tf-digits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numbers with TensorFlow 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from six.moves.urllib.request import urlretrieve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Data\n",
    "First step is to actually get some data! We are downloding the famous [MNIST](http://yann.lecun.com/exdb/mnist/) data set created by Yann LeCun et al. It basically has representations of digits (in numpy arrays) as well as the corresponding number. Our job is to create a function that can tell them all apart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_file = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'\n",
    "file, output = urlretrieve(mnist_file, 'mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.load(file) as f:\n",
    "    x_train, y_train = f['x_train'], f['y_train']\n",
    "    x_test, y_test = f['x_test'], f['y_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This helpful function takes the numpy arrays and draws them as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_digits(X, y):\n",
    "    fig, axes = plt.subplots(6, 20, figsize=(18, 7),\n",
    "                            subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                            gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        ax.imshow(255 - X[i], cmap='gray')\n",
    "        ax.set_title('{:.0f}'.format(y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ueJSx5xd169a1pX6PP/44AN27d7fzmbB69WquvPJKIHQz2rdvX7vpGSR++uknAIYOHWrXusLatWtTzMPg3pvI8XEcx5YRybELAocOHeLBBx8EQnPc5MmT4/LmXzqXSCeT559/3s4BQr9+/aJ2b+gl/r49RVEURVEURVEURVEiImpKAMmWye50ZkoA2WHzylJEJn/PPfdEK8wc0bZtWyC0y58eooo4cOAAN9xwA+BmdWW3Llq8/fbbVtIUDWTnatSoUVYJEZSsbXp8/vnngLtDKkjMM2fODIyrqOwQdunShZ07dwIhlUWNGjWsnKt79+72d+T5nTt3pjDg8oO9e/fSo0cPIPRZxLjKS6VKlfjss8+A0K5z5cqV7WeWn0FAMpdiGBRv1K5dO40S4IwzzrDZfFH2eHfWpYRrwYIFMYoy58SLGik9FixYYE2O5NqSOislyPMrV66kQoUKQOYy8WgjpSIjRoyw50pG5SIdO3aMSVyx4PPPP7eZ+okTJ9p1T7gSm8WLF0f0f8h8uWbNmhQqSMkslypVKkvvI/PxxRdfnCYz+NFHH3HLLbdEFF+0EWXc5s2bqV+/PhBSWMSjoV6HDh2sIbGoNOIR75pWrhfTpk1LMx97z4V169bZUpRYS+vnzJkDuOtzUU2JqWQ4zj//fNvR5LnnngNcFU8QlQByDxWuM0ahQoXsev2LL74AYODAgfZ5Ywz33XcfEKxygN69e1sDUFFi+F2GGAmLFi3iscceA0Jl5MaYNNeIp59+2n7O7KrFsoMqARRFURRFURRFURQljxA1JYBkLl588UXAzbJWqVIFcLObgjwmu3JFixa1RmFBMgXzsmzZMiDU0sy70ykmhbfccovN0srO/GWXXWZ9DL788suoZ6yOHTsW1feX+hxwM7pBZ+HChXaH06tMkePkd6330aNHWbJkCRD6bv/991+rHunduzfg1vyKl4HsYM+ePdu+TxBMGqdNm8abb76Z7vOStZwzZ471BJBdz6Ai9c2ps2bgmmOKosTvcZQe7dq1o1GjRikeK1CgQIZ189Lm9KKLLkpRsybvI/3fg4TsqEs9drzx0EMP2XNB/D/S81wQxcCuXbsYNWoUEDLM9ZtevXrZ+KR1bjiC7suSEWIoJhlQmb8F8TmRFrqXX345zZs3B0ItjrOLzJ3yM1LEBM07n0nLwSAa04mqUdQl5cqVY/DgwUB8KgAEUcEC1gtn4MCBWVZ0xBLxvGrWrBnr169P8dzevXv5559/gNCauGrVqlYJG47jx4/b62qsEQ+VihUrWq+uzLjzzjuBkBLg0KFD9hoZhKz0s88+C4TuuwDuvfdeAEqUKAG4LVzl7zIv16lTx6ouS5QoYT9nEJDrw4QJE7jxxhuB+Dzf5ft96KGHrOpFjkOjRo2sl4n4Lb3//vtWLZaUlBQ1A/modweQxWKtWrXsBXHFihWAK1WRfuBiugXughNg5MiR0Q4v2/z444/Url0bCC2QjTHUq1cPCMkz582bZycKuaCWKFHCLtDy5ctnNxG+//57wJ0wcwP5fqNtMiPyaMB+J0Fm3LhxaYwba9SoEfN+2ukxYcKENIuv2rVrWzm99yIjj3lv/mVilEnfT2Qx46VcuXL2plHkZ7IBAMGXQooh6X333WcvtsKzzz5L8eLFgeDKm/Pnz5/i+84KIj3es2dPisdlrMlNQxBZtmxZVMuhokXhwoXtRoYYaIbjxx9/tDdw+fLly/C1fnDnnXfazQu5PoTrBNK7d28++OCDmMaWE6TjTs+ePRkzZgwQSnpUq1aNJ554AnDXMYULFwZCxoh+k5SUZDstyWLTi5T/XHbZZTGNKzOmT5+eQjoLbjcK+X7jHblplpK4GTNm2HLTICBllJKc2Lx5c4avl83L008/3d78yCZy69at2bJli33tBRdckOvxZoVatWoBbjlAVnvLp77ebd++nXfffRcIlUH5iWzCyAZ42bJl7Uasd1Pp119/BVxDOoAdO3bY7+CZZ56JeIMyGkgnqQMHDtjPEo/ITf6qVausEW44o2Yxkv/888/tebJq1aqobe5rOYCiKIqiKIqiKIqi5BGirgQQvFnMU045xf5dJMMixwlquwdpS/PCCy9YKbn0DS9VqpTNvoqi4ZZbbsnUWEd268TISXYUc4rsLkVTDrt9+3bbVgWwLUWCiOxEjxkzxo4vydpKSyQ/kRj69+9vsxzt27cHXNlZOJlZuB1RKZ8RiZGfvPnmm1bJI7ueFStW5Iwzzkj3d7Zv3x6T2HJK79690ygBEg0xlpRjmHoukV7lQSF//vz2uiLz82+//eZnSNlGyn1WrlxpS0vC7f5LT/GBAwdaKe3VV18dKAknuMomUaVl1AYsSO0ls4K09h09ejSdOnUCQvOxV9EYJObOnQu4x2Ts2LEpnitQoIC9dohJW1AQtaG0v/Vy6qmnZigLfuWVV4CUWWu/TTPTI7UpWJBas0EoGxtOASDZ8YEDB9rWxF7DSjGXk+PhVQGUK1fOGj3Gmkiy3eXLlwdC6oVffvnF3hsEAbkGfPLJJ4CbQRZl0muvvQa4CmYxphM18mmnncZTTz0FhNaeQUHUrtdee22uqaX9wKtaymobU1n7R9OgMZh33IqiKIqiKIqiKIqi5DoxUwJ4eeaZZwC3ZnP+/PlAqOZIsoZBQUwpunXrBrhZdvE2kJq6yy+/PEdZ98zqq7LLmjVr7N8vvPDCXH1voVu3bjZze95559nvJEiIUqFx48ZpnpMMjtSF+YFkU6UtTcGCBalTpw4Qqpn37h5Kze/s2bNtLbDUEvbq1SvLu4uxoHTp0tnOlks9ajwQrp1evDNhwgQABgwYYLPoYp7kpUqVKhQoUCCmsWVG8eLFuf766wHXhDaekPlfjP3y589vszbhVD3io/P+++9bn4qvv/46FqFmiHh63H777YBbd3r06NFMf69hw4ZRjSsniNJi4MCB9novGc2aNWva+TpINbRepI5e4gxnFmyMsV4hJ5xwQuyCywISz7Jly9LMuWKY62Xw4ME2qy7qBq/x4aBBg+z5Fo/mYn4we/bssO0sZcxIJj8zRY9XASA0bNjQKmrjAbnuBe36J4jRunjhrFq1yrYBlHusRx99NI258TPPPGPXxEFB1D8y9kRVlhpprynjSDzlgobMX47jWIN4WdP/+uuvjBs3DggZz5955plWHR7NucqXTQCRzI0aNcrKO8RwpGbNmtbdvEOHDkD4HruxQkz7vAYO06dPB0KdAIJMbrh379u3j08//RQI3Sh4Del69epl5fVBQmL2Th433XQTkLJDhR/8/fffvP7660BofNepU4cPP/wwzWvFxEUcpmWSgJD8SzocBB1ZmHldhOXzi8M2hC5iQTV2k4Won3NTdtm4caNdsMmCwMvChQuB8J+pWLFiDBgwAID69esnjCGX3/z000/ccccdQKhsqVOnTmGvLSJl9sq5RcIZBMTxeMOGDQBZ2gAAGDJkCEOHDo1aXDlBzH0HDhxoO7FIoiKoN/5exKA1o05BSUlJtnSxWrVqgHtzJqbOF198cZSjTB9JEn311Vd2zhWTRa9EVlzOFy5cyIwZM1K8R5EiRWy54tq1a2nSpAkQKnkKajeXoDBo0KA0Dv7Vq1e3m/wZ3fzv2bPHStMXLFiQ4vcBGjRokMvRRhdJCnpNWIPQFUCQ0gxvUk7MsCUZ5l1z3X///QBpugYFgXfeeQfAlsZJKQaEroFdu3a1psXy2V988cVAmjOLWaYxxnY1GTRoEJByTS/zUqzK+xInjaUoiqIoiqIoiqIoSob4ogQQKlSoYHd0WrduDbjSIslWSbawVatWvvVMFQMNkV3XqFEjVxQAx48ftzvb8t7RYPfu3ek+t3z5citREcnQli1brCmN7MQdP37cZv7E+KVQoUI20xOEvvSp+fDDD60hinDddddZyY3XnNIPkpKSbOZPePXVV21bx7feegtwWwVJWy0xBDPG2J3cli1bAsE0pJLsgZiC9e3bN01LFO95IJQqVcrOC0GTp8YjorBo2LBhxKVH119/PQ899FBuhhV1pJVbkJA5UxRV999/fxqZ86JFi2zrJpH+7969m/fffx8IXS9atWoVqDZiUgYgJmI9evTIUtvC1G1bg4SUahljaNasGRAfCgBBVCaShVq6dGma644XyUgtW7aMPn36APDII4/w+OOPA2Ro7Jqb7N+/HwipSiDU4uyee+4BoFKlSikMm8FVaYosWNpSdu3a1bZzrlWrVorWxkFBzukgKsseeughO2Zk3fTuu+9y5plnZvq7I0aMsIanwoUXXmjnsqy8R5CQElNvyW3dunXTvE6+r+XLl7No0SIAq0DxmiZGi3LlymX4fP369YFQmXN2WwfHAmm/KpL4QoUK2XsTmZveeOMNW+oka8vWrVvbNnvhjo1fiHJp//79LF26FEh53kuLxli3zFQlgKIoiqIoiqIoiqLkEXxVAkAoeyA7N127drVZ6SeffBKATZs22b/H0sxl5syZLF++HAjt0OaWiVG+fPnse4qZR24hWXtjDA8//DCAzSx5WbFihd2Jyp/fHQonnXSSbRPUpk0bwK0TvPHGGwEoWbIk4B4HMUOUmp0gkJEZYPny5W38flOwYEFr+rVjxw4Azj333LCZADH/ktqzP//802Y7br311liEm2XESO6HH36wx0AyfYULF7bZnGuuuQZwfRtS1xseO3aMqVOnAq53Q8GCBWMSe14gI9VRRmaHM2fOtDvtkkUIOqlrg4OA1Ps98MADgDtHy/ct18ClS5faTIF8hj/++MOeRzJvSKYkaHTu3BlwM7Wps65Hjx619ZqSnQ0yV155JeAeE4lbrq+SaQ4yMs/Kufv777/bLKUY+06dOtWOJe/8IPPB4MGDrUJA1mbRNkQVf5JHH33UPiZKpKeffhpw4/caNoNbCy0ZV6m3XbdunV0HnXzyydYXKEheAEFUAAiNGzcOu57KiI8++ghI2UpW1pht27aNKwWA+ABs2bIlrAGrjK2qVavyww8/ACEF7ubNm219vng7pW7RmZuI94eY6oW73jdo0MAen6CycuVKq5qTcQMhjzbJ8Hvr5u+++27AnTtEwRUkJYAoYhcvXmxNMiVmCKm2Yq0E8H0TQBDzmcmTJ9sBKiUCb7zxBuvWrQNgzpw5MYvp4MGDVn4iMjjvQcsuhw8fTuGYLs70YriVW4jhXNmyZTN0XC9Tpox1lJeBd/XVV2f43tI3fMeOHSmMOoKCuOqHW6SkLg/wk+LFi1sTQDFl2r17NxUqVABCfUTvu+8+TjvtNACaNm0KuDfV8vegIOeJmDHKhAahbiA1a9a0JkJykaxVq5YtdxB27NhBz549AXeMimmNGL8EgXA3zGJ8FDRTGplb582bZ0ut5OKYnrR59OjRAIE1bAtHzZo1geB2B5g0aZK9pom7dPHixa3cURyDH3vsMTuWvLJBuVGQm7hzzjnHOiPLvBEk6tWrl+Yxx3HsYlhuEH788Uc2bdoE+Htj9u233wJw2WWX2Y1HMTV79dVX6devHxBaeC5evNhumMcLZcqUscZ6Qr169WyJ47Bhw4BQVwFBDPrEnFLKA6JFOCdwufkX7rjjDnvMhOnTp9vPIjJs6RoCbmmDfIYgc8kll/gdQo6Qa7Z3c0NMgYNSViZJrL/++stucsl4mjt3bprXSUlNauTmbu/evfYxSaA1aNDAysDPPffc3Aw/LLIulCRKuM2lIG84CbJBCSnLJ6TbmRi2hqNdu3a+mplmxtVXX53CBFuQRHes0XIARVEURVEURVEURckjBEYJIBQvXtwav4hk8ujRozYzMm/ePCtNjyWShYzEoFDkRM899xwvvvgi4MrpxfQpWqZuPXr0yPX3FDkghJfc+4W0CPK2LhQkqx4LQ5bsICaLUg6QHjL2JRuTL1++QKkwjhw5YrP9Mr4hlG2W/rPFixe3n1Uk5T/99JPNukl26aeffrIy6BYtWvDf//43xfOSMQU3a+cH4VoEyu67ZAxiLevKjLJly9KrV68svVYUS/GkBEid4Txy5EggMszCG2+8YWOU1n6SMfIybNgwmy0L159bJJ41a9YMpAIgI5KSklJIhMFVRfhlAPrnn39aJZb0zh4yZIg1XBUVVseOHa0SQAxapTVPLimFAAAgAElEQVRVIiCfVzKJ//3vf1O0dBNExRFtpIxExrpcwyF0rd+4caN9XqT/NWrUsGaBzZs3t+8hzz/yyCMxiD7nxNt5LUg2M5xSLggttQ8ePGivbbLG8Jr8eZHyS1mf58+fP03b0wceeCBFOYAfbN26FXDLw6ZMmQKE1iVVq1bl0ksvBUJm02I+HS94S8C9rQ+z8vqgIurXjEowY4UqARRFURRFURRFURQljxAYJYDUgH3wwQcsWbIEIMWum2TVbrjhhtgHR2SGgLJjLe1rJk+ebN9HsobxjNR9BYGbb74ZSJmdkUx7NI1YYoHUpHmzz0HwBBATmt69e9s6yyJFigBuay1pqVW8eHEAlixZYlUBYqBTqVIlhg8fDoRquvft22e9LN555x27Yy/HGEItbbwtpGKJtGYTjwwv8tjLL78c05hyk88++8zvELKN10AI3AygqLCCwG233Wa9MjJqybRz505bZypMnDiRiy66KMVj8ZDxSE04Jcr999/v22epWrWqNSgUPxnJinvxnsuiTEp9PBIBOYeqVq0aVglw3nnnxTSejOqXvebKsn4sU6aMbUspNdhfffWVvQYp0SMpKcle171rFTl3KlWq5FtsQqNGjayvmKh7GzRoYMeKKE4KFSpk2+zJ3HT++edblYkoMQcPHux7e2ZR54oaE0I18x07drTeU6IECJpCMRyO40TcOn3+/PlZUgz4iZjLynlSo0YN3wywfd0EWLNmjZWbTps2DYBt27aled0JJ5xgZfixlE14B6KcSK+88kqWfnfw4MH2RBTDkBYtWvD2229HIVJFeoJ7x0eHDh2A6JVbxArpgxo05Gb3pZdesj1O33jjDcC9YRcps1x8Zs2aZRdoYvLUunXrNDdExYoVs6UEdevWZeLEiYC7ISAMGTIkKp8pqwTdEOzIkSP2Rl7csOXCkxljxoyJG9msl9RlP2vWrLELUDFL9ZMuXbpk+LxcJyZPnmxvTGWxedddd0U3uGywa9cua3Aom5EivU4P6WwQbtPMayIaazp37myv09LVQH5C6MZl3bp1tqREnKdFLhw05LseNWqU7dyT1fEjG7upjflkc0A21qONJEukvGzGjBnW6E86Nu3fv9++XtZVjuPYzjlyUxSPm2VB2rzMDOnuM2HChDTG3c2aNbOban5KnoXZs2fbm3tJxKVXUihJSCmr3bJlizUInzx5MuDv2lJMYb3zlSRMZKNy27Ztacqv5PMHGWNMtg0MpTPV8OHDbUl5EFm1apU1XpZOP+3bt/ftuPh/ViqKoiiKoiiKoiiKEhNiqgSQLL+0RHrttddsX/dwXH755YBrohSJHD+neHejJPbOnTtbMydp/bF48WLbekt2qbds2WJNoCST2759+9gFH2Ucx7FtG6tXr+5rLK1bt7YGG16kR3K8E1RptneHWTJIkrl59tlnw5pIiSmPtADMiiGYlBXIzyAgZQ3S9mj9+vX2OVELderUKeYGT9If+Pnnn7dZGSmZSE+CLu0apdd2165dbXZHKFy4cJaVBH4j8+3WrVsZPHiwz9FkHVErjBgxwmacvvzySz9DCkunTp1sG1+Rx5511lmcddZZAFSsWBGAZcuW2eelJE4UDoA1xi1dunRsAg9Dz549bbtG6UHtNb+V8rL69etbczn5fEFD1iiiovrpp5+ybF4oLbnkfPG2SIOQ8snbbi+aiDRWFGb//vuvbS+bUYbw5JNPpkmTJkDIfDYemTVrlr3GBBVRYjz44IOAW8oriFKvY8eOgVAACMYYWxqSURu5Q4cO2XH08ccfA26JwHvvvQf4ZwLoRUywRUFWo0YNa3IqWfGZM2fa50XVLEqZIHPBBRdY9feECRMAt/VfOOSzyvObNm0KpOJajkPdunX5448/gFAJmrSd9YOobwLIxeXnn3+2k9rq1avTff2VV15pXcBF3hmESURucl5//XXrwClyQLkZ9lK9enVq1aoFkEaOkwgYY8LeeMcS8VyYM2eOHSOyeOjQoQMlS5b0Lbbc5LfffvM7hLCceeaZgNvdQOSLsgkGoUWY+Hg0atTISp78cgPPbaQu2OtN4Od8JXOsuM9C6AYsvTo52SyQmyDvIls6sbRr1856NsQLxhjf6uyyg3QwePPNNwE3bukOEEQpc6dOnex4l5KfG2+80Z7bcsO4cOHCFJJtcD+blGvIhuCJJ54Yg6jTp1u3br7+/7mFlPB4e1DLcZLv3LuRJ14zL7zwgr359x4vuWk4+eST7WZnrKhWrRoQShgNHjzYdsfx0qpVKwAuueQSwJV2B8GFPjuULFnS1mmn14s+iGzZsgVIefMvm95eiXqQOO+88+waRebYXbt2WQd9Kb968cUXbdcAKYF5/fXXfetGFI7UHYqMMfaGWMqXu3TpYrspSbe1eEhGlipVyiaKZLMY3JJqCK2JV6xYwfPPPw+EriOfffZZIDc65L72jz/+sGV03s/mF/7fXSuKoiiKoiiKoiiKEhOiogTYvXu3dc+WbK1XLutFJNuyI1KnTp3AyE6rV6/OFVdcAWA7FkBIdicqBwiVBsgOT1YNBOMZMeq57777fPn/pZew9ziIJFXc6hMBkWAGoaeoF3GP/vDDD20WWWTMbdq0sTvQ8ZCNjRTJJog8OohI94WscsYZZ3DrrbcCoXnM72xtJOzbt89mRPw0n8sMMXESRUDLli3p06ePnyFlSPXq1W0JmGRi27dvb0v7MirxO/XUU1m1alW0Q8yTiAGomJZBSLYsGcxTTjnFPifyVHF0T40oh6ZNm+Zbdl3kzfIzESlYsGCaNe+cOXMCXQ6wevXqNKVWlSpV4tNPP/UpoqyxevVqevfuDYTWiMePH08Td8OGDW35j5TXBI0dO3ak+HeJEiWoXbs2ECoLhJAxs1zT44WOHTum+HfXrl3TPHbyySdb1Yl0ngnaevPzzz8HQmUNhQsXtqUmQSAYdxOKoiiKoiiKoiiKokSdXFMCfPvtt9YU7LvvvrPGB+GQXc/OnTvz5JNPAsFs43b22WfbNiLS+kzaCXnp3LmzNaUIQi/UWBBpD08l+4iBjRhSbdiwwdZESYsRP5BM0T333BPolizRRGo5K1euHIgMp+z6Dx06NEvmOBUqVLDmW6I4efDBBzM0TQo6kgktVKhQXPRElnZ70jbTDxPc7CJZQPECOXDggH1OMsvS2hNCGejULcSU3EMUJaJGFBMzSD/bnxppBfjII4/QuHFjIHZtAfMyVapUAVwzTUh5PgWRvn37plCcgOsVIm00g0y/fv1S/IxXUrcp/uCDD+y6/LTTTgNcbyyZF+IVyf6nVgHEAxs3buTuu+9O8di4ceOs310QyLVNgGnTpjFt2rQ0j8tAvfXWW60ZmBjxiEtnkBGHSjExkp95lXr16gHw/vvv+xwJtgfyNddcw8KFC32OJvo89dRTgGvwIptnw4YNA4iLm51ERBY9XjMuPxHZ7/Dhw+3iXWRye/bsoVGjRgBWNnjbbbdZg8dEQYwoV61aFZjSsoyQc1l+xhOFChUCoHv37mmeE1M3JTace+65QGgjsGHDhtbl/7zzzgNg+vTp9vXemwgxMRYDwSAZoOUF5Nouhq533XWXn+Gki8TnNZCUkjgpR1Fiw7333gtAUlIS4G5qSEc12Uh+9NFH/QkujyOmqy+99JItu5JN1aCVJmo5gKIoiqIoiqIoiqLkEXJNCTBgwAAGDBiQW2+nBBQxAfTLDNCLZDDDtQ5KRGQH8b333rNmI6JMGTNmTCBLahR/KFSokDVnlZ95Ba8MWlHyGqLOaNq0qS0NEBKlHWKiIe01xWw5qIwfPx6ATz75xKrgunTpAoRUJEpsEONlaT0nPxX/ETXW8OHDrYluVsoz/UCVAIqiKIqiKIqiKIqSR4hKi0BFUXKfYsWKAa7xmdQQSvu3Z599Vn0BFEVRFEWJCjfffDMAgwYNsuagqgBQFJfvvvsOgOeffx5w/ZgefPBBIKTQChq6CaAocUaxYsUYOnQogP2pKIqiKIoSLcT87+jRoz5HoijB48orrwRgy5YtPkeSdbQcQFEURVEURVEURVHyCCY7/d6NMTuATdELJ2qUdRzHNlRPlM8BcftZEuVzgI6tIKLHJHjoMQkeekyChx6TYJEonwN0bAURPSbBI2GPSTiytQmgKIqiKIqiKIqiKEr8ouUAiqIoiqIoiqIoipJH0E0ARVEURVEURVEURckj6CaAoiiKoiiKoiiKouQRArEJYIypZIw5ZIyZ4HcskWKMOc0YM80Y848xZpMxprnfMUWCMaayMWauMWavMeZXY8ztfscUCcaYjsaYpcaYw8aYsX7HkxMS5bMYYwoZY0Ynnx/7jTE/GGPq+R1XTkiQuWuCMeZPY8w+Y8xaY8wDfseUXRJpbBljDqT6c8wYE5e9QBNhbAnGmHLGmFnGmD3GmG3GmGHGmLhss2yMaWqMWZW8XvnNGHO93zFll0RZq0BCrR/nJV8PZe5a43dMOSFBzpNEGVsJc12E4IytQGwCAK8BS/wOIoe8BiQBJYEWwHBjzIX+hpQ9khc004GZwGnAQ8AEY8x5vgYWGVuB54AxfgeSCyTKZ8kPbAZqAKcAvYHJxphyPsaUUxJh7uoPlHMcpxjQEHjOGFPN55iyS8KMLcdxisof3OvJQeB9n8OKlEQYW8LrwF9AKaAK7lhr72tEEWCMqQ0MBFoDJwM3AOt9DSqbJNhaBRJg/eiho2cO+4/fwURKIpwnySTE2Eqk62KQxpbvmwDGmKbA38AXfscSKcaYIkBjoLfjOAccx1kIzADu8TeybHM+UBoY4jjOMcdx5gJfE3+fA8dxpjqO8yGwy+9YckqifBbHcf5xHOdZx3E2Oo5z3HGcmcAGIC5vChJh7gJwHOdnx3EOyz+T/1TwMaRsk2hjy8OduDeeX/kdSCQkwtjycC4w2XGcQ47jbAM+BeJuMQ30Afo6jrM4+Vz5w3GcP/wOKpskzFolgdaPiUbcnycJPLbi+rpIgMaWr5sAxphiQF+gq59x5ALnAcccx1nreWw58bdAMOk8dlGsA1ESH2NMSdxz52e/Y8kuCTR3AWCMed0Y8y+wGvgTmOVzSDkinsdWKu4F3nbiuJdvAo2tV4CmxpiTjDFnAfVwNwLiBmPMCcDlQIlkCf2W5LKGwn7Hlk0Saa2SKOtHob8xZqcx5mtjzI1+BxMJCXSeJNrYEuL2uhi0seW3EqAfMNpxnM0+x5FTigJ7Uz22F1fmEU+sxt1d626MKWCMuRlX8niSv2EpiYYxpgDwDjDOcZzVfscTAYkydwHgOE573PnqemAqcDjj3wguCTC2ADDGlMGdf8f5HUtOSKCxNR938bwP2AIsBT70NaLsUxIogJtJux63rOEyoJefQUVAIq1VEmX9CNADKA+cBYwEPjLGxKPyJ1HOk0QaW0BCXBcDNbZ82wQwxlQB/gsM8SuGXOQAUCzVY8WA/T7EEjGO4xwBGgENgG24Wc7JuAseRckVjDH5gPG4dWodfQ4n2yTY3GVJltUuBM4G2vkdTyTE+9hKRStgoeM4G/wOJKfE+9hKHlef4W5iFAFOB07FreuMJw4m/xzqOM6fjuPsBAYD9X2MKdsk2FolIdaPAI7jfOs4zn7HcQ47jjMOt0QjrsZWMglxnpBAY8tDvF8XAzW2/HS2vREoB/xujAF3x+oEY8wFjuNU9TGuSFgL5DfGVHIcZ13yY5cSh1JUx3FW4O6yAWCM+Yb43XFTAoZxT/bRuLuh9ZMXc/HGjSTO3BWO/MRh3XaCjC0vrYABfgeRy8Tl2MI1nzsHGJbscXDYGPMWrmHr475Glg0cx9ljjNmC680Q1yTQWiVh1o9hcAhfuhFoEug8ScSxFdfXxaCNLT/LAUbiLgaqJP8ZAXwM1PExpohwHOcf3AxBX2NMEWPMtcBtuBmpuMIYc4kx5sTkusduuE7IY30OK9sYY/IbY04ETsC9QTvRxG87p4T5LMBwoDJwq+M4BzN7cUBJmLnLGHNGcquaosaYE4wxdYBmwFy/Y4uARBhbABhjrsGV1Mal+zEk1thKztZsANolz8fFcetSl/sbWUS8BXRKPj6nAo/guuzHFYmyVkmU9aMxprgxpo6sT4wxLXBdzz/zO7YIifvzJFHGlpAI18VkAjO2fNsEcBznX8dxtskfXNnKIcdxdvgVUw5pDxTGrVObCLRzHCced9vuwTVv+gu4CajtcXeOJ3rhym6eAFom/z3e6rmEhPgsxpiyQFvcG+dtJtTvtYXPoWWLBJu7HFx59hZgD/AS8IjjONN9jSqbJMrY8nAvMNVxnHiWbSbE2PJwB1AX2AH8ChwFHvU1osjoh9vWdC2wCvgB+J+vEUVGoqxVIDHWjwVwlTE7gJ1AJ6CR4zhrfI0qchLlPEmEsSUkwnURAjS2TByaKyqKoiiKoiiKoiiKEgF+dwdQFEVRFEVRFEVRFCVG6CaAoiiKoiiKoiiKouQRdBNAURRFURRFURRFUfIIugmgKIqiKIqiKIqiKHmEbLUZO/30051y5cpFKZTosWzZsp2O45SQfyfK54D4/CwbN25k586dKXrHxuPnAB1bQUSPSfDQYxI89JgEDz0mwULXKsEjUcYW6DEJIol8TMKRrU2AcuXKsXTp0sij8gljzCbvvxPlc0B8fpbLL788zWPx+DlAx1YQ0WMSPPSYBA89JsFDj0mw0LVK8EiUsQV6TIJIIh+TcGg5gKIoiqIoiqIoiqLkEXQTQFEURVEUJQ/Sq1cvjDEYYyhbtixly5blwIEDfoelKIqiRBndBFAURVEURVEURVGUPEK2PAEURfGfZs2asXjxYgDee+89AK666io/Q1IURVHigGPHjgHQr18/AAYPHkzdunWB0HVk/fr1XHLJJf4EqCgB4rPPPgNgwIAB1K5dGwh5Rdx8882+xaUouYEqARRFURRFURRFURQljxBoJcDatWsBePjhh3nnnXcAKFWqlJ8hRcS8efMAuOmmmzh+/Lh9rEaNGj5GpcQrGzduZOPGjQC0bNkSgF9++YUCBQr4GFXmTJkyhYMHDwKwbNkyAF5++WVq1qwJQJs2bQC44IILqFq1qj9BKoqiJDATJ04EoG/fvgA88cQTPP/8836GpCiBZebMmQAsWLCA+fPnA9i1+zXXXEPRokV9iy0SPvnkE2655RYASpcuDcDIkSOtuqFEiUy7yik5RO4DZQ7u06cP/fv3B9z5OJbkeBNg//79ABw4cIBTTjkFgJNOOimnbwvArFmzAPfke/PNNwHo2bMn+fMHeu/CMnbsWACGDh0KQL58IeHFY489RqtWrQDo0KEDQNx8rkSif//+PPXUUwA8/vjjDBgwwOeI0mfz5s1A6AYa4NdffwXg6NGjgdoEOHjwIKtXrwagd+/eAMydO5fDhw+neF2+fPnshVV+nnvuudSqVQuAgQMHUqxYMQBOOOGEmMSeyMgmjEgc+/Tpw48//giAMSbN60ePHg3Aqaeeah+rWLEiF110UVTj/PDDDwF49dVX7Saq4zjpxtmoUSPq1asHhCSau3bt4rzzzgOIu4WaEjx++eUXwN24BNi6dSsff/wxALfddhvg3hQIDz30EMWLF49xlBnz3Xff8eijjwLYjdZnnnnGz5AUD0uWLAHgyiuvTLFeFPr06QO4Zo5KdJH7j3HjxqV5bsGCBQDs27cvLq8tMra2bdsGQMOGDbn11lsBmDp1qm9x5QU2bNhg51xJbufLl4+vv/7al3i0HEBRFEVRFEVRFEVR8gg5Tj0PHDgQcE0zXnzxRQC705xTqlWrZv8uO6DNmjWjYsWKufL+0WTs2LG8/fbbAKxYsSLN8ytWrKBbt26Am8UCKFu2bOwCzAabNm1iyJAhALz++uuAm3lu2rQpAO+++65vsUWKKFiGDh1qM4svv/wylSpVAuD+++/3Lbb0+PvvvwE4cuSIfUzGTqFChXyJSZAxLjvks2fPtlmy7LJhwwabgR49erTdNb399tsBuPjii3MablT5/fffAahevbrNuEc7c56aNWvW2LnZy7///gvA+++/bx8Ll3ESHnjggTSPXXDBBUyZMgXAZtpzkw8//NCqpP755x97foZTAAjTp09n+vTpAPYc/vfff/m///s/IOX5MXjwYCBl1lZRMkPUiDI3Qejc+eijj1L8BHjhhRf43//+B0Dbtm1jFWaGjBgxgj179gDQtWtXwP9rhxJC1rn58uXLUAmwY8cOGjduDMANN9wQuwBzmc2bN9u15aJFiwBYvHgxV199dYrHYs1vv/1GixYtAPcalBp57owzzohpXJEg68b27dsDoTVaaqQcQIkuXbt2tWsVoUCBAtSvX9+XeHJVfy4TVPny5a08Lids3749x+8RC/7++28rqW3dujXgTtKppc/nn3++rQURv4MgM2bMGMDd1JGF9RtvvAG4k7cc76effprzzz/fnyCzydGjRwEYPnw4kHKMlSxZkurVq/sSV0ZIzFIz5KV58+ZAxjdysUAuLF26dEn3NWXKlMlQ0v/nn38CcOjQoRSPyzg7/fTTgdhvAsi5euKJJwLu58iIhx9+GICCBQty8sknRze4dKhduzZ//PFHVN77l19+4YorrgDg3nvvBVzZfm6xY8eOsAuvrLJu3Tr793Dfwd133w24skf5HErmSO/4w4cP2zpZue5lh86dOwNu2U884d04Ey677DIAzjrrrDTPzZ0713Zv8XsTQEqtxo8fz1133QXEvvY0mvz0008AfPPNN7Rr1y7Fc47jUKdOHcC9PknZUFDYuHGjjU/k2ZkxbNgwuyaLl02AyZMnA+5NvveGPxznnHNOzOIKxyuvvMK+ffvSPH7TTTcBoRLHeCjhlQTNd999B7jrrHDrRUm2XHjhhbly/5YZss7bsWOHfUwk8Y7jpLiOp0eBAgWsL5b4GwS1bFTWkeGSwv37908zb8UKLQdQFEVRFEVRFEVRlDxCrm5jSaagdevWzJ49G4hMYiLvM2jQoDTPTZ48mSeffDIHUeYeYl41cuRI5syZA4RcH8PttHXv3t0+/+CDD8YoyuyRlJRkv3dxrnz00Ud5/PHHAazR0ffff28ztPFkjCI70D179kzz3PDhw7ngggtiHVKmSHmNuDoHGdlBnj59OmeeeSYQkpR37949w7Ei2eTcKifKDaZNm2az3TLe04tPxtYXX3wBuJk2v0p87r77bit79yLmrZLJGDlyZESqJJmjv/zySwBWrlyZayUPIluMFlu3bgXguuuus5kdMejxGiAGDenvvn79+jTPlSlTJiqybin1WrhwId988w0QyrpGihhuffXVV0B8SGrDUalSJftZvJ9B1GWXX345y5cvB0ImwQ0aNPDFfVuO2bFjxzj77LNj/v9Hg/fff58PPvgACDm4Hzp0KE3ZkDHGrs9WrFhhSzREtek3R44cCXtOxzNiYjxkyBB7jOSx1Ij0X66rolTxg+7duwMwadKksM/LOIonpPwnq+q6tm3b2vsXMQuMBvfddx8QUohEihh7N2zYEHDLtaQMMAjI9UDk/hs2bEjzGj9LMVQJoCiKoiiKoiiKoih5hBwrAcLV9e3bt4+nn34aiCzDIrUg0i4laEyYMAEI1cN6kUx/OKTFVWav85O33nrLtp+RVkidOnVK87rZs2fb7Ee8ZBY2btxo61G9SDZQ+tUHiVGjRqUwogoqYpQjhm5bt261NfTlypXL0nukV59dpEgRwJ/+te+88441X8xMoSBmL+LhIMZNfvDAAw+ErTGTGkbxNbjjjjusoetjjz0GuJmYTZs2AbB79+4M/x/xcdi5c2fuBI6ruBg/frz9t/h0eDN3S5cuBVwlg/Dbb78BoYx5Zhw9etQaN27ZsgXImRJgxowZNhuRE/bt22eVdOLLkpSUZA1BJYPupW/fvlFpGybntdeozOuJIfXIJUqUoHLlyum+z8qVKwFX7SNtTWVtECTlT3YoUqRIGhXDnj17GDVqFBBSnEDIaLZx48Y5znxFgigWSpUqRZs2bWL+/+cmsv7q2rWrnXfuueceAOrUqUNSUhIAPXr0AFLWHG/fvt2e60EhsxaNMp6WLl1qPZmCjphOen00mjRpAsCdd95pH/Mz6x8O8TjxXs9k3gu3Dg4q4s00bNiwNM9ldu+xfft2e/2PJuKZVrVq1YhVbMeOHbNeBzNmzABcReYtt9ySO0HmAnv37gXCKwDEnDgaBstZJcebACLp2Lp1q5XLAnYRIy7S4Vym06NkyZJAaIPB++X5PWlMmDCBRx55BAhNDieeeKKNWVznvYtnuRk6+eSTrdmI30ZuqZF4e/fubSfpcDcRMjmIU3I8ceutt9pez0KxYsWsBKxw4cJ+hBWWt956C4COHTvaRY30df7+++99iys9Ut88FStWLEu/d+TIEVveE854C0IdSGQREUu+/vpru8DMDFn0ezf7/GLJkiXWMCcjypYtm2ahMGnSJPu7coFNjZwrskC98cYbcxBtSp588slMS76uv/56IOUNpJh9SgcEgOeffx4IOSSnh0hWc2I6Wbt27Wz/zl9//WWvlVKWMX/+/DQ3+lWrVrWlNmKo5N0kf/7556OyCSCLk4IFC9oSkuxcg0UCHM6ROqjdcLLK5s2b7eeSUph69erZDSovcr2XDcVYIVJguWa0adMmbkx800P6tu/cuZMBAwYAIbPJQoUK2evlqlWrALejkUih8+XL55tZq/DJJ58AZHij8tRTT9lyTGHfvn32Bi6oSSTZSBbDvyZNmtg5Ooimy4JsUqZeH0LoHidceV1QkZKY9O4zZC113XXXAe7G8tSpU+3z8nfpACamzLmJbAIXKFCAAgUKRPQeR48etUk8uWbOnz8/UJsA3u9VkJt/WXfI/aMfBOtOVFEURVEURVEURVGUqJFjJYC0Y+jcubPd2RG5H8Brr70GuD2+s2rWIEYK4eQTfiEmgPfee2+a3bWrrrqKzz//HAgZAHmN/yQbdccdd9jng4JIl3XNy60AACAASURBVK+99lrANTiSjFq49ieSIVy/fr2VfMULP//8cxrToHbt2kWUwcspBw4csNIzyQB+9913VioqGRwIGeaJsUjFihVjGWpUEEO5IUOG8PHHH6f7uvLly3P77bfHKiyLZPXDGU2lh6ieTjrpJCCkAPKDzFQAYuy3Y8eONJnd/fv3Z9iep2jRoowYMQLAl2OTHuGUSw899BAQMkV65JFHbCZOvgMIKde8arbsEomSqEaNGqxZswYIKUiMMfbv8v2OGDHCSs+lHd2SJUvs66QMJ7eR2CJhw4YNdmx51UuiaPBj3s1Ndu3alWkJmZRCibGutHSNFVLq89dffwH+t17LDURtCaE1imQSxSAQQu3GvIZoLVu2tEpOv8lIDZpaBQDuvBA0BamXyZMnM2TIECBk9jdo0KC4GHOiaAvXovHSSy+NdTgRsWXLFpsNl5IZCJl5Sza/WrVq9r5MrlnekhkIZdVFyh4NJYCsk3LCP//8k0Y157daPDWiSBLKly9vVa/hFACiWuzRowelSpUC3FLIaCnngjujKIqiKIqiKIqiKIqSq+Rai8BTTjnF1jl4lQDSmmbz5s1hlQBSv+U1PPHDOCc9JHPv3T2WDN9VV10FhDK1Xi699FJrHOjNUEm9/ahRo9Ktt40lUpMi2ei5c+dy2mmnpXmdtIqSWq+iRYvSrVu3GEWZM6ROzXEcm9WVOiIxsIw1mzdvtmZR3hZt0sJNlCTdu3e33hhBMzSKBPE6aNu2LZC+iZscl0aNGtlWg7GkdOnSgLuLLpkkMbJJz8Tm4MGDQChzUKFChUx/J9ZI9lvGV3bmWhmbb775JnfccUfuBxcFpP5XzCVr1aoVVnniV3YwX758NhtzySWXAG49sHgTSBYtX758Nqv77LPP2t8vX748kLm5WCwQLwZRxT300ENpMkwQynL6XZsdLeTYffrpp3bNE2nNa05JXY8azZZfscKbPZOxJG2NixYtGlbFJOoTyYD6SUZrDrnueJE1cm6ar0YDUQFAqP5/0aJFdt0SZE8AWdfGI9KGtGXLltbTwKsYEU+jcJ4Gcq6IWlnJXbZv387cuXNTPNa2bduwa1q5H+zQoQOQUj03duxYfvjhByD32xjn2iYAhMwO3n777TTPLVq0iCpVqgDYfsPffPONXZQ+99xzGb63mNnEuo9zv379gJSSMjGtCtdrXow26tWrF1bqIX3Sg3JTICY7YgAlx9DLtm3brLmLGNJ07NjRVzOLrCD9xsW13RhjF9pSuuKXZLty5cqsWLECIMWiRQz1vA7cGZHV3q9+Ip9z+vTp9nwKd/Mvx6J+/fpW3hyu+0gsadKkiV1gyg1N//797c1XOKSM6b///a+dK4IifRapWSQbrbKIiJcNAC9iyJq6ZEBkq351Bhk0aJAt78mozGfGjBn2Rl+MAcuWLWtvuEU26CeyOSHnS3p07NgRCF0LIdQn+b777styN5FYsmbNGrvJFw5ZdI8cOdKWcIgM1y92795trw9S7pedcfL7778DWb8WxQq5kd++fbs1x5TzOz3q1q0L5I4EOadIFxwpB/Ti7XYiDB06FAjujZrIrxcvXmznU+kY1a1btzSPBa08YM2aNRma+socu3//fvu8JMAKFiwYoyjTR8xJV69ene3flfmgbdu2YTtPyDXHW14QJKRMO6iMGjXKji2ZeyR57GXPnj12bRzO+Hvz5s02oZTbaDmAoiiKoiiKoiiKouQRclUJIG0A582bx8SJE1M817FjR5sB8CKZ5cwMT8Rc4cMPP7Qy6mjz448/WhMaiTOzHtRZNW1zHCcQbV6kT7bI6ryyRdldb9y4sZWiiYz7iSeeiGWY2ea7776zCgCv2YsYhfnRcz41ogaR9lKZIfLZM888034m+YzSxiYoHDlyxJYFSTsab5mQGIp6x5uMwSAZTvbs2dPGLWYukydPttkPUS+sX7/eZgnkvGnYsGFgFACC7EZL5jVcO7P0EFXDpZdeymWXXZb7wUUBMUQLd37ky5fPPp6633uskAxlekgJQN++fa0CQDJps2fP9l0p48V7fmdEaiMnCLVNW7VqlS09kznCD8QwVwxMH3zwwRRmreDOx3Xq1AHcEg4IlXQEgdWrV/PHH38AoXKX9L7TP//8Ewipff7++28mTZoEuNcpkah6S1H8QjLKCxYssOeEzL2TJk1Ko/Zp1aoVrVu3jm2QGSAZV++aV8o0qlWrlub14dQBfrNo0aI07QAHDRpkr/UyR23ZssWWCWSmEPKLX375xWZrw5kAf/TRR/anjDNpTVmtWjVbchNrlbKoqOXe6Pjx4/ae4oILLgDca0RW1D/e3z1+/Lj9fbn+BBXvnCyKpUqVKtnHZs6cmWEGvXHjxtELjlBLdQiZxEp7Yy9dunRh1qxZKR6rXr26vZ6EU2nkFqoEUBRFURRFURRFUZQ8Qq4qAYSuXbvy3nvvZem1shua1TZcixcvjroSYOXKlYC7SyQ7TbnVmkV275KSknxv9/LFF1/Yv4txDoTUAZL1//3336lQoQLg1kNDqHY9qIwZM8ZmN4TKlSun+JzxhphMlStXzioB/KplzoyBAweGNSuTXdC7774bCN/WLUiceuqpVgEgmbEPPvgghZkjuJkzmcNEnVGvXr0YRpo1xPRTPDGWLVuW5jX33nsvR44cSfO4tG6dOHFioJUAotYaO3Ysb775JkAKE1ap43z88cdTtHINEpJ96tWrF+Aqa8SHQmqEvRmPICC+PuIfE46//vqL8ePHA9js7KZNm+jRowfgnluSlZMsfLhWtdFkw4YNtmXYwIED031d//79Az9/Cf/5z3/SfW7KlClWfSXKgfvuu8+2Mxw5cqRtfxoEJYBQsGBBey5LNtRrflajRg0AXnrpJd+MGVNTv379NArQSpUqpTFw9OLN0Ap16tQJq6yNFYsWLUqhAICQAXNeQLw2FixYYJUa999/f1Ra6aWHnIsyV3nvJ2TtnpkKQNbIo0aNsr9/wQUX2PVBELxmhFWrVlnlz+7du4GUSgUxoPS20jtw4IBVb4gHzWWXXRZ1XyNRTst1HDK+Xov/CmBNA999913mzZsHRFcJENuraxhEPi8L6Pr161tDnXC9UmNB586dgZQHJrcQN/4gdAY444wzrJxZ5M0HDhywBmgiV3ccx15wxCE8qLz88ssAjB49Os3G0pw5c8K678YzQZmkZXPrt99+A0JdNbzUrFnT3gAEJe6sIBJaWRSH6/U9fvx42w3kyiuvBGJ/85IdMjKja9SoEV26dAFCCwwvL7/8Mg0bNgRCRqhBQsaelP6kRoyq+vTpE6uQssXMmTNp0aIFgN2MKVOmjJXMB+3mXxAJaTgWLlwIuBsYYhzsNZyTcdS2bVtrHCwbbRm9b24iG2J33HFHljqxBPU4hCPc55Ebz+bNm1upqmy8XHfdddawduTIkXY8Bg1Zo8l1f926dfaGX24QwnWlijXz588HXBM6udnKLAEma8VwiSi/N58ee+yxNNL/cMhnCDKlS5e29xxinOtFJNnnnntuWCM6KZP7/PPPbSlTLMpNwxnICXITeuTIkWxvgI0dOzbmZU2yfhSTw7Vr11pDRtmYXL16dZbM8YwxnHXWWQDcfPPN1K9fHwh10pGEZjSR67a3O07Tpk3TvE6uOd9//729+ZdzpkyZMowePTraoWo5gKIoiqIoiqIoiqLkFWKaqhIpqmQAunbtSrNmzdK8Tvoh+qUECMcLL7yQ4/dYvXo1jz/+uP237L771abu4osvZsSIEQB2x6lKlSr2mEj2v1q1alZeFFQ2b94MYOW/x48ftxlcMaxMJBWAZA/8MjRLjWRfJYPsRWSZ06ZNS9je4OvXr/c7hFyjUKFCVu0grQT37t1rn8/MHNUPXnzxRds6LFw/bSlfWr58eWDas6Zm5syZANx+++32O5asRdBMALOKSIalne4LL7wQtuVc1apVAWjRooXNcImppmSCooW0amvUqBGALUeAkArolltusWU+8Yi37aJIaSW737x5c1555RUgdJ4cPnyYli1b2t8