Handwritten Digit Classification Using MLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing necesssary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data.sampler import SubsetRandomSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_workers = 0\n",
    "batch_size = 20\n",
    "valid_size = 0.2\n",
    "\n",
    "transform = transforms.ToTensor()\n",
    "\n",
    "train_data = datasets.MNIST(root='data', train=True,\n",
    "                                   download=True, transform=transform)\n",
    "test_data = datasets.MNIST(root='data', train=False,\n",
    "                                  download=True, transform=transform)\n",
    "\n",
    "num_train = len(train_data)\n",
    "indices = list(range(num_train))\n",
    "np.random.shuffle(indices)\n",
    "split = int(np.floor(valid_size * num_train))\n",
    "train_idx, valid_idx = indices[split:], indices[:split]\n",
    "\n",
    "train_sampler = SubsetRandomSampler(train_idx)\n",
    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
    "    sampler=train_sampler, num_workers=num_workers)\n",
    "valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n",
    "    sampler=valid_sampler, num_workers=num_workers)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n",
    "    num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing a Batch Of Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fca3e2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for idx in np.arange(20):\n",
    "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(str(labels[idx].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing an Image in More Detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0w0UsEREREWmHi1giIiIi0o7b/dpZIrq0xMREZe2RI0cqaw8YMEBZGwCOHDmitE9ERO6FR2KJiIiISDtcxBIRERGRdriIJSIiIiLtcBFLRERERNrhIpaIiIiItMNFLBERERFpxy0XsXFxccjMzER2djYmTZqkTVt1X9e26v6N2m7Xrh1effVVvP7663jggQectvfq1QszZszA9OnTkZSUhICAAADAPffcg+nTpxu3BQsW4M4773R47LZt2zB8+HAMHToUn376qVP73XffxVNPPYWnnnoKw4YNc3j+kpISjB8/Ho8//jieeOIJFBUVOTw2NjYW27Ztw44dO/DnP//Zqe3j44OPPvoIO3bswJo1axAUFAQAePTRR5GammrcSktL0bZt28ueN3f+nF6rtuo+267v69pW3de1rbqvaxsicsU3APsB7AaQDmBHHfaX2m4eHh5itVolODhYvL29JT09XcLCwmp93LVu6zx2zote7cTERBkxYoSUlJTIhAkTZOTIkZKXlydTpkyRxMRE4zZ69Gjjz3/7299k165dDtsTExPlpZdekpKSEuN+amqqrF+/XgICAuSzzz6TtWvXSmhoqCxcuFBSU1NrvI0ZM0bi4+ON++3bt5c333xTUlNT5bvvvpOUlBRJTU2Vhg0byh133CE5OTnSvn17adq0qezevVu6dOkiDRs2NG7jx4+XBQsWSMOGDeWpp56SJUuWOGxv2LCh3HvvvZKbm+vwMZ0/p/w3emO1dR4754Xz4qp2Xdah9XEktpeItBeRjvXQQnR0NKxWK3Jzc1FRUYHk5GQkJCTUR1ppW3Vf17bq/o3aDgkJQUlJCQ4dOgS73Y5t27ahQ4cODvucOXPG+PNNN91U/Y2kg86dO2Pbtm0OH8vIyIDZbEZAQAC8vb3Ru3dvbN68+aJjWbduHWJjYwEA+/fvh91uR6dOnQAAt9xyC26++WZj36ioKOTm5iIvLw8VFRVYsmQJ4uPjHXr3338/kpOTAQDLli1D9+7dnZ5z4MCB+Oqrry46potx58/ptWqr7rPt+r6ubdV9Xduq+7q2ATc8ncBsNsNmsxn38/PzYTab3b6tuq9rW3X/Rm03bNgQZWVlxv0jR46gYcOGTvvFxsbijTfewKBBg2o8LaBz587YunWrw8dKS0vRtGlT436TJk1QWlpa4ziKi4tRVFSEu+++GwBgs9lw6623YurUqXjqqafw/vvvw263G/v7+/ujoKDAuF9YWAh/f3+H5vn72O12HDt2DI0aNXLY5+GHH8aSJUtqHNOluPPn9Fq1VffZdn1f17bqvq5t1X1d28DVL2IFwGqTyZRmMpmeqY8BmUwm5yep4QiSu7VV93Vtq+7fqO2aHl+TdevWYeLEifjiiy/Qv39/h20hISE4e/asw6Lycsexfv169OjRA56engDOLTp37dqFP/7xj5g/fz4KCwuxatWqS477wuerbZ+oqCicPn0aGRkZdR7n5Tz/ldK1rbrPtuv7urZV93Vtq+7r2gaufhHbVUTuBhAP4HmTyeT0cz+TyfSMyWTaYTKZdtQlmJ+fb1zIAQCBgYEoLCy8ymGqb6vu69pW3b9R22VlZQ5HJxs2bIgjR45cdP9t27YZR0ur1XQqAXDuyOvBgweN+4cOHULjxo1r7K5btw59+vRxeGzLli0REBAALy8vxMTEYO/evcb2wsJCh+/CAwICUFxc7NA8fx9PT080aNDA4e/2yCOPXNGpBIB7f06vVVt1n23X93Vtq+7r2lbd17UNAFd1YdcFF229DGDC1V7Y5enpKfv27ROLxWKcBBweHl4vJxirbOs8ds6LXu3ExER58sknpaSkRMaPH3/RC7smTpzocGFXTk6OcX/EiBFy+PBhmTBhgsNjUlNTZd26deLv7y/JycnGhV0ff/yx0wVd//rXv8TPz082btxofGz9+vUSGhoqy5Ytk9TUVLnvvvvkhRdeMC7saty4seTm5kpkZKRxYdc999zjcIHWhAkTHC7sWrp0qbGtUaNGUlBQIO3bt3e62Evnzyn/jd5YbZ3HznnhvLiqXZe1pxeukMlk8gXgISLH//vnfgCmX2mvmt1ux5/+9CekpKTA09MTCxYswJ49e642q7ytuq9rW3X/Rm1XVVVh0aJFmDBhAjw8PPD999+jsLAQDz/8MHJzc5Geno7Y2Fi0adMGdrsdJ0+exD/+8Q/j8a1bt8aRI0dw6NAhp7aXlxdeeOEFTJgwAVVVVbj//vsRHByMjz76CHfddRe6du0K4NxR2N69ezv8uMjT0xPPPfccxo4dCxFB69at8eCDDzr8vSdOnIgvv/wSnp6e+PTTT5GZmYkpU6bgl19+wapVq7Bo0SL8/e9/x44dO3DkyBE8/fTTxuPvvfdeFBYWIi8v77Lm+/znd9fP6bVqq+6z7fq+rm3VfV3bqvu6tgHAdKXnJphMphAAS/971wvAv0VkVi2PubInIyJDYmKisvbIkSOVtQcMGKCsDeCSp1MQEZFeRKTWiz+u+EisiOQAiLzSxxMRERERXSm3e4stIiIiIqLacBFLRERERNrhIpaIiIiItMNFLBERERFph4tYIiIiItIOF7FEREREpJ0rfp/YK3oyvk8s3SACAgKUtXft2qWs3bBhQ2Vtle9vCwCLFi1S2teVxWJR1t6/f7+yNhHd2OryPrE8EktERERE2uEiloiIiIi0w0UsEREREWmHi1giIiIi0g4XsURERESkHS5iiYiIiEg7brmIjYuLQ2ZmJrKzszFp0iRt2qr7urZV93Vp9+zZE5s2bcLmzZvx/PPPO23v3LkzVq1ahby8PDzwwAO19tatW4fOnTujU6dOmDt3rtN2m82Ghx9+GN27d8dDDz2EwsJC4+O9e/dGz5490bVrV/zzn/90euyqVasQFhaGVq1a4fXXX3fanpeXh759+6J9+/bo3bs38vPzjW2TJ09GREQEIiIisHjxYqfHtmvXDm+88QbefPNNPPjgg07be/fujVdeeQUzZ87E1KlTHd6uLCgoCNOmTcOrr76KV155Bd7e3rXO04V0eb3Ud7t79+5Yt24dNmzYgGeffdZpe3R0NFasWIHs7GzEx8cbHzebzVi+fDlWrlyJlJQUDB061OVjvx7bqvu6tlX3dW2r7uvahoi47AZAart5eHiI1WqV4OBg8fb2lvT0dAkLC6v1cde6rfPYOS/13w4ICHC4BQYGSm5urnTp0kWaN28uv/76q/To0cNhn+joaImNjZUvvvhCRo0a5dSovpWWlkpJSYlYLBbZsWOHFBYWSps2beSHH36Q0tJS4/bQQw/Ju+++K6WlpbJkyRJ57LHHpLS0VAoLC6WgoEBKS0tl//79EhQUJLt375bS0lKx2+1SXl4uISEhkp2dLadPn5aIiAjZvXu32O124zZw4EBZsGCB2O12WbNmjQwbNkzsdrssX75cYmNj5ezZs3Ls2DGJioqSI0eOiN1ul+HDh8vjjz8uxcXFMnbsWElMTJS8vDyZOHGiDB8+3Lg9/fTTxp/feust2blzpwwfPlyeeOIJycvLk6SkJBk+fLg8++yz8vjjjxv76vx6Udm2WCwSEhIi+/fvl27duknLli1lz5490qdPH7FYLMata9euct9998lXX30lzz33nPHxli1bSqtWrcRisUh4eLjYbDaJjo4Wi8Wi9bxcy7bOY+e8cF5c1a7LutLtjsRGR0fDarUiNzcXFRUVSE5ORkJCgtu3Vfd1bavu69Lu0KED9u/fjwMHDqCiogLLli1DXFycwz75+fnIyMhAVVVVrb2ff/4ZwcHBsFgs8PHxwcMPP4zvvvvOYZ+srCx0794dANCtWzdju4+PD2666SYAQHl5udPz/fTTTwgNDUVISAh8fHwwePBgLF++3GGfjIwMxMbGAgB69eplbM/IyECPHj3g5eUFX19fREREYNWqVcbjQkNDUVJSgkOHDsFut2Pr1q2IiopyaJ85c8b480033VT9DTDatWsHm82GAwcOAABOnDhhbKsrXV4v9d2OjIxEXl4ebDYbKioqsGLFCvTt29dhn4KCAmRmZjq9HioqKlBeXg7g3GvHZKr1/cfrdezXY1t1X9e26r6ubdV9XduAG55OYDabYbPZjPv5+fkwm81u31bd17Wtuq9L28/Pz/hxPgAUFRXBz8/visdWVFTk8GP2gIAAFBUVOezTpk0brFixAgCwcuVKnDhxAmVlZQDOLVi6d++OyMhIjBkzBv7+/sbjCgoKEBQUZNw3m80oKChwaEdERGDJkiUAgKVLl+L48eM4fPiwsWg9deoUSktLsXHjRodTDRo2bGiMAQDKyspq/C1hffr0wZtvvokhQ4bgX//6F4BzcygiePHFFzFjxow6nXJxIV1eL/Xd9vPzc3h9FBcXX9brz9/fH9999x1+/PFHzJ8/HwcPHqzzY915Xq5VW3Vf17bqvq5t1X1d24AbLmJr+i6/vn41rsq26r6ubdV9Xdr1Pc6aHnvhc/zf//0ffvzxR/Tq1Qs//vgj/P394eXlBeDcfyybNm3CTz/9hOTkZIdFSV3as2fPRmpqKqKiorBp0yaYzWZ4eXmhX79+iI+PR0xMDIYOHYouXboYz1lT52LPt3btWkyYMAGLFy82vmv39PRE69at8f7772PGjBmIiopCeHj4paap1r/HxZ7/Srhz+2ofX1RUhPj4ePTs2RMDBw5E48aNXfbc12NbdV/Xtuq+rm3VfV3bgBsuYvPz8x2OAgUGBjocwXLXtuq+rm3VfV3aFx459ff3R0lJyRWPLSAgwGEshYWFTkfW/P39sXDhQmzYsAFJSUkAgAYNGjjtc9ddd2Hr1q3GxwIDAx2+cy4oKHAYe/Xzf/XVV0hLS8PMmTMBALfddhsAICkpCT///DNWr14NEUGLFi2Mx5WVlaFRo0bG/UaNGuG333676N/z/NMNysrKkJmZiRMnTqC8vBw7d+6ExWK5+CTVQJfXS323i4qKHI62+/n5XdHr7+DBg9i7dy86depU58e487xcq7bqvq5t1X1d26r7urYBN1zEbt++HS1btoTFYoG3tzeGDBnidD6eO7ZV93Vtq+7r0k5PT0dwcDCCgoLg7e2NhIQErF69+orH1qFDB+Tk5CAvLw/l5eVYunQp7rvvPod9Dh8+bJzfOHfuXOOq8sLCQpw+fRoA8Ntvv2Hbtm0OC81OnToZ5zCVl5dj8eLF6N+/v0O7tLTUaL/22mt48sknAQB2ux2HDx8GAOzatQu7d+9Gv379jMfl5OTAz88PTZo0gaenJ7p06YKff/7Zod2sWTPjz+3bt0dxcbHRCwoKgo+PDzw8PHDXXXc5neZQG11eL/Xd3rVrFywWCwIDA+Ht7Y3+/ftj7dq1dXqsn5+fcQ51gwYN0LFjR+Tk5Lhs7NdjW3Vf17bqvq5t1X1d2wDgdu9OAEDi4+MlKytLrFarJCUl1dvVfarbOo+d81K/7ZreVWD48OGyb98+yc3Nlddee00CAgLkr3/9qyQmJkpAQIDEx8dLYWGhnDx5UsrKyiQzM/Oi705QWloqn332mYSEhIjFYpGkpCQpLS2V8ePHy7/+9S8pLS2VBQsWSHBwsISEhMiwYcOMdyT48ssvJTw8XNq0aSPh4eHy1ltvGc3qdx9YsWKFtGzZUkJCQmTGjBlit9tl6tSpsnTpUrHb7bJ48WJp0aKFtGzZUkaOHCmnTp0Su90uJ0+elLCwMAkLC5POnTtLWlqa0ax+F4HZs2dLYWGhFBcXy+effy7Dhw+XJUuWyFtvvSXDhw+XVatWic1mk/3798uvv/4qkyZNMh47b948sdlsYrPZ5JtvvnF4VwOdXy8q29XvMjBixAjJycmR/fv3y+zZs8ViscjcuXPl6aefFovFIg899JDD6y8rK0ssFosMHz5cMjIyZM+ePZKRkSFTpkwxmjrPy7Vu6zx2zgvnxRXtuqwrTfV5bkJtTCaT656M6Bq68Mfv9WnXrl3K2jVdZFVfEhMTlbUBYNGiRUr7urrcUy4ux/79+5W1iejGJiK1vhWK251OQERERERUGy5iiYiIiEg7XMQSERERkXa4iCUiIiIi7XARS0RERETa4SKWiIiIiLTDRSwRERERacer9l2I6HItXLhQWfv2229X1lZpzJiSXgftAAAgAElEQVQxSvsZGRnK2uHh4crazz77rLI2oPZ9Yg8ePKisrbMdO3Yoa1/Nb/qriy+++EJpn6g+8UgsEREREWmHi1giIiIi0g4XsURERESkHS5iiYiIiEg7XMQSERERkXa4iCUiIiIi7bjlIjYuLg6ZmZnIzs7GpEmTtGmr7uvaVt135/b27dvx5JNPIjExEcnJyU7b33//fYwePRqjR4/GiBEjMGDAAABASUkJ/vjHP2L06NF4+umnsWLFCqfHrlq1CuHh4WjdujVef/11p+15eXno27cvOnTogN69eyM/P9/YNnnyZERGRiIyMhKff/55je2wsDC0atXqku327dvX2I6IiEBERAQWL17s9NgtW7bg0UcfxSOPPFLjW5H99a9/xbBhwzBs2DAMHDgQvXv3Nra98847GDJkCIYMGYI1a9Y4PfZC99xzD7766issXboUiYmJTtuHDRuGzz//HJ999hnmzZsHPz+/S/batWuH119/HbNnz8aDDz7otL1Xr16YNWsWZsyYgalTpyIgIMDYFhQUhGnTpuGVV17BrFmz4O3t7fDYrVu3YsiQIRg0aBD+9a9/ObXnzp2LxMREJCYmYsiQIYiLiwMApKWlGR9PTExEr169sGnTpkv+PTZs2IBu3bqha9euePfdd5225+fnY9CgQejTpw8effRRFBYWXrL3ww8/YMCAAXjooYewYMECp+1FRUUYNWqU8ff7/vvvAQC//fYbRo0ahXvvvRevvfaay9uq+//5z38wdepUJCUl4bvvvqtxn+3bt2PatGmYNm0a/vGPfxgfnzNnDsaMGYO33367xsdFRkZizpw5ePvtt5GQkOC0vW/fvnjzzTfxxhtvYPr06TCbzQCA0NBQvPHGG8atU6dOF52bi+HXC9e3Vfd1bZtEpF6Dl3wyk6nWJ/Pw8MDevXvRt29f5OfnY/v27fjDH/5QL+8BqbKtuq9rW3XfXdtr1qyB3W7Hk08+iddffx2NGzfGn/70JyQlJaF58+Y1Pubrr7+G1WrFhAkTUFFRARGBj48PTp8+jVGjRmHOnDlo3LgxevXqBbvdjrCwMKxatQqBgYHo0qULFi1a5PB+poMHD8YDDzyAJ554AuvXr8fChQuxcOFCrFy5Em+//TZWrlyJs2fPonfv3lizZg0aNGgAk8kEu92Ou+66CykpKQgMDETnzp3x6aefOrQHDRqEBx54AImJiVi/fj0+/vhjfPLJJ1i5ciXmzp2Lb7/9FmfPnkWvXr2wdu1aNGjQAGlpabDb7Xj00Ufx7rvvomnTpkhMTMTMmTMREhJS45wsXrwYe/fuxV/+8hds3rwZycnJmDNnDioqKjB69GjMmzcPt956KwDgueeec/r8LVmyBM8//zxKSkrwySef4KWXXkJubq6xT1RUFP7zn//g7NmzGDhwIKKiopCUlOQ0jvDwcJhMJuMLf1lZGf7v//4P8+bNc1jg3XzzzThz5gwAoEOHDoiNjcWbb74JDw8PTJ8+HfPnz4fNZsOtt96KkydPQkTw7LPPwm63Y8iQIZgzZw6aNm2Kp59+Gi+//DKCg4NrnJcvvvgC2dnZTmM9duwYBg0ahK+//ho333wzAOf3ibXb7ejWrRs+++wz+Pv74/7778e8efPQqlUrY59nnnkGffr0waBBg7B582YsXrwY77zzjtM4Dh48CLvdjgEDBuD9999Hs2bNMGzYMLz66qsIDQ019psxYwZat26NQYMGYd++ffif//kffPvttzh9+jQyMzNhtVqxb98+TJ482Wmsqtoq+zt27EBVVRWmTp2KsWPHomHDhpg1axZGjRrl8I1NSUkJ5s+fj/Hjx8PX1xfHjh1DgwYNAJx73+Py8nKkpqY6vMfy6tWrYTKZMHfuXMycOROHDx/Gq6++irlz56KgoMDY73e/+x1Onz4N4NzrPC4uDq+88gp8fHxQWVmJqqoq3H777Zg9ezZGjx6NqqoqALW/Tyy/Xri+rbrvrm0RMdXav+oR1rPo6GhYrVbk5uaioqICycnJNX6X6W5t1X1d26r77tzOyspCQEAA/P394e3tjZ49e+LHH3+86P4bNmxAr169AADe3t7w8fEBAFRUVBhfYKr99NNPCA0NRUhICHx8fDBo0CAsX77cYZ+MjAzjKGavXr2M7RkZGejevTu8vLzg6+uLiIgIpKSkXLQ9ePDgGtuxsbE1tnv06OHQXrVqlfG4X3/9FYGBgTCbzfD29ka/fv0uedRw9erV6NevHwAgNzcXHTp0gJeXF373u9+hZcuW2LJly0Uf26ZNG9hsNhQUFKCyshKrV69Gjx49HPZJS0vD2bNnAZw7atasWbOL9kJDQ3Hw4EEcOnQIdrsdW7duxd133+2wT/UCFgBuuukmVB8kaNu2LWw2G2w2GwDgxIkTOP8AQkZGhsO8xMbGGkf8arJ27Vr06dPH6eMbNmxAly5djAVsTX755RdYLBY0b94cPj4+SEhIcPj8A0B2djZiYmIAAF27dr3kG+z/5z//QVBQEAIDA+Ht7Y24uDhs3LjRYR+TyYSTJ08af/cmTZoAOLfQ6tChA2666SaXt1X3c3Nz0aRJEzRp0gReXl7o1KkT0tPTHfb5/vvv0atXL/j6+gKAsYAFgLCwsIt+Hlu0aIHi4mLjm4gff/zR6Yhq9QIWOPfNVfXrrby83Pj/xNvbG5d7IItfL1zfVt3XtQ244SLWbDYb/9ED536sVf1jEHduq+7r2lbdd+d2aWmp8QUPABo3bozS0tIa9y0pKUFxcTHat29vfOzgwYN45plnMHToUAwePBiNGzc2thUWFiIoKMi4HxgY6PQj34iICCxZsgTAuaO8x48fx+HDh42F5alTp1BaWoqNGzc6/D0LCgoc2maz2eEIz4XtpUuXXrJ9/qkGhw4dclgoNm3aFIcOHapxToqKilBYWIiOHTsCgLFoPXPmDH777TekpaVd8jdGNW3aFCUlJQ7z2bRp04vun5CQcMlvMho2bIjDhw8b98vKytCwYUOn/WJjYzF79mwMHjwYixYtAgD4+/sDAF588UVMnz4d999/v8NjDh065DC2S81LcXExioqKEBUV5bRt7dq16Nu370X/DtWPP/9ooL+/P4qLix32CQ8Px7fffgsA+O6773DixAmUlZXV2Dt48KDD57RZs2ZOYx89ejS+/fZbxMXF4X/+53/q/CNFlW3V/d9++w2NGjUy7jds2BC//fabwz4lJSUoKSnBa6+9hldeeQX/+c9/6tRu1KiRw2vx8OHDDs9VLS4uDm+//TaGDRuGf/7zn8bHW7RogbfeegtvvfUW/vGPfzh9k3wp/Hrh+rbqvq5twA0XsSaT89Hj+jrlQWVbdV/Xtuq+O7dr2remJvD/z1H09PQ0Pta0aVN88MEH+Pjjj7FmzRocOXLkstpvvPEGNm3ahI4dO2LTpk0wm83w8vJCv379EB8fj27dumHYsGHo0qULvLz+/2+grkt79uzZSE1NRVRUVI3tmJgYDB06tE7ti1m9ejV69+5tzEmXLl1w77334qmnnsLUqVPRrl07h/mqi4s9f3x8PMLCwvDJJ59cVq8m69atw4svvojPP//cOOLg4eGBVq1a4f3338fMmTPRsWNHh9MzLue1snbtWvTs2dPp715aWoqcnBx07tz5kuOry3P95S9/wdatW9GvXz9s3boVfn5+Dp/Hy7Vq1Sr0798fKSkpeOeddzB16tTLWjhdq/bV9OvyWrfb7SgpKcGECRMwatQoLFy4EKdOnar1cXX9vyklJQVjxozBp59+ioEDBxoft1qtGD9+PKZMmYKHH37Y6fzs+njuK+XO/6dfq7bqvq5twA0Xsfn5+bUeYXLHtuq+rm3VfXduN2nSxOGoTmlpKe64444a9924caNxKsGFGjdujObNm2P37t3Gx2r67rb6aF+1gIAAfPnll9ixYwdmzJgBALjtttsAAElJSUhLS0NKSgpEBC1atHD4e154ZPb8I3fV7a+++gppaWmYOXOmU/vnn3/G6tWrndo1HR09/2j1+dasWWNcvFRt5MiR+PTTT/Huu+9CRBw+Pxe68CjbxY5uRkdHY+TIkRg3bhwqKiou2jty5IjD569Ro0YO31hc6PzTDcrKypCZmYkTJ06gvLwcO3fudDhXtWnTpg5HlQ8ePOhw5P18Fzvaun79euM0kUvx9/d3eB0XFRU5nUbh5+eHDz/8EKtXrzaOPJ7/o+7zXfg5LSkpcfqcfv3118ZpIZGRkSgvL3c6Kunqtup+w4YNHY5eHzlyBLfffrvTPu3bt4eXlxeaNGkCPz8/h/FczOHDhx1ei3fcccclX4s1nW4AnPu3febMmUv+O7oQv164vq26r2sbcMNF7Pbt29GyZUtYLBZ4e3tjyJAhTufjuWNbdV/Xtuq+O7dbt26NgoICFBUVoaKiAhs3bsQ999zjtJ/NZsOJEyccjswdOnTIOFfz+PHj+PXXXx3+I+jUqZNxnlF5eTk+//xz9O/f36FbWlpqHDF67bXXMGLECADnjv5U/yhy165d2L17t/FFuqb24sWLa20/+eSTdWqHh4cb56lWVFRg9erV6Natm9Oc5OXl4fjx42jXrp3xMbvdbiwesrOzYbVaL3nUcc+ePQgKCkJAQIBxlPjC829bt26NpKQkjBs37pKLAADIyclBs2bN0LhxY3h6eqJLly745ZdfHPY5fzEYGRlpLEh2796NoKAg+Pj4wMPDA3fddZfDKRp33XUX8vPzUVhYiIqKCqxbt844J7WmeWnbtq3TtjVr1tR4nuyF2rdvj9zcXBw4cADl5eVYtmyZw+cIOLforv78Vr8jxMW0adMGBw4cMD6nKSkp6Nmzp8M+fn5++OmnnwCcm8ezZ8/WeCqGK9uq+xaLxTiHurKyEtu3b0dkZKTDPh06dEBWVhaAc//Oa1pE12Tfvn3w9/dHkyZN4OnpiXvvvRc7duxwGne1u+++G0VFRQDOfXPt4XHuS3/jxo0REBBw0VNXasKvF65vq+7r2gaAK//5kCJ2ux1/+tOfkJKSAk9PTyxYsAB79uxx+7bqvq5t1X13bnt6euJPf/oTpkyZgqqqKsTFxcFiseDjjz9Gq1atcO+99wI4dypBz549HX7scuDAAcyfPx8mkwkigscee8zhSnUvLy/MnTsX999/