PronosticTemperatureLSTM.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"12WhtGp_HRQ6k11Bl6PB2NjjG2GtFP_1D","authorship_tag":"ABX9TyNg0kBHd0EZN5OGcP57yObz"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0648pVbg_iuR","executionInfo":{"status":"ok","timestamp":1719507771599,"user_tz":300,"elapsed":87313,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"0906d3dd-c2f7-4afe-937c-cf6be0bf36d0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","91/91 [==============================] - 5s 14ms/step - loss: 0.0193 - val_loss: 0.0111\n","Epoch 2/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0111 - val_loss: 0.0102\n","Epoch 3/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0107 - val_loss: 0.0091\n","Epoch 4/100\n","91/91 [==============================] - 1s 5ms/step - loss: 0.0104 - val_loss: 0.0099\n","Epoch 5/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0102 - val_loss: 0.0092\n","Epoch 6/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0098 - val_loss: 0.0088\n","Epoch 7/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0094 - val_loss: 0.0081\n","Epoch 8/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0098 - val_loss: 0.0084\n","Epoch 9/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0093 - val_loss: 0.0084\n","Epoch 10/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0089 - val_loss: 0.0078\n","Epoch 11/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0091 - val_loss: 0.0079\n","Epoch 12/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0092 - val_loss: 0.0081\n","Epoch 13/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0090 - val_loss: 0.0080\n","Epoch 14/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0090 - val_loss: 0.0081\n","Epoch 15/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0090 - val_loss: 0.0110\n","Epoch 16/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0089 - val_loss: 0.0079\n","Epoch 17/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0091 - val_loss: 0.0076\n","Epoch 18/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0088 - val_loss: 0.0079\n","Epoch 19/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0090 - val_loss: 0.0073\n","Epoch 20/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0089 - val_loss: 0.0099\n","Epoch 21/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0090 - val_loss: 0.0076\n","Epoch 22/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0087 - val_loss: 0.0079\n","Epoch 23/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0088 - val_loss: 0.0080\n","Epoch 24/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0088 - val_loss: 0.0078\n","Epoch 25/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0089 - val_loss: 0.0085\n","Epoch 26/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0088 - val_loss: 0.0098\n","Epoch 27/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0088 - val_loss: 0.0077\n","Epoch 28/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0086 - val_loss: 0.0076\n","Epoch 29/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0073\n","Epoch 30/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0087 - val_loss: 0.0086\n","Epoch 31/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0087 - val_loss: 0.0080\n","Epoch 32/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0090 - val_loss: 0.0080\n","Epoch 33/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0086 - val_loss: 0.0083\n","Epoch 34/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0086 - val_loss: 0.0074\n","Epoch 35/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0087 - val_loss: 0.0078\n","Epoch 36/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0090\n","Epoch 37/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0086 - val_loss: 0.0075\n","Epoch 38/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0090\n","Epoch 39/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0086 - val_loss: 0.0082\n","Epoch 40/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0072\n","Epoch 41/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0079\n","Epoch 42/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0077\n","Epoch 43/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0074\n","Epoch 44/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0075\n","Epoch 45/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0079\n","Epoch 46/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0077\n","Epoch 47/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0081\n","Epoch 48/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0083\n","Epoch 49/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0079\n","Epoch 50/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0076\n","Epoch 51/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0077\n","Epoch 52/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0080\n","Epoch 53/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0074\n","Epoch 54/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0071\n","Epoch 55/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0073\n","Epoch 56/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0083 - val_loss: 0.0079\n","Epoch 57/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0072\n","Epoch 58/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0072\n","Epoch 59/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0074\n","Epoch 60/100\n","91/91 [==============================] - 1s 13ms/step - loss: 0.0083 - val_loss: 0.0080\n","Epoch 61/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0083 - val_loss: 0.0071\n","Epoch 62/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0081\n","Epoch 63/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0078\n","Epoch 64/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0078\n","Epoch 65/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0073\n","Epoch 66/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0071\n","Epoch 67/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0072\n","Epoch 68/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0073\n","Epoch 69/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0075\n","Epoch 70/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0073\n","Epoch 71/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0073\n","Epoch 72/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0072\n","Epoch 73/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0089\n","Epoch 74/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0073\n","Epoch 75/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0085 - val_loss: 0.0071\n","Epoch 76/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0073\n","Epoch 77/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0072\n","Epoch 78/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0080\n","Epoch 79/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0084 - val_loss: 0.0073\n","Epoch 80/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0074\n","Epoch 81/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0071\n","Epoch 82/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0070\n","Epoch 83/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0085 - val_loss: 0.0075\n","Epoch 84/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0076\n","Epoch 85/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0080\n","Epoch 86/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0082 - val_loss: 0.0072\n","Epoch 87/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0082 - val_loss: 0.0071\n","Epoch 88/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0072\n","Epoch 89/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0084 - val_loss: 0.0079\n","Epoch 90/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0079\n","Epoch 91/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0070\n","Epoch 92/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0072\n","Epoch 93/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0080\n","Epoch 94/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0081\n","Epoch 95/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0074\n","Epoch 96/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0072\n","Epoch 97/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0070\n","Epoch 98/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0068\n","Epoch 99/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0071\n","Epoch 100/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0082 - val_loss: 0.0072\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 376ms/step\n","Temperatura predicha para 1991-01-01: 14.93\n","1/1 [==============================] - 0s 18ms/step\n","Temperatura predicha para 1998-01-01: 17.91\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkgAAAHHCAYAAABEEKc/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACPZ0lEQVR4nO3dd3iTVfsH8G9W96SlCwplVMosGwooCggIIksZogx5wQEK8hNfJ6C8vjgRFRTxFcSBIFMERNkyyqZsyqZAaUsp3bt5fn+cPhlt0qZtmqTw/VxXr6TJyZOTpyN37nOfcxSSJEkgIiIiIh2lvTtARERE5GgYIBERERGVwACJiIiIqAQGSEREREQlMEAiIiIiKoEBEhEREVEJDJCIiIiISmCARERERFQCAyQiIiKiEhggEdE9TaFQYNasWRV+3NWrV6FQKPDDDz9YvU9E5PgYIBFRtfvhhx+gUCigUCiwZ8+eUvdLkoTQ0FAoFAo8/vjjduhh5e3cuRMKhQKrVq2yd1eIyIoYIBGRzbi4uGDZsmWlbt+1axdu3LgBZ2dnO/SKiKg0BkhEZDP9+vXDypUrUVhYaHT7smXL0K5dOwQFBdmpZ0RExhggEZHNjBw5Enfu3MGWLVt0t+Xn52PVqlV4+umnTT4mKysL//d//4fQ0FA4OzujSZMm+PTTTyFJklG7vLw8vPrqq6hduzY8PT3xxBNP4MaNGyaPefPmTTz33HMIDAyEs7MzmjdvjsWLF1vvhZpw+fJlPPXUU6hVqxbc3NzQuXNnbNy4sVS7r776Cs2bN4ebmxt8fX3Rvn17o6xbRkYGpk6dirCwMDg7OyMgIACPPvoojh49Wq39J7rfMEAiIpsJCwtDVFQUfv31V91tf/75J9LS0jBixIhS7SVJwhNPPIHPP/8cffv2xdy5c9GkSRNMnz4d06ZNM2r7r3/9C/PmzUPv3r3x4YcfQqPRoH///qWOmZiYiM6dO2Pr1q2YPHkyvvjiCzRu3Bjjx4/HvHnzrP6a5efs0qUL/vrrL7z00kv44IMPkJubiyeeeAJr167Vtfvuu+/wyiuvoFmzZpg3bx7ee+89tG7dGgcOHNC1eeGFF/DNN99g6NCh+Prrr/Haa6/B1dUVZ8+erZa+E923JCKiarZkyRIJgHTo0CFp/vz5kqenp5SdnS1JkiQ99dRT0iOPPCJJkiTVr19f6t+/v+5x69atkwBI//nPf4yO9+STT0oKhUK6ePGiJEmSFBMTIwGQXnrpJaN2Tz/9tARAmjlzpu628ePHS8HBwVJycrJR2xEjRkje3t66fl25ckUCIC1ZsqTM17Zjxw4JgLRy5UqzbaZOnSoBkHbv3q27LSMjQ2rQoIEUFhYmFRUVSZIkSQMHDpSaN29e5vN5e3tLkyZNKrMNEVUdM0hEZFPDhg1DTk4ONmzYgIyMDGzYsMHs8NqmTZugUqnwyiuvGN3+f//3f5AkCX/++aeuHYBS7aZOnWr0vSRJWL16NQYMGABJkpCcnKz76tOnD9LS0qplqGrTpk3o2LEjunXrprvNw8MDEydOxNWrV3HmzBkAgI+PD27cuIFDhw6ZPZaPjw8OHDiA+Ph4q/eTiPQYIBGRTdWuXRu9evXCsmXLsGbNGhQVFeHJJ5802fbatWsICQmBp6en0e1NmzbV3S9fKpVKNGrUyKhdkyZNjL6/ffs2UlNTsWjRItSuXdvoa9y4cQCApKQkq7zOkq+jZF9MvY5///vf8PDwQMeOHREeHo5JkyZh7969Ro/5+OOPcerUKYSGhqJjx46YNWsWLl++bPU+E93v1PbuABHdf55++mlMmDABCQkJeOyxx+Dj42OT59VqtQCAZ555BmPGjDHZplWrVjbpiylNmzZFbGwsNmzYgM2bN2P16tX4+uuvMWPGDLz33nsARAbuwQcfxNq1a/H333/jk08+wUcffYQ1a9bgscces1vfie41zCARkc0NHjwYSqUS+/fvNzu8BgD169dHfHw8MjIyjG4/d+6c7n75UqvV4tKlS0btYmNjjb6XZ7gVFRWhV69eJr8CAgKs8RJLvY6SfTH1OgDA3d0dw4cPx5IlSxAXF4f+/fvrirplwcHBeOmll7Bu3TpcuXIFfn5++OCDD6zeb6L7GQMkIrI5Dw8PfPPNN5g1axYGDBhgtl2/fv1QVFSE+fPnG93++eefQ6FQ6DIm8uWXX35p1K7krDSVSoWhQ4di9erVOHXqVKnnu337dmVeTrn69euHgwcPIjo6WndbVlYWFi1ahLCwMDRr1gwAcOfOHaPHOTk5oVmzZpAkCQUFBSgqKkJaWppRm4CAAISEhCAvL69a+k50v+IQGxHZhbkhLkMDBgzAI488grfffhtXr15FZGQk/v77b/z++++YOnWqruaodevWGDlyJL7++mukpaWhS5cu2LZtGy5evFjqmB9++CF27NiBTp06YcKECWjWrBlSUlJw9OhRbN26FSkpKZV6PatXr9ZlhEq+zjfeeAO//vorHnvsMbzyyiuoVasWli5diitXrmD16tVQKsVn1d69eyMoKAhdu3ZFYGAgzp49i/nz56N///7w9PREamoq6tatiyeffBKRkZHw8PDA1q1bcejQIXz22WeV6jcRmWHfSXREdD8wnOZflpLT/CVJTId/9dVXpZCQEEmj0Ujh4eHSJ598Imm1WqN2OTk50iuvvCL5+flJ7u7u0oABA6Tr16+XmuYvSZKUmJgoTZo0SQoNDZU0Go0UFBQk9ezZU1q0aJGuTUWn+Zv7kqf2X7p0SXryySclHx8fycXFRerYsaO0YcMGo2N9++230kMPPST5+flJzs7OUqNGjaTp06dLaWlpkiRJUl5enjR9+nQpMjJS8vT0lNzd3aXIyEjp66+/LrOPRFRxCkkqsRwtERER0X2ONUhEREREJTBAIiIiIiqBARIRERFRCQyQiIiIiEpggERERERUAgMkIiIiohK4UGQlabVaxMfHw9PTEwqFwt7dISIiIgtIkoSMjAyEhIToFmk1hQFSJcXHxyM0NNTe3SAiIqJKuH79OurWrWv2fgZIleTp6QlAnGAvLy8794aIiIgskZ6ejtDQUN37uDkMkCpJHlbz8vJigERERFTDlFcewyJtIiIiohIYIBERERGVwACJiIiIqATWIBER0X2vqKgIBQUF9u4GWYFGo4FKparycRggERHRfUuSJCQkJCA1NdXeXSEr8vHxQVBQUJXWKWSARERE9y05OAoICICbmxsX/q3hJElCdnY2kpKSAADBwcGVPhYDJCIiui8VFRXpgiM/Pz97d4esxNXVFQCQlJSEgICASg+3sUibiIjuS3LNkZubm517QtYm/0yrUlfGAImIiO5rHFa791jjZ8oAiYiIiKgEBkhERET3sbCwMMybN8/e3XA4LNImIiKqYR5++GG0bt3aKoHNoUOH4O7uXvVO3WMYIDmYtOwCpOcWwMtFA283jb27Q0RENZAkSSgqKoJaXf7bfO3atW3Qo5qHQ2wOZs6fZ/Hgxzvw0/6r9u4KERE5oLFjx2LXrl344osvoFAooFAo8MMPP0ChUODPP/9Eu3bt4OzsjD179uDSpUsYOHAgAgMD4eHhgQ4dOmDr1q1Gxys5xKZQKPC///0PgwcPhpubG8LDw7F+/Xobv0r7Y4DkYDQq8SPJL5Ls3BMiovuPJEnIzi+0y5ckWfZ//4svvkBUVBQmTJiAW7du4datWwgNDQUAvPHGG/jwww9x9uxZtGrVCpmZmejXrx+2bduGY8eOoW/fvhgwYADi4uLKfI733nsPw4YNw4kTJ9CvXz+MGjUKKSkpVT6/NQmH2ByMWiWmJhYWae3cEyKi+09OQRGazfjLLs995v0+cHMq/23Z29sbTk5OcHNzQ1BQEADg3LlzAID3338fjz76qK5trVq1EBkZqft+9uzZWLt2LdavX4/JkyebfY6xY8di5MiRAID//ve/+PLLL3Hw4EH07du3Uq+tJmIGycHIGaRCLTNIRERUMe3btzf6PjMzE6+99hqaNm0KHx8feHh44OzZs+VmkFq1aqW77u7uDi8vL932HfcLZpAcjKY4g1TADBIRkc25alQ4834fuz13VZWcjfbaa69hy5Yt+PTTT9G4cWO4urriySefRH5+fpnH0WiMJwkpFApotffX+xIDJAejVooMEgMkIiLbUygUFg1z2ZuTkxOKiorKbbd3716MHTsWgwcPBiAySlevXq3m3t0bOMTmYDS6GiQOsRERkWlhYWE4cOAArl69iuTkZLPZnfDwcKxZswYxMTE4fvw4nn766fsuE1RZDJAcjFyDVMAAiYiIzHjttdegUqnQrFkz1K5d22xN0dy5c+Hr64suXbpgwIAB6NOnD9q2bWvj3tZMjp9HvM+oVRxiIyKisj3wwAOIjo42um3s2LGl2oWFhWH79u1Gt02aNMno+5JDbqaWG0hNTa1UP2syZpAcjG6IjSlQIiIiu2GA5GD0RdocYiMiIrIXBkgOhtP8iYiI7I8BkoPRLRTJDBIREZHdMEByMGpmkIiIiOyOAZKD0XAWGxERkd0xQHIw+llsHGIjIiKyFwZIDoaz2IiIiOyPAZKD4RAbERGR/TFAcjD6vdgYIBERUfUICwvDvHnzdN8rFAqsW7fObPurV69CoVAgJiamSs9rrePYArcacTBq7sVGREQ2duvWLfj6+lr1mGPHjkVqaqpR4BUaGopbt27B39/fqs9VHRggORhuNUJERLYWFBRkk+dRqVQ2e66q4hCbg9Ewg0RERGVYtGgRQkJCoC3xQXrgwIF47rnncOnSJQwcOBCBgYHw8PBAhw4dsHXr1jKPWXKI7eDBg2jTpg1cXFzQvn17HDt2zKh9UVERxo8fjwYNGsDV1RVNmjTBF198obt/1qxZWLp0KX7//XcoFAooFArs3LnT5BDbrl270LFjRzg7OyM4OBhvvPEGCgsLdfc//PDDeOWVV/D666+jVq1aCAoKwqxZsyp+4iqIGSQHo1ZyoUgiIruRJKAg2z7PrXEDFIpymz311FN4+eWXsWPHDvTs2RMAkJKSgs2bN2PTpk3IzMxEv3798MEHH8DZ2Rk//vgjBgwYgNjYWNSrV6/c42dmZuLxxx/Ho48+ip9//hlXrlzBlClTjNpotVrUrVsXK1euhJ+fH/bt24eJEyciODgYw4YNw2uvvYazZ88iPT0dS5YsAQDUqlUL8fHxRse5efMm+vXrh7Fjx+LHH3/EuXPnMGHCBLi4uBgFQUuXLsW0adNw4MABREdHY+zYsejatSseffTRcl9PZTFAcjDcaoSIyI4KsoH/htjnud+KB5zcy23m6+uLxx57DMuWLdMFSKtWrYK/vz8eeeQRKJVKREZG6trPnj0ba9euxfr16zF58uRyj79s2TJotVp8//33cHFxQfPmzXHjxg28+OKLujYajQbvvfee7vsGDRogOjoav/32G4YNGwYPDw+4uroiLy+vzCG1r7/+GqGhoZg/fz4UCgUiIiIQHx+Pf//735gxYwaUxUvftGrVCjNnzgQAhIeHY/78+di2bVu1BkgcYnMwnOZPRETlGTVqFFavXo28vDwAwC+//IIRI0ZAqVQiMzMTr732Gpo2bQofHx94eHjg7NmziIuLs+jYZ8+eRatWreDi4qK7LSoqqlS7BQsWoF27dqhduzY8PDywaNEii5/D8LmioqKgMMicde3aFZmZmbhx44butlatWhk9Ljg4GElJSRV6ropiBsnBqA1W0pYkyeiXhoiIqpnGTWRy7PXcFhowYAAkScLGjRvRoUMH7N69G59//jkA4LXXXsOWLVvw6aefonHjxnB1dcWTTz6J/Px8q3V1+fLleO211/DZZ58hKioKnp6e+OSTT3DgwAGrPYchjUZj9L1CoShVg2VtDJAcjEapT+oVaiXdrDYiIrIBhcKiYS57c3FxwZAhQ/DLL7/g4sWLaNKkCdq2bQsA2Lt3L8aOHYvBgwcDEDVFV69etfjYTZs2xU8//YTc3FxdFmn//v1Gbfbu3YsuXbrgpZde0t126dIlozZOTk4oKioq97lWr15tlBDYu3cvPD09UbduXYv7XB04xOZgNGp9QMRhNiIiMmfUqFHYuHEjFi9ejFGjRuluDw8Px5o1axATE4Pjx4/j6aefrlC25emnn4ZCocCECRNw5swZbNq0CZ9++qlRm/DwcBw+fBh//fUXzp8/j3fffReHDh0yahMWFoYTJ04gNjYWycnJKCgoKPVcL730Eq5fv46XX34Z586dw++//46ZM2di2rRpuvoje2GA5GDUBr8QnOpPRETm9OjRA7Vq1UJsbCyefvpp3e1z586Fr68vunTpggEDBqBPnz667JIlPDw88Mcff+DkyZNo06YN3n77bXz00UdGbZ5//nkMGTIEw4cPR6dOnXDnzh2jbBIATJgwAU2aNEH79u1Ru3Zt7N27t9Rz1alTB5s2bcLBgwcRGRmJF154AePHj8c777xTwbNhfQpJkvguXAnp6enw9vZGWloavLy8rHZcSZLQ4M1NAIAj7/SCn4ez1Y5NRER6ubm5uHLlCho0aGBUkEw1X1k/W0vfv5lBcjAKhcJgLSTGrkRERPZg9wBpwYIFCAsLg4uLCzp16oSDBw+W2X7lypWIiIiAi4sLWrZsiU2bNhndv2bNGvTu3Rt+fn5mN8RLSEjAs88+i6CgILi7u6Nt27ZYvXq1NV9Wlcgz2ViDREREZB92DZBWrFiBadOmYebMmTh69CgiIyPRp08fs2sb7Nu3DyNHjsT48eNx7NgxDBo0CIMGDcKpU6d0bbKystCtW7dS46WGRo8ejdjYWKxfvx4nT57EkCFDMGzYsFJLqduLPJOtUMsMEhERkT3YNUCaO3cuJkyYgHHjxqFZs2ZYuHAh3NzcsHjxYpPtv/jiC/Tt2xfTp09H06ZNMXv2bLRt2xbz58/XtXn22WcxY8YM9OrVy+zz7tu3Dy+//DI6duyIhg0b4p133oGPjw+OHDli9ddYGRo1F4skIiKyJ7sFSPn5+Thy5IhRIKNUKtGrVy9ER0ebfEx0dHSpwKdPnz5m25vTpUsXrFixAikpKdBqtVi+fDlyc3Px8MMPm31MXl4e0tPTjb6qC/djIyKyHc5VuvdY42dqtwApOTkZRUVFCAwMNLo9MDAQCQkJJh+TkJBQofbm/PbbbygoKICfnx+cnZ3x/PPPY+3atWjcuLHZx8yZMwfe3t66r9DQ0Ao9Z0VwPzYiouonr86cnW2nzWmp2sg/05IrcFfEfbmS9rvvvovU1FRs3boV/v7+WLduHYYNG4bdu3ejZcuWJh/z5ptvYtq0abrv09PTqy1I0ui2G2EGiYiouqhUKvj4+OjqXt3c3Li9Uw0nSRKys7ORlJQEHx8fqFSqSh/LbgGSv78/VCoVEhMTjW5PTEw0u/NvUFBQhdqbcunSJcyfPx+nTp1C8+bNAQCRkZHYvXs3FixYgIULF5p8nLOzM5ydbbMmkbo4g5RfyAwSEVF1kt8/qnvjU7ItHx+fCsUGptgtQHJyckK7du2wbds2DBo0CACg1Wqxbds2TJ482eRjoqKisG3bNkydOlV325YtW0zuMmyOnHYruYS5SqWq9o3vLCXXIDGDRERUvRQKBYKDgxEQEGByKwyqeTQaTZUyRzK7DrFNmzYNY8aMQfv27dGxY0fMmzcPWVlZGDduHAAxHb9OnTqYM2cOAGDKlCno3r07PvvsM/Tv3x/Lly/H4cOHsWjRIt0xU1JSEBcXh/h4sRtzbGwsAPEpISgoCBEREWjcuDGef/55fPrpp/Dz88O6deuwZcsWbNiwwcZnwDTWIBER2ZZKpbLKmyrdO+waIA0fPhy3b9/GjBkzkJCQgNatW2Pz5s26Quy4uDijTE+XLl2wbNkyvPPOO3jrrbcQHh6OdevWoUWLFro269ev1wVYADBixAgAwMyZMzFr1ixoNBps2rQJb7zxBgYMGIDMzEw0btwYS5cuRb9+/Wz0yssm1yDlcxYbERGRXXAvtkqqrr3YAGDYt9E4eCUFC55ui/6tgq16bCIiovsZ92KrwTiLjYiIyL4YIDkgjW4WGwMkIiIie2CA5IDU3IuNiIjIrhggOSDdEBuLtImIiOyCAZID0g2xcZo/ERGRXTBAckBqZpCIiIjsigGSA9KwBomIiMiuGCA5II1aZJAKmEEiIiKyCwZIDkiexcYAiYiIyD4YIDkg/Sw2DrERERHZAwMkByTPYitggERERGQXDJAckFrFITYiIiJ7YoDkgDRK7sVGRERkTwyQHJCaQ2xERER2xQDJAclF2hxiIyIisg8GSA5ILtLmLDYiIiL7YIDkgNTMIBEREdkVAyQHpOEsNiIiIrtigOSAdAtFci82IiIiu2CA5IC41QgREZF9MUByQFxJm4iIyL4YIDkg/V5szCARERHZAwMkB8SFIomIiOyLAZID0hdpM4NERERkDwyQHBBrkIiIiOyLAZIDUiu5UCQREZE9MUByQNxqhIiIyL4YIDkgrqRNRERkXwyQHBD3YiMiIrIvBkgOSFO8kja3GiEiIrIPBkgOSKNmBomIiMieGCA5IP1ebBIkiVkkIiIiW2OA5IDkhSIBoIjDbERERDbHAMkBybPYAC4WSUREZA8MkByQ2iCDVMDtRoiIiGyOAZIDkmexAVwskoiIyB4YIDkgpVIBFbcbISIishsGSA6K+7ERERHZDwMkB8X92IiIiOyHAZKDkqf6F7JIm4iIyOYYIDkodXEGKb+QGSQiIiJbY4DkoDRKZpCIiIjshQGSg5IzSFwokoiIyPbsHiAtWLAAYWFhcHFxQadOnXDw4MEy269cuRIRERFwcXFBy5YtsWnTJqP716xZg969e8PPzw8KhQIxMTEmjxMdHY0ePXrA3d0dXl5eeOihh5CTk2Otl1Vlcg0SZ7ERERHZnl0DpBUrVmDatGmYOXMmjh49isjISPTp0wdJSUkm2+/btw8jR47E+PHjcezYMQwaNAiDBg3CqVOndG2ysrLQrVs3fPTRR2afNzo6Gn379kXv3r1x8OBBHDp0CJMnT4ZSafd4UYez2IiIiOxHIdlxu/hOnTqhQ4cOmD9/PgBAq9UiNDQUL7/8Mt54441S7YcPH46srCxs2LBBd1vnzp3RunVrLFy40Kjt1atX0aBBAxw7dgytW7c2uq9z58549NFHMXv27Er3PT09Hd7e3khLS4OXl1elj2PO41/txqmb6VgyrgMeaRJg9eMTERHdjyx9/7ZbyiQ/Px9HjhxBr1699J1RKtGrVy9ER0ebfEx0dLRRewDo06eP2famJCUl4cCBAwgICECXLl0QGBiI7t27Y8+ePWU+Li8vD+np6UZf1UnOIBUUcoiNiIjI1uwWICUnJ6OoqAiBgYFGtwcGBiIhIcHkYxISEirU3pTLly8DAGbNmoUJEyZg8+bNaNu2LXr27IkLFy6YfdycOXPg7e2t+woNDbX4OStD3o+tUMshNiIiIltznKIbG9EWT5t//vnnMW7cOLRp0waff/45mjRpgsWLF5t93Jtvvom0tDTd1/Xr16u1n2oWaRMREdmN2l5P7O/vD5VKhcTERKPbExMTERQUZPIxQUFBFWpvSnBwMACgWbNmRrc3bdoUcXFxZh/n7OwMZ2dni5+nqjSc5k9ERGQ3dssgOTk5oV27dti2bZvuNq1Wi23btiEqKsrkY6KioozaA8CWLVvMtjclLCwMISEhiI2NNbr9/PnzqF+/fgVeQfXSbTXCDBIREZHN2S2DBADTpk3DmDFj0L59e3Ts2BHz5s1DVlYWxo0bBwAYPXo06tSpgzlz5gAApkyZgu7du+Ozzz5D//79sXz5chw+fBiLFi3SHTMlJQVxcXGIj48HAF0gFBQUhKCgICgUCkyfPh0zZ85EZGQkWrdujaVLl+LcuXNYtWqVjc+AeeriGqQC1iARERHZnF0DpOHDh+P27duYMWMGEhIS0Lp1a2zevFlXiB0XF2e0NlGXLl2wbNkyvPPOO3jrrbcQHh6OdevWoUWLFro269ev1wVYADBixAgAwMyZMzFr1iwAwNSpU5Gbm4tXX30VKSkpiIyMxJYtW9CoUSMbvGrLaNTyOkjMIBEREdmaXddBqsmqex2kaStisObYTbzVLwITH3KcwI2IiKgmc/h1kKhs+llsjF+JiIhsjQGSg+JWI0RERPbDAMlB6af5swaJiIjI1hggOSi1sniITcsAiYiIyNYYIDkoNYfYiIiI7IYBkoNy4lYjREREdsMAyUGpudUIERGR3TBAclBqbjVCRERkNwyQHJQTZ7ERERHZDQMkB6WfxcYhNiIiIltjgOSg9LPYmEEiIiKyNQZIDsqJRdpERER2wwDJQak5zZ+IiMhuGCA5KC4USUREZD8MkByUvFBkIbcaISIisjkGSA5KrRQ/mnxmkIiIiGyOAZKD4kKRRERE9sMAyUFpWINERERkNwyQHJSGK2kTERHZDQMkB6Wb5s8ibSIiIptjgOSgNEoOsREREdkLAyQHpVFzoUgiIiJ7YYDkoORp/txqhIiIyPYYIDkoDaf5ExER2Q0DJAel4Wa1REREdsMAyUFxFhsREZH9MEByUPIsNkkCirTMIhEREdkSAyQHpVHrfzScyUZERGRbDJAclFqp0F1ngERERGRbDJAclFykDXCxSCIiIltjgOSgVEoF5CQSC7WJiIhsiwGSA1Nzqj8REZFdMEByYBolF4skIiKyBwZIDowZJCIiIvtggOTA9KtpM4NERERkSwyQHJh+PzZmkIiIiGyJAZID43YjRERE9sEAyYHphtgKGSARERHZEgMkBybvx1bIvdiIiIhsigGSA9MNsbFIm4iIyKYYIDkwDaf5ExER2QUDJAemn8XGDBIREZEtMUByYOriGqQC1iARERHZlEMESAsWLEBYWBhcXFzQqVMnHDx4sMz2K1euREREBFxcXNCyZUts2rTJ6P41a9agd+/e8PPzg0KhQExMjNljSZKExx57DAqFAuvWrbPCq7Eejbq4SJsZJCIiIpuye4C0YsUKTJs2DTNnzsTRo0cRGRmJPn36ICkpyWT7ffv2YeTIkRg/fjyOHTuGQYMGYdCgQTh16pSuTVZWFrp164aPPvqo3OefN28eFAqF1V6PNcl7sbFIm4iIyLbsHiDNnTsXEyZMwLhx49CsWTMsXLgQbm5uWLx4scn2X3zxBfr27Yvp06ejadOmmD17Ntq2bYv58+fr2jz77LOYMWMGevXqVeZzx8TE4LPPPjP7XPamn8XGITYiIiJbsmuAlJ+fjyNHjhgFMkqlEr169UJ0dLTJx0RHR5cKfPr06WO2vTnZ2dl4+umnsWDBAgQFBZXbPi8vD+np6UZf1U2excYhNiIiItuya4CUnJyMoqIiBAYGGt0eGBiIhIQEk49JSEioUHtzXn31VXTp0gUDBw60qP2cOXPg7e2t+woNDa3Q81UGp/kTERHZh92H2Oxh/fr12L59O+bNm2fxY958802kpaXpvq5fv159HSymVnIvNiIiInuwa4Dk7+8PlUqFxMREo9sTExPNDnsFBQVVqL0p27dvx6VLl+Dj4wO1Wg21Wg0AGDp0KB5++GGTj3F2doaXl5fRV3VT64bYmEEiIiKyJbsGSE5OTmjXrh22bdumu02r1WLbtm2Iiooy+ZioqCij9gCwZcsWs+1NeeONN3DixAnExMTovgDg888/x5IlSyr+QqqJE7caISIisgu1vTswbdo0jBkzBu3bt0fHjh0xb948ZGVlYdy4cQCA0aNHo06dOpgzZw4AYMqUKejevTs+++wz9O/fH8uXL8fhw4exaNEi3TFTUlIQFxeH+Ph4AEBsbCwAkX0y/CqpXr16aNCgQXW/ZIupWYNERERkF3YPkIYPH47bt29jxowZSEhIQOvWrbF582ZdIXZcXByUSn2iq0uXLli2bBneeecdvPXWWwgPD8e6devQokULXZv169frAiwAGDFiBABg5syZmDVrlm1emBWoudUIERGRXSgkSWJ6ohLS09Ph7e2NtLS0aqtH+uzvWHy1/SLGRNXHewNblP8AIiIiKpOl79/35Sy2moJ7sREREdkHAyQHxiE2IiIi+2CA5MCcWKRNRERkFwyQHJia0/yJiIjsggGSA+NCkURERPbBAMmByQtFFnKrESIiIptigOTA5Fls+cwgERER2VSlAqTr16/jxo0buu8PHjyIqVOnGq1mTVXHWWxERET2UakA6emnn8aOHTsAAAkJCXj00Udx8OBBvP3223j//fet2sH7mRNrkIiIiOyiUgHSqVOn0LFjRwDAb7/9hhYtWmDfvn345Zdf8MMPP1izf/c1uUg7nxkkIiIim6pUgFRQUABnZ2cAwNatW/HEE08AACIiInDr1i3r9e4+p2aRNhERkV1UKkBq3rw5Fi5ciN27d2PLli3o27cvACA+Ph5+fn5W7eD9TKPkEBsREZE9VCpA+uijj/Dtt9/i4YcfxsiRIxEZGQkAWL9+vW7ojapOU5xB4hAbERGRbakr86CHH34YycnJSE9Ph6+vr+72iRMnws3NzWqdu99xoUgiIiL7qFQGKScnB3l5ebrg6Nq1a5g3bx5iY2MREBBg1Q7ezzSc5k9ERGQXlQqQBg4ciB9//BEAkJqaik6dOuGzzz7DoEGD8M0331i1g/czjYoLRRIREdlDpQKko0eP4sEHHwQArFq1CoGBgbh27Rp+/PFHfPnll1bt4P1Mw1lsREREdlGpACk7Oxuenp4AgL///htDhgyBUqlE586dce3aNat28H6m5iw2IiIiu6hUgNS4cWOsW7cO169fx19//YXevXsDAJKSkuDl5WXVDt7PNGrx4ylgDRIREZFNVSpAmjFjBl577TWEhYWhY8eOiIqKAiCySW3atLFqB+9nGqUYYmOAREREZFuVmub/5JNPolu3brh165ZuDSQA6NmzJwYPHmy1zt3v5Gn+WgnQaiUoiwMmIiIiql6VCpAAICgoCEFBQbhx4wYAoG7dulwk0srkIm0AKNBq4axU2bE3RERE949KDbFptVq8//778Pb2Rv369VG/fn34+Phg9uzZ0HLGldXI0/wBoICF2kRERDZTqQzS22+/je+//x4ffvghunbtCgDYs2cPZs2ahdzcXHzwwQdW7eT9Sm0wpMbFIomIiGynUgHS0qVL8b///Q9PPPGE7rZWrVqhTp06eOmllxggWYnKIEBiBomIiMh2KjXElpKSgoiIiFK3R0REICUlpcqdIkGhUMBJxan+REREtlapACkyMhLz588vdfv8+fPRqlWrKneK9NS6/diYQSIiIrKVSg2xffzxx+jfvz+2bt2qWwMpOjoa169fx6ZNm6zawfudXIdUwOJ3IiIim6lUBql79+44f/48Bg8ejNTUVKSmpmLIkCE4ffo0fvrpJ2v38b7mxNW0iYiIbK7S6yCFhISUKsY+fvw4vv/+eyxatKjKHSOB+7ERERHZXqUySGQ7cg0SM0hERES2wwDJwelnsTGDREREZCsMkBycfhYbM0hERES2UqEapCFDhpR5f2pqalX6QibINUgFWmaQiIiIbKVCAZK3t3e5948ePbpKHSJjGrVcpM0MEhERka1UKEBasmRJdfWDzNAoWaRNRERka6xBcnD6WWwcYiMiIrIVBkgOTlM8i62QK2kTERHZDAMkBycHSAWFzCARERHZCgMkB8e92IiIiGyPAZKD0w2xsQaJiIjIZhggOTgNtxohIiKyOQZIDk7NrUaIiIhsziECpAULFiAsLAwuLi7o1KkTDh48WGb7lStXIiIiAi4uLmjZsiU2bdpkdP+aNWvQu3dv+Pn5QaFQICYmxuj+lJQUvPzyy2jSpAlcXV1Rr149vPLKK0hLS7P2S6syDbcaISIisjm7B0grVqzAtGnTMHPmTBw9ehSRkZHo06cPkpKSTLbft28fRo4cifHjx+PYsWMYNGgQBg0ahFOnTunaZGVloVu3bvjoo49MHiM+Ph7x8fH49NNPcerUKfzwww/YvHkzxo8fXy2vsSp0s9gYIBEREdmMQpIku47ddOrUCR06dMD8+fMBAFqtFqGhoXj55ZfxxhtvlGo/fPhwZGVlYcOGDbrbOnfujNatW2PhwoVGba9evYoGDRrg2LFjaN26dZn9WLlyJZ555hlkZWVBrS5/gfH09HR4e3sjLS0NXl5eFrzSynn/jzNYvPcKXny4Ef7dN6LanoeIiOh+YOn7t10zSPn5+Thy5Ah69eqlu02pVKJXr16Ijo42+Zjo6Gij9gDQp08fs+0tJZ8oc8FRXl4e0tPTjb5sgUNsREREtmfXACk5ORlFRUUIDAw0uj0wMBAJCQkmH5OQkFCh9pb2Y/bs2Zg4caLZNnPmzIG3t7fuKzQ0tNLPVxEaFmkTERHZnN1rkOwtPT0d/fv3R7NmzTBr1iyz7d58802kpaXpvq5fv26T/qk5zZ+IiMjmyi+2qUb+/v5QqVRITEw0uj0xMRFBQUEmHxMUFFSh9mXJyMhA37594enpibVr10Kj0Zht6+zsDGdn5wo/R1VxoUgiIiLbs2sGycnJCe3atcO2bdt0t2m1Wmzbtg1RUVEmHxMVFWXUHgC2bNlitr056enp6N27N5ycnLB+/Xq4uLhU/AXYgG6hSG41QkREZDN2zSABwLRp0zBmzBi0b98eHTt2xLx585CVlYVx48YBAEaPHo06depgzpw5AIApU6age/fu+Oyzz9C/f38sX74chw8fxqJFi3THTElJQVxcHOLj4wEAsbGxAET2KSgoSBccZWdn4+effzYquq5duzZUKpUtT0GZ1ErWIBEREdma3QOk4cOH4/bt25gxYwYSEhLQunVrbN68WVeIHRcXB6VSn+jq0qULli1bhnfeeQdvvfUWwsPDsW7dOrRo0ULXZv369boACwBGjBgBAJg5cyZmzZqFo0eP4sCBAwCAxo0bG/XnypUrCAsLq66XW2GcxUZERGR7dl8Hqaay1TpIyw/G4Y01J9GraSD+N6Z9tT0PERHR/aBGrINE5VNzJW0iIiKbY4Dk4HRDbCzSJiIishkGSA6ORdpERES2xwDJwWm4UCQREZHNMUBycFwokoiIyPYYIDk4bjVCRERkewyQHJyGs9iIiIhsjgGSg9PPYuMQGxERka0wQHJw8iw21iARERHZDgMkB8chNiIiIttjgOTgOM2fiIjI9hggOTg1p/kTERHZHAMkB6fLIHGrESIiIpthgOTg9DVIzCARERHZCgMkB6dWigxSkVaCJDFIIiIisgUGSA5OrkECmEUiIiKyFQZIDs5Vo9LVIV1OzrRzb4iIiO4PDJAczcHvgKUDgLMbAABOaiV6RgQCAH47dMOePSMiIrpvMEByNLfPAVf+Aa7v1900vEMoAGDtsRvIKyyyV8+IiIjuGwyQHE1QK3F564TupoceqI0gLxfczS7A1jNJduqYjWiLgJy79u4FERHd5xggOZrg4gAp4QRQPGtNpVRgaLs6AIAVh6/bq2e2seo54NMHgNQ4e/eEiIjuYwyQHE1AM0CpFlmUNH3N0bD2Ypht94XbuJmaY6/eVb/4o0BRPpB0zt49ISKi+xgDJEejdgZqR4jrCfphtvp+7ujcsBYkCVh1+B4u1s5NE5d56fbtBxER3dcYIDmioJbi0qAOCdAXa688ch1a7T24JpIkAXkZ4rp8SUREZAcMkBxRkEEdkoHHWgTD00WNG3dzsO/SHTt0rJrlZwJS8Z5zDJCIiMiOGCA5ouDSM9kAwEWjwsDWIQDu0WLtXINhNQZIRERkRwyQHJE8xJZ+A8hOMbprePt6AIC/TicgNTvf1j2rXnkMkIiIyDEwQHJELt6Ab5i4fuu40V0t6nihabAX8gu1WHP0pu37Vp3kAm2AARIREdkVAyRHZaYOSaFQ4OlOIov09c6LyMgtsHXPqo/REBtnsRERkf0wQHJUugUjT5a6a3j7UDT0d0dyZj7mb79o445VI8OgKJ8b8xIRkf0wQHJUQZHiskShNiA2sH338WYAgMV7r+BKcpYte1Z9OMRGREQOggGSo5IzSHcuAPnZpe5+JCIADzepjYIiCR9sPGPjzlUTFmkTEZGDYIDkqDyDAPcAsS5Q4mmTTd7p3wxqpQJbzyZh1/nbNu5gNeA0fyIichAMkByZrg7puMm7Gwd4YHRUGABg9oYzKCjS2qhj1YRDbERE5CAYIDmyINMLRhqa0jMctdydcDEpE7/sv2ajjlWTkkNs2hoe8BERUY3FAMmRBZue6m/I202D/+v9AABg7pbzSErPtUXPqofhEBskoOAeKT4nIqIahwGSI5MzSIlngCLz6x2N6FAPLep4IT23ENN+O15zN7ItufYRh9mIiMhOGCA5Mt8GgJMnUJQHJJ8320ylVGDe8NZw0Six52Iyvtt92YadtKJcBkhEROQYGCA5MqVSvy9bGXVIANA4wBMzBzQHAHzyVyxO3Eit5s5VA8MibYABEhER2Q0DJEdnQR2SbESHUDzWIgiFWgmv/HoMmXmF1dw5K5OH2FTOxt8TERHZGAMkR2fBTDaZQqHAh0NaIcTbBVfvZGPG76equXNWpNXqM0bedcQlM0hERGQnDJAcneGebFL5xdfebhrMG9EGSgWw5uhN/B5zs5o7aCX5GQCKX5+XHCBxPzYiIrIPBkiOrnYEoFQDeWlAerxFD+nYoBYm9wgHAMzZdA65BUXV2UPrkAu0VU6Ae21xnRkkIiKyE4cIkBYsWICwsDC4uLigU6dOOHjwYJntV65ciYiICLi4uKBly5bYtGmT0f1r1qxB79694efnB4VCgZiYmFLHyM3NxaRJk+Dn5wcPDw8MHToUiYmJ1nxZ1qHSAL5h4vqdixY/bNIjjRDk5YKE9FysOHS9evpmTXKBtrMX4OwprjNAIiIiO7F7gLRixQpMmzYNM2fOxNGjRxEZGYk+ffogKSnJZPt9+/Zh5MiRGD9+PI4dO4ZBgwZh0KBBOHVKX2+TlZWFbt264aOPPjL7vK+++ir++OMPrFy5Ert27UJ8fDyGDBli9ddnFX6NxeWdCxY/xFmtwqRHGgEAvt550fGzSHJBtothgMQibSIisg+7B0hz587FhAkTMG7cODRr1gwLFy6Em5sbFi9ebLL9F198gb59+2L69Olo2rQpZs+ejbZt22L+/Pm6Ns8++yxmzJiBXr16mTxGWloavv/+e8ydOxc9evRAu3btsGTJEuzbtw/79++vltdZJboA6VKFHjasQyhCvF2QmJ6H5QfjqqFjViQPsTl7iS+AGSQiIrIbuwZI+fn5OHLkiFEgo1Qq0atXL0RHR5t8THR0dKnAp0+fPmbbm3LkyBEUFBQYHSciIgL16tUze5y8vDykp6cbfdmMn8gEVWSIDRBZpJceEcHV1zsvOXYWSZdB8uYQGxER2Z1dA6Tk5GQUFRUhMDDQ6PbAwEAkJCSYfExCQkKF2ps7hpOTE3x8fCw+zpw5c+Dt7a37Cg0Ntfj5qsxPFFwj2fIhNtmw9qGo4+OKpIw8LDvgwFkkuQbJhTVIRERkf3YfYqsp3nzzTaSlpem+rl+3YeGzPMSWeg0ozK/QQ53USkwqziJ9s8uBs0i6Iu1qzCBlJgFZydY9JhER3ZPsGiD5+/tDpVKVmj2WmJiIoKAgk48JCgqqUHtzx8jPz0dqaqrFx3F2doaXl5fRl814BgFOHoCkBe5eNd2mMB/IzzJ515Pt6qKOjytuZ+Th5/3Xqq+fVWGySNuKAVJhPvB1FPBNV0DroEEiERE5DLsGSE5OTmjXrh22bdumu02r1WLbtm2Iiooy+ZioqCij9gCwZcsWs+1NadeuHTQajdFxYmNjERcXV6Hj2IxCUX4d0k+DgC8igaw7pe5yUisxuYfIIi3cdQk5+Q4YIJgs0rZinVfWbSA7GchMAHJSrXdcIiK6J9l9iG3atGn47rvvsHTpUpw9exYvvvgisrKyMG7cOADA6NGj8eabb+raT5kyBZs3b8Znn32Gc+fOYdasWTh8+DAmT56sa5OSkoKYmBicOXMGgAh+YmJidPVF3t7eGD9+PKZNm4YdO3bgyJEjGDduHKKiotC5c2cbvvoKKGuqf+Zt4NpeEQRc2lb6fogsUl1fVyRn5uOPE+YXnEzLLsDei8mQLFi126qMirQ9im+zYgbJcCPcnBTrHdccrZbDeURENZjdA6Thw4fj008/xYwZM9C6dWvExMRg8+bNukLsuLg43Lp1S9e+S5cuWLZsGRYtWoTIyEisWrUK69atQ4sWLXRt1q9fjzZt2qB///4AgBEjRqBNmzZYuHChrs3nn3+Oxx9/HEOHDsVDDz2EoKAgrFmzxkavuhJ0AZKJDFL8Uf31y7tMPlyjUmJkx3oAgFVHbph9mleWH8Oo/x3A+uOWrdptNbnVPMSWm6q/nnPXesc158/XgU8aAzeOVP9zERGR1ant3QEAmDx5slEGyNDOnTtL3fbUU0/hqaeeMnu8sWPHYuzYsWU+p4uLCxYsWIAFCxZUpKv2U9ZaSDcN3oSv7BJ7tikUpZoNaVsHn/4di4NXUnDtThbq+7kb3X8xKQO7zt8GACw/eB0DW9exWvfLlWc4xFYcIGkLgMI8QO1c9eMbDqtl2yCDdHUPAAm4FQPUbVf9z0dERFZl9wwSWaisDJJhgJR2HUi5bPIQwd6u6NbYHwCw2kQW6adofQH3/it3EJ+aU/n+VpThNH8nD/3t1soiGQ2xVXMGSZL0xfS2CMaIiMjqGCDVFHKRdmaifjgKEG/GN4uH2FxricsrpofZAOCp9mL9ptVHb0Kr1dcZZeYVYvXRmwAAfw8nSBKwLuam9fpfHsMibaVKHyRZq1DbaIitmoOWjASgsDi4zGYdEhFRTcQAqaZw8QbcA8R1wyzS3aviDV/lBLQXhe3m6pAAoHezQHi6qHEzNQf7L+tnvK09egOZeYVoWNsd0/s0AQCsOXrTdsXahkXagPXrkAwzSNWd1bl7xeC5Ss8qJCIix8cAqSbxL15R27AOSR5eC2oJNH5UXL/yj5hFZYKLRoUBkSEA9MXakiRhafHw2ujO9dGvZTCc1UpcTMrEqZs22FJFWwTkZxZ3sJoCJMMapOoeYkthgEREVNMxQKpJdGshGUz1jz8mLuu0E18ad5FRSjxl9jBPtqsLANh06hYycgsQffkOLiZlwt1JhaHt6sLTRYNHm4lZhGuOmZ/xZjWGw2jyGkjVmUGq7iE2ZpCIiGo8Bkg1ialCbTmDFNIWUDsB9buI78uoQ2oT6oNGtd2RW6DFppO38OM+kT0a0lYER+K6mMH2x/F4FBaZzkZZjRy8qF3EawCqIUBK1V+3ZQbJxMKdRETk+Bgg1SQlA6SiQiA+RlyvUzyVvGF3cVlGHZJCocCT7USx9v92X8GWs2Lrlmej6uvaPBheG37uTkjOzMfuC9VcaGxYoC3TBUhWGuKz5TR/w+1gmEEiIqqRGCDVJH4GNUiSBNw+J2ZLOXvpg6cGxQHStX1lbmw7uE0dKBXAhaRMFGklRDX0wwOBnrr7NSqlrlZp9dFqHmYz3IdNpttupDqG2FKtc0xzDIfYCnOA/OzqfT4iIrI6Bkg1iW8YoFCKguaMBIPhtdaAsvhHGdgCcPMDCrKM10cqIcjbBQ+G19Z9P6ZL/VJt5GG2LWcSkZ5bYK1XUVpuiRlsQDUPsVVjBik3XZ81UqjEJbNIREQ1DgOkmkTtBPgUBzJ3LuoDoDoGKzUrlUCDh8T1MuqQAGBY8ZpIwd4u6NU0sNT9Let4o3GAB/IKtdh8MqHK3Tcrz8QQm5OV92MzzCDlZ5aZXasSOXvk5g94FJ9TBkhERDUOA6SaxrAOSd6DrU6JrSwalF+HBAD9Wgbho6Et8f2YDlCrSv8qKBQKDG4jskjVOsxmuIq2zJoZpKIC/TICsuoq1JYLtGs1EJk8gItFEhHVQAyQahp5LaSEk0DiGXE9pK1xG7lQ+8YhID/L7KEUCgWGd6iHZiFeZtvItUoHrqTgQqIVN481VGaRthWe0zB75Fw8jFddAZKcQfINA9yKVzbndiNERDUOA6SaRl4L6czvgFQEeAQBXiHGbXwbAN71xGav16Kr9HQhPq664bel0VerdCyz8uQMkmENkhWLtOUAyckTcBd70VVbHZKcQfI1zCBxiI2IqKZhgFTTyENs8rBNnbaAQmHcRqEAGsp1SDur/JRju4YBAFYfuYm0HMuKtSVJwvWUbMu2KqnuDJI8a83FG3D1Lb6tmjNItRrogzEGSERENQ4DpJpGnuovq9PWdLtGPcTl6d/FVh5VENXQDxFBnsgpKMLKw9fLbJuRW4Af9l5Br7m78ODHOzB52THkF5az0GTJfdgAKw+xpYpLV5/qH/ZKuSouDTNIWaxBIiKqaRgg1TSewYDGTf99yQJtWZN+IluSFgdc3Falp1QoFBjbJQwA8MO+qyjSls4KXbqdibfXnkSn/27DrD/O4NJtUfu08eQtPP/TYeQWlBGk5ZpaB6kaAiQXn+rNIBXmA+nFxey1OMRGRFSTMUCqaZRKoFYj/fchbUy307gCrUeJ64e/r/LTDmxdBz5uGty4m4NtxStvy45cS0H/L3fjlwNxyM4vQuMAD7z3RHMsfKYtXDRK7Ii9jed+OISsvELTB5drhCowxFah7U9yDWqcXIszSNVRg5QaB0haEcB6BLJIm4ioBmOAVBPJhdq1GukzIqa0Gysuz/8l3ryrwNVJhREd6gEQWSTZ+cQMPPfDYeQWaNEhzBfL/tUJW159CGO6hKFvi2D8MK4j3J1U2HfpDkYvPmh6wcmyVtIuyDIaIswv1GLK8mNoM3sLoi9ZmJmRa5Bcfao3g2Q4g02hEGshAcwgERHVQAyQaqKApuKyboey2/mHFy8aKQFHf6zy0z4bVR8qpQL7Lt1BbEIGbqbmYPT3B5GWU4A29Xyw9LmO6NLYHwqDovHODf3w8786wctFjSPX7qLXZ7vwxPw9GLYwGs9+fwAv/HQE2RnFwYpRBslDf704i5RfqMWkZUfxe0w8MnIL8caaE2UP3cl0Q2zepbI6Fj3eUvIebL4NxGVNXAdJWyS2sSlLymUgtexaNCKimo4BUk3U6Xmg26tAj3fKb9t+vLg8+qNYMLEK6vi4ok9zMeX/y+0XMPr7A0hIz0XjAA8sHtMBbk5qk49rU88XyydGwc/dCUkZeThxIw0Hr6Zg94VkbD6doKtB+nhXAk7HFw+HqZ0BlbO4npeB/EItXvrlKLacSYSTWgk/dydcu5ONBTsumnzO9NwCbD6VgOz8QoMhNh9dBkmbfRdvrT2JZjM243+7L1fpvOgYLhIJGARIKYC2AkOCFXQuIR2Pzt2F38opoC9XTirweQtg5RjzbfKzgW+7A989Un2rkRMROQDT72jk2Fx9gV6zLGsb0V/Uw2QmAuc2As0HVempx3ZpgE0nE7DxxC0AYpuSH5/rCF93pzIf1yzECzunP4yTN9KQW1iEnHwtcguKkJyWCbd/8gAAy46n4uvjexBZ1xs9IgLxksYDmqI85Gen4qXfE7D1bBKc1Up8N7o9svIK8eIvR7Fw1yUMbB2CxgH6jXbvZuVj5Hf7cS4hA3V8XLHGPxGBgNE0/2s3bmBZrBh2/PTvWPRvFYxgb9cqnRujITZAn62SisRaT2UNh1bBBxvP4kJSJj77OxZD29aFSqko/0Gm3DwCZMSLIVlJKr18BCCGavPSgTwAiafMz6IkIqrhmEG616k0QJtnxfXDi6t8uA5hvmgWLIbCfNw0+PG5jgjxsSyw8HTRoEtjf/SICET/VsEY2q4unu+k3zD3wRYNoVYqcPxGGj7feh43s0X8/trPe3XB0fdjOuChB2qjb4sg9IgIQEGRhLfXntKtt5SanY9R/zuAcwliWO5mag5ir4mZZXclNxxLFr/yLoVp8HHT4IFAD+QWaPHx5tgqnZfcgiJIJTNIamf9sGE1FWofLs7EAUBieh7+uXC78gdLKc6kFeaar9FKv6m/fuNw5Z+LqDJSLgN7PgfyMstvS1RFDJDuB+3GAFCIzWuTTQ9JWUqhUGDGgGZ4MNwfP4zriPBAz/IfVBZ5FW2NG756piP2vdkDc4a0RP+WwchVisArLTUFLholFo/tgG7h/rp+vPdEc7holDhwJQWrjtxAWk4Bnv3+IM7cSoe/hxM2vNwNL3RvBB+FWHLgrU3X8crvVwEAtRRZ+GNyN3z6VCQAYO2xm4i5nlrh7mu1Ev63+zIi3/sL+beLAwy5BgkwqHmqnkLtz7eeBwC4aMSf8qrDVdgz747B74ZhIGQoPV5//SYDJLKxHf8Fts4CTv5m757QfYAB0v3Apx4Q3ltcP7Kkyofr3NAPP43vhNahPlU+VslVtAM8XTCyYz0sGNUWTeqJLVSebeOLFROj0LWxv9FDQ2u5YWqvBwAA/910FqO/P4CTN9NQy90JyyZ0Ros63njjsQhE+IhC7NuFLrgrieJvZ+Qh1FOBVnV9MLRtXQDA7A1nLFv5u9ittBw8u/gA/rPxLLwLU+CMPBRBif13DNapKq5DunnzOmb8fgq/HqzabEJDB6+kYO/FO1ArFfhihFjuYcuZRKRmV7I26M4l/XXDQMiQ4e03DlXueYgq6+4140uiasQA6X7RobhYO+YXoCDHvn0xZGqKfzFF8W29Grkj0kwwNr5bA0QEeeJudgGO3xDDZj+P74QHDDJbTgViuG1y//aY+0w3QFlcelc8jPR63yZw1ahw5NpdbCiurZL9c/42/rX0EF5dEYP/7b6MA5fvICO3ABtP3ELfebux9+IduGpUeDtKFJTHa/0w6odjWPTPJUiShDSFeA3z/tiPH6Ov4c01J/HHcTPBRwV9vkVkj55qH4o+zYPQNNgL+UVarK/s8VMMAqQ0M5kow8xSymUgi0sYkA1lJojLjFtltyOyAgZI94vGvQCvuiIouLTD3r3R062i7V36PgtW09aolPhgcEuolQp4uajx8/hOaBZiEGxJkm4W28OtwtG7RbC+WLq4LijQywUvPizWlvrwz3PILShC3J1sTPjxMEYvPoitZ5Ow9thN/GfjWQxftB8tZ/2NScuOIi2nAJF1vbHxlW4YWE9kbXI866FIK+G/m86h+yc7sfWaWBzTT5GBiCDxeqavOo5TN9Mqdbpk+y/fQfTlO9CoFJjcQ+zP91Q7kQlbWZlhtqIC40/llmSQAA6zke1IEpDBAIlshwHS/UKpAsIfFdev7rFvXwyZWkVbZuF2I+3q++LvVx/CzumPoEWdEoFWfqaYRQaIaf6AycUiJzzYEMHeLriZmoOxSw6i1+e7sOVMIlRKsc3KtEcfQO9mgahTXJCuVAAv92iMVS92QcPaHroZbOFNWmL2oBbQqBSIS8nGXYjX9Xx7H2x85UE89EBt5BZo8fxPR3AnM8+yc2SCnD0a1j5U16dBbepAo1Lg5M00nEtIL/cY0ZfuYM6fZ3E3K18ER5LBmlDlBUieweKSw2xkKzl3gaLi4WM5UCKqRpzmfz9p8KCoQbq629490StjiK0i+7E1rO1h+g55FW2lRmy/ApjcbsTVSYU3HovAlOUx2H9Z3N61sR9mDWheqhA9JSsfCsB4aYPiGWyKWg3wbOf6aF3XB7sv3sZTBa2BvRvhi3RAqcBXI9pg4II9uHonGy/9chQ//6sTNKqyP6dk5hVCpVDARaOEQqFA9KU7OHAlBRqVAi890ljXrpa7E3pGBGLz6QSsOnwD7zzezOwx/z6dgEnLjqKgSML+yylY8XAaXAwbmC3SLr696RPAwW85k41sxzAoSmcGiaofA6T7Sf1u4jLhpPg0Vk3r8lRIiSJtI7oAqfxsiPnjF2eoXH306/qY2W7kicgQ/H0mERcTMzG1Vzj6tggyWhVcVsvUmk93jaf4t6zrjZZ1vYEjYmFNeRabt5sG341uj0EL9uLAlRTM3nAG0/s0gUalhFqpgEqpwLU72Th4NQUHr6Tg0NUUXLuTrXsaV40K2uJC8uEd9Nkj2ZPt6mLz6QSsi7mJfz8WYTL4+ut0Aib9chSFWgkKBXD8eipW/r0LzwJie5TsZNMZpPws/arkzYoDpJtHxCKYSiajqZoZDqvlZ4gPTs5VnEVLVAYGSPcTz0DA/wEg+TxwbZ9YRNLeyswgFd9mQQbJLN02Iz7628xsIqtQKLDg6UoufFhymxHdc8mraeuLmcMDPfH58NaY+NMR/Bh9DT9Gm5+RE6pIxDeaZZhXOBSxUj3kFG+N4umsxksPNy7V/uEmteHv4YzkzDzsOJeE3s2DjO7ffCoBk5eJ4OiJyBCM6VIfz35/UMxgUwPasG5QnlknMkUlF4uUP7U7eQChncWmvHnpuHHxOJZfccMjEbXRrn4ti04XUYVlGm+SjYwEBkhUrRgg3W/CuokA6eoexwiQdNuAVK5Iu1zyEJvh8a29YW1uuj4AqlUiQHI3vWFt7+ZBePfxZvh48znkFRpvQ+KkUiIy1BsdwmphROo21Dt3CI82DcDdAUuQk1+EnIIi+Hs4wc/DuVRX1ColhrStg0X/XMbKIzfQu3kQirQSsvILsSv2Nl5dEYNCrYSBrUPw2VORUKvEyuT48T8AgN/vNsRgACjIxu/7z2D/rSIkpOViYOs6GOB1AyoA8AoBVGpIIW2guLYX3/y8HL/kP4T5Oy4iqqEfXu7RGFGN/Exm34gqrWRhdsYtsd8kUTVhgHS/CXtQrKjtKHVIuiLtagqQDIfYZLoAyUqrW8vDa27+pT/RyhkkE9Phx3drgLFdwlBQpEWhVkJhkRb5RVp4uWjgolGJRiuSAADqa7tR200FeJYOikp6ql1dLPrnMracSUTEu38it8A4ADMMjgCga2N/5HjcAbKBn6964mEnT/gqMvD1+n8QK9UDAOyIvY3zvgfxOgDJKwS303Nx6G5d9AfQXBuLBwL74UpyFqKLZ9e1reeD4R1C4ePmBHcnNdydVfBy1aChv7tDBE45+UX49O9Y3M3Kx6uPPoDQWm7lP4jsq2RhNgu1qZoxQLrfhMl1SKfEEJObnYdErFSkbZZuiM1UBim18sc1lHBKXPqVHvLSBUh5aWIqvUpjdLdKqYBKqTJ/7NTihSXz0kW9T71O5XYnPNATXRv7Ye/FO0bBkUalwPAOoXjviRbG+7UV5MI1W3w6vyIF45ZUC76KDDwUmI+HmzSEs0qJH/ZdFXVJGmDrTTWmz/sHHXND0N8J6ONzEyOmPIRb6blYtOsSfj10HUfjUnE0LrVU39rX98W8Ea1R19d0QHIuIR3pOYVwVivhVPzl6+Zkuu4LYouXeVsvYMOJeDzcpDaGtQ9FyzreZQZhV5Kz8OLPR3Tb0Ww8eQsv92iMCQ81hLO6jJ8F2VfJgMjcTEsiK2GAdL/xCAD8mwDJsaIOqenj9u2PRUXaVsggWVCDVGlx+8Rlvc6l73PxARRKQNKK5/MMrNixUw1W3r60zaIACQAWj+2Aa3ey4apRwd1ZDTcnFZzVStOBw90rACTA2RtLnuuLkL9/A25cw9sPegPtmgIAxj/YEFd+WAEkAmeyPJFaWIDsoDZAKuCXdREoyEQdH0+8N7AFJvVojCV7r+LUzTRk5RUiO78IWfmFSEzPw+Frd9Hvi934cGgr9GsZrOvCsbi7+OSvWOy7VDrTplAA/VsG45We4UYLgB6/nor/W3kcF5PEvlw/74/Dz/vjEBHkiWHtQ9GvZTCCvI3m5mHTyVt4fdUJZOYVwt/DCQ39PXDwago+/fs81hy9ifcHttBtZ2NNtzPykJyZhyaBnlBWdjPh+50cINVqKBYpNZNBSs7Mw58nb2FAZAh83MreRNsWcguK8Mlfsajl7oQXuzfiz78GYYB0PwrrJgKkq3tKB0g3jwInV4k3+0Y9AGcz0+etxaIi7SrMYpOzREZDbPI0fyvVIF0rDpDqdy19n1Ipni87WdQhVSRAyk037uOl7cAjb1n0UGe1yiiYKJO8B5tfQ0TW8wWCwoAbANL0U/29XTVo7Z0NJAJNH4jAv+tFYHy3BsBXoUDadSD+GNDgIQBiu5h/940o9TRxd7LxyvJjiLmeipd+OYqRHUMxsmM9zN9+EX+fEQW4Tiol6vi6Ir9Qi7xCLfILi5CeW4gNJ25h48lb6NcyGC92b4TNpxLwza5LKNJK8Pdwxss9GuPItbvYfDoB5xIy8P6GM3h/wxnU9nRG8xAvtAjxxt3sfPxyQAScHcNq4aun2yDA0xnrj8dj9oazuJychWe+PwA3JxV8XDXwdnOCj6sG/p7OaODnhga13dHA3wMN/Nzh6aKGQgGjgLNIKyGvsAj5hVpk5xfhxI007L98B/suJeN8ogjiujTywydPRZaafWhKbkERVhy6jqz8QozqVB/erppyH+OoriZn4Z8LtxGfmouEtBzEp+UiOSMPT7ava3KygUlyQBTSpjhAKj3VPy27ACMX7ceFpEz8cfwWfp3Y2ThbamP5hVpMXnYUW8+KofKbqTn4z8AWDJJqCAZI96MGDwKHvy+9YGRBDvDbaPGGt38BoHICGnQHmjwGNB9cPcNxZWWQnIqDs7yM0jOqLD5+qrg0OcRmhQxSRoL4Zw0FENrRdBs3v+IAKblix067Li5VzkBRnhhiq47lGeQ92OQhQi+xB16pIYziNZB6d24LPCBWHkfd9qKfNw7pAiRz6vm5YeULUfh8y3l8s+sSfj14Hb8eFK9RqQCGtq2LKb3CSw2/nYlPx5fbLmDz6QRsPHELGw22g3kiMgTvPdEcvu5OGNMlDGnZBVh//CZWHbmBkzfTcDsjDztjb2Nn7G3dY57v3hDTezfR1WANbF0Hj0QEYO7f5/HT/mvIzi9Cdn4R4tNyyz11CgWgUiggQQRIZdGoFNh36Q76fv4PZj7RHEPb1jGZ0csv1GLF4euYv/0CEtPFYqLf7rqMiQ81xLiuYXBzqjn/trVaCT/su4oPN59DfonJCADw8eZY1HJzwoiO9co+kCTptxkJaQOcWl0qQMotKMKEHw/jQnFG8eDVFHyz8yIm97CskDsnvwg37mYjI68QWXmFyMwtRG5hETo28LMooC2psEiLV1fEYOvZJDiplSgo0mLZgTgoFcDsgS2qpRbvyLUULPrnMi7dzsIrPcMxoFWwQ9T81VQ15y+NrEdeDynxpHEd0v5vxJudm7/IHN29ClzcIr72fwNMPlS5IKUsugxSGUXaklYEb06VKKQta4gt527lAy+ZnD0KamGcpTJkYqq/ReThtYCmQEG2mH145R+g2cBKddUseQ+2WsVBj1cdcVlysUg5YJIDKACo2wE4vdbiBSM1KiVe7xuBro398eqKGCRl5KFv8yC81ucBNA4wnfFqFuKFhc+2w9lbIlD681QCark74YNBLfCYwTAdINaZejYqDM9GhSEnvwhnE9Jx+mYaTsenIykjD093rIdezUpn8bxcNJj1RHO81qcJkjPykJZTgNScAqRm5yMhLRdX72Th8u0sXEnOQlKGfgV0SQIKTWxwrFIqEObnhi6N/NGlkR86NfRDWk4B/u+3GByNS8VrK4/j79MJmNIrHAooigv1tbiYlImvtl/Ejbtiv8Q6Pq5wdVLhYlImPvkrFkv2XsEL3RuhQ1gtuDur4OYkhk89nNW6gM/Q9ZRs/HEiHutj4pGcmYdxXRvgXw82sLjWqrBIC60EOKlLHzuvsAjH4lKx/7L4ve4ZEYgWdbx0b8iJ6bl4beVx7L4gPhi0q++LlnW8EeTtgmBvF5y4kYbv91zB2+tOIcTHFQ89UNt8RwxX0Q5uLS4NAqQirYRpv8Xg4NUUeDqrMbpLfSzYcQmfb72Aro390aZe6Q8VKVn5OHxVrDV26OpdnLqZhkITQa6zWonJjzTGxO6W16hptRJeX3UCG0/egkalwKJn2+FOZj5eW3UcP++Pg1KhwHtPNIdCoUCRVsLBKynYdPIWnNRKTHv0Abg7W/7WrNVK2Ho2EYv+uYzD1/QZ51d+PYbfDl3H+wObm19Il8rEAOl+5FEbqB0B3D4HXNsLNB0AZCUDu+eK+/t8ALQaDtyOBWI3Abs+Au5cEAtMBreyXj8K84DC4k/ppobYnNwBKABIIotUmQCprGn+Rfli8cOqDCOWNbwm09U8VTJA8qkngpbk82KYzZIA6Xas+Gr2RPltdRkkOUAykUEqyNVnwEoGSIDIIFUg2Oza2B87XnsYdzLzUc/Psp9r02AvfPNMO9y4mw1vVw08XcoecnJ1UqFtPV+0NfHmaI6Hsxoe5bw5ZeUVIq9QC60kQStJkOMjXWG5SmkyWKnl7oSVL3TBt/9cwudbzuPvM4m6ocWSans6Y/IjjTGiYyjUSiXWH7+Jz7dcQFxKNv6z8Wyp9iqlAiE+LqhXyw31armhtocz9l26Y/SGCQCf/BWL1UdulFtrdfl2Jn7afw2rjtxAVl4hQnxcEebnjvp+bqjl7oRjcak4dDXFaImKeVsvoI6PK3o3D0RDf3d8tuU8UrML4KxW4p3+TfFM5/pG2YwnIkNwNysfa47dxEu/HMXKF6LQNNjE/wFAFwzlanww+Y/b+B8AbXoCpCItlEoFZm84g00nE+CkUuLb0e0Q1dAP1+5kY8OJW5iyPAabpjyo+7lm5RXi079jsXTfVZSMh7xc1PBy1eh+D7Lyi3D2Vjo+23Ieq4/ewHsDW6B7WYEcAEmS8Pa6k1hz7CZUSgXmP90WDzcJEPdB7MX4Y/Q15BVo4easwsYTt4yC7h3nkvDV023QPMTEh8YSYhMyMGnZUV0dnpNKicFt6iDQyxkL/7mMPReT0XfebjzfvSEmPdJYPzuWLMIA6X4V1k0ESFf3iABp5xyxOm1wJNBymHijC4gQXzcOA7EbRbBkzQAp16C2yNQQm0Ihbs9LEwFSRQucAdPT/DVu+mGrnLvWCZDqRZlvo1sLqYJDeoYBUoPuwIFvgIvbyw9ECnKBpU+IIYl/bQfqtiv7eUoFSHIGySBAkj+tq12Nh/iCWoltXLJui/761rf45bk7qyv0SVlmbgacrYh+V+6xKqUCLz3cGA8/EIAZv5/CpduZUKuU0CgV0KiVcNWoMKhNHYyJCoOrk/7NbHCbuni8VQhWHbmBXw5cQ0pmPrILipCdV4T8Ii2KtBKup+TgekoO9kIfiCsUQFRDPwxsHQKlQoGPNsfqaq36twzGsA6hRoHdzdQc/Lz/mi7rI7txNwc37uZgz0Xj1+Pv4YSoRv4oLNJiZ+xt3EzNwZK9V3X3Nw/xwhcjWpvMDioUCnw4tBXi03Kw/3IKnvvhENZN6opAL5dSbTOTb8ADwJU8T/yToQRcAKU2Hz3/uxZ16oTin/NiCPWzYZHo0kj8vX0wuCWOxaUiLiUbM38/jc+GRWJHbBLeWXsKN1NFhq5xgAc6hNVCxwa+6BBWC3V8XI2COEmSsP54PD7YeBZX72RjzOKDeKRJbUQ18kN4oCceCPREiLcL7mTl48DlFN0m0heTMqFUAPOGt0Yfg8Van2xXF1pJwr9Xn8CKw9d1t3u7avBos0DsvZiMy8lZGPz1PrxrIqg0dCzuLsYuOYS0nAJ4uqjxTOf6GNclDAHF529ou7qYuf40dsbexlfbL+LPUwn49tl2aFSN2aSkjFx8sjkWCem5mDmgORoH1OzMFQOk+1XYg8Ch/wFXdgO3zwOHl4jbe39QetuIiH4iQDq3EXj4Dev1QR5ec/IQm+ma4uxZHCBVslDbVA2SQiHe5DMTRB2ST2jljp2dAiSdEdfrdzHfrtJDbMUrbPvUB8K6ikAkLU7UPMnBjCkxv+jrNW4eKTtAysvUt61VIoOUnyECTBdv4+E1w3/YGhcgqCUQf1RkkSoQINnUuU3A8V+B7v8Ww6F21izEC6teLON3xgSNSomRHethZIl6nfxCLVKy8nH9bjbi7mQjLiUbt9Jy8ECgJwZEhhgFHH1aBGHu3+fxY/RVbDwpCt9NUSiAHk0CMLpLGJoGeeJaSjauJmfh2p1sJGXkonmIN7o08kPjAA/dG3huQRH+OX8bf51OxMmbqejVNBBTez1gcnhO5qRW4ttn2mPIN3tx6XYWxiw+iEmPNEaXRn66hVCPxd3FprW78TaAZPji+R5NkXnAGx5FadBkJ+Gf8+L1vdO/KQZE6rOb3q4afD68NUYsisbqozdw4242DlwRH1Lq+rrig8Ety80GKRQKDGxdBz0iAjBv6wX8sO8qdsTexg6DmjZXjUq3wr3udamU+O+Qlkb9kQ1rHwqlQoEvtp1H+/q1MCAyGN0a14aTWom7Wfl4beVxbDuXhHd/P419l+7gvYHNEeBpHDTuvZiMCT8eRnZ+EdrU88GSsR1Kzdir7+eOJWM74K/TCZjx+2lcTMrEoPl78fnw1iaHmfMLtcXLjlS85KBIK2HZgWv4+K9YZOQWAgAGfLUH7w1sjqfa1S0V5EmShLxCrcNntBgg3a/kIaGk08CGqWIn9yb9RAF3SQ/0FVPVE04AqdcrH1CUpFsk0kxaHaj6VH/dEJuP8e1utURgUJWp/tcPAJAAv3CxfII5usUiK1ikbZhBcnIXMwuv7gYubjMfIBUVAHvn6b9POl32c8j1R25++iybk7s4X7mpIjAqGSCVVLeDCJBuHgVaPmnZa7OVvAxg8xvAsZ/F9xm3gPFbrF9LZ0dOaiWCvF0Q5O2CDmFlT6SQa62GtQ/F3C3nceNuNgqKFyjNL9RCrVTi8VbBeKZzfaPFMwO8yj+2i0aF3s2DSm1vUx5vNw1+GNcRg7/ei3MJGXj512MAxJBq02BPrI+Jx0RFIqABWkQ0wYO9mwCX6gGJJ/Fp3wCsSW+AMH83jI4KK3Xsjg1qYdIjjfHV9os4cCUFSoVYoPXVRx+oULG7p4sG7z7eDMM7hGLTyVu4kJiJ84kZuJKcpQuOIoI80bmhHzo39EOnBrWMN7Mu4cl2dfFku7qlbvd1d8L/xrTH93uu4KPN5/DnqQT8fSYRPSMCMKJjKB4Kr41t55Lw8rJjyC/Soltjf3z7bDuzmViFQoG+LYLRtr4vJv1yFIeu3sW/fjyMqb3C8UqPcOQXabH9XBLWx8Rje2wSPJxFJurZzvVR24JFaQHg1M00vL32JI7fEP/PW9bxhoezGtGX7+D1VSew50IyPhjcAh7Oapy6mY4NJ+Ox8cQt3LibAzcnFQI8nRHg6YLaXs5oWccbvZoGoFFtD4coLmeAdL/yqA3UbgrcPivqkBQqoNd7ptu6+wOhnYC4aCD2T6DTROv0oawCbVlVAqTCPKBQpNJLFVBbY7uRa3vFZf0yhteAqhdp+xRnDBr1EAHSpe3mfwanVhuvnZR4puznKDmDTeZVpzhAuimKxOWCbXn4zVBgc3F5+1zZz2Vr1/YBa18ozsQpRJbyxiFxe1gZNWP3gWYhXvjfmPb27oZOaC03rHqhC37afw17LybjXEIGzt5Kx9lb4n9Ex8ACIA3wDSz+W/AMAhJPooVnFlp0b1bmsV/pGY4ryVm4k5mPN/tFoFVdn0r384HiYTVZfqEWcSnZ8HN3KjMgqgiFQoF/PdgQHcJq4b0/TuNoXKquXi3A0xl3svJRpJXQt3kQvhjZ2qLC8QBPF/zyr874z8Yz+DH6GuZtvYAtZxJx7U42MvMKde1SCvPx5bYLWLjzEga2DsGozvWhlSTcvJuDm6k5uHk3B4npuUjOzMPtzDwkZ+Qb7Q85vW8TjOokssgLd13C3C3nsf54PI7G3dVtxG0oO78IV+9k42rx7RtP3MKHf55DfT839IwIRM+mAejYoJbJTbdtgQHS/azBgyJAAoD2zwG1HzDftkm/4gBpo/UCJN0Ms2rKIMnHB0pnqawx1f9atLgsq0AbELMCgYoFSIZrIMkZu8Y9gW3viSCpMB9Ql/iHrNXqC+1bDgNO/gYknS27ZqnkDDaZV4jIPsmZo7IySLWbiMvk85a/vuq28yNRVwcJ8K4HDF4InFwJHFkiMmz3eYDkiML83fHu4yLYSc7Mw75Ld3D02l20DvVB9/O/AmkAPIqzU57FlxZsN6JRKTG/sptQl8NJray2OpvIUB+seakrzidmYMWh61hz9IaumPvJdnXx4ZCWJicDlNXX9we2QMs63nh73SmcjhfBZx0fVwyIDMHjrYJx7U42/rfnMo7FpWLlkRtYeeRGucdVKIDHW4Xg3f5NdfVPADDpkcbo3LAWXvk1Rjcr00WjRM+IQDzeKhidi2d2JmXkISkjF/GpOdh78Q6iL93BtTvZWLz3ChbvvYJJjzTC9D6l11WzBYcIkBYsWIBPPvkECQkJiIyMxFdffYWOHc2sKQNg5cqVePfdd3H16lWEh4fjo48+Qr9+/XT3S5KEmTNn4rvvvkNqaiq6du2Kb775BuHh+vUwzp8/j+nTp2Pv3r3Iz89Hq1atMHv2bDzyyCPV+lodStiDwMFFgJNn+bVFEf2BLe+Kou6cVPNT2mVJZ4H9X4sZV417lb4/IxGI/lpclzMsplgjQHL2Ll3jVNUMUn4WcCtGXC+rQBuo3Mrd8hpIrrX05yCwpQi2spNFJqTkm3zsRrEAqLM38NhHYvp9foY4lo9x3YqOLoPU0Ph2ORCSF4vUZZBMBEj+D+j7nJdZ/YuLlifxNLDzv+J662eAvnNEEO4ZBBxdClz4W2wP4wC1SGSav4cznogMwRNyDc+R4tl+cmBkbq2ue9ADgZ549/FmeL1vE2w/m4Ss/CIMaVOn0otNPtU+FM1CvPDX6UQ8FO6PtvV8dcdqUccb/VsF48i1u1i85wq2n0uCr5sGdXxdUcfbGY/l/w0vN2ekNR0Jfw9n+Hs4o7ans9khvnb1a2HTlAfxU/RV1PNzR8+IAKO2vu5OCPN3130/8aFGyMwrxJ4Lt7H1bBJ2nEvCI03KKF+oZnYPkFasWIFp06Zh4cKF6NSpE+bNm4c+ffogNjYWAQGlT8y+ffswcuRIzJkzB48//jiWLVuGQYMG4ejRo2jRQvzD+/jjj/Hll19i6dKlaNCgAd5991306dMHZ86cgYuLiHAff/xxhIeHY/v27XB1dcW8efPw+OOP49KlSwgKqtgYeo0V0R94+E1R2+JufsovAFHzIm9RcnFr2bUm16KBX4eLAOXoj0CLJ8WblFynE3dALEiZmSCCsy6vmD+WLkCqRJG2qSn+Ml3QUskA6cYhQFsIeNU1H3zonkseYku2fCp8yeE1QBTPN3pEZEIubTcOkCQJ2P2ZuN5ponh9/g+ILFDiGQsCJBNDbIA+MNJlkEwMsbnVAtxri5lsyeeBOtXzad1iJ1eJyyb9gEEL9Lf7NQKaPgGcWQfs/QIY+p1dumcXOaliS5mQNvbuSeXImSLP4nWvKpBBulc4q1Wl1v2qrOYh3mUuI9Cuvi/a1TeYrZp1B1g7EbiyVXz/6JOAj2V98XbVWLxYJyCW2ujbIhh9WwRDW87iq9XNPgN7BubOnYsJEyZg3LhxaNasGRYuXAg3NzcsXrzYZPsvvvgCffv2xfTp09G0aVPMnj0bbdu2xfz58wGI7NG8efPwzjvvYODAgWjVqhV+/PFHxMfHY926dQCA5ORkXLhwAW+88QZatWqF8PBwfPjhh8jOzsapU6ds9dLtT6kSmaOGD1vWvslj4jJ2k/k25zYBPw0SwVGthqK4+9QqYH574MgPwIFFwA/9RHBUOwKYuLPsGh7ddiNVyCC5mvhHYGkGqSAXOPFb6U+quvWPupQf8MgBUmGuWPDREnflGWwlAptGPcTlpe3Gt1/eIbb70LgBnV4UtwUW12aUVagtbzNScojNu8RU/7KG2AARPAP2H2aTJH2A1PKp0vd3myouT63Wn+P7wfrJwKKHxczVmsZwFW15qQ/P4t9DE9uNVNqV3WI2r4mFP+9rcfuBhd3EB2OZ/P+vmimVCrtuy2LXACk/Px9HjhxBr176IRilUolevXohOjra5GOio6ON2gNAnz59dO2vXLmChIQEozbe3t7o1KmTro2fnx+aNGmCH3/8EVlZWSgsLMS3336LgIAAtGtnekp0Xl4e0tPTjb7uOxH9xeWFLaIGpqQjS4EVo0Qg8MBjwAt7gQnbxdpKuWnAH1OAP6eLzEvzwcC/tgH+5ezDVKUhtlRxWXIGG2CwH1sZw165acDPQ4A1E4DveuizLYBBgFTO8BogZoWpimeEWFqHZCqDBAANi4eA448CCzoBf74BnP8b+OdTcXu7sYB7cUAWUBwgmSvUzrmrf/21zAyxpceLmXGZxUMcpjJIgL5+7XZsmS+r2l0/KJZCcPIQsy9LCmkjPhBIRUD0gtL3WyI/27HeRM/8DmydJWrQzInbLy7/erv8wn1HY7iKtoccIMkZJCsFSJIErB4vZvTK58qWJAn4+11g03TH+d3SaoE984Al/YCMeDFbt+kAcV/JbaruUXYNkJKTk1FUVITAQOM1GQIDA5GQYDp1mpCQUGZ7+bKsNgqFAlu3bsWxY8fg6ekJFxcXzJ07F5s3b4avr+lVd+fMmQNvb2/dV2iolaa61yR12gPuAWK465rBH4i2CNj5IfDHK2JbkDbPAMN/Fitfh7QRixX2mQNo3MVsuT7/BZ5cYlmtSlUCJDk7ZGqIrbwMUkYisKS/fqZaxi3gh8dFkFSYL4bYgPILtAGRYXKvYKG24RpIhryCxfmFQswaO/ANsOwp0U+lBoiarG8rzy5LMvOGeOeyuPQMLv2zMFwsMiMBgCT25jNXL1a7uIjS3hmkU8XZo4jHza+83nWquDz6Y8WXXrh+EPikkXgjdQRFBcDvk4E9nxv/TRrKShbDn4D48LLqObF1T00hB0FufoC6+IOGPNSWmQQUFZp+XEWk39R/CLi6u+rHq6ire4B9X4qaUEeZDXroO2DrTPFhouUwke1v86y4T/6/eI+z+xCbPUiShEmTJiEgIAC7d+/GwYMHMWjQIAwYMAC3bpn+RPLmm28iLS1N93X9+nWT7e5pSiXQpPhT+bniYbaUKyJw2DlHfN9tGvDEfEBlUN6mUgNRLwFTTwKvHAOiJlm+Do38xp2fWfH+mlpFW1ZW4XTKZWBxb7FXnXsA8MwaEQBkxIvXemq1eKNx89MXKJdHfr6sCmaQTC28OHAB8Ppl4KmlQNsxYpYWAHScqB8aA/QZpOTzpjN+5obXAH0GKS9N1J0B4k2p5CKiMvk8mPvnrtUC5/8yXj29Kkx9yi4qBE6tEdfLqpFr+LDYz6swR7whWUpbBGyYJoZJT/wmhl/t7fpBfX1ewknTbZKKZ6p6BIkMzO2zIpNUU8gBkjyDDRA1bwoVAAnISqr6c9w6ob9uj+zIns/1168fsP3zmyKXUnSbBgxZJP4Xh3YCoBD/I9OtOLzpoOwaIPn7+0OlUiEx0Xg/osTERLOF0kFBQWW2ly/LarN9+3Zs2LABy5cvR9euXdG2bVt8/fXXcHV1xdKlS00+r7OzM7y8vIy+7ktNiofZYjeJ8fpvugJx+0R26ImvgF4zzQc/7n4VX2m52ofYSmSQEk4C3/cRG/X6hgHj/xLT68f8IepsMuKBdS+ItvWiLA/0KroWkrkhNt3xagHNBwFPfAlMPQG8fgXo/R/jNt51xYw2baHYS6+kFDMz2ABx3uX6L3kjWnPDa4B+qn/KFdPB2MnfgGXDRH1aUYH545Qn8zYwryXw6wgRsBi6slMUwrv5lV1Xp1Doa5EOLrJ8duHhxSJoBkSQ5Aifog3rQhLN1JrJAVJIG7HcAQAc/h44+0f19s1aMkrMYANEoC5/b4036lvH9devHzT9O1xd4mOAS9sMnv+QdY4rSeJvLS9D/I5XZOhOqxULvwJAiyH6/3OuPmLlfMAxfv+rmV0DJCcnJ7Rr1w7btul/ObRaLbZt24aoKNO1HVFRUUbtAWDLli269g0aNEBQUJBRm/T0dBw4cEDXJjtbFMoqS3waViqV0JY1jk9Aw+6iEDj9phhmKMgSw0wv7gXajrb+88lDYckXzA8LZN0Bvu0uhhoM6dZZ8jF/3Jy7+n8cuenAT0PEJ9LAlsBzf+lrczwCioMkg4yRJcNrsooESLlp+uDO24KhXIVCBEwlszsKhVjkETBdd2JuBptMDojk4URzBdqAyC45eYp0vBx4GZKLym8eEZsfV9aRJSJ4PL8Z2DPX+L6Tq8Vl88GAquyNbNH0CbFQas5dYOP/lf+8WcnA9tniulwHc2FLxfpuqDBf/K6tGl+1mhOjAMnMBBN5rbOApqLIv+sU8f3vk0UmoCBHLKpaVFB2HZO9yBkkeVhNZs06JMMAqTBHTHiwFXnle6/ilbWrmkHa9QnwQTDwni8w2x+YUxf4uIGopbRU8nmRmdS4ib8TQ2HdxCUDpOo3bdo0fPfdd1i6dCnOnj2LF198EVlZWRg3bhwAYPTo0XjzzTd17adMmYLNmzfjs88+w7lz5zBr1iwcPnwYkyeLN0eFQoGpU6fiP//5D9avX4+TJ09i9OjRCAkJwaBBgwCIIMvX1xdjxozB8ePHdWsiXblyBf3797f5OahRNK4iowKIwuM+/wXGbABqNaie56sXJf5xZCaYL6rdOlOsSXTsJ+PZZmVN85cDJKlIP0Sx+1MRHPk1BsZuMP7ECogZNHImSakGwntb/joqslhkavHwrZtf1dcUMjeTTVukH0qQh+JKkgMiSwIkhaLsQm15UU1ALEdg+L2ligpEFke2Yw5w44i4XpCjz4i0sGC7E6UKGPS1+DmeXqOf+WbOtvdF4BrYUqwxBQAXqxAgXd4psganVolsZWVkJIrtf2RJ50zX4yQZBEgA8Mg7IpuUmwp82Qb4IAj4T4B4M/2wnsigOBLdFP8S+4fJAZM1AiT5PMrHtNWb/51LosgeAAZ/U3zbhcpvgaQtAqLnF8+WLRF4n/3D8uyt/Dcf0ta4XALQfzC00Uw2e7J7gDR8+HB8+umnmDFjBlq3bo2YmBhs3rxZV2QdFxdnVBfUpUsXLFu2DIsWLUJkZCRWrVqFdevW6dZAAoDXX38dL7/8MiZOnIgOHTogMzMTmzdv1q2B5O/vj82bNyMzMxM9evRA+/btsWfPHvz++++IjIy07QmoiXrOEsXAL+wW9UTm6lKsQeMKPFq8BcruuaXXPYk7IAIj2fnN+utyFsZUDZLGRXw6AsQ/o5QrwP7if1B9/mt+IUzPIPG6Xz1d/gw8QxXJIJU3vFYR5mayxUWLoNPZG2jwkOnHygGRnIkra4gNMD/VP+2GmFmmUImFQyUtsGai8Urnlji3UbwZutcWGSCpCFjzL7E45YW/xaKY3qHFdRIWqNMWeOh1cX3jNPOLDt48Kgq6AaDfJ0CjniKwunPReGZjRZxeq79e2ZoXOSsX1FLM2ivK09eVySRJX6QvB0hqJ2Do96K+rqT8DLEchyPRTfEvmUGyUoCUlaxf76v9eHFpqwBp7xfi7yG8j/g7lLO5Nyo5zHYrRvzfc/YCpp0D/n0NeOuW+DsvzLW8APxm8bC6qY2u5YVxb5+r+CSHGsbuARIATJ48GdeuXUNeXh4OHDiATp30/+B27tyJH374waj9U089hdjYWOTl5eHUqVNGq2gDIov0/vvvIyEhAbm5udi6dSseeMC4mLZ9+/b466+/cOfOHaSnpyM6OhqPPfZYtb3Ge4p/Y6DPB/q6k+rWYqjYELUgC9g2W397UaF+eEQeRos1DJDkITYzC6IZ1iFtmSGmEjfqUX5mSO1cOrtUHl1RuAX/UKwZIJmbyXaqeDiq6QD9zKCSSgZEZWWQAP3vQ8l/wvK06eBWooDfp74ImDZNL/t4JR0sXtix3VhRe+VVRwwRbX5DLJ4JiN+VigTsD04Tn5Jz04DfJ5Ue7tJqi/spAa2Gi2UdXLz0bxKGQ1yWKswTwZ6ssgGS/NzhvQ0C4RLDbBkJ4rUplGKatsyvEfB/54C34oE3bwBvxAFP/ybuO7+5dH2XPekySCX+5ryCje+vLHl4rVYj4IE+4nrcfuvMjitL+i3g+K/ierdXxaUc3Fc2i3dph7gMe1CcH1ef4tnExR/8LR06lOsO63YofZ+7n/737R4fZnOIAImoTAoF0PdDcT3mF/0f+aH/iaJZFx9geHEW6fJOsQ0IYDDE5mP6uPIw27kNwNn14k2k9wfVs9O7LoNkQercqhmk4qxB2nV9wFhUqE/rtxhi/rElA6LyMki6AKlEBimueDitXnFwMeQ7ca5PrABOrCz/NQAiA3Ztj8hCtRsnfnaDvwWgEBlEOeAoa/aaKSqNmKGjdhEZGcOFFDOTgD2fiU/TTh7Ao+/r75OD6At/V+z5APEmllcctAAiQKpoHZK2SJ9BatxLv21KyQBJDoxrNRJZU0NKlVijy9lTfIho1ENcZt+pfAajOsgBkEfJIW8rZZDkACk4UnygcPEWs2YTjpf9uKra/7X4UBbaWb+emhyQVLYO6fJOcdmoxJZZ8grqlgRIeZn635s6ZjY0vk+G2RggUc1Qt734BA8J2Pym+Ke54wNxX88Z4hOTT30xzCD/kyhrmj8AuBUHSHu/EJftxulrdqxNXgcp41b5n0zNrYFUGa6++sBGrkW5sku8Cbr5AQ26m3+sd52yvy9JLmC/c8E4A3HNIEACgHqdjIe25JqrssiBS0R/fT8aPKifjSZpxVIMgZXYX80/XB/8/P2uGP77ojXwaTiwvXhmYPd/G2cw5ADpym6xcGRFnFknLls/LdauSr9R8TqkWzFikU9nL/GmKmcKS85kk7N5ARZs9qnSiKEeoOzV8m1JksxnkKw1i00XILUSQWO9LuL7q9WYHcm5q6+ne3Ca/nY5g3TzaMUzWPlZ+mxtwyoESLdixN+TV119lq6k+jY4Rw6AARLVHD1nAmpXkZFYOkAUV4e0EUMuCoXxViharQVDbMUBkrZQjNE/8lb19V3+tJtyGVjQATj6k/mpxNbMIAEGwy/Fb57yWkHNBpYuwDRkmDFSqkXtT1l8w0ThfmGu/jXk3NV/Gq3XWd/2oenijT0vXaywXlYGJTcNOL5cXO9YYibOw2+JNY0AoNWwymf/OkwQwWJhjshs3b0ibq/dFHjwNaDzi8btazcR9U5FeRVbWNBweK31M0Cd4hqPig6zXSyepduwuwhs5MCwZICkqz+yMPCX/4bOOUiAlJ0CaIsLi+XZgzJrbTciF2gHFw9DyXscVtfwUWGeWE8rP1P8XAyH9GtHiKC3IKvsLYJMubZPnCvvUDGEakgOkBJOiecvi5w9NFV/JJMzSImnKr/hdw3AAIlqDu86+oxB8nkACqD/XPGpD9D/cz//lxjCkGdxmB1iq6W/3v318jfsrQq/xmL4zs1PBEnrJwNftRV1NSWnVls7QNLNZDtT/AYtz/YaWvbjDIfYPIP159kcpUpfZCoXal8/CEASQzweBkXBKjUw6BsRUF3api+CNuX4cvGGUTtCZAoNqZ3EQp4DFwBRL5fdvzL7rhRDf21Hi+zWqNWiwHXSfqDnu6WXDVAogPBHxfWKTPe/tF0EhZ7BIltQ2Tdjuf6oUfGMUjkASr9pPIybJGeQSkzVNqdxT5HVunNBLK1hb0araDsZ3ydnkHJTK78yeG6a+HsEgKDiAEk3fBRt/VqszNviw93pNWK4uNcs46BeqRTZcqDidUhy/VHDh0t/UPCpL/4PagvMr6wvK6v+SOYZWFzTJtlnaxYbYYBENUuXV/SZjfbjjHeOr9dFfPrKuq2vz1A5l669kMl1QbUailWoq5NCAXSZLFYT7/2B+DScdh3Y9Brwz8f6dhVdA8kShjPZLm0Xz+ERpB/yMsfZS9TeAOUXaMtKTvWX649M7VnnHw70eEdc/+tt00NtkqQvzu7wL9MZInc/sf1KyTfQivIMFIud9ngbCO9lfmhWpqtD+svyGiJ59lqzQeLNUF5TpiJ1SDl39Z/y5SU3XLz0Q7JyFkmS9ENsJdeyMcfFW9+n2D8te0x1MjeDDRB9VbuK65XNIiUU12x51dXvYRjUSqzplZdmfvHNykg8LfZ0vH5AZKyfWaUvCjdU2UJtc/VHgPi70Q2zxZg/hiTpAyRz9Ucy3TDbvbsvGwMkqlmc3MQ+bw++Zlw0C4g3yMbFmxTHFM8OKetNrvXTYs+uIf+r+purpZzcRaA05YQ+ONjzuT5rJF9aYw0kmRwgJZ3Wz15rPrj8jJBCoQ+MLA6Qimtd5ACpZP1RSVGTgLodxfRyU0Ntl3eKbIaTJxA5wrI+2EqDh8T+dKlxlmVbCnL1QUfzQeIytJMYvky7rq89K8/lnaJGxL+JcZYxsEShdtp1MYyj1JQecilLk+JZwdYMkApyRdF7RekKtANL36dQVH0mm2GBtkyl1g8HW2uYLXYz8H1vMXuzVkPgX1tFUbwplSnUzkgsHpJTAA0eNt3Gkjqk9JsiKFWqjc+JKffBgpEMkKjmqdNWDHvI25AYkofZ5KX7zdUfAeJNY8QvZY+1VxeNiwjy6ncTNTtbZorbrT28Boh6GYVKZI5OrxO3lTe8JpOzdeXNYJPJhdrJseJNMb54uwJzAZK8YGPJobbsFOCfT/Sr/7YeafrnbU9O7vo3CUtms+mG10JEUCgfo6J1SPLwmvxhQFZyJptclO8fXv7K4obk/Rav77d878Cy5KQC3z4IzG2qr3+zVEYZGSTD282tYVUewwJtQ/LQpzWyI+m3gN+eFcFq2IPAv7bpM62m1G0PQCECZnmblfLI2aPgVvpMWEmWBEhyZjKwufnNnmVyBunW8cptBVUDMECie0vjXiIYkIrreszVHzkChQLoO0dM9z69RhRZVkeApHbW1wZpC8TmtnXLSZ/L5NoVS9e8MpzqH39UTGN2D9Bv2WJKyaG2Da8CnzcXM8iybouhoy6vWPb8ttZYrkOyIECSh9eaDzJeq8lwmK08kqQv0JaH12QlZ7KVXEHbUj71xOKTkrZyyxgYkiSxvlTyeTEZYvW/gDPrLX+8uRlsMt12I5XMIJUs0JYZTmOv6vYrsRvF30FwJPDsWv2aaOa4eOt/ZjcsHGa7LNcfmRhek8kBUtIZ8xstW1J/JPOuK/42Ja1YsPcexACJ7i1utfSfbIDy60jsLbiVfg+7P/8tVvQGrDPF35Dh8gUtBls+2+vhN4FRq4DIkZa192ssAr48g2xVfQs29Y2aJP4p52eI6c8F2eJNesh3wMtHAB8r1WNZm1yHdG1f2Z+ijYbXBhvfV5E6pKQzot5G7Vp6L0B5iC3prJgiLgdIltYfGdINs1VxNtu+r8Q6Yyonca6kImDVOOOFMsui24fNXIBUhbWQCnL0Q8FBJTJIIW3ESvs5KcYLn2q1Fd/IVp4R2HyI5Zm80OIMoyV1SJKkL9A2VX8k864rtjzSFpqvrbK0/khWkQxqDcQAie49D/TVXy9riM1R9HhXFG0mnABilonbrJlBAoCA5vrrlg6vAaL4N/xRy/+xq53FdH8AOFE8Nb+8YnCgeKhtoXgzD+8NjF4PPL9bTN2vyPCQrfk1AnwbiMycvPimKZe2ieDPq27pNx9L65AyEoG1L4jrYd1KTz7wbSDe1Atzxcys25XMIAH6oeqL28xnG8pzdS+wdZa43vdDYORyoOVT4g36tzFitml5MouHmKojQEo8IwI2N//SNXYqjT5IObwY2PkR8PNQ4OMw4KP6+j0Ay5ObBlz5R1yPqMA+n3UrECDdPifqhtQuYtFJcxQKIKS1uC4PfRsqKhBrIAGWZZAAsRI/ABz6TqwJdo9hgET3HvmfO+DYQ2wyd3+xzAAg3kQB62eQ6hSn1/0fKP1p2drkPdnkdajqlfFP2+hxjcW0+lErxfo+1bGiubUpFEC7MeL69v/oV3E3JEnAgYXierOBpbdCsaQOKfki8H0vEUS7+Yvp4SUplfqC/IQT+uxIZQKk4NYi+CjIqtg6T7KMRJEpkorEAq/tn9MHwc0Hi4ByxTNif8Ubh81nZcqrQbKkSDs/G1g+SkyvN9w7TA4GgiNN/67VL86OHPoO2PlfUfuVmyaym+tftmzj14tbxWv1f0AMJVtKnskWf6z8jJWcParfxfyMXVlZM9kST4ng2sXH8qL+Jo8BrUeJYbbV4ytXiO/AGCDRvcevkb5YuCZkkACxzIBcJwRYP4PUqKdYK2j4z9UfeBjWKzl5AIEtq/f57K3Ti+LnlXEL2Ptl6ftjfhFZBLUL0PFfpo9RVh3SjcPA94+K+jTfBsD4v/UF2SXJdUjnNog3O7WLPqNXESUXXrVUQa5Ye2n1eJH9qd0UePxz/e+cSi2GTZsOEHU5294D/tcT+DAUWNwX2PoecP2QGMoyXEXb1Cw2oPwMklYLrHtBnI8r/4gsUG66uM9cgbasxRCxOKpPPZH5euwTYOxGsX5a0mkgen7550MeSpSHLC3l10g8T1Gevk7KHEvqj2RygCQHh4Z09UftK/Y/ot8nYvZqZqJYhb6qNVsOhAES3ZvkdY3kNx5Hp3YC+vxXXFeorF9zo1CItYJsscGw4XPU7VD2at33Ao0L8GjxJsp7vwDSbujvy0gA/ipeof2Rt80Xq5uqQyoqBE6uAn54XNTChLQBxm8p+9N9UHEwKtc7+T9Q/nIO5shv6qfX6bNRJWmLRFC4dADweQvggyDg604i6+TkIfZIdHI3foxKAwxdDDz2sXgONz8RzMVFA3vmikzZ582A3yebX0VbJg+N3b0mzlVJ298XQ59KjQg4bsUAv44Q9UfmCrRlfo2A6RfF2mVD/wd0mih+Tr2Lt5/Z+ZG+ZtCUwnz9IqIRj5tvZ4pCoR/iK2u/s8J8/XYfZdUfyXSF2mdLb5FT0fojmZM78NRSMbx7eYfYv/AewQCJ7k0dJwDv3Lbsn4ajCO8N9PtUZHpKvqnUJP4GAZJhwfy9rNlAsVBpYY7Igsg2vSaGZYJbA51fMv94wzqkC38Df78jgoTV48UxG/UExmwAPMrZ7kXOIBUW1w1ZusWIKQ0eEgFWTorIYMm1NLLMJOCnQcCWd8V9adcBSGKB0TrtRLbS3LCS2gno9Dww8ldg+iVg8hHxe99iqFjzKuMWEPOzaOvmb36dMp/64txLReJcbZ2lX/366E9ijTEAGDgfGL1O9O3aXuC30aIGCaj4kHPrp8V0/cIcYOP/mS+sv7pbLOvgEagfQq0I+W9n2/tiKLLkqt55mSIDV5AlMl2GdYbmeAaL/khFpTc2vlmBGWwlBUQA/YsDox3/td7ikRXdxNnKGCDRvctWiz9ai0IhArvWFs4Yc1SGb4qW1h/VdAoF0Pe/ABTAyd/Ep/EzvwNn/xCBz8D5ZWfSDOuQlg0Ts78yE0XWo9s04OkVli0cWjIgqkz9kUztDIz7UxQM56YBPw0Gjv0i7ru2D1j4oAiMNO5AnzkiuzX9EvBGHDBhu+UfThQKUX/W5hngycXA65fEzMm2o0UA1PbZsh/75BKg26vi+z2fA8uGi/O+Yaq47aHXxSKjwZHiPKpdRBBalCcCJt8GFTsvCgXw+Dz92l3y4qsl6YbXHitdd2aJDv8CmvQXWbRt7wFLHgPuXBJDWDG/AvPb64f52o217DmMVtQ2WA8pbj9w56K4brg7QUW0flpfj7TKCvVIsZvF+ll2rGu6x3PfRGRzLl5iI9a0OH2x6f0gpI14k4j5RWSO5MULu07VD32V5YG+YvVkpVpcjxwpsooVCfRdfcQ6V2nF62lVJUACxASCMX8A614Ua3X9/pJ44z+/WWQhakcAw3607tCt2lnMnJT3uiuPsnhPs8AWYlju4hbxBQAtnjTehLp+F5HZ+nWEmE0X1LJywYt/Y+Ch14AdHwCb3xCrYhuub6TV6oc5Kzq8JnNyFwvZHv8V2PS6+N1Y2E0M/SWcFG18w8SQX0WeI6SN+PnJAVLcAVGbJfe1vHWaytLvE+DmETGzbvV44Nl1FR/iTY8XS56cLV4v659PgX4fl/2YasIMEhFZ36AF4o1V7WzvnthWj3dFRiX+mMgA+YUDD0237LFdXgZG/w7833nxxtj08cplQQMNhlqqGiABosZq6PfAg/8nvo/dKIKjlsNEpsgWdW2WaPkk8NxmsZQCIDJfAxeULjgOf1TUFLkHWL6+lyldp4rh5KzbwKbpxkNgt44BGfGiDqvBQ5V/DoVCBN0v7RPDegXZIjhy8gB6zgReOiAK3itSVB3cWlzGHysOjoaIVb4bdBcF9FXh5C4CZo27yC7u/NDyx2qLgP0LgfkdRXCkUIm/iV4zq9anKlBIkp0H+Wqo9PR0eHt7Iy0tDV5eXvbuDhE5il2fADv+A0Ah3rBtPcy4bTaw+1PxJvrmDevOWoxZBkQvENP22z/nmEsxZCWLIbSI/mXPYpWkqvf/WrQY+oIkli8Y/K34UCD/DJoNAoYtrdpzyLRa4MgS4O4VIGqy+bWhypORAHzWBIBCBDRycDRyefnbi1jqxEpgTfGMzVGrxebPZcm5C/z8pHEd1OOfW5Z5rQRL3785xEZEZE1dJgPpN8SQjz1qsORaJnPr+1RF66fFlyNz97esj9Y4N/WjgCe/B9Y8L7aSybkrhvDk+qPKDq+ZolQCHcZX/TieQWI/wIz46gmOAKDVU2JW4uHvxX6Kz/9T9szcP/8tgiNnb5ExajeuckOfVsYMUiUxg0REDkmSgGM/iRXMK7I4IVXepe3A8mfEjLLaEaIGR6kWReuOuN3RyrEioKuO4EhWmAd831ssrVCnvSj4NzVkfGa92MxXoRSF/pbuE1kFlr5/2z9EIyIi61EoxAwwBke206gHMPYPsaaTvHdbWDfHDI4AsebawK/FrL7qCI4AMdQ4bKkY5rx5WKw+XnJV8Mzb+tmG3V61SXBUEQyQiIiIqqpOO+C5vwDv4qGkZoPs2p0yeYUAbUYBGtfqfR7fMGDwIpEdOrFcFIRnp4j7JEkER9l3xHB09zeqty+VwACJiIjIGvzDgYk7gWE/iSweAU36Fg/jeYjFM79/VKzndGKF2AJGqQEGL3TIdetYg1RJrEEiIiKyUOJpsYhn2nWxIa4kAXlpYmmMh16zaVdYg0RERESOIbC5WDerTnsgN1UER3Xai/WkHBQDJCIiIqp+HgHA2A1i+DGwhVg3yoE3s3bcnhEREdG9ReMKPPGVvXthEWaQiIiIiEpggERERERUAgMkIiIiohIYIBERERGVwACJiIiIqAQGSEREREQlMEAiIiIiKoEBEhEREVEJDJCIiIiISmCARERERFQCAyQiIiKiEhggEREREZXAAImIiIioBAZIRERERCWo7d2BmkqSJABAenq6nXtCRERElpLft+X3cXMYIFVSRkYGACA0NNTOPSEiIqKKysjIgLe3t9n7FVJ5IRSZpNVqER8fD09PTygUCqsdNz09HaGhobh+/Tq8vLysdlwyjefbdniubYfn2nZ4rm3HWudakiRkZGQgJCQESqX5SiNmkCpJqVSibt261XZ8Ly8v/rHZEM+37fBc2w7Pte3wXNuONc51WZkjGYu0iYiIiEpggERERERUAgMkB+Ps7IyZM2fC2dnZ3l25L/B82w7Pte3wXNsOz7Xt2Ppcs0ibiIiIqARmkIiIiIhKYIBEREREVAIDJCIiIqISGCARERERlcAAycEsWLAAYWFhcHFxQadOnXDw4EF7d6nGmzNnDjp06ABPT08EBARg0KBBiI2NNWqTm5uLSZMmwc/PDx4eHhg6dCgSExPt1ON7x4cffgiFQoGpU6fqbuO5tp6bN2/imWeegZ+fH1xdXdGyZUscPnxYd78kSZgxYwaCg4Ph6uqKXr164cKFC3bscc1UVFSEd999Fw0aNICrqysaNWqE2bNnG+3lxXNdOf/88w8GDBiAkJAQKBQKrFu3zuh+S85rSkoKRo0aBS8vL/j4+GD8+PHIzMysct8YIDmQFStWYNq0aZg5cyaOHj2KyMhI9OnTB0lJSfbuWo22a9cuTJo0Cfv378eWLVtQUFCA3r17IysrS9fm1VdfxR9//IGVK1di165diI+Px5AhQ+zY65rv0KFD+Pbbb9GqVSuj23murePu3bvo2rUrNBoN/vzzT5w5cwafffYZfH19dW0+/vhjfPnll1i4cCEOHDgAd3d39OnTB7m5uXbsec3z0Ucf4ZtvvsH8+fNx9uxZfPTRR/j444/x1Vdf6drwXFdOVlYWIiMjsWDBApP3W3JeR40ahdOnT2PLli3YsGED/vnnH0ycOLHqnZPIYXTs2FGaNGmS7vuioiIpJCREmjNnjh17de9JSkqSAEi7du2SJEmSUlNTJY1GI61cuVLX5uzZsxIAKTo62l7drNEyMjKk8PBwacuWLVL37t2lKVOmSJLEc21N//73v6Vu3bqZvV+r1UpBQUHSJ598orstNTVVcnZ2ln799VdbdPGe0b9/f+m5554zum3IkCHSqFGjJEniubYWANLatWt131tyXs+cOSMBkA4dOqRr8+eff0oKhUK6efNmlfrDDJKDyM/Px5EjR9CrVy/dbUqlEr169UJ0dLQde3bvSUtLAwDUqlULAHDkyBEUFBQYnfuIiAjUq1eP576SJk2ahP79+xudU4Dn2prWr1+P9u3b46mnnkJAQADatGmD7777Tnf/lStXkJCQYHSuvb290alTJ57rCurSpQu2bduG8+fPAwCOHz+OPXv24LHHHgPAc11dLDmv0dHR8PHxQfv27XVtevXqBaVSiQMHDlTp+blZrYNITk5GUVERAgMDjW4PDAzEuXPn7NSre49Wq8XUqVPRtWtXtGjRAgCQkJAAJycn+Pj4GLUNDAxEQkKCHXpZsy1fvhxHjx7FoUOHSt3Hc209ly9fxjfffINp06bhrbfewqFDh/DKK6/AyckJY8aM0Z1PU/9TeK4r5o033kB6ejoiIiKgUqlQVFSEDz74AKNGjQIAnutqYsl5TUhIQEBAgNH9arUatWrVqvK5Z4BE95VJkybh1KlT2LNnj727ck+6fv06pkyZgi1btsDFxcXe3bmnabVatG/fHv/9738BAG3atMGpU6ewcOFCjBkzxs69u7f89ttv+OWXX7Bs2TI0b94cMTExmDp1KkJCQniu72EcYnMQ/v7+UKlUpWbzJCYmIigoyE69urdMnjwZGzZswI4dO1C3bl3d7UFBQcjPz0dqaqpRe577ijty5AiSkpLQtm1bqNVqqNVq7Nq1C19++SXUajUCAwN5rq0kODgYzZo1M7qtadOmiIuLAwDd+eT/lKqbPn063njjDYwYMQItW7bEs88+i1dffRVz5swBwHNdXSw5r0FBQaUmMhUWFiIlJaXK554BkoNwcnJCu3btsG3bNt1tWq0W27ZtQ1RUlB17VvNJkoTJkydj7dq12L59Oxo0aGB0f7t27aDRaIzOfWxsLOLi4njuK6hnz544efIkYmJidF/t27fHqFGjdNd5rq2ja9eupZarOH/+POrXrw8AaNCgAYKCgozOdXp6Og4cOMBzXUHZ2dlQKo3fLlUqFbRaLQCe6+piyXmNiopCamoqjhw5omuzfft2aLVadOrUqWodqFKJN1nV8uXLJWdnZ+mHH36Qzpw5I02cOFHy8fGREhIS7N21Gu3FF1+UvL29pZ07d0q3bt3SfWVnZ+vavPDCC1K9evWk7du3S4cPH5aioqKkqKgoO/b63mE4i02SeK6t5eDBg5JarZY++OAD6cKFC9Ivv/wiubm5ST///LOuzYcffij5+PhIv//+u3TixAlp4MCBUoMGDaScnBw79rzmGTNmjFSnTh1pw4YN0pUrV6Q1a9ZI/v7+0uuvv65rw3NdORkZGdKxY8ekY8eOSQCkuXPnSseOHZOuXbsmSZJl57Vv375SmzZtpAMHDkh79uyRwsPDpZEjR1a5bwyQHMxXX30l1atXT3JycpI6duwo7d+/395dqvEAmPxasmSJrk1OTo700ksvSb6+vpKbm5s0ePBg6datW/br9D2kZIDEc209f/zxh9SiRQvJ2dlZioiIkBYtWmR0v1arld59910pMDBQcnZ2lnr27CnFxsbaqbc1V3p6ujRlyhSpXr16kouLi9SwYUPp7bfflvLy8nRteK4rZ8eOHSb/P48ZM0aSJMvO6507d6SRI0dKHh4ekpeXlzRu3DgpIyOjyn1TSJLBUqBERERExBokIiIiopIYIBERERGVwACJiIiIqAQGSEREREQlMEAiIiIiKoEBEhEREVEJDJCIiIiISmCARERkJQqFAuvWrbN3N4jIChggEdE9YezYsVAoFKW++vbta++uEVENpLZ3B4iIrKVv375YsmSJ0W3Ozs526g0R1WTMIBHRPcPZ2RlBQUFGX76+vgDE8Nc333yDxx57DK6urmjYsCFWrVpl9PiTJ0+iR48ecHV1hZ+fHyZOnIjMzEyjNosXL0bz5s3h7OyM4OBgTJ482ej+5ORkDB48GG5ubggPD8f69eur90UTUbVggERE9413330XQ4cOxfHjxzFq1CiMGDECZ8+eBQBkZWWhT58+8PX1xaFDh7By5Ups3brVKAD65ptvMGnSJEycOBEnT57E+vXr0bhxY6PneO+99zBs2DCcOHEC/fr1w6hRo5CSkmLT10lEVlDl7W6JiBzAmDFjJJVKJbm7uxt9ffDBB5IkSRIA6YUXXjB6TKdOnaQXX3xRkiRJWrRokeTr6ytlZmbq7t+4caOkVCqlhIQESZIkKSQkRHr77bfN9gGA9M477+i+z8zMlABIf/75p9VeJxHZBmuQiOie8cgjj+Cbb74xuq1WrVq661FRUUb3RUVFISYmBgBw9uxZREZGwt3dXXd/165dodVqERsbC4VCgfj4ePTs2bPMPrRq1Up33d3dHV5eXkhKSqrsSyIiO2GARET3DHd391JDXtbi6upqUTuNRmP0vUKhgFarrY4uEVE1Yg0SEd039u/fX+r7pk2bAgCaNm2K48ePIysrS3f/3r17oVQq0aRJE3h6eiIsLAzbtm2zaZ+JyD6YQSKie0ZeXh4SEhKMblOr1fD39wcArFy5Eu3bt0e3bt3wyy+/4ODBg/j+++8BAKNGjcLMmTMxZswYzJo1C7dv38bLL7+MZ599FoGBgQCAWbNm4YUXXkBAQAAee+wxZGRkYO/evXj55Zdt+0KJqNoxQCKie8bmzZsRHBxsdFuTJk1w7tw5AGKG2fLly/HSSy8hODgYv/76K5o1awYAcHNzw19//YUpU6agQ4cOcHNzw9ChQzF37lzdscaMGYPc3Fx8/vnneO211+Dv748nn3zSdi+QiGxGIUmSZO9OEBFVN4VCgbVr12LQoEH27goR1QCsQSIiIiIqgQESERERUQmsQSKi+wKrCYioIphBIiIiIiqBARIRERFRCQyQiIiIiEpggERERERUAgMkIiIiohIYIBERERGVwACJiIiIqAQGSEREREQlMEAiIiIiKuH/AXWPkkP8MdiWAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 36ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 40ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 48ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, LSTM, Concatenate, Input\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.models import Model\n","import matplotlib.pyplot as plt\n","from datetime import datetime, timedelta\n","\n","# Cargar y preparar los datos\n","url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv'\n","df = pd.read_csv(url, parse_dates=['Date'], index_col='Date')\n","\n","# Crear características de tiempo\n","df['day'] = df.index.day\n","df['month'] = df.index.month\n","df['year'] = df.index.year\n","\n","# Normalizar los datos\n","scaler_temp = MinMaxScaler()\n","scaler_features = MinMaxScaler()\n","\n","temp_scaled = scaler_temp.fit_transform(df[['Temp']])\n","features_scaled = scaler_features.fit_transform(df[['day', 'month', 'year']])\n","\n","# Crear secuencias de datos\n","def create_sequences(data, seq_length):\n","    X, y = [], []\n","    for i in range(len(data) - seq_length):\n","        X.append(data[i:i+seq_length])\n","        y.append(data[i+seq_length])\n","    return np.array(X), np.array(y)\n","\n","seq_length = 10\n","X_temp, y = create_sequences(temp_scaled, seq_length)\n","\n","# Dividir los datos en conjuntos de entrenamiento y prueba\n","train_size = int(len(X_temp) * 0.8)\n","X_train_temp, X_test_temp = X_temp[:train_size], X_temp[train_size:]\n","y_train, y_test = y[:train_size], y[train_size:]\n","\n","X_train_features = features_scaled[seq_length:train_size+seq_length]\n","X_test_features = features_scaled[train_size+seq_length:]\n","\n","# Crear el modelo\n","temp_input = Input(shape=(seq_length, 1))\n","features_input = Input(shape=(3,))\n","\n","lstm = LSTM(64)(temp_input)\n","dense1 = Dense(32, activation='relu')(lstm)\n","\n","concat = Concatenate()([dense1, features_input])\n","dense2 = Dense(16, activation='relu')(concat)\n","output = Dense(1)(dense2)\n","\n","model = Model(inputs=[temp_input, features_input], outputs=output)\n","\n","# Compilar el modelo\n","model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')\n","\n","# Entrenar el modelo\n","history = model.fit(\n","    [X_train_temp, X_train_features], y_train,\n","    validation_data=([X_test_temp, X_test_features], y_test),\n","    epochs=100, batch_size=32, verbose=1\n",")\n","\n","# Guardar el modelo\n","model.save('model.h5')\n","\n","# Función para predecir la temperatura para una fecha dada\n","def predict_temperature(date_str):\n","    date = datetime.strptime(date_str, '%Y-%m-%d')\n","    input_features = np.array([[date.day, date.month, date.year]])\n","    input_features_scaled = scaler_features.transform(input_features)\n","\n","    # Obtener los últimos 10 días de temperatura\n","    last_10_days = temp_scaled[-10:].reshape(1, 10, 1)\n","\n","    prediction_scaled = model.predict([last_10_days, input_features_scaled])\n","    prediction = scaler_temp.inverse_transform(prediction_scaled)[0][0]\n","    return prediction\n","\n","# Ejemplo de uso\n","print(f\"Temperatura predicha para 1991-01-01: {predict_temperature('1991-01-01'):.2f}\")\n","print(f\"Temperatura predicha para 1998-01-01: {predict_temperature('1998-01-01'):.2f}\")\n","\n","# Visualizar el rendimiento del modelo\n","plt.plot(history.history['loss'], label='train')\n","plt.plot(history.history['val_loss'], label='validation')\n","plt.title('Model Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('Epoch')\n","plt.legend()\n","plt.show()\n","\n","# Visualizar predicciones vs valores reales\n","dates_to_predict = pd.date_range(start='1991-01-01', end='1991-12-31', freq='D')\n","predictions = [predict_temperature(date.strftime('%Y-%m-%d')) for date in dates_to_predict]\n","\n","plt.figure(figsize=(12, 6))\n","plt.plot(df.index, df['Temp'], label='Datos reales', alpha=0.7)\n","plt.plot(dates_to_predict, predictions, label='Predicciones', color='red')\n","plt.title('Temperaturas reales vs Predicciones')\n","plt.xlabel('Fecha')\n","plt.ylabel('Temperatura')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","source":["# Visualizar predicciones vs valores reales\n","dates_to_predict = pd.date_range(start='1991-01-01', end='1992-12-31', freq='D')\n","predictions = [predict_temperature(date.strftime('%Y-%m-%d')) for date in dates_to_predict]\n","\n","plt.figure(figsize=(12, 6))\n","plt.plot(df.index, df['Temp'], label='Datos reales', alpha=0.7)\n","plt.plot(dates_to_predict, predictions, label='Predicciones', color='red')\n","plt.title('Temperaturas reales vs Predicciones')\n","plt.xlabel('Fecha')\n","plt.ylabel('Temperatura')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0zORQq4pBy1Y","executionInfo":{"status":"ok","timestamp":1719447079747,"user_tz":300,"elapsed":62733,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"cac4bc8c-00c9-4cdd-d411-4eb018be5633"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 42ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 35ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/step\n","1/1 [==============================] - 0s 35ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 37ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 46ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 47ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/step\n","1/1 [==============================] - 0s 40ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 49ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/step\n","1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 40ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 47ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 45ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 46ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 41ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 40ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 43ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 43ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 35ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 43ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 42ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 44ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n","import numpy as np\n","\n","def evaluate_model(model, X_test_temp, X_test_features, y_test, scaler_temp):\n","    # Hacer predicciones\n","    y_pred_scaled = model.predict([X_test_temp, X_test_features])\n","\n","    # Invertir la normalización\n","    y_pred = scaler_temp.inverse_transform(y_pred_scaled)\n","    y_true = scaler_temp.inverse_transform(y_test.reshape(-1, 1))\n","\n","    # Calcular métricas\n","    mse = mean_squared_error(y_true, y_pred)\n","    rmse = np.sqrt(mse)\n","    mae = mean_absolute_error(y_true, y_pred)\n","    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n","    r2 = r2_score(y_true, y_pred)\n","\n","    print(f\"MSE: {mse:.4f}\")\n","    print(f\"RMSE: {rmse:.4f}\")\n","    print(f\"MAE: {mae:.4f}\")\n","    print(f\"MAPE: {mape:.4f}%\")\n","    print(f\"R²: {r2:.4f}\")\n","\n","    # Graficar predicciones vs valores reales\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(y_true, label='Valores reales')\n","    plt.plot(y_pred, label='Predicciones')\n","    plt.title('Predicciones vs Valores reales')\n","    plt.xlabel('Tiempo')\n","    plt.ylabel('Temperatura')\n","    plt.legend()\n","    plt.show()\n","\n","    # Graficar residuos\n","    residuals = y_true - y_pred\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(residuals)\n","    plt.title('Residuos')\n","    plt.xlabel('Tiempo')\n","    plt.ylabel('Error')\n","    plt.axhline(y=0, color='r', linestyle='--')\n","    plt.show()\n","\n","    # Histograma de residuos\n","    plt.figure(figsize=(12, 6))\n","    plt.hist(residuals, bins=50)\n","    plt.title('Distribución de residuos')\n","    plt.xlabel('Error')\n","    plt.ylabel('Frecuencia')\n","    plt.show()\n","\n","# Después de entrenar el modelo\n","evaluate_model(model, X_test_temp, X_test_features, y_test, scaler_temp)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"lESs9MTeCR7Z","executionInfo":{"status":"ok","timestamp":1719447138415,"user_tz":300,"elapsed":1404,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"7c5e0cc4-c552-43fa-8500-6f17966d2316"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["23/23 [==============================] - 0s 2ms/step\n","MSE: 4.6769\n","RMSE: 2.1626\n","MAE: 1.7079\n","MAPE: 20.7118%\n","R²: 0.7221\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA+cAAAIjCAYAAABh8GqqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydebgdRZn/v2e7NzchCfsmMQKKgKCCiA5uoLjivm+/cRkcx2VwF9xBkeioyCAggiOgoIgogkEEZJWdEAgkkJCwZd+Tu9+zdf/+OKe636p+q7r77Pfm/TxPnpx7TndVdXd1Vb31bhnf930IgiAIgiAIgiAIgtA1st1ugCAIgiAIgiAIgiDs6IhwLgiCIAiCIAiCIAhdRoRzQRAEQRAEQRAEQegyIpwLgiAIgiAIgiAIQpcR4VwQBEEQBEEQBEEQuowI54IgCIIgCIIgCILQZUQ4FwRBEARBEARBEIQuI8K5IAiCIAiCIAiCIHQZEc4FQRAEQRAEQRAEocuIcC4IgiAIQsCpp56KTCaT6NhMJoNTTz21vQ0SBEEQhB0EEc4FQRAEoUe5+OKLkclkgn/5fB7Petaz8PGPfxxr1qzpdvMEQRAEQWgh+W43QBAEQRAEN9///vex//77Y2JiAvfccw8uvvhi3HHHHVi8eDGmTZvW0rq+/e1v45RTTmlpmYIgCIIgxCPCuSAIgiD0OG9+85tx1FFHAQBOPPFE7L777vjxj3+Ma665Bu9///tbWlc+n0c+L8sDQRAEQeg0YtYuCIIgCJOMV73qVQCAJ554Ivhu6dKleO9734tdd90V06ZNw1FHHYVrrrlGO69cLuO0007D8573PEybNg277bYbXvnKV+LGG28MjuF8zovFIr70pS9hjz32wMyZM/H2t78dq1evjrTr4x//OJ7znOdEvufKrFQq+MEPfoADDzwQ/f39eM5znoNvfvObKBaL2nELFizAG9/4Ruy+++4YGBjA/vvvj09+8pPJbpQgCIIgTCJka1wQBEEQJhlPP/00AGCXXXYBACxZsgSveMUr8KxnPQunnHIKZsyYgSuuuALvfOc78ec//xnvete7ANSE5Hnz5uHEE0/E0UcfjaGhISxYsAALFy7E61//emt9J554Ii699FJ8+MMfxjHHHIObb74ZJ5xwQlPXcOKJJ+KSSy7Be9/7XnzlK1/Bvffei3nz5uGxxx7DVVddBQDYuHEj3vCGN2CPPfbAKaecgp133hlPP/00/vKXvzRVtyAIgiD0IiKcC4IgCEKPMzg4iM2bN2NiYgL33nsvTjvtNPT39+Otb30rAOALX/gCnv3sZ+P+++9Hf38/AOCzn/0sXvnKV+Lkk08OhPNrr70Wb3nLW3DBBRckrnvRokW49NJL8dnPfhbnnnsuAOBzn/scPvKRj+Dhhx9u6HoWLVqESy65BCeeeCIuvPDCoL177rknfvrTn+KWW27Bcccdh7vuugvbtm3DDTfcEJj1A8Dpp5/eUL2CIAiC0MuIWbsgCIIg9DjHH3889thjD8yZMwfvfe97MWPGDFxzzTXYb7/9sHXrVtx88814//vfj+HhYWzevBmbN2/Gli1b8MY3vhHLly8PIrvvvPPOWLJkCZYvX5647r///e8AgJNOOkn7/otf/GLD16PK/PKXv6x9/5WvfAVAbRMBqLUXAObPn49yudxwfYIgCIIwGRDhXBAEQRB6nHPPPRc33ngjrrzySrzlLW/B5s2bAw35ihUr4Ps+vvOd72CPPfbQ/n3ve98DUDMPB2pR37dv346DDjoIhx9+OL72ta/Far+feeYZZLNZHHjggdr3z3/+8xu+HlXmc5/7XO37vffeGzvvvDOeeeYZAMBrXvMavOc978Fpp52G3XffHe94xztw0UUXRfzSBUEQBGEqIGbtgiAIgtDjHH300YFZ9zvf+U688pWvxIc//GEsW7YMnucBAL761a/ijW98I3u+EoJf/epX44knnsDVV1+NG264Ab/+9a/x85//HOeffz5OPPHEpttpBn1TVKvVVMfT36+88krcc889+Nvf/obrr78en/zkJ/Gzn/0M99xzD3baaaem2ywIgiAIvYJozgVBEARhEpHL5TBv3jysXbsW55xzDg444AAAQKFQwPHHH8/+mzlzZnD+rrvuik984hP4wx/+gFWrVuGFL3whTj31VGt9c+fOhed5WmR4AFi2bFnk2F122QXbt2+PfK804WaZpnn9hg0bsH37dsydO1f7/uUvfzl++MMfYsGCBbjsssuwZMkSXH755dY2C4IgCMJkRIRzQRAEQZhkHHvssTj66KNx1llnYdasWTj22GPxq1/9CuvWrYscu2nTpuDzli1btN922mknPPe5z3Waib/5zW8GAJx99tna92eddVbk2AMPPBCDg4Oaqfy6deuC6OuKt7zlLWwZZ555JgAEkeC3bdsG3/e1Y1784hcDgJi2C4IgCFMOMWsXBEEQhEnI1772Nbzvfe/DxRdfjHPPPRevfOUrcfjhh+NTn/oUDjjgAGzYsAF33303Vq9ejUWLFgEADj30UBx77LF4yUtegl133RULFizAlVdeic9//vPWel784hfjQx/6EM477zwMDg7imGOOwU033YQVK1ZEjv3gBz8YRIc/6aSTMDY2hl/+8pc46KCDsHDhwuC4F73oRfjYxz6GCy64ANu3b8drXvMa3Hfffbjkkkvwzne+E8cddxwA4JJLLsF5552Hd73rXTjwwAMxPDyMCy+8ELNmzQoEfEEQBEGYKohwLgiCIAiTkHe/+9048MAD8dOf/hSf+tSnsGDBApx22mm4+OKLsWXLFuy555444ogj8N3vfjc456STTsI111yDG264AcViEXPnzsXpp5+Or33ta866fvOb32CPPfbAZZddhr/+9a947Wtfi2uvvRZz5szRjtttt91w1VVX4ctf/jK+/vWvY//998e8efOwfPlyTTgHgF//+tc44IADcPHFF+Oqq67C3nvvjW984xtBEDsAgdB++eWXY8OGDZg9ezaOPvpoXHbZZdh///1bcBcFQRAEoXfI+Ka9mCAIgiAIgiAIgiAIHUV8zgVBEARBEARBEAShy4hwLgiCIAiCIAiCIAhdRoRzQRAEQRAEQRAEQegyIpwLgiAIgiAIgiAIQpcR4VwQBEEQBEEQBEEQuowI54IgCIIgCIIgCILQZXaoPOee52Ht2rWYOXMmMplMt5sjCIIgCIIgCIIgTHF838fw8DD23XdfZLN2/fikEc6r1SpOPfVUXHrppVi/fj323XdffPzjH8e3v/3txIL22rVrMWfOnDa3VBAEQRAEQRAEQRB0Vq1ahf3228/6+6QRzn/84x/jl7/8JS655BK84AUvwIIFC/CJT3wCs2fPxkknnZSojJkzZwKo3ZRZs2a1s7mCIAiCIAiCIAiCgKGhIcyZMyeQR21MGuH8rrvuwjve8Q6ccMIJAIDnPOc5+MMf/oD77rsvcRlKwz5r1iwRzgVBEARBEARBEISOEWfxPWkCwh1zzDG46aab8PjjjwMAFi1ahDvuuANvfvObrecUi0UMDQ1p/wRBEARBEARBEASh15g0mvNTTjkFQ0NDOPjgg5HL5VCtVvHDH/4QH/nIR6znzJs3D6eddloHWykIgiAIgiAIgiAI6Zk0mvMrrrgCl112GX7/+99j4cKFuOSSS/DTn/4Ul1xyifWcb3zjGxgcHAz+rVq1qoMtFgRBEARBEARBEIRkZHzf97vdiCTMmTMHp5xyCj73uc8F351++um49NJLsXTp0kRlDA0NYfbs2RgcHBSfc0EQBEEQBEEQBKHtJJVDJ43mfGxsLJITLpfLwfO8LrVIEARBEARBEARBEFrDpPE5f9vb3oYf/vCHePazn40XvOAFePDBB3HmmWfik5/8ZLebJgiCIAiCIAiCIAhNMWnM2oeHh/Gd73wHV111FTZu3Ih9990XH/rQh/Dd734XfX19icoQs3ZBEARBEARBEAShkySVQyeNcN4KRDgXBEEQBEEQBEEQOsmU8zkXBEEQBEEQBEEQhKmKCOeCIAiCIAiCIAiC0GVEOBcEQRAEQRAEQRCELiPCuSAIgiAIgiAIgiB0GRHOBUEQBEEQBEEQBKHLiHAuCIIgCIIgCIIgCF1GhHNBEARBEARBEARB6DIinAuCIAiCIAiCIAhClxHhXBAEQRA6yLL1w9gyUux2MwRBEARB6DHy3W6AIAiCIOworNg4jDeedTsA4OkfndDl1giCIAiC0EuI5lwQBEEQOsQ9T27tdhMEQRAEQehRRDgXBEEQhA7hd7sBgiAIgiD0LCKcC4IgCEKn8EU8FwRBEASBR4RzQRAEQegQIpoLgiAIgmBDhHNBEARB6BCiOBcEQRAEwYYI54IgCIIgCIIgCILQZUQ4FwRBEIQO4YvqXBAEQRAECyKcC4IgCEKHENFcEARBEAQbIpwLgiAIQocQxbkgCIIgCDZEOBcEQRCEDiGyuSAIgiAINkQ4FwRBEIQOIT7ngiAIgiDYEOFcEARBEARBEARBELqMCOeCIAiC0CFEcS4IgiAIgg0RzgVBEAShQ/jidS4IgiAIggURzgVBEARBEARBEAShy4hwLgiCIAiCIAiCIAhdRoRzQRAEQegQ4nMuCIIgCIINEc4FQRAEoUOIbC4IgiAIgg0RzgVBEAShQ4jmXBAEQRAEGyKcC4IgCEKHkGjtgiAIgiDYEOFcEARBEDqEaM4FQRAEQbAhwrkgCIIgCIIgCIIgdBkRzgVBEARBEARBEAShy4hwLgiCIAgdwhe7dkEQBEEQLIhwLgiCIAgdQmRzQRAEQRBsiHAuCIIgCB1CZHNBEARBEGyIcC4IgiAIHUI054IgCIIg2BDhXBAEQRA6BM1zLv7ngiAIgiBQRDgXBEEQhA5B5XGRzQVBEARBoIhwLgiCIAgdwrd8FgRBEARBEOFcEARBELqAmLULgiAIgkAR4VwQBEEQOgURyD2RzQVBEARBIIhwLgiCIAgdQjdrF+lcEARBEIQQEc4FQRAEoUNIQDhBEARBEGyIcC4IgiAIHUK05YIgCIIg2BDhXBAEQRA6hGjOBUEQBEGwIcK5IAiCIHQI8TkXBEEQBMGGCOeCIAiC0CFEcy4IgiAIgo1JJZyvWbMGH/3oR7HbbrthYGAAhx9+OBYsWNDtZgmCIAhCakQ2FwRBEASBku92A5Kybds2vOIVr8Bxxx2H6667DnvssQeWL1+OXXbZpdtNEwRBEITUeKI6FwRBEASBMGmE8x//+MeYM2cOLrroouC7/fffv4stEgTg5CsfxvbxEs7/6EuQyWS63RxBEHoc6mcusrkgCMLUZd3gOAq5LHbfqb/bTREmEZPGrP2aa67BUUcdhfe9733Yc889ccQRR+DCCy90nlMsFjE0NKT9E4RW4fs+/rhgFa5fsgFPbR7tdnMEQZgM+JbPgiAIwpRhpFjBv827GUed/s9uN0WYZEwa4fzJJ5/EL3/5Szzvec/D9ddfj8985jM46aSTcMkll1jPmTdvHmbPnh38mzNnTgdbLEx1PJ//LAiCYEOitQuCIEx91m4fDz77YiYlpGDSCOee5+HII4/EGWecgSOOOAL/+Z//iU996lM4//zzred84xvfwODgYPBv1apVHWyxMNWpEolcLNoFQUgCXaTJek0QBGFqQpeFosAR0jBphPN99tkHhx56qPbdIYccgpUrV1rP6e/vx6xZs7R/gtAqaDAnkc0FQUiClkqte80QBEEQ2ghV2kjwTyENk0Y4f8UrXoFly5Zp3z3++OOYO3dul1ok7OjQsVaCwQmCkATNrF0WbCy/u+cZXLNobbebIQiC0BKqojoXUjBporV/6UtfwjHHHIMzzjgD73//+3HffffhggsuwAUXXNDtpgk7KKI5FwQhLaI5d7Nq6xi+89fFAIC3v2jfLrdGEAShMajSRvZhhTRMGs35S1/6Ulx11VX4wx/+gMMOOww/+MEPcNZZZ+EjH/lIt5sm7KBowrlI54IgJMATn3Mn28ZK3W6CIAhC0+g+5zLYC8mZNJpzAHjrW9+Kt771rd1uhiAA0AN8ZER3LghCAvSAcLJgM5FbIgjCVIBqzkU4F9IwaTTngtBr+KI5FwQhJZ6YtQuCIOxQNOJy/ps7nsIPr3209Y0Rep5JpTkXhF5C4nsIgpAWMWtPju/7EmxTEIRJCR25GrGS+v78mmD+riP2w6H7SrapHQnRnAtCg4iZkiAIadE15zKGuJAhVhCEyYqeSq3xcsbLleYbI0wqRDgXhAYR4VwQhLT4ojl34ls+C4IgTCbo+N7celGsh3Y0RDgXhAbxvPCzLLIFQUiCZtbexXZMBiRgniAIkxU6enkpVed07MuKbL7DIcK5IDSIvsiWRaQgCPFoZu0ifEbwZfNCEIQpAB3L0pq1V8kJEndjx0OEc0FoEAnsJAhCWmTccKOZtcv9EQRhkqJpzlMOZnqqXmFHQ4RzQWgQOtbKGlIQhCRo44YMHE7EIkkQhMmKrjlPK5xLqt4dGRHOBaFBdA2YLCIFQYhH3GHcyOaFIAhTgWbGMmrWnhXpfIdDhHNBaBBPNOeCIKTEE+FTEARhytOMWXtVJocdGhHOBaFBxHdUEIS0SLT25Ei6SkEQJit6KrWU53rxxwhTFxHOBaFBdFN2WUQKghCPL+4wiZHbIwhCO/F9H3et2Ixto6WWl+014XNONedi1r7jIcK5IDSImKcKgpAWj2hEZNjgEMsCQRA6w18WrsGHf30v3nL2v1petqY5T6k6r6ZVtQtTChHOBaFB6OApw6ggCEkQdxg3ehAluUGCILSP6xavBwCsG5xoedk04GdaWbsZrbsw+RHhXBAaRBbZgiCkRV+kycDhQu6OIAjtpJ2Cr+5z3ngqtcnC+bc9gWN/cgs2DrV+o2NHQ4RzQWiQZgZeQRB2TPTct11sSI+iRfKQ+yMIQhvpVeGcWmZOlvXlj65biqe3jOHn/1ze7aZMekQ4F4QGEc25IAhpEeEzBXJ/BEFoEZWqF3GVaecGKTVrTzvWa7FJJtk4WCxXu92ESY8I54LQIHqe80k2egqC0BX0VGoybpj4Mq4KgtBixkoVvHzeTfjkxfdr37czrkVTmvMUPucT5Sque2QdhifKqepoF5KjvXlEOBeEBhHNuSAIaZEsD8mR+yMIQiu4/fFN2DxSwi3LNmnftzMquteEC5O+ievm1GuW4DOXLcRnLl2YrpI2IZHmm0eEc0FoEIkkLAhCWnzZ1EvMZPG1FASht7HJi231OW+iHpp6LW6t+acHVgMA7lixOVUd7ULG7eYR4VwQGkQ0YIIgpEXM2t34KTRGgiAISbAJjG31OW8mz3mKTdxcNpOq7HYjmvPmEeFcEBpE29mUZaQgCAmYzIF+OoFsegqC0GpsAmN7LSAbN2vXo7W7jy30nHDe7RZMfkQ4F4QGkUWkIAhpEZM/N1qEY9n0FAShBdiG3fb6nNPP6epJE0yu1zTnMsc1jwjngtAgaQJ2CIIgAEY0chk4Imj3RO6PIAgtwLbR1zGz9ibynMedms/1lignZu3N01tPVBAmEXq0dhmMBEGIR4/gK+OGiZ5KTRAEoXlsptZtDQjXRPDPaor1ZV4051MOEc4FoUE8WUQKgpASsbhxIykqBUFoNfaAcL1p1q7HNHLTa8K5aM6bR4RzQWgQWUQKgpAWPVaFDBwmEs1eEIRWYxtrvTYGL/ObCAiXRrDP5UQ4n2qIcC4IDeKLc6QgCCmRVGFutFFVbpAgCC3ANpa01QS7Qz7nhWxviXJi1t48vfVEBWESISmRBEFIi2R5cOOLT74gCC2m2gWzdlpy2jznaWKT9Fq0dtGcN48I54LQIOI7KghCWjyxuHEi0ewFQWg1NnmxnXKkLmCnO7eawue854RzGbebRoRzQWgQ0YAJgpAWGTfciNJFEIRWY/U5b2u09sbrSZMNqNBjqdTSWgkIUXrriQrCJMJPMXgKgiAA4nMeRzPphwRBEDhsAmOHXM5TrxHTBBzuBc05vT4xa28eEc4FoUGqssgWBCElmqmjLGIi6Ckq5f4IgtA8drP2zuQ5T2/WHn6OO7cXUqk1kzZOiCLCuSA0iJinCoKQFl34bJ6q5+PuJ7ZgtFhpQWndRzTngiC0GpvA2E4tbzNm7bRdcefmeyCVGm2vaM6bR4RzQWgQMWsXBCEtacwVk3Dd4nX40IX34MwbH2++sB5AwuUJgtBq9ECTndkAbCbPeZo29oLPuSacy3q4abr/RAVhkiLR2gVBSIu2SGzByLFhqFj/f6LpslrJLcs24qnNo6nPSxMISRAEIQm2TdFOBYRLO5ZVU4yD1Ky9W65SVXHXain5bjdAECYrkudcEIS0eLp03jRq4dZLY9ADz2zFJy66HwDw9I9OSHVui2+PIAhCxCc6i0zwuRN1pjX1TpdKLdSzlqoepmVzqepqBaI5by2iOReEBtE15zIYCYIQT6stblR5vRSE58GV2xs+VzTnneHh1dvx0V/fi8VrBrvdFEFoO7ZxlwZeazXNBITTc6Qn15yX23lBDjQf+e40YUohwrkgNIhustS9dgiCMHlotcWNKmMqBuGRcZXn3ie34JZlG5sq45qH1uKOFZtx7SPrWtSqxrhrxWaceePjU7L/Cr2DzYe7nRuAtOTUec5TzBO5HBXOu2TWTt7fbm0QTCVEOBeEBhGfc2GqsmrrGD7663tx2+Obut2UKYffYosbtSbqJdmmmfWujKtufN/HBy64B5+46H5sHik2XE6l3mG67R/64V/fi7NvWo6/LFzd1XYIUxsqL+qB2nrf5zxNG0uV7mvORThvHhHOBaFBvA7tvgpCp/nqnxbhjhWb8bHf3NeW8u97aiue3DTSlrJ7Hd33sRXlKZ/z3hmDmlnwSiwPNxXSaQbHyw2Xo55Rr2isV20d63YThCmMPSBc++psyqw9xQm0nq6ZtWtt6I0xZTIjwrkgNIhoeISpysbhxjVy8WVP4IMX3I0Tf7ugbXX0MlRr00qBupd8zptZ8Oqp1HrnmnoFuvguZBtfwoWxCppukiD0PNq4QoXzduY5J59T5zlPoTmnG5qlbgnnRCDvlvZ+KiHCudDTPLx6Oz54wd14ePX2bjclQqujLgtCr9BOLez2sTI8H9jcxg2AXsZr8bChFpe9JGQ1pTnvUA7iyUq5Et4U6muaFrWG75lNnUzj1yIIcdjciTpl1p4+IBxfDn9s9wVjuplQqno9Zck1GRHhXOhp3vvLu3HPk1vxkQvv7XZTIngeP9gLwmSnnb15KgcwS4Lf4k290Od8itxPCbTphGrGck0ItGEKvt64ySKaC+1ES01Gunw70341k3mCri/jpkr6e/eitev1iml7c4hwLvQ0aiEyXKx0uSVR0uxsCoJQQ21kVXZQ4VzXnLciIJwSspouqmU0YyoqKSrd0MV3M/dHCSu9kpNYFOdCO7FZLLXV55x8bibPedzGa0/4nBvVSlC45hDhXAAAnH3TcvznbxfssNqsRhDzS2Gq0s7+vKNrzls9bqgieklz3jKf8965pJ5BE86buD+9FuU/I7pzoY3YTNnbmkqtRXnO4/bg6LHFHojWDojfebPku90AoTc488bHAQC3LtuI1x2yV5dbE6XQhG9duxCXc2Gq0k6NpXpvKp4P3/eR2cFUZp7FvLJR1AKwl4TzZvqPbHq6ocJ5K3z7e8WsXRDaiW3c7dTmVOpUaik057pZe3feZ1M4F815c4jmXNAYLVW73QSW/nyu202I0KndV0HoNG3VnKNxbcJUoNWber0YdbuZtrTa7H+qQRffrcgn7/XIGnoH26MTWoTn+YnS8GljEvU5b+PA6fnJBezoueHnuDN7LSAc0L2o8VMFEc4FDTOoQ68wrdB7XbXVUZcFYUeAzuGVHh1v2kkzCza+vNr/PbVB2ExbRHPupFUaKSWU9IrFhcjmQiN85+rFeNX/3ILf37vSeVw3Ylk0F609jc95+LlXAsKJWXtz9J7Ek5Af/ehHyGQy+OIXv9jtpkwpKj0aYbH3NeddbIggtJhO9ecd0e+81YEkVRm9dCubaYpserpplVl7EPuhRyYv0ZwLjXBZXSj/2Q3LnMf5LR53k6AL542btadJpdY7AeF6Y1yZrExK4fz+++/Hr371K7zwhS/sdlOmHL2yi27Sn++9rqprqnrzvglCr6Frzne898Zr8bihxqFe2uhoTmjsDXehNdvH8fZz7sDVD63pWhs4SpXki3YXqr/0ypS/o8WeEFpLXPdptcVSEpoJbqkL53E+560JCHfqNUvww2sfbehc0wpONOfN0XsSTwwjIyP4yEc+ggsvvBC77LJLt5sz5ejVxXJ/oRc15+HnXlngCEIraGsEW7Jk6VVLnXbSag1OLwb2ap3Peff43tVL8PDqQXzh8oe62Iooeiq1xgljFfROvxGERonb3NGE3XY3po62IZByUNQ2KWPrCT83qjkfKVZw8V1P48J/PYViJX3sKdNDTXzOm2PSCeef+9zncMIJJ+D444+PPbZYLGJoaEj7J7hpJj9tO+lFn/NuDPaCMNkRn/PWjhu9aNbelOacfu7iNQ2Ol7pXuQM9lVrjN6gXAwlyrNg4jHNuXo7RYqXbTRF6mGwKzXnHxhUaeC6tWXsKwV7Lc96gxrraZKBJ8/okWntzTKpUapdffjkWLlyI+++/P9Hx8+bNw2mnndbmVk1+6IvdS5rzCnm5e92sXZQPwlSind2ZLpJ6yRS7U7R6kRjmq+6he6lZB6RLl9cr7kK92jd1n/PGy+m1fmPrIsefeTsAYPNICae+/QUdbJEwmcjEhBTUlSmdMmunpvTpzqWybdyp9BVuVGNN29qQcC5m7S2l9yQeC6tWrcIXvvAFXHbZZZg2bVqic77xjW9gcHAw+Ldq1ao2t3JyQgeNXlqQjJVD05reDAhHP/fOfROEZmlvKrWQHdGsvdWpwnpRA6r7dzZ+bjeH1V7tmqVqazYvgmjtPdJx4oSrB1du61BLhMlInOZcE3Y71OV118d0laYZQ/WAcI1dXDPB6wAuIJwI580waTTnDzzwADZu3Igjjzwy+K5areL222/HOeecg2KxiFxOF+D6+/vR39/f6aZOOqhA3kua87FiKJzn40beLtBq81RB6BXaqVmg834vbQZ2AnOB1ppo7cl8zn93zzPYa2Y/3vCCvZuvNAZzwzeXYvxuJv1QK+kVodWkopm1N15Or/mcSzw4oRnifc5b896koRmBl44/8QHhws+NaqybDZgnmvPWMmmE89e97nV45JFHtO8+8YlP4OCDD8bJJ58cEcyF5PSqmeloKfQx65V0L5RmdkUFYcelNzcDO4F5uebVjxYr+MAFd+PYg/bEV9/4/ERlqjJcC6rlG4bxnb8uBgA8/aMTEra2cZpalPbIuNorQqtJ6wPCNdmgFhErm4v0LjiI6x5UoTwpzNpTWBDRcbJxs3b+c1LMaiUgXHNMGuF85syZOOyww7TvZsyYgd122y3yvZAOKpD3knA+Xgo1573ULoUI5MJUpdmu7fIz3pE156bAZ44hVz6wGovXDGHxmqHEwnkSIWvzSGeDmzVjmq75PraqQQ3Qq12TmrU3s4GgFF2TZR4T0VxwkY2RznVNdLtbE62nGc153LlatPYGNdZaFpEGipBUaq1l0vicC+2j2quacxKdtZfapegV30hBaDXNdOd1g+N46Q9vwpk3Ph5b9o4WrT0qnOu/N2JJkCSwVzeVjmkXpa1ONdcovWrWThffTeU57zXNuUjfQhPE9R8613SqyzcTNFjTnMccq/ucNyqcN7fpZ57TqO+7UGPSaM45br311m43YUpAd8l6SQge63HNeasDOwlCr9DMov/sm5Zj80gRZ9+0HF9+/UHOsnvxvW4n5n01x41GQmuEPuf2Yzot9zSz0NM0XN2M1t6jO67lFvmcq2fUzXcwjdZehHfBRVz3oDJrpzbeaC1p60wTcFjzOW+jWfuStYNYt30Cxx+6V+Q3CQjXWkRzLmiLkF7yAaXCeS/6/01lzXnV83HNorVYs328200RJhlxyvBeTd3YCeI053GmmRxhnnOX5jwstxNmzPrCMt25vvWPztKLcw5gplJrvI1BtPau+vWHn+P6vsjmgotYs/Yu9PNmglumMcPXfM4rjV1nEhP8E86+Ayf+dgEeWzcU+U0CwrUWEc4FPVp7D+12aQHhenAR3w0fpk5x2b3P4KQ/PIjX/M8t3W6KMMmI03bSX3vxvW4nkYBwEeG8kTLjNaC03E7cc33jMq3GqEd8znu0b5ZaZC6qLq+rrgNTbeIUukZsQDhmvdbud7wZCyK9vXGacyKcN7iGTxOtfcXGkch3ZrXFSjVyjJAcEc6FlvirtIOxnvc5Dz/3Xuua4/bHNwPY8TSbgqJ9z53O+ztanvOI5tz4PS4dEF9mvSyXWTspthPvdFOa8x7xOZ/qZu00ldrGoQlMlDu/mE4jsDTybgg7DvGp1KLuMraxsFXWRWlM003SRGtvSUA46x9cfdEDTM35SFGE82YQ4VzQBq1eSn8wRhYLvbhQakY7JAi9TDu7M9Ws9+KmWzsxo+Ca40a7zNqpUXAntJXNaIy0IEpd3Pbs1ViFWkC4Ju6Pei5PbBrB0WfchNf//Lam25YW2jVE+BaaIc7qiM41riCa3//bo3j1T27B0ES56TZpPudNBMaMmyZbHxAu7tjod+ZcPlJs/v7tyIhwLugmMQ36q7SDsSINCNfFhliYyppzWScJjRK7BqGa816VgNpEnOY8R2bkpCaXPtGA2ui0WTvVSKU1HdUXtC1qUAP0qsl1hREyGkHNqRuGigCAVVs7H18klea8je0QJj9xG5tVRpnCac5/c+dTWLV1HJfft7LpNqUReE04Tb+9nvBzwwHhUmj5ud/NezkyUYkcIyRHhHNB0xD0kuac+pz3ov+fpvXqveY1hSyEdmza2Z3b6XN+9xNb8MAzW1taZiuJy3NOF5jlhBsXSfKcU61kJ/ZDOC1VUnrFIqlXrTpKmll7423sBWsvPSCc+1jZMBaaQc8CUcP1jrfi9ddddBo3a0+jOW80EFua5SzXHnNuGymKcN4MIpwL2iDQqL9KOxgn0dp7UcOmBy7q/kJHEFpFU4v+2LLDz630fx4cL+NDF96D9/zy7p6KnUExr9YVrT2pP37oc55Qc94BoUwXztPV1ysWSb2qOdfN2hunFzYfaBtE9haaIU5zTuca9Wq3+x2g68K0S1g94HByTXajw5be1jhNvV1zPlDIAQCGRXPeFCKcCz3rcz6qpVLrYkMsaIvIHmyfIDRKezXn7fE5HxwLfdx6VTiPM2vPkhk5qXCujkp6KzshlDUjnGuHd3Fc7QXhlaPVAeG6Sbo85yK+C3ayMdKMxwws5jvut0DI1cuz1R9PGoGbCv6Nbr6mWc9yvyuBfufpBQAinDeLCOeCRGtvkF5J+SMIk4l2ac7p2r0H5A4Ws13m3xk0Y9aezESzE0IZtXRKW13PBITr0T5UrtJFe+ON7IXro22IE75FNBdMaP/PxPQQztUmKpy3rm1Ac/Ez6FI8bsym96HRtXKacZfbAFDtnT1QE87FrL05RDgXtJe52ENm7XTnrReF815J+dMOREmxY9NMf45P+0IXEq0bb2if7QWtIEdUc67/Tce5xJpzJZw7/SfbY61goynNOf3cxcfYi3FOAMPnvIlyeuEd6YU2CJMXulHVSJ5zU8hsdW9MkzvcdW7cma3YfE2zkeBKpaY05yKcN4cI50LPas5pKoteFM7F51yYqjTncx6zy08+tzLPebbDQc8awRzGzNtcbWAsVtfqemSt0KykoamAcMwiut1sHJrQYpwAvZm+E2idWXsvzKlpgv/JhrFgQi10GslzXjXmn1Zb7TRj1p5mg7OZTQCFn+JdZFOp1b8MNOdi1t4UIpwL2kK2p4TzcSKc9+BCiduJnSrEmYgJQsOQd6WVAgJdm/ViAEkgqo01r57+ntTkXy0iXYuyZhaJjVBplea8Re1xsWb7OI4+4ya84sc3a9/3gvDKQefoZp5lL8xZaSJEy5wkmNBxJq536KnUot8B7R1vmslaEdcwT5tT09UTVKHNEXHHRg9Qz0IJ56Wqh2KlGjlOSIYI54I2QDWahqEdDE30eio18rl7zRCEltPO/kw1EuU2vde9uJkHMAKR8UVDmnPLQpM7Bui85jytFUanU6nduXwzAGDraEn7vke7kOFz3ng5vbD50IyFhSBUUpi1e8yGoelW1Yqo5xQ9z3nj42D8ueTYRn3O6edYTT3zXf3LWdMKwXeiPW8cEc4FbYIst9DMtBkqVU/zWenFxXanF5GL1wxi+1gp/kBBaJZmunPsrnv4udpCSx3N76539hg1zEWWucihfycVzgOfc8d9b4XZYxronJL2EafR4LQCm/lqL845gGHW3sSL2gv+3mLWLjRDJYWLB59KTT+m1a+EHgG9cbP2+Dgu5LyGo7VTk/7kxyrU/S3ks5jRV0unlsbv/OalG/C+8+/C05tHE58zlRHhXNBetF7RnJtpGEzfoF6g1busLh54Zive+os78Pqf397eiurIQmjHpq2ac1J4K6O1a37VPSB4cEQCwpnCekMB4fiybPV2wnOpuVRqnTVst3XBXhBeObRNmyaa2AvX1wNNcDJSrODjF92HPy1Y1e2mCAzlFOMMp1Fut/uTttGYOs85+RwrnLdAc65tijauOc9nM9hpWh5AunRqf1m4Bvc/vQ23LNuY+JypjAjngvYy90qecxoMDujNxXYzu6Jpufbh9QCATcPFttYjCO2GvimtNK3VF0K9N14ATEA443ctWrvn4fL7VuJNZ92OtdvHHWXGa841a4UO3JtmfM7T5NttJz045QAAypXWmIL3wiuSxqKjGxvGv7rtCdy6bBO+duXDna9ciIVqzuM2Mzmfc1NgbvU7Ty1b2mnWzpnspyXNRgK33lX3N5vJYKf+mnCeRnOunl8vuNv0AiKcCz3pcz40bmjOe/CF1SN7tpdhY7Oi3YjmfMemuWjtyctupebca1O5rcS8r+Zt1jNn+DjlL49g6fphnPH3x6xlqkvtpYBwzVgV0QVtJ55irwrhNlpl1t4Lc2qajZhuBITbJm5kHeGR1YP4+Y2PY6KcLoAYdcOMG9d0g5PasabmvNVZd/QUZ+nOTaOQ0jZfW2LW7i6DjdZev8BcNoOd6n7naTTnqt29YNHTC4hwLvRktHalOZ9VN4/pxRe2kxoeyRkpdJK2mrWTz60UEDod9CwOTnufSnNOFp5Fx6apGht9376p0uk855UUi2aTTmvOe3FucVFKmUpty0gRn/7dgoi5aC9ct64d7GJDLPTALdoheNs5d+B/b1qO8297ItV5VLiOG9do8DfbhmbLrSGbCQjXaCq1Fizhueri3MaocK7W7SPF5Eoldb29OA50AxHOhR7VnNde6l1n9AHoTU1YmgAazZJmB1IQmqWZdUmaHKnt8jnvtuAxVqrgVf9zC75w+YPa93E+51q0drLKyiZUGtou24tZWLWaZqJw0+a1WpM1FdA15/H88NrHcP2SDfjERfdr3/eC64eW0z7marphzdUDt2iHYum64VTH003AeOGcWvMoE2r9mFa7J9LSUuc5T2F91IqN6TjrqrjfK1RzrszaG9Cc98LGei8gwrkQya3bC5O20pzvPL0mnLu0Qt2ikz7nwx3WnEtOWaF90AVVe6K1d3uC/8fi9VizfRxXP7RW+z6N7yBdeGYdkkkS38RO++NXNC1Vuvr8FIvSVtBbs0o8aUx5gVoed44emOZ7Jr6AjV5bc0x1ckl3IevQjaq4TUetr9X/j5q1k8+tVZynz3OuBYRLrjlviVk7U0Ts7zQgXF04T7NuVXO2vHM1RDgXIgvZXggKp3zOd5ke5kzs9oLbpJODyEiHfc6FHZt2aiw7oTnv9lhhq98cMsy/6dBLgx1lHYvWJFF9fXT23jSzcdlJi6RaJb01r8SRNlq77ZBOB1ldvGYQ37rqEWwcngi+63Q60rR0exzZ0XCNcxx0/oh7VlQQ71RAuGb6d5px0G9ivOXqYDXnWn12zXk2E0ZrT6M5TxLYdEci3+0GCK3j2ofXIZMB3nL4PqnOM1/EctXDtEKulU1LzWDdrH2XuuYcqL38+e42SyNNHspm6bhZuyjOd2iaMmtP8XsrUyRq5XZ5hrdpOiJm7cbd0s3aW6c5p4vQzkRrj/p3JqUVC800TLbFYNqAcLZ72Glh+K2/uANAbW4/58NHAkgXOND1DrSLydY3Jju5lI9Y05zHPCw6Bqq+H9Gc95JZO2OGb6MVMUV0tzDud9o2exvyuQxmNhCtXbW723N3ryCa8ylCsVLF536/EJ+9bKG2M50Ec9DoBb9zZda+y4xQOO+2H6lJmuiWzSIB4YRO0kxvjntN26U577RftQvbdZlfR/7WzNqT+ZwnMcXsuM95Cl9Qk143de42mll7gqnadg+559IJgX3l1jFSH6k75rxu+JxLzIPOklZzXk2hOdd8uOv/R2OApKo+Ft2dKN256XzOmxfO9dOiZcRtAleJ5nxGAz7n6v70ogVNNxDhfIpAJ+wla4dSnWvugpVbqM1qFDMgHNB7O2qdXESOlcIUIzJ49T69kvWgF2mXibVm3t1tzblVOHcvBvVUauEF5RySSVwU3Vq54edOjKPVmIWcCz2VWvvbOpnG06rn6xq1BOfYrTiSfddqdp7Oz+m9tvkOTI7NobtWbMbbfnEHHlk92O2mNI1rnONImhXC933W0tHMje5bPjdKM3nO4wKwUZpJ2UZqTFwG9zON1p7P1UTLNBvBQUC4yfDSdQARzqcIVFOxfEO6iJfmy9ALgsVQfcdtZ+Jz3sLYUS2hU3nOzYV+r21SCDrrByfwotNuwDeveqTbTWmcNnaxjmjOe9bn3FwMGu+2JpyHnzNOs3b6Ob7eTghBzbj86GbtLWqQg8k0nJpzc5KNBVYIT7h51A52pXN6Cu1gNzytenHDwOTDv74Xj6wZxMcuuq/bTWmaZgLCueaSSArLwL/Z3CxN1h8rCdfIzYxlScdQcwxotM/qWn7OqsZeJ6AL5+oxppmH1fObTONxOxHhfIpAF3WPbxhJda75Irpy6naKIdbnvPvtonRKcz5kBIPrRFo5cTlvnIvufApjpSp+f+/KbjelYZrRWMadSX9vZbR2Srd3323GR9FFovF7I2btdEFpuZ36PbeX1SoqTWhEeyFae7c3d2xEhPME53AL6aQxEVoFbTfVnHcy40kj9Gg3YNk2Vup2E5qmXQHhzN/UX+ZaKsnjPufm5XjB967HY+viLVSTWDXZSJqO0vytFWbtXAlxVgBBKrVMJogPkeaSgzznk+mlayMinE8R6Au5dH06s3YuIFy3CVKpDZBo7T02eXfK53zrqD7pdkQ474aD3xShF9xCmqWdrxpdsLRLc97tfTyrZjI2F2/4md4bd0A4+jle6OqIWXvChSVH3CKx1XBCYTfmwC0jRcy77jE8scm+uW6OLY1qzm1zabveezqHzSJzehrrs27MSWk3K+Y/vBavP/O21NaLJlc+sDq1mXo3Aua1mrRm7fQ9dY2t5nNUf5tjYZLn/dMbHkex4uH0ax+NPTaNabpJ0kjvtmtLS5x1VZzpPA0IpzZZ0sw16tjJYK3SCUQ4nyLQl+DxDSOJzW7Mc4HeCAinopPPnFZAvv6icwvuUsXD5fetxLpBPpdrO+mU5nzbmKE574HNE8FOr1l4dJo4gUGP+tpK4Tz83O1nkDQgnMskkQpiWcdMncRkPYkA30qa8SXuhfRaEY1aB9rx9Ssfxq9uexJvq0c154iatceXy20cJwkc2Eo2DRfZytPMoV0RPVPejusWr8fyjSO4c8Xmhqu8c8VmfPVPi/C2c+z9gGPyi+bpzdqpz7hLeROZZ3z390lIshnSjC940tgS5jvbkoBwTBFx84x6FtlMaNaeZjwJN0wSnzKlEeF8ikAHplLFw9NbRhOf24uacxWdfEZ/LtyFY170H123FKf85RF8+ncPdLR9QOcWjtsNc7VOaGanwkTfLaaE5rytZXdAc97l3fckvt+1v/Xf6cKKbsIl9znnj+l0Dnj6XNOOkzFrxJbDNc9M8dcJS8uFK7cB0IN/mjRi1s7tU9n6QLuuc/NIKJzbggV2+53lSL2xpNJBNXEpjzeodZ8CivPU2n+6Cesa12zm6zZz99pn90NM0lYtuGXqTUr62X5uxDXKb2xtqputc7/b6yxXveAe57OZwAIizfsjmnMdEc6nCKZJz1CKFAamLN5tzbnv+xitC+c79eeDF53LifybO58CADzchUilndLwRM3au795ItiZCpYN7ezPmua8lXnONY18mvN8fP73C3HG3x9rWVuSCj/mUfY85/a6kiwAmzGvbARNc57yddCuoQPrNG4RXjYa3Yl7lsRsO2rWHl8ud0infc63jIRzGH03vYTaQaA7wmejuambGT8bvcyp4IqWSymRlBOmbDTXx+rxRITzFJYcSZT8rTNrT3ZckuOtxLSVxjOh/btc9XD8mbfhgWdqm4vZbOhznmazT819IpzXEOF8ihDxnUnj62G8DKUuCxcTZS94qWf05wNTJ5fZUjfmpao2WLWvnlEjx7mZ/mNwvIx3nnsnfnPHU+1rhJCYTsQEaDfNXEHcufRdaeW9SqIdrno+xkr6+7R4zRDmP7wOF9z+ZMvaYhfOTY2sfdzWA8I5NOdU2LHcziQ+54+sHsQ9T26x1pMUM21RMymEOpNKLfqdOcZ2RDhPcIy5cZ6kXWxAOFvgwDZN/VRzrmvLSd2xl9L5ST7tUkhdTzP9pVEhe7KK5rR/pg4IR33OHRpjc+0YmFCblkwpxpu0Jvip85yTl2PzSBF/WbgaE+WoVU2amBIu0gWEC79/ZssYntkyFvydyzTmc+6J5lxDhPMpgrnITbPmNQe0dmnOBw3faRsjRBgdKORC4dy4KFre4c+a3YIWpqNTqdTMZ2uaNl5w+xN4aNV2fH9+fIASof1MBeG8KWIun/7cymjtScwA/9//3YtX/OhmbYwpVe1mxI23xaLBNv82vtDM2hMHhIsXhOm9sS2YPnbRffh//3cvhieSjdP29tjbl+z88Pi7ntiC7//tUXZR2iq41sX5dlc9H7cu2xjMQd+86hG86azbMe4wSY8jiUzWWLT26Hcd15yPUs053197MVp7elNkJWA0XmejiobJqjin41zqPOfGjXZtylJ8y/dpnluSTZRm3DboRu1dT2zBl69YhB9dtzRynCutWRriorHbrACmFXQxMpdrzOc8yHM++Q0PW4II51OEZoJCmMe2w2f2igWr8KLv34ALbn8i9lilKZ7RV/M3V8K5eY0Pr9kefJ5NIsB2ijQ5WpvBfB7m83L5KDbKZJ3oe4GpYdbezrJ5AbRZvATlLl4ziG1jZazdHgaQdDXB9318+6+P4MKUWnV7QDh+kci1pZxQc57EdDJukej7PraOllCu+kEwzkYx3W7SPmLavKsfWovf3PlU6vuflErV433OY6I4//bup/Hxi+7HBy64GwDw+3tXYun6Ycx/eG0TrYkfdM00p8mitTOL9w4L55uHbZpz9wY3vb5uzElp74YtCngaGr3MyRqtnVqppM9zbqyNbJpzS4BH2/dJSGvWnvbV4t7Fax9ZxxyX7Nw49LZGz7etd8399VzGvmZ3ocrpxU26biDC+RShkZQQrTg3KV+/8mEAwBl/j+78mYTB4PIAwkmnUvVRqXrBonXRqu3BOd0whelUBGRT2GvE7zAtk3Oa7w16IaBiL6NrzlvXefUFg0VzXf866cbafU9txaX3rMQPU/qj2+v3zS/088jf1IIpuc+55RjNrD36uxbpvsnNWXOxll5zHv3uqc3JA5wm5ealG3D4qTfgmkVRgTpug+Hqh2rnLF2vB+9qJtd0ksV+I9Ha+bRI8RYWrWQTNWv3+P7KvTP0q27MSd3wOW90F2Kyztk0vkPqgHDG+2AzxEqqOdddatwk2UigY3PqPOcJNeKt0pzHzYm2edscK/PZTGBVkMYwTpXZiYClkwERzqcIZodO5esR4wfZaWgwOCAMElKqenjdmbfhbb+4A77vY+3gRHBON17ouF3/VlE2rk0CwvU2O7pZe6zfXgsFQa3YBH7VnGbLtZDeQFNApcBWvvnqmjXT8ybKZNHqWAgm2SSkX8ct+sxgaGlpXnOeXNPbDAue3obxchWPrRuK1G1ugJr3NW95HtsTum4pbl66AX+4byWAZDKZ6XKWxEeWNWu3+Zy3ae6nmxa2aO1czd1ei6Ttuy0xa2/0vB1Qc27Otba1UaQf1f9sysIhSbT2JhQ43KVwVnm85jxVVQD094+N1m6ZQ8zrypJo7WnG7WoL3p2pRL7bDRBaQ0Q4T6U51//u9oQ4WtI15/l6kt/H1w8HgSfGy1WMEb/Rbsir2m1q4z0z/XKnQqquqUwrBc6piJ5KrT0+51bzRrUAoMEcHWU26n9N6/f9UPCKmLUbldPzJiqhu0pSn/MkUeJ5DWV8GUlpxlQUsAlpTTTIAnedng/kMtF32AyUZhMitqUUzj958QIAwEvm7oJMArFssmjOB8fK+I9L7sc7j3gWPvryuShXeMsNfROLa0/4ZVfM2hsUqJoLCEfL8xMHSZuksrmeMjLtueZGoGU6icZk8p3fA4h9uZL4x6cLeKiTVHPOvt9NSuc21yeufFN+yGdDn/M0748EhNMRzfkUIRJ1Ms1LEfFXb0mTGmakWFuUzujPAQDqsjlWbwsjQlY9PzgOaI9mJY5Oac7NhaK5eyo+Or2FmLW70XbgW2nWDn7xQFFfm8Jz+Fk/j/pfp1nw0GNt0anNNpvnFctUOHdUpi2qLO2JEb7p783232bdpFq22IyB2+T0g0W7Oyp6Psc/kMHxxszaN48UncLVio3D+Oiv78VdT+jR9JPcWu6QpNkEGuWBlVux4JltuPz+mlUAvZ/6u+Gum36VZPOi1aQ2a2+B9o9uxKWxYpmksrlmGZj2vplrI2ssBYv5uvl9mvqT7ZnYNcxxcMdzVnnqODp+NBat3b1RRr+qaMJ5VHPeSLR2SaWmI5rzKULUrD35uZEBqo12JUl2dyNm7fWTVm8jQZw8aCmRuuNz7h7MWoW5iDQH6HZUPVlN5HqBHd6sPebybZN8s2h+0zE+3/rGmr44pHIX1ZxXfR/ZhEvgimXBaQr/Ec25zaw9oebcnufcvUjUfAibtPyIpvVMdz53Ce1wW+IyBahanBo1ALksr9dIa9auyMSInn99cC3uWLEZC1duc7aLQ9scqmtibae1ah5V649ivQ/T51dN0F+59nRHc572+Pr40qKAcGn6/WSds/V0aOnuW3RtxA820ZRp6vjGNxKTWDQ0atbu+37icVAdl8tk4MGH5zfW/1yb1ObvnkM4z2UazHMuPucaojmfIjTjc27LAdkOBgq52GOUcD69rx4Qrj4IriYRliuep+X/bueGAqXq+Vi0ajtKFc8wjW1f/eaE02nNrGjm0zEVorW3k3ZpztOkFNPGC8eiZIRoztMIrS7hn2sPd95EQs25rn3kj6Ffs5pz0mWbdTVoZsFrO74dcxK3gaOqMcdY81Dqc077TKPCeTbjFq421WMfmJk5ktwVzlfUpllr1W1Wz0tFl7f5qMZpzrWAcF2QPRsNCNcqs/Y0Lmwp3bV7BnqNadcaSc3aba420c1SOm67SWbWTjfGYg8PsM2LLs15lkRJb0RzTs/g/dj5DTazrlw2E8SJShetXT2TxKdMaUQ4nyJEzXPSvxSKJC/2ttFSQwJxMuFcmbUrn/PagLNyi27WPlrqvFn7nx9YjXeceyfOv+0JfSBvY/VxqdTaUTeddmQjMx2iOY/5XfM5b929ShIQjtudp0eaZ1Gz9jRCq82XNppKzf5uU59zF4k2JSyaS+67lmvOWzBAteOV4q4z8EWNZMSILkCDckjjBseTC+f0mWQyGafwuXnEEpgwwX3hNsPanedcXVux3oer5H7aXD64qrtt4po+IFxj51GoDcUOoTkn42radVyjqdRszynNvnqSyPJx2mgbae5DcA1kg6+R/mfbUA6/I+1zac5ptPYGrlk05zVEOJ8iNKP9jpwb83Is3zCMI35wIz520X3O4ypVL5icFdOSCOclZdZe9zmvv+jrh8Lo7BXP1zTnnVJWKr/3NdvGdfO8NtYZm0qtrbW3X3Neqnh4cOW2KTMoS0A4N+3SnMeVqwnvFqHAHDeHGtScVxIK5+arS6ugZu2u25TEdFLTUDKF6alxWi2cpzu/m5pzRdw1FIjvA7Wu2J4ilRqtv6Y5tx9rE86TRWuPPtu4mAzNospRmnPNR9XSX1mBgEx9XfE5T3lD1PU0M2dqm5cpFjaTVXNOx9X0Puf6/alaxujofMBvUqVxVbR4tjC1qLLjj09aN0X10Wwm1OY3a9bOnW5zjeLuYdiO5PW3IpjiVEKE8ymCudBIZdYeidbuPv7KB1YDAP61fLPzuPecfzde+9PbNAF9oC9eODfznHORcauGcN4p02sVvKTseU1F4kxD0nQh7aLdMvPXr1yEd513F8765+PtrahDSEC4ENZ3jXxuV7T2OBNZTaPhyBM+RDShaYRWm8AcI5triypq1u5asCSx4PEYAc32e7P9t2mzdqb6jvmc16uJmrXr9VNBkVpXjJaqieci3Z/a7XW+eYQX+pPcFnqMEl5s57VMc14vR/Vh+vySBksEDMXBJDBrD1OpNX4f6fuTbqNsckrnFUt/SHsu4AgIFzFfr/9vHJdmnEmrOU+XQSn9XKOZtTc0XtJ5hNvcDj/TDRVz03qi7BGf8xTX3IJ3ZyohwvkUoRmzdvNFjHuxZ08vxJZZrnpYtGo71mwfx4bBcNc/jc/5Tg7hvGKatXdI66p2aitV3/BPal/95kIxanLZhkppOpc2D5Z/fWgtAOC8W59oaz2domJZiO4ouITd2ndEi9ZCK4M0AmhcCieFlp851YZnMi2DOfbqPufhe++qOW5TolYPOSZOc95ys/Z053NjaTvGoDLTMFV33AZDiYzJQ0a6vWGyaezC1Jzb8H0fm2ya8wS3RXNZ8KIB2sy6WgH1Ofd93xrdWYvbwlTd7YV62r6rukUz+1teg+/iJLVq17TfaR+3uTay+mpHrA1rxKW2dJFMOHcLvDbSmbX7QXvUONJYtPbwMz9vR+s0P7/pBXvjiDk7B1YFSd9f3/eJy03yNk9lRDifIjQTrT2tj+DOA33BZ5vZFQ1cQxcv0wrxXW40geZ8rFSxRoBtJ8qkvFzVNefttCw3J5ZIrtsW1PGr257A//xjKfub7dZuHS1hvJTMLzYJU8esvXEfuqmArpV2/94un/M4AdTmkmKOfTRvdRqNss2nPW5jjZ5XTKg5j9M+1trgHivbadaeOld0zMKwVXAbQ6ruiObcePTKXBvQNecAsMWi5Y7Ub/ic2wT04WIFpQrf95JsCnP+oe3Ocx5oJv3anJlssypmU6YLQ2naKrlsEGnR38Wpb9ZetsQjSEIklZrl/GiqYf3/oP4UCpck91ufW+KPD45NcTBNpabWyo1sssW5R2lzCDOmHLLPLJz//15SS6WWSafB16x7psg6sFlEOJ8iRNPXNL5LFzexzBoIM/BtY6LTDo6VNZNz6i+XxOc8YtbO7FCagXfSjEWbhou47N5ngnrSoCbLctU3BvL2ETVrb21tvu9j3nVLcd6tT+DpzaMAdLNNrj9sHini3+bdhBPO/lfPRie/98ktOGbeTbjx0Q0drbfMTFxJGJ4o479+9wD+/si6djSrK/C5ixu7P3FoJoRMl7Rpj21m4b7vaz7EjWrOqd+smbfYLFHTnBN3INf45tpcCL+nbeN+b0wg4GhmLgLADqbtWLDx0dp9tr6I5rzCbz4DwNZRS/A2A1qHK1r75mF7eUluS7kS3Sxse0A4Uk6xUtXznFveN67b6bJ55xftaYUc9UybsUBw5ZB20Q2f/Fag943GzwXs98vsW7bYAHGb6XQsS5ZKzbYR5SaVIF8/NpuhQnHy8xXxFm/hZ7onou45zWARbhIkq9u2ebcjI8L5FKGZgHCRaO0pXuxtRgCcR9cO4UXfvwEf+00YLG4DCeSWKCBcPVp7EBCOGQSHxnXBOs0kdt6tK/CtqxbjygWrEp+jKFdq9VQ8T7tvKzaO4Kx/Pt6QwB+HOQGZwnCzYxm9dSoYH10ncsU/s2UMxYqHJzeP4o8N3MdO8MEL78HawQl86rcLOlpvo2Z6597yBP6xZD0+e9nCNrSqO7CTPPncrjzn3CLLpmG26e5GihXD/zP5wGjzo0yjOU+qUUpiOhkX0b2VZu2R9EYpi2Pb18QgN1Gu4oy/P4YHntmqfc89T1WLeQ/MNrk05zb/cBNNc+4QrZzlJbgvdEOoEudz3qJ9Vlp+seJZozubmsqxkn4vG01F1Soa9zlvTZ2uVGrbRkt4qr6ZDkxms/bGBbOk8S1sec7N5xQXv0N3RUkgnNO2Nbi5G1tHYNYerpUb2cyMc4/S3cLIhl/9RLpOT2tenyTjyI6GCOdThIhZexP+LXEvB63KNOH77d1PAwCWbxwJvtswFO78JzEFMvOcc5rzIUNznmYw2lhvzwaHRsKGWujUfM7D729euhFn/XM5zvj7Y6nLjMNcKEYFGvu1FytVzPv7Y7j7iS3WY+L8b+MW82f9c3lP+lZ3a4ynzyvQVHk+nt486tSobByesP42mdCF3ej16hruVgrnbmHW5nNOm0iPMXNWpwsIR4WOkLhAY7a1uKvmOK14rT30GPe9aSYg3FObR/G1Kx+2lp2EOGuLtJx36xO44PYn8Z5f3q19z0f0r/1vWjiYhxbLVDg3fM4nkm3QmkKqba1vTaOGZBZbVMDjUglS2qE5nyhXrdHa6drjrw+txaHfvR6/ueMpUk54bDc052k3BFR7WxUQzjU+HvGDG3HcT28N/k4iLPYiZW0zO6VwHrs2qmEGf7S5H8QN8XRDL8laNs6P235e8oOp5jzXQCA2RdzGBP2KcxEjCSxSB4SzxaHYkRHhfIrQjCmheWjcufR3U3PORWOnKdCSNGvECAiXzzHCubEgSjMYqXNHEi6iKGoyKFU9ts4HV25PXWYcavLqy2e1NiThsntW4le3P4kPXXiP9Zi4e+czgyXVDm8aLmK83Drf82KlOqn9jsrMrvK3/roYx/70Vlx059PW86bihnG85ryV0dqjCwbtd48/1mbOt3VUH9saTaUWF6hOb2N6oSmJ6WScKwGVx5t59z532UKsIBuztbrTlcEd3kyblm8YZr/nnqe6T3F5zmlAOFMYHy8lFM6N52Y1a3cJ5zG3xfN0f2/V/2xCUKvGIFr+RLlqdSmhj1X51X9//qOxx3aKbkRrp9dsbhJNRSpNPOOkAeGSbFq6zldomvM2mrWnGe9Cn/Nmo7WHxG2qa3NpYNYeipPZlKnUqjFz946ICOdThGhAuBQvd0qtOx1kthgL2OmMcL5hMBTOk+yMmgHhuB1hU3OeZuBTwn8zPucVMyBcnXakdFMTgop0H/FbdVS5bnA8tvw4kyLuOzPScauuulip4rU/vQ3v/9Xd8QcnpD8fHeaGJ8q4YsEqDDIxE5qlwpgj/+G+lQCAn92wzHpep9IBthvNTzTmHWnXJkycdthqLUI+bx9vXHNONx1o+VGXlGTjtqtrxJkjJjlGM6dv4pms2DQS+S7tYourvpluYo3g7EilFjWX1Y+j6UFNzflowiCZVc2c154Iy+1z7r4x5lzROc15+Fm5qZltAOLHvLixpNcIAu41IVMn2Sij/U8xSRXnmoDdNrN2y3GRbBm0b3LlkHeWs+g0oWWk8yNPL5zXzNrTn8/VGbe21TNA1Osnyyy1SZC0HbYYMDsyk0Y4nzdvHl760pdi5syZ2HPPPfHOd74Ty5bZF7o7GknNIznSpr6hv28zhHMuVdqG4eSac88LU6TNqPucc9Hah+raipl1AT6NJabSdCQ1P6QoE8GK57dsIROHWtCrSPdpUqkN9OXtP9bRzStr/9M7zl1nnJDRKJuGi1izfRwPrtzWkvIAYOa0aOq/L1+xCF+/8mF85rIHWlYPgEjaoDRmc71qLNDMs40zRW2tz7lb6LdtQmkaAfKHGSGby4ttg5oS0/tXsqT04epP8r35m+2wNPemmQCPe8+a5iw7EQk3CJNi62JsQDj1W4z7Qcnhcz6WcNNXyxjgMGvf5PA5j7stUbPfWrutPudtMGsfdfqRu+uLi+beblJrzpVw3sR9pOOMzcWEW7tMVrP2irFJBdTu40d+fQ9O+sODMee608wqIhlu6odFx9+Ytqacr2wCbRxJ41bU6qj9TwPCNWbWHn7mo7WHcHEj6DpddcWkm+/cGnRHZ9II57fddhs+97nP4Z577sGNN96IcrmMN7zhDRgdHY0/eQfAHMObyamYxqzdNP3khEHqcx43aFBzQSXoc5OOitY+a6CQqFyKMmcfKabXmqrJwIzW3k7UQl/dj0rVw2X3PoO3n3OH0+QRAGYwlgwmeq7Z+o4y/Z25TDNQTasG1DIJWNQqP3aaXUChIrjf5fDFb4SSaWZn9BGXsNqrgVCaaRa/A69/btVz9mI0TjbtsW35bz4PV3Amk4qmDeK/B5KbVbqeQdyiymxDOwPC7T07Kpyn7T+85ryJDaKE2jR6bJxGjgaEM12sEmvOqXUFjAwZpP5mfM5tZr/tTqVGyxkzNOdJUv81cmw7SGvZE/qcN1FnAssi03IQmLyac93KqHa9j28cxp0rtuCaRWuda9mk8ZJMtzs/eE7287mibCkybehzXfJO8di6ocTHhsJ5qM1vKFq7z3/mvuMCPNJ1eqg5T1Z3kj6/oxGvVusR/vGPf2h/X3zxxdhzzz3xwAMP4NWvfnWXWtU7mFqdTpm1m8I5Z0K8aZgK5zFtIWWrFzzPRmuvTU4zpynNefLrVWaIjZm11+qJ5Dmv0w75Sk1e0wKzdh/fumoxAOCn1y9zCnzKNQComcL156PCOpfzWRuomfIjJqEtE86J1sDz0J+N31zgoBMhpzlvF6a2NZLCxTFp9qhsDs/3kU2VpocucLgdeP27iuejrwVJel2CtfkdXbzoCzJ72xtOpUZaZgp95r1oRHOu+zXGH8P6nDNmio3Aas7TapuYwaSZbI22+YzbbFFNtWnaFE7NeVKfc8NvkwpXdOxz+5wn3+wGwmebJDZBM9ByTM15mrRJtOt0w9w1bZWqr9naeteKzfjSFQ/hjHcdjtcdshdfBnlkto2yIUZzPkllcz0rRf2+6fFB9GBjlKR5zoumcE7KpsT1R1tKQBv6xmns4QFKOD9wjxl4YpNbAUl9zpuL1u6et23WV2pMyWvR2tOatYefxay9xqTRnJsMDg4CAHbddVfrMcViEUNDQ9q/qUpz0dpr/yf1E6Flm8J5XK3ci7dhaAI//sdSrN42pg1gSpPAmbUPGsJ5mqiQSrPRmFk79TmPFzxagRr8phHNuWLD0ITbrJ24GdgC4HFmhnE5L+NSDTUKXRQ3osH764NrcMldT2s75bOmdW4PsmgK5wl39oH29J1WkLZV+saO+3egdTvlqczaLT5uuoZZPz+N0FpmTDVr37uFPru5sb2uJD7nutaDKUPTnDcuCStXJFv7ksDnuw4LGZ4oYyJFAEpb/ZybgnoH4/Kc65pzfVw1faxtuPI70/o54TyfMI9wZJyO9Tl3l5cUWn7E5zxBf1XYsh50irTzmmqvbf314V/fiw1DRfzHJfb0nrRf2sYcTnM+ec3ao+8BvRTX/JA01lJUc642UfTjYte+2rzhPLR2DOm1afqSEs4P3Xd25DdzszPwOc+iuWjtWpnM75b3VrUnx6RSS7wub9D8fyozKYVzz/PwxS9+Ea94xStw2GGHWY+bN28eZs+eHfybM2dOB1vZWUw5Jl209tqxhZyKsJh8N9sUzuPO5d67k/7wIH556xP44AX3aJOxGqDZPOcTSjgvJKpXQbXlzURrLxup1DjmXfcYTr1mSeo6bHUGZu2OhY8JneRsmxH03qnJJ85UNiJkOFuRHCrUNCKcn/KXh/G9a5bg6c1jwXectUC7iPoppxDOe3ROaqZdXKR/s7hWRWyP0w7Tr/RI2bRt9sVUGqGVM9WslVH7rN5Ls5V2s3b7Q4jTeJjHtDMgXNymSBJc7RueKOPwU2/Ay+fdlLw8y/Wwgk/9K5fbTqWq5+02A8Il1Zyb2iIqXNG2bR6O+p+qzB1xG3rmOF1hxnetTS0ahDSzdtPnPIV/qWuzrBOkrTJ0G2i8Tl1zzo85pisFgEmrOuditNB1i6tPmoKcbfycKPObolHrKHIMazEYP9ZSdMuPZOf4vo+l62oZJg7dZ1bkd9MaRtWRzWSC+9bQe6y1NXo+vR9cKjU6fmUz6TT42pgw9RMUJGJSCuef+9znsHjxYlx++eXO477xjW9gcHAw+Ldq1aoOtbDzRM3ak5+rXoxCPdxi3Ln0xY2kG4oV7KO/L6wH/1q9bVwbzNQLzpk0DY3XA8Ips/aEgxEVzocbMGtXix1zgFSoZhQrVfzqtidx8V1P8xNpCtQErdLUUaF1pFhxLiDo4Ggz4+dMWuNMZSPmuW3QnKdNI+P7fjAJb9DS93VuVRevObef27M+501svcSlZAFaqTl3l+kxi0Dzs57zXD8/VbR2LSAcLaPWPwq5bOQ3oAU+55ZXJvbeGMJno3CntmJsUGPUo2trGiUzB70L23vFplJT9UXynIfHmmO/mosUYwl9ziumzzmZ51TbRouVQOtH96iV+1jcrTU3GToVrV0za3f6nCdfLzTajzYNF/HFyx/EfU9tTX1u+o2lxs6z1WnXnE9Ns3Z16TT+QirNueW+m5Y2al5yWrYxRaUNXGY2J0m3WL1tHMPFCgq5DJ67506R3801hnovsk2mUouP1h5+1vOS1z7TlMdq7Z70NUjj6rKjMGl8zhWf//znMX/+fNx+++3Yb7/9nMf29/ejv7+/Qy3rLuaiKE0HVy9GIZ8FiulMewbNlGYNCOczpxUCIZ9OwFmH5jwICBdozp3VBlAtR6niWf2wbaiJxNSQKlTrK01qgLU66/c01JyHdY+WKjFplsIfbZsE2sKc2fnnnmk0mrG9DWkok/ua9r7RfklT/LUyIngcZoqbdPlKW92axjAXwWnnSno4d64psKcJtOasly4eyB/jpSo+c9kDOGivmcF3nLUIYGo69Hal6Y9li2+iutb+XBalihdN5ZNA822SxHQyLmpwnEtAUnhf/+bLUF8lyS2cpDzAreV3bT4WDS1co6nUTCFVH4drdSiT9mmFLPrzuWDeq81Z5dh7a9OcJ3F/aAZavqk5T2Ma3IqF+o2PbsBfH1qLUtXD0fvb3SDZ+lPuUwVuYU20u6KZtSfXnE8Ns/bofXMpXlRf6stlUTIsWii2gHBR4dk9llYY/3g30fE9Ln7Lk5trPub77z4jsJChROLaqA2NTPoUZraWusZgwJJKjQkIlzhae4oNux2FSaM5930fn//853HVVVfh5ptvxv7779/tJvUUaXxbbefmE77Y3CIi/Dv5uQrqE0x/z2TsAeHUYJvW59w0ZU/qH6hQ18vlGQXCwV0Tzpu00wlTqdUDwpGyR2O0//R+JjNrr9UVdzdNgaoZ7aoeCIkKMunumx4LgQQh7KDUa5s4k9ArgVDMNjfTrLhJHmiPzzl95g+t2o5bl23CxXc+zdapK0vsi4Q077FVc17v0wWL5rMRX+Akpr9x6as0s/YmNkviAvElgTs88Kskg0XS99r22Fx5zqN+neFnU3NuarKSplIzU0hxG0ZKON99p/4glSYA9BeSmbVHA2apVGrpN4HSQO+XabEV1xcpmlVIg21Tc7Vp2rx+cALn3/YEto+5UtWlq5MLaJaWRAHhplC0djrnU0Ez+C6B5lwJsYnN2o36zPq538zyk3QN85gkQ5YKXjejP8+K8VGz9nBszLYoWjv7u82svf6ZS6WWPCAcX/aOzKQRzj/3uc/h0ksvxe9//3vMnDkT69evx/r16zE+Pt7tpvUESaNWcgSa85x7gFNoPpSmWVHcrMQJ5wNhNG1Oc84FhFMon/OkZu2mgJrW77wSozkPjiP3odnBRtUZ5jkPy66ZtcdPXoD9Wrk0SnGmh+bCtpk1XY7MxJrmPOV9o02imvNODvZxZu0uemVOMtucduMlbRCntvicMxuIdFFjC0qlW4zo5afpR7Zc90rwVfE9kkZrT+pzniwgHCOca2N6M2bt7rKT4PI5p1NB0vGhEbP2SCo18repOTdJatZuaou0Z1Bv26a6v/nuO/VrFl5Jzdoj0dpJqkoO2/dVz08lqOqac7s1UdwjTCsMcYRxYvR78aEL78GPrluKr/7pYe37vyxcjX//zX0YHI+3TIi0t97IZoJaJRFUOM15ZpIatnOac3olSczalXBue9dNs3Z1nMtyiSuKWkQl8zlPNr5TgvV4NstaQ0QVAOHY2DqzdmZs1ObG6LE5RnPeSEC4HtFRdJ1JI5z/8pe/xODgII499ljss88+wb8//vGP3W5aTxAJjEEGn9/e/TQeeMbub6XesyAgXMoJU59M3OdyL+sskuqK05y7zLWm1/2wfT/ZYGn6mQ+nzHVeDjQP/O/c4q5Z4VDVSVOpKSbKnlMCovfbNL8M2sdttmjCS/ScqNmnvQ1xUFNVPVp7E5rzkS4J58bCPU3dvaM5b+7Z0sO5c5tJUeaCFlON2RyzBY9zmTWmMWu35TlX/bsvpebcVXMS7aIXcwwdw1uhOf/Gmw/Gf776gEj7ksAdrtpH54LEwYZswrmjX7hiRZSqvPDdV9/cNlOH2dDab8yjqm1Uc07TlCZ1xTLH0EZ8zitVD28663a89/y7E9UJ6M/ctO7S3tNYK734jac41Pxpvr9P1c2Hb398k/b9l69YhNsf34TzblmRemMymD6bGMtpv7QFZ2R9zienbM4GWaNXndSsHbCP0RGf8+gyRysPcG8S1n63Niusx/g7SR9WzzyXzYDTS5nCuSqypjlX36Xvf/QM7to01ydt/kTQXkWYSi1Z3VyAuR2dSSOc+77P/vv4xz/e7ab1BKbpj+rfj60bxnevXhLkxWbPrR+crw9waaK1A6Yg6haoeJ/z0KxdaW3ooERfejUIK+iCJclAYAqojWrOrdR/bqVwHonWniJSOn2WNrN2zhIizo/VvA/NmLXTHVeq6UkrJNB20kCFnRzszYX75PQ51/9uNChSrSxGEHSMH81gE7jjFlm+1l7+M9B4QDj6hqoygoBwjnZRkmrUk5i1s9rtFGO4C1XO9P58aNqY8vlyl6qKoMJ50oCRtmGE37Sp/e9KFWmayCrUPDaW0FXK7KP6glf3Od9jZl9gyg6E817cvTXH0EbynD+9ZQzLN47ggWe2xVqMKWi7XJrzOCFCH0sSVR2hWr8HNouQrGUVPDRRiR3LTLzg/qZro1YG7QcporVnGOl8ydpBfPmPD2H1trHIb70C3ZBXj0h3fbCfG4yn+brGOKHmXB2VVrOtuSslWPNE59LYU4J3P5/LsFH+TAuQMMI9MWtv5GWJ0V5rGybMfMEJ58mjtZPPKdv+1wfX4ISz/4VVW3u3jzfCpBHOBTe2lBLK38uV09s0a08TQRUwTLhTTLYKKpyrgDcZxkQGiObRpQuWJAOBKYzbIpjbiBMY1a9UgG5G+PB9PzhfRWuP+Dg6NDW0ubbo9HSsD3wSjTQ/Jq0MCJfXNOdUkEknJNAFYdfM2i0pW5LQI7I5Y9aejjiz9qTa4rTomnP+c/CdRQvg0kKn6Y96QDjyvdKcW6K12/pLUjNkmwBhM/kPyyHvneYv7+MXNy3HPxav4xtgEGhRyEIx7eN1+a1TQaoaMxZPlKt4aNV262aDy+fcnMdc0doVah4rVb1EQqypheP8/qnmnG5Mhz7nbmwLeauFBdNs6useF+MkrCf8bFoSpNGGtyLPudJE2ubhnEXlnM3oc0qSsTzu/iZBczOzas45s/YoJ5x9B/7y4Bp86Y8PNdyedsMFWaP90LWmVM9HWZLYNqvMgHAInpNZHvncAs15I2bt6t3PZzOs1WjUda72fwbNmrW720m/0oXz2v9ZJs957bz4ttCxOO3e8Bf/+BCWrB3Cd6+2KyAnI5MuWrvAE0kpYZivuQaFUDhPtutmDoCVBJPJ7IECBsfL7ItKhe9to7VJR9OcZ6hwnsc2kkZnGjHvSzLwRXzOUwrnSRfordCc37J0I/70QJj+T5m1m5oIqiX2fV/b2EiiOecWhbrmPHqOaW7XjBkfHdSb2dSw5R7upHBuLtzT7GD3jlm7/nfaZsX57Zkah2azGXD1xmmKbWnVXP0+nVk7fw8qgc85H9DL1l+S5C/n/g7Op3UwYxjttvTdfmrzKH524+PYd/Y0vOmwfdiyuXbmsuEY3sqAcJS48eEzlz6AW5Ztsv7O+5zr82ZYf/jZ5nM+k7hnjZeqbKRliqk5ZwPCWXzObZs7JpFo7crn3DKNcfeZziejpQp2mdHnrtQoJ2LWnkLASau55lBziu39tWUAyGczxtjgxvfDiPtpp5ytoyU8uHIbjn3+nsmEc2Yut1kAAMATm0bTNaiDVJiNTC5ooLm2qZ1b+y0wa7fcL2XtoqK6q6NcPudxsXYaMh1PsHwMA6zxcd0Hx0vavaAbl01Fa9fmbU445+dUNWdxPufq2DyXD5nQCveVpFkyJguiOZ8iRBcT+iLDJaBEorWnNGunGgxTm/Gho+fgyGfvjO++9VD23Fp94edt9cipNs35Tv36fhJdACXSnBsLBW6ScxG3QGejtTcofHzi4vvx90fWB38PBMK53mYqnEd2gslAZ/M55wSZOHPCiGl9E/JV3upznlI4p9q/FroVpKEZn/NeSSESaUfKZrm0z+bvQHueT5xZu64lCD+72p60ndTaxSwnzHOunAP5dlNtpdku1/dJhK64oG1UeFeL2ojmyYIqh5pYNhrxWis3WKBHv7PhEszNZxR+z5fNac5VvBPFQCEXPNexcvy8Ypp3c5vcms95IepzHmdaa81zbt3sYb4jXybNbuLKc54mbVLcRl8S1DXbsn9w2WCAmtAep02kaMemHNPe9os78B+XLMDv7n7aCNBqMWtnNed2Ach0B3SxetsYbnt8U8c2i8uGpU7t//D3qufjb4vW4iWn/xP3PrlFO1f1pULCgHDK+tBjxhPzfK6o5qO1x58Uuj5l2I2jT168AN+7Zgmpoy6cU2ulRqK1089MM+lXupVa1KxdUxAluE+6pVtj/a6BLJs9jQjnUwSr5jyBmVXUrD2mroi5Z3QXTfGl4w/CXz77Cuy2U223nVtM0HOUOTJ90egARU3gAT0wTpKBz/TVSutzbjNpVKgWtDJau2Kgr/Z8TJ9GXTg3+oEmnFtSqTFCbZyAFUml1sQl0udb0jY10pq1h5/T5yNtDWaKvTR194hsHtndT+9zThYwXPnG362K1m4TQON8rK1adOO6E/s4OwQ79d7Yogurv6f35dnvTZJqznXBgfmdsZ6h5SXOV6u0PpkM0ewkOjWA15zr7am1s/F+Y3cT4K9Xz3Nee8fNZ5TNht8lEWJNdwq9/9auTW0mzxrIa/FV1OZN3L21pTpN4v4QtiX8LqmlGW1XNM85OS5WEeAeS5JQDnzO+RJs2WBymYyhTXTXk0b7Z1orr9leyzp0/ZINiSwReZ9ze33KJzsJJ//5YXzsN/fhgtufTHxOM3DR2k23o//+w4PYOlrCxy+6XztX9c3+mExDE8E7qza1UP/fXDeHn7mibJuuNpJm49DqqIbCru2p/fbuZ8Iy623OkIBwjfic2zasFZoFmLa+RdBeBf2cJkK9re4kuAJHT0ZEOJ8imKlmgsUMowk1Ub8FuSJjXiZzAncJotPrmm7Xjh59ebcFwjl50Q2zdgrVMCVZOythXF3rSMpo7XEC49bREj79uwW4fsmG8JwWCR/KhN/04aNm/lFNWvzCitupd0WtBphUak14TNs057ZItTaqloV7qwKOJaFkbFqk0aB0YhNhxcYR3LJsY6p2pG1VrClqgxrpNPXGa84tArmlPCDex1nhymSg+mVcQDhlJcO10VY293f4vb7YNbFp61S9SbumOj6XzQTjdnqzdvvzsj3jtNgEe1ViZIOFHK58Pncy4p/ks1nM6OOtmziS+JyrvpTPZnWzdiWox9xb0/ddCf22aYzVnJM6klyXeY4yNw2zwSRfiMdtEidBXbNt7rYt6nPZjB4gMmYkjBtztLItdeZzuik9Zz1WrFTZoIRcQDhFkuj+1y9Zj4Urt+HOFTXt9LzrliZ+3s3A5TnnzNoBXTni+34YrT1hnnM1rqrizaWZ/tzc42SS4Sdafvw51eCdzzifaVhmuPbPJbR+5dDjO8TMmYxvvh4Qjj/PRivM2qeYbC7C+VQhMO8xIq63w6zdLItOIOZvajDMOhZqtL7ArJ3WR85xmrUneKmV9nif2dMApNOce54fO7gOT1Rw/ZINOPum5WG7WiB8ZDLhtbqiAUc1abRtyc3atSmK20GORDO2NikWLQIzzXOeWnNOFjUWzWi7MRf9aXawm2lm0kXU8Wfehk9cdD8WrdruaIcpXDYuXMWZxwHNb56ofq31Y4sWnfsuqVl70s0isw9oGgelOWd8hqnPqhn40vYIEmvOY8wGbS4h6mPSfqzqyZI0QKk152y5Sjjn25kUc16M1K2u12XWXlFm7abmPBNsRifSnBvCHGftESzUcxk2lVrcHTDvUVy09riNrKQB4arM/epjAs52wqxdvbe2YK42zXnW9Dn31f8+Ng5PRI7Xxw53m2x15rOZWJ/z1dvG2XNdskmcWfuKjSP49O8ewLvPuwtzd5sefD//4WSBIJuhyljq2DYTbeN2nHA+bpi1q6Nccx2nU9HTvrFV6eU1oDkPAsLlsokETtUkatbebIaaOM0590y4aO30dxdpNrZsiOZcaCu+72O0WMHgWDmd1s0wTVcvT2DW7igrXAAkjdbOn29+Hijkghc2zL/oLo/TnNPd/2Z9ztUgvefMfgDAdsZ3y0ZSs1aTVmhuC9ls8HzGHP6f5v3Vg6TZzNrDz5zZYzKzdv3vR9cOaeb2dz2xGW/7xR2sUEiDhdBrS5tKzab962QqtbIZSTVFl2nUx+/RtUN48Wk34kfXLU18zpK1Q9bfzO6atlW6wOn+HWhu8+rKB1bjhafdgKsfWqMvji1jUvCdRTOj93uznQmDQTrSDKoxJMhzTo6j9Q1EzNr5usyv7ce532dtY6sZs3alRcmE/pJpN8dYn3Nu0d5Av1Hl2Mdkfd4M2xR+DjXn+jPKZzOpNOemiSw3DquNnnw2E/ic57KZxIGfIpuFDQjntIiRxD7n0e8KjAAVN+RpG2eJao4SBISzpVKzabENn3PFebc+gaN/eBNuXrpB+153U3C31mpKn83G+pzfuWIze67brN291H9mSxgwrkAEec63vdXoAeFU/wx/t73n9Ly4YMYRs3Y1nhjH6f2NexfSBYQzD0lm4h2+80kEztDnvDnNuculC9DvB7fmp9Yg2bQ+502O64DbcmQyIsJ5D/KC712PF33/hkCLnAQaRAIgmga1A+8YFALToMBvx11X1KzdZz8fd/Ae4UGOyL20bVvHVCq18He6wJhmmHsWclki+CffoTtg950AAE9vThbF1PP8IJJ8WlqhOc/nMigkSJPh2qm1WQlwA61Nm6gwFzr0+EfXDuEtZ/8LR51+Y/Ddhy+8F4+sGcRX/rQoUhYd1MdJxM3UqdQsWrVWRQNPQjOa80b3EJZtGEKp6uHh1dsTn+NqlyuCbRJs0c9t3zWzebVk7SB8H3hs3bBV2xIXlM5m1u4a51yYm3ia0GVGa7csSqYXTM15MmEqic95vOY8ugBN2gfUfJPNhGN4+v5j/47+1ojPubp2m1VOYClgNML3fQyOlfHkphGU6nElTOuGbCYT+pwniBxsLqA5oSw0cc2iL1err49o1OJurTXPeYzlgK1dic3amfL7DKs+IK3mvLFxohI8c/58q+Y8o2vO1eel64cBAMs3jFjbGjfn24SuQi5ec/6v5bxw7ro9/SkzB3DftwstxoXHtMfmgkG+73OkUvM8P9hQU5ue1AqCEme2HhfL5uqH1uADv7obm4aLWj1cmb7v4zOXPoDT/rZEO0Y9c5fPOVcmDcLZyGOL21Q3p3LTConGDtJ8zhM0Jm3KQg4JCCe0lUwm3BVPs2hVndv0Gw/+d5QVmLUzPmEctuBztB2nvPlgnPeRlwTfB5F7mfLoABlozi1+yOZE2pfLhrkdE7zV6p4ess9MAMDyjSOJJv0Tf7sAL593U+xxrjqbIZfNBJpzF2ZV9Fma+TGDY8hJ6l7TYlif88hCJ/z77npEVe6yuX5Inyld/KUVqm2+ep0MCBfxOU8jnDdYp1qkpKnLNWFGfkrZMHp+nHkckFwjzUEXWVq92oZT9LxGzNqT9keXSXTF4XNOjzMFP6vPuXFtSYT4uNRyekA4/f841BiczTa+UOStLaJCZSOCQ5wlgKqbS6X2snn/xGt/dlsgnE3nNOf15zaWwPzb1JxzrgV0oa4054VcJojMHXcHbJpzW1eOM2tvJCCcgotpEzdmpQnIZkO9t7bNHDr/0PoiPuf1z2pzJmJdESPY2eo0v3dpEStVD/c8UZtfX7TfbO23yLBNyokTzm3uA52wOuM15+QeWNpAz3OlUqPrnoGCnsLStV7iY+2Q+8S06YoFq3DvU1tx1xObtXqCMsn5S9YO4brF63HRnU/rdZAN3ESa83od2Uy4bm5sbKSfuXFA/7sSjCX1MUrTnLvLMokL4JoEMWsX2o7y/U6jGYj4nBuLDNcLoo4tNGjWzgXfKhiCpMvnnL6MyhSavmZ0cDXTnuRzxM8mwUutFscH7rkTCrkMxkpVrB2M+o+Z3LzUHUTLRTPCh6KQy8bmigSi95d2oVLVi9WahSmL3AsNl19tn9FOWtacXafDhA6qNId7Ws2Ynqs5WbT8Vu+2mm3uREC4YEGT4na5NoyaDQgXmy/VbEsTlg2BjyL0drsySAB2TZErWFXSRUPErJ38qfxf+1T0ZLoQJuWbZu22mqPRht1CJxA/BnMBPpNeu+rvORI5uJmYBWY7NCuEBhZxptAbqRv8757vB0GlVEDFnYxnlMtmguc2lkRzTvuaxy9Qw0wqoc95Xz7ndBOjmG423Piut8ndzqQ+51wfCzIUaPEPYsphAk+lJa4P0zmAHpPLZrT3S31Sm1dmgEhTK+rCJpwXclnruwjUtPbDxQpmDxTwwv121n4z66TrJnM9ZmITzJIGwWwGPZWa/r/ZHoquOY9aZSgmiKucGRDOaSXGVMvF46AEgRyr6j3Tf6d/28YgTXNuWZ/QgKGqmGwmA7X0auRd0V2f3L/TOtQ9p+vTTCZse5INnlYEhBPNudB21EBqmqSt3T4eG3HbNB1LFBCOLADijgU4ATC6EDblyKQ+5yoap83n3JzU8tlwdzHJO63aOq2Qw/67zwAALN8wHH9iE7TCrDqfzaCQjX9do2ZU+hdm9N7aMeFnLpUat9CILl5JW42FAPU932+XAab+8OQxzaw93X2z+TK6hfPWjuimuWyaXeBGDSwCbWDLNOeOBUsCfMvn4LsGhV6OQGjzfOszZ00dff5316Iw6WZRdOOKLLYjmnN+UWKatdtuUVTzwx8X56tNhyib1Uki88T6ITlNc954/zHLbdbnXF2DbUwOrFAc1g8qIOdAX05bEOaIz3kSIda8z3q0dk/7P5fNBEHg+nIZYtbuvgdmEMNA22W5d3EbNzTQ3bbRkrV+7vu+XFRzHtf+lgSEU/fSKpyHD5HOOdlMxphTap/VHGqOt2kiebv83HX3Br0glWr2WTsPaPF2anXqx1IXsbiAcFaz9k5ozpnsEEnGHbpx0e/INKTiDBVyofWhOso1frLvAtNW7XxDm2weQc+xrTwCn/OcXThXqYkB6nOeaUpzru9LRM+3zdvqnTH7dLp1efi54vm4+qE1WL1tLEGrKVNLOhfhvAdRQjIdtNZuH8cxP7oZL/vhP9lzwh12PegKp3GInBvRuqcTzjUtVX0yyRmTQcaxUONMFWlwh8Rm7QkGJKVRzWUzeN6eNdP2FRtHXKc0TSv8tpJqzl0+VEA0Dzeg33/V50wN4oqNw/jH4jBya0QAIYO5uUv/FPHrLzDbm7pwTs3aG/c5p7gWGK22hGrKrL1hzXn6ulzHunb705YdF1gGaM7tI7x209rDLcTZFqCapqzBTQTXxpUZrV3TDJLPKqpw2Ba9zMGxMtZuH0+8kaIvOpnfNW1dss0KjnD8RsN5zpNYeQHpxwfaPls8C5vmnDZppBSm46QbkblsJoiJYnMhori0RZFo7dks0Zxng3sbd2ujZu1qfOePd6WxA8JNh9sf34QjfnAjTr/2MbYcrnzV/nTR2knbnEfaCX3O483aS9pag69fHWMKjXFBVPU6+e8jqdSMOpS7xIz+XMSK0KySplyNSwNnWnGEnzsgnDMbA0nMnANfZxIITZW1bP0wFjy9FUCoOZ9WyAXiG7cJYNbF1VqJ6ZCmG6lrfKZrD86tyBUQjq6xVJmZTGha3simSpwLSeReGZpzs0/nUli0mtabX7j8Ibzqf25J1G6FaM6FtpNnNOf31wcaW6CZQDjP6y9nkh1IugCoHedun2tAC0xyIrto/Lnmd5VAcx7+Tu9DRHOeC80nkwxIVTLwPXfPWlA4M7BLq2mFz3k+l4lMxhzRnWBTOI8+XO75mYvy48+8Hf916cLAlyrik0mKLeT0jZUniXDO3Qta13ipNdHak3wPtD7CZ8SsPcUlpNVSmuelOcf1rqQxa982WooEiaJ9ga2mQaGXgwYso8XEpWahj0lbb9F+b9E6xuHSnKuFfSEf1ZzTZzK9z+5z7vs+PnjhPXjdz27TNr7M9tvOj9us4DRZZvtsqONzWiq1dM/XdbhLcElCnCWZKt61qFYfp+Wz2mZjLpsJxr4kmT0qzILc/C3Ic54Lfc778tlAyIjb0Csa+bDjU6lFv6PdWVk2nXnj4wCA/7vjKUs50YLCIIj0vXU0HvEuMkkIr5kfI+magmqqTcFIxXewas5TWHVY85xns1obzD6u1n/T+/KRtZBZpWaFFjOX2jZUO6E552Jc0GptbaiSscaMUv7Gs27He8+/G+sHJwJ3lGmFXGwgxTh3vrh5xbRYNQ+hZdJwb9z4nMvaU6lxmyk0lVojj8019wGMFYBhJWC+L2kCgrL1pbwG8TkX2k4h2AUMZ8U4IUK9AGYUYDqx2ga5iFl7zFthbkBzizlTkAy0KMyahdPq0MstOXzOC7lsqpQ9yrQtn83ioL1qmvOlbTZrb0m09oQB4SI+VKbmvMwI58zEYNMgPlpPweVKF0VN6IoVT4uIHycYjDURrd06iTvuf7t9ztM8+7RayvDYel1pznG0y/zJtigemijjiB/ciBeddoNxPuk7zHnmd81pztUiyI9YeyjiAqDZNAbmaUk1tVwwsaAMw8LJt7RzIBKtPfz89JYxPLZuCOPlKs67ZYVRl1vo5NoH6H2HWywD8Zu2tOwcCWzajOUFxXzGjYyr6jrjNv7U+ObK1T7Ql9c155lwjC5X4tvGRWc361ff54lZeyGXZa0SKlUPn/rtApxL+sSEYSmlNqdt73/SgHDPIbmweW17tGwu7WkqzXmDwwS9t9ymCRVyXa4rao5Tx5iHxrWV3qesZeLJGbnVzX6hNkJ36s8zmnPfODa5i5htndgKxUIc9Bq57BC2cUe9I1Q4N5/JM1tGwxznhRzM+OfRTbjwc5w7H7vpa2jOzSPs7iS0jvoGrkNzzm0SNBsQzrd8tn0Z2UA0+mO4YRJfdyvWyAm8PicVU+xypgac5pzutLoGjT7TrN2ymClVPPxt0VpsGi4GAwOX3ocjSSo1c1fXJQTp2pCoz/ns6YXgc854Awu5THBvzIGV81kJrARyGRz2rFkAgMfWDrHm3q3CnOBuemwDVmxMtyFQyGUb0pybAht3nT4zGdsmqVw2g2KlGlnk0Gros58oVzXtHi+ch5/HmtCc2/ptVMtPFklt15ynF7LNz3FwG3FxpAoIZzl0WT1qdbnqY6JcDXLlxm0yRF0vGg+YqK7Z8/W1gxbUzLGIqpVBNxPsi6/kmnPj/iHcQAhjg0TNkukiy0wZSdtyB8lzfMuyTcZxfJviTIltUdC171NozrPZjNOVyYVd+6//1g7NuWnumg/cD6LHT+/LaVZCuVyGxIuJ79OcxVL4mwfPC61BctlMINzWzNpr39P7ceOjG3Djoxvwk+uXBd+pzdjAVY5ZF1C420KvXZm1z91tRvDd5pFoylfW55wK58GmGtuMyHFAvGm2jbi0mnQOoM/N7COqKYHm3EyZGCO40XGBzuX0XsWlUlObI9P7cpG1kFklzRgQt9Fty27RebN2/X/A1Vfra81MhphQRy1FioFZezYQ4MLsD3qZtrg1yX83riky14V/a2btfrSP5nL2VGq0WPU5m8kEpuXNZLIwP9u+M9M1R9f8ycf/VlhoSJ5zoe0EpnGG/5OCE1oiPuf1Q2wBLC7815P47z88iHeee2fE57yZgHCNvKicNoROmN854VAcc+BuOP+jL9EmtVx9AcjtFv7ouqV45Y9viZjdUU3Es3edjt1m9KFU9bB4zZDzmpuBThgPPLMN/3HJAhx/5u2pysiThZ+LaPRR/XferD38HOxiWwTF65esx/O//Q88uHK7US9/fLHiYdtYuHiL15w3nkrNth52CVmtFs5LFfu7EUcan0WujjRmn65Fl1mMrVj6ir/9nDvwmp/cigVPb3X6bQOMRrqJBaC6Zh+G5jxmgWnTsnObUmliWgBRbZeqio7bSlDhNNo0bRbXrrtW8HmOa8fxbYzVnGsBefg5Q31e8PRWvOHnt7HtoMJk4G6U8vna+nHF85xaxSTE+pz7qq7aB7XZXWLqGujLBa5gQE1I4Kze4toC8GbtdMGaz2Vx6D4z0ZfL4oXPmh2atZN3bZgJQjcR5GTPa3XaHgkbKZ98p8yqqaC9nNlo5vohtaji8lnHtafRYYL2E25OsZm1R+IO1P+3a87jhPPwBKo5p/XkzWjtpua8GD5Pczlg1jmaYqPb1vYGXrHUlJk1alxAT0BXBAVrQN+PpIYdJz7nKmhYIDsbGz5xc3BctHZTYI0oSxJYrARr+Ww2InA+r+6KyWnOMxmqqEr/snACv+13IGolYFqDpHE3bcUmkJi1C21HCWF0oqAvqWmqBlCzdt3Em47J9IW+8dENAIA128cjqRDiZKKIWbu20xsGXKO4/E+4nWL6nu09exp+/6mX402H7a2Vq66VG5B+dfuTAIAfzH+UbWu+PvAd8exdAAAPrtzGXWpLoPfn0XWNbQLks1lMKyQQzo2/I2btTN/hzNhsk/U9T2611EsFsvDzRLnq1ATUjg8/jzZj1h4ziXPltno4b8bnPG5ytKHqaFVkeHOBbtNY0THp8XrchqsfWpvAvFP/uxWp1Eytqh5QiDnP4/sr528ZbpYmFM5NSw0/KhCGG23RRVk2k8G0PB8QzvN83P1kLc8xl7s4SZ5zlzkm4DJrr/3x4QvvxeMbRvDhX98bLSe4BrohyzbJiu34qufHjiVxmBGGTdS35pzIbWrO6MtrQTpz2UwQS8DcpHO1BeDN2iuGpvW5e87Ewu++Hqe+/QWsbyn37JXWcEY9xVswvtuEBHbjJvzusXVDOPOGZdomKhdQlSue05zHm7Xz7yZl4cpteO8v78JDq7azv2u+/ZxZu0VzbmY2MaO1O4OJMU3VlC2WbDR5I8951Ofcrjk320OfUdzcoNWpWQ+0XzqvsD7n0XHRJHxHQ6vCqhfVQrt8ziPCs+9+hvR+uDaygtg9keCn/P2k7VCul2YqtVnT8vjfDx5RPz56z1pq1s5cm/mN6ZpiWnaqtiRRGrRCcy4B4YS2k48JKsP5DQdRgPO69lsztSKnzegPF3/mIjS1WTtjChZ5UR0LNW4xb9sF04Tz+uSURkMT7LbWr/XIuTsDqE3w7YK2q9EBpJDLYKf+fOxxrgUDwPcdbiKkZyW5rzYztGLZ0xb7cZpzulBJa9bu0hrSayxrm16pqojFnHwbzXMeN1mNlSpYtbXmthGYtafRtjuONZts15xHb142Ey8ItiNau+/71kBGvFk7+UwX1UzZoatQsoVqNCCc+j4snfM5p8HUTM25Om7rWAnbx8oAgCPrG4sU2630LNcb/G4RFDmNDqdFNo/JJshzbj6z4HvLZlC5ag/6l5RAOLeMLao9ZmwALgVlzaxdj9YebKwn0ZzTMSmygehrZah5b6f+vJZDWB+7o3UowUQFGPSMBbUJPz/rX5598wpctXBN8DcXUDVWc64sXmz9NVi/hN/ZnvY1D63Fgme24W+L1rK/a9pwzqzdYpVoPkP1i8rIEY0t4R5zbPMOHS8iAeGM9lLNecTnHLX79rU/LcK86x4zAsKlMWtPPg+1Aj07hOqf4e+2NtAUXqF1k2dsNHpstPZgjDGK1jYjYzTnXKvM9yuibfb4PqBvJtetO3O6z3nNQiDattAdKsOus1dtHcM3r3oET25yBz6Os1KxrS1tqdRCVwNntbWyG1wDtNNFsduIcN6DKFM52yDNpsMKBGw9XYltkTq9LxT0IibxKXZZAX3Aspm4uPKyci+m7T2jwrnaxAgCwiWYSII8w/Vzjgw059tjz20UVwTYpGTrwYbiBHTzVpp/x0ZrVwGDDO13HLad7olK1Zh43MI5Ja3Zqqvf2rRULY/Wbpq1p1jc6IKh+7z/unQhXv2TW7Bq61ioPU4xwbnulStSNYXbaMpkMrFa/+iCpQmf8+DaeS2v+Zn9nfzMmTWqDc+kmwi2RTvtdwXG55wGUzO14qYvdDaDiABPfzeJz3NOhRL+3iW5fLrBEJc+8+3n3ImP/t+9sa44tN1cfIw0mKaY0fZD+13NE5xwXjNrJ/MRjdae1uecSXlGf48EWK3/bxszVHlFw6w93uece1bR44YnQq0sa9bO3F+qOY/bJOAsuGzjyva629TwRNlSlhf5TO+tLSCcKRirtpTq99SVMjHOrF2L/0PN3Y3NTbOf6ppzMyAc8MDKbfjTA6vxq9uexNB4eD/izdrDz/p6znlai4iOubqLEt92qgiiqdRof614PgkIF43V4JrruFqr1Whbtd+DsV5dB99mQI/WzikP8tmsZtmXy9rjKwG1TaYcsxb+9O8ewO/vXYn3nX83c0W0nPAzHyuGvxbqjkVJE3Ok0YBwVIE5xWRzxKvihI7D5TmnA7tLwOozhXOPH0xoqh5qHmSeAwAbhyYwNFEJUo+5Bpw4zTn3nnIvr02IpeWqzQRuQOIwA+wAwLN3rUWd3cIEtWkVtF1pNOf5bCaySJk5LR8EheGILnT1vzlBWzcV9erlhL+PJxDObQuTmuacLo444ZwvM+3i2yUIVzwfylI4LuJqM6iFVi6b0cxwa4su97nawiBmUbRyyyh8H1i1bSxYQKW5XWmEc9uRtnc0Ll+qCc1D/Lt7nsFAIYc3vmBv7DKjL/ZcPZUaL7hx/cKm3aeHqs99jJuRi2hAOL1NuSxNeRMVfrPZDPojAeHq/9efdTbDp1a0PVY/5hhbnvM4gSNav+rvvBZHsXWshEfWDAKo9UVqHm7rcDWfc/J3Ez7n9lRn+rwZmKlXo2Pg9L6cFq09SzTnac3azfexpjnnBUgAwUpU76/6Aj+fCzXnOwU+59HxncJ9z70/u8zoC3zclSUHhXvmaaK1c5pHmwXG9roQapsXuYBwmv83GceoIB+xEKkXo95vU2jU/aSj7aB10kuhGz+ez8/HitEgz3k+Mpd7vo+l68ONkvVDE2zbOGx9sRMB4fQxRv3vHr8BXSikrqD6PKBrzoNxN6jPWC8xWnxKnM95KLCq98zeR/SAcNFjzDznNYuZqNm6+pghYy79XblSbhl1r3E110TLERRzI8UWBDqNRWtabJsdUwHRnPcgBSYIDf3sErBM7bdNg0Q15+rrIIKw8Z4cfcZNOP7M27BucLx+fHQhEbQj2MXjX1R+tzHyVSLNeSCcJzSfMQOv0PIaNd/qSxCkraINyMkHELqYUQuCWdPCyPWcD7rLjArgN3a4nXp6VhLhXPedDb+Nas7dZvWUJNonijs9GF24ksVQixcfquxpQcCvUFiJI27nmhIKtOFiJM3us+tYs+o0r0Y2k4kV6CILlnrfvu3xTTjtb4/ilL88go9fdF+i+lR38o12xmnObZuWmhBrCGhJ76+ZJcL0Vc0TrfLWsTKW11M5alpxQ3NuLiZrwjmjObdqhO0LfiC6OOdSGiW5fvVqxeU5d0WBt/X9qmffgElKsHi2mrXrbVIbIHazdl1zng80eE0GhCM+57S/KAKXASZmAW2vWisoN7YtoyUMjpcdlgPuZxW2j9cCu8rJk7zNoQUD2wxL7BP+WLU5QLX5elujfd+28UGfQ8Q9pf6/Wou5+i03p2mac0ZTqspwxVVQMVlmMHnOfR9YvHow+HvNtvGwjrho7Zb3qhOp1DhrJXPc4TYi6WanWruWKoZZe8UP1jzT8sSsXW3+GGVyGwV6nW6fczP3t63NtfPD77lnns/pPuc0PaXNrJ2L1m6m5bTBaeNtv9fqqLeXpLSjpEml2aiSpKxZpTZURM8iwnkPkmc0NbpZO7O48tVCUgmb9fMsiyCqOVfCl1rs2QaWx+o7cOZkzUVDteY5Z4rmBgKr5jxHhfNMveza32l2h1X7aCTmNNGuFTtNizc+0X3Ok48g9Eg1sc8aCOvjBt04k2TeJSL8HAQyIeeNl5KYtfN1Fsuec7EB2J9b+mjt9uM5032g9T516jkpzSf1wY0jzlebEvp7eawgxcEtgkaLFbzjnDtw9k3LHXUnX8hnDLNM7kzzO9UnhohZ6prtE0gCXcxZfc5jhAebCaX6GKTHSiBwrdg4jJ/esEz7Tp1G/ZhVb1i0ajvecNbtWLt9XNME9dsCwtX/z2TCuBm269K/j7aHYr4HZca9JVnsibC/u6ylXM/H7uaSPM+57bekAeHMzW7erF33+81mwnRn0bgDvhagy2yjKcxTn/OI1hyhlogbu4FQgFRrBRUQ7l/LN+OYeTfhtsf1FHwKVhvIPI9S1f0cuEeYp2lP1aZajFZUExgs49BgXXM+ZBXOo3FMyuR52gLCmfOP70OzhjLXQVqGCKapttgrmkYdhouJ6XOuzNr7c5E1luf7WlC8tYOhcB43l6bZrKEUK1W867w7ccbfH3Me54LWoKrT76XP5oWnGluawlCL1k7cQ2h6x6CeiMBJhWf3WsU1roXrKMf5liC6FWJ9pwnndMOTmaeymbAv0/IG+pIJ57qVivt3ei3Bc4hsICZXfDXqPtHo2noyIMJ5D8KlY9HM2pmgXurnwOec2XmmHZnTuuaNSO8mqjlOzTlZYFJcqdS4icH2mtEX0NR+xwnX9H7mjUjvtbY5T2dJEqSt0uDuHjdJU805J5ybtyAqnCfzOaenJfE51xf/RDg3cqKr9oyXqiTqLV9mEmHI1gYT20J4ouzh+DNvw/euXpyqLhtqAaY0n+rRJ5k30gTiUe8cDZIVJzxxO/V/vH8VFq0exJk3Ph78ZhZjK5arL5uJN0W1TvIe/S7Zs6fmrx5zfQB/L3VNMWkbU3ZfwlgcQC041UTZw8sP2BUvmrOzVmYQ78LQiPg+sG4wFM6zmUxkfDbNfFObtVs2LoLzjBMDC5qYBZtJOP6nz9AR1GMpu+L5WhtcWj2b1U3g/2+Lmqw0X6ZwzpQ3vaCbtec1IUFv2yl/fgSHfvd6rCD+2VoUcYfPOZdCk8tzTsd21d5Qcx7OU6OlKhthHUg+P9P7F+c2oqAprzgNKVdnGp/zEavPeXR+s80tFU1zbvZL3+miFR8QLt6s3Y9ozvV2BgHh+vKRaO3DExU8TvrXuu0pzNpjnoON5RtG8ODK7fgLCRCYFi4/vL5u1ddpQdBGorHty4fvqbZhVfXJmBl+rwTjSGaSGEuNSjXaVkrgYsb0X8CuOec22fLZrLbezWb5wMqqzRmESjD6POk6cfNI0bqeo5sFtN1bRoq14J3GyGz6nEesZZngdTYa1ZzbgpdOBUQ470HyTOoe3eec0X56+kKS9TmP2dk1z43UEZSpf88thKO7aLX/uaK5tth9zvXFED02VqDRUtPUBXuifUqbugtIJpxTQSPN7h5tjlpMzCSa+mnMjqj57CJm7czGDmfySr+bYM5RcBH+q9q5Vc2EtFL1UaxU8ZLTb8Sr/udm+L5v7W9pNeeuwdllQrpi4wj+sWR9qrpsBGbtBT06cpLnHrcrzx1LtYlxsiMneHJ9PuJzbimX15xnYhc4tmjtabW0tHzT5zwuoBC3uKl9jn5fyPMCF4fyf33pc3ZFzhBOg0A/uWxks6ZMniOnOTc3RnMkCBIlSSq1uIBwQDjepNkwovXoEZTdAp9pYm6rppoiz3mcNY7td/VtKBjXroHb1Jzer5u1Z4lZuyls/3HBKgDAhbc/FXynR8g37n/VD/oLrzk3W6xvoppm7dP7k2rPoveFex5U88yN07xZe6j54zTjlDD2iXtM9Dw/0JwPT1Rwz5NbIm4lXCo12mZabEkTvvVn6Pn67+b7oKfhcgvnmguCIWC4zdppQDi9/MHxsnaPqI9xnIuY/Z2LWVMFm8QNqj7Bz0vm+E03IgO3gkCIzWgWLqZVArU2Mq15osoMvl2KuDk6nFd9tgybOxWXyo4za6f53M1y9DE3PI9ayR51+j/xlrP/xVwZv1lwy9KNeMnp/8S3/ro4uqmu5IH69/Y4U/HzRhqXPEpcDIDJjAjnPUho1m7zOY8OhGoiMX3ObdHauQVrEBDOMsn4zHe1uqMLv7xhchma4TETOac5T+BzrnZLuSAYHLSdqhg6oDQyQCQxa9d9zsPv4/ydOT/pWQNuzXmc1pPNc848v6Sa8zA7AKlT05x7Wpqgqudj5ZYxjJWq2DBURKnqWRcGd6zYjDP+/hjbZg6nmStjhUBpJtc2JTBrz+sbXUksJtIEqlP1VDzPuktvwglonN9y0jzn3HqsZtaunx0t3ywnGqQquXAeLvI57QMQr9mz+YqqIpQ/YxJtvvJ7nTWtEFmcBGN0NhMJXkPNZbNMtHbVKtWmTCa6GDKvRTvfHBcimnL9dy5rQyKz9kBzHmPWbhFAXAs5M5WaS3Nue5/NaMomqvpwHuPN2mt+rtnIZrHayLGVT01MXb69tD9wzznQQJNmUfcjNT5MGGbtcXC3VLXj1QftgTcculetfGbOiCtHjzbtHrPCzajwO65vDBcrwTEbh4v44AX34JU/vkU7hjNVt2lAKxaFiKpfC95mWnzEbErSIIG6X7SuUbe5YAGh5nyn/qjm3EWc73icG4i1XDUPNRPWXVs71P+nc4Gvm7WrZxDEN8oYZu3k3hYrXlB8BhlizaP+N967mA2WuDna1Ca7AsJpwrDnY7RYwb+WbwrW92ZAuGyWT08ZzgnEJ53UY5q1P7lpNNJus0z18Wc31ly0fn/vSqviR82LpuY8TSq1hoXzGEuGyYxEa+9B+hjTOJqiyeU3bKZSs+Wv5AbrQi464XOTY8RUh9kRNjWFnBleWEf0yyTR2k2/8bh3M9zACH2PaD1WvyvHwDEzkeY8PJ9uLlQ8H30Oqa0Rs3ZTGFJtHyjkMF6uWgLChZ85X1NXQLi+fBZjpao1MNFEuRrpd/QejBar1kF162gJF9z+JPac2Y8TX3WAtQ3hddifk7abzghZrQp8o+6f0pzTBYTC9302MKC+q+6uRx2bxqzdNBUEeK2cWUwarUoGGaspqu/7WLl1LFI+l94paSwAfZGl16vuM7c4SGXWniKVmkrnNHNaPjLmuTXnHjw/dNOZVnD7nNe0JFwqNb5d3CKUJuqxxRHhLAlc0P7uDAhns3JwVFH1kvuc283W3b+rcUyV3WcRzqcXcshkMprmPJfNoJDlfc4VM4gGm9t8Ddsf+pybG91ce4Fa8E1FqeJpwuSMBPMU4LaqyGXC8cIW3Cxol01zblhT2LoUZ8HFHUrThdnQhd1aubqrVbRegDFr9+3a79/e/TT+sTi0vuL6vC2YmB4Qzr4h5vt+qDlnfM5dxAnPtt/jxuHAh7+JzW16pi0QJRe0j+YD7yfWTaZwjmBMQiTPubq9KpuKHq092lbdZzyK6RajjinkMihXfa0PmPPkZy9bqMWCyOeMVGoZPpUaVQBwVqRJA8JxY71rA8i0pDWtZV1uTbay0qK510wx1bkI5z1IYBpn8zl3+A0HAeE8/cUB4rUgnFk7JzBQMxYz5YtNI+fKRc5NAFbNuRYQLquVHb/LqwacqIDvOt81QaXVnCfZDABqgyOtlg0Ix5q163+rtk/vcwjnMVoQl3Ae+ENqk0X4uVj2In5B1ApktFiJHZhXk6izLpIGiOI1503s/BOUFiTQnNfrNdOmcOtt7T7F9WWyax2nhVLomulaXaxwbtQd5+ZCMVPG0c/XLFqLL1z+UPB3JqNrihoya1fH+fqmoiojn8vERmu3mc6aG55JrCuGxmuL55nTCpEgmNRc0aRS9bUNEzOHubnZmk2pOY+MC54PumYzxzjW1SDJIqt+DTT4kss3k9blar86Lmmec9tvXLRuSqA5r39Qz8r0OVdjL53naoGposdTwZ5mSeF8ocO/PRJcNZnPObWoK1U9bazfKaVZ+11PbMYPr30MZ7zrcN0agtkIT645z0aiTds3xKPlcH2DS+NmomnD6wVqZu2k3JJmrh+dE+jvVDv63auXGO2PttUm2Ou+6Pp6iv42UQ79qblo7S7iNhZtv8fNQ8EmcT0waZpsNEEdzOac+dy5Z6RuDdWcm2btxUo12DCrpSKr/6CmDT/s217V7hqliIvWXjX6tTokn82iXNUVFb5xnhmk0czSkMvyqdRUHbYN0aQB4bh3rUD6mHm5gXUVGR8onBbfRmvM2qeWcJ7arL1cLiOfz2Px4tYEUBKicNHa0+c5V9/zO3Wc5oDLc84NAmpQ6mNSDFUsL6rL55z7zqY5p7tzYSq1eltjXs4gWrKxoFI0YtqVzOecCue0Pcl9JtWENFNLpZYkWnvtfzVAF7k0fMyCV1/0OTTnnFk7OXmiUo0sQqnf+wgxS7RhCio2XM/J84D7ntqKfy3fxN73coOTAwAsXT+EW5ZtBBAuygPNORFW4tpp80dzHUs153GTIC94Nq45533OAV2DHX5+arNuThcIvYEgGP6W3qw9Grsg9InjhAf+XnMm7mkCwg0RzbmZ7oqOP+b4VqFRhTPRVGrmojWbybBCvu1ZmQvJyDgR0ZxH753r+n9zx1P41G8XBP0/F5PnXNN4aIKStYr6RhRto338jAsIZ/U596EF5bJFa1d+nIW8Ppdwc/cg0e72a7m+7f7NVbLpzQlhZr5mIOpzTv9OqjlXt+XDF96LJWuH8NFf36ubzTLzckVbm1TxwDPb2PufJ2btQeAsywNP6nO+fdydu5mWVau3rjm3BJJyuRpENOf18+Ks0RSaWbsmQOptsW18jJJo/wOFtJrzxoTzOKG+HDynxgUsfV5S6w/9XmmZCALhPDT/LgRxmjxtk7ZY9jTrkozx3lDBtlZ/tC2UOOs2m1l7PscJ1e753ozWTn3K6flButYsLxBPTxqtnclzTuuLri31OcIeBDq+7kaz5ohZO6FQKODZz342qtVkfqBCetRAw6UAASwClhLODV9X2+KKG0iDaO10cmAGEFrXWEkXvmy7aGmjtdvmHc2sPaebptMBKZfNRMoNtIVkUZup7zZ6jsnF9dIn05yHzzGTUHNu/qTucepo7fXz1LFcvAIuyJJm1u5Ipab6m82svVj2IpYVdNE4VqrEmstOyyfd+bWXU6pW8f5f3Q0A+MWHjoj83ozm/E1n1QKsXP/FVzt8zuOfe9LgJlR4qFTDVGpxExynBaVma5Wqh3wum8h8GXCZtZO2avXrxxayGZQQCmemH10STYwunBu/KQEgRrNXtba3LqBZ0mNxBD7nA4VIuqsS0ZzHBYTrM6I9RVOp2aK124VOSmRsNA5Qi25tseq4/O/Pf1T7uxZZWG+71k5Nc84LSiamyWpSSxlKJcYM14fej2xm7QN1DTjVLNHnRvvK4DgNzMW3P5Ln3HP7nKtvtI1QqjmveMHfZoDBfWdPw9pBPlWhef+HixUtyCs3L9P79YubVuCcW1YEf1NLGqr5C4UwthmsNRB3aJzm3PP056nus75mob/zFg+qLUVGc87Nj1w/tkVrL1vWeeZvyt98el8OWUtASBtxwW7jNrOs5RrR7RNO1Rp6lPDa/+ZalY7hpbrsQTe+9Wjt9FgP0/1ao7JEc26Op1z+cO7SbfEx7nlyCwq50KUratYe3eDVNiWY8aqQM33O9XVxYBlWPzVDNkRpN+KUONzcym3e0w1g2xyi/ret+ZNZXDUonE9hs/aGAsJ961vfwje/+U1s3bq11e0REJqxlbXJO0ZzrnbosvogoJm1k8+JzdqZnalg0coErrPt9nOLCbNc7Xib5pyUa5q1bxktYbA+WXMTV8Wy2AkiXFoGEddLn9bnnNbs2pWO+vXXhfOUec5V3Wr31BXpnx5PS0kSrd22oTNRqRr9Qze3HClWW6g5t/82Wgyve9tYVNvi+Y1PEIoVG0eCawmitQfCeXic1R82Zic9PN/XPgdm4TGyo1Y+8y6UqlGBDEinOa8txvnrMIW0vKk5j2ymJZjU/fD/SKoXx6aFzUqBDwjXoOZceXzUywzMlHPZSEC4CnFPqC0i+YUT7U+8zznfxqimXP89upGphKf4PskJ33Ga86QaebNNntH3FcNGGi2b8B0uni0+577+ftqitc9QZu3U5zyTCdzKaP3biABpM22O5jn3nD7npnlurY16QDj197R8VnvPn7fXzEh5Cu5RcGbtZlsVNLc2oM9R+VwmiDDOubLoZarfw++4frY9xufcjC8SBoTjN4Toc+NS/JWZ9RDn9hVnEq0FhNOE86gFhSKM1F5bA3DuDia779RXb497fms0IBx9j9KmPlVwz9jUYOua8/oz1DTn4aYYvbdF4gqQySCyYaqODPOD07ZE28ppzocnyvjgBffgPb+8O7L5E66VM9r35jVya8FcVvc5z0B//8K5j84JUUVVgekn3PjIjfV0jjHPME34TWuwNKnUGtWNSLR2g3POOQe333479t13Xzz/+c/HkUceqf0TmiOY4Cv8oO3SnIdB3aILXs58mcKZ3ujRK2v/c4vWjUMT+MN9KzFarE0gEeHcsVDjJg2r5lzzOa99VgPrt/+6GC+b90+s3DLGahtsPnyqrTbTL9eckzbPOa0hjeZHCU6a5pwxV4pozutfBGbtrEsEaSuzaJpwREtXk6I2sJO2j5c8Y6LVzS+T+JybaaVsuMoZI9oN22HNBoWj70q/4fJBhS1bf0oatdrUuJmTtA1tA4XZRFNBJ23ma9F2MF9mDB9vY5FFKQTjTd1M0agmyfNQdZkxGoBwY5H3ieX7q8+0t8/YRHC1ZaSofM7zwUJQlakEgj5Gc17zOecXObQtqqxcNl2ec/N7c8PCvHdqvuHmD7N5ys+eQrVUcdZSnHUWR8XQgqoy/v7IOhx+6g045+blbPl6vWFZHJ7va++nLVp74HNOLBxqzySqOafaXc5nuXa83p5K1ScxUhifc9W3yHdRs/bQxYa+5wfttVOkPAXrR6v6ZZY3a3dZ5NE5Kk20di7lHXfoILPRamsbEAp09H7TcisOQdmM1q7aOMZqzqNtoUF99Q0Bu/aP9tOxunCu4gfYZHP6rA/YPXzWLuHZtpkVa9auBS1uUMJixlzdrF2/X2YqNS3PecXTxqxiJQxWm0E0VoNnjGm6T3jN1/3OFZsD1xQzPgAQWktRqsZ4bSrNzGvcOhrtx2a0dpoKjrZdFZnNRAMuqusw4SwlfOaPQjZaX/B3jOY8eNcTbbLzx8RZ8Wlm7VNMOm8oINw73/nOFjdDoKidLm23MKnPuRIKGK1RrFl7Nqo558xG1EvQT6IYn33zclx6z0pSlmniEn42TWq4d8quOaepa8LoxoqJsodPX/oAuLNtWn1zwWDiMstJ4stnG5BdE59Z5zkfrplia3nOk5i1K+G84BDOuT5CynGZtQeTnVZn+HmspE9cUc15JTbKfl8+2R6iWzgP22FLzVbxPPQ1kV2SPlszWrtuMcG4Fni+IRja69F9+EONa5z5mOnHB+iCVrFaBVCI3EdbqdwY4vu+tS+Yh+eNcS7OL5qDLlBsPufxvoP8QkZ9rTZL41wfRkthX541rRARTpUlVN7QiABK8OTHp1oZelmm/2FwnKXjuFL6cH+HqdTC735+4+PYNlaKvK9bRouR+rLZGM251ofdAlh4jscKNSf/+WEAwE9veByff+3ztN9MgoBwVrN2U3MemstSAp/zBGbt24kAqefR1t9jStWLMWtnXAbMgHBKWDeFc7fmPHpfOM2c2Vbus6pbwUVrt28mRccErm2DcZpzJn987X+b5txuYu5bzuNisnBChZbPnDzuUsVeJ+0jI4FZu1tzvsv0Pmweqb2TB+wxA/c9XbNwdSkDbGNb0oBwZluB2r06+c8PY7+dB/DZ457LrlcA3qzdtL7ifc6JcE4yHNFm1KK11z5ns5lItHb1H2c96fnApfeuxHf+uhjHPX8PXPSJow2ht14ut2EVuGrV/s7HaM7XM24m+VwGGfKIM9BjLpnviC0gHLtRxAnn2lwdnYsicUkM4dwcp1wKORO7SykfPDdoQ0zWiMlMQ8L59773vVa3QyDkSXALBR20uclAvThmgC7P0wc5hTmQHrDHjIjWvfY5PCbQpgSmOuFuoLnzZ/M/AWqDwIahCZSrHubsOp19qWyacy4gnDk2PrZuiD1XvchmEKxAc27Vtthf+iSC42Prh3HOzcvxyVfurwsrjnJ9ct8fPvUNgcac5jk3g0YBjIBSr0NN6Jxg6jOLE92X0S6cm7mcaZ0AAk1i8FtV9znndp1Nku6Isj7QmVp/GyUbDOMl28I9/eBuu+5pBWVRwLST+ZLLc2yjaphehoFh3H7atNpQcA2/U4ses3m23Wublo1+rS+89ONNS52IK0eiHfewLRHNubF44M4zP/Oa81yi9gzV+3JfLotphVwkzzeN1h7VnHuaDyUAzP/vV+KaRWtxwe1PRlL/pM1zbjbdtpERtIdJZfWv5ZvZsrcwWp9cls+5G9RnEejifc6jZbhcmExU+bbff3PHUzjn5tBnOoi+HgkIVxeQTM15EC8mLJ8KkKUKLxSafbTshQIJb9YeXfjaAsL15/XUfc8nwrkru4LZzlyGN2v3yLhjPr/pmuY8Gq09lVk7c1ycz3l006m+OWOJc6BpgiOac97ygdOcc2OOLf1cyVFn1fPx6389if945f4Yq8+lKh2fzed8txmhcP7s3aaz15akvWY7OVw++gue3oa/LFwDALht+Wb89bPHsHOTNuYGY3b4XaWqj+2mcJ7PZoK4IKWqHq29VAk39DJAZEw2hVBzM+jH1y0FANyyrBZJnYsOzk235n0LUhxbFDXrhxjhPKs7P2nR5hGdNzMZXtHEvWPmZqPtnAIZ30z3jeBZkc0BSs7YiHPhcil1xVaYymbtTaVSe+CBB/DYY48BAF7wghfgiCOOaEmjdnS41D3UZMil/TQHAS7NGaAv8AHgPUfuxwZw0PJ+GhNmmP/X0yJwA27hvOL5ePm8mwAAS057I7vIT+ZznmHrsmHVnMekfHCZ1iRJHfLYuiE8tm4I++0yXVtoJdWc70TS71DNeRL/ffWYw2jtdqsL+pmW4kqlFuxEM0INgMDNQVHxfEyQ/mv6inIkCcQF8JNAIZdFqeJhnGjObWb6SVJlRdsWvXf5bJiDmrufXDvN79zmvboPv2n5YMuLzAkDtM9zOe7NtmtttgjntqA65uGuaO1m22xQH0WbOT7XffQNSH6CV58DzXlMe2iOcyCa51X1r9p168+oTMza1eM77FmzUfF8XHD7kyS1VLgQyue4zTm+bRF/fLO/WTXn8c9gywgjnMeYtds2il23uOrxqdRYzZXFhFf1Bdvv5gaE0k6am5oDnM858X0tVcPUUppZO9WcG8G0zPYHmzmOgHD0LC3POUml1l/IBcHEAOC5e4amznGbNrW21P6vWUNEfq4fUxt3ImbthubczNNs3UxiNuy4Q+N8zk2NsFrDaOM2KbesPR/GrF0TsGv/8z7nXFt4gUmrk+mXp1/7GF5z0B7BBnOoOecfxs7Tww383Xfqt14PxZatxKlA8O1p3wDgmS1hdo5Fq7ZjvFzVUgkGdTBCoe3+ACQgHBkL6SYaPbdYqYb9JhO1OFHND4MK0+tjlAuM4ooVzo3+q57Vsg3DOPWaJfiv1xyovbsbWeE8GzFr16On6//bzNq5CdzcSDEPU/eMXpu5CRXIA5Z1tSsgqImtn8XNP9om2xSTzhsSzjdu3IgPfvCDuPXWW7HzzjsDALZv347jjjsOl19+OfbYY49WtnGHo8Bozmkn5ITzIE2Pw6xdW5wbnf49R+7HatO0qLoRzXmoJTDbFPE5J+tIOpltGSmxE1kyn3OVSi2ZcB4KT6bPudun1LV7nKzmGkMTZewyvY+0xz5Z0sGVaiuo/zUb1MP8u9726S6fc2bBSy/ZbdYe1eBoQWyKxm6r72sxE5JozpMGjOIG8r66cE7bYbueRiK2cxqYfC5cxHKBtbjrMRdlrjnJTE0Xp/nifuO01WrCNsuwTa62TQZzsbVw5Tbc99TWyCIrbywkGtGcU9N1m+acFTgs4yJnRktja7gsE2ikdiCqpVE+n/ksozknJtt07DQXOA3nOTe6dsSs3XLvk7x6vFm7O0OHbdPYuhME4NRrluA1B4VrC5ePvk1LWI0xa6dkMuF8Y46b0wvKrJ26WelR9pXAStN92TTnkYBw1bhUarX/bWbt5Qo1a89qQqzLFYvrPlRzbptrK14tUrfLrJ2mhooLYsn1P64fcfNZrVwf2WwmInQGmnNLnnNd2NTP9WE8v/qx4yV+DjPHCirY+374u82sfa9Z/dgwVHu3xkpV7XkCdqVEP7nnB+4xI8hc04hZu20MvmP5Zpx0+YN4wb6zrMc+vWVM+9tWP/1WHcJZ8ylUQLhgPZfLoD8XXnPRUGSFsnkmsqmlNi25aO1rt4fBDefsOgDAHj/IxFxHqbXqlQ+sBgAsXjOILxz/vOB4TnPOpVLTfM6N+S1LNOfVmPeGDwhHz6n9r6/l9H4eWDAw85ZqL/3dha1vxK0BKoxyZKrQkIPlf//3f2N4eBhLlizB1q1bsXXrVixevBhDQ0M46aSTWt3GHQ4uWjs1fXJF3C6YpqLMTh/9HQA+fsxzsPfsaWwAB01zbgjnfcTn3GxTxP+EfKbmd9xue+34eM25Wjgl0V7T9puaRVNIMHG99AmrBqAih/ILAROfDLgmJxy+D56z23S88rm7W89TqLYPJIzWzqVSm7AsgADe95EOxubOc62vpNOcJzU359YX6n2gPuc2M/1Gcp3r0XtVndmImRx9LM1rzvUFZJKI2rQtAB9wKRTOjUWppUib0GXKWu8+7y786Lql+HN9YaIwo7WbxSUKJOOFbbS5dHD9J05bXitTF85pmRxD44bmXJWj8pwTzbkpUFLNuZY+J6JpDL/nFudJn1Wczznn3mJjK6M5pwtJVuCzCOeu+jYOF/En0odCzXn02EYDwlHoJootzzmdS8zc82pssAWEcwmCVJgqMBYSGebeambtVS8Yt6flc3jdwXtioJDDMQfuZrtcANGNd9UWoK45twiENhcSataeZ1wd7MEm1ZhgDCYGto3DwB+WCbRX+z3e55zbMOWen82yzOxitkjsnLa+kMvghi++BrPrG31V3yduMdn6//yzyGaAX37kSHzjzQfjJXN3De65a35L69L30f+7F1tHS5qlifmOrNw6qv1tq56zuqPHmibYZkC4LMmSAOgBk+maK5tBsGBRdapHzG0kbhwONx0LjoBurvHNFP4VC1du085bPxTd4MwbqdQy0Mc6z6ifzgm2+U3B+5xHr40+U1NzHgjnVf4aW5Hn3Ox/g+NlfOmPD+Ffy+1uBlOFhjTn//jHP/DPf/4ThxxySPDdoYceinPPPRdveMMbWta4HRU2zzl5SbjUVlVjIWlGUqTHAGGnPvP9L8K7j9wPQPji27RK5WBBX/u7r67FrXp+pE3mJE4HGVNzyb1UtkikVOgPNOcJt5hsgSvifGNc0dptmwgcNVPH8G+X9sa2GwkA537kSHiej4dWb4+21Sgy8DkvKJ9zzteItIkRlDgTKAVnXknPHS1Ffc7p5GkK7xxJzc25fqT6CPU5twnn5kIuCVyk3T4ifI2Wqtg2WnL6l3LfuSY03efci02RqKC/KC2u9pzr5oLmgtdWImsu7vtGZwg/DhlWEpFo7aaWi7mWTcNFzJyWj6Sqc2rOOeHcuqFBvq9fX4HEdlAaQg6lOQ/N2vXFCd0cNN/qStUdEC70OffrZfOLc9sCJXJvfLO/8UJMkvUO63NuCU4U1G8RztO8gWp+5DXnFrN2ZVGWYMVINxgiAeHq2mcqOOdzGe3vUtXDAHKaz7kWUEwTzg3z66rn1JyHgTjDMqjLUpmMs/2FLHaZ0YeF33k9G6eEQn1Ny0E+6VC4sWnOQ0s9/XszWrupTbP1L87qhTs0bkM9kkotEIij73nte16LXavfZ58f53Ou2p4jb7r5jFXTuSCB2UwGs6cXsPP0AgbHy5oJuVq/2Mzac5kM3nz4PsHfhWwGJbgtw6wB4VIIPBHN+WZdc86Nw5G5pv4nt2ls/q35nJP3jrp3ULP2Wiq1ej1Q/a9+v40UfyZq3cRt6vCb1Pr82mcsUj3f2AiwmLVTVIpNFUcnsBrz1JzAuxKxPuecWTs5TH2kfTOqOdfdC8yxIc5dlGI1aze+/97Vi/HXh9biqgfX4OkfnTClhfOGNOee56FQKES+LxQK8BrMdSiEqJ1RW3ASU/tJIz0rbXaoafG14xRqQayZUAbaPmDB01sxXqpGTGhrvyshpL6JkEBzThdQdDKrej77YnILLvP7tD7n6n6avpqxAeGMl36vWf146wv3wa///aiUmvNqrJBm/ma9D5a0Nub4pP4e6Kv7Tib1OU840MUFhIv3OU9i1p5sTOH6kZq0x0g7bJqORvK00o0DNeFRzeiVD6zGET+4Uct1nEQ4d5ogevoC0uWuQtGsNtRGm0/brxYb+nnpNefJJkw1Rth83c17sHFoAkef8U+8+7y7Im3zfD96vkMQs5u1I/I9XVi5NK7KCkQFbzSF02D8yWYj40bVCwMZ6WbtpuY8PMYV1d0kzufctjGSRIjlhPNsNsO6vAT1aRvFvBYzDpfwatvQq9bn0SRxLPLEx9psVqA5N54V1TgrgSeJ5ty8z2OlajKfcyLMmBldqOYcqAnKao4/7vk19wBqlgyEz4rW6cf0OSDcMDTH4IFCqP+p5Tk3LPtiNGYe8z5yx+01q1/7vmLb8AvM2i3uBXEB4ZigbjY3qWjd+t/BuFCJ1qnuf2Cm7FFhtDYe2dYGpiUhtVD69b+exNUPrYmcYxvX0gRJNdOMUZ9zgH9+EWupYNPG/hxKgaCs+mVWs3IxsxYoMoha86hazFgIJmpDX9ecqzZHjw9jhNT+5zZS6Wm2gHDaM87obTXrp9kUuHZS+FRq0TnRqTk3LRgMaZLbKNg2WsI1i9ZGFCRJ1943PLpBP8+RhnCy05Bw/trXvhZf+MIXsHbt2uC7NWvW4Etf+hJe97rXtaxxOyr5QFiMmpQDUQGLduAgnyKzMKWfK4wpChX23nv+3fjP3y3QzrGatVe9iEbWnDjon1Q4Ms1gw+P5iYcOcnGTlMI37oU1IFwC05pZ0/I4/6MvwTkfPhLHH7pXKp/zYsXQnDsGE/XoXRsP/OKcF1AG6oFYuGBopqm9mQ7LRWjWTurU+ky0PXRgNrWpHEnNzTnBVPXRMU1zbvGva1Jzru5tIZ9xWnNwz938Lmme86oREM53yBychYSmobCYMscFbTK/iwvipMjndFNBzurjqgdX401n3Y5ntozizic2w/eBR9cNBX2ImvaZ55vRZPXfLJ/96GdNc+4Q6oYimnMEbaPn9uWjPudlMg7SsU8teEwTymzGlufc8qyYe6v9blwWF63dxlbD51yNSw1pzlO8grZFIaC/T5qJNhFm48iai2PCDCZae23TJRPZdNJ8zi2LSXWs8iceIxvjvOZcX6Cbm+OlihdozlWZlAv//Sj86+vH4bjn76l97xtzOxC+PzTglIlqq2kKrjaFa9cRjdYeZ84aN5ao777y+ufjP165f/C9akbEIiHQqPPl2gL2qbaYPuejxYpVODfba1pfqGtUFku0TnWfaYCvqOacn2TMuUcdv3TdME6/9jF84fKHIufY5r40QbaodefmkRJGS1U9ujgnnJv1Bdro8DtTy1s2hMJctvY+qI34CYdZeygw6vVlY9aARWNDQGsrO5+rNtefGbMg8GP6tpnVQ716akyq1BVbNEho4mjtMRaU6hT6/phWkKYbXDSWU3Sj4EMX3oOT/vAgfnL9MqPu+LU3EN0gmMrR2hsSzs855xwMDQ3hOc95Dg488EAceOCB2H///TE0NIRf/OIXrW7jDoeaGLU0FZrPOT/I187VTUnomMtpi8ydf8q/lm/WFxDGbnQf2ZE1NwxcmvNxQ3PODdo2kZSL1h4nnKuFjxowranUbBNUvX27zujDQ999A4549i5hO9Nozo1Iok9tHsXdT2xx1um6Nu63iGBVf1YqgBGnOec0lkk1WJx2zHVu1egr1Of84L35/LuJA8KxmvNa+8YSmLVzkXLj4DbNCrmsdRELNG/WTjc8zDQzTs25tjmnzO6IcG4NCKf/Xap4uPHRDZo1QFCuF/U5t1EgljdmW4DatXzpj4uwdP0wvv3XxUFKMwBYvmEkOEa10Tw/CCoVc7+1zQ0tz3ntM81l7dpQGwqitZsB4XSBoJbnXO8flaoXRr3VFmSmlqT2IZMJMwJQbJs6sT7n5r1j8pzbMKO1qwWiy+dc2yj2ovc8Ca5o7epdftn+u+LR778J7z7iWfV6o4tsG3mi/Vc8a+cBnHD4PnjtwTWhls4l6nEUcvr8PTQeLmq5VFy0varvjJUqJOAV53Ne+189V3PDsVylqdSifhj5XBZzdp0e8ddXQyBdaKs25xzR2kNBRf+eRmvPZYg1Rf042+Pm3Ku4LWPVb2cNFPDtE0I3S1s++1Zqzp/cPIoXfO96/OzGx9lrMN+5qFl7fVygmnNjQyZHxpBAoaIsBq0+56bmvPa3Sq/GYZv70uSOphseSmu+7+wBEn8leo75vptCMxDd4I/mOa/11f5AONctSIKiMnTU1cc3TqilqHeJrr1dt6Zq9N8C8+LE3dpItPZ669VXH7nwHrzpf28P+lU2AzZae2MB4fzIcaYVpLlhYU5H3Pi/dP0wAODah9dpxyaJ90TTNe8xs2YpY8uAMBVoyOd8zpw5WLhwIf75z39i6dJaHsBDDjkExx9/fEsbt6MSBISzTBSmcEE7cJB7PNAW8z5uFWNgA3gNBKdtDwYcovkyd+5taRUAPThX1fPZBZk1WntW34nn6jIpVz305bPEWsDY4YsZmKmJeVToSi6dm5rzr/5pEYBaPuPDnjVbr5Ps9tpgA8IYl6CuiQaEM6PImsrAmvbcdSUhgXklqTjOXJ9q70eI5vxtL9oXS9cvi5yTOJUa0+jQ5zw+IFyzqdTUZFXIZp0ZBLj7YwoLcRscQf2er/WRpBsZnOa8zAjsQHRRPP/htfjyFYsifnRc/W6zdjV+WDT2pKyhiQq2jYWT82PrhnD4frM1odWsKQhSxWnONa0F/Rw9JputaUMrno+bl27Enx9YjR+/54V4zu4ztDJNn/NgcVL/XQtIaWrOq+EYSrWW6jBTo5TNRDcZgahwxF0XEG/WbusLHKZZu+r6rmjtNnerNNoPJWBx7xoNpkY3y8JUavEVccLo8YfsidPecVjwN52P1Od8LgOUiaUZY50CGKb99XFkZn8em4aLGCtVIybOFLVQVyWYY1qpElqzcZrzoBzj3gU+5yTA1kg904XL57xiedcGjGjtqsvSLAscnFaSe2TUzSNTD4hFg+mZ42qg4bcs6vU0lYxQU0neQSPCuXEup903XRky5H6pcVIJejafc3ONovql6VJH10227Aa28UT5PVOo5nz1tlqk82fvOh2bhouogo8Wzz1T39cVNqYyyjSnVtNQIZ8FiobmvBK6EmYQtWZS/4f+0fz1VrxaQD4aL0e1kLUMMvo3Hx+Er0uRy5p5zvW2qmj4c3ebUf89mqrQVk+cWbu6JLqZaGbeKVVq6SKDe2iMDa5o7VG3rmgba9+H5y5atT34HGZQmbpm7amF83K5jIGBATz00EN4/etfj9e//vXtaNcOTZ4JCEc/RzTnpPOrhZ3SJNk0RFxwNE7I5RbvQfA5LVp72Kaa+ZBeFv1bN2v32JfKpjGmbTQXgTbMyNS2gHBxEUs5U+V0mvMqO5AvWTsYEc5tuSMpvOZc/zs0a88Fv1c8X1vYm5rFqpfcrJ3bHeVu47RCNtjRpoM89Tn/9KsPwGixgoP2mokv/vEhrT1J4AZ4pXVKZNberOacmLW7+iSfH5zXqnBoC8iqp2nWXMIUF5WYrsmSRmvfVI9ia5pp0nJt51JoKkaA3yQK2u75WmCtR9cNaW1lfc4dmnNb7AfdHLD2v4rCXfF8fP3KhwEAX/nTIvz5M8cAqKX42jxSDMY4pf1UK6tFqwZxRX5VmAe9Px8NCOd5GC5W9PMRtUwJU6nZfM7dwk5YH7+Jks3U6go2NmLePc/zNY0GQMzaHUGWrJrzFCHh1Hncq1YONmJ1QSbUCMW/6zSAmcLUYut5zmv/9+X0zXV6722ac9WemfXo3MWKF7xfzoBw9SI44TxMvWWJYIjoHEL7vEJpzJzR2pnsDwAwjUZrzyWP1s6m8uOEcy/st0BNQKjCJ0I4bxJtpjVTlBzCd01zbk8rGmmbKbx6/LqNM6VX95+aBodadbdSwqY5N/tbLhs+G9t7bpsTs5kMk36RWMTV+8zsgUJtHKjydXDvu+frz8R8V4uG5lxtPqj5RA8IF6ZSUwHVACqc6/fbZSlA4zjU2sn00TqBIqv+N2f9EqfpNVNu2ta7NDCmGnNtm88Kbu7mrFTovTdj9RQr+trdppDjrtPsuUnM2hevGSR1V+vti87XU4XUwnmhUMCzn/1sVFMMUkI6THNPwNTQRQPCKehOvufrO4G085q+TQAv7HF+cTSia60s3eec84Wi7y1dSNRMqCOHW33OuRzAcfHg1EBE8wxzZVojRqrdeaZNKWTzull79HvOPLVqTBocSRbn6vlPL+h5QGl0U86kNal5KRsdlLnI/nwuEIqp5YQa8DOZ2gT29TcdjG3GYj9pUBpugO8LzNrjA8I1ojmnbQs05w2YtUeEJYfsYL6T2SwRNJ3Cefh5eKKCfz66QcvRGwjnRt1mka7nERV67MeGmvP6QsYiXKvPtF8sXW8K59HJ2abNM8u2XQ4VWAu5rLapowTSUsXDxXc9DQA4ZJ9ZAMJNS/Xu/nnhavx54epAizh7el9kfKtU/UAI2onkoTYXOLRNaXzOza9tGxn9+RzGy9VAEIh79cbL1Uh/Ns3a2cWr8WzDdrnr49rMmrWTlFRAOM9xKQRt0IjzClMDVtCEcz3FVRijJTzebtZe+zxrWvjs1cZlwTmf1s7jzdpr3/U7hXP9b/UOVhhzVleec5smfHrBHa3dNs5x1jQuDaV6vrlsRhMEI5rzavT6bJpzk1q09uQd1JwHzXO5oFtqjZKj11M/NnRzcGvOTSWtuTFVa4unbdpYsxvYNOfMd9T1cqK+GT7Ql3O6t3Dfeb4eJDguIJwqX4279F3w/VCjn8lE1yvU8qJ2vP35TpSreswBh/UHjYUCpDdrpybqCqVHN4sqBxs6ZJMhZkyNS6WmXgO6rjcz65RIRgnAEcuJaYA5/1nN2kmbxo1YAuZ5aeIjTAYa8jn/1re+hW9+85vYunVrq9sjgDdrNyOxUjSfcyMnr+ZnziyIXD7nZtlBDux69Soti7mDxpnH05eRajFtk53drD3a3iRm7QAik5uC05xvHJ7A6352Ky68/UkSeChaT5zWnlKqeOxOMaeRD/JvOq6Nm5zN0tUkQSfiuFR2Fc9zThwU7vI5YYim8DF9lwD9PprPxxWES6uXGZzVuzRGtPVW4byBwd3mc25xB7S2M405uJZBwfPY95qDljk4XsaJv12An94Q+ksWLQHh4iJ9a22LLECthwaB1rjgT2Y9nu9jO9GcP7ZuGL7vE9/V6IZSqP2N1q0HIaOfEfmczWQiKajUmEG1+eo9VgKM2QVUv9t5oMBozv3AxUMXzpWKtPafarbN5zxuoyGoj8mrDdTSbtV+T2bWzr1LQTArw+dea49Nc84c+9w9d8LX3vj8yPfOaO3GbzlDKEzyrudyUZ9zU1DW3Kzqx5o+55zFChDdfAJqZuDqcobqfYvzLVYLdVWEGeizqPmc25d55vzCWRaoRXnOpTm3+HgPWPKc2975sLza/9q7yRxn5qnOG+VHzdo97f9aG8LfXRYVnu9OKxo93hDOjXPVO1BmNOc54x3yvPC3vCG4m0Q058EmKGmL5f2PuwZbHbW2R7Ws0wq5yLtHsQrnlg2tWtv190qtF9R7VzTGpEABgKg7iPo/DAgXbY/CDF5rat8pZhyVuIBwJpxyKtCcG89dyQaZDB+ETd2nzx/3XPzbAbvVzuFSqZHP3DgwZqzb1m4fx9fqlmRAc3nOkwSdpeWo8Y5ao6SJjzAZaMjn/JxzzsGKFSuw7777Yu7cuZgxQ/e9W7hwYUsat6NiTu7m50i0dmLaRdcOdLeVHgeEHZnTRFNokA9btHZT2LJFEVUmk3RBZ5sMbTIvNyG5BFhaB01lxJVJTYvPvmk5ntg0ih/+/bHAfJWbkNIHhIt+zy+y68/HFRAugeaMbkjsObMfG4eL+MH8R3HWB14cTkhe9JykwTU4v1JWc078HkeZ6Lb0UgrGRJZUaDYXGFkyWSUJCJfUt912TuAznMs6N4yaFs6NgHBePrrjzREnaKnFo3lYKs258ZvTrN3YFDOLNd1wthOf88HxMlZvGw81FGw71Waie1FoS6sWBl+L9sl8IJyHbVJdQR1q6wKzBwqsaeJwsVbfTkR7atP0tFpzHozpgTWUW3hScJGqE0Vr154tb2IclJfJYM6u0yPfV4JFqV1QUItiU4uTZMOvpjk3BB2n5ryuwSOBUgHTbNrtI5nLZjC9L4+RYiUQBlgNqdqzqRdujmnliodive2NmLXTd1wJ5zZXCnq8+aynmT7ngYYS7PGKUHMefsdu8hjzZM6wOoyatfva/2YbXJpx3/dZc2AbNkse83c9wKeuOVe3u+r7wXgWF6094nNevyeaht64DmsqtRTrM84EeqCQY63rFJyywvftG1oA0ZwbGxnqvTPfBaVJzxIz8aD/kbUz4N54pvMPLYPrwpGAcDGp1GznU9Q4Z64J6TjIxU9SH/fffQYeq7uDcWsd7V2r/6/lOTfG+luXbdL+tmVo4q9F/zuJ5tw33tOq5wfuNLX2i3COd77znS1uhkApMD7ndNd1wgjqRQVtOnmaQlYjPudDRDNUNibgAuPPaysHqL28nu9rCzrbTrTV55x8n7UMViblwJTRbdZOxytqGmWaPlFSCeflKrvAcAU0cqZSY86LRj8Ny5n37sPx6d89gGsWrcX7jtoPr3peLdetuR4pe8k8P//93+biqc2j9XpJ25lrnEYiBps7sIChOc+ak0+yQde8dvo+tCIg3EixguUbhvHiOTsH7x4bEI7RuGn1MBNRNJWavR160CIPnh/eW9cEFTd3JU2l5so7by7mXD2JBpTk69En3u1GdPiHVw9qQqvtfNasnTHhAywB4TIZLUgbEL6XtE1h1Fxd62Wy8/RCxPqp7Pko18dFTnOubkVczunEmnNmQw4gmvMUZu0mqlmuPOe6tUe0HZRMJm6M1MvNZjNB+9UGkGkdlUhzno2atZubNPTvwL9dmbUzMRxsqdQU2WwG0/tyNeG8PvfybmK6BtDcsKf1uALCmdfHacxGiXBum45sMQpoQDiVao4eZ3sMqnozBsSTm0aw3y7Tg/cxtGpTdbg158H3Fp9z16aNj3Sa80hgTXN8qv+tm7Xr8z7dVDKD+Fpkc8YVQ1k42pUito1pz6+1+/vzH8VBe83Eh45+dr2OaEegZu3jgVl7NrIxRrFrzqPzalCP8V6psYEza6+dH163arWal1QtNCq+DWolRevn+nAgnNf/5t7htMKkOa4qQrP2DDvmhoFNw3tUYtY6ugVZfRxI0d+t7qLMdSYVzumazDymWKlqyoCpZtaeWjivVCrIZDL45Cc/if32268dbdrhyRsBZczPvl/7W6VNo4IcHTQ939d9qwxzWHWOIpPJRKJw0t1CNXGpcdymObcJlKpp46V4zbltYZvV2quOZQ+N1GEGCQram1ELN37CppYJJmZKJBelqscOIKxZO9Ha2eAm56iGrH5sJoPXHbIX3v6iffGXB9dgwdPbAuE8ojmv+rED3R0nH4dn7TyAj190f6Re7lS6iOU15/ZNosQB4YyLp5qeJK4UcQv2d593Jx7fMIJffOgIvO1F+9bOYdxNCjGa87jUXtzftt/M+ABJfc45rKnUIvXby4imC7IfGwS+tGi4Tb85Fa39wD1m4IlNo3h49fagfM+zC/dx91vTMjBtz2YyVs05J5wHz97SBXaeXsDGIT2tUaXqBRrKmU7Nufqez3NuW1z6wbXUyjDvifpTpd0Kx3p3pzH9EGt1xGvOdZ/zWl2LVm3HO869M3JszYQ/WncgrJCxo1T1MC2biwgykfzaCYVz8z0273meEc4Dyzdm00nTnFs2aqfXTcFVAEEu0rP6Rl0GFxBOPQfOZ11hzrOqSXTdoKK157L2edm2ETa9L+zLXLR2W3/lNOcA8Nqf3YaXH7ArLv/Pf6v/Hgom9H9bKjX1jtpiHrg15+msq1yWQADwvl/ehbe9eF8jIFx9DDGup+qHWsLQ55x/rrY+W9Q058nmm6rn49bHN+GiO58GgEA457oBnQsniObcFWyNq9XzYzTnwTPUx9sgz3nF1JyHcW1MN6FgXMza26gwhXOX9YcZR4V7h9MqelUJ5lhIU6lxGyHh2B/OY5zQzc19aSxF7GbtjHBuTI5JApmaXXSi7GmKgqlm1p7a5zyfz+MnP/kJKpXopCy0BjWYqsHH9/1IpE86AFXJIoW+IOaCVdOcWwRVc/KluYzDVGq1/5UZEdVKcmUq1K5eM2btXFuTmrWHJt58xF19kR7d1GCvK5XmnDdrd+Urdwl5/G6s/rcZNOnFz94ZAPAQSUvB+pxba62xcz2oFWeyZss3Hmix2QV9+Dm6M5xsgjAPyxKz1CSL8bho7Y/Xc2tf/dBatm1qEVDzObc/t9se34SNQxN63ZZgQXw7ycLF011XXJsqcTv1nI8sgMgKyqU5Nxd9Lk1EVHNu1qML0Gpx9OqDaptKi1Zv16I+m1W5NOe2QFNclNtshjFrr/9N/eDVgidOcz5roBAZN+wB4XQNqWbWniICcCSIp01zntcFyzhXiJGJ6Lts+su6zD5pW77xl0fYOrIZ/l6qMuh4ESzcjYBwpi9mEmucHNH0Ksx7TgM9hZpzfQFMb7WmOWfaoMzagdDH1R2tvVbGsDGmlqthHBjX/GjeVtVP6FhIo7Xb5iOr5ryPpprjorXz7XJtrN3z5FbS3tr/ZlT+UHNumG8HZu1Ryzjz+yh+Ks35xy+6D39/JMznbF7K2sEJ/Oq2J4PsF7R90YBw0YC2tsca6bP1Aycc6y7bpkTV97Fq61jke+595MzapxVyxH0uWr4tmJrtnQFIoM/AjUgJ59HrrP0djss2N6HAHNzxeIesmnOHcF7/25w/bOe5sM0r1GJLVcO5amWocB4TEE7FcOH6BZdCNZOJ9juXz7nZd12bQ+Z1KCbKVUP5yBYxaWkoINxrX/ta3Hbbba1ui1AnjIIeTnjme8yZe2QNzXktNya/cLcF0zGFCppb2DR7LVjMiKz5NxnNOWdeA9ijtXPlJTVrV9dsTaVmWCcoXJHTU8jmtRzjzPfcrmow8TiujfstYpJsPOcXz9kZQE2wUYMxq7WNmTeCPKz1v+nhbGA2ok3mNNTO1GMJNefmQi6X5bWLNh5ePYirH1oTexydm2xm7a4F8cV3PY1jf3qr9p15jZffvwrn3bqCPd8M0ugxfZUjbjFQCnzODQHb6AwuC4M06ejyRn9wmbVXqqFZ+2vqwvniNUOaX29kjLQIDOZ32s/kM9VSR8zaM0pzHo6PZTIOA/axYfZAIaI5KHt+IGBR4dwUwuhmG2vWbrn96lrCDRH9QFVukB6TiTTO4dac2zUn2qZxsGlqmzf41IRqgUl/MiM5RwLCWYQ2jpqmWP/O9B2lwrrqz30kWrv5LsVqzrOh5jw0a7cL5wqVPUBFey9VPLLBy1xcnajPeXRRrgQtc/OfYhPOqRURG63dMibZxgQTU3OufM5tmzCcRp02OS4gXBrN+dL1w/jsZQvD8y0v05rt42H9hjY4DAgXruXUb7Y1ks0Vg67Toj7n/HVVPR+DhjsRwI9tWp8h0do5P2gF93h9T79XZn54NXZVjXvVV7f6MdejEyQgXFCHUb/LBFsRNWu3X4PpqpVmHWIjtBTVy6IpJflo7eodQWBtywaEM+Y+24ZNP+Mmw61FXX78kWjtMZt0tfYxwrlDeJ/sNORz/uY3vxmnnHIKHnnkEbzkJS+JBIR7+9vf3pLGcZx77rn4yU9+gvXr1+NFL3oRfvGLX+Doo49uW33dQC0AghRgTM/Vo42GgzYdA8xo7dwLa2pfVU5KxSCjOQ+0LK5Zn0ENHGOJfM4TFJgJJ30XZUObEonWzkwePnPfeA1G8kG3xCzWbGVUyYBrI5lZezgwA8DBe89CXz6L7WNlPL1lDPvvPiMyqJWr8QHhotoxcr8si858NoNS5Jca5nW+9YX7YP7DNa1D4oBwRr2ZTHzfoFxw+5MAgL1mTcPL61FNOeg7Q9/DItGcx1VrxmkwF0c3L92Im5duxAeOmoPddurXfjOj++pWC/Y64+auosWs3SzTtVkSXUjZj1WCjdIgujTnQxPloB8c9ZxdMVDIaYKhy+ecz3NOPtMFAKJjZDYTClwKLlq7zSSVMlDIoT+fi/T3StULtNAznD7n4ffpAsLVvi8Ewgv/e39MBH0TTjgPhQd7m/SMA7XPtsBltveYe75msCglmARmq4YQWchlrIvQXDZrjXwd/E36haqDmrVH3iW/9qzzuSw7ruUyGUyvP//hICCcw+e8XoQSzveZPYChiWGUqr62kWPDvK+eb39nubzviorlXctmMnjNQXtg1dYx7D17WkRzHqcxixv6TZczMz2jOa6Wqz42DE1g8drB4Ds6d7nmGt+P+j+nIYnwEInWTvptEEchZt1lCkqqLN3n3BgrbZpzT8+SoeC6gS0gnMu9hc1db4zlpubcVLaEAeF4zTlN12rmOQ/9sdML56rx3ClmMMhG8pybqLabwwG12OLM2tVHzaydDQinr39tJu39+RyGoY/7tjWfKityLWbdiTTn+m/Fiqe5UiRV4kwWGhLOP/vZzwIAzjzzzMhvmUymbTnQ//jHP+LLX/4yzj//fLzsZS/DWWedhTe+8Y1YtmwZ9txzz7bU2Q1MzTl9SZRPOGcWmM/WAkIEfoW+7js8Wqpiw9AE9po1zao5j5q1E5/zwMy+3s68e6I2UWUnMWtPkqIscO2M1ZwbZu02zbllIHAGhIttZUjNrD16b7jBy3fUqeB+swWdoblAD9t3Fhau3I6HVm2rC+d6GVUvPiBckC4q0OzZ2wDoZu20/TbTy3M+fCRef+gafOHyhxJrYznNeZzLA8cTm0acwjktkwqj6j0t5N0+5xy2iXqCWQxqPpJG2rt2BIQzT3NHa+dNEDkKRmRlV55zJaj057PYqT+PXWf0aRonn2m3K885gCB4mM2vjS5qTM25Esp0n3P1ztbPY9bQO08vAIiOGxXPT+hz7gff8+9/tE56Xj4wazc054FwntOuJVZzzpi1q2aZ5qwT5WoggHPp/+zCOf8ec8HdTM15GNla72vq+vpyWZQt65VchguupX9B/bmDuohZO/c+lqs+8jl+QZrNZoLc4GqetFkUAOFz3TJSm6f3nj0NyzYMo1SpourV+pFrLjWvz/d96zubcWjOPcu7ls1k8H8fOwq+X7fsI2batfr4dsX5pJv1mmbgtsB/Fc/DW/73X9gyGq5r6DNyuTvULAoaF86TuVbp8zX10TcjuduIpP/LKeHcrjk3x22KGYiTq8MsU/M5NzbGKEnM2k3/aHNTTr13oc+5qTkPhVd161Tx4WanqjvSnAAlnKt1izqWvQY1p9X/Njd3a9dhr4tDlWBuvijrU+r+Q6+Dumcpk3Q+IBz5DHswOC7AJPeahsEfmUKM25EkWjtr1u4Q3ic7DQnnXpeM+88880x86lOfwic+8QkAwPnnn49rr70Wv/nNb3DKKackL2h0FMgxC4FcDpg2TT/ORjYLDAw0duzYmH1WymT0QEljYygPT2CgVPNRnVbI1gIhDI0ABR+YPj3o2AOVEjA6ip0qRZSrPvyRERSKY8G5P7l+GX5y/TLcecprkZ+YwECpjML4GDAavik7lYvwSxWM99Xuw7axMvorJWQ9D5mxMWB0FP3FcVRKVQwUixgoTQTHAkB/pYT8eJW9H9PLExjxc4HJU1+lDG94JGgfpb84XrtHaiAqFoF6nAN1fH58HBgdxbTSGDK+Bz9TGzQK1TLyZMFVHR4GRqcDo6MYKE1A21MolTBQqt3f7Oho0O7CxDgGShMo5gthALxqJXJdufHRoD3FfAFettav8tUKClV94ZodqyI7VnsepXwB1fqxXrEcKdcfHam3K1+77nz9Va1UavcCQK5Sjdw7v0Qm0koF/RMTyFQ95MfHgNFaP3nejCweK01g87aaD3XV85H1quiv1M71RobRXxyHT8zDKrkcyrmaYJH3q8iO1/zQ+tW9Gx8Lr6EULnwyvodp5RIGykXMqBRRIe2bNS2PrVUf5Vw9tZTnAeOhwNU/USs7V+93yOeB/roW2fdr75H2LMK+Xs3mkM301SZu38dAWQ/ARfGyWRTzfcHf00oTbP8dKE3Ay+o5zL3RsP9mRmvHzChPoDAxjv5yEcVCv3a+BqnDG9WvZVp5Ahkf8IdHgII+3vrkuquej3y9rwKAPzICjJJhPZMBptfSUHm+j/5yEVnb2DOiR95Xx9L3AgCy9ffIfO+znof8eEa7Tn/Efuy0Yu359lWzwOho8G6MF/qBTC3idl+ljJwXvst7TesHRkcxozKhHev5PnLlkn6PR0eA0dH6u1zCRKFPGyOqIyPI5rLoK4b3L6v62sBAuEFWKWFGpaiVPb1c6yNj24aQ9arwsjmUqh4K1TIK9XGpQJ6LYpf+6fXHktHHiNHR2r0CsFOlCIwC6O8PF+mV2tiTqd+jaaUJbWxX44nn+9oYEdzzUm28n1nxsa1aDRc69WPVPZpZrV1nZqx27+i7TMcIxcT2oaANaozIZjNAtYp8va35bAb/e/WD+M2dT+OPn345Dt57FvwiSUFXqc0Xs71i9B0B0Fcth5uB9fEEAAoTtfPy5L2vjI0DmFETZHwf08rjwZxVG0tGg3P6KmX0TS/UAlQyY8SMcjF4t9QYEZi1q3mCPIPs2BiQq2J6tda+WiYFP3JNpcFhDEwvoDAxjlwmo40R00pj2Nn3tHP6i7VroOuITCaDaeUJ9E3UfhvZuh0DpQnM6audmxnPwJtRG9Ny2Yx1zZGfGMe08gQmCrX30/OB8sgo+xz6J8ZQ8PR1kxojvJERYHQAhYkxDJAxPpsNA81iYiJ45zP1MaW/OI5qfT1AxwiMT0SerUZ9beD5tXVEbnwMGC1gen0+qo2D02pjmu8jk83UXF8mihjbNgSyGkN/LlyvlMvhnG2uIzJjo8iMhvfGHE/yNqXU6CgwbVqo1DCOff7eM/HK5+2G//vX0wBq64jARcKrzfHZsdFgfgv6A4CcVw3WEWo8mVYa18br6eXae1WaCPt3uVjSjimMjyNDpZu+AsZRK3dwOHwG3vBIbQOpPIFS/TmXc3lUcvnaxka1CkxMwBupzYvTKxOYXq6vX0dHgdIMoK8+11ar8Eejfc0bqT33QrWMcq6AYtXT3nu11lDzRX9xHCiVwkjkxbJWZn6iioFSGfnxMeSzmdq6M9j88YL130BpAn0TWe3cajaHUr627hkcq80vM/vzGC5V0Jev95v6GoCuIyqeX1ubFseRq/qYVprQyvWyWU3Dz/bx+vNR6wi1FA7up7q+8dr19RXHkZuor39UZxsbC+ah/Pg4plfUsxipjQnTwxSVhWJYbmGigNLQcLiuyCAYI6YVcvw6gq6ZZswINqkzE2F/VOUNFLPh8TNmBIoBtTZQ+PVxBait7yjFkTHtfewrGnLH9Oms/MCS5tiBgXDnvVQCytHNK+exLlmR4qfgzW9+s799+/bg73nz5vnbtm0L/t68ebN/yCGHpCkyMcVi0c/lcv5VV12lff/v//7v/tvf/nb2nImJCX9wcDD4t2rVKh+APxi6KOr/3vIWvYDp0/njAN9/zWv0Y3ff3X7sUUfpx86daz/20EP9jUMT/tyT5/tzT57ve4ceaj927lzf933/4VXb/bknz/eXPOsg67GbB2YFZV65YJV/77MPtx47WugPjn3N/9zs33TAUfY2AMGxc0+e789//iucxx78pSv9t579L3/uyfP9Px32Ouex/saN4T377Gedx77iv/4vaMP5R7/beexFv/pbWO73vuc89m3/fqZ/3SPr/Lknz/cveZe7DR/40BlBG779+v9yHvvx934vOPbh089y34crrgjbe8UVzmMf/P6Z4bHz5zuPvfW/v+v7vu9/9Nf3+B/40BnOY3947CeC9r774+72/uXtJwbHHv/Jc53Hnn/0u/25J8/3X/KDG3z/qafc9+Gznw2vbeNG57F/Oux1/kt+cKP/5T8+5B/8pSudx85//iu0Puw69qYDjvK/9McHg2aUpg1Yj717zmFauZsHZlmP3faCF2nHrpq1p/XY7fs/LzjumHk3+Sv32d/e5voY4fu+v/CZrf5Dez/PeuzwzJ193/f9C29/wp978nz/7jmHWY+lY8Tck+e3fIyYe/J8/9qH18aOEUf892X+3JPn+8f+5Bb/2le9y3lsmjHCX7zYP+Hs2/25J8/3n/zcV53Hvu3fz/Tnnjzff84p8/0fHvsJ57Gnfukc3/d9//6ntsSOEf78+f7m4dpc8JW3fNF57GfecYo/9+T5/rvOvSN2jPjKW77oX3H/ykRjxF8+eUpwz5KOEcf/7Fbfv+8+57F3ffTzQbkXX+Buw1Wv+5B/54pN/tyT5/uv+K//cx67+WMn1ob1qxf7R/z3Zc5j/3TY6/yX/fCf/tyT5yceI655aE3tvjmOffhFtWN/e/fT/nip4o8W+q3HmmPEyKxd7GWTdcQ1D61xjhFP7DnXf/d5d/pzT57vX/fIOt93rCNWzdozqP9jv7nXLx/5EuuxEzvvqrU3boxYtn4oHLPf8hbnfaPlLnvVG93vxsiI7/u+f9TpNyYaIw7+9nX+3JPn+39/tXuMePMXL0k8Rhz/yXODY3/+ig+523vffcHziBsjPvChM/z3nX+X7/u+f+n/+5rzWLqOiBsjfvap04NjF5/1a+ex33j7l4Njv/9f/+M89tuv/y9/7snz/VOvWez7t9zivg//8z9hf4gZI37+ig8F42rcOsL/6lf9r1zxUG2cOuX3zmMvOeIE/7OXPlDrP19wH/unw14X3Id/P/sm57F0HfG8b/3deexNBxwVzLNzT56faIz49G8X+L7v+9tmzLYeO/7iI/25J8/3X3jq9YlkDcrqfe3rCDpGvOV/b3euI/zdd/d93/