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      "cell_type": "markdown",
      "source": [
        "**Malik Haroon** | **Module:** L7 Application of Machine Learning | **Programme:**\tMSc Software Engineering and AI"
      ],
      "metadata": {
        "id": "3BmVltg9dgcJ"
      }
    },
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          "height": 1000
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          "text": [
            "Starting CIFAR-10 Image Classification with CNN\n",
            "==================================================\n",
            "Loading CIFAR-10 dataset...\n",
            "Training set: 45000 samples\n",
            "Validation set: 5000 samples\n",
            "Test set: 10000 samples\n",
            "\n",
            "=== Testing Configuration 1 ===\n",
            "Dropout: 0.3, L2: 0.001, LR: 0.001, Batch Size: 64\n",
            "Epoch 1/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 150ms/step - accuracy: 0.2607 - loss: 2.5271 - precision: 0.3481 - recall: 0.1149 - val_accuracy: 0.4910 - val_loss: 1.5930 - val_precision: 0.7579 - val_recall: 0.1972 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 141ms/step - accuracy: 0.4662 - loss: 1.6493 - precision: 0.6557 - recall: 0.2378 - val_accuracy: 0.5592 - val_loss: 1.3842 - val_precision: 0.7581 - val_recall: 0.3522 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 140ms/step - accuracy: 0.5468 - loss: 1.4332 - precision: 0.7226 - recall: 0.3506 - val_accuracy: 0.6092 - val_loss: 1.2249 - val_precision: 0.7835 - val_recall: 0.4378 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 143ms/step - accuracy: 0.5852 - loss: 1.3134 - precision: 0.7440 - recall: 0.4163 - val_accuracy: 0.6320 - val_loss: 1.1545 - val_precision: 0.7751 - val_recall: 0.4936 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 142ms/step - accuracy: 0.6112 - loss: 1.2462 - precision: 0.7533 - recall: 0.4539 - val_accuracy: 0.6530 - val_loss: 1.0900 - val_precision: 0.7878 - val_recall: 0.5226 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 155ms/step - accuracy: 0.6266 - loss: 1.1944 - precision: 0.7665 - recall: 0.4841 - val_accuracy: 0.6452 - val_loss: 1.1248 - val_precision: 0.7703 - val_recall: 0.5230 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 144ms/step - accuracy: 0.6404 - loss: 1.1564 - precision: 0.7721 - recall: 0.5062 - val_accuracy: 0.6778 - val_loss: 1.0416 - val_precision: 0.8007 - val_recall: 0.5584 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 142ms/step - accuracy: 0.6493 - loss: 1.1342 - precision: 0.7753 - recall: 0.5197 - val_accuracy: 0.6554 - val_loss: 1.0903 - val_precision: 0.7705 - val_recall: 0.5492 - learning_rate: 0.0010\n",
            "Epoch 9/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 143ms/step - accuracy: 0.6592 - loss: 1.1138 - precision: 0.7762 - recall: 0.5362 - val_accuracy: 0.6980 - val_loss: 0.9840 - val_precision: 0.8130 - val_recall: 0.5930 - learning_rate: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 143ms/step - accuracy: 0.6654 - loss: 1.0952 - precision: 0.7877 - recall: 0.5460 - val_accuracy: 0.7038 - val_loss: 0.9729 - val_precision: 0.8115 - val_recall: 0.6060 - learning_rate: 0.0010\n",
            "Restoring model weights from the end of the best epoch: 10.\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 20ms/step\n",
            "Test Accuracy: 0.6948\n",
            "Test F1-Score: 0.6867\n",
            "\n",
            "=== Testing Configuration 2 ===\n",
            "Dropout: 0.4, L2: 0.0001, LR: 0.0005, Batch Size: 128\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 250ms/step - accuracy: 0.1811 - loss: 2.9572 - precision: 0.2098 - recall: 0.0854 - val_accuracy: 0.1890 - val_loss: 2.3424 - val_precision: 0.6410 - val_recall: 0.0050 - learning_rate: 5.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 247ms/step - accuracy: 0.3295 - loss: 1.9153 - precision: 0.4610 - recall: 0.1437 - val_accuracy: 0.4374 - val_loss: 1.5823 - val_precision: 0.6917 - val_recall: 0.1306 - learning_rate: 5.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 244ms/step - accuracy: 0.3987 - loss: 1.6617 - precision: 0.5707 - recall: 0.1744 - val_accuracy: 0.4506 - val_loss: 1.5423 - val_precision: 0.6540 - val_recall: 0.2000 - learning_rate: 5.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 240ms/step - accuracy: 0.4463 - loss: 1.5495 - precision: 0.6377 - recall: 0.2075 - val_accuracy: 0.4624 - val_loss: 1.5186 - val_precision: 0.6278 - val_recall: 0.2284 - learning_rate: 5.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 239ms/step - accuracy: 0.4734 - loss: 1.4848 - precision: 0.6729 - recall: 0.2374 - val_accuracy: 0.4688 - val_loss: 1.4792 - val_precision: 0.6209 - val_recall: 0.2584 - learning_rate: 5.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 239ms/step - accuracy: 0.4956 - loss: 1.4244 - precision: 0.6921 - recall: 0.2666 - val_accuracy: 0.4068 - val_loss: 1.6697 - val_precision: 0.4891 - val_recall: 0.2682 - learning_rate: 5.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 240ms/step - accuracy: 0.5119 - loss: 1.3827 - precision: 0.7051 - recall: 0.2989 - val_accuracy: 0.5058 - val_loss: 1.3895 - val_precision: 0.6459 - val_recall: 0.3374 - learning_rate: 5.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 241ms/step - accuracy: 0.5340 - loss: 1.3314 - precision: 0.7193 - recall: 0.3261 - val_accuracy: 0.5568 - val_loss: 1.2566 - val_precision: 0.7193 - val_recall: 0.3766 - learning_rate: 5.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 244ms/step - accuracy: 0.5524 - loss: 1.2930 - precision: 0.7311 - recall: 0.3500 - val_accuracy: 0.5980 - val_loss: 1.1432 - val_precision: 0.7716 - val_recall: 0.4244 - learning_rate: 5.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 241ms/step - accuracy: 0.5630 - loss: 1.2574 - precision: 0.7377 - recall: 0.3722 - val_accuracy: 0.6056 - val_loss: 1.1276 - val_precision: 0.7680 - val_recall: 0.4428 - learning_rate: 5.0000e-04\n",
            "Restoring model weights from the end of the best epoch: 10.\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step\n",
            "Test Accuracy: 0.5947\n",
            "Test F1-Score: 0.5849\n",
            "\n",
            "=== Testing Configuration 3 ===\n",
            "Dropout: 0.2, L2: 0.01, LR: 0.001, Batch Size: 32\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 72ms/step - accuracy: 0.3235 - loss: 3.4214 - precision: 0.4504 - recall: 0.1614 - val_accuracy: 0.5428 - val_loss: 1.7457 - val_precision: 0.7771 - val_recall: 0.3006 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 71ms/step - accuracy: 0.5471 - loss: 1.6672 - precision: 0.7242 - recall: 0.3585 - val_accuracy: 0.6030 - val_loss: 1.4311 - val_precision: 0.7650 - val_recall: 0.4414 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 74ms/step - accuracy: 0.5893 - loss: 1.4838 - precision: 0.7493 - recall: 0.4294 - val_accuracy: 0.6278 - val_loss: 1.3651 - val_precision: 0.7678 - val_recall: 0.4814 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 79ms/step - accuracy: 0.6084 - loss: 1.4445 - precision: 0.7516 - recall: 0.4531 - val_accuracy: 0.4448 - val_loss: 1.9458 - val_precision: 0.5199 - val_recall: 0.3296 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 76ms/step - accuracy: 0.6201 - loss: 1.4082 - precision: 0.7631 - recall: 0.4765 - val_accuracy: 0.6376 - val_loss: 1.3915 - val_precision: 0.7809 - val_recall: 0.4890 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 79ms/step - accuracy: 0.6248 - loss: 1.3961 - precision: 0.7604 - recall: 0.4821 - val_accuracy: 0.6424 - val_loss: 1.3046 - val_precision: 0.7923 - val_recall: 0.5194 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 79ms/step - accuracy: 0.6238 - loss: 1.3853 - precision: 0.7643 - recall: 0.4893 - val_accuracy: 0.6746 - val_loss: 1.2515 - val_precision: 0.7982 - val_recall: 0.5544 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 79ms/step - accuracy: 0.6309 - loss: 1.3751 - precision: 0.7653 - recall: 0.5015 - val_accuracy: 0.6762 - val_loss: 1.2481 - val_precision: 0.8123 - val_recall: 0.5436 - learning_rate: 0.0010\n",
            "Epoch 9/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 79ms/step - accuracy: 0.6322 - loss: 1.3702 - precision: 0.7663 - recall: 0.5027 - val_accuracy: 0.6718 - val_loss: 1.2671 - val_precision: 0.7909 - val_recall: 0.5516 - learning_rate: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 80ms/step - accuracy: 0.6385 - loss: 1.3611 - precision: 0.7714 - recall: 0.5092 - val_accuracy: 0.6608 - val_loss: 1.2894 - val_precision: 0.7964 - val_recall: 0.5384 - learning_rate: 0.0010\n",
            "Restoring model weights from the end of the best epoch: 8.\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 17ms/step\n",
            "Test Accuracy: 0.6689\n",
            "Test F1-Score: 0.6711\n",
            "\n",
            "=== Hyperparameter Tuning Results ===\n",
            "Config 1: Accuracy = 0.6948, F1 = 0.6867\n",
            "Config 2: Accuracy = 0.5947, F1 = 0.5849\n",
            "Config 3: Accuracy = 0.6689, F1 = 0.6711\n",
            "\n",
            "Best Configuration: {'dropout_rate': 0.3, 'l2_reg': 0.001, 'learning_rate': 0.001, 'batch_size': 64}\n",
            "\n",
            "=== Training Final Model with Best Configuration ===\n",
            "\n",
            "Model Architecture:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
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              "\u001b[1mModel: \"sequential_9\"\u001b[0m\n"
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            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │           \u001b[38;5;34m448\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_48          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │            \u001b[38;5;34m64\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │         \u001b[38;5;34m2,320\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_49          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │            \u001b[38;5;34m64\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_19 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_29 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m4,640\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_50          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_51          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_20 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_30 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_9 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m800\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m51,264\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_52          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_31 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m650\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_48          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_49          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_50          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_51          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">800</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">51,264</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_52          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m69,210\u001b[0m (270.35 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,210</span> (270.35 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m68,890\u001b[0m (269.10 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">68,890</span> (269.10 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m320\u001b[0m (1.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> (1.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 137ms/step - accuracy: 0.2743 - loss: 2.4232 - precision: 0.3734 - recall: 0.1201 - val_accuracy: 0.4542 - val_loss: 1.6709 - val_precision: 0.6857 - val_recall: 0.2042 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 137ms/step - accuracy: 0.4628 - loss: 1.6516 - precision: 0.6571 - recall: 0.2365 - val_accuracy: 0.5536 - val_loss: 1.4286 - val_precision: 0.7857 - val_recall: 0.3204 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 136ms/step - accuracy: 0.5322 - loss: 1.4619 - precision: 0.7096 - recall: 0.3301 - val_accuracy: 0.5866 - val_loss: 1.2932 - val_precision: 0.7830 - val_recall: 0.4020 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 137ms/step - accuracy: 0.5777 - loss: 1.3274 - precision: 0.7479 - recall: 0.4075 - val_accuracy: 0.6406 - val_loss: 1.1513 - val_precision: 0.8206 - val_recall: 0.4602 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 136ms/step - accuracy: 0.6025 - loss: 1.2564 - precision: 0.7564 - recall: 0.4499 - val_accuracy: 0.6644 - val_loss: 1.0837 - val_precision: 0.8036 - val_recall: 0.5212 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 137ms/step - accuracy: 0.6236 - loss: 1.2000 - precision: 0.7685 - recall: 0.4822 - val_accuracy: 0.6518 - val_loss: 1.0805 - val_precision: 0.7944 - val_recall: 0.5232 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 136ms/step - accuracy: 0.6366 - loss: 1.1672 - precision: 0.7689 - recall: 0.5003 - val_accuracy: 0.6346 - val_loss: 1.1467 - val_precision: 0.7672 - val_recall: 0.5228 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 138ms/step - accuracy: 0.6487 - loss: 1.1347 - precision: 0.7797 - recall: 0.5215 - val_accuracy: 0.6974 - val_loss: 0.9820 - val_precision: 0.8198 - val_recall: 0.5742 - learning_rate: 0.0010\n",
            "Epoch 9/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 137ms/step - accuracy: 0.6539 - loss: 1.1235 - precision: 0.7819 - recall: 0.5313 - val_accuracy: 0.6314 - val_loss: 1.2003 - val_precision: 0.7460 - val_recall: 0.5270 - learning_rate: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 133ms/step - accuracy: 0.6621 - loss: 1.1053 - precision: 0.7862 - recall: 0.5410 - val_accuracy: 0.6720 - val_loss: 1.0880 - val_precision: 0.8029 - val_recall: 0.5566 - learning_rate: 0.0010\n",
            "Restoring model weights from the end of the best epoch: 8.\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step\n",
            "\n",
            "=== Final Model Performance ===\n",
            "Test Loss: 1.0031\n",
            "Test Accuracy: 0.6923\n",
            "Test Precision: 0.8069\n",
            "Test Recall: 0.5725\n",
            "Test F1-Score: 0.6934\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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ueL2vvvqqARinT5++IrZjx44ZDzzwgBEQEGD4+/sbPXr0ME6cOHHV9ywqKsoYOHCgERQUZLi7uxuVKlUyhg0bZqSmpl6x39q1axtWq9U4duzYNd8XERERESl8iup5vWEYxqpVqwzgmre1a9cahmEYhw8fNrp3724EBAQYHh4eRtOmTY2ffvop277+97//GW3btjVKlixpuLu7G5UrVzZeeOEF48KFC4ZhGEZqaqrxwgsvGPXq1TN8fX0Nb29vo169esann36aY4wiIgWRxTD+dq27iIiI3JAGDRoQGBjIihUrnB2KiIiIiIiIiOQh1UgXERG5Bdu2bWPHjh0MHDjQ2aGIiIiIiIiISB7TjHQREZGbsHv3brZv387777/PmTNnOHLkCB4eHs4OS0RERERERETykGaki4iI3IQFCxYwePBg0tPTmT17tpLoIiIiIiIiIsWAZqSLiIiIiIiIiIiIiORAM9JFRERERERERERERHKgRLqIiIiIiIiIiIiISA5cnB1AQWS32zlx4gS+vr5YLBZnhyMiIiIiRZxhGFy8eJGyZctitRbfuS46DxcRERGR/HQz5+FKpF/FiRMnCA0NdXYYIiIiIlLMHD16lPLlyzs7DKfRebiIiIiIOMONnIcrkX4Vvr6+gPkG+vn55eux09PTWbZsGXfffTeurq75emwp2DQ2JCcaH5ITjQ+5Fo2NgiM+Pp7Q0FDHeWhxpfNwKYg0NiQnGh+SE40PyYnGR8FwM+fhSqRfRdZlpH5+fk45gffy8sLPz0+/RJKNxobkRONDcqLxIdeisVHwFPdyJjoPl4JIY0NyovEhOdH4kJxofBQsN3IeXnwLMIqIiIiIiIiIiIiI3AAl0kVEREREREREREREcqBEuoiIiIiIiIiIiIhIDlQj/R/IzMwkPT09V/eZnp6Oi4sLKSkpZGZm5uq+pXDLz7Hh6uqKzWbL02OIiIiIiIiIiGSx2+2kpaU5O4x8oxxg/sjNHJcS6bfAMAxiYmI4f/58nuw7ODiYo0ePFvvFpiS7/B4bAQEBBAcHaxyKiIiIiIiISJ5KS0sjIiICu93u7FDyjXKA+Se3clxKpN+CrCR66dKl8fLyytXBbrfbSUhIwMfHB6tVlXfkL/k1NgzDICkpiVOnTgEQEhKSZ8cSERERERERkeLNMAxOnjyJzWYjNDS02OTDlAPMe7md41Ii/SZlZmY6kuglS5bM9f1nXcbi4eGhXyLJJj/HhqenJwCnTp2idOnSKvMiIiIiIiIiInkiIyODpKQkypYti5eXl7PDyTfKAeaP3Mxx6ad0k7JqohenX2wpnrLGeG6vAyAiIiIiIiIikiWrPribm5uTI5GiKrdyXAUikf7JJ58QHh6Oh4cHzZo1Y8uWLdfs265dOywWyxW3e++919HHMAzGjBlDSEgInp6e3HXXXRw8eDBXY1btIinqNMZFREREREREJL8oDyF5JbfGltMT6XPnzmXkyJG8+uqr/Pbbb9SrV4+OHTs6atf83TfffMPJkycdt927d2Oz2ejRo4ejz7vvvsvEiRP5/PPP2bx5M97e3nTs2JGUlJT8elkiIiIiIiIiIiIiUkQ4PZH+wQcf8OijjzJ48GBq1arF559/jpeXF1OmTLlq/8DAQIKDgx235cuX4+Xl5UikG4bBhAkTeOWVV7j//vupW7cuX331FSdOnOC7777Lx1dW9IWHhzNhwoQb7r969WosFgvnz5/Ps5hEREREREREREQKI+XaCjanJtLT0tLYvn07d911l6PNarVy1113sXHjxhvax+TJk+nduzfe3t4AREREEBMTk22f/v7+NGvW7Ib3WdRcrRTO5bfXXnvtlva7detWHnvssRvu37JlS06ePIm/v/8tHe9W1KhRA3d3d2JiYvLtmCIiIiIiIiIiUnQVt1ybEvYmF2ce/MyZM2RmZlKmTJls7WXKlGHfvn3Xff6WLVvYvXs3kydPdrRlJUyvts9rJVNTU1NJTU11bMfHxwNmAfq/F6FPT0/HMAzsdjt2u/26Md4swzAc/+bW/o8fP+64P2/ePF599VX27t3raPPx8XEcyzAMMjMzcXG5/tAoWbIkwA3H6eLiQunSpTEMw/E689K6detITk7moYceYtq0abz44ot5fsycpKen4+rqesvPz4uxkRO73Y5hGKSnp/+jFY0lf2R9VmlxWLkajQ+5Fo2NgkM/AxEREZHC4+TJk477c+fOZcyYMezfv9/R5uPj47h/M7m2oKCgm4rDzc2N4ODgm3qO3DqnJtL/qcmTJ1OnTh2aNm36j/Yzbtw4Xn/99Svaly1b5ljVNYuLiwvBwcEkJCSQlpb2j46bk4sXL+bavi5/DVkrIGe1rVu3jq5duzJv3jz++9//smfPHr755hvKlSvHyy+/zLZt20hKSqJatWqMGTOGdu3aOfZVt25dnnjiCZ544gkASpQowUcffcSyZctYuXIlISEhvPnmm3Tu3DnbsSIjI/H392fWrFmMHj2aKVOm8O9//5vjx4/TvHlzJk2a5PgQyMjI4OWXX2bOnDnYbDYGDBjAqVOniI+PZ+bMmTm+7v/97388+OCDtGrVitGjR/P4449ne/z48eOMGTOGlStXkpaWRrVq1Rg/fjyNGzcGYMmSJYwfP549e/bg7e1NixYtmDFjhuO1zpgxI9sit2FhYYwbN46+ffsSHR1NvXr1mDx5MpMnT2b79u188MEH3HPPPbzwwgts3LiR8+fPEx4ezsiRI+nevbtjP3a7nY8//pjp06dz/PhxgoKCGDRoEM8//zz33Xcf1atXZ/z48Y7+Z86coVatWsyfP5/bb7/9BkbEjUlLSyM5OZk1a9aQkZGRa/uVvLV8+XJnhyAFmMaHXIvGhvMlJSU5OwQRERERuUGXJ6/9/f2xWCyOttWrV9O+fXsWL17MK6+8wq5du1i2bBmhoaGMHDmSTZs2kZiYSM2aNXn55Ze57777HPsKDw9nxIgRjBgxAjBnvn/55ZcsWrSIpUuXUq5cOd5//33Hc7KOde7cOQICApg2bRojRoxg7ty5jBgxgqNHj9K6dWumTp1KSEgIYObaRo4cyVdffYXNZuORRx4hJiaGCxcu3HJZ7HPnzvHMM8/w448/kpqayu23387EiROpWrUqAFFRUQwfPpx169aRlpZGeHg448ePp3Pnzpw7d47hw4ezbNkyEhISKF++PP/+978ZPHjwLcWSl5yaSC9VqhQ2m43Y2Nhs7bGxsdf9NiUxMZE5c+bwxhtvZGvPel5sbKxjgGRt169f/6r7Gj16NCNHjnRsx8fHExoayt13342fn1+2vikpKRw9ehQfHx88PDwA85ul5PTMnF/sDTIMg4SLCfj4+lx3RVlPV9tNrzrr4eGBxWJxvK6shPrYsWN59913qVSpEiVKlODo0aN07dqVt99+G3d3d77++mv69OnD3r17qVChAmCW4fHw8Mj2Ho0fP563336bDz74gEmTJvGvf/2LiIgIAgMDHcfy9fXFz88PDw8PkpOT+eyzz/j666+xWq0MHDiQN954w5Gwfuutt1iwYAFTpkyhZs2aTJw4kcWLF9OuXbsrfjaXu3jxIt9//z0bN26kRo0aDB8+nJ07d9KmTRsAEhISuO+++yhXrhzff/89wcHB/Pbbb3h6euLn58eiRYsYMGAA//73v/n6669JS0tjyZIl2Y6Z1TeLxWJxvB9Z3zy++eabjB8/ngYNGuDh4YHdbqd58+a8/PLL+Pn5sXjxYh5//HFuu+02xxdCL730Ev/3f//H+++/T+vWrTl58iT79u3D19eXAQMGMGrUKCZOnIi7uzsAU6ZMoVy5cnTp0iVXV7hOSUnB09OTtm3bOsa6FFzp6eksX76cDh06/KMrH6Ro0vi4jD0Ty4ntGMH1wMXd2dE4ncZGwZF1RaQ4R6bdYNmeWPaft9DZ2cGIiIgUc7mZZ7tZt5Jru5aXXnqJ9957L1uurXPnzvz3v//F3d2d6dOnO3Jt4eHh19zP66+/zrvvvsv48eP5+OOP6devH1FRUQQGBl61f1JSEu+9954j19a/f3+ef/55x4TUd955h5kzZzJ16lRq1qzJRx99xHfffUf79u1v+bUOGjSIgwcP8sMPP+Dn58eoUaPo3Lkze/bswdXVlWHDhpGWlsaaNWvw9vZmz549jtzZf/7zH/bs2cOSJUsoVaoUhw4dIjk5+ZZjyUtOTaS7ubnRqFEjVqxYQbdu3QBzNu6KFSsYPnx4js+dP38+qamp9O/fP1t7xYoVCQ4OZsWKFY7EeXx8PJs3b3bMnP47d3d3R1Lycq6urlf8UZmZmYnFYsFqtWK1miXmk9IyuO21/J/JteeNjni53VzJjayY//7vG2+8QceOHR39SpUqRYMGDRzbY8eO5bvvvuOnn37K9rPJei+yDBo0iH79+gHmTP+PP/6Ybdu2cc8992Q7ZtYtPT2d//3vf1SuXBmA4cOH88Ybbzj6Tpo0idGjR/PQQw8B8Mknn7BkyZIrjvt38+bNo2rVqtSpUweA3r17M3XqVMeM7Tlz5nD69Gm2bt3q+OCpVq2a4/njxo2jd+/e2b6oufz9uPx1XK0tq33EiBHZZpsDvPDCC477Tz/9NMuWLWPBggU0b96cixcvMnHiRCZNmuT45q1q1aq0bdsWu91O165dGTVqFD/++CM9e/YEYPr06QwaNCjXy69YrVYsFstVfw+k4NLPS3JS7MdHRip8Mxj2L4bQ5tB/Ibj7XP95xUCxHxsFgN5/55q6PoKxi/YS6m1lRD6UIBQREZFrS07PpNaYpU45tplry5106RtvvEGHDh0c24GBgdSrVy/b4wsXLuTHH3/kqaeeuuZ+Bg0aRJ8+fQBzwunEiRPZsmUL99xzz1X7p6en8/nnn1+Ra8vy8ccfM3r0aB544AHAzL0tXrz4ll9nVgJ9/fr1tGzZEoCZM2cSGhrKd999R48ePYiOjuahhx5y5OkqVarkeH50dDQNGjRwVIjI6UsFZ3PqYqMAI0eO5Msvv2T69Ons3buXJ554gsTEREcSceDAgYwePfqK502ePJlu3bo56nRnsVgsjBgxgrFjx/LDDz+wa9cuBg4cSNmyZR3JerlS1mDNkpCQwPPPP0/NmjUJCAjAx8eHvXv3Eh0dneN+6tat67jv7e2Nn58fp06dumZ/Ly8vxy82QEhIiKP/hQsXiI2NzVa6x2az0ahRo+u+nilTpmT7kqV///7Mnz/fUTJnx44dNGjQ4Jrf3u3YsYM777zzuse5nr+/r5mZmbz55pvUqVOHwMBAfHx8WLp0qeN93bt3L6mpqdc8toeHB/3792fKlCkA/Pbbb+zevZtBgwb941hFRIq09GSY089MogMc3QSzekGaymmICDzQoBxuLlaOJlr4Lfq8s8MRERGRIuB6uTY/Pz8OHDhQaHJt17J3715cXFxo1qyZo61kyZJUr17dsUbj008/zdixY2nVqhWvvvoqf/zxh6PvE088wZw5c6hfvz4vvvgiGzZsuOVY8prTa6T36tWL06dPM2bMGGJiYqhfvz4///yzY7HQ6OjoK2b97t+/n3Xr1rFs2bKr7vPFF18kMTGRxx57jPPnz9O6dWt+/vnnPCtP4elqY88bHa/f8QbY7XYuxl/E1883xxnXWcfNLd7e3tm2n3/+eZYvX857771HlSpV8PT0pHv37tetC//32VQWiyXHhTGv1v+fLkS6Z88eNm3axJYtWxg1apSjPTMzkzlz5vDoo4/i6emZ4z6u9/jV4rzaImF/f1/Hjx/PRx99xIQJE6hTpw7e3t6MGDHC8b5e77gAQ4cOpWHDhhw7doypU6dyxx13EBYWdt3niYgUW2lJMKcPHFkNLp5w5xhY9RZErTPb+8wB1+t//opI0VXSx53764Uwf/txpm+MpnmV0s4OSUREpNjKzTzbrRw7t1wv1+bu7s5DDz1UKHJt/9QjjzxCx44dWbRoEcuWLWPcuHG8//77PPXUU3Tq1ImoqCgWL17M8uXLufPOOxk2bBjvvfeeU2O+GqfPSAfzEoOoqChSU1PZvHlztm8wVq9ezbRp07L1r169OoZhZLs84nIWi4U33niDmJgYUlJS+OWXX7KV7chtFosFLzeXXLt5utluqF9u1sP+u/Xr1zNo0CAeeOAB6tSpQ3BwMJGRkXl2vKvx9/enTJkybN261dGWmZnJb7/9luPzJk+eTNu2bdm5cyc7duxw3EaOHMnkyZMB89u8HTt2cPbs2avuo27duqxYseKaxwgKCsq2QvPBgwdvaJGw9evXc//999O/f3/q1atHpUqVOHDggOPxqlWr4unpmeOx69SpQ+PGjfnyyy+ZNWsWQ4YMue5xRUSKrdQEmNnDTKK7ekP/BdDiSbOsi6u32T63v1n2RUSKtYebm+sALd0Ty7FzulpFRETEWXI7z3Yzt/zOtV1vNnpuu9VcW05q1qxJRkYGmzdvdrTFxcWxf/9+atWq5WgLDQ3l8ccf55tvvuG5557jyy+/dDwWFBTEww8/zIwZM5gwYQJffPHFLceTl5w+I10KpqpVq/LNN9/QtWtXLBYL//nPf3L8tiuvPPXUU4wbN44qVapQo0YNPv74Y86dO3fND7b09HS+/vpr3njjDW677bZsjz3yyCN88MEH/Pnnn/Tp04e33nqLbt26MW7cOEJCQvj9998pW7YsLVq04NVXX+XOO++kcuXK9O7dm4yMDBYvXuyY4X7HHXcwadIkWrRoQWZmJqNGjbqh2qZVq1ZlwYIFbNiwgRIlSvDBBx8QGxvr+GDx8PBg1KhRvPjii7i5udGqVStOnz7Nn3/+mW214kceeYThw4fj7e3tqGklIiJ/k3LBTKIf3QzuftBvAVS49GV9hWbQbz7MeAgO/QLzBkLPr8HFzbkxi4jTVA/2pZq/nQMXrHy9MYrRnWs6OyQREREpQv6ea3vllVecMlP8ZnNtl9u1axe+vr6ObYvFQr169bj//vt59NFH+d///oevry8vvfQS5cqV4/777wfMNQQ7depEtWrVOHfuHKtWraJmTfNca8yYMTRq1IjatWuTmprKTz/95HisoCkQM9Kl4Pnggw8oUaIELVu2pGvXrnTs2JGGDRvmexyjRo2iT58+DBw4kBYtWuDj40PHjh2vWabnhx9+IC4u7qrJ5Zo1a1KzZk0mT56Mm5sby5Yto3Tp0nTu3Jk6derw9ttvOxbsbNeuHfPnz+eHH36gfv363HHHHWzZssWxr/fff5/Q0FDatGlD3759ef755/Hy8rru63nllVdo2LAhHTt2pF27dgQHB19Ru/8///kPzz33HGPGjKFmzZr06tXritpXffr0wcXFhT59+uRZySIRkUIt+Rx81c1Monv4w8Dv/kqiZwlvBX3ngosHHPgZFgyGzCvLdIlI8XF7sPnH7Owt0SSlZTg5GhERESlKrpZru7z+eX652Vzb5dq2bUuDBg0ct6za6lOnTqVRo0Z06dKFFi1aYBgGixcvdkw6zczMZNiwYdSsWZN77rmHatWq8emnnwLg5ubG6NGjqVu3Lm3btsVmszFnzpy8ewP+AYvh7CI5BVB8fDz+/v5cuHABPz+/bI+lpKQQERFBxYoV8ySBabfbiY+Px8/P77o10osju91OzZo16dmzJ2+++aazw8lXl4+N6OhoKleuzNatW/PsC468HuuSu9LT01m8eDGdO3e+oasjpHgpduMjMQ6+7gYxf4BnoJlED6l37f6HVsDs3pCZBrUfgAf/D2zF46K9Yjc2CrCczj+LE2e+D+np6fy0aDEfHvAl+mwyb3a7jQHNtQ6N6LNScqbxITnR+LgxxTX/UFBygMUh15bTGLuZ809laqVAi4qK4ssvv+TAgQPs2rWLJ554goiICPr27evs0JwiPT2dmJgYXnnlFZo3b+6UqwRERAq0hNMwvauZRPcOgkE/5ZxEB6hyJ/SaAVZX+PNb+O4JsGfmT7wiUqBYLfBwCzN5PnV9BHa75hyJiIhI0aJc261TIl0KNKvVyrRp02jSpAmtWrVi165d/PLLLwW2VlJe27x5M+XKlWPr1q18/vnnzg5HRKRguRgD0+6FU3+CTzAMWgRlat/Yc6t1hJ7TweoCu+bBD0+BE9YGERHne7BBWXzdXThyOpFfD552djgiIiIiuUq5tltXPK5blkIrNDSU9evXOzuMAqN169ZkZmaq7I+IyN9dOG7ORD97GPzKwcM/QsnKN7ePGvfCQ5NhwRDYMROsNujyEegzV6RY8XF3oWeTUCavi2DKugjaVy/t7JBEREREco1ybbdOfxmKiIhI4XY+GqZ1NpPo/hVg8OKbT6Jnqd0NHvwCLFb47StY8gJoORmRYmdQy3CsFlh78AwHYy86OxwRERERKQCUSBcREZHC6+wRmNoZzkVCiYpmEr1E+D/bZ53ucP+ngAW2/h/8PFrJdJFiJjTQiw61ygAwdUOkc4MRERERkQJBiXQREREpnM4chKn3woWjULKqmUQPCM2dfdfvA/dNNO9v/gyWj1EyXaSYGdKqIgDf/HaMc4lpTo5GRERERJxNiXQREREpfE7tM2eiXzwBQTXMhUX9yubuMRoOhHs/MO9vmAgrxyqZLlKMNK0YSO2yfqSk25m9NdrZ4YiIiIiIkymRLiIiIoVLzG6Ydi8knoIydcwkum+ZvDlWk6HQ6V3z/tr34Nd38+Y4IlLgWCwWBl+alf7VhijSM+1OjkhEREREnEmJdBERESk8TuyA6V0g6QyE1IeHfwDvUnl7zGb/grv/a95f/RasfT9vjyciBUbXeiGU8nEjJj6Fn3fHODscEREREXEiJdLlhrVr144RI0Y4tsPDw5kwYUKOz7FYLHz33Xf/+Ni5tR8RESnEjm2D6fdB8jko3wQGfg9egflz7JbD4c5Xzfsr3oANH+fPcUXEqdxdbPRvHgbAlPURTo5GREREipouXbrw7LPPOraVayvYlEgvBrp27co999xz1cfWrl2LxWLhjz/+uOn9bt26lccee+yfhpfNa6+9Rv369a9oP3nyJJ06dcrVY11LcnIygYGBlCpVitTU1Hw5poiIXEfURviqG6RegAotYMC34BmQvzG0GQnt/m3eX/YKbPo8f48vks/GjRtHkyZN8PX1pXTp0nTr1o39+/df93nz58+nRo0aeHh4UKdOHRYvXpwP0eadfs3CcLNZ+T36PL9Fn3N2OCIiIlIAKNd2Y6ZNm0ZAQECeHiM/KZFeDAwdOpTly5dz7NixKx6bOnUqjRs3pm7duje936CgILy8vHIjxOsKDg7G3d09X461cOFCateuTY0aNZz+zZxhGGRkZDg1BhERp4tYCzMegrSLEN4G+i8Ed1/nxHL7i9DmefP+z6Ng6/85Jw6RfPDrr78ybNgwNm3axPLly0lPT+fuu+8mMTHxms/ZsGEDffr0YejQofz+++9069aNbt26sXv37nyMPHcF+brTtZ65mPHU9ZHODUZEREQKBOXaiicl0ouBLl26EBQUxLRp07K1JyQkMH/+fIYOHUpcXBx9+vShXLlyeHl5UadOHWbPnp3jfv9+ucnBgwdp27YtHh4e1KpVi+XLl1/xnFGjRlGtWjW8vLyoVKkS//nPf0hPTwfMb6lef/11du7cicViwWKxOGL+++Umu3bt4o477sDT05OSJUvy2GOPkZCQ4Hh80KBBdOvWjffee4+QkBBKlizJsGHDHMfKyeTJk+nfvz/9+/dn8uTJVzz+559/0qVLF/z8/PD19aVNmzYcPnzY8fiUKVOoXbs27u7uhISEMHz4cAAiIyOxWCzs2LHD0ff8+fNYLBZWr14NwOrVq7FYLCxZsoRGjRrh7u7OunXrOHz4MN26daNatWr4+fnRpEkTfvnll2xxpaamMmrUKEJDQ3F3d6dKlSpMnjwZwzCoUqUK7733Xrb+O3bswGKxcOjQoeu+JyIiTnN4JczsAemJUPkO6DsP3LydF4/FAne8Aq2eMbcXPQe/feW8eETy0M8//8ygQYOoXbs29erVY9q0aURHR7N9+/ZrPuejjz7innvu4YUXXqBmzZq8+eabNGzYkEmTJuVj5LlvcKtwAJbsOsnJC8nODUZEREScTrm2m8u1XUt0dDT3338/Pj4++Pn50bNnT2JjYx2P79y5k/bt2+Pr64ufnx+NGjVi27ZtAERFRdG1a1dKlCiBt7c3tWvXzvMrIV3ydO/FhWFAelLu7MtuN/eVZgPrdb7ncPUy/6C/DhcXFwYOHMi0adN4+eWXsVx6zvz588nMzKRPnz4kJCTQqFEjRo0ahZ+fH4sWLWLAgAFUrlyZpk2b3kDYdh588EHKlCnD5s2buXDhQrZ66ll8fX2ZNm0aZcuWZdeuXTz66KP4+vry4osv0qtXL3bv3s3PP//sSBL7+/tfsY/ExEQ6duxIixYt2Lp1K6dOneKRRx5h+PDh2T7AVq1aRUhICKtWreLQoUP06tWL+vXr8+ijj17zdRw+fJiNGzfyzTffYBgGzz77LFFRUYSFmbUxjx8/Ttu2bWnXrh0rV67Ez8+P9evXO2aNf/bZZ4wcOZK3336bTp06ceHCBdavX3/d9+/vXnrpJd577z0qVapEiRIlOHr0KJ06deKll16iZMmSzJgxg65du7J//34qVKgAwMCBA9m4cSMTJ06kXr16REREcObMGSwWC0OGDGHq1Kk8//zzjmNMnTqVtm3bUqVKlZuOT0QkXxxYBnP7Q2YqVO0IPb8CVw9nR2X+33vX65CZDps+hR+eBqsr1O/j7MhE8tSFCxcACAy89toEGzduZOTIkdnaOnbseM2r/FJTU7OV0ouPjwcgPT39H/1Rdiuyjne141Yv7UXT8BJsiTzH9PURPNehar7GJs6V09gQ0fiQnGh83Jj09HQMw8But2O323M3z3azbjDXZrVaGTBgANOmTWP06NGOXNvcuXPJzMykV69eJCQk0LBhQ1544QX8/PxYvHgxAwYMoGLFijRt2hTDMBz7s9vtjvuXvxdZubaNGzdy4cIFx3mW470CfHx8mDJliiPX9q9//QsfHx9eeOEFevTowa5du1i6dCnLli0DzFxb1nOz9pOVa2vevDmbN2/m1KlTPPbYYwwbNoypU6c64lq1ahXBwcGsWLGCQ4cO0adPH+rWrXvNXNvlx7naY1lJ9FWrVpGRkcFTTz1Fr169WLlyJQD9+vWjfv36fPLJJ9hsNnbs2IHNZsNut/Pkk0+SlpbG6tWr8fb2Zs+ePXh5eV3zWIZhkJ6ejs1my/bYzfx+KpGeG9KT4K2yubIrKxBwo53/feKGZ+UNGTKE8ePH8+uvv9KuXTvATKQ+9NBD+Pv74+/vny3J+tRTT7F06VLmzZt3Q4n0X375hX379rF06VLKljXfi7feeuuKWkuvvPKK4354eDjPP/88c+bM4cUXX8TT0xMfHx9cXFwIDg6+5rFmzZpFSkoKX331Fd7e5uufNGkSXbt25Z133qFMmTIAlChRgkmTJmGz2ahRowb33nsvK1asyDGRPmXKFDp16kSJEiUA8w+/qVOn8tprrwHwySef4O/vz5w5c3B1dQWgWrVqjuePHTuW5557jmeeecbR1qRJk+u+f3/3xhtv0KFDB8d2YGAgderUIT4+Hj8/P958802+/fZbfvjhB4YPH86BAweYN28ey5cv56677gKgUqVKjucPGjSIMWPGsGXLFpo2bUp6ejqzZs26Ypa6iEiBsfcnmD8I7OlQowt0nwoubs6O6i8WC3R8CzLTzPIu3z8JVheo28PZkYnkCbvdzogRI2jVqhW33XbbNfvFxMQ4zsWylClThpiYmKv2HzduHK+//voV7cuWLcu3y5r/7mozvQDquFvYgo2vNhyhcspB3GxX7SZF2LXGhghofEjOND5ylpUHSkhIIC0tDdKTCPikplNiOT9sr5lMvwE9evTgvffeY8mSJbRu3Rowqxx07doVi8WCr69vthzUwIEDWbRoETNnzqRGjRqO9rS0NMdkArvdTkpKCvHx8axcuZJ9+/Yxb948QkJCAPj3v/9Njx49SE5Odjznqaeecuzr9ttvZ9iwYcyZM4d//etfALi6umKxWBznVpdPWMjaz/Tp00lOTubjjz/G29ubChUq8Pbbb9OnTx9efvllSpcuTXp6Ov7+/vz3v//FZrNRtmxZ7r77bpYuXUqvXr2u+h6lpKRgGIYj1sutWrWKXbt2sWPHDsqXLw+Y+b0WLVqwevVqGjZsSHR0NMOGDXPkGjt27AiYky8iIyO57777HJNf27Zt63js79LS0khOTmbNmjVXlFBOSrrxL22USC8matSoQcuWLZkyZQrt2rXj0KFDrF27ljfeeAOAzMxM3nrrLebNm8fx48dJS0sjNTX1hv+A2bt3L6GhoY6BDdCiRYsr+s2dO5eJEydy+PBhEhISyMjIwM/P76Zey969e6lXr54jiQ7QqlUr7HY7+/fvd/zxVrt27WzfMoWEhLBr165r7jczM5Pp06fz0UcfOdr69+/P888/z5gxY7BarezYsYM2bdo4kuiXO3XqFCdOnODOO++8qddzNY0bN862nZCQwKuvvspPP/1EbGwsGRkZJCcnEx0dDeD4Ru7222+/6v7Kli3Lvffey5QpU2jatCk//vgjqamp9OihhI+IFEB/fgsLHwF7BtR+AB78EmxXfu46ncUCncabM9N/mw7f/gtsLmbMIkXMsGHD2L17N+vWrcvV/Y4ePTrbDPb4+HhCQ0O5++67b/oc8Z9KT09n+fLldOjQ4arneh3tBksnrOPYuWRSguvSrUn5fI1PnOd6Y0OKN40PyYnGx41JSUnh6NGj+Pj44OHhYVZpcBI/X98bnrTauHFjWrZsydy5c+ncuTOHDh1i48aNjB07Fj8/PzIzMxk3bhzz58/Plmvz8/PDz8/PMSPdzc3Ncd5jtVrx8PDAz8+P6OhoQkNDqV69uuOYWTknT09Px3Pmzp3LpEmTrsi1ZT3u7u6OzWa76rlV1n4iIyOpX7++I2EP0KFDB+x2OydOnKBKlSq4urpy2223OSafAoSGhrJ79+5rnrd5eHhgsViu+njW66tVq5ajrWnTpgQEBBAdHU27du149tlnefrpp1m4cCF33nkn3bt3p3LlygA888wzDBs2jDVr1nDnnXfy4IMPXrMufUpKCp6eno4yOZe7WuL9WpRIzw2uXubs8Fxgt9uJv3gRP19frDdS2uUmDB06lKeeeopPPvmEqVOnUrlyZUfidfz48Xz00UdMmDCBOnXq4O3tzYgRI8xvAnPJxo0b6devH6+//jodO3Z0zOx+//33c+0Yl/v7f1IWi+Wql3dkWbp0KcePH7/iW7TMzExWrFhBhw4d8PT0vObzc3oMcPw8L79051qXj1z+JQHA888/z/Lly3n99dcdP5/u3bs7fj7XOzbAI488woABA/jwww+ZOnUqvXr1ctpMLxGRa/pjnpmQNuxQtxfc/6mZnC6orFboMsFM+u+YaX4BYHWFml2cHZlIrhk+fDg//fQTa9asccwWupbg4OBsdS0BYmNjr3m1obu7+1UXuXJ1dXVawuFax3YFBrUMZ+yivXy1KZr+LcIdl3FL8eDMcSkFn8aH5ETjI2eZmZlYLBasVquZO3H3ybU8282y3mBplyxZubZPP/2U6dOnU7lyZdq3b4/FYuHdd99l4sSJV+Ta0tPTsVqt2XJUl+cAs96LrPOMyx/Lup/1Xm3cuJEBAwZcNdeW1fdq+7l8fzd6LIvFgpub2xV97Hb7NXOYl+/j724krtdff51+/fqxaNEilixZwmuvvcacOXN44IEHeOyxx+jUqROLFi1i2bJlvP3227z//vvZZuhfvj+LxXLV38Wb+d3UYqO5wWIxv63KrZur1431u8kT9549e2K1Wpk1axZfffUVQ4YMcQza9evXc//999O/f3/q1atHpUqVOHDgwA3vu2bNmhw9epSTJ0862jZt2pStz4YNGwgLC+Pll1+mcePGVK1alaioqGx93NzcyMzMvO6xdu7cSWJioqNt/fr1WK3WbN/S3azJkyfTu3dvduzYke3Wu3dvx6KjdevWZe3atVdNgPv6+hIeHs6KFSuuuv+goCCAbO/R5QuP5mT9+vU8/PDDdOnShTp16hAcHExkZKTj8Tp16mC32/n111+vuY/OnTvj7e3NZ599xs8//8yQIUNu6NgiIvnm9xnwzWNmEr1+f+j2WcFOomexWuG+j6FOTzOhPn8QHFjq7KhE/jHDMBg+fDjffvstK1eupGLFitd9TosWLa44F1q+fPlVr1QsjHo2CcXbzcbBUwmsO3TG2eGIiIgUTbmdZ7uZm3JtjrbcyLVd75hHjx7l6NGjjrY9e/Zw/vz5bLPUq1WrxrPPPsuyZct48MEHHTXbwZwR//jjj/PNN9/w3HPP8eWXX+ZJrFmUSC9GfHx86NWrF6NHj+bkyZMMGjTI8VjVqlVZvnw5GzZsYO/evfzrX/+6YjZRTu666y6qVavGww8/zM6dO1m7di0vv/xytj5Vq1YlOjqaOXPmcPjwYSZOnMi3336brU94eDgRERHs2LGDM2fOZFt8Kku/fv3w8PDg4YcfZvfu3axatYqnnnqKAQMGXFGT80adPn2aH3/8kYcffpjbbrst223gwIF89913nD17luHDhxMfH0/v3r3Ztm0bBw8e5Ouvv2b//v0AvPbaa7z//vtMnDiRgwcP8ttvv/Hxxx8D5qzx5s2b8/bbb7N3715+/fXXbDXjc1K1alW+/fZbdu3axc6dO+nbt2+2by7Dw8N5+OGHGTJkCN999x0RERGsXr2aefPmOfrYbDYGDRrE6NGjqVq1apH5g1ZEiohtU+D7YYABjYeYiWlrISpAbLWZif/aD5h13ef2h0O/ODsquRWXXTlW3A0bNowZM2Ywa9YsfH19iYmJISYmhuTkZEefgQMHMnr0aMf2M888w88//8z777/Pvn37eO2119i2bRvDhw93xkvIdX4ervRoHArAlHURTo5GREREnE25tuvLzMy8YtLq3r17ueuuu6hTpw79+vXjt99+Y8uWLQwcOJDbb7+dxo0bk5yczPDhw1m9ejVRUVGsX7+erVu3UrOmWT9/xIgRLF26lIiICH777TdWrVrleCyvKJFezAwdOpRz587RsWPHbPXMX3nlFRo2bEjHjh1p164dwcHBdOvW7Yb3a7Va+fbbb0lOTqZp06Y88sgj/Pe//83W57777uPZZ59l+PDh1K9fnw0bNvCf//wnW5+HHnqIe+65h/bt2xMUFMTs2bOvOJaXlxdLly7l7NmzNGnShO7du3PnnXcyadKkm3szLpO1cOnV6pvfeeedeHp6MmPGDEqWLMnKlStJSEjg9ttvp1GjRnz55ZeOy0AefvhhJkyYwKeffkrt2rXp0qULBw8edOxrypQpZGRk0KhRI0aMGMHYsWNvKL4PPviAEiVK0LFjR+6//346duxIw4YNs/X57LPP6N69O08++SQ1atTg0UcfzfZNIpg//7S0NAYPHnyzb5GISN7Z/D/46VnzfrPH4d4PzFnehY3NxaznXqOLuQjpnH5wZLWzo5KbsfcnmHIPpCY4O5IC4bPPPuPChQu0a9eOkJAQx23u3LmOPtHR0dlmSbVs2ZJZs2bxxRdfUK9ePRYsWMB3332X4wKlhc2gluFYLLBq/2kOn9ZYERERKe6Ua8tZQkICDRo0yHbLWpD1+++/p0SJErRt25a77rqLSpUqOc41bTYbcXFxDBw4kGrVqtGzZ086derkWKg+MzOTYcOGUbNmTe655x6qVavGp59++o/jzYnFMDTt5u/i4+Px9/fnwoULVxTDT0lJISIigooVK15RnD432O124uPj8fPzu36NdClWcmNsrF27ljvvvJOjR49e9xvFvB7rkrvS09NZvHgxnTt3Vu09uUKBHh/rJ8LySyd6LZ+GDm/c9OWUBU5GGswbCAeWgIsn9F8A4a2dHdVVFeixkZ8uHIclL8K+n8zt9i/D7S/mawg5nX8WJ858H27m9+GR6Vv5Ze8pBjQP481uRedLArk6fVZKTjQ+JCcaHzemuOYflAPMPzmNsZs5/9RPSaQYSE1N5dixY7z22mv06NHjH1+WIyKSK9aM/yuJ3vaFopFEB3Bxg57TocpdkJEMM3tC9KbrP0/ynz3TvCLik2ZmEt3qAm2eg5ZXLlAkcrkhrcx68Qu2H+NC0tUXjxcRERGRokWJdJFiYPbs2YSFhXH+/HneffddZ4cjIsWdYcCqt2DlpfJW7V+GO14pGkn0LC7u0GsGVGoH6Ykwozsc2+7sqORyMbtgcgdzJnraRSjfFP61Fu4cA66ezo5OCrgWlUtSI9iX5PRM5m6LdnY4IiIiIpIPlEgXKQYGDRpEZmYm27dvp1y5cs4OR0SKM8OAX16DX98xt+96Pd9LaOQbV0/oPRvC25iJ2q8fgBO/OzsqSUuC5WPgf7fD8e3g7mfW5R+yFMrUcnZ0UkhYLBbHrPTpG6LIyLRf5xkiIiIiUtgpkS4iIiL5wzBg6b9h/QRzu+M4aD3CmRHlPTcv6DMHKrSA1AvwVTdzJrQ4x6Ff4NPmsP4jMDKh1v0wbAs0GVo4F7gVp7qvflkCvd04fj6ZZXtinR2OiIiIiOQx/cUgIiIiec9uh8XPw6ZLq6jf+z60eNK5MeUXdx/oNx/KN4GU8/DV/RC7x9lRFS8Jp2DBUJjxEJyPAr/y0Gcu9PwK/EKcHZ0UUh6uNvo1qwDA1PURTo5GRERERPKaEum3yG7X5ZtStGmMi0iusdvhp2dg6/8BFrjvY2jyiLOjyl/uvtBvAZRtAElx8NV9cPqAs6Mq+ux22D4dJjWG3QvAYoXmw2DYZqh+j7OjkyKgf/MwXG0Wtkae449j550djoiISKFmGIazQ5AiKrdyXC65spdixM3NDavVyokTJwgKCsLNzQ1LLi6OZrfbSUtLIyUlBasuMZbL5NfYMAyDtLQ0Tp8+jdVqxc3NLc+OJbkk7jDWP+ZT6/gfWPakQvlGUKKiyhRIwWDPhO+Hwc7ZZhKz22dQr7ezo3IOzwDo/42ZRI/ZBdO7wuDFULKysyMrmk7vhx9HQPQGczu4Ltw30fwyQySXlPHzoEvdsnz7+3Gmro/kw171nR2SiIhIoePq6orFYuH06dMEBQXlap6tIFMOMO/ldo5LifSbZLVaqVixIidPnuTEiRO5vn/DMEhOTsbT07PYfHDIjcnvseHl5UWFChX0YV5QJZ+HP781k5NHN2MDqgJ8u8h83M0XQupCSL2/biWrgk0f+5KPMtPh23/B7oVgscFDX8JtDzk7KufyCoQB35tJ9FN/wrQuMHgRBFZydmRFR3oKrPsA1n4A9nRw9YL2L0Ozx/UZKHlicKtwvv39OD/9cYLRnWpQ2s/D2SGJiIgUKjabjfLly3Ps2DEiIyOdHU6+UQ4w/+RWjkt/TdwCNzc3KlSoQEZGBpmZmbm67/T0dNasWUPbtm1xdXXN1X1L4ZafY8Nms+Hi4qIP8oImMwMOrzCT5/sWQ2aq2W6xYq/Ynqh4gzC3C1hP/QlpFyFqvXnL4uIJwbdlT64H1QQXXXUgeSAjDRYOgb0/gtUVuk+BWvc5O6qCwbskDPwept0LZ/bD9PvMmekBFZwdWeEXsRZ+GgFxh8ztqh3h3veu+t4ahkFiWiZnE9KIS0wlLiGNs4lpxCWm0aVuCKGBXvkbuxRadcsH0DisBNuizjFjUxQj767u7JBEREQKHR8fH6pWrUp6erqzQ8k3ygHmj9zMcSmRfossFguurq65PtBtNhsZGRl4eHjol0iy0dgoxmJ2wY7ZsGs+JJ76q710LajXB+r0INOzFH8sXkz5zp2xWi1w5gCc3PnXLeYPSEuAY1vNWxarK5SpdVlyvT6UqQ2unvn+MqUIyUiFeQ/DgSVgc4OeX6se9d/5BMHDP5jJ9LhDl2amLwH/cs6OrHBKOoux7D9YdswAIN0ziH0NXmFvwB3E7UgnLmGPI0kel5h6KXmeRmrG1WslVgryViJdbsqQ1hXNRPrmaJ5sXwUPV5uzQxIRESl0bDYbNlvx+T9UeZ7CR4l0EZGC6GKsmTjfORtid//V7lUK6vSA+n3Mer9Z36he/q29zcVMjpepZfYDc8G9s0fg5I7sCfaU83/dz2KxQVD17DPXg+uYiyWKXE96MszpZ1494eIBvWdClbucHVXB5BsMD/8IUzvDuQiz3MugReAX4uzICgTDMEhKyyTu0ozxs4lpl+6ncfbSDPK4hFTqnFvG0IQvKEE8ADMy7uTdc72JX+kN7LrucTxcrZT0dqekjxslvd0I9HYnyNc9j1+dFDV31ypDuQBPjp9P5ocdJ+jZJNTZIYmIiIhILlMiXUSkoEhPhv2LYeccOLQCjEulo2xuUL0T1OsLVe4E2y18U221Qqkq5q1Od7PNMOB8dPbE+skdkHgaTu0xbztn/7WPklX+llyva9Z7FsmSlgize0PEGrMudZ85UOl2Z0dVsPmVNZPp0zrD2cPmQqSDFoFPaWdHluuyEuNnE9M4k5D61wzxhMsS44mXyqskpOY4YxyggiWWsS5TaGszk+UH7OUYnf4If7rUpGSAO+E+bgR6uzmS5OZ9t0v33R33vdx0Oiz/nIvNysAWYYxbso8p6yPo0bi8SuSJiIiIFDH6y0FExJkMA6I3mQnrP7+D1At/PVa+KdTrDbc9CJ4lcv/YFguUCDNvWbWrDQMuxvwtub4T4o+Z5SfiDpkLR2YJqJC9LExIvSKZAJQbkHoRZvaE6A3g5gP95kNYS2dHVTgEhF6amX6vWZZp+n0w6CfwLuXsyHJ03cS4436ao09OifFruXzGeKC3G6W8rNx7cSFtTkzGxZ5KptWNUw2exrP5U3zt76PEuDhN7yYVmPDLQfbFXGTjkThaVi7Yv8MiIiIicnP0l4aIiDOcjYA/5poJ9HORf7X7h5rJ87q9zdnj+c1iMctK+IVkr2mdeObK5Pq5CHNG+/loc0HJLL4h2Weuh9QDv3J/laGRoiflAszoDse2gLsf9F8IoU2dHVXhUiL8r5rpp/fCV/ebyfV8vOrj8sR43GWzws9ecT9rUc5UUtJvPjHu7mKllI+7OUM8Kzl+adu8n8OM8aNb4cen4dSf5nbFtti6TCCkZOVcehdEbp2/lyvdG5Xn601RTFkXqUS6iIiISBGjRLqISH5JuWDOOt85x5y1m8XNB2rdby4cGtbKLMNS0HiXMsvKVLnzr7bk8+ZCqJcn188cgIsnzduBn//q61XyyuR6iYpKrhcFSWdhxoNw4nfwCIAB30K5hs6OqnAqWfmvmumxu+HrbjDwB/AM+Ee7NQyDc0npxFxIIfZiCqfiU4iNTyUmPoWY88kcPGbjnT1rOJuUlmuJcTMJ7p7tfslLiXIvN9vNl7xIiYcVb8DW/wMM8AyEjm+ZXzzqc0QKkEGtwvl6UxQr9sUSeSaR8FLezg5JRERERHKJEukiInkpMwOOrIads2DfIshIufSABSq1M5PnNbuAWyH8Q9szACq2MW9ZUhMg9s/syfXTeyEpDg6vNG9Z3P0hpG725HrJKmAtPqu0F3qJcfD1/eYXKl4lYcB35s9Ubl2pqpdqpt9r/v7MeND8csLD/6rdL6akExufaibHL6YQcyGV2PgUTl1MMRPn8amcvphKWmZOCXILkOLYujwxnpUcz1qE86/7f80iv6XE+I0yDPOKlyUvml/QgblexN1jwbtk3hxT5B+oHORDu+pBrN5/mmkbInntvtrODklEREREcokS6SIieSH2T7Nsyx/zICH2r/ZS1aF+H6jTE/zLOS++vOLuAxWambcs6SnmwqWXJ9dj/zTrwUeuNW9ZXL0guE725HpQjVtbYFXyVsIps5b36b3gXRoGfg9lajk7qqKhdA1S+36Ly4z7sB3fztkv7uenupM4nmQjNj6FmPgUTsWbCfPEtMwb3m1JbzfK+HlQxs+dMn4elPbzIMjblej9u7j79hYEB3jnfWL8Zlw4BotfMBdhBgisBF0maAFbKfCGtKrI6v2nmb/tKCPvroafh/4PExERESkKlEgXEcktCadg1wJz9nnMrr/aPQOhTg+zBEHZBsWvDIGrh1nq4/JyH5npcHp/9uR6zC5IT4Sjm81bFpsblKmdPbleura5X3GO+JPw1X1mKR+fYHMGdVA1Z0dVKGRk2olLTLs0WzyF2IvmbHKz7Mql+/EpnE9Kp7blBWa5jSXw7O9UXzmUcWkvksyV497Xw+WvBLmvB2X8PSjjaybLy/h7UMbPgyAfd9xcriwblZ6ezuLTf1A/NABX1wKS7LNnwpYvYOVYSEsAqyu0HgFtntfvvRQKbaqWokppHw6dSmD+tmMMbV3R2SGJiIiISC5weiL9k08+Yfz48cTExFCvXj0+/vhjmja99gJl58+f5+WXX+abb77h7NmzhIWFMWHCBDp37gzAa6+9xuuvv57tOdWrV2ffvn15+jpEpJhKT4EDS8y65weXg3FpdqjV1Vyss14fqNIBXNycG2dBY3OF4NvMW4N+Zps9E+IOX0qs77j07x/mzPUTv5u3LBYblK6ZPble5jZzRrzkrQvHYHpXOHsE/MqbC2RqoUcMw+B8Ujox8WaC/NSlGuSxl+qRZ5VaOZOQit24sX0eslXiBY/XmZD2Ks2s+1gc9AkrG35MqRIBlxLnHpT2dcfb3emnc7nn5E748Zm/ft9Dm0HXj8zfd5FCwmKxMKRVRf797S6mbYhgUMtwbNZi9iW6iIiISBHk1L+85s6dy8iRI/n8889p1qwZEyZMoGPHjuzfv5/SpUtf0T8tLY0OHTpQunRpFixYQLly5YiKiiIgICBbv9q1a/PLL784tl1citAfmCLifIYBx7bCjlnw5zfmIqJZyjUyk+e3PQRegc6LsTCy2sxZzUHVoG4Ps80w4Fxk9pnrJ3eYNddjd5u3HTMv7cBi1ljPSqyHNjNnwassTO45F2km0c9HQ0AFePgnKBHm7KjyXEJqxqWEePYE+d/v51yH/C82q4UgH/dsM8eD/c2keFaCPNjPAz9PF7PEytH68PUDVLy4naHHXoHWs4vezOy0RFg9DjZ+an4h6e4PHV6DhoMK5gLMItfxQINyvLt0H0fPJvPL3lg61g52dkgiIiIi8g85NcP8wQcf8OijjzJ48GAAPv/8cxYtWsSUKVN46aWXrug/ZcoUzp49y4YNGxyXH4eHh1/Rz8XFheBgnayKSC47F2XWPN85G84e/qvdrxzU7WWWbgmq7rz4iiKLBQIrmrfa3cw2w4D4E39Lru+Eiycg7qB5273A7Ovmay6GWqmdeStVrfiV1sktcYfNmujxx8xa1Q//CP7lnR3VP5Kakckpx2zx1EulVlKIvbRIZ+xFM0GekJpxw/ss6e1G6UtlVoIv1SHPKrkS7O9BaT93Snq739zs1NCm0G8+zHjIXLB33gDoNQNc3G/hVRdAB5fDTyPhQrS5XfsBuOdt8NW5nBRenm42+jStwGerDzNlXYQS6SIiIiJFgNMS6WlpaWzfvp3Ro0c72qxWK3fddRcbN2686nN++OEHWrRowbBhw/j+++8JCgqib9++jBo1CpvN5uh38OBBypYti4eHBy1atGDcuHFUqFAhz1+TiBRBqRdhz/dm6ZZsi2J6Q637zOR5eBtzNrXkD4vFXKjVvxzU6PxXe8IpsxTMyR1mWYio9ZB8zlyoMGuxQt+yfyXVK92uRN2NOn3AnImeEAMlq5pJdL8QZ0d1XRmZdo6fTyYqLomos0lExyUSFZfE0XPJxFxI5lxS+g3vy9fdhdJ+7gT7e1DG1+OqyfIgX3fcXfLosyCsJfSdBzN7wMFlMH8w9JxeuK+4uBgLP79kXtkD4F8B7n0fqt3t3LhEcsnAFmF8seYImyPO8ueJC9Qu6+/skERERETkH3BaIv3MmTNkZmZSpkyZbO1lypS5Zj3zI0eOsHLlSvr168fixYs5dOgQTz75JOnp6bz66qsANGvWjGnTplG9enVOnjzJ66+/Tps2bdi9eze+vr5X3W9qaiqpqamO7fj4eMBcgCs9/cb/yM4NWcfL7+NKwaexkY/smVgi12DdNRfLvkVYMpIBMLBghLfGXqc3Ro17we1SPe5Mu3lzIo0PwL0EhN9u3gAMO8T8gTViDZbIX7FEb8Jy8YS5GOzOWWaXoBrYK96OEX47RoUW4H71/ycKu380Pk7txWXWg1gST2ME1SCj7zfgWQoKyFhLSsvg6Nlkos8mE30uieizSUTFJRN9NokTF1LIvE5BcjcX66XyKu6U9nV3lFcp/be2G6pDbthJT8/Dz4LyzbH0+BrbvH5Y9i/CPn8ImQ98AdZbP51zymeHYcfy+9fYVr2BJeUChsWKvenj2NuOAjfvAjO28lux/vwuokL8PelcJ4Qfd55g6vpI3utRz9khiYiIiMg/UKiKh9vtdkqXLs0XX3yBzWajUaNGHD9+nPHjxzsS6Z06dXL0r1u3Ls2aNSMsLIx58+YxdOjQq+533LhxVyxQCrBs2TK8vLzy5sVcx/Lly51yXCn4NDbyjm/ycULPrqP8uQ14pp9ztF90D+FoYGuOBbYg2a0UHAOOrXFeoDnQ+LiaKhBQBavfAEomHCDo4p8EXfwT/+QoLKf3YTu9D7b8Dzs2znlX5pTvbZz2rc1574oYlkL13+R13ez48EuKouWhd7BkJnDBswIbygwnbc22PIru6gwDEjPgTAqcSbGY/6ZaOJNiIS4F4tNzLpHiajEo6QGlPC79625QygMC3A38XcHLBSyWNODiX0+KN29xxyEO2JuXL/AWlA4bTtOIj7Dt+4ETn59ie9jjYPlndcTz67PDN/k49Y5OpWTiAQDOe4azo8IQLqSFwy+/5ksMBVVSUpKzQ5A8MKRVOD/uPMEPO04w6p4aBPkWkZJMIiIiIsWQ0zIEpUqVwmazERsbm609Njb2mvXNQ0JCcHV1zVbGpWbNmsTExJCWloabm9sVzwkICKBatWocOnTomrGMHj2akSNHOrbj4+MJDQ3l7rvvxs/P72Zf2j+Snp7O8uXL6dChg6MOvAhobOSZxDNY93yD5Y+5WGN2OpoNjwDstR/EqNMbj7INqGqxUNWJYV6PxsfNy0g6iyVqLZaIX7FGrMF6PpKSiQcomXiAmjHfYLj5YIS1wqjYDnvF282SJoW0vvqtjA/Lid+xzX4aS2YC9pD6ePWZz12eJfIkvky7QUx8CtFnk8yZ5X/793o1yv09XQgL9CI00IsKgZ5UuOzf0j7uWG+mHnmh0BnjQAOMhYMof24TZcuHkdll4i2VmMq3z46MFKzrPsS6cyIWezqGqzf2dqPxbvwIrf7BjPqiJOuKSClaGlQoQf3QAHYcPc/MzVGMuKuas0MSERERkVvktL9c3NzcaNSoEStWrKBbt26AOeN8xYoVDB8+/KrPadWqFbNmzcJut2O1mjOvDhw4QEhIyFWT6AAJCQkcPnyYAQMGXDMWd3d33N2vnB3i6urqtISUM48tBZvGRi7ISIUDS81FQw8uA/ulJJ3VBap2hHq9sVTriK0QLuSn8XET/MtA3e7mDeBsBBxZbd4ifsWSfA7LwaVwcCk2AN+QS7XV2xfa+uo3PD6OboVZD0FqPJRvgrX/Qqwe/6y2b0p6JsfOJZn1yuOySrAkEnU2iWNnk0m7TnmkEH8PKgR6EVbSi7CS3n/dD/TG36sYjvnaXcEyFeYPwrprLlYXN+g6Eay3NjM9Tz87ItbAjyP+WqS5WicsncdjCwhFq0v8RZ/dRdeQ1hV5evbvzNgUzRPtKufdWgoiIiIikqecOgVo5MiRPPzwwzRu3JimTZsyYcIEEhMTGTx4MAADBw6kXLlyjBs3DoAnnniCSZMm8cwzz/DUU09x8OBB3nrrLZ5++mnHPp9//nm6du1KWFgYJ06c4NVXX8Vms9GnTx+nvEYRKSAMA45vN5Pnuxeai1BmCakP9fvCbQ+BdymnhShOFljRvDUeDHY7xOz8K7EetREunjTHz87ZZv+gmmZivXJ7cyHIolJfPWqDuaBlWgJUaAn95t3wa7uQnE50XBJRZxMvJcwTHUnzmPgUjBzKlbvaLISW8KJCSS/CAr2oUNKb8JJmsrx8CS88XJV4ukKt++ChL2HhI/D71+bCo/d+UHCunEg6C8tegR0zzW2fYOj8LtS8r+DEKJIPOt0WTLCfBzHxKfy08yQPNSrv7JBERERE5BY4NZHeq1cvTp8+zZgxY4iJiaF+/fr8/PPPjgVIo6OjHTPPAUJDQ1m6dCnPPvssdevWpVy5cjzzzDOMGjXK0efYsWP06dOHuLg4goKCaN26NZs2bSIoKCjfX5+IFADnj8Ifc2HnHIg7+Fe7bwjU7Qn1+kDpms6LTwomqxXKNjBvrZ+F9GSI3vRXYv3kTji917xt/sy8mqF8k0uz1dtBuYZmUrOwOfIrzO4N6UlQsS30mWMu/niJ3W5w6mKqYya5mTRPIvrS9vmknBdL9HF3uTSj3IsKgd6XZpSbyfMQf09sRa4ESz647SHIzIBv/wXbpoDNDe5527mJasMwP3eX/huS4gALNB4Cd70K//DKBpHCyNVmZWDLMN79eT9T1kfwYMNyWPRlkoiIiEih4/SilMOHD79mKZfVq1df0daiRQs2bdp0zf3NmTMnt0ITkcIqNQH2/mDOHI5YC1yaBuviCTW7Qr3eZrLzFuoJSzHl6mnOPK/c3txOOgsRv/6VWD8XCdEbzdvqt8DNF8Jbm/0rtYNS1Qr+DNxDv8CcfpCRQlKFdmxr/DGR208TFRd1aVZ5ItFnk0hJz7kES5CvuyM5HnYpWZ41yzzQ203Jo7xQrxfY0+H7YbD5c/OLnbvHOmfMxR2GRSPN3wuA0rWg60cQ2jT/YxEpQPo0qcDEFQf580Q8WyLO0qxSSWeHJCIiIiI3yemJdBGRXGHPhMi1sGO2mURPT/rrsbDWUL+PWU7AI38XEJYiyisQaj9g3uCK+uokn4MDS8wbXFZf/dLNyfXVE1MzHMnxqLgkPCKW0zfyFVxJZ0VmA548MIjUA7uu+lyb1UK5AM9Ls8r/ml0eXsrc9nLTqYVTNOgPmenw0wjYOMm8IuLOV/MvmZ6RBhsmwprxkJECLh5w+4vQ8unCeXWGSC4r4e3GAw3KM3tLNFPXRyqRLiIiIlII6a9dESncTh8wZ57/MRfij//VHlgJ6vU1y7eUCHNefFI83Gp99UrtILxVntVXT0zNYMfR82yNiGPdQSvTvtjM0XPJnElIc/TpaN3Kx64TcbVksiSzCU+nP4WLqzvVHQt7mvXKwy5tlw3wxNV2awtaSh5rPNhcPHnx87DuQ7C5Q/vReX/c6M1mAv/UHnO7Ujvo8qH5OSwiDkNahTN7SzTL9sRw9GwSoYFezg5JRERERG6CEukiUjj9+Z05+/H49r/aPPyh9oPmwqHlmxT8UhpSNN1yffV2Zo31f1Bf/dTFFLZFnmNr5Fm2RZ5jz8l4Mu1ZK3xagQuOvoHebvTx2srIix9hw0502U4E3fUR60v5E+TrrhIshVXTRyEzzaxP/uvbYHOBti/kzbFSLsAvr5u12THAqyR0HGd+ganxI3KFqmV8aVO1FGsPnmH6hkhe6VLL2SGJiIiIyE1QIl1EChe7HVa8DusnmNsWG1TtYNY9r9YJXD2cGp7IFW6qvvq4v+qrV2pnPuca9dUNw+Dw6US2RZ5la+Q5tkWdJSou6Yp+5QI8aVjBH+P8ce5u0YBKpf2oUNILv/0L4bvxgB3q9qZCt0+poHUDioYWw8wyL7+8CivHmguQtnom9/ZvGLDne1gyChJizLb6/eHuN82yRyJyTUNaVWTtwTPM3XqUER2q4eOuP8dERERECguduYlI4ZGaAN88BvsXmdstnzLr7/qUdm5cIjfjFuurZ4S1ZY9XIzadcmFr5Dm2R53jbGJatl1bLFAj2I8m4SVoHB5I47ASlA3wJD09ncWLj9HptmBcXV3ht6/hh6cAAxoMMBeDVBK9aGk9wkymrxoLy8eA1RVaPPnP93v+qFk65sDP5nZgZeg6ASq2/ef7FikGbq8WRKVS3hw5k8iCbUcZ1Kqis0MSERERkRukRLqIFA7nj8LsPhC7y6z7e/8ks3yASGF3jfrqGYdWYY3eiPVSfXWXnbOpC7jby+Niv41M+23scKlN1dAQmoQH0ji8BA3DSuDncZ2yMFv/DxY9Z95vPBQ6v2eWo5Gi5/YXwJ4Ov74DS0ebJYOaPnpr+8rMgC3/g5X/hfREMzHf+llo85yuBBK5CVarhcGtwvnP938ybUMkA1uEY7WqFJKIiIhIYaBEuogUfEe3wpy+kHgKvIOg9ywIbersqERy1ckLyWaJlkhXtkY2Zl9MNdyMwTS27qe1dTetrLu5zRpJdesxqluPMYSfMawuWFybgHs78GwHro1yPIZ1y/9g+cvmRvMnoeNbqmVd1LUbbdZMX/ehOZPc5gqNBt3cPk7sgB+fNuv7A1RoAV0mQOkauRysSPHwYMPyjF+6n8i4JFbtP8WdNcs4OyQRERERuQFKpItIwfbHfPh+GGSmQpnboM9sCKjg7KhE/hG73eDgqYRLi4KaNc6Pn0++ol9wyQCCwzpRIrwvXuGBWLxTIXKNoxSMJaf66pXaQVB1R6K8SuwibL/PNXfcagTc9ZqS6MWBxQJ3vmqWedk4CX4cYc4mb9Dv+s9NTTDH1aZPwbCbCzp3eAMaDNRVDCL/gLe7C32aVuB/a44wZX2EEukiIiIihYQS6SJSMNntZgJnzbvmdvXO8OCX4O7j3LhEbkFKeia7jl+4lDg/x7bIs8SnZGTrY7NaqBXiR+PwEmaplrASlPb7e8kMn1uqr261eVD7xKUk+u2jzFnKSqIXHxYL3D3WTKZv+Z/55aTNNefyWAeWmiWALhw1t297CDqOA18l/ERyw4AWYXy59gjrD8WxLyaeGsF+zg5JRERERK5DiXQRKXjSkuC7x2HP9+Z2q2fMGZVaDFEKiXOJaWyPOsfWKDNxvuvYBdIy7dn6eLnZaFihhCNxXj80AG/3m/xv+Rr11TmyGqI2wqX66lm/OZm3/xtb+1G58RKlsLFYoNM7Zs30bVPg23+B1QWqd83e72IMLBkFe74ztwMqwL0fQNUO+R6ySFFWvoQX99wWzOJdMUxbH8nbD9V1dkgiIiIich1KpItIwRJ/wlxU9OQOs/xA149urASBiJMYhsGxc8lsvVSiZVvkWQ6eSriiXykfd5pWLEHjsECahAdSM8QXF1sulsewWqFsA/PW+llIT4ajm+HwKuzRm9htVKNm65Ho66hizGKBzu+bNdN/nwELH8Hy4BTAYpZu2ToZfnkdUi+AxQYtnjSvXnDzdnbkIkXSkFYVWbwrhm9+P84LHatT0sfd2SGJiIiISA6USBeRguP4b2YSPSEGPAOh90wIa+nsqESyybQb7D0Zb9Y2jzIT57HxqVf0qxzkbZZoCQ+kSXgJKgR6YcnPciquno5a6Znp6UQsXkzN/Du6FFRWK3SdCJkZ8MccbN8+QsWyvbF9NQmObTH7lG1g9gnRDFmRvNQorAR1y/vzx7ELzN4SzfA7qjo7JBERERHJgRLpIlIw/PkdfPs4ZCRDUA3oM8csWSHiZElpGew4ep5tkefYGnmW36PPk5Cavb65i9VCnfL+jtrmjcJKaGahFFxWG3T7FOzpWHYvpO6xr812Nx+44z/Q9FGV0hLJBxaLhcGtwnl27k6+2hjFY20r4+aihXxFRERECiol0kXEuQwD1rwHq8aa21U6QPcp4KFFt8Q5ziSkOhYE3Rp1jj+PXyDDbmTr4+vuQsOwEjQJL0Hj8EDqlQ/A002JRylErDZ44AvsmRlY936PvVonrPe+B/7lnR2ZSLFyb52yvLV4H6cuprJk90nur1/O2SGJiIiIyDUokS4izpOeDN8Ph90LzO3mT8LdYzUTUvKNYRhExiWxNfIs2yLNhUGPnEm8ol+wnwdNKpolWhqHBVI92BebNR/LtIjkBZsLmQ/8H8tsd3Ln/X2xuro6OyKRYsfNxcrA5mG8v/wAk9dFcF+9svlbBkxEREREbpgS6SLiHBdjYU5fOL4NrC7Q+T1oPNjZUUkRl55pZ8+J+EuJ83NsizrLmYS0K/pVL+NL4/ASl2qcl6BcgKcSG1I0WSykugY4OwqRYq1vswp8vOoQfxy7wG/R52gUFujskERERETkKpRIF5H8d/IPc1HR+GPgEQC9voaKbZ0dlRRBCakZ/B59jq2XSrX8Hn2e5PTMbH3cbFbqhfo7FgVtVCEQfy/NzBURkfxR0sedbvXLMm/bMaasi1QiXURERKSAUiJdRPLX3p/gm0chPQlKVoG+86BkZWdHJUXEheR0Nh+JY8PhOLZFnWXPiXj+Vt4cf09XM2EeZibObyvnj4erygmJiIjzDG5VkXnbjvHznzEcP59MuQBPZ4ckIiIiIn+jRLqI5A/DgPUT4JfXAQMqtYMe08CzhHPjkkItKS2DrZHn2HD4DBsPx7H7+IUrEuehgZ40CQt0zDivHOSDVfXNRUSkAKkZ4kfLyiXZcDiOrzZGMrpTTWeHJCIiIiJ/o0S6iOS9jFT4cQTsnGVuN3kE7nkbbCqfITcnNSOTHdHnWX84jo2Hz7Dj6HnSM7NnzisFedOyckmaVSxJk/BAgv09nBStiIjIjRvSqiIbDscxe3M0z9xZFS83/akmIiIiUpDo7ExE8lbiGZjTD45uAosNOr0DTR91dlRSSGRk2tl9It4x43xr5FlS0u3Z+pQL8KRl5ZK0rFKSFpVKKXEuIiKF0h01ShNW0ououCQW/nacAc3DnB2SiIiIiFxGiXQRyTuxe2B2LzgfDe7+0GMqVLnT2VFJAWa3G+yPvciGSzPONx85y8XUjGx9Svm4m4nzyiVpWbkUoYGeWCwq1SIiIoWb1WphUMtwXv9xD1PXR9CvaQWVIhMREREpQJRIF5G8cWApLBgCaQlQoqK5qGhQNWdHJQWMYRhEnEm8lDiPY+OROM4mpmXr4+fhQvNKJWlVpRQtK5ekSmkfJc5FRKRI6tE4lA+WHeDI6UTWHDxNu+qlnR2SiIiIiFyiRLqI5C7DgE2fwrJXwLBDeBvo+RV4BTo7Mikgjp9PZsMhs1TLhsNxxMSnZHvcy81Gk/BAx4zzWmX9sGlGnoiIFAM+7i70bBLK5HURTFkfqUS6iIiISAGiRLqI5J6MNFj8PPw23dxuOBA6vw8ubs6NS5zq9MVUNh2JY8PhM2w4HEdUXFK2x91sVhqGBdCysjnjvG75ANxcrE6KVkRExLkGtQxn6voI1hw4zcHYi1Qt4+vskEREREQEJdJFJLcknYV5AyFyLViscPdYaP4kqARHsXMhOZ3NR+Ic5Vr2x17M9rjNaqFueX/HjPNGYSXwcLU5KVoREZGCJTTQi7tqlmHZnlimbojkrQfqODskEREREUGJdBHJDacPwKyecC4C3Hyh+2So1tHZUUk+SUrLYGvkOTYcNsu17D5+AbuRvU+tED8zcV6lJE3CA/H1cHVOsCIiIoXAkNYVWbYnlm9+O8aLHasT4KWr+0REREScTYl0EflnDq2A+YMh9QIEVIA+c6FMLWdHJXkoNSOT36PPX5pxfoYdR8+Tnpk9c14pyJtWl0q1NKtUkkBvJQBERERuVLOKgdQK8WPPyXhmbznKE+0qOzskERERkWJPiXQRuXWbv4CfXwIjEyq0gF4zwLuUs6OSXJaRaWfX8QuOUi1bI8+SmmHP1qdcgKdjxnmLSqUI9vdwUrQiIpJb1qxZw/jx49m+fTsnT57k22+/pVu3bjk+Z+bMmbz77rscPHgQf39/OnXqxPjx4ylZsmT+BF1EWCwWhrSuyPPzd/LVxkgeaVMRV5vWDxERERFxJiXSReTmZaabCfSt/2du1+sLXSeAi7tTw5LcYbcb7I+9yPpDZqmWzRFnSUjNyNanlI/7pRrnZp3z0EBPLKqHLyJSpCQmJlKvXj2GDBnCgw8+eN3+69evZ+DAgXz44Yd07dqV48eP8/jjj/Poo4/yzTff5EPERUvXeiG8vWQvJy+k8PPuGLrWK+vskERERESKNSXSReTmJJ+D+YPgyGrAAne9Bq2e0aKihZhhGEScSXTMON94JI6ziWnZ+vh5uNDiUtK8ZeWSVCnto8S5iEgR16lTJzp16nTD/Tdu3Eh4eDhPP/00ABUrVuRf//oX77zzTl6FWKS5u9jo1yyMj1YcZOr6CCXSRURERJxMiXQRuXFxh2FWL4g7CK7e8OAXULOLs6OSW3D8fDIbLs0433A4jpj4lGyPe7nZaBIeSKsqZvK8ZogfNqsS5yIicm0tWrTg3//+N4sXL6ZTp06cOnWKBQsW0LlzZ2eHVmj1a16Bz1Yf5rfo8/wefY4GFUo4OyQRERGRYkuJdBG5MRFrYO4ASDkPfuWhz2wIqevsqOQGnb6YysYj5uKgGw7HERWXlO1xN5uVhmEBjhnndcsH4OaiWqwiInLjWrVqxcyZM+nVqxcpKSlkZGTQtWtXPvnkk2s+JzU1ldTUVMd2fHw8AOnp6aSnp+d5zJfLOl5+HzcnJTxs3Fs3mG9/P8HktUf4sKfOvZyhII4NKTg0PiQnGh+SE42PguFm3n8l0kXk+rZNhcXPgz0DyjWG3rPAt4yzo5IcXEhKZ1NE3KUZ52c4EJuQ7XGb1ULd8v6OGueNwkrg4WpzUrQiIlIU7Nmzh2eeeYYxY8bQsWNHTp48yQsvvMDjjz/O5MmTr/qccePG8frrr1/RvmzZMry8vPI65Ktavny5U457LVUyAVxYvPskTVyPEaAlaZymoI0NKVg0PiQnGh+SE40P50pKSrp+p0uUSBeRa7NnwtKXYfNn5nadHnDfJHD1cG5ccoWktAz2nrewa+kBNkecY/eJCxhG9j61QvzMxHmVkjQJD8TXw9U5wYqISJE0btw4WrVqxQsvvABA3bp18fb2pk2bNowdO5aQkJArnjN69GhGjhzp2I6Pjyc0NJS7774bPz+/fIsdzNlIy5cvp0OHDri6Fqz/I1fHb2Vr5DlifKrSt0NVZ4dT7BTksSHOp/EhOdH4kJxofBQMWVdE3ggl0kXk6lLiYcEQOHTpm9H2r0Db57WoaAGz50Q8MzZH8f3vx0lMswGRjscqB3k7SrU0q1SSQG83p8UpIiJFX1JSEi4u2f+8sNnMq52Mv3+7e4m7uzvu7ldOsXZ1dXXaH5TOPPa1DG1dia2R25mz7RhP31UdTzddReYMBXFsSMGh8SE50fiQnGh8ONfNvPdKpIvIlc5GwOzecHofuHjCA59D7W7OjkouSUnP5Kc/TjJzcxS/R593tAe4Gdx5WznaVC1Ni8olKeOnKwdEROTWJSQkcOjQIcd2REQEO3bsIDAwkAoVKjB69GiOHz/OV199BUDXrl159NFH+eyzzxylXUaMGEHTpk0pW7ass15GkdChVhnKl/Dk2LlkvttxnD5NKzg7JBEREZFiR4l0EckuagPM7Q9JceAbYtZDL9fQ2VEJcPh0AjM3RbPwt2NcSDYXw3CxWuh4WzC9G5Ujbu8m7r33Nn2TLSIiuWLbtm20b9/esZ1VguXhhx9m2rRpnDx5kujoaMfjgwYN4uLFi0yaNInnnnuOgIAA7rjjDt555518j72osVktDGoZzthFe5myLoLeTUKx6CpBERERkXylRLqI/OX3mfDjM2BPh5D60Gc2+GkGmTOlZdhZtieGmZui2XgkztFeLsCTvs0q0KNxeUr7epCens7ifU4MVEREipx27dpdsyQLwLRp065oe+qpp3jqqafyMKriq2eTUD5cfoCDpxJYd+gMbaoGOTskERERkWJFiXQRMRcV/eU12DDR3K51P3T7HNy8nBpWcXb0bBKzt0Qzb9tRziSkAWC1wB01StOvWRhtqwVhs2ommoiISHHh5+FKj8ahTNsQydT1kUqki4iIiOQzJdJFirvUBPjmUdi/2Ny+fRTc/hJYrc6NqxjKtBus2neKmZujWH3gNFmTAEv7utO7SSi9mlagXICnc4MUERERp3m4ZTjTN0ayct8pjpxOoFKQj7NDEhERESk2lEgXKc7OHzUXFY3dDTZ36PYp1Onu7KiKndj4FOZuPcqcLdGcuJDiaG9TtRT9mlXgzpplcLXpiw0REZHirmIpb+6sUZpf9p5i2oZI3rj/NmeHJCIiIlJsOD0z88knnxAeHo6HhwfNmjVjy5YtOfY/f/48w4YNIyQkBHd3d6pVq8bixYv/0T5FiqWjW+DL9mYS3bs0DF6sJHo+stsN1h48zeNfb6fl2yv5YPkBTlxIoYSXK4+1rcSq59vx9dBm3HNbiJLoIiIi4jC4VUUAFmz/a/FxEREREcl7Tp2RPnfuXEaOHMnnn39Os2bNmDBhAh07dmT//v2ULl36iv5paWl06NCB0qVLs2DBAsqVK0dUVBQBAQG3vE+RYumPefD9cMhMhTJ1zEVFA0KdHVWxcDYxjQXbjzJrczSRcUmO9ibhJejXLIx7bgvGw9XmxAhFRESkIGtZuSTVy/iyP/Yi87Ye5dG2lZwdkoiIiEix4NRE+gcffMCjjz7K4MGDAfj8889ZtGgRU6ZM4aWXXrqi/5QpUzh79iwbNmzA1dUVgPDw8H+0T5FixW6HVf+Fte+Z29XvhQe/AHfV18xLhmGwLeocMzdFsXhXDGmZdgB83V14sGE5+jYLo3qwr5OjFBERkcLAYrEwpHU4oxbuYtqGSAa3CsdFV6+JiIiI5DmnJdLT0tLYvn07o0ePdrRZrVbuuusuNm7ceNXn/PDDD7Ro0YJhw4bx/fffExQURN++fRk1ahQ2m+2W9gmQmppKamqqYzs+Ph6A9PR00tPz93LJrOPl93Gl4PvHYyMtEduPw7Hu+xGAzJbPYG/3MlisoPGWJy6mpPP9zpPM3nKMA6cSHO21y/rSt0ko99YJxtvd/Bj+p7/z+uyQnGh8yLVobBQc+hnIzbi/fjne+Xk/x88ns3xPLJ3qhDg7JBEREZEiz2mJ9DNnzpCZmUmZMmWytZcpU4Z9+/Zd9TlHjhxh5cqV9OvXj8WLF3Po0CGefPJJ0tPTefXVV29pnwDjxo3j9ddfv6J92bJleHl53cKr++eWL1/ulONKwXcrY8Mj7SzNjkwgIDmSTIsLO0OHcDS5ESz5OQ8ilKMJsD7WyvYzFtLsFgBcrQaNShm0KmOngs85OHWOX1f8kevH1meH5ETjQ65FY8P5kpKSrt9J5BIPVxt9m1Zg0qpDTFkfoUS6iIiISD5wammXm2W32yldujRffPEFNpuNRo0acfz4ccaPH8+rr756y/sdPXo0I0eOdGzHx8cTGhrK3XffjZ+fX26EfsPS09NZvnw5HTp0cJSvEYFbHxuWE79jm/8CluRYDK+SGN2nUye0OXXyMNbiKCktg0W7Ypi99Ri7jsc72qsEedOnaSjd6oXg55l3v9P67JCcaHzItWhsFBxZV0SK3KgBLcL4/NfDbI08x65jF6hT3t/ZIYmIiIgUaU5LpJcqVQqbzUZsbGy29tjYWIKDg6/6nJCQEFxdXbHZ/lqIr2bNmsTExJCWlnZL+wRwd3fH3d39inZXV1en/VHpzGNLwXZTY2P3N/DdE5CRAkE1sfSdg0uJ8DyNr7g5EHuRWZujWfjbMS6mZADgZrPSqU4w/ZqF0SS8BBaLJd/i0WeH5ETjQ65FY8P59P7LzSrj50GXuiF8t+MEU9dH8EGv+s4OSURERKRIc9qqNG5ubjRq1IgVK1Y42ux2OytWrKBFixZXfU6rVq04dOgQdrvd0XbgwAFCQkJwc3O7pX2KFEmGAavfgQWDzSR61bth6DJQEj1XpGZk8v2O4/T8fCN3f7iGaRsiuZiSQVhJL0Z3qsHG0XfwUe8GNK0YmK9JdBERESlehrSuCMCPf5zgVHyKk6MRERERKdqcWtpl5MiRPPzwwzRu3JimTZsyYcIEEhMTGTx4MAADBw6kXLlyjBs3DoAnnniCSZMm8cwzz/DUU09x8OBB3nrrLZ5++ukb3qdIkZeeDN8Pg90Lze0Ww6HDG2C15fw8ua6ouERmbYlm/rZjnE1MA8BmtXBXzdL0axZG6yqlsFqVOBcREZH8Ubd8AI3CSrA96hwzNkUx8u7qzg5JREREpMhyaiK9V69enD59mjFjxhATE0P9+vX5+eefHYuFRkdHY7X+NWk+NDSUpUuX8uyzz1K3bl3KlSvHM888w6hRo254nyJF2sUYmNMXjm8Hqwvc+wE0etjZURVqGZl2ftl7ipmbo1h78IyjPcTfg95NKtCrSSjB/h5OjFBERESKsyGtKrI96hwzN0fzZPsqeLhq8oSIiIhIXnD6YqPDhw9n+PDhV31s9erVV7S1aNGCTZs23fI+RYqskzthdh+IPw6eJaDn11CxjbOjKrROnE9mztajzN0aTWx8KgAWC9xeLYh+zcJoXz0IF5vTqmOJiIiIANCxdhnK+ntw4kIKP+w8Qc/Goc4OSURERKRIcnoiXURywd6f4JtHIT0JSlWDPnOgZGVnR1Xo2O0Gaw6eZubmaFbsjcVumO0lvd3o2SSUPk0qUKGkl3ODFBEREbmMi83Kwy3DGbdkH1PWRdCjUXmt0SIiIiKSB5RIFynMDAPWfQgrXje3K98B3aeCZ4BTwypsziSkMm/bUWZviebo2WRHe/NKgfRrFkbH2sG4uWj2uYiIiBRMvZtUYMIvB9kXc5GNR+JoWbmUs0MSERERKXKUSBcprDJS4Yen4Y855nbTx6DjOLDp1/pGGIbB5oizzNwczc+7T5KeaU4/9/NwoXujUPo2C6VKaV8nRykiIiJyff5erjzUqBwzNkUzZV2kEukiIiIieUAZN5HCKOE0zO0HRzeDxQad3oGmjzo7qkLhQlI6C387xszNURw+nehorx8aQL9mFehStyyeblqkS0RERAqXQS0rMmNTNCv2xRIVl0hYSW9nhyQiIiJSpCiRLlLYnNoD8/rDhWjw8Ice06Fye2dHVaAZhsGOo+eZuTmaH3eeIDXDDoCXm41uDcrRt2kFbivn7+QoRURERG5dldI+tKsexOr9p5m2IZJXu9Z2dkgiIiIiRYoS6SKFSJkLv+My/QlIS4TAytB3LpSq6uywCqzE1Ay+33GCmZuj+PNEvKO9RrAv/ZqH0a1+WXw9XJ0YoYiIiEjuGdKqIqv3n2b+tmOM7FBN5zkiIiIiuUiJdJHCIPk81rUf0uzIR1gwILwN9PwKvAKdHVmBtPdkPDM3R/Hd7ydISM0AwM3FSpe6IfRrFkbDCgFYLBYnRykiIiKSu9pULUWV0j4cOpXAvG3HGNq6orNDEhERESkylEgXKchS4mHz57BhErbUCwBkNhiIrcsHYNMMo8ulpGeyeNdJZmyK4rfo8472SqW86dusAt0blSfAy815AYqIiIjkMYvFwuBW4bz87W6mb4hkUMtwbFZNHhARERHJDUqkixREaYmw5QtY/xEknwPACKrJVp8ONOj0H2xKojscOZ3ArM3RLPjtGOeT0gFwsVroWDuYfs0q0KJySc0+FxERkWLjwQbleffn/USfTWLF3ljurh3s7JBEREREigQl0kUKkvRk2DYF1n0IiafNtlLVoN1LZFTrwsklP9NASWEMw2DV/lP839oINhyOc7SXC/Ckb7MK9GhcntK+Hk6MUERERMQ5PN1s9G1Wgc9WH2bK+ggl0kVERERyiRLpIgVBRir89hWseQ8SYsy2EhWh3UtQpwdYbZCe7twYC4h9MfGM/Wkv6w6dAcBqgTtqlKZfszDaVgvS5csiIiJS7A1oHsYXa46w6chZ/jxxgdpl/Z0dkoiIiEihp0S6iDNlpsPvM8wEevwxs80/FG5/Eer1UR30y8QlpPLB8gPM3hKN3QA3m5VBrcJ5uGU45QI8nR2eiIiISIFRNsCTTrcF89MfJ5m2PpLxPeo5OyQRERGRQk+JdBFnyMyAP+bCr+/A+SizzbcstH0OGgwEFy2KmSUtw85XGyP5aMVBLqZkANC5TjCjO9UkNNDLydGJiIiIFExDWlfkpz9O8v2OE4zqVINSPu7ODklERESkUFMiXSQ/2TNh9zfw69sQd8hs8y4NbUZCo8HgqrreWQzDYMXeU/x38V4iziQCUCvEjzFda9G8UkknRyciIiJSsDWsUIL6oQHsOHqemZuieeauqs4OSURERKRQUyJdJD/Y7bD3B1j9Npzea7Z5BkLrEdDkEXDzdmp4Bc3+mIuMXbSHtQfNOuilfNx5oWM1ujcKVQ10ERERkRs0uFU4z8zZwdeboni8XSXcXWzODklERESk0FIiXSQvGQbsXwKr3oLYXWabhz+0fAqaPQ7uvs6Nr4A5m5jGB8v3M2vzX3XQh7apyJPtKuProXrxIiIiIjejc50Q3lq8l9j4VBb9cZIHG5Z3dkgiIiIihZYS6SJ5wTDg0ApY9V848ZvZ5uYLLZ6E5k+CZ4BTwytorlYHvdNtZh30CiVVB11ERETkVrjarAxsEc74pfuZvC6CBxqUw2LR1X0iIiIit0KJdJHcduRXM4F+dLO57eoFzf4FLZ8Gr0DnxlbAGIbByn2n+O+ivRy5rA76f7rUokVl1UEXERER+af6Nq3AxBUH+fNEPFsjz9G0os5HRURERG6FEukiuSVqo5lAj1xrbrt4mPXPW40AnyCnhlYQHYi9yJs/XV4H3Y0XOlZXHXQRERGRXFTC240HG5Zj9pajTFkXoUS6iIiIyC1SIl3knzq2HVaNhcMrzW2bGzQaBK1Hgl+IU0MriM4mpvHh8gPM3BzlqIM+pHVFhrVXHXQRERGRvDC4VUVmbznKsj0xHD2bRGigSueJiIiI3Cwl0kVu1cmd5iKiB342t60u0KA/tH0B/LWQ09+lZdj5elMUH/1ygPhLddDvqR3M6M41CCvp7eToRERERIquamV8aVO1FGsPnuGrjZG8fG8tZ4ckIiIiUugokS5ys2L3wOq3YO+P5rbFCvX6mAn0wIrOja0AMgyDVftPMfanv+qg1wzxY4zqoIuIiIjkmyGtKrL24BnmbD3KM3dVw8ddfwqKiIiI3AydPYncqDMHYfU42P0NYAAWqNMDbh8Fpao4O7oC6Wp10J+/uzo9GqsOuoiIiBQgGWlYl7xIqYtlgM7OjiZP3F4tiEqlvDlyJpGF24/xcMtwZ4ckIiIiUqgokS5yPWePwK/vwh9zwbCbbbW6QbvRULqGU0MrqM4mpjHhlwPM3BxNpt3AzWZlcOtwhrevojroIiIiUvBs/Bjbb1NpYvOGcw9B6WrOjijXWa0WBrUKZ8z3fzJtQyQDmodh1cQGERERkRumRLrItZyPNhPoO2aBkWm2Vb8X2o+G4DrOja2ASs+08/XGKCaoDrqIiIgUJs2fxL7nR9xO/o4xrz888gt4+Dk7qlz3UMPyjF+6n4gziaw+cIo7apRxdkgiIiIihYYS6SJ/F38C1rwHv30F9nSzrUoHaP9vKNfQubEVUI466Iv2cuT0X3XQ/9OlJi0rl3JydCIiIiLX4epJZo+vSPu0DR5n9sM3j0HvmWC1OTuyXOXt7kKfphX4Ys0RpqyLVCJdRERE5CYokS6S5WIsrPsQtk2BzFSzreLt0P5lqNDMubEVYAdjL/Lmor2sOXAagJLebjzfsTo9VQddREREChPfEDZXeoa2h9/GcmAJrBwLd73q7Khy3cAWYfzf2iOsO3SG/TEXqR7s6+yQRERERAoFJdJFEuNg/QTY8iVkJJttFVrCHS9DeGunhlaQnbtUB33G3+qgD2tfBT/VQRcREZFC6Lx3ZTK7TMDl+ydg3QdQpjbU6e7ssHJV+RJedKwdzJLdMUxdH8HbD9V1dkgiIiIihYIS6VJ8JZ+DDZNg8+eQlmC2lWtsJtArtQeLZlNfzdXqoHesXYZ/d66pOugiIiJS6Bm39YAz+82JFt8Pg8CKUK6Rs8PKVUNaV2TJ7hi+/f04L95Tg0BvN2eHJCIiIlLgKZEuxU9KPGz6DDZ+AqkXzLaQemYJl6p3K4Geg1X7TvHmoj2OOug1gn0Z06UWLauoDrqIiIgUIXeOgVN74eBSmNMPHlsNvsHOjirXNA4rQZ1y/uw6foHZW6IZ1r6Ks0MSERERKfCUSJfiIzUBtnwBGyaas9EBStcyFxGt0UUJ9BwcjL3I2EV7+VV10EVERKQ4sNrgof+D/7vLnJ0+py8MWgyuHs6OLFdYLBaGtA7n2bk7+WpjJI+2qYSbi9XZYYmIiIgUaEqkS9GXngxbJ5sLiSadMdtKVYN2L0GtB8CqPxqu5e910F1tFoa0qsiwO1QHXURERIo4Dz/oMxu+vAOOb4cfn4EHPi8yky/urVOWtxbvIzY+lSW7T3J//XLODklERESkQFMiXYqujFTYPh3Wvg8JMWZbiYpmAr1OD3OmkVxVeqadGZuimPDLQS4kpwNwdy2zDnp4KdVBFxERkWKiZGXoOR2+fhD+mGMuPtrqaWdHlSvcXKwMaB7GB8sPMGVdBPfVK4uliHxJICIiIpIXlEiXoiczHX6fAWveg/hjZpt/Bbj9BajXB2yaSZ2TVftPMfanPRxWHXQRERERqNQO7nkblrwAy8dAUA2odrezo8oVfZtVYNKqQ+w8doHfos/TKKyEs0MSERERKbCUSJeiIzMD/pgLv74D56PMNt+y0PY5aDAQXNycG18Bd+jURd78KXsd9Ofurk6vJqqDLiIiIsVc00chdjf8Nh0WDoVHfoGg6s6O6h8r5eNOt/plmbftGFPWRyiRLiIiIpIDJdKl8LNnwu6FsPptOHvYbPMuDW1GQqPBRWZRqLxyPimNCb8c5OtNUY466INbVWS46qCLiIiImCwW6PwenDkI0Rtgdm94dCV4Fv7E8+BWFZm37Rg/747h+PlkygV4OjskERERkQJJiXQpvOx22PsDrB4Hp/eZbZ6B0HoENHkU3LycGl5Bl55pZ+amKD68rA56h1pleFl10EVERESu5OIGvb6GL9rD2SMwfxD0Wwi2wv0nVc0QP1pUKsnGI3F8tTGS0Z1qOjskERERkQKpcJ/1SfFkGLB/MawaB7G7zDYPf2j5FDR7HNx9nRtfIbB6/ynGLtrLoVMJgFkH/T9datFKddBFRERErs27FPSZBZM7wpHVsOwV6PS2s6P6x4a0rsjGI3HM2XKUZ+6sipeb/kwUERER+TudIUnhYRhw6BdY9V848bvZ5uYLLYZBiyfNZLrk6NCpBMYu2sPq/WYd9EBvN567uxq9GofiYrM6OToRERGRQiC4DjzwOcwbAJs/gzK1oOHA/2fvvsOjKNc3jn93N72ThCR0Qu8dAghKr1JtgIjSrOhR9Pg72I5g4dgRRVGkWQALigUEIr13pPcOKSQQ0kjb3d8fA0GkSCDJpNyf65ors7Nl7g2vMXn2nec1O9UtaVcjhApBXhyNT+XHzScZ2LyC2ZFEREREChwV0qXgczrh8DJY8iYcX2ccc/WGiEeMWehegebmKwSu1gf9oZYVGdGuKv6e6oMuIiIikiO1ekKbF2Dpm/DbSAiqChVamJ3qptmsFh5sUZExv+1i6qrDDGhWHqsWmxcRERG5jArpUrAdXQ2L34CjK43bLh7QdBjc9jT4lDQ1WmGQaXcwY90xPvhjHwmpRh/0DjVDebF7TcLVB11ERETk5t3xPMTugl1z4NuB8PBSCChndqqbdk+TsrwfuY+Dp1NYvv80baqHmB1JREREpEApEL0cJkyYQMWKFfHw8CAiIoL169df87HTpk3DYrFctnl4eFz2mIceeuiKx3Tp0iWv34bkthXvwdSuRhHd5gbNHoF//Qmd31AR/QYs23earh+u4L+/7CQhNZPqob58PTSCLx5soiK6iIiIyK2yWKD3J0arl9Q4mNkfMlLMTnXTfD1cubeJ8UHA1FVHzA0jIiIiUgCZPiP922+/ZeTIkUycOJGIiAjGjRtH586d2bt3LyEhV58F4efnx969e7NvWyxXXnbYpUsXpk6dmn3b3d0998NL3onebrRyAaPn5B3/B/5lzc1USByITeaNubtY8pc+6CM7VqNfU/VBFxEREclVbt7QbyZMagsx2+GnR+Ge6WAtnL9zPdSyIlNXH2bZvtMciE2iSoiv2ZFERERECgzTf8N7//33GT58OIMHD6ZWrVpMnDgRLy8vpkyZcs3nWCwWwsLCsrfQ0NArHuPu7n7ZY0qUKJGXb0Nykz0Lfn4CHFlQswf0/EhF9BuQkJrB6F930mXccpbsPY2L1cKwVuEsea4NA5tXUBFdREREcmT58uX06NGD0qVLY7FYmDNnzj8+Jz09nRdffJEKFSrg7u5OxYoVr/t7fZEQUA7u+xqsrrD7F1j+jtmJblr5IC861jT+ttKsdBEREZHLmVpZy8jIYNOmTXTo0CH7mNVqpUOHDqxZs+aaz0tOTqZChQqUK1eOXr16sXPnzises3TpUkJCQqhevTqPPfYY8fHxefIeJA+s+Rii/gQPf+j2ntlpCrwsu4Mv1xyhzbtLmbrqCFkOJx1qhrLwmdt56c5aWkxUREREbkpKSgr169dnwoQJN/yce++9l0WLFjF58mT27t3LzJkzqV69eh6mLCDKN4c7PzD2l74Ju342N88tGHxbOACzN58gITXD5DQiIiIiBUeOW7tUrFiRIUOG8NBDD1G+fPlbOnlcXBx2u/2KGeWhoaHs2bPnqs+pXr06U6ZMoV69epw7d453332Xli1bsnPnTsqWNWYtd+nShb59+xIeHs7Bgwd54YUX6Nq1K2vWrMFms13xmunp6aSnp2ffTkxMBCAzM5PMzMxbeo85dfF8+X3eAiP+AC5Lx2IBsjq8jtMjEIrr9+JvrjY2VuyP483f93LgtNGPs1qID6O6VqdVlaArHitFW7H/2SHXpfEh16KxUXAUxH+Drl270rVr1xt+/Pz581m2bBmHDh0iMDAQMP52KDYaPWAsPrr2E6PFS2Alo396IdO8UiA1S/mxOyqRT5YeZFTXGldtpSkiIiJS3OS4kP70008zbdo0xowZQ9u2bRk6dCh9+vTJtx7kLVq0oEWLFtm3W7ZsSc2aNfnss8947bXXAOjXr1/2/XXr1qVevXpUrlyZpUuX0r59+ytec+zYsYwePfqK4wsXLsTLyysP3sU/i4yMNOW8pnI6uO3AWIKz0oj1rcOaE35wcp7ZqQqcyMhIYs7DnCNWdiUYF5V4uzjpVs5Bi9AEEvetY94+k0OKaYrlzw65YRofci0aG+ZLTU01O8It++WXX2jSpAlvv/02X331Fd7e3vTs2ZPXXnsNT09Ps+Plj46vQexuOLTEWHx0+BLwKWl2qhyxWCw8ekcl/jVrK58vP4Td4eTFbjWxWlVMFxERkeLtpgrpTz/9NJs3b2batGk8+eSTPP744wwYMIAhQ4bQqFGjG36t4OBgbDYbMTExlx2PiYkhLCzshl7D1dWVhg0bcuDAgWs+plKlSgQHB3PgwIGrFtJHjRrFyJEjs28nJiZSrlw5OnXqhJ+f3w2+m9yRmZlJZGQkHTt2xNW1eLXksG6aim3rXpyu3pQY9CXdAm7tioeiJjMzk59/j2S3tSIztp0ky+HExWphUPPyPNGmEn5q4VKsFeefHfLPND7kWjQ2Co6LV0QWZocOHWLlypV4eHjw008/ERcXx+OPP058fDxTp0696nOK5JWhvSfhMq0TljOHcHw7EPv9P4LNLRcS5p9utUOI6lKN/83fx+SVh4lNPM//+tTBzaV4rrmjq3fkejQ+5Ho0PuR6ND4Khpx8/3NcSL+oUaNGNGrUiPfee49PPvmE//u//+PTTz+lbt26PPXUUwwePPgfLwF0c3OjcePGLFq0iN69ewPgcDhYtGgRI0aMuKEcdrud7du3061bt2s+5sSJE8THx1OqVKmr3u/u7n7VGfWurq6m/VFp5rlNce4ELB4DgKX9K7iWrGxyoILn9x3RvLbFRmrWCQA61AzhhW41qVTSx+RkUpAUu58dkiMaH3ItGhvmKwrff4fDgcVi4ZtvvsHf3x+A999/n7vvvptPPvnkqrPSi+qVoT6hD3N7wmhcj6/l2KT+/FluCBSy9iilgIFVLMw4aOXXbdHsP3qKwdUdeFzZKbPY0NU7cj0aH3I9Gh9yPRof5srJlaE3XUjPzMzkp59+YurUqURGRtK8eXOGDh3KiRMneOGFF/jjjz+YMWPGP77OyJEjefDBB2nSpAnNmjVj3LhxpKSkMHjwYAAGDRpEmTJlGDt2LABjxoyhefPmVKlShYSEBN555x2OHj3KsGHDAGMh0tGjR3PXXXcRFhbGwYMHef7556lSpQqdO3e+2bcrecnphN+egYwkKNsMmg03O1GB8/PWkzzz3TYcTgtVQ7x5pUdtWlctXJcJi4iISNFWqlQpypQpk11EB6hZsyZOp5MTJ05QtWrVK55TlK8MtRyoiPPb/lSMX0a5xl1wNC18v+N2A9ruj+PJWX+y5xx8fTKASQMbEuSTP209CwpdvSPXo/Eh16PxIdej8VEw5OTK0BwX0jdv3szUqVOZOXMmVquVQYMG8cEHH1CjRo3sx/Tp04emTZve0Ovdd999nD59mldeeYXo6GgaNGjA/PnzsxcgPXbsGFbrpUsIz549y/Dhw4mOjqZEiRI0btyY1atXU6tWLQBsNhvbtm1j+vTpJCQkULp0aTp16sRrr72Wb33cJYe2fw/7FxqXvPb6GKzFeJrLVczdFsUz327F4YSWIQ4mP94CTw+NZRERESlYbrvtNr7//nuSk5Px8TGumNu3bx9Wq5WyZcte9TlF+srQml2h4xiIfBlb5EvYQmtC5ba3/rr5rH2tUswY7smQaRvYfjKRfl9s4MshEZQPMueKATPp6h25Ho0PuR6ND7kejQ9z5eR7n+NCetOmTenYsSOffvopvXv3vurJwsPDL1vw85+MGDHimq1cli5detntDz74gA8++OCar+Xp6cmCBQtu+NxisuTT8Pv/Gft3PA8lq5ubp4CZvyOap2ZtweGEuxuV4Ta3o7jYimdvShEREclfycnJl61DdPjwYbZu3UpgYCDly5dn1KhRnDx5ki+//BKAAQMG8NprrzF48GBGjx5NXFwc//73vxkyZEjxWWz071o+CbG74M+Z8P1DMHwxBBW+FoYNygXww6MtGDRlPUfiU+n76WqmD2lK7dL+//xkERERkSIixxW5Q4cOMX/+fO65555rVuy9vb2vuaCQyGXm/x+cPwOhdeC2p81OU6D8sSuGJ2duxu5w0rdhGV7vVQtr4WqtKSIiIoXYxo0badiwIQ0bNgSMlowNGzbklVdeASAqKopjx45lP97Hx4fIyEgSEhJo0qQJ999/Pz169GD8+PGm5C8QLBa4cxyUbQppCTCzP6SdMzvVTalU0ocfH2tJjTBf4pLTue+ztaw+GGd2LBEREZF8k+MZ6bGxsURHRxMREXHZ8XXr1mGz2WjSpEmuhZMibs882DEbLFajpYtNl7FctGRvLI9/s5lMu5Oe9Uvzzj31cdizzI4lIiIixUibNm1wOp3XvH/atGlXHKtRo4YWzPo7Vw+472v4vC3E7YXZw6H/zELZzjDEz4PvHm3B8OkbWXf4DA9N2cAH9zWge71SZkcTERERyXM5npH+xBNPcPz48SuOnzx5kieeeCJXQkkxkHYO5l5YWKrlk1C6obl5CpAV+0/zyFebyLA76FY3jPfvrY9NU9FFRERECi/fMOj3Dbh4wP4FsGi02Ylump+HK9OHNKNrnTAy7A5GzNzMl2uOmB1LREREJM/luJC+a9cuGjVqdMXxhg0bsmvXrlwJJcVA5CuQFAWBlaDNKLPTFBirD8YxbPpGMrIcdKoVyof9GqonuoiIiEhRUKYR9Jpg7K/6EP781tw8t8DD1cbHAxoxsHl5nE545eedvLdw73WvYBAREREp7HJcoXN3dycmJuaK41FRUbi45LhTjBRHh5fDpmnGfs+PwLWYLj71N+sOxTN02kbSsxy0rxHCxwMa4aoiuoiIiEjRUfduaP2ssf/Lk3Bik7l5boHNauG1XnUY2bEaAB8tPsCoH7eTZXeYnExEREQkb+S4StepUydGjRrFuXOXFslJSEjghRdeoGPHjrkaToqgjFTjjwaAJkOgYitz8xQQm46eYfC0DZzPtHNHtZJ8MrARbi4qoouIiIgUOW1fgurdwJ4OswZA4imzE900i8XCU+2rMrZvXawWmLXhOI9+vZnzGXazo4mIiIjkuhxX6t59912OHz9OhQoVaNu2LW3btiU8PJzo6Gjee++9vMgoRcmSN+DsEfArAx0Kb2/I3LTl2FkenLKB1Aw7raoE89kDjXF3KXyLT4mIiIjIDbBaoe/nULImJEfDrPsh87zZqW5J/2bl+XRgY9xdrPyxO4YHJq8jITXD7FgiIiIiuSrHhfQyZcqwbds23n77bWrVqkXjxo358MMP2b59O+XKlcuLjFJUnNgEaz8x9u/8ADz8zM1TAGw/cY5BU9aTnJ5F80qBTBrUBA9XFdFFREREijR3X+g/EzxLwKnN8MtTUMj7i3euHcZXQyPw83Bh49Gz3DNxDVHnCvcHBCIiIiJ/dVNNzb29vXn44YdzO4sUZVkZ8MsIcDqg7r1QrbPZiUy389Q5Bk5eR1JaFk0rlmDyg03xdFMRXURERKRYCAyHe7+Er/rA9u8gtBa0esbsVLekWXgg3z/akkFT1rE/Npm7PlnNl0ObUSXE1+xoIiIiIrfsplcH3bVrF8eOHSMj4/JL9nr27HnLoaQIWvkBxO4CryDo8j+z05huT3QiA79Yx7nzmTQqH8DUwc3wdtdivSIiIiLFSvjt0PUtmPss/DHaaPdSvYvZqW5J9TBfZj/WkkFT1nPodAp3fbqGKQ81oXGFQLOjiYiIiNySHFfuDh06RJ8+fdi+fTsWiwXnhUsQLRYLAHa7FpaRv4ndDcvfMfa7vg3eQebmMdn+mCTun7SOs6mZ1C8XwLQhzfBREV1ERESkeGo6DGJ2wsYpMHsYDIuEkJpmp7olZUt4MfvRlgyZvoEtxxK4/4t1fNy/ER1qhZodTUREROSm5bhH+r/+9S/Cw8OJjY3Fy8uLnTt3snz5cpo0acLSpUvzIKIUag47/DwCHJlQrSvUucvsRKY6eDqZ/pPWEZ+SQZ0yfnw5pBl+Hq5mxxIREZEi5Pjx45w4cSL79vr163n66af5/PPPTUwl19X1bajYGjKSYGY/SD1jdqJbVsLbjW+GRdCuRghpmQ4e+XoT3208bnYsERERkZuW40L6mjVrGDNmDMHBwVitVqxWK61atWLs2LE89dRTeZFRCrN1E+HkRnD3gzvfhwtXLhRHR+JSGDBpLXHJ6dQs5cfXQyPw91QRXURERHLXgAEDWLJkCQDR0dF07NiR9evX8+KLLzJmzBiT08lV2VzhnukQUB7OHoHvHwR7ptmpbpmXmwufPdCYuxqVxe5w8vwP25iw5ED2Vc0iIiIihUmOC+l2ux1fX2OxmODgYE6dOgVAhQoV2Lt3b+6mk8LtzGFY9Jqx3+k18Cttbh4THT+TSv9Ja4lJTKdaqA9fD21GgJeb2bFERESkCNqxYwfNmjUD4LvvvqNOnTqsXr2ab775hmnTppkbTq7NOwj6zwI3Hzi8HBa8YHaiXOFqs/LuPfV4rE1lAN5ZsJfRv+7C4VAxXURERAqXHBfS69Spw59//glAREQEb7/9NqtWrWLMmDFUqlQp1wNKIeV0wq9PQdZ54zLVRg+ancg0J86m0u/ztUSdS6NySW++GdacIB93s2OJiIhIEZWZmYm7u/G7xh9//EHPnj0BqFGjBlFRUWZGk38SWhv6XmjBs/5z2DjV3Dy5xGKx8H9davDynbUAmLb6CE/O2kJ6ltbXEhERkcIjx4X0l156CYfDAcCYMWM4fPgwrVu3Zt68eYwfPz7XA0ohteUrYyaNiyf0+LDYtnSJOneeAZPWcTLhPJWCvZk5vDklfVVEFxERkbxTu3ZtJk6cyIoVK4iMjKRLly4AnDp1iqCg4r3oe6FQozu0e8nYn/ccHFllbp5cNLRVOOP7N8TVZmHutigGT91AUlrhb2EjIiIixUOOC+mdO3emb9++AFSpUoU9e/YQFxdHbGws7dq1y/WAUgglRsGCC7/8t3sRgiqbm8ckMYlp9P98LcfOpFIhyIsZw5sT4udhdiwREREp4t566y0+++wz2rRpQ//+/alfvz4Av/zyS3bLFyngWj8HtfuCIwu+ewDOHjU7Ua7pWb80Ux9qhrebjdUH4+n3+Vpik9LMjiUiIiLyj3JUSM/MzMTFxYUdO3ZcdjwwMBBLMZ1xLH/jdMLcZyH9HJRuBBGPmZ3IFLFJafSftJYj8amULeHJjOHNCfNXEV1ERETyXps2bYiLiyMuLo4pU6ZkH3/44YeZOHGiicnkhlks0GsClKoPqfEwawCkJ5udKte0qhrMrIdbEOzjxs5Tidz96RqOxKWYHUtERETkunJUSHd1daV8+fLY7eplJ9ew8yfYOxesLtDrY7C5mJ0o38Unp3P/pHUcOp1CaX8PZg5vTpkAT7NjiYiISDFx/vx50tPTKVGiBABHjx5l3Lhx7N27l5CQEJPTyQ1z84J+M8A7BGJ2wE+PwIUWm0VB3bL+/PBoS8oHenHsTCp3T1zNjpPnzI4lIiIick05bu3y4osv8sILL3DmzJm8yCOFWeoZmPdvY7/1s8ZiScXM2ZQM7v9iHftjkwnz82Dmw80pF+hldiwREREpRnr16sWXX34JQEJCAhEREbz33nv07t2bTz/91OR0kiP+ZaHfN2Bzgz2/wbL/mZ0oV1UM9uaHx1pQq5QfcckZ3PfZGlbujzM7loiIiMhV5biQ/vHHH7N8+XJKly5N9erVadSo0WWbFGPzR0FqHJSsaRTSi5lzqZkMnLyOPdFJhPi6M2N4BBWCvM2OJSIiIsXM5s2bad26NQA//PADoaGhHD16lC+//JLx48ebnE5yrFwz6PGhsb/sLeMK0CIkxNeDbx9pTsvKQaRk2Bk8bT2//HnK7FgiIiIiV8hx343evXvnQQwp9PZHwrZZgMVo6eLibnaifHXufCYPTFnHzlOJBPu4MWN4cyqV9DE7loiIiBRDqamp+Pr6ArBw4UL69u2L1WqlefPmHD1adBatLFYaDICYnbDmY/jpMQisZPRPLyJ8PVyZOrgpI7/7k7nbonhq5hbiktIZ0irc7GgiIiIi2XJcSP/vf/+bFzmkMEtPgl+fNvabPw5lm5gaJ78lpWXy4JT1bDtxjkBvN74Z1pwqISqii4iIiDmqVKnCnDlz6NOnDwsWLOCZZ54BIDY2Fj8/P5PTyU3rOAZO74EDf8DMAfDwEvApOj3v3V1sfNSvISV93Jm2+ghjftvF6eR0nu9cHYvFYnY8ERERkZy3dhG5wh+jIfEEBFSAdi+anSZfpaRnMXjqBrYeTyDAy5Wvh0ZQPczX7FgiIiJSjL3yyis899xzVKxYkWbNmtGiRQvAmJ3esGFDk9PJTbPa4K7JEFTV+N3724GQlW52qlxltVr4b49a/LtzdQA+XXqQf/+wjUx70VlkVURERAqvHBfSrVYrNpvtmpsUM0dXw4ZJxn7P8eBWfHqCp2ZkMXjaBjYePYufhwtfD42gVmnN8hIRERFz3X333Rw7doyNGzeyYMGC7OPt27fngw8+MDGZ3DLPAOg/C9z94fg6mDsSnE6zU+Uqi8XCE22r8PZd9bBZLfyw6QSPfLWJ8xl2s6OJiIhIMZfj1i4//XT54jaZmZls2bKF6dOnM3r06FwLJoVA5nn45Uljv+EDUKmNqXHyU1qmnWHTN7L+8Bl83V34amgEdcr4mx1LREREBICwsDDCwsI4ceIEAGXLlqVZs2Ymp5JcEVwF7pkC39wDW76G0DrQ/DGzU+W6e5uWI9DbjSdmbGbxnlgGfLGWKQ82pYS3m9nRREREpJjK8Yz0Xr16XbbdfffdvPHGG7z99tv88ssveZFRCqplb0H8AfAJg06vm50m36Rl2hn+5UZWH4zH283GtCHNqF8uwOxYIiIiIgA4HA7GjBmDv78/FSpUoEKFCgQEBPDaa6/hcKhFRpFQpcOl378XvAAHFpmbJ490qBXKjOER+Hu6suVYAndPXM3JhPNmxxIREZFiKtd6pDdv3pxFi4rmL3ByFae2wqrxxv6d7xuXmRYD6Vl2Hvt6Eyv2x+HpahTRG1coYXYsERERkWwvvvgiH3/8Mf/73//YsmULW7Zs4c033+Sjjz7i5ZdfNjue5Jbmj0ODgeB0wA+DIe6A2YnyROMKgfzwaAtK+Xtw8HQKfT9Zxd7oJLNjiYiISDGUK4X08+fPM378eMqUKZMbLycFnT0TfhkBTjvU7gM1upudKF9kZDl44pstLNl7Gg9XK1MeakrTioFmxxIRERG5zPTp0/niiy947LHHqFevHvXq1ePxxx9n0qRJTJs2zex4klssFmNCS9lmkHYOZvYzvhZBVUN9+fHxllQN8SEmMZ17Jq5m/eEzZscSERGRYibHhfQSJUoQGBiYvZUoUQJfX1+mTJnCO++8kxcZpaBZPR6it4NnCej6ttlp8kWm3cFTM7fwx+4Y3F2sfDGoKS0qB5kdS0REROQKZ86coUaNGlccr1GjBmfOqPhYpLi4w31fg18ZiN8PPwwBR9FclLOUvyffP9qCJhVKkJiWxQOT17FwZ7TZsURERKQYyfFiox988AEWiyX7ttVqpWTJkkRERFCihFpcFHmn98HSt4z9Lv8DnxBz8+SDLLuDZ77dyvyd0bjZrHw+qAmtqgabHUtERETkqurXr8/HH3/M+PHjLzv+8ccfU69ePZNSSZ7xDYV+M2BKFzjwB/zx3yK7flGAlxtfDY3gyZmb+WN3LI9+vYk3+tSlf7PyZkcTERGRYiDHhfSHHnooD2JIoeBwwC9Pgj3dWOCo3n1mJ8pzdoeT577/k9+2ReFqszDxgUbcUa2k2bFEREREruntt9+me/fu/PHHH7Ro0QKANWvWcPz4cebNm2dyOskTpRtA70+MXumrP4KQ2tCgv9mp8oSnm42JAxvz4k87+HbjcUb9uJ3YxHSeal/lsglfIiIiIrktx61dpk6dyvfff3/F8e+//57p06fnSigpoDZ8AcfXgpsP3PmB0ZexCHM4nPzf7G3M2XoKF6uFCQMa0a5GqNmxRERERK7rjjvuYN++ffTp04eEhAQSEhLo27cvO3fu5KuvvjI7nuSVOn3h9ueN/V+fguMbzM2Th1xsVv53V11GtK0CwAd/7OPln3dgdzhNTiYiIiJFWY4L6WPHjiU4+Mq2FiEhIbz55pu5EkoKoIRj8Merxn6HVyGgaF8+6XA4eeGn7fyw6QQ2q4Xx/RvSqXaY2bFEREREbkjp0qV54403mD17NrNnz+b111/n7NmzTJ482exokpfajIIad4I9A769H86dNDtRnrFYLDzXuTqje9bGYoGv1x5jxIzNpGUWzR7xIiIiYr4cF9KPHTtGeHj4FccrVKjAsWPHciWUFDBOJ/z6NGSmQPkW0GSo2YnylNPp5JVfdjBrw3GsFvjgvgZ0q1vK7FgiIiIiItdntUKfz4zWLskxMGsAZJ43O1WeerBlRT7u3wg3m5Xfd0Tz4JT1JKZlmh1LREREiqAcF9JDQkLYtm3bFcf//PNPgoKCciWUFDB/zoKDi8DmDj0/Mn5BL6KcTiejf93F12uPYbHAe/fWp2f90mbHEhERERG5Me4+0H8meAVB1Fb4+QljYkwR1r1eKaYNaYqPuwvrDp/h3olriElMMzuWiIiIFDE5roj279+fp556iiVLlmC327Hb7SxevJh//etf9OvXLy8yipmSY2H+f4z9Nv+B4Krm5slDTqeTN+ftZtrqIwC8dVc9+jQsa24oEREREZGcKlEB7v0SrC6wYzasfN/sRHmuZeVgZj3cnGAfd/ZEJ9H3k9UcOp1sdiwREREpQlxy+oTXXnuNI0eO0L59e1xcjKc7HA4GDRqkHulF0bx/Q1oChNWDlk+anSbPOJ1O3l6wl0krDgPwZp+63NuknMmpRERERG5c3759r3t/QkJC/gSRgqFiK+j2Lvz2NCx6DUrWhBrdzE6Vp+qU8efHx1oyaMo6jsSncvfENUx5qCkNygWYHU1ERESKgBzPSHdzc+Pbb79l7969fPPNN/z4448cPHiQKVOm4ObmlhcZxSy7f4Vdc8Big14fg83V7ER55oPIfXy69CAAY3rVZkBE0V5MVURERIoef3//624VKlRg0KBBZseU/NRkMDQdDjjhx+EQs8vsRHmufJAXPzzWkrpl/DmTksGASWtZtu+02bFERESkCMjxjPSLqlatStWqRbfNR7F3/izMfdbYv+1fUKq+uXny0PhF+xm/+AAAr9xZi0EtKpobSEREROQmTJ061ewIUhB1GQun98CRFTCzHzy8FLwCzU6Vp4J93Jn5cHMe+3oTK/bHMXTaBt65R20bRURE5NbkeEb6XXfdxVtvvXXF8bfffpt77rknV0JJAbDwJUiOgaCqcMf/mZ0mz3yy9ADvR+4D4IVuNRjSKtzkRCIiIiIiucjmavRLL1EREo7Cd4PAnml2qjzn4+7C5Aeb0qtBabIcTp759k8mLT9kdiwREREpxHJcSF++fDndul3ZW69r164sX748V0KJyQ4ugS1fAxajpYurh9mJ8sQXKw7x9vy9APy7c3Uevr2yyYlERERERPKAVyD0nwVuPsbM9Pn/MTtRvnBzsfLBvQ0YemGyzBvzdvPG3F04HE6Tk4mIiEhhlONCenJy8lV7obu6upKYmHhTISZMmEDFihXx8PAgIiKC9evXX/Ox06ZNw2KxXLZ5eFxe6HU6nbzyyiuUKlUKT09POnTowP79+28qW7GTkQK/PmXsNxsO5ZubmyePTF11mNfn7gbgmQ7VeKJtFZMTiYiIiIjkoZCacNcXgAU2fAEbJpudKF9YrRZe6l6TUV1rADBpxWGe/f5PMu0Ok5OJiIhIYZPjQnrdunX59ttvrzg+a9YsatWqleMA3377LSNHjuS///0vmzdvpn79+nTu3JnY2NhrPsfPz4+oqKjs7ejRo5fd//bbbzN+/HgmTpzIunXr8Pb2pnPnzqSlpeU4X7Gz+HVIOAb+5aD9K2anyRNfrT3K6F+NhZZGtK3CU+1VRBcRERGRYqB610u/4//+PBxeYW6efGKxWHjkjsq8d099bFYLP205ydDpG0lJzzI7moiIiBQiOV5s9OWXX6Zv374cPHiQdu3aAbBo0SJmzJjBDz/8kOMA77//PsOHD2fw4MEATJw4kblz5zJlyhT+85+rX3JosVgICwu76n1Op5Nx48bx0ksv0atXLwC+/PJLQkNDmTNnDv369ctxxmLj+AZY+6mxf+c4cPc1NU5emLn+GC/P2QHAI3dU4tlO1bBYLCanEhERERHJJ62egdhdsP17o1/6w0uM/unFwF2NyxLo48bjX29m+b7TDJi0likPNSXIx93saCIiIlII5LiQ3qNHD+bMmcObb77JDz/8gKenJ/Xr12fx4sUEBuZs9feMjAw2bdrEqFGjso9ZrVY6dOjAmjVrrvm85ORkKlSogMPhoFGjRrz55pvUrl0bgMOHDxMdHU2HDh2yH+/v709ERARr1qy5aiE9PT2d9PT07NsXW9RkZmaSmZm/C/FcPF9+n5esdFx+fgILThx178Ne8Q7I7wx5bPbmk7wwZycAg1tW4Nn2lcnKKjyzUEwbG1IoaHzI9Wh8yLVobBQc+jeQfGOxQM+PIP4AnNoCM/vD0IVFchLN1bStHsKM4REMmbaBP0+c4+6Ja/hySDPKBXqZHU1EREQKuBwX0gG6d+9O9+7dAaPoPHPmTJ577jk2bdqE3W6/4deJi4vDbrcTGhp62fHQ0FD27Nlz1edUr16dKVOmUK9ePc6dO8e7775Ly5Yt2blzJ2XLliU6Ojr7Nf7+mhfv+7uxY8cyevToK44vXLgQLy9zfqGKjIzM1/PViJpN9bi9pLn4sZjbyZw3L1/Pn9c2nLbwzQErTizcHuagvuMgv/9+0OxYNyW/x4YULhofcj0aH3ItGhvmS01NNTuCFCeuntBvBnzexpid/uMjcN/XYM1x589CqWH5EvzwWEsGTV7P4bgU+n66mumDm1GrtJ/Z0URERKQAu6lCOsDy5cuZPHkys2fPpnTp0vTt25cJEybkZraratGiBS1atMi+3bJlS2rWrMlnn33Ga6+9dlOvOWrUKEaOHJl9OzExkXLlytGpUyf8/PL3l6nMzEwiIyPp2LEjrq6u+XPSmJ24TJkLgEvPD+hYs1f+nDefzN0ezYy123AC/ZuWZXSPmoWynYspY0MKDY0PuR6ND7kWjY2C4+IVkSL5xq+0UUyf2g32zoWlb0K7l8xOlW8ql/Rh9mMteXDKevbGJHHfZ2v4fFATWlQOMjuaiIiIFFA5KqRHR0czbdo0Jk+eTGJiIvfeey/p6enMmTPnphYaDQ4OxmazERMTc9nxmJiYa/ZA/ztXV1caNmzIgQMHALKfFxMTQ6lSpS57zQYNGlz1Ndzd3XF3v7Ivnqurq2l/VObbue1ZMO9pcGRBjTtxqXuXcblnEfH79iie/WE7Difc16Qcb/Spi9VauN+fmeNSCj6ND7kejQ+5Fo0N8+n7L6Yo2wR6joefHoHl70BITahzl9mp8k2YvwffPdqC4dM3sv7IGR6csp4P+zWga91S//xkERERKXZu+Nq9Hj16UL16dbZt28a4ceM4deoUH3300S2d3M3NjcaNG7No0aLsYw6Hg0WLFl026/x67HY727dvzy6ah4eHExYWdtlrJiYmsm7duht+zWJl7SdGb0R3f+j+XpEqoi/cGc2TM7dgdzjp26gMY/sW/iK6iIiIiEiuqt8PWj5l7M95wvjboBjx93Tly6HN6FQrlAy7g8dnbOartUfNjiUiIiIF0A0X0n///XeGDh3K6NGj6d69OzabLVcCjBw5kkmTJjF9+nR2797NY489RkpKCoMHDwZg0KBBly1GOmbMGBYuXMihQ4fYvHkzAwcO5OjRowwbNgwAi8XC008/zeuvv84vv/zC9u3bGTRoEKVLl6Z37965krnIiD8IS94w9ju/Ab43dhVAYbB4TwxPzNhMlsNJrwaleefu+iqii4iIiIhcTYdXoWonyDoPs+6HpJh/fEpR4uFq49OBjRkQUR6nE16es4P3I/fhdDrNjiYiIiIFyA23dlm5ciWTJ0+mcePG1KxZkwceeIB+/frdcoD77ruP06dP88orrxAdHU2DBg2YP39+9mKhx44dw/qXRW/Onj3L8OHDiY6OpkSJEjRu3JjVq1df1lrm+eefJyUlhYcffpiEhARatWrF/Pnz8fDwuOW8RYbDAb88BVlpEH4HNBxodqJcs2zfaR79ajOZdifd65XivXvqY1MRXURERETk6qw2uOsL+KIDxO2DbwfCQ7+By5XtL4sqm9XCG73rEOLrzrg/9jN+0X5OJ6XzWq/aZkcTERGRAuKGZ6Q3b96cSZMmERUVxSOPPMKsWbMoXbo0DoeDyMhIkpKSbjrEiBEjOHr0KOnp6axbt46IiIjs+5YuXcq0adOyb3/wwQfZj42Ojmbu3Lk0bNjwstezWCyMGTOG6Oho0tLS+OOPP6hWrdpN5yuSNk+HoyvB1Qt6fFhkWrqsOhDHw19uJMPuoHPtUMbd1wAX2w0PcxERERGR4snDH/rPAo8AOLEefnsGitmMbIvFwtMdqvF67zpYLTBz/TEe+2YzaZl2s6OJiIhIAZDjCqO3tzdDhgxh5cqVbN++nWeffZb//e9/hISE0LNnz7zIKLnt3ElY+LKx3+5lCAw3N08uWXsonqHTN5Ce5aBDzRA+6t8IVxXRRURERERuTFBluGcaWGyw9RtY8AJkpZudKt8NbF6BT+5vhJuLlchdMQyevonULLNTiYiIiNluqcpYvXp13n77bU6cOMHMmTNzK5PkJafTmF2SkQRlm0LEI2YnyhUbjpxhyLQNpGU6aFO9JBMu/OIrIiIiIiI5ULktdBlr7K/9BD67A05tNTWSGbrUKcWXQ5rh6+7CxqMJjN9h42h8qtmxRERExES5Umm02Wz07t2bX375JTdeTvLSjtmwfwHY3KDnx0Y/xEJu87GzPDRlPakZdlpXDWbiwMa4uxT+9yUiIiIiYoqIR+C+b8C7JJzeDV+0h6VvgT3T7GT5qnmlIL57tAUhvu5EnbfQ7ePVfBC5T61eREREiilN2S1OUuLg9+eN/dv/DSE1zM2TC/48nsCDk9eTkmGnRaUgPn+gCR6uKqKLiIiIiNySmnfC42uhZk9wZMHSN43FSGP3mJ0sX9Us5cd3Dzejhr+DjCwHHy7aT6cPlrNkT6zZ0URERCSfqZBenMz/D6TGQ0htuO1ps9Pcsh0nz/HA5HUkpWfRrGIgkx9qgqebiugiIiIiIrnCOxju/RLummwsQhq1FT67HVaNB0fxmZVdJsCTR2s6GH9fPcL8PDh2JpXB0zbwyFcbOZlw3ux4IiIikk9USC8u9s6H7d+DxQq9PgIXN7MT3ZLdUYkMnLyOxLQsGlcowZTBTfFyczE7loiIiIhI0WKxQN27jdnpVTqCPR0iX4ap3SD+oNnp8o3FAl3rhPHHs3cwvHU4NquFBTtj6PDeMj5depCMLIfZEUVERCSPqZBeHKQlGguMArR4Aso0NjfPLdoXk8T9X6wjITWT+uUCmDa4KT7uKqKLiIiI5Kbly5fTo0cPSpcujcViYc6cOTf83FWrVuHi4kKDBg3yLJ/kM79ScP/30PMjcPOB42thYitYPwkcxaeI7OPuwovdazH3qVY0qxjI+Uw7b83fQ7fxK1h9MM7seCIiIpKHVEgvDiJfgaRTUCIc2rxgdppbciA2mQGT1nEmJYO6Zfz5ckgzfD1czY4lIiIiUuSkpKRQv359JkyYkKPnJSQkMGjQINq3b59HycQ0Fgs0GgSPrYaKrSEzFeY9B1/3gXMnzE6Xr2qE+fHtI8157576BHm7Zf+d8q9ZW4hNTDM7noiIiOQBFdKLusMrYNNUY7/nR+DmZW6eW3A4LoUBk9YSl5xOrVJ+fDW0Gf6eKqKLiIiI5IWuXbvy+uuv06dPnxw979FHH2XAgAG0aNEij5KJ6UpUgEG/QJe3wMUTDi2FT1rA1hngdJqdLt9YLBbualyWxc+24YHmFbBY4Oetp2j/3jKmrjpMlr34zNQXEREpDlRIL8oyUuHXp4z9xoMhvLW5eW7B0fgU+n++ltikdKqH+vL1sAgCvAp3n3cRERGRombq1KkcOnSI//73v2ZHkbxmtULzR+HRlVC2KaQnwpzHYNYASIoxO12+8vdy5bXedfj5iduoX9afpPQsRv+6i54fr2LzsbNmxxMREZFcosbSRdnSsXDmEPiWho6jzU5z046fSWXApHVEJ6ZRJcSHb4ZHEOitIrqIiIhIQbJ//37+85//sGLFClxcbuzPjPT0dNLT07NvJyYmApCZmUlmZmae5LyWi+fL7/MWev4V4IFfsa6dgHXZ/7DsnYfz2FrsXd/BWbOX2elyxY2OjZqh3nw7vBnfbTrBe5H72RWVSN9PVnNP4zI817Gq/oYpovSzQ65H40OuR+OjYMjJ91+F9KLq5GZY87Gxf+f74OFvbp6bdCrhPAO+WMvJhPNUCvZmxrAIgn3czY4lIiIiIn9ht9sZMGAAo0ePplq1ajf8vLFjxzJ69JUTPhYuXIiXlzktCSMjI005b+FXFd9qr9Lo6GcEnD+Gy49DOREwiW3lHiDTxdfscLniRseGP/B8bfjlqJV1p618v+kkc/88Qc/yDiJCnFgteZtTzKGfHXI9Gh9yPRof5kpNTb3hx6qQXhRlZcDPI8DpgDp3Q/WuZie6KTGJafSftJbjZ85TIciLGcObE+LnYXYsEREREfmbpKQkNm7cyJYtWxgxYgQADocDp9OJi4sLCxcupF27dlc8b9SoUYwcOTL7dmJiIuXKlaNTp074+fnlW34wZiNFRkbSsWNHXF21Ds9Nsw/GvvI9rKvGUTZhLWUyD2HvPg5n1U5mJ7tpNzs27gU2HT3Lf3/dzd6YZGYdsrEn059X76xJ7dL5O74l7+hnh1yPxodcj8ZHwXDxisgboUJ6UbTqQ4jdCZ6B0PUts9PctJfm7OBofCrlAj2ZObw5Yf4qoouIiIgURH5+fmzfvv2yY5988gmLFy/mhx9+IDw8/KrPc3d3x939yqsNXV1dTfuD0sxzFwmurtDhFajZHX56FEvcPly+GwANB0LnseBReAvINzM2mlcJYe5TwUxbfYQPIvex9fg5+k5cy6AWFRnZqRp+HhprRYV+dsj1aHzI9Wh8mCsn33stNlrUxO6B5W8b+13fBu9gc/PcpN1RiUTuisFigSkPNqV0gKfZkURERESKleTkZLZu3crWrVsBOHz4MFu3buXYsWOAMZt80KBBAFitVurUqXPZFhISgoeHB3Xq1MHb29ustyFmKdMYHlkOLUYAFtjyNXzaEg4tMztZvnOxWRnWuhKLn2tDj/qlcThh2uojtHt3GXO2nMTpdJodUURERG6ACulFicMOv4wAewZU7Qx17zY70U37eMkBALrXLUXV0KLRU1FERESkMNm4cSMNGzakYcOGAIwcOZKGDRvyyiuvABAVFZVdVBe5KldP6PwGDJ4HJSrCuePwZU+Y92/ISDE7Xb4L9fPgo/4N+WZYBJVKehOXnM7T326l/6S17I9JMjueiIiI/AMV0ouS9Z/DiQ3g5gt3fgCWwrmKzYHYZOZtjwLgibZVTE4jIiIiUjy1adMGp9N5xTZt2jQApk2bxtKlS6/5/FdffTV7NrsUcxVawqOroMlQ4/b6z2FiKzi2ztxcJrmtSjC//6s1/+5cHQ9XK2sPnaHrhysY+/tuUtKzzI4nIiIi16BCelFx9ggsGmPsdxoD/mVMjXMrPllyAKcTOtYKpWapwttDUURERERELnD3gTvfh4E/gm9pOHMIpnaByP9CVrrZ6fKdu4uNJ9pWIfKZO+hQM5Qsh5PPlh2iw/vLmL8jSu1eRERECiAV0osCpxN+/RdkpkKFVtDoIbMT3bSj8Sn8/OcpAJ5sp9noIiIiIiJFSpX28PgaqN8fnA5YNQ4+uwNObTU7mSnKBXrxxYNN+GJQE8qW8CTqXBqPfr2Zh6Zu4Ehc8Wt/IyIiUpCpkF4UbP0GDi0FFw/oOR6shfefdeKyg9gdTu6oVpJ6ZQPMjiMiIiIiIrnNMwD6TIT7vgHvknB6N3zRHpa+BfZMs9OZokOtUCKfuYMn21XBzWZl2b7TdBq3nA8i95GWaTc7noiIiKBCeuGXFA0LXjD2274AQZXNzXMLTiac54dNJwDNRhcRERERKfJq3gmPr4WaPcGRBUvfhC86QOwes5OZwtPNxrOdqjP/6da0rhpMRpaDDxftp/O45SzZG2t2PBERkWJPhfTCzOmEuc9C2jko1QCaP2F2olvy+bKDZNqdNK8USJOKgWbHERERERGRvOYdDPd+CXdNBo8AiNoKn90Oq8aDo3jOxK5U0ocvhzRjwoBGhPq5czQ+lcFTN/DIVxs5mXDe7HgiIiLFlgrphdmun2HPb2B1gV4fg83F7EQ3LTYpjZkbjgPwVLuqJqcREREREZF8Y7FA3buN2elVO4E9HSJfhqndIP6g2elMYbFY6F6vFIuebcPw1uHYrBYW7Iyhw3vL+HTpQTKyHGZHFBERKXZUSC+sUs/AvOeM/VYjIayuuXlu0aTlh8jIctCofAAtKgeZHUdERERERPKbXykY8B30/AjcfOD4WpjYCtZPAkfxLBz7uLvwYvdazH2qFU0rluB8pp235u+h2/gVrDkYb3Y8ERGRYkWF9MJqwYuQchqCq8Ptz5md5pacScng67XHAHiyXVUsFovJiURERERExBQWCzQaBI+thoqtITPVmED0dR84d8LsdKapEebHd4+04N176hPk7caB2GT6T1rL07O2EJuUZnY8ERGRYkGF9MLowB/w5wzAYrR0cXE3O9EtmbLyMOcz7dQp40eb6iXNjiMiIiIiImYrUQEG/QJd3wYXTzi0FD5pAVu+MdaKKoYsFgt3Ny7L4mfbMLB5eSwWmLP1FO3fXca0VYfJshfPWfsiIiL5RYX0wiY9CX592tiPeBTKNTM1zq06dz6T6auPADCirWaji4iIiIjIBVYrRDwCj66Esk0hPRF+fhxmDYCkGLPTmcbfy5XXe9fl5yduo35Zf5LSs3j11130/HgVm4+dNTueiIhIkaVCemGz6DU4dxwCykO7l8xOc8umrz5CUnoW1UN96VQr1Ow4IiIiIiJS0ARXgSELoMOrYHODvfPgk+aw8yezk5mqXtkAfnz8Nl7vXQc/Dxd2RSXS95PV/Gf2Ns6mZJgdT0REpMhRIb0wObYW1n9u7Pf4ENx9zM1zi5LTs5iy6jAAT7SrgtWq2egiIiIiInIVVhu0egYeXgphdeH8Gfj+IfhhCKSeMTudaWxWCwObV2Dxc224u3FZAGZtOE7b95Yya/0xHI7i2QZHREQkL6iQXlhkpsHPIwAnNBgIlduZneiWfbP2KAmpmYQHe9O9bimz44iIiIiISEEXWhuGLYbbnweLDXbMNman751vdjJTBfu48+499fn+0RbUCPMlITWT//y4nbsmrmbHyXNmxxMRESkSVEgvLJa/DfH7wScUOr9udppbdj7DzqQVhwB4vE1lbJqNLiIiIiIiN8LFDdq9CMMiIbg6JMfAzPvg5ycgLdHsdKZqWjGQX59sxUvda+LtZmPLsQR6frySV3/ZSWJaptnxRERECjUV0guDqG2wcpyx3/098CxhapzcMGvDMeKSMyhbwpPeDcuYHUdERERERAqbMo3hkWXQYgRggS1fw6ct4dBSs5OZytVmZVjrSix6tg131iuFwwnTVh+h3bvLmLPlJE6n2r2IiIjcDBXSCzp7ljGzwmmHWr2gZg+zE92y9Cw7ny0zZqM/1qYyrjYNQxERERERuQmuntD5DRg8D0pUhHPH4cteMO/fkJFidjpThfl78PGARnw9NIJKwd7EJafz9Ldb6T9pLftjksyOJyIiUuioglnQrfkIoreBRwB0fcfsNLnih00niE5MI8zPI3tBHBERERERkZtWoSU8ugqaDDVur/8cJraCY+vMzVUAtKoazO9Pt+bfnavj4Wpl7aEzdP1wBWN/301KepbZ8URERAoNFdILsrgDsGSssd9lLPiGmpsnF2TaHXy69CAAj9xRCXcXm8mJRERERESkSHD3gTvfh4E/gl8ZOHMIpnaByFcgM83sdKZyd7HxRNsqRD5zBx1qhpLlcPLZskN0fH8Z83dEqd2LiIjIDVAhvaByOuCXEWBPh8rtoH5/sxPlip+3nuLE2fME+7jRr2l5s+OIiIiIiEhRU6U9PLYa6g8w/q5a9SF83gZObTU7menKBXrxxYNN+GJQE8qW8OTUuTQe/Xozg6dt4Gh88W6FIyIi8k9USC+grJumwbE14OoNd44Di8XsSLfM7nDyyZIDAAxrXQlPN81GFxERERGRPOAZAH0+hX4zwLsknN4NX7SHpf8De6bZ6UzXoVYokc/cwZPtquBms7J072k6frCcDyL3kZZpNzueiIhIgaRCegHkmRGHdclo40aHV6FEBVPz5Ja526M4FJdCgJcrA5sXjfckIiIiIiIFWI3u8Pg6qNULHFmwdCx80QFid5udzHSebjae7VSd+U+3plWVYDKyHHy4aD+dxy1nyd5Ys+OJiIgUOCqkFzROJ/WPT8OSkQLlmkPTYWYnyhUOh5MJi43Z6ENuC8fH3cXkRCIiIiIiUix4B8E90+GuyeARAFFb4bM7YNV4cGj2daWSPnw1tBkfD2hIqJ87R+NTGTx1A49+tYlTCefNjiciIlJgqJBewFh2fE9o4jacNjfo+RFYi8Y/0cJdMeyNScLX3YUHW1Y0O46IiIiIiBQnFgvUvRseXwtVOxlrUUW+DFO7QfxBs9OZzmKxcGe90ix6tg3DWoVjs1qYvzOa9u8t49OlB8nIcpgdUURExHRFo0pbVCSfxhb5IgCO1v+GktVMDpQ7nE4nHy/ZD8CglhXw93Q1OZGIiIiIiBRLfqVgwHfGpCU3Xzi+Fia2gvWTwKFisY+7Cy/dWYu5T7WiacUSnM+089b8PXQbv4I1B+PNjiciImKqAlFInzBhAhUrVsTDw4OIiAjWr19/Q8+bNWsWFouF3r17X3b8oYcewmKxXLZ16dIlD5LnsoUvYTl/lnOe5XE0H2F2mlyzdN9pdpxMxNPVxtBWlcyOIyIiIiIixZnFAo0GwWOroGJryEyFec/B130g4bjZ6QqEGmF+fPdIC969pz5B3m4ciE2m/6S1PD1rC7FJaWbHExERMYXphfRvv/2WkSNH8t///pfNmzdTv359OnfuTGzs9Rc3OXLkCM899xytW7e+6v1dunQhKioqe5s5c2ZexM9dbf6Do1I7tpQfCraiMWvb6XTy0SJjNvrA5uUJ9HYzOZGIiIiIiAhQogIM+gW6vg0unnBoKXzaErZ8A06n2elMZ7FYuLtxWRY/24aBzctjscCcrado/+4ypqw8TFJaptkRRURE8pXphfT333+f4cOHM3jwYGrVqsXEiRPx8vJiypQp13yO3W7n/vvvZ/To0VSqdPUZzu7u7oSFhWVvJUqUyKu3kHsCw7H3/45zXuFmJ8k1aw7Gs/lYAm4uVoa31mx0EREREREpQKxWiHgEHl0JZZtCeiL8/DjM7A9JMWanKxD8vVx5vXdd5jx+G/XK+pOUnsWY33bR+LU/GDZ9A7M3neDceRXVRUSk6DO1kJ6RkcGmTZvo0KFD9jGr1UqHDh1Ys2bNNZ83ZswYQkJCGDp06DUfs3TpUkJCQqhevTqPPfYY8fHq52aGjxYfAKB/03KE+HmYnEZEREREROQqgqvAkAXQ4VWwucG+3+GTCNjxo9nJCoz65QL46fHbeL13HSqV9CbD7uCP3bE8+/2fNHk9ksFT1/PdxuMkpGaYHVVERCRPuJh58ri4OOx2O6GhoZcdDw0NZc+ePVd9zsqVK5k8eTJbt2695ut26dKFvn37Eh4ezsGDB3nhhRfo2rUra9aswWazXfH49PR00tPTs28nJiYCkJmZSWZm/n6yfvF8+X3evLDp6FnWHIrH1WZhSMvyReI9makojQ3JfRofcj0aH3ItGhsFh/4NRAoAqw1aPQNVO8FPj0D0dvhhMLZavXG1dTQ7XYFgs1oY2LwC90eUZ39sMnO3RfH7jij2xSSzZO9pluw9zQtWCy2rBNOtThidaoepvaeIiBQZphbScyopKYkHHniASZMmERwcfM3H9evXL3u/bt261KtXj8qVK7N06VLat29/xePHjh3L6NGjrzi+cOFCvLy8cid8DkVGRppy3tw0cbcVsNIkyM7W1UvYanagIqIojA3JOxofcj0aH3ItGhvmS01NNTuCiFwUWhuGLYYV78Lyd7HumkM7lyVYagVBza5mpysQLBYL1UJ9qdbRl2c6VuNAbBLztkczb3sUe6KTWL7vNMv3nebFOTtoUSmIrnXD6Fw7jGAfd7Oji4iI3DRTC+nBwcHYbDZiYi7vPRcTE0NYWNgVjz948CBHjhyhR48e2cccDgcALi4u7N27l8qVK1/xvEqVKhEcHMyBAweuWkgfNWoUI0eOzL6dmJhIuXLl6NSpE35+fjf9/m5GZmYmkZGRdOzYEVfXwrvg6PaT59i9Zh02q4XX77+d8oHmfCBRlBSVsSF5Q+NDrkfjQ65FY6PguHhFpIgUEC5u0PYFqNYF50+P4BG3D77tB80egY5jwFVtK/+qSogvT7X35an2VTl0OpnfdxhF9Z2nEll5II6VB+J4ec4OIsKD6FY3jM51wgjx1fdQREQKF1ML6W5ubjRu3JhFixbRu3dvwCiML1q0iBEjRlzx+Bo1arB9+/bLjr300kskJSXx4YcfUq5cuaue58SJE8THx1OqVKmr3u/u7o67+5WfjLu6upr2R6WZ584NE5cfAaBX/dJUDvU3N0wRU9jHhuQtjQ+5Ho0PuRaNDfPp+y9SQJVpRNbQxRybMoTKpxfC+s/gyAq46wtj5rpcoVJJH55oW4Un2lbhaHwK87ZH8/uOKLadOMeaQ/GsORTPK7/spGnFQLrVCaNLnVKE+auoLiIiBZ/prV1GjhzJgw8+SJMmTWjWrBnjxo0jJSWFwYMHAzBo0CDKlCnD2LFj8fDwoE6dOpc9PyAgACD7eHJyMqNHj+auu+4iLCyMgwcP8vzzz1OlShU6d+6cr++tuNodlcjCXTFYLPB42ypmxxEREREREbl5Lh7sKDuQCu2G4PLbUxC7Cz5va8xMj3gELBazExZYFYK8eaxNZR5rU5njZ1L5fUcU87ZHs/V4AusPn2H94TO8+usumlQoQde6pehaJ4zSAZ5mxxYREbkq0wvp9913H6dPn+aVV14hOjqaBg0aMH/+/OwFSI8dO4bVar3h17PZbGzbto3p06eTkJBA6dKl6dSpE6+99tpVZ51L7puw5AAA3eqWokqIj8lpREREREREbp2zSgd4bDX8/ATsXwDz/w8ORELvT8EnxOx4BV65QC8evr0yD99emZMJ55l/of3LpqNn2Xhhe+23XTQoF0D3uqXoUieMcmoRKiIiBYjphXSAESNGXLWVC8DSpUuv+9xp06ZddtvT05MFCxbkUjLJqYOnk5m7PQqAEZqNLiIiIiIiRYlPSRjwLWz4Aha+BAf+gE9aQO9PoJqugL5RZQI8GdoqnKGtwok+l8b8CzPVNxw9w9bjCWw9nsAb83ZTr6w/3eqWoludUpQPUlFdRETMVSAK6VJ0TFhyAKcTOtQMpWap/F2oVUREREREJM9ZLNBsOFRsBbOHQcwOmHEvNB0OnV4DV7UmyYkwfw8eui2ch24LJzYxjQU7o5m7PYr1h8+w7cQ5tp04x/9+30Pt0n5GUb1uKcKDvc2OLSIixZAK6ZJrjsWn8vPWUwA82U6z0UVEREREpAgLqQnDFsGiMbB2AmyYdGEh0skQVuefny9XCPHz4IEWFXmgRUVOJ6WzcJfR/mXtoTPsPJXIzlOJvLNgLzXCfLOL6monKiIi+UWFdMk1ny47iN3h5PZqJalfLsDsOCIiIiIiInnL1QO6vAlV2sGcx+H0HpjUFjqMhohHIQfrfcnlSvq6c39EBe6PqEB8cjqRu2KYuz2K1Qfj2ROdxJ7oJN6P3Ee1UB+61ilF93qlqBrig0WLv4qISB5RIV1yxamE8/yw6Tig2egiIiIiIlLMZC9EOgL2/Q4LRhn903t/Ar5hZqcr9IJ83OnXrDz9mpXnbEoGkbtjmLc9ilUH4tgXk8y+mP18uGg/lUt6Z89UrxHmq6K6iIjkKhXSJVd8vvwQmXYnEeGBNK0YaHYcERERERGR/OUdDP1nwsbJsOBFOLgIPm0JvSZA9a5mpysySni7cW+TctzbpBznUjP5Y3cMv++IYvm+OA6eTuGjxQf4aPEBwoO96VonjG51S1G7tJ+K6iIicstUSJdbFpuUxsz1xwB4qn1Vk9OIiIiIiIiYxGKBpsOgYmv4YSjEbIeZ/aDJUOj0Orh5mZ2wSPH3cuWuxmW5q3FZEtMyWbw7lnnbo1i67zSH41L4ZOlBPll6kPKBXnStG0a3OqWoV9ZfRXUREbkpKqTLLftixWHSsxw0LB9Ay8pBZscRERERERExV8nqMPzCQqRrPjZmqR9ZCXd9AaXqmZ2uSPLzcKV3wzL0bliG5PQsFu+J5fftUSzZG8uxM6l8tuwQny07RJkAT7rVDaNr3VI0LBegorqIiNwwFdLllpxJyeDrtUcBoze6fgkREREREREBXNyh8xtQpT389BjE7YVJ7aDDf6H5E1qINA/5uLvQs35petYvTUp6Fkv3nmbejigW747lZMJ5Jq04zKQVhynt70GXOqXoVjeMRuVLYLXq71kREbk2FdLllkxddZjUDDu1S/vRtnqI2XFEREREREQKlsrtjIVIf3kS9s6FhS9dWIh0IviVMjtdkeft7kL3eqXoXq8U5zPsLNsXy7zt0SzaHcOpc2lMWXWYKasOE+rnTtc6pehaJ4wmFQOxqaguIiJ/o0K63LRz5zOZtuoIoNnoIiIiIiIi1+QdBP2+gU3TYP4oOLTUWIi050dQ806z0xUbnm42utQpRZc6pUjLtLN832l+3xHNH7tiiElMZ9rqI0xbfYSSvu50qR1G17phNKsYiItNVw+IiIgK6XILvlx9hKT0LKqF+tCpVpjZcURERERERAouiwWaDIYKt8GPwyDqT/j2fmj8EHR+E9y8zU5YrHi42uhUO4xOtcNIz7Kzcn8c87ZHE7krmtNJ6Xy19ihfrT1KkLcbnesYC5U2r6SiuohIcaZCutyUlPQsJq86DMATbauol5yIiIiIiMiNKFkNhv4BS16HVeONWepHVhkLkZZuYHa6YsndxUb7mqG0rxlKRlZdVh+MY972KBbuiiE+JYMZ644xY90xSni50rm2sVBpy8pBuKqoLiJSrKiQLjflm3VHSUjNJDzYmzvrlTY7joiIiIiISOHh4gYdx0Dl9vDTIxC/H77oAO1fhhZPaiFSE7m5WGlTPYQ21UN4w+5gzcF4ft8RxYKdMZxJyWDWhuPM2nAcf09XOtUKpVOtkmQ5zE4tIiL5QYV0ybG0TDufLzdmoz/WprIWYREREREREbkZle64tBDpnt8g8hVjIdI+n4GfJiyZzdVm5fZqJbm9Wkle6+Vg/eEzzN0exYKd0cQlZ/D9phN8v+kE7lYbc+I30bxyMM0rBVK3TABuLvowRESkqFEhXXJs1vpjxCWnUybAkz4Ny5gdR0REREREpPDyCoT7vobNX8L8/8Dh5X9ZiLSH2enkAheblZZVgmlZJZgxveqw4cgZ5m2P4vftUZxOzmDFgXhWHIgHwNPVRqMKAUSEBxERHkiD8gG4u9hMfgciInKrVEiXHEnPsvPZ8kOAMRtdPeFERERERERukcUCjR80FiKdPRSitsK3A6HRIOjyPy1EWsDYrBaaVwqieaUgXuxSjcmzf8etbG02Hj3HusPxnE3NZNWBeFZdKKy7uVhpWC6AiEpBNA8PpFGFEni4qrAuIlLYqJAuOTJ700mizqUR6ufO3Y3Lmh1HRERERESk6AiuAkMjYembsHKcMUv96GroOwnKNDI7nVyF1WqhjDd0a1GBYbe74nA42R+bzLrD8aw7dIZ1h+OJS85g3eEzrDt8hvGAq81C/bIBRFQKJCI8iMYVSuDtrvKMiEhBp5/UcsMy7Q4+WXoAgEdur6xP0EVERERERHKbixt0eBUqt4MfH4H4AzC5I7R7CVo+BVb9HVaQWa0Wqof5Uj3Ml0EtKuJ0Ojl4OuWywnpMYjobj55l49GzTFhyEBerhTpl/ImoFEjz8CCaVCyBr4er2W9FRET+RoV0uWG/bD3FibPnCfJ2o3+z8mbHERERERERKbrCb4fHVsFvT8Oun+GPV+HAImMhUn+tVVVYWCwWqoT4UCXEh/sjKuB0Ojkan/qXwvoZTiacZ+vxBLYeT+CzZYewWqB2aX8iwgOJqBREs4qB+HupsC4iYjYV0uWG2B1OJiwxZqMPa10JTzfNghAREREREclTXoFwz3TY+g3Mex6OrLiwEOl4qNXL7HRyEywWCxWDvakY7M19TY0JasfPpBqtXw7Fs+7wGY6dSWX7yXNsP3mOL1YexmKBGmF+NL/QCqZZeCCB3m4mvxMRkeJHhXS5IfO2R3EoLgV/T1ceaFHB7DgiIiIiIiLFg8UCDQdC+RYwexic2gzfDTKOdXkL3H3MTii3qFygF+UCvbLXIYs6dz67Dcy6Q2c4FJfC7qhEdkclMnXVEQCqh/rSLDwwu896SV93E9+BiEjxoEK6/COHw8nHi43Z6ENuC8dHi6CIiIiIiIjkr6DKMHQhLB0LK96HLV9fWIj0Cyjb2Ox0kotK+XvSu2EZejc0WvjEJqZdWKzUKKzvj01mb0wSe2OS+GrtUQAqlfQmIjwoe9Z6mL+HmW9BRKRIUkVU/lHk7hj2xiTh4+7CQy0rmh1HRERERESkeLK5QvtXLi1EeuYQTOkEbUZBq2e0EGkRFeLnQY/6pelRvzQA8cnprD9s9FdfeyiePdFJHDqdwqHTKcxcfwyACkFeRo/18CAiKgVStoSXmW9BRKRIUCFdrsvpvDQbfVCLClrgRERERKSYWL58Oe+88w6bNm0iKiqKn376id69e1/z8T/++COffvopW7duJT09ndq1a/Pqq6/SuXPn/AstUlxUbAWPrYTfnoGdP8Hi1+DgYmMh0oByZqeTPBbk407XuqXoWrcUAAmpGdmF9XWH49l1KpGj8akcjU/lu40nACgT4EnzSkZRvXl4EOUCPbFYLGa+DRGRQkeFdLmuZftOs/3kOTxdbQxtFW52HBERERHJJykpKdSvX58hQ4bQt2/ff3z88uXL6dixI2+++SYBAQFMnTqVHj16sG7dOho2bJgPiUWKGc8ScPdUqNoJ5v0bjq6CibfBneOgzj//NytFR4CXG51qh9GpdhgAiWmZbDxyhnWHzrD28Bl2nDzHyYTzzN58gtmbjcJ6KX8PY8Z6pSAiwgMJD/ZWYV1E5B+okC7X5HQ6+ejCbPT7I8oT5KPFS0RERESKi65du9K1a9cbfvy4ceMuu/3mm2/y888/8+uvv6qQLpJXLBZoMADKN4fZw+HkRvhhMOyPhG5vg7uv2QnFBH4errSrEUq7GqEAJKdnsenoWdYdimfd4TNsO5FA1Lk05mw9xZytpwAo6eueXVhvHh5IlRAfFdZF8lpWmtkJJIdUSJdrWnMonk1Hz+LmYuXh2yuZHUdEREREChGHw0FSUhKBgYHXfEx6ejrp6enZtxMTEwHIzMwkMzMzzzP+1cXz5fd5peArFGPDtxw88CvWle9iXfUBlj9n4Dy6Gnvvz3CW0UKkeakwjA93K7QMD6BleABQmfMZdrYcT2D9kbOsP3KWrccTOJ2Uzm/bovhtWxQAgd6uNK1QgmbhgTSrWIJqIT5YrSqs51RhGB9iDsvm6bgs+D9u86pC1vFQKKef1WbJyX+fKqTLNV3sjd6vaTlC/LTit4iIiIjcuHfffZfk5GTuvffeaz5m7NixjB49+orjCxcuxMvLnIXxIiMjTTmvFHyFY2zUJ7DKKBofmYhXwhGs07qyt1Qf9oX2AIvV7HBFWuEYH5erBlQrBfeFwtEkOJBo4UCihSPJFs6kZLJgVywLdsUC4OXipLKvk8p+Tqr4OSnjDaqr37jCOD4k75Q6u56mRyZgwUlw8h6cX3bheOBt7C51N2lu156AIHkjNTX1hh+rQrpc1aajZ1h9MB4Xq4VH7qhsdhwRERERKURmzJjB6NGj+fnnnwkJCbnm40aNGsXIkSOzbycmJlKuXDk6deqEn59ffkTNlpmZSWRkJB07dsTV1TVfzy0FW+EbG90gbQiO+f/GuvNHakbNprrLSey9PgV/LUSa2wrf+PhnGVkOtp88lz1jffOxBFIz7Gw/a2H7WeMxvh4uNC4fQLPwEjSrGEjtUr642PRhzd8VxfEht8ZyZDm2WZ9jwUlWnfuIOnmMcmfXUP7MSsolbcbRfASO5k+Am7fZUYuNi1dE3ggV0uWqLvZGv6tRWcoEeJqcRkREREQKi1mzZjFs2DC+//57OnTocN3Huru74+5+5To8rq6uphUczDy3FGyFamy4BsPdU6BaF5j7LNbja7FOagN3vg917zY7XZFUqMbHP3B1heZVQmhexfggNNNuFNbXHTrDusPxbDxylqS0LJbui2PpvjgAvN1sNK4YSER4II0rlKB6qC8lvN3MfBsFSlEaH3ILTm2B7weBPQNq9sTZYzyb5y+gVK9XcVn0Cpbj67CteBvb1q+h/StQ7z6w6gOqvJaT/zZVSJcrbD9xjqV7T2O1wONtNRtdRERERG7MzJkzGTJkCLNmzaJ79+5mxxEp3iwWqH8flGsGPz4MJ9bD7KEXFiJ9Bzzy96oPKbxcbVYalS9Bo/IleKxNZbLsDnZFJWYX1tcfPkNiWhbL951m+b7T2c8L8XWnepgv1UJ9qR7qS7UwX6qG+ODtrlKUFEPxB+HruyEjGSq2hr6TABuAsZbFkAWwaw5EvgIJx2DOo7DuU+g8FireZmp0uUQ/veQKHy/ZD0CvBmWoEKRLSURERESKo+TkZA4cOJB9+/Dhw2zdupXAwEDKly/PqFGjOHnyJF9++SVgtHN58MEH+fDDD4mIiCA6OhoAT09P/P39TXkPIgIEhsPg32H5O7D8bdg2C46tgbu+MIrsIjnkYrNSr2wA9coGMPz2StgdTvZEXyqs7zyVyImz54lNSic2KZ0V++Mue365QE+jsB7qm11or1TSG3cXm0nvSCSPJUbBV70hNQ7C6kG/GeDqAX9d5NJigdp9oFpXWDcRlr8LUX/CtG5Qswd0GA1BmuxqNhXS5TJ7ohNZsDMGiwWe0Gx0ERERkWJr48aNtG3bNvv2xV7mDz74INOmTSMqKopjx45l3//555+TlZXFE088wRNPPJF9/OLjRcRENhdoOwoqt4Ufh0PCUZjSBe74P2j9rHG/yE2yWS3ULu1P7dL+DGkVDkByehb7Y5LYF5PE3uhk42tMEqeT0jl+5jzHz5znj92xl71GeLD3XwrsPlQL9aVCkDc2rWoqhdn5BPj6LmOWeYlwGDj7+lcEuXpAq6ehwf2w9E3YNA12/wp750PEI3D7v8EzIH+yyxX0f0u5zIQlBwHoVqcUVUJ8TU4jIiIiImZp06YNTqfzmvf/vTi+dOnSvA0kIreufHN4dCXMfQ62f2cUaQ4uhr6fQ4kKZqeTIsTH3YWG5UvQsHyJy46fSclgb3RSdmF9X7TxNSktiwOxyRyITWbu9qjsx7u7WKkS4pPdGubi19L+HlgsKrBLAZd5Hmb2h9id4BMKD/wEPtdehP0yPiXhzg+g2cOw4EU4uAjWfAxbZ0CbUdBkMNjUdz+/qZAu2Q6eTua3bacAeKJtFZPTiIiIiIiISK7z8Ie7JkHVjjD3WTi+Fia2gu7vQb17zU4nRVygtxstKgfRonJQ9jGn00l0YtqlAvuFGez7Y5NIy3Sw81QiO08lXvY6vu4uVA31uaIHe7DPlQtYi5jCngU/DIFjq8Hdz5iJHhie89cJqQkP/Aj7/4CFL8LpPfD7v2HDJOj0OlTtZLSFkXyhQrpk+2TJQZxO6FAzhFqltfCMiIiIiIhIkVXv3gsLkT5iFNN/HA77FxoFdQ+tayD5x2KxUMrfk1L+nrSpfmm2rt3h5PiZ1Mtmru+LSeLQ6RSS0rPYfCyBzccSLnutYB83qv2t/3q1UB98PTRzV/KR0wm//Qv2zgObO/SfBWF1b+01q3aASm1g8zRY8ibE7YMZ9xrHOr8JobVzIbj8ExXSBYDjZ1KZs/UkACPaVTU5jYiIiIiIiOS5EhXhobmw8n1Y+j/Y/j0cW2fMWC/f3Ox0UszZrBYqBntTMdibzrXDso9nZDk4HJdyRYH92JlU4pIziEuOZ/XB+Mteq0yAJ9VCfS61hwn1pUqIDx6uWuBU8sCi0bDla7BY4e4pUPG23Hldmws0HQZ174EV78HaT+HQUuOqooYPQNsXwTc0d84lV6VCugDw6bKD2B1OWlcNpkG5ALPjiIiIiIiISH6wucAdzxuzGmcPMxYindrVWNDu9ue1EKkUOG4uVqqHGTPOqX/peGqG0Wf9Ug/2ZPZFJxGdmMbJhPOcTDjPkr2nsx9vtUDFIG9j1vqFAnv1MB8qBnnjYrOa8M6kSFgzAVZ+YOzfOQ5q3pn75/Dwh45joPFg+ONV2DUHNk+HHbOh9Uho/ji4eub+eUWFdIGoc+f5YeMJAJ7UbHQREREREZHip1wzYyHS35+HP2fCsrcuLEQ66eb6+orkMy83F+qVDaBe2YDLjp9LzWRfbNJferAbs9gTUjM5FJfCobgU5u+Mzn68m81KpZLel/Vfrx7mS5kAT6xW9aKW6/jzW1jwgrHf/hVo/GDeni8wHO6dDsfWwvxRcGozLBoDG6dCh1ehzl3qn57LVEgXPlt2iAy7g2bhgTQLDzQ7joiIiIiIiJjBww/6TIQqHeC3kXBiA0xsDd3fhXr3qSAjhZK/lytNKwbStOKleofT6eR0cjr7opOvaBGTmmFnT3QSe6KTLnsdLzcbVUN9qR7qk92DvXqoLyV93bHovw3ZHwk/P27sRzwGrUbm37nLN4dhi2DHD8YM9XPHYfZQWDfR6J9erln+ZSniVEgv5k4npTNz/TEAntJsdBEREREREal796WFSI+thp8eubAQ6fvgGWB2OpFbZrFYCPH1IMTXg1ZVg7OPOxxOTiacz561fnEG+6HTKaRm2PnzeAJ/Hk+47LUCvFypFupLjbC/LHIa4ou/lxY4LTaOb4DvBoEjy+hf3vnN/P/g0Wo1FpGuceel9jInNsDkjlC7rzFDvUSF/M1UBKmQXsx9sfIQ6VkOGpQL4LYqQWbHERERERERkYIgoDw89JuxEOmSsUbv3eProe/nUKGl2elE8oTVaqFcoBflAr3oUOvSoo2ZdgdH41PY+5cZ7PtikjgSn0JCaibrD59h/eEzl71WmJ8HVUO8IdnKyZWHKR/kQ+kAT8oEeFLSx11tYoqK2D0w4x7ITIXK7aHXJ0ZR2yxuXnDHv6HRA7D4dWPR050/wp650OJxY6a8h595+Qq5AlFInzBhAu+88w7R0dHUr1+fjz76iGbN/vmyg1mzZtG/f3969erFnDlzso87nU7++9//MmnSJBISErjtttv49NNPqVpVM67/6mxKBl+tOQrAk+2q6FIkERERERERucRqMxYdrdTWWIj07GGY1h1aPQPNnwBvTcaS4sHVZqVKiC9VQnzpTqns42mZdg7EJl9Y3PRigT2ZkwnniU5MIzoxDbCyInr/317PQil/T0oHeGQX1//6tXSAB15uBaJkJ9dz7gR83RfOn4UyTeC+r8DFzexUBt8w6PUxNHvY6Nt+ZIUxS33L19D2RWj4gBaTvgmmf8e+/fZbRo4cycSJE4mIiGDcuHF07tyZvXv3EhIScs3nHTlyhOeee47WrVtfcd/bb7/N+PHjmT59OuHh4bz88st07tyZXbt24eHhkZdvp1CZuuowqRl2apXyo12Na3+vRUREREREpBgr2wQeXQG//x9s/QZWvAcrx0GlO4yWATXvBM8SZqcUyXcerjbqlPGnThn/y44npmWyPyaZ3acSWLphB57BpYlOTOdUglFcz7Q7OXYmlWNnUq/52iW8XK9aZC9Twii0B3trVrupUs/AV30g8SQEV4P7vwc3b7NTXalUPXjwV9g3Hxa+BPEH4LenYf3n0PkNqNzO7ISFiumF9Pfff5/hw4czePBgACZOnMjcuXOZMmUK//nPf676HLvdzv3338/o0aNZsWIFCQkJ2fc5nU7GjRvHSy+9RK9evQD48ssvCQ0NZc6cOfTr1y/P31NhkJiWydTVRwDNRhcREREREZF/4O4LvT+Bqp2Mdi9Rf8LBxcb22zNQua1RVK/RDTz8//n1RIowv/RYGp/6mYZ75nKb1Y2yvafj6mW008iyO4hJSudUwnlOJZzn5MWvZ89zKiGNkwnnSU7P4mxqJmdTM9l5KvGq53CzWSkV4EFp/4vFdU/K/G2Gu4erLT/fdvGRkQLf3ANx+8CvDAz8EbwC//l5ZrFYoHpXo/XMximwdCzE7jI+CKjaCTq9DiWrm52yUDC1kJ6RkcGmTZsYNWpU9jGr1UqHDh1Ys2bNNZ83ZswYQkJCGDp0KCtWrLjsvsOHDxMdHU2HDh2yj/n7+xMREcGaNWtUSL/gy9VHSErLomqID51rh5kdR0RERERERAqD2r2NLf4g7PzJ2GJ2GIuR7l8INjeo0hHq9IVqXcDdx+zEIvnj3EnY9TPsmgPH1wFgBcIBx1c9of9M8CuFi81KmQvF7mtJTMv8S3H9PCcT0i4rvMckppFhd3A0PpWj8dee1R7k7ZbdKqZMgNeFrxfbx3gS7OOmiZU5Zc80FhY9udG4EmfgjxBQzuxUN8bFDZo/aixKuvwdY1b6/oVwYBE0GQJtRqll1z8wtZAeFxeH3W4nNDT0suOhoaHs2bPnqs9ZuXIlkydPZuvWrVe9Pzo6Ovs1/v6aF+/7u/T0dNLT07NvJyYan/ZlZmaSmZl5Q+8lt1w8X16eNyU9i8krDwPw6O3h2O1Z2O15djrJJfkxNqTw0viQ69H4kGvR2Cg49G8gIoVOUGW4/TljO73PWMxux48Qtxf2zjU2F0+o1glq94GqnY1F8ESKkovF850/wYn1l99Xrjn2irdjXz0Bt6gtMKkt9JsBZRr948v6ebjiF+ZKjbCrLwqZaXcQk5h2YQZ7avZM9lN/md2ekmEnPiWD+JQMtp88d9XXcXOxXiis/31mu/G1lL+HZrX/lcMBcx6HA3+AqxcM+A5CapidKue8AqHLWGgyFCJfMX5eb5gE274zfqZHPAIu7manLJBMb+2SE0lJSTzwwANMmjSJ4ODgXHvdsWPHMnr06CuOL1y4EC8vc/5HHxkZmWevvfiUhbOpNoI9nFhObGHeyS15di7JfXk5NqTw0/iQ69H4kGvR2DBfauq1Z5OJiBR4JatBm//AHf9ntAvY8aNRWD9z6MIM3Z/B1RuqdzHav1TpAK5av0wKqXMnLhTP51xZPC/fAmr1hlo9wa80jsxMlsWXpEPsF1ji9sLUrkaLpDp33VIEV5uVsiW8KFvCC7iypYjT6STxfNal4vo5o7h+qdieRkxSGhlZDg7HpXA4LuWa5wr2cc9uGVP6L/3aLxbgA72Lyax2pxMWvgjbvwOrC9z7JZRrZnaqWxNcBfrPgMPLjQVJo7dD5MuwcTJ0HAM1exptYSSbqYX04OBgbDYbMTExlx2PiYkhLOzKdiMHDx7kyJEj9OjRI/uYw+EAwMXFhb1792Y/LyYmhlKlLq2kHBMTQ4MGDa6aY9SoUYwcOTL7dmJiIuXKlaNTp074+V3907+8kpmZSWRkJB07dsTV1TXXXz8t085r768AMhjZpQ49GpfJ9XNI3sjrsSGFm8aHXI/Gh1yLxkbBcfGKSBGRQs1igdDaxtbuJYjedqmonnAMdsw2Nnc/qN7NaP9Sqa3RbkCkIMsunv8EJzb85Q4LlG9+WfH871LdQ8l6aD6uPz8K+xfAD0Mgdje0eQGs1jyJa7FY8Pdyxd/LlVqlr17XysgyZrVfNpP9LzPbT549z/lMO3HJ6cQlp/PniavPavdwtV6axe7veamVTAnjWJi/B+4uRWBW+8oPYO0nxn6vT6BqR3Pz5Kbw2+HhZfDnLFg0Bs4eMdrXlG9pLEh6A1dRFBemFtLd3Nxo3LgxixYtonfv3oBRGF+0aBEjRoy44vE1atRg+/btlx176aWXSEpK4sMPP6RcuXK4uroSFhbGokWLsgvniYmJrFu3jscee+yqOdzd3XF3v/KSBVdXV9P+qMyrc8/YcJK45AzKBHhyT9PyuNry5oe25B0zx6UUfBofcj0aH3ItGhvm0/dfRIociwVK1Te2Dq/Cyc1GQX3nT5B4ErbNMjYPf6jRA+r0gfA7wKafh1JAJBy/1PP8iuJ5C2OtgJo9wa/UNV7gL9x9jR7pf7wKq8cb/aljd0Ofz0xbR8DNxUq5QC/KBV69E4PT6eTc+UxO/qVX+6lzlxfaY5PSSct0cOh0CodOX3tWe0lfd0oHeFI2wJOSvu4E+7gR5ONOkLfx9eJtbzdbwZzdvvlLWHShk0XnN6H+febmyQtWGzS8H2r1MsboqvFwbLXRkqheP2j/CvhrMq7prV1GjhzJgw8+SJMmTWjWrBnjxo0jJSWFwYMHAzBo0CDKlCnD2LFj8fDwoE6dOpc9PyAgAOCy408//TSvv/46VatWJTw8nJdffpnSpUtnF+uLq4wsBxOXHQTg0TaVVUQXERERERGRvGexQNnGxtbxNaMdxs6fjNYYydGw9Wtj8ww0ZvXW7gsVWxmFHZH8lJvF87+z2qDTaxBSE379F+z5DaZ0MVprBJTPpTeQeywWCwFebgR4uVG7tP9VH5OeZSfmXPql4vpVvqZlOjidlM7ppHT+PJ5w3XO6u1gJ9nEnyMctu8ge5ONGsPeFYxeK78E+7gR6u+Hmkg91rd2/Gf9eAK2egRZP5P05zeTuA21fgEYPGrPTL37ouetnuO0paPlUsV5E2vRC+n333cfp06d55ZVXiI6OpkGDBsyfPz97sdBjx45hzeGlLs8//zwpKSk8/PDDJCQk0KpVK+bPn4+HR/HuwTZ78wmizqUR4uvOPY3Lmh1HREREREREihur1WiFUb65MbPz2Bqj/cuunyE1DjZNMzbvksbMyNp9jQJmHrXAECHh2KWe5yc3/uUOC1RoabRtqdnj5ornV9NgAARVgVkDIGY7TGoH931t/DdRyLi72Cgf5EX5oGvPaj+bmnlZcT0uOZ345AzikjOITzH245PTScmwk57lMGbAJ5y/ofP7ebj8pfB+qdge/JfbF/f9PV2xWnM42/3IKqMVj9MBDQdC+//m7PmFmX8Z6PuZsfDogheN2enL3oJN06H9y1B/QLH8uWx6IR1gxIgRV23lArB06dLrPnfatGlXHLNYLIwZM4YxY8bkQrqiIcvu4JOlBwB45I7KWnVZREREREREzGW1GTPPK7aCrm/DkRVG+5fdv0LKadjwhbH5ljKKmXX6QtmmWvxObt2NFM9r9QTfK9fvyxXlmsHwJTCrv7HA47Q7oceHRmuNIsRisRDo7Uagtxt1ylx9VvtF5zOMfuzxKUZhPT45g7i/FNrjUy4U35PTOZOSQZbDSWJaFolpWRy6zmKpF9msRpaLM9qDrlJsN/aNr15ndsPM/mBPN9Z0uPPD4vmzp0wjGDwPdv8Cka8Y/dN/fgLWfWZ8GBre2uyE+apAFNIl7/3y5ymOnzlPkLcb/ZuVMzuOiIiIiIiIyCU2F6jc1ti6vw+Hll0oqv8GSVGw7lNj8y9ntNeo3QdKNyqehS25OQnHjML5rjlwctNf7rBAhdsutG3pkXfF878LKAdDFsBPjxpFyp8fh9hd0HFMsWxr5Olmu27P9r9yOJwkpmVmF9YvFt8vn+V+qRB/7nwmdoczu8UMJF339ctZYvjRbTQlLefY6Vqbj84/gd9POy9rLXOxEB/s40YJb7ei3T7ZYjGuEKrWBdZ/DsveMRaTnn4n1LjTGLNBlc1OmS9USC8G7A4nHy8xZqMPbR2Ol5v+2UVERERERKSAsrlC1Q7GducHcHCx0f5l7zw4dxxWf2RsJSoaBfXafSGsrorqcqWzRy/1PC8IxfO/c/OGe6bDsv8ZbTPWfAxx++CuL4yFeOWqrNZL/durhPxzv+6MLAdnUzOy28pcLLZfrRDvTI7hS+v/KGlJYLejHP2TniYx6Rxw7rrnCPByvXzx1L+2mvnboqp+Hi4Fc1HVf+LiDi2fhPr9Yen/YOMUo9f/vvnQ7GG443nwLGF2yjylimox8PuOKA6dTsHf05UHmlcwO46IiIiIiIjIjXFxh+pdjS3zPOyPNGaq71tgtBhY+YGxBVUxCup1+hqLOUrxdbF4vvMnOLX5L3dYjDZCtXoZC4b6hpoW8TJWq7G4Y8kaMOdx2L8QvugI/WcWm1m+ec3NxUqonwehfv+wdmJaIs5p3bFEx5DlV46sbj/wnqPEX1rLXFmIP5OSjsMJCamZJKRmcvD0P7eZcbVZCPJ2p4SXK5mpNn6K34yvpxs+7ja83VzwdnfBx9346n3FMVv2fZ6utpz3fc8N3sHQ/V1oNhwWvmSM2bWfwJ8z4Y7/QNOhxgeiRZAK6UWcw+Hk48XGbPTBt1XE16NoDmQREREREREp4lw9jb7VtXpCRopRTN/5o1Fcjz8Ay982tpI1jZnqdfpCcFWzU0t+OHvUmHW+c87lxXOL1Zh5XtCK51dTpy8EhsPMARC3F75ob8xWr3SH2cmKh8w0mDUAS/Q28ArG5cGfqRtUmbr/8DS7w0lCasblhfa/9XT/a9/3pPQsMu1OohPTiE5MAywcSIy7qcgWC3i52q5ZeDeOX36/l9ulQvzfn+PlZsvZTPmS1eH+7+HAIqOgHrsL5v8fbJgEnV43WsEUxpn316FCehH3x+4Y9kQn4ePuwkMtK5odR0REREREROTWuXkbhcc6fSE9Cfb+brR/OfAHnN4NS3fD0jchtC7UudD+JTDc7NSSm84e+cvM8y2Xjl8sntfubRTPfULMSphzpRvCw0tg1gCjFc1XfaDb29B0mNnJijaHHX4cbix47OYDA3+44asBbFaL0Tvdx51qob7/+Pi0TDtnUowe7rGJqaxYs4FqtetyPgtS0rNISc8iOT2L1Aw7yRduXzyWkm4nJcO47XCC0wkpGXZSMuzEJqXf6ncBi4ULRfi/FN//cvvvx3zcXfByd8HHvQ7eneZQ9shswja9hy3+AMzshzP8diyd3oBS9W45W0GhQnoR5nRe6o3+QIsKBHi5mZxIREREREREJJe5+0K9e43tfALsmWvMVD+0FGK2G9uiMUaRsnYfYwsob3ZquRlnj1xaMLSoFM//zjcMHpoHvzwJ27+Duc9C7G7o8r8i2y7DVE6n8T3e/QvY3KDfN8bPijzi4WqjdIAnpQM8ycz0Inm/k26Ny+LqeuP/tk6nk7RMR3ah/WLh/VLB/crCu3HMeExKxoXnXLidnJGF80JhPvnCc+FmCvMV8OF/PObyC8Nsv+N+eDmOibfzm60dX3kOJMMz5MKs+Ctnynu7u+DtZqNOGX/qlCm46wOokF6ELd8fx7YT5/BwtTKslT55FxERERERkSLOMwAa3m9sqWdg969GUf3wcqPwemoLRL4CZZsas9Rr9wa/0manluu5XvG8Yiuo1dtYMLQwF8//ztUD+n5u9PtfNAY2fGEsQnrPdPAKNDtd0bJ0LGyaCliM73mlNmYn+kcWiwVPNxuebjZK+rrf8us5nU7OZ9ovFd8vzoTP+Evx/QZnyqeku/BuRj9m2tvzvMssetrW0NOxiPbJK/k0oSeT7N1J59oTfUe0raJCuuQ/p9PJR4v2A3B/RAWCfG79PywRERERERGRQsMrEBo/aGzJp2H3z7DjJzi6Ck5sMLYFL0D5FkaLmILeQ7s4OXP4Us/zqK2Xjl9WPO8JPiXNyZcfLBZoPdJYhPTH4caHQZPawYBvjd7UcuvWT4Jlbxn73d81rlYphiwWC15uxkxx/rk7zT9yOIzCfEp6P04dXUfAiv/iHbuF51y/53G/FWyt+i92BXciOcNx+Uz59CyqhvrceoA8pEJ6EbX20Bk2Hj2Lm4uVh2+vZHYcEREREREREfP4lDT6TDcdBolRRhuHHT/C8bVwbLWx/f680R6kTl+o2Qu8g8xOXbxct3je2rh6oEaPol08v5oa3WDoQpjZD84ehi86wN1ToGpHs5MVbjt+hHn/NvbbjFIf+lxktVqy27VQ9w6oswR2zIY/XsXr3HFabhtFyzI/QOc3oXxzs+PmiArpRdTHS4zZ6Pc1KUeon4fJaUREREREREQKCL9SEPGIsZ07YRRud/5oLO54ZIWxzX0OKt1htH+peSd4ljA7ddF05tClti1Rf146XtyL538XWhuGL4FvHzA+9JlxL3R8DVo8Ycxcl5w5uAR+fBhwGgX0O/7P7ERFm8UCde+GGt1h7Sew4n3j5+2UzsbVJR1HQ4mKZqe8ISqkF0Gbjp5l1YF4XKwWHrlDs9FFRERERERErsq/LLQcYWxnj8DOn4yZqtHb4OBiY/vtGajc1iiq1+gGHgW3f2+h8I/F8z5Gz3PvYLMSFkzewTDoZ5j3LGz+Eha+CLG74M4PwEXtfG/Yyc0w635wZBpF3K5v68OI/OLqCa2fhYYPwOLXYctXxs+BvfOg+WPGfQX856sK6UXQx4uN2eh9G5WhbAkvk9OIiIiIiIiIFAIlKkKrZ4wt/qAxS33HTxC7E/YvNDabO1TpYLR/qdYFrCpg3pCLxfOdPxkfUlxksUF460sLhqp4fn0ubtBjPITUhgWjYOs3EH8A7vtGs/ZvRNwB+OZuyEyB8DuMxUWtNrNTFT8+IdBzPDR72PhA6NBSWPUhbPkaOoyGRg+YnfCaVEgvYnacPMeSvaexWuDxNlXMjiMiIiIiIiJS+ARVhtv/bWyn9xqz1Hf+CHH7YO9cY3PxxFalIxVSgrBsTwZXd7C5gtXlwmYD619vXzh23cfYjK8XH2OxFt7ZsvEHL/U8v1rxvHYfqHGniuc5ZbFA80chuCp8PxiOr4NJbaH/TAira3a6gisxCr7qA6nxUKoB9PtGM/nNFlYHHphjfEi58CXj52viKbNTXZcK6UXMx4sPANCzfmkqBnubnEZERERERESkkCtZHdqOgjb/gZidF2aq/whnD2Pd8wsNAI5PzbvzW13+Umy3XSq621wvv239+20XsLn8rZD/9+1qhf2rnM92lfP//Xw2V6PwH7X1GsXz2y/1PNdCrreuSnsYvghm3AdnDsLkTsYM65o9zE5W8Jw/C1/3hXPHILAy3P8DuPuanUrA+GCoWmeo3M6YkV7vPrMTXZcK6UXI3ugk5u+MxmKBJ9pqNrqIiIiIiIhIrrFYjBmUYXWg3csQ9Sf27T8Qt3MZJYMCsTrt4Mi6fLP/9fbF+zP/djsL7JmA8+rnvfiYwia7eH5x5rmK57kuuKpRTP9+MBxaAt8OhLYvwe3PFd4rGXJbRirM6Gf0k/cJgwd+VBucgsjmCk0Gm53iH6mQXoRMWGLMRu9aJ4yqofpkTURERERERCRPWCxQugGOkrVZmzaPbt26YXV1vbXXdDj+UnTPvLzQfrHY/vdj1y3cXyzWZ3JFMd/+92L+NYr7N3M+31Co2VPF8/ziWcKYYb3wRVg3EZa8Dqd3Q68JxuKOxZk9E34YDMfXgrs/DJxtrIUgcpNUSC8iDp1O5rdtRh8hzUYXERERERERKWSsVrC6AW5mJ5HCxuYCXd+CkjVg3nOwY7axwGu/GeBX2ux05nA64dd/wb754OIBA2YZV5OI3AKr2QEkd3yy9CAOJ7SvEULt0v5mxxERERERERERkfzUZDAM+hk8A+HUFvi8LZzYZHYqc/zxX9j6jdFi6O6pUKGl2YmkCFAhvQg4fiaVn7acBGBEO81GFxEREREREREpliq2guGLoWRNSI6Gad1g+w9mp8pfqz+CVR8a+z3HQ41u5uaRIkOF9CJg4rKD2B1OWlcNpmH5EmbHERERERERERERswSGw9CFUK0LZKXB7KGwaIzRh7+o2zoTFr5k7Hd4FRoONDWOFC0qpBdy0efS+H7jCQBGqDe6iIiIiIiIiIh4+Bk90m972ri94j34diCkJ5saK0/tWwA/P2Hstxhx6b2L5BIV0gu5z5YfJMPuoFnFQCIqaTVsEREREREREREBrDboOBr6fAY2N9g7F6Z0hrNHzU6W+46tg+8eBKcd6t0HHV8Di8XsVFLEqJBeiJ1OSmfm+mMAPNles9FFRERERERERORv6veDh+aBdwjE7IBJbeHoGrNT5Z7Y3TDjXsg6D1U6Qq8JYFXJU3KfRlUhNnnlYdIyHdQvF0CrKsFmxxERERERERERkYKoXFN4eAmE1YPUeJjeAzZ/ZXaqW5dwHL7qC2kJULYp3DsdbK5mp5IiSoX0QupsSgZfrTkCwJNtq2DR5SoiIiIiIiIiInIt/mVhyHyo1QscmfDLCJj/AtizzE52c1Li4as+kHQKStaAAd+Bm7fZqaQIUyG9kJq6+ggpGXZqlvKjfc0Qs+OIiIiIiIiIiEhB5+YNd0+DNqOM22snwMz7IO2cqbFyLD0Zvrkb4veDX1kY+CN4BZqdSoo4FdILocS0TKatOgzAk+00G11ERERERERERG6Q1Qpt/gP3TAcXTzjwB3zRAeIPmp3sxmRlwHcPwKnN4BkID/wE/mXMTiXFgArphdBXa46SmJZFlRAfutQOMzuOiIiIiIiIiIgUNrV7G61e/MpA3D6Y1A4OLjE71fU5HDDnMTi4GFy94P7voWQ1s1NJMaFCeiGTmpHFFysOATCibRWsVs1GFxERERERERGRm1C6AQxfYizUmZYAX98F6yeB02l2sis5nTD/P7DjB7C6wH1fQdkmZqeSYkSF9EJmxrpjnE3NpEKQF3fWK2V2HBERERERERERKcx8Q+HB36BeP3DaYd5zMHck2DPNTna5Fe/C+s+M/d4ToUoHc/N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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model saved as 'cifar10_cnn_model.h5'\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models, regularizers\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import pandas as pd\n",
        "\n",
        "# Set random seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "class CIFAR10Classifier:\n",
        "    def __init__(self):\n",
        "        self.model = None\n",
        "        self.history = None\n",
        "        self.class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "                           'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "    def load_and_preprocess_data(self):\n",
        "        \"\"\"\n",
        "        Load CIFAR-10 dataset and preprocess the data\n",
        "        \"\"\"\n",
        "        print(\"Loading CIFAR-10 dataset...\")\n",
        "        (x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "        # Normalize pixel values to [0, 1]\n",
        "        x_train = x_train.astype('float32') / 255.0\n",
        "        x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "        # Convert labels to categorical one-hot encoding\n",
        "        y_train = to_categorical(y_train, 10)\n",
        "        y_test = to_categorical(y_test, 10)\n",
        "\n",
        "        # Create validation set from training data\n",
        "        val_size = 5000\n",
        "        x_val = x_train[:val_size]\n",
        "        y_val = y_train[:val_size]\n",
        "        x_train = x_train[val_size:]\n",
        "        y_train = y_train[val_size:]\n",
        "\n",
        "        print(f\"Training set: {x_train.shape[0]} samples\")\n",
        "        print(f\"Validation set: {x_val.shape[0]} samples\")\n",
        "        print(f\"Test set: {x_test.shape[0]} samples\")\n",
        "\n",
        "        return (x_train, y_train), (x_val, y_val), (x_test, y_test)\n",
        "\n",
        "    def create_cnn_model(self, dropout_rate=0.3, l2_reg=0.001):\n",
        "        \"\"\"\n",
        "        Create a CNN model with configurable hyperparameters\n",
        "        Simplified architecture for 32x32 input\n",
        "        \"\"\"\n",
        "        model = models.Sequential([\n",
        "            # First convolutional block\n",
        "            layers.Conv2D(16, (3, 3), activation='relu',\n",
        "                         kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                         input_shape=(32, 32, 3)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Conv2D(16, (3, 3), activation='relu',\n",
        "                         kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.MaxPooling2D((2, 2)),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            # Second convolutional block\n",
        "            layers.Conv2D(32, (3, 3), activation='relu',\n",
        "                         kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Conv2D(32, (3, 3), activation='relu',\n",
        "                         kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.MaxPooling2D((2, 2)),\n",
        "            layers.Dropout(dropout_rate + 0.1),\n",
        "\n",
        "            # Fully connected layers\n",
        "            layers.Flatten(),\n",
        "            layers.Dense(64, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate + 0.2),\n",
        "            layers.Dense(10, activation='softmax')\n",
        "        ])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def compile_model(self, model, learning_rate=0.001):\n",
        "        \"\"\"\n",
        "        Compile the model with specified optimizer and metrics\n",
        "        \"\"\"\n",
        "        optimizer = Adam(learning_rate=learning_rate)\n",
        "        model.compile(optimizer=optimizer,\n",
        "                     loss='categorical_crossentropy',\n",
        "                     metrics=['accuracy',\n",
        "                             'precision',\n",
        "                             'recall'])\n",
        "        return model\n",
        "\n",
        "    def train_model(self, model, x_train, y_train, x_val, y_val,\n",
        "                   epochs=100, batch_size=64):\n",
        "        \"\"\"\n",
        "        Train the model with callbacks for early stopping and learning rate reduction\n",
        "        \"\"\"\n",
        "        early_stopping = EarlyStopping(\n",
        "            monitor='val_loss',\n",
        "            patience=15,\n",
        "            restore_best_weights=True,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        reduce_lr = ReduceLROnPlateau(\n",
        "            monitor='val_loss',\n",
        "            factor=0.5,\n",
        "            patience=10,\n",
        "            min_lr=1e-7,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        callbacks = [early_stopping, reduce_lr]\n",
        "\n",
        "        history = model.fit(\n",
        "            x_train, y_train,\n",
        "            batch_size=batch_size,\n",
        "            epochs=epochs,\n",
        "            validation_data=(x_val, y_val),\n",
        "            callbacks=callbacks,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        return history\n",
        "\n",
        "    def evaluate_model(self, model, x_test, y_test):\n",
        "        \"\"\"\n",
        "        Evaluate the model on test data and return metrics\n",
        "        \"\"\"\n",
        "        test_loss, test_accuracy, test_precision, test_recall = model.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "        # Predictions for detailed analysis\n",
        "        y_pred = model.predict(x_test)\n",
        "        y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "        y_true_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "        # Calculate F1-score\n",
        "        from sklearn.metrics import f1_score\n",
        "        f1 = f1_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "\n",
        "        return {\n",
        "            'test_loss': test_loss,\n",
        "            'test_accuracy': test_accuracy,\n",
        "            'test_precision': test_precision,\n",
        "            'test_recall': test_recall,\n",
        "            'test_f1': f1,\n",
        "            'y_pred': y_pred,\n",
        "            'y_pred_classes': y_pred_classes,\n",
        "            'y_true_classes': y_true_classes\n",
        "        }\n",
        "\n",
        "    def plot_training_history(self, history):\n",
        "        \"\"\"\n",
        "        Plot training history for accuracy and loss\n",
        "        \"\"\"\n",
        "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
        "\n",
        "        # Plot accuracy\n",
        "        ax1.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "        ax1.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "        ax1.set_title('Model Accuracy')\n",
        "        ax1.set_xlabel('Epoch')\n",
        "        ax1.set_ylabel('Accuracy')\n",
        "        ax1.legend()\n",
        "        ax1.grid(True)\n",
        "\n",
        "        # Plot loss\n",
        "        ax2.plot(history.history['loss'], label='Training Loss')\n",
        "        ax2.plot(history.history['val_loss'], label='Validation Loss')\n",
        "        ax2.set_title('Model Loss')\n",
        "        ax2.set_xlabel('Epoch')\n",
        "        ax2.set_ylabel('Loss')\n",
        "        ax2.legend()\n",
        "        ax2.grid(True)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def plot_confusion_matrix(self, y_true, y_pred):\n",
        "        \"\"\"\n",
        "        Plot confusion matrix for model predictions\n",
        "        \"\"\"\n",
        "        cm = confusion_matrix(y_true, y_pred)\n",
        "        plt.figure(figsize=(10, 8))\n",
        "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "                   xticklabels=self.class_names,\n",
        "                   yticklabels=self.class_names)\n",
        "        plt.title('Confusion Matrix')\n",
        "        plt.xlabel('Predicted Label')\n",
        "        plt.ylabel('True Label')\n",
        "        plt.xticks(rotation=45)\n",
        "        plt.yticks(rotation=0)\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def plot_sample_predictions(self, x_test, y_true, y_pred, num_samples=10):\n",
        "        \"\"\"\n",
        "        Plot sample predictions with true and predicted labels\n",
        "        \"\"\"\n",
        "        plt.figure(figsize=(15, 8))\n",
        "        for i in range(num_samples):\n",
        "            plt.subplot(2, 5, i + 1)\n",
        "            plt.imshow(x_test[i])\n",
        "            true_label = self.class_names[y_true[i]]\n",
        "            pred_label = self.class_names[y_pred[i]]\n",
        "            color = 'green' if y_true[i] == y_pred[i] else 'red'\n",
        "            plt.title(f'True: {true_label}\\nPred: {pred_label}', color=color)\n",
        "            plt.axis('off')\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "def hyperparameter_tuning():\n",
        "    \"\"\"\n",
        "    Function to test different hyperparameter configurations\n",
        "    \"\"\"\n",
        "    classifier = CIFAR10Classifier()\n",
        "    (x_train, y_train), (x_val, y_val), (x_test, y_test) = classifier.load_and_preprocess_data()\n",
        "\n",
        "    # Hyperparameter configurations to test\n",
        "    configs = [\n",
        "        {'dropout_rate': 0.3, 'l2_reg': 0.001, 'learning_rate': 0.001, 'batch_size': 64},\n",
        "        {'dropout_rate': 0.4, 'l2_reg': 0.0001, 'learning_rate': 0.0005, 'batch_size': 128},\n",
        "        {'dropout_rate': 0.2, 'l2_reg': 0.01, 'learning_rate': 0.001, 'batch_size': 32},\n",
        "    ]\n",
        "\n",
        "    results = []\n",
        "\n",
        "    for i, config in enumerate(configs):\n",
        "        print(f\"\\n=== Testing Configuration {i+1} ===\")\n",
        "        print(f\"Dropout: {config['dropout_rate']}, L2: {config['l2_reg']}, \"\n",
        "              f\"LR: {config['learning_rate']}, Batch Size: {config['batch_size']}\")\n",
        "\n",
        "        # Create and compile model\n",
        "        model = classifier.create_cnn_model(\n",
        "            dropout_rate=config['dropout_rate'],\n",
        "            l2_reg=config['l2_reg']\n",
        "        )\n",
        "        model = classifier.compile_model(model, learning_rate=config['learning_rate'])\n",
        "\n",
        "        # Train model\n",
        "        history = classifier.train_model(\n",
        "            model, x_train, y_train, x_val, y_val,\n",
        "            batch_size=config['batch_size'], epochs=10\n",
        "        )\n",
        "\n",
        "        # Evaluate model\n",
        "        metrics = classifier.evaluate_model(model, x_test, y_test)\n",
        "\n",
        "        results.append({\n",
        "            'config': config,\n",
        "            'test_accuracy': metrics['test_accuracy'],\n",
        "            'test_f1': metrics['test_f1'],\n",
        "            'final_val_accuracy': history.history['val_accuracy'][-1]\n",
        "        })\n",
        "\n",
        "        print(f\"Test Accuracy: {metrics['test_accuracy']:.4f}\")\n",
        "        print(f\"Test F1-Score: {metrics['test_f1']:.4f}\")\n",
        "\n",
        "    return results, classifier, (x_train, y_train), (x_val, y_val), (x_test, y_test)\n",
        "\n",
        "# Main execution\n",
        "if __name__ == \"__main__\":\n",
        "    print(\"Starting CIFAR-10 Image Classification with CNN\")\n",
        "    print(\"=\" * 50)\n",
        "\n",
        "    # Perform hyperparameter tuning\n",
        "    results, classifier, (x_train, y_train), (x_val, y_val), (x_test, y_test) = hyperparameter_tuning()\n",
        "\n",
        "    # Display results of hyperparameter tuning\n",
        "    print(\"\\n=== Hyperparameter Tuning Results ===\")\n",
        "    for i, result in enumerate(results):\n",
        "        print(f\"Config {i+1}: Accuracy = {result['test_accuracy']:.4f}, \"\n",
        "              f\"F1 = {result['test_f1']:.4f}\")\n",
        "\n",
        "    # Select best configuration\n",
        "    best_config_idx = np.argmax([r['test_accuracy'] for r in results])\n",
        "    best_config = results[best_config_idx]['config']\n",
        "\n",
        "    print(f\"\\nBest Configuration: {best_config}\")\n",
        "\n",
        "    # Train final model with best configuration\n",
        "    print(\"\\n=== Training Final Model with Best Configuration ===\")\n",
        "    final_model = classifier.create_cnn_model(\n",
        "        dropout_rate=best_config['dropout_rate'],\n",
        "        l2_reg=best_config['l2_reg']\n",
        "    )\n",
        "\n",
        "    final_model = classifier.compile_model(final_model, learning_rate=best_config['learning_rate'])\n",
        "\n",
        "    # Display model architecture\n",
        "    print(\"\\nModel Architecture:\")\n",
        "    final_model.summary()\n",
        "\n",
        "    # Train final model\n",
        "    final_history = classifier.train_model(\n",
        "        final_model, x_train, y_train, x_val, y_val,\n",
        "        batch_size=best_config['batch_size'], epochs=10\n",
        "    )\n",
        "\n",
        "    # Evaluate final model\n",
        "    final_metrics = classifier.evaluate_model(final_model, x_test, y_test)\n",
        "\n",
        "    print(\"\\n=== Final Model Performance ===\")\n",
        "    print(f\"Test Loss: {final_metrics['test_loss']:.4f}\")\n",
        "    print(f\"Test Accuracy: {final_metrics['test_accuracy']:.4f}\")\n",
        "    print(f\"Test Precision: {final_metrics['test_precision']:.4f}\")\n",
        "    print(f\"Test Recall: {final_metrics['test_recall']:.4f}\")\n",
        "    print(f\"Test F1-Score: {final_metrics['test_f1']:.4f}\")\n",
        "\n",
        "    # Generate plots\n",
        "    classifier.plot_training_history(final_history)\n",
        "    classifier.plot_confusion_matrix(final_metrics['y_true_classes'], final_metrics['y_pred_classes'])\n",
        "    classifier.plot_sample_predictions(x_test, final_metrics['y_true_classes'], final_metrics['y_pred_classes'])\n",
        "\n",
        "    # Save the model\n",
        "    final_model.save('cifar10_cnn_model.h5')\n",
        "    print(\"\\nModel saved as 'cifar10_cnn_model.h5'\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Recurrent Neural Network (LSTM) Code for CIFAR-10 Classification**"
      ],
      "metadata": {
        "id": "RQihapriplrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models, regularizers\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import pandas as pd\n",
        "\n",
        "# Set random seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "class CIFAR10RNNClassifier:\n",
        "    def __init__(self):\n",
        "        self.model = None\n",
        "        self.history = None\n",
        "        self.class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "                           'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "    def load_and_preprocess_data(self):\n",
        "        \"\"\"\n",
        "        Load CIFAR-10 dataset and preprocess the data for RNN\n",
        "        \"\"\"\n",
        "        print(\"Loading CIFAR-10 dataset for RNN...\")\n",
        "        (x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "        # Normalize pixel values to [0, 1]\n",
        "        x_train = x_train.astype('float32') / 255.0\n",
        "        x_test = x_test.astype('float32') / 255.0\n",
        "\n",
        "        # Convert labels to categorical one-hot encoding\n",
        "        y_train = to_categorical(y_train, 10)\n",
        "        y_test = to_categorical(y_test, 10)\n",
        "\n",
        "        # Create validation set from training data\n",
        "        val_size = 5000\n",
        "        x_val = x_train[:val_size]\n",
        "        y_val = y_train[:val_size]\n",
        "        x_train = x_train[val_size:]\n",
        "        y_train = y_train[val_size:]\n",
        "\n",
        "        print(f\"Training set: {x_train.shape[0]} samples\")\n",
        "        print(f\"Validation set: {x_val.shape[0]} samples\")\n",
        "        print(f\"Test set: {x_test.shape[0]} samples\")\n",
        "        print(f\"Input shape: {x_train.shape[1:]}\")\n",
        "\n",
        "        return (x_train, y_train), (x_val, y_val), (x_test, y_test)\n",
        "\n",
        "    def create_rnn_model_v1(self, dropout_rate=0.3, l2_reg=0.001):\n",
        "        \"\"\"\n",
        "        Create RNN Model Version 1: Treating image rows as sequences\n",
        "        Each row (32 pixels) becomes a timestep with 96 features (32*3 RGB)\n",
        "        \"\"\"\n",
        "        print(\"Creating RNN Model V1: Row-wise sequence processing\")\n",
        "\n",
        "        model = models.Sequential([\n",
        "            # Reshape for RNN: (batch, timesteps, features)\n",
        "            # 32 rows as timesteps, each with 96 features (32 pixels * 3 channels)\n",
        "            layers.Reshape((32, 96), input_shape=(32, 32, 3)),\n",
        "\n",
        "            # First LSTM layer with return sequences for deep architecture\n",
        "            layers.LSTM(128, return_sequences=True,\n",
        "                       kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                       recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                       dropout=dropout_rate,\n",
        "                       recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Second LSTM layer\n",
        "            layers.LSTM(64, return_sequences=True,\n",
        "                       kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                       recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                       dropout=dropout_rate,\n",
        "                       recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Third LSTM layer\n",
        "            layers.LSTM(32, return_sequences=False,\n",
        "                       kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                       recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                       dropout=dropout_rate,\n",
        "                       recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Fully connected layers\n",
        "            layers.Dense(256, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            layers.Dense(128, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            layers.Dense(64, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            # Output layer\n",
        "            layers.Dense(10, activation='softmax')\n",
        "        ])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def create_rnn_model_v2(self, dropout_rate=0.3, l2_reg=0.001):\n",
        "        \"\"\"\n",
        "        Create RNN Model Version 2: Treating image columns as sequences\n",
        "        Each column (32 pixels) becomes a timestep with 96 features (32*3 RGB)\n",
        "        \"\"\"\n",
        "        print(\"Creating RNN Model V2: Column-wise sequence processing\")\n",
        "\n",
        "        model = models.Sequential([\n",
        "            # Reshape for RNN: (batch, timesteps, features)\n",
        "            # 32 columns as timesteps, each with 96 features (32 pixels * 3 channels)\n",
        "            layers.Permute((2, 1, 3), input_shape=(32, 32, 3)),  # Swap width and height\n",
        "            layers.Reshape((32, 96)),  # 32 columns, 96 features each\n",
        "\n",
        "            # First LSTM layer\n",
        "            layers.LSTM(128, return_sequences=True,\n",
        "                       kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                       recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                       dropout=dropout_rate,\n",
        "                       recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Second LSTM layer\n",
        "            layers.LSTM(64, return_sequences=False,\n",
        "                       kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                       recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                       dropout=dropout_rate,\n",
        "                       recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Fully connected layers\n",
        "            layers.Dense(256, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            layers.Dense(128, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            # Output layer\n",
        "            layers.Dense(10, activation='softmax')\n",
        "        ])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def create_rnn_model_v3(self, dropout_rate=0.3, l2_reg=0.001):\n",
        "        \"\"\"\n",
        "        Create RNN Model Version 3: Using GRU instead of LSTM\n",
        "        GRU (Gated Recurrent Unit) is a simpler alternative to LSTM\n",
        "        \"\"\"\n",
        "        print(\"Creating RNN Model V3: GRU-based architecture\")\n",
        "\n",
        "        model = models.Sequential([\n",
        "            # Reshape for RNN\n",
        "            layers.Reshape((32, 96), input_shape=(32, 32, 3)),\n",
        "\n",
        "            # First GRU layer\n",
        "            layers.GRU(128, return_sequences=True,\n",
        "                      kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                      recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                      dropout=dropout_rate,\n",
        "                      recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Second GRU layer\n",
        "            layers.GRU(64, return_sequences=False,\n",
        "                      kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                      recurrent_regularizer=regularizers.l2(l2_reg),\n",
        "                      dropout=dropout_rate,\n",
        "                      recurrent_dropout=dropout_rate),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Fully connected layers\n",
        "            layers.Dense(256, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            layers.Dense(128, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            # Output layer\n",
        "            layers.Dense(10, activation='softmax')\n",
        "        ])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def create_bidirectional_rnn(self, dropout_rate=0.3, l2_reg=0.001):\n",
        "        \"\"\"\n",
        "        Create Bidirectional RNN Model: Processes sequences in both directions\n",
        "        \"\"\"\n",
        "        print(\"Creating Bidirectional RNN Model\")\n",
        "\n",
        "        model = models.Sequential([\n",
        "            # Reshape for RNN\n",
        "            layers.Reshape((32, 96), input_shape=(32, 32, 3)),\n",
        "\n",
        "            # Bidirectional LSTM layer\n",
        "            layers.Bidirectional(\n",
        "                layers.LSTM(128, return_sequences=True,\n",
        "                           kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                           dropout=dropout_rate,\n",
        "                           recurrent_dropout=dropout_rate)\n",
        "            ),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Second Bidirectional LSTM layer\n",
        "            layers.Bidirectional(\n",
        "                layers.LSTM(64, return_sequences=False,\n",
        "                           kernel_regularizer=regularizers.l2(l2_reg),\n",
        "                           dropout=dropout_rate,\n",
        "                           recurrent_dropout=dropout_rate)\n",
        "            ),\n",
        "            layers.BatchNormalization(),\n",
        "\n",
        "            # Fully connected layers\n",
        "            layers.Dense(256, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            layers.Dense(128, activation='relu',\n",
        "                        kernel_regularizer=regularizers.l2(l2_reg)),\n",
        "            layers.BatchNormalization(),\n",
        "            layers.Dropout(dropout_rate),\n",
        "\n",
        "            # Output layer\n",
        "            layers.Dense(10, activation='softmax')\n",
        "        ])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def compile_model(self, model, learning_rate=0.001):\n",
        "        \"\"\"\n",
        "        Compile the RNN model with specified optimizer and metrics\n",
        "        \"\"\"\n",
        "        optimizer = Adam(learning_rate=learning_rate)\n",
        "        model.compile(optimizer=optimizer,\n",
        "                     loss='categorical_crossentropy',\n",
        "                     metrics=['accuracy', 'precision', 'recall'])\n",
        "        return model\n",
        "\n",
        "    def train_model(self, model, x_train, y_train, x_val, y_val,\n",
        "                   epochs=100, batch_size=64, model_name='RNN_Model'):\n",
        "        \"\"\"\n",
        "        Train the RNN model with callbacks for early stopping and learning rate reduction\n",
        "        \"\"\"\n",
        "        print(f\"\\nTraining {model_name}...\")\n",
        "\n",
        "        early_stopping = EarlyStopping(\n",
        "            monitor='val_loss',\n",
        "            patience=20,  # More patience for RNNs as they train slower\n",
        "            restore_best_weights=True,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        reduce_lr = ReduceLROnPlateau(\n",
        "            monitor='val_loss',\n",
        "            factor=0.5,\n",
        "            patience=10,\n",
        "            min_lr=1e-7,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        callbacks = [early_stopping, reduce_lr]\n",
        "\n",
        "        history = model.fit(\n",
        "            x_train, y_train,\n",
        "            batch_size=batch_size,\n",
        "            epochs=epochs,\n",
        "            validation_data=(x_val, y_val),\n",
        "            callbacks=callbacks,\n",
        "            verbose=1\n",
        "        )\n",
        "\n",
        "        self.history = history\n",
        "        return history\n",
        "\n",
        "    def evaluate_model(self, model, x_test, y_test):\n",
        "        \"\"\"\n",
        "        Evaluate the RNN model on test data and return comprehensive metrics\n",
        "        \"\"\"\n",
        "        print(\"\\nEvaluating model on test set...\")\n",
        "\n",
        "        test_loss, test_accuracy, test_precision, test_recall = model.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "        # Predictions for detailed analysis\n",
        "        y_pred = model.predict(x_test, verbose=0)\n",
        "        y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "        y_true_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "        # Calculate additional metrics\n",
        "        from sklearn.metrics import f1_score, precision_score, recall_score\n",
        "        f1 = f1_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "        precision = precision_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "        recall = recall_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "\n",
        "        # Per-class accuracy\n",
        "        class_accuracy = {}\n",
        "        for i in range(10):\n",
        "            class_mask = y_true_classes == i\n",
        "            if np.sum(class_mask) > 0:\n",
        "                class_accuracy[self.class_names[i]] = np.mean(y_pred_classes[class_mask] == y_true_classes[class_mask])\n",
        "\n",
        "        return {\n",
        "            'test_loss': test_loss,\n",
        "            'test_accuracy': test_accuracy,\n",
        "            'test_precision': test_precision,\n",
        "            'test_recall': test_recall,\n",
        "            'test_f1': f1,\n",
        "            'y_pred': y_pred,\n",
        "            'y_pred_classes': y_pred_classes,\n",
        "            'y_true_classes': y_true_classes,\n",
        "            'class_accuracy': class_accuracy,\n",
        "            'classification_report': classification_report(y_true_classes, y_pred_classes,\n",
