diff --git a/Project.ipynb b/Project.ipynb
index 3825d82..0f5ffc7 100644
--- a/Project.ipynb
+++ b/Project.ipynb
@@ -1818,33 +1818,399 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "1050/1050 [==============================] - 3s 2ms/step - loss: 0.4421 - accuracy: 0.7946 - val_loss: 0.3914 - val_accuracy: 0.8261\n",
-      "Epoch 2/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.3240 - accuracy: 0.8640 - val_loss: 0.3884 - val_accuracy: 0.8321\n",
-      "Epoch 3/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.2634 - accuracy: 0.8938 - val_loss: 0.4161 - val_accuracy: 0.8268\n",
-      "Epoch 4/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.1957 - accuracy: 0.9242 - val_loss: 0.4836 - val_accuracy: 0.8217\n",
-      "Epoch 5/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.1335 - accuracy: 0.9493 - val_loss: 0.5705 - val_accuracy: 0.8133\n",
-      "Epoch 6/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.0935 - accuracy: 0.9670 - val_loss: 0.6644 - val_accuracy: 0.8160\n",
-      "Epoch 7/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.0698 - accuracy: 0.9758 - val_loss: 0.8007 - val_accuracy: 0.8194\n",
-      "Epoch 8/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.0575 - accuracy: 0.9803 - val_loss: 0.8931 - val_accuracy: 0.8157\n",
-      "Epoch 9/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.0432 - accuracy: 0.9848 - val_loss: 0.9727 - val_accuracy: 0.8069\n",
-      "Epoch 10/10\n",
-      "1050/1050 [==============================] - 2s 2ms/step - loss: 0.0341 - accuracy: 0.9886 - val_loss: 0.9949 - val_accuracy: 0.8038\n",
-      "263/263 [==============================] - 0s 922us/step\n"
+      "Epoch 1/300\n",
+      "1050/1050 [==============================] - 2s 1ms/step - loss: 1.0956 - accuracy: 0.6962 - val_loss: 0.6570 - val_accuracy: 0.8210\n",
+      "Epoch 2/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.6116 - accuracy: 0.8071 - val_loss: 0.5299 - val_accuracy: 0.8264\n",
+      "Epoch 3/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5482 - accuracy: 0.8136 - val_loss: 0.5036 - val_accuracy: 0.8305\n",
+      "Epoch 4/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5365 - accuracy: 0.8175 - val_loss: 0.5118 - val_accuracy: 0.8240\n",
+      "Epoch 5/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5329 - accuracy: 0.8183 - val_loss: 0.5042 - val_accuracy: 0.8270\n",
+      "Epoch 6/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5337 - accuracy: 0.8162 - val_loss: 0.5141 - val_accuracy: 0.8206\n",
+      "Epoch 7/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5291 - accuracy: 0.8184 - val_loss: 0.4960 - val_accuracy: 0.8279\n",
+      "Epoch 8/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5273 - accuracy: 0.8188 - val_loss: 0.4946 - val_accuracy: 0.8301\n",
+      "Epoch 9/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5245 - accuracy: 0.8167 - val_loss: 0.4972 - val_accuracy: 0.8238\n",
+      "Epoch 10/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5216 - accuracy: 0.8185 - val_loss: 0.4997 - val_accuracy: 0.8230\n",
+      "Epoch 11/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5208 - accuracy: 0.8175 - val_loss: 0.4927 - val_accuracy: 0.8240\n",
+      "Epoch 12/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5185 - accuracy: 0.8215 - val_loss: 0.4870 - val_accuracy: 0.8287\n",
+      "Epoch 13/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5203 - accuracy: 0.8204 - val_loss: 0.4905 - val_accuracy: 0.8225\n",
+      "Epoch 14/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5167 - accuracy: 0.8185 - val_loss: 0.4948 - val_accuracy: 0.8199\n",
+      "Epoch 15/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5148 - accuracy: 0.8211 - val_loss: 0.4854 - val_accuracy: 0.8277\n",
+      "Epoch 16/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5121 - accuracy: 0.8228 - val_loss: 0.4869 - val_accuracy: 0.8282\n",
+      "Epoch 17/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5130 - accuracy: 0.8209 - val_loss: 0.4826 - val_accuracy: 0.8273\n",
+      "Epoch 18/300\n",
+      "1050/1050 [==============================] - 1s 1000us/step - loss: 0.5134 - accuracy: 0.8199 - val_loss: 0.4910 - val_accuracy: 0.8249\n",
+      "Epoch 19/300\n",
+      "1050/1050 [==============================] - 1s 998us/step - loss: 0.5083 - accuracy: 0.8208 - val_loss: 0.4715 - val_accuracy: 0.8290\n",
+      "Epoch 20/300\n",
+      "1050/1050 [==============================] - 1s 998us/step - loss: 0.5074 - accuracy: 0.8198 - val_loss: 0.4844 - val_accuracy: 0.8243\n",
+      "Epoch 21/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.5079 - accuracy: 0.8215 - val_loss: 0.4783 - val_accuracy: 0.8273\n",
+      "Epoch 22/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.5040 - accuracy: 0.8204 - val_loss: 0.4733 - val_accuracy: 0.8311\n",
+      "Epoch 23/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5019 - accuracy: 0.8219 - val_loss: 0.4747 - val_accuracy: 0.8307\n",
+      "Epoch 24/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5041 - accuracy: 0.8201 - val_loss: 0.4817 - val_accuracy: 0.8229\n",
+      "Epoch 25/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4994 - accuracy: 0.8217 - val_loss: 0.4717 - val_accuracy: 0.8336\n",
+      "Epoch 26/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5013 - accuracy: 0.8205 - val_loss: 0.4728 - val_accuracy: 0.8265\n",
+      "Epoch 27/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5016 - accuracy: 0.8210 - val_loss: 0.4667 - val_accuracy: 0.8279\n",
+      "Epoch 28/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5018 - accuracy: 0.8220 - val_loss: 0.4721 - val_accuracy: 0.8269\n",
+      "Epoch 29/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5008 - accuracy: 0.8222 - val_loss: 0.4767 - val_accuracy: 0.8224\n",
+      "Epoch 30/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5017 - accuracy: 0.8202 - val_loss: 0.4666 - val_accuracy: 0.8354\n",
+      "Epoch 31/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5003 - accuracy: 0.8212 - val_loss: 0.4795 - val_accuracy: 0.8235\n",
+      "Epoch 32/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5006 - accuracy: 0.8193 - val_loss: 0.4645 - val_accuracy: 0.8301\n",
+      "Epoch 33/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8206 - val_loss: 0.4684 - val_accuracy: 0.8280\n",
+      "Epoch 34/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5007 - accuracy: 0.8189 - val_loss: 0.4653 - val_accuracy: 0.8302\n",
+      "Epoch 35/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4968 - accuracy: 0.8207 - val_loss: 0.4710 - val_accuracy: 0.8251\n",
+      "Epoch 36/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8205 - val_loss: 0.4684 - val_accuracy: 0.8283\n",
+      "Epoch 37/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4966 - accuracy: 0.8224 - val_loss: 0.4685 - val_accuracy: 0.8277\n",
+      "Epoch 38/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.5009 - accuracy: 0.8216 - val_loss: 0.4652 - val_accuracy: 0.8311\n",
+      "Epoch 39/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4959 - accuracy: 0.8243 - val_loss: 0.4724 - val_accuracy: 0.8220\n",
+      "Epoch 40/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4974 - accuracy: 0.8204 - val_loss: 0.4732 - val_accuracy: 0.8235\n",
+      "Epoch 41/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4987 - accuracy: 0.8192 - val_loss: 0.4731 - val_accuracy: 0.8255\n",
+      "Epoch 42/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4979 - accuracy: 0.8211 - val_loss: 0.4656 - val_accuracy: 0.8304\n",
+      "Epoch 43/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4927 - accuracy: 0.8222 - val_loss: 0.4676 - val_accuracy: 0.8269\n",
+      "Epoch 44/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4977 - accuracy: 0.8233 - val_loss: 0.4701 - val_accuracy: 0.8281\n",
+      "Epoch 45/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8214 - val_loss: 0.4675 - val_accuracy: 0.8273\n",
+      "Epoch 46/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4981 - accuracy: 0.8190 - val_loss: 0.4682 - val_accuracy: 0.8252\n",
+      "Epoch 47/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4971 - accuracy: 0.8234 - val_loss: 0.4628 - val_accuracy: 0.8295\n",
+      "Epoch 48/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4988 - accuracy: 0.8225 - val_loss: 0.4684 - val_accuracy: 0.8275\n",
+      "Epoch 49/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4964 - accuracy: 0.8214 - val_loss: 0.4683 - val_accuracy: 0.8252\n",
+      "Epoch 50/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4977 - accuracy: 0.8211 - val_loss: 0.4656 - val_accuracy: 0.8308\n",
+      "Epoch 51/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4965 - accuracy: 0.8211 - val_loss: 0.4622 - val_accuracy: 0.8285\n",
+      "Epoch 52/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8218 - val_loss: 0.4791 - val_accuracy: 0.8200\n",
+      "Epoch 53/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4968 - accuracy: 0.8205 - val_loss: 0.4682 - val_accuracy: 0.8292\n",
+      "Epoch 54/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8206 - val_loss: 0.4623 - val_accuracy: 0.8306\n",
+      "Epoch 55/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8197 - val_loss: 0.4681 - val_accuracy: 0.8286\n",
