ch12_DeepLearning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5d34fee4",
      "metadata": {
        "id": "5d34fee4"
      },
      "source": [
        "# Deep Learning\n",
        "\n",
        "Artificial Neural Networks (ANN) is a biologically inspired programming paradigm which enables a computer to learn from observational data. The origins of neural networks can be traced, at least, until 1949, when D.Hebb proposed a simple learning principle for neurons: 'fire together, wire together'. Later, in 1957, F.Rosenblatt invented the first learning machine: the perceptron. These simple models were further developed for more than 30 years until 1988, when the backpropagation algorithm was proposed by Rumelhart et al. This algorithm was the first computational approach for training modern multilayer neural networks.\n",
        "\n",
        "In spite of the fact that the discovery of the backpropagation algorithm started a rich research field leading to interesting models such as convolutional neural networks or recurrent neural networks, its severe practical limitations at that time provoked after a few years what is known as the neural net winter, a period of reduced funding and interest in the field. The neural net winter ended in 2012, when a neural network called AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge. The network achieved a top-5 error of 15.3\\%, more than 10.8 percentage points ahead of the runner-up. These results, which were unexpectedly good, led to the current deep learning boom.\n",
        "\n",
        "Deep Learning is a powerful set of techniques (and tricks) for learning in neural networks with a high number of layers. Its notoriety builds upon the fact that it currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing, but at the same time some critics describe deep learning as a black box where data goes in and predictions come out without any kind of transparency.\n",
        "\n",
        "# Neural Networks\n",
        "\n",
        "Neural nets are a specific method for learning from data, a method that is based on a very simple element, the *neuron unit*. A neuron unit (or 1-layer neural network) is a mathematical function of this kind:\n",
        "\n",
        "$${\\mathbf y} = \\sigma(\\mathbf{w}^T \\cdot {\\mathbf x} + b)$$\n",
        "\n",
        "where ${\\mathbf x}$ represents an input element in vector form, $\\mathbf{w}$ is a vector of weights,  $\\sigma$ is a non-linear function and $b$ a scalar value. $(\\mathbf{w},b)$ are called the parameters of the function. The output of this function is called the *activation* of the neuron.\n",
        "\n",
        "Regarding the non-linear function, historically the most common one was the Sigmoid function, but nowadays there are several alternatives that are supposed to be better suited to learning from data, such as ReLU and variants.\n",
        "\n",
        "# Perceptron\n",
        "The perceptron is a simple model which, given an input vector ${\\mathbf x}$ of $m$ values $$(x_1, x_2, ..., x_n)$$ often called input features or simply features, outputs either 1 (yes) or 0 (no). Mathematically, the model is defined as:\n",
        "\n",
        "$${\\mathbf y} =  {\\mathbf \\sigma} \\Big( \\mathbf {w}  \\cdot  {\\mathbf x} + { b} \\Big)$$\n",
        "\n",
        "where $\\sigma$ is known as the activation function, which in the case of the original perceptron is the linear threshold unit defined as:\n",
        "\n",
        "$${\\displaystyle f(\\mathbf {x} )={\\begin{cases}1&{\\text{if }}\\ \\mathbf {w} \\cdot \\mathbf {x} +b>0,\\\\0&{\\text{otherwise}}\\end{cases}}}\n",
        "$$\n",
        "\n",
        " As you can note, the Perceptron is really similar to Logistic Regression, but instead of providing a probabilistic output, it provides an output based on a hard threshold, the reason why Logistic Regression is usually preferred over Perceptron.\n",
        "\n",
        "\n",
        "# Multi-Layer Perceptron\n",
        "Simple neurons, such us Perceptrons, can be organized in larger structures by applying to the same data vector different sets of weights, forming what is called a *layer*, and by stacking layers one on top of the output of the other.  \n",
        "\n",
        "It is important to notice that a multilayer neural network can be seen as a composition of matrix products (matrices represent weights) and non-linear activation functions. For the case of a 2-layer network, the outcome is:\n",
        "\n",
        "$$ {\\mathbf y} = {\\mathbf \\sigma}\\Big( W^1  {\\mathbf \\sigma}\\Big( W^0  {\\mathbf x} + {\\mathbf b}^0 \\Big) + {\\mathbf b}^1 \\Big)$$\n",
        "\n",
        "where ${\\mathbf \\sigma}$ represents a vectorial version of the sigmoid function and $W^i$ are the weights of each layer in matrix form.  \n",
        "\n",
        "What is interesting about this kind of structure is that it has been shown that even a neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function of $\\mathbf{R}^n$. This fact makes neural networks a sound candidate to implement learning from data methods. The question is then: how to find the optimal parameters, ${\\mathbf w} = (W^i,{\\mathbf b})$, to approximate a function that is implicitly defined by a set of samples $\\{({\\mathbf x}_1, {\\mathbf y}_1), \\dots,  ({\\mathbf x}_n, {\\mathbf y}_n)\\}$?\n",
        "\n",
        "From a technical point of view, not only neural networks but most of the algorithms that have been proposed to infer models from large data sets are based on the iterative solution of a mathematical problem that involves data and a mathematical model. If there was an analytic solution to the problem, this should be the adopted one, but this is not the case for most of the cases. The techniques that have been designed to tackle these problems are grouped under a field that is called optimization. The most important technique for solving optimization problems is *gradient descend*.\n",
        "\n",
        "> The training of models like $ {\\mathbf y} = {\\mathbf \\sigma}\\Big( W^1  {\\mathbf \\sigma}\\Big( W^0  {\\mathbf x} + {\\mathbf b}^0 \\Big) + {\\mathbf b}^1 \\Big)$ (or bigger!) can be readily performed by applying *automatic differentiation* to a loss function.\n",
        "\n",
        "> In the case of regression: $L = \\frac{1}{n} \\sum_{i=1}^n \\Big({\\mathbf y}_i - {\\mathbf \\sigma}\\Big( W^1  {\\mathbf \\sigma}\\Big( W^0  {\\mathbf x}_i + {\\mathbf b}^0 \\Big) + {\\mathbf b}^1 )\\Big)\\Big)^2 $\n",
        "\n",
        "> In the case of two-class classification: $L = \\frac{1}{n} log(1 + exp(-y_i {\\mathbf \\sigma}\\Big( W^1  {\\mathbf \\sigma}\\Big( W^0  {\\mathbf x} + {\\mathbf b}^0 \\Big) + {\\mathbf b}^1 \\Big))) $\n",
        "\n",
        "## Training neural networks\n",
        "\n",
        "Training neural networks involves various methods, with one of the most common and simplest being the backpropagation algorithm. This method, published by Paul Werbos in 1982, was not entirely new; the algorithm he presented had already been discovered in the 1960s. In fact, Werbos himself mentioned this method in his PhD thesis in 1970, highlighting its potential for neural network learning.\n",
        "\n",
        "Backpropagation is based on the chain rule and entails iteratively adjusting the weight connections to minimize a predefined loss function. This loss function quantifies the difference between the actual values and the values estimated by the network.\n",
        "\n",
        "## Playing with neural networks\n",
        "Before to continue, I recommend you to visit this website http://playground.tensorflow.org\n",
        "and try to create a NN architecture to solve the following problems:\n",
        "\n",
        "![Three toy datasets](https://github.com/DataScienceUB/introduction-datascience-python-book/blob/master/datasets.png?raw=1)\n",
        "\n",