e/89dH/Lknz/dXvvCl9mOnT/d93/dffNr1/tyT5/u3HOg4FvoYseGNb3P3ifoY4fu+73/sY+5jU8ga/lNPhcd+1b028BcvDo+tyxqDgA/AHxwc9F2kchq+/vrrUSQ78meccQa2bg0jZ1YqFSxbtixNkYnZvHkzqtUq9tprL+37vfbaC+vXr2fPmTdvHmbPnh38mzNnTlva1mrMoDNxKBPFpP7P2SwS5bEGwPobNctYqbuR/tOaOgempKmM2JPj+0mfRpKykh+rLLLT+uo4MvPU25D+etL47icll3UHQ2q43Da0NfELaRyaxswyrgr1ewu7Y1MkjTUA1ANZtbh+tYGfdLhIct92qpusJ+1CSdx7KIPj5UR9Iqm/YyP3NIlLhx4Qzt3eTCb5O+cF5szJ3gvTXSGOJHOzemY2s/ZWENcvfN8dK8Vkn9mh5ryV81GjcbCStiBpbmNliutKlZbk92ZI0xfCiOytm2vouHD3k5udx9LnNsy4rnAkyYDQCElvmzJrTxIXQM0WSZ6JuheD48nXrUnWVKZvfBxJuoJq62ix0tL3mNKfcMx0ZeuglCpekBWqz+IqyzHV8pqbZPwUTzCbzWL9+vWBf/fMmTOxaNEiHHDAAQCADRs2YN99922Lz/natWvxrGc9C3fddRf+7d/+Lfj+61//Om677Tbce++9kXOKxaK2mTA0NIQ5c+ZgcO1azJo1K1pJj5i1j+b68ILvXQ8AeOyUV2Pd9jGccPYdmD1QQD4HbBkp4+rPvwI7T+/DpQ9vwvP2mon//sODOGhWDjd84VV46en/xEixguu+8Cp89P/uDfzRlNnY/37wxfjGZffBr3q47WvHYs9Z4TW/8sc3YctImTVD3X/36fj7F16Nw793PSqej4s+cRQ+cdGCyLH9GR8Pn/rGyKUd86ObsLaUxbN2mY4128fRVynjC6/ZH+fcsgL5bEZbjL/nyGfh9I+8zGpq4vt+INCdd8sK/ORfK60mZj9+7+F4+4uehW9f9Qj+vHANPn/C4fjc6w6q/Vgq4ZTLH8DVD63FV99wEP7jVbW+/MXLH8T1SzagmC/gfz9yFP77Dw/imDkz8fuPv0S7pnue2IxPXLyg1sQYs3YA+OyxB+K8W5/QzNrPe9/heMvBuo/zzY9twOd+/yAO3282rjjpWNasHQAO+c4/tPN+/KGX4O0vfU7tHpXLOPTkawDU8pKrwGLz/v4Yfnv3M/jEcQfh6287HB+84G7ct2JTYLL6m48fhU//7gFtoULN2mcXMlh0ymsAAF/4w4O44dEN+O5bD8GHXjYXAPDRSxbijpVDAEIz1De+YC88smYQa7fXTZByWTxn9+l4dPM4yrkC9prVj3tPea1m1n774xvx6d8txKH7zsSfP/OKWLP2k/7wIG58dAOAmjnabrvNxLHP3xN/uPcZ1qw98B0zzNr/7z2H4HWH7hU5/pDv/ANeNot3H3Mg5r37hQCAC657GD+/cXlwTaWqh88ceyBesM8sfPbyh5xm7Y/94E3B52seXoeTrg43NpVZ+9WffwUO2mumdt7v7nkG37nxKQDArjP6cPCsHB58Zlvtt/84Gkc9Z9fwYGLW/sAzW/Hhs2+1mrUfd/AeOO8/X41f3fYE5l23NDBd+98PvBhvOGzv4LivXLEIf39kHTtGzJ5e0IJIfvUNB+GnNzzOHvuNNx+MedctBQA8fOob8K2/PIK/PbwuMFU/6wMvxtcvu18zaz/u4D1w3kdegrf/4g4s3zgSHDtn1wHslvexbPX2wPXHxDRDffAbx2Gn/jyO/ckt2FDPOx7044EBvOnsO7B0/TAu+39H4JoFz+CaRWEKvTcdtjd+/oEX4w0/vw0rhirBe1+olvGbj7wYr3reHvjB/CX4/b2rtDa8/WX748cfOBIPPLMNHzjn9mCMULFEdplRwF2nvK5+o/oxWPLxou/fgHy1gse+81pc9eAafO/qJXjtIXviG286GK//+e0AoI0nh+81HX/7Tz1I6lE/uBGjpSoO2msnLNk8gdPe+2J85GVzg/Hk6NP/ieFiBe9/6X644v7VeMeL9sWP3vtCfPWvj+LKRzYC4M3aTzh8H1z7yDr05bIYQwblXAGH7DML133+GGzZPIhX/viWWpv2m41HVg8G9/d/bnoC591Vuzf/9ar9ccqxc/H9vy3BH+7T7xcAvPiAPfCVtx6G955/t2be2pfPYtH33oDjz7wVa7bV3q2LP/VveNkh++KUPz+My+9biVNePQf/dexzccldT+NH9b6mqGZzePa+u2DFxhHAj5q1v/J5u+NNL9gb3/7r4mCM+PW/H4XjD90rmO+f2DiMt/6ilpt9wbePx4z+PL7+l8W4YslmfPMtB+PDL5uLl37j6lrfqAefu+4Lr8Les6fhiO/fCO//s/fncbccVbk4vqp7D+905nnOPM85ZE4IkJBAQJAQHAImgMFgQCZBEAQUvwYF788rYsQJuHqvCKKo0QC54I0ikRkFQiJzQubxnJNz3vO+7967f3/0ru5Vq1ZVV/ee37Oezyc5+927u6q6u7pqDc9ai9Daf/Epm2CmUYP3/8v3su/e/twT0vrSSI7452/cD6//0OfhKTvXwF/8/Nlwxm/cCvNLbfijl5wB1//FV2FuqgZbtq6D//rxHvjza3fD03eucMoce+Zb8Jm798HrP/qfcOHR6+G3n300POM9/2Id9+vPOxFmGjG86u//O/tuRbIEraU2/P5Pnw6Xnrgpkz00bn7LZXDkhrn0j4MH4Vc/9nX4u6/dC6+79Gh4xUVHwinv/FS2z+A14tXnbYc3POMoeN1Hvg6f/JbtfPn2774AQCk4/Tc+Dfv3HoB//sVz4KhNK+Cn//h2+M979sAf/Ozp8IzjN6Vr2W13w9bV03DfnoOwYyaCR54w942ZRgxf+bVLodNJ4Mjf/BenHPHn1+6Gd918B/zgkfT8UFr7t991OcDUFDzvptvhP3+8xzj27MPXwodedhb8/dfvhTd/PC0luFCrw/nHboK/ePnZ8OoPfwH+73/+GN52xfHw6TsegC/+4HH43RedCs8+eQsAABz565+xaO3XXXQ4vP7SY7P+f/VvvwF/97V74bCta+Dbj6T7a9xpQ6O1BL948ZFw3UVHwOm/casx5pUrZ+DBg+kauqquYHF/et6X3nYJzDVrcOHvfBYe2Ze+h5rW/tNP2QHvfv6JAAcPwvnv/gw8tn8J/uFV58Pr//rr8N2H98MHX7obzjl2i0Frf+jhJ+Cpv/P/QKlUuV5sdeCzb3gqvOdTd8E/fvvhTObA7/0ZO1fD/77uHHjlX34F/t9dD8NvPO9EuOrcI+Cdn/wOfOjzP4Q1UzEc3JvL49P1GOaX2vDWZx8HcaTgHf/83/CM03bATS8+Ew77lZthemkBLj9xM3zyWw/A1tVTmYwCkK4Rzdlp2LfQgm2rpuCxh5+AozfOwXceehLmmjX40tsuga/86DF48Z9+0ZIjfvC2p8JJ7/gUdBKAd/zECfDr/3BH9lsniuDnLz0+e885WruWD459+6dgod6EK07eAu+/+gx43m9/KivrivHuK0+Gp52wGU757X8DAIA733U5TC0twItu+nf4xr174aYXnw7fe2g/vPfT/w3PO20rvPuFpxq09us/8G9w213pWn/Mpjn4natOgef/wecBAAxa+3lHroOv3HmfJUdgeQZmZ+E3/vEO+PN//wG86pxt8MuXHg0Aubx69MZZ+IdXXwgAAPe3Ijj3xs9CLVJw/Jo6fPf+vVkzf/Czp8P+xRZ8/juPQjtJ4GN3Pp799r6fPA4++60H4JPfTNeH2UYMX/61S/MxjCmtfe/evbBq61bYs2cPr4d2USnmfBRYv349xHEMDz74oPH9gw8+CJs3b2bPaTab0Gw27R9mZ9P/ihByTJVj0QvBoYazak5NweJUAvONKZidbkISAcwvLsDS1DT8wt9+E7529xPZse3mFMDsLCw0p2A+aUFregYO1KdgvmFauiKl4EDcAIgB4hVzALP5PVpozsD8oimk6AVnX60JMDsL++tN6CQA9ZUrzDix7rGLir8fi81pgKXFzHO+WKvDfGMK5htT0KxFhsWzjV8WgFQpQ88S29eS2dlskwQAWIrr2aIOAHCwMQ0wOwvzjWmYb0xBXENxc40GtKdnYL4xBQeb09m4F5rT2bVl1tVGw7quzuy8dQ8AII3Biu3X68la0zq+XatZ7S51x9Sams4Vc4D0M/qbttWp5dfdiWvZ7/GKOYCZ9Dmqubn0ekEnZwLoRDHMd0v4LE2l86YV88JcVM/HuzSd3qel6Zn8O3TdiYpgvjEFnZlZWJpagvnu3lVr1qA9MwNLcbeGrlLpIobuQzSXzq/9tSl7PillfafnUna+6tb9VIp9RnPNGsxzpaBWzlltJ0mStYG9GQcb+TyZBwCIIT13btYQuvX4DKA+FslveiNsTc1YY1lAxy61O3CwPpO1vcQcr9FJwBoTxr64mR0HkB/bmjHbnG/Yc1ivEXFcy+YRAMBB9B7RY9XcbPZbe3rGOrbVSbrxfvmcVnPps2lNz8B8A2Vr7wAs1Row35iC2lQN5pXfy7EU16E9PQMwXYf99SmYb3S9nWge69deNRvd9SMfW6d7Tx5s16ATJUa7ajYdY3t61rr2VSt0zDC/RmxYYd5r1VWGW3ENkplZWOzeo9bUNMQr59CchMzd+I0HD0AyM2OwUQ40pmAe2tCemYFW3Mq9Dt315Ml6E+aTGkTdtWF/I33n8LuM1wiNx1S6hs/MNmCpm2QrjgAgjrO2ANK5Nd+YggPdtbiF1qk2QHZ/ufe0U69nbCe9ngAALEbpGvBkPJWtKwvddb/VSdKb3J0v9PlpZDV7mTWiPT0DnZkZ4/ssOZueI7P5uhCvmAOox6C6CrSueqF/r3XXm4XmNLSm7fcCAKA9MwvNWfP9Uoy8oiBdIw42p2C+PgWPqTpAow6btqyD+cYUtKMINuLkYh6ZY9UsQPzjVNjvJAm0HM8BZmYhIc8/mZqGedWCxan0uT5ZS+eRhuH5nZrK7udiMz++w6T/Wayl79zBpmMsqGTUYq0OaoV+52ZgvpHKRzA7m+79SmXJBh9t2c85qaf7zoGFlleOaE/Pwp6oCfMN2/C3ZvUcPLTPNgADQPbs9CuH221315Fkxpyfmdexnr5fi1PTXVluCqK5fD5oxRwgX0860+Z80fd8PsmfRbv7Lj+m6t21lKxTjQZAN355z1ICoNfpqRmAmXq6Di2YcuViu5M6uGZn4XHVgPlGDFOrV3ZlmTa0plC8OUB6bPe6IwXQqUVwMOpAe2YGFprTxr3H7/3++pS5Xsym7WpWy3w7gYPoetq1CBZVB9ozswCRgsVavcs5TrL3Xssx8/V8LdFY3Yhh30IL9hxMczGpFXMw/0QLWpGCj97xGDRqETtHOzOz6VgBIJqds47BBmR2jmtZVO/Z3ce3NGXuffj42VW5IX//Qgum5ma6cu8iJLNzEK1Ir3d/fcpaEw6iff1gYxoWG/a8AEg956wcQdYovbQuNZrZb7q9hWa+zz3y4z0AALB+rpnpBBpLU9PwS3/zVeBwIKrDgXo+Zv0esyD6gxdljm00zDkdcmyg87oUpytN7KGs74aBRqMBZ555JnzmM5/Jvut0OvCZz3zG8KQvB+AMsK12kmWEbsTKqNeKFXOAvIwILmHA0WsS5hwNH32wRTL4ukp6uEkBadu4jNQSykBqdh0+r4qoX1kptS7VkWZr1wKXUVIJXYM/W3u5+c9RmXzZPsvS2rj6wQDme5olTukuEjTDLleWCiNGc4aWJsFjwPerFpmZfpv12GiHu84ayeZdBKuMT+Sfz67s0PicVjZ38rbx/GkxFGJfySGMxHhW7jqzFEYZqraZ2baTJPD7n/kOvPCmz1tzrYgGtpiVUjOPo1PBR1+06ud6jsXlZVode85x49WUOrbGd8G6ZB3PZIk3S9B0w1mUsipTaNbF3oO21dwX9rFqms/WroHLqNHjdA1qALvOOVdxAYOWzqTvVOU65wftDPNcKTldxknPMa7aiItS7KK1tzvp/WCztbfNtd4VyuSjtXPvMZ1bjdhew/RcWWp3IEGvQ7O73iy2Ou5yZUrBNFGAfSU8kwTg0f0L2VjWdg2wuI8QWrtur9NxZ+6OmPuh75/eQ+h10a6zfrqHueZXaJ1z/TOdc3m29vRa9HuFZY+57nd6DPsZQ63RF/DZ2t92xfHwhmce4z0XgF/L9Xyi05tmn8el1OKCuDJXhQGO7h0r5dzDOOj7ye1veq1PkiSrNDBVj9Ezd8uiaZWh9Lhv3bfXKPtGQcsh6rFy9dz1eNIxm9Uv8OPI65zb/em5oxkh0yhE4k0f/y947V9/nR0nDq3hKi7Ml6S16/vjevy1KK0SM91dZ/Rc76A9o44qSVBQGW7RoUQ2a7zc5BovX+c8//zwk6kRaP2KBlMO1d3+AllHlxvLvZTnPEkSuPbaazNv9MGDB+H666/P6pwvLDgsh33C61//erjmmmtg9+7dcNZZZ8Hv/d7vwf79+7Ps7csFUZSXQ2u1O0aJJj152RqpJE6pnSSsgIuFUfqi+zbypXbHaK9svJ5uGW8S+tqi7uLcRgtpKIqOXXKU18nPt5VAHMGqFwhuQyprm+Lotnw5kW6fpePj+XYNxVgr5w5lrCh+F98//YkzCuBa1DVSSm2qHhntcBtOVv4oMK6aGhlipbyCzEyD32T0tTxxYBGe/ru3wcXHbIB3Pf8kNNZ83FxJkshR+skabyfJNm3XJXJlwPCG1CZKbSdJ4KNfvgd+/Pg8fOu+PXDmrrXoN/949LtII51c9cM5WMq5J/YXP/9221bOuTVOG1To/cWKa2jOjrw8Wf5dwvyelqAx50o9imDfwSXWEOmrc76um0HbZdReQZRz3EaSmOXdsGF12+ppo7Rcq5OW68qvJf1XK5Mu5V0LXqHK0b6s/Fvu5dLPBjkhYaG77tGylviz6z2nhgiMVicxDFt6Di+Rtd5lpPMq50pZ6xLdO3asnYYXnL4NZppx1pZ+LkttM+Y8W3fbHuU8UjDbNOcaJ9hjJUPXOF872zCuR6/vIfH6uBa1y6DG5fCoZ+tX97mS+ULfAX1+u8PLJhpFdc51WBstFZqvp6bBR5cw1Ni2eho+/srz4JwbP5OtG08WKeeJXXMbIJ0TXC16Cu5d0vOJrgd5KbVcsdXPhc5BCro26rFxjoG4G2phtwGZDIqh7ys3Av1uL7Ty8p7TjTh/5qyck7enr/kX/uIr7HXRfqhy7nqXs3dNqcyZkoC5z8VIZqaYJkb8mUaY2oTfcW79OrhYMuZct+V4n/Xcn23WYH6pnc3nfM/I13/OyET3vsUW//JxpdQ40LKJLjzcZZxsmGvC3Y+ZISdPLuTGbx2eoHFwqW3M3eUWg15KOb/mmmuMv1/84hdbx/zcz/1cbyPy4Kd+6qfg4Ycfhre//e3wwAMPwGmnnQaf/OQnrSRxywH1OKV5L3Vya20tUtBJ3NYo/XLiupKcgIs3Cbqp+PbxJSJAN0pm2+KEVS1ApDUauxRHx7EuFHkG9Auc1Qkl465F+QaoYSi5Hg+Er2caRw/AW0s5mVRvEmWTj5neWHvzAcALdFcAsrxo/iQe+D5wnnO9Xk4h5TyOTGW8SWqBc8+bqz/vg+21Ud6EcHTT1dDX8vGv3guP7V+Ev/3avfC255yQj4vxrJvjDntuut5xOnaX59z+HgvPS52OZRjJmCJE6CpKmbbk8JbSs3zPg/7kS7BUNzznHaCXyglKU90bRvWVVDm32/WB8/bhLvXnKLI951Gk2Pq/ANhIan7/gtO3weXd2H3X7Jib8ijnkK+/Spnv4dGb5uA3n38SvPRDXwKA9FliZoheF6jyoqH/bhLPeVFKGi08zTVtzzlmgGlPGKecF3nOteGWQ7tjKpNZ+7rmfKwVZv58X3KjOLb7rcV0v1TwP37qNOM7rSTQ/VILtT7PeaQUTNfNOcAZGPWoEoCsZjdVzrUyFmIoxMmbXAkFueeg3zX9DOh1WR5hpGxy77eGbsc1/xbbHWjW4uw4fYv0vcq9q9246WlTOZ9pxNn7qY8t8px3koT1ONbiyFuLHp9Poe8ffUT6b6zgZM6Fgr6sORu5Pee1LgOIayOOFHTIO6nHwD0V/f5iI8BULcprb7OOory/oi1Ty1OZMc/hOafIdHPI5yM2dAKg+8yMkRrLXHIDBV7POFm5vOc8/ddl2NXPebYZwyNP5vMZs630PsYZmfC7lqBjqBzrYhy6xsvNe3wNmXK+ogk/fNRUzh/amzt8m/WIKOfmOupbTyYRpZTzD37wg4MaRzBe9apXwate9apRD2PgyJTzVifbLOtxlNO1GEEmo0Ihiho3YfFXVOH0Kbqtdsdor6znnGtaX1sU6ZCJxHmsu12sLNqKpabp6Re5bl2zTfU0lU3TOo/hC+vAyqlGWVp7CCURg6OXp+PMv28QzzmdI0XZk7FwwC3AeuzYM01p7TONmuk5Z+4jFfyKQO9jFPk92FMFnnN86iISbPD+zQmyEeNx47DY6mQUVpfCyxpucB3QxLw/2MNC2yzau9y0dl6RC4GP9aAVTG1EtDz0zLlawaFGxU6SjzvUaKhvI2Ue0M/Y45AjgT2OShaZ55hMvd96wcmZYONaNuYorR0d10ECZRwp4/3pJAAXHbMh+5squvo8rVzidz5BFM9MOdeeywLSyv6FdD3DtPaM3o3WCU2x1AJfm1kvXEohNURgtIgBeiFjSXX3zAJau1c5Z8L4QlgZ+rkskf1SsxLKes7pfgVgGkUf6yZ8XTfXMBQULciG7CGG59xjOKBt6fei42Dp0XUdOw58rAx931xLzVI7gWbNVO4AENuKGHzWzJhxoTON2KLYF3rOgVdqGnEUZBDkrkXPJxoeRw18bcQQKXqe9Hf9zrO09ihy7mFxZHvVqWKModvR864RR1CLkXLOXH/2lSp2yDRrEbQW2zmrgrBjitb91LiU94yN1dhoRDFNPOWhyine+6hRD6C8cq7nq+v56+9nGyYNHxt0NQNssYDWniQ545TKsaHZ2n33FF8BVs7puojzOGi5RssMqefclIVwouhJxwAKDQn6gTzetpNNykYt9z6ynnMap+TYMF3xswB+j99SOzFe4LKec+6lwdQ7PJQyLxg+b4qJh9Ex+9TSqqEvw7xXtmeHu1zfMDnjBbcgc88oo4+VXGdMJTn/Hl8zLTlC92Zu48XzxPCce8aALcyU1j5dj4kH3m6nrOecKhKxUl4K4LSDnoWpwxo4Bg4rx0sOgTSE+fGpbz0AH/3SPd02+WvkaOG0T8Nw0ME5FqiC5r+PuXLuH3domSpuDBgKCfupcm7+ztlkclq7+X2qYIZ5lvL2TSGPImdDKmut63TcJYay2EAHVZX7TYN6zk3l3Iw5x9eZJImRt4MaRZLMcNHdGwyDTn5c7jlPvyzyRuiYczzu3IuZj0eve3otxve85RH4AfQ84ftvtTvGfFykIUyxzlHAn+8zMNciZRmJQ+jLWVxnO6f3xpGCOjKKuu5rHCkr3IYTxvVXCeQx52tnG4TV4zYq2+3lgrTLoBYzxs4GMuZwz8+itRshd+7x6PnpWrOoYZmL0cb/Us95Src23x9taHIhSRKW3VGL/fuMBrfO1Jyec/N6Oh1Ma/fPQfq46w62TNqWze7TbXCyYIvcV/O3rnK+qOPNzfwg3Dn6nigodsjonA10vdDOlSJnkVK5EaSTmMqoj4I926C09kDlHMvZzJ7Ehhl4boJ+HK5D9LzQBl49n7E8o+cCT2s3DdPaEEVp7JyhgR+v+7njKfzwkzmtnb7vD+3Ls9jr+TXTnQc05jwdd9DQJgITk639UEMet5a/JPU4QpZLdxIPHI/GCQFaKVOK8Sh4FkhKoQ2lj2pwe4re7BRRaMoopTSWmSrAlOZLx13kOdcWxLK09p1rZ7J4QI0FNubcPteXhM6HBAD2HFiCux87ANvW5OX7DFo7in0ECPOMTtfjLL6U83gbTIPu+dMez/l0IzaeMe8514t7tZhzl4Ch4YodyzymaLxYAcbzhKM5xpE7RhbjTR//LwAAOH3naqdiwl06fT5GlYME0f7IBly0cel3sZeYc7tNj+cc0nmxCKkwbs9Dt+ec3t8ECVtlE8Lhy+GYJykd0GyznfDJ4ADQnCNTIGROeGPOO6YXBAvpGSuoW46IGnAyz7mm/TrCX3JlK4zWrteQlSjmPN+HFNSjtLygboYzHOXxwS46tVvBpEphnhDO9Kq5ck/45gqXAC3Ec95ABg7Mvmgipd1lEIqUstYlX8x5kiSwt1t/edV0vctAM+OjQ7YQ7N30hRfQ9VTfv47DE077zpPV+o2Ff/3le+DwDbPONYsmFqQx2pkSqY1StciIWZ1p1IyxdZKcBjzTiI3EcXmf/GBqcWStDxx4Wnv+rmDkilhuzAiltdNn5Ft3Ui+ki/1ln6eP5d5VbXjT91jv/xGaqy6EGLRpvow85jz9vWjdV9n/TGMuboP3nMfev13A96jOrD/zjCxYj/kwA4DcsOC6T3nMeTq+/cRzjvcx7h3H0wCHt9AEcLT3ozfOwXNP3Wq150uyh5kiuiTfhhVTFrvvwb2253y6mz3/4FLb2ufanaS0zDyuEOV8TKEX7VbbjDn3e85tSyW3HvoSxfiSKqRUF1uQCwUbc55law/zLLHtopcxXUhMoVnT2lsOWhhOUqOBF+mMes/S2vkx/d0vngdHbpyDU975aeP7g0wWUi1A7DmwBD/x/s/BJcdvgmM2pbVhy8acd5IEnvf+z8EPHz0Af3j1Gew4c1p7N1t7JuB0Y9uYhbuJlHND0EWGIDwGAOo5N2POZxqxsXlx62lmNAmltZO5y3l6MNwx5/n90MCJ/Lh4WQxOqPfhR48eKOU5p/djwfCcJ9mGZdPa/fdxwUlrp2MKV859ijymK7c6tjeREyC0oECVLZzEMFg5z6iz/HufU2ZtllAnSWCfQznXQ7M95/nnUFo7jTnHySlN5aKrnHeNHdhohJ+7Fs5ca52+v1k+isBHjceNx1yPFWA9J8trwLxDvlhnl8CFvdMAKCGc3jNjU2mj8FE0Wc95wNzS69xiu2OyL7Dn3HFja5Gy5gDnKc0SWyX5/dPzPlYKWsjzFURr73aRZr/3eM4dxgocC2yM01I6c9mkiJXx7lvuhAuPXs/+lj73/Hx9iXHGDCG5ByIFc1M1Q3HEY+skSUbdXTFVY5VzzJ7CpV/rkWKVLwruevWzpdOTMgE6Sf7OFiaEcxhQONQY6nraBt+Pz3Ou55v2COv9FRsYKJLs/ShmSzbrpuEwV87T74sMZ5FSRq4GPJw8FNQe42xlWnveFus5Z+ZYI47YpMEAtsGGIo85N2nt3D4W5DnPaO3+vFS3vv6p7HiyUBkHM0Pjvj1pItP1c3a29ocxrb3bjmYupDHn1AERLpuMO4TWPqbQCyqOT8PUYF+2dr3QcF49AKScexZfF/AGFUof1eCOxoqv6UkNbxcLXk2GqpzR2h3ZTvXi/sUfPAb/8/9+J9340e++++XynR+2btbwJmnMMwuyFjL+6kt3w48ePQB/9rkf5EJ4SStgJ4EsqcYnv/lA2gbZ+PQCTamBtdgW3DWmG/l9NbO1d4VEdGyo57woIRyNHywClynYJ8hwcwUAxfWiMWFFzPDUcQnhPMoEh/2LLacBgtts6Ia0iCn3iJZqJYQruI26HXq76XllsqL6EsIpyEu7tBGtXc9PLkYy85yT29tBnrjQbO15RmikkBtt2h6H7LgEMo8lBZcQjr6DrhKMFq3dGI8ZB28qF+m/+h3GChZ+XDp+mcuWDmB7zkOFHaOUGg6hIfeNiznX89n1DihwC6RUkKUeVRrqReEzMOd5UHKE0JdzWnseVhYpM9eHj9a+caVZY5ftU3sAUZJAWjaOxmP7gOOvXc8hUjb7rYGMPdw12Z5zyI4PWUdceR0opVU/38x50f0Jh7JhVspMPbaMW/uZygO0T40pangOSQjXPR0/jiyJryMEBidTWwo0ttjZ2t3HK8U/b5dBzJXPJP1O09rzWGXjGsgpi62ciZm+485hAoDHc95tv9BZhNZMytzIciEwc3imYkI4vAZzexIX4njFKVsAAODIDXa9bt1CUcx5TmunMef5fhsSc57T2s3rDXU85KECjHIOqez/og/cDj9+PFXO+Zjzg9a5ejwHW21r7i4n5Vw852OKLOYcKee4fI4v5ly/FJziAJC/mNyiXURZxRuUjukNVZ782dpNq3wpzzk6lo05J1RK6v3Qi/sd9++FO+7fC+vmGiat3eM5d20oLqWaz9aedvYkimGtUlIOAGABta/pTXQxbxJqkxaSGhklllGK0H01ler0Xy7TveE5j8zs7NP12KD4c1ZznHchBHadc7/n3JUzAStlGnvRs8F0cS4+M6XhBg0ZAFILt4u6zwlO9H3DCjBWcul9K4w5b/MKGT2rjOfc9+xMz3lOa691va2cAKE3ZjqnO4imWN5znn/H0dqVymO1Ndodt+dcrydYAafjdS1vVp1zokAkzNwEyOc+Vgzza7I9OK4stzTmPNhzziSEA7C9vkuMcJ/R2l10TgfFFgCsesh6zuSl1PgwCA1aIo/CReP2oY72bqwg6+8XPbT2OFLQrMWwYUUz8xrxMee5koETJQHkoSK4zSLgmHOsBOJ5EkVuWnu7kxh5DFx94yRRIXPr3sfn2e+XECtBjw33l5V26+SyDp6jabb2fGxJArB/0U5uiLGAjPR4HtRiFWQQxOuTnqfubO2mco7DlfA7deMLTob//YUfwTfv3YvONdvysT3aHX6NpmGG+fGmYozhorXjuHmNh/ctwFPf8y9w7OYV3v4wcs95dwwk9K8oBxI2ACRgrm2+pHXUc14p5pxhVtCY8zhS8GvPOQFO2b4annH8Rnv8yvyXQs8lvYfsJ3XOlbJDGjFM5TwxEsKZA+H7p6CMCWPNUwr++8F98MUfPAYAACdtWwk7185Y958z7uv7v7DUsRgAZULuxh3iOR9TYPo6rudYynPusIBrgaaa5xwp55EKztwIwC8qebZ2sqmUUG4MWjvjDf2L//gRfOVHj2ebEDVKUKv3Dx7Zb3rOPYl1XFQsl0DE1znX/eS/LZTItIuBax3rsdEx0mztun9f4hjDU4A953qzY5Qaw3MemzXHZyzPuX0tpUupWZ5zN53VJ0xlZXzQLNjr8JyzXoeStPY980tOxYRTqGmfxrzBsfGOjN0u5DHn/jGUytbuo7WDuc5R5Zqj3uW0dvP+JknumQpXzvW/2LBkG5kiREnGx+11JYRjPedhyjmNOVeIUYQVGvpOt7N7p9d+3nPOsWOw0GTVOQ981kadc4OlY45TC3y43Uw5dzC9fO/xEokDzku1EVq7Y0q42DN6jFad8wAlDM9fbOhr6GztBZ5zAICtq/N8IZxypUeR0p3Nfa1MzLFGbmTNnz31EHKMoEacK0zcNVn1uxm5xodHSc4WDex1TcefGyb0eAByD3pMwgWmGzVjnoZ4zvV+2YgjYz7Vo6hUnXOcfT+POTePzWj6Oha6w8ec/8xZO+HmV18IKx3MFdwHh3an40jk56a1Jwn/7LRRf57Q2vW14XP+6ot3w4HFNnzt7ieyY0I957RsX1EptfyaFJJXEsPqnOdCcCuDGsGe8wJaO3XUxCrNN/EzZ+2EjSum2PHjsVLk2drNmHPOQIj3h0e6CdlMWjs4E8K5WF/WeJABEcCUzxTkmdi3rZ6Gf3zVBVCLo8LQO4A8V9BCq80kvQ0a2kRAlPMxBbbkZcnBlEKeD1uQqZGF3pV1NfOcM4tZsec8X1CUUnDeUXxMGAc+5jy/tqqec3wZLmPBr33imzmtnSyUdi1bZSwSuSfBbtc1SpcwueDJ1o4tqY8fWGTHZvVPfr7nsVw5f6LbBh0LtZ7mYRPuZCF4gebo6Fx9dbyJ0dJP0yGl1LoCT6p4Fa+6dLpTYRKPpxFHTk+y/hrfB5yZG78j/aC1P/bkoifmnFHOyXf4XEM5J2tE0caX1oxmvHrkz1Ke84BSarpNqlzznnPeE1olUWW7K2jS27LU7sCdD+xFlGRltZkkEJAQLh+j5TkPpLXr/tNOTVo7Rk5rtw2z+N5gZUoDz5/MO+VgUbiAjQqG4kLWY1+dcxfLwleacLFtrqdZKTWS2bpKQrh2xy7LExJbbNDaDfaFHapGodfEbaunrO8wsJJBs1ZTlkGZbO0J8pzTxFdcDg8jLMWh5BltGJ7z6pI0vYcxUVzamQKXGy4wK2WGJCTtJICUc5fnvFsirBYZe2otVkFGG70UYrkrjznnnxmmBut3kpsP+LnY5f/cc3apnThp7XxCOLdRRc8bHUs9TVhO+HlTxVQx46bIDYc05rxrmCl4BkqZuRo44w53aTRBo6sEK4WREI55BjQ5cNHSom9PaMy5rZzjmPP0yz/9t+/D7t/8v/Cn//Z9y8Gij6Fs1FDRBhuVAcw1XymAh/amlPVjNs2hsJriNUGvS5/77iPwI1IXvUzI3bhDlPMxRWRsYt3vsOfcsaAC5IuVk9buoWkXKec0/vrKM7Z7j+fGh7GEaO14cS7jMDZo7Q6r5h3377UWcw262dVJ7U9fzLlrP3EttGwpte6C9OiTuZdAZ3kvVM7J3z9+PF+sdBu2JZ16zk2LPp8l2/SCZ/1nQiK6Hq2ce+ucx8bmz3rOUT8c1Z6Co7XjPneszb1RjVrktLLq+4EVy73z4Z7z0GztGo/uX3TGeXLvoy97PTb+VLEq719o2zHnRDsvV+fcdyxOcJl7E7W31Udrp+8FFrbKxJxb15ok8MaP/Sdc/nv/lnntsFCj0UkSZ8x5TknNv6MGMtdrTZOBAZheUj1emzmgr91UrtPf8uNqjKCM7wGlxfcac07XVi50Iq8ukP572o7V8LLzD89+V8pj7CRzRLefeTlreZI0Cpd3MBtXkrCG2yLUkHfKZF90v291nGXE9Jq4dRWqtOGhtQNg42pXSaFU8oC1CAvS+jlQjyGbrb0w5pwonRnFubf40CXiOdfdULZVVk4uIjHnhNaOE8KtdCnnSzkVHe9d9dg23nFImPVJf3bdp4wa3HGXgqXnlwnFaHf4BICR4vtpOTztADatXSuxXL1rmnsHl9V0QRsOKbVez/fCbO0KySvAZ2vn9rZd62aMv8NjzpHnnLk2Kp8XJf9VzL5inE+Uc1rnHBuZtdz9m//07exfmntlwUFrD/WbucoaAnQ9591M7JglECJb6HVJD/endu/Iz19GMeeinI8ptHKHY42wMBFS59xFa+9HzLk+9enH5bExW1alL5krwyr3UmeGgsjMjFvGc47h8pwftm7Gymqb9cXQ3PF98GZrd3jAXAstt7HpBQVnpnQp1lb/pJ+7H8uV8ycOLHWPMc+hCbcypSYTtOx+pgwvOL5/XUs0+kZfD6XC02zthuecuU7spQpZtKmwRwWMHWvyTTZVzvk2dVchnnOOek3nchEe3b/o9Bry9WTd98JHa9fC4fq5Blx+4mb2/H0LS9Z9od2VqXPuousDmJ7zpTaOOXfHxek5Rdcu7IkLrcPKee86CcAnvn4fGSdTSq1TLlu7i7ZKwXntsKHWFXOelVKLTOVan6dRYzznuGwj3V9C7TCumHO61tI65AD5PNUGuHc89wR42nEbjPZc6yA14Oi/F4hyzhlLaR4Mik4nMZ6TLsdVBEy/xZUftJe01XF7jfX932LQ2rl9pztGw3PeFd6pcl7Cc57StkB8hAAAxIRJREFU2ruecysRlD3vcJIutp6xwyPcSRKnsTwEC23TwJHH20dZ+3pc6fdmzPl0w0wIl3RyTyOXyBUgn1PNmll5pBZFXiOPngNZ4lU0GfX7SM/OaO3M/eKUUHw+HYrPWNnq8J5zYAwxAH7PectJa8/nlsaBRdOwGUJrz/JhEKYNTQboAnYAEVa7V97csKIJ62Yb2d+hynmR59waX5Gsp49zjFX3kSWEW6QJ4XIZb4HN1o4+IwYNDf0JpbXTMrt4zY+UymjtOAFmiG6NjYYrpmrwpsuPtbz0ywGinI8p8niNxBCe/DHnOrlIegzneQIwE5tQYMGbWwPyGunpj41aBH/6c7vhxefshM+84anwZ9fshptefCbbL0dbyhVfIsyyLfDAmwutyXhTt5xYC1mICz3ncWQIt1U85/RYGreDodcTnJny8f2p4F/Wc45LwGhqvBUnmCWE0/Sw9HvO64bP0UPBMXMRsyjq02kpNbz5TNWLs7Xj331Zv7N+ycJMPdg71prKuWsd19eCvfWubO2c8skJsT48+uSCU+FudxJ4cO9BeNfNd8APHtmffecCV49dC4f6tCM2zMHLLjicPf/JhZZFf//snQ/B7d97NB9TYGk7gAJaOyBhHXmx6x7PeTNTtsz7myCvchnPOb2X3JzgPed5kkBqEMzqfDPf5eDHONe0FQM9lfB46fSyaO1o7uJL0veGi/nGYVNZtvYA7bweK4P6aMScB9Dav/Kjx+G3P3lnlhCzHkeGAKiUW3B1KefasKOfGxcTXIv9uSHaiXkth6+bDaqegY1L2HPuKxuooY8xae3ueKokYTLTW17YwiGThHC85zyltZvnaTaAu5Qa7Qe6/eQ5Iqoo5zRun9La7aRhkcFK4Tzn+xf8CeHyJG4m5btW4DnXt0W/S9jYkiXxJTeKGlpSD7fbmGGyDm15xoVW206qlbbhzknk2qv0/Zm3aO2QXYMGrfEdVOe8lntMO528rGROay+e6NiohZVWX8+RUnB0t7QtQHidcyyXhijnhVn4FT9X6Pm559ysvhIpZZRSo/u8SWsHZ0K4UNEGz10Acy9RCuDBLq1944pcOQ9RrrGM//MXHAHr5pqoFF7Y2CYBkq19TKHQZMPZ2lUpz3lRzDlvGdXAWUXpuVgAuOSETXDJCZsAAOAZx29yXhO39mBaO/69KP7IGDN6oamVD5d60aDKOFd6BBspfGEAHFL6VHrsH734DPjyDx+HA0tt+D9fuJsff8f2nD+6X2fqLe7Lhce7nnMqrDWthHCmRZ7bfBvdOuVL7cTYRDhau170ZxolPOfMdeDfgzznlNZOrP/b1yBauyf5iP4eJ5vC8cWYVs4lsSpbm/6x/YtOhbfdSeD1H/06/Pt3H4WPf/XH8PW3P9NrqMDe5la7A999aB/89B9/AV558ZHZJpgKXvz5Tx5sWd7SW+94EG6940H4y5efDRccvb5kzLnPc54rLCll25yHPs85vccdFDtelLU3P8dWximFH0BTkmnMee45Xz1Thwf35u+uHhteV0Kztftizi/8nX/JjHw2rZ+8w937vufAEigcA+6JOY+iXBEsQ2unhjYfrZ1rd7HdgZv+3/fyc2KzrKZyePH0ucbfWjmntHZmSqTGO+dlQaeTGOM4gilvxKFu0Nq1ApAnY0rzK/iVcyMhHKeMIcZSC8kHuA3apg8RWse1QY3G2tIwIYB8PmHPrtmuYv/GTgeaXT4E+N4C5O8TZX44Pef1mlUJ4cnCUmp5zDl+Lepx5FW+7umy2XLjYX5sUbb2PLlvPs/ZmHP0lSsvAAeXsu0rpebai/cebMF9T8xnDAS9/2PDj8Y89ZxDsdKH1/XUGGQ6TQqztStlyCt5LLbydh5HCo7ZtAL+4/uPAUC1Umoh4TDFtPZ8PBzymHOaEC5fH/Q9wkY9DSwPJUmS09prVDkPk2+o4wbPsyTJE8JtQLT2ENFCM3QBAF56wWHdvhQA8KE1kwrxnI8p9MTGtPZCz3kWv5T+7VLOfdnajZq3XBKL7rkVjN3ehHB2zHkJ5dwzZkzjzLKdEvM/5zlv9eA5x4vs5Sdtgbc95wTvgq4zxe43vN6hnnP374uE5aDhSghXJ14zDBwzjjcaNiFc9zP1nNOYc0yP5xZ8TA/3eWBpv3hs+DvTcx57Ys67fRqe81yYwMoxG68XhZcXBEhzDbhi6ttJAv91zx4AyMMU/DHnpuf83bfcCY88uQDvuvmOTO30eSn2LbScG9wvfeRrML/YLkUd85ZSAxRTmSSW8Fo2IRwuxRaCTmJv5txj4xLCtZMky0OwZqZhHs8Kz0Q5d4xphlkn8Km62gPtgr7DS+0OvP9fvgun/san4e+/dm92HFdKDSccxWXA0t8cA0WYqsfG/fHR2jnPOUUtiiyabjCtncScZ0wLZr7XogLPeceMOQ9VzjGtHSfw0+udT8HRexFWzrkjc2U6V1LyzPREOQ/YS3FCpixbO/WcM0YS7lq5ceb9QNYPLYWlj//kay+Em199gXe8i60O4BryWTyulo90yT4Uc04951hZaydJRgMu9pxHlgHKt+b8xB98Dp7zvs9ldG+DEp8l8eWNGPpQPM/Z7P1YOSdD8Y2t3UmcnnM+J1HHu6Zf/J7/B/91b7pfre1SwfM8A/n8OGDFnBfLOtjxQqsYARSv+5EyjVp6uirwy7OxSpVzjSqe8yLKPUAxrV3fH9dt0vPCrnOuzweo1/KTD5K1E7+9nSR/RtRgHCqZU6MMrYqinVGbEK09RLnetW4G/vza3fCp116UhaBoUVISwgkGDlzawcjWnlmGeQUKIH8p3KXUbO+3Bp7b1FsEkAv/VWLCubVnERkKsM5cRvk3YlksRTsXRmniHA2u9IhBXW57lHNmqeIWWd/G0UlMrzkASghXdCMC7hPdy2mcIPZgAPCGn3otLxdjKNXdf/EZ+j5PeTzn0/UaUfL5seM4zSLQVyJS5n3dgOhTvphzPX7slXPFnLMJ4ZRiBZ4VUzW46zcvh2ccZ9YwXWx3YM8BPn653UlgpmkKA/6Yc5QQrp0YhiitvCqPsvPkwZZzg3ts/yLc8/iBknXOfZ5zlMkXeRN9tPYpRyk1zvNeBDz3MzBzQinb6NfuJLCvK/ysmjY9bZnnHK2Rtufcvv9zzVqQYs991ybXvtTuwHs+dRcAAPza338rO07HpBt1zjPPuW38DSltM9esWQqWBt1DqEGQQyOOjHXNZ0yic0S/dxmtveZeP2pxVEBrJ8r5+jnnsbRdPRbsnTP2IietPT0Xx7hyyiKOnaV1zkPmGgX2crlo7VwG7zxPSYfNVeKOOc/nFh3vcZtXwpEb/PcaJ9vjmBp5nXPkOSfKOQAOHwyPOW/UTOW8HkfeLP57D7bgyYUWGzOuP7vo//p+LxR6zt1rjc+j3OokrJyoFG9wcIUvaCy2O1nt6vVzmqnVfSZJykD7xNfuzcp34f4Kae2G5zzP1q+fYVE4kwLEyEH7RaSU18kRRQBHbcznY5U650G09kCZuqiUmma80IRwSimYrsfZ2nLXA/uM801ae5JVD5prVqO1x+hdB7DZWjqMc+PK3BMest8opeDpx22CYzfnBhMu6eCkQ2jtYwqcREOvy1GULyHeOueBtPYiuhu32GUJ4Sq4zvmY89z6bZRSK9E+Viboabllv5MpkFY2WzKuWhwZnlofrZ1bqLih+xbeTieBh8lmFXJeKFwZdgFMD0RWFofZrOvYc44uEAuJGlm2dlJKzShrZtU556+zFitYbIfR2ukxcZQnHUmvIb/uZuyOOdcbBFa8XTHnbCm1SFkKG0CqbzRrMWv0erC7UdVjZQhL7U4Cs40aACwY37lgxpx3jPJBWBhxKucLLa9CvdjqlIo591HwlTLj0krR2pnx62EH09o7CSSkC9ZzHuWZtjX2HWxl88flOTeopgGecy5TOwC/xtC1VI+lhjyZHDjPuRbi5po1i/YeYoeZbcZOwdimtQd4zmNlxZw7Pedkjjy2fxHmF9tZ+3ouUE+dHluZhHClae2dDvGc5/uyy/mop65SCm5+9QWw9+BSpuRgZPRcYGLO6f4W4rXT8kYnZ01YCeEidzxzO+GzfruUzpQR2B2fws86/TzdiOHIDbPwvYf3s+PFMef4XcgyRJMkbHFE65xjynVCaO3+Umr1OLLC/0LZOunxyvrsytaurwczooqytdO1wee1bbU7LCvN7Tl3JJBjoI3h+nKTJIFf/uh/wqfveNA6VkGx0oc95ylTI/1MkwG6kHrnu2NBAUwFrHaII2Uo56GecyMUIYTWXvCeZnkIXLJS93c9zw8stlPnHmGYnLFrDdx6x4Pw1R89bpxvKLZJnrRvluxLZcI7AVCICZo3j+5fzGSDDWh927xqyigHzMFnrC5TSWbcIZ7zMUWEJjbe4LOEPcwCGRpzvuCJOcfgrH2LPdDauXc6SwhXQDH0gWaBxKgTLzEAQ2sn90GBqVTo2GO2znmgIu4VApMkKytB0QfHuU1rRxey2O5YXjfWcx5HrPCHaYoauj1sYa7Hdik1fJ+Kkpy45jIGR2s/bcfq7G/8nMOyted97jU85zium/e0bl8zA7/zwlMMJV0LXNx7pZ8/56GlwkBwtvZOYgiaethc2Rr9rHye87T9dv/qnIPKBLdQWruPpkyNTEXoJPac4WPO7Tb3dCntjThiPIzpv3iIITHnXLx52h63nph/03KIrvelhtZDDX0tq6brRsxu4ogjpphtmJ5zfIpFa29ppcndnhVzDu7qB3SOfOu+vfBTf3x79neW/JLLDRHxtZw1UqpzrtQfUeDN1cjLFeVzWikzE36R5xwA4KRtq+C8Ix2VT7r/cnXO6f5TJlt7kiRZkstarIz1KI4UUaRx+c2ENThYdG3ECMSMDXpdAABPOWytc7wLyKhs1BxH4wHI10oac669i/rUpVaShYy4Y87zUAn8TobWOc+Pz+9pLUviax5Dae1FMecYPmM8hZvWXj7mnCLznKNnwinmAGG0dhpzjhO6AYTR2vUM6yT5fqGUX46KIwXr55rwu1edCr/zwlO6xvJiYNkg6B10PKZXPe0o2LxyCn7hqUekxxXISjrmvN1J48aN2HoA2L1rDQAAfPlHjxnnU8/5fOY5t2nt2vt+xs7VzuvJ88mYhjKAPBncmpm64az4s2ueAucftQ7OPtz97nPTPwudWD66uSjn4wo8sbNNCFn6MX01P8cUXF3Z2nPPuf/xs7T2QK87B25RwclszLip8PaxsuS17Gv6H1nEOYoo9gIs+DzngbR2H7WwnSTwvYefZH8rLK8RcJs42r7GIqq5W2e8aho427rhOWdKqenTafk1bBSZJkmkXNdRZ5QJFyglKlIKXnD6Nvi9nzoN/u1NTzPGHZSt3eU5x4Ybh+ccAOBFu3fAZSfmCRL1/OHeK11TmyvZRYUBn8Jr0to72Uat20rHYQsL2ojgizlP2++Usk57FXll0tppHWBu/eLmIO0rVFBuJzatnbt0LiHcE91KCCunbSo65+GwlHNm3SjjOXeWUtOKIXPfI8WHruhcBqum64bisOSII+bGjZVwbOCg81m/Lz4DUJ2JOU9pr/axej/auXYG3njZsQAA8F8/3pP9rp/bBUevh2NR3ChAN3bYty6TMXJsGHb8iNaeGdYjTHf3JIQL9krl6y71nNP5GJKtPTey5utLLYqMvSJWZuhZjGjuPoODMRbkocfhehx2e5RzPDcNBpae3901WssGXMw5Hs++hXx9D4k5x1daj/y0dgq8dun766S1E1kujhQrS/hCAqvQ2qly3kSJdSlDYtbhSV6/omFcg38rsCsBUMSRGRpClc5ioyxOCGee75Oz9Py88szt8KLdO4JjrvV98jF/uH4ofvmyY+H2tzw9qwfuakpfP5YXnlxoWUaM3YelyvlXiOc8IZ911n0qfygF8NHrz4Vrzt0F7+9WQ+JAY86NRLrdOYdrnAMAHLNpBfzvnz+nQDln5n+2fi0f7VyU8zEFrhGoN71IqUzBmV90x5xrgURbviiyhHAF6wW3qGvrcpls6hq+9SlWpue8jO6PdRU6LE1HxZtKnTRu10w2BTOcUZ6C9Zwzg/cJXUkC8KUfplbMC44yPSWFGTwdWwVWJrgYRJwUjtaH5uiJjVixnnNuUdSCJ/Yo0ltiZ2v3W4ODSqkxtPZaHMHzT98GO9bOGIpHIy7nOadU8/x7f7Z2rjwgJ0RQGq5Gq5MYCjbtn4KWUsOUtCfmF7MxUWFEx1k+ebDlbX+hZScEmmnEcMymOXa98DEeFOB4xDBau4YvmWWw55yJOecuXSm7zSe63uYVU3Wn18v8zm6TwqUU8OuO+V1Oa9fKSYcdFxebx3nOAUwB2IdZEnOOz6FrrX6mPqNNvRaxyUG5Z47LW121e7vxm0LGiKl6DJ963UXw86iEYGG29iSBC49aD9ddeDj8oUcIpcjK2aFyRZEy88W43rFQozdmLBXVey5Fa0ee+HqsDOMKzdYeRcow9oQY7XA/mBFIrwsA4CldJYLDIjIS4nPsbO359zytPf1bG6gatcgqHaXbzGLOSaWPWuxX7ijqjOecvs80RNGXxDe9DttAgcfnQqvDl1JTytzH9D7S6iSw2DKfc7Mew99cfy5cecZ24/y13XCfkHjgSBU7ZGJlliPEYVoAxYwC7CFPIDciFj250EobFHnIJm9QofDNIS50wzpfG3QilRlT5hfbRiJYAIATt66CRhzBI0+aNRLwnMae8zR5ojmWIzfMwa8/7yTYsmoaXNDX8+/ffRT+6ot3s2u+ky3mvRf2dzF575cDRDkfU+iXMaWL2Z7z+aWWdY7+TceK6RgqikVkGfeBE3J1HEoFx7l3gUppTfnfZTznuLQCPQ+XBuISyADYC0GHWJP9CeFs8LR29/gXW50s/ueS481kYUWCles24YRC3DFNRB3Wxh+ceZcCZ1s3Y867H9ApXLZ2Sp+badQMBoPrMqmw5QM9hl439Zy7msxjznnl0EwWyHgdjPtjf254hCXqoe10EqOkUavdCae1txPDeKMrACiw5+jKaZ1EZsmvnC+1rfv2gZecCZ967UVWGUMA/3PD3pk0fCf9XisELuYPAL9503lcBI6KG15KLf13xVTNekeDPOfMFHB5zlkaH/mOGjaW2h0rVhB7cPAc0sr56pm6IcjjmGkfZps1493C57iytfs852kWdXPcAPyegPeyJin504gja8/BymZQtvZIwVuvOAGeffIW53EUjdjec7ByvsQYhbIxBbI+smU3ycNVnJ7zEpTaTpI/o1pssp1oacpImUJxmHKe98PT2vPPO1F1DYrFdl41wizjZ4Zm4XJbmvkQRyqriqDvzQN78prL9F3VzySLOSesqzKUdnq8/mzlpCAGKT3PqbErOx59drEHObTaYaXUtKH91/7+m/Ds3/8349g4UrD7sLXwnFPyd2TtTCMzhuspdGCRl0cBoJs5v0A5j3PnFA33BCiuc44TSyaJWf/b1zV9n1TB8RpafgidHSEZ3XX/3Ln4e/285pfy90T/PFWPYdMqO4+FSWvP83RMNWKS0yBomMYa/pa//Qa7PhSVhePgy7mwnDznkhBuTKHXb1xyBG/w844ENwB5uaH9LuU8kJrO0W+1wl8lUZnXc04EpTLNP/fUrfDfD+6Dpxy+Fj7/3UeM37iNky7idCFIk55gr6lHOecEZ27x8Fz8Hffvhf2LbVgxVYOzDl9H2i9Qzh3fr5lpwP1dgYN7Vo1aBLCQChzU68YtombMeX7/ML0SwKSL4VjpTpIYylazFgV5zjMvlKd8S96H+bdLyAJIr9/1fnC0dgzsOWZLqTkYIPpr7r3KxsV4zvF9xFl/a5Fdso3GnGMqmaZiczHnWnDF7XPgEmvFXcGK21CLEsJx4TtaAF3wKOdcX7ScWBGw9y4DM9xIKafCP1WLrbmbZ2s328Dg3muqTPuOddPau+9LO4EVzZpRZcCoK2/Q2tN5sXqmYdBzW+0wqvJcMzbGaMSc02zt3Wfqa7ceR5anBsDvOa/FKtv3NLj3DCs3tdivnPsyOPuQ5+7oGHGtMUq46RIiQ43SWMmgnnM6/ct4zhMU/lWPlGFIxDR2/Xd55Tzvx2Us11BKwV+8/Cz4yo8ehw9//oeZcRHANnxoZMZcbWDN1soI1s014XWXHANzU7VMBtCnPtCNgd20csqSU+pRBAehkyVlo6yrMpR2PZbsXD0OcoxeQ/TcL5LZTEOg+ZvPWNnuJHz4kAJIUF/acMjKBt3jNqKSWDiJoR7bvY8XJfry/kw852Y+B4CAOueAEikmZsy6713n5CcFfIlDjBZ690MQ+u5zl0nl3JlGDR4/sNQtfWq3zznmDMN0Alm2dp0fqJ0xDQLH6dijjHG75rNnMvgSwgWIiRMDUc7HFAatXWc1NTznjJDcfUG1x5ITpAFMgcYHLOQqZZYbqUZr979wnJcxBHGk4E2XHwcAAP/xvUeN37gFmy4IluccJcUByO8XP377O24x9127Lj1y5q41WW3QrK2Cfd91n9bN5e1wG7oWCg6iLLD+hHB8nXPdvRZCXTXn2x2TppxSJG0lnwJbyotAj3GxKAC6MeeOdjhau9FPV3hNEj5eDz8zTlH3eTLob53EzBi972CeTX2qHlvsGIPW3jbLG2nqJvZ4aWha+76DrYwSx4FbU2hmYQx/nfNcOWp3bO/vIpNXI+vT8z4VeVDiSGXKBJ0zrlrNrmcWRW4mjo+KyI3eGXPO9Uu+tEJT2klKGdxjnoNpoRqY1h5FKlvrW6gUmA/UqJAYigtdW/2KXD5G+71hPed6L4tU11Oee4C4eYznRhxFsHqGjyNfP9eEX3/eiexvRaghAwk2rNfRvc8qsCjTmBHuPUv/xYnVdL9U8A5LCJf+i2nytTgitHabPo2ZTSFeK4X6obTk9ADz+AuP3gAXHr0B/vI/fmR8j2ntHEOl3Tb3I/3YX3PJ0eZ1d4/HnnOLYp55znUpNWU8s7KVa8xs7WaeoKzN7p/62eUyG78OccYsDZ+ct8TEkOvx4GZ85cP0/dmESmLpeHPdFgDAPR7lnKsEYPWD5htm9+Wec//5SqH3BlCd8wJPuMtTWzTftWwQrMwGziPuPtF3XhsqdcZ2eh7XFZ7TnSTJHIDTdUprDxqmNQ99Faas731GU+YnWqVhOUBo7WOKjNZOYrMyz/mSvaDi+DoAt+c8NKkbFki14Pjkgj/2yYci6pDpaSrdfLcP80ROqKaLuB1zbgqPi21zgzf7s78rG3OucdzmlVYMTnHMOQ9c3olTfLVX6SAy8mCPD0U9xnXOkXJOEsK5hBbqOQcw77tLn+I2Yxe4bO3mNeR/1yL35qqfvUs515uMi17uojOHJK6h3r7U+53/vffgUsbqoLGRAKZy3iae88cP5DHnlnIe6DnnGDs0PtIYf6jnHHlCtABaNuZco8iDon9vJ3ZMNTcltMeZfa8J44fLx8CNl3ute4k5z0ICIq0Y2rR2/Nwf3LsAH/ni3TC/2DYSwqVt5Ea60IRwGEbMuSMPgWuOUY+mHnf6r308ZjUppQyFnDXMkjXgomM2wI0vONkIJ9qxdhq+9NZnwDEkgVwo8DVnHk/i9WsTY4pGcMw5WndptnbqFAtRHnHpVq1U1GJlXAvN1o5zV5SNOW9jWnvQJZsH4cSU+BbSsI02uTeu8WDPuZ1A1QyzoTHnZWHEnLsSwmVrKhh9u4w3hiEwQAbSaHc6jjrn5rrjYvUA5OvF2plGNj7sOdf3857HDjjb8Fcaz9vhqreExpxHKu8lSQAprf456PPU+pDR2gNl2V6Uc7v6Svq8Di5hz7m/LxpzfqArG9plb4OGaR3HyQJO5bys5zwLyxHlXDBgxGiyZXUKkbdxnonf0ZM2V879nvMixQ8LNyuaeUxq2lfQZbDj486PFP29mnZuUdI470mBZ4EKGnn5OEZI5sbAvFUhAlKjFsEUGW8hg4D5WSkwPELc3swr525FuIY958xCrRdFvDia5ZXski1RVPy8OU+fCzSOlV43VZQLY84dB2RlehyKpysLvf7oo7VTSjZNWrZ3HnvO7XZwtvaljklL1koYFbwAkHJ+sOUtc8V6zrtNcetJlYRwmkrrizn3bd71mv+dwRl/6WbOGQRyxgMnFEVOwYVLapb9ViJbO3ep9PqtmPNOx6LbKnTeDx7ZD2/+22/Ajbd8O0tup5VzXK4zNOYcw5etHaBbvtHxbmnjAr46fev8tPa0H2yw4vrG96QWpcrnz5y1E3aty2uYhyZwcoFWw9DXkBk/23YIh0bZhHAJUnK5hJ1lBf5Okod01aPIUOxSZdwcq+6z0wnM1h7pcfPZ1l2jpZex1LZLROG2tFGS3htXu5nnfGWTobWnX+A6570oAdhApOejpZxnBqnuWtj2K+emY4Mo5x7afavNl1JTShnKmq98WBY3H6mstvkGhtb+0D6+XGzaX0FlD0ivnSbnw+27QqvyTiCbYAmYYRFeWjvXZsBrpdmXoUtJ2ZAWDLo3YfZsTt/3y1v47neS3Mg6XScx54FMgPueMJkS3DxzPa/SyrlevyQhnGDQyDfL3CMYq7yeJkdrp55zV0K43NJeoJwj4UbX/tQKfxXlGS8OVEFJs7XjY0s3z46LE6jpZdP7QJWCpbb7fnFCHE9r58dLz6vFpHxNBStILVIZTdk1Hm14wcq5j0Jej6Js7nHKp17I8bm43+1rpq0Y4jIx5yG1tYs956ag+aZu6SWcyAYg90K6lEM9Fpdn12VkComN4zzn+Nr3HcwTtnG03QVCa8fn+jznZsx5ngGbgkvqk2XTZo73etNUbkDBmcH1c/Kd6lXOizzntVxJonPmILOuRp7nFiueHQFgrmE+z7n+7Mpcy8ecm3/n1Oauct6y6ao0oRcAwM3/dT/sRQnhAPL3cqnTCYrho8o5PodLYLXUcieayz3n9j3lnvkC8ShOoaRwhZ5zHJ6DjqlqGNbA828BhURhQyN9XtmYyirnkFj7OfYSh+aG0d0mCRh1zvEaQNeN9O+cZdEOYDcZ2do75nf4uijo9ziRKRdzTtlNvrh2gNxzvnHFlB0O1X1GOa3dnUw0BIaByJEQTg9Xf5+xHR3UbTPcgPTnMVampdR4gyTeO2aaHlo76nBjl9q+fgVWzp2nZlBQHLoWRSq7fjxmha7XR21XkN+nJEHZ2lUBq5P5LeSa9D7af1p78bk6T82BxRYyYvn7ctmbUuU8/zt0eTx95xrjb06equY5Z47XRn5RzgWDBrYEsdnaOXpp5j1I/93vyY4JEBJzjmjtXcFRU+XLxlkBmC8VzaxLs3VW9VxYZUTITlWPba8IvQ90EVn0JYQLGIPrXAo9DixgFp3nojjhmrw+Wjs28uhNnI05r+Vzjyq5GFjZiSKAj/7CufDbV54MZ+5aaynnLg8zhn5+RbR2nIgu75/OBSxYApy0bRXc+a7L4R3PNWNLteJQ5DmnluG8bbOffDzpv76EZY2aXTatYyjnZsw5BU0IZyb+yj3nTuUclVLj5h6bEC5jVNjbic+oogAlKDNiZ4u3JZ/SEVqFIjUImOPjktBlyjljDEnp1Obf9DxuvPivcw5fB81aBKduX82Ol7tU+t7lhg2kAJJbr5Qt3C8s2bR2HLceRms356EvWztAaux0zQsuQZa+VG6toyFauGIA97xozDntg/ZdBXidyZTzyGQmZbR28o6F7qta4Ncx/Lgt/IjLeu2w5xxX6ACwQzgiZRrXytY555RrF6iCg2vIc0yBvLayXznXXz/c9epuWtm0q3yQmPO+es4dyjkNFcpKBgYkn/MZpSlanYTdWyOlDOOez3OO97PjuqEgR2+cy74LTUjoY1kBdJkuWfUd23MOYN4f2yCaz6JOkhsRC5Puejy1Puj7GioqhxrSuPtJ9zwu75Qr/EqDC9VoxJG1DoTK5qfuWA0f/YVzs78XmBwyIQkOKdjnkb33