+      "Epoch 56/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4958 - accuracy: 0.8193 - val_loss: 0.4640 - val_accuracy: 0.8273\n",
+      "Epoch 57/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8215 - val_loss: 0.4642 - val_accuracy: 0.8318\n",
+      "Epoch 58/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4947 - accuracy: 0.8204 - val_loss: 0.4724 - val_accuracy: 0.8256\n",
+      "Epoch 59/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4958 - accuracy: 0.8201 - val_loss: 0.4665 - val_accuracy: 0.8299\n",
+      "Epoch 60/300\n",
+      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4952 - accuracy: 0.8205 - val_loss: 0.4602 - val_accuracy: 0.8311\n",
+      "Epoch 61/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4959 - accuracy: 0.8226 - val_loss: 0.4657 - val_accuracy: 0.8313\n",
+      "Epoch 62/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4996 - accuracy: 0.8192 - val_loss: 0.4655 - val_accuracy: 0.8268\n",
+      "Epoch 63/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8224 - val_loss: 0.4598 - val_accuracy: 0.8295\n",
+      "Epoch 64/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4939 - accuracy: 0.8222 - val_loss: 0.4628 - val_accuracy: 0.8318\n",
+      "Epoch 65/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4956 - accuracy: 0.8213 - val_loss: 0.4615 - val_accuracy: 0.8307\n",
+      "Epoch 66/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4972 - accuracy: 0.8201 - val_loss: 0.4602 - val_accuracy: 0.8288\n",
+      "Epoch 67/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8211 - val_loss: 0.4642 - val_accuracy: 0.8289\n",
+      "Epoch 68/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8204 - val_loss: 0.4684 - val_accuracy: 0.8292\n",
+      "Epoch 69/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8226 - val_loss: 0.4706 - val_accuracy: 0.8231\n",
+      "Epoch 70/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4976 - accuracy: 0.8214 - val_loss: 0.4666 - val_accuracy: 0.8315\n",
+      "Epoch 71/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4950 - accuracy: 0.8225 - val_loss: 0.4672 - val_accuracy: 0.8264\n",
+      "Epoch 72/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4935 - accuracy: 0.8214 - val_loss: 0.4621 - val_accuracy: 0.8279\n",
+      "Epoch 73/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4972 - accuracy: 0.8200 - val_loss: 0.4637 - val_accuracy: 0.8289\n",
+      "Epoch 74/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4931 - accuracy: 0.8222 - val_loss: 0.4616 - val_accuracy: 0.8351\n",
+      "Epoch 75/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4965 - accuracy: 0.8214 - val_loss: 0.4627 - val_accuracy: 0.8307\n",
+      "Epoch 76/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8215 - val_loss: 0.4649 - val_accuracy: 0.8282\n",
+      "Epoch 77/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4924 - accuracy: 0.8208 - val_loss: 0.4584 - val_accuracy: 0.8331\n",
+      "Epoch 78/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4933 - accuracy: 0.8221 - val_loss: 0.4696 - val_accuracy: 0.8277\n",
+      "Epoch 79/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4948 - accuracy: 0.8203 - val_loss: 0.4619 - val_accuracy: 0.8281\n",
+      "Epoch 80/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4904 - accuracy: 0.8227 - val_loss: 0.4715 - val_accuracy: 0.8249\n",
+      "Epoch 81/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8207 - val_loss: 0.4615 - val_accuracy: 0.8323\n",
+      "Epoch 82/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8208 - val_loss: 0.4607 - val_accuracy: 0.8280\n",
+      "Epoch 83/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4944 - accuracy: 0.8217 - val_loss: 0.4641 - val_accuracy: 0.8280\n",
+      "Epoch 84/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8208 - val_loss: 0.4715 - val_accuracy: 0.8269\n",
+      "Epoch 85/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8201 - val_loss: 0.4704 - val_accuracy: 0.8254\n",
+      "Epoch 86/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8203 - val_loss: 0.4649 - val_accuracy: 0.8264\n",
+      "Epoch 87/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8223 - val_loss: 0.4593 - val_accuracy: 0.8323\n",
+      "Epoch 88/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4942 - accuracy: 0.8201 - val_loss: 0.4631 - val_accuracy: 0.8302\n",
+      "Epoch 89/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8204 - val_loss: 0.4592 - val_accuracy: 0.8295\n",
+      "Epoch 90/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4923 - accuracy: 0.8212 - val_loss: 0.4647 - val_accuracy: 0.8262\n",
+      "Epoch 91/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8197 - val_loss: 0.4600 - val_accuracy: 0.8315\n",
+      "Epoch 92/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8192 - val_loss: 0.4663 - val_accuracy: 0.8270\n",
+      "Epoch 93/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4963 - accuracy: 0.8194 - val_loss: 0.4556 - val_accuracy: 0.8332\n",
+      "Epoch 94/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4931 - accuracy: 0.8224 - val_loss: 0.4615 - val_accuracy: 0.8293\n",
+      "Epoch 95/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8211 - val_loss: 0.4575 - val_accuracy: 0.8344\n",
+      "Epoch 96/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4961 - accuracy: 0.8220 - val_loss: 0.4600 - val_accuracy: 0.8304\n",
+      "Epoch 97/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.8215 - val_loss: 0.4620 - val_accuracy: 0.8289\n",
+      "Epoch 98/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8214 - val_loss: 0.4631 - val_accuracy: 0.8261\n",
+      "Epoch 99/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8210 - val_loss: 0.4611 - val_accuracy: 0.8270\n",
+      "Epoch 100/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4962 - accuracy: 0.8192 - val_loss: 0.4660 - val_accuracy: 0.8268\n",
+      "Epoch 101/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4940 - accuracy: 0.8227 - val_loss: 0.4659 - val_accuracy: 0.8305\n",
+      "Epoch 102/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8211 - val_loss: 0.4666 - val_accuracy: 0.8294\n",
+      "Epoch 103/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4934 - accuracy: 0.8221 - val_loss: 0.4595 - val_accuracy: 0.8352\n",
+      "Epoch 104/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8203 - val_loss: 0.4606 - val_accuracy: 0.8265\n",
+      "Epoch 105/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8211 - val_loss: 0.4587 - val_accuracy: 0.8320\n",
+      "Epoch 106/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8198 - val_loss: 0.4635 - val_accuracy: 0.8296\n",
+      "Epoch 107/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8205 - val_loss: 0.4584 - val_accuracy: 0.8312\n",
+      "Epoch 108/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4938 - accuracy: 0.8217 - val_loss: 0.4611 - val_accuracy: 0.8290\n",
+      "Epoch 109/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4928 - accuracy: 0.8209 - val_loss: 0.4626 - val_accuracy: 0.8288\n",
+      "Epoch 110/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8216 - val_loss: 0.4573 - val_accuracy: 0.8344\n",
+      "Epoch 111/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4933 - accuracy: 0.8214 - val_loss: 0.4553 - val_accuracy: 0.8321\n",
+      "Epoch 112/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4953 - accuracy: 0.8210 - val_loss: 0.4614 - val_accuracy: 0.8317\n",
+      "Epoch 113/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4921 - accuracy: 0.8221 - val_loss: 0.4611 - val_accuracy: 0.8298\n",
+      "Epoch 114/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4954 - accuracy: 0.8202 - val_loss: 0.4634 - val_accuracy: 0.8287\n",
+      "Epoch 115/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4918 - accuracy: 0.8224 - val_loss: 0.4540 - val_accuracy: 0.8317\n",
+      "Epoch 116/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4948 - accuracy: 0.8197 - val_loss: 0.4601 - val_accuracy: 0.8308\n",
+      "Epoch 117/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4937 - accuracy: 0.8208 - val_loss: 0.4622 - val_accuracy: 0.8269\n",
+      "Epoch 118/300\n",
+      "1050/1050 [==============================] - 1s 999us/step - loss: 0.4936 - accuracy: 0.8222 - val_loss: 0.4583 - val_accuracy: 0.8330\n",
+      "Epoch 119/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8189 - val_loss: 0.4669 - val_accuracy: 0.8294\n",
+      "Epoch 120/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4937 - accuracy: 0.8227 - val_loss: 0.4673 - val_accuracy: 0.8255\n",
+      "Epoch 121/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8206 - val_loss: 0.4651 - val_accuracy: 0.8300\n",
+      "Epoch 122/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8219 - val_loss: 0.4688 - val_accuracy: 0.8254\n",
+      "Epoch 123/300\n",
+      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4952 - accuracy: 0.8207 - val_loss: 0.4623 - val_accuracy: 0.8296\n",
+      "Epoch 124/300\n",
+      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4947 - accuracy: 0.8211 - val_loss: 0.4664 - val_accuracy: 0.8235\n",
+      "Epoch 125/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4952 - accuracy: 0.8214 - val_loss: 0.4617 - val_accuracy: 0.8319\n",
