From 8308268831ea023e6704a468792da1528b4c3d4e Mon Sep 17 00:00:00 2001
From: Shimon Lyons <shimonl@tzurmanagement.com>
Date: Mon, 19 Jul 2021 21:55:09 +0300
Subject: [PATCH] Remove unncecessary view()

---
 2-Regression/1-Tools/notebook.ipynb | 145 +++++++++++++++++++++-------
 1 file changed, 109 insertions(+), 36 deletions(-)

diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index 121fae3e..692123f3 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 54,
    "source": [
     "print('Hello notebook')"
    ],
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 55,
    "source": [
     "import matplotlib.pyplot as plt\r\n",
     "import numpy as np\r\n",
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 56,
    "source": [
     "X, y = datasets.load_diabetes(return_X_y=True)\r\n",
     "print(X.ndim, X.shape)\r\n",
@@ -88,10 +88,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 57,
    "source": [
-    "Xview = X.view()\r\n",
-    "X = Xview[:, np.newaxis, 2]\r\n",
+    "origX = X\r\n",
+    "X = origX[:, np.newaxis, 2]\r\n",
     "print(X.shape)\r\n",
     "print(X[0])"
    ],
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 58,
    "source": [
     "print(X.ndim, X.shape)\r\n",
     "print(X[0], X[1])\r\n",
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 59,
    "source": [
     "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)"
    ],
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 60,
    "source": [
     "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
    ],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 61,
    "source": [
     "model = linear_model.LinearRegression()\r\n",
     "model.fit(X_train, y_train)"
@@ -210,7 +210,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 210
+     "execution_count": 61
     }
    ],
    "metadata": {}
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 62,
    "source": [
     "y_pred = model.predict(X_test)"
    ],
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 63,
    "source": [
     "print(y_pred.shape)\r\n",
     "print(y_test.shape)"
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 64,
    "source": [
     "print(y_pred[:10])\r\n",
     "print(y_test[:10])"
@@ -285,9 +285,9 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[106.13314033 158.77362293 206.44802227 178.63795599 146.8550231\n",
-      " 141.88893983 306.76290422 180.6243893  140.89572318 108.11957364]\n",
-      "[138. 151. 242. 296. 141.  81. 242. 270.  60.  93.]\n"
+      "[102.22273764 110.70519001  89.02781173 146.51998889 176.67981953\n",
+      " 189.87474543 120.13013708 119.18764237  80.54535937  97.5102641 ]\n",
+      "[ 60. 102.  63. 198. 172. 202. 190. 125. 101.  70.]\n"
      ]
     }
    ],
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 65,
    "source": [
     "plt.scatter(X_test, y_test, color = 'black')\r\n",
     "plt.plot(X_test, y_pred, color = 'blue', linewidth = 3)\r\n",
@@ -315,8 +315,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ],
-      "image/png": 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@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 66,
    "source": [
     "plt.scatter(X_test, y_test, color = 'black')\r\n",
     "plt.scatter(X_test, y_pred, color = 'green')\r\n",
@@ -348,8 +348,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ],
-      "image/png": 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@@ -388,9 +388,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 67,
    "source": [
-    "X = Xview[:, np.newaxis, 9]\r\n",
+    "X = origX[:, np.newaxis, 9]\r\n",
     "print(X.shape)\r\n",
     "print(X[0])"
    ],
@@ -400,7 +400,7 @@
      "name": "stdout",
      "text": [
       "(442, 1)\n",
-      "[0.06169621]\n"
+      "[-0.01764613]\n"
      ]
     }
    ],
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 68,
    "source": [
     "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\r\n",
     "print(X_train.shape, y_train.shape)"
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 69,
    "source": [
     "bloodSugarModel = linear_model.LinearRegression()\r\n",
     "bloodSugarModel.fit(X_train, y_train)"
@@ -454,7 +454,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 238
+     "execution_count": 69
     }
    ],
    "metadata": {}
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 70,
    "source": [
     "y_predict = bloodSugarModel.predict(X_test)"
    ],
@@ -477,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 71,
    "source": [
     "plt.scatter(X_test, y_test, color = 'black')\r\n",
     "plt.scatter(X_test, y_pred, color = 'green')\r\n",
@@ -491,8 +491,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ],
-      "image/png": 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rlo8iIChb+pBZB2dICNHkyey/6NiRaaupqZnuhoh8gHxhc8+NVhiEe36ZHq9cyfZjfeyMnExru3MnkY8PUWSk4fVy2xGo696o+nCNgBbZ0oesnld8PNHvvxNVr05UpgzRDz/kvC8jr/PXF1TkG7ZMTreCLtzzi2NLGT16/77+ddLS2s4UCN37E8aUzNa0thMnEnXoYFjzzA8UPu5DLQIeESBkSx+y6zmQy4lOnSLq3ZvI2Znoyy+J7tzJdLf09zcf5K8vyMgOhU8U7nmM/EBJi4wkkskyzvuSltZ2hpTgEEpAKsHjKqH5TLpxI+tO0aQkosaNiebN07+Ovuvl2tw117RI7sMqAqZl6z3T90Jn9kV/9oxo+nROudymDdHu3aRVGSuz8FlUmtCzL6HEEcKwOmkCPjfz14swDFG45zHyWhNNTWVt+auvMl43TXOfDUKfbgTXO4SBLcm+6W/k48PmgMmTic6fz7zd99kzoqJFmWKoC7o0XPNa5mQxxyLXtMjffttCQBQBxTLUsvNDXdQPH5h2WqcOUcmSRIsXE715Y1I30vDiBdHs2USwe07wPUoodoHQbWDadRdmi0FQ+QWGhLuY8jcXkNdpfL/9Fnj/Hvjuu4zXnd9qPmzMFbm2y/8NOD+G+asGWPWTFUJCgK1bATMzTlcgkwFjxgDHjwMpKcb3p3hxYNMmoH9/3alxdaWEdejugCRKAj44APc6A3IB8cnxCDgakCNh3GZmfVGjRjxkMnODaWmDgoLg5+eH0NBQEBFCQ0Ph5+dnUh+yI02ApSUwYABw4QKweTNw9SpQsiQwYgRw82bG2xMBZ84An30GVKwIvHwJeI4cDAxuBXidAz44pa3r45g7z62ILEKf1M/NVtA197y0uR85QuTpSfT8ufHbqDvRigU0ITunBJ3Jrm7fJpo/n6hWLWZyDBlCtHcva5HGYO5coiZNjMtCiVkCocenrE06PSZU2kIIsGQNPgeubePG7ADOCNlhcsupmV14ONGcOXz/mzcn2rFD+1onJHCKiJo1OYPl0qWUFtSWZnNv8g2h+QzR5p4PAdEsk/fIC7ZMRuYPYzF7NlGPHobXCQnhFLdNmhA5OnIu8z//NByAk5pK1K4d0ddfG9737dtElqVPs+1/aH1CgBWh4h8En39J8qV7tvsz7tzh67b+clCGNn5DgtnYe57TPpnERKLNm4kaNGC20oIFRFeucN5+d3fOWLlvn24zW2BwIDl1mU9osERky+RDiMK9ECIpiahhQ9ass4qEBNbqDhwwbv2XL4l+/ZUFt709UdeuROvXczGP9IiIYIHz99/a/8XGsgBydSUaMPkiWc+1V9ncZwpk1mQpweIuASWzVeudNImo85CbRjFF9Dp/XV2NnlHk1sxOLidatIjfemX788+Mt1u9mmjo0GztiohsgijcCyHGjyfq1Cn7gl0OHGABb2rBibdviTZt4khLBwei1q2Za//ihWqds2dZg3z0iH/L5WwSkck49F+5ri7OtYtLAAEvCKiXLVpvYiJRkSJExaY2Z6E+2UlDwKdniugTzK6uriZp4zk5s3v/nmjVKs2UBo8f88Dv5cUmqD/+YIVAF/74g6hXr2zrjohshCjcCxn++ovL3enSlLOCHj3YRJNZxMWx3bd/f+ZnN2zIrI5Hj4iWL2e77+3bPCiVL2+cOSkwMJAsLbsT8JqAHlnWerdvJ2raVEEJnWFGsHxLaDbLIFNEl2DOa4YUEacrGDeO0xd068bXMz2VNTmZaNs2PufixYm++Ybo1SvNdQ4eZJrlx4r8EkCYExCFeyHCvXusBV+8mP37DgtjE8nDh1nfV2IiCw0/P9aUy5dXmQq+/ZZo/frNRr+QgYGBVLRoRwKekrPzXNq0KfMvb8eOTCmULZMRhtUluNwnuN8kNJtNmGU8xzuvYhtSU4n272fqq5sb01ZDQozb9to1omHDuP7rgAEc9EbEM6s6dXKuzzmJ/BJAmFMQhftHhsyGfMfFEVWpwlPwnMLChSw4sjPD44EDKsGubGZmiwmoY9ILGRrKpodRozIXyBMWxjOKuDi+B+btAgh1fyBMLEJwv0lmLb6lTdeNuxd6c9as9M+RQKzoaHZoly7NSdrWrs18mcOoKC7eIpMR1avHQVK+vtnSzTTkh8RwBQGicP+IkNmQb7mcaNAgon79cja1bmIi22137Mj6vp4941TAJUoQ7dnDy65dUwr4nQTcJiCMgB8IaEYuTYtkKBijo4lateICGLGxpvVn7lwif3/V72qNn5H7534kzBbIa25N8ir1lqZPN/76phdg/iv9sz2c/+ZN7rOzMzOU/vsv++5/Sgo7uitW5Hsyc6amrySzyE1tOj+Yx3ISonDPp9CZsEoZIVryEAu5ofWNCvles4a1VlMFWmZw/DgzXEw9liohlzk5O88lO7sEmjFDW8MEPlcIdlvivPPTCFaXCNavCTXWED7rQJhuoVcwJiYSDR7M/HtjC36nprJ2evky/05OZgdwRIRqndeviSpXJpo2LXMCNO3eTrcgfFk+0+H8yck8uLZsyZTNWbP0xzFkR+KvuDiWFP7+bLLp25fo9OnMDyK5qU2Lmrso3HMd+rSXNK1urEzTVDG6tN59Xb7M9tWcSB6lD/37sz1XH9ILFf+VyhS6jQkIJuAgWVnpzpHOL+RvpEy1C4AwTnFN2o0jeJ1mR2flzeQ2yF8nl14u5wAeX1+iW7cyPp8jRzjbohIXLhB5lX6rJRhfv2bT15Qppgs3YbZAGO9FKH6O4POvyeH8ERHMUffxYc56UBAPZPqQXYm/5HIiMzMOTnv7ls0/pUqxA3zdOtMZVLmpTYs29wIu3PNj2lJ9GoV0olTjZcTwWmkCPr0mScT5Q0qWJNq6NXf7Hx7OA4ouwalLqGB8EYLTOoWZpZdBDSowMJCsrV2I0+1+wevOUvHbYRFDqBJIaDORUOqAQS79hg3ssD12zPD59OnDRauV6Dv+MpnVX6lTMEZEcNrkyZNNE/BF/D8l2L0gFLlOqPOT0Zr7lStEn3/OWvPgwUSXLhl3PK9vahA+7Uzo7GeQzmkMXF01WTSpqRyN3K4dX99p04iePjVuX/khpXNBQaEW7vk1bak+7QVVoLO/E348nibka9ViU0ZqKgu10aM1951bD/OKFRzWnl7ApZkfRpfiFAGdviDYvCLUXESAnVHaWmBgIBUr1oyA11S0aGdyneequialDjCLxe45uX42jt68McylP3qUGUQbN+o+j8hIjqpVT7RlXeEooeVUTpw13UJLMEZGElWrxgFPGQl4uZw1bke3eLIc0oFQZROh6+cGn8XERKItW5gu6u3NDKLXr/UfIzGRGVIrVrDfpVQpxYzP5jVTOWfB5JmCOkqW1J8q+u5dfgadnZkPf/Kk4WtS0LXp3EShFu4aWQ6n2hFmSvJF2lJD2ouhmcbPP5OGuaZ2bc2peW6+OCkpzMwICtJcnpY2uOgV7qf7DYJ/ZRYwJmprSs7+51u+Ut3HbgMJ5f4mDKtL7qVDqHFjdsQSsR9g+3YWcE5OKi79nj3M/pgzR1vwLF/O6yuRnKx2jW3D9QrGyEg25UycqF+YRUfzoFOvHrNxAoMDyazIQ8IXVXXOIsPDOZbA05OoRQs+F125d54+ZX76V18RNWpEZGvLfHZlvxs3JnLtP0ZjYMqK5l6jRsb02nfveHApV45nNmvWGCgKU4C16dxEoRbuaYLmKw+CNIEffosYgkMoVa7ML0HnzmxD/vJLooAADtFes4ZDsw8fZvvr/fusORmycZqCrAhhuZyFuvJFDghQCZfcnvKePcuCKDpatcxrfhVC7Z8J1pHcR5f7hFFlSBgvZOp8x40jsq54hE0y080J/+tFsIwmfO1MPktK0C+/sGlg9GhV0isivlcHDqi49EWL8vWqU0d1H+VydpIeP67abulSxbWt+Cehwl8GBWNUFAu+CRO0BfzNm0Rly7IjUplMLSaGyMZGU2DL5URnzhB9+ikPSCNGEN24ofo/Pp6LcyxaRNSzJwcbubszI+ibb9j+X7n+C5LYvCXU+Zk8J7WlwODAbJ21tmhB9M8/xq2bmkp06BC/V25uPLt58sTkQ4owAoaEu8D/5y1q165Nly5dypF9+y73RWhMKP+IKQ6cmQRcGQrrsmexbWEbODgA0dGaLSbG8G9zc8DJiZujo+q7Mb+dnAArK0AQOF1sQEAAwsLC4OPjg/nz52ullNWF8HCgdm1g9Wpuu3fz8jVrAD8/CXTdU0EQIJfLM3cRM4CfH2BtDSxfzql8x3yVgLiSm5DSYjJg8R7YtRZ40Bld/Tbi+t/LTT7fpCTAstRZTkGcZAtc/BLwPQ6UPgih1lrIZ8kRFQUEBAC7dnFq44EDAYlaQuvUVE5pu3Ej8NtvvOyLLzi97Q8/AA8e8D3Ztw/o3Bnw9I1BhMdWpDjeAxouAwDYmNtgdZfV6FdFs89v3gBt2gBNmwJLl/J+tm4FRo8GFi8GBg1SrXvyJDBlCnD2LPDhA6+3YgU/Y19+yamU37wBzp3jdvYscOcOUKkSUL++qjk4AOvXA7/+CiRJ3yC8/CwkV1wPWMZq9BUAAo4GICwmDD6OPpjfar5W/41B9+6cUrhHD9O2e/QIWLkS2LABaNyYr0nLlnyNRGQdgiBcJqLaOv8r6MI96EYQ/Pb4IT5ZlS/bOtEXrV7uxZldldCjB79spUoZtz8iID4+4wEg/W/1Zamppg8IymV2dkC3bkCLFsDs2dyn9+/5hb99W9nL9gAOafRbJpMhJCTE2MtmEqKiADc3wN4eKFsWWLUKuG8VlCZUvO1lKHPuEE7uLItVq4Bhw0w/htesBng+9yz/sI4CuvgBF0dCNmYoQsaFpK136RIwahQL9p9+AmrW1N5XSgrQpAkLTyVGj2Zhu2MHb/vzz8DEGW+Q1GEIXjntzlAwvn3LAr5ePR789+wBtm8HqlfXXG/JEuDff3lQ+f13vl716vG9vXCB+2RlpSnIa9bkwZOI/1+1ivfftSvg7w/0OeuLsHehPB9SE5oyR5nGtckKBg8GmjUDPv88c9vHxQGBgTyQEfE9GjCAn2cRmYch4Z7nJhnKYbMMkX62TGQk0YwZzATo35/zmuQGEhI4c+Ki3buo6LiuhP5tyW3gSBoy8xx9/z0zD0aO5KRZnTqxTbVSJZ6OK00xjo4c/FOjBk+Zu3UjattW0x7PwT+fkKVlO5o/fx89fsxOQ/XoTf+V/szQmcVMHf+V/nr7rQvv37PNWXlMfcmn5HI2XQC8vqkRpGsvbNE8twArgtUbWvGPdt3A1FQuJO3hwSYRXTl2lFRJgG3yxYppXrsffmD6nylmuNu3VdunP6ZczmYN9WNYWLCtvHFjviZ//aWbcfLuHUcdV6vGEaiLF2sWGcc0W0KnEQS3W4SW03KkYtKYMWyuyirkcmYude/OPoJx4zgHjojMAVmxuQPwBnAcwG0AtwCMVSx3AXAEwAPFp7NiuQDgRwAPAQQDqJnRMfKa5x4dzTU93d05YlLpnMtJZMYeumsXc5zDw/nlfviQaXFHj7Lj7fffiZYsIapdO1RDiLi5vadq1dih6OhIJJEwq8TOOYppeT4nCWV3EapuJNT+kWp12EeLF7PfYds25oBfvMgvodLvIJfzf15eHBkbHs7c6zVr9J+zXM6DKcAsH0O53tNj/HiiImWPEaqt4PMaIyXPigdp2TL927x5w36UIkU4bW36DJm//64pbB8/5uuovuzTT/k8M+rrqVM8QIwbx/6QUaOYtvrHH/xcqe+zVCmmXV66pH8wJCIKDlZFn/bowfdB/RxCQzkXvsQ2klBuJ8HjGqHbgBypdTpjBgdLZSdCQphO6ubGOX0OHMi+LKaFBYaEe4ZmGUEQPAF4EtEVQRDsAVwG0A3AYABviGiBIAhTFMJ9siAIHQGMBtARQD0APxBRPUPHyEmzjCmIjWUb5pIlQJ06bMOtWzdnjqXhC1CDvqn048c8Rd+9mz+NwT//sKkAAEqUAL7esA0LLk9C6Nun8LaqiKdP3wPkCNzpDjxpBZQ6BFi+g/DOBePqzdZraoqK0jxOgwZsVggJYfvwsGFA6dL6zUy//ALMmQNUq8bmBW9vw+dx8iTQqVM04uJKAZACeA1gPoB/UaTIL3j1qoTB7a9dY3t2cjKbW+rU4eUNGwIREcDDh/x71So2uT16BLi7A2FhbDLZsYPNIS1asM25c2fAxYW3IQJ+/BGYOxeYMAFwdgYOHVL5QZQYPx4YOZLNNNHRXKpQFz58AP76i69RSAgwfDhfz+LFVcc7e5b9G0ePsj3fu80uTL/6GeK/vwUMaAe43dfrH8gsli4Fnj4Fli3Llt0h6IbKbOdlXQbtP6zHxZ0NEB+v8j04OGTPsQoystUsA2AXgDYA7oGFPgB4Arin+P4rgE/V1k9bT1/La809PeLjmdLl5cWmjlOnsv8YaSyeKfbMAS9+llD3R0K3gXT7tqYGk5DA5pcff8zcsdatU9McSx4iTFPMGJQUvy/LEbxP8f/epwgdRtLLl9r7iY/n/CIuLkzXe/KEGSH//ccBLYGBbMoAmCExfDhR7958DevWZeaIhwebI9Q1WUHglLL/+x9vM3Eiz6R++onNEbzOAgIqEVBVsd1TAroS8JIWL/47Q1pdaioHOTm6xZNd/SDCoBYEEJWoFElRUTwrKVOGj7V1K3PllfluiEiLS1+pEs9UNM1gt0gqfaGxrGNH1b08doy30YWHD/maubvz9UpfEi8xka9v7dqs+f/4I5trlPjp2F/MlpkpyZFAvd9+40Cq7IC+Weum64F06hQ/B87OPOvKzajrjxHILiokAF8AYQAcAESrLReUvwHsBdBY7b+jAGrr2JcfgEsALvn4+OTWtdBARpGriYk8nS9RgqhZM54WZ1dSJg3+/VdFCW3Hq3jh4ApGzZvzC+/kxDzprBzbZ3EppiQqBU+tVUwrVPZhFgi9exCcHxDApps2bTi74Nu3XIatZEkOUjEUiRgdzeaJ2bMPGBS4CQlcLELZn6FDWaj+8gtnnpw6lU0SKkH5HwE3CUhIJ1CV7SgB2wn4nSSS5dSz5zVavpwF+t9/E504QTT/z31k9WV9rr+q2M56hkfafX/1ipc1bswDkNKunZDAVMUlS1jwqPPJVe0tAUkEXCYLixG0du0WWn1mK1n6XiLUXkU+S3zp0wmXNQLOkpO5KEm7dmyamDhR2/78+jXTHT09OSHanj26TRd//837ySls25ZxqcWMIJfz+bn2G0uos4JQZg/TW3WYkZ4+ZYpvkSI82O3Zk7lMnwUd2SLcAdiBTTI9FL+j0/3/lkwQ7uotLzR3U2zeyckc3ViuHFH9+qylZlXI6zv+d9v30NdfazpPAXa8ubtzut1Zs7gP6Ysq6EJSEgtoOD8gyE4Qqq3XFEotp2i8YJhiTg17biNnZ3Y0e3ur1v38c+OShY0c+R8JwlUCpAQY5rRv3kwklfL+583TvK5793IAU0wMkVSq3JcLAZEEmBHwrdq5tCIu1jGEgAlkbb2IRo/mvORdunBtV/Nitwnm73UODk2b8kxN98DBrX59vvZTpvCAO2YMkZ1dOkcvrhIwnRzrNCTreTY8M/P+j1DrF5JW2UZ+35ym58/ZmevlxUFWmzZp52e5dk2VcmDYMLa/G8LUqTyrym4oFSAMaENWZf4zaUbw7h07kefNU3He02ZtjiGEPp9oPP+6HMAJCZxConZtVi6WLNGMZSjsyLJwB2AO5tZNUFv2UZtl0jTn0vs5EVWLAIJfDfJZ4qt3m5QUdpBVqcJmku3bs+YAMjRzSEnhBE1KodGhAwexBAXxi9y6Nb/4Pj4c2LJgATtWlcFEiYlcx9TXlzU+jy97q3KzlN9BqLSVk1cphVK3AST5SsWWeflSU7AtX86aoaMjOxl37VIF5qSHj49MoUmPShPugP5Aqp07VS99//6838hIngEog4v8/f3TCXflfncq+lhV41hscdQEPm+iOifPS1xCr/G3pCmctVu7dsxIqlpV3zpvFDOLA8S5cxTLXe/y/seUYAEPIjP3R+TkRPTFF9qOe2WK3ebNeXCfP187l5A+tGzJs6vshIYCMqwOwfOiXgUoNZXZQmvXsmmtShUO1mrUiKNoN29mJ7OrK5Fjmx8I06xNipyVyzlg7rPPdAd6FVZkSbgrTC4bASxPt3wRgCmK71MAfK/43gnAAcV29QFcyOgYeSHchdkCP2DmsZxCtv4Sgusdgt0L+vxznoaqR12qIzWVX8LatTnXdVBQ9k8Zo6PZBhwYyNpyYCALGWXyqGPH+Jj37/Pxx43jF0mpBSvb4sVsK9d4UafaEdxvknnXMbTin+3k4aFa//Bh3neFCmwvPnyYBw9vb9YwX73ivC1NmrB5YuhQNlepnz/nzSlPXPrOI03YGsr6d/Agz05cXfk8Wrbkc1JHq/GtCCNcOPJ1HPg3HBV9f6xXuKemMqtEQyCbv2d2iXksSYveSlsudX5K9dqF0Nq1bCIqWpRNMnfvqrYtVYrvR2QkUfHi9Yl9AY0I6ERAPwJGElpMIzT8nlBhG6HGbwSzuLTt0z9XMTE8kJcsyea3zZsNs2jSIyWF/QDq9MisIjmZyPvbyoQJnoQxJdkv43I/TQi/ecPsllmz2Gzi5MT9/+wz9gdcuKCikR46xP6WTp3YLJPVyNn0KRrS+ycKE7Iq3BsrXpZgANcUrSMAV4XJ5QGAfwC4kGow+BnAIwA3MjLJUC4Id7150wc1Y0em2kNWLKAJrVjBmrK9PdvaFy5kLSG9KUYuZ6HUqBEL4rVrTXsp9UEuZ/vmF19o//fiBU9Nq1dngTtlCmdmjI9n7drDg5cPH85h9zVqEFlbK/jwve6TS5+vCV9Uo2JTWpKDSzz9+y/v9+ZNTeE3Y4bm+Z46xU7R2rU5MRQR20UXL+ZlHh6smZ0+rdTcQcB3BGzKUHNX4sQJFu7KPqhnPwwMDiSLbywIXyuE+2yQxTcWZFvfloBBim3Kpx3L1dWVoqK06Y6QJPNn60mE4bXJbKYN73emwMvdbxJANHzuGSLS9AsAROfOafZZVxoJa2sbchjRllBtHcHqDVNMWwQQPK6SZclzNGQIDzgPHrBpx9mZqH77J1R0bDeTMpempPDAcOQI9+3qVXZuHzrEAm/TJh6gFi9mM9DXX7OTcvBg9p106MDmqFq1uMyhtzf3Jb3DGxbvONUyiFB9LcH1DtnZsWCdOpVncboc8CEh/ByXLKnpnFbez6xmak1M5IGwQQOewS5YkL0D3McAQ8K94EeoBgXBz88P8fGqCFUbGxsMWjwIv+10R/J7R6D9V7w8HX0sPh44cQLYv5/D0lNTgY4dgU6dOITa1pb3R8R0vXnzmFY3ZQpH8llaZtA3NTqYegTksmXA5s3Af/8Z3sfNm0yZ+/ln1bL9+4EOHTTXS0gArl8HLl5UtadPOWoQ4H6/fAls2QKUKaOK3HR0BC5fVkXvyuXAH38AU6cCNWoACxdyhCXA4ftbt/I+IiJiER29Gikp2wFsBjAINjYXsXr16gzTDfz9N4e6FyvG1MVNm4B27QC3790QFR8FPKsP/H4WmM2hmHYSO3yYm4iUlCTFHr6CIFRD0aKfIDzcMW2/jo7AvXvAyDnBOHLjKmJbfw4fRx/EJsUiKiEKeF4L2BEIDGwNLHums2+LFzPdMX3ovDKNRGhoFFxcRsHefhIS5OZ4W2kBkiuvAcwTgBOzIXlTAdMDpJg7sF3atjIZULN9MPY+DUJyggWnV0i2hTTFEbXdm8FVKkNcHLRabCxfHxsb/g4AVavyM6lsdnaav/Uts7AAbt1iiuXZs8Ddu2onZx4LQABsIoAYX6DzCDjK7iJy+UmDdM5Fi5iuOW4cMGkSR93mJC5f5ujXXbuYrjp6tHZ0cEFEoY5QNZRIq1qTZ+Q2eIRR2oNczrSsJUvYhm1vz9PR5cs1U6GeOcPmjOLF+T+9WfH0TE1nbjhERYpknGjp3Tui775jNkGPHtyvgQN5etyhA6eLNVRDMyaGzS/p7cetWvFsoFcv1bKaNTXLqyUksJbk6sqap7q2JJcTXb9O1KXLDZJKVfbn777bo90JHde4Uyd2vjk6xpONzS4CiCSSCELpfayxKx2iXxVlh1yj76h8+Zca5/Dpp5epRg3V70WLeP8pKazhXb2qOiZmSghdhrF5rtYvGuYTZXNwYA1V+XvSJE5SNmQI54F3L/5OaxsXFyILq2SCkKrXiQvw9nb1g7jCVNryVILLPbKqcIzGjGEzx/79PHsMCSFatWobeXuXJYBnoi1a3DeJJiuXs4N26VL23aTvk4UFP9vWsu8JfdoRvnbmZ1T6gfC1Jbk2d9W77z17WFPv3j1vkoW9fs2zLS8vZj398Uf2zKbzK1Cos0Lqy5sOCTk7G1+GLT3eveOp77Bh7PgrXZoF3cGDLPwuX+YH3MODBaE6J5lIzaE7w4xze0+3IEx0J6nTc9q7V/9xo6OZGufmxo7Nmzc1/1e3zzs7M+Pi2DE2A6hPhb3mVaMWPe+Tpyc/Bf368dR67162o3bsqE35s7JJomIBTdIGw5UnttHIkdyXRYu0HaxKJ5hye5ksipyc5hPgo0WPTE5mgQkQNWz4WLcwLL1Pe5ntSxo1ip12urZR1hbt0kW1rFIldjTrE7jqTT0tQYkSqu8zZ7JfIG1dn5NMJfWvRFZfVaCVJ7ZRXBybrqZN43VKlVLRaWNj2awxYAAR/GoSXO4Raq4m9OpNaDeW0HAhoepGatWK/TpKc4mrayxJJBeIHckrCZhBAFH37tfp0iUut6fL/hwaymbDZs20z9HTk814v/9OGjEWgiAQqoCrYM0CwfoVobyHTt/Jw4c8KJcty+9AXiM5mf1mTZuyovXNN8axyz42FGrhrk9z9/RsTSVLZs8x5HJmPnz7LWsL9vb8oK9cycLy009ZAM6ZoyoIkRbENNmRNTXXuwSzeEKT+TqP8eYNC11XVxYIxgR3vHjB9tZq1Yhci8aSWdNFhJEVCV2HEGzDyazBz7T69B8UF8c2/CVLtM8rJIRpoFqCr+wuMm8/lQLWHqYLF1h4lijBtLXXr3m7mzeJzp/nABjWwA8SkGKUUM2O1rMnxykEBbED3MWFZzcXLvBsR7me4BTCNuXJTmnL+k+6mOZzkMs574uPD9unletUrkzk/rkfD9CzQPiiStosrOi4rvTppyyU+/fn9dM73WNj1frbvX+G7JGEBKLixRsRUJ+A7gSMJGAZD7xWp6h6dVYmzMx02M3TtWHDmF0TE6P/+dF+d+4TUFbDdxIXp8rPtGBB9qXEzk5cu8bn6+TE786FC3ndo+xDoRbu+vKmDxlyjgYMyJljRkVxQM7AgcxNr1iRc6nIZCz4p01TlEBTvsiel9JeOrsGQRovXEQEr+/iwmaAzCZZ8pzUlrVB5Qvuco8wvniaAAkJYWbIkSO6t5ctk7Ez0wThWqECO1u1IzmNaQ8JWEPADdWyctu5eAaI0LMPmc81p43XAmnDBt37kHzplpYQbfDCSeToqJarHTzo7dtHtPFaIPks8SU04usz+VfNxOUpKew0bNpUc//FihFhZCVmXVXaQnAIY83b6wzB6TEtXcozrSNHmF2kjnfveNAvXZrIxSOWpNW2pBWSMcQe0Z6JNiPgDAFW9Pvv+mckpUqxCaZrV3aMe3vzAODszM9nq1Y8CE2axNdoyxaigIDDZGVVlVTVsy6QpWUTCgwMJLmcqcAyGZuWjC2xl5eIjGRyhEzGMQsZ1aD9GFCohTuRbrbMoEEc2p7TSE1lTWH2bH6p1F84adU/2XasYGhYjWNTiZcXa5yTJvHL5+dHtHT/ziyxC9JmCgNaE4oEa/SjUiWeSh88yDb8R48MbD+iOkGSqCk8Wk4jDGpO6OhP0pobMhDalwmYR8AqAq5o/V+9OpGb2ygCWhNwQSG4uhJwk6x9lpJju6UEEFlXOkybrgfSzz/zNbK3V+2jRHW1/dZaxedc9GLasrJlie7dU53bmzds6wdYOCrx4gVP5729WRisX89+jPv32a+h8/xkxwl9PiGfJSXS9rNggSat8/p17sPw4by/uDgutmFTcydhhpnB+6vSpu0IGK+zD5068YCiz9+jRGoqKw/BwcywWb+e/ThjxnAkbuPGREWKvCNBiCNA5TeoVk11rMGDmS578yYrNdkVwZ2TSEnh2IqWLVmhmTVL06f0MaHQC3ddKF0646i/nMCrV2y6aNRI+6XceDWIXrwgDWfg+fPZUwdWI92Bso0vTjY1dusUELt2aQoHzXKFtux8TL+d9APBNpyk9q+ocWNDAp4UQrsveXq2J1dXnpEo/Qma63UlwIKsrb1JJouifv00/y9TRlUvtEgRFpqSrr3Yh6HjuGvWaEaDXrvGDsCxY9mO/tVXHAzWqxdP4/38uEB1eiidvxr77/SFznvTqxf7QeRyPr6bG/9WR3w8p3vo21fbZi6XM89+5kwiQUjVcV6p9O23e3NMsMrlbL5RnxV4e/OA1a8fC8kKFfh6WVqqNOPu3Tl19TffsGlu3z6+luHh+SeVwM2bTDl2cuKZ1JkzH8cApYQo3NPh5UuOtMzr9KIaNle15urKfOpp09is49JnEtt0J3gyd7ruD4TmM8m5xwzavJm1rkuXmJ3w7p3uhzOjAeLFC9Zg0velYUNminy96ihZz/DgYJZPBrFGrE9wu92mChWI7OxY2CqXm5n9SkAcAR8I2EGAiuUyYYJqUJs5k2j58p3k4hJAwEmSSN5Qs2YPaMySk2Tm9kTnMatXZy10zBjFMmUiNPXWfjQ1a8b3vndvFs5WVkQjF5zi0oCK9bxKvaWfftIdxJaayn4U9ePaOnzg32bx5LOojNagW6IEp0zu14/t9Pr8JfHx7Ahv357ZMaNG6b6+rVrdo2LFmhAgkFT6kpYu3an3GcuOWqVyOZtplMc3pOXGx3Pq5NOn2U+xYgU/P4MH87lVrcrPtJkZO3Jr1uT7MGwY2+5XrmSt+tw5dgJnh9nEGE7927dsjipViv0q69drp4TIjzAk3As8z10Xdu7k8nQHDuTaIXXiyy+B8+e5ZN6vv2r/X68ep3rdsQOA7ATQfjzw9zpAbg5U2A7Eu6N3SX9ERUGjpaQArq7a7aX8Fk5F/I1o4RHc3STwb9obn9ZtC1dXTlWr5C1fvJhxqmPz4rcglZ3Bh8e1gJc6yh2BU+1Wq8bfb9wAOvW5jacPfIEUG0iGlkPt14sQ/6QrKlYE/vxTtV3x4pyWV9mKFgUWbLiMLRvsQJHldB6rRw/u9/v3nFIXzg+BuCKA7wngflegwWKg3iTQMsKzZ8zdT0rSsaM+3WFd5TDWdNVMl/v+PZeKW7oUePKEl12/ztzy6dOB48e5jB/AaYw7d+bvyipVZcpwGb4ff2RuuhJyOadJPnuWU/hu3ardpcmTuWxghQqaHPunT4FatYBXr3SXrdMX42FMvIESt24xZzwqivnwffsCX31l1KYGkZzM/Q4PN9xev+bUv56eGTdl3InGNdBRic1QOmS5nOXCihXA1aucbtnfH/Dyyvo55wQKNc9dFyZOJJo7N1cPqQWl+UEqZV758OGcBS86mnm6Tk6swbduTWTm/lCludk95+LQnb7Qm4sjIYHo2TO27R47xpSwX37h/U6YwI7eTp146lymjL4shya2YucJSCXzdtPJ1TM2bbmDA88o0mYO/dppbOfiEUu1anFkJRFriY8esUP6q6/YiWlnZ1wfjh7lkPi0ZV95cKoFEME9mFBZQk+fct4TgE1zEps3XLDE6g2vV/IwodMXVHx2bSJiLXTCBL5GrVpxtG+7dqrkVXI57+fCBZ6JKdM/tGzJTurevfn3xo28fmQkmyemT+ccMunPwc6OfS2OjlxdyxBHe9s2ZmXpQ1aKpcfEcIEUd3dOvZyczDO7GTMy3DRbkZrKpsxr1/jerl3Lz/GoUcyGatiQZ0ZWVux3KVuW6Z59+3L/nbrMJ3Tvx9HoejJQ6sPdu0zPdXZm09rJk/nPZAPRLKOJBg1YEOQF7t0jqlOHr3yXLqoAoCNHNHN9v3/PNEZPT6KazcPIom9/TbYLiHwrRNL165l74J4/Z57+11+zALW2Zhpd48Y8oKjb/bOtfd6IA2JGVtRYHhKiv59JSTz4Gdqvem4cgMjW9k+C5wE2ZXUZSii7m+BxjCSSSNV6ZXeRpGdHfuFngVA5iE1NvbsTqgRq7O9//2PBUro0Dzjq9uIrV1i4KO9BSgoP1On7aFv7L/LwidFaXqwYDx7792tWe0pI4FiDnj31C3hl3nt90BfjYSjHj1zOA5GnJ+cNUueGL1vGZq/8CLmcB+LffmPbucYzYRZPKHqZzZqz9Weg1IeYGDYvlSvHzuQ1azJ2VucWROGuhoQEzlZnTOra7MTCnbuZDSHlfOT9Jl3S+D8ujhNnpQ92UhYOcS0aS1blThI+b0IuvSdrCIjixVmg/P237nJwCQlsA1XmI/f2Zk20Y0cWnDNmsH2/Rw8WNu7uTJn79ltOBFWsmKb9+flzzaCh/v05x8eBA2o2b2Nb/aXk4sLHVz/3iAi2vRvc1uY1WVc6rP9/v5qcO6j675rLx3lr5rHv9AUnEZvsyP6EolfIzCWUunZlQaHcrmxZ7Tq7kydzI2JH4c6dOhytas25SBwNGcLXS1c+FnV8+MD76t5dt+25cWP91FUi0zX3q1fZ0V+rlnYOHSLWmgcNMtznnIZcztf52DGeUXz5JQeDeXjwrKduXe7jwoVEu3cTOY2vpUExVTbXefqjbPUhNZX9W8r0xZMm5U0UrjpE4a6GU6f44c0tBAcT1WsXQrB9TWg5lVBmL6HGGp1sl6ZNWYPThfXrN5OLy9cEPCRLy7P05ZenyMeH7+CAAapQcjs7Zn/Urs0h5LVr82BWqxYXv5g5k9vIkbzMxoZfiDFjWOA8fqw9E/D35wda3QHdqxebka5fZw2yWDHe3w8/sLb3338c9FOzJpvA9Ak7iV0EPX7MpiKAmRbqlNGqVVlgunjEkrTWOoJVVJrmrb4fe3s2lahHxGq1rp8zT354bcL44vzSD6/F/1UO4v9KHSDLQZ/QxmvM5f7uO6bLffutqo8AC0H1qFejmtstKhbQ2KTn58MHPk63bpoCPimJlQF9mUuJ9Md4pHeqqtea/fVX/UyW7du5H7kBuZwdqgcO8LM9bBibYJydWTFp3JiZTMuWscB9+lT3DNa1uSthWjqW2DQYTKFgDB4+VJnrPvmE89bnhclGFO5qWLAgd6aWV66o0g84dZnP9MFPBrFtd0IxnXa/2bNZG/Bf6U/SidK0AJxW41upvaRSAvqTINyl0qVfa9jLGzRg84pOISphYeDtzdr7kiWszRvKP6NEYiIH4Uyfzr/37GEThfq2KSnMdx4wgP0F1Zs+4wyUrnfT+mA5oIdJwvDWLQ40cXdn4eriEUuObZenzX4AJS+eWSilS/P9hUOY5r4qbyaMLkXwr0go93eGx5WapWjw5rPcrN4Q2o8htJpCsI6gWbOMu+5KpgtgSdbWh6lWrbA0AX/lCtMPjd2HLrZMaiqbMTw8eADPKKPiP/+wnyA7MjoqoUxbvWsXD6QDBrBCYmenqj41ahSzaE6cYMXBFCGqlUJhHAhVDJumTEFsLPuzKlXiYLBVq0wr/J5ViMJdDV27srMup6AMxff0ZI0jLk4tAOiLqlwkwzKaC2b07qGRj+XkSSJ3nxBCgLamgQ4gjBVYQLUeQnD+TacgKV2aI1krVWJh7uSk+X/HjjydffzYtPN69Yo18fXr+dOQz+K3c1vJvMMUnf2z9L1I6DDKJOHo0IJpl/378/1T/2/cOCP3Y/+U4HaL4HlR+z/XW1ShAmvolpaG91OiBJtK0vPt1ZunJ5HHqF6E/m0Jvsc4H7rrXULD76n4jIbUqxfvZ9cu/YJKW+s2J6l0N9Wo8ZQ+fGAhkpWaphcv8gypfn3Og2TsNrLyUZmKuUhMZE75n3+yEtOnDzu2rayYP9+hA5v6fv+duebZVW0pK05lUyCX8zvRrRtr8+PHs3af0zAk3AsVFZIIKFKEKU7ZTW06exb45hum/E2eDAwdClhb83++y30RGhOqWvmDA3C7FyxvDYdddH306gUMGMC0NmuH98AEH+DKMOBBByDREfjgBHxw5O+OoYDXOcDxIXCfMGnQHCxapN2fHTuYjmdurloWHQ0cOcJpgQ8cYPqjMoVx48ZMdTOEy5eZtlmrFqDvdiUmAkV7fI/o/V8DZXcD5XcB+38CUqw11vPxYZrj9u0ZXlqdsLHhlMxKSOwiII9111zJ+RHwthTgch/oMQBCaHPQhZFAjEy1zrC6sHxXDv7FNuHMGeDCBdVfxYtHIyoqGB8+EIBmmesoAHT6AkiyB47wjVq2DDh1SkFxBTBqFFCyJNPwlG3Bgu8RHR0NQKLWrMB1cRgODsDw4aptiDT3oau9fg0cPKjqWs+e/JnRdkTA/fvAs2cAfE4BJAHK7QIa8znJHGUIGReC+HhOGXz7NlM8lZ8hIZzeuGJFbhUq8Ge5cropjNmF7KCDmorQUGDlSmDtWqYzjx4NtGkDSCTZfyxDVMhCJdzv3QPatuWLn104dQqYO5cf/KlTdedxN8S1bezQD5s384Pw8KHaRtIPQGrGSbAbN+ZjdukCtGrFg4sSixYB48cDUqn2dnI5cOUKC/r9+/mFbNmShX2HDsw1B1iAKvnzhw/zwAVwnm4iTX69umA0BpUq8UCxcaNp26lj5sZDWBTWAwkp8cCbEsCPj9P+E7r6gfb/wIIo1RKwfwb06A9sOJHp43l6xsDMzBFPn2a+zwDnwa9eXZPfP2IE51uXSIBFixYCSAUgT9fMAMwEwHnvGzTg9Q01QeB79dtvfI9cXfmZdXHRva6+/URFAX36AKgSBNz6H9B2EmDxHoioCERWgG9yJ7x8yZx+dQFeoQIvy6i+QU5BmW8/LCwMPj4+mD9/vkmCXV/dhYyQkMB1GVas4O+jRgGDBvGgnJX9qkPkuSvw++/Mfsgq5HL21jdvztPr337LOJIuvZ3yh8Pbads2noo2asSOTY3pvUUMmy8muapSAw9qziYdHaYAJycu0abrvwsX2L4aFcX2zTNn2G6+fj3b3pVMGX1mBg8PtiemX/7997yP9NWKBMv3hPLbOfvkBE9C1Q2EOj+l+RiCg7X35ejILVts3LYvtZbZ2upYz/cowTqSvL6pkcZ9t7Fh56yV1THi3C1VCBAIMCegD1lantPYhz7WFZsDwojL73UmQDerx8mJfQbK340acWRn0aKdiOvFapsTIiJ43bZtjYuiPHOGqa1NmrAD3BS8fs227pUrdVA8i13ge9tqMrkPGUb37xe8cnfZkfpDLif691/2dTk7sw/h+127srxfIsNmmTwX7JSLwn3oULY3ZxZyOXvmlWX11q83rhBAfDyzdBYtYt5y8eL8Qnfpwjzlf/5RpV7tMWkh24Y/68COQMtodgL+rychwJIfhABLwqBmTK3MhPCTSPhF79+fc6p88w2/vH/8wX25dIn9EuPHs1BwdlZt+/Ilv8ANG2rb80eNYqfqukubuXTdbBD6dCO43CfzGU5pD+6jR+w0U27Xp4/mflz+N1V33yXJ9Nln/F0pjNF0rup/NUdrZlqtWkz3i4wkhUAHAT7Eic7CiYt+7yUHB14/fS59dQDuBLxV24+yOVFgoOZxlc+lOvvG3DyZuPD2e+LMmHvJzOwX6tv3Cg0bprqH7dvrF/AvXzItsFgxdkzrs+/L5cw2OXSIC8z4+fG+XV35HjdsqEpbDBBZDm+lERCUGaGUnxEezukWRoxIF0DYp5tJQVBKpKYy6yk2VpXLRrCII5Q6SPi0Ezt6M7FfIsPCvVCZZSpU4GlSjRr61xm5aiRWP16NVNtUSOOk8Cvph5+/WIn9+3kq++4dh5v36QOdZcaIgEePuFSdst25wyaI+vVVrUQJXj8mRtO08eoVm1mEar+AzFOBqBLAk47aB7J7BS8PS5Qs7gQzM7aHx8Ror2ZlxWXP0kMQuJRdhQpA+fLclN89PTXD2c+cARo14lJ1uo5x9izbFpXbBN0Iwud/f47kd07AL9eB3j1h7nsJaz9Zh+TL/fD118C0acDYsRxy37Ytb9e3r+7we12w8LqBpGdVdP/pdQbubdfhj/5r0LIlL/r1VzZ7GELdukBEBIe9a1+zGwD4eJa+l5EYUgtFx3fBlPZDMKJpd60ych4eg/D69UAArTWWy2QyhISEAGB/Q69e7OuwseFnpEsX4MULfk7r17+Fq1c/x/PniXB3r4MOHUbAza0Oli7lfTk5KVItgFMblCsH+PqySW3fPmDbNjbLzZ7NpoDUVLZ9q9vClZ82NtqmlIoVAQ8P4M0b4JNPeL+HDgHztu/A4hsTsmROkMs5TUZKCqciMPQ9o/9N+Z6czPf3+HG+FibB6TFgEwnIzQC5Gcq7VDWqr0Ts+zIzU32+SXwFSJIBaRIwoB3gyjZZAQLks+RGd0m0uYMFZ4kS/KDqq/04ctVIrHq+ClA6IQnAra5wO78CRR19MGMGO6DUbdgxMWzHPHWKbdeXL/NyMzO2M5Ypw46kuDiVAI+M5M+3b9np6uammQNm82agcmUWRsplF94ewE+nf8er69Vhfq83nKTFMGKIHQYMUNUxBfgFPncOCAzk+qoA8Omn3IdVq7SFc7FimrU0Q0LYPli+PFC6tMrWDrCNNjVVex8VKwIzZrCQdnFROJCjQ4EV9wCf/4BuQ4F4F9gcDEQp6oCgIKBKFa7b2qMHC6g7dzT3OWX1Uazc/AjvLnUGYosZd5OHNAR8zsLG3AaLGm6Ec3hPbNvGuYSUWLJEMzfK3Lnch+bNeWC5dIkHgsTEGLx+vQopKQcB1AHAjkNJ7TWQXxrOGxe9AiHeA9KEorC2kqJIEcDdnZ32u3crjzAOwGsAr2Fl9R6LFn2NESN6pjm6r11jgf7FF6x0/Pkn8Ndf/J+yxu3SpWxfVwqN9u2B/v1Z4EZGsp8E4Fqzhw4Zd6mUaN6cBbmDg27hdO+eKmdO1658XjIZPxtZEcByuaawU35m9buZGb9XV64onL8moGZNdnxWr87+ATMzYNjeQXid8ByQpADSZP6UpMDT0R3/DD5oVJ90+by0SBYKKB3TxkIU7uBkTj/+yGwRfTCbZIZUu1RALgB3uwMnZwCvKwNl/saPo3ohIoKF+IkT+vfh6MiCS1firvTNxUU3Q6XfxMvYfe424toM0qkZEbFQ2LSJC1L7+PDL3rcvCxclwsNZ26pcmYX46dP8Il+8qFrH2Rlo0oSdzPfvc5+ePzd8LS0suFB0+fIqDbFuXRbQ1aoB/1lOA+JdgLMTNTes9Ac+XO4DS0vW9hs21H+MffuAx4+5SPgffxjujz4Ilu9BsuOcOEyBkpUj8fimm8Z6JUqokoEBzDKqXh24fv0W9u0rCrncFYIQB4tafyHxgzlw8zNeseKfgNwM1hIHVHdsjefPWetOSclcf41B2bJ8n9zcWKt+9Ej3zCyzsLNjwV26NM9iTp4ELEueR2LNpXC3d0LEul8xaxY78rMijJXOW1NBxLPb69dZ+z5xgpPvGYK1NQ9iLVpwq1LFeOeuqYnHjEV27VcU7mAmi6UlT1H1QZgtAAKAFzWAP7fz9Oudt9Z6EgnTBzt14oemaFHWfDLzsKZH0I0gDPv1Z3zYuhYYXQGA9k0nYg06OZlf7IMHgXXreOCqWpU19fbt+SW6cwf43/9YW23ViimQO3eartUo4e7OL2h4eNbPNcchOwFAAEKbmbTZjBmcMXLhQv49dCgL+9ErtwN3eqpW7DgSsHsJSFKxf+AuDeHVpAkPSuXKaQs3iYTNe2/fcnvzhgfXmUyCQdu2zFJ6/ZoFmS5TmCG8ecODtjFITuZnITSUZ23Kz/Xr1VayewE4hQJOIcDNT1Gr5VN8M9Ebvr6sxatnucwq5HJ+tu7eZcF94gQP8IYglaoEd/PmLLzt7bOvT9nBasmp/YrCHWyTnDGDp126kJQEWA9pAHlUHeBZfW4JLoDXeQhFLmDfjBmoW5c17pyE73JfhL4NA+Yq7G42rznFr9wcQrIZiMwAmEEikcPcXAJBmowkiodcSIKQag1KtNPap5IKB/DD/+EDm270oX591sB1pSFOjxo1mPu+Zg3/btCAtXJTUb0636MmTXh2Ubmy6fsAeObx5o3ih+dl4GU15tqHNgHii2iuXHo/0HAJHO3kcNhzHAMHsmmtbl2eodWqxXzlN2/Ydr37RBhr5XVWAe89AZICnUZpTaVfveJZzZs3pg34L1/yNXjwgAdkpbnq1Ss+L7mctWldcHDgWaOSounqyoK3SBHNpjQbqf9WxmMA/JwsX87mq9Q+nfDS+ijwuA1gHg/EFgV2BEGweofWTRwQGgqEhbG2rxT06T9lMu6XEqmpPLt5+JBnksePc8tIDDVtqhLelSvz+WWHMvWxw5Bw12N91th4LYDOAF4TUWXFstkAhgNQPmrTiGi/4r+pAIaCSbpjiMhEK2DWoGs0/F+5frhyhZ1+AD9IT59qOj2vXwecXf5ElPs+oNQRoNk3gOs9IJXwRXF/dOhg3LGyOqKHxYQBEgJK/APYh3MgzvN6wPO6oKR4AKcBnIEgXMKAhVWxOe43yBVTO4JKy2/uwvz5TZtY80tKYuFx4wYHGrVqxeaHTp34d4cOKm1eeU0A4PffFQ5etRcpPp6v1+XLbKNWF+Zp34fVBd6WBLYb5yG9do2dv61ba5pItFB2N5tZJrnD6kNJuO0/hFXLndClC/+dJtgBILwWf6pr2+p4Xxwofh4xFnG4c55t2Fevchs+nPnJS5ZwGzMG6Pj1WQxr1QKo9Cdg9Rb46S6sGqzH/B7jNHZ7+TIPDLqEDxFrpkpnprpjMzGRBzaATV0LF/IAK5OpAmDGjmWhqPTtODjwQPLwIccpdOvGfPY7d7jfcXE8A4iI4EHixg3V79evuVlYsKB3ddU02WHpPtX3bgP5vai2HuR9Fof38MivDIxSavyPHvHs8MQJ9t0YiwYNWHA3a8Y+nGLFWCNXvmOnYsIQ6uiDMq7z0dwtZ4KPChoy1NwFQWgKIBbAxnTCPZaIFqdbtyKALQDqAigG4B8AZYko1dAxMqO56xKsAHTascYW2YXvhrfGwoUqwZWayg+Ukr1SuzZrILrYMiv9V+o8fk7Y4tIcLedHsdb5icJ59xbAD6UB1FM1s0qA230gzh3wCAaqbQTK7YHM3R0PvgzBf/+x7XrJEs1jDBzIAU5F1BTZ1FQWHD//rLtfY8ey469JE91+grg4ZhEtX57xOQYFsfY2aVLG66pj0spj2PJmAp7NPwrZt7UQUPd7jG/bG+/fsyB9905TS8wQTk8Ay3dAx26I++kJhg7VZOvYOSYi1etfJLT6ArISqfjU9jf8sbwG5MNrISwmDE7XZ6B4+BcIPuOpIcjnzgViY4GRI7UF+J07fP2UbBR1Zoo6SykoiGcLW7bwQAyw8FeycmbMYLbMggXAZ5/xYHTsGEe9XrrExwd4QPfw0H8J3rzh2cqmTSrHuRISl8eQvynJP4rc4OcsrqgJF1gdcsD5CSTOz1ClWGl42hVHeDgPCCkpujX/O8kHsfCWPxIsQthciux5xwoSshzEBMAXwE2137MBTNSx3lQAU9V+HwLQIKP9m8pz1xdY4LrQlfm3tX/Wy2Vu0oSTUB04wGlN793jQA1j+OrqSKspOtWOMKpMGlfVVJ6q3nMbWZHg9EiVW6aKdn4MBFgShtYjOD/Ue74ABxk9fsznqCweAXCemS1buGza+PHMax49WvX/oUPMw1f+trLiQKNevYjWrePEYxs3ci6b9MeUVttCGFmB0HSO1n8lSnBWypUrOYukl5fpnHR7+7U0atS/BBCFhamuX0oKkd83p43bj8U7QpV1ab/79ye6cSPdOmp8brM6v1HfcaqCqklJHO+weDHnxp8/XzPnTPHiXBd17FhOLvXvv5zK2FicOMGZGtev52yhyiygN26o1omI4IyJRYvyesriFr/8oupHy5Z8v6KiOF3z1q2cBbJKFU7QVbkyr+fjw9z4e/f43tuUCjD5vgD8jBQrRiRYviMIyZxts8MoDS63elCf97eV6du/9tKuXUQ//sjZFrt1U2wP4nzsZXdnmgtekIGs8twFQfAFsJc0NffBAN4BuATgKyJ6KwjCTwDOEVGgYr3fARwgor907NMPgB8A+Pj41Ao1ISeABo0owQl4XldlJ39Wj+NGkq0BkgAQOPRcAWtrdnK5u7NNU+nUio7m/5yduTk5qb7rap12NASs3gAvawAHfuRjeZ0DvM7h6PRvULcuzwQyg6AbQZj2TwDCZpxH8Qk98fzv00yzTo+xApBQA7jfCbjXFQjXPYDrQ/XqbA5RYutWdkY1bQpMmcIaf+/e7IxetYo10owQF8e5U9b9EYlws7OIv9oFEFLx/d/78FXnrmjVSj/bqFkznlU8ewbMmmXSqeiE1Ok5Up3vAk9aGbeBkAKJ9Xt0HxyG41Gb8eavuUDTb4Bm84FEW+C7WDh1XoAxNaekaeK3bvGmbduyn6JiRTZjXb3K1zer2L+fTWcAkwF27tS8Z0pcuMBlGy0sePZVvTrPxmrWBIKDtdc3N7+H5OTzMDevh+Rk3aULNeB2Byh2HHhQE0gAXr+uDzc3bdOTXA78+y/TedcERgFFbgJVNgOV/gCsVZ5hG3MbjVmvNblivM8WWL9og9On2bwXY3aXqbQ+p4ESxwCnMACmc8ELMrLsUNUh3D0ARII1yG8AeBLREFOEuzpMNctI5khAICBVCix5oXCUyYFyu4F6P3LdTInivAiQLI7Cnxtc8OIFPzRnz7Idun59Ns00bMhJrACVsNfV1AeDY3euIinWBkhwBj44s9NTD0qVYi5ynTq6B4r0ATBK9O3L3OVJk9wQFRWlWGoHDozpBMG8M+D4DlR6L1B2L+DzH2yszbGs2TrgVm+sX890wnr1eOr/8iXz31MNGskYn33GgkKdNVG7tnbCsDJlIvDkSQJSUnwAAK1b38fAgWUxcCD/P3gwX+NjxzhgycpKk6nj6so25Fev2I783Xcqp2H9+uk547kDs6aLkeJ0G7g2GAhryuabaEXUWYXtmN6zZ5oppWxZDkRq04bNKOHh7PCLjMyawy8hgW3uK1YwzfXECRbSgwczM0oXkpPZPLNhg+nHK1LkPbp0sUejRvy8ensDZcuGISVlIICTamsOhK1tV8TGqvwYSmpuUBArCG5u/Pz8GNcQzyXpvOtyAZL3vpCHNATufQI4PAWeNgJeV4Zl8bsY879aaNRI8U4GZQ8XvCAj280y+v5DLpll0kwis0GYbkEYVpfQfjRZVN9GgstDDtkvcYTQZB6Zd/yaJNJUrdDrFy+4+MDEiZxOwNaWC0OMGMHT23v3DOeN1jANzQJhqi1ZTSpD3/61l44fZ3NGzZqGp692dkQWFmzu8PTk/C2NGnFhjP79Veln27S5S4JwRW3b5ySRLKZvv91Lm64bzq199y5Xny9enEP+x4/XLIYB6Mhrk1GziiLLEufJ3iWSJJKziuWfE1CBOGRec/0WLTjFQZ3WoRrL67d/ktbP1FS+Zp6eRMePczqGEyfY5KHKUZ+sv092LwjWEQQQCVWDqOPsFeTUdR6h9P5MmRbSWosANi9V3UBoO15nBZ/btzmdREQE5+xp08akx1kDcjlX1PL1ZfNXaCgvj41V9enkSU4XvHix7pw/hpqT07cEtCVOqUAEPCbgFVlYBNO8eapKUxs2EJUv/1Kr2IeFRW/yrXqN38Expcixw2IqViKafH05R9HNm3wOERFEMzceJIuefoRG33FKDV19arSA8HljQoCVVum77MjrUtCBHDDLeBJRuOL7eAD1iKivIAiVAGyGyqF6FEAZymaHqiFnJgBM2bUYz+4Ug2NkW8QcHguAA33q1VO1mjWBnTtV2eK8vUth6NAVcHRsjzNnWLuPj2etU9nq1NE0tRjLlpHLVUEXx47xtPX9e0AikcPM7DaSki7CySkcQ4Y0RLduzfH6NWururMlXoe5uTVsbb3w4YMN5HLds4H0ZiVBYHOBOhwc2AkJsJkqIoJZGVWrMuvh/XsDN8HmNaexTZfKV4mAAKb0qWc9TMOX5QHb1xDWnsVAv3dYPqkOBg5kbXfbNlVGSiWIAIlkv+JXfQAuBjqmQIvpQKVt7HDetwJmZhJYPvgUcZFGEsB1wa8yXO+/ROTxSK2/xo5lzdnDg6mm331n+u4fPODUz+fPszO2dGmOED15knnfGaFFC3Z6lyypisaeosgQvGIF01XXrDkNuP8LfPgM6NcZsLoJ/CMFbjbG6NEnsGMH89YfPGCne9GiQZg+XZVRsUyrGfhn7VCg+DkO8PO8CjOv6/ikRhPYvKuG+/eZkaOejjkNbsFo2Ow+npT6FeFWR4GFb4BxvmnmGl0aeU5xzAsKsmSWEQRhC4DmANwAvAIwS/G7Ong0DwEwQk3YBwAYAiAFwDgiOpBRB7OLLaPrpn/xBVPFOnbkl0bZgoNTkJx8E0RnAZwHcB7W1k8xePBA7N+/H2FhYShWrA569lwEM7OmOHuWBXTZsipTToMG/CKZOv1OSQG+/fYg5s07g+TkRgAaA9BOat26NfDPP/xil9NjFl13aQtm7FuK56/j4SEtj/5lR6OaY/M089Ht23oEbD7E1KlMgXNzUzU7Ox4QR4x4DaAIgG0A/oc0+kSHkcABNYqPYwgQ46u9875dgePzgFdVNZcXvQK8rGlSP318AH9/zgVTvjwL0nfv+LutLbB4MaepINLOa/LyJQvOhw+53btnOOLZEBo1UqUlSJ8GQPmZlMTUSC1TnMt9wOURkGoOpJrBLNoaNcvXQ1KSbpu+TkiSAbe7gE0kzBM94ZxaHhERfN41awJubndx4sQ8JCUdA8BRbzY2Nhi0eBA2vN2A+LnP04S7yILJHAp1EFOVKmyjrJ3u9GWycggLc4EGtRAuAC5CKeyB87CxiUtL7J+YyDkrlHb7s2f55Umv3RsTsSeTlUBYmCuATgB6AKhmcP3Ro1XHkMl4QNGawSQ4wiKsE+rFzcXt06WQZqZXwMmJB6PUVNaSX7zgF9EgzOKBlGwMQcxJuN4FnJ8A5nEccBNZAUjIIOqs6kYgeGDu9C8bMHAgR7vu3ct+ijJlmNuePkeLubnm9/BwHU7qnn0BmyhAkgIHcxscGroPEgnPbBcv5udk5kymYKbB4zrwqhoXIAmvBTxtCMR4A17nMHtgWzRuzNtzYJMvdBElZDIZ5u+ZjwF1u4DGyiAr6ihq5JlEoRXu0dHsGHrzRrMiEQBIJBJon7s72KKkFPZ1AETDxuYm5s3rgnr1OCJTPaLv6VOkmXHOngVu3mTnpbrA9/VV8bCPHGHu+bp