05-SupportVectorMachines.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM Classification:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fundamental idea behind SVM can be understand with help of below figure. Figure shows part of iris dataset. Two classes can easily be seperated with a straight line (they are linearly seperable). \n",
    "\n",
    "Left plot shows the decision boundaries of three possible linear classifiers. The modes whose decision boundary is represented by dashed line is so bad that it does not even seperate the classes properly. The other two model works perfectly on this training set, but their decision boundaries are so close to the instances that these models might not perform well on training set.\n",
    "\n",
    "In contrast, the solid line in the plot on the right side represents decision boundary of an SVM classifier, the line not only seperates the two classes but also stays far away from closest training instances as possible. \n",
    "\n",
    "We can think of SVM classifer as fitting the widest possible street (represented by dotted parallel line) between the classes. This is called `large margin classification`."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding more training instances \"off the street\" will not affect the decision boundary at all: it is fully determined (or \"supported\" ) by the instances located on the edge of the street. These instances are called `support vectors` (circled in right plot)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Margin Classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we stricty impose that all the instances must be off the off street and on the right side, this is called `hard margin classification`. (The \"right side\" refers to the side where the instances of a particular class should be positioned.) There are two main issues with hard margin classification. First, it only works if the data is linearly seperable. Second it is sensitive to outliers. Below figure shows the iris dataset with one additional outlier: on left it is impossible to find hard margin; on right the decision boundary ends up very different than we saw in above figure without outlier and it will not generalize as well."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid these issues, use more flexible model. The objective is to find a good balance between keeping the streets as wide as possible and limiting the margin violations (i.e., instances that end up in the middle of the street or even on wrong side.) This is called `soft margin classification`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When creating a SVM model using Scikit-Learn, we can specify number of hyperparameters. `C` is one of those hyperparameters. If we set it to low rate, then we end up with model on right (see below figure). With high value we get model on the right. Margin violations are bad. It is usually better to have few of them. However, in this case the model on the left has a lot of margin violation but will probably generalize better."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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dPv30U8yYMeOyN5Mbe002Vjm6u7u3mPRryYQJE5CWltYusXeFiIgIjB8/Hps3b0Z2djYOHjyI3r17N6m4lCQJRUVF2Lp1KwAgNjYWsbGxTW6gy2QyREZGIiEhAUVFRdi2bRueeeYZeHt7N9vd/fDhwzh9+jRsNhsiIyMxZsyYzrtgIupxWP9NRNRDxcTEYObMmXBzc0N5eTmee+45HDp0CNXV1U0avouiCLPZjOrqauTm5mLJkiWYO3cudu3a1SN2tmxshq/X66HX62EwGJr0LBVFEQaDwfF4bW2to7cTERERUVsoFApMnjwZw4cPh1wux759+/DMM8/g/PnzzeYejZtHlpWV4ejRo3jppZfw4osvoqSkpAuvoO1MJpNj3qTX62E0Gh3zRkmSYLFYmsy9fr1RUCNBEPDkk0/Czc0NFy9exLJly3Dy5ElYLBZIkgS73Y7S0lIsW7YMBw8ehFqtxpQpUxAREdEsnvDwcNx6663w9vZGfn4+PvjgAxQXF8Nmszl2gT9//jy++eYb5OXlwcXFBX/6058cLQiIiFrCCk8ioh7Kw8MD9957L/Ly8vDjjz/i6NGjmDt3Lu666y4kJiYiKCgIMpkMJpMJRUVFOHv2LHbt2oWjR4+iV69eiImJ6RGVkXl5edi0aROqq6sdXzt16hSAhmTojh07UFdX50jeqtVqzJ49GzExMV0SLxEREfVMcXFxeOqpp1BZWYnjx49j0aJFOHfuHKZNm4b+/fvDw8MDkiRBr9fj/PnzOHbsGHbt2oXz58/jzjvv7DFte1JTU5GWluZIYtbW1uL06dMAgOrqanz55ZcIDAx0JEEjIiIwa9YseHp6NhlnwoQJeOCBB/DNN99g69atEEURc+bMQWhoKAwGAzZt2oRvvvkGcrkc48aNw+zZs5uNATS0apo2bRoOHz6MtWvX4ptvvoHFYsHMmTPh5+eH8vJyrF27Fhs3boTNZsPkyZPxu9/9roOfJSLq6QSp8V2MiIh6pDNnzmDRokXYuHEjcnJyIIoiPD094e7uDrlcDovFAp1OB6PRCI1Gg169emH8+PF48cUX0adPn64O/4pSUlLwzDPPIC8vr03He3l5YcmSJZgxY0YHR0ZERETOxmazYcuWLfjiiy+wf/9+VFZWQqFQwNvb27G83Wg0oqqqCna7HX5+fujbty8efPBBzJs3r8P6qbenP/7xj/jiiy/avCLmlltuwddff43evXs3+booisjNzcW7776L9evXo6ysDF5eXvDy8oLRaERpaSlcXV2RnJyM//3f/8W4ceNa7QcviiIOHz6Mjz76CFu2bIFOp4OPjw88PDxQW1uLqqoqeHp6Yvz48XjllVcwZMiQHnHjnoi6DhOeREROoLy8HEePHsXu3btx7tw5FBUVoa6uDoIgQKvVws/PD6GhoejXrx8GDBiA2NhYhIaG9ogl7WfOnMHatWtRWVnZpuM1Gg3uu+8+xMXFdXBkRERE5IwkSUJWVhYOHDiAtLQ05Obmory8HGazGUqlEu7u7ggKCkJYWBji4uIwYMAAxMTEwMvLq0ck4dasWYP9+/c3W6bemujoaNx7773w8vJq9pgoiigoKMDWrVuxZ88eZGVlQa/XQ6VSoU+fPhg+fDimTJnSrHdnS0RRRFZWFrZt24Z9+/YhNzcXRqMRWq0WERERGDNmDG677TZERUW1qQc9Ed3YmPAkInIiOp0OlZWVqK2thcViAQAolUq4urrCw8MDHh4ePaLy4NesVmuLvaNaIwgCXF1d22VHeSIiIrpx2Ww2VFVVobq6GgaDATabDXK5HC4uLnB3d4enpyfc3Nx63Jyjvr4eZrO5zccrFAq4uro22ZDot8xmM8rLy1FZWQmz2Qy5XA4vLy8EBgZe9S7yjdWhjX3plUolvL29ERgYCLVa3SOSykTU9ZjwJCIiIiIiIiIiIqfBXdqJiIiIiIiIiIjIaTDhSURERERERERERE6DCU8iIiIiIiIiIiJyGkx4EhERERERERERkdNgwpOIiIiIiIiIiIicBhOeRERERERERERE5DSY8CQiIiIiIiIiIiKnoejqAOjaSZIEo9GIqqoqqFQqyOXyrg6JiIiIqNOJogij0QgvLy+4u7t3dTgAgLq6OtTU1ECtVkMmY40BERER3ZhMJhOUSiV8fHw6NW/FhGcPJooiNm7ciKeeegoWiwWiKHZ1SERERESdThAE+Pj44LnnnsMf/vCHrg4HALBq1Sr885//RHFxMSRJ6upwiIiIiLqEQqHAxIkT8fHHHyMkJKTzzttpZ6IOYbfbYbFYMGrUKERGRjLpSURERDcUmUyG6upqpKSkwGw2d3U4DhaLBdXV1Zg4cSKCgoI4RyMiIqIbiiAIsFgs2LVrF+rq6jp9LsSEZw+n0WgQEBCAp556CrfffntXh0NERETU6S5cuICffvoJarW6q0NxcHFxgUwmw6uvvoqEhISuDoeIiIio09XV1eHxxx+H0Wjs9BY/bCjkBCRJYtUAERER3bC6U2Xnb1kslq4OgYiIiKhL2O122O32Ljk3E55ERERERERERETkNJjwJCIiIiIiIiIiIqfBhCcRERERERERERE5DSY8iYiIiIiIiIiIyGkw4UlEREREREREREROgwlPIiIiIiIiIiIichpMeBIREREREREREZHTUHR1ANS1JEly/G/jfxPRjUMQBAiC4PhvIiIiIiIiop6OCc8bmM1mg9FohMVigc1mgyiKXR0SEXUyQRCgUCigUqmg0Wggl8uZ+CQiIiIiIqIejQnPG5TJZEJFRQXq6+shiiITHEQ3qMbKbplMBldXVwQEBECpVHZxVERERERERETXjgnPG5DVakVpaSnq6urg6uoKd3d3qFQqJj2JbkCSJMFsNkOv10On0wEAgoODIZOxxTMRERERERH1TEx43oB0Oh1qa2vh4eGB4OBgLmElusFptVq4ubmhsLAQNTU18Pb2hlar7eqwiIiIiIiIiK4JE543GFEUYTQaIQgC/Pz8uHSViCAIAlQqFTw8PFBXV4fa2lomPImIiIiIiKjH4prFG4zNZoPZbIaLiwtUKlVXh0NE3YhWq4VCoYDRaHT09iQiIiIiIiLqaZjwvMFIkgRRFCGTySCXy7s6HCLqRhQKBQRBgCiKTHgSERERERFRj8WE5w2KPTuJiIiIiIiIiMgZMeFJREREREREREREToMJTyIiIiIiIiIiInIaTHgSERERERERERGR01B0dQBE9Ivi4mKUlJRAJpNh4MCBkMm67z0Ju92OwsJClJWVwc/PD7169YJSqez0OCwWCwoKClBVVYXg4GAEBwe32/MmSRIqKytRUFAAlUqFyMhIqFSqdhmbiIiIiIiIiDoGE57/1bh7uclkQm5uLjIzM5Gfnw9BEBAREYHExESEh4c7djFu65ilpaXIyMho9RiVSoW4uDh4e3u316VQJxNFEQUFBcjLy4NCoUBCQgJcXV2vaWOoRYsW4Z133oFCoUBpaWm3Tq7V1tbinXfewb///W888sgjePPNNxEYGNjpcZSVleHFF1/E999/jxdeeAF/+9vfoNFo2mVsURSxZs0aPPvss4iOjsbKlSvRt2/fdhmbiKijSJLk+G9uUkhERETUfYiSCAEC52idgAnP/7Lb7Vi9ejU+/fRTnDhxAjKZzJFsMhgM8PDwwMMPP4xHHnkE4eHhbaogE0URO3bswAMPPAAXFxeoVKpmL+rg4GAsWLAA48aN64jLok5gt9vx/fff4+9//ztcXV2xbds2DBw48JrGksvlEAShWyc6f63x90AQuvYNu/Hccrm83ceWy+WOf/yjRETdnSRJKK8vR3l9OSK9IqFRts8NICIiIiK6PpIk4VDBIUR6RyLQrfOLhW40THj+l9VqxYkTJ1BVVYXp06cjMTERUVFREEURR48exbp16/Cvf/0LBoMBL7/8Mvz8/NqU/JDJZJDL5UhKSsKUKVOaLfl1dXVFVFRUR11WtydJEqyiFZAAF4VLV4dzzRQKBeRyOZRK5XUtpw4ODsbw4cMhk8m6fXJNLpcjPDwcI0eOREREBBSKrnk7cXFxQXR0NEaOHInQ0NB2bQMgCAJ8fX0xdOhQhIaG9phENBHduKyiFdtzt2NN5hq8OPZFDAka0u3/nhARERHdCHQmHf530/9iVuws/HX0XyETum8LO2fAhOd/yeVyTJ06FbfeeisSExPh4eHh+IAwZcoUxMTE4LXXXsOqVatwxx13YMyYMW3+AKFQKJCUlIQ///nP0Gq1HXkZPY7ZbkZaURogAaPDRt/wv/CzZs3C2LFjIZPJuiyB2FZarRYPPfQQZs2aBQ8PD3h4eHRJHD4+PvjDH/6Ahx56CN7e3u3aR1QQBIwfPx4DBw6Ei4tLlyzZJyJqK0mSUG4ox4azG7AtaxtGhY1CjG8MXF1cuzo0IiIiohvexuyNOFF4AnqTHnNi5yDK58YtfusM3Tuj0omUSiVGjRrVYhJTq9Vi5MiRiIuLw08//YScnByMGDECLi5tr0gURbFJTy1qUFlfiQWHFsBV4Yq+Pn0R7B7c1SF1mpKSEuj1eqhUKgQEBECj0cDb2/uK/VxFUURpaSkMBgNEUYRSqYSHhwc8PT07NUkql8vh7+8Pf3//Nn+PTqdDZWUlrFYr/P394ePj0+Txmpoa5OXloby8HCqVCqGhoejduzcUCgVqampgNBqbJVcVCgWCgoKuKnadToeKigqIoghPT0/4+Pi0+NwJgnBVydz6+npUVFTAaDRCJpPB3d0dPj4+rb5XiKIIg8GA6upqmM1mCIIAd3d3BAQEsCKLiK6aXbIjvTgdO87vgM6iw5ozazAxciLiAuL4nkJERETUhcoN5fj2xLewSlbkVeZhyYkl+Fvy36CQMS3XUfjM/teVPgjIZDLIZLJrTloaDAbk5ORAkiSoVCqEhITA09PzmsZyFnbRjvSSdGzK3gRvlTemFk7F7f1uh1zW/n0Yu9L58+exbt061NTU4MEHH4TNZsPSpUtx+PBh6PV6hIeH4+mnn8aIESOQmpqK1NRUuLi44IUXXmiShBNFEYcPH8b69etx/Phx1NbWQpIkKBQK+Pr6om/fvpg1axaGDRt2xSpHSZKQkZGBpUuXwt/fH9OmTUNMTEyrx+7fvx/btm2DWq3GE088AS8vL5hMJmzduhX79+/H8OHDcdttt8HNzc0R64YNG3Dw4EEkJiYiOTkZqamp2LhxIy5evAi73Y7bb78dzz//PICGPqj79+/HkiVLcPLkSdTW1kKhUCAgIACTJ0/G7bffju+//x779u3Dfffdh3nz5jni0+l02LhxI06ePIlbbrkF48ePd1x/bW0ttm7dirS0NEyYMAHDhw9HSkoKUlJSUFhYCEmS4Ovri+TkZMyZMwchISFN3gtEUUR6ejo2btwIPz8/3HvvvfD19W32HFVWVuLnn3/Gzz//jHPnzsFisTiSpQMGDMBtt92G0aNHw9XV1XG9R44cQWpqKjIyMlBcXOxIeHp6emLw4MGYOXMmkpKS2vAKIyJqUG+tx/JTy1FeVw7IgSOFR7A1ZysivSOhdeEKEyIiIqKusiV3C44XHocoE2GRLFiXsQ4zY2ZiaMjQrg7NaTHh2Ubnz59HdnY2/Pz80Ldv36uqpLNYLFizZg12794NSZIgk8kQFhaGadOmYd68efDy8rrmuHpyxYbRZsTS40tRXV8NvVmPlLMpSOqVhF7uvbo6tHZVWFiIZcuWobCwEGq1GmvXrkVGRgYAQKPRoKqqCqWlpQCAQ4cOYcGCBVAoFHjuueccrzNJkpCSkoK33noLGRkZqK+vh7e3N9RqNerr61FXV+foLzlw4MA2LeuWyWRYuXIlLBYLtFotoqKiWnxdV1dX4/vvv8d//vMf3HrrrXjssccAAGazGampqVi4cCHmzZuHMWPGOBKekiQhNTUVX331FcaPH489e/Zg3bp1qKiogIeHB+rr69GnT58mx7722mtIS0uDzWZDfHw8XF1dkZubi3/84x84ceIE8vPzcejQIQwd2vQPQl1dHTZv3owff/wRKpUKN998s+P6DQYDduzYgSVLlsBisWDz5s1YuXIlysvL4ePjA4PBAIPBgD179iAvLw+vvvoqAgICHGNLkoRTp07h888/R9++fTG5smcMAAAgAElEQVRlypRmCc/c3Fy8//77SElJQUVFBcxmMwICAiBJEmpqarBz506cP38effr0Qb9+/RxxvfDCCzh27BiMRiPUajVcXV0dP8vU1FRs3rwZL730EmbPnt2jf8+JqPMcKz6GjVkbAQGAABitRiw5uQQz+s9AlE8U30s6GZ9vIiIiAoCyujKsPrMalebKhi8IQG5VLlaeXon4gHioFNwroiMw4dkGRUVFWLFiBc6dO4ff/e53iI6ObvMk1m63Q6vVIjIyEmFhYZDL5cjNzcWuXbuwb98+7NmzB5999hmCgy+/lNtut8NqtTarMLVYLNd8XV3tePFxrM9aDygaluFtyNqAO+LuQJBrkNNVeZrNZlRWVuKtt96CWq3Gm2++iXHjxkGtVkOv1yMkJARAQ4LNaDQ6KgEb5eXl4YsvvkBaWhoGDRqEv/3tb+jfvz80Gg3q6+uRn5+PlJSUZkvELycoKAjJycn4+uuvceDAAcyYMcMRx6/l5OQgLS0NdXV1mDhxYpPY7HY76uvrYbVam32f3W6HwWBAamoqZDIZpk2bht///vfo06cP6urqYLPZAAD5+fn44osvsH//fvTu3RvvvvsuJk+eDAAoLS3F//3f/+Hrr7+G0WiEKIrNfvckSYLFYkF9fT3sdnuzx6xWK4xGI5YuXYq6ujo89NBDmDdvHnx9fVFSUoLFixdj+fLl+PLLL3HzzTfjjjvuaJL4lSQJZrO5xd+/kpISfPjhh1i8eDEEQcA999yDhx9+GKGhoQCAgoICbN68GTqdrsn32Ww2qNVqzJ07F1OmTEGfPn0cCc9jx47h3XffRXp6Oj744ANERERgyJAhV/x5EhH9O+3f0Nfrf5ndyYHTRafxfcb3+OuYv0IuONff1u5CFEWYzWbH/xcEATKZrMW/G0RERHTjSb2QivSCdNhEW8ONaQAGuwHbs7ZjRv8ZGBM2pmsDdFJMeF6GJEmora3FN998g6VLl2Lw4MF49NFHERQU1OYd2seMGYM9e/YgNjbWUYFnsViQkpKCJ554Aps2bcK7776Ld95557I9QXft2oVXX30V58+fbzJ+fX09fHx8elwVgV204+NDH8NoNAIuAASgTF+GZSeWYVjIMPhr/XvcNV2OIAgwmUzQaDRYtmwZJkyYcFXXd/bsWeTm5sLDwwOLFi3CsGHDmjweFxeH6dOnw2w2t6m3bGOvyMmTJ2PlypU4evQocnJyEBwc3CQuq9WKU6dOISMjAzExMRg1alSL47d0LYIgQBRF1NfX43/+53/w2muvNeu12biUfefOnXB3d8fHH3+M6dOnOxKOHh4eePHFF1FbW4slS5a0+MHxcs9j42OSJKGiogLz58/H888/D7VaDQCIiopCUFAQampqsHbtWqxfvx7Tpk1rUwW31WrF7t27sWzZMsjlcrz44ot45plnmrSqiI6Oxvjx42EymSCX/5Jo8PDwwPLly1us7o6Pj0dycjKmTZuGjIwMbN26FQMHDuz2m1gRUddKK0zDmsw1DTO7xrdFARAFEf9O+zfmJcxDiEfzm1p0/c6cOYPp06c3ufknCALq6+sd7UqIiIjoxlReX45NWZtQUFfgWIXTKLMqE+vOrkNCUALcXNy6LEZndWNviX0F9fX1+Pbbb/Huu+8iMjISf/3rX5GUlNTmiasgCOjTpw+GDBniSHYCgIuLC+644w589NFHsNls2L17N06fPn3Zsfz9/TF27FhMnjwZU6ZMwZQpUzBp0qRmy3t7ikOFh5ByNqVpyl0ObDi3AUeKjkCCc1ZEzJkzB8OGDbvqDz8WiwU2mw0uLi5NXku/pVKp2jy2UqnEwIEDkZCQgJycHKSnpzckoH+lrKwMe/bsQW1tLZKTk9uc7P+1mJgYzJ49u8WNhWpqanD06FFUVFTglltuwdChQ5sl9gIDAzF16lSEh4df1Xl/a+jQoZg7d64j2dkoPDwcw4cPh5ubG86cOdNitWpLampqsGnTJtTU1GDChAm45557Wu3Lq1arm7QZUCgUTZKddrsdZrMZJpMJJpMJXl5euPvuu1FbW4uzZ8+iurr6Gq6YiG4UZpsZ7+9/H1aztckkGgAgBwqrCvHRwY8aqgqo3Xl4eGDSpEmO+Vnjv8abVazyJCIiujGJoogDlw7gwIUDsEv2pvM0ATDZTNiRswNphWkQJbHL4nRWLBlqRW1tLb799lvMnz8fgYGBeO655zBjxoyr2pn9SqZOnYrQ0FBUVVUhNzcXiYmJrR47cOBA/OMf/2jyNVEUsWnTJjzzzDM9ajJttBrx8cGPG5Z//frplAF6ox4L0hZgdO/R8FQ716ZOcrkcI0aMgFZ79RtHBAYGwsfHB/n5+Xjvvffw2GOPISQkBN7e3nB3d7/m6r+goCCMHTsWhw8fxs6dOzF79myEhYU5qjMvXLiAAwcOwNvbG2PGjLmqJfONwsPDER0d3eJjer0eFy5cAAAkJCS02s82OjoaQUFByMvLu+rzN0pMTGwxIdm46ZNWq4Ver4fJZLriWJIkQa/XIz09HS4uLhgxYgR69+59VfHY7XaUl5fj4sWLKCkpQWVlJUwmEyRJgiRJyM7OhiiKqK6uRl1dHfz9/a9qfCK6cRwqOIRN5za1PquTA1+lf4X7B92PhKCETo3tRhAWFoYvvvii2deXLFmCF154oQsiIiIiou6gwliB7TnbkafLa35TGgBkwJnyM9icvRmDgwbDR3P1n7epdUx4tkCn0+Grr77Cm2++CV9fXzz//POYM2cONBpNu55HpVLBzc0N1dXVbUqy/JYkSY4+iD3J/kv7sS1rW8uvPjmwPXs7tuVtw12xd3V6bB1Jo9HAz8+vydLmtoqNjcXUqVNx4cIFfPvtt9i3bx8GDx6MqKgoDBgwAP3790dsbCw8PT2vqgLT09MTN910E3r16oVDhw4hJycHoaGhkMvlMJlMOHToEPLy8jBmzJg2b4b0a4IgwM3NrVlP0kZGoxHV1dVQKpXw9fVtdXxPT0/HhkjXytPTs9XxFQoF5HK5o1duWxgMBpSUlMDT0xNhYWHNKkcvx263Y8+ePVi+fDkOHTqES5cuwWg0NulBKooNd/jMZnOT3nDUfuqt9bDarXBXuUMmcMEDtb/SulKoFCp4qq7uvflqGCwGLDq2CAaz4bIJz6raKnx66FN8NvUzaJTtO5+hlomi2KNuSBMRdSfFtcXwd/WHQsaUBbW/amM16q316OXRcRsm20U7TpaexLbcbbDZbUBLaQABMNvNSDmXgqn9pmJM2Bin28+kK/Hd4zd0Oh0WLlyIDz/8EFqtFq+88gruuuuua6rKu5KysjKUl5fDzc0N3t7e7T5+d2SwGPCf4/+B3qRv+dUna9i9/YP9H2B8xHj4anxbOKhnUiqVV50wbOTl5YVHHnkEAQEB+Omnn5CRkYENGzZAkiR4e3sjMjIS48ePx8MPP4zo6GjIZG1L3sjlcsTGxmLQoEHYuHEjdu7c6ahCra2txebNmyGTyTB8+HDHrupXQxAEKJXKVpO8kiTBbrdDJpNBLpe3mhCQy+WXfbytsVzpsav5YGoymWCxWODm5gaNRnNVsZ0+fRrPP/88jh8/jl69eiE5ORmhoaFwc3ODQqGAKIo4efKk42fMD8ztzybacLDgIPKq8nBn7J3w1twY78HUeUw2E1aeXglPtSfuir0Lri4t3/i5XgcKDmB37m6I8issg1IAm85uwr6B+3Br5K0dEgsREVF7MFqNeGP3G3gq6SnEB8R3dTjkhFafXY3cqly8Pv71Dkuq6816bM3eipyqnMs3k5QBWRVZ2HB2AwYFDuLnknbU7ROeVqsVOTk5yMvLQ3V1NSwWS6sf/iVJwpAhQzBw4MBrWnqu1+uxaNEifPLJJ3Bzc8OHH36IW2+99bKVnaIooqSkBBkZGfD19UVMTIyjmq2xMsvDw6PZ95lMJnz22WeoqKhATEwMBgwYcNXx9kT7Lu3D3ry9l/9gJgeOXDyClRkr8dSwpzovuG4uNDQU8+bNw6233oqioiJkZWXhxIkTOHjwIE6cOIEzZ86gvLwc77333lUtPQ8LC8NNN92E1NRU/PTTT3jyySeh1WqRmZmJQ4cOITQ0FMOHD4e7u/tVxywIwmUTgUqlEq6urrBarTAYDM12WW9kNBphNBq7VeJPpVJBqVTCarXCbDZDkqQ2Jz0XLVqEI0eOICEhAS+++KJjub1KpYJMJoPNZsN3332HDRs2dPBV3LiqjFVYfWo1jhQewYCAARgVOoobi1C7OldxDutOrYNRMmJM2BhEeke2+2uszlyHFadXoKSupOELl3uLFBqa5n+V/hWSQpKcrm0MERE5jz0X9+C7w9/BReGCDyd+2OZiDqK20Jv0+OzQZzDWGzG7/2wk9Upq93OIkoicqhysObvml+rOy8zTbJIN32d8j9mxszGy90iuPmsn3TrheeHCBbz99tvYtWsX9Ho9bDabY5lna/785z8jOjr6qhOeZrMZy5cvxyeffILS0lI88cQTUKvV2LVrV5PjJElCv3790KdPH8cS2D179uAvf/kLkpOT8fe//x39+vUDAJSWluL//b//hxEjRiApKcmxacv58+exbNky/PDDD/D09MTcuXOve0OWnsBgMeD7jO9RYii5/IECYIMNnx/6HDP7zezQMvOeRqvVol+/foiOjsbIkSMdy6oXLVqExYsXY9WqVXj44YcxZsyYNo+pVqtx0003oU+fPjh16hQOHz6MGTNmICUlBTqdDqNGjcKQIUM6ZKLh5uaG4OBgiKKI/Px8GAyGFqupi4qKut3GPW5ubggMDER2djYKCwthsVjatKzdaDRi586dcHV1xaRJkzBz5sxm71cmkwlFRUUdFfoNT5REnKs8hw05G1CsK8aW7C2I84+Dl7rlHrJEV8tsM2PX+V04WnYU9eZ6pJxLwR9u+gMUQvtOuw4VHsKB8wdggaVNyVQ77Nh/fj925O/AHQPuaNdYiIiI2oPJZsKX6V+izlaHH07+gAfiH8CwXsO6OixyIqvPrsapglNwUbpg6amlGBQ0CCp56xsDXwuL3YI1mWuQX5UPQdGGG95yoEBXgCXHl2BYyDCoFO0bz42q2yY8S0pKcPvtt+PMmTOOJa8qleqyiUy73X7FhGhr9Ho9Dh48iIsXLwIAFixYgC+//LLFirJXX30Vf/nLX6DRaCBJEgwGA4qKilBZWdmkp6YoitixYwfWrl3rWJIrSRJEUYTVaoWPjw9ef/11/P73v7/mTWd6kv0F+7E/bz/MdnNDSfcVKlFyS3Lx9Ymv8fLNL3dWiD2GIAhQqVRQqVTw9vbGnXfeiT179uDIkSNXnSgTBAGDBw9GTEwMMjMzsXr1akyaNAlr166Fh4cHRo4c2WEJeW9vb8TGxkKlUiE1NRVPPvkk/Pz8mnxwt9lsSEtLw/nz5zskhmvR2Jt08ODBOH36NA4fPoyioiJERkZe8XtNJhOqqqqgVCoRHBzc4nuaxWLB9u3bOyJ0QsPNlzUZa3Cx+iIgAD9k/ICZ/WciMTiRVZ7ULvJr8rE5azN0Jh0gAJ+lfYb7B98PP61fu55nYMBALL97OWySreVG+L8lATJBBn9XboJGRETd067zu7Avbx8kpYSyujJ8mf4lEoIT2MuT2kWduQ4fH/wYdsEOo2jEz1k/42D/g0juk9yu51HKlJiXMK9hX5K2fryQAJVCBaX82trgUXPd8l1DkiR8/PHHOHXqFDw9PXHPPfdg7ty5iIqKciQHWlva6urqek39NrVaLWbMmIHg4OAr9swbPXq0I0Epl8uRkJCAl19+GX379m2yk3KvXr3www8/YP/+/cjOzkZFRQUkSUJAQAASEhJwxx13XPXOzj2V0WbE/ov7UWevg7+bf5t+6QVJwM95P+OBgQ8g3Mv5K2BbI4oiampqIIoi3Nzc4OLi4qi2bEyg6/V6GI1GyOXya1p6HhAQgJtvvhk7d+5EamoqFi5ciNzcXPTv3x9jxoy5po2W2kKj0WDEiBGIj4/H0aNH8cEHH+Dtt9+Gt7c3BEGA3W7H7t27sWzZMuj1+g6J4Vp5e3tj0qRJWL9+PbZu3YqxY8fioYcecmwc1fg+YrPZYLfb4eLiArlcDq1WCz8/P5w7dw6ZmZkwGo1Qq9WO7zGZTFiwYAEOHz7c1ZfotC7pLmFFxoqG9yEZkFmeiTWZaxDjFwM3l+vbHIvIYrcgrTANey7tabi5JwNySnOw4MgCvHTzS+26RCnALQABbgHtNh4REVFXqrfWY8XpFag0VgIArIIV27O3Y++FvUjuk8wb03RdJEnCD5k/IKsoqyETJgFZNVlYe3YthoYMbdfPAXKZHP38+rXbeHRtumXCs76+Hjt37oQgCHjsscfw9ttvd3gFpKurK2bPno3Zs2df1ffJ5XIkJiYiMTGx2WNKpRLjx4/H+PHjATQkrq7U09BZKWVKPJX0FH4/5PcQ2nyLo6GSzkfT9n6UXaWtvSWvpQelIAhISUnBihUrkJSUhJtvvhkhISFQKBSw2+3Izc3FggULkJOTg2HDhiEuLu6qzwEAycnJ+Oqrr5CRkYH58+dDLpdjwIABLb62f6ul62pM+F3pmhMTE3HfffehsLAQX3/9NfLy8jB58mR4eHggKysLW7duhYuLCyIjI5Gfn9/s9+dy47fnz+W3x7i4uGDcuHG499578d133+GNN95AYWEhZs+e7eihWlVVhQMHDkCn0+Ghhx5CZGQkVCoVpk2bhoyMDKxfvx69evXCtGnToNVqodPpsHr1anzyyScIDQ1FQUFBm+KnthMlEf85/h8U1xQDv7p5+t2J73B33N0YHDS464KjHk+SJBTXFuOH0z+gzlT3yyxLDnx28DPMjZ+LSJ8rV4ITERHdaCRJwr6L+xpatUgWR4FMUW0RVpxegcTgRHiom++NQdRWFfUV+Pb4tzDB1PAFoaEN0c/ZP2Nq9FRMiJzA3plOplsmPE0mE8rLyyGTyTBr1iynWe59IzdbVsgUTr2ErnGXcZlM1mJC+0q7kDcSBMExzq8pFArk5ORg27ZtUKvVCAkJgYeHB0wmEwoKCmCxWBAfH4/nnnvO0Sv2ag0YMADDhg1DTk4O6urq4OXlhYkTJ8LTs/WNLVqLt/GaFQrFFV/3Wq0WDzzwAMxmM1atWoXjx49j7969UCgU8PX1xfDhw3HXXXfhu+++Q15eXrM+mYIgNHn+2/pYS8e1dEzjNbZU5RoaGoo//elPsNvt2Lx5Mz744AMsWbIEvXr1akh8FBdDr9djypQpTdpdPPLIIzh69Cj27duHt956C0uWLIG7uztKSkpgMpkwffp0DBo0CPPnz78hb5B0pNzqXCw9sbTpToky4EL1BXx17Ct8OOlDyGUdU9FMzs8u2ZFWlIad53c2fY3JgVJdKT49/CnemfgOXORXv7EiERGRM9OZdUjJSsFFfUPLocaEp0k0YVf+Luy5tAdT+k5hQoquiSiJ+Cn7J5wuPg1J9qtCFhmQWZWJzdmbMSR4SLu3H6Ku1S0ziUqlEu7u7o4+hUTdmUwmw5AhQ/Dkk09CpVLB19e3yeMhISGYO3cuampq0KdPn8smsJKSkvDUU081WbYOACNHjsTzzz+P48eP49KlS6ipqYHVaoVWq0VUVBTi4uIwadIkJCUlXfWGXY3UajXmzZuHwMBAWCwWeHh4YPLkya0e31jhqFAoMGLEiCatJARBwLhx46BUKjFkyBBoNJrLnjswMBBPP/00RowYgfT0dBQXF0OlUiE6OhqjRo2CKIpYuHAhJElqltB1c3PDlClTEBwcjJEjRza5QaLVanHrrbfCzc0No0ePbvX9JC4uDo888ggUCkWTlgCCICAuLg5PPPEE/P394eXVdFMbQRAQHx+P+fPnY9SoUdi7dy8uXryI+vp6KBQKJCUlITIyEhMnTkRwcLDj+/r06YP3338fK1euxLFjx1BTUwO5XI6RI0di1KhRmDNnDkpLS1FQUIDY2Nhm56VrYxft+Dzt82bVnY2T6hUnV2BewjwMCR7SVSFSD1drrsW3J76F3qRvPsNSAD+c+gH3xN+DEaEjuiQ+IiKi7kiSJBwrPobd+bthtBub3jQUGnpjbzy7EcOChyHQLbDL4qSeq7i2GOsz16PKXNX0AQGw2q34KesnTOk3BeP7jGfxgxPplglPV1dXjB49GqdOncKpU6cwdOjQrg6JqFUymQyjRo3C8OHDAaBZwjE8PBx//OMfIUkSlErlZROeycnJGD16NAA4EneCICAiIgIPP/ww6urqUFFRAb1eD5vNBoVCAU9PTwQGBkKr1V53NeCYMWMwfPhwSJJ0xRsOarUa06dPx+TJk6FQKJokGgVBcDwml8uhVF658bK7uztuueUWjB07FjabDYIgQKlUQiaTYfXq1SgoKIBGo0F8fHyT7/Pw8MA999yDu+66q1kc7u7umDVrFqZPn97ssV8bMmQIBg4cCABNrlkQBCQmJmLQoEGOeFoSFhaGefPmYfr06SgtLYXJZHIkTwMCAuDu7t4kgS2TyRAfH4+IiAgUFhaitrYWMpkMvr6+6NWrF+RyOQIDA/HPf/4TCoXiigljapvM8kwsPb4UkKN5H2E5UFZbhnf2vYNvZn/DCjy6Jvsu7sPPOT83/aDWSAaU1pVicfpiDAwYCFcX106Pj4iIqDvSmXXYkbcD5yrPNZ+jCYDZbsb2vO2YEjMFU/tOZUKKroooidh1YReOFB5p2Ojxt/M0GZBdmY31Z9djSNAQ+Gp9WxyHep5umfCUy+V48sknkZKSgsWLF2P06NGIjo7u6rCIWiQIwmWTaTKZrM2VypcbRy6Xw9PT87JLzK+XTCZrtmS8NY0JwJaSgJd7rCUVFRUQBAFeXl5NngNJkpCZmYlly5ahoKAAI0aMQFRUVLNztVbV2tY4Wnver/Sz/bXGJGVgYNvuOjfu9B4TE9Pi4y4uLtdcrUvNiaKIxccXo0xf1rS689cUQMqZFKQmpGJS30mdGh/1fFa7Ff+X9n+oM9e1+hqzClak5qRi78W9fI0RERH9V1ZlFrbmbIXRamz5pqEA5Ff/UuUZ7B7cwkFELSupK8GW7C0oMhS1vHmy0NCWaHXmatwZeyduDr+ZrROcRJf/FEVRRH19PQwGQ5N/kZGR+OSTT1BYWIjHH38cq1atQmFhIerq6pod2/ivrq4OFovlmjaGIaKus2XLFjz00EN49dVX8f333yM1NRU//fQT3nvvPTz22GPYuHEj1Go1Hn/8cS7vpmtyuvw01mWsa7jN11ohtAAYLAZ8cugT1FvrOywWo9WII0VHUG/puHO0pspYhUMFhzr9vABwsuQk8qvzu+TcneHn/J8bqjuvUHRyqfYSVpxeAZ1J12GxlBnKcKb8DEw2U4edo7vhBxMiop6pzlKH1LxUnCg90ZCdaCUhZZNs2JC1ASdKT8Au2jssnpOlJ1FUW9TpOQWzzYyTJSdRbazu1POKkogLNReQV50HCc6XR5EkCUeLjmLvhb2wibbLfg4oqi7CkuNLOnz+tP/SfpQbyjv0HN1JV27c3eUVnjk5Obj77rthMBiafL2xakuv1yM1NRV79+6Fu7s7PDw8Wtw8BGhInv7xj3/Eo48+2qQPHxF1b/X19Thy5Ag2bdrkeENs3OFdFEUEBgbilVdewe233+40m5hR51p6cilK9CWtT3IaKYCDeQeRci4Fc+LntHsckiQhtyoXf/rpT3hjwhuYEDGh3c9xOWsz1+LzI59j6++2wlvj3annfjn1ZQS6BmLRjEWdet7OYBNteHffu7DYLFecWVlEC/bl78POCzsxM2Zmu8diF+3YeX4nVpxegfnJ8zE4aHC7n6M7yq3MBQBu9EZE1MMU1RZhVcYqWKyWy980FIAiXRGWn1yOYSHDOmRzGZPNhLd3v42+Pn3x+i2vQ7jixLH95Fbl4qUdL+H3ib/HjJgZUMg65zOP3qzH4vTFMFgMeDn5ZfhofDrlvJ2lor4CW7K3IK8m7/KfAwRAkktYfmo5Hhv2WIf1WzdajXgy5Uk8MOgBPDf6uQ45R3dTY6xBrbkWKqHz9+fp8syB1WpFUVFRs4TnrzX2rzMajTAajZcdT6fTQRTFdo2RiDrW7bffDq1Wi3379uHcuXOoqKiAXC5HcHAwkpKSMHPmTMTHx7d5iTzRrx0vPo4t57bALJobqsAkQBKkJnexZdIv1WF6ix6Ljy3GuIhxCHANaNdYREnENye+wbELx7Aucx1u6nUT3Fzc2vUcrakz1+Ffh/6FSyUNFYaPD3u806ri9l/aj925uyHIBDw9/GmnSsJJkLA5ZzMO5B2AIBcACbhsgYQE5NXkYW3mWozsNRIBbu33GpMkCaWGUqScScH2c9txS59bEOMXA7Wiba1KeiqraMWnhz8F0D0Tnt0xJiKi7sBit2BLzhakF6U75miQAFEQG5JTEiBAgCD98j7645kfcd/A+3Bb39vadR4jSRL2XNiDYxePYWfeTjw69FGEe4W32/iXYxNt2HVxFw7lHYIgCRjde3SnbM7UeCN+fcZ6WGwWTIuZhnF9xjnNqglREnG6/DQ2ZW2CIAoNFcRXKGI1G814c9ebWH3v6nbv6S9JElKyUpBVlIWF1oW4f+D9CPEIaddzdDeiJOJQ4SFklGdgaMDQTr2JAHSDhKe/vz+effZZWCyW6x5LkiSMHTu2zT0Iiah7CAoKwv3334/7778fNpsNdnvDMhWFQtFqRTdRW13QXUBf374I8Ahw3NnNqsjChZoLgAC4Kl0xKGjQL4lHCfBT+yG3KrfdE54XdBfw3cnvYIIJO/N24sClA5gQOaHDJ5aSJOGHzB9wsvAkZHIZVmWswuS+kxHhHdGh5/3/7J13eBTnubfv2V1Jqy7UKyogkBDNAmwHjG2MG27ELTZ2cnKcz4lLTsq5HMfOSfIl5yTxObbzOSfuNdgOGIMxpogigakCIdRAvfeuVdtdbd+Z74/1LhISIOtkFXMAACAASURBVMMKyfLc16ULsTszzzuzo32f+b1PAcdK9vqi9RgsBiRJ4o28N/j77X/H22P6NOOq6KngO/HfQVKOLxVMEiUUkoJ2fbtbBU9REinpKmFf/T50Fh1flH/BjYk3khaWNq1Ft6OtR9lXsw8/rsziwdfFYrFgsVgu2rhQRkZG5tuG1W6lTdvG6qTVCEoBhaBAY9RQ0lmCxW5BQCAmMIZ5ofNcC9WiTaRCU8HNs252q/+ks+jYVb2LlqEWjGYjLx9/mb/d/jc8lBMbcCEh0TDQQGZ1Jhqrhv11+znSdITvpbk/0+hcDFYDWXVZlGhKkJDYVbWLRRGLpk3THovdQqeuk/jgeOJDxileS44mWS2DLcwKnnXx7b8GA6YB1hetx6Kw0KJp4YPTH/D8iucn/B6bTLoN3WTVZdE80MyS8CvfjHzSBc/w8HCef/75yR6GjIzMFGG8TYJkZMbLbbNvczjSX6mdfcY+/vWLf6WprwkU4Kn05KmlT41KH3J3KpFdtPNm3pt0DHSACqr7qtlTvYeroq6akLSs4WgMGt7NfxcER9REaUcpO6p28NTSp/BSTWx6yam2UxysO4hNYQMJMqszOZF2gtVJVzadfyJ5cumT/Dj9x1+r9pUgCPh4+Lh1HHqLnk0lm+jR94AKclpzOFh/kFkzZk0rgXk4eouet/Lewm6duHpul4qzRMsHH3xAeXk5sbGxhIaGEhISQnBwML6+vqhUKlkElZGR+dbi4+HD72743Yiorzfy3qCmqwYLFgRJYHbQbF5b8xoxATEj9lUK7guKcNZ5PNZwDKPdCCrYcGYDP1j0A66JvcZtdsbCardysuUkOS05jo70VjOv5b7GrbNuJUg9cb0LJEmiRdvC5pLNjgxZwVH6aO3ctayMX4lS8c0POvFUenLP3Hu4ffbtX2s/CQlfD1+3jkWSJLLqsyhsLUQURMyY+bzkc9bOXcvCiIVutTVVsIt2CjsKOdxwGNE+OVnYU1ZVMBgM2Gw2vL29L5rGKkkSVqsVs9mMh4cHXl5esvMoIyMjIwMwKp13T80eTrWccqS1KKB/qJ/Pyj7jllm3EOkXOWHjqOqtYsPpDa6C/Ga7mcyaTNbMWcPqxNUT6lhmVGdQ0lHimPUl0Jg17Knaw81JNzM/fP6E2dWb9Xxe/jmduk7HCwJ06br4pOQT0qPSr3gd0YlAQMDfa2rUDS/uKmZ31W7XPWa0GNlQvIHbZ99OckjytPSNjjUd41j9MZiCwREKhSPyaP369WzatImIiAgiIyOJiYlh5syZJCQkEBsb63otNDQUb29vObNBRkbmW4MgCCNK+9T11bG/Zj96mx5wLNKWdZVxoOEAP1nykwmrazloHuRA/QGqe6sd2UACDBoGeS33NeaHz8fX073i13A69Z1kVGbQPdTtmL9VcLLpJJ+Vf8aP0388YXYlJDKqMijtKnXVTm3sb2RT6SaWRC+ZMr7N5aAQFPh6+uLLxH1+46XH0MMX5V/QZ+5zvCBAfV89m0s3kxycPC0XpvtN/WTWZDqalk5SlYQpWZxBFEXee+89fvrTn1JQUHDR7e12OydOnOCZZ54hIyMDk+nb05VURkZGRmb8GKwG3s5/G71Ff3YGVMDBuoMcbDg4YXbtop1/FP2DLm3X2YL8AtT01bC7cjd9xr4Js92h62BL6RYMNoPLrjOSYX/d/gntSF/YWcixhmMY7GdtmCUzx+qPcaz52ITZ/TYiiiLvFbyHZkhz9t5Wwun202yv3I4oTb/65lqzlk0lmxwdbae4lmsymWhqaiI3N5dt27bx6quv8oc//IFnn32WX/7yl/z0pz/lmWee4a9//SsZGRloNJrJHrKMjIzMFcUm2sisy6SkswQR0fW93mfqY2f5Tmr7aifEriRJVGmqOFB7wOErOecTJRyoOcDhpsMTYhcc0Z0F7QUcbDzoEloRHNfileOv0KZtmzDb3UPdrD+9HrtkH2H7i/IvKOwonDC730YkJLKbs8lrycMqWV33mN6uJ6smi6LOoskd4AQgSiKVmkp2V+923GOTxJQUPCVJ4siRI2zatImmpqaLbi+KIjU1NWzZsoXi4mK31AOVkZGRkZl+7Kvdx/GG4yO7gCpgyDLEa7mvMWQ5fwO9y6Gsu4wvyr4Yafcrh/aLyi8o7S7FLk6MM5BVl8WZ9jOOBgDDbPcZ+8iozJiwBwi9Rc++2n3U9NeMcKQRoEnbxI6KHXTpuybE9reR/PZ8tldsH+nZCY6GPu8UvEO7rn3SxjZRHG8+Tk5TDlbBOtlDGRNJcpQ4CAgIGBW1KYoig4ODNDQ0kJ+fz549e/jHP/7B//zP//D+++/T0dEx6lgWi4WBgQFsNtsVOwcZGRmZK0XjQCNZ1Vn0mHrOviiAHTv5Hflk1mZisbv/OV9v0XOw/iDF3cUjF88U0Gvq5cOiDx0LaxOAzqJjS+kWNHrNSNtKqOqq4uMzH0+IXYCNxRup6qwa5Tf06Hp47dRr03KhdLLoGephT/UeWnQtZ18UHEJohaaCXVW70Jl1kzfACcBoNbK9YjsNfZMX3QlTVPAEh2PndBRlZGRkZGQuF6PVyEvHX8JsM4+OBlNCflM+G0s2ut2uKIl8UvoJHYMdo2ddBbQOtLKxeCNDVveLrR26DnZV7qLb2D3yja+Ex5zWHA7VH5qQKM/S7lIO1R4a87wsooXDDYc52XbS7Xa/rfwt92/oDLox77GGngZez3t9UsY1UejMOnZW7aRF2zJloztFUUSSJN588002b97Mf/3Xf/Hggw+SkpKCv7//qBIDNpuNgYEBBEEgMDBwxHuSJHHixAnWrFnDD37wA/70pz+xc+dO6urqGBqamIUaGRkZmSuFXbRzvPk4uW25DqHtnO/1PmMfeyr3UNVb5XbbrbpWvqj8ApPVNMquDRu5jblk1WW53S7Amc4z7K45W4rGhQCSQuLjoo8dafZupmfIIWrasY+yixIyKjL4sv5Lt9v9tlLQXkB2YzZWcfQC7ZB1iP01+zndeXoSRjZxNA028Wnpp45o7UlkStbwVCgULidwPPWmJEnCbDZjNptRqVSumkkyMjIyMjJO9tTs4VTDqbFr/QlgE2z8+eif+V7a99xaJL68p5y9VXsxSsbRwsxXDu2npZ/yo6t+xPKZy91mFxz1DfNb88+mK51j22gxsqlkE2vmrGFOyBy32TVajRxpPEJRd9HZyM5hdgHq++vZWbGTq6OvJso/ym22v42cajvF9rLt5723JaXEG7lv8OP0HzM7ePYVH99EcKrtFMfrj2MWzSMjp6cgKSkppKenc99997l81ubmZsrKyigqKiInJ4fKykq6u7sRRZGIiAhCQkZ2yJUkiYqKCk6ePMnJk46FAkEQ8PLyIjk5mbS0NJYsWcI111xDcnIyISEhF62BLyMjIzNVaNO2kVmTSedQ59i+kiRxvO04RxqPMCd4jtsaLpptZg7WH6SgreC8oWBthjY+K/uMVYmrCPcNd4tdJ2/nv41uSDf2/K2A5r5mPjrzEX+56S9utfvRmY9o6mqCsS6jAGaLmT8e+SM3Jt6Ih0KeSy6HXmMvWXVZVPdXn9cnPt11mn01+1gUsYgAdcBkDNPtfHT6I9r62hz32CQm4kwJwVMURfR6PQaDAUEQXM4ggFarpatr7JQ3SZIQRZG6ujoyMzMRBIGwsLBvnYPn7AIqIyMjIzM2OrOOt/LfQhKk80eDKaGlp4U3897kuRXPuaWJkDNlvam/6fx2FTBkHOL/5fw/lsYsxVPpedl2wVEEf1/NPlr0LaMdLHCt4ue15bG7ejfxy+Ld8gAhSRK1fbXsrtyN2WIe29P4aiyZNZl8N/W73JF8x7ToBnouJpsJhaDAQ+ExYfO0xW7hrfy3HOV8zuf+KGBoaIiXj7/M63e8jodyYvwkSZKwilbson1Ci+/rzDrHw8NA9ZSN7hyO3W4fsZDv7e3N3LlzmTt3Lvfddx8Ag4ODVFdXU1dXR0JCAr6+vqOOUVQ0ssaXJEmYTCZKSkooKSnh008/RaFQEBwcTHJyMqmpqXznO99h3bp1o47nzKKS/UcZGZnJZngnZyTGFh4VMGQe4vPSz7kx/kbSwtPc8v2lMWj4qOgjRwfp8ywaiqJIXnMeWXVZrJu/zm3+Sl57Hjsqdzj8pLFORQCDaCCjIoP7Uu8jPSp9RDf7S6Vd2876ovWO8z2PXTwgtyGXz8o+Y938ddNyrrDYLdhEGz4ePhNmQ5REyrrLyKzORBKlsRdoBcccv7NyJ3fOvZNrY69FIUxMAJ/J5uh146Wc2CbfzYPNfFj04ZRYkJ4SgqdWq+X9999n8+bNLsGzrq4OSZJ48cUX+eCDD7Dbx65tZrPZ6OzspLOzk4ULF5KWloaXl3tWfL4pmEwmamtrKS0txdPTEy8vL7y8vFCr1Xh4eKBSqVyRr7I4KiMj821DkiT21uylqKXo4rOeCj4+/TH3p97P3NC5l227vKecAzUHGLQOXliYUUJGZQaHGg5x2+zbLtuuXbST15bHsaZjiKJ4fodDANEu8kHhB6ydu5ak4KTLtm2ymchuyia/I//ChXMUjoiOz0o/Y2n0UiL9IqfV/GS1W9lZtZMgdRA3xN/gtmiUc8ltzeXL2i8RVRdJGfKEHeU7+MGiH3DdzOsmZCw20UZ2czZt2jYemv+Q28T74YiSSHFXMQdrDzrKU0wBZ9odBAYGsmzZMpYtWzbm+85F/aVLlzI4OIhWq0Wv12MymUb4yKIootFo0Gg05OTkcOrUKdauXTtK8Ozr66OrqwtPT08CAwPx9fXFy8tL7hAvIyNzRZGQ0Bg07KzcSYd+jNI/w1HAydaTHGw4SFJwkluEqj01eyhoL7iwfyhAy1ALGVUZrIxfSXxg/GXbNdvMvHj8RcwmM1xoqvyqk/fW8q2khqZe9jnbRTufln1Kk6bpwvPnV7VT3857m9WJq4nwi7gsu1ORL+u/pHmwmcfTH5+wRXetWUtmTaajFMNF7u3ynnIyqjJIC0sjUB14gY0vDUmS2F29G5to4/5596MSJkYKtIt2Xs97ne7B7gvf21eIKSF42mw2enp6qK2tdT3s6PV6ADo6Oujt7T3vvgqFAl9fX1asWMEjjzzCwoULp9UD08UQBIGenh5eeeUVNmzYQGBgICEhIQQHBxMcHExQUBBBQUHMmDGDgIAAAgICUKvVo2pDycjIyExXegw9fFryKVqT1uFsXKg8tADNmmbWF63nDzf+4bKi1IxWI1m1WZR2loLIRW1bzBZeOPoCS6OXEuITcv4NL4Lz4WFfzT5HoXDgguVzBCjvKOfD0x/yhxv/cFlOnyRJtOna2FSyCaPJ6PAyLqLD7a3ay73z7uXuuXdPmPM1GTQNNvHPwn+CAlJCU4gLiHO7f6Iz6/ik5BN6tD2Oe+sih+/T9/Fe/nvMD5/v1rINTrRmLe/mvUuXrov0qHTSwtPcbkNn1nGw/iClXaWOF0Qc5z3NeyuoVCqef/55fvjDH9LS0kJdXR2NjY00NDTQ1dWFRqOhp6cHrVaL0Wh0RXCmpKTg7T3ye0ySJA4cOMCbb76JWq0mOTmZ+Ph44uPjiYiIICwsjNDQUPz9/fHy8pJLRcnIyEwYouhYxNpXs88xjzl/zoPJYuLj0x+zOmk188LmXda8qjFoePPUm4g20eGvXMCuJEocqz/G0cajfC/te5e9iHm8+Ti7y3Y7RMeLzF96i56sqixun3U7K+NXXlb0X8NAA1+UfuHoRn+xwwhQ3FrM5rLNPLX0qQnLDpkMDFYDf8/9O50DnaxKXOXWsk5OREmkureaz8s/d0QQK7ngPSbaRTaXbOauOXdxbZz7ozw1Bg0bzmygcbCR62ZeR0xAjFuP76RCU8E/C/551jcTuPCz1wQzJZ4s/Pz8uOeee0hMTHR9ab3++uuUlZXx8MMPn3e1G0CpVBIQEMDs2bOZO3fuqBXsbwN2u522tjba2tpGvedMnfL19cXHx4fo6Gh+8pOfcO211455LIPBgNlsRqlUun4UCoXrR44QlZGR+aaR355Pc18zwX7B49tBgpMtJ6nurWZR5KJLttuibaGgrQClUkm43/hqPlV1V3G48TD3z7v/ku1KkkR9fz2F7YWEeIeMy6HFDjsrd/L4kseZGTjzkm2LkkhheyH1ffWE+4ePL93YDhlVGaxOWk2A1/SoW2S1W8luzqawvRCdRcfRxqOsW7AOpeDeCILynnLKOssI8A4gQBjHtZOgrLOMwo5Cbkq8ya1jAUdR/qzaLARJYG/tXhJnJLo1VUySJNp17ZxsPkmAVwABCsc5CyoBm7cNG9O3e7kgCPj7++Pv78+cOXNYvXo1NpuN/v5+NBoNHR0dtLS00NLSQlNTE+3t7bS0tLBs2TJUqpHuvtVqpby8nPz8fAwGA1lZWSiVSkJCQggLCyMmJoaYmBji4+OJi4sjOjqamTNnEh4eTlBQ0KjjycjIyFwqZruZ/bX7sdvt4/aVWgZbONJ0hJTQlMuaV/fX7adtoI3wgPHZtYpWshuzuSnxpssSi8w2M1srthKgCiDAY3xzd6+hlxPNJ0iPSsffy/+S7IqiyLGmY/TqewnzCxvXPgpJwaH6Q6ydu5b4oMuPbJ0qHG48zNG6o3goPPis/DOeXf6s27NSbKKN7KZs+gx9Dp94HOhMOvbV7nNriStw+E9Hm49ypv0MjQONbCzZyK9X/Nptx3ciiqKjTIONs+f8VS15rVLrGMsVVj+nhMeiVqtZsWIFK1asABwfSHFxMUajkXvvvZc777xzkkf4zUWSJAwGAwaDowOv0WhEp9M5UhzHYGBggJ6eHhQKhUvsdAqfw1Pjnb87X/f09JTToGRkZKYkycHJ/Nct/4VNGr8Y4qXwItQn9LLsBnsH85OlP2HdwnXjrjMoiRKzQmZdll2AmYEz+e2Nv3V0Oh0nkihddsSCIAikhafxxl1vICjGe9Lg4+GDp2IK5L24iTZdG1k1WXQaOhFFkXcL3mVN8prLitwdi2j/aH57w28djXvGiUpQuSUd71zsop33C9+n39gPCkfk7s1JN7M4crFb7YT5hvHz5T93pLN/dYsJCoG2+jaef+95V2TjtwGVSkVYWBhhYWGkpqa6auAPDg7S399Pd3c3M2fOxNNz5N/WwMAATU1NGI1G12t2u53u7m66u7spKysDwMfHh4CAAIKDgwkLCyM5OZnnnnuO2bOnR+MrGRmZycdD4cHdKXezIn7FuINqJFEiJjDmsoNwUsNSee+7743bX5EkiUCvwEsWHJ0IgsD35n2P22fdPv461JJjzr/c1Otl0ct4+Y6XHd3Zx4mvyhdfz+kTVKa36Hkj7w2MViMmlYk9lXtYM3sN6VHpbrWjEBSsjF/JrLWzvtY9FuIT4vYF8m5DN3uq99Cmb0NC4s1Tb/LAvAdImnH5pazO5abEm1gQvmDEvW3QG3jj5BtXXOyEKSJ4jsWzzz7L448/TmJi4mQPZUojSRJBQUHccssthIeH09HR4UptctZ3GhoacjQzwKG6i6I45gQhiiIWi8W17XCGF9sHRkR8qtVqYmJixiywr9VqXaLo8IhROVJ0bA4dOsShQ4fw9PTk+eefl6MoLhFJkujv7+ejjz5Cr9dz5513kp7u3knsUmhvb+ftt99GFEUefvhh5s+fP9lD+lYwO3i2ozO1wLiKvUtIbkm9CPEO4cbEG8dtF3CLWCMgEO0fTbR/9Nf6nnWX7Xlh8752mtl0Eqlsos3RfKHxMCKOFKbcply2VmzliSVPuNVWbEAssQGxV/zeHouc1hz2VO9xRRTntuVysP4gycHJbn1QCvEO4dZZt466v2qoAabXvfR1cfpkarWaiIgIUlJSxtxOrVazdu1aAgMDKS0tpaGhgY6ODkwm04jtnAvmnZ2dAHR1dfHss8+OOl5bWxs7duxgwYIFJCQkEBYWhlqtdv8JysjITDtUCpWjrvTX8ZXcNJctiljEoshFV9yuh8KDGxJuuPI+2leL0l+n4dN0nFMPNhwkpz4HVI7PtLS7lIyqDFJDU93acFEpKFkavRRirvy9fS4F7QUcbzyORbSAEpo0Tbyd/zYv3fKSW+0IgsC1sde6fncyODjIdv/tY+pME82UVFMEQSAhIYGEhITJHso3gqCgIO6//35uu+02bDYbdrsdu92OwWCgt7eX3t5eV3F6rVbLzJkzx4zGFEURq9U6ShB1NpKCs196wyNEVSrVmF+aZrOZhoYGJElyRYoOjwr18PBw/Xh6erre8/Dw+EYJona7nf3797Nt2zbUajW//vWviYm5tFXHnJwc3nzzTQRB4Nlnn5UFz8tgcHCQ9evXo9FoSEhImBKCZ3d3N2+++SaiKJKeni4LnleIr/u3KDi87itu91L3cdcxvum2pwoag4adFTvp1HU6xD8BLKKFvx7/Kw/Oe5Bg73GWVhgHk3Vvn4skSbx0/CWGjEOuLrdD5iE+K/uM25JvIy3MPbU8L3S+0/GhbKLw9/fnzjvv5JZbbsFsNmMwGKitraW6upqioiKKi4upr69Ho9G4GiIJgkBUVBQxMaPTOE+ePMlzzz2Hl5eXq8xUSkoKS5YsYcGCBcycOZPQ0MuLmJeRkZmeXJKvNEl+2rfRP5xO/hnAkGWI9UXrGbQMumpqai1a9lXv49bZt7rEOncwmff2cDQGDZk1mdT21zqOLQAq2FC0gUcXPHpZ5bvOZSreL5OupkiShNVqdZuj6owinIoXe6KQJAmVSoWfn9+o984VjSVJor6+fszro1AoCAsLw8fHB4vFgtVqxWazuURUm802SgyVJAlBEMYUUC0Wi8tRFkURm82G2WweIZ6eK6x6eHiQkJAwqqmSKIqYTCbXuTpT6y/GlbgPRFGkuLiYjRs34u3tzeOPPz7mA8F4sNvtmM3mb9X9O1FIkuSKWB7ewXayMZvN2O12+TOWkZmGiJJIpaaSndU7kQTprNOqhNrOWt4peIfnVzw/7f7+s5uzyajMcIidw845tzWXzNpMkoKSUKvU0+68v6kIgoCnpyeenp74+fkREhJCXFwcq1atAhy+SE9PD9XV1Zw+fZqcnBwqKipYunTpqGweSZLIz89Hr9ej1+vp7e2loaGB/fv3u7aJi4sjOTmZJUuWcO2115KamkpcXBy+vr7yPSEjIyMjc0WQJIlDjYfIa8pDVHwVvPXVFFTYVcj++v0sCF8wrdL3JUmioqeCrNos7KLdIfICKECj1/BO/ju8dsdrE9alfiow6YJnXV0djz76qKvG5OUgSRJPPPEEjz322Jjin8xZEXKsyEGFQuHq6u5EkqQRUaNOEdRqtWKxWLDZbKjV6jGPZ7ONrJcnCI6fs09DI51cSZKQJDtK5Wgh02Qy0dTUxNDQ0AUjRT08PEbUGz2fAO4UXd3laDsjVNVq9WV1M124cCEPPvig3BXVDfj5+bF27VoGBwflemMyMjJXhCHLEBvObKBf3++KdHShgvfy3+OheQ+RFOz+mkmThdlm5s28N5FsEgwvFSmAZJdYX7iee+bcw6zgy69NK3NlUCqVREZGEhkZyfXXX8/Pf/5zjEYjVqt1zO0rKirw9fXFarWOGcTgbKZ08OBBwDE/X3/99bz44osjMh0kSXL5msMzfmRkZGRkZC4XnUXH1rKt9Jh6Rr4hgNlqZlf5Lm5NupVlMcvc3iF9shg0D7Kvdh9VfVWjmphaBStZNVkcbznOypkrp+18O+mCp9VqpaOjg6GhoVHvKRQKdDodZrOjGL+npydqtRqlUokoipjNZle9IQ8PD/z8/NBqtedtyCPz9XFGXXp4nPvk5uBCwqG3tzdRUVEusbSnx0ZTk4jZPPbnI0kSHh4qhoY88fBwiKOhoZCUdLb2qCiKrqjg843XmT4vCAIhISFjRlsajUYsFosrHX94TdLhNUavdGr92rVrWbt27RWzN10RBIHw8HBefPHFyR6KjIzMt4jynnK2V2w/u4I+HCW097fzfuH7/PHGP+Kpmh5Nmo41HeNw/eHRAi+AEko7S9lUuonfXv/bcdewmm5MhwVMb29vvL3Hrm329NNPk56eTlNTEw0NDWg0GgYHB9HpdCPqyDvR6/XnDXRoaGhgz549zJgxg4SEBIKDgwkKCsLf3x9vb+9RDZhkZGRkZGQuhiRJZDdnk9uU66hjee60rIDTXafJrM0kNSyVAK+ASRmnOxElkbq+OrZXbEcSpdG+qQDt2nY+Lf2UxRGLCVB/8895LCZd8AwLC+PZZ58dU8Dq7Oxk48aNDA0NsWjRItLS0oiJicHLywubzUZnZydlZWUUFxcjCAJ33303N9xwA15el9dlVmb8XEgM9PX1xdfXF0mSEEWRggKRl1+WaG8/f/kCp8AqCAIqFaxdCy+/fLZm6PkeGoanxw8XRs/XoKmnpweNRjMiCnR4ZOi5rzvfG08E57n2zGYzAwMD2Gw2QkND8fLyoqWlhcbGRgYHB/Hz82P+/PmEhoai1WrR6XSuWlnnHstqtdLW1kZLS4tL3Pfy8iIwMJCIiAgiIiJQqy+eNihJEkNDQ/T39+Pp6UlQUNAF/26GhoYYHBxEkiQiIiJGRfRarVa6u7tpbm6mv78fm81GQEAAcXFxxMbGjnlsm82GRqNBFEUCAwPx9vamo6ODhoYG+vv7UavVLF68mLCwMMAR5dvW1kZrayt6vR5RFFGr1QQFBREREUF4eDheXl6ucz/3+Oem4Q3/fDo7O2ltbWVgYABRFPHx8SEiIoKYmBgCAwNHfeaSJGEymWhsbKS9vR2DwYCHhwdhYWEkJiYSHHx5Nfp0Oh0NDQ10dnZiNpvx8vIiIiKCxMRE/P39x/x8dTodWq0WQRCIjo7GYrHQ2NhIa2srQ0NDREVFsWDBAvn7UUZmgrCJNt4peIdefe/Y4h9gxsyuyl3cm3ovy2KWXdkBTgA6s45PSz+l39A/9gZf1Yr6R+E/WLdgnaN52LcI53d1TU0NYWFhBAYG4u/vPNM8bQAAIABJREFUP2YZoG8qgiBw6623csstt7h8gY6ODurr62lubqa+vp62tjZXB/j+/n50Ot2Yc6XdbqewsJDf/va3iKJIaGgosbGxzJo1i7i4OBITE4mJiSE8PJywsDCXEDodBGUZGRkZmYmj39RPZk0mjdrGsWtkCmC1W9lSuoU75txBelT6Nz7K02K3sLt6N5U9laMF3q8wikYO1x3mxNwT3DbrtmkZ5TnpgmdoaCg/+9nPRr3e2dnJM888g0Kh4Be/+AX33nsvSUlJ+Pr6uhzFoaEhWlpa2LNnD5988gkGg4EZM2bIq79TDGeNT5NJSWcntLePbz+VCnq+ijh3Cj7ONHpn1Kiztqjdbh8hcjoF0vM1/TGbza6apE6Gp2ANj/R0Rn/6+/sTHR09SjCyWCwYDIavUvIljEYjZrPZtV99fb2rec7TTz9Ne3s7mzZtoqqqir6+PhISEvj973/P6tWr2b9/P7t27UKhUPDOO++MiKzt7e1l165d7N27l5qaGvr7+12iX3BwMDExMTz66KPccccd4xK16urqePHFFwkNDeXRRx/lmmuuGXM7q9XK3r172blzJyEhIfz+978f8ZDS29vL/v372bdvH+Xl5Wg0Gmw2G0FBQcydO5fbbruNu+++m4iIiBHH7evr44UXXsBisXDfffdhs9n47LPPKC4uRqPREBoayp///GfWrFlDR0cHO3bs4MCBA9TW1jIwMIAkSXh7e7tqj/3whz/kpptuwsvLC0mS6Ovr46WXXkKn0/H973+fG264YYR9SZLo7Oxk586dHD58mJqaGnp7exFFEV9fX2JjY1m6dCnf//73mTdv3oh9m5ub+fTTTzl8+DDNzc3o9Xo8PDyIiopi0aJFPPTQQ6xYseKSHsLOnDnDpk2byMvLo7W1FZPJhFqtJjY2lmuuuYZ169axYMGCUfsdPXqUbdu2oVQq+dOf/sSOHTvYtWsXDQ0N6HQ6rr/+el5++WUiIyO/9phkZNzB6c7T+Hn6XXHRq2WwhT5jH/PD509ojaKijiK2l50nutOJAA39DWwu3UxqWCp+nhNTfqd7qJuuoS5mz5jt1o6j53Ky7STZjdmYMZ9/IwU09jXyWu5rvHLbK9O6TtS5OLNEXnrpJZKTk4mKiiIxMZHY2Fji4uKIjIwkLCwMb2/vb/xDhrMuaGxsLLGxsSxbtgxJktDpdPT399PR0UFbW5trYfM73/nOiBJK4Mi+qampwWg0IoqiKxU+JycHQRAICAggODiY6OhooqOjiY2NJSEhgdjYWG6++WYCAqZndIqMjIzMRCJKIs2DzdhEG0kzkq6o0Ge2mantq8Xfy5+ZgTMnxIYkSZzpOsORhiOY7Kbzin8ooKKngq1lW5kfPh+1Sj0h4wE43nKcWTNmEek3cc9lzYPNfFLyCaIkjn3OX7kdDQMNZFRmkB6VTrhv+ISNZ7KYdMFzLCRJ4pNPPmHLli08/vjj/OIXvxgzYsrX15eUlBRiYmIQRZFXXnmFtLQ04uLiZKdnCnIpvrwzCMLT05OQkBCXqDlc2Bz+M1wEtdlsY9ZydTZQGt59/lycxwdcYupYqVxOgXNwcNBlv7W11XX/eXh4UFlZSUZGBu3t7VitVgoKCtBoNCxZsoSkpKQRkR6VlZVs3boVtVrN22+/7XrdaDTy8ccf8+qrr9LU1MS8efNYtGgRPj4+6PV6WlpayMzMJCYmhptvvnlcgqevry8lJSU0NjYSGRnJwoULxzzHrq4utm/fzieffMK//Mu/jBDxent7ee+993j33Xfp6OggMTGRhQsXolarXWlpOTk5dHZ28uSTT47o0mowGNi7dy8DAwOu6NC+vj5SUlJITk52RXEODAzw4Ycf8vrrr6PRaEhNTSU9PR21Wo1Wq3UteiQnJ7N8+XLXuQ8NDZGZmUl3dzcrV64c9bl1dnbyl7/8hW3btrmE5wULFuDr64tGo6GmpobKykrS0tJGCJ7t7e0888wzfPnll5jNZubNm0dqaiqDg4MUFhaSm5tLbm4uf/zjH7nzzjsv+jkMp6CggGeeeYbc3FwUCgXp6elERETQ1dVFXl4eJ0+eJDs7m9dff52FCxeO2LempoZt27ahVqvx9fVl27Zt+Pn5kZycjM1mO2+Eq4zMlcBqt/Li8RcJ9Qnl1TWvXrHUZgmJ7ZXbOdx0mA/u+YAgddDFd7oEbKKN10695oh0PE90p5Mh2xAHag9w55w7WZW4yu1jsdqtHG06ys6qnfxq+a9YGLHw4jtdAoOmQXZV7qJ5sNnxwvk+UgFQwIbCDTy68FGujrl6QsZTral2mJtCwqFzLKdPn+b06dOuBpNBQUGEhIQQGhpKfHw8M2fOJDk5maSkJGJiYggNDT1vKaFvEk6RMiAggPj4eFeNTuci4bk+h3MhOjIykp6enhHZX5IkMTg4yODgIA0NDYDDN/Tx8SE0NJT9+/eP8v1NJhN6vZ6QkJApdV/IyMjITCUGTYN8cuYTjFYjv1j+C0J9Qi++k5voM/bx8omXSY9K5+llT6NSuF+eGjAN8GXtl1T2Vl64A7rg0Ak2FG/g4QUPszhysdvHAo7smN8d+B33pN7Dv1/77xNiA2Bz6WZqemrOL/ACCGCym9hft581c9ewZvaaCRG8+439aM1avIWJW4Q/H1NS8DQajezYsQNJknjggQcumh7q5+fHtddeS0REBMeOHWPdunWy4DnNcDqqF0oDG9793fkzVoSdIAjExMSM6ETvLLQ/PHLUbre7juO0PdbxnOKqc1uTycTQ0BB2ux2FQoFWq3U5+F988QVLlizhN7/5Dddddx0eHh7Y7XbXPe6MKlUqlWi1Wnx8fFCpVFRUVLB9+3ZaW1t54okn+NnPfoafnx8qlQqr1crQ0BBlZWWo1epxPSQJgkBERAR33HEHL730EqdOnaK1tZXk5ORR29bU1HD69Gl8fHy46667XCKy2WwmMzOTv/3tb1gsFp566ikeeeQRwsPDUSqVDAwMsH37dv73f/+Xd999l5SUFNauXTtifM7tMjMzWbVqFS+++CKzZs3Cw8MDs9lMcHCw69x7e3v593//dx599FGCgoJQKpVYrVa0Wi1lZWWEhISMOvfhUbrnfmZvvfUWH374IWq1mqeffpqHH36YyMhIlEolFouFnp4eioqKSEhIcO1nt9t58cUX2blzJ76+vjz77LM89NBD+Pv7Y7PZKCws5LnnnqOgoIAXXniB2bNnM3fu3It+HuAQj//yl79w7NgxgoKC+Mtf/sLNN9+MWq3GZDKRlZXFn//8Z7Kzs/mP//gPPvnkkxHfc85z7e3t5Z///CePPPIIP/rRjwgODnb9LYSEhIxrLDIy7iavPY8D1QcQBIH/c9X/mTAn8lxaBlvYU72Hw3WH2Zu6l3UL1k2InWNNx9hdvBtEwKnRKDnrZIqA/avfJajorODzss9ZELHA7Q8WGoOGXRW72FWxi2tjriU5ONntUZ6SJJHblsvhmsMYTcYLO9Nf0aft46XjL/HPe//p9vHYsfNe4XsAU7pOqM1mY2BggIGBARobG11lfLy9vfHz88PPz4/Q0FCSkpKYM2cO8+fPZ9asWSQkJODv7z/Zw79sHOWKVKMiO534+/vz+OOPc8stt9DQ0EB1dTWlpaXU1tbS1NTEwMDAiO0tFgsWi8VV2uZcTp06xd/+9jf8/f2ZO3cuCxcuJCkpiZkzZ06L6ykjIyNzuUiSRNNgE1tLt2K2mbl59s1cH3/9FVkkEiWRvPY8tpVuo1vbza2zbiUlNMXtNsp7ytleth2z0Xw2C2csH00CBGjraeOvx//K+u+ux0Pp/sXHzLpMTjWeonuomwfnPUhsQKzbbTT0N/DaydcQreKFM48AJKjvrueLki9YHLGYmIDR/U8uBwmJos4iKjWVpIenu/XY42FKCp4Gg4Hm5mZX05mLIQiCy1lsb2/HaDRegVHKTDWcX8wX+4J2RhwMF0iH/+uM6HQKmU6H2tfXd8xajk6x02nXGR06XCx1HjciIoJnnnmG+Ph4IiMjz9sAwG63U1NTg7e3N15eXuTl5dHY2EhQUBCPPPIIoaGhKBQKPDw8UKlUREdHM2fOHERRHHdUiI+PD6tXr+aDDz6gqKiImpoaZs2aNeIcDQYDeXl51NTUsGzZMubPn49SqUSSJHp7e3n33Xfp7+/niSee4NlnnyUiIsK1f3R0NGFhYfT19fHqq6+SkZHBihUriIqKGjEOm81GWloav/rVr1i5cqVLuHNeu46ODlpaWkhJSeH73/8+aWlpIz5jSZJISXFMjucrYXAutbW1vPHGG4iiyLp16/jNb35DSEjIiHNPSEhg0aJFI14rKipi48aNiKLI/fffz3PPPTciFTEuLg5PT0/uvfdeSktL+fzzz/nNb34zLqchMzOTEydOIIoizz//PI899tiISN24uDj6+/v5z//8T44cOcLevXt56KGHRh3HarWydOlS/vu//xsfHx85qkVm0rGJNj48/SEDRkcpirfy3+KtO966InX3DjYcpLi9GJPNxP8c/x/umXsPvp7uj3Yu7yknfWY6kkICAeyinZreGtp0bSBBsE8wS6OWYpccqqdznujSd7lV8LSLdso15eyp28OAaYBtZdu4Kekmtz9AWEUrPUM9xIXEER40/vQnwS7QNNBESph7x5Pdks3eqr344HPh6I0rjDNT5JprrmFwcJC6urpRUYtOH2NwcBCA6upqcnNzXQ0jvb29XXUsly5dysKFC5kzZw4xMTHTroST05+JjIxk6dKlIxakm5ubqauro7CwkDNnzlBVVUVLSwsWi4UFCxagVo9OPSwqKuLIkSPodDrX9fT19SUmJoa5c+eyZMkSFixYQHJyMvHx8ZNwxjIyMjKTi9FmJKs2i+LuYiQkdlXtYkHEAoK9L68fwXiw2q18UPABOqOO3LZcDjYcZNaMWW4VGa12K91D3UTPiCYyyJE+rlQoKeosokffAwIEeQe5UtglSQIJ+s39dA11uV2M1Jv1/KPwH5gkE/Xd9Xx05iN+u/K3brUBUNRZRFpoGsrIcZYREsEsmuk39RPtH+3W58fuoW4y6zJpGmgiPTz9ii9MT0nBU6lUolarEUWRqqoqFi1adMGLLkkSPT09dHR0EBQUJBcvl7koF+q+Pl7RzHkcZ2qas2ZnYGAggYGBrhpUw7npppuIiorCYrFcNFrVmfplNpsxmUyIoojJZKKmpoYZM2a4ju+sHXluMyFRFDEYDFitVpcwqlKpXH8fgiAwe/Zsli9fzq5du8jJyWHFihUEBAS4BMfOzk6ys7OxWq3ccMMNREZGut4rLy/n+PHjJCcnc+edd44SMp2d0m+66Sa2bdvGyZMn0Wg0rmM4USgUrFq1ikWLFo24JsMFZEmS0Ov1WK1WRFF0iaLO7b5u6t/nn39OX18f8+fP58EHH3Q1Rjp3/OeWBsjKykKv16NWq3niiSfw8fEZ8b5KpXI1Tzt06BDZ2dloNJoxjz8cs9nMsWPHGBgYIC4ujieeeGKUbS8vLx5//HHee+892tra2Lt3Lw8++OCY33dPPPGEnMIuMyWQJInc1ly+rPkSm2ADAfZU7CF7QTYrZ66cUEG+VdvKnuo9tBvaQQXFLcV8fOZjnlz6pNvt/vTqn/LTq38KOM75dOdpfr7757QNtgHgofDgvbXvTVh9KicGq4FNZzah0WpABUebj3Kk8QgJQQlurUXlqfTk0YWP8ujCR912zEvFLtp59dSrYza/nGyc89e7777LrFmzaGpqoqSkhFOnTlFSUkJlZaWr7vXwhovOBVej0YhWq6Wrq4uCggK2bNmCIAgEBgYSHx/P4sWLWbp0KfPnzyclJcWV/eCcIy/k60xlnP7U8LT3kJAQrrrqKh544AEAtFotzc3N5OfnExsbO+o8bTYbNTU1GAyGUdezo6OD/Px8Nm7cCEBwcDBxcXEsWbKElStXsnr1auLi4q7sScvIyMhcYSRJomeohw3FG7CLdhBgS9kWHpj3AFfHXj3htTxPtp5kT80eUDlS2zPKM1iVsIqU0BS3zV1eKi/uTb2Xe1Pvdb2mMWhY8f4KenSOZiGhnqH8avmvuGfuPRM6Z0qSxL66fRS1FCEqREySiU3Fm3hw3oPMCZnjVlv3pd7Hfan3ff0xcv7m0peCJEmUdJdwoPYAol28+A4TwJQUPP39/UlPT6eyspLXX3+dq666ioSEhFEr2U5BqK6uji1bttDY2Mgjjzwip7PLXDEEQUCtVuPn5+dK1YqPj2fWrFmutPi+vj5XqvmyZctcaVcXElaHP6RIkkRMTAxRUVG0tbXxwgsv8NBDD7FgwQLCwsLw8/NzPRwMRxRFurq66OnpwcPDw7WNU/j09PREkiTmz5/Pvn37OHToEA888ACpqal4enoiiiJ1dXWcOnWK2NhYli9fjr+/v+sBrqCgAJvNxowZMxAEgdra2jHPxWAw4O/vT3t7Oz09PSOiYcHx956UlDRmvVVwRDbOmjWLEydO8G//9m889thjXHfddQQEBODr64uPj8/XEjwlSeLEiRMoFAri4+NJTU0d934lJSXYbDZmz559wf1Wr17NwYMH6enpoaWl5aKC58DAAA0NDVgsFpYvX37e77CIiAgWL15Mc3MztbW19PX1jaiLCg5hdMmSJeM6JxmZicZsN7O1fCuduk5X5F3PUA//PPNP0qPSJ6xpj120k9OSw6mWU44Xvqoj+ffcv3PP3Hvcnq4zHIPVwMGGg5xqO+Wy2zXQxR8O/YF37353QtKjnFT0VLCtYpsjTUsAq83K+sL13JJ0C4kzEr+R4tfFONx4mCP1R6aoR+vAZDLh6+vLvHnzmDdvHg899BBWq5W+vj4aGxs5c+YM5eXl1NTUUF9fj1ardTVANJvN2O1217EkSXKlxZ85c4aPPvoIpVJJREQESUlJLF68mNTUVObMmUNcXBz+/v6ubBEvL69p0yE+ICCA+fPnM3/+/DHfN5vNzJgxg8TERNf1NJlMWCyWEdcTHE0U+/r6OHPmDDk5OURGRo4SPC0WC+3t7Xh5ebmup6en54gFWBkZGZlvEhISOyp3UNJZ4kp7bulrYWPxRuaFzyPAa+I0FbPNzMsnXsZmtoEXIMKx1mMcaTpCwowEvFUTU+vRLtp5O/9tmjRNjnOWoEHXwOdln3N1zNVE+Udd9BiXyoBpgM/LP6fX3Ot4QYDm3mY2FG/gd9f/Dk/l5GdtuDv6ss/Yx57qPdQN1I2r/NFEMCXdQ5VKxWOPPcaXX35Jbm4uTz/9NP/6r/9KWloa/v7+qFQqRFFEr9dTV1fHxo0bycjIIDIykjVr1owSAGRkJpLhqevDfx8uLCoUCnx8fJg5cyYxMTEXfeBQKBSuWpjOlO/7778fk8lEdXU1L7zwgqvOV0pKCqtWrWLlypWulGrnWJzNmYZHjpjNZtf7CoXC1eG0qKiI/fv3ExgYSEJCAgaDgUOHDtHV1cV3v/td10ODM6LS2TSgsLCQxx577LwCrtlsZmBgAJVKhVardUVoOvHy8iIgIOC8+8+bN49169bR399Pfn4+J06cIC4ujrS0NK666iqWLl1Keno6MTEx4xI+7XY73d3dqFQqwsPDx13Hy2Kx0N/fjyRJzJw5c1R053CcD0omkwmtVnvRY+v1eoaGhpAkiaSkpAtum5iY6HrgNRgMo94PCAi44NhkZK4UEhIF7QUcrj+MQTS4BE+zZOZo/VGONB2ZsOLoXUNdZNZk0qxtPpvirIKG7gY+PPMhv17+6wkRHiVJonmwmc/LPsdis5z1slSwpWQLP0r/EStnrrzgMS4Vm2jj3cJ36R8a1jhJ6aifurNqJz+75mcohekhdjnRW/RsKN6AzqibNEf6UvHw8CAiIoKIiAiuueYaVxPE7u5u6urqqKqqor6+ntraWjo7O+nv72dwcBC9Xj8qg8Rut9Pe3k57ezvZ2dkIgoCvry8REREkJyczd+5cEhISSE5OJjw8nKCgIAICAvD390etVk/LzCgfHx+ee+45fvSjH1FbW0tVVRUNDQ3U1tbS0dHBwMAAWq0WnU43Yi4NDQ0lMTFx1PGqq6t59NFHXenws2bNYtasWYSFhTFjxgz8/f1d11MWQGVkZL4J9Bn6eL/wfVftSgAUsKV0C+sWrGP5zOUTZvtY8zH2V+8/668oQG/Ss7lkMzcm3MjckLkT8l1a11/H9vLtWASL44WvyhAdazzG0aaj3D/v/glpnCRKIkeajpDfko8Vq+N6S6Cz69hbtZe759zN0uil02r+ECWR6t5qMqoycHPg6NdiSgqeAKtWreL//t//y6uvvsrBgwfJy8tj9uzZREdH4+3tjcVioauri+rqagYGBpg5cyY//vGPWb169bSraSQzPfg6UQAKhYLY2Fg8PDxcDzVPPvkkixYtIisri7KyMlpaWqitrSU/P59du3Zx3XXX8bOf/Ywbb7zRJfw5o07hbI3Sc+0kJCSQkpJCY2Mjx44dY+3atUiShEajISsrC39/f6655hr8/f1pampCFEX8/f3p6XGkAajVaiIiIkaJjc5mQc6HKQ8PD4KDg0ddA4VCgVKpPO+18fPz4wc/+AEJCQns27ePiooKWltbycnJITMzk7CwMFatWsXTTz/Ntddee9G/f6cQDA5RerzRLs6UQ0mSUKlUF/wsndfCWafvYjjrxgIXHb/zfaeIfS4XG5uMzJViyDLEvtp91PXVORy7Ybdl02ATOyt2six6GeG+468BOR5soo3Tnaf5sv5Lx/feMC3HIljYWrKVu+fcPSHdy62ilS/rv6SgvWBkkXgFGMwG/jfnf1kcsRh/L/c3TDnTeYZtZdtG2hVAtIu8k/8O9827b8JT6q80OS05ZDdkn31w+QYjCAI+Pj4kJCSQkJDA6tWrsdls6PV6urq6aGxspLm5mebmZhobG+no6KCrq4ve3l56e3uxWM5eA2cZGGdgwL59+1AoFAQFBREZGUliYiKxsbEkJiYyc+ZMoqKiCA8PJyQkhBkzZkwLP1oQBFcjqMTERG655RbsdjuDg4Ou69na2kpDQwONjY10dnbS09PDvHnziIyMHHW84uJi18/evXvx8PAgICCAqKioUdczMjLSdT2Dg4Mv6OPIyMjITBabSjdR2lE6yl/p1nXzRt4bLIlZgpfS67z7Xyp6i543Tr2BxWqB4dONEk62nGR/3X4SgxLxUrnXttVuZXvFduo0dUjCsOdiAZq1zWRUZfCduO9MiK/Ua+hlX80+WnQtI+wCVGmq2FG1g5TQlAnxDycLo9XIjsod1PbWTuqi9JQVPJVKJY8//jgxMTFs3bqV/Px8qqqqKCwsdG3j6elJXFwcN998M/fccw+33nrrRVNHZWS+SQxPVQ8MDOT222/nuuuuo6WlhaamJhobG8nLy+PQoUNkZGSg1+tJSUkhLi7OleIWFBTkSq931rFy/l8URcLDw1m8eDE5OTmcOnUKjUbD7NmzKSgooKysjDlz5rBs2TLgrOhnsVhcDQISExN56qmnCA8PH5EmJggCnp6eREVFuaJcY2NjUSgUrk61zuMZjUb0er2r9pjzX+e5BwQEcOedd7Jy5UrXA19dXR25ubkcOXKErVu3YrPZXA8dF0KpVOLv748oiq40t/FEhjrT2ARBoL+/H6vVet6Hwv7+fsAhPo7VSOFcvL29XcdyCsnnY7jQPB0eSmWmL+U95RysO4jOqhvVSMYsmjnUcIiclhzumnMXSoX7Ig8HTAPsrtpN82DzaAdLgLreOraUbWFOyBy31rUE6DE40vVHRHc6UcGhukPsr99/SXWVLoTVbuW13NdGRnc6UUBldyXvF77Pf974n9NGeBk0DbKzcqejMdT0C1AEcHU0DwoKYu7cua4mR866np2dnXR2dtLU1OQSQtva2ujs7GRgYGDEnCyKoit1u7y8HHDMa4GBgURFRREZGUlERIRLtIuPjyc6OpqoqCgCAwOnxX2jVCoJDg4mODjYVZbGZDKh0+lc1zI4OHjMGtjFxcUj/m+1Wl1ic2lpKeCYywMDA13Xcvbs2Tz33HMX9UtkZGRkrjQdug7ePPWmo2bj8K93AVDCropdHL3qKLck3eJ220ebjpLdkD3aXxHAZDWxvmg9d825i8QZo6PtL4favloyazLR2s7JvhMc0Yhf1n/J3S13E+kX6db0clESKego4FjjMcx28yifRWfVkVWTxZrZa1get3xazLfg6BT/Wdlnbq8L+nWZsoInOATNu+66i/T0dOrr62lqaqK7uxuj0YiXlxfBwcEkJSW5UnLlh3+ZbwN+fn6kpqaSkpKCzWbjnnvuYdOmTbzyyiucOHGCyspK4uLiUCgUrlqQzlR7Z1Tg8BR3SZK49dZbyczM5PTp0+Tl5XH11VfzxRdfYLVamT9/PgsWLMBkMgG4UuSdHU1NJhPe3t4kJia6UuidNtVqNUlJSaNEP6vVikajwWq1Yrfb6enpoampySVyKpVKlEqlqyyA88fX15e0tDTS0tKwWq1897vfZePGjfz1r3/l6NGjtLS0EBMTc8FGDQqFgtTUVLKzs2ltbaWjo2NcdX+d9VmVSiX19fV0dXWdt6nB6dOnAYdQGx5+8ei1wMBAIiIiUCqVnD59GrvdPmbkqdFopKysDIVCQUxMDIGBgRc9tozMZDBkGeJww2GKu74SCc51poHG/kb2VO1hWcwyov2j3WJXlESqNFXsqt6FKIljCmF6m56s6izuTL6T78R9xy12neyu3s3pttMjoyWcCKA1a1lftJ6VM1cS5uu+BdqC9gIyKjLOaxcB3s1/l4fnP8y8sHluszuZFHYUcqzhGEa7cdoKnufibKYXFhZGWFgY8+fPRxRFzGYzWq2W3t5e+vv7XRGMNTU11NXVuRYKjUbjiOOZzWa6u7vp7u7mzJkzCILgEu2Cg4MJCgoiJibGlQ6flJTkKs3jbCb0TUetVqNWqwkLC2PBggXn3W7FihU88cSeKSMiAAAgAElEQVQT1NXV0dTURGtr66jraTQaMRqNdHZ2AlBbW8svf/nLUcdqaWnhzJkz+Pr6kpCQQExMjPwMIyMjc0XZXLqZqq6qsdUgBehMOl7Kfonlscvx9XRfI1StWcuG4g0MmAbGnruVUNJewqeln/Kblb9xm12baONA/QFKukoQEUctxCNAp66Tz8o+47r469zml4KjjuWB2gPU9teOaRegtLuUfTX7SAtPI0gd5Dbbk8knpZ/Q2Ns46YrjlBY8wbEaGxcXR0xMDMuXLx/RpVmlUuHh4eFWFby/v5+ysjIqKytpbW1FEATi4uJYunQpKSkpl+yQ9PX1cfLkSYqLizGZTMTFxbFq1aqL1uubTthsMI7s3hF8VW5SZgyctTSjo6NZvnw5mzdvpr29ncHBwRHbDP/3fOnbS5YsIT09ndLSUvbu3cvdd9/N/v37CQ4OZvny5YSGhmI2mwkICHBFZd50002sX7/e1SV1wYIFKJVKrFarS0gdK40ecEWbOnF2T4WzqffOMTs7yztF1qCgIFf0aFxcHNdccw3R0dHU1NTQ1taGVqvFw8PDVat0LNauXcsHH3xAXV0dR44cITk5eVw1zFasWMEHH3xAb28vu3fv5sknnxy1TVtbG7t378bLy4uUlJRxRXb4+fmxcOFC9u7dS2FhIbm5uSxfPrpuTnZ2NpWVlahUKpYtWzYtanWKkkN4V/x/9s48PKry7vufM0smk0z2lex7AkkgQNgUlFWhitSli3ZvH/u8rdfje71dbB9rW23f1qe1Wn3VVlurtbW44IoIgiAqyGKAkJCELJN932cmsy/nvH8M55CQAAESts7nuoYJs93n3HPmnPv+3d/f7ytcfOMHue2pVBdOBkmS8Em+aakRdLnQYm5ha/1WrG7rxANawZ/+vc24jfUz15MQmjAl34Pb5+bVqlfpGO44bfBPQqKmv4bNdZspTiieMuMki8vCU589hVucQN15Ap/g41DbId5reI9vlnxzStr1iB7+Wv5Xhh3Dpx/VnTBOevKzJ3l63dNXfM1Gi8vCjsYd1A3VjZ88/JuhUqnQ6/Xo9XoSEhKUxU2Xy4XdbsdutzMyMkJXVxf19fVUVlZSX19PQ0MDvb29Y67FkiQp7+nu7gb84wadTkdISAghISGEh4eTlpZGfn4+RUVF5OXlkZeXN6nFvSuZNWvWsGTJEux2OyaTic7OTurr66murqa+vp76+np6enrGjHuys7MVU8vRHDlyhF/96lcMDg4SFRVFcnIyM2fOVPozJydHSYUPEABOlEiSxIs+XgF/fUNBEKbdsXsiPD7PtBr9nQ6f6Lsk49KLRedIJy8cfQFJJZ3+GqqBvca9bK7bzJ3Fd05Z23va9nCg+QBewTvxCwTw4uVPZX/iK7O/MmXp5U3DTXzQ8AGDjsGJ91kASZB43/g++9r38fmCz0/JOF2SJOoG6njf+L4/++c0P2GHx8Hm2s3cnH8zC5MXXvHHXpu5jb+X/x1RmCC4fJG5YmZbsvLrXNyYzwWv18umTZt49NFHMRqNY1wc1Wo1MTExfOUrX+Hee+8lKWnyEX9Jkqirq+NnP/sZH3zwAW632z/BVquJi4vj4Ycf5q677pqWfbrcyM2F22+HoSGYzG9YpYKlS6d/uy53RFHk2LFjWCwW5s2bNy7Vyufz0djYSE9Pj1JP81wxGAxcf/31bN26lf379/PAAw/Q39/PnDlzWLp0qVKLU1ZqSpJEaWkpd911F4899hjvvPMOpaWl3Hrrreh0Onw+H263W5koNTc309nZyezZswkPD1cWLgRBUCZnowOk8kne5/NRV1eHzWajpKQEnW5sLReXy0VjYyPd3d2EhobicDhoampCpVLR1dWF0+lUTIra2trQarVotVpKS0tZsmQJ+/bt469//Ss5OTlcf/31YyYXPp+PgYEBJElS6nndcMMN5ObmUlFRwVNPPcX8+fOVdH/wmw/dd999dHZ2kpSUxPr168dt80SoVCpuuukmXnjhBYxGI/fddx+bNm1ixoyTToEtLS08/PDDjIyMEB8fzxe/+MUpvRianCacLueUu/Odjaq+Kg51HeLG7Bun1Tn7VCRJ4rPOz2gebmZ9/vppcwufiCHHEH+v+DtfKvwSKeFXX6qj0+vk09ZP2d+xf1ztzjEI0GHq4I2qN1iQtIAEw7mfu06lZbiFjcc2+mszneFQtnlsfFD/ATfl3cTStKm50Lxa/SpVHVXjU7ROoc/Zx+vVr7M6a/WUfP9lnWXsrN+JqD7DiqIAaOHNY2/yjTnfYHHK4gtu91JS01/DroZdOL3Ofxt152QRBAG1Wq0EKMF/vps5cybLly/H6/Xi8Xgwm800NTVRXl5OeXk5NTU11NXVMTIyMubzfD6fEgSVqamp4YMPPlDqYMfFxZGVlUVpaSmzZ8+mqKiIrKwsQkNDr/hJm8zoMZBsnCjXWfX5fAwODtLc3Ex5eTlHjx6loqKC0tLScUpYr9er1A4dHh6mtbWVyspKduzYgUajQafTERMTQ25uLvPmzaO4uJji4mKys7PR6XRXTX8GmDyiJFLVV8Xetr3cPe/uix4AfKXqFXQaHXfMuuOitgvw010/5b8W/hcZkRkXtd0Xjr5AbnQu16Vfd1X+5jZVb6Kpr+nM108BnJKTp8qeYm3OWqL0URfcrtVt5a3jb9Fp6zzzC1XQMdjB4wce57EbH7vgdn2ij4MdBznQeWBidaeMAFaHlT9/9mduzL5xSuppWlwWthu3c7z/+On7WwBU/jnRlrotzIybSbju7NmHlzNPlz1N91D32Bqtl4hLHvCUJElRhI2u3ScPIM4VORX2XE9ObrebgwcPUltby9KlS1mzZg3FxcX4fD52797Nxo0beeSRR/B4PNx///0Tmq9MtG89PT088MADvP322yxZsoRvfvObJCYm8t577/G3v/2NH/3oR4SFhXHzzTdflSfU0Vx7rf92NXI6JeOpz5/tdaNfPzr4t3//fh588EFSU1O58cYbKSwsJCoqCpvNxsGDB3nttdfo7Ozk1ltvpbCw8Jy3XxAEli1bRnJyMmVlZWzcuJGgoCBKS0uZNWuW8prRr4+MjOQ///M/qa6uZufOnXzve99j69atrFmzhpSUFNxuN21tbezbt4+dO3eyZMkSHn/8ccUZNjMzE61Wi0qlIiIigpiYGDweD263G6/Xq/TBkSNH+NOf/kRBQQE33XQTxcXFREZGYrFY2Lt3L6+99hpDQ0Ns2LCB9PR0JV1/tNLUarWOUV5otVruueceurq6OHz4MN/4xje4/fbbufbaa9HpdPT29vLZZ59x6NAh7r33Xr72ta8hCAIRERE89thj3HLLLdTU1PDlL3+Zb3/72xQVFTE0NMTGjRvZvXs3wcHBfOELX2DdunWT/l3PmTOHe++9l/vvv599+/axbt06vvOd75Cenk5rayt///vfqaioQKvV8uCDD1JQUDDhMTPZY2w0PtHHgH0Aq8NKkHTxrkxe0ctHLR/x6P5HSTAkkGhIvGjqhWHnMJsqN3Gg/QA50TkXzRlRkiT2tu7lkU8eIVwXznfmfueSqCamC0mS6LP18UrlK34TFQ1ndGaUBIm3at/iztl3sipr1QV9/6Ik8qeyPzFoHvQPsM70UxCgoq+C7cbtzE6YTVhQ2AV9/2anmScOPOFXS3DmtkVJ5Ej7Ed43vs+35377gr5/t9fNxsqN9Fn7ztouwLB9mGcPPcvcxLlTbgZwMZAkCbvHzkfNH3G07+jYSculLRF1WSOXeZGFA3q9nvDwcFJTU7n++usBfyCut7eX6upqDh06xLFjxzh27BgdHR24XC6l7rd8L19nwb/Y19zczK5duwB/bdCkpCSKi4uZO3euErRLTExUanrL4315+64k5KCyWq1WMr/CwsLIyMhgxYoVANjtdgRBGFfSx2az0dzcjNVqVbJXRvenw+HAZDIpZlOAUjbo6aefZvny5WM+TzZilIUhV1pfBjg7Hp+HF4+8yIf1H1KaVMqCpAUX7Xt2ep38Zs9vSAhN4IasGwgPvnhBmP0d+3nykyfRa/T8asWvLtpYqcPSwfOHniciJIIlqUumtJbjpUaSJDpGOthUtQmbaFOcwk+LGmo6anit+jXunn/3BX0HkiSxr20fB5oP+DNhJtH284ef59tzv01hXOF5H/OSJNFuaee92vfot/b7FZZnaffDpg/ZWr+VLxZdmLhEbvv16tf9RrNnaVsURf5V+S/uKLyD2Qmzr8jzuYREy3ALfz3815ORxks8PrvkAc/e3l6efvppnE4nubm53HLLLSQmJrJz50527tx5TmlXkiSxatUqli9fPimjkNGo1Wo2bNjA7bffzoIFC8a8f+XKlRQXF/Ozn/2MjRs3ctttt7FkyZKzHoSiKLJjxw42b97M0qVLefTRRyktLQXg5ptvJiYmhkceeYRnnnmG0tLSMWquAFcWsrFAcHDwuONCEAQlyHe29CSNRkNMTMy4oFVCQgKpqak0Nzfzxz/+UVEgq1QqdDod4eHh3Hrrrfzyl78kMvL86n6kpaWxatUquru7cblcREVFsXbt2jOqqnNzc3n00Ud55JFHFOOkTZs2Kdum1WoJCQkhLCyMwsJCRW2i0WiU1DjZXCkrK2vMwN/r9eJyuSgsLCQ9PZ22tjYee+wx3G43oigq6tGwsDBuuukmvv71rytmRPKgPzg4GIPBoEys5H71eDwsWbKEZ555ht///vdUVlby7LPP8uSTTyou7DqdjoyMDOx2O3V1dUiShFarJT8/n9/+9rc89dRT9PT08OCDDyr7q9frSUpK4otf/CI///nPUavVipO6PNmMi4s7bY3O733ve7hcLp5++mmam5v54Q9/iNfrRaPRoNfrSU9P55577pkwlV4+dmTl7GSRJAmLy4LNbcPtc4MIwUytkcvp2m0cauSDhg9o62vj5YqXWZC0gLiQuGm/wIuSSP1gPe/Wv0ubqY2djTsvmjOi0+vkz4f+TL+lny3Ht7A6c/WUF2W/lIiSyIH2A1R2VxKri51cGosL/nX0XyxJXXJB30HTUBObqjcRGxw7OdWfD3Y37GZ9/noWJC04++vPwJvH32RoYMi/z5PA4/HwUcNHrMpcdUHff3lPORVtFRjUBgzCJFTKElS0VrC3fS+rMledd7uXkjZzGx82fEgEESfVtBrwaXx+5UYg8HleaDQakpOTSU5O5oYbbkAURex2O+3t7Uqpp9raWurr6xkcHMRmsyk1K+XAp4zL5aK5uZnm5mY2b94M+GtVp6WlUVRUpNQhz8rKIjo6mpCQECUtX6PRXJGTvFM5XcmZ4OBgNmzYQHh4ODU1NRiNRgYHB5W+nKg/HQ4HAwMDE9bt7u3t5cUXXyQ7O5vs7GyioqKU/gwODr5q+vPfmRZzCy9XvYzFZmFz7WYK4wqntKbi6ZAkiVerXuV413GGDEO8Xfc2Xyn+ykVZmHZ6nTxz6Bl8oo+XKl7irqK7mBU//fWnfaKPLfVbaBpsYrBzkO3G7azPXz/t7V4sJCS2N2xncHCQGE3M5MZoErx1/C0+l/s5UiMm9i6YDCPuEXa37GbANkCsZnLjQ5VbxbOHn+WxGx47b2WzT/JxrPcYZR1lxGonOT70whOfPcG6vHUXpLT0iB52NOygb7iP2KBJ7LMGRswjbKraxMy4mVdksF2SJP5R8Q+0Nq1/n8FfLsAnMSL4M0gutonRJQ94DgwM8MQTT2C1Wlm5ciWLFy8mMTGR3bt38+ijj57zRVqn07FkyZJzDnjqdDplVXai5xYvXkxJSQlbtmyhrq6OBQsWnLWep9vt5vXXX0ev17NmzRrmzJkz5vnvf//7bNy4kbq6OsrKyrjlllvOaZsDXB6o1WpWrlxJdHQ0QUFB40oe5OTk8KMf/QibzcasWbPOGMRfu3YtSUlJSn1a8AfKVq9ezYwZM6ioqMBoNNLZ2YnD4SA4OJjU1FTmz5/PihUrLqiGliAIfOc736GoqAiPx0NoaCgrV64863sKCwt57LHHFJf3xsZGRkZGlPT63Nxc5syZw+zZs8cM1mNiYnjggQdwu93KQsBoZ/agoCBCQkK44447yM3N5dixYxiNRrq7u3E4HISEhJCens68efNYsGAB4eHhigrF6/Wi1Wr5wQ9+gMPhIC8vj6CgICV13ufzodVqWb16NQUFBezatYsjR47Q3d2N2WwmJCSEzMxMiouLyc/Px263K8FSs9nMddddR3JyMvv376ehoQG73U50dDT5+fksW7aMVatWIQgCbrcbs9mMIAhoNBoiIiL4+c9/jkqlorCwEJfLpQRnZcXLD3/4Q6699lo+/PBDJb0wLCyMvLw8Vq9ePSaFfjQrVqwgKipKMXeaLD7Rh9lpxiN6UKHC5XXh8XmmfYLk8Xk42HGQ/e37QQ1b6rfw5dlf5nO5n5v2tHq7x867te9iHDIiSRJv1rzJmpw1zJsxb9oVBB+2fMhHTR8hqkV/KnLTTr5R8o0rclAzEYIgkByezG/W/AZBPcnvUYIQbcgF972ExIPXPYigmXy7GpWG6OBoJKQLOu4yIjN4aN1DJxWek2g7Kjjqgr/3cF043138Xb9xzyQnLlqVlujg6Atq91ISrgvnrnl3ccfsO5R9VqlV9Lf287sXfnfJ3UCvFlQqlWKSOHPmTOXaOTw8TEtLC0ajkaamJhoaGmhvb2dgYACz2YzZbMZqtY6pDQr+a6esGgV/pkVMTAxZWVlkZ2eTmZlJXl4eycnJxMTEEBERQWRkJCEhIeeVuXW5otPpWLZsGcuWLcPtdmMymWhublb602g00t7ezuDgIGazGZPJxMjICImJiWRkZIz7vPLycv77v/9b6c/s7GylT3Nzc0lOTiY6OpqIiAgiIiIIDQ0N1Aa9gvBJPv56+K90m7tBBdvrt3Njzo1cm3bttI9XhhxDPHXwKRBgwDXAG9VvsDxj+ZTVVDwTBzsOstu4GzFIpMfUw9/K/8ZvVv2GYM30Lsi3W9rZ3rCdflc/ok/k4T0PszRt6ZSkc18OSJJEQWwBP179Y3ySb9LjhrCgCxcESEhcn349mZGZk67pKEgCUSFReEXveQc8JUkiJTyF+66/79wEIUh4fJ6zv/AMCAjMip/Fb9f9dvLld0SINcResdlfkiQxb8Y8/u/6/6uMiQVBwGVz8c+D/7wkY7RLHvCMjIzki1/8Ik6nk6KiIqKj/YPwBQsWcNddd52zwrOkpGRanA5HB2ImmzI6PDzM0aNHiY2NpaioaJxSLjo6moULF7J9+3bq6upOq/oKcHkjB69Ol0oeHx8/6WC2nPZ1KmFhYSxevJjFixcr9TFlVWFQUBAazdT8lHNycsjJyTnn90VHR3PTTTexbt06JegoKzxPpxA1GAzceuutZ/3s8PBwZXLg9XoVhefp9n10WndxcbFi3iD3mRwQNRgMqFQq0tLS+Na3vsVXv/pV3G43ra2t2Gw2goKCEARhXL1R8C9mZGVlkZmZidPpxGAwkJaWRnh4+JjzhMPhoLOzU+kPjUbDddddh1qtxuv10t7erjjQy670KpWK+fPns2jRIqUvZTUrnD71b9asWUr5gckiSRI2j40R94g/4CMIuEU3vbZesqVsNML0XCIkr5eewRbeOf62v3i4GswOM8+U/ZnrY+cTZoiFoGmqUeV00dJdw6bqTf7vUw0VvRVsO/4uM3WphIZFwTS55Y64Rnhs/2O4PC7QQI+th/fq3uP6jOvJi8mbljYvNipBxbVp13Jt2sWvX5Ibk0tuTO5FbxdgReYKVmROvGg6ncyMm8nMuJkXvd1LhRxQn8jwqTWsld/xu/Mq6zHdXA3BOnnhTnaIX7BgAZIk4XK56O/vp62tjY6ODtra2mhubqajo4Pe3l76+/vp6+sbs3AIfpVzT08PPT097Nu3D4DQ0FDi4+NJTU0lJSWFjIwM0tPTSUlJIT4+nri4OGJjY9Hr9Ve88Rb4F3bj4+OJj49n0aJFiKKIw+Ggv7+f9vZ2Ojs7aWlpobGxkczMTMLDxyuNKisrkSQJt9tNd3c33d3d7N27V8k6SUxMVMxfMzIylD4tKSkhLi7uEux1gHOhYbCBlype8gdLVFA9UM1243aK4oumPQj3Tu07VHZV+hX0ko9DHYfY0biDb8z5xrTWEbW6rbxa9Sr9tn4AXLjYVreNW2feOmU1tyfC4/PwSesnHOo8hCiJoIGDrQd5veZ17p5/97S1ezFRq9QsTV/K0vSLb5IRoYtgbe7ai96uVq1l7oy5zJ0x95K0fSn2+VKiVqknVEVbLVb2R+/H4XJc9G265AHPxMREHn74YURRJCgoiLAw/wrC2rVrWbZs2TmnZoaGhk7KJORckCRJWX2Ni4sjOzt7UgGm7u5uent7KSwsnNCpWa1Wk5+fz9tvv01nZydOp/OclFmyS3eAfy/UavW4IviXC/IAe7qQg4NnQq5Vduo2jd6uiSbEcnA2IyNDCY56PB4lQCrXFZZvci0zOa0+JCRk3ARs9GtldYzL5RrTvrytssoT/IHn8PDwMdvs9XoZGBhAFEUlQDrRTVaMTubc6RN9mF1mXL6TjvYaQUPlQCURfRHMSZxzhnefBy4XDA7i7R+grHUru5t3nzS1UcOHjbvZ9snzfDF1PcSEQ0wMGAyTczk7E6IIIyPQ3w+DZl6p+zONw43+Wjon3ML/Vf4iG5wZzE4sgdjok21PIR80fcDepr3+K69wop5n214+afmEtPA0grXTX0ogQICrldHnscsFWZn46quv0t7eTlJSEqmpqcTGxiqLalcycumY1NRUUlP9qY4+nw+bzcbAwAB9fX1KMLSlpYWWlhY6Ojro6Oigv78fl2vsdybXt2xubgb8/WcwGJSgoBwMzczMJC0tjZSUFNLS0pQMmysdlUpFaGgooaGhiprT6/VitVpPK4pwuVwkJSXR398/JhVeFEVsNhuNjY00NjYC/jGUwWCgsLCQ3/72t+MCnh6PB5vNhl6vvyqOzysdURJ59tCz9Jp7lfIdDo+Dd2vfZW3uWq5JvWbaVGDdI928VPkSHsmjKNN67D28V/se16VfN62LtIe6DrGnaQ9O0amMEVvNrWyq3sTshNnTZubSNdLFjoYddFu7lXZFRP5y6C/cmHPjRVG2BghwteKTfH5V8SXgkgc85dXiUzEYDBimeLJ5vnR3d/P6669jNBr5whe+QF5e3qQGASaTCY/HMyaQOxpBEIiOjlZSWtxu92kDnq2trezdu1dJj5U5duzYpIMbAQIE8HOm34tc+wpOqkXlgKVcX1T+Ww6GBgUFjZuIyO8d/fhoYyF5G+S/ZeMB2QzhVDwejzJBHJ0GP9r8QQ56yoFhuVbqRE65kiTh8DqwuC2KuhPBvzLXbe3m7xV/53/i/gedegoWkETRH2xsbITBIWyOETa2v8Wwy3QyxUMAh8/B72ufY40zjaiOSIgIg/R0SE4+f9Wl0wkdHdDaCiN2Wu0d/LNts3/1Xm5bBQ0jrbzU+Ca/d8RARxtERkJWFiQmwhQsLFlcFv5y+C+4ve6TdQcFGLQP8nr166zMWklWVNYFtxMgQICLz8DAAG+88QY+n2/MItbBgwex2+0899xzvPnmm0RERBAXF0daWhq5ublkZ2eTk5NDenq6UtP6SketVhMeHk54eDhZWf5zmsfjwWq1YjKZMJvNDA4O0tbWRkNDgxKQa2pqwmKxjDEs9fl8Spp8Q0MD4FdFGgwGIiMjx/Rnfn6+ksotZ1xcDf0p14g/HV/96ldZsmQJ7e3t1NfXK/3Z2tqKyWRS6oiDP3gqzzcmChC3tbXx+9//3p/2eqLGqnx8hoaGXhWq2iuJqr4qXq963b84K6OC4/3H2VK3hcL4QqKCp0fluc24jaruqpNlWgR/EP3T9k/5uOVj0iPSp8X4bsQ14q+haW46uSAugd1rZ3fjbg7mHWRN9popb9crejnSfYSPWz/2B2XkQ10Nx3uP80rVK/z4mh8H5tsBAlyBXPKA5+mQU1YvNVarlX/84x+8/PLLzJw5k29961vEx8dP6oTncPglu3Iq60QEBQUpTvWjByWnUl9fzx//+Edl1VvG7XaTmJh4DnsUIECAszF6wgpMOGmSVZqjg5enfkZERAR6vR6v14vH4xlzP/rv0a63cuDyVOTgqqwUPdu2y38nJCQQEhIy5nFRFDGZTPQO9+L0Of0DuxPOgYIkIIgC26q3cUveLReeput2Q3MzGI1gs4EQRJm1mu3WivE1fAQ46mjjZfMnfF99M/T1gckEg4MwaxacxgTitIyMQE0NdHWBxwOCjmeHd9DuGRrXtijA3y0f8x/Ra8iTUqG3F8xmyMiA3Fy4QOXyB00fcLjt8NirrgCo4OOWj/mw6UOS5yRfkc7ZAQL8u9PV1cUDDzwwZhwnCAIulwubzYYkSZjNZuVx2Sldr9cTEhJCXFwc+fn5FBYWkp+fT1FRESkpKVOesXSp0Gq1REVFERXlD87IxoSjjXr6+/tpaGhQDJJqamro6OgYlwrvdrsZGhpiaGgIONmfISEhilFhQkICeXl5zJw5k1mzZlFYWEhCQsJlmx1zIWRlZZGRkYHX68Vut+N0OrFarfT391NfX8/x48epra2lurqajo4OnE4nycnJxMaONVgTRZGWlhbeeecdTCaTcnzKZQZmzZql1HOdOXMmiYmJ6HS6QABomhAlkX9U/INeS+/YgKfgT71+tepV7ph1B/OT5k/5d9A90s3bNW8z5DxlrCTAgG2Azcc3T1spnoreCj5p/AS7136y7RP39UP1bKnbwvyk+UTrp7YO9YB9gK11W+kyd42ttSiA3Wfn9arXWZ+3/t+qhEyAAFcLl23A88UXX6SsrIzVq1ezZMkS4uPjL/pKrc/n41//+he//vWvSUxM5Kc//SnLli2bdL1EOd1cVoNNhDyIO5tKc8mSJbzyyiu43W7lMUEQ2LVrF48//vhlWbMqQICrmVODohM9P4cu1AUAACAASURBVFEK/ujf6mgFqcfjUVLoJypVIQc5J1KJjr4/NV1eVoOeitlsxtJvGfu8ChBBaBBoerqJe968h6WzlpKYmKgYUK1evXryi1FuNzQ0QG0t+HyAX0X65PD72ETX+ALeAvgkkaeGtnFb+CIStdH+zzAaweGABQtAp5tcirvVCkeOQE+P//UqDa3uLv48tANJVg2cQr9nhN8MvMGLKT8ASfC3WVvr34bCwvMOelpdVv5V+S+GXcMTFi13epz85fBfWF+wngRNwnm1ESBAgEtHXl4en3zyyZjzr1qt5u233+YPf/gDAwMDyuNyvUXZ1A6gqamJsrIyRa1/zTXX8MgjjzB//vyLvi8XA7kGd1BQkGJmmJGRwfz585UFQKfTSXt7O5WVlVRWVlJeXk5VVRUDAwNjUrdH96dMY2MjBw4cULIg9Ho9mZmZlJSUMGfOHObNm0d+fj7R0dFTVgP9UjK6P8HfJ9nZ2SxcuFDpT4fDQXt7O0ePHiUmJmacWMLj8dDY2Ehvby/gT5U3mUwAGI1GDh48qCzIhoaGkpWVpRhSfuELXwiIL6aYqr4qttRuwSNMYJqigpbBFv5R8Q+KEoqm3MhnV/MuyjvL8XHK4roAkiCxq2UXn7Z9OuUqT5vbxodNH3Js4NhJdecoXF4XOxp2cHPBzazJmjqVpyiJ1A3U8W7Du4iI49qVBIm6/jo21WziZ8t+dlFc6gMECDB1XLZX+draWp555hmeeeYZYmJimD9/vmJckp+fj8FgQKvVKiYfU43b7eaVV17hBz/4AXFxcdx///3cfvvt5zQwCg8PRxAEZRX7VOQVf41GQ1hY2Bk/22AwjDOTEUWRpqamye9UgAABLiqnU35OxNnqjxkMBvLz88coRU+9+Xw+RQEqB0g1Gs2E6exy6uWY5+TMJZeA2CFyvP04xz89rjy9cOFCli9fPm5b9+zZw65duwgPDycpKYkZM2YQHxtL6NAQoY2NaDUaVMHBqNTBlFvL2WYrVxSl41BBq7ufF8wf8ZPY21AJAqjV/rR0tRoWLoSznYedTjh61K/sPPFaERV/Hd6FyWuf2CnxRDdsNO3le1E3sjh0FkgekCR/Kr5W61eZnmN6uyRJ7GzeyZG2I/iECZS5AqCBsvYy3qx+k/+14H9Nq2Jm9CJbgAABpobg4GBmzhyv/ElMTESj0XD//ffjdrs5dOgQjY2NmM3mCRX+8uJ4SEjIuDRmSZI4duwYzz33HPn5+ZSUlCip8BqNBq1Wq9QMvRJ/33I5F1ncEBwcTGRkJMXFxXzlK19BkiQsFgt1dXVKEPTo0aO0tLRgMpnG1NqWS87I10GXy0V5eTnl5eWAP0AYHR1NQUEBxcXFzJkzh5KSElJSUpT+lGtlX4n9OTo7ZXR/RkVFMXv27AnfIxs8JicnY7FYlP4cnVki1zZ3Op0MDg5SVlaGIAhcc8014wKebrcbq9WqHJtXcn9ebHyij03Vm+g0dU7sZn2i7vk/K/7Jd+Z9Z0rrrXePdLO1fiud9s4Jg46owOFy8M+j/2Rl5krSItKm5PuUJInawVp2NOzA6XGOVbWCkhFTN1THltotzE2YS0xoDMJk7b7PgGySNLpW6qltWzwW3q97n5vzbmZu4txpO4YnqvEfIECAC+OyDXguWbKE6667jo6ODkZGRvjkk0/YsWMHOp2O7Oxs5s6dy6JFiygpKSEpKYnw8HAMBgNBQUEXHAC1Wq289tpr3HvvvcTGxnLffffxta997ZxXgePj44mOjsZqtdLT0zNukCGKIs3NzYqLYnDwua3QyTX/AgQIcPWjVqsJOU1K96k1RUdP/CaqC+wVvdg9dsW053SBx1MX95OSkiYMnn766ac8/vjjilpJpVL5J1d6PXEGA7Hh4cSEhREfEc1nQi1eRIgCkgDDKe1LYPe5eX1oH7eELiBfn4oKEUGjQejo8JsJ5eefvqNE0Z9CLwdIAQQ1Da52Nln2TTx5kBHAK4r8ceg9XtBnESJoTvSP5FeZxsT464mewyDU5DTxbt27dDu6z/raJw4+wR2FdxAXOn3OuRaXhW5rN/kx+YHBdIAA04xc8/nOO++ksLAQr9dLX18f9fX11NXVUVNTQ21tLZ2dnVitVux2Oy6Xi5SUlAlTjg8dOsSTTz4J+BfJkpKSyMvLIy8vj6KiIvLy8khISFCM9EJCQpRx8ZX+e5fLxCxcuJCFCxcCJ53ea2traWhooKamhrq6Onp6ejCbzdjtdmw2G263e0ymlSiKDAwMsHfvXvbu3Qv4s7JSUlIoKChQygtkZ2czY8YMxUgoJCRECSxfbQQHB/Pd736X9evXU19fT319PVVVVdTX19PV1aX0pd1uH9OfSUlJSq3W0ZSXl/PQQw8hCIJSpiEnJ4f4+HgMBsOY/rwcSphdThzrO8YHxg+w+qynH7OowGQ18Yd9f+BvG/5GkPrCTbt8oo/9Hfs52HYQSZTGBx1l1LCnbQ87GnfwrbnfQiNceDjB4XWwt3UvR3qOTLwoDX6FqSixpW4Lt8y8heXpy6dEbdkw2MCbNW+evt0TbVf3VbO1fisFsQWEaM+xxNIk8Yge2s3txIfGYwgyXPHn7QABLgcu24Dn5z//eVauXKnUnamsrKSuro7Ozk56e3t57bXXeOmll4iJiWHmzJkUFRUxe/Zsli9fTm5u7nmnqJjNZl5++WV+8YtfEBkZyU9+8hP+4z/+47zcHxMSEsjPz6ehoYG6ujpWrVo1ZpBkt9s5dOgQkZGR5OTkXBUOkwECBLj4jFbGTOY8MuwcxqV3odKpQGTszQGEAWmAE/BChDoCnaQjKytr3ODL5/NhtVrHucPa7Xbsdjudg4MTb0QksB7I97fh3xHAA+wFo6aHR2e8ww2xJUTq9YSG+OuJBQ8Po/f5CImKQq/Xo9PpxipHhof9aeiCcCIwKeBB5BXLXjrdQ2fvTBV8aD3GTutRbglfBJIXVCp/DdDqan/Qc5Kp7aIksq99H/tb9uP2uc88mNZAXW8dzx56lv9e9t/TljK1pX4Lm6o28bfP/42YkJhpaSNAgABjsdvtSq3J5ORkkpOTWbFiBZIkYbfb6ejooLW1laamJlpbW1m4cOE4s0tRFDl69Kjyf7fbrTif79ixA4CwsDDS0tLIyMhQDGfy8vJYvnz5OS+qXwlotVrFIX7NmjVIkoTVaqWjo4OWlhaampowGo20trbS3d2NyWRiaGgIi8WCy+Uao6byeDyKQ/y2bduUAGtqauqY/kxLSyMhIYGoqCiio6MxGAxXRS1LQRDQ6XRkZGSQkZHBDTfcoPRnW1vbmP5saWmhr6+PoaEhZs2apZQlGE1DQwMVFRV0dXWxdetWpT/T09NJT08nJyeH7Oxs0tPTiYuLIzIykpiYGMLDwycs6/PvgtPr5H3j+9QP1E+ssByNBt6sepNvz/32hddbB/rt/exo2EGrufWswT+vx8uzZc9yU95NJIUlXVC7kiTRbm7nzZo3cbgdZ45OqKFpuIlNVZsoSSghWh99Qb89n+jj+fLn/bVSz3TYnVB5bqvfxg05N7AgacG0/Oabh5v53Z7fcfus27kx98YpCSYHCPDvzmX9KwoPD1dWct1uNz09PTQ1NdHQ0IDRaFRuhw4dYu/evQQHB/PLX/6Se+65Z0JX9LNhMpnYuHEjv/vd74iMjOT+++/nrrvuOmMAQRRFent7qa6uJiYmhry8PEVRFRwczOc+9zl+/etfs2fPHm666aYxq6Bbt26lrq6O+fPnM2/evHPvoFO243TmKQECBAgg4/K66LX34tP6UI0e0Qog+AQkj4QvxYd0k+QPfrogLzaPWzNv5frS68cpMTweD2FhYeTm5jI0NITVasXlcOD2ePCewVwJFaBjrLpTAOzAp2DxOviH6mPeDD5AWLCeMP2Jm05HxFtvEZmcTEREBFFRUURGRrJixQoKCgrQ1Nf7U9o1GkRJwuUTOe5sZ5vpCHbcJ1WtjGpbGvu3yWPnxeGPWKDPZYYmEhD9atGhIb/be0HBpPq639bPtrptNA02+YPJZ+gO8Dfzl/1/4ea8mymZUTKpNs4Fs9PMnw/9mdqOWt6tf5evz/k6KiGgrAkQ4FIhCAKhoaHk5+eTn5+vqPUlSZpQ9abT6SgqKqKnpweLxTKmbiXAyMgI1dXVVFdXA/6SJgsXLqS0tHRcwFMO/kVGRhIWFnZVBJkEQSAsLEwx1gH/otzQ0BBdXV10dXXR1tZGW1ubEgTt6emhp6eHkZGRcbVBTSYTJpOJY8eOAf7+jI2NJSkpieTkZFJTU0lPTycjI4P4+HgSExOJj4+/6vqzsLCQwsJCJbNsaGiI7u5u2tvbCQsLG3esyhlsVqtVeWx0f1ZUVAD+gLXcn0lJScyaNYs77riD0tLSi7qflwuSJFHTV8P22u0M24bPHOwEv6GO087/7PkfZifMvqBFTK/opayzjO312/F5TriUn0idHzNm8qGMl8rbynmh/AV+uuynqIXzX6R1+9zsatxFWWuZ/7M9+NuXt0Eeo4kn79859g4b8jdwY+6NF5TWXt5TzutHXz/Z7lk42nmUbXXbyIvJIzI48uxvOAc8Pg97Wvfwfu372Nw2FqUuIjYk9uxvDBAgwBm5rAOeowkKCiItLY20tDSWLFlCW1sbn376Kdu2baO1tRWn06k4E57J7fx0uFwuNm/ezOOPP05XVxdf+tKXiI+PZ/v27eNeW1BQQFZWFmq1Gp/Px8GDB/nFL37BNddcw49//GOys7MBfwrqLbfcwrvvvsvu3bv54x//yG233UZ4eDiVlZX8/ve/R6fTcdtttynvOR+Gh4d544036OjoICwsjIiICCIjIxVHzLCwMHQ6HTqdTlGBBQKjAQL8e+LyudCr9YRoTknHEUDySlhVVtJj07l16a3+x0VIi0rjC4u+QHZU9riBZVBQEHfccQcLFixgZGSEEYsF08GDDPb0MGS3Y3E4sNjtDNlsDNlGMNvsWOxOgsM0xEaGU0snHnwICFwXOhMG4WNfDQA+UcRsd2C2n1ID+cRkHlDcjh97/HFyU1LQdHf7FZmCgMXp5I19+3m1fh81UgeSToIQ0Ok1EAyuIC8EQ0hwECWhmcTpwtGpNahVKqIIpcc7fCLgeQKVCtraICsLJqGktbgshAWFcVP+TZM/5/r86oXpCHhubdjKZ62fISKy6dgmVmSsID0yfcrbCRAgwPkhq/UnQq1W893vfpd169bR1dVFa2srra2ttLe309TURE9Pz7gxsNfrJTIycsKF+3379vHSSy+h1WrJzMwkIyODlJQUMjMziY+PJyQk5KpI3Var1cTFxREXF8ecOXOQJAmn08nQ0BCDg4P09fXR09NDS0sLzc3NtLe309LSQm9v74T9KQdIjxw5AvjrrcbExBATE0N8fDxJSUlkZmaSnp6uqG1jY2PPS4hxuSGrlBMSEkhISKCk5PTXqeLiYr70pS/R3NxMU1MTfX192Gy2cara7u5uuru7OXz4MMeOHWPRokXjAp5ut5vt27cTGhpKZmYmcXFxhISEXHWp8BISI+4RsqKyiNHHnD3geYIgTRC9tt4LCnh6fB7sHjvzkuYxP8lvluaTfOxs3YnV5U+tTwhNoDSxlGD1icUTEfpsfbi9bvTa8zN1lNsxO82sy12HoBJQCSq6bd2UdZXhEl0ICKRGprIgcYESbJVEiW5rt/94uoAprXHIyNLUpQiayX2IJEqIkojdbZ/ygGe7pZ0PjB/Q6+hlu3E7e1v38vmZn5/SNgIE+Hfkigh4+nw+TCYTdXV1lJeXc/ToUWpraxkYGGBwcFCpd1RUVMSCBQvQ6c7dMc5qtbJ3714aGxuRJImPP/6YgwcPjnM/lySJ//2//zff/e53UavVSJLE0NAQx44dY8aMGbhcLuW1giCQl5fHz3/+cx566CFeeukldu7ciU6nUy789957L1/96lfPO51dEARGRkZ46623eP/995XApk6nIzg4GL1ej8FgICYmhtjYWGbMmMH8+fNJTU1VDJNGp4NeDXWeAgS4Yhm9WKOkZE8tIdqQ0wa53E4Xbap2FqctYcXcFcrAUqVSnbaupFqtJjs7W1m0kex2PAYDLpsNtyTh8vlweb04nU4cLiculwubw4lJcrEj8jDH7R3+D5IkFofksiZ6Nm1xA1gcDhxuN063G1GaqMioH4/Hg0ajQR8aiqqvD1wuxajI7HTyQWUFOw9WIKnxKxXUIGpP1KbS+G9enYgnyocuUkNsWBgFyclsWLyEKG0IfkmBHx8gDQygGRnxp7afhZTwFO5ZfA9e8dxqLYcGja+7eqGYnCaeOfwMHtEDKjjcdZhtxm3cPe/ugONogABXACqVitzcXHJzcxXX7ZGRESwWCyaTid7eXurr62lqaqKurg6j0Uhvby/FxcXj1IZer5eKigq2bNmCzWYjJCSEsLAwwsPDiYqKIjk5WUk5zs/PJycnh+jo6KsiLV4QBPR6vVJaAE6WYRndn11dXTQ0NIzpz8HBwXEmpHL5lvb2dsD/PYWGhir9KafF5+fnk5mZqdzHxsae13zlSkAQBNasWcOiRYuwWCwMDw/T1dU15vhsamqiv79/zLxJLslwKn19ffz4xz9GEARiYmJISUlRjs+ZM2eSmZlJZGTk9PanJPnHaF6v/2+12j/WmMJxmoBASWIJGasyxs0/z/AmkCA29MKUgEHqIFZlrWJh8kJlHljTV8Pult3+F0gQrYvm6/O+zsLkhSdNLgUBrfrC1Mw6tY6vz/06d86+U/nM//fZ/6O8sxwXLv/+6WP5wdIfnEyflyBYG3zBWSorM1eyKHnROalENWrNlJcE8vg8lHWWsadtD6IgYnaaefKzJ1mbu5ZgzZV/3g0Q4FJy2QY8XS4XnZ2d7N+/n/3793P48GHa29uVgtmyedGXv/xlFi5cyOzZs5UaMOdzwdPr9WzYsIHMzEwkSTrjhWb+/PlKjVC1Wk1paSkPP/ywktIymqCgIFavXk1qaiq7du3i6NGjOBwObrjhBtasWcPChQsnrH1zLsg1oOx2+4TPy4oB2Snxv/7rv7j55ptxu92KW6JarVZcFOWbVqtVHpOLigccFgMEmEIkyT949nrB5/PfZFQq/2BaHlRPkZJBo9KgUWnGty1J2EasSG43UQ4fiV4B4uIgIuKk+c8kECwWglwugnS6004EREmg2tnGrzqMeEXRry4FXh3ax91pq9j5y18xYrPQb/HfBkZGGBgZoddkottkotdkos9qZcTrxWazERoaSmx0NGqLZUw7VqeTYasVyceYdHLPKbnlbrwcVTdTrWpHo1ZxbX4+/7lkDUGqIPxvFkCr5Z1PP+XVTz4hcedOYjIzSUxIIDExkRkzZhAXF4fBYFCMQgD0Wj0pESmT7rvpZGvDViraKxQTgn5HP1uOb2FF5gryY85gAhUgQIDLDjmoFhoaqrhj+3w+Vq1ahcvlwuFwYLFYaGpqIjMzc9yiutlsxmg0YrValTqNVquV7m6/uZparVYWzoODgwkLCyMvL4958+bxzW9+k4yMjIu9y9OKSqXCYDBgMBiYMWOG4krucrlwuVxKBpnRaOT48eMcP36cyspKWlpaGBwcHGeINDIywsjICF1dXQAcOnSI4OBgpU+joqLIzc1VzJFKSkpITk4mOjr6UnXBlCKXaggNDZ2wPx0OB2azmcbGRsW86+jRo2RnZ5OSMv6a2dzcTF1dnfJ/jUajiDv0ej2RkZFKWYji4mKKiopISUkhPDz8wlXKDgd0dYHVChYLyHMtnc4/PtLrISkJwsMvrB1OGHMFRxARfGHzwvNBrVITGxKrpFCLkshj+x/D5rQp44ZWSyuHuw6zLmcdYbqpUyyrVWqSw5OV/9cP1FPWVoZdtPvHh4JEy1ALBzoO8H8W/58pnYPGh8bD1K8xnzN9tj7eq32PnpEefyq/GvY07eGt2re4s+jOS715AQJc0Vy2Ac/nnnuO++67T3ECVKvVJCYmsnr1alatWsW1115LXl4earVaUSVeyAlQr9ezbt061q5de9ZVtdEqSJVKRVFREYWFhcr/RyMXAS8uLqawsFAZFMlqyouRjiHX3PGeUFlVV1ezfPlydDodPp8PQRDGBHnle7lP5W2VA5+nBkR1Op3y+FR9HwECXNVIkt8Ex+U6qRaQEQT//30+/2vAH/DU6fxp1FPxu3I6we1WAp0ALq8Xk8UCHg+hXV3+Qb5OB/HxkJPjD35OZuIwPOxXQajVY/drFC4E3hspo8rZNiYVqcXVz5Mj2/hj0veBWCRBAJUKSaXyn6NEEcnnA68Xb1QUw3l59AwNMWK1Mjs/H6G6esw2erxeEASCNBrc3jOrLD0+Hx6fDzwQHWZAq9XgD8Oe2EBB4KDRyKayMoRDh8acHwVBQKPRKCmNcXFxSsrfrFmzuOGGG5gxY8bZ+26asLgsvFT5EiPeEcWIQBRF9nfs58PmD8mOyvYHwQMECHDFolarlQWXqKgoZsyYQV5e3oRjMVEUSU9Pp6CggPb29jG1FsEfPD11Ib2+vp6amho+//nxKZZ2u53Ozk6SkpKUOvZXMvI5XaPRKPsjSRI5OTnceOONiKKo1PCvqamhqqqKzz77TDHpmag/bTYbNpsNgK6uLmpqasbMBZKTkykuLmb27NksWLCAwsJCUlJSCAoKuuLH0xP1Z1JSEgUFBXzuc59DFEV8Ph9utxuDwTDu/R9//PGY/8tzGrk/Ozs7qampUeYfWq2WjIwMnnnmGa699trzC3paLNDU5C9jY7f7xzMnyuUA/v+fWCCgpsYf9MzJgdiro+bi0Z6jbK3dilflVVSkdredHfU7WJe7juUZy6elXUmS2GrcSk1fDZJwcgw55B7inZp3WJezjplxM6el7UuFT/JRM1DD1sat/n0+cYh5fB4e/OhB1uetxxA0/ncRIECAyXHZznA6Ojqw2+1oNBoWLVrE3XffzcqVK0lISFDUhlM5ADjfAN1k3zfaRXkqkSSJhIQE7r77bgoKCuju7lYKs8uOlGazGavVqgSP9+zZw5w5c7j22muJiIgY05fy/alBX1EUlZXZifZNHrTJSlI5+KnVagkKClICohO1dbr9OtvzAQJcsTid/mAnTD6AOTICWi0EB5+f2lMQ/AFOp/NkIFUQECUJp9uNxeHAZbcTabEQ6vH4g6xeL7S3+wf1WVmQnw+hoWfeZo/nLPukYsAzwIvmT/wZUfJLT9w/N/wh/xG5mqLgTAS8Y1WvykeoCAoKImTGDJIzM/2PORwn9wtAkiicMYO/33OPohI1jdh5pWMPmzoOgBX/7YQxEy7AB8FoSYiMRNBqx3ye5PHQbzafNgPA7XaPSWuUmTt3Lrm5uWMCnpIk0djYyBNPPIFKpSIlJYXk5GQSEhKIjIwkIiICg8FAUFCQcl6Vb/L1b7JIksTWhq1UdlQiCqNKJqhgyDHEuzXvcn369cyMnXnFT6oDBAhwkjONT+Pi4vjFL37BT37yE1pbW6mvr6eyspLDhw9jNBrp6+vDbrfj8XjweDz4TpyHk5KSJlR3fvzxx2zYsIG8vDwKCgqYM2cOJSUlZGVlERcXh06nU8aCF2uxf6oZ3Z/yWF72Fli7di3gvw40NTVx/Phxjh07Rnl5OY2NjXR1deHxeHC73Xi9Xnw+n3ItkYUQskP85s2bATAYDGRlZZGfn09JSQklJSVKfVV5bC2PteXtu5I4tT+1Wu1pSyasXbsWr9fLkSNHlFR4p9OJ2+1Wjk9ZRQooxkoTeRb09PSwb98+goODyc7OJiYmhqCgoJP9KYoInZ3+IKbF4l9EnWjudmJB9kSD0NzsHyvl5vrHS3r9tJQluhh4RS+vVL1Ct6V77BhNBVX9Vbzf8D5zEuYQpY+a8raNQ0a2129n0DU41qVegsreSrbUbyE7Opsg9fmVgrscsTgtvFz5MoPWwbGRGQ3Ud9XzQvkL3LPwnoDJZIAA58llG/CMjY0lNTUVk8lEZWUlf/jDH9i6dSulpaXMmTOHtLQ0oqKiiIiIIDg4+IocPE0Ver2eefPmsX79+jGPy2lKJpOJ4eFhhoeHGRwcpKenB0mS0Gg0uN1uZfApr1jLDqGjb/Jjo9N2JhpcTRQwPTXAeaoyavRjoyf3owOko1Wjo18r/z26vVOVqoHgaIBLjqza9HjGqCrPG7X6pNrzbJ81uu2JVI6ShCCKqD0eoux24s1mVJJ0soao5kT6e0ODX705bx5ERZ3/QF7Q8qp5L7WuTjj1tC2Azefi0cF3+XPS9wg+UxtyPa3R/z8FrVrNjOhoZsTGgkqNRXLzaNNmKDrxAhFwA84TNwfcoJ7DtwpWgW9sPVVJFNEHBZGbmIgHcAkCLpdLmcCOnsSOJjo6epxiRZIkOjo6eOWVVxgYGBjzXGhoKNHR0YrxXExMDAkJCcTHx5OQkMCNN944zuROFEWGh4cBlKCCfB4ddA7ybv279Ln6xva3BKjg0/ZP2dOyh8zIzAsyHQgQIMCVh06nIy8vj7y8PG6++WZEUcRsNtPc3IzRaKS6upr6+npaWloYHh6mqKiIyMjxRh1lZWV4PB7FIf6NN95AEARiY2PJyMggJyeH2bNnk5ubS0pKCtHR0YSHhxMdHX1VuJnLBAUFUVBQQEFBAbfe6jf+GxgYoKWlhcbGRqqqqqivr6etrY3BwUGllIDT6RzjEA9+b4HKykoqKyvZtGkTgiAQHx9PZmYmeXl5FBYWkpOTQ0ZGBmFhYYSFhWEwGNDpdGg0misuAHomSktLKS0txefzMTQ0REtLCw0NDVRVVdHQ0EBraysmk4mRkRGlP7OysoiPjx83P6ytreWhhx6iqqqKhIQEMjIyyM3NZfbs2WRnZpIiikSaTIRpNIQGBxMMaFQq/3hDHhudijxWcrv9gVKzGWbN8qe8X4Hz0/LucnYZdykp5QoCeH1e3q19l3V561iWtmxKg3Aen4ePWj7iSPcRpb3RbZtcJrbVbWNNKAu5hAAAIABJREFU9hrmJMy5ao7xmv4aNtdtHj8mBlDD80eeZ33+ejIiMy72pgUIcFVw2QY877zzTjIzM6moqKC2tpaWlhY+++wztm7dSnBwMJmZmRQXF1NcXExBQQHJyckkJiYSFRWFVqu9ak6Ck+HUQKSMIAjKICg1NXXc83LRe5vNhsvlwufzKTd5Au/xeLDZbIpS1GQyYbVacTgcOBwO7HY7TqdzzIRfbnv09k0WOfh6auBzdBq9wWAgIiJCcaOX6+bp9Xr0er2SYn+uitpAYDTAtCAHHPv6oL9/YsWi/NIT/0pM4liMjobk5DOmjiMI/kBnTw8MDY17nQAgSWh9PnQeL3qvF0EUxw/oBcHfTn8/HDwIpaX+tK2JzrNnrKGsotfTz3PDu8Zki5+6UVtHjvCpvZpVhhKQJg7SotEo5kTAybT/Uyckoui/CQJvD3/MAXPD2EGlDgg+uS0Num4iUwyjvYr8QWHgRxs28JVlyzBHRTEQFsbg4CBDQ0OYTCZlYUmebNntdmw2G+np6ePceUVRZGhoaJz5BaCkPZ6qFJV57bXXxgU8LRYLTz/9NMPDw0qwVA6Y1lnrOFB+AI/dA1ogCMW8CcGvLHit+jWuz7ye/Jj8f6trZ4AAAcaiUqmIiooiKiqKefPmAX5juJ6eHtrb2yesOS+KIjU1NajVamUMCP4xVX9/P/39/ZSVlfHyyy8TFBREUlISKSkpLF68mO9///tkyir9Ue+TJOmqETLExsYSGxtLaWkpX/rSl3C5XPT29tLR0UFjYyONjY20trYqQbuBgQFMJhN2u31cf/b29tLb28uBAwcAv+BhxowZpKSkKOaB6enpSj3Q2NhYwsPDCQ0NvSr6U61WExcXR1xcHAsWLAD8qtquri46OzsxGo0YjUaam5vJy8sbd7yKoqi4wo/+e//+/QDodTpmREaSFhNDTmIimQkJZMbFkRQXR0FSEglhE9StHJ3irlL57zs6/PfFxf7anlfQddXusbO1YSvGIeNYhaWMCmoHa9lcu5nZCbOJCp46lWebuY3tDdvpt/efdnx4qPsQOxt3khOdc1WkeXtFL8+XPz9e3SmjAWO/kY3HNvKja350VSlbAwS4WFy2Ac+kpCRuu+02NmzYwNDQEK2trRiNRmpra2loaMBoNLJ9+3Y2bdpETEwMGRkZ5OXl8eUvf5mlS5detc6HU8noovenQ5IkJVVTvlmtVsxms3KTHS1lFanValWcLuXn7Xa7Uvjde5ZaemdDp9Mpdark7Y+MjCQ8PFwJgEZFRSkTf4PBoLhlyreQkBCleLxWqw0EOwNML4OD/oDn0NDJx9TSKQM6AZ/kZsBnJUEzwQBSAsQTo09J8isJEhIgMfH0g2lRhM5O6O2FE7WuTn2tIPkjj2bRgQ0VBlUwnC7gqtH4lQtHj8LChX71wqnIioaJlBCCmhdNu6lzdU+8ku3vBoZ9Np4f/pB5wTlEqcc6pSuEhPhT/GW0WjAY/MHdcRM7Ff3eYV4w7R6b1s2JXR21u8etnTw7sJ0H4+9k9HKJIAhkxseTGRPj3/dRaZ3ywpAc5JTPkSaTidjY2HFmdpIkodfrKSkpUc6RDocDp/P/s/fmUXLc5d3vp6r3faZn31eNNmu3bEtesDHYBtvgQHiN2UIIJG+Ck3AvCTfnkpNzAifJGwiGmMB7DRj8QsCAF4yNLa+yLMuWZGvfZjSj2dee6el9eq+q+0dNlbpnekYjW7v7e065re6a2rr6V8/veb7P95ssyPTJRSFDB7/fz+OPP87Ro0cBdJ00p8NJSkwRlIJqotM6u2hJ3nqgBXYP7+bl3pdpKWnBbDDr55JbQLqcE6Ez6RnSUhqP1XPBW8ISmYRuFHY5X8OlQlEUMnIGSZaKjOErBCaTiYaGhoKFcw1333035eXluqTS2NgYwWCQmZmZvPgqnU4zMDDAwMAAiqLw2c9+Nm87Gvt9165d2O126urqqKmpobS0VDeDu9xhsVj0VvitW7cCatFqcnJSTywPDw8zMDDA+Pg4o6OjjI6OEg6Hdc1KDYlEgr6+Pvr6+ti5cyeiKOJ2u6msrKSmpoaGhgY9GVpVVZVHDrFarVfEmGQ2m2lubqa5uZnrr78eSZKIRqNIkjQv4ZnJZEgkEvocIpFI5N2fiVSKPp+PPp+PHSdOIAClDgfVpaX8x598mjuu3UwsGUYQDDgNVpAVAjNpbCYTNs2oUes+GRtTO3HWr1df3wUURSEtpYmlY+fcHXwuOv2dbO/dTiQdWTDpKEsyT5x4gj9a+Udsbdh6Tp6raSnNW6NvsWtwl1r0X2Df0VSUpzqf4gNtH7giWJ6HJw7z1ImnFo6JgRlphqdPPM2Hl32Y9dXrL9zBXSBIskQ0HcUkmnCYL6wGdCqbQlIk7KYr4/myFCQyCURBxGy4/PWhl4pLNuGpIbead/XVVxOPx5menmZ0dJSenh62bdvG888/z86dO3n99depq6tj8+bNxYTnOYJmumSxWCgtLVzFk2WZTCajT9aTyaTOUorFYvprMBjU2VCRSET/t/aeNslOJBJ5Ve250LREtRbOQsh1F7VardjtdpxOp54gdTqdeL1ePSmqtVZ5vV5KS0spKSnB4XDorFGj8ZL/qRRxqSIeV4Xvtft1VjdzbNrGTNqgx3SiYKAvNc7vIgf5m/KPYNBU4mf/azHI1JYmMRtmg/NEAnp7VfZAgRZDFEV1Fe3vV1+1FvW5EATAwO9j+0jJKf609A6MLFKUMBjUc+npgbVr5wfyHo+qM5pMztmfgaH0BD8Kvlw4kM1BBoldM128Gj/Kx1zXzV9BFNVzzt2+IBS+DgCCyLPRAxxJDC6+YwABHgu/ycfd17He1prPMNXkCOYUiUwmk86qXAoMBgPXXHMN3/ve9/QxcmZmRpcdCQaDRKNRfYycnp4mFAqRTqepra2dt73p6ek8feVsNqsXpBaECdgANEMim+CRQ49wV8ddNLgb2LZtG7/97W8pLy8vOEZq72mTRrvdfs71qc8ldgzu4NjkMf722r/FaiysEXc+IMkSz/c+T4mlhK2NW7EYrvy4JCtn2Tuyl7HoGH+86o8xiJfufVHEuYEoitx777186EMfIhQKMTU1hc/nY3h4WGcunjp1itHRUYLBIOl0GoDm5uZ5caUsyxw6dIh//Md/RBRF3QROS2g1NjbS1tZGTU0NJSUl89znL1doxfj29nZATcxpcbLGkh0aGqKvr09/HRsbY3p6Oo9EIMuy3nHQ3d0NqM8nj8dDaWmpLo3S1NREU1MTzc3NtLa2Ul1djdfrvaTH8aXCYDAs+Cw2Go3cfPPNeL1e/d4cHBzk1PHjqu/BzIxqXjgLBQjMzDCTyjAdLef4qI3XQl04RTtbnSsIx0I89MIvcNssNFdX01RVTVtlBVUeDyV2O8bxcbUo3dj4rs5JQWHf2D52De7i/mvvP29JoXgmzo7+HRz2HVbfWChWE2EwMMjPD/+czbWbsRjf/bPNH/fzzMlnmJyZXDT5hwj7x/bzYs+LLPMuu+AJsnOJrJzle3u/R2AmoMZkC0ARFLr8XTzV9RTLy5ZfccXESCrCfx/9b9pK2/jwsg9f0H3vGtrFYHiQL2z4wgXd78XES30vIckSH1n+EQzC5T/mLwWXVRYnk8kQCoXo7Oxk3759HDlyhEOHDukukppgdZGtd2EhiqKeFC3U7gSnk6Lakk6n8xKkqVSKaDSqT+7D4TDT09NMTk4yNTXF5OSkzh7VEgSLQUuKLjTh11wcNb07s9msJ0e1c5mbBM11YK6srNTZo06nE5vNdkW0CxVxjqEo4PerrENZ1pmHKRH+15Mr2dNzulIvIBCREoxkr+YtS928OLO5Is53/+wADSXJ0yyCYFBlEbhc80X1JUnd79TUwslOAER8UpjfhN6gNzPBna7N1BpLKciqzMXYGNTUqG31ubBYVOZpf/8cpqXAr8KvM5BeoFVpDsalII+H3+R6+0qqDO7TxyPLKruzomL+H1VXq58lkzn7FvFlgzwe2U1Yjs//m7kQYDAzxc9DO1hlacAsiOgUUFlWW/kXKP4sFaIoUlZWRlnZ6e9/KWNkPB6nqqpq3vYMBgPLli1DlmWi0SgzMzMk4gkkeeHCEQpqgG0ERDg8dpjfHP8NX73uq+zdu5cnnngCk8mkj49ms3leIcnpdFJWVobX66Wjo4O/+Iu/mNcxoJlKWK3WizKZjqVjPHniSZ7reY7bWm9jQ82GC7bv4cgwvz74axRRYWXFSqqd1Rds3xcLM5kZHt7/MFORKdZXr2d5+fKLfUhFXAAYjUY9XmptbUVRFH3MikajhMNhAoGA3sLd1dXF1q1b5yWmksmkrhkqyzKnTp0C0AvXbrcbj8dDZWUlra2ttLW1sWrVKl3L8kohO5hMJv0ZoSVBNTKBdj2npqbo6+ujv7+fEydO0N3dzcTEBKFQKG9bmUwGv9+P3++np6cHQRCwWq04HA5cLhcej4eKigo6OjpoaWlh1apVelL5SmmF12AwGGhtbaW5uTnv/gxt346/r49Tk5P0+Xx0jo5yeGCKsWAISY6QkVz86xP3YLV4mcgsxyQYqTC6SWf3c3LsbQxiAofFgttmx2O3UV1SQmt1NW3V1azu6qLtox+lsq5unrzNUpGW0jxy4BHeHnibaxqu4ZbmW87xlVHRH+zn+e7nCSfDiycdZw2MHj/2OJ9f/3m2Nmx9V/uVZIkjviO82Ptinkv5QvtOZpP84ugvuHvl3awoW3HZstTeHnub3x/7PSwhNIqkI7x48kXuaL+D6+oLEAEuUyiKwkBogP/c85/c3HQz19Zde95ZzBokReLBvQ8yEZrg/S3vf09opIYSIf778H8zEB7gfc3vw2vzXuxDuiC4pBOeWsDU2dnJ7t27eeONNzh8+LDuHqnpTrpcLjZu3Mj111/PRz7ykSum7eVKQm5StBA0zSZJkpBleZ6WaDabJZ1O60Gzpp03MTGht09NTEwQDAYJh8NEo9GC+ni5+0un0zrToBAEQcBgMCyqJ6q1zmuBaXV1NbW1tdTW1lJVVUVpaakeoFsslsv2oVzEO0QyqbaTJxJ5CUcZhb5JJ/v75j5oSoFaDhTaVNpAWhLJIX6q+pw+H9TWzmc3plJqO3s2W9hhVIfI9tgxjiYHGckEeCjwAv9c9VlQUov8zez2x8fV5GYuA1oU1XbvoaHTSV7BQHdqhEeCO5BkeUkJz4wssTPayQuug3zac+PpeFCS1MRmoYmDzaYuOb99BfhDdD8HZnqRFPn0m3O1qXLyu0kybIsc5E7X1dzqWAsa41WW1f2eB8b3mcZIdfdywTFkzZo1PPzww2QyGYLBIH6/n1c7X+U/XvgPEtMJ1Y0+OrtoBk0CagQgAVnViODbu77NJ9d+komxibwxcqECU+4YuWnTJv70T/90XsLztdde4+GHHyadTlNWVkZNTQ21tbXU1NTorZXaGHk+Wiz3juzlrYG3mAhM8N093+Xnf/Tzc7r9hSArMntG9vDmyJsEE0E+ufaTfHT5R694xuPxyeP8vuv3GDDwUt9LtHnbMIqXdKhZxHmAllSzWq14vepzTpZltmzZoncEaUnMXCSTSfr7++fp0muFn8CsLExu0Vorvrzwwgt0dHRcmBO8CNCup1Yok2WZG2+8kXQ6TSqVIpFIMDw8TE9PD8ePH9cd4oeHh/NYoIqi6Dr8mmmeIAjs3LlTL2xp2v+amc+GDRtobm6murr6ijCZEkVR7+AqkyQUlwtl3TpuBNKyTEpJ8bMXvPz74w6mY73ISpzO0VbULKB6Pw8B0IfqfJgmkU7jj0bV7QsCpmPHMJtMWE0mLN//Pu3LlrFs2TI2bNjABz/4QVpbW5ecTD7pP8ljnY+RTCV5uutpNtVswm1xn9Nrksgk2N63nZ19O1msyUeHAMFgkH/a/k889+nnMBvfOds6nonz47d/zHQgR8dSRNca16WHco7rxNAJHjnwCP/ygX/BKFx+z5hUNsV33vwO0UhUlRw6A1dLQWH/6H6ePPEkK8tX4rEWJhhdbsjKWR47/hh9vj4MsoE9I3u4s+POC7LvXUO7eOHkC1gNVh478Rh/t/XvEJYyQbmM8frQ6xwcOUh/oJ9Hjz7Kl6/58sU+pAuCS3aEOHjwIA899BC7d+/m5MmTpNNpnbkpCAKtra1s3ryZD3/4w1x77bW0trZeEa0Y71XkOq8vhIWYu7nva63ufr9fT4hqekjDw8NMTEwQCAQIBoOEQqFFtfIURTmj3mghY5HcCbvFYtEZUNpkv76+Xtdvqq6u1s1FSkpKrohAsohZKIqqmzk5eVrMPn+Fs9ve3Gewdp+FwxAKqa3t2j4URX0vEDiDQ6jIlBTmD9G3GJECKILCg4FtfLHkVhrMlajZsAUgy6o2aTgMZXOqsWVlKvNzaGhWz1PGLpj4duVnyQiLbLPAKbebq1AUGYRZXVCzGTo6Cp9XIKCed85niiKx3tzId2r+BHk2apYReDS8k23RQyBArbGUv6n4MA2mct0wyiQYqDG6yYuwDQaVNZtMqm37FxgLjY8Wi0VnftbNMm5XXLuCjbdvRFZyEgeaK30cSAE2ICc/KWUlTKhJhIqKCsLh8KJFodwxcqF2yP7+fl599VUmJyf193LHSKvVmlc4qqyspKOjg0984hOsW7du0etxJsQzcbb1bKMn3AMm+NXhX3H/NfdzTd0172q7S8F4dJznu59nJDoCMvzX3v/iQ+0fwiZeWa1oc/HQvocIzYTAAM92Psv7W97PqopVF/uwirgEkFvUcToLm4243W7uv/9+rr32Wvbu3cuhQ4fo6enJGz8gv2gdi8Uwm81UV89nUB86dIhf/OIXrFq1inXr1tHS0pLHrL+ckXs9XS4XiqJQX1/Pli1bAPUahcNh+vr66Ozs5I033tCToFqiU4NGMNG6rfx+P/39/ezcuVMfr0tLS1m+fDlr167luuuuY82aNbS1teHxeC7fYr6iwMAAQiaDYDJhASyiiMtkxm2tRhTXADfPrlzoHCOAG8gnWMiKQiqbJZXNEk0kIBJhZHSUHTt2UFZWhslkmmfWpZE9Cs0DHtz7IOFYGAzwcs/L3NVxF7e23vruzz8HoiCytWErv7r3Vwji0r9PAYGMksHMO094mgwmPr3u03xs5cdABKNo5IjvCD/c80NCmRAGDLyv9X18ceMX9ZhGkRXKHGWXbYJKVmS+sOELfGrNp5b8N4qsUOOsueBa5OcTUzNT/PTgT1FEhe5gN8+cfIbNdZupdFSe+Y/fBWRF5ru7v0sqkyJFiqeOP8XdHXezonzFed3vxUQ4FeYP3X9gIDKAJEj8265/476r7sNrv/JZnpdswvM3v/kNP/7xj3WDmoaGBlavXs3111/P1q1bWbNmDW73ua1uFXFpY6GAKvd9rWJbSOdOQywW013nA4EAPp9Pd3gcHx/XNfQCgQDhcFhnmua+yrKsL3MTsbn/TiaTuuD8QseumS5piU+NJaqJ9ee21btcLt2B3mg06szTXGf7xa7VkqGJrivK6UUQZtl6whmSaEUA6jWLx+e0V59jCIJqXhSL5bXMI8tqu/sZ2J0y8Ea8m7fip9T7VoBQdoZvTz/NAzVfWvgBod1fySREo6pjfO49ZzTCihVq8nFWP7TeVE59SQ1LonfqUECRAOn0fbh2bWGzJFDlAyQpj4EpCgKbHMvY5FiBRuscyPj4X/4nZ88FpqQINtHMx0rfz+k0pjKr35mTMBQElT0aCKis2ksQ2m+/xl7DPW33nPXfK4rC93/wfb4Z+qZuRufz+fTxcXR0VC8aBQIBIpEI6XSa+vr6eQlZSZIIBoMkk8l5+9CgMY3Gxsb096qqqli7du28hGcsFuPP/uzPyGQyNDQ06AYcWlHJ6/Xi8Xj0cXH/1H5e632NlJQCEaS0xDd3fpPHP/H4OdEcWwiSLHHYd5jtfdvVN4ywo38HT598mv+x+n9cvsmBM+DwxGGeOPGEHlm+MfIGO/p30FraekG1U4u4fGEymVixYgUrVqzgs5/9LLIsMzExwcDAAEeOHGH//v309PQwNDREJBIhlUqRTqdZvXp1wTnBnj17+NnPfkYwGEQURerr62ltbWX9+vWsX7+eZcuW0djYmGfQZjQaEUXx0v6das/DnDhN0KRrZuM0QRQpLS1l06ZNbNq0ic985jNkMhl8Ph99fX0cOXKEgwcP0tPTQ29vL8lkUk8iZ7NZnWWrjdeBQIDdu3eze/duHnroIcxmM7W1tbS1tenXs62tjfr6ehwOhy4ZZTKZLt22eElSn+cF4ySNVrjYffA3wCeAAQziQZor3qS65DDD0wFiySSpTIaMJJGVJOTZ61hZWUl9fX1egVBRFMbHx/n7v/97Jicn2bRpE+vWrWP5suUknUl+ffjXuvxMZ6CT53qeY0PNhnPajmoxWthUt4lNdZu0g1LvL+0eg/x5wKJSSWcHq9HKx1Z9TP93Rsrw9tjb6rMbkJDwx/ysrlzNmso1l/Zvc4mwmWwXXK/yUoOsyPzw7R/iC/pUM00Jnu9+nntW3sNtbbedt8SugsKOgR1s79mua6d2Tnby9MmnaSttw2S48shHiqKwd2Qvbw68SVbJghFG/aP819v/xddv/PoV3310ySY86+vrue2229i0aRObN29m7dq11NbWXjH6PEVcPGi6m4XcjkGtsuY6zYdCISYnJ5mcnMTn8+H3+/VEQCAQIBaL6S1FmtaeFjBmMhmy2eyi7FRNl3RkZKTgOgaDQddacrvduvC8tpSVlVFSUqJP+O12e54WqeZErwXyizouy7Ka0JmZURNZsZjauqwoahLJbledsF0utXXYbD5nAc8VB1lWr+MiBlznBFpiNZM5nejTDIsW1TMW8GejvBTdR3/WdzqmF+C34Tf5tOcmrrUvZ9HepmxWTXrOdWQXBDUpuXw5HD+urqPTC9/hOQqC2irf1rbwOpHIAp/NJk0BRTDzi+BLnEiN6BpVGUXi/wu+xG3ODayw1LKgfqkgqN9nJHLJJjzfLbQijMPh0Nmic5E7RmrayyUlJVjnsF5lWcbj8bBy5Uqi0eiSx8iF2FqhUIht27YRnW0b1JA7RrpcLn2MnBKmOBI6Ai1AI2CCV069wvOnnueuZXcRDoeJx+NYLBZ9cq5JlrzTCbqiKAQSAZ7ueprhyLB6jwmgSAr//sa/88G2D16RmknpbJpvv/lt4ok4GtEnmoryxIknuLnlZlaWr7wiJqlFXFiIoqjLBG3dulVvxx4bG6Ovr48TJ05w8uRJrr766nl/K0kSPT09esFFlmWGhoYYGhpix44dALhcLmpqamhra9MZi8uXL2fNmjV6K/4lAy0BlUrlx2jJpPq+FqO5XGqcZrfPi9FMJhP19fXU19dz0003AWohaXx8nL6+Po4fP05PTw99fX34fD7C4TCRSEQvTOUinU4zMDDAwMAAr7zyCoIg4HK5dFd4TQu0vb2d8vJy3G43LpcLu92O2Wy+NJKg8bhaNH7HY5MZaAKaMBtv5KPXfJR/vvd1RqZD9Pl8HBsZoW9sjFM+H5PJJJF0mvb29oKkDL/fz44dOxgfH+eVV14BoKy0DIPHQNwchyrAC5Jb4g/SH7jJexM3tt2oEyHOCRRFjXGSSfUei8XU+0wzRTQaVdNG7R6z2cBkOqfzAEVR2Du6lxdOvkBCSuix6anpUzxy6BG+ecs3L2ujoiJOYyg0xE/2/yRPxmAwPMgTx59gQ/UGKh2V5yVuiKfj/PTAT4ln4rp+ajAd5NmuZ/lg6wdZX73+iotXQskQr/S+Qnew+/R8zwiP7H+ET6z6BCsrVl7U4zvfuGQTnvfddx/33XffFdN6UsTlA1EUdcfMhSb8mUxGd5XXGKO52qJaolRjQuWuq/2/NvFPpVKLttZLkkQkEiESiSzIFDWZTNjtdj1RoZks5bKecpOimmC95lhvs9mwKgqWaBTL1BTmSERNoBVK1mlBdVWVuni9qlFNEfnIZtVAUZbPoKF5DhCLqUG7bbZdVlHUfS/ywJaAQ6kBXp45hqQoeQnPgBTjx8GXWW1rwimYWLD9XpbVCUM2O9+t3WiEhgb1s95eNXiemxhdChRFDajr62H16oXZslqQvuj2DYxkJngktCP/bQE6UyP8MrSDb1R/DkFZJDEriuo5v4eRO0YuBqPRyL333sutt946b4wMh8N5Y2Q8HmdmZoZYLEZZWRnl5eXztjc9PT2PLQr5Y2ThAwEaABFSmRQ/OfATNpRt4MXfvcirr72K1+vVi0maA702PmqL3W7HYrFgs9lwOBwLBsMKCif8J3iu5zn1DW01AxwcOcgvj/6S+zfff8UF0/vH9/P0iafzo0oR9gzvYXvfdlpKWq44Z9kiLjwEQcBut9Pe3k57ezu33Xabrvk+F+l0GovFQnNzM36/X2eE5kIr2nR3d7Nt2zYMBgObN2/m3/7t37j55pvnbW90dBSn04nH47nwDvGxmCqR4/OprMREYn6MJgjqs9fpVI39qqtVo71FklJOp5Nls/qSt99+O9lsllAoxPj4OP39/boOaF9fH36/XzcR1dj9GhRFIRKJcOLECU6cOMEzzzyD0WjU9e3b29tpaWmhqamJxsZG3QTU4/Hg8XgujqSTlsw7J+OxjEIap93GCruLFY2NfHjzZuRZbc+J8nJ6FQWzyUTjHNd2WZbp7+9neno67/3p4DQEZ//RjVpAs0G3u5tvvfUtdl61k8//yedZs2bNOTh8WS3mTk6q+uyhkHpt5ujpIgjq/eR2Q2Wleo95vedM23wmM8Oz3c8yEB7I01qPy3FeOfUKdy+/m5ubbr7inqHvNUiyxI8O/ghf2KcXSbXv+pmTz/DxVR/ng20fPC8u4m8Mv8GugV1IYs74KcChiUO81PsSy8qW4TQXll25HCErMscmj/HiqRdJZ9OnTbIMMBYe42eHfsY3bvnGFd2Jc8kmPIuJziIuZZhMJr0NfSFobKZEIkGauABiAAAgAElEQVQ8HicSiRAOh/WKeTweJxAI6JN/zXlTW08zXorH4yQSiUX1RDOZjL7tQtAE/rWWf5vNpgeZHo9HZUYpCiXpNF6zGY/TidPhwONw4LHb8djtOC0W7GYzNpMJezKJMRpV9QwbG6GpSa36FpGPuYHi+UBuW9vc9xZBWE7yYmQ/vWnfvM8ySGyfOcqumRPc4dowy5BcAHP3nQuLBVpa1GRsT8/Zs1217VZUqGxRm23hiYl2HIsEwYog8nDwZfpSkwUdSB8Jv8ZnSm5muaWeRZmtF+J7vQIgCAKlpaWUFnC1VxSFTCZDIpEgmUzqY2QoFEKWZRoaGub9jcVi4bbbbis4Rsbj8cJjpADY0b9vWZTZN7KPp44/xZE9R/jVr36lr2o2m9XizxxHaI/Hg9vtxm63s3LlSv7yL/9yHps1HA7j9/sRLSK/3v9rhgPDahCfU0hAgB8f+DF3Lr+TVk/rO7qmlyISmQQ/3v9josmo3h4GgKAyKX5x+Bfc3n477d724iS1iHMOTcJiLsxmM5///OfZunWr3sY9PDysywxp3Tq5yVJJkhAEoSC7c2RkhK997WtUVFTQ0tKiMyVra2spKysrOM6dE0iSmuTs61OTUVqBXBDmF1M1hl4opOprj42phcfm5oWlYObAaDRSXl5OeXm5nkhLJpMEg0EmJiYYGRlhdHSUwcFBRkZG9CUQCOjjt4ZsNovP58Pn83H48GEEQcBms+H1eqmurqahoYHq6mra2tqora2loaGBmpoaysvLcTqd59+bYQlxw1lBmd2mfPpZJAoClW43lR0drF25MIvK6/Xyx3/8x4yMjDA+No7f7yccCecbeMnAjLq8Of4me1/eywdu/cC8hGcsFmP//v0YjUZqa2t14sOCyGRUk8uBAVWbXXuWLnSPZTKqhFAgACMj6v3V2Kgm2t8ljk4e5dW+V4llYvPUBE4FTvHsyWdZV7XuiuyUeC/h5PRJHjvy2PxMlAi+qI9HDj3CdQ3XUWJdeJ79ThBOhnnixBP4YnPmPgJEUhGe7nqaD7R9gA3VG66YeCWairK9bzudU53z5j4pUjzX9Rz3rLiHrQ1bL84BXgBcsgnPIoq43KG1lC8UZOSK7mvJ0ZmZGX1JJBLEYjGCwaDuPh8MBnWGlMYk1VpLZ2Zm5jmbFtrXgklRwGw06ovNbMZhteKwWLBbLNjMZlw2G6V2O6VOJ26HA6/TSVlJCWVtbZRedRXuigqcLpcuG3BJtCxdLFworVMtIJ3bUr6odqdAT2qUp2JvI1HYNX0sG+C34TfYbGunzOBgQZbn3H3n7UhWJ17T02dorz8DNNaB3b4wi0AUZw2SFtqPgb7UKD8Jbi8sySXASHqaB6Z/z/+u/atC+dCcTV3ZWjcXAoIgYDabMZvNi0/EctDU1MQDDzxQcIzUxsNgMEjPSA/bT2wnE51NDLjytzOdmuZn+36G0Jd/I5xpjAS47rrr+PKX57tavvzyy/zkJz/BYDKwd3qvOjG1oxpD2WcXK/QEevjpaz/l7973d7icrivCbPGt0bfYfmr7adZALgxwcPQgT3Y+ydeu/9oFP7Yi3rswGAx0dHTozu1arDQ1NcX09DTj4+N6S3Z3dzejo6P4fD5dR30ujhw5whNPPIEoitjtdjweDxUVFZSVlVFXV0dzczPNzc20t7dTX19PdXU1Ntu7ZDVrHRI9PWqXBCweV2jPYkE4bZzY3a0+g6+6SmXkvQNYrVZqamqoqalhw4YNSJJEIpHQCz1+v5+JiQn6+voYGBigt7eXkZERxsbG8lrhFUXRC1QjIyPs27cPURRxOBz69SwvL9evZ0tLC21tbbpe89xC07vGGeOGcwRJWnQfoiiyefNmWltb8fv9TE9P89yB53j4pYcJT4UhgOqPNAPkNIS5PW6WL18+b3sDAwM88MADjIyMUFZWRkNDQ8HrqbOUT51S7zOte+VM95h2n2ndRJ2dqm78ypUq2/MdIpaO8UrfKxyfPJ7H7lT3C/FsnBd6XuCOZXfw/pb3X1HmPe8lSLLEr47+ipHgyHzygQCIsO3kNl5f+zp3L7/7nO77rdG3eKP/DZJycv5cQIQDYwd48dSLLPMuw2VxFdzG5QRFURgID/C7zt+pmrhz4zQB+oP9PHbsMdZUrrkizrkQignPIoq4SBAEIc9hcy4URdFdkDWtOy05mrtEo1FCoZDeHjpXb1RjfoZCIRKJxMJ6oqC7Si4EURAwiCJGgwGTwYDFaMRiMqnn4XJhmmWOaq7zpaWlVFVVzdMb1VhTjiuZFWowqIwKLZg+n5VCpzNfVkAQVI2lqamCq6cQeCq8h/7U5ILJv5SS5fWZTl6Pd3KPazMFE54Gw8JJSEVRGQBHj6osAO24zgbapC0WU7VARVFljBbajsGgOqcvOKkQ+HnoNcbSgYLsztlV+HXoTT5f8n62OFaBUkBqQlHUcy7igsNms+nJCw2yLOtjZDabZSYxwyOHHuHlF19WdRuyzEt4ZuQMx/3HafI0sXXLVkLhkD5GzmhJhQXgdrsLtl4ePXqUl19+GUmSUARFvccMOYtRfU0ZUvzkpZ9wqP0Qq1as4nOf+xxXXXVV/vHNMvY1pumlXDhKZBI8euxRJmYmFhxLMnKGH+37EZ9e+2nq3YW1s4so4nxDK8TW1NQAKvswmUwSi8X04vHQ0BClpaUFizD79+8HTmsYx2IxXWbIaDRitVpxOp243W68Xi/19fWsXr2aP//zP6empubs2UKSpCaiurpOt12fzTa09RVFjQUOHoSNG9WOiXcJg8GgX8+6ujo9VtWuZzQaJRgMMjIyQn9/P0ePHqW7u5uhoSF8Pl8eq1aWZV1aQNOyN5lMWK1WXZfZ4/HQ2tqqa4MuW7aMpqYmysrK3l3RyO1W5XjOKIfzLiGKsAgDWGO+aqzhlJTi2eyzZGYykESVP58BQsA0MKW+Jp1JhqQhWsnvGND0bU+dOgXkX0+3260mQRsbWdbezlUVFbSn0zSUlFB2tkQF7R6TJJVNLEmqseQi3W+L4VTgFC/2vEgsHSscpwkqM/C5k8+xrmodFY53fy8XceHR6e/kua7nSJEqvIIAkWSEB3Y/wG1tt50zk8lIMsILp16gL9Q3P6E+u99ENsGjRx/lruV3sbpi9WXP8kxJKZ7veZ4TkycWnPvEpTjbe7dz1/K7uLX11gt7gBcIxYRnEUVcohAEIY8FVQhaUjTXNX6uo3w6ndZbRUOhEBMTE4xPTDDW2clYVxfTsRjTsRiB2SW9SMJTVhRkSSIjSSTmfjg+rh+35hoviqLuJq+9ms1mPempJUQ1UwKtlUnTHy0pKbl8jcq0ZKDBcH6NiwRhftJREFSJgYIPagPjmQn+O7ITWVAKJylm0Z+d5OnIXq6zL6fa4GaemY/BoCZaC+0nHlcnatPTcyZqZ8mk0CZsySQcPqwG0oUYBJpRUsFtGDiVGuPX4TcW37sAETnB9wLPcrWtHZMg5h+voqgTlzNoVxZx4SCKYt4YmTVleXzwcSSHVJhxKAAKZC1ZOj7WwTdu+gbV5mp9jAyHw7or/djYGOPj4zrjZmpqira2toIB8PDwsD4eq22NFFRFUFDw+Xw83/08I0Mj3HHHHfmfKwpDQ0N89atfxefz4fV6qaqqoqamhrq6On2M9Hq9ehvtxRwj3xp7i129u0gpC0xcAETo9/fz/be+z79/4N8v3MEVUcQiMBqNetKuuroaRVFYu3YtQMEkWllZGRs3bqSvr49QKJT3WTab1ZOgExMTCILAvn372LNnD/fdd5+eZNUQjUaZnp6moqICu91eeFI9NqayMzVTnUV0g0FY+FGu/V0oBMeOwebN56T1OH8Xgm6OqRlBKYrCpk2byGQyeoF+enpaT4AePHiQ7u5uTp06RSwWy9teJpMhk8nkGdQdOHAA02yB3Wq1UllZSWtrK2vXrmXDhg0sW7aM5ubmsyuk22xqDJNInN+Ep8FwVtf8wPgBdvbuJE5c7RKwASVAHaefLylIxBN8a++3eN+y9+l3gKIoDAwMEAwG9e3lXs+xsTEEQcCwd696PY1GLKJIa1UV//O22/jcLe+bbfXPOSDz7DNGyuZ8NieaUhRVeqGnR2UTnyW7OZFJ8Prg6+wb21c4GYX6XkbK8FTXU3x05Ue50X5jkeV5mUFWZJ7qeoreQK9aHC4EATDCrt5dPNn5JPetue+c7PvI5BFe7X2VRDaxMPFBhOMTx3nyxJMsu2HZOUu2XixMJ6b5P4f+Dxk5s3BMDJwMnOSZ7mfYWLuRUut5kme5iCgmPIso4jKGlhQ9awaQLMOePeD3o0gSqXicSCJBOJHAH40y4vczMh1gYNrPXl83E5EQroSVqWiUQDSKlNM6r8Y9ih76KIqSZyIw1yQAWNB8SYPNZsPlclFSUkJ5eble9W5sbKSmpoaKigp9KSkpwZiT7NMmDnMnEBelSmezqcnIcHheG/Qi05OCEApFgLKsButOZ/72RVFNCppMarI159wV0czPAq8wshjTUd0hkiLzSuwYe+Mn+Yjr6tN7VxR1sVrV5N/caytJMDSkTthEUXWpViAjCciKeBZnrmAyKogCIIhq0nPfPrjlFvXc5qK8XL0Ocxi1CvCr8OuMZqYXTfACIMIr0aO8MnOU212bEHJZnhq781Jz7y0CUMee33X+jgODB/K1JOdCUNfdPbabw6HDrF+3fkGDOg1a++ZCxaemxiYc9Q4iUxFIkV8bKBDTS5Kkm8bNRTAY5K233mJ8tohUCFarVTeOKi8vp7a2lvvvv59bbrklbz2tIJaLczVGprIpnj75NH3hvpwdFl5XMSj8YPcP+OKGL7KsbNlZ7aeIIi4EBEHIiyXm4itf+Qpf/vKXGR0d5eTJk+zdu5dDhw5x/Phx+vr6kGVZL3pojMe2tjbcbnfeb0tRFHbu3MnXv/51IpEIV111FVu2bGH9+vWsWLGC+vp6xFQKYWQEIZvVu0Tynr+z21MEE0+F3yQsx/iT0tsXNtzT9u/3q1qgq1efd2kW7XoajUa9tb+qqopVq1Zx5513Aqo26MTEBF1dXezevZuDBw/S2dnJ8PAwmUzmdBEJdBa/1iI/MTHBkSNHeOqppwB0g6rVq1dz7bXXsnbtWlavXk11dTWiKOrxsnZsgFooLitTC7Nz4mgh579LPGMEBJS50Z0kqezOJbbjp7Npnu95ns7pzvwPcsfWWfMibLCtaxvberbxofYP6ed1ww038OUvf5n9+/dz4sQJhoaG8u7N3O4xjbyQlWVm0lkyshk5fTruURR4+s2dOG1WVtQ1Ue0pwyhaURBRlCwmQxqDUT6tXzoyol7T5uYlyzopisJYdIzHjz1OIp1QEzMLVadF6A/089jxx1hbtZYSa8llz8J7r0BRFLr8XbzQ/QKRTGTxn5cAWbJ8c+c3uavjrnfdah1Lx3i1/1WO+o+envsscI9JSDy0/yE+t/5zNJc0v6v9XkwoisJvj/6WzolONeO3COMjlU3xUvdL3NVxF7e23HrF/aaKCc8iinivQVHgxAlVnNxkQkCdPFttNioFgWWzTAJFNDAuRfin0YepTnr4hvfTVMluovEZpiIRxkIhJkIhxoNBpiIRpiMRpiIRpqJRYpJEFrWqPJdxupCjai4SiQSJRILJyUm6u7sLruN0OnE4HLjdbioqKnR9qVyWaHl5ORUVFTidTkwmkx58a2zT3OWcD+6CoCYiKytVjaOcSUpGyRCV5rtNL4a4nCIhJU4H01rrdkmJGkzP1fD0eNSgc2JiNukoAAZGUiP8KPSKznQ7E4ayfp6K7OFqWwe1RjeC9keiqCYYC7EdYzGVmaIdi6CQyQp85sFr2NfrXTLHc2VdlH+67xjXtQdAEtTJiTZh6+iYn2gtLVXb9Xy+nMmcSHdyhD9E9jGjsdDOcABhZYaHgy9zna2DEoNN/QPNCKqxMV8+oIhLBrFUjO/u/u7pIHqx71mA4EyQ3x37Hdc3XE9HWceiY4Bm9rYQ7v7S3TwoPghh1PbDOBCbXaKo7YjxnCUF9jI7dme+PIKiKESj0YUd52eRTCZJJpNMTk7qbYsf+chH5q0XCAT4h3/4B44fPz5vjMxdXC5X3hipsagWK6btH9/Prr5dJKRE4YlL7vUXYCY+w3+8+R98/8Pfx2y4wA7XRRRxDmAymXStzttvvx1AdzQ/duwYhw4doquri+7ubvx+P+vXr5+nO6koCmNjY4yMjOiMx2eeeUY3eWtsbGRNYyNrS0pYXlNDR00NbpcLm82Gw2DIqeUIJKQ4/xXcxqn0OHe7rqHM4GJeJ4a+unA6IVVVpcYmF3lSa7Va9et5xx13IEkSkUiE4eFhjh07xuHDh+nu7ubkyZOEQiGSySSpVIp0Oj3PpC6VSnHy5ElOnjzJk08+iSAIun7lunXrWLNmDcuXL6etrY2SkhKsVqvaHdDcjHFg4HSMpijIyETlJNnFDBvnQFZkonKCjJzBrNlOa8Xh1tbCRdo5UBSFw77DvNLzCvF0fPHEn/5H8K87/5UbGm/AbVGT6xs3bmTjxo2A+gwYHR3l8OHDHDlyhJMnT9Jz8iTByUmS6TTpTIZUNovTaiUY2cBN/+/7mZglMKuXI87A1P9EFAJ47C7ctjpEYS0KVwGr+NSNFv764xNYBYfqAZBOYxgcRCgvV6WVlnCPZeUsrw++zp6RPWoyaglB4m+P/ZZ7r7qXGxpvOPPKRVwSyMpZXjj1gtperWGx71qEnvEeHjn0CH+1+a8wiO+sSKMoCif9J3m261kymcyZf1cijE2P8eCeB/nWbd/CKF6e6bLpxDQ/fPuHp984Q0zcNd3FH7r+wLqqdZTby6+opOfl+Q0WUUQR7xzJpJrszJ3IFjA7khSBA7Gj/CG4H0mROVTSyydLbqbKW0p7rjD+rH5PNp0mkU4zE48Ta21lyu1myudjcnKS6elp3XBpenoav9+vuzNrhk1aEKsti7nSA3r7mM/no6enZ97nBoMBu92uuy2Xl5dTWVmZxwzV2kK9Xi8Oh0Of5OuBcM5iNBrPfvC3WNRJxdiY2uI9m3ScTE8heTvZ0Lb01iuhpI9eaZCVNGPQJjQmkzpxKdTCZbFAba2aINS+X0Hk9ZkuGgxlNBrLlrxvfzbKeMZPjcF1Ona1WqGmZr5+p6LA8LB6vjmfKcDwtIP+KQdLZU24bFkS6TkJF1FU2aNNTfMZEwYDtLeremU5CeYTqRHKDC6usbUv+ZxlRaYrPcp1tg5AOs3ubL1y3LWvNOwf348VK1fXX720W0yGWCbGSGSEdm87BuGdM57eHn+b5ZXLydZm89vxtP+XUM0msrOvSWiqbEIoyT9QRVGwWq1s2bKFUCikj4/JZDJvfEyn02rgnoNCRiuxWIyjR4+yd+/eeZ/ljpEulytvjNy6dSv33nvvvPbQQCCA3+/HYrVwdPAogiSwrmIdBpMBo8GocpxmmVQaz0lWZGRJZQCNhkcZiYzQWlr8HRVxZaCkpISSkhJWrlzJJz7xCdLptJ7I9Hg881jciUSC0dHReXrBiqLohpSHDh0CwGoy4XU6aamqYlNLC3/+wQ+yuqkJJAlFMPDSzGEOxweZzkZ5JPwq/1f5H6nkpQUMLBEE9dns96sF0UUYrRcDBoOB0tJSSktLWbt2LZ/61Kf06zkwMKDrUp46dYqRkRGCwaCuvzrXuFNRFN1Q6eDBg4DKAtVa4VesWEFbWxsdHR3U+v14JAm304nTbEEmSdDay/KWOrIJN/Js4nLRPJwhSdLZw2g2SIuxBpDV78HjgTN0EGiQFImh8BAmg4mr65b4HFOADByaOMRNTTfN+9jr9eL1enUn91Qqhb+7m/6XX6azp4e+8XG6xsawGI1Uemo4NWHDH819FnYDEWRFJjgTJjgTBrSElZMHn3PyZreXVXUNtFVV0V5VRW1VFc1VVXiXL0dcApM4LaU55jvG+sr1Z2SiAep1ycK+0X1sadiCUbi07uMiCsMf9zMRmaDF20KrsMQYQFIlHmLpGB7r0gwu5yIjZxiNjGIymrim/poz/65mY7ZjU8cIJAJUOt6Z2dvFxlsjb+E1ebmm8Zql/YEMk7FJJuOTlNvLz+/BXWAUR4giinivwedTGYeLBCEKEFaSPBZ+E19GdSz+dXAXW22raDNVnGb55cBoMOCy23FZrWAy0b5x47yKdjqd1ifv0WiUQCCQ50Kf60QfCoX0IFZ71Zif2rJYUlSSJF0If6GkqCbkrrXQe71eSktL8Xq9eDwe3XxA0xPVWKXaq8b6stlsWK3W+UlRQVBZkHV10N8/OwmRqTWW85NPBSC7c/HvKhdihnJnOaIso0eDXq+67ULfpdEI1dXq9z02Nvtmhluda9hsX3aWLfUKVQZ3PrO0vr6w+UE2qzJICiSHTYYFJmELoKDRvSiqEgF+v3oMc1Fbq7IwBwdnNyJxs2MVm2xtZ7dvBDyiDT3ZKYqwbFlRv/MSxpqqNTx676MIorBkqVgF5ZxUsu9ZcQ93tN2x8Apzk6CAQTDgtebLI4iiyMaNG/nlL3+pG39oLvThcJhIJKInRbQxMhaLkUwm5+kEgqoVmE4XbnOdO0ZqTFFRFInFYnz84x+f9zcvvvgiDz/8MKXeUhSLQouthfIyVVPUW+pVTUbcqjGG0+nUx0aH06Ff4zL70ostRRRxucFsNutM6kIQRZF169bxyU9+kvHxcXw+n/6bnqtlmcxkGAsGGQsGOTk6yr033DD7bBUIZaP8OvIGkXgcuuGnE9u5ZkUHrSW1lFgt2MxmxFzjIlk+bTDj90NDw2XxPMu9nlu2bEGWZeLxOFNTUwwMDDAwMMDw8DC9vb1MTEzg8/n0Ins8Hs8z6kylUgwPDzM8PMxrr70GqEZ0FW43raWlNFVW0lheTmNlGattbrbeE6bU4cZts2MwGVG0gERjbuaZJCpYLCkqxVL0ZKfJBKtWLbkrRBREbm6+mXVV65Z+gWa7dbz2pUntWEwm6qxW6trbuaGlBSSJSCJBIpXirR43alUuN6YcBipRreLjc7YWI5aMsePYBDuOnUAUBFw2GzVuN/9iNnNXUxPmnKKZJEnE43Hd5Et7JliNVr6y9Sv8zXV/s/TzBkwG07sqVBZxYVFqK+Wvr/trvnT1l5bcZQZgEA1YjUuThCgEo2jkxqYbWVe97qzmPpIi4bZc+mPkQri2/lp+e99vzy6+Fbjikp1QTHgWUcR7D36/+rroAGjkSOI422IH9cn5rngXO2KHaSj5AJaC7Ys5AaDfr7Y1z3Gl1NiSbrebyspK2tryk1CyLOe50WuT8UgkQjQazXP/DAaDuslIJBLRX7UEQCwWIx6PL9o+nyvkPjk5SW9vb97nmhC/2WzOcwx1u924XC69pV5jJGju89qiTfqdNhtOiwX7zAyiwYDT6GBlpQDi7NNeVhYIoHOvrwFkB6Co6zscKpvRtYiujdOpuppHo+oCVBk9VPFOBKlnE61ZSWWVtrcXbtHS9nW+WiG0trxAoHDC02CA5cshElHXEUVKDU5KDe9E/0cBRVa/k7o6VZOqiEsWZfayi5ZMO1cMAEEQdBZSZeX8bSqKoo+RuQ7T8Xic5gL3p91u57bbbqOhoWHJY6TRaMTr9RY0Q9Lc6PV1RaM+rlstVpwup64t6nK5cNgdtLS28Pf/z99TWpI/7oRCIcbGxvLMYxwOx7tzXS6iiEscVquVu+66i1tuuYWpqSlGR0eZnJxkcHCQwf5+hru6GB0YYCIcZioS0Y0kSx0O2quqZmMEkdfjnexN9ZCZlOB56HGM89XKn9FRVkdDeRlNFRU0VlZS6/VS7XZT7nSqGqXirB52MnlZJDznQhRFfbxoaWkBVH3PaDSqX8+JiQmGhob0ZXR0FJ/Ph9/vn1cAikQiRCIRejWHeIMBt91ObYmH+rISKjxuWiurqKmooKGyglpvGdVuN16HA5PJCOKsbrgkgWwBWVDjBkFQY4YlsjtBTXie9+dYOg3BoGrUBCAIuG1WXDYrBlEskINaB3wLGAAGgRFgFJgA/OS688mKQjgeB1nGGI1imBPPTk1N8cQTTzA+Pk5TUxNNTU3U1NRQXV1Njbfm9NifTqtM5ERCLaKDGm/abGpnj9l80eUYijh7WI1W6j0F4vbzDFEQKbWVUmq78sx4FsPFjIkvNRQTnkUU8V5CNqsmgs4QKMgoPBJ8lalsVBd3jspJfhbawR2uq6k3lrBgaU4QIJNRk16lZ/dwEUURi8WCxWLRWyznHdusE73m/BmPx/VFa5OPRqO6K73GnJientZZUeFwmHAkQjgSIR6L5TEAcqEoit4+GovF8GvJ4rzTFXTtO4vForeIaovVasXtclEiSXgUBa/TSZnLhdftxut0UuJw4LHZ1MVuP83KEMXT2pt6IlQGadaoaNUqlcG52HcpimpysqND1W2Nx9XvcyFnxMWgBfReL6xbt3CiNRxW77PznbSIRtX9FGrJ83hUU4YjR9TjEXlnwbF2X1RVqUnUJZoOFFHE+YIgCHqC0el0Fhwjc1FfX8/f/u3f6snNeDw+b4zUWPXaWJlKpVi1ahWmAgWNuYZzWTlLNpklnpxl/kzNP4a1ra3c/6HbKV2x4rSZGrB//35+8IMfEAwGcbvdlJSU4PF48qRGNHZ9biHJbrcX1BZdzHCmiCIuFWgxg9YKv2yZauKVSqWIBYME9u1j+tgxJkMhhqem6JucpHN0lPaqKkodDpBhWorxbHQfY1IAJoEEZBISb/t72Sf0YTYacdlslDudlDqdVHk8NJaX01ZTQ1t1Nc21tTS3tOAoL1+yscylDKPRqBeeOzo69OJ5NBrVx7apqSkGBwfp6+ujt7eXwcFBhoaGCIfDeTFgRpKYjkaZjkY5OqwmQa0mEy6bTY3dZq9nc0WFej1ramgoL6ehpAS33Q5GA4gmtduko+OSkw3Qk4m55o4LSSAA0Aw0obrxRYFpIIia8BxmbdN+mqsO0zcRZMTvJxyPUyqESWEAACAASURBVF9WRo3djphO5+1nbGyMRx99lP379+tSU6WlpVRXV9PU3Ex7czOtZWU0mUzUWyw4DAYEUZx1hlfU2NfhUOUYKivVhP0VcP8WUUQR5xcXfRSenJzkpz/96TwtqncCWZa56aab2Lp1a0FmQhFFvOeRTkMqtXjyRzBxIN7FU9G35+mc7Iv38rvwbv66/KOgzHdfz0N8buvLuYEoivqE3+Fw4C3gmC3Lsm6OlMsY1XTv0uk0kVCIyPHjBLu6mAyFmAqH8UUi+MNhArEY07EY09EowZkZUpnMgp0XuUnReDxOMBgseMyiKGIQBMwGgyrsbjKprwYDltlg2m2zUep0Uunx6Eu5202Zy0WZ04nXbqfU68WyYQNCXd3SAmmTSdW7NBjg2DGVeXu2AbgWbFZXw8aNi7NCNF2y81l9VxT1Xs5kCp+LKKrHKopqojcQmOfcvqR9iKI6aVmxQjWHKgbWRVxm0NpBc6G59C40RkqShMfjKZhU3Lx5M6FIhND0NIHhYaZDIYKxGIlFYrhymw3j4KBafKioUJMAZWWMjY2xZ88exsfHEQRBN5AzmUxYLBZ9nNf+7XK5qK6u5otf/KLu8pyLwVkZiytJaL+I9w4sFgsWt5uy0lKWtbYiCwJpWSaezRJNpTCjsg9RFN5OnGJXvItUNqsmPHOgKAqpTIZUJoN/1vxMFARMRiMOiwWn1UpbTQ3/3NDAjW1nJ/VyuSC3eF5eXq6PeVqcpnUL+Xw++vv7OXbsGF1dXfT29jI8PEwymW8qmcxkSGYyTOVcT7PRiMNqxWm14rBYqPd6aa+uZlVDAx1r1rCsvZ06iwUjZ+f1ft6RyagLnEVMJADW2UWTMlKFqW+9qouvfPotIpMJxqenOTU+jlEUafB4EHKuoyzLTE1NMTQ0lGe6B6e/L4fdjtNsxmM2U+3xsKy2lqsaG7l+xQquamhQ48tAQJVpcjjU2HahbqMiiiiiiFlc9ITn1NQU3/nOd+bp1rxTyLLMxo0biwnPIooohMVapnPW+Y7/GSJznXcFSCtZ/jPwHJ8peR+lBidqwLMAFq0Yn19oCUZNo3MulEwG5cgRlIoKlLIyJEBSFGTtNZUinUgQTSaJJhL4Z13px4JBxoNBJsNhJuNxJjMZpvx+QsHggjp5oI5LsiyTBVILJAYE0M0+DKKIQRQRZ/9fFEUsRiNOiwVnSQmVzc3U1dbqulZVVVW62UhVVRUejyefnWUyqbqWVVVw+LBqKqQl9BaDxuo0m1X9yvZ2taVosSB5EQmBcwatrX2xe9lgUBOzTqcaIL8TiOLpbRSTnUVcIdDGmcXGyIXwhS98gc984hPIXV2ke3qIJRJEEwmmolHGAwHGZ8fJyVCIqUiEsWCQFfX1aqImmVQNx6ankVesIBQIEJ9tq1QUhWw2SzabVZluBWJCzcX6nnvumfdZKpXia1/7mr5eEUVclpBltXNBlhENBqyiiNVsxqvNaRQIygleih2kJz2uvncNUAuMozKsp4AZ8ppw5JwkaCAWw+t2q8y5ORgeHubRRx/F5XKxZs0aVq5cSWlpacHix+UEbczTTCm9Xi+KorBy5UpuuOEGXd4oFosxMjzM0QMHOPTqq5w4dozjo6NMzRmPZEXRk6DTs3JBXaOj7DhxApPBgNFspuS736WxsZF169axbt06Vq1axYoVKygpKbm411PTc33XMAAGTEYrjW4XisnLyro63rduHYosY9L0YmchyzKJRAJRFBEEIY9Vq32WSCTQ+qiOCgKvzl7Pr3/841zV1qY+Q0DdbiRC+uBBTD4fwrXXqrFpEUUUUUQBXPSEp8lkoq6ujvg5YIPJsnzxHyRFFHEpQ2uTXgiCiX3xLh6P7NFb2fM/h97UBD8IPMfXKz+FoCyS3LoUf4eKAskkwptvIvh8KjtQEOYPhFYr2GxUaYL/uS3moqg6pGazpBobiTc2Eo7FGB8fZ3R0lNHRUcbGxvD5fExMTDAxMcHk5CSRSIRsNqu7iGqMA32ZfQ9FQVogGPUBTE5Cd3fBz61Wq+5KX1dXpy+aRlJ1TQ1VlZVUt7bi8PkwzcwgGAww2zYkKAqCJCFIs0Y9VitCVRW0tamtqEv5Ti/E964laxdKbCiKyjA+ckRNsGjf4dlAk2bo7FS319FR1I0q4j0Pm6JgO35cNUPzeKgqLc0fI7VXWUaZ1RkFsJjNpzcyM4N88CCN6TQ333QTA0ND+CYmiEQiZDIZfSI8b4xUFARBKKhtOjMzQ1dXV1H/s4jLGwWd+tCLewoCx1JDvBw7QkaR1EppFWrCUwTSYIobuCu9ia3hlfQMjnFosJ+u0VES6TTy7O+ooayMitLSvH3Jskx/fz8PPPAAPp9PbxNfsWIF11xzDWvXrmXt2rW0tLRgs9n0wrJWYLjcCg1aElRjkgOUlJRQX1/PdVu2wJe+hHzqFKGjR+nu7uatU6c4PDDAkeFhTvl8zKRSqrK5oqhFbUUhnc2qmqupFOFolMHBQV5//XVALcRXVlayYsUKrr76atatW8f69etpbm7GYrHoRajzfj3fSTy0KNS4VVAUNQWqESsEIU/ayGAwcOedd7Jlyxa6u7vZf+AAh199lUMHD3JqcpJUJqPfn/Lsol3PjS0t+V06gkAkkeAz//mfRKNRrtu0ibUf+hBrN26ksbFRN0TSrufldm8WUUQR5xYXPeHZ3t7Orl27FtTQOxsoioLZbC6yO4soYiGYzaoGzgK/t7SS5sHANiRFXrgHR4DvB7bxSc/1tFvqoFDSUxAWN9O5WEgmYe9eNWloMqFSIApcC0U5g3ug6vNnHRnB6vHgbWnRxfNzIcsyyWSSRCJBMBhkcnKSyclJJiYmmJqa0hfNpTUWi+lt9xrbKff/ZVledKzU2oQCgQADAwPzPhdFEYvVik3T9XI6qbLbqXK5qPB4qPB4qPR6qWxspKyxEXtDA5ayMoyZDKZAAKPRmLcUDCZLShYIps9hwCkIp4XrCyGdVlvZBwdnkzCwZDvIXGjaUV1danJ82bJLT4+riCIuFNJp2LcPJibU34HGAM9B7q9MEARsVmt+Z8HsZNsoy3y0o4O77rmHdE0NiXSacDisuyz7fL55Y+TU1BQOh4Pa2tp5hzY1NUU6ncZWZPkUcTnDYDjNVCsgwxKRU7wSPcDx9MjpR6o8uwAIkHFIjFZMs+W6Nfzflo+Rjs8wHYlwfHSUY8PDdA0Ps7KlhfKysrztZ7NZBgYGCMx2RGSzWf33pyXtbDYbNTX/P3vvHWXJXd55f6rq5hw65zBRkzQzKCIhjGTjgISBXdJ6MT5+sQ1ry8jsAu97wD5rvMawFpz1a+NleTFY2KRdgsAgJCMsUEBhpMmhp2emc+6bc6iq94/frep7u2/3dA8zmp6Z+z2n5k7frlup6/7q+X2f7/N92tm1axc33XQTO3fuZMeOHezatQuPx3MFL8xVgM2GfNNNhLZt4/b5eW7P59FjMQqLi8zG45xeXORkPM7pkRFOnzrF7Oys6ZGcz+cpFosrVIxGEvypp54CRFO59vZ2du/ebV7P7du309zcjNvtNhWpVqv18pF2xjwANm71s17o+lKToQoM79rW1lZam5u5+6abYMcOcpEIc/E4JycnOTk5wenJSYYmp5hNxMkUCqiqxs7OTtBUzCeMYmF0YYEjo6NMRCI8dfYs0te/jsvloqura8X9GQ6HcTqdOBwOnE4nFoulQYI2cMOiely6Ub4HV33mZnTba6CBBl4FKIpo6DIzs6KpjIaF59OneCJ5HLAtBdB1sFDI87nIj/mL1v+Ia7kYQddFQLXZCM9SSSgjjck6oOkSZWMuvp4xXwdZAqtxzvk8DA+Dy7XkGVkFWZbN5kXhcJgtW7as2KSqqmbzJaN5iLEYTUQWFxdZXFwkkUiQSqXM5kyFQoFCoVDzf1VVVyVFNU0jl82Sy2aJzs9zvs46isUiSFG3m1AgQDgYJBwO1zQRaWpqorm5Gb/fj8fjMYNyu92OQ5axqyoOTcOmKCgVUlSQo5dRfeX11m+MpOuiZH9kxFScabpOWZc2RHnKgEViyTB/aEg04Wpru1xn0EAD1w5UVRD/U1NrNiTTgYVynIxWoN/WirRagqbSKVq5cAFnIICzqYlQKFQ3cVQul82u9KlUitbW1hXrWCwWfvmXf5lnnnnmUs+wgQauPiwWYaFS8eqshobEUGGORxNHUFUFpNWncIeTk3x/9jm2t7QRtjrobG6ms7WVXzl4ELVQQHe5UJb5nxsJ2u7ubpLJJKlUikKh1qs9l8tx4cIFLly4wPe//30URcHj8fDUU09x880316xbKBSIRCLY7XZ8Pt8lkUxGhf9GNDGybBbvXB5YLMLLG5B0HYem0SfL9EkSv4YgD1KpFBMTEwwNDXHhwgWGh4cZGRlhYWGBRCJhXs/l1kfZbJbz589z/vx5Hn30URRFwe/309vby9atWxkYGGD79u309PTQ1NSEz+fD5/Ph8XgunQS12UTMKssbu7Abga6LfdjtK/8Qui78nA8fhkQCp91Ob1sb3R2d3HfL7egVn9XJSIShmWni6Qwhr59CSQe9on7VJc7NzddYROm6TiaTYWhoiKGhIb797W+jKAo+n4+BgQG2bdvGli1b+LVf+zVuvfXWRjVAAzcsxhPjSJJEl6+rIt+5/nHVCc8GGmjgVUZLiyDpqjO7ss74nJO/f76d6PxvczE1nI7ON61Bbt3p5+0Hk8iyiskYapoghlyuK3oaG4KuC1WnofgTbzKXdPD44TbimVWUgnWwpS3Fm26dBrVSvplKie36/cJEfYNQFMUkRZuamsyOrUuHrptKz2w2a3aZj8ViJJNJEolETTd6I7hOp9NkMhmy2WzNaz6fX5MUVctlsuk02XSayNwcw3XWsVgs2Gw2XC6X2UHZ7KTs8RCIxwlVGjAFXC58bhcuu4dkLgA0Ae6qxQamrX/tg1eCleVXui4mIHWaVQGQTC6VocsySDoLSTtPHG4jkl6f+l8COkI53nRwGqddNckZTp0S93ajiqCBGw2GlcZaVhJAXtf5RuI5xksRPtb87wgoLlZ9nkgSxONw/rzwyl3le2Uoyl0uV90mdQB9fX381V/9FXfddZdpG9JAA9ccFEUk8xwOYctiftd0UjkbX3qymxPzD1Qqa1b/HpbQ+Ja1nX27Q7x1fwa7roIq1ldAVGIsU0NbrVbuu+8+WlpaGB8fZ2RkhPHxcebm5lhcXDTJO7VK1a2qKoqi0FYnETg8PMwXv/hFJElicHCQrq4uWltbCYfDNDc34/F4UBRlTdIuGoX/83+WrBvXg8FB+KVfErzxZceyMm3xloTP52PXrl3s2rULgGKxSCwWY3x8nImJCSYmJjh//jyTk5PMzs6aSexkMrniehqx3OHDhwHRzCoUCtHb20t3dze9vb309/eb19NIQLvd7vWReFariFctlqXmRZcTxtwiFKpfEVMswuioGPsBNA1N0jk67OVnp5uRJUkssoyiyKiqyv/3Y0PdKe4VWZF59kySnV23MKiOsZBIs5hMksrnayyhVFUlFovx8ssvm53hfT4ft9xyS80hlUolTp8+TS6Xo7m5maamJtxud43FQAMNXA8oqkX+4fA/EC/E+fR9n8ZuuTHmMw3Cs4EGbjQ0NYlgOpczAzddVnluUuPpJ+6ntPjudW1mRtZ4ZGGc+/YdosluX5rTappojrOZSn+LRaH6q1YrSDAWdfLfv7ed87Prj4wfuGWaN712Yonw1DRBBkSjYgJxmT0sjTIgq9VqkqLLYXSlN0jRdDpds2QyGZLJJPF4nHg8TiwWMwnT5UsikaBYLFIul1clRY0S+2w2y+LiYt11LIqCVVFw2e14HQ7cdgdjC98FAoAH8ALBys+hyquvZlE1iUIxR0lVsWBZarLQ1ibu43o4f762E72sMxFz8tkfbOPU5Brd5Zfhju0RfmnvHE6HKu5tWRZ/5+lpqKNCa6CB6xalkvhelUoXGddlZssLfDn+FHm1xL2effyqZ9/qtIwkicnx9DT09f1C6mlFUTbUfKmBBjYlJEmQ/8GgIDwN8kiWOB/P8fXv/Qrl1P3r2tQ54JupM9y39xRNFmvF2UUXcV84vCIprSgKAwMDDAwMoGmaqdCcm5tjfn6eyclJxsbGGBsbY3JyksnJSWZmZti1a1fduOT06dN861vfYnJyEofDQXNzM21tbbS0tNDV1UVfXx+9vb10dHTQ3d1Na2srDoejpg/D7Cx84hMQiaz/Et5/P7zmNVeI8FwnbDabKN1ubeWWW25B13VyuRyRSMT0dZ+ammJ0dJTR0VHT/312dpZcLleTtCkUCszMzDAzM8Pzzz9vVg41NTWZDSsNIrSvr8/0bm9pacHpdK4k7GRZxE8ul1Ba6rrJnasalNSNEXyl8rKYV9dF8mq1eUAiITygVdWMl8uyztNnmvjoP+9Z935VfQv/8AEfOwaHmZuPMzk/z+jCAmOxGFOFApMV+6hcpTEeLHm0WpYdVyaT4TOf+Qxnzpyhra2N7u5u+vr66OnpobOzk66uLpqbm01v0AYauFZxdvEsT5x9guHoMO/c9U7u6L7jah/Sq4JNxEjUR7lc5vz58xw7dsz0R1kte6/rOrfeeiuvfe1rbygfp4YhcwMbQiYjJq5V94ymq8wVE+TLEqjrmzTqWpl4KUeknKHJ7sBkPA3V45XyBroUxGJiWXZMug5FVaZQXn9py4pgUJKE/GBuTqhnr4L6r7or/VqkqKZplEol01fUKI03XtPpNIlkklg0ysLCAtFo1CRHDdVBNBo1m4uU1lAHlFWVsqqSKxbNLqYwUbWGhNCaWAEH4Fz26mIiYue/f7vMd39upcnjI+T1EnK7CezdS0jTCIVCBINBvF4vdrsdS7GINDu7RDxrGsgqkqRQUpUN/J11SqpcW+1lkDMjI6LjfaMcqoEbBdGoYBwuds9LNv4l+SIn8hPISPwgeYiDzkFaFA+rqjxleSkh9QvaRTSUnQ1cF3C5BFm0sFClwJOZKi2SL2nrjtEApospsmoWLP4lL12vV8Qqa3yfZVnG6XTS1dVFV1cXINRyRpVJPB4nEokwMTFhlqtXQ9M0hoaGSCQSJtk3Pj7O+Pg4IMhVt9tNMBgkEAgQCoXo6upi9+7d/Pqv/zq7d+8GKjFasTZXfTGUSleuUvtSIUmSWcnT3d0NiOuZyWSIxWJmInpycpLz589z7tw5RkZGuHDhAouLizWxlqZpZjLb8Go3rAWM69nU1ERXV5dZFt/X18fAwADBYBCr1SpI9Y4OkRzWNETjIWgL5HnTgRkyhfXTA3t6EkIAAEsXPhyu7+deLsPiopiH1FTuCLJ1I7E4kpNWXxMH+8tIXZK4nsUicVUl3tnJoiQxNTXFuXPnTBsGn89Hb2/vik0lEgl+/vOfc7bSENRisax5PQcHB+nt7SUQCDT8QBu4ZlBSS/zo/I84GzlLNBPl4Z8/zNc6voZVsV7tQ7vi2NSEZyaT4dOf/jTf/e53WVxcvKg/HcDv//7vs3///huK8DQysUZpSQMNrInZWRERVt0rsqTQa2vFLTtYbyJdlmTarEE6rQGE4adRHi8L8i+X2xxl7ZomMspVWd5qbDRMWbG+ofKMRERkvkk7eRukqMViwel0EgwGa1eodMXUdZ1SuUyp0jzJaKJUKpUoFApkMhlSqRQLCwvMz8+bTQ2MEi3jvXQ6TWFZedGyHQLlypIDYivWSGQlfnJc4ulTMlaLBVtFNWp1u7FWPEPdbjc+n4/mlhZaXC6aMxma/H6a/H5avF5agx4iCR1VywAuBMl68b+PLOkr15JlQf4kk0KB00ADNwKMsW1NyCTUBJ+P/SslRInmE5kjvCn3Gn7Fs5c1de+SJNTTxvjZQAM3MhRFkFHz88IzV9cBlW32dryKiw1Ud7PV1oZbmV56w2oVCbtA4BIOS8Hr9eL1eunu7jbtduolGnRdp6WlhZ07dzI0NETcKF+uQFVVs6pkbGwMECTT4OAgAwMDJuG5BA0YRVSD+GCNEWUThl91YfhLGiSccT1zuRzZbJZsNks0kWB0bIyTx48zdPIkZ06fZmRsjGQ6jbasFD6RSJBIJABBsBo2INWWSYODg2Yjn51dXfQoCh5NQ3I4QdPYPZjmv//eETTtYo07l+C1qlCsIjxdLujtFa/L/xj5vIihyuXa30mXFourmgZqGUlTsEgSfrsdvyTR29qKvm0bZVk2r2Umk0FVVZPAr8bk5GRNo89yuWwS+wYMQYHL5cLj8RAKhfjgBz/IO97xjg0eeQMNXB0MR4f58fCPiRViYIUfnv4hPxn5CW/c8sarfWhXHJuW8CyXy7z//e/nG9/4BqqqEggEkGWZeDxOOBzG6/USiUTMwSgUCtHW1kYoFLqhSD9JkpienuajH/0oX/jCF2hrazPLKDo6Omhvb6e9vZ1wOIzb7cZut99Q16eBOohViKWqYEOSJGyyBXmDIYciKVglBfSqgFeSRClWKrVxwjOdFpPrRGIp8yzLYjuBgPAE8no3FtGWy+JY1Drd5C8XZFmccz5/deuoNgJVXVJuVa63XCl3UwIBHF4vNDcLJUil7MhINum6bnaMN5SjxmIE7LH5eWZfeIGZ48eZSyaZi8eZicWYiceZiceZTybJ5vPkSyW0VZNYgoQtlDUK5fLS28lkzVpGUyRZEvbbctXPNouCLDmIZ9uAdqCtsrRW/dyB8BZ1VRZQZAsOlwsckuBkNU0spVKD8GzgxoGqLpU9rgXJxj/Ff8jp/JQ5cz1XnOP7yRc54NxKy8W8PDMZMeav5s3bQAM3ElwuYUYZi4nvhi5hl6zI0sYsc2ySBUVSkNCW7Ib6+y9LhYJht1MPsizz27/927z97W8nEolw4sQJjh07xtGjRzl27BiTk5M1pcYg5n0Wi4XOzs46W4wAbwaKwD5gb9VrG7BUWbPc9vtaQbV9kc/nA12nPxZjv8/H/f39qHfdRSEaZT4a5eTkJMdGRjg6Pc2R0VFm5+bIp9PmtnRdp1Qq1ZCg58+f59ChQ1gsFtMCpN3vZ29HB3t6e9nX18ee7i5aggHszkpTI7UspJe6BtoazwBdQnT1lKG7u24TT0BIdTMZ44Qv49UzT9x8ZknlMla3W3jL+/1riqU6Ojr48Ic/zLFjxzh27BjT09MrGkwtv57AinVAiLWee+45NE1jz549tLS0rFBAN9DAq42yVuaZ8Wd4afoldISNRa6U49PPfpp7eu/BYb2+LYE27Tfw0Ucf5Stf+Qp+v58HH3yQ973vfXzzm9/kL/7iL/jABz7AH//xH1MoFHjsscf427/9W6LRKH/yJ3/Cu971LlybQVX2KqJcLpty/XqwWCy43W7TP6etrY27776bBx98cMW6hlLUKJM3DJuNV2kZSdbANYZ8XgQb9QKRSy0BWv45o/Q3mRTB9Zqf1UUQHo3ChQuie3w+L94zolajDEuSlnyBBgcFGXeRBhqAUA3l81e+xL5UEsRqOLx5o20jGBwfF6XZkchSpr36es/MiPUdDhG4Dg5COIykKFA1NqyGYDBIR0cHuwYH4eRJUZoHYt/lMqVymVyhwEIqxWw8zmw8zlzldToWYzoWYyYaJZpOkykUyJfLqKwkWvWKIlXXdVRVZXVKO4OYMJ1c5fcWRAOlZgQR2sb5WR+f/GaBgVYfnYEg7aEQAbcbjyzjmJtDbmlZc4xsjI8NXBcoFFZVxy9BJl6O8vDi99GlJT84Xdf5TupF3ua/g9e7d6+uyZKkJWK1QXg20ID4TrS1wZ498MorUCog2kVeCnQoqyJm2rfvVbHdkSQJu92O3W4nEAgwODjIm9/8ZnRdJ5/PMzo6yvHjxzl27Bgvv/wyp06dIhKJmKXtKzGKeH7rwFngf1fedwFbWCI/D1AsbiWTCZDPW7HZbNeW7ZdRwz87CxcuIC0uopRKotGUouCSZYKhENubm3nrLbeArpN3ubggSRyZneXY8eMcfuUVzp49SyQSoVgsitioUh1p/B9Eh/hoNMrJkRG+9uyzSIDH6WRLWxt7e3rY19fHwf5+BtvaCPp8yE4niq6j6DpSZVlKhFXi664u2LatvlL/UvwJNgrj75zNrmjItNY90N/fzyc+8Qk0TSObzXLhwgWT/Dx06BBnz54lHo9TLpfNa+jxeNi+ffuKbU1NTfHpT3+aH//4x3i9XrZt28bNN9/Mnj17OHjwIAMDA/h8PqxWK4qi1J1fN9DA5YSu64zGR3ls6DGiuWilcx1ghRdHXuS7Q9/lHbvecV3fg5uS8NQ0jW9961tIksRb3/pW/vRP/9SU58NSh+BAIMB73vMeXvOa1/ChD32Iz372s3R3d3Pvvfc2VIxVKJfLZlbq3LlzgCh/WE546rrO5z//eR599FF8Ph/hcNg0xG5tbaW5uRmfz4fX68XhcGC1WrFYLNjtdjweTyODdS1AVa+s0rEaF+v+qGmCIBwZEUs+LwhMWV5dfVAqCbJuelqUzGzbJhSfa33fNU2Qeq8GXq39XArKZUEsnzolgmkQ17qeQkNRRHBaKokO9LOzQhUyOLgxha3XC9u3i7/t/Lz5OavFIlQMXi+DXV1LxLWmgaqiqyqlcplMocBCMsmirrPY1MRCocDC/LzZRGF+fp5EIkEqlSKfy1HKZCiXShTLZUoVD9GyqqJVyvXXuDhAorKIMXIiAn/5bfFbCdGEyetw0OT10tTaSlNvL01NTeb4aIyVPp8Pj8eD0+nEUjnP6sUIcBvdPxu4JrCOZ4YmKfxT4mlGiwu1laYSzJTi/HPiafa5thGSrKvXEOj6lZ0IN9DAtQjDb/DkMZi/hM8bid72Djh48KpXoEiShNPpZOfOnezcuZO3v/3t6LpOPB5naGgIVVVprZsoP4IYXJaPRVngWGUBkHn66Wbe+95t7Nq1kwcffJCdO3fWPGuNJOmmIpmM5H8sBkNDwspAVUUstjxGq642ARzZLDfZ7dy0dy/vfvObMfabvQAAIABJREFUUW02ovE4w8PDnDlzhlOnTnHy5EmmpqaIxWKmf7shcDEPAUjlchweGeHwyAj89Kcoskyz18uOzk62d3ayq7ubHR0ddIbD+Px+nHY7LosFmyShdHfD3r2rV3YZCXdD0HClYCTQLsHIVZZlPB4Pe/fuZe/evYDgJCKRCENDQ5w5c4aTJ09y8uRJ0+N2OWZnZ4lGowCkUimzQzwI/qK1tZXt27ezfft2du/ezc6dO2lvb6e5uZlQKLR57skGrhuUtTKHpg7x9PjTK7wjsnqWLx3+Em/oewMtnparc4CvAjYlQ5VOpzlz5gySJPGWt7wFi8VidgyWJKlmgJZlmR07dvDe976Xhx56iB/84AccOHCgbtOO6xG6rmOz2diyZQtut5t8Pm8uRiMSo6Nytc/O4ODgim2pqsrp06d59tlnKSybeBjZWq/XSzAYNJuFBINBbrrpJt71rncxMDBQ85lCocD58+ex2Ww4nU6cTid2u92c9DdI6auEV+thutZ+dF0Yl588KYgwWF9Xd4MQNZrHJBKwc6dQQjQI99VRLAqi+PTp2g7ma8FQfMqyCKyHhgRhum/f+lWsFUWnSWgak69qZUAdMkUCbFahzgh6vWzz+wVx2tFhbkvXdYrFIqlUimg0SjwSIfbznxOdmGAxlSKSTBJJpYimU0xGSrwyIpPOl4ACkEf4huYrS4lK3Xrd09CBkqoSzWSIZjKcnZ2Fo0eXXS7JLEWrHh+DwSBNTU2Ew2GampoIBoP4/X58Ph9OpxOHw2Euxs/LM/8NNHBVseY9qDBTXOCf4j8VVY0rPguPJl/kXb7Xcq93P+hrJIUa93oDDaxEby/4vFCauPi6y+F0wvbdsLP/qjRUXA8kSSIYDHL77bevsVYL8BZgGogjPL+TiOd49XNbI5mc49ln5zh58jjvfOc72blzZ82W4vE4p0+fBjCf0X6/H5vNdvXmJZoGk5MiJk4mRZyz3pjWaPx28iTMzqLcdBPNLS0033knd955JyBKr6empsxqwLNnzzIyMsL09DTxhQVii4sks9kVFkOqpjGbSDCbSPDUqVMAuGw2OsNh+ltaGGxrY3t7O73NzXQUi/i9XgJNTaYwpqYS6Br1GpBlmebmZpqbm7nrrrvQdZ1sNksqlaKpqWlFjBaPx9E0DafTST6frymlL5fLTE1NMTU1xU9+8hMAnE4ng4OD/M7v/A4PPfTQiv2nUinTgmCtyqoGGqgHXdeZy8zxndPfIZKNLKk7K9AkjaOTR/ne2e/x3pvfi0W+PufSm/KsSqUS8XgcWZbN7IkkSSiKgiRJZLPZmgHEYrHQ399Pb28vx44dIx6P3zCEJ0BzczN//Md/zG233UYymTTVnPF4XBABFePlRCJBJpMhnU6zZ8+eFdvJZrPEYrEaQtmAUYaSz+dZMMpTKzh48CBvfOMbVxCeExMT/NEf/VFNJ0bjNRAI4Pf7cbvduN1uPB4PHo8Hr9dLIBDAarU2JvpXAlarCKJejRaWawXXi4tw5Igg0NZTlr4cxvrRKJw4IYLFjo76AaKivHpk6GZsuFEsCnL4zBmhtLyUgN4gmRcW4NAhOHBAeHuuBcOqoJrU3sjfuZoUjUQEwahpouECtSVz5nivKKLbsyShaxq6plGkyHNnnPynz2/hzLQGpBBKzjhLE6d41ZIBMjhtCVr9i+SKObLFolCNlsurKkUNAtZo3lQP1Ykjo1mB1+vF7/eb42L1WOnz+WrGSK/Xi8fjweVy1ShGG2jgisFiWXPM0CT4bupFzhSm668gQaSc5u9jj3OnZzcuZFb1T3Fc3x5SDTRwyQiGYLcP7BskPFqaoacN7Nd6PH0/8AYE4TkFjCPK3MeAGYT8dRbxPBfl//39/bS1ta2YS5w5c4aPf/zjzMzM0NXVRW9vr7m0t7ebVRt+vx+73X7l5yK6LhLSx44J+5BLeaYbxxiJwPHjsGsXtLeb27LZbPT399Pf38+9996Lpmkkk0lmZmaYevppxg8fZmxxkbGFBaZjMeYTCebiceIVErQa2WKR4ZkZhmdm4OhRJEki4HLRGQ7TOThIz5Yt9Pb20t/fb1a+tLW14fN6sdtsYpw3rJRqyuIvE3RdPLeuEDkoSZIZl9XDnj17eOihhxgeHmZ0dJTZ2Vnm5+eZnZ0lkUhQLBZrOIxcLsfIyAiFQmHFvabrOo888gjRaJTBwcGaPh1er9e0bGiggdWg6iqvzLzCkxeeXLUz2GJhke+e+i6/1PdLDIZWCuKuB2xKwlOWZewVssQg32RZNid509PTlJeVjhqKwVgsVtdEeL0ol8uk02lmZmYYHR0lmUzi9Xo5cOAAbW1tG9qWruuMj4/z/PPPr7qOy+Xitttuo+Vi5MEasFqtdHR0sH///hX71zSNfD5f06UunU7TUyENqiFJErfddhuASZIaRGk2m6VQKKwYqI1zCNTp+DgxMWFmsAzIsozD4cDtduNyuVYQnoFAgA9+8IPcfPPNNYN4sZhnfHyCclk0sHK5XKZatJHx2gCsVnC7BeG4HJfwzJTqfa7S+GbV0ql0ehnZCcjaxvevVv7uiQScPSvKaOopD222pW6RV8rHU9fFftzuzZXB1jSYm4Nz5wTZaWTYldU6p68CHcFsKIoouTp2DG69FXy+VdbXReA+PHxRsrOsa6hoWFGQV7t2sizumxMnxH0VDtdfz2YTBK/FgoQY0xxWG36XH4vSB/jrnRhigpRFkJ3idUvrDP/3214CKUEilSWWyRBNpYhns6S8XhLFIvFYzBwn1xojly7L6okjA0bJnzE+Gq/VSSG/308oFCIUCuH3+1eQpoFAwFTU22y2xhjZwKXDGD/rQmG8uMCjyRdJaWv4fErwROooT6Ze4X7fHaAvi9GMCepq40kDDTQgnmsbDS8UyyXFdpsPMuAFtlcWEJUZcWARQXjOAON0do6yY8cE+/dvJxAIrChnn56e5syZM0xPT5tKT4vFQjgcprm5mZaWFtrb2+np6aG/v5/Ozk76+vro6enBcyUsAebnl5GdOsj6pcfE8fhSTBwM1o29ZFkm4PcTSCTY2dGBHgpRVlVi2SwLmQzzsRizkQjjCwtcmJ9nJDLP5GKE+ViSZC5LqboUXteJZTLEMhlOjI/Dv/0bNpuNUChEU1MTra2ttLe3CxK0tZXObJZep5O2QAC/w4Fss4GiociXIXlrxF4u11Wr+jKI5WKxSCwWY3Fxkfn5eaanpxkbG2N0dJTJyUnGxsaYmZkhkUjgcDjYtWvXim1ls1n+7u/+juHhYVNlalzPvr4+ent7zfuzra0Nt9vdsJdroAbZUpavHvsqkcxKdScAEqiaystTL/PE+Sf4Xf/vYlM2oXjnF8Sm/Fa4XC66u7s5e/Ys85XJsiRJNFVk8q+88gqJRAK/X0xeVVU1JeIdHR2XrHiJRqN89atf5cknn2Rubo5oNEo+n6e/v58//dM/3TDhqWkahw8f5iMf+ciq67S1tfHwww//QoSn4UezHIYqdq1MVDVcLhfvec97eMtb3mKWw+dyOTKZDIlEgkgkQiQSIVaZ5C8uLhKJRNi9ezfBOh2LJyZWlt8YhtDZbLbuMXg8Ht7znvcAotJ1fl5U005PT/OFL3yW06eP4K503TPKRIUiKkgwGK546jURDAZwOBy4XC5sm1F1d7UgSSIAGh2tfV8HVZXWbMJYD6omoWmIwMz4rKYJ4q/e5LVcFkrDSMQkO0uqRCxpJ1daPykjS9ARyKPIVUrPCxcEGbZcJWSQr4py+Tw2l18nXRfnvNkUSqmUuC7ptEl2llSJsTnPKsXbdaBLeB1lmnwFrHKFzI5GBZm5d299D1BNW/JahTVIYJnTxWkO585xn2c/HRYfq6q/ZFmcz/HjcMcd9RXE9Tqnazq6vtrZSogIwOjOblQG6AQ8UX7lZp2QN49WhoKqUiiXyQOFO+4gX+lGn8vlyGaz5hi5uLhokqDGz4uLi8RiMbLZLLlcbs2knFEulc1mV1WKGklBh8NhqlyNcniDLA0EAoTD4aoxMrCitN7lcuFwOBoqgQZWh6KIsdxIGFVBQ+df00c4nh9HY40JugRprcCnFh/l9e49eGUHNWWoui7G6KvsL9jAxhGPiwrc9Yq0JAn8frE0sDGUS+LRuqHPlC+PgC6bXeo9uF4Egxuz/K6HtQWAVkSjwWZgJyJ2yLN3b4qPfzxOR4eN8LLkaLFYNEmmapTLZebm5pibmwMqyVKHw7SfaWlp4aMf/Si/8Ru/ceknUw/ZrEjkZrMVgk4nV5KZT9oplOR1k542i0ZPMI9sxMSLiyLOd7tXr7aKRoXNUSaDJMtYZZkWr5cWn49d7e3omka+XCZRhn+efoxEKs+t5W2cm53k/Nwc52dnOTszw3wiQSqfr9l0sVhkdnaW2dlZTpw4AYg5pt/rxW+zEXQ6aQ8E2NLWRl9bGwNdbczE3cAgooHkLwBZFjdevdj0VYTNZjPVmLt27TIFSEYlZiwWY25ujuHhYeLxeN3Ky8nJSS5cuEC5XGZmZoaZSkNRSZJwuVz4fD78fj/hcJi2tja2bNlCf38/d955Jzt27MB6la9BA1cfL029xI+GfyRyRmvEaPPZeX449ENe3/d6djbvXGXFaxebkvC02Wzs3buXH//4xxw6dIhf//VfB2Dnzp10d3fz9NNP8+d//ud85CMfwefzceLECT73uc8xNTXFvffeaxKhG8XCwgKPPvooP/3pTwmHw/T29nL06FFsNtsKT8v1IpvNMjU1xR133MH999+/gnxzu930GqbkVxmKopgT5GoYStFSqWT6gZbLZUqlEqVSyWwgtRzd3d28853vNIOIWCxGJpMhm82uUOgacDqdhMNhJEkilYLf+z2RqCwWo8zPHyOb/bl5rBaLpWaxWm1YrVZsNisOh4Pm5mYeeOABHnzwwRUkeCQSIZvN4vP5TOXwDYP2dhHklEpLJR+6RCRlo1heP+mo6RKJrJVE3orTUaglPDs66gdZ8/OiCY5Rxi7rTMUc/NX/vomfnFw/6e93FXns/3mGJn8etEq59dQUdHaK81vuGxQMioxvMll5Uxysrsqo6sai8VJZosaszlCNtrSIc94sxFG5LJoNGc2CJAlkjbmoizd/6m4KZWldsbSuw5sOzvBf/t0pOr1FQFq63q2toivncqTTQlWqqmuWFZV0lZ9mjvO30cdotvho9eyvm4AElvxEZ2fFPbRt28p1WloEOZNKXVpZWA1ESbykgyJJuCwWXEYX0i1batY0xsjqsbF6jCyVSuTzeaLRKAsLC2ayaGFhgdnZWebm5pidnSUej5tjZGmNpl+applk62qQZdlsLLc0Ri41TzLG2qamJpMINUr5jLKpQCBgqkwtFkuDFL1R0dwslJ41cZDMRCnCD1MvM68lVv2oCQleyAzzf5LP8TuhXwGtivTX9YYP8zWKf/xH+PKXIZNZ3/o+H7z//fC7v3tFD+u6xPy84MU2gsXFy9On8pln4A//cP3ryzJ85CPw7nf/YtahF+t9WQsJcGKxOOnqaqG7e+UaFouFe++9l4cffpjTp09z7Ngxzp49y8LCQk0iUtd18xk7OzvL6Oho3XlCIpHgb/7mbwgGg9x8880MDg4SCoXMKsWLYnRU/JGMeEXWOTXj5b99cxfHx9c3l9WBwdY03/kvz+Gyq+I6GEnnjg4Rpy1/dpfLwuoolar9XRXDLAFOqw0sZb4mP0tz0MdDXffzBulm8uUiuXyeTDrNxOIip6enOTs1xfGJCc7NzDCfTFJcNs8zErkzlZ8VWcZuseCw2XDabeSL/wRsA3ZUlr1AP8K/dZ1zE10XsX4odNUJz+UwKlVdLpcpoFJV1awMqqcenpubIxAIEI1Ga2JCXdfJZDJkMhmTBFUUxUx+f/SjH6W/v7/mntU0jWKxiCzLDSHQDYTPvfQ5EtnE2oyfBJqu8czEM/x09KcMBAewWzan5/OlYlNGl5Ikcd999/Hwww/zjW98gz/4gz+gpaWFwcFBHnjgAY4dO8aXv/xlvvWtb+H3+4lGo2QyGdra2viN3/iNS/bvbGpq4sEHH+TDH/4we/bsQZIk9u3b9wt18jNUlgcPHuT973//ioegJEmbvtzQOIeNKmfvvvtubr/9dpMMyGQyLCwsmMvc3BzT09NMTU0xOTnJ7OysaSAO4nk9NiYIT+HLszSpUlXVfFCsBrvdzo4dO+pe30984hM88sgjdHZ21pQJdHZ20tnZSVdXF21tbfj9ftMz77rxypPlul0Sy6qEVrfrxCrQoaxJlLVln1mtdFzThL+i0cSmspGiKjEVczI84133roPuAury/ebzIsBraVlJeNrtdYOfS1E+aMCK7hySJNSdm+m7XCgsdfo0jquiqL0w5yZfWv/9PJuIrfw7FwqCfKxHVExNCdJzze+MzGh5kSfTxzibn+br8We41bWLsLxGkGrcU2Nj0Ne30jPVbl/5nlRRIm/wb10sy2jLKWFNq6virR4jV5voGEr86kXTNPPVUL9Xj5FGGdTk5CRTU1NMT0+bXemz2Wxdv+WlQ9UoFAprjpHGs814Di1/dbvd5vjY0tJCS0sLHR0ddHV10dnZaY6RhrfoZn+WNXCJCIUE6Tk2Zn7Xy2g8nj7Ms+khtPL6yi/Lusp/W/gWv+zeS5e1Cah0G7ZahTdvg1C/5hCJiNxWOr2+9f1+4YrSwMZRugSFZ6l0eRSe6bQo6lgvZFmof3/RfWvaxrdhNDGvf1wy27dvZ+vWreY8Ip1OMzQ0xMmTJzl8+DAvv/wyIyMjZvMZEGKM7du3r9je2NgYf/M3f0MymcRisRAIBNixYwe7d+/m4MGD7N+/n+7u7rqiEHI5ESsta+aTL8tMRFycm11/TGyVtdr4XZLE9icnxdi9PBaLx4Vk96IX18p34z/jlfQIbRY/j6UO8+7A63BZdbBa0b1etnR28rr9+9F0HbVQIJFKcXZujhOqyivj47z8yitMTEyYncsNqJpGtlgkWyxCGiAKjAA/RlTeKEAYuAmh4H0NsA/oAbzCScpiQ7JYQGXpXCwWEQdeA88TRVFMErQe7rzzTo4cOcKpU6c4efIkr7zyCocPH2ZsbIxkMlnTjFhVVbLZLIqiEAqFVpCakUiEv//7v+fZZ59lz549vOY1r+Hmm2+mo6MDX8NO5rrEE+ee4NEjj4qsyDoSX/FcnH9++Z+5q+cudrXsuq5EDpuS8AR4/etfz//6X/8LWZZNNaCiKLzvfe8jkUjwuc99jlQqxezsLLIsMzAwwIc+9CHe+MY3XrJ/RTgc5v777zd/Xj44/yJQVRVd128oJaGhKjLg8XhobW1ddf1ypUTU5XKZX7Kl71oIOAg4EMRnEuG1p61YZFmQCZIk0draWtcEenx8nFgsRmyNyFuSJLMMtLm5mde97nX84R/+IZ2dnTXbyufzpNNps8OjLMs1SzWpsCkGj9FR4XO47F60yDrSauXE9SCBLOko0rLPGI1jduyoLVGMx0Ume9k1uJQrsuIyGuq/uTkR5FWfm66L0p2aGZmowZdlsWwENkWH6nOWJEEqzs2JSbuiXHqgpS/b7qVC14W3aSxWtynUhntErXijoiCIRIRqNhRa+l25LIiRi3TkLKNxKHuan2VPo0vw7eSL/HbgHn7Jsx+JNawHFEWc2/z8SnXp4qL4W1cH96rElpYMn/wPx4lnrEiI5/7X40/zePowINFuCfKH4V+l29qMof5t8efxOUq1DWAVRUxQdu0S3W83gPV8/z0ez0XtTeLxuKkSXVxcZG5uziRDJycnmZ+fJ5lMkkwmTVLUIFSNZTnxCtQlTzOZjGkrUw+yLOPz+UyV6PLEUWdnJ01NTQQCAVNNv3x83JRj5OVCvcnktXh+sgxbtwqLikoCRdc1Djh6ebjjPRvalFKt4TbURN3dDf/OGwTXaKPmTQFF2XhO9Wrl6S/X3/hSeCtFWf28qxN8xvzE5XLR0tLC3XffDYi5yOzsLENDQxw/fpyf//znyLJcE/sbOHXqlGlTUywWyWazTE9Pm/0LZFmmt7eXHTt2mCTTjh076OvrwxONIqVSyw5Q/LPRy7dqTDw9DTt3itJ2A5omYiWjQmTVCyyRUVM8HPkXdHTmtATfTDzNG9y7aLMEoJISljQN2SDeLBZcoRDtTU3cs327qMSxWhkfH+f06dMcP3qUl//t3zhz+jST0SjZYhFVVSkbsQk6UK4sIOZ5E8DjlZ9tQA8WeTt9LYMMTftoOR+kKxjG5vFgkWWUQgElmYRwGOkaF6pYLBba2tpoa2vjDW94AyDsAgwf2uPHj/PCCy8wNDTE7Ows2WyWtrY2Ojs7V/ANiUSC559/nieeeIInnnjC3H5/fz87d+5k3759HDx4kG3bttHS0oLD4TDn8deN4OcGQ9gV5ov//ovIyvoeHLquY1NsBJ117MGucWxawtPhcPC+971vxftut5s/+7M/4zd/8zd56aWXiEajtLS0cOutt7J9+/bL+qVcrfHEpaBYLDI3N8fCwgIWiwWPx4PP52uYC1fBYrHg9a6W0TwIfBkoIgjPWGWJstSdcY6Wlnn27YuTzcbJ5XL09/ev2JKqqiZRrq2RLtd13SRFh4eHCQaD5Jf51Oi6zlNPPcV//a//FYBQKGR2JDS6E1Z3pTcaLdlswlto3WUvlwsG+VcnapYuoVm6LOv1A61cTpTKVBOeiYQgWq8UJEmoDqPR2smzpoljqVcbVe/417EblpOkkiSIv2JxY0SYrgsCoVRaejXUt1ariNqtVpGx3siBGh3SS6UrN+ORJKGqTSZrjfGTSVHfuMbMTEdippzk0eSLRMtpkCGt5fl/oz/iNvdu3NIanZxh6fw6O2uvSyQifle9b10i7C3ywO0TlaoomXPFGT4z9mnIL4AEUQm8rQv8+6Y3Y+o3NaCkUEP3Gvd2PL5hwvNywRhPtm7dWvf3qqqa41YsFiMajTI/P29ai8zNzdU0pMvlcpRKJYrFYs1ruVw2ydLVoGmaua1z587VXcdqteL3+wkGg4RCIQKBgOlr1dbWRnNzs+kxGggEasZIY6kOujd94K3r4ntXLi99r41GbhZL7XItoblZTJxPnABdxyrJvMa5jde4btrghnTQS6BXrkswKCwibqBkcAMNXArq5C6vyGc2Ey4lfyzLv1jYY7FY6Orqoquri3vvvZcPfvCDqKpa99kzMTFBc3MzFovFfJZWzx01TWNkZISRkREee+wxAHp6evjUpz7Fv9+5E6VaoafrpIpFsuUSOioiCPkFqiZkWXggJBK1hGepJOK0i3gd6Ch8J/kCx/PjIIOm67ycG+FH6aP8VuB1qxMIui5i4akpUYljsdDT00NPTw9vvPNOuOMOUiMjjEUinJ+d5cTEBKcmJhhbXGQuHidbKJDM58gWC2jq8jiwCJyjrJ1jZA7+0xfB53Ix2NrKltZWdnd3s72jg75kknCxiDsYxOPxmHHF9ZBQtdls9PX10dfXx6/+6q+a5e0XLlxgeHiYVCrFjh07VjTrSiQSjIyM1GyrXC4zPDzM8PAw3/ve90yhT39/P1u3bmXPnj284Q1v4I477ni1T7OBy4CDHQc52HHwah/GpsCmjbiNCZehmluOffv2sW/fPqDWY1LXdRRF2VSDWrlc5mc/+xljY2Nks1ncbjfbtm3jnnvu4Y477thwM6RqbKbzfHVgY8mkfCX27oUvfCGHwyEUTsv9SEEEIPfccw8tLS0kk0mzQZPhL5PL5SgUCjWTfoCWlhacy0iOUqnExMQEx48fX7URU7Xxuc/nM1WjH/vYx9i9e3fNurlcjjNnzpiG1EYDEpvNZnrvGYqoS0IuJ5Yrfd9Ikgiy2tvFz7ouSLDLYSa11j5VVey3GqWS2HedMv7Luu9CQexnPR7Cui7WTyYFcReLCQVqobCkCrNaBWEcCIiu5D6fINnWI/HQdbG9y5i0qQtVFaRntYVBMimu+RqEjgocyZ3lycyJJT5Rgh+lj/Bk+mUe8N25spNzNYzz07SlmY2uV3m0Ll9fglLleCQbX59/nhPJBXMuUdDh83PPcZ/9tey0dbJq7YehbK2+tzcZFEUxFZf1oOs6xWKRRCJhKkGrm9AZDZbi8TixWMwcI43mTMZroVAwfUrXKq0vlUqmGnU1OBwOc3z0+/1mU7raxnQB89Vo2GSUgjmdzhXk6KsOTRPf/2RSlAomEmKyafiYybKwXPB6hSI6HBaT0Guk9A4Qap1EQpRJapr4/uiXMKYbyk6XSyhHjaZI1yFuvBitgQYuHy41hLncoc9qiba3ve1tDA4OcubMGUZHRxkfH2dxcZFoNGr2LVjux2212XC6XMjFYs2BRjMZvvDEjzg9bWU2fgHhZxmuLAHAiZgDbQBGXNTRsfReoSCeTcuTwzWQWSzHeSTxM0q6KuI0CabVKP+SfJHXuW9iwNoMq7W/NMjWQkGM88ax5POQSuF1Otnd18fu/n7efPvtaOUyC8kk49Eoo/PzfH/kEN8bfYFENAd5IFdZSku7NLjQZDbL4ZERDo+M8L+ffx6rotASDtPV18fgli1s27aN3t5eenp6CAaDhMNh/H7/deNNLkkSHo+HvXv3snfv3lXXs1qt7Ny5E6fTSTQaNT3jq3tqVAt9XnnlFb7xjW/woQ99aAXhqWkaw8PD5HI583o6nc4bqoq1gWsLm5Lw1DSNH/7whwwNDfHAAw+wY8eOi65/6tQpnnrqKfbv388tt9zy6ivnVoGiKHR0dNDa2orL5cLtdrOwsMA//uM/8vWvf513v/vdPPTQQ3TXc9euwvT0NIcOHSK1rPzhyJEjV/Lwr0nYbE5aW52rls/bbDY+9rGPmYbPqVTKnPQbSyqVMgOWZDJJJpPhwIEDK3xWyuUysVhsTZ+8fD5PPp83uz+CeEA99NBDK9adnJzkIx/5CNls1pzcGwSA4W9qlIZ6vV6TJAiHw3i93ot76BmE55X22pOkWqMuVa0l864klhOJ+zFaAAAgAElEQVTPpdKVVZYa0DRxbev5ly5fb3FRlP3Pzy8Rd8s/p+tCSTg9LYjO5mZRwr2e5h4GIXilAzlVFedcLi95Z66DaE1rRb6WeIbFcmpJwCBBXivx6cVHucu9h5DsZNVg2iCYqxWsBvm65jkrTJbm+HL8qVobVglOFSb5Wvyn/Hnrf1ybxJGkjXeO2ESQJAm73W56ctaD0U00nU6TyWRIp9MrxshkMmkGxsYYmUqlzCWdTpulfoVCYU2lqDFGrlY+L0kSTqcTt9uNx+PB7Xbj9/tNVahRMl89Rrrdbrxer7l4PB6zq/1qidRLRi4nLC3Gx8W4tzwJYCCVWvru+3ziu9zVJZIkm125CiIJs2ePOC/DH3ijNcoG2enxCLKzs/PaU7vWQTQa5fHHHzfvc2MS/fzzz6/ZgKyBBhq4djEwMMDAwAC6rlMoFJifn2d2dpbx8XEmJiYYHR1lcnLStJ6JRqN0dnXR2dqKZDwnAF2SWEgk+MKT/8bowgLwHUSznjagD+hENPDpqrzXhrD6cnNRFWg8XvtzqXTRblA68Hj6CEdzI+hVFk6arvNcboifZk7S6b8b+1pjf7ksnoX19q1pS8nAyhm0BgK0hsLsHOzl6MAo+gyQqSxxIAb2hIWt+Xa8OSczsRjRdJrksuaNJVVlan6eqfl5XnjxRUBUh7a2ttLe3k5PTw/d3d309/fT0dFBW1sb7e3tZpPG69WLfNu2bXzmM59hZmaGsbEx8/6cmpoye2oYJH11vHbgwIEV28pms/z1X/81Z86cobe3l+7ubvr6+ujo6KC9vZ22tjbzel7rhHID1wc2ZZSp6zpf+9rX+MEPfsDAwMBFCU9VVTl06BB/+Zd/yQc+8AH27t27KQhPo+nRZz/7WbZs2UIgEECSJCKRCM8//zyf+tSn+OIXv4jf7+fDH/7wCvVgNc6dO8ff/d3fMT4+br4nyzLJZBKHw9EYUDYIt9uNu7rEYxlUVSWfz9eoP41JczUURWHfvn383u/9nkkAGERpKpUylVGFQmGFRUJ7HYXY7Owsx44dqyFHjf04HA4cDgdOpxOn04nH48Hj8RAMBnnHO97B2972tpr7vlwuc/78eSKRCKFQCK/XiyuXw1kq4TDKK80dqEiylUtz1FwF1eer6xt3279ULFebXc19L0exKDxUL1wQWXeDFFkrwNJ1QbCNjQmypKdHlICuYnJufuZKqmmrYZTsVv+81ngkWXk5e4IfpY6svN0keCE7zLcTz/B/hX4V9DX+blXdRIGlTgVr7lvmi7Efc6Ewt2KOoKPzpfi/8R/897Dd0QX6Gj6ir9b9dJVQ3U10NRhjpDFO5nI50ul0zZJKpYhEIkSjUVKpFIlEoqbU3hgjDcXoajYyuq6bCvyFhYW661SPkYb60xgjDZLUSA4ZpKjf7ycUCplEqdfrNcdXu91+8YmPkZQ4f14QgMbk7mLfZ00TxGgyKb7TAwOC+LwWiD+PB3bvFq9jY0uJlYsRn8Z5y7JoLDc4KAjf66Rb7NzcHJ/85CdryE1ZlonH4yuscBpooIHrC5Ik4XA4zNLtW2+9FU3TSKVSpu/2zMwMMzMzuD0e+jo6RLevSgyoSRIjc3PMmORkFhitLM9X3gsAQQQR2oogP/uAByr/XwZFqR8LLo+dVkBmthznO8kXiKqZZScKs+U430u+wN3uXWyxNrGm/VD1vqtj8XrPCk0DDY7nRngyeZSkkgN/5bQ7EPnvMhy0D/BbtvvIJfJML85zbnaW6ViMC3NzTEYiRNJpcstEDkbJ94ULF3j22WdRFMVMkho2ZJ2dnfT399PT00N/fz/t7e00NzdfF6Xwxv1plMLfcccdqKpKKpUiFouZRP309DQjIyOMj49z4cIFFhcXzWraaiwsLPDSSy9x9OhRnnnmGWRZNqtwDDu3np4e+vr66O7uZmBggI6ODrNC51q/ng1ce9i00bVBMlVLrddCuVwmm81SLBYvq/fmLwJJktiyZQtbt26tmTj19PRw0003oSgKf/RHf8Tjjz/OW97yljWl6DfddBMf//jHa8qmJUniueee4ytf+cqmOefrBYqiXJQUBdEJ/nWvex0HDhygUCiYBKlBckajUZMANcpG5+bmKBaLdbs2zs3N1VWDqKpqKlKXw+PxcNddd62YnGezWR555BG+//3vm6XxAaeTcKFA0OPB7/HQ5PXS4vfRGvAyuaBR1nwIu4B1lGVfDNUPtIuRepcTy/fzanZJWOscCwU4c0aQI6XS+q+JcfyGLcDQkCAZ9u2r9WVa/plX63ovN9m6iFJN1zU+H3uCmJpZKUyQoKxp/M/Yv/Jr3oN0WsLULS83iOLq/RpmZauNhZLCSGGaL8SerFV3Vu17shjls5Hv8T87P4DR2KourlMFwEZwsTFS13VUVTWbOZRKJZMgrSZJ4/G4OU4mEgkWFxfNLvWLi4umejSdTlNcQ6m91hhZfczV3qB2ux2n02kmkux2O6FQyCRBq5vWtbS0mEp6g0C153Jw9qxQd2qaKFk3JpPVSzWMe9awR1hcFN/rclkQn5v93pIkQXZu2yZK88fHhVK9WmWzXKmu6+K8/H7RoKinR4xd14KqdZ3o7OzkM5/5TI0yRpZlHn/8cb70pS9dxSNroIEGrgZkWTYrtHp7e9F1nXK5TLlcxm7E+VXPAwnY3d3NxGKUSDqNqi2f/8Yri+HDaANcwC6E+nMJRVVlNhLBqSiEBwZqQ611xMQ/zZ7k5dx5yqgrEtM68FT2JM9kTtAduIdV5UXL49Dq/a5SCZXWC/wkc5QThYml8Mt4laBgLXPENspvt+u8afAAJbVEplwmlc8TT6WIJBKMzc1xIZFgOJdjqKJgXFhYqJknV3udX7hwARDVfx6PB6/Xa5a+DwwM0NfXx/bt29m2bZtJ2m16L/F1QFEUs0Kmv78fXdcplUpkMhlTwJNOpxkcHFzx2eHhYZJVFlKapplx3Pnz5wExPzYSzcFgkObmZt72trfxW7/1W2sm0xto4Epg0xKeBnmznixA9UNkM/l3SpK06qBot9t561vfyic/+UlmZ2cZHh5ek/AMh8O89rWvrXlP0zRyuRxf+cpXLutxN7B+GF6b9QZvY8JvLMY9WiqV0DRthVoUYOvWrfz+7/8+o6OjzMzMmBP+ZDJJKpWq65Vns9nwer0rfOuKxSKnTp3i+PHj5nuKoqBIEoosY5FlrBYLVkXBqijkSnbimQDwy8D/qHO288AiooTGj4QXRbJhszvAroNaFciAKMFe2jE4HGsTUpcDRqlkNaxWQURcaciymMTXG3/KZUGMnD8v/n8ppEY18Tk5Kf5/4ED9c5Mk4RW4hm/iZYGiCKVp9b3n8axBOlr5eeYk/5J6ZXUxsQRn8lN8LfEM/zn8m6vv2+GobXSiKKL0f7V96zL/GH+K6WJs9QowCf4p/jS/E3gDt7l3iuYqy6FpqxPNDZiQJMn001wtuF0+RqqqSqlUMhcjkWkkjSKRCJFIxFTKzMzMEIlESCQSpifpWqXzqqqaROtqMBojKYpiHr/hn2y1WnE4HITDYcKhECFZpglo8/tpDwbpCAYJe734nE78TidulwtrPY9OQ+livGazcOyYuJ97ey/lcr+6kCQx7rS3Cy/SZFKQvtGoSMaUy0skp8MhyvdbWqC1VSg6r0OfL6/Xy7333rvi/YmJiUZzygYaaABJksznCBZLTeNDWVW5Z88evjXYx9Mny/y3bzo5Mz0FHAbOAFPAco/yIuAABqlJDCsKo/PzfPSRR5iJRhnYvp0dt93GgQMH2Lp1Kx1+Px63WyTaDO97c24hM1tO8ljyZSbV6ConAnE1yz/Hf8Z9nv10WfysSA4bjfqWP/uN8b+u6l3iXHGeJ1JHSGv5+jGiDifzk/xL4kX22Ptokp3Y7ApBu50evx+ts5PS9u0Um5vJbdlCDpifn2d4eJizZ89y5MgRzp49y/j4+IrkaLFYNEm7sbExJEnimWeewWazmZY67e3tbNmyxexqvmXLFrq7u3E4HHUO9tqCJElmMjgYDJokfT1Opa+vj/e97308++yznD17lpmZGdLpdM06hULBFP6Mjo7icrnYv3//CnGOpmkcOnSIxcVFduzYQXt7+5oVrw00cCnYlFGYLMvmF8K2jnKnSCTCSy+9RLlcxu/3XzPBpcvlIhgMMj8/v2KgWI7VSNyGsnPzonrCv17s2bOHXbt2oet6ZVEpldLE41Fzsj89Pc3ExASTkxNMTk6iKFb6+rpX3COlUonJycma91RVXa0dSwUzwM5Vfvdl4K8r/w+h6WGOjXv4z/8gs6U9SHcoTGc4TNjrJWi3E5RlvEsXQwQ+irJU0iLLIF9G9aURYC1XzlqtgqCS5bolzxvde931dV0QAKtlLWdmYGRkSdmJDlZ1Yw04dZa6huu6KKENBGDHjpXX0FBhXenkj6KI867eTyAggtp65eW6yqci3yWnFdckHTN6ga/FfsZvem9li60DWKZ0MM5vuXpgtYZRksKFwgxfTTyNvlb5lQQZrcBnot/nq66tKMtVnsY95vOtvo0G1o31jJHGM676tXrRNI10Om2Wzi8uLjIzM8P4+DiTk5NMTU2ZjZgMv9G1YBCv6zl2qfr/lZ9lScLrdBLyeAh7vYS9HjpCIXpCYbqamugKhQj7fITcbgIVX2/FIOpPnRLNJa4VQtAgNO12MJpkVfs1K4oYC6pbRW+ShPTlxmZJtDfQwPWES/1abfqvo0F4VpLYEuC2WnGFAgx0NONz7Qe8iPijDFwAzgKHgJcq/59GeHm2szxGGllY4PTUFGempnjh/HnkH/0IWZZxulz09/Swo7mZ27q7OTgwwGBrK51NTWKsLqu8mBrm2ewQqq6tGSD/JHOCJ1Ov8FuhX0ZZnhw2mtJVz+ElSfzsdAo/62XI6xrPpo/zQu7c6juVoKyrfCf1Ir/pu527XNuRqubBsq5jlyTsXu//z96bx8l1lne+31On9r26qvd9U0sttdSyLFuW5QVjw9iAE4yNCXMHTGDuxCQMyWRIgh2chAmJGTAh+E4C+F6GxAFiGAeMbRzM4g0sI1mLW0u31C31vlR3dVd1LV17nfvH2+f0Ui2pN1vdcv0+n+OWu6vOe85bp8553t/zPL8fjpISsFioqalhz5495HI5TR5nYGCAM2fOcOjQIQ4fPsz58+cZGhpakDBVdVmTyaTmn9Hb28vBgwc1nsJqtdLU1ERLSwvXXHMNV111FU1NTRQXF28aLuJiuNBzrbm5mc985jP88R//Mel0mp6eHrq6ujh8+DBHjhyhu7ubsbGxBbGUzWajuro6T3Iwk8nw93//9/zgBz/A4XBQX19Pa2sr+/btY/fu3dTX16/J3PlSCMVDJLIJyuxv3hgFXH5siG9jOBzmiSee4Omnn9Z+9/rrr5PL5fjCF77At7/97QsSe8lkksHBQbq7u6msrGTbtm2bJtMSjUYJBAKYTCachQV0AUA8ruPpp3VEInMFfTqdBVkuFvzg7FZbK7oCczlwuXLs2ZP//bBYLNx+++1UVFQsaBtNxONkk0myuRy5XE78VBSyOYWcoiCCp8XIAFMIXaEYIHT0xkLwv19Y+Eq9TofLasX9yCO0tbfz2c9+lmuuuUYQYSYTpNOk0ml6JyawWY2E4yVkszNABJARTNj8TWKpqCu7uJBLUcTiezHhqRJUBoNYiIsXowBKViKbW1lknM5KkFOJMGmuZdPpzHdcVhQR2J0/L1o+Zz/UDAovHytjNLj8LKbTmubm7RM4zJm5dti+PlFhVVyc395dVDQbwGbWP/pXz9liyXdYtttFdWkwuLBlVdLzcvQEz4WPLYvoPZce54nwr/lT310LH1Rq5ZjHk39ePt+cbtW8v2UV+O70K4ykg5dmuHXwy8hJfh59g3c59iDND+TVIN7jufQJFLAuUAPuixFKZrP5gq70IAJq1XQpGAwSCAQYHh5mZGREE+yfnJzUSNGZmRmN+MzlctrP3Oy9UpldOGl33UXxSTISIRCJiETHEtBJEm6bDbfNhsdqxed0UllURKXLReWvf03p1Vfj8Xi09nqz2byg8lRdbKmbSrheNsxvVdTpNg9hW0ABK0Q2K1RlDh5cmZTz/v2wffubd1xvNpapLqZhfhH7WrDIyHxZyGZXfrxL4exZUXh/EU/SBZBlIa9+9dXLeLEkiThCr19gICQpoOSys7G4GvkYEG3r24H3I2LPINCDqPK0ImJzAQXoHx8nNFvBOL+TIj09zRsnTvAG8MTs60ucTmqLi7m6oZHbr9vNz3xvcD69yEBwscKPBLmcwqNT/85vOffhli1zL1A/MPX85j+bTCYRM05MLIjTFGAoE+LJ8G9I5tIXjxEl6E2O84PQr9hhbsCt0y8M6/T6ucQ7cwlJtZDK5XLR1tZGW1sbd999NyAKp86fP8+pU6d4/fXX6erqoqenh3A4rEnyZDIZjTRV53N6epojR45w5MgRvvvd7yLLMj6fj4aGBnbu3MmePXvYsmULDQ0NOBwOrcJXfY5v1kSZmqwGUZi2a9cudu3axb333ouiKAQCAXp7ezl58iSHDh3izJkzKIpCU1NT3jlnMhk6OjpIp9PaOvXIkSNa92p5eTn19fXs2rWLPXv20NLSQl1dHR6PB6vVuqY5VBSFbx79JqcnTvPY+x7DIBdilysVG4LwVFtvf/azny34nSRJdHR0cOrUqQu+V20RqKio4MMf/vCS5dKrxXL2oygK09PTjIyMYLPZKCsr07IX6XSaaDS6ZLtxJBLhn/7pnxgdHeW66667pDHT2xUrDXQuqcW9wREIwJ/8iTDxXS7279fx6KMLO8gB3G43n//85wGRGFBb48NjYwR+8QvGAgH809P4p6cJRiN09GXp6M+SzLQvMUoGoR0EgpS8cAVUJpdjMhplMhrFYrfPZficTnGQ8TiDfj/ve/hhckoOo97JSLCaOffJEoRKuRch0O5CtO2YZv9mI5eTBPG4+DlXWbl0lWVR0aIga/ZYMzqxnxUgnpYhu+jeoNeL9s7FFemKIsacmm0Nmn0wJyWFv/3RNn7esfyM4pbyCM9+7iUclsxc4BmNChd3j2chyaBWO3o8otV0kbRGbhXfq7zJ1ukE2bo4WaPXCzY+GJwX0ErEsgn+Mfg8mYs5oM/DdG6GZ8Ovc7t9D7vNdUiqY3suJ8ZdymHc4xHX2NjYvDZ7Hd3JIZ4Nv85MLnlRaU4VodwM3wr+kr2WJopkm3iDuoKrqRHEegGbBnq9XiMPLwS1ikO9TwYCAcbGxvD7/fj9/jmzpclJgoODJOJxUtksqXSaZCZDKpMhk82KbTaRdCHkFIWpaJSppTo7fvQjQCwinE4nDocDt9uN1+ulrKyM0tJSSktLF5gteTwezX1e/Wk0GrV2fL1ev6kXVwVsXLzdYrR0Gn7xC/j0p1d2Ho88sj6E50r98tZrri8in3xBZDJr9/ebmVn5OaTTqzvexfj5z+Ev/mL5ykBmM3ziE8skPEHEKk6nGGBF92YJ4dB+jfab+TGdkslQ6XJxYOtWukMhwrPPNtUccHEHw3g4zHg4TM/YGONF05yxjYgio/mx0jBzkqFGtNqEI8nzfCf8Ir/vvRNyabRYyWQSpnSLk18mkzjv+SZ/QAaJX0WPczB+dlkxGhL8IPIad7v3c8C2HUnJzt1c7HYRC65AZ9Pr9eL1etm7dy/33XcfiUQCv99Pf38/J06coLOzk97eXgYHB4lEIprhYiKRWFAVms1mtZjh4MGDgHiWqy7mbW1ttLS00NjYSFlZmfaMN5vNmEymK0IbVJIkiouLKS4u5pprruFjH/sYmUyGQCCAa4kurImJCYLBIBaLhWQymSdLpMoXvfrqq4Doji0vL+f3fu/3+IM/+IMFRW5qVa6iKMsyngzEAzx25DGS8SS/2vUrbq67uRAnXaHYEISn2WzmwIEDZDIZJElCURSeeeYZ+vv7ueWWW2hpaVmywlNRFAwGA16vlx07dnDgwAG8Xu+qj0NRFM6dO8fo6CiKopBIJEin08TjcU6fPo3FYiGXy1FRUUFjYyOyLJPJZHjhhRd48MEHOXDgAH/2Z39GQ0MDIL7EX/va12hvb6eurg6HQzT4BoNBXnjhBb761a/i8Xi48847lxQFfrtDr4c9e+Y65ZaDnTs3t+mrTrdyo97lePKYTCbtAURjoyAFz5/XAoJcLsVTrxXx9ecrCEadwOSiPaSBNuB9s38Lks4myWTjZHNxZpJJ4uk0yXSaVCZDcjYTajKZ8KjVcLIsnIgnJpgIhznn989msccRmerFsAB2hGaoE0GA/jlwE5KkoJMAJOLpNGdHR5lOpbB6vdi6u4WhiMmkbUaLBbmyUmjNqal/SVmVn5FOEu9dUN3pdosgbnGwkkgIQ49UKm8gg7yylYBBn1u4i/mmJ5FIfsWjySQI4KmpOed0Bcz6HPuaJ1ewiJDYWhHGtPh4zWZB8i51wVZWihb+cHh2TiSG0hNkc1luti1/1efQWehLjbHLVI08X/+1rm7pL7paYhEIzCNbFXpSY1Toi1Y0tgGZcyk/RZZGtCDeaoX6+mXvo4DNA/VecbFK0Xg8TnRsjNgvf0kkHGZqNrETiESYikSIxGKzRGYMf2yazugg4/EwpEGX0eFV7KTTGY0gvRgpmkqlCAQCBC6w2lZdV1XjKIfDgdfrxefzaU70LpdLqxR1Op2aDpl1tpVeXWSpRGmBFC1gpaithRtvXOhZdTHYbOLxsFkhSeKRt6gw75JYj+5Wux3a21c27pYt6zP2WmTHN+O4kO/HuJxxV1TcbrGImHhyckG1o8OUob0uhNWY5dLMn0CFJ45OUkARHQS3t7dzw+7djNfV0R+JcObMGQYGBujt7WVsbIxAIMDU5CThUIiZ2RJWvUnGXmqm3OCh2OacPSeJZCrNa890k03lRA1CEZphvMVp5N9Sv+H9xuvwmOxYDHoxcaWlIiZdykTU4xHb6Kh23mklQ0/Kz3XmLcvXmVLgZLyf663z4jqdTiTD19g1aTabqa2tpba2lhtvvBFFUYhEIoyNjdHX10d3dzf9/f2cO3dOM1icmprSiND5nEUqleLcuXOcO3eOX/ziF5qZVVlZGQ0NDTQ3N1NdXU19fT0lJSX4fD48Hg9Op/OKcDRXC9PKy5fqHhTGuw888AC9vb3a9alKFKnzOR8zMzOcP39e44zmI5lM8rOf/Yze3l7q6uo000m3260Ry/Px+BuP0zPeg8lg4jsd32F3+W7c5nxD4QI2PzYE4Wmz2bjzzju54447AEE8hkIhkskkH//4x3nf+9635PsURdFcV9cDuVyOf/mXf+Hf/u3fFpSrR6NRvvrVr2KfrVa75557+OxnP4vVatWOtbOzk5qaGpLzeh8ymQzf//73+da3vkV1dbVGxqomRY2Njdx777185CMfWbdzuJJgtcLnP7/8dhL1PUuYnxewGA0NItiYJaR0ksRN22PUlY2Qzo4ASy3EG4FqIAEkSaZnSGUipDJhpuNxotEo07EYUx4PwXSaSCRCdXX1wqqq4mKoqyP40kuzZOfFEJ/dJmb/Xw+IiihJAoMsAqWxSISvPfssB3t6cP/oR7i9XlwuF3a7fcGC32Ew4BgdxQm4rFZKXA4kySOCxBVAr1N1jSRQcoJ4q67O18xUFFGeEJqtjH0zghZJElWekYi48OePIcsiw+73zwWWOR0+Z4pv3H+Y3JJW5flQAJc5g9eSXhh7V1XllxWrsNkEsX7y5KxJE1QYPDxc/pElr6wLD67g0lnm4t9cTmgcVlVd+D3l5eLz6Oub1Q6EfdYtbDNVL+3OfgHokCjSWYHc3GKkubmg3/k2hsViwaIoFDud+QkGRSGXzRJPpUhkshyJnuez/f/MeCgMSTCkZH7X9g7K0l4C0RDBWIzIzAyRRILwzAzT8TiRuEgezaTTIoGUSl3QhElRFM2A6UKkqE6n0xzoLRYLDocDl8uF2+1ecI9Uq0XtdjtOp1PbHA4HNptNe7/FYtn0i68C1hd33QW33LL8Sj5VjaSAlaOxEb72teWr1CiKaG5xOC792kthpUsUlRhe6+1iNWStTrei4r7Li5oaEZ+NjoqTzUk0lcR44O7TJNLLZ3tN+hxmfQ41Ea/T6XC2tODcupUmo5F3vvOdmjP5+Pg4g4ODjAwO0nfwIAM9PYxMT2My6vn9ptspKnajzH6hJb2enr5+7pz4ItlMbq4WQgZMoHfKjPiCPFjyHZpKyqgpLqaivJyK4mIqcznsuVx+lZ3NJs47FBKZEkXBhMQnit7FfZ5bV0B4KpglPTplHjHsdov4b53X1JIkac/FLVu28K53vYtUKsX09DQTExP09/czPDzMwMAAAwMDmlxOIBBgcnJyQVVtLpfTOkY6OzsBMBgMFBUVUVpaSnV1NZWVldTV1VFdXU1FRQUVFRX4fD5cLpcwvbqC4PV6+eQnP0k6nSYYDOL3+8X1OTKiVdWOjo5q8xkKhdDpdGzfvj2ve3Zqaorvfve7/PCHP8TtdlNWVkZdXR0VFRXU1tZq81lZUUnKlOJbR78FekhKSV7ufZlX+l/hvVveW4hzrkBsCMJzgXPdLD75yU9y5513ct11171lmpySJLFr1y5Nr+tC2L17t1Z2Lssyu3bt4sEHH6SpqWlBhanP5+Phhx/m5MmTTExMMD09jaIoNDY28ju/8ztce+217NmzZ8kS7wJEwFJdfbmP4gqF0wnbtsGxY6JcQKejyJakyH4BZ0RgTktTh+hrsQLzCK9MhkxpKcnt20nJMonZdpUFhKdeDw0N1O3YwZ//9m8TTSUZnEpw8IzMSHAGmEaQmuo2n+22ItLKqqaf+E84kaB3fJzOwcE8LQC9Xi/aO00mzGYzdp0Om16Pw2Lhhm1buOOqT6HLe7CdRAjE+xCVpQ5EpakdMKLTzZZ3IIOkExdpdXV+al9RRDCXSLy5CvrptCA9s9l8Ex+7XVQkRiKaSLxBhlzZQLUAACAASURBVJby6PKDShCxZG72889kRAa9ufnC5Qw6nSAlg0Ho7wdFwaEz49BZWdnAIMh3RZyf0wltbUs706vQ66GlRZD5U1Mgy/hkOz7ZscKxFbEps4RnVZUoZyoEQm9v+P0XFIjTATaTCcmUozs1RLdzDNW5La1kmClK8anS95DMJEnOVsLHkkmiySTRRIJYMkk8FiMUjxP0eAjpdEzP6o5OTU1pjvTRaFTbkhfJCOZyOWZmZpiZmbnga2RZXlDlabfbsdvtWuWozWbTSNKioiIcDocmD6BWjjocDu19m0VDvYD1QVGR2Ap482Gzia6ny4HLWeG50n2sV4XnWwKLBXbsEPHKzAzIMlZDjvri2CpiNN1c11FJSV5cqupL+nw+WltbQVGI33wzkYMHmQgGSWYy7CiuxKg3znXTmIycmjhHavEzLwvMQGQmTtdYnC5GsBqNOK1Wij0eSp5/nj/67/+d2267Lb+gR6cTielYTAilptPISNQZfKw8PlQQSWkEyVlfv7S++5sAo9Godc61traSy+VIJBKaJM74+Dh+v18j7c6dO8fg4CBjY2OEw+EFPEM6ndZa4Ts6OpAkCavVitPppKSkhJKSEkpLS6mvr6e2tpaGhgYqKyspLy/Xukc3OwwGg3aubW1tAMRiMSKRCBMTE9r89Pb2EgqF2L59ex6ZPjY2xvDwMMlkUnv9G2+8oc2ny+XSxsjaspwJnRHLyhIYUAb4cdeP2Vu5t2BgdAViQxCeS2Hfvn0oivKWsuySJHH77bfzzne+86KvMxgM2g1cp9PR1tZGU1MTer1+gfuYxWLhrrvu4vbbbycej2sLE5PJpC0kCijgsqGyUhBynZ2zVXg6llcCN0sEaf87q21YXIx+xw70RUXYLva9NZtpfs97+BOfj3TAz9lxM3/xzw2MBO0IgjOOEGQPzW4BwD/75grmAiJxDNFMhsgFBJsymQyZTCZvwa+TJGqKi5D08hKu3U8A30G01KuiRR7EU9FNz6iFf3gmSInDja+iErfHg3dqitJZclVDLjdnVGQwrJ+K/3yobe0zM+IzXExA6nSitWjLFjh1ak4zKbdKneNMRgSTu3eL1dfFjstiEeMmk0JTE0BapaBXNivI2127llfC7XKJRcQbb8D09OpWTTC3eCgtha1bl9aHLeDtg2xWJBcuci0pisJoapLvB18lkolrt6ucovB48CX+L9dNXGPditUw25e6qGJYyeXI6nRkWltJV1WRSqVIJpMkEgkSiYT272AwSCgUIhQKaYuriYkJxsfHmZ6eJhQKEQ6HmZ6eJnMRB49sNruAFPX7/XmvkWVZ0wM1Go1aK7z6U11IeDweTW9UXVSo7WQul0vbrgT32gIKKKCAdYHXC1ddBUeOiJhRlmcTzCuEGmMWFYmk72JDycWQJCx1dVgUhZKOjrkybWXe8yKdZFtNJX9/332cHR2lc3iY/okJhqemSCzSVphJpZhJpRgLhbBPTJBMJvMIqcnJSX76058SiUbZ1thIraJQlcshzzf8zGZXJv6ay4k5a24WlaOXqbxXdW23Wq2aq3g2myWRSBCLxQiFQprnR19fH11dXXR2djIwMMDw8DCpeesYRVGIxWLEYjFGZ80PZVnGYrFonRkul4uamhpqa2tpbW1ly5Yt1NTUUFpamueEvlmhciVlZWW0tbWRzWY1PmUps+dEIqF1/l5oPkdGRsQvdYhKZTOwBZJ3JHmh9wVeG3qNO1vuRCfpiMfjmg56AZsbG/YTVF3VcrkcY2NjvPjiixw9epShoSEkSeJzn/sc27ZtQ1EUotEo3d3dmM1m6urqsK5yUapqYq2kQmGp6tT5f9Pr9TgcjismA1PAFQKVhGtoEP/u7BQ6kyslhtQAq6RECKguM7Oq93hw7N8PHUfxRqKY9JWAKuw1m7ElO/szM7spaOVSIGzaXU7qbr2VT3o8nO7sZHR0FL/fz9TUlFYVFYlE8iq2JUmixOmaO4cF6AZ6F/1OfTLq6B6TeeBx0MsyeoMBvdnMdfv38/nPf55t27bNmxqFqclJuk6exGQw4HM48NhsOG020BuQpXVsuUmnLxwgGgxC81KngxMnBDm60od3LqeR2uzde+lAGsR4breoyNTphLkSrO76crvF9VVaurz3SZJ4bXu7OOepqdVd2yBa6Ftb37KqgQI2MLJZ8V27yHWQQealWIcwX5gPCUKZGb4SeIrvVDUgq84Mi+4/kqKgB/SShHmJuEHVBlNd47PZrJbYmb/NzMxoC6zJyUnGxsYYHR1lZGREu0eq21L3yIWnLSR+LlZNqtPpNOdZvV6/YJNlOU9ipLy8XNtUEya1td7pdK6b+WQBBRRQwIaGJImKx6uvhuPH57TPVxqvZLPC9GDnThGrLeceqteLqkg1PlQJV3XsXI6GsjL+8x13kMxmScy2cQ9OTHBmZISjvb2cGRqiZ3QU/2wXEaDd2xeb8AwPD/Ptb3+bw4cPYzabKXI4qLbZ2FZVxVX19WytqKChtBSP04lOjVNzuQt2VZDNinNoaxPrmQ3W7i3LskbalZSUaA7v6XRaS16Gw2H6+vo4c+YMx44do7Ozk3PnzjE5OZlniKR2dozNFhEcPXoUg8GgcRcej4e6ujpaW1vZvXs3W7ZsoampCfcVovWmxhJ2u33Jv+/evZuvf/3r9Pb20tnZybFjxzhz5ow2nwuQY26paQaM0Bfq45muZ7im4hoqnBV85Stf4ZVXXqG9vZ329nZaWlpoaGjA6XQW2t43GTYs4QkioH/88cf567/+a86fP4+iKJrz1v3336+9bnBwkD/6oz9idHSUf/iHf+Cd73xn4UIsoIBLQZJEa/CWLULk6cQJ0YIsy5cOlNTgSjWJ2bpVVPyt5HvndMI110JyCMU4P0iREOTiJbK09fWwr4kKs42PzrY/qGSAoihMT08TDocJBoOMjY0xNDTE4OAgg4ODBAIB9v72nWCpWXTMOaB/icHUJ6PgWacXdYiGQqE8wkBRFF47e5aP/c//yXQ8jsNsxmm14rZaKfd6ON7bBrQgiN5ShDyAulnyjkBiVhRLliGziJy41LyrQa3bLWQM/P7lCV2pn7NeL66T1lbRNrTcz1kVbLvmGtG6dOaMIIwuFczPv75qa4W9rUr+LGdsSRJjl5WJ6/L0aRgYEEHzcsc2GkXFQHOzMGgqPFMKWMY1EMvF+f9CL5DOZUWeZMH74d/Db/CL6Ane5dgDysqthNXYRpZlZFleMtl6IZPH+T/VxZNaDarqZqn3ybGxMc2MYWJiglgsdtHjUglY4KLE6OLzUH/qdDpNI83tdlNeXk51dTXV1dVUVVVp1aLFxcX4fL4lFzyrTXYXUEABBVxW6HQiuWq3i1h8eFgQfctJ1Kqiro2NIlayWlcWr8xKTeHxiK6YsbG5GEqSkBERqUWWwWKh1GZjS1UVt1x1ldD6lGXGqqo4H49z+NAhDh06hM/no7S0dMFaXFEUpqam6OvrIzSrbT82NsZp4Pnjx5EkCaNeT4nLxZaKCq5pbOTqxka2VVZSV1KCWX3Wze+W8vlEx5HXuyliNLUQSq/XY7GIOL+0tJTm5mZuvfVWFEUhk8kwMTFBT08Phw4d4vDhw3R1ddHT06NJhalQE5yqsc/o6CinT5/mueeeQ5IkZFmmsrKSLVu2sG/fPnbv3k1rayu1tbVXTBXofJjNZrZs2aLprCqKQi6XY2RkhJ6eHg4fPszLr73Mq4dfZXpsWizrJMQSTIKskuXZ7mf5wI4PUGYv45mnn+G137zG888/rxXFlZWVsXXrVq699lquuuoqtm7dSn19fR65X8DGwoYlPFOpFF/+8pf58z//c8xmMw0NDVRVVdHV1UUsFtOy/5IkUVVVxc0338wXv/hFjh49ynXXXVdoFy+ggOVAkubc091uYfQyOCjaNtVFs6RWIs17n8EgMtL19SJIW2k1jhqYmKxQ0yzkMlcCg1FU71lmlUWXCHS8Xi9er5f6i7hq//rXiuhY16AAnwCuQrTVBxCmSVMIs6Ysen0Gszmraf1ms1mKioryFts5IJrNEksmSWUyTM46OgMc6+sDji06GiNzWqH/G7hlwV8TqSTHznQRKU3hszsxGI3Iej16WUbW65GzWeTZFiK10moBJEm0Ot18s9DV7O8XBPeFMueSJIi+4mIRDPt8q2sVUon1tjZxrfT1iYA6Hp8LWud/fjqduL6KiwXZWVq6uqy9uk+nU1SlVleLcx4fF+e8VEWbTjfnPl9bK855EwTRBbxF0OnE4vBCshSSkeciv+LgzNl8shNAgmguwTeCz7Pf2oJdZ2ZJgzj1vrxKLHU/XPw7WZa1qsoLIZ1OaxUloVBIqxBVq0RVF3mVGE2lUmQyGa3qdP49cj4hCvkEbC6X0ypOAY4fP553PAaDAbvdrpkvlZeXa4YOFRUVGtFaSHgXUEABmwrqPcvlEgnikRERr0xNiSTx4jhJUeZipcpKEa9UVi697+WOX1QE+/cLsnVgQJgKqd1D88aWVEMju12M2dBApdVKJXDDgQOASKgtvg9ns1kCgQDxeByr1Uo6nSY92xavIJ4FiXSagUCAgUCAn3d0AGAzm/mre+7hj++6SxyPwQBuN0p1NTPFxUgGA4ZMBr1evynv/YuTf7IsU1VVpXEbIJzJh4eH6ezs5OjRo3R2dtLV1YXf7yeZTJJMJjXyUy0OU8m+vr4++vr6eP755wFwOBxUVVWxfft2du/eTUtLC9u2bcPn8wnPA6NRa+PebPM5/3jnz2dtbS21tbXc/I6bKe0opePZDqZHp4UJ1xjCjzcH6GAsPMaTp5+kllqGhoYAtPlUHeLPnz/PT37yEwCcTidVVVW0tbXR3t7ODTfcwFVXXaUR2gVsDGxYwvPll1/mL//yLykqKuK+++7j4x//OA6Hg/vvv5+XXnppwWvtdju7du3C6/Vy+vRpwuHw24rw3GT3owI2Kux2oXvY1AgjozATE0Y3idlyRlkGh0uQVyWlIjhaj7ZDSVq5Tvm6XfSLx5aBj89uCkJTNDa7BYFxduwY5777xgkEJgkGQ0xMTLBv3748PZmsohDJZIQpiF5POpu9hDN9CkGsBoF8gm80NMnvf/ObZJUBrEYzRXY7JS4XJU4nJQ0NFDU2UuTz4fV6KS4uxuVyYTKZtKoojQCVZZSGBqipQZqaEqRnLCb0PdWKAptNVAl4vSIAX6/MpdcrtkhEjBuJCOIzm50jRu12Qb673es3riyLwLy8XATxoZAg9RMJsXCQZUF0OhzrZ2tbwJUHWRbXxuLWKAB0RHIzPDr574LEu8AtKivl+M3MWX4SPcbdzv1L8qLI8sU1ct8iGAwGPB4PHo+H6upqzUhAhaIoJJNJTQNUdQBWt8nJSc1sKRAIEAwGNU1SdUulUqTTae3nxVrrVRfXYDAIwIkTJ/Je43K5NuQirdChX0ABBSwLRqOQIaqsFIRnMCikiGKxuXjFZhOb17u+cjsmk0hwV1UJ/XM1VlL1EPV6ERs6nSJWukD3y1LVbjqdjuuuu45HHnlEq1js6ekhGAwSiUQ0jcX0Im3QWCJBdVub6LYxGkVM6nYTz+X4u0ceIZvN0rJlC/X19bjdbhwOBzabDavVuilJu6VgtVppbm6mubmZO++8k0wmQygUYnh4mFOnTmlt2729vUxOTi6Yz8Ua3pFIhM7OTjo7O/k//+f/IEkSxcXF1NTUaFqgzc3N1NbW4vF4cDgcmjbpZneIHwwP8uyZZxnJjEAZUA60IVTT1NBDB8/2PEur1EpTYxM2u41wOEwsFmNmZiZvPsPhMKdPn+b06dM88cQT3H///TQ3Ny8gPLPZLJOTk4RCoQXXpyzLV8T1uRmwIQnPTCbD448/TiaT4f3vfz8PPfQQTqeTiYmJJS8MnU6nLer7+/sv2Xp1pSGVEsm42WQYIJ6JJpN4PqyFM8hkRPdrMLj856naWbHWavlsFnp7l29yrSji+V9VtXYZl4EBIaOz3HGNRsGlrJUnWYlO9/zx18MHJxoVn3U6bQEaQK9AURZQgw8JFCOgE4WPAXG+5eVrW8wpizyQlv2+VfrfLB8SQtjFDHiBGkBoon/60+IVqiujJEl57SE6nY6dLS380Qc+QGB8nMlolEg8zsysK/OZEZlIPIMwaUrM/kzOjlecdzQzySixZByIMUmMwfmky69+pY1pMpmwWCxYrVa8Xi8f/ehHuf/++xdoE+eyWTq7uujr69Mcma1uNxaLBcusFpBqSPKmaOk5HHNflvlGTqs1F1oudLo5W2H1i6NWTmwqa9cCLhu8XlF5sxiSzNOh1/hNvOeSCZzxbJgnp1/lgHUbFXo3C6o8FUUsIi+gUbWRMF/3vKioiKqqqrzX5HI5kskk8XiceDxOMBjUiNBgMMj09LSmtxwIBIhGo9rCIhaLae9LJBKaWcFSLfsbFeotpbdXxAkqFEV8xBUVwt9tLQiHYWhoZXGA2y0UP9aaU/L7Bf+/3LF1OlG871tpV8ciRCKiUSCZXH6c5nCIQri1YLXx1npcsqmUuPVks8t/j8slJNbX+jmv9PjnP14LWCUMBtHhUloqJlIlAnW6ue3NgtEovqjFs7GoetGpre6rgE6no7Kykg9+8IMAmvnewMAAPT09DAwMcO7cOYaGhpiYmNCeEel0ml3ve5+QzpqHwTNn+H8fe4z+/n4sFgter5e6ujoaGhq0n+Xl5ZSUlFBUVITb7cZms10Rrcd6vR6fz4fP52PXrl1a8nFycpL+/n7Onz9Pf38/PT09jIyMaPMZCoXyNLsVRdGSlK+//jqAtn6or6+noaGB6upqmpubKS8vp7i4WEuE2u32TUPYpbNpXh18ld8M/gYld5Ebkw7GgmO8MP0Cjz3+GP5B/4Lrc3h4mEAgoF2f0WhUi0lMJhM1NTV5vi2xWIzvfve7PPXUU9r1WV9fr82nen1ardaCfvmbhA1JeM7MzHD8+HF0Oh133323VjmllhQvBavVit1uJxQK5WWHrmRIkgg2H3sMfvjDud+r0ibf+tbaSLhIBL73PfjpT1cm2/eNb6w9sIzF4H/8D9HZsdyAtrUVPve5tQfT3/wm/OY3yx/X64X/+l9FN8hasFJzQpjzk1krTpwQn9v4uHreEqAHad5tYvbrp3bT7N0Ln/3swoXcSpHNriyAB3G+6/U1X2lAPj+5p7oyLgWDwcDevXvZYzSSPH+eVDZLLJ0mHI8zkZjhzx5r4uBZCYgA04jqzinEJJfk71AKY9DlSF9krnK5nEYQqATC5ORkXkCSSqX453/+Z/7lX/5Fc3t0ulxa5tHjdmsBjdPpFH93OikpKWHHjh3r+0C+XA/3AsFZwGpQUiIYqkRi3rWrI5CZ5v+Z+ndySm7pdnYVEqSVLAdnzvLzaAcfdt+QH4gVFW2ICs/1gE6nE4mUWVavcom2y2w2q1V+zszMEA6HNYd5tVIlFAppxOj8v4fDYY0kDYfDF60QvRxQbzNf/KIoilKhKMJb5FOfEjLYa0FHB3zpS6JYfrl45zvhk59ce5L2qafgxz9eHvGoKKI47IMfhP/4H9c2bkcHfP3rc3KDl4KiCBWcL31pbeMqiogBVho3pFYu15uHyUl44AERGy/3nG+5Bf7Lf1n757wMWd48rGaeNjvetPOVpLUF2mvFm0ASms1mzdzo2muvJZfLEYvFmJycZHh4mLGxMfr7+wmHw1QukUw7e/aspmkZj8cZGhpiaGiIX/3qV8iyjNVqpbi4mKqqKsrLy6mqqqKhoYH9+/fT1tZ2RRCfKtTkY2VlJZWVlezfv3/BfA4NDTEyMsLQ0BB9fX0MDQ0xPDyM3+/H7/fnaYOqXRuDg4O8/PLLSJKE0+nE5/NRVVVFWVmZ1iZeXV2tmQ8WFxevyPj5rcRYdIyfnv0pI5ERVM/IC0IHL/a9yMlrTvLb1/82119/PdlsllgsRiAQYGhoSLs++/v7tflUFIXm5ua8OYjFYhw8eJAXX3yRF198Eb1erxlZqddnTU0NtbW1VFVVUVFRoc2n8XJ+768gbEjCM51OMz09jU6no3SZrryL9aHeTkgmBVm1GDU1aw+y0mlh4P3zny//PUajCMjWikwGDh6E7u7lvyceX11gthgdHSs758pK+PCH1z7uais81+PSn5iAF19cunhpKciySECvNcBbbRXAZvi6S2YzclkZ1vFxrKkUbpuNSrebGhmKHAeQtEpOtZI2jWhtd7H4aVzhqeA/33YXBmmUqWiMqWiUqViMEDA9e89USQC15ULNei9uQ8lmswwODjI6Osro6Gjecaui6kajEYvForVf7Ny5k29/+9t5hOfp06fp6OjAYrEsIEldLhd2ux2DwbBpssAFFHBR2O2iNK63d+53ko5nI4c5Hu9bNoc+mgnyk8hhbrBto97gQ3Ns1+tFm8IVtBi7FGRZ1khRt9tNRUVF3mtUfbJUKkU8Hl+yCrSrq4u/+qu/2lAVoOr1cORI/t9yOdFZsVYEAvDCCyJBvdxjKitbn6ThuXNi7JmZS78WZr0Kr1n7uIEAvPoqnD+//Pes12WxmthjPeKVRAJ++UvR5bxclJRcWKZ7JVhpUhqujArPleRF11D8WAAiOeZwOHA4HNTV1QFoUidL6SFKksT27dvR6XSMj4+TnXeRZrNZIpEIkUiE87M3CYPBgM/n40//9E/ZsWPHgn1ls1lGR0eRZZni4mL0+g1Jj6wIi+dTNUSKRqMLCiL6+/vp6+vj3LlzGnkXCAQWtG6rRrDT09OcO3cOAKPRiM1m0ypNi4uLqa2tXVAVWlNTg9frvezxfyab4djoMV7ofQEF5dLHI0EsGeORXz3CzdU347a4kWVZM1ZsaGgAxPWpksoTExMkk0laWlryzLqi0Sg9PT1zx5PJaPPZPUtyGI1GHA4HvllpstLSUm688Ub+8A//cP0n5G2IDfmNNhgMuFwu7Uu3HASDQSYmJqiurt70GhPrBYNhfQqYVrqPS5kgrwQrfeas1xpxNeOuR6BjNq98bINh7fIBsLpu4vWICWR55Z+bJK1dtgBEZc3f/M3yF4qKssLKZZ1OlBsXFYlS5dn2aZOi8Ed3nOaD+ywXmHQl7389thTX77gekyFDMiO2hNVKqrmZlF6vVTiFw2EmJycZHx8nkUhw9dVX5z3cc7kck0vqEAqo5EIikSAcDmu/r6qqWpJMePHFF3n00UdJJBKYTCaMRiNWq1VzXFZ1RUtKSvD5fBQVFVFcXExDQwMul2sFE1pAAZcZsixuAn6/yLDpdKBkaTFW8I2K/xtl2fdQhSKdHatkQPu+Z7OC7CzOl7R4u0NNwpjN5jy9ZBCLisbGxg1HeF4M66XgsdJ9rCcxs5p4bz3itNUU6K9HzKD62q107PUo2Jaklcdc6/U5rybG1OvXPn5dHfzd3wmyd7moqREh11rxjnfAo48uf2y9HrZsWfu4BcxBNdFZCgcOHKCpqQm/38/AwABdXV0LtCzD4fCCZ0E6nUaSJNxud151ZzQa5eGHH+bUqVPU19ezbds2WlpaaGxspL6+/oKdXJsJkiQt0OVubGxEURRSqZTWWRGJRAgEAvT19Wnzef78eXp7e4ksWiilUilSqRTBYJDu7m4kSdJIUKfTid1u1ypBVW3QxsZGampqsFqtby0JKkGTt4kv3PYFlOVqqCkg6y6usalenx6Ph6ampgvGHi6Xi7vuuovq6mp6enro7+8nuijbmUqlmJyc1NZmOp3uguuj119/nYmJCRoaGqitrd2wVbUbCRuS8LTZbLS2tnLixAl+8pOfcOutt1709ZFIhEOHDjE4OMitt966ZDBcQAGbAUbj6hYPm7kYaDULlzWaGGsoLoYPfGBlVQgrfkbb7cLNPhgUBIkkIStwU9s4irT8gSUkZEVCQsYmy6KtdscOEWFLkubIqCgK2eyci7zRaMx7YFssFj7zmc/w3ve+l9HRUfx+P+Pj4wQCAcbHx5mYmGBmZiavar6srOyC5Onw8HBeQKTT6ZAkCVmWkWUZvV6v/bumpoa//du/5d3vfveC96RSKX70ox9hNpspKyujuLgYn8+nadtc7kxxAQVQVCSMHU6fnk1iwNXWZvZYV7raVdBuY9ms6Pdta1ufTNLbDIX7QgFvBVTScaWX23okpS8nVpOUXg+ytagI7r135WOvxy20qUmEbitBocLzrYPb7cbtdrNlyxZNFkUl78bHxzlz5gzHjx/n9OnTnDp1isHBQXw+HzU1NXnPi5mZGV5++WVOnjzJr3/9a43IstlslJaW0trayu7du2ltbWXbtm2Ul5dfEcVVqv+AyWTC4/Fo0oH79+/X5jOZTDIyMsKZM2c4duyYZtAzPDy8QEJQ1RFNJpNMzZahnz59Whi3zhZC2Gw2Kisr2bZtG7t27aK1tZXt27dTWlr6pkoMyJJMi7eF5qLmFb1PQUGvW/7N5EJxiM/n4w//8A9JJpNEo1H8fj+dnZ0cP36crq4ubT7nV9XmcjmuvvrqvH2l02m+/vWv8+Mf/xiLxUJFRQXbt2+nvb1duz6vlCrl9cSGnA1Zlrnrrrv413/9Vx5//HGuv/567rrrLmRZ1lopdTodiqKQTqf5+c9/zte//nXMZjMHDhzA4/Fc5jMooIDVYZMUpqwrLuc69c3WfQfECVZUCLfLzk5QFKGOKkmsoBxsDooiyJHaWkG6zE6gSiwCl3zQGQwGbrvtNm677bYFv89kMlq7aCAQYHh4WNOmGR4e5qabbsprZ08kEvj9fpJLaEmohGl2iX44t9u9ZOY+EonwoQ99CEVRNI1Uu91OcXGxpk9UXV1NWVkZZWVllJeXU1ZWRklJCSaTKa+VZCkUyJECVg1VS622ViQxhodBpxPB1GouK7XvU5bh6qvXLrZXQAEFvKl4Oz4+VnvOa50rne7ySVe+JfFhAWuGTqdDp9NhMBiEBr3HQ2VlJbt37+ZDH/oQIOLKgYEBxsfH2blzZ94+BgcH6e7u1niFME/NJAAAIABJREFUdDpNLBYjGAwyNDTEkSNHePzxx7U28YaGBvbu3cvv/M7vcNNNN1007txM8aYkSVql5vz4vLy8nD179vDhD39Ya9Hu6+vj9ddf5+jRo3R0dHDq1CmCweACvxVVblAlRqemphgcHOS1117T9u12u2lsbGTv3r3s3r2b9vZ2WlpasNvteeuN1c6lJEnIkozMW18dpM6pzWbDZrNRVFRETU0Ne/fu5SMf+YgmF9DX18eRI0c4evQoR48e5ezZs1x11VV5+xsfH6e/v59AIICiKAwMDGjzqbbdNzc3a/O5a9cuWlpasNls2rG8HbEhCU+AO++8k7vuuounn36a3/u93+Pll1/m2muvZWpqCkVR6OvrIxqN8tRTT/Gv//qvhMNhPvShD3H99ddfEVmXAgoo4AqCTgctLYKo7O5e6Hi5XKhBlKJAdTVcddX69OjNg16v1zR/ysrK8nSOloLBYODee++lrq4Ov9/PxMSEJoQ+PT1NIpEgnU6TzWbJZDJa9anNZsPr9ebtz+/3I8symUyGXC5HNBolGo0yNjbGiSXEig0GA2azmS996Ut89KMfXdDakUgkOHHiBMFgkJKSEpxOJxaLRWuPXary9O0cEBSwAjgcosI6mxVOb6BJViwL87/PJpOo7Cwvf3OOtYACCiiggALepnA4HGzfvp3t27cv+XeDwcC+ffu0VmO1UjGdTi/odMrlckxPT3Ps2DGOHTvGjh07uOmmmxbsK51OMzo6iqIo2Gw2rYJS1bLf7PGlJEk4HA7a2tpoa2vjYx/7GNlslomJCc6ePUtHRwenT5/m5MmTDA4OavOp6rEu7hwLhUIcOXKEI7NC10ajkeLiYlpbW9mxY4dWBVpZWYnVasVoNF5x8+l2u2lvb6e9vZ2Pf/zjJJNJgsHgkgV8o6OjxGIxjEYjqVRqAcGezWYJBoMcOnSIQ4cOAWI+S0tLufvuu3nwwQfz1l3qZ2IwGK5oh/gNS3iaTCa+9rWvIcsyL7zwAo8++ij/+I//qLVt3n///doNyel08t73vpdPf/rTmtBxAQUUUMCGgsEA27cLAbCeHuHykMvNESRLPbTnkyKSJNrYa2pg69YN0yNnMBi44YYbuOGGG7TfJZNJEokEkUhEE/NWtWlUsXSVVF2MQCCA2+3WWmmWCpDmQ80cm0ymvIf15OQkjz76KN///vc17T9VYN3r9S7YiouL8Xq9WjVpU1NT3v7UbLVKjBbwNofHI6yfz54VGr2p1Nx3+kLXx/zvtCyD2y1kKSoqCuVEBRRQQAEFFPAWo729nWeffRa/38/Zs2fp6enhzJkzdHd34/f7NX3LSCRCIpFAURTMZjO7du3K29fIyAh/8id/