SheepFat_3_kmeansSegmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "from skimage import io, color\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_img(img, img_lab):\n",
    "    \n",
    "    plt.figure(figsize=(10,10))\n",
    "    \n",
    "    # plot 1:\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.imshow(img)\n",
    "    plt.grid(False)    \n",
    "\n",
    "    # plot 2:\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.imshow(img_lab)   \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add working directory here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sheepfat import SegNet\n",
    "from sheepfat import kmeans_clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('./data/')\n",
    "print(sys.path[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (1) Get all whole images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GTS01.1_OLD_H&E_10x.tif\n",
      "GTS01.4_OLD_H&E_10x.tif\n",
      "JTS01.3_FemurTest_H&E-10x.tif\n",
      "JTS04.3_PCAControl_H&E_10x.tif\n",
      "S34.3_DC07_H&E_10x_s3.tif\n",
      "S35.4_SHAM_H&E_10x_s3.tif\n",
      "S37.3_SHAM_H&E_10x_s2.tif\n",
      "S41.1_Control_H&E_10x_s2.tif\n",
      "S42.4_DC04_H&E_10x_s4.tif\n",
      "S43.4_H&E-Bild-Export-11_s2.tif\n",
      "S46.3_SHAM_H&E_10x_s2.tif\n",
      "S47.4_SHAM_H&E_10x.tif\n",
      "S50.4_SHAM_H&E_10x.tif\n",
      "S51.1_SHAM_H&E_10x.tif\n",
      "S53.1_2_SHAM_H&E_10x.tif\n",
      "UTS01.4_OP_H&E_10x.tif\n",
      "UTS02.1_OP_H&E_10x.tif\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original tissue\n",
    "all_original = glob('./data//images/KW/H&E/inputs/*.tif')\n",
    "\n",
    "print(*[x.split('/')[-1] for x in np.sort(all_original)], sep='\\n')  \n",
    "len(all_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "GTS01.1_OLD_H&E_10x.png\n",
      "GTS01.4_OLD_H&E_10x.png\n",
      "JTS01.3_FemurTest_H&E-10x.png\n",
      "JTS04.3_PCAControl_H&E_10x.png\n",
      "S34.3_DC07_H&E_10x_s3.png\n",
      "S35.4_SHAM_H&E_10x_s3.png\n",
      "S37.3_SHAM_H&E_10x_s2.png\n",
      "S41.1_Control_H&E_10x_s2.png\n",
      "S42.4_DC04_H&E_10x_s4.png\n",
      "S43.4_H&E-Bild-Export-11_s2.png\n",
      "S46.3_SHAM_H&E_10x_s2.png\n",
      "S47.4_SHAM_H&E_10x.png\n",
      "S50.4_SHAM_H&E_10x.png\n",
      "S51.1_SHAM_H&E_10x.png\n",
      "S53.1_2_SHAM_H&E_10x.png\n",
      "UTS01.4_OP_H&E_10x.png\n",
      "UTS02.1_OP_H&E_10x.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annotation mask\n",
    "all_label = glob('./data/images/KW/H&E/masks/*.png')\n",
    "print('\\n', *[x.split('/')[-1] for x in np.sort(all_label)], sep='\\n')\n",
    "len(all_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original tissue: ['inputs', 'S43.4_H&E-Bild-Export-11_s2.tif']\n",
      "label:\t\t ['masks', 'S43.4_H&E-Bild-Export-11_s2.png']\n"
     ]
    }
   ],
   "source": [
    "abbrevation = 'S43.4'\n",
    "\n",
    "tissue_path_orig = [x for x in all_original if abbrevation in x][0]\n",
    "tissue_path_label = [x for x in all_label if abbrevation in x][0]\n",
    "\n",
    "print('original tissue:', tissue_path_orig.split('/')[-2:])\n",
    "print('label:\\t\\t', tissue_path_label.split('/')[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2) Get a small random subsample of the cut-out images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1629"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original cut-outs\n",
    "all_original_cutout = glob('./data/images/KW/H&E/subwindows_512x512/original/*.tif')\n",
    "len(all_original_cutout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1629"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annotation mask cut-outs\n",
    "all_label_cutout = glob('./data/images/KW/H&E/subwindows_512x512/labels/*.png')\n",
    "len(all_label_cutout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 630 µs, sys: 175 µs, total: 805 µs\n",
      "Wall time: 720 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['./data/images/KW/H&E/subwindows_512x512/original/1619_S46.3_SHAM_H&E_10x_s2_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/0184_JTS01.3_FemurTest_H&E-10x_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1594_S46.3_SHAM_H&E_10x_s2_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/0966_S34.3_DC07_H&E_10x_s3_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1406_GTS01.4_OLD_H&E_10x_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/0704_S35.4_SHAM_H&E_10x_s3_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1092_S51.1_SHAM_H&E_10x_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1090_S51.1_SHAM_H&E_10x_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1020_S51.1_SHAM_H&E_10x_walluks.tif',\n",
       "       './data/images/KW/H&E/subwindows_512x512/original/1256_S41.1_Control_H&E_10x_s2_walluks.tif'],\n",
       "      dtype='<U92')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "rnd_original_cutout = np.random.choice(all_original_cutout, size=10, replace=False)\n",
    "rnd_original_cutout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2.1) Optional: Read images and convert them into LAB color space and plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 40 ms, sys: 4.02 s, total: 4.06 s\n",
      "Wall time: 1min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "imgs_original = [io.imread(path) for path in all_original]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4h 52min 46s, sys: 51min 25s, total: 5h 44min 11s\n",
      "Wall time: 10min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "imgs_lab = [color.rgb2lab(img) for img in imgs_original]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.07 s, sys: 1.69 s, total: 6.75 s\n",
      "Wall time: 6.75 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "idx = 0\n",
    "\n",
    "plot_img(imgs_original[idx], imgs_lab[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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j1rUUKZSyv7CK1vRksQlrodN8eQvz+/2Mt3v9erLR4ovxXeA5Hk5N/RDwG/EF+70Mw1mY3wv8X97jz340/nOcLfojb/H1GWen/hLwSx7zvALfcPD3NwPfiC+XjwNWj8YN4N/FWb+paPUzdYzfCfxPeNrzR2FXkPNhie/A2b9fwNP/LX+zeBb49Xjhw3/Bl6OHe2JA6+2GTekVOaBZbC/ufkunbIcuZA9CBDQoiC/QE2NSipE2ifPPnbF69QLZwPrOBZu7l9jlyGwcsJLIF4nLtTBcZNbzjtjUoQ1Qko+zDS1RhSC+uOgsovOIWUGaQLOcEY9bjAQmSAGy7TWW035PQPKtpEkPM3SPPnWYJ5yO3p6IeehvFCigUQlNeKh60orrcwiBtmtom8hGlVKYpOkc5MkcUJntdFcOxA4AyAHIsioUt905nkBXPShWKFYYS6laqESrEQsNW+2I8yUnN29ydO2U5164zY1bRyyOO0iZsQyM/ZbcbwnDliPNNI3QWN4ByyIQ2pb5fEGulXlSQEQpBLa54eh4yRw4/8G7bL9wiWwLlvbHT2R3QOsxfz28VYwuQltZWd2dM0OsAtWSIQv53sidv/8aY7/lYz/5q1iczDm6vYBLY/XSmtV2zXrTI7OIobRBCI0SRFl0HTNpmEtizGPVkUGoOrJ9unz6Xlll1vTxAN/20+Ng1oAVcp/IKOu7Fwz3N5BbbBEggkW/edCoRA31u5YhV33kNI63Sj5cBZ4e/Jb6+zAugd8J/Kn3fEQexuNZofcyBPhqXPT+dsKAPw/8PJxp+lIxsVxvZ0wBZ7y+uv59DPxa4Jfi4PhJFU48DXEJ/CGc9fsmHFh+EAFlwdOgPc4Yv/N4aoAWMF3JD8BW/fPwNW9msDrlpg7X9h2GMbQYpQDJ6B/03P3MHe7+4Gvks4HUr5nnwKwXWkm0IWEmlKyk+5eM99aspMFEHFAFQxslWCEGoW8CQVuiNDBXZKGERYc2kVxc02VjgrEK4LMguYp4lHqzJLvD8PBO7WNnSvDQww9zWYei/4dfcng3/Oh2DTHXSSUKWY2sQhLo5i3XbpwyrEdy7ikmrteaPnNKFVZma0faMKUaHZhNuqxJRF+mIYs/mnEMamSiFFoRrAi9CV3TEJo5zI64+YmP8slPfxXztiEGRcn05z3jpmdzb83m7Jy8vmTOlhwKEAjFE1c6V+Jxy/UXrhO2gfPzC3LKNDjjqU2Hzk8IG+Pe3/0BNp+9YHx1i/YZyHXfpiSpUAyUtGdhJ6YQn3eNVIAlRipVZ1YcUKpBKQlLRiMt3Bk5X73Gq4sZH/mGFwhBmZ3OOH3uhDGN3H/1PqvsyO2kBK7bgkZmnMxaNIBtL7ECnQglaK0olAp+XYTPTvM2nSM5zD77cyIEKiss9TzupqNRxsT6/iWbly6JlwtyK4RFhGNFo0IjaFOrDi8zaZsRMWeVoxDKm3yPr4KDuzA89fSoFcHv5MtdAL78OMcF46fv8zjebmbX8CrF34hbVLyb8QBnP57D3T/+S+AOrvea86X1ZB+meAX4i3wwQRa4hu4X4N+/L6/o4KkBWrv02SG6sgOSB94cZE1h7IkRBVPba0yKEbKRB2O4s0XvG906sjlbM8+FzkY0FYrAqH7HX8Q1JVEybSlYqWCgCGQlqbIuhTRmum1HezSDdSB3gfYk0MykFh9aZYhwPVRygCWNp6Fst69ygIMetmzYs0g4O1D/8yO3f60VqwBTdoJ4f+IQyMmuUrBg5FKwDEWN3jIpCtZFclSOrx3R6QtcPBg56++TFAYbgUJTKnTy3FBdpIXgFCLZCtnSDmi51osKOqo+ykDMGAgMIkQzWjFmErksDedhAfNrPHP7Fh974SYv/pgXuPbcLfpV4uzeGXJ/5NoGxtWW4XxDudxQNlssjRSd0XZLTrrKSJ7C4sUli2cXvPrdr5JzIcaWUOdICEITjc1Lazalx9Zb8jhglGppMVWyahXwO7hUyxi686GajvMwEX2V8USqt1YBagUgpWDlHBWlGZW7f/sljpYL5jcXsBSOnl+ySRteOr/Hq3fPWOmWo3nkE6e3+VTT0ZUREal6sIJEoWiDVLDk1hh5l+oNAKYVrEude1I9vISgE0yWCoZdb2UoTQhe3LEaKPdHYhcoOWNWyENBgpDIDHnEspHXmdxntBHCUUTngbi9orTePAZci/8JPB0F+xuljFepvd+M0u8APodX1n2a17NuTzoM+JO4QP0zvD0PqgFPO/4W3Avr3Yr/GE8XfifwEdwO4g/hTNZv5iuHzi28//Pzy413Z/xPDdB6Xc6CRzJob/UG2NexXTakrnlYXdy0+kmVTSadD8jG6FIgJaUZjUjCrIpMTRAphPrxUkCndJ8JWRwx2JBIuCB5HEdOoy9wm3HLSGCWjNAFpBWKZEobPE2VzIFJ0ErgTQBrQlx2uFv7f+0A2JQCnABqfcUh+TA9M7GENUUnnp172BO0gBRB20DUGfOoBBmRPrC0Bc2N67x2Z8NmtYGxUHIhF0OnSjLx46bsyXNneLyScAcDbaou3IvqVVy9k0zIEhASyZTL1JGbBbdv3+KFjzzD888+w61nr3Pt9jXCvGXoYdOP2OWWtlf68y2rs56zBwN5a0Rr+PitGddPO9ZEwqxDTx3g3vvMPe7/yBklGxJld8hzGhkvL0jZKJYJGG2IiGaocvjpx/dVdqSk1n17aEranrkLE1Mrh89XAG4J1YANyuVLZ3zhM5/jq77lazg6PmU8HulO3BpkY5n7w5azfqTLkU/ePqUV3ZmyBoMigoo9dC85cYl7TtN2E+R1c6Z+eWSitNgTxVIBfh4KZVuQsVByRhHK6Bsqo5H77HNsMCQJoQug1dpi/EpZbL6caIHJyPlRncDncLH1+x0jrsv5Il6J934ArTPc++jtMieGa8x+CfDTeXeMVzOuzbqL68BOgT9+8HlX8/4rMZ4eoPVuhnLA8vgCY1btAkqmbIzLzz7g/EfOuLhzjq22zOlRBnLJmDS7svciShHXxIgUT3gJEJyxMa2sUMqU4uCjX22ZNZEQFRtHxh5EO4K683hjARmNtMloUWKISKwGBhMyfByyPGC5PGwqFNsxFVABatBdGnXfz9JNJWFy+zZUlJwyq34LBWLXoEtlYTPGrSJFKaeGlESMkec+9RznZ/e5fCWRiyASMB3RklETtFQyUaq3FsZoRm/O3Ey2AtkgF0+zBYlEDViAXCLnEgl0tNpwY3nC8vQan/ia2zz73ClHR0csFksYQS4Li9Rwu7vO2YP73L17yeXZJeNlT1iPzKUw08i4Sbw2XHA5DkgIhHlwUHBhlMvk2LWUmtY0zDKkgbhjVKcUmmJWK7/MX5eBsRiDCYEIGg5AbUEoiNhOQjuRrYCL+idWUtQZocr+MY6cfe5HefUooh//ahrzgotbt65zeVHgImPDltBnNn1P081om0Axh3k5Z0plGqlzWbWa907lkNO3Y7qxYD9IswxUfyzbm+H6+As5A3NFlwIzoYmNg8Rt2lX0lgBjGhAJ/l1Qo2TD+vI62HAVj4vpduXR2AD/PvCD7+1w3jDuAr8IT5E9Tlz+JCPhlgLfBPwKHPCBs4CffQvvH4F/Bq+e/Ldxr6vpmL+VWfqngf+h/vub8OrEP4inDn9hffyDzupcxZcbHz6gVVMgMjFDxi49Qk0jMhqbl1esXj5n++AC3fbEPBAVRAK9RU9xqQEBk+BqJBFUHbSoqvtjiZCxXSsfFehXPc1yTjfryAqSC+SMJbBRIBk2Gtn87kY71yFNIGqvW2L3XT/UY+1TiXbARsj+tSIHAPMRNgxPKxZXotM2HVorH6WIWwGo0ISI5UzolPa4gaTQBK595JRbH7lFf7ZiM/j4izmgiOagQetH5arHSmb0xTVanp7zFFc2pZgQpcHUHditaQizBUEDbWg5WZ5y85nrPP+xZzi9MSNoQFGG9UBKmbIpjGcbNmdb7t1ZsX5wgfaJG5JYRNdErVcJVSMnV4WVy4KKUJL7PAXxtjcmhkk5YOOmRK2zh4a6Ps/AaqsGwWjVASNSX1N1UGUyNSXvRPAJZ/FkmqNyAF4sV/YvE00IK+Hih+4g/ZxbH3+G2WnHrWevsb5IlGFELHKkDUMuJCt0dYHw81v8HE/mt3LgH1dKnT57C4pdmnkCVRNI5xBk+e9iYCWDRpgJJXp1LRjWO3DVNtC0AaTBBrwljxiWqtXD021Y+hTFtNhvcbYo4EDr7/LWaf73Ii6Av4PbF7yX57bBj81X87CG7RM46/dWWK4Nrnf7AVy0foRrqX4NXh0YcDuI1/B9u14/6wvAf4RXL1Jf+1n2/lFXAOsqPD58QAvgkYWB4kxKKy05iet47m4ZH2xYlg3CyGaE5cmSo7bh7mp0byHxijdEnQlSXzjdM0pqST915TFUIq1Gtqst/cWG5ayl0egMwiZhuRDajn7Vs1pd0h53zHWBjYUyCjbV2U85qYf2iMdfV6ds3B5nAeyE2iaH+cGaHhNBFKS2jYmx4ag94vLlFQ/u3Kd5tuHk1jHSCt2spZ3NPEWaR4455lPf9CnUhJd++Aus759DEjQryay62biovVRx+GhCIlaj1EqvSAALhNCQm45NbDm5fp0XXrjNjRdv0847aCPNYs583nFt2cI40l9sGVYjlw9WyMbYrnq+8PJrXJxvyb2R1xvaVNAGogoiDoJzFgLONoVS/aQUEplUarKvHqeM0BdFJFb9WK5gSCtBVCozNHGPxY1JMaKMDrNMGKqZgTAJsg4l5ft+hmMa2eSe7ZgQ3LIhWKQ5i+TVyMt3vkBYwovf8FGemV1HU0O+GMiy4ig2hFbJltluCyWNpOKskhc3WKUX995YiCIUSgW/r7txN8AmV7NH0qB4z8JSbyqsFLbbLQOZUCLD+UAIgSgN82tHnMxP2a56tuve3ehVkADyIb3yPLk4x0HAHHew/nvv73AeG9+Og5Kfg6fi3ooVwrsV34mbiU7xHbwxyGpwJuswtry+evO/Bf5JPKX4eeCv1/d+E17l+F247mqK96vn5FU87fGhu9ztOIiDu3UrBSmCpMBwp2f72prxYott064XXhN8cSwSmXWBksedJ5BXYfnGTcPuc8qEfirQslTImpBsDBcbihnd0YzQRqwJaBewWfQ+cOKl91Igj5kyOCiSqGijnvqUcsAusGPoHtX4vKEdRBWZ75432dk3qASk9iWc2uZIdBCUt4mhH5zVEpDgn2sCYaYsn1nwyR/3SRYnc37wu7+fdDFA74t8qZYFngLLmEGWsBNbFxXPmWnEpCXOFlx79pTj20c888Jtjq+fcHzjmje3buoPgmZnYCQ3SG/oEOgvNpyfr3jl4pyLixWdBaJ5pSISvQ/idAZlL+pGi/e9rCmyKW0olc3DDMnVIV8jKVfAJNBIIZJIuXhBhE06qCpEn9hEq/NmOr7lELQ48A+1mfPkHN8XqWJ8L7hIxbx9kwIvr+H6mnk340giJ7OWzXaNSMJsREQJEnYifdtRnU7p1nbVuLWbN8guY8I8z8xUaSiq8Ajw3DGldZu5ZIoEggihBGyEMY2+70GRRqFVZKbEZaRrvNKzYLueo1dA6+3GLdz1/Xfhi/7TxGZN8Tk8bfYq3kj51hP+vOkYPIunDA/B0xuBrJ8K/DTgv+FhYPa4eAn4rx/z+P/3bYzxKq7iQwi0JpBVpvYfNQWS+8J6lbn4wgUXP3Kf4WwD25GcPNnTBRhTogyZ2HaAkUqu5FLlx0Qw0SoOLlWnA7azfsokG9AQSast/aZnuOyZLWe0Rx0hNQyN0J52zBczojRIhtJnsriiJxIdbMmk8Kk7YY8olt9i7OwdJqRgzjSVUsAKoaYNtVNOnj9mfjKnHzdIo+Rcaq5PoYICmSmxjVx79jrLZ5f0MnL3h+6zfnVN3mxIFaD2JbNKXgnXhYa5tkRVNERCiDSzObNTB1Uvfu0zPPPRExYnM1bbnhwzFiAEyONA7jNNatAUyIOQNyBbZVyNXD5Y8/KDB6TNhufbIxbaMFcHL3kHaB2dFq2XX8E5t1oBqSpEBDUIJu5+XkYaFJPIhkA2IWcINjCzXFPHgUBBK2uYDEZT8gSYxCfGxOxNp09let7BWAzeG3M0r1xsQ0bMGIFcNsyzIC/19PEBs+dOaHo4ahsGVbbDwHoYmcWW2EYX4rv4aweWpLJwgjJmB1NRXJtlJt6PEGXXYbPOE08p+qAFEBUHmDljjRBEaKxxTaP1DlLbiLYRnQXohNxUUBsr5BR3YNO9odhVvKVQ4D/g/fPMeiuRcRD4R4CfjacRn2RMiscW7/n4E3Dw9EaC8yPgZ+LNpX8G7t5ueLrvUYbrKq7i3YsPIdDykCJMIngNillm+2ADW9ierdFUak+3eqcuRhoHxgxtyVUA7IvVvp+dojudjqelZCrVx/NIIg64QgioKuNm68yYGLFk6ECbgIiinaevShUOW1BKKJSUMa1S8qmEspbdP7KXHK6NU+yaO08lAbsnfdEspbheqUDGDS9jDMipEI4UvQyUIdcqNiXUlE9RQWIgNA2XeUN4oeObfvY38+AH7nP2uXM+/8Mv8/nPvcTdew84KwOv5Q1dG3l21nL7xg1unBwzm89ou5blzSXXPnKNk9snLE+XaKOstxs20tN1keXyiJLg8tX7bC96wlaRIVA2mXI5kvpEaAPL4znHDxpOiHzd8S3yaAzjljEPXslHqN1eDCvO9vjpdgF6aHSnTQqluM+aOf/jGjJFbc96NhgzFAktFlpyTqQ0kMvIZA0VZH+sd+fp8F9TurdIfU9xhlGmOouyY8EEQ3ImD5nN+YbSKiW3RIFkgctBSSXTauZI005wNYHpUNnKUoxc6sJUU4ay49ymOeQdFfbaLtmnses0LMXNQFSUECPSCnHRMp8tIAtkZ8sEYJtIGcqYKKMXi7i9R4H2vUwrfVjig1Kx1rAHQU9Sr5XwasPfjqf+fjrwj+LNrv8jHi4WOMY9rX5zHdM/C/wv6xh/A/C7n+A4r+IrPT6EQEvQmmIzjERCo5ARNmcb8tnIcJGwsSCp4BbhrreKoYKnPDpzVXN0BXybKEGiL04ypWV8YfVyeG9B4j+FMvqCVgZjc7EipIZGM6W+XrtAwDAL5CAQjBKEHHG/JTVsZ2I6waypvtD2AuaDxXIn4C610aPsn50EziJKDA7khsuecYT5Yo7OtO4M9JsBCsy6lsY/iiKBpEKuDYo1KPObc04WJ6SvTmxmwmdeuctrOZDjERI7LIDMF3zk0x/lU1/zCWZHHdooOg9Yp0gXSV2DAGMOlEUDx5Fw1KKXniJkZfT3B8bLkTwkmjZCMGKILHTBizdvcKMYN+KMV+6t2OSRoQx1ckeXp+vU66+CrKpCLxSyH3o/uiIOIjSwsYZSlE62rmkyP8cj6hWICF7nYJRUalce/4w9mXoAhnnoCfKUdowQY6BpMloGGskuHDdPeUZVZ6os0uTOfb+AWRPpm0wZR1IaGUfvg7gDVimjAS/YKAXLGTEFcyNeDaEeg6roE0HV04im+wVSVChWSFWoX4rRhECcRVKTka4gpqRtZjmb19010tlA368Yz7fYOjkTRgGF7sbiy/+qf6jDcNuCOe994+YvN+7jYv1/7gl/zhnuT/Vf44J2xVOWvwv3HTsEWv8Y8I/jOqpYn38GB69z4CZuNPpugNkF8ElcLH/3XdjeVXzQ40MHtKRqmXbaGMwZDTPGzYitMmWbkLFMIiumCjCvyppqzMBQNASaGL10PZsbgRZ2bJcAIcjuva4KKgyp0I+eerRkpOqVZI0Q20hpW6zPzmZhlEaxqEgskKpLwG6npv89wmjZo89XkHVo6XAAtCagxqS7UmF+fb4DpVbtHtquQ5eBfrUljYmxL+gIVhktB5qeOiNC7tyS4OiZBS9+1TOEEOnzQFg0fPzjL/LCs9d5/uaS5Y0TmqUijWB1fy0qRT1ttWiPaVkgjTHkRFoPbC+29Bc96SJR1gUNSjtribNA27QsUiaT0AcrLleFlTWsCQw504rRifdljOgOlE5AyxPAnkIr9VgpQtJIT4MZNHlLlBHFSDUBVwyijYglAkYKiulsepbJIT7bdLj32rp99Wi1l7VCMGOmiaU6EIkiZIOg0+cV1v2G2XpDt1hg4kxVF5U2Kps+UVIij6OneevcL7sZ6t0QDr3OrO7voaO/qqIqzuiJOBjTmlo0ILvHnAZ1rdjxEdK5N9xiuWBd1lxenFOG7I73Y2Jzvma4tyGvRiRDiP454/2riqwvHXeAP4AzL596n8fyTuIv4iDj0dY4053Gu8F03cIZqn8C964CB00f5fWeWFPLmwL8V3gV4X+BL4H/d+BfBP4q3rfx/Msc1/N4Q+VfBfyFL3NbH5QQXCv3Kn6MP40D3auULHzQgNaEJXYLhOwee7g6yg7e4mX8Yz9iYyFvRywV1Fwnok4L1dfWCkPcpEDVmY0YG4IGihQXrkvVvRxk5KwuqKrRjSItkwuQc/UiypDBhkTejIxNjyyVIAUpERoBKRRLKM6+0CgWHCjqjjWjasPqhx9er6b9EGdCpr+n5XaH1WoaCHEBfCEzjAORhlYa12U1RrNsvdHyuvc0XCvEEyU2guRJx1P1cEG4efsaX/8Nn+DFZ58hlUR7POOZ526zmLeI9VgrlOitWWiqPYZWkTRGpzNkEPJ2ZLtaM9zbkjejV2t2gfVqzdD3xBhol0tCGwklcpxPuHfec+9yRSIg2gDqAnDxY7mrwjyYGRWG7/49moOjEWG0hoaBVrwS0H3BZDfnAg4mskRCo260WgqUBFb2FYk2OcRPk/fReWqIZbQkZpJJ1V5CUQIO4LHCph+4XG2ZLbaEWSSK0KF0KFuRCqI8BSpWCHUOCHuvtWnSljoBnGGa9t8BnZr/LmUP1lX382UC6RojsW3pujnSzFjd33L+yjnDxbp+xxJpOzCst8jGgXoQRWKAoAx94iq+VCzx6r3n69+7hlUfkPge4H8H/Cy8v9/pwXPvlj5PcED1sx/ZpuG+XvfwqsAeZ6v+M7z/4LeyZ7Mu63Nfi7Nav4UvH2h9DreG+N4vczsfpDgC/h3gX8dB7BWTdxgfHKB1CLI4+D3RBBN4qCm16d9qgbQaGM574iBszzfokAnid9dicZ82CRGNsZbqe9pHJSAmWPEWJP6JVu0RfCHLhrvNe2Kk4kCjESEGJZEZS4aUYa2kpGyGQjkydGyIqaErC1hD6YyyaYg3WmSm5CYjEWRqZu05vD3IqToeDVNbGE/rSa2em5b1CShI/bfVQ0Vxp6dOO98bMyyCHCk2JGwFbBrGfsAWhXhDCYsGNkrZZoYxI8F727UnM65/5BrXbl3z6kUSGg1rMxoDGxsIIRJD4wxi8ZRsqY7rw0XP6osXaA/jemTcDnS0dEcd+SgRTgVr4fiZI5anR2gJpD5zfHqNL758xqvru9xatHRBKdLRqNJooFF1RpLKRIpOojtnP81Zqr6eoqBGG0aighF3InYnS33eFTPGoozaMVNvSj2ORhRX8k29HIFd78fdQa/+Yt5dvDhTlhu0tnmyqv/rxAF/QVinwtl6YLYZuNFGZqKYBSiBVXStX6CCrJKZRUiipFoJqrXSExWywFgy/dgT1BlbQSiWSblQUqFPblgaTGqauVZmRijZ6EtmWwpxm9hcnvPq998h39sQ+jWMGUrC0ojkTMSxu5hgKs5gvltr7Yc2FsCPPfj7TwJ//30ayzuJM+C/x8f9ncBvwsHM4YlfA/8d8E+xB5Rvd2K8ETP2v8c1Wd+BN3P+IbxaMOCs1TfgjOHvBn4Ur0T8+bgf2DuJT+E9E/8EzuL8uXe4nQ9qXAC/jv3NwGvv41ievvjgAK2JvYKHSYHDP2rKzNmbvQ6nbVpojaRbZ7OmJJpMPIf/PzYtTdt6aX32RWf/oZPXPA9dC6a1c7r7P3RgUHGmQAvuy5UVUqEwYl1gMZuTzLg8u2AMmfaoQ4un0hjMhUNioNXH6+AzrYqdU04O6tpIbCIiB9VjjztcE+tl0/+m9OnBfjilAo0gM7ATcRBIIZ0lwhipvg2VcXONW9NG7KhjWA0UCk0MNG2knbVIgFwS2gZC64yTjZ6G9d56gXE9kC5HFsyr95USmkhsAykqi/mCuGwoXUHnSjNvkBxI5xu4FpAuknOCkgkSnBE6qKbbH4cdx7c7/0Ech3TV2D3IZPb66Hun/pD+XCqwHqFTiA2ULO4HWqdr2X2a7fKG0/nbJbfr52hlIUudtz76Ogarn5u9X6fW6eGsl1Ymy6oGzysptUw3Ibbr6mT1+VLtN/TgOHBQADKNS9lvc8KmxWAcE8M4cHZxxmt3z8nnRliN2HZAc0ZxHZkgqBWf/9TOBJN/11W8jfguPpgsQcIB1wPgD7NP6RnOfPw4XKj+bsU0sxQHqz8H+D8B/ynesigB/xruAP89eH9E8LRh5J0DrUveOeP4OF+vD2J8kBjX9zY+QECL3YJZ6sXfzYp8BTAzF4AzEV/TEmo0TYTWK+iYWKpp5TjQzOxzIzse6OHlWMTTKLZ/n00A7CAlNxUKTmMp4D5KUrCUERTJhVgCcaacxQesLi+QAI22JIG0zmjd1xB8gcy5VBd2reOvlpJykDi1PXjYL+v7ONRu7fZhOpSTuWXFIaoKc5AmUFZGuchs7q8ZL0aaWUNsI2pufqHBgVY4CbSLBsOIbaDrOgQYxpFSIhaEGDydl4vvT7/O5GyM5wOWxBtsm6BNQDvFoiCNIm3AFLedGDw12cyVbRKuPXfE8MIp8d4a6xXNXiGoNS1mBW8wbjUVu4M/fhwDbm4eJsIU2CQXu08+slMxwXSAasKNlI1lNCIwmOwZUYWUq32GuN3I5Po/mZxOZ85fc+DXNs1180+JQYhqtAE3UC2ZZP4TcbbSvw6CBkWq99c0A6ymFEG8ilSKd0LA0+BBdPf9QKi6RhD1FKYPRgjmAnop6vN1Ce3dEdQY8gYtGSk+d2PY38TsrSNePyev4q3E/wFv9fJ33+dxvJN4gAOZH8aZn+lG8Dp7r613k+JMVA61/vwrwM/Dqwy/m8ezTS/hthSPhtZtvRkQeqn+vJP4ZuBv4+O+ig9jfECA1vSF8TgEVZPCZDKONLNqyFnfoX73vd32DNsRTBGNSAhVDF92q9/Q94xDNV6sQM0OfYQOyt/zNA7YeTUZ3r8vc8Bo1XW5WBXSm3+Ztit49QdeYvbMkuObS4ZVJm8y5JHQF8Y4Iskb8Ia2AQuk0pNKomtaJCghNMS2mTBe1eh4ikYRilT9lOyd8n1fprYsEzszIS5hauUoeHNixJsBhxBompbcJW+uXdOUksGKV+5lKZRotEdNZde8+m3YDoxDJoZA2g6UEHwfiiHZ2NxZU1YF2QiShPV2Sy6ZOG+QRSSRIArWKsRASX6smouR0AaaMXLr9gn6iZ7RAvlsS8ri/RdLodQT4oJ3I1Z/LTCKQLFAwu0PxIyoQhvch6svwiL4a1175Uey4EC9I0P05treJNsrEkNw649NNoIYQR3yDaXUYgV1LZR5WycVB2pZHcsVIBepaURh3ihdI8xbQaxQcmKbE1vLNCXQmJJxhivEFs0F0ugg02raO3slaskJyPWewn3kYog71mzSJ4K760cEihHNSKUhSEvXLljcmHP946fI2Ya7L7+KlZUXGpSy24epZ6jWVPv+a3wFtt5efJEPJqM1xcvAv4UbhU5VlF+qp2Cdo++o4vIP4anArzvY/jPAr68/2zd5/y8C/tcHY3wFTzP+pTd8x5cXf/MJbfcq3jwC8AuAP/ZEP+UDArQqxTIhiv0j9Y/H5hTZGYriFXXSOEsS2oj1AdPiMhmm3m91IRV1+wKVh68B4jk1ry6s8KT6W8mU5KlC+SnFtIctezMiy4W0Hhh/5JzLuytOPnGNo9ProMI4ZIopeZ08N9QILCBKwLTZMQQ7VkV3W94JsHfH56GD9OaHeMoa2jROXJuWils9qCrdrEOyO4GnNLo3Uk05WixY6yA45+LHL8OY8w7MWXEfrzGNjFv/sa3RWkcqiZRHKIV2PqM76ijRPabmxzOIQkojTdsC3gom94k0JEQLy5MZ52FNBpoYvfG094nZp84OZok8NHfY7zO4k7zt02j7BPI+beizoRy8XXbgQqT2MjwEsoCIPTSPp3HszTuoU326hXC/L9XgTclVMHNgl5wXI4r7wVkBC9UfrAJhpv2ptGUuxlCMIfuXf67QRasFHPvjMLWXmubV1EXAKxMjBEFbgRBYbQpfePk+oc9cI9JM1Z0HezZV9r5u56/iLcbX4v38fvT9HsiXEX8DH/9NnM2CN75AHX5T3250wAs8/M3tcA3VDb408xRwcf23Hjy2xRm5JwW0ruL9CwVefOKf8gEBWgD7SjsRIdSGztOXSQ1kSmPVdxQrDIP3novLhvnzSy4enBOblrHfosW8Sqy20KGyFJOfkAbdMTdMnyTiqadymFakOsYX189UWkiqF1dUL5O36p1kgBWjH7bYpfDgcwP9Sc/JjWs0XUceRnpWNHmO9cJqe8FcjsjzwpAGmhixGNBgmAZKrTozcUDh4u7a5HcSxMuB97fU3T1IW9mONNzDEJlyobUTslkm6cTo+D6qaXV0hbGYgx4SMhMfI0rTuPApEogWKX2mv+jpH3jfwkYaSs6sLy7RmTvny0zIuje5TDn5+FphdtxRxBiGwW0NJDE7bmiePWL1ww/Il6Cji8XVqqDcSnXDr3PpYai+O78ORmDMrtuaaSGWwkjECDs39x0YlSnVpmTv6ETXQAjGtsCsCUhxofk0X4IIGspu2hneeNvBucM3n2tuJiuiZCLadIDQj4WLLYxFaKpNh4iSTCvIN2IT6HIkj2sM982y4r0oJxYriNAEowkFsRGt3mBW04mYM5KjBmLTcjmsGC1w0l6HYyVeW5CHQr7YsrSWICMNbnAyZead0ar6szrZ5CA1ehVvNWZ4JeIHOV7GbQ9+Ai6W/5k48HncXIi8s+VJ8CpH2AMtA/4/eBXgzwe+jTcGcRl3jv+TdTu/DGcSP42DsA+KaexVvLVIuHbwycYHB2gdaGPclHECODIhIHaeUfXu3ap3lQChbZjfWjB/dgmxp9xvMe9xgmnxuv6JiVCtqbkwfZrHhEjq4jF9WXeNegREqv3jtMogtU2LMyuSs5s/YqCZnLI3R7ZL8nakW85pZq2X3heQbSQPCWkVOxKKFvJcXFPaKOpCG0z2rV4e9vs+SBc+VOr1MLezA1u7vx3YCpNZq2uOJDgyaKzZueOXqhXTpIQUvHdgMjQesBhilJS9jc6msL63YfXKiuGi5/jo2PchF9rFgu5GR4jBr2m9VwSOyV3PQxMYNZHJZEY0CDEGtPhr3M3ezyMq+/NX9vPC994OrsOyA2AToM97v9ddT8vddusc26ditZ5/Z5iCuNbLDK++K7b7CKXSTwhVtkWyqsWy6cztbygEz7sVCUiIgDEmYzN6arHRvb97Nm8hhOC2JE1DIVSHfOfbpm44ItAotEGI4hYTByOsGnpPh+ZijKUwNg3MOubPLZl9bMny9jH3vvAy27sXLOp0ihPulsmXTvbfzQrkdzYlV/E24sMATAvw+/B+iAlv2Pxr8OrKR/fvy9lfeeT3OfA/46zUp4E/ioOnfw9PC3bALwd+bn3d78JNV1/E00ob4M/z7oOsZ3D92vAub/cq3noY7v31ZOODA7RgvxgeeETtGZoJXFVGy5w1ChIoqewaPD/z8We5zPfQk5FhhJQSTJ5Du5+JpJFq3Mj+Lp/DS4BMuKtqW6p/FboHGEypFwGbEjC45UDbUcTF3cNqy+ZyTXO5ZnlyRDsk1ueXdCdL5v0R69UFzbKjPZ1hN4w0z+jcU1HaCuaUnn9eFcrLwWJmu//Lzq7gdYeWPfaAyby1LvYCIQZi44UEZXQwZVamjCpRojeirtVuVrwysgyZtB4pfSFsAhf3NnzxR17j4pVLZCzEF5TZsuP0+gmL0wWl2UE/ck3ntiESYvTBjTCaYYMQTJEc2N7d0n+xp8kdIULSzU6rZ8UvkI51Dxp118rCw/O58x2oDbizBUYiLT3u+BVrOk0OjpUfOT/eSrbqlJ4TZ5cPaENk2S12BQzTnDiM3f3CNHepNhQENETatqWJkVSMMReG4sc+4PfZhgNtxDViouqp1uaEgRGxkWADWD7ozWmUkhmLMZrg7YqmjgjTpBCGYmQbaW8sOfnqGzz7Lc8xu7UkrQvnf/mMfDbQEkBK7ZJQqxQP0oeTBbA8tKNX8dbjC3iV3gc9PnPw7+8Fvh34Z3Dz0GOezMT4IeCv40L853FWK+CpzN+PX+0/CfxC/BgvcaD13+FC9W8H/jLvftr7yS/wV/GlYo6D6CcfHxig9ej03t/3exQzSjUHnbyv8pDJqVBGAxNvt6IFa4Vm3lFmI3njFVql1EVA9tt1zqLwyFK8/9fOKsGmfMjuDt4XvrqVYlCbOGOlVvfV0vy6NaUQrCApk1c9YxavtjMlWkS6AAPeOkgadFBkBNOIYJQIFpzZ2pmbPjLufbrw8I7v4SM7LZDT8ya2y8WaGJmyB3AHyHNi8cQ8jesmrc4UygBhKz6nL0c2r264++qW3MON5YLF0YzupKNdNqRxpH+1R4AYGobNwGa9oYsdbdsSZoHmqIFgjJc92yFj2Ri3BW07mtuBEjakbULNCAppMESNrunI1ZYgl7r87wgXT7+h6j0Gq2la2EFNwSZFlu15wsnd3Yo/L6L0JvSlMITMha5Y0LGQ2Q5kT8e92GQFIV5xyH67RZRikUKDhpaTozmzLjJm97iyXFw/V2dpsIJS0NCCBooKJom2jYhCTkYaRkr2c0OBorWROkqulbhdJzQaPTU+AdEQmR/NufWNz3HtG27SPT+HIt5hIRld0xC2I6ZeBLC7wagAa/pPkfodOUzwX8UbxxbXNH0b8D8BP/D+DueJxB08dfMy8J+zF7C/W2F478N/B9dtBTwNaziLdad+5k/FWba/hVs1TGP7d/E051Ungw9PPAt8Ff59ugJar4t9omsfO1BkVu0CjEYDUqCMhTxk92sSSMGNQ4saJUAWq+k+2aeFDoDS4ZJxCL/8gyuNA0CuYGsCX7XSqzqEewlW2ac/zXbVgeCEuqeaKtAqIzYaFpU0FCRDu5xTBnemjx1ojl44FhUris5dvC9h0vfIw8N9ZD8mxuihBOIByHQGztzOojqDTyJs8OrGqa/e7nilQukzZdzbTpCNMEDYgm2MsspsH/RcPhhomshsuaSdBaQtjGXL+GBkuOfu/cUKfd8zDIMv0CixDcwWM29jFAtx0TA7XtBdV5pFRzkZ2UZjc8etCkSBlBArxBAI4g7uY3JQHjgQr2tlguq4Ma/Mc3sD3aVm9+dfYLKpteI9FQVHMFnpbMbt5lmCZMLuiO/PebFaobqbOvvz4bqygElEQsty0RJVuFwlhmRVb+atnazOqailzuFAQWnawPUuMgw9l+s1o8FIQnNGSFjOJDKDJbZERIxFyCxCpLODyts2MP/oMS/8pBcJL3ZcbtZwWVjfvyBvB6JUCFkZxIlmznbIZrEzYnXTjSug9ebxEvB/w1mXD3MMwJ/FU3q/HgdG71YI8In68yhb9jPxRXcNfCMOtL6Dh60cXn4Xx3IV7398Cu8Q8L/AtYJ33pNPfYqA1qNAZvpnffzAnOqRQrH9+0VRM0zE9UQilFwog7lYuSSCCpTCZrVi2GywNBDMyKredHjapqMV/6nyFZP9j8ehQdsBSpmWyz2NVIXxlSEQrWkrv8N3NusABFlhHAbyCJqSC/8J6GjImGEmhDFjvZC2A2Ee6E472uOOOHetGMHZKF/rnFXLyUVB3kJlr9masjm7Rd724y71eFtNpU0LZF1L/S/154paZWIKYz8wbLfYaDRFib3QX4z0Z4nVaoRgLJ5pWbw4Q06VVBLbu2vs1YzeExjcaqEphpqSNVEkU2xkmwdvj3QzIrc6cjAWNzuu3+7IJ4FL69i8uqBsBgIFYsvmMrPtR9rYEGNHyQPFMoZrkBwkTXWDzlIxgUr3gqheVXu91XTe3VjUXd7F2z9TmkDJcJwXiBSKDbv5sXOMF0Mr+JbdUZ3mix/XtmlYzrzadExGnwqpQCqFXNxRP9R52ga3eKC491o377h2+4Qhjej9c9Z3zxnOLulCocUoeSCZkij0RSg5kzcD1ighztyPS5R4o2HxYxZwu2EcBoZXtrz8PZ/jle/+Au0dI25GgmW0mqO6xcUhL1rTiLt04pVE663F83xleSv9ZuB/xAXoz+Ltcm7juqppNn0GZ/p+3MFjbxT72xb/PfJwD8Q/jfc9/CfwZtTw/qZnb+Nmqn8a+Cvv4zg+rCG4e/0/imsD37sK3qcIaLFfvQ9+wbTmSxUR+z9th3ymh9xd3CiY+mLmbI9hY8GCkfJIrJVcuR+8QlAM1MgEzJSp6W6p/eJUXExsGUQN21k+2C6FNKWeJoA2eVNNqSlU3ePbXBMzJSVt2o+iRBTXmat7OqWEhECrAU0Go9GnhPUjOhMYG9gIummwWSFtBVlBWUTCPBLnEaJQWmdJSi7ktbvIaxRsEdCoB2Dr0PHc2TYx9kgL27E4Mu1zPT8xKKowbgupz4zbkbIthJVi2QgEbFvoLxJnZyPnlyMlGte+6ohrX3fCUdORL0byvcTYr5FxpPSJUlOuYtWLqjI3E0DdrlesvpAJi8D8xQWZZ7j2/A3aG0uOX8xs7z9gNvak1LJKhfFyC0MtjpCAqgOWVF3Vg9auAVaBVvVkm9gakb02ayemN5mKLinAWFmpUAIzUbRxIIPpjiU9TDzqBMg5BB+72j9ms8jxUUc22PaJ7VgYELbZGM090qK4SWnRyFhAah9Gs4LMhOV8STid0ZzMuPNKoGxGNEG/3bJJDZs0klQQKWQJjChFWu9nOGtY3Fowbxf0FxkZN7z8dz/P+ffdoftiokvBU8Tq7u9i7L5LsWq1VA7my5utjVdxEHPgm97vQbyHUXCAcQgyfiLuLP9RXDj+K/DU3h8EfjxvPqEyuztlLnGD1AFnsY6AX4yDr3v1uXcCbFscxH25tw+vAb+d1zfivop3Jwz4Dfj36q/yXqaDny6gNd19HJSCT+ilWPG1D4BSrRfcMHP3NgQJoeIBBzJDP9Jf9nRNS9MEVFrSMLK6uITVSJdHVGOtx6o6G53SO8Wdz2vWb3+Pbo98paRikUc9k3xoauxEwKpaAc3UnAXvm1icKisHAKaNkePZnKKBy8sNgxntoiXkOV1RJCllbeQhuWXC5RadR5qjluaoJcwDugzoXAmEndu3Oz+YMzZTidgujWgVaDhatcl5svIS+3xhZS9KYagVgXlTGPtM3hTS5cjwYEsZCpYKkmtfxm7g9AXl1u0bfOQbbnH07AI2haY0BIukZEjypoOSXQivh8rx+plgxFzQAuVy5MHFPR7cPeNrfsrXUQbhu37ws9y/v+Z2FL7po8/zwrMn3M+KZNddpdEZw4llBFxLV3VEO8G4TNNrAqSHtKrtiUyT6vc60YPeGkdDZQiLVBPPQ6g1zRF/tJiSiCSU0HQcL65zcjSja4V+9NR4kMxxG4jF0+AWvLqvqSxmsULODha3vXHv/D4LWTCbL3jm2Ztcv37CkAaGNHJxtuLB/TXlvKdJiTwOkAqmAZqGsOhob884+uR1Zs8sabYDZz90yfbv3yPeTYRti0quKfgJlVcwKp4m3IOsia193Pf+Kt44rr/fA3if4+/izan/NO6e/tfr478eT/O9lfg9wN/BxfZzvJLw9wH/PG7j8I/j3l73efsVgP8SrvX6jbw7vSi/UH+u4slEAf637/mnPj1Aqy5OwCMpuOkhqSaMSq7amYlUmJ7f5yqsMkxGzomcExa9Lcz2fMP63oq0TdBnAoWoBUiI6g7QaQh0844gkYGRPGZQB0WI1EUZT68Y5JJ3Jp1SNSs7ebNSQY1U8OKruC+wVism6yGoq34TlCZG2q4hhcDqwYrzcWSmhfm4oLWOJkSwREmltq2pwvtslKEQl5FGWpCIiBAk1KPqYMUwSjamrscTz+KCdme0Sobce9NkUaHtWlTZ3SiWVEjjSB4TkgJpWxgeJDb316werMh9clsHE7pZi8TM7WeOOP2aa3RHHUGVpokUTZScvWE2DlIcy1SfBdvBQHQC3pYpJZMphK0xvppY/egGnbesVgNnZ5ltHnnuZM2Lp8cwi1gWYqNIL4R1Ig/GWEYHSgVvLYOnQS3vknxMgGxyhn9omta557q16XxXI1eZDEP9vZni59+ypymt7M57xkXwiYY2zjlazGmC0o+FTe8msE3lPAmZgHmi0sybcxsMCBn3rYoaKJeFIkYajBjnNEEw8ybft5+7zXK+4Wh2Qb/t2W4GUs7Mmpb5ckF31NE809Hcbiid0d9bcf75M+xiRKvbvlmp34m9b1ttelTd96fv6cPtsT6ceOtxeyVv4/nHxU/Hhbs//E4H9QGPjAOqX8nDzvgvAZ/njc0mE17V91uAP1PfO93c/qn6/r+MVyR+J/D/PHjP24kbuD3Flb/WVbxxPDVAS7OwK/CbFn3BbQtqej3gJqLbYYM0Qtd2DzEEu0tWTfcEVUITaLuuNjY2zl57wNkXLgg5uEC9BIoaQQZUQ21bY7Tzlva4hSJIzhOl5aSOuMO2qjhIKIVhOzKOI03buHt31YyBYSVj5J3pp/+UA6g1pRuBUggoIQaC+v7HRYeVwv07G+yyJ54rOlNO5g3JCrkkOpuhGXQMXo24ypQtFA2+DwomoVoc1M/U+u+J0lF9uAqvAEMhnY8M/YAGpTmJENSfS4YNmf5yZLvZ0sUF4yax+vyay9cuudyuGQcHUKUU74eocDveoJstaEKLJMFGcwuO5Fo6NFZWsaDe42cnt9i3DDJCKEgoSMnEqMi28NL3vMzRx27w4u1TuuGCl1/LfN/n7sMLwQFZgOWNBXGI5DIwloKljW87CTFENCgjkDU7UKokn7vguxZr0kU5m1nPtZi3qzEXyOecCbGFopSSSWaMlsiWEKxWNO7PQbGCKrRiLGNgIYlxVC4HuFyNdJJZhMKwGRlzYsiFvlpXRB2IIlMPaUygzR2015AcsFmkm0eCZvK2px8FawrzBo5vHzOkIy63I8OYaWYtRzeXNMsGTgSiMZxvufP9r3H2+ftsxxEpiUhB1QiiaE2ZZqz2bMxIbbHi820H4539kg+jGP4+rjGa4S7u38DOzRcDfhvwx/GU1X+CO5W/WdzAmyN/Jcclry8I+CGcSfpVwD/Ej/MK+D7cRf/7688dPOV4CIS+u/6+V39/tv68k/gjeFXi977D91/FV0I8NUDr8F7v4EaYYkZaJfp7A1xAaCND1zO7MUPaaqz5MMzaoa4QIsujI/qxx7IbZg6bgbQdvBdcrdoKVY8FEGNDO5vRzDr67ZY0FshVaMI+q+njdJYoNHHKIrmTPFQ/rbrIFAdb4zCixQ0tjT1g4AA8WFX7KO7DtOm3LBYzPvrsdUZ6tv3WWYlHj5g5g1bc/dKBnAKrkRiE0Ki3yJnSh2aYVuf4CiJFAXUfqJwTlsFGb944ix2iyrgeGIFG3XXdRiOY0EpDtEDfD/SXPevzLZeXK4ZhIFeT1qYNXDtaIhmG1RadCWVr9OdrWEFsGpKOiBZMnMmaMnQwHbPaJslK1cAZQb2/XquRzXrg4s4Z1z55g3jaUn6wJd9fc7HusZKJCrk5oWtbyiajJaApUsZcLSkm4bbuhPFTux23OygH5+mh7HbVu1XgPzFwRvWWourrC6UyPnJQAMEuNV2tELw5ICln+gRjTgTJXhBQSj3Hfq5z7Ugg1UBuwqXFMuVeZhUbQjtjsTzm5skMLFCScTlsCTHQtE7VigS6JhBb71XpzakFu3SrlOHeSFolJo+xXTeBXYWr7nKuXvDxyBf7Qx+CL/j3cXbkB4Gvwe8g/0PcpuHl+prPAv9q/flSoHPkylrgjeKv4Gm2s/r3FjcnPWV/Hp50vIK73X8lFS1cxduNpwdo6aR38VXJXbPdMmA4GxnuDqR7idAGupsd3bWOIEqq7JDWtJjrZepSqEI3n1G2hfFiQHrBesjFWQmTzGg9UgpdiDS4KD1K9B56Jdc0mreSqTKsqnw2By3i4CrGZgdaJkuHUhfPpg2YxcqeHNxZ2UOcVmW5vH7NimEpk/rEsOo5Wh7xkeMT0vERzTwwiy1B1H20TJ09MMGykVMmjZmUM1ELXQabR3QRdk3trbq5Sz3ubmfuOhtLRhkyllyfFfGG0hhsNxtKycQopFIY+h4RYTHrCKOyXvWsz9ZcnK25OLsgjYN7mwVBcoPMl+RVYvPyJZJB20DaJGSEGCJFJq0cByxWPVY25YptB2RkAjhmhBCIOTPcvyQMJ9w6XaK3lTvbzLpPYM5obTSzeOYYOxb4/oF42THI1vVNxUGuKmDi4CtnJOgOgDmrVhs+11O585yacsDUxtBWmDXKkAJ5LJQK36bq1mzBs8uSmWpeRZXQNIwGl8PIesiMOaNa2KbiAKqmIL21UGbENVwq7g6vJlAKQx4ZVYihhX6gSccsj444ig2ZQFZlGAvbzZoYI7OuI5pShuxtoxA0KtoLbNwTLUpAIqhltAryqbq5ybh3YkWz5QMQWr8vojx0x/KhiWvA/4rdl4zX6uOvAb+DfSl5wVmY34ZrfI4PtvHoLedXFFJ9mzHirNVhtDib+OjjTyoK7w2gu4oPcjw1QGtXUcjh4