diff --git a/exercises/Jupyter notebook CVML.ipynb b/exercises/Jupyter notebook CVML.ipynb
index ef4940d..b60bb97 100644
--- a/exercises/Jupyter notebook CVML.ipynb	
+++ b/exercises/Jupyter notebook CVML.ipynb	
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "34c5864c",
    "metadata": {},
    "outputs": [
@@ -113,7 +113,7 @@
        "(1000, 2)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "fa762402",
    "metadata": {},
    "outputs": [
@@ -135,7 +135,7 @@
        "Text(0.5, 1.0, 'Blue circles and Red crosses')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "9efb32c3",
    "metadata": {},
    "outputs": [],
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "43c0a1d3",
    "metadata": {},
    "outputs": [],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "585aa0e2",
    "metadata": {},
    "outputs": [],
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "84e68325",
    "metadata": {},
    "outputs": [],
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "fef8e12a",
    "metadata": {},
    "outputs": [],
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "0e1bcf7b",
    "metadata": {},
    "outputs": [
@@ -243,54 +243,54 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.6605 - accuracy: 0.6200\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.6674 - accuracy: 0.6450\n",
       "Epoch 2/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.5513 - accuracy: 0.8240\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.5857 - accuracy: 0.7630\n",
       "Epoch 3/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.9510\n",
+      "32/32 [==============================] - 0s 516us/step - loss: 0.4744 - accuracy: 0.8540\n",
       "Epoch 4/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.2894 - accuracy: 0.9940\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.3558 - accuracy: 0.9190\n",
       "Epoch 5/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.2142 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.2594 - accuracy: 0.9960\n",
       "Epoch 6/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.1680 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.1930 - accuracy: 1.0000\n",
       "Epoch 7/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.1372 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.1507 - accuracy: 1.0000\n",
       "Epoch 8/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.1153 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.1224 - accuracy: 1.0000\n",
       "Epoch 9/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0988 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 578us/step - loss: 0.1030 - accuracy: 1.0000\n",
       "Epoch 10/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0865 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 580us/step - loss: 0.0887 - accuracy: 1.0000\n",
       "Epoch 11/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0765 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 547us/step - loss: 0.0778 - accuracy: 1.0000\n",
       "Epoch 12/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0686 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.0691 - accuracy: 1.0000\n",
       "Epoch 13/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0622 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 483us/step - loss: 0.0622 - accuracy: 1.0000\n",
       "Epoch 14/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0567 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.0566 - accuracy: 1.0000\n",
       "Epoch 15/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0519 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 579us/step - loss: 0.0519 - accuracy: 1.0000\n",
       "Epoch 16/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0482 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 515us/step - loss: 0.0480 - accuracy: 1.0000\n",
       "Epoch 17/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0447 - accuracy: 1.0000\n",
+      "32/32 [==============================] - 0s 547us/step - loss: 0.0446 - accuracy: 1.0000\n",
       "Epoch 18/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0418 - accuracy: 0.9990\n",
+      "32/32 [==============================] - 0s 515us/step - loss: 0.0416 - accuracy: 1.0000\n",
       "Epoch 19/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0392 - accuracy: 0.9990\n",
+      "32/32 [==============================] - 0s 483us/step - loss: 0.0390 - accuracy: 1.0000\n",
       "Epoch 20/20\n",
-      "32/32 [==============================] - 0s 1ms/step - loss: 0.0369 - accuracy: 0.9990\n"
+      "32/32 [==============================] - 0s 515us/step - loss: 0.0368 - accuracy: 1.0000\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x26d8d1e0588>"
+       "<tensorflow.python.keras.callbacks.History at 0x22d8c7c8308>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "20f9bb50",
    "metadata": {},
    "outputs": [],
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "1e5b54db",
    "metadata": {},
    "outputs": [
@@ -366,13 +366,13 @@
        "Text(0.5, 1.0, 'Blue circles and Red crosses')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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XKXt6SJKhECWkRYu4THc3pcX+fn4G+F1+B0ikKSmUADMyKBEODFASrazkfqIxDVRUAL/7HQmzv5+k2dOj7YSJinm0H9PAAB8gHR3Ahg38HEmtnkkxmUqVo7W1Di0tLZN9KGGhVAaUKk/Y9gxhGviGXaXMzSVp2G2X8vvixVTRc3LokCkuplpcUcHvbW2USoNBvX4wSFLNyQGOHuVvPT3AuXN6/zk5wLZt/Nzaqpfv6SFh9/dT6h0Y4Ht/P6VVgFJovJ5pMVds2wbs2UMJ9qqreOx+nDeRzB3TCUqlQanp5+WOF4YwZzASHcJiVykXLwbefptEWVxM0gK4/WCQDp2SEhLlwoXATTcBJ07wWObM4TrNzSS39HTtPT97lsvMmwcsW8bvBw8yPEhspQMDtIFmZVGV7+ujOn/ZZVT5ARJzYyM/J9IzXVlJM8P1149VrQF/zpuLPSZzuiMhhKmU+jGA2wA0W5Y1rpqnopHjOwBuBdAL4H7Lst5MxL4N3JGMEBa7SllQQIfP8eMkxqIi/h4K8fPnPz/eUSTEfd11wO7dJM1jx7jOyAhJ9vhxblfIqLmZ9s/mZn7PyaEtdGiI+x0YIFmnpGibKaBtg8nwTB89SulWnFWLF3M8pqvzxsA/EiVh/hTA9wH8zOP/WwBcMvpaD+BfR98NkoRkEIVTpVy4EPjEJ8Jvz424d+8GNm6kxNnbq4ln4UJKi/Pnc93WVpLgyAg/5+ZSOh0a0tsXtV5U/ZGRsbbBRx9NrGe6pobrKsXtDAxQ0l66lMfvXPZiC1Kf6UgIYVqW9aJSalGYRe4A8DOLPv7XlVJ5SqlSy7LOhVnHIA4kI4QlFgLwIu4TJ9xDfR56iGQXCpGIAgF6vMWJ1NnJ5dLSSLYtLSTJ/Hyq5Y2NY22DXp7pYDA2u+auXVqyDoVI3m1ttLVmZ2vnjwlSn5mYqOIb8wCctX2vG/3NIEkoK9N2RUE8ISw7dgBf/jLf6+oYxuOneEZDgw45EoQj7k2bSC7V1SS19HQSo6Q6XrhA8hwepn0zFKLzZXiY6vi995KIhZRke11dOiOorg5oaoqtEEhDAyXgtWu5z/p6mgMKCrTzJ1FFPwymHibK6eMWqOUaUaqUegDAAwBQWrogmcc0I+Al9SUyhGXHDuAb36Ckl51NAujupqTlpeLbA9xPnmRQOkAVu72dBOMWiiNq/1e+wiD0oSGS2vAw1d+MDNo+W1pIoJmZlC4XLyaZfvObdBiVldErf+KE9rbn5AArVnD9YNCfucI5vsEgt1dYSKeTqOHp6boQyDe/SUIuLmZQvdhVwz0ojPo+PTBREmYdgPm27+UAXKeOZVlbLcuqsiyrKj+/aEIObroiXMm0RJUVq6kBtm7VZDkyQsIbGSEpuBGA/bhWrybBvPIKQ3Eky6e42Fuqq6ykV/2KK3jcJSUkQfGcb9rE3z/8YeDWW0mWR44AL79MVTktjRLwt7/N92XLuM3cXK4bCvmTet3Gt6mJEmpXFx1RjY3cR28v933sGFV0iRx4+23aXwFvCd/0/Zk+mCgJ87cAPquUegx09nQa+2X8iOTYSUQIy65dlPKELAMB/t7Tw+9XXhn5uKqq6Ojp7SWRLF5Mqaury1tCFQk5GNRxlQMDlFR7ekiiPT3a1tnRwdjOQIBhSKmp2ru+cOHYsfGbceM2vvPnc5+hEHD+PMl53jzud/9+knJRkQ67UooSdjDoLeGbHPPpg0SFFf0KwA0ACpVSdQD+HkAaAFiW9UMAz4IhRcfBsKK/SsR+L3ZMRG5yQwNV3pQUSpYAP1+4wBtbqp2HO67CQtoZAR0n6XasTrV040aq4K+/zvXXrNHEc/fdJOHaWqrrXV1U4WfN0tLvwoU62wgg4T7/PLctefDz53ubK8Lls5eVAddeq1NDg0E+WLq6+BApLKSd8+RJkvaVV3oHqc+0HPOZjER5yT8W4X8LwN8mYl8GGhORm1xWRnW8u5t2x54ekmVaGvDAA1zG6W12O6709PHbth+rV/jRffcBW7ZoIi0qAtat07bJ06dJVIEASUspErt4r0MhkmQgQIlwzhyq6JmZJLveXto13cgs3PiK8yc7m+aA2lqOk73WQ2Ehj+nKK8MX//BzHY2Nc2rAtKiYxnB6gE+fZhHd6mqSWCJsYJs2kWyWLgVmz6ajo7SUHvNFi9xtbxUV4z3T+fkkXPtvXV1j+/F4eZUrK0k4Dz7I5Xfv5rrLllECDQS4/5QUEiZA4mpsJLk3N1PS6+jQyy1cSGl3xYqxXvVw42s/ZnsUwtAQv5eV8Vj27aNTynmOfq+jcz1j45w6MKmR0xj2QPKjRyl9SLm1l19mQYoNGyihJSJYPS2N0pJINw895G5727OHRCYl39as0XUvvfKo/aqlTntfVpbO9ikp0VIlQHuiFPmwLJKZqOJe2/c6d7djfuQRSpaSDx8IkIQbG3nuhYXch4QSeV2DSPvxY+M0EujEwBDmNIXzBikqovNBnCDp6bT7VVfHHzDt5Tyqrqa0Y08RtCwS5vXXAzfcoO2D4bYD+DcvuNlHRd0eGQEWLKBjac8eLpcxWtmrvp7E2tSk1z17ltLnV7/qTTLOY66p0SaI/n7gzBnuNycHuPRSLYm//jrXy8nxF7QebmwiPUxMkPzEwRDmNITzBjl9mhJlfr6uRZmeTvKSdg6J9rjW1JCEnCmCIyMk6mg9vn7jRp3EungxpcqyMuDqq/V6zrCh/Hx9vCMjtDu+/TbJzbJof4xEMvZxT0ujDVQpEnRWFq9DXh6Xy8+PLBFu20aPPkApfMsWfnb+LrGfs2bpdNG2NtpjnUHy0Yy5QfQwhDkNYb9BWlt17GFvL298KbsWCFDyy8nRds1EqWzOFMFgkO/NzcD73z92WT8eX7+lz5zE6qyMJOtt20a1WCk9Fjk5JMiaGuCdd2hTLSzkcUvRj3AkYx/3vXu5vUCATqasLO6nuprS7npHpQSnRPjd