P+x2O0aMGIE2bdrgf//3f9GxY0f0798fqampeOmll2AymdCtWzfj6vKKigrjC/Xvf/97LFy40OHonZeXF95++23Ex8cb77BQ3Y6KisJDDz2EjRs3OrSr36apoqLCuHiqQYMG+OSTT5zaL774IsaMGYOqqir0798foaGhmD9/PsLCwtC9e3cA534M2rdvX4c5qaysxOjRowEAvr6+mD59+iWPOtrtdsyePRvvvPMOPD09sXz5cuTk5GD06NHIyMjApk2bMGbMGPzud78z3iKppKQE48aNq7FXVVWFTz75BBMnToTJZMKmTZtQUFCARx55BLm5ufjll1/Qp08ftGnTBna7HSdPnsQHH3wAADh16hRWrVqFl19+GQCwc+dO7Ny502Fexo4di3HjxsFut+PBBx9ESEgI/vGPf+Cuu+4yFvrVF3Rd+CO6oqIiHDx4EB06dLjofJz/XDNnzsTQoUNRVVWFwYMHo3Xr1pg9ezYiIyPRr18//Pjjj3j11VdhMpnQpUsXzJo165K9SZMm4Y9//COqqqqQkJCA0NBQzJs3D+Hh4ejZsyfGjRuHGTNmYNGiRTCZTJg+fbrxd7j//vtx8uRJVFRUYMOGDZg3b57x7gAq26r7np6eGDp0KObMmQMRQdeuXWE2m7Fs2TI0b94c7du3R5s2bfDrr79i2rRp8PDwwKOPPmq828brr7+O4uJinD17Fi+++CISExONb16qqqqwYMECvPTSS/Dw8MCGDRuMt0Xbt28f0tLScN9996Fdu3aw2+04ceIE3nvvPQDnvmEaMGAA7HY7qqqq8NFHH+H48eO1vm6q8euF69uq+7q2ATd8iy2i60Fd3sP0Sl3stIP6cLHzMOtDWlqasjbg/BZb9en8o+T17dlnn1XWBpzfYqs+XerCuhvZhUdF69Ol3imiPtT2FltErqLlW2wREREREdWGi1giIiIi0g4XsURERESkHS5iiYiIiEg7XMQSERERkXa4iCUiIiIi7XARS0RERETa4SKWiIiIiLTDX3ZANySVbwAPAN9//72ytr+/v7K2yl92oPr/mlOnTilr33LLLcraqun8OSVnJ0+eVNqv6Vdj15f6/E1NdP3jLzsgIiIiousSF7FEREREpB0uYomIiIhIO1zEEhEREZF2uIglIiIiIu1wEUtERERE2nHLRWxcXBwyMzORnZ2NSZMmadNW3de1rbp/Ne3u3btj3bp12LBhA5599lmn7dHR0VixYgWys7MRHx9vfNxsNmP58uVYuXIlUlJSMHTo0Br7GzZsQI8ePRATE4P33nvPaXtBQQEGDRqE++67D3379sX69esBAOXl5Rg3bhz69OmDfv36YcuWLU6PXbVqFcLDw9G6dWu8/vrrTtvz8vLQt29fdOjQAb1790Z+fr6xbfLkyYiMjERkZCQ+//zzGtthYWFo1arVJdvt27evsR0REYGIiAgsXrzYpeMGgDVr1uDuu+9GZGQk/vrXvzptP3DgAPr374977rkH999/PwoKCoxtt99+O7p27YquXbti8ODBLh276nnR9XOqa1t1f+3atYiKikL79u0v+Tq/99578cADDzi8zhs2bIiYmBjExMRgyJAhTo+NiYnBN998g++++w5PP/200/aoqCh88cUX2LlzJ/r16+ewzd/fHx988AGWL1+O5cuXIyAgoMa5uRR3/XpxLduq+7q2ISIuuwGQ2m4eHh5itVolODhYvL29JT09XcLCwmp93LVu6zz2G3FeLBaLhISEyP79+6Vbt27SsmVL2bNnj/Tp00csFotx69q1q9x3333y1VdfyXPPPWd8vGXLltKqVSuxWCwSHh4uNptNoqOjje02m032798vd955p2zevFn27dsnYWFhsm7dOrHZbMZt6NChMmvWLLHZbLJu3ToJDAwUm80mM2bMkMcee0xsNpv88ssv0q5dO8nLyxObzSaVlZVy9uxZCQkJkb1798qpU6ckIiJCdu3aJZWVlcZt4MCBsmDBAqmsrJTVq1fLsGHDpLKyUpYtWyaxsbFy5swZOXr0qERFRUlZWZlUVlaK3W6X8vJyCQkJkezsbDl9+rRERETI7t27xW63G7fqtt1ulzVr1siwYcPEbrfL8uXLJTY2Vs6ePSvHjh2TqKgoOXLkiNjtdqXjrqyslGPHjsmRI0fEYrHIzp07pbS0VNq2bSs//fSTHDt2zLgNGDBA3n//fTl27JisWLFCBg8ebGzz9fV12Lf6pnrsKts6f051bavsHz16VMrKysRisUh6erocOnRI2rZtK9u2bZOjR48at4SEBHn//ffl6NGjsnz5chk8eLCxzdfX12Hf829t27aVvLw86devn0RGRkpmZqb0799fwsPDjVufPn1kwIAB8vXXX8sLL7zgsG3btm3y1FNPSXh4uHTs2FHuvvtuY5uuXy+udVvnsV9Nuy7rSrc7EhsdHQ2r1Yrc3FxUVFQgOTkZCQkJbt9W3de1rbp/Ne3IyEjk5eXBZrOhoqICK1asQN++fR32KSgoQGZmJqqqqhw+XlFRgfLycgCAj49PjW8on56eDovFgubNm8PHxwcPPfQQVq9e7bCPyWTCiRMnAADHjx9Hs2bNAADZ2dmIiYkBADRu3BgNGjTAzp07jcf99NNPCA0NRUhICHx8fDBo0CAsX77coZ2RkYHevXsDAHr16mVsz8jIQPfu3eHl5QVfX19EREQgJSXlou3BgwfX2I6Nja2x3aNHD4f2qlWrXDJuANixYwdCQkIQHBwMHx8fDBw4ECtXrnTYJzMzEz179gRw7kj8t99+i7pw5ZzX97zo+jnVta26n5aW5vA6f+SRR5xe51lZWejRoweAy3udt2vXDjabDfn5+aioqMC3336LXr16OexTWFiIvXv3Ov2yi9DQUHh5eRk/OTp16hTOnDlTp+et5q5fL65lW3Vf1zbghqcTmM1m2Gw2435+fj7MZrPbt1X3dW2r7l9N28/PD0VFRcb94uJi+Pn51fm5/f398d133+HHH3/E/PnzcfDgQYftxcXFDj9K8/f3R3FxscM+Y8eOxZIlS9CpUyckJiZi+vTpAIDw8HCsXr0alZWVOHDgAHbv3u0w1sLCQgQFBRn3AwMDUVhY6NCOiIjAkiVLAABff/01jh8/jsOHDxuLkFOnTqG0tBQbN250mMOCggKHttlsdvhR5IXtpUuXXrJ9/o9IVY4bAIqKihAYGGjcDwgIcOq3bdsWy5YtAwCsWLHC6APAmTNn0KNHD/Tu3RvffPONw+NUjl31vOj6OdW17Yqxn///nNlsdvj/ATj3Oq9eFFe/zsvKygD8/9d5bGys0+u8WbNmDq2SkhLjm+vaNG/eHMeOHcOcOXPw5ZdfYvz48fDwuLxlhrt+vbiWbdV9XduAGy5iazqiVV+/2lBlW3Vf17bq/tW0r3ZcRUVFiI+PR8+ePTFw4EA0bty41taFz7ls2TI89thj2L59OxYuXIgXXngBVVVVGDx4MPz8/PDAAw/g5ZdfRlRUFDw9PS+r/cYbb2DTpk3o2LEjNm3aBLPZDC8vL/Tr1w/x8fHo1q0bhg0bhi5dusDLy+uy2rNnz0ZqaiqioqJqbMfExGDo0KFX1L7Scde1P2vWLPzwww+IiYnB5s2bERAQYHT27NmD1NRUfPTRR5g8eTJycnJcMnZ3mBd3/Jzq2naHsc+cORObN29GTEwMfvjhBwQEBBj/h/z6669ITU3Fhx9+iClTpji8zmtS1/8Xvby8EBUVhTfffBODBw9GUFAQBgwYUKfHXuzvcTnPf722Vfd1bQNuuIjNz8+v9btXd2yr7uvaVt2/mnZRURH8/f2N+35+figpKbnsMRw8eBB79+5Fp06dHD7u7+/vMJaioiKnIxqLFy9G//79AZy7WOLs2bMoKyuDl5cXXn75ZaSkpGDBggU4duwYgoODjcfV9N3t+X8X4NxRyC+//BI7duzAjBkzAAC33XYbACApKQlpaWlISUmBiKBFixbG4wIDA52OzF54cUZAQAC++uorpKWlYebMmU7tn3/+GatXr3Zqqxx39WMvPEp4Yd/f3x+ffvopNm/ejGnTpjn0q/cNDg5GTEwMdu3a5ZKxq54XXT+nurZdMfbzj6QXFBQ4/RTp/Nf5X/7yF4f2pV7nJSUlDuNs1qyZ00+ZLqa4uBgZGRnIz8+H3W7HunXrEB4eXqfHVnPXrxfXsq26r2sbANzuwi5PT0/Zt2+fWCwW4yTgup4Qfi3bOo/9RpwXi8UioaGhkpeXJzExMcaFXX379nW4sKv69sUXXzhc2NWlSxdp3bq1WCwWiYiIkJycHImLi3O4sCs3N1fuvPNO+eGHH4wLu9auXetwYVfPnj3lrbfeEpvNJuvXr5emTZvKgQMHZO/evZKVlSU2m00+/fRTiY6ONh5TWVkpZ86ckeDgYMnOzjYuGtm5c6fDRSPFxcVSXl4ulZWVMnnyZHnppZeMC05KSkqksrJSfv75Z2nTpo2cOXPGuAjo7NmzEhwcLFar1bgIaNeuXQ4XAZWUlEhFRYXY7XaZMmWKTJ061biA6ODBg2K32+WXX36RNm3ayNmzZ42LgFSNu/rCruoLXnbt2mVc2LVt2zaHi7Ryc3Plt99+k2PHjsmECRNk4sSJcuzYMcnLy5NDhw4Z+4SEhBgXhakeu8q2zp9TXdsq+0ePHpXDhw9L8+bNZefOncaFXVu3bnW4QCsnJ0eOHDkiR48elfHjx8vEiRPl6NGjsn//fjl48KCxT0hIiMNFYe3atZMDBw5I3759L3phV/Vt6dKlDhd2tW3bVjIzM6Vr164SHh4uS5YskRkzZlzWhV3u+PXiWrd1HvvVtOu0rnS3RSwAiY+Pl6ysLLFarZKUlFRvLwLVbZ3HfqPNS/Vic8SIEZKTkyP79++X2bNni8Vikblz58rTTz8tFotFHnroISksLJSTJ09KWVmZZGVlicVikeHDh0tGRobs2bNHMjIyZMqUKQ6L3uoF58KFCyU4OFjuvPNOefHFF8Vms8mf//xn+eijj4x3JOjYsaOEhYVJeHi4LFq0SGw2m/z4448SEhIiLVq0kJiYGNmyZYvDIrayslKWL18uLVu2lJCQEJk+fbpUVlbKSy+9JEuXLpXKykpZvHixtGjRQlq2bCkjR46UkydPSmVlpZw4cULCwsIkLCxMoqOjZceOHQ4LHrvdLitWrDDaM2bMELvdLlOnTpWlS5eK3W53ap86dUrsdrucPHnSaHfu3FnS0tKMpspxVy9ijx07Jl988YWEhoaKxWKRv/zlL3Ls2DGZOHGiJCcny7Fjx+STTz6RkJAQCQ0NlSeeeMJYuK5Zs8b4QhweHi7vvvuuw7sTqBy7yrbOn1Od26r61YvN81/nU6dOlaNHj8rEiRPls88+k6NHj8rChQuN1/njjz9uLFxXr17t8Dp/5513HBa/4eHhMnr0aMnNzZW8vDyZM2eOhIeHy7x58+T555+X8PBwGTRokBQVFcnJkyflyJEjkp2dbSxUn3rqKcnMzJSsrCxZunSpREZGXtYi1h2/XrhDW+exX2m7LutKU32em1Abk8nkuicjugSLxaK0//333ytrX/gjyfpU0/lL9UX1/zWnTp1S1r7llluUtVXT+XNKzk6ePKm0f8899yhr79mzR1mbrj8iUut/Xm53TiwRERERUW24iCUiIiIi7XARS0RERETa4SKWiIiIiLTDRSwRERERaYeLWCIiIiLSDhexRERERKQdvk8s3ZBSUlKU9vv06aO0r4qHh7rva6uqqpS16eLq+itDr0Rqaqqy9o4dO5S1VRs/fryy9oW/Xra+qaAmkhwAACAASURBVPx3+sgjjyhrL1u2TFmbrg2+TywRERERXZe4iCUiIiIi7XARS0RERETa4SKWiIiIiLTDRSwRERERaYeLWCIiIiLSjlsuYuPi4pCZmYns7GxMmjRJm7bqvq5t1f2raW/fvh1PPfUURowYgcWLFztt//vf/47nnnsOzz33HEaOHGm8RUxJSQmef/55PPfccxg1ahS++eYbp8euWrUKYWFhaNWqFV5//XWn7Xl5eejbty/at2+P3r17Iz8/39g2efJkREREICIiosZxuaJfbeTIkWjatCnatm1b43YRwZgxY9CiRQtERETg559/vmRP53nRtQ0A69evR0xMDO655x688847TtttNhsee+wx9O7dG4888ggKCwuNbX/4wx/QunVrPP744zW209PTMXbsWPz5z3++6FsdbdmyBePHj8eECRPw9ttvGx8vLS3FrFmzMG7cOIwfP97pLcFyc3Px0Ucf4cMPP8S2bdtqbANAVlYW3nzzTRQXFwMAjh49ijlz5mDhwoVYuHAh1qxZU+PjVPZVzvn5dPs32qFDB7z33nt4//33a3zbrbi4OMydOxd/+9vf8MorryAwMBAA8Pvf/x4zZszAZ599hlGjRl3y73Ax7vq16Fr3dW1DRFx2AyC13Tw8PMRqtUpwcLB4e3tLenq6hIWF1fq4a93Weew34rykpKTIt99+K/7+/vLxxx/LN998I8HBwfLBBx9ISkpKjbc//vGP0q9fP0lJSZFvvvlGVqxYISkpKfL1119Ls2bN5N///rexb3l5uYSEhEh2dracPn1aIiIiZPfu3WK3243bwIEDZcGCBWK322XNmjUybNgwsdvtsnz5comNjZWzZ8/KsWPHJCoqSo4cOeLwWFX9mqSmpkpaWpq0adOmxu0rV66U++67T6qqqmTLli0SHR1d434qx616XnRuFxUVSX5+vjRv3ly2bt0qeXl5Eh4eLhs3bpSioiLj9uCDD8rcuXOlqKhIvvjiCxk4cKCx7fPPP5eFCxdKnz59HB6TnJws//73v6Vp06Yyd+5cWbRokdx5553y5ptvSnJysnH729/+JhaLRT788ENJTk6W+fPnG9vCwsIkKSlJkpOT5eOPP5aFCxdKcnKyTJgwQcaNGye33XabPP300zJ27Fhp0qSJjBgxQiZMmOBwGzNmjAQGBoq/v78MHz5cJkyYIKNGjZI77rjDad/zb6r6Kudc5b9REVH6WkxISJCHH35YioqK5JlnnpGBAwdKTk6OPP/885KQkGDchgwZYvx55syZkpaWJgkJCTJo0CCZPHmyzJs3T7755huHx+j6tcgd+u7arsu60u2OxEZHR8NqtSI3NxcVFRVITk5GQkKC27dV93Vtq+5fTTsrKwsBAQHw9/eHt7c3evbsiS1btlx0/w0bNqBnz54AAG9vb/j4+AAAKioqnN4g/KeffkJoaChCQkLg4+ODwYMHY/ny5Q77ZGRkIDY2FgDQq1cvY3tGRgZ69OgBLy8v+Pr6IiIiAqtWrXJp/3zdu3dHo0aNLrp92bJleOKJJ2AymdClSxf89ttvKCoqqnFfnedF1zYA/PLLL7BYLGjevDl8fHyQkJDg9As/9u7di5iYGABA165dHbZ369YNt956K2pitVrh5+eHZs2awcvLC/fee6/TLypYv349+vXrZzRuu+02AEB+fj6qqqoQEREBALj55ptx0003GY8rLi5Gw4YNcfvtt8PT0xN33XUX9u3b5zSGzZs3o1OnTvD09KxxjBejsq9yzi+k07/Rli1boqioCCUlJaisrMTmzZvRuXNnh31Onz5t/Pnmm2+uPgiGs2fPIiMjAxUVFXWalwu569eia93XtQ244ekEZrMZNpvNuJ+fnw+z2ez2bdV9Xduq+1fTPnz4MJo0aWLcb9y4MUpLS2vct6SkBCUlJWjfvr3xsYMHD+LZZ5/F8OHDMWjQINxxxx3GtoKCAgQFBTmMs6CgwKEZERGBJUuWAACWLl2K48eP4/Dhw8Z//KdOnUJpaSk2btzo8OM6V/Qvx4VjCQwMdBqLq8atsq9rGzi3WDv/34W/v7/xY/Fqbdq0wcqVKwEA3377LU6cOIGysjLUpqyszOG136hRI6fHFRUVoaioCNOmTcPUqVORnp5ufPyWW27BW2+9hcmTJ2PRokUO3xAeP34cv//97437t956K44fP+7QLikpwfHjxxEaGuo0tqNHj+KTTz5BcnJyja9xlX2Vc3653OnfaKNGjRz+nz18+HCNC/D4+Hj8/e9/R2JiIj788MPL/0vXwF2/Fl3rvq5twA0XsSaT828Zq/4uzJ3bqvu6tlX3r6Zd03419QBg48aNiImJcTgS07RpU/z973/HP//5T6xZswZHjhy5rPbs2bORmpqKqKgobNq0CWazGV5eXujXrx/i4+MRExODoUOHokuXLvDy8rrssV9N/3JczjzqPC+6tuvanzZtGrZs2YK+fftiy5Yt8Pf3v+LXxYVtu92O4uJiTJs2DWPGjMEHH3yAkydPwm63IzMzE8OHD8esWbNw8OBBbNy4sc5tEXH4Ccn/Y+/M46Kq/v//GmagiEQFhGEAZRVBHUTAJcUNxV1UckVTq2+lmZqWC5RaKi59/BRalpUU2iKmqAgFroCGCy4Iyq6I7LIKaAoznN8ffrg/r4OCycW59X4+Hveh955znnPmcM6Zc889996HMTAwwNtvv43XXnsNgwYNQmRkJO7fv/9UeX8Wf2uX+ZPQpjb6uM99lD/++APvvPMOdu7ciUmTJjUrTVNo62/R8/aL1Q0ALd9anpG8vDyNM8aHF7trq1tov1jdQvufxW1iYoKSkhJuv7S0lDej9DCxsbF49913Gw0zNjZGp06dcOXKFXh6enL5ePjsMz8/HwqFgpdOoVBg3759AICamhqEhYVxl1n9/f3h7+8PAPDz84O9vT0vrdD+p+HRvOTl5WnkpbXyLaRfrG7gwSzgw7NphYWFMDMz48WRy+UIDg4GANy5cwe///47DA0N0RRGRkYoKyvj9svLy9G+fXteHGNjY9jb20Mmk8HU1JSblTQ2Noa1tTWXF3d3d2RlZXHp2rRpw5sZramp4V1ir62tRVlZGXcD0Z07d7B//35MmDABcrmcG0DJ5XK0bdsWFRUVkMvlreIXssyfFm1qo2VlZTAxMeH2jY2Nnzj7fPLkSbz99tvN+ZpNoq2/Rc/bL1Y3oIUzsQkJCXBwcIC1tTV0dXUxdepUjfU42ugW2i9Wt9D+Z3E7OjoiPz8fRUVFqKurQ0xMDPr06aMRLzc3FzU1NXB2duaOlZSUcLMu1dXVSElJ4e6gBQAPDw9uHVBtbS1CQ0MxduxYnre0tJS7dLphwwbMmTMHwINZq4ZBQVJSEpKTk+Ht7c1LK7T/aRg3bhx27twJxhjOnDmDtm3bwtzcvNG4Yi4XsboBoEePHsjOzsbNmzdRW1uLgwcPYvjw4bw4ZWVlnH/Lli2YOnUqmoOdnR2Kiopw69YtqFQqxMfHw83NjRfH3d0dKSkpAICqqioUFhbC1NQUdnZ2uHPnDqqqqgAAV69e5V1qlMvlqKioQGVlJTdr+/Bl/RdeeAHvvvsu3nrrLbz11lswNzfnBph3797lvk9lZSUqKyu5gVZr+IUs86dFm9poZmYmzM3NYWpqCplMhv79++PcuXO8OA/nzd3d/bHrd58Wbf0tet5+sboBLZyJVavVmD9/PqKjoyGVShEcHMx1ftrsFtovVrfQ/mdxS6VSvPvuu/D390d9fT28vb1hbW2NkJAQdO7cGX379gXwYCnBwIEDeZdFbt68ie+++47bf/XVV2FjY8Pty2QybNmyBSNHjoRarcacOXPQtWtXrFq1Cm5ubhg3bhxiYmIQEBAAiUQCT09PfPnllwAe3Cg2cOBAAIChoSF27typcUlOaP/DTJs2DTExMSgtLYWlpSU++eQT7saKd955B6NGjcLvv/8Oe3t7vPTSS/jhhx8e6xJzuYjV3eAPDAzEtGnToFarMXXqVDg6OmLTpk1wcXHB8OHDcfr0aQQGBnI3/wQGBnLpfXx8kJWVhbt376Jnz57YvHkzBg8eDOBBO5ozZw4CAwNRX1+PwYMHw8rKCnv27IGtrS3c3d3h4uKCpKQkLFmyBDo6OpgxYwa3FnXGjBlYu3YtGGOwsbHhbhgCAB0dHXh5eWHfvn2or69H9+7dYWJiglOnTkEulz/xCkJeXh7+/PNP6OjoQCKRYNiwYdDX1+fFEdIvZJk/ipjaaH19Pb777jusWrUKUqkUR48eRW5uLqZNm4asrCwkJCRg1KhRcHFxgVqtRk1NDYKCgrj03377LfT19SGTydC7d2+sXr262Wv6tfW36Hn7xeoGAElLrk1o8sMkktb7MIJ4Ao/eJdzSDB06VFC/UOjoCHdx5tEnOBCtw6PPXW1JYmNjBXM/+oQDMbFkyRLB3A8vhxACIdtpY8+EbSke93xiQrwwxppcQK11ywkIgiAIgiAIoiloEEsQBEEQBEGIDhrEEgRBEARBEKKDBrEEQRAEQRCE6KBBLEEQBEEQBCE6aBBLEARBEARBiA4axBIEQRAEQRCiQ+tedkAQrYHQz0duzecvtyRCPiNS6DJ5+BXCLU2HDh0Ec7fkg78bY/PmzYK5Q0JCBHOLGSHrYsNraoVCyHba8FIEIbh48aJgbgC8V/ES2gPNxBIEQRAEQRCigwaxBEEQBEEQhOigQSxBEARBEAQhOmgQSxAEQRAEQYgOGsQSBEEQBEEQooMGsQRBEARBEITo0MpB7PDhw5GWlobMzEwsW7ZMNG6h/WJ1C+1/Fvf58+fxxhtvYM6cOQgNDdUI3759O+bNm4d58+bhjTfegK+vLwDg2rVrWLRoEd566y288847iI2N1UgbFRUFZ2dnODo6YuPGjRrhOTk5GDZsGFxdXTFkyBDk5eVxYcuXL4eLiwtcXFywZ8+eRvMupD8qKgpOTk7o3LnzE909evRo1K1UKqFUKhstU6HL5fjx4+jfvz/69u2LrVu3aoTn5uZi0qRJGDJkCCZOnIiCggIubNq0aXB0dMTMmTMbdQuZ91OnTmHs2LEYPXo0duzYoRFeWFiIN954A5MnT4avry9OnjwJADh9+jSmTJmCiRMnYsqUKTh79myjee/evTvWr1+PjRs3YvTo0RrhgwcPxpo1a/Dpp5/C398fCoUCANC3b198+umn3BYcHIyOHTs2+hmPQ1vbv9BuIctczG30xIkTGDhwIPr374+vvvpKIzw/Px+TJ0/GiBEjMGzYMBw/fhwAUFtbi8WLF2Po0KHw9vbG6dOnNdIOHDgQJ06cQFxcHObNm6cR3qtXL0RGRuL69esYNWoUL2znzp1ITk7GDz/80Gi+m+Lf+jv6PN1gjD1xAxAM4BaAKw8dMwJwBEDm//5t35Tnf+lYU5uOjg7LyspiNjY2TFdXlyUmJjInJ6cm0z1vt5jz/m8sl6ioKBYZGcnMzc3ZDz/8wA4dOsRsbGzY9u3bWVRUVKPb3Llzmbe3N4uKimLff/8927FjB4uKimI///wza9++Pdu7dy8X9/79+8zW1pZlZGSwu3fvMqVSyZKSkphKpeI2X19fFhwczFQqFTt8+DDz8/NjKpWKHTx4kHl5ebF79+6x27dvMzc3N1ZeXs5LK5RfrVaz2tpaZmtryzIzM9lff/3FlEolS05OZmq1mtsa3Gq1mh05coT5+fkxtVrNwsPDmZeXF7t//z6rqqpibm5urKKigqnVakHzrVKpWGFhIcvLy2OdOnViZ86cYTk5OczZ2ZnFxMSwwsJCbhszZgwLCgpihYWF7LfffmO+vr5c2J49e1hISAgbOnQoL42QeU9KSmKXLl1ilpaW7Pfff2cXLlxgnTt3Zvv372dJSUnc5uvrywICAlhSUhLbv38/UygULCkpiYWGhrKjR4+ypKQktm/fPmZqaspLN2vWLDZ79mxWXFzMPvjgA/b666+znJwctmLFCjZr1ixue/vtt7n/f/7551zah7eAgABWXFzM7Yu1/QvtFrLMhWyjarVa0Daam5vLbty4wTp27MhOnTrFrl27xpycnNixY8dYbm4ut02fPp2tW7eO5ebmsmPHjjFLS0uWm5vL1qxZwyZNmsRyc3PZpUuXWPfu3VlOTg7Lzc1lVlZWrFOnTuzGjRusX79+zNbWll29epUNGTKEWVlZcVvfvn3ZsGHD2N69e9nbb7/NC5s6dSqbM2cOO3r0KO+4lZUV/Y4+B3dzxpXNmYn9EcCIR44tB3CMMeYA4Nj/9luEXr16ISsrC9nZ2airq8Pu3bvh4+Oj9W6h/WJ1C+1/Fnd6ejrMzc1hbm4OXV1dDBw4sNEz+wZiYmIwaNAgAIClpSUsLCwAAMbGxmjXrh1u377NxT137hzs7Oxga2sLPT09TJ48GeHh4TxfamoqhgwZAuDBjExDeGpqKgYMGACZTAYDAwMolUpER0fz0grpf9Q9ZcqURt1eXl6NugcOHMhzR0VFtVq5XLp0CdbW1ujUqRP09PTg4+OjEScjIwP9+/cHAPTr148X7unpiZdffhmNIWTer1y5go4dO8LS0hK6uroYMWIETpw4wXNLJBLcuXMHAFBTU8O9gMHJyQmmpqYAAHt7e9y/fx+1tbW8tLa2tiguLkZJSQnUajXOnj0LV1dXXpx79+5x/3/hhRcafeh97969HzvT+zi0tf0L7RayzMXcRhMTE3ltdNy4cTh8+DAvjkQiQU1NDQCguroaZmZmAIDMzEyu7ZqYmMDQ0BCXL1/m0vXo0QM3btzAzZs3UVdXh0OHDsHb25vnzsvLQ1paWqMvdvnzzz+5z31a/q2/o8/TDTRjOQFjLA5A+SOHfQA0vKolBMD4lsqQhYUF780YeXl53GBBm91C+8XqFtr/LO6ysjLem5hMTExQVlbWaNzi4mIUFRXBxcVFIyw9PR0qlQrm5ubcsYKCAlhZWXH7lpaWvMvWAKBUKhEWFgYAOHDgAKqrq1FWVsb9qNy9exelpaWIiYnReFuMkP78/Hye28LCAvn5+Y9179+//4nuhy81Cl0uRUVFvL+/ubk5ioqKeHG6du2KyMhIAMDvv/+OmpoalJc/2sVpImTei4uLuR9qADAzM8OtW7d47rlz5yIiIgJDhw7FvHnzsGLFCo08HjlyBF26dIGenh7vePv27XnfsaKiAu3bt9dI7+XlhU2bNmHy5Mn4+eefNcJ79+6NM2fONFo+j0Nb27/QbiHLXOxttGHZBNB4G33//fcRFhYGDw8PzJo1C59++ikAwNnZGYcPH4ZKpcLNmzeRnJyMwsJCLp1cLufltbCwkNeuhOTf+jv6PN3A318Ta8YYKwSA//1r2lIZkkgkGsda6jV4QrqF9ovVLbT/WdyNxWvMBwCxsbHw9PSEVCrlHS8rK8OmTZuwePFi6Oj8/+bUHPemTZsQFxcHd3d3xMXFwcLCAjKZDN7e3hg5ciQ8PT3h5+eHPn36QCbjvyFaSH9z3J999hliY2Ph5ubWqLt///6YPn3633ILXS4rV67E6dOnMWzYMJw+fRrm5uYansYQOu9Nuf/44w/4+Pjg6NGj2LZtG/z9/XmzSVlZWfjiiy+wcuXKJl2P49ixY1i6dCl+++03jB07lhdma2uL+/fvawyWmkJb27/QbiHL/J/eRg8ePIhJkyYhISEBISEhWLRoEerr6zFlyhTI5XKMHj0aq1evhpubG69PFvq37En8W39Hn6cbaIUbuyQSyVsSieS8RCI535z4eXl5TZ4F/l2EdAvtF6tbaP+zuE1MTHjvOC8tLYWRkVGjcWNjY7mlBA3cuXMHK1euxKxZs+Dk5MQLa+zs8+GZWgBQKBTYu3cvzp8/jzVr1gAA2rZtCwDw9/fHhQsXEB0dDcYY7O3tW81vaWmpMTP78MxJg3vfvn24cOEC1q5dq+G+ePEiDh8+rOEWulzMzc15P/iNzcTI5XIEBwfjyJEj3GymoaEhmkLIvJuZmaG4uJjbLy4u5l0lAB7Mpg0fPhwA4OLigvv376OiogLAg9mt999/H+vWreO1hwbKy8t5dbt9+/Zc2sY4e/YsevbsyTv2d5YSANrb/oV2C1nmYm+jTc2WhoaGcgN6Nzc33L9/H+Xl5ZDJZFi9ejWio6MRHByMqqoq2NjY8FyPzvI+ekVDKP6tv6PP0w38/UFssUQiMQeA//372FrCGPuWMebOGHNvjjghIQEODg6wtraGrq4upk6dqrEe5+8ipFtov1jdQvufxe3o6IiCggIUFRWhrq4OsbGx6NOnj0a83NxcVFdX8waqdXV1WLNmDYYOHYoBAwZopPHw8ODWAdXW1mLPnj0asyylpaXcTNqGDRswe/ZsAIBareaWNSQlJSE5OVljXZeQ/kfdD/+gPM49Z86cv+Vu6XLp0aMHsrOzcfPmTdTW1uLgwYPcwK+BsrIyzr9lyxZMnToVzUHIvHft2hU5OTnIy8tDXV0doqKiNE6a5HI5N6C5fv06amtrYWRkhKqqKsyfPx8LFizQWHPZQHZ2NszMzGBiYgKpVIrevXvj0qVLvDgPDyRcXFx4g2qJRAIPD4+/NYjV1vYvtFvIMhdzG3VxceHWrdbW1iI8PBzDhg3jxVEoFDh16hSAB+tg7927B2NjY/z111+4e/cuACAuLg5SqRSdO3fm0l2+fBk2NjawsrKCrq4uxo4diyNHjmiUnxD8W39Hn6cbAJq+htY44QBmAdjwv38PtlSG1Go15s+fj+joaEilUgQHByMlJUXr3UL7xeoW2v8sbqlUinnz5iEgIAD19fXw9vaGtbU1du7cCQcHB/Tt2xfA/7+h6+HLInFxcUhOTkZVVRXXSS5ZsgR2dnYAAJlMhqCgIIwaNQpqtRqzZ89G165dsWrVKri7u2Ps2LGIjY1FQEAAJBIJPD09ucdB1dXVcQOYNm3aICQkROOSnJB+mUyGLVu2YOTIkVCr1ZgzZw7ndnNzw7hx4xATE8Nzf/nll5x74MCBAB7Mbu7cuVPDLXS5BAYGYtq0aVCr1Zg6dSocHR2xadMmuLi4YPjw4Th9+jQCAwMhkUjQp08fBAYGcul9fHyQlZWFu3fvomfPnti8eTMGDx7cKmXu7++PuXPnQq1WY/z48bC3t8dXX30FZ2dnDB48GB988AE++eQT7Nq1CxKJBGvWrIFEIsHu3btx8+ZNfPvtt/j2228BAN988w2MjY05f319PX766Sd88MEH0NHRwcmTJ1FQUIAJEyYgOzsbiYmJ8PLyQteuXaFWq3Hnzh189913XHpHR0dUVFTwrlw0F21t/0K7hSxzsbfRNWvWYMaMGVCr1ZgyZQocHR3xn//8B0qlEt7e3vj444+xbNkyfP/995BIJPjvf/8LiUSC0tJSzJgxAzo6OpDL5QgKCtL4m3388cfYtWsXpFIpQkNDkZGRgcWLFyM5ORlHjhyBUqnEd999h7Zt22Lo0KHcI7sAYO/evbCzs4OBgQHOnj2LDz/8EHFxcc36e/9bf0efpxsAJE2tTZBIJL8CGATABEAxgFUADgDYA6AjgJsAJjHGmrwzQiKRtM7iFIJogofvxhWChk5RbDR3Hd/fQei1aX9ngNVcHr2035K0ZIfeGJs3bxbMHRIS0nSkfyGzZs0SzB0cHCyYGxC2nT58E1ZL88orrwjmBqBxgxohPIyxJn+QmpyJZYxNe0yQ11PniCAIgiAIgiBaAK18YxdBEARBEARBPAkaxBIEQRAEQRCigwaxBEEQBEEQhOigQSxBEARBEAQhOmgQSxAEQRAEQYgOGsQSBEEQBEEQouPvvuyAIARn2bJlgrkffb0j8YCPPvpIMPf588168/TfJjs7WzD3w6+2bGliY2MFcwNAbW2toH5CEyGfn/vXX38J5gaAX375RTD3o6+vbUnefPNNwdwAsGrVKkH9xN+DZmIJgiAIgiAI0UGDWIIgCIIgCEJ00CCWIAiCIAiCEB00iCUIgiAIgiBEBw1iCYIgCIIgCNFBg1iCIAiCIAhCdNAgliAIgiAIghAdWjmIHT58ONLS0pCZmdnizwoV0i20X6zuZ/Vfv34d3333HbZv344zZ848Nl5aWho2btyIwsJCAMDVq1fxww8/cNvGjRtRXFzMS3P8+HH07dsXvXr1wpYtWzScubm58PX1xcCBAzF+/HgUFBQAAJKTkzFy5Eh4enpi4MCBOHDggEbaqKgoODs7w9HRERs3btQIz8nJwbBhw+Dq6oohQ4YgLy+PC1u+fDlcXFzg4uKCPXv2NPp9hfRfu3YN27dvx9dff43Tp083+vnAgzJfv349V+ZqtRqHDh3C999/j2+//Rbx8fEaadzd3bFjxw788MMPmDJlika4r68vvvvuO3zzzTfYuHEjTE1NAQAuLi74+uuvuS0iIgKvvPKKRnpPT09ER0fj6NGjeOuttzTCPTw8cODAAaSmpmLEiBEa3yc8PBzh4eH45ptvWjXv3t7eSE5ORkpKCj744AMNt56eHn766SekpKTg5MmT6NSpEwDAy8sLp0+fxoULF3D69GkMGjRII21zEGv/Ilb3s/pdXFzw+eefIygoCD4+PhrhQ4cOxWeffYaNGzfik08+gYWFBS/c2NgYISEhGDNmjEZaMfddmZmZ2LJlC4KCgnDy5EmN8EuXLmHjxo1cW7xw4QIXtmvXLqxfvx4///xzo/luCm2uL/9Ut9YNYnV0dPDVV19h5MiRcHZ2xrRp0+Dk5KT1bqH9YnU/q7++vh5HjhzBpEmT8OabbyIlJQWlpaUa8e7fv48LFy7wHqbdtWtXzJkzB3PmzMGYMWPQtm1bmJmZceFqtRrLli3Dr7/+ilOnTiEsLAzp6ek87+rVqzFp0iTExsZiyZIlWLt2LQDgpZdewldffYWTJ08iNDQUH330EW7fvs1zL1iwABEREUhOTkZoaChSUlJ47qVLl2LmzJm4dOkSPvroIwQEBAAAIiMjcenSJVy4cAHxQVgkCwAAIABJREFU8fHYvHkzqqqqeGmF9NfX1+Pw4cOYPHky3nrrrSeW+fnz56FQKLhjaWlpUKvVePPNNzFnzhwkJiaisrKSC9fR0cH8+fMREBCA//u//8OgQYPQsWNHnjcrKwvz58/HO++8g5MnT3IPMb98+TLmzp2LuXPnYunSpbh37x7vB6jBv3r1arz55psYOXIkxowZA3t7e16cgoICLFu2DIcOHdL4Tvfu3cO4ceMwbtw4vPPOOxpuofKuo6ODoKAgjBs3Di4uLpgyZQq6dOnCc8+ZMweVlZVwdnbGli1bsG7dOgBAaWkpJk6cCDc3N7zxxhsIDg7W+F5NIdb+RazuZ/VLJBK8/vrrWL9+PRYvXox+/fppDFL//PNPfPjhh1i2bBnCw8Px2muv8cJnzZqFxMREDbfY+67IyEjMmDED7777LpKTk3Hr1i2N79itWzeuPbq5uXHH+/Xrh4kTJz623J+ENteXf6ob0MJBbK9evZCVlYXs7GzU1dVh9+7djZ5laptbaL9Y3c/qLywsRLt27dCuXTtIpVI4OTkhMzNTI97JkyfRu3dvyGSNv4QuJSUFzs7OvGMXL16EjY0NrK2toaenhwkTJiAqKooXJyMjAwMGDAAA9O/fnwu3s7ODra0tAEAul8PExARlZWVcunPnznFx9PT0MHnyZISHh/PcqampGDJkCABg8ODBXHhqaioGDBgAmUwGAwMDKJVKREdH89IK6S8oKED79u3Rvn17rswzMjI0yjQuLq7RMq+rq0N9fT3q6uqgo6ODF154gQtzdHREQUEBioqKoFKpEBsbqzEjefnyZdy/f5/La4cOHTQ+29PTE+fPn+fiNaBUKpGTk4Pc3FzU1dUhMjISXl5evDj5+flIT08HY0zD+ySEzLuHhweuXbvGtZE9e/Zg7NixvHRjx47Frl27AABhYWEYPHgw95kNM+EpKSl48cUXoaen91TfTaz9i1jdz+q3t7dHcXExbt26BbVajfj4eHh4ePDiPPxmrxdeeIFX393d3VFcXIzc3FwNt5j7rvz8fBgZGcHIyAgymQzdunVDWlpas8oUAJenv4M215d/qhvQwkGshYUFr2Hl5eVpnGFqo1tov1jdz+qvrq6GoaEht9+mTRvU1NTw4hQXF6O6ulpjxu1h0tLSNM7+ioqKePkwNzfnBgMNdO3aFREREQAezALU1NSgvLycF+fixYuoq6uDtbU1d6ygoABWVlbcvqWlJbcUoQGlUomwsDAAwIEDB1BdXY2ysjIolUpERUXh7t27KC0tRUxMjMaPjZD+mpoajTKvrq7WKLvq6mo4ODjwjnfp0gW6urrYsmULtm3bht69e0NfX58LNzExQUlJCbdfUlICY2NjPI4RI0YgISFB4/igQYNw4sQJjeNyuZz3NywqKuLNvjfFCy+8gLCwMPz2228YOnQoL0zIvCsUCt7fID8/X6ONKBQK7rKqWq1GVVWVxudPmDABly9ffupXzYq1fxGr+1n9RkZGvJPmsrIytG/fXiOet7c3goKC4Ofnhx9//BHAgzru4+ODvXv3NuoWc99VVVWFtm3bcvtt27bV6LuAByd727ZtQ2hoKO8K2rOgzfXln+oGgManrZ4jEolE49jTzpg8D7fQfrG6hfYzxnDs2DGMHj36sXEKCgogk8k0ZsUay8OjeV29ejWWL1+O3bt3o2/fvjA3N+fNPBYXF+Pdd9/F1q1boaPz/88Jm+PetGkTFixYgJ07d8LT0xMWFhaQyWTw9vbG+fPn4enpCRMTE/Tp00djtlNIf1PuJ5V5YWEhJBIJ3nvvPdy7dw8//fQTrK2tG/2BfdLnAQ/Wenbu3FljfaiRkRGsra1x/vz5xzqb42+MgQMH4tatW7CyssLOnTuRkZGBmzdvCp735rSRpuI4OTkhMDDwiW3hcYi1fxGr+1n9jaVtjMOHD+Pw4cPcZfJt27Zh0qRJiIyM1LiK8aQ8iKXvag6Ojo7o3r07ZDIZEhISsH//fsyePfupHI2hzfXln+oGtHAQm5eX1+RZmja6hfaL1f2s/jZt2vDWPFVXV+Pll1/m9mtra1FaWopffvkFAHDnzh2EhYVh4sSJ3PrY1NRUjaUEwIOZ1/z8fG6/sLAQcrmcF0cul3MzGDU1NYiIiOBmKaurqzF9+nSsWLEC7u7uvHSNnX0+vF4XeDCz1jAbUlNTg7CwMG4Wwd/fH/7+/gCAGTNmaMwyC+lvqszv37+PkpISrsxramqwd+9evPrqq7h69SpsbW0hlUphYGAAS0tLFBUVcYPY0tJS3slEhw4dNGa2AcDV1RXTpk3DBx98gLq6Ol7YgAEDEB8fD7VarZGuqKiIVw5yubzRNXGPoyFubm4uzp07B2dnZ24QK2Te8/PzeW3EwsJCo43k5+fD0tIS+fn5kEqlMDQ05D7fwsICv/32G15//XVcv3692d+3AbH2L2J1P6u/rKyMNwtvbGyMioqKx8aPj4/n1mfb29ujd+/e8PPzg4GBARhjqKur4y7Li7nvMjQ05M2s3r59G23atOG5X3rpJe7/bm5uOHLkyGPL7WnQ5vryT3UDWricICEhAQ4ODrC2toauri6mTp2qsV5GG91C+8Xqfla/ubk5KioqUFlZCbVajdTUVF6n9cILL2DBggXcIn2FQsEbwDLGGl1KADwYbFy/fh05OTmora3F/v37MXz4cF6csrIy1NfXAwC2bNmCadOmAXgweJ49ezYmT56McePGabg9PDy4dUC1tbWNrnEsLS3l3Bs2bOBmA9RqNXepMCkpCcnJyfD29m41v0Kh0Cjzh5cNvPjii1i0aBHmzZuHefPmwcLCAq+++irMzc1haGiInJwcMMZQW1uL/Px83o9teno6LCwsIJfLIZPJMHDgQI2nH9jZ2WHhwoVYuXIl76awBgYPHtzoUgLgwVMjrK2tYWlpCV1dXYwePRrHjh1rNO6jGBoacuvh2rdvj549eyIrK6tV8n7+/HnY29tzbWTy5MncMpYGIiIiMHPmTADAxIkTERMTA+DBJdMDBw7go48+euKTJJ6EWPsXsbqf1X/t2jXI5XJ06NABUqkUr7zyisbs/sMn5K6urtwym9WrV+O9997De++9h99//x379+/nrSsVe99VXl6OiooKqFQqXLlyReMGyYeXF6Snpze6bv3voM315Z/qBrRwJlatVmP+/PmIjo6GVCpFcHCwxp2L2ugW2i9W97P6dXR0MGzYMOzZsweMMXTv3h0dOnTAyZMnIZfLNdZkPkpubi7atGmDdu3aaYTJZDJs2LABU6ZMgVqtxvTp09GlSxds2LABPXr0wIgRIxAfH4+1a9dCIpGgb9++2LBhAwDg4MGDOH36NMrLy7F7924ADwa53bt359xBQUEYNWoU1Go1Zs+eja5du2LVqlVwd3fH2LFjERsbi4CAAEgkEnh6emLr1q0AHtwY1fCYpDZt2iAkJETjkpmQ/oYy3717NxhjUCqV6NChA+Li4mBubv7EMndzc0NkZCS+//57Lm3DY6aAB3cPf/nllwgMDISOjg6io6ORk5OD1157DRkZGThz5gz+7//+D/r6+vj4448BPJgdXbVqFQDAzMwMHTp0QFJSUqOfr1ar8cknnyA4OBhSqRR79+5FVlYWFi5ciOTkZBw/fhzdu3fHtm3bYGhoiMGDB2PBggUYNWoU7OzssGbNGtTX10NHRwfbt2/nDWKFzLtarcaiRYsQEREBqVSKH3/8EampqVi5ciUuXryIiIgI7lFxKSkpKC8v5wa0c+fOhZ2dHW+GavTo0bz1u00h1v5FrO5n9dfX1yM4OBj+/v7Q0dFBTEwM8vLyMGnSJFy/fh0XLlzA8OHD0b17d6jVaty5cwfbtm1rllvMfZdUKsWoUaOwa9cu1NfXw9XVFaampjh+/DgUCgW6dOmCM2fOID09HTo6OtDX18f48eO59Dt27EBpaSlqa2uxefNm+Pj4PPFei4fR5vryT3UDgKQl1yY0+WESSet9GCF6hHjOXgNLliwRzA08WPsoRhoGX0LQ3DWsf5fs7GzB3DY2NoK5Y2NjBXMDeOqbvAjtZvLkyYL6G5YJiY2GR84JRcPJKNF6MMaaXPytdcsJCIIgCIIgCKIpaBBLEARBEARBiA4axBIEQRAEQRCigwaxBEEQBEEQhOigQSxBEARBEAQhOmgQSxAEQRAEQYgOGsQSBEEQBEEQooOeE0toLSEhIYK5/fz8BHOLmad9DzlBEK3Lo6/Gbmny8vIE9QtFfHy8oP5H3+bYkvz111+CucUMPSeWIAiCIAiC+EdCg1iCIAiCIAhCdNAgliAIgiAIghAdNIglCIIgCIIgRAcNYgmCIAiCIAjRQYNYgiAIgiAIQnRo5SB2+PDhSEtLQ2ZmJpYtWyYat9B+sbqf1Z+UlIRly5bhww8/RERERKNxzp49ixUrVmDFihX4+uuvAQClpaVYuXIlPv74Y6xYsQLHjx/XSBcVFQVnZ2c4Ojpi48aNGuE5OTkYNmwYXF1dMWTIEN7jZ5YvXw4XFxe4uLhgz549repuDf+T+LfWxX+qW2g/uVveP3jwYJw8eRLx8fGYP3++Rrienh6++eYbxMfHIzIyEpaWlgAAXV1dfP755zh+/DiOHj2Kvn37aqQVc9919uxZ+Pn5Ydq0afjpp580wrdu3YrXX38dr7/+OqZPn45Ro0ZxYcXFxVi8eDFmzJiBmTNnorCwkJd22LBhSExMRHJyMpYsWaLh1tPTw86dO5GcnIzY2Fh07NiRF25paYlbt25h4cKFjZZLU4i1rgvajhhjrbYBYE1tOjo6LCsri9nY2DBdXV2WmJjInJycmkz3vN1izru2lktISAj74YcfWIcOHdhnn33GduzYwaysrFhgYCALCQnhto0bN7KOHTuybdu2sZCQELZ161YWEhLCduzYwb7//nsWEhLCtm/fzkxMTNgXX3zBQkJCmEqlYvfv32e2trYsIyOD3b17lymVSpaUlMRUKhW3+fr6suDgYKZSqdjhw4eZn58fU6lU7ODBg8zLy4vdu3eP3b59m7m5ubHy8nIunZBuIf3Pu75oa138J7vFnHexup/FL5fLmUKhYNnZ2axXr17MysqKXblyhQ0YMIDJ5XJuW7ZsGQsJCWFyuZy9/fbb7MCBA0wul7Ply5ezX3/9lcnlcta1a1d2+fJlZm5uzqUTa98VFxfHTpw4wRQKBdu9ezc7duwYs7OzYzt37mRxcXGNbgsXLmSjRo3i9nv06ME2b97M4uLiWFRUFDt8+DAXZmBgwK5du8acnJyYoaEhu3z5MnN1dWX6+vrctnDhQvbdd98xfX19NnPmTPbbb7/xwvfv38/27dvHli9fzjv+vOujtrqbM67UupnYXr16ISsrC9nZ2airq8Pu3bvh4+Oj9W6h/WJ1P6v/+vXrMDMzg6mpKWQyGXr37o2LFy/y4sTGxsLLywsGBgYAAENDQwAPHtyvq6sLAFCpVKivr+elO3fuHOzs7GBraws9PT1MnjwZ4eHhvDipqakYMmQIgAczHw3hqampGDBgAGQyGQwMDKBUKhEdHd0q7tbwP4l/a138p7qF9pO75f2urq64ceMGbt68ibq6Ohw8eFDjYfwjRozgZiojIiLg6ekJAOjcuTNOnToFACgrK8Pt27fh4uLCpRNz35WamgoLCwsoFAro6urCy8uL+66NcfToUXh5eQEAbty4AbVaDQ8PDwDASy+9hBdffJGL6+7ujmvXruHGjRuoq6vD3r17MWbMGJ5v9OjR3Ozv/v37MWjQIC5s7NixyM7ORmpq6mPz8yTEWteFbkdaN4i1sLBAbm4ut5+XlwcLCwutdwvtF6v7Wf0VFRUwMjLi9o2MjFBRUcGLU1RUhOLiYqxZswaffvopkpKSuLCysjIEBATg/fffx+jRo9G+fXsurKCgAFZWVty+paUlCgoKeG6lUomwsDAAwIEDB1BdXY2ysjIolUpERUXh7t27KC0tRUxMDO87CuluDf+T+LfWxX+qW2g/uVveL5fLkZ+fz+0XFhZqvMlLLpdzfYJarUZVVRWMjIyQkpKC4cOHQyqVwsrKCkqlkve5Yu67SktLYWpqyu136NABJSUljZZhUVERCgsL0bNnTwBAbm4uXn75ZQQEBOCNN97Atm3boFarufgKhYJX5vn5+VAoFDznw3EaytzY2BgvvfQSFi9ejMDAwEbz0hzEWteFbkdaN4iVSDTfMtZSr8YV0i20X6zuZ/U3Fu9Rn1qtRlFREVasWIG5c+ciODgYd+7cAQAYGxtj3bp12LRpE06dOoXbt28/lXvTpk2Ii4uDu7s74uLiYGFhAZlMBm9vb4wcORKenp7w8/NDnz59eK9sFdLdGv4n8W+ti/9Ut9B+cre8vzlpHxfn119/RWFhIaKiovDpp5/i/PnzUKlUT8yDWPqu5rgbOHbsGAYNGgSpVArgwe9IUlIS3n33XWzfvh0FBQX4448/nuhpbpl/9NFH2Lp1K/e79HcQa10Xuh1p3YvS8/LymjxL00a30H6xup/Vb2RkhPLycm6/vLwc7dq104hjZ2cHmUyGDh06wNzcHMXFxbC1teXitG/fHhYWFsjIyOAuFzV2hmhubs5zKxQK7N27FwBQU1ODsLAwtG3bFgDg7+8Pf39/AMCMGTNgb2/PpRPS3Rr+J/FvrYv/VLfQfnK3vL+wsJA3m9XQ5z0aR6FQoLCwEFKpFIaGhtxVrFWrVnHxwsPDkZ2dze2Lue/q0KEDbt26xe2XlJTAxMTk0eIDABw/fhyLFi3ipXVwcOBmVz09PXH16lUuPD8/n1fmFhYWGjd+NcTJz8/nyry8vBweHh6YMGEC1q1bh7Zt26K+vh7379/HN99802jeGkOsdV3odqR1M7EJCQlwcHCAtbU1dHV1MXXqVI31MtroFtovVvez+m1sbFBcXIySkhKoVCqcPXsWrq6uvDg9e/bk1hlVV1ejqKgIpqamKC8vR21tLQDgzp07yMzM5F1y8/Dw4Nbq1NbWYs+ePRg7dizPXVpayq2l3bBhA2bPng3gwVl7WVkZgAdPT0hOToa3t3eruFvD/yT+rXXxn+oW2k/ulvcnJibCxsYGVlZW0NXVhY+Pj8ba0+joaEyePBkAMGbMGG5tqL6+PvT19QEAAwYMgFqtRkZGBpdOzH1Xly5dkJeXh4KCAtTV1eHYsWPo16+fRvndvHkT1dXV6NatGy9tdXU1KisrAQAXL16EtbU1F37hwgXY29ujU6dO0NXVxauvvorIyEie9/fff8eMGTMAABMmTEBsbCyAB081cHJygpOTE7766it89tlnTzWABcRb14VuR1r3dAIAbOTIkSw9PZ1lZWUxf3//FrsbVGi3mPOujeXS8PSBxYsXMzMzM9ahQwfm6+vLQkJCmI+PD1u4cCELCQlhP/74Ixs+fDhTKBTM0tKSzZ07l4WEhLAPP/yQWVpaMisrK2Zpaclmz57NORvugg0PD2cODg7M1taWffrpp0ylUrGAgAC2f/9+plKpWGhoKLO3t2cODg7s9ddfZ3fu3GEqlYrV1NQwJycn5uTkxHr16sXOnz/Pu7tWaLdQfm2oL9pYF//pbjHnXazuv+tveIrA9OnTWVZWFsvOzmbr169ncrmcbd68mb322mtMLpezTp06sfDwcHb9+nV28eJF1qtXLyaXy5m7uzvLzMxkGRkZLDY2lrm7u/OeaiDWvqvhKQIbN25klpaWTKFQsDfffJPFxcWxWbNmscDAQC7O7Nmz2fTp0zWeVrB582Zma2vLbGxs2IgRI9ixY8e4MH19fTZ+/HiWkZHBrl27xlatWsX09fVZYGAge/XVV5m+vj5r164d27dvH8vKymIJCQnMycmJ9xQCfX19tnbt2r/1dAIx1/W/627OuFLSkmsTmkIikbTehxGiJyQkRDC3n5+fYG4x8zRrYwmCaH0evYGrpXn4maxiIj4+XlD/o09/aEn++usvwdxihjHW+ILmh9C65QQEQRAEQRAE0RQ0iCUIgiAIgiBEBw1iCYIgCIIgCNFBg1iCIAiCIAhCdNAgliAIgiAIghAdNIglCIIgCIIgRAcNYgmCIAiCIAjRQQ+FJJ4JU1NTwdwTJ04UzC1mvv766+edBYIgnhMlJSWC+vft2yeY29fXVzD3K6+8IpgbAJYtWyaYe/Xq1YK5/+nQTCxBEARBEAQhOmgQSxAEQRAEQYgOGsQSBEEQBEEQooMGsQRBEARBEITooEEsQRAEQRAEITpoEEsQBEEQBEGIDq0cxA4fPhxpaWnIzMxs8cdaCOkW2q/N7sGDB+PPP//EmTNn8N5772mE6+np4dtvv8WZM2fwxx9/wMrKCgCgq6uLL774AjExMTh+/Hijj0k5cuQIXF1d4eLigs2bN2uE37x5E2PGjEGfPn0wcuRI5Ofnc2G5ubnw8fGBm5sb3N3dkZOTw0sbFRUFZ2dnODo6YuPGjRrunJwcDBs2DK6urhgyZAjy8vK4sOXLl8PFxQUuLi7Ys2ePRloh3QCQkpKCNWvW4JNPPsHhw4c1ws+cOYMVK1Zgw4YN2LBhA+Lj47mwAwcOYN26dVi7di327t0Lxlijn/E4tLkuPk+/WN1C+8nd8v7hw4fj6tWrSEtLw9KlSzXC9fT08MsvvyAtLQ3x8fHo1KkTAMDIyAhHjx5FZWUlgoKCGnUnJiZi0aJFWLBgAQ4cONBonNOnT2Px4sVYsmQJtmzZwh0vLS3FunXr8P7772Px4sW4deuWRlox97sNZGZmYuvWrQgKCsLJkyc1wi9duoRNmzbh66+/xtdff40LFy480dccxFrXBW1HjLFW2wCwpjYdHR2WlZXFbGxsmK6uLktMTGROTk5NpnvebjHn/VncpqamTC6Xs+zsbObh4cEsLCzYlStXWP/+/ZmpqSm3LV26lP3444/M1NSUvfXWW+zAgQPM1NSULVu2jP3yyy/M1NSUOTs7s8TERGZmZsZMTU1ZdXU1q6ysZDY2NiwpKYmVlZWxbt26sYSEBFZdXc1t48ePZ9988w2rrq5mERERbOrUqVxY//792cGDB1l1dTUrLCxkxcXFrLq6mqlUKnb//n1ma2vLMjIy2N27d5lSqWRJSUlMpVJxm6+vLwsODmYqlYodPnyY+fn5MZVKxQ4ePMi8vLzYvXv32O3bt5mbmxsrLy/n0gnp3rp1KwsKCmImJiZs1apV7PPPP2cKhYL5+/uzrVu3cpufnx/z9PTkHdu6dSt7//33mY2NDQsKCmJBQUHM2tqaLViwgG3dulXUdfF5+8XqFnPexep+Fr9UKmW6urosKyuL2dvbsxdffJElJiaybt26MalUym3vvvsu++abb5hUKmXTpk1joaGhTCqVsjZt2rABAwawuXPnsi+//JKXRiqVsl9//ZWZmZmxLVu2sJ9//pl17NiRbd68mYWGhnLbF198waytrdmOHTtYaGgo+/bbb7kwZ2dnFhAQwEJDQ1lISAjbuXMnFybmfnf16tXctnLlSta+fXu2YMEC9tFHHzEzMzM2b948XhwfHx/m4eHBO/a47XnXR211N2dcqXUzsb169UJWVhays7NRV1eH3bt3w8fHR+vdQvu12d2zZ09kZ2cjJycHdXV1OHDgAEaMGMGLM2LECO7M9tChQ+jfvz8AoHPnztxZbGlpKaqqqtCjRw8u3fnz52FrawsbGxvo6enB19cXERERPHdaWhoGDRoEABgwYAAiIyO54yqVCkOGDAEAvPzyy3jppZe4dOfOnYOdnR1sbW2hp6eHyZMnIzw8nOdOTU3l0g8ePJgLT01NxYABAyCTyWBgYAClUono6OhWcQMPZhNMTExgYmICmUwGNzc3JCcnP+YvxEcikUClUnGbWq1GmzZtmpUW0O66+Dz9YnUL7Sd3y/t79eqFa9eucWn37NmDcePG8eKMGzcOu3btAvDgBQYNfc3du3fx559/4t69e426s7KyYGZmBjMzM8hkMrzyyitISEjgxTl27Bi8vb3x8ssvAwDatm0LAMjLy4NarYZSqQQAvPjii3jhhRd4acXc7zaQn58PIyMjGBkZQSaToVu3bkhPT280bksh1roudDvSukGshYUFcnNzuf28vDxYWFhovVtovza75XI5CgoKuP2CggLI5XJeHHNzc+4yv1qtRnV1NYyMjJCSkoIRI0ZAKpWiY8eOUCqVUCgUXLrCwkJeXiwsLFBYWMhzd+/eHQcPHgQAhIeHo7q6GmVlZcjMzETbtm0xffp09OvXDwEBAVCr1bx8NixrAABLS0ve9wAApVKJsLAwAA8uwTe4lUoloqKicPfuXZSWliImJoZXhkK6AaCyshLt27fn9tu1a4fKyko8yuXLl7F+/Xrs2LEDFRUVAAAbGxs4ODjgo48+QkBAAJycnDT+Xk9Cm+vi8/SL1S20n9wt71coFBppH+43H42jVqtx+/ZtGBsbN+kuLy/nxTM2Nub6jgYKCwtRWFiIjz/+GAEBAUhMTOSOGxgY4D//+Q+WLVuGn376CfX19by0Yu53G6iqqoKhoSG3b2hoiKqqKo14qamp2LZtG0JDQ3H79u1GXc1FrHVd6HakdYNYiUSicexp1+s9D7fQfm12N5a+OTDG8Msvv6CwsBCHDx/GmjVrkJCQwBtoNpaPRz9v3bp1OHXqFPr164c///wTCoUCMpkMarUap0+fxrp16xAbG4sbN27gp59+eir3pk2bEBcXB3d3d8TFxcHCwgIymQze3t4YOXIkPD094efnhz59+kAm+/9vcRbS/Tge9Xfv3h2rV6/GihUr4OjoyM3KlJSUoKioCGvWrMHatWuRkZGBrKysJv2P+5zHfd+/A7XR1ncL7Sd3y/ubk/bv+psTp76+HkVFRVi1ahUWLlyI7du3486dO1Cr1UhNTcXMmTMRGBiI4uJixMTENOkXc7/7uM9xdHTEokWLMG/ePNja2mL//v3NdjXHD4ijrgvdjpr/F2ol8vLymjzNYxG6AAAgAElEQVST0ka30H5tdhcWFvJmARQKBYqKijTiNMyiSqVStGnThju7X7lyJRcvIiIC169f57kevlErPz+/0VneX375BQBQU1ODgwcPom3btlAoFFAqlbCxsQEAjBkzhndZrLEzRHNzc55boVBg7969nDssLIy7dObv7w9/f38AwIwZM2Bvb98qbuDBzOvDsyOVlZVc2gYMDAy4/7/yyivcbPXly5dhY2PDXeZzdnbGjRs3ND7jcWhzXXyefrG6hfaTu+X9+fn5GmkfvULVECc/Px9SqRRt27ZFeXl5k25jY2OUlZVx+2VlZbyrPsCDm8McHBwgk8lgamoKhUKBwsJCGBkZwcbGBmZmZgAADw8PZGZm8tKKud9t4NGZ16qqKo0lWQ8vXXNzc8PRo0cbdTUXsdZ1oduR1s3EJiQkwMHBAdbW1tDV1cXUqVM11rRoo1tovza7L126BFtbW3Ts2BG6uroYP368xlqi6OhoTJ48GQAwduxYnDp1CgCgr6/PNfYBAwZApVIhIyODS+fm5oZr167hxo0bqK2txb59+zB69Gieu7S0lLtktXnzZsycOZNLW1lZiZKSEgBAbGwsunTpwqXz8PDg1urU1tZiz549GDt27GPdGzZswOzZswE8uDzX0NEnJSUhOTkZ3t7ereIGgI4dO6KkpASlpaVQqVS4cOECunfvzovz8OWr5ORkbvDfvn17ZGZmQq1WQ61Wc2vgmos218Xn6RerW2g/uVven5CQAHt7ey7t5MmTcejQIV6cQ4cOcX2hr68vTpw40Sy3nZ0dioqKcOvWLahUKsTHx8Pd3Z0Xx8PDA1evXgXwYABXWFgIMzMz2Nvbo6amhhvgXblyBZaWlhppxdrvNqBQKFBWVoaKigqoVCpcuXIFjo6OvDjV1dXc/9PT02FiYtKoq7mIta4L3Y60biZWrVZj/vz5iI6OhlQqRXBwMFJSUrTeLbRfm91qtRorVqzA7t27IZVK8euvvyI9PR1Lly7F5cuXER0djV9++QVffvklzpw5g8rKSrz99tsAABMTE+zevZu7PDV//nyeWyaT4T//+Q/Gjx+P+vp6zJw5E05OTli7di1cXV0xevRonDp1CqtXrwYA9OvXD//9738BAFKpFOvWrcPYsWPBGEOPHj24DqvBHRQUhFGjRkGtVmP27Nno2rUrVq1aBXd3d4wdOxaxsbEICAiARCKBp6cntm7dCgCoq6vjbiZr06YNQkJCeJeehHQ3fLdJkyZh27ZtYIyhT58+MDc3R2RkJDp27Iju3bsjNjYWycnJ0NHRgYGBAfz8/AAArq6uyMzMxPr16yGRSODk5KQxAG7q762tdfF5+sXqFtpP7pb3q9VqLFy4EL///jukUil+/PFHpKSkYPXq1Th//jwiIiIQHByMkJAQpKWloaKiAtOnT+fSZ2VlwdDQEHp6evDx8cHIkSORmpoK4EHf8vrrryMwMBD19fUYNGgQrKyssGfPHtja2sLd3R0uLi5ISkrC4sWLoaOjAz8/P24mcubMmVizZg0YY7C1tYWXlxcv72LudxuQSqUYNWoUdu3aBcYYXF1dYWpqiuPHj0OhUKBLly44e/Ys0tPToaOjA319fYwfP75Zf9sn/c3FWNeFbkeSllyb0OSHSSSt92FEq2BqaiqY+9q1a4K59fX1BXMLzddffy2Yu7Fn/BIEoT1IpVJB/Q1Ls4TA19dXMLfQrF27VjB3wyQMwYcx1uQNN1q3nIAgCIIgCIIgmoIGsQRBEARBEITooEEsQRAEQRAEITpoEEsQBEEQBEGIDhrEEgRBEARBEKKDBrEEQRAEQRCE6KBBLEEQBEEQBCE6tO5lB4S4ePnllwVzP/zaPrFx8eJFwdzLly8XzE0QhHajVqsF9QcGBgrmfvXVVwVzC42Yny3+T4ZmYgmCIAiCIAjRQYNYgiAIgiAIQnTQIJYgCIIgCIIQHTSIJQiCIAiCIEQHDWIJgiAIgiAI0UGDWIIgCIIgCEJ00CCWIAiCIAiCEB1aOYgdPnw40tLSkJmZiWXLlonGLbRfm90DBgzAkSNHcPz4cbz99tsa4R4eHjh48CDS09MxYsQI7rhCocDBgwdx6NAh/PHHH5g2bZpG2qioKDg5OaFz587YuHGjRnhOTg6GDRuGHj16YMiQIcjLy+PCli9fDqVSCaVSidDQ0FZ1P0p8fDx8fX0xYcIE/PjjjxrhRUVFeOedd+Dn54dp06bhzz//fKJv6NChuHjxIi5fvozFixdrhOvp6SEkJASXL1/GiRMn0LFjR164paUlioqKsGDBgibz/ijaXBefp1+sbqH95G59/7O4X3nlFRw4cADh4eGYM2eORnjPnj3x66+/4vz58xg6dCgvbNGiRdi3bx/CwsKwdOnSRv1i7dPT09Px2WefYdOmTThx4oRG+Pnz5/Hpp5/iiy++wBdffIFz587xwu/du4d169bhwIEDjZZLU2hrfXmebjDGWm0DwJradHR0WFZWFrOxsWG6urosMTGROTk5NZnuebvFnPdncdva2jJ7e3t248YNNnDgQObo6MhSUlKYt7c3s7W15TZPT082cuRIFhYWxubNm8cdd3R0ZF26dGG2trasW7duLDc3l/Xp04fZ2toytVrNamtrma2tLcvMzGR//fUXUyqVLDk5manVam7z9fVlwcHBTK1WsyNHjjA/Pz+mVqtZeHg48/LyYvfv32dVVVXMzc2NVVRUcOmEdCckJPC2M2fOMAsLC7Z//34WHx/PHBwcWGhoKC/O+PHj2bJly1hCQgILDQ1l5ubmGp6EhARmYGDA2rRpw65du8a6du3K2rVrx5KSkpibmxszMDDgtkWLFrHvv/+eGRgYsFmzZrG9e/fywg8cOMDCwsLYihUruGNirovP2y9Wt5jzLla3Nufd1dWV3bx5k40aNYq5ubmxtLQ0NmHCBObi4sJtI0eOZK+++ioLDw9nS5Ys4Y6/9tpr7NKlS8zV1ZW5urqyxMRE9sYbb3DhYu7T169fz4yMjNjSpUvZunXrmLm5OVu8eDHbuHEjt02aNIn17duXd+zhrV+/fqxHjx4accRcX4R0N2dcqXUzsb169UJWVhays7NRV1eH3bt3w8fHR+vdQvu12e3i4oKcnBzk5uairq4OERERGmfn+fn5SE9PR319Pe94XV0damtrATyYOdTR4VfJc+fOwc7ODra2ttDT08OUKVMQHh7Oi5OamgovLy8AwODBg7nw1NRUDBw4EDKZDAYGBlAqlYiKimoV96NcvXoVVlZWsLS0hK6uLoYNG4bY2FheHIlEgjt37gAAampqYGJi8lifu7s7rl+/jhs3bqCurg579+7F6NGjeXFGjx6Nn3/+GQCwf/9+DBo0iAsbM2YMsrOzkZqa+tjPeBzaXBefp1+sbqH95G59/7O4u3XrhtzcXOTn50OlUiE6OprXdwBAQUEBMjMzGyanOBhj0NPTg66uLvT09CCTyVBWVsaLI9Y+PTc3F8bGxjA2NoZMJoOLiwtSUlKaVaYAkJeXh+rqajg4ODQ7zcNoa315nm5AC5cTWFhYIDc3l9vPy8uDhYWF1ruF9muz28zMDIWFhdx+UVERzMzMmp3e3NwckZGROHXqFLZv345bt25xYfn5+bCysuLlNT8/n5deqVQiLCwMwIPBWnV1NcrKyrhO6O7duygtLUVMTAzv0pGQ7kcpKSnhlYmZmRlKSkp4cd566y388ccfGD16NBYtWoQPP/zwsT6FQqHxXRQKxWPjqNVq3L59G8bGxnjppZfw/vvvY/369Y/1PwltrovP0y9Wt9B+cre+/1ncpqamKCoq4vaLi4thamrarLRJSUlISEjA0aNHceTIEZw+fRrZ2dm8OGLt02/fvo127dpx+23btsXt27c1yuDKlSv4/PPPsWvXLlRWVgIA6uvrERkZqTHR8DRoa315nm5ACwexEolE49ijZ3va6Bbar83uxtI/DYWFhRg9ejSGDBmCiRMnwtjY+In5ePTzPvvsM8TGxsLNzQ1xcXGwsLCATCaDt7c3Ro4cif79+2P69Ono06cPZDJZq7gfpTmfFR0djTFjxiAyMhJffPEFVq1apTFz/bi0jX3G4+IEBATgq6++4mZ9nxZtrovP0y9Wt9B+cre+/1ncz5LWysoKtra28Pb2hre3Nzw8PNCzZ88mXWLs0xtzOzk5Yfny5Xj//ffh4OCAPXv2AADOnDkDR0dH3iD4adHW+vI83QDw5L/QcyAvL493JmVpaYmCggKtdwvt12Z3UVERzM3NuX25XI7i4uKnzsetW7eQmZkJDw8P7jKOpaUl7yzucTOO+/btA/DgMnxYWNj/Y+/Mw6qq1j/+RQ5ol5QCRSaVUQXsAIJYjokKoiIppiZWlreUskG9poJlOXQdSkKvQ2XezDLFAUHFMQccMBDFo4kKgshwAAGRQQXO4f39wWX/3J6DULrhnHvfz/Ps52HvtdbnrLNZZ+13r7323jA1NQUAhIWFISwsDAAQEhICJycnoZyU7kexsLAQ7ZOCggKN6QIxMTFYtWoVgLrRgqqqKpSWlsLMzEzDl5ubC1tbW2HdxsZGNBr+cJ68vDwYGhrC1NQUJSUl6NWrF1555RUsWrQIpqamqK2tRVVVFb799tsG6/8wutwWW9Kvr26p/exufv+TuAsKCmBpaSmsa7tq1BC+vr5QKBS4f/8+AOD06dOQy+U4f/68qC762KebmpoKI6tA3chsu3btRG4TExPhbx8fH8TFxQGou5ksMzMTZ8+eRVVVFdRqNVq3bo2AgICGdqUGutpeWtIN6OBIbFJSEpydnWFnZwcjIyNMmDBBY06LLrql9uuyW6FQwM7OTpjvOXLkSPz2229NKmtpaYnWrVsDANq1awcvLy9kZGQI6b169RLm01RXV2Pbtm0IDAwUOYqKioQRy6VLlwp306rVamE+lkKhwKVLl+Dn59cs7kdxdXXFrVu3kJubi5qaGhw+fBgDBgzQ2BdJSUkAINTp+eef1+pLTk6Go6MjunTpAiMjI4wdO1boMOuJi4tDSEgIAGD06NHCHFw/Pz+4ubnBzc0Na9euxVdffdXkABbQ7bbYkn59dUvtZ3fz+5/E/ccff6Bz586wtraGTCaDv7+/xvz9hlAqlfDy8oKhoSFkMplGfw7ob59ua2uL4uJilJSUQKVS4eLFi3BxcRG5y8rKhL+vXLkiTMN47bXXEBYWhrlz52LEiBHo2bPnnwpgAd1tLy3pBnRwJFatVmP69Ok4ePAgDA0NsXHjxj81ebql3FL7ddmtVqvxxRdf4Mcff0SrVq2wY8cOpKWl4eOPP8alS5fw22+/4YUXXsC6detgamoKX19ffPTRRwgICICjoyPCwsJARDAwMMCGDRtw/fp1wS2TybBq1SoEBARArVbjrbfegpubGxYsWAAvLy+MGjUKx48fR3h4OAwMDNC/f3/861//AlB309jAgQMB1AXIP/30k+jykJTuR5HJZPjkk0/w4YcfQq1WY9SoUXB0dMT69evh4uKCgQMH4uOPP8aSJUvw66+/AgAWLFjQ4FQNtVqNWbNmYffu3TA0NMTmzZuRmpqK+fPn4/z584iLi8OmTZuwYcMGXLx4EXfu3MHkyZOb/D99HLrcFlvSr69uqf3sbn7/k7jVajWWLl2KdevWoVWrVoiJicGNGzcQGhqKK1eu4MSJE3Bzc8PKlSvRrl07DBgwAKGhoQgODsaRI0fg4+OD7du3g4hw5swZxMfHi/z62qcbGhoiKCgIP/zwA2pra9GrVy9YWlri0KFDsLW1haurK06fPo0rV67A0NAQzzzzDMaNG/fX/4la/i+62F5a0g0ABk9zbkKjH2Zg0HwfxjQLDg4OkrnT0tIkc0vNw5fPnjaP3in8NPmr82QZhvnvwN3dXTK3lP2i1Hz11VeSuaV4BvF/A0TU6A03OjedgGEYhmEYhmEag4NYhmEYhmEYRu/gIJZhGIZhGIbROziIZRiGYRiGYfQODmIZhmEYhmEYvYODWIZhGIZhGEbv4CCWYRiGYRiG0Tt07mUHjH4xbdo0ydxSPsO4oqJCMjcALF68WDI3P8uVYRh9pDmfS8/8b8AjsQzDMAzDMIzewUEswzAMwzAMo3dwEMswDMMwDMPoHRzEMgzDMAzDMHoHB7EMwzAMwzCM3sFBLMMwDMMwDKN36GQQ6+/vj6tXryItLQ1z5szRG7fUfl12X7t2DcuXL8eyZctw7NgxjfRz587hiy++QEREBCIiIvD777+L0h88eIDFixdj9+7dGmUPHDgAV1dXdOvWDcuWLdNIz8rKwtChQ+Hp6QlfX1/k5OQIaXPnzoW7uzvc3d0RFRWlUfbIkSPw9vaGp6cnIiIiNNJv3bqFUaNGoU+fPhgxYgRyc3NF6WVlZXBxccHs2bO17hdPT0+sWbMG69atw5gxYzTS/f39ERkZiYiICHz55ZewtbUFALRt2xaLFi3Cr7/+infeeUeruzF0ub20lFtqv766pfazu/n9T+Lu06cPdu/ejdjYWLz11lsa6T179sSvv/6Kc+fOYciQIaK0jz/+GDt37sSuXbvwySefaPVL2adL6b527RpWrFiB5cuXN3icW7hwIb755ht88803SExMFKU/ePAAS5Ys0Xqcawq62l5a0g0ieuwCYCOAQgCXH9r2OYBcACn/WYY35vlPOWpsadWqFaWnp5O9vT0ZGRlRSkoKubi4NFqupd36XPcncS9fvpyWLl1KZmZmNGfOHPryyy/JysqKZs2aRcuXLxeWcePGUZ8+fUTbHl769u1LHh4eojwqlYqqqqrIwcGBrl+/Tvfu3SO5XE4KhYJUKpWwBAcH08aNG0mlUtGhQ4coJCSEVCoVxcTE0ODBg+nBgwd09+5d8vLyopKSElKpVFRaWkrFxcVkZ2dHKSkpVFhYSG5ubnT27FkqLS0VlqCgIFq7di2VlpZSTEwMjRs3TpQ+depUGjt2LL3zzjsa5UaPHk1KpZLeffddCg4OpoyMDHr//fcpKChIWCZMmCD8vXjxYkpOTqagoCAaN24czZ07l9auXUt79+4VldHn9sK/Ud1z63Pd9dWty3X39PSkW7du0fDhw8nLy4uuXr1Ko0ePJnd3d2EJCAigsWPHUmxsLM2aNUvY/sYbb9CFCxfI09OTPD09KSUlhaZMmSKkS9mnS+3+5z//SWZmZvTJJ5/QkiVLyMrKimbOnEnLli0TlldffZVeeukl0baHl/rj3KN59Lm9SOluSlzZlJHYHwEM07I9gog8/rPENcHTJHx8fJCeno7MzEzU1NRg69atCAoK0nm31H5ddmdnZ6N9+/YwNzeHTCaDu7s7/vjjjyaXz8nJQUVFBbp27aqRlpiYCEdHRzg4OMDY2Bjjxo1DbGysKE9qaip8fX0BAIMGDRLSU1NTMWDAAMhkMpiYmEAul+PgwYNCueTkZDg4OMDOzg7GxsYIDg5GXJy4KV+7dg0DBw4EAAwYMAD79+8X0lJSUnD79m0MGjRI6/dydnaGUqlEQUEBVCoVTp06hd69e4vy3L9/X/i7TZs2wsPAq6qqkJqaipqamsfvvAbQ5fbSUm6p/frqltrP7ub3P4m7R48eyM7ORm5uLlQqFQ4ePIiXX35ZlCcvLw9paWkaLy8gIhgbG8PIyAjGxsaQyWQoLi4W5ZGyT5fSnZ2dDXNzc9Fx7sqVK03ap0Ddca68vBzOzs5NLvMwutpeWtINNGE6ARHFAyh5ap/YCDY2NsjOzhbWc3JyYGNjo/Nuqf267L579y5MTU2FdVNTU5SVlWnku3TpElauXInNmzejtLQUAFBbW4u9e/dixIgRWt15eXno1KmTsG5ra4u8vDxRHrlcjl27dgEAdu/ejfLychQXF0Mul+PAgQO4d+8eioqKcPz4cdH3VCqVou9pbW0NpVIpcvfo0UPo5Pbs2YPy8nKUlJSgtrYW4eHhWLhwYYP7xczMDEVFRcJ6cXExzMzMNPIFBARg/fr1ePPNN7Fhw4YGfX8GXW4vLeWW2q+vbqn97G5+/5O4LSwskJ+fL6wXFBTAwsKiSWUVCgWSkpJw5MgRHD58GAkJCcjMzBTlkbJPl9J99+5dPPfcc8K6qakp7t69q7EPLl++jIiICI3j3L59+xo8zjUFXW0vLekGnmxO7HQDAwOFgYHBRgMDg+efVoUMDAw0tj2tV9VJ6Zbar6/uelxcXDBv3jzMnDkTTk5O2LZtGwAgISEB3bt3F3UOjdXj0fouX74c8fHx8Pb2Rnx8PGxsbCCTyeDn54eAgAD0798fISEhePHFFyGT/f+blpvyHRctWoTTp0+jf//+OH36NKytrWFoaIgNGzbAz89PmMOqDW37VRv79+/HtGnT8NNPP+HVV19tUpnG0Nf2wr/R5ndL7Wd38/ufxP0kZTt16gQHBwf4+fnBz88PvXr1Qs+ePRt1SdmnPy23Nh51u7i4YO7cuZgxYwacnZ2FebVnz55Ft27dGjzONQVdbS8t6QaAx/+HGmYdgEWom7ewCMDXAN7WltHAwOBdAO82VZyTk9PomdRfRUq31H5ddj96Rnr37l20a9dOlMfExET4u3fv3sJl+aysLNy8eRMJCQmoqqqCWq2GsbExhg8fDkD7WZyVlZXIbW1tjR07dgAAKioqsGvXLmFkOCwsDGFhYQCASZMmwcnJSVTu4Ru18vLyNNxWVlb4+eefBfeePXtgamqKpKQkJCQkYMOGDaisrERNTQ1MTEzw+eefC2WLi4vRvn17Yd3c3BwlJQ1f1Dh58iSmTp3aYPqfQZfbS0u5pfbrq1tqP7ub3/8k7oKCAlhaWgrrHTt2xO3bt5tU1tfXFwqFQpgmdfr0acjlcpw/f17II2WfLqXb1NRUGFkFGj/O+fj4CNPTsrKykJmZibNnzwrHudatWyMgIKChXamBrraXlnQDf3EklogKiEhNRLUAvgfg85i83xGRNxF5N8WdlJQEZ2dn2NnZwcjICBMmTNCY0/JXkdIttV+X3ba2tigqKkJJSQlUKhUuXrwIV1dXUZ6HpxdcuXJFuDw1ceJEhIWFYd68eRg5ciS8vLyEABYAevXqJcynqa6uRlRUFAIDA0XuoqIi1NbWAgCWLl2KyZMnAwDUarUwH0uhUODSpUvw8/MTyvXs2RM3btzAzZs3UV1djZ07d2p0KsXFxYI7IiICISEhAIDvv/8ely9fxqVLl7Bo0SJMmDBBFMACQFpaGqysrGBhYQGZTIZ+/fpp3K36cAfr7e2tMZ3hr6LL7aWl3FL79dUttZ/dze9/Evcff/yBzp07w9raGjKZDP7+/jhx4kSTyiqVSnh5ecHQ0BAymQxeXl7IyMgQ5ZGyT5fSbWtri+LiYtFxzsXFReRu6Dj32muvISwsDHPnzsWIESPQs2fPPxXAArrbXlrSDfzFkVgDAwMrIqo/2o4GcPlpVUitVmP69Ok4ePAgDA0NsXHjxj81ebql3FL7ddltaGiIoKAgbNiwAbW1tejVqxcsLS1x8OBB2Nraws3NDadPn8aVK1fQqlUrPPPMMxg3blyT3DKZDJGRkRg+fDjUajUmT54MNzc3LFiwAN7e3ggMDMSJEycQHh4OAwMD9O/fH6tXrwYA1NTUCDcktG3bFps2bRJdHpLJZFixYgWCg4OhVqsxadIkuLi4YMmSJfD09MTw4cNx6tQpfPHFFzAwMECfPn3w1VdfNXm/1NbW4vvvv8eCBQtgaGiII0eOIDs7G6+99hrS09ORlJSE4cOHw93dHWq1GhUVFYiMjBTKf/fdd3jmmWcgk8nQu3dvfP7556LHwTwOXW4vLeWW2q+vbqn97G5+/5O41Wo1li5dinXr1qFVq1aIiYnBjRs3EBoaiitXruDEiRNwc3PDypUr0a5dOwwYMAChoaEIDg7GkSNH4OPjg+3bt4OIcObMGcTHx4v8UvfpUrnrj3M//PCD6Dh36NAh2NrawtXVVTjOGRoa/qnjXFP/L7rYXlrSDQAGjc1NMDAw+BXAywDaAygAsOA/6x6om05wE8DUh4Lax7me7kRLpsVZvny5ZO6ZM2dK5q6oqJDMDQBvvvmmZO6YmBjJ3AzD/G/j7u4umTs5OVkyt9R8/fXXkrmleAbxfwNE1OiNJY2OxBLRa1o2//CXasQwDMMwDMMwTwGdfGMXwzAMwzAMwzwODmIZhmEYhmEYvYODWIZhGIZhGEbv4CCWYRiGYRiG0Ts4iGUYhmEYhmH0Dg5iGYZhGIZhGL3jr752lmEAAHZ2di1dhb9EdXW1pP6LFy9K6mcYhpGCR9+w9TR5+PWzT5uePXtK5gaALl26SOpn/ho8EsswDMMwDMPoHRzEMgzDMAzDMHoHB7EMwzAMwzCM3sFBLMMwDMMwDKN3cBDLMAzDMAzD6B0cxDIMwzAMwzB6h04Gsf7+/rh69SrS0tIwZ84cvXFL7ddld0pKCj766CN88MEH2L17t9Y8Z86cwYwZMzBz5kxERkYCAC5fvozZs2cLS0hICBITE0XlDhw4AFdXV3Tr1g3Lli3T8GZlZWHo0KHw9PSEr68vcnJyhLS5c+fC3d0d7u7uiIqK0ih79OhRvPTSS/Dx8cGqVas00rOzsxEcHIyBAwfilVdeQV5eHgDg0qVLCAgIQP/+/TFw4MAGv/OAAQPw22+/4dixY5g2bZpGuo+PD/bs2YO0tDQEBAQI221sbBAbG4t9+/bh4MGDmDhxolb/49Dl9tJSbqn9+uqW2s/u5vc/iXvIkCFITk5GSkoKZsyYoZFubGyMf//730hJScHRo0fRuXNnAEDnzp1RUFCAU6dO4dSpU4iIiNDqT0hIwNixYzFmzBhs2rRJI33lypUICQlBSEgIgoOD4evrK6StXr0aEyZMwIQJE3D48GGNslIeL+r3x0cffYSYmJgGv9usWbPwj3/8Q3RMKSoqwpIlSzBz5kzMmjULhYWFWss/Dl1tLy3pBhE12wKAGltatWpF6enpZG9vT+rOlbsAACAASURBVEZGRpSSkkIuLi6Nlmtptz7X/UncUVFRtHXrVurYsSOtXr2atmzZQl26dKGVK1dSVFSUsERGRpKdnR1t3LiRoqKi6PvvvxelR0VF0caNG8nExIQ2b95MUVFRpFKpqKqqihwcHOj69et07949ksvlpFAoSKVSCUtwcDBt3LiRVCoVHTp0iEJCQkilUlFMTAwNHjyYHjx4QHfv3iUvLy8qKSkhlUpFhYWFpFQqqUuXLpSYmEg5OTnk6upKJ0+epMLCQmEJDAykVatWUWFhIe3cuZPGjh1LhYWFlJCQQGfPnqXCwkJSKBRkYWFBaWlpQjk7OztycHCgmzdvUv/+/cnZ2ZmuXLlCQ4YMITs7O2Hp27cvDRs2jHbu3EmhoaHCdmdnZ+ratSvZ2dmRq6srZWdnk4+PD9nZ2el1e+HfqO659bnu+urW5bqbmppSRkYGvfDCC2RmZkYKhYK8vb2pbdu2wjJjxgzasGEDtW3bliZPnkw7duygtm3bkpubG/3xxx+ivA8viYmJlJCQQDY2NhQdHU2nT58mJycn2rp1KyUmJmpdZs2aRYGBgZSYmEgrV64kHx8fOnPmDJ04cYK6d+9OR48epcTEREmPFyqVirZs2UIWFhYUGRlJP//8M3Xu3Jm++uor2rp1q7BERESQnZ0dbdiwgbZu3UrffvutkObi4kJhYWG0detW+vHHH2nTpk1Cmj63FyndTYkrdW4k1sfHB+np6cjMzERNTQ22bt2KoKAgnXdL7ddld3p6OiwtLdGxY0fIZDL06dMHSUlJojy//fYb/P398eyzzwIATE1NNTxnz56Fp6cnWrduLWxLTEyEo6MjHBwcYGxsjHHjxiE2NlZULjU1VThTHzRokJCempqKAQMGQCaTwcTEBHK5HAcPHhTKnT9/Hvb29rCzs4OxsTFGjx6NAwcOiNzXr1/HgAEDAAD9+vUT0uvrBACWlpZo3749iouLRWXd3d2RlZWF7Oxs1NTUYM+ePRg6dKgoT25uLq5evYra2lrR9pqaGuGFDMbGxjAwMNDYX49Dl9tLS7ml9uurW2o/u5vf/yRub29vZGRk4ObNm6ipqcHOnTsxYsQIUZ4RI0bg119/BQDs3r0bL7/8cpPr9scff8DW1hY2NjYwMjKCn58f4uPjG8x/6NAh+Pn5AQAyMzPh6ekJmUyGZ555Bs7OzkhISBDySnm80HacO3funMh99OhR+Pn5aRzncnJyUFtbC7lcDgBo06aN6DjXFHS1vbSkG9DB6QQ2NjbIzs4W1nNycmBjY6Pzbqn9uuwuKSmBubm5sG5ubo6SkhJRnry8PCiVSnz66acIDw9HSkqKhuf06dPo27evRrlOnToJ67a2tsIl/Xrkcjl27doFoK5DLS8vR3FxMeRyOQ4cOIB79+6hqKgIx48fF33P/Px80fe0srKCUqkUud3c3LB3714AwL59+1BRUaHx3c6fP4+amhqNt5dZWlqKfPn5+bC0tNT43g1hZWWF/fv348yZM/j222//1OUnXW4vLeWW2q+vbqn97G5+/5O4raysRJfY8/LyYG1t3WAetVqNsrIymJmZAah7s9XJkycRFxeHl156ScN/+/ZtdOzYUVi3sLDA7du3tdZFqVQiLy8P3t7eACAErQ8ePEBpaSmSk5NF/aKUx4tHj3NmZmYaxwKlUgmlUonPPvsM8+fPF45zSqUSf/vb3/D1119j7ty5+PnnnzUGLhpDV9tLS7oBHQxitY04/Wcqgk67pfbrsltb3kedtbW1UCqVWLBgAT766COsX78elZWVQvqdO3dw69YtuLu7/2n38uXLER8fD29vb8THx8PGxgYymQx+fn7CvNWQkBC8+OKLkMn+/03LTXF//vnnOHPmDHx9fZGQkAArKyuRo6CgAO+//z4iIyPRqlWrx7oa+syGUCqVCAgIwMsvv4zg4GC0b9++yWV1ub20lFtqv766pfazu/n9T+JuStmGrgzl5+fDzc0N/fv3R1hYGH744Qe0bdv2L9UDqBuF9fX1haGhIQDgxRdfRJ8+fTBlyhTMnz8fL7zwgpDWkPtpHS+08ahbrVYjPz8fn332GT788EN89913qKyshFqtxtWrVzFp0iQsWbIEhYWFOH78eJP3g7bPauj7/hX01Q3oYBCbk5PT6JmULrql9uuy29zcXHQpvbi4GM8//7woj5mZGXr16gWZTAYLCwtYW1uLRikTEhLg4+Oj0WloO4uzsrIS5bG2tsaOHTtw7tw5LFq0CMD/X8YJCwtDcnIyDh48CCKCk5OTUM7Kygq5ubnCulKp1BgptbS0xI8//oijR49i3rx5AIB27doBAMrLyzFx4kTMmzdPGCl4GKVSKaqrpaUlCgoKNPI1RmFhIa5fv45evXo1uYwut5eWckvt11e31H52N7//Sdx5eXmwtbUV1h/tqx/NY2hoiHbt2qGkpATV1dXC6GRKSgoyMzNFfS5QN/L6cD9YWFiIDh06aK3L4cOH4e/vL9r29ttv45dffsG//vUvEJHoe0p5vDAzMxMd50pKSjSOc+bm5vDy8hKOc1ZWVsjPz4e5uTns7OzQsWNHGBoawtvbGzdv3tT6nRtCV9tLS7oBHQxik5KS4OzsDDs7OxgZGWHChAkac1p00S21X5fdjo6OUCqVKCwshEqlwpkzZzSCOh8fH1y+fBkAUFZWBqVSKbqkpG0qAQD06tVLmE9TXV2NqKgoBAYGivIUFRUJl2aWLl2KyZMnA6g7K67vdBQKBS5duiTMrQIAT09PZGRkICsrC9XV1YiOjtboMIuLiwX3qlWr8NprrwEAqqurMXnyZIwbNw6jRo3Sul8UCgXs7Oxga2sLIyMjBAYG4siRI4/fmf/B0tJSmDPVrl07YZ5aU9Hl9tJSbqn9+uqW2s/u5vc/iTs5ORkODg7o0qULjIyMEBwcjLi4OFGeuLg4oS985ZVXcOLECQB1QVz9FSk7Ozs4OjpqBGuurq7Izs5Gbm4uampqcOjQIfTv31+jHllZWSgvL8cLL7wgbFOr1SgtLQUApKWlIT09Hb179xbSpTxeODo6Ij8/X3Sc8/LyErm9vb1x5coVAP9/nLOwsICjoyMqKytRVlYGoG5e8J+9pK6r7aUl3QDw+LHyFkCtVmP69Ok4ePAgDA0NsXHjRqFR6LJbar8uuw0NDfH2229jyZIlqK2txaBBg9CpUyds27YNjo6O8Pb2hru7Oy5evIgZM2agVatWmDRpknCZqbCwEEVFRXB1ddVwy2QyREZGYvjw4VCr1Zg8eTLc3NywYMECeHt7IzAwECdOnEB4eDgMDAzQv39/rF69GkDdzVH1Nxy0bdsWmzZtEo30ymQyLF26FOPHj4darcbEiRPRvXt3LF26FB4eHhg2bBjOnDmDxYsXw8DAAC+99BKWLl0KAIiJiUFCQgJKSkqwdetWAHVB7qMd7oIFC/DTTz+hVatW2L59O9LS0jBjxgxcunQJR44cgVwux/r162FqaorBgwfj448/hr+/P5ycnBAeHg4igoGBAb7//ntcu3at2f6n/41uqf366pbaz+7m9z+JW61WY/bs2YiOjoahoSE2b96Mq1evIjw8HOfPn8f+/fvx008/4bvvvkNKSgru3LmDt956CwDQt29fhIeHQ6VSQa1W4+OPP8adO3dEfplMhtmzZ+PDDz9EbW0tAgMD4ejoiG+//RYuLi7CjbQHDx7E0KFDRZejVSoVpk6dCgAwMTHBwoULNfp0qY4XhoaGeOutt/Dll1+KjnNRUVFwcHAQjnMKhQKzZs3SOM5NmjQJixcvBhHB3t4egwcP/jP/Up1tLy3pBgCDpzk3odEPMzBovg9jmgVtz9J7WowZM0Yy96MT8p82Pj4+krn/7GUohmGYpvLoHNanyW+//SaZu2fPnpK5AWDHjh2SuSdMmCCZW58hokYfy6Nz0wkYhmEYhmEYpjE4iGUYhmEYhmH0Dg5iGYZhGIZhGL2Dg1iGYRiGYRhG7+AglmEYhmEYhtE7OIhlGIZhGIZh9A4OYhmGYRiGYRi9Q+dedsDoF8HBwZK5pXyGsZmZmWRuAHj22Wcl9TMMw0jBkCFDJHN37dpVMrfUaHsZD9Py8EgswzAMwzAMo3dwEMswDMMwDMPoHRzEMgzDMAzDMHoHB7EMwzAMwzCM3sFBLMMwDMMwDKN3cBDLMAzDMAzD6B06GcT6+/vj6tWrSEtLw5w5c/TGLbVfl90HDhyAi4sLunbtimXLlmmkZ2VlYejQofDw8ICvry9ycnKEtLlz50Iul0Mul2Pbtm1a3a6urujWrdtj3Z6enlrd7u7ucHd3R1RUVLO6AaBv376IjY3Fvn37MGXKFI10Ly8vbNu2DRcuXMDQoUNFaTNmzEB0dDRiYmIwd+5crf7HocvtpaXcUvv11S21n93N738St6enJ1avXo01a9Zg9OjRGul+fn6IiIjA119/jSVLlsDW1hYA4O7ujhUrViAiIgIrVqxAjx49tPqPHDkCb29veHp6IiIiQiP91q1bGDVqFPr06YMRI0YgNzdXlF5WVgYXFxfMnj1bo6yUffqpU6cQGBiIESNG4IcfftBIVyqVmDJlCsaNG4fg4GCcPHkSAJCQkIDx48djzJgxGD9+PH7//Xet+6UxdLW9tKQbRNRsCwBqbGnVqhWlp6eTvb09GRkZUUpKCrm4uDRarqXd+lz3J3Gr1Wqqrq4mBwcHSktLo/v375NcLqdLly6RWq0WluDgYNq4cSOp1Wo6fPgwhYSEkFqtptjYWBo8eDBVVVVRWVkZeXl50Z07d0itVpNKpaKqqipycHCg69ev071790gul5NCoSCVSiUs9W6VSkWHDh2ikJAQUqlUFBMTQ4MHD6YHDx7Q3bt3ycvLi0pKSoRyUrp79OhBcrmcbt26RcOGDSMPDw+6evUqjRo1inr06CEsfn5+NGbMGIqJiaEZM2YI20NCQuj8+fMkl8tJLpdTSkoKTZ48mXr06KHX7YV/o7rn1ue666tbl+seHBxMSqWSpk2bRq+++iplZmbSBx98QKNHjxaWiRMnCn9/+eWXdP78eRo9ejTNnDmT3n77bRo9ejR99NFHVFRUJCpXWlpKxcXFZGdnRykpKVRYWEhubm509uxZKi0tFZagoCBau3YtlZaWUkxMDI0bN06UPnXqVBo7diy98847wjap+/QLFy6Qra0txcXFUXJyMnXt2pWio6NJoVAIS3BwMIWHh5NCoaDo6GiytrYmhUJB27ZtoyNHjpBCoaCdO3eShYWFqJw+txcp3U2JK3VuJNbHxwfp6enIzMxETU0Ntm7diqCgIJ13S+3XZXdiYiIcHR3h4OAAY2NjjB8/HrGxsaI8qampGDx4MABg0KBBQnpqaioGDhwImUwGExMTyOVyHDhwoEH3uHHjtLp9fX21ugcMGCByHzx4sFncAPDCCy/g1q1byMnJgUqlwv79+zFo0CBRnry8PFy/fl3rix1at24NIyMjGBsbQyaTobi4+HH/BhG63F5ayi21X1/dUvvZ3fz+J3E7OTlBqVSioKAAKpUKp06dgo+PjyjP/fv3hb9bt24t9F+ZmZm4c+cOgLrR1Pq+62GSk5Ph4OAAOzs7GBsbIzg4GHFxcaI8165dw8CBAwEAAwYMwP79+4W0lJQU3L59W6MvBaTt0y9fvozOnTvD1tYWRkZGGDZsGI4dOyZyGxgYoLKyEgBQUVGBDh06AABcXFxgYWEh7N+qqipUV1dr1P9x6Gp7aUk3oIPTCWxsbJCdnS2s5+TkwMbGRufdUvt12Z2bm4tOnTqJfI9e/pHL5di1axcAIDo6GuXl5SguLhaC1nv37qGoqAjHjx8XXd7Jy8sTuW1tbZGXl9ege/fu3Y91P/w9pXQDgIWFBfLz84X1goICdOzYsQl7FLh48SISExNx9OhRHD16FKdPn0ZmZmaTygK63V5ayi21X1/dUvvZ3fz+J3Gbm5uLTpiLi4u1vuFw2LBhWLt2Ld544w2tl9ZfeuklZGRkQKVSibYrlUpRXaytraFUKkV5evToIQSXe/bsQXl5OUpKSlBbW4vw8HAsXLhQa92l7NMf7b87duyIwsJCkTs0NBR79+7FkCFD8N5772HevHkadTx8+DC6d+8OY2Njrd+hIXS1vbSkG9DBINbAwEBj29N6/aiUbqn9uuzWlvdR54oVK3DixAl4eXkhPj4eNjY2kMlk8PPzQ0BAAPr164eJEyfixRdfFJ25N8W9fPlyxMfHw9vbW6u7f//+CAkJaVa3NldDn6mNTp06wcHBAUOGDMHgwYPRu3dveHl5Nansk372f6tbar++uqX2s7v5/VLXHaibe/ree+9h8+bNGDt2rCitU6dOeP3117F+/fq/VI9Fixbh9OnT6N+/P06fPg1ra2sYGhpiw4YN8PPzE+bgNsX9NPv0xtz79+9HUFAQjhw5grVr1yIsLAy1tbVCenp6Or755ht89tlnje6Dxj4L0I/2InVbfPx/qAXIyclp9ExKF91S+3XZbWtrKzrTys3NhbW1tSiPtbU1du7cCaDuMsuuXbtgamoKAAgLC0NYWBgAICQkBE5OTkI5bWdxVlZWGu4dO3Y06p40aVKzuYG6M3dLS0thXduZe0MMHjwYCoVCuGx36tQpyOVyJCcnN6m8LreXlnJL7ddXt9R+dje//0ncxcXFMDc3F9bNzc1RUlLSYP5Tp07h3XffFeWfM2cOVq1ahYKCAo381tbWoit1eXl5Gv2ulZUVfv75ZwB1/e6ePXtgamqKpKQkJCQkYMOGDaisrERNTQ1MTEzw+eefA5C2T+/YsaPo+xQUFAjTBeqJjo7GunXrANTd5FZVVYU7d+7A3Nwc+fn5mDFjBpYsWSL63zQVXW0vLekGAJ27scvQ0JBu3LhBdnZ2wiRgV1fXpzLBWEq3Ptf9SdxqtZqqqqrI3t6e0tPThRu7FAqF6MaugoICqqmpIbVaTfPmzaP58+cLN4UVFhaSWq2mCxcukJubG1VVVQk3dj148IDs7e0pLS1NmKh/8eJF0UT9/Px8qq6uJpVKRXPnzqXw8HBhkn9BQQGpVCo6f/48ubm50YMHD4RyUrp79OhB7u7ulJ2dTf7+/sKNXUFBQaIbu+qX3bt3i27smjVrFiUkJJC7uzt5eHhQQkICvf/++02+sUtX2wv/RnXPrc9111e3Lte9/sauqVOnCjd2ffjhh6IbtN577z3h7yVLllBaWhqNHj2aQkJCKDMzk5YtWybK//CNXUVFRdSlSxfRjV0JCQmiG7du3LhBJSUlVFpaSrNmzaLZs2eL0ktLS2nNmjUaN3ZJ2aefP3+ebGxsRDd27dq1S3SDVt++fWnRokWkUCho9+7d1KFDB7p48SKdOnWKunbtSl9//bUo/5+5sUtX24uU7ibFlboWxAKggIAAunbtGqWnp1NYWNhT6zSkdutz3f+quz5I3bNnDzk7O5ODgwMtWrSI1Go1zZ8/n6Kjo0mtVtO2bdvIycmJnJ2d6e2336Z79+6RWq2myspKcnFxIRcXF+rduzclJycLzvrOIzY2VnAvXLiQVCoVhYeHU3R0NKlUKg13ZWUlqVQqqqioENw+Pj507tw5UWcmpbs+GA0NDaXMzEy6desWRUZGUo8ePWjdunU0ffp06tGjB40fP57y8/OpsrKS7ty5Q2lpacKTDaKioujGjRuUnp5OmzZtEpz63F5a2q3Pdef98t/l1tW6jx49mhYtWkS5ubmkVCrp559/ptGjR9O2bdvoyy+/pNGjR9OePXsoKyuLMjIySKFQCEHuL7/8Qvfv36eMjAxhmTx5siiILS0tpaioKHJ0dCQ7OzuaP38+lZaW0uzZs2nLli1UWlpKmzZtIgcHB3J0dKTXX3+dCgoKmhTEStmnKxQKWrNmDXXp0oVsbW1p+vTppFAoaOrUqRQZGSk8kcDDw4O6du1K3bp1o/Xr15NCoaDp06dTmzZtqFu3bsJy7NixPxXE6mp7kdLdlLjS4GnPk3kcBgYGzfdhTLOgVqslczdn23zaeHh4SOa+fPmyZG6GYf630fZc2KfFv//9b8nczz77rGRuALhy5YpkbrlcLplbnyEizQm1j6BzN3YxDMMwDMMwTGNwEMswDMMwDMPoHRzEMgzDMAzDMHoHB7EMwzAMwzCM3sFBLMMwDMMwDKN3cBDLMAzDMAzD6B0cxDIMwzAMwzB6BwexDMMwDMMwjN4ha+kKMExLkJWVJanfwcFBMje/7IBhdJvQ0FDJ3J999plkbkDalwY888wzkrmlfBkBAAwZMkRSP/PX4JFYhmEYhmEYRu/gIJZhGIZhGIbROziIZRiGYRiGYfQODmIZhmEYhmEYvYODWIZhGIZhGEbv4CCWYRiGYRiG0Tt0Moj19/fH1atXkZaWhjlz5uiNW2q/LrsPHDgAFxcXdO3aFcuWLdNIz8rKwtChQ+Hh4QFfX1/k5OQIaXPnzoVcLodcLse2bdu0ul1dXdGtW7fHuj09PbW63d3d4e7ujqioKI2yJ06cwJAhQzBo0CCsX79eIz0vLw8TJ05EYGAghg8fjmPHjgEAYmJiMHLkSGFxcnLS+ogXT09PrF27FuvXr0dwcLBG+rBhwxAZGYmIiAj885//RKdOnQAAbdu2xeLFi7F161a8++67GuWagi63l5ZyS+3XV7fU/v9V961bt7Blyxb88ssvOH/+fIP5bty4gXXr1qGwsBAAkJ2dje3bt2Pbtm3Yvn27qE+r5+jRo+jXrx9eeuklrF69WiM9Ozsbr776Knx9fTFmzBjk5eUJaa+99hq6deuG119/XWt9Dh8+DE9PT7i7u+Prr7/W+r1GjhyJF198EQEBAcjNzRV9blBQELy8vODt7a31cYZS9umnTp1CYGAgRowYgR9++EEjXalUYsqUKRg3bhyCg4Nx8uRJAEBCQgLGjx+PMWPGYPz48fj99981yg4aNAinT5/G2bNn8cEHH2ikGxsb47vvvsPZs2exf/9+oT83MjLCN998g+PHj+Po0aPo06ePRtmmoMttvaXcIKJmWwBQY0urVq0oPT2d7O3tycjIiFJSUsjFxaXRci3t1ue6P4lbrVZTdXU1OTg4UFpaGt2/f5/kcjldunSJ1Gq1sAQHB9PGjRtJrVbT4cOHKSQkhNRqNcXGxtLgwYOpqqqKysrKyMvLi+7cuUNqtZpUKhVVVVWRg4MDXb9+ne7du0dyuZwUCgWpVCphqXerVCo6dOgQhYSEkEqlopiYGBo8eDA9ePCA7t69S15eXlRSUkIqlYpu3LhB169fp86dO9OxY8coNTWVunfvTgcOHKAbN24Iy/jx42nhwoV048YNOnDgANnY2IjSb9y4QXFxcdSpUyfRtlGjRtErr7xCeXl59M4779CYMWMoIyOD3n//fRo1apSwjB8/Xvh78eLFlJycTKNGjaJXX32V5syZQ2vXrqW9e/eKyuhze+HfqO659bnuuuoODQ2lqVOnUrt27SgkJITeffddMjc3p/Hjx1NoaKhomTJlCllZWZGFhQUFBwdTaGgojR07lt544w0KDQ2lcePGkYmJiZBfqVRSTk4OdenShc6ePUtZWVnk6upKx48fJ6VSKSwjR46kyMhIUiqVtH37dgoODhbSoqKiaNOmTTRkyBBRGaVSSaWlpWRvb08KhYKKi4upR48elJSUROXl5cLyyiuv0Pr166m8vJz27t1LEyZMENL69etHMTExVF5eTkqlkgoKCoQ0Kft0hUJBFy5cIFtbW4qLi6Pk5GTq2rUrRUdHk0KhEJbg4GAKDw8nhUJB0dHRZG1tTQqFgrZt20ZHjhwhhUJBO3fuJAsLC1E5S0tLyszMpF69epGNjQ1dvnyZ+vXrRxYWFsLyySef0I8//kgWFhb07rvv0u7du8nCwoLmzJlDW7ZsIQsLC3J1daWUlBTq2LGjUE6f27qU7qbElTo3Euvj44P09HRkZmaipqYGW7duRVBQkM67pfbrsjsxMRGOjo5wcHCAsbExxo8fj9jYWFGe1NRUDB48GEDd2Wx9empqKgYOHAiZTAYTExPI5XIcOHCgQfe4ceO0un19fbW6BwwYIHIfPHhQKHfx4kV06dIFnTt3hrGxMUaOHIkjR46I3AYGBqioqAAAlJeXw8LCQuP779mzByNHjtTY7uzsjPz8fBQUFEClUuHkyZPw8fER5bl//77wd+vWretP9lBVVYXU1FRUV1dreJuCLreXlnJL7ddXt9T+/1V3YWEhTE1N0a5dOxgaGsLJyQk3b97UyJeYmAgPDw/IZP//7qEOHTrAxMQEAGBmZgaVSgW1Wi2kX7hwAXZ2dujSpQuMjY0RFBQk6tsA4Pr16+jXrx8AoG/fvqL0/v37N/hSg3PnzsHBwQH29vYwNjZGcHAw9u7dK8pz9epVvPzyywCAAQMGYN++fcJ2lUol9MfPPvss/va3v2l8X6n69MuXL6Nz586wtbWFkZERhg0bJlw9q8fAwACVlZUAgIqKCnTo0AEA4OLiIvTvTk5OqKqqEvW/PXv2RGZmJrKyslBTU4Pdu3dj2LBhIvewYcOE0eE9e/YI+79r167CiG9RURHKysrg4eGhdf83hC639ZZyAzo4ncDGxgbZ2dnCek5ODmxsbHTeLbVfl925ubnCZZN638OXlwBALpdj165dAIDo6GiUl5ejuLhYCFrv3buHoqIiHD9+XHTpKC8vT+S2tbUVXRZ71L179+7Huh/+ngUFBbCyshLWLS0tUVBQIHJ/9NFH2L17N/r27YspU6ZgwYIFGt9/3759CAwM1Nhubm6OoqIiYb24uBjm5uYa+YYPH47169dj8uTJ+P777zXS/wq63F5ayi21X1/dUvv/V92VlZVCIAoAJiYmQvBUz+3bt1FRUQE7O7sGPRkZGWjfvj0MDQ2Fbfn5+aK6WFlZIT8/X1TOzc1NCC7j4uJQUVGBkpKSRuutVCpFbhsbGyiVSlGeF154ATExMQCA1LtbmQAAIABJREFU2NhYoc9NS0uDqakpJk6ciL59+yI8PFwUfAPS9+kdO3YU1jt27ChM0agnNDQUe/fuxZAhQ/Dee+9h3rx5Gvvg8OHD6N69O4yNjYVtlpaWonrm5eXB0tJSVM7Kyko49qnVapSXl8PMzAxXrlzBsGHDYGhoiM6dO0Mul8Pa2lrjcx+HLrf1lnIDOhjEGhgYaGyrH53SZbfUfl12a8v7qHPFihU4ceIEvLy8EB8fDxsbG8hkMvj5+SEgIAD9+vXDxIkT8eKLL4pGJJriXr58OeLj4+Ht7a3V3b9/f4SEhDTJ/Sh79uxBcHAwTp8+jR9++AH/+Mc/UFtbK6SnpKSgTZs26NatW6Ouhj4zLi4O06ZNw6ZNmzBu3LgmeRpDl9tLS7ml9uurW2o/u7VDRDhz5sxj50eWlJTg7NmzGDhwYKP1eLS+n332GRISEjB06FAkJCTAyspK1P89rl6NuZcsWYJTp06hb9++OH36NKytrSGTyaBWq5GQkIAlS5bgxIkTuHnzJn7++ec/7f+rfbo2HnXv378fQUFBOHLkCNauXYuwsDBRn56eno5vvvlG4/W+2tpDUyAibNmyBUqlEocOHcKiRYuQlJSkEdw3hr62dal/R4236GYmJyen0bM0XXRL7ddlt62trehMKzc3V+Ms09raGjt37gRQdwln165dMDU1BQCEhYUhLCwMABASEgInJyehnLazuIdHT+vdO3bsaNQ9adIkkdvS0lI0wpCfny86iweA7du3Y+PGjQDqLidVVVWhpKQE7du3BwDs3btX6ygsUDfyWp8PqBuZfdxIyMmTJzFt2rQG0/8MutxeWsottV9f3VL7/1fdj468PjoyW11djZKSEuFS+b1797B//34EBATAwsICFRUVOHDgAHx9fYX+rJ6HR/yAutHTR/suS0tLoe+qrKxEXFwc2rVr12i9ra2tRe7c3FytI45btmwBUNfnxsTEwNTUFNbW1pDL5bC3twcAjBw5EklJSaKyUvbpHTt2FF1NKygoEKYL1BMdHY1169YBANzd3VFVVYU7d+7A3Nwc+fn5mDFjBpYsWSL63wN1+/jh45q1tbXG6Hf9KLZSqYShoSHatm2LO3fuAIAoKN67dy8yMjLwZ9Dltt5SbkAHR2KTkpLg7OwMOzs7GBkZYcKECRrzZXTRLbVfl929evUS5rxUV1dj27ZtGoFdUVGRcLa7dOlSvPXWWwDqLrkUFxcDABQKBS5dugQ/P78G3VFRUY26J0+e3CS3XC7HzZs3kZ2djerqauzdu1eYt1uPlZUVzpw5A6DuDL2qqkqYElBbW4v9+/drnQ8LAGlpabCysoKFhQVkMhn69++PxMREDX893t7eGpft/iq63F5ayi21X1/dUvv/V90WFhYoLS1FWVkZ1Go10tPTRdMGWrdujbfeeguTJk3CpEmT0LFjRyGAraqqQlxcHHr37q0R4AGAh4cHMjMzcevWLVRXVyMmJgb+/v6iPMXFxUK/uGrVKkyYMKFJ9fby8sKNGzdw8+ZNVFdXY+fOnRgxYoQoz8N97tdffy085cDLywulpaW4ffs2gLqnv3Tv3l1UVso+3c3NDVlZWcjJyUFNTQ0OHDggzN2tx9LSUnjyQEZGBqqrq2FmZoaysjJMnz4dH374ITw9PTX2y4ULF+Dg4IDOnTvDyMgIr7zyisY85IMHDwpX0wIDA3Hq1CkAwDPPPCPMDR4wYABUKhWuX7+udf83hC639ZZyA4DOPZ0AAAUEBNC1a9coPT2dwsLCnsodcs3h1ue6/1V3/dMH9uzZQ87OzuTg4ECLFi0itVpN8+fPp+joaFKr1bRt2zZycnIiZ2dnevvtt+nevXukVqupsrKSXFxcyMXFhXr37k3JycmCs/5O1djYWMG9cOFCUqlUFB4eTtHR0aRSqTTclZWVpFKpqKKiQnD7+PjQuXPnBGf9UwQ2bNhAdnZ21LlzZ5o5cybduHGDpk+fTt9++63wRIKePXtS9+7dycXFhX788Ueh7C+//EIeHh4aTyuofzrBqFGj6IsvvqCcnBzKy8ujzZs306hRo2jr1q20ePFiGjVqFMXGxlJWVhbduHGDFAqF6OkF+fn5VFZWRvfu3aPbt28LafrcXlrarc915/2iP+76JwkMHz6cTE1NqV27duTj40OhoaHk5eVFw4YN03hKgbW1tfB0Ah8fH5LJZGRubi4sb775pvB0AqVSST///DM5ODhQly5daM6cOaRUKmnGjBn0448/klKppO+//57s7e3JwcGBJk6cSDdv3hTK+vj4kJmZGbVp04asrKxoy5YtQlp5eTnt2LGDHB0dyd7enj777DMqLy+nOXPm0NatW6m8vJw2b95Mjo6O5OjoSG+88QYVFRUJTyCIiYkhNzc3cnV1pYkTJ1JxcbHo6QRS9en1TxFYs2YNdenShWxtbWn69OmkUCho6tSpFBkZKTyRwMPDg7p27UrdunWj9evXk0KhoOnTp1ObNm2oW7duwnLs2DHBa2FhQa+99hqlp6dTZmYmffnll2RhYUFfffUVvf7662RhYUGdOnWimJgYysjIoPPnz1OvXr3IwsKCvLy8KC0tja5du0YnTpygnj17ip5qoM9tXUp3U+JKg6c5N6ExDAwMmu/DmGbhz87r+TNI2Ta1PbvwaTJjxgzJ3E/1LJZhmKdOaGioZO5H52o+bRp6asHT4JlnnpHMre053U+TIUOGSOZ+9OYzpg4ianQiss5NJ2AYhmEYhmGYxuAglmEYhmEYhtE7OIhlGIZhGIZh9A4OYhmGYRiGYRi9g4NYhmEYhmEYRu/gIJZhGIZhGIbROziIZRiGYRiGYfQOnXvtLKNfLFq0SDL3p59+Kpn74TfnSEF0dLSkfqmIj4+XzP3o6yefNlI++7f+lclS4O3tLZkbAAYOHCiZ++HXKj9t3njjDcnc+kyrVtKOPdW/KUsKqqurJXMfP35cMjfAz3LVVXgklmEYhmEYhtE7OIhlGIZhGIZh9A4OYhmGYRiGYRi9g4NYhmEYhmEYRu/gIJZhGIZhGIbROziIZRiGYRiGYfQOnQxi/f39cfXqVaSlpWHOnDl645bary/utLQ0rF69GpGRkTh58qRG+oULF7B8+XKsW7cO69atQ3Jy8mN9Bw4cgIuLC7p27Yply5ZppGdlZWHo0KHw8PCAr68vcnJyhLS5c+dCLpdDLpdj27Zt/zVuqf2JiYl44403EBISgi1btmikr1mzBn//+9/x97//Ha+//jpGjhwppA0ePFhICw8P1yh77do1LF++HMuWLcOxY8c00s+dO4cvvvgCERERiIiIwO+//y5Kf/DgARYvXozdu3dr3S9ZWVn45ZdfsHnz5se2rfT0dKxZs0Z4dE52djaioqLw66+/IioqSrS/6hk0aBBOnjyJM2fOYPr06RrpxsbGWL9+Pc6cOYN9+/bB1tYWAGBkZISIiAgcPXoUR44cwUsvvdRgverp2bMn1q1bh2+//RZjx47VSB82bJjwO1u2bBk6der0WN/Vq1exfPlyLF26FEePHtVIT0pKwueff46VK1di5cqVov3+ySefCNv//e9/a5S9dOkS5s2bhzlz5mDfvn1aPz8xMRHh4eEIDw/H+vXrAQC3bt3C4sWLER4ejk8//VTjfw3wb/Rx/nrefvttWFhYoEePHlrTiQgffvghnJycIJfLcf78+cf6pK73oUOHIJfL4ebmhhUrVmj1BwQEoFevXvDz8xP8J06cQO/evYXlueeeQ2xsrKjslStXsHjxYixcuBCHDx/WcP/++++YN28eli1bhmXLluHMmTMAgOvXrwvbli1bhpkzZ0KhUDx2Pz0KxxfN79a558S2atUKa9aswdChQ5GTk4OkpCTExsYiNTVVp91S+/XFXVtbi7i4OLz++uto164dvv/+e3Tr1g0WFhaifG5ubhgxYkSjPrVajQ8++AAHDx6Era0tevfujcDAQLi6ugp5Zs+ejUmTJuHNN9/E0aNHERYWhp9++gn79u3D+fPncf78eVRVVWHQoEEICAhAu3bt9NrdHHWPjIzEihUr0KFDB0ybNg19+vQRPVv3/fffF/7etWsX0tLShHVjY2Ns2LChwfYRHR2Nd955B6ampli9ejVcXV3RsWNHUT53d3e88sorWh0HDx6Eg4NDg/74+HiMGjUKzz77LLZv3w57e3uYmZmJ8lVXV0OhUIg+t02bNhgxYgRMTExQXFyMPXv2YPLkyUJ6q1at8OWXX2L8+PFQKpXYv38/Dh06hOvXrwt5XnvtNdy9exd9+vRBUFAQ5s+fj2nTpiEkJAQA4OvrC3Nzc2zZsgXDhg0DEWn9Hq1atcK0adPw6aefori4WAgqs7OzhTwnTpzAgQMHAAA+Pj6YMmUKPv/88wb3S3R0NN59912Ymppi1apVcHNz07rfR48erVHeyMgIM2fObNC9efNm/OMf/4CZmRkWLlwIDw8P2NjYCHny8/Oxb98+hIWFwcTEBGVlZQDq2srf//53WFpa4s6dO/jiiy/wwgsv4G9/+xsA/o0+zv8wkydPxvTp0xt8tu7+/fuRlpaGtLQ0/P777wgNDdV6wtBc++Xjjz/Gvn37YGNjg379+mHkyJFwcXER8sybNw8hISGYNGkSjh8/js8++wwbN27EwIEDhXqXlJSgR48eGDJkiFCutrYW27dvx/vvv4/nnnsOX331FXr06AErKyvRd+zZsydeffVV0bauXbsKAVZlZSUWLVqE7t27a91H2uD4ovndgA6OxPr4+CA9PR2ZmZmoqanB1q1bERQUpPNuqf364s7NzYWZmRnMzMwgk8nQo0cPXLt27S/XLTExEY6OjnBwcICxsTHGjx+vceadmpqKwYMHA6gbKatPT01NxcCBAyGTyWBiYgK5XC4c9PXZLbX/6tWrsLa2hrW1NYyMjODr64vTp083+D86evSo8DmNkZ2djfbt28Pc3BwymQzu7u74448/mlQWAHJyclBRUYGuXbtqTS8sLISpqSlMTU1haGgIZ2dnZGZmauT7/fff0bNnTxgaGgrbOnToABMTEwCAmZkZVCoV1Gq1kO7p6YmbN2/i1q1bqKmpQUxMDPz9/UXeYcOGISoqCgCwd+9e9O/fH0DdAfLUqVMAgOLiYty9exfu7u4Nfk9nZ2colUoUFBRApVIhPj4evXv3FuW5f/++8HebNm0adAF1I54P73cPD48/td8fR0ZGBiwsLGBhYQGZTAYfHx9cuHBBlCc+Ph6+vr7C/q0PaiwtLWFpaQkAeP7559GuXTshwAX4N/o4/8MMGDBA40TtYWJiYvDGG2/AwMAAL774IkpLS6FUKrXmlbreSUlJcHR0hL29PYyNjfHqq69i7969ojxXr17Fyy+/DKDuZR2PpgN1L5Tx8/MTTniAuhHcDh06oH379pDJZOjZsycuXbrU4H5piJSUFLi4uMDY2LjJZTi+aH43oINBrI2NjWi0IScnR3RGr6tuqf364i4rKxOddT96UKonNTUVa9euxbZt23D37t0Gfbm5uaLLpDY2NsjNzRXlkcvl2LVrF4C6jq28vBzFxcVCB3rv3j0UFRXh+PHjoste+uqW2l9UVCQaOe/QoQOKioqgjfz8fCiVSnh6egrbqqurMXXqVLz33ntC4FbP3bt3YWpqKqybmppqbR+XLl3CypUrsXnzZpSWlgKoG2XZu3fvY0fwKyoq8Oyzzwrrzz77LCorK0V5bt++jYqKise+te3GjRvo0KGDKMi1tLQU7WOlUikEYA/nycvLA1A34lRWVgYzMzNcuXIF/v7+MDQ0RKdOnSCXyx/7GzM3Nxft8+LiYpibm2vkGz58OL777jtMnjwZ3377bYO+srIyPPfcc8K6qamp1t/dpUuX8PXXX+Onn34S9jsAqFQqREZGYvXq1bh8+bKozJ07d0QBlJmZGe7cuSPKk5+fj4KCAixZsgSLFi3SGlhkZGRApVKJ2h7/Rhv2/xkerYutre3/sXfn8TGdDf/HvycLP7UEIdskJMFNEks2QW1FQ4NG0ZbSVt36dG9abRXRUr3b0rpVqaellJv2JqoEbe21C0I0TdCQECGykEVIqWQm1++PyHkck03ljLni+3695sXMnPnk5DqZzJUzZ2bM1sVS652RkaEeZlNRv0OHDurhQuvXr1f7t1q9ejWefPJJzWWXL1/W/Jw3bty43J/z33//HTNnzsS3335r9rMKAEePHkVQUJD54FSC8wvLtwErPJxAURSzyyp6ys2a2nr3ZW2X12/bti06dOgAOzs7HD58GNHR0Zqnbataj9t7s2bNwuuvv45ly5ahZ8+eMBgMsLOzQ//+/XHkyBH06NEDzZo1Q9euXWFn938/8rK2rWHdy+zcuRO9e/fWTPZWrVqFZs2aISMjA2+99Ra8vLzu6JeWj48P/P39YWdnhwMHDmDVqlV48cUXceDAAbRr107zIHWnhBDYt29fpXuOc3NzceDAAYSHh2sur879pKJlVq5ciTZt2mDz5s1IT0/HkSNHYDQaK1yH6t4nN27ciI0bN6J3794YMWIEvvjii3J71dmmvr6+CAgIUMc9KioKL730EgAgMjISDg4OyM3NxcKFC+Hi4lLpR87e3i4pKUF2djYmTpyI/Px8zJgxAx999JG6F+3y5ctYtGgRnn/+ec3Hqt7rn3NrvY/eqTu5T1vDuMyYMQPjx4/H999/j+7du8PNzU3TyczMxPHjxxEaGlrxN11Bu3379ggMDIS9vT327duH77//Hq+//rp6fUFBATIyMjSHN1QH5xeWbwNWuCc2PT3d7C/Gsj0b1tzWuy9L+/Y9r1euXEHDhg01yzzwwAPqL6SgoKAKn9YqW5db/4q7cOEC3NzcNMu4ublhzZo1iIuLw0cffQQA6t6+yMhIHD16FFu3boUQAq1bt5a+rXe/efPmms8Jv3TpUrl7AYHSQwn69u2ruaxscuPm5gZ/f3+kpKSo192+B7CgoMDsOL/69eurPx9dunRR99KkpaUhJiYGM2bMwM8//4y4uDhs3LhRc9sGDRqgsLBQPV9YWKg+hQ2U7iXOy8vDunXrsHz5cmRnZ+OXX35Rv9/CwkJs2rQJDz/8sGaPMVD6wHnrZNzV1RXZ2dlmy5RtB1tbWzRq1Aj5+fkwmUyYNm0aQkNDMXbsWDRq1KjcwxzK5OTkaCaJjo6OyMvLq3D5PXv2oGvXrhVe7+DgoNmzeifjXnb7svXw9vbW/H5o0qSJZt3y8vLM/tBo0qSJOkFu3rw5XFxckJWVBaD0sIg5c+Zg2LBhaNWqleZ2vI9W3L8Tt69Lenq62bpYar0NBoPZHvHy+qtWrcLBgwcxffp0TR8A1qxZg/DwcNjb22tu17hxY83P+eXLl8v9OS+73YMPPqj5XoHSFx536tRJ84d5dXB+Yfk2YIWT2MOHD6NNmzbw9PSEvb09Ro4caXY8jjW29e7L0nZzc0Nubi7y8/NhNBpx7NgxtG3bVrPM1atX1f+fPHmy0j06nTt3Vo+nKSoqwqpVq/Doo49qlsnJyUFJSQkAYObMmRg7diyA0qdzy56CSkhIQGJiIvr37y99W+9+u3btcOHCBWRmZqK4uBg7duzAgw8+aLZtzp07h6tXr8LPz0+97OrVqygqKgJQOlE6duwYWrZsqV7v7u6OnJwc5OXlwWg04vfff9e8YASA5o+gEydOqE8vjxo1CpGRkZg8eTIGDx6MoKAgDBw4UHNbJycnFBQU4MqVKzCZTEhOTtYcNlC3bl2MGzcOzz77LJ599lk4Oztj0KBBcHJywo0bN/Dzzz+jW7duZi8EAUqPk/Py8oKHhwfs7e0xZMgQbNmyRbPMli1b1Kc4Bw8erB5OUa9ePdSrVw9A6fGLJpNJ84Kw2yUnJ8PNzQ3Ozs6ws7NDr169EBsbq1nm1nUMDg6u9IHBw8NDM+7x8fGVjvvx48fVcb927Zq61/jPP/9EWlqa5gVhXl5euHjxIi5dugSj0YjY2FjN4SVA6Qtpyl7IcfXqVWRlZcHJyQlGoxFffvklunfvjs6dO5utN++jFffvRHh4OJYvXw4hBA4ePAgHB4dyf8Ytsd7BwcFISUnB2bNnUVRUhNWrV5sdInRrf9asWRgzZozm+h9++MHsUAIAaNGiBS5duoTc3FwYjUYcPXoUHTp00Cxz6x/RiYmJZi9ujIuLQ2BgYLljUxnOLyzfBqzwcAKTyYTXXnsNW7Zsga2tLZYsWYITJ05YfVvvvixtW1tbDBw4EN999x2EEAgICICTkxN27NgBNzc3tGvXDocOHcLJkydhY2ODevXqVfgqdACws7PDvHnzEBYWBpPJhLFjx8LPzw/Tpk1DUFAQwsPDsWvXLkyZMgWKoqBnz56YP38+AKC4uBi9e/cGULqHePny5ZqnpGRt6923tbVFREQE3n33XZSUlCAsLAxeXl5YsmQJ2rZti+7duwP4v72wtz5dlJaWhs8//xyKokAIgaeeekozibS1tcWQIUOwePFilJSUoHPnznBxcVFfCe3n54f9+/fjxIkT6s9HeQ9WFbGxsUHPnj2xYcMGCCHg4+MDR0dHHDp0CE5OTvDy8qrwtomJiSgoKMDhw4dx+PBhAKUP/re+Uj4yMhIrV66Era0toqKicOrUKUyYMAG///47tm7dipUrV+LLL79ETEwMLl++rD4d7+joiJUrV0IIgczMTM3Tl+UpKSnBggULMH36dNjY2GD79u04d+4cRo8ejeTkZMTGxmLw4MHw9/eH0WhEYWFhhYcSlI37Y489hkWLFqGkpAQhISFm475v3z513B944AGMGDECQOmL5dasWaNu0z59+mge+G1tbTF69GjMnj0bJSUl6tPL0dHR8PT0REBAANq3b49jx46pP48jRoxAgwYNEBMTg1OnTqGwsFCd8D///PNo0aIFAN5HK+vf6qmnnsKuXbuQk5MDd3d3TJ8+HcXFxQCAl156CQMHDsTGjRvRunVrPPDAA+W+TZolx2XOnDl49NFHYTKZMGbMGPj6+uLDDz9EYGAgBg8ejD179mDq1KlQFAU9evTQ/GynpaUhPT1dfdHkrWxtbfH444/jq6++QklJCbp27QpXV1f88ssvaNGiBTp06IDdu3fj2LFj6s/5008/rd4+NzcXly9f/lt7vTm/sHwbAJSaPDahyi+mKJb7YmQRFb2lT014//33dWtT+fbs2aNbu2xiqJe0tDTd2mvWrNGtHRwcrFsbgDqp0ENlz6LcrYreLup+d+sxw3oo2wOqh7JnafSwaNEi3doAEBERoWufzAkhyj9w+xZWdzgBEREREVFVOIklIiIiIulwEktERERE0uEkloiIiIikw0ksEREREUmHk1giIiIikg4nsUREREQkHb5PLN2Vij6OtCacPHlSt/btH4tJpSr6PPWaYMnfNfR/uE1rF5PJpGv/9OnTurUXLFigW3vevHm6tene4PvEEhEREVGtxEksEREREUmHk1giIiIikg4nsUREREQkHU5iiYiIiEg6nMQSERERkXSschI7YMAAJCUlITk5GRMnTpSmrXffmtt9+/bFwYMHERsbi4iICLPr69Spg8WLFyM2NhZbtmyBh4cHAODxxx/Hzp071dPFixfRvn17zW1//fVXdOnSBZ07d8bcuXPN2ufPn8fQoUPRq1cvhIeHIyMjQ728b9++eOihh9C9e3csXbrU7LabN2+Gr68v2rZti08//dTs+rS0NISGhiIgIAB9+/ZFenq6et2kSZPQqVMndOrUCT/88INF25ZYdx8fH/zjH/+otO3v719uu2PHjujYsSNWrVpV68ZFxnZZX8ZtKmtb7/6WLVvQvn17+Pj4YNasWeW2BwwYgKCgIISGhmrakydPhr+/Pzp27Ijx48ebvVXa3r17MXDgQAwYMACLFi0ya2dkZOC5557DsGHD8Nhjj2H37t1m1wcFBWHJkiXljsvZs2exfPlyLFu2DEeOHCl3GQBITk7GvHnzkJ2dDQDIysrCihUr1NPfeTswa34cvZd9WdsQQlR6AuABYCeAPwAcB/DGzcubAtgGIPnmv02q0RJVnWxsbERKSorw8vIS9vb2Ij4+Xvj4+FR5u3vdlnnd76bt6OgomjdvLs6cOSMCAwOFi4uLSExMFN26dROOjo7q6Z133hFLly4Vjo6O4vnnnxfR0dGa6x0dHUWPHj1Eamqqej4nJ0dkZ2cLT09PceTIEZGRkSH8/PzE/v37RU5OjnoKDw8X8+fPFzk5OWLt2rXiiSeeEDk5OSIjI0NcuHBB5OTkiLNnzwoPDw+RmJgocnJyhNFoFDdu3BDe3t7i1KlT4tq1a6Jjx44iISFBGI1G9TR8+HCxZMkSYTQaxdatW8Xo0aOF0WgU69evF/369RN//fWXKCgoEEFBQSIvL0+9nZ5tPfsmk0kUFRUJb29vkZycLK5fvy46duwoEhMThclkUk9lbZPJJLZt2yZGjx4tTCaT2LBhg+jXr5+4ceOGuHLliggKChL5+fnCZDJJPS4yt2XeprK29ezfuHFDXLt2TXh5eYk//vhDXL16VXTo0EHEx8eLGzduqKdhw4aJxYsXixs3bojNmzeLUaNGiRs3bohdu3aJbt26iWvXrolr166JLl26iK1bt6q3S0xMFB4eHmLLli0iPj5etG3bVmzYsEGcOHFCPT3xxBNi6tSp4sSJE2LDhg3Czc1Nc31oaKjo37+/eOeddzSXR0REiNdee000atRIjBkzRrz66quiWbNmYvTo0SIiIkJzeumll4Sbm5twdnYWI0aMEBEREeLll18Wr732moiIiBDjxo0T9erVU8/L/Dh6r/vW2q5qTimEqNaeWCOAt4UQPgC6AnhVURRfAJMA/CqEaAPg15vn71pISAhSUlKQmpqK4uJiREVFYciQITWR1rWtd9+a24GBgUhNTUVaWhqKi4sRHR2NsLAwzTJhYWGIiooCAGzYsAE9e/Y06wwbNgxr167VXHYrE9xqAAAgAElEQVT06FF4eXnB09MTderUwdChQ7Fp0ybNMidPnkSvXr0AAD179lSvr1OnDurWrQsAKCoqQklJieZ2sbGxaNWqFby9vVGnTh08+eST2LBhg2aZP/74A3379gUA9OnTR73+jz/+QK9evWBnZ4f69eujY8eO2LJli0Xall73ESNGlNvu169fue3evXtr2ps3b66V4yJLW+ZtKmtb7/7hw4fN2j/99JNZu0+fPgCAhx56SL1eURT89ddfKCoqwo0bN1BcXAwnJyf1domJiWjRogU8PDxQp04dhIWFYceOHbhdYWGh+u+tt9++fTvc3d3RunVrs9sAQHZ2Nho3bgwHBwfY2tqiTZs2OHPmjNlyBw8eRFBQEOzs7NTL7O3tYWNTOm0xGo3l9itjzY+j97IvaxuoxuEEQohMIcTRm/+/itI9sgYAQwAsu7nYMgCP1cQKGQwGnD9/Xj2fnp4Og8FQE2ld23r3rbnt6uqqPoUPlD6V5OrqarbMhQsXAJR+4syVK1fQtGlTzTKPPfaY2SQ2MzMTbm5u6nk3NzdkZmZqlvHz81N/Qf/yyy8oLCxEXl4eAODChQvo1asXOnXqhIiICM16ZWRkqIc1AIC7u7vm+wCAjh07quu0bt06XL16Fbm5ueoD+bVr15CTk4Ndu3ZpxlDPtt79CxcuaNoGg0HdduW1o6OjK23f+jSmzOMiaxuQd5vK2rb0ule0PaOjowEA69evV9tdu3ZF79690bJlS7Rs2RKhoaHw8fFRb5ednQ0XFxf1vIuLCy5evKhpv/baa/jpp5/Qp08fvPTSS5gyZQoA4Nq1a/j222/xyiuvoCKFhYVo0KCBer5Bgwb4888/NctcvHgRV69ehZeXl9nts7Ky8P3332PFihXo27evOqmtDmt+HL2XfVnbwB0eE6soiieAAACHADgLITKB0okuAKeKb3lHX8Psspr6aEM923r3rbldndtXtUxgYCCuX7+OpKSkKtfj9tb06dMRExODPn36ICYmBq6urupf7waDAXv27EFsbCyioqI0v4yr0/7ss8+wZ88eBAcHY8+ePTAYDLCzs0P//v0RFhaGnj17YvTo0ejatatmj4GebWtY91mzZmH37t0ICgoqt92jRw+MGjXqvhsXa2xXt2+N21TWtjWs+8yZM7F3716EhIRo2ikpKUhKSsKZM2eQmpqKXbt2Ye/evZW2b/fLL7/gsccew86dO7FgwQJMnDgRJSUlmD9/Pp599lnUr1+/ykZFhBDYu3dvuc/WAaWT6qeffhojRozAkSNH7miPrDU/jt7LvqxtALCrehF1RRoAWAPgTSHElep+HreiKC8AeKG6Xyc9Pb3Kv17/Lj3bevetuZ2RkWG2tzQrK8tsGYPBgMzMTNja2qJRo0bIz89Xry/vUIKy1u17eW/dSwCU7uVdtqz0SYHCwkL89NNPaNSokdky7dq1w8GDBxEeHg6g/L8Qb9+D7Obmhh9//FFtr127Fg4ODgCAyMhIREZGAgCefvppzdNnerb17ru7u5vtmb11+5a116xZU2V79OjRtWZcZG0D8m5TWduWXveKtmfZC8IKCwuxbt06ODg4YPHixejSpYu6N3TAgAE4dOiQOml0cXHR/P7OysrSHC4AAGvWrME333wDAPD390dRURHy8/ORkJCArVu3Yvbs2bh69SoURUHdunUxevRo9bYNGjRQD0UoW7dbJ71FRUXIzc1VfxavXbuGn3/+GYMHD4azs7O6XNOmTWFnZ4fc3FzN5ZWx5sfRe9mXtQ1Uc0+soij2KJ3A/lcIUTbTyFYUxfXm9a4ALpZ3WyHEN0KIYCFEcHW+1uHDh9GmTRt4enrC3t4eI0eONDuO6O/Ss61335rbv/32G7y9vdGiRQvY29tj6NChmmPmgNJX6Y4cORIAEB4ervnLX1EUhIeHq0993SogIABnzpxBWloaioqKEB0djUceeUSzTG5urnq869y5czFq1CgApRPe69evAwAuX76MQ4cOaR4IOnfurB6rU1RUhB9++AGPPvqopp2Tk6O2Z86cieeeew5A6SERubm5AICEhAQkJiaif//+Fmlbet1XrVpVZXvs2LH33bjI0i6vL8s2lbWtdz84ONisPXjw4Arbn332GcaMGQMAaNGiBfbs2QOj0Yji4mLs2bMH7dq1U2/Xvn17pKWlIT09HUVFRdi0aZN6bG0ZV1dXHDx4EABw+vRp3LhxA02bNsX333+P7du3Y/v27XjmmWfwwgsvaCawAODs7IzLly+joKAAJpMJycnJ8Pb2Vq+vW7cuXnjhBYwdOxZjx46Fi4uLOoEtKChQv6crV67g8uXLZjssKmPNj6P3si9rGwCq8+4ECoDlAL647fJZACbd/P8kAJ/VxLsTABBhYWHi5MmTIiUlRURGRtbYq/v0bsu87n+3XfZOAiNGjBApKSnizJkz4qOPPhKOjo5i1qxZYvTo0cLR0VG4ubmJdevWidOnT4u4uDgRGBio3jY8PFwcPnzY7N0Kyt59YOXKlcLb21t4enqKyMhIkZOTI95++23x3XffiZycHLFkyRLh5eUlvL29xejRo9V3JPjxxx+Fr6+v8PPzE76+vmL27Nlqs+zVwRs2bBBt2rQR3t7e4sMPPxRGo1FMmTJFREdHC6PRKFatWiVat24t2rRpI/75z3+KP//8UxiNRlFYWCh8fHyEj4+PCAkJEUeOHNG86ljvtl79sleq//TTT2r7X//6lzCZTOK9994T0dHRwmQymbWvXbsmTCaT+PPPP9V2ly5dRFxcnNqUeVxkbsu+TWVt69UvexeBdevWidatWwsvLy8xffp0cePGDREZGSl+/PFHcePGDbFy5UrRqlUr0bp1azF27Fhx5coV9Z0Nxo0bJ9q2bSvatWsnIiIiNO9qcOLECfH111+Lli1bCg8PDxERESFOnDghXn75ZTF//nz1HQkCAgJE27ZtRdu2bcWiRYs070Jw4sQJ8corr5T77gQREREiPDxcNG7cWDRq1Eh069ZNREREiJCQEDF48GCzdykwGAzquxOEhoaKpk2bimbNmonmzZuLQYMGqcvJ/DhqDX1rbFfn3QmUqo5NUBSlB4C9ABIBlL28OxKlx8X+AKAFgHMAnhBC5FXRqvyLkXQcHR11a588eVK3duPGjXVry6y6hwn9HTV5HBRVH7dp7WIymXTt/533Xq2uBQsW6NaeN2+ebm26N4QQVf7yqvKYWCHEPpTujS1PvztdKSIiIiKiu2WVn9hFRERERFQZTmKJiIiISDqcxBIRERGRdDiJJSIiIiLpcBJLRERERNLhJJaIiIiIpMNJLBERERFJp8oPO6jRL8YPO6A70L59e93a06ZN060NAEOHDtW1rxe+MX7tI+s2PXr0qG5tAJqPvq5pt3/sdk0qKirSrQ3oO+516tTRrV32Mb1Ue1Tnww64J5aIiIiIpMNJLBERERFJh5NYIiIiIpIOJ7FEREREJB1OYomIiIhIOpzEEhEREZF0rHISO2DAACQlJSE5ORkTJ06Upq13X9b23fa7d++ODRs24JdffsG4cePMrg8KCsKqVavw22+/ITQ0VL28c+fOWL16tXo6cuQI+vbtq7ntb7/9hoiICLz22muIjo4u9+vHxMTgzTffxPjx4/HFF18AAI4dO4Z33nlHPY0aNQqxsbGa223evBm+vr5o27YtPv30U7NuWloaQkNDERAQgL59+yI9PV29btKkSejUqRM6deqEH374odz10rO/efNm+Pj44B//+EelbX9//3LbHTt2RMeOHbFq1SqLrrclxkXGdllfxm164MABPP744xg2bBiWLVtmdv3nn3+O0aNHY/To0Rg+fLjmPv7ll19i5MiRGDlyJLZt22Z226SkJMycOROffPIJfv31V7PrY2NjMXXqVMyePRuzZ8/GwYMH1eveeecd9fJvv/3W7LYAEBwcjG+//RZLly7FiBEjzK4fPnw4Fi1ahAULFuDTTz+Fk5MTAKBTp074+uuv1dPPP/+MBx98UHPbkJAQLF++HP/9738xatQos/YTTzyB//znP/j2228xe/ZsODs7q9f9+uuvWLx4MRYvXoyPP/7Y7LYPP/ww4uLiEB8fj/Hjx5tdX6dOHSxduhTx8fHYsWMHWrRoAQBo0aIFsrOzsW/fPuzbtw9z5swpd1z69u2LgwcPIjY2FhEREeX2Fy9ejNjYWGzZsgUeHh4AgMcffxw7d+5UTxcvXrzjt2K8Xx9Ha2sbQgiLnQCIqk42NjYiJSVFeHl5CXt7exEfHy98fHyqvN29bsu87tY6Lu3btxcdO3YU586dE4888ojw9/cXSUlJIjw8XLRv31499e/fXwwbNkysX79ejB8/XnNd2enBBx8Uly9fFsHBwaJ9+/Zi9erVIioqSjg7O4v58+eLFStWiJYtW4rPP/9crF69Wj3NmzdPeHp6iqVLl4rVq1eLxYsXa65fvXq1WLJkiahfv774/vvv1ctu3LghvL29xalTp8S1a9dEx44dRUJCgjAajepp+PDhYsmSJcJoNIqtW7eK0aNHC6PRKNavXy/69esn/vrrL1FQUCCCgoJEXl6e5rZ69U0mkygqKhLe3t4iOTlZXL9+XXTs2FEkJiYKk8mknsraJpNJbNu2TYwePVqYTCaxYcMG0a9fP3Hjxg1x5coVERQUJPLz84XJZNJ1vfUeF5nbsm7T2NhYceDAAWEwGER0dLTYv3+/aN26tYiKihKxsbHlnt5++23x6KOPitjYWPH555+LkJAQERMTI3bv3i3atWsnduzYoS47a9Ys4ejoKCIjI8Wnn34qXF1dxYQJE8Ts2bPV04gRI0T37t01l5Wd6tSpU+7ls2fPFqGhoWLAgAHiwoUL4plnnhFhYWEiJSVFjBs3ToSGhqqnd955RwwePFiEhoaKuXPnip07d2quDw0NFcOGDRMFBQXqcr179xZ9+vQR6enpYuTIkaJfv34iOTlZPPvss6J3797q6Y033hD9+/cXvXv3FrNnzxa//vqret21a9c0y956cnBwEGfOnBEdOnQQTZs2FQkJCSI4OFg0bNhQPY0fP14sXrxYNGzYUDz33HPixx9/FA0bNhR+fn7i+PHjmmVvPTk6OormzZuLM2fOiMDAQOHi4iISExNFt27dhKOjo3p65513xNKlS4Wjo6N4/vnnRXR0tOZ6R0dH0aNHD5Gamqqev9ePddb6OCpzuzrzSqvbExsSEoKUlBSkpqaiuLgYUVFRGDJkiNW39e7L2r7bfocOHXDu3Dmkp6fDaDRi06ZN6NOnj2aZjIwMnDp1qtI3Xu/fvz/27duHv/76S70sJSUFLi4ucHZ2hr29Pbp3744jR45obrd9+3Y88sgjaNCgAQDAwcHBrH3w4EEEBASgbt266mWxsbFo1aoVvL29UadOHTz55JPYsGGD5nZ//PGHuteoT58+6vV//PEHevXqBTs7O9SvXx8dO3bEli1bNLfVs397e8SIEeW2+/XrV267d+/emvatb/xem8ZFlrbM2/T48eNwd3eHwWCAvb09+vfvjz179qAiW7duRf/+/QEAqampCAgIgJ2dHerVq4c2bdrgwIED6rLnzp2Do6MjHB0dYWdnh4CAABw/frzC9p1q27YtMjIykJWVBaPRiN27d5vtTf39999x48YNdSyaN29u1unZsyeOHDmiLgcA7dq1w4ULF5CZmQmj0YgdO3age/fumtvFx8ertzlx4kS57fIEBwfjzJkzOHv2LIqLi7FmzRoMGjRIs8ygQYOwcuVKAMC6devw0EMPVasNAIGBgUhNTUVaWhqKi4sRHR2NsLAwzTJhYWGIiooCAGzYsAE9e/Y06wwbNgxr166t9tcF7t/H0draBqzwcAKDwYDz58+r59PT02EwGKy+rXdf1vbd9p2cnJCVlaWez87O1jwtVl2PPPIINm7cqLksLy8Pjo6O6vmmTZuafepLZmYmMjIy8N577yEyMhK//fabWXv//v3o0aOH5rKMjAz1KTAAcHd3R0ZGhmaZjh07qr+E161bh6tXryI3N1edJFy7dg05OTnYtWuXZvz07l+4cEHTNhgMuHDhQoXt6OjoStu3PnUs87jI2gbk3aaXLl3S3N+dnJxw6dIllKfsvhocHAwA6qT1r7/+wuXLlxEXF4eLFy+qyxcUFKBx48bqeQcHBxQUFJh1ExIS8O9//xvLli1Dfn6+ernRaMScOXMwd+5cJCYmmt2uWbNmmnW9dOmS5vfN7R555BEcPnzY7PKHHnoIO3fu1FzWvHlzs3Zlk9RBgwZpDneqU6cOFi5ciK+++srsd5erq6vZ9nVzc6twGZPJhCtXrqBp06YAgJYtW2Lv3r3YuHEjunXrZrYurq6ump+PjIwMuLq6mi1T9vN5e7/MY489dseT2Pv1cbS2tgHArsZKNaS8j0isqY821LOtd1/W9t32a2LdmjVrhjZt2iAmJuaOv57JZEJmZiY++OAD5ObmYurUqfj8889Rv359AEB+fj7OnTuHTp06VbmOt7c/++wzREREYPny5ejZsycMBgPs7OzQv39/HDlyBD179kSzZs3QtWtX2Nlp76p69qvTnjVrFl5//XUsW7as3HaPHj3+dlvmcbHGdnX71rhN7+S+vnXrVvTt2xe2trYAgK5du+LEiRMYN24cmjRpgg4dOqjXVZefnx8CAwNhZ2eHmJgYREVF4eWXXwYAvPfee3BwcEBubi6+/vpruLq6olmzZpX2Kvp++vXrh3/84x945513NJc3bdoUnp6eZs8Q3Uk7NDQUbdu2xRtvvKFe9uSTTyI3Nxeurq6YM2cOzpw5o04sq/M7t6KPMs7KyoKfnx/y8vLg7++PFStWoEuXLrh69Wqlt61O/9ZlAgMDcf36dSQlJZW7HhW5Xx9Ha2sbsMI9senp6VX+VW+Nbb37srbvtp+dnQ0XFxf1vLOzs2ZvSnUMGDAAO3bsgNFo1Fx++57XvLw8s7/2HR0d0blzZ9jZ2cHZ2Rlubm7IzMxUr4+JiUFISIjZpKG8vz5v39vg5uaGH3/8EUeOHMG//vUvAP93uEJkZCTi4uKwZcsWCCHQunVri/Xd3d3N9szevifGzc0Na9asQVxcHD766COz9tGjR7F161aztszjImsbkHebOjk5ITs7Wz1/8eLFCvc4btu2DQMGDNBc9s9//hP//e9/MX/+fAghNL+HHBwccPnyZfV8QUGB2eFC9evXV+/bXbt21eyhLFvW0dERrVq1MtuznZOTo1nX5s2bIy8vz2y9AwIC8NRTT2HatGkoLi7WXNerVy/ExMTAZDJpLr99z2vz5s2Rk5Nj1g4KCsLTTz+NyMhITbvs915mZibi4+PRpk0b9bqMjAy4u7ur52//nXf7Mra2tmjUqBHy8vJQVFSkfo/x8fFITU01+1m8fc+um5ub5tm2smXK9taV9W/dC/53DiUA7t/H0draBqxwEnv48GG0adMGnp6esLe3x8iRI82Or7LGtt59Wdt32z927Bhatmyp7rkJCwvDrl277ujrh4WFmR1KAACtW7dGZmYmsrOzUVxcjP3796tPRZbp3LmzepzclStXkJmZqXl6s7xDCcpuV3YcUFFREX744Qc8+uijmmVycnJQUlICAJg5cyaee+45AKV7f8seZBISEpCYmKge52eJ/u3tVatWVdkeO3bs32rLPC6ytMvry7JNfX19cf78eVy4cAHFxcXYunVrucdHpqWl4erVq+jQoYN6mclkUiepycnJSElJQZcuXdTrPTw8kJOTg9zcXBiNRvz222/w8/PTdK9cuaL+//jx4+q7B1y7dk39o7iwsBBnz541O8zp5MmTMBgMcHFxgZ2dHXr37q05JhcAWrVqhTfeeANTp07VTKjL9OnTx+xQgrK2u7u72u7bt6/ZM02tW7fGW2+9hcjISE27QYMGsLe3B1A6EW/fvj3Onj2rXh8XFwdvb2+0bNkS9vb2GD58uNnvz40bN+Kpp54CUPq0/u7duwGUTuhtbEqnFZ6enmjVqpWmDZS+I4y3tzdatGgBe3t7DB06VHOMNVD6bhcjR44EAISHh2Pv3r3qdYqiIDw8vMJ3k6nM/fo4WlvbgBUeTmAymfDaa69hy5YtsLW1xZIlS3DixAmrb+vdl7V9t32TyYRPPvkECxYsgK2tLaKjo3H69Gm8+uqrOH78OHbt2gU/Pz/MnTsXDRs2RO/evfHKK69g6NChAEr/yndxcSn36ThbW1uMGzcOH3/8MUpKStCnTx94eHggKioKrVq1QufOneHv74/ff/8db775JmxsbPDMM8+gYcOGAEr3CuXk5MDX19esbWdnh7lz52LgwIEwmUx47rnn4Ofnh2nTpiE4OBiPPvoodu/ejSlTpkBRFPTs2RNffvklAKC4uFh9oUTDhg2xbNkysz29evbt7Owwb948hIWFwWQyYezYsWo7KCgI4eHh2LVrl6Y9f/58td27d28AQKNGjbB8+XKztszjImNb5m1qZ2eHCRMmICIiAiUlJXj00UfRqlUrLFy4ED4+PujVqxcAYMuWLQgNDdU8dWk0GvHiiy8CKN2j+uGHH2ratra2GDZsGL755hsIIRASEgIXFxds3rwZ7u7uaN++Pfbu3Yvjx4/DxsYGDzzwgDqxys7Oxo8//ghFUSCEQN++fTXPGAFASUkJ5s+fj08++QQ2NjbYsmUL0tLS8Oyzz+LUqVM4ePAg/ud//gf16tXD+++/D6D0d8q0adMAlD7r1Lx5cyQkJOB2JpMJc+fOxaxZs2BjY4NNmzbh7NmzGDt2LE6ePImYmBi8/PLLqFevHqZPn66u85QpU9CyZUu8/fbbKCkpgY2NDVasWIG0tDRNe8KECYiOjoatrS2+++47JCUlYcqUKTh69Cg2bdqE5cuX45tvvkF8fDzy8/PVP3i6d++OKVOmwGg0wmQy4c0339TsQS3rT5o0CatXr1a//smTJzFp0iTEx8dj8+bN+O9//4uvvvoKsbGxuHz5Mv7nf/5Hvf2DDz6IjIwMzTpX1/36OFpb2wCg1OSxCVV+MUWx3Bcj6d3p+//dibIHCr2UTaJlU9GxbjXBkr9r6P/Iuk2PHj2qWxuAZu9eTbt9z2JNKioq0q0N6DvuderU0a19+4tySX5CiCp/eVnd4QRERERERFXhJJaIiIiIpMNJLBERERFJh5NYIiIiIpIOJ7FEREREJB1OYomIiIhIOpzEEhEREZF0+D6xdF8q+8ACvQwaNEi3dlWfz343OnfurFv7Tj8u+E7t2bNHt/bw4cN1a5f3QRw1Sc9xKfsULj38nTezvxNXr17VtU9Ed4fvE0tEREREtRInsUREREQkHU5iiYiIiEg6nMQSERERkXQ4iSUiIiIi6XASS0RERETSscpJ7IABA5CUlITk5GRMnDhRmrbefVnbevfvpv3www8jLi4O8fHxGD9+vNn1derUwdKlSxEfH48dO3agRYsWAIAWLVogOzsb+/btw759+zBnzpxy+506dcLs2bMxZ84chIeHl/v1P/30U8yYMQPTpk2DwWAAUPo2WsuWLcOMGTMwY8YMjBs3zuy2aWlp+P777/Hdd98hLi6uwu8xJSUF8+fPR3Z2NgDg3LlzWLVqFVasWIFVq1YhPT3d7DYJCQmYOHEiJkyYgJ9//rnc7qFDhzB58mRMnjwZX3/9NQAgJycHU6dOxfvvv4/Jkydjx44dZrdLSkrCZ599hpkzZ5Z7/eHDh/HBBx/g888/x+eff45Dhw6p17377rvq5UuXLq3wey4TGBiIr776CgsXLiz3bbIeeeQRzJs3D1988QVmzpwJDw+PSnsJCQmYNGkS3n333QrHJTY2FpGRkYiMjMSCBQsAlI7LtGnT8P777yMyMrLc71vP7QkADz74INavX4+ffvoJ//znP82uDwwMRFRUFOLi4vDwww9rrnvzzTexdu1aREdHl3sf6969OzZs2IBffvml3J/VoKAgrFq1Cr/99htCQ0M1140fPx7R0dFYv349Jk2aZHZbve+jlblffy/ey7befVnbevdlbdvVaK0G2NjY4H//938RGhqK9PR0HD58GBs2bMAff/xh1W29+7K29e7fTdvGxgazZ8/GkCFDcOHCBezatQsbN27EyZMn1WWeffZZXL58Gf7+/hg+fDimT5+OsWPHAgBSU1PRo0ePCvuKomDs2LH45JNPkJubi48//hhxcXG4cOGCusz+/fuxfft2AKUP9M888wxmzpwJAMjOzsbkyZPLbZeUlGD37t0YMmQIGjRogB9++AFeXl5o2rSpZrmioiIkJCTA2dlZvaxevXoYNGgQGjRogNzcXGzYsEH9nsray5cvx7vvvoumTZvigw8+QEBAgDrBBoCsrCz8/PPPeO+991C/fn1cuXIFANC4cWO8//77sLe3x19//YUpU6YgICAATZo0UdvR0dF44YUX4ODggHnz5sHPz0+zfkDp5H/o0KFm37e9vT3eeuutCsf8VjY2NnjxxRcxdepU5ObmYvbs2YiNjcX58+fVZXbv3o3NmzcDAEJCQjBu3Dh88MEH5fZKSkrw3XffYcKECWjatCmmT59e4bhMmTLFbFzee++9SsdFr+1ZNhaRkZF48cUXkZ2djRUrVmDXrl04c+aMZt3ff/99jBkzRnPbTp06wd/fH48//jgA4D//+Q+Cg4PV97e1sbHBlClT8MILLyArKwtRUVHYuXOnpp2ZmVlhOyAgQP0DY/ny5WZtPe+jlblffy/ey7befVnbevdlbQNWuCc2JCQEKSkpSE1NRXFxMaKiojBkyBCrb+vdl7Wtd/9u2sHBwThz5gzOnj2L4uJirFmzxuxDCgYNGoSVK1cCANatW4eHHnqo2uvWunVrZGVl4eLFizCZTDhw4ACCg4M1y1y/fl39f926dVHdDx/Jzs6Gg4MDHBwcYGtrizZt2mgmDWUOHTqEgIAA2Nraqpc1b94cDRo0AAA0bdoURqMRJgtNEZ0AABn2SURBVJNJvf7MmTNwdnaGk5MT7Ozs0KVLFxw9elTT3b17N/r164f69esDABo1agQAsLOzg729PQDAaDSavRn+uXPn0KxZMzg6OsLOzg7+/v44fvx4tb7nO9WmTRtkZmYiOzsbRqMRe/fuRZcuXTTL3Dr+/+///b9Kx7+8cfntt980y1R3XG7/OnpuTwBo3749zp8/jwsXLsBoNGLz5s1mP8sZGRlITk4222ZCCNStWxf29vaoU6cO7OzskJubq17foUMHnDt3Dunp6TAajdi0aRP69Olj1j516lS541tZW+/7aGXu19+L97Ktd1/Wtt59WduAFU5iDQaDZk9Jenq6Zk+Htbb17sva1rt/N21XV1fNU68ZGRlwc3OrcBmTyYQrV66oe8datmyJvXv3YuPGjejWrZtZv0mTJpoH5NzcXHXP261CQ0PxxRdfYNSoUVi2bJl6efPmzTFjxgxMnToVbdu21dzmzz//1HzqWIMGDfDnn39qlrl06RKuXr0KLy+vCsfg9OnTaN68uWZSlJ+fr9kD2LRpU+Tn52tul5WVhezsbPzrX//Chx9+iISEBM33OWXKFIwfPx6DBg3SfM9XrlxB48aN1fMODg4oKCgwW6/ExETMnj0by5cvx+XLl9XLjUYj5s6diy+//BLHjh2r8PsCAEdHR+Tk5Kjnc3Jy4OjoaLbcwIEDsXDhQowZMwbffPNNhb3bx6VJkybljktWVhY++uijcsflvffew1tvvYWBAwdqxkXP7QkATk5OyMrKUs9fvHjRbO93RRISEnD48GFs374d27dvR0xMDFJTUytsZ2dnV7v9+++/IzY2Fjt27MCOHTuwf/9+TVvv+2hl7tffi/eyrXdf1rbefVnbgBUeTqAo5p8yVlMfjatnW+++rG29+3fTrs5ty1sGKJ2s+Pn5IS8vD/7+/lixYgW6dOmi+SjLim57u23btmHbtm148MEHMXToUHz99de4fPkyXn/9dRQWFsLLywtvv/02JkyYoNlzWBkhBPbu3Wt2bOOtcnNzERMTY/ZXcXnjd/v3YjKZkJWVhcmTJyM/Px8ff/wxPv74Y9SvXx+Ojo74+OOPkZ+fj7lz56Jz585wcHCodtvX1xcBAQGws7PDgQMHEBUVhZdeegkAEBkZCQcHB+Tm5mLhwoVwcXGp8GN4q/uzsXHjRmzcuBG9evXCiBEj8MUXX5Tbq87PVUlJCbKzszFp0iTk5+fjk08+wUcffaSOy0cffYT8/HzMmzdPMy5VuZvtCdzd/cTDwwNeXl7o378/AGDhwoUIDAxU987fbdvb21v9vhYtWoSYmBj1mGC976OVuV9/L97Ltt59Wdt692VtA1a4JzY9PV3z4gp3d3dkZGRYfVvvvqxtvft3087IyIC7u7t63s3NDZmZmRUuY2tri0aNGiEvLw9FRUXIy8sDAMTHxyM1NRWtW7fW3DYvL0+z58/R0dFsz92tbj3cwGg0orCwEEDpcX3Z2dlwdXVVl61fv77mwbiwsFB9ChuAun7R0dFYtmwZsrOz8csvv6gvBiosLMTGjRsRGhpqNpFq2rSp+r2VfR+37j0tWyYwMBB2dnZo3rw5XF1d1XaZJk2awGAw4NSpU+plDg4Omj2rBQUF6lPut35vdnalf1936dJFcwxx2bo6OjrC29u70m2dk5OjmeA2a9ZM833drrzDDW7/nm+9fX5+vtme9SZNmqgT8ObNm8PFxaVa46Ln9gRK9466uLio552cnHDx4sUKv9db9e3bF4mJibh+/TquX7+O/fv3o2PHjhW2nZ2dq93u168fEhIS1Pa+ffs0bb3vo5W5X38v3su23n1Z23r3ZW0DVjiJPXz4MNq0aQNPT0/Y29tj5MiR2LBhg9W39e7L2ta7fzftuLg4eHt7o2XLlrC3t8fw4cOxceNGzTIbN27EU089BQB47LHHsHv3bgClkygbm9K7j6enJ1q1aoWzZ89qbnv69Gm4uLioT+9269bN7FXntz74BwQEqE/LNmzYUP0L1snJyWwy5OzsjIKCAly5cgUmkwnJycmap5nr1q2L559/HmPGjMGYMWPg7OyMQYMGwdnZGTdu3MBPP/2Ebt26aSbGZby8vJCdnY1Lly7BaDSqx2HeKjAwUD0w/+rVq8jKyoKTk5M6eQBKnyJPTk7WfI8eHh7IyclBXl4ejEYj4uPj4evrq2mXvRgKAI4fPw4nJycAwLVr12A0GtV2WlpapU9bJycnw83NDc7OzrCzs0PPnj0173QAQPP9BwcHV/rLtbrjkpSUpI5LdnZ2tcZFz+1ZNo4tWrSAwWCAnZ0dHnnkEfVnuSpZWVkICgqCra0t7OzsEBQUpHnK/9ixY2jZsqXaDgsLw65du6rVzszMRHBwsKZ967HAet9HK3O//l68l229+7K29e7L2gas8HACk8mE1157DVu2bIGtrS2WLFmCEydOWH1b776sbb37d9M2mUyYMGECoqOjYWtri++++w5JSUmYMmUKjh49ik2bNmH58uX45ptvEB8fj/z8fPVVz927d8eUKVPUF9G8+eabZntZS0pK8J///AeTJ0+GjY0Ndu3ahfT0dDz++ONITU1FXFwc+vfvjw4dOsBoNOLPP/9U36rKx8cHTzzxBEwmE0pKSvDtt99qjpG0sbFBr169sH79eggh4OvrC0dHRxw6dAhOTk6VHjeZkJCAgoICHDlyRH0VeHh4OB544AEApXuznnnmGcyaNQslJSXo1asX3N3dsXbtWnh6eiIwMBAdOnTAsWPH1O9txIgRaNCgAY4dO4aVK1dCURQIIRAWFqb5K9zW1haPPfYYFi1ahJKSEoSEhMDFxQVbtmyBu7s7/Pz8sG/fPpw4cQI2NjZ44IEHMGLECAClx3GuWbNGbffp06fSSWxJSQkWLlyIDz74ADY2Nti+fTvOnz+PUaNGISUlBbGxsRg0aBD8/f3VPd8VHUpQtu5PP/00/v3vf6OkpAQ9e/aEwWDA2rVr4eXlhYCAAHTo0AHHjx9HZGQkbGxs8OSTT6rjEhUVVeG46Lk9gdKf9RkzZuDrr7+GjY0N1q1bh9OnT+OVV17B8ePHsXv3bvj5+WHOnDlo1KgRevfujVdeeQXDhg3Dtm3bEBISgh9//BFCCMTExGgmwCaTCZ988gkWLFgAW1tbREdH4/Tp03j11Vdx/Phx7Nq1C35+fpg7dy4aNmyotocOHYpt27ahS5cuWLt2LYQQ2L9/v1lbz/toZe7X34v3sq13X9a23n1Z2wCg1OSxCVV+MUWx3BcjqsStL6LRw+2voK5JFR0DWhM6d+6sW7u6TzH/XXv27NGtXd57zNaUsomnXvQcl9vfyaAmpaWl6dYGUO1jY4no3hBCVPnCEqs7nICIiIiIqCqcxBIRERGRdDiJJSIiIiLpcBJLRERERNLhJJaIiIiIpMNJLBERERFJh5NYIiIiIpIO3yeWiIiIiKwK3yeWiIiIiGolTmKJiIiISDqcxBIRERGRdDiJJSIiIiLpcBJLRERERNLhJJaIiIiIpGOVk9gBAwYgKSkJycnJmDhxojRtvfuytvXus235vqxtvfuytvXus235vqxtvfuytvXuy9qGEMJiJwCiqpONjY1ISUkRXl5ewt7eXsTHxwsfH58qb3ev2zKvO8eldrVlXneOC8flfmjLvO4cF46LpdrVmVda3Z7YkJAQpKSkIDU1FcXFxYiKisKQIUOsvq13X9a23n22Ld+Xta13X9a23n22Ld+Xta13X9a23n1Z24AVHk5gMBhw/vx59Xx6ejoMBoPVt/Xuy9rWu8+25fuytvXuy9rWu8+25fuytvXuy9rWuy9rG7DCSayimH/KWE19NK6ebb37srb17rNt+b6sbb37srb17rNt+b6sbb37srb17svaBqxwEpueng4PDw/1vLu7OzIyMqy+rXdf1rbefbYt35e1rXdf1rbefbYt35e1rXdf1rbefVnbAGB1L+yytbUVp0+fFp6enupBwL6+vjVygLGebZnXneNSu9oyrzvHheNyP7RlXneOC8fFUu1qzSutbRILQISFhYmTJ0+KlJQUERkZWWM/BHq3ZV53jkvtasu87hwXjsv90JZ53TkuHBdLtKszr1Rq8tiEqiiKYrkvRkRERERSEkKYH1B7G6s7JpaIiIiIqCqcxBIRERGRdDiJJSIiIiLpcBJLRERERNLhJJaIiIiIpMNJLBERERFJh5NYIiIiIpIOJ7FEREREJB1OYomIiIhIOpzEEhEREZF0OIklIiIiIulwEktERERE0uEkloiIiIikw0ksEREREUnHKiexAwYMQFJSEpKTkzFx4kRp2nr3ZW3r3Wfb8n1Z23r3ZW3r3Wfb8n1Z23r3ZW3r3Ze1DSGExU4ARFUnGxsbkZKSIry8vIS9vb2Ij48XPj4+Vd7uXrdlXneOS+1qy7zuHBeOy/3QlnndOS4cF0u1qzOvtLo9sSEhIUhJSUFqaiqKi4sRFRWFIUOGWH1b776sbb37bFu+L2tb776sbb37bFu+L2tb776sbb37srYBKzycwGAw4Pz58+r59PR0GAwGq2/r3Ze1rXefbcv3ZW3r3Ze1rXefbcv3ZW3r3Ze1rXdf1jZghZNYRVHMLrt5KIJVt/Xuy9rWu8+25fuytvXuy9rWu8+25fuytvXuy9rWuy9rG7DCSWx6ejo8PDzU8+7u7sjIyLD6tt59Wdt699m2fF/Wtt59Wdt699m2fF/Wtt59Wdt692VtA4DVvbDL1tZWnD59Wnh6eqoHAfv6+tbIAcZ6tmVed45L7WrLvO4cF47L/dCWed05LhwXS7WrNa+0tkksABEWFiZOnjwpUlJSRGRkZI39EOjdlnndOS61qy3zunNcOC73Q1vmdee4cFws0a7OvFKpyWMTqqIoiuW+GBERERFJSQhhfkDtbazumFgiIiIioqpwEktERERE0uEkloiIiIikw0ksEREREUmHk1giIiIikg4nsUREREQkHU5iiYiIiEg6nMQSERERkXQ4iSUiIiIi6XASS0RERETS4SSWiIiIiKTDSSwRERERSYeTWCIiIiKSDiexRERERCQdTmKJiIiISDpWOYkdMGAAkpKSkJycjIkTJ0rT1rsva1vvPtuW78va1rsva1vvPtuW78va1rsva1vvvqxtCCEqPQHwALATwB8AjgN44+blHwC4ACD+5mlgNVqiqpONjY1ISUkRXl5ewt7eXsTHxwsfH58qb3ev2zKvO8eldrVlXneOC8flfmjLvO4cF46LpdpVzSmFENXaE2sE8LYQwgdAVwCvKorie/O6OUII/5unjdVoVSkkJAQpKSlITU1FcXExoqKiMGTIkJpI69rWuy9rW+8+25bvy9rWuy9rW+8+25bvy9rWuy9rW+++rG2gGocTCCEyhRBHb/7/Kkr3yBpqbA1uYzAYcP78efV8eno6DIaa+XJ6tvXuy9rWu8+25fuytvXuy9rWu8+25fuytvXuy9rWuy9rG7jDY2IVRfEEEADg0M2LXlMUJUFRlCWKojSpiRVSFMXsspuHIlh1W+++rG29+2xbvi9rW+++rG29+2xbvi9rW+++rG29+7K2gTuYxCqK0gDAGgBvCiGuAPgaQCsA/gAyAcyu4HYvKIpyRFGUI9X5Ounp6fDw8FDPu7u7IyMjo7qrec/aevdlbevdZ9vyfVnbevdlbevdZ9vyfVnbevdlbevdl7UNAFUeNHtzxmwPYAuAtyq43hPAsZp4YZetra04ffq08PT0VA8C9vX1rZEDjPVsy7zuHJfa1ZZ53TkuHJf7oS3zunNcOC6WaldrflqNiacCYDmAL2673PWW/48HEFUTk1gAIiwsTJw8eVKkpKSIyMjIGvsh0Lst87pzXGpXW+Z157hwXO6HtszrznHhuFiiXZ1JrFLVsQmKovQAsBdAIoCSmxdHAngKpYcSCABnAbwohMisolX5FyMiIiKi+54QwvyA2ttUOYmtSZzEEhEREVFVqjOJtcpP7CIiIiIiqgwnsUREREQkHU5iiYiIiEg6nMQSERERkXQ4iSUiIiIi6XASS0RERETS4SSWiIiIiKTDSSwRERERSYeTWCIiIiKSDiexRERERCQdTmKJiIiISDqcxBIRERGRdDiJJSIiIiLpWOUkdsCAAUhKSkJycjImTpwoTVvvvqxtvftsW74va1vvvqxtvftsW74va1vvvqxtvfuytiGEsNgJgKjqZGNjI1JSUoSXl5ewt7cX8fHxwsfHp8rb3eu2zOvOcaldbZnXnePCcbkf2jKvO8eF42KpdnXmlVa3JzYkJAQpKSlITU1FcXExoqKiMGTIEKtv692Xta13n23L92Vt692Xta13n23L92Vt692Xta13X9Y2YIWHExgMBpw/f