QUObCEhCuDFFhIXWktnasxfTQWvXcMVhAaSCDV7UdTyk9sZX0M2NF26qHsEepNvEEaVmlW+fOy9SZlZrV3IPDCqgc4lnNEIEZ9d3FLmAGcO+7D4XbRz2d7Uoykpj4XYxclp7fq0NpLRwberNg6MhZrR2dG6kFJx1+Fo46/C1AGAbPYI854Sm6AK3KPsYEvrzVD22vKW6LVcpNS0Y/N3X7mV/d3pSQSt5uTIz16wZDBdaZo0mz8Ke86KYc+o557x4Giv1+72YK+c6SzEGZ/DzxQT7oBSitXdsWrsPPiWmUUhrT+f59x/ZD797613Gb5znXD9CTsil3uhY2e9GOl7aaP7x9c88Bk7ZvsoyWOI+7DHZ8wSPcamdWO9MFNme88V2Jy+lNp3mqci9j4Gl1Ijgjt9Fjlq+1O446Yda0MfD5Dzn9VjBUjvJS0xpwzT2nDN947lVd72nPWrnSqlsfAsoJAp7mTuZIcrsrHxCuDzkJ49TdisoRe11SLZ2Iy8IWTdSZ0G+Z4RQSg3PuZZr0GNyeRnpZSy28jmEf6PG3GLl3Px+44op6x2h5TwbpJRaWfB1zs1j9Putn6nPQQDgV7x8c6rd6ThCeRQsoXrmPkcObv/XnnsCvOgp2+H0HWusa/FCBXjOlSvmHI2FsA7bhjySH4fDKiJV3hkUcnSWEC6UvRLoKmXZmuQZ6wR+WLYoVM6ZvrQHPnLsa0U4bcfq7DNn+HbpIL4562MySMy5YODQk62NEkIZnnMuWztSOAD8CZUA+BfgD68+A9bPNeF/vewsQ7jR8ZD7A5VGDvgMKohGilrlq0lIVmwnESg4yzO1OrrozKFKd9ls7fS86Qb2TBeeZrdDPOfceDQlGht5cI1kirqjzrmWKvSaiPdXSxHweM5d1xlKa2draDJzgftMFT1tQV50eOt1qb2//WqqnF949HqzXzSdIuYasaeBxhlTBbCdUFp7KzMaTLOe8/x5Us+5hlL2vdHzJUkgK7/FUSg5g5/Pc+6DQufiOuchnnPf5h1Ka/+j274HD+414yAPMok2fZ5znIsBgD5v93pGBSWXYp4eW/xdnmAsF16pRwwbdzWW2km2l6zq0tqN2OgAWcdOFoWEembwi+2O08uaJchCu4X+ZNKXNX3fVFpwHga+UodDcWWMAb2AJhFTSqG1rJPPdZr/pKQyDZDTyfOYc9ReqGKQKc2Y1h5ZdGk6p7HiGuY57446yQ25IWOkCgnOW2CUd0OefPyvO1bb/H7Tymah59mVEO5Pfm43vOScXXDytlWe6yDzOPOc03F1r4fsr07lHO85jKLvYmu12rkxBr8vSpl5XnxrKp7Dc80anLlrLftMfFAQEHOO1tpFZDgw76eZU8Lsg48tT6uGFI8RI8hzntHa6bn88aHvfkgptSlEa8+ADuHeOU6x1TIGfp5llkd8TZxc3e9SakJrFwwcODYGW4jzbO3FMeehfWA8++Qt8KW3PgPOOWKd4WXQnvP9i/2JOafxsrZyXrp56zz92bD+czHo5C1wKechm0zaHmfZLD4vEzB7pLXTmHPuUWWl1JAyhxUl+/hcuOSUakojBLDvwysuOgIAAJ55wqbuON30+Pxawmjt3Mbis1jjodG5mNHaPdnav3HvHnjkyQVYPVOHpx270fjdNY+zmHNs9CJxxlQBpAnh9i34E8LhzLstB32YJnYCSBUs/Vy1J5XbOA94YrLLhroo5HXuJDnFvEi5LuorlNbOgaPe5UYV3nPu8kjg6WdRLNHnIuUkxCiYJKaA3WKSrilw3zel8qSfWIkMyUpNDUy4W86g4cvWntHa8Toe2euOnps0UVazwHPujjl3G1KqgCauwnu3QWsn+1HZBG6p59z0DruMRWHtIVp7ZMeYG/srUtbbSVi2drMKjT1G162n3y+2cgOH+f7l7IT0X18ZVHPvb9QiWDVdZ2jtRDl3xJxfesImeNfzT/LKLqn3F7XF5FhIr4NX2l1rV9H8dZ2HY87x+5LS2vOL7CUBZ6jxpYjWHiEDlxFzjprHbBguz4dhHMKe88IR0vEWH5PtxXQ+FVQOKIJL5sOYYZJCm++u3Qa31M9knvPy46TnuRgaHHxzxhXKCSCec8EQgOlfGa0d0cg4Dw/nPfChqCwHV0otG1+FmWMoRGSMur5uNoaKkX9FiYS4a6aLJSeg4/bM/uzjuH0wZEHTG880qilaSGtnvosjlWXf1n9TZAnhMmNLfhxf5zzK5h6+h3YpNTeN8Pmnb4PPvOGp8IdXn2Ed41KkfWPCYD3nHoEC/9asxfDxV54Hp3YpWHoP8QkLmi62Ya5pz2WHx1R/bHjeK+rto5UD9h1cyu5Fs8AI56o9zHnOI6WysegYZE6I4KpE6CGHUnLxOLSQYNY559vZsXY679PzXhTR2n20eZ/nnCt1htlM+FgAf3ZcXw10C8zP+pSXX3A4AAD86rOPBwCczM32ZPqyGesa53g8VT3nhTHnLbcix2Wv1p8NBlRXkdBrtVag8LvIJUzkKMWu/nqBVnQWlnLlsIYUx06HXyeDPefdf41cDVmpy/K09tzIalLBKY1dIQU9RvMpvJSaPe4QyqylnOOYc4NRodcT4jkPSKSma5zTutc0qWFRzLlvr7Io80yOBQCb1p6d72QA4M/FyptGu5NkSmTTUM5NJwV3/nGbV8BFx2yAq8/exbatEULrjlSx17MW5TKIM+YcXQNde5SC7EZ3UI4amu8oBCHHayOXxZpyyM5lDXPmuWajM4zn3JcDBYBXzrWjzwjXKnGrMHO1jOccv69UJmKrISF9ablAYs7HFHrO4hJDsUe4Asg3Z0p3pbE32fcFsZ1ctvbs3AoSDF4cbFo7r8j00of+XHMIZBpUfnSFA/SWrT1kg+oaV2pYOS88ze4/IrR2TjnXMeeIFprNOS7mPM7nXhyZmziAna3dNT+O3MAninFdZ55EyW9V5yiVvs2OPrczd62Bsw5bA/95zxOZt5Cr/6qB6yu7kvnQfvRxXC4HDRqja5VSQwnbpjxUaIA027Yr2Q+nFORzQis89v3jS6nxgmQRFIDhOccxnRiHrZuBi47ZkCmiAL3R2n2/+2LOf/XZx8MffPa7cNt/P5z9RuNwOeYOgN9zXrQ2+GLO33bF8fDS8w+D7WtmAABna7e905FyPyO8XuA65yEKl89zztPa25465+nxJq1dZeOn7eq4WI41xnrOHYbaqp4hFzJaezs3fuprw3kkaJhVaGiIHmICjOe8wrUo9B5iAyu3n8aRgk476ToL9DW52RAYuYcLWOXaOT6ivi62XLR2XjkPidXGbLNIKSe7oSjm3HcflCLvhyshnL7XVJkvcKhwbekxc2h1OpU95xtXTsH/etlZbLsYIUqnAlVogJ9pxjljhimlBkBYktaam/+dQD7/lCovb4Zsdfp6LCZGFAEA41zrwXNODdqa1m56zt33BoBPCKeVfCMkMGiUObT+wWZrDzCaTTdiWJznn3f2HTLyLxeI53xMwdHaqSWbgsaca7g8UUWeLp/nvEo2dSx3UK9GrBSJm6omIBmX1P3MJQIz+qaec8Z75jqXG2XlbO3dY7D3p+g83nNves59m/U8ClPQx3GbZCOO4KStKyGOFBy7eYXVv17Y8YZXhJCEcKGec1dstQvcbaXWV+o537l2Jvu8iJKr0bYMYZHZ1OpMLgcNznOOFew9B1AptQKGDE0mh8ehyLgjlT8PvUeztHZvtvaSyrnCm2qC+jWva9e6WfiN551k1qIOMFK64KszTwWIVHBL+3rKYWvhdZceY/xOFRgXrd32nJtt+MD9rM9RSmWKOQCitTNZmLmYcw2snGPqaIgngjK1sJDH3evFljsLfH5deNzmbwA2rT1Lpon642LO8Z5mJrZEa1EfpKIsw/cSijmP7Ptq0dpLKtNcnfOy8b4AppEVK/ucspMngMTKeZjXCtPn2YRwjuun0xbfQ85ooxPUuWL78/7yz3hdxnOYq/jiu1bfb9RRoo0zdk6K/HgMFwPAeF+4mGTH9aehT+kcxe+OUsowiPOMw1Bl0vz71U8/yjpGqWLP+blHrMvuhyshnMmMsddcLK/gsIiy0mZYzHlXFiLfu/MGhI2Ce6ethHBMOWVzjth9LTH3X7M4Q1iOzvF2j+cSwoXUOZ9p2Pmp7D7Sf5eT51yU8zFFlq29k2QlcXDMDYcsWzuZzK66ykVCoS82topHF7/U1ICgFLHSV5yZXLwvXgA4QwWXwbiobQ1WOfZY9nzQzwMzH4rP4zfNFc0aKkFkn0VLqWFBi/NS1+IIXnfpMfC1t18KZ+5ak/eOhESAvDxJiDHCjI3kj8nLOpXP1u4TdLnfcu9R+jc1CByxIVcOcQknK16QmYPp5/QPL62dKDPtxKQn3/79RwEgjQ9eP9cEH5aYOt54HDQ2NYRi661zXtqglsdfYgo+fUd5g5q7VV+puvT3cM+5Tf+n4zBjSF0hDbRLLA4WKVB8zDl/LFasrZhz5X4vN6K6xLiUWoisQ+c/Pocvpeb2srYZbxNWBukYsxJTugxlkec8tttw9dcLcs85jjlPv8NGMxrPXMRm09BHdZJcueKMZFW8cdqokxrM7bUMv++6r2/fvxd+5ePfKB43EqLL0drJHt3iS6np8aalBJHn16n059/jcJeOMYfNc5s9eM4jpUzFn6lOAJBfB+915dvlPmu44sJbnSRjnzQIrR0zx7is2sE0bHLchUdvgNveeLHxXVHM+XuvOhWm6shzjsamjHUBr6t07c5X3QT9HxtgQ+E7XN/HJZQMEsOpkJY0zPnanC6gtbOMJkZ5zhLCocPLLo8c24Ebk+t7Wzl3Hx9SMWJSILT2MYWegO2Er3POQW/slO7q8hQVLQZYiVhBlIjQhRkDn0ENBnFEqTPVBCROyCr0nNPav86EcIxCx4yT9ZwHrGgc86HoPJfnPOoq6HsPtvwx511lBM8tV0I4pcxEc7j/LCFcCapimVJqVWLOfUI294wici1LZB4cvn4W/t9dKaU5r19sl6cyGCCMJxXHRFsJ4Wi29raZcV1f5zOO31iYW4KWUqPjS+9PLiDTDZtTrLhs7VzCrhAoZdLa9VDpesW163u2PdHayTO3PVr0Wfs85551p0dl0CVMZrR2JiM6F86gsXHlVN4GeueqeCI6hteRFwJdr3OmnBsJrtJ/Oc85rXM+VVTnHL2cZu6MHFVYYRTaI2rEnGeGE3ed8/BSat3jktwgyibsDPZu5seZITt2n5kHPVLB7dN+OsjTHkZ7NpGWUrPHbmSPx7RsJ302/+xaF6gBpSjm3K+c82EflnLOyC7c37hd3zH42q4+eyfcv+cgfPbOh9KY88xznssdSpnZ2nvznJM5HqusbGPWH/D37Y9fciYAADzzxM3dc82YczoE1/sNQJTwBEzPeclX3rdGNGsRLLY6zjHuXDcDj+5ftM4LNczxxmqqnOvSx46EcMz4WeVcJ4Qz1spyN0tfF+f0csacG57zYtZuxsATz7lg0MCxWTi2yh9zbgsoAO5Np2gxwBZjGnNeidaOzuGSaPVineP6yGLODaGOsQCTzlzKeU+e84DrYT3nRco5852+Rl0ayUdrP7iUbyA+WntRllh9BhcH6IIrkRYGTqKk8cCeg/C9h580juOEJd8Ytqyasr7DoSQAkAktGoetm82et97IYmV7/V25E/Q7g98davTiPOf62jetzL2bl5+0mS0LaJzbcZVSswU/GjsN4Ig550o46vcsULjIxgEmrT33nNshLxQ+6jpXWxvDlzCOes5dQjMem1M595yH/yymtYcbJ7T3r9W2QxqUcgtDm1nlPCyOmMJICMc8i4OOhJsAvOccmPmq59qizo/QnQ9GtvbYzsmAn707N4RzeMHQnkpc5zxXHPMa3fR5BMeId/9NY85NA0VRbCkH/DplSoXDGZB70MuHsuDQIU65dsn99La0UMJDTjGl+RJCSqm5SjhSz7krW7uGT0GIImX8niV+czB06NrnYgVxxizjPPSArz3vMHjbFWkCyaW2J+bc8JwXK4QusHHz5FSl+Iosp+1cnSnm2bmQG87pfat7POcKTMZJB601ZRVO36XbpYzTgz/8srPgp3bvgF96xtHseeEsF/s7umdqGdKZEI6Z6pzyrL3WVRPC4XPLxJzjZ0eZwNz150b+cmMbZ4hyPqbAsVl4E+LoRRqZ94DS2h3CamHMeZ9p7fgc6t2nHqh+xJzrJjihDoMu4u5SaoxyHngcVRT/50+fBje+4GTjOy1gYuNKkZLr8pwD5AluWOW8uzrvO5hm5m7U8pq2vOfcRadL/83rnId7Q/CzcB2+pmtgeAxZms+58TPwjN+9zfiO95zb7f3Jz+2G6y48HJ532jbn8XnMudnmzrUzFkULx+prxI55nLWPxkrfK6vOOVJan3vKVgBI58dFx2woVIaXOg7POfNeRJGt8MaMF4K9z4YnPhy4lBpOfEnXpX7T2l1hPgC2cl6UaZeymVzeCY5i6eqDgs9Oyx+b1f/uJFYoiFLuvrByXi9Ja6fA53D3msuIr5F7znPkyiBaxyOTNs4Zpos853iecIyrXkBp7UqZ4QJZLDSZq2W9kW0mrroKrR3PzyVMa2fOzxLPOZR3/7jTfzsJr1y7QJ0B2PBoMirSe9DpJIUJzWi7rioOVOaqO+qcZ2PzJBKNlWINpq6koqHZ2ovmb50YpXCIRYvJ1k5p5txeE1LyEsC+hloUWc9cKdsYDuC+L7lX2m7bNT6chT+B3KGgoLzC6VPm9X1cbOXvPgDAU4/ZAL/9wlMsg7xGsCHN805qzGQJ4fiYc64vbm+fymjtxbKaC7ovzunlDDdBncwGxJzrxy60dsHAoedfmigp3yxDsrVTxXf9XBN+9OgB5/Eu+LJKV6G1+z3nxbSboD4Yb6yP6sR9t+jw7LB7ETPMEFp7I46gFZsLiW7foLUX7H/cJqE3Up3kiXtWegO5f89BAABYM9PI+uJiv9xZYtN/aUK4MKaAKQxw2LI6LZ917xPzAGAqTz9+/ACsnU3pcWw1AqbNS0/YBJd266xTZHkeulZ12uaOtdMQRwqW2kkeSxrZMefOhHCZYJofS8tQWZ5zNI4Ljl4Pm1ZOwa51MzDTqDk9Pdm5bYfnHHLhOhsz5zkn1ut9B+14cwDkOa8gqOPcGnqo1NtamtbeQ51zerto13RO6RAS/LeGIQx5xltogONCZ1xME02dZrK1719oO9f8TYhJUjYhnMbzTtsKf//1++AXn5YnfOLmxDzDvtDIMxzb6zhuKvecd73GTEhXUcy5sf4Y2eF7B0trZ7y69HmE0sT17cHUY06hC91GOVp7arBzKwK+MAn3uPUaC6xy7WqNfp/OTbNN3Bb1nLtjzvPPToYhOTX1nHuUc89vSvHJ5OjwXB71yjHnpGKNfl9anXwvozHnRcaNcGWSjsU2+qSx+Ny5/J6kY87ppfpizpUCSJL0O7POuZ3UtQhlPOf0WNc7HirzcsfR93SK1DlXil9TizDTD1p7ppzb634VWruPSVaF6TWuEOV8TIFpGpgq7I057/5Wj9Pj9HkbHImjfFmLAUxLqk7GoRfsXuMkaUK4WJlKTlXnBdeG6Tm3r5kulqVo7azgzJxLE0Ixgk2cec5L0Nq5vrpfZp5zZkCru0rtjx9PjTZrZxveBc41V5y09oAH6PIwY2xbnSoN93WV873zS9lvWODAXtcWel/KIL9+3oq/bfUM1KMIDkIno9XFyo45L8rYvXoGla0i85F6b9pEkL/uoiOy34qU4SXiPcrHAdnY8Zh9XrwZn3Lu8PIUQUEupHWQsESVa27N87EGsHI/04jhwGIbnn3yZvjnbzxg/V4Em45uP+sipgSAfW9CFAeunXwc/LF5tnY75vzxA4vOxIs4ZMLIeO1QNC4+dgMcu2kFXH5STjn9vZ86DX7tOScYiQq5e73gUc45Wrv+yMWc517j9O9ydc7972kv0J5KLKDnimOHZYmUNW4BmOyerNSlh7HhAj5uEa1tfG4OlZ1TVjnPw/USVrl2Z2tn1kUmvwnOm6INF0qFKUQuYyftuxEX0No9uUvjiP+dXnZWSo0MKSRbOxtzjl78OFZZ7fZWu8N6zqmy3EvMORc3bxkjHOdaxtECz7k3WzsgZwLkrKA0UVzJvcuzRuj7mFVqIG271pfQygrsfuioc44T/ha1wYFLCFfWeqn7YmntAcYmSmvnbh9eV5YLRDkfU+jNJKW1p98Vec71BFVKwVQtgv3dskcbVvDKOSe8YBhlZ5SC6XoM+7o0mSoCDB46pTtypZ2qgPO++6ypAJznfBAx53SBZqy+3XGUUs6Z73Q72nPO3cv1XeVcCxlrZxuG18HVptV/92u9KFbO1u44fGvXc66V8yeQco49/LiebVUDkh5DkphlqP7Pz58Nc1M1mG7EVnITmjSJXosRf97995hNK+Ctzz4eNq5sWpuJz3NO97Ei4cilXGVeGaKc0GeG3//Uer3A9qPbq1JKDWdr10MNydbue7Z4HOcduQ7ef/UZ8N2HnsyUcx+tnRuj2a89Dle2dsOj5/AAAZg5Jvgx2Nfqer8wdZoa2ZLE7XkzY85zarxLCZlr1uAtzz7eGietIMDT2gOUc/SdnqccrZ3+bWRrZ/o2lGFHWE0fdPNsPFxCOMNzXrA3ueATUGPjugIVKHQcXtt8exk2OIQiX2PLJQ+lh7QctHa8h2WJ8jxjND3njuPI10Wec99vqdJbTGvX4wpVqlx5TjQwrb2GjCqdJJd3GjUsdwD82bW74ZV/+VV41/NPcihWYc+erl/1OLLnpcsoQ/qww8rM4/EzpMaWSKnMidDp5OE3SkFphdM3ZWnSuqI9RKMqEwEArNhtmuGcnhIa7qITy4XkB3LBr5z7zwFgaO0eeUCUc8HAoec/ja3yvcCUhqqV840O5bzIc05rwk43sHJefA0U+J226pxHyinYlgHnucL3jC2lFug5D405D6lzzlEC9XnTJeqc71o3C/d1qen0nJXT3YWVuZfriBC9BinnnOfc5VWgpdTaSbhibAjHjuvUyvlD+xZgsdWBJw7kyjl+TjiZWJ7BuXAIBhRa4LHi/5TD12bvgiUcRAyt3eE9xcdpD/jN/3WfcS6rnDuE2KKYvyXkFeHGZ3icGMOfkZTFo0BW9ZzjMWB6rkVrL6GcKmVTOJu12LsGvOVZx0GkFPx///xtqz16TbawbH7nTAhH2plt1uAdzz0BOkmeuNEF7lKLaO2LBZn6KXCd82kmXpEiVJDkPH2+mHOO1s4zoHihvYjW7vacVxc+Oeh+NJUzrXOefodridfi8LUew0dtreI5j7rezCQxs7X76MwRs2YUAa+xWbheZD9r13kauJIA/gmzPmiiPA74vXQZ7ajnM405dzbpLfsZKcXS3ukI3bR297qnUeRZjdBcBMgTNOLrV0rBeUeuh6+//VJQSsEnvnYvey0h4JhloQqrvd6aiq91fzzvk1KQU/wA0Pxx+81dl+gPq8rXYAD72brODU4G6Xn3Neh+3avn3KykVA7emPOAmsnThNbuMxgWVNydKJQUXwXDgovW7qNz4hcOZ63duNKhnBcI99Sqj61x1Tzn+TmU1h4pqtRUVc7zz5xHL6ReZznPOSO8BHjOlXJvvGU857/7olPhipO3wOU4o2n3ua3pesc5IXXdnFnKZO1MI7uWFkPpds2VzBPS/bvNeDNcCBHK1s02oNGtK/vg3oPwxIE8CdwCEvK1sEupeWWAKdYcXRSPeQFnayfdmInWeEU9b5v3AGrgUoqWQlKQ+KzlqHPOKTucIE5p7S5UjTlXKr8/2IhA51qIsStrE8z7nJWXQnOBvg9T9ThYCONCUYx1y+Fh4F6fl55/OLz8gsP5jhHY0jGO8TaQ14ZTIFyec9yHVtQfY8r9aIR6XjgDkqZaKgXw9zecD+//2TOy3zKBGR2PadQarvrgzaKEcI6Yc4wAebEQ2gC0gDx8uG9N7a8za0sIvJ7zim3qZ5olhHMo5/r+pL+Xu1m6OVznHF+LU0kif2NGEXe9xu+euWrS2osVX4D02Z535DoAMMNBNHyU9yjif3etM9aaXGAk59oCsMMn8LXOd504NFs7bpebB5ycwIGeWottGrnrCVnnUsWXPhtynWYfKCEcSkCqIDycwjUuY4wkH4bPeI9RJQQl65O8h0VU8GDlvJG2i1+NsqK5r865S2bABq6QOuf6tVhOnnNRzscUmKYRmq2des41XLT2Is859QhhC1YVLxlepKyEcMSaWsUzT/vgPOdsghuy2nAlJWg7WX+Bx3EJUFwbsqmcs0PJsHX1NLz/6jNg92FrUDvpvX3+advgBadvg6vP3mWdt36W8Zxr5ZzxtjoFl+6/elFM0FwtQkidc6UUbF2Vx51jWjtOMKLHXKtA6cyPT//FnnNd3z0fs/aIYVo7eY4OQYkTAGjpFzp3WihuPDQ5kAat9ZuPKe8Pj80yFKCx0c3eaK97Whm6OEAqLFGFgPYLEPbOZmNRqnst6e9aOcTrlVWqjWE/5GMkf1MhR5nPDA8d385QZTZkDOk4+Pb0vcPeaZzBPGQca2ZSw92jT7qV81Bmk4/WHisFp+5YDVecsiX7rcUI3fojnv/1iJ8jZik1v+d8KNna0TqB53GWZd4TI+uDHcfKK1JlroUyp1wJ3/QcihRPe/chk2s65Qy5XDcZ/Z5RsF3Kuz2e/LO7XKiJOFLw21eeAq+/9Bj4m+vPs473Ka2h2dr1uEI951HB/DVCbGLzueq9rEkSwhX1u9gKU4TCsrWHKcd5sko+r0yR5xznyNGjjyK3vOKaOb71L6sk0eINCK5tOzwhHNen+WWR5zxUfl/blRN7cZzp4zm52vVuLnqUc95YnetLywWinI8psKLQQRbgkJhzAHOhpXGAGkW0WEqPMz3n3lNZ4HMorZ0qq1XlI24RKcrW7kvWhMHdLm6cIZ6+1HPOH2Nmaw9dsG0DxNbV0/A/fuo0OHn7Kuv4ldM1416sna1n11cu5rz7vaa1l0jGFhJzDoDizvfMwx4HrT2jiUYRol0WDsGAvodJkiv7nCIHYFrFXeEJuE3XeCjNlh7TccRWpuf6L3DJUaea80TGkZ38Cb83Plq7bk8/p1BgzzkOI3Dd86Lv8Fj077VsLriV83rspjXSzZ6ltTs8lUbt4arWRuCzPztp7d1nhjOir0OGOFddWYw1s9pzzucYAAgPGcGGFm0kyJIUMfckjzm33yEfrT1Ppun3nBuJsRzvadWQKqOfjNaOs7XnfWvWD8fKCYGtPPEGmFKe84jeU76UWpYDIFJOQ7YLWIjW0zoOuPfcODjPJA6TyeP63ZNVedaF/Bjz71gpWDPbgF96xtGwY+2MdbzPoRwpxcsXlgJnz3nub+507hB8DbWIlyMN5TygX66qCwcr30ZsG0Nds9RlnAipc255zlV+H5IEOxJsmj0+p8z3AJzn3PzdZdCqIutl51qsusgwTlqyZuAat67LvqxSASIfi3k/jHE4rhkb6+2QUPt4Pb7llK1dlPMxhVHWKckVHn/Mef448YuwerrBHV7sOUc/1yJlCOhlE8EAmItKk5R7S2uq5n9XjzlHbei2PUJdubbtczmxnuuC7vucVyJXzt2bpAt4aGGeCGVQ29fMNJwCvz6eAzYiAZRL8kPj4FzIk8IdhCfmEa3d8JxrT1Q+j0onK0LvHK2fnI/ZVCZj5Wd8FM3pGhEmuNhKVxx/0fXh+FGMPJ7RHKd1rWhsIbT2nYyg6oNS+TUYMecB2dp9MecA+bWwtHZL+bc9ORp0r+cMMW6mBDqvB4WP97TxxzaIxxYAYD1iTtFxHLNpDv7smt3Gd6u7nvPHkCGMIvTdwgqP3j/02DgFocVRnbuf/Qnh0t+aJUqpGTHn6Jge7CgZKK1dkfdrAa1Xeb8lOqbCNjY0YIW/jOecWV98nvNYARy5YdYIqSqC7gIzAkP2etZzzuQWibM13Exs5wL+yWXsLOt19JVSiyK+lJq9b9hrdDpGXmbDLfLMPfN37hizznlxv6HKOfUU15n1NpQ+bsecm78bRirSMaa1Y+OQAl6OA3DPTd8MyNh1Wcw5NRL0Xznn1lJDjiwpO2hox14va6JeL6omFeRYp64+lpFuLsr5uALHvwZna0eby4HFXHFxKaRFyjkVOjG1tYryjE+hi32keqPOcOdxMedFVGAf2IWE+YrbvC1LsbKvUY/TMIIEU514odMH7FHD2dpLAXmbAcpla8ePwjefNK393ifmzYRwTMx5DXl7ypdSS//FyYToO2LVVybeJdvKyysAGjTpIn3eab11s2/uXBe4JCy6C+o59HlpaFIWDH2fOS+SDy5aO84sTMfh+w7Avjb9vuO5Rp8pDV3AoIoxPSzNfJz/7fLA9uI558vh8e1xa732fgCY9+2IDbPw6dc9FZ5x/Cbj+NXTxZ7z0PUZz1GtOC8xdGQNXnGxFRXbc57+XeQ5N5TzmFdGBpEQLlIpM0VfQ26gqJYjgwr8rpJsZbY7LvmhL0RLh5D80UvOhF96+lHWcRyw8pzTzvPfXXeA+36R8Z7i+5nFznvuq49Rk/VNFcSC5/TCM7Z7+guMOXfsYa51D782PtqvbhuH/mjg94XOr14855xiaI3RcUtdtPY85pz/HcA2tkSRSWvHpfycHnL+a++7Wicygs9gH9omRkjMOYBZH5w2HdrX2u7eQUPgyiBjGzLzxSWrPvWYDXDK9lXwknN2BRlyMifRMtLORTkfU+DJViVb+/7FPNPullVTcPbha63Y82YRrZ1s+P1MCFeLFREizAW7qixrCFkocU3Wr6Phl5xjx2VT8LFc9nFsdmnG+udaMMskhMuPQ59DlfMSnvOifhMwPechbeENxXe09uTtO9giMedIOdcx5yhBV9XYqCQBWGrl7WFoa3xWv5go51ziP9dvADQGNrKE6ZSeySszIUYYzC7Ix5H+GxOKL20Pe7B9nnONKp7zPCGcm9bOb8b8tWehLDq7vvaco2vjaPNuz7mf1k7DcVyhGr0ofGxogmPAnIKxeVVeJs24Dw7NLSTmPNxznh+n84xw8aKn7lgNAADPPnmL9Zu+dfgWujzneO3k9jcXrd2k0XsuKBDUiKfbpPGoNcd8KYKlMDpKspUxuHLVGopo7RpTAesDAF5je8vWDmCuwfk48ue7p7tX+D3nxco53Z2K3uV3/sSJ8L6fOR0uOmYD2x9f3pIel/5rVehwKefoc1HOG1eyuUbZmHMmNw0HlxMCf60A4NWMgcea591zXZ5zauw22jISwuVru+8VcefCcZ9DwyjCE8K52yzqmzPKYmeaHZsfti7oOVEUnucDDQXEcO1jjVoE//CqC+Bdzz/JOkYx9ymjtUvMuWDQwBRbI1u7xxSOF6MDC7lQrpSCj7ziHPiLl59lHF9MazeV52M2rcj+vr9bd7oMTM+5GeOaCrno2NIFG/J26OeQWrLvev5J8NtXnuxtu5eEcFzMuZ2JlVHOQ9/QCp5znItg7WyjWpI/0PMUuv/quVp8Lr5+35KqFcP5xZYRc75oxJwztPbSynn6bydJYKnbHvXi0syjsTKv1ed9ZjdVIkjTjZxWazDODfGcM2Wr8hhe8ztr7IYXIiqcV66SjS4oyENAsHeYCslsQjjHWGh8cpYQzhDCmWfqmCt0sy+i/pbJ1h4KX1I/CstbpABef+kxcOSGWfiVy4+z6K0cVndLu2H2ld1/qHKOPecmJRX3/+fX7IYbX3Ay/ObzTwIASjPvCvPoW1fZP0zN5fY37L2uOd7NfsScNzLPuendrWV0925SPCMpXXi/vkRhVRlolpdWKXbe6u/wu+DLSWH0ka2xedmjqvc7X4Pz85u1GFZMpR7Dh/amJUZ9igiVSYqOASje26YbMTz31K0ZA8U4VylYOWWzkGyjLq9ANx33GddO515rbn7TdbZZczsFWM+5o6oNBTVYZsYdZb5/r7/0GLj9LU+HZ520GX3v33997wEXc54fj2jtijdC6XM4+By01IBCm3Ctu6HySiiTzJdwN2RdcBmXy8rmemwciy9EVi1Ha18+yrnUOR9TYFo7tvDRxA/NWl7XmcsGq1FEY+KwgSSSe/kFh8N7PnUXAKSUyLIwhdVU2F/M/qYeyNLNZ+3Q/rCH0KfQFC1YrHLOetO57+y+EvJdb7R2/zg5rEOl1mYasdXX4etnYaYRw1MOW+tsA1uiAcBIXlgEPB8Tz6KqLcDzS+3MGwLgyNYe5UafsjKfUee8+0654p8zuiqZtxxDgvusgd/BWmQLCa1OJxMELM92gPWbp7Ura6yRcsfX68+NWgQtn8JW8qXFBipsaLHpiH7Bw2iT/J69U40YIpW+W7byH3kSwpl/c0KrGeNrjyX9vrrCVyohHHNt6+ea8Jk3XGwd65o/a2b5HCUYoWsMfk7a6IgrHWism2vCz5y1M/vbZJzY37lp7fna6fKE1uIIFlsdQmvHwmfvoH1TZWuRWV/KTBGfwF81jt1auyKAlVO2gplTrvPvQpVzvMby+Un48XK7w4KDNrx+rgn7DrbgoX1pWEavnvNQ5YrCpUS95dnHw48fn4cXI7aey0NMv6eJdDnwDL9ihc7rOWfWiiql1Ix3zjgmNUxvWTXtnbO555xn6Jmeczt0Uo+4k5iVZVxdusbiUwLpvaJNuPoKLUvIMvAKFHb6c4ixeK0jHKrsVkZlJtcYXaCHcKdk+tIyorWLcj6myCzMpF4nFUrXzDTgga6FuGii05e6SDnfsXYG3vPCUzJa8VQ9hi++9Rnw55/7IbzwTHdclbv//HONeJyUMjf7viSEU3lfuF/3uUX3L+w73ttgK21UOc9p7eUTwlWKOe8aX9bNpjXO6d7QrEXwT790YUG/6b96o6uard232WmBe36RKudMzHmc09pLJ4RDBrG8PV5hxV4bVxIw+neR5zwdu/l7p5MLQbaXICTm3N4QdSvYiJFmnbcVO0DHNmuR15taHrliiz3nNGFbSIWFrMXu11nMebetlVN1+J8/fTrMNWvw5ELLOCeO3Z4TajSilDofrb1fMeec59y1VHFxli643o/ZRgz1WBl5AELPpVBKZW1ZnnPPemt4Z5TC/wCA23OOlXPX/lbvGoXdCeF6V8/puqG7olncqyrS9FBX7HqZNdBickURvOTcXfD/7noYLj0hz0vAeT59pRYxdBftTlKq7CYw+wNHawdI97MfPLIfHtRykee+uhRH8xh77w6BKzZ208op+JtXnuc9Vo/Lp0CX7ZeCsrZ8ccWc4liF1m57c+0EkL61Ur8/S0zJxfR3xX5OoUB1+0vrnHe/VXbd9fwMBzyXPmhaO+s5Zp5PzcPKCTEWY+Xc9KKHjNIe2yATwuXZ2suNbZwhyvmYIlcUULbmyKaerp6pZ8o53qB/4alHwAdu+z684qIjsu+oMB9Sl/iq3TuMvzeumII3P+u4EleSgy7Sxuag+hVzbgtcLq8CRVnjRtqH/R1ngaeLYUqxMo/RzwfH74WWhsBNhVpgdcy5jjG1x1j8EHCCFYBcyS7rOfddpqa1H1hsmwnhGFo7FjLKx5yn/yYoW7uzlBqy3PtYC6bi7p8rtchOCNfqdJxJ9kLixjjPeUax7bYXk7/zsZnva4hgWAZK5X3j+ta1OIKpepTV6vZljKbQmzT1nAMAPPfUrQAAcMs37jfOSbPk82Ok7x8X9z/obO0tRgh2zW3K9PAZcFzKiFIKVs804OF9vSeEA0jn+FK7bSeE86y3rOcc09oZhgBAMa0dAKBeiwAW205vUA85Q/M+HHkTLM+5J0bWB1/CLo49FgJr7YrSpFJ/9YpzzOMY4+dUMK29u184wnVctwC/htrYs8TQ2gHyfe2hveU8541AWnu455zpr4DxQ4+j1+bynGPbRVHMuQatXOBjeRWVXvPBlfPHMFwH5nygycXshHGemHP0ZwJ5nhwF9jNGA2PhcyZQL7ZdHcS/dxWBNbQwcxfLga755YOpnOPjy+1lNfLMuN98CKK1dy91OdHaJeZ8TIG9eFk9UEZ414qV/l3jTZcdB//4qgvgTZcdm7dJnna/he1CoKHXYtNTZ8WcV/ac2xtMsOe8YKHgae355zN2roZnnrAJrmSytdrWU7s9HbIwhWK/OGsjhyqe89N2rIZapOCMXavTNuhmFtCG7jb3nAPbFodQz7mmTD650IJ9B3Ov5wKq5awFvTqKOS+drT3K37mMJk+VgCzRE6K1OwRj+jc3HCqc03nS7qDydJZnq/j6uNua3Z9uX5kg6KAK68+0/CGH1196TOExGgr1TTMrr0B0Wn4zdgg4RHHghRZ6H6vT2tPnz7dtrEU9eM59Gcyt7yO/sQjDN3+4eFncXpnL0XNcC/NZvKhn+8HNFykWeFyG59xFa+92XHd4rftT59xWdAFywZ0r81WmV8rgcNVLL5PrwFq7XHNMrxvYc15SOe8gz6VZ55w/D78DWblAh4KmGWEP7guJOc9/C6a1B84PzkhelMiS/k3XDZfMliBXLvdu8qwtZMgirC3aBp4b5x6xDo7ZNAe/9ZP+HD15W/w9drHKfAYlWsrUdnC45aC03fS7NCFc/r2rRzet3TlEq/pFs24+M9flDTPmvLznvPgddQEb5ChCHEn0EG5+43VluWAiPOc//OEP4V3vehd89rOfhQceeAC2bt0KL37xi+Gtb30rNBrF8XGTCD0B2zghnFKWAL0KCVF0gz55+yrjWMtzPmTl3FQgIzMhlWW5rdqH3Z/pOffFnJt/1yJl0G2LvBBHbpiD91x1KvubbcVlFtjud1iwC1XODS9d4M07ZtMK+NrbL4W5ZroM2IpHSL+5Qpv+G05VxPfAt6ZqyqSmKWpgr3Cm3KFs7eUTwuULvN78qTcly7acCde8QSj/O//MjYaWdqL3rd3poJJD5o8hpdQ4ZMo4UbR8MeeRCvOc3/C0o+DCo9fDV+9+At518x3eY5XKjY2Uur9iqpZ5bsskhNPfZgpYAd1P/x3qYbS8aMrtOQ9VkotQJiEcQDovuDwkFL61EBt9jXMiBe1OUup69HpGs7V7wzI8ioIeB/d3HCnYumoKHj+wZAiX3HgMz1KgchAKZ8y5Tgi3ZD+fMt1aCiPe+1FDvdDa3R6+7r9YOQ+ltSMPVwcxAouA94dGLYL9i+3sHtLz188Sz7nnxuJTfUwS8+/C4ab9cuFtjpPp19SAquEykJYppZaPD+89RP7y7AUvPHM7XFkirNFlvHYZxIJizlvlY84VmOw47VBQym2Qc03NhOG1v+HSY+Ca8w/LEhFqUIaBm9YeNrFYY2Whck7aKOk5r8rGcY0t5DdXf3zYRvpdKNN0EjARyvmdd94JnU4HPvCBD8BRRx0F3/zmN+G6666D/fv3w3vf+95RD28g0C9DgpTzOFLWC2ck8SiY6FZMcRy2ofYLVFg14+T6473gLHyhnnOOQoYTYBV5zn1gE4VRQb/bPr52jgrEQVUUyLCH0jIgBPhy9BEWrb2kMuJL5KG9MjQGFivnutxYHRl5yupDmUGskyvnrpJNZsw5boMKcv5NDSuPOJmdBr5mi1IdWA6FQjejnxFHU03/NscWQmOMIwWn71wD37xvb/E4UJ/6OvXYDM95GeU8yHPO0AyDBW5bgTEEF4ey1VNCODbm3N0eVs59e4LXcz5jrgs5MyVtu4yAdtTGOdh38AnY0S2156r/i4HXnuwzVqQciRoBAP7mlefB/FIbZpu8eKPnhDvm3D2uUNi09u73xLjn8275YMcEm4pWlTbthHB+JQJfYnnPORhOBw3XnoM9YqmCuuT0nmae84BcPPg3F63dHH+4bMK9865TXXHJVWjtbPvMd2ZN8MibH8W4TyWdOvj2G55zx/h87x/1nNNx+uqcpyKXlqvze5Ymo3ON3TEfGbGsUYtg5VQdHiMlKKlBpYiRUoTQBKm+mPMQpXgtMtAacyNkkAjesJIKyjl3m3Qfy0g3nwzl/PLLL4fLL788+/uII46Au+66C2666aZlq5zrl6ndSQxvJJ7oM40aG1vpwlh5zmNTqE0pvfjYan1wHhBaz9k5PvJbsx7DfqScc/fLECJ9wib5TTHfcc9w08qw8lSGB6DizbMNCAH9dm+JlRCupDLiW1NdXhmc7Cz3nPdAa0f0K91evcYrAVj58SVgKow5r5nn0iFj44ztzaj2/uo5S+mp1PBnGB0iFSyAp30UI1K5MWKJUFRxuaFypdTM3zlvGFfPPXSmWPTAyJOt3eOJKoMWI3H41rFGLQLohotX9Vpgz3kjjuDC49bD3oNLcGCxDf/14z2ljG8fftlZ8OTBFvzd1+4FgPIx5/ozPpoK3ritraunvePR8xgn3sQd9sdz7lC2iHEPz88y/dJb51LIyzwnrpQa37e5bgBUiTlPkCE3/911C7DyqddMLjQAII85z5J6etZJyuYrOqaX+1l0fqRMqnXahnlMCK2db9uv0NVRIlXueIMCX1JudDE5XFRpv+fcH9LgCu/Qx2o5JYHc4MP4SdhxYXDVZfSxVGbov+ecOZfLc2QkdC3f15o+Z2vnUNZz7mI56GaE1j4G2LNnD6xdu9Z7zMLCAiws5Elt9u4t9uaMC1wWZrxITjdiZMUunuR0sx22cq7IRoj7p7UmqwpIJjWr2xexEIecC2AvqquYOEzToujbVOyNgl4i3sz/4uVnwfceehLO3OWf47g9V1+hsGSTgGeALdEA4KRgF8G3qM7U+WUK1/DGnj0um3AIFFrgtQJBk7vkMee559wQLDzxUdxw8Ptcj5XlAcFhDVVKqXHQzej2cpo7jX/H41Rw5IY5+PKPHg/qI+TWK5WvSa12boAEgKxWMR4fRpHioOdfUYkZ/Xew14IcRvOAGJ5zY7xBzbPgWCW+1wuvW17ByDOo1bM45h/gj39uNwAAvOAP/72wXXs8MTTn4uweU0MMB86oZbKDeEZLCN7wzGPh9u89CqduX436y3/vg27uTAinlfE8IVw1oddOCMc/8zKMDbo1up5xTNYNgPLZ2jtJPq9D3j2sfGr2QUZrJ+fTcIZQIxBVqrhjemEiFJ0fKWUxz+jYQ2jtbNscxZ7sPb6wQhxOWdpz7mB1uO6rz+ZcVOfclDHte61/trK1O58L/z13u/VY6L5Ma9PTfBHZ+cHKuX0ct89VrXN+9uFr4dv374Xnn7aNPb6sXBXKXHEfk392GzbSkEBfSd5Jw0Qq59/97nfhfe97X6HX/MYbb4Rf//VfH9Ko+gs9IU0Ls+lZm2nELEXP2abH2zAM4N5qkYLrn3ok/PLH/hMAUgrN3Wp/fmzFoXEUOeqhd4EKJ3gTmmnY9ZHTPtBnz5i5zKE+q++FR2+AC4/e4G6QjoMoUlVgbXQl+tXCU5ls7Ri+NXWqwe9mZsx57kXJFbRSQ0AGsSRLNkc9Qlqgwf35hOGiTMR4I48j5aVlWYnMqsacZ14Z8/2gmzn1/J68fRX89ZfvKdVHEfQlZDHn3TGsaCJaNSfkOua4PjRjFIV4zmM3rdFu3xZyDMOYQ4jpZa3lPOe++4vXLb/n3D1/VjoS8uV5CspfDw1h8CbqKvguVJHkcNmJm+GyEzeTtqsLnxzsckrd77Vyka0fpoE6FPRQfCtdYRZFsOtC8+dylOuydc7bHVSFxtizeXDZyF3e0/VzJtvM763Of3MZO815V+Z+hq9bAFRZtccH4POc+8FXCsFKc+Q1QuB1JDRLO9eWmWOBf+5BMecOWru5VtnvoOZIJWDWOXc9Ftf3nDNBXw/NcxLsOQ98/7mjipLEcYwvF2568Zkw16yRuvfo+JLLY6/KeUgVpzc/67jKVaTGFcN1nRK8+c1vzqxWrv/uvPNO45x7770XLr/8crjqqqvguuuu87b/lre8Bfbs2ZP9d889YYLlOMCktaffpd6sfHbONGo9ec6HDSrkvfDM7XD7W54OH3jJmXDZiZv65Dm3P4fGnNMNDC+qnNecO8c9LluoD03AEwKfVykUtnc/vF8dg7UYIHhz8Fk8aSZZDUxr155zHC5RNm8BZqtoxZ9mWs1j3vJkRr5EOkXxlErleSRqUeRlEPgEJgoqxHEeOr0e6GZi8p5Qg8/J28wEkz6E3HlsoFpqm4L6isq0dnM9DDmXsgSKQI19g87W3vZQKDngLOXemHPPOzqLPKGc0F7FLqTHkimmnovgZEGf8tCroZlTjHoBjV+m81LD9ApV7w8bWgwacSlPL20z7B0DKBNznn/WETshzw6vi5R9QOfiuhKec/yTM1u7Z975wL17vkvlwmCK2HzBY2H6xfel7thjuWPLjgHfVppnSANfZgjbh9L/2b4c72B6fhIUc+76njOi62FTBsYUkSFc72Tomoq71ms9t5b7WDm+fSFStvxghJ6EDTM/N8DY4kM/clFNIkbqOX/DG94A1157rfeYI444Ivt83333wdOe9jQ477zz4I//+I8L2282m9BshsXsjhuKEqcAdD3nkX45w5I15e33a6Th4DbCLaumYcuqNEbQsKRWHJ9iXmSqdLjgo/07lXOjb/e4aLdRxFNkqwKfWjVRmI8i5oI+QnvOD3Zj9Gca5ZYWn8dYKQUzjRo8uZCWUdM1sM06511aexRZymcoMFtF19i2krkwBgzTIOQ2cLhsJrU4rQpQi5VXObc850RJxInDpmqRQYlvdGtNA9jvBRtzHinDmBArBcduXuEcG0XIrVfIQNUiyZ3mCmjtrvdY93vekevhjvv3WhUr0nOpwSUqJ3QrAG0WqkWjSQjXH8+5RzlHydS4ZGOVPOfdcxYDktWxhiz0ue/KuaftKnAmhPMoDOXmoPv6q3vOydgc5+pLw0OgCogLeNz0nU/b5PvEbwBlH9Bxrp5pQKTyPcVbPtXwnLuU82pyE/fOh2aOdxlznJTyIlo7068Zc06YUh5DcPlwMbMf7nsIZK7QtdsOI3PLekphWjupc+5QOV1j4bZpfaRFaycyhOvyQu8rrVyw2O6wLChaqtj4zWsYtX/rRUGmjF2MIMZvnwyYk4aRKucbNmyADRvCqLv33nsvPO1pT4MzzzwTPvjBD0JU0Ts4KdATspMkWWwWXahnSsac+5JlDANF1lGfkhOKIs85lzjDNSa8qGKaJ4Y5TI/iTzcKsDNz9yLAG4pUxWdbxXOOjUgAAAcddPAiFCXymKrHmXK+brYJ9z4xb8Sc65jlOFbII1xNiEg95+l1UC+BlYhKmUINm5WftE9RjyI4CB2oRXbMudmW+Td+XkdvnINHnlyAR7qZYqcbMexFNeF1+SHcDvXO0JI39H0t80xDDTu0znnuOedLRGoUUW5fc8nR8KqnH1VI9wMob8xKry1nTphUdnwcGlcvnvOSyrkZc+5e73wG3TmknHMKZDVaO+nf60ZEH5X+173GVU2OqOFKTlUV9N66PefVFD86xlrM35tSymSgfJDJHBUEdvweaINq2VJqNKkeZ6hYO9vI1kJ/zDmvOJrH2H2HgDvWd5/M0ArzXw1ntvZKCeFwzDlNCGceGzo3OLgTwgH72TeVuNw9rr85mStTzgGMmHOX6OYaCsf00/O4Mq098L5iWUkn/yxbSs3PILW/KwrP88HrEAtoLLTM3nLDRGi49957L1x88cWwc+dOeO973wsPP/wwPPDAA/DAAw+MemgDA45/bTvieKfrcals7a56k6NAkcBd9R00FxEtEKFNqMSihBfVlQG0dq/nnPW4mgteLwI8PrWqkk/PCyqlpg/p7hfzXeW8TGZvgOISGDOIZru+m40X09pbqJTaURvnoBYp2LVuptQY8DunPed2zDl5jlQ5Y6z12WdHv5oG56O105CWtG2VCZTTjRiuPe+w7Dd6/7n4MVwbGv8N0DU6kP4BAE7bsdpxFSZCZqBS+Zxb6ugYQq2cFySEK/Cc+46x6pwz99YHShuOyH3TcMWi9wO+paLfnnMuyVKV/aNMUiLjvdHKuXGueXzP+5nRX+/Piu4zWTwqUdqrKLgA9vvlUp6qKpN+D28vcyD/3CqTEA6ti3rNw0k5KXwlQl3jcXrOKxq+ecOg+3jOOZGGd+bfV00Id/1Tj4RV03V4+QWHZ9/hdbAWkZKgHuNXWQXJ8GbjUmoO2cnvOeffKw0fSzLCOniSK7nG9wSud9IXcx5FJpuqiH1X9D0FR2svMkJTWc7PWrJhGE9KEttDcgiEnj/q0NxhYiISwt16663w3e9+F7773e/C9u3bjd+WU3Y+jExR6OSeEzovy3rOMUYxybECxnmr+u85Z5QOnxWP9BkScx4+LntTcVmUq6BqnXOzDf/fHHRX2nI/n3nOy9r9/O8xVjZ1Nl6O1h5HEbzvZ06HPfNLWb3bUOhr6XQSp+fcqq+saCk1sH7P23coi/odjt0J4Vzvay1K6eqxUnDNeYfBhz7/Q2jWYmu+msq5OR59SZQOy3khPvzSs+DfvvswfO47j8BHvuTO4RHmOc+FGL2M6/uHS6nxWY/5NkPWNc57WeaVMYVYcBpnqnrcgsbgaa+BhEHfcWcfvs7526zhOc+/z0IhKlxPmRwb7C8+5aGXdPjgV/yrwPacp/9a3jyPt9IHX93iflDlfUSEqjk9aB8tJveAq8kO5zl31DkHAJhtxtbxRePpd8w5zw4MGwue35TGzKFICt68agq++muXOg03jZrpObfml4OZEQI8l4yEcOiYUAMIpUjTQ421ij5Pld/jBPKYc6Xcz8X1uPhs7fnnepyHmdG8Na7LC6e1E8858OtfzbEXARQZiRhFvwfHWVUDcT/6nmRMhOf82muvhSRJ2P+WK7AXj5bX0JguWeccYxSec0zP5GiIVQUVVxt6vaeJT1zwxXeFKOe+IXM1xPspvOO2qgqqtuAY1DMA2LT20p7zjv/3KeQ510q3oZzr0mexgloclVbMAUyKvm47xHPuE95C6GB6TtYid8x5kRc4UgpWTNXhX375YvjMG55qWesbRkaXrjEgMsdMEydy83PVTB2ec8rWQop7yHRWyp5jHK29DD3U935r0LWyFkelvAHUku/yOPYrIVzRGCiaBZ7z2954MfzBz54Ozz55s/WbhovWfsLWVRApgONK5B/QKKWcY4Gs+2x8GdV7pTv2mzrpulbLuGcI0GXmoLu/qopUec+5v70GcwC+tzipZhEwbVvfQ1/ugtmGP6FkNh40xJBs7b3kBUj785yP11tHP05ae4As7AsFoeu9Nb/Qj3htCIE5N3kPfGRvT3xbBe+9T4bEtPZOgrO1V0gIVxBqhKnt9Jm53vNQGd7Iv5BVa+Jizqutl9xPveSD8ofIFO/XPkbHcsZEeM4PRWRevCRx1o5OS6m5aS0+jEI5x4pHkVW5KrWQi5sKFVbsbO25AhKknHuGzCUuMep/9jFmsl8x5yEKS+Y57z7b+W5cc2jdW42imPOZOlbOu57zJUxrr1ZfHUOhd04bGWzPuS10c6EU+d+4fZdCmW+wGxxGBadynnmx0r+1UksNNIY3VZltaoEndghO3N+vvPhI+If/vA+uPGMbcAh5fRW4hS1May/zTOu14mO5xHpllhtat9cVLmQK9eHtB43B017DiDm3D9y1bhZ2rZv1tu/yPL7ukqPh5RccXolJVCaJG/5Fn2YYizye4yowBMA+KOdU0dPvvr1+8GMogu0559/dMu8OTQjpQqacO+7T807bCptWTsGLdm+3fsOnZOUTFf87RsKw7ny09jkjoaF7bzVjzjljQi8x5/Z3wZ5zx2GcwaMqzIRwNOacrMuRgrc++3jYe3AJdqytFi5G+zT9KO6+MYry4viYapHK55FZ59wt61RJCAfQDVVbSD9zoQhxpKxcIsEyLzpNz3O8XmdjMKp22P27wN0LH0W+CGXYqux4+mw4nRSIcj6m0JtjJ3GXjcC09rLKXdVyW73A9Jxzynn+ueoryAlZvjgkDL/nvPhV8S1atoWXbiSFzXuBz+9XnfOQKYKTqAGAM1a7CEUx51jZXz/Lec51KbXq85rznFNKmuURU/7whBCLcw15zk/atgre/pwTYPuaaXjFX3wFjc1/bpHiw8Wc53GNkPWPz+dijTU2rZyCL7/1Ene98ZA3WLnZGkUJ4VwI85zbz7TMK0PXGJdl35cosFd4PefoHlRdC7By00K0FqVU5RAf7t1xgbs+w9hRwgsfAkNR6MOjcikRvvJOvQieLrpyVU+v39uV/kuViTddfix89Ev3wFuvOB42rpgq7CNL4hkg+BvKefd4V7Z2AFptwHUlRKFiDlSKlsp0t0XB04N9x6PPjvvv+r4KhxRTxIsSwgEAXHfREfaXAXAp58b9wQaQEusCnYOGB56cmyZ+S+9UJwG4b8989r2rS6dyXnAsNs5xbIdI5RU/ygKzSN767OPhSz98DE7bscY6zrcOhJaxzM/3/+5DmTCFovMPIce5KOfjCv0ytTpub/PxW1bCPY8dYH8rQh8NsMHAChhbA7SiUGG0wQg7Bl3Xc+F0SCEJ4ULBeQgNhbrHB2Ja96u1VcVzro/Qj7ZqQrgiWt404zlvdVJWSRyp7D3pxYOWxz8nOa2dWL258iz4K2+2dsf91EKh3qhe1k3aE6n8nXG93zrxlKWMk/nUNLzi5rVweSuowsoyXbwsFOdP+TFMxQLOc17GUheinNNrSc+prsS46kqHCNtV4bu/2KBUVWnFys2BxapipAlfyAdF0fyhp/Yec94fJVnDLqVm70UAlNZerg+8PtCQlLz98PZCjdizDm/dL158FPzixUd5+8DN5p7z4gv3se6482cDPedm/XTeIIS/LVU3nnVA+OZ89TlYJcITJy2sx4ooYP1br3C7+L1w6OawZTVv2AEo9pz7jF2KfPeB276fHee6Wtdt4BPC5Z+xAZhzVOCKH0V9UeCuzz5iHZx9BJ87xGQpuNcde2z2d73I5r6+ghJZD2hejjtEOR9T6AmpY2kB8o3ho79wLvzXj5+AZ520Gf7scz8AgPLCSa806iooUsDwe1dVQOI8IHEU5kmiffaX1s4odQ5vRxX4vJyhoKeFPAJ9azNa+4BKqU0zMecAKbVxuhFn70kvQrq+XoPWTjzn1IhCPcwWfSyAHaGt7DTDcxwp6GTeJf591eOhzwqPWynzvmhRhJZSszznZCxlEJQQTtnCru4HK+cHSyiHrrhR3zGRKqcYUUHFlVdgVJ5zbJipuq5gIXq+T8p5Gc85B9MAGS5slm27H3YUt+fcna297J6nlMokdZeXrKoy6RvLy85PwxquPMOmrZfpo0XKJwKE0trpGmwfP4cMB779UI8BwBMjb6zv4feTZQd6zsfyUdk5WMVzftTGuezzdCMeGH3YxepwyXsv2r0D7npgH1xw1HqmLfNvOk6fUTdSfE4XBe79ynUXuHbwWDBTzeU5D+2LIjRk0OdxLktrD3lHneMoyZCyxhMgQy1HiHI+psjr/yJKYfcdP+vwtXDW4WsBIN8Ayi6moygTz9XrxehlAeDa4D3n7obpgoUVnF4TwqXjMUMUQmLMQtGP5FO2ASFg4exetd6rsoRwjXIT7KRtq7y/G57zbrZ2gLSc2nQj7ovnnE0IV1AGhXqYufAF128auQfbVvyXMuWcHzNOCIeBx12LFCsU0XrwNCFULxUAQo5WTLu6S2wYK+O5Les512XUytF/88+12F1Kr4h50Au8ynlgKbVQtIpiTgJhVTLwrMWmkTX9AwuNlieoR2Wi34oJrXXsijnvxSOFj3bWOS/x/F1KFMWOtTPw2kuOCW4XA9/npY69toUoQ3aJLPss03PuvpY2apentdPcESXWCdZz7j4ev2Wl9/AKrvNfvPgoOHbzSvi/dzwIP3fuYbB3finvv4/LlUFrj/n5jm9rPY7gN553UmFb3N9n7FwDP3PWDjhs3az9rBQv0/iMs2Vizl3JBamBH6C39eqcw9fBC87YBsdu8ifldIYQgH9+cT8ZGdMDx5mda+W/ibJ8EUHZ2ntYIycZopyPKTJaO7Lsci+0TuJUVggbhee8XbCB9IMGyi34oUKH7TkvqZwXLBzYE0q9hmWTbFh9o89VBXIuo3xhv91jslJqi+U857e85kL49+8+AtegGt0ccJ3z1TP1LKGKVqJz5bwPMeedJEs2Z3nOGcHQF6sZYnDKsrWTTcxFlzbOjXTMufk9HnekFLvBUTq7kVQq4rO1hyLUIh6SRfbAYiu437Ix5/qel7k66pl01Tk3qNL9Vs49l4kNG6NI/OlCqbjHgidiJ4TrMSwIf+6DAGiXfOq+Y/Qd74HW7no/Q7OuU/TixS8DbaRul6C1Y8khhNY+F6qce8IGAcCqKFH1foaMBYtHw1BCokjBpSdsgktP2AQAAF/64WPZb/3s3kx8yyt5od25jLn53wpufMEpAADwt1/9sXUs108VWjufEC4/GO9DXEI4NqdG4E2PIgX/40WnFR5XY0LZNPwx54wBw0ORLxwH6bwR58p5SMy5z/mxnCHK+ZhCT8gllIyHW9hp3GjZ9oeJIuOuGZ9bDdyLTLOSukB/wsf2Wuc8Hw8q3xFYPiS87RT9y9ZejCwhXHealo05P37LSjh+y8pSY1vRrEOzFsGBxTYsdBPQ9YPWrrvooJhzurGynnOvhTr/XFT+yxeP6trEuHhxOu5aRL27+l/VHTPTX0Ro2WU95yGGHaZdrhsu2Y0LIdmMTc85HxbgA2WpuDyVVYX6smOgKMrWPiqUoaIbnnPmuzJe+BCYDJeemgIAzoinv+dj0ennEOC4VdxuVc95P8OsvP2olF7M09r5frHsQFkJ3Fix59xnrC5i8ymgCeGq3c+sPc8zxrT2sutFP7gtg1KC8LU4S6mFKqYBxlwN+lOkFHQUn2bdTWvnvy+MOTeUc56RwQyjrzANfyXWXuY7fHjZqUGfEWYVhMWc83vqcscIyM2CEOgFbamF45DclthJiDkv3Aj7YLnn4lOqes5xSEGvCeFo37bnvDcYVNs+sA4AQmntKfST1dnay5ZSKwJmXcw242zDW2ilxoB+eM5x5vmFLHa+oJSa8idOC9lY8lqlHuXcqdgrY+wamLlgKZDEaKWFyBrpD7c4EFq78scQ/sdbngEff+W5cPJ2f8gDRsg6yIW5lKOr5p992foNo00Py+2vXH6cPYZg5Xx8tviiGsUY+Bd9mKGwW57z3lbQfguALuq1S2mv0i8+3BVnXmZe05wTg4Iek862HtKXobgyOSMogmnthaF2QNZB/zgxWM954PMoOxeqJISz+yw2klSBSzYxugjszlbOw49V4KK1V6hzXhRzXkRrH4LR1KS1m7+VMWoA9Car0jWv7jDQuNAPx9MkYnx2boEBvYj4srUDAGxZlWa23LxyulT7/aZZhqAo6Zdpua3WB2ctrAVa6nxW2RCadtE6Qy3FIR6DYHgUxFDYC3jIOelBWng6WDFbexE6uAxfHGWe4YzW3g6PYXIhyHNOszBH/twBIXkFsmztlkfIpJlzyEupmd9jowKNOadjy+ucm5tgL55fPJ85z4E+xvfObV41BWfuWluq3xBaO6bsZ/OlxOVRYcF1nwyhvof3+5UXHwlf+7VL4XmnbUVjcB/f7FPMed/j5El7vrEZikLuO8/HVkLRD4FL0a0KaiTUTVLFspd4StNTh+6N8R6Ht+fKl9Bv6HFrxZhjSVBgycEXt68RnBAuIA9O1XWQX3Pdxxsx50w/vmSXSR985+Y70HNzeVsOow//jvsREtLgglK8jBYp9/U6mRzMd3gs1Wjt/Biqwlei0Jcsrt+0dquSTMn9CS+lQmsXjBx6zuIas