71LCzc7mb/v20c5qWbS12n8vKOD3nBzGeaak0DMvsaw9PbTnRjvmBtHDEOY0hF1FO3WK0mRxMQOqMzPphGhtpbSzfDlvzPp6kkmiVDa7l/jUKV2bMj9/vEfcr+feT9yoG7HaKyMJtmwhAZ09S9U5I4PH+3d/x3VPnyYRKaWP99QpXYLO7aFiH3c532CQD6n0dF2FXmpkijTY3c3vku20axdJMSdH71spqvdKkfzl9/5+nsfICIk9NZXnsWaNjmU9dy68BBppTI390z8MYU5D2NVSuXEBXeXn7FlKKqLOSTWdRKhsbimPElt5+jSrDb34Ism6slITSaQmatHcqH4D8rOzdQm49HQttTU0kCxDIU1MQ0Mct8WLvR8q9nGXAsp9fdxOdze3tXw5pcvvfY+klZmpK9Tv2QN87nMkuP5+nTIJ6GrxIhED1Biam/ku5AyMrRGQk6PtpB0d7hKoXzODsX9Ghgkrmoawh6GIvW9gAFi1isHea9dSotqzhzdEbq4mU0EsKptbyqOE0EhnyEWLtDr6+uskE7ebL9mhMrt28ZyvvZbjde21/C6ZPkKkAwN8uDQ3k2SWL/cullFRQVV8xw6GK507x1dWFk0iPT18iPzud7pYyOAgpb6hIX4XabOzU1deAjhOGRl8iZe/o4Prp6Zy3awsfu7vpxQJcJ8rVnCMm5sZEZCby2LKkuEUruCHKRISHYyEOQ1hV0ulX87SpZSaTp9mqMu6dTqLZe9eSk/2eo2iJkcj5bmlPFZXA4cO8TjWrNH7KCrS0pjb9pLtqAjnWb73XpLz0qX0mre3k5gkQ8e5PMBx2r17/DoFBSSxjAwSV3U1/wsE6LFXitfEsvTDbc0aSp+trVyvr4/fAwESrVSKP39eE6102+zu5jabm/VDU2y98+axa2dKivs5RDtOBuNhCHOawq6W2kmvtpY314kTvKkWL+ZNfvgwSVPU0/x84Pbbo1PH3EJ6rr5a3+DR3HjJvlF7e4EnnqA0lp3NkJ/iYt110hlb2tqqVV6B3fZqJ3h5KOzYwXGUfPrWVtqKJfd+eFiXnhse1iFIp04BK1dSfe7upiRZXEypsLERePNNkmcgoAPx29p43bKyeE7nz4+X3mPJ/JpJnSwnAoYwZwCEPGtqgC9+kTepdGJ84w0tkUjvm/R0/rZnT3RSXqSbK5obL5k36o4dNA/09elxePVVntOXv8xl3OIrw4U1uRF8fr7Or29t1cU2xF4qMZ+WxWNRillSAwN8oIlEax+H2lpqBhLX+sYbOoZ0YEDv9z3v4br2c4ilpN9M6mQ5ETA2zCkKCZD+6lf9pznu2sWbSSm+hocpiTQ18QaUQO7ly4HycpJKLMV9namYR4+SMKTsmVt6X6Rt+U0j9IPHHuNDY/58SpBK8bz7+yNn23iVw3MryFxSwu13ddF2qRSJMiuLD6biYl09PhjUNTTtcBZYlh7u3d1cXsKFLIvbz8rSkqbzOsVS0i9RZQAvFhgJcwpCpJ3hYZLdoUMkwwceAG6+WS8jKZGSaSOhPmfOcJmODqp1Q0O8+ezhM5dfzs+S7SJ9w4VY3LJf7KpsdfX4ij+WRXJwtolwQzJbzjY10YaakqIJamSEzqlwCOd9d5PEAgFeEzF/FBfTPglwjAcG+ABbuZLjX1urx7mykmPllLSdPdw7Ohg7KjZRQPdEv/ba6M4hlvM2GAtDmFMMNTWs2C2pdwUFvOl6eljId9EiLieEKi0SOjtJWCdOkMQ6OujJTU/nDZc6eqWl/3dPD2/Agwcp0WRnk1BaWqjuedk05eZ66CHe7M5akbNmha/MY0eybtSSkrGkA3AsSkpi36YfgrcTn1PV7uoaW9quq4uk7iRitx7uEg4lCQmWRc0hEdK4QXQwhDkB8OuJFslSKppLqbJgkDdTe7sO9xgeBg4c0HbJ7Gwu19fHG+7qq3VRiqVLqT4DvNmkpqTUmBQJU1rk9vfrEBPA3aY5lb2rd9/NausAx+XCBb4+9an4thutBGq3BXr959aJ062Huz1BIBhkURUjFU48DGEmGdEEBosntqBAd0MU7+rcubr3dWcnJUvJYBkeJpnm5lKKOXSIpLl8ue4tIymBnZ3MRNmyhS1o58/XXt/nn9f2M4EXCZaVkYTt6nxx8fhWs5MBMVs89hjPv6SEZCm/JwORJNBw/4UjYpk/wSDNKEK2knduMLEwhJlkRBNvKFJbfr5uy5CaSsmvqYn/SbXwlBSS5dAQlxkYoPq2fz+lknvv1Z5zuVEliNsZhhIKUXppbeXLrrp6ea4rKhigLep8dzdJ+qabpkaq3c03J5cg3eBFfPGMRzJtvQbRwxBmkhGN6ioEJnF5bW0Mjk5Lo3TZ3MybrbqakmJ2NiXLgQHGHaanu6fEhVMj7UUg8vJIep2dlHDDpTWeODFWnc/NJYnu2cMAb5NqRyQi9dA4ZaYODGEmGdHEG4odrL2dpJeRwc+zZlFqFO/q8uW0WzY3k1AlqHnWLAY/R2owJqispDTZ0UEpMz+fbWEbG6nW33RT+D40dnUeoAnhhReYAWRKjRFeGsa2bbxOU6ngxVTQDKY6TBxmkuEWb3j2LFVftxjLjAxKeNJpcNMm4JZbSJIrVuhtBgKczJs301EjTbn89MC2IxSig0ja2i5dyu/Ll3u3bgDc4xLlezSxnTMdzjhLgBrBa69NrZYTpg2GPxjCTDKcgcGhkK5IY5+YO3Zo4/6NN1LaO3+eKY07djBAvKLCfZtz5gCXXKLJsrWVksLLLwN33gl8/eveE9+L+CJl3HgFnq9ZE9v2ZiqCQWYZPf88r2Fr69gCw1Ol4IUpwuEPRiWfANhtUA89xJvIqaI99hiXke9Ll/IG6+0lUZaU0Da4aJHenj2XXKSDpibaEXt7qcIPDbGiUFOTe93IWFLjWlpoU739duCllxiQvWRJ5BCaiw01NTSb9PTQRtzfz2vR2wtcd93YZZMphftRtadymNhUgiHMCYbXxGxq0tk3AO2K5eW6ig7gbZcUiXPbNpLl4KBuo9DRQXLr6Ai/bjgvrDNDRiTdigpdXf3ECf2/G5FejLawXbt4DYuLxxZZlnx+OxIhhduJUQqJtLTo5njz53s7nUwRDn8whDnB8JqYJSXjq2YPDTG1r7VVdyB0PvHtN0l9PeMtxWNu335Kyth1/UgdQpRCkOFgX8aNSO2kW1QUeXszAfJwTEnR5pKREY69NIZLlBTu7DckHTszMmgCOn6cUq4chzw87QWhnamuF6tmEA7GhjnB8LL93X0330+fBt56Sy+fnc0qOK2t45/4TkN9WxvJ0rIYzA7QOdTfTwKVdSMZ+CVFEvBHluFQUTH+Jdu3v2YivOzDUvA3kQUv7DbI2lpdib25WbfCkKLD8uC1z4Nly0iWR44Av/0t8NxzTJvdts04fuwwEuYEI5wKvGgR88iHhihxXrhA9c2yKAEsWjT2ie8MWZkzhyR8/rzOOxbizM/XucfhgukLCvg5XqIMB+e2T5yYmRKom324ro4q+qOP8tpLgkEssGsJ1dWsgg+MbVsC6GpJksElD17nPMjOpjmnr4+mBCC8/ftihCHMSYBMPJns4ol0Vs0W1VyaazmlkIYGql979/JmCAR0ZW+RXoaGmAr56U/rdb3sqCdP8nMyydINbgTqxHQkUefDUQpnBIN8MMUT1O8MiD95klleVVW63xCgW3GEQrr3j6jajz46dh6cOkXClHJ4AD/b7d8Xe6ymIcxJQLjsD7uNs7BwfNUbe6vc3l7apkT9CoVIkHl5DDO64Qb3CR3OjjrRZOmGSBKoYDqQqJ8IiViC+p3S4fLllAarq7mt/fv5++WX63kiy4tG45wHUoDFXrNTJFO7Cn8xZ3EZwpwEhFOJvcJ81q0bP1lrarTKLUhPZzO0r33Ne/9u+2huBj760cSeZ6LgRuJuJDrVCTSa0J1IkpxTu5DU1LNnKSVWVdE0c+gQl1+7lgU77KFora0MoM/P5+/BoC6CDJBoW1p01MW2bcntwzQdYJw+kwC37A+5cbwqYJ84MT6wODWV7+npJL30dEoU0nXQC859BAIkS/FsTwf4dSZNJYeS3yQBP1k3wSClyIEBzh1pe7FmDfDgg+zcefo0yS4nh/Zw2Ya9AtJVV3F7e/ZwXxUVfAh3dOjWGFKJ6rXXtKovuNhiNY2EOcGoqeFEPHSIdqzFi6l2228ct2ILTnsToHvK3HCD/k0K00aSUOz7aGmZGqp4vPAriQomWiL1myQQb0fNmhoWm1aKcyQUokq+dKm2l9u3b+/wuWkTJcnnn+f68+ZRYykspPRaUzN23C62WE1DmBMIebIXFzNfvLubIUSXXEIpz3njOGMsBwfHFruQyuL2/uRe6ruXrWkqSWDJgNeDIByRChJNqH5LtflR3UMhahNubS927dItgKUpG0Bvd1oaP3ttv7KS5pxQSMeQ2o9/z57x8+1iitU0hDmBsEsOUkG7rY32wy99yb2LoZDe4CDj4gAdWGzvKWO/AaOVUJIpXfb18T0zM3n7iAWRztkPoQLRk6qfUm1+sm5kGbe2Fw0N9HKfPUv1OhikI/DChbFZY87tB4Paqej2gE5Ppwo/a9bFW5vTEOYEQCTFp5+mdLlkifaAj4zQjuhVfV0mtUzc5mZKCfbJapdEJWtDug0K3GxNiZAuhRC9YJdkpH+5H0w2wfp9iLiFQNkRi5TqR3UPt8y2bdRgQiHOlaEhxn8WFOhYXLf4UAl5Skujw+iddzjPLrtM10a9mDzibjCEmUC42Q0B4Hvfo63xwgWqUK2tuie1lw3ITS2TtrEPPjh2n071u76ehGOXDsJVTo8WTpJ0HqcX/C4HRCbXySZUgR9J1QteZOqlugNjw8o2bhyvXQiZSSvenh5megUCfIh6tcwIBjnmp09TxS8oYA/1zk7g9dfZQ+hiJ0vAEGbC4BWj1t/Pnjw5ObQ5njtH8jx8mN0ZvWxAfoshuKnfS5fSyJ+f7y2hxCJd2okyGvKLBZG2H45QpwqZAuEJNVyAvlN1d5tfu3e7k5jdvhkIMGtn0SKq2LItO1lWVFD7mT1blx9sb+d8LSjQBaEvdrIEDGEmDF52w1df5ZM6PV3nc7e0UAq84QZvG5Bfj6qXJNrbG9nW5Fe6FKJMNklGg3DHMl3JNFyKaDR26XD2TTfi3bqVc1MprcZLi98FC2ILHZqpGUEJIUyl1GYA3wEQAPBvlmV90/H/DQCeAjCa/o/fWJb19UTse6rAy7MpT3VBVhaf3IODrGheUzNWzbJPrIwMXXVmzRp3acIuiUoqZVsb88q9Jmk00uVUJMtIiJVMBRNBqkePssBFfT1DdzZv1hX1gbHkKY4cv0Hv4R62bsQrc7S+nmYje4O9/Hw+9EMhztNwLaLtKaDNzZRsZ1pGUNyB60qpAIAfALgFwEoAH1NKrXRZ9CXLsi4bfc0osgS8g5LLyzkJBwZoVB8Y4Pc1a7wDlO3V12+4gSpRf7/7fqX6kVQ56u4e2wjNq9JMJOmyr4+vuXOjJ8uUFCspr0RAzifcS8493CseHD1Kqa6zU9sJt27l7wJnQP7s2f4r2XslP1RWuidNZGTootA5OfSsX7jAUKXjx6mep6RwXn75y3y3wzmPq6tphgqFZl719kRImFcCOG5Z1kkAUEo9BuAOAO8kYNtTHvJkPXp0bKFWeap/4hMsl9XRQTJLT+f/W7Z4q1nO6uvh1C+5Ob75TU702bN1MLyfRmhuiFaqdCOz3ByXBeNAd4/7fpwYGVFx78uP/TQcaUaSUJ97jtdp9mx+l/fnnhsrZQoqKoCPfxz453+mqaW4OHIMpFeBFzfb+OAgH7KZmTyWzk5qKxcuUCMKBPi/OJG2btWV/2Uf9nkcCumwuWh7TE11JIIw5wE4a/teB2C9y3IblFJvA2gA8F8syzqSgH1PKuz2oMpKTqjjx3kzLV8+tmyb08juFmYEuFdfl9+9JpyzylG4dSJl9fglSyd5JZognfC7/e6e5JNqvA6p+npKlnbk5vJ3L6xYAfzn/wz88pf+Ktl7OSE3bqSzCNDqen+/zj3v7mbM5mWXMUhdHEES/J6TQ4nT/iB2mgtyc7lNKScHzJyMoEQQptvsc87aNwEstCyrRyl1K4AnAVziujGlHgDwAACUli5IwOElD/Yna2urbnvb3T3W1uPWf2fWLC0pvP02iyNImJG9+rog0oSTZluhECdsfr5uuhbO9uSGcIRgJ8pkk2QsiHRMfiTVZBGqSKbFxdQ4pDo6wDkzb1747a5YAfzP/8nPkeI/vbSXEyfGhxRt2MD5c4ntjuzq4jxsb+dcEoRCVN3tD2KnHb23lyp5Zmbk/vbTDYkgzDoA823fy0Ep8s+wLKvL9vlZpdRDSqlCy7JanRuzLGsrgK0AsGpVVWIMV0mCPFlbW0l66ek6v9vLyG2fyEuWcD2ldFtdqb7ulALCTThns62ODpoIZs9my1yRLm6/PbJ06XWzx02UnZ0xrBQGosdGCT/HHklKjZVQZWw/9jHg+98fn2K4ZYv/bYVCwA9+wODy1FTaxO3ViMI5ibxClmQZOZ6776b63dOjywcODNCkZH94i5OpowM4doxaTn4+74c9e5gdNBMcPkBiCHMvgEuUUosB1AO4G8DH7QsopeYCaLIsy1JKXQk6m9oSsO9JhTxZT53SYUMDA3wCi5E7XJ5wYSEly5MnSXhXXqlJcc+eyB5ygbPZ1rlzNOTn5Y0NTXnpJe+KROFsckKWvojSixhV/LbFP8Oy/BNwDMQa7jwjSah+yPTSS4HPfhb43e+YYVNeznbIixbxOkSygR49CnzrW7zWaWm6h4+9Mno0Tc0i5bhv3UohoKCAZBkI6KQM+/pf+xolykCA0umqVbr+50wgSyABhGlZ1pBS6rMAdoBhRT+2LOuIUuqvR///IYA7AfyNUmoIQB+Auy3LmtLSox/Ik7WtjWTZ1ETbTXk5idPN5uicyIWFnFRXXqnDjERlv+EG/bQPB2ezre5uSpoXLuhlcnJo+3JDOLulL7J0klciydEN0WzfD7FGQaqxkKkbiV56KV9O2B1KXsT53HPUaqS0X18fr6+9Mnq07ZNF6hQnprTQ2LQJ+MY3/MVUdndz7qenUxp9+20+7GeCs0eQkHqYlmU9a1nWMsuyKizL+sfR3344SpawLOv7lmWtsixrrWVZV1mW9Woi9jvZkCdrZqbOxZ03j0/Y/ft1q1M7vJqgufXb8RuS4Qxpys3VYSGC5mZ3D6wgJrLs7NSEpJR+ueH118czdm0tf08m7Mfl9gL0ebi9okBuzvgXMD7MKhzsYVxeIUz19bpPjx32h3S40CIveIW5AXyYP/gg5+muXcBXv0rbuISt7dpFjUaGVTSumpqZ4ewRmEyfOFFZSdVDnvLBoLb1HDnCiWV/KkdSf6IJUBY4pYniYt4kFRUk5Z4evjZvHr+ul90yJcUKT5RAdJLe3LnAU08Bd9xB3bO2Vn8P5x52IpJnJFpEOodwpOlDMnWOoV0KDae+yzURidMubc6bB7z7rvZeZ2byMO2dQQHvykheWTiRsonCtagQ26hU1JL+RefPj1XfpzsMYUYBr+IaBw+SmFpbOVGys2mIFzJyZjqEU3+isT0JnCS8cCFw0026MMPs2ayo7pQwveyWnmRpJw+/ZClkmJZG6//PfkZd9PBh4Oab+bs9knrvXhrAFtgiJM6cob3jiiuiI1dBPCTrdZ5edtQIJGofV7tzyYs8584dT5qbN7MA9alTWosYHKSEF4mcIpFeuIe1W7xlbS3wla/oquxr1/K4urt5L2zYMHPsl4AhTN9wm2jf+97YklhK6RL+mZmcRKJWAyy7VVgYPn3MLU7OT0hGuDqL4WIvnTeIp8oYjVTpJDUhxOXL6T3Ys4c9FJYvH79uSQnw7LPArbeSNM+c0d/t2/KLnp7wJBsrmbqNg5NEfZKnSJ1+SXPFCuC//lc+aA8c4DJXXUWbdyRyCidFRnpY2wlVIkNEkiwupuCwZg3jgWPx/E8HGML0CbeJ1t7Oz8uX67CiYJBpiqmpJM+9e5l5Y1nkieuv56R79VVOquLisaTqFicXT5FWr7xxN+nS02bphyztpORFamfO8K5av57v5eVjJUmA32+9lSS5Zg3wzDPABz/oLXFGQiSCTSSZOsfHJ3lq4vRW1YU0BStW0Bljh+Sfh6vBefQopb+eHj7QZW4+/zyD6d2y1eRhbSdUiQwB+N2tXuu6deM1qFjm8VQq5GEI0yfcuvR1dXHSSHjQqVOcMBJaNGcOP7/9NlX2vDx/6WN+qnJHAz/SZcxk6YcogbGS4oIFJEv7dzsWLCBZ7tlDUnzrLd4pInFu3Qrcdtv47fslUSCy6u8lmUZDovYx80GeuTnhpU0hTS/veUVF+ID2mhrOL6V047Q9e5hSW1DAOTc0RD/cm2/yEt19t56Ldlt5V5e214uiYK/X6tTITp9mHvq8eVzeL+lNtda+pmukT7h16Tt/nkQIkPSuuIL3woIFOrRCWpc2N4+9wGJ78ps+JlWNnN7JWOBlu3QlSy/Pd329JhRpjB4OTU1jyVEkyaam8cvaJdHGRubpPfssxfJnnyVZvvUWl5Pln32WBCjYu1f/b9/u3r38LKq/1zbknJwvOW/7+fuBm1feBXbPuhdiLf6xaxelR8vSc/PCBc47sZY0N1NCnTeP83X3bj3X7J53OR3JUAPGzl+7RtbezpRhpXRUSLjCMM5jjjZqJJkwhBkHJPvBHiJ0/jzv77VrdfvbnByd+SBYvJiTNRh0Dy+yw0/bVTeEK+PmJl2OQTjvcDREKbjiCndJEhhLbGfOMGG6vJxpSrfeSnLs7QV27qTkedVV/P0XvwB+8hN3STUSIdpVfyFiN2nXiXgJ1EmcLghHmvGU2WtooBRon5sANZ/CQq1m5+TQRllTw0iPb35zLGl+5jPAP/4jgx285q+9KpJ9u5Ly65f0wrWkngwYldwn3Lr0bdigA4jFvnLVVTq7QZ68XV2aWAEdfjR/Pu/fd9/lNnNy9CSyS6PxtF11quO+pEsvNdxOlImCENuKFTSiPfooMwE2bODAyHG89BLZ4skndQHHM2cogV5zzfj6d05b6MGD4wnRrvqvXx+ZLN1gHwu7Gh9JdVdKO4lcVHRRzxOJsjKqxs3Neg4PD2sVX+agdDSdNYtk2tY2Xg2OFB5nt3fKdqXOAeCf9GKJGkkmDGH6hFcV6xUr+MQVeOXl3ncff7NPsM9/nr898gi/5+S422hiic0MB6d0OaFkKSRoRyAAvPACiS81lSQ6PMwcz+Fh4MUXgc99jge+fTvw4x+zNNTcuVzn8GE+nQ4f1ukzhw/TiFxQoEnz1Kmxdk4/TqhoYB8bP8RplzQ97Jpe9kw/KZROVFQwHTM7my9p0ZyWBrz8Mh/+ra18HhUX61TfOXPcU32dRWXsDp6KivHRHpal7Z1+SS/ajKVkwxCmT/i9cJGevE6J8KGHvKVHea+uZr758uXu9iI3uIUSOaVLTztZIsnSSZBuOuU11wAPP8xCoBUVrEry8MO0Vb70EslOmtNccw2JrroauOsu1iVLSeHyn/yk3v7QEMkVYLzNK68w4foDH+AxnTtHIpYexrfeqp1JZWWaWKN1JglkrPwSpwtpekmZTo+5X5w4waG0S5hFRWzHK62fOzs5dBLlMTDAeRfuAe3Vb0iatOXmcn9Ll/L5Jeq7H9Lz28t9omAI0yeiuXDReLm9pMfqaqZbzpoFrF5Nh9O+fTQLxFMuy7kvV+nSDdGSpZ0o/RjeZs2iUff550lut9zC6OyREarbRUUckMOHub3ubkqOIyP87fbbKT61t4+tMNLdDfzpT7zzhSHmztVxYBUVJE6AxufvfIeVS/7hH8bHgMYCebpKL4pw8JA0w8VoRgOxYdq7ib7xBp83117L762tFPYbG0lw8pDu6tIPaGeYj5il3ErJifblXCca0kt01Eg8MIQZBZJx4bxsNN3d/E9+r6oiiR46xCyecBMurn7jbtJlNGQpROnXO1FXB/z0p0xfGRlhefqDB6nbiTH4ox/lstu3c7vr1ul4rVCI9s7qakqhR44AK1eSdJcvJyN0d1PvXL6cv19zDc+rqor7uuYaRoCXlHB7+fmMrampYeBsPGo64E/aFJumA4m0ZbrNtY4OSn2CwkJKhnv2cH6JmUge0G7S5Guv0XZvh1MinUqkFw8MYSYQsQTYeqn6Tgd0YSGdxo2NY22mXvCjjo+TLr1UcT+IVqIUtLSQLF96id83bCCJDQ6S5AoKtMNHYrCuu46eiIMH6YUTe8Wtt1LC3LmT5PfrXzMc4fbbWeLn179mHbWdOymFSprVzp3UQZ98kv8XFVGc37CB5Gw/t2XL/J+bE3KBwyGMPTNeuM01MRnbkZ5OAnTrOupmQsrP59y3B8zPlArrThjCTBDCBdgCTIuUwgTOYq9u3SF37Uq8dzCmkBQ/0mW0UqUd69ZRyhTU1lKynDOH22tvZ6xWZiYlx7vu4nLHjtGFK8fX00Nivesu/v7887yDc3O5LMDvIqIDlCrXrSNjPPkkifjdd/kSZ1JZGYlV8Jvf8Ng2btS/RZt55KWee0iZiYKbWemBB2jJePllCu3p6XxGfe5z7g97NxNSZSUlUmdB5JlQYd0JQ5gJglfoz7ZtNLKfOUOjOqCLvd5+O43jbrUvY/UO+lHHxzl73GyXflXxeMhS0NJConv7bTp+liyhGt7WxoF95x0OwLJlJK8dOxgJHQzqVKnWVt7tTz9NcVrSWdLT6dWQgNj586mWAywz9a//SrX9zjt5LpK0vWoVSW37dh6bkGZeHvcBMAwqIyM2O6cfm2YS4FZt3cnR4TjbTa0PJ5HONBjCTBC8nDcvvMD7WkivpYVmsqYm3sdXX+3uIf/MZ2L3DvqJvRyX1RNL0d9oyPLAAUp4dmmtro4DIlLmm2/yzguFdGHIPXt0x7DHH9f7nT2b9sr163VU9R//yJMvL6cE+Ktf0bZ5yy1cJieHhHfokA5dev55EvTatXxw7N1LUrUskuY77/BVXs5jPHiQ2ztwgGaD116L3s4ZSTVPolruxK5dPN1Vq/Rv4bqNej3IZ0oLikgwhJkgOBtBnTpFbbKzk5JlSgqJLyWFdqOhIUqdFRVjn9Z2Y3kiDeVRCYB+pMtoJcuiorG2w7o6/V0+/9Vf6f+2b9fS49//Pbfx3e+yPNwll7Agx/LlFOErK0lgZWU89vXrGas5dy73e+EC9/PHPzJr6JOfJEE+/TTwkY9Qqvzf/5tq//LlXP/mm3kcq1bx/Y03qKLL8Y+MaDtnaSnHI1r7ppuUmWS13IloY3ynWpjPRMMQZoJQUaEbRvX303QWDNIg3trK34QsR0b4npJCgcXerS8eO2VM3vFYmpPFooaXl5Nsdu7U9TBF2mxpGUukLS0kr7w8nRpy5AhF5d5eiubDw5REi4ooPZaW0vZxxRW0ZebmUnK87jo6fNraKG3ecAPF+0OHuJ/bbuOdf/o07af33afJUgg9L4/kWFWlj/HwYX4XO2dqanSk6ccBNAGItf7qxUKQTphc8gSgpoa2SClsIK12Fy2itpmVpetkjozwXk9NJd+IShMpn9wvwnWFBDyC1WNRx2OxWZaXkyz37eP7ypUkJVHVhaSKikhkixaR8LZvp2fi2DEGDM6dC/zwhwz96eujBHn2LHXL/ftJjrLuwYMkxmee4ZOpu5vL/9M/8Yl26BBbOA4PM73lpz/lskKWR47Q5inkuG8fj2fePDZikofA0BDP8Te/oeTqzI9/+mld+CNJiNR61w2RWqYYjMVFS5iJrP4jDp+FC7W90rJ4fwG0U0qRAsui8CPNohYtiq7vSrRwa0ERtqFZJHV8715mydhRV6cr2YaDXTLbuZMnLYTzxhuaiMrLtUR68CBPorMTuOceXbE2FCJJioE2K4uEOjJCsb6iAvj97xmadPYsw4t6eihl/vu/U2rcv59VJDo76bQpLqYT55FH9HbfeYfv588zhOHxx0m6oqqLdNzSwoGeM4eB8L/8JYnyzBkWCTl8eHz8TowIl+UTrhamG2Lp/XMx46JUyRNdY8/en7y7WzeB6uuj43fpUvoFjh7VebwXLvD1xS9SA5w26O0d68Cxq65OHDhANXnVKkpq77xDqW/fPhLev/wLbZGXXsqA9bffpuFXKUqfLS0k55dfpqh++jSD0+vr+S45goWFjMBOTdV19rZto53yuedos9y/n6QsarDUNUtL4xNMpL+yMkqoZ8/ymCWMaft2TpTeXhIn4H7e69bx/dVX6YG3LJLzxz8efwC8DdHmkYfDRKnYU6kQcKy4KAlz1y5qYDU1Oqe2uNhf9R832PuTFxTwnh8c5KQeGKDmuHYtC3XU15MHSkqAT30qcWQZrg1FQlFaqh04YosU+6MTRUUkrmeeoTNF0hkvXOBd09nJ+MeiIhKfUnyqnD9P7/Xs2cATT1A6fPdd5oXu3q1z9VJSdMe3UIgifloaj0kpEt6Xv6yrHHV08GIHAjw+pXihAgFKskuWULpeu5aSYn09yV5y2J9/nhLosWNc/s47uR2786qlhWNUUaE7Ynp50cPFZE4BJJLgploh4FhxURKm5GlnZOhiwMeOxV6YVUIt2tspTAC6fUVXFyXKZcso0GRkTKFJ4pbd4wd2W6Q4QtzChgCSz+9+R7KRQPSCAj6xlixhPGVTE7d3//2U5F5/nQNZV8ftSf/iJ54ggXV2cv2BAdoO+/pIhFLCp66Oyy9bxm1LOFJpKe2cr79O1b6/n+ff1sb1WlooQebmkuTsknFdHQPef/97kuzICB8Es2ZpKVSIc2iIY5OezmN/8cX4KyFFQCz2SzcISR49OrZdRbwEF0+JwqmEi9KG2d1N4SQ9XavPKSljq59HA7EDZWQwS6+jg9yQn08BaO7cqVEtOm785jckA7st8pVXGEguUqdk7Yiqft11tD0ODVEEl8o899xDqbO/X4vle/bw6bJoEe/WWbNIjHPmkLguu4z7lVaY8+aRJPPyWKEEoMSWmqq7cT35JP9fuZLxL0ePkryU4oXJzdWBsXV1ZIzlyzXxv/MO9/3+9+vk/rQ0/jc4SCZpaBirnkuVpL/5GwbUWpa2aUaJaPLIo7VfOmEvVC2mpePHeWninbtTrRBwrLgoJUwpkjowoPuSjIzEX+qxsJD3r2T01NaSC8TkJfuebpPkz5gzR5OBSFUASWXVqvFhQ0Ig775Lcjt+nIT2kY/obJ1580hWIyNaeuvoIDk1NPDubWqi56ymhgPa2soL2NvLJ9LwMMlOPre1aVHphhu0wyYQoF3k9GmSb10dnT0vvsgnWn8/z+O3v+WTLxQi0UpU9/bt/D5nDqXWtDRdYemmm7SkvWoVl+vvp5R7zz1U4ZuaIkuZljUuaN1eqShcT594UFPDyuqiALS18TRDIR0OG8/cnWqFgGPFRUmYK1bQqWqvC+gsexUtJGOipET3Zc7KIiFLDcvWVpoDQiF65qed0bu0VKupu3dzAIU4pV1herpW1QGSjJTczs3l/zt2cGD6+kiYGzYw0HxoiMRZUMDB7O4mMaalcZtFRfxt9mwO5pw5HNyeHpLf8DCdLg0NzBoqLCRZZmeTrIJBOpbe9z4ue/PNTMVatIgX68IFHsPVV5P4Zs/WNsydO3k+c+aQ/D77WbLX9u188r7yim6VKBB39oIF7kQ5BeIwAS1ZtrVRKxoYILmlpnIIRPNyElw0Ns6pVgg4VlyUhCkXT8pXycWT2LNYjN3iKU9J0QR57Bgds889R3W9s5N8cfnliTV6h2ulG0+KtyskkPv553VwqaivIn1K4QqxTYh+96Uv8f9vfYtEduedHOw//YliU3c3B2pkhKSclkaV/pVXdHjQvHkkS1HjJSOgqIgS54sv8re0NL5XV/NJODDA/VRUkDDnziVJt7dz35s2USr88Y95bpJbvn07z6eujg8HCSMCKBHfdRe3deSI7jkUCIwlznBIgMPnxIn41HGxL0qXUynA0d7OU3GWeAOid+LMlAyhi5Iww128WL15TpWjtZUTubiYT+mTJ3V6sn1yJ8roPSEeckDbL2+6iUT2/e/rKj+Aljg7OrQHeP16LakBwD//M216Z89ykA8dIqFefz3De3p6eKfOmsXtZmaSAFNTKbUtXEgxPRCglCsOG6V4x1dUcNmeHl7gYJAXdvZsSo9SLq63l1Kz9EUGKAW/731cPzOTF86ucgP6CXXLLZQ229t5fnPm8Ol4//0cp+rq+MrBTRDkYb94MQVwgEPf18c5K/ZLO8E5nTihEE1QX/kKh8pNyJgJGUIXJWEC3hfPrzfPKYU6e5hUV/PzunVaa0xLI48Ipp0989w5qqoSRlRWxvzuxx6jWn377VxOHCCrVuniGna0tPCu+vWvSUb9/RyMmhpKmAMDtGVaFgnyiitIbhcuUPpsaqLU9/rrVHXr6kiW4iDq6OBy3d3UMevrudzQEFXwnBwdFiSFg7/3PdblvOsu7vO558ggJ0+SoA8f1rms9jAq6TP02mucLPffr8fAr5Tpge6eibFfysO+sJBBDadOUT0vLaVSEKnMW2sriVZyCqZryJAfXJRe8nDw481za3srPUwkY0K6TIp6Hm0f8imJtjZNFgcO8ERTUqjKHjpElfcnP6Fa2tLC5ZykceAAnybbt5OEBgZIkoODOoA1EOB2xQP+7rv8/OUv0+ssKY/vfS/XzcggWaak6By/8+f5u6jsEkfW0EDRv6KCzp/Zs+nAKSigOv/znwM/+AGJ9tlnGfQeCnE7Dz/Mc7OHTkmMpvQNsXvM29rC90Z3IoaiG4kIJ5L0yNOn+Xxoa+PD/e67vQmvrEybYKWNrlJa2JjW0SBhYAjTAftEEDiJzau5vPQwefBBClCx9iFPFDIzfTTLiuYmvfRSTRbDwySQjRspoa1cSUkzL4/S2fDw2HUlfbKoiMbd6mo+TUTyk/AiaVOYl0e1uL2dx1hRwTu5oIDEd9llNAmINJmXx4sxNMR1srO5TcviPletooSclsY7+5lnSF7PPMPjP3uWF+z550mQO3cyb331an2xbrtNB73bz0tMFADXl3GaMyd8b3Q3xFDWLd5wospKXsbjx3XLiqVLKQR4pQzbc9C7ujjMAwOc58A01J584qJVyb3gx5vnLInV2sonc3Oz3oZzO8EguUESU6aE0Xv27OirFQnpBQKUvg4e5JPg8ceplr/+OvAXf8GePDU1JFKJ0RwYIFlJA5jDh6kup6TwWHp7ebe2tJB0TpzgoA0O8rdf/ILH+9d/zTv7xRe572XLeFHEcZSaqh1I/f3chtTDbG3lRbhwgemLc+bw4rS361a7p06RWEdGKAkrxde771JnFezbp22WAJ+aXV3aY15aSgYJ1xvdJ2LpEukGL4fmiRO0fNjDfux1Md3WEz+ADM/atf67mk5XGAnTAT/FCOxSqNhvWlsp3OzYQc1RKoXZt/O5zwFf+xol0M98Zprad4T8iooYOpSWRm/x1VdTAty8mZLcsmXAj37EPPCdO0keb7zBp8rKlSS8M2d0NZLOTr6fP08CO3hQS6AdHbxTN2wg2f361/RKj4xom2dJCUkwK0s3RJJOkfv3UwJtbtYtJwIBvqR4aU8PyfPYMZJsTw+JTwqNCMlt306psq6O5715M3/fuZPn9YEP6CpMsm56OoPy16zR24kQUuS0XwLj7ZfRquNupqRHHuHv4UxRXusBnMf/+I+MzJpI7WmyoKwJLFYaLVatqrK2bds32YcxDnZPenU177n2dt6zs2frpvXf+MbEkGK4PHJnaFHE5mfhqhWJJ3xoSDca++UvKTG+8w5J89VX+S72waeeIrF2duq+4d/7Hpfr7eX3lhZdOHTePK4rto6sLF2BvKpKd4Q8cIAkKCWgUlO5vWCQ2zh3jnfthQvcdkcHyWrzZopFg4MUqXbvJtHn5tKMoJSWUiVae80a4BOf4JPv8ceppg8MjLXnil5sz/h55RVu27Ko89olzJ6esSFFch+OquR+HD7RhhM99ND44HH799Onx8YmFxfr2GSv9bza6E71GOPVq9V+y7Kqol3PSJgxwC6FNjfT5FVSwvtXWskMDsZu9E5k6bmYINLP3r1jnRbLlpGIpHrIk0+SgLKzSYaDgyTLF1/knZadTZJ74QUOTlUV7/xDhzQBtrbyv85OrtPczMyC2bMp6XV2kmxSUigpPvssmSIY1PGdGRna2ZOZyc/33AO85z286zs7KT02N1PSCwbZM6i+nrbHuXNJrBKqlJmpa/QND5M9GhtJeNddx+3Z7bnr1o0tSbdzJ5nj2DFu4557OC633jrWpumEh/0yUep4OCmyooKn193Ny9bdze8VFWPXa23ltNi7l+baCZ+bkwxDmDGispJP19tu4/1ln+uhEE1xTs+6HxIMpzYlCmHzk+1ST