JZ1gXBEQuK9d9Y4yVSWfUxjXWSJvcrKpLDxw4YOcD79wryiwxEvSTjBQAUpaxefNmWx4UtNaAwlVXXWUVceGMcm+44QbALXOSeUzKyH766Sf7urlz5zJt2jQgfRVabtKgQQMgvNGqKK/27t2bpkRh48aNVqbuvVbKmFq6dGlMz/edO3dy4YUXAikz5xkhY0nW/RDKoMs8HjTkXuuuu+6y37uUwf3zzz+2pXn16tUBt4xJFOPly5eP2n2iKgEURVEURVEURVEUJY8QFSVAhQoVaNWqFRDKkFWuXNnudFx88cURv/fs2bNtjVQsW3LkpK5s9erVgGu+t2vXLsDN4MrOVcmSJXMeYITIcZKfjuPYdkJiBDZlyhTf1ApZ4ddff7W7mF7DNtmBzsjcKWhIHaa39UnhwoWB0Bjs2rUr3bt3B0I7pzt27ODff/8FXDMxqdeT2u1osmrVqrCtZG666SYAJkyYAJCpCkDaqfzzzz/WEM3bYkUoWrSoNai85pprIg88F5AatY8++sjuZgfdNDMrSLY2XD1h27Ztre+BX0jduGQwV65cmSIrIEj9vyiwwmWh/UayMpKZ9Sot5Ll4VAGAm80AOHjwIADnn39+hq+/8sor7ZjztnaNJvK9exUAUkfarVs3wFULxJsSoGTJktbD5+OPPwZcQzmZhyXT37hxYztnSWvEm2++2WZFn3vuOavmChLLly+3Kj9Rxj388MOBb1+cmnDX6Mcff9yas3mJpZeOqMDELyoriAJAPBrOOeecDI1Cg8DChQtZs2ZNus8PGzYMgFmzZtljImZzqXnrrbeA2CgBwrWOlNa/Y8aMAQhrVLhq1aqwJo6i9pW2p7GiYMGCdv6R9WyPHj1s7Pfccw+Qsr2pGDNWr17dmhuGa73nF7Jmr1ixom2du2jRIsD1JpM5WDxYIKQIl7XvoEGDbDvUWbNmWd+K3CYqmwDFihWzgzC3+eOPP0hKSorKewtygnvdWMXJWG6WM+PAgQP2td7Fg0jUZs6cmaFjr1/Mnz/fTnrS+/iKK67wM6RMWb16dRq3dgiGZC4jZByLmd6oUaOsC6jc0EPIzVzcTUXOCdib/RIlStj327t3rzUZieYmgMhomzRpEtZ4SiR2cmy8pQsi2fTe8MhmQWaSybfeesv3m39BNmt++OEHK+2SC0C8sm7dOlsOEE5aX7RoUTsm/ULGWzjjqSJFigCueZbIN6N1Ac0pmzdvthd9MUc644wzbGlDEK8R2UHGj5T4NWvWzBppicwWsB04pk+fHrYzRbSYMGGCnZ9kAf3555/bBZlsSETTnTlaVKhQwSYYZDxVrVrVrmVEWr5//35rOvnwww8D7rj83//+B4RKOYKCLPp79uxprx9STiJrl3jCe8P49NNPA+54S9096tZbb42pTFs6+AwZMsTeyIfbEJAbyiFDhtjHZF0yaNCgwJYBCOXLl+eiiy4CQjdq4Vi/fj1XXXUVEL5b2EknnRTTbjMyFuQmE0KG4wMGDADcMSMbyTKHHT9+PE38nTt3jvnNv1CsWDG7mS9r2zvvvDPDxKMkxM4444w0XcCCgKw3brjhBnt8ZH3esmXLFBsaQr9+/dI81qFDByC6JrRaDqAoiqIoiqIoiqIoeYRA9rCSXRTJZqaWBcrOtMhhcrsVl2Rm7r777hSmLOC2ORPpmfSc/89//mONA0VFkJSUZHflTjrpJMBtJSL9KYOa4WnevLk10POaGAYZr3ReqFGjhpVnB5Ht27dbEz3pPx+OUqVK2fEmn+fSSy/N9P2lXV00kUz/TTfdFNYYT3aeRVboLQcQ6XZ6srqMyEr7rlghMltwZbV+Mnfu3LDGjMOHDwdC8ymEWn0eOXLEqk6k3/Onn34alfaouYlkzMIhEuEGDRoEtoxJMuTXXnutHc9SqjBu3LhAyq8joUqVKkDIvGrOnDl8/fXXQEqpqpj/eZVB0WyPJPLf3r17W8WfZMfClS15+7iLoikopqwZIaVwUvLQrl07nnnmmRSvOXLkiC39ExXNiy++SKdOnWIYadaRUo0VK1bYMpmgKzW86lKvwhRc9aXMvd5srSBGzaKUixWSwV+8eLEtixHl1eLFi9OUX1199dX2Wh/07L+X0qVLWyXSnXfeCYQMTbNDnTp1aNOmTa7GlhGyTg+nShCVj7fFr/d18ndpZSj3JX4h53Si0bt3b2vSKIqzcOrKcPTp08eas0eTQG4CyMQ+ZcoUwB2gcpECbJ/hV199FQjJpHMLqWWeMmWKXdjLZsCCBQvsCRTOldO7oJCFXOq6+yAi9X87d+6M2vcaLXr37p3msfbt28fUMyK7vPvuu2Fv/sXxVS661157bbb7vMYKGR+vvPKKPT9kEeBFNmnCbdZkBennLt1F7r///ojeJxpIn1fw30F73759/PLLL2keD3dDKeUUO3fuDFtKEw7ZWPK79s570xIOkRa+8sor3H777UBoI9ZLmzZtrGeAbLTFqnPAzz//DLgbWrKJLT2mg1LqkhvITeihQ4cAd1Eqm07pbTSJpFZqQaOBzFfeBZnMvRBasEnXE6mVB+yYinUv7UiQRIV0Wpo/f76VBwv/93//ZyXpsugUeXRQOHbsGBMnTgRCjuyFCxe2UnQpBwgqMr+EuxHzdl4K97xc/2KN+HkMGTLE3hTLjX+TJk3sDbNI/8XVPB4RrwXZ7Bs+fLgtb5AyrfQQf5zXX389ihGmRbqoZJciRYpw1llnAaH5Tcoc4o1ixYpFlEiKFWXKlOGzzz4DQj4ZH330UZqkWZMmTWx5XNOmTQE3UROubCC30XIARVEURVEURVEURckjBFIJIMju1If/z96Zx2k1t3/8/Z0oItJiiRZ5pNBDhEIbfoqyJCnUZHmyFKJC1PO0aJFKyFJji8rSppCWR7TYoqjQitanZCqp0Sad3x+n63ufmfuefe77nPvM9X69es10zz0z15lzvt/zPd/rc32uKVOsyZt3516ycPGSTjZq1Mian4mspn///jl+j0huGzRoYKVdIr0NIpKhkezLySefnKk/cJD54YcfgIhTKGCljn5Ls3OjZcuW1jxTdmXbtGljTZuSiVKlSlm5qbi7el1Pc0Okg5LpOfPMMzN9XeS5sWRvfrFkyRIg0vv1kksu8TOcfCPu+rkhpVkpKSm2PCg3h/d4M23aNDtv5Yb0bY6F131bsjkDBgzgsssuK1yAeUD+rqVLl+bKK68EwqUAEERZIeqygQMH2jETSxZ50UUXWfMxyZ7GA+kv7zVglczfggUL7L3Fq1aQdYaYbiUTkjGP5QqeDCxZssQaSkrJQv/+/W3HqaDjHe+5ZZbBNQITk0a/OprIOMxPd4BkR4w0+/TpQ7du3YCIqeasWbPsfLZr1y4AWrRoYdfOhekgVhBESSoGjrGUyV7kGapp06aZ1CfJTMuWLXM9br+R8SudM+6//357zxdFQOfOnX1TWAZnVa0oiqIoiqIoiqIoSlwJtBJAuOCCC+wuypAhQ2jRogWQmBpc6dModVnVq1e3PdHFXKhmzZq2b7u0ALz00kvjHltRIH1Nly5dCrht35LFC2DBggVAZFcWIpmneGaRioJq1arZ2sYwIGOycePGgFv3tHbtWiBitAmRdkje9mAyZpKpLk38DeQ6k/ZiflK1alWrJBk3blyBW6mmpqbaWjSpCw1Si73rr7/eZpPl48aNGzP1ec8Lp556qs0Ei5dLmzZtrJolnoi557JlywL1t40X0lKyXLlyNqPrJ2JE9cwzz1h/iR07dgBuT+aseOvPpae1En9WrFgBuNlLaR8mfjlSi54MiL/F7t2786RekONW/EMUiD169Mj0MSiIqZ+s4du0acM333yT6T0vv/yy9TuQ5yWvIauSeE499VRrDC8qYD+VpEmxCQDYvtXy0S86dOiQEOf1RCEyTTE5qlWrlp/h5AsxiOvXr59dzIvMRvEH2UASR2MIXp/pokAeFEQCKMZbflKnTh1eeeUVwN2YfPTRR3P9nt69e1sHd6F58+YJMaQpDDIHy8eFCxfaEg1h2rRpTJ06Nep7pR9vq1at7IPFaaedBhBlmhZv/JL6FnfE+G7mzJn2nvHbb79FvU96Z3fv3j0hZSKKixg3iknW9u3bbTmGmBd6u50kC3fffbfdZJTEUqVKlWynK0XJL1WrVgUK1tEg2UlNTbUJmGTaTP/www/9DsGi5QCKoiiKoiiKoiiKUkxIGiWAEh9E1ix9t6VdVTKxbt06v0NQiik1atQAYvcW95Pu3btb2WxxoG7dulElGQVpJRmk9pNK/KlduzabN2/2OwzFw/r1622bZsmad+7c2ZYmSWlJsiLlcd4yOUVR8k+5cuWSpvQ6qKgSQFEURVEURVEURVGKCcmX9lWKlF9//dXvEBQl6Xjqqaf8DkFRFCU0HDhwAHBViaLqEaNT9fpRFEUpenQTQFEURVEURfENKUUcPXq0v4EoiqIUE7QcQFEURVEURVEURVGKCcZxnLy/2Zh0IBld2Ko6jlNR/hOW44CkPZawHAfotRVE9JwEDz0nwUPPSfDQcxIswnIcoNdWENFzEjxCe05ika9NAEVRFEVRFEVRFEVRkhctB1AURVEURVEURVGUYoJuAiiKoiiKoiiKoihKMcHXTQBjzH3GmIXGmH3GmNF+xlJYjDHVjDEfGWN+N8b8aox53hiTdN0XjDEZWf79bYwZ4XdcBcEYU8sY84kx5g9jzE/GmJZ+x1QQjDFjjTGbjTE7jTGrjDH/8jumghKicxKK8Q5gjJljjNnrGfMr/Y6pIITl2oLwjPmwHAeAMaacMeY9Y8yfxph1xphb/I4pv4Tp/i4YY04/NH+N9TuWghKWuStk98WkH+8QnmvLS7KP+SA9+/qtBNgE9Ade8zmOouBF4DfgJOBcoBHQydeICoDjOEfLP+AEYA8wweew8s2hG89U4EOgHHAXMNYYU8PXwArGIKCa4zjHANcC/Y0x5/scU74J2TkJxXj3cJ9n7J/hdzD5JWTXFoRkzBOe4wB4AdiPe1+8FXjJGHOWvyHlj7Dc37PwAvCN30EUlJDNXWG6Lyb9eA/ZteUlqcc8AXr29XUTwHGcyY7jTAG2+RlHEXEqMN5xnL2O4/wKzACSasKIwY24E/p8vwMpADWBSsBwx3H+dhznE+BzoL2/YeUfx3F+dBxnn/z30L/TfAypoITmnBDO8Z7MhOnaCs2YD8txGGOOAloB/3YcJ8NxnM+A90nS6+sQyXx/B8AY0xbYAcz2O5ZCEKa5KxT3xRCN9zBdW0A4xnyQnn39VgKEiWeBtsaY0saYk4GrcCfAZKYD8KaTnC0kTDavnZ3oQIoCY8yLxpjdwApgM/CRzyEVhDCdk7CN90HGmK3GmM+NMY39DqYAhOnaAkIz5sNyHDWAvx3HWeV5bQlJ+IDjIZnv7xhjjgH6Ad38jqWQhGnuCst9MSzjPUzXVpjGfGDQTYCiYy7uBLET2AgsBKb4GlEhMMZUwZVyveF3LAVkBW6W42FjzOHGmCtxj6e0v2EVDMdxOgFlgAbAZGBfzt8RSMJ0TsI03h8FqgMnA2nAB8aYZMvWhunaAkIz5sNyHEcDf2R57Q/c40o6QnB/B3gCeNVxnA1+B1JIwjR3heW+GJbxHqZrC8Iz5gODbgIUAcaYFGAm7gLnKKACcBww2M+4Ckkq8JnjOGv8DqQgOI7zF3A90Bz4FXfncDzujSkpOSTn+gw4BbjX73jyS1jOSdjGu+M4CxzH2eU4zj7Hcd7AlQte7Xdc+SEs11ZWkn3MCyE4jgzgmCyvHQPs8iGWoiCp7+/GmHOBK4DhfsdSWMIyd4XsvhiK8R6WawvCNeaDhG4CFA3lgMrA84cW0tuA10myhXQWUknuLAGO4yx1HKeR4zjlHcdpipvt/NrvuIqAw0jCuloIzTkJ43j34hBbRhhoQnJtZUfSjvksJOtxrAIOM8ac7nntHOBHn+IpLMl+f28MVAPWG2N+BboDrYwx3/oZVEEJydwVpvtiaMZ7SK4tCNmYDwp+twg8zBhzBFACKGGMOSIZ24k4jrMVWAPce+iYyuLW2y3xN7KCYYy5GFcanNSuwcaYfx66pkobY7rjOtaO9jmsfGGMOd4Y09YYc7QxpoQxpilwM/CJ37EVhDCckzCNd2NMWWNMU5l7jTG3Ag1xMzpJRRiuLQjPmA/LcQA4jvMnboaznzHmKGPMJcB1wBh/I8s/Ibm/p+FuJp176N9IYBrQ1M+gCkoY5q4w3RdDNt6T/to6RGjGfJCeff1WAvTCbVHTA2h36PNevkZUcG4AmgHpwE/AAeAhXyMqOB2AyY7jJJX0KQbtcY2ofgMuB/7P41SdLDi48tmNwO/AUOBBx3Gm+hpVwQnDOYHwjPfDcVvVpANbgfuB6x3HWelrVAUjLNdWWMZ8WI5D6AQciXt9vQ3c6zhO0mUGCcH93XGc3Y7j/Cr/cOXbex3HSfc7tgISlrkrLPdFCM94D8W1FbIxH5hnX5OkxrCKoiiKoiiKoiiKouQTv5UAiqIoiqIoiqIoiqIkCN0EUBRFURRFURRFUZRigm4CKIqiKIqiKIqiKEoxQTcBFEVRFEVRFEVRFKWYkK+WBBUqVHCqVasWp1Dix6JFi7Y6jlNR/h+W44DkPJa1a9eydevWTH3Ik/E4QK+tIKLnJHjoOQkeek6Ch56TYKFrleARlmsL9JwEkTCfk1jkaxOgWrVqLFy4sOBR+YQxZp33/2E5DkjOY6lbt27Ua8l4HKDXVhDRcxI89JwEDz0nwUPPSbDQtUrwCMu1BXpOgkiYz0kstBxAURRFURRFURRFUYoJugmgKIqiKIqiKIqiKMUE3QRQFEVRFEVRFEVRlGKCbgIoiqIoiqIoiqIoSjFBNwEURVEURVEURVEUpZiQr+4ASuEZOnQoAH369KFfv34AdO3a1c+QFEVRFKVQ5rxrlQAAIABJREFUZGRkAPDDDz8AMHHiRI455hgAvvvuOwA2b97MPffcA0BqaioAKSmai1AURVGUffv2AdCmTRvef/99AKpUqcLatWvj8vsCswlw7bXXAvDBBx/w4osvAnDvvff6GVKBkAXQ33//bV8rU6YMANWrV7ev7dmzhzFjxgC6CaAoycTBgwft3LRo0SIAjj/+eGrWrAnAjTfeCECtWrU47rjj/Aky5Hz77bcA3H777QAsXbqUVq1aAfDqq68CcOyxx/oTXCGZO3euva769+8PwB9//BH1voMHD7JhwwYATjnllMQF6OHnn38G4N///jczZswAYMeOHQAcccQRHHaYu8T4888/AShVqhR33HEHAJUrVwbg8ssvT2jMBWXp0qUAfP755wB07tw55vscxwGgbNmyfPHFF4A7FyiKEmy++uorwN20HDZsGAC//PILAJ9++imNGjXyLTYlNk888QQA//nPfwA4++yzefzxxwG4+eabfYuroMgz5AcffIAxBsB+jAe6Ba8oiqIoiqIoiqIoxQTflQD79+8HYPfu3YArDXz22WcBuPXWWwGspDCobNu2jS5dugAwefJkwJV0yO5NuXLlAFiwYEGm7zv11FMTGGXxY926dUBEdjp//nx7TiRbU6tWLebMmQNAxYoVExZb+/btqVq1KgAtW7bM1/dWqVIlobEqmUlJSeGhhx4CsDvOH330EXv37gVg3LhxgJuplczCaaed5kOkRUefPn3s53379s3xvZ9++ikAjRs3jkssixcv5sorrwRg+/btgJt1njJlCoDNNF999dVx+f1FzWuvvQZE/q5//PEHu3btAsgxE5CSkkK9evUAKFGiBB06dACgXbt2ANSoUSO+gRPJhqekpNjfW758eQDq169v1TGiZDjyyCO55pprAHj++eeBYCsBVq9eDcB7773HSy+9BETuK9llZ+T1nTt3ctNNNwERdcqFF14Y13gVxU9EmfTyyy/b19544w0A1q9fb1977rnnALjrrrsYMWIEEJn/zjrrLObNmwdAyZIl4x7z9u3bue+++wCYPXs2AFu3brVfl/HcqlUrTj755KjvF7XWJZdcYtf6SsGQ9VSbNm0455xzcn3/vn37GDx4MBA5Tz/++COjRo0CkksJcODAAQB7PF6OOOKIuP1e3zcBZOHslTvKjXfPnj1AcDcBPvvsM8BdIMuDZCxkoZqRkWEnxPLly9uFQdCQB+TNmzcDMGHCBCZOnAhE5J9fffUVVapU8SfAPLB8+XJ69uwJROSbxpiohdvKlSvtJsH06dMTFt+KFSt46623AHjyyScB9++edZPCGJPpc3BltBUqVADgzTffBJJDbpqRkWEl27NmzQIy1wOfcMIJQOQhDqBjx452s8Qvfv/9dyDycLtt2zZ+/PFHAKZOnQq4Mme53qpVqwZAkyZNaNGiBeBej8mEPPTn9sAfiyZNmgCRa7iomT9/vp1Tpdzi22+/tTfPW265BYAlS5b4du1s2rQJgAoVKuS4kJ09e7YtB5Oa+vzw66+/2s8HDhwIRDadE7EJkJaWBpCve4EsaGbOnAm4D8tBu8fv3LkTcDdrAb755psC/Zxly5YB2IeaeG4CyHiTzZXs3iP3EXnQ6d+/P8OHDwci95hrr73W93m3oMjfXMbDO++8Yx/u/JZzf/fdd3z44YdA5EF469at9u8uD5TyMJQdu3btYtCgQUCkTKVnz57Ur18/LnHnhKzh3333Xfs3/+mnn6Le573XP/jgg5k+elm8eLFNDiZiE+CTTz7h3XffzfV927dvt2sBL9dffz3gJnPGjh0LxPehraDIGuTZZ5/lvffeAyA9PR1w148iq7/hhhsSGtfvv/9ua99HjhwJuJumGzduBODwww/P8fslgZzsyFpr0qRJ9rV//OMfADbBEQ+0HEBRFEVRFEVRFEVRigm+KwFkd1AMhMA1GQICa6r10UcfAVipn+yEQsTM0HEc5s6dC0QkRk899RQrV64E3AxRkI5Pdt2mTJlid0Ulg+6ldOnSmT4GDdmJ7datmzWjqlOnDuBmlWWXc+HChQA0b97clyz6N998YyVLck3Mnz8/T9+bkZFhjdEkUyXHEyREySNZsNatW9td2xIlSgBw4oknWhPN3377DYgoI8BVnrz99tsJi1n466+/AOjdu7fdnY6VBRD27NnDddddB0SuwWOPPZZVq1YB7nEkS0nAnDlz8q0AaNy4sS9Ztn/9618AVK1alRNPPBGIZHHfeustHnvssYTHBDBt2jQArrnmGhtXLM444wweffRRIKK+KF26tD2GsmXLAq66ROT2kjWR+4sg11fz5s2L6ChyJ79qsAULFljjI5mLxTjXb8TQ8MEHH7TZ2pzGvJdSpUoB7prFq86IN5LJmz59OgMGDAAiar1YeJUAgjHGljfJ15566il7b2nXrh1nnnlmkcdeFGzZsgWIGE9Wr17dqi3kXvP000/nODdNmDCB1q1bxzVO+Vu+++67mUyjIXOGXMzNLrnkkpgxyzV61VVX8fXXX2f6WqNGjRKqBJA1Y9OmTQFX3SjImL799tutMk4y0d5SAS8XX3wxAAMGDODoo4+OS8xevvzyS8AtSSgK3nvvPVvKLHN6EBB1iSjldu/eHVVmtnLlSltOZozJd4lqYVi5cqU1+PUSLyVhUJH7uhdRzp5xxhlx+72qBFAURVEURVEURVGUYoLvSgAxC/HuaspriagHKgiSaZEd3HPPPde2RxLDtjlz5ti6oPvvv9++JrvAzZo1S2jMsVi6dKmtK5Maof3799taQDFLOXDggM2GiiGX1KQHjW7dugHueZCYZVczPT3dGjeKedsNN9yQa/1dvLj77rsL9H2LFi3ioosuAghshmbjxo088sgjAIwfP96+LuoXqUFt166dzXpKJlRqJb3vTzSyM/3OO+9Yc7N+/foBrqJBjNvEK2PQoEG2jlHq1ffu3WuVQcmiAoBITT9EzP28WanGjRvHzfQvLHTs2DFP7zvllFO47bbbALfNJLh1gN999x0ADRs2BGDEiBF07949259TrVo1m70+/fTTCxp23BC/g9tuu80aB77wwgtAfNsf5QepxZTWvflBTH67du1aZJnFvCDXmZz7omLz5s089dRTALz99tu27Wnv3r0BEpKpzY0DBw5Y4y9RP6xbt86qAmStcuedd+b4c+JVU7xw4UKGDh0KRO4TjuPY+4mohSpUqGB9sMRn6tJLL435M8U/xLteluMUc+pEsHHjxigFQI0aNXjggQeAiClr1apVbd9z+VpWjjzySCDigyBzXryQ9Yas42O1X41F8+bNrTJIVARZkWy7GKYmepyIMkgUCYMGDcpkgg2uckPWxKLkuvDCC+33fvvttwlVAiju/C2Gs8Ixxxxjn8Hiie+bAMmIPPzL4uX888+PGuyNGze2bo/eB36ZeMQROpGIsZkYr23ZssWWMshiol27dpx//vlARPK/ePFi+0Bdu3bthMacV0TyJBPZDTfcEDWRLV++3JohifT+7rvvDuyGRnbUrVvXXoNiShMUZLF//fXXs3jxYiBiVHbDDTfYSe3ss8+23yN9eL2GKDJmxGgoUSxZsgRwH/7BfeCPVY4gJoY9evQA4J577rHySFnslCxZMlCyQC9iZDpnzpxMzv/gzhNheMjPSRYdJE466SQg8rAyd+5cDh48CMAVV1wBxF6oHjx40G4knnnmmYF8+Jf5Vkrnfv75Z1seF5R5VwxwR48eneP75CGlevXqAMyYMcM+3Mk8IBuBiSInw9FevXrl+28s91GvO/r69evtpq1cj/IA6AfyN27Xrp2dx0QGP2fOHLsuk7JSv8pNBg8ebJMOL774IgA33nijLR3xrhmlZFE+xmLs2LGZ7kVyX5UETiKTZgMHDrQP/3IvnDZtmh0bXqQc8JVXXon5syQhc9VVV8Uj1Ci2bdsG5P7wL4Z0svEycOBAu8Eva+m77rrLlmh4f6bM34kkPT3dbr5IuWitWrWsebRsPpUuXTqqNGPbtm22PMuvErrizPPPP28TS5I8njJlCpUqVYr779ZyAEVRFEVRFEVRFEUpJviuBJAd5mRCZJnDhg0D3Ky+tKWR9jyO41iZp3DSSSdZSbEfyO7+ueeeC8BRRx1lZX7XXnstkNmkJhYi3Qoa0qotJ2lpw4YNbfmDvE92R5MBydKkpKTYdnSJbueSHaIAuOeeewBXPSJZqAkTJgDQoEGDqO/bv3+/zdj873//s69LZk2M0RKFZGnk+li8eLGVD+bUxqxkyZI22yMqk5EjR9osb1AQqb+3pWlWJUDQVQDeOUhat40fP95eZ8KsWbNsZubYY49NXID5RDJTkjV8+OGHbVYgq4ETRObvO+64wyoBxGgzCIgx3ptvvmkzl6KwKVmypM1cymu33HKLr/21JXsmrdYgch+UuDp16mTLm+T6u+6662xbLSn3ycjIsKWBcj7jifwthwwZElXGMGnSJFsmkNd2f6LUWr16deDazorRrBggTpw4kfPOOw+I/K29mXIxpMuNeF17H3zwgf1c1llSCpMXRNXYpk0bwDXVFBVE6dKlrZrU73Z0Mh6kFNbLxo0bc2y/d80113D55ZfHLbZYyHojN2QN4r0/yrUihm2VKlWySofRo0dbI2Bp6SbtpxNBr169rAJA2uRmV9okaxRRWhpjbFlJUEy/pYXsNddc43Mk8UNUJjNnzrT3HCkFSpTRsioBFEVRFEVRFEVRFKWY4LsSQFpxeTnqqKN8iCTvSEZWaqKmTZvGV199BURqmR3HsRkeyQZOnTrVZnH8QNrg5Lcdjtc4TzK9QUPqns466yzArUOTWMUbYNSoUcyaNQuAypUrA3DrrbcmOtR806tXLwCrYjjvvPOyNdnxCzE2klp6iNQAxlIASJuwjh072mxuEBBliLTMGjJkiM3yybi//fbbo+ovu3TpYneu33rrLSD/4yzezJkzJ5MCACJGX8lEamqqbV/6xhtvAO6xSYsp+btPmDDB1mznZg7mJ2Ic6zXQzEqLFi1sraaYOQVNZSItC0UBl9XoCFzlzzPPPJPptZdfftmqAhLNggULWLNmTdTrkvGTFnSxKFeuXFQWeceOHXk2GisKxBg2LS3Nzl3SonT58uU2Eygt6vKqCMiKZHn98nHYv3+/9TKSzHKNGjWsiayYn/3nP/+x9/a8mrLFK9N4zjnn2Na9olLq0KFDjmpKuYcsWrTIKsvEs8JLv379fM2QtmnTxs5Xa9euBdx5WeZjUc3dcccd1gPEi3g4jBkzJqHmeV9++aW9J8RC7ocPPvhgnjLi9evXt8fy2WefWSWAmIPWqFGDevXqFTbsPLFixQqrGMvN3FSeXbxKM79UpWKGmRVRWot68eijj7bKJzGPFwVwsiHqEZnLjDH2uffBBx9MaCy+bwLEolOnTn6HkCNiGCIPngMHDrQXrEjuHcexbs8iC/NzA6AwxLoJBQ2RLspENmXKFDvRycOot0ey34uavNK/f/+oEobp06cHLm5vj2CAE088MZP5n/Dqq68CkY2NXbt2WdmmLCbq1Knj+1iRv3lqaqrdTJKPkydPtosdWai99957LFiwAAhuJ4BYrv9ZSwGSgZIlSzJq1Cgg8gANkYdiMW2aMGGCfSASWabM3UHCayyVHRUrVrQPN0F7+BdkESPmZqmpqdYo7Lrrrot6v2yWPfTQQ1ZWn1epblExaNAgW3rhpaBrkI8//jjmQ0+8Ofzww60Jadu2bQHXOE8eKmUTIK/I+BLEtE1MgxPNrFmzomTlF154oS0hE/l1RkYGF154IRC7lGzx4sV2w1pkt/EaT9OnT7dG0OIWP2zYMA47LPtltzzciGFzVsSQUTZE/KJRo0bW0E/Kad5//327Aei9v2elbNmyNrGUaNPGp556KirxWKZMGVtWIn/XnEr/8oL8jqeeeiohZUHgSvzz0nGqV69etnuAbJ5VqFDBtzWlt2zGizwoi+FtiRIlbGJVOk5kh1x/QeTrr7+2XU2kdA4inSUSZZApaDmAoiiKoiiKoiiKohQTfFUC/P777zZrI5QrVy4wxhS5IWZT9erVsztqwsGDBznxxBOB5FUAeJEMgF/tdvKKSL0mT55spUOSpc7IyLByrbz28fYLMczs3bu3zQCKxCtoKgCIZGKEihUrRo3t5557zmZuxOTphx9+sGaZogS4+OKLfe9DLUZrZ599tpWrSTud7t27R2WPZs+eHVgFQCykLKBJkybWgEbUAUE3BoRIOyzJ4HiR8p9mzZpZkza5FsX8KEiIOsab6ZQsyPvvvw/A66+/buc2GWuJMg7KK3Xr1gXybogn2fbp06fz9NNPA4lXAsTi8ccftyaAyYhI/sX8Kz+IwbHcO8E11PXbwPmqq66iQ4cOQKQEaOzYsVbp40Xu9/fee699Tea7n3/+2bZulrHVokWLuMRcrlw5m+WU8r2VK1fa1rleGjZsCETK0bZv3x4lk65WrZpVzwTB6FRilvPRtWtXK8+OJdMWZcaYMWOseiDRSDtiLw0bNrTXQmFo06YNH330Ua6/L14YY+y4lbWUt022fG3WrFlRBtrekt9EIfc4MTPMjpxKsrKjsEqOePLaa69Fqatr1aoVUy2XCFQJoCiKoiiKoiiKoijFBF+VAPv27WPjxo2ZXrvkkksKbF6TaBYtWgS4dalZa80dx2HDhg1AxAQtVo10kJFzs3z5cttCMEitqHLihhtuiDI6KVGiRI4tBIPEypUrAXd3V2Jevny5/SgeCKIK8Ludk9RmT5o0CYDvv/8+ptpCsoWy8166dGl7XDJ2gmo+Kcczffr0TJkycDNol112mR9h5ZnevXvTt2/fTK95zQLla717905KrwBBjLfuvPNOPvnkEwC++OILIJhKAFGVeA2B5HNRyYwaNcrWD8p1NmTIEGtA5bdypjDceeed2ZpDxQupv/RmAMXD56KLLipwK9w777zTmlaOHj3avp5VKRhUJBslmURw69n9zjyXKFGCkSNHAnD99dcDsHDhQqss8/6tpdVW1qwsuD4AYqYotcbxRO7PksHfunVrTMPMM844A4iM488++yxK6dOlS5eEt8zNCRkj7dq1A1x1gpwbL8cddxwQOUd+qQDAVehmHYtFNTbbtWvHjz/+CMCTTz5ZpD87L5x33nlWBSNeILNmzbIxyBqxY8eOVtXop0G2xHPppZfaeIuCsmXL5slnJ9HInDpu3Lio55A77riDSpUq+RBVQI0Bg853330HRG6Yv/76q13IifzrrbfeshLH559/HsDexJIFkZ3u378/kwFXsiHyVMdx7E1ZFs9BRfq8li5d2so6u3XrBsCff/5pJxGZ4I0x1oHYD5dXMQATB/MxY8ZEbfA98MAD9qFGFjuTJ0+23QGkbMbvDY2sHDx4EMCa7kyZMsVKuKVE4/7777eGT0HdxOjTp499uPc+5GfdGOjbt29SlQZkR6tWrWwfZJGn9uzZM5DmgNnxn//8B3Dl0NKdYtq0aQA8/PDD3HTTTUBybwJAxEhr69atCSl38rpiC7LRXVh5uDyEen920DefxeRY5mxjjJ2Hpce935QqVQqIrLuuu+66qLmrU6dOdr2VV2bOnGkN9+JNXg3YvB0r5N4a1E5GGRkZANm67ku3lniVXeSHlJSUqLEoJm1FgfzsWHNAvPnmm2/sJoAY/4GbWIXIurBr1642Linp8KPEVJI+Y8aMsV2lZHM2O2TdLt/7yCOP2OtPKFOmjH3WCsKmv6wfpZPJ7t277dfEiFLW9n6g5QCKoiiKoiiKoiiKUkxQJUABaNasGYBtV3HSSSfZTJNkM0X+BfDVV18BrhGiSKOSAZHQpqSkJE2JhheRmYuhkDEm1/6pQUF2aOUjRAyP1q1bZz8XlcCUKVNsWzs/lAAiZZJWX/IxO6R1kGQJIPOxBgkZy9JqsmfPnnYHV+KvV6+ezTqLuVOQs+heJYDEKVm1OXPm2HaCySJjzo4rrrgCcKXz4Gaag9piLycuuOACK+EUk9ZNmzbZrKfIT5ORrVu3WnVGEE1Pw056ejpPPfUUEFFkVKpUiQ8//NDPsHJly5Yt9p53wQUXAORbBQDBaukq6ktvadB9990HQPny5X2JKTsko/n2228DZDJplLKFww47LNd2bn7zzjvvWOVlYTh48GC2rR0Thaw9XnrppaivSZvQcePG2bWWKID8pEKFCvYal495Zfny5YwYMSLTaxs2bODUU08tsvgKi6irvOoMUTL861//8iUmL6oEUBRFURRFURRFUZRigioB8skdd9zB1q1bgUi9z/PPPx+zVZUgWU+/dwnzi7SxqF27NlWqVPE5mvwjBjyyY33eeecFokaooMgub82aNW0NoxgKZTWqCzr//e9/AXcMVatWDYi0DAsSW7dujfLDEG8AiLTMfO2116xhW69evQA3q5MMmU1RAohB4Jw5cwKtYigMS5cuTUolAERMWb31/+ecc45f4RQZiW5PtXTp0piZssIg2c4RI0ZkUgFeddVVgGuuF1QGDBhgVXOypklNTQ28+m/evHns378fcP0/Cso//vGPogqp0AwdOhSAHTt2UKNGDaBoa9aLElFhiAIOIn4Noky67777rHljUFm9ejU///wzUDhVyMaNGzNle4OGqEeNMb60BIwHl19+eZQSIGgsXbo06jVRKF500UWJDieKwG0CBLW/o8jI33jjDWv00KVLF4CY/R0zMjKslFYWasm6+ExGli9fnqkMAPzphRpvRD6YbBO7GARC5KE5SIsxYdasWdZpVhYIJ5xwQtT7LrroIrsAkC4C48aNs3NEkJGH/7lz5/obSAJYvXp1wkzAihqR3a5evdq+Ju72iX5QkF7rPXr0ANwHgrwaLv79999ApKPI1q1b+fe//x2HKGPzz3/+0/aQFyPTwiIL0UcffTTT6yVLlgQocLeBeCLmpiNGjLBrGtmQDaoJHUSM6F566SVOPvlkAG677TYfIyo8kij6888/AdcQ+OGHHwYiXSuCxBNPPMGLL76Y6bUhQ4ZYeXNQ1/FnnXWW7dYlrFq1ypoWirF3QdYiPXv2jHotCB3BZH317bffAu5zSzInw7wkuqtMfpkxY0aU5L9Ro0aB2rjQcgBFURRFURRFURRFKSb4qgT47bffol4Lauu26dOnA27GVXb3RdLhRXYZR40aZSX0L7zwQoKiLBp27twJwIIFCwC3j2eyMWnSJNLT04GICUfLli39DKlISUtLy/SxVq1aSXF80uv8+++/B9xMWbly5fwMKUe8WVdp/SfjPyvXXHNNpq//8ccfcY4uZ8T8TzL8vXv3jpL5e00AvWTtUa0UnrFjx1p1kmReN2zYwCmnnJLpfXPnzmXRokVARMX03HPPsX79+kzvq1Onjm9tZyUDI21wly1bxjPPPANgZcyx+OWXX+w9/pNPPgHccrNkbUErbZ+klaOXMmXKBLIMQO6Lcu8wxtiWZtKqK2htWr1I3HPmzLFlHUHMlucHUfSJdPjiiy+25rNBYt68eQAMHz7c3t/kvtexY0dbHifX2ObNm33rfx6L559/3paQeEsoV61aBUSOpW3btlblJK0ps2PGjBlApMQRIubMfmd8ly9fbss25F6SDOvEvBJ008l77rnHjgXh5JNPDpRBvK+bAKNHj/bz1+cLqZmDSE2mLPZnzZrFsmXLgMyS8zp16gDJVwYgElPxMEimBZoMuFdffTW0ZQDp6enWKVyOsTA1kYnk9ddfz/T/c8891/bnDjriQJ0dmzZtAiJSaT/p06dPVA9tkf3nRuPGjTN1DwgisuASWXfFihW5/fbbgeBtJMsD/b333hvVR3ratGl2E0xKzubNm2flwTn1mn/ggQc49thj4xt8NtStWxfAbmBMnz7d3iNl0VmhQgW7WSAbGBMnTrRybimTmzFjRiC8M6TP+fz5821df+XKlaPeJw9qnTt3tmNeFqNHHHGEXR+8++67gfTWkISGXJeAnYNr167tS0x5QRIsstl03XXX2U21ZGbRokV07do102s33nijT9HERsasXCe7du2ypRjSFcDrVSJlcMuWLaN58+aJDDVHypUrZ+OVB/WZM2far8tmQL9+/axPgHgV1atXL+rnzZkzh7Zt2wKRkg6IlNr4VRYhGxPNmze3ZclvvPEGAA0aNPAlpqJEEsjjx4/3OZLYyFwl5T1e1q5dG5Wg9BMtB1AURVEURVEURVGUYkLgjAGDisjjvv/+e2sUdvXVV2f7/rvvvpsBAwYkJLaiZtKkSZn+HysbElTEuX39+vV2NzoZzNnyw913321NXqS/rddoL6isXbvW7kaLUVbWay1olChRwip+qlevnu37Vq1axeDBg4GI1NtPBVBWFUBe6N27N0DgVQAAH3/8MRDJju/YscNmbMSNun379rbDiZ+IPL5t27ZRSpi8ysXLlCnDJZdcAmBN9Px0Fpas37Rp0wB3Hvrxxx/t51mRbJQxhssvvxzA9qaPZbQZb8T468QTT7QlSiJv/uOPP6zSIRbeYxFEBt2/f/9897pONLHWJQ899BDgGtIFFSl9keulW7duHHHEEX6GVCRs3LiR7du3AxHpeW6qs0QjZT+S7S5durTNdnsVADK/SXlDpUqVAtEL3YtcM1IOcPPNNzN16tSo940bNw6IrFEOO+wwq+AS9u3bZ1VAp5xyii1zlPnAL7wlALLmEuVDGJCSji1btvgcSWzKli0LuNdMVo466qhcS0wSiSoBFEVRFEVRFEVRFKWYEBglwHnnnQeQ4w68n3Tr1g1ws+LSy1WoW7eurf2QFiPSaieZkXqmINRr5sbkyZMB7I5urVq1QucF0L59e8A9RtnVlTriZKBly5b89NNPgLsbCgTKNCgWHTp0sOZf0vNbas8h4kExePBgm0WTljzSKtAPevfuHaUG6N27tzUJFOO/ZMj6x0IUP/379wdg5cqV9u/9xBNPAG42RNrRlS9fHsDWbyYSyQpu+ozHAAAgAElEQVT17dvX1p2L+aqXEiVKAO6YEDWJGM5Vr149poGj30gN+fjx462pnIyX0qVL2/t6mzZtAGjRooX9e8jx+oHMn2+++aa9ZxcGuQ6DrgJo3bq1nYOFVq1asXjxYgCrcgyaT8umTZt48803gYjnRzIaFsfiww8/tJ+L2fTFF1/sVzgx2b17d6b/N2nSxCp/5OOkSZP45ptvAPjrr78At41jEFv/QkR18corr1hPBvHDGj9+PBs2bAAi3liO40T5skDm+d3v1ogyD4mBY6NGjWjXrp2fIcUFUWQceeSR7NmzJ9PXzj//fGvq6BfilRPLRPq+++7z/Trx4usmgFfOKAY6QZV3nX/++fajyH7DyooVKwCsg2VW9+qgkZGRYSWysnhu2bJlUmxeZMfw4cMB+Pzzz6lZsyaANavq0qVLUmxwrFmzBoj0MBfzTEiejhmVK1e2RkJyHN27d496X8uWLXnyySeBnN3RE0WfPn2S9gE/L2QtzTj77LP5/PPPARg2bBjgbgaIoZX0g/dzTqhUqZKVyT777LOAu+h84IEHALeHNcCdd97pT4CFoFatWnbTqSClKH7x5JNP2o2xWBszOdGuXTvbMSRo8u3smDx5ctSDzOTJk63k+YsvvvAjrFx56aWX7CbsI4884nM0RYt3TpK54MCBAzGlxEFh2rRpthQoFlKCKecsyJQrV85uKMnHjh07Mnv2bCDSMWPu3LlRY6dp06bWONvvY508ebJ9NpFuGVLGETYkefTee+/RrFmzTF/r1KlT1Gt+sW7dOr9DyBUtB1AURVEURVEURVGUYoKvW41t2rSxMkElGAwbNswqAURyG3RWrlzJypUrgYjM2U8pdlHQsGFDwJWZSxsRae105ZVX+hZXfli4cGGmjxDJdCSTRE3+3tu2bfM5EiUnRCL48MMPZ/oYJGR+ko+Kv9SsWZPff//d7zB8R9QnQWsRKHNuWlqazchKljMseFvPNW3aFHDbGktZUxAQJdWQIUOiviYtTjt16mRbG4qqKauRXrJw+umnc/rppwNYtU/QmTlzpi3bEEWDlGOFlSuvvNKqf5WCkZwjVFEURVEURVEURVGUfBPcoiPFF7zZTtnVDTrnn38+Bw4c8DuMIkU8KKR9VRioW7duUrQyVBRFKS7ceOONpKWl+R1GTETR16tXr0AaYxYFNWvWJDU1FYiY1QXNF+TYY48FsEarSvAwxthW5slkGK34i24CKJkYOHAgAwcO9DsMJQS0bt0600dFURTFP5LtIU66/oSZM844g9dff93vMJQkZ+TIkX6HoCQhWg6gKIqiKIqiKIqiKMUE4zhO3t9sTDoQ/J4H0VR1HKei/CcsxwFJeyxhOQ7QayuI6DkJHnpOgoeek+Ch5yRYhOU4QK+tIKLnJHiE9pzEIl+bAIqiKIqiKIqiKIqiJC9aDqAoiqIoiqIoiqIoxQTdBFAURVEURVEURVGUYkIgNgGMMacbY/YaY8b6HUthCcuxGGPaGmOWG2P+NMb8bIxp4HdM+cUYU84Y896hY1hnjLnF75gKQxiuLWPMnEPHkHHo30q/YyooIRkj9xljFhpj9hljRvsdT2EI03gPy7GE5fryzFfy729jzAi/4yoIYTknQkjm4bHGmM3GmJ3GmFXGmH/5HVNBCNG8VcoY8+qhY9hljPnOGHOV33EVhLCM9xAdR6DuJUFpEfgC8I3fQRQRSX8sxpj/AwYDbYCvgZP8jajAvADsB04AzgWmGWOWOI7zo79hFZikv7YOcZ/jOK/4HURhCNEY2QT0B5oCR/ocS2EJ03gPy7GE4vpyHOdo+dwYcxSwBZjgX0SFIhTnBEI1Dw8C7nQcZ58xpiYwxxjzneM4i/wOLJ+EZd46DNgANALWA1cD440xtR3HWetnYAUgLOM9FMcRtHuJ70oAY0xbYAcw2+9YCkuIjqUv0M9xnK8cxznoOM7/HMf5n99B5YdDg6sV8G/HcTIcx/kMeB9o729kBSNE11ZYSPoxAuA4zmTHcaYA2/yOpTCEabyH6VjCcn1l4UbgN2C+34EUhJCdk7DMwz86jrNP/nvo32k+hpRvQjZv/ek4Th/HcdYeuq4+BNYA5/sdW34Jy3gPy3Fkwfd7ia+bAMaYY4B+QDc/4ygKwnIsxpgSQF2gojHmJ2PMRmPM88aYZNt5qwH87TjOKs9rS4CzfIqnwITl2vIwyBiz1RjzuTGmsd/B5JcQjZEwEZrxTriOJYx0AN50tLWSr4RtHjbGvGiM2Q2sADYDH/kcUn4J7bxljDkB9/iSTdGgBBvf7yV+KwGeAF51HGeDz3EUBWE5lhOAw3F3qBrgSrrqAL38DKoAHA38keW1P4AyPsRSWMJybQE8ClQHTgbSgA+MMUmV8SA8YyRMhGm8h+lYQoUxpgquRPgNv2NRwjUPO47TCXeMNwAmA/ty/o7AEcp5yxhzODAOeMNxnBV+x6OEg6DcS3zbBDDGnAtcAQz3K4aiIkzHAuw59HGE4zibHcfZCjyNWxOVTGQAx2R57Rhglw+xFJiQXVs4jrPAcZxdjuPscxznDeBzku/aCssYCROhGO+HCNOxhI1U4DPHcdb4HYgSvnnYcZy/D8noTwHu9TuefBK6ecsYkwKMwfU5uM/ncJRwEYh7iZ/GgI2BasB6Ywy4u4gljDFnOo5zno9xFYTGhORYHMf53RizEbcmLZlZBRxmjDndcZzVh147h+STczUmJNdWNjiA8TuI/BCiMRImwjLeIVzHEjZSgSf9DkIJ/Tx8GEnmCUDI5i3jLrhexVWcXO04zl8+h6SEi0DcS/wsB0jDneTOPfRvJDAN1/kx2QjTsQC8DtxvjDneGHMc8CDwoc8x5QvHcf7EldT1M8YcZYy5BLgOd1c3mQjNtWWMKWuMaWqMOcIYc5gx5lagITDT79gKQNKPEYBD5+EIoATu5tIRxpigdI3JMyEa76E6lrBcXwDGmItxy5iStSsAEK5zQgjm4UOxtzXGHG2MKWGMaQrcDHzid2z5IUzz1iFeAmoB1ziOsye3NweVsIz3sBwHBOte4tsmgOM4ux3H+VX+4UqJ9jqOk+5XTAUlTMdyiCdwW9GtApYD3wEDfI2oYHTCbSXyG/A2cG+ytaoJ2bV1OG6Ll3RgK3A/cL3jOCt9japghGWM9MKV1fYA2h36PClragnBePcQlmMJ0/XVAZjsOE7SypsPEaZzEoZ52MGV/m8EfgeGAg86jjPV16gKRijmLWNMVeBu3MTLr56e7rf6HFpBCMt4D8txQIDuJUYNbhVFURRFURRFURSleOB3dwBFURRFURRFURRFURKEbgIoiqIoiqIoiqIoSjFBNwEURVEURVEURVEUpZigmwCKoiiKoiiKoiiKUkzQTQBFURRFURRFURRFKSbkq8dihQoVnGrVqsUplPixaNGirY7jVJT/h+U4IDmPZe3atWzdutV4X0vG4wC9toKInpPgoeckeOg5CR56ToKFrlWCR1iuLdBzEkTCfE5ika9NgGrVqrFw4cKCR+UTxph13v+H5TggOY+lbt26Ua8l43GAXltBRM9J8NBzEjz0nAQPPSfBQtcqwSMs1xboOQkiYT4nsdByAEVRFEVRFEVRFEUpJugmgKIoShxo0qQJffr0oU+fPn6HoiiKElr++usv/vrrLx5//HGMMRhjGD58OMOHD/c7NEVRlMCimwCKoiiKoiiKoiiKUkzIlyeAoiiKkjPGmNzfpCjFgDlz5gDQt29fAB577DGuvPJKHyNSwsh7771nP6akuLktnYcVRVFyRpUAiqIoiqIoiqIoilJMUCWAkisbNmzgsssuA+Cnn34CoGLFisyePRuA2rVr+xabogSFJk2aRL326aef+hBJ8WPDhg1069YNgAkTJtjXH3roIQCefvppX+Iqznz88ce0bNkSgN27dwPwxRdfMGvWLAAaNWrkW2zFlU2bNnHmmWcC8M477wDQrFkzP0MqEm666SYAFi5cyLBhwwBYtGiRnyEpScwvv/zC0qVLo14//vjjAbj44osTHZISg3nz5tG4cWMAatWqBcCPP/7oY0TJR8I2Ab755hsuuOCCAn//xIkTAXfhULFirq0PlSJg3Tq3w0TTpk355ZdfAKzUbtu2bdxwww0ArF692p8A80h6ejoAHTt25IMPPsj0tb59+9KrVy8/wsoTr7zyCo8++igA3bt3B1xJbW7s2rULgJ9//hmAKlWqUK5cuThFqTRp0sRKn4XevXv7E0wOfPzxxwCccMIJAIwfPz7qPVWqVOHUU08F4NdffwXg3HPP5eyzz05QlLmzYcMGILL4/+qrr6LeU7lyZWsMVq9evUzvDzIXXHCBfYCR+dbL6aefDsDYsWOpWrUqABUqVEhcgLmwfv16wJ1v5eFfSElJsQtpJfEYY+w11aNHDyAcmwDbtm0DYP78+fa1rl27+hVOtrz77rvMmDEDgDfffBOA1NRUu76VloT9+vWjQYMG/gRZBMybNw9w54JBgwYBsGzZMgBOPvlknnzySQCuv/56jj76aH+CPMTDDz9s14jCihUrWLBgQdR7K1WqBMCYMWMAbHIs2ZBzIaVZmzdvtl/7+++/fYmpIKxYscKW/axcuRKAtLQ0+2wSpPtiUNFyAEVRFEVRFEVRFEUpJsRdCTBz5kwAbr31VpvBF5nN4Ycfnqef8c4779ChQwcArrvuupjZq2RBpGoDBw7kiiuuANzd4SDx3//+F8BKOffs2WO/Vr9+fQC+/PJLHMdJfHD54KWXXgIi1+C0adOiMmt9+/alfPnyANx7772JDTAHRJ0watQofv/9dwAmT54M5K4E2Lt3L7fccgvgHjPA6NGjSU1NjVe4xRYpAfCqAESeFsTWgDLnCHkt5dm2bZvNovs9/3bt2jWq9Vfr1q3t3Fq5cmXAjbNNmzYA9uNXX30V2NIAyZ5t377dzlOxlACivKpfv74d50OHDvU96zF9+nTAvdcD/PHHH1HvOfzww61sU0k8J510kh0f27dvB2Dnzp0cc8wxfoZVaG6++WYAvv76azunValSxc+QMvHRRx8BcMstt9jMpXyUrDJE5oCWLVvasqZYZWZBRUp9brvtNgC2bNlivyZz2ebNm+16vmPHjjz77LMAlCpVKu7xrVy50sb06quvAq6i6uDBg9l+j1d5vGnTJgBatGgBwAcffMDll18er3DjRlpaGhA5P7HuM8lC1ueQe+65xypq/L4nJgNx2wQQyVP79u0B94YjN538PjyWKlXKXqT//e9/7cOMyKmCjkwwqamptg7v+OOPp2fPnn6GFZM1a9bwwAMPAJkf/s8991wgskFw880388MPPyQ+wFz44osvALj99tutlDkjIwPIfqJ75JFHAPfh+b777gPyvkEVL8aOHQu4D1/HHXccAC+//HKevvfgwYNWkhtG9u/fb0tVBJFIJwp56M9aAgDJ6wMgEvtJkybZcgFZXKenp/PNN9/4Gtcll1xi/y/yfnmgl81JLzfddJMtoZGfMXz4cCu9ffDBBwMhGZaFf+fOnYFIrHnhrbfeAqBLly6+LnimT59u7/WxHv6V4LFx40bAlWwHqdQnP3z99ddApA64dOnSdszL5r6fyEPjv//973x9344dO+y65JNPPgGgTJkyRRtcHLj++usB2LdvX57e//LLL/PPf/4TgE6dOsUtru+//x6Atm3bWim8F9kcv+qqq6K+VrNmTcBdV8nxydq4R48evt0XC8rbb79t15dC2bJlk/aBOeumWrKzdu1aAF5//XX69+8PQIMGDZg6dSoAxx57bJH+vuTd/lEURVEURVEURVEUJV/ERQmQkZFhdz7FsCUlJYV77rnH/aWH5e/XtmzZ0hpyrFmzhi+//BKImJ8FfYf022+/BSJZG3BNw2QHNEi0b9+eVatWZXrthBNO4PnnnwfgyCOPBNxdqr/++ivh8WXH3LlzAXenF2Dr1q15/l4xr5Kdd4i4igcBGUuixsiN/fv3WxVEiRIlAKyBWLKwd+9ewM3wfP7550BkHC1ZsiTKuTeRZjZz5swJZSeAcePGAfD444/b60bmApHTJ5oNGzZEyXqHDRuW5wz+jTfeCESynvXq1bNdBLp162Zf96tEYPny5dx+++1A/hQAwuOPPw7AGWecUaRx5ZcRI0bYsiWhYcOG1rBx//79ALRr1y7hsRUVcgyy7pDyE4gYaL7++uv2tZo1a9pMTqtWrRIVZq5Ur14diGRHJ06cmJRKgDVr1lgDMPn733zzzYG6xsRQWUoUUlJSOOuss6LeJ/LlLl262Ne+++47IJIZDHoXpvT09HyrfGvXrm3PYTwRyX56ejqlS5cGIuNg1KhRNtufk3ny+vXrrZo0mczzBMkkt2vXLkoV2759+8CWyeVG1msu6GXKEDErlxK6AQMGWMW4rH03b95s1Q3z58/nwQcfBDLfY4oCVQIoiqIoiqIoiqIoSjEhLkqA+++/P6pHa2pqqs0mFxZpezZp0iQgYkLiF2lpaVadMGTIEMA1pJBadKmVBKwhil+ZtewYOHAg4PbZFaQueOLEiVE1t1KnHhTee+89IH8KgFiIYYpfSgDZ/d+xYwfgZvDzm9lIS0uzO43SizsZenJLbd2kSZP48MMPATdTKju7sWq+/DBN6tu3b9Rrn376qTUETFa87TMl03Haaaf5FQ4Q8QEA1/wP8tf6S8axZPzr169vv99rMCg+AYn20jjrrLNiepXkZFTl/ZpkmmvUqGFNAv2gZs2a1oBVPGXq169vPVqEU045JeGxFQZRiT3xxBPW/0PUDbHmI+9rK1eutMasQVICSEZJMoMffvhhIE1McyMtLc22NpNM7zPPPONnSFFceumlmT5mZGTEbImXbHXlsXjsscesWiavlClThhNPPDFOEUW466677OfVqlUD4F//+le+fsbDDz8cKPVrfsmrp1Qy4W0RGFRPABkT4mPVpUsXq5D3PifntM6F+N1DinQTQOS6soCHiLxGDNfCxOLFiwFXVipGcv369bNfl0WZ9K/s0KGDvUkVtblDQXn//feBSNzeSU7cwC+++OLEB5YPnnjiCUaMGJHt13NaUGf9umzciHnMmWeeWQQR5o1t27Zx9dVXAxFzrR49euTZsEWcXr3dJsTIJihI2YZIh4cNG8Znn30GZJ78pHyhadOmUT/jnHPOsROiyCgTQaxOAFICkMwbAHI8Mn9DZN72Xv8io08Esqm6YcMG+/BfkK4E4oQuH708/fTT9qFUjq1+/fr2dyeCe+65hxUrVgDYceDFu0Eg3VpkvpKHOHA3wo844giAhMhrszJgwABr7CV/63379lG2bFmg8JuziWTjxo12LMimvpQkeSlRooR9oBD27t3L//73v7jHWBikDCBZkXHyxhtv2NeklC/WvXLr1q389NNPgOv07ufGZqwNAIBrr70WiIztlJQUrrvuOiD4ZQBidi3di7IiY0TKGvxANksLgmxuiim2l2SQ0MvfXdaH3vWuGConw3F4kYfnZ599Nkr+X7ly5cB0BnEcx/5tpeOX4zgF2rCIV1cdLQdQFEVRFEVRFEVRlGJCkSkBFi1aZCVZO3futK9Lr/bzzjuvUD9f5FRr1qyxr4kpUqLLAUSqLVn9P//802YsJQuyevVqu2MtWf/hw4cHRgEgyLnyKgAk81+Y3dNE0qdPnzz1Oc3tPSkpKdZg6JVXXgESs0MqcqHOnTvb3y99m2O1rMkOaTUmJQUAzZs3L6owC4yMlzp16tgsmddYR3ZFmzVrBrjtESWrKSY+fhKrHaBk/pNZASA8+eSTQOa2TjK3yby9f/9+25o1EXjLpQqiAMgrUhogEu8JEyZYJUCstoNFzaOPPprj/Uvue7fccotV9YjBblb87PV85JFH8o9//CPTa6VKlbIGk0FGJP+ihnv11VetXDMWMjdNmDDB9guXn/HOO+9kkhknQuqcX5K1FZj8jaV0YcuWLXbdJfPU77//bk1jpQ/8kiVLbDvjSpUq2bKnvBrtFjXSXk5MV8eMGWPNJmUMG2Ps/VDeL6bMQUPm0FitQS+88EJrZPboo48CmZXCQebAgQNARFnpNT5t2LAhQCDNvbMi17sol1NSUux1lt29JOhI+a83oy6fN2zY0Lc5TpSuMgcNGDAgRxWcKICqVq1q1wEyp3m/7/7774+bgkmVAIqiKIqiKIqiKIpSTCgyJcDs2bMzKQDq1KkDYHfKC8vIkSMBd4dlyZIlgH9tOmQn+s033wSgfPnymbwAAC677DKbAR00aBCArY8MChs3boxppHP++ecD2devBQUx+4pF2bJlbYZd6vwB691w//33A65hjOze3X333fZ9cm5btmxJgwYNijbwLIgvw/jx4+1uptTXnXPOOXn6GXv37s3UsurKK68EIuaOiebvv/+2rQ3lb7l582ZKliwJRNrzNGnSxNYwX3DBBUDwxkksI0AxWsyroVbjxo0DqxqIlfWsWLFipv+XLFnSnrtEIO3yYtXyxwNRGxhjrFlgIpQAV199NatXr456XVpWSd2z9+8wZcoUIDrzHzT/j2Rgz5491sjwtddei/q6/N0rVapkx+9ll10GwP/93//Z93388cdAZrOxBg0a2DrQIHHqqaf6HUKBmD9/PhDxYalRowZjx44FIlnA1NRUPvroo2x/xqZNm6zKr6iMqvOLrAfFjDk7xGPjpJNOAuCaa66Jb2D54KuvvrJ/RzEhhsh9UdQyb7/9tlW/3nrrrYCbLRXlw44dO+w6LkimoQcOHLA+E96WbLI2lusuaMrerOzfvz9mJlrmpc6dOyc6pCJBPLscx4nyBBD1XKKZPn26NfGOpYqRmv6qVataryVZP5YuXdqu1b3rMfmeeN5HimwTwPswWaFCBTvRFZWESX5OqVKl7GvykLd27doog554kZaWZm8e5cuXB6B79+72AUYe6P73v/9ZY5eHH344IbHll5kzZ2aSjoN77kSKK5P86NGjo763YsWKdpJMxGLZi2wCxXLNlwfIUaNG2ZuT3Ewh8vA/ePBg+5rI8rzIIBYJYjyQTSwx1oHIAk020fLKM888w9dffw24N2CReJcpU6YoQs03CxYssH9jr+vpE088AUQcqg87LC4NSoqMPn36ZCoDEGJtDORE3759A2kiuGXLFrtZGUTq1auX0N83bNgwJkyYkLDf99hjj8UsBxD5ciwWLFgAwEUXXZTpdbkHBclpXMa+mFHlZtLqB1J6JfLr1NRU+0AiHytVqhRzE0w2kO+88077mhgC9+rVKxDlTFkRo7NkYvPmzVHlSJ07d7YGprfffjtApg0AeRioX7++ldL61fVHmDdvHi+++GLU67KJJOuT5cuX26899dRTAFxxxRWBKQm48847raGpcPvttzNq1CiAmGVAYjQ9ePBgK01ftmyZXTN712l+M3LkSLsZ7EU2WhO1OV1Y1q1bF3OzSZ5dJCmWLEhyTExxY5UDxMtALzeMMTYGMVzs2bOnTajIxr6YXntJT0+3mzXyM0qWLGlLaOJZ3qDlAIqiKIqiKIqiKIpSTCiyNNzmzZvtDka9evWsHLkwiEma9IIF14RPkGxt06ZNbRu+eDFp0iQAevfubbMZkgF3HMeaFD733HP2e/7zn/8AEdnm4sWLrYzFz57OQqwMf82aNW22NreMgWScFi1alDDZ+caNG60CIOtONMALL7wAZG6TJTtvxpg8S/tFeifyr3gg2QvZ3SxbtqzNFkuGI6941QzVq1f3zfRIqFChApUqVQLI1DLr2WefBSI76V4DuCCSW8a/d+/e2X5t7ty5mVQE8nmQlABvvfUWP//8MxDJ2pYtWzbf11+8EMO+RHHKKack5HfKmL/tttuiZP3SCjA7xBQp6/f50RowN2RNILFKtj0oHHnkkQU2f92zZ4+dH0TCecstt9jSxaCX0wmihAgyS5cutZkyMbtt166dVe3ImDj++OPp0KEDEFl/lS5d2prRidmbX+zcudPK+0VKPnXqVCsPlhI67/pQjEoHDx6c5/KzeCEKX+96WxSh/fv3z5MR6Ouvv55vpWOi2L59O5C5BECMsh944AFuvPFGX+IqKLHWJ3Xr1rWGdMmGXH/eEgD5XLLl8S7fzY5mzZrZ+7oYR+aGqJW9z8uiZHj00Udjqp2LGlUCKIqiKIqiKIqiKEoxIS4Fud9++y09evQA4L777sv2fd999x1vv/12tl//8ccfAfj+++9z/H2//fZbAaLMH2IctWXLFvua7C7HanniOI7dkZK61h9++MHWeAQBb7tFQdoaevm///s/W88ldVLLli2zCo22bdvaDHa8mT9/vlVTCN4601iZ+7xmn7w/R2qO7rrrrrhkr4YOHcq4ceMyvTZgwACqVKmSr58jXhxivgfQqlWrwgdYSGrUqGFbFor5zOzZs60qYOjQoUBwlQA5ZVx69+6d5xaB3pq1IOI1Q5NYzz77bFtf7hcyZyZaCRBvxFAqlk+MGGcNGTIkx58hfh9ZlQCPPfZYUYRYZMyfPz/KIKlu3bo+RVN0iNFs7dq1WbduHYDN5KalpQXSB8CLrKsEr8Fh0JD2eF4PH8mObdq0yRoxyjm56KKL7PjwIhneypUr06VLl7jGnBMtWrTI0Sxb1lk7d+7MZFQM5NhqLN6IeZ+sWRzHsfXk4u+T13agQfQBEjWcZMiXLVtm51c5X0Fdq8RC1sjz5s2L8mERT5lkY/ny5Vb9K2sV7/pK1Nh+klcFgCCqpaVLl1K7dm0g4g+SqDaHRTYaW7ZsaV2LN23aZM1M5GM8ELnd7Nmz4/Y7BHloufrqq+1ra9euBeDll19m06ZNQKQvcM2aNaMWac2aNbM9IIOMmMmJC3JaWpq9IGUR55Vz/fTTT3GPaeLEiYBrnBOrH7a8JsY0BbnuYv3ceD3ETZo0ycqYRMabXc9W+fvOmDEDcKX/cpwib3Qch7POOgtwjSqDgJgcilnT7NmzrezJ27EhiMQqA8jqQpsbfks3cyTf514AACAASURBVEI2aOQhBiLjPjvH4ES6OLdu3RpwNwFEDptoA9J4IIsEmU+9ztpyTrZu3ZrjAkA2/LIaCrZv3x5w+44HgdWrV7N37948vVcW4SeccELgZfRSBrdu3Tor6ZYSgKBvAEB0Gd2MGTMCt4EkyJiYO3eunZ/kb/7QQw/Z+4gYM0oJgJc1a9bYTYQHHnjAmnYFmQsuuCBq7TFy5Ei7/vSuQxOBPACLYWH58uXthqZfHYiKEklGysNzw4YN7WZTx44dfYuroKSlpQFu0lLWtdmtL5OFcePGWaNu71rsvPPOA/B1cy+/vPTSS0Ak6WqMSfjDv6DlAIqiKIqiKIqiKIpSTCgyJcDkyZO5+eabAXj33XeL5GeKnF56j4IrX5Wsu8h0ZCconoiRmcg3IFKm8Mwzz9jdadnNEXlgMiJGKGJy4SWWJK1t27ZxjWfu3Ll2FzNW/81KlSpZE0lv71/JCsTKLElbtPT09Ew7vSJxk1Y9iTAZqVGjBuBKH6UMYd++fYBrULNw4UL79ZwQlYlfbQFzw7t7G4asbnaIAWBWNUGQlAEyXryKDCmliTWeN27caMueEpFRECVAt27dbBZq/fr1cf+98S4/iNUyT3b+hw0bBkRaCWVHrHZ7uX1PEJH5rGfPnlZm3KZNm0zmukFCsjYyzx511FFMnz4dSHwry6IkiIaSgtwPIaKylPLPjz/+mOOPPx6ItP7zqpTELLBXr15W/XDcccfFP+gioEyZMtacVUoZAGvanEglQEZGhm3HLZxzzjkFNv/2tqQ988wzfTep++CDD6xSRNZ/zZs3T0oFgLQsFZWGl5zKUIKMqE8GDRoUpY4xxgRG+ZZXpk6dasvCRdlQsWLFhCsABFUCKIqiKIqiKIqiKEoxoUgdOqRWMS0tzdY8xDKfu+SSSwCoUqUKixYtArAGL15KliwJQKlSpexrs2bNskqAomhDWBAkmyOZ8oyMDFuDnqwKgAsvvNB+Hqt94S+//AJkrkMVgxfv98aD3bt3x1QASBu//v3788orr2T62oEDB9iwYQMQabnhRXYPu3btajNqKSkpVgHgNSKKN5L5euGFF9i1a1e275NWh61atbKGgBJ769atbfuURPPYY49ZP4KcWpqIrwZE/ALCwpw5c2IqAMQ4MFHGmXkllqJHsmqxyMjISGittiivWrdubduAiWmWmLTGg4kTJ8Y1qyumZatXrwbcOUcysddff32efobMwV4PkzFjxgSu7VasbFSfPn1s/flbb70FRNqGQuQ+EzTmzJkTtd749NNPk87o8LfffrPrJyHR9eWFRVRMEPFgkPn1008/tR460i74wIEDVm2XLMdavXp1e49PTU21r1esWDHhsYwePZpvv/0202sjRozI988RLw2vwV7ZsmUT4jGTEw888IBVxElbY2l7mGyICtu7Xj7nnHMA8m067TdyTqQtY6y2gHfddVfSqeCeeeaZTAoAyL0dezwp0k0AkdIcfvjheR5EeXVTFOl9IiShuSEP/7LYr1+/vpWjJSsisZs7d27UpLxv3z5efPFFIHPPd9nwEOluopCHfzE/qVChQp57PYu5mXyvl3vuuSfHvu9FScOGDe1NUSYEL6eddhoAZ511lpVne2XasnEgmwB16tSx4y/RDB482Mb7z3/+M9NHiPzNv/jiC/ta0A0y5eFdHuwhtpw/loGg92cE7eFf8EqujzzySCC2Y71w+OGH+2LYNmzYMCvR924GFPVGgGwYbtiwwbpdxwPpADBr1izANZebP38+EJGbX3rppXH7/YlA/pZZDejA3bCdNGkSQFSXFwheWYPMzW3atLEmrLLRmWwbAOCOp23btgFw8sknA/48WOYVMbkdNWqUNY8UeXCNGjVYtWoVECnfi8WgQYPo1KkTELs0MKiI8bSXZLzmlixZAkTKTjZt2mQ3nBOZbMmOSpUq2b+1rIP79+9Pr169fIyqYIhhtHeDWJ6xYiXDgowYza9cuRJwpf9SDiBluslQsiHlLy1btgTcNWW1atWAiGm2n+dGywEURVEURVEURVEUpZgQvIad2SA9FKtUqZJJDpZo9uzZY02MypYtC0QMnZKNa665xmbEZSe0e/futt+mtHd68sknMxn0gCvnjqcsNyteEyyRxefVSEPacHXs2JEPPvgg6ut///13EUSYPwYPHmwzjpLh+Pzzz22bL8lYHHPMMVHfu2fPnqh2dV7DykRz44032haO5557LgBnnHGGLW0Q9Ygxhh49egDxLyEpLJLB9xrR5JT1j/W9oiYIOsOHDwcixoCxOO2006zaI5FUrlyZzz//HIiUkU2YMIGuXbsC5FkBlB3SflAkqpUrV7Y/Ox5Ill9Mv9atW2czHTKGp02bFpUR79+/f9QcHDRE8SNGX1u2bIl6z8GDB6MUAOXKlbP31EQYseYFUWnJtZCenk7z5s2BSNljMvLxxx/be4co+WQdE0QkY5aammr/7m+88UaO31O9enUg0lL31FNPjdn+1w/effdd29I6lhpR+Omnn3j55ZeBiPS5dOnSMVsgBpG//voLgH79+tmyIClDOf74462qS0yo/WTkyJFWuShqn+nTp3PEEUcAEXVSixYtbImG3NtlvQNuxjdrWaqXRLRu9q6TAW6++eZC3yP9ID09nQEDBgCZzXRlHEu5eTKoG6TNqSj+UlJSrHo6CPEnzSaAcPzxx9vF6hVXXJHw3z906FArUxFZYLI6A3fu3Jn3338fiNTZTZgwwU7QsZBBOHTo0IT22/XexGVQyUO0TMzZIZKhadOm2Z8jHSfyWocbD0466aRMH/MqAx43bpzduJAHI7+cRQGGDBliazNF+rd06VJbh9mtWzfAXdDl5BkQRD799FOaNGmS7de95SNBcv/PjgULFgCZH9AS0V2lMIg/gJSCde3a1W5cyMfWrVvb2kHxDojFhg0b7IN/9+7drXRdfodsOMSL/v37AxGpvHcDct26dQCcffbZmXxKsuJd6El/+iD4Abz22muAO/bzgiyEXnjhBcqXLx+3uAqCLJzleqhYsSL9+v0/e+ceZlPZ/vHPY1ByDFE6qRARJR0VqpeipF7EK/FTckhS5JBCGfJWSkUqHYQoQlEiSkiqN4eOopwTCknG+bB+fyz3s9fM7Dntmb3X2nvuz3W59tiHmXvttdaznvXc3/t7D/YzpDxDFjbjoexEjv8BAwZkuvgi9c6DBg2y1xjxLAoSy5Yts+Wk4tbesGFDu2AuCYF27drZsUkWDKXbQaxp164db775JgArVqwAXD+upKQkAFsqKh2yIDQnk9IfCNXbT548ORA3/8I555xjPcnkJn7JkiW2fFHmVlWrVrXjgSw0ydwN3OSMeJxJhybvMRjtRYCXXnrJni/ymN3ERdAYNmyYLfWR8crrnxOEm+fs0LFjR9tVSbjlllsCNVcMxvKooiiKoiiKoiiKoihRJ3hLpVkwZswYK9nxw1V02rRptltBNA2kYkGNGjWYN28eEDJq27p1azqpOYRW42Q1M5YZ9KJFi9pV5t27d9tMkzx6M/zhELfg8uXLW2nXO++8A/ibQY8UcdWGUAbTL1NAcMszJFMg/YQ3bdpk+zrHkxlTWho0aBD2fIhXxIld3IOrV69uj6F44dlnn7XqK1ECeBVMXvfprBBDV3mM9nchZlOSRVu+fHmmMv/MxrWgSJwFcaH2IkZmRYsWBVz5b8OGDe3P4O/YFY7p06dbw6by5csDbi/xIKgt8ivnnnuuL2V70UCMwiSrecUVV1izxrTZT3AVm+CfcqNEiRKpOnRBaoPorPrPy9xNnOuDpAIAd34iJUxyjP3888/WkFY66YhxK4QUG/IIrvGblBVI2aOoIWPBrFmzYva3ooVcz59//vl0arjatWuTnJzsW2w5YeHChYCr3knb2ezBBx+0ytkgEKxZhKIoiqIoiqIoiqIoUSPulACSXfSLzZs306VLFyD4tbTZQcyBpP5szJgxtmZWVn/POOMMW8911113xTzGevXq2dYns2bNYsKECaleX7duXbayYv379+e+++6LSoyxROoHg0jhwoUBqFSpks+RKOGQc1vOl27dutlsZzwhdf/y+Ntvv1klgGRwMiI73gHR5uGHHwbcLJNkyb0Kn+xw9dVXB6quu1mzZoA/Rqt5wV9//QXAwIED7T4R/594bM0Wjnbt2nHw4EEg1LJKiR3hapkzGq/E/CwIPjpi7nf//fcDri+T9HEPhyidnnjiCTp16gSENzkOCtJ2Wh7Xr19v25Jnl2uvvdZ6ASiR0bx5c8Adi8U4U4zKe/To4Vtc2UVUo9ddd519TubCYgAcNOJuEcAv9u/fD7imTOKan4h06tTJDtpBQgaHmjVrplsEyAgxRZHFGlnwiHcGDBhgJ3CJsk1KbJHzSRY0451ou/pHi7JlyzJ8+HAg1JNebjy99O/f395kez8bb6UcQWTXrl0AjBo1CoCVK1cyYMAAIPhdTHJKjx494mIynai0aNHCmk7+/PPP9nlZZBLD4saNG1vZvCys+8k555wDYLsrffjhhzYxI6UznTp1sj3pZRHAz4XW3HDOOefYbVZihxh8Jicnx43034sY/0o5zymnnMILL7zgZ0hZouUAiqIoiqIoiqIoipJPUCVANtm7dy/grhAGWY6d6FSuXNn2oM2v3HLLLXEruVX8Zf369X6HoKRBzEmnTJnicyT5kw0bNgChFp/du3e3ElRFyUuKFy+eY5l5ELn55puzNARUYkvaVnRKbNm8ebM1yBZeffVVa7oeVFQJoCiKoiiKoiiKoij5BFUCZBPJ1ixbtsznSBRFURRFyS1///033bt3B7Dmtz169EjXEk1RFEVRMmL//v1s3LgRwHpjXHPNNX6GlC10EUBRFEVRlHxH3759ba/vefPmAW5PekVRFEXJLpUrV+bIkSN+h5FjtBxAURRFURRFURRFUfIJRvoaZuvNxmwHNkYvnKhxtuM4p8h/EmU7IG63JVG2A/TYCiK6T4KH7pPgofskeOg+CRaJsh2gx1YQ0X0SPBJ2n4QjR4sAiqIoiqIoiqIoiqLEL1oOoCiKoiiKoiiKoij5BF0EUBRFURRFURRFUZR8gu+LAMaYasaY+caY3caYNcaY2/yOKRKMMaWNMe8ZY/YaYzYaY9r4HVMkGGNOMMa8fnwb9hhjVhhjGvsdV6QYY1obY34+vl/WGmOC37MjDcaYlDT/jhpjRvodVyQYYyoaYz4yxuwyxmwzxowyxsRdlxJjzFvGmK3GmH+MMb8YYzr6HVOk6BgcTIwxlY0xB4wxb/kdS27QMTiY6PEVHBLoupgQY3Aine+JcmxB4sy7jDH3GWOWGmMOGmPe9DMWXxcBjh+IM4APgdJAJ+AtY0wVP+OKkBeBQ0B54A7gJWNMdX9DioiCwG9AfaAkMACYYoyp6GNMEWGMaQg8CXQAigP1gHW+BhUBjuMUk3+4x9d+4F2fw4qU0cCfwGnARbjH2b2+RhQZw4CKjuOUAG4BhhhjLvE5phyjY3CgeRH4xu8gcoOOwYFGj6/gkCjXxYQYgxPsfE+UYwsSZN4FbAGGAG/4HYjfSoCqQAVghOM4Rx3HmQ98Adzpb1g5wxhTFGgODHAcJ8VxnMXATOJsOwAcx9nrOM5jjuNscBznmOM4HwLrgXg80R4HBjuO89XxbfndcZzf/Q4ql7TAHdA/9zuQCDkHmOI4zgHHcbYBc4B4nCT85DjOQfnv8X/n+RhSpOgYHECMMa2Bv4FP/Y4ll+gYHED0+AoccX9dTLQx2EO8n+9xf2wJiTLvchxnuuM47wM7/Y7F70UAk8FzNWIdSC6pAhx1HOcXz3PfEacnmhdjTHnc7fvJ71hygjEmCagDnHJc4rz5uAyqiN+x5ZL2wHgnftt6PA+0NsacZIw5HWiMe1GKO4wxo40x+4BVwFbgI59DigQdgwOGMaYEMBjo5XcsuUHH4GCix1cgSYTrYsKMwWmI6/OdxDi2LAky7woMfi8CrMJdYettjClkjGmEK1U5yd+wckwxYHea53bjytPiFmNMIWAiMM5xnFV+x5NDygOFcFdxr8GVQV0MPOpnULnBGHMW7vkxzu9YcsFC3EnBP8BmYCnwvq8RRYjjOPfinuPXANOBg5l/IpDoGBw8koHXHcf5ze9AcomOwcFEj6/gkQjXxUQag4GEOd8T4diyJMi8KzD4ugjgOM5h4FbgJmAb7sr0FNwDNZ5IAUqkea4EsMeHWPIEY0wBYAJufdd9PocTCfuPP450HGer4zg7gGeBJj7GlFvaAYsdx1nvdyCRcPyY+hh34C4KlAVOxq3pjEuOS+gXA2cAXf2OJ6foGBwsjDEXAf8CRvgdSx6gY3DA0OMreCTQdTEhxuA0xPv5nijHVirifd4VJPxWAuA4zveO49R3HKeM4zg3AOcC//M7rhzyC1DQGFPZ81wt4kxCLxhjDPA67kp78+M3CnGF4zi7cG9k4lXCFY52xPeKdGngTGCU4zgHHcfZCYwlziZtGVCQOKxNAx2DA0YDoCKwyRizDXgIaG6MWe5nUJGgY3AgaYAeX0EjUa6LiTIGe4n38z1Rjq2MiNt5V1DwfRHAGFPTGHPi8XqVh3AdLN/0Oawc4TjOXtyVtsHGmKLGmLpAM9xMejzyElANaOo4zv6s3hxgxgLdjTHljDEnAw/guqDHHcaYq4DTiV+HWo5natYDXY0xBY0xpXDr7b7zN7Kccfx4am2MKWaMSTLG3AD8B5jvd2yRoGNwoBiDO6m56Pi/l4FZwA1+BpULdAwOFnp8BYxEuS4m0BgMJMb5nijHFiTWvOv4vjgRSAKSjs+/fGnb6PsiAK5z6FbcutTrgYYe98d44l6gCO52vA10dRwn7lZAjTFnA51xJwjbTKhP6h0+hxYJybgtkH4BfgZWAEN9jShy2gPTHceJZ2kdwL+BG4HtwBrgCPCgrxHlHAdXgrYZ2AUMBx5wHGeGr1FFjo7BAcFxnH2O42yTf7gS2wOO42z3O7YI0TE4QOjxFVgS4boICTAGe4j78/04iXJsJdK861HccqZ+QNvjP/viZWLi1/BSURRFURRFURRFUZScEAQlgKIoiqIoiqIoiqIoMUAXARRFURRFURRFURQln6CLAIqiKIqiKIqiKIqST9BFAEVRFEVRFEVRFEXJJ+SoJUHZsmWdihUrRimU6LFs2bIdjuOcIv9PlO2A+NyWDRs2sGPHDuN9Lh63A/TYCiK6T4KH7pPgofskeOg+CRY6VwkeiXJsge6TIJLI+yQcOVoEqFixIkuXLo08Kp8wxmz0/j9RtgPic1vq1KmT7rl43A7QYyuI6D4JHrpPgofuk+Ch+yRY6FwleCTKsQW6T4JIIu+TcGg5gKIoiqIoiqIoiqLkE3QRQMlXGGMwxvDYY4/x2GOP+R2OoihKvmTr1q1s3bqVatWqcdZZZ3HWWWcxadIkv8NSFEVRlHyBLgIoiqIoiqIoiqIoSj4hR54ASuJx4MABAObOnQvAzJkz+eabbwCoXr06AP3790eMMYoVKxb7IPOIBQsW+B2CoihKvuXgwYP07t0bgE8//RSA1atX29fPOussX+JSEoPly5fTqFEjAHbu3AlAcnIyjz76qJ9hKYqiBBJVAiiKoiiKoiiKoihKPsEXJcDu3bsBGDp0KAsXLgSw7ovHjh2jfPnyAHb1tnPnzhQqVMiHSBOb4cOHs3LlSgDGjx+f7vWffvoJgMmTJ3PRRRcBMGbMGAAuueSSGEWZe0QBcO211/obiKIoeYIomD755BMAHn/8cZYtWwaA4zgAtG3bloEDBwJw3nnnAVCggK57+8nrr7/OqFGjUj13wgkncOWVVwJQuXJlP8JS4hxRL95yyy3s2rULCJ3rpUqV8i2u/IqMy40aNbLjsTFup8UXXniB++67z7fYFEUJEdNFALn5l7Yr69evt69dcMEFbkAFC9oJ3gMPPADA3r176du3byxDzTGbN28G4D//+Q8AX3zxRabvdxyHRYsWAXDNNddEN7gMGDdunF0EkAE6I7777jsA/v3vfwPwyy+/cMIJJ0Q3wDwiXBmAmgLGng8++ID+/fsDoQUmIN0koV+/fvbm7cQTT2TNmjUA9nxp3749SUlJMYtbCRZ79uyhVatWAHz88cf2eTl+5HHSpEnWaG7Lli0AdoFZiQ0//PADAMOGDQPg7bfftvtHpP8PPfSQ3hQEjKuvvpoOHToAcPfdd/scTcbs378fcI8hgD///NO+Jud6ly5dYh9YhKSkpADuoubs2bMB+PnnnwFo164djRs3BuD222/3J8BMSElJoW3btgD873//A0JGzF5efvllPd9jxJ49e/j7779TPffrr79SpUoVAM444wz7/L59+wBo1aqVTc5+/vnnANSqVSsW4aZC7hcnTJgAuGU9O3bsANxkMcBNN91Es2bNALjnnntiHmNe8u677wKhc/vll1+mU6dOQNb3Z7lB0yKKoiiKoiiKoiiKkk+IqRJg6NChQEgBUKZMGZ577jkAmjdvDkDhwoXZs2cPANdddx0QWu0NKocOHeLOO+8EYMmSJUDWKzfhVkiDSoUKFdi6dSsAv//+OwDXX38977zzDpB6NTFIiAJAVjUBPvvsM5+iyb9Ipv+9995Lpzw56aSTaNiwIQBNmjQB4N577+Wkk04CoHfv3jz//PNAyECsRYsWlChRIlcxHTx40P4sppjz58+341Fm3HHHHXZskvNey5Wij2QG2rZtm0oBAFCtWjX69esHuFk0gHXr1tnXO3bsCLjy1O7du8ci3Jhz6qmnArB9+3b73BtvvAG46plYs23bNho0aACQSqIt++f//u//gOBeP+Kdw4cP8+OPPwJw8cUXZ+szcs58++23gZ+fHDx40J7Lixcvts937twZgJ49ewKuujTIrFu3jq+//hqAZ599FnANDtMqmyZMmGCzosWKFbPXS78RFWyrVq346quvsnz/rl27bElQu3btcn0tV9LjNcUcOXIkkPqeRMquLrzwQgBat27NZZddBsCmTZvYu3cv4KoGIPZKgDVr1ljVi1cxLtsgpT5z5syxKmUpUa5du3YsQ80z/vvf/wKhbbv33nvtdxBNw9yYjY4//vgjzzzzDBDakbVr17byeS/FixcHoEaNGkBw5WgiMW3btq2VKodDtufiiy+2datykgWRChUqAFgpbenSpa3cTm6YvvrqK1599VUgNOkOGmk9ABo0aGAnpbFEJuX16tXjl19+ASKTLokfgzHG3lhLeUbZsmW57bbbALjhhhtyHXNecvjwYcAtP5EJ/4gRIwA47bTTbD2w0K1bNzsgLly40NZ7ygJbXkwaTjzxRCD1hdEYY89V4dixY1YmJ3hl5k888QQA8+bN45xzzsl1XLFm9erVTJkyBYDXXnsNgN9++82+7i3VaNGiBeB2CxGPkFgi3/lHH31kn5NFmAEDBljJuffmX5DPfPzxxxw5cgSABx98MKrx5pSjR48CMG3aNKpWrQpAzZo1s/XZt956i3/++QcIHdNly5a1ss9YIsfRk08+aW/+xZOhc+fO9lqiRJepU6fy/fffA9lfBDh06BAQ/MQLuDeTY8eOTfVc/fr17Y20jPFBY8WKFQA8/PDDgHvD/9dff6V6T8OGDe1NviyIjxo1yi7qJCcnB2YRQObw2VkAAHdxsEePHgBcfvnlXHrppVGLLTdIokAWmGbNmmUTSsuXL7fvE8n2K6+8EuMIM+all14CSOe/IsjNvcxH586da5N6IrX3A5HE9+nTx85DypQpA7jJF7kpljnZfffdZxOUL7zwAgBvvvlmLEOOe7QcQFEURVEURVEURVHyCTFTAsyZMyfdc1kZtohccPfu3Tar27NnT+699948jy8niCxVJJZeuXk4JKvz2WefWfmNyKL9xHEcm+kTuUnv3r3Dfr+yyvavf/0LcLOFQ4YMAdzSgHr16sUi5GzjVQFI9t+vUoBVq1YBrsxHsnSvvvqq/dmbbU1rkuc4jv25WrVqQOq+2u+//759n2TgpkyZYhUCQUCMW04++WS70ivSs4yQTNT8+fPtvhTz0LygdOnSgOtMLqajRYsWtbJxYefOnbz11lupntu4caNdYZesc69evZg+fXqexRctJJP05ZdfAq7qQjLQQlp1hDBt2jTAPeYk+zNu3LioxgshaeOLL76Y7rUbb7wRcI95yQqWLVsWgK5du1KpUiUgJBE+cOCAzcA5jmMlw0FAzt9u3brZY16OqbQKFUGM0B5//HGbxRWuvPLKdCqbaCJGcpL995bcDBo0CMAah8UzojgBV+0A2BLGc889l7vuusu+3rRpUyCkaowl06dP55RTTon53402Ml517do13Wt9+vQJrAIA3PGyT58+QGhcA7j55psBbEnTFVdcke6zzZs35/zzzwfcTPSHH36Y6rOxZt68eQBWbZIRxYoVA9ysP8Cnn35qX+vZs6c1n/MTMTAVY8YFCxbYOL3zRpmfebvMyLgdBCWAnBPe6/JNN90EwOjRo9O9/+yzzwZcNYeY7okRpR9s3LgRcEsSZI42efJkgLAq3unTpzNx4sSYxRctfvzxR1tWIxQpUiQm3YxUCaAoiqIoiqIoiqIo+YSYOqZIrYm0bpHMZkbIaugll1xizSGSk5N9VQL8+eefNps2f/78DN9XrFgxKlasCIRWCv/44w/b/jAIjB071tbWiPFHRj11JaMmmWdp8yjPBUUJIK3/vG0BJQvlF9IC8qeffrKrrBmtfksWM6eZ/KVLl9qVdlnRDQpSU1eoUKGwmSnJpkt9d9rMdFbjRCR4szCZUaZMGVu/KJnW5cuX29VpqUfLKFMbJMaPH2/VV5LR8H7XkjXeuHGj3earr74agLp169r3HT161LavigWiHvFmKMRTQtrpeOvM5bU2bdrY58TIsXnz5rZ9Vf/+/a2ZUP369aMVvh1jpbY3LXJNmDFjhn1OWsxKVj2j40taba5fv56SJUsCIeWZglyC/wAAIABJREFU7MNYsG3bNnue//HHH4CrBBg+fDiQel/EA/K9S5bpww8/tDXPkvUPp5hZv349AwYMsM+LMaPU4MaSadOm2fE2XBYwHH7EmVNEiTFz5kz7XK9evQCsyWxQ8bY5kwzto48+as3MMjMx/P333+01qHDhwta7yQ/27dtnFXKSPfci7aOXL19uz6UgqRMBazj8zjvvWGWubItXgSlUqVLFzoO9vjTSrtZv/v77b5tNFhXWTTfdlOo8SYvEXrduXauIdBzHzu/FByhWyPwkJSWF+++/HwipNr2IAk7UiZD6niTemDhxYrp5+3PPPRcT09yYLQI0adLEHmSyscOHD+fRRx8FUrsfihRYDoJVq1alc0r1izFjxmTrprJu3bqpBgpwzavCmVb5RSSGLDIIVqtWzU7KZ8+ebY3cChcunHcB5gC56feaFIqMK5yMSN7vXSyIhXGg3NDm9Y3t+++/7/u5kREywVm8eLG9GRA5OoRuEMQA0MsFF1zA008/HYMoM0YuqCIj93YQkPIeMaMKMkuWLEkl0QZo3LixlQ7KTeqhQ4fsDaWY6C1ZsoSrrrrKfk7KgqKN4zjWjNSLmGCKXO6qq66yBodyY+9FJsyDBg2yEu3Dhw/bSYf8jTPPPDOPtwA++OADIPxkcc+ePVZG791OOZ4ycwU+fPiwlaMDdvvXrFkDhB/38hoxPW3atClr165N9drw4cPtQkQsZI25ReTV33//vS338fadjwSZ68gCQjiZd7RwHMceW5F8VhYKg4IspslcA0IJJTmPk5KS7PxRyjaNMfZ9fuM4jk2GyWNW37WM2cOHD7cLUC1btvTVBX3gwIHpyuS8SNlP1apV7UJ5bs+lvEYW68KZdNevX98u0rRu3RpwE3tS+uud2w8cODDaoWaLjRs32gUXmQvKNmSELA5eeeWVdvHWGGO7VcQaKR2RZF5GzJo1C3DvFWXh77777otqbNHEawwqC/7S7SfaBP/KrCiKoiiKoiiKoihKnhAzJcAFF1xgsxrS+uGNN96wstLq1asDrqRDMrjhVqPEyMIvbr/9dturVTIu4Jo4AAwdOhQgMO1b8hpZqXvggQes2dYvv/xiM7vRlNVmRto2hRll9WWFMVxbQ+9zQcuCZMXQoUMDm22TVduTTz7ZZvvF9PPrr79OJ0E98cQTrZFonTp1fDd6knPaqwAQZAX3wQcftJL07LZ1ixWi2JFMsZfLL7/ctuARihQpYjO8kr1Oa2Sa3ZZjuWXt2rWpZPLgKkvSli21aNEiW9LFG2+80ZZytGzZ0rZIEjlxuO8ot4RTAEgJQIcOHWyJlUiBO3funC35/Msvv8wnn3wCuDJoURJIG6VYIN+ltL4FrHlZ+/btSUpKilksOUHUGUuXLmXq1KlAyJQqkpI9mb9IeYYg7UxjqQAQjDE5VodJz+1IPhtt5PyWTH/BggVtaei5554LuGZ1gwcPBkItZQsWLGjnKsnJyVbl5Addu3a1ili5xs2ZM4ePP/4YcE2W0yLlUG+99RblypUD/M8+Z1QOJqaHXrWsxCyZ9+Tk5ChHlz1EJu9t9yetmzMqvxIlgMwPa9Wqxemnnx7NMLNNrVq1rKGsKI+Sk5Ptc+HaxYqizzsH85YzBYlffvnFltFKW8YiRYrYksB4RMpPvPe6cu8YK8PPYN41KIqiKIqiKIqiKIqS58TUGFBWZsePHw+4GSqpw5OMxrx58+wK9GmnnQa4q6OyWt+8efNYhpyOKVOmpFIACLfccgsQ3oxJaqG82ZJ4p1WrVrY2Lwg+B97afghvBvjYY4+FVQDIe72viWIgq9okv5Esr7f9YNAMeEQ90q9fP5544gkAq6bxIjXpc+fOjWlrs6yQ2lLJaJxyyinWqPT3338H3CyNZAkkm9CnTx/fPDK8yNi0e/duzjnnHCDUovGOO+6wtZ033HAD4GZEb731ViBkfAih/Th27FhfDbjOP//8XBkxNmrUCHC/AzEJ/OeffwDXDyGa++zw4cNAyLxv8+bNVgEg7QrlHMkIUdN4x7hFixbZ+u9YeAEIaTNjEPLJSKswSYvURO/du9cqCtatW2c/L20oo4GYkIYzNQvH+eefb02apDWuF5kTyO8VwrWxCzLZ/T5izfbt21mxYkWq52rWrGk9pUQF06ZNG1sXLRw5csS2F922bVtU1D7ZpUWLFraefNGiRfb5p556Cgh5klSpUsX6fTzzzDP2faKKyst2uTlBFDTe64IXmS+JMSBg1UBy/QgKMk5md7zcsmWLVTXKXKtBgwaBMgW+++67AWy73G3bttlsv9xjQchTR9pXG2OsAkDOKb8R/wtRWE6ePDnd+FSwYEFrbizvD9L+yApRUEuLSoidF4AQ00WARx55BAg55kLIJX3btm2AK++QCapMxkqVKmVlty+//HIqJ+ggULx4cWsaFg6RGYrkC9yDN6jy7exQrFgxO9DLZM4vvDfqMjH2DuzhSgDkdW8P2LTviQfkhsFxHPr37w+EOgwEjVatWoW9wREpp3TRCNICAIRchGVCX7VqVTtuiezulVdesYthItVcvHgxb7/9NuCWQsQakbrLImTz5s3tthQqVAhwSy9EWi9j7IEDB8JO8kTG6fciU27/vkxGr7nmGrsIID2vf/vtN84777zcBZgBKSkptGvXDsC6OJ955pnW7VwMqLKiY8eOQGjhAtzuB2J4GAvkWPFOIgVZZAnHzp077cRHPisSYkG2o2XLlkDmjum5pWzZsnZhTKTlH3/8sZXvy81WlSpVMnVqTtvjGdx9K2VPseTvv//O9e8oVapUIDo6yNj19NNP259lAW3GjBl24i9jrncBQI7DefPm2UWq5cuX298ji7qxpGLFirz33nsAdiyYNWuW7UkvZUmnnnqqfZ+31NQvM0C5+ZLrmSyMQ+h7HDduXKYLqCJHL1WqVJ4co7EmOTnZyuZljuVnp7JwSEcy6ZDTunVrW359xx13AKnLH2SfzJgxI2y5gJ/IYt3rr7+e4XtSUlLsvEzMdBcvXpzlArTfyLXb2/FIuiB07949prHE712ooiiKoiiKoiiKoig5IupKADFxefLJJ3nhhRfSvS491LMiXPuwWCIrlz/++KN9rnLlyoBr3CISxuzSo0ePVC23/EJWdMO14fIiWRBvllayPwUKFLByllgaA4ZrCxhO2iWSVe/raRUA8lq8KAFkBVFMxYwxVuIVNDZt2gS4MlrJaojZ365du2jfvj3gn6lkdpEMFITGLXm88847bYmDqFHmzp1rJZ1dunSxq/SxQmSnksWZP3++NfjzGlClNaARlYOXihUr2sxVLAnXhiqvMvVt2rRJJbWNFvL9d+jQwZpRScZs/Pjx2b4GSk/kDRs22Oek1ejEiRPtuRULpJ2UV54p18BwSg05N5566imbiRXTvDPPPNNmqyA0NkupTTTMgOW4qlChQrrsalpJf3aYNGmS/Vn2Q69evWIu7QSYPn16pq+LQkjOc6+KQ46xf//7374bskIoa+ltwSoGgRUqVLDZQu/8UOTMIm9+9dVXbfuw9evX8+233wKZK1aiiRgTvvTSS4B7DRG1qBjuefvUS7ZZDAX9QIxipWzHi5jEZvV9Slla5cqVfZ/PR4KUAkBo7i+PQUPm6Q0bNrQqNymnMcbYfRbk/SAGtzL+n3TSSdY0T0rnZs+ebRWKotIYOXJk4Mt4ZV+IYqN06dJW0SDKtFihSgBFURRFURRFURRFySdEXQkghn5DhgyxtZiR1PTLqpC0VYk1Dz74IBBq1wJYc6ycqgD8Qmr3JWuxdu1am3VZvHhxhp9zHMfW/0v2BlLX3/nxHXjNAMOZvMjr3veFUwCEI5bmWpEg58G+ffsAN5smLTiDhhjOlSpVymafJBvVpk0bq9To3bs3kNpUKF449dRTbfxiTPPggw9aw6d169ZZI03xQIg2Yqwm6pZdu3bZunNRJfzrX/+iUqVKQCjTMX/+/HS/q0ePHuna8sWCLVu2xPxv5jVPP/00gK3xBayZkZj5QcinoUuXLva9UmvuOI7NoIu6DkJKFO+4HAtkmySDCaFsodQ7li5d2ppkvvnmm4Bb3y+qMTnuVqxYwSWXXGJ/T9oMUDTIq/ZLDzzwAICt6YaQB41kn2ONeC45jmMzy2JK7DUHkzr5cK0A165dy6uvvhrtUCOiTp06gOsX4D1/wM1GiwLA23JT8GufhKNChQqAe+zUrVsXCHljHTt2zJpMS8tDP8ls3uT1+MoOb7zxRtzMmQGrHPn777+tj1dmHmBBIKsW195rUVCR82Pt2rUZvuf//u//7PvkupGcnJxj08dYI22nRSVbunTpiBRoeUFMjQFzgxj0zJgxw5pWSfeAaNKvXz8gtZu5HHReiVp2EQd0P2REcvPvNSvKbCLg5dChQwDs2LEj7OtiehhLSbdXuh+uG0DajgFZDQhZlRUEhZ9//tneUMp+GzFiRKAMAdetW2fLf+SYf/zxx60MTXqJX3zxxfamUxaiwvVKjie6dOkCuP3sRb42depUO4GQ8zDaxqDly5cHQufm/fffb+V/YuzmNQkKhyzc1qpVK1phZkqdOnXSGQMtXbrUt3giQUquvBOzI0eOANguE15E6ujl2LFj9niRvtSNGzdm9OjReR5vdhAXcy9yA9O2bVvAXfyT66ZIuO+66y578y8UK1bMGmemdXYPIgcPHgTcMjNx4Zbn+vTpQ6tWrXyLDUIGYEuWLLELLrJI1KRJE3sDFs60UEoUgzIGh0tOSN/zlJQUDhw4kOq166+/Pp2RpDEmlft20Fi3bp09frwllrLYEQQyM2fLKWKEGi8899xzgNvFRPZJrHq45xRZNBejTO+inxcpMZHytHhGxjNJgv32228sXboUCOY8fteuXbaMTxaP/TSK13IARVEURVEURVEURcknRF0J4G2BcOmll+b69yUlJcXUsEYyNt4sjsjLRL6ZFa+88or9WYyCRCYZS0SCEg3EfESkeNE0qQpn+pGdFb+M1AKSWcjoPUFj2rRpVnp7yimnAATOFPDrr79m5MiRQMioyWscJOqAhx56yGauRI4elCxUbrnssstsS8Tu3btbEyvJjsZq5Vdarr733nvW2En2yf79+63MXwwapY0ghKSofpk2Xnfddeme++yzz2w/5EgQk1dvJlSUZtKmJy+Rln6RHNcffvgh4LbPlNikv3isjSa9SLZfTJm8LFmyxD5K+z9Rx1SsWNFmPYcMGQK42WcpkVm2bFnUWjTmFaIAlGsdwK233goQtgVqrBGDqQ0bNtgsmfQKz8qoUHq6i3md38ixBCFVUmbjprR29PLee+/ZOVi1atVSGbz6iSjgWrduHZct8+SaULRo0Wy9X1RoXoWmKOWCiKhHvNJ5v1U+WTF+/PhUj5C+HG3JkiV2riWlmEE3Zc4MUYTLPMZrMhtEli5dar9/aa/ZrVs33657qgRQFEVRFEVRFEVRlHxC1JUAXnMfWQmMZNXp5ZdfBtzaO/EEkDrCaLFnzx5b5yj06NEj261apJXTBx98kNeh5Zh3333XmrF5kRpAMRG79NJL0yk2Fi1aZFfk16xZA2BrIcGtV5Xa4qZNmwIZ1yLFElEMSK3/tddeazP9sgLqXZUeNGhQoFuLSPb/9ddft7WD/fv39zOkdEgLzYceesgqZTIzAhVFQKLSqVMnAMaNG8eXX34JYA23pC4vVpx22mnWyEyM2Pbt22e9JEaNGmXfK+oYGRf8omDBglZVJIZ4e/futQqttPW/2UHM9rx1wpdffjkQnWtKjRo1Uj1mB2nvKFn/4sWLWzMqPxUAgtRfSoY5o/H+xhtvBEIx79u3z37XP/30U7r3d+zYMVWGPUjItULG3AIFCth9Ku3egoS0J80Oe/bsAUKGlUFEWpTKNUMMKL1MmjTJHpvy+uDBg20N7qxZs3w30N25cycAt99+O0BcqgAgVBcvRrgZIfMWaR26ZcsWmwH1ywwtO0j7U28b1CBz++238/XXXwPYa/qTTz5pFW/ityLzEIiNr5riIj5M/fr1s15Acr8Uzp8lVkR9EUAGbMdx7A2WTAKy6o8sN63XXHONNaSrWLGilW5GmxUrVvD222+neq5YsWLZKgP49ddfadOmDZDaTE9klH4QzvxP5LZi2CYHKoQMxWbNmmXdYaW8w/u72rdvb98bbmKX14j032vkl92bd+9nBNm2IJqIeJHJ8aZNm6wxl0jpg8L9998PuCZhsliW2SThn3/+sXLCaC/q+UnLli3txTftwqIfyNi7e/due3Mpi5XVqlWz5oUyWfOLihUrWtdf6X8+c+ZMuxAczqAuMzZt2kTz5s3t/2WxIyc3TNFm27ZtdO/eHcB2BJg7d67t/RwEpIOHGOdmtAggZUoyKT1y5EiqjgLglmBID/HevXtHtZQsUvbs2WOlyyJHr1mzJnPnzgVCZVnxikjTg3xD+vvvvwOhhYqTTz453SLUK6+8YscxmXfVr1+fMWPGAPi+APDNN9/YRIR81y1atLDlWbI9cuMcrxw+fBhwF2DkJlpM68qVK2e7bJ1xxhn+BJgFR44cseWzUgpcrFixsKatfiP3KNOmTbPzcunKduutt9oFPiktg9B8v0qVKrEMNSrI4qy39DxIyOLLiy++CLjdJuQ8l3mHn+eBlgMoiqIoiqIoiqIoSj4h6koA6X9aqVIlKyWX7EC3bt1sJsG7+i+9E8XQYufOnTYj5be0fteuXXY7wvHMM88Abh932Q4xMuzTp0+2SwlihZgzyXd99OjRdO2ajhw5ElZFINvVrVs3m7mKxQp2uIx9uAx/uM9JKUrQ+4iGQ4xdjDHUq1cPIDBtAfft2we4Um1wy0wku5EZr7zyim0fFlQZcF7z/fff+x2CZerUqdY0S1RbPXv29F0BkBWSTc6uEkBajd19992pxm8xritTpkweR5hzpN1Zx44d7fZJNipIKgAvoigZMWIETz31FIBVaUCoPaK3TaJcS1q2bAnA6NGjA/H9h0PGtQ4dOtg2uVIC0KtXr7hXAAgXXXQRkH2Tt1ghJn4LFy5kxowZQKiFZu3atVmxYkW6z0jGWTLuAwYMyLIFcqwYPHgws2fPBrCtMv/73//y+eefA6H5kzEmpgbYkSAmmFJqUbJkSTp06ACEWkrLHNjLlVdeydVXXx2jKCNj+fLldj/JsSNt94KCKKXlHgqwLWOzMlz0w5g8Ghw+fNiWzon6BIJldPi///0PIJW6UtpOSqtsP4n6IoC4/g4dOtQ6a4oMaujQobZPotQNv/TSS1ZSLrLZ0047zdZMxKoUAFyJRu3atYFQP+3Ro0fnuDezOPT66Tx/yimn2DIG78kiyKANqcsXMqJUqVLW7Twvuj5Egsj4ve7+4ZAbfXl/vCFSRu8EQSTcQUEGYunP2rdv37D1/iINlAvXypUrE6YbQHbx+qT4xZw5c4DQTTBgb+LuuusuX2LKCKkblXIACN1AykRNbmK8bNmyxZ470pv6yJEjdsF59OjRtjQtCEhJ05w5c2z3A/FwCCpyTenRo4f1vxDPi3nz5lkXZO/7pZOM+NFkt8tOLBEJrUyW33//ffta7969AX9L+/IauVnzzgOCgJSJeDsseb1LwiGLynLuBGEBQBbwP/vsM+tiLqUkFStWtGObxFq9enW7HUFFkkTiDZBdgnYzHY533nkn3XOtW7f2IZKM+fbbb4GQx02BAgWoUKECAIULF7bvmzhxIhBKPtSsWdN3r5+sOHz4MCtXrgRC5aRyLymvg7vAJ91a5Nxp166db/ck4XjzzTeB0KLNGWecEagFPi0HUBRFURRFURRFUZR8QtSVAEKLFi344osvgFBmZ/369Xz11Vf2dUgtFxTuu+8+X7JT5557rpWViBIguxQqVMiuYEkWyk8aNGhgM+G5kWJJqcDSpUt9z2hKhl+MWxIVbxkAuC67IsELAikpKbYMRvBmZsWw5aeffrLmTuKm3bFjx8CVyGTFzJkzqVOnDoBdec8KUUhAZP3i8wqRN0v5zt9//20zsl7DvCDRqFEjINRpYcyYMdYwT14bOnSoVTC98cYbgLutaU0Y69SpY1U00tvdT44ePcojjzwChErJKlSoYM8JMeCLB0RhIeag8hhv/PjjjzbLL91OihYtas3ngpRlyiukrKNEiRI+R5IaGSuffvppBg8eDIRUGhAq4xHlzB133GGvjUlJSbEMNVMk9gMHDthz+tdffwXccoDJkyener+UPgSFWrVqAdj5ek644YYbgJCBXbFixfIusBggSoegOenLtU3mhSeccIIdo6QDxUcffWRNAuV91157bZYdHfxm586dtoNR6dKlAXjuueds+avcU0kZDYT2jygDgsCcOXOsAqBmzZqAW9oUpHFWlQCKoiiKoiiKoiiKkk+ImRIA4IorrgBCWbHVq1eTnJwMhPoI//TTT7YlVPv27QF3JSiSftB5gaz6N2vWDMj+Cu11111n66SDgnz/0mNbCT4pKSm2XlMUD2KWFBT27dtnM7PCPffcYzOcUju4a9cuWw8p/hh9+vQJVH1UZkjtXc+ePe1KuoxRGTFixIhUnwXs+BZrDhw4YONdt24d4K5OSz1qUE3O5Ph48MEHAVcZI/4YkvHo0qVLpr9DlBvDhg2zbVGDwLBhwxg+fDgQquOcN29eqvpHJTaIeeETTzxhFQCiaHr22WcTUgEQdMQvomfPnoFszxYJ4okVzrxNVAwyHw4K4hcjLaSlxV9GyH4bO3asHW9LliwZxQjzloULF9r5lijHgqQsCcf+/fvD+mWIQkvUl0FQwOUEOebatWtnn5N9U7p0abp16waE1EAyx/QTmbM3a9bMxiM+TEFSAUCMFwEEGQwuu+wy393+s0LMDGXivmDBAntQykHnRQwOpQOCouSG1atXs3r1aiB0k3bPPfdYiX0QygLKlStnBziRZ2/atMmaAIrcfODAgdaILWgDYXaQXq4//PADDRs2BMj2xPSSSy6x3Q/8WtAcO3asveG/7LLLAHjkkUcCe/OfFulpnFbiH8+I+RFgSwCC4BicnxB5edeuXQG3A5HI48ePHw9kvwtFvJMIfcODyAsvvADATTfdZDvoCC1atODGG2+0PwcRkfCLYV4447xEQDpLrFixwsrnvd1OgkTHjh2B0E2yJFQBLr74YsCdKzZt2hQIXjlDZhQtWtTObWWu60Xus5o2bRrIThPHjh0D3ISrdAKQfRI0tBxAURRFURRFURRFUfIJ/qSk4hDJXErLGsC2LVSUaCLSJ5FAN27cmLPOOgsItUjzm8qVKwOu2WeiU6RIERYsWACEN6WUVjwzZsywEvYSJUr4JidcvHgxQCoDxnHjxgGa+QsCkql59NFHfY4k/7Fv3z7bBnDhwoWAW8oXNGO2WCFjhZK3iLLkn3/+8TkSJTPCtZMUQ9q6detSrly5WIeUIdKCWVrLymMiULx4cX744Qe/w4gYKec5evSov4FkA1UCKIqiKIqiKIqiKEo+QZUAihJgypYta2u2RQlgjGHChAl+hpXvyayuX1rbyKPfiD/J3r17bZsaaa2l+MukSZP8DiFfcujQIcA1m5o3bx6AbaFbu3Zt3+JSFMU/pG6+du3a1ntGvHz0mqkkIroIoCgB5uyzz04oIzQl9sjikaIoLi1btgRcIzDplKGTfEVRINTBTFESHS0HUBRFURRFURRFUZR8gglnbJXhm43ZDmyMXjhR42zHcWwfrETZDojbbUmU7QA9toKI7pPgofskeOg+CR66T4JFomwH6LEVRHSfBI+E3SfhyNEigKIoiqIoiqIoiqIo8YuWAyiKoiiKoiiKoihKPkEXARRFURRFURRFURQln+D7IoAxZoEx5oAxJuX4v9V+x5RTPLHLv6PGmJF+xxUJxpj7jDFLjTEHjTFv+h1PpOg+CR6Jsh2CMaa1MeZnY8xeY8xaY8w1fscUCcaYt4wxW40x/xhjfjHGdPQ7pkgwxpQ2xrx3fH9sNMa08TumSDDGnGCMef34NuwxxqwwxjT2O65IMMZUM8bMN8bsNsasMcbc5ndMkZJA50lCjMOJsh2QOGOXYIypfHxe/5bfsURKIoxdiXQtgcQ5T4K0HUFpEXif4ziv+R1EpDiOU0x+NsYUBf4A3vUvolyxBRgC3AAU8TmWiNF9EkgSZTswxjQEngRaAf8DTvM3olwxDLjbcZyDxpiqwAJjzArHcZb5HVgOeRE4BJQHLgJmGWO+cxznJ3/DyjEFgd+A+sAmoAkwxRhzoeM4G/wMLCcYYwoCM4CXgYa42/OBMeZix3F+8TW4yEiU8yRRxuFE2Q5InLFLeBH4xu8gIiWBxq6EuJZ4SJTzJDDb4bsSIAFpAfwJfO53IJHgOM50x3HeB3b6HUseovskACTKdhzncWCw4zhfOY5zzHGc3x3H+d3voCLBcZyfHMc5KP89/u88H0PKMccX+poDAxzHSXEcZzEwE7jT38hyjuM4ex3HecxxnA3Hj60PgfXAJX7HlkOqAhWAEY7jHHUcZz7wBXG4TyAxzhNInHE4UbYjkcYucBVywN/Ap37HkgsSYuxKoGtJwpwnQduOoCwCDDPG7DDGfGGMaeB3MLmkPTDe0bYLQUL3iZJnGGOSgDrAKcdlgpuNMaOMMXGbjTLGjDbG7ANWAVuBj3wOKadUAY6mydJ8B1T3KZ48wxhTHnf74i3bYTJ4rkasA8krEuA8UYJHwoxdxpgSwGCgl9+x5JKEG7sgrq8lkDjnSaC2IwiLAH2Bc4HTgTG4kpu4W10HMMachSu7Ged3LIqL7hMlCpQHCuEqTK7BlXNdDDzqZ1C5wXGce4HiuNszHTiY+ScCRzFgd5rnduNuU9xijCkETATGOY6zyu94csgqXAVWb2NMIWNMI9yx+CR/w4qcBDhPlOCRSGNXMvC64zi/+R1ILkm4sSvOryWQOOdJoLbD90UAx3G+dhxnj+M4Bx3HGYcruWnid1wR0g5Y7DjOer8DUSy6T5QRMFVNAAAgAElEQVS8Zv/xx5GO42x1HGcH8CzxO24BcFz2uBg4A+jqdzw5JAUokea5EsAeH2LJE4wxBYAJuLWD9/kcTo5xHOcwcCtwE7ANNzs4BdjsZ1y5Jc7PEyV4JMTYZYy5CPgXMMLvWHJLoo1d8X4tOU5CnCcEbDuCYgzoxSG8FCceaAf81+8glFToPlHyFMdxdhljNuOOVYlIQeKv1vkXoKAxprLjOL8ef64W8Sl7xBhjgNdxVSdNjk9K4w7Hcb7HzaABYIxZQuKosuLxPFGCR6KMXQ2AisAmd/iiGJBkjLnAcZzaPsYVEYkydiXKtYTEOU8CtR2+KgGMMaWMMTcYY040xhQ0xtwB1AM+9jOuSDDGXIVb0hCvDvSA64pqjDkRSMIdwE887pQad+g+CRaJsh3HGQt0N8aUM8acDDwAfOhzTDnmePytjTHFjDFJxpgbgP8A8/2OLSc4jrMXV5492BhT1BhTF2iGm/2IR14CqgFNHcfZn9Wbg4oxpubx8/wkY8xDuF003vQ5rByTKOcJJM44nCjbkUBj1xjcRbGLjv97GZiF270h7kiUsYsEuZYkynkStO3wuxygEG6Ll+3ADqA7cKvjOKt9jSoy2gPTHceJN2lKWh7FlTv3A9oe/zlea511nwSLRNkOcGsfv8Fd1f0ZWAEM9TWiyHBwJc2bgV3AcOABx3Fm+BpVZNyL2yrsT+BtoGsctg7CGHM20Bl3Ir3NGJNy/N8dPocWCXfiGuj9CVwPNPQ47McTiXSeJMo4nCjbAQkwdjmOs89xnG3yD1f2fMBxnO1+xxYhcT92Jdi1BBLgPDlOYLbDqGG6oiiKoiiKoiiKouQP/FYCKIqiKIqiKIqiKIoSI3QRQFEURVEURVEURVHyCboIoCiKoiiKoiiKoij5BF0EUBRFURRFURRFUZR8Qo7aqZQtW9apWLFilEKJHsuWLdvhOM4p8v9E2Q6Iz23ZsGEDO3bsMN7n4nE7QI+tIKL7JHjoPgkeuk+Ch+6TYKFzleCRKMcW6D4JIom8T8KRo0WAihUrsnTp0sij8gljzEbv/xNlOyA+t6VOnTrpnovH7QA9toKI7pPgofskeOg+CR66T4KFzlWCR6IcW6D7JIgk8j4Jh5YDKIqiKIqiKIqiKEo+QRcBFEVRFEVRFEVRFCWfoIsAiqIoiqIoiqIoipJP0EUARVGUCNmwYQMbNmyga9eulClThjJlyjB79mxmz57N0aNH/Q5PURRFURRFUdKhiwCKoiiKoiiKoiiKkk/IUXcAJX8wfvx4AH755RcAhgwZ4mc4+ZINGzYAcN5557Fu3ToAzj77bB8jih6rV68GoFq1agA4joMxblemnj17Mnz4cN9iy4hjx44B0LdvXwDeffdd+9pNN90EwK5duyhZsmTsg8shM2bMAOC5554DYMGCBRQokH59eP78+QDUr18/dsFlwOLFiwEoVKgQAJdffrmf4ShK3LJkyRLee+89AJ5++mmfo8k9mzdvBqBy5coAHDhwgP/85z8ATJo0ybe4FEXJG+Qc79evnz2nTznF7YbXsmVL/vvf/wJQrFgxfwKMgJdeegmAe++9F4BKlSrx8ssv29dlLhmuY0lu0EUAJR0dO3ZM9f/HHnuMggX1UPGDAgUK8OSTTwIwevRon6OJDmPHjgWwN/7GGE477TQAGjdu7FtcGbF//37+7//+D0h983/eeecB8PvvvwPw6quv8tBDD8U8vsxYuXIlABs3brT/f/zxxwHYt28f4B5zsi+83HbbbQC0aNGCMWPGxCLcVHz88ccADBs2jEWLFgHYcalz584MHjwYgJNPPjnmsSlKvHL99dfbn3v27Algx99447vvvuPf//43AIcPHwYgKSkp7KJmkNmzZw8nnHACAIULF/Y5mujSqVMnAF5//XUAvvjiC6644go/Q4pbtmzZAsD27dsj/h1FixalUqVKeRVSniIllv369QPcRb2yZcsCcOKJJwLuPPmrr74CiJvWfjt27OCDDz4AsGPVunXraNSokX3P6aefDoTOk4YNG+bJ346vkVFRFEVRFEVRFEVRlIjxPb0rstq///4bcGUeaSVbo0aNYu/evQCUKFGCp556CnCzP0r0OHLkCAATJ06kffv2PkeTf5GyjETk0KFDTJw4Md3zU6ZMAeCqq66KdUgZcujQIQA6dOhgFQCSde7WrRuPPfYYANdeey0QOn+CwubNm2natCkQKjcJl/HPiN27dwOwZs0aUlJSgNjI7aQcaejQoUBoP0AoMzB69GhWrFgBQO/evQFXYZJ2+y655BJuvvlmAC666KLoBp4Nfv75Z1uG4UWys1deeSUAZ555Zkzjygu2b9/OiBEjAFe9IYiK45prrvElLgiVIH3zzTdceOGFAGzatAlwj3NRzIisNCscxwHc88n78yuvvAKEsjYVK1bMmw3IA2SfHDp0iA4dOgBQrlw5P0PKNQMHDrQqp3hh3759fPrppwB88skngDvnlbKrRx55BEit2Ih3du7cCcDbb7/NhAkTAKyCQ8o4lOzx4IMPAu618IsvvgDg22+/jfj3VahQgenTpwNw6aWX5j7APOTtt98GQmU9ZcqUYe7cuQCUKlUKgP79+9v52VtvvUXbtm19iDRzZG6YnJwMwMyZM/n+++8z/YwoTD///HMg75QAviwCyGRyxowZzJs3D8i8VqtkyZJWnlayZEn+9a9/RT/IKCGTVu8NQlJSEoBK7qOETMpSUlKYNWsWgB04xo4daweJJk2a2NpBJbr89ttvgHtDLRI2L+IPEAQOHDgAYBcfZYEC4K677gKwUnTAShm7dOkSqxCzxf79+9NNkKtVq5bpYuqOHTuA1L4gixYtspONG264IQqRhpg8ebK9+ReaNm1qSxMOHjwIuNcU2QeyTy688EJWrVoFhOSRM2fOtDc/d955Jy+88AIQO8ntggULAGwJxpdffmm3wYuUW0id4+zZs7nkkktiEmOkyDVNZI3333+/nbh4F2Nat24NhMo7Dh06RPXq1QGsBDqaHDt2jJkzZwKurPTcc88F4I8//gCwCYe0cWeG933en2UMkMWAtKV2frJnzx77c9GiRYHQXCSROPPMM7n99tv9DsMiC6ifffYZAMOHD7fjqRdZLJMbultvvZVWrVoB7thWoUKFWIQbFcRf5oEHHqBEiRJAaJG3TJkyvsXl5cMPPwRI5UmU9qa4Tp06dp/EkjFjxtCjRw8g9aJ4XrBlyxZ7jyX3an6yfv16wL12S438xRdfDMBrr72WbjH/2WefZfny5QA8+eSTgVsE2L9/v13Ye/7553P8+TVr1gDu3ExKIXKDlgMoiqIoiqIoiqIoSj7Bl9SzrKw98cQTYV8XWYfIgkaMGGFlkfHCkSNH2L9/PwAvvvgi4LqFi2RVZF8Affr0AbIvPVTSIxIZr1GbICul4czMjDFWjv7VV1/ZFVDJwAUByWaKI/rVV1/tZzh5gmQCJBvipX379hQpUiTWIWXIm2++CcCgQYMA95i57777gPBu2mLkGDQqV67M5MmTUz3XvHnzTD/z66+/ArHvECJZ2Z49e9rzt1evXkBIkZEWOb/HjRsHuKUk8rP8vipVqlj33TFjxvDjjz8CISXaWWedlefb4kUykpJtrlOnjj2fxaF98+bN1qRRFAySLQsy999/P0AqR+MqVaoA0LVrV8DtPCPXwJo1a9r33XrrrQBWhhpNdu/ebY2lANt9JacULVqUWrVqAa7DfmaI2iYISgDJRIsMO9GpUKECt9xyi68xyPn8ySef8MwzzwCEzf6H459//gHcc0c6N5166qn2WGrWrBkANWrUsN1S/KBr167W4TwztmzZkko5J3J2GSv8QObqck+yceNGpk2bluo1Y4ydZ4b7rJgFx4I33ngjnQKgcOHCttxIrnfly5fP9PdIll1MNAGKFCnCwIED8zDayJBxWcyhZS4CWOWYKAK8nHrqqVZpd++999prfI0aNaIab3ZJTk6OSAEgyBzunnvusaWnuUGVAIqiKIqiKIqiKIqST4ipEuCee+4BSGUEJrWYklGrXr26rXMQw56gc/jwYVu3JRmlNWvW2PpzL17TIOHLL7+MQZSJw9atWwHXVAtc8w+p8fzrr7/Svd/7nctKuWT8tm7dalfp165da2uggqIEOHbsmN3etWvXAvGtBBBPDG89qiAtXm677Tb7s99s2rTJZqDlnO3SpYvN5sQbWWX+BclAS92hl3r16kXd1E3aRm7dutWu9nuzR+GQbLPXTDKcoekFF1wAwPnnn28zuGLQM3r06Khm0/78888MXxMlWEpKCsWLF0/12oQJE7Lcfj8ZNmyYVV0Ibdq0sW1NRcnQqVMn2+dYxu9YU7x4cavqGT9+vDUl3rVrl31P7dq1ARg5cqR9TrZFlCSFChWyXkViKghuC00IXaeChhzforQM58kSbzz77LNA6uy6mLa++uqrvsR06NAh5syZA4TmtzmZ60nLWTnGVq1aZT1atm3bZtVZ8ti7d2/7cyy9Hbp37w64yirJ1sq5Ei67v3HjRmu8CTBgwIAYRJkxq1evtvtHrjteZP5YvHhxq0iW/ZiSksLDDz8MxFYJEI7evXvba4S0Xcyola8okWWs8ioBLr30Ujvn8Yt169bRpEkTILUCQNrnibHx559/HnYuInOGvXv3WqX1Rx99FM2Qw3Ls2DGr8JNzU0xps6JgwYKULFkScOfLee39YP9OVH5rBkjPRq8RkgzUIq8NKmJ6tH37dntxf+211wD3Yj9jxoyIf3fLli1zH2A+YfTo0dbYS0ynMkJMmcRsqm7dutbNvF69egA0atTI3ghceeWVnHrqqVGJO6eISeTJJ59sFybEUTeekQmn9+ZSbnikTEgc7IPA4MGDrdzvpJNOAqBv376+yi5jgSzYioEmhMq0+vbta7+LaCHmN8YYKxfNamFIepxnhUxMFy1aZB2p33jjDcCdAMkEyi+KFCliJZCzZ88G3DKnIC4CiHx/8ODB9rouE+Xnn38+XRlDkSJFbKcD7yJALLsfFCxY0MpdBw4caM0apTRp7ty53HHHHQCp+pVn1rvca9IW9LFh8+bNQMh4Lp6RmxeRDsuCDoT2gyz6xQqZK/bq1Stb8vhw3HLLLbaMVI6tVatW2d83adKkVItW4C40nH322UBsTGll4UXkycYYO7aGu/mXhf/nnnvOLqiHk3PHCpGJX3fddenmVrfffrtNUMpcsV69enbhTDo21a1bN1bhpuK6666z0n8Zl7z3UOHk5jL3+uijj2zpVjhTWlmk9ZO+ffva77hRo0aAe+8lyTtZtKlUqVLYz8vx17RpU7766qtoh5sh7733XrZMSevXr8/ChQtTPVejRg2WLVsGuNsRrUUMLQdQFEVRFEVRFEVRlHxCTJUAsurn7YcoEs6gIqZgsgozderUTN8vhmb16tWzUhvh22+/tSu5x44ds7LVoH8HQeKPP/5IpwCoWbOmzSZLu5CbbrrJmvyFa70o5lReQ6crr7wyJn3Ps8MZZ5wBuFI7kRH17dsXyH7GM4hI33AvIncMgmmWIO3Lxo0bZ+WA0jJIsi2JSo8ePcIaIN15551A9NsCQmrj1GiZ4l1zzTVWKihGcePHj7fjdunSpaPydzNC5H4jRoywxplCdss4YoVkziRjfvDgQS6//HIgdJ6Iyg9Cktrhw4fbnuheYnFMZUSDBg1SPTZr1izbLUrFgEuuSVOnTg1bBiBZuyAQLksYD8aTafnnn3+sKtBrRuk3kkmVTHNW9OnTJ122MG3bM4CqVavafdekSRN77nnVD6IWirYSoHv37lYBICWYrVu3zpa0XzK88hm/EEPFHTt2WGWCZJqHDBliyzHC8frrrwPud58XbdpySkam6oLchxw5coRRo0YBIcPccP3oCxQoYOdfYqrnB6JSWrhwoR2Pxez75ptvtteUjBQAaalVq5a93ogSIhatNSXmbt26Zfo+KVccMmSInYvIvYzcv0QbVQIoiqIoiqIoiqIoSj4hpkqAhg0bAqEVqaSkpJitdmSFt47LW2slBiBiyuZFalSvvvpqmyU7/fTTAbdmJy3Jycm2zujAgQO29tlrEqhkTr9+/exxJNSsWTPHmQzv/hQTQDF8UqLDoEGDrGFQ0JH6xaNHj1pzlvPPP9/PkKKGZJJuu+02ABYsWGANeLxINjcWiNFatMdGqaOU1ltffvklX3/9NRBqTRRNjh07Zs3DpLXkokWLrOeCZPseffTRqMeSE0QZ560plSykVwEgfPDBB0BIzRRk9uzZYw1MwyHHx9SpU63PUVb19bFusZlTJAsVTyxYsCBsi1bBr7nlDz/8YP++XDvCtb6UtoUPPPAA5cqVy9HfuOGGG2w22Dtvycx4NDe8/fbbQOi8nzRpks2oindB69at7faGQ2qe16xZY2vrW7VqFZV4M0PGemmPB66pHmTepnv37t32PPYaA8fyuphTRo0alanJn2TUO3Xq5LsZILh+EeCqM8RvQTLjDRo04Lfffsvx75T5zbx584DwZsF5hfjKiBLHq9IR5s6da9Wkct6XKFHCnkdi6hkrVXJMFwHSkpSUZE2E/EZ6gs6dOzdT98batWvbAaNMmTJA1hcbkd/MmTOHAwcOAK6c68YbbwQIO+FWwlOkSJE8ccefMmWK/VluTM8999xc/968plmzZraEJN6NAf/8889ULrSCn1LgjPCalknZjlyAMuo5LYZyN998MxAqcwgiYji3cuVKKxeU7StQoEDYm+9EXKwU2aRMhlauXGkn7NFcBBDZ4zPPPGMnPkJSUpItQwuqaexPP/2U6v9nnHFG2J7FMvH3yn5lArRx40bA3d6iRYtGK9RsIwZSR48e5dJLLwWwpqxexChU+rdnRJUqVexNU1AMZ/MTr7zyii9/V0pDnnrqqbCdCSRBJHL6SI0k27VrZ/8OhBzT85qDBw/amxvpfnXs2DGb+JLHnTt32nNCzJgdx7FlTo899hgA+/bts4sAfpwXch2TxxNOOMFes73IgoosgIwcOdIuHHivhUG8Lkq3m3BlAyeccII12xNzx6DMfaUMsVSpUtx0002pXqtbt26qhZuc8s033wDRWwQ4cuSIXez23vxLKVj9+vUB1/CvfPny6T6f2QJaNNG7T0VRFEVRFEVRFEXJJ/iqBAgS0stx9erV3HXXXQBhVwfr1KljTduyy/vvvw+4xoAiHXrkkUeoVatWbkKOCh988IEtjRBDvXDfQ7wiBmCSCS1fvjw1a9b0M6RMueiii2y28tixYwB06NAhbD/beOWhhx7yO4R0eOWZogqQ1fOMMoCdO3cGQnKuK664wmZJ/G47J4iqRGTw4bIYlSpVsuokMdPZu3dvjCJ0ESngF198Yc3Xoon0eM5Nq9fsIGPr3XffDbjKMylHknaFAwcOzDPzIilrkex1kSJFrLwyN6Q1yC1QoIC9Xkj2/Mknn7SKBikb6NOnj20PKqVz5cqVs1kSP5DsjWSIwmX/c4KoSqZPn84555yTu+CigMxB5FGuK0reIMrPl156KZ3y7fbbb7fGa7ltJSlzGFHUAFFR1s6aNcu2UJXrRYECBWwJjJRnfv311/aYEpUshIwD5TVjjC2lPXz4cFjj5lhy4MABq1IQg8Zp06bZ71WMDM877zxq164NwPLly+3n0xqAxxq5Pm7bts22PpUSsyNHjlhZuaitevXqFQjpvxc5lv73v/8BbrY8bUtWmX/lBJm/QPQNAZOTk9OZrhYrVszO1aXVZE6Q/fjdd9/Z50SJml3z2qxQJYCiKIqiKIqiKIqi5BNUCXAcqXVq166dXWHJ7Sq+tIiRusD9+/czYcIEAC655JJc/e5o4a07l1Vf76puPJOSksKCBQsArPHWBx98YOvTgopkZeUxiDVo4RBzLWmnM3HixHTvGTFiRCBr56XFC6TOtAhNmjQBUrfWkhpP2e4vvvjCeoBcccUVgVCchPM7kUz0I488AoRUAhDazrlz58YguhDSEm/JkiW88MILALRt2xYI1fHnJWLYmpSUZI29osHixYuB0PdZqFChXJl5rVq1CnCNlFJSUgBSqYS+/fZbIJTNqlSpEr/++mvEf08QtZwYFm7atMl+h5LxO3r0qG0LLHW19evXz3WmPa+RLEtexTVr1iwg+22sYk3amuh48ySS2veM2qSNHDkSCJ3TsUY8brzfq/zcoUOHPJlvrF692hoCepUc0TBDLFOmjM3Kim9G2p/TIsdWONO8MmXKWNVTNMbyrJB5vagR9u/fbz0P5NGLZP8vvPDCsAaPfrUMliyxZIbDmZmWLFnSqpz9VFtlxbZt24DQcXPSSSfZ8TiSWnnx3Jk8ebLdzxl5OeUWMfQVbw4v5557bkQKAHAVKqKQ8LZFv/DCC4G889OI6SKADFAitf3rr79Yt24d4L8xhQzSMunNC8TwSSaVF198se1N6tcFKiuqV69uT0QZVH788Udq1KjhZ1i5QibHp556Kvv27QNCjrrS0zfIyDEjk5t4QVx4u3btmu61KlWqAHD55Zdb+XyQkJscb792KYsZNGiQ7ePsjV2k9iIp79y5M8uWLQPccUUuTH4ippoyOatWrVq2eko7jhNTF2SZSA8aNIiVK1cCMHPmTCA6jtKyP4sWLWoNhKJBWhn9kSNH6NmzJ4A1Qrr++uvt6x9++CHgSj7F3Vhu/CG0QBXOhdiLSO+9Czy5QSbx3q4FUuogVKtWzUqWM3M/37t3r90OPybUIuetXr064CYEZOFeynjq1atnpd0dOnRI9zvk3Dh48KB1eR4xYkR0A89nyLVb5OfhTMJGjhxp949f1xXvYr3E0L9/f4B0nY1yipjsjRo1iu3bt9u/A+4xKn8nL6lfv75dtBRp/4IFC1JJlAUp9ZH57axZs7jnnnuAkNv5tGnT8sTcOVLkb3v7z4dDzEHlJq9w4cJ2ThPufbFk6tSptqQs3M2/GM+NHz8+opt/+Z2ffvopABMmTGDYsGEAOS6Hzg5yjReuuuqqXBnlyXly6NAhm8SM1j2MlNzJ34SQmbQsfkfC008/bY+9aBJfS8CKoiiKoiiKoiiKokRMTJUAIjuVVcIjR45YA6jSpUvb9/3nP/8BQlmLUqVKxTLMPGHIkCG88847QMiUqV+/frZ1SlC59NJL7eq1ZDdi1a8yWogp1d69e7ngggsAbFu0eEBkTKIEmDp1qjWhu/zyy32LKytExh0OMTAKavzhlDry/WeUrZSVa2nddP/999tjLyUlxZrURNugJjMk65mVmZFkmaQ8yBgT0zIUKclq166dbeEpBpJVq1bNc1NVybLv2bMnqpkdackk2fxPP/3UZozlfPEee3L8ZGTeJu+VTD+E1GznnXeevb6K4kmkkblF2h6J8d/EiRNtplakw7179w6rAEirKNm9e7dtOeiHEqBq1aqpHm+77bZM3y/mYV4kk3XhhRda9cY999xjrzdK7pExwSuNFWSOUqlSpTw7xvMCkZ4PGjQo17/r4MGD9OnTBwipzrw8/PDDuTYbzAgpn5THtm3b2vKscMi49eKLL9rzvU2bNgC+qgC8yDh57Ngxe87KXD1ca9bvvvsu3dj10EMPxVRm/8knnwBuH3pRuArFihWzrVhFDeM11xMlk9fkV8ox9u/fz8cffwy4hqaiBJBSAghds6KhlJO2mYMHDwZC2xkpUl5w6NAhq/KLBj/88EOq70iQcsacqCZEbfPaa68BIcNa4a233gJC5QB5hSoBFEVRFEVRFEVRFCWf4IsxoGRaNm/ebE2RvOZIsmoq9biDBg0KtKmFFzF+mjBhgs2MDB06FHAzukH1AhDeffddW9spq8qS9Yk3ZKVU6ouTkpJsC5V4Zu/evTY7GtRMOsDw4cOByFq7BIns1sLLeSOZRKkVk5+lrZCfSoDsInWc4mngF88++6xtuSVja+PGjRkzZgyQd+1Lvav5eb3S7kXGf2lJddVVV1njTFGKSDYqLZKpFiPNTp062WMpUvOhSJFsq6he/r+9ewuxsW/DAH55vUOcoLQm248YSqQoBzbZRVIilMJkMw4Y2zkYYxtlv8mBA0zREBlMURJRdsluiEIpUfNhEt+EpOxifQdP170eM481y8za/J+1rt/JKl7zPstans3/f9/XzddE1K8oCUvQaTzdu3cHAMyaNcsCUKdNm2a7Zq5U0717965BLkWPHj0yPqatMU+ePGmw8wnEwrFYTdPcnvtk4444MzuaUtX65s0bAN4YU/Zo+zF7asSIEU09zKTh+y0pKQHgBZMyWC+oesEViVxHzp8/3+BcxermdCkvLwcAfPjwocHvnTt3rsF3oKqqyvJWWK105MgR+31WBNY/JwRJ5XvlM4Y/aLk5Fi9eDMCrfkjlOMR37979ltHDCle+fvnyJaHwy69fv+LGjRsAvMyM+iorK606JdnXy4yc+U+dOgXAu8FjWMP9+/cBeF9apupfv34dgBe0FZZFAD74+BOYWf7keisAkP554KnEebx8kOnXr19KQsXShSXBv379sgcil7F8248zyl1fGGAZc15enpXRrVixAoB3M1M/mfX169dYsGABgOAk/ZkzZybtgTXVrl+/bhckP4bBpVNeXp6FMTFY9sWLF5g0aRKA2L/xJUuW/NZSlohPnz5h1apVAGJhjm3btsWyZcuScuzx8JowfPhwC2bkLO6gAMnWrVvbew1je5wf280YXvX27VsnQjObgw8/LEMFvIkMQaFdrpkxY0ZGUtoTwevcvHnzGkxv6NKlCzZv3gwguHw7U9guUlVVhVevXgGIlcIfO3Ys4fMUH5p5v+w/J3fo0MEe4Bhi60IbxM2bNwHAFmm7du1qKfZh9/Tp0wa/9jeLn8nAh/WgiR6zZ89u8O+4trbWNiPj/bz6+PN5nerZs2dKg065iMr7qrq6uiaHxm/YsME+q+nTp6d1o+z27dsAYsGA69ats+u2H59xea9YV1fXIAQwEolYy82AAe2ZU44AAAhJSURBVANStliudgARERERERGRHJGRSgCuMvl3CidMmAAAKCoqstJGrgRdvXrVVtRdHCcGAHPmzAEQC5jq27evlXkOHjw4Y8f1t7hTC8TKoD99+pS0Mp10efz4sZXMc4Wcu31hw2ATtjVcvHgxtCW03MVkgI2reA7av3+/lZbx+3Pnzh3b9ecc9urqagvTo7y8PKseYKBTqvEYX758iaVLl/7xv2MInf97xBnI165ds3M0P68zZ86kveScGBZ369YtAEBZWZkF0nGV/eDBgxg1ahSA2CigSCTSoPri3r17NrJ13759tlPHKq2KioqUtgMEYUm/f9xeNmNpPEtXq6qqbLeQI/lSgTvJ/mC5Vq1aoXfv3s3+2Q8fPgSAwHJtl0QiEdtB5vXRRZzXzrLyoHGAgwYNQmFhYVqPKxEMN6uurrZSbO74jRw50ioBGN6Wn59vO4g8x3379g01NTUAYu0AgFcBAHjlzmwpckn9UM3i4mIb0RZWdXV1ALxxh8TrebrfGwOhjx49ahWKfDZqSjUVr3v++4CCggKUlZUBiIW0pwsrerZs2WJVeqyE+RO2RrAtZseOHVZltnfv3pRWOQ0cONAqf4KOc+fOndYG6sfPiq2OQSoqKuy5OJWcawTr1KmT9XBwfvKjR4+sFNrFRYDq6mqbOfr161cAXnkLH/7D0AZA06ZNs/5A9jifOXPGFjnCYvz48dZby3K5ZNzsZQIfxHgBuHjxovUYsmXGtXaZmpqauGVoYTF//nxrXeBc3gcPHljORxDOG1+5cmVab1LHjRuHu3fvAvBmasebgPH8+XMAwf1l//zzj7VDcDJFphYA/NiTuHHjRrsOsIS+trbWerEpGo3GXSyLRqN2bj569CiAxqcmSPKla0GTacv+a1n79u1RXFwMANYGkmjv66FDh+xBjQv+fpMmTQrFtd/FBX5uprBs1o+TMLg465qCggIA3qIerxP8nvhLylk6HyTo3NW+fXs7x7nYTjdlyhTLweEDUTacTzkViPdcQOzhON1ZGgcOHLBXPnOwZeb06dOWK+PHCQGcEOPHhSQuLmUaF5H27Nljiy58EOa9iN/r169RUVEBwGsrA7yWPrY51G/bTLaOHTva5sOFCxd+y4ECvOkE58+fb/TntGnTBuvXrwcALF++HED6nhvVDiAiIiIiIiKSI5yrBABgQUlcgfOnL7ro0qVLVgFAq1evDsUuQDyJpqK7hGXO79+/t13YZMzodcHkyZMBeCnhz549A+BuJcDevXttt9mPAUlhwlmvnKN76NAhK0f37+awlJmVTNwRSpeXL1/+Vn3x4sWLRv9Mjx49GsyW/vXrl/07YguKS7p3726lzPy3XVlZiePHjwOIhbK2a9cuMESPn8uwYcOs/DCsE1DCjKFHVVVVtutTU1OTss8iKJ3848eP2Lp1K4BYanZQteGRI0fsejh37lwAXjJ0/es+AAufLS8vdyKsrTEsu3Ud/y65AzpkyJBMHk6j5s2bZynha9asAQCcPXs24T/PQLNhw4YBAJYuXYpu3bol+SibjxVnly9ftiob/3z6sOM1NRqNok+fPgBiE1oyidcuvi5btiyw+pKl8azucxknSZw9exYTJ04EEGulCQpcBmLna7aXlZeX2ySddGCF7o8fP2wXP1Gsbhg1atRf/9lkUSWAiIiIiIiISI5wshKAfS31+ytcw7GG/tEZ7LHh6m0YMfSEOyRhwFVCHvuPHz9sZrCLq+fNUVpaanPcXVVcXGw9uAz4LCkpyYrws6KiIssHcMm6deuwc+dOAF7vKfv44432KywsRLt27dJyfMnE6gVWaZSWlqK0tBQALPgvEonYLoi4h+OnIpGIhWqeOHEiZTvTixYtAuAFewbxBwbWN3r06Lg/m72na9eutQo0f8iuNB97nxmUGwbckTx58iQAr0KR+SPxvm9lZWWW1dC2bdsUH2XT8D69srISgPdeec/owk55srVo0cKqlBLNDUmnXr16ZfoQkmbs2LF2Hd++fTsA4Pjx4w3uZbp06WK/xqqbTFm0aJE9szZ2n8sKS1anpTq7IB4nFwFY6smTZP/+/QPnYmbalStXAMTSKYFYmEP9Etsw4exdvoYBTxTfv38HAOzevTuUpeeJmDt3rpWkuqp3795Wli3pUVhY6GRadrqlO91fmoYp6QsXLsSmTZsAAIcPH07ZIgBTnMeMGWMlnEAsJJMtVn/CUtWgG7Zt27YBiE2mcBlbffjqIj5MhmkjIh7eD+bn5/82FSvM2MLz+fNnAN7CRTY+/PtlQ9BhWLD1guG/fHVVy5YtsXr1agCw1zBw78laRERERERERFLCyUqA+oEva9eudXI0YJChQ4dm+hBykr8aA/AC6FysHhERkZjZs2fbnPRWrVql7P/Dn925c2drVQJiY+hYfvonbF/ItvYykaZgS6LrrYnJMnnyZAv9FMkWekoSERERERERyRFOVgJwbMLPnz8zfCTxsbdrxYoV9mv//uvkX6mIiIhzCgoK/jj+KR3Yxx+Gfn4RyYypU6cq7FOyjp5Ym4Hl5qksYZTEPHz4MNOHICIiIiJZYteuXZk+BJGUUTuAiIiIiIiISI5oEY1GE/+PW7T4H4D/pu5wUuY/0WjUBntmy/sAQvtesuV9APpuuUifiXv0mbhHn4l79Jm4JVveB6Dvlov0mbgnaz+TIH+1CCAiIiIiIiIi4aV2ABEREREREZEcoUUAERERERERkRyhRQARERERERGRHKFFABEREREREZEcoUUAERERERERkRyhRQARERERERGRHKFFABEREREREZEcoUUAERERERERkRyhRQARERERERGRHPF/1JhNoQUvLWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 120 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_digits(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a Model (or function)\n",
    "Since we are restricting our model to only use `if` statements then lets try a decision tree model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to reshape the images because they are currently `28 x 28` pixel grayscale arrays. We want to flatten them so a row now becomes `784` pixels of numbers between `0 .. 255`. We also scale the numbers by dividing by `255`. We are scaling them to be between 0 and 1 this is one way of [Normalizing](https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/normalize-data) data. You will be able to visulaize this below by printing out the first item in the training set before and after it is reshaped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Current shape of training data set. 60000 examples that are 28 x 28\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape docs: https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html\n",
    "#One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.\n",
    "train_set = x_train.reshape((len(x_train), -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can do the reshape and normalization all in one line like this. (we split it above so you can see whats happening)\n",
    "train_set = x_train.reshape((len(x_train), -1)) / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function shape\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the function and the optimization method then we just fit the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /data/anaconda/envs/py35/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# how to optimize the function\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 4s 62us/sample - loss: 0.2645 - acc: 0.9246\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.1159 - acc: 0.9669\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0802 - acc: 0.9766\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 62us/sample - loss: 0.0600 - acc: 0.9819\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0471 - acc: 0.9858\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0367 - acc: 0.9888\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0298 - acc: 0.9909\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 62us/sample - loss: 0.0242 - acc: 0.9922\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0189 - acc: 0.9939\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0164 - acc: 0.9950\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f3c570e1358>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_set, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the Model\n",
    "Now that we've made a model - let's see if it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 31us/sample - loss: 0.0800 - acc: 0.9781\n",
      "\n",
      "Test accuracy: 0.9781\n"
     ]
    }
   ],
   "source": [
    "test_set = x_test.reshape((len(x_test), -1)) / 255.\n",
    "test_loss, test_acc = model.evaluate(test_set, y_test)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  }
 ],
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