diff --git a/Face recognition/Face recognition.ipynb b/Face recognition/Face recognition.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "165c3108",
+   "metadata": {},
+   "source": [
+    "# Система распознования лиц\n",
+    "\n",
+    "GitHub Repo - https://github.com/IgorVolochay/Face-recognition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f73e87c4",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "* python == 3.8.5\n",
+    "* dlib == 19.23.1\n",
+    "* tensorflow == 2.2.0\n",
+    "* skimage == 0.19.2\n",
+    "* pillow == 9.0.1\n",
+    "* matplotlib == 3.5.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "24f05414",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import dlib\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "\n",
+    "from skimage import io\n",
+    "from PIL import Image, ImageFilter, ImageOps\n",
+    "from matplotlib import image, pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c68df874",
+   "metadata": {},
+   "source": [
+    "### Установка предобученных моделей распознования лиц:\n",
+    "\n",
+    "1. https://github.com/tzutalin/dlib-android/blob/master/data/shape_predictor_68_face_landmarks.dat\n",
+    "2. https://github.com/ageitgey/face_recognition_models/blob/master/face_recognition_models/models/dlib_face_recognition_resnet_model_v1.dat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e33aa700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n",
+    "facerec = dlib.face_recognition_model_v1('dlib_face_recognition_resnet_model_v1.dat')\n",
+    "detector = dlib.get_frontal_face_detector()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3927fdf2",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detection 0: Left: 70 Top: 248 Right: 869 Bottom: 1047\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dlib.vector([-0.0902129, -0.0284728, -0.0201088, -0.0237396, 0.0195988, -0.0624656, -0.0341601, -0.0645328, 0.145692, -0.103387, 0.164995, -0.0667768, -0.19563, -0.0463215, 0.0462609, 0.0386064, -0.204915, -0.0965468, -0.0547373, -0.130571, 0.100364, 0.0206162, -0.0427951, 0.108554, -0.284338, -0.223312, -0.115767, -0.17538, 0.0108243, -0.0472676, 0.0455159, 0.0537128, -0.0945584, 0.00360177, 0.0861953, 0.0713386, 0.0278726, -0.0417318, 0.217425, -0.0155115, -0.131425, 0.0396619, 0.0762383, 0.219826, 0.112767, 0.0618684, 0.010629, -0.050927, 0.225475, -0.277578, 0.061519, 0.183117, 0.0907261, 0.0902939, 0.156285, -0.202908, 0.049825, 0.165025, -0.225213, 0.124595, -0.00210925, -0.00164449, 0.0281997, 0.0171772, 0.118259, 0.0737286, -0.16149, -0.0689448, 0.155305, -0.175737, 0.00137892, 0.170224, -0.0961124, -0.265967, -0.20611, 0.0573926, 0.432559, 0.168564, -0.122163, 0.0571624, -0.0393574, -0.0610366, 0.071344, 0.0299074, -0.113738, -0.0179229, -0.0436408, 0.0608074, 0.205917, 0.0866748, 0.0209013, 0.263666, 0.0228976, 0.0307268, 0.0403442, 0.035415, -0.105188, -0.0733519, -0.0467792, 0.022078, 0.053307, -0.108359, 0.00818931, 0.06266, -0.223834, 0.1759, -0.0215296, -0.0689591, -0.0981493, 0.0681423, -0.0973912, 0.0902572, 0.147521, -0.347928, 0.117237, 0.143078, 0.0655791, 0.166869, -0.0161545, -0.000808923, -0.00869995, -0.0707334, -0.165803, -0.0982898, 0.0219621, -0.0364028, 0.0172211, -0.0176742])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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i9G0BJOa0+3bW4TzX5oy0UP5Nn9D2rz4sNof3k8puzkOc+H3giVbk3grD669+i3YQMB50kWnCzv0HeFLw6CNX+eqLX+Vb3/oWnU6HIPAZjUZordna3qTTadFsNvmpn/opXn7ZOXV2dnZYaLsdNfQDhJLUlpZZ6iyglEInhlE0JvQD2vUanuc5JuILwqUWvYMuQiqCUHH//m0Go4h+P6bZDFlfX6fb7bK/d0ClMqBacbYJrCVNRrQaTZqNCkop2i3n4nz9tW/hS0FnaYGlhUXevP4Wa0sdRuOY5toK7Xabg4MDjBVYY+j2eigpaTUbjMZ9NlbWUGwhZcB4FBWuyzR1HoezjzxKmqbc33qA9ARXrz7GjRs3iOOUwWDA1atX2du6Ty2s4KuAZt1HJykf/eQn+fy//QN+7E/+MItLS0T9IbHVVCo1pOchlcQKCuN1HMeF3eP9pId1g37oJYcf//N/1hl9yL0NOnsv+ZWFRWacU2td2Bo0utilcsrPO43kkBsyhckW+RE7ytQYlox4EvCwdDzFwb17pL1d4v09TGrROqFSqXDv3j0ibdBxggDWV1ewWtNoNOh0Omxu3qNWqZImkqeefpQvfvGLLCwsoKOIMAwZDoc0FzuZmB2jtcazgnq9zmuvvcazzzzD17/+dR599FEMmsFgQKPRoD+Kicdjhv0BMvCpVquFRXs4HDIaRaysrXKwt0+j0WBvbw8pZYG+rNZrPPvss1hr+cpXvkKn1aRarVKv16nWa4Wh7tatm6Rpyvnz5+l2e0gpqdfrbG1tYa3l4ODAuVMbzYIpKqVYXV3lxo0bpFqwvr7Ot7/9beqdFt3BGGN9hCcg1VSqVWKd0q43GA6HPPLII7zy2uuMo5SVjTMMkxGJjkFKnnj6IzRaHZQfYIXAC3xGOnFSCXLKE2GMAXn4gZclB2kmOJt3KzkcRXPVDZsbFzP3ujD/fyg5lMV6UTIAHqOPKaWmm3i/XbVKImxmiEw1wiZsPtgkiCL6+11kHNHd76G1Jk1TB7pJUjylWFpYJPR96q02/X6f9dU1hDXUGzWU9fji73+BJ554gm9+85usLDm///LyMnGakEYxVmvOnTlDPI7o9XpcvnyZ7e0HXLlyCWs1ldDH6hCMxleQCkOzUWUUjenubXPlyhXHbKpLWAHKD6hVQqIoYn19lVqtxs7ODp1Oh8XFRTbv3WVhYYHnnnmaZrXqGJPnOVSiMQyHQx69fMkZgD0Pm0kDOk1YX1slTVNWV5bRWuMHITdv3qTTbuH7PqGSbKwsc//BNjsP7rHYbmCEIRACY+Fgv0sQBKSeRzQeI2oNQj+gu39Ao1rB2DHbDzZ5+tlnePnVb2KU4NWXX+Hc+Yt0ltw1rZI0FjvoKEYGITJzex9HU7YrcwSi6pR0lDt+Hk1vQLlb8l1d/kR6uJnDB4yEECAF0oJJNZ7RKGvobT+grXzMcIivYHFxkYODg8IlG3qKSqVC4EniccTezi5KKT7/+c/z7LNPs7v1gFs37rC03GJ3Z4snHn+UWqWOEFksBpYwDHnw4AGDfpc0Tmg2aiRJgrIB1WoFay1h6LPQbmKtJU6zOARtCKq1Av+wu71DvQ61WgOLpLO8VABqgiDg0oXz9Ho9Qj+gc+ECYRgShiHJaFiAlHK4clyvYYUp4j0WOh2SJCFJEhrNNsPhMMM5ePSGA5YWFxBCUKk4tWV7e5vlxSUXb5GmDEZD6mHMrTv3adVC4lSTRGMUlgcPHrC4uMibb75JvdUk8D20Trhz+yYYy5MfeZKbt29x+9Yt7t9/QKVS4fGPPMloMKDdbGE978Qd9jtJswbzk5jFO6WjUJ3z6CFnDrZQI8wxll2QJTCKQAiFthYh8+8mR85iF2bJGTYddsKJasZZnnCBSpNOTKPkAIS0zqFpNJiEVuBz/bW36FSrbN65RV2Ar0LSNMbqhGG/S6vVotlsZzp2xMriItsP7nH58hVeeOHH+PKX/pBOp8MTj19FSE2SpkhPsH+wDUC9Xsf3fXZ39vE9QbVSRco6AEHQxJeqkLbq1WrR11B5CCWdK0tKkiRBSsmlC2eceO8i1Ehjt7B936kelUqFpU67UDE6nQ6+7xMNB0gpne9f2oLx5YFeSeLEd8/zHDNJEuxiswB6jaJmgWQVmSU4lAtIr8JgMEBrTXcwZHdvn7AasLt3wDgZMx6nSOVg4Ts7WzzxhGMCcZTw2CNX8esNzp0X3L1/j5372/hhFaHG6HrC5u27nL1ykSSK8QQE0ie1Fi1ACImxtgA15YvXGltA5EUGqHJYGzc3joJeTyEjT1j4s6C+Q/BnYQFboKlP4mmqWBvW3dMpodQPOXM4HVk7ibG01g2skB5lDnBaJNk7oWLi4CQFZQ2eEextPqDpK+Jel06thkxTpITheEij0aDRaFCruV37/Plz3Ll7i/v37/LII5e5fPkKN25c5/z58wwGg2LCLC4uIoSg3WgCDpxkraXZbNJut/E8rwBQSSlRZCK9tegkKUR/aSy9QR/l+wS+j1IKpVwAVhRFrg3p9NtcLYgiZyz0fd9hQ7Jd3xhDrTLBk+SowzKsvRgja9na2mJpaQnf94vfG1ojMyYFliiKaNZqGOmx0GnR6/Wo1+ssdRZYWRmyd3BAb9Dn/uYOaWoYCIGVgjevfYvltVXu3H3Am9eugeehJTzY2ufixTP0hxFSeYjUcO/uXZrtFjfu3ubcpYssLC4jfX9q3szu6MJyrP1p3jnw9gKiZtspj2P5/OMCCN8L+kAyh7kLfEol0zgjxdtve/bBOq598nF5v0SaII2hEYZ0h0PG3QPsYEAlVERphPJ9as0aOtYuorLXo1IJaTRrLC0tQbvBpUuXEEJRr6+gEKytrbnFKzTCU1MI0Xq9XuA4giAoduVioVoKBJ9JnfswjmOENtQa9czA5lQGKSVKSNrNlnMlZpLABI5eQ0pJGIZukcx4g3JXprBphqYMinHSWjMcDgmDgIvnzxbowvy30Lr7soFTByqea1t7Lu6jVg2wqeGgNyAMQzqtJv1Bj1oQ0h+OuXs/YjAcEw1STBxT8QQ6HaONj/AUi60q6TiC1GBtSpQkNIM29+/eox5UWF1eQXo+qTaF19xJj7ObinAYmWLXnp5kxzGG2WOOCgfI6bgAsMN/H0Zyzj33WAl8mj4wzCH3Llg7J5cDk0FSQmKwh5BfRw3q8bEWAq0Nao7VWpSZURJjPYmXnecZy+7mHb71ykucWVrmwuoK16+/ybkz69SrNbrdLne271ANq3ieR7VWYaG9iK88Wp02GMtCvUmr3kAFIVJCmqaEviiMmEEQFLt2TnmshAnCYrJNBQaZSb6HslV+OBxQqVSKQLVc6vA8D6VUsZANtjD4SiYTPBd9c6kF4xVwXY3Ofgto+uFUX/PzVYZizdvxRejg1lpjjQDlk8qU1NN4nqJRi+iPhlRCS6vV4KA7YGmhyebWDpcvCW7cvMvSYpvd3X2sjbGxQqEg1QhrMAakp1CeYNDv0hv0ef3mdV749PewsLKCzh63tDABWGfzRZChYfPxzeZAacbl4z1vwc8ecxyVpd2jJN5iHs/O+HmqNLMs5Hh66JnDvEE5jt8ZbCYOT77JYune9rXd7uehzdFRoZB5SKRExAkIQzIYcefNGzz56KP0dnf41ivf5JlnnmJvb4/RYEir1eLc+bMZyEhw4ew5Kr7H0vmzTtWoVJEIKn6A9D2sFSRJQjVUxUQpL+IyzTM4TYKCDgfgCCFoNhtTTKS8UJVSeJ7nbAFqcv0cYZqfl6Zp4U2SXmbnADCysE9IJQrmMsucy7EyFl2oP9aIyXOwpmCOnufRbLWJkpRqENJpN2g2G0jf5+zGGe7e32R5YZH+OGJ7e4fhOCWJRwjl4QuBNpJRf4AWsLG2TrXdZn11hTjVSJH3kfmrSZhjJ2GZMR9HxzGPd31OOW/FNHyzkARP0rAfeuYwS27Aj34yGodJcJLfu0OxuwVyRJi2mCw0I1w0nrLgpSnJaMTGyjJ725vINGZ5eYFbt27i+z5Li4sgLEHgMRz2+cxnPkOzVqdWryClpFWvFQvfiekpvhfieXU86fAaZXH/KJrGgWSifymAzIqJsWueaFws9Exq8JVCp9btpBbIDXCZJCc95RiAUFORiUq5KeZ5ElG6pimi2bI3JUEI174GL2NENjNuaq2R1hSMIQgCNOBHEYGnSI112ap6A1Y6i7SbTba3txnGCZ94/mN84ff+gK2tLYSCSlijPxhjpEEoyf72Fvc27/Pg3n0uPfoIIgjpLC1ipDNuz4xstthO78acZcansTuUGfRRv8NkDs4yoqOjA06GA+T0gWMOOeYBpgda5x4GkwdD5SARAHMk6Oko9cJdJ0u3JuYsQqORwsVOKCnwDAit0eMhaTzm1puvEyqDtJaV9Q33AKXzvqyvb7C6upohIgNajVoWTZnh9QElJV4YIBEZlFhNTdQJ9xeHHrb7Wx5e+MW4ZRZ2IRBSorLgsgIOnh0rAJMdm8Vog1CAyY41ICQWjcxcloUhMs8nYCYGSYMBIdDWFkyjbLiUOUZFGuctMRYpZMHMJKqQMHzfx2Tu1SiKSJIEkya0m1XGMTSadRr1Cv3xmC/83m8z7o9YXOjQHYyxaUIt8Nnr7SFVFb/qs7qyAYFHNIyphx7SaIRSkG0OKvOIpbYYoVNnhTqJGcw+uzLTL/7OInzTJHWSmRBzw82PIykllonkeJJq84FjDt8peifWZJHzKWNJ4xjGIw42t3jr1ZfpVCtcvnyRVqvJ9TfedHkKFlqc3TjD5cuXi9wMnufhS4VUjjHobEfSMxZzKZ2MWyxAO3Gp2cxAVthdMvcl2WuWeZRzXFgyPbp021PgHAnCWKw1mMLNq3Dp9HJmpRBMpIzc/mCMKdzJIpMKrBR4wjvUH5gwJ5NPYAlWm8LuAW6MkiRxDCXra7VadRKF7yOUpD+MszyTy/j7XX74h36I2/fuc+vOPTw/oD+OkMqnWa8RpS6w7fbt2/wv/pO/xD/45f+WF77vU46Z2YnRtFiE772z622TeA+6MZvmYB59oJjDPC5ZFtfKOynkRkyRbZWH2zqqndJBh47Jj/OEQFiLTlN8JanWKjzY3uITzz5Lsr/L1TMb9A522E8SVrOApaeffpqFdoeVlZXCJSilzKDfTo+VclqclJnI6Bbk4Z3KLcRpxpZ7WHIJId9tCw/GbFYgVDZMeUBPaZwd3AGpFEqVXWuiaEcIl8DPBTUxdR3MhGlIoTBHzOpc2gHwvAlSUXgTZ74xBg0Iz0MBomT4E0KgtAYpaGUTP8lct77v088S4Lz67TeIdUKawdejfkKapgRBlf/3P/pl6rU6t67f5pIfUqnXEX7OAPMxLi2oEuNATD+DMs2zA5VzkcyqHfkY514orXWBVfCkcs/EAvLweafd3E5z3EOdz+EkOkl3ypO5ilOM13EW4ePO8ZGMhwPuXL/Jt19+hX/9m7/BhTMbvP7tV6nXqnS7Xay1PPLII1y5coWVlRUajUZhjJxcO1/8LiVdnrtCSR9PBQgURueW8kmuiXIuiaNegV9BSX+Sn8LK4rMUnpMejngded++n0kD0qk7QrkFIlThNVFKFQwQQEm/6Ef+WaBc6rYj+o5QCOk53Ep2HWMFJou+DYKgkBrCaoVKpUK1EhL4Hr6nWFtZYHllkScffRQlJLtbD/jkx5/nzp1dB+haXiAMQ7TWPPPU06RxwvXr1xn2BgwGfcgwI6fV0087b95pW/J0a/89oYebOQiRJWvJ9W1ZJG+ZfeU0xRGVdS959PFlOmTEKyXPsEa4LADGhRLbDD9gtEZpTTLsI+OIjdUl+gcuWGlra4t6rcb6+jpPPPEEtUqVWq1WXKvw82f/rJx4InI0ofAUNsugrEreguMmbL7j5As0/7vMjGbfZ8emQDtOte/sDG6tu3chDUI4m4jERwnXZyOcJIEULt5ESWcAlBapJuOd32+5v8oTeL5EKhBWghEIK5EIPCWohD6V0J9IJDkWQ7oAPSEVnh/gByEaS60SYk3KYqvBT/7Y5/j1f/5veOSRdbr7+1y5eJGLFy4QeIov/v7v0e/uEyqPu3duEgYeSdQjiYaur9luPncuZYliyjlD50/pyXllYNg8Kqc6LL4TFiucQdeayRwtP/Oj5ni5fME8T9csPeRqhdtJD92ntZS9EEcvdnPs73OveJxhyQiU9B1TiCOnZ6eGuucz7vZ57NIlvv3y13n2I48jfcnug22efPJJLly4QKVScbEMHGZCuctwdkEeeshTQK+JlbqcTPQkC/dpDFjl6wrhwqxnR2WWIed2DABjZGHEzZ+SxSLsxDcopZqaqNP9mp+T003m0nNHFdex0iBIpwbJ8zxCKUgTw8LCAs1Wh9u3b/MzP/1ZHnSH6OvXefP1V4lTw/LSggOS1eocDPskwzHRoI9f8QDniQmCgBRz4vjJWQDeOxASjlq8WmsXFFdK+2atxZrM25PZdo7ypsyqpscxiIecOcChUNTvKkmsxYn7vsFTkvFel3t377K3s01zcYHLF84zHA65dfMt/szP/AcuyjGDNh/lVs3BRjB/ARcLlQkeoEyzDGL2t1l9dFbHnWtveRs0i10oe1UKwJp1btApe0pJ355nHJNSTgU+lnX77AjyVShwRlIhMgOrEU6qy9L9eZ5LjLOyukR/PEarXXoHbbb3uoS+YtTr0e8d0AAC5TEc9Xlw/x473T1WN85SqbVYXV6EIOQ4ejveg+PoqEWrlDoCui0ht7FhkfKwixomhu7TPOuHaeXNJ2EOvU47iQ3zc+odeamZdg/toMJkE9AilUCahMAmkCastZsc7O+gTUL3YI+f+ZM/zebWfZZWFlldXSYPICuLlbnOXAbNlMX68rXnYRvmGWfnMZV53x81hmWRN+/DcceX+zkPM5HbfCRiSg2Yx7DmkSdAYZHWuByfRiLwsXiFumWcqQNfSHKjZnEdFAiNVE48r9earK2uUvMky0ttDnZ2MGlCpRKwvrKCMWCjCOKU+7fvMdg94PyZs1meiqbDswgxKXjE9DOdyxiOAB3MU1GOeuULX2fVzXIVYfL7dGbI2X7MBnPN+zxLDz1zOM6+8N3oSyEGW7DGEAQBOw82scbgZy7BS+cvUAlCVlZWuHjx4tx2gCIganYRzu7m5QWVvzzPKyQGOIzKK0+go+4FJuLrrG2ijJY8qo2yWFt+LzOy/DWbZ6Pcj9xwWXZZzjuu3M6s3aSMsSjfV/7K/65Vq7QbTdbX1mhVq3zv93wKm6SM+gOi8ZjeQZeVlSUunjtLs1qhUa3y0le/wuriAlFvUNgVyoCyk+i9kCRyklK5V+YOngVBHXWt2Q3o3w/J4SGlOI65feMmv/Ubv4GOE3QUUQ0DlhYWefrppxkNBix2FqjX68U55QWe6+izksK8hTWP8iAnYKrNchs5HWW4zA2A8/T+MiOcJxHM++7IYLQTJqLWuoj3mJ28s30oX7N8vfI45vdWPq7MJBqNBovtDosLC1QDn8cevYrvedhUs9RZoN/vc/f2HZLhmHQU0apUuXfrJv39PeI4PvZevtPkbArOQP+d3iw/cMzB2gxGW3J4lVPLz5IRTs+afeV0PCctJfIUBqxGGg1JjNApK+0WH7lwkcV6hcHBLq1GgyQdsr2zCcpy/vxZlDQI6cRaRIqQ2r1KevmUmGdcWjpfKjwhEcbliHD3lntrFFpbJl6co1Wio0TIfKEdVadh1lg4r/15zOA4VeQo8bt8jpQSD8+pD1oghUBZsEnqXjOMopAmsrRonpAoFNJm+BFhILN1SGWQKgXGrK50OLexQTQe0qpXMHpI4FtXWcvzGA2H6CRFGcVgu8vvff63ieMx0rjKYLmmcEiNUtKllyu9hJJveyHPjqFQEpdXX2SZbdMj27TWFuqWzTw4GltgTFyBpcn6OYo+cMzhu035w/AFjLt9KoHHY1evcv7sOQ72djl35gzWWs6cOVMUmJmvR+pDona+++dx+vMWbr4D1mq1KYPeSe6zoySA09xvrsK8l+R53iHpJpcg8utK6fI79Pt9Dg4OuH//vksSM+debIGuOnlK5yrM6soKly5cZH9vj7PnNqjWKlSr1SLR7Gg0Ik4juoMu58+f5f7d26RxgrSQHlOi8b1WhY+2Q3xn6QPDHE4ajPdjwPLoQyEEcRyTDocMel2+/eor1AKfM6ur9Hb3sdoQVitz9exCfCdFm2hKgsh38lmmULYfaK2n+lFWIaZQj+VdZ+bzUVJDuR/zJIZ57cz77TQ0tbCzvkxcoabIsZnHUVQqFZdHYobR5WMjxExBodz/n0G856lOvq94/NFHuHzxAsl4xGKnzde/+mU6nQ7j8dhJMULgAcPdPXbv3idUEp3GnCY48ih70XGv2XPfDyZwFH1gmIPNsA3lSTs7gHnuwu8Yl7VuISbjiOVWB5uk1KoV1pY6pCZBhj54hla7xtrqIkIaPE8hxOGJopRXGJVm77PMBGAakDQfmHSYeR4nJczT4Q/ZD4RAaQvaYLU5xAhmn8O8Phw5jKVFXV60E2MoWOve4zjG933q9TqdpcWjryFiEOnUS0hdeLcKHEmmfiph0dZgjWZ5dYkzq2uk44hoaOnuPmB9eRlprIuvsA4AF4YhL33tK/R6B3hSFRmhXP6Q+XlGymN2HCM4jt7N7/PsTaeVID8wzCGn7yYndXYHy3gw4OVvvETvYI96pUq/3y/E3dFoxKVLl4q+nqR/w/TDStN0KjfCoS7MtDWru5823n/2+kfR21FF3quUZUKIIkV9GIauliBHoTbfzXUsQRDQqNdpt9u0221++Ic/jTGGwWDgrmMmzCtNXYUtAZhS1fbce5EXTf73hT4QzKFsxPpuMgdhLfFwiB4OqVertJsNSJ3uee7cOba2tvjsZz9bQKSP63N518zJWlt4HHJjW3HtGaZQVlmO8yocRbNi9qyaUL7uaWhWfD4tzbumlLKojCWkRHmeYxAniOO5tDWrXpX7V76GFBZfClYWF6lWqywvL3P9+vUiX2betkK4bOJSoqOEB3fu4SvPSSEuuuQwgzDWSVzlOpqzxsuSNDn73bzxfb/n/geCOTws5CtFxRM0qhVGvQOwltGwT7/f59atmzz55ONuZxHzQUTFJMhiNcoLFErRhRk4ap44OI+KmITsdRSJ0vtsv6YYxZxz30tf/VSfhJhreC2YZFYDI7962S35dtXGWamq7N1qNZqMx2N6vR5nzpzh3LkzeJ6aWiBp6mqEri+vYFKNshZlsnRyx/TjKPTqd5tOGruHHD7tkqNI6QPT8QOz5G70iFasxZuTsGV2V5/dPW0eRGMtGIMSLnNQFI0IFMTjEeiYZqOO50sGwwOutM9TqYTO1mAsSkncP4EgiyYUfpF+3enehyHIZVtAOedjfowpibvzxPm5NgJjpxiEFe4mjcgmuLFI69xfSZK4EvVzpBiYziZd7ve8cc3v86jJWI7LADKXW5buD4HwMhCTyXNQWPL8FYXto5Rluyw5uGHOrmmBUsZmKwXWk0hjadTqmKjP008+yc27dzFY6rWAXn8MOOCREoo0GlOrBpAmWA+kcUlncqnGzIlllWV2W0qvNzHA6iLvxVE0f5yPPt4rPa7UTJ+Xj693QtDHO5YchBDnhRC/LYR4RQjxshDiL2ffLwoh/o0Q4vXsfSH7Xggh/qYQ4poQ4iUhxMffzvVOUimOM/gcd/xpSAgHl1UI9CjCQzLs9rBaF5Wml5aWUAga1VqBUcj7PTv5Z9s+yU5grS0SnJRhs6e1L8wDEskSym8WHzLrqZg3Vkd5NI67z+No9tnN814kRqNLhr951y2rN8eNR/l8YwytRo3VpWU8JUBYlJDUKgHdgz2sSaiFzs0pLPz+F36Pr335K/R6vUNQ8HnXLXuGvhuq8VFr4iQ19N2oFSnwn1lrPwJ8GvhPhRAfAf4K8Hlr7aPA57O/AX4CeDR7/RLwt09zkbc7kO/Un3+a830NvYMD9rd38IRL0Z6mKRfOnsNXHitLyzQbDaQQYCew6Nx3D0zZFIrdreSJgMOGPWunIdZlqaJs+Z/nBj2Oygxilsqhv8e95o3bSWN/1AKaPT/HQpSlFIOdyjVxlL5+XD+KDNqlc2vVChcvXgCdsrayTFgJGPT6vPCJj+J5LuGKJzyqoSu0u7a6Sq/XIy0ZJmf7MW9BlqWddzNPD190JgbpFJQDpY6id8wcrLX3rLVfyT73gFeBs8DPAP8oO+wfAf9B9vlngF+2jv4Q6AghNk5zrbwQrhvIo8VTON7Ic5xUMf9BZW7C1OAZg/IEyWgEpFQqIdYmVMOAJI1oVGucWzsLQmQ5GAVCSKScTMR5E1dKWYjzwky+nxdcM2toO2pilRdZjomY/b2MFC3vtvMkhtNeq8zEjqJZieOoBZLbQQ7ZIywuwnOGMeX3mLeVF/YpSyGH27Mo4Wyd1lqqVZe6L/RCPCsIg4A33ngDIQSpjugP9sAYlpptbt54k5X2kruunX62s/c6fWMTaeWo3TwIgiONl1PfSVu8Zo+dpklA1mkZKLxHBkkhxCXgY8AfAWvW2nvZT/eBtezzWeBW6bTb2XenaX/q79kJNm8yztt9jxR5S5bm2faEmKAWtx9sYeIUZeFgdw9fSOIo4sKFc3QHXdrLHSqVqtNPSwlU54VYzwYZFdfMDII2S9I62//8lU/+MgMpHYw1xmEVsohGmyZYq3HRocfbKPK/Z2M85jHYMjOeDf6aved51zpJKrFigiXIP1smO175/nMPg5TyUGBaTvMkIgfBFvi+x/n1NYYHu9SqIc1anZWVFYAin4O1TuXwpeLNN685HIU8PHZHzc+TpKt5toXZz0dJJMcx99NIf7P0rpmDEKIB/Crwv7bWdmdutCwBnra9XxJCvCiEeHE8Gr3b7r0nZIwhiiJGvT5ogxKS1aVlrDYEQjEaDFlc6iACVSz83NZwGvdevoMaYwoXmOTt2USmFpx1xX2sNgXoZ/ZVpnmTVUpZuAZnAUvvmSh8CsonUOHSFKLAPcz2J1cXZhfmSWPvciQYBIZOq8kTjz+CtGaSJFe4tHhRFDGKItCGZDSm1Wq952OhlCoqluX3cZwH6jtJ74o5CCF8HGP476y1/yz7ejNXF7L3B9n3d4DzpdPPZd9NkbX271hrP2Gt/USlWj0UKJW1O/9m7NGcOdeviiCUknvp6AfsRFsPqPoBgefhCY2wKTodU69V8HxBNfR46oknXICUnAB2yuL6zD1OXVPjPCLCTMTenFGUF/RJO0PRfhbw4wk5Jb6W+1NeQHlgl7WT4JyyepMkSSG257vxceHg5fNn+1t+L9O838r9ze0PnucVAUPltnPJZRZVOjtO5b+lFWAyNKV1cOrUxuzt7RHUQ/A0vpTUw4A0Tlx901qNYTQmiiI8KYjGydQ4yDkeAGvtVBq542ie7aVsszoNw5s3jhOajuT9jkgOwrX694BXrbX/demnfwH8hezzXwB+rfT9/1Q4+jRwUFI/3jPyhCxes5Gb5e/y+hDH00QkHfZ7xOMxSrgdNR6N6e7t4XmStbU19vb2isKws7EO+WKcp4/O0y1z63z5wR2VF+GkhzybUzDvX/79SZQvyPJ4BUFw4nmz/T6tFDRL5b7nTGneop/X/jzmlUtos8dL5fpaqVTwPIUvQMcuQ/XGxkbhJt3b2ytwKIPBgIWFhXd8b99pOg0TOY7eDc7h+4D/CPiGEOJr2Xf/R+D/AvxTIcR/DNwA/lz2228CPwlcA4bAXzzNRQ5zwKMNkeDEaQBjzdTuJbNd1JrDeQsKN/jsZBIGsFg0e3s7iChCYRhGQ0JhaTbrbKytUK9PdNO8nfIuVTaOgcVop/fnD8+VnRBgLPFoWOyUCou2pb7NFKqZlXymxgpQyqW101kF67KYau30/c7Ti2fHpLxIc2aRQ73L7cPEE1BmcvO8MUftcOW28j7k15wtQSClxNj5UuAhlUu49P+uffcZbbHW2Q4qlYBLVy6yeW+LUZywudulaaHT6bDfPaDVahGnKeMk4uDggCiKXLEf73Do/LtROcqbzDulYg4yGYe306d3zBystf+Oo1Yq/Mic4y3wn77T6+XRi+7e5nD+fLfOhCGXy3RSwUlb43orBRaJNL7rvAVEXLSRp+ESQrg6j0AgYa3dYndvl+2tTVY7TWphgKdTOq02oedTCyuFdIKxSG8i5hZMwBhMVndTybBYAJ67QcgqXuXitDEGobxitzTZw82DkQp7hpm/e6bWAgIjJAiJyOpK6CzP4PT4nqRiTWh24s4zrE4bSfNkqBMmVk5UU263/F3OTOf9nrdRMCWm+3+USzcvjmusRZeYCxikVUhfkqRD0sRQqzao1SJ2d3ep1lp0Wk16vQGh5xN6PvFgwNbdW2xcvoq1JjOWHr9THzIe52Sm790cddwpaUpSzP421k6lnT3pWT/k8OmSnki5rsN70LI4nDGp/L27piNrLXfv3mVzc5OrV68WiyNJEjzPmxIthRAoKac9INlLIvCVhycVaRIhMEgHxs8YmSlyOZTF3zKISggXHXiSWjAR5SW+H84dt9k25tkRyraK465TXrBHkbWCPNqyXHJgUrNj9vi3j5fIzzvJHpKPb37POVN241vB2ARhNSvLy5w9d4ZxNGTY7xaFfAFMlBD6AejT4QqO6sc7kTBOwzjKtqVCymKChv33ItmLE82NK+T6HjGI0xp0fKnAGHa2t3n++ed5/fXXiwQka2tr2WTyUUpgrXaF4ZzM7qpVQeGeJHMxGq3BaqSwmeG9nBx04qMvL97jDJz570e5CGd17HkqQ9l1OW8cCikny5Q9yaMwLbWUbSKzNoH8Nc9uctT4l/+eZ6ybvU7573n3mKbplJo3axeKoog40QQetOpVfCnAaJYWOmysr7G6ujq5DyHo7u5hknTqGu+Gyv0p/z3vuJOojMqEk200s/SQx1ZMyPMn9oZ5A5NvWFJOBsFaWXw3EWMndRnz4ipHOVulBD+QbN28z/rSCm+98SbrWeZibMy582dY6rSKhen7Pqjy4rXFyy26SX5DpZTz11tbAGOQgsTGSN9D+q6KtLAGkzGa8m0LIRBWZjES0+Ce3A05O8nK86Fs2S9bxWeZT55Uxkkvbtd34K6j7AeuMrkr5Ev29zQzKntK8sk/HA6oVCrFONisPVPKa+FnrszUTvqdxzLIEnPKpQLH8Cbelfw5zZs/nvDQMqJVq+Fv+Ng05vatLXZ3dvGDCt2DfZJUs37mLN3uPmTP48Zbb/LE088QYTDWqaH6iHV3Gu9OMZZHMOnTtAVM1JTsv3yTyDeu09AHQnL4ztL8HIrg7BRJklBvVPF9n1qthrAwHA5pNRoIIWi1WnPPBaZSns0Tt4Vw1aPL3DwMw0JtQKiCb1k40d+d7/xl19dpqLzjzuIZZhfx290ZT3tOtVotJKPZe87rir5dmpWCjnoG0welKAGh71Othiw0Gkhhqdfr1Go1egfd4n7SNKXZbDIcDt+VfeBhpQ8EcziN1faodGlv9zqz4nm+WAaDAcPBgDSKiccR8ThieXGJu7duT7n6ZvX2o/pxVB9nxe+ybaT82yx6sbzjH1USbdbOchTqsdyPebiIWSml7Gad96zmicizXpdZwNhxodnz+ly2H8RxPK1nz7g+5z2bXI1TSmF1ykK7xZn1Vaq1CgpLo1Yh8D1Go9HU+KVpyp07d0iSo3NKHkVHqV3fCUYzT7U4iT4QzOE7S4fFt6lFYsGkmkrgdnRPKjwpefTRR4lGYzY2NiZFZecM+FHBUIcNrBNvy3HpxubZBTiCEcwTRY+bFEctPinl29q5ZxnL7GI/meTM+zFHlhZqTjnepHyvJ107Z1ZSSipBkDF7h5h050Il8KjVaoW3KDdinl3fIAzD9ywT1sNCD7XNQQiBL7NJWVbkTsnS3I7kY0xKPi+m425yKWEyGa22eNLPEoxorE2x2uArhRSCJB6z3GnjSai3qgTVKr4f4iotubqe1roS9Yc8vfluJgXWyAzbIBBFLsmsWjUTZSdnIvMMjuWSjPm7sNNXnd4dBWk6W1fT9TenST0N5fzBwmLtZNzyvszSPDzE7AItSyCzWIUMJVA8h9w1adyw5RdxNTeLJnPVx7kQpcj6Lk1mH5mu/jSrJuX5LVRWZVMpVxNTegHGDGg2Grz++lu0mg2+9cZb1JotwsBnZWmJKNHgKSJtWOwscJCMQUqHMDWH80nK7O4MTPVh3pi5G5/F3BwacGdvyo2pYrodyvammVNzF/9JwsMHRnKYJ0Iel6nZkau1WM7wfJrdqwxhnhjpnGrRyGwNzWazMELOivOzC7ms7pQX5ux9HdefWTvCrOW+cM9hnT9ba0ypzkMZpJQvzqPiJco2hvI9nBbjP0/tmXdM+VrlOI5yct15dFyfAaxRCHyw3iHbW3m8Z59LPsZaa8IwJIqiomLZxsaGS1Ufx5PclkAURdy6devUfc4/z0sTWO7fadWM06og8xjRSfSBYQ7vnN4dkEQIl1h0PB6TpBHj8ZB6o+YqOIdhUevyqIf4blFu74RSYQvAVNmOUnbZfad023dKs9GSb99mJCevLPu0VPMNkDmTK4eyG2OmKp37vk+j0QCgXq8XG0KSJMXzXl5eRinFt7/9be7evftdC5A6ik5SI0+ih1qtyMlaDSV4bFm0zLnw5FhbGJekBBfTVN51c/nYONSggFyqnt2dPVfkna3tbfwkQSKohiGNRoNRPGKjvkKYJz9lGp1X3jXzeZ4rGsJOxMv8vPzzbBVkYaZ3m0L0PYbpeEKSWo2acVfOc+XlqkVZ2nH9kSjlkWfXO62vvYypKJCdZhrKPktCCFJEwceNzc4rVWnKRWfr/L9TzA4MxrgCx65jrhyAsa6eRVHE2IJEklqDtaCUR6o1Odgu9/QoC0bjpEM8RsOIpWYbEstYxcTjEUYqDrYGBEHA1SeeYOnCOmNjwE5Uvql7LL0XaE5x+l08p7IL+TgGmo+7MQbDYfUOPvAIyQkdJ2IdJWa+WwqCgCiK2NjYoFqtonWC70ta9RrLC4suCIujy8XNu4d5dJybcPa3MgM6SozNmWNZZM/pSJfqzNjNUy3erjU9n8hl1OfbscofpTqVfy9dDSEUs7fnzhFOtZQKkxWhlcKbc9x0+0mSMBwOAac+5AFXlUqFJEnwfZ/RaMQrr7xSlCbI7/udeMveDb0dNWTWc3EUPfSSg+v8xPImhEBJVWSHmieCvlfici5CDgYDrLWsLLWRwtJsVPDtxP1WHux8Yc7rz+yDmf3OmFyaEUW1J6+0+8Nhbn/UYhelz2XdOn+f0tFLUkX5OlIKcqNfmeYt0PKYl3f13L5RllqOk0KsdRbVsuEyF+NzBlPGj7hnhMvmjQNowUT/V9Kfki6d3GlQKAwusC4HqeXPzmQ1M8yMHQKyAsbSpQgcpqkrm5emhGFIHEWImQLJx9Fxx51GHZ06d87mVNhW3uF6eOiZA0wbFIHM0KQzMVIihCG3uLtJL8g9B+DE5rKoK4Qo6g04moieudU3t4JL6TEajkn6Xbx4QKXTwViFCD1U4KOx+KXM1lIqhCjbIITzigiKqSilzNSd6Z1GCMFoPAac1OIm5CSZbGpc9KAxBmGNy0HgeRMrfbHgLQiBQWCNndy/mFxvHqR6OnWZwdiEvMpXfszkmUzvUuXfppiBKaVEy5h8OTI2P85TLjAoLaEZ01Rj5cTuky/QMjO1VqO1QQi/GHdjJkFvOTq2bGtBSlJlsCisSbHGqRauTZdsRwjwrODM+gYH+z3ASQ/S94hGY0ap5sIjj7C5s8uZzjKjaIwnA8BJk5LZTeuwATqvewEUSM/y5lIe2xPdpDPeDSMpkLe5V6Pc5mnooWcOxU29TcOiG4Tyjv72fNC5bSNH6G0fHHBxpUO73abZbFKtVt2k9rwpX5Fl8mfhzioxnNxeUM5LYK3D/BtjCMNwalLlqc/CMMRkkGh3HQs2N6pNvBJF0Vs7uWauA1vrvCvzkqKWJ18eZn2UgS23fRxFZWkl+wKgkIYsprDDxLmBTzqAkRUT+LfWGuGpYrzyoKdp5pCFWwtDlBUYKoOeZnM1lj0/8yQYIQRSKVLjxkBrzblz5zgYjIgTzc3N2/h+gFIet27dQgZhUZ3LZNGe78IGfuR4nmTsfLuo2NPQQ88ccjrJiDLP4JLTPFvEUaqIZXoHFNayvLzMg+uKNE2p1+uF7nmSXplP3MKIpAErSeLJ5IyiCJmJqYPBgME4KvqjlMqi5gSDQZ80E1+LieLkaJIkKlyNaRpP2QzcsZkZTMgi/8Ks1DDv8yyVo0ULxjiHlCpLMhpb5NaYLOby+ORxINZa0pJLE8CmgsFgQKvVIoqignmmaVowOt9XjEZjbKoPVe/2vIndI48TmZIimI4z0VojSkZcz/NJ0wFRFBUQ+iiKUYGitbDA5s4u+uCA3iuvcOHSVSrVWnHteRLXO6UTjYczakhqjjdan4YebuZQFqfy+3QyUmng9aHJLUQuSk+SvFo7GyQEBjcplHD+cAd6EqVEs5ZqNeRLv/cVOmFANB7zyOXz1BtutygwDmWbA4DIAoKsRGb5I3IdvmwkzCf4cDgkSRJn5PK84nujU5IisYotUHij0YharYZSiv6w58KGcRM7jmM8kZWZlxJ0iihcrRT9TNCIUgTpPKbpxtJMAaDy91n7SVlFKO/OUpb0dTPZ3ZLIlbnPJanRaIQfhOgkwRq3e0vhkQpNGifs7O7hScUgGaKNW+TROEZ5kihKwLpw+DROiaIxtVoN3/eJ47hgZIVKWXI9G5ui9cT24Gws+dxxamsQeqxuLPPiS990KfWUT71SZ2drF08pzpxf48zVRxhFSZYjYtoLNc8udhp6N0zluOjanHmf1P7DzRy+y5SL/lEUIRshjaDKcDhkYaHlcjZwWIIUAFZkLkvhXgKw8lBmV2NMYew8ODggjmOWl5dJ05SDgwNqtRqj0QghBLVajTQd4fs+vV5vaufOE5JCBvISbkEmSVJIGUqpYhd1hk/X2Vz5mizmWZemmOovTAOXykbDPL6gvPOXpYJ8wuZqTfn44XBIaCy9fg8p3EJuNBoMYleirtFo0O92M5VGZOOiipwaa2srbG1tZSpYQLffo91uo7UmiqIitV0OXgJcWveZhWuti4A1ueqiLaEf0Gm1WWx3+Pb9N6g22uwf7OJX6gzGY5IkYWdnh0qtgZBqSs38INMHhjnkk63s553nMsrhw/MSeR5nHc5tDFPLXRi0SVhdXUZGvcLWUK1W8aRCSeUMT6VTFIAQWKPwMui3QJDYlDR2i2kwGBQBPEEQsL29Tb1ep1qtcv36dZIkodlscufOHdrtBcIwpN/fZjQa0e/3OXfuHLdv33ZxAJWgSA4rpaTdbpKMhlQqlcxOkUy8B/iFCiNUrpUIbKk6V76Y8512dgealRby65Y9CGXVwxkMnXSQp8DTWmOEJI7jAnEIsL29jbYGK5za9u1rryOkUxMO9nuMBmOUUgxHfZaWFhiNB0gpqVYl1958A0kWWt5z0lV1Z5tnn/oIw+GQg4MD51GIYxYWFojjmEktB2eIzPtf/BMghUSiEKnhwvoZNje3iLQlikb4Xsj58+fRmXRXSI5z6CS1GA6XBTwNFevCU1N/H32CxVh97FrI6QPDHL4bpLUmimNEli+h0WjQbDYLdWIec7IWpBZoa4iiJFuIgmFvyDiK6Ha7LC4uMhgMGI/HDIdDlpaW6Ha7jEYjkiQhjmPu3buHtYIbN24X0FzPUwwGA65duwY4kE4QeCwvLJKmKSsrK9y5eYOFZoPFlaXCc5EbzALfw9gUawPSpJRNWkxHdeZBVmXvxzw7zpT7ceY9ZxZRNHZQ5OGIfr9boAw1jkl0u1263S6ptoxGY4bjEZs7u8VCiZKJCxFNxlDGrK2t0Wq1UIFP/8YNglDS6zkVq9lqoLXmxo0bvPn6NT7xiY9z9+5dOp0OSil2dnZc0lj/ZFyABLQxNOp1eoMxly9f5stf/ybWWsbjMTdv3uTSU4+/J3aFh40+cMwhFyuNEdnnbCLnz0aWMAHaIETisi/NyTuZLxpbKtBaJMK3YISPlB6+5zEY9BmPnS57NAkMHggn2o/HY0bRkGEc8dWvfpUnH3uctbU1XnzxRVZWnBhcr9fZ2tpiOByyv7/v9Geg3+/TaDTY29sr7AxaayqVCu12G4But8v29gMqfoDC1VaoV2sMlxa4desOa2c26HRatNttxuMIz/Op+BXGUR+QheSipMLKaZ9+2Zg2i3IE5qgbJjM8UkgMSZIgrWHY6+IpxaDfZ3dnhyRNaXbavHbtDZaXl9ndO2B7e5vBaEycanZ39iexH76LY9jf32dvZ9+5eE3CW7dv4nke7XabxcVFOo06+7v7nDt3hiRO2d7eJggCxibmC7//BywtLZFm3Y+iiFgbVpcWqTeqJIlGiNwupLBpirBOKtTGuYW11lTDEGNSqtUq/fGYRMd4fpVonLDeaDCO08x9eDQA7jQSxKFzrcWKaTtG7qaU8vSQbaun63V+sG0OJZdYWWw9PRkoYuEOU47c86SP1hMXVHkH7Ha7xbVzUXtWdJtnmDPGMB6PuXPvLv3xiEcffZSbN2/yta99jQsXLvDaa6+xuLhIv9/nwYMHWGvp9/sMh+PCVZjvwHkyEasNo8GQ7v4BlUrFGTMHA3rJAUudBWe/0IZhv8vq6ipfffHLPP6RxxmNRrRaLe7fv0+tVmNxcZEkmWR4MqkBNV3nYdpgdTghzuw9565GmMZRWGPY291lPBgSRRHtxQW+8Y1vwB3F0soqN27cYGt7l9RY9g66YCWphV5/gOd5JOMxg8GIra0tlpZWHIMUIVonpFpz7949RqMR235Aq1Hj9t17BIHLr/Fge8sV+FGK8SgijmMef/xxbt++QxhUuPnWdT7+8Y+6cSierTNgKinROltEJncB9xgMBrTbbXa7XVRYYzAYEMfxlHt0dsnNekdOQ1PHHsGUv9P0cDOHU1IZEfheSndhGBIGAbvZrnnu3DmAY92Yuf48Go0YDAZcv36d2Gg2NzcZ9vpcvHiRnZ0dFhYW6PV6hd49Go3Y3NxEKZf1qN/vs7e3R7/fZ2FhgWazSW2hyquvvsry8jLLy8u88sortDtNgmqVXq9Hp9NxngitefDgAfVqjc3NTaSUtFotqoET7+/0b7G4skq9XneTWqhjB65sdT+O8riPHJthjGE4GDDqD/Ck4mBvn/v37zPqD1haW+X27dt8+ctfZml5ld1uH6Gcd6FSrXPm7Fm63S42cqpXu91G514kJL4vGfWdHWE8HhONXV7PwJMEgYfWmkajQafVxhhDt9ul0+nw8ssvc/bsWb72ta+xsbHBN155leeee8YZjK3DmkxiUibMXkpJo9HA8zzSdFSolIuLi1QqFaIowgqF9D4YEQmnkVw+UMwhjxfQKYDErUPpREJzGNjjUqKDtYJZC6XbJZ04rG2MLVUmzi34cZxQr9ZIEycF9Af7KOV2W0/IObuBRUiNNgm9/h47Ozt0u12CMARtOLdxBptqdre2GY/H7O/vAxDHDj/RqDY4ODggjeLCHdiq15A2ZfvubQaDEY899hjD4ZD7d96iUfERaUqj2eD+3gGB56PDlGa9itEJWM3dmzepeB6bd+7w0Wefw2jNwsIC4/EQKcnsJym+9JFWlCovuwwEQuD8vvmLHOJc+EWd688qTMlVmaYxSRqxvfsApKU/6JOQ0F5sE6UJd+/c4d7mXdYWF+n3R86QbCy+VPT3D9i8c5fFxUVSbRkcOCni3LklknTkwFLGVaJKksR5k4RHWPHBC/E9Rb1W5eDggMHBAdvbuzz33DPcvP4WFy6c486t61SrVe5v3uONP3iDKIr46Ec/Si10MRMpCQidSZIKaYVTv9KUs2fWuXn7PgrBYDAkNRZVrbF4dh0R+kfIqEzGjcMuznwOlRMLT503jykbW6gWR7klC3dtKa5lSjXkeJXkA8EcZhF57qbzxf3O2yx7PubpYPmu1Gw2GW5mE6QUWjz7UMoP+Xd/93cZdHtUag12trdJ05Ttzfv0+32EEIzHrtaiMQbf97l7927hkgzDsND7o+GIRMLK8jKVsMv9u5vcuL7F5SsrrK2cpdvdQ0qP3d0+QVCh2+2yv6O5eOEc/e4evoCldovXXnuN4cjZMYxNCTwfjEbiIaRE60xfFl42QV2F8PKCn73PeWOaj0kQBPT6B3iex+a9+/T3D6g3G9hU8+br17hz5w5XHr2CMdA9GKGEZX93h1inYF2p+2F/QKJjAg8gxZMGP6zQ3dunUg0YxwmLCwv4fki1VuPGzbeI45jxqF8UvvWlotGocevWLVotNw6e5xEEAQf9AVevXuXatWt0u11eeP4TeJ7ED0qLigQpHBRNKotOYjbWVugPRywuLrLd7RbP7ShvRT4mZW/ESWrGaW0SR3knjjKYT6srx7f9gZCB3mmcfA7BfaeUpime5zEcDgvQEcwPl819/tVqlZdeeomzZ8/y5JNPsLTQ5rlnnnWTvdfnkUuXCwPia6++zngwpNvtFpGfxhj29vbY29tjc3ObwWCApxR37txxCU1bIU8+tcHFS+cBS3/QxQ8Ejz9xkUazAiZmeWmBjz/3LJ4n0WnMN176Gp12k1u3btDt7mOtxqQJSoDEIqyrIJqmCcZOUIT5q+zJgMMTN/87n4y5F8L3fZrNJp1OxyEcR2OGgwEXz1/g0qVL7O/sEkURCwsLpElMp92kXqkSegaTDAg9U9T36LSbCFJe+/Zr3LvbZaHdIopG7O93eeXlN3jxxW9w714fYwyXL19mYWGBtbU1Op0Onufh+z4PHjxgNBphjCkwIOMMp3BwcOBUntGouAe38Byy0xiHxAw8P0v0owu4e65GHUc5s3m/qOwxeqf08EsO1pVuEyUvhPucp2rPU7IdJufVyB/wYZoF9RSXzN6l9dBC85kf/FFe/Df/HKWcC9OQp55z/v28BqcnJK++9E36ewd0u12GY2d3OGstrUaFhdZFxuPMtTce89yzT/Hiiy+zvtFhd+s+0XBIRUguXrqAUopRHLG5dR+lBBfPnWU46HPx7FniOCYMA/a3H/DYlUsIJfFVjb2dbT79/HPcvXmDa99+mbXlDh95+vt56aWXCAKPZr1K6DuItR9WCuCS9BT4KpMcpplA2UiZj9kso3DuUFsUKdbW4AlJYjXxcES73iAe9NncPKDTWSROnNTUaDSKZLAVIVhdXMAuWPrjiK2tLc6ur9PuHfDcc8/xxS9+kZ3NET/xJ37QeXBsymc//QLXr1/nM5/6GJV6g27f5VdAx3zfp15ACMHe3h5LS0vcv3uPRKccdLvcvn2bWq3B1u4OvV6P3d1dXnjhBV574xrnonNcrpwl9PxiJhhcGQNlINEx1956kyhK8DyHkv3o88+zu7fH0sYGJgs0K3I1zO7cJU+GmAzi5LsSqOxEMi7Ayxwxv8ttmVQjLMfmJ52lh585fBfJLf6YF198ccoinVMuLubJSXq9Hn/8x3/MYDAoDFX9fp833niDOI7p94YMh0OefPJJ1ldWWVxcZP0nVllZd/U2d3d3qQZV9vf3efTRR9Fa0+sdODQgkI6HSCm5desWtaDGx575CM1mk95w5DJTeU9w984tLlw8l3kOLHdu3UAnEQQetUqIUiILA09JdUwYVItkIMrzUHngVkZlQ2T+uTwOZaaaeywkgiRNAUOz2WRna5swDFlaWsrEfZ/d3V23A5uU/YMDhDUoAa12m6XAY2W5w9raGltbW9y/d4ePPvcMi4ttpKe4fG7D2WNaLR67ctmpnWGFS+cv4AcBo6HDjIRhyNJCh2G/x/mL59jb26PTaTEejahW6qjAp9vtsrS0xIsvvsjq6irj8ZgnP/II8WiM7x9eHu12mytXrnDQ/SZp9vx/67d+ix/96Z9iPB4jlJ+pvCdTWcX4bmQMO4kebuZgy1GZ7x/liyFNU6QwVCoVejOiY9nmYHHeiV/5lV+hElTodDq89dZbaK3dhBuOqFWqXDh3kaWlJfr9PmdWHIhneXmZRMd0u13OrK3jq4CNtXVncxCWTrtFs1Yj8DykcAartdVlTJZ8xBiD11SEnnLHP/YoWrvgqnE0xGjB2lNP4/s+g1GPNIkZDYaE9RoSSxyN8DIpYjZV26yoHMfx1HFSSsbj8RQiEjJpwjjw1DgaF6jMVrNOv9+nEvqcPbOGMYZ2q84jVy5i9ATanKfei+OYzqVzRTBV4FcyJibw/QoIQ6Vdz3ZIhwWPoyFrnQ5po8FwOCSohiwvtomspnf7gHajxceee5Y4Tnn9xg2Wl5fpdrtcuHCBzc1NFhYW+NVf/VX+w5/+mal7d14dCmkk/87zfYLAGUb9ICDV2lU/mzOfirGZMUSexj15rEoi559fNkjm17al306ih5s5lGgKLq3zOPVJ943NobBiihPn58wmvLDYQ2rFISOQ1HhW0VppsvWKZmf7PlI8gTRJkf5NCIlEcO/OXUI/YDTus7y8xLlzZ5EqoNPpUG/sU6/X2VhfJRqOWF9dYqHtdGEhDcpKlhYXiaIIrzCSSlcUWKcgFFaCMDAejZHGZAFHY6zNXG9aO/OsMFQDB9SqVaogS3kZqZJqjVQapVOETvGUj1IaKUBagRQeNs+foWweiuT6JEAqic0qUiVpghG4Nq1zCHkGYhNjSSHVCFIqoSIZJdSUIgE6K0v0R8PC9WmtBR8gi7WILUIpPCwCS6gy3ISJHaOwEqGz8G1tUIFPxQuyZ+4DmsATVDpNhPSI0wQda568eAWki8eor9QZDAYc9LqsLi6w1+sShiG9/oCvffUm3/PCA86cWXeG2iwnBxlMerHd4V64zWjkPCWt9kIWOFZDSadiGijiM46iWaxI/t3chVvyTMxpaMoels/7Q+fLiZpjs7lyHH1gmEOxS1s7pbedlubFZpyW8mIn5RDl2Ye0tbXlgqZ0TK/X4/HHHydNnU56/twZut0uC602rfPnqIYBw+EY3/dddGfggFVhBVQWSeoCpwzK8wBXAq7c59yDY4zri0ldMV4hBNFoUOzsfiWETGUI/YBazSMIQ3w/zCDZR+urs7aYk8LUy/1TSjEejtBpjDCWNIoZR0OUJxgMe2Cc1CMzlzAiLaEHU6zzV2O0JbHGXTtNSLUzEit8jExdfIg1LhekdteV1tXztKkmwWToxgq1Wo3BaIhOYnoH+6wtL9FuN9nvHqCUYn9nl3aryec+9728+OKL/MAPfJZarYqTW7McHLh6oSbVhdFPZh6neWP4Tukkb8ZJCMfcQP5u6KFnDsXuXq53OecYOZPkdfb8o/6G+YkyrANIYIzFJEkhOhbt2AkacOvBA3Z2tzh//jyvvPIKly89wp07d7h88SLLy8suxHp1hUajwe72Fp12G6kUWruIQp1aRMb0TBaOLKULd05jtwOn1uWWgMzQZEvh7MZV67ZZDL+FAt+hB67v0hqsdDttmiSuLmUp49C8nWt29zFmOmozV2tyl6tJNSr7LfcUSeuiL8fjcZHNyTEe7XZjDMK6TMBS5BmfHCOMoohEJ2xtbbG1tUUcJ6yurmKtK0/XWVpgYXERvxLSqrYyaLVFC1tEaxoMvudhTUq/30V6Pq1Wy10nC21v9xs82Nzh0SuX0Rbub+8UsTQ6Y1I2T5eXZYpyqpVTpba3t4tyfuXw+MNYyfk0D2Q2K8meZEicndfvBZLyoWcOzEZKvtvW3paoBzDBWERRVBxXhg1vb2+zvb1NFEVcuXIFKRRXr17lINNNL1y4UEyslZUVwsDDWIE2CdZYPOEhstiGFOdm09qSJJEr755qjKDI05CkE3tDEkWOyUjlQpBnfdtCEMdxlqwkRAU+oZKMxgOU9Alr9QIuXL63+T7y6XyW5QkopUTbtMBn5OeOx2PSJCGNE6JojMgyfiuEE2tFSeIZR+4erGDz3j08z+f5T33SMTSjIQtoC/0A5Xuk1hCnCb3BgHv3Njk4OODSpUsgLc1mk/5gSOBXsFbjSx+hJFanGGuziHVn3DUCWnWHfuwPRwyHt3jhhRcc49aHwXVCiKmEsn4ldLEwzYUiJZsT24+dau+KTjuP8/6+E3rXzEG4BI8vAnestX9SCHEZ+MfAEvBl4D+y1sZCiBD4ZeB5YAf4OWvt9eMbB+WVA3xw9oZZdaqY2BJr40PNzBvI8k457zjHzRWQ0B+MCap+hibMEmXg8PZ5ZOVnvud7GQwGVKtVOp0OlUqF5U6bSqVCnBnl/EC5iM3EMRlPSqwwRCZL0uJ5GKuQKiROxggZcOPmm+xubdOsN2g1nBEu8HwCobh//z5SQLNWZ5hENJo1bGoLm4HMgsqEEK5aV5IwTmKGwyEqFCwtLJMkAcMoZXG5isUgPTV3YuUu4XLgUJlRONXAoLXDSkSjMUKnxFHEsNdnOBxi0pQgCBwDEy6ZDlYBNkso43T7K1cus7KyDlYiZB0hIcBldcozQSmlXKp6KVjzPJTw2D/YxZiUt956gxu3brGxsUGnpbBRCsq5bI0sJb0VAVcunGc4jtna3mPnwRYH/QH1Ssgbb7zBzs4WTz7+RBZGrvGUwM+wEdVqjcHAxawMDlyOSZcqLstnIUCo03kgTgrGKksVR83X2bbK9E7xFe+F5PCXgVeBvNz0/xX4b6y1/1gI8f8C/mPgb2fve9baq0KIn8+O+7m3cyGXmPV4LlgkiC29O710+rijWrFlzmNBCJeRKI41/d4Ya5y4LqTbkd944w201ly/fp0zZ85gUk3voEujVqdWD0mShDBwIJxkHCGEQQow1mU40tqSWonnObXl9p077O/vs7CwwLlz53j2mY86LAKQRGMwzkWYDsc0Gi2MTkjGEdeuvc5gMGBldSnLqkyGhwgnYmtmkLLWohNNd/+AhlWoap0kivHDPDdENt62NE7Zd7kaobXBZmn0yjlsrLXFrprnnxhmqFDHWAVaW7xAZQzG2RDq9Wahtw8GI8Kgmv3ujMu+7yNUljQXl2fBCpeQt1pVpDamnhWh+cjTT6GTlC/+/u9zD8u5c2epVCp4YYDyQgeKUgotDP1+H2strWYdzpzhQhiwtbvH5oN7rK6uZoxVFmpFjm0JgoAwTul3uyy2F6hWq4fnUq5+zbg2J6X+Tq92SHF8FnKnopUhE/MDt04yQpbpXTEHIcQ54KeA/xL43wjHtn4Y+PPZIf8I+D/jmMPPZJ8B/j/A3xJCCHtanOgJVLhrcsv6CUCSfELbqYk9XZITHCeuVGoIFGFYBSQu+7VzbT7xxBNce+116pWqm4BSuTwDWWJaJTKkmkkJA480dq4/nY7AOsv79bducnBwwJUrV7h69So2q+SSJAnjKAULGrBCot3WlEk1OoNbV7l8+QpCwIMHD5C+S+riske5Y/JUc5DZWHRKrAU92aOh/GK8ciZaMNOyK9mCJ506Y9IsCjHLKpU/xjwDVV7zIa/3AKCU08eDIEAJ5xK1aKrVMCtf53ZIpSaSoNGgpCIaJ0hPIGRSuFKN8PBUSJwYfM95j+I4RipL4Hl8+oVPUfEk//RX/jHPPfcc9UYDPzSoahVjDUZIfD/IPBGOQfT6Q/r9PhcuOCDa/fv3WdtYn1j57QQG7Xkeg909quF0GL+UEsRk8csjNqa8YM9pJIDcVnP0cpmflKd8Rffd6Zfbu5Uc/gbwvwea2d9LwL61NlfUbgNns89ngVtZJ1MhxEF2/Ha5QSHELwG/BFBvNdE6KcRANykAO9NtYWAmu/RJ8FErxbTfF+XUGAA8jIkxaFIt2TvY4d7OHutLDjbrC4NA4WVuMSFEkXxkPBwRhB7aJERRFr5kweqE1OgJJsAo7ty7x7Vr1/nYJz/B+fPnqdRqmS0i88rIoMg4ba1FA9pCnnJOIp0kg6ZSa5EkCRtnLxFFkUska2xmIJNgFUYnjrmgMNYw1oZUjpCVMQ3rPCICC1aTpCnKqsxYmD+brNKDcJmHXIizJU3SqbgKk1qSKHZjIz2QIFFIbQkrWUVy66DHlSBwOrzWgEQoSWQTJB5WCjAjhzfxsrqXGqS1xCWpA3BZtnC5PoQI0CbGChhFEb/wi3+B//6//+94+umnaSKJRkPq9TqVWh0TG7SAJImxQtLuNLkUKrr9Hl5Y58HOFitiza0pK1HSz5LVRgihWFhYwCgPlxrfLUJlIVVQ0ZJYgTazFaeOnJZTOIhsPUz9NmciuzeOmevvcP99x7AsIcSfBB5Ya7/8TtuYR9bav2Ot/YS19hOVasXtYMYipYeUHkWp+qktv1QncdLOSdc59vcyQOWRRx7hR37kRxwe34KWE+azv+8wDEEQZO5InzD08X0FwtVVSJIoSz3vqn3fvHmdf/U//hs6iwt85rOfpV5vUqlUMBoSbRklMZE2JNkiGGtNZAyRBis9hnHMSBtG2hAjSaVklKRYJMY4o2UYhlSrVZeoNstBkaYGZ5cR6NRmUoUskt3mTLi80MvjZVPtJIWZGgnW2sItm1Oe5dq14WpQKN/HIpHKJ6hUCSpVrFDEqUtim2rLOEqIYkMUa6LEMEpSdg96RLEhsZJU+aTK5/7eAcb3iYBUKYbGMkgsYy2JkgRts2K6ns/dB1t87nM/xre+9e0iDiKOY/r9IZYEITW+kvhKkkQxUglarSbGaB577LHMfa6cipmFb+/u7maZqCNWV1en7nneXJoHKpt3TN5+2ZX8HgnXb5veDWbz+4CfFkJcxxkgfxj4fwAdIYpaY+eAO9nnO8B5gOz3Ns4w+d6QfTe3cjwNBgPGUeRASxk+XUpJv98vjJDl9HFOjJ/gKdxLMxgMeOmll9jb2+NPfO5zTk9VEoGaqDdSYaWiPxyx3+vSHQ1QlYDeeIjwA/b7A6RfBc8n1pZIa7rDEamFKNVTkynX1Wu1WjbhFNYIfC90GY+sZTyaGHBzW0HZCHko4axxRgmrTSFi59cqB8jliV+EUIgs65S2Ai8M8cKAWr2J8gKSjDEMxyO0NWhtSBOXY9IiSVIL0uPWvfsMI7h58wFvvnmXr3zlFfb2Rty/v89v/dZvE6WWUZoySFLSBIbjMVGiibRB+T6D/ogf/dEf5e7du4VLtd/vZ8l6nYs6h3Dnbtg0TRmNRuQ4B5cmQBWp/fr9fpG4NsgkoH+f6B2rFdba/xz4zwGEED8I/G+ttb8ghPgV4M/iGMZfAH4tO+VfZH9/Mfv9355sbxBI4QbfaJOlEIc8VLs4XVlsYUTIznyHD8oFcWWZnxBYpdBJzEuvvcqiSYltimcESIsBlldX0Sbm69/8Bh996hm80EcajUk0CusqKtmYNEl4/eWXWVpf5fzFCySpxOJhkUTWMByOQSgMisE44qsvfcMxoN6Qra0dzp8/7+IkUku9ETIe7vH41cep+pKzG2cYRWOUgX40pt2oI7AEynNWc+GiRfd6faQUSE/hWYMmxq9UsNYZCvOis9JKhBWAxdi4cM9pLKlwKpgSAqNNYc8wOsFXHjEZMlAp8AKkgUD56DSl3ag7RoUgGY1JjSGsVtDGYKwlsYJhnJAag00j3rxxg5v3brL3YMj2fpfu2FCpS4JKSBSN2eoNOXv2PC9+85vEGBYX2jz11JMMBn0a9RCbxM5tqhRBrc727i6PP/44e3t7pNbQqjdIrKW7P8APKvihm2sNvwJSoCVIT+SzzXlXRB7R6XJpWGvp9XqZ/QSMcDkxPA3xpGLyZEaX3b9lW8CM/WF2/kqyuJYZ9XnWDjHPqzGlmryNtfCdwDn8H4B/LIT468BXgb+Xff/3gP9WCHEN2AV+/jtw7XdE0k6en7VkFl1TQCy0FfiVCjIakeg0mwRQbTgIbjQa8NRTT7noN5sWKkcUR/ieszIfHBywsbHBve1tzl+4RJy4mhBRkjLWkOgETcxL33ydf/P532VzN0YCQQijGL514x6eJ7BJSs0X6GjIjbce8Lkf/iF+4zd+i/XlJV742PNYGbPfHxAqyfLCIsMoplqtIqVkpVZzmaeyylCxsSgvwIIDRc2hPAozrzYlrSvlZ0pQdZdqTmfqiBONPc+lahM4dKKvfDqdNsNeH2udHcYZTFOGwwEH3R5ShSTG8u/++EsEtSqf+t7v4xOf+R7+zt/+e6xfPEtHW67fvkMcpQivytdff5Ovvfwa0Sjl3/7Olzmz3uTX/8W/5Sd/4nt47MpjVAKFsjGexVUUkorecMDCwgLdbtc9ewRWCJIsOlQoD52maGsQgYecKdzj+z7tdpvuMCKwkmGcFEWOTBSdCJk+imbjdo6i9wK/cFp6T5iDtfZ3gN/JPr8JvDDnmDHws+/0GkdZaqWUGG0KluhKox0etNkz890QKNLZOzLYzHAoVY6PFzzx1HNc+8PfIYpjNBYvE78rlQrNWs25An2mLPe+7xONhxwc7BH6Aa9/69t83w/8MN3eEMIqUZSijSCNx3z166/w5W+8zJs3t2k1K7RbLohHhj59O+Z+t4cxlooF38LZ9ZDQC9k+2OLn/uJf5NVvfINv377Jzbfe5Oqliyw2mly/fpPO4jLWjFlfX6ebDPDDgFqryX63B4Fzc7ZarcxdVs5OlNW61AadMQglvKxoSynRTmYAd+AmhZQOulyr1eh1uwS+Rxj6NOstYuMqUi0vLDLuDbh//z7dvsvLuBdFRIlleW2Nn/rTf5r1jbNoIfnCF3+fRGoUmuv3d/jP/spf5a/+F3+NSsXQrtdAaKphFZNqdvd7dBaa/O4Xv8S1N+/z2e/9JJKIxVYTm8RgFSoLFmvVGujM4xJWQocetdahPAOfMKhglHAZ9EyOOLVZKvxqwRSTDG36bqIqhRCFHUc6aGXh6ShLAtbaKWnDuaa/c0ziA4CQdFTOhnwYrDRhHDlycJ7F9zgtJq/aPW3o9DAIPCUZ9xP6gwRtJZ7I4L5ykgA3CAIkFm2ty3ZtDBKX2r5SqdHr9Xj+U99LvzfCqoA41sRJzDD2+Ie//KvcvP2AWkPyC7/wp/nNf/0/4sWaoBpggwrj3TGvXX+DX/2nv8If/bt/y1df/H2s0VSbDbqjlEqjzae+77NEoyHPPf9xbr/xOr2tXbracO/BJu12m+3eLheuPILvO/CQ7/tuoUtXt0J6rvSeRGWLIBNjkxRVskEoAVYal5WZkgs5ey7CU8hEkmhBtVbDpinSC4hTp3aEnRavfOtl9rf22Nnfww9cJOaZC5eod5bxm22WVjrgBXjKJ440y81lGosdvvKtu/zgj/woyX/x1/i//fW/zt/6m/8NcV/TalRJhSXujfnxP/Xj/Mtf/zXu3L3NP/iHb/HpTzzJ41fP0KxWEEJRDUSB9DTC4ikFqUb6AuH5JMZlpdAGrPAcDLqErbHWFkjZPJXf2toavV7P2V2EKDC90mbuyixQTdjJfD2Kivknjl/0OUMRnB4vUZyXvZ8EjnrImcP8wTlpcMsDcOQ5x6DR5rVZrVbZ2dk5lBVp3rE6TRE4A58xBp1llHKGMNBIwqDCq6/d4Jf/4T/jzIUWP/fnfoynP/osW7tdrLXUajW8UBIbw2KnyqOXrriIxhAeOb9Gp+bClzudDvV6HWk0EkvgCR599FGihT0O9vZBGJfEdWONe1vbfPwTz9NotVBJgjTO8JarHQVyMGMMSZIUyVZt5kf0Ap9Yp4XDQggH+9aZquIJSLMQbU822d/bxVpLo9EgiiI+//nP0wgC9rf2OHv2LOcvXiLRKUsbZ4mtIvUCrFRuB5WSWqOOX60wGAyoe/Bjf+KHCIG/9Tf+awb9LjqJaKyvMIxibu7uUqtU+XN/7s+yu7nDb/yLf8VXv/IqNom5cnGder2CL0N8z3PBaBnuwnkjHCmlIPMYWClB2KxsoPs9TzE3Ho+xKmB1dZW1tTUO9CTupmAK2WebM1Lx9hDVR9kf3s65szRRn09mKA9fhokZmt31jxqot/v9PMp3v9mBk1IyGAwKl1Xe7qzbKd9JYJL4JG+30WhgrSsAOxgM+K1/+Zv83b//z/j48x/hf/lL/zOefe4ZJILV1VV2dno0W3U8TzoJwfdYW2yy0g5ZXWzSqNUJwxDf93nqqacKhphPdM/zqFQqLC0tMR6PabfbPPbYYzz97DP89m//tnNbKqc2VGoOvFV2teUowPx+5o29wDqkp85L2IO0xuUlysRvpRRSeVghiFPNv/uDP+ATL7zAxz/xPB//6Mdc8FSrxY23rjMajQo7hUUilA9C8eSTT3J3axMV+Lzw8WdZ7bRZX2kQerC6vMhiZ4Fh5FLOr662WFxcwpeKVrvBT/+pP8Fip8I3XnqDa6/fZDyOSWKdeSNs4UkqS6WzsSOz0OVcjciBUJubm2xtbU3NIWM0aI3IQs6lsUfOreNonvvzJJfoe0kPPXOYiFlploQscYE6UhfvMrMiC+mANLkvP393aEI59VuOFZMWhDR5gjOUNShhUGgkKZ5IMDrl7MVL7O8eII1wocIyq6eoJFqCEdqpJjZDDuLgxWlWYt6YlCSNiK3m7/7dv8+rr77OxY0q3/99nwAN1SCkUqmxsFDn5372Z1yMBYrFZpuVhQ6LnSYLjZBGtUbg+RitGfSGPP74k4TSc4hDBMJK4shgpOSTP/SDrJ87z0eff4Hz5y7ypRe/yic/9WmiJGFvbw/pKfwwxFqNklliWWExWoOxKDHZWfOFk8YJwlhEarBxikhNZtAVaCzGCqz0HXzd82m0WwzjiP5oyLMffY5vfP3rPNjexatVuf9gizubD/jxn/hT4J4MnhRYk2JwsQznzpzl6aeeo1Fpgk3pNKq0Qp8kGhMPBpxZX8EXlj/8wz/kF3/+5wBJu9WgXa9hkzGPXL2EF8Ab129x/dYWCYaD/gE6cx9rLEaYrO+TgjFWCHf/KAciy+aNRBAqSbPVplGroq2h1+8jSFE2xTcpPhofTQAIqbFCu9gRW5p/J4UB2EkCnfLLGFNUa8dmGdAzLFD5VWZsLiDPvaaYVNbGUfTQM4dpypOz5By+7Np8Z8ElOUzYE4IAS2gNgbVUrME3Gs9oAikYDYasrqy5nS1nWJnNwZMKg8yYUKltIYod3lrBaBjzhd/9A1aW16lUAi6fP0OrWafValGv11lc7JBGCc9/7Dk2792l3WwhhKXTCllvV1jpNHjmiavYdMzrb7zBn/v5n6VSC134uHZWM5OkjIdDDrp9fvNf/Q+MxhG9QZ/eYMAP/MAPsJgllclVpTAMHQDLTMaynNWpDM4pJlvZLJNNNCdhTHY1FfjFjlypVOgNB4RhyKe/93tpNBrcuXeX5kKbV7/1LV5+9RW2trbY3tpi0DtA5RKI1pg45ic/9zlu3rjuIOie5ypbr66yuNBySMwo4Ud/6AdZW16iXgkJPZ9K4LO+tkqr0eTSRZe89803rvO1r7zKYDRmNBoRR86zZLBFUpw8d0dRQX2K3P2EgYenJNFoRL1aodGsoYxBWYOPwbc2e6Uo6zacWQj1B4EecpvDfCqMXyXbwmxlrDJNVWAqDJiT34VOXdQfGs+6QKI0jfE9H43EoBiNRoUoaYxxYKWiD9lLeZjEIJkvog4GA37nd7/Fn/kzP0AY+Lz22mtYHdPd32N5fQNrXEBPQyr+7//VX6N7cMBf/at/jWeeucrO3hZSCt567RVskvKX/1e/xKOXzqPHA0wUsbP5gDSJSeOEZORqUixUG7Q7TbwwKHzw3W6X1LjaFWEYUq/Xs7HxJiJryXBWNjiWPTH5b+77UvHcbJy1NoVLM8+FMY5ilPLoLCzSaDQZj8ecPX+RXm/AYOyyQu/39wgPGnRW1qjXm3h+wEKzxl/6n/9Ffv03/iX9/T3a1Qo6GtOoVXjjjbf4yR//KT72/LPYJEViSD2JVhJfCfrdfT7yhEM5Xn/rJi+9dJ31lQ7NypBRbKkLSyAqVIKw6K/0fZdMAxdwpbMxUEqhraXbPSAed1labHNn+z5n11YYjAdIY0GnWJPSbDZJTYJNU1KvipFgRO7pee9plpG9F7zo4WcOwswVffLSdIcOLzGMI40y03gppLD4aYIdj1lbWyUIXCh2t9sjlZaxhcD3WFldnmozZ0hKehjpUJBkNQ7ylGc5hWHIH/3RH/LzP/8ZlleWwSi+//u/n+7+Ae0Vn/3dXfxGg09/9rP84Zf+iE67SXd3h7/+f/rf8ff+4T9CEKJI+NwP/SiXL12k3W4z3D9gd3eP8XgMWlOvVDFZUtdGo4a0EFQrhft1PI7xfQ9P+gSVicSgVMmRawyU1InyuM4bTaezT5LG5AspZzI5wrLRaDDoDuh1B4y9iHrFMSbPT2k0WiTWMO4P6I37dHf26PcGtBeX0NawvLjE6soSP/+zf5b+cMA3v/EKO3u7PPPR5/jT/2GdVr1BrFO8wMsS8rgyhgr4vu/5XvrDAd3uPucvrPHUU49wsLONES7vXpQkVOv1AszleQ7bYACkcmHXqUanMQ4VoTh37hzffus6lcBndXGJWtUnHhp8KfCkx5OPP0nF96g1W/z+V/6YHoLUOGiteX/MBe8JPeTMwQIm05Um+IV8l3J/WwzyWEYB4EnfnWdTECCtQqLxMMhkzNMXzvPoufNgNb3xkNFoxEHF5/72Dp7VpGlEEvUIgyrKyExaydo3LhW7kBajQnQSkyZO/wbBaDTizt37fOKFT3Lx4iVe/fbr1CtNFjst+n1JMhqRRDHeeMy//o1fx69UOdCWZqNGmsT85f/kL+FJhe8r9vf3SdOU3QdbjEcR+/tdLl6+hJ9FMqZJDZPGjDNwTnzQxyhnH9FYrE5ZaC0gQ2co9KVzyzppzLgMVEpmJehng37KY2zd5opBJ9q5OEvoPSk9kBJhDFIIjNaOUfVdBu44jqnVnLFzNBrhCY+gUmGhssTyikevN8DGEakx3HjjGu1Gk8SkRFHE009eJbWGSoY3SJMxwlqGUeTqhSYxoZLoJOXGjbfY399HKFCexIoGq2fO4guohArpuXiJwAsRQuD7ISjhonuFxBiJlAZPkkHGXSUwZSSNSsjjVy6Q7DygASw2mqwvLrJ34wYbVx4hGI354Wc+yq/90R8hq3UUisR6JQbx7jnFcZvgPJLOROqC7E64/EPOHMrkHoqj+RFos4N0SNTKfndl1VOEcFb1IIl4ZKFDS7gaALVqhW4SU221qCC4dvNmhjjsMI4j6qYy1b61LmJUp2kmjcoMNZiHKIesrq6hlOTrX/sGZ85fIB7FXL9+3XlBzmxABmEObJU07nHQdcVzjXC6dyUI2dvbo1qtcvbsWbYf7PD4s49z6+YdojQhGrnU+QKL50lEnmfOOkMbUqBQtFoNFwfgKzzlTY2VtRki0hgCPyjChHO1zMwZy5xyZu1epbHOJAelFJ7vE3ba7O3sAi5jt5TSWf+NJE0StLWMDw4IggrWWJLxGGUte7s7rK+v06hUsdqyv7ODF/gsLS2xtfOAarXK6uoqd2/eYtzvEUURi502tVoNrTVhNUAqwfr6OsIahE5J4hhPBXhegDaGIPARSoHIEhILSa6lKqVcRKuFUb9Pu17DM+AhCWLDmfVVlhcXqAcBF59YQmjDqNulNxrwwkee4I9v3WOYaISYdnV+t+g0Ho8PEHM4Hc3jolrrwhJtrc1AKgajDalJ+bHv+R7k3gHWWprtOkIpROgRYKh5bUy8zlu3bhEoZ4z0meRGgMxoh3CGRyKEEVjlIwPXHyV9pHKfn376aQajhIXWggv3NYZr199ibWWdrfv3aCwssrO3S7PZ5uDggE6rSRgEaM+n3Wxx+/ZtBr0+3b0uu1s7hGGVRFgqgctUpaTAClOAwcgWd2oNrVYLP/QRXmZwsxyJ98gZg1fKE1nO1l22ORy3cxWT0LrITayl2W4xHgwLI2d+fuD5JDqlXq0iUIyTMYHvk1rwPMHWvbv4vk9vMODbr7/Gx57/OJt3bmOtpbe3y87mfUySUPXDIl9EkiTUajWWVhap1ipUKgFSOG9LEsf4QYCVgqDqXLrKDzBCI6UgSXOGJ0EprHbSVeArxsMByvc5f/YsV69epRUGVD2PUHkIk1D1A/6/v/bP+eRnvpfO2iqetQXOIQdIvZ0CM98NeuiZg9GT0vATycHhwozRJYPZjD/YSiAPm87UCeuYghUC34AKwO7t0lGGaLTF1774Mr/wF/4C3Tgm8J2Y6eoxtpFS8o2XXyJNY1LPEiJQ5OnvM2OcktjUx0qJ9UBnornM9FkhLPFoTLtSZTQc4me5DS5eOIdOBRsbG2ztdvGFj9CWJx99nP6gy87ODm9tb3Pl/EXqYYCyhoVOgzgdI/0KTRVghBOb88hIrbXbEWWK0Zpmo0m1GhbYjDwgyEBmgHRuYCnznBaObAZGcl6KaUOwG/eJipfmGY91hJCTSFMBKCtIhYP/VqvVIntzzmhcKjbpGHPWdlANCKQlQmM1CDzSNKFZrfDCRz+K73kI32cYjUkELC8uuMIywnkc6vU6/X6XdqdZ4D9yVKsIBF7FMVGZPS/hB2gp0NkmYIRxbm4rUMpHiwgPwfrKBrVKhcVOi8euPsJCs4HI8msICbWwwt7d61w828Y3A2zcx4xGCK9S2COdQnw05TDpCTw6i/LNBnUWGXxIamZWJcztQPLU2sxDzxzmkbtZO+WBmKXjDGgu159FJppQQ11IknHEmdUV/uW/+HVe+P7vz3YsTRAIWnV33tr6CrV6JQvMmiwhjS1ARdJTbrGpkmEOnJgPVOoNTJIiO65Yru/7pFrj+yH7+13