+      "Epoch 126/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8203 - val_loss: 0.4657 - val_accuracy: 0.8289\n",
+      "Epoch 127/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4945 - accuracy: 0.8211 - val_loss: 0.4649 - val_accuracy: 0.8257\n",
+      "Epoch 128/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.8208 - val_loss: 0.4594 - val_accuracy: 0.8305\n",
+      "Epoch 129/300\n",
+      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4967 - accuracy: 0.8209 - val_loss: 0.4592 - val_accuracy: 0.8326\n",
+      "Epoch 130/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4911 - accuracy: 0.8233 - val_loss: 0.4719 - val_accuracy: 0.8262\n",
+      "Epoch 131/300\n",
+      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4924 - accuracy: 0.8214 - val_loss: 0.4650 - val_accuracy: 0.8273\n",
+      "Epoch 132/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4906 - accuracy: 0.8228 - val_loss: 0.4628 - val_accuracy: 0.8308\n",
+      "Epoch 133/300\n",
+      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4967 - accuracy: 0.8206 - val_loss: 0.4600 - val_accuracy: 0.8302\n",
+      "Epoch 134/300\n",
+      "1050/1050 [==============================] - 1s 999us/step - loss: 0.4904 - accuracy: 0.8226 - val_loss: 0.4644 - val_accuracy: 0.8299\n",
+      "Epoch 135/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4939 - accuracy: 0.8216 - val_loss: 0.4589 - val_accuracy: 0.8319\n",
+      "Epoch 136/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4940 - accuracy: 0.8226 - val_loss: 0.4638 - val_accuracy: 0.8285\n",
+      "Epoch 137/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4950 - accuracy: 0.8191 - val_loss: 0.4680 - val_accuracy: 0.8239\n",
+      "Epoch 138/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4930 - accuracy: 0.8226 - val_loss: 0.4584 - val_accuracy: 0.8312\n",
+      "Epoch 139/300\n",
+      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4946 - accuracy: 0.8214 - val_loss: 0.4660 - val_accuracy: 0.8235\n",
+      "Epoch 140/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4952 - accuracy: 0.8221 - val_loss: 0.4641 - val_accuracy: 0.8292\n",
+      "Epoch 141/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4956 - accuracy: 0.8213 - val_loss: 0.4589 - val_accuracy: 0.8289\n",
+      "Epoch 142/300\n",
+      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4946 - accuracy: 0.8207 - val_loss: 0.4641 - val_accuracy: 0.8298\n",
+      "Epoch 143/300\n",
+      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4940 - accuracy: 0.8214 - val_loss: 0.4514 - val_accuracy: 0.8358\n",
+      "Epoch 144/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8201 - val_loss: 0.4621 - val_accuracy: 0.8285\n",
+      "Epoch 145/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4945 - accuracy: 0.8201 - val_loss: 0.4601 - val_accuracy: 0.8301\n",
+      "Epoch 146/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8208 - val_loss: 0.4706 - val_accuracy: 0.8265\n",
+      "Epoch 147/300\n",
+      "1050/1050 [==============================] - 1s 994us/step - loss: 0.4914 - accuracy: 0.8214 - val_loss: 0.4681 - val_accuracy: 0.8271\n",
+      "Epoch 148/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4920 - accuracy: 0.8211 - val_loss: 0.4694 - val_accuracy: 0.8236\n",
+      "Epoch 149/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4892 - accuracy: 0.8240 - val_loss: 0.4599 - val_accuracy: 0.8304\n",
+      "Epoch 150/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4930 - accuracy: 0.8211 - val_loss: 0.4570 - val_accuracy: 0.8336\n",
+      "Epoch 151/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8202 - val_loss: 0.4702 - val_accuracy: 0.8248\n",
+      "Epoch 152/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4923 - accuracy: 0.8206 - val_loss: 0.4688 - val_accuracy: 0.8299\n",
+      "Epoch 153/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8222 - val_loss: 0.4604 - val_accuracy: 0.8300\n",
+      "Epoch 154/300\n",
+      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4919 - accuracy: 0.8223 - val_loss: 0.4634 - val_accuracy: 0.8276\n",
+      "Epoch 155/300\n",
+      "1050/1050 [==============================] - 1s 989us/step - loss: 0.4917 - accuracy: 0.8209 - val_loss: 0.4657 - val_accuracy: 0.8263\n",
+      "Epoch 156/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4931 - accuracy: 0.8208 - val_loss: 0.4601 - val_accuracy: 0.8243\n",
+      "Epoch 157/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8203 - val_loss: 0.4656 - val_accuracy: 0.8248\n",
+      "Epoch 158/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4922 - accuracy: 0.8189 - val_loss: 0.4562 - val_accuracy: 0.8282\n",
+      "Epoch 159/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4932 - accuracy: 0.8188 - val_loss: 0.4595 - val_accuracy: 0.8307\n",
+      "Epoch 160/300\n",
+      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4912 - accuracy: 0.8224 - val_loss: 0.4602 - val_accuracy: 0.8321\n",
+      "Epoch 161/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4912 - accuracy: 0.8217 - val_loss: 0.4568 - val_accuracy: 0.8304\n",
+      "Epoch 162/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4909 - accuracy: 0.8197 - val_loss: 0.4709 - val_accuracy: 0.8214\n",
+      "Epoch 163/300\n",
+      "1050/1050 [==============================] - 1s 988us/step - loss: 0.4901 - accuracy: 0.8238 - val_loss: 0.4730 - val_accuracy: 0.8230\n",
+      "Epoch 164/300\n",
+      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4874 - accuracy: 0.8222 - val_loss: 0.4667 - val_accuracy: 0.8249\n",
+      "Epoch 165/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4896 - accuracy: 0.8230 - val_loss: 0.4592 - val_accuracy: 0.8308\n",
+      "Epoch 166/300\n",
+      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4911 - accuracy: 0.8227 - val_loss: 0.4584 - val_accuracy: 0.8310\n",
+      "Epoch 167/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4920 - accuracy: 0.8224 - val_loss: 0.4582 - val_accuracy: 0.8270\n",
+      "Epoch 168/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4899 - accuracy: 0.8218 - val_loss: 0.4565 - val_accuracy: 0.8323\n",
+      "Epoch 169/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.8223 - val_loss: 0.4591 - val_accuracy: 0.8299\n",
+      "Epoch 170/300\n",
+      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4923 - accuracy: 0.8221 - val_loss: 0.4587 - val_accuracy: 0.8340\n",
+      "Epoch 171/300\n",
+      "1050/1050 [==============================] - 1s 990us/step - loss: 0.4913 - accuracy: 0.8230 - val_loss: 0.4605 - val_accuracy: 0.8315\n",
+      "Epoch 172/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4925 - accuracy: 0.8213 - val_loss: 0.4646 - val_accuracy: 0.8280\n",
+      "Epoch 173/300\n",
+      "1050/1050 [==============================] - 1s 995us/step - loss: 0.4938 - accuracy: 0.8216 - val_loss: 0.4582 - val_accuracy: 0.8293\n",
+      "Epoch 174/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4921 - accuracy: 0.8206 - val_loss: 0.4641 - val_accuracy: 0.8268\n",
+      "Epoch 175/300\n",
+      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4907 - accuracy: 0.8199 - val_loss: 0.4547 - val_accuracy: 0.8333\n",
+      "Epoch 176/300\n",
+      "1050/1050 [==============================] - 1s 991us/step - loss: 0.4937 - accuracy: 0.8217 - val_loss: 0.4668 - val_accuracy: 0.8245\n",
+      "Epoch 177/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4913 - accuracy: 0.8216 - val_loss: 0.4639 - val_accuracy: 0.8260\n",
+      "Epoch 178/300\n",
+      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4914 - accuracy: 0.8203 - val_loss: 0.4647 - val_accuracy: 0.8239\n",
+      "Epoch 179/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4919 - accuracy: 0.8200 - val_loss: 0.4575 - val_accuracy: 0.8357\n",
+      "Epoch 180/300\n",
+      "1050/1050 [==============================] - 1s 992us/step - loss: 0.4903 - accuracy: 0.8228 - val_loss: 0.4610 - val_accuracy: 0.8270\n",
+      "Epoch 181/300\n",
+      "1050/1050 [==============================] - 1s 998us/step - loss: 0.4926 - accuracy: 0.8199 - val_loss: 0.4600 - val_accuracy: 0.8290\n",
+      "Epoch 182/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4939 - accuracy: 0.8216 - val_loss: 0.4732 - val_accuracy: 0.8188\n",
+      "Epoch 183/300\n",
+      "1050/1050 [==============================] - 1s 996us/step - loss: 0.4914 - accuracy: 0.8199 - val_loss: 0.4595 - val_accuracy: 0.8294\n",
+      "Epoch 184/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4923 - accuracy: 0.8210 - val_loss: 0.4598 - val_accuracy: 0.8298\n",
+      "Epoch 185/300\n",
+      "1050/1050 [==============================] - 1s 993us/step - loss: 0.4940 - accuracy: 0.8200 - val_loss: 0.4643 - val_accuracy: 0.8285\n",
+      "Epoch 186/300\n",
+      "1050/1050 [==============================] - 1s 997us/step - loss: 0.4896 - accuracy: 0.8220 - val_loss: 0.4605 - val_accuracy: 0.8308\n",
+      "Epoch 187/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4909 - accuracy: 0.8207 - val_loss: 0.4629 - val_accuracy: 0.8254\n",
+      "Epoch 188/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4891 - accuracy: 0.8234 - val_loss: 0.4626 - val_accuracy: 0.8255\n",