1LwFEA9gLYB+A/wCkQBAExMfLcewYmyg0wsAzARcXLhLg7q5KUKbUigF2sE6blm4jy2jA9yTwohazgJSJu6QfgOIXALNEjloUYTIkxS9BXnofYBUNHFoGAHAv/h52FvZ4947jCJTtjtlmzDw5DWExYXA4swzNfFph1xpV6O7586y5T5rETl1lUJJ6gNLr18zc0cWYQeuv0yopCRCQPF0OPz8uHOPszOa53r3ZPHT7Nj+L16+n20fzWUCDJZAVcdMyqeh+xwBBECCXy+HkxDx3J6dMXkwRhVe4HzgAfP8927rTQ59WgSoAWgFwBBAtAPvLAg/qw99/Pc6f5yCUihU17fdlyqgiCBMSVNr9mTOaGQmVqFkTePDgN7x//xRs7XJVfLpBKvWApWVxpKSwQI6L40FKX7pdfZgzB5g4UXsWkZrKpp7ff+eglZgYDp559ozt7GVrvgBaTQNSrIB9irSSljFAzd9g4XUbSbfbArf6ZHh8NzdmXehM6mQWDxS5BctPJmBak2nwSeyg5Q8wGeV3AC4PAeu3EKzeQWKejNS/V6f9La29FpW9fXDzYTRSb/TK4sGyEUWvwrHqvzgweyxq12YlJCUFWP3fdny1YyE+xNgBcUWA7VsheNxE88aWcEgpkybE1at3VavGeY7U0wu8fMnU1B49mNra9M/yeBp/D7g+APhnAVBmP1BpG+yf9YLNneF49YoVkcGDOer0wgU2OzVqxMnCXjrtwsSunYAxJZntcqcH8LIG6jWLwYQhMnTooMrrYkhzDwkJEYV7NqDQVmKaPp0oIED3f9oFiBWBQtO0i1O7NlcxJN6+5SK/gwcTOThrF4ewsjKVkXGagCkEtCMrq4a0bNlOev9ek6lTooRmYYjoaGZl2DdfTSh6mYBUvfsfO5bo7FmiO3eIPvkkmKTS5wRcIBeXAJo27QgVKcJBL0oUndCRYPOKMKgZn/+osoQm8/Tu//vvuVCzio3BxR46duRglv79iXbu5Mr2GttKP5DELCntd+/eRE2bErVurRlQ9v49B53xekkGriMRJIkE+2day81cQ8jW4YPpTJn81pwek2P7JbRjB9F//3FVrehoDk5q2pTZNfHxqmt36BCRuzvR5s1qz72SgTK6NKHBYp3HadqUmS/79+suVG1plUze31VMY2j9fHwbrVnDRa7t7bnoyLp1RL/88qfWO2ZjY0OBgcx2cXTMvkLYhRUorJWYWrTgB1QfAgMDSSaTkSAIJJPJyG6GHQu0CcUI9ZcSqmwilDpA0uJXyNeXqYtmZhwN6FX6LUlKnCBU/JPgpTsi0tKSaM4cLnOmXi0nLo7pbH36XCFr60MEvCSpNJzq1AmlxYt5/Q8fVOsPHcqRe1r9Dw4kyxHNCS73+ZjFzpNQ5BZZ2yUaEBKHCehJgBdJJJdo+PAzWvuUDm7NAn5EdcIMKUnaTdK5rypViBwcuFRgZCRvv3s3kbc3R63eucOVfWrVUtvOPFZjH1KzVKpTh/dTooRq+ZEjRLNmsXDq3ZvIw6MXwW4/wV2NEmr+iJwb+5LgFMr3of6SnBGqkgwGFSOb1Pkpwfs/QvkdhBq/ESr+QfA9Ruaed8jDg0gQeD0bGyIUCSaU2UOo8xOhwjbeR8nDBKs35OXFEZzz5hEdPMi0ww8fuIRkgwYccf377xzdevKk6t4+e6Y7mriI97u07/qUIXV4enLlJl14+5YoMJDLNjo4EFWu/IJcXAII8CSZTJYm2IlE4Z4dKJTCPSmJhbEpD48wWxFqPrEIoeFC5kjbPyWA64j27Uu0YgW/MF7zqyjKyH2i8aKYFXlI7dqx4JNItF8ke3t+MX/6icP3g4M5NcD581yLdeRILm1nY0NUvz7z0rt1I6pTR7OvYWFEn31G5Fwkjlw/G0eYKSHZMhltuh5Ie/cSubgYJ3BsbPbQ8uV8/MREFu4W31gQenfXu03z5pq/K1XiWdLq1cy9HjfOSIFnHksS20iaM4cHh5s3iebOTbeOZTRZlT/O30vvIXzlofqv6EWynWNHqLUqS0LX3PwrAloTUJOAamRp2Znseo0gNJ9JcAwxfZ/WkWnfy5XjWqf16xNZ2b0j1P6BMKKaamYYAPJf6Z92X5OTeVB0GzyC0HwGodJW1X5d7pN1pcPUuzdrx61b834dHJgX36OH5jN35Qpz5kuV0uxf8eJ8r9RjOYKCVP8NGsRpCPShQgXDJQaViIvjWJJ+/XhG16gR1+x98oT/F4V71mFIuBdYm/ulS2w3vHnT+G30VUfxMquKoCbXERzMHN4bN4Czl99x0qkiNwHb18zScHoCOLzA3j6HERvLPPH37zmJ0+vXzFe+oSuNgBrMzNhmKQjpMhzqgIUFRzEWLcrc6l27gL//5v++/JJbuXLMlPCq3x+w6Qnc7665E7tDGNGvHc6eZZt7SpELSHS5AtzuBSS4aR1THRUqMGNDvZ/m5lxBqmRJdtZeuaK2QbUNQL0fgEftgKP6ieDeNW7j6dWKQJfhQLQvcCpAe6Xi5zlbpr7lFf4C7hwAEAcgDhYWjvj007GQyeoYlUbBJNi8BprN5SyY7b5ilma8M6xXvMGXX6oyTcbHn8IHaRIgKQPYRgHl1gGvgyCLt08rvaeEBhNq78+A8xNYVTwKP+/lcIltilu3OPXB48ccF2BhwUU89EYf2z+Dp/8QLOo/SIuVkprKrLLly5kAMHcuO09nzGC+ffrKZQ0asP3eULGV9EhK4gjlHTv4OfX25mfjzBnen4jMoVDa3JWFfk2BKRFxPkt8CWN9CX27EFpOI1TaQnC7RYJ5AlWqxFr+/PlspggJ0R3tGh7OmtXUqZzcycGBTRrt2nHypN9+42jNw4dN0xxLluSZhq0tT/VtbYlg+0JlvslCO3xY97VLSeH/lAWs1Vv9+kSCTRSho78qEdd0C4LHVd3HsXzL5gv1ZdIPHHms/O1xTXu7Ev+ovkuSCIgi4A4BJwnYRnZ2G2nWLNX6Vasqv28iwJ+APyhDuz6I/Rxl9ug+/sQihNkg14WutGIFUatWqgLUwGTiYtchxL6WC8RFtLfR3r2stavDf68/SedICZ6XSDKsEfnvVWn4cjlr+CtX8qzO2PsnOD+mRp0f0/LlbLePjeXkcaWrviafpapI54U7d1PLljwDVTftEPHzacjcmRGSkzn6GOBns3x5Ngddvmw4KlyENlAYzTK9e3MItalQz1anK5xfCf+V/oSAdM7XANDwH0bT1aucLXHiRH4RihVjwd2wIVc+//lnzhCYfkqaksLT3d9/54GpWjU2z6hPtdeuZUfatGm8T4AHhMaN2RQjk/HA8sMPRBcvsi323TsiTPDk7JXpbN651QT7l6rflTcTfE6atg+JIT+CovXsw5/9mhEqmxGgnUmTSLV+q1bq24cQcImA88Q+kKfUvz/Rr7+yEE1JIR7M+7fhDIcDWvM9H1ZX5zW1GzCAPnxgE8bu3Xx/ZTKZoh/VCVhCwAsC7pC5+W0qVYqfk8mTOWVEmqIxzZr3P8WBLP1aUN9xV6hrV87g6etLVLq05nFHjGBH6tmzRPaN1/Ny81i22xsa3K0jCR1HcpbRACuymW9Dm64H0h9/cNbOfv04ZYfy3dqyxfR3Kz0cHdkZfP48n3fp0nxOEybwwJOamvVjFHQYEu4F0ixDxNO+kyeZjpcT8PX1RahDqIo2GQPgKCB7J9OaYgNcaFpp0lG2mzeZT1yliqpVrcqmFAsLDjIaO1ZVMDk9evUChgxBWoUoIp6aq/Punzxh6uV181/wzuEssPs3YFALQHYaAOBtUQXN7wRj0yZOR0DSD3j/Vk8mM3BBi9hY5jtHakfh5z7sDgOxbfm7+03AIhYo2x04Hg1AWVy0KIAWEIRW8PUdmlYMxNeXqXgq/ArgX1hZXcJvv81Ev36a5gsNsx2BoztTrLg96ATc7gnITgH/LNTq5q+/AhcvnsOGDVuRnCwBYAXAFoLQFURVsuNKoFgxTsxmacmfVyPOcEyC0xMuUg4AKRZAVDlsaRqMW7eAefN07MgsHnC9Dzvfu1g0uC/Kl1elw5g2jZ/bunU5+jsrSE+FJOJ979gBbN/OVM/u3ZnG2ayZdqyKiEJmlgkMDqTiMxoSbMPJZ6l+zTurEARBp2YoCELGGyuQmkr06BGbZubOJfrf/3iKml6rsrdX+92rDxWf0YjGLjtBkyczI8jenrWefv2YVaN0jhIxVe7wYaKeI9XMGM4PCVU2kbR6UJoG+/Ah96VrV7VjGaBAZtQaN+bZxZ49zOYoPqMhwfWOah2v04S+XUgyzYHQWsHGafxtrs8ostQkiZwT3uY1wSGMNWP3GwTPi8yM0bGNs3Mc2dltIuA7cnRcSn36XKFly5hSmt5RrdHqLWfmjN1zkpqlkl29LYTG35LENpLGLjuh8/nSSLD3tTPh80aEzsPJvunv1LYtzxTS9l91I6HVFF4nwJIwrA6h40j6/HM2zVhbM0FBuX7RosbVQEjPSDOFLXP/PtHChUT16vGsdNAgngWpM88KO1BYzDJpU9kenxLKb8/RLHKqKbZmk8lkJvVX3QS06XogbdvGL45+gZJEqP0zmXcdQzPWH6LoaDYZ3LjBNvrhw9mWbGPDL8WYMcyE2LiRyMk9jrzmVSUMbcA2bD3HaNOGB4c1aziz5b17PPiYzuHX0zyuEszfZ7xekeC8F+BZaLrYUuqtf3/O7njhAtGtW+ybiYhgoebU+bs0ppZ6M/M9Q1ZfVSR0+oIzYQ6rm/aMy+XMcjl2jNlYbfreJUnJE1wswyKGUPwsSWtuoAZDtlOR4YMIbSaRhfc1clngqmlenK1dFCMujtldy5dr9sfLiwt/rFvHz6C630BXLElmee5hYay4NG/O2/XuTfTHH2xyLMwwJNwLlFkmbdp8YDkQ0hzwPgOYJcDBzhITm34Ja2tOHWBlBZO+S6XaxwoKCoKfnx/i1dLf2djYpOWhyQhaeWHCq0Ny6EfIQ5rAxobDvocM4ajAEj8ozmv7JuCdFyD7F3hRB5LwepDHcYZEZ2eOlC1blpuyHubJk/prktasmY7Nko9QqvFlPLpelOucArBwiETSOzfALA74ZCiwew2QnFGJ+zsAVgDYCBsbwsqVaxAQ8BmeP2eW0ddfc6bMJk2APn3YHLZtm272Rmoqm6PUWVBRURwBevD6Vey/cgXv31jDKlEGN3klJEQ7aeX2yXZU28CMrRhvWMSWhm08pyaoVElVwi/c+igCX0zDc1yEzMkHHct0xIbrGxCfmAD8ch1oPRkWFY6CiJAsT07btaFEXj/9xLn3HR2BH37gTJiNG3OKgxcvuFBI7dpAYOA4REUdBHAfLNsZWY1QVWZN3bED+O8/rr3aowfQtSun2yhMKDTpB9KKeESU56pMKVZckSnFBtMbzkNCAtLahw8Zf1f+lkp1C/64uAiEhT1AYuJb2NgIqFGjAipUKGHUwDFsfz+8TgoBJCnA4cVAWBPA6TGcmm7Bg98DNKq7p53Xo1bAHzsBaRJg9gEw+4ByRUohPFyVujcraNcOmDyZ+6hLwFWsyDZQHx+mOyphZsapZgcMALy8gA5d4xD1Ui3Lpc8pQHZc0c6yHTiyPHD+C+DS2Kx3XB2yk1z554MjbM40Qnx8GQhCCfj4JODlS5e0JFjTpgHz5zPdz8yMk2pt2AD88gv7LLJSaEMX3rzhrJFr17Jg2r6dB5a+fYGDB7kf6nAtGoe3pX6FPLIU8KQFkORg1HHs7bnZ2en+3Hr/N7zHcyDelSmjw+sCAmAnsUPCu4QMawcDXG/10CGmuj58yH6hx485rXDt2kz7vHQJ+PrrPwDUBudsugIu2nYJwGXI5Q/g7Jz19AMxMZxEbccOTqtRty4L+u7dmSJc0FFohLs+nnpWKrcQcfpUfYLf2AEi/fd9t4/y4JPgwtkLAcD2JZBiDXO5I1JSVANCTGo4UqWxCoGeAJgnAGYfYG0t4JPKbXUOIgBr7Lt363d8NmkCnDrF3+vVY2309m3NdX79FRg2jNMHnz/PTlp1J5yzM6fqPXyYX6409OsA2EQAZycANz/TPrgkCZBzpW0XF34R0x/bKFTeDJQ+CDg/BuI8gEdtgdu9YOZ5F79Ma4RevdgRd+MGV3YqXhx4/pw3LVFCNavZtYud2T16cA703r15dqbM9+/j44P58+cbNSvTh59+4jiEI0dYqHXrppnQq25djhvQSsfi/BDwPgsED+DKRQNbA08b8O+X1YEK2+HR8AieLt2G+HhVbIWuzy92TAQS7YAkO6BqIOCpyARGAOaoDmloFrp7N7B6NQtVgN+RPXuAceN4Nrh0KSsAqtwyLgBqggV9bUil9WBv74XoaFYSWrXiQUGZYC+ziI/ngXLHDi5PWLEi388ePXjfBRGFRrgbKuKR39KJZjQQpaaqBoLNV3di8oFZ+JAALo6RYgVLcsYX1cejnkczrYHkYuhNHH9wDu/v1gZeVc+R/stkLCRTUkzc0OcUp+R91B5otBBoPgt3+n9As2Y83TYa1ddx8JgkmUvgvSnFQUTJtoD9c+BN2bRV7e25DN7Zs5wgTSoFvv2Wc65/9x0H7HTowHnMwzntOFq3vo9//12EpKTzAO4CSDbJ7KYL8fGc790Qhg3j4KAhp5oA8W7A49bAxS/5T68zwJDGsLG0Vj3jMV4wvz0YHg+/grncCf378wyqTBnd+9f33CEawHLNRUrzSXqcPAlMn65SDJRQLw341VdAsWJb8OWXw3SaLtu06QcPD2DCBNb+L13iZ7d2bc3m5ZU5gZ+UxHUAtm9XBU0pBX2FCqbvL7+i0LFljOGp5zVMLSFm7HkFBgeS9fRiGiH5Eu/z5OzznCz69+KSgCOqERyfkLR6EHUecpPc3Ih8fJito9xGydAxM0vH1tHTmjXjBGK+vrnsuLR4x2kims8gfO1CmGJP+KIq2X06kJyc5hGwVe+28+dz8AzA6Q+UDkkrKyKJJIKAIAJuERBHwGUCfidn59l04oRhR2BCAtG1a5ywa/p0dmxrHV92nNC9P1mNbEi/ndtCjx7x9XN15Zw63t8q0lsMrc/BWx5XCS73yML7Go1ccIp8FpfSeBbkck43MG4cB7DVq8dO1YiIjJ87TFMkzYNm08f8unaNGTT6oGRdlSlDNGnSUaPZMs+fMxtm5kxOPOfuzvlxOnbkZbt3q7j2pkAZNDVmDDuAK1QoOEFTKCxsmY8N2T0QyeVEbgNGE+yea9as/GQQSWaZ84vc63+q5daRZN/0N7p7l7efM4eDrpQPfFwcB0KNHm2coC1alHPhjPr+FFlNLE+YIeXkY+3GEszi9G5nZcWCACAaOJDI1uEDSWuv1VhHqLKVpD0HE7oM5Yhg17ua+/E+RRhTQiWwZqkLKl8CIuibb/bT4MGax1Ye19GRW40aLLj4/6kEtCKgIgENCBhOwEpq0IAjK93cWFiULMkU1qpVOY+LlVU6miG4fq37sMGEmRJCyUOEtuN1slIePuSMo3ZOCSQUDWa65WcdCLNB1t/Y0oQfj1OzZnzc77/XPcgkJ3ME6aef8jl17Ur011+qZHTpnzvX5q5agh3Qz/x68oSVgYywbx9fj08+UeWTUUdGbBm5nFkyO3YwrbZtW6ZEFivG5zR3Lp+nelbTjJCaWrCCpkThng+Q0zOKhw/54YflW03BZ/GOswlWX6sdsu96l1B+O02YQGlC79AhDklXYt06FmJjx7Lg9/ZmLWrvXqKlS1nbrF3bgNB3u0WCVYz+/yWJVKkSf1fOENq21VzHte9ECgwO1HkNi01rRmjyDcEhlNMCtB9NmORKGKcuqBYT8H2asAI4GZu/P2vKACc8e/SIaYlbt6od3+uYZtoDE1vp0pw1s39/IhQ/y5GgFjGEWr+kCff0haHlcu5b2n6azyTvbytrPDOXLikSxznzvXn8WPdzERPD97BlSxaMfn5Ep05paqwZURbT480bFszGICGBs1e6urLyoM5Rz0ziMLmcz/XPP4m+/prPy9GRB5sePTg99OHDHPlqzL6Cg4lmz+aBuWhRvu5HjhjH4c8PKJDC3VBwRH5DTlZxT0zkB1rJQResYghN5xCmOLD22ut/LPDVhY75exbsxc+Reemj9Mknqv/KleN9KVMbqLcePZjLrJ6OWImUFIUAg5F8+A6jCCOqk/fCsjRnju51Zs/mNAqWlix0DV7bmRLCgFacptnyLcFhOwFdCHAmIJIA7zQzA8DBMcrrB/BUHVCZAUrVvKQ5QBa5Tih+hmB7goADBPxNnLPGeEFv5v6IA8MGNSNMdtSpuaekEI0axZk2Q0OZ8z5wIA+wc+eysFZHWBgLOVdXzud+RjODs9a6331HVLEip1eeMYP3T2Ta+5SSwhx+UzTdkBB+fkqW5MA2ouzLCpmaSvTgAadE+OorNhHa2/OxevfmGc6xYxzQZwjqQVOurqzw5PegKUPC/aN0qGaVY57bSHNiBX8GXP2c2S7m8bC1kWJg7V6wtuaKSTY2SPtuzOeVK8CYMcwyMTNj5kHFXtsx8diXSLjcE7j0BZBqDvN6a2FusRnxFZ8DFgASHJll8bwGLG7UR9IrrqxUtSqXEXR1ZcaDEqVKcSWeZ8+Y5fHsGVda8vbmKlFPn2pXu//pJ+aBL1zIDJUHD4y/Xnv3ct1OZQqF05wpAd26McWtQQMu/aZ0tKUvlv5uu4C3/7UCMBhcfBwAqkIme4d5u+djQLV+QKlDcK52GjXN+uPon2W1+pAGp8fA/3oDb0sBUWWAl2WAO2UAaKdE7NyZz1UiYXbK48ecafPlS/27l5Q5gm6tPTCwXVWUKcNMnbg4dgSqUwTv3we++YbZIOPGcU1dBzV25Pv3XCt4+XLmnX/1FV+v9BkdAR5qrl5lSuOWLcwaGjCAuf6uGZScVcLenp8DR0fj1lfi8GHue9myfJ/fvs2ZSkxyOV+zS5dU7do1TtGg7rCtUUNVOUodT58ys2nHDr5W7dqxA1690lR+QIFjy2RUviu/IY2nHusOPGkFRFRg+mNEBZhHV0ayKnYE9vbMRJHJOEdIfDy3hATV5/Pn6YoW64OQAuuKx1HNuwzOHTwOuMQDJeIB2wQgNR54nACE1wQwCMuXM2c4fRFlS0ugaVMW7lWqMC30zh0WCvfvq9azsGBmQ2KiimqojqFDgWJNDmPtkxl4TpfgJa2Jhm9W488faujtfo0azB7p1QsYNIgFW7duHECTkMDURvXC5HZ2vF1QUBCGD/dDQkIRAE/0X5/aq2Be9CEsT32L1i0s8emnPAg9eABs2PE8LYAKRYIB1/uA3Ax4UUex/B2ApQCCADyEofcoLo4pl48fA3/+dxm7z97E+3AP4HFbgCQ6t6lXj4VJmzZM6bNg1iju3WMhf/iwSsirC5vUVBZKS5dyQNG4cRwMp08gpaQwNXPjRi5L2bw5C/rOnfnepx84lYWsvb150PXx0X959SExkUv/TZ3K/V+wwLii8llFSgpw966mwL9xg981dYFfvbpmf9SDpk6f5jw3PXtybEdeB00VOOGeUeHd/AZDtMeHo0Lw+DFr33fuqNrduyysKlTQbNeusTYcEQG0b8+UtOBgzq/96BEHxfTrx9qQ+qAwatTXePMmHoANAGvFpzuAIbC1ZRqaei7wcuVY4zSmbqunJ0dF2tqy8N+/P3PXadIkfnGCgzmg6N493et9/z0POE+fcoTili2GaZT29uvQqVN17LoZiYSbaoW9fU6xNh6njHbZDgeH1+jduwb2WC7DK8tTAEmBm32AG/2AWA+g7BbgymZwUA5DKpUixWROKENZ07Z9e54JtW3LwllfdGvZsixYKlZkit/Jk8D48VwEPb0AP3eOheg//7CAHzOGj6EP797xjGHTJk4MV63lA5xx9kdisaOcox4qavF3n/bDli084GcWgsDne/8+9/OTT7I/eCwjJCdzXnx1gX/7Ns8M1QV+1aocQ5LfgqYKnHD/2DT3zPDviXjaqxT2e/ZwsQN9WLKEX3ClhqfVh6AgfL7scyQ3SU7LYikJ/BVW8YPg6WmJR494vcWLOYjm1CnmH9ety8FOTZoAtWrxy3fvHmswZ84A/5yMR3SEYbVr0iQeZC5e1FMwOzfhdodnTaX3A3VWAh7BwMN2wN4GADhzorV1cdTtvhCnznpDHl4ZqLCDo16LnQT2yQEdBVcy8x4RcfH2Hj04WrZfPxY2KSnAtm07MXnydHz4YAugGoCeANpnuM8WLXig9fFhYZ+czPfxxx9V63TowMW0U1JUx1P/npzMM41z5xQbOD/ioDQ3tq3JHGUovi0ECxdy2oHMQpl+4PJl1uBlMk5nUNaAlSw3kJjIGr26wL9/nyOM1QV+yZLMpc/LoKksCXdBENYC6AzgNRFVVixzAfAHAF8AIQB6E9FbQRAEAD8A6AggHsBgIsowe0lBt7kD+qe3Ga3rbVsKdUP/wp411eDtzeaRmzf5hXBwYA1WaSqJiODglQoV+EFTavtlygB/3Q/CkJ1DkERJgFwA9qwGrg7TOK6NDdCyJQvypk052lDfYKHsZ9qgdaMvsH0LAMC5SDyKONvo1LznzAEmTlSldADYLNC6NX9PTOSXaft2YOtWVVDRxwwLC46SNTNjW7C6MFXCxka1jrk5EBHxAqmpiQCSAaSkfZrZA3KXRMgphf0AStORETA31zxmo0ascVpa8nGVx1Z+9tnek4PEpEmA7wnAnG2BAgS0Py/Hl18CnTpl/rqo55ZJSuIBaMECwM8PCAjIOOArN5GQwLMZdYH/+DFQubJKs3/3jt/DfftyL2gqq8K9KYBYABvVhPv3AN4Q0QJBEKYAcCaiyYIgdAQwGizc6wH4gYh01ELTRGYiVLM7NDy/QENghjYG9vwKvCkLd88EmJM9fHwAf3/gf/9TpRlQIjaWteo7dzTNPCEhQKr9E6Skylkg6ICyJJ9S+Khrcbo0u5QUYEvwNsQlJHJk6DOFg7HUIVhJrdGgeFNcusSOvo8POwEkoG6bRrgRfg8JicmwEhzgQh54+SgKcrkUgDkAcwiCBdzcPGFhYY/kZGg1XVbC9EK2bl1tZ3lg4GqwfhQPIEH12SYecGSHPHyPA5ZxAFSRzSdOsEB5+9a0M54zh00spUqxRmpnZ9ic2OB8CDp35tlGZqErcdiLFzzTO3WK/QYfPgRh+vT8+Z7HxrKZVF3gP33KM6fERJ4Bx8SwcFcK+ho19BMBDCl8+pBls4wgCL4A9qoJ93sAmhNRuCAIngBOEFE5QRB+VXzfkn49Q/vPiRqqHyvSXqhTk4GjC3ih9APs6uzAvys/Qw39/kcNJCczm+bUKTbnHDyY8TaCwBpIlSrMKlBqcek1OuWyEfuHsGYnSQFIYIEjTQZiZPC5sRJhYTxtL1WKbegAAMdQIEaWYV9sbXkWUaYMt9Kl+dPLi7X+BQtYuz91SjUDAFRMkFq12I7777/Azp3GXTMVbsCq+GnIG59BkucJwPEpILApbZDzIOxfuD9TwoaIBcLgwdzvbds4N4+6b0T5ffz4AB0+Emugmg0nw0u2Abr4AQ78agkQIJ+lGklu3uRaqP/+ywN3kybsKL52jduFC7pTRzg5sWCytwccikbgiXAUqY73AZdHgPNjWBV5gdWfzsXpVf1QtSrXWDUEQwLMUFbIkyeB/v2j8eLFFcjlIwHwNDC/z9DfvVMlTlO2hw9V//v6spC3q3YYi8J6ICE1Lu2/zKRKyQnhHk1ETorvAoC3ROQkCMJeAAuI6D/Ff0cBTCYig5K7sAp3XbOP/jdGAdcHcKZI2wig8XdAtU0QrN5rvLzpER/P9ux//2XBceECU9yaNuUX+4vgSnhrfhu43R34cwdQZh/QfSCcBBmCml7RcObeucOCPr0zt0IFtuVKFAQPDc0uwQl40AHYsTmtT4LwHkQGeGMu92Hu8QQj27aDVKpKqRsZyVPglBRmLtSooWply7JwJ2LnsZUV54ZRd8RNn84UzZ9+Ui27fRuo9MV84PxozrAoO8GpkwU5F+G+PhhcTksHt88uHPD5D/A6i6IVQhCycAcsLQ3cWD14944TkkkkwB9/GKbU6TM9Wk+zRlSKtrdVX3K8GzdYyJ86xRqxvz/PDuRydsArhf1ff2myn8qW5YH5RdIdnHlxAu8i7GHxriKs3ldAcoI1EhJ4vbFjWdNXavwlSqgG24x8TRml/JXJSiMsrDOA6QDWAvgGQGy+9a3pw9u3rGipC/yQEPBzVX4n0GoaYM3MBVOTHOaocFf8fktEzqYId0EQ/AD4AYCPj08tXQ7Sggztl7cmpNLRkJt3A5U6yM4+2ak0lkL6m/72LTs1T51igR4czE4ypfOzUSPWCpVw+94NUQlRwA8PgGqbgKbfABKCq7UrIr/WTBtJxOwTdWGvNPO8eGHKWSYDeAzgAVDvIWvsd7vzX937A9WC+HizdD+D4eGsBam3V694ZqEU9OPHA33GXcU5WXeExYTBy7oMYhZcx6XzVrCyYibN5s3sjwgvthRUaxNQ9FradQUAyTsJpCvuIDm5F3x8EjB27GJ8dX0F8NYXSHBlh+qzesCzBsDTBrB5VxNVqzIFs2FD/iyuw/StrrUWo7qQbjmADi2c8dNPuvnnWtvrGPxRFZlKjhcczKaXM2dYyH/xhW764b17rI0fO6ZaZmnJzsTq1bmVKgWsXMlO1+HDeZBQcvrDwgB3d17n8odtiLO9zhk7SxwH7Jnwr3yWMxLuKlacB4DvAbQEMBHAnyDKf6w4UyB87c405FdVFUnweMBOPwPLcD+iWSb/gRk/rwD0AeAPrvP5K5wb7kRipzCtl3dh/Y0oEtkzTTN//Ji50ErNvF49w1zhNK59kg1godq3roeJiIW4UqirN2MKUFhYBCMp6S8AhwDcBGAHdOwB3O4NhLTglWZKAQkfV59w14WYGNY0lcL+z+3J+BCnKK5ZbQMzX+KKwqfcG8RGuKBnT+Czz/gamdUQgC7gQC4lkgDsAdoVI4wdy0wSwLC9+eawEFy6pFmr1sZGxblv0AC4a74FIw8O4/v4sgqweS/MG6zB2u/Lo3/VrJkUsmKrvX6dhfy5c1ysZMQIbd8NwA7OP/4AZsx4g2fP4pGaehDOzs9Qq9ZnCItywv2rRQAA1pUPo33jovisVVXY2fH9OXyY6YKvXwMQUplt0+RboAbb5pTPXEbCXZsV1wjAT7C0jMflyw1RqZKRFywfIrvSk+eEcF8EIErNoepCRF8LgtAJwCioHKo/ElHdjPZf2IT7/ftAuXLLAAwAcAHASgAHAMghCAI2XtuEyX+sxotbpWAb3h624R2QEmePxo1VTJYaNUwrGOw23017Oi8X4BRXFZtbXdMS4urFP6RSthUq7d8ODkz/unOH6ZfTp3Ok4oMHvG3fvnMANAXQQvN4zo/YoVt7JdByBmDzRufMwRT4LvdF6MWKwGZtcr2XFw96SpOOn18dPHe5pFXU3NXVFYkfViDW7QRkrQ5hfqv5AIzXkInYrqoU9GfOADfuxoE8rgKeV5kn32EMUOWPLNUWyE5cu8ZC/vx5LtDi56ct5FXBYHUBfAWgDiRF10FodxCp58cAd3sAQgpA2tMQNzcgseJavC+zBvC8DJipPMjGau66TFPW1vb43/+OYv/+OhgwAJg9WzNS92NBdqUnz1LKXwBbAISD59jPwGRgVwBHATwA8A9YuAM82f0ZwCMwG7h2RvunTOaW+diQnEy0fTtR69acv8TBYSVxtkIQIBBQhYCRZGOzhzw9iYoXJ+rbl2jlSs7noi+PhzE5QVJSiJzq1ib06kJoNZlQdQPB8yLBPDYt74lEwrk42rXjmpjLl3NWv/v3VUmUIiI4j4mLC2eKfP5c8zivXxP98guRldV/BLwlTpnbg4BKBFlnQqkDfLxiFzh5lu0rKl/rJY0Ywcc7dIjzn5iShlWYLXCOlk87EyoHEZwecU6dmVK6fZvrx06cyEXA7ew+EPCSgP0EzCOgJ5lVLkfmsywJzWcSms7VyPuTlWRvmGpPGNiS0HIqp+3VkyQsr3HlCmdtLFaM8/go86ikpBAVK9acgG4EBBCwmQAddXe79yOMqE4us4oTET8DR44QLVpE1LDTYxKK3OKMoB5XCdXWkXnHSTTt98Npyccyyi2j7/l+9YpoyBDu96ZNH2fq3uxIJoiCllsmv8AYOubz58CaNdxKlmSHVteuwJIlBzF//n9ITq4Hnm5GQCo9i6FDy2LKlIZaVWl0TcURjHSajRmsrKrAz2853Nyapmnh9+4p0xWkAg6hgNtDwO4BEPEACH+Au3f3oUQJ/Zz2N2+YlrZqFTsEp01TRTpGRDAbZds2DlLq0AHw8DiJ1at7ICHhTdo+rK1tYOlyFjHVFoPqbIIkVop+rpMwsM53Ws7c+Hi28aZ35pYsqW2r1pjebjoAVN4K1NigU0MmAn78cSfmz9+PiAgvWFs3RKJFBcg/2AOWMUDJf4BuHMiUVQ07J6qC5QRSU9nEt2kTpzVQQiIB5PLHAG6ptdvAtDvM1LnkD4CAZjzLAQE0W1uWBN0IwtQDc/H0oQNc3rVELWEI4p+Ww/Xr7Dxv1oxTHijt+TKZaVGq584xG8jGhh3o1apl8kJ8pChwEar5AYYCqT79tB+OHWNhePw4C/Pq1ZnB8e+/LARLlwaKFLmLK1d+QGTk35DJLPXS6rSmcMmWsHpXDWb/VEXsHW8AFRWtDAAppNIXaNHCS4NG2K1bJcjlD8FGZhUMhc7HxHAiqhUrONglIIDNM1FRLND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@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 72,
    "source": [
     "y_pred = bloodSugarModel.predict(X_test)\r\n",
     "plt.scatter(X_test, y_test, color = 'black')\r\n",
@@ -534,8 +534,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ],
-      "image/png": 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@@ -564,6 +564,79 @@
     "And I'm still not sure why we are using test data, we didn't actually test it"
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "I am probably jumping the gun a bit, but what happens if you include more than one property in the set?"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "source": [
+    "X = origX[:, :4]\r\n",
+    "print(X.shape)\r\n",
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\r\n",
+    "print(X_train.shape, y_train.shape)\r\n",
+    "multiModel = linear_model.LinearRegression()\r\n",
+    "multiModel.fit(X_train, y_train)\r\n",
+    "y_predict = multiModel.predict(X_test)\r\n",
+    "\r\n",
+    "plt.scatter(X_test, y_test, color = 'black')\r\n",
+    "plt.scatter(X_test, y_predict, color = 'green')\r\n",
+    "plt.plot(X_test, y_predict, color = 'blue', linewidth = 1)\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(442, 4)\n",
+      "(296, 4) (296,)\n"
+     ]
+    },
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "x and y must be the same size",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16176/27852843.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0my_predict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmultiModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'black'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_predict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'green'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_predict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'blue'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\pip\\packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[0;32m   3066\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3067\u001b[0m         edgecolors=None, plotnonfinite=False, data=None, **kwargs):\n\u001b[1;32m-> 3068\u001b[1;33m     __ret = gca().scatter(\n\u001b[0m\u001b[0;32m   3069\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3070\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\pip\\packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1359\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1361\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1363\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\pip\\packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4496\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4497\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4498\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must be the same size\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4500\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: x and y must be the same size"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ah right, obviously you can't make a graph out of it because there's no X axis.\r\n",
+    "But the model happily accepted it, so maybe you could still make predictions.\r\n",
+    "\r\n",
+    "But enough jumping the gun for one day."
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {