aN0ROdX1PXuCvMTgfwCgoxAADgEyfTLCUWI5JkzbHoZJrF5ZUIvTGmK6bNWqTcXKuqVg0pNdasHP2VYRh664endSD9V731w9PqEttXFkvJ1w/wR2VarOOll16SxWJR165di/x87Nixslgs6tChg9LS0sqVIAAAQEXTqu0lan9JV7Vqe4nZqQScLcmr9fOJ7ZKkn09s15bk1SZnBHhHmQqxRx99VA0aNNC3336rf//73wU+mzhxoiZNmqTmzZtr1apVioyM9EiiAAAACGyGYWjGj88pyBIsSQqyBGvGj8/JMOgUIvCUqRCrXr26Xn75ZUnSk08+qbNnz0qSZs6cqfHjx6tJkyZas2aN6tWr57lMAQAAENDyu2F2wyZJshs2umIIWGV+jtgdd9yhSy65RHv27NEHH3ygefPmaeTIkYqPj9eaNWsUH//X3aaLFi3SDTfcoEaNGqlGjRpq3bq1pk6dqtzcXI+cBAAAgC/99utu/Xf3j/rt191mpxIwzu+G5aMrhkBV5sU6LBaLpk6dqj59+mjcuHHKzMxUdHS0vv76azVp0qRA7KuvvqoLLrhAkydPVv369bV582Y99dRT2rlzpz788MNynwQAAIAvjbzzeufy9au27Dc7nYBw7r1h5zq3K9Yt/ioTMgO8o1yrJvbu3VvdunXT5s2bVatWLa1atUotWrQoFPfFF1+obt26zvd9+vSRYRh6+umnncUZAAAAKqf8bphFQTJkL/S5RUGa8eNzSoi7khUUETDKVYjNmTNHW7ZskSTl5OQoPDy8yLhzi7B8nTp1kiQlJydTiAEAUAnUjjQUEmqU+jyt2pGuT0HzxpiuuO7G25WZcVK1wmuXGmtWjv4k125VSvahIoswSTJkV2r2YeXarQoJDvVxdoB3lLkQW7hwoe69915FRUWpX79++vTTT/XEE09o4cKFLu2fmJiokJAQNWvWrKwpAAAAPxIbLy1dk6uTacUXJLUjDbceauyNMV3x0GMvuBxrVo7+JCQ4VPOv3ay0M8eLjYmqVpciDAGlTIXY8uXLNXToUIWFhWnlypVq1qyZVq9erUWLFmnz5s3q1q1bifv/8ssveuONN3TfffcV20UDAACBJzZeHn9osTfG9DR/yNFsMWENFRPW0Ow0AJ9xe9XEDRs26KabblKVKlX0xRdfqFOnTqpdu7bGjx8vSRozZkyJ+x8/flyDBw/WhRdeqFdeeaVsWQMAAACAH3OrENu2bZuuu+462Ww2ff755+rZs6fzs1GjRqlx48b69ttvtWDBgiL3z8zM1IABA2S1WrVixQqFhYWVL3sAAAAA8EMuF2K7du3SgAEDdOrUKc2bN09XX311gc9DQ0P14osvSpLGjRunnJycAp/n5ORo0KBB+v3337Vy5UrFxcV5IH0AAADfu/fv/XXjVR1079/7m50KAD/l8j1ibdu21YkTJ0qMueOOO3THHXcU2m6z2XTrrbfqu+++09q1a9W8eXP3MwUAAKgg/jjwm46mJikrM8PsVAD4qXItX++qkSNHasmSJXrxxRdls9m0detW52etWrViwQ4AAHwoJUmmreA3+23pv7uLP3bLNobuetDx367m6Y3z+Wm7lHSo+DGDg8IUVrOWatTgNgsAZeOTQmzFihWSpKefflpPP/10gc/WrVun3r17e+Q4iYmJmjJlirZv366UlBQtXrxYgwcPLjZ+/fr16tOnT6HtKSkpiomJ8UhOAABUJClJ0qArqpb6TKula3I9XozNflt6c0pVScUfe/VXhqRcDRjkWp7vzcvVvUM9ez4/bZeG31RyntKv+nBRrtp3+mvL1uQ1mrRtjJ7o/Jq6xl1R7J6uxgEIbG6vmlgWv//+uwzDKPLlqSJMkrKzs9W+fXvNmDHDrf327NmjlJQU56tevXoeywkAgIrkZJqlxKJFkqw5lhI7TGXl6ISVNq5F/91tcTnPpEOePx9HJ6z0PM/tmBmGobd+eFoH0n/VWz88LcMoeql6V+MABD6fdMR8ZcCAARowYIDb+9WrV0+1a9f2fEIAAKBS2JK8Wj+f2C5J+vnEdm1JXq1u8VeVOQ5A4PNJR6yi69Chg2JjY3XllVdq06ZNJcbm5OQoIyOjwAsAAFRehmFoxo/PKcgSLEkKsgRrxo/PFep2uRoHoHKo1IVYbGys3nnnHf373//Wv//9bzVs2FC9e/fWDz/8UOw+EydOVEREhPPVsCFPgAcAoPKZrvVfT9KCD992drnshk2SZDdszm7XuVyNA1A5BNTURHc1b968wFL63bp10759+/T666/r448/LnKfcePGacyYMc73GRkZFGMAAFQ6r2jVl0nasT1edevEKMgS7CywpL+6XQlxV8pisRTohpUUB6DyqNQdsaJ07txZe/fuLfbz0NBQhYeHF3gBAIDKyWo7U6DLle/8btf53bDi4gBUHhRi59mxY4diY2PNTgMAAFRos3X7XYsUcXOULMX8c8qiIM348TnZ7XbN+PG5UuO4VwyoXAJqamJWVlaBbtaBAwe0Y8cORUVFqVGjRho3bpySkpL00UcfSZKmTZumJk2aqHXr1jpz5ozef/99rV27VqtWrTLrFAAA8KrakYZCQo1Sn7tVO9LzRUHLNkbec8JKmoJnqGUbQ7Uj5VKe8Q09fz7xDQ1JpeV5pXpfnaXl+x+UccZeZIQhu1KzD+vU2SylZB+SoZLjcu1WhQSHupwnAP8WUIXY999/X+ABzfn3cg0fPlxz585VSkqKDh486PzcarXq0UcfVVJSkmrUqKF27drp66+/LvIhzwAABILYeGnpmtwSn6tVO9Lw+MOcJemuByUpN+95YkVr2cbIi3M9T0+fT/tO0oeLcgs8J+x88Q0Nte8UqvmtNivtzPFi46Kq1VXNkHDNv7b0OIowoHKxGPTByyUjI0MRERFKT03hfjGgMrDmqEGrNkpKTlZ8XJy+XPurFMo/ngAAgJSVmaEe7eoqPT291NogoDpiAAAAvnDsaIrsNpuCgoNVtx73lgNwH4UYAACAm4YO6q6jqUmqFxOvVVv2m50OAD/EqokAAAAA4GN0xAAAMEFKkkxZMMNbftouFxa3cG9MV6+Rq8f25DW/vM/VSj+Zpojaka7tAADnoRADAMDHUpKkQVdULXXJ9aVrcv2iGPtpuzT8pqoqbVn6DxflulyMuXqNXpmWqzH/KP3Yr83M1djRnrvmT094u/SgSmJr8hpN2jZGT3R+TV3jrjA7HcBvMDURAAAfO5lmKbEgkCRrjqXE7k1F4uhGlZarpcSu1flcvUb7fnPt2Pt+C6xrXlEYhqG3fnhaB9J/1Vs/PM1DqQE3UIgBAACgTLYkr9bPJ7ZLkn4+sV1bklebnBHgPyjEAAAA4DbDMDTjx+cUZAmWJAVZgjXjx+foigEuohADAABw078evE1333ql/vXgbWanYpr8bpjdsEmS7IaNrhjgBhbrAAAAcNPOH791PkesMjq3G5ZfiEl/dcUS4q6UxcL9dkBJ6IgBAADALed3w/LRFQNcR0cMAADATcvW/9fsFEyT3w2zKEiG7IU+tyiIrhjgAjpiAAD4WO1IQyGhJS9oEBJqqHakfyx6EN/QkFRarkZenGtcvUbNLnLt2M0u8uw1DwkNdb4qm1y7VSnZh4oswiTJkF2p2YeVa7f6ODPAv9ARAwDAx2LjpaVrckt8ZlXtSMMvHuYsSe07SR8uyi3xOWHxDQ2XH+YsuXeNXD12IF1zM4UEh2r+tZuVduZ4sTFR1eoqJLjyFamAOyjEAAAwQWy8FBvvHx0vV7TvJLXv5NnzcfUauXrsQLvmZooJa6iYsIZmpwH4NQoxAAAANy1fukBnTp9Steo1dM2gW81OB4AfohADAABw07RXxjuXr6cQA1AWLNYBAAAAAD5GRwwAAMBNo8dOcE5NBICyoBADAFRaKUnyi1X01q6Q9v1WfJ7NLjLU92rpp+1yafVAV+Mk12NdvZbuXPOK/P0wHRFAeVGIAQAqpZQkadAVVWXNKf4f+iGhhpauyTW1GFu7Qhrzj6qSSnowrqF/PparN6eUHvf0y7l68cnS4z5clCtJGn5T6bGvzczV2NGlX8v35uXq3qGuXXPJP74fACgr7hEDAFRKJ9MsJf4jX5KsOZYSOzK+4OiElZaDRf/d7Vrcnl9ci0s6ZMnrhJUeu+83165l0iHXr7m/fD8AUFZ0xAAAANxkzclx/ndIKA8uBuA+OmIAAABuurZ3S3VuEa5re7c0OxUAfopCDAAAAAB8jKmJAAAAbmrXsYvS/jyuyKhos1MB4KcoxAAAANz06tv/Z3YKAPwcUxMBAAAAwMcoxAAAlVLtSEMhoUaJMSGhhmpHlhzjbc0uMiSVloOhlm1ci2veyrW4+IaOlyuxzS5y7VrGN3T9mvvL9wMAZcXURABApRQbLy1dk1vic6hqRxqmPyy479XSazNz854nVrRmFxnqe7XUqUtu3rO/ihbf0FD7TtKFzV2Lk6QPF7kW6+q1dOea+8P3AwBlZTEMg18llUNGRoYiIiKUnpqi8PBws9MB4G3WHDVo1UZJycmKj4vTl2t/lXiGEFDpvDj+QaWfTFNE7Ug9PeFts9MBUEFkZWaoR7u6Sk9PL7U2oCMGAADgpo3rVuhoapLqxdCSA1A23CMGAAAAAD5GRwwAAMBN85Zukt1mU1BwsNmpAPBTFGIAAJggJUkuL0ThTqynj23mmBX52HXrxXp2QACVDoUYAAA+lpIkDbqiqqw5xRcPIaGGlq7JleR6rCvFhjvHdrV48caYrjLz2ABQHtwjBgCAj51Ms5RYOEiSNceik2kWt2I9fWxXeWNMfzg2AJQHHTEAAAA3bU5cLWvOGYWEVlO3nleanQ4AP0QhBgAA4KbnnrjfuXz9qi37zU4HgB9iaiIAAAAA+BgdMQAAADfd9cC/lJ2dpbCwmmanAsBPUYgBAAC46dbhD5qdAgA/x9REAAAAAPAxCjEAAHysdqShkFCjxJiQUEO1Iw23Yj19bFd5Y0x/ODYAlAdTEwEA8LHYeGnpmtwSn21VO9JwPoDYnVhPH9usMf3h2ABQHhRiAACYIDZeio13rUvjTqwZ43lrzIp87MFXtNWxoymqWy9WS9bs8umxAQQGpiYCAAC46dSpbGVnZerUqWyzUwHgp+iIAQAAuKlxk4tUs1a46kTXNzsVAH6KQgwAAMBN781faXYKAPwchRgAQClJYrEDD3H1WnLNAaByoxADgEouJUkadEVVWXOKLwpCQg0tXZNLYVAKV6/le/Nyde9QrjkAVGYs1gEAldzJNEuJBYEkWXMsJXZv4ODqtUw6xDUHgMqOjhgAAICb3pryjDIzTqpWeG099NgLZqcDwA9RiAEAALjpi88/0dHUJNWLiacQA1AmTE0ETGKpEVbgVTU8QtENG6ntZZdpxH336d9Llujs2bNmp1mqC1q0lKVGmGnHP336tJ554UVd3K69qkVGKa5pM911/wNKSko2LScAAIDS0BEDTDb89qGSJLvdrvT0DP1v7159NG++Pvxkni5s1kzzZs9W58suNS0/S40wNW7USL//+l/TcijOmTNn1HfANdq6bZtiY2I06Npr9fsff2jOxx9r2YqvtHX9ejVt0sTsNAEEoBlz/qOzZ3NVpUpVs1MB4KcoxACTzZ01q9C2ffv3a/yzz+mzf/9bfQYM0KY1X6tD+/YmZFexvfTKJG3dtk0JXbpo1Rf/Uc2aNSVJr735ph4dO053PfAPrV+5wuQsAQSii1q0MTsFAH6OqYlABdSsaVN9+vFHunv4cJ06dUp3PfAPs1OqcKxWq6a/+64kacbrrzmLMEka889/ql3bNtqwcaO2//CjWSkCAAAUi0IMqMCmvjJRYWFh+vGnn/TN5s2FPj90+LBGPTJGzVq3UbXIKEXFN9C1N/5Nm7duLRS7PjFRlhphGnHffUpJSdGI++5T/QsuUPWoOrokoZs+mjevQPzcjz923vv1x8GDBe5n693/6iLzfX/OXLXr3FnVo+oo5oImun/UQzp58mT5L0QRNm3ZovT0dDVr2lQdO3Qo9PlNg2+QJH2xfLlXjh9IakcaCgk1SowJCTVUO7LkGLh+LeMbcs0BoLJjaiJQgUVERGjAVVdp0eLFWrchUT26dXN+tuXbbzXwxr8pLS1NzS++WAOvvlrHjh/Xyq+/1orVqzVvzmwNuemmQmP++Weauvbuo5ycHPW+vKfSTqZp3YZEDb/3Pu0/8Luee+pJSdKFzZpp+O1D9eEn8xQWFqabbhjsHKPFxRcXGvfxJ5/UGzPeVu+el+vCps20aesWzZo9W//ds0cbVq2UxeLZ5yH9tGuXJOmSIoqwc7fv3L3bo8cNRLHx0tI1uSU+s6p2pMGDhV3gzrXkmvu3X3b9oNxcq6pWDVGrtpeYnQ4AP0QhBlRwHdq106LFi/XfPb86t2VkZOhvt/1dGRkZ+mT2Bxp6663Oz77f/oOuuv563fPgSPXt1Ut169YtMN4Xy5fryiv6avGCBQoLc3S8vvt+u/pec41efOUVXT/wGl3SsaN6dOumHt266cNP5im6Tp0i72U718fz/087t32r5nlF2vHjx5XQp682btqkdRs2qG/v3s7YuR9/rDvvf8Ct69Dr8ssL3O918NAhSVKD+Lgi4/O3/3HwoFvHqaxi46XYeLovnuDqteSa+7fR993kXL5+1Zb9ZqcDwA9RiAEVXHSdOpKktLSTzm2zP/pIKampevThfxYowiTp0k6X6OmxT2jME2P1yYIFeuShhwp8HhQUpLemTnUWYZJ02aWdNPL++zRp6mt6e9Z7en/m227n+eIzzziLMEmKjo7WA/fcrX+NG6/EbzYVKMTyu23uOL8Ll5WVLUmqUb1GkfH555eZleXWcQAAAHyBQgyo4AzD8Rvzc6f2rfp6jSTpxkGDitzn8m7dJUnbvv++0Gcd2rUrUDDlu+3mmzVp6mvaWMS9aK64qt8VhbZdfOFFkqSU1NQC2/O7bQDgr2689S5lZaarZq0Is1MB4KcoxIAK7viJE5KkqKhI57bf86bbde9buPgpat9zNW7UqMjYCxo3liQlp6SUKc8G8YVvZqlVy7GSYY41p0xjlqRmTUfH69TpU0V+np3t6JjVOmc1RQDwlAcefsrsFAD4OQoxoIL78aefJEmtWrR0brPb7ZKkm264QWFhRU/Nk4peVMNbgoJcX4T1m82b9f7cuW6N3+LiizX2X/9yvm/UsKEk6XBScpHx+duLKzwBAADMRCEGVGDp6ela+fXXkqQ+vXo6tzeIj9Oe//1PYx99VJ0u6ejWmMUtXpG/PS42tozZum7vvn368JN5pQeeo9fllxcoxNq3bStJ+mHHjiLj87e3a8NDVyu6n7ZLSYeKXz0wvqGh9p3MHzMlSaatcujqsc3MEQDgnoAqxBITEzVlyhRt375dKSkpWrx4sQYPHlziPuvXr9eYMWP0888/q2HDhnrqqac0YsQIn+QLlObRseOUnZ2tyzp1UkKXLs7tV/btqzXr1mvxf/7jdiG2Y+dO/bZ3ry668MIC2xcsXCRJ6pGQUGB71apVdfbs2TKeQdFG3HGHRtxxR7nG6J6QoIiICO3bv187fvpJHdq3L/D5oiWLJUnXXXNNuY4D7/ppuzT8pqqSSnq8gaEPF+W6XDh5Y8yUJGnQFVVlzSl+zJBQQ0vX5Hq80HH12O/Ny9W9Q83JEQDgvoB6oHN2drbat2+vGTNmuBR/4MABDRw4UH369NGOHTs0evRo3XPPPVq5cqWXMwVKtv/AAQ25Y5g++PBDhYWF6YPzVjG8/+67Va9eXU1+/XXN+mC2c6pivrNnz2rl6tXa/fPPhca22+16aMyjOnXqr3urtv/wo6a/+64sFov+ce+9BeLjYmN15OhRrz2YuaxCQkI06v77JUkjHxnjvCdMkl57803t3LVbvS6/3O1CFb7l6FqV9ow5S4ndLV+MeTLNUmKBI0nWHEuJ3aiycvXYSYfMy7EyGnFzH13Xu6VG3NzH7FQA+KmA6ogNGDBAAwYMcDn+nXfeUZMmTTR16lRJUsuWLfXNN9/o9ddfV//+/b2VJlDAiPvuk+QokDIyMvW/vXv16549MgxDF114oebPmaO2502vq127tpZ+9pmuu+lm3f/QQ3pp0iS1adVKkZG1lXrkiH7Y8ZNOnjypxQsWqE3r1gX2vXbAAP20a5eatW6jnj16KD09XWs3bFBubq6eeuIJXdqp4INJrx84UG/NnKlLunVXt65dVC20mppffJEee+QR714YFzw19gl9vW6dNm/dqovattPl3bvrj4MH9e1336lu3WjNfmem2SkCCFDJh//Q0dQk5eR4fjEiAJVDQBVi7tqyZYv69etXYFv//v01evToYvfJyckp8EM3IyPDW+mhksi/V6pKlSoKDw9XXGyMhg39uwYNvFbXXztQwcHBRe7XtXNn7dr2rV6fPl1frlihDd98I0mKjYlRrx49dMP116lf38K/qa1TJ0pb16/XE08/pZVff62MjEy1atFCo0eNLHK64MQXnpdhGFq6bJk+XfRvnT17Vr0uv7xCFGLVqlXTuhVfaeKUVzX/s8+05IsvFBUZqRG3364Xn3lGDRow/wqAd0RERCrXmqOIiMjSgwGgCJW6EEtNTVX9+vULbKtfv74yMjJ0+vRpVa9evdA+EydO1PPPP++rFBHAjFPZpQeVIiYmRpNeekmTXnrJrf3i4mL18QcfuBQbFhamt16bqrdem1rk57//+t9i9+3ds6dHzrMk1atX1wvPPK0Xnnnaq8cBgHMtXLHd7BQA+LmAukfMF8aNG6f09HTn69ChQ2anBAAAAMDPVOqOWExMjI4cOVJg25EjRxQeHl5kN0ySQkNDFRoa6ov0AAAAAASoSt0RS0hI0Jo1awpsW716tRLOW74bAAAAADwpoDpiWVlZ2rt3r/P9gQMHtGPHDkVFRalRo0YaN26ckpKS9NFHH0mSHnjgAU2fPl2PP/647rrrLq1du1afffaZvvzyS7NOAfAaX9yvBbgrvqEhyVBpz/xyxJk3Zu1IQyGhRqnP6Kod6fqYnj52fEPzcqyMPn5/mrKyMlWzZi3dcc9os9MB4IcshmEEzE/k9evXq0+fwqvEDR8+XHPnztWIESP0+++/a/369QX2eeSRR/TLL7+oQYMGevrpp916oHNGRoYiIiKUnpqi8PBwD5wFgArNmqMGrdooKTlZ8XFx+nLtrxLTlcvlp+0q8Zle8Q0Nlx+87M0xU5JU4jO4akcaXntQsqvHNjPHyuaqhKY6mpqkejHxWrVlv9npAKggsjIz1KNdXaWnp5daGwRUIWYGCjGgkqEQAyAKMQBFc6cQC6ipiQAAAL7w8utzlJuTo6r8IgZAGVGIAQAAuOmyrr3MTgGAn6vUqyYCAAAAgBkoxAAAAADAx5iaCAAA4KakQwdks9kUHBys+IZNzE4HgB+iEAMAAHDTnbdcwaqJAMqFqYkAAAAA4GN0xAAAANzU96rrlZF+UuERtc1OBYCfohADAABw09jnp5mdAgA/x9REAAAAAPAxCjEAAAAA8DEKMQAAAADwMe4RAwAAcNPD99yotD+PKzIqWm+8/7nZ6QDwQxRiAAAAbvrvzzuczxEDgLJgaiIAAAAA+BgdMQAAADet2rLf7BQA+Dk6YgAAAADgYxRiAAAAAOBjFGIAAAAA4GPcIwYAAOCmpYs+0ulT2apeI0yDbhpmdjoA/BCFGAAAgJtmTH3OuXw9hRiAsmBqIgAAAAD4GB0xAAAANz3x7Gs6c/qUqlWvYXYqAPwUhRgAAICbrrh6sNkpAPBzTE0EAAAAAB+jEAMAAAAAH2NqIgAAgJuyszJlGIYsFovCatYyOx0AfohCDAAAwE03XNneuXz9qi37zU4HgB9iaiIAAAAA+BgdMQAAADd16nK5Tv55XLWjos1OBYCfohADAABw08RpH5qdAgA/x9REAAAAAPAxCjEAAAAA8DEKMQAAAADwMe4RAwAAcNMzj92r9LQTioisoxemvGd2OgD8EIUYAACAm7Z+s8b5HDEAKAumJgIAAACAj9ERAwAAcNNny7+T3W5XUBC/0wZQNhRiAAAAbqodWcfsFAD4OX6NAwAAAAA+RiEGAAAAAD7G1EQAAAA3Ja75UmfOnFG1atXU84qBZqcDwA9RiAEAALjppaceci5fv4pCDEAZMDURAAAAAHyMjhgAAICb7ntovE6fylL1GjXNTgWAn6IQAwAAcNNNf7/H7BQA+DmmJgIAAACAj1GIAQAAAICPUYgBAAAAgI9xjxgQaGw2adMmKTVViomRuneXgoPNzgoAAsrAns119Eiy6tWP05eJe8xOB4AfohADAsmSpdJjj0lJSX9ti4+XpkyRBg8yLy8ACDC5ubnKtVqVm5trdioA/BSFGBAoliyVhg6VDKPg9uRkx/Z58yjGAMBDLry4laLq1FVUnbpmpwLAT1GIAYHAZnN0ws4vwiTHNotFevxx6bprmaYIAB7w9ofLzE4BgJ9jsQ4gEGzaVHA64vkMQzp82BEHAAAA01GIAYEgNdWzcQAAAPAqCjEgEMTEeDYOAAAAXsU9YkAg6N7dsTpicnLR94lZLI7Pu3f3fW4AEIBemzBWmeknVSuitsaMf8XsdAD4ITpiQCAIDnYsUS85iq5z5b+fPJmFOgDAQ1Z88ZkWfzZHK774zOxUAPgpCjEgUAwe5FiiPi6u4Pb4eJauByqZoYO6a8FHMyVJT//rHnVoEqoOTUJ1SbPqurx9ff39+gRNe2W8UpMPeTWPpYs+0vKlCwptv/vWK/XQ3YM9frzdP32vZx+/T9f1bqmuLWvruj6t9Obkp3T6VHaBuPtvH6D3ptPFAmAupiYCgWTwIMcS9Zs2ORbmiIlxTEekEwZUGmtXLlVy0h8afMsI57YGjZpowrQPZRiGsjLT9cvOH7Rw/ntaNP89vfr2AnXtcYVXcvnPoo9VIyxM1wy61Svjn2/VsoU6eGCvht/3qBo3vUj7/veLZr7+gnbt+E7vzV/pjLtn5BMa88AQDbnjfoVHRJbpWLM++UpnbTZV4ecrgDKiEAMCTXCw1LOn2VkAMMm82W/p6utuUbVq1Z3bQqtVV7uOXZzvu/fqr1vuuF93DblCTzx0u75M3KOatcLNSNejRjzwrwIPWL6say+FR0Rq/Ojh+mXXD2rV9hLH9oTeCo+orf/8+2Pdftc/y3SsC5o190jOACovpiYCABAgkg4d0A/ffaMrB9xYamxE7SiNHjtB6Sf/LHCfk2EY+nDWa7q+T2td1ryWBvZsro8/eKPAvjOnvaiE1lHa/dP3Gjqouzo3D9cN/dopcc2Xzpi7b71S279N1Ma1XzmnRs6c9mKBcVYv/1yD+rZRQuso3fv3/jr0x75ynf+5RVi+Fq3bS5KOHUkusP3Ka/6mL/79SbmOBwDlQSEGAECA+HbTOgVXqaI27S9zKb5ztz6qUqWKdv74rXPb5OfHaObrL+i6v92htz5YoutvGqY3Jj2phfNmFdj37NlcPfHQUF33t9s19Z1P1bBxM4154Bb99utuSdL4F99Ui9Yd1OHSbvro80R99Hmibhxyp3P/Pb/s1IezXtPDT7ysF6a8r0N/7NOTj9xZ4Bg2m01nz54t8WW320s8xx+/2yypcAer/SVdteeXn/TniWMuXSsA8DSmJgIAECB+3vm9Gje5SCGhoS7Fh4ZWU+3IaJ045njY+6E/9mnBRzP15EvTddPf75Ekde1xhc6cPqV333hZf7vtHgUFOX6Hm2u16t5R45z3onXreZWu79NKH7w9Sa+8+bGaXdRSYTXDVSMsrMC0yHyZGSe1YNm3zi7WqVNZevaxe3Uk5bDqxzaQJF3bq6VSkv4o8Ryu+9sdevHV94v8LO3P43rnjZfU+8rr1LjJRQU+u7hVO0nS7p++U8++17hyuQr46YetslpzFBISqvaXdHV7fwCgEAMAIEAcP5qqyKjC0/NKYhiG8zEX325aK0nqN+AGnT171hnTpXtfzXnnVaUmH1Jcg8bO7X37/7Uaa3BwsPpcdb3WrfqPS8dt3qp9gamETS9sKUk6kprkLMTefP9zWa05JY5TO6pOkdtzc3M19p+3S5KefOmtQp9HRkZLclyzsnhs5N91NDVJ9WLitWrL/jKNAaByoxADKiubjdUVgQCTk3NGISEhbsWnnzyh6LoxkqS0P0/IMAz1viSuyPgjKYedhViVqlULrThYJ7q+y4VNrfCIAu+r5uWdk3PGua3pRS0dhWIJ8jt05zIMQ889fp92//S9Zn+6VnXrxRaKqRri6BrmnDntUr4A4GkBWYjNmDFDU6ZMUWpqqtq3b6+33npLnTt3LjJ27ty5uvPOgnPSQ0NDdebMmSLjgYCwZKn02GNSUtJf2+LjHQ+F5nljgN+KqB2l5MMlT+U717eb1urs2bPOqXURtSNlsVg0Z+E6Va1auKC7oOnFzv8+m5urjPS0AsXYieNHFF0vphxnUFBZpya+9vITWrV8kabPXqrmeVMQz5eZcVKSFBFZdEetNEPueEDZWRkKq+n/q00CMEfAFWKffvqpxowZo3feeUddunTRtGnT1L9/f+3Zs0f16tUrcp/w8HDt2bPH+d6SN0UDCEhLlkpDh0rn/5Y5OdmxnYc/A36rcdOL9d2WDS7FZqSn6Y1JTyoyKlpXX3eLJKlLtz6SpPS0E+rV79pSx1i7cqnzHjGbzaZ1q/6jth3++sVn1apVZc0p+y82yzI1cfbMKfpk9puaMO1Ddenet9j98gvWc4tLd9z94ONl2g8A8gVcIfbaa6/p3nvvdXa53nnnHX355ZeaPXu2xo4dW+Q+FotFMTGe+w0eUGHZbI5OWFFTffLvE3n8ccdDoZmmCPidDp0SNOvNlwsseCE5pt/lr4yYlZGun3f9oEXz31N2VoZef3ehaoTVlOQo5Ibc8YCeHHOXht83Rm07XKazZ3P1x4Hf9N2WDZo2a5FzzKohIXpv+kTl5JxRfMML9NnH7+pIymG9/u5fMU0ubKEv/v2JNny9TNH1YlW3fqzq1S962mNRLmrRxq3zX750gd6c/JQGDv674hteUGA1yAaNmha4J+2XXdtVI6ymmrdq79YxAMBTAqoQs1qt2r59u8aNG+fcFhQUpH79+mnLli3F7peVlaXGjRvLbrfrkksu0YQJE9S6desiY3NycpST89dv5zIyMjx3AoC3bdpUcDri+QxDOnzYEcdDoQG/c1nXXqodWUffrF+pv912t3P74YMHNOzGnrJYLAqrGa4GjZpowPVDdOuwfygmrmGBMZ547nVd0PRiLfq/9zXrrZdVo0ZNNW56sa68puCzyapUqapX3vhYE599WL/t2a34Bhfo1bcX6OKWbZ0xI+5/VIf+2KenHr1bmRkndf/DT+kfo5/22vlv2fi1JOnLJfP15ZL5BT57fsp7GnTTMOf7TetXqu9VgxTML50AmMRilHYXrB9JTk5WfHy8Nm/erISEBOf2xx9/XBs2bNC3335baJ8tW7bot99+U7t27ZSenq5XX31ViYmJ+vnnn9WgQYNC8c8995yef/75QtvTU1MUHs48cVRwn30mjbiz9Li5c6RbbvF+Pv7ImqMGrdooKTlZ8XFx+nLtr5KLS4UDvjD1pcf16y8/6b35K712jJnTXtRH772uLT//6bVjeFNGepqu6NxI73y0XJ26XG52OgACSFZmhnq0q6v09PRSa4NK/0DnhIQEDRs2TB06dFCvXr30+eefq27dunr33XeLjB83bpzS09Odr0OHDvk4Y6AcXJ2Cy1RdwG8Nu+8R7dqxTXt+2Wl2KhXW/334tjp06lauIuzv1yfoqoSm+vv1CaUHA0ARAmpqYnR0tIKDg3XkyJEC248cOeLyPWBVq1ZVx44dtXfv3iI/Dw0NVSi//Ya/6t7dsTpicnLR94lZLI7Pu3f3fW4APKJuvVi9MOV9pf15zOxUKqyIiEg98exr5Rrj+LEjOppawlRvAChFQHXEQkJC1KlTJ61Zs8a5zW63a82aNQWmKpbEZrNp165dio0t/MwRwO8FBzuWqJecD3B1yn8/eTILdQB+7qqBf1PXHld4bfx/jH7ab6clStKtwx/Uhc2LvhfcVdF166teTLyi69b3UFYAKpuA6ohJ0pgxYzR8+HBdeuml6ty5s6ZNm6bs7GznKorDhg1TfHy8Jk6cKEl64YUX1LVrV1144YU6efKkpkyZoj/++EP33HOPmacBeM/gQY4l6ot6jtjkySxdDwAumP+f4hcBAwBXBFwhNmTIEB07dkzPPPOMUlNT1aFDB61YsUL16zt+Y3Xw4EEFBf3VCExLS9O9996r1NRURUZGqlOnTtq8ebNatWpl1ikA3jd4kGOJ+k2bpNRUxz1h3bvTCQMAAPCRgFo10QwZGRmKiIhg1USgsmDVRAAAUAxWTQQAAACACizgpiYCAAB42wdvT1Z2VobCaobr7gcfNzsdAH6IQgwAAMBNn378jo6mJqleTDyFGIAyoRADvMFm8+xCGKdPS+PGS/v2Sc2aSRMnSNWrl+/Yns7RW2MCAAAEIAoxwNOWLC16afgpU8q2NPwtQ6Rly/56v2aNNGuWdO210meflu3Yns7RW2MCQAU1ZcZ8Wa05CglhsR4AZcNiHYAnLVkqDR1asBiRpORkx/YlS90b7/wi7FzLljk+d/fYns7RW2MCQAXW/pKuuqxrL7W/pKvZqQDwUyxfX04sXw8nm01q0bJwMZLPYnF0iP77i2vT9U6flupElx534rgUEuLasXfvklq38VyOkufPu6Jj+XoAAFAMlq8HzLBpU/HFiCQZhnT4sCPOFePGux7n6rHfneXZHCXPnzcAAEAlwD1igKekpno2bt8+1+NcHfPAftfiXB3PnVh3xgSACu73fXt01mZTleBgXdCsudnpAPBDFGKAp8TEeDauWTPHwhyuxLk6ZpOmrsW5Op47se6MCQAV3H23D3AuX79qi4u/5AKAczA1EfCU7t0d90JZLEV/brFIDRo44lwxcYLrca4e+/77PJuj5PnzBgAAqAQoxABPCQ52LNUuFS5K8t9Pnuz6ghXVqzuWqC/Jtdc64lw9dkiIZ3OUPH/eAOAHrr7uFt1wy526+rpbzE4FgJ+iEAM8afAgad48KS6u4Pb4eMd2d5+n9dmnxRdj5z9HzNVjezpHb40JABXYmPGv6NlJ72jM+FfMTgWAn2L5+nJi+XoUyWZzrBKYmuq4N6p79/J1hE6fdqyOuG+f456wiRMcnbDyHNvTOXprzIqG5esBAEAx3Fm+nsU6AG8IDpZ69vTceNWrS9Ne9+yxPZ2jt8YEAAAIQExNBAAAAAAfoyMGAADgpgeHX6s/TxxTVJ26evvDZWanA8APUYgBAAC4ae//fnE+RwwAyoKpiQAAAG6qWrWqqoaEqGrVqmanAsBP0REDvMFqld6dJR3YLzVp6niQckhI4ThXV0NkhUMAqFC+TNxjdgoA/ByFGOBp45+U3nxTstv/2jZunPTPf0oTXv5r2y1DpGXn3FewZo00a1bh54MtWSo99piUlPTXtvh4x0OUy/p8Lm+MCQAAAJcxNRHwpPFPStOmFSzCJMf7adMcn0uFi7BzLVvm+FxyFExDhxYsmCQpOdmxfclS93P0xpgAAABwCw90Lice6Awnq1WKqlO4CDtXcLB0+JAUG1f6eEePSB0vKVww5bNYHF2s//7i+pRCm01q0dKzY1Y2PNAZAAAUw50HOtMRAzzl3VklF2GSoxC64UbXxrvr7uILJkkyDOnwYcd9Xq7atMnzYwJAJbRo/vv6+P1pWjT/fbNTAeCnuEcM8JQD+12LO3zYxfEOuBaXmupanDux7owJAJXQrLcmOJevv+nv95idDgA/REcM8JQmTV2La9DAxfGauBYXE+NanDux7owJAAAAt9ERAzzl/vscqyOWdo/Y4s9du0ds9geOe8SSkx1TBs+Xfz9X9+6u59i9u2MfT44JAJXQUy+9pTNnzqhatWpmpwLAT9ERAzwlJMSxRH1JHnpIiohwLFFfkmuvlWrWdCwnLzkKpHPlv5882b1FNYKDPT8mAFRCPa8YqKsG/k09rxhodioA/BSFGOBJE16WRo+Wgs77qxUc7Nie/xyxzz4tvhg79zligwdJ8+ZJced10OLjHdvL8swvb4wJAAAAt7B8fTmxfD2KZLU6VlE8sN9x79j99zk6Zuc7fVoaN17at09q1kyaOEGqXr1wnM3mWMkwNdVx/1b37uXvWnljzMqA5esBAEAx3Fm+nnvEAG8ICZEeGlV6XPXq0rTXS48LDpZ69ix/Xt4eEwAqiZNpJ2S32xUUFKTakXXMTgeAH6IQAwAAcNMt11zmXL5+1RYXH18CAOfgHjEAAAAA8DE6YgAAAG7q2uMKpaedUATTEgGUEYUYApenF6NwdQEOScrKku66WzpwwPFg5tkfOJajP19qqtQ1QUpPdyxrv3VL0Q9TPnZMuryndOKEVKeOtDFRqlu36GOnp0s33CgdPux4ePTizx1jn48FQACgzF6Y8p7ZKQDwcxRiCExLlkqPPSYlJf21LT7e8QytsizPPv5J6c03Cz6sedw4x3PD8pekz3d5T2n79r/e//yzVK++1KmTo4DKF11XOnXqr/dHj0pNm0k1akjHj/21PTbOUVzly86WGl/gKK5Skgseu01baf859yocPuzYv2lTafeuv7Z7+vp4a0wAAIAAxT1iCDxLlkpDhxYsCCQpOdmxfclS98Yb/6Q0bVrBIkxyvJ82zfF5vvOLsHNt3+74XCpchJ3r1CnH51LhIuxc6emOz/OdX4Sda/9+x+eS56+Pt8YEAAAIYDxHrJx4jlgFY7NJLVoWLgjyWSyOLs1/f3FtypzVKkXVKVyEnSs4WDpx3BFbr37pY+78SWrXvvS4b7dKXbqWHvfH744pkrFxpYbq8CHHmJ66PpLnr3lFx3PEAABAMdx5jhgdMQSWTZuKLwgkyTAc0/U2bXJtvHdnlVyESY5C5N1ZjnvCXNHpUtfiuia4Fnd5T8c9Ya648irPXh/J89ccAPzAuNHD9Y9hAzVu9HCzUwHgp7hHDIElNdWzcQdcfDbMgf2OhTlckZvrWpyrzeoTJ0ovFvMdOeJanKvXx51Yd8YEgApu+7cbnc8RA4CyoCOGwFLUioPliWvS1PW4Jk1ci61a1bU4i8W1uDp1HKsjuqK+C1MnJdevjzux7owJAAAQ4LhHrJy4R6yCyb9fKTm56I4S94g5xvTU9ZE8f80rOu4RAyApOytThmHIYrEorGYts9MBUEFwjxgqr+Bgx3LpUuGOUv77yZNdLwhCQhxL1JfkoYcccTVrOpaoL0mnTtKFFzqWqC9JjRpS27ZFP//rXBERjueJRUQ4lqgvSdOmUlSUZ6+P5PlrDgB+IKxmLdWsFU4RBqDMKMQQeAYPkubNk+LO6xDFxzu2u/tMqwkvS6NHS0Hn/XUJDnZsP/c5YhsTiy/Gzn2O2PFjxRdj5z5HLCW5+GLs/OeI7d5VfDF27nPEPH19vDUmAABAAGNqYjkxNbECs9kcK/WlpjruT+revXxdGavVsTrigf2Oe8Luv8/RCStKVpZjFcUDBxz3js3+wNExO19qqmN1xPR0R2G1dUvR91IdO+ZYHfHECcc9YRsTHZ2woqSnO1ZRPHzYce/Y4s+LLuY8fX28NWZFw9REAABQDHemJlKIlROFGFDJUIgBkLRmxRKdOX1K1arX0BVXDzY7HQAVhDuFGMvXAwAAuGnS82Ocy9dTiAEoC+4RAwAAAAAfoyMGAADgppGPPqfTp7JVvUaY2akA8FMUYoCZC0y4ugCIOwuFAAC8btBNw8xOAYCfoxBD5bZkqfTYY1JS0l/b4uMdz8Xy9pLr45+U3nyz4MOix41zPLfs3CXxXY0DAACA3+AeMVReS5ZKQ4cWLMIkKTnZsX3JUu8de/yT0rRpBYsryfF+2jTH5+7EAQAAwK9QiKFystkcnbCint6Qv+3xxx1xnma1OjpcJXnrLcezyFyJs1o9lxsAAAB8gkIMldOmTYU7YecyDMcDkTdt8vyx351VuMN1PpvN8UBoV+LeneW53AAALrkqoak6NAnVVQlNzU4FgJ+iEEPllJrq2Th3HNjvYtwBz44HAACACoPFOlA5xcR4Ns4dTVz87WmTJtLPP3tuPACAx7Rs3UExsQ0UGRVtdioA/JTFMIq6SQauysjIUEREhNJTUxQeHm52OnCVzSa1aOlYmKOovwIWi2P1xP/+4vml7K1WKapOydMOg4OllGQpJrb0uBPHWcrel6w5atCqjZKSkxUfF6cv1/4qhYaanRUAAKgAsjIz1KNdXaWnp5daGzA1EZVTcLBjiXrJUXSdK//95MneeZ5YSIhj6fmSPPSQVLOma3EUYQAAAH6HQgyV1+BB0rx5Ulxcwe3x8Y7t3nyO2ISXpdGjpaDz/goGBzu25z8fzNU4AAAA+BWmJpYTUxMDgM3mWB0xNdVxT1j37t7phBXFanWsenhgv+Ner/vvK7rD5WocvI+piQAAoBjuTE1ksQ4gOFjq2dOcY4eESA+N8lwcAMAnXnl2tDLSTyo8orbGPj/N7HQA+CGmJgIAALhp7ar/aPnS/9PaVf8xOxUAfopCDAAAAAB8jKmJAAAAbprz2RrZbDYF++qeYgABh0IMAADATfENm5idAgA/x9REAAAAAPCxgCzEZsyYoQsuuEDVqlVTly5dtG3bthLjFy5cqBYtWqhatWpq27atli9f7qNMAQAAAFRGAVeIffrppxozZoyeffZZ/fDDD2rfvr369++vo0ePFhm/efNm3Xbbbbr77rv1448/avDgwRo8eLB2797t48wBAIC/+G7rBm3esErfbd1gdioA/FTAPdC5S5cuuuyyyzR9+nRJkt1uV8OGDfXQQw9p7NixheKHDBmi7OxsLVu2zLmta9eu6tChg955551Sj8cDnYFKhgc6A5B0VUJTHU1NUr2YeK3ast/sdABUEJX2gc5Wq1Xbt2/XuHHjnNuCgoLUr18/bdmypch9tmzZojFjxhTY1r9/fy1ZsqTI+JycHOXk5Djfp6enS5IyMjPLmT0Av2DNkd1ul+T4RU9WVoZkpRADKpsCPwcyM0zOBkBFkZ3lqAlc6XUFVCF2/Phx2Ww21a9fv8D2+vXr69dffy1yn9TU1CLjU1NTi4yfOHGinn/++ULbG150cRmzBuCvUlJT1ePSBmanAcBEx4+mqEe7umanAaCCyczMVERERIkxAVWI+cK4ceMKdNBOnjypxo0b6+DBg6VebPheRkaGGjZsqEOHDjF1tILhu6m4+G4qNr6fiovvpuLiu6nYAun7MQxDmZmZiouLKzU2oAqx6OhoBQcH68iRIwW2HzlyRDExMUXuExMT41Z8aGioQou4HyQiIsLv/+AEsvDwcL6fCorvpuLiu6nY+H4qLr6biovvpmILlO/H1eZMQK2aGBISok6dOmnNmjXObXa7XWvWrFFCQkKR+yQkJBSIl6TVq1cXGw8AAAAA5RVQHTFJGjNmjIYPH65LL71UnTt31rRp05Sdna0777xTkjRs2DDFx8dr4sSJkqSHH35YvXr10tSpUzVw4EAtWLBA33//vWbNmmXmaQAAAAAIYAFXiA0ZMkTHjh3TM888o9TUVHXo0EErVqxwLshx8OBBBQX91Qjs1q2b5s+fr6eeekrjx4/XRRddpCVLlqhNmzYuHS80NFTPPvtskdMVYT6+n4qL76bi4rup2Ph+Ki6+m4qL76Ziq6zfT8A9RwwAAAAAKrqAukcMAAAAAPwBhRgAAAAA+BiFGAAAAAD4GIUYAAAAAPgYhVg5zZgxQxdccIGqVaumLl26aNu2bWanBEmJiYm67rrrFBcXJ4vFoiVLlpidEvJMnDhRl112mWrVqqV69epp8ODB2rNnj9lpQdLMmTPVrl075wM1ExIS9NVXX5mdForwyiuvyGKxaPTo0WanAknPPfecLBZLgVeLFi3MTgt5kpKSdPvtt6tOnTqqXr262rZtq++//97stCDpggsuKPR3x2KxaOTIkWan5hMUYuXw6aefasyYMXr22Wf1ww8/qH379urfv7+OHj1qdmqVXnZ2ttq3b68ZM2aYnQrOs2HDBo0cOVJbt27V6tWrlZubq6uuukrZ2dlmp1bpNWjQQK+88oq2b9+u77//Xn379tWgQYP0888/m50azvHdd9/p3XffVbt27cxOBedo3bq1UlJSnK9vvvnG7JQgKS0tTd27d1fVqlX11Vdf6ZdfftHUqVMVGRlpdmqQ4+fZuX9vVq9eLUm6+eabTc7MN1i+vhy6dOmiyy67TNOnT5ck2e12NWzYUA899JDGjh1rcnbIZ7FYtHjxYg0ePNjsVFCEY8eOqV69etqwYYN69uxpdjo4T1RUlKZMmaK7777b7FQgKSsrS5dcconefvttvfTSS+rQoYOmTZtmdlqV3nPPPaclS5Zox44dZqeC84wdO1abNm3Sxo0bzU4FLhg9erSWLVum3377TRaLxex0vI6OWBlZrVZt375d/fr1c24LCgpSv379tGXLFhMzA/xLenq6JMc/+FFx2Gw2LViwQNnZ2UpISDA7HeQZOXKkBg4cWOD/e1Ax/Pbbb4qLi1PTpk01dOhQHTx40OyUIOk///mPLr30Ut18882qV6+eOnbsqPfee8/stFAEq9WqTz75RHfddVelKMIkCrEyO378uGw2m+rXr19ge/369ZWammpSVoB/sdvtGj16tLp37642bdqYnQ4k7dq1SzVr1lRoaKgeeOABLV68WK1atTI7LUhasGCBfvjhB02cONHsVHCeLl26aO7cuVqxYoVmzpypAwcO6PLLL1dmZqbZqVV6+/fv18yZM3XRRRdp5cqV+sc//qF//vOf+vDDD81ODedZsmSJTp48qREjRpidis9UMTsBAJXXyJEjtXv3bu6lqECaN2+uHTt2KD09XYsWLdLw4cO1YcMGijGTHTp0SA8//LBWr16tatWqmZ0OzjNgwADnf7dr105dunRR48aN9dlnnzGt12R2u12XXnqpJkyYIEnq2LGjdu/erXfeeUfDhw83OTuc64MPPtCAAQMUFxdndio+Q0esjKKjoxUcHKwjR44U2H7kyBHFxMSYlBXgP0aNGqVly5Zp3bp1atCggdnpIE9ISIguvPBCderUSRMnTlT79u31xhtvmJ1Wpbd9+3YdPXpUl1xyiapUqaIqVapow4YNevPNN1WlShXZbDazU8Q5ateurYsvvlh79+41O5VKLzY2ttAvklq2bMnU0Qrmjz/+0Ndff6177rnH7FR8ikKsjEJCQtSpUyetWbPGuc1ut2vNmjXcTwGUwDAMjRo1SosXL9batWvVpEkTs1NCCex2u3JycsxOo9K74oortGvXLu3YscP5uvTSSzV06FDt2LFDwcHBZqeIc2RlZWnfvn2KjY01O5VKr3v37oUekfK///1PjRs3NikjFGXOnDmqV6+eBg4caHYqPsXUxHIYM2aMhg8frksvvVSdO3fWtGnTlJ2drTvvvNPs1Cq9rKysAr+JPHDggHbs2KGoqCg1atTIxMwwcuRIzZ8/X0uXLlWtWrWc91RGRESoevXqJmdXuY0bN04DBgxQo0aNlJmZqfnz52v9+vVauXKl2alVerVq1Sp0H2VYWJjq1KnD/ZUVwL/+9S9dd911aty4sZKTk/Xss88qODhYt912m9mpVXqPPPKIunXrpgkTJuiWW27Rtm3bNGvWLM2aNcvs1JDHbrdrzpw5Gj58uKpUqVylSeU6Ww8bMmSIjh07pmeeeUapqanq0KGDVqxYUWgBD/je999/rz59+jjfjxkzRpI0fPhwzZ0716SsIDkeGixJvXv3LrB9zpw5leoG3Yro6NGjGjZsmFJSUhQREaF27dpp5cqVuvLKK81ODajQDh8+rNtuu00nTpxQ3bp11aNHD23dulV169Y1O7VK77LLLtPixYs1btw4vfDCC2rSpImmTZumoUOHmp0a8nz99dc6ePCg7rrrLrNT8TmeIwYAAAAAPsY9YgAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAC44KWXXpLFYlHXrl2L/Hzs2LGyWCzq0KGD0tLSfJwdAMDfWAzDMMxOAgCAiu706dO6+OKLdfjwYS1atEh/+9vfnJ9NnDhR48ePV/PmzZWYmKh69eqZmCkAwB/QEQMAwAXVq1fXyy+/LEl68skndfbsWUnSzJkzNX78eDVp0kRr1qyhCAMAuISOGAAALjIMQ5deeql++OEHvfPOO6pZs6buuOMOxcXFaePGjWrSpIkzdu/evXr11Ve1bds27dq1S/Hx8fr999/NSx4AUKFQiAEA4Ib169erT58+ioyMVGZmpiIjI5WYmKgWLVoUiFu6dKlGjhypzp0768CBA0pLS6MQAwA4UYgBAOCm7t27a/PmzapVq5YSExPVoUOHQjF2u11BQY47AB544AGtWLGCQgwA4MQ9YgAAuGHOnDnasmWLJCknJ0fh4eFFxuUXYQAAFIX/lwAAwEULFy7Uvffeq6ioKA0ZMkRWq1VPPPGE2WkBAPwQhRgAAC5Yvny5hg4dqrCwMK1cuVLvvPOOoqKitGjRIm3evNns9AAAfoZCDACAUmzYsEE33XSTqlSpoi+++EKdOnVS7dq1NX78eEnSmDFjTM4QAOBvKMQAACjBtm3bdN1118lms+nzzz9Xz549nZ+NGjVKjRs31rfffqsFCxaYmCUAwN9QiAEAUIxdu3ZpwIABOnXqlObNm6err766wOehoaF68cUXJUnjxo1TTk6OGWkCAPwQy9cDAOBlLF8PADhfFbMTAAAgEJ06dUrLly+XJO3fv1+nTp3SokWLJEmXXXaZGjdubGZ6AACT0REDAMALfv/9dzVp0qTIz+bMmaMRI0b4NiEAQIVCIQYAAAAAPsZiHQAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAAAAOBjFGIAAAAA4GMUYgAAAADgYxRiAAAAAOBj/w/b04v0t8Dn5wAAAABJRU5ErkJggg==\n",
      "application/papermill.record/text/plain": "<Figure size 1000x600 with 1 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "dt_boundary_1"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], xlabel=r\"$x_1$\", ylabel=r\"$x_2$\", legend=False, plot_training=True):\n",
    "    \n",
    "    # Get a grid of values\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    \n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    \n",
    "    custom_cmap = ListedColormap([\"mistyrose\",'cornflowerblue','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "   \n",
    "    if plot_training:\n",
    "        plt.plot(X.iloc[:, 0][y.y==0], X.iloc[:, 1][y.y==0], \"ro\", label=\"A\")\n",
    "        plt.plot(X.iloc[:, 0][y.y==1], X.iloc[:, 1][y.y==1], \"bs\", label=\"B\")\n",
    "        plt.plot(X.iloc[:, 0][y.y==2], X.iloc[:, 1][y.y==2], \"g^\", label=\"C\")\n",
    "\n",
    "    plt.xlabel(xlabel, fontsize=16)\n",
    "    plt.ylabel(ylabel, fontsize=16, rotation=0 , labelpad=20)\n",
    "    \n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=16)\n",
    "\n",
    "        \n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
    "plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
    "plt.text(1.35, 1.0, \"Depth=0\", fontsize=15)\n",
    "plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
    "plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "\n",
    "glue(\"dt_boundary_1\", fig, display=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "worthy-projector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/papermill.record/image/png": 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swWxg281TAAAAACBWEOwAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAoNUU7C/QqGWjVLC/INpTAWyFYAcAAIBWYRiG8jbmad+xfcrbmCfDMKI9JcA2CHYAAABoFZv3b9buot2SpN1Fu7V5/+YozwiwD4IdAAAAWswwDC16f5GcDqckyelwatH7i6jaARFCsAMAAECL+ap1HsMjSfIYHqp2NbD2EOFGsAMAAECL1K7W+VC182LtISKBYAcAAIAWqV2t86Fq58XaQ0QCwQ4AAADN5qvWOeSo93mHHLau2rH2EJFCsAMAAECzVXoqVegulKH6g4ohQ0XuIlV6Kus8Z4d1Z6w9RKTER3sCAAAAsC5XvEvrpq7T8TPHg45JbZ8qV7wr4FjtdWcjMkfI4ai/6mdVNat1NW9T9VXtRvYaGXPXjOgh2AEAAKBFMpIzlJGc0aTX1Lfu7KreV4VjelFT8xprqlm1i7VrRvRwKyYAAAAiyg7rzlh7iEgj2AEAACCi7LDurCVrD4Hm4FZMAAAARIxd1p01d+0h0FwEOwAAAESMndadNWftIdBc3IoJAACAiGDdGRA+BDsAAABERHPXndmh3x3QUtyKCQAAgIhozrozO/S7A1oDwQ4AAAAR09R1Z2brd7dsyzIt/mCx7r3qXs0YPiNq8wBq41ZMAAAAmJLZ+t1VV1drScESVXmqtKRgiaqrq6MyD6A+BDsAAACYktn63S3dslRnq85Kks5WndXSLUujMg+gPgQ7AAAAmE7tap1PtKp21dXVenbLswHHnt3yLFU7mAbBDgAAAKZTu1rnE62qXc1qnQ9VO5gJwQ4AAACmYrZ+d/VV63yo2sEsCHYAAAAwFbP1u6uvWudD1Q5mQbsDAAAAmIqZ+t01VK3zeXbLs5o5fKbi4qiZIHoIdgAAADAds/S7K68s17mqcw2OOVd1TuWV5UpKTGrx1wOai2AHAAAAS6u5g6bH8Ph3zhzZa2SLq3ZJiUlaM2WNDp48GHRMZqdMQh2ijmAHAAAAS6tZrZMCd85sjard4O6DNbj74BafJ1oK9hdo7jtzNW/MPF3Z68poTwdhwo3AAAAAsCyz9bszm9prD+3+/YhlBDsAAABYltn63ZlNfWsPEZsIdgAAALAks/W7M5va1UyqmLGNYAcAAABLam6/O7uoXc2kihnb2DwFAAAAltScfnd2UXunUJ/W3DEU5kKwAwAAgGU1td+dXdTeKdSntXcMhXlwKyYAAAAQQ1h7aE8EOwAAACCGtGTtYcH+Ao1aNkoF+wvCPU20Mm7FBAAAAGJIc9ce1u55NyJzBOvwLIRgBwAAAMSY5qw9rK/nHevwrMP2t2IuXbpUPXv2VGJiorKysrR9+/agY59//nldeeWV6tixozp27Kjs7Ow642+99VY5HI6Ax9ixY8N9GQAAAECz0fPO+mwd7F577TXl5ORo7ty5+uSTTzRw4ECNGTNGR48erXf8pk2b9Itf/ELvv/++tm7dqh49euiaa67RkSNHAsaNHTtWRUVF/sd///d/R+JyAAAAgGah55312TrYPfnkk5o2bZqmTJmifv36afny5WrXrp1efPHFesevXLlSv/nNbzRo0CD16dNH//Vf/6Xq6mrl5+cHjHO5XEpPT/c/OnbsGInLAQAAAJqsdrXOh6qdtdg22FVWVmrnzp3Kzs72H4uLi1N2dra2bt0a0jnOnDmjqqoqderUKeD4pk2b1KVLF1188cWaMWOGjh8PvnBVkioqKuR2uwMeAAAAQCTUrtb5ULWzFtsGu2PHjsnj8SgtLS3geFpamoqLi0M6x/3336+MjIyAcDh27Fi98sorys/PV15enj744AONGzdOHo8n6HkWLFig5ORk/6NHjx7NuygAAACgCeh5FzvYFbOZnnjiCa1atUqbNm1SYmKi//jEiRP9f+7fv78GDBig3r17a9OmTRo9enS958rNzVVOTo7/c7fbTbgDAABA2DWl513t9ggwF9sGu9TUVDmdTpWUlAQcLykpUXp6eoOvXbx4sZ544gm99957GjBgQINje/XqpdTUVO3bty9osHO5XHK5+IsCAACAyGpuzzuYj22DXUJCgoYMGaL8/HxNmDBBkvwbocyaNSvo6xYuXKjHHntM77zzjoYOHdro1zl8+LCOHz+url27ttbUAQAAgFbTnJ53MB/brrGTpJycHD3//PN6+eWXtXfvXs2YMUOnT5/WlClTJEmTJ09Wbm6uf3xeXp4eeughvfjii+rZs6eKi4tVXFys8vJySVJ5ebnuu+8+ffTRRzp48KDy8/M1fvx4XXTRRRozZkxUrhEAAABA7LN1sLvpppu0ePFiPfzwwxo0aJB27dql9evX+zdUOXTokIqKivzjly1bpsrKSv3sZz9T165d/Y/FixdLkpxOp3bv3q1/+7d/0w9/+ENNnTpVQ4YMUUFBAbdaAgAAxLiC/QUatWyUCvYXRHsqsCGHwRY3puN2u5WcnKyy4iIlJSVFezqAvVVWSJI+LY7XgVKnOl1Yog6dSyVJqY7UKE4MAGAmhmHo+heu1+6i3RrQdYDenPqmHI76d5oEmuKU+5Qu6XiJysrKGswGtq7YAQAAxBqqRtHh6wUnid5viAqCHQAAQIwwDEN5G/O079g+5W3Mo/dYhPh6wTkdTkmS0+Gk9xsijmAHAAAQI6gaRYfv++4xPJIkj+Hh+4+II9gBAADEAKpG0VH7++7D9x+RRrADAACIAVSNoqP2992H7z8ijWAHAABgcVSNosP3fXeo/t0vHXLw/UfEEOwAAAAsjqpRdFR6KlXoLpSh+oObIUNF7iJVeiojPDPYUXy0JwAAAIDmq1k1qi9g+KpGI3uNpK9aK3PFu7Ru6jodP3M86JjU9qlyxbsiOCvYFcEOAADAwppSNaoZMAr2F2juO3M1b8w8XdnrykhNN+ZkJGcoIzkj2tMACHYAAABW1pyqUe1+dyMyR1DNAyyOYAcAAGBxTa0a1dfv7qreV4VregAigM1TAAAAbIR+d0BsItgBAADYCP3uGlawv0Cjlo1Swf6CaE8FaBKCHQAAgE3Q765htdce2v37AWsh2AEAANgE/e4aVt/aw2iieoimINgBAADYQM1+d/Xx9buza5XKbGsPqR6iqQh2AAAANtCUfne12aFyZLa1h2arHsL8aHcAAABgA83pdyfZo+ddzWpdzdtUfVW7kb1GRvSaa88nWvOAtVCxAwAAsImM5Az179o/6KNrUtc6rzFb5Sgc1UOzrT00W/UQ1kCwAwAAQL3ssO7MbGsP2bkUzUWwAwAAQL3MVjkKR/WwJWsPw8Fs1UNYB2vsAAAAUIdd1p01d+1hONSsHtYXNH3VQ9baoT4EOwAAANRRszpWU83K0VW9r4rafFpzHhnJGcpIzmjpFFusKdXDSARNWAvBDgAAAAGaWzkq2F+gue/M1bwx83RlrytbfT5mqR6Gi5mqh7Aegh0AAAACNKdyFM62CGarHoaTWaqHsB6CHQAAAAI0p3JU38YmrRG2zFY9BMyKYAcAAIA6mlI5CmdDbbNVDwGzItgBAACgRcK5sYmZqofNRfUQkUCwAwAAQLNFYmMTs1QPm4PqISKFBuUAAAA2UbC/QKOWjVLB/oJWO6fZGmrboal6S4TjPQBzINgBAADYQO3KkWHUv2atqef0bWxSH9/GJq3xtZoyH6fDGXDcV7WL1DyCzSda86g5n9Z+D8A8CHYAAAA2EI7KUVM2NokEqoehzUcyR/UQrYs1dgAAADEuXOvOzNRQu7ltEcI9H7M0VTfb2kO0vmZV7B599FE5HA5dfvnl9T7/wAMPyOFwaNCgQTp58mSLJggAABAudllvFM7KUUZyhvp37R/00TWpa4u/RihaUj1k7SFigcNoxs21Z8+e1Q9/+EMdPnxYq1ev1g033OB/bsGCBZozZ44uvvhibd68WV26dGnVCduB2+1WcnKyyoqLlJSUFO3pAPZWWSFJ+rQ4XgdKnep0YYk6dC6VJKU6UqM4MQAtZRiGrn/heu0u2q0BXQfozalvRrxyceSQUyeOBf89e6fUanW7wBP0+VD4rvPz4s/rVI4uSb8kKtcdLoVlhY1WD2sHzXC8D3zn3FO0J2j1sH/X/hH73tvpPRCLTrlP6ZKOl6isrKzBbNCsWzHbtm2rxx57TLfccot++9vfavz48YqPj9eyZcs0Z84cZWZmKj8/n1AHAABMK9q9zo4ccurqvl1Vce6f/6AevlD68UPS+/OlLbMlSa5EQ5v2FrUo3NXuMefTmr3mzKIpbRF8wvE+aE5T9XCy03vAzpq9xu7mm2/WU089pU8++UQvvPCCzjvvPM2cOVPdunVTfn6+unXr5h+7evVqrVy5Ujt37tSxY8eUmZmpX/3qV7rzzjvVpk2bVrkQAACAUJlhvdGJY3HfhzpVS1fNk+IrvR+33CvJ+/yJY3HNDnZmW3dmNqw95D0QS5q9K6bD4dDvf/97SVJubq5uvfVWpaam6r333lNmZmbA2MWLF8vlcmnhwoV666239O///u968MEHddttt7Vs9gAAAM1guvVGIxZICWe8f0444/28FTR33dmyLcvU+/HeWrZlWavMw6xYexj5nUsRPs1aY1fT8OHD9eGHH6pDhw7avHmzBg0aVGfMt99+q86dOwcce/TRR/XQQw+puLhYaWlpLZlCzGGNHWAirLEDYo5Z1hvt+aSNrvtRuqRqaU4Hqc0ZySHJkFTVTnr8lKQ4vbWjWP3/parZX6ep686qq6vVd2Ffna06q7Zt2mrv7L2Ki4u9DllmeR9EQnPWHsI8wrrGzmfFihXaunWrJKmioiLoF6od6iRpyJAhkqTCwkKCHQBz++476TtDqqpWSalHZ9p41Db5tCTCHWBFpltvVLNaJ3nDna9q97fftvj0TV13tnTLUp2tOitJOlt1Vku3LNUdV97R4nmYjeneB2HUnLWHsJ5mB7vXX39d06ZNU6dOnZSdna3XXntN999/v15//fWQXr9582YlJCSod+/ezZ0CAIRfgnf9w8DukuLjdKA0Q6d8zyW7ozUrAM1kGIYWvL+gwfVGC95foH6Z/cJerSk12krqIo183Fulq/nlDHmP/y03rHOorbq6Ws9ueTbg2LNbntXM4TNjqmrHujPEomb9DX377bc1adIktW/fXu+8846WL1+uTp06afXq1frwww8bff3f//53PfXUU5o+fTq3GgKwjIHp3ykzxaM2ZRk69W2Kzpa1j/aUADRRladKxe7iBtcblbhLVOVp/q2PTeKr1tXODjWrdhFUs1rn46vaxZLmrjuzS99DWFOT19h98MEHGjdunCRp/fr1GjlypCTp97//ve69915lZWXpo48+Cvr6Y8eO6YorrlBiYqK2bt2q9u35h1FtrLFDHR6PtGWLVFwspadLw4dLTme0Z2UflRXeyl1lhT4t9t7ocKDUqarkQqWl8HMArOZoeaFKz54I+nxK2/PV5bzwrzf6Yle8fl3Q5/u1dbX9c63dm+O/0sChLetlF4qaa+tqi8W1dk1dd2aGvoewp7Cssdu+fbuuv/56eTwerV271h/qJGnWrFl65plntG3bNq1atUoTJ06sO6lTpzRu3DhVVlZq06ZNhDogFGvWSvfdJx058v2xbt2kRYukCeOjNy+bGniBU58e8igzLV4HSjJ0oizaMwLQVPFKU4OrY89IJ4L/e7/VnD19Soo/V3+ok7zH488pMcUt6ft/My3bskyLP1ise6+6VzOGz2i1+dRXrfPPNQbX2jV13Vm0+x4CjQm5Yrdnzx5dffXVKisr06pVq/Szn/2szpj/+3//ryZPnqyePXvqiy++kMv1fW+OiooKjRs3Tnv27NHf/vY3XXzxxa13FTGGih381qyVJk2Sav819f2GcOVKwl0k1KjYKcGlTw95JFf4ew+ZTdERqfRk8N9Op3Q01LVb0KcB1GPVhx/pVNVBnYt3q+N5Dp0xzqido53/Y9+MnvrXIQP848O1Y2VD1TqfWKzahar2DpqxuHMmzKvVK3b9+/fX8eMN//rq5ptv1s0331znuMfj0cSJE7Vjxw5t3LiRUAeEwuPxVurq+92LYXjD3ezZ0vU/4bZMhF3REWn86DaqrAj+D5gEl6G1+VWEO6AJsnoPVWba5SpLPKLMLvE6ZhxTqiPV/7G2cO1YWV5ZrnNV5xocc67qnMory5WUaL9fOtfeQTMWd86E9bWo3UGoZs6cqTVr1mj+/PnyeDwBa/D69etHVQqoz5Ytgbdf1mYY0uHD3nE1bosGwqH0pKPBUCdJlRUOlZ50qGu3FrVHBRBEOHesTEpM0popa3Tw5MGgYzI7Zdoy1Pl20HQ6nHX63bFzJswkIrX09evXS5IeeughDRs2LODxySefRGIKQS1dulQ9e/ZUYmKisrKytH379gbHv/766+rTp48SExPVv39/vf322wHPG4ahhx9+WF27dlXbtm2VnZ2tr776KpyXgFhVXNy64wAAlhbuHSsHdx+sn/b/adDHoG6DWuXrWI2vWlcz1EmBVTvADCIS7A4ePCjDMOp9XH311ZGYQr1ee+015eTkaO7cufrkk080cOBAjRkzRkePHq13/Icffqhf/OIXmjp1qv7nf/5HEyZM0IQJE/TZZ5/5xyxcuFBPP/20li9frm3btql9+/YaM2aMzp1r+PYGoI709NYdByAsio5Iez9zBH0UNVB4B0JVX7XO59ktz6q6ujrCM7KHmv3u6uPrd9fETeaBsIjIrZhm9eSTT2ratGmaMmWKJGn58uV666239OKLL+qBBx6oM/6pp57S2LFjdd9990mS5s+frw0bNujZZ5/V8uXLZRiGlixZogcffFDjx3s3tHjllVeUlpamNWvW1LtTKBDU8OHe3S8LC+tfZ+dweJ8fPjzycwMgibWHiBy77VhpFk3pd+eKt9+mWjAX2wa7yspK7dy5U7m5uf5jcXFxys7O1tatW+t9zdatW5WTkxNwbMyYMVqzZo0k6cCBAyouLlZ2drb/+eTkZGVlZWnr1q1Bg11FRYUqKir8n7vd7uZeFmKJ0+ltaTBpkjfE1Qx3vnv5Fy5k4xQgisy29pCdS2NTQ9U6n9Zaa4dArniX1k1d12i/O0IdzMC2we7YsWPyeDxKS0sLOJ6WlqYvvvii3tcUFxfXO774n2ucfB8bGlOfBQsWaN68eU2+BtjAhPHelgb19bFbuJBWBwD8qB7GLnasjK6m9rsDosW2wc5McnNzAyqBbrdbPXr0iOKMYCoTxntbGmzZ4t0oJT3de/sllToANVA9jF3sWAkgFLYNdqmpqXI6nSopKQk4XlJSovQgm1Gkp6c3ON73saSkRF27dg0YM2jQoKBzcblcAc3cgTqcTloaIKpSOhpKcBmNVoNSOrKBAKgehsPg7oM1uPvgaE+jRQr2F2juO3M1b8w8XdnrymhPB4g5tg12CQkJGjJkiPLz8zVhwgRJ3nvY8/PzNWvWrHpfM2zYMOXn5+uuu+7yH9uwYYOGDRsmScrMzFR6erry8/P9Qc7tdmvbtm2aMWNGOC8HAMKqazdpbX4VFRiEhOohajMMQ3kb87Tv2D7lbczTiMwR9H4DWpltg50k5eTk6JZbbtHQoUN12WWXacmSJTp9+rR/l8zJkyerW7duWrBggSTpP/7jP3TVVVfp97//va677jqtWrVKH3/8sZ577jlJksPh0F133aVHH31UP/jBD5SZmamHHnpIGRkZ/vAIAFbVtZtoPg7LoXpoDr5ecJL8vd+u6n1VlGcFxBZbB7ubbrpJ3377rR5++GEVFxdr0KBBWr9+vX/zk0OHDgXsLnXFFVfo1Vdf1YMPPqg5c+boBz/4gdasWaNLL73UP2b27Nk6ffq0pk+frtLSUo0YMULr169XYmJixK8PAAC7M1v10I58veCcDqc8hkdOh1OL3l+kkb1GUrUDWpGtg50kzZo1K+itl5s2bapz7MYbb9SNN94Y9HwOh0OPPPKIHnnkkdaaIgDApFh7CDSuZrVOkjyGh6pdDaw9RGuxfbADAKC5WHsINKx2tc6Hqp0Xaw/RmuhiCQBAC3TtJvW91Aj6iFSo81UPG0L1EMEU7C/QqGWjVLC/oFXP66vW1Qx1UmDVzs7qW3sINBcVOwAAYgDVQzRXuKpGvmqdQw4ZqvsLBYcctq7asfYQrY1gBwBAjDDLzqWsPbSWcO1YWempVKG7sN5QJ0mGDBW5i1TpqZQr3n79fFl7iNZGsAN8PB5pyxapuFhKT5eGD/c2BgcANAnVQ+sIZ9XIFe/SuqnrdPzM8aBjUtun2jLUsfYQ4UCwAyRpzVrpvvukI0e+P9atm7RokTRhfPTmBQAWRfXQGsJdNcpIzlBGckaLzxNran/ffajaoSUIdsCatdKkSZJR63/qhYXe4ytXEu4AwKKoHgZH1Sg6WHuIcCHYwd48Hm+lrnaok7zHHA5p9mzp+p9wWyYAWJRZqodmQ9UoOlh7iHAh2MHetmwJvP2yNsOQDh/2jhs5MnLzAgAgjKgaRQ9rDxEuBDvYW3Fx644DAMACqBpFF2sPEQ4EO9hbenrrjgMAwAKoGgGxh2AHexs+3Lv7ZWFh/evsHA7v88OHR35uAACEEVUjILYQ7GBvTqe3pcGkSd4QVzPc+dYULFzIxikAgABFR8ROmwBMhWAHTBjvbWlQXx+7hQvN3eqApuoAEHFFR6Txo9s02htvbX4V4Q5AxBDsAMkb3q7/ibVCEk3VASAqSk86Ggx1klRZ4VDpSUdE2iwcOeTUiWNxQZ/vlFqtbhd4gj4PIDYQ7AAfp9M6LQ1oqg4AkDfUXd23qyrOBQ+arkRDm/YWEe6AGBf81zsAzKmxpuqSt6m6h/+BA0CsO3EsrsFQJ0kV5xwNVvQAxAb+lgNW05Sm6gAAALAFbsUErIam6gDQoBf/U9r7WfAqVt9LDf3qNxGcEABEAMEOCKezZ6XcOdLXX0u9e0sLHpfatm3ZOWmqDgBBvfif0tOL2kgKHuw2/NWQVEW4AxBTuBUTCJef3ySdnyo995yUn+/9eH6q93hL+JqqO4L8o8XhkLp3p6k6AFvyVuoaXnMmORqs6AGAFRHsgHD4+U3SunX1P7duXcvCna+pulQ33NFUHQDCLqWjoQRXw20MElyGUjqGv9UBAPhwKybQ2s6eDR7qfNat845r7m2ZVm6qDiAqNq6Xvv4qeJWq9w8MjRobwQlZWNdu0tr8KpWeDP79TOlo0JwcQEQR7BBZHo+1moA3R+6c0Mct+f+a/3Ws2FQdQFRsXC/lzGh43Zlk6MllVYS7EHXtpog0H29Mp9RquRKNRvvYdUqtjuCsAEQDwQ6Rs2Zt/RWmRYtiq8L09detO64hVmqqDiBqvJW6xtedff2VQ6PGRj+sIHTdLvBo096iBvvUdUqtpjk5YAMEO0TGmrXSpEl1m2oXFnqPr1wZ+XBXWSn94TnpwH4ps5f06+lSQkLLz9u7t3ezlFDGAQDQQt0u8BDcABDsEAEej7dSVzvUSd5jDoc0e7b3tsJI3UY457fS009L1TVuTcnNle68U3r8sZade8Hj3h0wQxkHAIiqoiNirRyAmECwQ/ht2RJ4+2VthiEdPuwdF4nbCuf8VlqypO7x6urvj7ck3LVtK/3kJw1voPKTnwRunGKHtYcAEAF9LzX+2aeu4fWEfS81VHREGj+6jSorgo9NcBlam19FuANgegQ7hF9xceuOa4nKSm+lriHPPCP9bm7Lbsv802vBWx785Cfe533ssvYQACLA23S8qsE+dX0vNfSr33h73jUU6iSpssKh0pMOU2yUAgANIdgh/NLTW3dcS/zhucDbL+vj8XjH3TGrZV/rT695WxrkzvFulNK7t/f2y5qVOjOuPQRga5/ulI58EzzsdOthaOCQCE6oGbzhjiAGwF4Idgi/4cO9FajCwvrX2Tkc3ueHDw//XA7sb91xjWnbNnhLAzOuPQRga0VF0i0/a7wtwsurq0wf7gDAbgh2CD+n03tb4aRJ3rBSM8g4/vmPh4ULIxNeMnu17riWMNvaQ8BkYqFyZBa9f2DIW8FqOLAlJqqRMd7nj3zj0MAh4a+I8R4AgNAR7BAZE8Z7byusby3ZwoWRu93w19O9u182dDum0+kdF25mWnsImMynO6kctaZRY6Unl1X9s59d/Xr/wNC5BppcRxrvAQBoGoIdImfCeO9thdHc/TEhwdvSoL5dMX3uuKPuximNrZVrDjOtPQRMxlulMU/lKBaMGqtGm4+/vSYycwmF2d4DtEUwh2VblmnxB4t171X3asbwGdGeDmAqBDtEltMZ/dsKfa0Mavexczq9oa52q4Pau1vm53v71NXe3bKpzLT2EAAQ1LGj3uohbRGiq7q6WksKlqjKU6UlBUv062G/VlxcXLSnBZgGfxtgT48/Jp04LuXlSbf/2vvx+LHGQ11N69Z5n28u39pD6fu1hj4NrT30eKTNm6U//cn70eNp/hwAWE7REe82/cEeRQ0s3UWglI6GElwNV/t8z4faFgHhs3TLUp2tOitJOlt1Vku3LI3yjGB3BfsLNGrZKBXsL4j2VCRRsYOdJSQ03NLg7NmGm4xL3ufPnm3+bZlNXXtIzzvA1mio3bq6dpPW5lc1eoslgS36qqur9eyWZwOOPbvlWc0cPpOqHaLCMAzlbczTvmP7lLcxTyMyR8hR+xf1EUawA4LJnRP6uGAtDUIR6tpDet4Btld60lwNtWNh18qu3dTo96r0ZIQmg6BqVut8fFW7O668I0qzgp1t3r9Zu4t2S5J2F+3W5v2bdVXvq6I6J4IdEMzXX7fuuIY0tvaQnncAIqBbj9DaInTrYbBrJSKmvmqdD1U7RINhGFr0/iI5HU55DI+cDqcWvb9II3uNjGrVjmAH66uslP7wnLepeGYvb6uC2rtaNkfv3t6NUkIZF270vAMQAQOHSC+vrgqpCvf2GnPtWonYVV+1zoeqHaKhZrVOkjyGxxRVO4IdrG3Ob+vubpmb621pUHsjlKZa8Lh398tQxoUbPe9gM02pHKF1DRwiUwQx3gOQGq7W+dRXtSvYX6C578zVvDHzdGWvK8M9TdhI7WqdjxmqdgQ7WNec39bfj666+vvjLQl3bdt6Wxo0tIHKT34SuHGKxxOePn30vIPNNKVyhNjEewCSVF5ZrnNV5xocc67qnMory5WUmCTJnJtaIDrCEfBrV+t8zFC1I9jBmiorvZW6hjzzjPS7uS27LfNPrwVveVC7j104d6yk5x1syCyVI0SPWd4DvrYIje1GmtIx+nONNUmJSVozZY0OnjwYdExmp0x/qJPMuakFIi8cAd9XrXPIIUN1/7475Ihq1Y5gB2v6w3OBt1/Wx+PxjmuopUEo/vSat6VB7hzvRim9e3tvv6xZqQv3jpW+nneTJnlDXM2v01DPOwC2VXREjW7jT0uE0ITaFsGM388jh5w6cSz4xiKdUqvV7QJz90Md3H2wBncfHNJYs25qgcgLR8Cv9FSq0F1Yb6iTJEOGitxFqvRUyhXvatHXag6CHazpwP7WHdeYtm2DtzSI1I6VTe15ByDmhFo5qqww6HfXykJpi2A2Rw45dXXfrqo4F/x94Eo0tGlvkenDXajMuqkFIitcAd8V79K6qet0/MzxoGNS26dGJdRJBDtYVWav1h3XEpHcsTLUnncAYlJTGmrT7w4njsU1GOokqeKcQyeOxcVEsDPzphaIrHAG/IzkDGUkZ7R0imFBsIM1/Xq6d/fLhm7HdDq948It0jtWNtbzDkBMM0tD7VB3rTx7xqDfHSLCzJtaIHLsHPBtG+xOnDihO+64Q2+++abi4uJ0ww036KmnntJ5550XdPzcuXP17rvv6tChQ+rcubMmTJig+fPnKzk52T+uvjfKf//3f2vixIlhuxZbSkjwtjSob1dMnzvuCNw4pbxc+tVU6cABKTNTevEFKcjPu0nYsRKwDCpHrSfUXSu9z5un3x1rD2OT2Te1QOTYOeDbNthNmjRJRUVF2rBhg6qqqjRlyhRNnz5dr776ar3jCwsLVVhYqMWLF6tfv376xz/+odtvv12FhYVavXp1wNgVK1Zo7Nix/s9TUlLCeSn25WtlULuPndPpDXU1Wx1cOVLaufP7zz//XOqSJg0ZIhVsbtk82LESsIRPd4rKUSsLZdfKI99EaDIhKDoi1h7GKLNvaoHIsHvAt2Ww27t3r9avX68dO3Zo6NChkqRnnnlG1157rRYvXqyMjLr3zV566aX6f//v//k/7927tx577DH98pe/1Hfffaf4+O+/lSkpKUpvQnWmoqJCFRUV/s/dbndzLsueHn/M29LgD895N0rJ7OW9/bJmpa52qKtp507v8y0Jd+xYCVgClSOYce3h/3wWp73JDp1p016dk+N0ykhRB0dbnTJSdEmvBA0ZVhn2ecQCs29qgciwe8C3ZbDbunWrUlJS/KFOkrKzsxUXF6dt27bppz/9aUjnKSsrU1JSUkCok6SZM2fqtttuU69evXT77bdrypQpDf5WYMGCBZo3b17zLgbeEBespUF5efBQ57Nzp3dcS27LZMdKAE1A5Qh1K8hd/vkxtcZHQ2/87SjhLkRm3tQCkWH3gG/LYFdcXKwuXboEHIuPj1enTp1UHOIGF8eOHdP8+fM1fXrg5hyPPPKIRo0apXbt2undd9/Vb37zG5WXl+vOO+8Meq7c3Fzl5OT4P3e73erRo0cTrghB/Wpq6ONqNhtvDqvuWOnxWG/OgMWZrXKEyAu1gnzogFNDhkViRkBssHPAj6lg98ADDygvL6/BMXv37m3x13G73bruuuvUr18//e53vwt47qGHHvL/efDgwTp9+rQWLVrUYLBzuVxyuWLzNwdRd+BA645rjNV2rFyztv4q46JFVBkBCwu1311KR0JjLOuUWi1XotFoH7tOqQ3sMA3AMmIq2N1zzz269dZbGxzTq1cvpaen6+jRowHHv/vuO504caLRtXGnTp3S2LFj1aFDB73xxhtq06ZNg+OzsrI0f/58VVRUEN4aE47KUWamd6OUUMbZzZq13nWBtTd8KSz0Hl+5knAHWFSo/e7MeJsnaw9bT7cLPNq0t0gnjsUFHdMptTometgBiLFg17lzZ3Xu3LnRccOGDVNpaal27typIUO8W59t3LhR1dXVysrKCvo6t9utMWPGyOVy6S9/+YsSExMb/Vq7du1Sx44dCXWNCVfl6MUXvLtfhjLOTjwe7/e7vl08DcO76cvs2d5bS7ktE7CkUPrdRUKo/e669TBYexgG3S7wENxgeQX7CzT3nbmaN2aerux1ZbSnY1oxFexC1bdvX40dO1bTpk3T8uXLVVVVpVmzZmnixIn+HTGPHDmi0aNH65VXXtFll10mt9uta665RmfOnNEf//hHud1u/+6VnTt3ltPp1JtvvqmSkhJdfvnlSkxM1IYNG/T444/r3nvvjeblml84K0fnnedtadDQBipDhtTdOKW4WLp8mFRWJiUnSx9tja0+dFu2BIbo2gxDOnzYO85Kt5YCMJ1Q+90NHCLt/cxcaw/pewhEn2EYytuYp33H9ilvY55GZI6IyVYFrcGWwU6SVq5cqVmzZmn06NH+BuVPP/20//mqqip9+eWXOnPmjCTpk08+0bZt2yRJF110UcC5Dhw4oJ49e6pNmzZaunSp7r77bhmGoYsuukhPPvmkpk2bFrkLs5pIVI4KNgdveVBfH7vUztI/f+6SpKNHpV69pXbtpGPfNm8OZhPiJkEhjwMsoCmVI7SuUPrdRUJT1h7S9xC1UTWKjpoNx2O9wXhL2TbYderUKWgzcknq2bOnjBph4+qrrw74vD5jx44NaEyOEESqclSw2dvS4FdTvRulZGZ6b7+sXamrHepqOnPG+3ykw1041h6GWn2MpSolbK8plSMzYc1Z62nK2sP/2WGuvoeILqpG0eFrOO50OOUxPHI6nDHdYLylbBvsYBKRrBydd17DLQ2Ki4OHOp8zZ74PWJEQrrWHw4d7z1NYWH+11OHwPj98ePO/BmBCVqscVVYYrDlrZVZbe3hBZt31cVSOIs9sVSO7vAdqft8lyWN4TPH9NyuCHaLLTJWjy0NsFHT5MOlgK7VHaEg41x46nd5wOGmSN8TV/Bq+34AtXMjGKUCYhFo5Mlu/O6qHrcdXQf6fz6rVJdmpM21OqnNynE4Zp9TB0UGnjFO6pFe7Os3JqRxFntmqRnZ5D9T+vvtE+/tvZgQ7RJeZKkdlZa07riUisfZwwnhvOKyvIrhwIa0OgDALpXJUejJCkwkBO1a2voFDpKTu1cpMi1NZ4mlldonXMaNUqY7vP9ZmtsqRHZitamS290C4qoe1v+8+0f7+m1nwxiZAJPgqR9L3lSKfSFeOkpNbd1xLNGXtYUtMGC99sVda/1fppRXej3v/TqgDUEdTqoeRUHTEu4tmsEdRA/8JtaqaFQzp+8pFY3sAoPlqf899ovW9N9t7oHb1sLXm4btOR5BblR1y8N6vBxU7RJ9ZKkcfbfXufhnKuHCL5NpDp5OWBgBMgbWHDTNb5cgOzFY1Mtt7IFzVw0pPpQrdhTJUf3AzZKjIXaRKT6Vc8fSK9iHYwRwmjPfeVtjauz82RXq6t6VBQxuotGsXuN7v22+9rRSOH5fOP9+7+2bnzq0zl9YcByAsWHPWulh7GBzrjSKvZtWovoDhqxpF6ntvtvdAONceuuJdWjd1nY6fOR50TGr7VEJdLQQ7mIcZKkfHvg3e8qB2H7uuGYHr7U6fli7s6b1Vs6iwZfMw09pDAPVizVl4mGXtYai7ViYmRqZ6aLbKkR2YrWpktvdAuKuHGckZykjOaPF57IRgB9R27Ftv1fDyYd7glpzsvf2yZnWsdqirqazM+3xLwh27VgKmZ7aqEVpXqH0PE1zhfx+YrXJkF2aqGpntPWC26iG8CHZAfdLTg7c0+PbbxnfGLCvzjmvJbZlmWXsIIGpCXXOW0pHgGA6h9D3c+1n459HcypFdep2Fk1mqRlQPEQqCHdBUV4Z4u+iVI707TraEGdYeAoiaUNeccatnbGtO5cguvc7sguohQkGwA5rqePD/qDZrXGPMsPYQQNSEsuYsEqgeRldTK0dm63WGlqN6iMYQ7ICmOv9870YpoYwDgBhh1eqhHXcuDeduhYCZqocIRLADmqpgs3f3y1DGAUAMsVr1kH53Xqx7QmszS/UQgQh2QFN17uzdKbOhDVSSk+tunFJWJv30/0iHD0vdu0tv/Nk7DgDQJFbtdxcJ7FYI2BfBDmiOosLgLQ/q62N3aX9p//7vPz982Pv6Xr2kz/aEd64AwoI1Z9Flln53ZnsfsFuhtbBzKVoTwQ5orqJCb0uDK0d6N0o5/3zv7Ze1K3W1Q11N+/d7nyfcAZZj1TVnaF1meh+wW6G1sHMpWhvBDmiJzp0bbmlQVhY81Pns3/99I3QAlmKWNWeILrO8D9it0FrYuRStjWAHhNNP/0/o4zbmh3cuAGBCdty1MlzYrdA62LkU4UCwA8Lp8OHWHQcAMaToiGy5a2U4sVuhNbBzKcIhLtoTAEyhslJ65lkpJ8f7sbKydc7bvXvrjgOAGNKUXSuBWFGzWleTr2pnGNG/rRfWRLAD5vxW6nS+dP/90vI/eD92Ot97vKXe+HPrjgMAhMy3Y2VD2LkUkear1tVsRyEFVu2A5uBWTNjbnN9KS5bUPV5d/f3xxx9r/vmTk70tDRraQKVXr8CNUzweacsWqbhYSk+Xhg+XnM7grwcA1MtMO1YCEjuXIrwIdrCvykrp6acbHvPMM9Lv5koJCc3/Op/tCd7yoHYfuzVrpfvuk44c+f5Yt27SokXShPHNnwMA2JRZdqwEJHYuRXgR7GBff3jOW5lriMfjHXfHrJZ9rc/2eFsa/PT/eDdK6d7de/tlzUrdmrXSpElS7XvrCwu9x1euJNwBAGBh7FyKcCLYwb4ONNJfrqnjGpOcHLylgcfjrdTVt2DaMCSHQ5o9W7r+J9yWCQCAhbFzKcKFzVNgX5m9WndcS2zZEnj7ZW2G4a30bdkS/rkAAADAcgh2sK9fT5fiGvkr4HR6x4VbcXHrjgMAC2DXSgAtUbC/QKOWjVLB/oJoT8UUuBUT9pWQIN15Z/27YvrccUfgximVld41dwf2eyt5v57eso1VfNLTW3ccAFgAu1YCaC7DMJS3MU/7ju1T3sY8jcgcYfudRAl2sDdfK4Onnw7cSMXp9Ia6mq0O5vy27rjcXG84bElLBMnb0qBbN+9GKfWts3M4vM8PH96yrwMAJsOulQCaw9cPUJK//99Vva+K8qyii2AHPP6Yt6VBQ5W4cPe7czq9LQ0mTfKGuJrhzvfbp4UL2TgFQICiI6LaBcB2fP0AnQ6nPIZHToeT/n8i2AFeCQnBWxpEqt/dhPHelgb19bFbuJBWBwACFB2Rxo9uo8qK4P+ISXAZWptfRbgDEFNqVuskyWN4qNqJzVOAxjWl311LTRgvfbFXWv9X6aUV3o97/06oA1BH6UlHg6FOkiorHA1W9ADAampW62ryVe2M+pa02AQVO6Axke5353RKI0e2zrkAAABiSO1qnQ9VOyp2QOPM1O8OAADApnzVOofqvxPBIYetq3YEO6AxZup3BwAAYFOVnkoVugtlqP7gZshQkbtIlZ7KCM/MHLgVE2hMc/rdAQAAoFW54l1aN3Wdjp85HnRMavtUueJdEZyVeRDsgFA0pd+dGXk80pYtUnGxt8n58OG0TgAAAJaTkZyhjOSMaE/DlAh2QKhC6XdnRmvW1t9CYdEidtsEbIJ+dwAQ+wh2QFM01O/OjNas9TY9r72IuLDQe3zlSsIdYFEpHQ0luIxG+9hVVhj0uwNQR8H+As19Z67mjZmnK3tdGe3poBUQ7IBY5fF4K3X17QxlGJLDIc2eLV3/E27LBCyoazdpbX5Vo5W4pvS769rNnjvJAXZjGIbyNuZp37F9ytuYpxGZI+Rw0PPS6gh2QKzasiXw9svaDEM6fNg7jr55gCV17aZGw1jpyQhNBoBl1OwFZ/feb7GEYAfEquLi1h0HADGGtYewI18vOKfDKY/hkdPh1KL3F2lkr5FU7SyOYAfEqvT01h0HADGk6IhYe4g67LDurGa1TpI8hoeqXYygQTkQq4YP9+5+Gey3bw6H1L27dxwA2ExT1h7CHmqvOzPqW6NucTWrdTX5qnaxeM12QrADYpXT6W1pINUNd77PFy5k4xQAAFT/urNY47tGj+EJOF6zagfrItgBsWzCeG9Lg4xajTy7daPVAQAA/1S7khWLFSzfNTpUfxXaIUfMXbPd2DbYnThxQpMmTVJSUpJSUlI0depUlZeXN/iaq6++Wg6HI+Bx++23B4w5dOiQrrvuOrVr105dunTRfffdp++++y6clwI0bMJ46Yu90vq/Si+t8H7c+3dCHWATvn53DUlwGUrpyD/mYF+1K1mxWMGq9FSq0F0oQ/X/XTdkqMhdpEpPZYRnhtZi281TJk2apKKiIm3YsEFVVVWaMmWKpk+frldffbXB102bNk2PPPKI//N27dr5/+zxeHTdddcpPT1dH374oYqKijR58mS1adNGjz/+eNiuBWiU00lLA8CmQu13xwYhsKvau0T6xNpuka54l9ZNXafjZ44HHZPaPlWueFcEZ4XWZMtgt3fvXq1fv147duzQ0KFDJUnPPPOMrr32Wi1evFgZtW9bq6Fdu3ZKD7KL4Lvvvqu///3veu+995SWlqZBgwZp/vz5uv/++/W73/1OCQkJYbkeAAAaEkq/O8Cuau8S6ROLu0VmJGcoIzn4v3Nhbba8FXPr1q1KSUnxhzpJys7OVlxcnLZt29bga1euXKnU1FRdeumlys3N1ZkzZwLO279/f6WlpfmPjRkzRm63W59//nnQc1ZUVMjtdgc8AAAAEF6sO0MssWXFrri4WF26dAk4Fh8fr06dOqm4gWbN//7v/64LL7xQGRkZ2r17t+6//359+eWX+vOf/+w/b81QJ8n/eUPnXbBggebNm9fcywEAAE3kW3vYWB871h7GtqasO+MWRZhdTAW7Bx54QHl5eQ2O2bt3b7PPP336dP+f+/fvr65du2r06NH6+uuv1bt372afNzc3Vzk5Of7P3W63evTo0ezzAQCAhrH2EBLrzhBbYirY3XPPPbr11lsbHNOrVy+lp6fr6NGjAce/++47nThxIuj6ufpkZWVJkvbt26fevXsrPT1d27dvDxhTUlIiSQ2e1+VyyeXiPxgAAEQSaw8hse4MsSOmgl3nzp3VuXPnRscNGzZMpaWl2rlzp4YMGSJJ2rhxo6qrq/1hLRS7du2SJHXt2tV/3scee0xHjx713+q5YcMGJSUlqV+/fk28GgAAAAAIjS03T+nbt6/Gjh2radOmafv27dqyZYtmzZqliRMn+nfEPHLkiPr06eOvwH399deaP3++du7cqYMHD+ovf/mLJk+erJEjR2rAgAGSpGuuuUb9+vXTzTffrE8//VTvvPOOHnzwQc2cOZOKHAAAAICwsWWwk7y7W/bp00ejR4/WtddeqxEjRui5557zP19VVaUvv/zSv+tlQkKC3nvvPV1zzTXq06eP7rnnHt1www168803/a9xOp1at26dnE6nhg0bpl/+8peaPHlyQN87AAAAAGhtDoP9W03H7XYrOTlZZcVFSkpKivZ0AFRWSAku/8dPD3kkqvAAWsGBku+UmRavssQjyuwSr2PGMaU6Uv0fAeCU+5Qu6XiJysrKGswGtq3YAQAAAECsINgBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAACYTsH+Ao1aNkoF+wuiPRXAEgh2AAAAMBXDMJS3MU/7ju1T3sY8GYYR7SkBpkewAwAAgKls3r9Zu4t2S5J2F+3W5v2bozwjwPwIdgAAADANwzC06P1FcjqckiSnw6lF7y+iagc0gmAHAAAA0/BV6zyGR5LkMTxU7YAQEOwAAABgCrWrdT5U7YDGEewAAABgCrWrdT5U7YDGEewAAAAQdb5qnUOOep93yEHVDmgAwQ4AAABRV+mpVKG7UIbqD26GDBW5i1TpqYzwzABriI/2BAAAAABXvEvrpq7T8TPHg45JbZ8qV7wrgrMCrINgBwAAAFPISM5QRnJGtKcBWBK3YgIAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIuzbbA7ceKEJk2apKSkJKWkpGjq1KkqLy8POv7gwYNyOBz1Pl5//XX/uPqeX7VqVSQuCQAAAIBNxUd7AtEyadIkFRUVacOGDaqqqtKUKVM0ffp0vfrqq/WO79Gjh4qKigKOPffcc1q0aJHGjRsXcHzFihUaO3as//OUlJRWnz8AAAAA+Ngy2O3du1fr16/Xjh07NHToUEnSM888o2uvvVaLFy9WRkZGndc4nU6lp6cHHHvjjTf085//XOedd17A8ZSUlDpjAQAAACBcbHkr5tatW5WSkuIPdZKUnZ2tuLg4bdu2LaRz7Ny5U7t27dLUqVPrPDdz5kylpqbqsssu04svvijDMBo8V0VFhdxud8ADAAAAAEJly4pdcXGxunTpEnAsPj5enTp1UnFxcUjneOGFF9S3b19dccUVAccfeeQRjRo1Su3atdO7776r3/zmNyovL9edd94Z9FwLFizQvHnzmn4hAAAAAKAYq9g98MADQTc48T2++OKLFn+ds2fP6tVXX623WvfQQw9p+PDhGjx4sO6//37Nnj1bixYtavB8ubm5Kisr8z+++eabFs8RAAAAgH3EVMXunnvu0a233trgmF69eik9PV1Hjx4NOP7dd9/pxIkTIa2NW716tc6cOaPJkyc3OjYrK0vz589XRUWFXC5XvWNcLlfQ5wAAAACgMTEV7Dp37qzOnTs3Om7YsGEqLS3Vzp07NWTIEEnSxo0bVV1draysrEZf/8ILL+jf/u3fQvpau3btUseOHQluAAAAAMImpoJdqPr27auxY8dq2rRpWr58uaqqqjRr1ixNnDjRvyPmkSNHNHr0aL3yyiu67LLL/K/dt2+fNm/erLfffrvOed98802VlJTo8ssvV2JiojZs2KDHH39c9957b8SuDQAAAID92DLYSdLKlSs1a9YsjR49WnFxcbrhhhv09NNP+5+vqqrSl19+qTNnzgS87sUXX1T37t11zTXX1DlnmzZttHTpUt19990yDEMXXXSRnnzySU2bNi3s1wMAAADAvhxGY3vxI+LcbreSk5NVVlykpKSkaE8HQGWFlODyf/z0kEfi9moAreBAyXfKTItXWeIRZXaJ1zHjmFIdqf6PAHDKfUqXdLxEZWVlDWaDmNoVEwAAAADsiGAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA42wa7xx57TFdccYXatWunlJSUkF5jGIYefvhhde3aVW3btlV2dra++uqrgDEnTpzQpEmTlJSUpJSUFE2dOlXl5eVhuAIAAAAA8LJtsKusrNSNN96oGTNmhPyahQsX6umnn9by5cu1bds2tW/fXmPGjNG5c+f8YyZNmqTPP/9cGzZs0Lp167R582ZNnz49HJcAAAAAAJKk+GhPIFrmzZsnSXrppZdCGm8YhpYsWaIHH3xQ48ePlyS98sorSktL05o1azRx4kTt3btX69ev144dOzR06FBJ0jPPPKNrr71WixcvVkZGRliuBQAAAIC92TbYNdWBAwdUXFys7Oxs/7Hk5GRlZWVp69atmjhxorZu3aqUlBR/qJOk7OxsxcXFadu2bfrpT39a77krKipUUVHh/7ysrEyS5D51KkxXA6BJKiukBJf/Y3m5R6p0RXtWAGLAmdPfqfxUvE5XndKpxHiVG+VyOVz+jwBQ7vYu6zIMo8FxBLsQFRcXS5LS0tICjqelpfmfKy4uVpcuXQKej4+PV6dOnfxj6rNgwQJ/BbGmHj/4YUunDQAAACAGnDp1SsnJyUGfj6lg98ADDygvL6/BMXv37lWfPn0iNKPQ5ObmKicnx/95dXW1Tpw4ofPPP18OhyOKM2t9brdbPXr00DfffKOkpKRoTwdNwM/OuvjZWRM/N+viZ2dd/OysK5Z/doZh6NSpU40u64qpYHfPPffo1ltvbXBMr169mnXu9PR0SVJJSYm6du3qP15SUqJBgwb5xxw9ejTgdd99951OnDjhf319XC6XXK7A2y1C3anTqpKSkmLuL51d8LOzLn521sTPzbr42VkXPzvritWfXUOVOp+YCnadO3dW586dw3LuzMxMpaenKz8/3x/k3G63tm3b5t9Zc9iwYSotLdXOnTs1ZMgQSdLGjRtVXV2trKyssMwLAAAAAGzb7uDQoUPatWuXDh06JI/Ho127dmnXrl0BPef69OmjN954Q5LkcDh011136dFHH9Vf/vIX7dmzR5MnT1ZGRoYmTJggSerbt6/Gjh2radOmafv27dqyZYtmzZqliRMnsiMmAAAAgLCJqYpdUzz88MN6+eWX/Z8PHjxYkvT+++/r6quvliR9+eWX/h0qJWn27Nk6ffq0pk+frtLSUo0YMULr169XYmKif8zKlSs1a9YsjR49WnFxcbrhhhv09NNPR+aiLMDlcmnu3Ll1bj2F+fGzsy5+dtbEz826+NlZFz876+JnJzmMxvbNBAAAAACYmm1vxQQAAACAWEGwAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2iIjNmzfr+uuvV0ZGhhwOh9asWRPtKSFECxYs0I9+9CN16NBBXbp00YQJE/Tll19Ge1poxLJlyzRgwAB/o9Zhw4bpr3/9a7SnhWZ44okn/C13YG6/+93v5HA4Ah59+vSJ9rQQgiNHjuiXv/ylzj//fLVt21b9+/fXxx9/HO1poRE9e/as83fO4XBo5syZ0Z5aVBDsEBGnT5/WwIEDtXTp0mhPBU30wQcfaObMmfroo4+0YcMGVVVV6ZprrtHp06ejPTU0oHv37nriiSe0c+dOffzxxxo1apTGjx+vzz//PNpTQxPs2LFDf/jDHzRgwIBoTwUhuuSSS1RUVOR//O1vf4v2lNCIkydPavjw4WrTpo3++te/6u9//7t+//vfq2PHjtGeGhqxY8eOgL9vGzZskCTdeOONUZ5ZdNi2jx0ia9y4cRo3bly0p4FmWL9+fcDnL730krp06aKdO3dq5MiRUZoVGnP99dcHfP7YY49p2bJl+uijj3TJJZdEaVZoivLyck2aNEnPP/+8Hn300WhPByGKj49Xenp6tKeBJsjLy1OPHj20YsUK/7HMzMwozgih6ty5c8DnTzzxhHr37q2rrroqSjOKLip2AJqkrKxMktSpU6cozwSh8ng8WrVqlU6fPq1hw4ZFezoI0cyZM3XdddcpOzs72lNBE3z11VfKyMhQr169NGnSJB06dCjaU0Ij/vKXv2jo0KG68cYb1aVLFw0ePFjPP/98tKeFJqqsrNQf//hH/epXv5LD4Yj2dKKCih2AkFVXV+uuu+7S8OHDdemll0Z7OmjEnj17NGzYMJ07d07nnXee3njjDfXr1y/a00IIVq1apU8++UQ7duyI9lTQBFlZWXrppZd08cUXq6ioSPPmzdOVV16pzz77TB06dIj29BDE/v37tWzZMuXk5GjOnDnasWOH7rzzTiUkJOiWW26J9vQQojVr1qi0tFS33nprtKcSNQQ7ACGbOXOmPvvsM9aMWMTFF1+sXbt2qaysTKtXr9Ytt9yiDz74gHBnct98843+4z/+Qxs2bFBiYmK0p4MmqLnkYMCAAcrKytKFF16oP/3pT5o6dWoUZ4aGVFdXa+jQoXr88cclSYMHD9Znn32m5cuXE+ws5IUXXtC4ceOUkZER7alEDbdiAgjJrFmztG7dOr3//vvq3r17tKeDECQkJOiiiy7SkCFDtGDBAg0cOFBPPfVUtKeFRuzcuVNHjx7Vv/zLvyg+Pl7x8fH64IMP9PTTTys+Pl4ejyfaU0SIUlJS9MMf/lD79u2L9lTQgK5du9b5hVffvn25jdZC/vGPf+i9997TbbfdFu2pRBUVOwANMgxDd9xxh9544w1t2rSJBeUWVl1drYqKimhPA40YPXq09uzZE3BsypQp6tOnj+6//345nc4ozQxNVV5erq+//lo333xztKeCBgwfPrxOG5///d//1YUXXhilGaGpVqxYoS5duui6666L9lSiimCHiCgvLw/4jeWBAwe0a9cuderUSRdccEEUZ4bGzJw5U6+++qrWrl2rDh06qLi4WJKUnJystm3bRnl2CCY3N1fjxo3TBRdcoFOnTunVV1/Vpk2b9M4770R7amhEhw4d6qxhbd++vc4//3zWtprcvffeq+uvv14XXnihCgsLNXfuXDmdTv3iF7+I9tTQgLvvvltXXHGFHn/8cf385z/X9u3b9dxzz+m5556L9tQQgurqaq1YsUK33HKL4uPtHW3sffWImI8//lg//vGP/Z/n5ORIkm655Ra99NJLUZoVQrFs2TJJ0tVXXx1wfMWKFbZeoGx2R48e1eTJk1VUVKTk5GQNGDBA77zzjv71X/812lMDYtbhw4f1i1/8QsePH1fnzp01YsQIffTRR3W2ZIe5/OhHP9Ibb7yh3NxcPfLII8rMzNSSJUs0adKkaE8NIXjvvfd06NAh/epXv4r2VKLOYRiGEe1JAAAAAACaj81TAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAouDRRx+Vw+HQ5ZdfXu/zDzzwgBwOhwYNGqSTJ09GeHYAAKtxGIZhRHsSAADYzdmzZ/XDH/5Qhw8f1urVq3XDDTf4n1uwYIHmzJmjiy++WJs3b1aXLl2iOFMAgBVQsQMAIAratm2rxx57TJL029/+Vt99950kadmyZZozZ44yMzOVn59PqAMAhISKHQAAUWIYhoYOHapPPvlEy5cv13nnnaebb75ZGRkZKigoUGZmpn/svn37tHjxYm3fvl179uxRt27ddPDgwehNHgBgKgQ7AACiaNOmTfrxj3+sjh076tSpU+rYsaM2b96sPn36BIxbu3atZs6cqcsuu0wHDhzQyZMnCXYAAD+CHQAAUTZ8+HB9+OGH6tChgzZv3qxBgwbVGVNdXa24OO8Kittvv13r168n2AEA/FhjBwBAFK1YsUJbt26VJFVUVCgpKanecb5QBwBAffi/BAAAUfL6669r2rRp6tSpk2666SZVVlbq/vvvj/a0AAAWRLADACAK3n77bU2aNEnt27fXO++8o+XLl6tTp05avXq1Pvzww2hPDwBgMQQ7AAAi7IMPPtDPfvYzxcfH680339SQIUOUkpKiOXPmSJJycnKiPEMAgNUQ7AAAiKDt27fr+uuvl8fj0Z///GeNHDnS/9ysWbN04YUXatu2bVq1alUUZwkAsBqCHQAAEbJnzx6NGzdOZ86c0cqVKzV27NiA510ul+bPny9Jys3NVUVFRTSmCQCwINodAABgMbQ7AADUFh/tCQAAgMadOXNGb7/9tiRp//79OnPmjFavXi1J+tGPfqQLL7wwmtMDAEQZFTsAACzg4MGDyszMrPe5FStW6NZbb43shAAApkKwAwAAAACLY/MUAAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAs7v8HvPCVjsoQcTIAAAAASUVORK5CYII=\n",
      "application/papermill.record/text/plain": "<Figure size 1000x600 with 1 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "dt_boundary_r"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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swWxg281TAAAAACBWEOwAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAoNUU7C/QqGWjVLC/INpTAWyFYAcAAIBWYRiG8jbmad+xfcrbmCfDMKI9JcA2CHYAAABoFZv3b9buot2SpN1Fu7V5/+YozwiwD4IdAAAAWswwDC16f5GcDqckyelwatH7i6jaARFCsAMAAECL+ap1HsMjSfIYHqp2NbD2EOFGsAMAAECL1K7W+VC182LtISKBYAcAAIAWqV2t86Fq58XaQ0QCwQ4AAADN5qvWOeSo93mHHLau2rH2EJFCsAMAAECzVXoqVegulKH6g4ohQ0XuIlV6Kus8Z4d1Z6w9RKTER3sCAAAAsC5XvEvrpq7T8TPHg45JbZ8qV7wr4FjtdWcjMkfI4ai/6mdVNat1NW9T9VXtRvYaGXPXjOgh2AEAAKBFMpIzlJGc0aTX1Lfu7KreV4VjelFT8xprqlm1i7VrRvRwKyYAAAAiyg7rzlh7iEgj2AEAACCi7LDurCVrD4Hm4FZMAAAARIxd1p01d+0h0FwEOwAAAESMndadNWftIdBc3IoJAACAiGDdGRA+BDsAAABERHPXndmh3x3QUtyKCQAAgIhozrozO/S7A1oDwQ4AAAAR09R1Z2brd7dsyzIt/mCx7r3qXs0YPiNq8wBq41ZMAAAAmJLZ+t1VV1drScESVXmqtKRgiaqrq6MyD6A+BDsAAACYktn63S3dslRnq85Kks5WndXSLUujMg+gPgQ7AAAAmE7tap1PtKp21dXVenbLswHHnt3yLFU7mAbBDgAAAKZTu1rnE62qXc1qnQ9VO5gJwQ4AAACmYrZ+d/VV63yo2sEsCHYAAAAwFbP1u6uvWudD1Q5mQbsDAAAAmIqZ+t01VK3zeXbLs5o5fKbi4qiZIHoIdgAAADAds/S7K68s17mqcw2OOVd1TuWV5UpKTGrx1wOai2AHAAAAS6u5g6bH8Ph3zhzZa2SLq3ZJiUlaM2WNDp48GHRMZqdMQh2ijmAHAAAAS6tZrZMCd85sjard4O6DNbj74BafJ1oK9hdo7jtzNW/MPF3Z68poTwdhwo3AAAAAsCyz9bszm9prD+3+/YhlBDsAAABYltn63ZlNfWsPEZsIdgAAALAks/W7M5va1UyqmLGNYAcAAABLam6/O7uoXc2kihnb2DwFAAAAltScfnd2UXunUJ/W3DEU5kKwAwAAgGU1td+dXdTeKdSntXcMhXlwKyYAAAAQQ1h7aE8EOwAAACCGtGTtYcH+Ao1aNkoF+wvCPU20Mm7FBAAAAGJIc9ce1u55NyJzBOvwLIRgBwAAAMSY5qw9rK/nHevwrMP2t2IuXbpUPXv2VGJiorKysrR9+/agY59//nldeeWV6tixozp27Kjs7Ow642+99VY5HI6Ax9ixY8N9GQAAAECz0fPO+mwd7F577TXl5ORo7ty5+uSTTzRw4ECNGTNGR48erXf8pk2b9Itf/ELvv/++tm7dqh49euiaa67RkSNHAsaNHTtWRUVF/sd///d/R+JyAAAAgGah55312TrYPfnkk5o2bZqmTJmifv36afny5WrXrp1efPHFesevXLlSv/nNbzRo0CD16dNH//Vf/6Xq6mrl5+cHjHO5XEpPT/c/OnbsGInLAQAAAJqsdrXOh6qdtdg22FVWVmrnzp3Kzs72H4uLi1N2dra2bt0a0jnOnDmjqqoqderUKeD4pk2b1KVLF1188cWaMWOGjh8PvnBVkioqKuR2uwMeAAAAQCTUrtb5ULWzFtsGu2PHjsnj8SgtLS3geFpamoqLi0M6x/3336+MjIyAcDh27Fi98sorys/PV15enj744AONGzdOHo8n6HkWLFig5ORk/6NHjx7NuygAAACgCeh5FzvYFbOZnnjiCa1atUqbNm1SYmKi//jEiRP9f+7fv78GDBig3r17a9OmTRo9enS958rNzVVOTo7/c7fbTbgDAABA2DWl513t9ggwF9sGu9TUVDmdTpWUlAQcLykpUXp6eoOvXbx4sZ544gm99957GjBgQINje/XqpdTUVO3bty9osHO5XHK5+IsCAACAyGpuzzuYj22DXUJCgoYMGaL8/HxNmDBBkvwbocyaNSvo6xYuXKjHHntM77zzjoYOHdro1zl8+LCOHz+url27ttbUAQAAgFbTnJ53MB/brrGTpJycHD3//PN6+eWXtXfvXs2YMUOnT5/WlClTJEmTJ09Wbm6uf3xeXp4eeughvfjii+rZs6eKi4tVXFys8vJySVJ5ebnuu+8+ffTRRzp48KDy8/M1fvx4XXTRRRozZkxUrhEAAABA7LN1sLvpppu0ePFiPfzwwxo0aJB27dql9evX+zdUOXTokIqKivzjly1bpsrKSv3sZz9T165d/Y/FixdLkpxOp3bv3q1/+7d/0w9/+ENNnTpVQ4YMUUFBAbdaAgAAxLiC/QUatWyUCvYXRHsqsCGHwRY3puN2u5WcnKyy4iIlJSVFezqAvVVWSJI+LY7XgVKnOl1Yog6dSyVJqY7UKE4MAGAmhmHo+heu1+6i3RrQdYDenPqmHI76d5oEmuKU+5Qu6XiJysrKGswGtq7YAQAAxBqqRtHh6wUnid5viAqCHQAAQIwwDEN5G/O079g+5W3Mo/dYhPh6wTkdTkmS0+Gk9xsijmAHAAAQI6gaRYfv++4xPJIkj+Hh+4+II9gBAADEAKpG0VH7++7D9x+RRrADAACIAVSNoqP2992H7z8ijWAHAABgcVSNosP3fXeo/t0vHXLw/UfEEOwAAAAsjqpRdFR6KlXoLpSh+oObIUNF7iJVeiojPDPYUXy0JwAAAIDmq1k1qi9g+KpGI3uNpK9aK3PFu7Ru6jodP3M86JjU9qlyxbsiOCvYFcEOAADAwppSNaoZMAr2F2juO3M1b8w8XdnrykhNN+ZkJGcoIzkj2tMACHYAAABW1pyqUe1+dyMyR1DNAyyOYAcAAGBxTa0a1dfv7qreV4VregAigM1TAAAAbIR+d0BsItgBAADYCP3uGlawv0Cjlo1Swf6CaE8FaBKCHQAAgE3Q765htdce2v37AWsh2AEAANgE/e4aVt/aw2iieoimINgBAADYQM1+d/Xx9buza5XKbGsPqR6iqQh2AAAANtCUfne12aFyZLa1h2arHsL8aHcAAABgA83pdyfZo+ddzWpdzdtUfVW7kb1GRvSaa88nWvOAtVCxAwAAsImM5Az179o/6KNrUtc6rzFb5Sgc1UOzrT00W/UQ1kCwAwAAQL3ssO7MbGsP2bkUzUWwAwAAQL3MVjkKR/WwJWsPw8Fs1UNYB2vsAAAAUIdd1p01d+1hONSsHtYXNH3VQ9baoT4EOwAAANRRszpWU83K0VW9r4rafFpzHhnJGcpIzmjpFFusKdXDSARNWAvBDgAAAAGaWzkq2F+gue/M1bwx83RlrytbfT5mqR6Gi5mqh7Aegh0AAAACNKdyFM62CGarHoaTWaqHsB6CHQAAAAI0p3JU38YmrRG2zFY9BMyKYAcAAIA6mlI5CmdDbbNVDwGzItgBAACgRcK5sYmZqofNRfUQkUCwAwAAQLNFYmMTs1QPm4PqISKFBuUAAAA2UbC/QKOWjVLB/oJWO6fZGmrboal6S4TjPQBzINgBAADYQO3KkWHUv2atqef0bWxSH9/GJq3xtZoyH6fDGXDcV7WL1DyCzSda86g5n9Z+D8A8CHYAAAA2EI7KUVM2NokEqoehzUcyR/UQrYs1dgAAADEuXOvOzNRQu7ltEcI9H7M0VTfb2kO0vmZV7B599FE5HA5dfvnl9T7/wAMPyOFwaNCgQTp58mSLJggAABAudllvFM7KUUZyhvp37R/00TWpa4u/RihaUj1k7SFigcNoxs21Z8+e1Q9/+EMdPnxYq1ev1g033OB/bsGCBZozZ44uvvhibd68WV26dGnVCduB2+1WcnKyyoqLlJSUFO3pAPZWWSFJ+rQ4XgdKnep0YYk6dC6VJKU6UqM4MQAtZRiGrn/heu0u2q0BXQfozalvRrxyceSQUyeOBf89e6fUanW7wBP0+VD4rvPz4s/rVI4uSb8kKtcdLoVlhY1WD2sHzXC8D3zn3FO0J2j1sH/X/hH73tvpPRCLTrlP6ZKOl6isrKzBbNCsWzHbtm2rxx57TLfccot++9vfavz48YqPj9eyZcs0Z84cZWZmKj8/n1AHAABMK9q9zo4ccurqvl1Vce6f/6AevlD68UPS+/OlLbMlSa5EQ5v2FrUo3NXuMefTmr3mzKIpbRF8wvE+aE5T9XCy03vAzpq9xu7mm2/WU089pU8++UQvvPCCzjvvPM2cOVPdunVTfn6+unXr5h+7evVqrVy5Ujt37tSxY8eUmZmpX/3qV7rzzjvVpk2bVrkQAACAUJlhvdGJY3HfhzpVS1fNk+IrvR+33CvJ+/yJY3HNDnZmW3dmNqw95D0QS5q9K6bD4dDvf/97SVJubq5uvfVWpaam6r333lNmZmbA2MWLF8vlcmnhwoV666239O///u968MEHddttt7Vs9gAAAM1guvVGIxZICWe8f0444/28FTR33dmyLcvU+/HeWrZlWavMw6xYexj5nUsRPs1aY1fT8OHD9eGHH6pDhw7avHmzBg0aVGfMt99+q86dOwcce/TRR/XQQw+puLhYaWlpLZlCzGGNHWAirLEDYo5Z1hvt+aSNrvtRuqRqaU4Hqc0ZySHJkFTVTnr8lKQ4vbWjWP3/parZX6ep686qq6vVd2Ffna06q7Zt2mrv7L2Ki4u9DllmeR9EQnPWHsI8wrrGzmfFihXaunWrJKmioiLoF6od6iRpyJAhkqTCwkKCHQBz++476TtDqqpWSalHZ9p41Db5tCTCHWBFpltvVLNaJ3nDna9q97fftvj0TV13tnTLUp2tOitJOlt1Vku3LNUdV97R4nmYjeneB2HUnLWHsJ5mB7vXX39d06ZNU6dOnZSdna3XXntN999/v15//fWQXr9582YlJCSod+/ezZ0CAIRfgnf9w8DukuLjdKA0Q6d8zyW7ozUrAM1kGIYWvL+gwfVGC95foH6Z/cJerSk12krqIo183Fulq/nlDHmP/y03rHOorbq6Ws9ueTbg2LNbntXM4TNjqmrHujPEomb9DX377bc1adIktW/fXu+8846WL1+uTp06afXq1frwww8bff3f//53PfXUU5o+fTq3GgKwjIHp3ykzxaM2ZRk69W2Kzpa1j/aUADRRladKxe7iBtcblbhLVOVp/q2PTeKr1tXODjWrdhFUs1rn46vaxZLmrjuzS99DWFOT19h98MEHGjdunCRp/fr1GjlypCTp97//ve69915lZWXpo48+Cvr6Y8eO6YorrlBiYqK2bt2q9u35h1FtrLFDHR6PtGWLVFwspadLw4dLTme0Z2UflRXeyl1lhT4t9t7ocKDUqarkQqWl8HMArOZoeaFKz54I+nxK2/PV5bzwrzf6Yle8fl3Q5/u1dbX9c63dm+O/0sChLetlF4qaa+tqi8W1dk1dd2aGvoewp7Cssdu+fbuuv/56eTwerV271h/qJGnWrFl65plntG3bNq1atUoTJ06sO6lTpzRu3DhVVlZq06ZNhDogFGvWSvfdJx058v2xbt2kRYukCeOjNy+bGniBU58e8igzLV4HSjJ0oizaMwLQVPFKU4OrY89IJ4L/e7/VnD19Soo/V3+ok7zH488pMcUt6ft/My3bskyLP1ise6+6VzOGz2i1+dRXrfPPNQbX2jV13Vm0+x4CjQm5Yrdnzx5dffXVKisr06pVq/Szn/2szpj/+3//ryZPnqyePXvqiy++kMv1fW+OiooKjRs3Tnv27NHf/vY3XXzxxa13FTGGih381qyVJk2Sav819f2GcOVKwl0k1KjYKcGlTw95JFf4ew+ZTdERqfRk8N9Op3Q01LVb0KcB1GPVhx/pVNVBnYt3q+N5Dp0xzqido53/Y9+MnvrXIQP848O1Y2VD1TqfWKzahar2DpqxuHMmzKvVK3b9+/fX8eMN//rq5ptv1s0331znuMfj0cSJE7Vjxw5t3LiRUAeEwuPxVurq+92LYXjD3ezZ0vU/4bZMhF3REWn86DaqrAj+D5gEl6G1+VWEO6AJsnoPVWba5SpLPKLMLvE6ZhxTqiPV/7G2cO1YWV5ZrnNV5xocc67qnMory5WUaL9fOtfeQTMWd86E9bWo3UGoZs6cqTVr1mj+/PnyeDwBa/D69etHVQqoz5Ytgbdf1mYY0uHD3nE1bosGwqH0pKPBUCdJlRUOlZ50qGu3FrVHBRBEOHesTEpM0popa3Tw5MGgYzI7Zdoy1Pl20HQ6nHX63bFzJswkIrX09evXS5IeeughDRs2LODxySefRGIKQS1dulQ9e/ZUYmKisrKytH379gbHv/766+rTp48SExPVv39/vf322wHPG4ahhx9+WF27dlXbtm2VnZ2tr776KpyXgFhVXNy64wAAlhbuHSsHdx+sn/b/adDHoG6DWuXrWI2vWlcz1EmBVTvADCIS7A4ePCjDMOp9XH311ZGYQr1ee+015eTkaO7cufrkk080cOBAjRkzRkePHq13/Icffqhf/OIXmjp1qv7nf/5HEyZM0IQJE/TZZ5/5xyxcuFBPP/20li9frm3btql9+/YaM2aMzp1r+PYGoI709NYdByAsio5Iez9zBH0UNVB4B0JVX7XO59ktz6q6ujrCM7KHmv3u6uPrd9fETeaBsIjIrZhm9eSTT2ratGmaMmWKJGn58uV666239OKLL+qBBx6oM/6pp57S2LFjdd9990mS5s+frw0bNujZZ5/V8uXLZRiGlixZogcffFDjx3s3tHjllVeUlpamNWvW1LtTKBDU8OHe3S8LC+tfZ+dweJ8fPjzycwMgibWHiBy77VhpFk3pd+eKt9+mWjAX2wa7yspK7dy5U7m5uf5jcXFxys7O1tatW+t9zdatW5WTkxNwbMyYMVqzZo0k6cCBAyouLlZ2drb/+eTkZGVlZWnr1q1Bg11FRYUqKir8n7vd7uZeFmKJ0+ltaTBpkjfE1Qx3vnv5Fy5k4xQgisy29pCdS2NTQ9U6n9Zaa4dArniX1k1d12i/O0IdzMC2we7YsWPyeDxKS0sLOJ6WlqYvvvii3tcUFxfXO774n2ucfB8bGlOfBQsWaN68eU2+BtjAhPHelgb19bFbuJBWBwD8qB7GLnasjK6m9rsDosW2wc5McnNzAyqBbrdbPXr0iOKMYCoTxntbGmzZ4t0oJT3de/sllToANVA9jF3sWAkgFLYNdqmpqXI6nSopKQk4XlJSovQgm1Gkp6c3ON73saSkRF27dg0YM2jQoKBzcblcAc3cgTqcTloaIKpSOhpKcBmNVoNSOrKBAKgehsPg7oM1uPvgaE+jRQr2F2juO3M1b8w8XdnrymhPB4g5tg12CQkJGjJkiPLz8zVhwgRJ3nvY8/PzNWvWrHpfM2zYMOXn5+uuu+7yH9uwYYOGDRsmScrMzFR6erry8/P9Qc7tdmvbtm2aMWNGOC8HAMKqazdpbX4VFRiEhOohajMMQ3kb87Tv2D7lbczTiMwR9H4DWpltg50k5eTk6JZbbtHQoUN12WWXacmSJTp9+rR/l8zJkyerW7duWrBggSTpP/7jP3TVVVfp97//va677jqtWrVKH3/8sZ577jlJksPh0F133aVHH31UP/jBD5SZmamHHnpIGRkZ/vAIAFbVtZtoPg7LoXpoDr5ecJL8vd+u6n1VlGcFxBZbB7ubbrpJ3377rR5++GEVFxdr0KBBWr9+vX/zk0OHDgXsLnXFFVfo1Vdf1YMPPqg5c+boBz/4gdasWaNLL73UP2b27Nk6ffq0pk+frtLSUo0YMULr169XYmJixK8PAAC7M1v10I58veCcDqc8hkdOh1OL3l+kkb1GUrUDWpGtg50kzZo1K+itl5s2bapz7MYbb9SNN94Y9HwOh0OPPPKIHnnkkdaaIgDApFh7CDSuZrVOkjyGh6pdDaw9RGuxfbADAKC5WHsINKx2tc6Hqp0Xaw/RmuhiCQBAC3TtJvW91Aj6iFSo81UPG0L1EMEU7C/QqGWjVLC/oFXP66vW1Qx1UmDVzs7qW3sINBcVOwAAYgDVQzRXuKpGvmqdQw4ZqvsLBYcctq7asfYQrY1gBwBAjDDLzqWsPbSWcO1YWempVKG7sN5QJ0mGDBW5i1TpqZQr3n79fFl7iNZGsAN8PB5pyxapuFhKT5eGD/c2BgcANAnVQ+sIZ9XIFe/SuqnrdPzM8aBjUtun2jLUsfYQ4UCwAyRpzVrpvvukI0e+P9atm7RokTRhfPTmBQAWRfXQGsJdNcpIzlBGckaLzxNran/ffajaoSUIdsCatdKkSZJR63/qhYXe4ytXEu4AwKKoHgZH1Sg6WHuIcCHYwd48Hm+lrnaok7zHHA5p9mzp+p9wWyYAWJRZqodmQ9UoOlh7iHAh2MHetmwJvP2yNsOQDh/2jhs5MnLzAgAgjKgaRQ9rDxEuBDvYW3Fx644DAMACqBpFF2sPEQ4EO9hbenrrjgMAwAKoGgGxh2AHexs+3Lv7ZWFh/evsHA7v88OHR35uAACEEVUjILYQ7GBvTqe3pcGkSd4QVzPc+dYULFzIxikAgABFR8ROmwBMhWAHTBjvbWlQXx+7hQvN3eqApuoAEHFFR6Txo9s02htvbX4V4Q5AxBDsAMkb3q7/ibVCEk3VASAqSk86Ggx1klRZ4VDpSUdE2iwcOeTUiWNxQZ/vlFqtbhd4gj4PIDYQ7AAfp9M6LQ1oqg4AkDfUXd23qyrOBQ+arkRDm/YWEe6AGBf81zsAzKmxpuqSt6m6h/+BA0CsO3EsrsFQJ0kV5xwNVvQAxAb+lgNW05Sm6gAAALAFbsUErIam6gDQoBf/U9r7WfAqVt9LDf3qNxGcEABEAMEOCKezZ6XcOdLXX0u9e0sLHpfatm3ZOWmqDgBBvfif0tOL2kgKHuw2/NWQVEW4AxBTuBUTCJef3ySdnyo995yUn+/9eH6q93hL+JqqO4L8o8XhkLp3p6k6AFvyVuoaXnMmORqs6AGAFRHsgHD4+U3SunX1P7duXcvCna+pulQ33NFUHQDCLqWjoQRXw20MElyGUjqGv9UBAPhwKybQ2s6eDR7qfNat845r7m2ZVm6qDiAqNq6Xvv4qeJWq9w8MjRobwQlZWNdu0tr8KpWeDP79TOlo0JwcQEQR7BBZHo+1moA3R+6c0Mct+f+a/3Ws2FQdQFRsXC/lzGh43Zlk6MllVYS7EHXtpog0H29Mp9RquRKNRvvYdUqtjuCsAEQDwQ6Rs2Zt/RWmRYtiq8L09detO64hVmqqDiBqvJW6xtedff2VQ6PGRj+sIHTdLvBo096iBvvUdUqtpjk5YAMEO0TGmrXSpEl1m2oXFnqPr1wZ+XBXWSn94TnpwH4ps5f06+lSQkLLz9u7t3ezlFDGAQDQQt0u8BDcABDsEAEej7dSVzvUSd5jDoc0e7b3tsJI3UY457fS009L1TVuTcnNle68U3r8sZade8Hj3h0wQxkHAIiqoiNirRyAmECwQ/ht2RJ4+2VthiEdPuwdF4nbCuf8VlqypO7x6urvj7ck3LVtK/3kJw1voPKTnwRunGKHtYcAEAF9LzX+2aeu4fWEfS81VHREGj+6jSorgo9NcBlam19FuANgegQ7hF9xceuOa4nKSm+lriHPPCP9bm7Lbsv802vBWx785Cfe533ssvYQACLA23S8qsE+dX0vNfSr33h73jUU6iSpssKh0pMOU2yUAgANIdgh/NLTW3dcS/zhucDbL+vj8XjH3TGrZV/rT695WxrkzvFulNK7t/f2y5qVOjOuPQRga5/ulI58EzzsdOthaOCQCE6oGbzhjiAGwF4Idgi/4cO9FajCwvrX2Tkc3ueHDw//XA7sb91xjWnbNnhLAzOuPQRga0VF0i0/a7wtwsurq0wf7gDAbgh2CD+n03tb4aRJ3rBSM8g4/vmPh4ULIxNeMnu17riWMNvaQ8BkYqFyZBa9f2DIW8FqOLAlJqqRMd7nj3zj0MAh4a+I8R4AgNAR7BAZE8Z7byusby3ZwoWRu93w19O9u182dDum0+kdF25mWnsImMynO6kctaZRY6Unl1X9s59d/Xr/wNC5BppcRxrvAQBoGoIdImfCeO9thdHc/TEhwdvSoL5dMX3uuKPuximNrZVrDjOtPQRMxlulMU/lKBaMGqtGm4+/vSYycwmF2d4DtEUwh2VblmnxB4t171X3asbwGdGeDmAqBDtEltMZ/dsKfa0Mavexczq9oa52q4Pau1vm53v71NXe3bKpzLT2EAAQ1LGj3uohbRGiq7q6WksKlqjKU6UlBUv062G/VlxcXLSnBZgGfxtgT48/Jp04LuXlSbf/2vvx+LHGQ11N69Z5n28u39pD6fu1hj4NrT30eKTNm6U//cn70eNp/hwAWE7REe82/cEeRQ0s3UWglI6GElwNV/t8z4faFgHhs3TLUp2tOitJOlt1Vku3LI3yjGB3BfsLNGrZKBXsL4j2VCRRsYOdJSQ03NLg7NmGm4xL3ufPnm3+bZlNXXtIzzvA1mio3bq6dpPW5lc1eoslgS36qqur9eyWZwOOPbvlWc0cPpOqHaLCMAzlbczTvmP7lLcxTyMyR8hR+xf1EUawA4LJnRP6uGAtDUIR6tpDet4Btld60lwNtWNh18qu3dTo96r0ZIQmg6BqVut8fFW7O668I0qzgp1t3r9Zu4t2S5J2F+3W5v2bdVXvq6I6J4IdEMzXX7fuuIY0tvaQnncAIqBbj9DaInTrYbBrJSKmvmqdD1U7RINhGFr0/iI5HU55DI+cDqcWvb9II3uNjGrVjmAH66uslP7wnLepeGYvb6uC2rtaNkfv3t6NUkIZF270vAMQAQOHSC+vrgqpCvf2GnPtWonYVV+1zoeqHaKhZrVOkjyGxxRVO4IdrG3Ob+vubpmb621pUHsjlKZa8Lh398tQxoUbPe9gM02pHKF1DRwiUwQx3gOQGq7W+dRXtSvYX6C578zVvDHzdGWvK8M9TdhI7WqdjxmqdgQ7WNec39bfj666+vvjLQl3bdt6Wxo0tIHKT34SuHGKxxOePn30vIPNNKVyhNjEewCSVF5ZrnNV5xocc67qnMory5WUmCTJnJtaIDrCEfBrV+t8zFC1I9jBmiorvZW6hjzzjPS7uS27LfNPrwVveVC7j104d6yk5x1syCyVI0SPWd4DvrYIje1GmtIx+nONNUmJSVozZY0OnjwYdExmp0x/qJPMuakFIi8cAd9XrXPIIUN1/7475Ihq1Y5gB2v6w3OBt1/Wx+PxjmuopUEo/vSat6VB7hzvRim9e3tvv6xZqQv3jpW+nneTJnlDXM2v01DPOwC2VXREjW7jT0uE0ITaFsGM388jh5w6cSz4xiKdUqvV7QJz90Md3H2wBncfHNJYs25qgcgLR8Cv9FSq0F1Yb6iTJEOGitxFqvRUyhXvatHXag6CHazpwP7WHdeYtm2DtzSI1I6VTe15ByDmhFo5qqww6HfXykJpi2A2Rw45dXXfrqo4F/x94Eo0tGlvkenDXajMuqkFIitcAd8V79K6qet0/MzxoGNS26dGJdRJBDtYVWav1h3XEpHcsTLUnncAYlJTGmrT7w4njsU1GOokqeKcQyeOxcVEsDPzphaIrHAG/IzkDGUkZ7R0imFBsIM1/Xq6d/fLhm7HdDq948It0jtWNtbzDkBMM0tD7VB3rTx7xqDfHSLCzJtaIHLsHPBtG+xOnDihO+64Q2+++abi4uJ0ww036KmnntJ5550XdPzcuXP17rvv6tChQ+rcubMmTJig+fPnKzk52T+uvjfKf//3f2vixIlhuxZbSkjwtjSob1dMnzvuCNw4pbxc+tVU6cABKTNTevEFKcjPu0nYsRKwDCpHrSfUXSu9z5un3x1rD2OT2Te1QOTYOeDbNthNmjRJRUVF2rBhg6qqqjRlyhRNnz5dr776ar3jCwsLVVhYqMWLF6tfv376xz/+odtvv12FhYVavXp1wNgVK1Zo7Nix/s9TUlLCeSn25WtlULuPndPpDXU1Wx1cOVLaufP7zz//XOqSJg0ZIhVsbtk82LESsIRPd4rKUSsLZdfKI99EaDIhKDoi1h7GKLNvaoHIsHvAt2Ww27t3r9avX68dO3Zo6NChkqRnnnlG1157rRYvXqyMjLr3zV566aX6f//v//k/7927tx577DH98pe/1Hfffaf4+O+/lSkpKUpvQnWmoqJCFRUV/s/dbndzLsueHn/M29LgD895N0rJ7OW9/bJmpa52qKtp507v8y0Jd+xYCVgClSOYce3h/3wWp73JDp1p016dk+N0ykhRB0dbnTJSdEmvBA0ZVhn2ecQCs29qgciwe8C3ZbDbunWrUlJS/KFOkrKzsxUXF6dt27bppz/9aUjnKSsrU1JSUkCok6SZM2fqtttuU69evXT77bdrypQpDf5WYMGCBZo3b17zLgbeEBespUF5efBQ57Nzp3dcS27LZMdKAE1A5Qh1K8hd/vkxtcZHQ2/87SjhLkRm3tQCkWH3gG/LYFdcXKwuXboEHIuPj1enTp1UHOIGF8eOHdP8+fM1fXrg5hyPPPKIRo0apXbt2undd9/Vb37zG5WXl+vOO+8Meq7c3Fzl5OT4P3e73erRo0cTrghB/Wpq6ONqNhtvDqvuWOnxWG/OgMWZrXKEyAu1gnzogFNDhkViRkBssHPAj6lg98ADDygvL6/BMXv37m3x13G73bruuuvUr18//e53vwt47qGHHvL/efDgwTp9+rQWLVrUYLBzuVxyuWLzNwdRd+BA645rjNV2rFyztv4q46JFVBkBCwu1311KR0JjLOuUWi1XotFoH7tOqQ3sMA3AMmIq2N1zzz269dZbGxzTq1cvpaen6+jRowHHv/vuO504caLRtXGnTp3S2LFj1aFDB73xxhtq06ZNg+OzsrI0f/58VVRUEN4aE47KUWamd6OUUMbZzZq13nWBtTd8KSz0Hl+5knAHWFSo/e7MeJsnaw9bT7cLPNq0t0gnjsUFHdMptTometgBiLFg17lzZ3Xu3LnRccOGDVNpaal27typIUO8W59t3LhR1dXVysrKCvo6t9utMWPGyOVy6S9/+YsSExMb/Vq7du1Sx44dCXWNCVfl6MUXvLtfhjLOTjwe7/e7vl08DcO76cvs2d5bS7ktE7CkUPrdRUKo/e669TBYexgG3S7wENxgeQX7CzT3nbmaN2aerux1ZbSnY1oxFexC1bdvX40dO1bTpk3T8uXLVVVVpVmzZmnixIn+HTGPHDmi0aNH65VXXtFll10mt9uta665RmfOnNEf//hHud1u/+6VnTt3ltPp1JtvvqmSkhJdfvnlSkxM1IYNG/T444/r3nvvjeblml84K0fnnedtadDQBipDhtTdOKW4WLp8mFRWJiUnSx9tja0+dFu2BIbo2gxDOnzYO85Kt5YCMJ1Q+90NHCLt/cxcaw/pewhEn2EYytuYp33H9ilvY55GZI6IyVYFrcGWwU6SVq5cqVmzZmn06NH+BuVPP/20//mqqip9+eWXOnPmjCTpk08+0bZt2yRJF110UcC5Dhw4oJ49e6pNmzZaunSp7r77bhmGoYsuukhPPvmkpk2bFrkLs5pIVI4KNgdveVBfH7vUztI/f+6SpKNHpV69pXbtpGPfNm8OZhPiJkEhjwMsoCmVI7SuUPrdRUJT1h7S9xC1UTWKjpoNx2O9wXhL2TbYderUKWgzcknq2bOnjBph4+qrrw74vD5jx44NaEyOEESqclSw2dvS4FdTvRulZGZ6b7+sXamrHepqOnPG+3ykw1041h6GWn2MpSolbK8plSMzYc1Z62nK2sP/2WGuvoeILqpG0eFrOO50OOUxPHI6nDHdYLylbBvsYBKRrBydd17DLQ2Ki4OHOp8zZ74PWJEQrrWHw4d7z1NYWH+11OHwPj98ePO/BmBCVqscVVYYrDlrZVZbe3hBZt31cVSOIs9sVSO7vAdqft8lyWN4TPH9NyuCHaLLTJWjy0NsFHT5MOlgK7VHaEg41x46nd5wOGmSN8TV/Bq+34AtXMjGKUCYhFo5Mlu/O6qHrcdXQf6fz6rVJdmpM21OqnNynE4Zp9TB0UGnjFO6pFe7Os3JqRxFntmqRnZ5D9T+vvtE+/tvZgQ7RJeZKkdlZa07riUisfZwwnhvOKyvIrhwIa0OgDALpXJUejJCkwkBO1a2voFDpKTu1cpMi1NZ4mlldonXMaNUqY7vP9ZmtsqRHZitamS290C4qoe1v+8+0f7+m1nwxiZAJPgqR9L3lSKfSFeOkpNbd1xLNGXtYUtMGC99sVda/1fppRXej3v/TqgDUEdTqoeRUHTEu4tmsEdRA/8JtaqaFQzp+8pFY3sAoPlqf899ovW9N9t7oHb1sLXm4btOR5BblR1y8N6vBxU7RJ9ZKkcfbfXufhnKuHCL5NpDp5OWBgBMgbWHDTNb5cgOzFY1Mtt7IFzVw0pPpQrdhTJUf3AzZKjIXaRKT6Vc8fSK9iHYwRwmjPfeVtjauz82RXq6t6VBQxuotGsXuN7v22+9rRSOH5fOP9+7+2bnzq0zl9YcByAsWHPWulh7GBzrjSKvZtWovoDhqxpF6ntvtvdAONceuuJdWjd1nY6fOR50TGr7VEJdLQQ7mIcZKkfHvg3e8qB2H7uuGYHr7U6fli7s6b1Vs6iwZfMw09pDAPVizVl4mGXtYai7ViYmRqZ6aLbKkR2YrWpktvdAuKuHGckZykjOaPF57IRgB9R27Ftv1fDyYd7glpzsvf2yZnWsdqirqazM+3xLwh27VgKmZ7aqEVpXqH0PE1zhfx+YrXJkF2aqGpntPWC26iG8CHZAfdLTg7c0+PbbxnfGLCvzjmvJbZlmWXsIIGpCXXOW0pHgGA6h9D3c+1n459HcypFdep2Fk1mqRlQPEQqCHdBUV4Z4u+iVI707TraEGdYeAoiaUNeccatnbGtO5cguvc7sguohQkGwA5rqePD/qDZrXGPMsPYQQNSEsuYsEqgeRldTK0dm63WGlqN6iMYQ7ICmOv9870YpoYwDgBhh1eqhHXcuDeduhYCZqocIRLADmqpgs3f3y1DGAUAMsVr1kH53Xqx7QmszS/UQgQh2QFN17uzdKbOhDVSSk+tunFJWJv30/0iHD0vdu0tv/Nk7DgDQJFbtdxcJ7FYI2BfBDmiOosLgLQ/q62N3aX9p//7vPz982Pv6Xr2kz/aEd64AwoI1Z9Flln53ZnsfsFuhtbBzKVoTwQ5orqJCb0uDK0d6N0o5/3zv7Ze1K3W1Q11N+/d7nyfcAZZj1TVnaF1meh+wW6G1sHMpWhvBDmiJzp0bbmlQVhY81Pns3/99I3QAlmKWNWeILrO8D9it0FrYuRStjWAHhNNP/0/o4zbmh3cuAGBCdty1MlzYrdA62LkU4UCwA8Lp8OHWHQcAMaToiGy5a2U4sVuhNbBzKcIhLtoTAEyhslJ65lkpJ8f7sbKydc7bvXvrjgOAGNKUXSuBWFGzWleTr2pnGNG/rRfWRLAD5vxW6nS+dP/90vI/eD92Ot97vKXe+HPrjgMAhMy3Y2VD2LkUkear1tVsRyEFVu2A5uBWTNjbnN9KS5bUPV5d/f3xxx9r/vmTk70tDRraQKVXr8CNUzweacsWqbhYSk+Xhg+XnM7grwcA1MtMO1YCEjuXIrwIdrCvykrp6acbHvPMM9Lv5koJCc3/Op/tCd7yoHYfuzVrpfvuk44c+f5Yt27SokXShPHNnwMA2JRZdqwEJHYuRXgR7GBff3jOW5lriMfjHXfHrJZ9rc/2eFsa/PT/eDdK6d7de/tlzUrdmrXSpElS7XvrCwu9x1euJNwBAGBh7FyKcCLYwb4ONNJfrqnjGpOcHLylgcfjrdTVt2DaMCSHQ5o9W7r+J9yWCQCAhbFzKcKFzVNgX5m9WndcS2zZEnj7ZW2G4a30bdkS/rkAAADAcgh2sK9fT5fiGvkr4HR6x4VbcXHrjgMAC2DXSgAtUbC/QKOWjVLB/oJoT8UUuBUT9pWQIN15Z/27YvrccUfgximVld41dwf2eyt5v57eso1VfNLTW3ccAFgAu1YCaC7DMJS3MU/7ju1T3sY8jcgcYfudRAl2sDdfK4Onnw7cSMXp9Ia6mq0O5vy27rjcXG84bElLBMnb0qBbN+9GKfWts3M4vM8PH96yrwMAJsOulQCaw9cPUJK//99Vva+K8qyii2AHPP6Yt6VBQ5W4cPe7czq9LQ0mTfKGuJrhzvfbp4UL2TgFQICiI6LaBcB2fP0AnQ6nPIZHToeT/n8i2AFeCQnBWxpEqt/dhPHelgb19bFbuJBWBwACFB2Rxo9uo8qK4P+ISXAZWptfRbgDEFNqVuskyWN4qNqJzVOAxjWl311LTRgvfbFXWv9X6aUV3o97/06oA1BH6UlHg6FOkiorHA1W9ADAampW62ryVe2M+pa02AQVO6Axke5353RKI0e2zrkAAABiSO1qnQ9VOyp2QOPM1O8OAADApnzVOofqvxPBIYetq3YEO6AxZup3BwAAYFOVnkoVugtlqP7gZshQkbtIlZ7KCM/MHLgVE2hMc/rdAQAAoFW54l1aN3Wdjp85HnRMavtUueJdEZyVeRDsgFA0pd+dGXk80pYtUnGxt8n58OG0TgAAAJaTkZyhjOSMaE/DlAh2QKhC6XdnRmvW1t9CYdEidtsEbIJ+dwAQ+wh2QFM01O/OjNas9TY9r72IuLDQe3zlSsIdYFEpHQ0luIxG+9hVVhj0uwNQR8H+As19Z67mjZmnK3tdGe3poBUQ7IBY5fF4K3X17QxlGJLDIc2eLV3/E27LBCyoazdpbX5Vo5W4pvS769rNnjvJAXZjGIbyNuZp37F9ytuYpxGZI+Rw0PPS6gh2QKzasiXw9svaDEM6fNg7jr55gCV17aZGw1