+      "Epoch 189/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4905 - accuracy: 0.8220 - val_loss: 0.4555 - val_accuracy: 0.8308\n",
+      "Epoch 190/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4909 - accuracy: 0.8203 - val_loss: 0.4583 - val_accuracy: 0.8305\n",
+      "Epoch 191/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4897 - accuracy: 0.8233 - val_loss: 0.4738 - val_accuracy: 0.8192\n",
+      "Epoch 192/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4904 - accuracy: 0.8228 - val_loss: 0.4718 - val_accuracy: 0.8223\n",
+      "Epoch 193/300\n",
+      "1050/1050 [==============================] - 1s 1ms/step - loss: 0.4898 - accuracy: 0.8228 - val_loss: 0.4642 - val_accuracy: 0.8273\n",
+      "263/263 [==============================] - 0s 522us/step\n"
      ]
     },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1852,7 +2218,7 @@
     {
      "data": {
       "text/plain": "<Figure size 1200x600 with 1 Axes>",
-      "image/png": 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z5mnmzJkaN26cZwG3gQMHavv27frss8+0cOFC/etf/9Krr76qGTNm6Pbbbz/uvTt16qQ1a9aoqKjouNvkrV+/Xv7+/l4frIwZM0Z//OMflZGRobCwMH3++ee64YYbPDsnlP/+3HTTTcddI+Horf3ORi+6dPzf0xP9XlfnPQFAfURIB4AGYunSpTp06JA+/vhjDRw40NO+c+dOC6uqEBMTo8DAQG3btu2Yc1W1He3XX3/Vli1b9NZbb2ncuHGe9hOtvu1Ly5YttXjxYuXm5nr1pm/evPmU7jN27FjNnz9f8+bN0+zZsxUeHq5Ro0Z5zn/44Ydq06aNPv74Y68eyKeffrpaNUvS1q1b1aZNG097enr6Mb3TH374oYYMGaJ///vfXu2ZmZlq0qSJ5/hkVtav/PqLFi06Zph3+XSK8vrOhpYtW2r9+vVyu91evelV1RIQEKBRo0Zp1KhRcrvd+v3vf68333xTTz31lGckR6NGjXTLLbfolltuUW5urgYOHKhnnnnGZ0i/7LLLtHz5cn3wwQdVbmG2a9cuff/99xo6dKhXiB4zZoyeffZZffTRR4qNjVV2drbXEPLo6GiFhYXJ5XJ57Wtel9XH9wQA1cFwdwBoIMp7sSr3UBYXF+uNN96wqiQvDodDQ4cO1aeffqoDBw542rdt23bMPObjPV/yfn+GYXhto3WqRowYodLSUv3973/3tLlcLr322mundJ/Ro0crODhYb7zxhubNm6errrrKa3/pqmr/+eeftXz58lOueejQofL399drr73mdb9p06Ydc63D4Timx/qDDz7Q/v37vdpCQkIk6aS2nhsxYoRcLpemT5/u1f7qq6/KZrOd9PoCNWHEiBFKSUnxGjZeWlqq1157TaGhoZ6pEIcOHfJ6nt1u9/TYFhUVVXlNaGio2rVr5zl/PL/73e8UExOjRx999Jh59oWFhbrllltkGIYmT57sda5Tp07q2rWr5syZozlz5ig+Pt7rwzWHw6Grr75aH330kX777bdjXjc9Pd1nXbVRfXxPAFAd9KQDQAPRv39/RUVFafz48br//vtls9n03//+96wOKz6RZ555RgsXLtT555+vu+++2xP2zjnnHK1du9bncxMTE9W2bVs98sgj2r9/v8LDw/XRRx+d1tzmUaNG6fzzz9fjjz+uXbt2qXPnzvr4449Peb52aGioRo8e7ZmXXnmou2T2tn788ce68sorNXLkSO3cuVMzZsxQ586dlZube0qvVb7f+5QpU3TZZZdpxIgRWrNmjebNm+fVO17+us8995xuueUW9e/fX7/++qveeecdrx54SWrbtq0iIyM1Y8YMhYWFKSQkRElJSVXOcR41apSGDBmiJ554Qrt27VL37t21cOFCffbZZ3rwwQeP2Sv8dC1evFiFhYXHtI8ePVp33nmn3nzzTU2YMEGrVq1Sq1at9OGHH2rZsmWaNm2ap6f/9ttv1+HDh3XhhReqefPm2r17t1577TX16NHDM3+9c+fOGjx4sHr16qVGjRrpl19+0Ycffqh7773XZ32NGzfWhx9+qJEjR+rcc8/V7bffrs6dOyslJUWzZs3Stm3b9Ne//rXKLe3GjBmjyZMnKzAwULfddtsxc+v/9Kc/acmSJUpKStIdd9yhzp076/Dhw1q9erUWLVqkw4cPV/fbapn6+J4A4FQR0gGggWjcuLG+/PJLPfzww3ryyScVFRWlm266SRdddJFn32Wr9erVS/PmzdMjjzyip556SgkJCXruuee0adOmE64+7+/vry+++EL333+/pkyZosDAQF155ZW699571b1792rVY7fb9fnnn+vBBx/U//73P9lsNl1++eV65ZVX1LNnz1O619ixYzV79mzFx8d7Fv8qN2HCBKWkpOjNN9/UggUL1LlzZ/3vf//TBx98oKVLl55y3S+88IICAwM1Y8YMT+BZuHChRo4c6XXdH/7wB+Xl5Wn27NmaM2eOzj33XH311Vd6/PHHva7z9/fXW2+9pUmTJumuu+5SaWmpZs6cWWVIL/+eTZ48WXPmzNHMmTPVqlUrTZ06VQ8//PApv5cTmT9//jGLvElSq1atdM4552jp0qV6/PHH9dZbbyk7O1sdO3bUzJkzNWHCBM+1N910k/7xj3/ojTfeUGZmpuLi4jRmzBg988wznmB8//336/PPP9fChQtVVFSkli1b6oUXXtCjjz56whoHDBig9evX68UXX9QHH3yggwcPKiIiQv3799d//vMfXXDBBVU+b8yYMXryySeVn5/vWdW9stjYWK1YsULPPfecPv74Y73xxhtq3LixunTp4tnvva6pj+8JAE6VzahNXSgAAFRh9OjR1dr+CgAAoK5hTjoAoFYpKCjwOt66davmzp2rwYMHW1MQAADAWURPOgCgVomPj9eECRPUpk0b7d69W3//+99VVFSkNWvWHLP3NwAAQH3DnHQAQK1y6aWX6t1331VKSoqcTqf69eunF198kYAOAAAaBHrSAQAAAACoJZiTDgAAAABALUFIBwAAAACglmhwc9LdbrcOHDigsLAw2Ww2q8sBAAAAANRzhmEoJydHTZs2ld3uu6+8wYX0AwcOKCEhweoyAAAAAAANzN69e9W8eXOf1zS4kB4WFibJ/OaEh4dbXA0AAAAAoL7Lzs5WQkKCJ4/60uBCevkQ9/DwcEI6AAAAAOCsOZkp1ywcBwAAAABALUFIBwAAAACgliCkAwAAAABQSzS4OeknwzAMlZaWyuVyWV0K6hmHwyE/Pz+2/wMAAABQJUL6UYqLi3Xw4EHl5+dbXQrqqeDgYMXHxysgIMDqUgAAAADUMoT0Stxut3bu3CmHw6GmTZsqICCAHk/UGMMwVFxcrPT0dO3cuVPt27eX3c6MEwAAAAAVCOmVFBcXy+12KyEhQcHBwVaXg3ooKChI/v7+2r17t4qLixUYGGh1SQAAAABqEbrxqkDvJs4kfr4AAAAAHA9pAQAAAACAWoKQDgAAAABALUFIx3G1atVK06ZNO+nrly5dKpvNpszMzDNWEwAAAADUZ4T0esBms/l8PPPMM9W678qVK3XnnXee9PX9+/fXwYMHFRERUa3XO1l8GAAAAACgvrI0pH/33XcaNWqUmjZtKpvNpk8//fSEz1m6dKnOPfdcOZ1OtWvXTrNmzTrjddZ2Bw8e9DymTZum8PBwr7ZHHnnEc61hGCotLT2p+0ZHR5/SKvcBAQGKi4tj2zoAAAAAqCZLQ3peXp66d++u119//aSu37lzp0aOHKkhQ4Zo7dq1evDBB3X77bdrwYIFZ6xGwzCUX1xqycMwjJOqMS4uzvOIiIiQzWbzHCcnJyssLEzz5s1Tr1695HQ69cMPP2j79u264oorFBsbq9DQUPXp00eLFi3yuu/Rw91tNpv+9a9/6corr1RwcLDat2+vzz//3HP+6B7uWbNmKTIyUgsWLFCnTp0UGhqqSy+9VAcPHvQ8p7S0VPfff78iIyPVuHFjPfbYYxo/frxGjx5d7d+zI0eOaNy4cYqKilJwcLCGDx+urVu3es7v3r1bo0aNUlRUlEJCQtSlSxfNnTvX89yxY8cqOjpaQUFBat++vWbOnFntWgAAAADgVFi6T/rw4cM1fPjwk75+xowZat26tV555RVJUqdOnfTDDz/o1Vdf1bBhw6p8TlFRkYqKijzH2dnZp1RjQYlLnSefuQ8BfNn43DAFB9TMb9Hjjz+ul19+WW3atFFUVJT27t2rESNG6I9//KOcTqfefvttjRo1Sps3b1aLFi2Oe59nn31Wf/7znzV16lS99tprGjt2rHbv3q1GjRpVeX1+fr5efvll/fe//5XdbtdNN92kRx55RO+8844k6aWXXtI777yjmTNnqlOnTvrrX/+qTz/9VEOGDKn2e50wYYK2bt2qzz//XOHh4Xrsscc0YsQIbdy4Uf7+/rrnnntUXFys7777TiEhIdq4caNCQ0MlSU899ZQ2btyoefPmqUmTJtq2bZsKCgqqXQsAAAAAnApLQ/qpWr58uYYOHerVNmzYMD344IPHfc6UKVP07LPPnuHKar/nnntOF198see4UaNG6t69u+f4+eef1yeffKLPP/9c995773HvM2HCBN1www2SpBdffFF/+9vftGLFCl166aVVXl9SUqIZM2aobdu2kqR7771Xzz33nOf8a6+9pkmTJunKK6+UJE2fPt3Tq10d5eF82bJl6t+/vyTpnXfeUUJCgj799FNde+212rNnj66++mp17dpVktSmTRvP8/fs2aOePXuqd+/ekszRBAAAAABwttSpkJ6SkqLY2FivttjYWGVnZ6ugoEBBQUHHPGfSpEmaOHGi5zg7O1sJCQkn/ZpB/g5tfK7qXvozLcjfUWP3Kg+d5XJzc/XMM8/oq6++0sGDB1VaWqqCggLt2bPH5326devm+TokJETh4eFKS0s77vXBwcGegC5J8fHxnuuzsrKUmpqqvn37es47HA716tVLbrf7lN5fuU2bNsnPz09JSUmetsaNG6tjx47atGmTJOn+++/X3XffrYULF2ro0KG6+uqrPe/r7rvv1tVXX63Vq1frkksu0ejRoz1hHwAAAEAtYBhSwREpJ0XKOSDlHZK6j7G6qhpTp0J6dTidTjmdzmo/32az1diQcyuFhIR4HT/yyCP6+uuv9fLLL6tdu3YKCgrSNddco+LiYp/38ff39zq22Ww+A3VV15/sXPsz5fbbb9ewYcP01VdfaeHChZoyZYpeeeUV3XfffRo+fLh2796tuXPn6uuvv9ZFF12ke+65Ry+//LKlNQMAAAANQnG+lHOw7JEiZR+oCOM5KRXtpYXez+t8heQfaE3NNaxOpc+4uDilpqZ6taWmpio8PLzKXnQc37JlyzRhwgTPMPPc3Fzt2rXrrNYQERGh2NhYrVy5UgMHDpQkuVwurV69Wj169KjWPTt16qTS0lL9/PPPnh7wQ4cOafPmzercubPnuoSEBN1111266667NGnSJP3zn//UfffdJ8lc1X78+PEaP368BgwYoEcffZSQDgAAAJwOV4mUm3pU8D5Y8cguC99FWSd/z6AoKaypFBYnFecR0q3Qr1+/Y+Yrf/311+rXr59FFdVd7du318cff6xRo0bJZrPpqaeeqvYQ89Nx3333acqUKWrXrp0SExP12muv6ciRIye1jduvv/6qsLAwz7HNZlP37t11xRVX6I477tCbb76psLAwPf7442rWrJmuuOIKSdKDDz6o4cOHq0OHDjpy5IiWLFmiTp06SZImT56sXr16qUuXLioqKtKXX37pOQcAAADgKG63VHC4UtA+WHVPeF66pJMcUesfLIXFm4/weDOEl4fx8rbQuHoTyo9maUjPzc3Vtm3bPMc7d+7U2rVr1ahRI7Vo0UKTJk3S/v379fbbb0uS7rrrLk2fPl3/93//p1tvvVXffPON3n//fX311VdWvYU66y9/+YtuvfVW9e/fX02aNNFjjz12yivf14THHntMKSkpGjdunBwOh+68804NGzZMDseJ5+OX976XczgcKi0t1cyZM/XAAw/osssuU3FxsQYOHKi5c+d6ht67XC7dc8892rdvn8LDw3XppZfq1VdflWTu9T5p0iTt2rVLQUFBGjBggN57772af+MAAABAbVeUUyl4HzXkvLznO+eg5C45ufvZ/cxw7Qne8UeF8bJ2Z7h0Ep129ZXNsHCC8NKlS6vcamv8+PGaNWuWJkyYoF27dmnp0qVez3nooYe0ceNGNW/eXE899ZQmTJhw0q+ZnZ2tiIgIZWVlKTw83OtcYWGhdu7cqdatWyswsH5+KlPbud1uderUSdddd52ef/55q8s5I/g5AwAAgKVKi8oCdlVDziuF7+Lck79ncJNKQbtS4A5vWtETHtxYstvP3PuqxXzl0KNZ2pM+ePBgn4uIzZo1q8rnrFmz5gxWhbNp9+7dWrhwoQYNGqSioiJNnz5dO3fu1I033mh1aQAAAEDd4nabw8qPN+S8vDc8/9DJ39MZXhayKw05D2/q3RMeGiv5BZy599XA1Kk56ah/7Ha7Zs2apUceeUSGYeicc87RokWLmAcOAAAAlDMMqTDrOEPOK4Xx3FTJXXpy93QEVD3X++iecGfomX1vOAYhHZZKSEjQsmXLrC4DAAAAsEZJgfew86OHnJd/XZJ/kje0SaExVc/1rtwTHhTVoOd912aEdAAAAACoaa5SKS+tiiHnRw1DL8w8+XsGRlYx1/uoxddCYiQHMa8u43cPAAAAAE7H4R3S+velg+sqesPz0iTjJLc49gssC9lHB++jthwLCD6z7wO1AiEdAAAAAE5VYZa04VNp3bvSnuVVX2NzmIuqhcdXHbzLjwMjGXoOD0I6AAAAAJwMt0vavkRaN1tK/koqLSw7YZPaDpE6jpAimlfM/w5pItkdlpaMuoeQDgAAAAC+pG40e8zXvy/lplS0N+ko9bhB6jbGHKoO1ABCOgAAAAAcLS9D+vVDs9f84LqK9qBGUtdrpO43SE17MkwdNc5udQGoPQYPHqwHH3zQc9yqVStNmzbN53NsNps+/fTT037tmroPAAAAUG2lxdKmL6R3b5Re6SjNf8wM6HY/qeNIacz/pIc3SyOmSs3OJaDjjKAnvR4YNWqUSkpKNH/+/GPOff/99xo4cKDWrVunbt26ndJ9V65cqZCQkJoqU5L0zDPP6NNPP9XatWu92g8ePKioqKgafa2jzZo1Sw8++KAyMzPP6OsAAACgDjEM6cBqae270m8fSgVHKs7F95B63Cidc40U0tiyEtGwENLrgdtuu01XX3219u3bp+bNm3udmzlzpnr37n3KAV2SoqOja6rEE4qLiztrrwUAAAAo+4C0fo4ZzjM2V7SHxUvdrjOHs8d0sq4+NFgMdz8Rw5CK86x5GMZJlXjZZZcpOjpas2bN8mrPzc3VBx98oNtuu02HDh3SDTfcoGbNmik4OFhdu3bVu+++6/O+Rw9337p1qwYOHKjAwEB17txZX3/99THPeeyxx9ShQwcFBwerTZs2euqpp1RSUiLJ7Ml+9tlntW7dOtlsNtlsNk/NRw93//XXX3XhhRcqKChIjRs31p133qnc3FzP+QkTJmj06NF6+eWXFR8fr8aNG+uee+7xvFZ17NmzR1dccYVCQ0MVHh6u6667TqmpqZ7z69at05AhQxQWFqbw8HD16tVLv/zyiyRp9+7dGjVqlKKiohQSEqIuXbpo7ty51a4FAAAAZ0Bxvrn429ujpb90lhY9YwZ0v0Czt/ymj6SHNkgXP0dAh2XoST+RknzpRYtWavzDASngxMPN/fz8NG7cOM2aNUtPPPGEbGVzYz744AO5XC7dcMMNys3NVa9evfTYY48pPDxcX331lW6++Wa1bdtWffv2PeFruN1uXXXVVYqNjdXPP/+srKwsr/nr5cLCwjRr1iw1bdpUv/76q+644w6FhYXp//7v/zRmzBj99ttvmj9/vhYtWiRJioiIOOYeeXl5GjZsmPr166eVK1cqLS1Nt99+u+69916vDyKWLFmi+Ph4LVmyRNu2bdOYMWPUo0cP3XHHHSd8P1W9v/KA/u2336q0tFT33HOPxowZo6VLl0qSxo4dq549e+rvf/+7HA6H1q5dK39/f0nSPffco+LiYn333XcKCQnRxo0bFRoaesp1AAAAoIa53eY+5utmSxs+k4pzKs616C91v17qMloKPPb/pYAVCOn1xK233qqpU6fq22+/1eDBgyWZQ92vvvpqRUREKCIiQo888ojn+vvuu08LFizQ+++/f1IhfdGiRUpOTtaCBQvUtKn5ocWLL76o4cOHe1335JNPer5u1aqVHnnkEb333nv6v//7PwUFBSk0NFR+fn4+h7fPnj1bhYWFevvttz1z4qdPn65Ro0bppZdeUmxsrCQpKipK06dPl8PhUGJiokaOHKnFixdXK6QvXrxYv/76q3bu3KmEhARJ0ttvv60uXbpo5cqV6tOnj/bs2aNHH31UiYmJkqT27dt7nr9nzx5dffXV6tq1qySpTZs2p1wDAAAAatDhHdK698xH5u6K9siW5lD27mOkRvyfDbUPIf1E/IPNHm2rXvskJSYmqn///vrPf/6jwYMHa9u2bfr+++/13HPPSZJcLpdefPFFvf/++9q/f7+Ki4tVVFSk4OCTe41NmzYpISHBE9AlqV+/fsdcN2fOHP3tb3/T9u3blZubq9LSUoWHh5/0+yh/re7du3stWnf++efL7XZr8+bNnpDepUsXORwOzzXx8fH69ddfT+m1Kr9mQkKCJ6BLUufOnRUZGalNmzapT58+mjhxom6//Xb997//1dChQ3Xttdeqbdu2kqT7779fd999txYuXKihQ4fq6quvrtY6AAAAADgNhVnShk/MYL5neUV7QJjZW97jRinhPMnOrF/UXvx0nojNZg45t+Jxils63Hbbbfroo4+Uk5OjmTNnqm3btho0aJAkaerUqfrrX/+qxx57TEuWLNHatWs1bNgwFRcX19i3avny5Ro7dqxGjBihL7/8UmvWrNETTzxRo69RWflQ83I2m01ut/uMvJZkrky/YcMGjRw5Ut988406d+6sTz75RJJ0++23a8eOHbr55pv166+/qnfv3nrttdfOWC0AAAAo43ZJWxdJH94qvdxB+uIBM6Db7FLbC6Wr/iU9skW6YrrUsj8BHbUeP6H1yHXXXSe73a7Zs2fr7bff1q233uqZn75s2TJdccUVuummm9S9e3e1adNGW7ZsOel7d+rUSXv37tXBgwc9bT/99JPXNT/++KNatmypJ554Qr1791b79u21e/dur2sCAgLkcrlO+Frr1q1TXl6ep23ZsmWy2+3q2LHjSdd8Ksrf3969ez1tGzduVGZmpjp37uxp69Chgx566CEtXLhQV111lWbOnOk5l5CQoLvuuksff/yxHn74Yf3zn/88I7UCAABAUupGaeFT5gJw71wt/faRVFooRSdKQ581F4C7+ROp27VSwMmPUAWsxnD3eiQ0NFRjxozRpEmTlJ2drQkTJnjOtW/fXh9++KF+/PFHRUVF6S9/+YtSU1O9AqgvQ4cOVYcOHTR+/HhNnTpV2dnZeuKJJ7yuad++vfbs2aP33ntPffr00VdffeXpaS7XqlUr7dy5U2vXrlXz5s0VFhYmp9Ppdc3YsWP19NNPa/z48XrmmWeUnp6u++67TzfffLNnqHt1uVyuY/ZodzqdGjp0qLp27aqxY8dq2rRpKi0t1e9//3sNGjRIvXv3VkFBgR599FFdc801at26tfbt26eVK1fq6quvliQ9+OCDGj58uDp06KAjR45oyZIl6tSJFUEBAABqVF6G9OuH5iJwB9dVtAc1krpeY841b9rzlEekArUJPen1zG233aYjR45o2LBhXvPHn3zySZ177rkaNmyYBg8erLi4OI0ePfqk72u32/XJJ5+ooKBAffv21e23364//vGPXtdcfvnleuihh3TvvfeqR48e+vHHH/XUU095XXP11Vfr0ksv1ZAhQxQdHV3lNnDBwcFasGCBDh8+rD59+uiaa67RRRddpOnTp5/aN6MKubm56tmzp9dj1KhRstls+uyzzxQVFaWBAwdq6NChatOmjebMmSNJcjgcOnTokMaNG6cOHTrouuuu0/Dhw/Xss89KMsP/Pffco06dOunSSy9Vhw4d9MYbb5x2vQAAAA1eabG06Qvp3RulVzpK8x8zA7rdT0q8TBrzjvTwZmnEVKnZuQR01Hk2wzjJzbjriezsbEVERCgrK+uYBc0KCwu1c+dOtW7dWoGBgRZViPqOnzMAAIATMAzpwGpp7bvSbx9KBUcqzsX3MBeAO+caKaSxZSUCp8JXDj0aw90BAAAA1A7ZB6T1c8xwnrG5oj0sXup2nTmcPYYphajfCOkAAAAArFOcLyV/Ka2dLe1YKqlsoK9foDmcvccNUpshkt3h6y5AvUFIBwAAAHB2ud3Snh+lde9KGz6TinMqzrXobwbzzqOlQN/DgoH6iJAOAAAA4Ow4vENa954ZzjP3VLRHtjSHsne/XmrU2rr6gFqAkF6FBraWHs4yfr4AAECDUpglbfjEDOd7lle0B4RJXUabi8AlnCfZ2XgKkAjpXvz9/SVJ+fn5CgoKsrga1Ff5+fmSKn7eAAAA6h23S9q+xNzPPPkrqbTQbLfZzfnl3W+QEkdKAcHW1gnUQoT0ShwOhyIjI5WWlibJ3K/bxj6LqCGGYSg/P19paWmKjIyUw8HiJwAAoJ5J3WgOZV//vpSbUtEenWgG827XSeFNrasPqAMI6UeJi4uTJE9QB2paZGSk5+cMAACgzsvLkH790Ow1P7iuoj2okdT1GjOcN+0p0fkFnBRC+lFsNpvi4+MVExOjkpISq8tBPePv708POgAAqPtKi6Ut881e860LJXep2W73lzoMM4N5+0skvwBr6wTqIEL6cTgcDsIUAAAAUM4wpAOrpbXvSr99KBUcqTjXtKfU/UbpnKulkMbW1QjUA4R0AAAAAMeXfUBaP8cM5xmbK9rD4s055t1vkGI6WVcfUM8Q0gEAAAB4K86Xkr+U1s6WdiyVVLaFrF+Q1Okycz/zNkMkOyNPgZpGSAcAAAAgud3Snh/NHvONn0rFuRXnWvSXetwgdR4tBYZbVSHQIBDSAQAAgIbs8A5p3XvmInCZeyrao1qVbZs2RmrU2rLygIaGkA4AAAA0NIVZ0oZPzHC+Z3lFe0CY1GW01ONGqUU/tk0DLEBIBwAAABoCt0vavsTczzz5K6m00Gy32c355T1ulDqOkAKCra0TaOAI6QAAAEB9lrrRHMq+/n0pN6WiPTqxYjh7eLx19QHwQkgHAAAA6pu8DOnXD81e84PrKtqDGkldrzUXgYvvwXB2oBYipAMAAAD1gWFIu3+Ufp4hbZ4ruUvNdru/1GGY2Wve/hLJL8DaOgH4REgHAAAA6rKSArPX/Oc3pdRfK9qb9pS63yidc7UU0ti6+gCcEkI6AAAAUBdl7ZNW/ltaNUsqOGy2+QVJ3a+X+t4hxXaxtDwA1UNIBwAAAOoKw5D2/GQOad/0hWS4zPaIFmYw73mTFNzI2hoBnBZCOgAAAFDblRRKv31khvOU9RXtrQZISXdJHYdLdod19QGoMYR0AAAAoLbKPlAxpD0/w2zzCzS3TUv6HUPagXqIkA4AAADUJoYh7Vsp/fR3adPnFau0hzeX+t4unTueIe1APUZIBwAAAGqD0iJpwyfmkPYDayraW55v9pp3HCk5+O87UN/xpxwAAACwUk6K9Mt/zEdeutnmcErdrpX6/k6K72ZtfQDOKkI6AAAAYIV9v5i95hs+qRjSHta0bEj7BPY2BxooQjoAAABwtpQWSxs/k37+u7R/VUV7i37mkPbEyySHv3X1AbAcIR0AAAA403LTpF9mSr/8W8pNNdscAdI515jhvGkPS8sDUHsQ0gEAAIAzZf9q6ec3zT3O3SVmW2ic1Od2qdcEKTTa0vIA1D6EdAAAAKAmuUrKhrS/Ke1bUdHevK/Za97pcskvwLr6ANRqhHQAAACgJuSmS6tmmUPacw6abXZ/6ZyrpaQ7pWa9LC0PQN1ASAcAAABOx4G1ZUPaP5RcxWZbaKzU+zZzSHtYrJXVAahjCOkAAADAqXKVSMlfmuF8z/KK9qbnSufdLXUezZB2ANVCSAcAAABOVt4hafUsaeW/pez9ZpvdT+pypZR0l9S8t6XlAaj7COkAAADAiRxcL614U1r/geQqMttCoqXet0q9bpHC462tD0C9QUgHAAAAquIqlTZ/ZQ5p372soj2+hzmkvcuVkp/TsvIA1E+EdAAAAKCy/MPS6rekFf+SsveZbTaH1PkKc0h7Ql/JZrO2RgD1FiEdAAAAkKTUDdLPM6T170ulhWZbcGNzOHvvW6WIZtbWB6BBIKQDAACg4XK7pM3zzHC+6/uK9riuUtLd5h7n/oHW1QegwSGkAwAAoOEpOCKt/q+08p9S5h6zzeaQOo0yh7S3OI8h7QAsQUgHAABAw5G2yVwIbv0cqSTfbAuKknpNkHrfJkUmWFoeABDSAQAAUL+5XdKWBeaQ9p3fVrTHniMl/U7qeq3kH2RdfQBQCSEdAAAA9VNBprT2HWnFP6Qju8w2m11KHGkOaW95PkPaAdQ6hHQAAADUL+mbzWC+9l2pJM9sC4yUeo2X+twuRbawtDwA8IWQDgAAgLrP7Za2fW0Oad/+TUV7dCdzSHu3MVJAsHX1AcBJIqQDAACg7irMrhjSfnhHWaNN6jjCDOetBzKkHUCdQkgHAABA3ZOxtWxI+2ypONdsc0ZI595sDmlv1Nra+gCgmgjpAAAAqBvcbnMo+89/l7Ytqmhv0rFiSLsz1Lr6AKAGENIBAABQuxXlmIvArXhTOrStrNEmdRhmrtLeZjBD2gHUG4R0AAAA1E6Htksr/imt+Z9UnGO2OcOlnjeZQ9obt7W2PgA4AwjpAAAAqD0Mo2xI+5vS1oWSDLO9cXtzSHv36yVnmKUlAsCZREgHAACA9YpypXXvmovBZWypaG9/iRnO21wo2e3W1QcAZwkhHQAAANY5vFNa+S9p9X+loiyzLSBM6jlW6nOH1KSdtfUBwFlGSAcAAMDZZRjSzm/NIe2b58kzpL1R27Ih7TdIgeGWlggAVrF8zNDrr7+uVq1aKTAwUElJSVqxYoXP66dNm6aOHTsqKChICQkJeuihh1RYWHiWqgUAAEC1FedJv/xHeuM86e0rpM1zJRlS24ukGz+Q7v3FDOkEdAANmKU96XPmzNHEiRM1Y8YMJSUladq0aRo2bJg2b96smJiYY66fPXu2Hn/8cf3nP/9R//79tWXLFk2YMEE2m01/+ctfLHgHAAAAOKEju6WV/5RWvy0Vlg1p9w+Retwo9b1Tiu5gbX0AUIvYDMMwrHrxpKQk9enTR9OnT5ckud1uJSQk6L777tPjjz9+zPX33nuvNm3apMWLF3vaHn74Yf3888/64YcfTuo1s7OzFRERoaysLIWH8yktAADAGbNvlfTDX8wec8NttkW1kvr+zpxzHhhhaXkAcLacSg61bLh7cXGxVq1apaFDh1YUY7dr6NChWr58eZXP6d+/v1atWuUZEr9jxw7NnTtXI0aMOO7rFBUVKTs72+sBAACAM6gwW/rqEelfF0nJX5oBvc0Q6YY50n2rpX6/J6ADwHFYNtw9IyNDLpdLsbGxXu2xsbFKTk6u8jk33nijMjIydMEFF8gwDJWWluquu+7SH/7wh+O+zpQpU/Tss8/WaO0AAAA4juSvzICec8A87jZGumCiFJNobV0AUEdYvnDcqVi6dKlefPFFvfHGG1q9erU+/vhjffXVV3r++eeP+5xJkyYpKyvL89i7d+9ZrBgAAKCByD4ozblZeu9GM6BHtZbGfSZd9Q8COgCcAst60ps0aSKHw6HU1FSv9tTUVMXFxVX5nKeeeko333yzbr/9dklS165dlZeXpzvvvFNPPPGE7PZjP3NwOp1yOp01/wYAAAAgud3SqpnSomekomzJ5pDOv18a9JjkH2R1dQBQ51jWkx4QEKBevXp5LQLndru1ePFi9evXr8rn5OfnHxPEHQ6HJMnC9e8AAAAaprRkaeZw6auJZkBveq70u2+loc8Q0AGgmizdgm3ixIkaP368evfurb59+2ratGnKy8vTLbfcIkkaN26cmjVrpilTpkiSRo0apb/85S/q2bOnkpKStG3bNj311FMaNWqUJ6wDAADgDCstkr7/i/T9K5K7xNxO7aLJUt87JDv/JwOA02FpSB8zZozS09M1efJkpaSkqEePHpo/f75nMbk9e/Z49Zw/+eSTstlsevLJJ7V//35FR0dr1KhR+uMf/2jVWwAAAGhYdv8offGAlLHFPG4/TBr5ihSZYG1dAFBPWLpPuhXYJx0AAKAaCjKlRU9Lq2aZxyEx0vCXpC5XSjablZUBQK13KjnU0p50AAAA1HKGIW38TJr3f1Ju2YK/546XLn5WCoqytjYAqIcI6QAAAKha1j5zz/Mt88zjxu2kUX+VWl1gbV0AUI8R0gEAAODN7ZJW/kta/JxUnCvZ/aULHpIGPCz5B1pdHQDUa4R0AAAAVEjdIH1+v7T/F/M4IcnsPY/pZG1dANBAENIBAAAglRRI3/5Z+vFvkrtUcoZLQ5+Wet0qVdptBwBwZhHSAQAAGrod30pfPigd3mEeJ14mjZgqhTe1tCwAaIgI6QAAAA1V/mFp4VPS2v+Zx2HxZjjvNMraugCgASOkAwAANDSGIf36oTT/cSk/Q5JN6nObdNFkKTDC6uoAoEEjpAMAADQkR3ZLX02Uti0yj6M7mQvDtUiyti4AgCRCOgAAQMPgKpV+/ru05EWpJF9yBEgD/086/wHJL8Dq6gAAZQjpAAAA9d2BtdIX90sH15nHLS+QRk2TmrS3sioAQBUI6QAAAPVVcZ60dIq0/A3JcJnzzS95QepxE9uqAUAtRUgHAACoj7Ytkr6cKGXuNo+7XCVd+icpLNbaugAAPhHSAQAA6pO8DGn+JOnX983jiARp5CtSh2HW1gUAOCmEdAAAgPrAMKR170oL/iAVHJFsdinpLmnIE5Iz1OrqAAAniZAOAABQ1x3aLn35kLTzW/M4tqt0+V+lZr2srQsAcMoI6QAAAHWVq0T68TXp25ek0kLJL1Aa/LjU717J4W91dQCAaiCkAwAA1EX7VpnbqqX+Zh63GSxd9qrUqI2lZQEATg8hHQAAoC4pypG++aP08wxJhhTUSBr2otT9eslms7o6AMBpIqQDAADUFZvnS189LGXvM4+7XS8N+6MU0sTaugAANYaQDgAAUNvlpErzH5M2fGIeR7Y0h7a3u8jaugAANY6QDgAAUFu53dKat6WvJ0uFWZLNIfW7x1wcLiDE6uoAAGcAIR0AAKA2St8iffmgtHuZeRzfQ7r8b1J8dyurAgCcYYR0AACA2qS0WFo2TfpuquQqlvyDpQuflPr+TnLwXzcAqO/4mx4AAKC22POT9MUDUnqyedzuYmnkK1JUS2vrAgCcNYR0AAAAqxVmSYuelX75t3kcEi1d+ifpnKvZVg0AGhhCOgAAgJU2fSHNfVTKOWge97xJuvh5KbiRtXUBACxBSAcAALBC9gEznCd/aR43aiuNmia1HmhpWQAAaxHSAQAAzia32xzWvuhZqThHsvtJ5z8oDXxE8g+yujoAgMUI6QAAAGdL2ibp8/ulfSvM42a9zW3VYrtYWxcAoNYgpAMAAJxpJYXS9y9LP0yT3CVSQJg09Gmp962S3WF1dQCAWoSQDgAAcCbt+sHcVu3QNvO44whpxMtSRDNr6wIA1EqEdAAAgDOh4Ii08ClpzX/N49A4acSfpU6Xs60aAOC4COkAAAA1yTCkDR9L8x6X8tLMtl63SEOfkYIirawMAFAHENIBAABqSuZe6auHpa0LzOMmHaVRf5Va9rO2LgBAnUFIBwAAOF1ul7TiH9Li56WSPMkRIA14RLrgQcnPaXV1AIA6hJAOAABwOlJ+lT6/Tzqwxjxu0c/sPY/uaG1dAIA6iZAOAABQHcX50rcvST++JhkuyRkhXfysdO54yW63ujoAQB1FSAcAADhV25dIXz4oHdllHne+Qhr+ZykszsqqAAD1ACEdAADgZOUdkhY+Ia171zwOb2bueZ44wtq6AAD1BiEdAADgRAxDWv++tGCSlH9Ikk3qe6d00VOSM8zq6gAA9QghHQAAwJfDO6WvJkrbvzGPY7pIl/9Nat7b2roAAPUSIR0AAKAqrlLpp9elJVOk0gLJ4ZQGPyb1v19y+FtdHQCgniKkAwAAHG3/aumL+83t1SSp9UDpsmlS47aWlgUAqP8I6QAAAOWKcqUlL0o//10y3FJgpDTsRanHjZLNZnV1AIAGgJAOAAAgSVu/lr6cKGXtMY+7XisNmyKFRltbFwCgQSGkAwCAhi03TZr/uPTbR+ZxRAvpsr9I7S+2ti4AQINESAcAAA2TYUhr/ictfFIqzJRsdum830tD/iAFhFhdHQCggSKkAwCAhufQdumLB6Rd35vHcd3MbdWa9rS2LgBAg0dIBwAADUdpsfTj36Rv/yy5iiS/IOnCJ6SkuyUH/y0CAFiPf40AAEDDsHelua1a2kbzuO1F5tzzqFaWlgUAQGWEdAAAUL8dXCctf0NaP0eSIQU3li79k7l6O9uqAQBqGUI6AACof9wuafM86ae/S7t/qGjvMVa65AUpuJF1tQEA4AMhHQAA1B+F2eaK7T/PkDJ3m212P6nzaHPl9ua9LC0PAIATIaQDAIC679B2acU/zIBenGu2BUVJvW+V+twuhTe1tj4AAE4SIR0AANRNhmFuofbT382h7TLM9uhE6by7pa7XSQHBlpYIAMCpIqQDAIC6paRQ+u1DM5yn/lbR3v4SM5y3GcKCcACAOouQDgAA6oacVOmXf0sr/y3lZ5ht/sFSjxulpLukJu2trQ8AgBpASAcAALXbgbVmr/lvH0nuErMtIkHqe6d07s3m3HMAAOoJQjoAAKh93C4p+SsznO/5saI94TxzSHviZZKD/8YAAOof/nUDAAC1R2GWtPq/0oo3pcw9ZpvdT+pylXTeXVIztlADANRvhHQAAGC9Q9uln9+U1r5TsYVacGOp1y1lW6jFW1sfAABnCSEdAABYwzCknd+aQ9q3LJBnC7WYzmVbqF0r+QdZWiIAAGcbIR0AAJxdJQXSrx+Y4TxtY0V7h0vNcN56EFuoAQAaLEI6AAA4O7IPmluo/fIfKf+Q2eYfIvUcK/X9ndSknbX1AQBQCxDSAQDAmbV/tdlrvuGTSluotZCSfif1vEkKirS0PAAAahNCOgAAqHmuUin5SzOc7/2por1Ff3NIe8cRbKEGAEAV+NcRAADUnIIjZVuo/UPK2mu22f2lc642t1Br2tPa+gAAqOUI6QAA4PRlbJN+niGtnS2V5JltwY2l3rdJfW6TwuKsrQ8AgDqCkA4AAKrHMKQdS8wh7VsXVrTHnmMOaT/nGsk/0Lr6AACogwjpAADg1JQUSOvnSD/NkNI3lTXapI7DzXDeagBbqAEAUE2EdAAAcHKyD0gr/yX9MlMqOGy2BYSaK7T3vVNq3Nba+gAAqAcI6QAAwLd9q6Sfy7dQKzXbIltISXeZAT0wwtr6AACoRwjpAADgWK5SadPn5nzzfSsq2lteULaF2nDJ7rCuPgAA6ilCOgAAqJB/WFr9trTin1L2PrPNEWAuAnfeXVJ8d2vrAwCgniOkAwAAKX2LuYXaunelknyzLSTa3EKt961SWKy19QEA0EDYrS7g9ddfV6tWrRQYGKikpCStWLHC5/WZmZm65557FB8fL6fTqQ4dOmju3LlnqVoAAOoRw5C2LZL+d7X0eh/pl3+bAT22q3TFG9KDv0lDJhHQAQA4iyztSZ8zZ44mTpyoGTNmKCkpSdOmTdOwYcO0efNmxcTEHHN9cXGxLr74YsXExOjDDz9Us2bNtHv3bkVGRp794gEAqKuK86X175lbqGVsLmu0SYkjzfnmLc9nCzUAACxiMwzDsOrFk5KS1KdPH02fPl2S5Ha7lZCQoPvuu0+PP/74MdfPmDFDU6dOVXJysvz9/av1mtnZ2YqIiFBWVpbCw8NPq34AAOqUrP3Syn9Kq2ZJBUfMtoAw6dybzS3UGrW2tDwAAOqrU8mhlvWkFxcXa9WqVZo0aZKnzW63a+jQoVq+fHmVz/n888/Vr18/3XPPPfrss88UHR2tG2+8UY899pgcjqpXmC0qKlJRUZHnODs7u2bfCAAAtd3elWVbqH0qGS6zLaqVuYVaj7FSIB9aAwBQW1gW0jMyMuRyuRQb6z3PLTY2VsnJyVU+Z8eOHfrmm280duxYzZ07V9u2bdPvf/97lZSU6Omnn67yOVOmTNGzzz5b4/UDAFCruUqkjZ+ZW6jt/6WivdUAc0h7h0vZQg0AgFqoTq3u7na7FRMTo3/84x9yOBzq1auX9u/fr6lTpx43pE+aNEkTJ070HGdnZyshIeFslQwAwNmVf9gczr7in1LOAbPNESB1vc7cQi2uq6XlAQAA3ywL6U2aNJHD4VBqaqpXe2pqquLi4qp8Tnx8vPz9/b2Gtnfq1EkpKSkqLi5WQEDAMc9xOp1yOp01WzwAALVNWnLZFmrvSaUFZltIjNTndqn3LVLosQuyAgCA2seyLdgCAgLUq1cvLV682NPmdru1ePFi9evXr8rnnH/++dq2bZvcbrenbcuWLYqPj68yoAMAUK+53dLWr6X/XiW9kSStmmkG9Lhu0ugZ0kO/SYMfI6ADAFCHWDrcfeLEiRo/frx69+6tvn37atq0acrLy9Mtt9wiSRo3bpyaNWumKVOmSJLuvvtuTZ8+XQ888IDuu+8+bd26VS+++KLuv/9+K98GAABnV3GetO5dcwu1Q1vNNpvd3EIt6W6pZX+2UAMAoI6yNKSPGTNG6enpmjx5slJSUtSjRw/Nnz/fs5jcnj17ZLdXdPYnJCRowYIFeuihh9StWzc1a9ZMDzzwgB577DGr3gIAAGdP5t6KLdQKs8w2Z7h07jip7x3miu0AAKBOs3SfdCuwTzoAoE4xDGnfSumnN6SNn1faQq21uUp7jxslZ5i1NQIAAJ/qxD7pAADAB88Wam9I+1dVtLceKJ33e6n9JWyhBgBAPURIBwCgNsk7ZC4At/JfUs5Bs83hlLpdJyXdJcWdY219AADgjCKkAwBQG6Rtkn76u7R+jlRaaLaFxkp97jC3UAtpYm19AADgrCCkAwBgFbdb2rbIHNK+Y0lFe3x36bx7pC5XSn5sMQoAQENCSAcA4GwryjW3UPt5hnRom9lms0uJl5nzzVucxxZqAAA0UIR0AADOlvQt5vZpa/9XaQu1CKnXOHNYe1RLS8sDAADWI6QDAHAmlRZJm74ww/mu7yvaG7U1t1DrfoPkDLWsPAAAULsQ0gEAOBMO7zCD+Zp3pPwMs81mlzpcKvW6RWo3VLLbLS0RAADUPoR0AABqiqtE2jxP+uU/3gvBhTWVzh0nnXuzFNHcuvoAAECtR0gHAOB0Ze6VVr8lrf6vlJtS1miT2l0k9b5Vaj9McvBPLgAAODH+xwAAQHW4XdLWr81e821fS4bbbA+JkXreJPUaL0W1srREAABQ9xDSAQA4FdkHpTX/lVa9JWXvq2hvPUjqfYvUcSR7mwMAgGojpAMAcCJut7TjG+mXmeacc8Nltgc1knqONReCa9zW2hoBAEC9QEgHAOB4ctOkNf8z55sf2VXR3qK/Ode80yjJP9Cy8gAAQP1DSAcAoDLDMPcz/+U/0qYvJXeJ2R4YYe5p3usWKSbR2hoBAEC9RUgHAECS8g9La2dLq2ZKh7ZVtDfrbfaad7lSCgi2rj4AANAgENIBAA2XYUh7fzZ7zTd8KrmKzPaAUKnbdWaveXw3S0sEAAANCyEdANDwFGRK6983w3n6por2uG5mr3nXayRnmGXlAQCAhouQDgBoGAxD2r9aWvUf6dePpNICs90vSOp6tRnOm54r2WzW1gkAABq0aoX0vXv3ymazqXnz5pKkFStWaPbs2ercubPuvPPOGi0QAIDTUpQj/fqBuX1ayvqK9pjO5nD2btdJQZGWlQcAAFBZtUL6jTfeqDvvvFM333yzUlJSdPHFF6tLly565513lJKSosmTJ9d0nQAAnJqD683h7L9+IBXnmm0Op7kAXO9bpIQkes0BAECtU62Q/ttvv6lv376SpPfff1/nnHOOli1bpoULF+quu+4ipAMArFGcL2342Azn+1dVtDduZw5n736DFNzIuvoAAABOoFohvaSkRE6nU5K0aNEiXX755ZKkxMREHTx4sOaqAwDgZKRtMoezr3tPKsoy2+z+UqdRZjhvdQG95gAAoE6oVkjv0qWLZsyYoZEjR+rrr7/W888/L0k6cOCAGjduXKMFAgBQpZJCadPnZq/5nuUV7ZEtzeHsPW6SQqOtqw8AAKAaqhXSX3rpJV155ZWaOnWqxo8fr+7du0uSPv/8c88weAAAzoiMbdKqmdLa2VLBYbPN5pA6Djd7zdsMkex2a2sEAACoJpthGEZ1nuhyuZSdna2oqChP265duxQcHKyYmJgaK7CmZWdnKyIiQllZWQoPD7e6HADAySgtlpK/NMP5zu8q2sObS73GSz1vlsLjrasPAADAh1PJodXqSS8oKJBhGJ6Avnv3bn3yySfq1KmThg0bVp1bAgBwrCO7pFWzpDX/k/LSyxptUodh5vZp7S+W7A4LCwQAAKhZ1QrpV1xxha666irdddddyszMVFJSkvz9/ZWRkaG//OUvuvvuu2u6TgBAQ+EqlbbMN+eab/9GUtmAr9A46dxx5iMywdISAQAAzpRqhfTVq1fr1VdflSR9+OGHio2N1Zo1a/TRRx9p8uTJhHQAwKnL2ietftt85FTaKaTtheZc8w6XSg5/6+oDAAA4C6oV0vPz8xUWFiZJWrhwoa666irZ7Xadd9552r17d40WCACox9wuadsic/u0rQskw222BzeRet5kzjdv1MbaGgEAAM6iaoX0du3a6dNPP9WVV16pBQsW6KGHHpIkpaWlsRgbAODEclKk1f+VVr8lZe2taG81wNw+LfEyyc9pXX0AAAAWqVZInzx5sm688UY99NBDuvDCC9WvXz9JZq96z549a7RAAEA94XZLO5eac803z5PcpWZ7YKTUY6zUa4IU3cHCAgEAAKxX7S3YUlJSdPDgQXXv3l32sv1oV6xYofDwcCUmJtZokTWJLdgA4CzLyzBXZ181Szqys6I94Tyz17zzFZJ/kGXlAQAAnGlnfAs2SYqLi1NcXJz27dsnSWrevLn69u1b3dsBAOoTw5B2LzN7zTd+LrlLzHZnuNT9enP7tNjO1tYIAABQC1UrpLvdbr3wwgt65ZVXlJubK0kKCwvTww8/rCeeeMLTsw4AaGDyD0vr3pNWzZQytlS0Nz3XXKH9nKukgBDr6gMAAKjlqhXSn3jiCf373//Wn/70J51//vmSpB9++EHPPPOMCgsL9cc//rFGiwQA1GKGIe1dYQbzDZ9IpYVmu3+I1O1as9e8aQ9LSwQAAKgrqjUnvWnTppoxY4Yuv/xyr/bPPvtMv//977V///4aK7CmMScdAGpIYZa0/n1z+7S0DRXtsV3NueZdr5UC+XsWAADgjM9JP3z4cJWLwyUmJurw4cPVuSUAoK7Yv9qca/7bR1JJvtnmFyidc7XZa968t2SzWVsjAABAHVWtkN69e3dNnz5df/vb37zap0+frm7dutVIYQCAWqQoV/rtQzOcH1xX0d6koznXvPsYKSjKuvoAAADqiWqF9D//+c8aOXKkFi1a5Nkjffny5dq7d6/mzp1bowUCACyU8qs5nH39+1JxjtnmCDC3Tet9q9SiH73mAAAANahaIX3QoEHasmWLXn/9dSUnJ0uSrrrqKt1555164YUXNGDAgBotEgBwFpUUmAvA/fIfad/KivZGbczh7D3GSiGNrasPAACgHqvWwnHHs27dOp177rlyuVw1dcsax8JxAHAc6ZvNXvN1s81F4STJ7iclXmYuBNdqoMQWmwAAAKfsjC8cBwCoJ0qLpI2fm9un7V5W0R7ZQuo1QepxkxQWa1l5AAAADQ0hHQAamtJiadf3UvJX0sZPpfxDZrvNLnUYbvaat71QsjssLRMAAKAhIqQDQENQlCNtW2QG8y0LpaKsinNhTaVe46WeN0sRzayrEQAAAKcW0q+66iqf5zMzM0+nFgBATcpNkzbPM4P5jqWSq6jiXEiMlDjCnG/eZojk4DNbAACA2uCU/lcWERFxwvPjxo07rYIAAKfh8A4zlCd/Je35SVKltUEbtTFDeeJlUvPeDGcHAACohU4ppM+cOfNM1QEAqA7DkA6uqwjmaRu8z8f3kDqVBfPoRPY0BwAAqOUY3wgAdY2rVNrzY0Uwz9pbcc7mkFqdb4byjiOkyATr6gQAAMApI6QDQF1QnC9t/6Zs4bd5UsGRinN+QVK7i6ROo6T2l0jBjayrEwAAAKeFkA4AtVX+YWnLAin5S2nbYqm0oOJcUCOp4/Cyhd8GSwHBlpUJAACAmkNIB4DaJHNv2TD2L6XdP0qGq+JcRAspcaQ5xzzhPFZkBwAAqIf4Hx4AWMkwpLRNZihP/tJcBK6y2HPMYJ44UorrxsJvAAAA9RwhHQDONrdL2rdS2vSF2Wt+ZGelkzapRb+yYD7C3DYNAAAADQYhHQDOhpJCaed3Zm/55rlSXnrFOYdTajvEDOYdhkuh0dbVCQAAAEsR0gHgTCnMkrZ+bQbzrV9LxbkV55wRUodhZjBvd5HkDLOuTgAAANQahHQAqEnZB82e8uSvzJ5zd0nFubD4ivnlLS+Q/AKsqxMAAAC1EiEdAE5Xxtayhd++MueaV9akg7lNWuJlUtOekt1uTY0AAACoEwjpAHCq3G7pwJqKYJ6x2ft8s97mNmkdR0rRHaypEQAAAHUSIR0AToarRNr1fdke5nOlnAMV5+x+UuuBZm95xxFSeLx1dQIAAKBOI6TXYoZhyMaeyIB1inKlbYvMYL5lgVSUVXEuIFRqN1TqNMr8NSjSsjIBAABQfxDSa6nNKTl64L01enRYR12YGENYB86WvIyKhd+2L5FcRRXnQqKljsOlxFFmz7l/oHV1AgAAoF4ipNdSr32zVckpObrtrV90XptG+sOITurWPNLqsoD66fDOsmHsX0l7f5IMd8W5qFbmMPZOo6TmfSS7w7IyAQAAUP/ZDMMwrC7ibMrOzlZERISysrIUHh5udTnHlZVfojeWbtPMH3epuNQMDJd3b6pHh3VUQqNgi6sD6jjDkFJ+rVj4LfU37/Px3ctWZB8pxXSWGMkCAACA03AqOZSQXsvtO5KvVxZu0Sdr9kuSAhx2je/fUvcMaafIYPZYBk6aq9TsJd9UFsyz9lScszmklv3LgvkIKbKFdXUCAACg3iGk+1DXQnq53/Zn6cW5m/Tj9kOSpIggf907pJ3G9W8ppx/Db4EqlRSY88qTvzLnmRccrjjnFyS1u8jsLe9wqRTcyLo6AQAAUK8R0n2oqyFdMld7X7olXX+am6zNqTmSpOZRQXp0WEeN6tZUdjtDcgHlH5a2LpQ2fSFt/0Yqya84FxQldRhuBvO2F0oBTB0BAADAmUdI96Euh/RyLrehj1bt0ytfb1ZqtrnydNdmEZo0IlH92zaxuDrAAln7zL3Lk7+Qdi2TDFfFuYgEM5QnjpRa9JccrJcJAACAs4uQ7kN9COnl8otL9Z8fdurvS7crr9gMJRcmxujx4YnqEBtmcXXAGWQYUnpyxcJvB9Z4n4/pUhHM47uz8BsAAAAsRUj3oT6F9HIZuUX666Ktmr1ij1xuQ3abdF3vBE28uINiwtnHGfWE2y3tW1kRzA9vr3TSJrU4zwzlHUdIjdtaViYAAABwNEK6D/UxpJfbnp6rP89P1oINqZKkIH+H7hjYRncObKNQJ0N8UQeVFkk7vysL5nOlvLSKc44Aqc1gc0X2jsOl0BjLygQAAAB8IaT7UJ9DermVuw7rxbmbtGZPpiSpSahTDw5tr+v7JMjPYbe2OOBECrPNhd+Sv5K2fi0V51Scc4ZL7S+ROl0mtRsqOZnWAQAAgNqPkO5DQwjpkrkS/LzfUvTS/GTtPmSubt0mOkSPX5qoizvHysYcXdQGhiHlpUtHdksp681t0nZ8K7lLKq4JjTP3Lk+8TGo1QPILsK5eAAAAoBoI6T40lJBerrjUrdk/79ZfF2/VkXwz+PRt1UiTRiSqZ4soi6tDg1CYLWXuNoN4Vb9W3iKtXON2ZijvNEpqeq5kZwQIAAAA6i5Cug8NLaSXyy4s0Yyl2/XvH3aqqNQtSRrZLV7/N6yjWjYOsbg61GmlRVLmXilzlxm8j+zyDuEFR05wA5sU3lRq1EZqO0RKHCVFdzgLhQMAAABnByHdh4Ya0ssdyCzQKwu36OM1+2QYkr/DppvOa6n7L2yvqBCGEaMKbpeUfeD4veE5ByWd4K+RoEZSVEspsqUU1cr764jmkp/zLLwRAAAAwBqEdB8aekgvt/FAtqbM26Tvt2ZIksIC/XTPkHaa0L+VAv0dFleHs8owpPxDZcF717FBPHOv9xzxqvgHm4E7smWlAF7pVxZ4AwAAQANW50L666+/rqlTpyolJUXdu3fXa6+9pr59+57wee+9955uuOEGXXHFFfr0009P6rUI6d6+25KuF+duUnKKuYJ2s8ggPXxJB43u0Ux2O4vL1RtFuUf1gO+qFML3SMW5vp9v95MiEqoI4K3MX0OaSCxGCAAAAFSpToX0OXPmaNy4cZoxY4aSkpI0bdo0ffDBB9q8ebNiYo6/7/GuXbt0wQUXqE2bNmrUqBEh/TS43IY+WbNfryzcrINZhZKkLk3D9YcRnXR+uyYWV4eTUlosZe09dj54+a/5h058j7D44/SEtzLnjNsZYQEAAABUR50K6UlJSerTp4+mT58uSXK73UpISNB9992nxx9/vMrnuFwuDRw4ULfeequ+//57ZWZmEtJrQGGJS/9ZtlN/X7JdOUWlkqRBHaI1aUSiEuP4XlnK7Tbnfh9vXnj2fp1wXnhgpHcAj2olRbYyv45IkPwDz/z7AAAAABqgU8mhfmeppioVFxdr1apVmjRpkqfNbrdr6NChWr58+XGf99xzzykmJka33Xabvv/+e5+vUVRUpKKiIs9xdnb26RdeTwX6O/T7we00pneCXvtmm/730259uyVd329N1zW9mmvixR0VF0GQOyMMw1wF/Zie8F3m11l7JVex73v4BUmRLY5amK3Sr4ERZ+GNAAAAADgdlob0jIwMuVwuxcbGerXHxsYqOTm5yuf88MMP+ve//621a9ee1GtMmTJFzz777OmW2qA0DnXqmcu7aEL/VvrzgmTN/TVF7/+yT5+vO6DbL2ij3w1qo7BAf6vLrHuK88z535Xng1f+tTjH9/NtDnMldK8A3qriODSGeeEAAABAHWdpSD9VOTk5uvnmm/XPf/5TTZqc3FzpSZMmaeLEiZ7j7OxsJSQknKkS65VWTUL0xtheWr3niF78apN+2X1E05ds07sr9uiBoe11Q98W8nfYrS6z9nCVlM0LryKAZ+6W8tJPfI/QWB/zwptJjjr1RxYAAADAKbL0f/xNmjSRw+FQamqqV3tqaqri4uKOuX779u3atWuXRo0a5Wlzu92SJD8/P23evFlt27b1eo7T6ZTTWQf3YN7xrbTzW3NVbZvDXLTL7jCPvdr8Kn6tqs3uKGv3q9R+nDbPPcrb7ZLdT+fGOfXBHb20YNMh/XnBFu3IyNPkzzZo5rJdeuzSjhrWJU62htCD63ZLuanHmRe+y5wXbrh938MZIUW1qLRfeKtKYbyF5B90Ft4IAAAAgNrK0pAeEBCgXr16afHixRo9erQkM3QvXrxY99577zHXJyYm6tdff/Vqe/LJJ5WTk6O//vWv9auHfM9P0vevWF2Fh03SpWUPd5Cfig27XDk2uT5wKOcjPwU5A+Tv71/DHxScoO24H1bYj7rv0TWcoC7DLWXtMwO41xzxPZKryPc3yuGsej54+a9BUWf+NwsAAABAnWX52NmJEydq/Pjx6t27t/r27atp06YpLy9Pt9xyiyRp3LhxatasmaZMmaLAwECdc845Xs+PjIyUpGPa67xmvaSkuyV3qfkwXJLbVXbsOn6b21XWXlqpvfL1pWaPsNd9j7rOXSpfK4XbjVIFSmZyV9mlhWWP+s5ml8KPnhde6dfQWHMEAgAAAABUg+UhfcyYMUpPT9fkyZOVkpKiHj16aP78+Z7F5Pbs2SN7Qww97YeaD6u43UcF+FKzh9kr/JcqPTtfb/+wXYs27pfdcMvf7tZl58To2p5xigh0+Pig4KgPBU76Q4iq6vBx7+q8nmGY+4JXNS88ornkYNE8AAAAAGeG5fukn23sk35mJKdk60/zkrV0s7k4WpjTT3cNbqvbLmitQH+HxdUBAAAAgHVOJYcS0lGjlm3L0ItzN2nDAXM/+viIQE28uIOuOre5HPYGsLgcAAAAAByFkO4DIf3Mc7sNfbZuv15esEX7MwskSYlxYZo0opMGdYi2uDoAAAAAOLsI6T4Q0s+ewhKX3vpxl6Yv2aacwlJJ0oD2TTRpeCd1bsr3HgAAAEDDQEj3gZB+9h3JK9b0Jdv09vJdKnEZstmkK3s20yOXdFTTSPYFBwAAAFC/EdJ9IKRbZ8+hfE1duFlfrDsgSXL62XXrBa119+C2Cg9kxXQAAAAA9RMh3QdCuvXW7s3Ui3M3acXOw5KkqGB/3X9Re41NaqkAvwa43R4AAACAeo2Q7gMhvXYwDEOLN6VpyrxN2p6eJ0lq2ThY/zcsUSO6xslmYyV4AAAAAPUDId0HQnrtUupya84ve/Xq11uVkVskSeqREKknRnZSn1aNLK4OAAAAAE4fId0HQnrtlFdUqn98t0P/+G6HCkpckqRLOsfqseGJahsdanF1AAAAAFB9hHQfCOm1W1p2oV5dtFVzVu6R25Acdptu6JugBy7qoOgwp9XlAQAAAMApI6T7QEivG7am5uil+clatClNkhQS4NBdg9rqtgGtFRzgZ3F1AAAAAHDyCOk+ENLrluXbD2nKvE1avy9LkhQb7tTEizvoml4JcthZXA4AAABA7UdI94GQXve43Ya+/PWg/jw/WfuOFEiSOsSGatLwThrcMZqV4AEAAADUaoR0HwjpdVdRqUv/Xb5br32zTVkFJZKk/m0b6w8jOumcZhEWVwcAAAAAVSOk+0BIr/uy8kv0+tJtmrVsl4pdbknS6B5N9ciwjmoeFWxxdQAAAADgjZDuAyG9/th7OF+vLNysT9cekCQFOOyacH4r3TO4nSKC/S2uDgAAAABMhHQfCOn1z6/7svTi3E1avuOQJCkiyF/3XdhON/drKaefw+LqAAAAADR0hHQfCOn1k2EYWro5XVPmbdKW1FxJUkKjID06LFGXdY2XnZXgAQAAAFiEkO4DIb1+c7kNfbhqr15ZuEVpOUWSpO7NIzRpRCed16axxdUBAAAAaIgI6T4Q0huG/OJS/fv7nZrx7XblFbskSUM7xeixSxPVPjbM4uoAAAAANCSEdB8I6Q1Lek6R/rZ4q2av2COX25DdJo3p00IPXdxeMWGBVpcHAAAAoAEgpPtASG+Ytqfn6qV5yVq4MVWSFBzg0B0D2ujOgW0U4vSzuDoAAAAA9Rkh3QdCesO2ctdhvTh3k9bsyZQkRYc59dDQDrqud3P5OezWFgcAAACgXiKk+0BIh2EYmvdbil6an6zdh/IlSe1iQvX4pYm6qFOMbDZWggcAAABQcwjpPhDSUa641K13ft6tvy3eqiP5JZKkpNaN9IcRndQ9IdLa4gAAAADUG4R0HwjpOFp2YYn+vnS7/vPDThWVuiVJiXFhGtQxWoM6RKt3y0YK8GMoPAAAAIDqIaT7QEjH8RzILNDLCzfr0zX75a70pyIkwKH+7ZpoUAcztCc0CrauSAAAAAB1DiHdB0I6TuRwXrG+35qub7ek67st6crILfY63zY6RIM6xGhQx2gltW6kQH+HRZUCAAAAqAsI6T4Q0nEq3G5DGw9m69st6fp2c7pW7TkiV6Vu9kB/u85r09jTy966SQgLzwEAAADwQkj3gZCO05FVUKIft2Xo2y3pWro5XSnZhV7nExoFaVCHaA3uEKN+bRuzBzsAAAAAQrovhHTUFMMwtCU1V99uSdO3W9K1cucRFbvcnvP+Dpv6tGpkhvaOMeoQG0ovOwAAANAAEdJ9IKTjTMkrKtVPOw5p6eZ0Ld2Spr2HC7zOx4UHmsPiO0br/HZNFBHkb1GlAAAAAM4mQroPhHScDYZhaNehfH27OU1Lt6Rr+fZDnu3dJMlht+ncFpFlc9lj1KVpuOx2etkBAACA+oiQ7gMhHVYoLHFpxc7DZXPZ07Q9Pc/rfJPQAA1sb/ayD2gfrUYhARZVCgAAAKCmEdJ9IKSjNth7OF/fbTVXjF+2LUN5xS7POZtN6tYsQoM6xmhQh2j1SIiUg152AAAAoM4ipPtASEdtU1zq1qrdR8xt3raka9PBbK/zEUH+uqB9Ew0u2+YtJjzQokoBAAAAVAch3QdCOmq71OxCT2D/fku6sgtLvc53ig8vWzE+Wr1aRsnfYbeoUgAAAAAng5DuAyEddUmpy611+7L07WZzm7f1+7NU+U9sqNNP/ds21qCOZi9786hg64oFAAAAUCVCug+EdNRlh3KL9P3WDH27JV3fbUnXobxir/PtYkLLVoyPVt/WjRTo77CoUgAAAADlCOk+ENJRX7jdhjYcyNa3W9K0dHO6Vu85InelP82B/nb1a9O4bG/2GLVuEmJdsQAAAEADRkj3gZCO+iorv0TLtmfo283mfPaU7EKv8y0bB3t62fu1bazgAD+LKgUAAAAaFkK6D4R0NASGYWhzao4nsK/cdVglroo/6gEOu/q2blTWyx6t9jGhstnY5g0AAAA4EwjpPhDS0RDlFpVq+fZDnqHx+44UeJ1vGhHoWXyuf7smCg/0t6hSAAAAoP4hpPtASEdDZxiGdmTkeXrZf9pxSEWlbs95h92mXi2iPKG9c3y47HZ62QEAAIDqIqT7QEgHvBWWuPTzzsNaWrbN2470PK/zTUKdGtihiQZ1iNbA9tGKCgmwqFIAAACgbiKk+0BIB3zbezhf325J19LN6fpxe4byi12eczab1L15pGcue/fmkXLQyw4AAAD4REj3gZAOnLziUrd+2X1Y325J17eb05WckuN1PjLYXwPam8PiB3ZoopiwQIsqBQAAAGovQroPhHSg+lKyCvXdFnMu+3db05VTWOp1vkvTcM82b+e2jJK/w25RpQAAAEDtQUj3gZAO1IxSl1tr92aavexb0rV+X5bX+TCnn85v10SDOkZrYIdoNYsMsqhSAAAAwFqEdB8I6cCZkZFbpO+3msPiv9uaocN5xV7n28eEalCHaA3uGKM+raPk9HNYVCkAAABwdhHSfSCkA2ee223otwNZWlq2zduaPUfkrvQ3TZC/Q/3aNi4L7dFq2TjEumIBAACAM4yQ7gMhHTj7svJL9MO2DM82b2k5RV7nWzUO1qAO0bq4c5yS2jRiLjsAAADqFUK6D4R0wFqGYSg5JcezYvwvuw+rxFXx11B4oJ8u6hSrYV1iNbBDtIID/CysFgAAADh9hHQfCOlA7ZJbVKoft2Xom+Q0fb0xVYcqzWV3+tk1oH20LukSq6GdYtUoJMDCSgEAAIDqIaT7QEgHai+X29DqPUe04LcULdiYor2HCzzn7Dapb+tGuqRznC7pEqvmUcEWVgoAAACcPEK6D4R0oG4oHxa/YEOKFm5I1caD2V7nz2kWrks6x2lYlzh1iA2VzWazqFIAAADAN0K6D4R0oG7aezjfDOwbU/XLrsNeq8W3ahysS7rEaViXWPVMiJLdTmAHAABA7UFI94GQDtR9h3KLtHhTmhZsSNH32zJUXOr2nGsS6tTFnc2F5/q3baIAP1aKBwAAgLUI6T4Q0oH6JbeoVN9tSdeCDSn6ZlOacopKPefCnH4anBijYV1iNbhjjEKdrBQPAACAs4+Q7gMhHai/ikvd+mnHIS3YkKKvN6Z67cce4LDr/HaNNaxLnC7qFKvoMKeFlQIAAKAhIaT7QEgHGga329DafZmehed2ZuR5ztlsUu+WURrWJU6XdI5Ti8asFA8AAIAzh5DuAyEdaHgMw9C2tFwt2JCiBRtS9ev+LK/ziXFhZmDvEqvO8eGsFA8AAIAaRUj3gZAO4EBmgRaWBfYVuw7LVWmp+OZRQWVbu8Wqd6tGcrBSPAAAAE4TId0HQjqAyo7kFWtxsrlS/Hdb0lVUaaX4RiEBGtopRsO6xOn8dk0U6O+wsFIAAADUVYR0HwjpAI4nv7hU323J0MKNKVq8KU1ZBSWec8EBDg3uGK1hXeI0JDFG4YH+FlYKAACAuoSQ7gMhHcDJKHG5tWLnYS3ckKKFG1N1MKvQc87fYdN5bRqXLTwXq5jwQAsrBQAAQG1HSPeBkA7gVBmGofX7srRwozmPfVtartf5ni0iNaxLnIZ1iVPrJiEWVQkAAIDaipDuAyEdwOnanp6rhRtStWBDitbuzfQ61z4m1LNSfNdmEawUDwAAAEK6L4R0ADUpJatQX29K1cINKVq+/ZBKK60U3zQiUJeUDYnv27qR/Bx2CysFAACAVQjpPhDSAZwpWfklWrLZXCl+6eZ0FZS4POcig/11UWKsLukSq4HtoxUUwErxAAAADQUh3QdCOoCzobDEpR+2ZmjBhhQt2pSqI/kVK8UH+ts1qEO0Lukcp4s6xSgyOMDCSgEAAHCmEdJ9IKQDONtKXW79svuIFmxI0cINqdqfWeA557DbdF6bRrqkszmPPT4iyMJKAQAAcCYQ0n0gpAOwkmEY2nAg27O1W3JKjtf5bs0jylaKj1W7mDCLqgQAAEBNIqT7QEgHUJvsysjTwo1mD/uqPUdU+W/kNtEhuqSzGdi7N4+U3c5K8QAAAHURId0HQjqA2iotp1CLNqZp4cYULduWoRJXxV/PseFOXdw5VsO6xCmpdWMF+LFSPAAAQF1BSPeBkA6gLsgpLNGSzelaWLZSfG5RqedcWKCfLkqM0bAucRrYIVohTj8LKwUAAMCJnEoOrRVdMa+//rpatWqlwMBAJSUlacWKFce99p///KcGDBigqKgoRUVFaejQoT6vB4C6KCzQX5d3b6rpN56rVU8N1cwJfXRD3wQ1CQ1QTmGpPl17QHe/s1rnPv+1bn9rpd7/Za8O5xVbXTYAAABOk+U96XPmzNG4ceM0Y8YMJSUladq0afrggw+0efNmxcTEHHP92LFjdf7556t///4KDAzUSy+9pE8++UQbNmxQs2bNTvh69KQDqMtcbkOr9xzRwg0pWrAhVXsO53vO2W1Sn1aNdEmXOF3SOVYJjYItrBQAAADl6tRw96SkJPXp00fTp0+XJLndbiUkJOi+++7T448/fsLnu1wuRUVFafr06Ro3btwJryekA6gvDMPQ5tQcLfgtVQs2pGjjwWyv812ahpsLz50Tq46xYbLZWHgOAADACqeSQy2dyFhcXKxVq1Zp0qRJnja73a6hQ4dq+fLlJ3WP/Px8lZSUqFGjRlWeLyoqUlFRkec4Ozu7yusAoK6x2WxKjAtXYly4HhjaXnsP52vhRjOw/7LrsDYcyNaGA9l6ddEWtWwcrEvKFp7r2SJKDlaKBwAAqJUsDekZGRlyuVyKjY31ao+NjVVycvJJ3eOxxx5T06ZNNXTo0CrPT5kyRc8+++xp1woAtV1Co2DddkFr3XZBax3KLdLiTWlasCFF32/L0O5D+frn9zv1z+93qkmoUxd3jtElXeLUv21jOf0cVpcOAACAMnV6SeA//elPeu+997R06VIFBgZWec2kSZM0ceJEz3F2drYSEhLOVokAYInGoU5d1ydB1/VJUF5Rqb7dkq4FG1L0TXKaMnKL9O6KvXp3xV6FOv10QbsmGpIYrcEdYxQbXvXfpQAAADg7LA3pTZo0kcPhUGpqqld7amqq4uLifD735Zdf1p/+9CctWrRI3bp1O+51TqdTTqezRuoFgLooxOmnEV3jNaJrvIpL3fppxyEt2JCirzemKi2nSPM3pGj+hhRJ5jz2IR1jNCQxWj0SGBYPAABwttWKheP69u2r1157TZK5cFyLFi107733HnfhuD//+c/64x//qAULFui88847pddj4TgAMLndhtbvz9LSzWlasjld6/dlqvK/CJHB/hrYPlpDEqM1sH20GofygScAAEB11KnV3efMmaPx48frzTffVN++fTVt2jS9//77Sk5OVmxsrMaNG6dmzZppypQpkqSXXnpJkydP1uzZs3X++ed77hMaGqrQ0NATvh4hHQCqlpFbpO+2pOub5DR9tyVd2YWlnnM2m9QjIdLsZe8Yoy5Nw2Wnlx0AAOCk1KmQLknTp0/X1KlTlZKSoh49euhvf/ubkpKSJEmDBw9Wq1atNGvWLElSq1attHv37mPu8fTTT+uZZ5454WsR0gHgxEpdbq3Zm6klyWYv+6ajtndrEurU4I7RGtIxRgM6NFF4oL9FlQIAANR+dS6kn02EdAA4dQezCrR0c7qWJKfph20Zyi92ec457Db1bhmlIYlmL3uH2FD2ZAcAAKiEkO4DIR0ATk9RqUu/7DpS1suepu3peV7nm0YEanBZYD+/XWMFB9TpjUQAAABOGyHdB0I6ANSsPYfytWSzGdiXbz+kolK351yAw66kNo3KVoyPUesmIRZWCgAAYA1Cug+EdAA4cwqKXfppxyEt2Zymb5LTtO9Igdf5Vo2DPcPi+7ZupEB/h0WVAgAAnD2EdB8I6QBwdhiGoe3puVqSnK4lm9O0Yudhlbor/skJ8nfo/HaNNbisl71ZZJCF1QIAAJw5hHQfCOkAYI2cwhIt23aobF/2NKVmF3md7xAb6ull79UySv4Ou0WVAgAA1CxCug+EdACwnmEY2ngw27Ni/Oo9R1Spk11hTj8N6NBEgzvGaHDHaMWEBVpXLAAAwGkipPtASAeA2iczv1jfbc3Q0uQ0Ld2SrsN5xV7nuzaL0JCO0RqcGKPuzSPlsLPFGwAAqDsI6T4Q0gGgdnO5Da3fl6klm9O1dHOa1u/L8jofFeyvQR2iNSQxRgPbRysqJMCiSgEAAE4OId0HQjoA1C3pOUX6dos5LP67renKKSz1nLPbpJ4tosxe9o4x6tI0XDYbvewAAKB2IaT7QEgHgLqrxOXW6t1HPL3sySk5Xudjwpwa3DFaQzrG6IL2TRQW6G9RpQAAABUI6T4Q0gGg/jiQWaClm9P1TXKalm3LUEGJy3POz25Tn1aNNCTRDO3tYkLpZQcAAJYgpPtASAeA+qmo1KUVOw9rSbLZy74jI8/rfLPIIA1JjNaFiTHq16aJggIcFlUKAAAaGkK6D4R0AGgYdmXkaenmNH2zOV0/7Tik4lK351yAn1392jTWkI7mAnQtG4dYWCkAAKjvCOk+ENIBoOHJLy7V8u2HtGRzmpYkp2t/ZoHX+TZNQjQkMUZDOsaoT+soOf3oZQcAADWHkO4DIR0AGjbDMLQtLVffJKdpyeY0/bLriErdFf8UBgc4dH67JhrSMUaDO0araWSQhdUCAID6gJDuAyEdAFBZdmGJlm3NMHvZN6crPafI63xiXJinl/3cFpHyc9gtqhQAANRVhHQfCOkAgONxuw1tPJitJWW97Gv2Zqryv5LhgX4a0MFcLX5Qh2hFhzmtKxYAANQZhHQfCOkAgJN1OK9Y329N15LkNH27JV1H8ku8zndvHqHBHWM0JDFG3ZpFyG5nizcAAHAsQroPhHQAQHW43IbW7s3U0s1mL/tv+7O9zjcOCdCgDtEanBijge2bKDI4wKJKAQBAbUNI94GQDgCoCWnZhVq6xdyT/fstGcopKvWcs9ukXi2jzF72jjHqFB8mm41edgAAGipCug+EdABATStxufXLriOeXvYtqble5+PCAzW4bE/289s1UajTz6JKAQCAFQjpPhDSAQBn2r4j+Vq62exlX7btkApKXJ5z/g6berWMUrfmkeoUH6bO8RFqEx0if1aNBwCg3iKk+0BIBwCcTYUlLv2887Bnxfjdh/KPuSbAz64OsaHqHB+uzvHh6hQfrk5NwxUe6G9BxQAAoKYR0n0gpAMArLQzI08/7TikTQezyx45yq00n72yhEZB6hQXrs5NzeDeOT5czaOCmN8OAEAdQ0j3gZAOAKhN3G5De4/ka9PBbG08kK2NZcF9f2ZBldeHBfp5Anvnpuav7WND5fRznOXKAQDAySKk+0BIBwDUBZn5xdp0MEcby8L7poPZ2pqWoxLXsf9s+9ltahcT6hXeO8WHq1EI28ABAFAbENJ9IKQDAOqq4lK3tqXlmr3ulXreswpKqrw+LjywLLCbC9R1ig9Tq8YhstsZLg8AwNlESPeBkA4AqE8Mw9DBrEJPb/vGskdVC9RJUnCAQ4lxYWave9lw+cS4cAUFMFweAIAzhZDuAyEdANAQ5BaVKvlg+Rx3s9c9OSVHRaXuY6612aTWTUI8K8t3bhquLvHhig5zskgdAAA1gJDuAyEdANBQlbrc2nUoTxsqLVC38UC2MnKLqry+cUiAp7e9PLy3aRIiP/Z0BwDglBDSfSCkAwDgLS2n0BPYy4fM70jPlbuK/yEE+NnVMTbMa4G6xPgw9nQHAMAHQroPhHQAAE6soNilLaneq8tvOpitvGJXlde3aBTsWaCufLG6ZpHs6Q4AgERI94mQDgBA9bjdhvYczvf0tpfPdT+QVVjl9eHle7pXGjLPnu4AgIaIkO4DIR0AgJp1JK9Ym1IqtoTbdDBHW1NzVFrFePnyPd07HxXeo9jTHQBQjxHSfSCkAwBw5hWVusr2dM8pC+9Z2nQw57h7usdHBHotUNc5PlwtGgWzpzsAoF4gpPtASAcAwBqGYehA5T3dy3re9xyuek/3kACHEuO9V5fvGBvGnu4AgDqHkO4DIR0AgNolp7BEySk5XsF983H2dLeX7+neNKJsoTozvMeEBVpQOQAAJ4eQ7gMhHQCA2q/U5dbOjDxzdflKK8xn5BZXeX2T0ACvofKd48PVmj3dAQC1BCHdB0I6AAB1V1pOodcCdRsPZGlnRl6Ve7o7/ezqGFfR294hNkxNI4IUG+FkhXkAwFlFSPeBkA4AQP1SUOzS5lTvBeo2HcxW/nH2dJfMnve4iEDFhQcpPiJQ8ZGBiq90HBcRqEB/gjwAoGYQ0n0gpAMAUP+53YZ2l+/pXjZUflt6rlKyCquc616VqGB/xUUEqWlZaDfDe0WIj48IVHCA3xl+JwCA+oCQ7gMhHQCAhsswDGXml+hAVoFSsgp1MKuw4tfsAh3MNL8uKDl+L3xlEUH+XqE9LjzI0ytfHupDnQR5AGjoTiWH8q8GAABoMGw2m6JCAhQVEqAuTSOqvMYwDGUXlOpgdkFFiM8s+zrbDPEHMwuUV+xSVkGJsgrM1emPJ8zpZ4b4yCDFh1fulQ9UfIQZ6sOcfrLZ2BMeAEBIBwAA8GKz2RQR7K+IYH8lxh2/tyOnsMQM7FmFSskq8OqVP1h2nFNYqpyiUuWk5WprWu5x7xUS4PCE9vIQH3/U0PqIIH+CPAA0AIR0AACAaggL9FdYoL86xIYd95rcolKleMJ7wbGhPrtQmfklyit2aXt6nran5x33XoH+9mOCe1xERe9808ggRQUT5AGgriOkAwAAnCGhTj+1iwlVu5jQ415TUOzSwcpz5LMLPccHMs3jw3nFKiwx947fmXH8IB/gZy+bG18R4ptGlh+bvfSNQwJktxPkAaC2IqQDAABYKCjAoTbRoWoTffwgX1jiUmp2xUJ3VS18l5FbpOJSt3YfytfuQ/nHvZe/w6bY8EA1rTS0vnLPfNOIQDUOdcpBkAcASxDSAQAAarlAf4daNg5Ry8Yhx72mqNSltOwiz5z4yvPjy79Ozy1SicvQviMF2nek4Lj38rObQT6uPMCHly18VynQR4c65eewn4m3CwANGiEdAACgHnD6OZTQKFgJjYKPe02Jy620nCLPnPjyLedSKq1kn5pdqFK3of2ZBdqfefwgb7dJMWGBni3n4sKPni8fqNjwQPkT5AHglBDSAQAAGgh/h13NIoPULDLouNeUutxKzy3y3kM+q0AHyo4rB/mUbHPO/Jrj3MtmkxoFBygs0K9soT0/hTorvq54+Je1H33OX8H+DubQA2hQCOkAAADw8HOUryJ//CDvchs6lFvktd2c1xz5bHOIfYnL0KG8Yh3KK652PTabuQBfuFeQLwv2ZV+HHxXyy78Or3QNPfoA6gpCOgAAAE6Jw25TTHigYsID1T0hsspr3G4zoGfkFim3qFQ5hSXmvvGeR0lZ+1Hniiq+drkNGYY8x6cj0N+uUKe/wstCe2ign8Kc/l6B33POeWxPf1ign4L8HWxxB+CMI6QDAACgxtntNkWHORUd5qzW8w3DUGGJ2wzwlcJ8bllgzz4q5Jd/nV1YqtxKob+gxCVJKixxq7CkSBm5RdV+Tw67zdNLX9677wn8lYfqO48/vD/U6ceCewB8IqQDAACg1rHZbAoKcCgowKGY07hPqctdKcxX9NqX9+5nV/o6p7DU60OAyte5DXOYf1ZBibIKSk7rvQUHOKqeg+/09w78R11TeXi/089Orz5QTxHSAQAAUG/5OeyKDA5QZHBAte9hGIbyi13ewf6oYfvZR/X05xRV7vUvVW5RiQpL3JKk/GKX8otdSsupfq++n912gkX3/BQR5F/2CKj4Otj8NSSAoftAbUVIBwAAAHyw2WwKcfopxOmn2PDAat+nuNTs1c+toqe+/Otsrx79ytdUDPE3DKnUbehIfomO5FevV9/PblN4WXAP94R5f0UEVQ73BHzACoR