149n56eDoPBYPVtvfuytvXus235vqxtvfuytvXus235vqxtvfuytvXuy9oGrHASqyiK2WU3D0Ww6rbefVnbevfZtnxf1rbefVnbevfZtnxf1rbefVnbevdlbQNWOIlNT0+Hh4eHet7d3R0ZGRlW39a7L2tb7z7blu/L2ta7L2tb7z7blu/L2ta7L2tb776sbQCwuhd22draitOnTwtPT0/1IGBfX98aOcBYz7bM685xqV1tmded48JxuR/aMq87x4XjYql2teaV1jaJBSDCwsLEyZMnRUpKioiMjKyxHwK92zKvO8eldrVlXneOC8flfmjLvO4cF46LJdrVmVcqNXlsQlUURbHcFyMiIiIiKQkhzA+ovY3VHRNLRERERFQVTmKJiIiISDqcxBIRERGRdDiJJSIiIiLpcBJLRERERNLhJJaIiIiIpMNJLBERERFJh5NYIiIiIpIOJ7FEREREJB1OYomIiIhIOpzEEhEREZF0OIklIiIiIulwEktERERE0uEkloiIiIikY5WT2AEDBiApKQnJycmYOHGiNG29+7K29e6zbfm+rG29+7K29e6zbfm+rG29+7K29e7L2oYQwmInAKKqk42NjUhJSRFeXl7C3t5exMfHCx8fnypvd6/bMq87x6V2tWVed44Lx+V+aMu87hwXjoul2tWZV1rdntiQkBCkpKQgNTUVxcXFiIqKwpAhQ6y+rXdf1rbefbYt35e1rXdf1rbefbYt35e1rXdf1rbefVnbgBUeTmAwGHD+/Hn1fHp6OgwGg9W39e7L2ta7z7bl+7K29e7L2ta7z7bl+7K29e7L2ta7L2sbsMJJrKIoZpfdPBTBqtt692Vt691n2/J9Wdt692Vt691n2/J9Wdt692Vt692XtQ1Y4SQ2PT0dHh4e6nl3d3dkZGRYfVvvvqxtvftsW74va1vvvqxtvftsW74va1vvvqxtvfuytgHA6l7YZWtrK06fPi08PT3Vg4B9fX1r5ABjPdsyrzvHpXa1ZV53jgvH5X5oy7zuHBeOi6Xa1ZpXWtskFoAICwsTJ0+eFCkpKSIyMrLGfgj0bsu87hyX2tWWed05LhyX+6Et87pzXDgulmhXZ16p1OSxCVVRFMVyX4yIiIiIpCSEMD+g9jZWd0wsEREREVFVOIklIiIiIulwEktERERE0uEkloiIiIikw0ksEREREUmHk1giIiIikg4nsUREREQkHU5iiYiIiEg6dhb+ejkA0u5g+WY3b0O1A7dn7cNtWrtwe9Y+3Ka1y/2yPVtWZyGLfmLXnVIU5YgQIvherwfVDG7P2ofbtHbh9qx9uE1rF25PLR5OQERERETS4SSWiIiIiKRj7ZPYb+71ClCN4vasfbhNaxduz9qH27R24fa8hVUfE0tEREREVB5r3xNLRERERGTGKiexiqI8oijKSUVRUhRFmXSv14funqIoZxVFSVQUJV5RlCP3en3oziiKskRRlIuKohy75bKmiqJsUxQl+ea/Te7lOtKdqWCbfqAoyoWb99N4RVEG3st1pOpTFMVDUZSdiqL8oSjKcUVR3rh5Oe+nEqpke/I+egurO5xAURRbAKcAhAJIB3AYwFNCiBP3dMXoriiKchZAsBDifnh/u1pHUZReAAoBLBdCtL952WcA8oQQM2/+sdlECDHxXq4nVV8F2/QDAIVCiH/fy3WjO6coiisAVyHEUUVRGgKIA/AYgOfA+6l0KtmeT4L3UZU17okNAZAihDgjhCgCEAVgyD1eJ6L7mhBiD4C82y4eAmDZzf8vQ+kvWJJEBduUJCWEyBRCHL35/6sA/gBgAO+nUqpke9ItrHESawBw/pbz6eCGqw0EgK2KosQpivLCvV4ZqhHOQohMoPQXLgCne7w+VDNeUxQl4ebhBnzqWUKKongCCABwCLyfSu+27QnwPqqyxkmsUs5l1nXMA/0d3YUQgQDCALx686lMIrIuXwNoBcAfQCaA2fd2dehOKYrSAMAaAG8KIa7c6/Whu1PO9uR99BbWOIlNB+Bxy3l3ABn3aF2ohgghMm7+exFANEoPGyG5Zd88bqvs+K2L93h96C4JIbKFECYhRAmAReD9VCqKotijdMLzXyHE2psX834qqfK2J++jWtY4iT0M4P+3c8coEUNRFIb/g2IzrbWg4C4sprK3ELSa0j3YWNmKG9BSwUpnCy5BwVbs3ITPYjKixaQZIbnwf1XII/DgcuGQ3Lz9JLtJtoATYD7wnrSGJJNuMJ0kE+AQeO1/SgXMgVl3PQOeBtyL/sEy7HSOsE/LSBLgBnhrrV39WrJPC1pVT3v0r9GdTgDQHRlxDWwAt621y4G3pDUk2WPx9hVgE7izprUkuQemwDbwCVwAj8ADsAN8AMetNX8UKmJFTacsPlM24B04W85TatySHADPwAvw1d0+ZzFHaZ8W01PPU+zRH6MMsZIkSVKfMY4TSJIkSb0MsZIkSSrHECtJkqRyDLGSJEkqxxArSZKkcgyxkiRJKscQK0mSpHIMsZIkSSrnG7FH0vdxRyI4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fc96552b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.squeeze(images[1])\n",
    "\n",
    "fig = plt.figure(figsize = (12,12)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax.imshow(img, cmap='gray')\n",
    "width, height = img.shape\n",
    "thresh = img.max()/2.5\n",
    "for x in range(width):\n",
    "    for y in range(height):\n",
    "        val = round(img[x][y],2) if img[x][y] !=0 else 0\n",
    "        ax.annotate(str(val), xy=(y,x),\n",
    "                    horizontalalignment='center',\n",
    "                    verticalalignment='center',\n",
    "                    color='white' if img[x][y]<thresh else 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Network Architecture "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (fc3): Linear(in_features=512, out_features=10, bias=True)\n",
      "  (dropout): Dropout(p=0.2)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "  \n",
    "        hidden_1 = 512\n",
    "        hidden_2 = 512\n",
    "       \n",
    "        self.fc1 = nn.Linear(28 * 28, hidden_1)\n",
    "\n",
    "        self.fc2 = nn.Linear(hidden_1, hidden_2)\n",
    "\n",
    "        self.fc3 = nn.Linear(hidden_2, 10)\n",
    " \n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = x.view(-1, 28 * 28)\n",
    "       \n",
    "        x = F.relu(self.fc1(x))\n",
    "        \n",
    "        x = self.dropout(x)\n",
    "       \n",
    "        x = F.relu(self.fc2(x))\n",
    "        \n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "model = Net()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.089078 \tValidation Loss: 0.095229\n",
      "Validation loss decreased (inf --> 0.095229).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.083261 \tValidation Loss: 0.089377\n",
      "Validation loss decreased (0.095229 --> 0.089377).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.078295 \tValidation Loss: 0.085598\n",
      "Validation loss decreased (0.089377 --> 0.085598).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.072538 \tValidation Loss: 0.083376\n",
      "Validation loss decreased (0.085598 --> 0.083376).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 0.066775 \tValidation Loss: 0.081590\n",
      "Validation loss decreased (0.083376 --> 0.081590).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 0.062374 \tValidation Loss: 0.076432\n",
      "Validation loss decreased (0.081590 --> 0.076432).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.058748 \tValidation Loss: 0.075717\n",
      "Validation loss decreased (0.076432 --> 0.075717).  Saving model ...\n",
      "Epoch: 8 \tTraining Loss: 0.055687 \tValidation Loss: 0.075745\n",
      "Epoch: 9 \tTraining Loss: 0.052414 \tValidation Loss: 0.071935\n",
      "Validation loss decreased (0.075717 --> 0.071935).  Saving model ...\n",
      "Epoch: 10 \tTraining Loss: 0.048404 \tValidation Loss: 0.071062\n",
      "Validation loss decreased (0.071935 --> 0.071062).  Saving model ...\n",
      "Epoch: 11 \tTraining Loss: 0.046749 \tValidation Loss: 0.071887\n",
      "Epoch: 12 \tTraining Loss: 0.044299 \tValidation Loss: 0.067538\n",
      "Validation loss decreased (0.071062 --> 0.067538).  Saving model ...\n",
      "Epoch: 13 \tTraining Loss: 0.041759 \tValidation Loss: 0.069013\n",
      "Epoch: 14 \tTraining Loss: 0.039864 \tValidation Loss: 0.067172\n",
      "Validation loss decreased (0.067538 --> 0.067172).  Saving model ...\n",
      "Epoch: 15 \tTraining Loss: 0.038025 \tValidation Loss: 0.067154\n",
      "Validation loss decreased (0.067172 --> 0.067154).  Saving model ...\n",
      "Epoch: 16 \tTraining Loss: 0.034839 \tValidation Loss: 0.065605\n",
      "Validation loss decreased (0.067154 --> 0.065605).  Saving model ...\n",
      "Epoch: 17 \tTraining Loss: 0.033831 \tValidation Loss: 0.065291\n",
      "Validation loss decreased (0.065605 --> 0.065291).  Saving model ...\n",
      "Epoch: 18 \tTraining Loss: 0.031692 \tValidation Loss: 0.065065\n",
      "Validation loss decreased (0.065291 --> 0.065065).  Saving model ...\n",
      "Epoch: 19 \tTraining Loss: 0.030424 \tValidation Loss: 0.063153\n",
      "Validation loss decreased (0.065065 --> 0.063153).  Saving model ...\n",
      "Epoch: 20 \tTraining Loss: 0.029185 \tValidation Loss: 0.062702\n",
      "Validation loss decreased (0.063153 --> 0.062702).  Saving model ...\n",
      "Epoch: 21 \tTraining Loss: 0.027906 \tValidation Loss: 0.061937\n",
      "Validation loss decreased (0.062702 --> 0.061937).  Saving model ...\n",
      "Epoch: 22 \tTraining Loss: 0.027010 \tValidation Loss: 0.062204\n",
      "Epoch: 23 \tTraining Loss: 0.025444 \tValidation Loss: 0.061376\n",
      "Validation loss decreased (0.061937 --> 0.061376).  Saving model ...\n",
      "Epoch: 24 \tTraining Loss: 0.023680 \tValidation Loss: 0.061612\n",
      "Epoch: 25 \tTraining Loss: 0.022739 \tValidation Loss: 0.062608\n",
      "Epoch: 26 \tTraining Loss: 0.021576 \tValidation Loss: 0.062248\n",
      "Epoch: 27 \tTraining Loss: 0.020827 \tValidation Loss: 0.062362\n",
      "Epoch: 28 \tTraining Loss: 0.019347 \tValidation Loss: 0.063171\n",
      "Epoch: 29 \tTraining Loss: 0.019205 \tValidation Loss: 0.060908\n",
      "Validation loss decreased (0.061376 --> 0.060908).  Saving model ...\n",
      "Epoch: 30 \tTraining Loss: 0.018719 \tValidation Loss: 0.060166\n",
      "Validation loss decreased (0.060908 --> 0.060166).  Saving model ...\n",
      "Epoch: 31 \tTraining Loss: 0.018040 \tValidation Loss: 0.061638\n",
      "Epoch: 32 \tTraining Loss: 0.016939 \tValidation Loss: 0.060277\n",
      "Epoch: 33 \tTraining Loss: 0.016167 \tValidation Loss: 0.060745\n",
      "Epoch: 34 \tTraining Loss: 0.016347 \tValidation Loss: 0.060920\n",
      "Epoch: 35 \tTraining Loss: 0.015947 \tValidation Loss: 0.060572\n",
      "Epoch: 36 \tTraining Loss: 0.015508 \tValidation Loss: 0.061600\n",
      "Epoch: 37 \tTraining Loss: 0.014563 \tValidation Loss: 0.061980\n",
      "Epoch: 38 \tTraining Loss: 0.013508 \tValidation Loss: 0.062071\n",
      "Epoch: 39 \tTraining Loss: 0.013959 \tValidation Loss: 0.061349\n",
      "Epoch: 40 \tTraining Loss: 0.012041 \tValidation Loss: 0.061065\n",
      "Epoch: 41 \tTraining Loss: 0.012941 \tValidation Loss: 0.059749\n",
      "Validation loss decreased (0.060166 --> 0.059749).  Saving model ...\n",
      "Epoch: 42 \tTraining Loss: 0.012606 \tValidation Loss: 0.060988\n",
      "Epoch: 43 \tTraining Loss: 0.011480 \tValidation Loss: 0.060736\n",
      "Epoch: 44 \tTraining Loss: 0.011204 \tValidation Loss: 0.061397\n",
      "Epoch: 45 \tTraining Loss: 0.010792 \tValidation Loss: 0.059786\n",
      "Epoch: 46 \tTraining Loss: 0.010405 \tValidation Loss: 0.061807\n",
      "Epoch: 47 \tTraining Loss: 0.009874 \tValidation Loss: 0.060479\n",
      "Epoch: 48 \tTraining Loss: 0.009773 \tValidation Loss: 0.061497\n",
      "Epoch: 49 \tTraining Loss: 0.009679 \tValidation Loss: 0.062993\n",
      "Epoch: 50 \tTraining Loss: 0.009087 \tValidation Loss: 0.061041\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "\n",
    "valid_loss_min = np.Inf\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    train_loss = 0.0\n",
    "    valid_loss = 0.0\n",
    "    \n",
    "    model.train()\n",
    "    for data, target in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        \n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()*data.size(0)\n",
    "        \n",
    "    model.eval()\n",
    "    for data, target in valid_loader:\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        valid_loss += loss.item()*data.size(0)\n",
    "        \n",
    "    train_loss = train_loss/len(train_loader.sampler)\n",
    "    valid_loss = valid_loss/len(valid_loader.sampler)\n",
    "    \n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(epoch+1, train_loss, valid_loss))\n",
    "    \n",
    "    if valid_loss <= valid_loss_min:\n",
    "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format( valid_loss_min, valid_loss))\n",
    "        torch.save(model.state_dict(), 'model.pt')\n",
    "        valid_loss_min = valid_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load('model.pt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the Network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.058821\n",
      "\n",
      "Test Accuracy of     0: 98% (970/980)\n",
      "Test Accuracy of     1: 98% (1122/1135)\n",
      "Test Accuracy of     2: 97% (1011/1032)\n",
      "Test Accuracy of     3: 98% (993/1010)\n",
      "Test Accuracy of     4: 98% (965/982)\n",
      "Test Accuracy of     5: 97% (873/892)\n",
      "Test Accuracy of     6: 98% (945/958)\n",
      "Test Accuracy of     7: 97% (1000/1028)\n",
      "Test Accuracy of     8: 98% (959/974)\n",
      "Test Accuracy of     9: 97% (985/1009)\n",
      "\n",
      "Test Accuracy (Overall): 98% (9823/10000)\n"
     ]
    }
   ],
   "source": [
    "test_loss = 0.0\n",
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "\n",
    "model.eval()\n",
    "\n",
    "for data, target in test_loader:\n",
    "    output = model(data)\n",
    "    loss = criterion(output, target)\n",
    "    test_loss += loss.item()*data.size(0)\n",
    "    _, pred = torch.max(output, 1)\n",
    "    correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n",
    "    for i in range(len(target)):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "test_loss = test_loss/len(test_loader.sampler)\n",
    "print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (str(i), 100 * class_correct[i] / class_total[i], np.sum(class_correct[i]), np.sum(class_total[i])))\n",
    "    else:\n",
    "        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
    "\n",
    "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % ( 100. * np.sum(class_correct) / np.sum(class_total), np.sum(class_correct), np.sum(class_total)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Sample Test Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fca211128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "output = model(images)\n",
    "_, preds = torch.max(output, 1)\n",
    "images = images.numpy()\n",
    "\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for idx in np.arange(20):\n",
    "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())), color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}