jpyQhNBoBl1OwFZ/feb7GEYAfEquLi1h0HADGGtYewI18vOKfDKY/hkdPh1KL3F2lkr5FU7SyOYAfEqvT01h0HADGk6IhYe4g67LDurGa1TpI8hoeqXYygQTkQq4YP9+5+Gey3bw6H1L27dxwA2ExT1h7CHmqvOzPqW6NucTWrdTX5qnaxeM12QrADYpXT6W1pINUNd77PFy5k4xQAAFT/urNY47tGj+EJOF6zagfrItgBsWzCeG9Lg4xajTy7daPVAQAA/1S7khWLFSzfNTpUfxXaIUfMXbPd2DbYnThxQpMmTVJSUpJSUlI0depUlZeXN/iaq6++Wg6HI+Bx++23B4w5dOiQrrvuOrVr105dunTRfffdp++++y6clwI0bMJ46Yu90vq/Si+t8H7c+3dCHWATvn53DUlwGUrpyD/mYF+1K1mxWMGq9FSq0F0oQ/X/XTdkqMhdpEpPZYRnhtZi281TJk2apKKiIm3YsEFVVVWaMmWKpk+frldffbXB102bNk2PPPKI//N27dr5/+zxeHTdddcpPT1dH374oYqKijR58mS1adNGjz/+eNiuBWiU00lLA8CmQu13xwYhsKvau0T6xNpuka54l9ZNXafjZ44HHZPaPlWueFcEZ4XWZMtgt3fvXq1fv147duzQ0KFDJUnPPPOMrr32Wi1evFgZtW9bq6Fdu3ZKD7KL4Lvvvqu///3veu+995SWlqZBgwZp/vz5uv/++/W73/1OCQkJYbkeAAAaEkq/O8Cuau8S6ROLu0VmJGcoIzn4v3Nhbba8FXPr1q1KSUnxhzpJys7OVlxcnLZt29bga1euXKnU1FRdeumlys3N1ZkzZwLO279/f6WlpfmPjRkzRm63W59//nnQc1ZUVMjtdgc8AAAAEF6sO0MssWXFrri4WF26dAk4Fh8fr06dOqm4gWbN//7v/64LL7xQGRkZ2r17t+6//359+eWX+vOf/+w/b81QJ8n/eUPnXbBggebNm9fcywEAAE3kW3vYWB871h7GtqasO+MWRZhdTAW7Bx54QHl5eQ2O2bt3b7PPP336dP+f+/fvr65du2r06NH6+uuv1bt372afNzc3Vzk5Of7P3W63evTo0ezzAQCAhrH2EBLrzhBbYirY3XPPPbr11lsbHNOrVy+lp6fr6NGjAce/++47nThxIuj6ufpkZWVJkvbt26fevXsrPT1d27dvDxhTUlIiSQ2e1+VyyeXiPxgAAEQSaw8hse4MsSOmgl3nzp3VuXPnRscNGzZMpaWl2rlzp4YMGSJJ2rhxo6qrq/1hLRS7du2SJHXt2tV/3scee0xHjx713+q5YcMGJSUlqV+/fk28GgAAAAAIjS03T+nbt6/Gjh2radOmafv27dqyZYtmzZqliRMn+nfEPHLkiPr06eOvwH399deaP3++du7cqYMHD+ovf/mLJk+erJEjR2rAgAGSpGuuuUb9+vXTzTffrE8//VTvvPOOHnzwQc2cOZOKHAAAAICwsWWwk7y7W/bp00ejR4/WtddeqxEjRui5557zP19VVaUvv/zSv+tlQkKC3nvvPV1zzTXq06eP7rnnHt1www168803/a9xOp1at26dnE6nhg0bpl/+8peaPHlyQN87AAAAAGhtDoP9W03H7XYrOTlZZcVFSkpKivZ0AFRWSAku/8dPD3kkqvAAWsGBku+UmRavssQjyuwSr2PGMaU6Uv0fAeCU+5Qu6XiJysrKGswGtq3YAQAAAECsINgBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAAAAsDiCHQAAAABYHMEOAAAAACyOYAcAAAAAFkewAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2AAAAAGBxBDsAAACYTsH+Ao1aNkoF+wuiPRXAEgh2AAAAMBXDMJS3MU/7ju1T3sY8GYYR7SkBpkewAwAAgKls3r9Zu4t2S5J2F+3W5v2bozwjwPwIdgAAADANwzC06P1FcjqckiSnw6lF7y+iagc0gmAHAAAA0/BV6zyGR5LkMTxU7YAQEOwAAABgCrWrdT5U7YDGEewAAABgCrWrdT5U7YDGEewAAAAQdb5qnUOOep93yEHVDmgAwQ4AAABRV+mpVKG7UIbqD26GDBW5i1TpqYzwzABriI/2BAAAAABXvEvrpq7T8TPHg45JbZ8qV7wrgrMCrINgBwAAAFPISM5QRnJGtKcBWBK3YgIAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIuzbbA7ceKEJk2apKSkJKWkpGjq1KkqLy8POv7gwYNyOBz1Pl5//XX/uPqeX7VqVSQuCQAAAIBNxUd7AtEyadIkFRUVacOGDaqqqtKUKVM0ffp0vfrqq/WO79Gjh4qKigKOPffcc1q0aJHGjRsXcHzFihUaO3as//OUlJRWnz8AAAAA+Ngy2O3du1fr16/Xjh07NHToUEnSM888o2uvvVaLFy9WRkZGndc4nU6lp6cHHHvjjTf085//XOedd17A8ZSUlDpjAQAAACBcbHkr5tatW5WSkuIPdZKUnZ2tuLg4bdu2LaRz7Ny5U7t27dLUqVPrPDdz5kylpqbqsssu04svvijDMBo8V0VFhdxud8ADAAAAAEJly4pdcXGxunTpEnAsPj5enTp1UnFxcUjneOGFF9S3b19dccUVAccfeeQRjRo1Su3atdO7776r3/zmNyovL9edd94Z9FwLFizQvHnzmn4hAAAAAKAYq9g98MADQTc48T2++OKLFn+ds2fP6tVXX623WvfQQw9p+PDhGjx4sO6//37Nnj1bixYtavB8ubm5Kisr8z+++eabFs8RAAAAgH3EVMXunnvu0a233trgmF69eik9PV1Hjx4NOP7dd9/pxIkTIa2NW716tc6cOaPJkyc3OjYrK0vz589XRUWFXC5XvWNcLlfQ5wAAAACgMTEV7Dp37qzOnTs3Om7YsGEqLS3Vzp07NWTIEEnSxo0bVV1draysrEZf/8ILL+jf/u3fQvpau3btUseOHQluAAAAAMImpoJdqPr27auxY8dq2rRpWr58uaqqqjRr1ixNnDjRvyPmkSNHNHr0aL3yyiu67LLL/K/dt2+fNm/erLfffrvOed98802VlJTo8ssvV2JiojZs2KDHH39c9957b8SuDQAAAID92DLYSdLKlSs1a9YsjR49WnFxcbrhhhv09NNP+5+vqqrSl19+qTNnzgS87sUXX1T37t11zTXX1DlnmzZttHTpUt19990yDEMXXXSRnnzySU2bNi3s1wMAAADAvhxGY3vxI+LcbreSk5NVVlykpKSkaE8HQGWFlODyf/z0kEfi9moAreBAyXfKTItXWeIRZXaJ1zHjmFIdqf6PAHDKfUqXdLxEZWVlDWaDmNoVEwAAAADsiGAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA42wa7xx57TFdccYXatWunlJSUkF5jGIYefvhhde3aVW3btlV2dra++uqrgDEnTpzQpEmTlJSUpJSUFE2dOlXl5eVhuAIAAAAA8LJtsKusrNSNN96oGTNmhPyahQsX6umnn9by5cu1bds2tW/fXmPGjNG5c+f8YyZNmqTPP/9cGzZs0Lp167R582ZNnz49HJcAAAAAAJKk+GhPIFrmzZsnSXrppZdCGm8YhpYsWaIHH3xQ48ePlyS98sorSktL05o1azRx4kTt3btX69ev144dOzR06FBJ0jPPPKNrr71WixcvVkZGRliuBQAAAIC92TbYNdWBAwdUXFys7Oxs/7Hk5GRlZWVp69atmjhxorZu3aqUlBR/qJOk7OxsxcXFadu2bfrpT39a77krKipUUVHh/7ysrEyS5D51KkxXA6BJKiukBJf/Y3m5R6p0RXtWAGLAmdPfqfxUvE5XndKpxHiVG+VyOVz+jwBQ7vYu6zIMo8FxBLsQFRcXS5LS0tICjqelpfmfKy4uVpcuXQKej4+PV6dOnfxj6rNgwQJ/BbGmHj/4YUunDQAAACAGnDp1SsnJyUGfj6lg98ADDygvL6/BMXv37lWfPn0iNKPQ5ObmKicnx/95dXW1Tpw4ofPPP18OhyOKM2t9brdbPXr00DfffKOkpKRoTwdNwM/OuvjZWRM/N+viZ2dd/OysK5Z/doZh6NSpU40u64qpYHfPPffo1ltvbXBMr169mnXu9PR0SVJJSYm6du3qP15SUqJBgwb5xxw9ejTgdd99951OnDjhf319XC6XXK7A2y1C3anTqpKSkmLuL51d8LOzLn521sTPzbr42VkXPzvritWfXUOVOp+YCnadO3dW586dw3LuzMxMpaenKz8/3x/k3G63tm3b5t9Zc9iwYSotLdXOnTs1ZMgQSdLGjRtVXV2trKyssMwLAAAAAGzb7uDQoUPatWuXDh06JI/Ho127dmnXrl0BPef69OmjN954Q5LkcDh011136dFHH9Vf/vIX7dmzR5MnT1ZGRoYmTJggSerbt6/Gjh2radOmafv27dqyZYtmzZqliRMnsiMmAAAAgLCJqYpdUzz88MN6+eWX/Z8PHjxYkvT+++/r6quvliR9+eWX/h0qJWn27Nk6ffq0pk+frtLSUo0YMULr169XYmKif8zKlSs1a9YsjR49WnFxcbrhhhv09NNPR+aiLMDlcmnu3Ll1bj2F+fGzsy5+dtbEz826+NlZFz876+JnJzmMxvbNBAAAAACYmm1vxQQAAACAWEGwAwAAAACLI9gBAAAAgMUR7AAAAADA4gh2iIjNmzfr+uuvV0ZGhhwOh9asWRPtKSFECxYs0I9+9CN16NBBXbp00YQJE/Tll19Ge1poxLJlyzRgwAB/o9Zhw4bpr3/9a7SnhWZ44okn/C13YG6/+93v5HA4Ah59+vSJ9rQQgiNHjuiXv/ylzj//fLVt21b9+/fXxx9/HO1poRE9e/as83fO4XBo5syZ0Z5aVBDsEBGnT5/WwIEDtXTp0mhPBU30wQcfaObMmfroo4+0YcMGVVVV6ZprrtHp06ejPTU0oHv37nriiSe0c+dOffzxxxo1apTGjx+vzz//PNpTQxPs2LFDf/jDHzRgwIBoTwUhuuSSS1RUVOR//O1vf4v2lNCIkydPavjw4WrTpo3++te/6u9//7t+//vfq2PHjtGeGhqxY8eOgL9vGzZskCTdeOONUZ5ZdNi2jx0ia9y4cRo3bly0p4FmWL9+fcDnL730krp06aKdO3dq5MiRUZoVGnP99dcHfP7YY49p2bJl+uijj3TJJZdEaVZoivLyck2aNEnPP/+8Hn300WhPByGKj49Xenp6tKeBJsjLy1OPHj20YsUK/7HMzMwozgih6ty5c8DnTzzxhHr37q2rrroqSjOKLip2AJqkrKxMktSpU6cozwSh8ng8WrVqlU6fPq1hw4ZFezoI0cyZM3XdddcpOzs72lNBE3z11VfKyMhQr169NGnSJB06dCjaU0Ij/vKXv2jo0KG68cYb1aVLFw0ePFjPP/98tKeFJqqsrNQf//hH/epXv5LD4Yj2dKKCih2AkFVXV+uuu+7S8OHDdemll0Z7OmjEnj17NGzYMJ07d07nnXee3njjDfXr1y/a00IIVq1apU8++UQ7duyI9lTQBFlZWXrppZd08cUXq6ioSPPmzdOVV16pzz77TB06dIj29BDE/v37tWzZMuXk5GjOnDnasWOH7rzzTiUkJOiWW26J9vQQojVr1qi0tFS33nprtKcSNQQ7ACGbOXOmPvvsM9aMWMTFF1+sXbt2qaysTKtXr9Ytt9yiDz74gHBnct98843+4z/+Qxs2bFBiYmK0p4MmqLnkYMCAAcrKytKFF16oP/3pT5o6dWoUZ4aGVFdXa+jQoXr88cclSYMHD9Znn32m5cuXE+ws5IUXXtC4ceOUkZER7alEDbdiAgjJrFmztG7dOr3//vvq3r17tKeDECQkJOiiiy7SkCFDtGDBAg0cOFBPPfVUtKeFRuzcuVNHjx7Vv/zLvyg+Pl7x8fH64IMP9PTTTys+Pl4ejyfaU0SIUlJS9MMf/lD79u2L9lTQgK5du9b5hVffvn25jdZC/vGPf+i9997TbbfdFu2pRBUVOwANMgxDd9xxh9544w1t2rSJBeUWVl1drYqKimhPA40YPXq09uzZE3BsypQp6tOnj+6//345nc4ozQxNVV5erq+//lo333xztKeCBgwfPrxOG5///d//1YUXXhilGaGpVqxYoS5duui6666L9lSiimCHiCgvLw/4jeWBAwe0a9cuderUSRdccEEUZ4bGzJw5U6+++qrWrl2rDh06qLi4WJKUnJystm3bRnl2CCY3N1fjxo3TBRdcoFOnTunVV1/Vpk2b9M4770R7amhEhw4d6qxhbd++vc4//3zWtprcvffeq+uvv14XXnihCgsLNXfuXDmdTv3iF7+I9tTQgLvvvltXXHGFHn/8cf385z/X9u3b9dxzz+m5556L9tQQgurqaq1YsUK33HKL4uPtHW3sffWImI8//lg//vGP/Z/n5ORIkm655Ra99NJLUZoVQrFs2TJJ0tVXXx1wfMWKFbZeoGx2R48e1eTJk1VUVKTk5GQNGDBA77zzjv71X/812lMDYtbhw4f1i1/8QsePH1fnzp01YsQIffTRR3W2ZIe5/OhHP9Ibb7yh3NxcPfLII8rMzNSSJUs0adKkaE8NIXjvvfd06NAh/epXv4r2VKLOYRiGEe1JAAAAAACaj81TAAAAAMDiCHYAAAAAYHEEOwAAAACwOIIdAAAAAFgcwQ4AAAAALI5gBwAAAAAWR7ADAAAAAIsj2AEAAACAxRHsAAAAAMDiCHYAAAAAYHEEOwAAouDRRx+Vw+HQ5ZdfXu/zDzzwgBwOhwYNGqSTJ09GeHYAAKtxGIZhRHsSAADYzdmzZ/XDH/5Qhw8f1urVq3XDDTf4n1uwYIHmzJmjiy++WJs3b1aXLl2iOFMAgBVQsQMAIAratm2rxx57TJL029/+Vt99950kadmyZZozZ44yMzOVn59PqAMAhISKHQAAUWIYhoYOHapPPvlEy5cv13nnnaebb75ZGRkZKigoUGZmpn/svn37tHjxYm3fvl179uxRt27ddPDgwehNHgBgKgQ7AACiaNOmTfrxj3+sjh076tSpU+rYsaM2b96sPn36BIxbu3atZs6cqcsuu0wHDhzQyZMnCXYAAD+CHQAAUTZ8+HB9+OGH6tChgzZv3qxBgwbVGVNdXa24OO8Kittvv13r168n2AEA/FhjBwBAFK1YsUJbt26VJFVUVCgpKanecb5QBwBAffi/BAAAUfL6669r2rRp6tSpk2666SZVVlbq/vvvj/a0AAAWRLADACAK3n77bU2aNEnt27fXO++8o+XLl6tTp05avXq1Pvzww2hPDwBgMQQ7AAAi7IMPPtDPfvYzxcfH680339SQIUOUkpKiOXPmSJJycnKiPEMAgNUQ7AAAiKDt27fr+uuvl8fj0Z///GeNHDnS/9ysWbN04YUXatu2bVq1alUUZwkAsBqCHQAAEbJnzx6NGzdOZ86c0cqVKzV27NiA510ul+bPny9Jys3NVUVFRTSmCQCwINodAABgMbQ7AADUFh/tCQAAgMadOXNGb7/9tiRp//79OnPmjFavXi1J+tGPfqQLL7wwmtMDAEQZFTsAACzg4MGDyszMrPe5FStW6NZbb43shAAApkKwAwAAAACLY/MUAAAAALA4gh0AAAAAWBzBDgAAAAAsjmAHAAAAABZHsAMAAAAAiyPYAQAAAIDFEewAAAAAwOIIdgAAAABgcQQ7AAAAALA4gh0AAAAAWBzBDgAAAAAs7v8HvPCVjsoQcTIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle = np.pi / 180 * 20\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xr = X.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_r = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_r.fit(Xr, y)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1])\n",
    "glue(\"dt_boundary_r\", fig, display=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1bcfef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X.to_numpy()\n",
    "#y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "color-fluid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=40)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "oriental-cooling",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/claire/.local/lib/python3.8/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/papermill.record/image/png": 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\n",
      "application/papermill.record/text/plain": "<Figure size 1000x600 with 1 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "dt_boundary_tweaked"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X, y)\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n",
    "plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n",
    "plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n",
    "plot_decision_boundary(tree_clf_tweaked, X, y, legend=False)\n",
    "glue(\"dt_boundary_tweaked\", fig, display=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "exciting-myanmar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alternate-father",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}