gW5N9LZiK0zVoxxNTBuR7RVwvIYDUIWT+vE6lZUfBlyABMMiUIaR9ZfXLO+vyblRLGRwkCf10ChxI5AiyVjWPsdpFld9KL3KsSqvVJxeKRVmJ2yt9Ixm8TA008h5cTR0Zzj0yOGV89Jqy1CwUrZeWuZ1X8D1RLUwaM1aImszjcM4R5h3tLFQNfNaGPEcvbmyeotY0ox0ljot4l+O3iqbj1Q1kZ32hFKRlqvLiypantadWawEXRyZM+GjsAolb0Qcj9iGXQQpAQkC2ksbFcbQjSka9neS5x/7j6be5dIP3JEoGhLxigK0SL0ifHOhjWCnRXmp0csT5YULZxvz8gp+aQ8LGuc4qEU3r6Zt5hheAp3ITDLyuXffwVtIjEITcmshprmVeFuWrF45pibX/UsMu+wv3WH7b1ESQMpJ2IRQoAoShoLQ0loaJEwJfvYA2qq7QNWOxIYUmr1nUAqmaNZZJuFtE2Tegkjk83YWkBFaKuuClHaGAkhcp4KdzdbNv1AKELOkCTTSkbIbk9hhUIiW/b5IMJCIw3BSVh1vWKwjG63nPeFy7MN7WzGybVj2jYyU2Exa0nZi0usT2xLQreBeNowP51hIZPHER1Arannxx2xKFWwhRywo7LTMu7aP6mDLGcLP4xAS3j4knq7/k54lduj8XncpPPX1Ncabl/wp3BN0i1cg/TT+XCalT6JcCucr4y4CfwbwG/lCuw93fH0AC14OGeo+8fMjJQyJRuCwlg4f+2ctMzcePaaa2pK2r1/yuBIveBL8GqvMQ30Q884DgQbwWoaZscq1c/OZXcfKRONdVD5Nv3LKtAax5FxTKgqGnT/omJsNmtiHDk+dV8i154pezel/XYPl55S9WrZjHjUcvKRY0on9P1A0UwqI7ZNtPMZSiSNmXFMjGMmDYU8FmdcBiP3mTIUNAmEKnlKzryY1sRY3cfJDoMRGGu6q4x+PM2F8oJQSiGocrQ8QqxgOXF+d8WDu1uacWARBnIYKQpJAkNo0BBoCUhfGB9s/RD0xrxt6ZYdmxDJ9dSbOFB53TGveeHJjUkn4ZzUS6wZZUikbaEfe0IjSLOk0TldbNBW2G57Vv2aWyc3OfrGU9KYOf/Oc8ieFrOpGnMC61OqtRZAuBWGHzOpaeDD5I9VZbxVgXp9xHVv0zSbRIiI9zkEJCgaGjQ2bIfEhsJYCik7y4b49qavxyS+d/PbieWr/SknEDSxa0UoGjjqhNWQuD9sGYgsO2U5j3TdHNFA34+MfSIFCPPIvJtD7ki9FzxMLGMpbqzrWjMnRKdj87j5zHTWRD6cZNY7igH4D/Avwq/Az+qP4mDrJXwhBTdX/APvxwA/gLEFvuv9HsR7FB/FQfqfwXs0XsXTGk8N0Do0wrRpNVJo5oHu9gwKbF7ekodMGxr6i56zz95Hi3B0Y0nTeQ86qnhei4A58Akx0Cwiy48ckS8Hzlcjw3qg5IIW9yHqSiS426MbcGrVZQnkyeYAdimPqQKymBGbhqZpmFr4DMNIKQlyIoqxXq956c5rhG6BBaEfki+4VUytUjVd5oujWCCLUkS4sJ5rzxyx/MQ1hiHTbArz+YJtv2W9WlPGERkchJZsno5DaGLYrcZpHBm2QhjnDgbxCk4MrDds42lW7RyUjoNvR+pCrZPeTCbBkYG4C7luhbwqrB9sWL/0gHBxSdiuCZaJYjutWgGkQBwLIWc0G/SFdJlYZeiuLQjF28hg2XVXpiSpKbhc9kyWCCJTOrciD5lAkaedQ4LOGlpgLsKoCStCpzOgJZwVyt2B5lrHrW9+hgd37jH8aCBeiKficgKye3mJVEd874vZxMYbSB96FYh5mlkU1EjGxE1ysc1QYB6hz75fxQArNPUYaTPjxknLeijcWQ00FigxeCsmE5rgDaQjQjRv9D2LkWiKbUciEJpAQIhB6tQVt/XA+x9q1ZjFADMMHddsE4wbpZn1aIy1u4KgsUOCkLYjw9ijk8ltIzQheNujMZPTSLWIg+rntetxaN7cGqmM8E6ONqnevlLiWby/3h99zHMD3ubl9+PM1b+K2xGAC7i/AJxwlUa8itfH/w+fM8dv9sKreJ/jKQJa+0vJznldQFulvd6iKDkbw0UPoyEbYbxfWOsaXSvzW3NkIWjrK7NMvkVWkKA0ixajIDPdVY+VVJAiRFUiYWdpgDhzJlUvNGm+Dm/FRbQKnAsagovgq7WDSnLtWElEb1HI5bZnGTtS9tYnXqk1betASG1KwVvBWFRKJyxuHWOivPq5V+kf9Fz76A26Rcc2JRrVmqryBVxFQN1okwChOVj8siBJ0CDe08/Mm0P3XvGWt2BSGNNIDJEweZVVbzJTo4TK6qhTGyknxlVic6dnvL+m2a6h99SrqhuiCnjvPDxFqiJIAnpzMbxeYCXTth2hekqoRNfq1R+dxmA1PTVptGqPwFwfU7xXX4zgfmsOekMxshnSg/YRLjL5otAsQa81HL94jfUKxrM1ZcyITmoqEPW2zSVnQggEVcrESNa5sS9K3YvAXbgPm8G1fq1CLrpjW70FkGusLCU2g7ItgQElF4gaaZoONWjEaAXv31hZRbNCKMYsRJDiLJbgIFVrYjVXs1xxM94hQxElqGFppCAkYMiZ0ATXolmgJNeilRBo7kaaLjILnkK0UgghgLXklJy8ZSoGCQ9BqEnUL1ORSD1oZl9JoKHDvZvuAX+BxwunfxgHVddwJuseLrK+iafCPlKfj2/w/qv4yosM/APgX8QZ0M+9v8O5ijeMpwZo7cTP9S8HW1XkvAiELiDHwuZeoH95iyZhvu2wVwqXr14yPjvSPeMieWnwH4E+jaCF+XFHbJVXc2Z1vkZSwsytA8IkQK8L934UcoD6rKaKHGD5AlL75vUDZrjdw8QKlFK9ngTVCBIpKZGzNwT2tjjZnegNtGq0sglZhO7Wgmsfu00+ijTa8er33ePuD54x9COvru5z+twpzz3/rGtgDCQpWg4c6K2gUWm6iDaR0LoGSAwsu3g5p0zpMzZUrdkoFKtjbMFiRKMQEG+vY5XFUoEBr0S73zPc7+kfbMjrgTAkQik1VaY1jcWOYdlFoVpEAIOhvTiIIZDzQCbXSlE/LjEGPAfsmrhSK+2oYGbasqoSQvQuAZZJGcRGdy3Pif68hxQJnXD5o/fJZUSXDaefOMG2I6uXLrBhpMHoOt9Ozm7VMLFbU8o473y2fH5MKrvpv8nbNZfJysFqdat7XSFKweiCoVq4vwVrZyyPG3IpLGcdOu8Yt5Fm7NHiJq6qQkmesjaMRdsBmZRHpoZAzowKTmvqvqhAqhu9GVYSte+3i9rrnLGcKHlE1krXz9iOis07Fiwo7ci6vwSNEFxLpvWGQ8wrHwWb7k921SmK67P2/SK/khgtgH8B+IXA9+AViP/9Y14zAL+7Pnet/vuTuL7rZwB/Aq9y+z2Pee9VfOXGXwAueFh7cxVPUzw1QKvsfE7qcmxSe82xS7M1XaQct5SUXUOUDEavsEpnCRLoWgk3BL2hNG0kijLaiGWhaSNH7YKlzrlkTXKZNlAYMNrK0uxMSSeerbqjO6vjwt8QPFVYsleq5eLptpJdoJxLIddxm0ErkcYCUYSEL9JF9mX+7qUEKcAQhflzS65987MMMXP3ux6wvZMZiqcSUz/Q9jOsC3THDYhRLhNhULrQAt7qZ7+gFcx0BxDAW/IMm8Ebbmdn1GIIxNB4SnQ0xmFAu0CJkNJIyZm2tITYUlaF8Wxk/eolm3tr1vfWlD4TSxWoT+cQR1lWK82mlCsakKiIBvJgbO6v6S8H8srdyV33VZxxw9OiphW0RSESsJyxLDt2ZNIH5aq986o8cNsBI1ZQOmwy/Ssr7othF5n243Paj7fMSsf8cwvyF4zWRq4ddwTtuNyM9MO4S++OY6Zk9wfzXZSdMHBvxek2HtP5LbViMuDNq7WmZJMVb/acYSgZlYFGhDYEjmMgipGiQlGvbBTFirAdM0EcvGipFa4lICoEU8QmcCXO9onruCiC6uTutLetUDOkZGT0FOjupiMlhrW76M86pZu3pNSRio9pNutQC/VmIxFrK5/JxsLHUHycTYQ8nZuvNKAFzmx9M/CtuDfUn+PhfqngF7aXcNC1xNOOgjdF/iSeLvogAa2P4vt01Zz5ycXngZ8M/C7grwG/9v0dzlW8Lp4aoPWQL9RknuTa3orThRAD3VGHRCWNiXE7MJwPpCFhZ4VxpcgAs8WMIMEXXNw9HIM2tsxCx0xbLgUy2TVMGOPESNQ101uv1FSi4QyAD7QyWtVrSdzzyLXR3ujXinsyFdhZEpQ+UUxrDzof09SaxO/2I6buDN6LkU8C4fkWUk8aC+OqsE2JlfVEEUwVDYEQI03jHuGyLb6Q13RbzoU8ejWiWXEwV1NtZcyM28FBlgkhKDlDGxuaOHfxcy6+j0GI0mAhevvkvlBWmfRgZHt3w+bBmv5iQxidndtrdCagui/3Z0qxqniaSrzFUe4LeRixwY1dyd66h+w1pKmOXRUiUxXowayR6qlmr/+pJwct3mgpDYXh/pY8ZrrS0CwD4WMtp89fI7+YuX8/06+Ny0E4mgeOl4qSWCdXF5WJ4cq1iCJ4qndXhYGL1Kcif+Ahj65QqwsdzEDKwqa4r1kLRA3M24ZlEwiWSUEo6gUURYRU9V0KhMoaBgnE2NRjMX2HqCnO+kWamD/bv2wq7hSbdHnOWjozVfVqfSJnaIJ3KtBwRD9ktpuey8HIaSCVRINxXBk1PxR+81SKYfFhofxXdvxy4KfhhqZv1PIl4u1iDo/ZKU/RJfstxq/Baet/n9eDyqt49+Icr0zt3u+BXMVj4qn51k4sSMEXJWCqIN+tBiIQm0gTG9LNRJ8C4SSwudywureCLDQ5sggLJARnYGhpLNBXw8rhckvK2Y1Da/omiGDFLRdED0CJ1nyLTWlFqeuVeOsaK4gp80ULKgz9QBkGt2uoTFa2KdWTSOILmeK+UloBURAlhkCP0qfEqJnuuHWBew/d9Rnrs0u6EFmWOce3j7h56wbBlHyWaOaBNnZYlxmHrfe+CwEpvjjnMZGzeQuhqC5qVqWZNeQ+I6U2JS6ZsQizowXtcUsuiT4P9LmnjQ2Ksr63oj/fEldCOR8Zzzew2hCH3kXuVvZLQ90//13BiCqEsOs0rE0kzju0cfF+WvWMwwabJFDignKlVv0Vr0AVsbqg6yG+mcr5HplcNY1W035qEA1ka6zurNAfjqRNRheR8TKRiZxthbv3VyybDbdPGo46JS461tvMkPPDIGUSGHLwd/3LcbrtKxEnKbi5YD0SmKmDdQkNs/mcxXLBrIm0TYONttMtmgmleCqSqXpvsksIAdW4Y6iY2MvDvOrBOHnoofrgTm9WzVSLM2DCSLLMqxdbSqvM5y3zzoX2Zxu4LGskGscaaYvQTCnL6pYvJlgRSvZ+jkbVCH5FR4frr774Bs+f1+e+Gm8V8ztw1/PH2UQ8zfHHcUPRq3TWk43PAL+Eq96YT2c8NUBLZWJBCnlaVFX2d8ETe5GNUmBgoJwUjp49IqwCW9lia/O2IVFdd0LEtoX+7sjlvQsajWzON+Q8orgIXqwKxyvQKurCbdNchdcTQzPRa4qJkFKpGhgIMUBwKwWd/IMqoSPibFGMERVhsOQVgdTPrWLxIpl1KvRaCMvA6c1j10GdZ2Y3FiyHAhthNjQc3zrm9PoxJj7esQyU4pV6oY3Vkqw6mxuM40jJBYqzgswDzXGDNh2DDdhgxKalSGWPJNEetw4ktkKbW9rQwAb6PhCGSFmP9Bc94/kWGQbaPCKWCbb3TDeZmgu767hIcFG0OtAzEQiB0DZI66Jp7ZMXPkhxIKFV7zT5GlCZkUItPvBPmwDCZMfgI2DXIBwEihuCqhYaAytGf7EhfaGwHEbaWYutM9ESR1oowSi5cP98hGVk1jWE4CyUysG2dcINtgNaxah+aHX61qMyGcX6eVevFgyRTiIa/Th3osQKxnK1iCiVWc3F2z+JTFWlDkNLgVQc1LfRU7SqU9p4Ohr74/LwwneIwKw6fdQ0OoJkxUx4Zb3lfknMFh0vnCy5Pmu5dn3BfCvuuj8W8sbBnaoSKvDTkrDq8UZNIX9ls1uGs1Uv4MDpZ+OWBJ9lf156XHfz54HfgIOsD2J8pVgtPA1h7NsKXcXTFE8N0Jpct/crk/sQTRIpAfd/Kt4wuV20hNOABLCTGTdnNymb4o2WT3whL0MmrRJnL59RNiOxDWwuNjQxsirePDqoEkSnjTvg0qq0KYe5lgq1KotQsrmYG1it1kgjhOBVfVYF41Jbxbg1QXZ/qJJ3ZfZpnHoAKtIEUmi4+Ylneebrb3HysRPSqnD/++5TgFtf8yy3l7fc/btEokb6bU+IVQ9VPcdFBSkORDQqHZGcC+N2hMGwXLCm6qWaQGh8DCHsG06PZcSK0Rw1LOcNpTfSamTsB8qQYVsom5G0HrDtiKbkZqiUvUmH1LMqFVjFgMboFZpBKeKVbrn0uNw+VEYlO9NFBSVaJscOdovQBKSmtK5MvNHrUxM7xy3Zt/PRiWnKRs6F/v6WYJFwpOScGIcetewVkFawkrncKttxIAp0besSwTRUICk7dkt3QnBvUm0THcXETFWa1pzFVVNEGzQ0hBiJMdCo7BsyT3Nv3+7RQdekC6tg32WEBcsw1BTpxG7JwZYOj8zr6K1d7KsCpQhiiZCV4xKhBNrSsogd8+WC0+snqMDF5SVndy54sN6gGeYaOY6RgAFaNYPCZCZZ7Cs1jWQ4gPo9wD8N/HbgJ+G6rH+PvV/Ws3il4b/DBxNkCe7SftU+6Cqu4qkAWmaGZZAwaZYE07AvV6v93Gz0FiAIhC4Qm8BYEhqV2bUZZekpP6JBzm6AFQrdaUuOSlllL8BqZFcZ6KkXb4RLwQ08azWbr3b1R30x9Y419X3q65xXn3nVnJMH6hWNvnOU4mBmEkprXchKTq7dERAi16+fcOPZWxyfnHBxd8XmQY/lzM3nb3Pjo6fkKtmwHsrKRfva+iJecsKSeAlZBTkAUxEBZpTedoL4kouX86tQpNCPW2caAtVtvLg2S711zDYl76kXFbKR11tku6G1stNCSd33yeHcKhgIGoizlrh00GYqyFKZt5E4a/3v4Fot6xUL4iL37KfhwILdgXBFMEXKDmjoBJiFaixqjwALdsdjZ82BuYEqAn2GWakAx89JESEEIyokCv1QKAEaojNNFVaasTMiLZX4DCrMGy+oSInqTwVTkYXLrNwjK4SAhojEhiLKWAwVr5IEvxkwkSour3BLDDXZeZxNBqZVfb4DppNFyeQ95mlUQ8kV2NaqRLH9nGHKxdbfxRtDnQZlGQOxiSzEW0a3bUfoIkOAkBKWC6rRtX5mMCSM4o3Op02q7johfLji8Bi+Ufxl4Lfgpfk/C/iJ+Nl7EW80/X+s29niIue/96QG+4TDuLKhuIqr8HhToCUivxf4ecCrZvaN9bEbuPveJ3C++583s/v1uX8L+JfxK8mvN7M/+6ajqGkcxzO+IEx9BMErqqwU0oMR1tCcNlhbSJPuCPHfASQIMgmpk2GWoCtcvPaA/uWeLLBhIOEi3SjFZUOIV7oVZ1F2AEtqXmhiLqy435ayE3O3NDvdlpnWZs0TtzPpXUq9i7eJ23AcqUqJAYmR5fGCcdXzxe9/hXitI84j1z56wvxWx9iOSBeQIOhcGcaBkIAFoELoA2WQXRPonTbIChYEaRTpvZy/JLd2aGgITSSNie16SwyBoA0BoWkaEBhLJg2Jko3YNZS2UIZCurhE1pfMrDCYMWZn8TRoNe10LY63MIq01+d0txcMr46YQvuxjsXtU5qjjtV6DSbkvjDoiuHCvcbMBBt9DsgEgNR2lZ2Ha3WMkRg9VeVFhpVRPABck90EwaGOIQSJDAXydmRstyxO5shywbZsGW0kF6OQUSmgxpiNMfU+5yTswLMVryzMnvFmrtBV5nCLYcmBZO3cDdTUoggxNoS2RWNDFmObs/crlNqCMgSKurZ8Ys0c0PscnRqiT4BUQ7Nb7nO1GDFRVKfWJKnqC+vbDkVmFVzJxEqaYdlbB7lpqoOk4SJR0ug3JqGFeWT2zJJrN5TTxTFHMkMfDIwXG9L5FrYjKRVvFWSFJrZvell4t+I9uYbtmhuf8niwZXVzfwj4fwM3cKPSw3Yx1+oPuEbrszwl98LvMPJjHhO8tdDHuUorXsVXSrwVRervB37uI4/9JuDPm9nX4iKC3wQgIl+PG8Z8Q33P7xCRN288NV2Dppv1mu7ZXfpNaFJEzpXx/kjpCwdSHN8RFWdb1JfQkgrDqmf16orV5y9Zv7Lh8t6K7WbDMPY7Px8RTyVpTVft8B2V1TpIWpVdJVs1spSpvk72eR3Zu0XJwcN2wPxYsYNUkJFTYrPZcPe1e6wuVwQNdPMZs8WMdtEhUSAKRGfcxjzSj1uGNFAkIxFo3LVbWoFG/Pod8F53UdAmII3WXinOeogJbdMwW8xoZt4iZ0rHOdtXbSD6wffShM1qw+ZyxbjpsVT9qSiIK+u8mKGyNiJKaAMshHi7Qa4La9uSKcR5h5wqdhOaj7bMvmbB4lNLuhfnyFGEWM+EHC5bDy9g7grvKd80Dmy3a7b9ljSOuz6Su59d6o4pp7arzgwiWC4Mmx4rxmzmabH58ZJ2OUPbliJuk5AxcgVIPDRjaioPb3ezGYWzrXC2ge0opEy1WqislwZC1zJbLunmM0LT+HnesZFVHzj5lsn+eEzH4dHknxOJeyZz6qtItaXIKZFyIltkZMYgM7I0+20fHGU5PO42bakQKMRS0FyQZI7ZBk8xzrs5t67f5Pq1a8xns+o1lr36dWe3YZ7kLo/lG59U/H6e9DWM14Bv443F7WscWP239e9zHJy9EQN2D2e+3mh7H9T4J3FPsGfe74E8gbgO/HNA834P5CqesnjT2yUz+w4R+cQjD38r8DPrv/8A8BeBf7M+/kfMrAd+WER+ADf4+Gtv+jnFhcdTTbxMQGpHLCk6D2iJSDPpbaz23qMmHGsz6WKkMbG+WHPx2hnjnZ60yWDi/j6lVo2pEEXdGV61ejzJnj2pi3NBKk3ijJcKNLUh3yTInjRFUskwF8N7S5YiVhezCbxR0ztSMzOFlBLbixXx5pKTo5bF7Q6CUnJdMA2kuA4nj4liiUJ1ew94SrTzMnpL/rc3DnTfJFHFUsRScpLO1K0dGiU0SmgVRj+OUJt0h0BKeXcOUp8YLnrGPoFlt6kwc+ihBRMh40AiiNJoIMQGnXXEtqWMfl7KFtqzHr3ZYaMSjxqaZUduMuGsdTro3A1KXbfn52OaFCrB/aTIfr4nEFuKa5LsUdjAflJ54nf3OijOGglYKmxXPbOZ0M5bpFWGrZAVUgXIUochMjmu17sEcYYoqKcDC8I21RrDspPCkwVMiqcNMZZtQ9O23gJpGqo5s6vqqV1ne7Xu8+FPLThgmrMHtwhWdWISnW31ZDBNjHRtQxBI4+jNzXeaLHv4kBn7x2p638yrPWMQF+0nYVxlcm8QQVph9eCMcTXS311Rtgn6AR0SQYzYqNuhpMexHU8m3ptrWAb+Dq63eobXX1qXwMdwO4cOZ3We+xLb+yJubjp+6Y99qmNKQR/GXdxD7KX3fjhPPH4y8Ntwpu5H3uexXMXTFO+Ul37WzL4IYGZfFJHp9uQF4K8fvO7z9bHXhYj8auBXAzx3+xm3S6gpIkGomTf/miqUttA8H4k5VKPrmt6YzK+oTJhByoUhZfp+YDsMrNcrGN2DqWkjpWugCZAzjQaaEOnUXcBVDpIzNeVnkl2YXe/QFfUUm0o1dywkc2fsYFZBlzfxxQpqhoXgC+YhaPDjRxRFonJvTKzXrstqTgMyaxl60EWLRKkLvdBIoMw7kiZCVEJQZKYQ3aYiD5m8Lc40BPGWRRGaqNimkMdMSca4GR2kRddIlTFTUvLU4DoxP52BQWwC/apnfX/N9v6WcbMlmKdCxzw4kFSjSAUxtbS/1QaRSJSG4d7IkDJN7mCEzRcGhv4cOxFu/Jib6NwoUYm3OuInZlhfGMeB0k82HH7OVUL14zKKCVIyRWBqqOxR3M5iNzWkWkuwK7gotcIiVKDVaSAjrM7W9P3Aya0T4jySG6VfB/pUkGw0SIU4+16LE0SZGisbLg/0VHLxf1cGNatr1EJQtBXCvCG2kSiGFgdYAQf8MQTXAaohGhHNDrhUIVfgVcmWSfTudhrRLTTqeQgqdFGZd4EmKGPODEPyCt461n3sEq271L1NKe9ilAySCtpn6MHGwKiBHJTYBmIjrFeXrM5X5CERVWnM069mMCYHpuG9ZbQeF+/qNexjHwN3bv+bwJ/ECbFH41fiuizDheLPf4nh/Vk+eFYOj8azwAoX/0/xd97kPRNP+0HU8H0eX1I/yRXQuorDeLcFAG8kTnj9g2bfhnPtfP0nP2Ulu1HiZC65Y7QOtxo8RehaZ9u1FDm8Aa+2V4SgNKEhhuipulRIY0LNaGJDbiI25p1OZards5pwwXzxgkxTdS5T2kdE/XOKcypjSaSSdrqfeJBS3P1XGZjdPtk+JTlZEqgKw0XPxUtnLD55xOJoxmzZUBqtRqOCFiHEFpkLKUSapqFpY00LQs6ZURKWR/KYd3ZGOyQgzuql0ZsBtzTECjKzJVJKlK1BMDbrWU2LmrNZ656x7xnWG5o8IlitKpvOhe/RPp1qFArDODCeJ4ax0OSWRltkUPKdRNoY+ZOZTEYGJbYNsxcX5Fd70ll2Kw6J06Z9Okj9lLJHNocTz1825WZdTzflmneKLTl4tQlT6yUBcsn0my3z7pi26+hHA3W2LgiEiU3abWZXx/jwOaXaPNjEjHraLKh7US2P5pwcRX93ragVcVZsN8BpuE5L7qocH0ocTmBSXM+lsSE2DTFG2qh0UZg1SlS4XG1ZXW5YDRkjOn9ph472B7FLH1plYK2mlbOnAosisaPEiIXgrZEGw7YjIQ0wbr15uYTavsiq/xwHerGnLt7RNexbvkUMNngK8Q4uZu8e2VyD+2K9WRhu//BBj8kN/hT4p3BB6V/BtWd68HMY02MDb2zm+rTGTwL+Bx7G6VdxFe8caL0iIs/XO8HngVfr45/Hey5M8SJvgSMuY6G/2/ud/SKgM0Vnulu4D7IXFWSVXRfbqbINQAs1xSdoEtbDSEkDhZEmCG0XePBgy+Z8yyIZDUo0RXIgEVg3HQsxZn2glETPgIZIbJpq+plpmw7EGENhkwea1p9LQ8+8jRQSvaWdEJviyqVS/cFcEO9N/qx4r0WrC/EiNOTLnvs/vEauw0dmke6jJ+7gbkKY0i7R4CjQhojGgEUhi39O6kfGlLDGaBetM1h59APXQHMUCV3w1jsCfe6RNKXTIpoFLg1bFYZmpFs0flN6p4e7lzSXl8h2TciZUASjIRVPr86aQBcDTXThtREQi4Tcor1Q0kBcBGZHHVggDZmyGelfvqA9hng7oilw8+SY+DWJ1G84v3OBMXr7ouKp3uoBT9oxWYlH9UsTc+jVnZ4KLRRUIm0biRoIKvuelLmgFGbi5pppm7k874lLIxx1vPbgnB99cI9riwU3l0eciNIVY05ALRCsqYBa6A1GLVxaZiwjQQaaUGgaoWkb5osjTo6OUVNy7x5YzoziqLitezF5cwmEZUPShI0Q8YKF6WZD1NOpQZQmepo21j6VorBOhddWA2ebgc22J5RMJ4UupJ256s5KBan/nswYjEkipgVKqN/DAKaJnFcEbWhCNdgtRhcFnTWsxy2Ct/bRCuYm1/nHC6Xf03hXr2H7uAR+KfC1wJ/BU4ZvN9Z1GB+GeA7v3fh/xlNq57gr/i8CPg186g3e9zeAX8YbYNynNL4D+Ndw49Df+z6P5SqepninQOtPAv8S8Fvr7z9x8Ph/IyL/GW4C87U4l/6lY9JkJfOGx4etVR4TVkUoO0Yh7NfYqXWIALFp6LqWo+MlcYCwLRi9m42WQoDKejnzNcaIWEIkEgJEcYYqJ0/ZNDEQEQjKeVkzu37M8toJ61cfIDk5AKhaoarA2jEdk5mms24T+1Gc5ap0kJqbS1pKpIuevE0QhCa23isuOqg0NU+fBv9xz4m69SAuQM/mfQLLZIHgi5wG9WOXKwixwlS1iQlqbs+gUWlDS5DAsNkwrAfSZoDBQeTOzoBIb5GRQM4O+DqEWNlHKYb1I6OA4QwVAZaLBf1qoGwL46s92yYgmxmiShIhD5mYA1Ejxca68E8skuw4pZ2RwQH78pDEaHfM/bgXdQZLD/VzNf08yccLjuXFjC4oi2tznhtPuX954b0ZoxKbls4gFpBkSJ23uRY8BDXmUYlNJCUjEZAQOD05ZjGfuf4qy86tYTLGRai2InbAKO0d9jlgS3f8mcFkgDrpE1Mp5B62xRhzcRF8TgQKbTBcd28PqWisgrfdl7Jaj+xO9l7mVsfjc9gZsUIInsZOKZOS6/ceqog0qWn/x5FG73m8u9ewXRiur/pHePPLa8/uJsGbL9XHCx8e/6mAZ15XeDotAD+Ig8mP40Wf8Hoi8QGP13g9zfFZ4FcB3/8+j+MqnrZ4K/YOfxj4mcAtEfk8XjrzW4FvF5F/Gfgc8IsBzOwfiMi34yrOBPxa2xlKvXFoUGIX6zW7AqhycJGXKbUnFZ8Iees6IiI0Rw3aVb+gyeIhCLOjGZZOCKXBViPjduWGnaU2LDZDm0jbzmhESSpIBg0GITKLkWEY6fueruvoYoMUkDbSdLB89oiimVwGb6ZbK9yK7SsPC7UAzo8mMKU4J0ZifyGRUnBzR2E8H3jwxftsrhmL5ojl8RFN0yAqDKWCs+oxZUX2/ZtDIDQBGwuD9A5QwoHuTBxkFNxLi+yLdoiK6+Pd92vY9pSLQtgI45016/MV68sNuh131ZNYQUuho9CIQI6MFkgZmmjMmwKWSMNAEqWdd8zmCzRGBhuRudLSMtzp2aw2yMtC80wDp4acGe3G/Zi85Y0bnzq0KtWgc/rLQdHU9Hl/QPe/ZAIyU9WoVRPN2hB8Sp95+5rakmnMMI50kvjIjSWr85tsxsT8eM7Rcs7SBDYFSwlyBRdjQoF5CCxbpSBcZuiL0MTA6Y0TukYYVr0vIzL5qNVz6LlJjzxVxgYge0FI0yDZXKdlpbJgkygeUs5YzhU7Go0UGsw1j6Hal/z/2fvzYOnSLb0L+633ffeQmWf6phpv3e7b6lYjtQVCCCEHGgjEZLCZhLGNsYUlJiODMWaQCGQIKYTaZhByEAZhsBkkWYjBBjtsQNBIYUJGahBYbdFSq1vdfae6VfUNZ8rMvfc7LP+x3r0zvxpu1b23btVX1WdFnPrq5MncuYfMfJ981rOep1rNC8/jqnro9XateZ+Hlij1/mV5oJJzBklIdrRNAyEwjltSzHVjwvEmlhazfnIL6CfxGfbeesQ3H+oegb8H+IPYBf+dmA5fgFNMON/y2WufvbueYIzWT9bfM3aM/wfgp4F/Bjj5dHbtY6/Ct4TJ7+rnTH2UqcP/0Qf86dd8wP1/O/Dbv6W9EJBWKElN5JwLxPqJ7Geu4XjBdOTbxPjWRHEJ98oJ/kEVSQtIYxOKFI/0DdHtGMaRuJ1I+4jkRKqml01JhBRBAi4rqplJMaqomE9W0wRjOkqhCR2lKH3piI9Hdrs97VCQWMzkclma7NuYTX65KlY+0BeqSi4GuAQ1+RSm5ykFWt8jWyG/PVEuMrnPSPLm3TUvWkBxli2odYofNY2RitKtO0rOZM0WZZPro2ygz6YXU8ElRxMCNELpgElhVNhaf1Z2it4mpus9XTKPsqy5smF2joJYyHXw1t60YUEDAuKErmlou84Acyq2hmyAAO7adnzYDUxvTuhbheHplnRrNhwejxM7RlRJ1ZC0zNunsoTvAVkHCwfKEQNUCqUkRCwg2i5JYRG2F6VMGXWZGJUxedqu49WHjxhyRM6F/rxFRiU/mXAu4By4UmiKtc8MtdpFaVqBmm3ZiuCKEoIjz6HUAtVnxN6RTWVGU6IRb/YO2aYQm6ah5BoVXfLC/iqCuplDPXBGlqxpx1fyAWDNXXnm1+pMWun8qj3wgvNpXeZOjl5/OeV6HOBDa9c8CyG0SMrMPnIG4pTZDuObc9Yfb30in2HvYV/+I0yn9X6j/hMGPn4/B8H7P4HZHtyv2/qNwA8C/xifTWf4uQbM3f726La5E/v7Mebq9/Hepege8BfxEQbW7+quXvh6IdzwRDCfqDoJNutqCOAOPSFbEIu1xKbtxHQ5oI1S7pfDt2fBwEhQfGeBxauzE9grkYGSMuRMrgxGKUouBRHzg0LVcvZUEDWdmHNSp8cyPmfUCet2xXY/kW4nOgWnM6w4asXI3LKqa+ncC5pvZ5aazaYKdflRIbiGUAI+BoIZYi0C/HrWFsC2tJ0qLaFYa9G3AVobBNDqCK51rROpfmOVGXPiEKfghNB4Greyx02ZMmTykJBkZqdL8F4tLzYtF8TyI53J5Mjz5JB4vPc0IVh7S8F1QnipRSZhGkdKzLTZwV5JY6w2ElNtEc7n7SAqn5ksrUDAztuhxWUCd5ZMvaPkG2OCZq18bSnO5150ZlUVUiEnJSah2zScnXhanchnmXDWIENBtxmC0K17QmgWzVPJmKGuKrlksxRRxU3VBiJY6LeKvQZnR3kcRnDkw3Eegrktq1K9r+xnPRYz3GK2eTi8XQ7tmAWHHp2iw32eb9ssvOsRoFpatEdxQmBt8lKPZw5jR8ykVhcH+MpOH31hem+r6LNepxiYmKflLoH/K6ZFWr/rviPwI5h4fq53MO+se9i5uVcf+3v4bAMteH7qcH6R53r7j/H+E4b3MRz8Exgrdld39dmtFwJoIYLvzXBUi5qzebV70DqWb9EhFRyQYQXNowY6wZ04M+aszIWogDjatqW76DjrTrjODcPP3Fp2G45CqlNglmW3ZMfV/QnemVdVqi7k2IKzLwMhNDQ5IzGxbhp8NtdwUeMPvFZmKtsCs1l1tF3D06tbcjpuBRyrbapLuPPgPSUmc3xvWjy2gKvP1j6rsTMzYAIQdQZKUTQEarI0Uhw+eMqk5Nt8WCIbTBOnxVivVMixUKJlC/rai5Ti2F7tydtIKwE0slBoWIhxqD6o3tWw5WOn8QpUnbPW2Oz5FXMkbhOhbVidrBiud0ZgiiNTJwqLeZ6FeXuKAaiFGaygaG6hzUCrThFqJZZmtsGGL3XR0SFSQdoxf2P3rnZuuFLQKSFS8C34ERiFsitoUlJw0DbsRHBkfPCE1oxau5VZegxXO3yudhe7SL6ZyEO0/Xf2CnDKbPSOlCOGquZEejD9Vyn4rq1MYcHbgVvbeqY6Fyhjr0lVE7QXqbYpx228bwp46jlm1sItEIzZ/mQGuqUU0jSxuXdCd9YyDQP7mMhTIjHZlwEpBDF28rM4vP/N63ux1t8t5n/6J4C/CxN//0aeP88b4DdjbM2frH/7KzHN0txu/NPAv1P//nmpM+xcvIpN5v0x4G/leXf8uU6wOKK/GJPN/fuYwetd3dVnr14QoAWu90gu5KFQptriEAME8+IsddGmC3T3HOEkoL4+1h0zFtZnEhHa1iYIlUyKCZK1Hm3N1soI5MoGeKghyE4wnY6aWHWedANrze1vbxERumD5cVrNiLwWpAhJC1FNW9Q0ns2653I7oqND1DJyZkAilYlApJqmOnKM5ui+Chj2zGaG6Vzlvyp7VykMhyzsnw+mx9ICbu3QRmDIpDFR5vOqCtEArd3XWrYpZkiZmBJtEwhFiNsRpoyLMx0iVb9j++pE8PWcLSBrBq1L+84d9HcOylhIb2dKq4TiFxuOknM9FqqeLoOf7R2kMk5UkCULI3UMtJYmjsy54MoR58esa5uRzcHsc3681HgktXZyNHNW3wdCcQYettZgK94zifDs6TNcEO49OmcdVpTa5iyaGZjw3oA/eMpeiFO2KKFSDUCdB+ewOQczPJWaAYk3xy5JBSQRutZY2MqHGiNnE6zoQuxCjSma3f7NlqJU37Fv9oY8+qMeR3UfM7aybFiw1mSMI9060Nxrub2F8SqSExRJZt+BDZi45bXxeaoG+K0YW/XrMaA1AP8plubTcwBbDgNm/yiWZbiq95ljiRKm2frdn8yuf9drjbVIXwF+C3a8fy12bv47HMDl8RcFh8UR/VXArwJ+AdZu/UOf0D7f1V19fPXCAC3xUkFObellzHA7HSlGvC3kThyyEgMRTg96k/ojKpBsoi47SHFknAZiNJsDGzWfq4qgl0aLGYPGPBGCsOq7yoUIqRRSMjF9msaaUdeAKLH2ZkSp7a55HUo8fXaFTju6ZsXUdhAjU6kfu3Nkz9xyKoUyTUxlwF84+u9ZUQZBWzVQKcdtruoaXixwuBJ59VwyJ2DjfGXBPDaxWKe/CNai7NqeVjoiyrCbuL26pHOwXnWEyTHtB3TKSCnVtuL4wmEibSfPM1nLnwUn/jBJN7eksuCSs9zFYL5KKnZSQvam8zos6VCOtqzvBgq1Lfr8sy/3fe6By691P8Qf+KzKDiH1ihclTZGy3cHoOPnCPdyDFYzC/jYRaAgrz+PLS37yZ36W/qTDn3Q0mxU6QbmJiCir1YrQBzxmlirOmeVELuRckJLJmIjeN562b3BezBC1gi0LF/D4JtC1LT54drcD4xjJMeJECRXsOo6OeWkXzm3FGfAW82Y7XtuW42e5zrN31uF01xNav9XI3G+VzCiZ6XTi5I0zyhVs3xpIqUAJqGZED5BNPpdgC8w769gd4keAHwV+Sf1b4AAk/pb6c1wFY3t+5Lu+p59M3cesxn4DB8AJJvb/dRy4zYK1EwsHwDm/RtbA3wv8ecCv/u7v8l3d1cdcLwbQgmWxc85Zbh9KSZl8m2l6h+vkEMmmlWGaNUocWKo4pcqEKN55xmkALbjQUGgQdTWGRgnB7AO8hMVxfGEDKhuieaYEpEapWOwL4tmniWmXOTttGdMEqjRa8KWgOVFyQrUwZLiisDrtUdcQS50is1hhBMGLQ7ynFItXyR2MfqQpe9b9qU0c1tUuV3+lefpujkWRORIIA1UmhsIWRGfnNmsm50zwgeA9OWU0Kbkk8nYk3gxcX+3oWuXea6eUp4khmQGlSxPmmFUM7M5BxeLIx/FFlZ1zEhDnDCi4yiBlLOrHmbhbFVzjCb1HyZQp2eSftwWfYhFFbjbsREm15TuDAueE2cLiuGV5/Oqa/3v8A3XBd/55VqwyRJqrX9dO0Kcg9zrC+Yr+wQZZTdaXlAY3tvh+RRyVt3/2kidfuaJr4NGjM1569SFnD09QX8hDJN+Y/1jbBBLmMYWY3caUEqQJdUq36hHvcCHgK9uHWEZgKpnQtfQ1LHoo1Zut6vPMG7QCIeeX/1+agJXBPQZPMoMseB5MH01yHm6u74f5G45CSZmkyuO3nuA2DY10NJuAasGPEAfI41S388mK4T/dusTYmz8Hs3z4YQxwzQj4XV9OuMacJr7E58MmYIUZef4AxmD9EMbm/UIOgHPABgQS5h/2KtZmfLfh6ycXRH5Xd/Vx1osBtI7kJc47pAWcMm0zeZdptTWWprGF8ejz3bollTlBa7tvzIy3I6EJFDKrVY9vA7kUWlFEE4rgfUMTGoKa4Pw5mZYLjNNEjBHnrD3GDMTU4mbGZN/Sz7uW/X6sIEssSkUznsykBcVxPSrr08yqC8QpgJrQmwoignM47yjS4HxLd9GzuTjhbHWCbg1UeglIEIqYuiznTNwlNFZH7qj4xrO66GlOzBYhOGfnzouBlWI6Hxcc3ntzbY+RPGWmqy3pNpKi4+TUc/LSmsdPHjPEiT6PaBzwzkzLfAWo4hzqHdnJc0BVxOHdDLR8zYQEsthnag29tonElva0JelEnoRWPdO4J94OpNtCiYq2YseuptHLWK6hw9qSh+XKANdCcM4g46il9tzSJu5w0edhgXpdIKHiyeOEXkK4mdB1h9sI4bwFL0yjkgP4bo2boMSWnCPto8DJ951x+topfd+RxmiTjqPik9B2AaeQZ0E8hThFhnGCxtNt1jjfEJpgr+OUl3ipPI2cnp3Qhw7xLTkreRrIJYKWJevR4fD1+A54qVKDOjNVM188s33P+5DZXxY3uOUtu7CCVD+2ZCz05VevYAzcf/SA1dka3wXYZqIP7MstlpH5HEX2c6CuMAPOn8YsHX4A+8bxDMs8PK4MfAUz9/w8lMM8tP4brH34hzGT0j+KtVtvgd+GgctLDgzXPwT83Rjg+ryyn3f1c6VeDKDF/JFdW1FerdXlbJEouSCT4BtBAthycKQXKdYm9Cqs6Hn7K2/x7CtPePV7XuXstTOkUeJ+i+6v2PgIfWCbMlJHvWZbBmYmAEGqm/v8bHD4G4AWpQ2BbrOm+BVZt2x0i2iDYgCj8a1lJSbYTZH97TX3ThpCl7jKNvnmqsj/cB6EIo5w0rO+d0rXd7z99cdMY+Li0X1WZz3qi01B7gvT44l4OVF2GZKxQ/HeRP/SitV5jxNBg82vlVxswi0pWTNpzDZZ6JSUEnHK5JQJreP03pq4j+yvB0iHxXs2zDSA4mq0kC3rh8bcwWBTZoNNJ5XVWq7aoi8rdZK0YPmASTJZ1MxE58fiOCi5jxgqOdJeLZChCuZ1Pqd6+Kw+/syWY4ix7PoCQChmVaBOKLGwu7zF3+vxfoN2ims9EoSzh6e8OmXSFBGBpl9x/wsbzn/+OclndvuBNMQlI1K8WGvQueqjZW1f58zw0+MMDwlmS1GHFiwg3IYQgHpOrW0bSyaOAwI0zlcG8IizMtR58G2Tw5l6rkV7fP/5fVnPyXPnivk8WTmxWPfGBeJ+Yne95eErj3A+kMqEF0/wwrQfiFPk27Km+szUA8xQ/ms8f8bewTRG349903jwPo/9w8D/i+cn9T7J+kUY0Lv8gL974NdiMUMfpb35azBmaq49dmxan+NvAv4I7zVo/SeAfxX424C/AvjFH+G57urjr1+KTcD+wU97Rz7T9eIArdm2wB1WO9d63FRIJVOmgm8bvHOUah9w/I2apOQI5Vlk9+aO8Vlkej1Ba+L1NEw4LTjncU1PyAOiponK4pkXcFXTBrmSETLOFWNMFtYDDEgUgoAryRbQUtAiZNVqazCbPRScmAg4xcQwZjOV1CpkpmpxKqtXipKAcLHCn7YW1pMycTvBRR3ln8GME6SxdlzeZmQSilObHkzQ5ob2okNPHBMTKSYLnJ4yU5qIQ0SzIj2MKRJzIQPdqef+qyeMj0fiVaIttZMnAU9YdHJV/Y4sPd0ZWLkDUzSDgTqSKA6b0KxeXgRHiYV4E8nYuUmdkNSaq855PM8/x8IuYiD10K2sQL1eo3lW7t0/jgMInDHuDNS02l7MIKKogT+XlHQTKTcT8lIhtAFageB48Oopm9MVMUeKFEIr9GcB8ULKmbiPpF2kjBkfHK5T0ggSBEn1mIC2afDO0gpcrshmKmSfIBeT1XlneQIpG2ALStN54miMbvDe2rqLl4OBstkaROt04nwOj9HScg6OAOpRM/Zw/8Mb4Tkc4bGYHpmsBTwlu54Thc1JS9NkEkZBa0of+pnw2a1fiy1Ovwf4f3NgaRTzjGp5/2m7Afh/YO3D4/pVGCv2//0u7e+3Ug7LLnznI9zXY95g715mvrdup8WA1p735gMOwJ/CpjP/AHbevtVa8byFxl196zXPX9/Vd1IvBtCqAIOZ8DBjJss8TJ5xN9jUW86w6WCNTdLBoqHUVMi3if3XB1x2PHjpET437J7tCRewuX/C/YcPuL18ypQLTr05bEtBQ6E4Ty5CLpBzoWhCi4GpLghhdgyvC5MXQAtlvyMNSqNK1KYuTXUCbHFwVxogZ+XpLlFquLSTOUB1BgyCeI/rO05ev0dz3hFzxuPptcerY4wjrgl475GN4BuPHwPx0tioPCb86NB94elVZvOop3k9oAFKBLJDx8TN41vG24HutCW0DcO4ZzdGdlPk3r2e/p7j8Y9ek58muqRIcTipUUDiTPvjnLGOC4PlahvP/mW2J/AC3tcJugqSJVMQO4djYtrWycRTR/uop+2UcT+htyO+OMN0VYvmpbJjpUKsI0rKWLfD74dQzCOwJTMjN9trHDYxe3Zl9GD/kYU8ZvTpyH51w+phz+bBBdpBSRNNCLQnhhy1DiCIgEQgCmW08+59S7sStEQYJoiCa2wIwGVH03VAZ8eaDOKUmCnOvNucCF3X29CAs31zATYnHY2cMjUNmjIlRko1M53d41WxKUe0MmEVmB5ps9CDbusQIkU12z3iCt81sjhvxynoGJEC0+2ex28/IznHOzc7zjYdX3p4QSst2e2Ztjs+v/V9mMbqL8eAxleO/vaH6u+/lucd0b8K/JMYo/Xu+lngnwP+Eb77nlo/9iF/j8D/8SNu65djx3n1rtt/LdY2bLFW6i/HzEnfD3wr8Mcxvdq726wfVr8R+Ke/xcfc1fP1Rz/tHfhc1AsBtOa2lom4q7XCvBbWCbqcM+N+AhFCCCawFhYDUxmwSa+p0J12BN8wfG3HcA0XP3RC17d0XcOtQk6Fedbs+KdpAkE8imM3jux3e4Io3rtlknBZYvTwzb6oTeMVZk8nxSYQj3gUqe07LdUhe3n4UdvUIy7Q9A3dpseFQN4n8lSQJKQxk4dM15tWy4kzDcyJEE8jsUTGONj6mmC8HRninl461hdrGgIITHFi3E3styORjOuaubdEKZnOtYxXI3E/2XlSDu2/gwhrYYWOW4VLzS29+XaRGpFj9ynFlFaqYsMIzqGuEO41tF/o4KowXDWkb0wV/BhoknIApSxA4biea4Bx3PCajT0RY9kWvd/879GlPW4XWyxPJk2FuB2ZrgfWU8atAl4E5zDrDTjE1ijmBVcE7zwuGMskWSEWXOch2nkwIKiViTqwT1CNPktta88AKZjmrVAomvHOsVr1NN4z7PZMxVIMqNYkfrlOc+f2cD4Wv3ydX4tzU34GVnOb1257L8g6PG45f7kw7PZICMiqZ0rwtae39G3PvaanXylliu++cJ/xusRahddY2/AEAwa/jOeBFlgW4g8Dv4kD2Pp3gH+R9xev/SxmbdC9z99e5Pp+bJ+/yvPOaf858DdjrVPBmKcPq3/jI97vuN7Nkn1S5bBpyy13jNpdwYsCtArkvZlx+q6Os4tQfIbOxv0lNzAIxQlu7QnJkUikOFFuCu3Q4WJDSeDXHhLcfPUa1wtNJ+yeXrF95xbJ0Ndv/qjiUTwFKcqUE7jAo5dfYTOsuARyTqgrJD1MCApgpt8HEAW5useXusgtPqt2gJI5bmDV5c22qdVsHU+k4BtFWpjGkXyr7B5v8beWc+jxrO8bMIklmVnouefklVNiNzHFCSkQQkPMkf3TiRSF9qUN/coT80jcj5QBJAWePRtI4nh0tmLqMqtVy6meUL62Z+NacjuRQ6iTliwGoIZXDh5ZUhmihRkSzBPMOfAe9bVtWM9bLoXkCi54XNsAQvaR7uVA93qDPymMb3Vsm52Bg+roqRkkelyh+py9S+vzzQ2izN4jeII3Rm7JbZTKZilVm1WjY5zipE4GSsBHkBtoJockTOhf9Wr2WhAzCa3TlaTq3O8NsNIJXhoa7Ul+gpDIW0Wz3U7BbEioAI/6ZUJtyrKgSHD06x4XMkknmOxtHHpHIz1XU6ZMkZaRUiwOx7uA5vqaVT0YAlcAZp5kWsGXVJsV3nfdF5nbi0d/rHdWLUv7tkMJ3nPSe/a3kW9c7jh50LJpHG79WQMNH1Z/FpuoAxNy/ybMluCHgf8SeIvDorsDfgdmcfB3YCfvR3l/kDXX7/n4d/m7Xn8O5ur+N2KtwLl+L+aR9TtYcrh4gJ2jD6o/8G08/3/2bTzm46hHmBnrDwP/wqe0D3f1ItULAbQoSolaQ4MtrsS+5NeFZbYCyEocEvky0peWdhUIOHJW4nUkD7bASHFoTLQPWjavrwltw/jOSLyZbNjNe1K2GAgnFp9jRI1SSFxdPqX1Hau2ZZrU2kjVLHN2Fy/U2BQtiJieq9SYnpILDhu9rweIyCEFkarHmk1GDW45G8tvA27dGAhIoJPSNh25z7gzR/dgxZQnZBJcsFaV2wjtqwF/5jjtT5mejaSrRJwSaSxoymgP0giazLg0R0WzY7eP+D7SP7ogrRXRhnwTma4GypjRnAw8+HrkYq03cQcOSJbfK1CZGRQ32zpUZstJXext35s20GxauldW6EYZr+qiPyjxNhL3sWqJsBPvqK7/RnfOIOEgG3reNOD5ZUufA4GuTuItiUaLbsvuubh6KNX1X2m8R8fC7TeuOL86pztd4b1DxdXDNnpH1TYsETQLi4O6KFNJiGZKULQTKN6yHycQ8SaMnzso1cbDCTQ2BULSwpQS15fXPNWRoWRek54uOFzvWJ/0vBQc19fCbrtl2o40Cq2HUJlJswWpwF/9ApBnl/y5RTizfceM1QyyZuf9+dQduo+K1IRFX5ROHOernmf7yJPLLW+cbjjpHD7M743PSymHFtnvwZisH8I+Yn8Yawv+iaP7F8yU9M/FHOG/GfNxio3qvlsw/klXwKYiP+TLzFJ7DDwdC/vfwKYJfxUHjdr3YezT78IGAX6W54HZZ61ugX8WA893dVcvCtCCyiwreczEyUCJmVYKecpoLKbJyYUhjohCQwPJUbaZ8Wpg3I6kMeKKx3nP5ntWrL7Uc/3lK26ubkhTolFjK1zNMjzMXEGaEq5pmPZ7tLG2k5KrD5HlIC4sFiwLaC7mnp2LRe9oZcoO01wFxNqLloxjyMFTp8NwFBGSE8L9FevXNng87AXdw+b0BPeKJ7zkKRsYhj2NtvRNMNDmBTaKXznO1ifsTwLbsCVdJ4L3NC4goQYAT4U8GYOiGSgF76FZtaxUaDshvXXLcLsn7fdoTHYss/hpFpFz3CmsTIjM7UW3TKHNLb4Zc1qEECAmkG98w/q8p3mlRVbCs7efcvuTN7STp902qHjEKUXK0vbDVTsHrUasdTr00Mx9/5oPQYvlWXI07Tm3j+3/ZU4wqhq7Cj6KksfI9snE7ZNbwqMOWQnq1UR79TFS44uY2cwKYFSVWNJBCB4E6RxMHp2MJaXMh1Nbw2JaPu89CqQ4MY4Tlze3vDUlIsKDjcc3DqGhW8O9dUsT7iFhTcxXxDk4m4nmCJzq8j9Ve1hZvRlkSTUlneNGjwcWDWy9t5WoOudnGiCUacJ1HY04NE7shpGN72g/bzjrufpTmHfWHCj9QSDpT2NTeZ5vDrT+MQyA/KGPbxe/5brA/LB+FvgZ7Jg+rP63GJg81uP9g5j56HGz3mMC+X8Kc9f/W7HInc9qbTHQeFd3ZfXiAC1VpMwTeLJ47eRUmPYRotKKffvu3AbdO3ZDJA+R/dUONyguFmRK+Naxuujx9z0ue3ZPd+SccU4t7TcXAjadpcyu7tCEFsHMK3MaTQitNWC6tkXM7HH+dm+BwGOBqdRv+mqmpogZHviZvqqu8V5tZNKJx4cOJ8HaUl6JTYt/1HPy/Wc49cTHkTKYCWs4heADjQuEVVOn8KituOV/ofFsXtrQn624fXpL+2yPLwHXCMMYmcZCTg7NkGImeFitPb4PdK0jbxPuqSeKWrtUS2X7DtommEOwqTl3tc3lwPtqZFrF71JZrRnksKzzhTIWYt5z85MR/42GxjXokJGdY9ztYVcIzqOuHBZ1cUgQtAhSHFKqcPtDv2QfGJqiZmtBnZ6c2bb5XgfdXm2vFQCHTJk2mAXD+GxH3J/jLoAguCCVqawgNFWQYhTgYhrqQqjWJWrCeaeQFAqkyVIH1FsUT8DT4EAhSSGVwqCFfckQGl4ODY0XWudISSnRWN0mFFabFa9crDlpHfv9yLDdk3ZmHWLWF8fBOge2avZCczMSW1qCcgDWVes2g+u5in0tsdeEKnEc2StIG1kj+JMNN/sJL8rrF+8OWv681cCHszLK8yDkg+ofx/rQn0Y1WFTQX4/5fz3D2p3/wUd47Mh7AeZxFNG7K2Dt1jfqz7u1bXf13amHmH3Gaxj4vxPAf9z1wgAtqd+QHbaYl1TYX41sH98y7AeaJuBO19aqSaadkiIwFeI24pMiMUMpuJUQHnr8RSANmfwkEwZvbZyiS9vOJqiMPVOUULUlYJYQZnaAsV/LvcpzImLBwIjTUreVl3su9gJHLFiVjuNwNKFDxTOUSHJC2ZzRXdyn7dZMVxPBt4SThmbT4M480hooMBbMamYljIHArqgXQhM4a05Z9T1pl8n7xDROBiibhn3Zk0tifdpxdn5SRdyFUpJNreWMlrKADZnF7HJoET7/cXlAOnO3j3rsmisj6QTvg00sQm0ZJ4ariO4GQhvIMePFE28HdBcJFdwVSl34lcO0hJ3RDwdZ837N1MzhmqiX90zfLezO0kqrz5cV5w1npDFRan/RVS8sEVnwS9FCjqm2ASt8q1YXeHtlCGYeK61DooIUnIYa/wM+CSTTomWgiOIcrBqhFcsGJRVyskzDUoSUCnunjDrRdYrznr5vkVIYkkVHOcrclTw0VIXFxsSw1cJ3MWvy5uv53Pk8um1+lZcK/ksuEBPBF9Ztz7r3DCmTFHZ8nn20Pq5qsLbil+FTO1+PgL8aa1/e1P1ovukj3r96zMrhV3/I/Tzwt2Mh2/8CxuLdCcq/e+WA/ykGosFaS3dA6+OuFwNoqeUPzlyJFM+wnbj8yjW3X9+RY4LgiI8S9x5tcDTEMUIyk1LNibRL5gg/jbSvrpB7inQwPRmZnk6EwZOjq+ABKrIwUOBtwcglY8KoeQGquYt1N0sFWbN4eoZSjkgrZtqQ5+lDbGE0HZBpsKA2DJ1HmoayXuMvNkgf6c9WPPhFX+L00TmkCddCc9rizxukOsIvGYYytzWP6KzZqkAwgNgIjXjC5BnHiSEnNGeaJnB6L4AUYvL0j845f3hqmqjbPfl6ZNrtbSqsWgTYNJzUYz60xRYOaBHEV5sOzDaDOhSQgVQU8Y6mE4IzXVqplEmpwPfs5Jz9zY6bx1cQCz6Bko+sXASKQ3O29lS1j/Bzft/7AK5ZtK8qCxA4nLPDfeabl2sszhJ2KiEp1CQ2EYrUTrebmTCbhFyeQLC24NpZXmeu7cQsls3pHeSCuoJWOwzvPTopZEUSaFZK8Azbgf24t2tRChojbhyQaECvZGtdqzhjq9RB8aRJSTkxoTRNw2pzhsezvczEKVI0Iyjem1UIonW44HB6lmt8BJyXE3YETOfbFFBXneKzQi5ILkwE7p2c8PDhOfspohSuhs+yBueTql+JAY5/CjMI/TTqlfrcvx7ToA0Yq/Wt1C8F/gHgr+OjTQ7+Yky79ldgOYl/37f4fHf10UuAX4H5vf27vL/Fxl19p/VCAC1jeKpMN2VKUsZ9ZL8dSKNC8Uz7jGsG7t3raZqOrFJbVoJzwhAn8mgeVb6t7aoEUuY2pGX0LTXHgCyj/bqI2Rc7Aw5gCmZvoTkv7rCJUsXQ9s2+HD2qardEyNXzKdQ2VREYUmSzClz84ENOv3DO5vvPiduJ6enIxUsXuJWn+EPMzHyuTFhNXdht72bt12yJoa7mPk4FjRmnlqfonaNpHbJaUyg09xt844lPJ3Rb0F1Gs+nSpNTJOw6GrYocROh1f5aMw3oOSimVdCoViNRW4uz0z6FNpU6QBqTz0DgL6i6OFDMl5+rhVMGwGKtUygw4LedQ9eCW/p7X1rJvBw2VnarKWh0hiOPsTPuj0VfVKYwslk5ZhOr1dhTfc+RJKSKE1iONNzu1saBRbWLSTgeajluKrrYSC3lMxJgs2LwouxTZjSM+Z7wWiBmfQLLgimnJCjX/UTKOgpdM0zhCZykFFpKeaUNgtVqBJlLMi4WE1Na2zjpEjuZrZ5Z3AaXHYniZD7jaYBxOs1BwRQHHdr8npZGTzuMkM0zJbC7u6puUYGzSf8CnB7LAJgcnLD7ow9qcrwH/c8x09Y8CfwPwl2GRO7+EQ6D0RymHWUP8chYa+q6+C1Uwn7L/CrPh+FyLJz+1eiGAlqtamVKUOCbipAtDMI0joo6YlCZJBTiKFGMJGu+RriWfTfgTT9MH2ouOpmvQSemalq5t2Ze9tcHqKNn8tl2+mMthoT38ZUEzS1NpVhHPIGoGWJXDgue3fNROqaqfyobknLm6uebm2nHv5dfY/PwLcsl89c9+lbJLrN9Y07YdquC8LAwcR4seUAHXYV91bqmJLZyllGVBBUUchM4hEtAGmrMGsjAmpUwFnUoVZNeldgGdcqwdx/DCzGw9h1bseMtsjeAIweO6FgneYnUEM13tAtJ45NQhJ8LoJsbbwR5bgeMimKq2EnVMtEbUVFuF+vu7W7QLiH43+3KEuHT5V4+uI5W1mY+sel0tyKxeS33Xdji0ni3H0TRkBsjL8+CkzMdCjZXCciCjoKUwTYnddmC3G8yqIZse0OWCpIyUgpSCK8VeU/V1dZ0zz4YBTYF7esob9+6zCS06ReJNolt1QGEchZIiWhLjOC6tZ5mRoByTpYf27Hz084lT5hbr4Xo5wIdASyAjxHFkHPcEr2zWHUphHD7tCboXvS6AvwX433+K+yAYg/XTfPjEowP+fowB+//V234FZkh6XO/zIfah272r7269hV3jzKfXov581wsBtLQoZSioA4/DNbYI3X/5jAbYbvc4VU4frXF9MOd2CjplxutEs2o4u7jPzfUN19e36JcdrXaUptC6hvv3L3j69p40uiqcTszj9gVZ2Aid24hz+2sW3NSVpKgyltmYFObWoqsga/7/JZi3KKUCOLSYLqt+vkTxaOMovlCaQnLZ4lhiZcsqriqVW3BwyEQsBhjn1t0MKKSugFpFMpqt1VRiQXPGBbHFPhXEKXRQYobB9EDqPHTBMvyaAFNl5w69UyNxmBm0GUzmajCrNhEo1tgEY03iVGgaT9d2ZMmoF7pNiztp0ACurz5dBVzxlekxJ3QXTAxuNlIGUJx6EGdtKq8gHpWM+ZmVmbKCOgGnhsIWkKB6AEYl52oge9DdLYBOrNVX5teHM88sHxwu2+BAExuLvQmBUgO7tRgomnVl6hRtq/i96KLtWhjJXCBrdXXPEBXGRLnZwXbAV7sQh7GMJSdcyeRs7OOYE7c5cpsndgo3EtjlwpPLPS4rr55vWDeBsSnGMmahwRG6jjgF0ngIo55B+WJGIkKuUHqx8BDqZKldF2u3Uoc97Cx6Z63XkjPem4P9iA1YJAc+vDt+5q6er2eYqeenaeyqGJv2J3h+Ad5guqljFvkca3P+Q9jU3Qdt7xsYs3XvI+7DR5luvKtvvxT4T/nW28F39a3UCwG0SlEzLA11EfMepXD/0SlnZz3XN7ckzfQnK5zzlK1ph+KQGN/eETYBguPZN67Y7rb41x3xNCIX1rYb444YB/OEytkmB2UOWZm/MQmoe04wfTBSYgEPTkzsXrTyV1posJbN8m3/iA3gXSxLoKAiON/S9IGT+6ecnZ3RlIZpP9G6Hn8SCKGp4vMDy7J8BzwiXRZG6fgL4kysJTVBdm0TOVfBR86LZUGJCfYgETyCBmcmoqExEKOlOt+bgLomzNjzV7A1B3Iv58n5arZZKGqToxIjTU5kyeZs3jncSijBIo/iNpH2iXw12RSe1pakcwZG6mQexXRuoBaujVBkhgKZhXarLNJstyHMXmlHPKXO1ynXa0sVts98nbUK89wOq+DLO49XR95FZOrRxvaFZZu1bTwjVI95iAloLHbfGYzO4CabeD4PkbRLlF3E7SNhP1ULDgOwVWVOLomcIzklxjwx5sg+RxDHRSv0KZKGkcdPAi6PPDhbEyUwpD0ujrSqNG1PQ7HzmTKaEkXtS8ziGF9K9RqzAQ6zRXNL6xenSLaGtlPzhZtfG1kLsSSaxvIYU3DErGgbaPpvpY30c7W+W6zfGnsXfBAgOq49Zr3gOYCtvwb4j4GnR/d7hsXd/LEP2d4lBso+KtB6+yPe766+/frJb/I3hw0/3DHQ30m9EECLAnkoSCgkZgZoVokIZycnhN7Yj3GYwBc0OLquxa+FaRp4+rUnpKtC5zpOz07xDz3tvUC5yjz5xmOG2xuaccCXmkMoM6tQqMPszK03Y4cObURrxyneK6eNtQz3SRlT9T7ioORajB45iL1N42Pal4wDF2jWK1554yGn3/uAVbsmPUukq8ym3dCf94ivwn03a3mOzpe8G+yA1Jad5jqFFgtlmylDtTKYCR/mtiDkrQmy2VUAUNuObh1ockPcR8hK4MAGLWCSIzzKIcB5/g3qBJ6HpjXB9TSNjCXiaHBDwDct3VnPuB+Ju0QppbJAB+CmubJzs79U9VtbnmOh8uTAZIGxVBUs6xE4fE8tE3MHTZ3M53PRpdmPtbiFeWpUVMipkMeptgQNnHlnAU/H4vy5BVvm7mPNyLZA9EwZE3k3EW9Ghss9aRhJw4imZMc6H6dmtCS0ZGMstdjLGTP1hYJPA6cBcAEtE7ubiTxe058/5OzePXZ6w/52pERHUyCIs4DvuWVb9Vplfk0t7w0oOAOjmC6raI2VquCydnGXczuzxK4x1q9tHU1R9rvbD7oid7XUG8D/APiXMYDycdUvB17FHNo/rH4aE0kfM1r/Be8P0n4vH6SVtNpjgO3iIzzvTwD/O+BHeL7tf1efbHls4vQOaH0n9UIALS1K3mYTcXOY6hPm9pFUFsBAi2sDeYq4xkTHKTq82iLmG/CdoC7T0LG7GYnbkRITpSSEjIg5tduz1egU5uX7wB45EbKacXfBfJtisHsWNffreSGfW4lGGhkE0RrL4lGcFqI4outAOnzbcvE99zn/3vv4laOo0p63uLUzhm4xqpKl3bUs+4c+IXUXFiYlT4XpNhJvJnQouD1IwsiePLM7pi9Kt9HaWElhNNE9TgjnAbdxbJ8OxiQdt9UqqBE4OLXPZ21mOWZYM++nF2hmRsbsMohCvIk4Auwh5ICsHXkSRpmMScsFnadAn9OoLQ5XHE9eLmMNVZ82g60D9GUBqTMbd6zWWyRX72IJpT6n996YKRTnfJ3Ws785Mf81sjGHZsxqLTWnR1OJzlchfW3jqbXAyzZSbid0NyH7iEwJSTX2SecvBFSAVYFWFa9Lfc2pmpGrx0LMrS0ZUS3sknJbniJNy6ZzpMkx7jNKxnshhIYMdv0y5JQWoGUn0b5siFS6tIrmi5bnmMPltV/MZFZVERcIbWuHP7+Gyp0W5MNri0X7fNztsz+BCZ8/Sj2uP38VZsL6m+s+vV/NIKsFfh7wF7/r7w54mQ+ePEwYS/bvA/9n4I98xH28q+9eRT7dYYzPR70YQCtDvkmLaeS8wDnvwDlKKGQSDkcQR+gDJUY0FmIZSMOEQ+g3gc35mrASnFf8AOM7e+LNRI4ToSTszVyW5zl8A5+X7cM+zKxEVLMncJIJJVaQZmG9DiUV+2YvdcvLlBuAOByFACQX2BEIbc/ZxQmnX7ygf3lFdBOhb5EzTyON6YKcHjRZC2e0/Hp08mqrKtt51L2it4V8mdGxkIcMk4nS26azaCCvxCkS35lIo+X4OaBrOjMdvdfStB79ySvyaGzcojvDLyDPDrPuV2311d5h1TixMDfiQbynDyvECTlFtHNIJ7jsyduCbwOsE65xlJgtMzJmY+Lm7c7XZkGXh2vlJICa/1mRYwA2A6d5AlEXBnA5ifN/dQZWh1M8W5j6+npMqjhvlgwCiLN2d0mWdemkRg/Vlppkhyuugj61vEix9rOWTBkj5Xak3IzobUSGhEsFPxtSoegMTDRRkgEtY31L1dTPbvyOgD1WtICOaFEGVa73T3l2c8ujds2Dfs39kzVaHCUrKSt4X5mxasFRClkTudR2uX3dqFYZ1TesfumYMbBdEkFKMd1cgO7BipMvnuHPAul6Iu8icbjzRvrweoqxSR93DcB/D/hX+OhM2S8C/seYc/vVh9z31wG/DXjpXbe3WBuqUt3vqTcxjde/zZ0o+64+T/VCAC2Bg9h5ZhYUM3aUgk4wTjBeR8TBxfkpkmHaj6SUSaWwH0dWq57TRxvakwbXeIZxYne7hZiri6J9+6a2WxaKY2lTViYEqlDeWlZmcFrwJBoigpJ1VmXZ4jkHSC9mnYvXkLUKi3OU0FHahtUrpzz40iPW9zbGzpVIjgWPII0s+YDIQfNijNERcJj7dlDHGE3HlMcCERoNZM2kkkk54nG0TWuTegoyC48qVea8x3lHQnBtIJw20DvKjZofmB6ahHMtXNARIFxYLZGDeLqYeaV3nq7ryCUz5InQ9/i1J8eIrgru3CFdS3vdMo2ZMs0ntCIfPXQJl2tnZ5iEgS8V03Q5Pezpkbfpcy860SOAdVwV38yk4QIgOQITC4NzmJAUu9QGPotSjAqtlKhNymos1vIbjEXM+0zaJ9KQyEOixISWsoBEM6et7vyzV9gRaWgieQhO6eprtQEz32UeSTCD3yFl00OODcOUSXGgDYHGibXUa/CjOCG4xlrXWRZvMXThLq0FOp+HhRk8aOqYWTbvWV9s6C86pAGlEMeJONyJnD+81nywpULDtyeUF8zp/TcAP4VZMXwU76T/Oxa/c/kR7nuCuY3Pb7iCHcfmaB+OKwNfA34L8Ie5A1kvQh0vMHf1ndYLAbTm3phWWwE1m2ubgnPCFBM3u4E3Hz8ll8hLL53wyisP8MUhzlMErqcB3Xi6lwPNhSc7uL254enjp5QpIbmakUoil2ImomKslFk0uee0QbVjxpxb6KrCvFDF2UctKz87WmIgw8mcweeAgNIRQ09uA+0rHY9+0Su8/ue8gZ56hjgyxhHBsep7vAs4bwuWtbje1SajBiIfeoumY4pQhsx0PVK2Bbe3FpAPnpyEnM3MtaqLkFgNUAOIF0Lb4n1LSRG/aujur5BzT36nBnur2nGhywJ+5G5QF39n+ZTeL6J4nJCzUsaEk0BYB1Qtd1FSJm8zcYi0L3Xc/wX3yLvIPgtXjyOpJCR405gVWQDdMdiSCkZTyTRecaGFKEg2Bqa4I/80Xf5zVPre32Zsp9WI1c3B0dZytdeohZ879UuLW4OH4ChSKFMh7zMkpRSHK9j6sa8U6VRgqxb+fTWRbifybqJMeQH7ztcRT3WmzapvFieytKWhEAR6cXhX9VpF8PUlWcRcV50UootMOuHLnnGEq9zifOCka3iwckxayEVr5FR9Ty7zh/YaFDXgquVw8Q+vyzptqKCaGYqQSsO6O6XFk3cTMhUL207fTMtzV1YfBLJewT66P2r777gUMwH9BsYgvYS1Jh32An3G++us/lT9+Sj1xzDW6359vqdYG/Cv5v2XnBvg38I0Xp91kHWKgeCnH3bHF7R67DUxcDeI8PHViwO0ZluF+lMKuJozqKlAEhgc05i4dNc8ODuj70+gc8RVYXVywubeCf39jv6iJwbPN26+wbNnV2jKSLWEEDE9Sa56Eudmc8ayaIBUpVo4VEixmJ0etRbrJNth8TbaxIkgzptNhXiKBEq/QtentOcNX/iVb/Doh17CnQRKEsLUVgJAaNsOH/wylTZP0EkVFS+na86ds3XWFraklLGQhgQTSPZ4F2i7jhA80zDaIp4yTh2heANajSDe4fuA4CkJ1DlcG3BrjwQHsRy6rXJ0vLAs/lQjVhY2y0CXCgaaU4FsbEYRpZGGcqXs2bN+9QR34dmxp73v8N/rkS8bGHTJoUWsNXvkEXbskeWco1+tWfewG83w1ok9t6VZzqCk6ukWkGw/cnwcCLMb7HK1Z0AH9XVTReKq5FjQcSJRcI3Dta76pBXKmM2XLFc/sizIrsBYKLtEvp7YPrklXQ+wT+hkOZzzvs2O+4iZwB5otgN1Kmr71WjVB1bv1tq0NE0jwqlreE2FVArrAL2bCJIpqTAVz9OyxjuHx8yDfbVvkNCwCiayTylbG7Ecrvzxm9iJwztfI3jUzGXVU5Kik13zmOtr4Q5ofZN6GfjbMDH4+7EKfzffms7q3fVlTGD/12PeV9eY99UWAzw3H/L4FvhfYTqsfwbLxzuud7BF+j7Glv0UBuY+6Jo/A/45PvsgC0zH9jdj+ZT/9ae7K99W9Zgj/wT865/yvnx+6oUAWuIEvzGNi05i+XNFF0bJOehaz+mmwbnWtDHacNpv2E8TY0j0fc+q73Ctt66jZdGQCnXBUTIciaorUCqKOlOfFDXWZBb2em/LVMnVz0oFN3s1yRytY4uvAN4FinjUeRwBEQ9tw+rlc5pX7+HuNdz7/vt0D3rKqIyXiTSYj1N31tCsAjSHmJcq6jl81M6Zi6VyCAUDparkmMljrlScQAMhBJougLf9izmSpmiRRsUAhUqBxuF6D8nc9tO+oCN0m45h7WAy4833WAdWU89DJ+C5vt7SbhPxSwRNHCZ7vs6RvaLrwsn3bIiSefbVp2zOekqaiCGhbTH3c8fheWS+hgcBPqK0TcAHB/u9AbtjsVW9ngchkZ3bo85xBVvHvV+7z9wwnfGNc47QgARHLoW0HZGSkBzwbSBowItDJ0WioEnIQ4IMrgh+V9B9YroZSZcD09UO3SdcWjrDLFoywfIRl34h807YF5CSKThUZ/2WLsJ9eR46shHPSjzqleDBOUVJZDJDSdwOnk3X4ZuqnSkG6rwI3lu2XZBIKWYsXObTWSdBCuCdt8nHomT1eAdBhDQmxtuJbt2Tp0weo72/7+oDaouBnw+q3/odbv8l4C8C/j/Af4IBox5rRX6UduT3YjqsH8Haicf1ANNZ/WD9PQB/AfDn896MRMWYr8Rn35hUsGP9fRgQffPT3Z1vuy4x7d5dfZz1YgCtYJNuOqlFxqTK0ORMjpngPd0qsG5PeXYlfOWdW77+1cfcb1bomIljZD8M9IOQ9SXyEAlaOD09pV2fcqM7gpp+xxpndZqrCnllIbNctWuQCrQa1HkmhZwFLwlHwlGNTmuLMJds7INvyNJSXEvbtDQhQO84/b4H3P8LXkNOGpqzjtu3tuy/MXLz9S1kx8mjNX7tiD6ZxqUm/mrRaqaJtfxmBisDlJorCKUU4hiJu2TsiRNc4/CNI2kyJs8Z46BFSVNanNSlEfza0Zw2xJvCNGX2TwbWJz2nDzaMD3rc9UBwrpKOY92JwBy/85zFwrFzemWdghhgNZsri4qRU0//PT3dyz2ls9Zn/mpmaieyjEzXEyGqte6CszDnWcdn6Nn+5u3KpWkiThbhJGJtw5mRkgqiZ6NZOQJcUqnBg47rCGgt+Ma24sQhTUPoPL73FpmzV9qmIdBaluGgJM3oBJIEHWF8NqIRWgm4MZG2e6bLPeU64bfGELl5x+ToOd3s0F4oRWq6gLXL51FXs2GoLfdZ/D8DNKiv9UrSNfWLBPYloajh8oLShoL35uZ/MMhV09fZiaepOj7UnMcsButg7WB6RIdzULIQgrMEgOwYriNh3DPdDOg+4j4P5MV3rW6BfxFrQ70f4PpO2EAB/klM2P57scicWUP1UWoF/CZM7P77eZ5xOwP+b8B/m+e+GODrT8HYtB12XH+o/v/rmOXEV961vc9aBQyo/qt8to/js7zvL2a9EEALB66vTt9ii7E6wasntGomozEh2bFe9Zysld12xzgMpvfQQk6JOExIcXRNi5I5PT1hc3bCjXsHlUPSoejs5m7IZYYypSrklQpWvMc1LckHyhCr47qrMTpiGYfB4X0AEUrjKaHh7Pwhq7YDMu29jpOXTukfrGjPe0oDcZssPui0pWla1o9OCBu/TKMZNKB2sY69jarVw0wnVGZLk1p7JlvLzAdPCIFMNrd2xRZeqW24bNE8vnG065bupKPtG/IQmWJi/9YzCImT8xWbsw2jiwcAMPcw6w5qbZmqHA2NOo5+mZ3FaquxnvbQNpxcnBJOGoZ3BsplIdwE/MYT2pbJN4g/MoGdJUFLW0yP2DQDGTnNJrQz2zU/zgCKMuucDtd+3q68h5njALpqW1SCOdW7LiCNELqG1aZD2gD4Ov1ZTAQ/Qdlnbp/esHuyxWUhS2OM4nbPeD3i9kC0szgfhxw9/VyHjz1z9he1Lwt5aWPX/8yiuaPpzOMj8mLgd2ahZgbPWNLEFEdydrS+oW16QgXmpdh9x2RfRoy9NZDtnbGgOvuOzcC7mLatbRr6tqdreva3e8o42Tb17sP8m1d9fbLC/Kc+qBrs6rZYNuCTj7Dd/xBjsP5tvjVBfYOxVX89Ng357mv4lwN/Ie+dKDwGXT+K+XD9FMaodVim47P32d5nqZTDdfosH8ddfTfqhQBa4oVw0VBiId9mdFtjSJwQxENy5AHy6GmawKOLDfsbIaWCd4JKJFB1L48Toe/Iq8TqtOGVVy943DimXcZrwms0YbvUMF3jteoC5xBxlOp5hQj3X77g/OEJb/3UO+xvt0xTR8kJFYcPHaFpmYJnGyPaFV76oUe89OgVdm/uubq+5ZVf/BLt955RmsrwqND1HeHlwPkXG3zrkVYorVJ8tbycwcHS7VKjsRafVTFPrKikIZOq8Dpni1HxTiiuEFOk3XQoQkLJl0JOkCcIvsBa6B+u6M57YsoMOpJKYdgO3KrDXUK5LPjKAgKWkQgHrdNC+9RF2zvEz+HRlVEyomPGO/ZvARkVvclMX9tR3s64ndCIo2XFIFuSHyuLo9U3YzYVPfogq7uQi1KqDcGsujvAaa2eVTVrctHe1Q0cjRgarvIm6PcefECaBmk8/myFW7VoJ0xtwndKe9IxpMy0nQid0KwMDMXbyO5nd+yf3JBuR3wRJgbyOKLjWI1krZ3qnP3Mwd3POZ0q5sp/wJRLuXka0jlKEfxszipH14Rw3AnlAOLtS02jgsdRSiHHgYTZkKSS8c7TiKd11vpNCimbdqxTm3QsWvVoatd5xrbFO3Jw0DSsTi/wbeDqyRXTbgtjQspHmXT7uV5fxNieP/gBf99goc2/Dpvy22D6rR/9kO3+m8Af4KMDgv8WFgr9+zEw8VuwluNxnWNMV/tNtiPAr8LA3U9ignwFfuYj7sdd3dVns14IoIUIbuOQKJSkEOs4uZifFGDhw96DJPrG052fGEvllLbzbFY9UuD2rS3d2uFeyoQWVqvAatWRtwGJA2auaBorUQNbM6MldSEXF3Ah4Pue9cMLzr50n9vLSHLC9CySNZOd0nQtbr2meM9q1fDg+y946Rfch2cJvRo4PT/j7EtntA96JDtSzkg2EOJ7j+sE3wo0Qqls1iyMXzRFtVTVtEcqSDHAVlIh7gt5tEVb8IhTcjGjzGbd0mxaxhTJJl0ysXtWMxBrC+HE4zaO/fXImCebxKTBTYHdO3vKzUBX9CC+F2/YRA7TaMua7rBpSVd9pOrkJDX7WavGieCQJIxPRvQW0s1kOh9V4t5BtEnDUrtRzOL/ugtVosUCktRyKHOp0THVYsIIuFlH58kiOJK1Fo/sRKAcBgwQi5VxDvEBQgNNQNpAOOmQdUMKBbcW/MojjSOIN5AhGZkSZZdJz0bGd3ZwnWkmwWVFSkanBFNGUrUYKYp6fzhJ9nY4Yp1mUf672C6lMkiO2fK/zMyY6vPGsQv8mY1nbYuzJYnDMjljdXiPuTCOGS+OVehouxXOmSmw5U3PgyBGZTrxUFlGLcZGq/PQBHzf4ZqG3TBwuxsY9zskFfrP3bf+78MMOv+ffDir9FHrBPg7sCy69wOmEyaI/0XA92PX+F+r+/C/4Zu3A7+V8/9DGIv1pzEm62c4CNdPMSbr1wN/Lu+hY58rwSYmfwVmSPp5ew3c1V29f70gQAtc41BRXCfmn1SW794H5kTMNNIBrvE2Ldc4mrro5JS5fHxJ/8izeamjlMK4H0zg6z0lCb7Yolw3ZxihsgBz1M4MGNTbYtFueu6/9oDufM2z5obb7Y7cZ/z9nv5iQ78X7r1+wRd/1feiwFtffRvfday+Z0O33phnlTcDSJKFDoszIXosxUyynbfFcV77jwiXpU1YQEvBZYdmJU82aViGglOHwzIGRZTgHd26RzrPRK6O5ofta53c8603g1BXSMV+5qibtEswVA+oWZBdAcmxI/yhO1cBjgg+BELbgJjGJ6vp2HwTcG2DqjC8vbclP4jFKm2U/XjL7ho0FtN/Hcimg3Zpbvken6DZg42qLTpih1Qh4ZkIrJgITEyV7Voy/erroFqXVwTizZAqOFzf4DcNbBpck1ndX7M6W4M34EtSyrYQtyPT5Z7p8UC8MubKZTVxecroPLCgVNG+/Tufv7k9u5xdPfpZKM7nmolLd1OObn0eZB3/a0ct1afBnse8sub3WqGCdTFmq5SC80e+ZPUxqOKdJ7gWFSGWREwRdQ68o1ut2JyfolK4urriyeUlOk50zhHcZ138/O66B/xjGLP0u/jOdFRzTVhczQdtK2If4fUNiwN+Qf33n+Gj666O6yWMnfozR7f9m9g04gOqK9vR317DPLn+Kj5c0J4xjdaP8d5Jxbu6q89vvRhAC/PfoQE2po0q64JkIMKgA3E/4YJndX5KKlN1EfDs9hM/8/gxP/3mO5w2He29FeFshT9dsd/uyFtjUYpTRjXjxxwTfevxS+fLFnTTHhuY0Kw8ux6Qb1xz+voDTr90xssvf4Hb/cRbX3mb7BKP3njA2ek5T3/iGSUqlz9zTYrKsE+Es57+fEOcIpoVaet2RwNFbdtanqEUcsp4N1tPHsEX1UUAT3bEIZKnjCstacqMtxNlbzYCkoU0Znb7LW3fcPbyOeKVPE40BVzTcOsmkktEn/EaaGLAlwaKmDu4wpALXWtxOOwyXmebjcPiXtTMOZHKWMnMdMgCDlTV4n2EKky3MO2sEY/Qrnq6dUOaEvHpaOAhQxwm0pDoQluZktkW9sDwwIw9ysE4FCojVPnJCsLMULTgSsYVpWgmL4HVoOUwn6dUOwU8WqdGNXjkNNA9PKF9bYNuHKEDOXWk1lzg85TZ7wZ0m4hPduzeuaXcTDAVJJtuK6uBVT+zelqjm/VwPKIF8yKz12PO1UBXS7V3OFg/LA9a4M+8789h9aO/zaDsMCKgYtueEb0X47nybC+hmZImtmotfB8amyy0i0+u2Y5SGcFclH0BySCtcHHRsz7tSPsb4rCniMf3rfm5fe7sHbYY6/RXYmzN5cewzbO6rW92rn4UE7b/E8Avw9pxv43393G6B/xA/dscJHwfA3N/tv7+Nh/sn/SDmFD/eNt/HqbLmr8AZaimIs/XM0zf9R/Vffx2zFbv6q4+m/XCAC1xDhEhdGLAqM2UrZK2kbiNjPtIUxpCANcKrk5Qba8Hvn55yZs3W+Ja+aIDd9agK8/0VsHnlgdffEh0A4+/vIfokJpBVyiW78bcrquLkTic86Qs3OxGppy5eOOE5o2OVW55/eWenCKrs451t2J7HRmfRvbXCbzQPtrQn7W0Jx2+Nbf3oomclLIrxN2E2zi6VUcJsMzKl0rEHI3FicqizWJS8i6RR2Oy8pBgstbh7nrg9nLL1c0VXd/wiiRefflVWwoLgOAbh28dcRKKCJo8Gi0YWcSTC+zGiXa1Imwa4jsFFzNSDkDr4OdVGbiFTjnwKUWxAQYARx1uMJAUcyIgrM5OOPniKdMw8exmIl8pZWvB0hZWbBOYTvwR63QMHw7/Lhl7hpyowqYKRIoBLS2EkuvAQ81dnMXbih1LNSZlAVsO9c5Ctu/1hPs9ZQN0EH1CXab1ARXFO4fzLamM5MFCopsizOHMs/lncGawqjX/UBcYObeN9bnuy/z4sji3H47bDsHahM45FHewXXi/99jscaazio0KUm3O0Ai1+mKrz6WaGeNEFk8vjuDtupv+TWwwI0dUYZ8zQy60bcNm3dOvWpwraE40waOusVEFb230z1fdYGDiV2ATdP/Bd7i9NfA/xFqB36wKJi7/LZghqGIu7u8XAvy3YmzbTwO/Gms7/gasNfi3fcjzdMAvBn7Pu27/Rv0bmH4rcxDmH9czTHx/9SHPc1d39fmrFwNoKfaBXQkJVaVMSnpmguLd5Y5MJjeJqdnTbhx9t8J7T3fScXq+Zt09o2s8UpShDJRJmZ5FSp9541d8gdXPtObO/Y1IYkd0N2St4cUACA77tq54Ch7XBHKTuXVbLu6dM8hEKYGmbVmvO3xvLZeT1zasHgilTktqRU1FzRy0cQ1ZPC5ncnIMT/eEOBHOG0pf41UyVaNlwu0D3jKPLNmB7jLpJrK/vMXhaX1TJxCVdy53fO1rz9jvd6zXgpw57u0esF6tGKeJHDPtuqWMa4hqOY1B2O8Hs4IgUKJSUqJbOdYnHdtSmKaJ1Sxgrz1CeQ5YHTWsjlXXRyv9wnQJEGxqM5PZh4HYTowykmIkj4nGW1hznCaK8/StW9pscwt1AVMzwzP3Ouvvi4v+7OJePdOc6Kypn19oHHbUgJq4WQjvkNbjVg1h3eJ6mwqdpx0tOBq211ve/KnHpKvCw9UFMkHf9ORWkGzB0b4p5FzIU64dHhPBmwmqHk7VzDrJIXbncOJl3vHaZTxiw5ajmO87XzA4TCBWkHV8yVQWs5PqCQHVaV+r1qvulGndZM7ynJv6jiKZlLKZxm5WnJxtWJ1ucKvAO1dPyCXx8PycNjS03pnNRdsh5f2AwGe5ngI/gU0J/hcfw/Z+AGOK/hcf8f7/Vf35oBLgf4a9PuaMQ4+Bs//sI2x/D/zz73P7HwH+DUyE/yOYIP9Xvs/9/l0+3Aj1rj75+rCp1rv6OOqFAFqKjeb7GsxLMasCzVDGYu02B95LjQBRNCi+81w8POONceLy6485IdBoIF0lunVLSQn3CPovdTzo7jM+2bNPt+yfNGxzRLNNLpaiFQMEY9N8A6Gxdp7AdhiJO9PhhOLI+0wJmVBsHm/cRh6/fcn6dEN72uMaC7/WsTBuJ1LM+N6iSfzGs35lQ9M3SMC8mcQMQUm2MBdn0M9X5JKmhNwq3ChuK/gBAxVtpmk8zbphtVrRtCuGMRJ6T7sxAf60S8R9QpMiXnCieHGEVSD0Ac2KL55xP1GmxKr3nN9rCMGiXnCeIrWFWaU/CwCoLcPDIi5HV3Q2dZAKE0ww7XsTmsfrBE8ipUBIbU0rqo8pSjBv/SV/7wCoKmjCGEk045YWYlme/8AiHaCUEzv+2UNskT/N4Ec84hokBGg92nvCpqW7WOHXDUqhRDs231uQ9H63462vXjK8XQiPei6axliftjFReyo4D+M0MuWRkrSeCVmEVWK7bCDLObxzJtt6bhJyZqGE4K1lW6p+agFZRx5m89nXeptdokOigLl+qEX0cABrNilq7NYC/PA2jYtUc17Fe/MVI9sVabqO1cWG/tEpxQm3KbJFwDXcVN+2B6cbJHbkcWLKnzegNTNJ/xc+HtbmpzCQVTDQ9c9j0Tn/znewf78Dm2T83VgLULDcw+/EXDMB/wLmgv41bOH+ZTy/tGS+uQHrXX169X7h3nf1cdcLAbQEA1AlFaZxIo+JaTvB3pGnZOJhBz4LEoHrQsmFeBLJDTy8v+Ev+EU/n/wsUobE7ddvaJ03BqtzZArn33OPk/unfOXBV3j8x5XVZUvZTjy+vWJbRlwIJnz2StP1hNWG1ekJ7qRFrx3PfvwW33mQLbFMnD5Ys6Ij58z1sz276y2r9dqOQwtN09CqZ9gVO4YQcN7hTxzN2doW0SCVxDPxMcW0LU0IkBXZQ7lOxCcj+WmkbC1epqltO1UlaySshNe+eI/N/RVvP+k4u7fmldcekkZlnEbylAjB03WBiUKOke68RVrHNI24vaMMmZN+zb3v2/DqF9Y8efOSNCU6cczZe1TS4zBFcAwYDsCB+b4LILNF3jWesG5wF4HwwABM3mXiyQgRnDpkssk9JwZiycaOaZ7d3Y/bqgVKrkwWR0wWi05MF3BWsZQa82hDhzoPLZrFgvf1dRDQxuP6hv5izelL56RQGFOEmlsY6HHOcXpygU5v8uTrz+inp3DWc7HuaLsO9Tat2a07wn5HKQmXJyQWQzpFlgEIO78HQORqK12qKe4Cn8QAjhMqU3ek36maucMQSbUePbpe9WlsFGDxRHNmoyE1HJ3DNO7MsM3GtBnTUzZdR9d25gQvnq7rYSVcDpdklLbd8FJ7iqoQpwHVwvmqQ8UzRaVp3t1a+qxXAv4oNnH4ceiPbuv2wLRafxnm5v5rgP/lt/kc/9a7flfMy+o7rT9Zf8A0X29hU5iXwN+HGZH+CT6eAYG7+njrjs36JOqFAFpgGi0zfIQ8GUNRNDGNEZnE4jxWJhQuE2hMTLvM1BTaVctrD15iV255Z/8O8WpkXI8m032auf2JPf7lhuZBy9kP3beMvzcnxic74nVD2I/cXkeuJ4tTOV/3XLx2ztkr98G3XG0HfubHv87qvOPVn/eIft0SvCfuIlOcaNqG0wdnEBTfCaH3Fq5LMZ8sZ0aXNmmo5CrwlmwLYKkMjPNCqJ5F+SqRLzPlMlKeZtJ1NMBWQJ1HfMA1jpwL036L9I7VWnjj5IIQGvPSisrTdy7Z3ezpVw0vvXKBtAW/FiSA6ZewWJSUESl06858oi/3kLK1GI+MMPW4K3V8/Z67QRaKxHy1BPUgwdGHnrDpcOce13jooX2pJ3SBfJvIz/akJ6OZcrqDjggtaLGf51t+VrpotSowO2K/FlA2ByTb+KaBL2eMovcBHxqc96bLaj3tuqFdtYQmkHRCShWFi8dFAaecrla8/MZ9rt/eUtzEqEJ0DVQvs8YHmnVDyS0eb4xszfM8tGArJ3V0Qp13uHwAW1SbCirAKgvonBuGcnRKZhbrmOU6Pll6dJ9ZGl/bsfXHGZKrjNjctTWBvF1Ya7Vq3f8YR4rY9kIvZB1p9gWSR7yiTcCrkIvp1NLnbeiQhE35vYGxUe+nkvt26ycwoPU68HdhTNfv/Bi3/+4SzDLiz2KAb64apPlcNfVnh2Uf/u3Y5OVTTBP2H3MnfH+R6/OmlXwx64UBWlq/yXtxFA24lWNsRqZpoo8NATNPDL0n7SHuorXEvMOn1swivbE2bAvp8YQ/9aShsH28p5vWhNOes9fuc3J6CtvC7ZMbNk/uc/32nh//L7/OW+8MRAqy6Xnl++7x8PvuEQfPkx9PPH02ctooXzptWJ22NNKSxgg4tPdoK7jW0awc3aZBJ4vFyS6bo3hji16ZWRbzTLUWEEp2hTa0JlQeC8Obe/LThOzM1FO3imRzdd/FvVlbtC1jHHl8+YTuwnPv1TPOHlxQihL3E5qEZ29fc3s90K8cJ2ee9UnHplnV95fShNaYlayIJPxqRQLGm0xXHI0kfEVRKlrF4jMAsms3t7aO6awqpbb8weBIrqAC434ivlPQrSCtRf9sXjpBzwt5TGx/KjG8k2mPGpGiFqpcqvP7Ahv06Lm0yrdmPddRzJKTalqgeZlUnDGFDx6cx4WGpm1RH9AghFWgP1nR9C25JJyDPEykPNF0DQVPWAXoE69+6YLtzTXlciKEhtxiOrsGQuNgJZRbpYxqgcpVS+adq5pAXfRX866FmhsobvZ/n7M5i7GfswZtbhNW/ZTOAGvmppaW7nxF6jmbnfAPMixQG2BYzFHrNtPS/s1oUXJKTCmhkgle8d6j00gujpOTNWOY+Nr2HS5yR8+Gzvdm/IoBSB+EW/28Lb6CCeH/J5iw/ONkCm4wLy0w5uh3Y3qsHfCn+Pjbcr8E0139ZuDfq7cJB+uILSZuf8YhHzEA/zAm4D8B/nDd58/bdb6ru/rW64UAWqVYhI7zzowqpwQN9I86Tr90QvrySLwacFshI8TbyPbylpvbLWOKiPNsNmuapupoRiXvJtavb+hWazQ7bvZbxieJ8FLAnzjC2uFXK04uAqxWtF++pez2THGirFa07cYGd6LQ+I6ShJt3trz9p99ifdZy/to93Drg1o7QdXhtbSEvmTgO5ElJKVOq15A4hwvOps5c1ZolW8lSTmie0EnZ3W4ZrvfoOwrXim4zeZ8oU+Hm5pbr6yuutreWKdet2Ebldrvj0c2KTVnTimPymdxGkAZGwSePyw6PZ9X3uNaxvxxJMRFCa9NuGbRk1GWmJExDZoXURdfNaIpFHK3HDMy8EC+/ApWJEqp5qd0nTQkpShmUHAp9u2b1PStc7wnZM+4b9KeUEgsqzgYJqUDrSOxen+HQKqy/V36wCq+o/mgVZFSQNbsOqTeNWdMG1pszXNMyJjOjFWd/zxSoNhWalTxmXHT4QYg6Mq0mNq+t+PN/2S8gX2XyLpGGxDQNpGlk3ZjNyO3VFXmMuJihGFMoyqJVX2yQai1u8XYiF+LLmKuZmZvBUwVORyTK4VIJz1Fllc1aNHRiaQRa8yGd1oZj3cDcPpwvqmohl0xKCZUJ1cZ2LCayeqQ49vuRr7z9DoM+5OHqgs57NEccSlALaz9pT7/FT4kXvRrgZZ53OXfY9N3HCbqugFcx4fkfAf5x3uvS/p3WD2K6rTfedfsjDET+Sozp+irwd2LarAL8ccxzqwMeA8PHvF8vcn0Jm+B8BWMcfy4d+119WH0o0BKRN4B/HXsFFeBfUtXfJSL3sXfV92KfLn+zqj6rj/nN2KsuA3+fqv6HH/Y8mu0DP+DBt7heCMFTHkSuvrEj6QA3BfaFtB3YX98S94NNc5VCvt3StC1N20JxpNETTj1taHF9wzjuiU8m+tWa0HnyGBhuBnSIaOd4+UsPGWPm+nLLqmkYrgamEHBdx3rT4vvCcL3j8is3TJsW53s2r57StxYgHVCmaaLsE4yCZtBUVUO5UEpGVAm+wYcaaiOgCUrMsIccI+N2YLzaE3YBmaBMkXEY2Y+Fx1dbnl1ek9NA27a0wX7ONiecrdd0tExXA6lR6AQfPCsC4qFBYKuot7ZTuk2kqUArNmmXlRITKU7EcSSnRC5CIRwYDp5vYXEEeA6L8rEmyJEso4UignPe/KAAaYTV/RWrh2ua0wCdtY3DJiCtkGKCUmjw1eIgVyB71Lo4CjTW2cqhMj86t0UPr2S0KuJKcBQxK4TUCKwD4WSF855pHEGzRRLtBnILm7PGmJi2seDoUet5KEy7EXbC6cUKf9YSfSQ2EyErMSqyy1y9/YThyS1hFCgGxi2T6OBhpViG4WI+WW0brNNZgLy4fgize5WZy5YKNufjs0tTKrsFqBxhsEMO4jFTVtSuncm2KupTe0+62dZ/bhvlQkkJqSHsXhpcFctHhfOTc75w/zXiZaD4BhcCKSamFCE4vHh8u/6wj4SPpT6pzy8zF/0/YWzODKw6zNDzpz6247F23h8D/lLg7+WbTxp+u/X76s9xNRhb99/FjEsBvgcTv4Od2t/9XdiXz0r9emyw4C3gH+AOaN3VcX0URisB/2tV/eMicgr8lyLyB7GvNv+Jqv6wiPwmLOjqHxGRX4jxxz+Efcr8xyLy81X1A5vBIoLPsjAGeZqQ5Cm5IFOGMaLRNEolTsTdLTrscDESsuIRSIUyRkYZ8KEl9B3j2zvcqKzvn5CaQkNL93JLksJ4GSEGTs/O4ELILtAWz3g10T309F1DLNC3wivff59deI0nX21Y6wlS4MlPX/Hka1v8yvPoS/c5fbixTsyI6XycGBBwFdwU86NSFapEhzgVy3fcZRO77zLslWbfkLcZmcwSYLsbeLaduJomStfx4JVTLs42nJ+dsVr1TNNI17c4B/vdFt0pYQyoFzbqCaK4BNM3Rm4uweMYdyMlK9oXfNfQ9IGz1Ybzi3Nub25pgzeneDze1RXeAdWQQKQcgS2pRInUvlPVcgE5m1NZCA0inpgj3lvOXn+vx19AzgkZBJeFxrdo5xi3IyELaIBSKCVVsKV1+PBgVDprjDy5QhZrotmQwcGFSwTUQfEN0TsmKaxfPWd1dk4ZezKFUfcMQybeZq6nTDPueePBhsZ7mt5AfNxOcM9CwVFH27VM04BGmwRMOtKsBV+EqzefcPvld5CrRKMt5mhfW391slBV0CIg2XRpJSNqWibULDdMqC5L+w3EQDrFOqVzq1UAzXNDcWkNQtVTKUf2DDMoqxBNHE5r27Huo8PVtEQ5GNKrUqbRgt67nizOJjWdZ1DlJArfc/oy1yjeBdabFeMIt7eJFALFOcZPLurwu/75dSjleQsDjwGvj7v+Yay9990AWR9ULWbGev/otgbDqT/5fg/4OVTfhzFaP4b5mW0/3d25qxeuPhRoqeqb1PlfVb0RkR/HVJl/HfCX1Lv9a8AfAv6RevvvV9UR+GkR+Uls3vcDx1sEIaVEidkYoVhwJSBF0VjQbO0WjYm8n8jDRJkikvPiNaVZzYRThBwTcZrIOaJSCH0ghoL6QtknXOuQExuyd519W1+dt/DGKfFBQvuMj0KZlP1+oO2El157yOnZBpkgj4nLp9cMu4G0y5ASt1/vaNqAD471yRrfB0owR3innlaCTdFNQp4ypSjDdiSNGd0r7Aq6L5RdJu8ywy4y7CI3l7vqdaW8/PKa1UnHxesnbM5W9E2LKOxvYbjckW4T03aEAi70+M7TibdRfCeEIsTtxBCT5R1ibBbioBFc8OSUECf44BiBbEl4BlIWGmShQwxcaRVWPyfCxrIFvcN3AbcK5GDgzPUeWUNz0uA6bzEvuSAJkmbUYQMDBQoOmXVtR3E01hmcIdQsep9vm1uI82uYpd1ZvGMsBXfecu8Hzrl48JB8CSUK22nknV1it49MUZE2sXGZ+7uBXjIObyJ2xKZKVy2SJ7MEqTorkWItwuQYn27Zf2VLO/qFaUMPTFKdbDiwWirVbsKc4MtiGjonAz3Xr60zB+867sVYtprdyqH5Z7cfHjs/rOIva2fPZnZiHKBWry1jMecJVNOK4QxYajH9XS6ZOI6s2oau7Wgb23jKEVWl6ztrB6Okj4JbPob6JD6/Prj2mDnox10T1qaEw3j+d/t87oF/FstU/Hn1th3PR/X8XK0NhsvfAL7AxzsIcVefh/qWNFoi8r3An4/NHb9cP8RQ1TdF5KV6t9eB//zoYV+tt717W38n1uDntZdeQprMdDsyPt6jY0G9wi6z+/I1+WZCktkSTONIjtF0MyXjSjVRrLbqipBTIiKUPJHjSBr3NCc9jdwjvTngp4C7aNEA0hVEHJ0EJHhunuy4efsW3WY2zRrKxH4cOXt4zubhfbKPFC2cfGHDOESGm4H9NwaGtyO5BeeVcl3ozjqak5ZwEvCNoKOQBmW6GojbjM66HxSdMnqb8dmT94Xd9cSTx1dc7wZKFF4+f4mH37uhfZiRtbJ+uKLrG7jN3P7sDcNbt9y8dcl0tSePGe8DpVvhQ2O5c4BvvIEdzUwxmku9QiiZkDsaaemahimO+N6xftQzvTWgY6F4y2acF3lXF/xZAC9iPkviA3hnuZKiEArNJtBdbJCXAlMb6WND6xvciae9WOM6Ycgj6szfKzsQ75bBgZwKUtSu9wy0SgUB83W3/tqBKVIDAgJIDcQuKmTv2BVhDMqrr5zzys97nd3bA/t3BtbNKX3XcnFxSr+O7GNkc7bh/OGGdd+Q4ggl4Ghom4BGSNvFyh8fMC+04hkHGC933H7livjWgM/YelgyHqmZlLabKc/AsIr6sxIXawpFSw3B1hmk1Tafk6W1Z9s60mxVmwcDxm65TnOMY30HVvBk7vW6uMbPcjwLD5d5wlFANFftFsbMlgoMKYuhrMGwHhca+hODwHnMUAoBZzE8rlD6I8D4CdXH+flVt7d8hn3xiy8DvxWzXpjbRt8t/6iv8Lx56D+KTfr9g3z3xOcZA1ofVz3AArP/KT77k28/hA0KdFjM0L/BnTnrXR3XRwZaInKCueX9/ap6LfKBH5Tv94f3QHxV/ZcwBz5+4fd9v1IKjfdMSdBJcJMj3UxMTwZ0yJDNZyunVEf851iYaqbJIYjXcuOgTMqYE2ma8NuOcYoUV1jdniCXwRy/z23iL8VMGoXxamL6xsQ0jfQv9bR9S+gdYdUQVh7nC/jA5t6aosr+euTJ7oppjLgiaEpMMVqLx9lylscMtwnNhelysrBmlHbT4BtHmYAJSsrEfWIaEsF5Hl6ccrJecfHgjNMf2CC9kvYT+XLg2faS2zev2X99R3w2wD7jxoxLirrCuFMkmNM53uEaISYPHnJRcoHQNGiBFBMuOUrxZBXaJpjPViPopKg3JmRpTS1nm8V+QJwD5yxKyTuKFKSGMbN2yP3A+lFnbJs6XOcJm4birJ2qXhACeGPBRBxopmS1CKAj94HDC6w81yY0YqgyXjMAQKvo3KPOkaXh/Ev3uPeFB4TbQHqcbRrQZbqVZ3WyIZbCWBLrszWrsxU+KGWCMtR+VxGmwcTdrhdkbf+af2oh7yb2T2+Jz0Z0TJScgWImoNX80wCTAcSFparAO5eq21MDUN5Z228OcraH2VvKOZltxCrbNPNl7iCGP7pWLKL6+WLWSVKMmVxE9PWuNsBpIFecw9Uw79lOwsCg2W6EJtB0HTkpU0o0q9b2KyYLFs+VIXNw/6z/oM+P70p93J9f8Pxn2C/9pT+gFsj8SzCR+idVGTNJXfFiMymC5SI+wYDiLWbw+lnz1vpBrFU41z3gr8HYvQ7oef+X0Itegn0H+XHuvLU+/vpIQEtEGuxD6veq6r9bb35LRF6t3wZf5ZBE+lWeH1f5AvD1b7b9tI+M7+xpXY8vzvQ4MZN2E3mXINe2xvyBP7MZehyRe5BjZyzAt+SCZmO4NI5c72/Y7rds3jqlPe05uXdGvJgY0khKmZwd41UmPY0kyeR7GQmO7rzHbQQNxb7RC4zDHgVC8Kz7FUkKw25PcIBX0gDj1kCM9cJMIJ/GRMnFFkjnTDCf1RbvcWKKE3jl3qMz1p1ntRI23+PRVyL7N0eGn9px+/VnbC+v2V9tKTEjqdAVj1ePqCNnSDkiyeFCQbwjZyXWmKOC4JqWfnMKLhDLfK4UjdYa830wYXpMLDTH868Klly+2a/JYRotJ9U7TAjrQHPawGlAzpy1UkXw3gP1+NORP49YC9M8x+aFXG0aroKoZZHUuXV2MDM9+GjV6B2pTcTKlK1ONnzhF36Rs/s9+rVMMzYMRKJGBE/A0/lQTWMhbvfEWFAyaSyUGBHnaDx2rTuHdoJulDwm0jCxv96yv9yRtxGNOh/Wc6fqeE3UpW93FMdT24aCGYRKaE2bVTJTyuSU6lVgAWrPtQXlcPt8tZ67vz5/QXX546EJbL/ostMHk1M9PE8FWqVkvDSE4EkxoYOy6kPVmEVySuzHyG6KeMm8/sp3Q7v0/vXd/vyy+kngb+TTATufBY3US9hMwm/CgNbIweT0s1It8NuBX1t/n6/1n8FsNv4izKT1swYewaDAbwX+HuDLn/K+fP7qo0wdChaO9eOqeswd//vArwN+uP777x3d/vtE5J/FxKQ/gI3JfGDF25Ev/0d/mnuPHrHabPANUBK761umYaCnMTASE4Gj2bJZ3UyNV6kDb2YGqWRVclFrzxTBRWGKhXK5w7ct+9Uz2vWpGYAGj+sDvmmIZQIK0/WEL458G5n6QLsK9JtA0mgu4YAUh8ue1arBk2laT9OItT6zhT9TFMmeUgzIUExDE28TyUHKhf2Y2I8jq3PP2WsNDx6d0bued77xDvHJlus/c8Ozn76iPBkJ4wDTREipRvsaeJnDaVQs8heMDXI4O3/JdEupCFGUtL/l7P6pxRVlJW8jvnFI71g/WHP/59/j9qcvKVcTvtSw7cXscnl9oKGCrAZoBYKgTihrKI+E8EaLPGgYZMKrGX5SHEkMJjscOQopFgTP5uKc8XJkHBMqc1vQ2oGuohRrl2U48sZa/i3WRhQs4qjkRMwZOW14+ec94mJ1wvZrN+y/sSOrEFYNbdeSy8jVO9eUffXkQiEo0psAvWkafLAWc8wFTZk4CY4etwtwmeBpJL2zI13tcTkjtS0iKgdT/WUtnl/DMytk16xUbOONAjMvq6R0FPoApTjSDDqdXRctFoatcGC/FmrqXShZ5jzFuds4ty+P9WPGiDlxeF/3Izs756VQI6yhJKZRSTnTieCbhpwzKWfUeYoW9jd7pt3A1W7PW7tbIpkoF9/sI+Fjq0/i8+tQLzKj9GnXU+Bv4rOr6fKYAeuvxtqC/xXmI3aGZVv+CGZpcclnUwwfMTPctz7tHflc1kdhtP5ibK73x0Tkv663/aPYB9QfEJHfgEHg/z6Aqv5JEfkDwH+DTfz8xg+d2EmF/Zev6fYt/Zc6c9JOmTxlgmuQIkt4sjD7E8+6E6kLgmUDiijFmYs1RVHMVsGMwet39QJMyrhP7PeQQksWuP/wlHurFTGMCJ40JHZxj7/0tF2gnATkXmsO8Jg+peSCZEcIDm09wVvYsLP/UCVSNlVW91tVKUUpk3kS3e723MQIwXN+smbzsMFvlLiPvPXmM/aPt6S3d+hNJKjiJCM5Qc6EqscpUpbgX2MnrLUq6kzjBLhsAdpeBS2Rkm6Jvcd5cBLQaLYP8SSyub/h/LUz4tMd4+1k51rcwZ5hvnazdssDXsAL0tqC2120nL56Rv/ymmlVCKXBqYPi6j5WZlIcUmwCU7MS2kC7aknbEZ3SIvg22e/sAFXBgR4MQNEDKNOKVmbgURDarqM7aZmu9wxPBoZxNKPSk47VyxuunwyMb464nUOLUrSgHhPxO+jWDV3fEdqAuga6Bi2F5B3sM9wW9CZTthkdE06rzcRzrCvMmqzDYVTX+8rClZkoxJnDvyoxFxon9QuFvfYWl6tZX+XcYdtHz/Z+NbOCxz5kB1Z4/l3AObzzS1uz1PDzWdhv1huQo+KbhjZlqOduGmJNLhiZhpFp2pPyjijK25eX3/Qj4WOs7/7n1119hIqYw/1ntXrMtuEh9tL4h4AfBX4hZnnxZ/h4bTw+jfrap70Dn9v6KFOH/xkf/In9az7gMb8d41g/UmkBRhhvRjRmzvp7jDGy8Wv0pGW62ZNLWfwcS2VBytE3dedlYTtc/Sli4l0/wzJVSp7wAo1rQIShEW4l8fh2x7ZMNE3D+dkFKsLV9pb9fk/wLSm1xBzJOdGfdKgTchXH5Claa6VxhD7Q9625vWtZwJh6sexFB9OQmfYjN5d7bm5uuBl2NGdrXn79Fc5OTmlRLr9yydOffso7P/YNTlzLZoq4MhkgyWWZflNRAzuKmV/OZkt1cXeqy9SY1jF+L8q6EcQVppsbttPE5v4ZQTpiHrh8nOhe7uhPOlbnK9Jb+xqJ40wIf9Bd25N6+5Eg5oLfB3Kr5FWxYYDOg2ba2o7TctQiU2qbEWg8gyZKiaxO18hU2O+uqxDb9FilAgxlZq44BEjPtuoVmpViNhIqnnbdc3bvhDJFbm6uKKODxuM3ntXrPe1rnnQzQcpIUtI0EFNCHbhWEO9IOVH2kdA3dP6EsHLkAtP1SGoFv4d8M1LGaE77xewRDl8Lno/dnvVUBpLEvhi8mxSpVgyunvRc7Dfv67SiyLKtRY/1Hv3RvNFjgFbmN+sR2KoXde5tyqzZmllEh5PyXGNEdXbdN41hShkVey+KFjQncjJHfSGykkTnFV8+mfbKJ/H5dVc/F+olzLAVzD/sX8F0WWB4/J36+3cS0H1Xn9d6IZzhQdlv96QibJ5e0591xOtEvk6LFYIucqA65Va/vM/rvTlpOwMa80Ti0VRckcKEidTNE9LhUssJGec6RtcT94l3nlxSxLFemxHfOEVuU6TvMqvcoUWY9iayH1NERGhaz3qzwnnIrqCtI6fMOExLr2h1tiK4QLwRcoqoZsZ9pERYdytO753w8NEpLYHxnT2XX7lhfGvHOmfctCPnjGZj59xCiAhFnLVPBZijfRyIzHDULauMHFZ5UlIQsxGgJOL1QC8eDY7tbWTYDbjzYpPLQZBsYAOnNQCcqoGy26XxaOOgE8KmwZ0o+gBKV8iYsNuAxAyyqrbK2TUN3oNTXO/IAZp1Sxk6dk6XCfY58zCXQiFXe3ed//gcm2VeZUJKQPCsVyvO7p3QSM/+tpATyKbBP2roHjZsv3HD/s1rwpjJU8STcT4bqEgOKYJT8NnyoNPVwKAB13tSk+36FmF/u0eHRChCwBjWBbswv2aPGK36B5lvcmLC/yVL0ITqXqy1GJNNEbbBk/MBQOMc6JGNxOEM19MjdSJQ8MyZhQcicH7c7BQvS59zdpEXRErNQHQLyJ0NY8ESDqYYabqOpgmoQCyRVCaijmbzUEDE4/WDsM9d3dWLVj+ITXTOusI1xmTJu+7z92KDCb+Tu+ihuzquFwJoCbAqI22G6atPuB7Bn7aIC6Spej7prECqmpR3AS1xNYRYddGfuNpOcaok6pSizO7WDofSjdc0TY+Glqf7yON3HjOME/ceXLDZ9LRtSyqZ2/2IqrBue263e55e3zDlBB66k8Arnef83hk4RRuhbTt8E8g54VsT0g/DLcNuj2alb1tKr/RNT3cROPt5J6wferZvXnH9s1dcf/Uabnd0MUHKBiKYY2WkDgJU/FJZD6mgQ6pw3AnLRKadjzkPzyYPUfMo0wzFT2jfgTZM14n9N/aECDIKVP2POHcQwGtl0oJHGoc0Dr9p8Sct9ML60ZrTHzgnNYWYIy4484WafZ6WvTc2UipIltY8vbxr6KYVrvMsWYBqoDuVuDjE23FzBLTq/wLFCVGFJrRIYx5nOmaGcWBaeVavn3Dx/acgka//5z/L/muXbG4DXhXvssX3FGs7kh2uOFwGEpQyEfOe7nyN64Eh2wDE7UCJmaDGQllndeZiTTeIatWiVybSLqoxUopZYxjqYnZy986uXM4FH1wNLafmHs5twtkZ/vB+KcxGo9Wn67kzzwKWDPNp1ZHNv1dedAaD9UYns7/azIYZQ1ZyIqZI23d45yoYLpQSySVS1AC3Lw5f7oDWXX0WSjBt1q/jsFy+X1v+Qf15BfgXuQNad3VcLwzQagQalLYIIXuatscFTyQyxAmN0YxLtRpRytIxWxbZnMGct2tbDcAaG8ti4goghawR0UIewBdl7eC6JPY5s725RUU5O3mNi5M1favsxom+b+k2gYhjusrcDBN4oTttCI2j7bwNGGpCNaNSSGVCR2F8tuf62TX7y4lWzNz05NGK/mJFniaaLExvbRnevGJ6fI3b7ZEp4vLcTrKFV+r/qxyOb9YAHbyt5rM6S+Vr60dtcqxUEABQtFonUMiu4FeBfLvn5s0b2uyZrmtwtndkZ4BIRMiazQHfe5Kz/DrxJoxPZBPrD5PlPIZqDuuqpcHRni8tKsRc5J1DukAclbJS9LwhPks1jLk+QucWWOag1aJOpQqIRxGyCKn1nL6x4fSlM3b7RNkn4iYTXgmcfbGnA97+qXcY3r5GxkjJieDcob2pR8oqiWakqtUSQpTQGJBjyMTtREnRWpwLLDZCTuarJEctw4WNO343zNdTbbijCuK1elYVcQujuYAqdAGXdirkuW3WuzJzU5b3OB/X4RVyUGg589GqXvTza2t5TG0pztYetVFr77SSyDlSsjd7EUBU8MXRYBpG7z3tHc76DuoB8BcCp9iw5B/+dHfnc10K/MvYef5twPk3uW/BtFqfXOzBXX026oUAWiiUDHFSWicUp6R9psHYqDLZAiaa5z5HjRGxRca6hZlcDrl389JRdDZRtA98Yz8KWSZQRxOb6kiQkJjwIpSU2O0GghNO+pYmQN+3NJ2nWQt9aPA3zaLNWp2safsGRPHek0okloQTRVxBotAMINeF+GwikpFW2LzSc/GlFc/+zI6bP3vF+GRL2Q24/UCzGyEnWwqlshJ1wtKkOUcj+JSDIPqIQVGtsSu15gXX6UHnNFNADgtT7k46GK65fHZF37WUXTIj0iLGnPmAc0ISNWd054gxIsFihzLZ9GhOiKngNODFLXjFyQwPDi0nhzF0ORkb6VctUxrQjRAe9QxjZBwGC4Au1kqT2WZAzKOqEnSgVb+EI3uIm8Dp99/j/P4pj398T9wVmi82rF/vOH3UM335hmc/9ib+KuGL4lxCcZRSd1gPgGR2RBcRNCc0R5IUmnBKcJ48xoV5nIOAAHw1DRUwjVXd9fn8HwTosx6rMI8mLqJ1sWlSAzwHgOWkRg3V4z+AOFn+PpdbjsI9pxqbrSc4uqf5cNl+OzkGYmJu/XMEk6tvsspqoZkcIykEutAZuM8Or55WlRAgBFkGNO7q26kfAv5azCv1jwB/CZ9NS4HPSn0Fs28YqZ++H3C/LfBP88nlHD7CXgt/6BN6vrv6duvFAFpo/YxW5gBchzBu98xGpjEe9CgH7HCwMahbYf78FrVR9HezPvO6U9QWs1wyPinBKY0qHR7XtJzeu8+m73GaaZuGxgdcC74R1k3H977+gFcfXoDA6cWKvm0oKZE10XYNzgdKTkzjnv3lnv2bN+SbPeHmBs2ZROKp31KmG579xKVNq90kfJ7wGvEmglr2fp5dexdsOvzfsQ5oWQFtMdXluOWozWb/uto6KhSmONFuMq9/3ytsv36L7B0+tKgzhoag+N4jfcB3Qnd/Aw7ylbX6/L0G9zDgThw0jtIU89QSE4QvET5i11m0Xq/qSK6qJqjvAnmAmDLNxQmP5IR0PnL19Bn7q2u8BBhtorKQEFVyPUc4IYvigrB6ecNLv/h1Xnr1IcPPXjPeJnzTcu/VC/pHAZXE45/5BvuvX9EUAzgOA/Oq/rmzTQVZiBp4LeaGHgVwwrrv6V3DrooHVWY92jz5OF8aOQJZtl1FF/PdRTi/dOzqNqRmImLX7d3WZnL0X51byMt/9b3eXUftj3l6cZkgNZEfglSN3xKutLQSxdVonrLgRtvXkil5YtwroQn0Xc/QjuRUUBKSjJ1UuQMG3141WK5ewt7jb9Sfn/00d+pzVg74y7AA7z+Lgdg/iQGpHrN0eL9STBD/SX2JeAP4G7gDWi9+vSBAq+qLFhBQDUeLuWa7EMxtPM1tihk4zG1BWzhc1RCVcuATXF3Y5/vhpP6dJdx5nsZbScD7hqbtuVivaZxQSkGljvpn0GjC89Y7Qms+Rq0IpEKWjAPW3Zr/P3t/HmxbtqX1Yb8x51zN3vs099y8N7vXV9WDoisohCRLAlsCCySBXaAQGOywJSMMshGWjVrC4bCFBRI4whFCIMsKC0QIEEKWARskRI/oBBYqQFRBUVWvz/42p9vNWrMZ/mPMtfY+N2++7DNv5rsj4+Q595x91l57rXX2/NY3vvF9WuCNN855/Ruvsbt/hZ4PuJhwccRLwWlh9xpcxMLw+o5Gg7WEtPopzX+rdRkUHln49zXNnE0LLOxBly3i+wV6brXtl0b7finE0Vq0R6tbnLx4xPqNNeNVRMdCjJFMxjnTUYUmIE7ILuNvB9rjnvasRU49LIyBczjUGYib2ls399r2o8w6IxAvSGsWHeeXW8p55Nn2mOdfvItfeV7pleH+JWRHm8xVzdyxHYjHhUBxQnOn5/RLz3D3i8+il4V4bn5izcKzWDY0zrO5Grl8/QLdRHKodhiFOj5wqMOQ/UF9ZKKvFCWOpqNrnJs1hN4BxXQaB7GMN07BBLD2Oiedz6F7RIM1XxIHmAjdgxyt+ydodaDf7/cM0CcyrTwKISt4whz5pdpITE7yh5YPj9aNozHZU5SC1EEL5zw4T1JhTEojalPA8tbbfFqPq4At8r8Ec6toMebkx3nqffRBVcCu6B6LG/oxzGR1C/wsbPJw+TbbmJzhP4rr+2vAv/kRPM/Ter/1hACtvc7EwnQTOSdccKSgZh3gPZmJEFEmrVau17Nzzt7Uxd7Qc65GkaImJC424aZqN+y52MKiWu/KRTgJHeob2qZnKUqjhRICg6u2k1lwgz2fd95aIGKRMjrYHXvTevoGrs53vP6Ne3zth7+J30Zuuw4piSIJ8Wa8KbtEeX1NmwTvbIKvMC3MUyuw5s+JzovrDRw2H8GC1oBi5yd/KvbsSeXEbvyiHnxSzJPrqvCNv/JVNAhd3xJoLTsxQ24L4VmHP/Kk88zu9S2xS6y+fMzqSytCH8goZTYiNfuJIuXgeScIvBeum22B2pRo4yid4vuA0PPGa+ecjw+4euaYW59/hs9+6bv55t/8cS6/8hBPg2a7JrIXshPCsmVxe8mLP/2zHD13TLweuPrWNfFyZLVY4lYOIaFbuDof2N7f4cdE1B2OgAVg7vGU1Km/QyuG/QMMUI5jYdxsOFr0BFdbm3t1Uz3607l485U/AZQJpPh6LUeFlO2qn6T/ToTiJibKHaCsUhV5dQhE7DqJumdCJwDmnM6ArxyASfG+WnjstzH9VHWCxnrwnNOxOHxBGS0e19i+lZRpuo4umVjeqxD0o1qIPsoKfHjanM8Dvw/TB70A3MaCpf8M8Fv56FpVn+bqsEigz2MX9JewycJ/DPvr7bHzWz163nTTO13Pn8euhfHD32UefATP8bQ+iHpCgJYtJeaNVD86rP0yKL5v0NIybB1FZX6srRb7RXBS0kyGivN/09/A/H1qvMsEQmwp9PXnAQgIZHCNo+1aQhPQArmGGzeN0IQqMy9KznU5jZ6Hrz7g5a+/xsOX7sEu0qgQ1DLrhOr/BJASOQreBxPYHFgxVOLi4Is9Y/X4Jaq2dw4XSJ1+fd7IfuP1W1KF5SVlQlbW9y/ZMdDfWtH2DWWEIQ3G1i0cq1srlssFulvjOs/xnZb+uQXNUUPUzOQ6Ptl5GXOoVVPFnlU5bDhN9OLU8XT26aTz3A/CK2+seen+OT/1SPny85/hM198gT4viPeGKk6HbiGsTjvO7p6xvLui6XrGy4HtdotmR7c8IidFPSQtpO3A+v4VZSgs2g7nixnjpj2GeFOj9hFGS4GSEmMqXF8KnfcE35BTRkuhlaqxOgRYk9/XBH8E9AB43Nj6xHKhTNOHzBC2PmZ/WOejekhfOZUaul7/Rg4usCm/0v6M9k14+/d88dT9+zZ1wJ6qqvlnlWKpBM6x6jwuCzKCRqDsmelPTy35cAKkwdzG/woW8bPCDvgFxrb8Nx/Sc36n1S/CXN/bR77f1c/Te+i3u24dltj0Nn8vT+s7rp4IoKWAc5mikd12xA0Detzi1COu5ahbEi871lfX5JjRkmr7gXoH7uwOvq5oTgrBF1JWEjXjj4MJrYlVqWyLurTXpChIGkm7zHBd8F4JJ0KzNNf1VBwpJZx4fGiRrAyX15QxEcSx2w689vLLPHj1DWSbOEsFj4JMYbwGsqQiPQkQXLAlskxruWlgDpt8pbZWDUxNC+YeYN5gF9RA6vTdvbZryie8+UYggI6Z8eGG9PAaOW5ZnrXcvnPG9fWG84fXhJMGeUYYm0jaJEZGFs+s6L/YE44b0MpgUYGF6sxWqWHfvYBf9iLsyV6gYIAVLxAUl5WjvuNzn72Nkvnaa1fcu3fJl66OOH3uNrfunCLRROvjMMJY8Iodt+y4ev2K4XpA2kApmV0ZyU7ouo7sPbqNxFevbarTCVqC+W+heKBRV4XoMpNY8xE9mOx0QEAZtiOXfoOItSX1QIJ02PKbYNOhCH0fmaMz4Ek1y3Dyep+e0znB+wnATQjugGWbwG19bueMndqDvUcAF27+h2YlV12j7Z+YUa3MO79fZuZDYI/RGjVUSoGcyHGkjCMSGsYQuL9JbLaJBdDIpNv7NNWHBbKmbf8rwB/DWBcwh/J7WBzM03p/1WIs4aMg69HyHNz5PqZ2wO/ho2GzntYnqZ4YoDWNtOdik2dHzx+RMlxdbWhdRhuHOk+ub/gz+SGTIzygtlCKFpxUnQiFWCe7vJQ92Jp7QwUkVzDiQZMJemMmDhliIVBwroB4XHYQM7GMZGdxIxIzxMxuF9mst5y/ckm+HFgWxeeMNdLyLIieFrypzeO9BUHrgX/RtI4WqmicvQeW36/zM4RyvpmDmotWI9Iqxr6Bq6absoOpRYcJrbGhOZJE1g+uuSvP4TtPEaU5aele6FieLclXmXbZ0Zx1lKNCCRmvAXHuRmtpJs7mVtNsfTkzJSKHBqYgXs0+KislZo6OOz7z4m2Kb2hcZNgG8Il+0bBYdoxDZtwVdrstaRspuRBcwGNgJ20KDx9ccHm5oVsteLZd4LueuEuU64grkFXROE3z2SEqbq8Nn4DMvN8wgxNBsGjOwrgdCa3QOE+WsmeLwF6n7v89bXUSlmup3mjOfkmLqcynKU2pE4cij+yXmHmoCDVDk71ovl4rBuYg66T3u6nRmi4Jy5M0jy/LTtSK3+wR07l9FLBNR6foZN8hFEmQbfhguViwXA7kccCloXq8fRrv+p/DbAA+DLYuY8zW9JY9sncmf1rvvQLwR4HvewePnW5fHge2Jtb5c5g7/Dmfvvb403qv9UQALSubjPJeCE3g5NYJGcfVN7c8vD7HbQo4j4hHauaequJVESkobupBzS2Mm5EsE5dQh+PVFi6P4Ge9jXFdE1ARLZScyWOC0cxQ82Vh2A5I7y1AuRSCCjlmri+v2FxuKUMkZMWVbCHMEyMi1Cmtyp7J5FQ0LXa6ZxKYBOxAZTycTABTOezmiJimJ4QGHzwpRoZxMjc9IB/mPtOsOELFFnhbwAVHQDUzXm8ZtjvzxgIIggve9EON0CyWhLOG2MW6fxUNHu7XI5/3Da7at5r3n9meQsXhvJmb5phpfMMzZ2ecnN5ms90Qk6c8HMkXhR0D4y6x2ayJKeJ8oO86vDoCDTFnri+uuPfgguvNmrOFwy88ftGyu6/kXYRcKMWm+FzdmZvqqoNeG3sAM7Wt5/ZnhpyMSXWz51T9vYmx0rIfUpg7c/vHGSit1+50qvZXQmXWDtgreMTv64DkmrnM/bZvnI0blg0cdiHn7+2vzMfV5Gm2//fcbpz1Zoawu6Acd0IMlmWp5dts9hNdR9gCO3wI2/aYTmhiAhN7U8wT7I/v+kN43k97/UTgp2FC97eq6djusGPdPeYxgp2PH2Qvqn8KtJ6W1RMBtByQioIv+JApRMbLHW7Vc/ziKek6sXttQzgvaFT8mNilxJiV4GI1hPQVQEHWTCzZLBwoNE5RLahmcskkhKgeh+lqli4wpIaoAk7xWhCX8GFkHArbVyLtCwsWZz1yorhWaZqGFAvrqw3+eqBcbdlcXDIMkS4NOInGYkiNqRGzCzAvL2udNCHQOEFKrt5WFXQ4PwvbJxYkq7BshM4XdtE8w6ZSVTZDxKXCnZMeHAwz43HTjvJw8bR7MKnsnkA1AHUR4nnkwY/f4+SFZ2jaBt8GRB3xOtN2LXLiKKsMzvRdOec5VBo3sSZ+xgQWK6PTOszhm5BF8DjAkx1IG1jvzrn/yjnD5YD3wundnv4oUHwwBkszDQaUfONwiyWu8Xg8ITrCKGy2iXsPtrzxcKSExHd9/oizLyzQheeNiw0X52v6IeKLhYRPgUXTAMLMtE2U0I03zspuFWvrqirqC64UOu+RBsZoNwNtCGjJ5FxYdo6MZz0YU+o179usj9wgiJ9YoymKx650FVev9TKbt6Ky18Gxh4T5gEJzTGap5pEFzLYSbga71dphGgKQAzA1M5NlfpbpcrLREqAyyarKEAs6ZEIuLI6WuPNzhlJzOr1/2/eFT159jQ/Hz+o2Zpb5Sw6+9wbwav36/4IBhV/xITz3p7k+j00XPodd2BeYCeyj1+aIXeg/jk0fvtVdQgf8XOA/5amv2dM6rCcCaNkCY/fdjRggituRxWrB4mQBC0+Xe87vFcjKmAeKjDS+tsfKYfPRPLKsjWFAy/LmJrBl7Nd0rx9EaHCMxUwcrQNTVS5ayCkxjpn0KhCVoztL2lXL1atXrO9viZuBLmV0GEjbwaasirk6lWmEfdL6HPznELx4nBgTd5N1qqP1dmjwTlj0gSY4M8xMW2ZR//SYKgofYqleYjJvbWIvZsil0z4xP+ss0taCE4dPwtW9S2gbQmPgLZPBK9oopSuEYJePFrO+MErSzfu/919iljcc7PX8+gSp8UlmveFCQ9P1ZHWcn29J48jr9xNnd4554fm79Ivesh8V+q5HU4bG47oWlyxIW3aZOCYurzbsUqZbBfpbLd2JxzXBjlGZ9q86sUsdgjhkrjjYXXn0G8yCqEnQL0Vxfh+Ro+i+9aaFmD3eC32NJJp0bG9dE1ieANA+e/DAHa7ytbaDs62tPtKWZcKMN7nGeSsz0pf5mp1/+027WBkz3f97by1hQLFki3oqCqFtzaJFK7X7qaO0luwNLT+oCnW7P4BFwKwOfnYBPKxffx9PdUHvphzw04H/LeaXNdWOfZ7hYfX14x/4NtucrucfhapX/PDqJwL3MY3e0/ok1JMBtJRZW0VSGAr5OhKeaSiNh86htyH1hRwdm2GJzzuOXSZFYciAlLk9U9RcuwuZQiHXKag8LWw4gjg6H3BSXbJdXWTFFoKclDhkXOdp28DwYOTifIfzjiZ4zr96n3R/ZBE6CJkUd0gccJm64EyL4SP9fOeMeajMQTlYdG5AH9mP5AuFUHasr2ETlc4VwsFaJQJ9EJx3bKLJp734unhOwA5EfD0+inPGIsUCUvb+TSL2WO9gc7khSubZz38O8Yp6pTvtkZXguhq5k5nZnkNDVeuYHrTb6v4e8HDMP6jYq5RCjAnvG84+f0KKiTiMbC535KzEXcv1tbJYNfRdQxki6qA/WuLbBgkBxkKOkegy2grNseP2rSOO77Ysn1kgreBV6Zxj2XYElxA101MDpQVLun7kvE3H+wZoplqGmA8bqqRS8GIsoffOArAn3RKOTRSWDlYd7KJjF2vLvJq4KkLOqT7lAVDddwtnkm3/z5vA5SZI3NNRex7KFoKMn/itAyhWn1mldiP1AGkpU0i2HpxUh5kDF6QOAThqfhCCkLXgHfgmQHC1NfppA1oDHyyLcQz8Ksz9/XO82b/pAmtnufr5j36Az/1pr5+ATWz+ZA6nec0v69FzOI+VvM02M/AyxmpOj3d8OJYfvwZj4p7WJ6WeCKBlN/UFyYKMBbnOxAc7xrMBPV3BUnDec+u7bzO+EQlHOzb3BoZdYRejeQ+RbQHRjJaRUgaKFiL2MRmTooITT+taTkIgiCeqmTQ24hDnKSIWZj0k+qZj0XS0nw1s88jlvQ3jZiBdb+m0wDCy22XIW5q8w2t10pbApBWbgn1t8a0gC0cWj+LrPt1kHaiaIeeEUhyboRBzwen+T37mrMRZyK8GmjkIL9zYnohY0G9OpJLommCB2UNENZpOqT7OxvMH2ggMmWFY07ke3RV2r48sX1whE7s+tblkEt3vEYFFwlRXcpk0cpNGq+57wYBaUeIQGXcDvo20L7TcaU4gjlw+uCTGxMnxMbdunzJl6okKOcM2jnQCnQi5FKImdmT6047vffELuBNPOHUcPdeiAeLDa9hs8FFxqQ5PqDGCOplNHVouTL5Ve+X3DGBLNcgqlT0ak23Hh4a294xDdUU3t1xab9BzzEIRT9GCx+GDR1BSsgxOreBsBsrzcOAEavcR0qW2Oqe/JjslE3ivIxS698wKGBuYCHjRqvmb2pEVfMkeYBU95IsP4PTcsrRnsqEHD+JB3BzqXhQIgWa5wq13xBIpn7qpQ9j/ZX4QjMZvAv4ZjMU6PFYRE1v/RuwE/CPA/wP40x/Ac36n1I9hbdZ/A/PJarHpzcSb24avYG9Sn3ubbf4Q8L9g7231izC27HcCf/0D2OdJo/cDwPfwVI/3yaonCGgpaEZSgSGTLgdiGSmhZbhINCvH2ffdRe5n5MGO177p+dqPfIPEFTlucUQaZylupSQLty2FJEqcGlZF8DgDWhJY+AbvPLHmzznnzC7CeVxoGJKy3mzBC3e+8AxnfcvLP/KA8zcuaNcDnQI5UXIGHWlJCN70NGpmB4jiEJuW3K/SNqHnPOLbOX+Rkpn8wfYgCpw41LcEpwRVHAm0zEupaWks19DVbx1soe4Hpv0qGecCTdtScibGjN8bjR049GckQrze8uDePU7LbW6fnPHyt+6hPdzqTigls15fo8VaeIt+SXKlYpQ9UDkU7j/qnzTLzhXymPE7IYyCuw3Lux2f/VkvkOMdVAvOBSQ54mWk7DLjerBJw5QZk00clliIY7YMwtsNp9+9ZPFcj28dPgjxemBz/5yy2RI3OxhHmkOeTS3waM9q2V5PDKOIm4EWWFA2YFmQaJ0ANNPP4B0+OKIWShKLI6oMWC6T1soyDMV5iwByzu6BZc9nHVw5zHSW7v929jximaHQBGmFyX2eiremXy4UsdxLDh8th9udmKupMak3nnO6NZiuwzl/cortEYdzZlrrgqPtW1zTQsyfQkYrY+2oW7x1yLNgC+Y7YTlewFitqaaW+/8by92bgNX3Y15aTycQ33kl4G9hwOj3Aj8b+C3Af4KZwH7h4LGTfuvt6ptYDNL02D8D/KN1238dZu74vYBwB/xLwK/EbD3+LDZ08bQ+KfVEAC0EVAoZxzaN7HZbwj2H3ruiCUKKmdD1uGchNIFm0XMrnHFrs2F0wrYo19uRbTZ2xomJ0VW08lwmc26cp3WexjesQkvjAyoBrUL6Iga0xHt817CLhfXljsvdwMlnjnju5JTtrSPu3+vZXg2EUuiY7CGy/QlNbRW9qZuZp/9ygRoQ3N/t6foV169eInnPIOwtWCdTy5o5N/kh6d7YdAZulVGaXSvq789VW3NoZcmyVoBY9gwUOuvTvCquKGmEeL5jfe+KZz53l+WdJd4FNCn5OpO+kUguIXctXHsChwhTBrbtjx4s3ofAq67mIoJvPL53eHVoKiSXYWmieikOzUoZEm4lSGPnkUYZ1glJGXO0sGNXfGH0I6P3pnGjJ58XLl+64JW/+TV2XztHYsI5CyS31qrM04+PwwF2egpSEzYnQi6zNwWd4FEBSuNYdB1Nbrle70ixgp4C4hRxFkLuqkt+qWfcibdrp+5EAdN/uX1m4XRzolV7OBl3Par5OoCK9brQ2ZA0kOasUJkdr2+2TGcWrbbckcr8TXBLxFgsygHV6lDvoXG4tsGFhqIWpdX2HXEYieOnTVN0BPwMLA7Hsj3fXJ8B/mHMa+ntajJzma6m/xgDWD/OTUfwP8J++vBpvbu6xMBWhzFaA/DXuAm03ukS+f3Ad9ffp27vzwE/DwPfz2AasNd59+fr+4D/EwawfgWWv/i0Pkn1ZACtgwU+lYjGLXIO/WvXrEIDS4fkYm3BzpNOAo32PLs+YzNkLkdlE7dsxpGYE+IyziVb9Go4sBdP6wO9D3ShZdkEQmhIVQSvFciIE8QLvnGMqXA1JlyMPHjlAWeLjpOjhtOzFVf3rhlyhWguoRSyCpLF0INOkEnnxde8sYppolDaZ3pOzm4xXI3k9UCO5UB/oweanLqgzmzE1H5kFvDIIdgCa9XVm+AJuu0zBZWcEgp4Ny2uBxYYoji1N/pQHGEQNm9cs91sWZz0DNeRzRsb9DLDy1CaQmwS8dmE9M4EXrVdWE/vLDzX+nr2FI2Cs9ZqaD3qKxcX7IVoxrydppcdoLR1mhOH9wGHR7bgozcAKQU8SKuELhBCQHfwxg+/zhs//irXX3lIuBxZSkC8xTgVmSJqDgXnB1eoUj2/hNmBdQI8M0yR+feLQAmOftWQinCdrGUryWwPfG0Xe+9rFuVBy88F0yseAOWiak7rVduH7EGWloPH3hDW20FTkcNNVUd4A1owgambrJWBqv0lJtWoSyc7+en04aq2vZ5dkeqS6iA4pK3DByg+ePpFz7DZMQyfttiYHmv1/Ungj7/FY67Z66reTs/1h4EvY1Ew/yXwr2NC61Pg+YPH/ch73uOnBfuBgqn+HQww/TTerMv6dnqtM+Dvx6YS/zImVv+VdTv/BfAtDCS/l2owcP0vAT/8HrfxtD7OekKAlkEtVGj8SBMcjXe4ywvcyYJx59hExceAaxyhb3DBc3T7mO4FaFzLxkXGSxg3sEmwHc2BeuU9x76hc4G+fu5cILgG1FdzR1cXEAsm9k1Dt1xw1MLFkFlfX/Gtb7yE7s75ri9/kRdOG65WDWPK5GL+VhRLmzMrg5vTXlCJ48p2ebEYmKvdmjv9c9x98S7nL9/neogHWpvpwOj8+2/a4g0kwEwVaQUBontFzcyu1V8rNSvSwN805WgLODPJJTQuIH6BXhUe/OgD7m83nL98yfFyxcnxglW3xIuHrbJd7+hDX3Pu9Mb+TTqeadf304e2tFtEoEOcolkNXMHU25ojxtSs2GcLhiCB290Z5TITHw7kEhl3I+7EcfTsitXtI7ouMLy64+Ffv8/2a2v8WvAJku4QOfC2qp+LVG5HpvaZ3QRQ9UtqB/BAk8YBA2b7WxQSijQNbesJwxaip0QbUfQzk6c32LMJLE/gaPIc259/nZ/D2s02STu1A29e9GbR6gABAABJREFUI28RdFNfmGipjNR0XUywcTrgj1xg0zkUZlZLZQJy9n3nHXiPDw2+qx+NB+9o+o4jdWguXJUPezLro67nsDbRFzGW6W885jHnwB96h9v7fZjAvcOE75M31zVPwdWHWX8O+OewY3/7Xfxej7Uft8C/jenmficGtH4X1t79J4H1u9yfHvgHsdbkU5D1Sa0nB2jVlofdoWc0Z8pmR7pYo4slSQvXskEdtKuOtm3wGfzpgh5luTlmEwdiVAbFzBKd0PiWVdPTV4DViseLIxdnbcUyrd4emAxRDYAdLTpOTlfscuQyb/HnO45ePaf1nobMQKF4jy8mZKZaSmiVHQs1flT2/IiXOnWYreUVd4MBo8lvy01BNlo9jm7WoyzL/uvaQprbPPXRB8aU82Onn05AbNo/MU6mSLV9FTWmTwXZwuVL17z+8JLtlQGZ3geOTzxN25A0WysSQM0E1NDExOYdognbi0n/4ziwd8iZkiuz542JVKcUV2ajS3ET+JBqXj55YFnLMY8J3zSElUcaY4vGYUCHhI+KmEsFUtSc6OsxqkRkdWOfmMGD/dY9dJFKbE3A1bv9cbebBigFIsJqFVjFBeNWKbtk14UDoeynHWf+U+drae4CCntD13p8zYmjiuZ1P2gwnd99gBOPXBPTlxMAn6/SeT8OThGz+e+8pWmw4/CYTOyq4F1AQoPvW9qjBX7RQush1Ia4F0LX0PWPM338JFeHvZ2eAd/L44HWu62Lx3zv0wZQn7RyWGvvcbcobzfA0QAvYZqsB8D/q379s7F237PAV9/l/jyPAb/HXQtP65NSTwzQmvpKqSglFeKQ4Hqg9VfIbXvDFt+gKMN1YpdHvMDqqKc9OuGZ/BxN6DldrLnaXXE9rAkKSwLHNDRilg4ec4AfU7J2DB5XwdUEtkqBGBN93/PCnTP6Wye89OorvLHZ4r615pnOFnRpHAUPKeMLFBIFJVMsA+/Ai8iAk+CdxxUleEF0gS8wFDM3deJt8ZVqdTFzUY/UmxbNCrSKzu8FE1BwogagKjKYGa5ysKDW/fRirakE5BLIKEUFjQXZYCawY4Nzjuys19aFDuc96qBpGkLwJC3kUhDnD0BcbZhMejC19onhhAl0FUou5NEWE69+Lxs6JMjEhOoOhxZl3EV8NLCFCl4Cy9Ml7UkHHsY8cnH1kPX2kpJHGhJCMZf7qQ0nxgROcnJ3yMDNx/qgFXrwY+f255lSKtGUKDGxiZmjXlgct1zdjwyqOKl+U5pnJDVNNu6bSlMr0Y6Pm8xEZ02WGqNV91cPwJY8spPzVzJdGBX0HLjMy8Ejp1djbv96wH76SmrJ3CWcbB4URVzA+d4Y4UVPd7qyQHhv7dyioB6avqVfPmpX8Gkoxfys/vzHvSNP6z3Xl4B/AXOAf7f1DUyH9UPs24xfwdq+xxgr9W7rf4L5Zv3V9/C7T+tJqScEaO0ZBXt7N91JECHgGccRrwOL0yNwns2DgVdffY315oqj447bZ6csVwtuPRs4PbnF3ctrxssr0miTaOZzNBmEipldpslDyDRcznkI1vZAhbhL4AeWt444u3uLRZ94494Fkno2OdKuOnxXiLsdJUeUbC1IXAUX9oKm9doyGguiCcWRioVh51hIMZJTFaZrZbNEbizs+6bOTdckuXkY68MnB6vpm1PLrk4VViHXnvjaq7/0Bos2cWuVnkkFr0J24DuhOWkItxrwcHR2TLMyB/lc4rQxDpXvpZ7d/ZPZ88WY0RSt31bjcFBIsZBjqqyRHG7kBpsnYro6nEO9EiUijSNLsXzLDDoUJNmE5SR8N/PaQ+bn4HkmSufGUda5RTaxcQLGSlXX+wKUkoljIWox3Vla0PcLTk4c5xfXMAz1+esLkYPXo+XgXB+8vnkPHqfvmQ6I3vjeJKafr4X9RWHfr6Brfk0zy7V//j302jNb+2uv6gKrZZBvW5pFh+8awrJFggGzrKBqbFfTeYo0MH7anLO/hbEOC8yn6eWPd3ee1nus7wZ+Du9+afw68KewluNL7N/kCqbZanhvnlpn7+F3ntaTVk8E0DLmxVEoVcxjs1xaChKVZkj0u4J7mEia2ZwPvP7KObtxy5gioenoThe4Z3okg3RQnDJeb0gxU0o2kOUbRDwpJfIYaZqG1gfmJWbGJA5NnnSRiDKyWGWefeEW3XFDu23R7Ui6Hsm7TGw8F7vILkccjlYCbWUerJVoC16WQknJ7BmaHnWBo8WSlITdNpLGSM6j5S7Oppg3IcCkhfFTYPHhz+fWkpu/r5UFkcnzYXqc6hzvYmySMFkDZLXByJkXUSWVhPfK6dmKtg3cWz/g5MWek+85on+xMw+u1kFj8UfqTKjuyrRYK1nyvgVWvwt2qjUpecxoUZyzvEtUkOLw2Si6IhUUZTUiKKvlgZudO644SpfJIeOOHdphmq6ghOgJUZAxWTylM8BiQKvODKoA5tQvs8fTIaTlEcQCk4WFFLtulYoVEVyBZgDWO67vb2iPjgiLgARPHjy4ADUmyuwX3HzeqVfkpBFzYs7/GfNL8+Jq21Fq/E/dp+kzOrtrTLo8PXg104jGjIFl/5z7R7h5exPTVebr5aCjKjaQIN7THPUs7p7i2saSBATIBU3zncLcAnXu0+ajdR9jL74fE0T/2Y91b57WOy1rm1tL1gP/FKazezelwO/GvM8iBqo+hwntr+r33utk6Jfr53er7XpaT1I9IUBLZnGzlY2rxyEyupG+6UjnGx5eroniuBgL68uBTGbRLjlendAer4wJyyY0bhFS62mzIuoqoxVwIjRF6ZK1baQKiqf5elF7PMUzxkLcDGwu19z63G2a40B+I+NXPfk4cn21YbgYGTfKVoVGnbUEnUOqfka1UIBYhF1JbPIGGIm6ZOkKiGe93kJKth9OuOGLYAdor/o6IFzeBLamH+xX0PrhbriKH9o52Bc6MypTC2pas513+MZTNJPzyGrZ41en9C90LJ5fEJ4JNulXlCKKqrX93AFKnHQ+VJ8veeR1BO8IjbX9ilhsEkVw6nA1u8dwq2m/NBnzZS23+mRB0KBoUwg+2NRhAj94HvzYfe7/3Tdgl9Bo21FX7HAqc/D31LzbmzQcHtSpJsiyP0Y88tPJGDeUgm5HLu9f02YIviE0AXWJMSaLCjrcxA2aboLak4auzMBJsAGMcuNZb+7KbGw/tQknJpH9+RcOInzkAHhVoKWCDQuom/etVJDlXJ1w9cFahiHQLDva0wWuCaRcSEOkZLU1rGi9tK3d+1j/jE989dhB/hJwF8sjfFpPdv1srD33Eqar+ifewzYKBq6mv8gvYKDrtwD/7fvcv9+FBVX/ofe5naf1cdYTAbQAu+uubMHEy6SUiCmxckIpme12YBTHelDDJN7TdwuWiwXdoiVpQouxKQGBxkGBQJhZXKmmpAUlDgNpM0DM5DGZ+FursLqaTpaixNGCjdvQcp3WOOdojjpaN+Ki4FpH0wRCyTQOgqdOzSlIrq8pkxGucmIQJSnc2a5ZdT15N+JSroMA04K3b93ohAhqzS2cyr4hFu9S6lScranTYjZpeN5+YTtsGZlOzHRm3jlSTGyvrnBHLavbC8KiQ4KDIHth0WHXbWZGDrY+dcoOdkUQJDi8N9F9KplU0oxnZvPTekzEidk2AVLmIBy0KDmb4adrHOpM66XnmQd/5w3Ov/aQblRKNisOmY/nxB5WAFO/d6N1xwStZP9SdN8GVfYs0fw7ClDQnIibEWl3hIVjdbxkp7C+vMKr1hCcm8fuhiy9Mnl2zuvXovPz75t5h6akE8EpSJja5XU6USrwrgAcMSsGETEnd7E23+SQXx3cUK0O8c7OF96MSCegRRPQNpCrYL9Q9X2lagfNXp58ALo/XaXYxBnYdNkfAv4K5tX0tJ7ceh74X2Fs1jXvTZvlsTDp/xoL+n4Nmzx9v1mEAfgLdVtP65NcTwbQkqnVZSJkXwqhegwUEZxkkGD6F62MkQscn3R4zaTdhqINbuGQ4JFVIB05WBR0p+TBWci0ZNo+0K5apAXJLeH1lnh/a1YNqqY1CUJ33NIOStwliii73YA0nu0ucbnZsGw8jRfOmhW6jGyyIxSl947WiwXqZlv8Y4rkONB7xyAOpdDgef31e3Cx5XiTySmiJVGcQ4o3QMGBRkb2wKn6juKwfEMRx1gCYzGjy1YyrWSbKlSLiXmrda1M8h4mQGdTj4ECTtFc0E3EeQ++IJsd/qiBFEipkDK1RVcXdxNAzQBL5hch84menk9E5rDpUu8GSwaqCL6otf4Mz0yZeg6CMZduEFwRtGTSkNhe7Qhtw+LWgnyaUZe4fHnL5Y9f0Kw9ngHn920soA4guAo73MwwTeahHsGLPaIwxdRA0VzzOe31mj7cmNmZjxSAgh8SzXWi94XTz9xm++yS9LXI7uFAiZ7gDZCbwo/qsF7mrE5VZVRBiQiZxCFjaNeG2ZPURmgFoN4FVkcL2kZ4cJVI4w4ngYZgk6Ri1w9B7MaiAi5VJakxfk5c9Vpz5rXrA+1RB50Qibi2Q8QhPpC85ypFJCcmYtICH2yK2AHFSb0APm2tQ8FYDbDpsv8UEzD/AHsA9rSevPpjwL+JBUzfeh/b+XsxZ/kI/IvAf8j7zzn8dcA/DvwC3kSdP61PVD0ZQIs9+XKoUsklM44jcRxIoqg6+lWPcz2DQtM40zRpAVeQ4JBGKKKkrOTGYl3QGm3joGkF7YXSGntEZ07wxgpBaBpWp0f0px26UcYwUlolx2ytNDwlKWPcgRNKTGjMBkiwO37n/Hzn73IB58zuQAWXXF2kYTdktnHLcQmUWa9TAc/kiQUHuibZ/70pqOxbXDb/aCubmxmbwwfzJrR1qJ/et68OG0rWZsvZ1ew7hVItCdQOey46P9c0OTc1uGYcMO37o5/39FttudUf15gbXLU7mL3EZM5GVLS2pcx3axxGxmHk5PiU1cmKcbUja2YcduR1IuS6wMuh8/7eemL+LHLzLe0xu7s/frWdBgcQjYNt2PHKw0jEEdsGpdAuGxa3lsS1UmI1X5VJ5b8/ZTPkmzqlEzuJzLmF085pZaImDRQIzge6vkMkmZGvik3XStWlTAa9UyKC8/MQRs24RkJH8S2jJlKrNEct7TNHNMctSRK+BOK6UMaq8Sv74RNXxKYWi50j1Rr7I0INifwUVY+xISP2tnqKhRY/mp33tJ6susRsGH4h8Pe8j+14bBBigQHtDyJM+uewB+9P65NcTwTQsvVbUHXWgXLWYkg5wTgwDB2pQHN8xPHtE7Q9YpMjOW+RLhCOW6TzqLPw6a4NNL1N7g3sGIbB1lhvWhRxnr7tEIXBF5IzlsxnpZGGznXIKMTdiOaCw1GGBKPii+IVtust1+OIpgQ5s2xsoq/kyKgR17SELtD4gIyOIsDO0WigA8QV7rPFl1wFzQmRjKqv4MXvAcokuKntLsMLMreTBKEhGdCqrMospZ/MNmXfGJvZpgo6CgfM02QTULddMOAqbmobeVwbcD7gVNCYzXHeCcjejmFa76eW21vej81uoft/uonpchV4ZCixVE2TTYmWVMibSBg8PnmGzUBMibZrCG2guAaJApEKyIpNNB5AFJk/9oatImKva2qtqVlSzMBp0jfpfgmdOr3T50mcPllXFFVK2UBQmocdy7vHHN+9xW5TWO829RXtW4YzBFET/E8HSCdnesSYXQwk2RkuZITgA04djfeErgEN7HaRcUj04uZwaqmCdFev/Yk9nk+cE8R73OoIOTmCMNCfKYtnOpa3V3SrjqSZfF6Q10fi/ZGcCpJAiuJUcVnQYuct5UIcR4pmQuNp5NMGQDbAbwB+GRa185N46nn1Sam/i+npthhQer/1fVhQdcFYsgYDdO9W0P53gL/EUzbrk19PDNDKxdsSOE2XOTOB7IIHCThalssl3XFLEqFfBXaj0Kxa2mcWyGlDGjPiIPkCXmlWjZlTFm8LczJTzbiL1iZRJY2ZnAviHKE4ZITtg405lJdCcfax2hwhCunymny9ZX1+yWa7oeTIIpgcTHMhZ9uH5a1TlsdL+qMVaSz4Zsv6est6kxGU3ie6OJJTZKsRITIZWJLMf8oE/A51gmiua/xNdmsfq4NNyx0aWwJzht3EiFRJ1Z4QmbmnqqeZTBgqWyNaPbPAN4JbmBll8A0ueco2k1y2qBUfDJTV5wgzcHgLh/JHarZ1ChX4VYZMk5J2GVccXjx5LAyXW3avb+jGhoVfUMZC0zbEnFjv1hSfYSzE3ViDshWnGadl1mFNT7qPL7IWmYGtCSAKsTKtXsCJtdR8NbmaSDidXwB2PGdyyjRuRZXtZkd7uaW7fcTq2SOux8jF/S0+mieYqM7ADMWGASpQzjIpssQ0VCo4CQa0xJb07IW+X1hgetuBc1yud4wjtL4nAE4F54Tg7DPegbOsT3UWR4V30HhcF3DHLf1nOlbPL+mebehOGlzryCOM60LyhaYLaJsgJnzSmmmkkIpdUwAps91s2cUd/aLn9vHpO7giPkmlGDPyh7Er4mdjE4jDt/ulp/VE1IBNG/6jGFD+5Ry8Qb6H+n4sm/DrwK/HUgP+BPA/491NH/6feQrWP+j6PPCrMYPh/yuWPfnh1xMBtJyzxcjelA/0LlRz8brgdX1DswioOm6dHSPNimdfuMXR7QVl5WCXGXYj427AB+GoX+EilK0iYlOGJWfyODI6hQx5jJQqEraWhpCHOHssuV5onCeuB8bNyPrhFWk34IZr+rSl5MQwFjbFmBLvTdfC9pqwa3nus8/R+8C6ecBm5XjtYWIcIqVATgu2NOzyWO0KIq2DTpRQNVlCZTXm6Dmtx8P+r2Viq6a2kR1TvcEU6awXkjluZdraYYvx8PHVxHMCDBPgCw4JlTFLoGMme8V5nVuRk7XAxPK8o7esR34HqQyas2+2TSDkQLkuXHzjgte/8ToPXr6HJDg+OuL09jG3n3sGdUrMEVcESYLGCnp0nq+7+WSHOzCxObpnmErdoYKZv2odEjhQzzFdocoN2Lo/EGI5k2mIXJ1f0V0vWXz2hJM7J9xfPETjuP+9uq+TTf2kAduHDNfWoQo5JyAjfYN0DYvlkmW3pMmClELKBecbmtbThBbSaK3fA++r+RU4R3GOIoI0HmkDJQSGMaGXW46fPyEsPLowA9OyE/QS9LogyVX2rSCxoLkYg5gNaImAxkgcBoZhIKfE6v2sY09sJfYtoz+HiaI/iBbS0/rwaw385xgb+cvf57a+B/i19esjjPv+e+vndwO0Pm3B609CNcAvxt73fttH9qxPBNCytUQIVZvjqNNjmPeVOocsPOG4Q73gAxz1S8JCaM5aSlfF1B6kgSYEvHfkklHBstYUcsnMC2oFBUkLUTNIwXuHd9mEwb5jN46kFHEjxPWG3fWGtN6Sx4jL5smkHlpR1BdybZFIaNiuN1xtR7zrefaZW7TesThecOYjDx5mrq8LyXljiSrI8zlAFSIX7M/SNC0WXm3aGali6Wk60SFO8AegRitQnY7t5OlFDZA2/c2k5annoE4yFrkpE1J7CrtSnEBwlE7YrUfSemB5d0VzJ+A6P08qkq39O7UMDW+8M/p72gcB3OQxpUrCnPyvLjZ840df5d43H7B5eIkrGbkjnNw6xjXO/MFEcc4E7LopBqhNuj7tWH2uqcW6Bzn1iNI4a+il+kitjNKcJijcnJKtrTzn7LwUZQ7D1pLt9TjHGEeuL9asHm7AeSQIWTPFYUCFghMlTf4+Ivvzp2VuFzrn8a4H15JPA82dnuMXz3DqKC8NbNOGuMsIjo4Wj5CTJw4jOSdyLPaavKLBWePRNxAaFqcLwumCSCK7TPtcx+LWAq+O8XwkbgbctSBrRa4TZQPsMrqL5M2IxmRGwXWStgAxJXQc0RRJJbFdfyqRVq0OY7T+Ak/bPp+U+gLGIP19vD82C+ym6M8Bn8XYLbDcxH8G+D3YdOPT+njqKxjQuuSjtF95IoAWWqqAe+IR3GyxEEKwBb5z+GUDXvC9JzQeOiV3ZdYFFQHfelrfmCXBNoFXfGNsmUvOmA3ncC4gKD4EXPAkl8EVWzAFJDg0Kikm3DYTd1vS9RYdxtnzyhicKSPG2kreVef5ynC89tKrDOtrTk+POD494ei442KzZn2RcAJtcAQXWDaeUJQxRXbJWLZcvY2qZadplLSCrQkgqJomxk1M1eFbu1StFfsFG6qInlmQPAGqImpmngd6rQm92RSkAS1tHcN6JG2Vri/0dz2+c1XMX7d94IqvenOv3vIy2O+ygcoK0FQzWTJZlCEnrtYj253ipKXzhT70NCEY61dtB2xTjjJoteWqIKVeLIeTlm/itgR8NQY154o8afBnTskAZFVyFQODzpu/lYqz6+OgBWvXh8UYDduR3eWAO2oIvUeDHLBuxdgx8Ux7PIFl0YJKVXQ5jw89rl8RXmhoPrNk+aXbpMvI9lxpTxq6Udm9dIXLjsYHfLIbkjgopU7Zljrv6YK5+ksb8Mc9ctrY+ewgPNdQloqmQtwV4mXCbQpsC2UbKdeFvEmUbSRvd5QxWVZpSjZMAaScIUVLAPDehkM+1fUZ7O75aevwk1E/EfilwOoD2JbHjEaP2b+znGDeWi8D/58P4Dme1nsrBX7sI3/WJwJoKTZa72dnbLVoHKOncMcd/s4Cd9RQHITO4RceWnCdVCsART0mSC82St73PTklRjdiUYbGcjhnzIdzjpPbp/Sh4+L1B+gQKQp53DGuN+bLhJKHQtzsSLsBjRFyRsg4V1tsusclivkVdRR6yejmAZfjBffve45v3eZLn3uRXCL31lcc+Y6z5Yq2aThqGxbeM46JbYzEmMglGWjTRNE0u4j7OvWHRf6Z2SQGGiZj05kIqQdY1VzXbaIRZl+uCYg5oDEtkQ411LlyPq6GTQO4NtCsGna7keXRCike3YJsBPXFntfftPt8x3f1dZ9LnXAUNZNLFzzdMuDV457r+Pz3ZI76C9wusgqBNjiarrP9VLEIwPrhnKtZjA7B3NiZmLfalpu0b9M+j7kwlIFlaGi8n3MPD2X0Oje3D/MG3dzSFdgPA5SDSKZUyNtEvBpognD77ITtIIxXGzSZTm9uPTLtl9Fnqh7Bgw/QNeRjhzwbOPmZdwif7WlvrfBfTeTPKcffvaIZHW/klyhX0VIR1NGuILBAvKOIMuQIjefk9hnStGQH7UlPOXIs+iOalccvYBgGxssRRsUNyu7BhvhwQ7pO5HWi7AqMEbajQcOslJRACwlrN5rtgwHQNH6aAUjB8u6etg0/OfWXgf818CLw38f0Wu914k+wYPFH6xbwP8ValE+vjTfXEhtGuP9x78gHXk8E0BIEr5Y6WBBUzKxAfMsQHP2dJavPnqGLgGQIfcD1QnGK99VKQW3uCkwjFdQjxTRXE+BwQSh1lH165UE8LrcMRx27IbLdRrNsyIVQ3a9zTORUXcmzudYHmV2VSFPUTtXWTLBEEDImDM4lc3m5Ydxu2KWRBzniCJwC2Qk5OEowq9VePI1kcsnklEllZBt3JI2IQlsUD0jBdFPWcGJygrJnt8xFVZm9RMsN09K6nFexcnHgO8+o0Iy5MkmKcxbGbdZHgm890gr+JNA1xnpsX9+Rtg3N3QZtFW0mHfvNxLy3r6oLm/ZNMd8sEWuxqqM9hjtfPKZdQr4YaVLV1ImSB5tow5s2q+RMWAZCG1DfGEvq8iz4NzBZbTFEUTwqMOqOi3jJGSuO5XjW7wl7IbpMEv/5mE4CtX3jdQJaNx6RlDJk0nokdI6AxUVtU6bTPBuphgkyTyhYW9u0D2hoiN4RTlu6F5cs7x4hC4sPcscBdysQc0GK0N5aEt1ALsaSQUdz2hBOWmiEMI6I95zcvk3TdWzHkewU3zoWqx7nhM31tZm/bjNsC3mbSRcj8XKkrCN5k9EhkVMkDwPBO7yClnRIj9ZjbcanOb7XSJJPQkXgr73H322xRfjTZn/xpNcV8B9hf62/B/j5mHj9573H7R3+1Y/UO1ngH8BMUr/1fnb2HdZPwmKAXv0InuuDqMy7Y4AnIPzkv5c8EUCL2iz02IJmC14DTQurBd3zpyw+c4v1kEnrhO89rqeGOWuNqdq7fTsVyI48JNKYqmDc8v3ML4j5lecUKRKRxjYzJoWkBFW64CilMI7JfLSKosV8ufCljvebID2Vg6S4yjDZ8NXEcsBul1jfu2IYduScGKtbfHFCaR2lDTiUoKAaKEXJUtglzzpnhpIqo2WsiZvDoIXJ7tIMEKZuqlTKa98eNLDATHWZ9YDi2sDiVk8qjvEyEtTy+izdRsiV/fFtgNbRtS3domV3PrB9acfwRmQVVvhTZ+fQ1Xijqaa+4P4fNzRi+yqTmooijqzOYnLEWnh0yvFnWtqTFdvXHeVKyRuHJChDhh4kGaDJRQlLT+gC6jtEIqhHJpd0tSM3u+aLTRxep8JXrnZ896rjVjPFhTsEb9ornfKvK7ieRV+lRhBNgvq9AkzB7CWSGFC5HimdwzfOPLREa8KnVEncQcBOEUp25vvmW6RdEFaBxbMnHH32lP5kaRmTu4zrHRogPsw4L/izltSbR1t7tMR1Dnenwd8OiIN2m9ER3KqjWXWkCHGzpaREbB06KsN6oFGP2ynxciSdD8TzHeU6UrYZdhFiIo87dnFH11qLXzTZuaO2glGCQNJMiU+nqR5f34XFwVx93DvyHVqKHf/fhbX73ivQOtzeX8XA1fdgGYi/HZtK/O/4cAH1j/DJ0ggOvHOg5THW8acCv4MnPQvyiQBathhVkFDdqrtVSzld0n/uDotnjomY2WFxhawZwZPVzCBBcAEma548RnSMdmNYs0LFuep9ZIDLeeN+nHf44OlFyVkIzcjwcINud+Q6pi7F7sRlWjXK3td6L2WadEEHupoqoBYxS6gyJq7vDdzuAz/9+JirnWm/gmtRaeiWHaER2CY0KjkVciwQhAXJyJOSaXI2e4E5EPUwDnh/UIVJhjVBrOn7U6TLvsXVtI67ZwtW3YIfub9DdnCSTA+WxZFaoTkLtM90tKuWJrT0y95afevC7nLL9krplz3dqjO9XWafngOo5BmATMfm8AqYPvy0n1WrpaqkmFHNBBxtH+hdh46KthBuBdJ1Iq5HO0ZOartOwB+YcZL3BvvVmNNsRSpwxRzxb/dLvq/7LLdKT1M8xScmbzE5PNPV1XNy11cxmGg2GwdNxFmiNrGImRRH8uhJeJpGODvpOX9ok4RLF0GLWX8hNhxwHBDnSRmaZeDsC8/SfmaFXxmrmKOZ+w7nI/e/dp9QAqcvnHD6pWP6bkkm7+OSWgeNoKPikhAvItfrK4azAb/0dMsGKQ7dKPkq0QwOdoV4b8v2wTX5ekDXGbYJxgRjtnZ6MUsSsQMx85lmogtehVD93Nw7HI74zqtP2uL4aawTTMT+99V/X2ITpLewDMt3U4KBq+7g378ICx7/jcD/kw8PbH1aWFHBNI898NOwCc7bwM/AdG+PgqwTzDD4Hk9KKsMTAbQAingUR5EWXE/oTmmfPab7/Anh9pLsoUHpmhbfNIAYQJnelBI2yg/mQl2z1SQI4s2jSKZFz4MLFl2jSdBOkLbFuRHXFOJl9dbKELTgi9JaF5LsTYNEqc2jiZyZPmqbSaXqt6qvlDFChau8pY+Cqo37x3GgaM9iCS8sz1i6ltJF8pAZh0SOSis94oV+aM0LbDBmS0QNvFjSYQV/rjoUVENTLZNfKX6KWGHKotPZdV41M+bE8pmW+Lzj4lU4Sh0hKjvniL2y+EzPyZdPCTVXskiBFbQvBGIQfAPSFUqbjANKvjKVAXxmGyJBPKEEFEgUigPUzZOUgsPfWGjsa+9Mo+S9x7uAbwKlOPIio0PBiYdY28nBwImZcVYW03s7gVRz2qoDc5jhql0ftZ2oQodHRSvreEAJThqqSePmatySYoxnNpd3m1z1iOxjjhQQnykyMKZAFz0XG6A4VqGjuMF0TFIoqgxZSDhSo9z60jFt23P1+o5y3NJ99phwqyPGxHAx4J3HjZ7txYaYRhbPLll+YUXzQofrxMJ8itktpLGQh0LZ2WRg8QXXOHwwfy28kKMyPtyRXh7hKhIvd8TLNeV6gE1ExgSxAqxs7dgggoTW7E0mkm9/4GwydtIT5qeM1uPrKcj68Os2FiD96NdT/TLg38XuSr4O/HEsVufXYnE976YE+OIj3/OYFuxfxqKaHr7LbX4nVYMBqt+Nsb1Tv+bXY7FJj2sb/nLgfwT83zE93MdfTwjQEgu29Q1yvMKfntDcPcM/15GOhEETebAYnKbrGOKIJsV5W8yc7KfvVNWABZW6CHumRFwFX172XlANaBKca2hlQRNatq9uyZtE2k2i7MpoTdONNXTXgIxtfTLBFJmATn2uyp7YSL4yMEIW0MLSjZRSKJtrXn/lASldcff4jKOwpPOdgUHXoFnp2hZRiD6xbTKb3ZY0VMG2+Dknb2KvFBOCN13D5BKvlX4rpU4vQtUeKbv1wEtfu+IsLPjy557lwWJH/uoFmyGh4mlvtfTPrxjbyG5IuMaDh3DkCb2jaRy7hwN6mQnS4ltPLIUsGQ3JINTOdDujJlzrCcsG1ziyrdf1WN50+KpCKvtUjT/HHO2ctqZKy77gRkEaUKfgPEgxoDMPPogd9/qandTXrqk2KydkUBk1FYpk0tRqnY8qM1sotWutOk2zCsH7eXrWza/CgDWoGeFKZhgjbBL9ckGOyvV2IJVEI2rXRIGoSvYBTlrOftbzHN06QX70PqOD/AVPd7tB1KxLdKOUjdI2HbeeOeXo+WOaZ1pyV8iBqmN05smWFM31tQZPs3T4YMwuKOODyPVX18TLEbcuyHlmvNiQr3cwJnRMSExIziZu1zyzpU5C9V+z4YCKXKFen66GW2Z5Ciie1sdRDvjnMSZEMEuH346xIP9NfcyfA/5Z7K/7TwNnwD+B6av+j1h765/HmKrHyR/eSQnwJUwH9tvf4zY+aXWExRN95eB7C2DHW99gfC9m9nrCzWM9ad8eV18DfhUfpX3D29WTAbQExAekb1g8f0xz+4Tu9pLcQ9wmnBtNE4MivrbQvOKxRc2Caiu4oU4f1pMyxY0cdqfEi30gljWnanGJziMroT/ryZuRuNuiOeFzRordtVfp+I19t4X3kZiZSZil1kIRPIsADgU1AfaixsyMWtiOIw/u3SNtIqt+xVF/xKpbctQtSSXPIHFbMi+PiQebHWW74/klnDVK6wTU9FETM2Shwb4yDFonGCcAKDOLoyhlzFw/GEk/9oDnvnDM803Pg7YnjQnpPMuzY5YndVx5sk8QwQVnLvynge2Dgd1rW/Lrmf60J9xpcMHOQ3w4sru/pZRsj1+29GdLXNegjTNNj7eWm9w4iNNxnoYalFRtKZxYtqUrnuJNh6Q1DNvc5QXxoFLIFSzPHTyZlVN1uzI/j6jp0ia7tcdcrjf2brrGvPeEUN3aSzEft1JZxXpWGhEysBszu/XAi8crnjlestsVXh0GdIhMZ0WcIo0jHHc0z3b440B3v+Xk5Ah/y5EX0dqkuWYMDkofFnRHHc1pi3olV0azNsqrHZddx97bpK8Ti1IaLyKIkh4k/IUjtB2535F2I7qNlDFRxgTJANaEoSYgL9jfaDKzFsvHfOSYTQfOPVaf97Se1kdRI8aURKy99DngH8LG/s+BH60fU72K5SD+x8AfxDQpfxHz3jq8jlvMIuIfwlpXb1ce89b6UeBP8ul3gf9fYh5iE9D6buDfxvSID9jbX0zVYbYbj4IsMPD7VvXHP4id/UDriQBaghC6I/xZz+kX7hKOjymNJ212lHVGd0pYNciy3nWvHNJVxqow643UGWvjfQ12ngDWJKiqi6c4syzQAimZYL48yNCOuB76z/aM48jl/WvKuKXLAyGPoGUWLk97jkz6r0NvpgpeFFQcsTT40PH8QlkPO64GE+gHMJZBLUMxbwfuDQ94hQsW/ZLnT0744p07LPoFOM+QImUs+OJYNUdEGi5SxLuRYwetCs3sseVQFWKyKcm6t0zybFe1cFoByKIJ+FJIF1e88sPXdL7FZcGFBk6XNM8c4dqWsk1IUw+oWouueKUsHYs7R/AN2P7YGm4VzlanCIXdGzuuv3JJWkdySSQKzaJhOBtIDfTPH9G/sCLcCoirYPYt1mEnAsG8pJyYRL04ZXCJGCM4jFXqq3x9YYxX1khXW6zUyUEVyOrq29sk13Y1/mif0Dhh5sdeuxXMWl4g1R/KQHmaaDpkig+kD55YhKuhMAwX3G8bTlcrnjntuX8vEMdATAUVCK3ijwKLu7dY3l7hk7B9Y4NuC7dv3SEctZTGoR2URikrA3dF1GxPWrt5cNgloEUpyQC3AE0XyKUwxER8Y2C42FJixqtwfGtFcgMP793HnY/odaLEREqxDjmYSa5zjlAjBJRMJDHSoeLw02WiBjipk5TGtn7asg6/U+rnYczPxce9I++xCvBb2d8s/yasJfXvYCHg/xwGth6t38vN266/VD8e97jfVrfzTupnAr8P+FeB/4DH39p9WuqnsGcNwXRvfx74H2JTnr8XA74/H/i5GMh6q2GEv/vh7eaHUE8G0HKOxckR/TPHuLZBRYhjQpMSCLSuxbmABsE3Hr8I+JUjl0IesgU/+z2bM0EdM3bkptaaA4ZLITSBTGHYblj0ntALi9s942sjGVu4Qsnm6cQesBw6nc/38/YDQEgEIoK4wHKxYtl1+HRtC16dVLPWnQnlPVCK5SA6MW3W+ZXyLfHcPnV0zQLfBY7pWfiGUpTrceTBboMPEe9qC6nuk6u7qAcA0B0cA9tp3S98Yo78XgWfHRqT6ZoC0Ht83xh7pZUNmwKuJ14oQDgNtM/0DK/uGDcjV1+7RGMmPhxIb1i7VwQa5/EEXOtoFo60icRdQneCa8A1jwc2+zZsbTFWawucmqeYMLuxey948TSdTUcOYURKmo/RTSH+4TjBY573LS9cOZiI3MMxERDv6bxxaCWbPgqx/MeE1H8LwzASY8Qva7t159DirJ3psRZ3I+RSiEPi/HzDa197iRd95LtPv4smCLGYTsF1fj+0EZhbnDoFkOfqeu+NdfN4xmFke70hXyZ0XdChmN0Eazb3Lxm+taFdF/JmIB9OWdZkgemewxjdafGqkH6OgZqusumwSd3Od3qdYHfzn6Rj8VU++Sas03W6wF7LT8LOxQ8AvwX4wcf8zjs9RxFrPf5KjOF6uxJMZP8rsEnHTzurdVjXmI7qrwP/GfDrMGbv/8Be1/a4d9+EtRs/OeXe7gEi0ovIXxWRvyEiPyQi/3r9/m0R+RMi8qP189nB7/wGEfkxEfkREfkFb/sc3rN48ZTu2RPGXWZ3vmN8uEOHjPdCaBy+E1wnuM4RFt4+eo9rnbVXukDbt3S9LVgSsHgRp/NCbNocQ16q+0Vfc2E7bvCdp+kDXd+Ym3x16ZZZZiITerEdr5qneYFlf1kojkRL0/R89tlT7p6teDDA9aAEHA02YekFvDgCngZHB6xEaTWRUua1ix1fffU+96+uoQmsTpfcvr3g2ZMFd7olq9DSuA58Y1l19YxOvNbcKtJiGXWl3ABf8zkAPELrPF1oceIpTgzcLhxuIZRQ/R4OQ6vlwAB26Wmea2hfaBnywMVXz0kvj8iF4EbwGULx9K5lETqCOhpCbT8VVDJaIcu3e1ubhOWlVD2TFksMaBukCJJNuwVCaAJN3+J8BVQT+1k7wNOCf/M590qxRzD6Yy5eh3MOHwI+eLyv//ae0DQ0obHv+WkQwRMVBiweaoyRTRpIrbK4vcCvWkZpGIpnp54oMObMmDPbMvLG9ZYf+car/OAPfpXXXr5g3GXSGMkp1xcgcxj39EocYscF8N7Ttg1t6y0P8XJk9/qGeLFFNwm9jqxfv+SlH/4am69csLru0F0mjpGSEhYRhMUtTXmYIjbMIh6VYFosmY6eYTLn7DpxUyrBR1QfxfvXe6sWE1j/VN67zufjqK/wSVvk3rq22BL4NeyPpwV+DfDCe9yeYLqrn8I7WFof+b0PwpH+k1DPcvN6vwS+gbUJI+aa/836s7f6u8iYdutn8kk5bu+E0RqAn6uq1yLSAH9BRP4L4J8E/pSq/lsi8q8B/xrwr4rIT8Zk/z8FG634kyLyE9QMhh5brgn03/UMEVh/7QrdJTrXICsofiDTEpoOWbWwhNxl1Ft7RTrTxlAgqy02RQrF7XUxdamxFh8mTC7F0gTzLjFud0gnuF4InaOMmUKuTAUz8zTrpavQ57CxNE/vgbUCga5kjkOm8Z5vnu/YDgO9KAET8Beph0QACTj1xJJJJRPJDOWanW5ZLo8oIaMtLG8dU+4scbSkBwP+Gzt0pwyqIIWA0ospEIqa71euyMRXxLDf04OnR2idA3WkMRJLQb0nNNDdbmluBbRV8L62hTyIMS2mEzLPL5bgThyjjrjzQuMckhWf7VlKKYhXXFGG7Y6cob27ommdBYbLzSP7ptLKpig2uZkVjQUJQrfqSA9GNJp+q+RsM4POBPRdddRXVUtOmu3h935k89PwzpY/Y6+mhOaq96tgvNQoJrs+plTwhqGMXJWBrgRcTFwPO9YaOX52RSzKZWoZdwPRR1Z9T3HCuBViKbz+4BLB44dAuc64bK1wRMwvztneF1WzUJgYp2L7EbxHtRBH08wNL6/JL+3Qy0wZlXG9ZdzscGMixZHrWHDRkhas/akzkHK1H1rUrhuLKco1CL7USCG1AREnoAUtdkzko2O0PvT3r3dfd4F/D/gfA/8Cxmb8CW66hQdswX+1fv/dHK9Jf/S0blaHAaG/c/A9jwEum942jdU/iLWs3q2Y+gw7j1/i3YHnN4DfzKfHjuGt6m9hbcLAzetzh+nUHmJt2/8O+DkHP/8j2J+xB17BtF0/GdPafZ0n3UML3gHQUrvln1Iwm/qhGM/6D9fv/27gz2KN5h8Afr+qDsBXReTHMEOSv/xWzyGNh6YhPhyIl9kiT/raP3IK3lCCNAJBUG/TZYqJ2lWVnBMl2vi62TdMIEvMz6moTVz5vX4qOEFLxgfH2TMnLI8XhB6aFGhDU3MFa1Pp4H1Ob/7PXkMFCKKT+N0WI68wbjdcX64Zx0Qn5rbuZWKGzHvIVwZAa65e1sQ6DzxII71coz5RNBOGDancon32mOPPNizGU8bXLhnjjpwLY064BtrW7Xd2Ym2mVs+bJr72/cTpqyIgXvB9YHm6ZLHqyc2Uuye0TUfSxNXVNa5xuOCNoXOCXwRCF/BOCbSI5jn6hmmhrlOipEzejYybHWVT8F2wyBx0ZptuvGUdiqgn4CuYOWdjbJGWmptZJz9dcHYdCQfRRI8eg1mCPn/9tqWYAL+U2SrDOethPrrPU5yRqb/qL6tCUobLkfXrO5a3Vpyc3WJ1p+XhxQXXV2tu3TmjW3VcfXOLykBbOk6OT/ni5z/P3dt36bywy6Mdiyp7ml5fKZlxKDUpXPHOUUaHbCDdH3n41fvE+yPlMpsXVlTyJqK7hBsLaRgoMdGKp0Zpzj5kM98nMh8txddvFQ5ccffg3oIg7Th8RGL4j+L9693XEfCPY2+/p1j0S49pfi6xhf8YY7x+KxZJ8k7bdQH4H2AtmKd1szLweW4CrRFr3z7E2odLzFx08R6f4zWs7fVuGK0WYzb/v+/xOT8p9Tvqx6PvrS9h1hk/ip2j5cHP1tjfwF+o/16w98b65LTc35FGS0Q8linxPcDvUNW/IiLPqeorAKr6iog8Wx/+GeC/Pvj1b9XvPbrNXw38aoDnTu4wxkIalXDUEAL41lG6Qg5C9oILioSCVA8rLfWN2lPfxcW+zuYX5ZxDs7Eew9WIrpXGN/hjbzc2xRZJ8Uq/CgTn8a2txxcvP2R77wI3JmNjSnWbB7S2ZWyBnQxJqxBsWmDFluqkwtWg5PMNbhzpxVptpTIp5h3lESmVGansB0IowlFoEAclK3E7cJHWuJ3puI4baJ57hqMv3OZ66Yj3LohXjrzeEXKmS7Fun5mNkOnzvMhVlmHmuOy/LAI+4Bc9zXJBCcKwS+BdNb0spBr23RDIY6bE0bzHCtAp7d2O8f7A9XqHS4U2A1qQ4Ci5kGNCAlAK21eu2O22tOueoxdOCWcrduMOVaVpHskbmxit+sLMosECxsMSNBSyJpw4fGtWG8uzFdeLxsw1hZqpqdXNdZ8Quc8tfPdlurXKDcoEWSfvNgNYxjQVGu84bXpKhhA83jeUJGwud7hjz9nnjlncvc35vRVeWsZYuHplx9Ftx0/5732R5qjnhc9+huWqYffwil0aCScNzYlHndmglJzRCGzUbh6zolFYD2u4zKSHA+n1HQyZMCo6mPFoqFOFlIyriQq4bK9EXG0TmplImRyCJ9us6lUncwhnZfZSAm/asKx2wyNvAvsfXn0Y7191u/N72Oc//272aIvdmU/Mxy/ErANewcTAr2PA6ysYo3XKOwdaPxX4xTwFWo+rxJsn0hQ7xv8i5tb+x7FF/S4GAN4NkfkAY8J+I/C/551LoE8x36ffxv6e4NNYb/U3PwJ/BmNwfx/GeoFd8/8KJpifavOh7d2HWe/oSqi0+c8QkVvAHxSRn/ptHv64W9U3HWFV/feBfx/gJ332e9QfNXTO0Zx6nFe8F1Kx9qDvGqTxM5Ml6L6nV/3LnHNItraQV4dTZ+5IBYhKWWeKWE4fDZYTiAGtpjdFD14pSbh49SHrh9dIzLiik8TLAInqLNWaDEv9HNBcwZYIYVL2qDIOkYaMd7YAT7HB7sZifOjuLoQ6yRikRUJAFVxOSB5huyVcLenOlO5WT2lOSI1Hw4arMZPUJt48VF+vuvwdgCzDGorKvuVV6mEtAniHbxt835CdMuxGRD3SK6FtiNtojJf6OnGp6CAwKGHVko8XbNzAELe4IXNEQ+v9/JpLKlXMrsim4LzQDoFGfe3ovZlzevQqk2rOKh5o1GKZpDCmTEoJae266Y46fO/Ry73yao4H0jKfETk4A4cN1rfkXqSew9q6O/yBTIj74N+KUIoxqcehYaMFFwJNaBHXEHNCt5HLh1eoOEQC21TYxoG1SyzOel748inNsqdfKON6zXA5UKSgy9pCdFWfphUjqbObiiETrxK7yy35wUi5iuhVtuicaCBLYsbFjM+FlJK1B11lAycB/Bz1VE1mXb35qASidexlzzZiLWwDV9XPDNnHHn0E9WG8f9Xtzu9hP+tnvRvk+HlMAD2VA+7Uj+npBsxKwAF/P/DH3uG2fwFmgvm03l39NSzr8A9jgOm597idXd3Or8FYyXckQMDaYb8O+Lf4JDE1H2ytsfbrCmsf/ntY7uQnv97V1KGqnovInwX+MeA1EXmh3g2+gN2Ggd0Bfu7g1z7LTXOMN5V4aJ8NNHic7/DexLNpnSlboTtewFLIjWlQtBSkOFyw0ObJhd1upK2VUZTaomtwx444JlwUNFsorks2taZazK+otUVkd7lj83BLXA9Iyjgt1RVIUSmoaBVsm/7FWCJj0ET2C27rwAdb0FM21qQ4i9Xd/yHtWyy2nWKASM3HyTlvXVPn6AJ0IZEdXA8etxnQzY608Cxv95w9f4fz16/4yrBBr0diLLhSvZimVbC2O8U+HQjA7RUVqkqg8bi+wS9bwrIh9HaZxN2I08Ky6UnrxDgMFpXqG5x44nkhPUzEdmTcjJATLihlSMQEfdciTii5VAIwmP2AKiF7uqYHhJTMmHbWPNX9nUHSARCq3UHzi3LGyMXdyG67o7jqeN77akvubCAAi1UytF3Mun26Fq27hR48x/TTChHmBx7G8TwKHGaT04npmoCH2hXQiyc5JQSP8w6cY7E8hbZlc1GIJSONMKSBIQ5oEKQ4mjYgAQbdgoOmbwjSGFOaFCeOJgma1D5iJo8ZGaFNjnGjjA+3pIuBdD2Q40CJI64oQcVyNEs1vxWZM7Pngd5H26JKbYsenh+dj4tMNxAz2DdLkY9j6vDDev96d/W/wfyEbvHWi7BgtPsvxHDcV3jnQOsvA//V+9vFD616jL157ePekcfUBRaHM9X7OeVvcFNv907qHGtZVrb9O7IuMcf3ZzHge/nx7s4HWO9k6vBuvRNERBYYr/d3sPGAf7o+7J/GbgWo3//lItKJyJeAL2Opmm+9E+JY9oFF7+k7TxuM+fDe4xtvcS+a8EHx3sKUgziCOCgFzQnNtZVXmabiqB9q03CnDeHII0XJl5HyMCJXim4KKWZKY9zF5RsbthcjZcg4zTgpIJkimSKFpMXG3OvKLw6apqFte5qmxbkGkYainpSEmK1D5aoxpBdvj3EBwSJqjHUybY/DXlfA4cQjYoajzfRRwG8Tbj2ilzvGh5c4F1m92HLry0csnuvJwaGlRYpU5GTu9lKKtZBSoaSCpgI5W1A2BiINaDmak45w3OKPWvplS9cFgnf4IuigyHXGXWa4n0gvj2y/umX9rTXDxcDVq5es3zinDYmz05ZbpwvEQ4yRFBMlZkoskISQA3EsRApuYYHVLlRD2aoxV5hJptnaQZldzolKGhM5J4IPhBIINLjgzdC092Rnjv6lQE6ZlBMlJZtanADdNI1ZO4iqk/7IdkSmtmBlq1QciK/O8NVQtVhLUovFQJVSmcLq3WXAruBQGhFa7/FeIIA2ijYZv4LlWcPRWcdyFeg6x8mioccTryx2OquaFu5WQBdK1ETcZspaiQ8iu9d3xIdDnThNaBpxu4hebsiXa/LlNeV6DbsBHxMuZUiJkhNosQlB5/H1w4n9TU7+Yk4LrhrvBjUtopuYyHJwE4IjhGb2zZpAcs4fjfD3o3j/eue1ZJ9z93ZvvYIxIr8MA1xvN121wFpP997nPn6YVfj2Iv0FN5m+T2r9Eh5vsvm4WgN/A3Oif9SrCwzn/9/4dByXd1I/hLURPz0gC94Zo/UC8LurzsEBf0BV/4iI/GXgD4jIP4vNZ/5SAFX9IRH5A8APY7D+177dxE7cjbz2t75pJp5tS7No6ZYLtPHkVmiXLa7H4ksU0jhCKjR9i/FQbhZpq2ItRg9obft1gj92iIN0XijXCdlC9kpsEv7M0ywbxsvMa1+/x+bBjrCLlh8oxZgscs0HZH83PmtxsDt9NTCV1YwwS87zgmNWDsZoldpAmxZs0arRUgu9dijTvFqZX6EznZgKbYKwTcj1SDgKiCRSt6E9aljc7dm9skU3AcpoLcm6AFpNVFZlHZyxgTpZNjhBWk84avFHgbD0dIuG5DI5G3rVbaJcDpTrgbwu5DXEbUalIL1jfb1m3G1YBIdrl7hgE4rr7Zau8TQhmCO5FsR7losV/nRBOOrxC08OE3PII727QyZLjInKoFkpKaNZ8d5TNBCkwXmb7CyukDQZw5ewiTe1cypu4mDmQ2FQwrlZsH1Tt11P1Mxvyb6lDHMbFaBUJmvuIlofDdFirKVzNF7MPT9A8YXiB9wy0B8taBYNbujxG3DZLEB0sGtApVAa3WvuNhCvDMjmTaQMibYPHD27MteZiw3bB9ds750znF9RriM6JLyzHMkbfh/icDZNUi+ZPfc5P0Tqq1epHnM2lJJuPKwOhXjPFGQ+ed1p/sju2j/09693XncxoTpYi+lV3uwufliCAazvxtqHf4m3tlb4CVicy3/ywezq+y6HAafDibAR2/+Ox2vOfiF2nf1nH/refbj1Rd7Z0qoY5v/9mC7scR3t5zCh+H/OU93dJ7feydTh38SizB/9/n3ewrZVVX8TZrn7jipejTz8r17Dq0M782O69fwZ7XML4qkyXBrDEbqG0ATUg1sIeSn43uO7gIpaSLFUvVHKmJ6kTCoo6xSNkLeFvClcj2s4htM7pyzbJV/5+jd46e++jHuQaYeBNg60onROCdOiy0FMboGMshsiPhZj4HxD1zTEFMlqWMAW2v2bqdRF2Tlzp89zpExdizEgIVBd7Os0W33mvm0gZ64vLnAnLWFs0HOlvxX4/BdeJNx3bLeXpCK46rSuWuZFX2cEY9lzgpCLUpyH0NJ0HU3Top1A78ztXBUvjjwq42bH7t4Vw8WGcp3IW8UnR+gD+TqTrq/ZbndcZkVY04eengBxABrT0blC1Ix3gaPjM2LOXJw/YHl8hOJwjT9QOE0XVt332gadjqNMB88DCw8pk2LGD0IqkXE3UHKEkio/NTVJ9yhur7CZZuiK0ZVzq7I6x9uT2ZWgtelawZXMNE4F5EBT0Vt+1IMNAefAebvPLxlfCmSQ0aYYdaks7vT0pWd8MDJuR3wXyJpRjCFEoJMWlxy7B1viZiTQkIfC5vwavdqRhh3rNy7ZvnRFvBxgM5omq9RrWaW2OG/+XYrsAdhsbTtdO0qNvLKWpuHeR8FT/d3KGjrBLFYEvH9bMv0DqY/i/eud12uYpYPDBPHfwNyvfzEW2/J9PB50LTBA9u2INY8NTv7QB7a3768C1ip8dPTe8dY2Bj/4mMd/0uoWlov4djWtIs9hrWHF2MhH/4augb+NeX09rU9qPRnO8EUJsZhYPApu6/AXkd31wDpvUW9LX+h6uuMFze2WYbnFLT39nSWLkwVu6XDeFrtcJlGusVCiDkmCJIfuoGwLcRPZDSOL0w7nhLLNXH7zing/w3okpoGhDKwEejyhupDHyrSUsjcDmPQsThytdyz7hvUmWZeoLuY663TsD0wUxFtIctGCVNuDMjEfE1E2ATXnSEXJueBCIevAuE6U1zva4yOOk0OuldWyYXnasG4LY4ImA5opmskzGzcFUAuNeARHyhl1QmhbQt8TmpbYFLSFFE243rgW4shwb2B8fUu82qLrhIwFTRA9FMmEOLDIhaa2cWOMJJ9ZukxWz5gSQQtSjPEb3IbYK11Z4hzEmGvbsIq6H1186utQ9toh8d4YniNFsmnyZGgIjadxjjY0JNnBxBVOeY3zRbjXUYnohLFusGkFzDMKqf8yZqxky1l0DoJ3OCdV+qU0DWjOaJZ5n7VeD+qE4sVcknJG4ojLgk+F0EbaZ3p8CKZJ7E18nlxGUzILE2et51RM0N8uPK30tLHn/GHk+sHa8hZjIl5v0esEQ0Zyfb0H7v5Tm++gN3r4F8oMrqZL+GBBKDXWaD6Oh6dKQbOZymqNvvLe2onfebUD/tQj3/sG5r30/ZhG6Pu5CbYyJgz+o3z7ycP/FsvNu/qA9vX91osYC/cow/btpup+/MPbnY+sOm5K/N6uEsZaCjZ1+mj9CBbN89L737Wn9bHVkwG0FEKxe7JGTNybzwdAWTjTv4xZUZdhlZALT3QDgxvZ3LpmcbZg9fkj/O2AW3ooarooHCE4KMoQB1BBTjy9rsjDGh8Tq9Uxy8WS6/tX3PvGfWRjQdPiPaFp8boXqBexFkkWW5z2tgjVpVwypQjb7aaaolZzxhk1MbftJmFwwVzb3RQto/v1/RAEiHiUqi0atvimRZxne7nlta++RomJo3HJ4sUlt585YXhmx/m4M+4lKxkYNe9NJLGF1VddWEEJbcfiaEW/XEEIdEee1ckRmjN5LGQyD1+9z/pr1+jFANH8lvyoaLasu+wyXgstglNHFiWSSI2gvVA0W8ixBFr1yFjYXazx0nK8WOAc5OKmeEY7dvNx2x9vDo5PERDnzf6jdzA48rYgSViEBbt0RY7R9FF7PnJ/lCeQJXuwIAc/nkp1MoKQygMq4jxu8nIriTENpFLYFs+oDUmFDq0tQ3sucVXUvuhpjo/QNpAEcs50rsWrY3OxRq6E0i5wwdP2gdA4lqdL5MThWk+OltOp0YB60wh5V9hebdherBnOd+TNaJOFY6TEVDV6dQpwbpruD8c80DHTicZqid7EV48DXPMP5t8XYJ9IILW9aM75Hw2j9cmpHwT+EapvKuad9R9h4va/iYml386E9EkBWQH4aRij9bTeuqY/srezdPidWNv1aX1S68kAWlM0Tp1MokBK0dytvTEIXi0Dz0dFLyOaE+jIeBXR84gUpd119M8vzHYAbwzCqIxxYDtskOzouo7m2LPYdrQLz2qxQneOqzc2+F1m5YXSeIKDxgut2oRaqSpp56Aa0NdFqpo1qsXB5JTJGsG5uV1nq5TceL3WwrO2nonh36xZMbZskmNnMpBxaFaaxtGEhrEU4tWW4XJLuwyEo5a+XXDrzhlXD88p2yoC1wRiwK+okhGiOnYxsVo0LG+d4PqG7nRJe9Sjnel0/OApEcpQGLdbNi9fMj7Y4IeMywVJVFPlqiirBq++Hi8RoVGzwHDjlJGYAQUvZApFHN4FW0d24BpnoFj3E3t6Y1E/aHPp/t9OHOo83gVKSsRtomwbxjGhwUEIZBchT8q3+rvVJmLa9gTFpG57siQQEVwFKI33OJRUjAkcMuxKIcbEmDO7XBgVSnGcBKGpZq4SDMCHkyX98ZLu1hFRlCgFddC1DcE7djmju0i6Fnzf0J0sER9oFp6CknYRKYLPnjQW8pjRsTBeDVzdu4aseBG22x3EjMtqr7uK9FVLFblXjm1ONphuH2QGWXO9lazqgIEF+5VDSCwcJCdooZS9ju1pHdYl1rH8Cqbz+c0YC/ZJdAy/jw1wHvHp9oZ6tEb2vk8/h73Fw+NY4qnOees/Lvjkt1Of1hMDtKQx24MxRVwVkXsxgKWYiLbESC6ZUvVKwWF32ykS4xV6f8DvMhy1+OWCNCaG6515LLUCEVIeKWMhS8J1jvE6c/+lcy7eeMBt6UjtjiKeXBcDR0GdJ6niHDSS2WTI1StrVvHUv5tCdeCenMLra9RqISAi88BRzqXqlXwVkOU38S2WhQieTMSRCbTe07pAwKKHXIZ8OTD4lkVzRFkop3dP2G6e5eq1a+LVSNYdSqSo4Zl1Vi4zNFL4zKrjmRefYdRIXjpSq4TGMTzYsn7tCt1ms2hY78gP1nSj4nJCc8JV5tDV8zSN84MSKKhCUCUNBRdBQrRWbfTsBg/B095doZK4/tYlR+E2ctdXjdZ+8WcSmYvMAva5hZgrWPeu+qo1bOPIerdh3EauHu5YnpwyXnqG4ZpckvmcaY2RkTqcMPM7QtHKXIngXAMiBFVyZTUXfcChrIcNm5x4bZfYxAxSNXGuoDmyyY4ggYUPBPH4bkF7vGL13Cn9c8f4o5btZkuvBkJ8wGKPtCdlpZxHdKnIWYs/atCQkOwo20iQFhcdcW0Aj21huD+wvbri1ukZcee53G7QMbGQhq7U8ItkgNs3TY2WmqFl1V6ZvQpiNz0qb87GfKuaHqaH4MsZuylgWZvVLPhpPa7W3LQZ+CRWwhi6WwffW2Fs3e/AhgA+rfUQCxf4aVhg8hEWF3PI4B4yWf8G8P/7KHfwaX0M9UQALeoiqaqUXONMJrGxltrSqa2XVKpvlmlpNFurrwyQrwuX24F8FOhOTkwgP2QWd3qGvCWej+TrzLgZGDfmnN6tllxcXXJ9fsnZKDQ50dTFPJdqSDmZlE5aq0lj9RjdqhbL0dPKhrzFC2YWvBy0rGpXcX6EMHVhKqVTF6/Jm2lKJyIp43aEsKV5uCGOGQ3Q9j3LZWHMgRgD6xFSSRQy+Iy4gG89tJ5tHigOmuBQb2BQRyVeD4wXO8ouwZiQXSENI17z3qgVbHWui/bhqxYUP4GkPAExy/2bROR5m8mXBe4regf0NqZFrxvaa8gnOGSvXyedlds/V8HsGzRD2SnXb1yxuTfQjB3eR3wXK0As1Rn+Zk7l4Rc6n6I9eDRy0nRnTXA0bYcblKLZIo7EExCCd3StWXIstaeXHicebVpc1yBNoIgSPITGYwK6ar7v7EXl2qrUCONrOzg3MbzrLaYojSPjJuKyhXPnEsma6G/3lGVm/eCKqCOuYG33mVWtB3Rule7/3g6vWuHgmHy7qm3vx4GxuYNYGUmd9XVPGa1Pdy2x1uHUzjwFfj7w735se/TRVcH0Zr8VA1m/HgOXX8YA1xRn0mMs5ncS4/edWU8I0IKu78m5MGyHGYMUykGcicyTYZpLlewo6gR1UAbQTSHuEnLc0g1Luq5BhsjlxX3uP7xPuizoFkpM+FJw4kj9hu1uTdpuiNLgc43UUTMpRabJrGroWaydp0wLl0WTTPtoZqmuGoBO/IjuDR9nAFkBW32xmYOFfdLDCLMr/TS92MgUO2Psg6/7oKmQhsS4HVCBSIQitIuGLjSUsmARO2LJDGnEjwOShK4LLFYLmr5DvKPre9om0DQep4V8PbDZDqTNSMiK1wzFQm6nSTVFKWLDAUaOzAKresImpDSd2HpcnQUvx23Ed5mSTFju8sGirRxEHd3UTkmdNDS/Kmtben9w0FTIm8z1g4FQAk0siPOEtsNXkbjT6n6u5nml85PW7auSSz7AClo1g4VcPC4EUEcvgeA9wTUELzTe0TeO1jU0urDMx8bDSQMLTyoJnyJpAB0iaZcoqeC8MzuEyV7COVQKw/VAWHja4w7tHe2qp4wwXG/pfU/jA5KN/Tw+PeHhxQPW11d4tYlZwUxitQoBp4lCFWa3isP26b50/50D5LU/D4JzQAXPj/xZzzcT07GzTZanQOtTX/exDL/ngR/DePT/kCfb5+uDrGvMIeSXYtOifwr432HHY6qAear9Ncww9XkMhP0tPpnt4qf1VvVkAC2E0HSIZEYzOpoXZnVV64MgWt0rczGdlFPzzBJIVOEUjjFuSNvIarWCrKTrNe06E6KZdVIyUnVCMkROC5TcGYBSNTWUmIcWOvnAK7kIWR2eEUeuhuKCSFtbUGZimXH1rn3K0mOOEjY5k8zfry/fbB70MG2vRhBX5i7ZOknna4sMRXO29pH3FBdwEmhocMXRNL212CTjmoKo44ieokpkyak6Eo7FcU/bBWIebQpOC2OMaI6QMpIzurFoGC0ZT6EJe82BHQKpov698ad1SGUGxJMQXabIHgoUR9bMuNuiueX0lodjA4lurcb8tGJWHc4hvsYITUeunnuKIMn0R8452t4Te0fJSn+6YPfyBZfn15wI6Bg5FsdR11AYcLngJ8d4xVrEGKs06ehyqvYbEzBGyDhjDkdFJXDW2zXQOCH4gIRgEUDHjvZWZ+76TijJ9gsH42bD17/6MsPVlrIrkNTc8n2zF5Q7cI3Qnjasbq04feGM9vYSZYTiaPHoLpFygm2mUSHdH7n4xgXysLAopiXz2DBJFswQVh25FDPMn/3CJoZL9iBpQlfTp5numiwhTF8pwhzh9GZqcA+W0VLx1tOF5OOtZ7E25Yep/3kFaxn+Zswt/bfx7rIDP6n1ZQw0/TjWKv0MZmD6p7Hjfgx8F3Ab+JeBX84eaDnMpPZPf+R7/bQ+vHoigJaibIYtYC7e1NaCqlBKhqI4tRbU/KZdHairhydTOLIOxuzE3ZbhYo1XhxRoc8GnBCUb2CgTMMjV9xv2Xgp6sLbsVVOqao7cNZJnAhs3b+QfbRfq/u7/4PXObJZtuH5/fupHNmWaoUk2Iwc/mha2VIqBonqMclZiGvEZmkqJXY8ZVfBtS7foWQRPaCrISBnXhFlvNi+02dzjJWcDS1JmYmNuqB30jAxY7b9+00tRrRZVdWpPlTiO6HrD9mqNp0eDEr+1I4aW7m5nYdVO90yVE6QBCQa8NRfTAJVMHEYDEF3AFUfewfJkxf3LgdEHUsy0ObOcbOenk1NftExoov57anXZo/TgVVkL1AWPb1pC1xpLp5nQGmvmThbIFxzLzx3R9R1pyNz72w+IDw20Pnz4Bq+9+hKheHwSGhxeHMF7GxqQqXHp2D1oGLs16Xzgzve+wNELt8wGI0IeM2mbYGct06v7F8SrAR0LXicdYakgSc3LSvemGXp4fiY/rX1X8bHX4+Pa4sK8y/MVogfHa+pYwlM+6+OvnwR8nQ9faN1gdgefLqfvb1+/CjOP/V7MZHaq/6B+XmFt1H8KeObg51Psz9MJw09bPRFAC0AabyP6nYfGMYwDaZeQ1OKLIClBTGgpZJQiNjZOmpBHFUmL4ovisuKBnEZSzLjqBm5yHiWIgbTJVNR2YhK17ycGpxbdFJ0iJZHrXfm0QE351rVxiEMoYqaYqgXnjAmz56iMj1p24rQYCcYW5YMFSic7gGorqpjnlahN2LkQGDJkzVwPFqPTpR1+l7m/3nBvuOLu0THPH/c0HpIEi4LJStoNxmzkBvFCg8fTQHbk3QjBPserDaGUikFtv6Yw7Uk3JdVjDN1P8s3/r8zWLKSqLVgpigtKAyiOso7c+9svw1145vkvUIYd5187J/1IoQ0N3tkwggWKF/xJYPXZE6RrGFMkjJ68jmw2a5LPltO4Mobv7uePaVfCrcWS4WrD5Y+9TNoogUAhk61TTGiEwNS+rXqvnC3cvBRcHYjw4lh0Lc0ULSOOJnikbUiNUF5wLD9zxK3PPENpYbeJ7K4Km4sdX//at7h47ZxWhJATXYRG1dqygNTrRlAaIqKFosKgDWUcufrGyLje8ez3vkDol2w3mZLtmmeM9L6jzUKXvUUcQRW8uxrsDB5rTc5N+RkY73Mk90B+Qk7zH8n8N6u1z13c5KN18LgZvVVG+vBGQ59CrY+//jx8JOfgHPiffwTP8yTV78dMaN+q1lhg+B/8SPbm4y2PZXv+XZ7cDM4Pv54IoCVO8GctrvW4ow6/amhYsbsc2Z2P7K53NFtwkcp2lblVNTFPc1azCKGyDV0IjEXZlmgtINg7VIt91nmNqEyOs1W3qBxmPlfQVaCkCoZqbMvh62APuCZAV8TaaLkuco5ysBbtN26yZ9tDatPRQJxD1Jn9xbwIik0qOs+YlRHlKlnLNYvSeY+K57XLa86vdgzrBc8sOzp3jGDRKpoSWW2qLhDwzkEyrZMrgkRBx0Tcjvhi7JtN/R1o1kpdmidrjgMm61GWzywSdNamAYhaZqUWJcdCu3PIvUK6t6M/bolXmetXtnbgUkFzNsrEZ8SDTwb90i6Rx0TZZsucXCrhVkMogewL3TBwd7XimbNjxutAfHCPMoxIDnOYNo46YWhN3lQZz+lFibOpOSf2uLaxBIDixGwkF47ubkf/XI88G0hN5lv3X2Hz2o6r17esr7dcX1zz+rdeJu9GThc9J66hUyGoTWY6Cioyg+2M4Gqs1NTq1q0yvA4Xxy0nzwbK6KgRllxd7ehJNNuMz46WBkdiCnJC3Xy+JjsV5it5uoLtmpeZleJgPX6UydL5HsSGHOr1rDce8rSeyHrauv3w6m9h2qzN+9zOC5jW6+PyR/sSJuLfvo9tKPAXMc3ed249EUCL4PCf72hudTRnPe1xT3+8RAfh+o01D7/+BvlbV/AtpQyKFpnZnj0Iss9uAgBOKSmZi3apeiwmQXVtek0LaVUEz2Jr440maygma4EytzSpbaZpAs5MzKdMQXsLM1FVqdua1qf93bzuxf2U2SGeqgjbQypjhEwH46fUQ8t3FE8iM2QllcqIOIcPDYuuw0vDxeXIPelpfYNvwUkm5UzUjDpohkjXNrStZ5cTCThdHaEU4i6SY8LnbCBTy4227txMm9kuO3ZTi2oCwtTv2yc96C2an5MoaM6s71+y+ZuZncCzf89nuP29tzh98YyyTezuXTO8soYEyxePWH1+SX9nyTBmHI72pME5Z75WodD0AUYYXSLrgHhlXA00Zy13Ht7h4fiA/Hoy13bNBOdRJ+ANUPkQkFIIXliGpYUz571PmCKkkhmBq6AkFdx2hDcK/HhhuBxYrzeUS2WtI+fjBZvNNZ0KJ23LSjxtKTRYULPTA7BTMgXHqIG5f0lCS0KyoCVyfn5Fe+sUpCE7x5gdDwdlPL9glWAxRkJlcPfhTbXhPWGsSjlO0Hd6XVPLfDaPl/1E4puZLbtF8DrFGjFv6eZavkdc0/M8rU9jfRkDBg3wzY95Xz6uipjJ7NuVx47T4/IrT4HfgDnr/8VHfvYLgP/y/ezgO6yvfgDbKFiE0Hd2PRFAS7zQnK1Y3F3SnLbQCrkpiDOW4DiesNsUdq9EihSUALXt5pwtIQ7rAUrN5TMdVqSUyYZgAi+lakUqMJgwUBX2mgdWoZQ6Wj+38Qw0lMowNGIZhL4aUbrKDhTV/dQgZgdRKlACJRclV7mRF3P0LlpImkma9y3Iat7qMfbB4fDq7WsJOBfIriEh7PKIIHQh4FyD9w3HC+GLt25z6Xb0zibSRJScE9thZJ0TlsTiWXYNZ8eBzRi5GiJOheO2IUY7fqIGRqCgZR9kLAdinpnFOgSUB9otmdq7sy7K4oTsmICqmXmW68zm62u2z61pPt/S3e0ht7Rnx1xlZXhti7ZC8WZDISuH974ym0KQgDo7fkUKtIITT9km8jV0twPL52+xfX3k4uED8lCQrEgbqis6OO9omoaUEt7DYtWSYmS3HYhR0VyI1V09S22d7UbStRqw29oEoeRM3A5s4xrVkZX33GkWHIeWxjvIqYLXmtE5M6X7mwGndt3MDUUt5BzJuRBOFqyOFmSFcVtQ/v/s/Xu0beua1oX93u/7eu/jNi/rsu/n7FOn6hRVFEoDRC2MRNJI1ACBSIAoMcZIrBhJNM0YAROTINpaJdhsKklakwQNJFyEQERBQAFJTIIEQShuVVJ1bvu69rrN2xij9/5d3vzxfr2PsdbZp8797HX2me9uc685x5xjjD5672N8T3/e532eNQ/7HU8/2DGWwpmHThWvFnAdmWxKqGxs5bLkMNE7tRKN8YKDbvGgrzocU1cF8A5PwlEo4ucn0JndmhBXfWUifHcwKj9drt/Htd7FmJh7WBzNT360m/ORVwB+uH7fYnmUh/eDff0d2PThH+YQtZOA38mHg52//C3a1tv6VtULAbTwjnC2wK0asleSJkoccOpwjcOvPW7VUIIjezebHtpofr0mryBL6uJuvJCBAxGdmQg4ACEjq5RpyspVKZHU90GpSvtjdwKmsGdq2HP9msi1UkHesaJ4ErKXevWvRecWpquAJGupE29mzjq5rZiYWSrAcnjxOPE4CeAbfOMhQXCZrglY4xQa57jXrehWjhjL7B9RSmKIA9sxE3MhZxi6wDIsGHNh1w/040gH9LueOMaaSVjB6qSOrmDrGc0Ox+qeektlRKzkWBDEBLZssRYcgdwr+w/2XL11gTtxnL/kCU1Dc9YRHyyIH4xoEoarEVkF3HnALx06GoPivQNvjlriHM2yoWs6xguhXEDyymqzIb2WGB+M7OINeRhR5yhe5ozF4sSGA7wjitDnwnYcSTGhSXEJfJ1EEArOa01RLKQcZ7PaEgfK2NO2jlXXce5a1hKeaWXb18GyZOJ8TPt/AF/TblOUIWfCacvpGyeMeaS7VrwTtk+e8qgkXM6c+DCfR9Pun07j6SBMgN6Is+kiBXJJhwuQmVs9KKvmgQmZzF2PhjumRz8C27MPmhyg5Me7OuC/hY31P/iIt+XbWVssAPkh8Aswl/vvNrB5XPeB34sxVB8AfwsHBqtgH8z/HeB/goHT34K9sz6Bga0PGyL4uBm+nmKTmB/fPMcXAmiJs4iX3eMdshba85bFqrWx/eJoNg65l4mv7hmve3J0jP1A2kdKFKSyC66uJAXLJaQK4KWyVwdrAJ3W+XrVnasmyqNaCCGQnbf2icMsCbBFIxSZF5/jgf8yP6otvFrjY6YFpapkbOF0Zd62oiaYP2rsgLr6lx6VAHicC2Yb4AKEFlksaVYr7qyEsGxJOdG2nhIT+7IzDVaMjClz04/0Tmi8GWJ6aVjgcGT2OdLvex4+TeTgcaGh8Y6x79nvdozDiC8Ff2gMVmDpEKmmFXq8ANvvSymVZyxH+9sm6OYVvz6WYoCzxNE8tG4c159/BJLoQsCfNfhlQwwROXF4AvmysF/0LNYrNBhg8MEmVksNMaYIrW8py2wGZNdws9sx3PGw8Zz+jHuEs4b9oy1+7yqzJdBiwxmjoBmeXvZsb3bkfo/Tgmgh1NfhJioo28djUHAarNWrimtWdE2DNJ42BJpUKLkq8JyvgwSlDmtMe9HNhrjmj6tMTWMVIXnHtt/z8OJ9lt/bsNy0NCctzarj1fIq4yBcv/OIpEqiav0mgCPTcdBD0PlRkKFi7u0pJVDFeU8TmhlUHxR2tb2oNQEAR56P65eXZk3DJu5jv/iugX8MayP9gY94W76dJRhAuMH8oT7ux/kr1WPgl2KTlz8D2zfHlTBn+ALcYW6784TDvvy41/8K+JXAPwH8hx/xtnxr6oUAWhQoTyPqQIojtwkXhJIKmpRGA82mYfnqCrcxc0m/27N7vCU+StVV21gjoF62T0BrgkMVytT+1uROfezvM03TNW1rdgKmjrclVSpjZo5ER41IsxhwszzF9GHTVXt1w5pBnaPUxdlCpw1oGWM0S5PVUTSAGENlrcIG51vEB7RdUBYLmkXHSoTilJQd3nk0F3JKlRlzDKVw2fd4LSy8sOo6Gtey9plOMk6t9Xi1HUghsDxtrMVYMlomWKQgxZZ6ZyHUOHfEc9j+liomN23PxPJVz6+JaTxqJzoEcQ5XZroQEQhZkYsEXxwppyPlpcx4J5I7wb3qcc5blmVT929WpJjPluFXA1sUIWSHBIElaBFygpIj3XnH2b27tHda3BcC6Z2RcpNxnUM2sNgszY19SLRaSChZjF0UsZbQocVrWMUfmch6EYL3dN5TZIF4bwCVhLjKfJEoFDs/6/k6uzqITdbOkUbZgH0uCpJpe+XBW++yeHPDz7j/BhIcoW14Se4zPkls339CLBB1MpY9YiQndkw5MJTzEQBEcN5A63w8Z7PYemGRJy1hoUFJWBvbkzjYYOh8fkxtZZmA9cee0SpYG+0bFUR/p1Rb/91gICFjrNZ3e016rR8D/uiX+ZsW+EOYFcT0+fjdtO8+C7yJMaC3QOtbVlogXkYDPTsYLwYWZwvajRlWRh1pNp6THzxlGZc45wh4+nf3PP7Lj+g/2JOHRM4ZLdnYlqMLqcm+QVUtZ6+2AKcmI2raoyJlbnGYM7ergEUR7+ZFSlWMp6mLBirPeEdNjR453FDB3IHxopp4ajkY+NnfW7NHxIMY2MKZiaUPAWkactchXYvrWlpgmTPJORBjQrzzLBqP64Q2ZtxuTx4y19seirBsOzxCcBbnEkrgag+pHO2rojZNNr+qwzYeC6knrduzDIZZPtjLPLCH8z/HujgEEWMdJ5ssyVD2hbKPFobtIWdYnZ0SThv6hzvKkIlPM5qFZtlQPPR+OMQzoUixqAtpHXLm0DUs/Qp/5vArR9M2tPdbVq9vuPlr14zv9Ox3W5pOyK7gg6ddtEifIDSMMVbq0mKgVMoMpifm0jlz7W+co/HBgIq3Y2MtYg9kMubfpkcJBHV+wgYPKui06Jxq0VAsxDqliKiy/fHMO7zDZ773VZYnC3a5QFNIOtItFjQZJI6o5moiW1voM8bRo/fB4bbJKHdiHOeW73Tc6mPYEMSUw9mYKJ4jYu5LamLF9Ljj/DGtC+Af+ag34ttYGcv3+28CP8qHC7xv68Pr36pf3631OzDW7x/AWqcfPxb0hQBaMWU+/8WHpCFSNNJ0wuufeJXT+yewwK79vdB2Hs2ZPiaWzZJVuyCeb+hKIA6RFCMlZjSNlOhI42iGpypMomMtdSZQnwNFVaOiik3aeV+1KkqQQqheD1NM9GGSC2YlcZUsFyYNsT12yRkXBO8DqJBSDdcVz+Qx4dQe1dgvRxNavG9xrsWHBte1SNPi2oBbL2iWLa4xl3EffNXXyMyMZLXnu7te4UUY9wMyjsamaK7MjNAGQSQQtWVISspq9hjBT0siOrm+C5VtM5A4tZwmsGX70Pwyik6/s/vbkMAkhJbK1Mhss0H1spKZdVFiTib0j8Llu3s+eGtL13QsQ0PjAxnLsuy6Dmmh+AxVCD+1J4sqbi244ChFaRaBdtVaqgDQdIEQAnqjND6Q3k84lylDxuVCSpl+t2Po95CnKT5nLd3Z22PyETM7honZ886ZXYjU111MmK+VFTRXNJu8VC2V0VVwHqUc6afmkQwmWbzLyn0NNF+44a0/8xPc/8zrrO+8hA52v+VmRRuVsFUYC6UknNdDZ4IDQH4e88xNYsGsIJyzfXqMmGcBfXWO09oyZ7ICsd9NOYrHj/5lcdhtfQdXBn4C+DfBDE9u62uuBvgMZqswYuvJf47ptD7OjvoD8McxA92PH8iCFwRojWPivfeuYEw0oqy6wGW+JD0aCI3DO6GQzFDSB/KQkEXGL9YsFi3tS548JnLMlJTRPjJue3Y3W2KM5JQpmshlsnqYWlo1amViaCrjFOPAMqyIBVKGVXUjn67fkak1qBMFwcFfajJ5BFfqol8nE4P3pIwZnoojV2An4m1BUwV1OPGsujWhXZrgPTT4boHrGnwXaM6XuDZABt1nfAhQ22VaCrFkxjzgnWcdOlbrFbnr2MfRpgZVkZRrZI3ivRIXDTdDISbTBLTNYp4sVDU3fXNmyGhNe5yX6Mp+lFz/xpng/3hFnW0tJi8MpharLcbOcCITfHUIcZ/ZXUTWpydwEXj03lOG8pR7r284v3/C6ryjLBRdKn4dcJ21dUux8wBApeA6RzPaFCUtaKjHR4ScFUohnAdEF6z8mvHJFraJNAzk/UiKxiB556r5J4ibYnKO2ZkKw8ThHLgwvfYDa+R8sPuj+GqPmkumlGTb7qhN58lXLaOlUGREnbFfQRxeYOMS3W7LxZ99H70ItD93A6Pj7OwO6Qz8kJFxJPZ7c/6v+rm5P1lF7FM7d5q6NEaywscpjoCDgYcdealY1pEsBwlX7Tpm+DaDavvxMLWoPw3rdVvfubXnG/Nc+m6v7wP+JCaKN0siizH6APgXOBh+Kh8f4NVgUUS/7aPekG9pvRBAy4nw6tqMJimKwyH7zDDsiKLglKIJ3wqLrqORwDDsyTcRODi4i5prtzQNbnHMtgjDMHB5fcNuv0O00HrBi/EDk0d2QWvmoLWG2jbgCixCQyOZnBIp1pH8uRdWn8d+4KAN05nVASEPe9anC7r1kt3lFTFHRpcRFRa+NY8khKZpaZqO5foU5xuKOHzb0qzXhOXCTF1XHglCjgVJiitKzEJOB/CXVQ1g5rGamwrL9QJxgsbEuO3NANQZmFo0nrvesUvKPjoaX5ucKhX2GFtRlIMA/Fh8U38wNu/Aejgx0XfjbKHOubJd006bDNCeIT2sh1icUryi3mwNtBS8dyzWC5anS9pVwHViQncPOgnllArmaoUDaJBQW3laj3WMlKntrOaVlYdI3O7I254yRHKMNQj7ABwmAP082Dz+eWqtHWwOrN0sM5NlwwKHWyYH9eoTxwHQGOC17x1matprZp8z1/vA9r0ndK/e4WRzRtc0bE7WuCYSLz1JBCNy5eg8ZWYkny8jGI+A0nz7QWt3CNeZvnvOsFSOdsnR4x6e7hZp3dZtPVu/lEPe4VSfqV+/B9NtKWaZ8VuAL/KdbwSascGJj3e9EECrdZ573QKfIilmUiq2wKEWKl29pkpwSFNQ3xBlqG0NW0AbF/CuZvUVBedoupbgG0Jo2O737FJBY6SkkVLZmlz5JwFSMU+noHb4T05aYnIsvFL6gaKp2j9MMTTKBPQO/g+msZnCla39mMmiNN5x9uo9HkkmpkiSBXEX0dQgJdCo0C7WtKs1ruvAeZwKbtHSbhYVaAllWabBRGMlnDBGZSSz9EJRzxgN8LhOWCxbfOsp3hbsPMAwWFvUiYnoO+9ovHDCgsY3UFVEo4rlRVbAimMeKHgGaABH4h/7sbqp46ptglKDuifLfWbQdWjfVvd+7/Ct0J4HmrUQrmFz2tKsV9x5+YzupEHaglt6aITsJsNX2zZ3tC0iggtKEDM1rV4bBhGyOc5rTMSbkXi5J90Yk1ViRHOawetBFC6zvqx2KGdwOYEOrXo3qYdIkbmdPP3d4RziYAdx1Nab4btOTKrUwQ4DPwUT6WtRdk/2PPibDwjf6/CsaFeBJgjSOEYf6GU0SDc9H0fDFxwOpZNnAZbArNU7eMrZBUypjr7TMMlk0jV7ch0QGvNk6tHxvq3b+u6oKbbky7XFGsx77Gdx+DwtmE3GGguifqX+C/DX689XfOcDrcK3Pm/zo68XAmi5ooSbRE4JSYmmTLSp5QseKVOIJKLuQcC7QAgNzjuy9zhvuqJUIjGnWRjunUdFOTlpkbCm3zlKzORSDKfUhSIWAx+oMlL4nldPudkr10+v2Q0jmjNBMlLBgk5Nwnl7D47zRY1VKgKhg7Z1PNw+4f33I1fA9/3A67z+M1/jg4c3XH52R9iNLNSzaBZ48QxjIgTPcrkwr6xVg3ZQWnBrbyxNcshSkcGz3+24jAOvLBpa39Hlht2wZ7vteTz0hNaxOm04OdlYAHJ/EN2LCxZmHBqKZoIqpURGhH0W2iI4NWsBnScxp+m7wzTazGZoBQ7ersyyFnIyxsgVy3Q0dqy6ikvtzGKh3SkV0IgslixeX+DvCSdNx+knNnTLDlkagHSLBukcBMt7zCUjiBm7Tu5RzrRl3nlcMCBOob4OofENKrDd7hke3tC/f0XZDrgh4UqFTVM7TXxlyuTIR+rg8E8FegpoPsrAdFPrzdjAyWKhqAFNQfFT71TN1Fam1p5WYxBxCK5q2wqh7uqsShiFMTkufuqSV++f4048WTJeCt4VfAhE9TVX0gxM3QQQ58sM05U5cUfH0168TlOhU5ucQiladY41NkqLRUWVo3NCD6cFdV9NFyByi7S+w+qHgJ+LTcdN7cEWEzD/GLfu3z9d/UYMNP3uD/ndDwP/c+DvxMDW9Mb465gD/Pdh9iATyNpj5qa/Dfipb9kW39Y3t14IoKW5wDjOGg8tB8uD+SqgLkLTBzVgDuBFUW8ePrMIWrROhYlpl8jmIh4qE1HFyjozDNNVPgcdSym0TUcTM33fE1MkOOZx/Pn6fmKyykGuPNXc/NGCL4mU4XPv9fSt45U3Ot789Pfy8ve9xMNPFUp/zdI3xIeR67euiI8Trm3o1gvEO4orpJxw2dGGDheEpCaKVlFKzozDSC+Jbuk4WXV4H9jvCh88uWbIPSfXjtdevsf9O3fwbYMWb9OZVd+VxsjF9RVN61h6R9ZCVGWsWYdBLOJotgiYjoNU2fzMxtRXLvNNM0NTqIHaM/dTIbVgDIkYmxjLyHK1JmwCJWQ49YTOE1pPLhHXeppFg3p7/SIg+TkbTDEGTuo2SakblGCyUvB4Yj+yv9gRtwMaM4a4J8uF466ggcwZZH2V7a8pC5DpPlI1SrN31tRy03mfHjOk9SyaNWxSW4i1w2pMWc7kHlKfWGyEHEd2ux6XkrXHJ13cvOUTQJxfDDphOziKBKqXD9OBPHB2h23m8Nrm87/qv6b26fw4dVd+PCWvH8eyKVn4xZhO6E9xAFor4F8B/vcY0JpaXrdH99l6BQOlx1cdTf35l2OTmu5D7neFWWUc/+7zmI7rs/Xn78b0ge+8eiGAFqqkFDHAkp9ZgObm1KRdmRZkVUQLJUe0HBZ2w1pubuuU+gCqjlKceSu5xhgECo4BxJ4zI5TiCCI0Ck+f7NkOZv1gDECeXbwzNdia2iMqoFLIYo7fvii+CEUWJNchKqTsSCVycXXDX/nx9/j+H3/Ca28suX/a4V47RyVxNQyEtrBad3TLFr/yiGCeYjkjGmh9QBxoyuguws3ICqEPjsurG4Yxc//+HTbnC3II7HZ7Yl94+DiCXrPpNoTQsS0jLiY6CsF7tMBNKtzsrvnU5gRE6SWRx8yJK2wCBIIBYmHWG8mkKaoAzDQ+VtP3xt4IqViLTyqTBUJR258RTwiOslyQ72Wa71nTnW9QHM6DCwE8NG1DaLx5rc3AwBmDNmmRJjbFVyCRlBwzJCUUb/OdnUNvMtefu2b79g2LGOhCoC/7Oj1JxSG+vhB/FO6djwi8CUnKrI+vJ/Z8fs9Ac9b32bntmVrMOrcNRe0rU5jJJNFqnXDIlwSzk2iQ2YJkf6Xcf3VDXiiXF1t2+0iOSucDTWXDDtpBw0MTC6s5k7XU91m9KNFDbM5x29C8KArH4HB6WLurms0KisY0p6pr8BTnGPXjIub9ONcS+LXY+P3vAv59nvV3ugJ+IXB5dNv3YNNjt8fXymEg632+9MLsHwH+ST4cZCVsGu+XYO7ygnmy/TPAf8SBL/+H+HCm7LZepHoxgNYEsGatxzTVx7MGh8IsCJ7iQnSKQTnQDHUxkgoG6sW194hvcOLxHnyx+IMxO2I2b61GbdGSAjpGHj26JBaxsf7a9sh1kcwUVMoRg2ULZQSSKK0XmuDBLRialpsGHpfCWJR2uYFmzfUjOPV7xnTDdhwZhoxcRcIobNYLpHMkiThxdF2LF0FL4frtS/a7PbubG9I+EvtIX5Q8Zvb7gW2MDGXg+954hbPNmnjnDO8c7+56A3sxE2PmycU1C8C3niYEcx0XuBh63licgINRlDEVmpBZqj2/UnOKRNAqvrLx/6mNeGAIqd9NVhEFrVFDmBVC5ZsyyliPa2oc55+6x0s/83XCuoPRmZk+tRXcCi486/8k4o6YniMmaDoHUDQWNBbG7YjzECTQP9qxe29LuSxkjQYKFGMoa9tO6sDAxGpZO8wZ61pf3xGPNyEXJtpJjxjPKUPgGRYLa1kfGNZ6/xlRPbs/CwddlYgNgJhFhhB3BR2VZdOxDy3XyXRojfe46vsFNZHgGYaLeTurWMv259HrMf0c9TVZM//4In0iyCbmLecIKjganKs6FbELkv2L8tFzW1+h/mL995JnARUcdETHP3+Or5bp/e6oz2Ctvt/83O3/OPDPYfEzU037LWFtwYJptKZ6H/hrHBisVzGTz9/LLav1YtcL8Wmn8//rgiITL3L4GY5MBQSzEDhaqGxhOgJb0yKnWuN4YNLZBNSm6sSxS55dCuTiWXvT0OScIWXGvqc4oXF5JmiLGiibr+pRCt7sGrCQYfVCCkIfPNw9obt3xvp8hd97Xr7Zs+wWrBYryj7z/hc+oN8Vto8LQZds1oFuIYRVg/pCTCNSAt1qDUXpb7bs379gf7Fl3+9IEs3+UhWvyklwaAhoPxIU1ouOcL9huV7RXPc0rtDgeXpzyfXNFdK0JLcwzY1XQvA0zjHESLPoaH1Dz1gtNpUkZWYZRW0a0RgknUGxsYkHN/yJA7SF3dXrN5u3cxOgEGgDFMloK7z05qvcf/1lepeqWawj5TzbDRiA1hkw2HSAHeOjDTwwMLUVqDGzv9jjMpRHmd3nr9GriI/KsOsp+wHJxlI+GzhzdL7qhLmqwn9irOSwCQY6DwJwnf/OLiam61FXmbHpAVQnN/WJ4arnLQdd0/NbNTG+ThwlFnaXN3TrQONbnAsUSebnBXa8jtx1RagxSDq3+yYAN9uwzkL/YwJr8gbTirlNBzlqQV1DKZBypoQCvsFpIBTBeWPNHo3fDdEi3wnVYkc2fsjv9piP09dStyDr2YpYu/V5p/fPYG7o8tztGWO47gD/NeClo99NYGpajd4D/hfcgqwXv14IoAVH4EqevcauK9W8OCNQ6rTd3IA5vryfL6ttQShayGpGlk4F8eYO37a2jGSx4OBUlCBCzomYFEcguACilU0z4BCLgTenimhGBbJryC5QRMjioPGEOydsXrvD6Q++yubukkXxDA8jcrGjZMfVLlF6Ad/SoTSbjNNC6BKlEQaEEIRFaOBaGK5HLi4uuHr0hNWQ8DGxIJPUWnjemYN9IZOLI2sgPt0zlCuaxZJ7mwUny40t8hq5bnacnaxYqBmJpZRx3nG26ojDmt0wcvf0hE0b6f2eIpAdJEyj5dXMOw3cVr+typBYi0lmTyUtFjekatYdTtxs7VpEoQgez2kjbD2E+2tYerb7PeICTfBIcpSxENTXlm2Fb9Xs05zl3QyAxNftSNXbKSlOCylGuhRITzOX71/AxchicDAa21WiWTzI0Xoxs6ciIKVq/MwbraiSjhmrGesf2t9TOxDKwZwUIamjqZq/4/S/uQUH1eBVpzfIvI4dEWzgHBmP4OkEUj8gJMqQaEIg+wMwnPhYLTodOlxNU9dJk1ZfHzBdXVT9lt3HWC1XmT/Ts4XGU6TQl5Hsl7hgyQV+o2xeWnMaNugNPL684Pp6ZO9vF4ePvgT4e7Ej+h98xNvyca3HWMv1+QuLPbbf/XO32+WXMVW/E1hgb7QeYw9/qN7nC/Xvn2cZb+tFrBcCaE0twgliHdon06pVI0kq8PIytQcPkTHmuzBF4kyPWmZGQ0ptdwgU50nOsucWHTQhk7KSijACuIKIxzuHajJDSTKxZIZsC4QXX7fZob5DnSN7R160dHdOOPu++7z8gy+xeWmD9pH8/o52W1jSMjrHNkPTBJwXUh7pWrFY3k6QLuBXDrcSvBdSn9lf7bi+vGZ7taXVQqeKL5lczUTVqYHJCnUaJ6TtwN4JSw/tYsmyM1+s/ah0i5ZTEfxogvqUMy5B0zjunZ4y9pG26Vi2C/Phqvs2y2TLyVG7rAIDnW6rIGsGw1XLxoHtchUoFSyUuBGzC9CTltNPneHuBkaJdATEiWmjyqHVOLWyppaXPa8BGJvsOwLbY0F3mXI1Mj7dUx5BviiUp4XQg6SMS4pkA2YUrU79zMBmbq0dOa8xt03nrCKO/3r6VqYtnAY5KgOWdXoD6pGw6+iBJmZuZpaOfq0HJmv6Q+ccXoQSM0mgZMU5j3rTd4Vg+yYnIWfL2fRTW5SZxqqPdtjXx9s8vRMFUHUgprkqBAZXKF3Dyfk5q3YNAfxLjpc/c8ZJs+LxT17z1v6Gh6NdTNzWR10K/JGv8DffAzzg1oj066m/B5sa/IMYczge/e4VvlSbJRysIFoObFYB/l3gNwBvc8safufVCwG0ZgWKHBbi49/Y1XPVlEwLj33SV+3UdH+Z2yvW3hO8+EOrBMi50JfCo5QITcNJ61g2jpOAeUYVoY0LclLGfiRGR8kdTkeC8ywXDTFkYoxoFlwT8Ocb1mcbzu6d0q0bTk5XNCceVxT/uS3xpjBcJ7hxxKIULzjX0ojQBoeEgKaR0Dpk4WHpWL6yxHeO1A/sn15ys78kD3taKZCjeXOVQs7J/p3Ey87jXcJ5ofRKXxIxZdrNQHt3hWsDPjjudGuGPhKvR8o+29RhyWjKnG5WLO6c41TxLEjXGxiFzps9wwRC5OgYTWRLPSxIKXMWpGXnuSOy0RZuUbP2cNU24skIm1fvcucH7iJ3XHVxz8QyMO4yuc9It4KGGmw9eZjV4Yf6NbUVjbUp6E6J70b6t64ZnuzoWEJU6BPEYro/VTQVSAl0MrKdzq2DbQNan/foQ/JwiWB/dSCyjoxt67mblRkS+jo8kHEUddW6dO5LHnAWTCgWmOzTdAaS4iwEulk2LFYtAuRU0AzB1SEBAbzZjaAOLzrHBU2twWdYvONj68RAc0ogFu0Uswn1cxuIXmg2C9r7K1759D1OQiDvIu3dU1ZvLnFnheuH1/zN6wd89vqaqyJ85lN34L/40s+C23rR6gG3kTpfT7XAPwH8KuBfBP4/wD/MoUX75zgYlH5YTUM3YIae/wbw1rdqY2/rW1wvBNCC6SrZFlypgGlmpyZPoVLNRacr73kxmy7s64npbBEvKvMibAaLxv70KfIkD/imIbEiNoHkPM0ysOwaHJ5hyJRLSL1DkmchLYvOI6cbyikM+z300C1aTl7fcHL/hHa5ZrfP9H1mGHp8SGyWJwieYVTGoadRZdEuWDcN3gtOlawF1zi6dUduIHcFd+bJY+H6wZYnDy+I11vS0BvI0uqIj1kueDeB1FJ1NErJxuCpQilCSqaScl2gWXYsThf4oAxFSeKJowU0FzJSMo3LNMHjSkverOivzUOs6Di3DZ8Jz67fT4JuxR1E6WKC7TK3nQyJuIlxkobkAuqVkztntMuGlO2McF6hFESUtmtogrfWcbWUcOLQKUF8noKsLNckReqVfJmIF5F0lVguFc2KlGJt4RkYaUXoB2B1CJ6ZGC2dXsTMxFLjarQa1VKnBifrj0lfpRX8Tyysq5N5RSEj1a5BJqsupsxHoDr018ebu4g6m6L64OiWDd2iJSrkqGjC8ha9I5bIkCJFMy4rrphCzjk5Mik9vOrjHuUUED5RiOLNg00R2pMFq/sL7n3yPqvX16w+sWH7wQ27RyPda4F0rpSSGEal7xNJhOXJkpc/eedr/oy4rY+ibpmsr68i8AhbYt8A/g6MrZqA1p/FBO+v8KU6red/fowxWbf1nVovDNCCSe4ztTAm7VUFWcLhkvtIE4TKwaNoYsSwRcqcuF310iqWbVcs87Afe3bbS8Ybx7ZpwbXce/mM+4tTNusFd84WlPOWi/3A07Fw5/yMk80Sd7rh9M1zhjyw/dwWeU9psiDXhe3TS3apUEJD23h8ahiGgevrgcePbwg+sAoNrWYWTYuzJBjzOArQp57ssOidUtg92PLkrz+lf7Kn7HuII06zsXtaKtDgmPaoi281nshKycbipRQZc8QvAouzBc3aszlbceflu8RUePLwgrSN6KC4khiGHZ6WRRPYdC1l30Cx8Bd7ukO77ECE2CI9mXBKyfMCrnJof5UjRgtxZOfRpuPs3gn3X3uZdtmgLtH4hrZdUApoE+n7geEqsryzJPijUOfpvKnboAiabcqQomwfb9m+e4lejJSdMWQi0LUtjTrKEMG7aiyqRwBxYlQrmlGHZVoacD/2THt+L+gEsI7biPP26XyOGjw+TBJOR3HWXx23wJk5u7mVB85ItiA0i0BoAimWeSLSIpAM5eWcySUhRc1+RBM5mrWEiMcHy9mU6iV32B5z9m+6jqKQFFzb0q1WnH/mLsvvX7N6fYOsYZSBsRlpzjv8yhFTpFxl5JGwvGl4Y3PC2Z0l33PnjNu6rY9vdViG31C/f74uMNB1rAf4sMoYk/XeN3n7buvbWS8E0BJsLThghiNbxXnMqTzT25iu5MXV5UyZGQRjBWSWvnhnVgIFAfWsQuBu6Viq4gqE4lg4pb+85u1+4PRkx+m64875htXZEi2Fe6+es1523AyR3ZMrmsWC1ndcD9eklGmjTdM13hO6DtXCsNux2+1IY2QVHKtVIIQWnDBopKGKtrOS+8xOdjj1LKNn+8E11+9ckJ5scdsBnw1guQoep0nMZ0Q7TF0nsX1AXdZLglRgnylR2A07ht2OxcmK81fu06wWLFpHSh7xQklCvxvZjYllcDSrBtd3jP2An6nGieBQpkDteTtEUDJxyusrAhhzM4qSilZ3GYfSoMsF7d0F5z94l+aVBUkdWjx5EFINni5pskDIlLFQ7MAhvkKUTG0BQoMjpZG4HxnfG+l/8orxgy0+qmVlupYxR8Kmow2BtHOUbaGMzmKeckFKRI4ifQxkMb/wUizyeWqFFpn0gPazmzp9OkErrZOZWg1v7ffTM/iJlau/mHI3hQNjO+niS8FaeeINqGpAgsetO0onlm6Aw/sGNKF9QQbBV4NaXxIuFUop1oLWjDhHUY9zvmq9/DxFmtw0vGDTtWHdcfLyKaev3eHk06fIKwFdQ0Ype6FlgfcOfZwYhi3Dgx36IHFahM1mRdc4Lj774Kv/gLit2/qOqwH4H2NC+J+DsVLHgvh3sdbiHwM+XW8rWJvwIfDvAb8MMyz9Vdz6kn1n1wsBtIAqLK/fM0Gt6cr92Wt+W6BsAfZOcAIxTwuxCcGn9qNMAEypwl3BZ1gg5NDiNKNkxiLs9olhH9lf73g/KKd3TlhulkgQvCuk1RrJgj4RtqknJ4euHOSMy7BYLEzXk2AbI0+u9lzdXNN64aXzNet1i7QL02kVhcbhnI3kS4Rm2dGcNrSLhsu3H9O/d4XLI2RjsvzEkEgVTU69uuNWD+CPNUNa7QOy+SLpCKmH7fWOq/ef8uCtD5A20LQd6/WS082adtFRWDPsByQmVqsOt8+kIROeYc+qFkqmQYMDVE4okQQFXPG46uu6pzBSaFyg8Q1ZWtxmzeZTJ5z90Bl6z5EKaPIMMZP6kdA0aBaatgNnYdpaIq5RXDNpiKgWbFWgPyTiwz3bv3pN+tyeEKFdect9zA1RC34T6E5WhH1LeaKENNLvdlAynhFPQtWE3jNqr06oJYu9IFctKZxp0kptmZrpBzPAqk1dnBS807lLOYV0e8AyPS2aSNXiiBS7CDGzUWtEZnXkLLjQgNi0qyw62nsbNDTklFDxOCcEn0ASMTtCbOy9UyKqidZlnJg2sWghRrPg8M6ioEQcxQkjaiajpRAWHXe+9xVe+cH7rF5d0oeRvMj4NsBWSU8G8k2m9Arbwni1JT3eITeZZkzc7K7Y5p7c3+p+buvjWEvMmuFd4Cnw++rX81UwkeL/HfgfYJ+bD4Afwdir9zBG7I9gLcjb+k6uFwZoTQvY3HipGpe5zXL0p6UuQDPOQI5aKdMjyMz6WIfGHsEheOfAOYLYZJYqtFIQl+k0M5ZCP2Y++GCLPlSKZN76YsPp+oSXz1/i9OSM0K2BwJgjcd9TcNxsB57stmgQ9jge7gceX93QeNhn4Y32hLVXmq5luegIzrYx9ok+j3QbYfXKklQy6UFEnii5H5FSKrNxaObMJfKlt1G7anoQTRsVUvel2kh/0sLF0x3booQ2cH6yIo6Ju+d3OF1vuCyQhgH10Kwa2tjipUNHYRJ6ixaKZI5VWwpkCnlivtzUknJ4heAKLjjUebrlkvPveYW7nzqhPeusdZqEPLljNtZa9Y1HHZSSGcaRYYw0O0eLx4unNFBaM5HdlYH0/o7tT14R3x9gsCy+ksE1Dr/y+NGjjTD0I8O2ZywJt2hwXWAYi2X+FWY7hvmsEmOLzLKWGmBepumLg97r+ZZhvU3F9tnhV1XbJUf3nc9lnd8a6g+3TDYSduo7mnXH6mzD8mRNLoIPkZzNAT4DGhy+bdGU6HPhc4PjOsHra2XpPF4bOu/QHNn2+2r6rtbaRsBb3JHftGxePuf+Z14inDTsh4EhjqRUWOkSNxTcfocMmbJXdo97+qs9fquUXSJue9pcWElnrd/buq2PXf08jLl696v4WwX+N8BvrT8XzK6hAP8S8K9hmYe39Z1eL8yn3dwKRI2FEUybdcRzCdMUGNWlWo+IhnkJwqa6pqmNMk8oThExpltxlTmYzCkrCMOWUVGlpDhH7eQY0T4i28h4tuf85Xs0JyfcxB3xak+bHVmNyVqfdLTLlnbhiFdXpqMKxjwchuQLOG/ys9bjNg3+DMQX9hd7xqtoLR8tZh9QeT3Tnk2u3V+hnmnpTVVvybbYB1VaVUrMbK97cgIvLZvVkrbx5OwojWNxugBRYh6tOZszUg4xRKoWhpxLnUbDBvtEsODmJqAFWu0IAfypsDlZs7pzysmnTmlf6cid7ZaQHSVF8IosBFzBBW9WGOrwOBJCHgox23lQkqBFKJJw28z2s9f0b22RLXh11cHe4VcN7d2OuIeUE+NVIm4T2SlNF+jOVoxpIO69MZUloZrqeTONXpeZtUKwvMhZRng0nAHWRj06Zw9h5Fpb2VN7t3qO6bGq63iKdho2qMfUO3A29dqeL2lPlqQCOWWcM/F7HCMhFyge8RAWnkXTchKWxN2ei/ExF5JZhIbvOT3HS+Txk0JJjuAbvDjEOZZdx/p0zeKlDZuXT1i0gdwXYiq41gEjw3ZL2keuPrik7BPSAzcJ7RNpLKQ4ktNII+BxSLk1LL2tj1utgV/NhzNYApxggvimfr8C/ioWrXNcDvjjwF/6Vm3obX2b64UAWlNzULBsNVeZEot6cUw5KopNeGmx0X0jsgw8HbRcVYuFfwaiiRxaj+LAi+BqeHWuDENWR1RPKoVcCpSMzMBIyQm2+0sYIoSBe6dvsrqz4p33rxie7olJkdBw92zDydmaVSOcnDkWiwXrRWuTaCVREgx9IS86C4wODpYN/jSS+5Hrd27or0Z8SrWldNBCTa+Ho1brl6uiB/ajPoz9q3bgBRAvLJwQVYnjSD+O7FpPPuvIGW6GntFH7ty5y/n5GdfNgv56R9r1FldT8jzEYMabdhhSKThsmrJtPavVgrZp8bmFtad7o2V9tkKCUE6E3MK4texGv48miVo7dF3IJNM2icN7aJsGDcY0umL2HS4aPs+pMH5xz/DjO8p1pNWG4Bw0DrrA0GTCm8Kq3TA+Gtn9jR2uNKzPFziB5cmKZrPm4t1L0pOETwktuQ5WFHB2PhYy1Agh5yZFXB3MqPvjIGebNIRiZ7rmIzBVwdMUQTW3X+WZVux0IWLGoQUJzsBXcGxePWd595R9jKQ+sW5ashsZykgMjs3mhGWzYLzcEfc937eBT5clFzctH2x3PNruubfu+P7veYNN/zqXlyPX714w9JHWO1ZNR1FPUc/QK+ODK0qE/W6kWQaKLzy6eMDb733AFx88YeEDn9ic8anFCoaRYTeQxwwlUTQRKUfDC7d1Wx+n+iwWsP18TXYP/yDmCO+ALwL/ZSwz8rgK8P/6Cs+zBs6Bd76Bbb2tb1e9EEDLamrN2IKSc5kXqnkE3wl4W9AnqwZXw4yfGb5jauPo/P1xK8Y+5F3VFtWq65roxGyZoHxeDkVwam1HLcruesvy4ormxJx7o2aGnNBSiDnhpbAKnu7OKeobLq627LY7Vm3D+ema5WqBaMGJOd2nUmCMEAsl5Xk/TBvnvmRU4NlX/GH7c3LbP37t01ru1PymQn20IMqi2gn4Yc/lo8fkdk1KmZunO5puxem9c1anJ5SiOJcpydprQTyhcbQnLTSBIkqqYmtphXbV0IYWKYImRzhr6F5tQZUYR/K+UB47+hQZrvfooCxPlwTXIgso2aFjJjvBNQ5tvA05eE8TGhx1YlCgbEf2FwN+FCgBpzaRRyPIKrC8u2T16gq66pK+3FF24Bet+YYtPaerJUOC3f6alMaq2YoWhq0GqEqVtAsO1DMF3Oi8vw9C99kWYmbCDuyrnZ4TK3jMhE13PoBrG/qYnsNTGjGd2Z0V2gjjLtK1LR6PbzztukU3wuJ8QSsLhn6gRE9whUbgzC9ouwW4Lb0rNJ3n9dO73DkVHkrH5dNLRAvrkzVFC1e7HZBZdEtaDaQ+0u8GRik8+OCKBw+u2O4Sy5MljWvIYyLt9qQh1muVPL/Pj/Mob+u2Ph61xfyuPqwG4A/Uv7mLMVuBr0/k7oD/EeYS/499DfcL9b7jV/rD2/om1wsCtAw8CRnnGkSEcRjxXuptHsmCW3pyW5DkICmlRMQ5HFIdIKb/CqKRSSRuC9rB18iJ4Jw3+wOdIQgOM5FUKSQxjypbHg2F+bmNU9Aedu/f4J4qJY2UVoklm5C79bgKLC4urrnYjzy5GYnjSOcK4/3My682hNVAs2hYLJd4Dahm9ttrSo50wSHBE1P1VpKAm31cD+2on26fYk2+w0KvZbZZCN7AW6P2VzbYWWw/DcqTB9c0pw4NDU93PWN6zCKc4kkMbqQ5AwkthQWtdpyfdbz0/Se40459HiAKcZ8Y2wSLwP4yE58k9Bo0O8pFRppMkcSwzwxP9wxDYn/Tk3YjJ3fWrOOKRViSnGM3JLQIzapheXeBa81GInsTcDdNoImeq/cG0sMeeqUpFYAF8KvA8g0T3Td3WvJYKGOBpeLuB1h69tc7XAebl85Ydy9zdX1DvxtZ6ohPIyVHm2x0QnbOtGc4VI29k9qetONTQbHaeT0xo5lD63Bud6uJ4Ceg5SqTNT2GsbmT578nu5YxwuLlNec/+DKbN+/SXw746wHvHGO/x7nM6dmS9uUFeMfN0yseXD/h5uKGs7VnswyUILi24XxjoPbmehLEO05eWtFtHJKVhQsUVRqJZK+4EnFJ6bIjlEKg8InNhlURroee10+XnIiw/eCCuB+MEaz7otQYH7kFWrf1Qtc5xjQVTOC+wCwZvpHz9nPA/+HoZ8/XB7TexCYa/+bXeL+/Hfhh4LdBVdLe1renXhCgRW3NHBaZnA+j8AVBs0Oz2kRchHEfq2CeWR/jaovNNPDlmffEcZQI3uJ3iqqxLjNrwNz+CmD5fhxYIVdpBi0gScjXA+NNpgSPBvBdQ9uuaNcr8AFRIUhDo5HOQdd1LBpP164IfmH+W3vTPdEIbejofOFkmembK1I/hZ2YrszXdssxUyLzln/YB8BhuIAjJgVxlcVypveq4GDy50pFSCqM24HUKpobnlxs+dzb7/Dy3Q3L0wVnd04ZyLz/eODmZkva7lg7YbEWskRuHm/plh3tHY9fC+0iMFCIYSSnjJaCZgOBaUgM+8I4ZNJNJu4zex0pQ2H3ZIsEZz5XTaA7aVDJrM6WhGVDKoVSvcXKFnbv3jA+HuiipQKoWDyRtoqulLIs7J/uYRSaTcP5m3cYHg9sr24gQHtnyeL1Bd0bHaW8xAOXuPzCIxYx0EpCNSO5MGYT+zvxtK61AGfC1ECs+9OOkRcbZtDjr0njpdOkoR3NCRhL1Sday1Cq9YINEGTXsNiccP/Tr7H+xBmjTyTJxBS5eHLN9dMrBMfpyZKXUJxzXD7d8YUPrnjw8CneK23nWbUdd7oNZ92KkODp42t82OFbR7N0+E7w6uqUZ6HpAk3jkAyM1or3IjQirJdr7i2W5DJQxpH+ckve92iMz5yOSqWNv6EF67Zu61tZHfC7MaH6nwf+IeCfBv5Z4D/6Jj7P1wt0XgH+BPD7sW39sAneFjjD2KvLett/hrUaP4m51P9pLDS85+sPpl4Dfz/wV/jSqIcvty5999ULA7TmNkoNu3XextOnvDURWzDa0DJEA1mTCL7M9g1ylFSiVUyvB8Hx7Nxdqi6risyn73USxVueYkBwOFJtPXoELw4n4LDImqFAqgzZZt2xOrlD23XkbNEup6sVi67hZNghzrNebliu17RdiwdSSmSfWa4WrJZL3n9wyQfvPaHZD/iSZjag5soc7ayjb4+8rSYLchHTu5mOiApXM1kcKlW/JjAxMNN9J7PKxnlizsgY2bQdl0Nhu+85uf8q9166g0jmvS+8y099/i3OlyvO7t5F1CM70JjZP9qyvLOkLQ3jRc/u/Rv6D3bkoUdLJo2FcSjkqBAWeL9g6QMiDtc0NC6g28z+YU/xQjht8ZuWkjNFMpKFs1cWECDliEZl96Rn9+SGtBvoZFHbjAKtQOPIJMZ+ID5NNK5j9caa/c2eHBPeefzKU3wm+p7VvYbXfs5d8MpVPzJ84NDoIe9wpafPAzFnEEfwkbZVOl9oCQScYYn62VUkI05nkFXqeagTQNRJT3doDU/tQeq/iqf4gK5amvUJr7zyKq998g30NHC5vyb2keubPQ8eXbPbjrRtRymZdnfNqm3p1HOvPaW907BYKMvO0/qaeOAbG/goA+NQCBLY3DmnbTvyqFxcXrPb9Uhn7vPn6wV4yK6QysgQ99zdnLMELh4+4YOHl+y3PYuc54nfuRU+scJf9wf7bd3Wt7oGTEvV159/H2bV8Bc+si16tj4A/hWM0foVGOjqn/ubHwZ+DxYW/iP1tojpwjzwH2K2Er8B+PV8ffE+AWtd/hbgR4F/lYPz/T1sJf3g63jcj1+9MECrVGaKUnDO0TXTplkwrmsc3XJB7DNjPwIWDWJC4nrnI0+pyb1bKutlICtDKRQHUiw/UdQWvjLbFZjbehBTamUtNl2nFmfSiHlfuQBJM3sVUhLaRcdquWG1aBGElIwtK2pGq6tuifNC2zmCLzifYOkJJ45w4iAqT5885t23PuDJO5ec7UZWTiqrd2DSjsbbjrRawLG4WI61XHowOBfIBDINzqW6dycpW+W7xGGGm5mggvfG5MXQsTpd093r6NuRn/ob7/H5n/qAm5sRKQPvP+1Jf/WGfJ24ubymLwPdnbeQxhEHZf80obvMohQ6lIAxIQ5hV56wK4Uoglsv6O6e4n2gbRo6hDEnSlJ0m0iqUIS0v2GxWtGdB3Iu+BRIlyPlOuISuBakc9A63CLYtOGixWch9oo0ShiEdBPJ20jbNnRnC9y5R7wnqkfOW+58v+d7dpmLv/aA63fMYb/JdgUp3rR5N3GHF+VMFpZhiTdGqsbWqNM6nalk8mwZUSqQVms8UuX2tf3talakx2K3F5RGaF7ecPfNT7B0LfvtDUkcNzc3DNcD+yGyHc3PLHgITpGkaB5ZAJ9pIfsGcYp4wXt7rRI80ja41ZJIshZ4ivimIbuWi155+GTHbujZLD2f+eQ92q4lh2L5isGRR8fVkx1XT3e4IbFEcF4QcfPrQZQsmYxWvdZt3daLWtdH3++wUOcXhZ35wtH3f4wDuDmu/y/wP8OYpucrY6DxV2OA8lN87UArAL8G+F8Dp8A/isU1/VvAz8fA1/8F+D9/jY/78ayvGmiJiKdyj6r6y0TkLvDvYPHunwd+jao+rX/7m4Bfhx3Rf0pV/8RX+zxatUS+gqii09RVIaVMSokY0wyqtIZNP/sIhzy5icFSLeaQrgoZcxY/Eubq5D5fgYmbL7+rVYRYm8Q790xESaotn+BsdH4CiKVMI/vmWeXCNJ2m5BwhFRrp8K2nSCFejTz5/CN2j/bkvZJiJAchhEn3Uzd3Hkhzz7zmepSe35lzS3ZavpGJs7PXxDw3UMHchNvqEj+p508WC5brFQRl0JHr/cDNLtJvEzpGhps9j3hK2CdaHBKE3cNdxb2CJgvsFgk2PShCcMYM+pKQbCylL+Z+P17vkcUKCR1aMt41iBeIjrJTUhzZX+4hNCCFsk8Ml3t8cYj3836yOKKMCwF/4mk2DeM2kmNivOlhNAZHMRAQmhYXAjiHXwZWrznuDXcp/cDTyyvGCJKsrWymo6Vq8yKxNBQxK4j5eNVzsJRShy+m862gJRsYxwDulI6Q68FLOeN8wHkP0rC6e8L973uFs5fOuH5/x/XlDU0JOIQ2tKyWcHriWErmpPF0KoRtJo/WqpWc0ZxJakoxcYJrBmMQ1w3dYsFy1eCzYxdHStexWAfuvr5mxw2P3rphfxU5v9px736g6TyLsxV+4di+fc328pp+N1Biqpq1On2JHJzy5/P127tofbs+v16sEkxbFHnWlfy2vvZ6UUDWa8B/BWsbFr68sD0BPwH894C/CPwpzGW+HP3+L2KtxHvABnOl/2rKYe3L/z7WnhRM5P8B8N/F2LSIGbDeFnxtjNY/jc2tntaffyPwp1T1R0XkN9aff4OI/BAGk38W8DrwJ0XkZ6h++UtYJ1RgBZQyLzSzZqsoRTPjPpNznttpxgaUOQqloiObzKptRfPJKtXfaJr2UnKeAEa2Vs4BcRy1GRNOHJ33ZIXWtwZwxOFKS8xwExMLl1h4YbPoLIgZKDmTc0KANjS0bUNKidQnBh0pKN0w0PUNSUe4KbR7WOTAkD1S1KYPXYtS26NM7RdXJycngHgMsI5sH9zBh2liv7wZRtRYHA6RhRU4PDPPKJiGLUWWbcOiZNJ2wN1p+PQPvsqiaXjwN96DfY/bJloyAWhcwWdfrRfsSUwQLgQcTsVaaSqoOBppWHvHQpUmBZqrRCoDuRmha0kCbhFoV0vTv3WO5Ar6Xo/Ljm7Rsnv/muHdK5pSD2MNhy6lkMdMiC3SB3YPBogOp4HHn7sgXRWapqXvBqIviA94VWRX4WhQVt+z5pXuDfaMPP1JIb0DfgCXByCCM5ZGSyZhMUO+OHOGV3PJLxPAmjVaiazVow2zFhGxGVCPB9cR6cjS0C2WvPyJ+9z7/nucfuqM4XpkXxJaIGSh61qyRu6cC/fvnnPmG67e/4Drp1dcbvekMaEpwZjQrKBVayZmBuucoA2Ut4V2tcA1HnyD3Cs095R7Z547dz7BarXhwfuXtOsNy7M1m9OOVbck7UfeeecJ20dP8TFCyRRhzuHUygiDTcK66eLl21vfss+vF7d+GPhNmPv47/qIt+W2vjn1AGPXvhrg/BOYpuxXYQaov4RnBfS/D/hJ7HrjvwB+LRYV9JXqB4H/IfB3cYAQf6Q+zhI71xIfzrR9d9ZXBbRE5BPALwX+ZeCfqTf/CuAX1e9/J/BnsIbvrwB+n6oOwOdE5Cex6PI/+9M9hxMOi1ExsOPqhzJSXYpKwVcwVKb2y9TycwcN0xRvctDB1Kmn+kE/6bpKASWjMum1plZaXRBLwXlHcAFXlDY01fzb41mgpTCS2ATHctGwaLw9d8pMUTBSxTqaEyUntAS0CDlnyi4jvkFU6TrP6WtnXF5suSTXXuoRDaCKOWMemI9jK4H5WDGBpInBgsnvSc0IobZTD7quaQBM5r9VHLlq5gplHMELuneUYUnjG+6+umK8vObaRZxLhJIRjThXaILDSTnSiEkVihuMmDzAclHLx0OrnQZIqsDZCRITlIIXSIMjDpmQhGYJEhz6eCAVR2lGtu9dkp70hBTsok3sXPLOTDfZKf3bPSMJv/E0rUOvHG4Umnsdeu5pzlq6M7PryGNmvB7AK34J61c3vPnzPkXbBB70mfhBhKx4gaXPjNXxPReIovUVGbNWKFXsbiALTCM4meEO6kg4Vh7wgb0sCO0KF05Yrk5Yn685e/OE7qWO1ChJBR9A1OHwlJhJccQ7ZdkUbh5dcPHwiv2u53p3Q04RXwpNLviiBFHcxK5lSAlKhKzKuB3MCNU36C6hY2L98imLjed7X3uZddPRtBBah28tOeDmwTX9xY4SE14nJutwTs7aSMxWxNjg59jXb2F9Oz6/Xsx6FcvL+88/6g25rW9aFb7U4PTL1R74k1iEz6/ERP2/AwNrqT7WXwD+deCfwqYS/zjwCeBn1/v9eZ5l8zzw38CuRZb1tszB0f7n1ef9z76eF/exra+W0frXgH8Os7Od6hVVfQ9AVd8TkZfr7W8A/+nR371db/uyNbVWqM7ph6ksmMxKZQIdVXclVV+lTI7aQJFJCz4zWhQDWvNjcWC6Ds9V/X1m7y2YXOlNqeXM5NQHyyiUhjasWQZl03ru3TnnzuYESiYOo7V8giOEAB6Gfs8wKqFpCY0luav5VqJRaZeB09dXNK0Q3gHXAIMc2YDN/T+ep7C/3FyHmbQeabgqZTXd8mxy5BEPNono61/Z8GayjMfYkscEGDBsGkXSQJMzLWabIc5akk6rIYHU4QKjHOs+Pzz3QWkmh22oEh4tGXLCOceibcn7kf14Qdkk2vWKfAnX+56shf7JjrxNZvPQGNjKKdEul/hFR1HoL3rUCzkUxpjQwRidEhRZONzSQWNsX9s2bK9uGMvAMnS0Jy33f+g+0gn9OPD2bsfYw9J3NCqEauGgFPI8VGGvJ6tWWwe1bES1QGoVoTjPyJKezhi8dUDv3ePlV17mzddeZt11gNIsA6yVUTIFoWnMpyr2I0JBXKFpA2XY88E775BuirXZ00jKBrQkG8t3gOeWp1gKIA4v9eNALSC9v7zBOeg2DQPQrjrunKxIkigl0feZcp24eHRhx/5Y9H58TlWGeJrGtLb3tw9o8S3+/Hpx67NYhMtrH/WG3NZHWpNc5F/AdFt/H4dTPGNA6ylmZ+Exf65/A9N5/QjPCu0z8G9ihqq/A5s6fIpdq8Czb53bmuorAi0R+WXAB6r6F0TkF30Vj/lhn6BfggVE5Eeo4xAvhVNr8amBgRnrqGmdZAJBE6CqfeY5NJqD4N3uV1uG0zTipOLSMjNWEx9UNJLVBMruKNpGCDgXUAIpt5ytWrply+WQUQm4ruHUB9CWdbOgJMjDQByiLW5qXlxFKmvjYblastkUmtDUcz8w5IinkNoCC0GaTBsKQXxlw3JloypYVFeBl3zIXj3e4ZVNmpDOxFpVEOWqKVepwPX48OlRdF+pFgQUQZMZqgb1tK6hdTYdaG1BA6K4DNX525z+Da5Ox2EW8wPgZlPVaYE2ttEOvt1WTDOnCVSsJXuzJ2WlcR0kT8oOSYFWoERjicT5+vzO8g3PAsEF+jzSrFqkdbiNQxqH23j8KYRFIIu1G10WfNOwwNGoZ7yJ5FBY3z/lB37Bz8Jrwzt/5S3yw2vGoacl4STX60QhC2id6BxKNkuR4EmaiTnRExidQzpjr7puSXj9lPufussrnzxnc2fN3funFAqxHwmjp2gmXSfK2FOGPdpnSI4YR3PSj0oYRtgnZFRKPxDUNG9z4kLdz1IDsp1SJYAm1afmaqoWisDu6oaL91tO7irCghxH0yv6lhBbdmNiIFK8+X4NKdMIBG96REEpVRowvTdzKV/mY+KbX9+qz6/62PNn2Jtvfr1b+M2qFvs4P2Y7fgxjKW4nPL97aoUd87/IQdD/YxhZe4Y50f9ingVECvy/sanBR8B9rOX47/LhGrALjOD92zhc6u/r7yIG1j6JnY8rTBz/FvAf8+Jo3b699dUwWv8l4JeLyC/BlJWnIvJ/Ax6IyGv1avA1DnOcb2N7eapP8CEJm6r624HfDvD9y9fUvIKePwx6yECcfbEqSJIj8ske8MibyOJzrGQGJWWiiKiLDkrSRNFovysepw7Eg2vMYgJHkIZlu8aHgEuJLA4az7IJdNIQE4xppOSMDgOkEVWLG+lLpi+JmxRxPnD3zpbNZkW36DhdnNvEl/fEIdsmx0yjinfenOOZttlV9q6+1qLPtGfqK533hygWYFyBDDIBTJlFylNrUo8+h0WhiKtgtUbGlGQDBONI6iOMSqOezrW0rqEwIijBeYocPYfKISe8bveBODti0ODZ8f9ik5qOMjOXxEIQB86T40AqCbd0dE3LOAq+BBonDP1A1ESzcoh3UITiC3SZUixUen1/jTvxZvsQhEQywNUFxjwyphFGWHUrlm1D8I7ryysePXqMJ9DJgjufuEe8iTy6zowpmeeyy/SaiVV7JbXVO1IvGBSS7xgI7FKC5YrV3XNee+0+r33yJe783NdZvbRE047oE/FEKV4ofUP6ILJ7eMOjtx5y/dYNZaesZcHGL6DPDKXgs+DyiE8FktKU2qicGDaZzvsaOj7RmxW3TwegUMg5g/OUMbJ9uqcNS5brSOMV5xsketKgiHhWr50yDpF0o+Q+44KviQN1GKQ+7nTdk4vi3LeN0fqWfH7Bs59hP//ny0e8gvxabJH8I0e3HS+At/Xxqw5jK7fAw3rb3wL8XzER/H9cb8sY+wQHs9Ln66cwjdVvwSYF/yCm3/rpQPrz55ZgIvm7GMP1CQzcBWxS8ufy1QvuP171FYGWqv4mTFFJvSL8Z1X1HxaR34odzR+t//7hepd/D/g9IvKvYmLS7wf+f1/peUxzJZVB4YCiOICjubU3rdGH3tps0aBHbUX7Izc/nhw93qGJJUw2kVSPKS8BcQHvOxbBsw6OrA27oQFXNVJV7OuAXApjKmjKlJiQnGhdpPWR1mVcKowUSo7015fE/RbXtGyHgbO7Z9xtzukfJNqiuEvBxwMYkroKzstSnXY0CPSli9XU9DPTy7ofj+0e5rxE5rbdMWqdJzif6Tra/o3jyP5qy+7pkmG9JvURKVKd6+sd1FUtGTbaf9z1rIjq+LVQgaAJpxUtU2vJVmVbj3VuN9nxTVMjjmbh0ZyIfWLZBsbRbBEm93ytwC8OiZQibhkYGek2C+SUw+BORR+igvOuvjOUcYzshsiDtx/z4LMPOek2rNuIEzh/+R7jm4nH7zVcPCnV9sFSDjKOUb058YcGdTCEQlx2LM7OeO3luyxXgdc/+QovvbqhPXdcvarsu4QOC5wTChE/FMrjkS/+uS/yzk+8zbtvPWDYK2frc964c5/utJ09s1ScMXnOUTSZkSsZqX5dh/fS4TwxAHY4PIWpvWyscM4ZiSNxjIx9j1s1NE3HfhsZx57TT59y/5P3CRvHe+MNOgqUQ2te5KDGmmztvnJK5zevvl2fXx99/RjPjv3f1se//nbMD+sLwH+CsUx/JwZ0nvfVmuqnE6j/O8AfwvRbX8+U6q8C/neYBuzvA/5ubDqyB/6PfDeD/m/ER+tHgd8vIr8Oc0H71QCq+tdE5Pdj4oAE/PqvOLFTGZpSQZMe3141JXOKnB4WikkQTxWuqx5E6MePDVRdyvS7+pg6QY2ACATnzbOLgKOla9Z0TcALFBqceIJ3pJJtesubYLzEZCP0OSM54SbQV2zbWpQT7ylA0EIaM8M4Msol6mCxWpKuEydR8INZReALvmplRMRAoHNM3lha26zPhSLWl3wAS6paW5DmO1Zxy9EUY0WL5RAJMy3GUh0gLNfbHNhTPxAvB8bTgbSzVqLH453Mthiqk+LKHbFV01EVA0JV03PMaNk2zjjNBPXTsZoAptpjpZyJPtGcO9qFp4+R4D1+9LXtaXxKdorkQsim3YolE68iYROQzlqHNEy0KU5cbe3W80yFWODdt6/4/I+/zytndznfRDbnHZvzJS//rNeRruP9/UgZt7i4hxxJLjA6T1ZltVjQngbCqRBePuHuJ17l09/3PcQY6ZYtumqILag3IOxcHVgYRsrDyP5v7njnr3/Ae198ynBTWISWtQZkLAz7kfb0hOA9JSi4JbQtWaJZUJRMmQKr54sMmcnhqcWr1bLETTyUirGTopSc6Pc7Vn1HCMJ1Huj3I+oVfxJYnHXcefmUi5OO8V0lpWweXU7rjGs9hxAD5s/QnB9ZffM+vz6yeg1rGb6LtYpu67urfhLTSv29wA8AvwgTqP8/+eqmB5+vzFfnVn+OgbmCvXUm1usJ8BJwBwN8r9Tf/XaMSXtB30bfhvqagJaq/hlsOgdVfYw1ez/s7/5lbMLnq3tcKmjiiJGZvK0qOyUw+zrNUOBo8lBLjXWhOsvPuYBT9MxBm2IuUjVzrViOIKGhTwo0nK5OOWlXrPzCphPrdJlvPdoFhjjiS5x9ifIwkoYEWvAoIgXNhVjMpjKIcBYcqoJ3sI0wZCUPkThGYlSeXu5gV4GW95SS8QWL3RFQ5+qLkqMW6uGfiek6OgjG6DgTOx97GmnV5xxwWgVyR55kRirJ5LjFBNQaPM1eGB5vGS52dNLgwqShslaZ6X8EJzZ1JzPYq0fhGaDFrNECqvWDPaOrzOPcLsY8yHIaKdLw9oNL8jrw2mt30WXEFYdvAyStk6dKloLEjNsL2kKWgrvMxDbiFh53FpDW2W7N4CUQ2qpbK6Ax0rjE/bO7PDm/QhceTlu6O2vWr67pTgIvf+KcV15b8+jzD7h+54Ld5Z4E5OBoupbVyQn3PnnC2fefcfdn3iOcNiw3J+z7wm4buY4FlzwkQUtC+oTbZYYvXBB/6obt21vcB3te8ivuvvES91YdkjPXu4H+MrNadiw3LX4hrJcLdhcdN9c3lKKUIZuVA/X9VYHwxOVOx9XNx8Phao/PUYe0U2S33XKyX6AhcJEhjYnFeYsuIDGyXDcs1x27NiCl1Jax8WPO1cQBjAYuTAMX3976Vn1+fXT1s7FF7Y9xiFm5re+eeh/4rRjQEox8/ctY4LTHiNw/jLWV/22sPfjNqB8BfjPWsvzlmGgejPj9S5gzyquYlcQ/yldvR/HxrRfGGd4sHag4ojb5TA3P4RYmFHb4fRW9zwJ4Jlf4A81z3CbB1k/yxHjgycHDwpO9J7RrQnvKolkRkjAMAynbguFre8whaCnEnMwzqCSEZNsgNsk15dW5mis4MWpaBNFCo+DwdEVpVZCk7IbEso7HqwpFZA6BLoeXXcmXqaU4vbrpxU7M1HTrgaGaf3rm90clUNEnE7A5tA/B4WjEEwgzm9W2LRqLeQRgoKnyZ4f/P8dozfP/Mv182IRpUm+eQZTDMVO1fZtKsfzCXrh6b88mPYVBGKCajZomqggUzWhxBsZFcKHG2hTFF4+om7ep4oz52ZUCTlmuPG9+733arqGI0nUdJ5uOduMoTU97D+5+35p9XrHPCd8FGg+LTcNqvaLzLd3dFYvzNYuTNSygSCE0QusE0YBPgfh4pORCuUlcvXdJ/5NPyR/sGW96FuPAEuWExKI4xqGn7EaytIz7geXCsW4961PPYtOAs3avkVJTVubUKqxDH9WP7TAFeDgQIpMXXc0aTxY4LSkTvEDjCG3NDI3mRecbR9s2SC64YhMV82WOHg79t91B62NbU+7erdj9u7f+GtYy/Lvqzy9jskSw1t0nMGPRH8ZAz9vP3f8E+OcxXddf/yqf02NyR4c5n/yn2Dl4g+VBvsmhBXlb8CIBLT2oQ44hkk6gBQ5ISQ+mohZzo0dsjF2NZ5XZg2t6LKlO6DHDvtRZuaYjB0daZs4/dcrL915CHinsHHkHSWBfIuIdvih5O1ByROMAGqEYm+Ykow6yMrvSCx6nikqmOJsalCJ4haU4muxob3q6ISNJeRoHEKVzwogjKRRnD1aUOneo+NkPi6PXxzPdmOlbw2N1z8w5R8f7k3kxVA7gR6pvl0x7XwQpio4ZiTYhGnLANWJWDDNoOj5+jkkJNjGMZQIxk6ZrMl5llkkR1G6f4oDMhypTVFDnkdBamHEeGC8jD9MArmW9PmN9skZF6ONI1kRMhaAN3bqje3mN00A/Dog6utxBdsSS63Smte6MYfNELWQPrmtpm5ZXu5abJ1v2+z2sGtypx/sW8YWQV5yn+zQnK8bdyGIpnJy3NG3DsBfLKVw3FG+TlmMccV4IpSA3gl5nrt57yHC5h6tMf72jv7om7faUGGlKthb2buBmKORckGzO+v31yLJtkXsBOXN0ZwuKVzIm0jdHL09bGcwiBuJrTxhx/vBeO7I4mbrTBrSg75USMstzT1k6lqcdS9/CmMAJzbKtzJgx1FLPW0olYycGVZ5r79/W11m3AOu23gf+l5hWK2BM0u/C2ng/gbXvBPiFWKv5eaD192Dmo3/wQx77nEP773McgNN08d5iPr+/E7N4UAx03Vo8PF8vBtDSg/M5UBe7+ZczM2NsjDEbhRprMts4TGxW5VP02cU8I/SqBq6CQ50ntIG0Etavb3jtZ7/O2b01i9xwOV7Q7/f0Q2IcR4Y8ElxAUyENkVISJY/m8qgFLwVxOm/tlFfn9MDPTPonVQuqXjpYOI8v0D+9pBHPdhxoURonoP6gV1Mli8wTmPaQR+wD1XOrgswP38fMfyOzKOoIdB3zfjOrpoensv4uJRXyGM1YNEbGPCKaje1TPUwTHoE2e4ypHWh2qFDB2RFNJyI0PrAMHQqkXBhzmlulhWywzXtEYCkGslOfyBRQR+hawqKDAJqUnM1OIGuh1x6nDXLm8Pc8Za0QihloKof9IiaoL0BxjtJ5nIfh7T1PPv+Ioe/pzhyt93jnoIXmzpK75wvu/IAhE6fQVEH+uFXioIiHYTsSooNUGG964tsj/fs9uyd7Lj+4IG57dD9SUiJn+9epsnRCcI5SRgZXLV4LOC3E3oYBSunQ1hGWwWZAtFSQJebYX9uDaT6olc06tgo5sleZPk9LMT5yHBU3FNrG05y2NOsWSVQg69mcrblqPHmYzH/tcUt9/Mlodzpnb+u2busbqTPg01iw86yGrN+vMU3U78Vain8rFo/z54/u7zEj03cxf60nWBTPxDmfAH8aaxG+xQFo/REMfL2DBXBvv+mv7ONWLwTQ0vn/Oi/OWjUeEwiY7EWrJWQFZuYrTnVfPwjf6/91GmYPFC/mW7RoCG3AeQjLhtPX1px+711e/YHXSVcD27e33Fz2xJuB2I+UmAzMFdO6lDyauDin2ta09tI0XTeNzTs9XLkr1r45sEYmFEeFUoTtxRX+5IROAlETyXkaKUhxZvkgh5iiuammRwaRU3vNVMwHDyumVt7zi5qbhejT/sp1F4LiSNZ6FQve9moKn1xgHDNpHFmdr2idkG8iecw0CEEmN3zjwY6fdmL5nulQ6QyL519KCLgm1EOZ0ZxMcydAUkouOGcasOAcOrW9SmLcbblpWjbO4T0UZ0ckFzETUQeuE7qXF+i5kLtiDvT19FOtHzC1dWrHstI6UsDBctmx7DoWbQcD9Pue0DiW6w5cobhMKY7xRolbR9M2CAmXRrQvlO3IbhjpL29IlwPpwcj28Y7dzZY0VhCfRvOzSuZz5aZ9Vezsn3zLpisPTQNx9ORxQb7JSLTsw+gCcnQVWsSYxQkAzYMIH/aenKVxpV46FLRqEjUVnHhEPDkWWu3o1p7l+Qa37Ij7hHoPsczviwnETq3w27qt2/pG6xcDvxuzeTh0bqz2mEvJxGRJ/dlxYEIz8I/X372JTR3+HAwWPAR+PfAHPuR5/2r9uq2vtl4IoAWgYhoaqZNONm1nzt1KIWFxN6r2vWqxQOIafUJtSVBBiKF1BwRUAmHZcPf+iuX9E7p1iwbBdZ7m7or2zpK0VcZ3M8PbiXSplBG7CnewFEHEsgs1DZa1eDTNaDjPQE8jBqqKzBxc9S6yJS3DLNTeDSMqhdAqTdhxZ7lhyIno4NRbZlyfFS9K68F7Yx9ycfPzOVdFyxOjp6bB8b7aIVQ5ed1YKzexQkdNvgJJjM1yRUjZGfhS8NjrTQrbfaLd7Xn1B15i3XVcpAsuhoL2Hq9xbu8eWLPnWD4ODIpOk5nOFm0RIWlhn1NtWWVrnaqJ1H3ONvFZeTGtgdEItM5yGfubHcvlgtW6RcQz4IlZkGih4n4RkIXtEzcNAOSKZo8hgCqumNu9FvPFWr/esTy/D1lRp+SbzM27N3T3O1Zn62or4mHM5MuR/LjHny2IVwPDoz15PxIf7ijbkTxG+u2OuB0oY0RSwseEK6UmH1RIfcQClQnviSKSUWet6ZR29IPS709o3o/IXli1S5LfoW6o3mhCdhOLSAXj02lxzCZLBd4TU+wA6w1LzkgqjNs9btNAakkR0qgEL4TTJX6zJF33SBYYM3L00NNVx3SxcVu3dVvfSP0nWJvwV2Imo2Ds0r8N/BUsfudnYpE5LwG/APPZ+rGjx5gmAR9hk6t/tt73AfDvf2s3/7uoXgygNV3tMrmU20Rg8IE2NBQKqpExm+lk0QPDdeBr1MTOzuEk4KQB51EXoAks7q45+967NHdsklCzgjg6WSBbz/WjG8b39owXPWNOSBBccGiy3D5KNlarTJ5ExwvUESOjB8HvRArU31QXe2b/Ky2Wd0fK+BhpWyHhQWzK8XB/gyh+3k+CVjsFw5YmvD94hU1i7rqFzy1qB67LzZtutgpyZI8x8XNT/K/QiB2X3aj0qXB6b8HZvVO2j0GHdMScVWBZga8C1OMmbnbvenabKs4pVewuGCiYwOCR1G7eqbY7qqmtYlmDY2R/s2Oz6WicZ0iFYYzEXUHigoVzxJyMKRwrUPXTPpm2V+ew7YlZLYB4kAWQhNhHNCqexgT1cysWYwMz0MM+7xnf3TI82Jt4/XpPGRI5jgz7PWkcK3Av1nKsZ4dQRevzYx695ufOOrCBjKy1VZoUxwSkn407el7VMzNj1H09sYwzQCpVOF9BcVE0GatVYiYOCXaK9hnfBk7vnbF9siUOfW0nP8uYHc7n27qt2/rGaoW1D6d3tWLtvz+BhUT/IGYSel5//xI2gfiTfGle4g0GtM7q/f4MX16Hcltfa70QQMs5YfSemCKtRmuXVI1VFEdBSarEYt+XelVc1JEEgvc0LuBUrIMYWrTt8OuG5l7H+o0zFus1bbNkHEaGbY8kIYgw3iRyiuz3e1xS/NLTbIK1aG4seLhsC2WvlEz1AJpE3kcA4AjMmFHmMciyvz9yCJvZL9VCHxO5h5NF5CQEPFC8MmqhFPBdQ2isHTYttaXqf8o83TVtG4BN503PfvykAjbO7yZLBfu9QwkTI6auTqiV2uL0tX1ljFMaM9cPtiwWgZOTMzbLyPbpDQWPqYeeZUhQ8N4TalZkLoeQYXGuducKWZ1Nq6kxTE0bSH1iHAeCN7jnvQm3p9c2Kb6K2pcjs91uuTOcsD5tIMD1bmB3pYRtx1nucDlQxmh857IhhIACOduXoDinc1v2GECqgIqSSkSBcBag9RR1EIQsI2kYKHEkPdyyf3pDfrijXCbKmEhxoJRITglNCakgSyizBcYMtJh6pgdoMuVHMu+FijwDyKlQ7md2VzeUcTQdXfWXc1hr2c1t42ffg8KHfKzO7UO7ABrHSNdGVq4gQ6JcjeaCr5780LHwyukbK95/v2F/ccPKgXe+9tHtsbQiu9uP8Nu6rW+0voBNDP7rWGb6+9jU3x/F3nC/EPifYp5X1Nt+NcZ0/cnnHmvyu/oRzGLurH5/O3DxzagXA2h5TwmBMY74UlCby6dIqnqZyuC4BsGDJjMnxVRaWTyEhtYHmralubOiOVkQTlralztWr53gU0N5DPlSiVeZMhaaoKRgVgBOoFkHwh3P6rSDAv1be/r9DYkRLTK35Sam5rD8HS3F+gyc4qAkn34+vO4gSsww5gIxQYksXMCrMIrFr4h65jH8KiFy1RC0YPLEeWuOLBNKndybwriPI28O2/7s9ngmoZabG6/GiNXJziKUUvAhk25GhqsRt/Q03gJXVH2d/iyHl6rWohMRUs4Wyq160Oocs21FKbWF7JzDe0+3aGuL0Shu5/xsUnvY82qvF8GJknJht9uxXHoWyxZFudkOhMdblmd77p6cEYJHS0GyIKFGDhVFiwFRhNnqoR5ZqMyXitK0LZlshqfBMQwJj1iuYnHE3cj2/QvGRztkF3FjQVIix56Uoj03ipvB1Ye08uT5yVkO4PgYKHnBNZ5w2uLueuJnB3KyfV0mHaNahuUEdL5k33/FMiNSLRYgnoZILA5cQRYtaaeUJbSnHr8MNkTgy+H812eB3C3Uuq3b+mbUZzFw9R9gk4GTp9USaxWuseiLtt7+OvBrgD/HIQtxKsVaiH8J+DwfDaPlgf82JuL/+HxGvBBAy3tHaBrytjIxalfurSt0wSa/+gKjgHqHkwZHML2TE5brFauTBe06cOe1u6w+cUq41yGNBfmOw0j/cCBtM7vLnuFmRBRCF+bFx6087p5n+eaCZunZv7en/6BneNSbR9TUUjtigQ7NJnh2MamX7s+tY5M+Zrqvw1o1pUDO5uDtRQkI0TtC2xCcI+eRnD3OTSHM02McTsQD9DuGffWZ5PCcX7K2TttcW29zHmK9h/kp1Z+UWT9UhkzqM27REFwwAJTzYVuq4ac4IfgGFwI5Z9Iw2jSc+ArE9FkwOlt3KOMw0nUtbdcSh4FJg3a8aB+g7vR6Cmjh5npL2xQ27RklJ64ur7jO14wkFndaVk0g5kjKmZCLsZA48zlzoEHmsOUy9b/qP+LEAGCwychclJuLLd4Lq65Fn2a271zz9J3HuG2hLVj+oGZKivN+mqZrZ+x+OGIH2CVHZ9sxVj56zc4JTWjolgsW6wbn3bxvpynd2VbuOc3UTwe15OgLbMq31BzKXBL9PtE0Hayg8Q1u0bBYLlhvVlx217gyTEj9uSf+0h9v6/laYi2cL2DtoNu6rS9XP1G/jmuHGZZusNbhS/X2L2Imt69/yH3AbB7+UP3+o3iTOuB7P4Ln/dbWCwG0ci7cXzS4xZLt1owWE0pRR9ZAQeh9Qe93bF7asOqW1gJpA93dNe1mSS4RDZnVG6c0i4ZSFE2CXivjw8j4eECvCi4qQTx4pfhCaRIsBXnZ035/x/qlE97/C+/w9Mcfkd4f8WNGSgJNQJkXuWfYIJ2WorosT8DkiGF6/noehVTBWyOFMcHNbuB0sWS5aBiHQMrQtg5y4bK3ds26FZaN4J1DVMjqyWVqvdVYoUPH6fD8z7WLzCKDyhZNbE6BfKTkmckIy+JRUbx3tE2DDxCHTLpoEFqarqOPIx6zDChYKLSqMGQFPD40nJ4u0DTQ7/dGGdVcwynkWlWr0z/EGh8zLfBa2bB5/3No7ZXJlqDY9g/7kZuto7sz4oMxXtdPItnf8PKn97Snp7gmUHImDxGS4LKn7BL7YSSnTNM2uNaRQ6ZdtnZe+WKHshfSPjGO0fZPUorPDDFx89YFT774hLEfabIwFoWSTX8l0PjnzoeKkg/w1tWf3ayzeoYirMh4ErRrEYaobJ+OrM4aTrolY3tTJy+tnWvuIw75EBbry4EtAcIRqLvWgevSUE5OWJ4sSdc7xpto9g050KdEswmcrU+5Xl5S9gWXYeZdZwGj3AKtn7bexBbH17Hpr68FaP3XMRPJ3/zN36zb+g6rR5gm6/lacWC4Pqw+yjdnBP5PH/E2fPPrxQBaKRNKphPHDYFCNMiijjE7QtuxOF9w/jPvs3l9Q6MNcR8pjbB4ec3oCvG60PiGwRfG7QhXICOwK/SP9ozXI64HXxz4QHYZnNKsGsK9Fv96y/LlFWTh6rNX7N7eEvqCV0VIBj6mzEA4WgAnpmpqhtgk22E9O2o1TtRRva0UY6UaB2OB7X4gxpGwbnEuAAXvlM4HLvqR3VhIRWi9Enxtv02xPMVc0M0FnIPtAxzYDA6r5uTrVUqh5ApiSkGK3W5tszo3KYeAZieONgS8F3JSxiyEsGSxHhn7vd3n2BKgFHKBGPeoFoY6QelcsEdXreHctvdKnRhQVcRJBVjTBNxBw6RHsGRi9vI8pFAoxSKPQhNYh4bV9ch2yIzbxLiLaIamC6QCREViwQ1CuhjJ/YgmoYRM9onkErnL6H21jERX27geck6QPUE9vvH4RtGxkPfRZiNVSTrF4Cit2OudmZ6Z0TvUIZJIngVa0+nEFJ9UW8ilMO4jF49vuHM30PoG5wXxk6DepjQPp8HR4/10JSavagRyBUkDhbTqOH39lHIF8YvXxD7TXGf6sRBioRXPsm3o1ZnJrcI8Rjw989fStfyuq89gjMQf/Tru+w/w3Rzee1vP14cBlm29/fvqz5/nxcohfPBRb8A3vV4IoFW08OTygv0AGkwd5FygbdvaDlmyvHPCcnWCRri52VHGTPJKcpApuJ1D8eye7PGjI/SBEiN5TOy3W/JgwcKShEKpPgxCWHqaVTD39CeJx289pbybWY4dSG+ThtPV+BRwXLdbxCb/iv1gt82tkuM69G1EDAgd3OMLjXOsQmA7jlxvd5wsWoIIjTcfK+eF+2vHO1eFi72yCZmwoGp4lODMCV9chSsVZT0rrT5sy7PkVm0P1oVUZRLWT1tszFtRNbsH5/CN4JoGFUcphSZ4lps1aRwoVQhuOM5AUNBEoFAExuLZ01oQNQmhurKLbfuMqVBj147MNOePjEl7Nje1DgzRFBZuujRH1yx47d4pebHk5asNftVw/84Sj5DGCuJyQceC9oW0zzAqrjiGoTewgWeUHr8Q/NrhF4Fm3dBIoDxR0uNMvswE9XSuoVFHU5zlaBZjnGaDj2cm8Y7bpscU5LPB6jIfUyoInd43h3ibNGauL7YMuyU+1/tMDKCAiJ/31VeLcZ7fslUXGJ1wtbvmNKxZv7Jk2Cd2V7EmKTqur25gzHRdywDViq0+UtGDuP/jdcH6Ta4//Q3c90e+aVtxWx/X+iQW5flfxd7h/w/M4f3mo9yoj3W9IEBLuU6ZiJCcQ1zAN4HF+YZ7L52zOtmAC+TexuqfPL5g0S5MW7UbjCkZCpqFJIlGAyEGxqG3iStnESSCkFIiaSGEFo+j27S0G0/WSHzSc/XFS8ouoylBjqAZlRpsLTov8DN8cZNtQ5WnC9Vtm2c1MFKBWWUTnCqq5hflURaS6EXZD4nLm4Hz5YLghXF0dDjaxtEG2I+ZYSy0vqFtPF4KXhxehKJhzkR8TorDBKgmo1Jxk/7KBMsGtsxvScRyAgGyqoVjqxJVCN7RtJ7QtozO2z52jhCEdtkx5ATZ1UXePKEU5iEBg1Op6sFqbJIP4D0pJnLRZ7GH2j5XnmURnyk9GKVO/lOqShwT2+uBO28ETs8bmnCKnDaELhAHs2cIhiTRQUnbSLyJlKhIgXAaCCeNHVnf0p62uM4hjcw2bb5xjDoSx5HQNJCE0o+4VFjgLaIJqkec6cemDEmtor1jd3YT5U+eaOVggDtlMB6kbJbjiCe0DSKefj8iKrTe2SCD2tShF8H5eqyPzt+vjtWyljEIXiMS4erhJbv3N9y7f8qq6di6Adc6mkXDcK14hNVyxb69RGM1Op3anTa2eQu0buu2PpLaAP8k8CuA03rbP4iFU98CrW9VvRBAS4EYPGG54M7pks2qo+kalqdrFmdrSi70u5Hdo0hKBQYorZLHRNzvyTHTjyNjThSBVbti3a7oFi3dckUukf1ub95JVfuzu9mxbtfIqdK9Kpyc3+HBX3nMzXs3+ARSMqoRqjmqymQ8alt80Ggd0JTgUFFbUI8IipnPUnM2L1rqolr9okphzMomCCXteXJROG3gZNHRh5ZCR9dkTnNPyntSHhjHQvCF4AXvCgWPqYAcuVSn96NoIjkCegfAV9d3OWK1JufwymxlKtBCSQS8c4TG45uOUmA/XNGr0jaBdhFoyoI4DmjOBBHMnd/8zFUVh/mQSZ4MVYUxWWSN8w3eWczPfGLItL+n/VlnIfXZPzGQVVktcZSc2d3sefTgCetPn3F6viCNPaUTihTizsxvc1JyPyIR8q5QesXh8OLZnJ/QvNZQ2gILcAtBGjGz0lIDHlUpKdE2nlXXEhTyfsRHaIo36w1xlW3MFDGG1Jz3p38PAFhLnvMgQ6gRP2It3kPr2V7n5BQfnGMhhcurK548vc+d0qFFkRpRpc7jvJCKzMD1iFObTtMPL6ECrYIrimTl+vEV23dPOZcOciY0AQ2KBGHRLmjF4cKSq/eeEreDHedySE+wQeLb3uE3t/63GLf+mz7qDbmtF7IEO0f+buBv41mN1o8Bjz+KjfquqRcCaDVdwyd+6FO03ZKl97QCOUaGAk+e7kh9TxoScTA9US6F3dUOYiSNZj46pkRUcwkfRyUsoGsDTQgWvBsF9dkcqyNsd1vYKymfAkIeM9ePrii7SCveZE9Tq2a6/j9a3DOCFENRU5DuHKzMMaN0uJNqnS6sU2cik/eV6VhaMQfyVBLb/R4R8GFFkgbXNqzXwphg3GbGAiHV8fnKoE1EmnMyPx/qmDMIJ+ZkkgZNlgbztKEe1j8nlDoJaV5V5gbmnENdYMiZUSGWwpgSxMimCawXgbAMDGVkLIr3BrbIueZPHvaL8S5CyYVUCl3NMJydXZ+vo33FrMyqq7dW3VQB0xsYM9Tf7BluejZ3N4izVnAZMiVlY72GBENEk0IyIb8WJeXIuO3J1xE5dzRdg7TGBJZc0FR5oeRw4lmdLelWDWXbwyhmYuoUDdb+NX27w+GtlViB7LPZRPVvnE2keh8I3lUtW7VpqIBVRM0Ljfr4RSkx8sH7N4wh4sdIizDUFnIuOgNRGwi1oYGDguug5VPqlOIEcg3rISL4ImhMDLvIOESyyzTOU6KSXKEJAekcblEoLfQ3ZX5lpj2rViUfeoBv6+uvjE0q3tZtfVgF4O/HMg+n+gD4ccw36zav8FtZLwTQatcL3vhb36QojBc9w/XIfjvy6GrL9S6yItGgSBZKKsQx4XOPywOaHVkFR6GdWJisXI+RlEeSDGzunrC6s2J0kf2THjIkEjf7LTcPb1i0juRHbt6+JN8MQINInmM6VQ4Nw4M+Rmo7UHHeWncAxdUg4lLbQHoAWkKZHbuVUoXKFh/jSfii1b5CuN71DFlZbRq6ZQAX6BYtm6w8Hkb6mCCWanal+ODnga66pKFFKeXILd5NIdfmQl4qwJrYEqn3VQGcUmIil4QHtIrunXeob9jFSC8wOmWr1vKMJbO6e8JSViQS/bandY6gHs3pmcVVEAIOJ44kSpRMTCNBJjG/1AnE6Q515deJ03l2oTZgUKN5SsYFRXDkPpJuIpJhsWqJTpF9psRo+KxPtHjimAxM+MowIeRtIj/KkMEthWa1ZCYK9yBRYRC8NDRndoU4biPswOeABpueFHVzs9kpIIfkTqRqrebWquDmVqKQ8sQEOaYMSannua+3xQJjEnIWLj+4YdvueXUcWTrH1jubbi0Fz9ShNM2dTOf0RLtWfzZjNg/M2ZTlCZ4Gh8TC7mokjxm3doQojCMkMtIpdJm2EYZF4SYXFkVoj1rXx/E/t/XNqn/+o96A23qhK2Gi9wloFeB3cHvefHvqhQBaORYu39nhvUOyEkfTIl1fj1xd9ojP4MGroDmT+4zkPaIDqsG4EV+Ft0DMmZgjRRPqI34hLM7WrE5XuBK4HK65/8pdVqslm7Rm+OzAxeWO4Z0eN2b6OBA00x654k4iZhU9AluY5sRPwuEKvhC8EzOMrJlx5vrtabtg+XmqB+NKNXZiui2R8aFjN0T6fMWpFrxb0jWBxaLBh0CMhbEoba66J7UJySlVxyHgDy7i9UUwUxdCFY1rJZBqO0km8bXW6CNzhzetVWaMI/sxkZce33WE3DH0I/ucOWlaCI7z07ssVksevv+YYbsjpUR7aKACxuQ5cXgcBWPNSkkUcdW64rBtdi8DHzPQ0cNjHanLTUcniqgFL7usXD28RM47WK3wzqNPM/kioqnQrTylzQTnKo5TXAWVZV/wycHOsR/2SHH4dUMcE/1bI/nCWCYNhfbUUzL0NyM6cMi79JMeycCWFK0apcouOTt2xiweGblOr+4Z1//peCozI1iPaVaB4tjfDNA67jvYuGDTnTmS1bR8c+fxyFjXeLLD6fGMBH4+HwwwenEkiVznK/bLM07u3yF9UOA6UaRQFlAah18HupMVfqG4cTTbDT1YUtyKtG7rtr6dpcCvwyJ5fgSzfviXPtIt+m6qFwJopbFw9SCybDyOyJgT427Ax8RKleZIb1JqRIuWPC8HHsWp4Cdpj0KWguRE3kf6yz3eefwi0LYNGgrLkwUnZxvWbcf24cD1+1vyztocKUccxQKNoTbNOFz1C9QwkwlhmW/XZNngzVx00taoas1gNAA2gapSI4WkGHthMumM04KnYcjKzZDAOZZtoAuO4IRl16K5oDlRFFIpaLaAZacmnJYaQO2nVmExc00tJur3IradVCf3qQNXAYzmSSkkM5k0tUdTSeADzbL9/7f35rHWZelZ3+9da+3hDHf65urqKne7aZuYyRAzCBxkHDBDIJBIEBScIIUEKSJAlEhgB2SUICSIkEOUQQQlRGaKAwGDgQTsMApIgsEDuO12D3Z3u2v6pjudYe+9hjd/rLXPOV91DT3UN906T+nWd+++596719n77P2c533e52VqE81ygR8ilc3qDMbRzKYcXvOcxgEfStZSUVHsKE5JIolsVSuFTQ7ZqPLs3I/H8ub44I1aOLIHKa6vIsiMz3e/WLM+XTCtpmCUMATCwhMXifqWITnFmkzi6rqmqpocmBoiKSj9/RXDKsDU0l4XQsrql60tSSC5hI8e8ULyAZMEg81r3mW6WSpkzGMbya2UnLJtvTevScjEeXvyyc73dp4zTRgMIcGy75nbFrGOZA2VC9mvF2N2FgrszkbbjSHR8dzeJOvmEuhG8SoKpBGDj57FuuMQh5ADfRMJrCNF8J3ibI2rOiRH0JWcM8DII8d1jz32eBK4B3xf+ajImVV7PAk8E0SrGwIf//Sr3Jo33Dpp8DERVj2TGDiwgtHsi/Gxz4N3U0KNknTsJcwLcakEOJK75kQVGSLhrKeLghpwk4bZccPhzTkHh1MOG0e/Niz7QIoJJx7r8o0+akBKR59IvuHsdsBtC3G68Z3HlKitxVqDSeBTPpmtdZlGxYD3xaCOYMSWtSgRwRmDM0pUT4xC72G17vB+QkoOgzCb1Igq/TqSyGN8okZMBOuUuhIqp7lUaBxgsjcsRDQp1oCxpnTa5xt4DgktJn22g52ttUXyMNR1jZ1OMM6QnJAqmM/mfHhSkXqPiQkJcL7uqJ1hejTBhwOWCH4xZCIjsRC3RDImrz0qGgu5ElBGw/hWX+GRz4u/bPRrbcW4zbmQhwhJJp19xCwUc80QpzDUAytZoYMypWIyrXPwaEiYekLd1mgFyfdIFHz01FTIAoIZ8OIxhw570+b5mlqIdQexTxCzG8uIZLI1YuQvyOZfGWu2Nq8ppexB3KiS5RiNhb6NqiXk6BGNoEptBFtVJDpm1iKmYUBpJStdQ+g33jzKWSuFOW9DTEfFc3yMlnLs+J8BsdQY4lq5+MyKG1X2q6WYVUmpK4jQnwboBUk+jyoq/jqjIM9SZM8ee7wvsUuyPkI2w5/z1ubYPb5SPBNECwFTQSIyrH3u6PIRlxIVMI5lGb3OUlSQSH7Db3buZWP+o5AQlTxYtw8MbkAvs9GeBpppgzhIJoFRYkwl52d7oj2aObS9GW3DJHf/7s64k918hc3Ndbt/24HKo98430jHEMqxpb+yhqayWBGGvmdtFWscQjZJB2uLspe9WEoqCgSMnWxCNnCDo65BU8KgGEnbu+4j+ziSxlLiYqNtIMZkj5YZ1ZxE13fUAnXrSEMialbXYkpYA+20JYXEyitpkNKFp5vF67aWtSFUaWRPjzyNccsB9G0uB0opde0eN4WkDJ3HrjvkqGV2Z84QPT722NYSQ0RDRCN0645koZpXBAJV3dDO2/z4YUBjTTNrqA5rZGLzWr0n9YHgI0Pf03c9hFAM5zkuY7vPu9EGuVT4yCHYlA7f5GIqBHicNrApVWOoa4Orp0xnDS+1PWYJtpOiXlqM2Oz72jxJ5k1Nf6Nba/fvjex13GfZPYExCbqLjssHl5i6DHQn+xUnkwkQcc5hnc3HfXdN+uiA9T322ONp4JcA/wnZEH8GfDe5pLjHe41ngmgdHNR847e8RDwLnH7inMX5ErpLLDnFHShZQDXR5kyrtVc671GBxmW1qk9bEmbHEhiJGD0mWiQINjlmk5b5fE6UwCBKqAS1ASMRSYpJisXgKJ6Wzc1Q2LCg3fLNqDiUTrCUEiEEYDQ2j4bzTBXGd/ejTyalRIgRH0P2VZXOs9okjMu/fbnwiB4wnzuMQO0s2tQMfU+Ij9TeQARTTGsKxf+TS2lqTC69lrymPOImfz4SxBwNUNZZiCtGMJoHPcvUUE0dvlKGroMkJDVIImdCle7BJGCbKbPjCmsqutWK/mKFGEPwHpfASCJpzMOkxxHIaaQkxQP3Jj/P1kEkb/HZLgnLW1VzflofOmpfcXB0QEyJ7mKFrpTF+SoT9tph54rvPUPyyA3D7KbDn8GwHuBQqE9q6oMGaU3O0hKDCRW9iUQTGYLHB08tmZxIqcfGkp/lpJBVtiOFdlcnxW/46Do2ExdBI5IkE7RyPH3KsRknx1OMrblcLAghK4dGhcpWUCshxpKltdNrKFL8X48+v5tnM+WE/1H8FMleyUohdZHVw3MmB1PagylRc1ewGEs9qZmfHLBaLFn1HnzuhtXk8RpJBPbYY4+nhZ8L/Bngo+Qh0t9KHt/0t8glxj3eSzwTRKtqLdc/MuW1H3vA2eWaofccqOBsDmWE7LVBLKImX9C9Z0iJaLIC5ARCcfM62HYMUshEiBASBEWSIQQPDrRyUFdUrYUOxBePD5LNw4ydb1sl6y0hoyaQ4wEike2suuyjGe+c299T1K9SmjTWYUo4pCoYjVQEVIXeG1b9gK1qprXBWSFZgzd5FuJuiUnKDEERUwSIVAIuyz6MN/iiwqWNCqeb78mmzrTtuDSSiZaZWNy0wlY2j3kJYCIlb2mUGqXsQ8KJZXpkcI0j+ET0iqstGgZiiiW4NKGSu/TYJVqbqIedbbAZC7RRXjbf3WKHfuYwVpTks3qVUmR1sUZiXXxzNu9/UpppTXN9wrrt6FOPd4H25oT6eoOZOUzJ4kqF2FrNZMXUjsmsJdglSO4KBCWluHm+MeX4KDCSp81eSgnAHQvUbM6dzfEZfV86qoL5XGhmLUriwb1zhouOKmU1KZdPLdZUpARb513anIePzD9U3WGsWdXcZLHZfDwFgymqVuoGaGuqsoIQleVqScDh2orZ4RR/tib2+Q1GSp5IyiXPK4UD4I8Dfxb4R095X/bY4+1wnWyG//fYjuD5EHnkE8AN9kTrvcczQbTUGVbOcX/hOb3omKI0zmIE+jSmWWeVxBpTbmzZlJvEFGNxNpOPN4lcTkxEDKpKTInBB+h6WII8VOpZzdTOMcZQtxXRGUzIXpkcyjSWacZmfNmVU7blnkLENjdH2WzcxBRskrG3Dyjfzz6oxlqclm45LaGe5BJoLGna3XpNSgl3eERT1xhTMolKbIMw3oNHf9NWHdrs5yZLaZds6aYk9Ih2pNvfOap6xuT5gc20xlR1vpkH0EHxacgZYSkHcSZyy4AK1G1L27ZYqTl/cEYa+o0va9yfsXQ4mrV37/m7T62USAs0l8PGsUdjR+jIF1NxYNdGSmCoQoLK1FRUhCEANbVrcqCnRFAwraG5U7O+6PCXHplZ5tdnuKOa4CLJ7B7iVEipUDc19uiIdX1OWg4YMTkBf3PUdzXQQnA23jPZPS3YPAE7P7E5YaCsJ78OmumEZjphtV6zulhAH3FWyjFI5bgZrDXkkZ36yJuARwqtUp5AzE5ZN8OUNwDj8zqWTaP3Wckik/nlakkXYUpFO53QtDXr5VB0ua2n8WohAP8Xe5K1x7MLAf4j8sDxqmxTxtxB+CTvPGx6jy8XzwTRSlF45QcfcvGJCxpNOAkMKWZPVvGGJCJVZZhMWqoIGiI25W4+Z3O5blSIBDYJ51EVnME2NbatiQIhwPkbl1SVI02XhC7fLEIcaCSVbjglSrnhjzRmhyxtBjeTU9iT6iaANPMSw5jHpGrKWJUM2fzWbcHLmnwDTDGVUt74d3LulqWoe3Gg77qct2RsDmQ1dqSZIEIIkbDqMdZQOYc1FnTsgCym9xSLmZrtDX38fOQ6KruMIn9uDa6tcgkMgaohGUsXPathDT5SUQGGNKRieROSgbq2XP/ATebXJ7zymZ+miwI4bEpQ0vjzH86EJ6qwDUTfaSAoJWKDbsYla9nXhBJFWMeQB0a7xJGbM51OsKZGB2X1U0uGswE3WLTKw8LVgHEWrODVswhLOITqqKKa1DARgokl8oKSj0XOyBLJKfBDQq2SKhiip1ahMoZcCxxJ7HYGZW5WMNtoB92eI1saX1L9XTVqoJk869hVadAYWTw8Yz101CkTUZsGUgiEwpYbI8xax3oA7wcEpa6q8vtGYlumRmoJsh27C8r5UWHoFc7xWIVGFe8rhi7gu4CbTzDTmhgU60G8wTpLezhhebnIY4/ILYhy5eId1sBffdo7scceO7gG/EKy38UCPxP4PWzrPQAd8F1kM/xnnvD+vX/wTBCtoRu4/2OvkO5eMkERDfiYcvKQbA0rPnpqrbFiqa1FnKMyBjFCr5FEzFUrM0oxxW9kBds4bFORjFBXTW7z7zyv3X9I8IF1PyApUmvWxnTTMfYWpcLRB1W+jCMR2YxT2apAo1qwa3reVbUKTRjdRLmbbqyW7SghRjR/oMTgiaHC1DZ7pkwu8Yhm/SKkRAge5yzOji+qrJ6MSfAUT9aoAI3q1WZ9ulVbsjenrM0KtnL5sSkhYkgKfQhcrleYQZk6waohdTFnhJXnw4dAsgOzueP2S7c4tffpL9ZoH9CYFZSYEiHpNoBgFHJGfxtK0hJqapSxExQKRVMIKF5zzpgmy8mkYTKf4yU/aHgw0D/o0V5JJge3jsoblK+N0J60YEtZ0SqYEjRanh/JSSOEENEhwRDBCK61DBbUj52lRXktC8pK3ViyK4b44p3S0RS3aaccldFMygzkfaH4Fo0h+UA/dIQw4KzdKmkxEbPYhjMOMZa2BkmZ+Ftry5+Jb1KvlNxUkDVJzZG1WCxKYpUijUoOdggDQ9cxrAdMaqlqx2TisIMSH6zwMWAqR7IQNG7O97d6We2xxx7vJV4G/jy5XPh2eAj8SeDyiezR+xXPBtFad8Szh8wZyyi7ak9WKowRhsFzGRe01STnGJVsKgAjijNZ7SAXRkilPOJqS31QYyeOGCL1rOXgYMryYsVnP3vG+WVH6wIHEbxPODP6gN5cutpoEaAjCdjd05z8vYk+2v2JMYRKxnjIRwtKptxgNe/1WJB6tHhTDOwxphyWagyGnPfU+XwTq10mQ0YMztid3/ImSO5+051v7z5KpYyxYSQFBmMsxjlsVWWPVIIYEn0f6C5XrBd57mRoApVUmKg4sTlotnSSrhMMyXAyP2D6oiOerLg8W7C47Ol7T+8DK18XNU9xlEBXIibphhDkymH2IKVSY0xiWIesmXhjEWdppjPm145pZjNWq3Nq47DJYGIeDyTC5lxyTUt90GBnFa6psa3bRG8kxvT2R8u+kUgInugHajE0c8fk2oThwYp0MRA0q3Sb5Pei8D36jI8SYinJFRVxN+NqQ8QBYy3OWBj9hymiMW6M6NsSMhsCPYRsxj9qDVIZhmAwpsr+sRJZmhPnR3Ktm/NW1G7WnlQJaYw+gSr0DGthtWxgVVHHCe1kSgyBhxcr4nqgTglXOVaa/Y8OsI+od3vsscd7j88Ab/DOROs68G8B/y3QP4F9en/imSBaksDhEKuI7sQOIMWGk5Wh2tbZ4D2mqUtRABRsIWRqlKhjKUkwDuqZY3p7glKzOl/R42lcDzZyazonLTzn/UClgitlKZEcLAowigy5VFQ2qJDSNkLBFmXFlJtJsVVtK44lsl2Kp6wwmLxKBdGE0YSKBUkbQ3TaUXKyQpbXq4XQmcoQQuL+akAlceOwYeocLQ5nLVKCOLWoFGP8wdixxlhyHUNLFXL0ZC6hZsrqckyAq7Bti2marJx5JQ4JXSumA4lCSJFBQya3k5qJa3FqiMuBwffcu+wxFob+kuATJiZMVcNhjYvKxA80KSe1hyHRrXpElVoSthCLEAMxRZLJ5M8Hn5Ud41hHGDQSVGnbCdc+eIdqPmWInhQCOnj6IaEp5jwxFFIi+kjqPWEJ1YHSeIvGrAbG0eS/q0XaHc+eA5xBXR52bo4tOlH6i4EUS4l1bDDIR614oSgKWSpl3UJuiyJrSxk6q44xm9mTkvxA5ercsZkiKQZAC/naeV0JuDFKBFBNdB5iyk0NztWE6McJ5Gx0RNENUTPkrLCEkjQQouKDsNJAtInrUuEkkEIglrFQ2ke6xcDFhcdEYVo7ptMJ9x5cQoCJgJU90dpjj8eLc949rqEBfi9Z0fqTZBXsBeAHyN6tPd4LPBNEywTBdXk47tgTBRTPDYxv0d0YT1rIgSldhmjCShn7V37Wq0GwmArqaUV9WLNcJNQZ6nmDnVrSQnlhWjM5nvATDy7Q4DHWEUnFCjy+q4dyV2RrHd7oUNu8Kh1VhwSaM9c3upVsCZyMQ6DLr9GUgxyTjiGVduM3onRnlQLkZkyKFkXL2qzcXQyBKJFjasQKlXFYk8t6+fFjjTAjz28s5HSztvI9CgEoSocRwTiDmVm46UjXDWmZSRaVUNWW6XTCyeSIvvc0dUPjHJURppMpbV1DCPTrNXbhcRr4kU+8wmq95rAVrh9OMcZxdP2YkxcamhuCOOj7yHodkCHilgOsPOvLwPlpT3/Z01qhaR0mDHkItE+sBmVQOLlxg8Nr11i5hrPTJUfzljqVxPhyjKyUlPriiUs+EHtBBocLYJIg1da29kghWSgzAclhoy6rkcko9sCirRAIaCrnrcoXZL7l577MnEy5AUJNHnWUy4q2nC9KCJl0h5iIMZJT/xVNPp8/kEcCKIUoZaZkGQ9tLhf3XmibFmeyuhtjeRexKcKO/45qn8GIy7+/dtQxUUWD9yt6EpVEnES89/Trnu5iSfCJbplYrRPqAwfGMJ3NmB0es764zFMNrpxHa489drF7tXikLvEEocD/CvxrO/vyZgiZWP1K8uzDbyGb5b8BePUJ7OP7A88E0RIF4wsZ2eT7jCRnNJXvBi7uQr/gq2yhEdQK0Qg0DUiFmEhVu9w5J4ZgQG2ibRwnTcPKR3wSGltUqZj3Q5Fyo9xmZG+M7nkF5cZLUZuyuyWVWIXR35QfWQYMb7ikgMSidkGmAan4dnRnAkupZ4ohGSEaUGuwztE4pa4t6xiRZJCU4wdEs/VnNFqPQ6RHf9YojY1WtFEtG/vNEpJv3pXFTircvME0jmHw+SbeCOIsMnV5dM26wq8GrLhMNpPHNQnTJJyxON9SnzvCwmOmNWenD1isc1elAKc+8YHb1/nqD91kfm2KInTdgA4R2wdYR4a1cHwBD1+5YPXaQ6qmYtoKgYG2S9izRN95jg+PoGr43N2H3Ft2vHDrGi/fnOLahrqq8etA9HksjY5l6KbCzZqc0n/hqU5qguSML+tsUTh3zkQFTYJJOXk/i6iWZj5hdjwjvLZCQ0KTbNQrRiK+IbJpO/eQcojVlNM/H5/MkbV4u3Kyf12mF/hQ8rgYO1DHPRwJU9q8KMajOm5IMeQB3KNiJ9kvlgrpR/M5YBAwFicwqYSTxhA0YGNPH5VIIpkBMwyk3qOmwqnlYDalX3fgDGIss8M5fujpF2uGsCdae1xl/HrgO8i32A7428Dny8f3sR2D9bjxMXIY6cnbfF+BU+AvkdPifwL4fmB4Ejv3vsGzQbRQrOZwRRFFTCExY5iivJmNv8nj8iYbUjayQ7JCcBZtGkQa6jYR0xrfD8Q+k53BKgFhVs1Ym0Tne1oLpY99W3aDHNopufSzIVtjTlYhKinPkNmUeZSEGMGo3aR6U9StjVdLRjWP4tPKapnI6NMpf8tkU3UQCEZJzuCqikkdmbSW9Vqx0SBR8qzH8WZZCNYmNLW0zW374MgqiI7Dr/M6hNIx11jMpMG1LToYuns9robptMG0iswFN6tpjiuGtUODggSUiLUKJmLEYUNWURyOO3cOeOXuXR5eDNjVgImBi/sXnDZL2q9yfOhwRjOpsRpJlWJOGpwYaioOdMLh69d45Z9bbIL57SmmSZggXH8t0J33pCGxWHskRnwfWKyyWmmrisrVuKpmWJcxFJozourW0R40rLQnnHvolGQjWMVUWSEc+c9IYkmCYiAWD5Y1VIdTZtcOWDWXhPWQVclNZ2eZZ1C6W3UzBFC3waFj0Gnamu+1RGag2YvYWCFE8CWeQ0pY7vblIiUTbfviGLsaQ/CITRhTwhZGv1Rum0USBEr5HUM0pgywNlTGcFQp0Vf4FOij4lPCyEATPDZIjkhBmM8mVFYwlWHwAVs7TO0YjGEY3vya3mOPq4R/BPw4meD8anIKO+QS3R8Gfgz4e2zzqx4XPgb8EPAreHtV62PA3yVfJ/5h+djjvcQzQrRyBWZ34Mu222xbRtxiV4qV7U1st64jYNqaybUp7qhlSKXcYh3dqsMRsbkdCxWYVBV3Dmd0vaPrPZUOmJJePcZKmhIYOXqvMv8Ziy35XX++OaatR0vzukzhWKaUQTclyFI61HG3S2lR0bL2zS/ZKGNJlCAJGsEe1DQm0vSWyhtMyvVWTeS09TdlZWnxwOmYTzZ6wHa8YNn4XnQb57CTGttWYIT15RqtDO31CbVYUpcYFh1Lv8B3eRZliokhJdYaMAi1Ea7NphwezJnaBt8GTg4cL9884K5G5jbQVuDXPfd+8j7f990P+PBHX+ODH7nBBz96jelhi4pBK7DqcSlyfGRY3mwZBkNz4xA9ijgL9UmiPe8Zlj3NwtEeXMN99gEXqzPOzpRJK9jKYmqLqyx1U+OcxThL0sBycYmpKzRG1veXzNoZ4gRiOacgR94rmZgmKeNnIIZAlxJQYVqH1HY84IWV5bNpe/6WaInNG4acUSZW8EMgacyeRbaneOUsdeWygpaUumkziS6qF9szC81R/VtVDC1eL5NngcJG6TSbNxEyFozZnhGmdM1anFgOKsVNp3TeEkJCY+6EDYOHIRF1YNEteHDeEZPS1Y4UPE1Vc/32TQ6Pj1j3q+zT3WOPK4lT4N8n51X9buAXle3fAPwxsnr0N4HfAo91SsIK+M/JsQ5fV/ZDyv5R9uMPklWvPR4XngmipWRSkP0rpZyl41vzUkKEtyfkm1+0ZUCGnJ01uTVncmcCA8RFpMIwRFivAhZwMWElkQg4a6irBhcSKckm+Xss6yl5eHMZklPCSDMpMsZgjc02cs2p27YYnk1JA9+OOtmJcCjpmpqUlHI2UuUs3seSzZWVPdn8XCKmgMTsKjYvVHDucQthusxdeDElLCUVPu2SrUKyyuc6BkKV/KSt57tECRhBK4OZOup5g2krfFTaaso0NYSznsuLBf3as1509Is1JiaIiXUYuBy6Tbnq4viAF1/6AHdeuo2zjsZaXjg5ZIbF+IHWGsQrD7uB89cW3Ft9hnD3IXr5Mjfu3KCZtUwaaC2oqXFtzeSwxVwk/GKNTx11K0zrlvnNmnAihEvDpIKgyoOzJVVtSCkySI9MoB4aRoarCtFnQ3dVKd6Afy1Q1Q53XGEnBpnnjk6JbM5PQUmiWOcIEgkm4YmkCaRG80gl2RYLxyLhNho2l4oTCeccVdvgk2cwBlGLlDy1jcfPZNITQjm37Hhypi3REslNEEk24a2739OUSOWlEkqGljVmkwvnjCGpIaT4yM9RzldnhMYZkmaiBZGYhKH3pD6CGrpVz73Tc/oo3DiacDipaNsaTYKtKw5fOIL/90u4SOxxhfGmksSVgge+c+frbwV+Xfn8+3l7w/kxMCeXGr9S/DDwHwDfCPwN4JDckfi58nd+iG2p8CPANwP/kv0L9L3DM0G0IHulxgxtTbJNr6YQgFESAt6pdJjVMYulopk0HHzVAYdfe0z/Ws9icUmlNSlVrINntVgwWXmmQdHYE73FRGVqBnoifakTmQ3RybP4UMGKJfcaZo+REwOuLuWkMrhZU9EFRmTvTN6Udrw3KUcIqFJZQ2XzTTIgJGypVW0VqBgTmgbiNFJ9dIJ5EGnuOg4vW4yHWOY1JrbjX7YJ5KVsqBGNWeHQ0hFni7PbuJpq0uBRaIXp9Tmz6wcEL6T7S2IYWF8E1qslp3cfEoaAhoiE3Dkpmphr5EATQRNrjSzWZ7zaK7PZjIPrB8zbA+rrLdebnuXZGVYVOQQzPORGO2GGku71fPbv/hQ/pT+JdRUHjXIwcVRHNzF3WlKthNPE5cUCrRLz4zm3bl/n+OaE41sTUt2wDI5ryTA/mQG5S1FrxU4sLlmWD5b4IdK4GnGCjZa4iLiqgjPH0i/Q40Rz3DJ5eYKpDWadSbXUuTwnQNVUSFKieNQK5tDBgUXvbUvfmXKWGIc85AkhE6GgYCvL5GRKXSVOX78gLRJVzMTGFcKmqvRD9pahCr7EM5RjLCLgXOluHUvTRXnNJ2f5ewlU8DFl/xVVrndrAmOorCPFnGuWZzoW1TMlIOA0IikSQsCkgSCG1XpgWHTYiaBRWAyJ+4uetrVcO2xQSSy6niFErl8/fk+uG+8PvEC+6X7yae/IY8J/CPxp3h9dbn+ufLwbOt57H9cPkAnWBfAfk9WuXwwsdh7zTcD/QO5A3BOt9wrPDNFKuUDBTlFtA935cnvL2jVlPfJNKN6iZj5hfu2Ag+tz7NrS1+s8z1AVDYnLiyUPHyyZmpraWhoXGLTnYuixEnFkU3wlUFu3MSSDomIykZKtST+XCLPPJaeWm01e0KhgjR1+I8naSSfFkFWVFJSqcog19H7YWWP+G3VV45PSDwNSW9qTKbNrM8zlGs5Hw7Ps/P6dMTuUhPiYvTmxbK+K6ibGYZoKqR1tUyOT3Ll4eXaJ7xPDwjPEFWmIxMFD19FGj0mBIQV69QRVXBLalMcl1cYyJOXy4QWf+rFPcHTrmMODYxrXkMyAFdCYmFcNvpmwXkXMeoXIwEyqov4Y3NITTaS7d4F+zuGcwUahDYneOC6rJd3sjFcPK45fPGY2nVGlTLtnTZV9Z8YQbMidlBNDIGSv1kyYn0ywtdDd98QhYjuDekV7RQdloEeNYnuDHFrsoUFt6dCzDuMtqY+YaLHUtNOWS9MRU1ZrjeYTQVFEMtHZVHCBLvQs6Ljx0i0OKuX+T+QOPStVOXRjqKnZqKGPTEXUEgtfXgzGGJrWQomPCN4TUyCnwVPU17xfPoEPQoyGurJMapM9aeVNQ/bupXLuxhyvkeKGvCl5aHW/7nDkcUiTtuLYVty6cY2jo5Z+3RFjxA+e4bz7oq8Ne7xQPj7F1VR+Xudqruut8PXAi+RxTe9EpLry8V6iJ2dm9Ww7Cu8CvwH4QeAV4C8D/5htaXGP9wLPBNFSNHf4GbIRWHc688rIwW2IKTyqaGlRtN4kP1fC5Nac9mSCqx11GaUynPaYBC2GJsHpasFKHZOm5aRWTByIRM5iz4MQmdqar5pOccZyuVzTayYw00qYOKgMVKYq4alQWYeRsam+tOynMSdJiWm3jLMtjBoEsYaYEjHlOXKQMFJKfSkrfmnsPkzgV4F0qRzcOqT78ID2pwyrFSYpRreDjKNmZUuLGT5nL+XMpqCCM4ZJU2HcBGyLPWowRw2DCfhuIN3t0JBIIRFDKuGYEWJWNZSYB1YTM/nUbNxeF24XUyaeqY8sX7/P+mzB+UnPnds3mDbCdFqxvFDa3nIsLQNLUIslMZNYgkETVhM2gmiP+rBpKLACTQrAQLpc4u8JD18956Gr6LwyPzrhxs3rTOcTqsbgKtBWqW459FRYn3tSv0QifPBDd6hmA6efucRiqKxFrKGpG/rzDt8FaldjK8EcWmKjGEoCf3IwKLTQDR533NBen7DuAlqGKhMDmkL2PIklh4Dm8zsEw0+/fs6lc3z45gF6MGHZrzBG8vOtqShVaVvKK29NtCiklKaHHM8wRqXkCQt1DcOQUJNHU3VdjzV53w2JprJQGZwx1M7ipMabHE7qxOIkgEZi9Kx8x2IYiJqPC0IuuxsHKWIk8uLNCW4y4/rBlArFq9JWFQ5LnZ6JS89zAgP8F+Sb4w895X15HPjrT3sHniBeIfuhnhax/Kk3ff2rgO8G/gzwT8nH4uNsx/a8H1TGx49n4mon5DwjUc25PaWrT2GTUC6bd+qP/tymhLLjp8qR1UJ9WGOnFjFgGoOptmGntQjH0xnd4SHrzhOiJw7KBDixOax0FUF9ZLla0UxnoxM6ExhJJISIUJcbWoq5dIYRjAEx2UCcZCRXpTS68yIbOWIOtRxb8/NYF5H0CKUcy4ujKtZddpy+fsbhodDemDK/HXn4uQ4dircm6YbgsUO2Uiq5TQIJB9ZQTx3aVmjbYKYNyQrr1cBwucYuQWL2nqUUUI1IyvNnNEWi5Ll4mhSTDJUqQWEY9zflY1sZocHQ94nzhwvqytHcnDE7mRIqC905pk/YPGcmR6WOSlw5roliPpfcoqCqpEgZxm2x1lEr2JXPzQDJINrRcw6rAds67MzhjMPebGmPJly+0SFqiCtFe0GTEnygrfOxSDES8ITek7oE02yCt9aRKl+aFjSb7FtBm0gtDXJthh4HhvsrUgi55GxNaXaAmMY1gKqgITL4wINX7nMDaG1NaiOScn6Wpky8zaYr15T3xONrIGXPYCHmWzIG1uRxTcYYhhhz6VAyuRcBQ6KyFmvyuW/F5LmNyZC8RxG6EAgh0IeBle9ZR49R0DKzsa5rqrommTyhYHrUMJ23tFVVvG8WTZYgisT3i4LxXuBXkU3M30FWJPZ4fnGvfDwrOCCHln4DWWn7O2X7twJfRe6Q3OMrxbNBtMrMtjGwdKcWsvn/mG+YudS2ZLI7G1BR1OSuQGkt9qBCGoMaMhFQJUWFEDGqTOuGa0fHnMuC1WKBxkw+JsDEWCaVYTCGxuUMISuJJmeJUgt5W9nTHJ0QICpBDcYZ6trmYdE7a5Gy3nEkCxvqVWYsMuYrlbZ9djoCC5EcLf+xj/SXPSEEZodz4jXlgbnLoDGXiEL2fjkjCImkcRP1oFpu2NbiJjXVtQlyOKOzjovzBf5hIKzz/D7tx+63hLIdKZNpj27LWCXuQkttNJVuOGEcuK1YBKew7gcuz86Zt4nbL11nOmlZrS9IUTG9LQGe2xJxHhAghRTIxtuUdKTbeQ6gSYoRi01Z5XEiMKwIpx6/XiGNo/VTmmpCO0yYHLS0hy21ddjOsn7Y0y16UozlfEnEIaCrnB6vneZB1DGXnzXmczDEiC1EL0hiejDFnwjhZMDOa5KPpJAIxpahjGnbkDDqTppoSYRuxd03TjkwDU1V4/uBcYLTNka3+Pvewkg8njdpU2LPOXAJgziXybbmyAqRnOYvIjmgNZsky/mXGy988sSQWPtINwz46AnJoykgJsedWGtKx6SjaoTpvKI9mZGMkELCD56YIs5WmFoI6/24jy8OBvil5Eu1f8r7ssfVw6fJ5cMfAP5HtknyR7x99tZVgSO/vh5/ZtgzQbQoxAkkz5ZjTEDf+pcedW+NxKOYgJMSNBFUwVlCK8xePqD6yBSOLNFGBu/p1gOrVQ8rjw0eh1CtOiZdV76OBCkBkgK1Qm2gEkFCIgR4OAScq7jTVMyMoTKWLibWyVMZyeRMLcRsrJfilRo7uihqRErKkLJKhWoxkYc8qFljudkVYqO6SRW3Qg5aBcygcJGwatEJxGmgN4nL6DkfFmjvmVcNNyeWxia8H7KKUjKyrDHUBxXTDxwy+egNgndcfv6M04cXuHXEDopNCZvycGgVMpElk6aszJjNEREE43IpLAweP/SoKs4mbKU5hT0YnHomBMLFwOv9moODlpM7x+jta3RrZb5WFv05ffBEl8+NWkyxjhswxc23ydAAYwymdHaOpESQPOpFPQQlrRR6S+8jcT1gBpj/nGvcOrnO+Q+eM6w86zggM0t9OCGG/NxLUtJ5KCoepJUn9YbhIh8MqYTgI8YKQwj4vscdTEnXDZOfeYwsPUN6QFosuL/suFx11D5wp5pyaCusyeVPh1BpJu125Ymm9ECk/FxLjrHfBIpqUbFG4m3H+I/xZZWUmAYUxYsFbTg4Oqau4XJ5ztn5GTokJEFtLG1dFVVLsWXYducHlkNH7BUtAaeQcJtzukZxtE1D0zRIO2V2yzA9cJxfeF65d0HlHG1VUbuGo/mE2gj339iThi8Ov4ucgQTZ13NV0QC/nJwv9cpT3pf3E/4J8K+SfVu7Stt3AXfIHZJ/n8ef9/Wk8GuBD5KvmN8E/Azgt5Mzzx4fnhGixbbsB6XLjmIcfvPjdKNoyY7JW5MSC0FLDtxhhT12UJeZgSHnOwWfhxvboHkY8KrD9ANVSnl23ObdPCgmZ2eVOrU1uYTmrDCpHVPrcGIZupANz2UhY3NW0hL/sGPWFyNF/QFLYIxXoHT/6RiCpSWPq+yP2SGahpJyFEEH8nQVSUQTSaIsg+fh0GNjIqkwswbjElFjfo5K+KS4ivnJIdc+cIP65IDFZ5d09z2yMkgIEH3pMsvKVSY2duP9MSVcdVyflIBZI7lU5YxDUawJWAlYMkMwmhXBIQoxKP3QYxulvT3Dni2wZ2tcVRGiJxA2SfXCqJjlslkehFnUoBJJsT2ZynlE3p9sFouFlOenuHcdzfmATCrEKdpAfbulbVrELxgeDJuuQoat/pi8EkOCLpL6SNf3DEOgnTW0Bw2qsFqswSTsoWV+c8LqXsWDs8BrqxWLruNILNcKcc0NFFkdNcmgCWwEDSHTqDJSR9BNnMju62Hcr7FUOGpcqglNIY/uIaE4ohFOTo5ojltWsaO77Eg+0cUczZDJmiImn4s+BvrQk4g4jRhifjOAwYrFWItUDZPphNmkoW4cnUncP1tw77UlCnzg+pzjoykWQUPi/GLJ2dnDL/UK8T7FNwOz8vlVDh6bAb+JbIzfE60ni7eKkLgg13Z+IZmMXQWidQL8cXKeGOSr5Jqs3j1ePBtEa/PufPwyd1flL0YpaFtGKlncxUhcvEObIoqgVrETgxlb4lOmJpUxOGMJGjACfujxvgO2Y0jGoTqGnIOVE7fz359I4GaTaJpIWyli857UVe7Miyl32w8x+2NiTASRzSDq3SgHUsSmHqeRWEpgGyVikxpfaJVuWaiOo1YoKo4xsAZdJkw0WR1JiVahthU2JrrO46psvh6zmKq2ZXZ8yMHxISY5Hn7mAXc/eUq4m6iRQvZiIXvliIzmahmHHcsj+yujPw6oqoqZOFQTokN+bjVu1EKNIY/ySTXnF+ccdVOa2yfMbk3wb5zR9i1KYj0sCs0zjAOZZeyQoASAlP0an5cNYS+7Mw7KFs0TAzTkmYbDomP5yQvsrGVyPGH6oRnzO3PSIpHj9ykG9JHAlL+hoF5hAAmKPx0w3nB575zVzGBnjqgBaqirPHQ5qudBNzCkyKStOGhn1K4lBTZJ8yNtVCn5V2xP/UfKzJtqoW5Y1Rc6nnZeLyU7K0bParXkxBxy4/pNgkYuH17QXaxZXlxkYhsjQcEXemsk4kSpZGdckBhQi5oK00yxkwNoatQp9y4e8qlXTnn1/JKXbt3m5/3sl3nhVg1x4PXP3eONz5+xfLjGrtZvdzXYY4Oi4L4vcAb8Pq7GDf2q4A3y3MOrgAPgvwO+ZmdbV7b9s8f+158NojU6T8odJI2+lU0W0Eh/Sj4VgtGcBZQ1jzEcwuRuvTpSH1mspaSkGwyWSWXpm5qz0xV9vyINHRBQE1GJxYxfLm+ixdcEPoZN4GRjFOcDC12ykB5rHEeuZd7UXPTKOgSGGKlsbpfP5G1raU9pHCKskCyqmTKaUaGjeMp0OwJFixIE5JucuKwaVQ7jLP5BNmTbQWhry7W2ZiZTkk8EEiKOqIba2KyQ1YbZrUNuvXgHJy2rs4779x/iL1eIJFL0pbMte8YiCaOCFcGZTNaCjk39WwIy1kdzGUuprZTQ1JwLNn5TyOTVap63J5fK8EqgrRLHsxnDtRnLdUIGSwBsKXXmbk7ZECnJB6psG5Ut2Go6WS0SY3KaO3mGoIZETB6xa1o/o6oMdmpxU4s/74lvJOK9gAQhWqic25oES0ky9Ym4TlS2oq5SJuNdjSygfzjgwwATg7luSWo5uH6Nr2mmrFdLYvA00TD1EM7XEMGG0cBu8u/S0amXP3KuaFlXNnTlV82mhDcS0ZGglyejJMMbIKSA79ecXp4RJ3Bw7YhJPWGYdtzVhO+WJD8gMSEhK8WZZOU156KhIMaRpCaZFl/NGVyLolwslnzy3iWXqaNtLR98cc7xrYbU9Tx89Yyf/NgrVKuBWYgQ9qXDd8dH2I5u+SfkDKSrigQsn/ZO7HEl4cjZYL+5fB7IStb/Qc4Le5zJ/Ns9eFeIyGfIQ5oiEFT1G0TkGvC/Ax8iXwF+i6qelsd/O/A7yuN/j6r+7Xf9I6PSobAlVuOsv/IQsnJFlEwYNBKIBEqeVfG3SCXUU7fp5FtcrBjOFkysYb1cslyuSP0SEz1KzDPnRDezDUcFqvQ9Eoti5hBqNUhMrFKPNwGDJfYwlciBgyCJ1uhmBnSKmRzkkiHEmFPAswoh27+nbNQeMLmrMebuw9HsLTvPRKQoFYAulCABQTi5fsIsVQyXS/res+wHfMxBqyFCU7dMjqZMrx1iGseD1x9ydv+SsF6jw4AGX573rUYycrztlMftsKTNzbx4o8b9MyJUJs9MVIQ0zgQsY4GkhLmqJlYPV5xxzsHNY5pbNXd+wR1+2nguPrtAgiWFUKqEMvKG7WSbkVOwJVmj3082+yVgJI8ZzDU1NAT8WukWq9w4ESr8pwNpDpPZlOqFistXF5igUOfSXYrFDyU2Nxr0CWnL2SpgxWCDZX25JgWlNTU2VAzJ0x5NuH7nJvO2JfiB1cUFBM/9199g/doF1QpY5/K1NZlc5nNEN6XZHWEzL2MkW/mM+YKX1FhO3JCxFAlDR7deYVaOwxeOmb1wyI3DG/hPJO5/+gHdvTWh79HUYUuXozOZYhnb4hWWwRLsMbae09YTpJ1zeH3CxXJB0wiH8yNe/OrrfPVHbzOnZv3wnLufucvywYqjGCAMqD65tvEncv16LJgDN8hH+4+T/Ut77LHHF6Imv1zf6rryTeQh3wM53uK/B/4quUz9+EkWfGmK1q9Q1fs7X38b8HdU9Y+KyLeVr3+/iHwd8FuBnwV8APi/ReRr9F2urLlTqqgjZlui2i2aZIoS6W3gIqxokwHjqMSh0eQkdRWmBxMmh5McA9Artk/IoAydsr5cwTAgKWCICBDJXqqoiaD5puUk50upJmxJqt84gjQ/cTlDKtEFUOOzWVsyJdIyZFhlNGrbwqEMttxAY9JtMoWO6kX2xggJa8EYmxUJG0kaGfAsjWKbitnMQBTCWuhWA32InC49YqCa1OAsTW2p4qigKW4yQdoJZ5eBe2d3WZ8uoBuo/Bop/qlc5nuTclS+SFq+N87GM7LxZu12CYJuih7ZuL1TChUwBlypeQ19z3rZMZz3cKgcvHzMwcNLzk8vccscgBrJDQ8GtnMYt7v4Bf9umFeZ36eFpifRorjlmqJfdhhnadOM2raYWtC5Yo8Nchd0AZ6AkxwPIlYgKNIJcYikg1zgq1pHpGNplpjblqP5nHY+oQsd08kUwea4DSKpC3jrqZzh+lff5nLSsn51yeruJVWy1DHvf4iBGIshX8cKrYIttcay3TD6vMzuM1COnWTSH8tIKBLdOpDOeyaHK+Rmi7vdcq29zeyFG/hTz/L0goefe53hrKfvBxZuAkawUjOdzDmsplysPMtBcO2ca0czDuYN1ia+ZlZxeOeAGy8eM8HQvXrJG5+7YHG64FqdcD6fi5q+kBg+ZjzW69fjxes8brPuk8Ehef7fPyCPeNljj/cK/zZwk1wKfDP+EfBzyuc98IC3Mls8TnwlpcPfSKaKkFsU/j7w+8v271bVHvgpEfkUOQTm/3m3X5g9IWUm4EYe2d68s78p0EnHvXTGtVAzc4c0xpQgUYMmoZ1PmRxMsWpJg9LiENNwvloT+jw+xJJwhdJA9kd5hSEpRor9W0qUqOimETQrUIpDscUZ5kXwqlx4wanBSv4ZI2CMIrhsGpbRg2My+5ASTpqdUGwzwWL2RyUl5XwEklOiiXgr2OOWg6MZh7MDNApxJXRLz9lyxU8vL2gcHFcOZwxN0+LIpaeYBFPVDGJ5cLbk/PKCuFxyVBmmNuQjIKawvm2pj1KyS+Q5ipkwukwCjWw7KkVKZMH4ozm8dMx6Ui3GdDFYA040xyikSPKB/rxHTgy2chzcPGJ6cMr69QGsJ6oSs45XONTYHrCDzYY3y1yFaJlIKh18FpP3dwjE5YCZTqkri0fpzlbQubyuMttwLKIaKXlUHkLwBBOIE0+qDPW8xVU1trHYtsqH+KFQTRvcxJF8hFUO9zQTQ1zB9OQAV00QOcsjn1aesOpzqc7n2AjRQoAFxGSiJWgx+ZewW5GSNZffFGzLupmIqVFcyq+x9dLjU8fhYc3qtEeOB9qjKYeHLdLD8vyQ+qihu9vTd5FhnsvFdhWYUNEPiXvrM7xRjk9mnBxOqCphalqmzZTDOwe004r+tQUXr57zxmunxPXAoekJKNGCWMtTxnt+/frSMSd3FP5N3jol/BeXf/8Bz1b20peLlpzVdPK0d2SPd8TzGFT6j8nDs98KHe9dg8W/QlbFvrTU/i+WaCnwfZLrSf+Tqv4p4Laqvgagqq+JyK3y2Bd5dEjS58u2RyAivxP4nQA37LyQjNLYNiokjCb5rKKIgsNS6YR5OmCeLBVVDlfU7KcSV1FNGqqmwgRIQ8J2oMtAd7Yi9gM2RrbBBDDemMeiWNLE4CN+SDgj1OJwRnYemW/dOboBRGPp7BqJWZ5jl4QcYCqKJDahq5tMpNEBraAyjlXJ38xqWCacwQrnwTO5eZMXXrrDBz98E9dMOX8YePiZBwznp8TLJbq+JIRLgrOkqmHW1Exmc2ZNg4jFq6EfPL0PGHFIEIhgbJ5fh5YyW7lFKyVAthBOii9rJJEyju0pRCvpbhPDow7tcWyMbj4bqVsO7hx8ZH22Rs9hebbiYH7ItWs3OZVzEItqKB2TYIufjZ3y5u6x2X5VVLjxadVMbmVTXkykIeT5hD4gkrDWkBaRdJqY3z5k/uEp9InLiwuW50tssjSuReeGet4Qm0R11DC9OUdF6f2AVUvoIqvXV6TzRH2rIZrAsF6zerjC9FDbFhz4ztMcN9y+9iIHX3sNpcM/PGP98QXpgRKWkbAKmFJallTOpcK8RhU1n3PlmZVShmZ7CAx57SkpTVjSX3b85Cd6pndXfOBs4M7X3mGxvMiVc5QAABE3SURBVCD0ATu3vPCzPoB8TYWxDjlMhC4S30g8+Kkz7n768zirfOB4yq2ThopE42pq2+IZWJ6e8vDumsVnzvH3elgsMaFjScJK7t59wibv9/z6BY9ew15++cvZrRXZe/V2o1h+Dvkc/ivkd+HPO+4C3/60d2KPt8R1spA7Iwu5f5gv/5yrydEQn3tvdu2Lwk8Df/AJ/J3P8uXkbn2xROuXqeqr5WL0/SLy8Xd4rLzFti/Q6crF7k8BfKS+pUOIiBgqMZsb/u5swTHKQQSsVBxVc5pidFaBaBJRijfLWQST2/CHiK6U7sGK9eklEgKSYlYqijpCKctUxmIKadOUb+u2lGVkQ/zGiE6zEzipObFbs8nbICQikQTJEkLCa0SMZDVEMhnD5ZjNlDKRzMZxLaXE0lEpQGVpphV3vvoOH/rIV3F40LDuEyF0rJdr+rMOO3QQO6Yp0MXIelBsglhHpALrBKksSg6hnBnQuiKlllZSNqXvKEBSSqBSuvzGUS7jtD0h52LJls8U4pnn3I3Hawxm1excz8qSpvI85p+NwHrwLBZL7IVl/UaHNBarDjEGCVmpVJQkJWfNpJLIPhaczY4Kuj3rdppZi8utkF12ukCDx/uBkAJiK1xtQSqMEXBgnMV4g+kNoQ+kuGJyPGf6woRgElRAPXY9GuKQiH0kLDysId4fWL6y5PT+fYb7p+ChmZxw0Mw5mNRIBD2MmImlPjzg5IMTLtwpp5+F8JoSUpdnLpaQU6s5BgIjJaC3rF22z8bmeJUy9fgaEo051JVEWAuLEDitHMezA2yK0PfEJQxhwLg51VH2OsZBWC4G7p+vuHfRg0YOVDEp5AiMVNGte07PL1nrmq5fkE7XNMFSEUt0iuwopW91mXhseM+vX/DoNewbvkHe8jHvjMQ738z+NvCQPIdujz3eaxiyqqpkKnAb+FqyyvpdfPlE61r5+X+HTK6fFBbv/pCvGF9eV+wXRbRU9dXy710R+R6ylP6GiLxQ3g2+wPYZ/Tzw0s6Pf5DtBMu3/v0iqK1RJc/e21hQclt6xjZJuyMnuwebb91GFC+RYJXpQYVxgl97TO3QkPDngctXL7i8+xATIiYFxJWL/ViSUaEZR5uUbsMx/dxINgUn3bbeZ8EmExcjxSwsYyREKmuBmGLOHg3ZW1TXNa5yGMwmZFOtxYgDFcIgBF8IpqkQa6iPptz8mlucvHyDOKk4O1/TXaxYvH6f9YMH0IElr+tQc+looQNBYa0Gs+xxbUV12IIRapswaaBygkwmqPf0Q59LUpv0dVPULFMGZ8u2C07YkCxTyJmqZjJV5lUmSdmTpKU8W7xmOfHd5NE8QibKGNZBWa9XzC9bhlc9pu7RtWJdhXQWUiQRihoY0WR2WJ7ZmPezHifbO+OmwYCyjq0pTjVBghCUvl/hY09tHdQQiZzeO2d9sWY6b4k+YicOe+CYHE6YvDTH3WiokrJedAxdzCN4TA7INbWhPqjxceDi82fc/9w9Fg/PqfoF0cMDN2CaB7w4mXN8o4XrjuHaAa00TF5uOPr5Nzj66An9ZzvOP3nK6x9/hfXlKhNyr/k8qyzG5QDXXN7dksj8hkGJul2yKeG3saiSMwLDkLj3+Xto77lz84hJU8NSCZeCTiJx7Wm6QLjf88rHX+EnP3ufn37Q46xgbMXt44G5s/hlz/ndMx7cPS9BtQOSBpIqlYlULk8FyDlg4yzQJ4PHff16fPjrvL/mAO7xZPH1wJ8llwp/A/CHgCnZrvjb+Mrmav5c8jif30RWmx5/+vqzjHclWiIyA4yqXpbPvwX4r4DvJUeq/tHy718rP/K9wF8Qke8ka5AfJU+rfFuoMZjjGbPpBOMjfrmGkMg1Et2EdlJKRyoGJ2NOeL6Luqammrd88MMv4GYTzl4/5+DwEINhdX/J4v4ZftXRaKZSmzgCMskaSYPJWg2SSl7RaDQufxvGx+zsUZFNRtKxebc+Wp0UKEN/o+YsIqkM7XxGPW0wriJ6w9AHUmhLIGQmIcZWVIc1k3ZK6gMXl6cMn1/SPVywPrug7Tsqm9W1kCITFGuViVGM8TB09D7Q9ULsLkEEh6GOlqYIPB6Ihdwa1Y0asn1rv+vXKoqJ7Az+ZqMp7RQd83HK28o4GE1kc3pkdE4ZFOdKNpYRUIvpASLOGNpJTT9YUm8waXzmy1OqmTBsadU4Dmi729lTt939/GMlZHVTflSST8TeY53FJPBdxJoGSRWr+x3JRNwHLO3tCYe3D4kTGBgy4Tbj+VT+nhPM1DD9wJRF9PTDkv58CUsPgyeFwFICZ4uec1Nz62LK5N6E5lagsbcwNx3qIvX1Cmezv89gOH/1jMvTC3y3hhRwBjB2481S2UaJjOri9gAV8i65I9QCUSNOBI2Jh2+c0l2saWdT1FouvedS36A6aPnqF+ccakPVDxy4wI25o0+OIToWS4+rIv1wSXd+yix5Ap6kSp+UGAaqCiRJGSH0RJWsJ3L92mOP5w/fSB6587XkmsJvAL4TeBn418nlvz8E/EXykOkv5Y3RefnZDwP/J/DfkGMU3r/4YhSt28D3lNKZA/6Cqv4tEfkB4C+KyO8gF2N/M4CqfkxE/iK5FzkAv+vdOnaqiePlX/phrt+6wd2PfY6zTw3YEgvQpQEdhyvLtiQyFkhIEFJk5T31ccPRSzNs23L3tQWLBwusWu594h7rh0saq9QkxEIy40V/HOO8sVlnH5At9+ExTRJAE1LM6ZtcAcYE+LRD/GRjXLZjcnfKBCwl6DWRjDI/bqlvH+CmDf3DxPKNS0xtmdR1TlVXQbA0sxY3COnBQHe55sHnT1mdr9C+YyIJ5zIZHWcJVigm50si5GiEEJRVH0CglhpXTWgqi5fIQGDQQJ1ySWr0x6WRW8k2q8mWAcLGmp3jsREGsYVsqDGodZkMpZQ7NE1WKMeXrCsELNIToue8V+ZD4HoCM3jaynB8a0ofl5wPmZy1psxqpERmwM4e6MZHpkV6M6Z4mGBL0hRSygolRtAEvvf0Fx03nCMgPFj1rFc9zaRiemCwreCOHBw6fJ1VOs0nEa6qoURxBA2ZQFpQCfSLJd3pGayXmL5DYodLgQMJOaMqeYbLBf1yRb0O2BSpu0RzUjHciNhaqG9W3BruMJ8fcnr3Ia9/7jXS8pwUA8a1WHFvov75GTE75DjLhxawebA1go8ei8dIbhKIK0/X9yxV+VzXcZos0lTY6hZf/+GXOTFHSAsvuTmrJQwrD8s16xTQoSd2PSYFLPlcxObkOzd6srQcA9nx8j1+PPbr1x57PF8w5ADPvwT8buBfsA3t/BSZJH0H8HvJ43l+Gzkd5YvFmhyhYIC/QPZ7/TmeTGnv2cS7Ei1V/Ung573F9gdk6vtWP/NHgD/yxe6EdZZ23vLG/Xu8cfcu9D1tNLlEtIkKZeOTypCtcTzFHNFQGahjNgwvDdIJl2eXnL16Rlj1TFRxZquIpKJgiW5VgK3yMXbIsb0npAQmFSVlmxsVk8033VEpKwVIIyVVXMGYHKo6oMSUICT6RaQ6SDAVorN4CxJKN6SWMS8K4iOut3TLFRcPHhIXS1zwIHkItk/FYC7ZDu3UIDoOqFY0BXKchAcRvFiSKKamPC5ufFNmowYVOW4M+SKLUVsD/A7pLdEcY5Uui3g5eHUzIkbHeIVsgR/VrJw2HnKelnd0Q8DEnAXfThuOrx+wWC25vExozGXicY+yslYOTxkaXmLgeacbuY5TjsZyp0IcImGd2xlCitw/PefhgxXzk4YXjo65fuMak5MWqQVPykxdLJIS0Qcq46icRZKwXCyxQ8JeRPzn1/h7K+h7TBwQjTiJzAUm4sBASAkflLhYc/n5U6rOcHzniEOd4o4sqU8slmtwwu2f8UHqGxNm9MRTz/kbq0x4tiFhjPri2FOrIhuPnADG2lJKzKTXSJmskCLEgGpAYgfR0FFxumxYy5qDo5aqthzPr7M4G3jw2imLBwu87yEMeVB2GQYuZOsa1uXXhT4xYvXosX4C16899ni+oGTT+58lq1qOrR9LyXEIv4VMxk758glSIpcf/13e72G0sjv65qnthMg98pG4/26PfcZxg+d/DXA11nEV1gBXYx1vt4avUtWbT3pnHgdE5BL4iae9H18hrsK5Bvt1PEu4CmuAt17HF339eiaIFoCI/DNV/YanvR9fCa7CGuBqrOMqrAGuxjquwhreDVdhjVdhDbBfx7OEq7AG+MrX8X6ZWLrHHnvssccee+zxxLEnWnvssccee+yxxx6PCc8S0fpTT3sH3gNchTXA1VjHVVgDXI11XIU1vBuuwhqvwhpgv45nCVdhDfAVruOZ8Wjtsccee+yxxx57XDU8S4rWHnvssccee+yxx5XCnmjtsccee+yxxx57PCY8daIlIr9GRH5CRD4lIt/2tPfnnSAif1pE7orIj+5suyYi3y8inyz/nux879vLun5CRH7109nrRyEiL4nI3xORHxeRj4nI7y3bn5t1iEgrIv9URH6krOG/LNufmzXsQkSsiPyQiPyN8vVztw4R+YyI/EsR+WER+Wdl23O3ji8V++vXk8VVuH7B1bqG7a9fX8Q6VPWpfZCnWX4a+GrycKUfAb7uae7Tu+zvLwd+AfCjO9v+a+DbyuffBvyx8vnXlfU05KFPnwbsM7CGF4BfUD4/AD5R9vW5WQc59n1ePq+A/w/4Jc/TGt60nv+MPKvibzyP51TZt88AN9607blbx5e45v3168mv4bm/fpX9ujLXsP31693X8bQVrV8EfEpVf1JVB+C7gd/4lPfpbaGq/xB4+KbNvxH4rvL5d5HHlY/bv1tVe1X9KfIQqV/0JPbznaCqr6nqD5bPL4EfB17kOVqHZoxzIaryoTxHaxghIh8E/g3gf97Z/Nyt421wVdbxdthfv54wrsL1C67ONWx//fri1vG0idaLwE/vfP35su15wm1VfQ3yRQC4VbY/82sTkQ8BP5/8buq5WkeRq38YuAt8v6o+d2so+BPA7yMPBhvxPK5Dge8TkX8uIr+zbHse1/Gl4Cqs47k9Rs/z9QuuzDXsT7C/fr3rOt51qPRjxltNmr0qeRPP9NpEZA78ZeA/VdULkbfa3fzQt9j21NehqhH4ehE5Br5HRH72Ozz8mVyDiPx64K6q/nMR+aYv5kfeYttTX0fBL1PVV0XkFvD9IvLxd3jss7yOLwVXZR1vhWd6bc/79Que/2vY/vr1CN5xHU9b0fo88NLO1x8EXn1K+/Ll4g0ReQGg/Hu3bH9m1yYiFfki9edV9a+Uzc/dOgBU9Qz4+8Cv4flbwy8D/k0R+Qy57PTNIvLneP7Wgaq+Wv69C3wPWUp/7tbxJeIqrOO5O0ZX6foFz/U1bH/9ynjXdTxtovUDwEdF5MMiUgO/Ffjep7xPXyq+F/jt5fPfDvy1ne2/VUQaEfkw8FHgnz6F/XsEkt/6/S/Aj6vqd+5867lZh4jcLO8CEZEJ8CuBj/McrQFAVb9dVT+oqh8in/t/V1W/ledsHSIyE5GD8XPgW4Af5Tlbx5eB/fXrCeMqXL/galzD9tevL2EdT8rV/3YfwK8jd458GvgDT3t/3mVf/zfgNcCTWe3vAK4Dfwf4ZPn32s7j/0BZ108Av/Zp73/Zp28ky5z/Avjh8vHrnqd1AD8X+KGyhh8FvqNsf27W8BZr+ia2XTvP1TrIXXc/Uj4+Nr6On7d1fJlr31+/nuwanvvrV9mnK3UN21+/3vlv7Efw7LHHHnvsscceezwmPO3S4R577LHHHnvssceVxZ5o7bHHHnvsscceezwm7InWHnvssccee+yxx2PCnmjtsccee+yxxx57PCbsidYee+yxxx577LHHY8KeaO2xxx577LHHHns8JuyJ1h577LHHHnvsscdjwv8P2UHwq3bUqvIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.91 s, sys: 1.81 s, total: 10.7 s\n",
      "Wall time: 10.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "imgs_original_cutout = [io.imread(path) for path in rnd_original_cutout]\n",
    "imgs_lab_cutout = [color.rgb2lab(img) for img in imgs_original_cutout]\n",
    "\n",
    "idx = 0\n",
    "\n",
    "plot_img(imgs_original_cutout[idx], imgs_lab_cutout[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (3) Convert from RGB to LAB, reshape the images to format (x*y, 3) and concatenate them in one array in one step to save memory\n",
    "\n",
    "Number of all collected pixel-values for whole images: 2.480.703.127 (2 mrd.) <br>\n",
    "Number of all collected pixel-values for cut-out images: 427.032.576 (400 mil.) /5x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [0 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 262144\n",
      "Accomplished sample: [50 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 13369344\n",
      "Accomplished sample: [100 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 26476544\n",
      "Accomplished sample: [150 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 39583744\n",
      "Accomplished sample: [200 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 52690944\n",
      "Accomplished sample: [250 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 65798144\n",
      "Accomplished sample: [300 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 78905344\n",
      "Accomplished sample: [350 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 92012544\n",
      "Accomplished sample: [400 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 105119744\n",
      "Accomplished sample: [450 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 118226944\n",
      "Accomplished sample: [500 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 131334144\n",
      "Accomplished sample: [550 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 144441344\n",
      "Accomplished sample: [600 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 157548544\n",
      "Accomplished sample: [650 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 170655744\n",
      "Accomplished sample: [700 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 183762944\n",
      "Accomplished sample: [750 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 196870144\n",
      "Accomplished sample: [800 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 209977344\n",
      "Accomplished sample: [850 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 223084544\n",
      "Accomplished sample: [900 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 236191744\n",
      "Accomplished sample: [950 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 249298944\n",
      "Accomplished sample: [1000 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 262406144\n",
      "Accomplished sample: [1050 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 275513344\n",
      "Accomplished sample: [1100 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 288620544\n",
      "Accomplished sample: [1150 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 301727744\n",
      "Accomplished sample: [1200 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 314834944\n",
      "Accomplished sample: [1250 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 327942144\n",
      "Accomplished sample: [1300 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 341049344\n",
      "Accomplished sample: [1350 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 354156544\n",
      "Accomplished sample: [1400 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 367263744\n",
      "Accomplished sample: [1450 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 380370944\n",
      "Accomplished sample: [1500 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 393478144\n",
      "Accomplished sample: [1550 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 406585344\n",
      "Accomplished sample: [1600 / 1628] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 419692544\n"
     ]
    }
   ],
   "source": [
    "# convert from RGB to LAB, reshape the images to format (x*y, 3) and concatenate them in one array in one step to save memory\n",
    "arr_lab_flatten = []\n",
    "\n",
    "for i, img in enumerate(imgs_original):\n",
    "    \n",
    "    img_tmp = color.rgb2lab(img)\n",
    "    arr_tmp = img_tmp.reshape(-1, img_tmp.shape[-1])\n",
    "    arr_tmp = arr_tmp.astype(np.float32)\n",
    "    \n",
    "    arr_lab_flatten.extend(arr_tmp)\n",
    "    \n",
    "    if i % 50 == 0:\n",
    "        print(f'Accomplished sample: [{i} / {len(imgs_original)-1}] with image-shape: {img_tmp.shape} - num-channel: {img_tmp.shape[-1]} - array-type: {arr_tmp.dtype} - length of array collection: {len(arr_lab_flatten)}')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [0 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 262144\n",
      "Accomplished sample: [1 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 524288\n",
      "Accomplished sample: [2 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 786432\n",
      "Accomplished sample: [3 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 1048576\n",
      "Accomplished sample: [4 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 1310720\n",
      "Accomplished sample: [5 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 1572864\n",
      "Accomplished sample: [6 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 1835008\n",
      "Accomplished sample: [7 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 2097152\n",
      "Accomplished sample: [8 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 2359296\n",
      "Accomplished sample: [9 / 9] with image-shape: (512, 512, 3) - num-channel: 3 - array-type: float32 - length of array collection: 2621440\n"
     ]
    }
   ],
   "source": [
    "# convert from RGB to LAB, reshape the images to format (x*y, 3) and concatenate them in one array in one step to save memory\n",
    "arr_lab_flatten_cutout = []\n",
    "\n",
    "for i, path in enumerate(rnd_original_cutout):\n",
    "    \n",
    "    img_tmp = io.imread(path)\n",
    "    img_tmp = color.rgb2lab(img_tmp)\n",
    "    arr_tmp = img_tmp.reshape(-1, img_tmp.shape[-1])\n",
    "    arr_tmp = arr_tmp.astype(np.float32)\n",
    "    \n",
    "    arr_lab_flatten_cutout.extend(arr_tmp)\n",
    "    \n",
    "    print(f'Accomplished sample: [{i} / {len(rnd_original_cutout)-1}] with image-shape: {img_tmp.shape} - num-channel: {img_tmp.shape[-1]} - array-type: {arr_tmp.dtype} - length of array collection: {len(arr_lab_flatten_cutout)}')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (4) Perform the k-means clustering on the random cut-out images to get the initialization-centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_clusters_cut_out = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15min 49s, sys: 1min 13s, total: 17min 2s\n",
      "Wall time: 15.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 79.15012405,   7.40237975,   2.99587954],\n",
       "       [ 37.95744684,  55.76114799,  -7.75606161],\n",
       "       [ 61.00461018,  36.24284601, -11.10606014]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans_cutout = KMeans(n_clusters=number_clusters_cut_out)\n",
    "kmeans_cutout.fit(arr_lab_flatten_cutout)\n",
    "\n",
    "print(kmeans_cutout.labels_, len(kmeans_cutout.labels_))\n",
    "kmeans_cutout.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 78.11497179,   5.90330813,   1.93818451],\n",
       "       [ 33.61496802,  53.38005403,  -4.4341589 ],\n",
       "       [ 62.08269847,  31.56794412, -10.76258893],\n",
       "       [ 41.72695166,  55.56367553, -15.28130996]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myinit = kmeans_cutout.cluster_centers_ + np.random.normal(0, 2)\n",
    "myinit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpraetor/anaconda3/envs/dltoolbox/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 26.2 s, sys: 1.91 s, total: 28.2 s\n",
      "Wall time: 2.34 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMeans(init=array([[ 78.11497179,   5.90330813,   1.93818451],\n",
       "       [ 33.61496802,  53.38005403,  -4.4341589 ],\n",
       "       [ 62.08269847,  31.56794412, -10.76258893],\n",
       "       [ 41.72695166,  55.56367553, -15.28130996]]),\n",
       "       n_clusters=4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time KMeans(n_clusters=number_clusters_cut_out, init=myinit).fit(arr_lab_flatten_cutout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L*</th>\n",
       "      <th>a*</th>\n",
       "      <th>b*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.150124</td>\n",
       "      <td>7.402380</td>\n",
       "      <td>2.995880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.957447</td>\n",
       "      <td>55.761148</td>\n",
       "      <td>-7.756062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.004610</td>\n",
       "      <td>36.242846</td>\n",
       "      <td>-11.106060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                L*         a*         b*\n",
       "Cluster                                 \n",
       "0        79.150124   7.402380   2.995880\n",
       "1        37.957447  55.761148  -7.756062\n",
       "2        61.004610  36.242846 -11.106060"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster_center_cut_out = pd.DataFrame(kmeans_cutout.cluster_centers_, columns=[\"L*\", \"a*\", \"b*\"])\n",
    "df_cluster_center_cut_out.index.name = 'Cluster'\n",
    "\n",
    "df_cluster_center_cut_out.to_csv(\"cluster_centers_cutout_K_{0}.csv\".format(number_clusters_cut_out))\n",
    "df_cluster_center_cut_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L*</th>\n",
       "      <th>a*</th>\n",
       "      <th>b*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.658496</td>\n",
       "      <td>7.061353</td>\n",
       "      <td>2.360295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.345774</td>\n",
       "      <td>57.396134</td>\n",
       "      <td>-10.750834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.442454</td>\n",
       "      <td>38.340907</td>\n",
       "      <td>-12.740432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                L*         a*         b*\n",
       "Cluster                                 \n",
       "0        80.658496   7.061353   2.360295\n",
       "1        40.345774  57.396134 -10.750834\n",
       "2        61.442454  38.340907 -12.740432"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OR: load the centers\n",
    "df_cluster_center_cut_out = pd.read_csv(\"./data/prediction/H&E/kmeans-clustering/C=3/cluster_centers_K_3.csv\", index_col=0)\n",
    "df_cluster_center_cut_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 80.6584957 ,   7.0613525 ,   2.36029482],\n",
       "       [ 40.34577448,  57.3961337 , -10.7508335 ],\n",
       "       [ 61.44245366,  38.34090679, -12.74043225]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_cutout_cluster_centers = df_cluster_center_cut_out.to_numpy()\n",
    "kmeans_cutout_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (5) Perform the k-means clustering on the big images with the previous analyzed initialization-centers\n",
    "\n",
    "## Cluster-table have to be in the format for K=2:<br>\n",
    "\n",
    "| Cluster | L*     | a*     | b* |\n",
    "| ---- | ---- | ---- | ---- |\n",
    "| 0     | 25654     | 125     | 234 |\n",
    "| 1     | 33563     | -2456     | -123 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_clusters = 3\n",
    "max_iterations = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpraetor/anaconda3/envs/dltoolbox/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n",
      "Iteration 0, inertia 56315961116.20732\n",
      "Converged at iteration 0: center shift 0.002362378958600199 within tolerance 0.028987022581724035.\n",
      "CPU times: user 10min 31s, sys: 30.4 s, total: 11min 1s\n",
      "Wall time: 6min 15s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 80.66136887,   7.05683519,   2.36231563],\n",
       "       [ 40.35033865,  57.39468583, -10.75335838],\n",
       "       [ 61.46228915,  38.30088348, -12.72296744]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans = KMeans(n_clusters=number_clusters, init=kmeans_cutout_cluster_centers, max_iter=max_iterations, verbose=1)\n",
    "kmeans.fit(arr_lab_flatten)\n",
    "\n",
    "#print(kmeans.labels_, len(kmeans.labels_))\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L*</th>\n",
       "      <th>a*</th>\n",
       "      <th>b*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.658496</td>\n",
       "      <td>7.061353</td>\n",
       "      <td>2.360295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.345774</td>\n",
       "      <td>57.396134</td>\n",
       "      <td>-10.750834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.442454</td>\n",
       "      <td>38.340907</td>\n",
       "      <td>-12.740432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                L*         a*         b*\n",
       "Cluster                                 \n",
       "0        80.658496   7.061353   2.360295\n",
       "1        40.345774  57.396134 -10.750834\n",
       "2        61.442454  38.340907 -12.740432"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster_center = pd.DataFrame(kmeans.cluster_centers_, columns=[\"L*\", \"a*\", \"b*\"])\n",
    "df_cluster_center.index.name = 'Cluster'\n",
    "\n",
    "df_cluster_center.to_csv(\"cluster_centers_K_{0}.csv\".format(number_clusters))\n",
    "df_cluster_center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (5.1) Test with MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpraetor/anaconda3/envs/dltoolbox/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:1600: RuntimeWarning: Explicit initial center position passed: performing only one init in MiniBatchKMeans instead of n_init=3.\n",
      "  super()._check_params(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init 1/1 with method: [[ 79.15012405   7.40237975   2.99587954]\n",
      " [ 37.95744684  55.76114799  -7.75606161]\n",
      " [ 61.00461018  36.24284601 -11.10606014]]\n",
      "Inertia for init 1/1: 791141.562490\n",
      "Minibatch iteration 1/20851200: mean batch inertia: 129.658668, ewa inertia: 129.658668 \n",
      "Minibatch iteration 2/20851200: mean batch inertia: 131.461683, ewa inertia: 129.658685 \n",
      "Minibatch iteration 3/20851200: mean batch inertia: 135.035590, ewa inertia: 129.658737 \n",
      "Minibatch iteration 4/20851200: mean batch inertia: 129.900086, ewa inertia: 129.658739 \n",
      "Minibatch iteration 5/20851200: mean batch inertia: 131.035363, ewa inertia: 129.658752 \n",
      "Minibatch iteration 6/20851200: mean batch inertia: 133.929775, ewa inertia: 129.658793 \n",
      "Minibatch iteration 7/20851200: mean batch inertia: 129.164564, ewa inertia: 129.658788 \n",
      "Minibatch iteration 8/20851200: mean batch inertia: 136.944066, ewa inertia: 129.658858 \n",
      "Minibatch iteration 9/20851200: mean batch inertia: 125.896753, ewa inertia: 129.658822 \n",
      "Minibatch iteration 10/20851200: mean batch inertia: 137.617574, ewa inertia: 129.658899 \n",
      "Minibatch iteration 11/20851200: mean batch inertia: 129.901179, ewa inertia: 129.658901 \n",
      "Converged (lack of improvement in inertia) at iteration 11/20851200\n",
      "Computing label assignment and total inertia\n",
      "CPU times: user 8min 56s, sys: 1min 5s, total: 10min 1s\n",
      "Wall time: 3min 47s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 80.73383088,   7.04561978,   2.36479449],\n",
       "       [ 40.36388823,  57.38926445, -10.74251569],\n",
       "       [ 61.29184432,  38.4309858 , -12.70041102]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "minibatchkmeans = MiniBatchKMeans(n_clusters=number_clusters, init=kmeans_cutout.cluster_centers_, max_iter=max_iterations, batch_size=2048, verbose=1)\n",
    "minibatchkmeans.fit(arr_lab_flatten)\n",
    "\n",
    "#print(kmeans.labels_, len(kmeans.labels_))\n",
    "minibatchkmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L*</th>\n",
       "      <th>a*</th>\n",
       "      <th>b*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.733831</td>\n",
       "      <td>7.045620</td>\n",
       "      <td>2.364794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.363888</td>\n",
       "      <td>57.389264</td>\n",
       "      <td>-10.742516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.291844</td>\n",
       "      <td>38.430986</td>\n",
       "      <td>-12.700411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                L*         a*         b*\n",
       "Cluster                                 \n",
       "0        80.733831   7.045620   2.364794\n",
       "1        40.363888  57.389264 -10.742516\n",
       "2        61.291844  38.430986 -12.700411"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster_center_minibatch = pd.DataFrame(minibatchkmeans.cluster_centers_, columns=[\"L*\", \"a*\", \"b*\"])\n",
    "df_cluster_center_minibatch.index.name = 'Cluster'\n",
    "\n",
    "df_cluster_center_minibatch.to_csv(\"cluster_centers_minibatch_K_{0}.csv\".format(number_clusters))\n",
    "df_cluster_center_minibatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (6) Predict specified images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = \"./data/prediction/kmeans-clustering/C=3/predictions/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/asbdata/Philipp/SheepFat/prediction/LAB_kmeans/C=4/cluster_centers_K_4.csv',\n",
       " '/asbdata/Philipp/SheepFat/prediction/LAB_kmeans/C=4/cluster_centers_cutout_K_4.csv',\n",
       " '/asbdata/Philipp/SheepFat/prediction/LAB_kmeans/C=4/cluster_centers_minibatch_K_4.csv']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load k-means center\n",
    "all_kmeans = glob(f'./data/prediction/kmeans-clustering/C=3/*.csv')\n",
    "all_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/asbdata/Philipp/SheepFat/prediction/LAB_kmeans/C=4/cluster_centers_K_4.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L*</th>\n",
       "      <th>a*</th>\n",
       "      <th>b*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.024203</td>\n",
       "      <td>6.493488</td>\n",
       "      <td>2.637092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.326309</td>\n",
       "      <td>55.713999</td>\n",
       "      <td>-6.826536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.915660</td>\n",
       "      <td>33.472404</td>\n",
       "      <td>-10.502167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.630440</td>\n",
       "      <td>59.254783</td>\n",
       "      <td>-17.174961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                L*         a*         b*\n",
       "Cluster                                 \n",
       "0        81.024203   6.493488   2.637092\n",
       "1        36.326309  55.713999  -6.826536\n",
       "2        63.915660  33.472404 -10.502167\n",
       "3        47.630440  59.254783 -17.174961"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_means = all_kmeans[0]\n",
    "print(path_means)\n",
    "\n",
    "df_kmeans = pd.read_csv(path_means, index_col=0)\n",
    "df_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 81.02420279,   6.49348818,   2.63709231],\n",
       "       [ 36.32630858,  55.71399885,  -6.82653592],\n",
       "       [ 63.91566004,  33.47240387, -10.50216714],\n",
       "       [ 47.6304403 ,  59.25478266, -17.17496082]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_kmeans = df_kmeans.to_numpy()\n",
    "arr_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 31 µs, sys: 3 µs, total: 34 µs\n",
      "Wall time: 43.2 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMeans(init=array([[ 81.02420279,   6.49348818,   2.63709231],\n",
       "       [ 36.32630858,  55.71399885,  -6.82653592],\n",
       "       [ 63.91566004,  33.47240387, -10.50216714],\n",
       "       [ 47.6304403 ,  59.25478266, -17.17496082]]),\n",
       "       n_clusters=4, verbose=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans = KMeans(n_clusters=number_clusters, init=arr_kmeans, verbose=1)\n",
    "kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and predict the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original tissue\n",
    "all_original = glob('./data/images/KW/H&E/inputs/*.tif')\n",
    "\n",
    "# original patches\n",
    "# all_original = glob('./data/images/KW/H&E/subwindows_512x512/original/*.tif')\n",
    "\n",
    "#print(*[x.split('/')[-1] for x in np.sort(all_original)], sep='\\n')  \n",
    "len(all_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from RGB to LAB, reshape the images to format (x*y, 3) and concatenate them in one array in one step to save memory\n",
    "imgs_predict = []\n",
    "\n",
    "for i, path in enumerate(all_original):\n",
    "    \n",
    "    img_tmp = io.imread(path)\n",
    "    img_tmp = color.rgb2lab(img_tmp)    \n",
    "    img_shape = img_tmp.shape\n",
    "    \n",
    "    Y = img_tmp.reshape(-1, img_tmp.shape[-1])\n",
    "        \n",
    "    # predict all LAB-pixel-intensities\n",
    "    arr_y = kmeans.predict(Y)\n",
    "   \n",
    "    # reshape the arry back to its image format\n",
    "    img_pred = arr_y.reshape(img_shape[0], img_shape[1])\n",
    "    \n",
    "    # save image\n",
    "    filepath = os.path.splitext(os.path.basename(path))[0] + \".png\"\n",
    "    filepath = os.path.join(save_path, filepath)\n",
    "    \n",
    "    io.imsave(filepath, img_pred, check_contrast=True)\n",
    "\n",
    "    if i % 50 == 0:\n",
    "        print(f'Accomplished sample: [{i} / {len(all_original)-1}] with image-shape: {img_shape} - array-type: {Y.dtype} - length of prediciton: {arr_y.shape} - unique predicitons: {np.unique(arr_y)} - img-pred-shape: {img_pred.shape} - filepath: {filepath}')    \n",
    "        \n",
    "    #plot_img(imgs_original[0], img_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build and train a k-means clustering with externals scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_id = \"TEST\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config = {\n",
    "    \"n_classes\": 3,\n",
    "    \"max_epochs\": 10,\n",
    "    \"input_dir\": \"./data/images/KW/H&E/subwindows_512x512/original/*S50*\",   \n",
    "    \"output_model_path\": f\"./model/kmeans/KW/{model_id}/cluster_center.csv\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[build_and_fit_model] Read images from path: ./data/images/KW/H&E/subwindows_512x512/original/*S50*\n",
      "[build_and_fit_model] Read images: 121\n",
      "[build_and_fit_model] Convert images to LAB color space: 121\n",
      "[build_and_fit_model] Flatten color images to shape: (512, 512, 3)\n",
      "[build_and_fit_model] Start clustering with vector: (31719424, 3)\n",
      "Initialization complete\n",
      "Iteration 0, inertia 5475283456.0\n",
      "Iteration 1, inertia 4359634944.0\n",
      "Iteration 2, inertia 4291540736.0\n",
      "Iteration 3, inertia 4254990592.0\n",
      "Iteration 4, inertia 4220588288.0\n",
      "Iteration 5, inertia 4180747264.0\n",
      "Iteration 6, inertia 4119240704.0\n",
      "Iteration 7, inertia 4007227904.0\n",
      "Iteration 8, inertia 3857108480.0\n",
      "Iteration 9, inertia 3734490368.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 5149789696.0\n",
      "Iteration 1, inertia 3725448192.0\n",
      "Iteration 2, inertia 3625200896.0\n",
      "Iteration 3, inertia 3580950528.0\n",
      "Iteration 4, inertia 3560601088.0\n",
      "Iteration 5, inertia 3551321856.0\n",
      "Iteration 6, inertia 3546411520.0\n",
      "Iteration 7, inertia 3544860160.0\n",
      "Iteration 8, inertia 3543914496.0\n",
      "Iteration 9, inertia 3543382784.0\n",
      "Converged at iteration 9: center shift 0.029496297240257263 within tolerance 0.03046228942871094.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 3968510208.0\n",
      "Iteration 1, inertia 3660625408.0\n",
      "Iteration 2, inertia 3596471040.0\n",
      "Iteration 3, inertia 3567386880.0\n",
      "Iteration 4, inertia 3554494464.0\n",
      "Iteration 5, inertia 3548536064.0\n",
      "Iteration 6, inertia 3545109248.0\n",
      "Iteration 7, inertia 3544230144.0\n",
      "Iteration 8, inertia 3543502848.0\n",
      "Iteration 9, inertia 3543152384.0\n",
      "Converged at iteration 9: center shift 0.018178055062890053 within tolerance 0.03046228942871094.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 4406694400.0\n",
      "Iteration 1, inertia 3550391040.0\n",
      "Iteration 2, inertia 3546052352.0\n",
      "Iteration 3, inertia 3545630720.0\n",
      "Converged at iteration 3: center shift 0.0281323604285717 within tolerance 0.03046228942871094.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 5081892352.0\n",
      "Iteration 1, inertia 3664107520.0\n",
      "Iteration 2, inertia 3609477120.0\n",
      "Iteration 3, inertia 3581287424.0\n",
      "Iteration 4, inertia 3564536320.0\n",
      "Iteration 5, inertia 3555397888.0\n",
      "Iteration 6, inertia 3550684416.0\n",
      "Iteration 7, inertia 3548268288.0\n",
      "Iteration 8, inertia 3546884864.0\n",
      "Iteration 9, inertia 3546119424.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 4617230336.0\n",
      "Iteration 1, inertia 4029565952.0\n",
      "Iteration 2, inertia 3881200128.0\n",
      "Iteration 3, inertia 3752267776.0\n",
      "Iteration 4, inertia 3666576128.0\n",
      "Iteration 5, inertia 3615731712.0\n",
      "Iteration 6, inertia 3585169920.0\n",
      "Iteration 7, inertia 3566752512.0\n",
      "Iteration 8, inertia 3556526080.0\n",
      "Iteration 9, inertia 3551360000.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 6003973120.0\n",
      "Iteration 1, inertia 4667031040.0\n",
      "Iteration 2, inertia 4663068160.0\n",
      "Iteration 3, inertia 4662043648.0\n",
      "Iteration 4, inertia 4661090816.0\n",
      "Iteration 5, inertia 4659668992.0\n",
      "Iteration 6, inertia 4657632768.0\n",
      "Iteration 7, inertia 4654319104.0\n",
      "Iteration 8, inertia 4649958912.0\n",
      "Iteration 9, inertia 4644386304.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 4820618240.0\n",
      "Iteration 1, inertia 3876897024.0\n",
      "Iteration 2, inertia 3702369792.0\n",
      "Iteration 3, inertia 3620805376.0\n",
      "Iteration 4, inertia 3581976832.0\n",
      "Iteration 5, inertia 3563398656.0\n",
      "Iteration 6, inertia 3554145792.0\n",
      "Iteration 7, inertia 3549656832.0\n",
      "Iteration 8, inertia 3547427584.0\n",
      "Iteration 9, inertia 3546321664.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 5851845632.0\n",
      "Iteration 1, inertia 3762696704.0\n",
      "Iteration 2, inertia 3646411008.0\n",
      "Iteration 3, inertia 3603001344.0\n",
      "Iteration 4, inertia 3577639168.0\n",
      "Iteration 5, inertia 3562379264.0\n",
      "Iteration 6, inertia 3554737664.0\n",
      "Iteration 7, inertia 3550254336.0\n",
      "Iteration 8, inertia 3547901184.0\n",
      "Iteration 9, inertia 3546691584.0\n",
      "Initialization complete\n",
      "Iteration 0, inertia 5883782144.0\n",
      "Iteration 1, inertia 4039879680.0\n",
      "Iteration 2, inertia 3850450176.0\n",
      "Iteration 3, inertia 3727738112.0\n",
      "Iteration 4, inertia 3651527424.0\n",
      "Iteration 5, inertia 3606829056.0\n",
      "Iteration 6, inertia 3579656704.0\n",
      "Iteration 7, inertia 3563655680.0\n",
      "Iteration 8, inertia 3554793984.0\n",
      "Iteration 9, inertia 3550492416.0\n",
      "[build_and_fit_model] k-means labels: [1 1 1 ... 2 2 2] and \n",
      "cluster-centers: \n",
      "[[ 60.005424  35.237984 -10.703571]\n",
      " [ 41.474598  57.885315 -11.729183]\n",
      " [ 79.44875    7.148966   3.151702]]\n",
      "[build_and_fit_model] stored k-means center to: ./model/kmeans/KW/TEST/cluster_center.csv\n",
      "CPU times: user 3h 29min 13s, sys: 6min 12s, total: 3h 35min 26s\n",
      "Wall time: 1min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = kmeans_clustering.build_and_fit_model(config=model_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on all specified samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"input_dir\": \"./data/images/KW/H&E/subwindows_512x512/original/*S50*\",   \n",
    "    \"output_dir\": f\"./model/kmeans/KW/{model_id}/\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[predict_samples] Found paths to images: 121\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [0 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0164_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [10 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0159_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [20 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0106_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [30 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0148_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [40 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0060_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [50 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0097_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [60 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0092_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [70 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0112_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [80 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0086_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [90 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0156_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [100 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0089_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [110 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0069_S50.4_SHAM_H&E_10x_walluks.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from int32 to uint8. Range [0, 2]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accomplished sample: [120 / 120] image with shape: (512, 512) to filename: ./model/kmeans/KW/TEST/0143_S50.4_SHAM_H&E_10x_walluks.png\n",
      "CPU times: user 1h 30min 25s, sys: 2min 42s, total: 1h 33min 7s\n",
      "Wall time: 23.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans_clustering.predict_samples(config=config, model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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