        "Upon closer examination of both non-linear scenarios—the concentric and X-or problems—it becomes evident that, despite employing multiple layers with numerous neurons in each layer, the utilization of a linear activation function proves insufficient for resolving the issue. Conversely, by incorporating a non-linear activation function such as sigmoid or ReLU, we can attain a solution with a solitary hidden layer.\n",
        "\n",
        "\n",
        "# Deep Learning in `keras`\n",
        "\n",
        "Let us see how can we implement our first neural network model. There are several libraries we can use with Python, such as Theano, Tensorflow, PyTorch, Microsoft Cognitive Toolkit (CNTK) and Keras, to name a few. These libraries provide powerful tools for developing and training neural networks, making them essential for machine learning and deep learning tasks.\n",
        "\n",
        "In this book, we have chosen to work with Keras due to its simplicity and versatility.\n",
        "Keras is a high-level neural networks library, written in Python and capable of running on top of TensorFlow, Theano, or CNTK. It offers a user-friendly and intuitive interface for building and training neural networks, making it an excellent choice for developers who want to quickly prototype and experiment with deep learning models.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa3652cd",
      "metadata": {
        "id": "fa3652cd"
      },
      "source": [
        "We will start with a simple dataset: the concentric circles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "c111d250",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "c111d250",
        "outputId": "4017c8a3-908c-44ce-ff52-55031b99ea72"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_circles\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X, y = make_circles(n_samples=1000,\n",
        "                            factor=.4,\n",
        "                            noise=0.05)\n",
        "\n",
        "# Let us visualize the dataset\n",
        "reds = y == 0\n",
        "blues = y == 1\n",
        "plt.figure()\n",
        "plt.scatter(X[reds, 0], X[reds, 1],\n",
        "            c=\"red\", s=20, edgecolor='k')\n",
        "plt.scatter(X[blues, 0], X[blues, 1],\n",
        "            c=\"blue\", s=20, edgecolor='k')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4afd6f70",
      "metadata": {
        "id": "4afd6f70"
      },
      "source": [
        "Let us divide the data into train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "30a439ab",
      "metadata": {
        "id": "30a439ab"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "134f6fbe",
      "metadata": {
        "id": "134f6fbe"
      },
      "source": [
        "The core data structure of Keras is a model, a way to organize layers. The main type of model is the ``Sequential model``, a linear stack of layers.\n",
        "\n",
        "Creating a MLP with two hidden layers is as straightforward as follow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "24b9fc79",
      "metadata": {
        "id": "24b9fc79"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(units=64, input_dim=2, activation=\"relu\"))\n",
        "model.add(Dense(units=64, activation=\"relu\"))\n",
        "model.add(Dense(units=1, activation=\"sigmoid\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "636ca097",
      "metadata": {
        "id": "636ca097"
      },
      "source": [
        "Here is a breakdown of the code:\n",
        "* The first line initializes the Sequential model.\n",
        "* The second line defines the first hidden layer with 64 units. Since this is the initial layer, we specify the number of input features (input\\_dim=2) for our data, and we use the ReLU activation function.\n",
        "* Next, we add a second hidden layer with 64 units, also using the ReLU activation function.\n",
        "* Finally, we add the output layer with 1 unit, employing the sigmoid activation function. In this problem, the sigmoid function is suitable as it allows us to output values in the range [0, 1], making it suitable for binary classification tasks."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df233c18",
      "metadata": {
        "id": "df233c18"
      },
      "source": [
        "To obtain a quick overview of our model’s architecture we can call `model.summary()`. This function displays useful information about the layers, their types, output shapes, and the number of parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "58b5231f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58b5231f",
        "outputId": "bba37dff-6d79-4e0f-81f2-5de14f7bfa21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_15 (Dense)            (None, 64)                192       \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4417 (17.25 KB)\n",
            "Trainable params: 4417 (17.25 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9682161a",
      "metadata": {
        "id": "9682161a"
      },
      "source": [
        "As you can see, the first hidden layer consists of 192 parameters, while the second layer contains 4,160 parameters. These parameter counts are calculated as follows: for the first hidden layer, there are 2x64 connection weights plus an additional 64 biases, and for the second hidden layer, there are 64x64 connections plus an additional 64 biases"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8db0245d",
      "metadata": {
        "id": "8db0245d"
      },
      "source": [
        "Once your model looks good, configure its learning process with ``.compile()``:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "efa2f9be",
      "metadata": {
        "id": "efa2f9be"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb0bead8",
      "metadata": {
        "id": "cb0bead8"
      },
      "source": [
        "Here we are defining our loss function and the optimization method. The optimizer is used to tune the parameters of the neural network to minimize the loss function. In keras we have several optimizers methods, from the classical Stochastic Gradient Descend (SGD) to adaptative models such as RMSprop, Adam or Adagrad."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15e6e7f4",
      "metadata": {
        "id": "15e6e7f4"
      },
      "source": [
        "If you need to, you can further configure your optimizer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "ad58b56e",
      "metadata": {
        "id": "ad58b56e"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=Adam(learning_rate=0.01),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "767c6bc3",
      "metadata": {
        "id": "767c6bc3"
      },
      "source": [
        "With your model compiled, you can now proceed to train it on your data in batches:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "6db02d29",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6db02d29",
        "outputId": "6c3dd569-ab61-4c10-cff7-624c92c736ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "21/21 [==============================] - 1s 2ms/step - loss: 2.5375e-04 - accuracy: 1.0000\n",
            "Epoch 2/5\n",
            "21/21 [==============================] - 0s 2ms/step - loss: 3.3326e-05 - accuracy: 1.0000\n",
            "Epoch 3/5\n",
            "21/21 [==============================] - 0s 2ms/step - loss: 1.1348e-05 - accuracy: 1.0000\n",
            "Epoch 4/5\n",
            "21/21 [==============================] - 0s 2ms/step - loss: 4.6309e-06 - accuracy: 1.0000\n",
            "Epoch 5/5\n",
            "21/21 [==============================] - 0s 2ms/step - loss: 3.2574e-06 - accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x79c9b3963610>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "model.fit(X_train, y_train,\n",
        "          batch_size=32,\n",
        "          epochs=5,\n",
        "          verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9c108cc",