0AAAA4CwI8LOrkV+AGoVUv1ff7TaUX+I6aiG+YwN/dtmwfO+H2V7scqvUbehwXrEOV2PlfQI+cGYR0gEAAIA6wl62eF2o00/xEaf+/PIF+Y4N8JUe+cUEfMBChHQAAACggai8IF9cxKkN3fcV8DPzi4/Te0/AB04VIR0AAADACZ2JgJ9Z1mtPwAcqENIBAAAAnFE1GfAzjxqOX3sCvr/CAv0VFOBQcIBDwf5+CgywKzjAT0H+DjnsBH2cHEI6AAAAgFqrJgJ+ZkFZsM8vOasBvzKnn90M8P6OsiDvZ74vfzPUe8J9gJ8C/cu/Ns8Heb7287RXXOOnQH87vf31CCEdAAAAQL1UEfCDFB8RdErPNQxDBSWuSgvqnTjg5xaVKr/YpYJil/lrictzv6JSt4pK3cpU9bbNO5Gjw35QgJ+C/O2eDwOCy84HlvXyl1/r/Ty/oz4AMNsDHHwIcDYR0gEAAADgKDabrSy0+p1ywC9X3pOfX2yG98ISM7ybAb4i0BdUajevOTbsm1+Xel1fVOr2vFZBSdmHAnk19R2o4LDbjurRP1HvfvmHBEePEii/xvtDAn+HveaLrsMI6QAAAABwBlQeqt/4DNzf5TbKAnupCovdyq8c/Itdyi9xqaAs8B/zIUH5BwElx/kwoMSlEpfheZ3colLlFpWegXch+TtsZYHeuyf/uFMAvEYA+CnY36GBHaIV4Fc/wj4hHQAAAADqIIfdplCnn0KdZybWlbjcR4V775788nCfX1x61AcAla6pYsRA+QcBbqP8dQyVuEqVXVj9DwHWP3MJIR0AAAAAUH/5O+yKCLIrIsi/xu9tGIaKXe5KQb9ykC+tonffpfyS0qNGCZT3+rsV5O+o8RqtQkgHAAAAAJxVNptNTj+HnH4ORQZbXU3tUj/GAwAAAAAAUA8Q0gEAAAAAqCUI6QAAAAAA1BKEdAAAAAAAaglCOgAAAAAAtQQhHQAAAACAWoKQDgAAAABALUFIBwAAAACgliCkAwAAAABQS9SKkP7666+rVatWCgwMVFJSklasWOHz+g8++ECJiYkKDAxU165dNXfu3LNUKQAAAAAAZ47lIX3OnDmaOHGinn76aa1evVrdu3fXsGHDlJaWVuX1P/74o2644QbddtttWrNmjUaPHq3Ro0frt99+O8uVAwAAAABQs2yGYRhWFpCUlKQ+ffpo+vTpkiS3262EhATdd999evzxx4+5fsyYMcrLy9OXX37paTvvvPPUo0cPzZgx44Svl52drYiICGVlZSk8PLzm3ggAAAAAAFU4lRxqaU96cXGxVq1apaFDh3ra7Ha7hg4dquXLl1f5nOXLl3tdL0nDhg077vVFRUXKzs72egAAAAAAUBtZGtIzMjLkcrkUGxvr1R4bG6uUlJQqn5OSknJK10+ZMkURERGeR0JCQs0UDwAAAABADbN8TvqZNmnSJGVlZXkee/futbokAAAAAACq5Gflizdp0kQOh0Opqale7ampqYqLi6vyOXFxcad0vdPplNPprJmCAQAAAAA4gyztSQ8ICFCvXr20ePFiT5vb7dbixYvVr1+/Kp/Tr18/r+sl6euvvz7u9QAAAAAA1BWW9qRL0sSJEzV+/Hj17t1bffv21bRp05SXl6dbbrlFkjRu3Dg1a9ZMU6ZMkSQ98MADGjRokF555RWNHDlS7733nn755Rf94x//sPJtAAAAAABw2iwP6WPGjFF6eromT56slJQU9ejRQ/Pnz/csDrdnzx7Z7RUd/v3799fs2bP15JNP6g9/+IPat2+vTz/9VOecc85JvV75jnOs8g4AAAAAOBvK8+fJ7IBu+T7pZ9u+fftY4R0AAAAAcNbt3btXzZs393lNgwvpbrdbBw4cUFhYmGw2m9Xl+JSdna2EhATt3bv3hBveA3UZP+toSPh5R0PCzzsaEn7e4YthGMrJyVHTpk29RopXxfLh7meb3W4/4ScXtU14eDh/0NEg8LOOhoSfdzQk/LyjIeHnHccTERFxUtfV+33SAQAAAACoKwjpAAAAAADUEoT0WszpdOrpp5/+//buP6aq8oHj+OeAAheihjAu2C9JnSIJSJcIaS3ThZhuOtLRrg1skzkvJLFaxCJt/spa6oq8hVP/8VeZw5jLmtGGynKQBuJEaWtrLIbocqa0rHHpjza2+7W179w9PofL+7Wd7d7nXOBztjO2z577PEfR0dGmowC24l7HWML9jrGE+x1jCfc7QmXMbRwHAAAAAIBTMZMOAAAAAIBDUNIBAAAAAHAISjoAAAAAAA5BSQcAAAAAwCEo6Q710UcfadKkSYqJiVFeXp7a2tpMRwJCbvPmzcrNzVV8fLySk5O1ePFiXbp0yXQs4K545513ZFmWqqqqTEcBbPHLL79o+fLlSkxMlMvl0syZM/X999+bjgWE3NDQkOrq6pSWliaXy6XJkydr/fr1Yn9u3ClKugN9+umnqq6u1tq1a3X27FllZWWpsLBQAwMDpqMBIdXS0iKfz6fTp0/r+PHj+uuvv/Tss89qcHDQdDTAVu3t7frkk0+UmZlpOgpgi2vXrqmgoEDjx4/XsWPHdOHCBb3//vtKSEgwHQ0IuS1btsjv96u+vl7d3d3asmWL3n33XX344Yemo2GU4hFsDpSXl6fc3FzV19dLkgKBgB588EFVVlaqpqbGcDrAPleuXFFycrJaWlr01FNPmY4D2OLmzZvKycnRjh07tGHDBmVnZ2v79u2mYwEhVVNTo9bWVp08edJ0FMB2CxculNvt1q5du0bGiouL5XK5tHfvXoPJMFoxk+4wf/75p86cOaN58+aNjEVERGjevHn67rvvDCYD7Hf9+nVJ0oQJEwwnAezj8/n03HPPBf2fB8JNU1OTPB6Pli5dquTkZM2aNUs7d+40HQuwxezZs9Xc3Kyenh5JUmdnp06dOqWioiLDyTBajTMdAMGuXr2qoaEhud3uoHG3262LFy8aSgXYLxAIqKqqSgUFBXr00UdNxwFscfDgQZ09e1bt7e2mowC2+umnn+T3+1VdXa3a2lq1t7fr5ZdfVlRUlEpLS03HA0KqpqZGv/32m6ZPn67IyEgNDQ1p48aN8nq9pqNhlKKkA3AEn8+n8+fP69SpU6ajALbo7e3VmjVrdPz4ccXExJiOA9gqEAjI4/Fo06ZNkqRZs2bp/Pnz+vjjjynpCDufffaZ9u3bp/379ysjI0MdHR2qqqrSxIkTud9xRyjpDpOUlKTIyEhdvnw5aPzy5ctKSUkxlAqwV0VFhY4ePaoTJ07ogQceMB0HsMWZM2c0MDCgnJyckbGhoSGdOHFC9fX1unXrliIjIw0mBEInNTVVM2bMCBpLT0/X4cOHDSUC7PPaa6+ppqZGJSUlkqSZM2fq559/1ubNmynpuCOsSXeYqKgoPfbYY2pubh4ZCwQCam5uVn5+vsFkQOgNDw+roqJCjY2N+vbbb5WWlmY6EmCbuXPnqqurSx0dHSOHx+OR1+tVR0cHBR1hpaCg4LZHavb09Ojhhx82lAiwz++//66IiOBaFRkZqUAgYCgRRjtm0h2ourpapaWl8ng8evzxx7V9+3YNDg5qxYoVpqMBIeXz+bR//3598cUXio+PV39/vyTpvvvuk8vlMpwOCK34+Pjb9luIi4tTYmIi+zAg7LzyyiuaPXu2Nm3apGXLlqmtrU0NDQ1qaGgwHQ0IuUWLFmnjxo166KGHlJGRoR9++EFbt27VSy+9ZDoaRikeweZQ9fX1eu+999Tf36/s7Gx98MEHysvLMx0LCCnLsv51fM+ePSorK7u7YQADnn76aR7BhrB19OhRvfHGG/rxxx+Vlpam6upqrVy50nQsIORu3Lihuro6NTY2amBgQBMnTtQLL7ygt956S1FRUabjYRSipAMAAAAA4BCsSQcAAAAAwCEo6QAAAAAAOAQlHQAAAAAAh6CkAwAAAADgEJR0AAAAAAAcgpIOAAAAAIBDUNIBAAAAAHAISjoAAAAAAA5BSQcAALayLEtHjhwxHQMAgFGBkg4AQBgrKyuTZVm3HfPnzzcdDQAA/ItxpgMAAAB7zZ8/X3v27Akai46ONpQGAAD8F2bSAQAIc9HR0UpJSQk6EhISJP3zVXS/36+ioiK5XC498sgj+vzzz4N+vqurS88884xcLpcSExNVXl6umzdvBn1m9+7dysjIUHR0tFJTU1VRURF0/urVq1qyZIliY2M1depUNTU12XvRAACMUpR0AADGuLq6OhUXF6uzs1Ner1clJSXq7u6WJA0ODqqwsFAJCQlqb2/XoUOH9M033wSVcL/fL5/Pp/LycnV1dampqUlTpkwJ+htvv/22li1bpnPnzmnBggXyer369ddf7+p1AgAwGljDw8PDpkMAAAB7lJWVae/evYqJiQkar62tVW1trSzL0qpVq+T3+0fOPfHEE8rJydGOHTu0c+dOvf766+rt7VVcXJwk6csvv9SiRYvU19cnt9ut+++/XytWrNCGDRv+NYNlWXrzzTe1fv16Sf8U/3vuuUfHjh1jbTwAAP+DNekAAIS5OXPmBJVwSZowYcLI6/z8/KBz+fn56ujokCR1d3crKytrpKBLUkFBgQKBgC5duiTLstTX16e5c+f+Z4bMzMyR13Fxcbr33ns1MDBwp5cEAEDYoqQDABDm4uLibvv6eai4XK7/63Pjx48Pem9ZlgKBgB2RAAAY1ViTDgDAGHf69Onb3qenp0uS0tPT1dnZqcHBwZHzra2tioiI0LRp0xQfH69Jkyapubn5rmYGACBcMZMOAECYu3Xrlvr7+4PGxo0bp6SkJEnSoUOH5PF49OSTT2rfvn1qa2vTrl27JEler1dr165VaWmp1q1bpytXrqiyslIvvvii3G63JGndunVatWqVkpOTVVRUpBs3bqi1tVWVlZV390IBAAgDlHQAAMLcV199pdTU1KCxadOm6eLFi5L+2Xn94MGDWr16tVJTU3XgwAHNmDFDkhQbG6uvv/5aa9asUW5urmJjY1VcXKytW7eO/K7S0lL98ccf2rZtm1599VUlJSXp+eefv3sXCABAGGF3dwAAxjDLstTY2KjFixebjgIAAMSadAAAAAAAHIOSDgAAAACAQ7AmHQCAMYxVbwAAOAsz6QAAAAAAOAQlHQAAAAAAh6CkAwAAAADgEJR0AAAAAAAcgpIOAAAAAIBDUNIBAAAAAHAISjoAAAAAAA5BSQcAAAAAwCH+Blg2ls9n1gmEAAAAAElFTkSuQmCC"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1861,25 +2227,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.9948511719703674\n",
-      "Test accuracy: 0.8038095235824585\n",
+      "Test loss: 0.4641617238521576\n",
+      "Test accuracy: 0.8272619247436523\n",
       "Classification Report: \n",
       "               precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.81      0.80      0.80      4192\n",
-      "           1       0.80      0.81      0.81      4208\n",
+      "           0       0.78      0.91      0.84      4192\n",
+      "           1       0.89      0.74      0.81      4208\n",
       "\n",
-      "    accuracy                           0.80      8400\n",
-      "   macro avg       0.80      0.80      0.80      8400\n",
-      "weighted avg       0.80      0.80      0.80      8400\n"
+      "    accuracy                           0.83      8400\n",
+      "   macro avg       0.84      0.83      0.83      8400\n",
+      "weighted avg       0.84      0.83      0.83      8400\n"
      ]
     }
    ],
    "source": [
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.layers import Dense, Dropout\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import StandardScaler\n",
+    "from tensorflow.keras.layers import BatchNormalization\n",
+    "from tensorflow.keras import regularizers\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "tf.random.set_seed(42)\n",
     "\n",
     "# Assuming data is your cleaned data and 'NLOS' is your target variable\n",
     "X = data.drop('NLOS', axis=1)\n",
@@ -1893,31 +2265,34 @@
     "X_train = scaler.fit_transform(X_train)\n",
     "X_test = scaler.transform(X_test)\n",
     "\n",
-    "# Define the model\n",
+    "# Define the model with batch normalization and weight regularization\n",
     "model = Sequential()\n",
-    "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))  # Input layer\n",
-    "model.add(Dense(64, activation='relu'))  # Hidden layer\n",
-    "model.add(Dense(1, activation='sigmoid'))  # Output layer\n",
+    "model.add(Dense(32, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(1, activation='sigmoid'))\n",
     "\n",
     "# Compile the model\n",
     "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
     "\n",
-    "# Train the model\n",
-    "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n",
+    "# Define early stopping with a higher patience value\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
+    "\n",
+    "# Train the model with increased epochs\n",
+    "history = model.fit(X_train, y_train, epochs=300, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
     "\n",
     "# Evaluate the model\n",
     "scores = model.evaluate(X_test, y_test, verbose=0)\n",
     "\n",
-    "# Make predictions\n",
+    "# Make predictions and generate classification report\n",
     "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Convert probabilities to class labels\n",
     "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
-    "\n",
-    "# Generate classification report\n",
     "report = classification_report(y_test, y_pred_classes)\n",
     "\n",
-    "# Plot the training and test accuracy\n",
+    "# Plot training and test accuracy\n",
     "plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
     "plt.title('Training and Test Accuracy')\n",
     "plt.xlabel('Epoch')\n",
@@ -1925,7 +2300,7 @@
     "plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
     "plt.show()\n",
     "\n",
-    "# Plot the training and validation loss\n",
+    "# Plot training and validation loss\n",
     "plt.figure(figsize=(12, 6))\n",
     "plt.plot(history.history['loss'], label='Training Loss')\n",
     "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
@@ -1943,12 +2318,12 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-03-15T14:45:58.153160Z",
-     "start_time": "2024-03-15T14:45:36.433177Z"
+     "end_time": "2024-03-15T15:35:44.337303Z",
+     "start_time": "2024-03-15T15:32:16.255784Z"
     }
    },
    "id": "c8745832a585d5ec",
-   "execution_count": 76
+   "execution_count": 39
   }
  ],
  "metadata": {