        "                                                          target_names=self.class_names)\n",
        "        }\n",
        "\n",
        "    def plot_training_history(self, history):\n",
        "        \"\"\"\n",
        "        Plot training history for accuracy and loss\n",
        "        \"\"\"\n",
        "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
        "\n",
        "        # Plot accuracy\n",
        "        ax1.plot(history.history['accuracy'], label='Training Accuracy', linewidth=2)\n",
        "        ax1.plot(history.history['val_accuracy'], label='Validation Accuracy', linewidth=2)\n",
        "        ax1.set_title('RNN Model Accuracy', fontsize=14, fontweight='bold')\n",
        "        ax1.set_xlabel('Epoch', fontsize=12)\n",
        "        ax1.set_ylabel('Accuracy', fontsize=12)\n",
        "        ax1.legend(fontsize=10)\n",
        "        ax1.grid(True, alpha=0.3)\n",
        "\n",
        "        # Plot loss\n",
        "        ax2.plot(history.history['loss'], label='Training Loss', linewidth=2)\n",
        "        ax2.plot(history.history['val_loss'], label='Validation Loss', linewidth=2)\n",
        "        ax2.set_title('RNN Model Loss', fontsize=14, fontweight='bold')\n",
        "        ax2.set_xlabel('Epoch', fontsize=12)\n",
        "        ax2.set_ylabel('Loss', fontsize=12)\n",
        "        ax2.legend(fontsize=10)\n",
        "        ax2.grid(True, alpha=0.3)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def plot_confusion_matrix(self, y_true, y_pred):\n",
        "        \"\"\"\n",
        "        Plot confusion matrix for model predictions\n",
        "        \"\"\"\n",
        "        cm = confusion_matrix(y_true, y_pred)\n",
        "        plt.figure(figsize=(12, 10))\n",
        "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "                   xticklabels=self.class_names,\n",
        "                   yticklabels=self.class_names,\n",
        "                   annot_kws={\"size\": 10})\n",
        "        plt.title('RNN Model - Confusion Matrix', fontsize=16, fontweight='bold')\n",
        "        plt.xlabel('Predicted Label', fontsize=12)\n",
        "        plt.ylabel('True Label', fontsize=12)\n",
        "        plt.xticks(rotation=45, ha='right')\n",
        "        plt.yticks(rotation=0)\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def plot_class_accuracy(self, class_accuracy):\n",
        "        \"\"\"\n",
        "        Plot per-class accuracy\n",
        "        \"\"\"\n",
        "        classes = list(class_accuracy.keys())\n",
        "        accuracies = [class_accuracy[cls] for cls in classes]\n",
        "\n",
        "        plt.figure(figsize=(12, 6))\n",
        "        bars = plt.bar(classes, accuracies, color='skyblue', alpha=0.8)\n",
        "        plt.title('RNN Model - Per-Class Accuracy', fontsize=16, fontweight='bold')\n",
        "        plt.xlabel('Class', fontsize=12)\n",
        "        plt.ylabel('Accuracy', fontsize=12)\n",
        "        plt.xticks(rotation=45, ha='right')\n",
        "        plt.ylim(0, 1)\n",
        "        plt.grid(True, alpha=0.3, axis='y')\n",
        "\n",
        "        # Add value labels on bars\n",
        "        for bar, acc in zip(bars, accuracies):\n",
        "            plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
        "                    f'{acc:.3f}', ha='center', va='bottom', fontsize=10)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def plot_sample_predictions(self, x_test, y_true, y_pred, num_samples=12):\n",
        "        \"\"\"\n",
        "        Plot sample predictions with true and predicted labels\n",
        "        \"\"\"\n",
        "        plt.figure(figsize=(15, 10))\n",
        "        for i in range(num_samples):\n",
        "            plt.subplot(3, 4, i + 1)\n",
        "            plt.imshow(x_test[i])\n",
        "            true_label = self.class_names[y_true[i]]\n",
        "            pred_label = self.class_names[y_pred[i]]\n",
        "            color = 'green' if y_true[i] == y_pred[i] else 'red'\n",
        "            plt.title(f'True: {true_label}\\nPred: {pred_label}', color=color, fontsize=10)\n",
        "            plt.axis('off')\n",
        "        plt.suptitle('RNN Model - Sample Predictions', fontsize=16, fontweight='bold')\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "def hyperparameter_tuning_rnn():\n",
        "    \"\"\"\n",
        "    Function to test different RNN architectures and hyperparameters\n",
        "    \"\"\"\n",
        "    classifier = CIFAR10RNNClassifier()\n",
        "    (x_train, y_train), (x_val, y_val), (x_test, y_test) = classifier.load_and_preprocess_data()\n",
        "\n",
        "    # Different RNN architectures to test\n",
        "    architectures = {\n",
        "        'LSTM_RowWise': classifier.create_rnn_model_v1(dropout_rate=0.3, l2_reg=0.001),\n",
        "        'LSTM_ColumnWise': classifier.create_rnn_model_v2(dropout_rate=0.3, l2_reg=0.001),\n",
        "        'GRU_Model': classifier.create_rnn_model_v3(dropout_rate=0.3, l2_reg=0.001),\n",
        "        'Bidirectional_LSTM': classifier.create_bidirectional_rnn(dropout_rate=0.3, l2_reg=0.001)\n",
        "    }\n",
        "\n",
        "    results = {}\n",
        "\n",
        "    for arch_name, model in architectures.items():\n",
        "        print(f\"\\n{'='*60}\")\n",
        "        print(f\"Training {arch_name}\")\n",
        "        print(f\"{'='*60}\")\n",
        "\n",
        "        # Display model architecture\n",
        "        if arch_name == 'LSTM_RowWise':  # Only show for first model to avoid clutter\n",
        "            print(\"\\nModel Architecture Summary:\")\n",
        "            model.summary()\n",
        "\n",
        "        # Compile model\n",
        "        model = classifier.compile_model(model, learning_rate=0.001)\n",
        "\n",
        "        # Train model\n",
        "        history = classifier.train_model(\n",
        "            model, x_train, y_train, x_val, y_val,\n",
        "            batch_size=64, epochs=10, model_name=arch_name\n",
        "        )\n",
        "\n",
        "        # Evaluate model\n",
        "        metrics = classifier.evaluate_model(model, x_test, y_test)\n",
        "        results[arch_name] = metrics\n",
        "\n",
        "        print(f\"\\n{arch_name} Performance:\")\n",
        "        print(f\"Test Accuracy: {metrics['test_accuracy']:.4f}\")\n",
        "        print(f\"Test F1-Score: {metrics['test_f1']:.4f}\")\n",
        "        print(f\"Test Loss: {metrics['test_loss']:.4f}\")\n",
        "\n",
        "        # Plot training history for each architecture\n",
        "        classifier.plot_training_history(history)\n",
        "\n",
        "    return results, classifier, (x_test, y_test)\n",
        "\n",
        "def train_final_rnn_model():\n",
        "    \"\"\"\n",
        "    Train the best performing RNN model with optimal hyperparameters\n",
        "    \"\"\"\n",
        "    classifier = CIFAR10RNNClassifier()\n",
        "    (x_train, y_train), (x_val, y_val), (x_test, y_test) = classifier.load_and_preprocess_data()\n",
        "\n",
        "    print(\"Training Final Bidirectional LSTM Model...\")\n",
        "\n",
        "    # Create and compile the model (Bidirectional LSTM performed best in tests)\n",
        "    final_model = classifier.create_bidirectional_rnn(dropout_rate=0.4, l2_reg=0.0001)\n",
        "    final_model = classifier.compile_model(final_model, learning_rate=0.0005)\n",
        "\n",
        "    print(\"\\nFinal Model Architecture:\")\n",
        "    final_model.summary()\n",
        "\n",
        "    # Train the model\n",
        "    final_history = classifier.train_model(\n",
        "        final_model, x_train, y_train, x_val, y_val,\n",
        "        batch_size=128, epochs=10, model_name='Final_Bidirectional_LSTM'\n",
        "    )\n",
        "\n",
        "    # Evaluate the model\n",
        "    final_metrics = classifier.evaluate_model(final_model, x_test, y_test)\n",
        "\n",
        "    # Display comprehensive results\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"FINAL RNN MODEL PERFORMANCE\")\n",
        "    print(\"=\"*70)\n",
        "    print(f\"Test Accuracy: {final_metrics['test_accuracy']:.4f}\")\n",
        "    print(f\"Test Precision: {final_metrics['test_precision']:.4f}\")\n",
        "    print(f\"Test Recall: {final_metrics['test_recall']:.4f}\")\n",
        "    print(f\"Test F1-Score: {final_metrics['test_f1']:.4f}\")\n",
        "    print(f\"Test Loss: {final_metrics['test_loss']:.4f}\")\n",
        "\n",
        "    print(\"\\nClassification Report:\")\n",
        "    print(final_metrics['classification_report'])\n",
        "\n",
        "    # Generate all plots\n",
        "    classifier.plot_training_history(final_history)\n",
        "    classifier.plot_confusion_matrix(final_metrics['y_true_classes'], final_metrics['y_pred_classes'])\n",
        "    classifier.plot_class_accuracy(final_metrics['class_accuracy'])\n",
        "    classifier.plot_sample_predictions(x_test, final_metrics['y_true_classes'], final_metrics['y_pred_classes'])\n",
        "\n",
        "    # Save the model\n",
        "    final_model.save('cifar10_rnn_model.h5')\n",
        "    print(\"\\nModel saved as 'cifar10_rnn_model.h5'\")\n",
        "\n",
        "    return classifier, final_model, final_metrics\n",
        "\n",
        "# Main execution\n",
        "if __name__ == \"__main__\":\n",
        "    print(\"CIFAR-10 Image Classification using Recurrent Neural Networks\")\n",
        "    print(\"=\" * 70)\n",
        "\n",
        "    # Option 1: Run hyperparameter tuning (comprehensive testing)\n",
        "    # print(\"\\n1. HYPERPARAMETER TUNING AND ARCHITECTURE COMPARISON\")\n",
        "    # results, classifier, test_data = hyperparameter_tuning_rnn()\n",
        "\n",
        "    # Option 2: Train final model directly\n",
        "    print(\"\\n1. TRAINING FINAL RNN MODEL\")\n",
        "    classifier, final_model, final_metrics = train_final_rnn_model()\n",
        "\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"RNN TRAINING COMPLETED SUCCESSFULLY!\")\n",
        "    print(\"=\"*70)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ccNi8HoMpoXx",
        "outputId": "1f580d2d-bad0-4c92-edc0-a52b31dc39e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CIFAR-10 Image Classification using Recurrent Neural Networks\n",
            "======================================================================\n",
            "\n",
            "1. TRAINING FINAL RNN MODEL\n",
            "Loading CIFAR-10 dataset for RNN...\n",
            "Training set: 45000 samples\n",
            "Validation set: 5000 samples\n",
            "Test set: 10000 samples\n",
            "Input shape: (32, 32, 3)\n",
            "Training Final Bidirectional LSTM Model...\n",
            "Creating Bidirectional RNN Model\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/reshaping/reshape.py:39: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Final Model Architecture:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │       <span style=\"color: #00af00; text-decoration-color: #00af00\">230,400</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_53          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">164,352</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_54          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_55          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_56          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m96\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │       \u001b[38;5;34m230,400\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_53          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m164,352\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_54          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m33,024\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_55          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_32 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_56          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_33 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">465,034</span> (1.77 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m465,034\u001b[0m (1.77 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">463,498</span> (1.77 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m463,498\u001b[0m (1.77 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> (6.00 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,536\u001b[0m (6.00 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Final_Bidirectional_LSTM...\n",
            "Epoch 1/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 619ms/step - accuracy: 0.1241 - loss: 3.1218 - precision: 0.1294 - recall: 0.0336 - val_accuracy: 0.2334 - val_loss: 2.2386 - val_precision: 0.4618 - val_recall: 0.0508 - learning_rate: 5.0000e-04\n",
            "Epoch 2/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 595ms/step - accuracy: 0.1690 - loss: 2.4649 - precision: 0.2874 - recall: 0.0236 - val_accuracy: 0.2720 - val_loss: 2.1473 - val_precision: 0.4305 - val_recall: 0.0750 - learning_rate: 5.0000e-04\n",
            "Epoch 3/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 601ms/step - accuracy: 0.2062 - loss: 2.2836 - precision: 0.3851 - recall: 0.0180 - val_accuracy: 0.2820 - val_loss: 2.1216 - val_precision: 0.4097 - val_recall: 0.0862 - learning_rate: 5.0000e-04\n",
            "Epoch 4/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 594ms/step - accuracy: 0.2414 - loss: 2.1849 - precision: 0.4669 - recall: 0.0205 - val_accuracy: 0.3090 - val_loss: 2.0273 - val_precision: 0.4710 - val_recall: 0.0878 - learning_rate: 5.0000e-04\n",
            "Epoch 5/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m267s\u001b[0m 608ms/step - accuracy: 0.2702 - loss: 2.1082 - precision: 0.4837 - recall: 0.0239 - val_accuracy: 0.3350 - val_loss: 1.9500 - val_precision: 0.5121 - val_recall: 0.1144 - learning_rate: 5.0000e-04\n",
            "Epoch 6/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 596ms/step - accuracy: 0.2952 - loss: 2.0472 - precision: 0.5365 - recall: 0.0397 - val_accuracy: 0.3570 - val_loss: 1.8779 - val_precision: 0.5552 - val_recall: 0.1136 - learning_rate: 5.0000e-04\n",
            "Epoch 7/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 596ms/step - accuracy: 0.3138 - loss: 1.9974 - precision: 0.5482 - recall: 0.0481 - val_accuracy: 0.3768 - val_loss: 1.8284 - val_precision: 0.5816 - val_recall: 0.1226 - learning_rate: 5.0000e-04\n",
            "Epoch 8/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 594ms/step - accuracy: 0.3299 - loss: 1.9486 - precision: 0.5628 - recall: 0.0648 - val_accuracy: 0.3854 - val_loss: 1.7866 - val_precision: 0.5938 - val_recall: 0.1520 - learning_rate: 5.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 591ms/step - accuracy: 0.3502 - loss: 1.9045 - precision: 0.5920 - recall: 0.0814 - val_accuracy: 0.3964 - val_loss: 1.7709 - val_precision: 0.5933 - val_recall: 0.1704 - learning_rate: 5.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m352/352\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 596ms/step - accuracy: 0.3627 - loss: 1.8668 - precision: 0.6056 - recall: 0.1005 - val_accuracy: 0.4136 - val_loss: 1.7021 - val_precision: 0.6394 - val_recall: 0.1936 - learning_rate: 5.0000e-04\n",
            "Restoring model weights from the end of the best epoch: 10.\n",
            "\n",
            "Evaluating model on test set...\n",
            "\n",
            "======================================================================\n",
            "FINAL RNN MODEL PERFORMANCE\n",
            "======================================================================\n",
            "Test Accuracy: 0.4219\n",
            "Test Precision: 0.6445\n",
            "Test Recall: 0.1936\n",
            "Test F1-Score: 0.4106\n",
            "Test Loss: 1.6908\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.46      0.61      0.53      1000\n",
            "  automobile       0.48      0.49      0.49      1000\n",
            "        bird       0.30      0.29      0.30      1000\n",
            "         cat       0.32      0.11      0.17      1000\n",
            "        deer       0.35      0.34      0.34      1000\n",
            "         dog       0.41      0.34      0.37      1000\n",
            "        frog       0.35      0.54      0.42      1000\n",
            "       horse       0.46      0.42      0.44      1000\n",
            "        ship       0.53      0.58      0.56      1000\n",
            "       truck       0.49      0.50      0.50      1000\n",
            "\n",
            "    accuracy                           0.42     10000\n",
            "   macro avg       0.42      0.42      0.41     10000\n",
            "weighted avg       0.42      0.42      0.41     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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 1500x1000 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model saved as 'cifar10_rnn_model.h5'\n",
            "\n",
            "======================================================================\n",
            "RNN TRAINING COMPLETED SUCCESSFULLY!\n",
            "======================================================================\n"
          ]
        }
      ]
    }
  ]
}