      "metadata": {
        "id": "b9c108cc"
      },
      "source": [
        "We can evaluate the model's performance on the test set using the following code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "d2ec75d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2ec75d4",
        "outputId": "e056e4d8-7409-4941-fe8f-87f8730436c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11/11 [==============================] - 0s 2ms/step - loss: 0.6779 - accuracy: 0.5303\n"
          ]
        }
      ],
      "source": [
        "loss_and_metrics = model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "254a7dfb",
      "metadata": {
        "id": "254a7dfb"
      },
      "source": [
        "Additionally, we can obtain predictions or probability scores for new data using the following code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "61f94929",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61f94929",
        "outputId": "91b1b3e3-8f61-4532-9f06-1be5d60f8bc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11/11 [==============================] - 0s 5ms/step\n",
            "predicted class: 0.0  -- prob(y==1): 0.48375526\n",
            "predicted class: 0.0  -- prob(y==1): 0.49654844\n",
            "predicted class: 1.0  -- prob(y==1): 0.5035339\n",
            "predicted class: 0.0  -- prob(y==1): 0.494181\n",
            "predicted class: 0.0  -- prob(y==1): 0.48309192\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "probabilites = model.predict(X_test)\n",
        "predicted_class = np.round(probabilites)\n",
        "for i in range(5):\n",
        "    print(\"predicted class:\",predicted_class[i][0],\n",
        "          \" -- prob(y==1):\",probabilites[i][0])\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "895f4734",
      "metadata": {
        "id": "895f4734"
      },
      "source": [
        "## Practical Case: Practical Case: Building an image classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c97d658",
      "metadata": {
        "id": "4c97d658"
      },
      "source": [
        "In this practical case we will work with a synthetic dataset called 'medmnist,' which can be accessed at https://medmnist.com. This dataset is a comprehensive collection of standardized biomedical images, resembling the MNIST dataset, and comprises 12 datasets for 2D images and 6 datasets for 3D images. All the images in this dataset have been pre-processed to dimensions of 28 x 28 (for 2D images) or 28 x 28 x 28 (for 3D images) and come with corresponding classification labels. This design ensures that users do not require any prior background knowledge. Specifically, we will focus on the 'BloodMNIST' dataset.\n",
        "\n",
        "To install this dataset, you can easily execute the following command:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "82cec424",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82cec424",
        "outputId": "ace6862f-377b-4f72-e583-82bd8dd98b12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting medmnist\n",
            "  Downloading medmnist-2.2.3-py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from medmnist) (1.23.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from medmnist) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from medmnist) (1.2.2)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from medmnist) (0.19.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from medmnist) (4.66.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from medmnist) (9.4.0)\n",
            "Collecting fire (from medmnist)\n",
            "  Downloading fire-0.5.0.tar.gz (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.3/88.3 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from medmnist) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from medmnist) (0.15.2+cu118)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire->medmnist) (1.16.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire->medmnist) (2.3.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->medmnist) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->medmnist) (2023.3.post1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (1.11.2)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (3.1)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (2.31.3)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (2023.8.30)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (1.4.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->medmnist) (23.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->medmnist) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->medmnist) (3.2.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->medmnist) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->medmnist) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->medmnist) (1.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->medmnist) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->medmnist) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->medmnist) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->medmnist) (16.0.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision->medmnist) (2.31.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->medmnist) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->medmnist) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->medmnist) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->medmnist) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision->medmnist) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->medmnist) (1.3.0)\n",
            "Building wheels for collected packages: fire\n",
            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116934 sha256=a8e085b75d59dd03bfa94e61b49d2c447a1a00435b2c0f2e27a69f93d0f98bdb\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/d4/f7/9404e5db0116bd4d43e5666eaa3e70ab53723e1e3ea40c9a95\n",
            "Successfully built fire\n",
            "Installing collected packages: fire, medmnist\n",
            "Successfully installed fire-0.5.0 medmnist-2.2.3\n"
          ]
        }
      ],
      "source": [
        "pip install medmnist"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08dae22a",
      "metadata": {
        "id": "08dae22a"
      },
      "source": [
        "We can import and load the dataset as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "977e1f95",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "977e1f95",
        "outputId": "a533be9f-58ea-4d2b-973b-31967fa226ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://zenodo.org/record/6496656/files/bloodmnist.npz?download=1 to /root/.medmnist/bloodmnist.npz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35461855/35461855 [01:24<00:00, 420093.16it/s]\n"
          ]
        }
      ],
      "source": [
        "from medmnist import BloodMNIST\n",
        "dataset = BloodMNIST(split='train', download=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e7a0117",
      "metadata": {
        "id": "9e7a0117"
      },
      "source": [
        "We can print some info from the dataset such as the description or the class names:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "449c4e15",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "449c4e15",
        "outputId": "d41691d6-d5cf-472a-bd5e-b1f4cdb9aefc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'python_class': 'BloodMNIST',\n",
              " 'description': 'The BloodMNIST is based on a dataset of individual normal cells, captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. It contains a total of 17,092 images and is organized into 8 classes. We split the source dataset with a ratio of 7:1:2 into training, validation and test set. The source images with resolution 3×360×363 pixels are center-cropped into 3×200×200, and then resized into 3×28×28.',\n",
              " 'url': 'https://zenodo.org/record/6496656/files/bloodmnist.npz?download=1',\n",
              " 'MD5': '7053d0359d879ad8a5505303e11de1dc',\n",
              " 'task': 'multi-class',\n",
              " 'label': {'0': 'basophil',\n",
              "  '1': 'eosinophil',\n",
              "  '2': 'erythroblast',\n",
              "  '3': 'immature granulocytes(myelocytes, metamyelocytes and promyelocytes)',\n",
              "  '4': 'lymphocyte',\n",
              "  '5': 'monocyte',\n",
              "  '6': 'neutrophil',\n",
              "  '7': 'platelet'},\n",
              " 'n_channels': 3,\n",
              " 'n_samples': {'train': 11959, 'val': 1712, 'test': 3421},\n",
              " 'license': 'CC BY 4.0'}"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "dataset.info"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07929b5f",
      "metadata": {
        "id": "07929b5f"
      },
      "source": [
        "We load the dataset and the corresponding training, validation and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "5915b3bb",
      "metadata": {
        "id": "5915b3bb"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "dataset_name = 'bloodmnist'\n",
        "data = np.load(os.path.join(dataset.root, dataset_name + '.npz'))\n",
        "\n",
        "X_train, y_train= data['train_images'],data['train_labels']\n",
        "X_val, y_val    = data['val_images'],  data['val_labels']\n",
        "X_test, y_test  = data['test_images'], data['test_labels']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bfa854af",
      "metadata": {
        "id": "bfa854af"
      },
      "source": [
        "After loading the data, you can check some information about the dataset,\n",
        "such as the number of training examples and the dimensions of each sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "27520379",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27520379",
        "outputId": "4249cb46-2239-460e-8db8-f118238b533f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 11959 training examples  \n",
            "Each sample has a size of 28 by 28 pixels\n"
          ]
        }
      ],
      "source": [
        "num_train, height, width, channels  = X_train.shape\n",
        "print(\"There are\", num_train,\"training examples  \")\n",
        "print(\"Each sample has a size of\", height,\"by\", width,\"pixels\")\n",
        "\n",
        "num_val  = X_val.shape[0] # there are 1712 test examples\n",
        "num_test = X_test.shape[0] # there are 3421 test examples"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8f912f9",
      "metadata": {
        "id": "e8f912f9"
      },
      "source": [
        "Following this, you can proceed to determine the number of unique classes\n",
        "in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "41111dad",
      "metadata": {
        "id": "41111dad"
      },
      "outputs": [],
      "source": [
        "num_classes = np.unique(y_train).shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b8eb438b",
      "metadata": {
        "id": "b8eb438b"
      },
      "source": [
        "To ensure proper data handling, we also normalize the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "90fde128",
      "metadata": {
        "id": "90fde128"
      },
      "outputs": [],
      "source": [
        "X_train = X_train.astype('float32')\n",
        "X_val = X_val.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255.0 # Normalise data to [0, 1] range\n",
        "X_val   /= 255.0 # Normalise data to [0, 1] range\n",
        "X_test  /= 255.0 # Normalise data to [0, 1] range"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "75952ccb",
      "metadata": {
        "id": "75952ccb"
      },
      "source": [
        "With these steps, the data is correctly loaded, prepared, and ready for further processing. To visualize some samples, we can use the following code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "2c32a414",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "2c32a414",
        "outputId": "1a16cd64-9aec-4a86-d29b-fd9024812737"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 24 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(16,12))\n",
        "for i in range(24):\n",
        "    plt.subplot(4,6,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(X_train[i].reshape((height,width,channels)), cmap=plt.cm.binary)\n",
        "    plt.xlabel(dataset.info['label'][str(y_train[i][0])][:20])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8ef4008",
      "metadata": {
        "id": "d8ef4008"
      },
      "source": [
        "We are now ready to create our first model. Let us create a simple MLP model. In the following example, we create a MLP model with two hidden layers with 256 neurons each and one output layer with as many neurons as the number of different categories in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "4fb64658",
      "metadata": {
        "id": "4fb64658"
      },
      "outputs": [],
      "source": [
        "from keras.layers import Flatten\n",
        "\n",
        "## DEFINE PARAMETERS\n",
        "batch_size = 128\n",
        "epochs = 20\n",
        "\n",
        "## CREATE THE MODEL\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=[height, width, channels]))\n",
        "model.add(Dense(32, activation='relu', input_shape=(height*width*channels,)))\n",
        "model.add(Dense(16, activation='relu'))\n",
        "model.add(Dense(num_classes, activation='softmax'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e254422a",
      "metadata": {
        "id": "e254422a"
      },
      "source": [
        "We can check the model summary to see its architecture and the number of parameters:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "41d48c20",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41d48c20",
        "outputId": "73df2a3a-7623-4fdb-d586-96f511ec0c73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_1 (Flatten)         (None, 2352)              0         \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 32)                75296     \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 16)                528       \n",
            "                                                                 \n",
            " dense_23 (Dense)            (None, 8)                 136       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75960 (296.72 KB)\n",
            "Trainable params: 75960 (296.72 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aabf71e3",
      "metadata": {
        "id": "aabf71e3"
      },
      "source": [
        "As it  can see, the created models has 75.960 paraments that needs to be optimized. That's a big number.\n",
        "In order to optimize all this parameters we need to define the loss function and the optimizer method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "76729c6c",
      "metadata": {
        "id": "76729c6c"
      },
      "outputs": [],
      "source": [
        "## COMPILE MODEL -> loss + optimizer\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c04f25e3",
      "metadata": {
        "id": "c04f25e3"
      },
      "source": [
        "Now, we are ready to train it!!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "92439994",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92439994",
        "outputId": "552df791-1c01-4615-953b-f26f336b429f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "94/94 [==============================] - 1s 7ms/step - loss: 1.8744 - accuracy: 0.2739 - val_loss: 1.6145 - val_accuracy: 0.4393\n",
            "Epoch 2/20\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 1.4855 - accuracy: 0.4771 - val_loss: 1.3759 - val_accuracy: 0.5333\n",
            "Epoch 3/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 1.3274 - accuracy: 0.5420 - val_loss: 1.2647 - val_accuracy: 0.5806\n",
            "Epoch 4/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 1.2196 - accuracy: 0.5911 - val_loss: 1.2187 - val_accuracy: 0.5754\n",
            "Epoch 5/20\n",