0nJeKfFiy+SRN58E/jQh2h7WLOG5LdmDb9ffz1te+npJJcbb+TT4x//kRLarFkkyb4+rtPdTWmuoYHOnGCQgxoIcF/795Ogliwh6bW16Uyhjg5KlpmZfO/o4HGdPEkzgAxkdjYJ7dgxSngvv0yS/7u/4/8jI9oYJ98lf7awkKp4ZiZDmqqqaHvZ59B+pFqRFCaZM4dkKWr4ggUkzaamqK9PIsKJwjk0T5zgpcjN5bMjN5ffT5zQ64n5SVKKg0HOzR07kj9npwoS4vRRSm0G8B0AAQD/ZlnWNx3/q9H/bwXQC+B+y7LeTMS+40W8qsSmTVxfmhKGQpxQ8+dro3ekFrz2/be2Jreqy8iIGlt1XRw/blWL5Aa3Oy1WrOC79N9Zu1anDcrvr7xCieqxx7j+Bz/I/3/zG5LekiUUm3p7aYssLaXkWlJCQgsEKF1ef73OGDpyhGpyXx8HWilKjw0NHPCyMpLi/Pm8ywcGdBn8L3yBRPad73C5116jB7yqiiQ3OKjPPy2Nvw0M8LhKS2kzXbiQx7Z+PSutp6QwSgDQ25FMn8OHeY5Ssso5phIkH+E6hUMs2T1OR+TZs/SMl5Vx+OfPH7t8KEQpsrSUwywmY/lv7VqS5mOPcW5O90pEfhC3hKmUCgD4AYBbAKwE8DGl1ErHYrcAuGT09QCAf413v4lAoqS5hQs54U6coHlv6VLe82L09gpD2rZt/P5fe01nHAomNURjwQKSgDgtUlLGBm6LGnrihP49P5+iyN13kzh+/GMayBYtohp84QJJ5/Rp3oGiljc16ZjKW26hXTQrSwernzvH7QUCFIE6OnQJtQsXaGdcupRMUFvLfXzhC8zseeIJkn0gQGZ47TXgf/0v4NvfJmHOm0ci6+wkG2RlcbuSQSTZPatW0WY5dy4l1Oee09XiMzJ0YPtll4VXwZ32ywnoEmk3Jb37Lsly6VL+PjzMIWlq4jyUtiu9vfx/6VJt30xP1x7xnByuMxMqEflBIlTyKwEctyzrpGVZIQCPAbjDscwdAH5mEa8DyFNKlSZg33HBi8j82h6FcIuLeY8UFpIPjhzRT2LA23Z08OD4/efnjyfseEI0fMViukF0tzNneKDr1/O9qoqSnh3l5cCWLZpEV64kOTU3k0gPHqT3WwrrHjlCNbuoiGRYWEjJbv58fp83j/8DlDQzMvj/JZeQZFeupESXnc2nS2Yml9u5UzdQW7aM67S0cN8jI+wY2dtLT/aGDXxiSf7q3/wNj29oiGLTnDk6s2jVKv7e20v76xtvcF9z5/LCP/+8TpWxP0x8quB2ONXxRGf3iClp+XL6vBYu5NwLBvl/UxNPNRQiiZ47x+dNdjaXnTWL6wG0Y/7ud3xuScui1lb+Z8KKvDEPwFnb97rR36JdZsIRb40+O+EqxYlXWsr7JhikM/jrX6cn/dVX9WQCNB85919Zyfs72U2pxtnJ7NESIv1IoPWtt451Wjh7+tghvX3uuosH/sYbJBqlSHbLl1M6PH1aE1JjIwlq/nxKe1lZXCc/n0RYWkoHztq1fO3ZQxIVD3heHtX5EydoW83MpPS3Zg1rlhUX87eFCyki/eY3ZAFp11lVxd9OnaIEK5Jlaiovxq5duubmz3/OFK73v59S5e9/z6ektNUQsjx3jgyyYIFmGJ/wo47HC+fcHx7W2TqhEC+DdDYV22VJCcnx9Gk+e1padCGqri5+f+st/j9Tw4oSQZhuV9cZq+RnGS6o1ANKqX1KqX0dHR5leBIEP1k9XqipoWAhHsMjRzjhJNg9FCLfVFfzPuvpoR+gpUWT4Jo14/efns4onWQ2pRp3Q3qpg01NYwOtRWJqa/PeuMRpilibl0eRRYppPP00T7Knh3fbyAhJtKODOmJGBtXp7Gy9H8kAOnuW62VlcQCDQf53zz0k28xM3uG1tbzDX36Z3vBnniEBh0I8huPH9bF9+MMkvoMHeZw33UTSHBjgMUl61tmztGX+xV/obJ+uLp7L5s18CEiM5oEDdI65ZfQ4L3iMYX3xZvc4535uLk8/P5+CuSRNjYzoCkZNTRTMm5t1l+OSEj4jSkq0VNrcPDPzyIHEEGYdALu5uByAU0bzswwAwLKsrZZlVVmWVZWfH+esiIBYW4yKKi6ewoEB3ieiyuTmUljJztats6uqSKaHDmkS3LLFff9btugUS78B7pGkjpjU8hUrxmelLFhAL7DXBsvL+ST4wQ9INufOkQQlzSknZ6xNr7IS+NjH+H9nJ8Wa/n4O4MAA/29s5N16/Dg/5+fTaRMIcNt//COJvKqKF6Sjgxdo9WqaEnJyaLTLyuKFSkmh5HjkCMlVKV2a54knyCaBgI7vXLKEF2ZoiE/AoiKuqxSJuqmJ4UYADX8vvsgL7JXR4yzpluRwIjc4535xMU9XKd0uaWSEr54ePi+amzk3581jQScpPi99qs6f5xDF2rlgOiARhLkXwCVKqcVKqSCAuwH81rHMbwH8pSKuAtBpWVYYvW5iEGuLUVHFly8nIQJ8Ajc363xamTRSdKOwkFrt8uWaBBPV4jSStOFlA/OllrshUsmytjYeVEEBYxSDQX33S3uHkRESXH8/SWrWLBLV8DCXnz2bg2kPRk9P5/pLl9KBdP/9JMdAgKYCpSghSo/w1lZ6xi9c4Pb37uU20tNpAujpAf7wB97phYVcTymmd0oOejBIPbO0lMvJBX/nHV7cjRt1O+Bly0j6lZUxpT/6ye5JFJxzb+FCNuWzOxxTU3WkVW8vL9X3v89ggV27+Cw8eZLDKM8babdkwoo8YFnWkFLqswB2gGFFP7Ys64hS6q9H//8hgGfBkKLjYFjRX8W730Qhlhp99mLBUg4rK4tRJEuXkieCQU6kFSv0em7q/mTVCPQML/KDvXt5BwE6jUhiEKU6UWkpfwuF6Og5cYLf29t5R61dS2Lt7SXhiY3yRz+iLfDqqxme1NCgM4ne+16tpotU9w//wJQq8Uj86U9Us197jSRWX8/9nzunjXZFRSTe4mJKnnPnUjLu6+PF7Ouj5Dp7NqXhtDQa9a6/nkGHIuWuX69tlmvW0J55xRVkijNnxpNmAtTxeIsF2+E29958k0N74gQJMyeHl6u7WwcydHbylZKiAxQk3n9khHNfnKczTS1PSBymZVnPgqRo/+2Hts8WgL9NxL6mAuzFggsLddfXUEjHXy9frrsiiFozHUvy/znA1A4JaAdINvbe5HV1JNTNm0mg3/oWvcypqTru6tZbKZn9/Oe6f8/rr5OgNmwg+f3pT7qK+h/+QPHnnntIWHv2kLAefRS4916G8Bw7RiPxFVdwmxkZ1COLiylpLljAO7uzk+S3YgVJtbSUbCBtfwMB7rezkyYA8ZgPDVHVzszkdoeGeF51dVTP6+sZg5qaSoIXZ5mTNKeAOh4OK1bo3nL9/RwqSckvLKQFo6yMAkJ9vfa9paTwc14eCXamhhUlhDAvNlRUAFu3aiN5SQnvM6c67QyKn8zqRBJe5GxZ0d2DsbnlYsgCeOfU14/fmDh/HnmEd5S9N/mBAyTLgwe5/ic/SalRpKu8PBLU8DAHTUhWYlg++UnaCRcvJtGlpfHJ9MwzHLw5c0hYSnFAH36YNlWJ5Txzhp0hLYt3/4kTPMbz5/kEKy8nCe7aRbLs6KBEK1k7l11GhpD4SxmTri7+PjJCtpBe6r//PdUKOX9huvJyf03PbNdhotTxcJDg9txcnR9w+jTnjVgjxHYvz7rWVt2eSRxEMzWsyBBmlJCA3qVLeT+0t/P+f+AB/yX5k1WoQOri+oVvtdxNylywgAf9wgvszGjPqwZ0JXLJusnO5n+nT1MaPH2aFc9bW3lX5udzMF97jYNSXKy92XPn8o781rf4PjAAfPGL/P222+hJy8igZDo4SNKUsvd5eZRSm5t1nx+pkFtXR7JMT9du46YmLpOVpRsxiRmgqIi/z5nD0KKHH2aVeQmzamzk56NHx0uXCVLHkwmZlz09uvlmcTGJULzl4g0H+FnSJPv7ddCD9DGfdtqUDxjCDIOaGsY2HzzI79IuV2Iv7ZVc/E7mWHsGRUJRkXcztKjhR8o8c4YEJ6mAUjZNIJXIX3iB27vsMjpPrr2WgyWtI9rbqev19lJKkxz0Z57hU+nkSRK5pFIODNCWeO4cT7qpid7wX/yCx5GdrQMKi4pIsADXTU+n2DY8TPFIanNK+115imVl8a4PhXQnSalidPnl3M6hQyTbI0eY0bRzJ8egvd275mUC1PFE2S+dkHk5PMw5KfkCc+fy9OvreTmysnROweWX6+zWzEw++3JyeAm2bJl59kvAEKYnamoYeH7mjO4zvm8fJ4hEkAjsdQMjSY72YHcpxdjWxvhqr/4piYIvtdyP88ce0N7fT1Fj504STFWVzq3+938nMRUVkSxKS0mI115LwpFiwR/9KLf78MNc5o9/ZFD54cMk1upqEqWkLDY3cwAffpgS5rvvkuD6+nh3z53LYxwZ0Xd+aakOVxoc5HmWl7Pi0k036YB5QJeDa27WrHHuHI9h71566NPTKQF/4QvczqWX8uEQQ4HgqaCO79pFsjx+nKeWn09J8/Rp+tRkmYYG3UE5FOK9sWiR7mTc0cEQ2PXrDWFeVNi1i8JCTo5uNaEUBY6amrFP+p4eqiXf/S4nzMAABaMjR4DPf37sxBEPu2RPyORsb0+MpBm3Wu4GkTJFLXcGtAMckJdeIom1tOgofcnCEQ/55ZdzW+9/P73gH/iAzr9esIBEnJtLtXbuXN7FTU10pqSnU0fs6GDq1Hvfyzs6PZ1kWV5OW2VLC59Wf/gDpduUFB5DYSEHPRCgSt7cTBJ94w3dlfJjH+PFHxjgb5mZJGEhXGl/kZUF3HEHiXTfPpoSpEBJefl4ddyt9uUUQkMDh1mirgBe7vZ2DodXPPBDD/GecBLt1q0k0plGmqa8mwekCI7k2AL8nJHB+9UZbH7+PH0RgI69PHuWKr2gpoZcsXMnk1BGRjjJBgcpbMXbOMqvuuam8o2JyZw9O/xNfcUV46UoIZtPfpLeb6UozS1cCHz60ySt7GwS4MqVDBP6wAd0ebiXXtIhPi0tJKRTp/gEkGDAQIDkVVFBb/qLL3Lg9+7lds6fJ5Hfcw/jY8SxlJ9P4nvrLQ64xHw1NWmi7OtjgHp9PS9gWhpDHfLyeNyXXUYiXL2ax3bppcw/X7OGUvP111NSjlTz0j7GznH3uDZA8u2XZWWc1/b57lam0Akn0SaiHuxUhiFMD5SVcQKIgRvgZ4l8cQabi+oukyY9nd/F/mkv1CEts9vaeI9LsPtEhGK4qXqRcpf/DK+2CvZA9qoq4OabeYevXauJafFiXVxj3z4S66WXsirRSy8xjEhaXpSU6AZJIyMcdImizssjCZ88yUHu7eV6CxdSin3ySXrlzp/nXbt4Mcnw7bc5wPn5vDA9PdxeQQG3v3Ahj6O1lTGh99/PCx4Kcd9nz/JBkZnJKICzZ3kegQCJv3S0loyz4IYPZ49fdTxZ9kuAArnMS2loJk6ecN7uWIl2usIQpgc2beJF7+mhJiZtXvLzY0tdFNvlwoUUViQg+MIFXSorUaEYsUojUWf+OCuyAwyz2bGDRYMHBkg+Tz5Jz7Jl8c66/36SXFERl8/IoAf9hRdIqmvXktQGB3Vic0kJpbi6Oup/+fm6cEdbG6XWvDx64I8e5b43beIgS3ZRcbFWEbKySHZSgPiaa7QzKRTicaWl0Z6bl0dp85e/pP31L/+ShP/ww9x+aenYh4az4IZPZ89korKSkR6WRTU8GBxfptANsRLtdIUhTA9UVrJpWVUV79vBQX522iQFa9aQ/AYG+H7mDLXEQGB8k6nCQt53JSW8fwsKEleVyI8U4lbyzXdBDrvEZK/IvmwZ8E//xFTEv/gLksrtt9PbPTTE9wsX6FFuatI2zltuoRg+MkJ1t6GB0mZmJlVjsTfOmsXlr7uOA9vWxqD1OXMY9P773+sUyL4+DkRGBklw5UoScns7bZ4FBToSOyOD+21s5O/veQ+rqgwNcXuLF3OZQ4d4XidPasnyk5+k6u+EPEjsY1Vby+O0wa86PlG4+WY6eDZvpvVh4cLINvVYiXa6wjh9wqCykl0e/WDLFmqR9fU6w6eggIbvRx7hPdfTo6tRFxbSEdzcPIXa7rohXIiRsyL7kSOUDlevpiRYXc2TrK3lk2R4mNKiFOe48kqG9dx2G6XQ1as5eIEAny6pqRw4SaHMyKCDpqKChPab31D0nz+f9s++PkqH8+bp5jOHDlEN+NWvdMMayTl//nkS9YkTJN45c/hkLC/XdcvS03keH/4wt7NvH8lZKs6fPz+2+CnA89y6lQS7fj3P/6mn6CRywI86nmz7pR3RpOrao0Kk+3AoND2aoMUK0zUygaipYXhQWxvvvcWLx6ZN9veTMO39zpNRBkviMcN5yyU80R5iFHj9FaCsFDmXLtY/HjxIRpde4kKY9kD2V19luuL69SScAwdINCdOUJIrKaEN8cQJqreSpqgUg9cPHiQ5PvEEf1uwgMRYXq5VainEITnc/f2sXitFSE+f5knNmcOYyoYGElVOjm6rAegA95YWts44d45qfG8v1egrrmAZt6IiHmNrKz9XVFBtt2c1uQWqS8jVZZdxTG64geNxxx3cr01y99MZEkhs/ngk+E2qsMcTJ3s+JwOma+QUQGUlhZv3v5/3nTS1l14/iahM5Ad+1XInRuaWIe3J7fROA3x/8smxrOq0xzkrsgN05nR2kmTuugv4+Mdp38jPJ8GIN3zNGpJLZiafMpdcQgJsbKTHuqmJBNzbq73ZK1dSAn31VQ5sMEgpTzzinZ1cVlrnlpZSKkxL4z4/9jGeQ1YWvdtpabxQWVkk5eeeA/72b5nrLlXUi4u5n337xnaLBLicSNmvvqrjUy+9lGT5yis0B0iWwyjcyHKi4WzMF00zM7HJh0IMZN+3j88ne1TITIRRyRMMe2EOgAJKdTUn1q5dWupraNBhF8l6IvuJybQHsluLFmPwQ3cBv9iO9GuuoEp7773uTbt6emi0sheZKC+nY6S9nU+Nw4fHriOE2dJCIlKKd9uFC5TI5s4lCR47RsKTmnlKUTK75x5u51e/4vJ1dTwBaWKWnk4Jdv9+Lr9kic7qOX+e/3/wg1TTDx4kQdbW0m5y1VXAz35GhxTAQsd3303PeTDIc7ntNr5LPc1LL+Wy9r5H69fze3U1JctrruG7LBMGExms7pZxtnUr7Y9+mpk1NPBZc/CgLpw9MMBw1Jqa+Ob0VO5xbggzAbBf4GCQgtH8+ZxA9jSy06epRa5Zw/9Pnwa+/GVOihUrdBW0REwUP6mSmZlaNRcML1iClPdcwdCcjRtpV+jsdLdl2gPY9+4lOVkWJUXprPjTn9KL8NJLvKtWriSZ/PjHvDuvvprqumXpnj/792ubYmMj9zU8TDIeGqJD6dQpfpZc8dRUEmMoxP0UFPB4z57ltq+9ltLef/yP/H/jRh5zdzeXaWsjWR46xNftt1NiXbCANsu77+ZFKSmhd/zDH9aecaeUHQqRiO+4g2S8YAEl9dxcYPHi8G2OHUiW/VIye2pqOATSXrepaaww7BXqVlbGWGJ7oLuUBIinrFuyUocTBUOYUcDtyQfoHNxTpyhADQ7yHhwZ0YWGCwt5f2ZnU2jKzmZ0jKQoHzrEeG+ZKIODEzdR7FKmqj2FwJt70XvlRmRJGqCQphMrVmhbZkkJRZTbbqO0tns3yaOigs6V/n7elZ/4BL3gv/gFnxiWReY+d44S6rPP0vkjFc5bWzkA0jQmN1d/njOHAfCZmXxSDQ3xOAsKuO2TJ0naf/M3HHRp1ztvHkn7Ix+hJCq1MwEG2VoW/wdYwk2cPZKu+eEPc7vA2DRRkbK//32S+qJFXGbhQkrqDQ0cS0SnjifDflldTQE9I0NLh7294/u1eYW6bdpEIVxyHKS99Jo18cVf2lOHganXstcQpk94PfmkK+zhw/xNwgalA6zdlilO2u5ukqsknbS1cd1AgCQbCukWqPFMlKKiyGq5XcpUtaeQ9tR2DNzxUagli4AVi1hx6K67SEKRPOYPPEDyGBlhFs711/MgfvITks1115EdXn2V6rX0cW1poQgueebHjtFuqBTwH/4DpbXmZpIXoMN6lOJxSOk2qWIrpdkCAYYtbdzIwgCdndyG5LYuW0ai3LCB5NrbS/sqwAt9113aXjlnDvdbVcVzFenSmSZaUAD83d9pFhSn6uLFYaXLic4d7+7mPBXpMD2dp9zdrSvZ1dRwaK+6ivbNEydItBJAkJura2bm5lIwCAa9Cd6Pqi2pw3ZMpdqaxunjE14teQ8e1BXBgkFqhsEg79eREW0wb23lRDx+XAtIwaB+MlsWyTcU0mpOU9PETZTGRiClsQGDd9wFaxGloO6ixSSNhgZ/cZl2W96mTVSxy8sZgiPZNg8/zPjFG2+ko+X8earVb77Jdf/wB7LH4sXAZz/LJ05zM9X6q6/m4EsfhcxMxnNlZfH38nJKmTk5ut9PaSnjQ0+c4PGVlXG7NTXAV75CybipCfhP/4lkd+oUL8I112iyrKvjE7GqitKovWumPU1UxkJsogLH2PnOrEoicnJ0yTYJOA8GOeyhkA4ZXb+eqvq3v00tqK6OhFlfz+Hq7+cD+fLLub5XLLEIHJEcSvE0JpwIGAnTJ+xFM44c4T1mWZQus7N5n8rTeniY96o4cKUEZDDIySbvgJYyAU5U+RwM0ndy5ZXxH7tfKXP4qmv+/Nufi3KMSkZ/Rjgp02nLk/4cUs3nd7+jyj53LhPqr7pKS3Xz53NgCws5YB/8INf7f/+PoktrK8N3lizh9t99lyR56BAl16ws3XAtPZ3fh4cp6eblUQxqayPpXnIJz+nsWRLghg28QHV1XC8YpL1T7lKpJp+aShXi6FGuH6mEm4+QvXB9x5MZf7lihS78JDbM+fO1/XLjRl1R68ABktZbb/HS5eaSYPv7+YyTUqPhYon9qtpSwBgYG640VWprGsL0ibIy3sd21Xt4mC/pbCDdV4eHKVRkZvJeF/+EaIEdHbq73qpVJN/WVp16aVn8nJamn9axeg6jqZMZc+k3L4+5tLEYGuLAffCDfD9yhAQEUOpcu5YnlprK99tuo/rb0cG78tgxDu7ChVzm2WcZFL57N0+wuppkKU3KAgGq0J2dOghWmqy1t1PK3bCB3xsb6YBSik+VTZvomNq+na+VK8fGXYojzFlN3SvPPkzcpR8kK/5SiKmyciwxbdrEzh/2ilp9fSTXjg6dzZOZqcuDpqUxTTgc/Kra0pxtqnQqcMIQpk9s2kSP9oULOjUZ4CQYGOBk6+vjRMrPJ2kWFFBjfPRRTqwUmwFkZIRC0sKFFLK6u8kbfX2cmKmpNAkCwNe/ThUpL0/7P6J1CEVjy9TH6FH6LZLHHNBZQDt2UMr7D/+BpFNWRolt1Soy+V13MW0yEKCoftNNPNnNm6meBwK6PW5mJh1Gt91GT/qdd5LUjh7lwYtnfGBA2zRnz+arp0e3/RWvvnS2TEvj/9nZJEhJwWxv5zLSeqOtjU+8BQsSJl1OFsIRk4TGiZ09M1NrP0rpRp25uf7VZWe4HeC97mQ1BvQDY8P0CZlIlkWyS00lWYok+Z730L9QUECyzMzk5Nq1S1cTs6OnR7fc/d73mMO7ejXvTcnplbTK6motqEjUSjSl4KKRUnyVfnODVw/zyy6j80fsgeXllNikw2R7u45HEU/35s18mtx4I+/K2lqq0dXVVNn37+cTSAr7zpnD/3t6uO2sLBKfiNdlZRw0sU92dvKCdXdTdF+xghd06VKdXfTcczoH/cABkr5UznUiDulyMvr2CCor3YvISM9y6eSRnc1hy8vTDf36++mX81v/wNkHPVG1EyYaRsKMAitW6PYuYq+Upk/SPbKsjALXokW6t0lTEzmhvNzbLuP2VH3oIZ1NIQ2pAN73l18evUMo6VKmG6Rqj2T3AByI8nLdcTE3l5ImQPX44EFKmq++SttnSwtJcuVKSpMrVpBot2/ngM+bx1TKpUsprt95p5ZOt2xhDKj0Kl+8mBfnO9/h8oWFlFo/8hGywL593P/mzZSEL72Uds777gtfST3B0mWy0iH9mnYyMnSd19JSmps7Onjcw8M83YUL/ZuGprqq7RcmlzwK1NSwqvrx47qCEcAJVVREgqyu1qWupGybswVvWZm/IPWvfpUcs3+/Jmaxb1ZVkUw/8xn/x9/SEjnzxy3HHABSUqyx3SU7OzVhCuxV2Z2QcB37hg8c4EGtXEkClRa6RUV8Mm3eTBFbqpinpFAsf+EFOn/KyugV+/nPeUe/+SbFIamK/p73cNAaG+mxACjFpqVxPcti9HUgQAnzppsozf7VX/G4GhpI6GvWsDjwmTN8+tlLtzmrqQPcbgTpMpyzB0gOYfrJ/7Yv40y8SE+fXvni4WByyScAlZVMAElLI1mmpOj+XZIvXlvLe01ipAH9n6g/mzbRX+E3xGLxYl1nUMI/YlVnInleo1IR3R62fooMC9at0w4VgAScm0sylBTFEyeo0ldVkRSPH2coUHc3bZLV1XQAvfMOCa+9nf/n51NizMzk6777KBq1tvLCjYzQjrl8OfvyXHEF1e73vEer5f/+7yTD9nbaVZ99ltsOd65TWADxCo3btUvnlX/lK5zDoRAJu6qK8/fQoeTWP5guMIQZJU6cYGz1xz5Gbe997+PkO3VKt4sRb3ldnfZ+243b4SauHWL3CQZ1GvL587zHY5m48dgyR0ZUZFumW5HhSBu24/3v1+0rdu+myv25z9EwvGoVw4lSU/nUuOceSrmNjRzE227TwYFz55J8MzL0/rq6tAp+8iQly+xskvDcuVz2Qx+i1Ctq+d13cztz5wI//CHtsU613O2c45QukwV7TVaBdCOWGEnL4uvttzl3CwsZiSX29ouZLAFjw4wabuERBQW8B8vLOcEaG3UtiOpqmszs9spYQyyuvTYxhQhisWUKXMOMnKq5Wx9zgFLmu++OjV9yquWrVvGEDx7kNjo6uFxBAfD447R7fPrTfCp96lOsq3nypG5QduECB2nuXGYY3XIL1//BD3T3SqV4jOfOUZKUOMvyctpRfvITkvOdd+rSdR/6kH4Syjn6UMVjQbLsl16e6u5u/icPbjH/nDrlrxNANCXhpmpRDb8wEmaUcMtEKCmhT0HSoiWLUMIxnNKgVzZDMDi23JZUfYm2HUY4xCtluiJSKws7nKp5URFtF9u36+pDkk5iWXTeSArjjTcyF72qiup8VRVtiy+/zMErLWVpNskL/6u/0iL/nXdymRMnuNwll/BYd++m+G734t92G6VWeyB+Y6NWx6NQxd2ky8mCl6c6J0c/38T8Y1n6/3DmH78ZPH6Xm+owhBkl3CZdIEDNTgSXvDwud911NKs5Sc5tG2fPMmNioiZUPLZMX2FGXrbMvXsp2QlzlJdzoJqa6FH7l3+h/nfjjRzIrCwa0C69VMdu2jF3LuttKsV0xqoqHbZUXk5iLSri9qVRWTBIAv1v/40k2N6ut9fYyOWqqnQg/tVXj+8G6UMV98JkhRKJxuKsybpihb5chYUUsqVRZyS7pV/zkt/lpjqMSh4lvMIjAHcPpFtKl9s2Skp4H9tTxzo6WMF93ryx6ZMTUfoN0L1/7OaDqMKM3FRz6QMkGBqiFHfddUydLC6mS/bdd3myHR0kylde4fISfmQ/mQMHSHCHD3NwJGwJoLQqKveRI/S+Wxa3d9ddfMlgCIkvW0ZidwvEr61lwKzz3F0eHLFIl8murl5ZyVPYu5dBBXv36t5ygK7JvGgR5yjAefroo+7zzq95aaoX1fALQ5gxwCsTIZo4M+c2vvrVsXV6W1uZETg8zMm7bzS66vLLE1cj0E+BYSDGlEmv2Ex7H6CmJorQ11zDWMnSUtogH3+cqjbAu0qqD0nDd4GdDO1ZRHbPe0uLTsOsr6dKD5A8Zdl168aSJTA2dEhQUDC+mLKLKh4uBXIyA9UBRm790z/p0qWnTlFo/ou/oN3aSwjwqk3pN4MnmkyfqQxDmAlEPCldzgl16hRVl9mzdWsagJ/lXo639JtfKTOmYHaBm5Rpr2pUVESyBFjF6J13GMKzezfV5pER1tOcP5953KJqA5oM3bKI5Ld16/h+4MD4Zevqxi7rFvpkPw8gZlV8sjzjTkfLc8/xmSQZaykpvGTPPTe2vYRXfypg7LzzWyxjqhfV8Atjw5wicNo129r4vnixrnBkr3KUCHVG6mX6ge8wIzcH0Isvju1ffuYMCTEvj/bJzExde/L976cUeOONHITDh2kIlpJuQoDy2d5fB9B2SyfCLdvYGJ4snecjSKAqDiReHXdztJw5Q0sHwAgtgDHFp0+PX6+9neGsAwM6zMg57+x20Zoavnp6dGyn23LJ7mmVTBgJM0nwqs7uFVbhtGvOmUNzXmGhLqcFaK10ItWZqMOM7Jg3j5KcOFAAVltXigMgoodd0pP3SOp2IuCHLL1CiByYao4er5Jqw8M6tVcKwoyMjF+voECHGAG0YgQCJNyHHtLzV+ZwXR2HKSfHXX2fykU1/MIQZhLgVp39u9/V+eRz547v5+OcfHbpYNEinaIm1YoSpc74qcouiMkBBPCkb7iBpJmezv8+/nHtULnvPhpp7Tvwo27HA6fN0gvhQohcVPGpFEbk5mjJyKBKLi3iR0Z4Onl549dbvJiSJaDbrohPzkmIU721RKJgCDMGRArAdZs8En+9ciWz+/bv5yTs7mbkjNvT2C5xSqEcSVlLdOGChAezO73mAGMfDx6kW9aeMSPl0uxB7W5qtd37HQ+iJcsIdktBPI6ecOp4rAHfbo6W0lKtUodCTPPNz2dShOynupq5AMuXM8To1ClKj9nZnIdiywQ0Ic4UL3gkGMKMEn662rlNHqlQvXs3CVPaz/T383txsQ4hst8UE/F0TkSYUUTVfHCQ9kipxl5ePj7N0C0TKNGIlyxd7JbhHD3xIp4uim6OlrIyHr60RklPJ2GuX6/3s3q17jV++eXcT1MTl7GTpZ0QZ4oXPBLiIkylVAGAbQAWAagF8FHLsjpclqsF0A1gGMBQLFVCpgr8qB5uk0cC1P/ccExRNZLeKu+8w4nsVHdk2xORTpY01by2FnjqKXZSTEvT1djtcY4CO2kCiSNOO3vFQ5YORFLF/UqXXohX1c3I0CFpa9YAn/+8Xt8+p5z7qaqipHnoEH1uV12lY4EBOoCqq7U9s6KCwgAwvb3gkRCvhPklALssy/qmUupLo9//u8eyN1qW1Rrn/iYdflQPtye72I0uXCBBSiplby/fh4bo6JEsCIBhHv39sfdojkaViyfMSOCpmjc26h7dUiDj1lvHt3kQCKElStr0K1UCkcnSh91SEI2jx0sd99JWnn8+/HW1S6Y33KAJDHDXXKQthUCKbjQ2MiVXtif7t5d96+rSbeztJQuFLB96aHrnj9sRb1jRHQBGhxGPAPhQnNub8vDT1c4ZQhEKkSSLixnKMTLCl7TVlYls7zWWk0PNNdZ0slhyd/2GGYlqboeQhmuo0VVX6R7dQkQFBe7B4XYIwTU2xqbfynrLliWcLAVuZBnNoUYab+d8a20lWQWD4a/rtm0U7Pft4/KRqvQHg6wz8vzzzP6xV9mSB68UqN+3j/OzqopzRubliRNj6x4AMyN/3I54JcwSy7LOAYBlWeeUUsUey1kAdiqlLAA/sixra5z7nTT4DcC1P8UfeohkCdBwLiEcw8N8lwB1u31IbhK3clxOQ7qbJBmPKpdQ1dytolG41EknnNKmHU7Ry/m/H5IUREmWXtk80ajignCxl875Vl3Nz8uXj9VG7Ne1poZF4vPyuI7EUa5Zo+eOfc4Eg3TySFsjKTw/fz7rv4qkWlnJZZqa+NnLnimYiZ7ziISplPojADed6CtR7Ocay7IaRgn1D0qpasuyXvTY3wMAHgCA0lIXdW2SEUup/YYG/n/woG6b3ddH4qyo0E9xqRwjJLxmDT+HM6R7OQV6esbzhR+vZbyqOQPaHdXZgfGhRtGQJjD+ZNwINBqCtCMGsgyHRMZcOudbKEQ1WMiqtZVk19zM7/KwzM/ndykzCHCuiDfcPmdefZVDsHQpozm6u3lJSkr48HSSXl4et2EnejcHz0z0nEckTMuy3uv1n1KqSSlVOipdlgJo9thGw+h7s1LqCQBXAnAlzFHpcyvAFhWRT2HiEa33WpxAa9eyEll/PydOYSHtPl1dnOCzZrnn8gLe0qzXU/zcuchkGw5+pEw3r7kgYqgRED1p2hErOToRI1l6SZfRkKXfzB6ntiImHGmDq5RuSCYPy5ISFtcYGeExZWWRbN20j1CIkmVHh7aSjIzwfNxIr7KS2avOB7xTy5qJnvN4bZi/BTDqy8V9AJ5yLqCUylZK5cpnAO8HcDjO/U4rVFTQLrR/P5/OmZm6o6sEoW/ZQrvPvfdynUcf5cTeuDF8OplXFW3JtoilS5/cxPGkTQIe9kynp1mIyqscXDKRYLKcCNhTaE+eJFlaFrt6CAk2NfHaFRRoTaC1lWq8xEza54xkj0naLaCJzc1mn57Otu6R0hxnSqdIO+K1YX4TwK+VUp8EcAbAXQCglCoD8G+WZd0KoATAE4qSRSqAX1qW9Vyc+502qKmhB3HpUk7k9nZG1ixdytBEexC6m3q9e3d4r7jXU1yyh2Lt0hetah5VfGYiJc1YEC4gPQ6yTIZ06YRdRW9upmS5ZIlW0XNyGImRksLjkZhLqdMKjJ8zixdrR4600bVLjG5ajp9IjZnSKdKOuAjTsqw2AOOeF6Mq+K2jn08CWBvPfqYz7OrPwoX8TSars+NjLEZycQp0dJCQOzroiX/ggfgD3/2mTYazZ3o6gdxIE9Al4ZJBnHZRaZLJMh7Yr6vbwzIjg8HntbWUGnNzubwU3XA6koJBOnhKSng+TmKLh/RmQv64HSbTJ8mIxvAdi5G8spJq+9atlFgLCjjxd+9mJE8iJms89kxXJ5AXaQJjpU0gccQZTqoEJowsBYmoSuQVsbFmDUnQHrXV1aX36Sb5ff7z/mu3emEm9OyJBEOYSUY0hm+/yzonZmsrbw77el1diQnfENU8nmLDgMMJBEQmTSB+4owkUQpiIEtBtGQZr3RpR6Tq/0B4p4xk61RX8/OWLbHPl3hSOKcTDGEmGdEUTvWzrNvEfO01xobbkcjwjaTYM4HwpAm4E6fAjUDdHEcxECUQmSzjKQicyJqXsVT/r6kBvvENOo0si9LoK6/QJvq5z8VGcDMx5tINhjCTDLsUUF1NO+OFC8BXvkLVyf5U92Mkd5uY+fn+4uLiQbT2TDfSBOCungO6WEc44hS4EajXsl6YBLJMdr8eO8Kp0du2kSwDAZLl8DBTdOvrYye4mRhz6QZDmBOE1lYWYA2FmDOelkbPpPOp7jbR7Sp4dfX4HlyVlez2ECkuLhGIhzQBbdMEwkibgDtxCvySoht8EKUcpxPx2CwTqYpHshVG+l+6EKel8X14mNlAZ84wNTIW26PTnOQszjFT7JmmRUWSISp0dTXv1UCAnuyREZJbe3v43HBnTngwyHjOVlsZk/R0quShEPDCCyTijAz3bX3962yVc+ed/BxNXm808ZlCKm7xia5xmgBJzB6v6dHrO2rItqQ0WwSpMllkWVTE8Xb2no8GO3aw8PRzz7FG5enTY/Oz/dYQSEsjUYZCugAMwPkVS763PeaypYVzsKeHD/eZkEMuMISZZIgKLQU40tIY9tPRwck5MBBebXH2c16+nL9XV48NBl6/nhlEVVWsTuOc+DU1wPe+x4mclqYl3O9+d/JJ0xdxRkuezvXCEKUfFRxIDFnGU4yipobREEoxGiIUYi3V4WH90LXPN6lpWVs7tsHZmjW8/oODmiwlI2j58tj6hdsLzhw65F6cY7r1IHeDUcmTBFGLnn6awcWBAF/DwyTMUIiv9PTwtkanbaiwkLnE+/ZRmgR0A8ZwRvdduyjN5uTo3GKlSNzR2q2i8ZxHUs8BuDuEgLEEZ1fX/cKFIAWR1G9BPGQpkIdMvI6RXbtYiyA/f2yOeFOTVq8bGvj54EH+L8U3Xn+dc7Kyknbzkyd5/aRXVFYWcOWVnF8jI7HZHsWcJHM2xSaOzRR7piHMJMDuyS4upmrS18cJ1N9P0gwE+PuCBeFTxdxCjXp7KUFWVWmb5YsvUsq0wz5JGxp4c9hbe0sXylgmcrSkCXjXBLY7hAAX4gTCkl80iJYogdjJ0unkidcx0tBAsgyFOIc6OvTnJUu4TFkZ6xWkp499MObljSXm7GwGq9fX8//sbJ0JFK/DcCbmkAsMYSYBdkliyRIWSJD88cFBOnpmzSLhecW+2fur1NczlXL+fE6848f5PVIFmbNnua+vfpXbGBnRUi3gT8INh2hjNMNJm8B44gQ8yDMG+CVKIDFSpZtHPByR+An6DgYpTTY2avOO9JRrbuY2Nm0Cfvc7zgfL0q0o7KXddu3iXFq1amwBj5MnuY94HYYzpQe5GwxhJgF2SaKwkFWKpATXbbdF9hjaJdRly3jjHj9OyXLFCt5Q8+dzsp86RSlxYIDk1dOjiy7U1mp7VWMjKxgpxUk8Zw5vuvnz4yuGYCfNEycYCVBfT0f25s08XjsiSZvAeFXdDj8E6lZ+LRJJ2o8pXqIE3MOHvIhk3brIQd81NZxDHR3UVCyLD9/hYT54y8tppywspPbS3Ew1u7CQdslgUB9TuPl55ZXx53vPxBxygSHMJMApSRQWcsJeeeX4/HE3OG1dCxfq8m+f+Qy9q6dPk0TT03kDtbdz2YwM3lSnTrEaUnY27ZtSHHZggHGgg4NsQfDpTycmG6imBvj1r0kE8+fT5Lh1K3PanaQJjJU2gfDEaYdd+gwHPwQpSIT6DYQnS3vV8nPnaBpZvpxE4lZurbaWsbo33aSLqIRC7PpYX89rnpLC6z80xOu6Zw8bcl5xhW4hsWjReKkx3vnpBzMth1xgvORJQLxlrbxKtolKtWkTyVIpTvS2Nn4uLSUp3nwzTX59fSTO/n5KmdnZ3M7y5TQVLF2auEm9axfttbm5wPnz2in9XJi6VJmZ+iXdJCKVSZOwn0ivSLDvz34csSISWYp3XIgkJ0drGmJf3rsX+P3vOZbivRZps7qay8yeTXU7N5efldKtI/LySIJFRdq+fejQ+PJrM7Hs2kTBSJhJQLwqiUgAoZBWuYNBHVJUWUmVt6uLkuPwMNfJytI1DQsKKGlKvJ28B4PxOXu84PSMtrfzZpTiQ5EgZGWXOoHEdtt1knEiKqPbw6u8sngieceDQV1erbubZNndrcODZs2iVJqezu/5+dqOKdk6HR1j02OdTczsmMkqc7JhCDNJiEcl2bSJ8ZFnz1IqTEsjMZ48yWDzUIg3VEkJ1ai9eyl9hELaC15Sook2JYUquGXxRorX2eMGp5qXnc2bNTfXv1MIGEtiTvK0IxyRhpNSE9k+IpxUaYdf73hfH69ZSgolx5ER3YsnJ4djeuYM3/PzqVmkpuoHqb0NLhDeMz1TVeZkwxDmFERlJQlPwkZyc6luHz/O71dfTQI8eJAkdeECb6TUVNqvxL6Zm8ubcHiYN5+08fUTzhQt3BwaQ0NsogVocvFLnEB4cpsoUnSDX6IURAqzkT49r7yiWy5LCNrIiO7Fs2kTHTsHD/Jav+99OsrC3gZ3pnmmpxIMYU5RhEK8EaQIbH297suSkkJHUFcXcOAACXFoiO9799KWtWqVDkM6e5YS5ZkzJNpw4Uyxwq+aZ1dhoyFPJ5JNik74Ub29ECnMRgg1GOR5DQzoVPqWFl47ebgVFnL57m7+J9kzRs2eGBjCTDJiLapqt2vl5DDko7d3bN2J+nrtCJBUt64uSpZS3X3WLL2+hJcA7BmU6CKvftQ8IRsJRbIjHgJNBpzHF2uloUhkJoTa20vJMRDQjrrUVN1+4pFH+FCsq+NDs7OTD1F7CFKk8b8YivwmE4Ywk4hEFlVNSxvfBqKhgTeUkGAwSMlECnNIhSS5wfLydKWayy+f3CKvTvKZCgTqlh8fjiS9yMfr93AVze+7jx7t9naGhs2dy2vb30/zjDiOamr4f3o6H5bNzTr11Q9ZXgxFfpMJQ5hJRDy5w2LXEpW8sFCnWEqjqpERShh2SL56ayvj8lpadCrmG28w9EeC2qWFwVQo8uqHQJ0IR6hHjzKkyRlEH2mbfqVIL/LZuJHtQSIFobsR6i230P7c3Mxrnp5Os8rChdpxJD3DAR3t4De98mIp8ptMGMJMIuLJHRa7lr0vi9xM0qiqvJzEKITY30+1XCngj3/UmSBKUSrp6eH62dl0FAEMbJ6KRREiEVc4Qj1xQgfRZ2dz3P75n4GPfpQkm4givl7k89hjJB8vUgon5VVUcDnpzZSRoTO8enr4e24uJUsJMcrN9Z+nfbEU+U0mDGEmEfEUIXBzFAQCwJe+pKWBigrgW9+iw6evjzdSWhrDUN5+W1dGysjg68IFbictjZLp+fMk1tmzoyvyGskOtmMHiaOpierk3XczmD6R8CK9mhrgRz+ialtQwBayQkxvvjm+lUes8CKfpiZqBs7f7XncbkS7bRsfeNKOuamJNsq1azm2Z8/SnLJoEX+XcoHz5/v3hs/kohgTBZPpk0TEk1Fhry/Y2Dg+WwMgCf3X/0pSkIo0114LvOc9lKxyc/muFMlTMDxMtVyk06ws/7UZRUI6fZq2USlou2MH/9+xA/j2t6kqFhXx/dvf1v8nE3JsbW0ci4EBPjhaWxMvSZWVje2S0doKvPoqpcFXXx1b4NlOSl5ZXAcP6lbMV15JMi4rI4lKVMSaNfxeXq4zfUR937Ur8rWrqGAUxY4dNM+cPm0yfKKFkTCTiHhDPfx4PW++ma+vfnVspk1Jia5QVFpKEhke5k1ZVkYJJhjURTj82rN27eJ2JI89PZ037de/TpvpkSOarAG+9/dTJX7ppeR6ZkV6mzNHq60As6WCwcRKUnYNYGBA526vW0eTwL59lDTT08PncQPujTG7uzmOkrkFUJpMSwMefHCsap+TE9mBU1ND22pxMa9dczPH5f77jf0yGhjCTDIi9ehJFIE4b8RVq7TqNjxMAs3M5O8LF7J3S07O2HJvfqSwhgaSbXo6t9vURMIdGKB6392tJSCAEldXF+1vyfbMipq8eDElS4AE097uX221XxuJPgiFxl8n+8NQxnL5cjrn8vOZ+33oEItnuIUQAeN7iff06OuXm8uxtNcvtUuq0Tpw5EHX3EzJf948bu/pp5kIYUjTHwxhTjCSFdrhvBGDQapgJSX6hq+ooJRhb5bW18fPzz8/Nl/dC2VlJIL8fJLKyAi3EQhQ9U9N5e9ZWXx1dDB7JSODUpika27bBnzta7GfrxM1NZSoDx7kg2FoSEvVZWXhx9et9mhWFqVEwDsEK1yF8WCQNmIn/PQSHxjgug0NlJZfeEHHZT7wAJeJ1oFjf9DZH5CxVNy/mGFsmBMMZ4+eRPU7cbN5fv7zJKV77+UyL71E4pKCDpI2mZKi89WlEK0XNm0iGdXWUnKT8Ca5EUtLx0qePT3cXyBAIsjJIYFKy4REQB5CxcXc99mz2o6Zn68Dv8OtK/ZlpaiyvvOOThqorQ1/nez2TCnI29PD43GzDVdWsiDGgw/yXYj3vvs4Vq+/TtJfvZoPmLNned2WLuUDr6ZmvA0VCO/AKSvj9bLnm0shD+Ml9w8jYU4wkhna4aX+2yVaUQH/9m8p5Ukr1NxcxikGg5EljqIi3WLHsrRTKT+fxLhgge4emJPD3/Lzw7dMcB5zNCYL+0OotlaHV/X20gkW7pzs6/b0aDNFQwM90oC2I3pdJ7t0f/Ikz8+yWEIvmljHykqS+8aNXG/vXtotAY6dpMPu2hV9VXOpqWk/x4EBbt94yf3DEOYEI9GhHZHIJZytSwp52FVJtwZYso+jR/k5EKANrK9P94QJBrUUuWIFb+7PfIbLf/GLmlw7Oynp5Oa698COxWRhfwgND2uS6emJ3NTLvq7EONptl/K7bM/tOtnV7OZmSpZLlmjJ1km04a6Z/XjsQepO0o7WoVhZSXV+61aq4fn5HKdAwHjJo4EhzAlGIvud+CGXcBKtH/KWfQwPkxx7enRsZ1oaQ5jefZdSXTA4/iasrAQ2bKAk29rKG1+CspUaf7xuBN/RAXzzmyRpt4eC/TyE9IDIROdcV5xFoZBuXifnICq713WS49m7lw+EU6f4XTK0ZP+RrpnzXM6f53cprFJcrOsERFui7eabKTWbXPLYYQhzgpHIqjJ+PKXhSDFcj5mvf50OlNZWSiPBIElO1LkLF0gGQ0MMBm9u9ia0LVu4n6NHSaZSIf7yy7Vd0IvgW1uBY8e4Hy/ny6ZN7Lku3vDz53mcGzZoolu3jsH5TqKwj0FBAe2Ex4/TWSbB8aEQP4e7Tjt2UHrr7eVreJjS9CWXcKyKi4HPfU5L6OLNFylUxsB+PHl5HLOUFI5tdzft0zfdFM0sGQtTBzM+GMKcBCRq0vqxh4aTaN3Ie906dh2UQrXDw7RFDg3RNpmZSYmvq4sqdmoqHT32DCS385Uca6W4jZwcBk7Pnj32eJ0Ef+oUCUNqeXrZBKVleXo6yS0UItmuWEHy27pVpxwODmrSBfggEI/4mjXAN74R3fWpqeH2leK+xOyQkUGbamEhSdMuoZ8/T2l27Voek4yB/Zq8/DIfVCMjvAYlJTy+SPnwBsmDIcxpDD8qdSSJ1kneDz3Emz0nh+STmUnyGRykdKOUjsG8cIFq+caNkQnmxAmtSorzZ2BAF8cVOAm+vZ0S2eLFehnnQ8HeNhYgUVZXs61DURGzWjIySEyhkG5TLOmIs2YBN9ygHybRwp7/LQ6tzEySXU8Pj02qDDkldLegehnL3/2OkqXkjQ8MMNzJeLUnD4YwpzH82kOjkWilIZfY/6R/jFK8ybOyKOkVFfF96VJ/Eo84KqS8XDConUB2p4OT4AsKqM7aQ4OcDwW7pN3aSoLs7SUZdnRQlV20SJM9QKJqbqaEKznnTvU4HOyOm+pqnsvZs7pvUl6efqBIr56cHD2efX3hg+q3beN1qKsj+UqUgfMBYzCxMIQ5jZGMKttlZQyNkQygrCwSijRcy8zk73l5JBm7OumEnVTq60l8a9eOb+y2a9f4gsb2UmgSJ+n1ULBL2keOUN1NTeXy0uahsVGXgxsaIrmlpo7NOXeqx15wOm6OHGHcaSCgg+br6rityy/nMYszSsbzwgWS+Zw54yMAamoYiykOr1CIx5+fz8/Gqz15MIQ5zZFoI/6mTQzaPnNG2wWHh0lsJSUkObsJoKvL3QPtJBXpQbRmjSaRujqSSFeXt5ffz0PBLmkLcVkWCaajQ5sPJGSouZlkWVbG4/Kbcy4PAHtWVEoKP6emjq2WHggAy5Zph1dxMZ1XoRAl3XXruIxbuNSuXSRygMfW0UGJtKuLpgPjtJk8GMI0GIPKSnpzpdkWoHsAAf5MADU1DANqa6MEtXixtl82N1MVLSsjidgJ2MuhE+mhYCdVIcCiIl1cubdXtxcWiW39ehKq35xz+wNAYkpFKh0epq2xoYH7DwS43LvvUnLOyOA2JH9bWoh4hfTYzRfS3VPSJeU6GEwO4iJMpdRdAP4HgBUArrQsa5/HcpsBfAdAAMC/WZb1zXj2a5BcVFZ653lHkvaEWNrbdeO1mho6PlauJGk8+CCX/epXqZ7aEWvWk5Dq8eOMVzx7lqp4SgrJbfZsXQt0/nySZ2GhNhEIuXsFyEvxipoaLiukeOoUCbClRUutopIHAtxfMEgi/uxnx7aw8OqrJCYGp/liwwYjXU424pUwDwP4CIAfeS2glAoA+AGA9wGoA7BXKfVby7LeiXPfBpOASNKexIZmZNBumZ7Oz62tLL5RVaWXTUbWUyhE4urr02mbGRkksYoKkuWRI8yrf+sthkSVlPA9XDZRdTVJMCODkvG5cyTJwUF652tqSP7BoDYLFBaObwUCRE42EBPDrFnafNHVZaTLqYC4CNOyrKMAoKQnqDuuBHDcsqyTo8s+BuAOAIYwpxH85nfbPdaSUy12vezsscsmMusJ4PGVl5PIlKKEGQjwPS9Pt+dobtYhRuJFf+CB8A+C7m5Kq1JkRGqK9vRQvW5u5rn29FASLS2lScCZ0ugn2SBRzjzTITLxmAgb5jwAZ23f6wCs91pYKfUAgAcAoLR0QXKPzGAc3G4yAPjud0kuAwP0oh85wmpIzhtQpEYhjfPnSZaZmZSWJD8bSLyXX8h6eJhB9kLYx47pYrynTpHwpBfOTTfxeCOFRuXkMARKHEeBAEl33jzmzD/0kJaW9+7lclLUBNCSs9/iK/E680yHyOQgImEqpf4IYK7LX1+xLOspH/twEz8tr4Uty9oKYCsArFpV5bmcQeLhdZP199MmmJNDAgiF+F1qWjqL7ko1d8uiWjowQHtcMDi+F08iCizb61mePEkyk7CoUEg3fZOivBI8LmTmx266YgUlRmkJkZurOzoCY6XlRYt0BXZnHvquXRPTV8d0iEwOIhKmZVnvjXMfdQDm276XAzC5ClMQXjfZq69SWpTwm/R0kuHBg+7l45Ti5+pqqsJr1mjHRyR12769tDSmBz79NPPVt2wJX75u9WoSVShE9Tkri+p4RQVtiRUVWm22LF0s2Q9hCSFWVo41H9iLjNilZbHVuuWhJ9IM4QXTITI5mAiVfC+AS5RSiwHUA7gbwMcnYL8GUcLrJhsc9F7HjWTLy/n+6U9rAikqYuxhOO+wfXuhkA6rmT2b5OumUjr3X1XFZbu6KAXm5pIYP/Qhqt19fZQQly7VAfl+CcuZc+48Fj9qdDKSDdxgOkQmB/GGFX0YwPcAFAF4Rin1lmVZNyulysDwoVstyxpSSn0WwA4wrOjHlmUdifvIDRIOr5usvJwqrdS9lFzoqqrwkoxbxk4km5psb/9+7WARqdBZ2ci+vKCwkDU+Gxt1+JITTpXfi7DsdUAbGkiy8eScCyaiYlCiHWoGRLxe8icAPOHyewOAW23fnwXwbDz7Mkg+vG6yT3yChSDa2ymdpafTqbJli3+bnF+bmpC2vXiu2BvdVMqyMlY9stsW7TUj3eAkrJqa8aXfAE3wTU2MvWxsZKxmZibtut/8ZvgqTZOJiZJkLzaYTB+DPyPcTRau8KwfScavTU1IOxiks0gpvi9f7k7EFRUk8+xs7QmPpmakl+SbkcHfmpvHetDr60mY0rp4KnueTe3LxMMQpsEYeN1k4X736oJol9qk1JmbJOpUkS+5hCRVW0tSDec0OnGC/9slzKIi4LHH3PugO/fV2uou+e7bp51IKSl8Scvi7GxK2wsWuJsJDGYuDGEaRESkMB83FdcptUlg9/z546u725c9fVoX6Vi5ktuqrmbet5sk19AwNryntZUZPMPD4yu019ay0O/QELc3OMiWwesdUcFiCqip4TFLWJJlkTgHBrj9xYuN5/liQ0rkRQwuZtjLq9lV1nAtct1aCZeXMwXR3gb4vvsoIdqXtWfjFBWx9uPGjXTmuElxznazUqG9oGBsG+Nt23RVdCmTdvy4rjFpR08PCbujg2mWKSlUwwHGeA4P83yc/XoMZj6MhGkQFrEEQHvZKxsbmRVjx6OPjl22u1vbIu3reklxTkdVWxtJzlmh/YUXxlZFl5jSoSHdcsMu+Ur7igMHSN7p6ZRk29u5/sqV0YUkGcwMGMI0CItYAqCjiQF0LivZOJKF47au00SwcSMl1YYGerHdKrQDOn9cyDIYpKq9YQP373R0bdlCb/iyZfSUd3QwEL+ykuQbqTGawcyDIUyDsIglADqaGEDnssXFujr6yMj4dd3so7t3a/umV4X2NWtIjsePczvihEpNdc8gAsY6tNLSgCuvNAUsLnYoS8pqT0GsWlVlbdvmWmLTYIJgJyinyuonv9tPPrhz2YoKLTE617UXuRDId1H3vQqISH/1piaq1mlprFJ0883xj5PB9MLq1Wq/ZVlVkZccCyNhGoRFrAHQkWIAYy095sdE4LVvIy0axAtDmAYRkegA6HhKj8WTI20CuQ3ihQkrMphwuIUdSQB4JGzapL3TIyP6s+mkaDARMIRpMOFoaNDB4QK/AeBiInDGcxrJ0WAiYFRygwlHvKXHjGptMFkwEqbBhMOo1QbTFYYwDSYcRq02mK4wKrnBpMCo1QbTEUbCNDAwMPAJQ5gGBgYGPmEI08DAwMAnDGEaGBgY+IQhTAMDAwOfMIRpYGBg4BOGMA0MDAx8whCmgYGBgU8YwjQwMDDwCUOYBgYGBj5hCNPAwMDAJwxhGhgYGPiEIUwDAwMDnzCEaWBgYOAThjANDAwMfMIQpoGBgYFPGMI0MDAw8AlDmAYGBgY+YQjTwMDAwCcMYRoYGBj4RFyEqZS6Syl1RCk1opSqCrNcrVLqkFLqLaXUvnj2aWBgYDBZiLdr5GEAHwHwIx/L3mhZVmuc+zMwMDCYNMRFmJZlHQUApVRijsbAwMBgCmOibJgWgJ1Kqf1KqQcmaJ8GBgYGCUVECVMp9UcAc13++oplWU/53M81lmU1KKWKAfxBKVVtWdaLHvt7AICQ6sDq1eqwz31MJRQCmK7mh+l67