OntsgSRKuXbtGWFEsLS0hpXTehSxuQUpJFEXUmw28oIIxUKs6VcdkInxYqbgFkXlY8oIxwKF8AWXKJQQppouv5MCmHBxVLotXtgXNwnNl4fHIVA4oUJl5FbH8njwvcHgAY7OcmgqkxgYBGFecNw+lDsOQOHUYklarhcq8Iu4YWxQ9brfbRWV0Fzaui2elsopWVoDMvDVOL3TPzvO8qYzj7r5c6jprLVevPEKzXkMKi6c858pUHhiHSvWFxfck6WjM+aVl3uqPGL2HLs3jJLapEIPySrDTtUiOow8kc3ATbxrbP8sOnfqQDUNJ9HW7VGZvsJpFPyRMEjxtqAaKhbUN7t27x8bZs3iej7UJSlpWFpqM4jWwKZ5isgMDBVc3xiH7jMTlpSkV3hX5gpR4gVfkDUjTlEa7g9aaheU2RkugwlNPPeGQjNIQBB6echJDmroanfV6HZ2V4QsqIRYy/7w3jfizhmq1muEzDtcymF74uft1NiX9hAnnmIacfN/PEtlMVA9rbeHSFGRuZj0Z/xxD4IcBcezySWgrSCxODQKU5zIuJemQSqWCzdLV5+qStZag4iQhlCQsRZUKMakxIoRXJCm2gJCl8REU7ktrLVZKTMkQa61jMmk6kVCFdCCjNE1YWVokDDzCjNkqKxAYbrz1Fhura/T2d0jTlHajypIVXB8OXKagt0n5ZvhekBBOtzmN8PCQM4cJfmFqlxOu6vFhWK90C8CCzFKakWHbhXSqiJuuCskYzwo++pErxMMe0aBLrdbEky7rUhCGoBOsNbTbbVKjWV5ayewXgJKHjUpKgpEYZTFaTi28vI9phhcQ0hm9KihSa7JdN3CBS7jM1Gkau1DpwMNahVEpng1cijpfIoyDGQspi9TyLhErpO4y1NRkMeQqR3ncyqK9lNahAKXIxjiLv8yOyTMdle+rOF+6+h75IsoXss2SvRhrnGonRZaKzp3nhUGWNj8oQGTKToBYQlZcsVhjIE2mmJRQcoI9yWZyWWIUQmCFq2NRVEDLJAghBFLbQmUyKpd8fKxNM7E/RaQWzwrSzDujrYcRmvOXLpImEUniEdbqCFKE8en1uvT7fYyQVIIqMkqpSMlyq4K4ZRAyJZUub0Q5tWGZIeXv05Ge+bHenO9OQZmXZZorHM+oHnLmYJlky52I8RP4adkGkf2WexfLrVi3ECXepDCMlVQCxeUzZ9G330Ra8IQkjlNUILh35z6dlQVUZtGXUrk083F8iCmVRTillDPyKYXNdth54p81bhJr4XI8Cisy8FWa5WkUKD9EKB9rs3vOMmGLzKviKR+Eh5ATzIWQjmlJAaGniliHMnlZsdz8e5Xp2EKoDJJrsEbgeT46W8jM6LDzcBDzKHf55olu83PzRQ0uohUx8UYpESBKO7VJNRXlYXVSSC3WukpYJvOylOHCRWJX6QyJUzYS5SHymBiZGQmlRKCdrUhmsGIcvsYKjQVSo53KmGjC0Gdz8x7dixdotdtY4YLpxlHMYDSkWq9hhKBSrRBFI1e7QwiiXh/bcnVC5KGkLfm8yO5byULlmqb5DOEkT135uFytkPbovJPw0DMHR3nZtJysNkXZOCGEwxOUErRY4Xa7/Ayl1MRAU0gfinQ4QsaJA7hkxUskCiVcPoIk0YV4qo2dGK+O0fVy8Vp6HqYsZufn5O/GhXcjXFRj7jmwpUkOOGx/1rb0VLG7prjFbFAgDeTeBAEISagEnsoKzBbj4I7JI0TzPqU6X0yZDm4F0nPxEXGaOjCVNyn3ZoyrbwFurIWSmCwH5WFsBEV6+XIWoyLbdeZNSTIe5LJKWSeF4QBqQS1wGAMFopRcWJYkDFlSB4wq2UpKz8ZFWk6kUStdun53jJ/ZHtzGM2E02iFAM0yLVBBHYy5evMhe94CL6mK24ATDaExQqSHSmGgUu8ArocBoKqFH1fcYSjB5Il9xOA1B8T4rgeXfl934diJVn8Y1OUviBEz3Q84c7FT2acilgFlual0691lcQ1ZJSaKKfA15uXmhU7xxCknskpwoV5MySRIXZxEExBisyCYxlvt37rG0vIwnFUpNFkv5stKTTj+WDhUnlFcY8DyHFCIPepHCBdEIQeZYsW5BiyyDsTV4yhk8vSwRbG5xNiJFSImnwkK6kkJnu7JESYGSCiEmFZuknBgcp6MRyfTu7EZUDtRxUaQVPyAajhCeKNLCk1XocvfvFpjJMRU48JM1GusrFwKOu6d8EueoyiIHZR4YZwValdSAzLtklYMylyUWYYTLRl12UWfeoUJKka4ylnv2ZJuMyEKzJ8zDSueNcczGOGlOCDDSBeQVtg43dkEQ8PLLL/P8Rz+GsZZRPHIqi3A5K13MjQciRFhNqFMurq1wYzSmZ0WWhi+fQJONT0lZZBorxvaozSjLgv52yAonFUnnrjr22IecOTiy1s7cxoxoJd6BwUYKQk8RxTGkKePhCK/axAtqWJyRT1jDcDikUg3wfZ9+v8/q2trJWX+kU3lyqzhSOPXElCZZxhGEMCiZV7XOmB9uUiiTpWEDlOehbJiFgUt8r0KaukhSLJNFC5m7Th0ak7KdYDY+BSYL1QhZMJFqteqiPT2D8MSk1mXmuZi67eK7SSCc1ZbUWhQCVSoVn5fNy8n1w+nEUkzQpzabw06wmtabcy9EGXcx62GxyiOgnPB2cs5xMyYPV5/dkD3Pw/juWpubm4VBNYoi6kGDiidIjIZqTEiKZ1wymVo9ZHV5hes3b/JeICPfD3rImcP89Nw5lLeYXNZB18rBQWU/77z6jr5QfOTxJwikoj92NSk9z2NsBc32AtILkDqlUqmh04Q4ilzB2yz825qS6F+2Pdi8DmPeAwec0SZ1Hopcp8zKzgkmYuVsRS9ZmvjgjIzW88CKwpI+0eEnkZPZyM0FOEkpiyzTDgvhTcTxTOUoJ3LJvw+CgCidZEAqM5687xMjYpbrwZgiS7X0/KnnmS/SCZmStOCeqZSSGFdxSirpangw8Z54QhVqiRCiAJpN+pFlYFIOHl0GcbkxmlAudeTtTQyCToooM0IpJWtrazQajYIhCuGwLL4vCestxnsHWCsxFgKpXM7MatXZV+LD9TePozIDn+rzMerte0EfsJDto2jimjrt0V5iuLCyitQOuVcultvpdLKdaMJYGo0Gg8GISV6I+ZTvJLOkpO8UVilK7wKUzNyik6jI8qtMxlpEdm6++/m+n6VFn7hXpXCemZzytsrSRVmtyBdsXrvCzyUZbZzUIKZViKwzU4zbZAukXJA3331NqguGNCut5AvdJYfJ8QheMSYqe0mhJjk4lEJ5HjqzTZTHLndNFkxDSWczyM4Tmdj+bpaVUqqoqh3HMdYYggxnAWQGSgeCy1WRMHSh5Pv7+ye2/7DIFQ+15GAphT2XuWQuMOQTzUqQZAi+XC+fHC7EdIi2tRqhU0TcRwC+VFSrdfywjk6GxGmCUD6ucoJDEQ4HAxS2SARanuCa3FjkJqvwlAvdPuSGVeReFxfYI1y+DetBZkTNwSllaSSXQVypS4MAPG9SI0OQVcnOxkOinceB3M6QqxuTAjY5YtBmRlFjDJVKVu/RWOfVcYYErDHO8FhKIpJrrBY3tgqBLySxSQstz2RFcKy1BfDMt84moBWQuXMtppCGhDBY6SGEi4zMA6OstYT54svGQ4Qy8/iIKXsGSISXSQ1y2tg3ZewVWSo85ewis8bj2XmTP0djDPv7+yTjiF63S80PWVhYcHMqY8D1Rgvb72K1YyZeKDEDg+V4qSHvWy5TzfomCntVZgcrvj+uzayaG4DIjcJWljyB8+nhlhxOYO+FOC5sNomP57llXTuORighXeah2JIaGEcJ9WbDWd+nAolcLoalpaUTLcMusYudmojzji/vdPOwHEdFmeYp4LzM6CjFdFvMuC4PSR+l9HXl33MVpbw4ynkwyhgJz/v/tffnwbZl+V0f+Flr7eGcc+c351Q5VKayJFVpoiwhCUSBgEY00XJ3g4HoaKkxtBrbDTZ2YAt3R9B2dzjA4XaD3W2w3IIWBEaARCCFoMEgwDIICpVU1DxXZlYOL/NNdzzDHtb69R9rrb3X2fece+97WVn5kn6/iPPeufvsYe01/NZv/P4yxFqM8gFLErwt8QPLFa+UE5r5onMDR2bR20hOT8PuWvrPUL1M2++Uxintd+sgRdFJJMvxHel1qVq1ZOwcjFsKgBNtMVprjo+PO/iA6M0qisKXcnSuC1Q7PDwEZTvYvXUqQfdbVFHXQMy9XZXiIt6Nh5s5JHTWQgOHEl8E9uzzehIRTKYo85yiGGFMRuuE69evY0U6a3FRFGRZxubmJmVZdoavdW3sYwf60mlZlnUIQ6smHfQqQZzIqYEtUp736Epa+o9SoeDKGh94arCLizZ1yaaLZ6h3x8UTVY5IRvmUaFwPlx5JS/C8KN2/l/MqVx3qQ6QTvlNNuvf1Bk0fxdq/Z2xj95zEFaq08Vb/6GVQ3iAc+34VMHA6V2IB4IuSiJ8rkVmnYEN1qPGB0RwfTTGmjys5HbewmuL7vpsqxkPOHBRIxrBwrrMgzuDEYJ3GOu3dltq70QiVsH017LBgVaxbZFG6ZVxmaIRZW4dF45jNTrh07brXYZ2Ac7i2RVpLmeVJSK7CqgarGpxeFhONMV3wilMJCrHyEQvxk+5W1svESxLEUi8o1T27m4RYRAsqWyeZCFovi9OxwG5ZlhTaW9EzFNr5IrTaWVzd4JOwHNY2YB3S2q7NWZb5hWRrRDla1+CwvpSdOK/ji1dzjNa9TWTkcw7EWYTlvIxO+hMBp5C2wSC+Ypj2CVAu1B01xqtRvtaIN3x6tUp1EkbXB9Izk2ifyfP8lLSgwtikbv+h9BT7P/6mtebVr3yF3c0xG5PCp2Tg29gqhUWRFyMfZGU00ljauqIsxz6CV5YlWa8e9Z+LpHQ/mO0k2OfOKLwb6aG2OaR0nkilgpeCRFcPZ+OhxcXrj6oflLb14CH1bAZ4Y6TF4zBmuaaN5d604dad29194wKNouRQj40TqdspAmRaasRbskCr/r3SSZkm/qS7XDrhsyxbitoMNwp2hv45kTHEgr8oL4nE+9RNg3IeoBbnUFlGpjUSkqcAnHXM54vAYHJfXTskYnXvJz2GQzyeKU2WG0QpqvmC0WTsPTrJu0jSD8PdNZVwvCvYO6iUVt0z4vO6awb3T+8Z1aRVKefpNeuoY2haM5vNwhh5NUbhg/QAxtubnCzmzGYzzGSvW8neJnbazTxQmM5swzeC3pPM4etFcYI456iqinJzk+l0ijYltmm7JCet4eDggHw8OnV9avwcqgydLhqNhbpnXNr0YmhEudKqDytOQVJSb8MqlSY+Z9mj0OvHUWwuS1/hKs9ztPSxBi78H+srKOknuDdq6iWmFd2hsSDtUlxBwjy11iEnQWNdS57njLKcpmk642Ikfw+6PulUHfrF3UVDau+5QMnSjr6ufx6E1jEJ34bwbm3Lxz72MXKTsbGx1Rcs1goljizPuHbjuq8uLkJjvdS2qIOUdAG9/92kh5s5KHyRXJEuwlEp4xOO0l1HRes//Y6U3MZ24aohqxDNonVoHQrNKE023mb3uadBKYxrkLqlwVItFohYNscjzLgE1aKdhtaEWpBqyf6c7uoQGIiLz/XJQjG2QUGoMqWD2OyCeNov7mhFBw8QKyqmQfcuyZhLYHouhYpxBqKx1pFlUfQltEsHhhLCniVGZVjE9e5a3/dprIl0SW/R89G2LU7abkGnYnuOZ3ra5Gi8Xm+t83aKRC7WotECRvnsSDqjbqiBgQ8t9+1oOxuCLIVk98wxulT7sHn6uSK9GpeiXSmliFWkkFC5TAGSebj+8L8EI2O5OeGF97+faj5jlOU4k/voSFWBUtTOUmxu49QYdMnNt27TWp8c6MPC1m94NuEb91PR6jySTg0+X0J6uJkDckr0UwpvP0h2x/s1nWidMZlMyMuCfDymUjBra57d2yPLc5q6Bg2u9dmfs9mUqlowHiW7XYxTUGptclxvjQ9+9sSYqcMCiKnnZ+16KRy+iJAHNGcIBk29XLUpJYNCnFAUOa30+Q/DNGzf1lgUR1FViwB9J93iqeu6M0xGxtDhO0ifUt00TXdNNAbGdzbGUAepYwn+fYUKPFS/zurj2IfpsZXqZ/DQprMnfdZ5CyYykZOTE05OTrp38ujaM5TaAJHgzcgYb2yQlwUn84rX37xJM9ryeTrnRdk+BPRQt1CEDitgSZ+UvlrRKhfPSndXKOChQ8IWRcZxNefE1ownE6aVrwzVNg1VVTGtFjRNRdPUWNfw6muvLFWwEu2LuLSybFxLLegprkKqp6auu3QBpR6ONB06/S3P81NejHQxxL7prkUFAJKA4xgYjXMu7OI9xsMw3dpXhvKLPwZ3RcYSj0cJIh6LkkTqIk0Th+K7xfstMyeWrh3actL+WUXp+esWuRZ8kpb04xLfO7VLDD03w3c/Pj5mPB6zubkZDJ2+rmZVz7tnF0XhK4k7DzBzdHS00mvysNJDzRxgObDFmLjTOKBFKev/196NJ2JxrkakAdX6T3CL+QJYCkJoU9UIv/BL/4RJuYUuSlSeU1nv6msaHy7dzGpc5aBVXLl8nencGy6t8vB1vUfkdKBNpDgZ4uROaWgIGzKV9G9jDLkI2lqUs2QKcq38d+cotE/a0srFhEacExpazChDl6FYizbkSqHrBdQ1tA1iBduEwKe2BvGBTBpfdNfhC79EO0NEcLLiaKxXMdq6YVFVnCwq5rRY5+t3GBG0c2jnMFowxgPKRoNw2lddf1jXuUEjDfErVzGB9LfYb+kxpTxDH8YypPfzqmIgs5yB6zckqGvPAJ947HE2yhFZmFsi3sMl0uJocQ7ybELT5qgCVLDBqAtIDWfFQqw6b/i5n+vW0UOtVijVc/MUbeht31e87nlnNmN06RJWWm488WQ3AXNtOJkdoQSyXDOejNi9vMf/9Csf5dpjN9aK8OfRRa3g0Fvmh7+vcnMCpySAeC+Pm+j7LHM+h1+airZSVNWcVgmNmrK9s4mS3EfTWdeFQxfSolBUdYVrFv6Z1npfm7Po1uJQzKZewhrlPvejLizFZETb1oExhtgJrajtani/1JiZGlXjb8M+HyZvXYSWpK4BYxoulqGa5//2c7CqGg6Ojn0augOsxdm+IvnIOqz2NUEaW4ET8lHJIrrM3mFaN9fSTew8elsrTSm1q5T6GaXU55VSn1NKfa9S6pJS6u8rpb4U/t8L5yql1H+llPqyUuqTSqnvOvcBiS94CFCStMGfmuwGF3nxFsX4ylXeODyhzQquPn6jE7cXiwU5Gm36BTmdHXuEn2RyDXeg+6FUbE13u3X3WqVGpPcaZiTG49FdCaDEF2dp64q2OeZ4/yZbkw2mU0GR49qGTJkQ2CTe1OpajBJwLZmik1qccwEureXk6JAsh1dfv8VnXvoabV5gpWZR79PUi+7jAliLCqpYrEI+FN+jVJH27fDdVvVD2h/rdsXekHl6vqT3H4r/UY21NiSttY5f9+HvZnN3l8n2NqOi9PEcAjqEn0d4QKsMlpLW9rCBq8bwfnb+bwS9XcnhzwJ/V0R+t1KqACbAfwz8ooj8KaXUjwM/DvxHwA8BL4TP9wB/Lvx/JqVRc/57r7t63/BwR4kTbGjh6ice+CIvtdN89nNf5Pu+6Vky8EVgW1+aXofS69ZadJGTFeOQbIPPBaBBBRSn+6GlxRCs4+tUklULIo2xWGUr6N8xnCMuxH85Fk1Fu5gzPzpks9zh5372b/Oll29Rq5zjquG/+X/+56jFAouvv2GMIss0TbVAo7CicK1gxYvX1loy7cFuf+q/+0scHIFg+Nmf/ftUteP3//Bv5Td8z/ux4vM2HAJZjojxqFfiut0/NY6a0KcGTUNvk+iyJSXiV/QeiRg0NFQzTkknqnfPWtcD5ab9qZTqYha66wLDcigW0xlf/vLL/M9/12/3AL+2ohWLEZ9B2jQNbWPJcofFYrUAJW2We89ZVEWju9nbgpfbmdrR6KNQfduimyf0m7/g1Ht2/fCAvOaBJQel1A7wA8BPAohILSIHwA8DPxVO+yngXw/ffxj4S+LpnwO7SqnHznpGDGQZLpKhAW7t9WdxYqNpHeyfnKCLgtaB4F1bEcw0Pqt13jB3+/btzrvwILFpkdJ3GkoQQzF5nYHtPHE6zQUQsT5YqWlZzE7QCH/y//yfIK7msRs7bI5hd2fM//4P/4fcuPFEH+cQJAPnHIiP8GvbmnoxJ6Ssslgs+PxnP8uHf90HefZ9l9FYyky4vJnxP/zdf8BnP/MppscnS0bkGBsx7I9OlQqeoHSCfz3UycgYgI4xrKLh2Cy11wmNs3zsU58KKdsZGxsbnsGFILB+pSd2E+0Bda34Re2Cm/TdLm5zFr0dyeFZ4DbwF5VS3w78KvDvAtdF5GY4503gevj+BPBqcv1r4djN5BhKqR8DfgxgsrWZiNkGDwsOaYBPL/4NwD7WNNpzWo1z3m9+az5DjSa4eo51DWVZ0kznfkI4iynywDAsly9d9Vb+uiXrjFXr3W2rxNclD8aK89P/h4wDezoN21e5Wr6/tT1jsNaiQ38dHR8wPzzi9Ze+yvd973citNzZP0KM5o1b+zx2KePf+2P/AT/yI/8G165dwTlCGcA62CFaj9NgG8TCrTt3OZlNeeqZp8A6lDJkRsg3tjg62OfJq1t89Jf/GU/+nvfR1g2S+QK7Lpl2XR+p1Tv9uo3Av+fpvo/hzkOytoekT/tqiPHQbUIrdHNvGM65cvUatw5hMplA05BlGePxmLad0bQtgtAKGGW8NAqgFdoYL9GKD/i6SMCWTyA797QzaRWUwUXsd2+HOWTAdwF/REQ+qpT6s3gVom+UiCh1f0KNiPwE8BMAl69f665VmCVVId2BjEkGdf19gzXZi3MiPvx2SsvcuU49WLIDhLLsZemh37/v+34Dd/bvsLO3jRnlp2Luh7TErEJcw5BSG0PKSIZtB0IAzumoQHHOeyaahogCFd93HsrEN01DbjRtnnH18h7jvMA2wsbGVXYvz9mYfA2rLL/5Iz+IbVqODg7Z3Jog0qIUWLGI9Vb6ejEH27Az2WSy6XfNpqqpLre4puVo2nLjuWfZm2jGhWCbhnpRUYwNZH1kqM57HIbUptR2gU298a5tWw/+CswXc05OTrh+/UzB81SfxtiSlEmvG7d0fFJG21rhy5//HNfG+HiOpsGqPqnKar0Ew59lPjYm1tiQtr3vte5VELpnDNsaJeyvN70d5vAa8JqIfDT8/TN45vCWUuoxEbkZ1IZb4ffXgaeS658Mx9ZS7BCvZ7mQI6Fp2waL9Tqs8rgBUZpQSgVbwTJXjgCkThxgsWKQpiFTGUdVzdg50BliCq/hiRc9VWspVA5FwWR7h49//BM4B09u7voY/8GCXra4G6DXadPzwsnd16EhcpUqNJ/PGY/HS78r5StbHx2d+HJupkDEhtwKhXXC8XRGUWRonZGbnEt719gezTiZLchHFdvtJa5c2uPuwV2KXHH56iWvTlnry+uJj4psmoa6bRGlGG/5dozxwVt1lnHpymXu7t/jajnisWvX2ByPuHJ1D9dalFG04jAitNJHJUZyyksenh2sZpixR8qy5JOf/CSj0Yit3T0flu5O7/TD7z6nZDl2YpiKLeJxMEXEB6klqeLWeki+w+kBf/yP/1scHR1w49IVqrqhrS1NtQDn0KZ3pU6rBcqMuH3rgKmt/ZwQH7l6lkqc2pOGcRb9OX6TW3WXpWOqt0XFEPmLqGkPzBxE5E2l1KtKqRdF5AvADwKfDZ8fBf5U+P/nwiU/D/wflVI/jTdEHibqx7qHBJixJNoQhTaZzxZUIcMx+V0iIInExempV0dCpwtok2Fkwlde+Rrf8b7H/QRU+AAncb6IqghNYynLjMmopF7M+coXv8CVa5f9rhnCIxUKo7wfXSk/kaxrQDSZ8rkPek26rgtgLSljsa2vBp7KXZubm7HvT91je3s7Eb19PojWJhjH2lBcN0ObPOBYZDiTUYw3aJxmNivZu3KZvd0rHnk6TKQ8N6RlByNydVXXIYUc0IqRLij2LrHxoW/zhkqEMjO0akxmTJDMNE1jO1+/oQ+4EjQe9VnTw+es3hFFhEuXLnUS1Cn4N+nPi3kncfTTvkuxFeLxuq6Xduc00Cve8+WXX+YHfuAHUMp7g9qq7s7RAkr7YDOTZ9QnU1SWc+fowIPvxuheuZg7MT5zXT9EifdMFDSJ6tNgQp1Db9db8UeAvxI8FV8F/gDeyPnXlVJ/EHgF+DfCuX8H+J3Al4FZOPccUl1koyQD7uteivcpO/GIzwPvxKnOTHpPFAG+PsNh+corX+M73/8MdROCcrSvfWCDHl0tGooNoakqfu3jH+M7v+u7UDjEtjijMMqXrJ+dzMjGpU8LV4osM9y5c5fLO1cxOg9ejhVvqXVXA7GNk7DvgTMptUukOIdRaskyn5q+WCww1pKXI1hUOOOhy7LMMdIeQFdlOaPJBGUMbVNhHZycnJBlvu4kzDmeToFerYtGN+0gz3ImuyNELIVWiHMs3MIzeWNonPMp9lhyk+Ph4DwmZ28b98xhFfWLVnj++eeXJa20P5O+WSwWfUUr6KWC5Jz071hyb/h7jP48PDzm9u3bLBYLdna2mM/ntG3LbDbzKoMTRmWByTIwmvl8jtvOuX10gMtyyAigRGcv0pQhpJ6UBzPMptdcPDHtbTEHEfmXwIdX/PSDK84V4N95kOcMDTdRRNSZF0OXwT9jfL0sJeJEiPhouPT91aKM5ubhCaJy6uaYjbykzEfUZo61M+9vz3xQkMlH/Gvf/ev58Ie/iwiTDoHvKMjLwu+IJur8zqNHWbUkFsc2sWI36BCgo7i7wjV3ynCX2DWWfw8ShMlRucKqCttYTFYitvG3FkvrGlSm0HlGnmehEExwybmWo+MTtkYFiqxLesvKgqwcYXQoSWe92teGZC5tMpq2onE1DhUS3ArQgslzivEIbYyvLBYkLq01gluqQbG0a8b/szzkc0hX/MYpkn7nVJ91zCFRI+L9z+rf+OwoZTQhqnOUF0yKEqqWeVXTOqGxsFGOfb9kHl8yG41oyGl8fcSuEtpFaGjMju1I2wVr2EyC9C2SAPyEYxcxhj7UEZJn0VKQyoN4FTv8PMfO3iVspmgbn8EZcxDqRlHZFnGWpm2ZTqe8//3v5/DwkMn2pGsHJLaGFdb1WFdhKAZ0sRrJBB3GKlyEXHDRCV4KIbG2a/F7hRaD1iOqxjM8hYdWEw0S/OamyCFEVGpjvM3F5DgUR8cntK0Do8mznGIyJs9LtPK1JFXuixE3VU1raxYLR6ahcS1N69g/OeHytcfJi4LJeBOT52RJgNaqdx4aBuOxGE6eRoWumgQ6UdWG0sDwvqv6O0pHKULUm2++yUc+8hEmkwmLxYLpvUMmkwmXr17FCNimpRwX5OMJd+8eoEZjbh8eYMW7ytfRWe14EFp1H88gv3FqxbtC0XV5lofi7E4WwHkGId5D+KWXX+b5nU02y5KNrU2sbWhpmDdzDo+OEA3jzU2eeuopPvaxf8ELuy/4RWb6Gg/GmCUG4Dn++lakjCAVG2P1bRFBkiKu8dwhM1HaoJd2Cr/YRQSJTNBojGhGm5vYsuVw/x5fe/kVX5DXVezuXUZphVU+NtI5RyvgtGGyvUN9ckTdLlBosqL0RX2UoShHtHbmXXPK209u799DhbiIRd2Ql2OeevY58rKkKH1RXxfUoFVRoauiUIGuClTad12/pW7gZJcV6aMth3aM4RyJEmmULtpg94nXtGGD+NxnP8u3f/BDVIsZV7Z2+2xQo9ne2KF1DfNFzbSqGI83+PinPk1Tll5ZEulacuYMTaSGVS7ededHSkO4ogSq9MUklkjvOebgjYamW3Ra50nye5w80UgTRE5RkLjHdChnKUClFWVR8rmvvMSL3/OdzOcV+aSk2BhzaTLi0tUetPToaJ+6WfDJT3yC5z/w/FKbmuDvTlWFfmBVVwsiUgpJtmpheLek7tCbwdet8DiJINQgBqeW3XPxeeCTnKwzGOODmDJRtNJilTDZ3OKFb/5mDg4OEAXjrS1vyTegdI7B55joWtD4WA9VW9AaXYxRWYnOMyrXYkYFmTa01mGdcOnK1W5BZkXBZLJBZkZkhfHgvehTGlVaOzI1HA7djqnFPtqOhsvGqT7vQto+elSZ/j5rd9ahFGd098wYDPaH/9Af5HD/wNc5sQ2j3KC10GKZtQsWTcvR8Yxsc4upBZuX1IDDhcpe69swpNMeiiFjk6X+Ous+3l43iAQ9g95zzOE88tw/uDbDu3u0o/XXLOoGtajIJ1vUxwfM5x5d2FkLyoQAbe9jfumLLy0FzzjnmE6nPiBmQC7ET9wPv5bBrqfWDLqfqIrICFOA03hsqFda6bNTTVaQ5YorV32xF4dQjEfLao3WZAFWTmcFKi/QyqCzHJMVwbXcM+BiPKEoRqh57iUSa1HK94FWBb4ekEKU5j6jzlfS2niQNCQ6OderdquvSRnDUnRsAuWvteaVV17hlVde4bu+4zt7JC3nyDIfRn5wfMTB4ZTdS5eYC/zapz/JXCyODHFRhYuNWv0e6futkm6WabWicFFmcRY99CnbQ4qW42E+QUrrJs06sjrDjCbsL+ZkZcF0PmNRV772Y0BvEhF2d3f54Ac/yNHREdPptOvg0Wi0FMTT2RrCTj4sG3cWRWkjhhKnNo34/7oJPVQ/0nO01lhR1K2P54hI2saYTjVqmoY2YDjEa532hWDysmA02UDlBaic0WSDYjRmNNkA7fV/HRCXNza3ceIZUAroGp+X+u3P271SiLohDXf62A/p+8fnpP8PaQmAh9OSXFT7IiDP008/jYivOFYURQfsopTh+HjKpStXMOMJlQiv3rlNYwwtHp8ztvksdTgdu683xfuvwkIZ0sMtOSgQHbi4W14kKbnumAIXjVSpe3N1ByiJRk2HG4156Y2b7D35PrTOuHXnHmVZsrW1xSjLyEYFzrU4EZ546vHO35+rHJNnyYCf7vDoQUnb75RHOx66ptIF7pzr6mamDKdtW18eTlbbXnxhH4M4yJRgwuJ1WLLCxz44FCrzyUdZMQ44B73dw4vUBt00WCcoCrRpKXSEwfdKXNM0jCYT6rr2WJQmZ1FXflErkFCexUqL0hnWWR95Onivi+jU6WROxeuzdteUMQy9Rek9U4bYSR1KUWY5ynlQt2o2x7UL0JZFO2NiRmhd0rSWRnx0xu7lq+isoMHwyc99knJjm0PrEAyOps+60BoxD74399Bx6zeetG8jI76oDQMeesnhbM7mX/Ttc1cRYd5YPv6pT1OMSjJTsLu12+2m0MeOjEYjPvjBD/r07WRCXZQbP0jboJdC0pDsVTrysA2utR04S1wEEQGqDWJxCoBiQim6U++hFcVojBVwqM66Mx6PMcYwGo269vYuxeU2Dg2wF3GnPQgNI03XjUd6XirFpN9jf06n04DenTObTWnbhsVizvHxMU1MTjManWXUTrBZxsmioQqFj9+L9J5p9XDXGIrbkdZFkq27J4AoL1pLnjOzlmw0Ih+NfOWikHEXz93e3qZpGt5646bPwqOP8oNlfXcVXUSUPouGnH+4g65alOmOsSrZKLY5BdaJ92ysrwbm0KAzRpNNdFYkMPF9pmW8Z2QO0ZaxqCta16NUQ29TGY7FkNaJ2UO16TwJ4qxjaRp8/H9o+ykCbucLL7wQAsKgsS2tszTOkmtDE4LiGqN59a03WaCgGJ/atYc796rvb2eOfL3oPcMc1lPyCqdq/2nOesV0sBqBfGubWyfHFHs72CyjGI2XkpzquqZpGo8baLKlyd5Z55Wv0/D1GNyz77Ac8Tn87utuui43IMK8HRwcLFnfRaTDpazrmrrxKkGWZYkdBZyTpcUTMSBjDoFzvhJ3fL6vOO3vGwFn3JL648Ol/aJ+ZxaC1roHAl5FKhgsleuY2ZAiw2yahpdffpmmrtne2jpVMPhoeuTVNVGQ5Xzl1VdRWd4lXK36POz08LeQsDto132iTaFzWWJxUiNiEdXiYlJWwHhU5Kd2mDQfwwW9/VAUP/eL/4DbJwsaAUZjGhytramaBXWzoGkrrl+/zquvvxqK4DrAe0isbcE6bNNinEcEMkMommRyiFYXq2y0YldU5IjSPrZAPOy9jW4t7V2SFuvdehpm1YJ53VCMJ6gs7+5b1y1tVZNrg0Go5zO0OOr5jLZaYHKNc7bLg0hxH6OaYq0F5ahtzbye04rPoPRVuRQHx0dUbYNF0WKSmpaaxvnqUI0TLKo73grd93h+qr6l/TLsn/SYi5/E5RkZo3K+RVpCNfHYt6F4MImurrXHvvz2D307Y51TaEWhDJkSXLvw9TmyjEYpbt87pBaolCMr8uCt8UWDV0kR6Sd9h3VjDyHHKPkYpTFKnzqeftKck4vQe4I5pLRqR76IYeusgUh/L3b2OKobJIRbxwWxWHj8xKZpyPOcF198EaUUx8fHQGLVjnUPBrRqVzrPPiH0koCW02LvqncYIl5ba31E52RCnufdjm+tpaoqRqNRB9cG3qailOoK67ah7mMsNFOWJUCHMp2+R13XlGXpgWyDl0JEumIvaZ3MFF07RjqmY5i+x9ALM/QKpe+ffk/tL6tUrTi+zjlwNUpcF1eylG6vFIvFoos9ie9h8ahYohVoQ22Fw1nFv/jkJ6isC/E1p0FuH0S1PEttvCjd7zPfM8zhvA45y+U1vP6sDm6Bv/ULf5tF67q6DNGAF8Xtoig4ODhgf3+fjY2NpckeGYS4ZcPgqgKqF4Ept4qzM+4SSidzuqi2trZ8lamyZDweU4eMyjhJ8zzvg7joJ1FRFEQ4fOjDicuyPAXuqrXuGFBRFCwWC46Pj6mqaintOGVwcaGtck2vw1SA1WOZHo/XnxU/kOKB+OdbdCjIHPtQKR+MtlgscM4nqlVVtXSfKjLbFqaLiq987WVMOaKyAipDqYfbIXgWPdzMQVa7pbqfo+iYejWkAMkQt9qif+oRImgriAWkpCXHbU744uuvUAHTVpi3gioyjyVZFoBPlc3znNZZH39tnS8xRxBhw6S7yGQ9S7XQ4iWIRvn7xvtbgrgcJnlq6ErF/rj4Yhuccx1DixmIoyxnUpQo67oCu4U2GAGxIbDKaAzetYd1GJNTVVWIjahpFnNyrTD4ythlljPKCwqTMcqLrhp10zQ+U9EKbWNRaIzOuv6KpQHSd0jHemk+JMN6Sm1c4a6LDDONCen+V97o6ssbuPQinHNs7mxzND3h1p1bvHH3FrUTGjRv3jlg0RpmTcWXXnmDOydzGiXoTKFimH4sBj0oCN1XSe8nwLoNY520O3z/rye9d9naGlrHTIYd50iSUJTX4QSfxi0q45c/8UmMg/c9dp1JOQLtmVArjlu3bjHa3OLNt25RFCWTyRiRHqUn7qqxHqafrB6/ID4yZQg+2rFv50Ws7sNz08meTv64Y/ucjSZAxQuEc7LCUBQFJ0fHfZFdEWwoP5jhowS1E2rn0Ztm1YLC5P4dgdwUtFY8+Kx1HRy9RmEJadAIbQjpNsaX6wsvgRO/c3fSlVadIU+pEBOh+ziFNCZQxPdlGo7eGaZTQ60I1vWuZx8S06tnfq5EkCB/fdvaTio8PDrCWsv7v+mbuXX3gOmiYWPnEhu7l7h5+x63pzPeOj6mMjmNyrtEOO+OT2MREizU2NwVw7tuw1i3wejBPZw6P/jrPPpXjjmkdFFO6gu3xF06wypDVoz5Z5/4JM59G09fvezLrGNYTBfs7F7j0uXr/LNf/icA3Lt3j+vXrwe9fLlUOxKZUK9fw3ItRGPMkk3hrLYPd8VU749AJVEqEBH2Dw+5fPkyAPV84ReaEzJtUCYD5zg+POpUCq29YSveN1bMalxDhvKFcJUm0ya4MzU4KLIQcakybNv6+phZhjI+kcypmJ1qyYOkoLUmU8arOWWx9C7OOapgw8iybODp6CkuoiVbBqejJP33gVci+T2iPIlAj5xku6Cz8XjMr/7qr/LCi9/k7S7acHt6zMl8wSuv3sRmJZUyLJTG2eUAJZd60ZLANxNydoZv9SD7f3qPs66/H+niPcEc/IC2weB2VpMvGFQjGt+FYfACY/dZCQ1CRqMdamODf/zpT/Gvvfg8T125zO7GFgaoZnN0phmXExYzYXN7cynOvhPvQ3ajDhPAOtfF9hvojFpLpBxK+d27kwxUHNSsMw52KpVzXeGalPnEoJ1ce7j9rCzJUGxubtI0TRfG3FQ1o8IbGTuod/E5HUopRJsuRyQ3isVs7pGkgFxrNMHWosEohbOOrSgpWI8x4ZzzSVxlTt00KGU6O07sr7qu+1J9ibqU1t5MYysiRaYqxnEyPenQstK5A6fDqkV6u5CPdrVonfR1MO6KcjipOKkcM2U4aRSvH97DFDmz4xMW1qHykpmy1MpLUOft1FFScZK2KZFy/Un93wqMb1rn/RKRC3i6ojdvuS8uSu8J5nAxOjum4XTHRAYxSE5SGiWCKMUCTTYe8dmXX+HzX/oiH3j2/Tx5/So44fabr9BUU/IyR6mzO97/1guaS3TOAKeTmSAdDD0fbdsug85qTZF7sT8T6Yxq43KERjEqvEExUxpdFEjrz1FZ5j0OeUYWqntnaEQp8sR7oUwWKliBUl4ysq2vnq2ANrhQlXi8iGLkmY9YXyEc5QFlm6bp3i8aPY0xtKEo72Qy6TwEcDpDMe0b5xxbW1tLUtU6D1H6PTLV6GmJElgZvDaLqqUYj5FmTlU1fO6rL7N7+RJNuwCT0WQaq7QHmQ043+9++FI65wbM4T4a955jDqsWoVIqQG9B7IrzuLe/z2nYb6Vzj2SkMiwWVM4CKMsJX37zNp9/+WsUWpgfvkW2mPHsM+9H6wJr3dKu5ZxDRVhypNsIVtlEvCSw3k0b79ckrkBjDFVVdSpElCCUUuTadAtJKyiCZ2IULPEuWOfb1rKYzTuPjHPeqFoHgNSiKMgzjwHpE7cgL4zP7QhVnSQA9irXcnx45LNIM0M5HoGIr6BlfRxBq32QmEOxqBehjT7jUWW9RCDIKSksivcRE3Jo4I2SVOoWPY85LI1V6K/5fN65fP0zHdPpHOsUJi+59MQTHM9nOOWzS1utacXXX7WDgkpfzyjHjhGuOX6RZyml7ktleeiZQ/ryWmc4GzpCDXYQJUFdWL5u3d/dDtOpGPFEjx4cwP58nIPWiDiqtkYZRYVma2cPYwq2t7exzXzJI9AZuCTC21ta25KZYun5abu8UW2Zs6eT3DlHWZQsFguvm+c5RQCEUcDhvX2uXbuGtBYtzhvWnAeCUf5BXqyvKqS1SNtSVTW2aWiqRee7V1qzsJbWedtETD5rmgbRilzn3Nm/4w1gpo8GtW3deRt0kXN8fExZlkw2NzCtoTUaMRlG5zgcm6MNjDEcnZz4frAtgqKxbRdYlRcFeZahxMeXmIQx+A5yCRNUS2MQ+3bVuBNiUWwoqiwChS7IdIYuNWTQBDf2ZFRwPD3h3tEJ73v+BW7tH6EnYxwK6zwaOkqFQLoQINYt1AdnDum8N84bhlOyyPK0HbxnKkPbB2zGQ80chPSlJZEOvg73XsE8/IJ1pL3uVCiAE3ANjcqwYlk4zWM7lzk4OGJnq+wiB4d+92ZRMxqNyI0mVWEUYRKFc7U/yFDNMUpT15VPrRbITcZiNqfI8q4q2vTE69ouAOTO5/Mu/FlLX+Tn5PiQZlEhjU8UslWDWEtbN95boLwxT4mQiaCUYXEyZXHiQWWj6jLO/eJXSkFErNLS2Qbq2RyHUM0XtHXDZHPDl4EzOTrz6ct5plFaU+behtJ6BBtfvFibDspObKjLIHR1O/o+7EYJkT7o6SK7qDcveNtOVVUUJvNo4cqHc0f7TVM3XL16lc++9BpV3ZLFYrhKozq80ndWkRje3fXTZi19PZbKQ80cIkX9SS6g0T2or7cP0kncg/gaGI3SGCWIa6nbFqUM42ITmykaDdoYdjY3WVQVhMlZFAXinN/dgyFyKZxeAKfQ0V4RXZmD17Nti9HaLwzrg3G2JmOsbWkbr0psb2yGKD8PBTYpfYl77a1rGIF6USFthZ0vfPxC21KfTMlNhrKOHIVyAtYiKorn3lUp4vMvJCRPVU3tDZ3iw62zLEOUDxcHQaqGzBhMbqhnc5r5ArQiG5U4oyhHE+rGM+KsLDzTDXU2InPdKAqf2ITHi1TKV4yKxsolaL41cyCVzpa+xzEPRkkt0Zvkj1d13YH1AlRV5SVEaxlNJsznFdbD7XYxLcNnD+kiRsrBW6w8z2fQcuq5w+sd/Xsute8+FIuHnDmkcfQOpeMAfn04ddppfZxAlArTegDe4KSV4LR37zVOU7uGm2+9yY1LL3BwdMju9g7O2s4GMJvNKMuyM7alz9MBjj6GyqyjxWLBZDLhzp077G7u9jEUQQcnGgkl4Ci2Fm0CdmWodzk7ngbm0VAHqaNZLBDraNoaQx/iOwu5FcV4Qls1iA6h0iF8PO0vYwyjoghSk4d+czGvRPt0cZzFaZ8D0ooF1dsOOpDYzCBKPGK1EwqTeQnGOqyC+cmU7b1dqqoKJeXOrliVju9ZwUFxQUVbTWqvUGGMoitLGS8h+aI+yf3h3C0rhrV/PVLUH2zrezB6yJmDJwkirq8HEeC1hx7ApXNPD9WqCTIMIvILlsARBBOK4jislyiUBm1oneOkOkGqOQdvvcIHnnmSjfGkE3tjKHK8f7S4pyhIHrDFdKAdS1WR40KdzRiNRhwdHTEajTpLutaaarEgU5rC+LoUWil0kHokZE3auiE3Ga6pqRcLbOvtDW3TIJXDOIJdwneoE2GUZz7Ss2qYHh1RjkZIyMhMcRr8ohZaC1VTU4xLyqJgGphIu2gYb25Q2Zqm9jstFb6gzSwjK3yYdV4WmCJHBa+Btg4X4guyoGpMRmOq2dznMKCwLgDTajklFaRSw5LbUvpgJ/+37+vpdNqNSz7yNp2yLPpsSqU4mE5Z1A13jk54rGkRwOBw0Yuyds727UqRsle1K53QMripUgob279kahiKoglDVCC0oX1m6V5pG86i9wRzgL6z14YhewPFWsYwjBiMx+P/cQDjqPhdIVqIEztCWIBOaQ6PpszfeINXX32VZ59+hrLwInLMyehQidVpHIV4ns5Oi8WpRyJlNLZtKYuCtmk8QpF43bmua5yEoBrncNJimxbXWpp2znw6Q1qf86iVItdZwH8UVDDIiQi3b93i2tXLHO4fcPnyVeYnU8piRKYN86pFbB97MA2GxJhrYsoRi+MZd2/d5urlK9y5cw9XW6xytBJwDlzwOrjKh57nFVlRoDNDOS6RxYIsL1HGoMoiVIhSwd0Y1L4whkZrqmYRpKjlnJJVYfbpHMJJqKTmOsZtraWZzXw+iUBT1d6Yq3wV7c985p/wG3/jb+SN+RRdjvyskD7OZPic9P90XNfNvSX0cH/yqXlxFg1dvOmT1YDRrGvfkB5u5iBD7rrad53qnRd17Qz1xJ6j9uf0Ya6n72W0JtM5ZrLhwWhdD5jSti3Hx8dsb293SU4dvFuQIFIpYhUNcwOyLEO1LGU2KvH6MW2DOKFpWubzOZOi9JBm1lIF+La2bhiXGcZk3HrzJruXr5Eb4+ssZAVvvvE6Tz32ONY2PHH9BjeuP86nP/4Jrl27zs033mJjY4tGfJLRYjbrOirTmpOjI8AnUW2MN7l3cMjlq9eCZ6VBlDCbTTmpW3a2thGBqvGqRVs3mDzD2RaT5bSmpshH1IuKbDKCUJovzvBMaVrxklGWZ0tu4Lg7x0jLVNJZ2jHDxyM7FR3kH4Riua1FOR/WrbRhUS2YzWbMZjPyIqe2FtHaB7etkVqGlGahDo+/E7SUuJbGVp+hZg3poWYOy96K3k047OTGCkXm3Y1LzCPx4axjLiLiw88YcHyrkor3PcpTjBZslcFsb9IcZ2xdvuF3r0YC3gRcurSXMAYvNub5MgitDwnWp3aU+H4moDQb/O7Z6hatQ2vE4doGqRfUizmL6YKNjS3axYLb+/tImPzVfOZdnYeHPLZzjRNr+ZZv/hBv3HyLRQi3PplNGW9tUdcNB/f2uXbjGj/783+L559/nuPjI0wGi+q4W4B3797l+tXrPolqOqOpa6bHh95gNxpRaMXd27f8e5mMk+kRe7tbnBwfclQfcDybglbs7OwwnkxCvsYcU+SMNia0YjFZDlaTafE1MgQaJ75QsLODvIl+TqTJbmnmaDqPIo4lxtA2NSbs/jG+wQK1bSmKknlVUSvFYl7Rao11PmfGSA9es3JO0W86KWNIpdhIQ2m2K0wU53eE7U83NBcljphJ68sfDje9ocF2qEqfRQ81c0gp2gRWGpmU6XzcaUfHGPfUgLUuMKYLaU1+XxVoE6kRsKIZb+5weHjIzrUrtHVLIz4YarFYrHRtxog8LQRX5rK9xGiNVv5TBUt6Xdde3EV1BYTn0ym0Dc10imtrZtMphwf3wDqaquXg4IDDw0PatuXNmzcxxvDLb/xPGGP4pV/6Jb7pxW/m4x//OI899hiTjREbGxtcu3SFk6MjDg8PyXTGqBjx0itfY2tri6Ojo24MPvZrH+e3/ebf0r3f4eEhV0fXyPOcg4MDrl69yquvv854PCYvC15//XX++b+4yWPvexznHKPJGJzQVDUmz9jd20OXGWO1gbQWqzzid1VVXq1WEoyG0Nq681q1bY8yFcdraHNIgWPj8W5cnEevcq3tJL6IG5plma8vmo24e/c23/yt34KejJkt+upXF5VSu2k6mA9pu5f+tpZ16zadx3Hh9/c7rcY45zoVcpVn5yx6zzCHdfokLNsH0smgE0PMEB7+Iu6duJjTey5dZxUbk12++MUv8vT1q2hjaMQym826QYw67Xw+74BQImnxhsi0klMbQoqzzCcwta3twojbRYVWPtTXtTXT4xPsfI5rW+7eOaCe1xwdHdFUDa+99hpaa3Z2Nrl8+TJFUfA9H/owe3t7jEYjFlXD448/zrVr19ja3eHTn/gk89kcZ+GNt97gzp07vPTSS7RJv04mE6y1XLt2jc9+/nNsb25hreXOnTt86StfZjabcePGDT73uc+RlyV1XTPemPB9P/CbeOLmm+SF4vbdO5xMp3z8Y1/g6ad9u27dusXV61e5JFd9WndA8sq0YjazbG1tLY1L9PQA3YJetYOnlEqOcfc22seQaPFqW8zvaNuWLM8oipL9wynW+jbcqRYold23x2C4U1+EqawT/dN39PPSn/dOqCcPPXOwQWQ0KhHVxCHK12Hw5DBmmSsrpXDKIk6tTImN9/JqxQojUvhvCQjVeBeXUmBboTCG2llu7Z9wNJuzlY1x4jrcgmhbiOJuvB94aHoALcuDqoxCWoe0LYUxiNa0dQ0iZLlhPj0JqEQLWltj25Z6XjOdTnnz9Tf9LphnfPt3fjsnJye88Nyz3Lt9hxs3bnB874C8LDg8mvHss0/7iFMHd+7c4YUPvMCtN/31N5640U3C3Ut7fP7znwc8oOrBvX3KsuRrX/4qTz3xJHt7e+xdvkQR0KeLoqAYlVy6dInj42N2Ll/irbfe4onHrlI1lmeefo6vfOUrfP//4bv56b/+13jfN38z+ajkYHbM8fExr776Kt/8Hd+KyTNoFuhygliHM857NLTCOW/AcyqB0V83tixvKPGriEMH96QxGfP5HJ1ntOJoxaGto6pbjpsZx/MZrx4cU+5e9d4sK2AMIm5JKu2foZb+T9t01t/L7VzNOE5LwKvrtyxvft6e0i7VFn2vGyQvSNE1te5l10kc68haiyHrdqOeYfS7qHcrGZzOcHnW1S2w1rJYLBiNRkvuv1j45kF83XGiV8FN6JzzuI95TqsqJNOMJxNeePFFNicTmqBrX7pyBSfC1RvXGW9usDHeZFSOUSbn8OSY7e1dtMm4dOkKVTXn6pXrbG1ucvfuXaqqYjwek2WaF198EYAbjz/GZz/9GbTWPPPkUzx23TORrZ1tKtuLuRsbG7Rty8bGFnXdsnvpsq+eJT5aNEobP/RDP0Se5xzPpmxf2aMcjzojbjYqKYuSIiRDxb70agHBi3H+mA4lzjiWMbszjk8c14ODA/b29qhqrz4IGdqUlBuKxoYAjgcYv4u619/JaMuLGiIjPfTMoQ+J7V9MhbJu0WDoF/Hyzt/BxnkU2JX3XuXS6ZJw7KD+QxcPECecCzUcDJVoPvmZz/Abv/PD3s03nXb67s7OTteeKAKvEyudcz16dGhHVVUo8eJzVVWI9ffY2tqiqirMqGBva5u9vT3qRYPBx0dEMXm8tUFb1TTOkrkcK47WNYzyMQeHh2xsbLExGVHXhvG4ZDGvKcoxSnsMhei6fP755/nCl77IE088wZ07d7CLlkuXr7JYLMhGY5+h2DRcuXKF27dv46TyAVzbWzTOglZMsozDw0N2d3cZZQZRPsgrz3PqpiLLci5fvoKZZIE5ZIjy0abo6MbunA1Bgliuoxn7Lo5rmm2ZGnu11kEKVDTBcFwHfNC6rqmqGkyGQ/jVT3ySGy98EFE5pwJs1syr1HtyEQjD1B4hLIc/d+09g8GsiqEY9sv9Mp63xRyUUn8M+EP4HvsU8AeAx4CfBi4Dvwr8b0WkVkqVwF8Cfh1wF/i9IvLyWfdPu7N1/QsXSQpv/F+S3PhlAyBebViVrbnGrSPiAwjS37SJOm2UJLyfvAJcJnzhlVf58Ic+SFFmbOhNH4psMpSK2YXeW5EyiHUTJhrNYkEdEUE7IS8EqZwXJLViIx8zzoswaaEceYi1jWLE/r17bG1s4zRkozHFeIyd11TWYcWgVYHIgsY6snzEojpiNvVG1OODIy5d3qOu50iWMRpv8dWXXiXXGxzsn/DsMx+gyDLeunXT7/zHU8rJBqC4+dZtlBZ0rskk87gS4igmI/b37/L444+zbRSxLE7lWjJj2N2akI9H5GVBPilppSWflCDaR1vivIegtVjjCykbNNiQCxkkgWGf5nnOdDrtoPGWxG3nVQqV+z4vTI4ZZ7R6hG6nHC9mqHJMozQLBa1q0dIbAdNszm6eJIs4NX5Gcom0Y5KmOpEQ5HWa0mel5JAO58E5t8RQ0gqtq1zxInIuNukDMwel1BPAHwW+RUTmSqm/Dvw+4HcC/w8R+Wml1J8H/iDw58L/+yLyvFLq9wF/Gvi9F33e8sAuv5Wv03j6TVcZMc8TreJzzsJ87J7r07FoG8szzzzHrXt3eezaJVzj/eSiVYfO5FwP657GRJxHWZb5gB0nWKsoyhIbB9YJ2ShDVI4KunNWetyFPMC/2caHUFsR8vGITHk7Q9vWWIST+Qn5oWF//x57u9uIOEwGSnlXaFmWzGYzlFLs7u6gjOJkdsLR9MjHNegNsskIYwx129LYllx5T8Ph4SHXn3iS/cMDJpubONUyXUxDPYtgrC1ysjyn3JhgSu/KdFoxyo1n0DaMg3iYPksA8FUgOl3oq9W1qNJFN3QEtTXGoJyfI9EI6RQYk2FxNK7h7uER1559P08+8yxzMRc2RK7ynK1sW/J9iAh1IRXggoLAug3wvCe8XRNnBoyVh9idADeB3wL8TPj9p4B/PXz/4fA34fcfVBeQc9IU3eXw19NiU6Shu3Ol+5NlK3JMEz7Pktzfw6f7NgJNZrhzdMidk5NuAu7s7HRGyXjNEKglRlKuo6hjRwaX3quru2E0ymiUMYjxmY5ZnrN9aY98VDLa3CArCx9ohFC1DeV4hMoUJteMx2Na8Z6IKBHlueHk5KQTjZ3zLsOD4wOyMkPnftrEHBKlFCaA7Y5GI+aLBXlZcPX6NVpnvVuyqZfE58q2mLKg3JhQTMbk4xKnFbrIQxEahVKmy3uAHhfRw8stT910jIeG6XQnj+N8yvhnfGyBFYeIZVov0KOSv/wzf4PR1m4AczFr59KwLUMsz1UUa43EJKrhnE4/Z9H92hK6dz6HHpg5iMjrwH8BfA3PFA7xasSBiLThtNeAJ8L3J4BXw7VtOP/yOQ9ZOeiNeDUjdn7U71PYseHAKHGIbXFt031sU/vv7mI1Lv3EzNA6Q0lAntKCKsbMxPBn/uu/zK27h2S5L2u/t7frreJa0SaAqdAbNSXsiLEykwkVszrjm3j4tbgoGnE4jT83LzBZ4ZGOjSbLDMV4RF5kKA3FqMBkislkwsbWBF1kmDJDa8hGI0aTMbow5GXmi9A4R123mKLsIjgXTY1oxfFsSlXN2dycsL9/F62hqubM51OsbTg42ke0MKtmSKa9dJOBlZqs1LRtTTYZkW9s0Cifp1KOJ2xsbTPe2CQvx52x0veR90q1KkhJK4rORvRmEwq29GYpST7Lm0BXc8JaXNOSRLp5csKoKMnMiEtXn+Crr97kZNEG3IewkIOnZNXijfMvhrwr5RPrrFpvrYjnpRufFdd90k1uiek46expKumPeO5FmNhZv78dtWIPLw08CxwAfwP4HQ96v+S+Pwb8GMAo1CQ8j0TkXBHLCpDsNs65oBQsG2vOkhxWdqRoBMvcCT/8u38X1mQcLxr0WMhC3kQUW1cZprTWIc6fRHyGNH05fVY5mnTgJkopFD55y5gMq12QKLydwzpHlpc0VU2mDFlRdDkFVmKEnc9RyMsMWst4XDKfzxmNRrRVjcLSNLFWg+HWrVvdZB6Px1RV5QvfZAYnvoq3bSyNeCQsXx8jo26967AMksXW9na3gMrSp3Ib4zE122BwdjE9meUQ6FXjoZTCRoPuKmMcXvXIYk3Tjjk7nO2BbRd1Rb2oOT6Z8+WvvsL3/6aPsHAWtEHh0luHrNpw71VtgQBEKCARo/Tr542IZQvSO0a7w3kS8IXu/+BN47cCL4nIbRFpgL8JfD+wq/pKHk8Cr4fvrwNPAYTfd/CGySUSkZ8QkQ+LyIeL0bKVOXLOVeJjcv1KlaJ1ULeOxvbFYa2oztB5yli1RkyNpFQ8ppFQ0m3viffx8c9/GVWOOJzNuwm9WCyW4vyNMRweHiISsizD7mdYFiWHPm0fIqt9yKwyCD5LVJQBpcnykrwYofMcneeYosAZQZcGySArRhTlmHyygR5nZIUB5TwcnhZEC/NqRmNrjk4OaVyD4JGxstzgXEueG4oiw7kWcGRZkJ4yhcp8BqtowSmHU4JkvvJ0MR5hc83CNmxsbKAyQ+Mspsi7nbgVX9sT4yWPiCV5EeOtCdgVYh2utbjWeknCCTjp/p6Mxv0xBVXb+FKBiwVVU6Myw/7RlON5zT/8R78EyizXRaHfwSMji+pBqiZECWMIIHzejt79JhpEh4zKWFd0eaHHZ8eSfxYvhcZM36E0Mvwo5SXOdfR2mMPXgF+vlJoE28EPAp8F/hHwu8M5Pwr8XPj+8+Fvwu//UB5AWVr1ssDSC6dcM42IS89LaRVTOI/SZ4korGi+9PLXaNHM6sZXXKbfnSMH11pzdHTE9vZ29/cwtn74iYbLoduu07+z3NselFdNVJajshzRBmUUJjdkZe6xJUZjilFJXhTo3GdUpn3VOEsbPD9WHE3tsC04q3BiOZkeM1/MqJuK+WIW6lssW76NMUwm4+CC9DYRjI9qzPO8q7aV2hOAJTfuqvE4bzPwi6r/PaqaqZ0hLf1nEXSeIVoxrxbd/crxhDdvvcWNJ570BtAz7E9ntentke4/0n+W3jc0KzKkrzc9sFohIh9VSv0M8Gv4KnIfB34C+NvATyul/m/h2E+GS34S+MtKqS8D9/CejXNplUFnpXg5EKf8tS0OjW1DoRLlUOQEQbA/D484vb4NIS9e9QZBichUCh+tqTRzZ7k3bfgH//Qf8kPf971Mq4rRqKRUvi6F0RolsLO55QfXecv7MG+kh18XlFgvvhpfIGbo7dBaI8H+go4xAD5QKMs0OOO9GtrQYhHxKdRoh1UWMQozynFNi2DIshJdg7U+KQzj27RoF6DAietS2JXu4dVVwHgEyIoMMuPVI6OQEFMwKUqUMihjMLEflX8vUX1hH43uvlvddqrX0nif+i4o59Gvm9TWlBgjYyh7lwjlhDbYnGbTGvSIRiy1rRGjeer5b+IoIEq30qD1sncpqqRDlWfVJrWq7cPjywwlvoPynhkRP38luVal1pVobIhXLd9z9Zo5XRUspbcV5yAifxL4k4PDXwW+e8W5C+D33N/9l64/ZRdYOneluUf8ZJY0bDZ21rpnLnfmMrNJJ6jXNv0iB9A4HK1WHM0aDmcVxZamqmrGk01i+nm8T5QmjPEgNnGRQQ9uKiLkKrVTOFyAaosxF36S6ESYpKsloZTC4Q2i1jWIUrS2pbEN2IaqXkCo6jQ9PsHgXa/iWnKdY9NAnswv0LZDuvJTMtpSUL6UnM4MyggKH/jUaqHIvbtSxAcxOZEujdiJ7wvb9ghX8R2VDn3mhExpH0wFXWRkKlEshbmvGM+2bU8t4ljTM89zD6Irwslsyt3pjF/93Gd537d+B602uGDH8WPuTs2NVYwgfdZwg4vG5qGElHqjhu33/3ujsbc1nWHhBJA+OStuJkNm5IbG2AE99BGSXy+Knft2pK90wFOVRYlfoFYpnFKo0Qaf+Mzn+L7v/CCjzNDkDdJaRqPyFDDIuh0mUlwwSimcbdGZCsxOgfRemfTatq76eysfSYhIb3F3HnJvsjFiMZ1hGyErcg+C0noj4OHsCI2hbesOpr6e+yCpgyPvoo04FhjtWaOCQhWo1ksNeZ5jTEZTzX17chNcCm4pMCf2a8yGVFrTtLWvWJ6ZDnxXi9flY8yIj5dwSyrjWVTXNZPJpFs0h4eHHBwcsLu7i9El9+7dQ5U5n/n8F3ji2eeQPA9ljxQK3T3/naK3r4rcH73HU7ZXW6lXUZuEUvfkQ3StcminQPzk1Fp1iVzgDZN9ElcUMSBDQBxIzwQUBkRjqRFnggjvjV2iMmZSoZxw7/CEae1YzFtGpqQ0PTycUgonbbAlDHYhAcSDw1rnaFX0XSiyBBfAWktrazQKrYqwU0S5PBg/AXG2i+Mwqs8w9RF1GlPk1PW0SxnIgyEzN5lP/3Z5xwjzUJxmtOGzM0UpsrwIbtiw4xsJ0Y8apQyNa4I7r0FaF4yqvo2p/cg5n2FobYu0wQsQxHjPEKy/NqiQWZYtua67GTOwA2RB8jIoNseTDm7NV9wStnZ3aJylsnOKoqTOJszQ7G5ephJDRmQI7pQnoMMZFW+gXhdvk24oMVFsVbJWPN+Hdg8XrndNrJJM1tFZYdvuTLEjfbv/P6ChB+AiJMGe0H+8buzUIHBJdGBCeOBZpZkBP/VX/nvy0YgW1VmyY9bfRdoLq7Pt4nt076KiVT8WfgWvs7queK5zrcdCWCG2FkWBMhqTZ95YWZaMJmPyiY+FyMuCrZ1tTJ4xmow7/MdYx7IsS2901N69mZUFKOftCIGGlb6HuBkisoS7mR4fqg+RwcZ+itLQKkN1SqcyGkM2rhPF4XRGawz/40f/OZMbNzjGMVcS0J7PjxlIDYirjMrpu561aHuV8Z2lOH/OWg8PueTQ0yobwKkotzOufRBp0Ls5wycsNq00ggql3bLQHl8KzSnBZTmNBdGGZ77pm7h3eICx2+SbPiYgD3UeoLc7RBKRJSPX2riIwWQTaciygqZpmE0XjMZFX/G79dBu3VNC8I7gaNoGcQ6nHDrXHnEqusy08piYueHo6IiFaxGjafGL2BR5N/FbBLSEato+IjIvirD4Apx3J/XEqt/eKOxzYnxinXMtzgUbBtFWIJ3htVPjku+95NF7pVLmq00fjDT0XKEd0+MKnRXMWst4K2cqMLHap9SHfopz6EwS3dvIBqcu2w3OBhFK3Y9x4aYRnecxjo5pSj+HVj3LXYAJvSeYw1A0ux9SEqoFPQB38KJi0NOlDYwiLF4ddGelENWEZwXVwYwAw6Fu+Zm/84v84d/7ezis51wfb1LPF5hR4S3+1lIM38u2KBVrStIxEh/t0nsqvEjddousrmceJLWdomu/wDwsvunUCt92Dw8vysO+S/DHowyis8SA5dUn19aUG5s+K3Q6Z2M0phyP0YFZxkI2mF69MEphjH+nlrw3ahov3fgdywBRygmVo/BqTZQylIA2PTO0tCijUFqwro9sheBFkeBDCkbOtMYmhL7UyiOEKcXJbIGojP3pFKtzfuWTn2Fr9xK1LrBKI85hVBY8KaeTrJbnogQ0bHvKUJjalVJPRkwHUoQgvUBDA6IH5bVL73O24TL03YC5pEWIL6JWvCeYA1zcWLOKS57HUNbf2080Sdx3MfsTyfyoJi44T9q7T51jbhuuP/UUf+9//Mf89t/8G2laHxabhUkfB/l0m1dLR/HvVAx37bKKM5lMVvaJh0HrJ7YxJSJ1Z9z0zz0NgmoKH0OR4xOwNre3/KQTi4ilyExn+Oylgz6Qy4UU85iHEa3yncttYOBNF58xPk4jeiJWWdyHUoNI4poKlC62tvXVvqq65nj/kKLYYlE1/ONf+Wc8/23fwdHhYu0+MszJiC/xoPNr+fhpe0WadyNIZ2MZSo+r7p/Kyp0UeUYezyp66JnDRQwv6Tnr3Fn3LXGoiBGhuu+dx0MpfFCB34l7JuQjHUUprNJoMcyVsLd7mcrRlXzz9gDPxR0subDEeWj5uKCHbUq/p0hEp/EE0/foPRpdW8nJMnA0F9Cng46aZ+gAkov1BloRwWQ+N8M510XcxT4vQn3QNDbDhXiElFa1O322cy6RpPz/MaU9nh8Xvw62C18b5DTAbLzndFazc3WbT37q07z4oW9jf16jinLpPOccOjuN75G2USnl1U0X8SbWv8s6StWJlXM4YUSpejK0GXTXBuly6B7t+u9fBYPkKkNTumOsS60+z7ATQ6fjZrpOTIttSEnh9e3GWaws72ZWDEoEZQpanXEoitePZ/zkX/lrHFYtrXgRsmkaBIvWYG2FrzbdgOrTilPR2DlHI47GWh/+m7QtRv2lbU6h7/sdQ6PImc8qrzJhyIoyIBinUyEsRu0RuLO8pBxNuHrlOpPxJmUxJi83yMcT8tEm+WiMMhlogzE5xuRoVXjUaJWjTY5RBT6ITBGDyZbGMkRqNHbZ2Djc7YwxXYh0HKd0QcXoy44pKkdDS+MaamkQsTS25tbdu+jJhBNyysuPcdxCpTQSdmUdiu26EMthQ2h3+knnoXUV1janojKj1NMxZRfu7YL3K3xWzdfu/qJQor3aGmKzxYLCdPVjh0xlqMZ0hltROK9L9p819FAzh2WRNz2+uiPS31LdbtU190Pp7utc6ytYr22zJAOtUaagUYZrT72Pn/xLf4VZK1R108Gmt8FgyMB1VRQFdV139RTWURQxU9H7LClCRBiPxz3jkeVKXPE8kxXeIxEAWFpnu3DnKK1olYUgLg3KkOUl2uQo7StaKe0Nl9bKkmifehjSZ8YyeWjVJV5dZGzW7cxL9UyCfaV1lul8Tts4KoE//Wf+HK+88SZzJ16yewdoaU7Gdg/OWZVNueozuOpCz0+zl++HHnq1Ir5UFJ3XRcJFSqtMpfrZKrUj7fD4jOGz/W/L1ZsV0dI+rFvgA4h645gPl7ZKM9Ug4wlmY8yibcjxRsJ8gP6T7npxEaW6rtZ6Kc04Luyqqvpw6vBOp3I2EIpQVi/mBzrncK2Q5WXCYAxKG2LEgggs6oaNYuJ3MZ13/SXi3Tk6C+I7gApCqwofnZEbjZU++9HRogITUF4WB3ygUj4qsc5179m9TxCRO1fuINdgaLT2/ZEFSctHsp7MFrx1+w5NDf/glz/KN3/oW2A0oRaNVSG2JUVRUip5kfNtX6sk2WU7wWopd2gnOY+8xym5/gLt8c+5+DMeaskhpfOg1SKl4ly6UFZdmx4b1sQYTrilRJhAq6SaoQQAvihJqzWT3R3+6//mJ5gtqsT4tnoIYoht+v5nUQSwXWWsijEJEUsxjfkwxmDyArRBZzl5OeolBG08o1CGshyHDFCNQ6GzHJ3lXpXIC7wnZLl/Yh/led4VJI6f2Nau15RXCbKyWHp/Yzzgi8lP72NnIWmlxXEj06uqmqOjYxSay1ev8v5nn+f69RuUZenBXKJh9RtMAl08xTty/weUmh9y5rAcEJPqbxcRv1LVYih+pvdK7RqpCCYSLeU+ClGRg2SdJNE/qw9+AbDiveNWWhqxHpPDaWrJuPrEU9y8dZO6bXCNw9peJUjDgIdWe/ALLU56//GLNfqy4/u1rf80jYA2oA3RBKh1kKq0okV8UTDt06pj5WttMu/KtA4nQmsti6qCzKNNZaMSlWeI0ZAZWpYLpmht0NpgTIYJ7YtSTScNsQyc4hCP6ZB7RpAVOSbPUMbv6A4fZGaDfSIu4qF7zxhDUYyIqe1+s/TG46qqwMIrL32NG9eu8Zt/8LcgWY7FB7H56FQfswJe1I+Riiok1Z9H64yK8e9YB0SL/96uyKjs7BgJgNFwrkbw34upCz5a9bwArSE99GpFpLQTzrL+pmJUByE/8E/D6sCUdXaM9NgpMQ06K3octP6COJk8krNHQFAcHk353OEX+I4PvohzGc55q7vWGsmMD2AJhsdo/ffvkVj9syR+IXSHgDc2KR96bbIMRHfXemyBYCvQKmFGQXAWP2mtUWgtGPoq4EUxWqq9EfMT+oIygo7SjupVIq39+7imJSJI+5y1PjjHOR9clhpQfYJRPCeOuyMWDknVi9j3qbSQLlKllAe6bVvefPNNnnvueapFw93ZFIs3TBOqt9uuJ+n65iK0br7EsUs3KN+u5XsP5+k65nJaRfbu9aFK5SSVehMmcB/qy0PPHJZF+/7YWefD6XiHi3R2SqtVhvXP7Dl7f9ynDKkwn1tv/MsL7u4f8n2/4bt5861XeOZ9H8BRd9e04rxxyrkONWg4sMaYpcHvQUC8fV3rjCzTQRLoLf7KxLqKfQSgUgrbODKlUUH/V85hdOYlp8C4vNTiUa7btqVpLabIsCJU9YJxkXfnKd2HqUtETjX4mBAVFn3IuYjXmCBddFGQyW8xxoRw73h8qbrZCrUr/t62LYeHh7x56y2uXrnO5s4um5ubyGzfX69CKpgQYs3On1+w7A68yJqLC18rfYo5DO+/yk3pnxMkrxCo5p+7IrZnjXHhftSLh5o5CF5fj7X+hqSUiqUUlw12LKsUK+89UDWG56+SItJJm3J4253b73IQd2oB412cgkaKgm//4LdCZrhz74C6+TJPP/UExvi2KxcSprSisdZHd4pCrKaVYM0vCj/xVQgVbntPQtt6Q1+Wa7R2tKagcb5QrCELSN2mm0++v5rgufAuTS1BT0/sJ15CiGnAivG4RGufIeqzM/sxUajwtwPlU6XFCJjeDWckD56YUBezbShCfoa10sc0pOMU3G4Ob/T11dAAnfmWChhtOkOrtQ4rltliTuuEW7fu8Js/8tu4d++AVgQxUCsV6i27DtH7LKvdKtUilW5W0XDDahFMZkh3kqHLtgeZA6RlWVoIqe7KnWIw3Xkk4dxrgurOYxQPNXOIdNYC97h8vStoSKlINzx+kfuvO5YyiVVqyyqyztFaYe/yZVp3xPtfeJ7DgznHR1N297ZxribPe1E9emd0EMe1yajrljwvQRva1iLa+DWT5TgRTOnL0ulQYLapm873j9IYo8PumOxAJuxkSKdj+Cx0TZ5nPickC8WKtfIgNbLch6uqT2mtA3Zn0ldaI9Z2UGpKKaoQ0xEpprWnnqpUSkjViVSF6L06/u+yLLlz7w7z+RylFD/yIz/C4cExxhgaazkJaOExixU4hQcZ2xHHfJWU6du0fqGt2oCGNqbhuelzh4WQVs25FIZQRDAJMM156vI6esiZw+l4hZS6BRrONe50vv39WmnPYgjDY+ngXlQNEQU6M2iryUYjtrdzcjTVoiHL9RJqcay1uVjU5Nk4kVD8XpBlBUoyyIMYGvIQtPbh29AvNM9w+gVog1Dc3VHFlG9BXB9hYJ1jNB53EoeXWBR1VfnK34P8hWF/dXcK7+0jDg22aUOcR3NKiksXUzTWdlGSa8YjGmu9PcSPzeHhIa1tqeua97//BQ4ODhAXGYsPHmvbFjFZryKs2YlXvVt6LNUAVl0zZBD3QzHSMV4/VJOjxHhW7sW6tp1FD7m34nTFIFjPweNOtIpWWWmHHT38W8vFuO55Vl//u8JpQyMeR1FlCrKSja0JC6mZNfMlu0UEFL1161aQJgwYTVYWHWhpuphSL4a0grRCpjK09ujPIn73ldbDzlnV102wCKL7vo7/R+9FrK+hg2FROb8rR3cx9EwofWfnoujf20aiazSiZ0fDZq4TAF56V6vKDCrz7161DfO66gFc0w8qaAQa6zSLyrKoLLduHbC1s0c5KX2fEz02jvF4vHLcl21Ip/Es08+wAvuqxTucI+l1F51Lq1Ldh+pu2ubeo9VLbWe1aRU91JJD7MT7wWAYXt99H/AMpdZU344QZIRaA8LSeZ1x7wwmdIoGp25sbFAd3EGHRTHZ3KSZLzieTmmsZTweB8xh2NnZ6aSIqmkoyxKt+gCYtB3d7mQU2uhQRUkt7bppkwTPALVSYAVRsTitn1yxxkNcqFYcddswGo28ahHO8eMkS7ub380ab5cIunE6jIImyzPaYNtwRtHW3vaRZTGtW3eZi9Gtt9TXsOQd0kbTNg0o3btfjebatRvMZ1Www/SGzFWq4HC+3Y8x/CJ0Uclh1UK+n79XMZ91Rs519JBLDvdPqwyKkdypj49xX3WOFe+DjvUdlFvmsqt2iHV2h86IFAYnCy5GCTHueV5ycHhMOZ54T0CSKxF35MViQZkX3r0pw3vrwce7FpXSGJ0TIc7jxwVAWu8VETJRna7duGW/elyUVeUDt0YhwnI2m3W7fuqiA7qQb2MMrhWUaKqmwbaCs8GDgQ4LMQPxu36WF5RZgb+Ntx2oYNlvW9uVsYuu1FZcFyshWnVAOrNqwcl8hs59BfGNjQ2yrOgMmsM5EnfbVdGNZ0kDsGwAP08yhdVetHVqUseYOf0x6uylu+7ZQ6noLFvZQy05RPK6abv095Ak/HMWV9b4he/FUOnszkuLPj0mF+Oe56kegge6VQqqxZzSeLQoTcCcRPHUU09xdHjI/v4+V65c6fARm8bXq9wI0GyxdBqAbe1SrEEn4mpvfPR1EyyIIJouC1QUqNaDsIhzVMFQqDUo2yLkKNX3RRf+iw4JYsLGxjgY9ELx3tEGIpam9clO83lNUWQYJ9jKkWcZ0lq/kAGUonUtThwHR4dsbWwyKktwQtM2lJOxtwfQxzDooIYsGl85O6JJt20bDHJ+x1wsaubzORsbGzz77NPM5/NuBoh2vn8smCynFUcjDTp6QtYs5PMonhkBcldFqq6yqaybN0NX5EpJIZkLF23rRQznkd4TksNF8tDP44Jxt+1cn2ukw3W653li3EVpaGVOB/XSpUs888xzTKdzqsqjOxWF3/GapqFpGhaLhcckaO2SQbCLsgxLOo1YVKYHlhXVR5sivSqg/EuRGYUEBiDisyucbdA+MgtEcNZydHRElmU+FyLPAxxdS4Sqy7KYZu0rjC9m8076imMVDa6bm5s0tQeUjVLKbDbzmavSqy9RPXLOsVj4OhNRmojfAQ4O7qG1ZmNjg7qumU6np8fBRA/I2Ul0D0LnwcCdp+tfhL4RYd7vCclh2JFDJuAzA5fPHbowY2Vm/1vPHCwEv/wyQs5Q/HNOunqNq8TSdW0FehchntsfHh6SJ6K4hJ9t66iqmqtXr/Hqq6+ilKLINNViRlEUXjwOVbSHGA1dnySiY5QmIubifD7vithoAazzMQ8R9s4YZtOZL4XnQKsckYbMKB9GLBaNr66lxfdprg22blBGdYWDtdGdq9M5H34dmZjHWFi2vEcwmDi2i8WCrYDU3bhenTAeXgpg6V2iZ0IpzcHBAY89foVRucHOzl5nQ1BKdahVKohFK+NakuG7qAfqtPtxGZBlXSzNWXS/zOOU5KFW//avkLfifIsqLEsWcfcU6ZmBqGUXZwwTXkWp/rycZ/H1GdDXXvkak9G40+eBgEXp9fDJZMLt27cZjUYcH3s//NHREWXpi9seHBxw+/btrq1RYugkAOnVrighFEXBfD5fSrzqrN9tXy6+aZoebh66YKLFYpEwH98fZVn6361FnGM2m/oCPmXhCwQ5h4S2TadTnHNMF/NO+kkT5IwxmCyjtj7Eemt3hyzEZsTU8uh2jEbSmHZ+dHTU3b+ua46OjtjY2GB7Z5O6rr2tpiyXLPdpyPVZMSpvZ3eP8+ZUcNMKe86qnKGHgd4TksNFuK0PBwbwtgKLoJXuipielaqaunlO1TYM/yh6JnSW5NBdl+zmgkOJIzOK2cmUZlEh4t1/AmjtQVMFaOqWGzdueLDY2YyjwxP29vaYzWbcvXebPM+ZjEq0QD33E9+GidahLFkv/0znM5TAYjanDEV0jfbVpFqxOGfJtemwL1MjmEEQqXxbneN4fshoY+Kh6cShA8jN8fFxgKZTNI23P+jM0DQNk8mExtY4aXHtwgPsNN6YWBZbNLWvUyFWaF0IZgpRl1VTdws39qUKNpLZ3Es3zgmbm1u0bcvJyZTt7W2+6Zu+iTyb4Kwmy9RSQNVisWBjY6OzUWxsbJwZkxCvS2mld0gkJGs5hD69XonHuRiqkevmcmowPJeG3rcLJIXFtp7XjkjvCeYA9ycORbqIDQIGi/ucx5yrQkjvmVjVnpdeus3+/j57mwnWQny0UkimcQKz+Zwsyzja32c08lGPs9mMK1euYIyhrr3RTUTIyoLZbMZkMgkqhN8Z60XVRRemngVjDFVVUeZFJwZHl+RisWBzc7O7LsZPRH0+vmPbNqFoja+OXRQ+UOrg4IBLVy6T53mAcZPOmNosKpw45osFeeFtAgB13UCi2kUGFfEeY9vqaoE2pouxiB6M2WzW9dHm5ubKuaKUYmNjo+sHaW33N6kKcPbwL3k0znNn+5/1UjUzOslOdYFX3vgdLcChaFGSMqCTyXreOrgvI+o5574nmMP96n3vJK2yDqftS+MgOski2BwEYXMzhrp6XdgNdiob5sj29jZtiGs4ODhARHjs8et+53aOW7ducfXqVSaTCdPFvNshsyyjWdR88Stf5urVq+zs7HTMYTGf92G2ylf4xmTUTagCjlCUWafjt21LkWAq2rpBCyzmc4os4+BgP1ngdWdcTHdfrTXHx8dkWUaeZ9A0aAWZNlTzXl2J8GzDrNqoTkAoade2qBDKHZlDlmU8+eSTHePztpL1u3N8xng8Oj2ugzyes+be8m8SRvp01uSQ5biQCLK8iydSSBBXO7uZPg1WtMqytY4edG28J5jDRWjVIF402MSqixufIqWSgU0fk/WZhVHQ83iNCpqWrUnhi8vg0Y485kDEOQiZnMrhjGM2r9m+vMfJ0bGvFdk0GKUpi4LrV7Z5863Xce6xbkHG95nNpjxx44Z3hzaNj2FoW7QIulsEOsQKtEzGXj8vitzr6dUiQZhadAvJW/5P2N3d5eTohJ2dXeq2WbJheO8KZMHN1lqfXdpU3qvhWsv2xiZ37tzpdnljDPsHBzz22GOcnJwAHmOzLMtOesiyjHv37jEaj9FBKqnrGmst165d6yMqk139tJEuZKGKwzi4ceMarnGdqxdCxWoXw81laV1H5tXZd1yyuJPM2v553WwZ/jL4fXWeRfzq6G1kQ+Nm/52l64eqUWdLi0npF5jq7wnmMNQD7+e6IZ0nCg51smhzEOl94KdsDeGW6yLQxHkodmcXfP/3f39S2OZ0O722D+J8mLWzvkiM0l4VEOt158koY3Nzk6Iouv7Jsow33niDcTnq3J3ZKOtiAYqi6Kz2WmvykJzlWl8qr6lqj37d+vJ+EdYNwAYouvFo5O8bGIlBMZtNmdcepm53d3fJ6CYiVFXVPbcsy073XywWfVAYcHR01BkLR6MRVVX5dw6Lsmka6qbh8rVrvPXWW4zHY5588kkfjo6BWK5wBaVMQ4teWjDWWg8IE3/XpzE4U+9XfC+jevCaVfpILzmcvRLP25g84+glMQixM64vjLTKMzJ89/ulh95b8U7RN8YyHHcMH+0XF+S5IazikXsyU5BlGRubm4yD3j6bzTDG8Nprr3W+/xhRGRfXzZs3sbYXueNuHC3jKUSbiHRW/RgFWRRFV5U6Lsw0qSc+r6oqqsUC27bdDj+fz5eiKmN7tdbM53Pu3r3beU5ibkO81jlfILdtW27fvs3NmzfZ39/vAp4iA3zttdcoy5KtrS0ODg6CLSUGnJ/dt5EhOOc4Pj5mOp0uw9Ul8TKpRyHtCzgNWzj0NnwjPA5n2be+HvTQSw5pR3897Apdh3Zl3PvfVnJggg1aQTSaRUNSNDTpNNtxyXWlaQxsNtAaB3XDj//RP8qbn/4EitbnNOA6kVApD1NOCOvOtMZhUE4wophsbnN4b583b95CtCEvRxwd7uMQJiNfbq80GrENh/t3uXTpEtOTI2xbk2eaum260O2MnPiC88XUqwOuJc8Ms2oBoXCt1hoUFJlm2tY0Vc8glFJYwIqlyBQKS26gbRYcz2aIVmSm4Gtf+xrXr18nVuFqmoZ6MUMZg3NtqNg9om29hybLchaLRZCwHCcnxxwfHyNAVdXYSti6tMfWeIuNjY1OXx+CCZ+2C3lvAtKSGcHUFmkdxvRVtgQBG1O4DSqMcXT5KvF4mQKorO2zOSO0HD6lHcCpPuYhpaGksCpse+l8PLiPr4lq0SrzUHwmGjN7W8eq9z+lSojucojOoveM5LCKM98Pt77o9fETjXhDEfntkFaKN95449Tx1IWYHtMEgFdtsEgnKYA3WH7hC19ARPiXv/ZxDg8P+coXv8SdO3d48sknO9345OSki5GoqqrbBeP7RVE/iqfRzZdKOTHIKF4XJYwYRyEinTTiq1d7d958PifPc65evdq9l7WWra2tLjgrz31R3qhqjcfjTt2IUaEHBwfd++R5ztbWFlevXuXg4KCzWcS2rpPKhv0b50S89+Bs+noO65dImimZ9sfwWHzW10OySN27w3e5yLH7oXOZg1LqLyilbimlPp0cu6SU+vtKqS+F//fCcaWU+q+UUl9WSn1SKfVdyTU/Gs7/klLqRy/UuguKbWd9v0jnDEXG4fNWnZtSKnoOz00nyWI2xzYtmTEr77fWtqIN1njX3+bmJgAnJydsbnrD3qQcMSlHvPnGTbY3t7h16xaXLl3ykYZbW8By0lecxGmtCxFZUh0AqqpisVhw+/Zt7ty5c8oPH6+NjKBt2049McYwmUyoqop79+51HgulVPf7fD7vAqJikeHXX3+d4+NjNjc3l6puF0URwGMLnnnmGfI85/HHH+/cs+vGaxVTiOfPZjPKslwZ6CYC4pbve9ZcPOv34f3T48PN56w5N5Siz7r3quvvly4iOfx/gN8xOPbjwC+KyAvAL4a/AX4IeCF8fgz4c+FFLgF/Evge4LuBPxkZykVonV953QCd1ekXHYhVx1I//FntjOc34jBWqLTPpDRK2BiNPU6EClmhIbAnfrQQ6jF4FOS4U2jxmYlZlvPMc8+hMOQm4/VXXmZjvMkbX3uDb/vQh3C2Ic/gzu2b5BnYZs58eoht5hgyXCO41iK2QWyDEsv+vUOOD44R61BaQBoULYoWcTXbW2OuXN7h8OAeb735BrPpMVoJWglFblA4mqZCxBf3LYrMF+uxLdPZMaNx0WWbxpDn4+Nj8jzn+NirDEopFgvvGTk+Pubw8JCDgwM0mqZq2BhvMJlMuHbtWoiv8Ax26KFI/15HcXyLMkSRdnEIGuVyOrekCB6x2cPeiSxLdktjL3SVrVfNm7OYyKp7njc/o4SS0lB6OYspnAFc1dG5NgcR+SWl1DODwz8MfCR8/yngHwP/UTj+l8S36J8rpXaVUo+Fc/++iNwDUEr9fTzD+avnN7HfoVKuGY+n56S/pXReeOw6yQOWQ5GH94znpkYq7W+yclBu3rzp3yWeq71XIkY39ju46SaV0ioEOI28dmu81+Dxp57i5PCQwmTkeclrX3uV3b1nODo64ujkkL29PYqi4N7BfmcMVBgPCttYGufIc4/jKNaye+USi/mU46MZ5TjrJphXMWA+nzKZjNjYGHeRmDE4aR7iJ6LePp0d914A53ExNycT5iGwazwec3C8j8NX9rp58ybXHnu8C7Ta3Nzk5OQEay37hwc89dRTZFnGpe1LZFmObVUXFr5qLFfhMaRjFZmIbVqaauZrIuYp9ujyHJGIVYnyxXiUNzAPd/EhDTcLGNRFTa5JvSfptcvXP5jXLiUR6Y0P59zrQW0O10XkZvj+JnA9fH8CeDU577VwbN3xU6SU+jGl1MeUUh9rwuSD0x150ZyHdb+vum94/tIgxWes+zu9ZtUz0t1ja2trSaePkzStS5G2KU660WjkF2Duw61Fe9vAZLJB6+CrX/0qk80Nbr75JmjFtWvXePzxxzk5OeGtm2/y1s032dnapigzfMq1oswLbNNSzRcocTRVjUYxGhXYtu2yNGNehcdx8JLBYjHj+PiY+XzObDbrMjzbpgERmqZisZihtY9XyPOce/fudYv23r175FlJ2zrquqUofAzFyclJp0pEr0o+KsnKgnIy7gKzhu674fidNSbxN601GYqdjdLngaxRCxDt8SfC/xF5yiGkdStXFT3q76O6z7B25nAupuHi50nAF6W1a8UJuPX3edveChERdRa65v3f7yeAnwCY7O5IOEb6/3ARDRdrcq+l89Nj59HQkLTq+mFcg4ggSVoyCpTSaOWwtuGt115jcXyEuAWZKRDXoBS+SrUIWilsCJH0yEstWjJGZYbROdZWff6E8/Buz77/OYwx7OxucffWbYqypCxH7O8fsntpj/2DO+ztXsK6mrZuqKoTb+QsJxwfe7DVnZ0dZvMTjAIl4tUaZ2lqi8MFYBc6e0Gee29ClmXM5w3T6ZRLly6FXtDghKOjIy5dukKWw/TEuwsXsxk2JHdZ6KIfNzY2OD6Zsrm5xUsvvdTZFzY3N3n8qSdDdKXHXlDG909uMkQtY3jGT4zrGHovCIWEnWvRqsDOD9janHDctGDKcF6fTp7OH6cCelSCeIUzPZw+zhsxA/T+8vzrVUgkhIkrh9L98lPdZn5aKu7/lvjHkrSaUrx+yf2MIYZmx2su4v58UObwllLqMRG5GdSGW+H468BTyXlPhmOv06sh8fg/vujDztv9H/TYUBpIUY7XPW+oI6YWaX9SsmMA1lmcErSCalFjmxqKXryLE9Faiw0MAjSLqmI0GqFc8AbkGu3y3u6hdUgGc1x/7Aa5yWiahuODQ7761ZfZ2trgKy99mSuX9phOp4xGoyTmgE7En81m5IVfCK214Dz+AsCimkHYoT22go8zWCwWHSLUfD5nNBp1EkJUkbTWHdaDD7rKfWh3PE/58nd3797tVKA7d+5053/wgx/0bUr6tityM1hIqeQV1Zk4vqsiD5Xy/Z3nOY/feIyX7s5oEneiiHQh2x1jUeDjVhJ1Ak0sqhyDqDyDEM7CiYjnpfPGDBjK2svCvInvOtwYlxhacv8h+Ow6ySulB1Urfh6IHocfBX4uOf4jwWvx64HDoH78PeC3K6X2giHyt4djZ5KiF7VSOstVlFIqgg2DWYb3iX+nO9BF1IWz0m3F+rLtrYXaCe9//7Pd5I0Guvj9zp07SeCRJc8NbVt7H7VyiCwDmToxCBmODAK+5Nb2LqIMGzu7PPHEU4zLCb7ATcHx8ZRLu3tsb26RaUPbVIhr2dwY09YVi9mU2ckROEtVLTg+PmKxmNO0NYtqTmsb7t69G2ISxl3MQrRbtG3LbDbrVA0Rv8Peu3evS8gSEe7cudP19Xw+5+joiHv37nF4eMjJyQk7Ozs8++yzniFmGePRBsdH07D7ZRhdBsyIMEdWGKmj9OCCXWWVuhCZ8osvvtipTsPyc+mx+I4p80nVgJXj3z1XobUvMdiXpTstIaSu4XXzbXgsXrdqjg7/TtWU2Hdn2TAu4sr8q8A/A15USr2mlPqDwJ8CfptS6kvAbw1/A/wd4KvAl4H/Dvi3Q8PuAf9X4FfC5z8Nx+6LYmOHutg6F2Q8L3V1pUCl5zGU4e9Dz/c6XbCfXG0YbEXTWG9UlL5sm1KqC2He2dnhpZdeWrI490lcofJ1Ih6LKJzFV73GIzTnecGNxx/jW7/1W/nKyy8xmy6o6zqkNJ+wmM24efMmTVUxGRUYJUyPD73a0NY+vbuac3JywtHRUbfw40KbTCYBVMVnab7yyitdMFQacTmdTrt+uHPnTpdFefv2bZRSHB4fM5vNuujG6XTKW2+9xVNPPc3GxgbPPPNcWNgld+/us7d3maizx94f7nnx+UMxe9Vi832qKIqCD33oW5dwFYb9vypCcji/0vPSaM9+UunlD72Re3ivU9cOKJ2T6e6/ai6vunbo9j3rmot4K37/mp9+cMW5Avw7a+7zF4C/cN7zlq6h54AmvIMBUOqU0HaWm3EdEm/aOevEs/Rv7XzWvoh0OmRqpff36wcq0xpnoXE1hXJ827d8AFSLuIhqlCRphTTsYSyBFdXH/dN6JhGSs5RWOLFoMSCOqm3Y2N7i6OAeTz/9dMBTXFDVFaOy5ObtW2xtbVG7BhsyGltpaZuWzfEEsY6m6u0ao9GI6b6Hazs+PmZv9wrj8bgLx452hqqqurZba5lOpz4dum3Y29vj+PgYgKwoODo6wh4esnv5EnXdcO+eD49+6slneepJj/d4uH/Exnib+XTO3t5ep6ZEqUq0R9ZGqSWL+3AnTEXr+FuUzpRS1NayazSZsbRYn9firVz9+KtgSxBBpEVCDVEPDhyLy/Rzx8+HqJIEKUb1QMZWWX+dsJSP4XEgYsHbmLm5GobemF4SGKoWqa1hKGnE8YHVOKxDes+ET1uXoD31aufK8y9637Rzh9evYxQdx00mUGQYiHQTxTlH61q0GAqtMbR8//d8NwZHRqx16e8ZdeYXXngBocGHLZtTg6xV5qNetQuiatRbI1S9onWWyeY221uaar7grfoWG9sj7rz1Jjs7e/59sewf7HPt2jVmAXXKWosLDEPnhrZpmJ9MUQJt3ZBpQ1lkzKYe3MW2Hi/COZ9jMZr4PIkYAWmM8VLDeEJRGG7f3qc5POSZ557lzdu3mU6n3Lp1i8PDQz7ykY9w+epjiHjPTNs6BM14shlCkOkYaBybdWUP05T5VW7BFHOzFcdmMUK3DqVdh4a9BDKrg6HYeXAcrVUH9x9BYIfPjwZJJ6FaWYLnIFpQ4rN0RSXzbeDtEHHEWT58l1T9HVb+Ogu/snsn1RcbPosebuZwAVEJ1vuZ02PndUTKmVeRtbaLmReRZYBPuyyF9GJhmIhti5aap558EnnrJlp6INi0bUVR4Jz1SNUQVIlkV7AKEXtaYkmYpXUOg8YCxWjE008/jbiW/f19fumX/gnf9m0f9OqCs5zMphSjktl0xrjwmI1HVUVJ0b1zDHG+evUqt27d5vLly95r0VZd2vSlS5c4mfWqRFFkTKdTjDHMZjMUPrJxYzzi9TdvUtcN29vb7O7u8t3f/d089thjmHzcgcaaAEKjle7eP90dY5j0RSzuZ421MYb6ZEZmocHv9B4gdynQAScOUb3475mCAythMQ5DBnx+jIhC1HLcBM7HuZzO4rx/89+qeb9OhXoQeqiZQ6pW+AFIvAvpeWsW9FmMJXLblFKuKyJLXLm7lxMPrppAvWfO+/kR8YlSSnnwVVE+Ed+1lJlmI884dAvaEPCkddGpFUr5KedFRnA2tqdvo3Nt185lihKQ915g/L2sgtx6qLLnX3iRJ57woSV3794NOQtHKHGUo4JFU2EEJpMxjfMBRnmWoeceL7KezdnbvdzZMYpixO7uJV5//XW+9rXXuHLtMuOxT/5q25ajkylN07CztUvd1qhMsX/3HmjFiy9+gCs3HiPP8w7Xcl5XCbivDQbH0wWEUqkhHctVCyBVKVbp6Rkwmx+BNIyVUOuQgKWiSudQNgsQLqBUhpYo9Um3WZxe6Ymk4FQn/fh2O5xYlJggHQStJVR9NcG+FKM20/YOXbNDb8Rwvq/qk1XS1Dp6qJnDg9A6hjB0aQ1dPamakV6TXqeUD3WO+A5R1XGSJMI4X0vSF5t1uFYwIuzf84a5VW2CIA6HCs9+EglVNe8MltCHCqcTZHlh+BqQzoEJSNkeWFej85zxZJPFYsHjTzxFfeUKzrWcHB1w+6030UaxMd5g/949sjz3xW2N6bIjF4sFk62iA2xNEZ6effZZDo+PsFYYjzeYTqc8duMJ5nNv3Lx19x5lMea555/nfe97H+iMcqMvRde0Let2TqV8BuhoNOpE6VXuyVWULqoh/meko6Mj9vf32So3sCkKuXeVdSgrEe8yZebIxQGCuvuK9TYIlRxzQqb9huGCHUMSHIjheKd9c967D89bpTavo4eeOXTBHgl3duKWDJLrXnSVLWF4327XHhgC07+XxFcniXrhuv/jfbQIzoZciKZlYzxhMZ+yOJ7yyiuvMEqelz7Hu+e8VwMC6OxAbFT0BqfYLm88xPu+8fEP4lQXH+DEi8vgd0JdFFilMOUIA2yZjI2NDebTE8ajEePxhK+8/BKmKCgnEzSa/f19Ll++zFHAP3DOMZ3e68Bmbt26hROPCP3Ky6+yvb1L3dSU5YTL1zb4ju/6brZ2dqhbh8dUVNi2RbSirkPqt12WilIJLg2THo51qmoM1Yx0XFfNg9iP0+mUZy5f5hOf/ZLPIBXpxH9RzdI4Oeu6Z2kieKzCub7Ybdqu1CXuJV6H0iH+JZaAVn3bwlt3zCFlDKsM6+vm/nkMYZ20ldJDzxxS6havyPI+o1eHnJiEH8RglTiATmJZOdVFNca4ikgqfoQlJqEF7y0IzMHaYLVuLUZ8jr+tW0Ym57n3PcFXPv85VGMZY5BMY5zuRE0UmCgSKmjbugswiu+cMqrhpPPnJbYQL7h017ciQXdXXUSetU1XcyIrN2jI2Njaw9YNrct44dkXUTim02NcMUaUphxPeOryFV577bUAJqN4+ulnu509z3O2d3Z47rnnMUbROsEBJqRSN1aQAHwrhMCv0G4PRLt6Mcf3Gv4f1b6zrO5DQ2SMU4jU2BZt4fDWjNe++GX+wI/8Pn7+b/1tVAe8GyYAy+HaPVMOhsEoNMa/Q7CUE+XxHyMSeqeGeFuKld6z0FiHMcFdK9FYGPsj3YhkiYFGRpWqwcNFHxGxBVlCyjpP6nnPMId0Vz9Frjf6pEzCrYgbH8Y4DCfeUPyMYuyyaEdXOr57fpRANcxOpvxn/5f/lD/7f/8v+Xt/++/wm773e/n8Jz5Lrhwt3uilwsQe7mrp7gPLiTrpmKcxHGly2Cp9PC3s4sN7DaKF2glGKXRmsM67TctyjBmX3Ltzl0tXrmJFceXaDabTKUoJH/jAB3jmmWcYjXzq+P7+Pk888URY9Bqtvb6cl14NaFqHFe+2lcAOnAjWtl1Fq/iO6wzLwz4Z9stFKF1I6T2yfIRRPmL0f/i7f48bN25w881bHgxloGKCtzXF+3WGLwG08y52ETSCiC8fSGDeMXJ0ad4NNm7nXF+ZTWXh+elDliMch+8zVIvTd+/6QC2n5Z9F7ynmEP9fa4BUvgBupFjJapWIlQ7SUFxLf1vlR07rGIpISLOGsiz54uc+xY/92I/xU3/hL1LPF1y7cpVf/qf/1KsETQ20KAQn+UpXakw8GrbXT4Y+tyEyguHCWSVax0Ad0OS5AWVBa0zmK121rZCh/LG8QGzN3uVLVPMFPlzYUZZjtPb3G497V+vu7q7PKMX4/VIpTJaHsnsg2uCXC7RB/Wpt24VWn9rlkkWcVsmKOJNDJnJWfEvq0Un7Y/i8b//2b+Xm4QFvvfo6RTFhNpt5CDvj80SC1uafZ5P+tbHQrQbxzMEz4NjvEjxuMZBsWeJJDe6dtBffS2J9Uo8mtcr+kM7tdC6dYpyDTQWWU93X0cPNHEQQ68FPh5bn4YtZcUG/7g03bWD+soK7ary70CXiWvw9/u8PCZm3D3Yg41YEbIQDUxgsrq2p24b/9f/yf8Unf+XXeOLqdT7+T3+ZetFwbW+H/+DHfpR6MaUoFY1SGG1xotBiVk7YVEzsSXXRhks+boKoaTykW8pAnUjiT7e0ddshRLcCgkKbEh/o7XVtMRk4w3iz7I2yLljoRaGkCZGZmqKckOU51vlEM43CaZ8FrZSv2WBdQ+Oim9C3K9oRVjHCiH05VB1iMFQ6lsNrl+bESsYQGU8G0mK05ujOHV577S029nbZ29xl54VdPvn5z3qgHAluS6T7qj1779RSh1cdIkPzz7GIcjgrvlZpGDOlVPd+6DReIyz07j36TUvpyCA8RcPo0EDdqZKtD5brrlFuSXLsUa7OliAefpg4t5y/sMoos+yWPh0+nX5EpAtxHYbMDuPko0HIOYfYduX9mqZhPj1mdjLlP/7jf5yPf/RXuPnKq/zNv/GzqEa4srvDv/9H/k1eePYaJlNopVAmR5vzA1EikGvXFaF9WZatFLWX+mSgMqVG0GF0qFPLfZgyHujjCrTOAppUQZ4XGOPb4US66t+i+7BwrX1djlhgN94rtaGkYxXzF1I1Lg34GVKsn9nZkdxp+LTzqGkably7ggCbowmf+dSnGY1GXL16tdvJl1RPF+dDuonY5XOS8Vpyv69g+qvm3ZnSsUgHyR/77Tw6UyU/g9SDXPSNIqXUMfCFd7sd59AV4M673Ygz6FH73h497O2Dt9/Gp0Xk6vDgw61WwBdE5MPvdiPOIqXUxx7mNj5q39ujh7198M618eFXKx7RI3pE7wo9Yg6P6BE9opX0sDOHn3i3G3ABetjb+Kh9b48e9vbBO9TGh9og+Yge0SN69+hhlxwe0SN6RO8SPWIOj+gRPaKV9NAyB6XU71BKfUH50no/fv4V70gbnlJK/SOl1GeVUp9RSv274fh9lwN8h9tplFIfV0r9Qvj7WaXUR0M7/ppSqgjHy/D3l8Pvz3yD2rerlPoZpdTnlVKfU0p978PUh0qpPxbG99NKqb+qlBq9m32o3s0SlCmtisx6tz94qMivAM8BBfAJ4FvehXY8BnxX+L4FfBH4FuA/B348HP9x4E+H778T+P/io6x/PfDRb1A7/33gvwd+Ifz914HfF77/eeDfCt//beDPh++/D/hr36D2/RTwh8L3Ath9WPoQX1zpJWCc9N3/7t3sQ+AHgO8CPp0cu6/+Ai7hwZ4vAXvh+959teMbMTkeoHO+F/h7yd9/AvgTD0G7fg74bfiozcfCscfwwVoA/y3w+5Pzu/PewTY9ia9X+luAXwiT5A6QDfsSXw7ge8P3LJyn3uH27YTFpwbHH4o+pK/Gdin0yS8A/7N3uw+BZwbM4b76C/j9wH+bHF867yKfh1WtuHD5vG8UBfHxO4GPcv/lAN9J+jPAf0ifmXMZOBCRdkUbuvaF3w/D+e8kPQvcBv5iUH3+30qpDR6SPhSR14H/AvgacBPfJ7/Kw9WH8A6WoFxHDytzeKhIKbUJ/Czw74nIUfqbeLb8rviDlVK/C7glIr/6bjz/gpThReQ/JyLfCUzpq7ID73of7uELQD8LPA5scLqq/ENF36j+eliZw7qyet9wUkrleMbwV0Tkb4bDbylfBhB1sXKA7xR9P/C/UEq9DPw0XrX4s8CuUirmzaRt6NoXft8B7r6D7QO/Y70mIh8Nf/8Mnlk8LH34W4GXROS2iDTA38T368PUh3D//fW2+/FhZQ6/ArwQLMYF3vDz89/oRiifb/uTwOdE5L9MfrrfcoDvCInInxCRJ0XkGXwf/UMR+d8A/wj43WvaF9v9u8P57+gOJCJvAq8qpV4Mh34Q+CwPSR/i1Ylfr5SahPGO7Xto+nDFcy/SXw9UgnKJ3ilDz9fBIPM78d6BrwD/p3epDb8BL759EviX4fM78TrmLwJfAv4BcCmcr4D/V2jzp4APfwPb+hF6b8VzwL/AlyX8G0AZjo/C318Ovz/3DWrbdwAfC/34t/DW84emD4H/BPg88GngLwPlu9mHwF/F2z8avOT1Bx+kv4B/M7Tzy8AfuN92PAqffkSP6BGtpIdVrXhEj+gRvcv0iDk8okf0iFbSI+bwiB7RI1pJj5jDI3pEj2glPWIOj+gRPaKV9Ig5PKJH9IhW0iPm8Ige0SNaSf8/UoFMbs16WiMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = io.imread(\"Men1/Men1_000.jpg\")\n",
+    "pyplot.imshow(img, interpolation='nearest')\n",
+    "new_img = detector(img, 1)\n",
+    "for k, d in enumerate(new_img):\n",
+    "    print(\"Detection {}: Left: {} Top: {} Right: {} Bottom: {}\".format(\n",
+    "        k, d.left(), d.top(), d.right(), d.bottom()))\n",
+    "    shape = sp(img, d)\n",
+    "    \n",
+    "face_descriptor1 = facerec.compute_face_descriptor(img, shape)\n",
+    "face_descriptor1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e5117e8",
+   "metadata": {},
+   "source": [
+    "### Формирование обучающей и тестовой выборки:\n",
+    "\n",
+    "> **ToDo**: переписать блок для большей эффективности и лучшей читабельности"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6a3ed10c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 Men1/Men1_\n",
+      "10 Men1/Men1_\n",
+      "20 Men1/Men1_\n",
+      "30 Men1/Men1_\n",
+      "40 Men1/Men1_\n",
+      "50 Men1/Men1_\n",
+      "60 Men1/Men1_\n",
+      "70 Men1/Men1_\n",
+      "80 Men1/Men1_\n",
+      "90 Men1/Men1_\n",
+      "100 Men1/Men1_\n",
+      "110 Men1/Men1_\n",
+      "120 Men1/Men1_\n",
+      "130 Men1/Men1_\n",
+      "140 Men1/Men1_\n",
+      "150 Men1/Men1_\n",
+      "160 Men1/Men1_\n",
+      "170 Men1/Men1_\n",
+      "Done!\n",
+      "0 Men2/Men2_\n",
+      "10 Men2/Men2_\n",
+      "20 Men2/Men2_\n",
+      "30 Men2/Men2_\n",
+      "40 Men2/Men2_\n",
+      "50 Men2/Men2_\n",
+      "60 Men2/Men2_\n",
+      "70 Men2/Men2_\n",
+      "80 Men2/Men2_\n",
+      "90 Men2/Men2_\n",
+      "100 Men2/Men2_\n",
+      "110 Men2/Men2_\n",
+      "120 Men2/Men2_\n",
+      "130 Men2/Men2_\n",
+      "140 Men2/Men2_\n",
+      "150 Men2/Men2_\n",
+      "160 Men2/Men2_\n",
+      "170 Men2/Men2_\n",
+      "180 Men2/Men2_\n",
+      "Done!\n",
+      "0 Men3/Men3_\n",
+      "10 Men3/Men3_\n",
+      "20 Men3/Men3_\n",
+      "30 Men3/Men3_\n",
+      "40 Men3/Men3_\n",
+      "50 Men3/Men3_\n",
+      "60 Men3/Men3_\n",
+      "70 Men3/Men3_\n",
+      "80 Men3/Men3_\n",
+      "90 Men3/Men3_\n",
+      "100 Men3/Men3_\n",
+      "110 Men3/Men3_\n",
+      "120 Men3/Men3_\n",
+      "130 Men3/Men3_\n",
+      "140 Men3/Men3_\n",
+      "150 Men3/Men3_\n",
+      "160 Men3/Men3_\n",
+      "170 Men3/Men3_\n",
+      "180 Men3/Men3_\n",
+      "190 Men3/Men3_\n",
+      "200 Men3/Men3_\n",
+      "210 Men3/Men3_\n",
+      "220 Men3/Men3_\n",
+      "230 Men3/Men3_\n",
+      "Done!\n",
+      "0 Female1/Female1_\n",
+      "10 Female1/Female1_\n",
+      "20 Female1/Female1_\n",
+      "30 Female1/Female1_\n",
+      "40 Female1/Female1_\n",
+      "50 Female1/Female1_\n",
+      "60 Female1/Female1_\n",
+      "70 Female1/Female1_\n",
+      "80 Female1/Female1_\n",
+      "90 Female1/Female1_\n",
+      "100 Female1/Female1_\n",
+      "110 Female1/Female1_\n",
+      "120 Female1/Female1_\n",
+      "130 Female1/Female1_\n",
+      "140 Female1/Female1_\n",
+      "150 Female1/Female1_\n",
+      "160 Female1/Female1_\n",
+      "170 Female1/Female1_\n",
+      "180 Female1/Female1_\n",
+      "190 Female1/Female1_\n",
+      "200 Female1/Female1_\n",
+      "Done!\n",
+      "0 SomePerson/SomePerson_\n",
+      "10 SomePerson/SomePerson_\n",
+      "20 SomePerson/SomePerson_\n",
+      "30 SomePerson/SomePerson_\n",
+      "40 SomePerson/SomePerson_\n",
+      "50 SomePerson/SomePerson_\n",
+      "60 SomePerson/SomePerson_\n",
+      "70 SomePerson/SomePerson_\n",
+      "80 SomePerson/SomePerson_\n",
+      "90 SomePerson/SomePerson_\n",
+      "100 SomePerson/SomePerson_\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "li_train = list()\n",
+    "li_test = list()\n",
+    "\n",
+    "for i in range(5):\n",
+    "    if i == 0:\n",
+    "        file = \"Men1/Men1_\"\n",
+    "        pred = [1, 0, 0, 0]\n",
+    "    elif i == 1:\n",
+    "        file = \"Men2/Men2_\"\n",
+    "        pred = [0, 1, 0, 0]\n",
+    "    elif i == 2:\n",
+    "        file = \"Men3/Men3_\"\n",
+    "        pred = [0, 0, 1, 0]\n",
+    "    elif i == 3:\n",
+    "        file = \"Female1/Female1_\"\n",
+    "        pred = [0, 0, 0, 1]\n",
+    "    else:\n",
+    "        file = \"SomePerson/SomePerson_\"\n",
+    "        pred = [0, 0, 0, 0]\n",
+    "        \n",
+    "    li = list()    \n",
+    "    counter = 0\n",
+    "    while True:\n",
+    "        try:\n",
+    "            if counter % 10 == 0:\n",
+    "                print(counter, file)\n",
+    "            if len(str(counter)) == 1:\n",
+    "                c = \"00\" + str(counter)\n",
+    "            elif len(str(counter)) == 2:\n",
+    "                c = \"0\" + str(counter)\n",
+    "            else:\n",
+    "                c = str(counter)\n",
+    "\n",
+    "            raw_img = io.imread(file + str(c) + \".jpg\")\n",
+    "            dtc_img = detector(raw_img, 1)\n",
+    "            for k, d in enumerate(dtc_img):\n",
+    "                shape = sp(raw_img, d)\n",
+    "\n",
+    "            li.append([facerec.compute_face_descriptor(raw_img, shape), pred])\n",
+    "                                                                                                                                    \n",
+    "            counter += 1\n",
+    "        except:\n",
+    "            for _ in range(len(li) // 10):\n",
+    "                rand_index = random.randint(0, len(li) - 1)\n",
+    "                li_test.append(li[rand_index])\n",
+    "                del li[rand_index]\n",
+    "            li_train += li\n",
+    "            \n",
+    "            print(\"Done!\")\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "72c2fd26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "809 88\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(li_train), len(li_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "86675ec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "809 809 88 88\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_with_train_inputs = list()\n",
+    "list_with_train_predictions = list()\n",
+    "list_with_test_inputs = list()\n",
+    "list_with_test_predictions = list()\n",
+    "\n",
+    "random.shuffle(li_train)\n",
+    "for i in range(len(li_train)):\n",
+    "    list_with_train_inputs.append(list(li_train[i][0]))\n",
+    "    list_with_train_predictions.append(li_train[i][1])\n",
+    "    \n",
+    "random.shuffle(li_test)\n",
+    "for i in range(len(li_test)):\n",
+    "    list_with_test_inputs.append(list(li_test[i][0]))\n",
+    "    list_with_test_predictions.append(li_test[i][1])\n",
+    "    \n",
+    "print(len(list_with_train_inputs), len(list_with_train_predictions), len(list_with_test_inputs), len(list_with_test_predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c1969df",
+   "metadata": {},
+   "source": [
+    "## Нейронная сеть классификатор"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d8072a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0565 - accuracy: 0.8529\n",
+      "Epoch 2/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0130 - accuracy: 0.9197\n",
+      "Epoch 3/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 0.9184\n",
+      "Epoch 4/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0087 - accuracy: 0.9110\n",
+      "Epoch 5/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0072 - accuracy: 0.9122\n",
+      "Epoch 6/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0074 - accuracy: 0.9085\n",
+      "Epoch 7/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0063 - accuracy: 0.9098\n",
+      "Epoch 8/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0069 - accuracy: 0.9135\n",
+      "Epoch 9/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0066 - accuracy: 0.9234\n",
+      "Epoch 10/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0064 - accuracy: 0.9147\n",
+      "Epoch 11/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9135\n",
+      "Epoch 12/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0059 - accuracy: 0.9147\n",
+      "Epoch 13/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0057 - accuracy: 0.9098\n",
+      "Epoch 14/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9085\n",
+      "Epoch 15/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0053 - accuracy: 0.9073\n",
+      "Epoch 16/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9147\n",
+      "Epoch 17/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0061 - accuracy: 0.9061\n",
+      "Epoch 18/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 0.9197\n",
+      "Epoch 19/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0050 - accuracy: 0.9122\n",
+      "Epoch 20/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 0.9159\n",
+      "Epoch 21/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9184\n",
+      "Epoch 22/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0051 - accuracy: 0.9147\n",
+      "Epoch 23/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0047 - accuracy: 0.9172\n",
+      "Epoch 24/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 0.9147\n",
+      "Epoch 25/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9221\n",
+      "Epoch 26/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9098\n",
+      "Epoch 27/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 0.9209\n",
+      "Epoch 28/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9172\n",
+      "Epoch 29/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 0.9135\n",
+      "Epoch 30/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9147\n",
+      "Epoch 31/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9172\n",
+      "Epoch 32/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9209\n",
+      "Epoch 33/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9197\n",
+      "Epoch 34/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9184\n",
+      "Epoch 35/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9234\n",
+      "Epoch 36/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9147\n",
+      "Epoch 37/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9147\n",
+      "Epoch 38/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9135\n",
+      "Epoch 39/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0042 - accuracy: 0.9122\n",
+      "Epoch 40/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9197\n",
+      "Epoch 41/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 0.9073\n",
+      "Epoch 42/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9135\n",
+      "Epoch 43/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9098\n",
+      "Epoch 44/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 0.9110\n",
+      "Epoch 45/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9122\n",
+      "Epoch 46/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9110\n",
+      "Epoch 47/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9122\n",
+      "Epoch 48/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 0.9172\n",
+      "Epoch 49/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9135\n",
+      "Epoch 50/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9197\n",
+      "Epoch 51/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 0.9172\n",
+      "Epoch 52/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0034 - accuracy: 0.9110\n",
+      "Epoch 53/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9184\n",
+      "Epoch 54/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9184\n",
+      "Epoch 55/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 0.9085\n",
+      "Epoch 56/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9184\n",
+      "Epoch 57/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9135\n",
+      "Epoch 58/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 0.9135\n",
+      "Epoch 59/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9061\n",
+      "Epoch 60/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9209\n",
+      "Epoch 61/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9147\n",
+      "Epoch 62/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 0.9023\n",
+      "Epoch 63/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0035 - accuracy: 0.9110\n",
+      "Epoch 64/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9135\n",
+      "Epoch 65/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9159\n",
+      "Epoch 66/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9147\n",
+      "Epoch 67/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9122\n",
+      "Epoch 68/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9159\n",
+      "Epoch 69/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9159\n",
+      "Epoch 70/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9061\n",
+      "Epoch 71/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9159\n",
+      "Epoch 72/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0037 - accuracy: 0.9147\n",
+      "Epoch 73/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9159\n",
+      "Epoch 74/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9110\n",
+      "Epoch 75/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9085\n",
+      "Epoch 76/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9135\n",
+      "Epoch 77/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 0.9098\n",
+      "Epoch 78/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9110\n",
+      "Epoch 79/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9098\n",
+      "Epoch 80/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 0.9159\n",
+      "Epoch 81/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9135\n",
+      "Epoch 82/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9110\n",
+      "Epoch 83/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9122\n",
+      "Epoch 84/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9122\n",
+      "Epoch 85/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9184\n",
+      "Epoch 86/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9135\n",
+      "Epoch 87/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 0.9110\n",
+      "Epoch 88/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 0.9135\n",
+      "Epoch 89/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0030 - accuracy: 0.9135\n",
+      "Epoch 90/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0029 - accuracy: 0.9122\n",
+      "Epoch 91/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0033 - accuracy: 0.9061\n",
+      "Epoch 92/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0032 - accuracy: 0.9073\n",
+      "Epoch 93/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0031 - accuracy: 0.9147\n",
+      "Epoch 94/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0031 - accuracy: 0.9122\n",
+      "Epoch 95/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0030 - accuracy: 0.9098\n",
+      "Epoch 96/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0029 - accuracy: 0.9147\n",
+      "Epoch 97/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0032 - accuracy: 0.9073\n",
+      "Epoch 98/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0030 - accuracy: 0.9110\n",
+      "Epoch 99/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0029 - accuracy: 0.9197\n",
+      "Epoch 100/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0029 - accuracy: 0.9159\n",
+      "Epoch 101/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0034 - accuracy: 0.9197\n",
+      "Epoch 102/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 0.9110\n",
+      "Epoch 103/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0029 - accuracy: 0.9098\n",
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+      "Epoch 147/500\n",
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+      "Epoch 148/500\n",
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+      "Epoch 150/500\n",
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+      "Epoch 151/500\n",
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+      "Epoch 152/500\n",
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+      "Epoch 153/500\n",
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+      "Epoch 154/500\n",
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+      "Epoch 155/500\n",
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+      "Epoch 156/500\n",
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+      "Epoch 157/500\n",
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+      "Epoch 158/500\n",
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+      "Epoch 159/500\n",
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+      "Epoch 160/500\n",
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+      "Epoch 161/500\n",
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+      "Epoch 162/500\n",
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+      "Epoch 163/500\n",
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+      "Epoch 164/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9110\n",
+      "Epoch 165/500\n",
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+      "Epoch 166/500\n",
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+      "Epoch 167/500\n",
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+      "Epoch 168/500\n",
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+      "Epoch 169/500\n",
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+      "Epoch 170/500\n",
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+      "Epoch 171/500\n",
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+      "Epoch 172/500\n",
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+      "Epoch 173/500\n",
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+      "Epoch 174/500\n",
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+      "Epoch 175/500\n",
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+      "Epoch 176/500\n",
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+      "Epoch 177/500\n",
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+      "Epoch 178/500\n",
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+      "Epoch 179/500\n",
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+      "Epoch 180/500\n",
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+      "Epoch 181/500\n",
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+      "Epoch 182/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9110\n",
+      "Epoch 183/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0027 - accuracy: 0.9110\n",
+      "Epoch 184/500\n",
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+      "Epoch 185/500\n",
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+      "Epoch 186/500\n",
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+      "Epoch 187/500\n",
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+      "Epoch 188/500\n",
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+      "Epoch 189/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0026 - accuracy: 0.9122\n",
+      "Epoch 190/500\n",
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+      "Epoch 191/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9085\n",
+      "Epoch 192/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9172\n",
+      "Epoch 193/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 0.9159\n",
+      "Epoch 194/500\n",
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+      "Epoch 195/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9085\n",
+      "Epoch 196/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9234\n",
+      "Epoch 197/500\n",
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+      "Epoch 198/500\n",
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+      "Epoch 199/500\n",
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+      "Epoch 200/500\n",
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+      "Epoch 201/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9197\n",
+      "Epoch 202/500\n",
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+      "Epoch 203/500\n",
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+      "Epoch 204/500\n",
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+      "Epoch 205/500\n",
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+      "Epoch 206/500\n",
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+      "Epoch 207/500\n",
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+      "Epoch 208/500\n",
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+      "Epoch 209/500\n",
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+      "Epoch 210/500\n",
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+      "Epoch 211/500\n",
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+      "Epoch 212/500\n",
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+      "Epoch 213/500\n",
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+      "Epoch 214/500\n",
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+      "Epoch 215/500\n",
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+      "Epoch 216/500\n",
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+      "Epoch 217/500\n",
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+      "Epoch 218/500\n",
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+      "Epoch 219/500\n",
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+      "Epoch 220/500\n",
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+      "Epoch 221/500\n",
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+      "Epoch 222/500\n",
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+      "Epoch 223/500\n",
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+      "Epoch 224/500\n",
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+      "Epoch 225/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9122\n",
+      "Epoch 226/500\n",
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+      "Epoch 227/500\n",
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+      "Epoch 228/500\n",
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+      "Epoch 229/500\n",
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+      "Epoch 230/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9147\n",
+      "Epoch 231/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 0.9135\n",
+      "Epoch 232/500\n",
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+      "Epoch 233/500\n",
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+      "Epoch 234/500\n",
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+      "Epoch 235/500\n",
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+      "Epoch 236/500\n",
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+      "Epoch 237/500\n",
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+      "Epoch 238/500\n",
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+      "Epoch 239/500\n",
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+      "Epoch 240/500\n",
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+      "Epoch 241/500\n",
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+      "Epoch 242/500\n",
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+      "Epoch 243/500\n",
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+      "Epoch 244/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9122\n",
+      "Epoch 245/500\n",
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+      "Epoch 246/500\n",
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+      "Epoch 247/500\n",
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+      "Epoch 248/500\n",
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+      "Epoch 249/500\n",
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+      "Epoch 250/500\n",
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+      "Epoch 251/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9147\n",
+      "Epoch 252/500\n",
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+      "Epoch 253/500\n",
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+      "Epoch 254/500\n",
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+      "Epoch 255/500\n",
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+      "Epoch 256/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9209\n",
+      "Epoch 257/500\n",
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+      "Epoch 258/500\n",
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+      "Epoch 259/500\n",
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+      "Epoch 260/500\n",
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+      "Epoch 261/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9135\n",
+      "Epoch 262/500\n",
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+      "Epoch 263/500\n",
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+      "Epoch 264/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9135\n",
+      "Epoch 265/500\n",
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+      "Epoch 266/500\n",
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+      "Epoch 267/500\n",
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+      "Epoch 268/500\n",
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+      "Epoch 269/500\n",
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+      "Epoch 270/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n",
+      "Epoch 271/500\n",
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+      "Epoch 272/500\n",
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+      "Epoch 273/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9098\n",
+      "Epoch 274/500\n",
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+      "Epoch 301/500\n",
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+      "Epoch 303/500\n",
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+      "Epoch 304/500\n",
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+      "Epoch 305/500\n",
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+      "Epoch 306/500\n",
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+      "Epoch 307/500\n",
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+      "Epoch 308/500\n",
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+      "Epoch 309/500\n",
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+      "Epoch 310/500\n",
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+      "Epoch 311/500\n",
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+      "Epoch 312/500\n",
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+      "Epoch 313/500\n",
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+      "Epoch 314/500\n",
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+      "Epoch 315/500\n",
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+      "Epoch 316/500\n",
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+      "Epoch 317/500\n",
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+      "Epoch 318/500\n",
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+      "Epoch 319/500\n",
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+      "Epoch 320/500\n",
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+      "Epoch 321/500\n",
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+      "Epoch 322/500\n",
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+      "Epoch 323/500\n",
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+      "Epoch 324/500\n",
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+      "Epoch 325/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9246\n",
+      "Epoch 326/500\n",
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+      "Epoch 327/500\n",
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+      "Epoch 328/500\n",
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+      "Epoch 329/500\n",
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+      "Epoch 330/500\n",
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+      "Epoch 331/500\n",
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+      "Epoch 332/500\n",
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+      "Epoch 333/500\n",
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+      "Epoch 334/500\n",
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+      "Epoch 335/500\n",
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+      "Epoch 336/500\n",
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+      "Epoch 337/500\n",
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+      "Epoch 338/500\n",
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+      "Epoch 339/500\n",
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+      "Epoch 340/500\n",
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+      "Epoch 341/500\n",
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+      "Epoch 342/500\n",
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+      "Epoch 343/500\n",
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+      "Epoch 344/500\n",
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+      "Epoch 345/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9197\n",
+      "Epoch 346/500\n",
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+      "Epoch 347/500\n",
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+      "Epoch 348/500\n",
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+      "Epoch 349/500\n",
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+      "Epoch 350/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9110\n",