            "94/94 [==============================] - 1s 8ms/step - loss: 1.1269 - accuracy: 0.6135 - val_loss: 1.0692 - val_accuracy: 0.6379\n",
            "Epoch 6/20\n",
            "94/94 [==============================] - 1s 8ms/step - loss: 1.0423 - accuracy: 0.6370 - val_loss: 0.9979 - val_accuracy: 0.6472\n",
            "Epoch 7/20\n",
            "94/94 [==============================] - 1s 8ms/step - loss: 0.9826 - accuracy: 0.6516 - val_loss: 0.9609 - val_accuracy: 0.6665\n",
            "Epoch 8/20\n",
            "94/94 [==============================] - 1s 9ms/step - loss: 0.9422 - accuracy: 0.6670 - val_loss: 0.9487 - val_accuracy: 0.6425\n",
            "Epoch 9/20\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 0.9021 - accuracy: 0.6787 - val_loss: 0.8848 - val_accuracy: 0.6963\n",
            "Epoch 10/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.8769 - accuracy: 0.6881 - val_loss: 0.8547 - val_accuracy: 0.6980\n",
            "Epoch 11/20\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 0.8501 - accuracy: 0.6936 - val_loss: 0.8107 - val_accuracy: 0.7120\n",
            "Epoch 12/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.8342 - accuracy: 0.6965 - val_loss: 0.7883 - val_accuracy: 0.7114\n",
            "Epoch 13/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.8062 - accuracy: 0.7099 - val_loss: 0.7968 - val_accuracy: 0.7021\n",
            "Epoch 14/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.8041 - accuracy: 0.7058 - val_loss: 0.7557 - val_accuracy: 0.7161\n",
            "Epoch 15/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.7706 - accuracy: 0.7215 - val_loss: 0.7374 - val_accuracy: 0.7237\n",
            "Epoch 16/20\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 0.7692 - accuracy: 0.7154 - val_loss: 0.7425 - val_accuracy: 0.7225\n",
            "Epoch 17/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.7373 - accuracy: 0.7297 - val_loss: 0.6985 - val_accuracy: 0.7301\n",
            "Epoch 18/20\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.7328 - accuracy: 0.7288 - val_loss: 0.6755 - val_accuracy: 0.7424\n",
            "Epoch 19/20\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 0.7059 - accuracy: 0.7391 - val_loss: 0.6977 - val_accuracy: 0.7243\n",
            "Epoch 20/20\n",
            "94/94 [==============================] - 1s 6ms/step - loss: 0.7091 - accuracy: 0.7354 - val_loss: 0.6871 - val_accuracy: 0.7371\n"
          ]
        }
      ],
      "source": [
        "history_mlp = model.fit(X_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=epochs,\n",
        "                    verbose=1,\n",
        "                    validation_data=(X_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73a69be2",
      "metadata": {
        "id": "73a69be2"
      },
      "source": [
        "When plotting the learning curves we can see that after 20 epochs training and validation are still improving without any overfitting signal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "f5be0b7c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "f5be0b7c",
        "outputId": "ad33532e-e7f9-4b7f-f81e-7468547f7fb4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_history(histories):\n",
        "    fig, ax = plt.subplots(1, 2,figsize=(15, 7))\n",
        "    for (history,name) in histories:\n",
        "        ax[0].plot(history.history['accuracy'],'o-.',label  = name+' train')\n",
        "        ax[0].plot(history.history['val_accuracy'],'x-',label  = name+' test')\n",
        "\n",
        "        ax[0].set_title('model accuracy')\n",
        "        ax[0].set_ylabel('accuracy')\n",
        "        ax[0].set_xlabel('epoch')\n",
        "        ax[0].legend(loc='upper left')\n",
        "\n",
        "    for (history,name) in histories:\n",
        "        ax[1].plot(history.history['loss'],'o-.',label  = name+' train')\n",
        "        ax[1].plot(history.history['val_loss'],'x-',label  = name+' test')\n",
        "\n",
        "        ax[1].set_title('model loss')\n",
        "        ax[1].set_ylabel('loss')\n",
        "        ax[1].set_xlabel('epoch')\n",
        "        ax[1].legend(loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "plot_history([(history_mlp,'MLP')])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ff1d70e",
      "metadata": {
        "id": "7ff1d70e"
      },
      "source": [
        "Finally, we evaluate the model on the test set, and the results should be similar to the validation set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "2a0f8acc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a0f8acc",
        "outputId": "515126c5-bd99-4d25-d34a-c658c342f323"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.7173313498497009\n",
            "Test accuracy: 0.734288215637207\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "898578e9",
      "metadata": {
        "id": "898578e9"
      },
      "source": [
        "# Convolution Neural Networks\n",
        "While multilayer perceptrons (MLPs) are powerful neural network models, they have limitations when it comes to processing raw image data. MLPs are structured in layers, with each neuron in a layer receiving input from all neurons in the previous layer. This design is suitable for certain problems, particularly those with a fixed number of relatively unstructured parameters.\n",
        "\n",
        "However, let's consider what happens when we feed raw image data, such as a 200x200 pixel image, into an MLP with 1024 neurons:\n",
        "> The first hidden layer alone would require more than 40 million parameters.\n",
        "\n",
        "The situation quickly becomes unmanageable as image sizes grow larger, way before reaching the kind of images people usually want to work with in real applications.\n",
        "\n",
        "To address this challenge, a common solution is to downsample images to a size where MLPs can be applied more effectively. However, this downsampling process risks losing valuable information. Ideally, we'd like to perform useful image processing before downsampling without significantly increasing the number of parameters.\n",
        "\n",
        "Fortunately, Convolutional Neural Networks (CNNs) offer an efficient solution. CNNs take advantage of the inherent structure in images. They assume that pixels spatially close to each other collaborate more closely to form specific features of interest, compared to pixels at opposite corners of the image. Additionally, if a particular feature is essential for defining an image's label, its presence anywhere within the image should be equally important, regardless of its location. CNNs leverage these principles to effectively process and extract features from images, making them a key tool in image analysis and recognition tasks.\n",
        "\n",
        "\n",
        "At the core of the CNNs lies the convolution operation. Given a two-dimensional image, $I$, and a small matrix, $K$ (known as a convolution kernel) of size $h \\times w$, which encodes a method for extracting image features, we compute the convolved image, $I∗K$, by overlaying the kernel on the image in all possible positions and recording the sum of element-wise products between the image and the kernel:\n",
        "\n",
        "$$\n",
        "output(x,y) = (I \\otimes K)(x,y) = \\sum_{m=0}^{M-1} \\sum_{n=1}^{N-1} K(m,n) I(x-n, y-m)\n",
        "$$\n",
        "\n",
        "This convolution operator forms the fundamental basis of the convolutional layer of a CNN. The layer is completely specified by a certain number of kernels, $K$, and it operates by computing the convolution of the output images of a previous layer with each of those kernels, afterwards adding the biases (one per each output image). Finally, an activation function, $\\sigma$, may be applied to all of the pixels of the output images.\n",
        "\n",
        "Typically, the input to a convolutional layer will have $d$ channels (e.g., red/green/blue in the input layer), in which case the kernels are extended to have this number of channels as well.\n",
        "\n",
        "Note that, since all we're doing here is addition and scaling of the input pixels, the kernels may be learned from a given training dataset via gradient descent, exactly as the weights of an MLP. In fact, an MLP is perfectly capable of replicating a convolutional layer, but it would require a lot more training time (and data) to learn to approximate that mode of operation.\n",
        "\n",
        "### Building Blocks of a CNN\n",
        "\n",
        "Now, let's explore the main components that make up a CNN:\n",
        "\n",
        "\n",
        "Input Layer: Although CNNs are versatile and not limited to images, they are commonly used for image data, which is typically represented as $h \\times w \\times d$ ($h$ = Height, $w$ = Width, $d$ = Channels). This is the first layer that receives the input data.\n",
        "\n",
        "\n",
        "**Input Layer**\n",
        "\n",
        "Although CNNs are versatile and not limited to images, they are commonly used for image data, which is typically represented as $h \\times w \\times d$ ($h$ = Height, $w$ = Width, $d$ = Channels). This is the first layer that receives the input data.\n",
        "\n",
        "**Convolutional Layer**\n",
        "\n",
        "This is the heart of a CNN and often consists of multiple layers in the network. These are the layers that will perform most of the architecture's computational work.\n",
        "\n",
        "**Sub-sampling or Pooling Layer**\n",
        "\n",
        "This layer serves a dual purpose. First, it creates a smoother representation, reducing sensitivity to noise and variations in the data. Second, it reduces the size of the representation, which lowers the number of parameters and system complexity, helping to prevent overfitting. Sub-sampling typically involves resizing the input to a smaller output size, controlled by the stride parameter. A common approach is to use max-pooling with a filter size of 2x2 (or 4x4) and a stride of two pixels."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b1c6d15",
      "metadata": {
        "id": "6b1c6d15"
      },
      "source": [
        "## Pracical Case: Improve our image classifier with a CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "158cd753",
      "metadata": {
        "id": "158cd753"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22b6bcd9",
      "metadata": {
        "id": "22b6bcd9"
      },
      "source": [
        "Here we can see a simple CNN to tacke our problem:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "e3e39bfb",
      "metadata": {
        "id": "e3e39bfb"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters = 32, kernel_size = (3,3),\n",
        "                 activation = 'relu',\n",
        "                 input_shape = (height,width,channels)))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "model.add(Conv2D(filters = 16, kernel_size = (3,3),\n",
        "                 activation = 'relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(8,activation='softmax'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ca54408",
      "metadata": {
        "id": "7ca54408"
      },
      "source": [
        "The network is composed of the following blocks:\n",
        "\n",
        "* **First Convolutional Layer:** This initial layer consists of 32 filters, each with dimensions of $3𝑥3$, and employs the ReLU activation function. Since it serves as the starting point in our network, we specify the dimensions of our input images. Consequently, the output shape of this layer consists of 32 new images, each with dimensions of $26𝑥26$. This output size is derived from our input images, which are of size $28𝑥28𝑥3$, accounting for the three color channels (r, g, b). Notably, the filters in this layer are sized at $3𝑥3𝑥3$ to match the input data's three channels (r, g, b).\n",
        "\n",
        "* **Pooling Layer:** Following the convolutional layer, there is a pooling layer with a pool size of 2, resulting in a halving of the input's dimensions. Therefore, the output shape becomes $13x13x32$.\n",
        "\n",
        "* **Second Convolutional Layer:** This layer features 16 filters, each with dimensions of $3x3x32$. Consequently, the output shape is $11x11x16$.\n",
        "\n",
        "* **Output Layer:** Finally, we have the output layer, which is a dense layer comprising 8 neurons, matching the number of categories in our problem. Note that we previously flattened the input data since the dense layer requires a 1D array."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "410b7b90",
      "metadata": {
        "id": "410b7b90"
      },
      "source": [
        "As you can see with the `model.summary()` the number of parameters has been substancially reduced with respecto MLP (see Figure 1)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "20835f63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20835f63",
        "outputId": "c2907f03-642b-41f9-ebe9-5e041c196b8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 13, 13, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 16)        4624      \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 1936)              0         \n",
            "                                                                 \n",
            " dense_24 (Dense)            (None, 8)                 15496     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21016 (82.09 KB)\n",
            "Trainable params: 21016 (82.09 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c90abf2",
      "metadata": {
        "id": "6c90abf2"
      },
      "source": [
        "Next, compile the model and define the optimization method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "4075b6b9",
      "metadata": {
        "id": "4075b6b9"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01267f72",
      "metadata": {
        "id": "01267f72"
      },
      "source": [
        "Reshape the training and test data into an image shape:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "b89ca50f",
      "metadata": {
        "id": "b89ca50f"
      },
      "outputs": [],
      "source": [
        "X_train = X_train.reshape(num_train,height,width,channels)\n",
        "X_test = X_test.reshape(num_test,height,width,channels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b99a255",
      "metadata": {
        "id": "9b99a255"
      },
      "source": [
        "Now, once the data is preared we fit the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "9f312633",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f312633",
        "outputId": "3e1146c2-5ee4-499e-c16f-9180eac14f65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11959, 28, 28, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "92f80c88",
      "metadata": {
        "id": "92f80c88"
      },
      "outputs": [],
      "source": [
        "## TRAIN THE MODEL\n",
        "history_cnn = model.fit(X_train, y_train,\n",
        "                    batch_size=128,\n",
        "                    epochs=epochs,\n",
        "                    verbose=0,\n",
        "                    validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7e499101",
      "metadata": {
        "id": "7e499101"
      },
      "source": [
        "We plot the learning curves to compare model performance of MLP vs. CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "05c51019",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "05c51019",