NP1uIHpe+zT9bgBoDKWlSISpmVZ741lw45tNIy+NyulngBwJQBXwrQsayuArQCglNpnWZanM2mqYroeNzB9j326HjcwfY99uh43wGOPZb2kq+RKqWylVK58BvB+0FlkYGBgMK0Qb1jRh5VSdQA2AHhGKbVj9PcypdSzo4uVAHhZKfU2gDcAPGNZ1nPx7NfAwMBgMhCvl/wJAE+4/N4A4NbRzycBrI1xF1tjP7pJxXQ9bmD6Hvt0PW5g+h77dD1uIMZjV5ZlJfpADAwMDGYkTGqkgYGBgU9MGcKczmmWURz7ZqVUjVLquFLqSxN5jB7HU6CU+oNS6tjoe77HclNmzCONoSK+O/r/QaXUeybjOJ3wcdw3KKU6R8f4LaXU1ybjOJ1QSv1YKdWslHt431Qdb8DXsUc/5pZlTYkXgBVgbNQLAKrCLFcLoHCyjzfaYwcQAHACwBIAQQBvA1g5ycf9TwC+NPr5SwD+91Qecz9jCNrOfw9AAbgKwJ5pctw3AHh6so/V5divB/AeAIc9/p9y4x3FsUc95lNGwrQs66hlWTWTfRyxwOexXwnguGVZJy3LCgF4DMAdyT+6sLgDwCOjnx8B8KHJOxRf8DOGdwD4mUW8DiBPKVU60QfqwFS89r5gMcGkPcwiU3G8Afg69qgxZQgzCkzXNMt5AM7avteN/jaZKLEs6xwAjL4Xeyw3VcbczxhOxXH2e0wblFJvK6V+r5RaNTGHFjem4nhHg6jGPN5qRVFhotMsE4kEHLtbhZKkhyiEO+4oNjMpY+4CP2M4KeMcAX6O6U0ACy3L6lFK3QrgSQCXJPvAEoCpON5+EfWYTyhhWhOcZplIJODY6wDMt30vB9AQ5zYjItxxK6WalFKllmWdG1Wjmj22MSlj7gI/Yzgp4xwBEY/Jsqwu2+dnlVIPKaUKralfEnEqjrcvxDLm00oln+ZplnsBXKKUWqyUCgK4G8BvJ/mYfgvgvtHP9wEYJylPsTH3M4a/BfCXo97bqwB0itlhEhHxuJVSc5VinUSl1JXgvdk24UcaPabiePtCTGM+2Z4sm8fqw+DTagBAE4Ado7+XAXh29PMS0MP4NoAjoDo8LY7d0h7Fd0GP6aQfO4A5AHYBODb6XjDVx9xtDAH8NYC/Hv2sAPxg9P9DCBNxMcWO+7Oj4/s2gNcBXD3Zxzx6XL8CcA7A4Ogc/+R0GG+fxx71mJtMHwMDAwOfmFYquYGBgcFkwhCmgYGBgU8YwjQwMDDwCUOYBgYGBj5hCNPAwMDAJwxhGhgYGPiEIUwDAwMDnzCEaWBgYOAT/z/jMcuSLs0uIgAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "id": "4deee696",
    "metadata": {},
    "outputs": [],
@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "id": "6b792675",
    "metadata": {},
    "outputs": [],
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "id": "81faf37f",
    "metadata": {},
    "outputs": [
@@ -465,7 +465,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "6417b5c6",
    "metadata": {},
    "outputs": [
@@ -623,7 +623,7 @@
        "4    0      2       1  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "202237c5",
    "metadata": {},
    "outputs": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "id": "9a94370a",
    "metadata": {},
    "outputs": [
@@ -886,7 +886,7 @@
        "max      3.000000    1.000000  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "id": "ce906842",
    "metadata": {},
    "outputs": [
@@ -930,7 +930,7 @@
        "Name: 10, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -942,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 23,
    "id": "5f8d9afe",
    "metadata": {},
    "outputs": [
@@ -1044,7 +1044,7 @@
        "8   52    1      0.5"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "id": "ce4a5f85",
    "metadata": {},
    "outputs": [
@@ -1071,7 +1071,7 @@
        "Name: sex, dtype: int64"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "id": "d129b322",
    "metadata": {},
    "outputs": [
@@ -1093,7 +1093,7 @@
        "array([1, 0], dtype=int64)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1105,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
    "id": "37d36915",
    "metadata": {},
    "outputs": [
@@ -1369,7 +1369,7 @@
        "[303 rows x 14 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "id": "c379d785",
    "metadata": {},
    "outputs": [
@@ -1391,7 +1391,7 @@
        "<AxesSubplot:title={'center':'age chol relation'}>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1415,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "id": "601da936",
    "metadata": {},
    "outputs": [
@@ -1427,7 +1427,7 @@
        "Name: sex, dtype: int64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1440,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 29,
    "id": "a128aabd",
    "metadata": {},
    "outputs": [
@@ -1486,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 30,
    "id": "09ee5be2",
    "metadata": {},
    "outputs": [],
@@ -1499,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "id": "37389405",
    "metadata": {},
    "outputs": [],
@@ -1510,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 32,
    "id": "a7ea6eb2",
    "metadata": {},
    "outputs": [
@@ -1578,7 +1578,7 @@
        "4  36  41.849864"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1590,7 +1590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 33,
    "id": "952802ba",
    "metadata": {},
    "outputs": [
@@ -1600,7 +1600,7 @@
        "<AxesSubplot:title={'center':'x and y in the dataset'}, xlabel='x', ylabel='y'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1627,23 +1627,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 34,
    "id": "8e9d0f89",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x251e44ef948>]"
+       "[<matplotlib.lines.Line2D at 0x22d8ddab408>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1668,7 +1668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 35,
    "id": "bf4cd5b6",
    "metadata": {},
    "outputs": [],
@@ -1680,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 36,
    "id": "b4842e99",
    "metadata": {},
    "outputs": [],
@@ -1691,7 +1691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 37,
    "id": "479278e7",
    "metadata": {},
    "outputs": [
@@ -1720,7 +1720,7 @@
        "       78.98989899, 79.24242424, 79.49494949, 79.74747475, 80.        ])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1731,7 +1731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 38,
    "id": "56cd60cd",
    "metadata": {},
    "outputs": [],
@@ -1742,7 +1742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 39,
    "id": "bd73d23f",
    "metadata": {},
    "outputs": [
@@ -1757,7 +1757,7 @@
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1768,17 +1768,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 40,
    "id": "7af98bd1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x251e4561388>]"
+       "[<matplotlib.lines.Line2D at 0x22d8dc975c8>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1814,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 41,
    "id": "62838ac9",
    "metadata": {},
    "outputs": [],
@@ -1827,7 +1827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 42,
    "id": "4bea9b4a",
    "metadata": {},
    "outputs": [],
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 43,
    "id": "392f50b2",
    "metadata": {},
    "outputs": [
@@ -1923,7 +1923,7 @@
        "        47.33487629,  54.09063686,  63.29717058,  52.45946688])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1934,7 +1934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 44,
    "id": "0806d238",
    "metadata": {},
    "outputs": [],
@@ -1945,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 45,
    "id": "aa0ef0f8",
    "metadata": {},
    "outputs": [
@@ -2254,7 +2254,7 @@
        "       [0]], dtype=int64)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2265,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 46,
    "id": "55ff0a77",
    "metadata": {},
    "outputs": [
@@ -2275,7 +2275,7 @@
        "3464.291087139079"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 47,
    "id": "ccde02ca",
    "metadata": {},
    "outputs": [],
@@ -2307,7 +2307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 48,
    "id": "3c2b554a",
    "metadata": {},
    "outputs": [],
@@ -2317,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 49,
    "id": "4da8d3b5",
    "metadata": {},
    "outputs": [],
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 50,
    "id": "afbf17ff",
    "metadata": {},
    "outputs": [
@@ -2336,11 +2336,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential\"\n",
+      "Model: \"sequential_1\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "dense (Dense)                (None, 1)                 2         \n",
+      "dense_2 (Dense)              (None, 1)                 2         \n",
       "=================================================================\n",
       "Total params: 2\n",
       "Trainable params: 2\n",
@@ -2355,7 +2355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 51,
    "id": "35dda627",
    "metadata": {},
    "outputs": [],
@@ -2366,7 +2366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 52,
    "id": "fe11b9d0",
    "metadata": {},
    "outputs": [
@@ -2375,94 +2375,94 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 276.5771\n",