wvHjx9m6dSuNjY2afqWaeFcLC4xG4xVBMsmyrHVd3XjjjYCQCRgdHdXmsaenh56eHq0YQp3PZDK5gLRLpVJaV9nPfvYzQBDWlZWVbNmyhaamJlpaWmhoaKC4uFibT7vdfsXMp8lkWnJ9BFBRUcEnPvEJDh8+TE9PDxMTE9r1qZLLi+dzZGQEv9+PaYl146FDhzh16hRNTU34fL68+bxS1joblvAE8aF+9atf5YknnuCXv/wl/f39TE9Pk0ql0Ol0lJWVUVVVxY033sg999zDtm3bLvchF1BAAQVcGHq9qPQsKhIuz4GA0PXMZC6sZ6DTCW0/j0e00W4CYkTNaLtcLqqqqvL+nsvlyGazS7be19bW8tBDDxEMBpmamiIYDDI9PU00GmVmZiZvi8fj2Gw2iouL8zSA1JYkVYsoHA4zNDSUN6baDmUymXA6ndxyyy1885vfzAsOTpw4weuvv47P58Nut2O1WvM2tYK0QIq+TeB2w65dwpxsYADCYUgmL27LrNcLbd/SUvGddrk2/He6gAIKKKCAAq5ESJKE1Wqlvr6e+vp63v3ud2uO5sPDw/T19TEwMMD58+fp6+tjdHQUk8lETU1N3r7Gxsbo7++nu7tbc+CWZRmXy0VNTQ21tbXU1NRoBj6lpaUUFRVRWlp6RUnyWa1WGhsbaWxsBET14fT0NENDQ/T39zMwMMC5c+cYGBhgbGxMK4pQeZ75iEQidHV10dXVBYhuNLfbnTefVVVVlJaW4vV6NfLuSuv6raio4Hd/93f5T//pPxGJRBgcHNSuT3U+x8fHtWKT6elprFYrW7duxbaEe973vvc9vvGNb+DxeLS5rK2tpbGxkerqakpKSigqKsLn8+FwONDr9ZtybbOhCU9JkqioqOD3f//3+a3f+i16e3sJBAIkEgntYq+traWpqangUFVAAQVsHvh84HQKDcCpKZiehlhsYYWYwSCITqdTEKRFRaLCcxM+aBZD1VxaCrW1tfzBH/wBuVxOc4qPRqNaNjgSiWiO9KFQiGAwSC6Xo6WlJW+fyWSSbDaLyWS6aKVoLpfTOgZUp/ulyNgf//jHPPzww3g8Hi1Db7fbtZ8ej0cT0vd4PLhcLkpLS9mxY8cVFcgWsAgmk9DTLS4W3+nJSYhEFiYzZFmI0dlsgiT1egXRebkE6goooIACCiiggCWh8gxut5vt27drjuahUIjh4WGSyeSSRp4qeTcf2WyWqakppqamOH78OCAKA7xeL+Xl5VRWVvL+97+f++677606vbccsixTVFREUVGRpqOqtm6PjIwwNjbGyMgIfX199Pf3MzIywsjICKOjo0Sj0QVeAJlMhkAgQCAQ4OjRo4AwYy0qKqK8vJzS0lJqamo08q6iokLT+7fZbG+qQdJbBYPBoM3nrl27tHVMMBjUzGiHhobo6+sjGAyyb9++vGs1nU5z7NgxMpkMExMTTExM8Prrr2tmVj6fj4qKCkpLS6murtYI0ZaWFrZv376kD8NGxYYmPFUYjUYaGhpoaGgA5gSBNyPDXEABBRSAJAmSpLRUkJ/JpCA751d66vWCDDGZVmcLu4kx39ldrRRdDFXeJJPJkE6nMZvNec+E6upq/tt/+2984AMfYGpqSmupD4VChEIhrYI0FAoxMzOjkaIXcowcHh7WKkuXgk6nQ5ZlzGazVu25c+dOHnroIfbs2bPgtaOjozzzzDMoikJRUREej0fb3G43FosFg8FwRQRmbxs4HKJys6xs7js9X6/XYJj7Thc+1wIKKKCAAgrYFFBJoNLSUkpLSy/4utbWVj71qU9x6tQp+vr6OH/+PCMjI8Tj8QWvU93PR0ZG6OrqYu/evUvu74knniAajdLQ0EB9fT2lpaVLxrubEWrrttq+ncvliMfjBINBbRsZGaG3t5eenh76+/vp7e1lbGyMeDy+oHU7Ho9rrfAg4nGVBPV4PHi9XiorKzU+Sa3mVQ1PNzvU81Wd20EQ7YlEgnA4jGMJQ8x4PE46ncZkMi0wnlUUhUQiwdDQkNYVp9PpNFOw97///Tz00EMUFRUt2J+qKboRW+E3HOEZiUQ4evQop06dIhKJYLfb2bp1K3v27MHtdgMFonO5yOUu3lW30bGa49/M5wtibbxSp/bN/jlfToyOws9+JgzUl4vGRnjPe9bpACRJkJl6vaj8KmDZWEyKLgW3280tt9xCNpslnU5rre2qeHoymdRc6aemphgfH2d0dJRrrrkmb1+qGPjFMN+RMhKJAIJ0XfzMUo33HnnkEYLBoOaIaTQasVgsOBwOPB4PJSUlCzRHi4uL2b17N+UFc5uNC9XFfRNlvgu4PMjlVv6830hQlNWdw2aOVxRlLoexEmQy638sbwcEAvBv/waJxPLf09AA73hHIaQq4PKgsbGRT3ziE0SjUa0baWhoiDNnznDmzBm6urro7u4mEAiQmb0xGI1Gtm7dmrevZDLJV7/6VXp7e7Hb7Vob99atW9myZQtbt26lubkZl8u1ZFfSZoNKqtlsNk0OK5vNEo/HtflU2+I7Ozs5e/asphE6NTWlzSeIeDwWixGLxRgcHARE1a7FYsFmsy2Yz9bWVpqbm9m6dSv19fV4PJ4rYj5lWdbmcylYrVa+8Y1v0N/fT1dXF2fPnqWrq4uenh4mJyfzDLvULjuz2Zw3P6lUih/84Ac899xzNDQ0sGXLFrZt20ZDQwNOp1Nbr12uIo4N9Wl2dHTwxS9+kV/84hfEYjFyuZymq3HgwAEeeOABrr766st9mJsGudz6BFkrDWaz2fUJaFezn9UQhkthpfO2XguXTGZ1JO9qAvCl9rOaz3qtWM28XUzyciXo7YWHHxbSe8vFbbetI+FZwJuK+aSo0WjMe+ir2WG1WlTVFl3qgazT6XjggQe4++67FzjRj42NMTo6ytjYGNPT08Tj8QX6Qy6XK2/cXC7H5OQkfr+f0BJsuyRJWtu/LMvaT1mW+eY3v8k999yz4PUzMzM8+eSTzMzMUF5errX0uFwuzGbzhsy2FlDA2x3rFSuthnBcj2c3rHw/qyUMl9rPSs97veLh1cQfs956ax57pXO3XuTyamLx9YiLR0fhC18QKiHLxXveA1ddVSA8C7g8WEwyKYpCW1sbt956q5Z4D4VCdHd3c+LECU6ePInf76elpSVvX36/n46ODmZmZvD7/QAcP36c5557DoPBoLU1b9u2TXNN37lzJzU1NVit1ivCwEeWZex2O3a7HRDzuXPnTt71rndp8xkMBjWH+JMnT9LR0UF/fz/hcHjBvjKZjEbaqTh27Bg/+clPMBgMmM1mfD4fLS0ttLW1sWPHDtrb26msrLwgabiZodfraW9vp62tjXe/+93afE5OTnLmzBlOnTrFyZMnOX78OP39/cRiMQAaGhryCk3C4TCvvvoqTzzxBHq9HoPBgNFopLi4mO3bt7Nz504aGhrw+/1Ldu296ef6lo+4BBRF4ezZszz44IM888wz2mJPkiQURWFmZoYf/vCHRKNRvvKVr7B9+/bC4m0ZkKS1+yDodEJmrKRk+e+xWkXn3npgpR/zel0WPt/KzrmkRJhvrxUXM/e91PvWCqtVdGIuNzDX64Vk3VrH1uuFnN1sBf6y4PGsT0eoJInFy+w9fNnvKeDKgPocUZ85l3rtrl27lnTlzOVyminS2NiYRoKOjo5SU1NDcXHxgtdns1kmJyeJxWIauaooyoItm81qlanz4fV688YfHx/ny1/+Mh0dHdrvzGYzLpdLaxdS9YsqKiqoqKigsrJSE8yfT/DOPwZJkhY8awvP3QIKWD/odOvjVWWxiGexxbL8cYuK1mfs1exjPW4jZrM452h0+e/x+dY+7uWM0fR6qKwUihjLhde7Pp+z1bqyGE2Whfz4epz3zMzKYrRFficFFHBZMT/xrvqNeL1eGhsb+Q//4T8AaPHWYnR3d5NKpZBlmVwutyA2TMyWPU9NTdHT08PTTz8NCG3Hhx56iE9/+tML2pjVxL6iKBrHoh7fZoIkSej1evR6/YL5bGpq4o477gCENuXIyAhvvPEGb7zxBkePHuXkyZOMjY2RSCQWzMX8+YxEIkxMTNDZ2cmPfvQjQFTf1tbW0t7ezs6dO9mzZw+tra2UlJSg1+u1uVwcL28WzJ9Py2wQoZK+d955J4qikE6nGRoa4vjx45w/f57rr78+zwwqGAwyPDxMJpPR/BcAJicn6erq4sknn9Re+4EPfOAtn6sNQXgmk0mef/55fvnLX2Kz2Thw4AB33HEHxcXFBAIBfvrTn/LSSy/x0ksv8dxzz1FfX39FMu3rDdXvZC0oKoK//Ev40z9dfuAiSYKQWitUqbOVwGJZn+Du7/5OVP4tFzqdkG9bK4zGlR+/LItAeK246SZ4/vmVZeT1+rWT27W18L/+18qqLyRJSOUVUMBGgE6nw2w2YzabKSkp0QTZLwS9Xs/NN9/MP/3TPzE+Pq5Vio6NjTE1NaUZM6XTaS14SKfTZLPZJV3vVSf6+UgkEiQSCfx+P2+88Ubee4xGI/fdd59mwqRCURSOHj3K8ePHqaiooKioCIfDgc1mw2g0IssyBoNBC5DmO9Jv1oCvgAIuB2y2lcc4S+Hmm+G111b27Dab1/4MVaWoVxKz6HTrkxy++WZ49tmVVTyux1yrUrwrvc2tx9gVFfDKKyv7nI3G9ZnvW2+FU6dWdt5mc0HZo4ACloMLxU07duzgO9/5DqdPn+bEiROcO3eOqakp4vE48XicZDK5oI0b5syBFhvKhMNhnn32Wfr6+ti5cye1tbV4PB7MZjMWiwWTyaTFcpsdBoOB2tpaamtrNdIuGo1y7tw5Ojs7tfns6+tjYmKCZDJJIpFYcj5TqRTd3d10d3fzgx/8AEmScLvdNDU1sXXrVrZv305bWxs1NTXafJrN5itmPiVJyvPSWQrpdBqLxUJVVdWC6zO7Xu0ka8SGIDwDgQCvvfYamUyGj33sY3zuc5+joqJCu0g++MEP8oUvfIHHHnuMgwcPcu+99xYIz2VgtZno+dDpBGm6VuJ0tVhNhed63FsuQ7X1qrFe91KDYf0qc1cCWS60HhXw9oJOp9OCsflQtYpUsXbV1XNycpKJiQkikciSYvkzMzN4vV5KSkq0lno1a61cYHUsSRI2m03L6KrI5XL88Ic/5G/+5m+QZRmr1Yrb7V5grKTqiqqby+WivLyc3bt352V9s9ksmUxmQXt+AQUUsH7xiskkqh03A9bznC+Xz8TlqvBUqyYvBwqyxAUU8NajtLSUD37wg4CIpQKBAL29vZw7d07TrhwcHCQUCjE9Pc309DQ+n4/a2tq8WGxycpLvf//7PPXUU+j1ekpLSzUSa+vWrTQ2NmpJbqfTqUkiXQlalpIk4XA4aG9vp729HRDzOTExQU9PD+fPn+fs2bP09PQwNDTE1NQU4XCY6elpEonEAhJUURSCwSCHDx/m8OHDgCBYy8rKaGhooLGxkZaWFhoaGqipqcHhcOByuXA6nVfMfC6F5uZmvvzlL2vXp6oFOjw8TCgU0ubzQqavbzY2xKxHIhEGBgaoq6vjnnvuWUB2gvjC33XXXbz44ov09vYu0F4ooIACCiiggCsB87WKqqurl/2+pqYmPvOZzzA6OqqRpaoTfSQSYWZmhlgspom+x2IxDAYDPp8vrwpAURTN5TKbzWp6R6ro+2Lo9XqsViv79+/ne9/7nmYuqKKrq4tXXnkFnU6Hx+PBbrdjtVo10Xi73Y7NZsNkMmmVo5s9I15AAQUUUEABBRSwXpBlWXOI37dvHyA6eUZHRxkeHmZgYIDz588DIiacn1xWFIVQKERPTw8gtCxVR/NXXnkFEAY2FRUVVFVVUVtbS11dHbW1tezevVsjCa8kyLKsST4dOHAARVGIx+OMjo4yNDTE4OAgvb29DAwMMDg4SCAQwO/3EwwGicfjCwx90uk0g4ODDA4O8tJLLwFgt9s1+Sh1Puvq6qioqMDn81FaWorb7cZisVwRMa/BYKCyspLKykoOHDgAiGKMkZERhoaGGBgY4MyZMzz55JOabNZbiQ1BeKbT/z979x0mVXn2D/w7vffZne29AQu4VFFEBAUFFguWBKOSxCTGVyOR+JMkxlexxFgSiJqixMSCvqJRIQo2mqiEJqEtLGwvs73N7PQ55/n9sc4J48wus+wuW7w/1zWXsmfOmeecOXPmnvvcz/ME0N3djeTkZFit1qhvfFxcHBITE3H69OmIMc0IIYSQbyubzYarr74aQE9gGwwGhSRn6NHd3Q2Hw4Hu7m50dXXB5/Nh6tSpUSsueZ6HyWSC3++P2sXnTMFgEA6HA4FAIOp39/79+/H444+jq6tLSG6q1WrodDrhodfrhSpSs9kMi8WCadOmISMjI+rrjdU75IQQQgghsVAqlcjMzERmZiaAnpvUPp8voroT6OlZZLPZ0N7ejo6ODvh8vrCkk9vtRllZmZAUFYvFMBqN+NnPfhaR8OR5Hk1NTcJzvjmBzWgUmiQ7Ozsb2dnZAHr20+FwoKmpCc3NzWhoaBASofX19aitrUVdXR06Ozvh8/nCttfd3Y1Tp07h1KlTAHoSrEajEXFxcbDZbEhKSkJqaiqys7ORmJiI1NRUJCUlwWg0QiaTjYkkqFqtRk5ODnJycgD0DL91+vTpPn9TDJUR8auB53kEg0GhuiOa0IxPwWAwLKtOCCGEkB4ikQgymQwGgyHqTIihALe3BKVEIsE999yDZcuWoaOjA62trULFaEdHB9ra2oSH0+mE1+uFx+NBfHx8RPI0NBu90+mEw+GImDHzTHK5HAqFAkqlEikpKXjooYciEp5erxfr1q1DMBgUutdbLBbhodVqhbGoKClKCCGEkG+L0DBE0WRnZ+PJJ59EU1MTqqurUVlZicrKSpw6dQq1tbVwOp1hiSie59He3o6EhISIbfl8Pqxbtw5Hjx4VunFnZmYiJycHSUlJ0Ol0YyIGCyV0jUYj8vPzwRgTJijt6uoShpyqrKxEWVkZqqqqwrpxn5mvCk1WGprEB+iJe41GIwwGA8xmszDMQGZmJrKyspCbm4uEhATodLoxMRyUWCwetv0YMWfj2UpbY5lFlxBCCCG9CyU5v9mVPUQsFmPKlCkoKioKmzAp9PD7/cLD6XSitbUVzc3NyMjIEGbMDAlVG4Rmsezre/7MbSoUioixRQGgs7MTzz33HBwOB2QyGWQyGeRyufDQ6/XCuKKhJGh8fDxmzpyJ/Pz8qFUPhBBCCCFjVWgMy6lTp4Lnefj9fmFime7ubjQ3NwvViMePH8fJkydht9vhdrtRVFQUsb329nZs374d+/fvFyY9CvXcSUxMRF5eHvLy8lBYWIiCggJYLJYxMfeKSCSCQqFAXFwc4r4eOJsxBp/PB4/HI/SsamxsFMZYDR3PpqYmuFyusO35/X40NzejublZ2P6Zx1Oj0SA5ORkFBQXIzc1FYWEhcnNzYbPZoFAoxkQV6PkyYhKeQM+PI6/XC7fbHfbDSCQSwePxCNWdPp8v4qQJCVWCUnKUEEII6b/QbOuhRGI0oe9onufB8zxEIhEkEknYc6RSKX7wgx/gsssuQ0tLC1paWtDU1CSMHWW329Ha2iqMExrqEiSVSsNmjg/p6OgQZtTsq92hu8ihR6ha9MyEp8vlwocffohDhw4J4yyFhtXRarXQ6XRjopsWIYQQQgjQc1M7NJO4yWQCYww5OTmYOXOmcJPb4/GgoqIClZWVKCwsjNhGWVkZOjo6APT0vPF6vcK/S0tL8fnnn0MikUAmk0GpVCIvLw8TJkzAVVddhauuumpMxVahJGXoeAI9Y6jOmjUr4ngeP34cR44cwcGDB1FeXo6mpqawfFdoHFGPx4P29nYAwKlTp7B7925IpVJIpVLo9XpkZ2dj/PjxmDx5MqZNm4b09HRYLBbKffVhRCU89+7di+XLl0Mul0ckPP1+PxoaGuD1enHrrbdG/bAwxrBgwQLcf//9UUuwCSGEEDJwoTvLEokkItF55nNCg8L3xufzoa2tTagUbW5uRiAQiDp+p8PhEO6w8zwvDHz+zceZ3YjEYjEMBkNEdafD4cCWLVvw0ksvhf1dLpfDarUKd/Dj4uKQnJyMlJQUpKSkICMjA5MnT47orsXzPDiOC+uNEjpGdBeeEEIIISPNmTeKQ3GSTqdDfHy8MDnSNzHGkJWVBafTia6uLnAcJ9z8ZowJPYK8Xi+cTidaWlqwd+9eiEQiXH755WE5HMYY2traEAgEYDKZIBaLIZFIhHaNxvgpdLO9r+PZ0tKCkydPoqSkBPv27cORI0dQU1ODrq4uIZ4MxbSh4wn0TPRdX1+Pzz77DEDP+2ez2TB+/HhMnDgR06dPx4QJE5CWlga1Wj0mjudgGDEJT7FYDJ/Ph8rKyrM+t7q6utdl48ePp0mNvnaeJ8AaEv3dB8ZG/36fyz6Tc0fHm5Dho1AokJSUhKSkpLM+d/z48fjggw/Q0dGBxsZG4dHa2irMSu9wOIS76n6/H2KxGGazOSIp6/F40NXVFfEafr8fdrsddrs9ahsmTpyIPXv2RCQ89+3bh23btsFmsyEuLk4YQ1Wr1UIulwtd8EPd8EOJ4lAgSr6dRvv3CcVoQ7cOOTd0rAkZWnPnzsXFF1+Muro6VFRUoKSkBIcPH0Z5eTkaGhrgdDrhdrvh8XgQCATA8zwMBgMKCgoiurYHAgE8/PDD2LJlC/Lz8zFhwgRMnDgRmZmZSEpKgkqlgkajgVKphFQq7fUG+2gTuqF+ySWX4Cc/+Ql8Ph/q6upQVlaGkpISHD16FBUVFaitrYXX64XL5YLX6xWOZwhjTIiFt2/fDgBQqVRIT09HdnY2CgsLUVhYiMzMTNhsNmHiUKVS+a3qET0iEp5msxlLlixBUVHRgAJ/xhimT58+JsaJ6A+RCFCpgG+ODzwWKsaVSkCni/35CkXP8RitRKL+77NaPbr3eTiJxT2fnf4c7156+BJChphOp8O8efMi/s5xnDALfVdXFxwOB9ra2tDS0gK3241JkyZFBHWMMej1eqSnp8Pj8cDn8wl30YPBYK/jjVoslqiTAuzYsQMPP/wwAoEAxGIxtFqtkPQ0GAywWCxC5ajVaoVer0dKSgqmTZuG+Pj4iP3xeDzCBFRSqfRbE5SONaHTSK0Gvvk7Tans+Q4azWQyQKuNPQbRaiNj1dFGIul5P/sTY4+R3+jnnVjcc870MopJVCoVxcSEDDWZTCbMED9//nzwPA+n04m6ujpUVVXh5MmTqKioQFVVFZqbm6HRaJCdnR2R5wkGg9i7dy8qKipQUVGBrVu3AujJDaWmpiIjIwMFBQXIyspCWloa4uLiYDKZYLVaodPpxswNY4VCIcwQv3DhQgA949bX1NSguroapaWlKC8vR1VVFVpaWoTJRF0uV8QwTx6PBydPnsTJkyfxwQcfQCwWw2QyITU1FVlZWcjPzxfeO7PZDJPJBKPRCLVaDblcPmaO6ZlGRNiRkpKCBx98cFBmXw+NGfFtwRhgMABXXQWMGxd+ZzM+fnQHWSoV8JOfAE1Nsa+Tmdm/5NVIo9EAd90FfD10R0zS0nrea9J/KSnAnXf273jn5w9dewgh/SeRSITEYmpqakzLw/mZAAAgAElEQVTr2Gw23HHHHbjyyivR0dGBrq4udHV1oaOjAx0dHXC5XHC5XHA6neju7hYqFjIyMqIGg3a7XYhheJ4XZqWvra2N+vpSqRRTpkzBU089FZbwZIyhtbUVGzZsgNvthsVigclkEgaw1+l0wiM0I32ognQsBqmjXaii8bbber6rzwxzc3IAq3X42jYY5s7tSf75/bE9X6kEZs0a0iYNKYkEKCoCVq8GOC729aZOHbo2jWXx8cA99wBud+zr5OX1JEkJIedPaPggg8GACRMmYPHixeA4Di0tLWhsbERXVxfyo/yA6ujoiBontbe3o729HYcPH8amTZsgk8kQFxeHxMREpKen4/rrr8eyZct6HWd+LAjNED9p0iQUFxfD7/ejtbUVjY2NqKmpQVVVFWpra4UkaGNjI9ra2oSeTiE8zwszxP/nP/8B0DN8U3x8PBISEpCRkYH09HSkp6cjJSUFNpsNCQkJMJvN0Gq1ET2aRqMRsQehAXTJuTEYgGXLgKVLw/8+2n/7KJXAD37Q//VG835rNMAdd/S/S85o3ufhlJTUk1QnhHy7aLVazJgxAzNmzBD+5vP54PV6w2YvDSU7Q4nPaGOLAoBarUZqaqrQjSs00WJvQlWk0QLJhoYGPPvss6ivr4darYZKpYJSqRQmUwo9tFotjEYjzGYzjEYj5s2bFzHBQGhGVolEAqlUSknR8yz0Xb5iBTB9euTy0f52XHJJz6M/RvM+SyTA5Mk9DzL04uJ6Ep79NZrPMULGColEctZx3OVyOX7605+isrIS1dXVqKmpQUtLC7q7u8MK4QKBgDDc0MGDBzF58uSIQjm/3489e/agtrYW6enpSE1NhcVigW40V0KdQS6XC0NATZkyBUDPBJzt7e1obW1FQ0MD7HY7KisrUV9fj6qqKtTU1AiTg57J7/ejrq4OdXV1OHDgAEQiEbRaLUwmE+Lj45GYmIiEhATk5OQgOTlZSIZarVZhbNDRZEQkPMngGItf8GNxn2Lxbd3v842OMyEkRKFQQKFQwGAwRF0eGpA/mttvvx2LFi2C2+1GW1sb2tvb0dnZiY6ODrS2tqK1tVXohhSqHjUajdB+oxSJMSZ0yQ8EAkLlaTShLu+hKk+r1RqR8HS5XFi/fj2qq6thMBhgNBqFbvWhsUa1Wi20Wi00Gg0lRIfQWDy0Y3GfzubbuM/DiY43IWOXxWLBypUr4XQ6hR429fX1wizxJSUlqK2tRWtrKzweD4CeQrlx48ZF3DDu7OzEm2++iffffx9GoxEmkwlpaWnIysoSunKnpqYiLi5uzFSGajQaaDQapKamoqioCMFgEC6XC93d3UKVbH19PcrLy1FZWYnS0lLU1NSgqakpbM4bxphwc7+mpgZAT8I6dIPdZDLBYrEgLS0NGRkZyMnJQUFBAZKTk2G1Wkd872pKeBJCCCGEnEVfd7Rzc3ORm5sLnucRDAbBcRyCwaAwLqjf7xf+G0poajQapKenh20nNCOn0WgUxvLsLcnKGIPf74f/6/7E0RK1XV1d2LRpE/bt2ycM+ROaOCmULNXr9TCbzbBarTAajYiPj8eUKVMwb948qFSqARwxQgghhJDoxGIx9Ho99Ho9kpOTwRhDMBiEz+eDx+OBy+WCw+FAeXk5ysrKcPjwYTQ3N6OgoCBiAqPm5maUlZWhtrZW6CYfinPUajXUajWsVisyMzORl5eHSZMmYdy4cUhOTobJZBoTN3ylUqkwtMCZxzPUe8ntdqO9vR0VFRUoLy/HkSNHhKRye3t7WLzJcZwwIeiZx1OpVAqTSVksFuTk5CA3NxeTJk0Sksp6vX5EVYFSwpMQQgghZBCIxeI+KwdCwWTov98MsMViMS6++GLs2rVLuDvf3NyM+vp61NXVoba2Fo2NjUL1aEdHh5DwtFgsEa8X6pYfqozojUgkgkgkglgshkgkwtVXX42LLrooIuH5wQcfYMOGDUhNTUVqaqow3lNo4HuDwQDFWJgxkRBCCCHnVajnikwmg1arhfXrga4nTJgAnufBcRx4no8aZ9jt9ojxQEM3nbu7uwEAVVVV+OqrryCRSCCRSCCXy3HTTTfhueeeGzNVn2c683iGuvanp6cLQwJwHAe/34+GhgacOnUKhw8fxv79+1FaWoqKioqwKlDgv8cz1EW+srIy7HgqlUqkpKQgJycHU6dOxfTp05Gbm4vk5GRhEk5/rIN+DyJKeBJCCCGEnAehBGdvlQQikQhKpVKYQTMaxhi8Xq8wS2eoC320CQHcbjfkcjm0Wi14ng97MMbAGIv4f7lcDrPZHHVs9f379+ONN96I+LtarYbBYIDJZILZbEZiYiKSk5ORkpKC7OxsXHHFFVCr1WH7zXEcAoFAWKL1zP8nhBBCyLdXKBYIJdT66jo9depUPP300/jyyy9x+PBhlJWVwW63IxgMCj1vzoyBQsk7qVQaNdn5xhtvoKamBlOmTEFWVhbi4uIglUohlUohkUhGbawiEonCjqdSqYRer0d+fj6Ki4sB9NwsD80Ov2/fPhw5cgRlZWVobm6G3+/v9Xh6vV50dnbi2LFjeO+99wAAer0e2dnZyMnJwZEjRyKGXjofKOHZi1A5NcdxwqRKA5mliuM4uN1uuN1u4c6ETqcb8WMeEEIIIWTkEIlEUKlUUKlUSEpK6vO548aNwz/+8Q+0traiqakJjY2NaGxsFMYTDVWROp1Oocu9WCxGQkJC1PikpaUl6uuE4puGhoaIZVlZWdi5cyfUanXY348ePYpNmzYBAGw2G4xGozArfSg+ksvlQvd7mUwmVGR8sytb6LgQQggh5NvHYrFg8eLFWLx4MYCeLu51dXUoKSnB0aNHcfr0aVRVVQljqbvdbgDABRdcELEtjuPw4osvYseOHZBIJIiPj0d6ejomTJiAwsJCZGdnIzs7G2q1GlqtFiqVCgqFYkzMaA70TOw5YcIETJgwAddddx2CwSDa2tpQXV0tHM9Qt3iHwwGXywWPxwOfzxcxYafD4cChQ4dw6NAhAEBhYeF5j9fGxrsySEIzVtntdjQ2NqKsrAwulwsWiwWLFi1CXl7eOW3X5XLhq6++wmeffYZTp07B5/MhMTERs2bNwpw5c/qcvYwQQggh5FyoVCrk5eVFjV8CgQDcbrfQ7b2zsxOtra3o7OxEfn5+1KRiKAgOzUTv8/ng8/mEMUqjSUpKgkwmiwhwS0pKsH79etTV1QHoGXsqNAB/aJB8q9UKi8UCq9UKg8EAq9WKSy+9FOPHjw/bFmNM6LI2ElEulhBCCDl/4uPjhTHJAcDr9aK5uRk1NTUoLS1FZWUlamtrMWPGjIh1W1paUFlZCaAn+dnQ0ICGhgb8+9//BtAzWVAoCZqfny/0ypkwYQJycnLG3NA+UqkUNpsNNptNOF4ejweNjY2ora0VusBXVlaioaFB6HnkdDqFYr9hbf+wvvoI09LSgj/+8Y/46KOP0NLSAofDgWAwiJycHOTn559TwtPr9eKDDz7Ak08+iZqaGmRmZkKj0WDPnj147bXXcOutt2LVqlVnrdLoi8sF7NwJOBzAmXMbGI3A4sVAlN8s/Wg/cPgwUFoa+zoyGVBcDHxj8llCCCGEjBAymUwY3D5Wt99+OxYsWIDOzk50dXXB6XQK44m2t7fD7XbD4XAIs312d3cjJycnorsYz/NwOp1hY4sGg8E+Z6UHeio4nn766YiEZzAYxF/+8hcAfU8udb6FEp1btgAnT4bHaMnJwLRpQD8Of1R2O7BrF9BLvjlqm3Jyel6bOhkRQgj5NlAqlUhLS0NaWhpmz54NoGdmd41GE/Hczs5OJCYmIhAIoKOjQ6gGDXG5XKisrERlZSV27twJiUQCvV6PH/3oR/jFL36BuLg44bk8z8PhcMDtdsNsNkOhUIyJHikqlUpI9M6ZMwfBYBBOpxMtLS2ora1FTU0N6urqUFlZicbGRuH/AfQ6GedQoYTnGXw+H5xOJzIyMjB//nyYzWasW7dOGFOqvxhjOHnyJH73u9+hvr4eP/zhD1FcXAyNRoPy8nI8/PDDeOmll2Cz2XDPPfec090AkQhobwdeegnYsCE8mM7JARYuHFjC0+UC3n4b+Mc/Yl9HoQAuuogSnoQQQshYEpqNPiQ0blOo0jNUMRpKdrpcLiQkJET8oGCMwWAwoKCgAE1NTXC5XOju7obb7QbHcb2+PmMMRqMx4u9utxtvv/324O3oIBGJeh5r1/YkF8+M0ebNA1JTB57wPHkS+PWvga/nEIipTd/5DlBYSAlPQggh317R4gkASE5OxhNPPIGWlhZUVVWhuroaVVVVqKmpQWNjI1pbW8NiFY7j0N3dDbVaHTHZo8fjwaZNm/Dhhx8iPT0daWlpyMzMRFpaGuLj42GxWEbUjdpzJZVKYTKZYDKZhCJBv9+Prq4utLW1oaqqCr/73e/Oe7IToIRnmMTERNx///0AALPZDI7j8MILL5zz9oLBIN577z0cP34c1113He68806kpKRAJBJh4sSJAIBbbrkFmzdvxqJFi855EFee76nu/KYzbi6cM8Z6kp6trbGvo1b3tIkQQgghY5dYLIZCoejzhm204FYsFmP+/PkYN24cPB4PHA6HUCna1dWFlpYW4dHW1oauri44HA4oFArYbLaI7bW2tsLhcESt1BhOoSKOzs7IZU4n0EduN2aBANDWFj0O7K1NLld48pUQQgghPXQ6HebMmSNMEulyudDV1YXOzk40NzejuroaZWVlOHHiBKqqqlBXVwe5XI7MzMyI8cq7u7uxfft2bNy4EQqFAlqtFkajEQaDAQkJCcjIyEBubi4KCgqQnp6OlJSUiEkeRyu5XI64uDjExcUhKSkJCQkJ8Pl8570dlPA8Q2isq5D29vYBbc/v92Pr1q0wGAy4+OKLkZycLJy8oWB//PjxqKqqwldffTXos1YN1s2C/n7exsBNCkIIIYQMgmhBu0gkEoJgoKdSNDTjZ2hG1dAMqsFgUKgS8Hg8mDBhQsT2/H4/srKy0NTUNOT7M5iG4/eMSERxGiGEEHI2Z04SabVaAfRUc4ZmJPd4PHC5XKitrUV7ezuKiooiqjW7u7tx4sQJ8DwvjH8emgBSLBZDLpdDqVRCpVLBYDAgKSkJubm5mD9/Pm644Ybzvs9DhTE2LNWdACU8+zTQN8Vut+P06dOw2WwoKCiI+AAolUpMnz4dr776KioqKuD3+yPGuSKEEEIIGcvEYrEQI0WrFg3FY4yxqAnU/Px8vPHGG5g3b96wD45PCCGEkLFJIpFAIpFAqVTCaDSCMYasrCwwxiJyPaGYJSkpCWazOaKYjud5eL1eeL1edHZ2oqGhAaWlpdi5cyfsdnvUhGdHRwdkMhk0Gs2YqAI9HyjhOYRaWlrQ0dGBrKwsxMfHRywXi8VIS0uD1+tFU1MTvF5vrwnPUIXDmUnY0IeIuiURQgghZKwKBfW9BfdisRh6vf58NikMx3Hwer1R/h4chtYQQggh5HwQiUS9xiYikQhZWVnYuHEjWlpaUFZWhoMHD+LYsWM4ceIEysvL4fP5hN4swWBQqIQMTax0Jo7j8Mgjj2Dz5s3IyMjA9OnTMXHiROTn5yMlJQUqlQoymQxSqRRSqZQSol+jhOcQcnw9oJJUKo0YwBbo+RDodDoEg0F0d3cjGOw9MC4tLcXbb7+Ntra2sMC/vLwcPC8CQCc0IYQQQr6dhrOy026345lnngHP82FDF5WUHIfD4TnL2oQQQggZq+RyOZKTk5GcnIxLL70UAOB0OtHc3IwTJ07g+PHjOH36NMrKytDU1ITu7m5Mnz49YjttbW04ceIEKioqUF5ejm3btkEkEkGv1yMpKQnjx4/HuHHjhAkmzWYztFotdDodNBoNJAOZyXoUo4TnEArN3tVX5j9U+szzfJ9d6Lu6unDq1Ck0NzeH/T00BgQhhBBCCDn/PB4Pjh8/HvY3kUgEu92OYJC64RBCCCHkv3Q6HXQ6HbKzs7FkyRIEAgF0dXWhrq4OlZWVmDRpUsQ6oRniv9njt6urC11dXThx4gQAQCaTwWg0IjU1FXl5ebj00kuxfPnyYe0JM5wo4TmElEolAAiD8Efj9/shEokgl8sjxn04U1FREZ566qmI7ezYsR0//OGj4DgKqAkhhBBCzreMjAy89NJLYT9CxGIx/vnPt/D440/gG/eqCSGEEEIEMpkMVqsVVqsVF1xwQdTn6PV6XHPNNcjKykJ9fT2am5vR0tKCrq6usPgjEAigpaUFLS0tOHz4MBhjWL58ecT2jhw5gtLSUqSkpMBms8FqtUKr1faZkxqNKOE5hEwmE+RyOXw+n9C9/UyMMbS2tkIul8NoNPY5YVFohrAzcRwHq9UyLLN8EkIIIYSQnu5qqampEX83m00QiylII4QQQsjApKam4p577kF7ezuam5vR1taGuro6VFdXo7KyEtXV1aitrUVDQwN8Ph+AnhxSdnY2tFptxPbefPNNbNiwATabDXFxcUhOTkZGRgaysrKQlpaG5ORk2Gy2qJNJjiaU8BxCiYmJsNlscDgcqK2tjRiLgeM4lJaWQq1WIzk5WagI7Q+Oo9lICSGEEEJGmr6GKiKEEEIIiZVEIoFWq4VWq0VaWhoYYwgGg/B4PHA4HHA4HGhra0N9fT1Onz6NkydPorOzE0VFRRFVmzzPY//+/aiurkZ1dTWAnipTtVoNg8EAvV4Ps9mMtLQ05OXlIT8/H9nZ2cjIyIDJZBpVVaCU8BxCJpMJRUVF2LFjB44dO4alS5dCKv3vIe/s7MS///1vWCwWFBQUfGsHkiWEEEIIIYQQQgghZycSiSCTySCTyYTxOXmeB8dx8Pv98Hq9CAaDUas7HQ4HSkpKwv4WGke0q6tL2L5EIoFCoYBSqYRarcbVV1+Nhx56CBaLZeh3cJBQwrMPGo0GYrEYYrEYMpks6nM4jsOpU6ewbds2pKam4pJLLoHZbAbQkyW/6aab8P777+Ojjz7CVVddFVbluW7dOtTX12PhwoW48MILB739TufAt8EY0Mfk8VF1d/esRwghhBBCInFcz2OggsH+xVw8D/j9FKcRQgghY82ZuSuNRtNrTxO1Wo3nn38ee/fuxeHDh3H06FHY7XZh0m0AQgVpMBiEy+VCW1sbxGJxRJFeMBjE559/jsOHD2P8+PGYPHky4uLiwibt7msS76FGCc8z8DyPHTt24OjRo2CMweVywel0IhgM4s0338Thw4cRDAYxefJkzJ07F3K5HBzHYc+ePbj77ruxYMEC5ObmCglPiUSC+fPn44YbbsC7776Ln//851ixYgXi4uLw6aef4rnnnkN2djbuvPNOWK3Wc2ozY4DBAFx+OZCX1xPIhv5uswEDLRpVq4HFiwGrFTGPFSqXAybTwF6XEEIIIWQ0C8VkP/gBkJT03yQjYz0xW3z8wF8jLw/4xS96kpixEImAKVOAcxhFiRBCCCGjSG9JRrlcjquvvhpXX301OI6D0+lEVVUVDh8+jGPHjuHYsWMoLS2Fw+GA3++H3+8Hx3HIz8+PGIbR6XTinXfewbPPPguxWAyDwYCcnBwUFRVhwoQJmDhxIuLi4uD1eocl6UkJzzPwPI8PPvgA77zzDvivo1TT15m7rVu3YuvWrQCA66+/HhdddBHkcjlEIpEwjkJcXFxEJWhcXBwefPBBKBQK7N69G6tXrwbHcVCpVJgyZQp+8YtfYPHixQNqt9kMfO97wJIlA9pMVBoNsHRpz4MQQgghhMSGsZ7HypXAxIlD8xq5ucCDDw7NtgkhhBAytkkkEhiNRlxwwQXCDPF+vx/t7e04deoUSktLUVpaioaGBkyaNCki39XZ2YmGhgYAPfm0jo4O7N+/H/v37wcAKJVKWCwWdHd34/LLLz+/OwdKeIYRi8W45ZZbcPHFFwvlvKEs9JnlwNnZ2UJmWyKRYObMmfj9738Pm82GhISEiG2OHz8ezzzzDPbu3YvTp0/D7/cjPj4eF154IQoKCgbcbp4fnG5RhBBCCCFkcHk8w90CQgghhJDYyOVyJCQkICEhAXPmzBHGBZXJZBETFnm9XigUCiQlJaGjowNerzcsd+b1elFfXy/8+3xXeVLC8wxisRhFRUUoKirq1zrp6elIT0/v83lWq3XAlZyEEEIIIYQQQgghhJwPEokEKpUq6rLk5GTceeeduOKKK4RZ3+vr69HU1AS73Y6Ojg4EAoHz3OL/ooQnIYQQQgghhBBCCCEkZnq9HhdddBFmzZoFv9+Prq4utLa2or29HU1NTaiurkZpaSk++ugjAOh1IqWhQglPQgghhBBCCCGEEEJIv4lEIigUCsTHxyM+Ph6MMTDG4PP50NraitbWVmGenPNJfPanEEIIIYQQQgghhBBCSN9EIhHEYjFUKhUMBgPkcvmwtIMSnoQQQgghhBBCCCGEkEEVqvYcDpTwJIQQQgghhBBCCCGEjBmU8CSEEEIIIYQQQgghhIwZlPAkhBBCCCGEEEIIIYSMGZTwJIQQQgghhBBCCCGEjBmU8CSEEEIIIYQQQgghhIwZlPAc5UQiEUQiEcRieisJIYQQ8u2kUCgA9MRFI0WoLXK5fJhbQgghhBAyPCQSCSQSybDEaNLz/opkUHEcB6/Xi9OnT2P//v3geX64m0QIIYQQct6IxWJUV1eDMQaO44a7OYJQWw4dOoRAIEAxGiGEEEK+VUQiEVwuF1paWqBSqcAYO6+vTwnPUYwxho6ODtTX1+O+++4bUVUNIxnHcVQV2w88z4MxBrFYTOdYDELHSyKRDHdTRgXGGHiep/OrH+gc6x86x/ovlJgbbd+TGo0GTqdzuJshcLvdCAaDuP322+nci9FoPfeGC8Vo/RP6PqDfAbGjmKN/KOboP7ru989oPsfmzp173m9MU8JzFBOJRFCr1YiLi8P3vvc9TJ06laoH+iAWi9HR0YE1a9agqKgIK1asQDAYHO5mjVgikQjBYBBbt27FwYMH8f3vfx/p6enn/a7MaCKVSvH666/jk08+wVNPPQWz2UyfyT6IxWIcP34cr776Kq666irMnj171H1xn28SiQQvvPAC7HY7Vq5cCYPBQOdYHyQSCT7//HO89tprWLVqFXJzc0dUBeBIIxKJ0NHRgZdffhl6vR7f+c53oFarR8V1n+d5eL1ezJw5c7ibIpDJZBCLxXjooYeQk5ND514fRCIRurq6sH79euj1etxyyy1QKpWj4twbLlKpFO+++y42btyI3//+94iLi6Pvgz6IxWJUVFTg5ZdfxsyZM3HFFVdAKpXSOdYHqVSKV155BUeOHMHq1asprj0LsViMgwcP4pVXXsH3v/99FBUV0XW/DyKRCG63G6+88goCgQBWrFgBnU5Hn8k+SCQS7NixA3//+9+xZs0aZGRkjJpzzO12Izk5GWaz+by+LiU8RzmVSgW9Xo+5c+eiuLh4uJsz4rW0tOAPf/gDCgsL8Z3vfGe4mzPi+Xw+tLS0oLa2FgsXLsTUqVOHu0kj3rFjx7Br1y7ceOONiI+PH+7mjHjbt2/H+++/jzlz5uDGG2+ETCYb7iaNaBzHYdeuXeA4DjfeeCMsFstwN2nEY4zhn//8J5YuXYoLLrhguJsz4tXU1GDHjh2w2Wy48cYbYTAYhrtJo5ZSqYRYLMbSpUsxefLk4W7OiFdXV4ePP/4Y8fHxuPHGG6HT6Ya7SSNeTU0NNm7ciOuvvx7JycnD3ZwRb9++fdiyZQtmzpyJm266CUqlcribNOJ99dVXqK2txfXXX4+EhIThbs6Ip9Pp8O6772LBggWYP38+3cg/i9bWVnz55Zfwer244YYbKK6Ngc/nw8svv4xrrrkG48aNG+7mjHhUNzwGhMqaydn5/X4wxqiyM0YcxwnjjtE5FhuO48AYg9/vH+6mjAqhrlKhzyY5u2AwCJ7nEQgEhrspo0LoM+n1eoe7KaNCKKYIBoOjpmpgpApd03w+3zC3ZPTgOI7OvX4IxbN0jsUmFM8GAgGKOWIUDAbBGKOYI0b0O6B/QmNvh6795Ozout8/lPAkhBBCCCGEEEIIIYSMGZTwHOVCdyfpLmVsGGNUEXsO6PyKXejcomMWm9Bnko5X7EJVsSQ2dKz6j74jBwfFaP1D8Vn/UczRPxRz9F8o5qBjFhs6x/qPrvv9Q8erf2gMz1FOLpfDYDBALpcPd1NGBbFYDKPRCK1WO9xNGRVEIhFUKhUMBgOkUrpcxEKj0cBkMtFsljGSyWQwGAxQKBTD3ZRRQ6fTQa/X07hQMVIoFDAajXQNi5FYLIZOp4NGo6FzbICUSiVMJhOdezESi8XQ6/V07vWDWq2G2WymcyxGoZiDxu6MnUajgcFgoLg2RnK5HEajkX6bx0gkEkGr1cLn89Es7TFSqVQU1/aDiNHth1GLMQan0wm73Y6kpCTo9frhbtKIFwgEUF5eDr1ej6SkpOFuzojH8zxaW1vhcDiQlJQEtVo93E0a8RobG9Ha2or8/HyagCcG3d3dsNvtsFqtMJlM9CP3LBhjsNvt8Hg8SE9Pp3MsBh0dHWhoaEBGRgZdw2Lg9/tht9shlUqRkJBAAfUAtLe3o7Gxkc69GAUCAdTX10MikSApKYkSLDFobm5GY2MjCgoKKMESA7fbDbvdDoPBAIvFQgmWGDQ0NMDhcCAzM5POsRh0dXXBbrcjOTkZOp2O4tqzCAaDaGhoAM/zSE5OppgjBu3t7airq0Nubi5UKtVwN2fEo4QnIYQQQgghhBBCCCFkzKDbWoQQQgghhBBCCCGEkDGDEp6EEEIIIYQQQgghhJAxgxKehBBCCCGEEEIIIYSQMYNGhR0jGGPweDzweDxQq9VQKpUxDZLM8zw8Hg98Ph/UajUUCsW3YnBlnufhcDjA87xwvMjQ8Pv9cLlckEqlUKvVNAkB6RVjDF1dXeB5HlqtNubB8YPBIFwuFziOg9Fo/FZMQsAYg9/vh9vthlgshlarpc/WEHK5XML3K00+Q86F21wksswAACAASURBVO2Gx+OBUqmERqOJeT2XywWv1wuVSvWtOffOvKZ/m2LT4eJwOBAIBKDRaCgeJr3iOE74XGq12pgnTeR5Hk6nEzzPQ6PRfGsmPvL5fHC5XBCLxdBoNDTJ5BAKBoPo7u4GAGi1Wpr4iIShs2EYcRyH3bt3Y/PmzZDJZFiwYAHmzJnTrwsiz/Oora3Fpk2bcODAAXR0dMBsNmP69Om46aabEBcX1+u6NTU1eOutt3Do0CE4HA5YrVbMmjUL1157LSwWy4gMLisrK/Hqq6+is7MTVqsVd999N3Q6XUzrOp1OlJSUYP/+/Thx4gQaGhrAcRysVismTZqEhQsXIicnJ+Ii2dnZiffffx8HDx5Eb3N8TZ06FUuXLoXBYBjwPg4Wxhjq6uqwYcMGNDc3Iz8/H8uWLYPVao1pfbfbjc2bN2Pfvn3geT5iuUgkQm5uLm655Zao74Hb7caHH36Ibdu2ob6+HgqFAgUFBbjuuuswefLkAe/fUKmqqkJDQwOkUinS09MRHx8f03p+vx+lpaXCF25fUlJSkJqaKvy7qakJtbW1CAQCUZ8vl8uRk5MDvV4/rJ9Lp9OJP/zhD+js7ATP82CMRbTnkksuwbJly/q1XY7jsHfvXnzwwQc4efIkAoEAMjIyMH/+fFx22WXQ6/VR1/N6vThw4AC2bNmC0tJSBINBpKamYuHChViwYAEUCsU57+tg8Pv92L59O3bs2AGfzxf1eGVkZOC6665DWlpaTNtsbW3FgQMHcPDgQZSXl6O9vV2Y0fjCCy/EkiVLol6Hampq8MILL8DlcvV6HVu+fDlmzJjR/x0dRFu3bsUnn3wCxljUdmq1Wtx7770wm80xba+iogL//Oc/Ybfbo25PLBbjmmuuwaxZs6J+91ZWVuL999/H/v370dbWBovFgpkzZ6K4uDjm92wodXZ2oqqqCh6PJ+pysViMzMxMWK3WmG4EuFwuHD9+HBzH9fk8lUqFtLS0sPehrKwMLS0tva6jVCoxefLkUXNDgjGGTz/9FDt27IDX68XcuXOxdOnSfm+nrq4O7733Hvbt24f29nYhRisuLkZGRkav61VWVuJf//oXDhw4IKx34YUXYunSpUhJSRnAng0NxhhaW1vxj3/8Aw0NDcjKysJ1112HpKSkmNb3+Xw4fvw49u/fj+PHj8Nut8Pv98NisWDcuHFYsmQJxo8fH3Xd9evXo6SkpNfvpbS0NNx7770D3seh8H//93/Yv38/ZDIZrrzySsydOzfmdQ8fPoy33nqr1+u6VCrF7bffjvz8/Kixw9GjR7F582YcOXIEbrcbiYmJmDNnDhYuXNjn74fh5HQ6UVVVhe7ubpjNZmRnZ8eU3OB5Hu3t7SgvL48a055Jp9MhMzNTuDHB8zxKS0vR2dkZ9fkikQhGoxF5eXnDen1rbm7Gpk2bUFJS0uv3Xej7qz83TziOw7Fjx/Duu+/ixIkT8Pl8SEpKwrx587B06dI+E5h79+7Fpk2bcPr0aQQCAaSnp+Pyyy/H3LlzY/79NpR27NiBf/3rX73GHDKZDPfdd19MvwV4nkdbWxsOHz6MAwcOoKysDM3NzRCLxUhJScHMmTNx+eWXIzExMWLdhoYGvP7666irq4vaDrlcjssuuwxXXXXVue3oIKmoqMC7777bazvFYjGKi4tx8cUXx5TY5jgO27Ztw4cfftjre2C1WvHjH/846nvAcRx27tyJLVu2oLKyEiKRCNnZ2Vi0aBFmz5497InPUJ7Gbrf3+hy1Wo38/PyYbjZxHCfE/mebdzwuLg45OTnCv88WL0okEuF370jMAQ0II8OmsbGRzZgxg6lUKqbX69lvf/tb5na7Y16f53l26tQptnTpUmY2m5nVamXZ2dlMq9Uys9nMFi1axCoqKqKue/z4cXbxxRczo9HIjEYjy8rKErZx8803s/b29sHazUHj8XjYfffdx8xmM1OpVCw3N5c1NTXFvP7rr7/Opk6dysxmM1MoFCwrK4vl5uYys9nMTCYTu+SSS9jHH3/M/H5/2Ho1NTXstttuYyqViqlUKqZWq8MeKpWKff/732d2u32wd3lAuru72aOPPsoMBgNTqVRs8eLFrKysLOb129ra2A9/+MNe91uj0bDFixdHfQ/8fj/79a9/zRITE5nBYGDp6enMZrMxg8HACgoK2IcffjiYuzpoGhsb2fz581lGRgabPHkye+WVV2Je1263syuuuIJlZGSc9bF27dqwdV955RU2ZcqUXp8/adIktnPnTsbz/GDvcr80Njay9PT0Xs8JpVLJVq1a1a9t+v1+9tprr7GioiKm0WhYUlISy87OZmq1mmVkZLBHHnmEtba2RqzndrvZhg0b2MSJE5lGo2EpKSksMzOTKZVKlpGRwR599FHm8XgGa9fPicvlYv/7v//LDAYDUyqVUa8dl156KTtw4EDM21y1ahVLSUlher2emUwmlpuby9LT05lOp2M2m41de+21rLKyMmK9ffv2scTExD7fu1dffXUQ9/7c/OY3v+nzWpuRkcGqqqpi3t5nn33Gpk2b1uv29Ho9+/Of/xz1u/fQoUPsuuuuY2azmZnNZlZQUMAMBgOzWCxs2bJl7MiRI4O56+dk27ZtbO7cuX1ebzZs2MB8Pl9M2/vPf/7DMjMzz3oNu+yyy9gnn3wStu5Pf/rTPteZOHFiv2Kc4Xb8+HG2aNEiptFomEqlYvfee2+/t3H06FFWXFzMLBYL02g0LDc3l8XFxTGz2SycQ9+8rvM8zw4dOsSuvfZaZjabmcViYXl5eUyv1zOLxcKuu+46dvTo0cHazUHj9/vZs88+y4xGI1OpVGzu3Lns8OHDMa//+eefs6ysLGaxWJjBYGBpaWksNzeXabVaZjAY2MSJE9nGjRujrrtkyZI+r21z5swZrN0cVP/+97/ZhAkTmEqlYhaLhT399NP9Wn/jxo0sKSmp1+ubyWRin3zySdTY4eOPP2azZ89mer2eWa1WlpeXxwwGA0tISGB33XUXq66uHqzdHDRer5e9+uqrrLCwkGVkZLBbb72VdXZ2xrSu3+9nb731Vkwx2vXXX8+OHTsW9ro33XRTr8/PzMxky5Yti/k6O1ROnjzJli5d2utnQafTsbvuuqvfv/E++ugjVlhYyPR6PYuPj2cZGRnMYDCwxMREdt9990X8bgp54403hM9wQkICy8zMZAaDgaWkpLDHHnuMdXV1DcZuD8hTTz3VZ8xhNpvZqVOnYtpWV1cXW7NmDUtLS2MGg4GZTCaWn58vxLTx8fHspptuinrdP3r0KJs+fXrUdqjVama1WtmaNWuG4hD0y+7du9nMmTN7PV46nY6tW7eOuVyumLYXCATYo48+2ud7MHHiRFZaWhp1/eeee46lpqYyvV7PkpOTWUpKivC7829/+9tg7vo58Xg87KGHHurzejN//vyocXs0DoeD3XLLLTFdx1auXBm27qefftpnvJidnc1eeumlXj/PoxlVeA6jRx55BEePHkVcXByam5vPmqn/Jp/Ph0ceeQQffvghLrvsMvzqV79Cfn4+Kisr8fOf/xwff/wxfvOb32D9+vVhdw26u7txxx13YO/evZg7dy4ee+wxpKWl4fjx43jwwQfx1ltvwWaz4ZlnnhnsXR6QLVu24J133oHFYkFZWVm/jhdjDI2NjZBIJLjrrruwdOlSJCUlQSqV4sSJE1i7di22bNmC1atXY+PGjcjKygq7uxEIBKBSqfDb3/4W06dPD7uDyxiDyWSKuXLyfGCM4dChQ3j++eehUCgQDAYRDAb7vR2O46DX67Fy5cqIu4qMMeh0OphMpoj1Xn/9dTz11FNITEzEI488gquuugoejwfPPvss/vrXv+L222/Hzp07kZ2dfc77ONh4nsfatWvx+eefIxgMwuFw9HoXLBqVSoVLL70UmZmZEXfGRCIRysrK8MUXX8Dn82HatGlhy71eL+rr62E2mzF79uyw84vneRiNRthstoHt4CDhOA4ymQzr169HXl5e2DKe52OuiA3Zs2cP1q1bh+PHj+MXv/gFfvSjH0GlUuHTTz/FQw89hGeffRbp6en4zne+I1TgMcZw7NgxrFmzBs3NzVi5ciVWrFgBjUaDnTt34s4778Rf/vIXpKam4tZbbx20fT8XjDH4fD7cfffduOGGG8LueDPGoFar+1WtVVpairS0NKxYsQKzZ8+GxWJBIBDA7t27cf/992Pz5s2QSCR45ZVXoFKpwtb1er0wmUx48803IyoreJ5Henr6wHZ2EISu6zfffDPuuuuuiGUymSxqdURffD4fMjIysH79+rCuxOzrSrDk5OSIauCGhgb86U9/wqZNm7BgwQI88MADKCgoQHV1NR544AFs3rwZZrMZjz322LBWQvn9ftTX18PlcuGaa66J+G6SSqXIyMiIuerIZDKhuLgYHo8nYh2RSIT9+/fj4MGDyMzMDKtSB3q6xdbX12PGjBkoLCwMW8ZxHBISEkbNsAtOpxOvvfYavvrqK2i12j4rV3vT3t6OJ598Eh999BGmTp2KP/7xj8jIyMDp06fx5JNPYvPmzYiLi8PDDz8cdt1saGjA888/j82bN+PKK6/Er371K+Tl5aG2tharV6/G+++/D4vFgscff3xExR2lpaX43e9+B4VCAY7jEAwG+xWnNTc3IxgM4vbbb8eSJUuQmZkJuVyO2tpaPProo3j33Xfx4IMPIjk5GRdddFHYuuzryqAHHngAS5YsCVsWGiJlpGlvb8fTTz+NlpYWmEwmdHV19ft3ANBzXS8sLMSLL74Y9nfGGMRicdQq4pKSEjz11FP44osvcOutt+LXv/41TCYT9u/fj4cffhh/+9vfkJqaijvvvHNEHbvTp0/jT3/6E06cOAGe59HR0RHzMROLxUhLS8PixYsRCAQi4jSfz4c9e/agtLQU8+fPh8ViCVve0dGBtrY2zJo1C5mZmWHLeJ4fMdXrwWAQVqsVa9asQVFRUcRyi8XSr8pKu92OH//4x2htbcXy5cvx85//HAaDAbt27cLq1auxdu1aZGVl4Y477ghbb+/evXjggQdQXl6Oe++9F3fddReUSiU++ugj/Pa3v8Vjjz2GgoKCiO+t840xhmAwiGuvvRa/+tWvIpZJJJKI77re+Hw+NDc3IyUlBatXr8bs2bORlJSEQCCAPXv24PHHH8fbb78NnU6HNWvWhMUyoXaMHz8e/+///T/k5+eHLROJRCPmd4Df70dycjJefPHFsN+AoXYmJib2a2gMnuchlUrxgx/8ALfddlvE+aBQKKL2qNm+fTtWrVoFtVqN1atX4+abb4ZIJMIbb7yBp556Cj/72c8wceJETJ8+/dx3dhA4HA7Y7XZMmzYNEydODFvGGENKSkrM11mZTIbZs2f3WqHd2NiIzz77DB0dHRG/Nf1+PxobG8FxHObOnRu2DcYYFAoFcnNzR8R1bNCdr8wq+S+O49jmzZuZVCplt912G7vrrruYUqlkjz/+eL+qHz777DMmlUpZYWEh27FjR9iyU6dOMZPJxJKTk9n7778v/J3nefb3v/+dKZVKlpSUFHHHZOvWrSw1NZUplUp24sSJAe3nYOF5npWWlrIrr7ySzZo1i61bt47J5XKWk5MTc4Unx3Gsrq6OdXR0RL3TfejQITZnzhwGgP31r39lHMcJy2pqatjNN9/MbDYb27Vr17BX2cXC6XSy2bNns6KiIvbQQw+xlJQUtnDhwn5XeK5YsYKlpqayN954I+b1Ojs7WVZWFtPpdOyJJ54IW+bxeNiCBQuYWCxmq1atCjvOw4njOPavf/2LpaWlseXLl7OioiJmNpvZCy+8MGjbf/DBB5lWq2WzZ8+OWP7CCy+whIQEdvfddzOv1zsorzkUGhsbWUpKCjObzWHVD+eqq6uL3XfffUwqlbJbb701rJLT6/WyF198kel0OnbNNdewiooK4bPndDrZqlWrmEQiYbfffjtrbGwM2+5zzz3HJBIJKy4uZnV1dQNu57lyuVzswQcfZCqVij3//PMDfm85jmPHjx/vtXJ106ZNTCwWs4SEBLZ9+/awZfv27WMmk4llZWWx7u7uAbVjKD3wwANMq9WyBx54YFC299lnn7GJEyeyqVOnMofDEdM6HMexDz74gCUmJrLCwkK2d+9eYRnP86ykpISNGzeOZWVlsc2bNw/rd8LWrVtZbm4umzlz5pBXFwWDQXbllVcyrVbLfv3rX7NgMBi2/Hvf+x7T6XTsz3/+85C2Y6gFg0H23nvvsZycHLZy5Up2/fXXM5lM1q8KT57n2TvvvMOysrJYUlJSRDz15ZdfshkzZjCz2cy2bdsmfBcGg0G2ZcsWFhcXxy644AK2Z8+esG2WlJSw3NxclpOTwzZt2jRi4hG3282uuOIKlpOTw5544gmWlZXFZs+e3a8KT7vdzmpra6Mu83g8bNq0aUytVrP77rsvInZYvHgx02g0bMOGDQPaj/MlEAiwtWvXsoSEBLZ27Vo2b948ptFozqnC02w2s/nz58e8jt/vZ7///e+ZwWBgc+bMCYujOY5jmzZtYikpKWzq1KlRK9GGA8/zrL29nd1zzz0sNzeXLVq0iFmtVrZ06VLW0dExKNuvrKxk06dPZzabjb388sth++31etnChQtZdnY227Rp04Bfb6icPHmSLVq0iOXm5rLdu3cPeHs8z7Nf/vKXTCwWs3nz5rGWlpaw5X/605+YXC5nWVlZYXGYz+djd9xxB1MoFGzRokVh63Acx5555hlmNpvZnDlzYq4EHCpPPvkkk8vl7H/+538GvC2Px8Nqa2ujVq7yPM+2bNnCMjMzWWJiIvv888/DzrGjR4+yoqIiNnfuXHbo0KEBt2Wo7N69mxUVFbGJEydGnA/nIhAIsDVr1jCj0ciefPLJmK83Pp+PzZ8/n0kkEvaTn/wkbBnP8+zOO+9kMpmMFRcXR8Qq55PH42GrVq1iZrOZrVu3bkhfi+M4tn79epaYmMhyc3Mjro1bt25lBQUFrLi4mDU0NAxpW0aaMZjCHfkqKipw//33Iy8vD7/85S/PaZw5xhjefvttiEQiTJkyBTNnzgxbnpubi6uvvhodHR3YsWOHMB5XIBDA1q1bEQgEsGTJkogKrUsvvRSTJk2C3+/HO++8c+47OYgcDgfeeOMNlJSUYOXKlX2OedUbsViM5ORkGI3GqONS5ObmoqCgAFKpFOXl5VHvGLOv775FWzaS+Hw+PPHEEygpKcGPf/xj4f08V/3d3y+++AIVFRWw2Wy4+uqrw5YplUqsWLECjDFs3779nKpmBhtjDBUVFXj++eeh0+nw8MMPD/rdrdOnT2P79u3weDy47bbben1eIBAY8edXyNnG+ItFTU0NDh06BLVajWuvvTZs7EmFQoEpU6Zg0qRJ2Lt3L2pqaoRj43A48OmnnyIxMRFz586NuOtdXFyMzMxMnD59GocPHx4Rx5TjuAG3QywWY/z48b3eOZ8/fz6sViv8fj+qq6ujPid0HRvJWC/jOA10m7Hq7u7G3r170dTUhMsuuyysmkckEiEhIQGLFi1CbW0tDh48GNO4vUNtMM6vs9m3bx+2bduGxMREzJs3r9dqzZF+fvWF53mcPn0aL730EhISEvC9730PWq2238c2NL5wfX09Fi9eHFHFPWHCBMyaNQtOpxO7du2Cz+cD0DP29Z49e9De3o45c+YgNzdXWCdUObNkyRLU1tbi0KFDcLvdA9/pAfL7/Vi3bh2++OIL/OhHP8KMGTOE/emPxMTEXqvdlUolLr/8cvh8PtTV1UXtfcEYO+vYjCMBx3H48ssv8frrr2PmzJn47ne/C6/XO6Bt9uf8bGpqwoEDB9Dd3Y3rr78+bIxssViMwsJCzJw5EyUlJTh27NiI+Dyzr8fT3bhxI6699lpcfvnlgzrGHGMMu3fvxqFDhzBhwgRccMEFUbfPGBuU2GeoDdZ3QXd3N959910oFAoUFxdHVJTfcMMNSExMRFNTE3bt2iX8vaKiAl999RUCgQB++tOfhq0jFouF79X9+/fj5MmTg9LWgRqMY6ZUKpGSkhJ13HmRSISMjAwUFBSgra1NqLT7ptA4xCPdUMRp/dn3Y8eO4cCBA1AqlVi+fHnYMpFIhO9+97vQaDTYvXs3Tp8+PajtPBfn4/upubkZ27ZtQ3NzM5YtW9br5IjnI14caahL+3nW0dGBJ598Eq2trfjLX/6CuLi4cwoMOY7Dvn37oFarUVhYGNF1EeiZPOSVV17BqVOn0N7ejri4OLS0tODUqVPgOA6XXXZZxDoqlQoXXHABPv74Y+zduxccxw1rF7RAIIBdu3bhnXfeEQYg3rt376C/jtfrhdvtFmZtj4bjOJSWloLneWGmQZvNhqSkpBEzcypjDJ988gnWrl2L4uJiLF++HJ9++umALmzBYBBlZWXYvn07OI6DSqVCfHw8UlNTo553e/bsAdAzGUu05PS0adOgVqvR3NyM8vLyYe+i4XA48Nprr6G0tBQPPvgg0tPTB5Qg/iae5/Hll1+isrISGRkZWLBgQa/PDQQCwvAWUqkUOp0OWq12xHUvCAaDOHbsGFpaWoShDRITE4VhImLBGENzczOqq6uRnp6OtLS0iHVtNhsyMzOxd+9enDr1/9k77/CoqvSPf6dlMjOZ9N5ID2m0GAQEAYGlV0ERXZYVRERRcaMrukqzS9FlZXUFASEoRQihE0qooZNGQhIS0nuZkukz9/z+YO/9ZTIzAUIZdO/neXzwmZt7594z577ne97znvctQr9+/SAUClFdXY2bN28iJibGaloEqVSK+Ph4nD59GmVlZaAoyu5baSsrK3H69GlQFAVHR0d4eXkhICDggRY6k8lk0Ov14PP5Nm2SwWDA5cuXQVEUuFwuXFxc4O/vD29vb7snd6ehKAq1tbWMI8jR0REeHh4IDg7ucpEDrVaLCxcuMH3B2dkZfn5+8PHxsShW1NbWhuvXr0MsFiMqKgqurq5mx8ViMWJiYsDn81FaWgq5XG734guEEDQ3N8NgMIDH40EikUAqlT6w39RkMmHjxo2gKApxcXFITEy0eR8ajQY1NTUwGo0QCoVwcXH5XVTZJoRApVJh27ZtyMnJwaJFixAfH9+lSUpTUxNKSkqg1+vRu3dvi+INTk5O6N69O1xdXZGdnQ29Xg+RSAS1Wo3c3Fw4OTkhMjLSwj6IRCLExcUBAEpKStDa2npPFd8fNLSjaPXq1Xj66afxyiuvICsr66F8V2trK7hcLkQikVV7TlEUysrKcOLECRiNRojFYkarPC79jxCCmpoa/Pzzz1Aqlfjkk0/g7u5+3xNhlUqFM2fOQK1WQyAQwNXVFQEBAfDw8LBoq/r6epSVlcHV1RWxsbEWfdPLywvh4eEwGo0oKCiAXq+3e3Xp0tJSfPnll4iNjcWsWbNw4sSJB+o80Ol02LlzJxwcHJCUlGRW6KM9FEWhra0NVVVVoCgKIpEIrq6udm+fjuj1ely/fh1arRaEEEgkEvj6+sLf3/+ethoXFxejsrISAoHAYnsscHt7fGJiIvbt24fs7GxMmzYNHA6HKari4eGBPn36WJzXrVs3BAQEICsrC1evXrX6N48SWo+eOnUKWq0WQqEQ7u7uCA4OfqDFQum5poODAxwdHa1eV6VSIScnB01NTeBwOJBKpfD394efn99jo9GA/1/U4/F44HK5cHZ2ZrTk3RQr6ojJZEJVVRVOnToFg8EAoVDYqe67du0aDAYDvLy8LLaJA0D37t3h7e2N8vJy5Obmonv37l16zgcFIQRqtZrRRg4ODnB1db2n9/FO18/JyUFubi7c3NwwduxYm/Mek8mEpqYm6PV6cLlcODk5wdnZ2e7zpIfJ4/Pm/A9gMBiQmpqKffv24YUXXsD48eOhVCq7dC2NRoOysjIIhUKbFTAjIyOZqoStra2Mw1OhUIDD4djM10bn+6quroZarbbbRI4QgvLycqxfvx4ikQgzZsyAr6/vA/8eiqKQk5OD69evQyKRIDEx0WIQIoRAoVDg66+/hsFggE6ng7u7O2JiYjBixIjHpmpqcXExPv/8c/j6+mLRokWQSqX35ewkhKClpQX/+c9/8OOPP0Kn08HV1RWRkZEYMWIEJk2aZJFXpaysjMmLZ82Qe3l5wcvLCyqVClVVVV2+tweBXq9HRkYGdu3ahWeeeQajR49+4IKiubkZ586dQ0NDA+bPn28zxyUhBJcvX8bSpUuhVqshFAoRGBiIfv36oV+/fo9Nrjaj0QiVSoXFixdDo9GAoih4eXkhLi4OEyZMwPjx4+9qAk4IgUwmQ3NzM3r37m3VQSeVSuHl5QWTyYSamhpGBNXW1kKlUkEqlVqt1i0QCNCtWzccOXIEjY2NjDPBHphMJlAUhV9++YWppuvk5ITw8HAMHjwYU6ZMQVRU1H33O4qikJqaCplMhpiYGIscirQdaGpqwhtvvAG5XA4OhwN/f3/07NkTkyZNwp/+9KcuCdUHCUVR0Ol02L9/PzIyMqBSqSCRSBAWFoZBgwbh+eefN8ttdTfXA4Dy8nK88cYbUCgUAABfX1/07t0bEyZMwNChQ80iMnQ6Herq6uDk5AQvLy+LyaxAIIC3tzecnZ1RX1//WER4VldXY9myZVAoFODz+fDx8UGfPn0wcOBABAQE3PeiSUVFBfbu3QuxWIwxY8ZYddQTQqDX63HgwAHk5+dDr9fDyckJUVFRGDBgAJ588snHbvGmI2fOnMHWrVsxbNgwjBs3rsvvg0KhQGtrKwQCAYKCgiwmElwuFx4eHpBIJCgvL2ei6AwGA2pqaiCRSODp6WlhF+jfViqVorGxscsa8kFRXl6OlStXQiKRYPny5VZzej8Ibt26hWPHjkEqlaJnz54WO6OMRiP0ej3WrVuHdevWQafTwcXFBVFRURg5ciSmTp1q98VV4HYE7/79+3H69Gn8+c9/RlJS0n3Zftq+3bhxA3PmzIFcLodAIEBAp+1+HAAAIABJREFUQACSkpIwZcoUDBgwwKwfK5VKtLa2ws3NDe7u7hbvpKOjI3x8fCAWi1FRUQGDwdDl+3sQKBQKfPrpp6ipqcG7776LmJgYZGRkPNDvKCoqwqlTp+Dv749BgwZZ1SOEELS2tuKXX37B0aNHYTQa4eLigvj4eAwcOBAJCQmPjVO9oaEBX375JfR6PQwGAzw9PREfH49Ro0ZhzJgxd/0uVFdXw2g0QiQSWc1jyeFwEBYWBpPJhPLyciaHY2NjI1QqFXx8fKxqNCcnJ8aBV1hYeN/PfD9QFAWj0YiMjAxcu3YNbW1tEIvFCAkJwYABAzB9+nQLPdUVdDodsrOzUVhYiOjoaAQFBVnk3KYoCjdv3sTSpUuh0+lACIG3tzd69eqFiRMn4plnnnmgi+RdgY7srKmpwcKFC800Vc+ePTF+/HgMGzbsnu6Toiio1Wr89ttv2LdvHzQajZnue+655ywcllVVVTCZTPD397dYlAYAFxcXBAYG4tatW7h169b9PfQDQKvV4tChQ7h58ya0Wi0kEgkiIyMxYMAAJCUldWm3b3vUajUuXryI0tJSjBo1ymouTnoeUFxcjK+//hpGoxE8Hg++vr544oknMGDAAAQGBj4WduxBwzo8HyHXrl3Dhg0bEBkZiQULFtzXiqBKpYJSqYSzs7NNh6SLiwt4PB70ej2z/UehUMBoNEIgENhMkOvm5gYOhwO5XA6NRmM3h6dWq0VKSgpyc3Px1ltvITEx8aFMlqqqqrB161bcuHEDEyZMsHB48ng8dOvWDRMnTkRUVBScnZ2h1+tRWFiIEydOIDMzE9XV1XjjjTfuuZjGg0Qul+O7775Dfn4+/vnPf6JHjx735eyk0wCMHj0aMTExcHFxgcFgQHFxMQ4fPoxz587h5s2beP/99xmnO0VRjEPd2gAEABKJBE5OTpDL5ZDJZF2+v/uFEIKysjJs3LgREokEf/nLXx6KU7GgoABXr16FVCrF2LFjrU6gyX+L1xgMBpSWloLP56OxsRF79uzBjh078Nxzz2HevHnw9/e360DE4XAQFxcHPz8/hIWFQSQSoa2tDbm5uThw4ADOnj2L2tpaLFiw4I6TOIqioFKpoNVqIRaLrdpDgUAAiUQCHo+HtrY2ZnLX3NwMAHBwcLAqEujIRYPBAIVCwRQdswc+Pj4YNWoUIiIi4OHhAUIIqqqqkJGRga+++go3btzAokWLEBsbe1+/bWZmJr7//ntwuVxMnz7dIkJFIBCgT58+CAsLQ2BgIBwcHCCTyXD58mXs2LEDmZmZ0Gq1mDp16v0+8n3h7u6OESNGID4+Hu7u7iCEoKKiAsePH8e5c+dw5coVrFy50mYETkccHR0RHx+PpKQkdOvWDUKhEAqFAleuXEFqairOnTuHDz/8EM8++yzTR0wmE1QqFUQikdV+w+FwIJFI4OjoCKVS+UAjwu8V8t9CTk5OTigoKIBQKIRMJsPBgwfx66+/YvTo0Vi4cCGio6Pva/zcu3cv6urq0L17d5tR6vQOCaVSicrKShiNRpSXl2P79u2Ijo7G66+/jhkzZjy2UQS1tbX48ssvIZFI8PLLL8Pf37/LY6hOp4NGo4FQKIRUKrX6botEIuY9pLfy0QtKtvoeHY0hFArR1tZ231uh74e2tjasX78ely5dwpIlS6xGgD0IWltb8emnn+LWrVsYMmQIxo4da9aehBAEBQVhwoQJiImJgVQqZbTK8ePHkZmZievXr+OLL76wutX0UWEymZCbm4uffvoJ0dHRmDp1qk2ddLdIJBImItHX1xd8Ph9NTU3IzMzEpk2bcO7cOXz11VcYOnQoOBwOsyih0Wjg5uZmddzl8/mQSCTg8/mQyWR23cJNCMHWrVuxY8cOzJkzB2PHjn0o37N161YolUoMHjy40wInjo6OaG5uhl6vh06nQ0lJCX755RckJiZi4cKFGD16tF0Xdfh8PiIiIuDi4oLw8HBIpVJotVrk5+czY2hNTQ1effXVu9K7dCEtkUjU6ZyRdgabTCZwuVyo1WoYjUab/Zses7hcrt1TWzk7O2Po0KHo2bMnPD09QQhBdXU1MjIy8M033yAzMxP/+te/EBMT0+XvoCgK169fx8aNG6FWqzF58mR069aNsWOEEDg4OCA+Ph69evVCSEgIo6+zsrKwc+dOXLx4EW+99Rb+/Oc/23VXoVAoREJCAnr37o1u3bpBJBJBoVDg6tWrSEtLQ2ZmJt5//31MmzbtrncfeHh4YNiwYejRowcT8V5eXo6jR4/izJkzyMvLwyeffGKWhk+hUIAQAnd3d6vjq0AggLOzM9M37QXtIBaJRFCpVCgvLwdFUaioqMDWrVsRFxeH2bNnY+bMmfc1T6msrMS5c+dACMGoUaNsLko7ODjAaDSirKwMIpEIMpkMBw4cwLZt2zB27Fi88cYbiImJeewXp+8V1uH5iKivr8fPP/+MyspKrFixokt5KNtDRw1xOBybnZLL5YLH45nljaBzUPJ4PJuTDloAGY1Gu+ZDOnPmDH766ScMHDgQU6dOfSgGvrm5GevWrcNvv/2G+Ph4zJ8/H15eXmbG093dHXPmzAGHw4GHhwccHBxACEFjYyOOHj2KTz75BBs3bkRcXBymTZtml60tFEVh37592LNnD5599lk8++yz931NiUSC2bNngxACLy8vODg4MBHDY8eOxcKFC7F9+3bExcVhzpw54PF4zEopAJvtQPc9e+cSVCqV2L59O7Kzs7Fw4UIkJiYy9/Wg0Gg0uHTpEoqKijB48GB0797d6sCcmJiIL774AkFBQXB2dgaXy4VCocCpU6ewbt06rF27Fm5ubpg7d65dK6Y6Oztj1apVTGQIj8eDwWBg7Nu3336LL774Av3790e/fv06vVb7nLg8Hs9qu3A4HPD5fHC5XLP8uXTECZfLtWr/OBwOBAIBKIpibKU9cHBwwOTJkzFq1Ch4enoyYkahUODChQv44osvkJqaiujoaAQGBnZ55T47OxvLli1DQUEBpkyZgldffdXCsR4WFobVq1fDy8uL6WN0Pry1a9di8+bNWLx4MZ566im7LtxMnToVY8eOhY+PD0QiEQghkMvlmDRpEt555x0cPHgQkZGR+Pzzz+/K1nbv3h0ff/wxnJ2d4erqCi6XC71ej4qKCmzatAk//vgj/vnPfyIpKQlRUVGMU4CeuNnqX/Qxe/Yv4Pbv+tFHHyEgIABubm7g8/lQq9XIysrC999/j61bt8LBweG+ou/kcjl2797NCGlbuxmeffZZTJkyBWFhYXB0dARFUaivr8e2bduQkpKCDz74AP7+/hg2bNj9PPJDwWQy4auvvkJeXh4WL1583847ul/Q2/2sQfeh9rmbTSYT0/dsaTT6mvbUaIQQHDt2DDt27MDw4cPx/PPPP5TvUalUWL16NbZv346AgAD8/e9/t6iQTQhBcnIy+Hw+o1VMJhNaWlqQnp6OxYsXY9OmTUhMTMTs2bMfyn3eDUqlEmvXroVSqcR7771nNR3LvfLkk08iJCQEPj4+cHJyAofDgUajQXFxMVavXo3U1FR8+eWXGDBgALPjhk7L1Nm4S/cxe+cVv3jxIr799ltERUXhnXfeeSj6p7GxEdu2bYNUKsUzzzxj1RHI4/Ewe/ZsiMViBAYGQigUwmg0orKyEuvWrUNqaira2trg4+Pz0Bz/d4Ofnx/efPNNCAQCuLu7Mzqovr4ee/fuxSeffIJNmzYhISGh0y2vNLRGp3WYNfh8PgghZn2F7mN8Pt9mH+NyueBwOHZdMARu53wfPHgwfH19GY2mVCqRm5uL999/H8ePH8fKlSuxdu3aLkf819TU4IsvvsDly5cxbtw4TJ061SKYKDAwEB999BGcnJzg6urK6Ou6ujr8/PPPWLVqFTPXHDhw4H0/d1eJjo7Ghx9+CKlUymgqg8GAiooKbN68GT/88AO+++47JCYmIj4+/o4L+TweD5MmTcLw4cPh6+sLR0dHRvdNnToVb775Jg4cOIDw8HAsWbKECXKg5wSd6UC6f9uzj/H5fIwePRr9+/dntBEhBE1NTdi5cyd++OEHrFq1Cp6enl2eu9PpR65cuYK4uDj06dPHajBIZGQkPvzwQybyWiAQQKPR4Nq1a/j++++xZcsWODo64u9///tD2VFrT1iH5yNAr9fj0KFD2LdvH1566SWzZP9cLhcODg7gcDgQCoV3vbXFwcEBXC63U6eRyWSCwWAwE84CgYAxTrbOow2Dg4OD3SIxmpqa8OGHH8LFxQVvv/222QScHnC4XO595b7QaDTMZDcoKAj/+Mc/8OSTT1r8BkKhEKGhoRZGOygoCBMnTkRRURFWrlyJc+fOYfjw4Ta3LD9McnNzsWHDBvj5+eG9994zcw7TosKWuLUFn8+3+txisRiTJk3C9evX8emnn+LEiROYOHEifH19mf5MCLGZm9ZoNMJoNDJ/aw8oisLly5exbt06PPXUU5g8ebKFQ/1BRFLW1NTg7Nmz0Ov1+NOf/gRPT0+r1+3RowcSEhIgEAjMVnzpAjXLli3D1q1bMX78eERERNgtypNe2W3//Y6OjpBKpVi4cCHOnTuH48ePY+fOnXd0eHI4HMaO2SoGRotok8lk1jb0QG7L2URHsXC5XMbm2QMej4eAgACL38vR0REjR45EY2Mj3n33XRw9ehTTp0/vksOzoKAAS5YsQUZGBkaOHIk1a9ZY3a7m7OxsIT4dHR3h7OyMBQsW4Pz588jKysKxY8fw0ksv3fvDPiCCg4Mt2kskEmHEiBF49913MXv2bBw6dAivv/66hePDGs7OzhY5uBwdHREbG4uZM2ciKysLGRkZuHDhAsLDw5kJGp/PZ5xPHaEXEel+ac+V8PDwcISEhDA6gr6/2NhYSCQSfPTRR/j1118xY8YM9O/fv0u2IzMzEzdu3ACfz+80QnPMmDEQCARmY2hsbCx69uyJxsZGpKamYu3atRg0aJDdUyd0ZM+ePVi/fj0mT56Mv/zlL2b3R/++97KYSS/sdeaUpO2XUChkvoPH44HP50Ov19vUaEajkel79tJoRUVFzO6I5ORks22rHA6nS5qjIxqNBmvWrMF//vMf8Pl8rFmzBoMHD7Z43zgcDrNY0R6JRIIXXngBWVlZWLt2LX755Re8+OKLDyxn2r2ybds2pKenY86cOcy7QkO31b3+np6enha6wtHREU888QReffVVXLt2jdmq279/f+a7aGeKrfGzfd+0l95obm7GihUrUFNTg127dlndUv0gOHjwICorKxEREYGRI0datec8Hg8TJkyweOdiY2PRu3dvVFZWIi8vDzt37kSvXr3slmtRJBIhPDzc4jcLCQnBc889h+vXr2P9+vXIzMzE4MGD76g5aDuo1+tt2jG9Xg8OhwNHR0czO0YvLtrSdnThFHvtvqEJCAiwWMQTiUQYPHgw/va3v+HFF1/E4cOHUVpa2qU8kG1tbXjvvfdw4MABDBgwAO+++y7Cw8Mt+plYLLZI10Pr6zlz5uDixYu4cOECLl68iL59+9ptDLWlqWJiYjBz5kzk5OQgPT0dly9fRnR09F3dpzWdLBKJ4OXlhUWLFuHll1/GqVOnUFZWxrQRPSforA4KHRxxv9vF7wc+n49BgwYx8xEaQggSEhKgVCqRkpKC7du3Y+jQoVZTQNwJuVyOkydPoqmpCS+88AJCQkKs2u3Q0FAEBweb6VZCCGJiYuDq6ork5GTs3r0bEydOhLe39x8qyvOP8ySPMZWVlUhNTWW2kh88eBBbt25FSkoKduzYwVRBvHDhAjZt2oSMjIw75swRi8VwdXWF0Whk8md0pLm5mQlfpp059BYWo9FoM/cTfZ6bm5vdkuGnpKQgKysLgYGBKC4uxi+//IKUlBRs3boVR48eBUVRkMvl2LJlC3bt2mWzInFnfP/99/joo4/g5eWFJUuWYOzYsVaNIi3erX3u6uqKxMREuLi44NatWzZ/i4eJTqfDwYMHmQn7+fPnkZKSwrQXnYS7qqoKaWlp2LJlC+Ry+R2va+u5gdsiaPjw4eByuWhoaGC2GHM4HEZANTU1WT1XqVQyeea6YtgfBHSxALFYjFmzZsHPz49xxNLON1qQ0ZPLe41yoKv9ZmZmIjo6Gn379rU50RIIBGYOC+B2Wzo5OeHpp59GfHw8CgsLUVZWZtdoi876hKurK/r37w8ul4uCgoI7Rh7xeDw4OTkx23asrcDqdDooFAqYTCazhNpeXl4AYJauoz10dI+DgwNcXFzsJgw7ay+BQICoqCiEhISgqqoKSqXynn/bkpISLFmyBHv37sXQoUOxfv16m6uyndmxyMhIxMXFgcvl4urVq/d0Dw8aW+3F5/MxdOhQSKVSKBQKVFZW3vX1rF2Ty+UiPDwc0dHRIISguLiY6bN0wTCVSmW1CjZFUVAqldBqtXBxcbGbAwW4fa8dnRL05HPIkCHo3bs3WlpakJ2d3SXbQe8eaG1txeDBgzstLiESiSwm+lwuF56enpg1axY4HA6uXbuGmpqae76Ph4lCocCKFSugUqkQFhaG/fv3m42hJSUlIIQgNzcXW7duxeHDh+9YHV0kEkEsFkOn00Emk1lte7VazeQDpycWdN9Tq9VWv8NkMkGhUECr1UIqldrFWUAXk8zIyEBISAiys7MZjZaSkoL09HSoVCrU1dVh//792LJlCxoaGu7pO7RaLX744Qd888030Ol02Lx5s82cqp3ZWZFIhOHDh4PH4+HWrVuMVnnUKJVKrFixAlwuF2KxGKmpqUx7paSkoL6+HgaDAZcuXUJKSgrOnz9/VwVNO7NvvXv3RkBAAAwGA/Lz85m/FwqFEIvFUCgUVsddOhWMXq9ndnI8akwmE1JSUpCRkYG5c+di6NChjB6jNRnw//kXbS2a3gmj0YiUlBQAwJAhQxAZGWn172ib2rEteDwe/Pz8MHv2bGg0GmRnZ9utjwGdj/Nubm5ISkqCQCBAdXX1XeX/pVOcaTQam/OG5uZmcLlcs74ikUggEAjQ0tJi9XfR6/WQy+UwmUx2z63bmeYYOHAg3NzcoFKpupQHUq1WY9asWdixYwfi4uLw5Zdfonfv3hbjZGc2DLi9sDFw4EC0tbWhoqICKpXqnu/lQdGZzQkODkZsbCy4XC5KSkruahdfZ8/O4/EwcuRISKVStLa2orq6mjnm6uoKDofDFHrtiE6nY3LV03MGe0Db3I4LphwOB+7u7pg3bx54PB7KyspQUVFxz9end5ymp6fDz88PgwYNsjm3pvVie0cmbduSkpIwYMAAVFVVMTnY/0iwEZ6PAL1eD61Wy1Robw8dwWQ0GrF7927s3bsXgwcPxvbt2zuNJhCJRIiKisKVK1dQUVHBJIpuT0FBAXg8Hnx8fODh4QHgtqOAzqlSWFiIp556yuJ+iouLYTKZ0K1bN7vlCWlpaYFAIMDJkydx8uRJs2O0uGloaEBycjL8/f2xdOlSm0WYOqLT6fCf//wHf/vb3xASEoKlS5d2OYyc3jrL4/G65BR7END55rhcLnbv3o3du3ebHaeT+efl5WHRokWMEL7fxNf0ClH7lAm084TOgaNWqy36UG1tLRoaGuDj43PfqR26SmtrK7Kzs6HT6TB//nyLd4dO1L948WKsWrUKs2fPxvz58+8pn61CoUBGRgbq6+sxceJEq9Gyd4J2qru6ukKtVjM5ax5H6IhNAEw+ujv9vbu7O7y9vVFXV4e2tjYLO6ZQKNDQ0ACBQMCsSgJgqpvL5XI0NjZarLrTuVDpyqT2XN21BR1FSG8Hu5etqXR+o+XLlyM1NRXDhw+3Gdl5t/dCr5bbM83EnaCj3x5UOgwul2uWMoHG0dERgYGBuHTpEmpra6HT6cz6EF1YRiaTwc/Pz+4V2m0hFouZbUv0Qua9cuPGDWRmZsJgMGD27NldXvGnx2e1Wv1YFHlqj8FggFKphEgkwtdff21xXKfTwWQy4ciRI8jIyEDfvn0RFxfXqT5ycXGBh4cHDAYDbt26xRQHoKEoCg0NDVAqlczOEtqGBgUFITc3Fw0NDTAYDGZa0Gg0oqamBgqFAn5+fnYpYEEXmACAQ4cO4dChQ2bHTSYT49BYunQpuFwuDh48eFe7X+hKtlu2bMGKFSsA3F6cthV5dzfQ45I9UwDo9XqoVCrI5XJ88sknFse1Wi0oisKOHTuQmpqKmTNnIiIi4r7GLh6PZ5aiisbFxQWenp7IyspCY2MjTCaTWd9Uq9Worq6GRqNBWFiYXRYM9Xo98vPzoVQqsWPHDuzZs8fsuFwuR2trK06cOIH+/fuDx+MhLS3NLMff3XDx4kVcunQJAoEA06ZN63Ifi4iIYCq42zOvbme0TxF0t6lYgoODIRQKGU0VFhZmdpyiKBQVFYHH4yEsLIzRb76+vpBKpairq0N9fb2F1lcoFExBpAdREOhhQb9DWq32njQHXfB14cKF2LVrFxISEvDvf/8bvXv37tJ90BqtfWTs4wh9nx011f1A62R6Vw1NaGgo+Hy+2ZyyPS0tLaioqACPx7O5kPE44OnpCVdXV2i12i4VITQajTh9+jQKCwsxatSou0ojYA2pVAoPDw9mUdWeuZsfBqzD8xHg6+uL1157jUm23d5QaTQaHDx4EGfPnsWYMWOYFUZa5LSfCNNbk+n/HzRoEE6dOoXs7GzIZDKz/Fx6vR7p6ekQi8WIj49nnJweHh7o2bMnsrKycPjwYbz00ktmYkYmk+Hq1augKAqDBw9+uA3TCePGjWNWZDoa9ry8PKxfvx7u7u5ITk6Gr68v+vbtyxynnS3W8psqFAps2LABycnJCAkJwZIlS/DCCy/YvA/6u20ZD7VajaKiIigUCvj6+trFQezg4MBUibeWbyknJwe//vorgoODMW3aNHh4eDApAuhIRrq97rTKSEMIwbVr12AwGODi4mJWCODpp5+GQCBAWVkZ8vLyzH4bQghOnToFnU6H4OBguzk8HR0d8dRTT6GpqclqZAD9rolEIri4uEAkEt3TAEIIQX19PdLT0+Ht7Y2BAwd2uSCSUqmEUqmEo6MjJBKJ3baX3c27kJubC4qiEBAQYDG5p89vb8d8fHwQHh6Oo0ePoqCgAD179mTsEV3cp7i4GEFBQYiKimImb35+foiPj8fNmzeRn5+P/v37m62Y19XVIScnB76+vmYi/FFyp/ai83/V1NTA19fX7LftuIjQsZJneXk5PvvsM+zcuRPDhw/HJ5980mmqgzvdS11dHUpLS2EymR5rYZiXlwe5XA4vLy+zSFZb4+SdqK+vR0VFBUwmk1llSroS9Pbt25GXl4empiYEBAQw5ymVSly7dg08Hg9RUVF2r5pqC7VazUTR0NEQ9wJFUTh58iSqqqoQHh6O0aNHd/le6MgMR0dHu29h7Ai99d9WpEhKSgouXbqEwYMHY+LEifDz82P0lq2+5+npiZiYGEgkEpw/fx6zZs0yc161tLQgNzcXSqUSTzzxBGP3JBIJEhMTsWvXLuTm5qKxsZEpCgjczml55coVCIVCu/U9Pp+PZ555BmKx2OqW1eLiYvz888/w9PTEtGnT4O/vzxQZ60xzEEKgUqmwfft2fPHFFxAIBFi+fDnGjx/f5S3CJpMJV65cAUVRdnMQA7d/188//9xmJP/q1atRXV2NcePGYciQIYiLi2N2WHXVvpWWlqKurg5cLtcsIMDX1xeRkZE4e/YsLl++jEGDBpnt5qqtrUVRURHEYjHi4uLs4vDkcrmIjo7GgAEDoFKpLJxzdMoaPp8PJycnq9GXd8JoNGLPnj1Qq9Xo0aOHRQDIvVBRUQEOhwOxWPzYpeugoecrOp0OXl5eZr+5rT5G74LIycnBuXPnMHToULN2Li8vx9WrVyEUCs2KPUVFRcHX1xfFxcU4efKkhdYvLCxEVVUVpFJpp7sG7M2NGzfQ3NwMDw8PMw3Q2TtJB3wsW7YMu3btQo8ePfDjjz/e13OqVCrk5ubC0dGRyW/+ONLS0oKysjIYDAazeUBX55oAcPXqVcjlcoSEhJjNoxITEyGVStHS0oILFy5gwoQJZudduXIFjY2NcHNze6yd6s3NzVAqlQgKCuqSD0GlUmHPnj0QiURMTueuoFKpIJPJmMKIf6Tt7AAAwmJXWltbyTvvvENEIhFZsWIF0el0zDGKoohcLie7d+8me/bsIVVVVWbnXr16lXh7e5OwsDCSkpJCVCoVIYQQrVZL9u/fT5ydnUl0dDQ5c+aM2XlpaWnEzc2NeHl5kUOHDhGtVksIIaStrY2sW7eOeHt7k27dupHy8vKH/PRdY9++fcTBwYFERUURuVxudoyiKHLixAmSkpJCzp8/b3asubmZrFy5knh4eJCgoCCyadMmYjQaicFgMPvPaDQSiqIIIYQYjUZSU1NDbt68SVQqFfM5IYSoVCpy4MABkpSURFxcXMiaNWuIWq1++A1wD1AURXbt2kW8vb3JmDFjSElJidlxtVpNzp8/T7Zs2UKys7OJXq9nzqutrSVFRUVWn/v8+fOkV69exMXFhSxbtozpQ4QQolQqyaBBg4hYLCZvvfUWaWhoIBRFEaPRSAoLC0m/fv0In88nX3/99aNpBCuYTCaiVCqJQqGw+E8ul5O4uDji7u5O1qxZQxQKBdFqtUwbyOVykpeXRwoLC4lGo7F6fYPBQH7++WciFArJ8OHDSU5Ojs170el0RK1WE5PJZHFMpVKR77//ngQEBJD4+Hhy/fp1s9/iUWIymUhxcTEpLy9n+gn9eUtLC9m0aRPx9/cnIpGIpKammp2bl5dHtm3bRtLT082es62tjXz66adEKpWSMWPGkLy8PEJRFKEoijQ0NJDFixcTqVRK/vrXv5rZP7VaTT7//HMiEonI5MmTSW5uLjEYDIQQQlpaWsi7775LhEIhmTlzJqmvr3/ILWMdiqJIS0sLKS4uJjKZjHluiqKITqcjV65cIc8//zwRCoVkwYIFpKGhgTkeMajiAAAgAElEQVRXpVKRtLQ0smvXLnLr1i2z61ZWVpJXXnmFSCQSMnr0aJKZmWlhwwwGg1k763Q6cuPGDVJbW0uMRiPzuclkIrW1teTzzz8nnp6exMfHh+Tn5z/chumElpYWkpOTQxQKhdn9a7VakpubS4YPH054PB6ZMWMGaWtrY443NTWR1NRUsmXLFlJbW8t8rtFoSElJCamuriZ6vZ55d0wmE6mvryfffPMNCQgIIKGhoeTChQvMd5pMJnLixAkSExNDwsLCyPbt24lGoyEURRG9Xk/27dtHunXrRnr27ElOnDhht3fSYDAQlUpl9pvS6HQ6snfvXpKQkEBcXFzIsWPHzMa1uro6kpeXR2pqaph3pyN1dXVkxowZhM/nk0WLFlm1UTQqlcqmPWxoaCB/+ctfCAAyZswYRqv8HqAoisyaNYvw+Xzy97//3eJ4Y2MjSUtLI1u2bDF7hwkh5OjRoyQhIYF4eXmRvXv3Ep1ORyiKIhqNhuzcuZPExsaSiIgIcu7cObO+l5GRQSIjI0lkZKRF39u/fz8JDAwkvXv3JidOnHgUTXDPHD9+nAQGBpLBgwdbjH06nY5cvXqVbNmyhVy6dMlMO6hUKvLTTz+RsLAwEh4eTn744Qcik8k61WiEEFJRUcGMx/TnFEWRtrY2kpGRQXr06EE4HA5ZvHjxI3n+rvDUU08RiURCVq9ebXGspqaGpKSkkJ07d5Lm5mbmc6VSSW7cuEEaGxvNbIDBYCDl5eUkOTmZuLi4kPj4eNLY2Gh2fP369cTf35/06tWLnD9/nphMJkJRFFEoFORf//oX8fb2JiNGjCBFRUUP98FtQL8n1jSaQqEgq1evJu7u7mTMmDGksrKSKBQKpg10Oh2pqKggeXl5pKmpyabdKikpIX379iVcLpd89913nd5PW1ub2fysPVVVVWTYsGFEJBKRt956y6xPP0oMBgOpqqoipaWlZu8CIbf7yu7du0n37t2Jn58f2bhxo5mOKysrI1u2bCGHDx8mMpnM7Lr//Oc/CZ/PJ3379iVXr14101offvgh4fP5ZODAgWZ902AwkI8++og4OTmR/v37k5s3bzJ9rKGhgSQnJxOpVEqef/55m+PGo0Amk5Hs7Gwil8stNFNBQQGZPHkyAUBGjx5t1i6tra1k//79ZMuWLWba1GQykdLSUvL6668TNzc30r9/f3LlyhWbGo3+jQwGAykpKSEVFRXMOEHI/+vIjRs3Ek9PTxIdHU327dv3iFrHEq1WS0pLS0lVVZXZfZpMJtLQ0EDWrFlDgoKCSGhoKDl9+jTTpgaDgVy/fp1s2bKFnD171kwDtLS0kOvXr9vUfcOGDSMODg5k1qxZZnN+vV5PZs6cSbhcLpkyZQqpqKhg5g+VlZVk2rRphM/nk9dee61T7fKw0Wg0Nn0DcrmcvP3224TH45FJkyaZ6Ve1Wk2uX79O8vPzzfRuRzIzM4mzszOJi4sjx44ds/l3tF60pvd0Oh1JS0sjsbGxxN/fnxw8eNCqrvw9w0Z42hmKophk0PS2qfbU1tbib3/7G8RiMb766iuzFabY2FjMnTsX3333HbM6HBoaylSfNRgMeP755y1WlYYOHYpp06Zhw4YN+Pjjj/HCCy8gICAAN2/exNatW0FRFJKTkxEcHPxI2uBeofMaGY1GaDQas+hCAPjmm2+Qnp6OmTNn4sknnwRwu50PHz6MNWvWoLm5Gd27d4dMJsPatWvNVttNJhPi4+MxbNgwcDgcGAwGHD9+HLt27ULv3r0REhICJycn6PV6FBYWYu/evSgoKMDEiRMxYsSIx3LVja6WaC2SUaFQYPPmzfjpp5+waNEihISEMNsmTp06hZSUFPTp0wdhYWHMcxcVFWHfvn3Iz8/HuHHjMGXKFLPIFScnJ3zwwQd47bXXsG3bNnC5XPTt2xdarRb79u3D1atXMWTIEEyfPv1RNwUDvYJlDdKuarhQKLTYrpqdnY3ly5fDz88PS5cutbqaptPpsHv3bvD5fPTt27fTaqyVlZXIyMiAu7s7wsPD4ebmBi6Xi6amJpw7dw7r1q2DQqHAyy+/DH9/f7tFeAK3txbW1taiT58+CAgIgKOjI5RKJXJycrBnzx4olUrMnDnTogrz/v37sXLlSvTs2dOs6IREIsHYsWORmZmJU6dOYenSpZgwYQKEQiEuXbqEbdu2oVu3bnj22WfNtkMKhUKMHz8ex44dw8mTJ/Hpp59i5MiREIvFuHDhAjZu3IiYmBjMmDHDrrl7srOz8eOPPyIoKAjR0dFwdXUFIQSVlZU4cuQIzpw5gyeeeALPPvusWYR+S0sL/vGPf0ClUuHTTz9l+hghBN9++y02bNgAiUQCNzc3XLx4ERcvXjT7Xj6fj/79+zPbp9ra2pituj179oSvry/4fD5kMhkuXbqEvXv3gsPh4K233rJImv8oKSgowEcffYS+ffsiMjISrq6uzPb9I0eO4MSJE+jduzfmz59vFp1SVlaGRYsWoaysDAcOHGCiP1taWrB+/XrU1NSgZ8+e8Pf3h1AohEwmw+XLl3HgwAEYDAa8/vrriI6OZvoll8tFQkICnnvuOaxZswYrVqxAVVUVQkJCUFNTgy1btkCpVOLll19Gz5497fZOtra24uDBg+Dz+YiOjoanpyf4fD4UCgWysrKwYcMGlJSUYPLkyYiLi2POU6vVSEtLw44dOzBx4kS89NJLViPfcnJykJOTAzc3N4wbN67T57x8+TLOnz+PpKQk+Pv7QyKRwGAwoLq6GmlpaUhLS4Obmxtefvllu6XK6Sq0LrOW06qkpAQffvghbt68iWPHjpnZmyeeeAITJkzAv/71L/zjH//AzZs3ERwcjIqKCuzYsQONjY1YsGABunfvbtb34uPj8cILL+C7777DqlWrmL5XXV2NzZs3Q61WY9KkSejVq9ejaYB7hLTLf90xMk+tVmPHjh345ptv8NprryE0NJTRD5cvX8by5ctRUVGBpKQkNDU14eeff7bQLaGhoRg2bBjTj/bt24fDhw8jMTHRTKPduHED+/fvR2FhIYYNG4ZZs2Y9kufvCnTefmvbQPPz8zF37ly4ublhz549TH626upqfPXVVxAKhejRowe8vLzA5XLR2NiIs2fP4tChQ0whCjqtFfD/EbpnzpzB9u3b8fHHH2P69OlwdnZGQUEBfvnlFwiFQsyYMcNszvEoofPK2cqPTOctpiM82+u01tZWbNiwAWfOnMG8efMwfvx4i2glQghOnjyJ8vJy+Pv7Y+LEiZ3ez/Hjx1FeXo5evXrBx8cHYrEYWq0WZWVlTK78qKgoTJo0yW4pdLRaLfbv34+jR4+id+/eTFoyrVaLvLw8pKWloaamBi+++CIGDx5slirj0qVLeOWVV9CvXz/8+9//NhsPnn/+eezfvx+nTp3Cxx9/jAkTJsDZ2RlXr17F5s2b4evri3fffdcsbyCfz8cLL7yAzMxMHD9+HO+//z7GjRsHR0dHXLp0Cdu3b0dAQABeffVVu0bElpSUIDk5GYmJiejevTvc3NxAURSqq6tx9OhRHD16FLGxsXjnnXfM2qSmpgZLly5Fbm4uduzYwbwnCoUCKSkp+OWXX6BSqRAREYHz58/jzJkzzLmEEHC5XAwdOhQxMTHg8XhQqVTYsGED08cCAgIgFAqhVCpx+fJlpKamwmQyYcqUKcy81h7IZDL8/PPPKC0tRc+ePREYGAihUAi5XI4rV65g//790Ol0eOWVV5i88MDtOdGBAwcYn8OyZcsY+11QUIBly5ahT58+iIyMhIuLCyiKQllZGY4cOYLjx4+jf//+ePnll83m+wKBAG+99RauXLmCI0eOYMmSJRg6dCg4HA4yMjJw+PBhJCQkYMGCBXaNViwqKsLBgwcZbeTk5ASj0Yja2locOnQIGzduhK+vLyZOnGimH8rLy/Hee++Bx+Nh8eLFNiOEt23bBrVajYSEBPTs2dPmfTQ3N+Po0aPgcDiMXhQIBJDL5bh27Rp++uknlJWV4fnnn0dcXJzdCiI+LFiHp52hczbRCZ6tweVyrW5jcXBwwNy5c0FRFLZv344vv/wSTk5OkMvl8Pb2RnJyMubNm2fhhKMdUiKRCGlpaVi+fDmcnJygVCrh7++P5ORku1bpvRN8Ph8SiQRisdhm4uSO7UVRFG7evAm1Wg2JRIIbN25g+fLlFkKaEIIZM2Zg6NChzHX4fD6uX7+OjIwMRoDRSd0DAwPx2muv4aWXXurUqWVPeDwexGIxHB0dbSYz79hetNgsLCzE6dOnmW2IdF6ugIAAzJs3D7NmzUJMTIzFNYcNG4bly5fj+++/x6ZNm7Bjxw4YDAZwuVxMnToVycnJFlURHyc0Gg3jJO5IXV0d0tPTERYWZjPfSm5uLjIyMhAYGIiBAwd2OsFvamrCli1bUFVVhcDAQDg7O4PL5aK1tRXFxcUwGAyYOXMmXnrpJQvn/qOELrZw9uxZ7N+/H05OThAKhdBoNNDpdAgPD8fMmTMxa9YsC2cy3cesiY7Y2FgkJyfDyckJhw8fxpkzZ+Dg4AClUomEhATMnTsXgwYNMrOPXC4XERERWLRoEdauXYvjx4/j1KlT4PF4aG5uxpNPPonXXnsNgwcPtpszis5lVFtbiyNHjjDvIXBbNEqlUkyePBkvvvgikpKSrBZ7sWb3MzMzIRQKQVEUjhw5giNHjljYMScnJ7z//vuMw5POqbRv3z789ttvTAEotVoNiqIQGxuLadOmYfr06XYVhhKJBE1NTfjhhx8gEAggkUiYfEJubm6YMWMG5syZg379+pmdZ6t/CQQCUBSF9PR0pKWlwcnJCQKBAGq1Gnq9HnFxcZg6dSqmTZtm8W65u7tj1qxZoCgKv/76Kz777DM4OztDJpPB09MTb775JmbNmmXmqH7UaDQapKam4sqVKwgODoabmxt4PB7a2tpQWloKmUyGsWPH4s0334SHhwfTlwwGA4qKipCeno6oqCirxVGUSiVOnz6N0tJSjBo16o6pDkpLS7F8+XJERETAy8sLYrEYRqMR9fX1KCgogL+/P+bPn39f2+LthVAohEQisTkxt2XbXFxcMHfuXHC5XGzbtg1LlixhCmC4urpi3rx5+POf/8ykHKKh+57JZMK2bdvw2WefQSqVQiaTwdvbm+l7Hc97XODxeJBIJBCJRFbbxdb7WlJSgvr6ekZ7lJSUWDhMuVwuRowYgSeffJKxp1KpFDdv3kRGRgZEIhFEIhGTl9Xf3x9z5szBK6+8YrcUOneDSCSyOQ+g26tj1XtHR0doNBqkpaVh27ZtkEql4HK5aGtrA4/HQ2JiIl588UWLrZ7A7dyMb7/9NoRCIfbs2YNr165BIpFAqVQiNDQU8+fPx7hx4x7bxQmDwQCTyWQ1jZNarUZWVhbS09MxevRoq7kqm5qacPToUbS0tOCVV14xSxthjdzcXKxYsQLh4eHw9PSEo6MjdDodqqurcf36dSQkJCA5OdlibHqU0H3k6tWrOHbsGEQiEYRCIVP8MTIyEm+//TZefPFFqwEttuyYt7c3PvvsM6xatQoZGRm4ePEi44yLiYnBvHnzMGrUKIvzoqOjsXTpUri6uuLEiRM4ffo0HBwcoNVqERcXh9deew39+vWzq+YQiURQKBRYt24dozkIIVAqlZBKpZg0aRJmz56NIUOGWJxrTaOpVCqUlpZCp9NBIBDg0KFDOHDggMW5PB4Pzs7OiIyMZCra83g8nDx5Evv372dSI9C5+3v16oWJEydi+vTpXU6P9SCgNdXx48exd+9eODk5Mfep1+sRExODBQsWYNq0aRbjky27LxaLUVdXx+g+sVgMk8kEuVwOd3d3zJw5Ey+//LLVd6tXr15YtWoVVq9ejT179jD5pI1GI55++mksXLjQ6hz1UVJXV4dPPvkEYWFhzGKJyWRCQ0MD8vLy4Ofnh3nz5mHChAlmTkaZTIZjx45BIBDg9ddft3ptevHUzc3tjhXe29rakJaWhvPnzyMoKIgpYq1UKlFSUoLm5maMHz8er776KpP27o8Eh3QcKVgeKfTEo6qqChEREQgJCTHLeaFSqXDp0iVwuVzExMRYJH2nKAqtra3Iz89HYWEhZDIZ3N3dERcXx0QU2aKpqQk3btxAQUEBFAoFPDw8EBMTg5iYGEilUrtGknVGXV0dsrKyIBKJ0K9fP7PVVEIIk7cjMDAQCQkJzOfFxcUoKyszq8JtjaCgIPTo0QMcDodp35KSElRXV6Ourg4qlQoCgQC+vr4IDw9HSEiI2WTycYL8N5dkXl4eXFxcLAot6HQ6FBcXo6KiAhEREQgLC2OKgsjlchQVFaGmpoZ5bj6fDx8fH0RERCA4ONhmEQJCCHQ6HYqKilBQUIDq6moIhUJERkYiJiYGQUFBj6oJ7hlCCD788EO0tbVhypQpFkLnypUrWLt2Lby8vLBgwQKrERBHjhzBtm3b0L17d/z1r3/tVKA0NTUhPT0dmZmZqKioQFNTEyiKgpubGyIjIzFo0CD0798fPj4+dl9xq66uRllZGaqrq9HQ0ACtVguRSISAgACEhIQgLCzMqu0oKSlBSUkJXFxckJSUZCF4jEYjqqqqcP36daZoWlBQEOLj4xESEmJz0qXX65kJR2lpKYxGI/Peh4WF2b1YUVtbG27dusXk6qQrRnp4eCAsLAyhoaHw8fGxmORqNBpcunQJRqMR3bt3ZyZjhBCcO3eOqZZqy44JBAJERkYyBQZMJhMqKipQUVGB6upqNDY2wmAwwMnJCUFBQQgNDUVISMg956p90Gg0Gty4cQO1tbVMYRYOhwNPT0+EhYUhJCTE6vtGr1Cr1Wo8+eSTTDQTXeClfZ81GAwQi8UIDg5GWFgYgoODO432bm5uRmFhIfLz89Ha2sqMr1FRUWZRU/ZArVbjxIkTyMjIQFlZGVPpWSqVIjQ0FE899RQGDhyIoKAgsz6mUqlw4MABHDp0CM888wwmTJhgEcne1NSE7du348qVK5g6dSqGDRvWaSROaWkptm/fjuLiYlRXV0OhUIDP58Pb2xsJCQkYPnw4evXqZRaZ+3uAEIKcnBzU1taiW7duFpOn1tZW5OTkQKVSoX///lYd4C0tLSguLkZOTg6Tb7179+6IiYlhIvqtfW9zczOj0ejzEhISEBkZ2enExt40NzcjOzsbYrEYsbGxZosJBoMBJSUlKC0tRUhICCIiIph+VVFRgby8PAC2bRuHw4G3tzd69OjB2PeWlhbcunUL1dXVqKmpQVtbGwQCAXx8fBAaGorQ0FB4eXk9lhqN5ty5c2hra0NkZCRCQ0PNjjU1NTGFdZKSkphIM71ej7KyMlRVVaGqqgrNzc2gKArOzs7o1q0bwsLCEBQUZHMcpCgKdXV1KCgowI0bN6DRaODj44P4+HiEh4fbdYH1Thw7dgypqakIDw/H3LlzzTRCU1MTtm3bhqtXr2L69OkYMmSIxRhbVlaGzZs3o7y8HPPnz79jfsW8vDz89ttvKCkpQW1tLVQqFYRCIXx9fdGnTx+MGDEC3bt3txmR+iigKApNTU3MfKW+vh4ajQYODg7w9/dHZGQkszDWkZqaGmRlZTE2puOYSFEUampqkJ+fj+LiYuh0OgQEBCAmJgbR0dE2+xj5b87xgoIC3Lx5k9FosbGxCA8Pt2t7AbejYgsLCxnNIZPJmIKatC4KCgqysB1KpRLZ2dlQKBRITExkCuZoNBoUFhaipqYGQOd2LD4+HkFBQUwRqdraWsaONTY2QqfTQSwWIyAgABEREQgKCrL7O0lHJrbXVDqdDhKJBEFBQYym6qgnTCYTysrKUFhYiICAAERHRzO/vVqtZtqstrYWcrkcXC4Xnp6eCA8PR7du3Trd3UYHNOXn56O8vBzA7V0AsbGxCA0Ntfu8qa6uDps2bWLm0wqFAjweD15eXoiLi8PIkSMRHx9vscumtLQUX331FXg8Ht544w2rjtusrCx8++238PX1xauvvtrpol5bWxtOnTqF48ePo6ysDI2NjdDr9WZ6cdCgQQgMDOy0aPbvFdbhaWfaG0P6Ze6Y/Lj98c5eeHrFk8/nMxV37+b79Xo9cx5dwe9x5k5t0n41t2Oxj7vt7h2v23GLVvsKy4+ziAY6b6+76X90pKPJZGKe+26NIflvNWWj0WhWve9xbzO5XA6KoiASiSwEmV6vR1tbG7hcLqRSqdXBVK1WQ6vVMivGnb1TtHNYq9VCr9czDnk+n89EFj1Og0/Hd4GOgu4YfdLxnLuxY3R1X0IIBALBXb9f9HkAmPMeFzq+QwDuaKM7a6+7rTJsrZ3bv4906gba5j8u72T79qKfla6U2tX+1fG5uVzuPdmi9uMrj8eDg4PDY9FehBAYDAZoNBro9Xom2onH40EoFDJRItbGSK1WC61WC6FQaDUSz2QyQa1Ww2AwMNGNnT0zRVFMtDetKWibLxKJIBaLH3ttYQtbmgK4e9vWXmvR/flu2uNx7XudcSfN0f7fjkWLuqrRADDjUnut8nvQaMD/9zFb9utOmpd+bvr9v9v+RZ9Pp9a6l/mDPdFqtdBoNMyOr/bPajKZGJsoFouZ7e/tMRgMUKvVMJlMcHZ2vqNmoK+p0+mY95EeR8Ri8WMVCWttvnInXXQ3dowem+lx5l7er/bRuI/je9lRo3VVc9yrDWv/L30+fR+0vhYIBHZ32nXkXjVVZ3af/rzjXPNOv0HH69N9DADTZo9DH2uvt2g7C9yeB4hEIjg5OVm9T6PRyCz607uTOqLT6dDW1sak9uisn9yNXrR3kMjDhHV4srCwsLCwsLCwsLCwsLCwsLCwsPxh+H0ut7OwsLCwsLCwsLCwsLCwsLCwsLCwWIF1eLKwsLCwsLCwsLCwsLCwsLCwsLD8YWAdniwsLCwsLCwsLCwsLCwsLCwsLCx/GFiHJwsLCwsLCwsLCwsLCwsLCwsLC8sfBtbhycLCwsLCwsLCwsLCwsLCwsLCwvKHgXV4srCwsLCwsLCwsLCwsLCwsLCwsPxhYB2eLCwsLCwsLCwsLCwsLCwsLCwsLH8YWIcnCwsLCwsLCwsLCwsLCwsLCwsLyx8Gvr1vgIWFhYXFPhBCQFEUTCYTOBwO+Hw+OBzOXZ9PURSMRiMAgM/ng8v931tDI4TAaDSCEAIejwcej2fvW2JhYWFhYWH5A0AIgcFgAIAuaQyDwQBCCLhcLng83j1pvD8KtFblcDjg8Xj/k1qVheV/GdbhycLCwmIFQggIITCZTFaP/xFEEyEE+fn5uHz5Mjw9PfHMM89AIpHc9fm1tbU4deoUCCF45pln4OPj8z8nptva2nD69GlUVVWhX79+6NGjh71viYWFhYWF5Q8PrdEIIRbHuFwuuFzu716TmEwm/Pbbb9DpdOjVqxd69ep1T+cfP34cFRUVCAkJwbBhw3737dEVKioqcOTIEXh5eeGpp56Ct7e3vW+JhYXlEcI6PFlYWFhsUF1djbNnz0Kn0zEikV4pF4vFcHV1RVBQEIKCgiASiR7Y9xJCoNFoUF5eDi6XC39/f0il0gd2fRqTyYSjR4/io48+whNPPIHevXvfk8OzoKAAixYtAkVR2Lp1K3x8fB74PdoLg8GAuro6yGQyeHt7w9PT02pkRVNTE7755hukp6dj5cqVrMOThYWFhYXlEUBrmLq6OmYBmhACDocDgUAAqVQKT09PREREwNPT84F+N0VRqKqqgkwmg6urK4KDgx/o9Wn0ej0++OADtLS04P33379nh+e///1v7N27F5MmTcKQIUN+9wv17ZHL5aioqACfz0dISIhNHZ6Tk4NXX30VSUlJWLNmDevwZGH5H4N1eLKwsLBYgaIo5Obm4oMPPoBMJjNzeHI4HDg4OMDV1RVRUVEYOXIkpkyZ8kAjHMvKyvDxxx9DLBZj/vz56Nev3wO5bkfoCIiuREJwOJwun/u4I5PJsHnzZpw4cQIvvvgipk2bZtUZTLcB/f8sLCwsLCwsDx+j0YjVq1fj0qVLFuMvj8eDo6Mj/Pz80KdPH0yfPh2DBg16YA4/k8mEDRs24MyZM+jfvz+WLVv2QK5rjfY6rSvntv/3j0R2djY+/fRTODk54YsvvkBkZOQdz2F1GgvL/x6sw5Pl/9q78+ioyvMP4N87aybLDNk3spKFEAKylEVCAirQA3GhYvGoDUhdQAVELXi00v6gKgIFxIIoiyxiUxBBISJbQssStgQIWZoESCCTZYbMZJJMZp/7/v7gzD0ZskAg0kqfzzmeOc7cee/73ns0zzz3fd6XENIJu90Og8EAq9WKCRMmoE+fPuA4DjzPQ6PR4PTp09i7dy/OnDkDrVaLuXPnwtfX957PyxhDc3Mzjh8/Dm9vb0ydOrUHRkO6w2q1ori4GIcPH8bIkSM7XdqAEEIIIfcfYwwtLS1obGxEZGQkJk+eDJlMBgAwmUwoKSnBiRMncO7cOVy6dAmLFi3CI4880iPn5nkeRUVFyM3N7VZlDOk5Wq0WOTk5UKlUaGlp+U93hxDyX4oSnoQQ0gWO4+Dt7Y3nn38eY8eOBcdxYIzBYrGgqKgIS5cuxZEjR7Bu3TqkpqZi7Nix97xxjascy7VOKD2Rvv9ci9u3fSWEEELIfw/XzMXY2Fi89957kEqlAG7O/mxqakJWVhb+9Kc/4fTp01i7di1SUlIQGBh4z+cVi8WQSG7+jHa9kvurbWxGG0YSQjpD/4cmhJDbcJWwq1Qqt6AqKCgIer0e5eXlqK6uxpEjR5Camtph4GWxWKBWq6HX68FxHIKDgxEREdFhIk2v1+PGjRtwOp1C0K7VagHcnNEgkUjg6+vbrkRJr9ejvr4eRqMRUqkUAQEBCAkJEX4A/CeZTCZUV1fDYDBAIpEgNDQUYWFh3WrDYrHg8uXLaG1thVKpRGRk5B3NrNBoNCgoKEBlZSUUCgUGDhyIAQMGgDGGhoYGGI1GBAQEQKVSQSQSwWw2Q6fTwWw2AwBaWlpw48YN4VdSXhwAACAASURBVN8ZY1AqlZ2uF8XzPBoaGqBWqwEAoaGhCAwMpB9FhBBCyM/Atba6t7e38F5gYCDeeustnDhxAj/99BOKi4tx8eJFPPbYY+2+z/M8GhsboVarYbVa4eXlhZiYGCgUinZxmtlshl6vF2ICi8WCGzduCJsnuR6Ue3h4uH3XarVCo9FAp9PB4XDAy8sLkZGRbn3+T3E6ndDpdKitrYXNZoNSqURMTAzkcnm32rhx4wZqa2sBAOHh4QgKCrrtA2On0yncm9bWVkRGRuJXv/oVAgICYDKZUFNTA5lMhpCQEHh4eMBut8NoNMJgMIAxBp7nodPphHvAGIOHhwd8fHw6LeU3m82oqqpCU1MTlEoloqKiaKYuIQ8qRgghpB2Hw8H27NnD/Pz8WHBwMPv++++Zw+Fod1xBQQFLT09nANiUKVOY0WgUPuN5njU1NbFt27axtLQ0plAoGADGcRzz8/NjU6dOZadOnWJOp5PxPC+cd/z48czf35+JRCImFouZv78/CwsLY2FhYSw0NJSlp6czvV7PGGOsubmZ7dq1iz399NMsKiqKcRzndo6MjAx24MABZjabhXO42Gw2tmrVKubt7c3Gjh3L1Gp1t67R4cOHWXR0NIuKimLHjx9v1z7P80yn07F169ax4cOHM5lMJvQtJCSE/f73v2cXL15kTqfT7Xt2u50dPnyYjRkzhs2bN49VVFSwHTt2sGHDhgnjk8vlLD09nf3www/MarV2eG6LxcJ27NjB+vfvz0QiEeM4jnEcx+RyOcvMzGSnTp1iM2bMYAqFgi1btoyZTCbGGGMbN25kISEhwv3y9vYWrn9YWBhLTExkO3bsYIwxVllZycaPH88AsM8++4yZTCb21ltvsdDQUAaAAWDBwcFszpw57MaNG926voQQQgjpmMlkYqNGjWIA2NixY1lzc3OHx61evZoBYGFhYWzz5s1unzmdTlZVVcVmzZrFIiIimEgkYgCYTCZj/fr1Y5999hmz2+1uMcaWLVtYcnKyECN4eHiw8PBwIUaLi4tjWVlZzGq1MsYYq6qqYn/84x/Z0KFDmUqlEmIDiUTC4uPj2fvvv8+0Wm27OIYxxlpbW1lsbCxTqVRsyZIl3b5GkydPZiKRiE2ZMoXZbLZ2nzudTlZaWsqmT5/OQkNDhRjLw8ODDR48mG3evLnD2LehoYFNmzaNpaWlsaysLFZbW8vmzp3LgoKChPGFhYWxuXPnsqampg7HxvM8MxgM7MUXX3SLjzmOYwkJCSw7O5sdPXqUeXh4sAEDBrBz584xxhgrKipiEyZMYL6+vsJ3AgMD3e7BzJkzmU6nY4wx9v333zMAbPjw4ez06dNs7969LC0tjUmlUgaAicViNnLkSLZz505ms9k67Csh5JeLppsQQsg9kEgkwsw9u90uPOHneR7V1dVYvnw5tmzZAsYY/P39ERYWBqfTiZqaGuzevRuFhYX46KOPMHHiRMhkMqFc3jVL1LUpTttZo2azGTzPAwDKysqwevVqXLx4Ed7e3ujfvz+8vb3R2toKjUaDQ4cO4dy5c1i8eDGee+65Dmcr/Bx4nkdFRQU+/PBDfPvtt5BKpQgJCUFYWBgsFgtqa2uxdetWXLhwAZ988gnS09OF68gYg0ajwdGjR2E2m2EymXDo0CFYLBYMGTJEuLbHjx+HWq3GsmXLkJGR4TaT1WazYdeuXViwYAG0Wi0iIyMxcOBAKJVKVFZW4sCBAygvLwdjDGazGTabTbh3DofDbSmBWzcMEIvFcDgc7casVqsxdepUHD9+HP7+/hg6dCiamppQW1uL1atXQ61W4+uvv+50ZighhBBCepanpyeAm7FF2/W4HQ4HDh06hFmzZkGtVsPX1xcJCQlQKpXQ6/VQq9WYPXs2zp07h5UrVwprtLtivbYbFradSehwONxihO+++w7Lly+HUqlEYGAg4uPjwXEcGhoaoNVq8fHHHyMvLw9r1qxBYmLifVtCx2azYffu3ZgzZw4aGxvh6+uLpKQkeHl5QafToby8HNOnT8eFCxewePFit5moVqsVeXl5KC8vR0pKCnbu3ImcnBwEBQUhKioKOp0OdXV1+PTTT3H9+nVs2rQJvXr1cju/TqfD888/j4MHD0KlUiElJQWxsbFobW1FSUkJXnzxRTzzzDOwWCwwm83CvXO9uq656/q3vQc8zwsxnYvJZMK2bduQk5ODxsZGpKSkgOM4qNVqnDlzBu+88w7MZjOeffbZ/4rKKEJIz6CEJyGE3CXGGHQ6HXQ6HQAgICAAIpFIWEh/8+bN2LRpEwIDA/G73/0OU6ZMQXh4uFC+s27dOmRnZ2PVqlWIiorCwIEDIRaLsWnTJuTn5+ONN96Ap6cn3n33XYwePVo4r1wuFwJHb29vjB07Fs888wxGjRqF4OBgyOVymM1mlJSUYNOmTcjOzsZf//pXDB06FAMHDrxv1+Xzzz/Hjh07EBUVhRkzZuDxxx9HcHAwrFYrCgoKsHbtWuTk5GD16tWIjIxEXFycW5JRLBbj0qVLuH79Op588klMnz4dffr0gd1ux8mTJ7Fs2TKcOXMGWVlZGDJkCCIjI4XzV1RUYN26ddBoNBg/fjwWLFiAESNGQCqVoqGhAd988w2WL1+OmpoaAO47d06ZMgXx8fFYuXIl9u3bhxdeeAHTpk0Typ3EYjFCQkLajXv9+vXw8/PDkiVLMG7cOPj4+KC+vh6bNm3C5s2b8d133+GHH36gTagIIYSQ+4AxhuLiYgA3YyeVSiV8lp+fj9mzZ0OtVmPs2LGYOXMmhg0bBoVCAb1ej++//x6rV6/G9u3bERsbi3fffRcymQyTJ0/GiBEj8Ic//AEHDhxAamoqVq5cKbQrEokQEhIiPMQNDw/HK6+8ggkTJqBv377w8fEBx3HQ6XQ4cOAAVqxYgby8PPztb3/DJ598cl9KqxljOHr0KObMmQODwYCJEyfi5ZdfxsCBA+Hh4QGNRoOdO3di7dq1+OKLLxAXF4dXX31VGJNIJBLK3Xfs2IGwsDAsXboUjzzyCHx8fKDRaPDFF19g/fr1OHjwIPbs2YPp06cL57fb7VizZg1ycnLg5+eHefPm4aWXXkJISAisVivOnz+PDz74AF9++SUA9xgtISEBa9euxb59+/D222/D29sba9euRWJionCMSqVyu9cAUF5eDp1OhwkTJuDFF18Ujj979iz+8pe/4PTp09i5cyeGDx+OhISEn+W6E0LuP0p4EkLIHbh14xqe51FfX48DBw7g8uXLkMlkGDZsGKRSKRhjKCkpwebNm+Hl5YXXX38dr7zyilvwFRQUhMDAQBgMBhw7dgz//Oc/kZiYCE9PT8TGxqK5uRlSqRRyuRxRUVFITk7usF+xsbGYP38+5HJ5u7WKwsPDER4ejqamJhw4cAC5ubno37//z76WJM/zyM/PR1ZWFgICAvDWW2/hhRdecAvig4OD4evrC71ej5ycHOTl5SEyMrLdelF2ux0ZGRn44IMP3Nb8zMjIQFNTEyoqKnD27Fmo1WphTVS73Y68vDycPn0aCQkJmD17Nh5++GFhlmxAQAAyMzPR0NCAZcuWwWKxuJ3Tz88PCQkJwmyO4OBgJCUlwcfH57bjXrlyJTIyMoT3XOO/dOkScnJykJWVhWeeeabTdaUIIYQQ0j23VsIAN2cw5ufnY/fu3eA4DiEhIUKSy2g0Ys2aNbh+/TpSUlKwatUqtzjL9beb4zgsWLAAWVlZmDJlCvr16wc/Pz+oVCr06tULHMdBpVJ1GqMBwMSJEzF58uR2swYDAgKQmJgIPz8/vPTSS8jLy0NpaSmGDh3ag1emY3q9Hp9++ikaGhqQnp6OFStWIDY21q1vCQkJsFqtWLp0Kb7++mtMmjQJ0dHR7dpijGHhwoX4zW9+I7wXGBiIhQsX4sSJE7h06RIOHjyI5557DjKZDABw/fp17Nq1Cw6HAxkZGZg3b54QI8rlcowYMQJLlixBRkYG6uvr3c7n4eGB2NhYREZGCvc9Li6uy3sA3IzRxowZgw8++AAxMTHC++PHj4fFYsHLL7+M4uJiXLlyRZiFSwj55aNfXIQQchuuoHn//v3Izs7Gvn37sHXrVvzf//0ftm7dCqPRiNGjRwtl2Q6HA0ePHsW1a9cwePBgTJo0qd2TZo7jEB8fjwkTJkAsFuPUqVNoaWkBcDN4dJVYd1SW05ZMJoNCoRASaHa7HU1NTcLMU39/fyQkJEAmk6GgoMCtnOvnYrVakZOTA61Wi1GjRuHXv/51uxkLYrEYAwYMwKOPPiqURplMpnZthYaGYuLEie1mVMrlcvTt2xeRkZHQ6XTQaDTC2CwWC06ePAmHw4Fhw4ZhwIAB7X4IqVQqpKenIyoqqtNxtC2f6uoeuIwaNQoTJ05s9354eDhSUlIgk8lQUVEBq9V627YIIYQQcmd0Oh0OHjyI7OxsZGdn49tvv8WqVavw9ttvo6qqCj4+PnjiiScQFxcHACgpKcHZs2fhcDjwxhtvdJgsE4vFmDZtGsLDw6HVanH69GnhM4fDISwt5HrtjLe3t5Ds5HkeJpMJjY2NQpz28MMPw9/fHzqdDleuXOmpS9Kl8+fPo7CwEADw5ptvuiU7XaRSKV577TUolUpUV1fj/PnzHbY1ePDgDmOfwMBApKamwul0Qq1Wo6mpSfisoKAAGo0GEokEmZmZHc5qHTx4MB599NFOx9A2NruT2DYoKAjjx49vF/dJpVIkJSUhOjoaer0eWq32vsTKhJD7g2Z4EkLIbTQ3N+PLL78U1i9ijMFoNEKn00EqlWLSpEmYN28eoqOjwXEcHA4H8vPzwXEcFAoF6urqhGTmrcxmM+RyOa5evSrs+NlddrsdRUVFOHbsGK5cuQK9Xg+bzSZ8XlJSAofDAY1Gc9vAvCdYrVacPXsWEokEHh4euHbtWrsn9G37LpVKcfXq1Q4TgWFhYQgNDe1wRqSnpydUKhVsNhtaWlqEsdlsNpSXl0OhUCAmJkaYqdmWa7ZHVFQUysrK7nHENw0dOrTDfnIch4CAAIjFYhiNRphMJlrHkxBCCOkhFRUVmD9/vvA32GazQa/Xo7m5GREREZgxYwamT58ODw8PAEBpaSkMBgPEYjF8fHzckpltcRwHPz8/6PX6u05GMsbQ1NSEo0ePoqCgAHV1dWhtbRWSag6HAy0tLfD09ITBYLirc3RXYWEhjEYjFAoFxGJxl+NXKpUwGo24du1ah8cMGjRIuK63Cg8PB2MMJpPJ7aH2lStXYLFY4O/v3+nMTI7jkJqaiu3bt3dzdB0LCAhAVFRUh3GaTCaDr68vysvLYTQa4XA4fvZqKELI/UH/JRNCyG2IxWL4+/sjICBAWBzdx8cH0dHRGD58OIYOHYrevXsLpTo8z6O2thY8zyM3NxeFhYWdljAbjUY0NTWhoaEBdru9231raWnB119/jY0bN6KyshImkwkOh8NtRqJrlujdJlS7y+l0ora2Fg6HA/v27cOJEyc6LQ1qamqCxWJBY2Njh0/UFQpFuzJ3F9c6nzzPuz3p53kezc3NkMlk6NWrV6eLzysUituWqXeHUqns9DNXHxwOB80cIIQQQnqQXC5HeHi4UM0hl8sREBCAlJQUjB49GomJiW6b5mi1WlitVjidTixYsKDL5FZNTQ2cTicaGhruqm/Xrl3Du+++ixMnTsBgMMBms7WLA5xOJ+RyebsNkX4u9fX1sNlssFqtmD17drsqmFuPlcvlbjM027p1M6K2XLGP0+l0G3NzczOcTidUKlWXsVNoaOjthnLHXBVRHXGVxt+6sRUh5JePEp6EEHIbvr6+WLRoEcaMGSO850q2yWSydut7MsZgtVrBcRykUqmQCO1Ir169oFKp0Lt3727vCul0OrF371588sknuH79Oh566CFkZGQgJSUFvr6+wq7v27dvx/bt2++oLLsnuEryRSIRpFJpl+Py9/eHn58fIiMjO/3BcSeB/92Mrad/UNB6T4QQQsj999BDD2HXrl3tdu52xSC3PnS22+3geR5isRhyubzLv99RUVGQSqUdblZ4O1arFbNmzcLhw4fh7e2NiRMnCsvpeHp6QiwWw2KxYObMmWhubr5vcZrNZgPP85BIJJDJZF2OPzY2FgqFAv7+/h0mY+829rlfY22L4jRC/vdQwpMQQu4Ax3Hw9vbu8il422O9vLwglUrx/PPPY+HChV0mPRljEIvFnZYEdUaj0eDgwYO4du0annzySXz++eftnoYzxpCbm9utdu+VSCSCQqGAp6cnZs2ahblz53Y5e+Jux9/V+ZVKJa5evYrGxkbYbLYOn+qbTKZOlxoghBBCyC+HRCK546oNVym3QqHAgQMHEBAQ0GUCjjHWZRzXmePHj+Onn36Cn58fFi9ejJkzZ7ZLvra0tNz3jQw9PDwgEokQEBCAI0eOoFevXrdNQN4uMdodPj4+kEgkMBgMMBgM8PT07PC4urq6HjkfIeR/FyU8CSGkh4nFYvTp0wfHjx9HbW0tWltb4efnd8ff5zhO+Afo/Cm4Xq/H9evXIZVKMXnyZPj7+3d6zP3cKEcikSA+Ph5lZWWoqamBw+HosuSpp8lkMiQnJ+PkyZOoqKiAXq9HeHi42zE8z0OtVqOysrLTdm53/QkhhBDyy9O7d28oFAoYDAaUl5d3uYHh7XQVI+Tn5wO4WZr91FNPdZjYrKyshEaj6XT5np+Da9ZmXV0dNBpNuxjp59a3b18oFApoNBoUFhYiLCys3TEOhwM5OTmdtkGzNQkhd4J2aSeEkB4mlUqRlpYGmUyGixcv4syZM26bCN2qo93Y5XI5ZDIZbDZbp9/leR4Oh6NdSb2L0+nExYsXcfHixfu6JpFcLseYMWMgFotx5swZ5Ofnw+FwdHr8nexG3x0eHh4YNWoUFAoFzp492+76M8ag0+mQk5ODqqqqDttwlbm51j7tqv+EEEII+eV46KGHhA111q9f3+VmQYwxIU5xcW3KyHFcl5UirtjDtT7krex2O7KysmA0Gu9hNN03bNgwBAUFwW63Y8OGDV2OwTX2ntz0cvDgwUKSdePGjdDpdG6f8zyP06dPd5nwdF1/xhhaW1t7rG+EkAcLJTwJIaSHicVipKWlYdSoUbhy5Qo2btyIo0ePoqWlxS3gtdvtqK2txdGjR1FYWOi2aZG3tzcCAwOh1+tRUFCAiooKaDQaaLVa6HQ68DwPHx8fBAcHw2KxICcnB9XV1UL7rp3bt27ditLS0vs6fplMhrFjx2LQoEEoKSnBhg0bkJeXB5PJJPTPtc7n9evXcfjwYZSVlfVYUlYqlWLEiBFITU3F5cuXsXbtWmRnZ6OyshL19fUoLi7G5s2bsWvXrk7XF/Xw8EBISAhEIhEKCwtx6dIl1NfXQ6vVQqvVwmKx9EhfCSGEEHJ/xcTEYPLkyfD09ER2djY+//xzVFZWtnu4aTQaUVpaiv3790Oj0Qjvi8VihIeHQyQSoaKiAqdOnUJtbS20Wi1u3LgBs9kMxhgSEhIA3Nz45/Dhw26JucbGRnz33XfYuHHjfS9pT0pKQkZGBiQSCf7xj3/gq6++QnV1tVscxhhDc3MzCgsLceDAATQ2NvbY+Xv37o1nn30WMpkM+/fvx4oVK1BQUICamhpUVVXh0KFD+POf/9zlLM6IiAjI5XJYLBbk5uaiqqoKGo0GGo0GTU1NPZqgJYT8clFJOyGE3EZ3Zx5yHIfevXtjzpw50Ol0OHLkCDQaDdLT05GQkAAvLy9YLBbU1dWhvLwchYWFyMzMRFxcnLBGkq+vL9LT03Hx4kVs2bIFBQUF8PX1BcdxCA8Px/vvv4/g4GCkpqYiNzcXe/bsgdVqxejRo+Hj4wO1Wo3c3Fyo1WrExsbiypUrHY6jbQKyp66PSCRCQkIC5syZg0WLFmHv3r2oqalBWlqaUEZlMplQU1ODsrIyFBUV4e2330ZMTIzbWp932rdbP+c4DlFRUZg5cybq6uqQm5uL6upq9O3bF56enrhx4wauXr2K5ORkBAUFIS8vz20JAeBmwnno0KGIiorCyZMnsXDhQoSHhwuzOjIzM5GamirM/LhdP3viOhNCCCHk3kkkEkybNg2XL1/Gli1bsHz5cuTn52PIkCEICQkBx3EwGAy4du0aiouLYTAYsHbtWrd10seNG4dNmzZBrVZj7ty5iImJgVQqFWKEkSNHIi0tDYMGDUJhYSGWLVuGsrIyxMXFwWaz4eLFi9i/fz/69OkDmUwmJElv5Yoz7iZ+6Cz2kMvleO2111BZWYldu3bho48+wqlTpzBo0CAEBgaCMYbGxkZcvXoVxcXFcDqd2LBhg9vSSd2Ja249RiwWIzMzE0VFRfjmm2+wevVqHDt2DCEhIbDb7SgrK4NcLkdmZiZWrFgBAO2Swn369MEjjzyCvXv3YsOGDSgoKIC3tzcYYxg5ciQyMzPbrel6J/EkxWmEPFgo4UkIIZ1oW2rd3QBILpfjscceg0QiwYYNG3DkyBEUFxdDqVRCLpfDbrcLJUzx8fHCLqAuKpUKmZmZMJvNOHLkCP71r38JwXBSUhLeeecd+Pv746mnnkJ1dTW++uor7Ny5E0eOHIFcLofRaERAQABee+011NXVYf369R3OIBCJRJBIJF1uKtQZjuMgkUjA83y7p/Cenp54/PHHIZPJsHHjRhw7dgwXLlyAUqmEVCqFzWaD0WiEWCxGv379EBERIWwI5Uo+SiSSTsv1XceJxeIOd2D18PDAuHHjIJFIsG3bNuTl5SE7OxtSqRQJCQnIzMxEWloa1qxZI/S37XkkEglGjx6N+fPn4+9//ztKSkqQl5cHp9MJX19fjB49GqmpqUI/JRJJlzM07uU6E0IIIaQ914zEu6kQCQ0NxcKFCxEZGYnt27dj9+7dOHToELy8vMBxHMxmM1pbW6FUKjFhwgQEBAS4fX/48OFYvHgxtm3bhn//+9+4cOECnE4nvLy8kJqaiuHDhyM4OBhLly7F+++/j7Nnz6KyshIqlQoOhwOtra0YN24c5s+fj9dffx1qtbrDeOdOYozOiMXiTmOP6OhofPzxx4iNjcWOHTuwY8cO/Pjjj/Dy8gJwc2NHo9GIoKAgPP744+3WYu9u7HPr2IKDg7Fo0SL06dMHe/bswfnz52G1WhEYGIgRI0Zg5syZaGlpwYoVKzrc2NLT0xMffvghevXqhZMnT+LgwYOwWq1C7D516lT4+Ph0K550HUcIeXBwjB5jEEJIOzzP48qVK8jOzoZIJMLEiRMRGxvb7YDTZrNBq9WisLAQZ8+eFTYQ8vb2RmRkJPr164eEhARERETA29vbLRhzOBwwGAyoq6uDXq8XAjkvLy8MGzYMMpkMPM9Dr9fj1KlTOHbsGGprayGTydC3b1+kpqYiOTkZNTU1KC0tRa9evZCeni4EczzPo7q6GpcvX4ZKpUL//v27tVO6TqdDSUkJGGMYMGAAVCpVu2DSYrGgvr4eFy5cQH5+Pqqrq+FwOKBUKhEdHY3k5GTEx8cjPDxcSDryPA+tVotLly7B19cXiYmJHe682tLSgrKyMhgMBiQlJSE0NLTd/bFardDpdKipqYHBYBDK0IKDg3H16lW8+eabOHXqFLZu3Yqnn37aLensWhdKq9VCo9HAaDSC53lIpVIkJiYiPDwcZrMZJSUl0Ol0SEhIQHR0dIfXqqqqCleuXIFEIsGIESPu6+YEhBBCyIPG4XAgKysL1dXViI6OxtNPP31XO6k3NzejqqoKp06dQklJCfR6PcRiMfz9/ZGYmIj+/fsjMjISQUFB7ZbBMZvNwnI3LS0tcDqdEIvFbjGJw+FAeXk5cnJycOnSJZjNZgQFBWHYsGEYOXIkQkNDkZOTA5PJhOTkZMTFxQmxlNPpxMmTJ2G32xETE4OYmJhuje3ChQu4ceMGgoKCkJKS0mEM65rJefr0aZSWlqKpqQkSiQSBgYHo27cv+vfvj4iICAQGBgrxo9VqRX5+PoxGI+Lj4zvt17Vr11BWVgYfHx8MHDiww93Ym5ubUVdXh/r6etjtdvTq1QuhoaEIDg7GsmXL8N5772HIkCH44Ycf2m1uxBgTlhrS6XSw2WxgjCEkJARJSUmQyWTQarXCA/ekpCSoVKp2fTCZTCgtLYXBYBBiUkp8EvJgoIQnIYR0wDWr0+FwCE9+by177k5bPM/D6XQKsxA4joNIJIJYLIZIJOqy3Y5mmN7aF6fTCYfDIaxZ5HpSLRKJ3DYEanuuW8uRuju+tv3q6ruu8bftX1fjv9N+3Xr+tq+3O9bpdGLnzp144403oFKpsH37dowYMaLT87R9bdun7lyDu73OhBBCCHHnitEYY0I8cbd/W11tOZ1Ot3hJLBbftt3OYoS2r7e275r56EpA3hobtW37XmKHtutYdhVLAejW+Hs69ukonrNYLHj44YdRVFSE3/72t/jqq686TGh3VlrfnTitO/EkIeSXhWrrCCGkA66g6G5mC3TUlitovJe+dKWr9jublXqvQd2dBt/dHf+d9qur8zudTtTX18NisSA0NNStnMputyM/Px8rV65EY2MjJk2ahIiIiC5LnTrrT3euAQXPhBBCSM/gOK7TjQfvtq27ae9OYpbbtd9ZfHSvscOdVCW52u/O+Hsi9rFarSgsLERiYiJkMplb9ZHRaMSHH36IwsJC+Pj4ICMjo9N4/HbX/076SjEaIQ8uSngSQgh54PA8j9zcXCxZsgTJyckYMGAA/P39YbFYUFJSgv3796O+vh4DBgzAc88957YRASGEEEII+fk4HA68+uqrEIvFSEtLQ0REBKRSKerr63H06FGcOXMGHh4eeOKJJ/DEE0/8p7tLCPmFooQnIYSQB45IJEJgYCBUKhWOHz+OH3/8EVarFRzHQaFQICAgAJMmTcKMGTPc1jUlhBBCCCE/L6lUiqSkJJw5cwZbtmxBa2srnE4npFIplEolkpOT8eijj+Kdd94RNlIihJDuojU8CSGEZPZ5zgAAAQBJREFUPHAYY2hpaUFxcTEqKipQX18Po9EIjuOgVCrRp08fDBo0CBEREXe18ykhhBBCCLk7jDFcvXoVRUVFqK6uRmNjI2w2Gzw8PBASEoL+/ftj8ODBPbZsASHkfxMlPAkhhBBCCCGEEEIIIQ8MmtZCCCGEEEIIIYQQQgh5YFDCkxBCCCGEEEIIIYQQ8sCghCchhBBCCCGEEEIIIeSBQQlPQgghhBBCCCGEEELIA4MSnoQQQgghhBBCCCGEkAcGJTwJIYQQQgghhBBCCCEPDEp4EkIIIYQQQgghhBBCHhiU8CSEEEIIIYQQQgghhDwwKOFJCCGEEEIIIYQQQgh5YPw/7UCdrl/R76UAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following Scikit-Learn code loads the iris dataset, scales the features, and then trains a\n",
    "linear SVM model (using the `LinearSVC` class with `C=1` and the `hinge loss` function) to detect Iris virginica flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, LinearSVC(C=1, loss=&#x27;hinge&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=1, loss=&#x27;hinge&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge'))])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:,(2,3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\"))\n",
    "])\n",
    "\n",
    "svm_clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5,1.7]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:**\n",
    "\n",
    "Unlike Logistic Regression classifiers, SVM classifiers do not output probabilities for each class."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using the `LinearSVC class`, we could use the `SVC` class with a `linear` kernel.\n",
    "When creating the SVC model, we would write `SVC(kernel=\"linear\", C=1)`. \n",
    "\n",
    "Or we could use the `SGDClassifier` class, with `SGDClassifier(loss=\"hinge\", alpha=1/(m*C))`. This applies regular Stochastic Gradient Descent to train a linear SVM classifier. It does not converge as fast as the LinearSVC class, but it can be useful to handle online classification tasks or huge datasets that do not fit in memory (out-of-core training)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NonLinear SVM Classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To implement this idea using Scikit-Learn, create a `Pipeline` containing a\n",
    "`PolynomialFeatures` transformer, followed by a `StandardScaler` and a `LinearSVC`. Let’s test this on the moons dataset: this is a toy dataset\n",
    "for binary classification in which the data points are shaped as two interleaving half circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, loss=&#x27;hinge&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge'))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "X,y  = make_moons(n_samples=100, noise=0.15)\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "    (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\"))\n",
    "])\n",
    "\n",
    "polynomial_svm_clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting moons dataset to visulaize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "def plot_dataset(X,y, axes):\n",
    "    plt.plot(X[:,0][y==0], X[:,1][y==0], \"bs\")\n",
    "    plt.plot(X[:,0][y==1], X[:,1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which=\"both\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X,y, [-1.5,2.5,-1,1.5])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0,x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    # print(y_pred)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    # print(y_decision)\n",
    "    plt.contourf(x0,x1,y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0,x1,y_decision, cmap=plt.cm.brg, alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(polynomial_svm_clf, [-1.5,2.5,-1,1.5])\n",
    "plot_dataset(X,y, [-1.5,2.5,-1,1.5])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Kernel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `kernal trick` trick is implemented by the SVC class. Let’s test it on the moons datase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernal_svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "])\n",
    "\n",
    "poly_kernal_svm_clf.fit(X,y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code trains an SVM classifier using a third-degree polynomial kernel. It is represented\n",
    "on the left in Figure. On the right is another SVM classifier using a 10th-degree\n",
    "polynomial kernel. Obviously, if our model is overfitting, we might want to reduce the polynomial degree. Conversely, if it is underfitting, we can try increasing it. The\n",
    "hyperparameter `coef0` controls how much the model is influenced by high-degree\n",
    "polynomials versus low-degree polynomials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernal_svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "])\n",
    "\n",
    "poly100_kernal_svm_clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10.5,4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions(poly_kernal_svm_clf, [-1.5,2.45, -1, 1.5])\n",
    "plot_dataset(X,y,[-1.5,2.45, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(poly100_kernal_svm_clf, [-1.5,2.45, -1, 1.5])\n",
    "plot_dataset(X,y,[-1.5,2.45, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "plt.ylabel(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TIP:**\n",
    "\n",
    "A common approach to finding the right hyperparameter values is to use grid search . It is\n",
    "often faster to first do a very coarse grid search, then a finer grid search around the best values found.\n",
    "Having a good sense of what each hyperparameter actually does can also help you search in the right part\n",
    "of the hyperparameter space."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian RBF Kernel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s try the SVC class with the Gaussian RBF kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=0.5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=0.5))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.001, gamma=0.5)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=0.5))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"svm_clf\", SVC(kernel=\"rbf\", gamma=0.5, C=0.001))\n",
    "])\n",
    "\n",
    "rbf_kernel_svm_clf.fit(X,y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is represented at the bottom left in below figure. The other plots show models\n",
    "trained with different values of hyperparameters `gamma`($\\gamma$) and `C`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yli1POBVUq6QiAAEdisD169cjVi+bbdu2hfx+11134a677pLZKoIg9ApFARMnIABLrOCZT2VrCD2QrgKQon+EGEj4KY+jxQW+owuWUzvQ19gJbnUDLGW1aSEA3SOOlAUgoEMRSBAEITW0eRNPsAAkiHhMOCbTUgCS+CNiESz6AFov1aIObZi4NAvjH786LcQfkHodYDAkAgmCyFgoCpgctKEhxJDuApDEHxEMCT/twSaHgTRsBCNFFBAgEUgQmkeopyHkgTZy4uEcdtrYEKJIx3WLon9EMCT6tA138n1MT/SgLXcsLRrBCEglAAESgQShC9LxarraeB3DtJFLABKAhFjSsREMRf8ym3DBB5Do0yqOFhcsB3bBmLsHJxs9MFXUp0UqqFR1gMGQCCQIhekf4DA8En2AZ6GVoaKcGu/LCaWBJgYJQEIs6SYASfylRu+AAUMjhqj326w8qsp5BS2KTyTBB5Do0wP2nUeQ0/km+htOAAtLYV22Nm0EoByQCCQIBekf4HDTLblwe6KLQLOJ4bmnJlFRztIypUptaFOXGCQACbGQACSC6R0w4PpbyuL6u5efsqsqBDmnAxxChSqtefrD0eJCHdpgmg+MLJqP6pXXqW2SJEhdBxgMiUCCUJDhES6mQwQAt8cfKRSigemyodIStKkTBwlAQizpJABJ/EnD0IhBlL8bGjHILgIjRfc48EC+f9apgdP/55bwk11VAm+JS20zJEUOAQiQCCQIIoOgNFBx0BgIIhlIABJqk0gqJ898gFtuiwil4Du6MOU4gYnFeWqbIhlypYEKkAgkCI2Sru3V1YI2d+IgAUgkSjqsVbQ+6IdoQg+gdSsTERrBWMZO4FRjJ7i6Bsyrv0Jts1JGzjRQARKBBKFBpoamgCK1rUgfaIMnDhKARKKkQ90yrQ/ahMQeEQ+hEcxUxSDGV0zBsjZ9hsID8gpAgEQgQWgWvV9Z1xq0wYsNCUAiUfReB0hz/7QBN+QC55iadTutRYQYahsKcHTRFOau/JLapkiG3GmgAiQCCYJIa2geYHxIABKJki4CkNYG9THZ8mEuMattBqFT2MSo2ibIgtxRQACIPryFIAjVsNr0ubHSGtQIJj4kAIlEIQFIxINz2MENpVeHRkK7eEssapsgGXIMhY8GRQIJQkEKrQxmE4s7N6nQqq3huXqENnrxIQFIJIseBSCtCfIRPmvPXGJFuc8Is4mH2xM93mA28Siy+pQwkUhDjJMOoEBtK6RDqTRQARKBBKEgFeX+QfDDI7NF4NSQvyaippZD5dkZgURy0GYvPiQAiWTQaydQWhOkIbxZS6xZe1XlPrz2VA9cI9E/L0VWH6rKSQQSicMN9fv/LbAAmF1TqleUigICJAIJQnEqyllgELzAhGMSKBKurpMATAWvcwQGGGmzFwUSf0Sy6LUTKAnA5InUoTN47Yg3a6+qnEQeIT2OFhfydv8FXHULDhSMwQT9dwRVOgoIkAgkCM2gx6vrWoU2e5EhAUgkix7rAKn7Z+LEE30EoTb2nUdgObUDbeecQu4Fc2BddkXajIVQMgoIkAgkCNXRa3qV1vA6R4B8wFKcrbYpmoQEIJEsehaAJP7iEy78aI0gtIp95xHUoQ1Di8ZhubIxLYbCA8o2gwmGRCBBqIhe06u0BnUBjQ0JQCJVSACmDyT6CD1TWOzFhLkQuWW1apuie0gEEoRK6PHquhYJbPiKs+GKUZuSiZD4I1JFb5kKVBMcGRJ+RFpho5EQUkAikCBUgASgNARf8eeZV2VrtAUJQCJV9JqpQALQDwk/Il1wtLjAd3TBcmoH3q7th6muHvPSoA5QjWYwwZAIJAiFIQEoDZTyFR0SgESqTDqnkGfJ1cU65XUMg4ePaoIRKvzo+0+kA/adR5Df3wp37h64LjfAujZ9GsEAyjeDCYZEIJGx9A9wEef1CRRaZ49ySBUSgNJAAjAytAEkpEQP6xSlg4uL+PUOGGleH6E7HC0u5Pe3wlh/AmOLyjF35XVqmyQZakcBARKBRIbSP8Dhplty4fZEF4Fmk3+wu1RCkASgNJAAjAxF/wgpKSzW/jqVyengiaR69g4Yce0tc+D2GKIeYzbxeO2pHhKChOYorTBiwpKFnJJqtU2RHDWjgACJQCJDGR7hYgpAAHB7/JFCKUQgCUBpIAEYGRKARKaRqWtBMtF+14gxpgAEALfHANeIkUQgoVl8ZQVqmyAZWogCAiQCCUJWSPxJR6Zu+mJB6Z9EJpJpawE1eCEyGTaZniOg1I4CAiQCCUI2JpyT4JBFAjBFMm3DJxaK/hGZSCatB3SRhyDOkmYjIbQCiUCCkJj+AQ7djkJkTWSh0GbGgCv0/kIrj0qJG86kK5m04UsEEoBEJpIp64GexF/vgBGnHIWYMplh4GZf8KSGM0SyOFpcyNv9Cqbz+tGWOwYL0mc4vBaigACJQIKQlP4BDjfdWgCPZ33UY8wmhpeeGiYhGIdM2fAlAok/IlPJhPWAczrAwV+7p4fveO+AEdfdOgduz7yox1DDGSIZ7DuPIKfzTUzbOtC3LheWZcvTaiyEViARSBASMeGYRH+HER5P7LQFf8MZAyrJKUYlEzZ8iUICkMhU0n094JwOIB8wFxdEjKZpFWo4Q0iNMBQ+p/NNOBpOwLRoPurSbCyEVqKAAIlAgpAEoQGM1aYfB65FhM0ekL4bvkQh8UdkMuksAIXvtqm4AMjQGYcEEU5VbRYKrAUYWTQf1WkkALUIiUBCFtQYxJ4IhVYGs4nFnRNYaI1toyD+AH8H0PD6P0I86bzZSxYSgEQmo5c1oXfAgKGR6BExm5VHVTkf+D38e80zeaNkRVYfzCY+7pzAIitF6whtwCZG1TZBcrTUEEaARCAhOWoMYk+Gb/7bNOyOyDZacoEli/iY9tH4B+nQy2ZPKUj8SY/z1JDaJhAJoJc1oXfAgOtvKYvr715+yo45xn7/7yp8r3/wbw70OyJv+fJyeZy7yE0pm4Sm8JakT0dQAS2lggIkAgkZUHoQuxiCI5MOJ/Df9+XA54tto8nE8HwEoRoe/Utn+gb89YvRkKLTqV42e0pBAlBahA5z2RPtaptCiERPa8LQiEGcv+sYxpz5ynyveweMcI34fdOg04B/v68MPl/s2j2TieH1p7ozVggGv2eRoC6nRCpoMQoIkAgkMgAxkclIeCII1UyK/vUNcLjxlsK4V7iT7XSqp42eUnBOB8wlNrXNSBucr+5CTs9eTJc54fi4EXhVbYuIeKTrumCy5cNcYpb9eXoHjLj2ljlxG7aE4/FwGdvAZabLaex0WepyqhBjDnAFFgBTalsiKVqLAgIkAgmNEF5DyJgBY55C5JsM4DhDSjWEYiKT8cik6J/AsNgr3El0Ok3XjV6yBHcHJJLDvvMI8vtbwU3PNBcyZbnQu6EfpqX1qK1cCeAl9Qwk4pIp60J41IlnPnR7ZmbtpRJ1EtOxkwiFupwSmQqJQEJ1okfq1gf+p2YNYSLRv0IrL7LhDB/1/nSGun+GQt0BU8fR4oLlwC6Yc/fAcn65/0abv5akL9cJ67IrUG6rx9jouIpWEvHIJAEYOVI3N/A/vUSdqOEMITWGsQG1TZAcraaCAiQCCQ2gxRpCgamhKRSdIz7yV1nO8OsnnWh3HEGR6fyIM5+kqKPTI5myyRODIP4Af42Q3N0B0xX7ziOwnNqB/sZOZBUVYHrt4pD762xXqGQZkQiZtDakU9SpqtyHHU924qijDdWmcyL6O6qlI8TiaHGB6+4Fb/gAB+pPwYT0GQ6vxVRQQIci8O2338ZDDz2EQ4cOobe3F3/84x/xmc98JuZj3nrrLdx55504duwY5syZg7vuugu33XabMgYTusY/9y8xB1ZZzsNoG0aZ2QtDalmoaUMmbfLiQY1fUkdo9mK2HYXrcgO4JQ2YW0+CT4/Q2qBvqsp9mLQNY4HZratB94S2ENL53bl7cHpdbiCDg5AX3YnA8fFxnH/++fjnf/5nfO5zn4t7fFtbG6655hrceuuteP7557Fnzx7ccccdKCsrE/V4giCShzZ4M4RH/4jE4Yb64dh9Ajk9e9F1ttYvt6yWNgs6Re/rA+ewgxvKAVCqtikEoVvsO48gp/NN9DecgGnRfNSl0YB4LaeCAjoUgVdffTWuvvpq0cc//vjjqK2txaZNmwAAS5YswcGDB/Hwww+TCJQJqQaxE9IiZtxDeZlwrAHtnak1F9D7Bk9KKPqXOEKtH4CQZi9cdQvsNxhgqqjHPIr+6Ra9rw/Cd7q8Npfq4jSImJEPFWX+v8mgkyKYalPbUICRRfNRnUYCUECrqaCADkVgouzbtw9XXXVVyG3/8A//gKeffhoejwcmk2nWY6anpzE9PR34fWRkBADAmA+MeeU1WGIEe5W0u7wMePZJb0i3z3AKrQzlZQyM+TuBioExLxhLvKGK2POHYzIxFBR4wCf4nPzZ95rX0Gelb8CAL91aHFeYv/jEAFzeXHz9jtjHBsMzX8hr9Tr93xdLcfbZ+5V5H4S6Oi3V13FO/1VA09mun9Fs06LtYpHD9sG3jiG76x1MFXZhbKn/c+gt8n+euPK5yC2di7LCheB94i8k8fp7axUhmr/jmVe2727wGiHVcyj1HQr/TlfAjR1PdooSHDwTb59/XU38tST7+k0mHoUF7oQfr8W1S+zIh1eeOAO7Nxff+FGFqPMm+zeRAy2+72IJt50ZGXwGAxiMCa3pSiPYlpDf4QHmVf418SKX1bQXgX19faioCP2CV1RUwOv1YnBwEFVVVbMes3HjRtx7772zbh/17IPPbZHNVjkZ8exW9PlybP6fWAyf7YY45ilEcCfQaIx5DmHYPRz3uHAMubkwma6AxxPdSRuNPnzlK8dgtfqNysvzYN68URhtk7An2bXR4dmf3ANloN1RCLdnfcxj3B4Oez7oBCBeAJpMPvhyD8DunhmhgXz/Py6Zul3a7bkYGYk2b6sQdutJlJVNRrlfYc6+F2I7f7Z7jstmitxIavsqALg08n3jwOg4MJpgO9WJCWq/Golo/s7pOYQpufydjGtEr6dZ+pMGE+k7bYvt7yYBnD57fLenEMGdQKPR7WlFThL+bjQ3FyZTVVL+btI2GbAzUbS0dp1yFMLtmRfzGLfHgL9/4ABQDK83/oXirCwfRnOP47RbWd8S39+1acffJUjgM7MKOIzzAQCnP9D+Ot1+zJPA0flAm/KvSay/S3sRCAAcF7qhZYxFvF3g7rvvxp133hn4fWRkBDU1NSgwrUK+uUg+Q2WAMS9GPLthNa0Bx2nzz51vEhepyzddhEJz4pHAwmrguSfH0d8586UoKAx1kP6OnbMvCCQDz7xwePajxLQCBo28506TODt+/vOLRZ/zh98ZwXkNHlSWN86K/slF34ARX7+jMk5Ek8cfnuxPeHahVAiRgkRm/vHMh3bPcdSZluquuYJUtrv++g7MffvhLXVh6NIcWJZeiLLChZLZOTaqnci8lojm74pNF6FABn/ndY7Isk7wzIdeTzOqTBdI/h0SvtNA6rM8p0ziBsZXm87BAnPim8cF1cCrT3bFjUxWleeH3SouGhaOFtcuse9xIv5u038P4JLqc5I1KSl6B4z4+h01cSOaO57s1FUH1vDPzOBbx7Aw6x0cWW9A7cJ1apsXFd7H0H7Mg7oGEwzG+BfK3aNOsOJiBSybjVh/p40dqoxUVlair68v5LaBgQFkZWWhpCRynm52djays2c7KY4zalZIxYPjsjRru62QE1VDaCs0guMST+2ccEzCmgVY58ea9Wc4+yMdBi5LMyJQDuc8vxYozxoF7wQMMCpS2zM8miVinIgBw6MmzKlQtjXrTN2fLelzGDijZjZSiZKs7a6mk8ht3odpwwdwrfHBtLQe82Wo9TPo822VnWj+To71y+sYhgFGWddF/+dQuvP7v9cGyep5SwohqoawpDD5dbu6AqiuiCcKpP1CaGntksOO8hJ5zhuL4VGzqHEiw6NmVFdoP4IWjvCZ4XwcjJwPHMdEiSu1MRg5UXYaDADLUuf1GEQugdrYocrIqlWrsGPHjpDb/v73v+Piiy+OWA9IKE9FuX8QfHANIWNejHkOId90ETguC4VWlvCMQGHIOyBu0LsWENO8RSszBr1DY0CRfhs7SAV1/RRPpGYvZk8PJuvG4b44D9Zly6nTZ5qix0YwcjR0qir34bWnekIidTzzodvTGpi1lymz9cQ0b8mE9yHTMU46wM2xAJhS2xTJ0HpXUAHdicCxsTG0trYGfm9ra0NzczOKi4tRW1uLu+++G93d3Xj22WcBALfddhseffRR3Hnnnbj11luxb98+PP300/j1r3+t1ksgIlBRHiryGOMx7B5GoZkHxyWWAiqX+JNboPUNcLjxlsK4EdGXnhrWjBDU04ZODqjrpzgcLS7wHV0hg929QSmBXEUV6qjTZ9pCAjCUqvJQccMzH3Lc2pq1J7dA6x0w4tpb4jdvee2pHhKChC7RcldQAd2JwIMHD2LDhg2B34Vahptvvhnbtm1Db28vOjo6AvfPnz8fr7/+Or75zW/if/7nfzBnzhxs3ryZxkOkGXIIv2DRN+jk8N378+H1xhdowpiFRBkeMYhIdfTbpFa9WzA5NjOAzKyzIvEnHmGw+/TZwe6WtVdTtC+D0JsAzNTvdrDoG3Qa8B/3l8VsmCIINGHMQqK4RoyiUh1dI0YSgQQhE7oTgevXrw80donEtm3bZt22bt06HD58WEarCLWQM+oXLyoXjiDQkhWBhPah1E/xOFpc4E6+j5yevehr7AS3uoEGu2coJAC1jZioXDiCQKsgf0ekADfUr7YJGY3uRCBBKFHrJyYqpycKrXzc5juJYDYx2KyJd2rVKyT+EsPVdBJ5zfswbTsK+w25sCyj6F8m4nUMkwDUAWKicnqiyOqL23wnEcwmHkVWikbKgbNtCLnjg+hHLwB9dd+Phl7qAQESgYROCBZ+gH4avWiFynJ/qmqkmsb2TgPueTC8Xfhs7r/Lhfk1fkdos/KoKs8MEZjJm0MxDL51DFgFTD3/Agxe/2eipGIEH605BdPSelgo+peRCGmgeoC+4+lFpOY7Aqc7s/DdB+OHL39ylx0LavzlDtSgRh4G3zqGvI9exVTdOIbrDLCU1aptkmTooR4QIBFIaJwJ5yS4sx/TZIWfnjpuykllOYtYSygmSmg2MTQ2eFQXfjaRtkoRpaSNYWyETp+mgv0AboRj9YfwFc18R63LriDxl6HoqQ6Qc9jT6jtOHTf9hDffERATJTSbeFzUMK36+yTWVr1FKZ2nhoAaYGrwZUw09oNb3UCNwVSCRCChOSYck2DwAvmArTi12UNKd9wcdHLgWRZcnkI4TVmzbNei4BSihI6OCTD4MGI5hXLTEnBBtmsl8ldVzuPlp+wYiiDqGfNhwHMCHytZhKry5NNeKfUzPvadR2A5tQPt55xC/jz/kOnqa/5RFzOeCGXQugBMx4s8SnfcHHQacJyZ0e0pxJTJPMvfaVFwBkcJw0dzCGjF7lgRTcH2ZSXzUVWugnESMHdpAVzn16Ok/kK1TclYSAQSmiC8zo9nDHYJZp8q3XFzpoPo+oj3a23EA+C/al9qBErnAznFWeh2D6Pa7IGB046NwVSVRxakPPOiwD2MSrMPySxtJP7iIwx2N9uOoneDB5aljahecDlOf6C/QcWEPOihDjAdBSCgfMfNmQ6icyPer9URD0KUUIujOcKJFtEUbK8y+wBo0/Zo8F3dQI3//76yAnWNkRg91QMCJAIJlUjXGr9YIySA2IJTbFpmoQSpjn0DHBwdE8JZz457AJjThwFPIYwlRsypSPlpdAGJv8g4WlyB/xvGBsC6e5HdsxcfNXbAurAM1mVrUW6rB+/T5sUCQnn0UAeYrgJQDWKNkABiC04lUx2jpcj6o2mFyC0xojpD/B0hP3qpBwRIBBIKkq7CT8CUxeCJIwJjEat5i4AU6aT+FFkr3B5blCMqYDYxvPyUXRMpoHJB4i8ywmD3/P5WlFjHArd3mz9E74YxFC5dhnlUv0GEoYc6QBKA0pGqv4uV6iggRVpm/BTZuZqNWBKE3JAIJGRDz6JPTFTOlMXwk++PoaTYL8ocTg7f+mFqqQ3RmrdIhdcxDEeHOYYA9OP2cBgaMaStCKTNYGTsO4+goOMQpm1H4VhhQH9xdtC9Bmr2QsSEBKA+EROVM2Ux/OL7Aygt9vuEQacBd/wwtfBZtFRHKaGh9AQRHRKBCTDpnEJ+ntpWRKZ/gMPwyGzBwpgBY55CVJdwqJQ53SFc9AH6En7BJBOVO9mq3dcafJU+x5W5X3vaCM5g33kk5HfjpAOWoMHuFO0jxKJkHWDvgCFKYyguaio7fe9jk0xU7nirWQnTCIKQkczdDSZJsNCxlOSqaMkM/QMcbrolN0bUaj3MJobnnppEhYQNSdJJ9EVC7qiclEQagzHo5DDcNwUAyLfkorrOCLiAts70+RuJhVI/ZxBGO2QVNSGvNAfcxLj/jgKg+wYDDXYnEkLJOsDeAQOuv6Ushq+bncpOAlAcSkTlpCJSjd+g04CRcb8PtObxgYjl6U7a5moWm0VtCyTFPeLQVT0gQCIwIQqLjSjI8y88LodPM4JweIQT2QGTS1oEprvg0zOxx2CkV+etRCHxF4p95xHkdL6J/oYTyCoqAL9kEYCZDm11JP6IJFAqCjgkstuzkMpOAjD9EDMGgyAIcZAITJJgARQuCAHtRAkTpf3kNEZGZztZ69nukXLMuYsUxeIZArP2igq5pJ9TyY6baiFmDIbaREvhEpB6DiGJv1CE6J85dw8cDeMwLZqP6pXXqW0WoXO0PA5CjABUY7B6pOcUulROmcwoKUTSz5muw8WDEVPjpzZqfK4IIhlIBEpApIiYK0LkDNCGOIwU1QOAgUEDbv1OETwKDVYH4kWx1qf8nEp13BTOk+6CMxnip3BB0m6knNMBwEDi7yzCYPf+xk5kFRXAspbSPYnU0fI4CG7IBRTFF4BKDlaP/5xzU35OpTpuCudJd8GZDGp8rggiWUgEykRkYTg7YhiPWKJRONfUkBFAfHE5NTSFCYcvahrngMsYUwAC0g5WB5QZ5q5UbZ8gOF3DDC7P+ygynT9rAK3UkVSvYxjeITMA7QqeRFO4koFz2MGBB/IBc3GBZgf/yokw2kHAOOmAxd4Os+0o7I3j4FY3YC41eyEkRKtRQCB+BoAaXSOVeE6lavsEwekYBro9rag2nTNr3c3EiBd1IyX0BIlABUmmhi5aRDH4fFaXuPNabUYU6atmVXdUljOUl3lhdw+jzOyFQaYszeCr8MKgdykxmxhsQRFLpdM5xRKS9llcALgVN0ET2HceQX5/K6aKmpBvObuxKADOLBpDVokVlrOD3QlCCrScBgoAJls+MnYxUJCqch8qynzIcQ9jgdmt24tvwRFLSuUkksE94lDbhKQgEahx0q35Snj9X3untnP7tUaw+AtswlzJn+/+u1yYXzPj0BjzYcBzAh8rWYSqcr+CVTqdMxLhIpQbEl50Dky2fBRZfajIwE2fUOuX4+5Ef8MJmBbNB19SHWj0YgVI/BGSouU0UK0RLiioU6W6/OQuOxbUeAO/+2sxW7GsZD6qyrWTyilGiFaUkRDVGnrrDAqQCCQUJHb9HxGLiOJPAubX+LDknGCn6EWBexiVZh+E5UGJdM5YRBahpSHHmE08djzZCdgkf3rNEjzYfXSFAQVL1qCk/kK1zSIyAC1HAbUCdbHUHgtqvFh6zszFQp75o5hVZh8AoyZSOcUK0Uzzd4Q8kAhMA/TSkEQPXSy1hlziT0+IE6F+x5xjU8YmNXG0uJC3+xVYPD002J1QFLXTQG2ifJ02mpHooYsloT0SEaJ69XfGSSdIwWoDEoFpQKwOmDzzweV5H3Ul56KynBySXkhE/InZGEUivO5Pi3AOO7ihHIRH/jKFSM1e8uztmLYdhWtdLg12JxRDC2mgVeU8Xn7KHjE13FiYG5LaR6SGsPYYaueipL5IbXMCiOlKGgmtXBwgCC1BIjBNiNYBk2dCkxIeAIlArZNM5C/SxggABp0GjI77hWFBHkNpcajgU6uZixiCG774mzxkHsJg95z5/TM3nm32YlpajzqK/hEKo4VshKrymXWLc9gDoyB45g5J7SOSR1h7vBWDyNpTCnvHBpRdca7aZgGIPgZj0GnAyLjfB1rz+Fn+jhq6EMRsSAQSAbSaVqqFVFY5kSLlM3hjpDXEpXD5o5IRh7yn0PhGjwQPdh9eY8BIeSlMixcH7qdmL4TSaCEKGI1k54Fqdc6d1M/paIm9gBrGBkJ+z23eB7PtKIbXGMCVl8Jz8gSKujowtXUZJi9YNevxvoJSoAZwnhoCx0LfS7kiiEqNwUgGrX6uCCISJAKJAEoOVo/FPXeNoXauJzBrr6iQk/051SBT6v2iRSqDKfINosroAZD8pi4dGHzrGPI+ejUw2N28dg0JPkITaG2N4hz2lNYKJQerx+Ind9lRN3c6MGuvpBCSPGfgYpK7E7biLGBqNOJxLD90xvBHa07BtLQeuWW1KLfVY2BxC1xHD2Dk1Lu4oHdk1uP5NjeGaq5E6QdbYfTO2D00kQt7xwbNpZPKjVY+V4RyuEccuuwMCpAIJMJQarB6LOpqeNQvDJ61l14f00wRf8FEi1RGjPxlIK4PWoDFgGn017DfkAWugga7E9pA7WYwkQheN1JBCxGlBTVeLF7olnTWnn3nEVhO7YC7zInRFf5sAoCDt8QS97Em1Ic0miq31QNr6nGmYifaIhzPWB4wDrR/IQ8c538vsxwTYANdKDiyHdm7l8HecZFm0kmVQAufK4IQQ3rtrglCw+hV/CWSzimWTBZ/9p1HAv83TvoHzJrt+4HFV2Ho0hzkLWuk6B+hCbSYBiqsHZm2bkQjfD2x2Nth8fTALnHn4Gjn4X0Mpz9wo3bhOhiMZ31EPTAw1IKhJR1ge99DWVMPnGfXOl/uTMREa8KQUjmJTINEICErwcPhB50csrIYvF5t1RzKjV7Fn4CYdE6xTWaSEX9iHHNWFo9BpxHTnkJMmcySpVRJSfBg90CzlwKAFeSge7X/vZu36vMzGymC0ABaXLO0KgCDh3wPOg3IyuLh9cojKIJrhy3F2f4bCwA2Jwd9dW7VOweX2+oBWz0GymphX3gAc9rfATc6Fbh/bMKIyRdbMbF8vWbSRbWQypku/o7QByQCCdkQMxzelMXwk++PoaTYX/Mn1BzyaVAC6HWOwACjJjdRiZJq45lUIn/hjnnQacD/u78cnqCLCV6vAf92TxWAKv9zmHi89lSPZhxjeHrW5No1IfdbeA6jZ1QyjiAi4HWOABab2maEkGodoJyIGfJtymL4xfcHAp0rBUGRqL8T1pP+xk6YFs3HdFDjKACo01AmgZBOOrCsJXCb0T6KyRPvo6/jVczd06m57qNq+o108HeE+oxPOUUdRyKQkA0xw+E9Xg4lxQyLzkmPxcvrGAYPH5APWIqz066eMVGkSvsMdszHW80hDjESwjBdtZ1i8GD3WOlZvI9hFG4VLCSI6GjpApZUdYByIWbIt8fLobSYx9JzkvuuC+uJ2XYUrssN4JY0oFontcMhUUkbgPoL0f3uDjgK/N1HJ1+8VFNRQTXRq78j9Edm71AJQiJCUj6Ls+HK8P18Jtf8CThf3YWcnr2YLnPSYHcVcY/4a5HcYxMqW6IvcoUUQw2Q6XWAjhYXuJPvI6dnL7o29Id079Qz1Suvw8DiFoy+vRujGowKEkS6QyKQIJIkWq0fz7xqmKMJMnWzZt95BPn9reCmZz4T4/kfwrlhkga7K4gg+IIRWnezbO1EtYjEybQ1RcDVdBJ5zfswWd0C+w0GmCrqJWv2ogXKbfXAp+oDUcHCwUHN1QoSRDQi+Ry1GZtywGwtFnUsiUCCSBC9N3qRmkyO+oU0Zzi/3H+jzd+G3ZdrgnXZat1frdcysUQfkR5oPQ1ULlxNJ8G6e5EdFP2zpEH0LxpCVHD87d0Ydv0dJRQVJHSCnn0OiUCCEAEJv9lkuvjjO7riNGdIn6v1WoAEX+aRKZkFzlNDQI3/X+PoIAAgNyj6Z112RdqKv2CCo4I5E/2o9LahoyWzhs0ThJKQCCSIGJD4m00miz9gJj1r2nYU9htywVXopzmDniDRRwDpv8bYdx6BufdtoOZSlH6wFUavDyw/Fx+tOZX20b9omBYvhrnzQ7A27c2pJIh0gkQgQYQRS/i9N/w2Hmr/b3yn7ke4pHCt0qapCom/zErPUhoSfUQw6Z4GKmQT5HS+icFzWwFciq4rOfhK8gAA1jL1o3/7epqw8d0tuHvl7Vg1p1HZJz+bVs+dfB+oX6/scxOEThmbSqxGkUQgkRTBQ+AjUWjlUWjlYTaxmGMitDQcPl7UjzGGRzt/jLapFjza+WOssF4Gjkv/wd5aE39ihummMoQZmKn1AxBo9pILZFx6ltyECz8SfUQwWlhvggfAR0KY9ZfMulRVm4X8LGC4fh58AOYsvxoGozZ8CmMMmw5uxenhTmw6uBUrr7tAMX9XbqtHe10HPBMHUfVmJyZfdGVsoxgl/B2RXpgLS+AZGRd1LIlAImHEDIE3mxheemoYLz01HFcsVparNxk+kXTPfcO7cHz8fQDA8fH3sW94F1bbNshqn5potR4nfJiuAM986Pa0otp0DkoKkfTMpODB7raPlQeuSLfl9oOrqKLoXwqQ6CPEoJWh8GIGwAcP6o60LgUjCMYAY/7vg7fYAojbsynG3p7DOObwD3g/5mjB3p7DuLT6IsWef179FRgoq4W95ACK3toDc4Y2ipHb3xGZDYlAImHEDIF3e/yRwkXn+FCpscUpWPgB4mr9GGPY0vVTGGAEDx8MMGJL10+xqnB9WkUDOacDyPf/ay6xqW1OVIKH6QrwzIcc9zAWmN0wcNE3YgKOFtes2/J2vwKP4QPYGyfBrW7AdFlt4D4LSPwlCok+IlG0lAYqZgB88KDuSOtSxPM2nQTX3Qs28R6arxwDMEcii6WBMYbNh7bDwBnAMx4GzoDNh7Zj9ZwLFfV35bZ6YE09urN2oKEJ6O1uhSMDG8VI4e8IIhIkAomMIZUmL8FRQADg4UuraKCw8RJisubiAvWMUQAh3TM/vxeYGg3c7rTZ4V6XB8uy9ST4koDq+ggp0EIUUC6cr+6Cxd6OyeoWnFptgGXZcpQWnIPTH7jVNi1AcBQQAHjGqxINFPCWWJBdVYSS0TFMKP7sBJG+kAgk0hopunuGRwEF0iEaGF7vxzMfoJ29iOQEN2PobzgB56L5ADh4Syxnj8ijwe4J4h51AsiHe9QJg4FEH5E8WooCSo2jxQXu5PshjaWEtYb3qVcSEU54FFBArWigwECeC7koVPx5CSIaWhwUnyi6FIGPPfYYHnroIfT29qKhoQGbNm3CZZddFvHYXbt2YcOG2ZGaEydOYHHYXK90xW7PhXMyK2rKgNp1eVKTTLpnLMKjgAJ6jgYq0exFbEMFpRCif+7cPRheY0DBkjUoqb9QsedPF6I5PlZcDJalz4shhHZIdT3qHTBieNQc9X6l1x3AX2dc0HEoMFZGy42lwqOAAmpGA3PLatGODrCevSh7sxP2jus0VxuoNX9HKIOWLnqOTTlgLkzMHt2JwJdeegnf+MY38Nhjj+HSSy/Fr371K1x99dU4fvw4amtroz7u5MmTsFpnnEtZWZkS5qpO34ABd9xxBTye6IuT0MRF70JQjpl+QhSQAweG2e8PBy5iNPD90ffxze7vaGqUhJJdPhNtqCAnwhX4nJ69gcHu5sWLUaLRTZgWiVXbx7wMaEvj8DGhCFI0g7Hbc/H1O2o0se4AMxeeLGMn0NfYCW51g6YzDYQoYCx/Fx4NfLenCfed2IIflN6O1TXyXFQrt9UDtnqcAeAq6kBey+8w+WKrZjqGasnfEUQi6E4EPvLII/iXf/kX3HLLLQCATZs24W9/+xu2bNmCjRs3Rn1ceXk5bDabQlZqh+ERQ0wBCMw0cdFaAxcxyD3M3cPc6JvuiegQAYCBod/dAw9zw8xl+29jDM/1PIe2qVZNjJJQY8RDog0VpGTwrWPgfP732zjpQJ69PXAF3rLsas1egdca1NSFUAqp0kBHRsyqrTvh2HceQX5/K9y5ezC6wgDLWu2vPR7eg75xe2x/NzEID++B2WgGYwy/aNqGruku/KJpG1bNbZTV182rvwIOHMZEeTcWnBzCRx1dgAZEoJr+jiBSQVci0O1249ChQ/jud78bcvtVV12FvXv3xnxsY2MjpqamsHTpUvz3f/93xBRRgenpaUxPTwd+HxkZAQDwzAueeVN4BcrDiwzu8cwn+rXJcc7Zj/WG/BuM1zkS+L+lOHvWY6QkizNie8OrGPJGz/0uMpUiizMGnn/f8JtonWwF4B8lsXdoJ1bZ1ktuWzw4p99mhplGLzyL7YCE++MdFw+xj/d/RqRxivbdR4EVQOnUfhTmn/3+ZgNNi9uQtXghckvnorRgoabqbwQEm9S0zV/bNwMrLg49wBvZNi3YLgZeX0u3YkT3d8mv34nCgYe5uCCltSCRx4pdd5JZx5ynhpB7+B2Y3d3oW3ISpvp5mLP8av9xUb4jWvkOZcGEX1/7CzinhqMeU5xjQxZM4H0Me7oPhYyR2N15SPZU0aIFjehx9MFnMIAZmSSfGT36O6lsVwN2Ni7Bc0YwnlP9cy+W8O8pz5/NhNEIzAfwXsFGcY/RlQgcHByEz+dDRUVFyO0VFRXo6+uL+Jiqqio88cQTuOiiizA9PY3nnnsOV1xxBXbt2oW1ayOn6W3cuBH33nvvrNudnkOYclsiPEK7DHsLAayPe5zL8z7s7ugLfzC+3FyYTLFTTE0mH3y5B2B3T4q0NDIOz/7ZN+bP/NelRBYaBxSYot/tRQ+63T0A/FHAX3beDwMM4MHDAAM2d96Lmtx85aOBQe9Tos1e2j3HU3rqbk8hgLkijmtFjsjPXVxW+P85veHisDsuhXccGB0HRjXe9ab9mEfFZ88P/TXBFM/eZjVtj8/EhLb/9moRzd/1e45gVCl/lw+JGlKJaxwidt0Zzc2FyVQV19eN5h7HacHX1QBDNXUA6gBcCh8guvOnut9/gULkxHgfJwCchhuMMfzso+0hvu5ne7ej8mPL5Pd12Vfg8DIA4DHiPpLy6XTp786Squ2qsM4GAGgvXgGc0b5fDmfme5qvsVKImYVUrL/TlQgUCF9gGGNRF51FixZh0aJFgd9XrVqFzs5OPPzww1FF4N13340777wz8PvIyAhqampQbLoIBWb1Uw8SYVDkX7jIdD7KzOKu+pZVA795ckjEEPhGcU8eAZ554fDsh21sCTj4HXBw1E+r7BvaFYgCAgAPHq2TreicHJM1GihE/YDUxjvwzId2z3HUmZamNHtoyhS9MUMw1aZzsMCc/CI6+NYx5Lb9FV0LTiOvphzu6utR12CCwaivBiW8j6H9mEcR2+NG/BKE9zH0NntQdYG23/exUQoFRiKav6swnQur2Sb783NOhyQjaXjmwyl0izpW7LqzoBp49ckuEQ0/zgHgjwLO7dqLifKP4DxvDooXni/OdgW//1Kxp/sQWt+f7ev6So/KHg3sOPUWzt3F45T3MpSua0j6PHrzd8FIZbsaON94B0PrbKhz7oejsQplhQvVNkkU4d9T96gzZf8pFePTfr9usvrtGRfp75ISgePj41iwYAEGBgYwf/58nDx5EibT7FDJ1NQUrrzySuzZswdmsxl/+9vfsH79+mSeEgBQWloKo9E4K+o3MDAwKzoYi5UrV+L555+Pen92djays2cLDgOXBQOnL91sEOlPDJxR9LEAMKfC/xPjjGd/EkOo8ePhA/KBvGKLbt5zxhge73444iiJx7sfxmrbFZJfIZ2ppTFIWu/n/zwk71jEPjbZ53G0uJC3+xVk247CsWwcltXLMHfB5Tj9gRsGI6ebjVQ4ctkeXN/HlYfW9kn1bAYjB4OGu4Ma9LGMKE50f2eUfe31r18GxTexiaw71RVAdUW83Cr/uThmgGFsCMYyH/jyvIS/y3pZuxhjeLT52YhjJB5tfhZrai6SNRrIcT4YfTw4HyfJZ0fr/k7pc8oJN9QP3mIDABiYD5yB6eIzH4zwPTUYoJmO2JwXIZ1BDSI/Eonv0gHk5eXhe9/7HgCgra0N27Ztm3UMYww33XQT9uzZA47jsH379pQEIACYzWZcdNFFeOONN0Juf+ONN7B69WrR52lqakJVVVVKthDS4XUMB34Af4MXPUT9whFGSQQLQCB0lIRUcA57QACaS6xpPVzZvvNI4Mf56i44X92FnDe3oGtNE1zrcmH59NWYp+GOe2rgHnGE/LDSksAPQWiJdFm7hAtTvZ730VY1qrY5siKMkQgWgEDoGAklME7qf05bpuHYfQLZnW8DAM7kDKhsDZH0Zb7bbrsNjzzyCDo6OvDAAw/g5ptvhtk8ExL/1re+hd///vcAgIceeghf/OIXU7cWwJ133ombbroJF198MVatWoUnnngCHR0duO222wD4U1u6u7vx7LPPAvB3D62rq0NDQwPcbjeef/55/OEPf8Af/vAHSewhkkPurp5Kk+woiURQo8unmgQPdq+Zf/bGAmCgYBgdiwZhWlpP4i8I6uZJ6Il0Ggxv33kEllM70NfYCSwshWXZcs13Ak2WZMZIyAFXYAHSW2unFcK4lOyxE+i93J+6OG/V53UXBRQQLq7qnaRFYHZ2Nn7wgx/glltuwZkzZ/DMM88EhNgvfvEL/PznPwcAfOMb38C3vvUtaawFcOONN8LhcOC+++5Db28vli1bhtdffx3z5s0DAPT29qKjoyNwvNvtxre//W10d3cjNzcXDQ0NeO2113DNNddIZpOWKbTyMJl8cecEFlr5qPdLgdQD3LVGMqMkxKJH8Vdk9cFs4uPOTSqyRk6zCm6vPrzGgJHyUnhLhCYVRbCWnZ+2m6xE0Jrwe6+rCQ/t2YLvXHo7LpmbfE0wkRlIvZ5Zre6U1p1EES5UVY6/gzOXD4Fb0pD2F6YSHSORCaTq79IdQQBOFTVhfMUUCtasw+gZta1KnXTwdykl/H/lK1/Bgw8+iI8++gg//vGP8dWvfhWvvvpqoMj885//PH72s59JYmgwd9xxB+64446I94Wnpt5111246667JLdBL1SW83jssZ0wTi6Pmjfub+IifZvbdBd+wZgN2Xhu2V/g8jrAmA8DnhMoNy0BF/SeF2eVwmwQLwD1KP4Eqsp9eO2pnpgNFTwe/3yl4GOGO0aQ27wLPq8dvsYWVFxTl/abqkTRmvATYIzh0f1b0TbUiUf3b8WK6gtUnY9JaBe5ooBlZZPY8WQnhkejiw9/IxfpNuNVtVnI7/GCKy/NiLXKbDTjN9dthuvsGAmeZ+hu8aC63gTD2cYCxbmFiglAbqgfzCa+J4QcxPN3g06/OAz3dwJSfya1SGmFERP1VeAXWlFUuFB3HUHDSRd/l5IINBqNuO+++/DFL34RnZ2duOOOO/DCCy+A53msXbsWzz33HAyGpMoOCQkpK5tEmdmbUOOXZMkk4RdOZXY1KrOrwTMvCtxuVJvPTbixgp6FXzhV5dEdW++AEdfeMifCldMqAP5uvuY9Xrx2w24AU7LaqQe0KvyCebf7MI7b/TPDjttbsK/rMFbXyNslkNAvcq1vVeU+VFcouMEc8383ZzIV0p+q/DJU5ZcB8HdMzOl2Y0GJWfHUPl+BTdHni0U0f9c7YMQ/3lkRN0r42lM9aS8E04l9/Uc04+/GphwhTWESIeXWX1/4whfwk5/8BM3NzXj66acBAA0NDXjllVcidhwDgO7ubvzud7/D66+/jg8//BB9fX0oLi7GpZdeirvuuguXXHJJqmZlDH0DXMxRDQUFPIw2+Z4/k0WflKST+BPD6f89Dbcn9lwltzsLLqcZVdWZKQL1IPwEGGN4/NBMt0ADZ8CWA9uxaq68dUGE/kglCtg7EDmSAvhb5o/m5mJBddKnTwihEYzDdhQfLvPABEpPV5IDBUdQcWwUk6eLML7m0yip1+b4LteIMaYABAC3xwDXiJFEoE5gjOGx479LC3+XsgjkOA633norvv71rwMAysvL8Ze//AU2my3qY375y1/ipz/9KRYuXIiPf/zjKC8vR0tLC/70pz/hT3/6E37961/jC1/4QqqmpT19AxxuvKUQbk/0D53ZxPA/j+WiTELHSMJPOjJN/Am1ASVDkwAiz+nMZNyjTgQnT2hZ+AXTPNqM44Mtgd95xqt+dZTQLsmsddGzB2Ywmarw6pNdqJY5O9C+8whyOt/EtK0DrnW5sC5bSzXKCjKv/goMlNXCUXIARW91w3KgEPaOc1B2xblqm0ZkAHt7DuO4qy3wu579XcoisKWlBT/84Q8Dv4+Pj0eNAAqsWLECb7/9Ni677LKQ29955x1cccUVuP322/HpT3867nkyneERQ0wBCABuD4eRETOQgggk0SctmSb8BIQOev2NnegdOw/YobZF2sA94gDPA0A+AP0IPwHGGF7ofSHizDC9Xh0l5CGVKKCYiIrH448Uxp/rlzyCAMyZ34+BxnLUrbxOtuciolNuqwfW1KM7awfKmw4ip60T9p0gIUjISqwZmXr0dykV7A0MDOATn/gEBgcHUVLi37iMj4/jgQceiPm466+/fpYABIDLLrsMGzZsgNPpxJEjR1IxjUiB4Ll9IbP7zv4QyZFJs/2CcTWdxNTWbTCPbIe9sRPc6gZUrP2U2mapSvAMPwBgxcUh/+qJd7sPo3WyNeLMMOHqKEEIpMO6VzMfyD5vKUyLF6ttSsZjWrwY2ectRW1DgdqmEBlA82hz1BmZevR3SYvA8fFxXHvttTh9+jTy8/Px97//HZ/5zGcAAL/61a9CxjQkgslkAgBkZaUcpCTO0tWVj5OtWTjZapz10zfAxRV9JPySRxB+nMOOQ1kf4Iau63Eo6wO1zVIEbqgfzld3wXzgDykPdu/tzsHxI9aoP73d+viMhgi/BAe4v9fVhBte+hre62qS08SEbHm38zAeP/QsOES+8smBw5YD28GY9N2HCX2h1FzAtk4TjreaZ/30DkTvVJwMrIRERyz29TThUy9/Dft61F+v9EbvgDHiZ1iuz7IW0dLnJ2BL92G80PuCpvzd2JQj/kExSEppeb1efP7zn8fBgweRlZWF3/72t7jwwgtx77334pVXXsH09DTuvffeQKMYsXR0dOB///d/UVlZiXPPpZC+VPz85xdHvc9s4vHiQ6OYv8ikoEWZAed0ADDAXGIFYwybjj2I01Ot2NT5IFZaL9VVykA8hHlZAGCc9C9KFns7uOoW2G8wwFSR/GD33u4cXLvhMrinY8y6zPZhx/++DWiw7XRwg5dkUz211I462JZf7n8GA2OO2DPDxjJrZhgRHSWigHc/FLkgMNUOjMIaZzm1A82NneCMBsyzpf9IiGRgjGHTwa04PdyJTQe3YuV1+myfrwZial/NJh47nuwEbMrZJQmjo/CJaFChpc9PiC1NWzHo0Z6/S7YzKJCkCLztttvwl7/8BQCwZcsWXH311QCA8847D5/73Ofw+9//Htu3b8d//ud/4mMf+5ioc3o8Htx0002Ynp7Ggw8+CKMx/a90aAG3x4ApYy4Ar9qm6B7hSjcHHsgHzMUFgdmMe4ffxrFxfwTw2PgH2Dv8Ni61rVPNVimx7zyCgo5DmLYdhaU4GygAWEEOOhYNIqvECsuy5Sk1TXA5zTEFIAC4p41wuczQSjxQCuEXzL4u7YxfCLblw8FTuHftt5HfV4XyJSZwEVrEKzkzjCCikUoHRvvOI8jvb8VUURNcl0/BsvZqagQTg709h3HM4V8jjjlasLfnMC6t1lfDDLVIpJtojk0Zm5RGS5+fYFtOOE/hG7XfwMULS4AiW8Tj9ebvEhaB99xzTyDC9/3vfx+33HLLrPtffvll+Hw+fP/738dLL70U95w8z+OrX/0q3n77bdx666246aabEjWLIFQjvNELz3whASnGGDZ3/QwGGMCDhwEGbO76GVYXrtXd1VFHiyvk97zdr8Di6UHf2Vq/6bLawH1WIOpGqajYDXO2L250r6jYDZdTHwuqXCMdGGPYcmC7JtpRR7LlpeOv4IHqBzG3NBuGLH19ngll4Bx23dYCOlpcyO9vhbH+BCYW5cO8+GISgDFgjGHzodA1YvOh7Vg9R771ipUUgH3QLsu5U6XI6oPZxMeN7BVZaTwEoM7nJxFbXrW/iv/vwh/CUFaqqC1ykZAIfPrpp3HvvfcCAG6++Wbcd999s45paGjAF77wBfzmN7/B7373O9x999244IILop6TMYZbb70Vzz//PP7xH/8Rjz/+eGKvgCBUQmyXz+AoIADw4HUXDRRGO9RW+EUbmxzG9EQPnDa7v9ZvWWJXxquqp/Dam+/EFHhFxW5UVU9pWgQqMcsvOPIGqNuOOqItgy1otjZjLmbmu77X1YSH9mzBdy69HZfMbVTURoKQmtIKIyYsWcgpqUYJCcCYBEdOAP8aoXY0R02qyn147ameqPMtAb9QpBmBfrT0+YlkS+tkK94dOILVFRsCt+vZ34kWga+//jpuu+02AMCVV16JJ598MuqxP/zhD/G73/0OPp8P//Vf/4XXXnst4nE8z+OWW27B1q1b8aUvfQnbtm2DwZBSw9K0JnxUQ77PCLOpIO6YCEI6ogm/fcO7sbH9Htxddw8usa4K3B4eBRTQUzRQaIne33ACjqKZZgje4mxwFXWoS7LWr6p6SreD4KVO94xGeORNQI1ooGALBy6kJsLAGfBC7wu4lq0AwGmqfpFQH6kawoiJqMiNr4yawQD+Rhkb392Cu1fejksqLgjcHh45EVAzmqMFqspJ5IlBS58fwZZZ/g4GbDn+O6xath4cp66/S7UpDJCACLzmmmvg8XhEHbt48WJ4vbFrzIIF4I033ojnnnuO6gDPEi72BMK7dM4vAV5+yo6hEQPaOo34/oNFSpiXkcSK+jHGsKnT3/Tlgfbvg2MGfGXuP2FBybmzooACeogGCtE/c+4eDK8xwFQ+H9UZPBNLKeEXTHjkTUCNaGAsW1onW/Fu92FcWnexpuoXCW0gRSpocETldGcWvvtgmQSWiYNNRvbJmUhwo4yv/+8P8cv1P0QFlgGYHTkRyPRoICHuO6Slz09UW8Dj+FBbwK+p7e9SaQoDpDgnMFl4nse//Mu/YOvWrfj85z+P559/PiMFYPhohlgjGqKNaagq57HkHC/m19BVJqkJHu8gzPWLtJkJFnrtU21omz6F53qeA8/z2Nz1s5jthDd3/UyT7fMH3zqGnDe3oL/+7xg91wDz2jUZKQBnzfNLYKRDqgRH3iKhZDtqMbY8fuhZ8DwfiFwCMxFLLX7GCfmReixEVbkPS89xY0GNCo3MbBbln1ODBG+O3T4Pfrz/MTDGQiInkeDAYfMhWgsymhjfIS19fsTYsuXA9rTwd6oM47vvvvuwbds25Ofn42Mf+xh+9KMfzTrmM5/5TMxaQr0QLaonoIUZfG2doQLcZuVRVc5HOTr9EVvrB0RP92ydbMU7w2+ib7ondjthdy88zA0zly2N8QkS3ugFE/3AYsA0+mu4LveBW9KAuUmme+oZNaJ+4Xh4D/rG7JpoRy3KlvFBvNNxQDP1i4Q20FJDmNOds7c8seqxHC0u5O1+BdN5/WjLHYMFtRGPyxQipcidGe1B00gTaviL0DceZ42YkHe9EsYTEfrDw3tU//wkZMtYevg7VURge3s7AGBsbAwPPPBAxGPq6up0IQLjiTxAG0IvFuFppGYTw8tP2TNKCCYi/IKJlu7JgcPjPb/ErxtewZDPFeGRfoqzSmA2qCMAhbbn+fm9wNQoAMCXZYRr8ZUY/mwtLOXVs5q99HbniGrmIiWiO4kWuZHKHkALwi8Ys9GM567fDFeMNBql2lGbjWZ8Y+Ut+MGbD8+67zurbkel4xx8rLEU//l/P9JE/SJBRCJSCmm0+YHC+jhtO4q+dbkpj7pJB6KlyD3Z/SQ+Z7gEv7luM1xT6qxXXIEFGJX2nL0DRsUbuiTSTXRS0mdWF7PRrOrnJ9yWb6+4BXe/Pdvffa36a7js4mUozrPhO2/cr3t/p4oI3LZtG7Zt26bGU6eE1zkC79TsL6YWRJ7NysNsYjGbxJiyGP7jlmH87HFbzHO5PRyGRgwZIQKTFX9A9Cgg4L9SdGz8CNqmWjVX8yfU+uW4O9HfcALORfMBcPCWWMCYERgHaheugyFs5pvYoe2vvfmOpEJQbCfRisopnE5QBCrR3TMVKvPLUJmvXO1TNBhj+M3RVyI6vNda/hcPVF+FtqGjmqlfJNRH6lTQYMRslE1ZDN+6xYmfPB77Ox0+PzB8fTQtmo+6DEyFDydaowwA6HX3Yk/3IaydtxxVGlivpEDs0PZIFxBSQWw30YoyH067ox6iS6ryyzTx+WGM4fljkf3d/zn/D7eUfBrv9TWp6u+kaAoDqCQC9UpucTYseeoLvkhUlfOBJjHBMObDgOcEyk1LUFTIzbo/E0lF+AUTLQoYeJ6zNX9a6gBq33kEllM74C5zYnSFAQVL1qCk/sLA/byP4fQHkT2L6KHtTrPk0UAxnUT5BPyw1qJ+Widmg5rBFjQVNOH33b+OuEEEZmoo1Lo6OjblwPjUhOLPm+nIlQoaaaPMMx+6Pa2oNp0DA2dEkdUXcyMdi9IKIybqTXAvDF0fM5loUUCBnx54HJfVXqwZX5cqiQxtlzoaKKabKK+fsjMYxgaAgnwA+miwFKtBTetkK/Z1HcKvmp5T3d+l2hQGIBGoC3oHDDHFm1DDJ/wEwzMvCtzDqDZ7YOCyMlYEhl+VTnVzIkQBoy0CgBAN1EYHUKG2xWP4APbGSXCrGzAvw2r9SPglR7TREAIcOLzQ9wKcbFCR+sVkroCaC0vg4bR5AS8dSSUKKDYFL3yjzDMfctzDWGB2w8D5H5+sCAQAjI4C0E49o5rEigIKnBntwZ7uQ1gz92KFrSMI6Yj3WefAYcuhZ9EvomZQifrFVCERqHF6Bwy4/paymGmemVjDJxapon7heJg7ZtOXYJSOBoY3e+FOvo+cnr2YLnPCvS4PlmXrM6q2hcRfaohpCuPwOPDMZ36GCT56lYqYeg4xAk+Kq5+E/CSz3qqVghcNmgvoJ16jDIFfHNqGS6svUjwa2G7sQBVcsNiz4Go6iaLGRYo+PxEdR4sLXHcvpgydaMu1a765kpimMPYJB7Z+5hGMuaNnlyhVv5gqJAI1ztCIIe4w+Eyq4ROD1FG/SJgN2fjNsj9jwN2Hr334Txjnx6Ieq2QHUGGwu604K9DsxWmyo3eDB6al9UkPdtcbJPykQ2hQ45wYwg/efBhnhjvBMwYDx2FeYQ3uXfstuFvzUFM4B4as+Ju/eEKPRF7momYKXjA0FzAUs9GM5659GF/a8Q0Mu6P7OvukU/HoR7mtHgMAeur64S1oQtWbPbA7r0PZFecqZgMRmUB3XdtRnF6XC66iAuW2evA+7eayCg1qnJND+N47D6NtuBM8GAzgMK9gDr5e+U0svqgcVYXq1y5KAYlAIm2QK+oXjarsOag0V6E6ey4+mvwQC3LOwcaFPwcDH1KbokQH0PDB7iPlpfCW5J29Nw/Wstq0j/65R50A8uEedcJgIPEnJZX5ZTjt6kDbUEfgNp4xtA11YGh6BLXmebMeE0vskdBLX+RsCKMoNBcwhFprNX7/6f/Bv/79v3F6uBMLCmuwce13wHigu8WD6noTSvNsqkQ/ym31wJp6nGnZCRfXgbyW32HyxVZMLF+Pkvqi+CcgJEe4IN237AS41Q2w6GgPUpVfhtPDHTg1HOTvwNA22o3RslFU5C1V0Tq/b5XKh5IIzBD6BowYHs2aNRNQ7ygt/MLZO/w2Ppr8EABweqoVQ14nVhWuQY6bYYF5WaA2RSz7hndjY/s9uLvuHqwqXBP3eEeLC3xHFyyndqC/sROmRfNhXrxYN4utFMzq8FlcDCYiIkWIR6gLjNQt7X8OPoOfzH0E49NOcEEzvEnoZS5qzwYU6gojzQWMh2FsQAaL0oPTw504PdwZ+P/Q9AhWVV6InG43FpSYZ3WUjse+niZsfHcL7l55O1bNaUzZvnn1V8CBwyg2j8DSYkRH/IcQMuBocaGqNgsF1gKMLJqPap1lIAl1gZH83Qu9L+BatgKIMkheb5AIzADs9lx8/Y7KuGmlAmYTg82q7dRStcUfMHtEhAEGbO76GVZaL036fJs6H8TpqVZs6nwQK62XxqytCE61cF1uALekQXeLbSTEzCIsKegOuY2VloB5GdCWZj2zNUKs7qAnHW1oLmzGVdZLRKWDEoSc9A4Ycd2tsesKgxFmroWgo06GShG+MTZwBmw+tB0rr0lOvDHGsOngVpwe7sSmg1ux8roLpKslHJV4YKCMqDGLUCnYxCi8JfqLqMfrDvpu92FcWpceDZBIBGYAIyNmUQLw/rtcmF/jC3Qb1RpaEH7BhI+I4MGf7Qb6DqosiaegBJ8vvKtocLMXw9gAWHcvcnr2omtDP0xL65GrQKqF6KHtxckLMVGzCM0+/PHVP6Py3Nykn4eIj5DOyRjDo+89Hbs7aO8L+HgaXR0lkoNz2FVfm8XUFQLAT+6yY0GNN2STrbcmFkoSvjHmGY9jjhbs7TmMKiRefxd8PuE8l1anPldNqmY+iQxtTxatNUIixHUHffzQs1g9T/kGSHJAIpAIML/GhyXneOMfqCCc0wEOMwuk2hsMgWiD4g0w4NHuR/Cjc+5L6XxCVHHRwLnIO/gWzO5O2CwznRcHbSOw32CAddkViqV+ih3ansqMQFGzCN1GuFgFKjGS9PMQoUSr3zMXlsDtdcM+6YrZLW3QMwgP74UR2u+GRhAAsKDGi6XnzFywsu88goKOQ5isbsHpiw2wLFueUWn1sYiVHvdo87P40dwHUzqfEFVcPUedOaKREDu0PRVxppVGSLIwJs0wc6UR0x20f1y98Q9SDYkXIBFIaBLO6QDy/f/XivALJtqgeH808AiaR5uxMPu8pM8nRBWbTvwIK3INGF0hNHsRUivyVCm0FjO0ndA+kRxJtBo+c5YZz9y4GUNROibyPgb+mAVmo0lSGwl9oeeGMHpuYqEEsdLjjjla0FzYjIW4JOnzBUcVpYgGSoWYoe1EdLgCCwB97ReE7qCuqRl/5xkfArPZwHwMAyc8WHRhmarjH6SstycRqHFsVh5mE4s7J1DrNXxiCN5EmIoLADdgLtbenKZ4g+KF9LjrS24SlR0XNarIODxWuxP1l96Cuo9dKeVLUI149X6DA9IvrL1dORiK8Zy2Yjeq5urLUSVKIqIvEhUFZagoiNwSm/cy2M1Ui0mkfsFOiRS8cIQmFvlZ0GUTC7kRkx73Qu8LuF5kOnisqKKU0UAtjPmIV+836BRXtyrlc8pdYyhE1B22o2hdlwtL2XLZnksuqvLLUJU/4+/cZgdYaQl4L0OBxY2KvPTJeCERqHGqynm8/JQdQyPRFwut1vCJIdpMP55p9+pbvEHxgfQ45oZRxFcsalSRY2jBMLrzilCXqtEaQEy9n8kk7d+9tysH16+JU2OY7cPLu99JKyGYqugjCDVQIgUvFnpsYiE3YtLjEkkHjxdVlCQaqIHxHmLq/UxZ0s7LU7PGUBhTlePuDETU02EucXj38XSDRKAOqCrXr8iLhtaavCSCMCje5XVGvJ9nPowxe8zZgEKjF8YYHhl5MOZVVq3VSiSLmHo/j0faESZDYmoMp40Ycpp1LQJJ9BFqImUqKKXgaYtI6XHB8DzDWJu4dHAxUcW08Xci6v08Xmlfo5o1hnxHF/Lze8EvnIL7gjUoqb9Q0vOriVZmDktdDwiQCCQURM/CL5yq7Dmoyp4T8T6e+XDafSTqY+07jyC/vxX5+b1wTw1hcO5pMGOMIuQJ9YqQCW2y6/Rb2Lz/WfzHin/CxXOWASDRR6iLrtd0nTaxUIrw9LhgeB/D6W5x6eCimm5I5e8K8s/OfKRh8UphrcrF8JxCyTq0Ckg9T1LPSO3nSQRmAFarW7W6wmjpnplI+GB356L58JZY8bPpmzDs8Xf+rKpZMetxxbmFaSEAPeNDapuga4JHNjxx+CWcGe7BU+//AasWr9X9VXOCkIpk6wr12MRCb8SLKgLS+jvW3Qs0LpLkXER82MQoWEmdtOeUc55kHNI9FRQgEZgRlJVN4g9P9mF4NHq6hpR1hST8ZiMUSwuD3S1rrw50n5unsm1yM7OQirsim2XywRsjLdSc7YMtxizCvu4cDA/7U3HbWvJE26k1wlM/hCuA7545hJOONgDAiYEWvNdxGCvnza6h2d/RhJ+/tQXfXHc7VtRm9tVTQj601hVU7bpCIjaxoopS0V4HeCZ2oerNCjhfBdii81FSr82IYFYWD683+UZIvQNGDI/6RfPpTvW29MZJByBDHz+x8yTlihZqJRVULkgEZgiV5T7MqZD36one0z33De/GxvZ7cHfdPVhVuEaSczpaXOBOvg9Lz170NXaCW92AeWlQLB2P4CtowiLKbOI+Ew8/04TS8ugiL1Y3T7s9F1+/cW3cOkCtMj7tBBc0qjM89YMxhif2hc7XemLfdlxSG1pDwxjD43u3ot3Vicf3bsXyGuWunhKZh9bW+0TqCg1jA0ChDRiS1STNka4pduW2esBWj4GyWthLDqDorVdhPuCCveMclF2R+FB7udn8fTtKi6NfgI91wcJuz8XX76iJWweoFP5ounSInSepZrRQKcamHLKUfJAIJFJC78JPgDGGTZ0P4vRUKzZ1PoiV1ksTXkQCzV44HqgBXH99B3l97Zi2HYX9hlxYll2d9rOnIom/RCktd2PJeckNgh8Zid8IRksEUjx9AJAPk7UYhqzon7v3Og7jxEDofK1I0cDg42JFCwki02HdveB7P8CB+lMwIb3XZ4FM2DSX2+qBNfXoztqBhqZO9HYDjpa5mosIlhbzWHpOciN2RkbMmhCAzld3IbtnL/rn+NBrnEIuaiU5r9h5kmKjhYmQCamgAIlAIgnSRfgFEzym4dj4B9g7/DYuta0T/Xhh2LDNMglflhFDNVei2HAMp9Z8BNPS+rRolRwLKcRfphApzZP3MgCxNwLhUUCB8Gig2GghQaQK57Dr1gc4WlzI2/0KjLajOL0uF6aK+ozI0gDk2TRrFW+JBdlVRSj1GtGhtjFphvAdyvb0oHdDP0xLpfsOiZ0nKTZamJQNGbCXIRFIiCKd6/zCh7UbYMDmrp9hdWH8hhvCbBxz7h44GsYxsmg+PMV5wDhw5loDrOVXpG30L1HhZyt2w5ztizuzL1a9nxwo8ZzR6vsSITwKKBAeDRQbLSSITEW4aGdc0A/XubmwLFuetut0OHJumrWM0sPjk21QJDdSPacgAKdtR+FalwvrMmn3OmLnSYqNFuoZOUZDCJAIJKKSzsIvmPBh7Tx4UdFA+84jgU6fWUUFgWYvvI/h9Adu1C5cB4MxvlPVW21GslG/qrlTeHn3OxhyRu/8FqveT0ruf/R9zK8fl/U5pRB+AkJ0L9Z8rSf2bceKmkZR0UKCyHRqGwowfE4JLA0FGSMAAfEpdnKhhr8zFBUAUFYEaqlB0U/usmNBjVfy5ywuM2KysQ7Ty2ol/Q6JnSe5qqpRVLQwUbSYCirXCCgSgUQImSL8BMKjgAKxooHCFTCz7Sh6N3hgWtqAuUmmQOipNkOKlM+quVOaGMo+v3486brDWEgp/ILx+DzoH4szX2tsEHvb94uKFhJEqug5FRTwt7MH0j/dKxixKXZyPr+a/k7puYGJNCiSkwU13qTrDqNhGBsACvIhh7gWO0/y7a79oqKFyZAJqaCATkXgY489hoceegi9vb1oaGjApk2bcNlll0U9/q233sKdd96JY8eOYc6cObjrrrtw2223KWgx0DtgwNBI9LQAKUc0JEqmCb9gwqOAAkI08K/HX8cK82oA/kWPdfcip2cvujb0I6vECuuytQlfAQu+EiosVoB2azOo3i82cgm/kHNmmfHMjZsxFCOlyZZjxd2v/yhutJCigYTc9A4YNREBiQcrkaGnvYYRm2InJVrwd/uMB7DAUIbc5n1wASii2YEp4Wo6idzmfeiubsFwrgEWiRrBCIiZJ1mUY8U3/i+2v0vmwobWooBypoICOhSBL730Er7xjW/gsccew6WXXopf/epXuPrqq3H8+HHU1s7+ILa1teGaa67Brbfeiueffx579uzBHXfcgbKyMnzuc59TxObeAQOuv6Us7rD2l5+yKyYEM1n4CQhRwKiLCOPwjP0+fJH7Mjj4/3YDc4bRsWgw6QLo4CuhPz/wDDiO02RthnvUCcPZaxaJCr/erhxNpHzKiRLCL5yKgjJUFESfr+X2ukVFCz0+D8xZ0gxjJjKTWLMBeweMuPaWOXFroV57qkcTQjBTEJtiJ6X/0YK/m1d/BQbKWnAaB1B4sB/mAz2wO6+TbFyEXi54SIEw8ir77IVw09J6WMqkTQUViDdP0u1zi4oWengPzMbE/J3WLnbLub/QnQh85JFH8C//8i+45ZZbAACbNm3C3/72N2zZsgUbN26cdfzjjz+O2tpabNq0CQCwZMkSHDx4EA8//LBiInBoxBBTAAKA28NhaMQQEIFyRQ7TsbNnsniYG33TPdEXEY6hzzKM5o9NwFAmXDEugrXs/KQXveArscedrSH3aaGg2T3qBJAPILmFsLcrB9evuSxu85eXd78jixC0WuVrPqOG8EsEMdHCotxCEoCEJETzH64RY9y29W6PAa4RY2BzrPRGWq7B1lpGbIpdMpvmaGjF3wnjIs5U7ITneAuq3tyByf5WjK9YC9Qkf161L3hYrW7Fms8EN4Kx3yB9I5hEERMtLM4tlOyznK7oSgS63W4cOnQI3/3ud0Nuv+qqq7B3796Ij9m3bx+uuuqqkNv+4R/+AU8//TQ8Hg9MJtOsx0xPT2N6ejrw+8iIv3aIZz7wzDvr+HgwJu4qFzt7/r4BIz53a/zI4R+e7ENlnIWF9w8gA3PawWCAqbhg1n1aRLBNThuzuCz8uuGPcHqdAICRrlFkf9gENtkBV30/smqqsOiia1GZVzrbPl9kRxp8X/gx0eoxghGujq6saFQ0GugXfwB/1ixfYREM3uivMRouuynunD73tBEuuwkVlZMJnz8WvI+hrGwSv/u/tzAynB31OFuxGxWVU+BFfJXHp50hv5usxTPPl8T7Ew3hXKmesyy3FGW5sz+vkZ4rnAOdTdi0+3F8Y81tWF4jvmGDVLbLDa/d5U5VkvF3HPioa7PYNdt/fh96B4y47tb4G+kdT3bG3Egn4jOYEeAL8+FjHBgfez1Xgmg+Q0qyYMKvr/0FnLE2zTk2ZMGUkB168nc1Cy6HvWQenIZ9yD++H8aD/UDNyqT3GY5hcRc8HMNARZm0CxDPfCgrm8QrT5zB8OjsfaxAkdWHijIf+CQ/Ws5TQ+C7upHb9lf0ntcF7pIlyC2di9KChUl/XqX6vFfklqIinr+L8hzv9jThJwcex3eX34aVZxsUuUedYMXFQBRfpsT3NBhh/5GMbxXr73QlAgcHB+Hz+VBRURFye0VFBfr6+iI+pq+vL+LxXq8Xg4ODqKqqmvWYjRs34t577511e7/nCEbdloTtHvAUAqgQcdwJFLiHccpRCLdntl3BuD0cPnKchM8mrii3J//shBxlu++nTLvnuLxPwAE5Z9fPnPn5wHx/bant7N0Tp4DTSb5p7cc8Ib83jTRFrMcIRrg6+vKe/Wi0KtkpND/kt95mT5TjYjNwStzjBk54UOCW58PI7KMowGjU+319QHfk5SIC+WG/y/sFcuxP7n1PFcYYHv1oK9onO/Ho/27FQx9bmvCmTC3bxTIxobPFTyGS8nf5iPpV6PYUApgb93m7Pa3ICfi7eTGPdXsMOOpow6QIfyfKZ1xugwsXAlMAzgCjGnGM4T5DegqRg8Ko904gE/xdDTC3BtNBH9Fk9xmJftblYLroA+TE6HUzCeB0Kh/vGv/PyKqzwZRxYHRcmu+M/J/3yDDG8OBHW3F6shMP7gn2d/lAW/zXlez+KHGE/Ufi77VYf6crESgQvjlhjMXcsEQ6PtLtAnfffTfuvPPOwO8jIyOoqalBhelcWM22hO0djRBtjES5aQmqzZ6EjwcAzhmWqnY24sczH9o9x1FnWgoDFztCoyXktHvwrWOB/xsnncgd7IDP24f+ywaQtXghaheKHxIfCd7H0H7Mg7oGU2BEBGMM//X6izGvigpw4PD74Rdx/aUrZLs6KkT+APivfAXZ3tvsQdUFJlHjLcIZNYv87C4xofpcadM0UrU9OOoXHPFTAt7L4NjvQckKEwxZyteDvtdxCK3v+9O1Widb0VZzFJfUikvRUtt2sYyPJp7FkQkk6u84pyPgXyIxZRL3va42nYMFZnfCx0dDrM9w/fUdmPv2B9b73NK5KCtcKMoGuYjkM/SCHvxdNBytRzA8sQhL9neh21eL0nUNCT1eqs9uMsi5RwqO/g0GRf+k+p6o/Xnf0x3q7/pKj2K5dX7IXigSqe4xEmV82pn0XkSsv9OVCCwtLYXRaJwV9RsYGJgV7ROorKyMeHxWVhZKSiLX9GRnZyM7e3Y6mYEzwsAl/pZxIr+gHGeEgWOijzcMj8DoFOqqDDFr/Py260cECkhptzDYvSq/FwWFZ78gFmCgYRgDde6kOn3GwmDkAgvFnu7IXdkiIdRm+Div5PnsQucrrnzmsx9pKTMYuaQ29JzIhZFL8vxiSMT24Do/zqh+nZ8hS773JRqMMTy5/9mQhg1P7n8WK+dflNCmTA3bE8Ggv+VPERL1dxwMMddkseu1sLYnenyyxwnrf1buHvSv8cC0pB7z6teLem6lCPYZekOL/i4efJkFOAMUWT3oGeQS3mtI/dlNBqnPbd95BPn9rXDn7sHwZQbkrb1Stto/NT7vjDE82hzq7x5tfhbPrvsBOJH+K9n9USKMTTnAGZH084j1d7oSgWazGRdddBHeeOMNfPaznw3c/sYbb+DTn/50xMesWrUKO3bsCLnt73//Oy6++OKI9YBaYNAZO8c8mExv7iIW+84jyOl8E/0NJ+AoKgBXXgpviZDqVIS6JOf8iSFeVzYAWFBYg41rvxPoQiplQTONeJhNsPhTW/ipzXsdh0NmC9JMQSIasbqCJksi/i5ZBAE4VdSE8XoDrGvVbWqRzqjt75JhcrQHxskiOFpcKKlXbo6glnC0uMB3dAX2SaZF8zF35XVqmyU54WNSAg2Kps5gNWLXFyqNEnsTXYlAALjzzjtx00034eKLL8aqVavwxBNPoKOjIzD37+6770Z3dzeeffZZAMBtt92GRx99FHfeeSduvfVW7Nu3D08//TR+/etfq/kyotI7YMC37xe3CJlsMQozCAAzzt+cuwfDawzgliQ/2D1Z4nVlA4AR9xjqi+ZJ6ghJ/IVCwm82jDE8sS/y8GiaKUhEQsoLj70DRvzH/dHbwEtJaYURE5X54JcsQgkJQNlQy98lQ1nhQozCjQ+WDqL4rVeRvbsdDnw644RgSPRvjQGWtVen5UWSaM2KDJwBWw5sx6q52vB3cs8GDEZ3IvDGG2+Ew+HAfffdh97eXixbtgyvv/465s3zF5b39vaio6MjcPz8+fPx+uuv45vf/Cb+53/+B3PmzMHmzZsVGw8B+Mc5mE0sbrdPm5XH0IgBXq/6H8J0wPnqLuT07EV/YyewsBSWZctVWdiUbmVM4i8UEn/RCY8CClA0kEiWIqtPdNt614gRXq/8kcBgfGXR6xmJ1NFj637L0gsxVHEGnuNNqHqzB86Tq8EWnZ/2YjBTon8C4VFAAZ7xOG5vwb6uw1hdow1/p9ReRXciEADuuOMO3HHHHRHv27Zt26zb1q1bh8OHD8tsVXSqynm8/JQ97ty/OcZ+DA/lABoLSWsdYSETME46YLG3w3h2ng1X0ZDUYHcpiTf4VAq0IP5sxfLN6UsErQi//R1N+PlbW/DNdbdjRa2S3V7j2yJEAWMNj6ZoICEgNhW0qtyH157qETX3L9YxhH5Rwt9JSVnhQhiKz8FAWS3sJQdQJDIqmMgFD60hR/RvX08TNr67BXevvB2r5qjn7yLZES9NmQOnqWigUuhSBOqRqvLZw93Dnaq5xAqTS9zVMVMW0+TCojRCrZ+tOGum2UsBcHDRKZiW1sNSVpuWaQ3BaEH8CVTNncLLu9/BkDP659hW7JZlULzA+LRTE01eGGN4fO9WtLs68fjerVhec4FqziWSLR6fB/1jcYZHjw3C4/PQgHkCgPhU0KpyaYe7S+Hv2KQ8LfqJ9KHcVo+BZcBQRYeoqGAiFzy0glAiYxk7gf7GTsmif4wxbDq4FaeHO7Hp4FasvE4dfxfNjnhpygF/x3tUjVIrmQoKkAhUnEjCLxl+8f0BTS0sSiMsZDnuTjgaTmBk0XwACDR7sZalf+G/lsRfMFVzp2QVeeEIi6Z/3m8+TNZiTXSpDE61VDu1Mpotz9y4GUNBm+MPek/gkbceC/x+zeIrSQASqiOZv7MlPueXyCzKbfWArX5WVNB5sg7Fn1w/63ipL3jIiX3nERR0HMK07ShGV0hb+xecannM0YK9PYdxabXy/i6aHeFpyp7xIXzgaMFP33828NhPLrpSE2nKSl7AJhEoM1KJvnBKi2PP3klnhOifu7ADoysMKFiyBiX1F6ptlmJoVfwpTXjKJ+9l0EqjpPCGK2o2WollS0VBGSoKygLH/fT/Noeky/z+gz/j9tVfgcGgbN0WoS04h13VTtSZ7O8IdQiPCi5q6sf41nYAAMsuBACMVZyDsivOVdHK6AjpngDATfuFT2VeP06u6YFpab2kJTLhDVcMnAGbD23H6jnK+rt4dgSnKU+bBvHAkWdD/N1LR/+Mf1uunr9TOgoIkAiUBbmEX6bjaHEhb/crsHh6YG/sBLda/Vo/JYkm/t7rasJDe7bgO5fejkvmqlt3pgRaqfeLhZbGLkSz5bNb/wnfu/LOQK1ipCYxU95pPHvot/jK8i8qajNBSIlhbEBtEwgJULrmLDgqeKrkAIAxZDmnAQBe1ygKB5sw+WIrJpav10wTmcA8zKIm9NfbkVXkb4TkLfbPArUukz5LKurYBYWjgdHseOrIb7GjdWfgc+MecWBf/xEct8/2d1ubf4t/uVA9f6f0noZEoAREKpgn4SctQqfP6TInXOtyYVmWni2MIxEr8scYw6P7t6JtqBOP7t+KFdXq1Z3JiR6En4CWxi5EswUA+scGsWXPM1hesxkAojaJ2X7gN/ini75A0cAMRY7ZgEpi33kEllM70F03juFcAyyoVdskIgnUrDkrt9UDa0L3GwNDLRh/ezf6Ol5F7ZsnYO+YXVenRJRw8K1j4Hwz74Pl1A70N3Yiq6hAkVEPscYuKBkNjGXHr5p/jSnfNDYd3IoL19UAAB776I8R/d0zTb/BP1+QOf6ORGCSyBXt03O3KTlwfdCCvMPvYsrwAZwbfDAtrZd1sLuWEJP2ua/rcOBqltZaHEuBnsSfgJbGLkSzReBDeyve6/B3To52HEUDCbkuasrp74LrxoXMkUzxHemIVmrOBMpt9cCn6nGmZSd6a1owr+c3IfdPtVXAvhMw1M6VPEroaHEhp+kt4NN1KMz6AwycN3Bf9w0GcBXKzUOONXZByb9TLDumfP7o7TFHC/b1HwErtM6KAgqoFQ0cm3KosschEZgAnNMJbsr/ZZPLKeqx25RUOFpcM79M9AOLAfPhP2Oyehjui/NgVWnOn9KIrfljjGHLgdD893RpcaxH8QdAU2MX4tki8Ku92+Kei6KBhBzI5e8EAZif34szS7syrm483dBKzVkk5tVfgYGyWgTtXmC0j2LyxPsoOLId2buXwXmyDlx1FQCAzy9P+DmC05lZdy/y7O2YLD0JoA5DN1wMzjCzvlsAxfZJYsYuKPF3imdHsD2PHfsdWFbs0TSZFA0kEZgA5uJ8mPPkT/PUU7cpKRDm/OX3t6LEOgYA8BkNcC1uQP9lAzAtqcuIK7iJNnwJjgIC2hx4miiC+NOT8AtGS2MX4tki8KG9FfnZeTGPmfJOY2/7fqxZsFJKEwmNo0QqqFz+rrTCCOvSeejIGyMBqHO0UnMWjVmiywag/kKcWbITnuNN+FjLCHJGzsrE7v3JPUlBPgBgytCJj9acQtaSxcD42RmHRnWEsKixCxPyj12IZ0ewPceH2pBvju/vdnfux9p5yvg7taKAAIlAQmWEK7bu3D1wrDDAAX8BM0MWgAYUrFmHiuJz1DZTVpLp9hkeBRTQYzRQr1G/SJizzLPGLoRTlFuoyNiFcFsYY7j37w/hjKsrxFkaOAMq8spQZilBu6sz6hXdZ/a/iEvnX6KbzxUhDVTfTqiJVmrOkkGIEp4uOQBAugsq1mVXoLTgHJz+QN1u2OFjFyJRnFso+9iFSHYwMNz99kNoG57t7yrzyuCzlKB9KLq/e/LQi7isNv39HYlAQhWE6J9QxMytbkBu0GB33sdw+gM3ygoXqmypfKQy6iE8Ciigp2igVsTf/o4m/PytLfjmutsDnTJTIXjsgtoE2/LumUNod3XOOoZnPE4521GQna+JCCZBSAU/OgTEvuhPaByt1JwlS6SmMlLA+2JHvaIhdYfV4LELahJux57uQzg9HNnftbraUWAW4e8UGByvZhQQIBFIqICr6STymvdh2nYU9hsyq9MnkPqcPyEKGA0OnKajgVoRf4D/vXx871a0uzrx+N6tWF6Tnt1VxdQqVuSXYvNnfhz19SsVwSS0Aed0wlwzV20zUsZbQgPi9YpWas7SBTU7rCqJmM9NZX4ptqz/MYDIr1+JCKYWIBFIyEpwsxfD2ABYdy+ye/aia0M/TEvrYQmK/qU7Ug159/Ae9I5Gn3ul5FWsRNCS+BMI7p6p1hw/JRBTq+icHMaCknkk9AiC0ARaqTlLF7TWYVUuxHxunJPDWFg8T9XPjdpRQIBEICETQq1fbYURLKg+qtv8Iew3GGQZWKpVpBJ/AmajGd9cdSt+8ObDgdvuuvQOnFexJPC7lq5iaVH8AbNn6Ek9x0/qNNNU0FKtIkEoAZschqGoEAjp20joCaHW682Od/Hj9x4L3P69S+7ABeV+f6clX6dl5OywKnWKaaqYjWY8t+FeuKZHwGy2iMeo/bkJ3hepCYlAQnLsO48gp/NNtNe+D0etf9PvLc4GAHAVVRkT/XOPOiF0GJZC/AkwxvCbo6+ELOavfvQGvtDwSU2ldmhV/AmEz9CTco6flGmmUolJLdUqEtrHXJyvtgkEgcq8UrzS+kaIv3ul9Q18aYm2/J3WkavDqtQpplIJykpLCSpqP5b045VAC/siEoGEZAjRP3PuHjgaxmFdtAjVK69T2yxVkVL8CWh9NITWxR8wOwooIFU0UKo0U63ULDr56Fct/W9fPly8Exwf9bCEKDZo83ND6IPguWqEvtH6eAg9IGeHVSlTTKUSlO4Rhyx7L6nQShQQANJ/EiKhCPadR5Dz5ha0V76K0XMNsHz66owUgO4RB9yjTgAAKy6W/PzBoyGCEUZDMJZcxzApGJtyhMz5S0YA7u9owpee+xr2dzRJbV4IgkgLdohAaDQwWYIFJjAjLOP9bd4ffR//36//NeS1RxKTUuPkHXF/8nJKYvz4P+d5OcVxjhP/E88egohLAUUy9U6weAlGEC9q+jsp2NfThE+9/DXs65HX3wlCLZK/E4RbMoT/fRL5u7w/+j4+88q/hrz2SIIyUYLLb7SMVi6QkwgkUsLR4sLU1m0wj2yHvbETln9oxNxP/XNGpHsG4x5xBK4+ySH+BIQoYKTFXIgGKo0U4g+YHfWSy8EHd8qMBAdOlGiLRrjAFCMsGWN4rue5kNeerJiMRixBFU+UKU08W0gUErFg3b2YGu/EPuMBtU0hUkAu8aIFwqNecvo7oVNmJIQOq8k8f/jfR+zfRfB3wa89FUEpIAhAigKKh9JBiaRxvroLOT170XbOKeReMAeWZWszUvwJyL3wCFHAWG2PlRwNIXXap1KdOsV0ykx2Jl6yaab7Ow+jdbIVQGjEL5maxWiiSA0xJweRXoczzLFSOmnmMvX8C+AqTuKj+WMwVdRjXv0VaptEJEG6j4dQqlOnXB1WU0kx3dsz4++CRWMqab96EIACWokCAiQCiSRwNZ1EbvM+cNUt6N0wBsvSxoxztEqKPwEP70GfGPEic7tsOWr+5O7UGYycnTLDm80IxBJwjDE88d6zMMAAHnxIxE+MmIwk+tJF8Ikl/PWSKMw8Bt86BqwC+pceBDe/MKM6UKcj6TweQs5OneEIHVZdU9H9XTKdMsNrNQXiiTfGGB5tDvV3QsQv2ZpFvQhALYyECIdEIBET+84jgf8bJ/1fNJOrCV1r2pFVYs04R6uG+BMwG8147vrNcE0O4+jAh3i2+ff4pwtuwLLyxYFjUm17/F53E35yYgu+W3k7Vs27MOQ+ORu+yNmpMxJydMoUM5A9krB9r+MwPrTPfu2REO773/ZduGjeeQAyT/CJIfg9GZ/yp4xOsAkVLSKUwlQ/D3NXXqO2GUSKBIuXDwY/xNYjv8c/n3sDziuVzt+929OE+05swQ9Kb8fqmgvjP0AilG52U5Vfhqp86fxdKlHaaK89EmLeFz0JQC1CIpCIiKPFBb6jC5ZTO2BeNA4AYHNyAAAdiwZhWpp5aTZaWGwq88tQkVeKH7+zGT1j/fjzyb/jhqXXSnL1kDGGxw5uQ9d0Fx47uA0raxvBcZzs3T7l7tSpFMmkmUZ77bHgwOGF/X/EZR9br4v3RW0CgtCdo64hhCJ4iy1qm0BIRFV+GSrzSnHv3s3oHuvHHz/6O25cJJ2/+0WT39/9omkbVs1tVGQ9lbNTp1IkG6WN9tpjEUtQamFPlghaiwICJAKJCNh3HkFBxyFM247CdbkB5rVrQu63AhkZ/dPKQhM8IiLSaIj3uprw0J4t+M6lt+OSueLn7OzrOozjg2fPO+g/73lldQDkXbySSaHUIsmkmUZ77bFgYLCPJlezSBAEoSfi1c4lO1dOqZq8WM8bjJ5GXySbYhrttccimqDU2r4sFlpMAxUgEUgEcLS4kLf7FVg8Pehr7AS3uiHjon3BqJn6GY3gERFCLUFwMxjGGB7dvxVtQ514dP9WrKgWN2cn0nkffe9pbP3y/8h6VTLZFEqtkkiaqZjXXltSg+9d/e1Zr91mSa5mkSDSFeOkE4BNbTMICYlXO5fsXDkla/IiPW86NLtJNMVUzGtfUFiDH18229+FC0q9CUAtQyKQAODv9Jlnb/dH/9blwrLs6oyK9gWjRfEnEG9QfLwoYSLnPelokz0KJ2enTi3j5B3w+DzoHR2I+dpHJ0cxv3ReWr12giAIMcSrnUs2mqfWAPp0bnYTDzGvfdg9ivrieTFfu54EoIBWo4AAicCMJLzZi8XejmxPD7o29MO0tB51FP3T1AIjpHd+e/VtIdE6ASEauLK6MWaUMBqMMTz63tOq1OTFSqE82vchXjj0e9y2+itpIYLCu3ja8irxxE2/xO7Wd7H5/x4L3P4fl9+BZdVL/MdQxI8gYiJksBhKTwK4UW1ziBQR0ju/e8ltMWvnVlU1JhXNU7MmL1YapdD85j8u+kraCUBg5rXv6nwXD7w74+/+a+UdOL/M7+/iNfoRZjHrBS2ngQqQCMwgHC0uWA7sQlZRE/ItPv+NBf6GL3117ozr9BmMFsUfgJD0zp/ueQwdw92zjhGigVubfxszShiNN0/vwklHW8TzKlGTFymFkjGGn/7fZvSO9uM3TS/j4x9bp/n0mEiEC7/wTp5lBaX427E3QjYyfzv2Bj7b+Eldvl6CUAqheVlO55voW3YCWHUeMA7ULlyntmlEkgSndz7w7mM4MxLZ3x1ztOCpI79NKpqndk1epDRKxlig+c2zR1/GNfP16e/iUZlXij+1hPq7P7W8gS8uju3vtLo/i4XW00AFDGobQCjD4FvHYN7zHPrr/47x+imMXLkUri9dAteXLsHQJ89H3ZovZ6QAdI84NL3ABKdpRhKAwTzT9BsYuNCvtBANZGx2CsbYlAOjk4N45oM/gkPkBVioyYv0eDmJNDheTzh5R0AA5uWUBH7COdB+GCf7WwJXpHnG42R/Cw606+v1EoTS8B1dqEMbrMunYFo0n8RfGhAs0CIJwGCeeD+yvxNmzkUiuC4tEkJNntL+LlJaazoivM5gfxfv9Wp5fxYNQQBqPQoIkAhMe5ynhgAAptFfY3hNF0yL5mPup/4ZJfUXotxWH/jJRIIXFy0uMMHNWsQw5Z2e1XY5OBooMDblCCxSXL5VdE2eUgSPTQBm0lKVdsyJIgg/J++IKfwEGGN4evfsv6+BM+Dp3dp/vQShNmarA9ycapgWL45/MKFpgtM0xTDli+zvYomKRGrylCL8dccTsnol2t831uvVowAU0IMABCgdNK2x7zyCnI6/AF++CmNLOWRftiZjBV8wellYwpu1CNx16R04r2JJ4HfGGH7w5sNoH+qM2nVLqA0cn3YCCF2ggmvyeB+D630Pis43wWD0Xy0NH2sgN0oPjk+FeOmesRCigOEERwNXzNfW6yUIgpCDaGma37vkDlxQHurvvvfOwzg9HN3fRavtC6/J43mG7hYPqutNMBj8x6Y6gD5R1GpSozSJpOFquTlfPPSSBipAIjANEQrlzbajGFrrX8yqr/nHwKY+U9HTwhI+skHAwBnw6kdv4AsNMzn0bp8bw9MjMa9u9o0OwDXRj/ziyln3B9fk8V4Gu8WNsjIzDFnKf170Mjg+WPwlIvwEhChgrHbZT+/ejuV12ni9BKFF2MQoWEmd2mYQKRKrWcsrrW/gS0tC/d1QHH8Xq8NmcE0e72PI6XZjQYlZlf1ROgyOF0MiozE8o/4L1Vrfo0VCT2mgAiQC0wznq7uQ07MXfY2dwMJS5C5dgekzalulPnqJ/glEiwJGavZiNprx3PWb4YrQYXPSPQQAsOVYIwpAraH1wfGpij8Bj8+DgdHYaUk0EJ4giEwgkShRsoPKtYjaTWqUQmwa7vhQP8xGk272acHoUQACJAJ1i6PFFfK7YWwAuc37YLQdhf2GXHAV/kHvvI9hFG6VrFQfPUX/BIQoYKyrZuGjHyrzy1AZ1nHMvygVKbYo7e9ows/f2oJvrrsdK2obE368VgfHSyX8gjFnmfGrf5xJw2U+hrGDHuRfbAJ39oo0jYcgiMjYdx5BQcchDJw/jF5jB3JRq7ZJRJIkM0A90UHlciCMsrh75e1YNSc5f5cug+PjES8N1zM+hOJsK0wVlVFkorbRqwAESATqEvvOI8jpfBO24ixgajRw+8k1PcgqscKybDnV/kF/0T8BD+9Bn5hmLVHSXYJz0qVclGKJPMYYHt+7Fe2uTjy+dyuW11yQsOPS2uB4OcRfMOXWMpRb/RsZ5mUYtrhRWG4Gp0IaLkHogfCxENwl/oudgD+1j9AfWh2gHkvkBY+y2HRwK1Zel4S/0+jrlotoabjecSdgLtLdPi0cPQpAgESgrhDm/OW4O+FoOIGRRfMBcPCWWAAAJtQHHGImo1fxJxArvVMgWrqLXFek4om8SCMdhLRNsRHCWIPjBZRoUiO3+CMIInEE/zdV1IThNVMoWLIGJfUXqm0WkSJaTO+MJ/IijXQQ0jbFRgi1+LqVxj3qhMGg370aoI+B8LEgEagThOifu7ADoysM5AAjoMfUz2hESu+MRbLRP7ECLZbIC2/mEtzEBUBCEcJIg+OVgsQfQWib0gojJuqrwC+0kv9LI5RK7xQr0GKJvPBmLsFNXAAkFCHUQlqrGrhHnQDywYqLwXSc+aK3TqCRoDmBGsfR4sLki3+E5dQOOBpOYOyK8sCcP2IGrc/8k5Pg6F8iAjA8uhdrwG6suX2CQAweACsIRT0MfY802J0gCI0yOhr/GIIIIzy6F2+gfLS5fbEGnmfK0PdkcY844B5xgBUXq21Kyui5DjAYEoEaxr7zCHLe3AJ37h64Lh+C5dNXo3rldWqbpSmERQXQf/QvUYKHviezEIkVaLFEXrhAFDBwBvxq7zZND33Xovg71NGEm5/5Gg6eaVLbFILQLL6yArVNIHSGWIEWS+TFGnj+i4PbMmLoezJE26e9192EG176Gt7r0pe/izRvWa+QCNQgjhYXprZug6drK+yNnRj6Qh3mfuqfqdlLGBT9Szz6JxAvuhftOAHh+HfPHAoRiAI84/GhvTWqeAT8qahfeu5r2N+hrAPQovgD/O/1k3u24YyzE0++Hf1KNUFkKixGvTBBRCNedC/acQLC8Xu6D4UIRAGe8TjubI0qHgF/KuqnXv4a9vXoS/CkQrj4C96nMcbw2MFtaBvqxKP79efvtC4AXcwp6jhdiUCXy4WbbroJhYWFKCwsxE033YShoaGYj/nKV74CjuNCflauXKmMwUngfHUXct7cgmnbUbivy4Pl01dTs5cwKPqXehpCtOje/s7DMY8TEI7/+VtbwEF8Tr8gHnmeF5WKGotERaRWxZ9A82gzTvb7r1Sf7G/BgXZKJSIIAb6jC9MTPYDNorYphM6IFd2LdZyAcPzGdxP3d5sP+f2dmFTUWOhJRMYSfwLNo804Puj3d8LsY60jRABNVm2nszp5ByzZ4mzUlQj88pe/jObmZvz1r3/FX//6VzQ3N+Omm26K+7hPfOIT6O3tDfy8/vrrClibGEL0b8rxZ/Ru6MfQF+pQt+bLFP0Lg6J/yUf/BGJG9957NuCgguf2RYIDh56R/qgtriMhiMdnD/02pVpBsfWMAloWf4D/9bzQ+0LIleqnd1MqEUEE18Wfmt+O9jqQXyREEyu692hzqL8T5vZFggOHrrHE/d0xRwueOvLblGoFxdYzqo0Y8QdE9ndbDmjb3+mlCUxwgzsx6KY76IkTJ/DXv/4V7777Li655BIAwJNPPolVq1bh5MmTWLRoUdTHZmdno7KyUilTE8b56i7k9OzFZN043BfnwUpz/maRTp0/k0HKIuTgWsBg/CmcLWguaMZVuETU3L6CnHw89MkfwmQ0zdzOGO5742G0OzujPnb7gd9E7CYqdtZSpHrGFdWzmyUFiz8tc+DMYbROtgZ+5xkfiAaumH+RipYRhHoIXbGnKgYxvmIKlrVXk28kEiK4FjAYQaA1FzZjIS4RNbfPmp2PRy+f7e++987DOD0c3d898X6ov0t0CHykesZVldppDpjo/uzd7tn+TogGrq7Rnr8T9l/+CKBbXWNEkJdTgnH3uKhjdSMC9+3bh8LCwoAABICVK1eisLAQe/fujSkCd+3ahfLycthsNqxbtw4PPPAAysvLox4/PT2N6enpwO8jIyMA/B9UnvmSfg3OU0Pgu7oDvxsnncgd7MBY/kdwXj6FrMULUbtwnf+5JBp8K5xHb4N0g+32txPGTEcpr7Zfi5TveXj6AZ/ia2eM4Vd7/Vc7IzksDhxe6H0BV3iWI8tkwlOf+wWGYswxKsq1oTy/NOQ2t8+DocmRmFdMp7wz3y8hOvhu2yFcUhvfAQivIdip/mrvdlz0mQv85/MyuPiZfPi8nGIwEe/boY4m/HLX4/j39bfhohjjMqSGMYZn9j4LAwzgMZOGJEQDL57bmPAgYiUR3lsx77GaMK/aFmgTufxdqjhPDcHiOIWqcwtxvN6DOcu/6LcryrqqV18HkO1yERzdi+XvPuNdjqwsE3597S/gjDW3L8eGyrwI/m46jr/zhfq7Y44W7O48FBg7IeY1hIvIFf9wgf98Sb7v7/Y04ScHHsd3l9+GlTHGZcRC2JsB4vdnjDFsORjZ323Zvx2XVGrL3wXvwYT9V6r7MLlw8c7Afkesv9ONCOzr64so3MrLy9HX1xf1cVdffTU+//nPY968eWhra8P3v/99XH755Th06BCys7MjPmbjxo249957Z93e4fkQFncK9Qg1Z38C2OCCDcB5AADvOHD6A3muMrQf88hyXrnx253v/6VN+1dggultluI9P/vaJbr65OE96HPFvto56BlE/3uTMBlMMKAQxSiMeU57BNsenP8QRrwjs8/PGH7R8Qt0TXeF2GCAAY/t3I75H1sW1wE0jTThQ/vMlV0hgrlz5wE0Whvh2B/0mQEwLOK9Y4zh8Y+24sxkJx7/y1Y89LGlijmippEmnIwSmT3Z34K3duxHo1U5UZosI7u1vcZMTOhr/VAK2fxdqtQAQzV16D37q1jfqFdfB5DtUuPhPegeju/vWo/6/R1QiJwY/m4CwOkI/uQnCxL3dz/bux2VIv1dcCRTEJF/etfv75J53xljePCjrTg92YkH96Ti72b8rNj9WdNIE05EicweH2zBa7u05u9m78H8ewwtkh/Y74j1d6qLwHvuuSeiAwrmwIEDABDxQ8oYi/nhvfHGGwP/X7ZsGS6++GLMmzcPr732Gq6//vqIj7n77rtx5513Bn4fGRlBTU0Nak2LYTXH3hCHI0T/srvegXPJSZjq5wEAvMUzzlWI/skB72NoP+ZBXYMJBqN2rq7EY2rYiZ4z+ai6QF92A/73vLfZk5Lt8hUgm/HM+dGje7yPAR/moXKlBYYUhriWoRpA9azb3+s4hM6WztnPCx6tk61oqzkaMxrIGMNLv38xcFU0mBeGt+OCggtQeJkZXIK2728/hNb3/ekprZOt+Gj+Uayokz8thTGG3/zmxZhXqn8z/iLWXbdCU1dHg2FehpHdHljXmBJ+35Uka4RCgZGQ0t9JifPUEOZ27UVe9RgOLRmK6yf16usAsl0+zPhtffToHuMZxs/kof5cS0q2L4ji7/Z0x/Z3faVHY0YDGWP4r9cj+7vfuV7ABQUXYP4yc8K27+kO9Xfx7BCIGPlLAMYYvvfn2P7udyMv4tr16vu7SHsw3svg2O9ByQpTSvsjORCigAJi/Z3qIvDf/u3f8MUvfjHmMXV1dfjggw/Q398/6z673Y6KigrRz1dVVYV58+ahpWX2lQiB7OzsiFFCA2eAgTOKfi5Hiwt5B96GO3cPRlcaYF1yiWpD3g1GToMLdGTcIw4YztZwG4yc5r5sYknGdiH3PLtYvhq2qqJyVCFyOjTvZbCfdsOQJf37zhjDk/ufjekAntz/LFbOvyiqA3j3zOGQKGAwrY52NBc2Y33WJQmJEcYYntn3bEi6zTP7nsWKhdHtkAqP14OB0dhXqu2jg/ByXpizzLLakipcFqdpEcip7u20iVT+Tmo4ZoBhbAhZzACO84n2X3rydeGQ7dIzx1qOOdYo/s7HcLrXLYvtjDE82hzb3z3a/CzW1ET3M3u6I9czAsBxVyuai5qx0HhJQrYLdgX7u1h2BNf7ceUz+5Jk3i23L37dZf/4IHwGL8xG9fzd2JQDnDF6DwY59kep4OQdyLeE2irW36nuFktLS1FaWhr3uFWrVmF4eBj79+/HihUrAADvvfcehoeHsXr1atHP53A40NnZiaqqqqRtjvscLS7wHV2wnNqB/sZOmBbNh3nxYpRQQXtMQrpKeZnu0j9TRcrmL1pETKOZ/rFBeHyeiIInuFtptHM83/M81rEVCbXxPtB+ODCaAVC2KYs5y4xf/eNmuMaGMHbQg/yLTeDCHLrNUqh5AUgQcmEoKgDgUtsMgkgIMY1m+icG4eE9EQVPvHpGwO/vrmcrkIgkC2+UEzwuIzgaKEczPrPRjOeu3wzn2BAGTnhQvmS2vyvOLVRdAAL62Ycl2g00HNVFoFiWLFmCT3ziE7j11lvxq1/9CgDwta99DZ/85CdDmsIsXrwYGzduxGc/+1mMjY3hnnvuwec+9zlUVVWhvb0d3/ve91BaWorPfvazsthp33kEBR2HMG07CtflBupmJpJMnfsnoLeFJxnMWWY8c+NmDMUY+FyUG13wxBORANDn7oPH50W2SZwTYYzhl/+3ZdbtQlOW5XWzO7gdPNOEX+7cgn+/4nZcPC/12oVyaxnKLKUYtrhRWJ54KitBpBuOFhfydr+CXttRHDfmgoP4bB+C0AJmoxm/uW4zXLEazcQQPPFEJHDW3/FeGCHe3218N7K/23xoOy7Onxfwd6y0BO91NeGhPVvwnUtvxyVzpanTq8wvQ3lOKQosblSXmjUVUdPbPkyK7ue6EYEA8MILL+A//uM/cNVVVwEAPvWpT+HRRx8NOebkyZMYHvZ/6YxGI44cOYJnn30WQ0NDqKqqwoYNG/DSSy+hoKBAUtscLS5wJ9+HpWcv+ho7wa1uQG5ZLQlAEZAA1NfCkwoVBWWoKChL6rGCiGwfa8fP/vdX6BrqCZkrxHEcysxlMBnFL2v72w+h09U96/Zo0UDGGJ58eyvOODvx5NtbcdE/XqB67QJBpBPCWIi2ee8j94I5sNDIJEKnVOWXoSo/SX93VkQ6J4fwvXceRttwJ/iQ5jJn/Z1BvL/b030I7SOR/d0xRwv29R/BqmUbAJxNG92/FW1DnXh0/1asqE5fX6fnPViq4690JQKLi4vx/PPPxzwmeFOYm5uLv/3tb3KbBeeru5Bnb8e07SjsN+TCsoyif2Ig8affhUctTHkGjAyORRRujDG0T7XjwJnDuOSci+Oeyx8FfDzq/Ry4WdHA4NRRmuNHENJi33kElePv4FTDCVgXLUL1yuvUNokgVKMqvwynhztwarhj1n08/P5ub89hXFYrzt9tfC+2v3vsoz9iZcN6cByHfV2Hcfxs/b2WZ/ilil73YammgQoYJDlLhuJoccH56i5k9+xF15omDH2hDnVrvkwCUAQkAPW58KiJk3eAMYYX9v8xas0fBw7P7Hs25GJQNNxeD3qHo4+XEZqyeHz+dtCMMTy92z+vCZhJGRXzXARBiMNalQvTovkkAImMJ7guMBJCc5l4Psg94sCYqw9do71RjwnU5PMe/yy/A6G+bsuB9PN1et2HSZEGKqCrSKCWEKJ/k9UtsN9ggHXZFST+REICUJ8Lj1oEL3hur1tUN81ozWWCeb/rCHz87GHY/3H5HVhWvQRAaFMWNRvIEESmwCZG4S1RcT4hQWgEMc1l+sYjN5cJbuwCAAc9PfCFjZkAgLsuvQPnVfj9nVCjuLfzUCAKCJyd4Zdm0UC97sOkFIAAicCEcOz+EF7PFCz2dpiyXOha0w7T0nrU1V+htmm6INPFH6DfhUctwhc8oZtmpOYyzMcwdtCDmsvK4grA4Khe8PwlA2fA3469gc82fjKk/iHW8dEayBAEkRjGSQcgbbk+QeiWWM1leJ6hu8WDc88tg9loniX6gvdZjDFs+eO9Ef3Xqx+9gS80zPi74Chg+LFbDmzHqrn69nXCHgzQ7z5MKgEIkAhMCGvWKyjIMYDNyYGjzk3RvwQgAUgCMFGiXfEqt5ah3Dq72J55mb/DZkH8TmnhUT2BaNG9RI8nCCI5uAILgCm1zSAITRCpuYx7xAGeB3Is+Sjmx+AeccTcWwXX9wUTKcKXyLF6Q+97MKnqAIMhEZgAw59uhM+aAwCoI/EnGhKA+l98lEbqlIdghKherCG+wdG9RI8nCCJxXE0nYbG3o39OL4Aitc0hCE0QHuETYMXFQJsbrLgYLMaYBSGyF8t/CRE+AKKP1Zuv0/seTK49EYnABCi1LoDVlqe2GbqCBKD+Fx+lkVMAAv55g4nUFSZ6PEEQiWHfeQSWUzvQtaEfWSVWWMpq1TaJIBQlmtgDouyfvOKatHh4D/pizNcNbggDQPSxag50T4R0SP+Uc09EIpCQBRJ/fkgAJobcAhCIXVcoENwQJtHjCYIQh6PFBcuBXTDn7kHvBg9MS+sxj2rsiTQlltAD5NkvmY1mPHf9Zrhi+K/gofWJHKt10mH/JfeeiEQgITkkAP2MTzvBGfW9ACmJk3fIKv6CiVZXKNXxBEHEh+/owsIlDL3mckyvW0w19oRuiSfwBNTYF1Xml6FS5ND6RI7VMukgAAXk3BeRCCQkhQRgKOmwACmBkgKQIAiNYaOREIT6iBVykaA9jzZIJ/GnxL6IRCAhGSQA/YxPOwHkw2QtVtsUXUACkCAyD0eLCwUdhzCRNYK2eXZYQHWA6UYqokoJeB4A8uEedcJgoL2L3kk3AagEJAIJSSAB6Ce4CJmIj1ILHUEQ2sG+8whyOt/EtK0DzefmwrJsOaWCapxogi5cSAWj9f0A8zJRHTYJbZNO4g9QpjeCAIlAIiVI/M0gLET+CKBbXWOS5EBnEx4+sQXfrrkdl8y/UNbnUnKhIwhCfYRGMDnuTjgaToBb3YA6agSjCZJtWqJnIfVedxN+cmILvlt5O1bNk9ffEdKTDp0/w1F6X0QikEgaEoAzBF+J4kW2btYajDE8/u42dE134fF3t2FFXaNss4BIABJEZlJaYcRE5RTcF6xBST1tvNUg6uy5DPLljDE8dtDv7x47uA0ra+Xzd4T0pFv0D1BnX0QikEgKEoAzpMti9F7HYXxobwEAfGhvwXsdh7Fy3kWSPw8JQIIgfGUFapuQMUQSfZnuu/d1HcbxQb+/Oz7Ygn1dh7G6Rnp/R0hLuuy3wlFrX2SIfwhBhEICcIZ0WZAYY3hi33YYOP+SYOAMeGLfdjAmT1Qz1kJ38EwTbn7mazh4pkmW5yYIQj3Y5DB1A1UA94gj8AP4/XXwTybDGMOWA6H+bssB+fxdLN7rasINL30N73WRv4vF2JQjbfZb4ah5YZxEIJEQJABnSKcF6b2Owzgx0AKe8QAAnvE4MeCPBkpJvE6gjDE8+fZWnHF24sm3t6rilAmCkAfD2IDaJqQtwaLPPeIg0ReDfV2Hcdwe6u+O2/3RQCVhjOHR/VvRNtSJR/eTv4tG8F4rHfZbwaidGUUikBANCcAZ0kkAhkcBBaSOBorpBHqg/TBO9vtTdE72t+BAu7JOmSAImSnIV9uCtCJatI+ITHgUUECNaKAgRgGoIkK1jhD9S0fxB6gvAAESgYRISADOJl0WpfAooICU0UAxix1jDE/vDk3ReXq3Oik6BEFIi/PVXTAf+APetfwNbbn9apuja6JF/Ij4hEcBBZSOBmopJVVrpLv4A7QhAAESgYQISACGIixO6YAQBeQQuSsaB06yaGC8xU6IAgan6FA0kCD0jaPFhamt22D0vIreDf0oXL0MdWu+THMBkyBS1I8QjyC8Yvk7pYSYVlJStUQ61/0FoxUBCJAIJOJAAjCUdBsG7/F50D9mB0Nkp8fA0D82CI/Pk/RziEkDDY8CClA0kCD0i6PFBb6jC3l5/XCty4V13RWYR3MBE4bEnzR4eA/6xPg7Pnl/JwYtpaRqgXDxRwJQOWhEBBEVEoChpOMVKnOWGc/cuBlDk8MAAN7H4Hrfg6LzTTAY/VdLi3ILYc4yp/Q8YqOA4QRHA1fMp/bdBKE3qoqHkZ+VC64ij6J/CUI+WFrMRjOeu34zXGf9HfMxDJzwoHyJCdxZf1ecWwizMTV/F4/gWsBggqOBmTCuYnzaCc7r/3867auioTUBCJAIJKJAzicy6bhQVRSUoaKgDADAexnsFjfKyswwZKU+ODeRKCAHLuIVWg4cnt69HcvrLqRhvgShI4RuoFyBBcCUusboCPK/8lGZX4bK/Bl/V2Bxo7pUGn8nhuCU1Gj+bsuB7Vg1N3393fi0E4C/QVQ67qkioUUBCJAIJCJADmg26VQHqDTxFj2Pz4OB0dgpOvZRf0pqqhFJgiCUwb7zCAo6DsFe3YLWiw2wlC1X2yTN4x51wnA2Q5D8b3qSSEqq3BFJpRGyqUzWYgDus/+mP1oVgACJQCIMEoCzSbc6QKUQEwUE/Cmpv/rHmZTUSNgsqaekEgQhP44WFywHdsEydgJdG/phWlqPOqoDjIl7dCYyQr43vQlPSY2EEimpShG8fxIupPPezKl51LIABEgEEkGQAIwORQGTQ+zCV24tQ7m1TGZrCIKQG76jC/lZH+HM5UOwrr2C6gDjIPhdAGDFxVH6VhLpRHBKaroSSfxlGloXgACJQOIsJAAjQ2mgBEEQiVFQkQvfkloSgDEI6fbpZUCbW2WLCCJ1SPzpQ/wJkAgkSABGgdJAk8fJO3SxABIEIS3GSQe4OdQIJhbkc4l0goTfDHoSgACJwIyHnFFsMn1BIwiCEIOjxYW83a/AaDuKprpccKhQ2yTNQf6WSCdI/IWiNwEIkAjMaMghRYeigARBEOKw7zwCy6kd6GvsBBaWwrJsOaWChkH+lkgHSPhFRo8CECARmPGQQ4oOLXDJIbYrKEEQ+se+8wjq0IaJS7MweEED5lEn0BBI/BF6J/yiOO2NQtGrAARIBGYs7hEHOaUoUDOY1NHjYkgQRHIUFnsxASC3rFZtUzQFCUBCr5Dwi4+exZ8AicAMJLglNUEQBEEki3HSgYmRLuAci9qmaAoSgIQeoXRPcaSDAARIBGYc5JhiQ1FAgiCI+AhD4ac87+DgIh9MdfWYR3WA5GMJ3UHCLzFcvBOcQf8CECARmFGQcyIIgiBSxb7zCHI634S7sAPuT+TBSo1gAJCPJfQBpXomh4t3AsgHkB4CECARmHGQc4oORQEJgiBi42hxIb+/FVnz+zHQWI66ldepbZImIAFIaBkSfqkR3PAuL6dYRUukhURghuAedcJgUNsKgiAIQu+UWMcwWVoI0+LFapuiCUgAElqEhJ80zNT/FWMYbpWtkRYSgRkEOajo0FxAgiCIBLBRIxiAOm0T2iHSPoaEX/KEN39hXqamObJAIjDNcY/6c5hZcTE4tY3ROLRYEgRBRMbR4gLf0QXLqR34YEM/THX1yFXbKBWh6B+hBcanneC8M7/TPkYa0qX7Zzx0lSD4wAMPYPXq1bBYLLDZbKIewxjDPffcgzlz5iA3Nxfr16/HsWPH5DWUIAiCINIE+84jsBzYBfPIdrguH4J13RWYV39FxjaDIQFIqMXYlANjUw6MTzsBACZrMcyFJYEfIjWcvCNjBCCgMxHodrvx+c9/Hrfffrvoxzz44IN45JFH8Oijj+LAgQOorKzExz/+cYyOjspoqTageYDioFRQ6Rmn95Qg0gJHiwsAUFXdibErymFeuyZjxR9AApBQFkH0CT+AP9pnsqZPcxKtECz+MkEAAjpLB7333nsBANu2bRN1PGMMmzZtwn/913/h+uuvBwBs374dFRUVePHFF/Gv//qvcpmqOgFHVVwMtKVXIasc0BU06Sg2lIR00iIIQt9U1WYBPYC3xIJqEoAkAAnZoLo+5Qnt/JlZ77WuRGCitLW1oa+vD1dddVXgtuzsbKxbtw579+6NKgKnp6cxPT0d+H14eBgAMDo8Lq/BEuIemwArKgY/NI6JCQ9GhjwwGPVTFcj7mGJ2T0xPwMTnSHY+3uu3fdTlgSFLP+85IJ3t4/wEWLZ076kY2NnPjNHlAaejzzpAtivBxOgEAP/FQWKGqP5ufAQAMDY1gpyJTviyTRgfm8bIkDb9oNw+wz3mT79jRcWAS9r3QEl/JzVke+pMnE3tFAiP8k1H+LzRPkMahnj/e2/J9r/nYxPRv9t68XWAeH+X1iKwr68PAFBRURFye0VFBc6cORP1cRs3bgxEHYP5h7U3S2sgQRAEoSijo6MoLCxU2wzNENXf3bw2yiM2yWoPQRAEIQ3x/J3qIvCee+6J6ICCOXDgAC6++OKkn4PjQhU7Y2zWbcHcfffduPPOOwO/Dw0NYd68eejo6NDd5mFkZAQ1NTXo7OyE1WpV2xzR6NVugGxXC7JdHfRiO2MMo6OjmDNnjtqmaIp08Xd6+RxGgmxXB7JdHfRqu57sFuvvVBeB//Zv/4YvfvGLMY+pq6tL6tyVlZUA/BHBqqqqwO0DAwOzooPBZGdnIzs7e9bthYWFmv/DR8NqterSdr3aDZDtakG2q4MebNeTqFGKdPN3evgcRoNsVweyXR30arte7Bbj71QXgaWlpSgtLZXl3PPnz0dlZSXeeOMNNDY2AvB3GH3rrbfw05/+VJbnJAiCIAiCIAiC0DK6GhHR0dGB5uZmdHR0wOfzobm5Gc3NzRgbGwscs3jxYvzxj38E4E8D/cY3voEf//jH+OMf/4ijR4/iK1/5CiwWC7785S+r9TIIgiAIgiAIgiBUQ/VIYCL84Ac/wPbt2wO/C9G9N998E+vXrwcAnDx5MtDdDADuuusuTE5O4o477oDL5cIll1yCv//97ygoKBD9vNnZ2fjhD38YMWVG6+jVdr3aDZDtakG2q4OebSdmo9e/p17tBsh2tSDb1UGvtuvV7lhwjPplEwRBEARBEARBZAy6SgclCIIgCIIgCIIgUoNEIEEQBEEQBEEQRAZBIpAgCIIgCIIgCCKDIBFIEARBEARBEASRQZAIJAiCIAiCIAiCyCBIBBIEQRAEQRAEQWQQJAIJgiAIgiAIgiAyCBKBBEEQBEEQBEEQGQSJQIIgCIIgCIIgiAyCRCBBEARBEARBEEQGQSKQIAiCIAiCIAgigyARSBAEQRAEQRAEkUH8/+z9eXxb1Z3/j7+uNu+WLFmyncROQjAhJAQCJBDWhBSmtKWlpS2d+Q5DZwr9UWb5tLRlSmeYFrowU5iWUoY1kLCXFkopFEppSgLZTeJACCE4IV6SeJG1etdy7+8P+cpX0r1Xd79X8nk+Hn5AbOnqyL66r/s6742YQAKBQCAQCAQCgUCYRRATSCAQCAQCgUAgEAizCGICCQQCgUAgEAgEAmEWQUwggUAgEAgEAoFAIMwiiAkkEAgEAoFAIBAIhFkEMYEEgo589atfBUVRol+Tk5OavubBgwdx2223YeXKlWhpaYHL5UJjYyPWrFmDu+66C8PDw5q+nhJeffVVfOITn4DX60VNTQ3OOuss/OpXvwJN06YcU+5zjx49ikceeQQ33HADzjjjDDgcDlAUhR//+MeK108gEAjlAtG+XNRqhpH6psVrEkoEhkAg6MZ1113HAGDa29uZCy64gPdrampKk9caHx9nvvWtbzF2u50BwDgcDmbhwoXM2WefzTQ1NTEAGACMx+Nh3n77bU1eUwl33nlndi0nnXQSs3z5csZmszEAmM9+9rNMOp029JhKnvv//t//yz6H+/WjH/1I9toJBAKh3CDal4sazTBa39S+JqF0ICaQQNARVgg3bNig6+tMTEwwl112GQOAcbvdzN13383EYrGcx+zZs4e58sorGQDMpk2bdF2PENu3b2coimJsNhvzzDPPZL+/b9++rFjfddddhh1T6XN/9KMfMZ/5zGeYO+64g3nttdeYq6++mphAAoFAmIZoXy5KNcMMfdNDpwnWhJhAAkFHjBLCr3zlKwwAZt68ecz7778v+tif//znTCgU0nU9QnzqU59iADBf//rXC3729NNPMwAYn8/HJBIJQ46p1XrYvzMxgQQCgUC0rxhSNcMMfdNDpwnWhNQEEkqWnp4e2Gw2LFiwAL/5zW8EH/f666+DoigsW7YM6XTawBUaw29/+1v8+te/ht1ux3PPPYelS5eKPv5b3/oWvF6vQaubIR6P4y9/+QsA4Gtf+1rBz7/0pS+hvr4eoVAIb775pu7H1GM9BAKBoDdE+zKUivYpxQx9I7o4uyAmkFCyDA4OYtGiRejp6cFXv/pVjI+PFzwmnU7jO9/5DgDg7rvvht1uN3qZAIDnn38eV111FS699FJ85Stfwa9+9SvEYjHVx6VpGrfeeisA4F//9V9x/vnnqz6mXnR2diKRSKCyshJnnXVWwc+dTidWrlwJANi1a5fux9RjPQQCgaA3RPtKS/uUYoa+EV2cXTjMXgCBoJRVq1bh0KFDWLVqFfbs2YM9e/bgoosuynnMo48+ivfffx+XXXYZPvnJT/Ie56c//SleffVV2a//q1/9CitWrJD02D/+8Y85/37uuefwgx/8AM8884zguqTwhz/8AUeOHIHT6cQtt9yi+Dhc9Pp9dHV1AQDa2trgcPBfek466SRs2rQp+9hiqDmmHushEAgEvSHaV1rapxQz9I3o4uyCmEBCSWOz2XDZZZdhz549+Oijj3KEcHR0FP/1X/8Fm82G//3f/xU8xkcffYRt27bJfm0pu5mLFi3CT3/6U3z605/GwoULQVEUduzYgdtuuw27du3CVVddha1bt+Kcc86R/fpARggBYO3atWhpaVF0jHz0+n1EIhEAQENDg+Bj2J+xjy2GmmPqsR4CgUAwAqJ9paN9SjFD34guzi6ICSSUPO3t7QCAQ4cO5Xz/v//7vzE4OIjrr78ep59+uuDzN27ciI0bN+qytttuu63ge5dddhkuueQSXHTRRdi9ezf+/d//HZs2bVJ0/LfeegsAsGbNGjXLzEGv3wc7E8rlcgk+pqKiAgAwMTGh+zH1WA+BQCAYBdG+0tA+pZihb0QXZxekJpBQ8rBC+NFHH2W/d+zYMfz85z9HTU0NfvSjH5m1NEFcLld2XZs3b1a8o9bX1wcAWLx4sWZr04vKykoAQCKREHzM1NQUAKCqqkr3Y+qxHgKBQDAKon2loX1KMUPfiC7OLogJJJQ8fEL4/e9/HxMTE/j3f/93NDc3m7U0UVavXg0gU+D+8ccfy37++Ph49kLtdrs1XZseSEkhkZKKotUx9VgPgUAgGAXRvtLQPqWYoW9EF2cXJB2UUPI0Nzejrq4OR44cQTqdxr59+/DUU09h7ty5+Pa3v130+WYVgzudzuz/p1Ip2c+vqqqCw+FAKpXCyMiI4nXko9fvg71h6e3tRSqV4i06Z28I2McWQ80x9VgPgUAgGAXRvtLQPqWYoW9EF2cXxAQSyoKTTz4ZnZ2d6O7uxre//W0wDIOf/OQnqK6uLvpcs4rBDxw4kP3/efPmyX4+RVFYsmQJ9u/fj927d+Oqq65StR4WvX4fK1asgNPpxOTkJPbu3YtVq1bl/DyZTKKjowMAcO6550p6TTXH1GM9BAKBYCRE+6yvfUoxQ9+ILs4uSDoooSxgd6T+53/+B1u2bMGKFStw7bXXSnruxo0bwTCM7C+1Bels17ZTTz0Vc+fOVXSML37xiwAy7cBDoZDoY3fu3CmppbNev4/6+np84hOfyK43n9/+9reIx+Pw+XySf7dqjqnHeggEAsFIiPZZX/uUYoa+EV2cZTAEQhnw/e9/nwGQ/dq0aZPZS2L+/Oc/M9/73veYjz/+OOf70WiU+dd//dfsWp955pmC527YsIEBwMyfP1/0NSKRCDN//nwGALNixQrmnXfeyfl5Mplk3n77beYrX/kKY7PZmOHhYdXvSw1bt25lKIpibDZbzvvet28f09TUxABg/ud//qfged/+9reZ+fPnM9/+9rc1O6ba53K57rrrGADMj370o6KPJRAIBK0g2lca2pePVM0wQ9+00kWC9SEmkFAWsMIBgLnyyivNXg7DMAzz4osvZtc0d+5cZuXKlcyZZ57JuFwuBgBDURTzgx/8gPe5UoWQYRjmwIEDzEknnZTzWqtWrWKWLVvG1NbWZr+/aNEibd+gQn784x9n13TSSScxy5cvZ2w2GwOA+fSnP82kUqmC57CCed1112l2TDXP3bp1K+Pz+bJfFRUVDACmuro65/u9vb2Kf08EAoFQDKJ9paF9ajTDaH1T+5qE0oGYQEJZsHXrVgYA43A4mIMHD5q9HIZhGKa3t5f5j//4D+bSSy9l2tramKqqKqayspJZuHAh8w//8A/Mzp07BZ975513MgCYz33uc5Jea3R0lPnFL37BXHzxxYzP52McDgdTV1fHLF26lPmnf/on5uWXX2YmJyc1emfqefnll5lLL72UcbvdTHV1NXPGGWcw99xzj6CwFDOBSo6p5rlvvvlmzu670NfRo0el/DoIBAJBEUT7SkP71GqGkfqmxWsSSgOKYRhGTvoogWBF3nrrrewQWnaIbCnz2c9+Fi+//DK2bNmCiy++2OzlEAgEAsGCEO0jEAhKIY1hCGXBe++9BwBYvny5ySvRhh07duCcc84hIkggEAgEQYj2EQgEpZAREYSyoJyE8NChQxgeHsa9995r9lIIBAKBYGGI9hEIBKUQE0goC8pJCBcvXgySpU0gEAiEYhDtIxAISim5dNC33noLV155JebMmQOKovD73/9e9PGbN28GRVEFXx9++KExCyboDsMwOHDgACiKwrJly8xeDoFAIBAIukO0j0AgqKHkIoFjY2M444wz8I//+I+4+uqrJT/v0KFDqK+vz/7b7/frsTyCCVAUhZGREbOXQSAQCASCYRDtIxAIaig5E3jFFVfgiiuukP28QCAAj8ej/YIIBAKBQCAQCAQCoYQoOROolBUrVmBychKnnXYa/vM//xNr164VfOzU1BSmpqay/6ZpGuFwGD6fDxRFGbFcAoFAIGgIwzAYGRnBnDlzYLOVXCWEbhC9IxAIhPJCqt6VvQlsaWnBww8/jLPPPhtTU1N48sknsW7dOmzevFmwBfGdd96J22+/3eCVEggEAkFv+vr6MG/ePLOXYRmI3hEIBEJ5UkzvSnpYPEVRePHFF3HVVVfJet6VV14JiqLwhz/8gffn+TujsVgMbW1teP3xt1BXU8/7HKtCMzR6kx+izXkqbFTp7H6X6roBsnazIGs3h1JZ+9j4KC77hwsRjUbhdrvNXo5lKBe9K5XzkA8z1p4Ij2b/n/F6FR+HZtIYTO5Hk/N02Ci7FktTDBUOZ//f5a0t+nhyzphDqa69lNYtVe/KPhLIx3nnnYennnpK8OcVFRWoqKgo+H5dTT3qazw6rkx7aCaN6kQ16l1u0y/QcijVdQNk7WZB1m4OpbJ2GzJrIymOuZSL3pXKeciHkWtPhOIAgMrqejA+9Q3yaCaFkUQ16l0e2CiTbylrGgAAVCgITAIun/gmBjlnzKFU115K65aqd9a2sjrR2dmJlpYWs5dBIBAIBAKBYAisAWR8fk0MoFVh31siFM++ZwKBUEjJRQJHR0dx+PDh7L+PHj2Kffv2wev1oq2tDbfeeiuOHz+OJ554AgBwzz33YMGCBVi6dCkSiQSeeuopvPDCC3jhhRfMegsEAoFAIBAIhsA1f7MF9r1SoSASoXjRqCCBMBspORP4zjvv5HT2vPnmmwEA1113HTZu3Ij+/n709vZmf55IJPCd73wHx48fR1VVFZYuXYo//vGP+NSnPmX42gkEAoFAIBCMgBsFm00GkAvj8xMjSCAIUHImcM2aNRDrZbNx48acf99yyy245ZZbdF4VgUAgEAgEgvkQ85cLMYIEAj8lZwIJBAKBQCAQCIVYIfVzPDQp6XHVvkqdVzLDTJ1gkBhBAmEaYgIJBAKBQCAQShizon/j4SmgNvNfG1IAAIev+AiWVChWYBaNMIWMz49EKAgAcHhrdH89AsHKEBNIIBAIBAKBUIKYYf74In0Ob72sERH5RjHfFOppCLPpoeERoPg4QQKhbCEmkEAgEAgEAqHEMDr1k2vSWBNHMykgof7YXFPINYR6mUHG5wdCg7ocm0AoFYgJJBAMpn/IjkhceNBoQ30aLYG0gSsiEAgEQqlglvmTkuaZz8AQhVhceCS1u55GcyC32R/7OqwZ1M0Ien1AohuJ8AgqfR5dXoNAsDLEBBIIBtI/ZMenr5+DRFJYFF1OGn9cf4IYQQKBQCBkKSXzB2QM4DXXu5FIUoKPcTkZPLc+VmAEua87HooB0DdFlHQOJcxGiAkkEFTCrckAxIvNI3G7qAEEgETShkjcTkwggUAgzHLMrvlTagABIBa3iRpAAEgkM5HCZhG9c/jcukYFGa8PCEc0Py6BYHWICSQQ8sg3dcXIF+ZEaBCoBRLhEdiQa/iSUeNaYhMIBAKhNDGt26fK6J9e6G4EOV1DSUSQMFsgJpAwKxEzemoFl60zYLw+MHnd0pgI+cgRCAQCoZD8jUNi/nJhjaBekKHyhNkGuSMllD1Chs/MYbrFSEZHkQhNEiEiEAiEMiYRioMGnR1VYIYulYIBZHH43BgPxXTtGkqMIGG2QEwgoezgM31WNnx8MJ4GAP0574UIEoFAIJQ++RrFzR4Rr6DTnlIygFx07RpKjCBhlkBMIKHkKRDUEjN8QhTWGgaz/0+EiUAgEEqDohrFpAxcTYZSNX+A/mmhADGChNkBMYGEkqMcIn1KYN8jK0wsRKAIBALBOlh9Y7KUDSCL3mmhwMzfjTSMIZQrxAQSSgKri6pUPPU0XE6m6NwkTz0t+HPueyeGkEAgEMylVPRJq9EPUnFL1Du3iN5ZAW5UECA6SygfiAkkWBputzSrCqscWgI0frc+iGhceFagp55GS0CaKBJDSCAQCMZSitkoZkT/mgOZQfAxEb1z19O8g+KlomdtIBe+TByisYRSh5hAgqVgL65stzS+MQulTktAusmTAzGEBAKBoA+lEu3Lx+joXz7NAUZ0ELwajKgNzIeYQUI5UV5314SShE9cGSYFJLrNWVAZQAwhgUAgqKNUjR9LOdT+ScGoaCAX7rlAmrYRShViAgmmwRXYUhPXUkKsyyhARItAIBC4lLo2zRbzB5gTDcxHyBACRF8J1oaYQILhlLrAljpippA7tJhAIBBmC6WuS2anfRIyiGXhAMQUEqwFMYEEQyh1gS1nuH8PNg2X25CHiBaBQChXSl2biPnLYEZKaDHyzydiCglWg5hAgq5YVWC5wqk1NNIlH03jNuTJT28BiHARCITSxqraVIx87ZrNxo/FCimhUuA7z4i+EsyEmECCbrAia6bACpk9PYUzEQpnXjs8BRtS2e9bbZdSKmQ3k0AglBNW0Cap8GkYMX78WDEaWAyirwQzISaQoDlmC6zUndKBIUqX+UUObz2QyPzXNh1NS4ViBesqNbFiIaJFIBBKEbO1SQp6mT699M4qlEo0sBhEXwlGQkwgQTPMSq9Rkh4zMEThmuvdSCQpwce4nJlBt1oIY/6a8k1hqRpCoLhoEcEiEAhmYtXUT1YD2BKC8fAUXD6v5q9jtN6ZSSlGA8UQ0lfSxI2gBcQEEjTBjB1WNQXxsbhNVBABIJHM7JzqMeg2f73jeTuYpSxiYt1HiSEkEAhGYpXon1hpAs2kstkjemC23hkFGw0sNyPIhT2PSRM3ghYQE0hQhdECW66d0LjvpZxSRwEyVJdAIBiPFaJ/pImL8XCNIFDa2ikFoSZuRF8JUiAmkKAYIw1guZo/PoqljgKlK2zEEBIIBL0xK/pHTJ81YH/vs8kMAjPnOynJIEiFmECCbIzcYeV22CwVQR0YsqE75EbY6YCNshf8XG4BfrmaQiJYBAJBa9j0uFIpTSh1tNY7LeEzgyylqJlSERpYT7SVkA8xgQRZGLXDOh6eyhY9l5KoDgxR+NsbvEgk1wg+Rm0BvhRTyFIKQkcEi0AgqCURHgFqc9Pj9IRE/YzROy0Q0kxuQx52s7kUNFMOfJutRFsJLMQEEiRjmAHk7qrqVCivF2YU4AvdfJSiOSSCRSAQ5JLfQl9PZnPUL59SbTjD/t24DXnYcU75TdqEsKqGCkG0lcAHMYEESRhhAFlx5XZLM5t4NI1IqFC8GnyFaS9WQ4k5rPRa45JABItAIEghq01eH5Do1vW1uBpFKE+k/G3FNJSLFY0i0VYCF2vc8REsjRID2D9kQ1RkMK2nnkZLgM7+20hxjYTSQJqB08kgWWRuUlNbBap9uWks46EJXmOYoXTNIQCMh8KWSo8hgkUgEPgo0CUmpdtrFdMnqw9id9fTcDmZonMC3fW04M8JM6g1ilYwh/naSnR1dkJMIEEUpQbwC9f7iwrO79YH0RKgDTGAXNNW7atCVZrCzf8yhWCIf43VVcCSxTSaeIS72lfF+5zx0ATiUeukvCjB4a2XlB5jtIgRM0ggEFjM6EwtZgBLYRD7d/9lDMEQv1GtqWKwZHGq5AfFWwnxzVbzNZWF8fmJEZzFEBNIECQRiisS2ajEOoFo3Aa3fVw388c1fiPpasTimTWFjgD/eUcl0mnxNTqdDJ5aP8FrBPmo9lWhMiK8G1yKZHa4vTnfS0VHgaOZHWNPXRoLFzsNWw8xgwTC7Mbo2vRi+mTFujhuZHI4TOHf76iVpHe/MdmomomR0VyrdfxmP0vs2CaiqbMHYgIJvCg1gHKYjCbgOFl7A5gf9RsconDt9VVFhTqfZJJCLE5JNoHlhvAO94xAuJw0nrnrOJoaM79zo4SLpLIQCLMLK0X/rIyUyCQfSQs2cDGKgSEb/vYG86K5xUyhkbpKNHV2QUwgoQAjDCAAODy1ADKCk78LRzNAJDkze0jqLhxrALkpm7E4JVsQ9Sa/4YwVG81I2+G2YdTuxlxf5r1w01yMEC6uaNGgs2NFCARC+VBuBlArveNDynWbkIvVorn5556RukqM4OyCmEBCFiOFlovwzuWa7P8V24XLj/6ZhbuekVSAz204w9doxoqmUAp8w3mNEC0AQGhQ19chEAjGwh39YJX0T7VooXdWgTScMQY+XdWzkzcxgrMHYgIJAMwzgID6XTi+6J9ZNAUYPPHICI6H9qLWeTYonqHF7nomJ8U0f93lYApNMYPTLeIT4RFU+jy6vhaBQNCXcov+sVgt6qSG5gCDZx8Jozu0Hw3OM2CjCnXK7M6o5USOroandM18IUZwdlByJvCtt97CXXfdhT179qC/vx8vvvgirrrqKtHnbNmyBTfffDMOHDiAOXPm4JZbbsGNN95ozIJLADMNoFqsZABZmgIMKj0xuF00KEr+Dijfe4mEJgQfb2WDyL2pYlNajDCDiVAEAClwJxBKDSOjf0Bp1/9ZgeYADbsnBr8rBRvJQjUE7ixldpyTHrrKGkFC+VJyrQzHxsZwxhln4L777pP0+KNHj+JTn/oULrroInR2duL73/8+/u3f/g0vvPCCzistDUrZALJYyQDqRbWvivcLyBjhYl9WgL3JGg9NShq0q4aZbmfxIo8kEAhWgatHxAASCMVxeDMbnXppKuPzEx0tY0ouEnjFFVfgiiuukPz4Bx98EG1tbbjnnnsAAEuWLME777yDu+++G1dffbVOqywN9DKAHoPqBKxibsxEigFm00uHhm2IjxT+Teo9mUiiu55GYPpUGBiyobtP+z2i/DRRPaOCJJ2FQCgNSjn6R+rirImUkQ+s3oXCpR3CdPjcumsq0dHypORMoFx27NiByy+/POd7f/M3f4NHH30UyWQSTmfhjLOpqSlMTU1l/x2PZwSKZmjQTGkZD3a9+etOhEcATNdRMSlNX7PJD7zwyACi0xfgyVgCdndu8nrmAkyDZjKd0aRAM+lMCgSAaHg6DdRbBabI+hlGmZlxOhnU1yXBMPLqGdj1FFuXUVR5nRgcovD179aJ3qg4nQweumsA6Zoq/PNNXskd5rh/F6nYvDUAgNHpm79qb4Ws5wutg/tfAIC3AVQ4hMlQFC5vnerX0Auhz2kpUCprp2Ht9ZmF2XqXo0WAYj2Seh6OhzPv1eGtl33d4iPgB559JCzBcCjXOzlIPX4+TieDurokaEaeWWXXqMXvUisyIx/ENczlZPDMw0OIpKpw64+lbQYo/ZvoQf7vnaupWuhpDtM6qtX1oFQ0I59SWrdUvSt7EzgwMICmpqac7zU1NSGVSmF4eBgtLS0Fz7nzzjtx++23F3y/N/khqhPVuq1VT7qTH+R+g/VkiW59XtAD1Hky/yt06x1MZP4bSbrB7YwmRCT5LoKJ6VbJ0+uPJYovxVZVBadzHZJJ4do5uz2Nr371AOrrMwesqUli/vwRVHomJL0GH/HkVmVP1IHjITcSyTWij0kmKezvOQ5AugF0OtNIV3UgmBCuWRRl+u8YkfE7DgarEI+7BH7qRrD+EPx+znqy57qSBRpLwee0hLD62scT42YvwZKYrncaa1F/cp/E19Pk5QAAdg/g9Yg/RpXeySCtQu/snonsOuUSSu5W9kQd6Jagd4kkhW3v9QHwIpUqrncOh0qtU0hxvdtfoHdy9FQytdBcQ62uGUKUwrql6l3Zm0AAoKjcDzgb2cn/Psutt96Km2++OfvveDyO1tZWtDlPRb2rtGoHaCaN7uQHWOA8DTbKXrjrqiPcHVcxwk5pp2GD8wz4XTORwGqvtFpA91zgyUfGEIsLX+gzHTvnSzpeMRgmhXhyK+qdF/J2BzWDWqe0aOgvfnGO5GP+4LtxLF+aRHNghdJlZUmFpUUEB4bs+Oebmovs8NJ44ZHBgs56VDiU+bkFI4L5n9NSolTWPpoaMXsJlsQMvdNDh2gmjf7kPrQ4zyw4D6VqkREo0Ts5+OcCv34kWjQy2Rwo3ABXAs2kEEruhs+5CjaL6J3U37EcvbvzP0dw2lz1WieHgSFb0awclzPTobU5kBvBTYW1jwhS4ZAm+lkqmpFPKa1bqt5Z4xOrI83NzRgYGMj53tDQEBwOB3w+fgGqqKhARUXhh8dG2Sz/hxfCRtmRCo/BBlumVsqI10RKUs1Fg5uSVFPR4KayIkOBkmWwmpsyX0ZCUQ7LmECK0r6+b2EbMKfJBi36S7l8XgDAZJEOorERh6QB9rERJ+Y05T3O1wQqFEQqPGbZ2gYbZS/pa4yV126DdddmJkbrXSIU11WHMufhzHV3PDQJG+yWaf6iRO/kMqcp8yWMNtftnCNSDsuYQD3OW7/PZvj7GxmxSxonMjLixJym3E1PG+yYDGvbNZSCTVP9tLpmCFEK65aqd9b4xOrI6tWr8fLLL+d8789//jPOOecc3nrAciURHskKrxHI6VTVHMgMxuXuXNJMGpHku9nZQ7Nl1tDgECUhYmmN30M8qn1evN4F7qRZDIFgDmY1fwGs1f2T6N0MUpq3zIbfgx6wWqolZGRE+VFyJnB0dBSHDx/O/vvo0aPYt28fvF4v2tracOutt+L48eN44oknAAA33ngj7rvvPtx888244YYbsGPHDjz66KN49tlnzXoLhpIIjwC1mbQbxqBdLCWd15oDTE76Hs2kEExYa/aQ3gZtcIjCtddXFd0hfnL9hGWMINuhVctZhcQIEgjlhdGjiKw++qEU9E5vgzYwROGa691F9e659TFiBBXi8LkxHorpPpuXULqUnAl85513sHbt2uy/2VqG6667Dhs3bkR/fz96e3uzP1+4cCFeffVVfOtb38L//d//Yc6cObj33ntnxXgIM2e7WFV85cA1faEwcNuPKkULyFmDFlB4nxOLU5JSP2JxyhImsNJTiWofnR1BUYpGkEAg6IvhBjA8lU0vJ0iHa/qGwxS+96Paonr33PqYCr2zSdQ7W0F9N8FcyAZq+VByJnDNmjWiLfs3btxY8L1LLrkEe/fu1XFV1iMrvF6ffh1AedB7CLhRSInK5cMaNKWiWKpU+6pK0ggCRMwIBD0xygCOhyYzLdFrrdH8pdSQEpXLhzVos03vShEtNZRsoJYX2neLIJiO0TuvLFZPwZGDlKhcKeGuZ+Byahc9zAw/njkeO7SeTQ/VCj3PJfbzYWbEnEAoV4w0gAAxf2qQEpUrJdz1tA56J29+olUoh/sxgn6UXCSQII5ZBpDFqAtOg8+OSGgiaz4I4jQFMqmqfDWNPX0UfvKz4ruE/3HLJOa3ZoSVr/5Rr4ggoO1OJheyq0kgaI8ROpS/6WiVId4E8+FrvsPS3WfDD39Wy/OsXH54yygWtGaMH2lQQyhXiAksIxKhuGnmz6w00HEJRrCUOm7qSVOA/32666W1LF++lC76e9LDCHLTQj311ZLW6pG5a0vSQgkEbTDDABJmIB03M+Q332Fho4TFNOSMpSnTf09S11qqUUqC+RATWAaYHf0zS5Az0cC0qBE0uuNmKAwwjA2jSTdqnbaC+XxWNJzcKCHDpDCa3INa59k5Mw7lrJs1glrCGsGWAI3frQ8iynOTwzBpDCUP4hTfYrQEpKc2kWgggaAtemkRMX/iGN1xczhMgWYciCTdCDsdBbPTrGg4uVHC/NEcLFZZt1hEk137At/paA6Qyi6CMogJLHFmqwFkYaNNkWnTkW8Gje64OdNBdA3vz6024oGFjRIyDI1YIga3iwZFqdtd1KNRzHgohpZAJVoChWujmRTqEjE0u9KQe2ljfH4kQkESDSQQVKBnNorZWlMKGN1xc6aD6Bren1t1xAMbJbTiaI58hCKaM2unQdp7EJRCTGAJM9sNIBduVBAoNINGIdZSGxA3nGzzluKpH+oFVShFlo1izvVRaG5Sfnw9ooEsenYLJRAIytCzwZKVtIYwgzS94zecRqY6CqXI0gwQSbqR9tkwR4XeEQilCjGBJY5ZBpDFSqLMjTqxkcHJqB2AMYbQ4WCKiqIYYs1bWLRIJy2eIrvGshFLNi1UD0g0kEBQhx56RAygNXE6GCRV6J1YqiOLFmmZxVNk11g2Ykkg6A0xgSWKmU1ggIwwW1mUWUNYH1GWjiglKudwMPjRbVPweTPCEQpTuPUH6iJUQs1btMSoFNlqXxUioQnNO4USCARroVcUkBhAY5ASlXM6GPz3baM5evftH9Spel2hVEctIUPpCQRhiAksQfgMYP+QTaBZBoWhpBt2n12zdIdyGQjPZTI6iXHOjLs6O/DwXZOIjwiLR30dg0DjTKpKJupI0hT1hq0NJAPkCQTroPWmpJABJKl92qMkKnfoMNncIxBKHWICSwy+Hdf+IRu+cL1fZLerCS4ng9+tD/I21JBDue7M1nvsaPDlfi//34VQAGaEUGrUkTWcWtYt8tX4hcLA6Fjme7U1yO7g9vRZtALeApBOoQSCdeAzgCS1Tx+MiMppBd9GwHCYyupdXQ2T1bvuPtI0hUAQgpjAEkKoEUxUYrpDNG5TZQLL1QAaTb3HDiAt2DhFrjmUMgbDDPRqDDNzfNIghkAwGz1SQYWyTUhqn3bIra22iu5LGYNBIBCkQUxgiaF3HaBQWulkNAHABV9bNZqh7Q4r364em9oTdjrQ4KYU7+paddiqUJ0ct8MpFzFjKKXGzyzY96n1AGM9G8QAJCWUQJCDHrpkhOkwY7C6+XpHozYtP51+PO96m4q6ABh/jZSyEWA2ZpxXBIISiAksEfRsv81SPK1U+7k/4rt6a1S/plEdyNjjqDWcfOaQzxiaNQJDKtz1Gj3AWC0kJZRAMA+jas7NuC7pqXepUAyNduCZu0YQHbGj0uPifZynnkZLwClz5Sgwjc1pmyTDafQGq9mUmt4RZjfEBJYQekcBpaaVaplqY0R6j1G1DqzhjMQYRJLvosF5BmxUrqlTYjjzjWG+KTRyDIYU2LWx6yYpXAQCQQ5GRAHNuC5p/ZrcbAjWpC3M1rKnlC5TEi0BGr9bH0QkxmAoeRAB5xJQeXrnqafhto8jFcp9rlVSS/WA6J31CXVFQPcek/08xs4Aq4HhLQdApZVHo21t8+Brb1D8fC0hJrAEMHscBEE6zQEGAX8KwUQMflcKNh2yVgqihUe1f438ofRCw+VZ2PmF+QaQQCAQCNrBZ/zMoiVAo8mfQl0ihrmuJGwU3wZn4RrzU0vNNoXcDB2SylneBDftR2Xfm0g1DaO2Wp4Jp+0OxPF5uB0vwEbJ22RhqmsAAGPDk3Bsa0Swdy38606XdQw9ICaQYCj5F1jSuUs9mUYzyviPWyYxv3VG0BgmhdHkHsz1nYWmQOa4UhrPOJ0MHrkrgkCjPgaQT5hTURcqI5lLmKeeRpPG+ySkLpBAIKhBS72zkvlTC3f946HJgvpuvUzhD28ZxYLWmfRUmkkjknwXC3ynozlgs0wqpxQjGiBxgQJCXRHe79tGhwAAVft2wOV5H6GlY3AuXgjaNxdpv/RZlwxNAT1A9IvngLJJ//vbgyPZ/58MHUfy0EH4O0cx2bsHYxd+TvS5ekcMiQm0OOUUBSRdvazH/FYGp5w8I4oMQyOWiMHtmrnASWk8k0xSgN2BBp/26S3C582MQXM5GbzwyADg0eY1SV0ggUAAgHhU2TVNS71jTZIe5k/JdU7Le5L895RvCrU0hAtaaSw+eebvSTNs1g4NwGaJVE6pRvTZR8Kwe3RZgihUKGi5zdFQVwTVHZsRqO3PfGNypOAxTG0V9i37APWL/KhedjECnnbZr0OnGYwgAb97EWx2GZ9rD+f/28/C0KldCC7qgPudLgQ6Hy58fGXGmEbDKUx0tGJ85RrdzCAxgWWAR2JDEo/JBdpW6uqlZ2dJGmmgFkiF47BNzxE0O91FCHZmIQuDFFALjIcnQE1fHsyuOZQqzNG4DXUeY9ZEIBCMx6xuzxHONVJqpoMWeqel+RMze3Ju6hOheM6xKNBALUCFQ6BgU20QxaKEVtVRLZFjRL0eY9ZkZdj0zu62d1HX5gMVaARAIeWrLnisG8swv32d8YvMI+BpBy5sR0/TJuS3fHSExgGMAgCYoWHU7e+Fa1ufbumjxARaGKkdQdkCbb7RDgyTxlDyIE7xLUZLwBoGzEjEzJ5eKTU0k0IkAVR7K2CjMh+x/BoILmqFTcqNER8uJ4PWNirnpoZmGAQTgMdrzza1qY+Q+j4CgWA+Yt2e81P7tKLSU4lqX8ZUjocmEAmlDal5VmsA+UyfFhGc/GPQTBpIAC5vHWyUHQme11VqDAujhDHUpu1wOWuRSMr7G5sxCoqgH6GuCGq2vpRN76xfvBhzz7vS7GXJgteQ5gUojwdezkkfHWk7O+fnao0hMYEWR+rFsyVA8w6Cp5lM0XazK41y/3PzGT6r1E4IrYOvHgKQZwyFboyGwxRGxzLGsK6Ggc+bm8NOCtwJBEKpIdTtOT+1Tw+qfVW8c1y1JhUdBRqU6RfX/JmRtpf/mvmRQ0CdKVzoA363fhgDvTONORyeWqJ3s4jwK5tReWI7Blb0AYsaFad3lgJzz7symz46d38XPOjK/mx03I6JZw6rShctb1dAkEUmrZQW3WEzYzdN7DXzDZRVTJ9U+NarpFCe78ZosfrlaYJZKVwEeQgV1ReDoWigFQgfiYJipN18W6U9NmH2Iv26ZKxxcDlpeOrSsrRMqfFT+pnnwv38N57sK/h5MVOoxBBmNr1nrjXjoTAWTV9SzE4ZJXqnHfnnp210CFX7doCa24X+taNwnrbUEumdesOmjw4tmzGA9uAIJkPHETv0Z/hUpIsSE2hRzGgI47aP47n1cdPbI//wllG0zUtmZ+01uKmc1yynLml88BtDa7XUloNYChcL2aU1D3ZmUmXfm/B4OZLAU1zPR9phR7T1E2h8bwPsKYFmCZW5HdhGO1p0LXYnGAsVCmqqV6lQTPdrHPe6FI+mUekpvO6yo2/0hKt39eOL0FAHLFwsfZg7a6jkRv3YWipPtbrIZrrGg2jruWg89DwmdgeKfq6569TCEAL5dYTm1hCWs94Z2RQmuGk/agcPo9bxUfZ7TG0VPrrwCBy+etQvW1e20T8hct6vB0D7WejxbUKsoRd1+x/H5IZlGLvwc7J0lZhAQg5GDVYXY0ErjfZF3Fl7jpKP+KnFrJbaWmGF84pQSPiVzagJdmPK8z5iF9oQDzRyfspfXJ8Pw9iBMaD7yzWgqMK/MbfQnSV56M/wv3kQ4UPnw/uZNereBMFUXL56yfXrUqj2VWI8NKnZ8cRgr0uRUDpb92c0XL1rGE2g1lcj6XlKzV+k8xCq9u1A0vYexpZOIL54oaTPOR+O0DiY6c9236k9GO3ZjbY3DyLYe6WkqISQIVSzocBqJVcnjdZHonfK4W5KDi49iHDe+elE+6yI/kllfvs6DPm7EF3SC2b7LvjfPIHwofMx2rZI0vOJCbQgWgqqVMZDk5Y0EtwOm7PN+AlhZEttgKS3lCqsmLLYJ0IFj6k4sR3H1g7CeVo7qvxtittmf/xeAm2LLuFvm81zyB7fJgQXDaJhyyuY3NCNcf+CnJ+nq3LTyqwwVJdQ3oyHJlDtE++CrFc9YCocB2ozzcSkINUAstcA7mffGenExNxhJM6pQfWyNeqiKe0zn/+5n/p79H38V/S3dsH/fiYqwX6u2c+zrW2eYJQi1xCaEx0kWmcubPQvUbUNsQttqL74ilkX7VNCwNMOeNrRA2R1tXrnyZKeS0ygRSmn2YBsWsRwmILDwSCVKn6BZUURMN/8yZ2jxG2ZDV+TTquaQe8oodnpLVKE2eFgMBy2YSjpxojTiQY3xdsoaTbA3UmtXDg484M6gKnL/SydOD9hSlpNdveyqRfJDzrRNtKX/Rk1khsFGg9PYeKZC0j66CzDiJRQlgafHZFQWtQIsgZQrDOoEr2rTUsfVyTX/FUfeRmuxWNg5sx87nsXD8N5WjsW6BBNyXyu2xDxdSAVynyu2c/zeHgKFVuXIdh7dtFNHfb9GR0dNFvr2ONL0btQmEIk6UbY6SgomeGi1TgsPefmsnP+qkcPYnBFH6jzZ0etn9ZwdXXs6FHgxeLPISaQoFvqjZShp04Hg/++bTTbyas2HUOjPQ329l3qzqgWCF3k5KbbcFtmp0QunHoYfb2ihGamt+QL82D3BP7jl4Gcm6tUisK3fugHsAZA5ubqd+uDio1gqQyKzy+cp3uPoa53Tza9c+LiC0WfXw2YttPK7l4O+dsQFXlc8sMPc9JHmcVn5PycGENroGVdoJEpoSxcIzizjtxuoMUMoBK9a2pMo9JbgUhCfH1yDGDN1pcw5XkfwS9WgWpqQZW/Lfvzeuj7mc82sYh25XyuJ4LT6WqdJxCeCIGa2wK6NpD9Od/nWG8zaPMWpt6ancqZr3ehMIXv/agWyTy9++4PPeDq3XPrY4JGUKuNdD3qAYOb9mc1a2QVif6phdXVo1XvAXiu6OOJCbQYZqSCAvrkzEsZeppMUfB5GSw+OZ0zF4mdtacn+Tf6elzghI7J1zYb0N4YlsvgXa4wp6Jp0d11YGZ4vJpooBnt1eWQbezgdeQ0cTl04QlV6Z1GU3SN57XnpI96O3dlfxQdr8JERyuJEpqM1nWBZpFv8iISon8scvUOAFKhdFbvxJBiAENdEVCH3kUlJ8W7WuI1IBEvTBWXCk0D2bQdDgWvO73hE1zUAfc7b6Cxi/NeKutEm0XpZQZHQ3G+pZsOV+8OHbbnGEA+2OHxeplXvTZFJ377MqqjH2CARP80x+8mNYEli5GpoEbvtvKRio4iFUronvZphOmTCt9razlPiQ++wbtcSsEUZkysy+xlmAqbOuNo6ERoaRDxxQvBbeJS7y+/rmncNJfo9PccoXEwQ8dQt78XLhUtsgnWxciUUD70HAovNU1PqgHkRv+cTeLNM/JNH9NYONpBKkyKAY4mkBgJw8bJonTVFx6TjRL2NG0Cu2WQ+Rx3Y6R3D+YV+RxrbganI7CpcBwun1fxcWYDWt4vDW85AKwGElW7MHZWmkT/TISYQAtRTlFAuehpAM0enisVPeYpicEXJaSRtuTOKDBz01Tpmb0mkI3+dbe9i7o2H+qWXAhf+1lmL8sQ2DSXLNP/ezzwMpKHDsL1QTcmNyyX3SKbYE3MSAk1Cm7WixSEdEtu9I9r/tQYPz4YrxeMIxOxooZD2dfiM4M5BpX9HO98GaG6g2g41lu0BjjfDGqhjWZvOMwG2A3MqqkuxFdfjtjn27DglDVmL2tWQ0ygxZhtUUCAvakXT4dRQqmYPyH0mKckBHszwqbhcruyWkkYq32VgPr5xiUHK56uqm0ILR1D/eLFmHvelWYvyxLMPe9KDJ3ahfFFHZja8j4q3zwhuUU9gWAWUgyg2Fy2/OifWIMnPc0fH9zXSAzPvDafIWRhP8cjb21FKiJtADabhqw2KljtrcBkOFV2RlCLpjBazQZkNzAT7l7Ez81s5LYtukT1cQnqICbQIszmKKDWGGX+8ptysDAUDbQC4SNRUIxNk6iEkYYQyIiijXJYpo4wFYoZ1iXWak1hgpv2o/rIy+g++Qjq2nwkdYYHbppZ8oMuVG/fQKKCBpO5Idd2aDxQfhGaVHQU1SdLM4BCsAaw+aQ43v3EAknRPyPMHx/s6xaLDgLTn+PPtqOnaxNCOGBoVJCNPJfb+aZGN7XQQm7nz+B07V/rSZfi4/d0bvpAkAQxgRZiNkYBtcTIyB97Yaut7S/4WdpuR7T1XDQeeh4TsQCCvSdrGpXQY8CuEFZoLKNVi2s5WCFyzI3+BVeMofr8FZhHCudFYVvUj/s6MPFOFyrffIAMpC9hyjklVAwpdYBevx1Vpy0A0MtrANWYv9FJaY1iaiulHzvfDIpFBdnPsdKoIDGC2qFGC7nRP27nTzqt34gNgjyICbQAiVDcFANYThc5qe2z1fLGnmdxz+S9+E7PCixsZxBevLDgMcx0GuWxi0aROrQP/s6DCEJ8UK5S+ArlgfIxhHJrZ8oFNvo3uKIPWNSI6mUXk+ifRPKjgv73MwPpo6cuN3tpBELR8gcpWmYbHQLqajGQ6OX9uVwDmG/6XO7M83b3duIXWx7Aty75Bla1rch9jVgIo5MhMGkAqMXYVBh1juKvx66JTRMtFhU8vvNlhHAQi/odGH3mcNGooFZGsNRRu3mqJgrIbmBWJvoQWnoQzsULMa8EyhfEuuSKbVqUMsQEmkyppoFyh+Ly4a6nJQ09dTkZeOqVt/HX0vyxA3ZrBw9njj2VexFlwOCRRY+hp6If956WwINX/BeaGk4pOA6dZvDxewnMWXkFhk89jEjDVtTtfxwVW5dhcivneBW5f4PRppMVG0Ujo4OAMYZQyAB6DDivzCJ8JIqa3W9lo3+kbbZyuIOr40d2oml3t9lLIpQgUrSuOcBoqndiehZ+ZTMqTmzHe2sH4aiqR7V/Zc7P5RhArvljjR8LwzB4cPsGdEf68OD2DVjZeiYoiip4PJ1iACRyjiclQsg0+iRFBeeedyV6fJvQeMCNyj47+G3vDFoYQcA6achSzys3z3mldvNUyX2VUPTPDOSOPhH7zCSGQ9lxKPmdcLmUmlkkJtACGB0F1MIAFhuKyw4vfeauY4iO2AU7OnrqadUDvbUygNUdm5Go2obqM2YG2MJTnf3frZNH8cFwHwCgayqEw+MjaCri17I1Dks2gRk8goUTTTM/jA7lPHaytxOVHStUp49q3Ua7GFobwmLRv5YAjd+tDyKad2PGMGkMJQ8i4FyCBjel6Lwyux6w4u1HMbjsKIn+aQQ3Khhq6QNeMXtFhFJCjtblD/nmkoqOotLjKqp3VDgEgP/ukq0DtHveR//aJJynCY+BkGMA880fy67evTg41AUAODjUhV29e3He/LMFj+es98LmoLIRQqC4GZRqBAHA1lAH5iNp0S21RtBK0UCh84pm0ogk30WD8ww0uKmcQfFmRAHNjv4JGT6tamGZRl92HAq3E27BOoYL12FlY0hMoIkYHQXU6qImZShuIkkh1DuOFec4p7+jbfdPrQwgt8X24Io+OBcvxNSppxY8jmEY/Hzzb2CjKNAMAxtlw717Hsf5c87K2R0Vgp1zNijwc3twBJOtxxE79GfN0kf1aKNdDDWGkPvYYjuYLYHCmymaSaEuEcNcVxI2Svmlzeh6wFBXBJWdW4DPLcDw8mOwnUeif1ozv30dRpvHADxn9lIIJYRUrWMHdXOHfLOkQjGgAaj2CUcTuQhdf+jeY2g+KY59p1ehXmCDKBEPaWIAGYbBwzseh42ygWZo2CgbHt7xOM5tK6537DFZMyjFCAKZm2exm+Ud9g6cZPOjZutLCKF4wyctIoJWiQbynVc0k0IwEYPfleLVO6VRQKX3VnTvMdTW9oNeNInEmfqOLuIzfGY1Psonfx3chkiA9QwhMYEmwRpAo6KA4+Ep2GA39IKmx+gHLaN/4Vc2oybYnW2xXb1MOG1h2/E9OBSdSUShGRoHQl3YfmIvLpgrvDvKRTSq4wHQfhaGTu3KSR8N9p6tuqnMjBnUPyrIRcwQSnnObIBNnRk6/TCAf0L1py9Dk/dks5dFIBA0QPY8QG+d4M/sEyGgDqCamkQbwRRjdDIkaP5YuFFAIKN3UqKBXFxun2QjCGRunoWMILuJ+jE64H5nEJVvPiBpDAxrBJVgpWigHLRopKb0/qrOnUJ8zlyk/cLnsVLyz2+rmL5i5K9T6sgUoyAm0ASMNoAA4PDWq4qOWAGto38VEgfsMgyDe/fM7IqyyI0GSoGbPpr8oBMtb57AZO8eTJy5Gg0rFqs6tlYzlZQw28xdMdjUmebafvRceAzOwHykAfjdi8xeGoFA0AA5BpAKh4Ba4Z+HuiKoCXaDmlMNQNiYFLsxltL1Mz8KyCInGsjCms3RmPRaQaHUUG5qN7P9APydLyMISNokLYdooBzURgHlEuqKZDcpjgYiqII2JtDo+ZZGIGdkihGUpCu4//77cdddd6G/vx9Lly7FPffcg4suuoj3sZs3b8batWsLvn/w4EGcypP2ZxRG3ICPhyZBI43gRBXCEw7YKDvv49jCdiujlQFko38Tc7sQ/KJNdMAuy/YTe3Eg1FXwfSXRQKmwTS2Cvg643+lC1T5gct8OADMNZcS6pAlhZIpo/5CtoG6Pi5p6UD3QaiiuVBqb7KhtdaM30IiWlVeQuUmEksasJmf5DAzZMDLCr3WAMXqnZVfj4Kb9qOvdgynP++hcUFXQCAaQFgUslgLKkh8FZFESDWRho4LFkFIjOL99HXoAzB2n8fFI8WMaFQ2U2kBIT8yIArK1qlOe97Hv9Mz5qaaWvRyNHx9WMYMlZwKfe+45fPOb38T999+PCy64AA899BCuuOIKfPDBB2hraxN83qFDh1BfP3Ny+/3GRUG4GCWU7IVrOOXBTTetQzIpLIrcwnYrooUB5Iv+LZBQd8VGASlQYFD4+6FA8UYD3x15Fze/tB63nvcNrJ6zouB5UuDufMYGjwAAHOGpzM8iHrgkzE4SQqsOakL0D9nwhev9RRsq/G590FJG0EiYiRgAG1K+6qKPJRBKASOzC/gIBqvwzzd5JTdy0ROpBpAKBXmUJbfRxsCyg6DOXyqqWVJumIsZQDYKKKZ3+dHAjr5O3H3wAXyn9Rs4d6FwHZjL7cNoTFqNIMXTXKNgLXXVwEjRh2Ve21ePhI6bnnIbCOmJmiignHsstpt69ZGXMTA9BF4so6oYs8X85WO2GSw5E/jzn/8cX/va13D99dcDAO655x68/vrreOCBB3DnnXcKPi8QCMDj8Ri0Sn6MSgPlzgGMdUHUAAK5he1Wg+2WpsYAKon+sSTpJAbGgryCCGTGRgyODyNJJ+GyZzqgMgyDJ088iY8n+nDPOxtw3pVnqkoXnd++Dshb7vGdLyMwPojm1FH0dikfK6GXEYxKbKgQjdtmnQnM7pzWDOKDxlFQaCr+JAKBUJR43CWrkYsepEIx2TfiLm8dO2UhC917DI1Ndow3699ogyWZTmJwtIjejQ4jmU7C5XBlxkjs3IhjU8fw4M6NWLVgRVGtk9osplijmI66/Wg5MYTwKwCz+AzNZ/DKQW4DIT1QEwWUmwbK7aYeuVTdGIjZav7yyTeDRhnBkjKBiUQCe/bswfe+972c719++eXYvn276HNXrFiByclJnHbaafjP//xP3hRRlqmpKUxNTWX/HY9nzBvN0KAZZR/gRDizZcV4fQCjbbMUlvHpKJHDmzFMNJMCLXHTiWbSoCWuS+oxGRnH5FsPANCg4fLWKfq9h49EQXXthyu4G8cuGoLj1EVoW3RJ5rhpaW/CASee/fQvEZ4UvsB6Kz1wwJk95rZje3B4IjNr8ECoC1v79mieLuo4ZQnsxz5CqmcEDKX8vHR4azLnZmgQjNc383tXeDwWhpFmetWcI/moXTsF5b9HqQxvOYCqo3/CRGME/RdUovq0VfC7F2XPHannpZUolbXT1tvjsgRa6R0NGozG2sZqmpRrhJzriFS9k6ufqTD7u5O2FiocgpOjb9zfN2NnUDevDiMJO1K+esHPV2IkDMbrBVLCix2bCmeOL/IYIKN366/+JaIietdQNa13KQa7evfgw2AmdfTDYBd2Ht2Dc9uEtc5R40UyHi66DgCgaOFrSutJl6KXsWPINwTPttdR0RnFMHURvIs8gsejQWd1Lvs9CZpR6XVgPBTO3lfxHluH+6zix0rl/hdpVHsrFB2fknifFT4SBX3sOCqOvY2BJYfgbJ8P5ymnoLFukazrP/vYyVhm3h7j9WZ+IOG8MBNDtM6T+V1MhqejgnVeRYeRqnclZQKHh4eRTqfR1JS7c97U1ISBgQHe57S0tODhhx/G2WefjampKTz55JNYt24dNm/ejIsvvpj3OXfeeSduv/32gu/3Jj9EdUJh6hZb9J3oVvZ8Wa8x861Yyg1gTdGnRpLvIpiQtpOUrqqC0ymeYup0pjFZ9S6OJyYkHVOIE7W9BTukkmkF0OoBcDkAIDUGhXVXblRCuDB8HMDH04tkGAY//+gJ2GADDRo22PC/2x9H8ynLNGsek6EVI4tagUUA0Idook/5oXjOzf7kPuXHAzCUdAMSIlxDyYOok3jeSUXx2muh/FyTymogvvry7D9HeoARzot2H0jqvAD9sPrax8dJzSUfmuldLbTXNx5NE0daAw+peidV69JVHQgmJrLrjUhdb941pzv5wcw/VgNb0QBgHZB3nSg4yNFiLyj9F2mDG94iv8cgEmAYBvd/9HiO1t2/6XEsLKp1tYhK+oMWuyCvBmqAUPZyWkQHRe7BimpGkaVEktrfZ0kllNyd+Z9aGeddPlK1r3X6CxcAuABpAJOi56Y4J3qm/yhFz19r0b/PCK2TffHLQarelZQJZMm/wDAMI3jRWbx4MRYvnumquHr1avT19eHuu+8WNIG33norbr755uy/4/E4Wltb0eY8FfUu+V2iEuGRnN0nrcmPAHIZlvgXbnCeAb9L2g6Sfy7w60ei2SLodGwUAFDpnhkI76mn0Rw4TdqL88CEgzhR24sFztMEG9rwET4SRdXet0GPfYTh5cdAnbskG/3TgsRIOHed3txdmh3H9uDwu4ez/6ZB4/DEYRyr2YHVTcuz31e6u8Ol98gWpD48An9HG5wpDyaWr0LDchUF2eER0KBxorYXLc4zZf3e8xlxOos/CEDAuQRzXdpcUGkmjf7kPsVrp8Ih0RbtahnecgCLHG9j/xpbwTlJpxl0H0hiwVInbHYtNwvEyT+fgcJzuhh0mkH/viRazsxdOxXOPbYW57waRkf0ycAodbTQO700bjw8JRqB4UIzKRzBIUmPlap3+VrHh7ueRqNjETAKVHsrJL0+kHu9oZk0upMf5Ojd8JYDWNZwBAPOjxG89BTBzsHZSKAIY1NhOOu1/fzt6uXXuqOt74tGAwEgGQ+jpqL4eqhwuOh1Ixg7gsDBUbg/TuDwyAo0XrJU8LH556lUzSh2Hoad0m605NxnFYNmUggld8PnXAU6Mi7r3OMiVffCR6KYd2w7xhf1I7ywDt5FZ8h+LVZv0u4GXs3QCjbyzYfSz0EyHgaTBmIHa+FeMgr2dJFyHquB1VG5+ilV7xSZwLGxMZx00kkYGhrCwoULcejQITh5bvomJyfxiU98Atu2bYPL5cLrr7+ONWvWKHlJAEBjYyPsdntB1G9oaKggOijGeeedh6eeekrw5xUVFaioKPxA2Sib7JvLRCgOG2xgdBjPwNb+ic3/s0n8fNkou+THAsCcpswXAKRC6ekaCG6InILSPQYqFAQNG2dd0n7nwU37UTt4GImqbRi5yIaaiz+hqksVCzdnnQrkDQLl/D/DMHhw7xO8rbUf6HoRq5etAUVRoIZDSI3NXKSU5n4vOGUNhgJzEfFOz07adQzRvvPh/cwaRcer9HkwGYpOr9muaqQIJfFvRlF22ChtUyuUrD1TFO/RdB35OEbDcMypAEVNCgqfzU7pagLzuwjmn89A7jktB5udgs3BeXbesVN5zR6M7oRmU76nUdao1Tu9NG48NAmXT58bLDl6x9U6PlKhEQB2WXWAmRqswt8vV++oNAUHzcDurgBlY4SvGTaAcYi/GSqF3M+mShiGwSO7+bXukd1P4LyFZ4tGAym7tPVQNiA1Fha9VlA2BnbQsNM0qDQles7aYAPCkYIa+GKaYQP/QHbu86Ug9z5LCnRkHDYo02s5ukcxNtjTDOzxKOhAs2ydSsRDmXO10QfbdNpngWYogG/sSYUOG1IVXt90GnMCrgYvbA4KiVgI46mZezkpI1BkE/Bl7xnlaKZUvRPe3hKhpqYG3//+9wEAR48excaNGwsewzAMrr32Wmzbtg0UReHxxx9XZQABwOVy4eyzz8Ybb7yR8/033ngD559/vuTjdHZ2oqWlRdVapKBnIxhu85dSm2EjRrYTqMyITKgrAgBomduH0XUBzPvsP6o2gIn4TLcmptFXtGh5x7G9+CDYlSOKQKa25oNgF3Yc25tzLPZ43NeRS8DTjgUX/h3in25B/9pBVJzYjskNG7O/D7noGQmbrYS6Igi/shkVJ7ajo26/KWvgO5eNLsLX8rwnWAuzu4KahZpREEaOo9EadowEn9axYyTEcLl90mYWanyNKuXfuRhGzeHNdLSWD1d7tGB0MpT9AjLnE/fLKPJfN39dWsHVTK1RZAIB4MYbb8yOZPjJT36CRCI3//Tb3/42nn/+eQDAXXfdha985SsqljnDzTffjPXr1+Oxxx7DwYMH8a1vfQu9vb248cYbAWRSW/7hH/4h+/h77rkHv//979HV1YUDBw7g1ltvxQsvvIB/+Zd/0WQ9xdDbAJYjSi/ULd7MBUpty3255g/IbHo80JFprc0HBQoPdDwOhsmNeml1Uzy/fR3qL1mH4BcnMOV5H5VvPoDgJuWGgwqTm3MtYLuo2ZOvIPjFCThPa890ezUA9nxKxEOmGT8hiBkkiDEemiwZfZN7Ay61E6N9ovhnwozPDXeMBB/sGIl8rdN1Tb46MOMS50UQACgfDA+PvPsrrv6ogWuwzDJ9xeCuRy8jqDWKTWBFRQX+67/+CwDQ09ODxx57LPuzX/7yl/jFL34BAPjmN7+Jb3/72yqXOcM111yDe+65B3fccQfOPPNMvPXWW3j11Vcxf/58AEB/fz96e3uzj08kEvjOd76D5cuX46KLLsLWrVvxxz/+EV/4whc0WxMfes0DlGsA3fU0nE7xNkEuJwN3vbI2/VoMJ2VRfFGaxnX8KIbq1K1HrvljSdJJDEhprU3z17/l3xQrgY0KRr+8AMEVfajse1OVEVSDp56Gyyl+E+ByMvAoPO9KgeCm/aju2AzH3Hcwui6A6mUrDTGASjYxtGLXsU588bmvY9exTkmP1+K8J5iHHjondTg3H/X1CUnXHaV6x0XJKIjsGiRudFLu4o+T+vmWMqxdCnLGSMwW3BL1TovzLh+ltYCAvA136tC7QF1t8QdyYA2gGvgiflZhd28n/vbJr2N3b67e5UcGtYJp9Gmuk6qS+L/61a/iZz/7GT766CP89Kc/xT/90z/hlVdeyRaZf+lLX8L//u//arJQLjfddBNuuukm3p/lp6becsstuOWWWzRfgxh6pYEqiQA2B2jcf/8m2CdWCuatu+tpVQNMtUxFUBIFDG7aj+ojL+O9tYNw+OpR7W9T9NpqUhZcdhee/MK9iEzEwKQZDB1MIrDECYqTN++tcmdnCQrBDspVMzR0fvs6HA+No3Uc6FJRg65mfmBLgMbv1gcRFWmokEhm5gkKPcZTT5f8DMH2lR4cT7jhPPVUTepTxTB73hLDMLhv9wYcjfbhvt0bsGqu9PmYWSM4bPywXII69Mh2URoF9Psn8OwjYYyMCDemUqt3gPLNT7UbnUqprZSWfikFl8OFx665F9Hp1EA6zSDybhINZ8w0+WiocsPlENc6qUiZGWg2zYHMIHihBkKhMAUGmXmCMZ59Ey3OSb1gs1kqRg/ivbWDcC5ox3wJWqbWrHCNnxVhGAYPbt+A7kgfHty+AStbC/XO5fYhEQtJmo0pBy3nCKoygXa7HXfccQe+8pWvoK+vDzfddBOefvpp0DSNiy++GE8++SRsNsXBxpLGCgaQxe+fgN+V0rwgWUsyBcryDCB7cXJNDyx1LlGeaqdFznpzrR/NtX7QKQZ11QnMbXQpKnrWYmhoJiWWlpRSxIfLW4dUeEzRc1laAsImrn/Ihi9c7xcdsOtyMvjd+mBJG0F6NAJocy8kitY1F0rYeTxTEwsgWwN7fqu8+ZjcTRAr3/QR9EFNFJClOUBjTpN+QyHV1AEC5VGX1lTnR1Nd5h6HTjEIVifg9yvTu3KhOcDwDoIfGKLw9ZvdRbXuufUxw4yg1M2InGZ7q2yov3idpM1MNXrE3aywqgEEZupiAWTrYM+bX6h3WhtBViO1QnU7ry9/+cv47//+b+zbtw+PPvooAGDp0qV46aWXeDuOAcDx48fx29/+Fq+++io+/PBDDAwMwOv14oILLsAtt9yCc889V+2yTEPP9Bg+AzgwRIm2r66ro2H3aL4kTVG6O0r3HsOc9iii1QFMXaw80qJFyoIeqL0hfmfOEbS8OYrgJh9sbfPga2/QYZXKiMZtoqIIAIkkhWjcZogJZHx+JBRsREhCZg2FHKxg/oDpXdE9M90CbZQND3Q8jtXzzpI9H5MYwdIgEYobHgUU0zuaycz188/VfEkF6N2II7hpP6pPbMeWxYNwVNWjGsqyW2YTVF017CeK3xy7fPVIqMhykUtMotbF4jZeE6kXUrVuTnsU7y8OYN55V8o6vhoDaGXzB8zUxXL17uEdj+PcNn690ysiqAWqTSBFUbjhhhvwz//8zwCAQCCA1157DR6PR/A5v/rVr/A///M/WLRoES677DIEAgF0dXXh97//PX7/+9/j2WefxZe//GW1SzMNLS8uxQzgNdcX32H6v/uNEUY1KL35ZsZHgHNPVfy6VjWALEpviOe3r8OQvw0Raivq9j8OV8cFCGGNpYwgQR1WMYAAsG9kHz4Y7sr+m9sRV240ECBGcDZSLAooRe+cznX49SNR0ZEOalBTAy8l2yV8JIqa3W/BVbUN/WuTmjaSqq30YTQWsvwNNqG0UZoGWioGEAB2981EAYHcrrh80UBAWyOoZYq06lzNrq4u/OAHP8j+e2xsTDACyLJq1Sq89dZbOHz4MB599FHceeedeP755/Hmm2/CbrfjG9/4BqamptQuzXC0jgIWSwGVusMUjxuQj2YCSlMdWUqlEYXSxhkBTztcF1+I0XUB2NsPorpjs6xGMS5fvWk1LOWAfSIkqbGDEqxkABmGwdP9T8NG5coJGw1U2iWQNIyxLno1PhOLAkrRu2TSLpoZowV6RgGr9r4Ne/tBjJxuQ/0l64oaQFe9tqlhRiJ1TAQLuQ6UDnJ1qZQMIMMweHjXE7x6V6wrrhXfn6qr5dDQED75yU9ieHgYPl/mzY2NjeEnP/mJ6PO+8IUv4KKLLir4/kUXXYS1a9ciHA5j/35zuhqqResUg1Jpk60UJbWAOc+vU5ZqZ6WbaCkoXWfA045K31xUNDWgfaVH20URTMFq5+7O43txeOJw0fmYSiBG0LponfFida1TGwWUgi9gR0VTA6qWnKF7I6lSwirXOoI4Sq7TpWQAgUzWy4cC86ClzMgEtB8foQbFJnBsbAyf/vSn8fHHH6O2thZ//vOfcdVVVwEAHnrooZwxDXJwOjNdvRwO1ZmqhqJ1fYSWonjsWC0OHXbg0GF7wdfAkPJCbjUtstVCRQcVP9esm2i57fP5UHKRTfvrgJHSnqHUP2TDwcMOwa/+ofJvQLXjRCeufP5r2DX4vuk3Rey5vLNvLx7c84Ts+ZhyMPu9EkqLnr5CnVOrdSxq9M4KDWFqK32ajYoohlD7fEJxBoYo3nOY71weD+ubNSd3/qKi7uo8BtBK58/MWvbi6f6nVc3ItJrZVeS0UqkUvvSlL+Gdd96Bw+HAb37zG5x11lm4/fbb8dJLL2Fqagq33357tlGMVHp7e/GXv/wFzc3NOP3005UsrSzQokMal1/84hzBnxndlYqL0lTDUFcE1KGDcEY60bG4G057O+Z75NVNGH1jqaZ9fvYYs7ROSmon0RceGQA86l4rEYprcrMWfmUzKjRs7MAwDH6x+xEcHTmBXx16AU+cdrHs80cruOfyr3Y/hqHRkKT5mMXGoxRjtp33VkWPsgcto4C338X/+VWjdUZEAbOMjAAw3zCqQUr7fAI/Uns9PPtIWHHTP10boclgbCqMCm/hNd1K5w93LQ/s3IDhpAS9Syc1G5GiN4pM4I033ojXXnsNAPDAAw/giiuuAAAsX74cV199NZ5//nk8/vjj+Pd//3eccsopko6ZTCZx7bXXYmpqCj/72c9gt/PPtLMietRHGJUaY0ZXKi5KxkLUbH0JU573Efp0FeqXSWtZzGJWWtmOY+rb5wPatwe2OpPRBKJwSe4kWudR/lqMz6+6BpIdW2Kv2obgF6vgbNKmscOWrs34IHIUgLrzRwu45/KHw0dw+8XfQe1AS8FcTBYp8zGLMdvOe6tjVGdFLVGrdUZGAdP+OsWvZQWkts/XmnSdB1R0EIxHu85AWm/KF0NON1GvJ/Pv8dCkbllZVHUdAGN/B3qcP2G6UD+8tuKbity1fDR8BN9s+yaWn3tSdiZmPlrOyDQC2Sbwhz/8YTbCd9ttt+H6668v+Pnvfvc7pNNp3HbbbXjuueeKHpOmafzTP/0T3nrrLdxwww249tpr5S7LdLQSRaMvOKVE9gb7pC5EP7EA1f42RXUTZkQBH+h4XJP2+SyyoyKeatBDEQCNil5Pazz1NFxOpuhup6cujWg0YeDKlJM9P9sPYqTBhuplKzWp65mKDeP+D36r6flTDKGaBYZhcN+uR3PW8twHL+Enc3+GeY0Vus8JI9HA8oLonTkkdOwSKrd9vpxuiUKf/257L1oQQe3AYYTrq9CwQtv2sGo25d0Stc5dr2wUksNbD1rlTF8rIff8EYNr/Gp4zrHwtM4JmUG+tbwSfAVfbPwl7M7yKEGRZQIfffRR3H777QCA6667DnfccUfBY5YuXYovf/nL+PWvf43f/va3uPXWW3HmmWcKHpNhGNxwww146qmn8Pd///d48MEH5b0DkynlKKCZKI24NDbZMd7oRpUCA6h3FJC9cWbSAFCLsakwqBTwXrA7GzkBtGufX8q0BGg8c9cxREfscHhqeR/jrqfRHKjF6OHSyQpobLJjvKkBVYvmw6dRY4cdg/uzUUBA/fkjhJQhvTt79uBQKG8tw13YV78P8zAz33XXsU7cte0BfPeCb+DceSs0WV85nPeljh6zAa2ud4amghpAbaW8rpxy4UZOAPH2+WzbfCkIff4DnnYMATixYBDu4U64Oj5CMHwl/OusUVLUHMikIYt1rc1onbqSHCXRQK3KH5TirPcWfE/O+SNGmA7xGj8uNZU+jE2GEKZDvEaQby2HJw5jd99erD5ppsxqd28nfrHlAXzrkm9gVZs2emcUkq3sq6++ihtvvBEA8IlPfAKPPPKI4GN/8IMfwG63g2EY/Md//Ifg42iaxte+9jU89thj+Nu//Vts3LgRNlvpuWuzooDsDpMZqBFGFiUXH2Yipmr4ttZRwNHJzNyXzR9vwT/8/hbsi/VmL2zOem9O5ISL2vb5QGl3TBwPTaKpMY2l51Rh8clp3i8z6lStBjcKyEWL84eFPYeBzE2ZkAFkd0Xzi+JtlA1P9z+dPU5+/asWaySUH6Wkd0algjIT8jaVzZpzK9S0gxs54SKlfb4aAp52LLjw7xD/dAuCK/owGXwOE8+8iFBXRJfXk0tzgBHUOS20jt1IkfOZsmJKt1bnD1/qpxBCRlFQ72DDw7ueyK4lv36x2BoTMWsNjJfsuD71qU8hmUyCYRi88cYb2S6efJx66qlIpVJgGAZ//OMfeR9D0zSuv/56bNiwAddccw2efPLJkqoD1As5u6LsDtPGX8Xxw1tGdVwVP2Z1BlWCHoaJvXF21nux/t0X0BM7gbs3/x/+7pmv492RdwEAnbEeHAod1bx9fil3TCw2/9JMMgXz+sxAUwIbBdRj/IJU88fC7ormF8Wzu6OdsW4AwJsfby6ofyWUPmZnvZitd4ahYpPTCLg3vd99+QfYzWmJz14j5LbP1yoyOb99HerOvxBt589FY5P6+8lSSldWYgSB4p/r3gMjSB46imN/2IChaJfoY9Wi9Pzho1gUkGVM4NwT1DvQ+DA4sxa++kW90TIjxpSwGxsB3LBhA770pS/hqaeeKkkDaIWbRXaHaUGrsnxyM1CaImMbHVL1uloaJ+7NM/ci0Bc9jp7oMTx54knQNM27k8SiRft8OVDuetgnrBE9tKIBtBpsFFCP80eO+QOEd0W5a3l41xNw1DVgfedM5FLLiCXBfMyOHpSS3lkxFVQLuHqXSCfxv2/fD4ZhpF0jeKI5cmsTjc6AKSWtkrvWYp9n/7rTkbjgWjR1XY66/TTG3+9AT9cmNUsUXovC80cNYwI1gVLXwt7jcfVObI1ajmfRqjbelGF8d9xxBzZu3Ija2lqccsop+PGPf1zwmKuuukq0ltAqaJkKatbFprsvdy9Ai/z0YijOQ6+rBaA+FVUNo5MzRfX5hcMshycOY3tvBwZHg7q2z9e7UYbLV49EKEgaH5lAkk5hYDKi+fmjZDhvMp3E4Ij4uTw0Nozt3R2FNYMmdzMlWA+zrgP5WgeI653aWbhmt+DXGu7NMXstOBY7gc54J9bSZ0vTOxXt80ldcHEcPjfGZZ63YrWBvvYGoP3zmHilASftfA8fnzeIIX+XJk3PuCTTSU3Pn7FJ/prA/MgfXy2gFL0bHM3ondz6RSulggImmcDu7m4AwOjoKH7yk5/wPmbBggUlYQLLgR/+LLcxR7F5SmYOiVeClnUTXAMIFBYOs1CgsKHjGTz65V8iNlkYMU6ORgEAc93zFRtAMwSxf8iGqEiBu6eeRktAfJde7maH1O5qnnoa5gw60Z5EPASX3Yknv3AvIhPCmx5yxy8oMYAA4HK48C8XXY87/nx3wc9uvugbmDt4Mhae24j/eP3HBRsiRnQzJeiL2amgWpGvdYC5s3ILqKsBoG8dm5rUSyG9e+T4I7jcdi4eu+ZeREWuV0Lt811uH0YtVis1HprE4LAdoxHhLDU5G+aRkLg6Nfjs2WNq0U1UaqMYqaORqLktqIxHsHDChkGhddX7kBhWdr/lcrgUnz/5eG0+hOmQYKpnsdEQYnr39blfx3nnL4O31oNb//gjXr3j62aqVS2g1vd8ppjAjRs3YuPGjWa8dNki5cLhdDD45+vHcc+DNaLH0nN2IGWBAaVKyRdPoSggkNkp+jDYhe5IH/+OUEDb1AAjkDq0/Xfrg7xGUOnuv1h3tVQ0UxvU3OZAkz+N4xpNk1DVNW1kBGn/XNVrYBp9aAbQXKtNFFapAQQy5/pv973EK3ivfvgX3DnnchyNvM97g0iigeWB2amgXKyud0bonJq0SKXXgPwoIEt/oh+7evfg/EUr0VRn7nmS9tcB+46rPs7gsB1/9915RfVObAMh3/hV+6p4Hzcemsg+tsIOSd1EA34aQQG9c/jcSIVisjqGFtM8ujYAHN8t6VhSScZzh8U31fk1O3+kzAAUQkzv/hr+K/6h8XPoONEpqHf50UCt7/W0zP4yxQSWA3q0ylaD0I0yzaQRSb6LBucZaHBTohcWKWjRFVQJ4Vc2wxnpxHvndcNRVY9qtJmyDilRQBY2b1xovo2c9thWICpxiG00bhOMBird/W8OMAI3aVXTkWkbaI028rUYGq8GPZsYKZ0NJnSu00ymSL6zthO/2fUs7w0iMFO/SKKBpYfWUUAtSh/49I6rdTbKDnc9rUrvjNS64S0HgNVAR9Um1Fd5JOubkQ3CiundL7Y+iNUnnaPq8y11ZqAR80KjI3bJQ9vztYlr/oSMHxfuY8ZDE6iwpxBomIkO8lFM7+QYQbM1z2qI6d3hicPY1bsH6zueFNU79t4vGQ8D0CYNVI/ML2ICS4CBIXHzxqYk8N0o00wKwUQMflcKNsqBmAZ6bmQqaPhIFDW734K9ahtCn66Cs6kd89vXSX6+VjfUQlFAoYsAkIkGSplvI2dYbsFrNGbSL+QIIhUdBOPRdpiu2YyHJlHpLZ/LmV5NjBStpci5ToHC0wNPI8QM61r/SjAPIzc8lepdrtZlvqdW7/TWulBXBNUdm+Gs2w3gGtSfexoWnLJGt9dTmgoqRe+OxU5gZ88erF5wDu/PiyF1U9TqdYGsAZRi/vjgPi8Smsj+v5ghFII1glKRlAETHZe9jlJCit49vPsJDEmoXxyLDMJld2pqALXe/Cifu6YyZWCIwjXXu1WlJGiF2p1RJSkyVXvfxpz2KPY32FC9bKWiYmStbqi5N9HFipi5WCEaeLRqEFODEVSNLUH4aBQNK8rHBMoVutmEWgMISCvYDyVDeOjL/4sJOnPTwta8Vrk82cfJrV/Mx8o3fuWKHlFAMaykd0qRqnOhrgjo3mOore3H8GmZ99u26BJJr2F0KmixRhksD+3YiPPmn13y0f7MeSrvWqXW/PHBHotNF1VqBKU0ipESDaSq3AD6RR+jpi7QCkjRu+BYCA9/6ecYSwgb4poUrZkBZNEj+k1MoMWJSUzB06uGLx+jG8L4Ana43C5ULTkDPo27UamBLWIOjg7j//3+PzCenBB8rNqOaGoJeNoxtAw4gQ64hzvh6vgIwfCV8K873ZT1EIRJjIRBBbS90KsxgMDMuR4Zj+KON+5Gd6QPDMOAoigsaGjFf176bTAf1GCeZw5sjsy1KlGhT5MHvVPACIVoHQUUSwW1it4ZtanU0uZA7YkUKL+CGj2ZN9lqGsK4HC489MW78bXffhPxSeEZjcGxsGqtU5MZoyUOT2ETISH0MIBcqn1VOXWDSsyg1PpAVfXwCkjEQqo1SkvE9G6+pxX/0vhvWLQ6gGaP8HUxEQsBFdp1AqVkZnvJgZhABVitHtAItIgCqiHtr1P1fD1oqvMjUNuIOfVNOBzqxoKGVvzg8u+CoYHIu0k0nOGEzU4V7WhlRGe0gKcduLAdPU2bwGw/APfwbzHxzGGMr1yTaQFd4mR2O8OAdN0ue/I72aqhqc6Po+FeHA33Zr/HMAyOO/Z+KAAAboBJREFUhnsRm4zjJNd8TV6HYB0S4VFUVmt3M1hq42GUbHgq1bmUtxoYk/ZYo6OALPMa5uLxr/wfvvXSf6I70serd75ajyoDWGp18gAQj6YRaNDPALLkRwXdXunPlZotY3RtYE2FF+OpsGGvJxUhveuO9GKkYQSB2tMEn8uev1oaQD0xZVg8wXgGhmw4dNjOOyepGOzFQ20UsFS7goqxq3cvDoe6AQDdkT5EJ+NY7D8Zi6oXYbH/ZCwOnIyAjG5Xu4514ovPfR27jnXqst757etQd/6F8C5tQWOT/N1EQmmgZtefD24nXC42yoaHdz1BhsGXKUZGAbVkYIhSrHdqMULnzEq16470oTvSl/3/fL2To3UAsLu3E3/75Nexu1cfveNDj018vQ0g32tFw/Ii4ZmNUg02YurqYA+OqD+OhRHTu6f7ny46DF5rA6hnBgyJBM4CgsEq/PNN3qJpNix8M2jUGMBy7TqVPyKCnQ+z8uoVio933+4NOBrtw327N2DV3DMl1VYoaQ5jZaQ2hrAijMiMI0nP93qhVTWNlik2xbqD7qvbh8txLgB9Rp/omQ5D0B8jo4ADQzb87Q3idYVc8vXO0Pri0RCoumoASX1fRoOsAD307sHtG9Ad6cOD2zdgZeuM3hVLCdVT87Lda/Ud15hlcIhCLC58rrrrGTTl6V21rwpjIf2MmBYpoVQJ1wUW6w66u28vVp+U2wBJqzmALEYYQICYwFlBPO6SJIg/vGUUC1rpnJtsrQRR6QWFmdB+SLFW5F8o2Pkwu/v24iTIr7fbcWwvPghmjqf7XLUR6QLi8tUjEQrCU98keWi7UsqhMQQ81aa+vJZpoIDE7qD9T+MyZhUwbWGtUNNDUAfjlZFvJgGjooBS6goBfr1jMTIVVA6JuHk31lrrHfd43C7aVkkJlTSL0smgqa0CkNAgjo/BIQrXXl9VVO+eXD9RaAS9VYglMhFBr0/arbyUJjFapIS66n2y0patVBcoqTvoridw3sJMAySto39cjNj4JCZQJlp3S7MSC1ppLD55JsVAizRQLYTR1mC9ekChQfFsetydc34m+3gPdOTusuo1V01pfWVLgMbv1gcRFYnSeeppwRmBQOacKoXGEEaTGAlDi4JGrdNAAWnd0oaTw0jSKdhldtSTAukKWtpYtRYwX++0QM5mp210SNPXFkKPKCCLGr3jiyoKddE2A6HZyyzxaBpNbRUF5kwOsTglUe8o0ddR2jnUCtRW+nTRLaVI0buhsUyzP4xlNtO1NoBGZr4QE6iA2dAURqs6QKB8awGlpsdJoePE/mwUkD2O7tFABbQExE2eGNW+SsveEJYDWoyD4IPtlhYVSHWl0wzoA9Vw2Z267eCTVNDShP28GxUFVEtKQit9LXHFg4DEPTmtZt4qQYreraTbQdGA11b8syoUVSw2U9do+GYvAxnTlWkGY342SrW3ChNhfdOJZxNiekenGUTeTeKk8/y6GkAjISbQ4khJSeCr4VODVgawnGoBayszHTxdbp+i9DgxGIbB+s7f8u6y6hUNNIvBYTuiRycE22+HwsreZyoUQ7W3ApFE4c/6h2yyopdKz1ulO/uJeCiTeneUZ/Ey0SulpqnOjyaBpg90ikHQNbN2PeoiCKWLHANoht5pgdwZuMFN+1F95GUcXzCG2Ok2VDWuwoiE7qBGjoXIvqZEvbuk4peYSEcQpkOiRlA0qjgdDWTXbuW0cinNYIrV+ynVOzGK1dTXpu1YaN1fqyUQ0js6xSBYnYCHHgXs+hlAIzc9iQm0EHypcsVSEgBtG2VoGQEEyjMKqHV63K7evTgUOlrwfStFA9m6QDVR8P4hG/7uu/PEaywc8s9jsbrV/iEbvnC9v+hN5e/WB3OMoOLztq4WgPGD67WuA7QSJApYmiiJ+hutd1yMaAgT6oqgumMzqkcPIriiD9T5S7GgfR3oNIMRqN8E4kPtdUGy3qVTqKn0YWwyJGoExaKK3GigFeoC1SCl3s+hQO/4qPZVIRKawFTaIaGmvha/Wz+sOKOHkKEcDCBATKBlEEuVE0pJ0JpUdBRo0MYAahYFrKvR5jgawRYwS02PK4aUXVYp0UDZ3dI81WCOGmtWohLq/ZIpeTuj3E0Lmkkpes1EkkI0blMlipHOQ6jatwOTTXEcrQqiGm2Snpdt9JBSfjNgpXoKAgFQlwZqlN7xoXdDGLr3GGpr+9GzKorqi6/IzG+VgJmpoNz0uBgdBQBUVXiyP2fSDOz7quFyZPROzAhKarrBiQYaTbYzqAjswPZiSKn3S8nUu+KvKUXvbKr1brZTU6Ft0yyzDCBATKAsEuFRVLQuMnsZulHpcaHap91cpXKLAnILmOWkx4khZZd1cHQYSToJl137phtyoVRGA7VE66g1oGzzgk3tmlgwhoFzbKhetlLSzZ2aG7tdxzpx17YH8C8r/z+cM2eZJaKAyXgYdTXapoKSKGDpUip1gID6KKAcratvqQIVaJRsAFnMSAVlYfUuTIdQkxcBYVIMYnl6xxrBfCTrXVp9jZvacT1iGDkXsBTRetNid28nfrHlAXzrkm9gVZuykSRakIxr08CND7O0jphAi1Gse6IS6usTurf256J1LeBAoheQGFnRG25toBawu6zBYA+qXB7ex3ir3JYwgJmUUGt0x9Uyap2P1Bu6UFcEdO8xVB95OSe1Sw5K2r1z50k+vPc5rD71YtnHIBD0Qko0RU+U1hUa1RCGGTdu0LaWm0NhWv2NfbEmUwDQUOWGy+GSlBybiAtsFJk8pqeU0er+Taq2FdusEJsnWQ6YXfdOTKCF0Kt7ot8/gWcfCWNkZCY9MRUdzf5/pcdVtLW/VNgLiNoo4PCWA8Bq4EDzVjiqqiSn1/GuSYehpVrOtWmgbWide07xB+pApplJg6zn6B0NdDhopFLCEWmXk0Zzm0Mwaj0wZEdsJHNpO9onvW22EvFraXOg1lGL4fOXYr4MA6hmp5Q7T/JQ6KhgRz2r7J4qwWxhJCjDCt1/mwO0rLpCNVFApTfMKd/sNSliWTRyYBp9ulwn9NiIF8PhYETTQjMbFsLlAoNDFIb6Eqj3uNDdp00ml+j928gI0v65mrxO9vVE7qWE5knmo7fe6VmjambGCzGBFkSPi1BzgMacpnT2+LlRlMJaKjWoMYBs4by9cT+Az8Jx6iIsOGWN8rXIGFrKpth994Jv4Nx5whcRNi3USgNOWQR3Rvmok5/WYEQ08O7bomj0Cm9IiG1YBINV+OebmiUNi+ZCRSNAg4Jzd1S+MLDno+Io4K5Hi87XKofdU5IKWlpYaRyE3LpCNVFAvcse9KoHlHvT7LX5NIkGWgGhjUwzxhj96LZJ+ERKzNz1jOCMwIze1cnWOyG0iAJqeb5KnSdplN7VVHgR1bCBkxU2O7UrACNoAitGenQqS4Vi2TlIeqS+aHEBoXuPYU57FPEzM6dm26JLVB9TCtwUu/t2bwDDiDfqYDtDqd0d0nJ3SY6pOFo1iONT76Bq3w5EOg/Jfi09x380emksOTkl+CUWsY7HXYoFUc7NHJsKynRtw77T5f8ulEam3/x4Mw6FjmZbq3M76nHh2z0lEPTGCgbQKIwcgaR1Jkv+TXMxvePCV+tXSmhh2sdDExqsJIPPC5xyMi34JTYkXo3eCSH2+5FaZyn1fB2bCov+nNWxctY7szc7iQm0IHoYtFQ4rtuxAe3SQFmcldoWXhfbceGm2LFjGYrBNYJqzJzRs5ACnnZUL1uJ2Dk2TMztgqvjBQQ37Zf8fPZvXE5zIJ0Ccwv5YKPVrvjjOPKJUVQvWyk5FVTNLunIxHB2niQXdneUvZHj7p7y/dzqWGF3lCAPs+sAlaJ2OLxcvbNPhEDVmZ8KqvSmme32WcwIamUUrdj5uMEnvcSglCim59k5uCL1lkr0TSibKl/HsusoM70zG2ICZcB4tW0LK0a1rzIbuVMC+9ys+fNW6F74btVuoMV2WhiGwQMduReRBzqkXURqK32Ko4JmzkEKeNqx4MK/Q/zTLQiu6Ms0N9m0H+EjUUnPV2IEPdMNG0SPq2GDIqm4nDQa6qWljgU37Ydr25MYbP8zRtcFJHcCBdSlgY5OhtBxYn9OFJAlf3dU6u6plTF7d5QgHSvUARKko/amuZgRZL8vNjBeClYrtZCLu56RpHdi9X5ijIeVRSP5NFbyJr6EEhI5+uasF76nztcxlnLRO6t0vyY1gRaGzU9njWCxndZ8w8jOTovoM4M2CxUKWtYAchFqEMONAgLKhrRz6wSZNFCsjTBrAGsrfZJrEaUgd17g/PZ16AEwd5zG6BwHBmS8FlsfKLVRTEuAxu/WBxEVadigVYOiYvz4G71YOGcKTk8tGurTaJFRP9S6EIgvXoi5510p+TlqDSDDMHjsvReLztda1boip4aCRaiWgkDQgtkWBSyVLAi+uvX8Ye35Q9qlwNYHjk2GkBnPWouxyTAox8zPhdC6gYdgHXxdraLGZ4C0vgzjoQnRURFNAQZPrp9ALC58vRWr9xN7Xbn86JYIFrZmNC5fY6UYwPArm1FxYjveWzsI54J28L3r7MxbCWTSQIXvkaTOkyR6px5iAi0OK1JcMyjl8UZRKmIo1CCGGwXMv4hIGdLOhY0I0ikGUSSQjIdBTWeOsELMjf7VVvoKahFXzTWngQdVV51pclIXkPU8JUbQCkNqF86ZwhnnuACZRd72iRBQJ6+7n1oDCABUbb2k+Vrbu3fn3OCxKLnRIxCKUappoFpg9Y1P7lxblvxGGyxKbppZo0fbGASRQIPNC5tN/LlaN/BQ2iE0o1tBwFtoEKU0h2nw2REJpSUZQbkmTwzWADb47KAZBhiU9ryFrWksObmwAWAxAxjqiqBm60uwe95H8ItVcDa185Y+yEkDlZLiK3WeJNE79ZSkCbz//vtx1113ob+/H0uXLsU999yDiy66SPDxW7Zswc0334wDBw5gzpw5uOWWW3DjjTcauGKgf8imKgJitLmTgtZ1gEaQHw3MjwKyKIkG5lNT4YXNkRG4UYHIX3r6dQCofj0usrqEAuio24/FnSFQdSmg1SPrtbhGEIBlhsmLkakBlGcA2d3Qd9YOwgljUkCBmQ2EYvO1PJX1uPXVHxfdPSW7owS9GRiiJI9oMAO1tYClSn4UkEXPm2Zu5I99HUC83b9ajlYNwj3Vj6p9MYRqA/C1ax8NlGoEtYAb/dOqJrHY/Vtw035UH3kZA9MzcKv9bbylD3I0jtW0TBqosP5KmSdJ9E4bSs4EPvfcc/jmN7+J+++/HxdccAEeeughXHHFFfjggw/Q1lY4S+7o0aP41Kc+hRtuuAFPPfUUtm3bhptuugl+vx9XX321IWvuH7LhC9f7iw6v/d36oCWiJFIoRQOYHw1ko4BiFxG50UAh2CghN/L3q12PgaKonPbHWrye3N3R+e3rMORvwxFfB9zbXgdwDYa3HEBgzXLJx2DPg0QojoFDUUTsjYKPNSrlUyu4u6H9a5Nwnsa/G5qPlgYQKD5fK5FKSNo9TaaTcDlcstdkBFapkyAURyhaMjBE4Zrr3UX17rn1MVONoFKUlj9QUYlhG77nKpx1W1vpw+h0SqjUFDstb5q5kb8HthXqnVavx930DHjaMbQMiKEDoYWdaHnzBIK9V8K/7vSC51HhEG9WotRREQ0+Ow4dYhA/mkClh39TQUnKJ4se5g8Qv3/LNj6r2obIpTZQS4Rn4CoxgC63D3Sq+O/DanrncvswFg6hWKlPqVFyJvDnP/85vva1r+H6668HANxzzz14/fXX8cADD+DOO+8sePyDDz6ItrY23HPPPQCAJUuW4J133sHdd99tmAmMxm1F2/gmkhSicVv25lht5FBPStEAcmEFNUknMSDlIkIn4bJrc9PMjTweHD6c8zMtoo8scmsDs8IZOA6MAVVH/4RgmoKtbZ6sXdRQugGf/+4cJJJiw9712/Cor0/A5WSK3IDKawTD7oZiUSPql10sqRGM1gZQClJ2Txuq3JY1gITSgy9SEpOod7G4LTvLz8jIoZlRwHSdR/Zz5My6FSIRCwE1dYZvEnEjjx8GC/WuWDTQ5c6YWLEO2nybngFPO3BhO3q6NiFC9WL+B29j9JnDGF+5JqtnLl89JkNRweNmjKB4NHBgiMLXv+spuuHx5PoJyUZQjvGTpne5jWCE7t/YsUeVfW9icOlBOBcvhOvUUwX1TonGadnsh+idNpSUCUwkEtizZw++973v5Xz/8ssvx/bt23mfs2PHDlx++eU53/ubv/kbPProo0gmk3A6nQXPmZqawtTUVPbf8XimwybNpEEz8gerM4y0XS5m+vgDQ3ZcfUPxyOELjwwUHYhLZ7qUZP+rFiocAgPA5a3T7JhcGDuDtM0GBpmLH53WdqfYUeNFYiQMDIXg8Hrx+Gd/ieik2EXEAwfjlLRzxcKuOX/tDMPggd2F9RhcbJQND+x+HOc2r1C9O0rRwGQ0BFedtK62jXUng65ehO4DSUQuGUXdgWfg6jwXQ8dPQuMlSyUdIxSzixpAIHMDGIkxaPLL/yyJQTNp+P0TeP6uA4iPuOBw89ftNdSn0eRPgxb5k4aPRFG9449weA6i/9IkHKcuz86sLHZOJkYys48YrxeQeN6wxxwdz9SROuu9ss45Fn9VI/xVwlFYAILH7ejrxD1bH8Q3L7wRK1ulN2xgj8ekhY8tFYrW/jPPQmt/uSoLlOodDf6fi32uch/H6p0Nf3uDt6jePftIGM0iG0fsWopptNC6pcDqnzLto8HYkdE3xp5zngtpRvbnNMAo/GxVO7yZRhzxEay/WoLeQabeTT82/zkMw+Ch7cX17qHtj2PlHGG9k3JdEbputJ50KU6EXoOHaYDzuANjFJ3zt3M0VANJ4b8njTQSoTAcXv4N70jMIW2DP5ZGwJ/7OxDq7unxzhg/0c8fk4LfP4En7zqK+EjmPrbSXWh4PPU0mvxpMKHpVExv3fTzZ95z+EgUVZ1vI1G1C9GLKGDxcrSI6J0cjWNnAXI1TeickYvResf+yrTQKD21DpCudyVlAoeHh5FOp9HU1JTz/aamJgwM8Pc1HBgY4H18KpXC8PAwWlpaCp5z55134vbbby/4/mByP0YS8mf8DCXdAJokPO4g6hIxHAm5kUgWrotLIknho9AhpD3SRkj0J/dJelxR2Ei4Xh1HVwN7cUb2n90Hkjq8yPSbOJoA4EYdhHf6UgCOK3yz/fty194Z78QHw4X1GFxohsYHw1344+bdWFGvtnOa8j9WYu4XEJqbXRXiCWlzBI8n3QDmFX0ce67rAb3giGjCxgSAj4v9SlqBaOtyAJmU2NQY8PF7Un+P3PNLHrGDen/A+GEYBvd9tAHdE3247y8bcNcpp8nehIgdrEVM9brl12pKZXzc2N9pqaBY7wT+VJGkG8Caoq8bSb6LYCKG7pAbiaT44xNJCt2h/bBL0LtQcrf4A2qhvGO2mo9nFYCVwF6cBQhcT4T1rlbR9STn+cjMBPOK6B0ABBV+/kK7C/XuQ556ey40Q+PDYBfeeF1M72oRLbomketGxTrsagPQBgB9iCb6Ch4ieH9U5O8t9VwfTe5BLF/vBEQqKPPXX7FgP8Sq8NMAjicg/l5agWjrAgALMv8uqndyNE74hfPPGaNQrneZ95J/b6cM/bQOkK53JWUCWfL/WAzDiP4B+R7P932WW2+9FTfffHP23/F4HK2trWgenYc6z0LZ6x3hiTbyEXAuwVxXUvbjxaCZNPqT+9DiPBM2Sl1OORUOwTW9i6QXw1sO4OS6Thxoj2CqYg0WLHXCZtenqDcxEtZl9iOdZtC/L4mWM2fWzjAMvv+HZ0R3RVkoUPht/Bl8es0qTWozqHBmJ05KRJBOM+g+kMz+3sNH3sXEof2oPlKJyuhyjAXEo4KTTmmpF03jczBvVDjNlPH6MvUaEnBxdja7kx9ggfM0Rec6G/2b8hxEaEkSjlMXZaN/UsjZHZXJ6HgYsYO18K1yZpsJGcmu3j04/G4mXevwxGEcbX0f57ZJS0mmUwxCu5NwLxlFbbW6zxMVDkuOXMtldETbyHO5IKR3Tc7TUe/y8D5nPDwlGB0JO6XdVjQ4z4DflZL9eCFoJoVQcjd8zlWwUYXH5M7MVYoqDYwNYfj9QZxcuxfvnTOVc23Jv+7yoYVejU2FRWezKYH9/HOvXQzD4LnnpevdcyPP4LK/Eda7ZDyMmgrxdQtdO3qPbAEzNATPtkm4Js7FxFkXwbvIk1n7tGbMGW2DDTYwXuF0xfFwJlrOPe+1Onelwp7DAMAgjWjtQdF7O1ZD+c7Z8JEoqvZmon+jp1GoWnw6vIvOKHgci1x940YA8+E7Z4xEqd6x6+be2ylFzn2ZEqTqXUmZwMbGRtjt9oKo39DQUEG0j6W5uZn38Q6HAz4f/we+oqICFRWFQmGDjVdcikFJvCGlKDtsFCP78VKwUXZFa8++VigIl8+j+PmSXydNwU7ToJCJZdvslG4m0GYDEA0rqtuSdHw7lb3Abe/bWzQKyMKAweDYMNK2lDa1iIFMzURqLCy5RpD9vTeeciZwypk4vvNljBx6Hb4DSxASqRWUar5cnjpU+oRvxhKhiOLzLXOuyzOB4Vc2o+rEdgxla/9WSx4CD2TqI2y26foUmesdnQxlR4nYHJThosgwDB7Z/UROw4ZHdj+B8xaeLWsTgrJD9dopG/T7vGvXV6GsENQ7Ec2wISX8M4mfvczx5T+++OMcvGuzwa6qFlCtBlKwgUojo29Umvc8F9M7LfSqzuHD6JiymuNicK9dO3v2Fo0CsjBgMDQ2jDSVEqzfknRtCfiQ4qmDX3DKGgwFujAW3grb/q2o2NqLUM/anEYxlV43UuExIBwR7G5d63NgPDQJOjwGIFMPq/W5ywd3NBj3HKaZFKKJws8pd2wX3/nKrf0LTtf+VZx6KhpF9E6uvrGaVuwcK1W9497bKWb6vsxsvSspE+hyuXD22WfjjTfewOc///ns99944w187nOf433O6tWr8fLLL+d8789//jPOOecc3npAKzAcFq+pMppSGQYvF7bgXmnnNakU60IKAAs9rfjRpd8Fpi+x3iq3Zs1ogJniebmjI1jmnnclQr69iDW8i7r9j8PVcQGCvSfzdlzTAqPOt0jnIVTt25Gdg0Q1CXdC40NNAxggI5Yz3dLMSVfUYng0gaCU4bD+N4FSZuxaHS0axAAz8wP5BslrQbEupACwoKEVP7j8u9mbbikNPEYnxRvEsPBpXMDTDny2HccDLyN56CB8B4DgJsB36WnZx0iZeZs/tzkVdQHQXqvyz1cpmxe55k947EPt4GEkqrYhdqEN1RdfIbrZKVffuDMA9Ti3tEALvRubCqPOYc33J5eSMoEAcPPNN+Paa6/FOeecg9WrV+Phhx9Gb29vdu7frbfeiuPHj+OJJ54AANx444247777cPPNN+OGG27Ajh078Oijj+LZZ5+V/doub63kodhK6R+y4Ts/kj/TRi/K1QCyGGEEi3UhBYD41CgWeedravzyUWsEfe1nAe1nTQvpn+HvPIiJwdyOa1aGFUAAoKYyIutKnsCxtYNw+OpRvWyl7OgfYGwHUK3Rcni0Wsh4iNJBSudEKQwMUfjej/Rtuc7eUKuNAloFLbRKTyNYbNA3kNG7k3zzJXdudLl9SMRCRY1gMY2be96VOI6X0ToOdPFky0mdecueS5URdbfQQpsTcs5VKhwChUzgQGzoO917DNVHXsbgij7JnT8B+QbQbE0Tw0p6ZxVKzgRec801CIVCuOOOO9Df349ly5bh1Vdfxfz58wEA/f396O3tzT5+4cKFePXVV/Gtb30L//d//4c5c+bg3nvvNWw8BJDpziS1jW80bkMqZf5JWOpjIOSgtxF02V148gv3IiLSyljryJ8Qao0gkBHSoVO7EGnYirr921CzNYZg79m6RQXVws49qkz0YXDpQTgaMvURqenaIGeTtLl/LEoEkouVxNKM4dGE8sYtUe/c9TRiOuudFgaQxQpaqKVWcY0goN31SK/W/awRLEaxObkpXzUAGvYJ/sdwZ95qtemfio4iFSrM9FB6XlKhICjQQG2m3k8sLTW4aT/qevdgyvM+IpdqH/0DrKVpYhC9K6TkTCAA3HTTTbjpppt4f7Zx48aC711yySXYu3evJq+d2SmSd2FoCdD43fqgpLl/Yo8xCrMNYO+BESRtPcAy415TbyPYXOtHc61+EWQ5sO8vMS2USswgm17Ts2QTmO274O88gcnePaAql8Jl/zwSaWFRkjOnTwnDWw6ASs/cWFYfeRkJfxgjq2yoW3JhJqKpELkCuetYJ+7a9gC+e8E3cO68FaaK5e7eTvxiywP41iXfwKq2FaYMjyaUFymeaGBzIDMIXsrcv1hc8CGaodYAWikKCGhvBAFoHhUsNuhbDVLSQovNyX1nzhG0vDmKyJ8AXOrhfcyMGRSOCkrd4G9uc6Dap+7eLv88dHnrCioIgptmunizJrf6xHYcWzsI52nim51amL98jTELvnUQveOnJE2gFZC7Q9QS0Ha4u9OROwBUK8w2gP51pyO4CQgcaUP/MqBnx29Rs2yFrDQ9pRhVI2gVuFFBQJkZnN++DkP+NgQXdWBOdx9Wj8Tx9tcPIjxRg7HQFCbSbZhceg7cbTPnU0N9Gi1F5lsqYXjLAWA1sMjxNuzUzPH3regDdb68Wr98lET/GIbBfbs34Gi0D/ft3oDTfK2gKMoQAximZ9brtfnAMAwe3L4B3ZE+PLh9A1a2nlk0bUuP4dGE8iGTEjrJ+7PmAFN0hq0cnI5M5FAOWg6F10IPGU8T7CMHAQ0abGutVXrXCWqF1LRQYMYIArnaltUsXwfqOt8E8HlE/vQ2fFes4X/NPDMIzBhCORv8cikwfXnnIHfOH5vt0lzbjzr3dJ5rHTBUF8OJ8xOoX7ZOl9RPYMYA8mmMGWZKaB1E7/ghJlABUgqI9eau2yKamkrAfAPI4l93OoYPzwHQhwUdjehBB4aWwVAjOFtgL/qsGaRpQHCAkQABTztwYTt6mjahHwAQBxBH5eAgKo+8DX/nW5gaPx++C5dkXtNTfGamHNjmLs7GQwCuwcHlk0hzOo9W+8XTX8RQk/q549hefDDdIe+DYBc6Yz26pppwjR8A1EzfJIUnQ9jT8142DYab9pKftvVe/0H8fMv92X9/6tRPzCpBJMiHLxqoNf992yiaA9IHK2tlAK0WBeSihxEEgFGN00O1Rq4R5Ct/CHjaMbQMGAkcB8YA18BuhF8BmMVnCNa3c++LuIZwjh2Y0yBcP1gMoXNM6n3Y8JYDqPnoFXSffAR1bT6kl7RxftqABQKbn0pr2oWyWbiplmamVgqtgy9NmegdMYGKYY2gWTR6y9MAsngXeRBN9KGyZi5Wp1vxroGv7arP7CDOhmggS/a9DmUu8ImRMCo98t5/QaStHehp2oTgokG433kDVe9GEBpIgxof1WLJWXxNcXx04RHYfZl5O3NWXqFJ22U1jV/YjrDcNtR6ppqwBrCG56aousKLJ3f9lnct3LQthmHwP3+9Nydd5vn3/oBvnP9V2GzFU5mKzfESgzSFKU3YaKDeRtDnlWEAw3HU+mo0e22raCIfemSvlEJUUCsjSNedjI/fS2D4cxPwvPMG5h3qQ3hrJsLGVGTOZ77GZ/nnBLeZjOz3IvH8Ypu7sM3NmPQI8OXlcI48i/6146g+bQXmFcl2UbOpKVbKkN9wxaxGK8XWoaXelQvEBKpASX2g1bCa+bMSpZIWml93pgbG6wWOZgoN1KSJsmRSb7oQQwdi2Avo1DvG2dSO1pMuxcfvqR+zoHbsA5AbBQT0KzwXM38sHd170TV0tGAtn9/wD/j+J27O1kzwFc1PpqbwxJ7f4Ksrv6LZmgnlhVFGsBipcByoVTcMnouVo4BcZpMRzK/1UmsEWeav/hL6mv6K2OBHAADH9GD4kd4Q5m3rQ7B3rWjjM73vn7jNXUKrMgYl6a0BsBzRCyqLzrXVwvwBwtFhq4wZElrHE+/8Bn/6cFNOjSDRuwzEBKqENYKA8nQAsygJAzgyBk2KKGRSKvWB+XVnq+Zqk4fPeL1gpoehJjid1hQ3kblQ/1ReOi09WpCP2o6fXEYmhnHfrkd1b0MtxQAyDINHtxa2xAaAwdFhPLDtMaxsvRcABIvmH+/4Nf7h7C8L7o4m42HITSEmlBdmGkE9ZgCWhDZy0MsIAtZJD+Wr9ZIbEQSEG6LNb18H5MnU8Z0vI1R3EA3HejG5YRlG2owxNPmdS/mau9BpBh+/l8D81V/izXzRqos1IP63t8rYBbF1bOx4FpOpqex5AyjXu3KDmEAN0LJGUM44CaWUmsCZhdlGkHsRFmL38fdy6s52HNuL81u1Far8ukGWckjf09L8AZm/WceJ/TgUOlrwMy13R6UYQCATBTw0WNgSm+XD4GHs6s10TuZrnQ3ovzsq1s6dUDrMDNLOmDIpZlDOOIl8uOav2lcJmkkhoj4RYOZ1ddDHdJUPzMh7ALS/weTqFaDN9QywTlRQsNbLnWtW5UQFi9XAZ8chvd8BHNmFueOF10imWnnqMTU+xv+DOoCpm6lpLdbcBUBBLwM9jR8Xq4xdEFvHZCoT2WXXw/4/H7MtGkhMoEZIHTJaDKO6TZWSAaQjIzAjGgiY0yhGzg7cY6+9mJP/ft+uR3F643zUVTVqvi6uqOQbQqB0TKEWYsnH6GQo8zd570VD2lAXM4AMw+CRtx8VXAvLQ9s3Fn0tod1RKXO7pFAq5w6hONyoICBuBuWMk2DJN39aUyppoHywn6NySw+VUnOmJCoopQaezWQZWtaFSN7P7MERVe8r7Zd2X7NAzPyNhMFelpX+vZWYP8A6YxeKrSN/PQwjnjVULBqopv49uxaLbH4SE6gh+UNGAWVmUOtxEgBAhUMAbCVl/gCAqqpHptukeRjZKEbOHDm+/PdDoaPoOLEfq+Yuz36/mCAqIf93YWVTyGfitfxbcv9miVRC9zbU+V1AhYiNDyI4EhYVRSATDaytEN/NnkxNYXv3blx40nkFP6up8CKaP7BKIlYRQoK2cM3ZeF66Jt9cQbFxEqlQDKm800QP8weUT5ZMudUJSq054xpBds1iyKmB543CeeS8C/VwtYyNYnJLN+SQn2mk5G9qlbELxdbBXc/BoS7Feqd16YMV7pGICdQBrcygGtjXpUADtZnBojZKeIA3oThGpYVKuRiL5b8/9t6LuGDJmuzO22hetMYsU8ii94WPuzPKotffLd+087Whzqehyq1aEMWigGPTa3Landj4lV9l18IwDG7/813oiRzLEUsbZUNTjR/+ah+6I32CO7qP7X4GFyw8N3tekSggQQq5hnBSUQ2fXqaPi5EG0BEaL6g/05pyMYJya87YdUmNCgLCNfCAedenYpuXTIrJGlipaGH8cp5vkN4pWYfWeleuEBOoI3xmENDPEPINF6WZNBRu0luC0FAawe4QxmNb0XN+r6qB32owIi10dFK6sMrJw+cek7tLymKEKeSSL7JaoXRnVEl3VbGILbcNtV6wRo81g2N5f1OvbXpddciuZWfPHnRH+gqORTM0joS7UVdRK3lHN8Gpv6FTyhrykCjg7MMIM6cEIw0gVV0HYFL31wE46aE6GEGjUFpzJjcqyGJk2YPYPYWWZQpc9sV6ZzqsamDkjdA7JevQUu+AzL2TmqwXLlbSPmICDSB3yGjhPBklplDtgNFSoepLVyL01w/g7xxFItKP7nOeQfWylYYMjudDr2igHFFld0aFEMvDzzct+aaw2qE+170YekXllOyMyu2uqrR2QkuyBg+ZQfB8389HSu1GU20j7r3qp4Lvn93RTUhswCAFEgUkmE25pICKwZY0ANpdf42IBqqtOcuPCgLyrltyMlyUorUe8t1LsL8HhmHw4Gu353RYLcdIl5Z6B2TOHzUbnnxYRfuICTQYPqFJ6DxktNTxrzsdEa8LVft2wJeK42N0YGiZQI6+jugdDZQqqMl0EgMjQ4I/l5OHn/+aY+EQgFqMTYVBpfSJEloJ7jy/Yt1V5dRrGoWY8eMipXYjPBHDSb75oueMVgbQSjuhhNmLGQYw3j+B5PxxhLr2wtd+lmGvq2V6KDct1FGj38ahVjVnas1g9vUsOC6K1WouUub5mTHHzyispndcrKZ9xARagNli5tTQsGIxIgC88Q+wcMKGQRPXYvbsQJfDhX+76Abc/ue7s9/79iU34fSWJdl/K83Dd9Z7ASTgrPciNRY2JHXULBiGwQMduR3nHuh4HKvn5e4qKzV/+YONzUSL2g2tDaBVdkIJsw+zOmXb2uZhoPci+F59E6GlWzGKCKr8bWisO9mQ19fDCOoJe916++Od+N8t92e/z9U7OVqXbwaZNFBKM065v2927c56L2wSyh+kdFhVipW0DrCW3nGxovYRE0goGejaAHB8t6lrMGNkRD4Mw+A3+17KuZi/evANXL38M5qmdvCZnvwmM0DpGkNuFBDI1Apwo4FqIn98g42V/m20Elg1tRvEAM4+qHAYqGkwexmaY2b6p6+9AWhvQKhrHpq3voSpY+8jcskgegPHAaw2ZA1aN4zRumNiPoHaRrx68A1N9Y69pk+FM9cjq2W+iJlrdu2Z1ETp5Q9SO6zKRUutA8pL7/iwmvZpP7GUQCDIQk5DGGDmYs52SuNezPXG5fblfAGZ9ed/WR1uFJALO2txZGI45z3KhS/tRuk6uQJbbL6RHmgtiGaI4FC0C31Hthj+uqWMy1sLKhQs6bl5XLjvxezsG197A8Yu/Bx8C67Ciu75hr8++xlUm5pmhGnSU+8ymS8z/zVax/i0k7v5yPelBG4UkAsbDVSjK1ppHbtOM/UuEQvpZgCtlgbKQkwgoaSgqtxAdNzsZZj2gdbzYq4EIaESEzcxdh3rxBef+zp2HevUdd1sFJDbchyYmbXYGetRfOz8v5HUv827I+/i/3v2/4fdvTPvXUuBVYKWgkgNh0wxgD1dmxDfsgnuF3sNf+1ShzVLpWwG882f2QYwH2bEHD3T6rOoxeBsIYzUO6kbnFK1TOy57Nc7J97HdX+4FftivZqYPSHyjTSLWkOtVOsAa+ldvvnTywBaLQoIEBNIKDGGB9NIDMeQ/PBD09Zg5gdZr4u51sg1h6OTIYxMDOOXOx/JdurUw9Cyr3PfrkdBgT9lhe04p/T1lexcMwyDJ088mbMDqkZg1aL1jqiZBnBBN7C4cw7Szk8a/vrlANc4lZIZtLr5AwDUmntT6Kr3abahmUkL1RYz9U5Iw6QYRDa7R+zLWe/F+ndf0D3qxe2UyYcavVMapbWS3ulp/gBrG0CA1AQSSghfewOCvSej9jhQG96G7pS54yKMRm27bCsgtsO5s2cPDoWOAsh06nzz481YNXe55GOzhfJ83dK4ULX1CE5EVHec432uzMHGLLv79uLwxGEAuTugetRwFEPrdBizDCBLYKwBofYzwdiNT7srJ3JHHek/91YpVDgEanp/25LGjwdHeBypCvNe3+xmZ3xYWe+0iNQZ1alTqw6rBc9TqHWANfROz7o/FqsbQICYQEKJ4V93OkJd80AdaoD/+e3oD23CxGnGD5Fn5y4ZKZx6XcytAF/nssfeexEXLFkjWeDZQnkp3dLUdg4TQslgY4Zh8PCuJ2CDDTTonB1QJQKrlASn6Y9W0T/AIgJY6wMmzF5E+cCaq/y5t2YYQvb1KdBALeDy1sFG2Q1fh1Iyw+O1j6JJRctGMVrODpxteqfXdV2LTpl8KNE6wHy9M8L8ARbTPxGICSSUHJkOa2sQfgVYOXIY0XQAg9Guso8Ici/m7w98iKf3PI//7+wvYlnzqdnHKDUvLB19nbj74AP4Tus3cO5C42ZY6dW5TAg1ncOEULpzvat3Lz4MFr53PvT6vegR/QPME8ChaBfG3+8Ajgzj485WjC9aDu95HlPWUs7kR9r4Zt5qbQz50lFdvnrQTFpOs0TT8bU3oH/TMUT6RjBZNwQsBIKxI2jyGjMugosWXa9rKrwYT2lnZoneaYfWeqcmSmuW3hHzxw8xgYSSxqyierNoqvMjUNuI//nrvegfGcQfP/gzvnD6pzXZJWMYBg/u3IhjU8fw4M6NWLVghSFpNmrSSqyEkp1rofcuhpZpUOU4C6mnaxOYwUE0bJlARfRcjK39HPztDRgdGzFlPbOJQlMY17yGsFRSPKXAZra0dG5B/0IgsW0Hek7tMTyzhcVq0UCid9ZEaZTWDL3TOsNFDLO1TwnEBBJKlnSV+UX1RqeEAsVrCaTM2QnThbu+e3rey+7QfRjswl+6N+Ps+cvhten7/pSmlVgNJWk3Qu9dDC3SoPQSRrNFcCjaBWZwEIv+Uotg9TpU/uMaVJqyEgJQXoZNL3ztDaBPvhJI7Efru150NwxiyG98ZosW0UA9BshroXdCx+XqnVE6Uw56pzTF1Ei9M9L8AeZrn1KICSSUPFRoBGiuM3sZqpC6e1qslkBscGu+8avhXBgZhsHTu1/MOe7Tu1/ERaesQXhKXNTVmEQrF/8rQU7ajZT3vsDbiv+67DsF711pGpSewkiFw3B5zBfAhf11qKhuAbP4DLOXQiDIoqKqBQv7aQwuM+f1tdjYrK30YVSjaKAavVNzXL0oJ72Tm2JqpN4ZlfoJlK75YyEmkFDSZIrqTV6DFqIpcfe0WC0B367pKa0Lso+vEbgodnTvxaHB3OMeGuxCR/derFoovisZFll7MYNYzsX/xZDy3mOTIzjJN1+T967bENxwGEAtXHX6zQuTC1XlNnsJBIJsqKp6AMfNXoZl0kKV6J2UKJrRNXksRO/01TsS/ZMPMYGEksXWNg8nOg4jcWwbEiEbepaY1CVUZQpNMbLpLhffKFpLsKp1RcHu5v07HsUv5t2B2qpGweMzDINHt/If99Gtj2PlAvFdSSFjCQgbRNYciqWVsM0Abjz/q2UniMDMe3/r4534+Zb7s9+/+ZKbsLxlCQD1jQ8AHc3ftADqP7VQGj1dm8BsP4D4gSUYd6WBNrNXRCDIoxfzUbNtP8bHX0PP+cbrGWB+WqgavZMSRTOzJo/onX56Z2j0LxwGZbOu+Tu+82WM9vRLeiwxgYSSxdfegBDWgO49GQ1/fRn9TBd6AFT520qyUyjfzik33eXuLfejL1q4S8zuYj6x5zcFu5tdQ0fxwUAPVi0UNoH5UUDu86VGA4UQMohcc+isscFf0wBgxhwyDJNtBvDrzt/hslMusXx6jBICtY147eAbOTcyrx18A19c/hlLNn1h4e6A0unMaA6zYDuBNmyZgGvicoy2ngz/utNRZdqKCARlNF6yFKE0heb392Ai0oXuc8ybhatJhovMaKBavZMSzTO7Jo8vjZLonTq9M9IAsljRAHK7Yrf1nCLpOTad10Qg6IqvvQH+dacjsfJqLD90Dlp2RTAR7DV0Da56X/amWClCFy+uYPEJIpfHO34NG5X7kWajeQwjkIIxHQWkwH8BpkCJPl8pNZW+gi8gU7cYpkP4S/fmgjSfcoT9+7I70twbEaUkYiFdo39WSoHp6dqE+JZN8D9fhUT9dRhfuQb+daebvSwCQTH+dadj7MLPgUlchhXd8zH+fgeGovKaaahFy882N0WvGFroHTtzjg9uXRofbE2e1npXDL601nJED71jMaLzZ6b0AZYqfWDp6dqU3Qxt6rocwVO+KOl5xAQSygK6NoDE3IVoQovZS1EFVzC5aStSmExNFbRd5kbz+EimkxgaEc/TD45kahT0hjWD1RXebJMaANm01lB6mLeraaki9PctdiMjRL750yv901XvM90ADkW70L31GXh+0415W1dgfNGV8K87PTNDlEAocdjzuIryYuFEkylr0HJzU4oR1ErvxEyFnJo8o8h/30qv/1ZHa71jScRCxo1+sKD5Y7WQ2X4guxla9Xefh3eRR9LzSToooazIzA00fm9Ds65qnDRJobSVb19yE06fzqEHMhfXO964G93hPsGuW0K1fS6HCw/9/UyNApNmMPpOErXnOEHZM4/1VKuvS5MDX5MaNq11acuCHCOo9/gKPdEqLUnvYnjujaDZ5g/I7HgmP+hCy5tNmJqzDmMXngE/MX+EMiMzAkm74etK0arxWbFGMVrqnVBtX35NHp1mEHk3iYYznLBN650WddhyMKtJjdHokYYrJ8qslMLSB+vALYXgzsSVAzGBhPKh1gfEzV0CNRwCPOp2ixKxEJz1XsHi9VcPvoGrOTn0iVQCscm4pGgen7gF6v0I1GdqFJgUg1h1Au6AC5TD+JqEYk1qHvz7X+YIO7e20APr7dIJoUWrcCM6oVkp9ZPLOScW4eM5y+H9zBqzl0Ig6Et03LSX1qrxWTEjKNasRYneiXXY5Nbk0SkGweoE/H4XbCbpXakPjpeCnqMxZpv25bOwvw5VzlPQd+FnFGXCEBNIKDscoXHAhL4wWnZVk7NrVmxwa4yOoqVBmzEDeiO3SQ238UxkNASgFhE6DB+sfeFW0yp8Nps/YPrzjcrpSAmBUL7YahvM7LmURYuREWJGUEu9A4yP5inF7CY1RqHHaAw9o4BW1j8u9uAIAHUjkYgJJJQN/b0pNI85wAwNo6drkynttYGZuWlKqanw4sGtt8naNRMb3GpUHd07PZ341aYH8K/rvoFz5q+Q/Xxukxq5aa0AUFPpRWz6jol9z1ZNF1WSlmSk+QOsKYDHd76M5KGjGImdDtSYvRoCQV+6OqJwzI0h+eGHGDoVpnQJZTc39TKCSqJEcgeV60F2lMUl38CqNmV6Vy6D44uhVxqu3p2vrUyoay8mDr4L25EFGB5VPhKJmEBCWeBrbwDaGzCwCWj4axTByAF0Dw4a3l7bVe/DZFSd6UrSSQyNhTXbNfPafAjTIYxNhkRn+hVDzOQxDINH3tqAnnAfHnlrA87++zNlC5ecJjVi77um0gvKQWFsMmRpMygnLcmIFthWFj9W8Gq6KuEYXIKB1otIF1BCWeNfdzpCXfNQ3QHUhrchkhpBT1Ppzg5kyTeCVh2gLmbyuKMsHty+AStblemdFd+3XlgpDVcIK2sgF3Yz1Hcgo4W2JfMUN0UjJpBQVvjXnY5Q2zw0b30JE5EuxNCBoWXG7qBmOkgpz+Fx2V146upf4USsBwDgrPUUPEburplaI1jM5HHTOPPTNqVGCPOb1PAhp0kN+z5ZM2hFI1gMI80fYE3xYwXPfsSHSnoFxi9YQxrBEGYF7Cxc6lAD/M9vR//azCxc04ygBtFAoNAIWi29s5jJ4xvpwKZtSo0QllNaq9Ho2RHUihrIwt0MrYzMzMRVAzGBhLLD196AyOhqzD1uh3cihkGT1kGFw0BA2QWludaP5lp/tluonIG7QvAZQakGTczk5TdzYZu4rFxwFgDIihBym9RoRXYG4WTpGEEjUj8Ba+98DkW7kHhrK+r207DFlmCydS2qyBB4wiwjk+WyBuFXgJUjh7EPk6auR4u0UCDXCDa5jUnvlGrQxExefjMXbhMXALIihFZIayVkoIZDltRBFm70b7J1LcZXztNkM5TMCSSUJXRtwOwlAIChc5akwJqgsckQRieGcwxasYHy3DlG3AHyrEHkDoBljSKfeTSLmkqf5ecMJuNhXWf9sVht6Hs+x3e+jPGXXgPzjguuiQuQuOBakv5JmPVkRiCZB3utUKtrLFx907vdf350r9hAeaG5fWIDz2fL0PdyQ6vzWQ+Gol049ocNqN00hKauyzHZulbTmbjEBBLKGzPba08PFrWiEfTafNjbu1+SQRMzefkGkcVG2bD+7Y2i5tEsrGwE3+l/H1996VYcGO7V7TWsNPQ9H67g+Q4sQaX/GlT93efJEHjCrMcq3XD1MIJaaxwfUg2amMkTG3j+0PaNs2Lou5Z09HXib5/8Onb3dkp+jtapoFbdDB2KdqGnaxPGX3oNDX/1IFF/HcZXrtF8M5SYQELZwrbNZdvomoFWgqm1SDIMg2d3vVjUoImZvEe3Po7dR/fkGEQWmqHx0dBhQfMIZFJRr3vs63inR7oAqEVNYxy9SMRCSMbDYBgGj737Ao5G+3DfbuGdaqVYPfrHFTwS/SMQCqGq66ZHpJiL1kYQ0F7juBSL7gk9joV9/M6ePTkGkYVmaHwYPCxoHoFMKqpcw1POMAyDB3duLBqZNQKr6SE7BN7zm240v38uxhddqWn0j0tJmcBIJIJrr70Wbrcbbrcb1157LaLRqOhzvvrVr4KiqJyv8847z5gFE0xnajyFydBxU9egpRGsrfRpkjrDt9t5aLALHT25u6P5UUAW9vG/+usDoCC9uxdrHmmalpSKKoYZJlJr2L9jTYUX+0b24YPhzE71B8Eu7DimXSpRKUT/PL/phu/AEowvupJE/wgEAZihYYS6zE8z1OM6oqXGcRGK7u3u2yv6OBb28b/YIl/vHt6R0TspqahilJuJ3DeyDx8GzU2dtVoaKDf653++Con66zB24ed03QwtKRP4d3/3d9i3bx/+9Kc/4U9/+hP27duHa6+9tujzPvnJT6K/vz/79eqrrxqwWoKZ+Nob0I2FmDpQi9AHPTj2hw0YihYOZTUKLXdO1e6Yiu12rt/2WFaguHP7+KBAoT82KNjimg/WPD696zeqagXzu5WWYsoNt/aPYRg83f90zk71Ax3qU4lI9I9AKA9sbfNwosuDmq5KTBx8Fz1dm8xeElz1vum5uNqiZVRQNLq364kcvWPn9vFBgcKJuHy9OzjUhSf2/EZVraDUesZSgU/vpKTO6tEV1Cq6mB/9m1z7Dd2if1xKpjvowYMH8ac//Qk7d+7EueeeCwB45JFHsHr1ahw6dAiLFy8WfG5FRQWam5uNWirBIrBzlto7NiPRvw2RyGvoOd+cOUuA/kN3pcKtjeBCMzS6gkexr24fVk62I0UVn9tXV1mLn3z+B3DanTPfZxj89LW70RvqE3zuU7t+zdtNVOqsJb6GMytbzyr6vLFJ83f++MY+7Dy+F4cnDmf/TTN0Nhp4fuvZil7HyuaP7fzp2U/DEWzNpruQzp8EAj+ZLqGfx+imk9Hw15dNm4Wbj9qRSEKo0TguYnr3YbAL++r24XKcK2luX11lLe76TKHe3fHG3egOC+vd4x25eid3CDxfPeOqucX1zqrs7ivUu/wurHpjlW6gQ9EuTAR7kfygCy1vNmFqzjqMXXiGYZkwJWMCd+zYAbfbnTWAAHDeeefB7XZj+/btoiZw8+bNCAQC8Hg8uOSSS/CTn/wEgYBw98ipqSlMTU1l/x2PxwFkTlSaSWvwboyDXe9sXXfDyfXAyZ/FyJZF8L3/J6QGjuPoBb9B9Wlnwe9epMVSC6DTTM5/uThqvEiMhIGhEBivV9XrVDsyzx8LZ272nfXFj8cwDB7antnt5BMsChSe7n8a69L3IG6P4udfuB2xiTiqKty8x2uo8sBf15jzvUQqifhEXHTHdCo18/lio4O7j+zBqgXFBUBoJMXZXzwz8/OU8OsyKaDB5gVNFz6mo68T92x9EN+88EasbBVuH66GZDyza15T4QWdmtmBfuCdJ2CDDTRm0pBslA0P7H4c5zavkD2ImAqHwSBzg8Z3HmqJ2PnOR++RLWB