        "outputId": "a9e2d5df-40e1-4718-f5c9-b7af9d105f21"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABNEAAAJwCAYAAACnPPcPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUxdfA8e/dzaY30ikhtNCLBALSq9JEBRSsNIVXEFAjCEhHKRZQQZr+aCIKSlMQUYpUKWpApPcWEtJ73d37/rFkYUlCOgnhfJ5nn2Rn587MTQiZnJ05o6iqqiKEEEIIIYQQQgghhMiRpqQHIIQQQgghhBBCCCFEaSdBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCGEEEIIIYQQQgghciFBNCFEqXLlyhUURWHFihX5vnb37t0oisLu3buLfFxCCCGEEKL0eRBzxxUrVqAoCleuXCnQGIUQZYcE0YQQQgghhBBCCCGEyIUE0YQQQgghhBBCCCGEyIUE0YQQopRLSkoq6SEIIYQQQgghxCNPgmhCCAtTp05FURTOnTvHK6+8gouLC56enkyaNAlVVbl+/TrPPPMMzs7O+Pj4MGfOnCxthIeH89prr+Ht7Y2trS2NGjVi5cqVWerFxsYycOBAXFxccHV1ZcCAAcTGxmY7rjNnzvDcc8/h5uaGra0tTZs25eeffy7QPV69epXhw4dTq1Yt7OzscHd35/nnn882z0VsbCzvvPMOVapUwcbGhkqVKtG/f38iIyPNdVJTU5k6dSo1a9bE1taW8uXL07t3by5evAjknG8juxweAwcOxNHRkYsXL9K9e3ecnJx4+eWXAdi3bx/PP/88lStXxsbGBl9fX9555x1SUlKy/Xr17dsXT09P7OzsqFWrFhMmTADgjz/+QFEUNm7cmOW67777DkVROHjwYH6/rEIIIYR4BD0Kc8ecLFy4kHr16mFjY0OFChV48803s4zn/Pnz9OnTBx8fH2xtbalUqRIvvPACcXFx5jrbt2+ndevWuLq64ujoSK1atXj//feLdKxCiKJhVdIDEEKUTv369aNOnTrMnj2bX375hQ8//BA3NzeWLFlCx44d+eijj1i9ejWjR48mMDCQtm3bApCSkkL79u25cOECI0aMoGrVqvz4448MHDiQ2NhY3nrrLQBUVeWZZ55h//79vPHGG9SpU4eNGzcyYMCALGM5efIkrVq1omLFiowbNw4HBwd++OEHnn32WdavX0+vXr3ydW9//fUXf/75Jy+88AKVKlXiypUrLFq0iPbt23Pq1Cns7e0BSExMpE2bNpw+fZrBgwcTEBBAZGQkP//8Mzdu3MDDwwODwcBTTz3Fzp07eeGFF3jrrbdISEhg+/btnDhxgurVq+f7a6/X6+nSpQutW7fm008/NY/nxx9/JDk5mWHDhuHu7s6RI0eYP38+N27c4McffzRff/z4cdq0aYNOp2Po0KFUqVKFixcvsnnzZmbMmEH79u3x9fVl9erVWb52q1evpnr16rRo0SLf4xZCCCHEo6sszx2zM3XqVKZNm0bnzp0ZNmwYZ8+eZdGiRfz1118cOHAAnU5Heno6Xbp0IS0tjZEjR+Lj40NISAhbtmwhNjYWFxcXTp48yVNPPUXDhg2ZPn06NjY2XLhwgQMHDhR6jEKIYqAKIcRdpkyZogLq0KFDzWV6vV6tVKmSqiiKOnv2bHN5TEyMamdnpw4YMMBc9vnnn6uA+u2335rL0tPT1RYtWqiOjo5qfHy8qqqqumnTJhVQP/74Y4t+2rRpowLq8uXLzeWdOnVSGzRooKampprLjEaj2rJlS9Xf399c9scff6iA+scff9z3HpOTk7OUHTx4UAXUb775xlw2efJkFVA3bNiQpb7RaFRVVVWXLVumAurcuXNzrJPTuC5fvpzlXgcMGKAC6rhx4/I07lmzZqmKoqhXr141l7Vt21Z1cnKyKLt7PKqqquPHj1dtbGzU2NhYc1l4eLhqZWWlTpkyJUs/QgghhBDZeRTmjsuXL1cB9fLly6qqmuZM1tbW6pNPPqkaDAZzvS+//FIF1GXLlqmqqqpHjx5VAfXHH3/Mse3PPvtMBdSIiIj7jkEIUTrIdk4hRLZef/118+darZamTZuiqiqvvfaaudzV1ZVatWpx6dIlc9nWrVvx8fHhxRdfNJfpdDpGjRpFYmIie/bsMdezsrJi2LBhFv2MHDnSYhzR0dHs2rWLvn37kpCQQGRkJJGRkURFRdGlSxfOnz9PSEhIvu7Nzs7O/HlGRgZRUVHUqFEDV1dXgoODza+tX7+eRo0aZftupaIo5joeHh5Zxn13nYK4++uS3biTkpKIjIykZcuWqKrK0aNHAYiIiGDv3r0MHjyYypUr5zie/v37k5aWxrp168xla9euRa/X88orrxR43EIIIYR4NJXlueO9duzYQXp6Om+//TYazZ0/qYcMGYKzszO//PILAC4uLgD89ttvJCcnZ9uWq6srAD/99BNGo7FQ4xJCFD8JogkhsnVvAMbFxQVbW1s8PDyylMfExJifX716FX9/f4sJBUCdOnXMr2d+LF++PI6Ojhb1atWqZfH8woULqKrKpEmT8PT0tHhMmTIFMOXRyI+UlBQmT56Mr68vNjY2eHh44OnpSWxsrEV+iosXL1K/fv37tnXx4kVq1aqFlVXR7Y63srKiUqVKWcqvXbvGwIEDcXNzw9HREU9PT9q1awdgHnfmpDS3cdeuXZvAwEBWr15tLlu9ejWPP/44NWrUKKpbEUIIIcQjoizPHe+VOaZ7+7a2tqZatWrm16tWrUpQUBD/+9//8PDwoEuXLixYsMBivtmvXz9atWrF66+/jre3Ny+88AI//PCDBNSEKKUkJ5oQIltarTZPZWDKUVFcMicQo0ePpkuXLtnWyW/QZ+TIkSxfvpy3336bFi1a4OLigqIovPDCC8UyYclpRZrBYMi23MbGJstE0mAw8MQTTxAdHc3YsWOpXbs2Dg4OhISEMHDgwAKNu3///rz11lvcuHGDtLQ0Dh06xJdffpnvdoQQQgghyvLcsTDmzJnDwIED+emnn/j9998ZNWoUs2bN4tChQ1SqVAk7Ozv27t3LH3/8wS+//MK2bdtYu3YtHTt25Pfff8/xayiEKBkSRBNCFCk/Pz+OHz+O0Wi0CASdOXPG/Hrmx507d5KYmGjxjuLZs2ct2qtWrRpgWtbfuXPnIhnjunXrGDBggMXpUKmpqVlOU6pevTonTpy4b1vVq1fn8OHDZGRkoNPpsq1Trlw5gCztZ75LmRf//fcf586dY+XKlfTv399cvn37dot6mV+v3MYN8MILLxAUFMT3339PSkoKOp2Ofv365XlMQgghhBCF9TDMHbMbc2bfmf0BpKenc/ny5Sz9NmjQgAYNGjBx4kT+/PNPWrVqxeLFi/nwww8B0Gg0dOrUiU6dOjF37lxmzpzJhAkT+OOPP4rtHoQQBSPbOYUQRap79+6EhYWxdu1ac5ler2f+/Pk4Ojqatx92794dvV7PokWLzPUMBgPz58+3aM/Ly4v27duzZMkSQkNDs/QXERGR7zFqtdos74DOnz8/y8qwPn368O+//7Jx48YsbWRe36dPHyIjI7NdwZVZx8/PD61Wy969ey1eX7hwYb7GfHebmZ9/8cUXFvU8PT1p27Yty5Yt49q1a9mOJ5OHhwfdunXj22+/ZfXq1XTt2jXLlgshhBBCiOL0MMwd79W5c2esra2ZN2+exfxq6dKlxMXF0aNHDwDi4+PR6/UW1zZo0ACNRkNaWhpgyuF2r8ceewzAXEcIUXrISjQhRJEaOnQoS5YsYeDAgfzzzz9UqVKFdevWceDAAT7//HOcnJwA6NmzJ61atWLcuHFcuXKFunXrsmHDBoscEZkWLFhA69atadCgAUOGDKFatWrcunWLgwcPcuPGDf799998jfGpp55i1apVuLi4ULduXQ4ePMiOHTtwd3e3qDdmzBjWrVvH888/z+DBg2nSpAnR0dH8/PPPLF68mEaNGtG/f3+++eYbgoKCOHLkCG3atCEpKYkdO3YwfPhwnnnmGVxcXHj++eeZP38+iqJQvXp1tmzZkq98HLVr16Z69eqMHj2akJAQnJ2dWb9+vUVOkUzz5s2jdevWBAQEMHToUKpWrcqVK1f45ZdfOHbsmEXd/v3789xzzwHwwQcf5OvrKIQQQghRWA/D3PFenp6ejB8/nmnTptG1a1eefvppzp49y8KFCwkMDDQf0rRr1y5GjBjB888/T82aNdHr9axatQqtVkufPn0AmD59Onv37qVHjx74+fkRHh7OwoULqVSpEq1bty7UOIUQRU+CaEKIImVnZ8fu3bsZN24cK1euJD4+nlq1arF8+XIGDhxorqfRaPj55595++23+fbbb1EUhaeffpo5c+bQuHFjizbr1q3L33//zbRp01ixYgVRUVF4eXnRuHFjJk+enO8xfvHFF2i1WlavXk1qaiqtWrVix44dWfJmODo6sm/fPqZMmcLGjRtZuXIlXl5edOrUyZz4X6vVsnXrVmbMmMF3333H+vXrcXd3N0/cMs2fP5+MjAwWL16MjY0Nffv25ZNPPsn1AIBMOp2OzZs3m/No2Nra0qtXL0aMGEGjRo0s6jZq1IhDhw4xadIkFi1aRGpqKn5+fvTt2zdLuz179qRcuXIYjUaefvrp/H4phRBCCCEK5WGYO2Zn6tSpeHp68uWXX/LOO+/g5ubG0KFDmTlzpjnFR6NGjejSpQubN28mJCQEe3t7GjVqxK+//srjjz8OwNNPP82VK1dYtmwZkZGReHh40K5dO6ZNm2Y+3VMIUXooanFmdRRCCFGq6fV6KlSoQM+ePVm6dGlJD0cIIYQQQgghSi3JiSaEEI+wTZs2ERERYXFYgRBCCCGEEEKIrGQlmhBCPIIOHz7M8ePH+eCDD/Dw8CA4OLikhySEEEIIIYQQpZqsRBNCiEfQokWLGDZsGF5eXnzzzTclPRwhhBBCCCGEKPVkJZoQQgghhBBCCCGEELmQlWhCCCGEEEIIIYQQQuRCgmhCCCGEEEIIIYQQQuTCqqQH8KAZjUZu3ryJk5MTiqKU9HCEEEII8ZBQVZWEhAQqVKiARiPvQ5ZGMs8TQgghREHkdZ73yAXRbt68ia+vb0kPQwghhBAPqevXr1OpUqWSHobIhszzhBBCCFEYuc3zHrkgmpOTE2D6wjg7O5fwaIQQQgjxsIiPj8fX19c8lxClj8zzhBBCCFEQeZ3nPXJBtMyl/c7OzjK5EkIIIUS+yTbB0kvmeUIIIYQojNzmeZLQQwghhBBCCCGEEEKIXEgQTQghhBBCCCGEEEKIXEgQTQghhBBCCCGEEEKIXDxyOdHyQlVV9Ho9BoOhpIciCkmr1WJlZSX5a4QQQggByDyvLJF5nhBCiAdNgmj3SE9PJzQ0lOTk5JIeiigi9vb2lC9fHmtr65IeihBCCCFKkMzzyh6Z5wkhhHiQJIh2F6PRyOXLl9FqtVSoUAFra2t5Z+shpqoq6enpREREcPnyZfz9/dFoZAezEEII8SiSeV7ZIvM8IYQQJUGCaHdJT0/HaDTi6+uLvb19SQ9HFAE7Ozt0Oh1Xr14lPT0dW1vbkh6SEEIIIUqAzPPKHpnnCSGEeNDk7ZpsyLtYZYt8P4UQQgiRSeYFZYt8P4UQQjxIJf5bZ8GCBVSpUgVbW1uaN2/OkSNHcqybkZHB9OnTqV69Ora2tjRq1Iht27Y9wNEKIYQQQgghhBBCiEdRiQbR1q5dS1BQEFOmTCE4OJhGjRrRpUsXwsPDs60/ceJElixZwvz58zl16hRvvPEGvXr14ujRow945EIIIYQQQgghhBDiUVKiQbS5c+cyZMgQBg0aRN26dVm8eDH29vYsW7Ys2/qrVq3i/fffp3v37lSrVo1hw4bRvXt35syZ84BHnjuDUeXgxSh+OhbCwYtRGIxqSQ/pobJixQpcXV1LehhCCCGEEFnIPK9wZJ4nhBDiYVViQbT09HT++ecfOnfufGcwGg2dO3fm4MGD2V6TlpaWJWGonZ0d+/fvz7GftLQ04uPjLR7FbduJUFp/tIsXvz7EW2uO8eLXh2j90S62nQgttj4HDhyIoii88cYbWV578803URSFgQMHWtR/9tlnc2yvSpUqKIqCoig4ODgQEBDAjz/+mGP93bt3oygKsbGxhbiLO/r168e5c+eKpC0hhBBCiKIi87zCk3meEEKIh1WJBdEiIyMxGAx4e3tblHt7exMWFpbtNV26dGHu3LmcP38eo9HI9u3b2bBhA6GhOU9aZs2ahYuLi/nh6+tbpPdxr20nQhn2bTChcakW5WFxqQz7NrhYJ1i+vr6sWbOGlJQUc1lqairfffcdlStXznd706dPJzQ0lKNHjxIYGEi/fv34888/CzXG9PT0PNWzs7PDy8urUH0JIYQQQhQlmefdn8zzhBBClHUlfrBAfnzxxRf4+/tTu3ZtrK2tGTFiBIMGDbrvqTzjx48nLi7O/Lh+/XqB+k5O1+f6SEjNYMrPJ8luQX9m2dTNpyyW/OfUVkEEBATg6+vLhg0bzGUbNmygcuXKNG7cON/tOTk54ePjQ82aNVmwYAF2dnZs3rw5S70rV67QoUMHAMqVK2fxbmj79u0ZMWIEb7/9Nh4eHnTp0gUwbeVt0KABDg4O+Pr6Mnz4cBITE81t3rvMf+rUqTz22GOsWrWKKlWq4OLiwgsvvEBCQkK+70sIIYQQ4m4yz5N5nhBCCJEXViXVsYeHB1qtllu3blmU37p1Cx8fn2yv8fT0ZNOmTaSmphIVFUWFChUYN24c1apVy7EfGxsbbGxsCj3eupN/K3QbKqZ3Ko9cjqZFdXcAWn/0B9FJWd+1uzK7R4H6GDx4MMuXL+fll18GYNmyZQwaNIjdu3cXdNgAWFlZodPpsn2H0dfXl/Xr19OnTx/Onj2Ls7MzdnZ25tdXrlzJsGHDOHDggLlMo9Ewb948qlatyqVLlxg+fDjvvfceCxcuzHEMFy9eZNOmTWzZsoWYmBj69u3L7NmzmTFjRqHuTQghhBCPNpnnyTxPCCGEyIsSW4lmbW1NkyZN2Llzp7nMaDSyc+dOWrRocd9rbW1tqVixInq9nvXr1/PMM88U93CLVHhCau6VCuiVV15h//79XL16latXr3LgwAFeeeWVQrWZnp7OrFmziIuLo2PHjlle12q1uLm5AeDl5YWPjw8uLi7m1/39/fn444+pVasWtWrVAuDtt9+mQ4cOVKlShY4dO/Lhhx/yww8/3HccRqORFStWUL9+fdq0acOrr75q8e9HCCGEEKKkyTxP5nlCCCHKrhJbiQYQFBTEgAEDaNq0Kc2aNePzzz8nKSmJQYMGAdC/f38qVqzIrFmzADh8+DAhISE89thjhISEMHXqVIxGI++9916xj/XU9C651jlyOZqBy//KtZ6X053DEfaP7VCocd3L09OTHj16sGLFClRVpUePHnh4eBSorbFjxzJx4kRSU1NxdHRk9uzZ9OiR/3dOmzRpkqVsx44dzJo1izNnzhAfH49eryc1NZXk5GTs7e2zbadKlSo4OTmZn5cvX57w8PB8j0cIIUTZYzAaCA4PJiI5Ak97TwK8AtBqtCU9LPGQyMs87/ClKAat+DvXejLPk3meEEKIsqtEg2j9+vUjIiKCyZMnExYWxmOPPca2bdvMhw1cu3bNIt9ZamoqEydO5NKlSzg6OtK9e3dWrVr1QI7ItrfO/UvVxt+T8i62hMWlZpsvQwF8XGxpVtUtX+3m1+DBgxkxYgQACxYsKHA7Y8aMYeDAgTg6OuLt7Y2iKAVqx8HBweL5lStXeOqppxg2bBgzZszAzc2N/fv389prr5Genp7j5Eqn01k8VxQFo9FYoDEJIYQoO3Zc3cHsI7O5lXwnRYS3vTfjmo2js1/n+1wphElu87G4lHQ8nWxxd7QmKjH75PkyzzOReZ4QQoiyrESDaAAjRowwTwTudW9+h3bt2nHq1KkHMKqC0WoUpvSsy7Bvg1HAIpCWOS2Z0rMuWk3BJil51bVrV9LT01EUxZzgtSA8PDyoUaNGnupaW1sDYDAYcq37zz//YDQamTNnjjlImtsSfyGEeNTIyqq82XF1B0G7g1DvefsqPDmcoN1BzG0/VwJpolDiUtK5GpUMwJA21Zj965ksdWSed4fM84QQQpRlJR5EK2u61i/PolcCmLb5lMXx5z4utkzpWZeu9csX+xi0Wi2nT582f56TuLg4jh07ZlHm7u6Or