+      "10/10 [==============================] - 0s 508us/step - loss: 6149.2983\n",
       "Epoch 2/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 70.1189\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 1049.6282\n",
       "Epoch 3/40\n",
-      "10/10 [==============================] - 0s 893us/step - loss: 38.0515\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 455.6070\n",
       "Epoch 4/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 27.3967\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 202.8622\n",
       "Epoch 5/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 25.7747\n",
+      "10/10 [==============================] - 0s 668us/step - loss: 91.5324\n",
       "Epoch 6/40\n",
-      "10/10 [==============================] - 0s 777us/step - loss: 20.8342\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 33.6845\n",
       "Epoch 7/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 15.4722\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 20.1020\n",
       "Epoch 8/40\n",
-      "10/10 [==============================] - 0s 997us/step - loss: 9.9356\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 13.5234\n",
       "Epoch 9/40\n",
-      "10/10 [==============================] - 0s 778us/step - loss: 9.9688\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 11.9283\n",
       "Epoch 10/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 10.1344\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 11.6073\n",
       "Epoch 11/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 10.7054\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.5410\n",
       "Epoch 12/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 9.7593\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.4354\n",
       "Epoch 13/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 9.4868\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.1968\n",
       "Epoch 14/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 9.8315\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.7576\n",
       "Epoch 15/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 10.3213\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.6691\n",
       "Epoch 16/40\n",
-      "10/10 [==============================] - 0s 999us/step - loss: 17.9203\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.5777\n",
       "Epoch 17/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 14.1653\n",
+      "10/10 [==============================] - 0s 667us/step - loss: 9.6235\n",
       "Epoch 18/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 18.5441\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.4145\n",
       "Epoch 19/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 15.2991\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.7170\n",
       "Epoch 20/40\n",
-      "10/10 [==============================] - 0s 778us/step - loss: 10.4892\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.7544\n",
       "Epoch 21/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 9.3215\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.7636\n",
       "Epoch 22/40\n",
-      "10/10 [==============================] - ETA: 0s - loss: 9.491 - 0s 1ms/step - loss: 10.7091\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.4378\n",
       "Epoch 23/40\n",
-      "10/10 [==============================] - 0s 885us/step - loss: 10.4797\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.5914\n",
       "Epoch 24/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 11.5123\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.4083\n",
       "Epoch 25/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 13.3325\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.5226\n",
       "Epoch 26/40\n",
-      "10/10 [==============================] - 0s 775us/step - loss: 13.3366\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.9660\n",
       "Epoch 27/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 16.0355\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.0456\n",
       "Epoch 28/40\n",
-      "10/10 [==============================] - ETA: 0s - loss: 24.20 - 0s 1ms/step - loss: 14.7609\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.2983\n",
       "Epoch 29/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 10.3856\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.5830\n",
       "Epoch 30/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 10.1707\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 10.5801\n",
       "Epoch 31/40\n",
-      "10/10 [==============================] - 0s 778us/step - loss: 9.4444\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 10.0829\n",
       "Epoch 32/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 10.1405\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.4324\n",
       "Epoch 33/40\n",
-      "10/10 [==============================] - 0s 890us/step - loss: 13.1112\n",
+      "10/10 [==============================] - 0s 665us/step - loss: 9.3277\n",
       "Epoch 34/40\n",
-      "10/10 [==============================] - 0s 778us/step - loss: 10.2187\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.1831\n",
       "Epoch 35/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 10.7566\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.8164\n",
       "Epoch 36/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 11.3941\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.3315\n",
       "Epoch 37/40\n",
-      "10/10 [==============================] - 0s 889us/step - loss: 15.5596\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 10.1487\n",
       "Epoch 38/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 15.3198\n",
+      "10/10 [==============================] - 0s 443us/step - loss: 9.5550\n",
       "Epoch 39/40\n",
-      "10/10 [==============================] - 0s 1ms/step - loss: 13.1469\n",
+      "10/10 [==============================] - 0s 554us/step - loss: 9.2581\n",
       "Epoch 40/40\n",
-      "10/10 [==============================] - 0s 888us/step - loss: 9.4861\n"
+      "10/10 [==============================] - 0s 554us/step - loss: 9.5368\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x251ea00efc8>"
+       "<tensorflow.python.keras.callbacks.History at 0x22d8c83fac8>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2474,7 +2474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 53,
    "id": "a22c9fb1",
    "metadata": {},
    "outputs": [],
@@ -2484,23 +2484,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 54,
    "id": "9787ea0d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x251f2e13148>]"
+       "[<matplotlib.lines.Line2D at 0x22d8dc97c48>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2521,7 +2521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 55,
    "id": "ccaf0068",
    "metadata": {},
    "outputs": [],
@@ -2531,17 +2531,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 56,
    "id": "5e966641",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.030289]], dtype=float32)"
+       "array([[1.0167854]], dtype=float32)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2552,17 +2552,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 57,
    "id": "d7d8eb87",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.44838893], dtype=float32)"
+       "array([-0.43452635], dtype=float32)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2578,6 +2578,54 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf431404",
+   "metadata": {},
+   "source": [
+    "# Week 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22d4d753",
+   "metadata": {},
+   "source": [
+    "## Opdracht 5.1: ZTSL 4: Deep Learning Intro - shallow model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7687f6bf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6042b086",
+   "metadata": {},
+   "source": [
+    "# Week 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea5be5e2",
+   "metadata": {},
+   "source": [
+    "## Opdracht 6.1: ZTDL6: Conolutional Neural Networks - MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03a13811",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {