2_CNN_in_Python.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "colab": {
      "name": "2.CNN_in_Python.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bronze-norman"
      },
      "source": [
        "# Modelling camera trapping data with NN in python\n"
      ],
      "id": "bronze-norman"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZ2jDinPQ4pX"
      },
      "source": [
        "Run this notebook in colab: https://colab.research.google.com/drive/1eL88mj3O2YREdr6-y6AnVO4nYM7fso7h?usp=sharing"
      ],
      "id": "eZ2jDinPQ4pX"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVRj57BuGWKt"
      },
      "source": [
        "### Mounting my Google drive\n",
        "\n",
        "We have to tell our *virtual computer* where is the data we are going to be working with, so we need to give it permissions to connect to our Google Drive"
      ],
      "id": "vVRj57BuGWKt"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSrdLYEDGVz9",
        "outputId": "fe7434b9-e271-4afd-e2db-cb2d9add30a0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "id": "sSrdLYEDGVz9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCBotuBqIn2G",
        "outputId": "cdad3740-0d1f-4c81-b854-051d17c793f7"
      },
      "source": [
        "!ls /content/drive/MyDrive/NN_WORKSHOP_DATA\n"
      ],
      "id": "fCBotuBqIn2G",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.Resize_images.ipynb\t\t     models\t  y_train.npy\n",
            "2.CNN_in_Python.ipynb\t\t     X_test.npy\n",
            "3.Transfer_learning_in_Python.ipynb  X_train.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KysCIWMJ4t6"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/NN_WORKSHOP_DATA')"
      ],
      "id": "6KysCIWMJ4t6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "competent-settlement"
      },
      "source": [
        "### Imports\n",
        "\n",
        "Here we import all the modules that we are going to need. This is similar to `library('package_name')` in R"
      ],
      "id": "competent-settlement"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unlikely-partner"
      },
      "source": [
        "import json\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import keras\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.callbacks import Callback\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "id": "unlikely-partner",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LaQVTLjdLEJL"
      },
      "source": [
        "### Data loading\n",
        "\n",
        "Load the resized images from our Drive"
      ],
      "id": "LaQVTLjdLEJL"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sporting-eating",
        "outputId": "fbf3c237-7ee6-46e4-eaaf-15b6cabaa735"
      },
      "source": [
        "# The data, split between train and test sets:\n",
        "x_train = np.load('X_train.npy')\n",
        "y_train = np.load('y_train.npy')\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n"
      ],
      "id": "sporting-eating",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (196299, 32, 32, 3)\n",
            "196299 train samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3N9fIJFOV4k"
      },
      "source": [
        "### Data preprocessing and split\n"
      ],
      "id": "t3N9fIJFOV4k"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imposed-bullet"
      },
      "source": [
        "# Convert the images to float and scale it to a range of 0 to 1\n",
        "x_train = x_train.astype('float32')\n",
        "x_train /= 255.\n"
      ],
      "id": "imposed-bullet",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "developmental-child"
      },
      "source": [
        "# Split the data in Train, Test, and Validation\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_train, y_train, test_size= 0.3, random_state=42)\n",
        "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size= 0.3, random_state=42)"
      ],
      "id": "developmental-child",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuyaBMDePgta"
      },
      "source": [
        "### Define model and model architecture"
      ],
      "id": "tuyaBMDePgta"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "little-hardwood"
      },
      "source": [
        "batch_size = 64\n",
        "num_classes = 14\n",
        "epochs = 30\n",
        "val_split = 0.1\n",
        "save_dir = os.path.join(os.getcwd(), 'models')\n",
        "model_name = 'keras_cnn_model.h5'"
      ],
      "id": "little-hardwood",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "terminal-instruction"
      },
      "source": [
        "#Define model architecture\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "                 input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))"
      ],
      "id": "terminal-instruction",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "center-framework",
        "outputId": "0ae38cc3-3dec-40a2-ad5b-a524e88f13f2"
      },
      "source": [
        "model.summary()"
      ],
      "id": "center-framework",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               1180160   \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 14)                7182      \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 14)                0         \n",
            "=================================================================\n",
            "Total params: 1,252,910\n",
            "Trainable params: 1,252,910\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "french-violin"
      },
      "source": [
        "class Metrics(Callback):\n",
        "    def __init__(self, val_data):\n",
        "        super().__init__()\n",
        "        self.validation_data = val_data\n",
        "        \n",
        "    def on_train_begin(self, logs={}):\n",
        "        self.val_f1s = []\n",
        "        self.val_recalls = []\n",
        "        self.val_precisions = []\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        X_val, y_val = self.validation_data[:2]\n",
        "        y_pred = self.model.predict(X_val)\n",
        "\n",
        "        y_pred_cat = to_categorical(\n",
        "            y_pred.argmax(axis=1),\n",
        "            num_classes=num_classes\n",
        "        )\n",
        "\n",
        "        _val_f1 = f1_score(y_val, y_pred_cat, average='macro')\n",
        "        _val_recall = recall_score(y_val, y_pred_cat, average='macro')\n",
        "        _val_precision = precision_score(y_val, y_pred_cat, average='macro')\n",
        "\n",
        "        self.val_f1s.append(_val_f1)\n",
        "        self.val_recalls.append(_val_recall)\n",
        "        self.val_precisions.append(_val_precision)\n",
        "\n",
        "        print((f\"val_f1: {_val_f1:.4f}\"\n",
        "               f\" — val_precision: {_val_precision:.4f}\"\n",
        "               f\" — val_recall: {_val_recall:.4f}\"))\n",
        "\n",
        "        return"
      ],
      "id": "french-violin",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZjD--60UZn-"
      },
      "source": [
        "![image.png](https://www.biorxiv.org/sites/default/files/highwire/biorxiv/early/2017/01/04/087445/embed/graphic-10.gif)"
      ],
      "id": "_ZjD--60UZn-"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "driving-breeding",
        "outputId": "44c9ad78-6fdb-4482-a5d9-83256d4d9c8c"
      },
      "source": [
        "f1_metrics = Metrics(val_data= (X_val, y_val))\n",
        "\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "hist = model.fit(\n",
        "    X_train, \n",
        "    y_train,\n",
        "    batch_size=batch_size,\n",
        "    epochs=epochs,\n",
        "    callbacks=[f1_metrics],\n",
        "    validation_data=(X_val, y_val),\n",
        "    shuffle=True\n",
        ")\n"
      ],
      "id": "driving-breeding",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "2148/2148 [==============================] - 32s 14ms/step - loss: 0.4872 - accuracy: 0.8413 - val_loss: 0.4121 - val_accuracy: 0.8668\n",
            "val_f1: 0.6560 — val_precision: 0.7017 — val_recall: 0.6335\n",
            "Epoch 2/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.4362 - accuracy: 0.8554 - val_loss: 0.3699 - val_accuracy: 0.8755\n",
            "val_f1: 0.6923 — val_precision: 0.7438 — val_recall: 0.6634\n",
            "Epoch 3/30\n",
            "2148/2148 [==============================] - 31s 14ms/step - loss: 0.4038 - accuracy: 0.8669 - val_loss: 0.3673 - val_accuracy: 0.8807\n",
            "val_f1: 0.7073 — val_precision: 0.7356 — val_recall: 0.6928\n",
            "Epoch 4/30\n",
            "2148/2148 [==============================] - 31s 14ms/step - loss: 0.3896 - accuracy: 0.8717 - val_loss: 0.3347 - val_accuracy: 0.8899\n",
            "val_f1: 0.7279 — val_precision: 0.7611 — val_recall: 0.7150\n",
            "Epoch 5/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.3628 - accuracy: 0.8770 - val_loss: 0.3279 - val_accuracy: 0.8917\n",
            "val_f1: 0.7438 — val_precision: 0.7669 — val_recall: 0.7355\n",
            "Epoch 6/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.3549 - accuracy: 0.8811 - val_loss: 0.3196 - val_accuracy: 0.8929\n",
            "val_f1: 0.7428 — val_precision: 0.7701 — val_recall: 0.7342\n",
            "Epoch 7/30\n",
            "2148/2148 [==============================] - 31s 14ms/step - loss: 0.3403 - accuracy: 0.8852 - val_loss: 0.3032 - val_accuracy: 0.8998\n",
            "val_f1: 0.7695 — val_precision: 0.7836 — val_recall: 0.7625\n",
            "Epoch 8/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.3365 - accuracy: 0.8862 - val_loss: 0.2933 - val_accuracy: 0.9040\n",
            "val_f1: 0.7750 — val_precision: 0.7907 — val_recall: 0.7693\n",
            "Epoch 9/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.3162 - accuracy: 0.8923 - val_loss: 0.2912 - val_accuracy: 0.9052\n",
            "val_f1: 0.7742 — val_precision: 0.7901 — val_recall: 0.7704\n",
            "Epoch 10/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.3202 - accuracy: 0.8892 - val_loss: 0.2843 - val_accuracy: 0.9072\n",
            "val_f1: 0.7864 — val_precision: 0.7942 — val_recall: 0.7842\n",
            "Epoch 11/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.3117 - accuracy: 0.8935 - val_loss: 0.2827 - val_accuracy: 0.9049\n",
            "val_f1: 0.7847 — val_precision: 0.7975 — val_recall: 0.7792\n",
            "Epoch 12/30\n",
            "2148/2148 [==============================] - 31s 14ms/step - loss: 0.3028 - accuracy: 0.8970 - val_loss: 0.2756 - val_accuracy: 0.9106\n",
            "val_f1: 0.7941 — val_precision: 0.7922 — val_recall: 0.7994\n",
            "Epoch 13/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.3000 - accuracy: 0.8977 - val_loss: 0.2704 - val_accuracy: 0.9125\n",
            "val_f1: 0.8006 — val_precision: 0.8063 — val_recall: 0.7998\n",
            "Epoch 14/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.2927 - accuracy: 0.8986 - val_loss: 0.2768 - val_accuracy: 0.9107\n",
            "val_f1: 0.7906 — val_precision: 0.8113 — val_recall: 0.7801\n",
            "Epoch 15/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.2892 - accuracy: 0.9010 - val_loss: 0.2740 - val_accuracy: 0.9110\n",
            "val_f1: 0.8004 — val_precision: 0.8117 — val_recall: 0.7990\n",
            "Epoch 16/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2769 - accuracy: 0.9046 - val_loss: 0.2685 - val_accuracy: 0.9133\n",
            "val_f1: 0.7987 — val_precision: 0.8164 — val_recall: 0.7932\n",
            "Epoch 17/30\n",
            "2148/2148 [==============================] - 31s 14ms/step - loss: 0.2803 - accuracy: 0.9031 - val_loss: 0.2704 - val_accuracy: 0.9120\n",
            "val_f1: 0.7957 — val_precision: 0.8048 — val_recall: 0.7922\n",
            "Epoch 18/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2790 - accuracy: 0.9038 - val_loss: 0.2680 - val_accuracy: 0.9118\n",
            "val_f1: 0.7966 — val_precision: 0.8115 — val_recall: 0.7884\n",
            "Epoch 19/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2704 - accuracy: 0.9062 - val_loss: 0.2626 - val_accuracy: 0.9128\n",
            "val_f1: 0.7976 — val_precision: 0.8030 — val_recall: 0.7978\n",
            "Epoch 20/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2679 - accuracy: 0.9072 - val_loss: 0.2589 - val_accuracy: 0.9176\n",
            "val_f1: 0.8069 — val_precision: 0.8248 — val_recall: 0.8001\n",
            "Epoch 21/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2683 - accuracy: 0.9062 - val_loss: 0.2559 - val_accuracy: 0.9178\n",
            "val_f1: 0.8073 — val_precision: 0.8263 — val_recall: 0.7976\n",
            "Epoch 22/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.2655 - accuracy: 0.9084 - val_loss: 0.2702 - val_accuracy: 0.9127\n",
            "val_f1: 0.7986 — val_precision: 0.8078 — val_recall: 0.7931\n",
            "Epoch 23/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2770 - accuracy: 0.9056 - val_loss: 0.2672 - val_accuracy: 0.9141\n",
            "val_f1: 0.8009 — val_precision: 0.8128 — val_recall: 0.7937\n",
            "Epoch 24/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2652 - accuracy: 0.9096 - val_loss: 0.2577 - val_accuracy: 0.9192\n",
            "val_f1: 0.8127 — val_precision: 0.8198 — val_recall: 0.8102\n",
            "Epoch 25/30\n",
            "2148/2148 [==============================] - 31s 15ms/step - loss: 0.2535 - accuracy: 0.9116 - val_loss: 0.2585 - val_accuracy: 0.9179\n",
            "val_f1: 0.8081 — val_precision: 0.8221 — val_recall: 0.8020\n",
            "Epoch 26/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2545 - accuracy: 0.9123 - val_loss: 0.2548 - val_accuracy: 0.9196\n",
            "val_f1: 0.8130 — val_precision: 0.8351 — val_recall: 0.7990\n",
            "Epoch 27/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2478 - accuracy: 0.9139 - val_loss: 0.2524 - val_accuracy: 0.9192\n",
            "val_f1: 0.8136 — val_precision: 0.8196 — val_recall: 0.8122\n",
            "Epoch 28/30\n",
            "2148/2148 [==============================] - 32s 15ms/step - loss: 0.2507 - accuracy: 0.9136 - val_loss: 0.2510 - val_accuracy: 0.9212\n",
            "val_f1: 0.8163 — val_precision: 0.8326 — val_recall: 0.8092\n",
            "Epoch 29/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2438 - accuracy: 0.9149 - val_loss: 0.2513 - val_accuracy: 0.9189\n",
            "val_f1: 0.8114 — val_precision: 0.8220 — val_recall: 0.8047\n",
            "Epoch 30/30\n",
            "2148/2148 [==============================] - 30s 14ms/step - loss: 0.2490 - accuracy: 0.9130 - val_loss: 0.2388 - val_accuracy: 0.9232\n",
            "val_f1: 0.8244 — val_precision: 0.8271 — val_recall: 0.8238\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "israeli-store",
        "outputId": "adf3c92e-a7a1-4afb-f736-a08e48c439aa"
      },
      "source": [
        "if not os.path.isdir(save_dir):\n",
        "    os.makedirs(save_dir)\n",
        "model_path = os.path.join(save_dir, model_name)\n",
        "model.save(model_path)\n",
        "print('Saved trained model at %s ' % model_path)\n"
      ],
      "id": "israeli-store",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved trained model at /content/drive/My Drive/NN_WORKSHOP_DATA/models/keras_cnn_model.h5 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "republican-karma",
        "outputId": "e9266820-733f-44c0-a275-3395d552d2be"
      },
      "source": [
        "history_df = pd.DataFrame(hist.history)\n",
        "history_df['val_f1'] = f1_metrics.val_f1s\n",
        "history_df['val_precision'] = f1_metrics.val_precisions\n",
        "history_df['val_recall'] = f1_metrics.val_recalls\n",
        "\n",
        "history_df[['loss', 'val_loss']].plot()\n",
        "history_df[['accuracy', 'val_accuracy']].plot()\n",
        "history_df[['val_f1', 'val_precision', 'val_recall']].plot()\n"
      ],
      "id": "republican-karma",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5345887850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0bMqTA4Jo0tC8oaCfH/2c+Sfn849e/+CRNo9glEbOpJ1hT8Ie9iTs4WTKSSSShvYN6effjwH+2tOCu4N7pWVHJefi7mSLh7MtoRtDySjKYMPYDTjYOJhVt6pQgUBRrikuhIgftBU3My+CdxvtF2nHCVVvaqmui/th6UNa89CUDeWPspESIn6E7W9rC6oN+BsMeFVb8jgxAn6arC0RMfIz6Pp4+dczGrW19Q06eP7QjYTttSVyLfw8BR78VFvGeu9nWnOWg7sWbEOe1iZoVUJKyb82nmHhvlheGNyI9akvU2gopEBfwKCAQbzf731cbV05HJvOwn2xbD+dhLUQDO/YiCf6BZKWq2PurmiOXMygoZMtU/oGMqVPIA2dzV+OemvsVl7f/TrjW47n3T7vltkMlVGYwf7E/exN2Mu+hH1kFGVgI2wY1GwQoa1D6e7bvdLmq/XR63l779v8d8B/Gdl8pNn1qwoVCBSlMAvCFmidknnJ4N9Du6m2Gl57zSQVObcFVjwGQQO0lShvXs8+LRo2vqzdQJv21iZU+bQpfUxeGqx6AmJ3Qc8nYeh/y26mOrMBfnocHvpOe5owU7HBSL7OQIHOQEGxgXydngKdgRY+Lrg7VXAzlVLrHL90QFubx8UP+r6gPQVUYWjp3F3RfLjlLFP7BlLovpRtcdtYMXIFB68cZPaR2TRyacRn935GGw/t3+Vyej6L98fxU9hlcor0APi7O/LUgCAm9myKk13VBkieTT/L5M2TaevZlgVDFmBrxg8Fg9FAZFok2+O280vUL2TrsmnZsCWhrUMZ2XwkTra3PoUU6AsY+ctIfBx9WPbgMrOal6pDBQKl/spL1Ub2hM2HomwIvh/6v6otmna7x6dH/KitPtlurNZkY2WtjaXf/6U2lt7aTpus1f2J8oOVQQ+/vaed07S31hFccn18KeHbe0CXC8+H3WiqKeFgTBofbztHRp6OfJ3phl9soNhQ9n3By8Webyd3o3uzCjrHk8/Aljeg/UPaJDNzEreUsOpIPK/9fJzRnRszrl82L/7+As90fobnuzwPQERyBH/b9TcyCzP5v17/x7iW467/6s4t0rPheCLO9jYM7+CHrXXVb6zphelM2jgJgzSwYuSKanXcFugL2BK7heVnl3M2/Syutq6MaTGGR1o/QmCDwOvHzT0+l68jvmbxsMV08+1W5euYSwUCpX4qyITv7tVGA7UbrS2HcNNSv7fdvi9gxzvQYzp0eQw2vARJp7SJWcP/B26NzSvn5CpY9wI4NND6Da4NUT23FZY/AmO+ga6P3XLa2mMJvL7qOH4NHOjUxB0nW2uc7KxxtLPBye7ae9OrrRZE/rvlDFcyC/n32A5M7Nn0ljJraufZJJ5acoQ+zT354rG2TNw4DhdbF1aOWomd9Y0nkfTCdN7c/SYHrhxgdPBo/tH7HzjaOFZQsnmKjcXM2D6Dk6knWTxsMe292teoPCklx1OO8+PZH9lxcQd6o56+jfsyqc0k2ni0YfTa0fT3789n935W47pXRAUCpf6RUmsOOb9VG7sf2P9216h8O2ZqM2oBXBtr69+3rUY78dVTWlaurAStjO5TYd79WuKZF4+W6gORUvLVzig+3XGe3s09+PbxHjRwMq+PJDNfxws/HmNvVCrT+gXxfyPaXJ9EVVNHL2Xw6LyDtPRxZfmM3sw+9iErz61k6YildPbufMvxBqOB7058x5zjcwh2D+azez8jqEHN1iP6z6H/sPzscv7T/z+MCh5Vo7JullqQyqrzq/j53M8kFyRjb22PQRpYP2Y9Td0sH1RLqiwQIKW8Y/66d+8uFaVS+76Q8l03Kfd/dbtrUjmjUcod70q55S0pC7JqVlZempRLHtK++6JR2mv496UO0ekN8rWVEbLZGxvlKyuOyaJiQ5UvU6w3yPfWn5LN3tgoH59/UGbkFdWs3lLKC0nZsvM/t8mB/9spU3IKZdiVMNlhUQf54aEPKz13X/w+OWD5ABnyQ4jcErul2nVYfX617LCog/z48MfVLsMcOoNObovdJmdsnyEXnlxYq9e6Bi13TLn3VvVEoNxdLh7QOivbjNCSlt/uvoC6ZjRoY/n3fgZuTeClY9c7kbMKinlu2RH2RaXx10EteXlwyypPyCppZdhl3l57En93R+ZP6UELn+qtM3Qlq4Dx3+yn2ChZ82xfvN2smLBhAnqjnjWj15TZyXqzq3lXeX3X60SkRPBom0d5rcdrZnXwXhORHMET254gxC+Erwd9XWeLvdUVizQNCSGGAZ+jZRObL6X88Kb9AcBiwN10zJtSys2mfW+h5TQ2AC9JKbeZU2ZZVCBQKpSbAt8O0Mb+P71LazOvr2L3aN+/UScA4jPymbYojJiUPD4c34kJ3S2TVSw8Lp1nfjhCYbGRLyZ14f42VUjmjtbU9PDcA1zNKmTF071p37gBs47MYuGphcwbMo/ejXqbXVaxsZjZR2az5PQSPBw86NO4D/0a96NP4z4Vdvgm5SURuikURxtHlj+4nAb2d9//b2ocCIQQ1sB54AEgHi2H8SQp5ekSx3wHHJNSzhFCtAM2SykDTe+XoyWmb4yWkvLaYiwVllkWFQiUchkN8MM47YngyV8p9unAsoMXaeBky0NdLZdKMTwunfNJubT0daFlZUMp/yROxmcxbXEYhcUG5j7enX4tbtwUC/WFJOYl0tS1KbZW1ZtLkZBZwIwl4Zy+ks3fh7bhmYHNy33SKNIbiEzM5tilTI5dyuBwbDqZ+cUsnhZCn2BPItMieWzTY4xpMYZ/9v1nteqzL2EfG2I2cCDxAOmF6QC09WhL38Z96effjy7eXa4/LRQZipi6ZSoxWTEsG7GMFg1bVOuaf3aWyFAWAkRJKWNMBa4AxgAlb9oSuDZXuwFwLSPHGGCFlLIIiBVCRJnKw4wyFeUWhfpCVpxdQUpBCkWGInQGHTqjDt2VExTlnaeobU9SD/6Xi+lZFOl16HPaE5/+Ai/c36JGzSAAKw5f4v9+OY5RCkAry8vFnlamoNDC15VWPi609HXFowoTl2rTr6eTeHH5MTyc7fhheggOThlsiD7AiZQTnEw9ybn0c+ilHkcbRzp7d6a7b3e6+3ano1dHs2e4+rs7suqZvry26jgfbT3L2avZfDS+E/Y2ViRkFphu+pkcvZTB6cRsdAbj9fN6BnnwaEgAfYI9KTYW8+6+d/Fw8KjR6pv9/LUlnI3SyJn0M+xP2M++xH0sjlzMglMLcLJxIqRRCP0b9+do8lFOpZ3i8/s+v2uDgDnMCQT+wOUSn+OBm/PSvQdsF0K8CDgD19bb9QdKLuISb9qGGWUCIISYAcwACAio4vosyl1n4amFzDk+BycbJ+yt7bG1tsXeaMQ++wo2Ll5cybcnLbcABxtXmnkUE+/wO7N2dyKroJi3H2xbrWAgpWTOrmj+t/UMjdosxtkph6GNp+Jm6MGF5HwuJOey6kg8ebobq1d6OtvRyteVvsGeDGjlTUf/BpWuVGlp3+45xSd/7MCvaTKtAzJ48o93ySzKBMDJxokOXh2Y2mEqAa4BnEk/w9Gko3wT8Q0SiY2VDR08O9DNtxvdfbvTxacLbnblr8vjaGfNV5O60q6RGx9vO0fE5UzydQZScooAcLC1opO/O0/0C6RrgDtdAxri61Y60Cw6tYhzGeeYfd/sCq9lLithRXvP9rT3bM9TnZ4iV5fLoauHrgeGPy7/AcBzXZ7j/oD7a3y9O5k5TUMTgGFSyidNnycDvaSUL5Q45lVTWZ8KIfoAC4AOwBfAQSnlD6bjFgBbTKdVWGZZVNPQXUhKbR3689vA3g26TdYSk5QhvTCd4auH08+/341x11nxyLkDyLD2ZEjOTLL1tjxzbzDP3RtMoSGHEWtG4EwLLpx4hIk9mvDfcZ2qdEM2GiX/2XyG+XtjCekQxxnDXPyc/biad5W2Hm15pfsr9GncBykliVmFXEjKISo5lwtJuZxKzOL0lWykhAaOtvRv4cWAll4MaOWNv3vNx7zr9EbS8opIybnxl5pbREJWOruzPidbnARAIAh2D6aTdyc6enWkk3cnghsEY13GchPZumwikiMITwrnaNJRItMi0Rv1CAStGrZiYNOBPNzqYfyc/cqt147TSczdFU0zD6frN/3Wfq4VTu6KyYxhwoYJ3Nf0Pj6999Ma/9tURkrJpZxLxGXFMaDJgFqb0ftnYYmmoQSg5CDXJqZtJU0HhgFIKQ8IIRwAr0rOraxM5W6l12kJyc9t0f6y42/s2/0/LSl6zydvWZJg3ol5FBoKeaGr6feCoZjcHyYjCgoYX/QM7Vv68c/R7Qk0rVfvYOvO9I7TmX10NhP7j2Xl3nhyCvXMDu2CvU3la+7oDUbeWH2S1Ufjeay3H4eKZ9HOsR3LRixjS+wWvjr2FTN2zKBPoz680v0V2nq2xd/dkXtb30iqkpZbxL7oNPacT2H3hRQ2nbwCQLC3MwNaenNPKy96BXnibG+D0SjJLCgmPa+ItFwd6Xk6UvN0pOfqtG152raUnCJScovIzC++pc7CJhOXZt+DbSodnMfxYp9hdPLuiIudi1n/07jZuXFPk3u4p8k9gDZD9mTKSY4kHyH8ajjzTsxjwckF3B9wP6GtQ+np1/OWp6wH2vnyQDvzO40NRgMz98/E0caRt3q9ZfZ5NSGEoJlbM5q5NauT6/3ZmfNEYIPWsTsI7WYdBjwqpYwsccwW4Ccp5SIhRFvgN7QmoHbAj9zoLP4NaInWwFphmWVRTwR3sPx0bSnls5sg6jfQ5WgrUQbfD62Ha/lp02O0QBD1q5bRq/fz0GsGODTgSu4VHvzlQUY2H8n7/d4nNbeIyIUvMDB9JW/bvMaAsU8ytL3fLTelQn0hI38ZibejN4MbfMC/Np1hQEsvvp3cvcL1ZwqLDbzw41F+PZPMK4Nb4eC9k68ivmLRsEV099VW/NQZdCw/u5x5J+eRVZTFg80f5MWuL+LvUnaOXSklF5Jz2X0+hT0XUjkUm0ZhsRFba4Gbgy0Z+TqM5fzn6Opgg6ezHR7Odvi4OuDlaoe3iwPervbX/7IMcbx3+FUK9QXMvm82vRqV2dpaI/E58aw8v5I1F9aQVZRFcINgQtuEMip4FM621UsYs+zMMj48/CEf9P+A0cGjLVxjBSw3fHQEMBttqOdCKeUHQoj30SYprDeNDpoHuKB1HP9dSrnddO7bwDRAD7wspdxSXpmV1UMFgjtMThKcWq0lcrm4H6QBXHyh1TBoPUJbs962jCaS+CNaQDi/FewbQO9nmWmdycaLO9gwdiM7T+k4tm0xn/EZ4X4TaTdtToU39bVRa3ln3zt8PPBjclLb88bqE3Rp6s73U0PKnFGbXVjMk4vCCbuYzvtjOjC0kyMjfxlZ7lIA2bpsFpxcwLIzyzBKI6FtQpnRcUalSxEXFhsIj8tgT1QK2QX66zd6Txc7PJ3tr79v6GSHnU3FTRf7Evbx6h+v4mrnypzBc2jZsPw1/i2hUF/I1ritLD+7nNNpp3G2dWZ08GhC24TSvEFzs8tJyE3goXUP0c2nG3MGz6lxh75SNrXEhFK3DMVaspdjP2jt/tKgJXJvPVy7+Tfuav6Kn4kRsPtjYqK38pB/Iya5tCSj4CUOnY5ms8M7WPm0xuGp7ZUmhzEYDTy88WEK9YWsG7OO386k8tLyCJp7O7Nkegg+rjc6LZNzCpmyMIyo5Bw+m9iFUZ0b886+d9gUs4l1Y9fR1LX8pQCu5l3lm4hvWBe9DicbJ/7S/i+092yPl6MXXo5eeDh41MpEpTUX1vD+gfdp4d6Crwd9ja9z1cby14SUkpOpJ1l+djnb4rZRbCymV6NejAkeg62VLTnFOeTqcsnR5ZBbnKu9N23LLc4lKS+JQkMha8espbGLmWsrKVWmAoFSN5LPamv9H18BeSnaL//Ok7S18ivIQGWOV7c+yb6kMDZdvIyjwQajQ0NcrIoQT+8Bd/PWaNkdv5vnf3uet0Le4tG2j7L3Qiozlobj42rP0um9aOrhxKW0fCYvPERydhHfTu7OPa28OZ12mtCNoUxtP5VXe7xq1rUuZFzg86Ofsyt+V6ntAkFDh4bXA4OXoxeejp54OXjR1rMt3X27V6nTUkrJ1xFf8+2Jb+nbuC+fDps+jNgAACAASURBVPzU7L6A2pBWkMaaC2v46dxPJOUnldpnJaxwtnXG1dYVFzsXXGxdcLXT3o9qPop+/v1uU63rBxUIlNpTmK0lLD/2A8SHgZWN1uzTdbIpr27Nf/1GpkYSuikUp/zheMS3ZF7QHzRJ2qktH9FycOUFmEgpmb59OtGZ0Wx6aBMudi4cvZTB1IWHcbKzYeaodry7PpJig5GFU3vSLaAhUkqmbZumnTNuE652VVtC4WreVZLyk0gtSCWtII3UglRSClJKfU4tSKXYqHX6+jj5MDxwOCOaj6CtR8VDXYsNxbx34D3WR69nbIuxzOwzs9oTwixNb9RzLuMc9lb2uNhpN3wnGyfV7HMbqUCgVE5KOLJIS92IBDtXsHcBOxfT682fXSDhiJaNSl+gZfrqOhk6PWJ+Hl0zTdowjVMpp+HyW8yf3J9ezT21+lbjpnIq9RSTNk3i6U5PXx95dPZqNpMXHCYlpwg/NweWTg+hpa92w//t4m+8/MfL19MU1gYpJVlFWVrS8thN7E3Yi96oJ9AtkBFBIxgeNLzU+vUAubpcXvlDS3T+XOfneKbzM+omq1RIBQKlYumx2jr417JhNWympUbU5Zhec0GXd2Ob1GaFYu+m5b/tOhn8u9XK4m7fHtrGV2dfwz57LCse+Xu1FzUr6fVdr7MrfhebHtqEt5MWtOJS81iwN5anBza/nstWZ9Axdt1Y7K3t+XnUz3W2CFlWURY7Lu5gS+wWwq6GIZG082zHiKARDAschkTy/G/PE5MZw7t932Vsi7F1Ui/lzqYCgVI2oxHC5sGv74GwhqH/hm5TKr6hS6klKNflaoHA1vJJtq9ZdvAiH0Q8i519DhvHbqRJQ8ssBHY5+zKj141mbIuxvNvn3XKPWxy5mE/CP2Hu4Lm3rf06KS+JrXFb2Ry7mdNppxEInG2dkUg+G/gZff373pZ6KXceS0woU+42qVGw7nm4fBBaPACjZkMDMxZmEwLsnLS/WmI0Sj7Zfo7vwtfj2PQyf+/5jsWCAEBTt6ZMbDWRn879xOR2k8sc6phemM63x7+lv3//29qJ6evsy5T2U5jSfgqxWbFsid3CufRzPNflOVp7tL5t9VLuPioQ1CcGPRz8Gn7/j5ZHduxc6Bxaa2v2X0rL59Md53B1sCHY24UWPi4Ee7vQqIFDmW3aRXoDf191gnUR8TRu/zsezs2Y2Gacxev1dOenWRe9js+PfM7n939+y/5vIr4hX5/P6z1et/i1qyuoQRDPdXnudldDuUupQFBfJJ+Btc9B4lFoMxIe/BRcy18vpqbS83RM+f4wSdmFWFsJcgr11/c52VkT7O1CsLfz9eDQ1MOJf286zcGYdEb3u8rv6fHM7PZxrbTNezh48ET7J/gq4iuOJR+jq8+NXMbRmdGsOr+Kh1s9THN38ydGKcqdTAWCu52hGPbOhl0fgYMbTFgI7cfVauauwmIDTy0JJyE7ndBBF5nUYRiedkFEJ+cRnZJLVHIu0Sm5hMVlsDYi8fp5ttaCjx9ux4K4L2nr0ZYhzYbUWh0nt5vMT+d+4rPwz1gyfMn1J5RPwj/BycZJ/fpW6hUVCO5mBj0smwAxf2g3/xEfg3P5mZosckmj5JWfIjh6KY2evbbwS9xBfolbSH///kzvMJ3Hmncv1SyUV6QnNlULEC18XDietYmE3ATeGfxOra4I6WSr3ez/eeCf7Ly0k0HNBrEvYR97E/byWo/XaOjQsNaurSh/NmrU0N1s29tw4CsYORt6PFEnl3x/w2kW7otlSN+THMhYxsvdXkYiWXp6KemF6XT27sz0DtMZ2HTgLTf6/OJ8RqwZQVCDIBYOXVjrY+P1Rj3j1o9DSsmq0at4ZMMjFBuLWTtmbZXy3SrKn11lo4bu7kW467NTq7UgEDKjzoLAgr2xLNwXy/Ce2RzM+JERQSOY1mEaT3Z8km3jt/F2r7dJLUjlpd9fYvz68WyI3nB9Vi1oq1CmFabx125/rZMJUjZWNrzc7WXisuN4ZsczRGdF82qPV1UQUOod9URwN0o6DfMHgV8nmLKh0kXZLGHLySs89+NRBra1Idru33g5erFsxDKcbEsPNdUb9WyN28qCkwuIyoyikXMjprSfwqCAQYxbN47uvt35ctCXtV7fa6SUTN06laPJR+np15MFQxaoWbrKXUdNKKtvCjJh3n3abOCnd9fqyKBrjlxM59F5h2jb2BH7pnO5lHOR5Q8uv2VphJKklOyO3838k/OJSInARthgkAZWjV5Fq4atar3OJUWmRjJz/0z+0/8/any+cldSE8rqE6MRfnkaMi/B1E11EgRiUnJ5cnE4jd0dad/xD9bHRDL73tkVBgHQMkQNbDqQgU0HcjTpKIsjFxPsHlznQQCgvVd7Vo9eXefXVZQ/C7MCgRBiGPA5WhKZ+VLKD2/aPwu4z/TRCfCRUroLIe4DZpU4tA0QKqVcK4RYBAwEskz7pkopI6r9TZQbyVxGfAIBvWv9cqm5RUz9PgwrIZj8QAqzIlbzRIcnGNRsUJXK6ebbjW6+3WqploqiVKbSQCCEsAa+Bh4A4oEwIcR6KeXpa8dIKV8pcfyLQFfT9t+BLqbtHkAUsL1E8a9LKVdZ4Hso57bCH/+Fzo9q+X5rWb5Oz/TF4STnFPLRJC/+dfQtQvxCeKnrS7V+bUVRLMucJ4IQIEpKGQMghFgBjAFOl3P8JKCs1bwmAFuklPnVqahSgbRoWDND6xwe+Vm1JouduZLN6iPx2NlY4eGspUf0cLajobMdHk52NHS2xcXeBiEEBqPkpeURnIzPZFZoa749+xIN7Brw0T0f1dkqnYqiWI45/9X6A5dLfI4HysyKLYRoBgQBO8vYHQrcnPD1AyHETLSk9m9KKYvKKHMGMAMgICDAjOrWM0W58NPjWvrHR34oOwdwBaJTcpn96wU2nkjE1soKo5Toy8mgbmstrufPjc8o4J+j2/Jr6iyu5F7h+2Hf4+VYu5PVFEWpHZb++RYKrJJSGkpuFEI0AjoC20psfgu4CtgB3wFvAO/fXKCU8jvTfnr06HHnDHGqC1LC+hch5Sw8vlrLJWCmy+n5fPHbBVYfjcfB1prn7g1mxoBg3BxtyCnSk5GnIz1PR0a+jvS8Yu1zvu769if7B6Fz+ZU/LvzBmyFv0sWnSy1+UUVRapM5gSABKJkYtolpW1lCgefL2D4R+EVKeX32kJTyiultkRDie+A1M+qilHTgay1V5KB3Ifh+s05Jyi7kq51RrAi7hBCCJ/oF8ey9wXi52F8/xs3BFjcHW5p5Opd/6cQDPPPrVwwPGs6jbR6t8VdRFOX2MScQhAEthRBBaAEgFLjlv3whRBugIXCgjDImoT0BlDy+kZTyitBm74wFTlWx7vVb7G7YMRPajoL+r1R6eHqejjl/RLHkwEUMRsnEnk158f4WNGpQtaYkgMTcRN7Y/QbNGzTnvT7vqQlYinKHqzQQSCn1QogX0Jp1rIGFUspIIcT7QLiUcr3p0FBghbxphpoQIhDtiWLXTUUvE0J4AwKIAJ6pyRepF6TUlpO+sA32fwmewTB2ToWdw9mFxczfHcOCvbEUFBsY29Wflwe1IsCz7OQyubpckvKTtL887TU5P/n65+T8ZDKKMnC2dWbWvbNumTmsKMqdR80s/rPT5UPcHji/DS5shyxTv32jzjB+AXi1rPD0x+YfZF9UGg92bMQrD7QsM++v3qjnxzM/Mu/kPDKLMm/Z7+HggY+TD75Ovvg6+eLj5MPApgNp49HGIl9RUZTapWYW34kyL9248cfuBn0h2DpB83thwN+g5RBo4F9pMReSctgXlcbrQ1vz/H0tyjzmTNoZ3jvwHqfTTtOvcT96N+qt3fSdtRu+j5MP9tb2ZZ6rKMrdQQWCPwtdPhz+Fo7/BClntG0Ng6D7VO3G36xflZPFLz98GVtrwSM9m96yL784nznH57D09FLc7d35eODHDG02VLX3K0o9pALB7WbQQ8QybVZwzhVo1h+GfACthoJni2pnEissNrD6aDxD2vuVGhEEsC9hH/86+C8SchMY33I8r3R/hQb2lksQryjKnUUFgttFSji3GX79J6SegyYhMOF7aNbHIsVvOXWFrIJiHgu5MQkvrSCNj8M/ZlPMJgLdAvl+6Pf08Cu32VBRlHpCBYLb4dIhbejn5YPg2VKbEdxmpEXzCP946BKBnk70bu6JlJJ10ev4JPwT8orzeLbzszzZ8UnsrGs/T4GiKH9+KhDUpZTz8Ns/4exGcPHVUkh2nQzWlv2f4UJSDmFxGbw1vA2JeQm8t/89Dl09RFefrrzb512C3YMtej1FUe5sKhDUhZyrWh/A0aXa6J/7/gF9ngO78mfu1sSPhy9hay0Y370Jz/0+mcs5l3mn9ztMaDWhVhPCK4pyZ1KBoLalnNcyhumLIOQpuOd1cK69xdkKiw2sOZrA0PZ+pOpiOZN+hrdC3mJi64m1dk1FUe5sKhDUtkNzwKiH5w6CV9lj+S1p80mtk/jRkADWRs3H1sqWB5s/WOvXVRTlzqXaCWpTYZY2L6DD+DoJAgDLD2udxD0C3dgUs4n7mt6nhoYqilIhFQhqU8RyKM7TmoTqwHlTJ/GkkAB2JewisyiTsS3G1sm1FUW5c6lAUFuMRgibB/49oHHXOrnkclMn8YTuTVgbtRYfRx/6Nu5bJ9dWFOXOpQJBbYn9A9KiIGRGnVyusNjA6iPxDG3vh9Eqm70JexkVPAprK+s6ub6iKHcuFQhqy+H54OQF7eumaWbzyStkF+p5tFcAG2I2YJRG1SykKIpZVCCoDZmX4PwW6PYXsKmblTt/PHSJIC9negd5sDZqLV28uxDYILBOrq0oyp1NBYLaEL5Qe+0xrU4udz4ph/CLGUwKacrJtJPEZsWqpwFFUcxmViAQQgwTQpwTQkQJId4sY/8sIUSE6e+8ECKzxD5DiX3rS2wPEkIcMpX5kxDi7lj4prgQji6B1iPA/dbln2vDj4cuYWdtxYTuTVkbtRYHaweGBg6tk2srinLnqzQQCCGsga+B4UA7YJIQol3JY6SUr0gpu0gpuwBfAmtK7C64tk9KObrE9o+AWVLKFkAGML2G3+XPIfIXyE+rsyGj2kzieIZ28MPR3sDW2K0MbjYYFzuXOrm+oih3PnOeCEKAKClljJRSB6wAxlRw/CRgeUUFmhLW3w+sMm1ajJbA/s4XNg+8WkHQwDq53KYTpk7ikAB+u/QbucW5qllIUZQqMScQ+AOXS3yON227hRCiGRAE7Cyx2UEIES6EOCiEuHaH8gQypZR6M8qcYTo/PCUlxYzq3kYJR7S/nk9ZdEnpiiw/fInmXs70bq51Evu7+NPTr2edXFtRlLuDpTuLQ4FVUkpDiW3NTEmTHwVmCyGqtAaylPI7KWUPKWUPb29vS9bV8g7PBzsX6BxaJ5e70UkcwJW8Kxy+cpjRwaPVCqOKolSJOXeMBKBkr2cT07ayhHJTs5CUMsH0GgP8AXQF0gB3IcS1Re8qKvPOkJcGp1ZDp0fAwa1OLnmtk3h89yasi16HRDI6eHTlJyqKopRgTiAIA1qaRvnYod3s1998kBCiDdAQOFBiW0MhhL3pvRfQDzgtpZTA78AE06FTgHU1+SK33bElYCiqs07iAp3WSTysgx/uTjasi1pHiF8ITVyb1Mn1FUW5e1QaCEzt+C8A24AzwEopZaQQ4n0hRMmfn6HACtNN/pq2QLgQ4jjajf9DKeVp0743gFeFEFFofQYLav51bhOjAcIWQuAA8GlbJ5fcZJpJPCkkgCNJR0jITVCdxIqiVItZ+QiklJuBzTdtm3nT5/fKOG8/0LGcMmPQRiT9eWRegiVjoeeT0PtZ8zt8z2+DrEsw9N/VvrSUktdXnWDbqat4u9nj42qPr5sDPq72+Lg64ON249XXzaFUJ/E/9n2Gs60zg5sNrvb1FUWpv1RimpLObYX0aNj2Flw6AGO+Agcz1vIPmweujaF19RPArD6awKoj8Qxp54uttRXJOYUcu5RJck4hhcXGMs95e0Rb8vX57Li4gxFBI3C0caz29RVFqb9UICgpbjc0CNDa+X99D5JOwcQl4FfmQ40mNQqid2p5iKuZhP5qViH/3BBJSKAHcx/vjpXVjScRKSU5RXqSswtJzi4iOaeIpOxC8nQGHu0VwLa49RToC1SzkKIo1aYCwTVGI8Tt1X7V93sJmvSAn5+A+YNhxCfQbXLZ54XNBytb6D6lWpeVUvLmmhPoDZL/TehUKggACCFwc7DFzcGWFj6ut5y/NmotgW6BdPbuXK3rK4qiqAHn1ySdgoIMCBqgfW7WF57ZA01DYP0LsPZ50OWXPqcoFyJ+hHZjwMWnWpf9OTyeP86l8Maw1gR6OVfp3LisOI4lH2NMizGIOprApijK3UcFgmvi9mivgQNubHPxgclr4Z6/Q8QPsOABSIu+sf/kSijKqnbymcTMAv618TS9gjz4S5/AKp+/LnodVsJKzR1QFKVGVCC4JnYPeDSHBjetdGFlDfe/DY+thuxE+HYgRK4FKbWZxH4dtaeGKtKahE5ikJKPJ3S+pUmoMgajgfXR6+nbuC8+TtV7GlEURQEVCDRGA1zcX/pp4GYtB2tNRT5t4OcpsOIxSI7Ungaq0SzzU9hldp9P4a3hbQjwdKry+QeuHCA5P1l1EiuKUmOqsxjgynGtiSfonoqPa9AEpm6GHTPh0BxwcIcOEyo+pwwJmQX8e9MZ+gZ78livZte3Lzq1iD0Je/Bz9sPXyRc/Zz8aOTfCz9kPP2c/XO1udBavjVpLA/sG3Nf0vipfX1EUpSQVCKBE/0D/yo+1sYPhH0KLwdpwUbuq/ZqXUvLGqhNIKflo/I1RQpdzLvP50c/xdfblUs4lUvJTMJRauw+cbZ1p5NwIX2dfDl85zIRWE7Czvjvy+SiKcvuoQABa/4BXK3D1M/+cltWbxfvj4UvsjUrlg4c60NTjRhCZe3wu1lbWLBm+BB8nH/RGPakFqVzNu3rjL197vZJ3hUbOjQhtXTernCqKcndTgcBQrM0i7vRIrV/qcno+/9l0hv4tvHg0JOD69ujMaDZEb2Bq+6nXO35trGyuNwkpiqLUJtVZnBgButwb8wdqidEoeWP1CYQQfDi+Y6lx/19HfI2TrRPTOtRNsntFUZSSVCCI2629VjRiyAKWHb7E/ug03n6wLU0a3mgSikyNZMfFHUxpNwV3B/darYOiKEpZVCCI3QM+7cDZq9YucTk9n/9uPsOAll6E9mxaat+Xx77E3d6dye3KWcJCURSlltXvQKDXweVDtfo0YDRKXl91HGsh+Gh8p1JNQuFXw9mXuI/pHabjYudSa3VQFEWpiFmBQAgxTAhxTggRJYR4s4z9s4QQEaa/80KITNP2LkKIA0KISCHECSHEIyXOWSSEiC1xXhfLfS0zJRyB4vxa7R9YfzyRgzHpvP1gWxq731gmWkrJl8e+xMfRh9A2avSPoii3T6WjhoQQ1sDXwANAPBAmhFhfItMYUspXShz/IlpeYoB84C9SygtCiMbAESHENillpmn/61LKVRb6LlUXtwcQ0KxfrRRfbDAy69fztG3kxsQepZuE9ibs5WjyUd7p/Q4ONg61cn1FURRzmPNEEAJESSljpJQ6YAUwpoLjJ2FKYC+lPC+lvGB6nwgkA941q7IFxe4Gvw7g5FErxf8cHs/FtHxeG9Kq1FpCRmnky2Nf4u/iz0MtHqqVayuKopjLnEDgD1wu8TnetO0WQohmQBCws4x9IYAdUGL5Tj4wNRnNupbkvozzZgghwoUQ4SkpKWZU10zFhXD5MARWsqxENRUWG/hy5wW6Brhzf5vSi8L9evFXzqSf4fkuz2NrbVsr11cURTGXpTuLQ4FVUpZeG0EI0QhYCjwhpbyWd/EtoA3QE/BAS2Z/Cynld1LKHlLKHt7eFnyYiA8DQ1Gt9Q8sO3SJK1mFvD6kdakOYr1Rz1cRXxHcIJgRQSNq5dqKoihVYU4gSABKNnA3MW0rSyimZqFrhBBuwCbgbSnlwWvbpZRXpKYI+J66TmQftweElZaAxsLyivR883sU/Vp40rdF6WGpG2M2EpsVy4tdX8Taytri11YURakqcwJBGNBSCBEkhLBDu9mvv/kgIUQboCFwoMQ2O+AXYMnNncKmpwSE9nN5LHCqul+iWmL3QKPO5iWnr6Lv98WSlqfjtSGtS23XGXTMiZhDe8/23B9wv8WvqyiKUh2VBgIppR54AdgGnAFWSikjhRDvCyFKpsYKBVZIKWWJbROBe4CpZQwTXSaEOAmcBLyAf1vg+5hHl681DdXC/IGs/GK+3R3D4LY+dA1oWGrf6gurScxL5KWuL6nUkoqi/GmYteiclHIzsPmmbTNv+vxeGef9APxQTpm37yfx5UNgLK48/0A1fLcnmpxCPa8+UPppoEBfwHcnvqOHbw/6NO5j8esqiqJUV/2cWRy3B4Q1BPS2aLEpOUV8vy+OUZ0b066xW6l9y88uJ7UglZe6qacBRVH+XOpnIIjdA/7dwN618mOr4Js/oijSG3llcMtS23N0OSw4uYAB/gPo6tO1nLMVRVFuj/oXCIpyIfGoxfsHEjMLWHbwEuO7+dPcu/S6QYsjF5Oty+bFri9a9JqKoiiWUP8CwaWDYNRbfP7AlzsvIJG8NKj000BGYQZLTy9lSLMhtPVsa9FrKoqiWEL9CwRxu8HKFpparn8gLjWPleHxPNarWalcAwBLTy+lQF/A812et9j1FEVRLKn+BYLYPdCkR5WTzldk9q/nsbUWPHdfcKnt2bpslp9dzgPNHqC5e3OLXU9RFMWS6lcgKMyCKxEW7R84dzWHdccTmdo3CB/X0quILj+znNziXJ7q9JTFrqcoimJp9SsQXDwA0mjR/oFPt5/Dxc6GZwaW/sWfX5zPD2d+YGCTgbTxaGOx6ymKolha/QoEcXvA2h6aWGZZo+OXM9l+Oomn7mmOu5NdqX0/n/+ZzKJM9TSgKMqfXv0KBLG7oWkI2FomEcwn28/h4WzHtP5BpbYXGYpYFLmIXn696Ozd2SLXUhRFqS31JxDkp8PVkxbrHzgYk8aeC6k8OzAYF/vSK3WsvbCW1IJU9TSgKModof4Egov7AWmR/gGd3si/Np7G182eyX2aldpXbCxm4amFdPbuTIhf3a6srSiKUh31JxDE7QEbR/DvXuOiPt1+jsjEbN4f0wEH29I5BTbFbCIxL5EZnWaoNYUURbkj1J9AELsHAnqBTZkZMc22LyqVb3fH8GivAIa29yu1z2A0sODkAtp4tGGAf+1kPlMURbG0+hEI8lIhObLG/QMZeTpeXRlBsLcz7zzY7pb9Oy7tIC47jqc6PqWeBhRFuWOYlY/gjhe3R3utQf4BKSVvrjlBep6OBVN64mhnfcv+eSfmEdQgiMHNBtektoqiKHXKrCcCIcQwIcQ5IUSUEOLNMvbPKpGB7LwQIrPEvilCiAumvykltncXQpw0lfmFqM2f0LF7wNYZGld/CegVYZfZFpnE60Nb08H/1vSWu+J3cT7jPE92fBIrUT8etBRFuTtU+kQghLAGvgYeAOKBMCHEeinl6WvHSClfKXH8i0BX03sP4F2gByCBI6ZzM4A5wFPAIbTsZ8OALRb6XqVJI7QYBNa21To9OiWX9zecpn8LL57sf+uaQdeeBvxd/BkeNLymtVUURalT5vx0DQGipJQxUkodsAIYU8Hxk4DlpvdDgR1SynTTzX8HMMyUuN5NSnnQlON4CVoC+9oxajY8srRap+r0Rv664hgOtlZ8OrEzVla3PrgcvHKQE6knmNZhGrZW1Qs2iqIot4s5gcAfuFzic7xp2y2EEM2AIGBnJef6m96bU+YMIUS4ECI8JSXFjOpa1qfbz3EqIZuPxnfC163sGcnzTs7Dx9GHsS1qL5YpiqLUFks3ZocCq6SUBksVKKX8TkrZQ0rZw9vb21LFmqXkUNEhNw0VveZY8jHCroYxtcNU7KztyjxGURTlz8ycQJAANC3xuYlpW1lCudEsVNG5Cab35pR5W1Q2VPSa7058R0P7hoxvOb4Oa6coimI55gSCMKClECJICGGHdrNff/NBQog2QEPgQInN24AhQoiGQoiGwBBgm5TyCpAthOhtGi30F2BdDb+LxUgpeWO1NlT089CutwwVveZ02mn2JuxlcrvJONlaLtGNoihKXap01JCUUi+EeAHtpm4NLJRSRgoh3gfCpZTXgkIosMLU+Xvt3HQhxL/QggnA+1LKdNP754BFgCPaaKHaGTFUDcsPX2b76STeHtG2zKGi18w/OR9XW1dC24TWYe0URVEsy6wJZVLKzWhDPEtum3nT5/fKOXchsLCM7eFAB3MrWleiknN5f2MkA1p6Mf2m5aVLis6MZsfFHczoNANXO9c6rKGiKIplqZlPJej0Rl7+6RiOttZ88nDZQ0WvWXhqIY42jjze9vE6rKGiKIrlqUBQwh/nkjmVoK0qWt5QUYC84jy2x21nZPORNHRoWIc1VBRFsTwVCErYeTYZV3ubW1YVvdmOizsoNBQyOnh0HdVMURSl9qhAYCKlZOfZZO5p5Y2dTcX/LBujN9LUtalKQ6koyl1BBQKTyMRsknOKuK+NT4XHXc27yuGrhxnZfKRaalpRlLuCCgQmO88mIwTc27ri2cubYjYhkYxsPrKOaqYoilK7VCAw+e1sMp2buOPlUn4GMyklG6I30MW7CwFuAXVYO0VRlNqjAgGQklPE8cuZDKqkWehM+hmis6IZFTyqjmqmKIpS+1QgQBs2ClTaP7AhegO2VrYMDRxaF9VSFEWpEyoQoPUP+LrZ076xW7nH6I16tsRu4Z4m99DAvvxlJxRFUe409T4Q6PRG9lxI5f42PhWOAjqQeIC0wjTVLKQoyl2n3geCsLh0cov03N/Gt8LjNkRvoIF9A+7xv6eOaqYoilI36n0g2Hk2GTsbK/q18Cz3mFxdLjsvQ6PkzgAAFfBJREFU72RY4DBsq5n3WFEU5c/KrNVH72Y7zybTp7knTnbl/1PsuLiDIkORmjugKDcpLi4mPj6ewsLC210VBXBwcKBJkybY2lbtB2u9DgQxKbnEpubxRL/ACo/bGLORANcAtaSEotwkPj4eV1dXAgMD1Uz720xKSVpaGvHx8fx/e3ceXVV1L3D8+yMEEBCZgqBBCRSUIYQhYRChCGVQMCoqEVsrtqJ2QSm15QnvuWpqsdUu3+tr+1hafApYaRliyxBQrBKk9DElFJllSEDCkIQkxqRMGX7vj3MSryE3uYmX3Nzc32etLO7dZ5999+as3J2z9z6/HRXlPYR+VXwaGhKRiSLyqYgcE5F5XvJMFZGDInJARP7kpt0lIns8fi6JyP3usSUikuFxbECtau4Hmw67y0Zv875s9GzRWSekRA8LKWFMZZcuXaJDhw72u9EAiAgdOnSo091ZjXcEIhIGLATGAZnALhFZq6oHPfL0BOYDI1Q1X0Q6AahqCjDAzdMeOAZ84FH8XFVNqnWt/WTT4Wx63diaru29bzO5PmM9gA0LGeOFdQINR12vhS93BEOAY6qarqpXgOXAfZXyzAAWqmo+gKpmV1HOQ8B7qnqhTjX1s8JLxezMyKv2IbLykBIDOw2k6/Vd67F2xhhTf3zpCG4GTnm8z3TTPPUCeonIP0Rku4hMrKKcR4A/V0p7SUT2ishvRKTKID8i8pSIpIpIak5Ojg/V9c3fj56npEwZW82y0YN5B0kvSLe7AWNMo+av5aNNgZ7AaGAa8IaItC0/KCJdgGhgo8c584HbgTigPfBcVQWr6iJVjVXV2IiI6iOD1samw9nccF04g25p6zVP8vFkCylhTCPSunXrao/PnTuXvn37MnfuXLZs2cKgQYNo2rQpSUkBG8GuF76sGjoNeI6LRLppnjKBHapaDGSIyBGcjmGXe3wq8Ff3OACqetZ9eVlEFgM/rUP966SsTNn8aTbf7BVB07Cq+8LismI2ZGxgdNfRFlLCmBCxaNEi8vLyCAsL48SJEyxZsoRXX3010NW65nzpCHYBPUUkCqcDeAR4tFKe1Th3AotFpCPOUFG6x/FpOHcAFUSki6qeFWd2435gf92aUHt7TxdwvugKY3t7nx/YdmYbeZfybFjIGB/9fN0BDp75wq9l9rmpDS/c29fr8Xnz5tG1a1dmzpwJQGJiIk2bNiUlJYX8/HyKi4tZsGAB991XeVrzavHx8RQVFTF48GDmz59PQkICAE2aNP7nbmvsCFS1RERm4QzrhAFvqeoBEXkRSFXVte6x8SJyECjFWQ2UCyAi3XDuKD6uVPQyEYkABNgDPOOfJtVs06Esmgh8s5f3oaZ1x9fRtnlbRt48sr6qZYyppYSEBObMmVPREaxcuZKNGzcye/Zs2rRpw/nz5xk2bBjx8fE1rqhZu3YtrVu3Zs+ePfVR9QbFpwfKVHUDsKFS2s88XivwrPtT+dwTXD25jKqOqWVd/WbTp9kMvrUdbVs2q/J44ZVCUk6l8MA3HrCQEsb4qLq/3K+VgQMHkp2dzZkzZ8jJyaFdu3Z07tyZH//4x2zZsoUmTZpw+vRpsrKy6Ny5c73XL1iE3JPF5wousf/0F/zbxNu85ikPKWGRRo1p+B5++GGSkpI4d+4cCQkJLFu2jJycHNLS0ggPD6dbt24WAqMGIdcRpLib0FS3bHTd8XXc2uZWojtG11e1jDF1lJCQwIwZMzh//jwff/wxK1eupFOnToSHh5OSksLJkycDXcUGr/HPglSy6XA2N7e9jl43Vr2M7EzRGVKzUpnc3UJKGBMM+vbtS2FhITfffDNdunTh29/+NqmpqURHR/P2229z++2316ncXbt2ERkZyapVq3j66afp27f+h77qS0jdEVwqLmXr0fM8NDjS65f8+nQLKWFMsNm3b1/F644dO7Jt27Yq8xUVFVVbjufxuLg4MjMz/VPBBi6k7gh2ZORxsbiUMV6Wjaoq69LXMajTICKvj6zn2hljTGCE1B3BpkNZtAhvwvDuVW9CczD3IBkFGXx3+HfruWbGmPqyb98+Hnvssa+kNW/enB07dgSoRoEXMh2BqrLp02zu/EZHWoSHVZknOd0JKTG+2/h6rp0xpr5ER0eH5LMC1QmZoaFj2UWcyrvoNdpoSVlJRUiJNs3a1HPtjDEmcEKmIyjfhGaMl45g+9nt5F3KY1L3SfVZLWOMCbiQ6Qg+OpxN7y5t6HLDdVUeT05P5vpm11tICWNMyAmJjqDgQjFpJ/MZ6+Vu4ELxBTZ9tokJ3SbQLKzqsBPGGNNYhURH8PHRHErL1Ov8wKZTm7hYctGeHTCmkatpP4JrYe3atbz88stej6empjJ79ux6rNHVQmLV0KZDWbRv1YwBXavehCY5PZmbWt3EwE4D67lmxjQi782Dc/tqzlcbnaPhbu9fooFQWlpKWFjVKw+rEh8fT3x8vNfjsbGxxMbG+qNqdRYSdwSDu7XnyZFRhDW5+mni8xfPs+3MNiZ1n0QTCYn/DmMajXnz5rFw4cKK94mJiSxYsICxY8cyaNAgoqOjWbNmjU9lbd68mVGjRjFp0iRuu+02nnnmGcrKygDnTuInP/kJMTExbNu2jXfeeYchQ4YwYMAAnn76aUpLSwF4//33GTRoEDExMYwdOxaAJUuWMGvWLABWrVpFv379iImJYdSoURWfO3myMxqRl5fH/fffT//+/Rk2bBh79+6taNf3vvc9Ro8eTffu3fnd737nh/89D6oaND+DBw9Wf/vjgT9qvyX99Fj+Mb+XbUxjd/DgwYB+/u7du3XUqFEV73v37q2fffaZFhQUqKpqTk6O9ujRQ8vKylRVtVWrVl7LSklJ0ebNm+vx48e1pKREv/Wtb+mqVatUVRXQFStWqKrT5smTJ+uVK1dUVfUHP/iBLl26VLOzszUyMlLT09NVVTU3N1dVVRcvXqwzZ85UVdV+/fppZmamqqrm5+dXfO6kSZNUVXXWrFmamJioqqofffSRxsTEqKrqCy+8oMOHD9dLly5pTk6Otm/fvuLzK6vqmuDsHeP1uzUkhoaqk5yeTO/2venRtkegq2KMqSV/70cwZMgQunfvDsC0adPYunUrDz30EGFhYTz44IMAfPTRR6SlpREXFwfAxYsX6dSpE9u3b2fUqFFERUUB0L59+6vKHzFiBNOnT2fq1KlMmTLlquNbt27l3XffBWDMmDHk5ubyxRfOrm+TJk2iefPmNG/enE6dOpGVlUVkpH9C4fjUEYjIROC3ODuU/a+qXjVoJyJTgURAgU9U9VE3vRQoHzj8TFXj3fQoYDnQAUgDHlPVK1+rNbWUUZDBgdwD/DS23rZLNsb4mT/3I6gcjLL8fYsWLSrmBVSVxx9/nF/96ldfybtu3boay3/99dfZsWMH69evZ/DgwaSlpflUL3DCYJQLCwujpKTE53NrUuOguIiEAQuBu4E+wDQR6VMpT0+cPYlHqGpfYI7H4YuqOsD98ZwxeQX4jap+A8gHvv/1mlJ769PX00SacHfU3fX90cYYP0lISGD58uUkJSXx8MMPU1BQUOf9CHbu3ElGRgZlZWWsWLGCO++886o8Y8eOJSkpiexs5yHVvLw8Tp48ybBhw9iyZQsZGRkV6ZUdP36coUOH8uKLLxIREcGpU6e+cnzkyJEsW7YMcOYOOnbsSJs21z7SgS+zo0OAY6qa7v7FvhyovBP0DGChquYDqGp2dQW6G9aPAZLcpKU4G9jXG1UlOT2ZoZ2H0qml903sjTENmz/3I4iLi2PWrFn07t2bqKgoHnjggavy9OnThwULFjB+/Hj69+/PuHHjOHv2LBERESxatIgpU6YQExNDQkLCVefOnTuX6Oho+vXrxx133EFMTMxXjicmJpKWlkb//v2ZN28eS5curf1/SF1UN4HgzDHwEM5wUPn7x4D/qZRnNfBr4B/AdmCix7ESINVNv99N64jTuZTn6Qrs9/L5T7nnp95yyy1VTo7UxT+z/qn9lvTTNcfW+K1MY0JNoCeL/clz0jaYBXKyuCnQExgNRAJbRCRaVT8HblXV0yLSHdgkIvuAAl8LVtVFwCKA2NhY9VN9SU5PpkVYC8beMtZfRRpjTFDypSM4jfMXe7lIN81TJrBDVYuBDBE5gtMx7FLV0wCqmi4im4GBwLtAWxFpqqolXsq8ZopLi3n/xPvc1fUuWoW3qq+PNcY0ANXtRzB69OjAVCrAfOkIdgE93VU+p4FHgEcr5VkNTAMWi0hHoBeQLiLtgAuqetlNHwH8WlVVRFJwhp2WA48Dvj314QdbT2+l4HIBk3tYSAljQo3tR3C1GieL3b/YZwEbgUPASlU9ICIvikj5KqCNQK6IHARSgLmqmgv0BlJF5BM3/WVVPeie8xzwrIgcw1lC+qY/G1ad5PRk2jVvx/CbhtfXRxpjTIPl0xyBqm4ANlRK+5nHawWedX888/wfEO2lzHScFUn1qvBKIR9nfsyUnlMIbxJe3x9vjDENTsgF1/nw5IdcLr1skUaNMcYVch3B+vT1dL2+K9Edq7xRMcaYkBNSHcG5f51j57mdTO4++apHyY0xjV8g9iNITEzk1VdfBWD69OkkJSXVcEb9C6mgc+9lvIeiti+xMdfAKztf4XDeYb+WeXv723luyHN+LdMXJSUlNG0aOl+PIXVHkJyeTP+O/bm1za2Brooxxg/8vR/ByJEjiY+Pp0+fPpSWljJ37lzi4uLo378/f/jDHyryvvLKK0RHRxMTE8O8efMAeOONN4iLiyMmJoYHH3yQCxcu+Lex11DIdHlH8o9wJP8I84fMD3RVjGmUAvGXe0JCAnPmzGHmzJkArFy5ko0bNzJ79mzatGnD+fPnGTZsGPHx8T4NB+/evZv9+/cTFRXFokWLuOGGG9i1axeXL19mxIgRjB8/nsOHD7NmzRp27NhBy5YtK4LLTZkyhRkzZgDw/PPP8+abb/LDH/7w2jXej0KmI1ifvp4wCWNi1MRAV8UY4yfXYj+C8v0EPvjgA/bu3Vsxpl9QUMDRo0f58MMPeeKJJ2jZsiXw5b4D+/fv5/nnn+fzzz+nqKiICRMmXKNW+19IdARlWsb69PXccdMdtG9x9WYRxpjg5c/9CFq1+jLkjKry+9///qov9I0bN1Z57vTp01m9ejUxMTEsWbKEzZs317lN9S0k5gjSstLIupBlzw4Y0wj5cz8CTxMmTOC1116juLgYgCNHjvCvf/2LcePGsXjx4oo5gPKhocLCQrp06UJxcXHFngLBIiTuCJLTk2nZtCV33XJXoKtijPGzqvYjuPfee4mOjiY2NrZW+xF4evLJJzlx4gSDBg1CVYmIiGD16tVMnDiRPXv2EBsbS7Nmzbjnnnv45S9/yS9+8QuGDh1KREQEQ4cOpbCw0M8tvXbEiQ4RHGJjYzU1NbXW5725700KrxQyZ/CcmjMbY3x26NAhevfuHehqGA9VXRMRSVPVWG/nhMQdwfej630XTGOMCRoh0REYY0y56vYjCFXWERhjvhZVDaqQLY15P4K6DvWHxKohY8y10aJFC3Jzc+v8BWT8R1XJzc2lRYsWtT7XpzsCEZkI/BYIw9nI/uUq8kwFEgEFPlHVR0VkAPAa0AYoBV5S1RVu/iXAN/ly/+Lpqto4u2ljGqnIyEgyMzPJyckJdFUMTsccGRlZ6/Nq7AhEJAxYCIzD2Zt4l4is9dhpDBHpCcwHRqhqvoh0cg9dAL6rqkdF5CYgTUQ2upvag7OTWcMLxWeM8Ul4eHjFk7gmePkyNDQEOKaq6ap6BWeP4fsq5ZkBLFTVfABVzXb/PaKqR93XZ4BsIMJflTfGGPP1+dIR3Ayc8nif6aZ56gX0EpF/iMh2dyjpK0RkCNAMOO6R/JKI7BWR34hI86o+XESeEpFUEUm1209jjPE/f00WNwV6AqOBacAbItK2/KCIdAH+CDyhqmVu8nzgdiAOaI+zmf1VVHWRqsaqamxEhN1MGGOMv/kyWXwa6OrxPtJN85QJ7FDVYiBDRI7gdAy7RKQNsB74D1XdXn6Cqp51X14WkcXAT2uqSFpa2nkRqVvgEOgInK/juQ1VY2uTtafha2xtamztgarbVO0mLL50BLuAniIShdMBPAI8WinPapw7gcUi0hFnqChdRJoBfwXerjwpLCJdVPWsOAuQ7wf211QRVa3zLYGIpFb3iHUwamxtsvY0fI2tTY2tPVC3NtXYEahqiYjMAjbiLB99S1UPiMiLQKqqrnWPjReRgzjLROeqaq6IfAcYBXQQkelukeXLRJeJSAQgwB7gmdpU3BhjjH/49ByBqm4ANlRK+5nHawWedX8887wDvOOlzDG1rawxxhj/C6UnixcFugLXQGNrk7Wn4WtsbWps7YE6tCmowlAbY4zxv1C6IzDGGFMF6wiMMSbEhURHICITReRTETkmIvMCXZ+vS0ROiMg+EdkjIrXfsq0BEJG3RCRbRPZ7pLUXkb+JyFH333aBrGNteGlPooicdq/THhG5J5B1rA0R6SoiKSJyUEQOiMiP3PRgvkbe2hSU10lEWojIThH5xG3Pz930KBHZ4X7frXCX8VdfVmOfI3CD5h3BI2geMM0zaF6wEZETQKyqBu2DMCIyCijCecakn5v2ayBPVV92O+x2qlrlE+cNjZf2JAJFqvpqIOtWF240gC6qultErgfScJ73mU7wXiNvbZpKEF4n9xmsVqpaJCLhwFbgRzirN/+iqstF5HWcaNCvVVdWKNwR+BI0z9QzVd0C5FVKvg9Y6r5eivNLGhS8tCdoqepZVd3tvi4EDuHEGAvma+StTUFJHUXu23D3R4ExQPkDvD5do1DoCHwJmhdsFPhARNJE5KlAV8aPbvQIPXIOuDGQlfGTWW5gxbeCaRjFk4h0AwYCO2gk16hSmyBIr5OIhInIHpzIzn/DCer5uaqWuFl8+r4LhY6gMbpTVQcBdwMz3WGJRsV9SDHYxy1fA3oAA4CzwH8Gtjq1JyKtgXeBOar6heexYL1GVbQpaK+Tqpaq6gCcGHBDcAJ51loodAS+BM0LKqp62v03GyeW05DA1shvstxx3PLx3OwA1+drUdUs9xe1DHiDILtO7rjzu8AyVf2LmxzU16iqNgX7dQJwN/tKAYYDbUWkPGqET993odARVATNc2fPHwHWBrhOdSYirdyJLkSkFTAeHwL2BYm1wOPu68eBNQGsy9dW/oXpeoAguk7uROSbwCFV/S+PQ0F7jby1KVivk4hEiBvuX0Suw1kQcwinQ3jIzebTNWr0q4YA3OVg/82XQfNeCnCV6kxEuuPcBYATK+pPwdgeEfkzzv4VHYEs4AWcKLYrgVuAk8BUVQ2KCVgv7RmNM9ygwAngaY/x9QZNRO4E/g7sA8r3EPl3nDH1YL1G3to0jSC8TiLSH2cyOAznj/qVqvqi+x2xHGefl38C31HVy9WWFQodgTHGGO9CYWjIGGNMNawjMMaYEGcdgTHGhDjrCIwxJsRZR2CMMSHOOgJjjAlx1hEYY0yI+3/Soq6+MnhMlAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anonymous-communication"
      },
      "source": [
        "### Reload model and predictions"
      ],
      "id": "anonymous-communication"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liked-probability"
      },
      "source": [
        "model = keras.models.load_model('models/keras_cnn_model.h5')"
      ],
      "id": "liked-probability",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "center-uncertainty"
      },
      "source": [
        "y_pred = model.predict(X_test)\n"
      ],
      "id": "center-uncertainty",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thirty-evening",
        "outputId": "25661d56-11c5-47b9-9101-21b151d2d621"
      },
      "source": [
        "y_pred"
      ],
      "id": "thirty-evening",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00000000e+00, 1.05146787e-13, 1.02645157e-15, ...,\n",
              "        3.92546781e-14, 1.78437371e-22, 0.00000000e+00],\n",
              "       [9.99999881e-01, 2.78555484e-10, 2.06041625e-11, ...,\n",
              "        1.01030136e-10, 3.05352916e-16, 0.00000000e+00],\n",
              "       [1.64925128e-01, 8.34496096e-02, 2.50445941e-04, ...,\n",
              "        2.51844205e-04, 1.64692141e-02, 1.41670466e-06],\n",
              "       ...,\n",
              "       [1.81562882e-02, 1.42762932e-04, 9.58941382e-05, ...,\n",
              "        1.40276956e-04, 1.96100652e-01, 5.33008526e-14],\n",
              "       [1.00000000e+00, 3.64094016e-19, 2.47879022e-18, ...,\n",
              "        5.21090925e-20, 2.93810443e-26, 0.00000000e+00],\n",
              "       [9.70289528e-01, 2.70125565e-05, 1.50705397e-03, ...,\n",
              "        2.95811356e-03, 3.73489570e-07, 5.42400823e-17]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "competent-billy"
      },
      "source": [
        "predictions = [x.argmax() for x in y_pred]"
      ],
      "id": "competent-billy",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naked-princeton"
      },
      "source": [
        "real = [x.argmax() for x in y_test]"
      ],
      "id": "naked-princeton",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chinese-client"
      },
      "source": [
        "classes_wild = {0: 'empty', 1: 'deer', 2: 'moose', 3: 'squirrel', 4: 'rodent', 5: 'small_mammal', \\\n",
        "                6: 'elk', 7: 'pronghorn_antelope', 8: 'rabbit', 9: 'bighorn_sheep', 10: 'fox', 11: 'coyote', \\\n",
        "                12: 'black_bear', 13: 'raccoon', 14: 'skunk', 15: 'wolf', 16: 'bobcat', 17: 'cat',\\\n",
        "                18: 'dog', 19: 'opossum', 20: 'bison', 21: 'mountain_goat', 22: 'mountain_lion'}\n",
        "keys = [0, 1, 3, 4, 8, 10, 11, 13, 14, 16, 17, 18, 19, 22]\n",
        "labels = [classes_wild[k] for k in keys]"
      ],
      "id": "chinese-client",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "radio-logistics"
      },
      "source": [
        "pred = [classes_wild[x] for x in predictions]\n",
        "true = [classes_wild[x] for x in real]"
      ],
      "id": "radio-logistics",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "growing-traffic",
        "outputId": "be115b87-cf66-4594-9530-6bb164a7f1dd"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(confusion_matrix(y_true=true, y_pred=pred),xticklabels=labels, yticklabels=labels, annot=True, ax=ax)\n"
      ],
      "id": "growing-traffic",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9310029a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jiIiIiEgaxV6IMk5nSM35Xb9m6GfjrKVqZfptcjm3V8qw1k4EJl7WnCvJ9P6XzZ8rydNIa207LmOtveQCLWNMDqAU4O57soqIiIiISKbj+kGZtxljGgE/Ap9ba085nUdERERExFVcep1XRrppB2XW2hEkXCuW2nyLgdu9nUdERERERG5Obr/7ooiIiIiIiKvdtJUyERERERHJAPFxTifI9FQpExERERERcZAqZSIiIiIi4j260UeqVCkTERERERFxkCplIiIiIiLiPfGqlKVGlTIREREREREHaVAmIiIiIiLeY+Mz9nEVxphixphlxphtxpitxpgXPe39jTFRxphwz6NFkte8aYzZbYzZYYxpmqS9madttzHmjSTtdxhj1nvaJxpjsqS2iTQoExERERGRm0Us8Kq19h6gBtDTGHOPZ9rn1toKnsdcAM+0h4GyQDPgG2OMrzHGFxgKNAfuAR5JspxPPMsqCZwAuqUWSoMyERERERHxnvj4jH1chbX2oLX2N8/PfwHbgcCrvKQtMMFae95auw/YDVTzPHZba/daay8AE4C2xhgDNACmeF4/EmiX2ibSoExERERERG4YxphnjTGhSR7PpjBfcaAisN7T1MsYs8kY85MxJr+nLRD4I8nLIj1tKbXfApy01sZe1n5VuvuiF5yLOe90hH/tzrxFnY5wXfaeOuh0BBG5CRmnA1wn63QAEbmhWRuXwf3ZYcCwq81jjMkF/AK8ZK09bYz5FnifhEPi+8Bg4GlvZ71IgzIREREREblpGGP8SRiQjbXWTgWw1h5OMn04MNvzNAooluTlQZ42Umg/BuQzxvh5qmVJ50+RTl8UERERERHvyVx3XzTAj8B2a+2QJO1JTxdrD2zx/DwTeNgYk9UYcwdQCggGQoBSnjstZiHhZiAzrbUWWAY85Hn9E8CM1DaRKmUiIiIiInKzqA08Dmw2xoR72t4i4e6JFUg4fXE/8ByAtXarMWYSsI2EOzf2tJ7zMY0xvYAFgC/wk7V2q2d5fYAJxpgPgDASBoFXZRIGc5Ke/LIEunaj6poyEZFrp2vKRMQpsReiMv0h6J/fZmboYSZbpTaZfptcTqcvioiIiIiIOEiDMhEREREREQfpmjIREREREfGeVG6+IaqUiYiIiIiIOEqVMhERERER8Z74jP3j0W6kSpmIiIiIiIiDVCkTERERERHv0TVlqVKlTERERERExEGqlImIiIiIiPfEq1KWGlXKREREREREHKRBWSa3e+c6wn5bTGjIQtatnQtAhw6t2Bi+lAv//EHlSvd5tf8iAYUZNe075q6exJxVE+n67MPJzletVmVmLBvLnFUTGTPj++vu1z+LP18M/4hFwdOYPH8EgcWKAnBfxbLMWDaWGcvGMnPZOBq3qH/dfaVFUFAAixdOZtPGZWwMX8rzvbplSL/pqWmT+mzdspKIbat5vXdPp+OkKqVtft9997B65UzCflvM9GkjyJ07l8NJr5Q1a1bWrpnNhtBFbAxfyrvvvArAsO8/Y0PoIn7bsIiJE4aRM2cOh5NenY+PDyHBC5gxbeQl7Z8PeY+Tx3c6lOraObHvDx82mKjIjYSFLUl2et26NTn653ZCQxYSGrKQvn1fuu4+s2TJwtix37J922rWrJ7F7bcHAdCw4f2sXzePsN8Ws37dPOrXr33dfV3N8GGDiY7cSHgy6/7yS88ReyGKW27J79UM6clNx86Ujj09uj9JxLbV2vYZLG/ePEycMIwtm1ewedNyalSv7HQk59j4jH240E03KDPG9DfGvOZ0jmvRqHFHqlRtQo2aLQDYujWCjp3+w6pV67zed1xcLAPf/ZwWdTrRqdlTPPZ0R0qUvuOSeXLnyUX/T/vw3y6v0PL+zrzQ7Y00Lz+wWFFGT79yENfxsbacOvkXjau1Z8R34+j9zvMA7IzYzYONutL2gcfo9vDzvPfZW/j6+l7fSqZBbGwsvV8fwH3lH6B2ndZ07/4kd99dyuv9phcfHx+++vJDWrXuwr3lH6Bz53aZPn9K2/z77wbxVt+PqFipEdOnz+O1V7s7HfUK58+fp1GTTlSu0pjKVZrQtEl9qlerxKuv9adylcZUqtyYPw5E0bPHU05HvaoXnn+GiIhdl7RVrnQf+fPncyjRtXNq3x85ahKtWj121XlWrw6mStUmVKnahA8//CLNy7799iAWL5p8RfvTTz3CyROnuPueOnz51XA++qgvAMeOHadd+yepWKkRT3d7iRE/f3ltK3ONRo2aRMtk1j0oKIDGjery+++RXu0/Pbnt2JnSsefXtSE0bf4w+/f/4XTENHPbtk/O50PeY8GCZZS7tx6VKjdm+2XHU5GkbrpBWXowxjh6LV5ExG527tyTIX39efgY2zbtAODs2XPs2bmfwkVvvWSe1h2asXDOMg5GHQbg+NETidPaPNScKQtGMmPZWN777C18fNK2yzVsXo9pE2cDMH/WEmreXw2Af/4+T1xcwt+6yJo1K9ba61vBNDp06Ahh4VsAOHPmLBERuwgMKJIhfaeHalUrsmfPfvbtO0BMTAyTJs2gTeumTse6qpS2eelSd7LS84XE4iWraN++hZMxU3T27DkA/P398PP3x1rLX3+dSZyeLXu2DNt//43AwKK0aN6Qn34an9jm4+PDJwPf5o03P3Aw2bVxat9fvXo9x0+c/FevffTRB/l1zWxCQxbyzdBP0nzcbN26CaNHJwzWfvllDg0eqANAePhWDh5MOD5v3bqD7NmzkSVLln+VLS1WpbDugz/rzxtvfZip9/vLufHYmdyxJzx8q6sGw+DObZ9Unjy5ub9OdX76OeEYGhMTw6lTpx1O5aD4+Ix9uNBNMSgzxvQ1xuw0xqwG7vK0lTDGzDfGbDDGrDLGlPG0FzLG/GKMCfE8anva+xtjRhtj1gCjMyq7tZZ5c8ezft08nul29W9dvS2wWFHuufcuNm7Yckl78RK3kTdfbkZP/56pi0fTrlNLAEqUKk6Ldo15uOXTtH3gMeLi4mjzUPM09VW4yK2Jg7y4uDj+On2G/AXyAnBfpbLMWTWRWSsn8G7vjxMHaRnl9tuDqFC+HOuDwzK03+sREFiEPyKjE59HRh0kwEWDyqTbfNu2nbRpk/DG/FCHVhQLCnA4XfJ8fHwIDVnIwahNLFmykuCQhP3lh+FDiPojnDJ3leR/Q39yOGXKhgwewBtvfkB8kje3nj2eYtbshRw6dMTBZNcmM+/7NWpUZkPoImbNHM0995QGoEyZknTs2Ia69dpRpWoT4uLiePTRB9O0vKTrGhcXx6lTp684Ve3BB1sSFraFCxcupO/KpKJ16yZERR1k06ZtGdrv9crM+09KUjr2uI0bt31Sd9xxG0ePHuPHHz4nJHgB3383iBw5sjsdSzKxG/7ui8aYysDDQAUS1vc3YAMwDPivtXaXMaY68A3QAPgS+Nxau9oYcxuwALjbs7h7gDrW2r8zKn+9B9oTHX2IQoVuYf68CezYsZtVq9dnVPeJcuTMztc/f8pH/QZz9szZS6b5+flR9r67eaJDd7Jly8rEeT8THrqZmnWrUbb83fyyaBQAWbNlS6yiDR0xiKDbA/D396doUBFmLBsLwMhhE5g6ftZVs2z6bSst7+9MiVLF+eR/A1ix5FcunM+YDxg5c+Zg0sThvPLau5dUPcR7Lt/mzzz7Cl8MeZ++b73E7NkLuXAhxumIyYqPj6dK1SbkzZuHXyb/SNmyd7F16w6e+c8r+Pj48OUXH9CpYxtGjprkdNQrtGzRiCNHjvJb2Gbq1a0JQNGihXmoQysaNHrI4XQ3hrCwzZQoWY2zZ8/RrFkDpkz+iXvK1qHBA3WoVPHexGuIs2XPxpE/jwIwefIP3FH8Nvyz+HNbsUBCQxYC8PXXP6RpP7rnntJ89OFbtGj5qPdWLBnZs2fjzT7P06xFxvZ7s0rp2CMZy8/Xl4oV7+XFl94mOCSMIYMH0Of1Xrzbf5DT0Zzh0upVRrrhB2XA/cA0a+05AGPMTCAbUAuYbIy5OF9Wz7+NgHuStOcxxly8k8DMlAZkxphngWcBjG9efHxypkv46OhDAPz55zFmzJhH1aoVMnxQ5ufny9c/f8qsKfNZOGfZFdMPRR/m5PGT/H3uH/4+9w8ha8MoU64UxhimT5zN4A+GXvGank/2BhKqbwO/7s/j7Z67ZPrhQ0coGliYwweP4OvrS+48uThx/NQl8+zZtZ+zZ89RukwJtmzcno5rnDw/Pz8mTxzO+PHTmD59ntf7S0/RUYcuqSgFBRZN3Lcys+S2+Y4de2ju+VBZqtSdtGje0MmIqTp16jTLV6xJuGDd88EoPj6eSZNm8NqrPTLloKxWrSq0btWE5s0akC1bVvLkyc2m8KWcP3+BHdvXAJAjR3Yitq2mzD11HE57dZl130/6pc78+Uv5+quPuOWW/BhjGD1mMv36DbziNR07PgMkVI5//OFzGjXueMn0i+saFXUQX19f8ubNw7FjCV+EBQYWZfLkH3n66RfZu/d3L67ZlUqUKE7x4rfxW+giAIKCihKyfgE1a7fk8OE/MzTLtcqs+09aJHfscRM3b3tIqOxFRh5MrFROnTqH13v3cjiVZGY3xemLyfABTlprKyR53J1kWo0k7YHW2ovvnmeTXxxYa4dZa6tYa6uk14AsR47s5MqVM/Hnxo3qOXJg/eiLd9izcx8/fzc22elL5q2gcvUK+Pr6ki17VspXKseenfv5dWUwTVs3pEDBhNNn8ubLQ0BQ2k49WDp/Je07twKgWeuGrF0dAkDQbQGJN/YICCrCnaWKE/VHdIrLSU/Dhw1me8RuvvhyWIb0l55CQsMpWfIOihcvhr+/P506tWXW7IVOx0pVctu8UKFbADDG8NabL/L9sAw7mzjNChYsQN68eQDIli0bjRrWZefOvZQoUTxxntatmrBjx26HEl5d334DKX5nFUqWrsFjXXqwbNkaChUuS9BtFSlZugYlS9fg3Lm/M/2ADDLvvl+4cKHEn6tWqYCPjw/Hjp1g6bLVPNi+VeJ+nj9/Pm67LTBNy5w9eyGPP54wUOvQoSXLlicMoPPmzcPMGaPo2/cjfl0bms5rkrotWyIICCqfuO9ERh6kavWmmX5ABpl3/0lJcseeHTsy5hr09Oa2bX+5w4f/JDIymtKlSwDQoEEdtm93z11r05u1cRn6cKOboVK2EhhhjPmYhPVtDXwP7DPGdLTWTjYJZbH7rLUbgYXA88AgAGNMBWttuBPBCxcuxJTJPwIJ1aoJE6azYOFy2rZtxpeff0ChQgWYOWMUGzdupUUqd/n6typXL0+7zi2J2Lor8RTDIR9+Q9HAhMHVhJG/sGfXflYuXcusFeOJj7dMHjudXREJbwJffPwtP0/+H8b4EBsby4A+nxAdmfo3XZPHzmDQN++xKHgap06c5uVn3/LkqcCzLzxBbGws8fGWAa8PvKKC5g21a1Xl8S4PsWnztsRTht5+eyDz5i/1et/pIS4ujhdf6sfcOePw9fFhxMiJbNuWud8cUtrmJUveQffuTwIwffpcRoyc6GDK5BUtWpiffvwCX18ffHx8mDJlFnPmLmbFsmnkzpMLYwybNm2jZ683nY56w3Nq3x89eij16takYMEC7NsbynvvfYa/vz8Aw4aPpsODLXn2ua7Excbx99//0KVLDwC2b9/Fu/0/Zd7c8fj4GGJiYnnhhb4cOBCVap8//TyBESO+Yvu21Zw4cZLHPMvs0eMpSpQoTr++L9Ov78sANG/xCH/+ecwr6z4mybrv3xvKgPc+4+cRE7zSl7e57diZ0rGnV8+nee3VHhQpUoiwDYuZN38pz/23t9Nxr8pt2z45L778NqNGfk2WLP7s23eAbs+84nQkycSMm+6C9G8ZY/oCTwBHgAMkXFf2C/AtUBTwByZYa98zxhQEhpJwHZkfsNJa+19jTH/gjLX2s9T688sS6NqNemfeok5HuC57Tx10OoKI3IRM6rNkaq590xIRYi9EZfpD0N8rR2ToYSZ73Scz/Ta53M1QKcNa+yHwYTKTmiUz71GgczLt/dM/mYiIiIjIDU43+kjVzXpNmYiIiIiISKZwU1TKRERERETEIVaVstSoUiYiIiIiIuIgVcpERERERMR7dE1ZqlQpExEREdKWPuIAACAASURBVBERcZAqZSIiIiIi4j26pixVqpSJiIiIiIg4SJUyERERERHxHl1TlipVykRERERERBykSpmIiIiIiHiPrilLlSplIiIiIiIiDlKlTEREREREvEfXlKVKlTIREREREREHqVImIiIiIiLeo0pZqjQok0vsPXXQ6QjXxd/X3bt0TFys0xFE5F+wTgcQERFX0+mLIiIiIiIiDnJ3WUFERERERDI33RI/VaqUiYiIiIiIOEiVMhERERER8R7d6CNVqpSJiIiIiIg4SJUyERERERHxHl1TlipVykRERERERBykSpmIiIiIiHiPrilLlSplIiIiIiIiDlKlTEREREREvEfXlKVKlTIREREREREHqVImIiIiIiLeo2vKUqVKmYiIiIiIiINUKRMREREREe9RpSxVqpS5yPBhg4mO3Eh42BKno6RZcpkH9O/NbxsWERqykHlzxlG0aGGvZoiIWE1IyALWrZvL6tWzrpieJ09upkz5kfXr57FhwyIef7zjdfeZP39eZs8ew+bNy5k9ewz58uUB4OGH2xEcPJ+QkAUsWzaVe++9+7r7Sgs37juXe75XN8LDlrAxfCkvPP+M03FSldw2L1++LGtWzSI0ZCHr1s6lapUKDiZMm9KlSxAasjDxcfxohCu2f1K7d64j7LfFidvdTYKCAli8cDKbNi5jY/hSnu/VzelI1+RGOPY0bVKfrVtWErFtNa/37ul0nGvm5v3f7dve7fklYxlrrdMZMowx5ldrba3reH1xYLa1ttzV5vPLEuiVjXp/neqcOXOWn3/+kgoVG3qji3SXXObcuXPx119nAOjV82nuvrs0PXu9kS79+fteWfyNiFhN7dqtOXbsRLKv6d27J3nz5qZfv4EULFiAjRuXUbx4FWJiYlLt7/77a/D44w/x7LOvXdL+4YdvcuLEST777Ftee607+fLlpV+/gdSoUZmIiF2cPHmaJk3q06/fS9St2y7xdTFxsde4xmnjxn0nqbJl72LsmG+oWaslFy7EMHf2WHr0eoM9e/Y7HS1FyW3zeXPG8eVXw5m/YBnNmzXgtVe707Dx9X8JkFF8fHw4sH8Dteq04sCBKKfjpNnuneuoXrN5iseAzKxIkVspWuRWwsK3kCtXToLXz6fDQ0+zffsup6OliduPPT4+PmzfuopmLR4hMvIg69bOpcvjPVyz/cG9+7/bt31G5o+9EGXSfaHp7O+JAzJ0wJG987uZfptc7qaqlCU3IDPG+F3teWayavV6jp846XSMa5Jc5osDMoCcOXPg9BcD1lpy5crlyZOTEydOEhubMDh6+eXnWL16JsHB8+nX7+U0L7NVq8aMGfMLAGPG/ELr1k0AWLduAydPngYgOPg3AgOLpueqpMiN+05SZcqUIjg4jL///oe4uDhWrlpH+3bNnY51Vcltc2stufPkBiBP3txEHzzsRLR/rWGDOuzd+7urBmRud+jQEcLCtwBw5sxZIiJ2ERhQxOFUaef2Y0+1qhXZs2c/+/YdICYmhkmTZtCmdVOnY90U3L7t3Z5fMp4rBmXGmJzGmDnGmI3GmC3GmM7GmGbGmAhjzG/GmK+MMbM98/Y3xryW5LVbPBUujDFnPP/WN8asMsbMBLYl89zXGDPIGBNijNlkjHku49f6xvb+e33YtyeERx5pT/8Bg7zal7Uwa9YY1qyZzdNPP3LF9O++G0mZMiXZuzeE0NAFvPbaAKy1NGx4PyVKFKdOnTZUr96cihXvpXbtamnq89ZbC3Lo0BEg4UPVrbcWvGKeJ598mAULll/Xut0stm6NoE6d6hQokJ/s2bPRvFkDgoICnI51zV557V0++bgf+/aE8OnAt+nb72OnI12TTp3aMmHidKdjXDNrLfPmjmf9unk80+0xp+P8a7ffHkSF8uVYHxzmdJSbRkBgEf6IjE58Hhl1kAAXDYrBvfu/27e92/NLxsu0VaHLNAOirbUtAYwxeYEtQANgNzDxXyyzElDOWrvPGFP/sufPAqestVWNMVmBNcaYhcDNc66nl739zie8/c4n9Hm9Fz17PMWA9wZ7ra+GDTsQHX2YQoVuYfbsMezYsYc1a4ITpzduXI9Nm7bSrNnD3Hnn7cyZM5Y1a4Jp1KgujRrdz7p1Cefg58qVk5Il72DNmmBWrpxOlixZyJUrJ/nz50ucp1+/gSxevPKKDJcXA+vWrckTT3SmYcMOXlvvG0lExG4GDRrKvLnjOHf2HOEbtxIX576Lhp97tiuv9u7PtGlzeeih1gz/fjBNmz/sdKw08ff3p3WrJq4bSALUe6A90dGHKFToFubPm8COHbtZtXq907GuSc6cOZg0cTivvPbuJWcbiKTmRtj/5QagG32kyhWVMmAz0NgY84kx5n7gDmCftXaXTTj3bcy/WGawtXZfCs+bAF2NMeHAeuAWoNTVFmaMedYYE2qMCY2PP/sv4tycxo2fSvv2LbzaR3R0wilif/55jJkzF1C16qU3V3j88Y7MmDEfgL17f2f//j+4664SGGMYNOgbatRoQY0aLShXrh4jRyaM/+vWbUeNGi3o3r0Pc+YsSpzn4oDsyJGjFClyK5BwTciffx5N7K9cuTJ8++0ndOz4DMePu/e0noz284gJVK/RnAcaduDkyVPs2rXX6UjXrOvjHZk2LWEAP2XKrCv2xcysWbMHCAvbzJEjR1OfOZOJjj4EJBwDZsyY56rtDuDn58fkicMZP34a06fPczrOTSU66hDFklTlgwKLJu5PbuHW/d/t297t+SXjuWJQZq3dSUIlazPwAdDmKrPHcul6ZUthvstHTkmfG+B5a20Fz+MOa+3CVDIOs9ZWsdZW8fHJebVZb3olS96R+HOb1k3ZsWOP1/rKkSM7uXLlTPy5UaO6bN2645J5/vgjivr1awMJpx2WLn0n+/YdYNGiFTzxRCdy5swBQEBAYQoVuiVN/c6Zs5guXRKqYF26dGD27EUAFCsWwIQJ39Ot28vs3r3vaouQy1zc9sWKBdCuXXPGT5jmcKJrF33wMPXq1gSgwQN12OWifeDhzu1ceeri5ceAxo3qXXEMyOyGDxvM9ojdfPHlMKej3HRCQsMpWfIOihcvhr+/P506tWXW7Kt+HMhU3Lz/u33buz1/uouPz9iHC7ni9EVjTABw3Fo7xhhzEugFFDfGlLDW7gGSXii0H2jleV0lEqpq12oB0N0Ys9RaG2OMKQ04fmX7mNFDqVe3JgULFmD/3lAGvPcZP4+Y4HSsq0ouc/PmDShdugTx8fEcOBBFj57pc+fF5Nx6a0EmTkz4IOPn58fEiTNYtGgFzzyTcF79Dz+MZeDArxg2bDAhIQswxtC370COHTvBkiWrKFOmJMuXJ3z4P3v2HE899SJ//nks1X4/++wbxoz5hiee6MyBA1F06dIDgDfffJECBfLzxRfvAxAbG0edOq29seqXcOO+c7nJE4dT4Jb8xMTE8sILfTl16rTTka4quW3+3//2ZsiQ9/Dz8+P8P//QvfvrTsdMkxw5stOoYV269+jjdJRrVrhwIaZM/hEAPz9fJkyYzoKFy50NdQ1q16rK410eYtPmbYSGJHyge/vtgcybv9ThZGnj9mNPXFwcL77Uj7lzxuHr48OIkRPZtm2n07HSzM37v9u3vdvzS8ZzxS3xjTFNgUFAPBADdAcKAl8A54BVQAlrbStjTHZgBhBIwqmHNYHm1tr9xpgz1tpcnmvIXrPWXhy8Xf7ch4SKXGsSqmZ/Au2A/Dh4S3xJXXK3xHcTb90SX0RERG5Mrrgl/pi+GXtL/C4fZvptcjlXDMpSc/mgymkalDlHgzIRERG5mWhQdiU3Dsrc/QlWREREREQyN5de55WRbohBmbV2ObDc4RgiIiIiIiLX7IYYlImIiIiISCZ1A1wu5W2uuCW+iIiIiIjIjUqVMhERERER8R5dU5YqVcpEREREREQcpEqZiIiIiIh4jyplqVKlTERERERExEGqlImIiIiIiPdYVcpSo0qZiIiIiIiIg1QpExERERERr7Hx+jtlqVGlTERERERExEEalImIiIiIiDhIpy/KDSUmLtbpCNclT9YcTke4LqfPn3M6goiIiGQ2uiV+qlQpExERERERcZAqZSIiIiIi4j26JX6qVCkTERERERFxkCplIiIiIiLiPbolfqpUKRMREREREXGQKmUiIiIiIuI9uvtiqlQpExERERERcZAqZSIiIiIi4j2qlKVKlTIREREREREHqVImIiIiIiLeY3X3xdSoUiYiIiIiIuIgVcpERERERMR7dE1ZqlQpExERERERcZAqZSIiIiIi4j3xuqYsNaqUiYiIiIiIOEiDMpfImjUra9fMZkPoIjaGL+Xdd151OtI1a9qkPlu3rCRi22pe793T6TjXJCgogMULJ7Np4zI2hi/l+V7dMqTfr7/5mB1717Fm/Zxkpz/UqQ2r1s5i9brZzF88kbLlylx3n1myZOHHEV8QGr6YRUunUOy2QAAqVb6PFWtmsmLNTFb+OpOWrRtfd19XM3zYYKIjNxIetiSx7Z23X+H3faGEhiwkNGQhzZs18GqG6/HiC/9hY/hSwsOWMGb0ULJmzcrypVMTsx/Yv4FfpvzodMw0cfPvLrg7v9uP/cn9HruNm/cft29/N+d36nODuNdNNSgzxowwxjz0L19bwRjTIr0zpdX58+dp1KQTlas0pnKVJjRtUp/q1So5Feea+fj48NWXH9KqdRfuLf8AnTu34+67SzkdK81iY2Pp/foA7iv/ALXrtKZ79yczJP+4sVPp2P7pFKcf+P0PWjV/jDo1WvHZJ0P54qsP0rzsYrcFMnPumCvau3R9iJMnT1OlQiO+Hfoz/d/rDcD2bTtpULc99Wq3oWP7bgz58n18fX2vfaXSaNSoSbRs9dgV7V9+NZwqVZtQpWoT5s1f6rX+r0dAQBF69Xya6jVaUKFiQ3x9fencqS31GzyYmH3d+g1Mmz7P6aipcvvvrtvzu/3Yn9LvsVu4ff9x+/Z3c36nPjdkWjY+Yx8udEMMykwCb69LBcCxQRnA2bPnAPD398PP3x/ror/5UK1qRfbs2c++fQeIiYlh0qQZtGnd1OlYaXbo0BHCwrcAcObMWSIidhEYUMTr/a5dE8KJE6dSnB68PoxTJ08DEBISTtHAwonTOnZuw6JlU1ixZiZDvnwfH5+0/Yq0aNmICeOmAjBj+nzq1q8JwN9//0NcXBwAWbNl9fr+t2r1eo6fOOnVPrzJz8+P7Nmz4evrS47s2Tl48FDitNy5c/FA/drMmDHfwYRp4/bfXbfnB3cf+93+e+z2/cft29/N+Z363CDu5dpBmTGmuDFmhzFmFLAF+NEYs8UYs9kY09kzjzHG/M8z32Lg1iSvr2yMWWGM2WCMWWCMKeppX26M+cQYE2yM2WmMud8YkwV4D+hsjAm/uPyM5uPjQ2jIQg5GbWLJkpUEh4Q5EeNfCQgswh+R0YnPI6MOEuDSg9PttwdRoXw51gdnru3/eNeOLFm0EoDSd5WgfYeWNG/8MPVqtyEuLo6OndukaTlFAwoTFZkwgIiLi+P0qTMUuCU/AJWrlOfX4LmsXjebV196J3GQlpF6dH+K3zYsYviwweTLlzfD+0+L6OhDDPn8O/btCSbyQBinTp9m0eKVidPbtm3G0mVr+OuvMw6mTBu3/+66PT+4+9jvdjfC/iPOy6yfGzJUvM3Yhwu5dlDmUQr4BngHCALKA42AQZ5BVnvgLuAeoCtQC8AY4w98DTxkra0M/AR8mGS5ftbaasBLwLvW2guePiZaaytYaydeHsQY86wxJtQYExoff9YrKxsfH0+Vqk24/Y4qVK1SkbJl7/JKP5KynDlzMGnicF557d1M9YG6zv3V6dK1I/3fGQRA3Xo1KV+hLEtWTGXFmpnUrV+T4sWLATBq3FBWrJnJpF9+oELFconXiT3apUOq/WwI3Uitai1oVL8DL73yHFmzZvHqel3uu+9HUbpMLSpXacKhQ0cY9Ok7Gdp/WuXLl5c2rZtSsnQNit1eiZw5c/Doow8mTn+4U1smTJzuYEJxEx37Rdwrs35uuJkZY4oZY5YZY7YZY7YaY170tBcwxiwyxuzy/Jvf026MMV8ZY3YbYzYZYyolWdYTnvl3GWOeSNJe2VMo2u15rUktl9tvif+7tXadMeZzYLy1Ng44bIxZAVQF6iZpjzbGXLwA5S6gHLDIs418gYNJljvV8+8GoHhaglhrhwHDAPyyBHp1iH7q1GmWr1iTcPHx1h3e7CrdREcdolhQQOLzoMCiREcfusorMh8/Pz8mTxzO+PHTmJ6JrgW6p+xdfPm/j+jUoRsnjiec5mGMYcK4abzff/AV83d9NOFC9WK3BTL0u09o06LLJdMPRh8mMKgI0dGH8PX1JU/eXBw/duKSeXbu2MPZs+e4+57ShIdt8dKaXenIkaOJP//w41hmTB+ZYX1fi4YN72ff/gMcPXocgGnT51GzRhXGjZvKLbfkp2rVinTo+IzDKdPG7b+7bs+flBuP/W53I+0/kvEy6+cGJ9jM9cejY4FXrbW/GWNyAxuMMYuAJ4El1tqBxpg3gDeAPkBzEgpBpYDqwLdAdWNMAeBdoApgPcuZaa094ZnnP8B6YC7QDLjqTuD2Stm/LUkZYKun6lXBWnuvtbZJkunnPf/GkUkGrgULFiBv3jwAZMuWjUYN67Jjxx6HU6VdSGg4JUveQfHixfD396dTp7bMmr3Q6VjXZPiwwWyP2M0XXw5zOkqiwKCijBo7lO7Pvsae3fsT21cuX0ubts0oWLAAAPny5yWoWEAKS7nUvLlLeNhT1WnbrhmrVqwD4LbbgxJv7BFULIBSpe/kwIGodFyb1BUpkngGMu3aNs+0H0z/OBBF9eqVyJ49GwANHqhDRMQuADo82Io5cxdz/vz5qy0i03D7767b87v92O92bt9/xFmZ8XODgLX2oLX2N8/PfwHbgUCgLXDx296RQDvPz22BUTbBOiCf54y8psAia+1xz0BsEdDMMy2PtXadTbgIeFSSZaUoUww40sEq4DljzEigAAkVst4krN/F9luBB4BxwA6gkDGmprV2red0xtLW2q1X6eMvILc3V+JqihYtzE8/foGvrw8+Pj5MmTKLOXMXOxXnmsXFxfHiS/2YO2ccvj4+jBg5kW3bdjodK81q16rK410eYtPmbYSGJLwhv/32QK/f/W/4T59T+/5q3HJLfrZErGLgR1/i5+cPwIifxvP6G70oUCAfg4YMABLu9tSw3oPs2LGbj97/nF9mjMDHxxATE8vrrw4g8o/oq3UHwJhRk/lu+GeEhi/mxImTPPPUywDUqFmZl155jpiYWOLj4+n9Sv8rKmjpaczoodSrW5OCBQuwf28oA977jHr1alG+/D1Ya/n990i69+jjtf6vR3BIGFOnziEkeAGxsbGEh29l+A9jAejcqQ2fDhrqcMK0c/vvrtvzu/3Yn9zv8c8jJjgdK83cvv+4ffu7Ob9TnxsyrQy+zssY8yzwbJKmYZ6z2i6frzhQkYSKVmFr7cUz5w4BF++eFgj8keRlkZ62q7VHJtN+9cxuuotTUp6NONtaW85znuanJJQXLfCBtXaip/1roDFwAIgBfrLWTjHGVAC+AvKSMHj7wlo73BizHHjNWhtqjCkIhFpri3tKlAsAf+Dj5K4ru8jbpy/KjStP1hxOR7gup8+fczqCiIjITSX2QlSq1ys57eyHXTP0s3HOvqNS3SbGmFzACuBDa+1UY8xJa22+JNNPWGvzG2NmAwOttas97UtIOK2xPpDNWvuBp/1t4G9guWf+Rp72+4E+1tpWV8vj2kqZtXY/CdeF4SkN9vY8ks5jgV4pvD6chIra5e31k/x8FM81Zdba4yRcpyYiIiIiImmVyf52mOcsuV+Asdbai/eSOGyMKWqtPeg5BfGIpz0KKJbk5UGetigSBmZJ25d72oOSmf+q3H5NmYiIiIiISJp4zqT7EdhurR2SZNJM4OIdFJ8AZiRp7+q5C2MN4JTnNMcFQBNjTH7PnRqbAAs8004bY2p4+uqaZFkpcm2lTEREREREXCBz/e2w2sDjwGZjTLin7S1gIDDJGNMN+B3o5Jk2F2gB7AbOAU9Bwll0xpj3gRDPfO95zqwD6AGMALKTcNfFVG+/qUGZiIiIiIjcFDzXhqV0zVnDZOa3QM8UlvUTCX/v+PL2UDyXWaWVBmUiIiIiIuI9mevvlGVKuqZMRERERETEQaqUiYiIiIiI92Sua8oyJVXKREREREREHKRBmYiIiIiIiIN0+qKIiIiIiHhPJvvj0ZmRKmUiIiIiIiIOUqVMRERERES8Rzf6SJUqZSIiIiIiIg5SpUxERERERLzG6o9Hp0qDMpFM5PT5c05HuC55suZwOsJ1cfv2FxEREXfSoExERERERLxH15SlSteUiYiIiIiIOEiVMhERERER8R5VylKlSpmIiIiIiIiDVCkTERERERHvsbr7YmpUKRMREREREXGQKmUiIiIiIuI9uqYsVaqUiYiIiIiIOEiVMhERERER8RqrSlmqVCkTERERERFxkAZlIiIiIiIiDtLpiyIiIiIi4j06fTFVqpSJiIiIiIg4SJUyERERERHxnnj98ejUqFImIiIiIiLiIA3KMrHSpUsQGrIw8XH8aAQvPP8M+fPnY/7c8Wzfupr5c8eTL19ep6OmKG/ePEycMIwtm1ewedNyalSvTIcOrdgYvpQL//xB5Ur3OR0xRcOHDSY6ciPhYUsS29ySHZLPP6B/b37bsIjQkIXMmzOOokULezXD1998zI6961izfk6y0x/q1IZVa2exet1s5i+eSNlyZa67zyxZsvDjiC8IDV/MoqVTKHZbIACVKt/HijUzWbFmJit/nUnL1o2vu6+reb5XN8LDlrAxfCkvPP9MYnvPHk+xZfMKNoYvZeDHfb2a4Xokt/+46dhzUVBQAIsXTmbTxmVsDF/K8726OR0pVclte3D3vgPuyX+5pk3qs3XLSiK2reb13j2djnNVKe3v5cuXZc2qWYSGLGTd2rlUrVLB4aSpy5o1K2vXzGZD6CI2hi/l3XdedTrSNUnpM9xNK95m7MOFjLXuDP5vGGP2A1WstUcvax8BzLbWTrmsvQrQ1Vr7gjGmPnDBWvtrav34ZQlM943q4+PDgf0bqFWnFT26P8nx4yf5dNBQXu/dk/z58/LmWx+ld5fp4qcfv2D16vX89PN4/P39yZEjO0WL3kp8vOXboQN5vc/7bPhtk9Mxk3V/neqcOXOWn3/+kgoVGwJQpkxJV2SH5PPnzp2Lv/46A0Cvnk9z992l6dnrjXTrM0/WHJc8r1m7KmfPnOXbYYOoXb3lFfNXq16RHTv2cOrkaRo1rkufN1+gcYOH0tRXsdsCGfrdJ7Rp0eWS9qefeZSy5crw6kvv8GCHlrRs3ZhuT75E9uzZuHAhhri4OAoXLsTKtbO4p1Rt4uLiEl97+vy5f7HWVypb9i7GjvmGmrVacuFCDHNnj6VHrzcoFhTAm2+8QOu2Xblw4QKFCt3Cn38eS5c+01ty+8/Aj/u65thzUZEit1K0yK2EhW8hV66cBK+fT4eHnmb79l1OR0tRctu+fr1art533JQ/KR8fH7ZvXUWzFo8QGXmQdWvn0uXxHpl2/0lpfx/y2QC+/Go48xcso3mzBrz2ancaNu7odNxU5cyZg7Nnz+Hn58fK5dN4+ZV3WR/8m9OxrlnSz3AHDkSl+/JjL0SZdF9oOvurR/MMHXDk/mZept8ml7vhKmUmQbqsl7U21Fr7gudpfaBWeiz332jYoA579/7OgQNRtG7dlFGjJwMwavRk2rRp5lSsq8qTJzf316nOTz+PByAmJoZTp04TEbGbnTv3OJwudatWr+f4iZOXtLklOySf/+KADBLe7Lz9pczaNSGcOHEqxenB68M4dfI0ACEh4RQN/P/KXcfObVi0bAor1sxkyJfv4+OTtl/rFi0bMWHcVABmTJ9P3fo1Afj7738SB2BZs2X16rqXKVOK4OCwxD5XrlpH+3bNee65rnw6aCgXLlwAyNQfSpPbf9xy7Enq0KEjhIVvAeDMmbNEROwiMKCIw6muLrlt7/Z9x035k6pWtSJ79uxn374DxMTEMGnSDNq0bup0rBSltL9ba8mdJzcAefLmJvrgYSdjptnZswlflPn7++Hn7+/19yxvSfoZ7qalSlmqbohBmTGmuDFmhzFmFLAF+NEYE2qM2WqMGXDZ7K8bYzYbY4KNMSWTtDfyvGanMaaVZ7n1jTGzjTHFgf8CLxtjwo0x92fEeiXVqVNbJkycDkDhWwty6NARIOEAXPjWghkdJ03uuOM2jh49xo8/fE5I8AK+/24QOXJkdzrWTe/99/qwb08IjzzSnv4DBjkdJ9HjXTuyZNFKAErfVYL2HVrSvPHD1Kvdhri4ODp2bpOm5RQNKExU5CEA4uLiOH3qDAVuyQ9A5Srl+TV4LqvXzebVl965pEqWnrZujaBOneoUKJCf7Nmz0bxZA4KCAihV6k7q1KnGr6tnsXTxFKpULu+V/r3FLceelNx+exAVypdjfXCY01Gumdv3HbfmDwgswh+R0YnPI6MOEpDJB/UXJd3fX3ntXT75uB/79oTw6cC36dvvY6fjpYmPjw+hIQs5GLWJJUtWEhzivt9duPQznEhKbohBmUcp4BtrbVngVWttFeA+oJ4xJunFP6estfcC/wO+SNJeHKgGtAS+M8ZkuzjBWrsf+A743FpbwVq76vLOjTHPegZ1ofHxZ9N1xfz9/WndqglTfpmd7PTM+s2Rn68vFSvey/ffj6JqtaacPXuOPq/3cjrWTe/tdz7hjhJVGT9+Gj17POV0HADq3F+dLl070v+dhEHi/7F33+FRFW0fx7+TRhNClRYEpKhYaKGjhN6bVJVqQzpiAR6wYFcERR98kN6kSyf03gKJJJTQi2IKvShNUs77x4a8ARISIJuTxd/nuvYie/bsGSIHQQAAIABJREFUzn2GmbM7e5+ZfaFGFUqXeZo1G+axYcsiXvCrQpEihQCYMn0UG7YsYvav4yhT9pn4eWIvd2iVbDm/Be2iasVG1PFrRb/+3ciQwcspx3PgwBGGDRvFMv/p+C/5hZBdocTExOLh4U6OHNmpWr0pAwZ+xozpo51SflpJr+eexGTJkpnZs8bS/92PbskYuwpXbzuuHr+rub29d3uzE++89zFFi1XgnfeGMvbn4XaHmCKxsbH4VqhH4aK+VPAty9NPP2F3SPcsuc9w/xaWZaXpzRU9TIOyPyzLCoj7u60xZicQDDwNlEqw34wE/1ZJsH22ZVmxlmUdBo4B97TigGVZYyzL8rUsy9fNLcv9HUESGjSoSXDwHk6fdkyFO3X6LPnyPQo4rh8/nU4vAwkLjyQsLDL+m61585ZStsyzNkclN02fMY+WLRvZHQalnn6Ckf/9glfav8WF845LnowxzJw+nxrVmlGjWjMqlavP11/+CECnl3tSo1oz2rZ6nZDgvfH7TJ/2KwCREaco6OP4Jtvd3Z1s3o9w/tyFW8o8dPAoV65c5alSJZ12XBMnzaRS5YbUrN2KixcvcfjwMcLDIlmwYBkAgUEhxMbGkjt3TqfFkNpc5dxzOw8PD+bMGsuMGfPj69/VuHrbcdX4I8JPUsinQPx9n4L5iYg4aWNEyUusvXfq2Ib58/0BmDt3MRUqpP+FPhK6dOkv1m/YQv16fnaHcs9u/wwnkpSHaVB2BcAYUxR4F6htWdZzwFIgY4L9rBT8ndh927Rv1+KWtPeSxSvp1NExQbdTxzYsXrzCrtDu6tSpM4SFRVCyZDEAatWqzv79h2yO6t+tePGi8X83a1qfgwftnR9X0Cc/U34ZRfc33+Xokd/jt29cv41mzRvEf2jLnsMbn0IFkniVWy3zX0P7l18EoHmLBmza4Piu5rHCPri7uwPgU6gAJUo+7tTr+/PkyQVAoUIFaNGiITNmzmfhohX4+TmmppYo8TheXl6cPXveaTGkNlc599xu7Jjh7D9whO9HjrE7lPvm6m3HVeMPDAqhePGiFClSCE9PT9q2bc7iJSvtDuuuEmvvEZGnqPGC43voWjWrc/jIcbvCS7HcuXPi7Z0NgIwZM1Kn9gu2v2fdj9s/w/1raU5Zsh7GH4/OhmOAdskYkxdoCKxP8Hg74Ku4f7cl2N7GGDMZKAo8DhwEKid4/O+4105TmTNnok7tF+jeY0D8tq+HjWLm9NF07fISJ06E0f7lt9I6rBTr+/YHTJn8I15enhw/foLXXu9P8+YNGPndZ+TJk5NFC6ewa1cojZq8Yneod5g2dRQ1XqhC7tw5+f1YEEM/+ZbzFy66ROyQePwNG9aiZMlixMbGcuJEOD16pt7Ki4kZO+E7qj1fkVy5crD3wCa++mIkHh6eAEyaMIP3B/YiZ87sDBvhmPoZHR1N7RovcvDgEb749Dt+XTgJNzdDVFQ0778zlLA/I+5WnOO4p8xh9NhvCQpZzYULF3m969sAVK5Snn79uxEVFU1sbCzv9f/4jgxaapozayw5c+UgKiqaPn0Gc+nSX0ycNJNxY4cTEryGGzeiePW1fk4r/0El1n5c6dxzU7WqFejYoTW79+wjKNDxYfqDD75i2fK1NkeWtMTq3tXbjivFn1BMTAx9+w3Bf+l03N3cmDR5Fvv2pd8vF5Nq72+99R4jRnyCh4cH/1y/Tvfu79scafLy58/LhPHf4+7uhpubG3PnLmap/2q7w7oniX2GE0nKQ7EkftxCHEssy3om7v4kHCsl/glcAhZZljUpbkn8WTgGav8AL1mWdSRu/+uAL46BV3/LspbELYP/rmVZTYwxJYG5QCzQO7F5ZTc5Y0l8EVdw+5L4ria1lsQXERFJK66wJP5fr9VN08/G2cavSvd1cruHYlCW3mhQJv9WGpSJiIikLQ3K7uSKg7KH8fJFERERERFJJywXneeVlh6mhT5ERERERERcjgZlIiIiIiIiNtLliyIiIiIi4jy6fDFZypSJiIiIiIjYSJkyERERERFxnli7A0j/lCkTERERERGxkTJlIiIiIiLiNFoSP3nKlImIiIiIiNhImTIREREREXEeZcqSpUyZiIiIiIiIjZQpExERERER59Hqi8lSpkxERERERMRGypSJSKr565+rdofwQK5FbLI7hPuWqcDzdocgIiKSKK2+mDxlykRERERERGykTJmIiIiIiDiP5pQlS5kyERERERERGylTJiIiIiIiTqM5ZclTpkxERERERMRGGpSJiIiIiIjYSJcvioiIiIiI82ihj2QpUyYiIiIiImIjZcpERERERMRpLGXKkqVMmYiIiIiIiI2UKRMREREREedRpixZypSJiIiIiIjYSJkyERERERFxGs0pS54yZSIiIiIiIjZSpkxERERERJxHmbJkKVOWzh05FEDwztUEBa4kYJt//PaePbqyd88GdoWs5asvB9sY4d317fMGu0LWEhK8hmlTR5EhQwbGj/uOwwe3ERS4kqDAlZQu/bTdYSbKx6cAq1fOYfeudewKWUvvXq8B8Nxzpdi8cRHBO1ezYP4ksmZ9xOZIE1eyZLH4Og4KXMn5swfo0/t1Spd+mi2bFse3qQq+ZewONUne3tmYNXMMe/dsYM/u9VSuVD7N6j/y1Bm69hpAs1fepPkr3Zg6e8Ed+0z4ZS6tOvekVeeetOjwFs8935hLf/39QOXeuHGDdz74koZtX+WlN/oRHnnq1rhOnqZCnZZMnD73gcpJztgxw4kI20VI8Jr4bV9/OYS9ezaw87dVzJ0zDm/vbE6N4X5lyJCBbVuW8FvQKnaFrOWjD9+55fHvRnzCxfOHbIouZRJr+67Sd5M6d374QX/+OB4Uf05q2KCWzZGmTP16foTu3ciBfZt5/72edoeTrMTajqv03aTazk1v9+tG9I1wcuXKYVOEKePm5kbgjhUsnD8ZgCmTfyR070ZCgtcwdsxwPDyUE5E7Gcuy7I7BVsaYPkB3YKdlWa+kxmt6eBVMtUo9ciiASlUacu7chfhtfjWqMmhgH5o278SNGzfIkycXZ86cS60iU02BAvnYsG4+z5auyfXr15kxfTTLlq2lRo0qLPVfzbx5S+0O8a7y5XuU/PkeJThkL488koUd25fTqvWrTBj/PQMGfMrGTQF06dyOokUf46OPh9kd7l25ublx4vffqFq9CT//bxgjfxjL8hXraNigFu++053addvYHWKiJoz/ns2btzNh4gw8PT3JnDkTy5fNcFr9X4vYFP/3mbPnOXPuPKWeKM6VK1dp+1offvjyA4oVLZzoc9dvDmDKrAVM+PGrFJUVHnmKwZ8PZ9J/v7ll+8x5Szh45Dgfvd8b/9XrWbNhG8M/HRT/+NuDP8MYw7OlnqDry63jt2cq8Py9HGqynq9eicuXrzBx4kjKlK0NQN06L7B23RZiYmL48ov/ADDoP1+karmpJUuWzFy5chUPDw82rp/P2/0/YvuOnZQv9xy9e79Oi+YNyJ6zpN1hJimxtj9z+miX6LtJnTvbtG7K5ctXGPHdz3aHmGJubm7sD91Eg0YvERYWScA2fzp07MH+/YftDi1JibWdihXKuETfTart7N9/GB+fAowZPYwnnihOxcoNbvlclN706/sm5cs/R7asWWnesjMNG9Ri2fK1AEybOopNm7bz85gpqVZe9I1wk2ov5iRn6tZI0wFHnlUb0n2d3E6ZMugB1E2tAVla6NatE98MG8WNGzcA0uWA7CYPDw8yZcqIu7s7mTNlIjLypN0hpdjJk6cJDtkLwOXLVzhw4DAFC+SjZInH2bgpAIDVazbRsmUjO8NMkdq1qnPs2B+cOBGOZVlkzZYVgGzeWYm4LROTXmTLlpXnq1diwsQZAERFRXHp0l9pVv95cuek1BPFAccH/McLF+LUXfqa/+oNNKpbI/7+4hVraf96X1p17snQb34gJiYmReWu3bSN5o3qAFDP73m2/xbCzS/P1mzcSsH8+ZIcGKamTZu3c/7CxVu2rVq9Mf44ArbvpGDB/E6P435duXIVAE9PDzw8PbEsCzc3N77+6gMGDvrM5ujuLqm27yp9N6lzpyuqWKEsR4/+zvHjJ4iKimL27IU0a1rf7rCSlFTbcZW+e7e2M/zbjxn4n89J78mEggXz06hhbSZMmBG/7eaADCAwMAQfn/RZ/2Kvf/WgzBgzGngcWGaMeccYs8AYs9sYE2CMec4Y42GMCTTG+MXt/6Ux5vO0jNGyLJb5z2B7wDJef80xbixR4nGqV6/I1s2LWbt6Lr7lS6dlSCkWEXGSEd+N5vjRHYSdCObSX443BoBPPxnAzt9WMXzYx3h5edkcafIKF/ahTOln2L4jmH37DtGsmeNNuXWrJhTyKWBzdMlr27Y5M2c5Lr/r/+5HfP3lEI4fDeSbrz5g8JAvbY4ucUWLPsbZs+cYP+47Anes4OfRw8icOZMt9R8eeYr9h4/y3NNPJPr4tevX2RwQRF2/6gAc/f0Ey9dsYOro4fw6eRRubm4sWbkuRWWdPnOOfI/mBsDDw51HsmTm4qW/uHr1GhOmzaHHq+nj+6OuXdqzfEXKjskObm5uBAWuJDJ8N2vWbGRHYDA9e3Rl8ZKVnDx52u7w7iqptu8qfTehhOdOgB7du7Lzt1WMHTOc7Nm9bY4ueQUK5uPPsIj4+2HhkRRIxwPMpNpOQum9796UsO00bVqP8PBIdu/eZ3dYyRoxfCgDB31GbOydk6g8PDx45ZVWrHCB+k9tVmza3lzRv3pQZlnWW0AEUBMoAgRblvUc8B9gimVZ0UAX4H/GmDpAA2BoYq9ljHnTGBNkjAmKjb2SajHWqNmSipUa0KRpB7p378Lz1Svh4eFOjhzZqVq9KQMGfsaM6aNTrbzUlD27N82a1qd4ycoUKlyOLFky8/LLLzJ4yJc8/cwLVK7SmBw5s/P+ez3sDvWusmTJzOxZY+n/7kf8/fdlXn+zP927dWZ7wDKyZs3CjRtRdod4V56enjRtUo+5vy4BoNubnXjnvY8pWqwC77w3lLE/D7c5wsR5uLtTtuyz/PzzFCpUrM+VK1cZ8H6vNK//q1ev8fbgzxjQpxuPZMmS6D7rN2+n7HOl8I7LYmwPCmHfgSO0f82RKdseFEJYhCNL3GfQJ7Tq3JPu735A6IHD8XPS5i9dedc4Rk2YRsd2Le/4gGWHQQP7EB0dzfTp8+wOJUmxsbH4VqhH4aK+VPAty/PVK9G6VRP+O2qC3aElK6m27yp996bbz52jf55CySerUt63HidPnmbYNx/aHeJDJ6m2c5Mr9F24te1ER0czaEBvPh76rd1hJatxozqcPn2WncF7En38vz9+waZN29m8ZUcaRyauQDMN/191oBWAZVlrjTG5jDHZLMsKNcZMBZYAVSzLupHYky3LGgOMgdSdUxYR90HuzJlzLFy4jAoVyhAeFsmCBcsACAwKITY2lty5c3L27PnUKjZV1K79PMd/PxEf1/wFy6hS2Tf+zeDGjRtMnjyL/m+/ZWeYd+Xh4cGcWWOZMWN+fJ0fPHiUho1fBhxZy0YNa9sZYrIaNKhJcPAeTp8+C0Cnjm14u7/jw9DcuYsZMzp9zocLC48kLCySHYGOb9jnzVvK++/14qOPh6VZ/UdFR9Nv8Gc0rleTun7Vktxv2ZoNNKrjF3/fsiyaNazD29273rHvD1866j6pOWWP5snFydNnyfdoHqKjY7h85SrZvbOxJ/Qgq9ZtZsRP4/n78hWMMWTw8uLl1s1S52BTqFPHtjRuVIe69dumabn369Klv1i/YQt+flUpVqwIB/dvASBz5kwc2LeZJ0tVtznCOyXV9qtVq+ASfRcSP3fePAcBjBv/CwsXTLYrvBSLCD95Szbep2D++Pfl9CiptgOu03dvbzvPPPMkRYo8xs6gVQD4+OQncPsKqlRrzKlTZ2yO9lZVq/rStEk9GjaoRcaMGciWLSuTJ/1A5y59+GDI2+TJk4vuPV63O0xbuGr2Ki39qzNl9+BZ4CLwaFoWmjlzJh55JEv833Xr1CA09CALF63Az68q4PhQ6uXlle4GZAB/nginUqVyZMqUEYBaNatz4MBh8uX7/2ps1qwBofsO2BVissaOGc7+A0f4fuSY+G158uQCwBjDfwb15ecxU+0KL0Xat2sRf+kiQETkKWq8UAVw/J8cPnLcrtDu6tSpM4SFRVCyZDEAatWqzv79h9Ks/i3L4sMvv+fxwoXo3P7FJPf7+/IVgoL3UPP5KvHbKvuWYdX6zZyLm5N16a+/iTiZsvk/NatXZqH/agBWrt9EpfKlMcYw5X/fsvLXyaz8dTId2rbgjU7t0nxAVr+eH+++250WL3bh2rXraVr2vcidO2f86nIZM2akTu0X2LlzDz6PlaV4ycoUL1mZq1evpcsBGSTd9l2l70Li586E5/4WzRsSGnrQjtDuSWBQCMWLF6VIkUJ4enrStm1zFi+5e1bbTkm1HVfpu3Bn29m79wAFfErH992wsEgqVKqf7gZkAIOHfEWRx30pXrIyr3Towbp1W+jcpQ+vdn2JenX9eKVDz3Q/J07so0zZ/9sEvAJ8GjeH7KxlWX8ZY14EcgIvAEuMMRUty7p4l9dJNXnz5mHunPGAY27JzJkLWLFyPZ6enowbO5yQ4DXcuBHFq6/1S4tw7tmOwGDmzVtK4I4VREdHExISythxv7B08TRy58mJMYZdu0Lp0XOg3aEmqlrVCnTs0Jrde/YRFOh4E/7gg68oXrwo3bt3AWDBAn8mTZ5lY5R3lzlzJurUfoHuPQbEb3vrrfcYMeITPDw8+Of6dbp3f9/GCO+u79sfMGXyj3h5eXL8+Alee70/HTu0TpP6D94dyuLlayhRrAitOjuWwe7brTORcR8E2rVsDMCaDVupWrEcmeO+fAAoVrQwvd/oxJv9BhNrxeLp4cHg/j0okC9vsuW+2KQ+gz4dRsO2r+KdLSvDhtrTP6ZNHUWNF6qQO3dOfj8WxNBPvmXA+73IkCEDy5fNBGD79p307JX++m/+/HmZMP573N3dcHNzY+7cxSyNG+i6isTa/qLFK1yi7yZ17mzXrgWlS5fCsiz++CPslvNSehUTE0PffkPwXzoddzc3Jk2exb596fvnFBJrOwFbl7pE302q7SRcKMMV/TTqK/74I4zNmxYBjveuzz7/3uaoJL3RkvjG/A744vhZuwk4Fv64CryJY77ZVqC2ZVl/xi2fX96yrM53e83UvHxRRNJOwiXxXU1qL4kvIiKuwRWWxD/l55emn43zrl+f7uvkdv/6TJllWUUS3G2RyC7xP2RjWdYPTg9IRERERET+Vf71gzIREREREXEeLfSRPC30ISIiIiIiYiNlykRERERExGmsWJeb4pXmlCkTERERERGxkTJlIiIiIiLiNJpTljxlykRERERERGykTJmIiIiIiDiNZWlOWXKUKRMREREREbGRMmUiIiIiIuI0mlOWPGXKREREREREbKRMmYiIiIiIOI1+pyx5ypSJiIiIiIjYSJkyERERERFxGsuyO4L0T4MyuYWrJ5fV5+VBZCrwvN0h3LeMHl52h/BArkffsDsEERER2+jyRRERERERERspUyYiIiIiIk6jhT6Sp0yZiIiIiIiIjTQoExERERERp7FiTZrekmOMmWCMOW2M2Ztg28fGmHBjTEjcrVGCxwYZY44YYw4aY+on2N4gbtsRY8zABNuLGmO2x22fZYxJduK3BmUiIiIiIvJvMglokMj27yzLKhN38wcwxpQC2gNPxz3nJ2OMuzHGHRgFNARKAS/F7QvwddxrFQcuAK8lF5AGZSIiIiIi4jSWlba35OOxNgLnUxh+c2CmZVn/WJZ1HDgCVIy7HbEs65hlWTeAmUBzY4wBagFz454/GWiRXCEalImIiIiIyEPDGPOmMSYowe3NFD61lzFmd9zljTnithUE/kywT1jctqS25wIuWpYVfdv2u9LqiyIiIiIi4jRpvfqiZVljgDH3+LT/AZ/i+NnbT4HhwKupHFqSNCgTEREREZF/NcuyTt382xgzFlgSdzccKJRgV5+4bSSx/RyQ3RjjEZctS7h/knT5ooiIiIiIOI1lmTS93Q9jTP4Ed1sCN1dmXAS0N8ZkMMYUBUoAO4BAoETcSoteOBYDWWRZlgWsA1rHPb8zsDC58pUpExERERGRfw1jzAzAD8htjAkDPgL8jDFlcFy++DvQDcCyrFBjzGxgHxAN9LQsKybudXoBKwB3YIJlWaFxRQwAZhpjPgOCgfHJxmSlZIkSuSceXgVdtlJd/ffWXbbiRR5QRo9kfwIlXbsefcPuEEREXFL0jfB0//HtSKn6afoRrfi+Fem+Tm6nyxdFRERERERspMsXRURERETEaWLvc57Xv4kyZSIiIiIiIjbSoCydGztmOBFhuwgJXhO/7esvh7B3zwZ2/raKuXPG4e2dzanlh4ftIjhB+Qn17/8WQYErCQpcSXDwGq5fO0GOHNkfqEwvLy9++eV/7N+3mS2bF1O4sA8AtWs/z/aAZQTvXM32gGX4+VV7oHLuVd8+b7ArZC0hwWuYNnUUGTJkSNPyH1T9en6E7t3IgX2bef+9nnaHk6zE2n6rVk3YFbKWG9f/pHy552yM7t6ULFksvp8EBa7k/NkD9On9ulPLLFHicbYGLI2/RZzcTY+eXe/Y7/nnK7E1YCmBQStYvmLmA5fr5eXF5Ck/smvPOtZtmM9jjzl+L7Nmreps2rKI7TuWsWnLImrUqPLAZaWEHXWfWtzc3AjcsYKF8ycDUKRIIbZuXsyBfZuZ/sv/8PT0tDnC5CXWj13Nw3Du/PCD/vxxPCi+HzRsUMvGCFPO1er+dq4ef2pyhdUX7aZB2W2MMV2MMQXsjuOmKVNm07jJK7dsW71mI6XL1KJc+bocPnyMgQN6Oa38yVNm0+S28hMaMWI0vhXq4VuhHkOGfMXGjQFcuHAxRa9duLAPq1fNuWP7q11f4uKFSzxVqjojfxjLF18MBuDcufO0aNmFsuXq8Opr/Zg0ceT9HdR9KFAgH716vkqlyo0oU7Y27u7utGvbPM3Kf1Bubm78MPJzmjTtwLOla9KuXQueeqqE3WHdVWJtPzT0AG3avsGmTQE2RXV/Dh06Gt9PKlZqwNWr11iwcJlTyzx8+BhVKzemauXGVK/alGvXrrN40cpb9vH2zsp3339K29ZvUMG3Ph07pPxDw2OPFWTZ8hl3bO/cpS0XL16i9LM1GfXjeD79bCDg6L9tWr9OpYoN6fbGu4wdP+LBDjCF7Kj71NKn9+scOHA4/v6XXwzm+x/G8mSp6ly4cIlXu75kY3Qpk1g/diUPy7kTYOQPY+P7wrLla22I7N64Yt0n5OrxS9rToOxOXYB0MyjbtHk7528b5KxavZGYmBgAArbvpGDB/Ik9NVVsTqT8pLRr15xZsxbE33/55RfZumUJQYEr+WnU17i5pay5NW1aj6lTHYO1X39dSq2a1QEICQklMtLxu36hoQfJlCkjXl5pt+Kch4cHmTJlxN3dncyZMhEZeTLNyn5QFSuU5ejR3zl+/ARRUVHMnr2QZk3r2x3WXSXW9g8cOMKhQ0dtiih11K5VnWPH/uDEiWR/RzLV+NWsxrFjf/Dnn7eW2bZdcxYtWkFYWAQAZ86ci3+sXfsWrN+4gK0BS/nhx89T3H8bN67LL9N+BWD+/GX4+VUFYPeufZyMPA3Avn2HyJgxbfsv2FP396tgwfw0alibCRP+f+Bb068av/66FICpU+fQvFn67sOQeD92JQ/LudMVuWLdJ+Tq8Uvac9lBmTGmkzFmtzFmlzFmqjGmiDFmbdy2NcaYx4wxWY0xx40xnnHPyXbzvjGmjDEmIG7/+caYHMaY1oAv8IsxJsQYk8kYU94Ys8EY85sxZsVtPyxnu65d2rN8xTq7wyBTpozUr+fHvPn+ADz5ZHHatGnGCzVa4FuhHjExMbz88ospeq0CBfPxZ9yHxJiYGC5d+otcuXLcss+LLzYmOHgvN26kzTLaEREnGfHdaI4f3UHYiWAu/fUXq1ZvTJOyU0PCOgUIC4+kQIF8Nkb079W2bXNmJvjyIi20btOEuXMW37G9ePGiZM/uzbLlM9i0ZREvxfXRJ54oRqvWTahTqzVVKzcmJiaGdu1bpKisAgXyEhYeCcT137/+vqP/tmjRkF0hadd/b7Kj7u/XiOFDGTjoM2JjYwHIlSsHFy9eiv9CLiw8kgIF1Yed7WE6d/bo3pWdv61i7JjhZM/ubXc4yXL1unf1+FObFWvS9OaKXHJQZox5GhgC1LIsqzTQF/gRmGxZ1nPAL8APlmX9DawHGsc9tT0wz7KsKGAKMCBu/z3AR5ZlzQWCgFcsyyqD4wfifgRaW5ZVHpgAfJ5ETG8aY4KMMUGxsVeccty3GzSwD9HR0UyfPi9NyrubJk3qsXVbUPyli7VqVqdc2WcJ2OZPUOBKataqTtGijwEwZ844ggJXsmjRVMqXLx1/jXvnTm1TVFapUiX54vP/0KPnAKcdz+2yZ/emWdP6FC9ZmUKFy5ElS+YUDzJFbvL09KRpk3rM/XVJmpbZuFEd5s/zv+MxDw8PypR9hlYvvkqLZp0ZMLAXxYsXxa9mNcqWfYaNmxeyNWApNfyqUbRoIQBmzBzN1oClzJs/kbLlno2fs9ahY+sUxfPUUyX45LMB9Ok9OFWPMzl21P39atyoDqdPn2Vn8B67Q5GHxOifp1DyyaqU963HyZOnGfbNh3aHJCK3cdUl8WsBcyzLOgtgWdZ5Y0wV4Oan5KnAN3F/jwPeBxYAXYE3jDHeQHbLsjbE7TMZuHNyEzwBPAOsMsaA49e6IxMLyLKsMcAYSJsfj+7UsS2NG9Whbv2UDWScrW3bZrdcumiMYeq0OQwZ8tUd+7Zp45hkX7iwD+PHfUedum1ueTwi/CSFfAoQHh6Ju7s73t7ZOHfuAuC4pGfOnPG8+mpfjh37w4l50EdzAAAgAElEQVRHdKvatZ/n+O8nOHv2PADzFyyjSmXfdDEgTombdXqTT8H8RES4zuWXD4sGDWoSHLyH06fPplmZ9er7ERISmmiZ4eGRnD9/gatXr3H16jW2bNnBs88+hcHwy7Rf+fijYXc856X2bwGOOWU/j/mWhg1undcUEXHK0b7CTzr6b7as8f23QMF8TJ/5M2++/g7Hj59wwtEmzY66v19Vq/rStEk9GjaoRcaMGciWLSvfjfiE7Nm9cXd3JyYmJr6OxbkelnNnwnY/bvwvLFww2cZoUsbV697V409tVpr+dLRrcslM2b2wLGsLUMQY4we4W5a19x6eboBQy7LKxN2etSyrnlMCvQf16/nx7rvdafFiF65du253OGTLlpUXnq/MokUr4retXbeZF1s2IU+eXADkyJE9fhW25CxZspKOHR0DtVatGrNu/RYAvL2zsWjhFAYP/oKt24JS+Sju7s8T4VSqVI5MmTICjkxgwgn46V1gUAjFixelSJFCeHp60rZtcxYvWZn8EyVVtW/XIs0vn2vTpilz5ixK9LGlS1ZRpYov7u7uZMqUkQq+ZTh48Ajr12+hRcuGCfqvN4UKpaz/+vuv5pUOrQBo2bIhGzZsAxyLivz66wQ++vBrAgJ+S4Ujuzd21P39GjzkK4o87kvxkpV5pUMP1q3bQqfOvVm/YSutWjku/OjYsQ2LFqsPO9vDcu7Ml+/R+L9bNG9IaOhBG6NJGVeve1ePX9Keq2bK1gLzjTEjLMs6Z4zJCWzFcXniVOAVYFOC/acA04FPASzLumSMuWCMed6yrE1AR+Bm1uxvIGvc3weBPMaYKpZlbYubm1bSsqxQZx/gTdOmjqLGC1XInTsnvx8LYugn3zLg/V5kyJCB5cscy1dv376Tnr0GOqX8qQnKP34siE8++TZ+GeYxY6cCjhP8qtUbuXr1Wvzz9u8/zEcff8My/xm4uRmioqLp02dwiibYT5g4k0mTfmD/vs1cuHCRVzr0AKBHj64UK1aEIYPfZsjgtwFo2OilWxYncJYdgcHMm7eUwB0riI6OJiQklLHjfnF6uaklJiaGvv2G4L90Ou5ubkyaPIt9+w7ZHdZdJdb2z1+4yMjvPiNPnpwsWjiFXbtCaeQiK7tlzpyJOrVfoHuPtLvsNnPmTNSsVf2WSwVfe/1lAMaPm87Bg0dZtWoj23csIzY2lkmT/r9dfDp0OAsXT8HNuBEVHUX/fh/esVBIYiZPmsW48d+xa886Lly4RJdOvQHo9lZnHi9WmIGD+jBwUB8AmjftlCb91466d4ZB//mc6dN+4pOP3ydkVygTJt65+mV6k1g/njjpwX96Ia08LOfOGjWqUrp0KSzL4o8/wlyiL7hi3Sfk6vGnNled55WWjOWi+URjTGfgPSAGCAY+AiYCuYEzQFfLsk7E7ZsPOA7ktyzrYty2MsBoIDNwLG7/C8aYVsAXwDWgCo5LGH8AvHEMYr+3LGvs3WJLi8sXncXVu4zLVrzIA8rokbYrGaa269Fpu+iHiMjDIvpGeLr/+LavWOM0/YhW6ujSdF8nt3PZQdm9iFtVsbllWR3TojwNyuzjshUv8oA0KBMR+XdyhUHZ3sebpOlHtGeOLUn3dXI7V718McWMMT8CDYFGdsciIiIiIiJyu4d+UGZZVm+7YxARERER+beyLJdLXKW5h371RRERERERkfTsoc+UiYiIiIiIff4FS1g8MGXKREREREREbKRMmYiIiIiIOE2s5pQlS5kyERERERERGylTJiIiIiIiTqPVF5OnTJmIiIiIiIiNNCgTERERERGxkS5fFBERERERp9GS+MlTpkxERERERMRGypSJiIiIiIjTaEn85GlQJrdQdlnENV2PvmF3CA8kg4en3SE8kH+io+wOQUREXJgGZSIiIiIi4jRaEj95mlMmIiIiIiJiI2XKRERERETEaTSnLHnKlImIiIiIiNhImTIREREREXEaLSSXPGXKREREREREbKRMmYiIiIiIOI3mlCVPmTIREREREREbKVMmIiIiIiJOo98pS54yZSIiIiIiIjZSpkxERERERJwm1u4AXIAyZSIiIiIiIjZSpkxERERERJzGQnPKkqNMmYiIiIiIiI00KHMRPj4FWL1yDrt3rWNXyFp693rN7pDu2dgxw4kI20VI8Bq7Q7kvvXu9RkjwGnaFrKVP79ftDuee1a/nR+jejRzYt5n33+tpdzj3zM3NjcAdK1g4f7LdoSQrQ4YMbNuyhN+CVrErZC0fffhO/GOffjKAfaGb2LN7Pb16vmpjlHeXWH8d+vF77PxtFUGBK1m2dDr58+d1agze3tmY9stP7Axew287V1OxYrlbHs+ePRszZv7M9u3L2LBxAaVKlXzgMr28vJg85b/s3rOe9RsW8NhjPgDUqlWdzVsWs2PHcjZvWUyNGlUeuKyUcvW+q/jtU7JkMYICV8bfzp894FLvX65c9+D68UvaMpZl2R3DQ8fDq2CqV2q+fI+SP9+jBIfs5ZFHsrBj+3JatX6V/fsPp3ZRTvN89UpcvnyFiRNHUqZsbbvDuSdPP/0Ev0z7iSpVG3PjRhT+S36hR6+BHD36u92hpYibmxv7QzfRoNFLhIVFErDNnw4de7hU++nX903Kl3+ObFmz0rxlZ7vDSVaWLJm5cuUqHh4ebFw/n7f7f8STTxbHz68ar77WD8uyyJMnF2fOnLM71EQl1l+zZn2Ev/++DECvnq/y1FMl6dlrYKqUl8HD845tY8YMZ8vWHUyeNAtPT08yZ87EpUt/xT/++eeDuHzlKl9+MZKSJYvx3Xef0LjxKykq77HHfPh5zLc0bND+lu1vvNmBZ555ir59BtO6dVOaNqtP5069KF36aU6dPsPJyNOUKlWShYumUKJ45fjn/RMddZ9Hfneu3ncVf/rh5ubGid9/o2r1Jpw4EW53OMly9bpPy/ijb4Sn+2sD1+dtk6YDDr9Tc9J9ndzOZTJlxsFl4k1tJ0+eJjhkLwCXL1/hwIHDFCyQz+ao7s2mzds5f+Gi3WHclyefLMGOHcFcu3admJgYNm4KoGWLhnaHlWIVK5Tl6NHfOX78BFFRUcyevZBmTevbHVaKFSyYn0YNazNhwgy7Q0mxK1euAuDp6YGHpyeWZfFWt0589vl33PwyLL0OyCDx/npzQAaOQaczv9TLli0r1apXZPKkWQBERUXdMiADePKpEmxYvxWAQ4eO8lhhHx59NDcA7du3YMPGBWwL8OeHH7/AzS1lbx9NGtfjl2m/AjB/vj9+flUB2LUrlJORpwHYt+8QGTNmxMvL68EPNBmu3ncVf/pRu1Z1jh37wyUGZOD6de/q8UvaS9eDHGNMEWPMQWPMFGAvMN4YE2SMCTXGDE2wXwVjzFZjzC5jzA5jTFZjjLsx5ltjzF5jzG5jTO+4fWsbY4KNMXuMMROMMRmS2f67MWaoMWZn3GNP2lEXCRUu7EOZ0s+wfUew3aH8a4SGHqB69UrkzJmDTJky0rBBLXx8CtgdVooVKJiPP8Mi4u+HhUdSwIUG9SOGD2XgoM+IjXWdRXXd3NwIClxJZPhu1qzZyI7AYB5/vAht2zQjYJs/SxZNpXjxonaHec8+/WQAx48G8tJLLfl46DCnlVOkSCHOnj3Hzz9/y9ZtSxn101dkzpzpln327NlP8+YNACjvW5rHHitIgYL5eOKJYrRq3YTatVpTpXIjYmJiaN++RYrKLVAgL2Hhjr4SExPDX3/9Ta5cOW7Zp0WLhuwK2cuNGzdS4UiTicfF+67iTz/atm3OzFkL7A4jxVy97l09/tQWi0nTmytK14OyOCWAnyzLehp4x7IsX+A5oIYx5jljjBcwC+hrWVZpoA5wDXgTKAKUsSzrOeAXY0xGYBLQzrKsZ3GsPtk9qe0JYjhrWVY54H/Au84+4LvJkiUzs2eNpf+7H93yrbU414EDRxg2bBTL/Kfjv+QXQnaFEhPjOgMEV9a4UR1Onz7LzuA9dodyT2JjY/GtUI/CRX2p4FuWp59+ggwZvLh+/R8qV2nEuAnTGTdmuN1h3rMPPvyaosUqMGPGfHr26Oq0ctw93ClT5hnGjptG1SqNuXrlGu+82/2WfYZ/+z+8s2djW4A/3d/qzK5docTGxOJXsxplyz7Lps2L2BbgyHYVKfoYADNm/sy2AH/mzZ9IuXLPsi3An20B/nTs2CZFcT31VAk+/WwgvXv/J9WPWcRZPD09adqkHnN/XWJ3KCKSBFdYEv8Py7IC4v5ua4x5E0fc+YFSgAVEWpYVCGBZ1l8Axpg6wGjLsqLjtp83xpQGjluWdSju9SYDPYF1SWz/Pu7+vLh/fwNeTCzIuLjeBDDu3ri5ZXngA7+dh4cHc2aNZcaM+SxYsCzVX1/ubuKkmUycNBOAzz4dSFhYpM0RpVxE+EkKJcjs+RTMT0TESRsjSrmqVX1p2qQeDRvUImPGDGTLlpXJk36gc5c+doeWIpcu/cX6DVuoX8+PsPBI5i/wB2DBgmWMHzvC5uju3/QZ81i8aCpDP3HOwDIi/CTh4ScJCgwBHJcS3j4o+/vvy7zV7b34+/v2b+b48RNUrVqBX6b9ykcffXPH677UvhuQ9JyyiIhT+BQsQET4Sdzd3cmWLSvnzl0AHN98z5j5M2+83p/jx0+k6vEmxZX7Lij+9KJBg5oEB+/h9OmzdoeSYq5e964ef2rTkvjJc4VM2RUAY0xRHFmq2nGZr6VAxjSK4Z+4f2NIYiBrWdYYy7J8LcvydcaADByroe0/cITvR45xyuvL3eXJkwuAQoUK0KJFQ2bMnG9zRCkXGBRC8eJFKVKkEJ6enrRt25zFS1baHVaKDB7yFUUe96V4ycq80qEH69ZtSfcDsty5c+LtnQ2AjBkzUqf2Cxw8eJRFi5bjV8MxR6nGC1U4dPiYnWHes4SXWzZrWp+DB486raxTp84QFhZBiRKPA+BXsxoHbpsg7+2dDU9PxwIhXbq2Z8vm7fz992XWr99Ci5YN4/tsjhzeFCpUMEXlLvVfxSsdWgHQsmUjNmzYGl/WvF8n8uGHXxMQ8FuqHGNKuHLfBcWfXrRv18KlLl0E1697V49f0p4rZMpuyoZjgHbJGJMXaAisBw4C+Y0xFSzLCjTGZMVx+eIqoJsxZp1lWdHGmJxx+xYxxhS3LOsI0BHYcJft6Ua1qhXo2KE1u/fsIyjQ0ak/+OArli1fa3NkKTdt6ihqvFCF3Llz8vuxIIZ+8m185skVzJk1lpy5chAVFU2fPoPvWHQgPYuJiaFvvyH4L52Ou5sbkybPYt++Q8k/Ue5L/vx5mTD+e9zd3XBzc2Pu3MUs9V/N5i07mDr5v/Tt+wZXLl+l21vvJf9iNkmsvzZsWIuSJYsRGxvLiRPh9OiZOisvJuXddz5mwsTv8fL05Pjvf/JWt3d57XXH6orjx/3CE08UZ8zYb7Esi/37D9Oj+/uA43LjT4YOZ9HiqbgZQ1R0NG/3+5A//0x+gYPJk2YzbvwIdu9Zz4ULF+ncqTcA3d7qxOPFCjNoUF8GDeoLQLOmHZ2+WIur913Fb7/MmTNRp/YLdO8xwO5Q7omr172rx5/aNOEjeel6SXxjTBFgiWVZz8TdnwRUBf4ELgGLLMuaZIypAPwIZMIxIKsDXAe+ARoAUcBYy7L+a4ypDXyLY0AaCHS3LOufu2z/HfC1LOusMcYX+NayLL+7xe2MJfFFRB5miS2J70qctSS+iEhyXGFJ/FV526XpZ+O6p2al+zq5XboelLkqDcpERO6NBmUiIvfHFQZlK/O2T9PPxvVOzUz3dXI7V5hTJiIiIiIi8tBypTllIiIiIiLiYjSnLHnKlImIiIiIiNhImTIREREREXEaZcqSp0yZiIiIiIiIjZQpExERERERp7FwucUQ05wyZSIiIiIiIjbSoExERERERMRGunxRREREREScJlZXLyZLmTIREREREREbKVMmIiIiIiJOE6uFPpKlTJmIiIiIiIiNlCkTERERERGnsewOwAUoUyYiIiIiImIjZcrkFm7Gta/5jbX0XYyIK/onOsruEB5IjkyP2B3CA7lw7bLdIYjIQyzW7gBcgDJlIiIiIiIiNlKmTEREREREnCbWxa/ESgvKlImIiIiIiNhImTIREREREXEazfhPnjJlIiIiIiIiNlKmTEREREREnEarLyZPmTIREREREREbKVMmIiIiIiJOE6vFF5OlTJmIiIiIiIiNNCgTERERERGxkS5fFBERERERp4lF1y8mR5kyERERERERGylTJiIiIiIiTqMfj06eMmUiIiIiIiI20qAsnTtyKIDgnasJClxJwDZ/AHLkyM5y/xnsD93Mcv8ZZM/u7bTyfXzys3LFbHaFrCUkeA29er12xz4vtW/Jb0Gr2PnbajasX8Bzzz71wOV6eXnxy7Sf2LdvM5s3LaZwYR8Aatd+noBt/uz8bTUB2/zx86v6wGWlVP16foTu3ciBfZt5/72eaVZuavDxKcDqlXPYvWsdu0LW0juR/8f0JkOGDGzbsoTfglaxK2QtH334DgBjfv42rr2tYtbMMWTJktnmSBPn7Z2NWTPHsHfPBvbsXk/lSuVp1aoJu0LWcuP6n5Qv95zdISYpqfaSluee1GRH3x353y/Yd2QrG7ctvut+Zco9S+S5UJo2r//AZWbP4c2cBRPYvnMFcxZMwDt7NgAaNKrN+i2LWLdpAavW/0qlyuUfuKy7GTtmOBFhuwgJXnPL9p49urJ3zwZ2hazlqy8HOzWG1ORq5/6k6h/g7X7diL4RTq5cOWyI7P64ubkRuGMFC+dPtjuUZCX3XuuK9Z9aYk3a3lzRQzsoM8b8bozJnQqvM8kY0zo1Yrpfdeq2wbdCPSpXaQTAgPd7snbdZp56ujpr121mwPvOe5OIjo7h/QGfULpMLao/34zub3XmqSdL3LLP8d9PULtOa8qVr8MXX47kp5++SfHrFy7sw6qVc+7Y3rVrey5cvESpUtX54YexfPH5fwA4d/Y8LV/sSrnydXjttbeZOOGHBzvAFHJzc+OHkZ/TpGkHni1dk3btWvDUUyWSf2I6ER0dzXvvD+W50jWpVr0p3bt3Sffx//PPP9Sp15byvnUp71uP+vX8qFSxHO+8+zHlfetSrnxd/jwRTs8eXe0ONVHfjfiEFSvW8cyzNShXvi77DxwmNPQAbdq+waZNAXaHd1dJtZe0PPekFrv67szp82jf6vVkY/tw6LusX7vlnl67avWK/PjTl3ds7/P2m2zasI1K5eqzacM2+rz9JgCbNmzDr1ozaj7fgr49/8N3P352T+XdqylTZtO4ySu3bPOrUZVmTetTrnxdSpepxfARo50aQ2pxxXN/YvUPjgFD3Tov8McfYTZEdf/69H6dAwcO2x1GitztvdZV61/SzkM7KHuYNW1anylTHQOZKVPn0KxZA6eVdfLkaUJC9gJw+fIVDhw4TIGC+W7ZJyDgNy5evATA9u07KVgwf/xjL7/0Ils2LyFwxwpGjfoKN7eUNbmmTesxNe4Yf523lJo1qwMQsiuUyMhTAITuO0imTBnx8vJ6sINMgYoVynL06O8cP36CqKgoZs9eSLOmD/7Ndlo5efI0wbf9PxYskC+ZZ9nvypWrAHh6euDh6YllWfz99+X4xzNmyohlpb8r1bNly8rz1SsxYeIMAKKiorh06S8OHDjCoUNHbY4ueUm1l7Q896QWu/rutq1BXLhw6a77vNGtI0sWruDsmXO3bO/Z5zVWrpvL+i2LeH9Q7xSX2bBRbWZNXwDArOkLaNS4DvD//Qggc+ZMTu8zmzZv5/yFi7ds69atE98MG8WNGzcAOHPbMadXrnjuT6z+AYZ/+zED//N5ujxnJqVgwfw0alibCRNm2B1KitztvdYV6z81xabxzRU9FIMyY0wWY8xSY8wuY8xeY0y7BI9lMsYsM8a8YYzpYoz5b4LHlhhj/OL+vmyM+TzuNQKMMXkTKefTuMyZe5ocGGBZFsv8Z7A9YBmvv+b45ivvo7k5efI04DgB5H30gROCKVK4sA+lSz/Djh3BSe7TtWt7VqxYB8CTTxanTZum1PBrQYWK9YmJieHll1qmqKyCBfIRFhYJQExMDJf++uuOdP+LLRsTHLIn/k3emQoUzMefYRHx98PCIyngAoOaxBQu7EOZ0s+w/S7/j+mFm5sbQYEriQzfzZo1G9kR6Ih53NgRhP8ZwpNPFOe/oybYHOWdihZ9jLNnzzF+3HcE7ljBz6OHkTlzJrvDui8J24td554HkV77br78j9KoSR0mjr/1w6ZfrWo8Xqww9Wq2pmb15pQu8zRVqvqm6DXz5MnFqVNnADh16gx58uSKf6xRkzpsDVzG9Dk/07fnf1LvQFKoRInHqV69Ils3L2bt6rn4li+d5jHcj/Tafu5V06b1CA+PZPfufXaHck9GDB/KwEGfERvreh+zE547XbX+JW09LKsvNgAiLMtqDGCM8Qa+Bh4BZgJTLMuaYozpcpfXyAIEWJY12BjzDfAGEH+NhzFmGJAV6Gol8jWHMeZN4E0A4+6Nm1uWVDmwGjVbEhFxkjx5crF82UwOHjxyxz5p8a1LliyZmTVzDO+++/EtmYqEatSoStcu7fGr6Rh41axZnbJln2Xb1qUAZMqUkTOnHd+Ozpk9jiJFCuHl5UmhQgUJ3LECgB//O54pU2YnG0+pp0ry+ReDaNz4zks0JGlZsmRm9qyx9H/3oyT/H9OT2NhYfCvUw9s7G7/OGc/TTz9BaOhBXn+jP25uboz8/jPatmnG5BS0mbTk4e5O2bLP0rffB+wIDGbE8KEMeL8XH308zO7Q7kly7eXf+o1vavj8q8F88tG3d9ShX61q+NWsxrpNjoxXlkcy83ixImzbGsTyNbPJ4OVFlkcykz2Hd/w+n3z8LevWbL6jDCvBemf+S1bjv2Q1Var6MnBIX1o3T9vLfj083MmRIztVqzelgm8ZZkwfTYknqqRpDP9WmTJlZNCA3jRo9LLdodyTxo3qcPr0WXYG76HGC67VVhKeO6Ojo12y/lOb3i2S97AMyvYAw40xXwNLLMvaZIwBWAh8Y1nWLyl4jRvAkri/fwPqJnjsA2C7ZVlvJvVky7LGAGMAPLwKplrbi4g4CTgu9Vi4cBkVKpTh1Omz5Mv3KCdPniZfvkc57eTLQDw8PJg1awwzZs5nwcJlie7z7DNPMXr0NzRr1pHz5x2XTRhjmDZtLkM++OqO/du0dcy1KFzYh3Fjv6NuvTa3PB4ecRIfn/yEh0fi7u6Od7ZsnDt3AXBczjBnzjhefbUfx479kZqHmqSI8JMU8ikQf9+nYP74/xtX4eHhwZxZY5kxYz4LFiT+/5heXbr0F+s3bHFMuA89CDgGbLNnL+Tdd3qku0FZWHgkYWGR8Zm9efOW8v57vWyO6t4k1l7S+tyTGtJr3y1d9hnGTBgBQK5cOahdrwbR0dEYDCO/G8OUibPueE6D2m0Bx5yyl15uSe8eg255/MyZc+TNm4dTp86QN28ezp45f8drbNsaROEihciZMwfnz19wwpElLjwsMr4dBQaFEBsbS+7cOTl79s4Y05P02n7uRbFiRShS5DF2Bq0CHAt4BW5fQZVqjeMzq+lR1aq+NG1Sj4YNapExYwayZcvK5Ek/0LlLH7tDu6vbz53PPPOkS9a/pL2H4vJFy7IOAeVwDM4+M8Z8GPfQFqCBiRuhAdHceswZE/wdlSADFsOtA9ZAoLwxJmeqB38XmTNn4pFHssT/XbdODUJDD7Jk8Uo6dXQMYjp1bMPixSucGseYn7/lwIEjjBw5NtHHCxUqwKzZY+natS+HDx+P375u7WZavtg4/hKaHDmy89hjBVNU5pIlq+gYd4ytXmzM+vWOifDe3tlYuGAygwd/ybZtQQ9yWPckMCiE4sWLUqRIITw9PWnbtjmLl6xMs/JTw9gxw9l/4Ajfjxxjdygpkjt3Try9HavHZcyYkTq1X+DQoWMUK1Ykfp+mTeolmj2226lTZwgLi6BkyWIA1KpVnf37D9kc1b1JrL2k9bknNaTXvuv7XG3Kx90WL1zBgHeGsmzpGtat3czLHVrFryqaL/+j5M6dsree5cvW0u7lFgC0e7kFy/wdq+8Vffyx+H2eK12KDF5eaTogA1i4aEX8arklSjyOl5dXuh+QQfptP/di794DFPApTfGSlSlesjJhYZFUqFQ/3Q8IBg/5iiKP+1K8ZGVe6dCDdeu2pPsBGdx57nTV+k9tWn0xeQ9FpswYUwA4b1nWNGPMReDmklcfxt1GAT2A34Eexhg3oCBQMYVFLAdWAEuNMfUsy/o7NeNPSt68eZg7ZzzguPRj5swFrFi5nsCgXcycPpquXV7ixIkw2r/8ltNiqFq1Ah06tGbPnv3xlxh+8OHXFCrk+OZw7NhpDP7P2+TKmZ0ff/gCcKw+VKVqY/YfOMzHH32D/9LpuLm5ERUVRZ++QzhxIjzZcidOnMmkiSPZt28zF85fpEPHHgD06N6FYsWKMHhwPwYP7gdAo8YvO33SeExMDH37DcF/6XTc3dyYNHkW+/a5zofsalUr0LFDa3bv2UdQoOMDxQcffMWy5Wttjixp+fPnZcL473F3d8PNzY25cxez1H81G9bNJ2u2RzDGsHv3Pnr2GpT8i9mg79sfMGXyj3h5eXL8+Alee70/zZs3YOR3n5EnT04WLZzCrl2hNEpklTS7JdVevh42Ks3OPanFrr778/jhVKtekZy5crBr3wa++fJHPDwdb7mTJ8xM8nnr126hZMli+K9y7HPlylV6vPleigYwP4wYw7jJ3/NKx9b8+WcEr3dxnCObNKtP2/bNiY6K5tr167zR9e1UOMKkTZs6ihovVCF37pz8fiyIoZ98y8RJMxk3djghwWu4cSOKV1/r59QYUosrnvuTqn9xPld8r5X0wzwMcwKMMfWBYTgWXIkCugNzAV/gHDABOAMMAKYB5YH9QA7gY8uy1htjLluW9bgBY9wAACAASURBVEjc67UGmliW1cUYMwnHJZFzjTGvAh2BRpZlXUsqntS8fDGtuRkX/XohTuxD0J5FxPXkyPSI3SE8kAvX0v8cUxFJXPSN8HT/4W2sT4c0/YD2Rti0dF8nt3soBmXpjQZl9tGgTETsoEGZiNhFg7I7ueKg7KG4fFFERERERNIn1/tRg7T3UCz0ISIiIiIi4qo0KBMRERERkf9j777jo6ryN45/vknoLk2QFgRXwIIKQqgioHQErGBFQSxLsa5trVjXhm11VVCq0pdepCptJSSS0IsoiqEsqAiCBUjO748Z8gshYSbA5M4lz9vXvJw5M7n3uTeXmznzPfdMgWFmg81sp5mtztJW1szmmNnXwf+XCbabmb1jZpvMbKWZ1cvyM7cFX/+1md2Wpb2+ma0K/sw7WWaCz5U6ZSIiIiIiEjHO8vcWhqFA+2xtjwHznHM1gXnBxwAdgJrB213A+xDoxAHPAI0IzOj+zOGOXPA1d2b5uezrOoo6ZSIiIiIiUmA45xYC2b9r5EpgWPD+MOCqLO3DXcBSoLSZVQLaAXOccz8753YDcwh8P3IloKRzbmnwO5CHZ1lWrjTRh4iIiIiIRIxPJvqo4JzbHry/A6gQvF8F+CHL69KCbcdqT8uh/ZhUKRMRERERkVOGmd1lZslZbnfl5eeDFa58ncZflTIREREREYmY/K6UOecGAgPz+GP/M7NKzrntwSGIO4PtW4GqWV4XH2zbCrTM1v5FsD0+h9cfkyplIiIiIiJS0E0BDs+geBswOUv7rcFZGBsDe4LDHGcBbc2sTHCCj7bArOBze82scXDWxVuzLCtXqpSJiIiIiEjE5Os4wDCY2SgCVa5yZpZGYBbFl4GxZtYL+B7oFnz5DKAjsAn4DegJ4Jz72cyeB5KCr3vOOXd48pA+BGZ4LAbMDN6OSZ0yEREREREpMJxzN+byVKscXuuAvrksZzAwOIf2ZOCCvGRSp0xERERERCImI7zvDivQdE2ZiIiIiIiIh1QpkyNkuGgb9SsiEv12/77P6wgnxO8fYusvl0h088n3lHlKlTIREREREREPqVImIiIiIiIRo0pZaKqUiYiIiIiIeEiVMhERERERiRhd9xmaKmUiIiIiIiIeUqdMRERERETEQxq+KCIiIiIiEaMvjw5NlTIREREREREPqVImIiIiIiIRoynxQ1OlTERERERExEOqlImIiIiISMRoSvzQVCkTERERERHxkCplIiIiIiISMRmqlYWkSpmIiIiIiIiHVCkTEREREZGI0eyLoalSFsXi4yszd/Y4Vq74nBWp87mnXy8Ann7qQb7fnExy0mySk2bTof3lHifNWW75R376fmb2TRuXkpw02+OkoeW2LX7Srm1L1qxeyPq1i3nk4b5exwnpWPu8b5+erF61gBWp83n5n094mDJ3gwYOYFvaClJT5h313AP3382hA1s5/fQyHiTLmyJFivDlkml8lTyHFanzeebpv3sdKc9OlWP/2ms7sSJ1Pgf++IH69S6KaIZBAwewNW0FKTkcv1kl1K/D7799zzXXXHHC6yxTpjQzZ4xi7ZrFzJwxitKlSwFw441Xs/yrOaQsn8vCBZO56KLzT3hdeeG34ye3c48fzpvZbdq4lJTlc0lOms3SL2d4HSfPSpUqyZjRA1m9agGrVn5B40b1vY4kUcyc898YTzOrDkxzzl0Q5uu/AxKccz+e4Hofd869FOp1cYWrnJSdWrHiGVSqeAYpqas57bQSLEv8jGuvu52u13Vm3779vPHmhydjNRGTW/51677OfM1rrzzNnr17eeHFtzxMGlo42xLNYmJiWLdmEe073kha2naWfjmDW7r3ier8ue3zCmeU5x+P3UvnK2/lwIEDlC9/Ort2/eR13KNc2qwR+/btZ8iQt6l7cavM9vj4ygz84DXOOacGDRu356efdnuYMjwlShRn//7fiIuLY+EXE3ngwWdIXLbc61hhOZWOfeccGRmO9997mUcefZ6vlq88aeu0bI+bNWvE/n37GTzkbS7OcvxmFRMTw2czR/PHH38wdNgYJkyYHta6mjdvwm23dqPXHQ8c0f7Pfz7Bzz//wmuvvcfDD/elTJlSPP74SzRpnMC69V/zyy97aNfuMp5+6kEuadb5iJ+N1DsZPx4/OZ17WrZo6ovzZnabNi6lUZMOvjhP5mTwx2+xeHEig4eMolChQhQvXow9e/ae9PUcOrA1+z/hqPNctZvztcPx9PefRv0+yU6Vsrx5PD9XtmPHTlJSVwOwb99+1q//miqVK+ZnhBMSTv7rruvM6DGTvYiXJ37/XTRscDHffPMdmzdv4eDBg4wdO5kundt5HeuYctvnd999K6++9h4HDhwAiNo3FosWJ/Lz7l+Oah/wen8ee/xF/PSB2P79vwFQqFAccYUK+Sr7qXTsr1+/iY0bv8mXDItzOX6z6tf3diZOnH7Uv8EHH/wbX/53Osu/msPTeaisdu7cjhEjxgEwYsQ4unRpD8CXS5P55Zc9ACQmLqdKlUp52ZQT4sfjJ6dzj1/Om6eSkiX/wqXNGjF4yCgADh48GJEOmZw6/NwpizOzT81snZmNN7PiZtbKzFLMbJWZDTazIlle/0iwfZmZ1QAwswpmNtHMVgRvTYPtk8zsKzNbY2Z3BdteBoqZWaqZfZrfG1utWjx161xA4rIUAPr07snyr+YwaOCAzCEe0Sx7fgh8mve/nbvYtGmzh8nyLqdtiXaVq1Tkh7RtmY/Ttm6nso86lVn3ec2af6VZs4b8d/FU5s8dT0L9Ol7HC1vnzm3ZunU7K1eu9TpKnsTExJCcNJvtW1cyb95CliXp2M8v0Xq+qVy5Ilde2Z4PPhx+RHvr1s2pWeMsmjS9gvoJbal38UU0a9YorGVWOKMcO3bsBAId0wpnlDvqNT173sCsWZ+f+AaEye/Hz2F+PW8655g5YxSJS2dyR6+bvY6TJ2eddSY//vgTH3/0JknLZvHhB69RvHgxr2N5JiOfb37k507ZOcC/nXPnAXuBB4GhwPXOuQsJTGLSO8vr9wTb3wUOj5V7B1jgnKsD1APWBNtvd87VBxKAe83sdOfcY8Dvzrm6zrmjzgxmdpeZJZtZckbG/pO6oSVKFGfsmEE8+NAz/PrrPj74cDi1zm1K/YS27Nixk9deffqkru9ky57/sOuvv4oxPqiSZZXbtkjkZN/ncXGxlClTmqbNOvPoYy8wauQHXkcMS7FiRfnHo/fQ/9nXvY6SZxkZGSQ0aEu1sxJokHAxtWuf43WkAiGazzcDBjzL44+/dFTVtE3rFrRu3YLkpNkkLZvFOeecTc0aZwGwZPFUkpNm8+EHr9GpU5vMa4vbtGmR4zqyL7tFi6b07Hkj/3g85FUEko1fz5stLruaho3a06nzLfTu3YNLw+zgR4O42FguvvhCPvxwOA0atmP//t949JF+XseSKObn2Rd/cM4tCd7/BHgK2Oyc2xhsGwb05f87YKOy/P/N4P3LgVsBnHPpwJ5g+71mdnXwflWgJnDMWr9zbiAwEE7eNWUAcXFxjBsziFGjJjJp0kwAdu78/0vjPvr4UyZPGnayVnfS5ZQfIDY2lquv6kDDxh08TJc3uW2LH2zbuoOq8ZUzH8dXqcS2bTs8TBSenPb51rTtmfeTklPJyMigXLmy/Pjjz15GDenss6tTvfqZLE+eA0B8fCWSEmfR5JIr+N//dnmcLjx79uzliwVLAhMfrNngdZywnErHfjSpX+8iPvnk3wCUK1eW9u0v59ChQ5gZr776LoM++uSonzl8HVhu15T9b+ePVKx4Bjt27KRixTPYmWWI3YUXnseHH7xG5y7d+fnn/Lu+yK/HT3Z+PW8e3te7dv3E5MkzadCgLosWJ3qcKjxpW7eTlrY9c2TBhAnTeeThgtspy/DdFV75z8+Vsuwdn2MPfj/y9bl2msysJdAaaBKsoKUARY8n4MkwaOAA1q3fxFtvD8xsq1jxjMz7V13ZIarfHOWUH6B1q0vZsGETW7du9yhZ3uW2LX6QlJxKjRpnUb16VQoVKkS3blcydVr0z3qZ0z6fPGUWLVs2BQJDcgoXLhz1bywAVq9eT+X4OtSo1ZgatRqTlradBo3aRX2HrFy5spQqVRKAokWL0rpVczZsyJ/rmk6GU+nYjya1zmlCzVqNqVmrMRMmTOeeex9nypRZzJ7zBT16XE+JEsWBwDDH8uVPD2uZ06bOpnv3rgB0796VqVNnAVC1amXGjhlEz5738fXX30Zmg3Lh1+MnOz+eN4sXL8Zpp5XIvN+mdYuofr+T3f/+t4u0tG3UqnU2AJdf3ox16zaG+CkpyPxcKTvTzJo4574EbgKSgbvNrIZzbhPQHViQ5fXXAy8H//9lsG0egSGOb5lZLHAaUArY7Zz7zczOBRpnWcZBMyvknDsY0S0LuqRpA7rfch0rV63NnDb+qade5vrrr6JOnfNxzvH992n07vNofsTJs9zyz/xsPt26XemLCT4OO9a2+EF6ejr33f8kM6aPJDYmhqHDxrB2bXT/cchtnw8ZOpqPBg0gNWUeBw4c5PZe93ucNGefjHiPFs2bUK5cWb77Nplnn3udIUNHex0rzypVqsDgj98iNjaGmJgYxo+fyvQZc72OFbZT6dgvXKQwb7/5AuXLl2XK5OGsWLGGjp0ic53NiCzH7+Zvk3nuudcpVKgQAAMHjcj15+bOXch559Zk8aIpAOzb9xu39bgnrIklXn3tPUaN/ICePW5ky5Y0brzpbwA8+cQDnH56Gf71r8CwxUOHDtG4SccT3cSw+PH4ye3c44fzZlYVKpRn/LiPgcDwy9GjJzFr9hfehsqj+x54iuHD/kXhwoXYvHkLve540OtIEsX8PCX+ZwQ6YvWBtQQ6YU2A1wl0NpOA3s65P4NT4o8BOgB/Ajc65zaZWQUCQw7/CqQT6KAtByYB1YENQGmgv3PuCzN7BegCLM/purLDTubwRRERkUjz+8gi/dGVgswPU+I/Wf2mfP1n+sJ3I6N+n2Tny05ZtFOnTERE/MR3716y0R9dKcjUKTuaHztlfh6+KCIiIiIiUU4fnITm54k+REREREREfE+VMhERERERiRi/fqFzflKlTERERERExEOqlImIiIiISMRk6KqykFQpExERERER8ZAqZSIiIiIiEjGqk4WmSpmIiIiIiIiHVCkTEREREZGI0eyLoalSJiIiIiIi4iFVykREREREJGI0+2JoqpSJiIiIiIh4SJUyERERERGJGNXJQlOnTEREpIDTGyYREW9p+KKIiIiIiIiHVCkTEREREZGI0ZT4oalSJiIiIiIi4iFVykREREREJGKcrlwNSZUyERERERERD6lSJiIiIiIiEaNrykJTpUxERERERMRDqpSJiIiIiEjEZOiaspBUKRMREREREfGQKmUiIiIiIhIxqpOFpkqZiIiIiIiIh1QpExERERGRiNE1ZaGpUiYiIiIiIuIhVcpERERERCRi9D1loalSJiIiIiIi4iF1ynykXduWrFm9kPVrF/PIw329jpNnyu+dQQMHsC1tBakp87yOclz8nh/8ffz4OTsov9f8nn/TxqWkLJ9LctJsln45w+s4eeL3fa/8pw6Xz//5kTnnz+D5zcxaAgecc/8N9dq4wlVO+k6NiYlh3ZpFtO94I2lp21n65Qxu6d6Hdeu+Ptmrigjl99alzRqxb99+hgx5m7oXt/I6Tp75Pb+fjx8/Zwfl95rf80OgU9aoSQd++mm311HyxO/7XvnDd+jAVjvpCz3J7qh+Xb52OD76bnzU75PsVCkLX0ugqVcrb9jgYr755js2b97CwYMHGTt2Ml06t/MqTp4pv7cWLU7k592/eB3juPk9v5+PHz9nB+X3mt/z+5nf973yS0FT4DtlZnarma00sxVmNsLMOptZopmlmNlcM6tgZtWBvwEPmFmqmV2a3zkrV6nID2nbMh+nbd1O5coV8zvGcVN+Kcj8fPz4OTsov9f8nh/AOcfMGaNIXDqTO3rd7HWcsPl93yv/qSUjn29+VKBnXzSz2sCTQFPn3I9mVpbAl443ds45M7sDeMQ593cz+wDY55x7PZdl3QXcBWCxpYiJKZFPWyEiIiKR0uKyq9m2bQfly5/OZzNHs2HDJhYtTvQ6loicYgp6pexyYJxz7kcA59zPQDwwy8xWAQ8DtcNZkHNuoHMuwTmXEIkO2batO6gaXznzcXyVSmzbtuOkrydSlF8KMj8fP37ODsrvNb/nBzLz7tr1E5Mnz6RBg7oeJwqP3/e98p9aNNFHaAW9U5aTfwHvOucuBO4GinqcB4Ck5FRq1DiL6tWrUqhQIbp1u5Kp02Z7HStsyi8FmZ+PHz9nB+X3mt/zFy9ejNNOK5F5v03rFqxZs8HjVOHx+75XfiloCvTwRWA+MNHM3nDO/RQcvlgK2Bp8/rYsr/0VKJnfAQ9LT0/nvvufZMb0kcTGxDB02BjWrt3oVZw8U35vfTLiPVo0b0K5cmX57ttknn3udYYMHe11rLD5Pb+fjx8/Zwfl95rf81eoUJ7x4z4GIC4ultGjJzFr9hfehgqT3/e98p9a/HqdV34q8FPim9ltBIYppgMpwETgTWA3gU5bA+dcSzOrBYwncFzd45xblNsyIzElvoiIiIhIdn6YEv+26tfm63vjYd/9J+r3SXYFvVKGc24YMCxb8+QcXrcRuChfQomIiIiInCIyCngRKBy6pkxERERERMRDBb5SJiIiIiIikaM6WWiqlImIiIiIiHhIlTIREREREYmYDNXKQlKlTERERERExEOqlImIiIiISMQ4VcpCUqVMRERERETEQ6qUiYiIiIhIxGR4HcAHVCkTERERERHxkDplIiIiIiIiHtLwRRERERERiRhNiR+aKmUiIiIiIiIeUqVMREREREQiRlPih6ZOmYiIiIiHShYp7nWE47b3z9+8jiBySlCnTEREREREIkZT4oema8pEREREREQ8pEqZiIiIiIhEjHO6piwUVcpEREREREQ8pEqZiIiIiIhEjL6nLDRVykRERERERDykSpmIiIiIiESMZl8MTZUyERERERERD6lSJiIiIiIiEeN0TVlIqpSJiIiIiIh4SJUyERERERGJGM2+GJoqZSIiIiIiIh5Sp0xERERERCLGOZevt1DM7DszW2VmqWaWHGwra2ZzzOzr4P/LBNvNzN4xs01mttLM6mVZzm3B139tZredyD5Sp0xERERERAqay5xzdZ1zCcHHjwHznHM1gXnBxwAdgJrB213A+xDoxAHPAI2AhsAzhztyx0OdMhERERERKeiuBIYF7w8DrsrSPtwFLAVKm1kloB0wxzn3s3NuNzAHaH+8K1enLIoVKVKEL5dM46vkOaxInc8zT/8987nnn3uUtWsWsWrlF/Tre7uHKUOLiYkhadksJk8MHOfDh/2LNasXkpoyj0EDBxAX55/5ZrJvi18MGjiAbWkrSE2Z53WUsOWU+emnHuT7zckkJ80mOWk2Hdpf7mHC3NWqdXZmxuSk2fz843ruvecOXvnnk6xetYDlX81h/LiPKFWqpNdRcxQfX5m5s8excsXnrEidzz39egFQpkxpPpsxinVrFvPZjFGULl3K46Thue/eO1mROp/UlHl8MuI9ihQp4nWkY8rp2PfLsZOTTRuXkrJ8LslJs1n65Qyv4+RZu7YtWbN6IevXLuaRh/vmyzr/9e9/suHbpSxJnJ7j89d168KiL6eyeOk0Pps7htoXnHvC6yxcuDAfD32L5NS5zJk/nqpnVgGgXv2LWLBkCguWTGHhf6dwRec2J7yucHmx708mv517Iikjn29mdpeZJWe53ZUtkgNmm9lXWZ6r4JzbHry/A6gQvF8F+CHLz6YF23JrPy4FvlNmZv3N7CGvc+Tkzz//pHXbbtRPaEP9hLa0a9uSRg3rcdut3YiPr0ztC5pz4UUtGTN2stdRj+nee+5g/fqvMx+PGjWR2hc0p+7FrShWrCi9br/Jw3R5k31b/GL48LFc0elmr2PkSW6Z335nEAkN2pLQoC0zP5vvQbLQNm78JjNjw0bt+e2335k0eSZz5y2kTt3LqVe/DV9//S2PPdrP66g5OnToEA8/8iwX1bmMS5p1pnfvHpx3Xk0efaQv8z9fzHm1mzH/88U8+kj0v0mqXLki/freTqPGHal7cStiY2O5vtuVXsc6ppyOfb8cO7lp3aYrCQ3a0rhJR6+j5ElMTAzvvP0inTrfwoV1LuP666/ivPNqRny9Iz+dQNerc//Adcv3P9Cpw800a9yJ1195j7feeSHsZVc9swpTZnxyVPstt17HL7/sJaFua95/bwj9n3sYgHVrN3J586tpcUkXul7dizfefp7Y2Ni8b1QeebXvTxY/nntOJc65gc65hCy3gdle0sw5V4/A0MS+ZtY82887yN8pIwt8pyza7d//GwCFCsURV6gQzjn+dvetvPDim5kXMu7a9ZOXEY+pSpVKdOzQisGDR2W2ZX0jnZSUSnx8JS+i5VlO2+IXixYn8vPuX7yOkSd+zJyTVpc349tvv2fLlq3MmbuQ9PR0AJYmLqdKleg89nfs2ElK6moA9u3bz/r1X1OlckU6d27H8BHjABg+Yhxduhz3KI18FRcXR7FiRYmNjaV4sWJs377D60jHlNOx75dj51TTsMHFfPPNd2zevIWDBw8yduxkunRuF/H1frkkid279+T6/LLEFPb8shcI/B2tVKVC5nNdr+/CnM/Hs2DJFN54+3liYsJ7q9fxitaMHjkBgMmTPqN5yyYA/P77H5nHXpGiRcKaROFk8Grfn0x+O/dEksvn/0LmcW5r8P87gYkErgn7X3BYIsH/7wy+fCtQNcuPxwfbcms/LgWyU2ZmT5jZRjNbDJwTbKtrZkuDs6pMzDLjSoNgW6qZvWZmq/Mza0xMDMlJs9m+dSXz5i1kWVIKf/1rdbp17cLSL2cwbcoIatQ4Kz8j5ckbA57lsX+8QEZGxlHPxcXFcfPN1zJr1uceJMu7Y22L5J8+vXuy/Ks5DBo4wBfD57p1u5LRYyYd1d6zxw185oNjv1q1eOrWuYDEZSlUOKMcO3YE/kbt2LGTCmeU8zhdaNu27eCNNz9g8zfLSNuSwp69e5kzd6HXsU6IX46dw5xzzJwxisSlM7mjl78q9pWrVOSHtG2Zj9O2bqdy5YoeJjpa91u7Mm9O4Jiudc7ZXH3tFXRocwMtLulCeno6Xa/vEtZyKlWuwNa0QKchPT2dvXv2Ufb0wJwF9RPq8N9lM1i8dBp/v//pzE5aJPlh3x/LqXjuOVWYWQkz+8vh+0BbYDUwBTg8g+JtwOGhaFOAW4OzMDYG9gSHOc4C2ppZmWC/oW2w7bgUuE6ZmdUHbgDqAh2BBsGnhgOPOucuAlYRmE0FYAhwt3OuLpDrWSjr2NWMjP0nLW9GRgYJDdpS7awEGiRcTO3a51CkSGH++ONPGjfpyEeDR/LRwAEnbX0n0xUdW7Nz548sT1mV4/Pv/uslFi1KZPGSZfmcLO9CbYvkjw8+HE6tc5tSP6EtO3bs5LVXn/Y60jEVKlSIzp3aMv4/045o/8dj93Lo0CFGBj+VjlYlShRn7JhBPPjQM/z6676jns+vT8xPROnSpejSuR01ajWmarV6lChRnJtuusbrWMfNL8dOVi0uu5qGjdrTqfMt9O7dg0ubNfI60imj2aWNuOXWrvR/+jUAmrdoQp26tZm3YAILlkyhecsmVK8e+CB/+Mj3WLBkCmP/8xF1L74g8zqxm265NuR6vkpeQdOGHWnd8lruf/BuihQpHNHtOhWcaueeE5WBy9dbCBWAxWa2AlgGTHfOfQa8DLQxs6+B1sHHADOAb4FNwCCgD4Bz7mfgeSApeHsu2HZc/DPDwslzKTDROfcbgJlNAUoApZ1zC4KvGQaMM7PSwF+cc18G20cCnXJaaHCs6kCAuMJVTvo7lT179vLFgiW0a9uStK3bmTgpcLH0pEkz+XjQGyd7dSdF06YJdO7Ulg7tL6do0SKULPkXhg19h9t63MtTTz5A+fKn07vPHV7HDMuxtkXyz86dP2be/+jjT5k8KbonXGnf/jJSUlYdkfvW7t24omNr2rTr5mGy0OLi4hg3ZhCjRk1k0qSZAPxv549UrHgGO3bspGLFM9gZxUOnD2vV6lI2f7eFH38M/J2cOGkmTRon+KpTc5hfjp3stm0LVF927fqJyZNn0qBBXRYtTvQ4VXi2bd1B1fjKmY/jq1TK3B6vnV/7HN5+9yW6XduL3T8HhruaGaNHTuT5/kd/WHvrTYFrQKueWYX3PniFLh1vOeL57dv+R5X4imzbtoPY2FhKljqNn3/afcRrNm74hv37f+O882uRmhLZgUPRvO/DcSqde041zrlvgTo5tP8EtMqh3QE5XkTtnBsMDD4ZuQpcpcxPypUrmznDVtGiRWndqjkbNnzDlCmf0bJFUwBaNG/Cxq+/9TJmrp548mWq/zWBGrUac/Mtffj88yXc1uNebu95I23btOTmW/r64pN2yH1bJH9VrHhG5v2rruzAmjUbPEwT2g3XX3XE0MV2bVvy0EO9ueqaHvz++x8eJgtt0MABrFu/ibfe/v9ro6dNnc2t3bsCcGv3rkydetyjNPLND1u20qhRPYoVKwrA5Zc18+VkPX46drIqXrwYp51WIvN+m9Ytov7fbVZJyanUqHEW1atXpVChQnTrdiVTp832OhZV4isx/NP36H3XQ3yz6bvM9oVffEmXK9tTrlxZAEqXKUV81cq5LOVIM2fM44ZgJefKq9qzaMFSAM6sFp85sUd81crUrPVXtmw57stmwhat+z5cp8q552SJti+PjkYFsVK2EBhqZv8ksP2dgQ+B3WZ2qXNuEdAdWOCc+8XMfjWzRs65RALDHvNNpUoVGPzxW8TGxhATE8P48VOZPmMui5csY8Swd7nvvjvZv+837v7bw/kZ64T9+72X+f77NBYvmgLA9+AIeQAAIABJREFUpEkzeOHFtzxOdWr7ZMR7tGjehHLlyvLdt8k8+9zrDBk62utYx5RT5hYtmlKnzvk45/j++zR693nU65i5Kl68GK1bNT8i49tvvUCRIkX4bGZg3ycmLqdvv8dyW4RnLmnagO63XMfKVWtJTgq8CXrqqZd55bX3GD3yA3r2uJEtW9K44aa/eZw0tGVJKUyYMJ2kZbM4dOgQqalrGPTRp17HOqacjv1HH+nni2MnuwoVyjN+3McAxMXFMnr0JGbN/sLbUHmQnp7Offc/yYzpI4mNiWHosDGsXbsx4usdNPhNLrm0IaefXobV6xfx8ktvExdXCIChg0fxyGP9KFu2NK+98SwQmDG1VYtr2LBhEy89/yb/mTyUmBjj4MFDPPL3Z0n7YduxVgfAJ8PH8cGg10lOncvu3b9wR88HAGjcpD73P3g3Bw8eIiMjg4cf7H9UBS0SvNr3J4sfzz3iLfNrb/JEmNkTBC7g2wlsAZYDc4EPgOIExo32dM7tNrNGBMaPZgALgATn3CXHWn4khi+KiIjIqalkkeJeRzhue//8zesIBd6hA1vN6wyhXBbfJl/fG3+eNifq90l2BbFShnPuReDFHJ5qnEPbmuDkH5jZY0ByJLOJiIiIiEjBUiA7ZXl0hZn9g8C++h7o4W0cERERERH/COe7wwo6dcpCcM6NAcZ4nUNERERERE5N6pSJiIiIiEjEZBTAOSzySlPii4iIiIiIeEiVMhERERERiRjVyUJTpUxERERERMRD6pSJiIiIiIh4SMMXRUREREQkYjI0gDEkVcpEREREREQ8pEqZiIiIiIhEjCploalSJiIiIiIi4iFVykREREREJGKcvjw6JFXKREREREREPKRKmYiIiIiH9v75m9cRjpt5HeAEqX6TP3RNWWiqlImIiIiIiHhIlTIREREREYkYp0pZSKqUiYiIiIiIeEiVMhERERERiRjNvhiaKmUiIiIiIiIeUqVMREREREQiRrMvhqZKmYiIiIiIiIdUKRMRERERkYjRNWWhqVImIiIiIiLiIXXKREREREREPKThiyIiIiIiEjGa6CM0VcpEREREREQ8pEqZiIiIiIhEjFOlLCRVykRERERERDykSpmIiIiIiERMhqbED0mVMp+JiYkhadksJk8c5nWUPGvXtiVrVi9k/drFPPJwX6/j5Jmf8xcpUoQvl0zjq+Q5rEidzzNP/93rSHl2T79epKbMY0XqfO695w6v4xxTfHxl5s4ex8oVn7MidT739OsFwLP9H2b5V3NITprNzOkjqVSpgsdJw+PnYx/8l3/QwAFsS1tBasq8zLZrr+3EitT5HPjjB+rXu8jDdHlzKpx7/Hb8ZOVF9kEDB7A1bQUpWY7frEqXLsW4cR+x/Ks5/HfJNGrXPueE11m4cGE+/fR91q1dzJLFU6lWLR6AVq0uJXHpTFKWzyVx6UxatrzkhNeVF34+diT/qVPmM/fecwfr13/tdYw8i4mJ4Z23X6RT51u4sM5lXH/9VZx3Xk2vY4XN7/n//PNPWrftRv2ENtRPaEu7ti1p1LCe17HCVrv2OfTqdRNNml5BvfptuKJja84+u7rXsXJ16NAhHn7kWS6qcxmXNOtM7949OO+8mrw+4H3q1W9DQoO2TJ8xlyefeMDrqCH5/dj3Y/7hw8dyRaebj2hbs2Y9XbvdyaJFSz1KdXz8fu7x4/FzmFfZhw0fS6dsx29Wjz16DytWrKFe/Tb0vP0+3hjwXNjLrlYtnrlzxh3VfnvPG/ll9x7OO78Zb78ziJdeegKAn376mauu7sHF9Vpze6/7GTrk7bxv0HHy87ETCS6f//Mjdcp8pEqVSnTs0IrBg0d5HSXPGja4mG+++Y7Nm7dw8OBBxo6dTJfO7byOFTa/5wfYv/83AAoViiOuUCGcj4YSnHtuTZYtS+H33/8gPT2dhYuWcvVVHbyOlasdO3aSkroagH379rN+/ddUqVyRX3/dl/maEiWK++J34Pdj34/5Fy1O5OfdvxzRtn79JjZu/MajRCfGz+cePx4/h3mVfXEOx29W551Xi88/XwLAhg3fUK1aPGecUQ6Am266hv8umUZy0mz+/d4rxMSE9za1c+e2jBgR6Kz95z/TufyyZgCkpq5h+/b/AbBmzQaKFStK4cKFj3vb8sLPx454I6o6ZWb2oJmtDt7uN7PqZrbezD41s3VmNt7Migdf28rMUsxslZkNNrMiwfaXzWytma00s9eDbV2Dy1xhZguDbT3M7N0s655mZi2D9/eZ2WtmtsbM5ppZQzP7wsy+NbMu+b5jgt4Y8CyP/eMFMjIyvIpw3CpXqcgPadsyH6dt3U7lyhU9TJQ3fs8PgU/tkpNms33rSubNW8iypBSvI4VtzZr1NGvWiLJly1CsWFE6tL+c+PjKXscKS7Vq8dStcwGJywL7+/nnHmXzN0nceOPV9H/2NY/Theb3Y9/v+U8Ffj73+Pn4idbsK1et5eqrOgLQIKEu1arFE1+lEueeW4OuXbvQvMVVJDRoS3p6OjfddE1Yy8y6renp6ezZs5fTTy9zxGuuueYKUlJWc+DAgZO7QWFkgujZ/17JcC5fb34UNZ0yM6sP9AQaAY2BO4EywDnAv51z5wF7gT5mVhQYClzvnLuQwIQlvc3sdOBqoLZz7iLgheDinwbaOefqAOF0qkoA851ztYFfg8tpE1x2jnV2M7vLzJLNLDkjY3+etz+UKzq2ZufOH1mesuqkL1sKhoyMDBIatKXaWQk0SLj4pIzjzy/r12/itdfeY+aMkcyY9impK9aQnh79H06UKFGcsWMG8eBDz2RWyZ56+hXOOrsBo0ZNpG+fnh4nFIk8P5975OR79dV3KV26JMlJs+nb93ZSU1eTnpHB5Zc1o97FF7L0yxkkJ83mssubcdZZZwIwbtxHJCfNZsqUEdSvX4fkpNkkJ83mtlu7hbXO88+vxUsvPk6fvo9GctNETkg0zb7YDJjonNsPYGYTgEuBH5xzS4Kv+QS4F5gDbHbObQy2DwP6Au8CfwAfm9k0YFrw+SXAUDMbC0wII8sB4LPg/VXAn865g2a2Cqie0w845wYCAwHiClc56V30pk0T6NypLR3aX07RokUoWfIvDBv6Drf1uPdkryoitm3dQdUslY34KpXYtm2Hh4nyxu/5s9qzZy9fLFgSuAB5zQav44RtyNDRDBk6GoAXnn+MtLTtHic6tri4OMaNGcSoUROZNGnmUc+PHDWBqVNG8OxzAzxIFz6/H/t+z38q8eO5x8/HT7Rm//XXfdxx54OZj7/euJRvv/2eZpc0ZMQn43jyyZeP+pmuXQOTO1WrFs/HH71J6zZdj3j+8LZu3bqd2NhYSpUqyU8/7QYCl36MG/cxt99+H99++30Et+xI0br/veLX67zyU9RUyo4h+28x19+qc+4Q0BAYD3Qi2LFyzv0NeBKoCnwVrKgd4sjtL5rl/kH3/4PeM4A/g8vJwKOO7BNPvkz1vyZQo1Zjbr6lD59/vsQ3HTKApORUatQ4i+rVq1KoUCG6dbuSqdNmex0rbH7PX65cWUqVKglA0aJFad2qORs2+Ov6lPLlTwegatXKXHVVB0aNnuhxomMbNHAA69Zv4q23B2a21ahxVub9Lp3b+eJ34Pdj3+/5/c7v5x4/Hz/Rmr1UqZIUKlQIgF6338TixYn8+us+5n++mGuu7pR5ri9TpjRnnlklrGVOmzab7t0DHbVrr72Cz79YkrmuKZOH88QTL/HfL5MjsDW5i9b9L9ErmipliwhUs14GjMBQwe7A22bWxDn