+      "Epoch 351/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0020 - accuracy: 0.9135\n",
+      "Epoch 352/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n",
+      "Epoch 353/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9172\n",
+      "Epoch 354/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9085\n",
+      "Epoch 355/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9184\n",
+      "Epoch 356/500\n",
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+      "Epoch 357/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9122\n",
+      "Epoch 358/500\n",
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+      "Epoch 359/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9184\n",
+      "Epoch 360/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9147\n",
+      "Epoch 361/500\n",
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+      "Epoch 362/500\n",
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+      "Epoch 363/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n",
+      "Epoch 364/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9172\n",
+      "Epoch 365/500\n",
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+      "Epoch 366/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9122\n",
+      "Epoch 367/500\n",
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+      "Epoch 368/500\n",
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+      "Epoch 369/500\n",
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+      "Epoch 370/500\n",
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+      "Epoch 371/500\n",
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+      "Epoch 372/500\n",
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+      "Epoch 373/500\n",
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+      "Epoch 374/500\n",
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+      "Epoch 375/500\n",
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+      "Epoch 376/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9172\n",
+      "Epoch 377/500\n",
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+      "Epoch 378/500\n",
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+      "Epoch 379/500\n",
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+      "Epoch 380/500\n",
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+      "Epoch 381/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9122\n",
+      "Epoch 382/500\n",
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+      "Epoch 383/500\n",
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+      "Epoch 384/500\n",
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+      "Epoch 385/500\n",
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+      "Epoch 386/500\n",
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+      "Epoch 387/500\n",
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+      "Epoch 388/500\n",
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+      "Epoch 389/500\n",
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+      "Epoch 390/500\n",
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+      "Epoch 391/500\n",
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+      "Epoch 392/500\n",
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+      "Epoch 393/500\n",
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+      "Epoch 394/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9197\n",
+      "Epoch 395/500\n",
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+      "Epoch 396/500\n",
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+      "Epoch 397/500\n",
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+      "Epoch 398/500\n",
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+      "Epoch 399/500\n",
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+      "Epoch 400/500\n",
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+      "Epoch 401/500\n",
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+      "Epoch 402/500\n",
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+      "Epoch 403/500\n",
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+      "Epoch 404/500\n",
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+      "Epoch 405/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 0.9197\n",
+      "Epoch 406/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9147\n",
+      "Epoch 407/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9110\n",
+      "Epoch 408/500\n",
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+      "Epoch 409/500\n",
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+      "Epoch 410/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9110\n",
+      "Epoch 411/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n",
+      "Epoch 412/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n",
+      "Epoch 413/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9147\n",
+      "Epoch 414/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n",
+      "Epoch 415/500\n",
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+      "Epoch 416/500\n",
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+      "Epoch 417/500\n",
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+      "Epoch 418/500\n",
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+      "Epoch 419/500\n",
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+      "Epoch 420/500\n",
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+      "Epoch 421/500\n",
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+      "Epoch 422/500\n",
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+      "Epoch 423/500\n",
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+      "Epoch 424/500\n",
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+      "Epoch 425/500\n",
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+      "Epoch 426/500\n",
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+      "Epoch 427/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9122\n",
+      "Epoch 428/500\n",
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+      "Epoch 429/500\n",
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+      "Epoch 430/500\n",
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+      "Epoch 431/500\n",
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+      "Epoch 432/500\n",
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+      "Epoch 433/500\n",
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+      "Epoch 434/500\n",
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+      "Epoch 435/500\n",
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+      "Epoch 436/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9085\n",
+      "Epoch 437/500\n",
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+      "Epoch 438/500\n",
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+      "Epoch 439/500\n",
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+      "Epoch 440/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9197\n",
+      "Epoch 441/500\n",
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+      "Epoch 442/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9085\n",
+      "Epoch 443/500\n",
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+      "Epoch 444/500\n",
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+      "Epoch 445/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9110\n",
+      "Epoch 446/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9085\n",
+      "Epoch 447/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9209\n",
+      "Epoch 448/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9147\n",
+      "Epoch 449/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9135\n",
+      "Epoch 450/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9098\n",
+      "Epoch 451/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9172\n",
+      "Epoch 452/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9159\n",
+      "Epoch 453/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9184\n",
+      "Epoch 454/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9159\n",
+      "Epoch 455/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n",
+      "Epoch 456/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9209\n",
+      "Epoch 457/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 0.9147\n",
+      "Epoch 458/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9209\n",
+      "Epoch 459/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9135\n",
+      "Epoch 460/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9147\n",
+      "Epoch 461/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9197\n",
+      "Epoch 462/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9159\n",
+      "Epoch 463/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9184\n",
+      "Epoch 464/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n",
+      "Epoch 465/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9184\n",
+      "Epoch 466/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 0.9234\n",
+      "Epoch 467/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9147\n",
+      "Epoch 468/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9159\n",
+      "Epoch 469/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9159\n",
+      "Epoch 470/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n",
+      "Epoch 471/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 0.9122\n",
+      "Epoch 472/500\n",
+      "26/26 [==============================] - 0s 3ms/step - loss: 0.0019 - accuracy: 0.9209\n",
+      "Epoch 473/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9234\n",
+      "Epoch 474/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9209\n",
+      "Epoch 475/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9221\n",
+      "Epoch 476/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9147\n",
+      "Epoch 477/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9172\n",
+      "Epoch 478/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9221\n",
+      "Epoch 479/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9061\n",
+      "Epoch 480/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9197\n",
+      "Epoch 481/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9135\n",
+      "Epoch 482/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9197\n",
+      "Epoch 483/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9234\n",
+      "Epoch 484/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9110\n",
+      "Epoch 485/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9184\n",
+      "Epoch 486/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9197\n",
+      "Epoch 487/500\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9147\n",
+      "Epoch 488/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n",
+      "Epoch 489/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9110\n",
+      "Epoch 490/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9234\n",
+      "Epoch 491/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9159\n",
+      "Epoch 492/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 0.9098\n",
+      "Epoch 493/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9135\n",
+      "Epoch 494/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 0.9184\n",
+      "Epoch 495/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9159\n",
+      "Epoch 496/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9159\n",
+      "Epoch 497/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9221\n",
+      "Epoch 498/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0022 - accuracy: 0.9061\n",
+      "Epoch 499/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 0.9221\n",
+      "Epoch 500/500\n",
+      "26/26 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 0.9135\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(units=256, input_shape=(len(list_with_train_inputs[0]),)))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(units=256, input_shape=(256,), activation=\"relu\"))\n",
+    "model.add(Dropout(0.2))\n",
+    "model.add(Dense(units=4, input_shape=(256,)))\n",
+    "model.compile(loss=\"mean_squared_error\", metrics=['accuracy'])\n",
+    "\n",
+    "history = model.fit(list_with_train_inputs, list_with_train_predictions, epochs=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d66ef167",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fPr/bx4UJqVyF9+fvfp3Db3g643bPhhwP8UR7csGvTf348QWMvfoJxl79BB9sSe31HfHjZ7j43jdyKleQ7zw8h7FXPxG6zfvI85VTnnM73cSUu4b0wnsbGXv1Eyk+hVzZVN/K72cu4+kFG9zrOtdrySAgfvXMYi5+sjFjB66prYPK0iL2r67Mar8/+sZn+dTvXslatpb2DsZe/QS/n7ksZf11/5yXeM5hvXtPG9ocEkBREotS52pNfgHR3tGZ0UldqDk5CiYgRCQK3A5MAyYDF4jI5MBuvwTuV9VDgBuAn7nrm4AvqOqBwJnALSIyqFBlzURX99zfM1y6sYFVW5sSUQmQn+q9rs5RM+99uXt21Z3J8k2NeanefhNEoUxM972yIud9vQYirAdfl+M0mS8t2ZyIKAnDMwWEXaMzEeWT3Hb3f5LPdd7a1Hu8tbGN2kWbcipXkIezjLfoiZMakgIi2HvtjoB44l1n3MHLyzZ3+/qbGlr4g+++eWM0EgIi0Mj+2g1Pb87QmDa1xRlQHGXv8uKsI8U31reGmg79NLo9/d+/uDxl/f2vJh3g89emf0te57G5rYNDRw9M2VZSFKHOjbTyt0FBJ7WfQqVcL6QGcTSwVFWXq2ob8CBwbmCfycDz7vIL3nZVXayqS9zltcBGYGgBy5qCJ427uuX+RqGupT3tZcvHvl3kZkDbFZy8ueDvnfsbiqQjsdeLlFaGsG+nrmXnDJ7zGogwgei9H2HaBUBxtHcsvD1NieEFYQQFQdLh2vV5B5UVAenhnJnwl3VTfSsD3eMBOgImpi0NraF1a8wQ3dTY2kF5SYyy4miPw0aTYzMyf+thyQU9AdHW0cmYvQcweURVYltpLJpoS/zvVbyzMxG6HaRQHbJY17vkzShgle/3amBqYJ85wHnArcAngEoRGayqiRFmInI0UAwsCxyLiFwGXAZQXV1NbW1tXgVdsq2DGcua6dQXiIiwYKXzQaxbt57a2mQP4ukV7TTFlY9PKAag3TfQ5q15ixhUkvoizFvwHtFNS7hzbivfOKKU8qLk9ua4cvs7rVw0uZihA5INxfpG5wWoa2zKuz7Z2NzcyZ1zW1GF88Z2QJZrZLq+3+l7xV3PJJZfmPkSFcVOHd/Z6Lzgzc3NO6Ue/mvmer45m5wy7Kir49o/PsNLa+IMiHTw8ppnGFGRvOdf/u1TXDipJOu5Ml1zjvuu7KirT9unvd25/uw332LHcsdUICQblfnz3yW2cWHO1+oOF9/+FAcOjnLU8BjxtlZAeP7dlby5+AOmVEdZUdfJhZNKmL0+zpxNHVxycAnvborzxnpn2c+a7c389tHnKPW1FrW1taxd5/TcX1y0gTP+99/EIvCVQ0qpKklvELducATDnEXLU84BTifjjjmtnDSmiMmDo9w3v5X9Biafzy+efA9/u7h67Tpqa7eybLvz/XUqPP5MLQNLEs5DAK7/y4tEBM4YW8Tt77TwlUNKGFgiNLd3sHHtKnY0KdvqO9Lu98tr2vmgLnlB//aNTZ3cM6+Vbx5RSllM2Nbihqu2xzM+t4dmzuHeZ9/hq4eWJtbNfvsd4mti1DU0sW1zK61Nyevd47MezFm6inNuWsWXDy5xNXTlhRdeSBM67y1azKDBrTu9zSikgMiFbwO3icjFwIvAGiDR6orICOAB4CJVTROdqnoncCfAlClTtKamJq9C/OTmmSzdLIyaNIX9h1ey5MXlsHAh1cOrqak5LLHfxa6N95ZLT3dWPDMD720cOnIfhlWVwNykjXzs+AnM3tjAom0fsK1yPGcfs29i29/fWs28zXN4uW4vbjnr8MT6pRsb4KWZRIuKybc+2bjm73NZvM2R2w8ujzLz/JBrPOnUM9P1dzS3w1OOXf7x5Unzy9HHHsuwSucjaJ2/Ht56k7Kysp1Sj5b2DnjqyazlCtK+YAO8OZuKikrm1MHKuh2AsHB7Gw9ccjS86tj7n1kZ5zeXnkppUTT9JF3ciwW1S2HhIkrKBqTtI889CR0dHHTIYRy732AABjz/JI2uWWr/SQdSc9AIwO1RPvnvbtUvrJwetavi1K6Ks+Lnp/LUimeANpbvcD6htzc617/ra2ck3ukHrkgu//Hrp8OTM1LOt33ASE6bVA2vvpoo49/XvQ1r19LWCYu2OedeFhnFlTUfSive0uhy/r5kIUVVg4ENKfVsae/gi089yZsbOlj44zO5+MknqfV1LYOd5iHDhlFTczgly7bAa68BMPHgI5k80u2FP/UEKMx433k3D/zQfry39T0Wdo7gGx+eCE89xYEfmsD7WxpZVLc+7X5fHPDl+Ld/88G3eW/rWloGT2Ta4aOdUfK1L6BI6nl8z+O5D5yOwn3/fQI86bzDB0x2nn3k5WfZZ/QwOjY3smzH1rT75j2rbZXjAKdtOfb4E5131XeN/SZMoKJ95U5vMwqp464Bxvh+j3bXJVDVtap6nqoeDvzAXbcdQESqgCeAH6jqawUsJxOHVQCwYJ1jK0z4Drrhg3BMTKm26raOzoRtMDgmoqLEkc31AVNHwixRIBNTSSzZCBaFPP1cbJmZ7Pd+H4RXj64iwXIln4gwz5nZqZqWIjo4dmXR+vzi4ZtanQ/YH5+euH5IFJN/4JP/mLBcPDuL7lofWgIpKCpKYmyqbw0ZKJd+4kxmVe/dCIva8ZsCtzR0bYKKB3wQkH20vBdVGIsITV50UEmUsqJo6HPLhldnL0Iunghpz+G7afGPik4OmCuORlK+yzCqSpMmtjCzWKEi5QopIGYBE0VknIgUA+cD0/07iMgQEfHKcA1wj7u+GHgMx4H9SAHLCCQFxMJ1TiPhNUaZbvmT89Yxb82OlA+vrrmdupZ4yqjZtnhn4mUOCgjv94otjfz48QXc9/L7/OPtNQl7dT4OboBnF2xgzqrtGbf7e8nFUZi7ejvPutEhkN44BFm/o4XfzUyz9gHJMMTmtg7uqF3ajVJ3TS4C86FZqxKx5JDa8ARV8lueTc1p44U7NrTGuevF5azc0piSaC8oON9bX8f0OWsTdu76lnbeXLmV2kUb047xJ7zz33+/E9VvL29p7+DFxZuYtSLZo1yxuZFH80j8t2prEzNXhwt0v//I3+gEfWkjB5U6AsLX+L+ybHOob2XVtiYeeHUF4CSbe39zI3e9uJytjU4DHtaQt/vO+z8PvtNlndZtb3YS3PnK/LMZCzOOzvac2vUtce6odd7d8uIYpUURmts7UFXeXLmNF3zPLhNeQ7x+RwsPzV6Vdg+2NrZxj8+h7ud7j8xNLLf5BUQsQkkse1Nc7xMu63a0pAQ7QOEmbSqYiUlV4yJyOfAUEAXuUdX5InIDMFtVpwM1wM9ERHFMTF93D/80cCIw2DU/AVysqu8Uoqwx11noNS7tXYxj+Oqf0uc4qGuJU1rUztCKEg4dM4hnFmygvaMz8eCigQbK67ks29TIsk3Jh/3Prx+X9dpdcen9swFY8fOzQ7eX+tSGoohwzm0vp+zf2Jr86FQ1rWH9wj2vszhDDhuvzLc8u5h5a+rcc+RTi5Bzd5HQqKW9g+8+Opcxe5fx0ndPBnxajGpaqOvSjal18EIRfzpjIX95/QNunJHqG2iNd6b0/s+8xUlY95kpjpJc3xrnv37rmF+8e5nIPuoTbmV+AeHTGvzpIupb4nzhnjdSzvXxO15me1M7nzh8FJFujND/zO9fZX1j+EPw93r9Pftg6OXIQWWs39GSsv9n73qdUydVp53zibnreGLuOk6ZVM13fQ2ix8a65Lm998v/rr/hE4qZeOuD7bz1wXYu/vDYxLr31tfzq2cWc/05B6bt74UiP+BLrTGg2NEgOjqV9g7l1ueWsLGuhZP2H5b12t479bN/vwfAY//94ZTtVz86NxGOG+QFX4Sady/bOlwBEWbe9OEXft97dG5alGGhhlwVNIxCVWeo6odUdT9VvdFdd50rHFDVR1R1orvPpara6q7/k6oWqephvr93CldO5/+2wECc7oxjcDSIdqrKirjrC1MoiUVoi3cmXqigBpEpBM9bXygTk78HWxLyTvobqjC1OduoVU/r2VkRQn66UuG9RmbDjmQD5N3DTtXE6PZMeAI7k/ksU+bPBvd+hQnCsHEQKSYm3zn9gtlvivDwQk0zvTeZyGZ68Z+rySesVm9rTtlvxMCyNA0CsqfdDqsDpJqg/I1kJvw96wOGV6aEhAbDRzOZWcJCWQeWFSW+hZZ4B5vqW3OKOgz21IPv+sYuBu09feWJALTFO+joVDo6leJotEsNwi8gwsxwhdIgbCQ1STu5X+2D7pl5vDDXKjfUozgWoTXembBD+3t9rfEO3lwZ3lPyGqjeSFVRFNIT9TdUYY1RNput98GHJadrjXcwd/V2d7/OxHI2tja2sXhDPe+u3tFlxtBEIrfOTrY3tbF0Y0NC/e/UrjWylngH72QxzfkbD78Q/fe74fMK+HvhfjOEP7S1pS3cxJRtjMaKLY3dSquQbf5mv1nJ/6yfCNRpWGUJW5va0pLiZXtHt+bgS/D8PtmE/6ABSdt7SSyS0PYhPb1JptxnYfezyicg5qzazqb6lowdQn9CzqAQ8p97a2Mby0OSd/qprnKCONo6OhPXK4pJtwREWD0LlbXBBARhGkT3U2XUNcfZ3txGlRuvXRKLpDip/c/0+unz+esbq8JOU5Detx//R+B3UnsvflPAFt4dvPvlf329e3vVQ3M457aX2d7Uxs3PLOac217uMgvlqTfP5PRfvcjHbvtPl6mZvWt3Knzu7tc59eaZCa2hUzXlWfpHp3q8snQzH7/9ZR6fG97gt/oE4/u+0cCZ2siTflmbWPY3Kv5G1R/T3pSiQWR+B87+9X/SEiNmI5s1qqU93MT1rzlrU/YbPrAU1fR8P9kco5tzyJL7MXe0frbvzD/+IRKR0MZx73In7DxTyo0w30RVWVHC3Pf5u99gS2NbRg3ilJtmJpaDssx/7k///tUuv1/vmm3xpIDIxUnt14LC7kGh0p2YgCDpjG4LCIZumZha2tlc38bQCieGvDgaSXFS+z+mtz/Ynvk8OY7wDSOXvPz+Ovlfdq+BaPT1arstINxzh3VavYa3qa0jkYpkXRcJ2/ypuNfXZd+33Xd/Pf+Hl9ZBNdUROsjX6His2JK9V+43Me0IjKz+5acOTfnd0akp0Wl+c2F7Rycf3m8wVaWxFB+EX4MIJn/sCZIlcYxfa8j2rPcfXgnAnDS7d2YBsSXHOTjqW9pT3snvn3UAd1x4ROL3oLLixLIqFEWd+viF/CtXn8xhYwaxqd4ZMBe0toQKiNJYirlV1TH7ZMLzYQWDFfznDvq1wvDK3xbvpNVN2FcSi1ASFlKY4Tph990ERCFxH77XCOTjg2hq62B9XYszFgLHxNQWT2oQ/kYiWxrwoO3WeeGVHc3ttPrsln463X0yjbL04zeb+RtVzwbd5Jues7v2bq8Hlq1Rao13Uuz7SLp77kz4heP4oU6+/3fdBi3e2ZlS74EDiukurfHORCMRfEb7udfzCJpiUu3unVSVFlFREqO53ZkAZkdze4oPwP8cwzRQ//quyKZB+K/Z3Jb5/h4wvBIRmBMwC2YTELnOs/He+voUDWKvAcUJMwyQ0MjB6chF3TlWRu9VllhfWhRl9F5lbGpoDX1ntzWll6W8OEZZcWrzl82k7CXPy2Zi8tKIZ0NEKI5F2NTQxrrtTqcnlygmv4DYFnJv84167AoTECQ1CM++7t3sfFJlJDQIz0nt5ePx2aFj2QREc6qJZ7/vz+DbD8/l0B89zf7XPsl+35/Bft+fwfPvJSMlPvHbV7j1uSU5xdL7G2V/9byUEf75m7sbI/7F+2axamtTigahAfNOa7wjkU6kewIie9381xjnTgjjOTFXbW1ms88mHqZBdMVry7cw7poZvL58S5rTc3B5asPQFHgO33/s3UQocXuHUhSLUFrkpHk4+9cvceiPnk5J3dzg0z7Gf98ZsBYJqGXjvz8jcU+yZczN5ic4745kIrpsk+4MKI4xbnB52ox02c69OQcfBMB76+pSfBBVZUUMLEsGV/pNTKgmvp0BxTGKfY3qkIoSNtW3Jt7j1LKkazORiFAaMOskfALR9O/z4OufZv2OljQB4W+4xw8pDx6Wgjf2qS3eyV/f+IBzb3ciCItjkS7HMfivUx9SR/NB9AIJDSIPJ7XHUHckcVHU8UH4c9d75KpBeCGBj76VHv/u5coHx5Qyf21dipkgUw/TE3qlRRH87b/XqG3x9U7yGby1aH19WmPmbzBb2zsTH3Z3ZgJr6aIs/vvrXb4xwzF+x2euPOM28C8t2ZymQVSVpUaLhzVSf3THBngDo0qLojS3dbBsk2MGm+8TENubUxtXVU27p5DsTGRz8ubayQmamP522TEpvyt9DfXR4/ZOO6aiJMZr15zCz847GCAx7iET1549yd2vPaVxqyyNpQwK83emOjX5u7wkyn++dxKPX3F84vpNbfFQE22mjk5pwBfVqY4mmknertnenDbuwf8uRCNCRJx795OPH5Sy3+mTq3n+qo+Enrc4Gu3StBzcXl4c5ayDhyd+93Rek0yYgCDpSPUckfn4IDw8NbM4EObqN4HEskxF6n8RNjVktruPGJhUw5vbnTA9/webacBbW7yTkQNLOWB4VUrstNeo+R19wUYj37kd/OdpjXcmNIhsmTSDhPWa/PhNeF19LAPz0CA8H0EsKmkfq9cz9AhqEOBkyAXn3SqOCWXFUTbUJ5+vP3w4aDNvbOsg7JXxBk91pV3lQtA0c/g+e6X8Lvb1qk+f7Ix/8GubE6srGD6wlBMmDgG6HhF9xoHDKS+OUtfSntIRKy2KppiV/ChKLJrUIIZVlnLQKCfstaw4Sqfmrrk4dUq/qa0+v2HY/tmc1I1tHRy732Cmjh/MZ4/eJ2W/o8buzTCf6SzlvLFIl87t4Pt/+D578akjk4kqzAdRQLwwV69h8Xpkq7c1c8Gdr9HS3sFXHpid07mGVDj27ZJYhP8s3ZwInfS/dLlqEBvqMvfCvDJ2dipt8U421afaX/3hqlc9NIfpc9by0d+8xDurtlEcizgCLMQH4Y/jXri+jgv/8FriI8jFJ3Hp/bNTUnIHZ8H69O9fTUTD/PWND/jan97s8pyQmpLk079/lXNu+w9n//qlhDPU38h09bEMzEOD8O7novX1/Pr51FHisUBDE6ZBrNnezCfueJkdze0URSOUFUUTznRwJqn3XougE7yuuT1cg2iJ88b7Wznr1y91uz5BgkKtOGAT9zo1xbFIYixHQ2s8IRy9V8mL0tnShQ+iJBahqqyIuub2lAACJ6InvFlSTZajPND7947Z6ArdcVVdN21hguCqh+Zk3L+toyOrk3pHU1siGik4mHFA2KAjl+JYJOc5aBLnK46mtCMmIApIQoNwe2L+xubV5VtYtqmBp+anjo78+kn7ccnx49LONcp1nl04dV8g+eH5X8Zsk3v4e9Vrtzdn3M8zz3iawqaG1tCRuarKY2+v5uczFjJvTR3LNjUmnGL+DrzXQ95U38KYvZ06vPDeRl5euoVZ7ztjNt7PY7KXeEdnmmB53T3f8s2N/Hve+pw0E3+qgTfe38rc1TuYv7aOJW7kiF9D68o06I+MyRVPQP173vrQ7f4Gq7EtHupnevuD7QkN6nPH7JO23dNsgrb+upb2tJH44AiOqx+dy6qtmd+TXOlK+Hs992JXuIEjCCvdcT/eM/SER1gUU6UvHWxxLEJVaRF1Le2Jzs45h47kwJFVGcduqDoht97xfrzrembZLxxYzFdOHB96nvu/dDQAh4wamDIaG+DJ+eHPFxwLQzYfxIotTSnC7aefODixPHJQ0ql+6/mH8dcvJ014xdEI1350Mt88ZWLodb0wXj/lJbGUd8yc1AXEe+RepEqmmbM8KkpifOeMA7jytPSslV4P4mOHjmSCm+MJUhuwbLb9ukDOlTAqSmKJHq13rrZ4Z0rvv8E3T0GnwjpfmGhxLEJxNEJju0+DaE3OznXA8CogGfrp5SnywlO7Q7xDu/Rl5GI/DSY19PDMPX47fJcaRD4mpi5MXBW+xm9bYzvxTuVbIe8HOP6pM90sruC3q8coikqaiamuOR46UraupX2nzdjX1TPyzDHFroMdnHvuNYjeN+I5fsNMJv4qFMciVJXFqGuOJ763K0/7UNaBfZ2qiXkT1ge0a09obXDf871LI1xz1qS0MS9Tx+3NiR9yppaJRCThC8mF1nhn2nMIant+AfHZqclOwIFeplng3MNGJbL7AhTHhL3Li0PbE0gGvvgZUBxN0VJMgygg3jNXdV769o5ODnBjvyHdVu7lM8oWjQSkTALy29plrNvh9PQyTWQCqWkOvP2DlJdEWbejmd88tyTFNOC3Y//fU4v4xZPvJXqGKR9n1DExNfkERFKDaGXkwFL2Li9O+CMWrK3jsbdX85fXuz8NarxTu4yG+tZD7/D43LVuOZU/vLTcSaPsI6OAcNf7czV19bHk46T27mPYIDtI9UN46S38PWY/xYEoGS9NdWlRlJJYlO0BAXHFX98KdbhfP31Btxz92ehKQHgaRFFUUvJJef4krw8ViUha797Db2osjjoaxKvLt/DSks2Jc3fFASOc73LlllRt1hNaXifJixsI+oeCOY9i0QgVJbHQsTtBWuMdaWapoCDMNODNS4OfWmZX6EazD5ILC59N0yAKJCD6ej6IXQJ/SurWeAfNbR3sP7yS+pY4a7Y3syUQkeH1pop8tueTDxjGp6eMTtnvwJFVTHdHpTa2dfCVB95k+uXHJ3rrp04axrMLM2eQXLejhcHlxWn23PLiGP+et55/z1ufYv/2N6q1izZRu2gTx08YknZezwfR4msT6lvitLR3UNcSZ2hlCUMrShKx7AvW1SVU7/LiaMbooDDinZnn0fWY8e56Xlm2hY8eMpI125v5yRML00bz1mfI7ZNITRIYjJYNvwax7+ABHD5mEP94Z22WI5J4AvkHZ03ixSWb+OSRzjOv9EXeeKbB8pLwz8t7b+78/JH8bdYqyktizF29g7IiJyfPjkDcfiZfVFj4ZjYmjahioasNThxWkTDPQS4mpuQ77x/UdcCIKoqiEX74seRswqVugEYsIoEZ0ZTbP3sEj761mmhEEs5oL0rP7zT+ykfGo5raQ1eFCUMrOHrc3vxPwBzj1yBKYpGEEA76bsL8Gx87dCSbG1oTkWqZaPWNawoyvKqU9XUtaQPevnvm/hlHeHvpxjMJ1BI3XU+ogCiOpfgg9iovBnJ30OeKaRCQkte7Nd5JXUucgWVF/NG1VQYHpni9EP8Duufio1LMBpDsGXp4L0pjW5wvHjeW7555QMr2oPN63faWRI/Jj9/h5R8EFJZI7+2Q/ELFsWhaBEd9S3uiwRlWWZryUvrPe+iYQWnny0Z7h+bk3PYa3kzzH2fWINKnw/RrVYMGFHHM+L1TjvEExODyYmZ+5yQmjUh9Tl0xalAZXz5xPA9cMpVzDxsFpGoLK13TXHlxBgHhNginHzicuy8+KlGehIDowWh6P0EF9x9fT2YefeRrH+YrH0na6LtjYvJrEAOKosz45glMHZ80mXj+gFG+wWweZx8ygnsuPgoRSTPb+Ttc10ybxPfPSjX/OFFMER76yrF8ONDx8WsQmaKgIFxA/Oy8g/n0lDEhezscNMp5P1rbOzPa+gf7glP8/HfNBH74sfQMs/4yhwmI+76YfC/CBMTAslQBcdcXpmQsf08wAUHqvA/z1uygrrmdqtIiyt2GeGsWO2M2gg1PW7yT/yzZTGNr3MlHHwuPxPBYX9fC8Kr0j2yAr+Hxhxr6x0Z4vP3BtrR1nonJT11zPCHAhlaWMCzDqNB9B2cfDBRGQxf2e4/FG+oTPdygaSCzD8IbC5D8cP0NbCwkf0/Q9FOWwWzkESxLmJnJf86Zi520zpkiV4oCwtkbR1FS5KR97o6Glo1gA13ki5UdUBxNMYd0qUFEfE5qX/2jIWYhT4CMDhEQfoJZY4u6+K6yxTJ45pr3NzdmNO1BZhNQpl48wIAi53xrdzSnldnDcyR3lVPJjycgwgbJFUUjCX9HmA+iqqwoazTkzsIERICL751Fa7yTqrKiREMc1CDOPypzb8PPkMCD3dLYxufufp1OddMNB4b6hwmeoZUlaS+CP2ImU8MJjk3XmwTJT3Es3U5c19KesN8OrSxJ9FpKAypzzf5DM14vE2GpAfxpm8Fp4E//1Yt8x51DIPiRZ+q5eRpEcByE52eIRiSRnmGUG0niPZcL3SlgRw7M3pAdFtCawkxHCU3Cty3TeYM+CP/AsLB3wBuY5h8YlQsjAp2LSESIuT6CokA4aXN7R6gd/sh9nfEQXuNdHIswtKIk4S+oDrGte+a2UYNSr3/e4aNSfp972MiU32E+iNMmJ+ec+FTAhOvHL7TCRsp730ymnEdhYyI8PEEfnGTKjz9JZ65c6DqxB4dEKUUjkhAcYRpEVWlR1vFUOwsTEIQPAKsqjSV6ilt9Zpzvn3UAFwVC47Kx5MZpoev3H16ZoqoDoXMiD60sYdGPz+RXn0kmhBvga4Q2B+yb9158VGJ57ODy0Dw0xdFIygcxtLKEupb2FA3CeynH7DUgsd9NnzqUIwIDqDw+deRolt44jSf/54S0bWG9rsrSIv7hTo4E6b3DTPb7IMkoplQB4vW6YpFIovf7/z46mbtPH0B5SYwlN07jylMdO/apk6t54/un8N0z9w+9xqFjUoVZeYhmcMaBw1n8k2lMrHYi1644eUIiyV2QdA3CaVxa451pDcyJHxrKg18+hsU/mcbtnz2CJ77hjBweNaiMC45OD5VNPW/6PSyKRpKNpV9AtHWklWvZT8/i4a8c6xwXkcTxgytKmP2D03j56pP5xikT0q4xzk054Q/tXHrjNG76dGpSw8tOHM+z30qOLi4KafBOnVzNkhunseTGaXz5hPCwVSBFG//d545M2z7YfR8yNeBeh2lgWRGLfzItpRPmNxWOH1qeSOnvpysBFMYlx49jyY3TXP9BKrGuBERZEVlk2k7DBAThqmtVWRFFrinG3wMuLYpmDcULEvzoPCaNqEoTCGEv77DKEmKBdMAVvhd2RSCaY8rYZAM+fGBp6Khez0ntMX5IORvqWlm6sQERR132Xkq/Q3doZUnGSJNoRIhFIynOWo9V29J9I8s3NbBXlmiiXO/x0o0NTmhyQE33tAQn/YG450z6eYqikZRrDKsqTRmd7ueQ0YNSfg/I4FtwkrA5Qva4kOAAjzQB4d6ztnhnmomiOBpJRAaJSIrdOthQBTscYeUsikpivb8cWxvb0nrR0YgkQik9J7W3z8ABRYwaVBb6nKrdhJV+B3EscL/BecZ+LSPTTHlF0Uja8wri1yCCmruzLrsJyPv2hlaWUByLpJTd3+ifMGFI4tvx3++yhF8y9yZVRDK2D7FoJBEZFlafqrJYt66VLwW9goicKSKLRGSpiFwdsn1fEXlOROaKSK2IjPZte1JEtovI44UsI4TPPe19tOXF0ZTMlDtLrXMa26CJKf3l9Zxf/n39tu3gWInK0qKEeSZTSKY/lj0iTtqOpRsbuO+VFQxzy+X1wP3aSlVZUdqoYQ9PyAZHuILj5A5++xOqKzM2tJBbHqi9y4tZvrmRx95ekxhn4jWaQyo9DUISGkRXCdGGVoQLiFGDythn76QmFVZHj4Pd1A+Thmd2fAc1EK+n397RmTDzeInfgvfN84ccPmZQ2v076YBU81/QPAjOs/eu7z/3xvrWhK8obGCW9/515SeAZJoOT1Bkoyv/T674O1t+QXOk22Ea4Zr7MoYeewLCfe/9ssj/LA8YUZXoZIwclHxfShJjQ3ZOuGksIhzhmvfC/IGOiWk39kGISBS4HZgGTAYuEJHJgd1+CdyvqocANwA/8237P+DzhSqfH1UojcJ5RyRtpN5HO6A4lmKm6elD+fwx+6ao1f/+5gnc8pnDgHD1NNnbS143rEfxxDeO54Vv1wDw/FU1PPutj4SarMARIl4D1KnJj/SMA6u5+yLHROVpEP7GsKo0llKOMMeev9F69GtO1MyqrU0pva0Z3ziB31xweKipxiPTwLRHv3ZsYvmnn3ASoi3f1Jj4MD2zhmfXjfqc1F0KiMCHeODIKu76whQOHFnFfV88KtHLHJDF/PWLTx7Kvy4/Pi2dxxPfOJ7plx/HHRcewUkHpM577NcgvDQkXvRa0P9UXVXKP79+HD897+C0+/ejcw7i8pOSJp+wDkdRNJJ4RmE99me/dSLPuNNiph6XdFJ3xbSDhvO3y45JyRVUaMKEIcAvP3ko0y8/jh+eM5lfX3A4n3P9TkG8ennvgHdvvnHyhJSMCaMGlSU6iX4TmvdudHcOlUxEI8IdFx7B41ccz17lxfztsmP4y5enJrbvCU7qo4GlqrpcVduAB4FzA/tMBp53l1/wb1fV54B0D2sBUBQROM03CXtCgyiJsq0x+3R/3eEjHxqaMsJ60oiqZBRLSINbHFDtITyqYdyQ8oTtd2hlCROGVaSZHDyqSotSQnC9OPuzDxmZSH7mfSj+Br+qrCjFTjwoJVe/0/j6hcZhYwYRjQjtHZrSU5w8sspx0meJ+Aib07gkFuHIfZMhqxOGVTCs0knz7I2k9oSnV/6eCIhRg8o4bXI1IsL4oRWJqJxsGkRFSYyDAw54gANHDuSQ0YM46+ARaQ23529p7+hkrasR7l/tPJ+wHEyHjhlEaVE0TYOoKIklHNoQ/j4VRZMaRFj6jgnDKhP2ej9eo5iL3VtEmDp+cEaTUSHIJLjKiqMcMnoQwypLOefQkRlH0RfHAgLCvTfHBOrhN7OOChEQOyuralFUqCiJJb7HqeMH8+H9kmbL8kAupkJRyIFyowD/vJqrgamBfeYA5wG3Ap8AKkVksKqmx2uGICKXAZcBVFdXU1tbm1dBV69uBVXeX7wgsW7e27NYUxoh3tKcEkGzZNF71NYvTTtHrtfesXI+tRsXpqybu9HpLTfV70jb/523ZrNxcYTF25I9k3UrFqXt99rLL6U1Jps3JB3YlUVQ77a56z9YzjJNjoressW53dtWLqR222LAcdzHBLZvXk9lMdS3wdtvvJIyj/WgWDveML9169ZTW5saUvvSizOpLILtraDxZIOfy73auC29byDamXLs27NnUSbtvLdyLW3bnQ803uiUYcOq94kKNDc10ug6yRe/t5DJlS0Zrx9Mo7Bt6+aUfdtanPNsXLua2trMAxzDyFbnLc3uREvtSXNh2+aVADRu35Tx2JVrU7WsV19+kfe2Jt/V9WtT08TX1tYSb22mqa6V2tpaFq9OF8KZrrXqA0eL3rJ5c17fWS7H5Pv9hp2noaGhW+era3Oeff1G59nG2536vjt3Dm2rkwJ9ydw3aWl23oPmrcm8TRtXrwBg7aoPqK3NnM8pV2bPmsXq8szSeObMmdS1Jt/XfOqcC309kvrbwG0icjHwIrAGyFlHU9U7gTsBpkyZojU1NXkVorZuPrJmBSdMncIvZjnz5J5z+klEI8Ija99iuW+e4oMPOpCaQ5ID4u4atoG9y4sT4YChPPkEANdMO4DzThyf5mzrfG8DvDWboUMGw5ZNKdtO+PAxjNl7AINWbYfXnQlGTvnwUfzqTaec1310MqVFUU6emh7R8lrzezz7wTIAfvjxQ/n2w06myimHHsjJh47ka+uf5Yzjp1BdVcKMd9fzhePGppTtFwNXc+DIgZTEIry6fAuneVEzbn3+8vWT+exdr7F4QwPVw4dTU+NEqdw6aA0Th1UyeWQVg9+sZfumRvaqKucXnzmAqrIijhqb7OX+auBqJg6r5KO/+U9K2dslBgTmXRhQQk1NTeL6J514HE9smMOWhjZGjRlKZNlSDhg3htfWrWCfcfsxcOUyBg0cwK8vmcp9r6zgqx/Zj5denEm29+SmqtUcNGogLy7exHlHjErpTcsbLwBNHD55IjXHpSdqDOPu6g0MLCtiiq/OYTQMXMFpk6vZ3NDKii1NnH3wCMqHL+MLx45NG4fh0b5gA7+fm8wyfPJJJ1G+fAvMeo2iqPCh/cbB8sWJ7TU1Nfxo2Ab2Li/iyH33Zsubq2FeavbSTPdmSWQ5LF7IsGFDqalJjxLKiPusst3z6RO2s2prc8p3lQ83V61m8sgqDhheRW1tbdZrhrF5wPucfcgIqqtKKXn5WWhrZcqRhztaq1uPs0+r4VfvzoSmRg6YuB//XPYeAP/vwlOofnE5XzpuXM/8Ku51jjvW+e6DPD5xB8s3N1Jz6Ei2N7XBC88Azv3Np85dUUgBsQbwGyFHu+sSqOpaHA0CEakA/ktVtxewTKGoOiYmf1igp75NHlmVMpF9LBDF44/T7orLQoSDn7AtCeeg77p+U8iXQjLKevhNTKdOGsaQimI2N7QlwiqnjoglYvzDMtOed0Qy7nxsyGxZQypKuPSE8Xz3kbkpkWDemABI+jfKiqKcMin9Xn3i8PDY9rDxHcERsmVFUYZWlPDeunraOzuJRSMJE8KO5naqSp18NeUlMb7us81n47/c1BlhIareALyhIbH/mQircxhe6PTIQWWJqKn/rsle5jBTl3++hDATk/997Y6Jwjtvtulk8+WQ0YPSIsXywf++5oP/W/JuTTBSKBqRRFi8/5ssikZyfsdyIdjOeBw0amDC7NQbJrxC+iBmARNFZJyIFAPnA9P9O4jIEBHxynANcE8By9MlYSGakwOjoXvipO5OeKyHZxv121jDBtaE4XfclcSiiQmRwuK488WrkWbIK+r5GTI5ETMR5i8I9qRLi6IMrSxhfV0Lzy7YQHE0khggt6O5fac78pICouvonN4gzFke9c2X0NWgre68jokout5zK/Qpnrk27Hv3Xs1so697Si7v7W4dxaSqceBy4ClgIfCQqs4XkRtE5Bx3txpgkYgsBqqBG73jReQl4GHgFBFZLSJnFKys7v+Dyoo4++AR/OXSpKskmE8pjza+S6aOG8zh+wzi6mnpqYe9l9Af5hqLRvjE4aNCBwT58au6xW7iL0jviRcSrwyZIqo8vLxXYUx1Ha/evbj7oimccWA10YhwwkQntHPZpkZiUeHjh43iqLF78eUTxnPOoSM548DujT7OhV1FQIRpEJ7jeUBJLCVz6c/POzht35MOGMZEX8DEVRnSTUOytxzmNM/GFSdP4DtnhA9A3JXx6uk11N85Y/9E+nZvrou9BhRzw7kHclmGeSd6QtigwSC7u5MaVZ0BzAisu863/AjwSIZj04fkFghVp2MUiQi3X3hEyrZgmt7OnROkkEJ5SYzH/vu40G3FGeLPf+WGxmbDHyUUjUjC2V4VoinlS0IryhAg5AmGTBFVHh/50FC+ecpEbn0uNZ3B7z53BJvqW3n9/a2JHtMpk6oTpptj9xvMBUeP4a9vrCIWibBXeTEPf9UJr700y8jbnrCrCIgwDcKbM7m8OJrQ2o4YFuX8kFHXVaVFTL/8eCZd9yQAV2SYsAaSHZTuNklXnb77CQdIdgS9d85vPvJyZR04sorxQyvSjt0ZhOW4CmKpNnoJRXN+8cMmbikkRb48/N0lOCm7R7ZkZt0laWLKUIai3DQIIKNt1wvnzNRj8syA3U1/nS+ZnMa9TZjQ9UbOOz4INxlcllc2116ot18hNOhdkaAGEUY+iStzJRfzUW9EEZuAwB0FnOVm+3Pe5CMgjhq7V0oSt+7g9dBzGaAUJNiAeI7oXBrrbBRHI3zYnRHr8H0GAXD2weERKGVF6WkJMuFNxTrON4p4+MDSxIjwTB/NUa4JKphUb2fT3WR5hSZMUHn37oKp+yR8ENle2Vzt2N45+ol8SDS+YX7D4ycMYXB5cUFNPLloB/n4NLtdjoJfYTegC/nATz9xENsa23hy/nq6GGsVimfy6AmZcrZkwzMxeLbq//fRyfy/jwYHs3efxb4EhOOHVrDi52dn3NcTDLmE/nn39pxDR/L5Y8YSiTiBA96cy5nSfBwwvIp5PzqjW5k08+GOC7sR3tkLFMcirPj52Yy9+onEupGDyhLP40U37Xi2OdBzjYTxghB6o1HaFfA0iLAO4Z8unZrTPOo9oTcc0LlgAgKvd5Ql/FRyH41bKPIREF6Zxw0tnCrcFZ62kkuWS+9jFJGUVBXePc/20ewqZp9dCe/574xXtr9pEAnXWgZBUGhB2Zuj0LNhJiYAN9VGNrz0Cf4EXb2JZ5c/bsLgLvZM4tnupx3UswFIPcGLPMplIpWJw5yxB/5UJAAj3Hu+M2Ll+xPdERAHZEhN7pFoJ3eNdqvgeClLKkp6L+IP4AjXZNsdPlRdGEc5mAYBJKOYsnHZCeM5fsKQxCCVQjHrB6dS19LOKTfNTFkvIjz7rY9kTEkdxmFjBjH98uMSGUb7Aq+nlUu78l9HjGL/6sq0XEZH7LMXj19xfNqYFMPhlatPDo3JT2i9XQiI56/6SJeRWUn50D8kxI/OOYiLPzyO4d343nYG918ylY11LV3v6FL77Rr2rshtXFQ+mIAguxPPIxKRggsHcEIowyZ6gfSedS70ea+7G7ZaEQlNdAf0yr3fXRkZmLnNI1cNIpdQTU2Y/7pXtt2V4lgk44RPhaSiJEZFN0JnwzIc7EzMxOSyK733uQySMYyu8KK/Kop7/nZ75spskzwZex6mQZA5TURfsas4qIzdmwOGV3HjJw6iasfyHp9r2kHDue6jk7uc5tTYs7CuKq4PwtpkYw/kwqn7UrkTNIhIRPjS8T3MVGrsdpiAIPMoYMMwjP6MmZjILYrJyI+LPjyWd9fsSKSzNgxj98EEBLueD2JPYnBFCfd+MXOmVsMwdl3MxARgPgjDMIw0TIOg61xMfUF5cfgMbIZhGL2FCQgy51vpS+bfcGZfF8EwjH6OmZhczMRkGIaRSkEFhIicKSKLRGSpiFwdsn1fEXlOROaKSK2IjPZtu0hElrh/FxWynLue/mAYhtH3FExAiEgUuB2YBkwGLhCR4GQEvwTuV9VDgBuAn7nH7g38EJgKHA38UET2KlRZLczVMAwjnUJqEEcDS1V1uaq2AQ8C5wb2mQw87y6/4Nt+BvCMqm5V1W3AM0DBjPKmQRiGYaRTSCf1KGCV7/dqHI3AzxzgPOBW4BNApYgMznDsqOAFROQy4DKA6upqamtr8yrohg0tqHbmffzuSkNDg9W5H2B17h8Uos59HcX0beA2EbkYeBFYA3TkerCq3gncCTBlyhStqanJqxAPr32LD+rXk+/xuyu1tbVW536A1bl/UIg6F1JArAHG+H6PdtclUNW1OBoEIlIB/JeqbheRNUBN4NjagpXUfBCGYRhpdMsHISKlIpLrDBWzgIkiMk5EioHzgemB8w0REa8M1wD3uMtPAaeLyF6uc/p0d11BUNQEhGEYRoCcBYSIfBHHL7BERL7d1f6qGgcux2nYFwIPqep8EblBRM5xd6sBFonIYqAauNE9divwYxwhMwu4wV1XEHRXHEptGIbRx3THxHQ5cADQALyCE6KaFVWdAcwIrLvOt/wI8EiGY+8hqVEUHJMPhmEYqXRHQIiqbgEQkcYCladP2AUzbRiGYfQ5XQoIEfkXzlCB8SIyHaezHRzwtltjPgjDMIx0ctEgPFPSTYUsSF9iGoRhGEY6uQiIk1T1+kIXpC9RQCxbn2EYRgq5RDGd0/UuuzemQRiGYaSTiwYxTES+FVypqjcXoDx9hPkgDMMwguQiIKJABXtwJKjalKOGYRhp5CIg1qvqDQUvSR9iFibDMIx0cvFBPFPwUvQxqmZiMgzDCJKLgPi7iFR6P0SkSkSCabt3e0xAGIZhpJKLgPgtTnoNjwZ33R6DmZgMwzDSyUVAiGoyEFRVO+n7eSR2KpaszzAMI51cBMRyEfmGiBS5f98Elhe6YL2JyQfDMIx0chEQXwU+jDPZzxqcaUMvK2Shehu1kXKGYRhpdGkqUtWNOJP97NGYBmEYhpFKlxqEiIwWkcdEZKP796iIjO6NwvUWNlDOMAwjnVxMTPfiTBU60v37l7tuj0EtjskwDCONXATEUFW9V1Xj7t99wNACl6tXUTUTk2EYRpBcBMQWEfmciETdv88BW3I5uYicKSKLRGSpiFwdsn0fEXlBRN4Wkbkicpa7vlhE7hWRd0VkjojUdKdS+WAmJsMwjFRyERBfAj4NrAfWAZ8EvtjVQSISBW4HpuHMQHeBiARnorsWeEhVD8dxhN/hrv8ygKoeDJwG3CQiuZQ1LyyIyTAMI51cophWkt+cEEcDS1V1OYCIPAicCyzwnx6ocpcHAmvd5cnA8+71N4rIdmAK8EYe5egS80EYhmGkk8uc1PcSko1CVb/UxaGjgFW+36txxlD4uR54WkSuAMqBU931c4BzROSvwBjgSPf/FAEhIpfhjsmorq6mtra2q+qEsm1bM50dHXkfv7vS0NBgde4HWJ37B4Wocy4pMx53//8F8N2denW4ALhPVW8SkWOBB0TkIOAeYBIwG1gJvAJ0BA9W1TuBOwGmTJmiNTU1eRXijkWvsmP7dvI9fneltrbW6twPsDr3DwpR51xMTI8CiMi13nKOrMHp9XuMdtf5uQQ4073OqyJSCgxxB+dd6e0kIq8Ai7tx7e5h4yAMwzDS6I7jt7uG+lnARBEZJyLFOE7o6YF9PgBOARCRSUApsElEBohIubv+NCCuqgsoEGpTjhqGYaSRiw/iXRzhMEFE5uIMGVBVPSTbcaoaF5HLgadwpi29R1Xni8gNwGxVnQ5cBdwlIle617hYVVVEhgFPiUgnjtbx+R7UsUssiskwDCOdXHwQH8335Ko6A5gRWHedb3kBcFzIcSuA/fO9bndRzMRkGIYRJBcBUV/wUvQxNuWoYRhGOrkIiDdJTpkwAmewnALjC1guwzAMo4/JJYppnLcsIm+7o573KMwFYRiGkU7OUUxuJFJxAcvSZzjJ+szIZBiG4SeXKKZ/uYuTgL8Wtjh9g4KlczUMwwiQiw/il0AnsFpV3y9wefoGc1IbhmGkkYsPYqaIHAqcLU4s6EuqOqfgJetFPA+8YRiGkSSXKUe/CfwZGOb+/clNrrfHoCYhDMMw0sjFxHQJMFVVGwFE5H+BV4HfFLJgvYmi3co5YhiG0R/IpV0UUjOpdrCH9bct1YZhGEY6GTUIEYmpahy4F3hdRB5zN30cuLsXytar7FESzzAMYyeQzcT0BnCEqt4sIrXA8e76L6rq2wUvWS9iGoRhGEY62QREolOtqm8BbxW+OH2DJeszDMNIJ5uAGCoi38q0UVVvLkB5+gQ1FcIwDCONbAIiClTQT8zz/aKShmEY3SCbgFinqjf0Wkn6ELUpRw3DMNLIFubab5pMtXyuhmEYaWQTEKf09OQicqaILBKRpSJydcj2fUTkBRF5W0TmishZ7voiEfmjiLwrIgtF5JqeliUbTjZXwzAMw09GAaGqW3tyYhGJArcD04DJwAUiMjmw27XAQ+4cE+cDd7jrPwWUqOrBwJHAV0RkbE/Kkw3THwzDMNIpZIaJo4GlqrpcVduAB4FzA/soUOUuDwTW+taXi0gMKAPagLoCltV8EIZhGAFyycWUL6OAVb7fq4GpgX2uB552k/+VA6e66x/BESbrgAHAlWEajYhcBlwGUF1dTW1tbV4FbWxsorKsM+/jd1caGhqszv0Aq3P/oBB1LqSAyIULgPtU9SYRORZ4QEQOwtE+OoCRwF7ASyLyrKou9x+sqncCdwJMmTJFa2pq8ipE2Zu1FEVbyPf43ZXa2lqrcz/A6tw/KESdC2liWgOM8f0e7a7zcwnwEICqvgqUAkOAzwJPqmq7qm4EXgamFKyk5oQwDMNIo5ACYhYwUUTGufNZnw9MD+zzAW60lIhMwhEQm9z1J7vry4FjgPcKVVCbDsIwDCOdggkINxPs5cBTwEKcaKX5InKDiJzj7nYV8GURmYMz3/XF6uS9uB2oEJH5OILmXlWdW8CympPaMAwjQEF9EKo6A5gRWHedb3kBcFzIcQ04oa69glmYDMMw0rGJ1LCBcoZhGGGYgMBNtWESwjAMIwUTEHgahEkIwzAMPyYgXEw8GIZhpGICApty1DAMIwwTEIZhGEYoJiCwcRCGYRhhmIDARlIbhmGEYQIC80EYhmGEYQICZxyEmZgMwzBSMQGBaRCGYRhhmIDAfBCGYRhhmIBwMQFhGIaRigkIzMRkGIYRhgkIAEvWZxiGkYYJCCzdt2EYRhgmIDAntWEYRhgFFRAicqaILBKRpSJydcj2fUTkBRF5W0TmishZ7voLReQd31+niBxWqHKqqRCGYRhpFExAiEgUZ27pacBk4AIRmRzY7VqcuaoPB84H7gBQ1T+r6mGqehjweeB9VX2nUGU1DcIwDCOdQmoQRwNLVXW5qrYBDwLnBvZRoMpdHgisDTnPBe6xBcOimAzDMNKJFfDco4BVvt+rgamBfa4HnhaRK4By4NSQ83yGdMECgIhcBlwGUF1dTW1tbV4FbW9vJ96ueR+/u9LQ0GB17gdYnfsHhahzIQVELlwA3KeqN4nIscADInKQqnYCiMhUoElV54UdrKp3AncCTJkyRWtqavIqRKz2KYqLId/jd1dqa2utzv0Aq3P/oBB1LqSJaQ0wxvd7tLvOzyXAQwCq+ipQCgzxbT8f+GsBywg4di7DMAwjlUIKiFnARBEZJyLFOI399MA+HwCnAIjIJBwBscn9HQE+TYH9D4BJCMMwjBAKJiBUNQ5cDjwFLMSJVpovIjeIyDnublcBXxaROTiawsWqCZfxicAqVV1eqDImyopFMRmGYQQpqA9CVWcAMwLrrvMtLwCOy3BsLXBMIcvnuxZiIsIwDCMFG0mNa2Ey+WAYhpGCCQgsF5NhGEYYJiBwphw1EWEYhpGKCQhcDcLkg2EYRgomILAoJsMwjDBMQBiGYRihmIAAGyhnGIYRggkIHCe1mZgMwzBSMQGBOakNwzDCMAGBWZgMwzDCMAGBl2rDMAzD8GMCAku1YRiGEYYJCCzVhmEYRhgmIFxMQBiGYaRiAsIwDMMIpd8LiOT8RIZhGIYfExCufLBxEIZhGKkUVECIyJkiskhElorI1SHb9xGRF0TkbRGZKyJn+bYdIiKvish8EXlXREoLUUZPfzD5YBiGkUrBphwVkShwO3AasBqYJSLT3WlGPa7Fmav6tyIyGWd60rEiEgP+BHxeVeeIyGCgvRDlNBOTYRhGOIXUII4GlqrqclVtAx4Ezg3so0CVuzwQWOsunw7MVdU5AKq6RVU7ClHIhAZhKoRhGEYKBdMggFHAKt/v1cDUwD7XA0+LyBVAOXCqu/5DgIrIU8BQ4EFV/UXwAiJyGXAZQHV1NbW1td0uZLzTERFtbW15Hb8709DQYHXuB1id+weFqHMhBUQuXADcp6o3icixwAMicpBbruOBo4Am4DkReVNVn/MfrKp3AncCTJkyRWtqarpdgNZ4Bzz9JCXFxeRz/O5MbW2t1bkfYHXuHxSizoU0Ma0Bxvh+j3bX+bkEeAhAVV8FSoEhONrGi6q6WVWbcHwTRxSikIkopkKc3DAMYzemkAJiFjBRRMaJSDFwPjA9sM8HwCkAIjIJR0BsAp4CDhaRAa7D+iPAAgqJSQjDMIwUCmZiUtW4iFyO09hHgXtUdb6I3ADMVtXpwFXAXSJyJY6/+GJ1woq2icjNOEJGgRmq+kShygomHwzDMIIU1AehqjNwzEP+ddf5lhcAx2U49k84oa4FxaJcDcMwwrGR1G6gq2kQhmEYqZiAsKHUhmEYoZiAcP8XkxCGYRgpmIAwJ4RhGEYoJiDc/01/MAzDSMUEhKX7NgzDCKXfCwjMwmQYhhGKCQgXUyAMwzBS6fcCQk2FMAzDCMUEhCXrMwzDCMUEhLdgEsIwDCMFExBqqTYMwzDCMAHR1wUwDMPYRen3AqI4FuHsg0dQPcB0CMMwDD/9XkBUlRZx+4VHcPDQvp591TAMY9ei3wsIwzAMIxwTEIZhGEYoBRUQInKmiCwSkaUicnXI9n1E5AUReVtE5orIWe76sSLSLCLvuH+/K2Q5DcMwjHQKZngXkShwO3AasBqYJSLT3WlGPa4FHlLV34rIZJzpSce625ap6mGFKp9hGIaRnUJqEEcDS1V1uaq2AQ8C5wb2UaDKXR4IrC1geQzDMIxuUEgBMQpY5fu92l3n53rgcyKyGkd7uMK3bZxrepopIicUsJyGYRhGCFKoGdVE5JPAmap6qfv788BUVb3ct8+33DLcJCLHAncDBwFFQIWqbhGRI4F/AAeqal3gGpcBlwFUV1cf+eCDD+Zd3oaGBioqKvI+fnfE6tw/sDr3D/Kt80knnfSmqk4J3aiqBfkDjgWe8v2+BrgmsM98YIzv93JgWMi5aoEp2a535JFHak944YUXenT87ojVuX9gde4f5FtnYLZmaFcLOTpsFjBRRMYBa4Dzgc8G9vkAOAW4T0QmAaXAJhEZCmxV1Q4RGQ9MdIVHRt58883NIrKyB+UdAmzuwfG7I1bn/oHVuX+Qb533zbShYAJCVeMicjnwFBAF7lHV+SJyA47Emg5cBdwlIlfiOKwvVlUVkROBG0SkHegEvqqqW7u43tCelFdEZmsmNWsPxercP7A69w8KUeeC5pdQ1Rk4zmf/uut8ywuA40KOexR4tJBlMwzDMLJjI6kNwzCMUExAJLmzrwvQB1id+wdW5/7BTq9zwcJcDcMwjN0b0yAMwzCMUExAGIZhGKH0ewHRVcbZ3RURuUdENorIPN+6vUXkGRFZ4v6/l7teROTX7j2YKyJH9F3J80dExrjZgReIyHwR+aa7fo+tt4iUisgbIjLHrfOP3PXjROR1t25/E5Fid32J+3upu31sn1agB4hI1E3H87j7e4+us4isEJF33QzXs911BX23+7WA8GWcnQZMBi5ws8ruCdwHnBlYdzXwnKpOBJ5zf4NT/4nu32XAb3upjDubOHCVqk4GjgG+7j7PPbnercDJqnoocBhwpogcA/wv8CtVnQBsAy5x978E2Oau/5W73+7KN4GFvt/9oc4nqephvvEOhX23Mw2x7g9/5JAOZHf+w0mdPs/3exEwwl0eASxyl38PXBC23+78B/wTJ918v6g3MAB4C5iKM6I25q5PvOc4A1ePdZdj7n7S12XPo66j3QbxZOBxQPpBnVcAQwLrCvpu92sNgtwyzu5JVKvqOnd5PVDtLu9x98E1IxwOvM4eXm/X1PIOsBF4BlgGbFfVuLuLv16JOrvbdwCDe7XAO4dbgO/iZFoApw57ep0VeFpE3nQTlUKB3+2CjqQ2dl1UVUVkj4xxFpEKnJH4/6OqdSKS2LYn1ltVO4DDRGQQ8BhwQN+WqLCIyEeBjar6pojU9HFxepPjVXWNiAwDnhGR9/wbC/Fu93cNYg0wxvd7tLtuT2WDiIwAcP/f6K7fY+6DiBThCIc/q+rf3dV7fL0BVHU78AKOeWWQiHgdQH+9EnV2tw8EtvRuSXvMccA5IrICZyKyk4Fb2bPrjKqucf/fiNMROJoCv9v9XUAkMs66EQ/nA9P7uEyFZDpwkbt8EY6N3lv/BTfy4Rhgh09t3W0QR1W4G1ioqjf7Nu2x9RaRoa7mgIiU4fhcFuIIik+6uwXr7N2LTwLPq2uk3l1Q1WtUdbSqjsX5Zp9X1QvZg+ssIuUiUuktA6cD8yj0u93Xjpe+/gPOAhbj2G1/0Nfl2Yn1+iuwDmjHsT9egmN3fQ5YAjwL7O3uKzjRXMuAd+li7o1d9Q84HsdOOxd4x/07a0+uN3AI8LZb53nAde768cAbwFLgYaDEXV/q/l7qbh/f13XoYf1rgMf39Dq7dZvj/s332qpCv9uWasMwDMMIpb+bmAzDMIwMmIAwDMMwQjEBYRiGYYRiAsIwDMMIxQSEYRiGEYoJCMPIERGZ6maLnSMiC0XkTnfUtmHskZiAMIzcKQU+r6qHquoknPEHf+jjMhlGwTABYRg5oqozVXW17/dvgQ+JyCUissPN0/+OiKwRkesBROQwEXnNzcn/mIjsJSIxEZnl5RESkZ+JyI3u8nXutnmuhiLpJTGM3sEEhGF0AxH5jk8QvIMzwnUj8JI6efoPw5lzwON+4HuqegjOiNYfqpNR9GLgtyJyKs68HT9y979NVY9S1YOAMuCjvVEvwwjDBIRhdANV/T9PELjCYG6mfUVkIDBIVWe6q/4InOieZz7wAM5cBl9S1TZ3n5PcWc/exUlCd2CBqmIYXWLpvg0jT0SkCmcWt2F5nuJgYLt3vIiUAnfg5M1Z5ZqpSntcUMPIE9MgDCNHRORiETncXY4CNwFP4iRES0NVdwDbROQEd9XngZnu8ecBe+NoFL9xM7J6wmCzGx31SQyjDzENwjByZz5ws2s62hsne+alQLYJ4S8CficiA4DlwBdFZAjwc+AUV1O4DbhVVS8SkbtwsrKux0lHbxh9hmVzNQzDMEIxE5NhGIYRigkIwzAMIxQTEIZhGEYoJiAMwzCMUExAGIZhGKGYgDAMwzBCMQFhGIZhhPL/Ac/u6/AghifuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pyplot.plot(history.history['accuracy'])\n",
+    "\n",
+    "pyplot.title('Средняя точность эпох')\n",
+    "pyplot.ylabel('Точность')\n",
+    "pyplot.xlabel('Эпоха')\n",
+    "pyplot.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "377390b4",
+   "metadata": {},
+   "source": [
+    "## Тестирование точности предсказаний нейронной сетиисходя из тестовой выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "910499e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26/26 [==============================] - 0s 1ms/step - loss: 0.0013 - accuracy: 0.9642\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.0013105241814628243, 0.9641532897949219]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(list_with_train_inputs, list_with_train_predictions)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}