9w8vuCo9fCx68+FYzf7k0NQ9CNRUFkU5RXqh7LKKfhmtnYfrd3s8ymHrYXLTtwER28sd29dUMbYFKE899a2kXSlT6XUQjQW3W2SpAp61WztRaBClLZGW57znmjt2tCQGxmoYkkzqqcbu1vADqEKaqHLjkdFnx3nuhJC4vsfOzznMTmXes56sVjj4UzVY6MmPT7GErZ6NBeHKOcA6bUutZOsf3p1tUg5BXd6X0IyEPeq6K6ZbQSvj/2itc/UY9i3EB7vXwT7WXuUc+47fG/JY+51P/N55avAVc+cUrJ7orUDP7+qes5dDJB+g+bTMfpydGsmhHPfQ43ZRi7a+uZZp0AoadZJFvMeae3e89FQ+BDGwfrSepmLoe26qpeEdkeXd9+7anvOFShGrVZWwTzcBt82G3NuGMuKaO2Dl78NzzkNA0K/zTZq2TrvGlaIk8EF6nQoW01EaO2CsYKetEtdoTa1+nHWNgVPOWwtrJwqR7seiec8gEKmUfUl5DaY0GzttM9GTcEHXrIbfvmyY6sNhrZPPvdz0emHIqCIlyCkmUw5hwSW2knmDem3ck7nTkZrz2LOGUGvJPS52HNOr4OPOXf/HpJHQQtdHGXe1a5GPfOcE+WcKGhe5Tyy+6BJcnqhtbtYFPQdAObvsmgEGmdoEjz6/oRms40jN63dVCZ7X29xC77mDOGnh/s51efQFCshnEf6MJXl7r+OtpTqx/2tLnxycOVS8MXdl31UuCmXIF7mvR1WVmTdj3Y8hMxRkznlXic1QmPOXQa4m64+A7atnoY/vWa38X1VY0fI+abn3P7ddx39oLX3ooD54KpmoSq8c2Vo7fRWu2ToKHK/e2VeA9f7yMlC3NrX71fOFd+f9p//PYNYJq55ZNrPyg3Uyg9S0gs/KKPRuEOU8zGFnrRZuZE+T8pReM6LsrX3J+bc/kxLJ7lgC07lXo+iRYteX1+ztROFoSpiIvQW9gtaoc2TwQH0n9ZOS601spjzVMDTRoFQzykHfb1L7U4mtFGrN+cR8yUMCvF2nnfkOphr1uCMXWuM7ynNnEMWM01+b2LPuaLKuTm23IhlKvS9zCkzJMRNa7ez7/b2HoR7zs0M+ca1KuUV+qzwFCdt02yzV4Suj4bnvAcmyUzflXPz77IZg13zsR97WQjDpQzoPNRzhFKTffTSIrgSNrqSwxXBaKPHBHs+6GG3ssoleB/k4c3Wzlyjma3dvSa4Ys6fdfIW+Pc3Px1O27G6r7T20OfBzX/fM+lPzLm//8rtOhiCxucB0NrtHDqud8zdeznafH4szpHA1zln1rc+p4Qz4/vdBi3MMnGhFyMifv8acdSTEXIEasvIILT2MUVGa+/G0vbb091vZT8ExTHnvVtuOWWo5hBcKOzyPOUGUXRLjc0ocm9UVUC9eZXbiVQWfxCyMWWe8yQvPxZHyhsfVwUvOGM7HFxqw1MOT2OQtXdQK+e6qkFP1969mHlUV9v2nNuZgl20PQAICl346bN2wlW7d9jGoQDPee75Nb/HCnFManHrj7TSQ0w8bi7abAjwtbpYFIppt9dlrhaonNMs9/RaQ8eB22l3EqeVvx804VBjCc3UXxX9NrBVNX6q7N9B3tve9x4M2oZun15zb7T2HDSZY5U2QxJQ9gN6TEulEsLln7nQIgrsOfc1X5QQLj0f3c8yCeGYjn23NSmQj3xrW3+ytVefiz645mMV5qDlOfexb6y/efU3Uu7rPX5zcSUZbmyoAnKwct5v+Ax/+L6F5VOqrpzjtalRi0obkg5Vz7ko52OKnGLb/bvPk3IUWQ/L0dqrjY97kQ2hw0trp8JjdWGp6Pc00Yxif6sC3FY/kqKlbYYcnx6UJGYZtX7HVMWRgpece1j2t/ZMaQEv95z3rpwfQAyABvWcM5Z7n2U3dGMp8rK4lXPFtj1VMz3n2Ouin01Ezo19nvOyikOIcs4IRb2uc6G0dn3fuDrxqWHCDc6LFysFbUiM54Tb6MfrENpes18x5yOktXNQxnvWX0XSvLf9UfZx3gL9GPye8+rvmJk8km+/CK5KA/1G5njQrMCAcpNGtvaAUmrYc87lu9AIUc5x62XuJ3ekb64WjYQaJYxz++A6NxNv9dxcBpeSWMU5UYZpRX+LFECHOZzL1n7dhYdDkgC8+hlHhw0MzGujCWztsXHjDe4qCHjP99HaQ+Y0XrbKevjxWtKoRYVOOqvvkjT45QKhtY8pfC9TPzAS5bxUQrhqfXCKEvaW+zY4ek/KKnplxuzyrFRFvzxJZan2meccACWDG/yyopXmRe05z2LOq/etb5veO+qxTbvm/vYlDOqFDWLQUx3zg6NlA9gJ4VijlTLHZZVS62FO4XPdMef2/e3VMh5Ka6eec1rtINTDZcXtuzxDfaAsupRTCoPW3sP9nAmgO5ZBmYRwGMp+RH1jCtE+0rb7szdywnGTMnF62PNcBtmQdYODwdQZgucc09qvPGM7AAD8kkMZMrK1O5LtYeA9CIdbURSF2qXj8/flQlFG77IIZQVVxaA8lK77VyUBcJn9gv7krGWu7PX5pG2r4G3POQFWlSihi5svUkC5NavfXmHDYEeaduUBcLdVfZ3Ca1OzFpVmeZjzp+TJEwzxnI8p6AvQ70k5GuW8KOY8/1w95pxRQgKthFQg6XeNYJ+w3uu6jE/vKWN5yUU4V2iTjA7e72RwHLR3cDHznKf/+owvRbBqhTMlUOxa5PrflNZsl3hyt18ELGC6NtCsDBtpG9dWjSPF1jylCir1nOEWe2GRuGPO7XH3+h7UHWXbKPT9yIwbhNa+eroBew8WZyrX48/i9x3vTz+WklDhudmnmPMNK5qVz+XgC9vwwz7OjI/u/eYOIhlWqiR3jDanyPxUPawPpuccv7uAPoe3GZH3f1DI8+nkCeHe88JT4JcvOwa2rOJLwprZ2osVNHwvD/qU8yDPeTWllfNm++5rkZ1gkHkAAMqz5kJhGqD49TG0uzIJ4exs7fx1cZ7zKs4SfE7RvMJjO2rjHDTiCM45Yl3pPn3wxpwbhvjitnrJB4WPb9Si0jEYhyqtXTznY4oij12vOK5k6bV+gCb1ouhPzHn+WTdn1Dn3CHKK/FQ+5tx/vI/G1evTxfeuX7WNwzao9JgOobUPGpTWrr0wvQjq9LY1GaWStp9RxInCS39PP5cbz3TDpKZzCCulFrFK49rZVPlaM9Mw2tL94SZ7eRd8xhqqtPa6+YaOUyut+f1Dv0UKbnrxmXDStpXwwWufYp1rZFUmHnOXMab/nnP3cWXryLrwlmcfB6fuWA2/c+UpldvAoM+27NjwdbmS8FWGcW/7s9dyoSTTDXf1h7KX4TKemQnhwtsbnuc8/Vcro7o6gksxx8fq4zGKrvHgUo+0docRpAi859x3vH8stAyfcW5fEsKV3fvD4JLrquyN9P55Pef0b+Uy5BSfGza2/Kyi54H7/P+96DT4p1+6wAqh6xWcQV6DhtMUsUT75TlvxOU9572skZMM8ZyPKfpdYkjjb3/xPLjlG/fDay4Jj6XpF152/uEQKYALj97A/m7GPFW7Xs47HZyt3WNdDEHx0W4LYK+boSlAVF/kXXVIi/pNIMk8FFQAHQQorT2PX+yPYQLATKyl4Sp3lj6/xPKMmDHI5caGacUur4k2FtBHbsYd81b0Z564Cd73M6dnFnuam8H0/Ja1luefG7Uojfcju7JSHNW5VDcWQgWcvHxd1B2LqZgcu3kF3PzqC9lzseCVJeTj2Ad9FiRChWezznn1G7pxxRT8/Q3nVz6folda+ysvPgo+c+dDcOUZ2/tmjMz6YPrrFTUiAAPYhsteYs7x8a4EaGXaxGvMIJl1tgGz+BzznSu3d/o8565Saq72e/Wce2POC4bieyZFin0IBlVKLXLM8SrztMzcoUufUu4651xm97LAQyuktRPDxCBiqX0x57TCTLMWw1LbzRQzjColx4GfWbMewVIAIw2jFzlkkiHK+ZjCshD2aVKesXMNnLFzTV/aKotGLYJXXHSk8/f+xJzbi39onfNePTtlVi3bAlyuK6trdH5PCeEMZTLg+G7HnU7uoRgGrZ1ma9dVDfqREE6D85y7GC36a99zLStA4YRcLuGlnsWcm7/7POe6rXocwXNP3Zp9T2NVDW9VWcotmj2NOIJIKUtgSYUi87xRxZxX9Y5RxoQrjrLPzl0vmrVixsUoYBk/A8emj1o724DPvuFiAAD4xo/3ZL/3w3A9CK8h58Gmynkvex4+fjWKjVVKZcawMveGCuyDQpUkkAatnbzjRef7lPOiJLUAlKFQeHgGTj/r5b769rb+1DnHnwfz/F1KXmhvvpwuFHgP0oexdc5VuVj2kP6Kchn0Q9YtghlzzsstAOn7M1WP4MkFd1u9jBf3VcVzLrR2wVih31mMJwH9oIEqxvvuovxR0J/67T1wUboAeqe99kuwKrtB4yOGGXOuY4szWnsfPOf0ckM853lyNfPfvM3q48HKueu6qHFAw1DOlZ/ilh1HPGdYbi0bu4wvu1mPnHTCUSWEozHnlNbuA/aI5WEN9lrDhdj0gtA2DFr7gONUy8AKYejTu9qPazTXvZ6bAwDeKz7lpbWX7Tg/fvWMmbhK73PlsrXj8wc3b+hlhswDIyGch52EoTO2X3D0eme7rY6b8q5R1XDHwfeMi5SWYZZSG5S46WIhBCeEszzAvr7wcV0jLHNcFClrDaly+abn3H8sN7Z+w3hPSBc0DxMn62BUeVZcX5WytQ+I0THuEM/5mGLQ2drHEf0QkEwlNf235slaiWHVOS8Zv1ykYCvP9fW6Pvct5ly5F3Rfv0mSoJjzIWRrj3lae6hyxsGO2y7jOe/+aymb6I+SEhSmtbvef1dCOJx4KlKkzrnjwVIaLt5Eeyml1ohjZ1xfmQQ/FO//2TPgf93+Q5ht1uCzdz4EAOHMCX0/82sOf3+4x6inndvK3/v6HWrAa8Ruj8koQedwv3Jj9CVbe8WkXz6wMeeebO2liVroeJpVOooAoF3uWnyetn6iSsgeVyFBw3X6/339U+Grdz8Ol5242dnuzrUz8L2H93v7Nt7iEvelbMx50f7ge1/6UUptGApjrwaAMsZcM8TR3acCO56/ytjw3Ch6Hr3EcIfCZ/izae1+uclVIrTsOJq1uKc651JKTTByWC/TIfCk+kFf4bzTG1c04fyj1sFPnr7N+3L3SmsvM+R+LzL9su674sOK+u0kOX1wGAnhmtRz3v23p2sn18ttWJR5ofUgfapt2UcbdsnxzAQkhNMecmqUwJ7zVichz5Xvz1R4/AJxEQxae83lOVelaIoUV5yyBf76F86Fzaumsu/Ke851zDkaQ6Hn3P4uY044zu2HbTW0DRyOUdZLMUhUzenBrZWheURC4TOcVgXHVrFo7X3IswIAsIp4zovmIwds1xpk1S46pJAxYk8kfcdd529eNQXPPnmLt/13X3kKPPfUrfA315/rPIbmowgFu070MLkGn629/wYqu4/8c5VY/jK5egw/g3Ifr5SCeo1+X/768bWVydY+qHtdj+31R4Nmay/K1aIczy0ERkK4WlRaOTfZsOXOnWSI53xMYVGsDwGLUT9ooFzMeRQp+N8/f07xuYF0OReKjvZ5vnpV1n0Jc8qgqoU0gbyU2jASwmkB7Rv37oErb/p8VvbKl9G2CPS2cVQvl+Vez51e5xBGCK39yjO2wX1PzMNPn7XD+B4bFpbaHW9ZFQ0jW3sUmbT2HgxVzW5COOsY1LYWZqq8B7jtsjHnXLb7Ys+5u0SSq/t+GONC28Ce85CY2mGhqiGGO6ps+E2ZPgYRc54lhPPQ2sv2+yRKrEQ953HBfOTQbzaCC72wZQAY9lIPz2vTyil438+c7j2mTMgLBrdO+J6xKyfJcZtXwJ0P7MtqwfPn9o5BJYQL6SP0Edr7b1hf+L6/8bJj4V//+2H4wg8ey9qgRvcqUwqvb8XKOeqrfFdBCI05j5SCZoFDpZdEp/i+VMlIHw/BkDGOOAT8sZOJQWVrH2f0IymPycru7Z71O+7Od0m99tTpwcuJQbOIFiFLCJcAHGwNL+ZcL/L/8f3H4Cs/ejz7vpe6znZSNXt5pLTpTAjWSrpl2c8/l6UeTiNau8ubu2vdLLz3qlPhqI1maUQcktFqV/OcYwGjrABMa5v6StiYwm+pbtJ20NsTSmunMbn4rKK1lk301D0HzyG8fqwhns0qCH0EWDkPyUY9LPRaDQPDMKb0waPYSx1fF2qM52qq5k4It2llubry8yjRGTUkcgkKi1CWNVUVtOmyfZXN1t4zKs6NXWtnre98U961PXzs+nPh4688F37y9G3Oc7etdpehC4XLq91PuOSz0N7o/QstpYY/3/C0o+DXn3eiMSa6b1RLCJfjpecfBgAAFx3DVyYaVNk6DNMgb/5mKNsBtHauClKVcTRrUWk56FBNCCee8zFF1bIzk4zMC9nDpfbT+tuLt5D93fNbr2ELWJHyJb0rgknvLL5+fUiS5Nnai5KL9AMuC2y9T6wBgHKec73BUuWyl80Ee857MRRRz7lrLbGztedzquz6Qz3n3G0wk6kl6HM59OI5rzO09iJlz0dXxQpoLY7gI684BxZbHVjdrSXfC0KFojIenGGiKquEmxKmIal3H8Ogae16rk81zLFGSsEHXnImfPKbD8AveCqZlAWtRhACkzkzOHnDlbcjFFa29gHLRrj1MsbDk7evgv/506fBbKMGP/+/vmy1ReF6U1dM1eHMXWu9ff3OC0+Bd/7Dt+Da8w4PHyDFAN4BCi4nUPo5dC3IKxGk7fmPzT+bv+G9PVLKmlNVLh/396LdO+CkbavgqI1z7LGjjjnH+1SsSirnJceLz23Wymdr58ITDgWIcj6msCxdh8CszGjoPVxrP5NHlD2/OCGcz8rb21iTHijIGGU3jfx+J30pZxYKlxLWmzfO/JvznFPDBzUoeWPOSyeEKy6lFoKlTodNjkNBrem96HW4j2Yt8gpfvWWsNt+r4Jjz2FReerXOZ8+fXKeuId8PVJnaRSV9homqStkxm1ZY3/UrAaYGbqFf3hluXjcYxfKyEzd7k5b10ncZuSEiAvug0Csr0K6Y0fOQvChrsMZ43mnbYM/8Uva3723sJanbllXT8IGX7K58PsDwY85D9iQOcaSg0y4Og/I9N6yMKuDYGP4xnLpjNfznPU8Y31HmwYlbVznP73dYDgdfhRYsxoRka+/FmICN3Y24fMx5r/LBpEKU8zGFnRBu+U/KXNHpj4I17Be5l+56HWq/aO1ljRv6kE6SU2h78dyHwuU5L5thH4NeL+s5d9DaQ7K1l08Ih7O1lzwZodVOysecK7sueRngPly0dtxXfl75vnDb1bO15yiMOWfuy9EbV8CPHj0Au9bNBPVfBVXuzTh5zsvWOf+3Nz0NHtu/CDvW2vcUv2d9ydbe4xzkUDOy5tv94O97AZeAM3asRz5Q49ygQO9B2bVt2KxCI5N/hb7weMfIVmZhKMq56/6V6E4FMq18yj/1FFOjbtHl/8nPnQkf/8q98PnvPQL/9p1HCsdCYYbRBJ9WCqGe8yhSRhJRDr2EPOC+mvW4tFxhGA0PAT1IQ5TzMYUdnzeigQwR2SX3pOQOftEbBHodKl7wernusgnh9DFJkkCra80eRm3lpuOF6MWLRk/ls7Xz76WL9dG/OufVF4CldhKU0CWKFCiVCpBxrHpS7PBlNxwJ4XC/+djK368qtHZa5xx3W1jnnPnuD372dNgzvwTr5srFDQ8a+p0cB5Stc75j7QyrmAOQPAV9UCQGYdSlpQk59CPelCaDAwCUoLCacj7IOudl4oY50P2FshH6jV7nGj7H6zkv3XJ/YSpgg+nDWee8hAQUWn7QpEMTwzsyaC110v0R0+WLxrNxxRS88uIj4TsP7kNjqXYNvbImXcB7oW0Qw4b4tNypD1FJudDVVyMuT2s31+aSJ08wDgGVbzLhs3QtV/Qj5hyfP2y2QVFv3kfY4/PFelQvAl9ZCpHuy/ScD/6+26VPUvRiGLCobwF1zqnH3CcM90Jr70X+XGp3gmsYZyWYlOrJy4N7aNZif589ekHxe14+W3t6fK/W+VocDVwxr6I0jnMptZ6qSmBae18SwvGfe0HNEI7R931eH1dO2z6WTSvT8oIbSszJYXnOe6e1m+/4+gG/d2UMdxxMz7n7fRz1qzoI9gjFsShExaSdh7cROk99xgZseF9speF4BusucDxV7xmewoO61z6DvI7dT8cS4jmvbkA365xXKKWmlLcU3nKFeM7HFIq8K4fCpOwHrV2f30mSgZWoqIrQzKJVsGNt79laAcozD/ThSZJAq9N7rfFQuCy9vVDq6fPh+rA95+ac7Scd0KC199Buq90xlHtfU3GkoNX1JPSi2BnZ2mM/rb2XZDMA5rtTOVt7CQF8ZEJ0hXszVrT2PtKR+01rxzd30J7zRi2C1mKbO6USVk7ZnvM/+NnT4cePz8Nh6+2M4S7QJFGDAn1cZZ8fPX7dXO/JFn1QPc6NSaHiDjIk8OZXXwDff2Q/nHV4ntiu6rof7uF3e3vxu7mkc+VEChaZsfVnLPS8wRtCfNnaAfLY/UgVJ4TrJfTM8JzXIqjCEYmUgnaSHFKec1HOxxT9LDszKXBRg0u30+UnDd2gUdCf7+deH+/GFVNw86svgNlmb6801m1CPIH6WSUwXM+5K+a8l/eEPh+uDxrTbiWE8+xxrjq2LhgJ4Xq4rpTWHuY5r0UKFkAbuCp3WYrWju/ZsBLCZZ5zTWtH4lvRHBqVN7rKvRmnUmraA6JvXy8KYL/jYwfiOfco5wf6qJxztPYtq6Zhy6pyBtv+Gzx4WHH3JfvCBrjVM/Xgd74yDMNd+dMnRXQbZMz5SdtWwUnbzARphmJbhtYeyO7zxUnjv7Vynu7t7e54wlD1ng0jvr/oHdZVUtJs7QW0duNZlQN2mDRqUSW5IlLpkzmUEsIJrX1McSjS2vWC2eul5kp+jwMaIvphSDhp2yo4vISnhANe0LcG1E7VRycJZNnae0nKFopBZIS3S6kVx5xrwTLEc94Lrb2XuLSlTjnPOUB6nT0lhEPjTUupuTsNjSF0AZ/jMtpQ1NB10jaGYVyqgjKj2tmN1b7i5C2DGUxFGN7ZHpaJXucMxSCEZa7OOUD/Y6Q55bwKQmLk+wE7NKj4HF03+qd27zDOXzc7WK85QO+Z/PHaN2rqug/9LEMbhmre2JD8KWmbYccttOwqM6HjiSoalocR34+VYl/5z6A65z1o5/geNWtxpaoEg2AmjjvEcz6moHNwCAmwRw79/vf6+o3qRS7qzff7uCw5+J5tXT1VeDymtbfHwHPeCyxaO9OHVQ5K5Rsc10YvwLR2bd2vgiSBcM95V3GIIgWdfiaE8zyuXhPCYYTOvbwOtF3nvGgMo5Kvy9ybf37NhXDPYwfg+C0rBzii8sA17XtJcmiGffXBc44+96+UGp+Qqd9r18o+Keem57wvTfL9kNsb4nh467OPh+ecshVO2b4Kvv/w/uz7QcebAxCltce9rSx7apgw2SOD38N9nm3/eb17zjGWukkzjQRqgWtK5ZjzIXjOsXGQ28qz/DIRn18Ho5fxYiNBPVaV3gDd5yGkm4tyPq6oWhN2khEhS15P7RCq8bBQtHCE1uQcJQzlPIAWqY/vJPkmNwzPeZGltwrofGGVc4uSaZ7bz/cU11k/uNQbDTY0ZixXWlVPmb5tWrvHc27ExpW/f7iWdz1wXmhWwmzGTsj7LUowNirvV5lbM9esjZ1iDtB9X7pTuV+e837ANM70p826w8PXb+X8xK39ec60vNKg4KMYu1CLIzhz15ru5/z49SsGr5zj4fU678RznsMwBpQ4L3S/UJ6Yc4ylLCFcBc95xXXDUOrDTyuFokSEWRJbpeBslAuAQ9VnpdvXqMXVEs32K+R1kiDK+Zii14ymk4h+vYD69GHHnBdZW/2e8/F4vvsWlrLPm1cVe841Ehiy57yg9EcV2HXObSE6isySK5nnnHjQ+z0eTb2ritA4vRrasPtHay/I1t4j1bmNjAihlOFrzz8cmvUYfvKMbQBQNhnSaCTs8VgheoNJR69+RaHU1lAYgnzfPOe8stsvWvv/ue5s+PIPH4crz9jel/bwuj3OCeHwONcPg9aO14Zl7CQZZEI4vr9+eJ3dx4UyARazhHB8dQUfTKN3+EWYYWYD8pyjm8MlBs1CuiIFZ+5aC3/58rOzcCiKqiwHgLQsa/Y5iqrR2pnws+UOUc7HFHQSHgrKuVL9eQFHRYHZUkQDD9xIRokH9ixkn6fqxQqwXjSTBLJs7cOoc+4qpdYrsOLt8s7Xoijb0PNs37lS60KVTUljsa/KefFxUaR6Gq8ZZxb5kyH2SGvHSc9Ck0Mdvn4WfvXZx2d/Y4GjyLg0umztY7JI9IB+JR0zBe9eRmS30S8BEDOIcJv9Yv2cd+R6OO/I9X1pC6B/z6awH/LAyhoC8NiGQWs3kkX26jnvdTADxDCo1v3oLzwhXJjyv8h5zivQ2stcg2kMDD6tFPB94mjt2HMOAHDB0b61pLoxNCZ7ay+09kNBD9I4BCKZJxO4th/AIUJrzy64t2sdNgXmz6/dDa+46IhCD4bXcz4mj/eRJxeKD0LQw04Twg3Tcz6YpYtmVebAecRiQm/n0ItgttAqT2t/x3NPyP41y6oUe86rbqJZH2Dex1DPeRXFBXsFqq6T+Kwi79ioBOwLvcLTZKDXmvbZuQYttPf1pkzOgVC43rlB5MvoB/r1bIpA7y8tG1sEbIAbNq2919syzrT2fhu8ijtkPxYilEoeSsXOlPMKnvOqoQCDWG/sPvJ2OUM7zZfjbwt9LrneUmPKT56estVO37k6uI1Mpj8E9CAN8ZyPMXRtP/15uSNXqnttZ7gUmKcftwmeftymntoYF1p7WeSl1BJUSm3wwuegBFycsMpZSz1WAF32/yBp7RgHl8p7zl96/uHw/NO2wZrZBtz23w9n3/tGiD3n1553GHz0y/fAT5y6tXTfmBLfLCilhu9ZFU9YP8qF4fW12HM+Ggn7KYethY+/8lzY4aAeTgL6VUu733WCjVrWfVpazGztE6acD1DeoE334jkfRrZ2jOWdEC4vdTgMJagyrT3Q0Bzqmc9o7ei9DB2OmcS8hOe8jwafEHAhatleH3DzI1M7LwX8vtYiBb/67OPhrMPXwoVHbSjdxiGkm4tyPs6IVJY7Z6DZU8cFuIxTL8hLso3Xm+xbBMdsqEYyMh/0uDuI1h4PgdY+MM85SljlEqK5kkNhtPbq46riOQcAWNMVXkPLJGnDSqwUrJtrwn+85RmV3iOcXb44IVz+eV0F5bzd6Y3yDwClWEqjFK/P3OVP3DPu6Bd12hTQexqS1Ua/9g1XDPfA63JXxLASwvWa7Bbf1zVDUM7x2rXcGYzaITSUhHD4cxlae6DSHcoEWMpizsOUfoyq65CZrG7wN5untaf/hixHeIRl5wY+Po4VTNVjeM4p5Qz+ecjr8n7/MEQ5H2OYZWeW/6Q8dtMKeM4pW7KsrFUxrpkdvXG3YzbW0GegR50kyXBp7QP1nKdwUayNsmQDzNaOsdhDKTWAcC9Cnq09/buqomIo53F4nfP1c+WF7R5vjYVC5Xx8nV9jj0EogP1Qpg0vXs+tpYgdNNlBVJroB0ZGa+/BE7e6T2XkfDAMd73GnI/52rFiqgZ755dgpj541cBMMhZ+Xmj+FNOrbWPlVA32HmzBeUeuAwDKdAkbi+EBL8VrRx+HsBywnvMAh4JGL8ZLfHzV90ff2nFzuA0SopyPMYadoGPUqMUR/MHPntFzO6MqpVYEn4V0XB7vX113Dnzo8z+A33jeSUHH68UyAVhGtHZ/H5xHLCQJYS+UxoUKtHaM0DIx+rhealADACy28mutxYOmtfeunUfMM3VhVLT25QBKcewH+tHKIGJA605ae/8rTfQDUR+E6LB+zL/L9tVE92/r6uJyn73CDHkYk416QPjDq8+AvfNLsGpm8EYP0yAWfl/D5WK/J/yW114En73zIXhhN1eQUec8cDiVY84dnwcFTjkvU7o4tCxdEaqu+fGYyvSDhCjnYwyDDnIozcoekVvZRjuOEBy7aQXc9eA+eP5p20Y9FAAAOPfIdXBu15IcAn2PkyRPzDWUbO0DoobiOROSEI6GYvjGVUWne9sVx8N7PnUXvPvKU8qfjGAIEZ61hHrO+4XQrLqjijnHo+vVMCFwA9/afinB/fCmmLTN/ozLTByZfz+okJxeYXgOByhv0OdVVrZp1CL49OsugiQBmG0OXoQddnzwKNHP7P+F6IPn3M9G9Le/bfU0vOScXdnfpuIYNqCqMedmG4OfVL5SauU95+X7P37LSvjRo/vhtBJJ4Mz+TSfIoQBRzscYvZYYOlQxri8yN5yP/+J5cNcD++CMiovWqIHvcZ71dPD3fVB9hNDatRCrVD7Xrj5nFzTrMZx7RLhhIwQ/f+ERcO15hxllmarAyBwdcFyv785Zh6+FC49eD0dtnEvb8zyvfQeXss9rK8SQ4jrnVWHGnPuPFb95dZg17cfVc96HBsE01E1CQjjTcz7Ifsy/qxhXjtm0ok+jKYZpuOvtxpy+Y3VP5y8nVA0lCZWL8bwKmWK1Cp5zM/497Jy0/XJj6xVszLmWk0tma68iG9z86gtgqd0JKs/LgYYPHgoQ5XyMcajR2vuFqMd42fTcPg2mAHPNWs8x9qMEvk06zngYtHbu2T7juI19aDf/XOQ5xxvzT5y6tTCreVWlrlfFHCC8Nuyqbgznqh5jOeNIwV+8/GzUp/vYxw/kynkVxaXdB5q5mfRJtPNBAQuCfaO196EZU1gegOccNbl2dvCU4SoYRcz5JDACq9ayxvjXNz4Nvvfwk3DeUZNfDrFfqMpWiQ1F0X2cWXKtuH3MaAkdTdV1YxBMHR/YUmolDPG9Jq2LIwVxVD2cR8taEnMuGAuYtPbRjWPS0I+Y80EsmMtxYcGX1BoirZ2O4Qu/+gxYN9t7zdsOMjE3HaXU6l3lbZLiD0OT6Lz1ihPg6cdtgvP7LET63qfH9y/21DZH2SsL0zvmP1Z08+oYREK4fqzVprDcc3MA4K5z/gtPPRK++IPH4LkVShQOEkYujQEaWKvG6Y4KJqum2oB3rpuBnesmtwTiIFC1lJoZLuLxnJeMkzbzsoQNaFLmMrdFlglh65XW3itymX6Mb3KfIcr5GGNYluzlhn68yIO428vxCeJNbGmItHaMSCnYuGKqL23h+OUiz3nZyxxlHrHYoShQHL5+Fg5fP9v3/n33qteY8X7EnIMhgPulFUkIVx2DqKXdH1p7/40GrvKFK6fq8LHrz+tLH/2EWeZuOP1MgrBtGO4mYLyTgqr2n1BvtalQFj83zPgLXQLKeufz8VQ7ryo4A3YZOXnY43X1fyipQeKPHWP0g051KKIfL/JgPOd9b3LkwNeky30N25DUT4Gp1S5WzrOM5qX7HZ1SV9VL0S8MkjXSjzrnZTJVi2peHQOhNPeD1o4+92tYcVxe2B8lQutH94pJS3Q77PjgQwfV5ltonHdZby+urhBK465e5xy1MYQ5xZZSyzznxQMYRKnJMigz1uUCUc7HGHgRGlR26uWIbCHpZScdwBqwHDd2vGjrmPNhz9V+MjCXkKLn2ggyz/kEbRRVLfyD6N+FqvWfW/1ICIc+F9LaRTuvjEGwwXqNhwSgxqv+jKs+YR5ivI4OK+Z8Eu6LuTaM/3gnBf2pc+4+saxCGVpuFKOsd547dhjhjtyeVUbhxUeMIjwzL1V76Lx/E6fxLSwswGmnnQZKKfj6178+6uEMFPidmWmMZ23UcYS+b715zvszFox+CJHjBnxFmjk1bAGmnwJeiOJVm0Ar7qhj40KeUZVM7QB9ijkvISz1Uq/+UEc0COW8H57zARivQks+jQswrXeQa5t5rwfWTd8wLKPFoQZDsS1xnhkWEdh+wAtYxanQj311ZJ7zErT2HWvTfAnT9Xgk72xegWn4fY8KE6ecv+lNb4KtW8crkcqggBcUUc7D0Y+Yc6G1h4G7pmEnhBt2HCCXrT0Eo/S4VrXw9wu+90lXK3gxqjlbBqtnqin1GGWYBeI5r464xH0ORb8FtkGXUhtXDI/Wnrc9rmXlMFRF+rXAj6pslThwLyurONcrlFKrHHOOPw9hTvlo7SHjnqrHcMdvXAZfe/ulI5Ef9KM5lN6/iUoId8stt8CnP/1p+PjHPw633HLLqIczcGAr7Uxjoh7VSNEPK9uhswT0Bm6xHEYpNYxhr9e1itnaR6nUjT7m3P3bn1/7FPjqjx6HC4+uliH+1593Ijy5sAQvO//wiqOjArj/2N98/knwxr/5L3j104+q3N+hinGltZtevP57zidBqBwerT3/3KxNgNMBjVc85/1DVa9z6HtV9p12JXD0oXKIxpCnEVvnvES2doDR6iD9qMA0aZgYje/BBx+E6667Dj7xiU/AzExYSYqFhQVYWFjI/t67d++ghjcQ4Ik425yATWxMoO9bb3XOB+A573uL44lhe86HHftdNSHcKOnQVSmE/UJRbfWn9VCjftvqafjIK86tfD4AlBLAr9q9A55+3EZYN9d76b5DDeNa43oQxitT2O9Pm4OEma16OJ7zqnkmhgl8JybByDKRKHFfQ5V6o5RaQPO1Sp7z0Rq9Q9FhtPPjN6+Af/vOw3DUxhUjGFE5aHlcYs7HDEmSwLXXXgvXX3897N69O/i8G2+8EVatWpX9t2PHjgGOsv/AL754zsNRhq7jwiDWgOW4sPCe8+VNa68ac755ZX/KvVUBnnujMBGMu3JiJicqHqwo5tUwCM95P4g6g4g5N4X9MX8BYIiec9T2RNDaR5yvY7miagbw8IRw+eegmHOcIyI0W7vRR9AppdrvF9oMbe/NzzoOvvK2S7OwsnGG9vccSsaxka6Mb37zm0Ep5f3vzjvvhPe9732wb98+eMtb3lKq/be85S2wZ8+e7L977rlnQFcyGOBNTDzn4cisbL200Z+hmG0uw3WFjTkfOq19yMaASFtxw47/s2t2w5VnbIfrLz5ygKPyAwseo6DXj/umKgL4cFCmZF04+kBrx2ENfVq+Js1zjp/HIA2sBq29Pv5yzaSVfpsUVDWIhZYvUyWV/0qe84qhK8PeDjlau1KqchLWYUNo7UPGG97wBrj22mu9xxxxxBHw2c9+Fm6//XZoNk1vxe7du+Hqq6+GD3/4w+y5zWbTOmeSYGZrF895KLJs7T0IWZNUJmuU4O7S0D3nQzYxauNDqKD2jOM3wTOO3zTIIRVi48opeP5pWyGOIlg1XR96/+PuORTq6nBgCLN9em/7sdwMwnM+iDYHCcMjOaRSalOT4DmH4dyXQw2VS6kFxnmXbd+scx4GFTgW67zgI/uDozfODbnH/iJTzg+h92+kGt+GDRtgw4YNhcf9/u//Pvzmb/5m9vd9990Hl112Gfz1X/81nH322YMc4kiBX/ZZUc6DMbbZ2vve4uhB75NSw19ANw2ZLl41W/uo8Xs/ffrI+h73PXXUpeYOFeB0FP1i2PTjNTQV6d7bS9upJriPCkopiFTqZRvk2mbEnE+A5xzfiklb88cZVWntoRnSy8ac95qtfRxnxt/fcD58/Ks/htdfesyoh9IT9FZxKL1+E6Hx7dy50/h7bi61Ah155JGwffv2UQxpKDBizoXWHoy+JITr01iMNpfhykIvqT5ESvuHXvoUeP+/fBd++8pThtYnQM4MOJSsuL1i3OmgRsK8ZfiejgvMeNERDoTAFOT7MzCjzvn4O4gBIB1zp530jdXAAd/eSUsIN+7r2EShouc8uM45mlohxjGcyDZ0DaiaEG5YW8ypO1bDqTtWD6ezAULf50PJODYRyvmhCjwPxXMejn7Ep0i29jDQ+zRM4eXiYzfCxcdWz/LtQ8PDlZ9Uz/koMQmeQw0RwAcHvDb3a43tx9waNK19UtaK9NqT4XnOJ0A5r6pECvyoXuc87Dzl+OwCdiyEjiZ3BJW7hmEnhJt09IMNO2mYgJXRxmGHHQZJksBpp5026qEMFK12nsVBPOfheOGZ2+GMnavh7MPXlT73jZcdCwAAN77g5H4Pa9lu7Pi6hl1GbVDwXYdOHCOe83CM+9wXWvtwkBm2+nCTn3ZsGhJ39dm7em6rrCAfgkmjtQNUr0RRBpNW53wwSQwFVd+5UPaN8c4N2HNe9v2WaVQOWtY6lO6buGPHGAdb7ezzzATEZo0Lrtq9A67aXa1s3g1POwpeev5hA0nAt1ytpamvJcWwk8ENCj7hNBdghzWaycf6MS89JrT24SDqo/L3Z9c8BfYttPqS4HAQyrNBa5+QKRUNIWQHt92sj/8iOixa+4vP2Ql/+R93w9Vn7yw+eBmgquc8NEN62TwSRsx56FgOwSzio0CW5HlSFtI+QJTzMcb8Yq6c10QTGBokM345REpBp1ufa7nM03oArf1Q2ih6xesuOQbOP2o9fPjzP4TPffeRUQ/HwiR6OScR2vPYDw9kFKm+VR4o6WQLQmjiqnHCMEJ2Jo3WbmTkHqAW9o7nngg/efo2OGX76oH1MU6omkzNyNbumT5lE84Z2doDB5QlKivpeJmQ5WBscCgaQcZ/ZTyEMb/ULj5IMDFYrguyQWtfJqtn3UdrF+W8NFbN1OHSEzZ57+sogUe1TKbwWCIegme2CoyEcH1iOE1iqMSwae1TE8AIHFbugHocwZm71noNw8sKgUo2hclICXseIcfh6hGhe7s+rvy0mJAFYUxwKJZSO0RWgcnEwlJn1EMQ9BHHbV4x6iEMBHjjWy7JtHxlnuJ48ALscsW4UsYHkRBMYCPLujtm784gsqlPIhtjy6ppiCMFG1cOLgxl4jzn6POkPMdJgOk5L0FrD3yvyhrHquTLUZlHVzzng0Q/KjBNGoS/O8ZYbItyvpzwy5cdC/U4guecunXUQ+kr8HK5XKz+IZ5zSQ60fDAs6uqhDr08jBvDZhCjmcSY8w++9Cnw6JOLsHHF1MD6UIZyPgme82oeXoEfZeuQa8RGiTRP+yWV/17qnJddziZkORgbnLh1FXzmzoeWrYOLgyjnAsGQsGKqDm97zgmjHkbfYdD+xkzorgpf7Hwc6WztwxrN8sEkzI5lMoXHEuOarwErYP0aGqZsT4rHZ/1cc+DJG81s7eO/iC7H/W0cYHjOB5AQzszWXtxurQJdPqe1y7wYJF5zydHwsgsOgxVT/ckxMgkQ5VwgEPQEvAmOm0esKnwMgPoQ4jKXKyZBhhk3xXE5YWxp7QNo88gNs3D12Tth3WxjAK1PLgxa+6Rla5e1oW/ASnaZuxoFGtLMhIzF7dZrvdU5LwOZRuVxKCnmAKKcCwSCHoH3meVS59xHa9cx56LELU+Mmd64rDC+nnP+c29tKvj/fvLk/jS2jIANM5NHax+vebtcUIrWHhhzbrBhQmjtPSSEKx1zPhEcMsEoMf5mS4FAMNYwPefLY0kJqXM+bgrGZGD875lQFAcH/c6MmxGvWwlSMATg12siaO3os6z5/UNUUnnOzguktQPkcy3ksdWqlFKrWOJLppGgCOO/MgqgsUySbAmWKdBGs1xo7b64Sx1zPm7U3EnAJAglQl0dHIZRR7sKsG4uxpnBAitUUkrt0IVZISP8PNNzXtBHdlxIQrjyNHvtixCjjaDfEK1vAjAJcVmCQxfLidb+/p89A846bC38xvNOdB4jnvPljWVC/hhLjGudc+zBXT19aMU2DhuTlhDOKMk1/sOdGJgJ4cLPw76qIkNamTrkBusvcDyqRPvGeeUOFxyCkJjzCcD0BFiXBYcusKA96bT2K07ZAlecssV7zMrpmvGvIByTIJSI53RwyBLCjdk9rscR3PKaC6HdSWC2Ke/1IDFpdc4xhC3VP5jG7aq0dv+xZZYZMyHcYLO1yx4jKILsQhOA6YYo54LxhZHN9hAQXi4/cQs88dwluOT4TaMeysRhEmQSYUQMDtrrNY7rxPFbVo56CIcEjDrnE+B4EFr74FGd1l4Uc64AIAmjtRul1MLGUrXOuUBQhMkyWx5ieNn5hwMAwK8++/gRj0QgcANvfL4s58sF040YXnr+4bBj7cyohyIYAE7etmrUQ1i20IL1OCrnguFg0mjt2Is6buEYkwyzJFr4fY3LJITL2i/XbiiqZmsXCIognvMxxq8953h41dOPgrVSJ1UwxjA8CyK8CDwY5xIy33jnM2F+sS3r7QARjWnMuWB4MEupjb9ynqB0gaKE9Q9G+cIS54XWOcfHhrSPQ/I6nbDyDSrznJeltZc6XHAIQpTzMYZSSgRFwQQAxZxLZQGBB+MslKyYqsOKKUkGNkjkMecjHohgZJg0Wjsusye09v6hjJJtnse3waGM8hyjRWkpUDmvOh/G2UgtGA+IJC0QCHoC3iyXSyk1wWAgsu2hDe01nfTEkYLqwEvA1CR4zpGeJtO2fyijZGMYtPaC51EmWzueiyumwvyWORMo6HCBIBjiORcIBD1BGcq57FICgYBHFqMpy8Qhi1a7k32eBM95B2nnErbVR1TktUcVYs5DOqjFEfzDq86HxVYHVgYyqPR8KOtBFyO1oAiinAsEgp6AN0jxnAsEAhfGOVu7YDhYaufK7iTEnGPlXGLO+4eomm5OsrX7j81p7WFtn7J9dYmRAJy0dRVccvwmuOiY9aXOk1kkKIIo5wKBoCfgjaYmwaQCDyTW7tCGZDcWLCLP+SQYc3H0sczb/qFqtvYoCj9PlaC1V0GjFsGfXrN7MI0LDmmMv9lSIBCMNRrI+zEJwpZAIBgN8phzWScOVSy2cuW8jFI2KiRCax8I8J0cVJ1z3e64GYWbdVG9BH7IDBEIBD1h/Vwz+xxLMKnAh/GSkQRDRhajKUrOIYtWp1N80BjBSAgn07ZvMDznJTYGs865/1g1pjkurrvwCDhm0xy88bJjRz0UwZhCaO0CgaAnbFyZK+d1obULPJDZcWhj1XS9+6+UCD1UgWPOJwG4qtYkePonBjjmfEAJ4cbVc756pgGfft1TRz0MwRhDlHOBQNATNq6Yyj6LR0zgw9EbVwDA/aMehmBEuOzEzfDuF5wMFx2zYdRDEYwImNY+CcAJ4QT9Q9U653GJ8zJjioglggmDKOcCgaAnbFiRe85r8ZjxxwRjhV946hEwv9SGS0/YOOqhCEaAqXoMP33WzlEPQzBCLLUnSzkX3XwwMLO1l6G14zbCSqlJIj/BpEGUc4FA0BM2YuVcPOcCD6bqMbz5WceNehgCgWBEmDTl/LQdq6FZi2Dn2plRD2VZQVWktatSCeG62dpLjUwgGD1EORcIBD3B9JzLNigQCAQCHsdvWTnqIZTCdCOG/3zHM6EurLC+wkwIF44qdc7FcS6YNIhyLhAIegKOORfPuUAgEAhcePE5u6DVTuD8o9aPeijBmKrHox7CskZUQm6IS9Q51wYAobULJg2inAsEgp6As7ULBAKBQOBCPY7guouOGPUwBCNGVc95VKKUWpX2BYJxgPB0BAJBT1g7k5dFeuLA0ghHIhAIBAKBYNxRNVs7PrQw5lxrOKKdCyYMopwLBIKegC3Zjx9YHOFIBAKBQCAQjDvMhHDh2jM+tDhbu9DaBZMJUc4FAkHf8Nh+Uc4FAoFAIBC4YZZSCwcuu6YKNBjdh6jmgkmDKOcCgaBnnLZjNQAAPO+0baMdiEAgEAgEgjFHeGI346wynnNdSk20c8GEQRLCCQSCnvF/rjsbvvfQfjhp22SVyREIBAKBQDBcVPec822wxyp9nGjngsmCKOcCgaBnzDRqcPL2VaMehkAgEAgEgjFHZNQrr6Y8F8ecd/8V3VwwYRBau0AgEAgEAoFAIBgKzIRwZU4MPy9X3kU7F0wWRDkXCAQCgUAgEAgEQ0FVbzlOCFccc66Pq9SVQDAyiHIuEAgEAoFAIBAIhoOKnvMyCeEiSQgnmFCIci4QCAQCgUAgEAiGgqox5+USwnWVc6G1CyYMopwLBAKBQCAQCASCoSCq7DkPL8GmfxVau2DSIMq5QCAQCAQCgUAgGAqwN7uMZ7uMIh9F+hzRzgWTBVHOBQKBQCAQCAQCwVAQKf5zEcoldhelXDCZEOVcIBAIBAKBQCAQDAcVae1RCU0+yrK1i5IumCzURj0AgUAgEAgEAoFAcGjAVJjDledTtq2Ck7etgh1rp4sPlmztggmFKOcCgUAgEAgEAoFgKIiMxG7h59XiCP7hVecHxZFrz7no5oJJg9DaBQKBQCAQCAQCwVBQpl65fW7Y8foo8ZwLJg2inAsEAoFAIBAIBIKhwCilNrA+lPGvQDApEOVcIBAIBAKBQCAQDAnVaO2leshc54NpXyAYFEQ5FwgEAoFAIBAIBEOB6TkfjPas6e9SUk0waZgo5fyf/umf4Oyzz4bp6WlYs2YNPP/5zx/1kAQCgUAgEAgEAkEgqiaEKwPdbJk66gLBOGBisrV//OMfh+uuuw5+67d+C57+9KdDq9WCb37zm6MelkAgEAgEAoFAIAgEVsgHpZxHUkpNMKGYCOW81WrBa17zGnjPe94DL3/5y7PvTzjhhBGOSiAQCAQCgUAgEJSB6TkfFK29+6/Q2gUThomgtX/1q1+Fe++9F6IogtNPPx22bNkCz3rWswo95wsLC7B3717jP4FAIBAIBAKBQDB6DIp2nmdrH0z7AsGgMBHK+fe//30AAHjnO98Jb3vb2+Dmm2+GNWvWwMUXXwyPPfaY87wbb7wRVq1alf23Y8eOYQ1ZIBAIBAKBQCAQEBie84ElhKMfBILJwEiV8ze/+c2glPL+d+edd0Kn0wEAgLe+9a1w5ZVXwplnngkf/OAHQSkFH/vYx5ztv+Utb4E9e/Zk/91zzz3DujSBQCAQCAQCgUBAECHtY3Cl1HS2doFgsjDSmPM3vOENcO2113qPOeKII+D+++8HADPGvNlswhFHHAF3332389xmswnNZrMvYxUIBAKBQCAQCAS9AXvLB6U8azp7JJ5zwYRhpMr5hg0bYMOGDYXHnXnmmdBsNuGuu+6CCy64AAAAlpaW4Ic//CHs2rVr0MMUCAQCgUAgEAgEfYBR53xQCeGy9gfSvEAwMExEtvaVK1fC9ddfD+94xztgx44dsGvXLnjPe94DAABXXXXViEcnEAgEAoFAIBAIQjDUUmqDaV4gGBgmQjkHAHjPe94DtVoNXvKSl8D8/DycffbZ8NnPfhbWrFkz6qEJBAKBQCAQCASCACg1eFq77iKSdO2CCcPEKOf1eh3e+973wnvf+95RD0UgEAgEAoFAIBBUAI4DH1RM+KDo8gLBoDERpdQEAoFAIBAIBALB5AOrzQPL1j7g9gWCQUGUc4FAIBAIBAKBQDAUDKPOue5DsrULJg2inAsEAoFAIBAIBIKhwNCXB1bnfKDNCwQDgyjnAoFAIBAIBAKBYCjAyvmg8rVl2dpFOxdMGEQ5FwgEAoFAIBAIBEOBQWsfcNC50NoFkwZRzgUCgUAgEAgEAsFQYNQ5H1AfopQLJhWinAsEAoFAIBAIBIKhwPScD6aPdbMNAABYP9ccTAcCwYAwMXXOBQKBQCAQCAQCwWTDjDkfjHb+ukuPgfOPWg9PPWbDQNoXCAYFUc4FAoFAIBAIBALBUDCo8mkYq6brcOkJmwbej0DQbwitXSAQCAQCgUAgEAwFOEO7hIYLBCZEORcIBAKBQCAQCARDAc7QLonbBAITopwLBAKBQCAQCASCoaAWpwp5pEQ5FwgoJOZcIBAIBAKBQCAQDAUrp+rwqqcdBdONGOJIlHOBAEOUc4FAIBAIBAKBQDA0/PJlx456CALBWEJo7QKBQCAQCAQCgUAgEIwYopwLBAKBQCAQCAQCgUAwYohyLhAIBAKBQCAQCAQCwYghyrlAIBAIBAKBQCAQCAQjhijnAoFAIBAIBAKBQCAQjBiinAsEAoFAIBAIBAKBQDBiiHIuEAgEAoFAIBAIBALBiCHKuUAgEAgEAoFAIBAIBCOGKOcCgUAgEAgEAoFAIBCMGKKcCwQCgUAgEAgEAoFAMGKIci4QCAQCgUAgEAgEAsGIIcq5QCAQCAQCgUAgEAgEI4Yo5wKBQCAQCAQCgUAgEIwYopwLBAKBQCAQCAQCgUAwYohyLhAIBAKBQCAQCAQCwYghyrlAIBAIBAKBQCAQCAQjhijnAoFAIBAIBAKBQCAQjBiinAsEAoFAIBAIBAKBQDBi1EY9gGEiSRIAANi7d++IRyIQCAQCgUAgEAgEgkMBWv/U+qgLh5Ryvm/fPgAA2LFjx4hHIhAIBAKBQCAQCASCQwn79u2DVatWOX9XSZH6vozQ6XTgvvvugxUrVoBSatTDcWLv3r2wY8cOuOeee2DlypWjHo6gR8jzXH6QZ7q8IM9zeUGe5/KDPNPlBXmeywvyPMOQJAns27cPtm7dClHkjiw/pDznURTB9u3bRz2MYKxcuVIm+TKCPM/lB3mmywvyPJcX5HkuP8gzXV6Q57m8IM+zGD6PuYYkhBMIBAKBQCAQCAQCgWDEEOVcIBAIBAKBQCAQCASCEUOU8zFEs9mEd7zjHdBsNkc9FEEfIM9z+UGe6fKCPM/lBXmeyw/yTJcX5HkuL8jz7C8OqYRwAoFAIBAIBAKBQCAQjCPEcy4QCAQCgUAgEAgEAsGIIcq5QCAQCAQCgUAgEAgEI4Yo5wKBQCAQCAQCgUAgEIwYopwLBAKBQCAQCAQCgUAwYohyPoZ4//vfD4cddhhMTU3B2WefDV/84hdHPSQBg3/913+F5z73ubB161ZQSsEnPvEJ4/ckSeDtb387bNmyBaanp+GSSy6B73znO8Yxjz32GFx99dWwcuVKWL16Nbz85S+HJ598cohXIdC48cYb4SlPeQqsWLECNm7cCM9//vPhrrvuMo45ePAg3HDDDbBu3TqYm5uDK6+8Eh588EHjmLvvvhuuuOIKmJmZgY0bN8Ib3/hGaLVaw7wUAQDcdNNNcMopp8DKlSth5cqVcO6558Itt9yS/S7PcrLx7ne/G5RS8NrXvjb7Tp7p5OCd73wnKKWM/4477rjsd3mWk4l7770XXvziF8O6detgenoaTj75ZPjyl7+c/S5y0eTgsMMOs95RpRTccMMNACDv6CAhyvmY4a//+q/h9a9/PbzjHe+Ar371q3DqqafCZZddBg899NCohyYg2L9/P5x66qnw/ve/n/39d37nd+D3f//34Y/+6I/gC1/4AszOzsJll10GBw8ezI65+uqr4Vvf+hbceuutcPPNN8O//uu/wite8YphXYIA4bbbboMbbrgB/uM//gNuvfVWWFpagmc+85mwf//+7JjXve518I//+I/wsY99DG677Ta477774AUveEH2e7vdhiuuuAIWFxfh85//PHz4wx+GD33oQ/D2t799FJd0SGP79u3w7ne/G77yla/Al7/8ZXj6058Oz3ve8+Bb3/oWAMiznGR86Utfgg984ANwyimnGN/LM50snHjiiXD//fdn/33uc5/LfpNnOXl4/PHH4fzzz4d6vQ633HIL3HHHHfC7v/u7sGbNmuwYkYsmB1/60peM9/PWW28FAICrrroKAOQdHSgSwVjhrLPOSm644Ybs73a7nWzdujW58cYbRzgqQREAIPm7v/u77O9Op5Ns3rw5ec973pN998QTTyTNZjP5q7/6qyRJkuSOO+5IACD50pe+lB1zyy23JEqp5N577x3a2AU8HnrooQQAkttuuy1JkvT51ev15GMf+1h2zLe//e0EAJLbb789SZIk+ed//uckiqLkgQceyI656aabkpUrVyYLCwvDvQCBhTVr1iR/+qd/Ks9ygrFv377k6KOPTm699dbkqU99avKa17wmSRJ5PycN73jHO5JTTz2V/U2e5WTiV37lV5ILLrjA+bvIRZON17zmNcmRRx6ZdDodeUcHDPGcjxEWFxfhK1/5ClxyySXZd1EUwSWXXAK33377CEcmKIsf/OAH8MADDxjPctWqVXD22Wdnz/L222+H1atXw+7du7NjLrnkEoiiCL7whS8MfcwCE3v27AEAgLVr1wIAwFe+8hVYWloynulxxx0HO3fuNJ7pySefDJs2bcqOueyyy2Dv3r2Zx1YwfLTbbfjIRz4C+/fvh3PPPVee5QTjhhtugCuuuMJ4dgDyfk4ivvOd78DWrVvhiCOOgKuvvhruvvtuAJBnOan4h3/4B9i9ezdcddVVsHHjRjj99NPhT/7kT7LfRS6aXCwuLsJf/uVfwste9jJQSsk7OmCIcj5GeOSRR6DdbhsTGQBg06ZN8MADD4xoVIIq0M/L9ywfeOAB2Lhxo/F7rVaDtWvXyvMeMTqdDrz2ta+F888/H0466SQASJ9Xo9GA1atXG8fSZ8o9c/2bYLj4xje+AXNzc9BsNuH666+Hv/u7v4MTTjhBnuWE4iMf+Qh89atfhRtvvNH6TZ7pZOHss8+GD33oQ/DJT34SbrrpJvjBD34AF154Iezbt0+e5YTi+9//Ptx0001w9NFHw6c+9Sl45StfCb/0S78EH/7whwFA5KJJxic+8Ql44okn4NprrwUAWW8HjdqoByAQCATjhhtuuAG++c1vGjGQgsnDscceC1//+tdhz5498Dd/8zdwzTXXwG233TbqYQkq4J577oHXvOY1cOutt8LU1NSohyPoEc961rOyz6eccgqcffbZsGvXLvjoRz8K09PTIxyZoCo6nQ7s3r0bfuu3fgsAAE4//XT45je/CX/0R38E11xzzYhHJ+gFf/ZnfwbPetazYOvWraMeyiEB8ZyPEdavXw9xHFvZDh988EHYvHnziEYlqAL9vHzPcvPmzVaiv1arBY899pg87xHiVa96Fdx8883wL//yL7B9+/bs+82bN8P/v707DY3q6uM4/hscZ0gYk4iJMQQjEVNNELeIOioqRsUNouCCC25Va0tAXMBYcAGX+MYlxhcqQlRUFIRWxSUuMQGjRtSIoUqaRGv6IhAI2ioJbvPvi4deOo3Pw1PUXKfz/cCFe88593LO/Dlw/9xz77x580YvXrwIa//3mH4o5n/WoX35fD716tVL2dnZKigoUP/+/VVYWEgsI9C9e/fU1NSkQYMGyev1yuv1qry8XHv37pXX61VycjIxjWAJCQn66quvVFdXx/yMUCkpKcrKygory8zMdF5X4L4oMj179kxXr17V0qVLnTLm6OdFcv4F8fl8ys7O1rVr15yyUCika9euKRgMutgz/FPp6enq1q1bWCx///13VVZWOrEMBoN68eKF7t2757QpLS1VKBTS0KFD273P0c7MlJeXpx9++EGlpaVKT08Pq8/OzlbHjh3DYlpTU6OGhoawmFZXV4fdXFy5ckVxcXFtblrQ/kKhkF6/fk0sI1BOTo6qq6v14MEDZxs8eLDmzZvn7BPTyPXq1SvV19crJSWF+RmhRowY0ebvR3/++Wf16NFDEvdFkaq4uFhdu3bVlClTnDLm6Gfm9hfpEO7kyZPm9/vt8OHD9ujRI1u+fLklJCSEfe0QX4aXL19aVVWVVVVVmSTbtWuXVVVV2bNnz8zMbMeOHZaQkGBnzpyxhw8fWm5urqWnp1tra6tzjYkTJ9rAgQOtsrLSbty4YRkZGTZnzhy3hhTVvv32W4uPj7eysjJrbGx0tpaWFqfNihUrLC0tzUpLS+3u3bsWDAYtGAw69e/evbO+ffvahAkT7MGDB3bp0iVLSkqy9evXuzGkqJafn2/l5eX29OlTe/jwoeXn55vH47HLly+bGbH8N/jr19rNiGkkWbNmjZWVldnTp0+toqLCxo0bZ4mJidbU1GRmxDIS3blzx7xer23bts1qa2vt+PHjFhsba8eOHXPacF8UWd6/f29paWm2bt26NnXM0c+H5PwLVFRUZGlpaebz+WzIkCF2+/Ztt7uED7h+/bpJarMtXLjQzP7ztyEbNmyw5ORk8/v9lpOTYzU1NWHXaG5utjlz5lggELC4uDhbvHixvXz50oXR4EOxlGTFxcVOm9bWVvvuu++sc+fOFhsba9OnT7fGxsaw6/zyyy82adIki4mJscTERFuzZo29ffu2nUeDJUuWWI8ePczn81lSUpLl5OQ4ibkZsfw3+HtyTkwjx+zZsy0lJcV8Pp+lpqba7Nmzra6uzqknlpHp3Llz1rdvX/P7/danTx87ePBgWD33RZGlpKTEJLWJkRlz9HPymJm58sgeAAAAAABI4p1zAAAAAABcR3IOAAAAAIDLSM4BAAAAAHAZyTkAAAAAAC4jOQcAAAAAwGUk5wAAAAAAuIzkHAAAAAAAl5GcAwAAAADgMpJzAACiwKJFizRt2jS3uwEAAP4Lr9sdAAAAH8fj8fzP+k2bNqmwsFBm1k49AgAA/xTJOQAAEa6xsdHZP3XqlDZu3KiamhqnLBAIKBAIuNE1AADwf2JZOwAAEa5bt27OFh8fL4/HE1YWCATaLGsPhUIqKChQenq6YmJi1L9/f50+fdqpLysrk8fjUUlJiQYOHKiYmBiNHTtWTU1NunjxojIzMxUXF6e5c+eqpaXFOW/MmDHKy8tTXl6e4uPjlZiYqA0bNoQ9tX/+/LkWLFigzp07KzY2VpMmTVJtbW27/FYAAHypSM4BAIhCBQUFOnr0qPbv36+ffvpJq1at0vz581VeXh7WbvPmzdq3b59u3rypX3/9VbNmzdKePXt04sQJnT9/XpcvX1ZRUVHYOUeOHJHX69WdO3dUWFioXbt26dChQ079okWLdPfuXZ09e1a3bt2SmWny5Ml6+/Ztu4wdAIAvEcvaAQCIMq9fv9b27dt19epVBYNBSVLPnj1148YNHThwQKNHj3babt26VSNGjJAkff3111q/fr3q6+vVs2dPSdKMGTN0/fp1rVu3zjmne/fu2r17tzwej3r37q3q6mrt3r1by5YtU21trc6ePauKigoNHz5cknT8+HF1795dP/74o2bOnNlePwMAAF8UnpwDABBl6urq1NLSovHjxzvvowcCAR09elT19fVhbfv16+fsJycnKzY21knM/yxramoKO2fYsGFhH6kLBoOqra3V+/fv9fjxY3m9Xg0dOtSp79Kli3r37q3Hjx9/6qECABAxeHIOAECUefXqlSTp/PnzSk1NDavz+/1hxx07dnT2PR5P2PGfZaFQ6DP1FACA6EFyDgBAlMnKypLf71dDQ0PYEvZPpbKyMuz49u3bysjIUIcOHZSZmal3796psrLSWdbe3NysmpoaZWVlffK+AAAQKUjOAQCIMp06ddLatWu1atUqhUIhjRw5Ur/99psqKioUFxenhQsXftT1GxoatHr1an3zzTe6f/++ioqKtHPnTklSRkaGcnNztWzZMh04cECdOnVSfn6+UlNTlZub+ymGBwBARCI5BwAgCm3ZskVJSUkqKCjQkydPlJCQoEGDBun777//6GsvWLBAra2tGjJkiDp06KCVK1dq+fLlTn1xcbFWrlypqVOn6s2bNxo1apQuXLjQZsk8AADRxGN//eNRAACAjzBmzBgNGDBAe/bscbsrAABEFL7WDgAAAACAy0jOAQAAAABwGcvaAQAAAABwGU/OAQAAAABwGck5AAAAAAAuIzkHAAAAAMBlJOcAAAAAALiM5BwAAAAAAJeRnAMAAAAA4DKScwAAAAAAXEZyDgAAAACAy/4AvOBbWYPJ7HgAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}