2HUTje/NgqzkFE5deBN8ij+7XKBqldQ00inLRu1LVOkDe2n2Xnobw/DlY3PUnTNQMoDdwHHTdyXovURD2c88Mh0E3qtO0fJRoHBfJepdcCYfTifVX/xLRSbG5fR4EavP0Lp1EtIjeTebp3cGhLuw8ugfntknTu4e25+rdQ9sfx9lXnZk5nojeiWGE3vHBMAwe3sWvdw9tfxwr5/DrXTIeztFNNbD6KFcb5WqdFBiaQmuPHVWHV+HEuWejYXlmQ0ftdUyq3pWMCRwYGOA1boFAAAMDA4LPu+KKK/ClL30J8+fPx9GjR3Hbbbfh0ksvxZ49e1BRUcH7nDvvvBO33357wfd7kx+iOlGt/E2YSHfyA7OXoAjN1r0aiK++PPvPkR5gRIfdSy7dB5ICP6nN/OeoVq8/fTwJ7ydJJzEQEd/tHE4OY3DXBJy2WjSgFsX2o2I8r/uzBXchnooXHp9h8MveX+LY1LGcNdhgwyOvP45TTllW1PB0xjuzUUBgxkS+9VoHVtSvQHyr0O8dAGoR5FkvwzC476MN6J7ow31/2YC7TjlNtvGSRuZvFeWsoTPeiYMh/p3qD4a78MfNu7GiXq5ISz8ntEL4fM9nNbBsNQaXsf/uQzTRp9OqZhhPmN9Qw4qUm96VqtYBMtbeCkRblwJYCowBH79n3OdciBM9tUCPXutQdj2Tp3dO2OCGF/wbnix8+vGzhfL17v5Nj2OhRL1ja+eAmQjmpk0ZvQvtlnrdzV2XMXpXSP77YWHf1xuvC+ldbY5uqkOdPkrXOim0YsTXClwKAJOIJPZrclSpeme6CfzhD3/IK0BcOjo6AID3JGUYRvTkveaaa7L/v2zZMpxzzjmYP38+/vjHP+ILX/gC73NuvfVW3Hzzzdl/x+NxtLa2os15Kupd4hcIq0EzaXQnP8AC52mwUXazlyMZPdcd+dPbcETfQ8WSMQyc40Hboks0PT6dZtB9IIkFS52w2cUvrImRTGRIbVSQZWwqczzhHVMXHjtDeLeTTjPAhzVoPq8aNkfh2iP0TP1HTaXwmt2YC2Buwfd3d+9BX1fhDT8NGocnDuOjhe+LRgMZhsGvf/1MdleUy7MjT+PMujPhvsgFKm/tY5NhNNiE17urdw8Ov5tJTzk8cRhHW9+XtEsrBW70Lx+GYfD9PzwjulP92/gz+PSaVUVFmq3NYUeTGIWU850b/bM7mjG++tPwLvIYus7RlHldgq1MuehdqWodIH/t4SNRVO19G81zjuPg8knMWXmFAavkh/v5T41HAGinZ/kU17d81OmdVPwCererV1zviukMwzB47nl+vft1PKN3jee6ZK9dT70Tg30/Ynr33MgzuOxvcvWOjQJqgRqdlHNvJ5UTHa/B/14cqRMrMHHWRZrpolS9M90E/su//Au+8pWviD5mwYIFeO+99zA4OFjws2AwiKamJsmv19LSgvnz56OrS7hJSEVFBW+U0EbZSk5cWGyUvSTXrse6G1cvQWgr0EIfxiA1qdmHOR+bnSp67EpPpk4Q0bDqOkEAqHNk6ihSY9MXOp5aipaGAFrAnw5NpxgEP07A5qB4hcWHmeOFUzP1dVJGLzAMg8d2PCEqAI/teAKrFp0taHg6ju7NiQJy+Sh4GPvq9mGN49ysCWRrAH0u4fUxDINHdj+Rk27zyO4ncN5C4XVIJRELgbJDsJg9kU5iYEx8p3pwbBhpWwouu0vwdajhEGAzt/6P73xni90btkygInoWRtrOhn/d6TAjvmRD6V3/jKDc9K5UtQ6QvvbGk30I9izCUC+DuuA2nBjaCNfFF5paG2izU5rrWT5cfZNaJ6hG79TA6oqY3hXTmZ09e3mjZgBwaPgw9tXvw+WOc2WtXU+9K0YilcRQEb0bGhtGmkrB5cjoXSIWQl2NhnMBNdBJKfd2xWDr4uv200jE2jDZugj+k7V7n1L1znQT2NjYiMbGxqKPW716NWKxGHbv3o1Vq1YBAHbt2oVYLIbzzz9f8uuFQiH09fWhpaVF8ZoJpQ3jaQJwEMzIOKzQIFfLhjHAjOFgi+oBfjOoFq+NYwjzGq7wmcJkunijmeDIMJLpZFYAcn7O6VYq2HDmxFO4hFmF8clwwRqFyG8coFWROl8DmHxcdhee/MK9CI9GMXQwicASJ6g8cfFWuQUNoJWbv/R0bcoWu9udizKtrsnYBwJBE9jGZ3RvplFMP7MJE6eZ1/iMRWs9y0erhjF6I6XRzOCouN6x3UrF9O4yZhUgY2yFXnonBZfDhceuyehd5N0kGs4ojKg1VLlzDKCWWKUZTE/XJjDbD8Df2Qpb7RKMX7DGNG003QRKZcmSJfjkJz+JG264AQ899BAA4Otf/zo+85nP5DSFOfXUU3HnnXfi85//PEZHR/HDH/4QV199NVpaWtDd3Y3vf//7aGxsxOc//3mz3grBAqSrfDh+aBRzMIYebEKVv83cDmvTF6bE9E19KZlBIM8Q0qGCLpw1lT64HC489Pf3IjohXHjvqXbzCiJQ3EQCwEBiALHxITRXSesGzDAMfr7lgYLvi3Vw293biV9seQDfuuQbWNVWWLsgxfxxaa71I1DZiLrqBOY2Sk/tsaoBZKN/7ndoVB1fgfE5C+D9zBpUmr0wAqHMyHQMbUAQwPJD+zBRHcOgv8tULQOIEQRmDI+Y3nENTz7FTCSQ0bsknYIdwlkiXOTqXTGtU0JTnR/+qkYEqxPw+4X1Tq6OFsMqMwGHol1Y0A24x5ega9Fa07til4wJBICnn34a//Zv/4bLL880+PjsZz+L++67L+cxhw4dQiyW+dDZ7Xbs378fTzzxBKLRKFpaWrB27Vo899xzqKurM3z9BOvgX3c6Qm3zYN/6Ejy/eR+RSwbR01Seu6hGmkGAP/rGRgprXDbUuHJ3vKSkkgLImsgTkW78718ewrHoiZy5QhRFwe/yw+8Q7vybz86ePeiLHi/4vtDuaP68ppWtZ+aIptbCxQdXzKxmAHOjf3MwduHnyNB3AkFnbG3zgEP7gKh1mh8ZZQStTFOdH011fkXPZU1kZDyaGUER6ePVO6dN+m28HL0rpnV6opcBtIxeRsdRO2cl0Gv2QkrMBHq9Xjz11FOij+F+SKqqqvD666/rvSxCiZLZRf0qxl/ZjEV/2YVDK7rQA1jKCALaRAUB480gF7G0zPxUUjFqXDbEJ0fRFykUMoZh0D3Zjd19e7H6pHOKHothGPzirQcFf06BKtgd5ZvXdN78s3PSVowwgJYRMw69R7Zgzu4I6HdJ9I9AMBqqyo3E8IdIfvghcJ65kUAWvY0gAEtHA9XSVOfH0XAvjoYL3YLeeiekdXqixyaq1TQz+eGHQF0dIl0jsLWdafZyLFAQRSCYDLP4DFDtF+CcE/rMDVSCq96XvWhpncZQW+nLXmQTsZDmefdy8dp8kr8aKC+e3fUiKIEaCAoUHt71RM5mkBCJdBInYsLjZbg1G8BMjYaNylw22RSaqegwgNzfqx5YTczyaUvPQ2DEjXF/xgASCARj8LU3oBsLMXm0CbWbhnDsDxswFBVufmckeukYoO+GmxXg1gXyoZfeCWmdlNdRSrkbwKFoF479YQOSh44i/tckurHQElkyJRUJJBB0o9YHxAFHaBywxiYqAP2iggB/ZNBRY+x4AblIKbYfGhMutufSeXw/0jwDWW++5CYsb1kCILdmQ6igvuPEfly6aK3St1QUKwmZGFR4FECm3pZAIBgL2yimumMzbNveRtwijWKAGR0jyMMsvdvZs8ew5jF6l1BYQTd7ujbBfrAXdftpuCYuN7URTD7EBBIIAPp7U6jsA5LVR3EcL2PueVeavaQs2aYxOqXVcM1gZqZdrfgTTESs2J5OM4i8m8RJ5/mLCiJ3p5M7f8lG2fDawTfwxeWfyal/EHv8hndfxNqT1uhSL1EKBjB85F0ASxDu6MLoCQ/QavaKCITZSabE4fMY3XQyzjnxJmKL2zAYNb9RDIte+jVapimhZuid2GOFmqUpJRkPi45QUopVdJNtkoYjw2jobMX4oivh/ry5jWDyISaQMOvJdljbBDR1tSJxbBuODW0wfe5SPnpGBYHMhZhOMYgikb04A8bVDUpFqNieTjGZjmO1xTul5Uf1sscQ2PEUe/wHwS7sOLYX57dqt0Nq5eYvXI7vfBmJrmPAsiWojC7H5AWXWGaHk0CY7VChEaDZGk3wjKgPLEeM1ju52igHNuqXCUjWoqbCq/l8RqsYQLZJmu9oLaqOn4uxtdYckURqAgmEafzrTsf4yjVwTVwA99Z5GH/pNfR0bTJ7WTnoWSvIpabCW1A3aHbtoFZIqrPg1D9MRYfx4NZHRR//QId29RJcETNbyIRg6xvi7xyC92BmRE/Vl660RI0DgUAARmIOMCeOZxpRWASrXs/KGTl6J1cbpcK9f6it9KGmQp+yE6sZwFO6FoFJXIbKf/yqZbWRRAIJBA5sOk1w0/6ZqODp1owKAtrOFeSDm6bBrR0ErBchlIrUIb5jkUG47E4k6RSC45HiQ3/ppOBQd6lQ4TBgM1/ExGAH3TYcq4Fv4jMYO+9iAH1mL4tAIEzjX3c6BjYBlR1vIjl+EMfxMpynnmoZDSPRQOOQM7QegKoB9yz5G8ZGNPCxigFkWTlyOkZaTgUFZSNCjIKYQAKBh5kie6DhrwcR/P+3d+9BUd13/8Dfy2UXEFy5LAsGBO8ab4N3jBesidXUPLn0Z5sm42CnzZOkTTo2eSaTJk3UTtNcJk3aThKtjVXnSTJJc7FpYszozwIGRUWFBjUiMciKt+W2sLDAAvt9/lh3WWAXdmF3zzm779eMEzl7Fj79yuynn/P5Xpr2o2aJPBbZuwr0FFFXoVIQDrbOoqvVBAAYEzMaSaN6D5z/3/v+gqZBDv1NitWOqABUNdrXYqoTkhARGZyzmHzlWN+QWNSOqLre9Q0a0QOTlUUgkZw4cpi+tBDW2iMwG+tRM136HBaoTWJC+aiIkfD10PrhHnAvReEHyK/4cxBmCzAasMV7f26xFFgEEnnQ2xWchLQzp9BZewaXVryHuJkLZPNEFRi4cQwQ2GIQGLwgBORfFDrWWQxIXNpJbu9Pi9chLd7/T/Qc/17RCUkArH7//v7i6P6pz8VCHb0MlpXy2d2MiNxz3Shm5pUC/Ge61BHZqUcnw+rHbqASDo6Xki+H1nt7r1RFnys5FoCOXHmlMhOWiVpEyHefPQAsAomG5HiiqqrMhu6jo7jWIJ+tt10FehdRT/p/+LsrCgF5FIbu4pIyeQH2fzdbT+DOXxoJd92/2FXy2t2MiLwjtyOQSBnkkjcd5Fj8ueZKjWkRzBPnQbdqltRhDYlFIJEX7E9U89D4OZBRfAkR5suowSHZFYJA8NYLeuIuOXgqDB38WSC67mzan9SHCytl10+gd3F73NFIaGyzZbu7GRENzdwcBWGsR0PVaSRPnit1OAC4NlCu5Fb0Ocg1fzoKwIn/Px51cavQtnSOYnIli0AiHySty0Pj54WYYG7BNamDGUIw1wsOZbAEMlSB6C3HttND/TwpyDV5udO3+6dH59gliFmXhxipAyOiYYkYl4HW0nRoi7vR0FSMjoYrkp+FywPk5UUO0zs9UUL+HH8tAZq4dIipc2S7E6g7LAKJfNQTmwxVXAKiGuqgSjBBpI2ROiSPfF0vePxKGV76ZhueTnsUuVnBeVrsr2TjOOMwUNtPD4cSkpcrd90/JSU0IhrI3a7Xl7rlsb7dX93A4RwaX3q5DK9+sw3/k/koFo2XR3c0mFwLPzkVfQ5KyZ+RdWYAgCpWK3EkvmMRSDQMhrNmxNW34rJxL2Knz5HN9BpPvCkGhRB46+Ru1HbW4q2Tu7F4XA5UKnnuVCl3SkleDo7un/akDepL7P4RhSLH+vbMylZMqY/AiToDIGERKGU3UAiB7cfs+W77sd1YmB0e+Y6Fn39dOfYZuiqrYbmYjBRbDzBO6oh8wyKQyEe9x0cUAlVlaJbJ9BpvDFYMltSexrn6KgDAufoqlNSexpLMedIEqkCuyQtQRgIDert/6QV6REaPRfuCXCTlTJU6LCIKEHvHwix1GE7+XBvo7VERxw2ncb7Onu/O11XhuOE0FmeFbr5j8edfRlMVrIeLkVBhQ0TzdHRkrlTkpmkRUgdApETJkxMR+8C96FavR/LZ6Yg/ZETtv3bBaKqSOjSvqEcnOz9oVfUNQF09tpXuQYTK/pEQoYrAttI9ECL4u1Yery3D//vgv3G8tizoP3s4VPUNfXYrcx1bOTOaqnCp+D2M3ncN6QU3u38/3YhEFoBEIa3+Rg+szVZEfmOQPGf587PS2+JGCIEdJX3z3Y4SafLdCUMZfvK//40ThsDkO2tz75r7+JhkWRWAqsZGZ/5UUu40XCyC5dP9iDjSDXX7bbDetkERO4G6wyKQaAR0q2bBetsGqNtvQ8SRblg+3Q/DxSKpw/Ka40P3ZGsNztVVwSZsAACbsOFcnb0bGExCCLxxYheqTZfxxoldkiRlbzgSl9KSl0NN1SG0FB2C7qNYxF6ZDOuCHyJpXZ7UYRFRgCVPToRlQR6uVo1BQoUNlk/3o6bqkNRhDZhJMRJDbTR23HAa3xj75rtvjPZuYDAJIbD96C5carqM7Uf9m+/kWvyp6hugamx0fq2k3FnXfBEAoN1rgK4sE9EZP0XsA/cqet08i0CiEXJ0BaMzfgpdWSa0ew0Aej8w5E4Igb+c6n0q6hChisC2kp1BLcRKak/j3M0pOlIUoZ64Fn1KLfwAdv+IqDdnWUfnQ1eWia5z9s8FqbqCwewG9u8COkjRDXQUowD8VoTKsfhzzZ0AoE5I6vNfJaipOgTLvoMAAHX7InSsfFSx3T9XXBNI5Ce6VbPQMC4Dscc+AwCYi4vQMb1GlmcJujp69TTONgxM/jZhw7mmahw7W4glabMBBPaYCSGEc0qqTdicU1JzM+ZKsmBfqWv8POHaPyJypVs1C01JasyuPIH2nGbckDieYOwU6lp4uXLtBgZjbaBrMerIdztK9mDRuOHlO7mt+RtsjZ+tR54zfNxxPTIpwpSBGzOB2PV3YZSnw4gVhkUgkR8lT06EbdKDgLUC+q9S0f3dNVyaL4+tuN1xdAFVUEFg4AezCipsO78XKybnocvc2OeD3d8FoWsXEOg7JTUYG9SEWtHn4LrzZ+yVHFjGZiOJO38SEQBbfKrUIQAIzE6h/TeJcRReg+W7kRRivuhfjI6kCHXt/EklFPNnTdUhiKNnoSvLRGT0RLQuuwvAZanD8isWgUQBYp37Xxh1+hgmpHSjDKUwzoTsCsEuWxeut9W5TYgAICBww1KPLlvXgA91q5s1HMMtDPt3AR0C0Q30tPYkFJJWf/27f21Lee4fEfXV0BKPqG/Poz0bMEK6PKUenQyrP7uBHQ19CsGuni7caB0i37XWo6unC+oo9Yhj8KR/F9BhON3ArpZGqCKlKQBDsfAD+nb/ouoyYZl4F3SrZkEtemCysggkIi8kzp4M0+XriFU1Ynx7h+RTbdxRR6rx/l1/QVNHMwDAZhO4UtWFWyZHIyLCnoSSYrVQRw5MiAOKQpdjJ/obKqn37wI6jKQbONhGA6GSrDxxTWIaE7t/RORe8uRENCAPo4qbMeYfZ9C04obkDyz9Oi20o3eNnFqbjL//+C8wtd/Mdz0CTf/pQuKcaERE2vNdYqw2oAUgMPIpqdbmBogeAIjHKE0SIqKCu1xCacc5+MK1+xcRPxeWlXnQhfCDUxaBRAHUE5sMoBEwWaQOxaP0eB3S43UA7Ekx5ooVE5LVzqToLU/JYLDiELjZBSzZOfiU1JKdWBKTNejTUZUNAOLt205HhF5y8lb/KSzs/hHRYOyF4N1QVWYjeV8ZbjQcQvutBsTqxgW9GHRMC/VnIQjAWQzqtTroE27mu26BujgrdDp10AqpkUxJdV33N0qTBBOsAY/XGVcIF35A77l/Yypsfbp/Sjv3z1csAokCKGJcBi6WfouOxFZYDxejZrpB9hvF+NtQCcPaY8WN9qZBp+gYO0xQxY9225F0sC82t0KdkORzARsK+nb/FsE8cR50q2ax+0dEQ0qenAiVbjoaioEF5gSYelIlm73i70IQcN8VlMJwpqS62/TF1h34zVVCdbpnf327f9NDvvvnikUgUQAlT04EJt+L1kOToC0uQEPTWdR+Y4B6+VLZrQ+USv8pqe54mpJKdv2nsLQtDZ8kRkT+IcboAXwjdRgAAlcIAr1dQceUymBSR6n7TEl1xzElVaodP0O96+fg2v1Tt69G68RJYdH9c8UikCgIdKtmoaEqA/rSQlhjj8CM8OwKeuI6JZU8c3eOV7gnMSLyn57YZAjz11KHASAwhSDQW1CZ2+zFTldLIzRJwSt29Am9U1L7szY3ADb7f4O92UsoF3/9c2fX+fPoqqxG8tnpiFJnwrIgPB+csggkChLXrmDivz9DXdNZXLpxQ7bHR5C8OHb6HGdOgcrc4bzeUZ2BjsyV6J6ZEZZJjIj8T9VgBtISpA4jYIUg0LuubpQmCZbmflMfgzRd1Nrv50q5y2eoFX5A7zKJ7oYWZF3t7fq2WiIR07QarZnh/eCURSBRkDkOlU8r/hSdtWdQd3MRPruC5I5rEksv0KN17BIAjk2HAGTaf6eIiPxBFZcAcfUKMGOa1KEACGwh6OBafLmuHewTxwgLQ3ffs//PDpZQ7vo5OB6c6s5EQ2PKgVmX3Zs31YBlAR+csggkkoC9K7gRls8LkV5wFNdQxa4gDdA/ibUvyEVSzlSpwyKiEBUxLgNXS79FR2K1rDYzC0Yh6OCuKPNUGPrjewdTOBR/jgenuFiP9LJMdI5dgpif8ogkd1gEEkkoaV0emsrSkVFcgs4x7AqSnSOJtZdfxbhvJzKJEVFQyHnZgqNosd4sZAJdDLqSungbqXAo/gDgyrHP0FVZjcTaUdCYFqFtJY9IGgyLQCKJJeZMBXKmsisYpoymKliMVwDkwnCxCNGNZmcS07evg2VlHpMYEQWVY9nCrZWfo9lyHlf1BkAm+ci1KwgEtxhUmlAu/mqqDjn/HtVggTDWo7vJjOSz09GRuRLae3lE0lBYBBLJhGtXsP2WKjSwKxjSjKYqtNcZ0HWuCnPb5uD0dGBWoQ2WqwImS28SC9cF60QkPevU+dBdbYKxwQLIowYE4NIVZDE4QCgXfkDvTJlbKmzQQX/zagzMzdlobU2H5bbw3OlzOFgEEskIu4Lhoe/B7jmoTssGpgMXu5dBlaxCRA4XrBOR9K4ZujFldAKAjiHvlQKLwV6hXvwBfc/E7Ykei6px83pfHAXo/osPTn3BIpBIhrhWMHTUVB2CuHHD+XVUYye6m8zQlWWic+wqtC2dg8RJo9FkrUDKihmIUEVKGC0RkV3y5ETUGWpxtWoM4uqrUWvcBfXypbJ8IBmuxaCqsRGqiN6vQ6X4c27uclNUYycADDgTl9M9R4ZFIJFM9e8KXjSdYVdQQRxJrOViHSaWjUNk9Fjna0Kj7bPWzyZ6JIqSiMgz3apZaKjKQFwpEFV8GQ1N+1GzRL4PJF2LIKtLZyyUCkJVfQNUNgCwn3sXKoWfQ99dsWc6rwuNFq36STwT149YBBLJXNK6PDRUzcEUnisoW471fa7E0bNIrB2FNNNitC/ItRf1LjhlhYiUwLFjaN2hCuirMmGtPYJLK95D3MwFSEmYJHV4HnkqCAFlFYWqejdnFiYkAbDe/K9yuW7uAsA+a6bf0Q6umDf9i0UgkQL0P1fQcLmMXUEZcN3cZYF5FoTZAgAw32hHiykTlol3cYcyIgoJjq6gzTAJuo8+Q93F/TDkzgaQK3VoQ+pTEN6cMuropqkaG4FU+RSFbou+ft0+W48IVjgBU1PyIW65FAE90l1yZzxau+dyV+wgYRFIpCCOruD44k/R2XwGTRflPTUnlBlNVbAeLsaYChs0phxc1GWjJ/Zmok4GN3chopBjfyCZiDoAaWdOof16JRpW56Ku+SL0SfLtCrpyFFT2QsreTet2U3g5BKpr6K7Ycwi1KZ6uDBeLAOQi5dNYtOoWovXm9Z7YZCDZ/rCBHb/gYBFIpDCOrqD5UAWSzxbAVnsJtbN2AQC6kzQAAJVej1jdOHYJ/cR1cxfHAnXH5i6WiXehe2YGn1oSUdhwdAXx7X8AAObiInRMr1Fs3vFUdLluNBOsnxmqHOvktUe60bA6F9a0hUhemyd1WGFNUUXgCy+8gH379qG8vBxqtRomk2nI9wghsHXrVuzYsQNNTU1YtGgR3nzzTcyYMSPwARMFUO+C/UIknwBUnc3O1zrHnEHTihswzoQiE7JcuHb7ouqSnJu7CI0WAGBZyfOIiCg8JU9OhG3SMjRZKzC2ZDY6v6tE8/zQyjvhVqgFgtFUha7z59FVWQ1dWSbapqwDYEPimmVShxb2FFUEWq1WrF+/Hrm5udi5c6dX73nllVfw2muvYffu3ZgyZQp+//vf44477kBlZSUSEhICHDFRYDkW7LtqqGqCqjIbuo+Oou7mdNH+OH3U/WYuABDVYHH+vauyGupzsVBHL3O7RoFTVoiIgPbZC6G6PA66j47i2s3Ny5TaFaTh659XoxosEMZ6JLgc7ZCy4la0WCskjJIcFFUEbt26FQCwe/dur+4XQuBPf/oTnn32Wdx3330AgD179kCv1+O9997Dww8/HKhQiSRjLwztawf1pYWIr77Q5/X2SVZcuvFeWG8q49iCOrk6Himm0QNviLE/IDI1TkdH5krEco0CEZFHibMnI2LONOf5tu3VVSHXFaTBecqrJksGOjJXOo924JFI8qGoItBX1dXVuH79OlavXu28ptFosGLFChw9etRjEdjZ2YnOzk7n183N9ml25raWwAYcADZhg6XLgpauZkS4nigqc0qNG5BP7NFjVTDOmgsj5jqvqSz1iCk/hajqC2i8+hVaF3cgZfQE5+u2HgGLpQstpi5ERKqkCHvYvI29troY4mQlks/eAnPqPJizUiHiUtzfPB5InKBFS5spMEHfJJffmeFQSuxtFvv2A0Iof1c9fwqVfKeU30N3Qin2yCl6GK2zgGt6JBd/A4PlOOp13yFj/FKpQx0gHPJdsNRWF6P7QjVSS3Qwp85Dy/Rb+7yeOMG+hKKlzaTY33clxe1tvgvpIvD69esAAL1e3+e6Xq9HTU2Nx/e9+OKLzq6jq+/nL/dvgESS+1DqACT2ttQBUJCZzWZotVqpw5AN5jsKjj9JHQAFDfOqXAyV7yQvArds2eI2AbkqLS3F/Pnzh/0zVKq+T0mEEAOuufrNb36DJ554wvm1yWRCVlYWDAaD4v7PQ0tLCzIzM3H58mWMHu1m2ptMKTVugLFLhbFLQymxCyFgNpsxduxYqUORlVDJd0r5PXSHsUuDsUtDqbErKW5v853kReBjjz2G+++/f9B7srOzh/W909LSANg7gunp6c7rRqNxQHfQlUajgUajGXBdq9XK/h/ek9GjRysydqXGDTB2qTB2aSghdiUVNcESavlOCb+HnjB2aTB2aSg1dqXE7U2+k7wITElJQUqKh/U4IzR+/HikpaXh4MGDyMnJAWDfYbSoqAgvv/xyQH4mERERERGRnMl7ZWM/BoMB5eXlMBgM6OnpQXl5OcrLy9Ha2uq8Z9q0adi7dy8A+zTQTZs24Q9/+AP27t2LM2fOYOPGjYiLi8MDDzwg1f8MIiIiIiIiyUjeCfTF888/jz179ji/dnT3CgoKkJeXBwCorKx07m4GAE899RTa29vxi1/8wnlY/IEDB3w6I1Cj0WDz5s1up8zInVJjV2rcAGOXCmOXhpJjp4GU+u+p1LgBxi4Vxi4Npcau1LgHoxLcL5uIiIiIiChsKGo6KBEREREREY0Mi0AiIiIiIqIwwiKQiIiIiIgojLAIJCIiIiIiCiMsAt144YUXsGTJEsTFxWHMmDFevWfjxo1QqVR9/ixevDiwgboxnNiFENiyZQvGjh2L2NhY5OXl4ezZs4EN1I2mpiZs2LABWq0WWq0WGzZsgMlkGvQ9Uo37W2+9hfHjxyMmJgbz5s3DV199Nej9RUVFmDdvHmJiYjBhwgRs37494DF64kvshYWFA8ZXpVLh/PnzQYwYOHz4MO666y6MHTsWKpUK//znP4d8j1zG3NfY5TLmL774IhYsWICEhASkpqbinnvuQWVl5ZDvk8u4k3eY75jvhsJ8x3znLeY7ZeU7FoFuWK1WrF+/Ho8++qhP71uzZg2uXbvm/PPFF18EKELPhhP7K6+8gtdeew1vvPEGSktLkZaWhjvuuANmszmAkQ70wAMPoLy8HF9++SW+/PJLlJeXY8OGDUO+L9jj/sEHH2DTpk149tlnUVZWhmXLlmHt2rUwGAxu76+ursadd96JZcuWoaysDM888wx+9atf4eOPPw5onO74GrtDZWVlnzGePHlykCK2a2trw5w5c/DGG294db+cxtzX2B2kHvOioiL88pe/xLFjx3Dw4EF0d3dj9erVaGtr8/geOY07eYf5jvluMMx3zHe+YL5TWL4T5NGuXbuEVqv16t78/Hxx9913BzQeX3gbu81mE2lpaeKll15yXuvo6BBarVZs3749gBH2de7cOQFAHDt2zHmtpKREABDnz5/3+D4pxn3hwoXikUce6XNt2rRp4umnn3Z7/1NPPSWmTZvW59rDDz8sFi9eHLAYPfE19oKCAgFANDU1BSE67wAQe/fuHfQeOY25K29il+OYCyGE0WgUAERRUZHHe+Q67jQ05jvmO3eY76TFfCeNcMl37AT6UWFhIVJTUzFlyhQ89NBDMBqNUoc0pOrqaly/fh2rV692XtNoNFixYgWOHj0atDhKSkqg1WqxaNEi57XFixdDq9UOGUcwx91qteLUqVN9xgsAVq9e7THOkpKSAfd///vfx8mTJ9HV1RWwWPsbTuwOOTk5SE9Px6pVq1BQUBDIMP1CLmM+EnIb8+bmZgBAUlKSx3tCYdzJO8x3w8d8F3jMd8r63JXbmIdLvmMR6Cdr167Fu+++i3//+9/44x//iNLSUnzve99DZ2en1KEN6vr16wAAvV7f57per3e+Fqw4UlNTB1xPTU0dNI5gj3t9fT16enp8Gq/r16+7vb+7uxv19fUBidOd4cSenp6OHTt24OOPP8Ynn3yCqVOnYtWqVTh8+HAwQh42uYz5cMhxzIUQeOKJJ7B06VLMnDnT431KHnfyHvPdyONgvgss5jtlfO7KcczDKd9FSR1AsGzZsgVbt24d9J7S0lLMnz9/WN//xz/+sfPvM2fOxPz585GVlYV9+/bhvvvuG9b3dAh07ACgUqn6fC2EGHBtOLyN3V0M3sQRyHEfjK/j5e5+d9eDwZfYp06diqlTpzq/zs3NxeXLl/Hqq69i+fLlAY1zpOQ05r6Q45g/9thj+Prrr1FcXDzkvUod91DCfDc45jvfMN8x3wWKHMc8nPJd2BSBjz32GO6///5B78nOzvbbz0tPT0dWVhaqqqpG/L0CGXtaWhoA+xON9PR053Wj0TjgCcdweBv7119/jRs3bgx4ra6uzqc4/Dnu7qSkpCAyMnLAk8TBxistLc3t/VFRUUhOTg5InO4MJ3Z3Fi9ejHfeecff4fmVXMbcX6Qc88cffxz/+te/cPjwYWRkZAx6b6iNu1Ix37nHfOcb5jvmOykw3wVP2BSBKSkpSElJCdrPa2howOXLl/skmuEKZOzjx49HWloaDh48iJycHAD2ufRFRUV4+eWXR/z9vY09NzcXzc3NOHHiBBYuXAgAOH78OJqbm7FkyRKvf54/x90dtVqNefPm4eDBg7j33nud1w8ePIi7777b7Xtyc3Px2Wef9bl24MABzJ8/H9HR0QGJ053hxO5OWVlZwMbXX+Qy5v4ixZgLIfD4449j7969KCwsxPjx44d8T6iNu1Ix37nHfOcb5jvmOykw3wVRsHeiUYKamhpRVlYmtm7dKuLj40VZWZkoKysTZrPZec/UqVPFJ598IoQQwmw2iyeffFIcPXpUVFdXi4KCApGbmytuueUW0dLSIuvYhRDipZdeElqtVnzyySeioqJC/OQnPxHp6elBj33NmjVi9uzZoqSkRJSUlIhZs2aJdevW9blHDuP+/vvvi+joaLFz505x7tw5sWnTJjFq1Chx6dIlIYQQTz/9tNiwYYPz/u+++07ExcWJX//61+LcuXNi586dIjo6Wnz00UcBi9Ffsb/++uti79694sKFC+LMmTPi6aefFgDExx9/HNS4zWaz83cZgHjttddEWVmZqKmpcRu3nMbc19jlMuaPPvqo0Gq1orCwUFy7ds35x2KxOO+R87iTd5jvmO8Gw3zHfBfI2OUy5uGa71gEupGfny8ADPhTUFDgvAeA2LVrlxBCCIvFIlavXi10Op2Ijo4W48aNE/n5+cJgMMg+diHs22Zv3rxZpKWlCY1GI5YvXy4qKiqCHntDQ4N48MEHRUJCgkhISBAPPvjggG2D5TLub775psjKyhJqtVrMnTu3zzbC+fn5YsWKFX3uLywsFDk5OUKtVovs7Gyxbdu2gMfoiS+xv/zyy2LixIkiJiZGJCYmiqVLl4p9+/YFPWbHNtL9/+Tn57uNWwj5jLmvsctlzN3F3P+zQ87jTt5hvmO+GwrzXXAx3zHfBYtKiJurGImIiIiIiCjk8YgIIiIiIiKiMMIikIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCCItAIiIiIiKiMMIikIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCCItAIiIiIiKiMMIikIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCCItAIiIiIiKiMMIikChEtbW1Qa/XQ6VSYcKECejq6nJ7X0dHB5YuXQqVSgWNRoPCwsLgBkpERDQCzHdEvmMRSBSiRo0ahWeeeQYAUF1djd27dw+4RwiBDRs24MiRI1CpVNizZw/y8vKCGygREdEIMN8R+U4lhBBSB0FEgdHZ2YkpU6bAYDAgKysLFy5cgFqtdr7+xBNP4PXXXwcAvPrqq3jyySelCpWIiGjYmO+IfMNOIFEI02g0eP755wEANTU1+Pvf/+587c9//rMzIW7atIkJkYiIFIv5jsg37AQShbienh7ceuutuHDhAjIzM/Htt9/i888/x/r162Gz2bB+/Xq8//77iIjgMyEiIlIu5jsi77EIJAoDH3zwAe6//34AwM9+9jO8++676OjowPLly3HgwAFoNBqJIyQiIho55jsi77AIJAoDQgjMnTsX5eXlzmszZsxAcXExxowZ4/F977zzDr766iucOnUKFRUVsFqt2LVrFzZu3BjwmImIiHzFfEfkHfbDicKASqXCQw895Pw6NTUV+/fvHzQhAsBvf/tb7NixAzU1NUhPTw9wlERERCPDfEfkHRaBRGGgqqoKmzdvdn7d1tbm1ZSYt99+G5cuXUJdXR0eeeSRQIZIREQ0Ysx3RN5hEUgU4oxGI9asWYP6+nokJycDsCfFF154Ycj33n777cjKygp0iERERCPGfEfkPRaBRCGsra0NP/jBD/Ddd98hPj4eBw4cwD333AMA+Otf/wqDwSBtgERERH7AfEfkGxaBRCGqu7sb69evx8mTJxEVFYV//OMfmDt3LrZu3QqVSoXOzk5s3bpV6jCJiIhGhPmOyHcsAolC1COPPIL9+/cDALZt24a1a9cCAGbPno0f/vCHAIA9e/bgwoULksVIREQ0Usx3RL5jEUgUgrZs2YKdO3cCAJ577jn8/Oc/H/B6REQEenp68Nxzz0kRIhER0Ygx3xEND4tAohCzc+dO57SX/Px8/O53vxtwz4wZM/CjH/0IAPDhhx/2OU+JiIhICZjviIaPRSBRCPniiy+cW1vffvvt+Nvf/ubx3s2bNyMyMhJCCDz77LPBCpGIiGjEmO+IRiZK6gCIyH/uvPNOdHV1eXXvtGnT0N3dHeCIiIiI/I/5jmhk2AkkIiIiIiIKIyohhJA6CCKSp7fffhvFxcUAgIqKCpw+fRq33XYbJk2aBAC45557nOcwERERKRXzHYUbTgclIo+Ki4uxZ8+ePteOHDmCI0eOAACys7OZFImISPGY7yjcsBNIREREREQURrgmkIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCCItAIiIiIiKiMMIikIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCCItAIiIiIiKiMMIikIiIiIiIKIywCCQiIiIiIgojLAKJiIiIiIjCyP8BjT42KLfPDcYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1050x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "\n",
    "hyperparams = (gamma1, C1) , (gamma1, C2), (gamma2, C1), (gamma2,C2)\n",
    "\n",
    "svm_clfs = []\n",
    "\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "    ])\n",
    "    rbf_kernel_svm_clf.fit(X,y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5,7), sharex=True, sharey=True)\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.sca(axes[i // 2, i % 2])\n",
    "    plot_predictions(svm_clf, [-1.5,2.45,-1,1.5])\n",
    "    plot_dataset(X, y , [-1.5,2.45,-1,1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma,C), fontsize=16)\n",
    "    if i in (0,1):\n",
    "        plt.xlabel(\"\")\n",
    "    if i in (1,3):\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing gamma makes\n",
    "the bell-shaped curve narrower (see the righthand plots in figure). As a result, each\n",
    "instance’s range of influence is smaller: the decision boundary ends up being more\n",
    "irregular, wiggling around individual instances. Conversely, a small gamma value makes the\n",
    "bell-shaped curve wider: instances have a larger range of influence, and the decision\n",
    "boundary ends up smoother. So $\\gamma$ acts like a regularization hyperparameter: if our model\n",
    "is overfitting, we should reduce it; if it is underfitting, we should increase it (similar to\n",
    "the C hyperparameter)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other kernels exist but are used much more rarely. Some kernels are specialized for\n",
    "specific data structures. `String kernels` are sometimes used when classifying text\n",
    "documents or DNA sequences (e.g., using the `string subsequence kernel` or kernels based\n",
    "on the `Levenshtein distance`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m,1)\n",
    "y = (4 + 3 * X + np.random.rand(m,1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(epsilon=1.05, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(epsilon=1.05, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(epsilon=1.05, random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.05, random_state=42)\n",
    "svm_reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.05, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X,y)\n",
    "svm_reg2.fit(X,y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X , y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred)) >= svm_reg.epsilon\n",
    "    # print(off_margin)\n",
    "    # print(np.argwhere(off_margin))\n",
    "    return np.argwhere(off_margin) # argwhere return the index where off_margin == True\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    xls = np.linspace(axes[0],axes[1],100).reshape(100,1)\n",
    "    y_pred = svm_reg.predict(xls)\n",
    "    plt.plot(xls, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(xls, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(xls, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X,y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "fig,axes = plt.subplots(ncols=2, figsize=(9,4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_reg1, X, y, [0,2,3,11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.annotate(\n",
    "    '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "    xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "    textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth':1.5}\n",
    ")\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_reg2, X, y, [0,2,3,11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph shows two linear SVM Regression models\n",
    "trained on some random linear data, one with a large margin ($\\epsilon$ = 1.5) and the other with a\n",
    "small margin ($\\epsilon$ = 0.5)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding more training instances within the margin does not affect the model’s predictions;\n",
    "thus, the model is said to be  $\\epsilon$-*insensitive*."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To tackle nonlinear regression tasks, we can use a kernelized SVM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m,1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=100, degree=2, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=100, degree=2, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=100, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel='poly', degree=2, C=100, epsilon=0.1, gamma='scale')\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=0.01, degree=2, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=0.01, degree=2, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=0.01, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_poly_reg1 = SVR(kernel='poly', degree=2, C=100, epsilon=0.1, gamma='scale')\n",
    "svm_poly_reg2 = SVR(kernel='poly', degree=2, C=0.01, epsilon=0.1, gamma='scale')\n",
    "svm_poly_reg1.fit(X,y)\n",
    "svm_poly_reg2.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(9,4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1,1,0,1])\n",
    "plt.title(r\"$degree = {}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph shows SVM Regression on a random quadratic training set, using a second-degree\n",
    "polynomial kernel. There is little regularization in the left plot (i.e., a large C value), and\n",
    "much more regularization in the right plot (i.e., a small C value)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Stuff"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:,0][y==0], X[:,1][y==1], \"bs\")\n",
    "plt.plot(X[:,1][y==0], X[:,1][y==1], \"g^\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]......................................\n",
      "Warning: using -h 0 may be faster\n",
      "*.......................\n",
      "Warning: using -h 0 may be faster\n",
      "*..............................................................\n",
      "Warning: using -h 0 may be faster\n",
      "*...................................*.......................................................*\n",
      "optimization finished, #iter = 212105\n",
      "obj = -4447.997680, rho = 0.075931\n",
      "nSV = 449, nBSV = 441\n",
      "Total nSV = 449\n",
      "0 0.1 0.30047035217285156\n",
      "[LibSVM]................................................*..........................................................*..............*..............................................................*..................................................................*...........*\n",
      "optimization finished, #iter = 258151\n",
      "obj = -4448.479655, rho = 0.058653\n",
      "nSV = 446, nBSV = 441\n",
      "Total nSV = 446\n",
      "1 0.01 0.27391886711120605\n",
      "[LibSVM]...................................................*......*............................................................*..............................................................................*..............................................................................*...........................................................................*...................*..............................................................................................*\n",
      "optimization finished, #iter = 457597\n",
      "obj = -4448.486560, rho = 0.058757\n",
      "nSV = 448, nBSV = 442\n",
      "Total nSV = 448\n",
      "2 0.001 0.36164021492004395\n",
      "[LibSVM]..................................................................*.......*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*.............................................*................................................................................................................................................................................................................................................................................................*.........................................................................*...................*........................................................................................................*\n",
      "optimization finished, #iter = 2065000\n",
      "obj = -4448.486967, rho = 0.058504\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "3 0.0001 0.6066179275512695\n",
      "[LibSVM]...................................................................*........*..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*...........................................\n",
      "Warning: using -h 0 may be faster\n",
      "*.............................................................*.............*...............................*...................................................*.................................................................................*\n",
      "optimization finished, #iter = 5714802\n",
      "obj = -4448.486967, rho = 0.058489\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "4 1e-05 0.9414219856262207\n",
      "[LibSVM]....................................................................*.........*..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*..................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5269415\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "5 1.0000000000000002e-06 0.8884665966033936\n",
      "[LibSVM]......................................................................*........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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      "optimization finished, #iter = 44838621\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "6 1.0000000000000002e-07 11.120692253112793\n",
      "[LibSVM].......................................................................*...........*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5795624\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "7 1.0000000000000002e-08 1.7236900329589844\n",
      "[LibSVM]........................................................................*............*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5798063\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "8 1.0000000000000003e-09 1.830979585647583\n",
      "[LibSVM].........................................................................*.............*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5800419\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "9 1.0000000000000003e-10 1.619931936264038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "\n",
    "for i  in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X,y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "\n",
    "plt.semilogx(tols, times, \"bo-\")\n",
    "plt.xlabel(\"Tolerance\", fontsize=16)\n",
    "plt.ylabel(\"Time (seconds)\", fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVM Classifier using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:,(2,3)] # petal length and petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C= C\n",
    "        self.eta0 = eta0\n",
    "        self.eta_d = eta_d\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        # Random Initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        \n",
    "        w = np.random.rand(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y*2 - 1 # -1 if y==0, +1 if y==1\n",
    "        X_t = X*t\n",
    "        self.Js = []\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w*w) + self.C * (np.sum(1-X_t_sv.dot(w)) - b*np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1,1)\n",
    "            b_derivative = - self.C * np.sum(t_sv)\n",
    "\n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "    \n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >=0).astype(np.float64)\n",
    "\n",
    "C = 2\n",
    "svm_clf = MyLinearSVC(C=C, eta0=10, eta_d=1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X,y)\n",
    "svm_clf.predict(np.array([[5,2],[4,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.xlabel(\"No of Epochs\")\n",
    "plt.ylabel(\"Cost Function (J)\")\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56770527] [[[2.28126905]\n",
      "  [2.7161964 ]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[2.27128546 2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf_2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf_2.fit(X, y.ravel())\n",
    "print(svm_clf_2.intercept_, svm_clf_2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    \"\"\"\n",
    "    At decision boundary:\n",
    "    wTx + b = w1x1+w2x2+....+wnxn = 0 \n",
    "    because it is decision boundary, and it equation is for decision boundary and thus all the points line on line. On positive side it is > 1 and on other side it is <1\n",
    "\n",
    "    In our case, we have 2 features, x0 (petal length) and x1 ( petal width)\n",
    "    Thus:\n",
    "    w0*x0 + w1*x1 + b = 0\n",
    "    which implies:\n",
    "\n",
    "    x1 = (-w0*x0 - b) / w1\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    x0 = np.linspace(xmin, xmax, 100)\n",
    "    decison_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decison_boundary + margin\n",
    "    gutter_down = decison_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:,0], svs[:,1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decison_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "\n",
    "fig, axes= plt.subplots(ncols=2, figsize=(11,3.2), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X[:,0][yr==1], X[:,1][yr==1], \"g^\", label=\"Iris Virginica\")\n",
    "plt.plot(X[:,0][yr==0], X[:,1][yr==0], \"bs\", label=\"Not Iris Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4,6)\n",
    "plt.xlabel(\"Petal Length\", fontsize=14)\n",
    "plt.ylabel(\"Petal Width\", fontsize=14)\n",
    "plt.title(\"My Linear SVC\")\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X[:,0][yr==1], X[:,1][yr==1], \"g^\")\n",
    "plt.plot(X[:,0][yr==0], X[:,1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf_2, 4,6)\n",
    "plt.xlabel(\"Petal Length\", fontsize=14)\n",
    "plt.title(\"SVC\")\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-12.52988101   1.94162342   1.84544824]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss='hinge', alpha=0.017, max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X,y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y*2 - 1\n",
    "X_b = np.c_[np.ones((m,1)), X] # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:,0][yr==1], X[:,1][yr==1], \"g^\")\n",
    "plt.plot(X[:,0][yr==0], X[:,1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal Length\", fontsize=14)\n",
    "plt.ylabel(\"Petal Width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2,3)] # petal length and petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y==0) | (y==1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVC:                  [0.28475098] [[1.05364854 1.09903804]]\n",
      "SVC:                         [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / ( C * len(X) )\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel='linear', C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha, max_iter=1000, tol=1e-3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"Linear SVC:                 \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                        \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries for these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0,0] / lin_clf.coef_[0,1]\n",
    "b1 = - lin_clf.intercept_[0] / lin_clf.coef_[0,1]\n",
    "w2 = -svm_clf.coef_[0,0] / svm_clf.coef_[0,1]\n",
    "b2 = - svm_clf.intercept_[0] / svm_clf.coef_[0,1]\n",
    "w3 = -sgd_clf.coef_[0,0] / sgd_clf.coef_[0,1]\n",
    "b3 = - sgd_clf.intercept_[0] / sgd_clf.coef_[0,1]\n",
    "\n",
    "# Transform the decision boundary lines back to original scales\n",
    "line1 = scaler.inverse_transform([[-10,-10*w1+b1], [10,10*w1+b1]])\n",
    "line2 = scaler.inverse_transform([[-10,-10*w2+b2], [10,10*w2+b2]])\n",
    "line3 = scaler.inverse_transform([[-10,-10*w3+b3], [10,10*w3+b3]])\n",
    "\n",
    "# Plot all three boundaries\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.plot(line1[:,0], line1[:,1], \"k:\", label=\"Linear SVC\")\n",
    "plt.plot(line2[:,0], line2[:,1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:,0], line3[:,1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:,0][y==1], X[:,1][y==1], \"bs\") # Iris Versicolor\n",
    "plt.plot(X[:,0][y==0], X[:,1][y==0], \"yo\") # Iris setosa\n",
    "plt.xlabel(\"Petal Length\", fontsize=14)\n",
    "plt.ylabel(\"Petal Width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0,5.5, 0 ,2])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.10/site-packages/sklearn/datasets/_openml.py:932: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "# Let's first load the data\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"].astype(np.uint8)\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first. However, the dataset is already shuffled, so we do not need to do it."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.10/site-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8348666666666666"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, 83.5% accuracy on MNIST is pretty bad. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.fit_transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.10/site-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9214"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we cut the error rate by about 53%), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an SVC with an RBF kernel (the default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(gamma=\"scale\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455333333333333"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] END ...C=5.3532239261827685, gamma=0.006930037811020538; total time=   2.1s\n",
      "[CV] END ...C=5.3532239261827685, gamma=0.006930037811020538; total time=   1.4s\n",
      "[CV] END ...C=5.3532239261827685, gamma=0.006930037811020538; total time=   1.6s\n",
      "[CV] END ....C=4.303348210038742, gamma=0.002566239988368079; total time=   1.4s\n",
      "[CV] END ....C=4.303348210038742, gamma=0.002566239988368079; total time=   1.4s\n",
      "[CV] END ....C=4.303348210038742, gamma=0.002566239988368079; total time=   1.5s\n",
      "[CV] END ....C=7.192709663506637, gamma=0.003974745692371884; total time=   1.6s\n",
      "[CV] END ....C=7.192709663506637, gamma=0.003974745692371884; total time=   1.5s\n",
      "[CV] END ....C=7.192709663506637, gamma=0.003974745692371884; total time=   1.6s\n",
      "[CV] END ...C=3.6682727510286663, gamma=0.017468984743535473; total time=   1.7s\n",
      "[CV] END ...C=3.6682727510286663, gamma=0.017468984743535473; total time=   1.6s\n",
      "[CV] END ...C=3.6682727510286663, gamma=0.017468984743535473; total time=   1.8s\n",
      "[CV] END ....C=6.29142094277039, gamma=0.0018584885538668373; total time=   1.3s\n",
      "[CV] END ....C=6.29142094277039, gamma=0.0018584885538668373; total time=   1.4s\n",
      "[CV] END ....C=6.29142094277039, gamma=0.0018584885538668373; total time=   1.5s\n",
      "[CV] END ....C=6.135781212657465, gamma=0.002338189636639463; total time=   1.4s\n",
      "[CV] END ....C=6.135781212657465, gamma=0.002338189636639463; total time=   1.5s\n",
      "[CV] END ....C=6.135781212657465, gamma=0.002338189636639463; total time=   1.6s\n",
      "[CV] END .....C=8.853351478166735, gamma=0.05104469172041387; total time=   2.7s\n",
      "[CV] END .....C=8.853351478166735, gamma=0.05104469172041387; total time=   2.1s\n",
      "[CV] END .....C=8.853351478166735, gamma=0.05104469172041387; total time=   2.2s\n",
      "[CV] END ....C=5.942368373819278, gamma=0.049331346615319634; total time=   1.8s\n",
      "[CV] END ....C=5.942368373819278, gamma=0.049331346615319634; total time=   2.1s\n",
      "[CV] END ....C=5.942368373819278, gamma=0.049331346615319634; total time=   2.1s\n",
      "[CV] END .....C=1.79645477009061, gamma=0.010244533341910568; total time=   2.1s\n",
      "[CV] END .....C=1.79645477009061, gamma=0.010244533341910568; total time=   2.0s\n",
      "[CV] END .....C=1.79645477009061, gamma=0.010244533341910568; total time=   1.9s\n",
      "[CV] END ....C=1.652865043868781, gamma=0.007181987669977643; total time=   1.5s\n",
      "[CV] END ....C=1.652865043868781, gamma=0.007181987669977643; total time=   1.8s\n",
      "[CV] END ....C=1.652865043868781, gamma=0.007181987669977643; total time=   1.8s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(),\n",
       "                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0df0&gt;,\n",
       "                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa703481a20&gt;},\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(),\n",
       "                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0df0&gt;,\n",
       "                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa703481a20&gt;},\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVC(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0df0>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa703481a20>},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "params_distribution = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1,10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, params_distribution, n_iter=10, verbose=2, cv=3)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=6.29142094277039, gamma=0.0018584885538668373)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=6.29142094277039, gamma=0.0018584885538668373)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=6.29142094277039, gamma=0.0018584885538668373)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8559907212601824"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=6.29142094277039, gamma=0.0018584885538668373)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" checked><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=6.29142094277039, gamma=0.0018584885538668373)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=6.29142094277039, gamma=0.0018584885538668373)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99935"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9702"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. By running the randomized search for longer and on a larger part of the training set, you may be able to find a little better hypeparameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train an SVM regressor on the California housing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedHouseVal']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"target_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedInc',\n",
       " 'HouseAge',\n",
       " 'AveRooms',\n",
       " 'AveBedrms',\n",
       " 'Population',\n",
       " 'AveOccup',\n",
       " 'Latitude',\n",
       " 'Longitude']"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting it into Training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/lib/python3.10/site-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(random_state=42)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9641780189948642"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9819256687727764"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variable (MedHouseVal) is represented in tens of thousands of dollars in training dataset, it means that a value of 1 in the target variable corresponds to $10,000. Thus an RMSE of 0.98192 can be interpreted as follows:\n",
    "\n",
    "- The average prediction error of the LinearSVR model, in terms of dollars, is approximately $9,819.2 (0.98192 multiplied by $10,000).\n",
    "- The model's predictions can deviate from the actual values by around $9,819.2.\n",
    "- It implies that, on average, the model's predictions for median house values can be off by roughly $10,000.\n",
    "\n",
    "Not Great!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can do better with an RBF Kernel. We will use randomized search CV with cross validation to find appropriate hyperparameters `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  37.5s\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  37.2s\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  37.4s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  35.5s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  36.7s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  37.1s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  34.4s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  34.3s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  34.5s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  33.5s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  33.2s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  33.8s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  36.6s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  37.1s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  37.6s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  33.2s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  33.3s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  33.1s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  34.6s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  34.6s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  34.5s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  34.1s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  34.2s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  34.6s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  34.2s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  34.2s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  34.5s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  34.0s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  34.2s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  34.1s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0700&gt;,\n",
       "                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa70287fb80&gt;},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={&#x27;C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0700&gt;,\n",
       "                                        &#x27;gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa70287fb80&gt;},\n",
       "                   random_state=42, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa7030f0700>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7fa70287fb80>},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "params_distribution = {\"gamma\": reciprocal(0.001,0.1), \"C\": uniform(1,10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), params_distribution, n_iter=10, verbose=2, cv=3, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=4.745401188473625, gamma=0.07969454818643928)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" checked><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=4.745401188473625, gamma=0.07969454818643928)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=4.745401188473625, gamma=0.07969454818643928)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785367"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528745"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  }
 ],
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