3JXATsBjYAFQ3sxrOuU3B1y0ws9OA4s65GWa2BPgWwMzOds4lAolm1oFA5+w7AkMhY4AqBDpzEiHp6encd/+TzJg+ktiYGIYOG8PatRtD/2CU8Hv+SpUqMPjjt4iNjSEmJobx46cyfcZcr2Plybgxgyh7ehkOHjzEvfc+wZ49e72OlKtLmjag+y3XsXLVWpKTAn+En3rqZXr2vIFatc4mIyODLVu20qfvYx4nDc3vx74f838y4j1aNG9CuXJl+e7bZJ597nV+3v0Lb7/5AuXLl2XK5OGsWLGGjseY4S5a+P3c48fj5zCvso/Icvxu/jaZ5557PbMTNnDQCM47tyYfD34L5xxr127grrseAmDduq95pv+rzJwxipgYyzzXb9myNeQ6Bw8ZzdCh77Bu7WJ27/6Fm2/pA0CfPj05++zqPPnEA5mz3XboeCO7dv0Uoa3/f34+diLBr9d55SeLplmQzOxB4Pbgw4+ASQSqXclAfWAt0N0595uZtQJeJ9CxTAJ6A2WByQSqXga87pwbFhwKWTPYNg+4P7iOT4LLXUfg+rX+zrkvzGyfc+60YKb+wD7n3OFJQzKfy00khi+KiIiIRBvzOsAJOhXesB06sDXqfw21yifk667euCs56vdJdlHVKcvOzKoD05xzF3gcJU/UKRMREZGCwHfvfLM5Fd6w+aFTVrN8/Xzd1V/v+irq90l2frimTERERERE5JQVTdeUHcU59x3gqyqZiIiIiIhIXkR1p0xERERERPxNE32EpuGLIiIiIiIiHlKlTEREREREIkZfHh2aKmUiIiIiIiIeUqVMREREREQixrkMryNEPVXKREREREREPKRKmYiIiIiIREyGrikLSZUyERERERERD6lSJiIiIiIiEeP0PWUhqVImIiIiIiLiIVXKREREREQkYnRNWWjqlImIiIjIcfH7W+24mFivI4gA6pSJiIiIiEgE6Zqy0HRNmYiIiIiIiIdUKRMRERERkYjJUKUsJFXKREREREREPKROmYiIiIiIiIc0fFFERERERCLG+X6ezshTpUxERERERMRDqpSJiIiIiEjEaEr80FQpExERERER8ZAqZSIiIiIiEjEZuqYsJFXKREREREREPKRKmYiIiIiIRIyuKQtNlTIREREREREPqVImIiIiIiIRk6FKWUiqlEW5QQMHsC1tBakp8zLbrr22EytS53Pgjx+oX+8iD9Mdm5+z56Rd25asWb2Q9WsX88jDfb2Ok2d+zh8fX5m5s8excsXnrEidzz39enkd6Zhyy/ts/4dZ/tUckpNmM3P6SCpVquBx0vDk9G/ZT/x87APcd++drEidT2rKPD4Z8R5FihTxOlKe+H3/+z3/po1LSVk+l+Sk2Sz9cobXcfLEy3NPTEwMS5fOYMKEIbm+5qqrOvDHH1uodxLez1SvXpWFCyezZs1CRox4j0KFCgFw7713kJIyj6SkWcycOYozz6xywuuS6KROWZQbPnwsV3S6+Yi2NWvW07XbnSxatNSjVOHxc/bsYmJieOftF+nU+RYurHMZ119/FeedV9PrWGHze/5Dhw7x8CPPclGdy7ikWWd69+4R1flzy/v6gPepV78NCQ3aMn3GXJ584gGvo4Ylp3/LfuH3Y79y5Yr063s7jRp3pO7FrYiNjeX6bld6HStsft//fs9/WOs2XUlo0JbGTTp6HSVPvDz39Ot3Oxs2bMr1+dNOK0G/freTmLg8T8vt3v06nnzy6HP/Cy/8g3/96yNq127OL7/soUeP6wFYsWINTZteQYMG7ZgwYTovvvh43jYkSjjn8vXmR1HXKTOzHmZWOYzXPWdmrY9z+e8G7//NzG49npz5ZdHiRH7e/csRbevXb2Ljxm88ShQ+P2fPrmGDi/nmm+/YvHkLBw8eZOzYyXTp3M7rWGHze/4dO3aSkroagH379rN+/ddUqVzR41S5yy3vr7/uy3xNiRLFffOHI6d/y37h92MfIC4ujmLFihIbG0vxYsXYvn2H15HC5vf97/f8fufVuadKlYp06NCKIUNG5/qaZ555iNdff58///wzsy0mJoaXXnqcxYunkpQ0izvuCL9D2bJlUyZMCFQyP/lkPF26BI6zBQu+5Pff/wBg2bIU4uMrHc8miQ9EXacM6AGE7JQ55552zs09kRU55z5wzg0/kWVIwVC5SkV+SNuW+Tht63YqR3GnIDu/58+qWrV46ta5gMRlKV5HCUv2vM8/9yibv0nixhuvpv+zr3mc7tTn92N/27YdvPHmB2z+ZhlpW1LYs3cvc+Yu9DpW2Py+//2eHwIVipkzRpG4dCZ39PJnxTu/vfZafx5//CUyMjJyfL5u3QuIj6/EZ5/NP6K9Z88b2Lv3V5o168wll3SmZ88bqV69asj1nX56Gfbs2Ut6ejoAW3M5znr0uJ5Zsz4/ji3yXgYuX29+FLJTZmbVzWy9mQ01s41m9qmZtTazJWb2tZk1NLOyZjbJzFaa2VIzuyj4s/3N7KEsy1odXF51M1tnZoPMbI2ZzTazYmZ2HZAAfGpmqcG2p80sKfizA83MgssaGnw9ZvadmT1rZsvNbJWZnRvOxmfNZ2Z1g9lXmtlEMysTbP/CzF4xs2XB7b80rztZRE6OEiWKM3bMIB586Jkjqk7RKqe8Tz39Cmed3YBRoybSt09PjxNKtCtduhRdOrejRq3GVK1WjxIlinPTTdd4HUt8pMVlV9OwUXs6db6F3r17cGmzRl5HimodOrRi164fSUlZlePzZsarrz7FY4+9cNRzrVo15+abryUxcSaLFk3m9NNLU6NGdcqWLU1i4kwSE2fy9NN/5447bs58XLv2OWHluvHGq6lX7yLeeOPDE9o+iV7hzr5YA+gK3A4kATcBzYAuwOPAD0CKc+4qM7scGA7UDbHMmsCNzrk7zWwscK1z7hMz6wc85JxLBjCzd51zzwXvjwA6AVNzWN6Pzrl6ZtYHeAi4I8xtO2w4cI9zboGZPQc8A9wffC7OOdfQzDoG248aNmlmdwF3AVhsKWJiSuRx9RLNtm3dQdX4/y/gxlepxLZt/hlC5Pf8EBjCNW7MIEaNmsikSTO9jhNSqLwjR01g6pQRPPvcAA/SFRx+P/ZbtbqUzd9t4ccffwZg4qSZNGmcwMiREzxOFh6/73+/5wcy8+7a9ROTJ8+kQYO6LFqc6HGq6NW0aQJXXNGG9u0vo0iRIpQs+ReGDHmLnj0Dbwn/8pfTOP/8c5g9ewwAFSqUZ/z4j7nuul6YwQMPPM3cHKrZjRp1AALXlFWrVpUXXnjziOdLlSpJbGws6enpVMl2nF1+eTMefbQfbdp048CBA5Ha9Ijyy3B9L4U7fHGzc26Vcy4DWAPMc4G9uwqoTqCDNgLAOTcfON3MSoaxzNTg/a+Cy8nJZWaWaGargMuB2rm87vBfqGMtK0dmVgoo7ZxbEGwaBjTPy7KdcwOdcwnOuQR1yE49Scmp1KhxFtWrV6VQoUJ063YlU6fN9jpW2PyeHwKzcK1bv4m33h7odZSw5JS3Ro2zMu936dyODRv8d32l3/j92P9hy1YaNapHsWJFAbj8smasX/+1x6nC5/f97/f8xYsX47TTSmTeb9O6BWvWbPA4VXR76qlXqFGjEeeccwm33tqPL774b2aHDGDv3l+Jj6/LOedcwjnnXMKyZSlcd10vli9fydy5C7nrru7ExQVqHjVqnEXx4sXCWu+CBV9yzTWBiVhuueU6pk4NHGd16tTm3Xf/ybXX9mLXrp9O8tZKNAm3UvZnlvsZWR5nBJdxMJefO8SRHb+iuSwzHTjqqDWzosC/gQTn3A9m1j/bMnLKmM7J//61SC77mD4Z8R4tmjehXLmyfPdtMs8+9zo/7/6Ft998gfLlyzJl8nBWrFhDxyicGc3P2bNLT0/nvvufZMb0kcTGxDB02BjWrt3odayw+T3/JU0b0P2W61i5ai3JSYE/VE899TIzs43njxa55e3Z8wZq1TqbjIwMtmzZSp++j3mcNDw5/VseMjT3C+Cjid+P/WVJKUyYMJ2kZbM4dOgQqalrGPTRp17HCpvf97/f81eoUJ7x4z4GIC4ultGjJzFr9hfehsqDaDr3PP30g3z11SqmT5+T62sGDx5FtWrxLF06AzPjxx9/omvXO8Na/pNP/pPhw9+lf/+HSU1dw9ChgUrcP//5BCVKFGfkyPcB+OGHbVx3XXR/LYwcHwtVTjSz6sA059wFwcdDg4/HH34OmA/scs49b2YtgTedcxeb2S1AJ+fcDWZWj8DQx7ODi866zIeA05xz/c1sKvCGc+5zMysNbCBQnYoFlgLjg6/LmuM7Ah23H80sAXjdOdcyl+3pEXxtv2Anb59z7nUzWwH0c84tCraXcs49YGZfEBxOaWblgGTnXPVj7bO4wlVUoxURERGJcnExsV5HOGF//LHFvM4QymnFz8rX98b7ftsc9fsku5NV9ekPDDazlcBvwG3B9v8At5rZGiARCOfjpaHAB2b2O9AEGASsBnYQ6NRFym3B9RYHvgV0Bb6IiIiIiERcyEqZ5J0qZSIiIiLRT5Wy/FGiePV8fW+8/7fvon6fZBeN31MmIiIiIiJSYOTrpBX5ycx6Avdla17inOvrRR4RERERkYIoQyPzQtLwxQjQ8EURERGR6Kfhi/mjWLFq+fre+Pffv4/6fZLdKVspExERERER76kIFJquKRMREREREfGQKmUiIiIiIhIxDlXKQlGlTERERERExEOqlImIiIiISMTomrLQVCkTERERERHxkCplIiIiIiISMaqUhaZKmYiIiIiIFBhm1t7MNpjZJjN7zOs8oE6ZiIiIiIhEkMvn27GYWSzwHtABOB+40czOPzlbevzUKRMRERERkYKiIbDJOfetc+4AMBq40uNMuqYsEg4d2GqRXL6Z3eWcGxjJdUSS8nvHz9lB+b2m/N7xc3ZQfq8pv7f8nv9kiPR74+zM7C7grixNA7P8DqoAP2R5Lg1olF/ZcqNKmT/dFfolUU35vePn7KD8XlN+7/g5Oyi/15TfW37P7zvOuYHOuYQst6jvFKtTJiIiIiIiBcVWoGqWx/HBNk+pUyYiIiIiIgVFElDTzM4ys8LADcAUjzPpmjKfivoSbAjK7x0/Zwfl95rye8fP2UH5vab83vJ7/lOKc+6QmfUDZgGxwGDn3BqPY2H6MjcRERERERHvaPiiiIiIiIiIh9QpExERERER8ZA6ZSIiIiIiIh5Sp0zkFGdmXcNpi1Zmdkk4bSKnGjM76stWzayIF1kKIp17RCQ/qVPmA2bW2cx8+bsys1gz+9TrHAXcP8Jsi1b/CrMt6pjZ82YWl+VxSTMb4mWmvDCzCmb2sZnNDD4+38x6eZ0rL8yslpkNMrPZZjb/8M3rXGH6OOsDMzsNmOFRljwzsxHhtEUx3557RMR/NCW+P1wPvGVm/yEwbed6rwOFyzmXbmbVzKywc+6A13nywsxWATlNT2qAc85dlM+R8sTMOgAdgSpm9k6Wp0oCh7xJFT4zawI0Bcqb2YNZnipJYApbP4gDEs2sJ1ABeBd/vakbCgwBngg+3giMIVtnIcqNAz4ABgHpHmfJqzQz+7dzro+ZlQGmE9gOv6id9YGZxQL1PcoStlPh3JPL3689QDLwgnPup/xPFT4z6wQ8D1QjcB49/He3pKfB8iBYVe3P0dvwVy9zSfRSp8wHnHO3mFlJ4EZgqJk5Am+URjnnfvU2XVi+BZaY2RRg/+FG59wb3kUKSyevA5ygbQT+AHcBvsrS/ivwgCeJ8qYwcBqB89RfsrTvBa7zJFEeOef+YWZzgURgN9DcObfJ41h5Uc45N9bM/gGZ3+3it47NIefc+16HOB7OuafN7FUz+4BAZ+Zl59x/vM4VSvB4eRwoZmZ7DzcDB/DH9zX5/twDzCTwIcTI4OMbgOLADgIftnT2JlbY3gKuAVY5/35308cE/tZ+hf8+EBIP6HvKfMTMTge6A/cD64AawDvOuaj+5N3Mnsmp3Tn3bH5nOV5mVg2o6Zyba2bFgDifdIgxs0LOuYNe5zheZlbNOfe91zmOh5k1B94HPgEuBMoAvZxz2zwNFiYz+wK4FpjjnKtnZo2BV5xzLbxNFj4z6w/sBCYCfx5ud8797FWmUMzsmqwPgaeAZcBnAM65CV7kyisz+6dzzk9DpY/g83PPcudcvZzazGyVc+5Cr7KFw8w+B1o55zK8znK8zCzROdfI6xziH+qU+YCZXQn0INAJGw4Mc87tNLPiwFrnXHUP44XNzIo7537zOkdemdmdwF1AWefc2WZWE/jAOdfK42hh8esQCjN7yzl3v5lNJYdhpM65Lh7EyhMzWwb0cM6tDT6+BnjJOXeut8nCY2b1CAy3vABYDZQHujrnVngaLA/MbHMOzVF9/Ie47tA5527PtzAnKDjssiZQ9HCbc26hd4nCZ2blgUcIDMPMmv9yz0KFycxWAHc655YFHzcAPnLO1TGzFOfcxd4mPLZg3ueBBRz5YUq0j7DJZGYvExjuOoEjt2G5Z6EkqqlT5gNmNgz4OKc/ZGbWyjk3z4NYYQuOz/8YOM05d6aZ1QHuds718ThaWMwsFWgIJB7+Q+aHTxoPM7P15DCEwgfXFNR3zn1lZjlWZZxzC/I7U16ZWWzwusrTAJxz+8zs9Gjf94cFZ/pLB84h0JnfAMQ45/485g+KAGZ2B3AfEA+kAo2BL/3QqQEws9kErqF8CPgbcBuwyzn3qKfBwhDs1AwmMAzTCAy97AWsBa5wzo31MF5IwX2/D1gFZFbLfDbC5vMcmp1fjn/Jf+qU+YCZvZL9j0BObdHKzBIJjMOfkqVTs9o5d4G3ycJzeAjC4U8Xg7PpLY/2iT4OOxWGUJhZYeBcAhWzDX6ZNMbMLgBGAGUJvDHaBdzqnFvjabAwHWsIlFeZ8srMCgG9gebBpi+AD/0wpDdYqbkTqE6Wa8D9UikLTjbRAFjqnKtrZucSqBRfE+JHo4KZfeWcq29mKw+f780syTnXwOts4TKzUgDOuT1eZ8kLP71HEDlZNNGHP7QBsnfAOuTQFrWccz/YkV+546eLXheY2eGL1tsAfYCpHmfKi8/N7DV8OoTCzK4gMHveNwQ6NmeZ2d3OuZneJgvLQOBB59znAGbWksDseU29DBWKmVUEqhA45i8msN8hMPtccc+CHZ/3gULAv4PPCaq2AAASz0lEQVSPuwfb7vAsUfgmA4uAufjrnHnYH865P8wMMyvinFtvZud4HSoPDnfctwfPQ9sIfMAS9YKdsWcIfhhhZguA53zUOZthZm2dc7O9DnK8sv8OCAzF9NPvQPKZOmVRzMx6E+gAnG1mK7M89Rdgyf+1d69RelblGcf/F0o5I6AUQTGgVipGQJDlobgKIqIFWSgHpUAt4qHSpShIpZ5wIda2ohVQQaVWRNSKiCdAUc4HFUhQAmiWykFUWqsCAgIKXP2w95u8iUNmJkyyn51cvy8zz/Mya90kk5lnv/ve192mqqVyi6TnAq7vWh9KCSrpxVspD3DzgNdR5gSd3LSi6Rntkj1z7J6BXlooPgDsNEotlPQkSjR4D4uytUYLMgDbF0paq2VBU7Qr5Rzr44HxMxx3UlL1erK97a3Hrs+v5216sGYvHREP4eeS1gO+DHxL0m1AT8EZx9QH68MpZyvXpQRt9eCTlHOg+9brAympzV3sUlJ2t98i6T7K4ri7SHz6/zuI5SztiwNWfxmsD7wPOHLspTuHnBy2OEmPAY4DXkD5wXoucGgP52rqXJ3reglmWBEt3i6ksuV6RQ8tRJLOBOZSWhgBDgC2s/3SdlVNnaS9eohgXxJJcynhJD+t108EvthDC6akY4DLbXczMPqh1LOhjwK+0VH78SmU31W31+sNgGN7aB+V9H3b20x2L5ad/B3EdGWnbMDqFvcdwH41BW0Hyg7HZUA3izLbvwb2b13H0qghDfMlPcH2z1rXszQkbQT8C7CJ7RdL2hJ4ju1BDwAeiwW/StLZwBco3//7AFc2K2wKJJ1q+0BK69lmlNZRgIuBwT/QjTlP0gfpu/3mCEoL7w2UN4VmAQe1LWnKDgXe1utugcoIhets32n7IpV5m8+gzO3rwVajBRmUMQq1nbcH90jawfalsCCF957GNU2ZyjiRP9FLcmfV9d9BLH/ZKeuApHdStr9HD3Z7AqfbPqZdVVMn6SmUMxwb2Z4taStgj47qv5jyIHEFiw6/HnwkO4CkcygtE2+vcciPBK4eenpkz7Hgkq6n7AyfA+xEfZgevd7LTrekMyjtN6fUWwcCW/cS1DBSUyRHZ5nmJz1y+ZB0NbCt64OGpFWAq3rYpYQFsfI72r6tXm8AXDT0n50ANeX405TdSSjD619p+5qH/qrhUBmFMrI6JQF5Tk/JhZK2ofzsfBTld8BvKSNSemmfjuUsi7IOSJpPeRC6t16vAXzfdhcHpusB4yMoiWc9pi92G8kOC9v/NDabJi0Uy5akN1LORDwR+MX4Swx8Rta4FaH9pvP0xa53Cx7i+2dBkuHQSfo7yhnK0+utfYD32j71ob+qLUmHjV8CozOsd1N+9nQz52ucpE2BD9neq3Ut01V3iLH9u9a1xLClfbEPv6S8U3RvvV6NRR/0hm5N21cslr54f6tipqOeKftY52fK7pb0aOpOTW0p6qb9rJ4BOo4y48jAd4A3276haWFLYPt44HhJJ9p+fet6HoYVof2m5/TFI8Y+X7BbQD8hPTfUNyhOrNeHAIP9d7s425+WdBUL/7xf5joIfsDWqR+3oIwj+AplcXYApdujVz8Hntq6iKmQdIDtzyy2QGb0DNTrwjiWvSzK+nAHcJ2kb1EeSncBrpB0PIDtN7Ysbgp+XRPzRouCvYFb25Y0NSvCmTLgMOCrlBTPy4ANKXPjevFZ4CPAKBzjFcDnWJgqOVidL8ig7DCdMpp1RG2BaljP0ug2fdH2S8avR7sFjcpZGv8AHA+8o15/G3htu3Kmry7Chr4QW8B1uHJtu9/W9p31+t2U1NouSDqBhS3fqwDbUEKTejDanVxnif9VxGLSvtgBSUt8CLJ9ypJeb63udHycMpvpNuBGYH/bXUQj936mDKCeI9uC8o7p/B5at0YmaneS9IPFHrRjGZD0iPrGRLftNz2nLy6uJo9eZ3vL1rXEsNVjD1uNzk/Wc5XXdHTsYfy5537gJts9jQKKmLYsymKZWXzrHliD8o7X3dDPFn6vZ8rG0gsnZPtLS3q9tXqoHsqcuNuAz1PeOX05sL7tf25V28qiJhaeAXzSdk+zBReQtDMl6GaR9MXx+XFD9RC7BTfZPqBdVVPXY+vxikLS2ykBYWfWW3sC/237fe2qWjqS1gc27Sik5Pglvd5Bd1M0kkVZByTtDryH8jDxSDqJRZZ0VP108d72l1DmTHXxYNGrsfTCP6fsUp5fr3eizD7avUlhUyTpRsqDnCZ4uZuwjJ5JWofSLnoQZVHwSeDzve2Y9Zq+2PtugaTvUlqPP1dvvQJ4g+3Btx6vCOoonefVy4ttX92ynumQdCGwB+WZZw7wK8rvrTe3rGsqeu9uinayKOuApJ9QJsDPc4d/YbX9b7ex3vZ1gLNsT5gsNhSSLrW9g6Q7GYszp5NF8YikcylRyLfW642BT9netW1l0ZO6Y/xZYD3gi8B7bP+kbVWTk/SPwGleOAB4fWA/2x9d8le2J+lgLzZPUNK/2j6yVU3TkdbjWFqjtGBJr6bskh3VU3LnVEg6wfYbWtcRw5Ggjz7cAlzb44Ks2gj4w9j1H+q9QbO9Q/3Y+2HdTUcLsup/gSe0KmZpSJoNbElJoANKMlq7ilYONX10N8pO2WbAB4DTKO++nw08pVlxU/ca2x8ZXdi+TdJrWJjGOGR7SbrX9mkAkj7C2L+BoRprPT5H0pEs2np8drPCoiePrG8g7gu8vXUxy8hftS4ghiWLsj78E3B2nfe1oO2mlzNZlAGWV0ga723/VLtypkfShAuYjtIYz5P0TRa2EL2ckoLWhdoGuyNlUXY28GLgUsr3VSxbPwYuAN5v+/Kx+198qBlaA/QISRobYPwI4M8a1zRVewFflfQg8CLgdtsHN65pKuawaOvx68ZeM5DzoDGZo4FvApfavrKeT/xx45oilqm0L3agtp/dBcwDHhzdH0Xf9qDz3vZ5Y5erA5tTzqU8rVFJ0ybppSwcnnux7TOX9N8PSf3z3xq42vbWkjYCPmN7l8alrfAkrW37rtZ1PByS3k85j/uxeut1wC22D29X1ZKN7TRBidX+MnAZ8C4A279tUVdEzBxJc3tMgY1lJztlfdjE9uzWRTwctufSz4yRRdh++vh1XWAe0qicpXU5JSjA9DdA9F7bD0q6v0az/wrYtHVRK4n1JJ0K7ED53rkEONT2z9uWNS1vpczGGs2M+xZwcrtypmS00zQiShvp39TrLkJuJK1K+XMfvSF0IfCxnkZyRBuS/h04hjKs/hvAVpTkzs80LWxmTRRiFSuxVVoXEFNytqQXti4iirrA7CY9TNK+lIXY3pT+/O/VAd6DV+cyXSNpPeATlIfVuZRo7Vj2/osyeHxjYBPga/VeT9YAPmF7b9t7UxZkqzWuaYlsb17TRY8EtrG9OSX58gf0Nfj9RGA7yvm9j9bPT2xaUfTihTXldXfgJuDJwBFNK5p5x7UuIIYl7YsdqOl/a1ICMv5IZ+l/vVts3toqwLbAo3tJL5T0A2AX27+q1xsC3+4lAU3SvNFupaTNgHV7mVfTO0nft73NZPeGrMayv2DUhilpbeBc289tW9nkRmlzknagjEU5FnhXL5HyEyUtJn0xpkLStbZnSzqZMuz9G71970h6CmUhORpnBIDt5zcrKgYt7Yt9eBSwP7C57aNr8MTGjWtamYynL94PnEUZqNuLVUYLsuo39LVLPlfS9ravtH1T62JWMr+RdAALQ2L2o3z/9GT18XNxtu+StGbLgqbhgfpxN8pu31mSjmlZ0DQ9IOlJtn8KC4ZJPzDJ10QAfF3Sjyjti6+vbybe27im6TodOInS5ZHv+5hUdso6IOlESsDH820/tc7ZOdf29o1Li4Gr7X//CTyORdMXr7H91maFTUP9xfxk4GbgbhbuFK8w82qGStIs4ATgOZQzTpdThv/e0rSwaZB0GaXmufV6O+DDtp/TtrLJSfo68AtgF8oO/T3AFb3sFkjamdLuekO9tRlwkO0LmhUV3aiBN3fYfqC+kbKu7f9pXddUSZpje7vWdUQ/sijrwCihZzRMsd7rahu/Z5K+xqKH7hdhe4/lWM60SbqWktq2Q711SWfpi7Mmum/75uVdy8pG0inAm2zfVq83AI61/aq2lU2dpO0pc7J+SVnQPxZ4ue05TQubgvog+iJgnu0f17lNT7d9buPSpkTS6sDhwM7A7cCVwH/Y7m3HI5azCUJiLgJO6ikkRtK7KcFUZ7LoOKOkp8aEsijrgKTvAc8FrqyLsw0pO2XPaFzaSkHScZQHuVHq036UAcxfBrB9UaPSpqQ+WH/Y9pWta4m+jL8RtKR7Q1cf8Laol/N7erDrmaQvAL+jDBwH+FtgPdv7tKsqelDPkq0KnFJvHQg8YPvV7aqaHkk3TnDbNcQn4k9kUdYBSftTWs62pfyA2ht4h+3Tmxa2kpB0le1nTnZvqCZo/wMg7X8xmRoSs+NiO2UXLT4mYugkzaYMH199dM92ho8vY5Kut73lZPciFpeQmFgZJeijA7ZPkzSH0gIiYE/bP2xc1spkLUlPtH0DLDisvlbjmqaji5TIGKQPAN+RNHoDaB/gvQ3rmTZJRwE7UhZlZwMvBi4Fsihb9uZKerbt7wJIehZwVeOaog/dhsRIer7t8yW9bKLXbX9pedcUfciirBO2fwT8qHUdK6k3ARdKGj+s/tp25UxPzl7F0rL9aUlXAaMI55fZvr5lTUthb2Br4GrbB0naiIWtyLEMSJpHOYe7KnC5pJ/V61nk91hMzRHABfX3rijfOwe1LWnK/ho4H3jJBK8ZyKIsJpRFWcTk1gVmA5sDe1DO9/26aUURy0ldhPW2EBt3r+0HJd0vaV3KwftNWxe1gtu9dQHRN9vnSfoLFj0Let+SvmYobB9VP/ayiIyByKIsYnLvtH26pHUoOwbHAicCXQxwjVhZ1ZEQ10hajzIraA5wF/CdpoWt4LI7Hw9XTe48hJIabOASSSf1ltwpaTfgaSx6nvXodhXFkCXoI2ISo7Q5Se+jRFN/tscEuoiVkaR5o2ASSZtRZh1d07SoiFiimtx5JwtbjbtL7pR0ErAmsBNwMqWV+grbBzctLAYri7KISfQ+wDViZZaREBH9WRGSOyVdY3ursY9rA+fYfl7r2mKYVmldQEQH9gW+Cexq+3ZgA8oh5IgYvmdREiR/KukaSfMkZacsYtjmSnr26KLT5M576sffS9oE+COwccN6YuBypixiErZ/z1haku1bgVvbVRQR05CREBH92Y4/Te6cP0r27GTO5tfredb3A3Mp/x8nty0phiztixERERExGJJmAesDo1a/i4HbR6/3ECYjabVRYqSk1ShhH/f2kiIZy1/aFyMiIiJiSPYETgUeA2xYP9/D9s09LMiqBSmvtu+zfQdJfo0lSPtiRERERAzJwcCzbd8NIOnfKAuaE5pWNQWSHgs8DlhD0jMow6+hzDxds1lhMXhZlEVERETEkAh4YOz6ARYuboZuV+DvgccDHxy7fyfwthYFRR9ypiwiIiIiBkPSYcArgTPrrT2BT9n+ULuqpkfSXrbPaF1H9COLsoiIiIgYFEnbAjvUy0tsX92ynumq4R57AZsx1plm++hWNcWwpX0xIiIiIgbF9lxKlHyvvgLcAcwBkrgYk8pOWURERETEDJJ0re3ZreuIfiQSPyIiIiJiZl0u6emti4h+ZKcsIiIiImIGSboeeDJwI6V9UYBtb9W0sBisLMoiIiIiImaQpFkT3e9o+HUsZwn6iIiIiIiYWdn1iGnJTllERERExAySNI+yMBOwOrA5MN/205oWFoOVnbKIiIiIiBlke5GQjzp37ZBG5UQHslMWEREREbGMSZq3+GItYiQ7ZRERERERM0jSYWOXqwDbAr9sVE50IIuyiIiIiIiZtc7Y5/cDZwFnNKolOpD2xYiIiIiIZUDS2gC272pdSwzbKq0LiIiIiIhYkUiaLelq4DrgOklzJM1uXVcMVxZlEREREREz6+PAYbZn2Z4FHF7vRUwoi7KIiIiIiJm1lu0LRhe2LwTWaldODF2CPiIiIiIiZtYNkt4JnFqvDwBuaFhPDFx2yiIiIiIiZtargA0piYtnAI8BDmpaUQxaFmURERERETPrScCmlGftPwN2Bi5uWlEMWiLxIyIiIiJmkKT5wFuAa4EHR/dt39ysqBi0nCmLiIiIiJhZ/2f7a62LiH5kpywiIiIiYgZJ2hnYDzgPuG903/aXmhUVg5adsoiIiIiImXUQ8JfAqixsXzSQRVlMKDtlEREREREzSNJ821u0riP6kfTFiIiIiIiZdbmkLVsXEf3ITllERERExAyS9ENKLP6NlDNlAmx7q6aFxWBlURYRERERMYMkzZrofiLx46FkURYREREREdFQzpRFREREREQ0lEVZREREREREQ1mURURERERENJRFWUREREREREP/D4d5I1auodi8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBmtkfCRRINz"
      },
      "source": [
        "# **TIME FOR SOME HYPERPARAMETER TUNNING!! ...**"
      ],
      "id": "nBmtkfCRRINz"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "judicial-performance"
      },
      "source": [
        ""
      ],
      "id": "judicial-performance",
      "execution_count": null,
      "outputs": []
    }
  ]
}