69vvvv08/NDURS2bNlC9+7dsbOzw9HRMdu6NWrUICMjg/nz59OzZ08OHDjA4sWL892nEEKUVbKyKm8MRgOzj8zOEkADUFFRUPjoyEd08O0gAUhRIKqqcjP2znyuZXV3xnWrzdf7LlmsSJN53h0yzxNCCFGWSRCtGHStX54n6vpw5HI04QmpeDmZlvYX9zuTd3N2ds61zu7du7Mcif7aa6/xv//9L9/9VaxYkWnTpjFu3DgGDRpE//79WbFiRbZ1GzVqxNy5c/noo48YP348bdu2ZdasWfTv3z/f/QohRFnzIFdWPYjVbsXVR3JGMr9c+sUi0HgvFZWw5DCCw4MJ9AksdJ/i0ZOUZiDDYLmlsGV1d5pXdePUzXiik9Nxs7fmqYYVcLHX5dBK0ZN5nhBCCFEyFFVVs0vfVWbFx8fj4uJCXFxclglIamoqly9fpmrVqtja2ubQgnjYyPdVCFHUiiswZDAa6LK+S46BIQUFb3tvtvXZVuj+HsRqt6Lq48+QP7kUd4lnazyLo7Vp9ctHRz7i29Pf5un6j9p8RPdq3fM3+Gzcbw4hSoeinufFJqdzLTo513qV3exxtbcu0JhF4cg8TwghRFHI6zxPVqIJIYQQ+VAcwSdVVUnKSOLPm3/maWXV2rNrCfQJxMnaCUedI/Y6ezSKJsfrsruH4l7tlp8+EtMTuRJ/hctxl7kSfwUrxYphjw0zXzP5z8ncSr5FfY/6POb1GABVXaripHMiISMh17F42nsW6l7Eo8tKk7efq7zWE0IIIcTDTYJoQgghRB7lN/iUYcwgOiWayNRIolKiiEiOIDIlMttHqiEVX6e85SqadWSWxXMFBUedI47Wtx86R/NzJ50TPg4+DGk4BDCtdvvg0Ac55hHLbL+JdxN0Gh2KoqCgmD9qFA0KCiigQZPtirjccpUBvL//fb499S1XE64SmRJpUcfLzssiiNa6Ymti02LRae9sl3uu5nP08e9Dl/VdCE8Oz7avzJV7AV4BOX4thbgfBxstOq0my5bOu+m0GhxsJOeeEEII8SiQIJoQQgiRB3kJDM08PNOcxH7Jv0tYcGxBtvXv10deeNh6oFf1JKYnolf1qKgkZCSYVmUlZa3v5+xnDqIFhwcTnRp93/bDk8Npu7ZtruNwtXFl3wv7zM9f//11/g77myENh9x3RR1Aij6Ff8L/uXNPdh5Uca5CFZcqVHWuiqqqKIopl+jUllOzXK9RNKDAuGbjCNodhIJi8bVWbp+XOLbZWDlUQBSYoihUcLU1n86ZnQqutuZ/q0IIIYQo2ySIJoQQQuRBcHhwroGhiJQIcxJ7R2tHVFS0ihZ3W3fc7dzxtPfEw84Dd9s7n2c+3G3dsdHa5GllVWZONFVVSTOkkZiRSEJ6AonpiSRkJJCUkWT6PD2BxIxEHHQOd8aYHFFkX5PMQFUmg9GAQTUQnx6fp+v71erHszWexc/ZDydrpwKNobNfZ+a2n5vtFtuxzcbKaaai0FzsrPFzh5uxqRYr0qw0ChXL2eFiJ7nQhBBCiEeFBNGEEEKI+7iZeJNfL//K2rNr81Q/M0j1dPWn6VqlK+Vsy+UrX1l+VlYpioKtlS22VrZ42Hnkqf285gdb0nkJTXyaYFSNqKqKimr+aFSz39r2WfvPSDemcz7mPN+d/i7XPrpU6UJ9j/p5Gs/9dPbrTAffDsV+0qh4dLnYWeNsqyMpzUB4QiqJaXrK2VtLAE0IIYR4xEgQTQghhLhHVEoUv1/9nV8v/8rR8KP5ujYzSFVaV1YFeAXgbe+d62q35uWb5zsI5WrrCoC7rXue+ijKXGVajZZAn8Aia0+IeymKgqOtFekGaxLT9CRn5G37tRBCCCHKDgmiCSGEEEBCegI7r+3k18u/cjj0MAbV9AeygkJTn6Z0qdKFJf8uITIlstgDQ8W5skqr0RZ7HrEH0YcQJcVOZ/p3m5pusMjdJ4QQQoiyT4JoQgghHmlG1ciYPWPYfX036cZ0c3l99/p0q9qNLlW64O3gDZhWWD2owFBxrqx6EHnEGv18hq8TnmSC/7EsfXx4vhHVfj4DIwvfT8T8L0GrwXP48KyvLVwIBiOeI0cUuh8hMtnqNGgUBYOqkqY3YquTYLAQQgjxqJAgmhBCiAfCYDQUe86qvPSRYczgVNQpGnk2AkynPManx5NuTKeaSzW6Ve1G96rdqexcOUv7ZSmJfbHnEdNqcP7mF74bOYKrfZqZ+/Bbf4Sob76EUSOLrJ/IefMBLAJpEQsXEjlvPh5F1Y8QtymKgp1OS1K6npR0gwTRhBBCiEeIBNGEEEIUux1Xd2QbeBrXbFyRBZ7y0kdieiI9NvYgJjWGHc/vwMveC4C3At5itGY0NcvVzHVrVllKYl+cq90yA1qR8+bjlzaU+k8+SeyKtUT98CMuz/XBvmlTkg4dBtWIajDe/mgAo2r5udGAem/ZXZ9rXV1xaNeOyHnzSTl6FNe+fUk7f94cQMtuhZoQhWVnbQqiJWcYKFfSgxFCCCHEAyNBtKL2xyzQaKHde1lf2/MxGA3QYXyRdztw4EBWrlzJ//3f/7F48WKL1958800WLlzIgAEDWLFihbl+bGwsmzZtyra9KlWqcPXqVQDs7e2pVasW48eP5/nnn8+2/u7du+nQoQMxMTG4uroWyT1duXKFqlWrcvToUR577LEiaVMIkb3iXCW24+oOgnYHZckjFp4cTtDuIOa2n1voQFpOfdxKvsU7u9/hs/af0dmvM47WjlR2Mq0wuxJ3xRxEy+8JkZLEPnvGtDTSL14k9dw50s6dJ+3cOTQODkR99RVRX31lrhe3bj1x69YXef9J+/aTtP8AqKoE0ETxuD3Psw98G4CU9LsOF5B5Xr7IPE8IIcTDSIJoRU2jhT9mmD6/O5C252NTeYcJxda1r68va9as4bPPPsPOzg6A1NRUvvvuOypXzrotKTfTp09nyJAhxMfHM2fOHPr160fFihVp2bJlUQ9dCFGCinOVWFJ6EjMOz8g2EX9m2ZQ/pxCZEolG0dC1alecrZ0BOBl5krMxZ6nuWt289TJFn8JvV34DTHnIFEVBVVU+/uvjbPvINOvwLDr4dkCr0fJpu09xt3PHSlM6fwU+iBxfhe1DNRrJuH79drDsdsDs/HnSr14Fw/1PLNRVrmxa7afVgkZB0WhBo7H43OJ1RQNaLYpGMf2Ovavs3tfjt/0GRiOKTicBNFE8bs/zHA1GqPkGKRkGjKqKZu8nMs8TQgghHgGl8y+I0kRVISM57/VbvAmGdNNEypAOrd+B/Z/B3k+g7RjT6+lJeWtLZw/5OPEpICCAixcvsmHDBl5++WUANmzYQOXKlalatWre7+E2JycnfHx88PHxYcGCBXz77bds3rw5y+TqypUrdOjQAYBy5UybGjLfDTUajXz00Ud89dVXhIWFUbNmTSZNmsRzzz0HQExMDCNGjOD3338nMTGRSpUq8f777zNo0CDzmBs3bgxAu3bt2L17d77vQwiRs/yuEjOqRuLS4nCxcUGjaADYH7Kfo+FHCfQJ5PHyjwNwOuo0g34bRFJG7v/fxafHM+Ow6c2HZj7NzEG0ndd28vV/X/NynZfNQbSE9AQmHZiU7/sMTwknODyYQJ9A8yEBBfFAktg/iBxf+ehDHxlJ2rlzd1aXnT9P2oULqCkp2Tft4oJNzZqmh78/KSdOELduHYpOh5qRgcuzzxRLgCti4UIw/mruJ2LhQgmkidwVcJ5ntXcWNdJTUVq/g7LrQ9j3qczzZJ4nhBDiESBBtNxkJMPMCgW7du8npkdOz3Pz/k2wdshXl4MHD2b58uXmydWyZcsYNGhQoSclVlZW6HQ60tPTs7zm6+vL+vXr6dOnD2fPnsXZ2dn8DumsWbP49ttvWbx4Mf7+/uzdu5dXXnkFT09P2rVrx6RJkzh16hS//vorHh4eXLhwgZTbf5gdOXKEZs2asWPHDurVq4e1tXWh7kEIYclgNDD7yOz7rhIbv288a86sISYthujUaGJSYzCoBvb024ObrRsA+27s47sz32FUjeYgmqO1Y54CaJnqutXFx8EHOys7c1lVl6q0r9SeGq41zGXWGmtaV2x9Z8yqKeB3PvZ8rn1EJEfkeTw5KuIAl5qRgSExEWNSEsbERIwJCdjWqYPTk0+acnz98w8OLVqQdOQISXv34dipE9a+lYn/7XcUG2s01tYo2T101ijWOtPrNjYoVpa/7u/OV5b5PPzzL4havBjHjh0xxMZydeAg0s6dwxAdne3YFRsbbKpXNwfLMgNnVl6e5rxyEQsXErdunXlrZebX6d6vX2Hd/fUvzn5EGVSIeZ79oc/g0Gd3CmSeJ/M8IYQQZZ4E0cqYV155hfHjx5vzXBw4cIA1a9YUanKVnp7OnDlziIuLo2PHjlle12q1uLmZ/pj28vIy58pIS0tj5syZ7NixgxYtWgBQrVo19u/fz5IlS2jXrh3Xrl2jcePGNG3aFDDl6Mjk6ekJgLu7Oz4+PgUevxDF5UGcNlmcgsODLbZwZifVkMrhsMNZymNSY8xBtKY+TTGoBhp6NDS/7uPgw+ZnN3Ml/gojd+UeWBodODpLjrGe1XvSs3pPizJXW1cWdV5kUfZX2F8M/m1wrn142nvmWifXNu4KPhlTUinX93mili4jds0anJ96CmtfX2K+/94UGEtINAXGkhIxJN4OkiUmYkhMwHj7uZqWdt/+kg78SdKBP83PE3fuJHHnzvwPXKO5J9CmQ6OzRuvmRuS8+eaAE0Dirl2W1yoK1pUrZwmWWftVRtHm/O/93sAWZB+8K6wH1Y8QpYHM84QQQoiSJUG03OjsTe8U5lfmFk6ttWlbZ9sxpq2d+e07nzw9PenRowcrVqxAVVV69OiBh4dHvtsBGDt2LBMnTiQ1NRVHR0dmz55Njx498nz9hQsXSE5O5oknnrAoT09PNy/dHzZsGH369CE4OJgnn3ySZ599VnJxiIfCgzhtEoo2ULfl0ha+P/M9HXw78HqD14lIjuD5fQaMisL61pos9fvsN6JRVZTXX6Sjb0fcbN3MD51WZ673hN8TPOFn+XOu0+io4lIFXydfvO29CU8Oz3bFm4KCt703AV4BBbongACvAAYesiNBn5TjfThbORDwau59qKqKMT4efXg4GeHh6MMj0IeHWzwyIsJBoyH666+J/vpr87XxW7YQv2VLge5BsbND4+iA1sERjaPpoXVyJGHnLjAaQaPg2LEjano6anrG7Y93Hsb0tCzlGI13OjAaUVNTUVNT7zsOracHtv7+2PjXvLMls0Z1NHZ2970uWwZjtsn9zc8NxmwuKoAH1Y8omwo5z1M11ihGmefdTeZ5QgghyjIJouVGUfK91J49H5sCaB0mmA4XyDxUQGud/amdRWzw4MGMGGHKybNgwYICtzNmzBgGDhyIo6Mj3t7e5u05eZWYmAjAL7/8QsWKFS1es7GxAaBbt25cvXqVrVu3sn37djp16sSbb77Jp59+WuBxC1HcHsRpk5n95CdQZ1SNXI2/ysmok5yMPMmpqFNMfHwi/uX8AVM+seMRx3GxdoEGppVZRkWh3z5TkOHuAFSf/Ub67TOyto2GHlW6FvgkSq1Gy7hm4wjaHYSCYvE1UzD9nzK22dhCreDTarS09m2L8ze/5Hgf8f3bQnIqaZnBsIi7gmL3BMtyWx2WHeuqVc2BL81dgTCNowNaR0c0jk53fX774eCI1tEBjaNjlu2WYFphlbB9hznHl23duvlaUaXq9XcF2e4OvqWZy2PXrSdu0yawsgK9nnIvvlhkq7bulxuuKFeGPah+RBlViHmevt37nPL/P7yPzsM7841TmecBMs8TQghRdkkQrajdfQpn5kQq82N2p3YWg65du5Keno6iKHTp0qXA7Xh4eFCjRo3cK4I5j4XhrlPZ6tati42NDdeuXaNdu3Y5Xuvp6cmAAQMYMGAAbdq0YcyYMXz66afZtilEScstj5iCwkdHPjKfBFlQuQXq5rSbQy23WpyMMgXLMj/em4fsROQJcxCtTcU2fNTmIxp4NABMK7jGP1keCLUIpN0dQDvwZAUmFWKVGEBnv87MbT8324Dg2GZjiyTg2Pz9TzkM9PvmF7xjVBLsoeFlFb8IMDjZ47LuD87dDrLlhdbFBSsvr3senlh5eaHz8iJ+2zail68wB7icez5V6nJ8KVZWpuCcvT3Z/UuMWLiQuE2bJI+YEPlx1zxP23YMLtHJqG3HYHC0QSvzvGzJPE8IIURZIkG0omY0WAbQMmU+Nxb/REGr1XL69Gnz5zmJi4vj2LFjFmXu7u74+vrmu08/Pz8URWHLli10794dOzs7nJycGD16NO+88w5Go5HWrVsTFxfHgQMHcHZ2ZsCAAUyePJkmTZpQr1490tLS2LJlC3Xq1AFMeTfs7OzYtm0blSpVwtbWFhcXl3yPTYiilFseMRWVsOQwZh6eSVWXqlhprNBqtFgpVlhprNBpdBbPMx8VHCtQ0dH0Tn5qRiofHvrwvgn/393zbrav22ptqe1Wm3oe9ajnXs9iBVklp0pUcqpkfp65SmxC9DvUuKnSb5+R5/Yb0aqwto2GDa21zC3kKrFMnf0608G3Q7HlkFP1emo3aM8t90O0PxFl8Zo2Idn8ldI4OloExXRZAmVeWHl6orm9iiI7EQsXEr18RbEFnx5Eji/JIyZEAd01z1MAP/fbq9g6jAWNIvM8mecJIYQo4ySIVtQ6jM/5tQewxD+Ts7NzrnV2795tzlmR6bXXXuN///tfvvurWLEi06ZNY9y4cQwaNIj+/fuzYsUKPvjgAzw9PZk1axaXLl3C1dWVgIAA3n//fcD0zub48eO5cuUKdnZ2tGnThjVr1gCmk6LmzZvH9OnTmTx5Mm3atJGjz0WJiUyJ5N/wf7kSfyVP9X8490O+2h/SYAijAkYB8PvV34lKjbpvfRUVraKlrntd6rrXpZ57Pep51KOaSzWsNHn7rz3lxEnqrd3Dsp81aNL0AGhVUIG6EdZ0dH2dNpU75es+7ker0RZ4W2hOjMnJxK5bT/SKFWTcvCevkVZLhY8/MgXKPD1NwTGHfG7buscDCT49iBxfkkdMPAB79+7lk08+4Z9//iE0NJSNGzfy7LPP3vea1atX8/HHH3P+/HlcXFzo1q0bn3zyCe7u7g9m0LmReZ7M84QQQjzSFFVVsy5lKMPi4+NxcXEhLi4uywQkNTWVy5cvU7VqVWxtbUtohKKoyff10VSYhPx6o55zMef4N+JfjoUf49+IfwlJDAGga5WubLuyLdc2WpRvgauNK3pVj95410PVYzAa0Bv1ZBgzMKimz/vW6svLdV4GYMXJFcz5e06ufcxsNZOeNXrmWu9uxuRk4n75hdi1P5B64oS5XFuuHIaYGFRFQbnr14JNzZqUe/UVXHr2RFOKfn70UVHErF5NzOrvMMTFAaB1c8OmVk2SDx4yb7PMLlBUGBHzvwStJts2IxYuBIPxvjm6xMPtfnMIkdWvv/7KgQMHaNKkCb179841iHbgwAHatm3LZ599Rs+ePQkJCeGNN96gZs2abNiwIU99lsQ8T28wkpphwNFWl3tlUeRknieEEKIo5HWeJyvRhBBlTn4T8sekxnA84jjHIkwBsxORJ0jRp1jUUVCo7lqdOm518FmzJ9eTIN9asKjAWxXrudfLUz0fR588t5l69hyxa9cS9/PPGG8ng1Z0Opy6dEGxsSFu/XpzwCnswxnEfPstWFmRdu4cYZMmEzFnLq59+1LupRfR+eS936KWfu0aUcuXE7dho/kAAJ1fZdwHDSYj/BZRCxcVa44vSWIvRN5169aNbt265bn+wYMHqVKlCqNGmVblVq1alf/7v//jo48+Kq4hFpreYORUaDwAdSs4Y6XJ+ntBCCGEEGWHBNGEEGVKfk7O/P7M93x3+rtst2g66Zxo6NmQRp6NaOTViAYeDXCydgLgsO+pXE+CLEyurwCvALztvQlPDs8275mCgre9NwG5JPw3pqWR8NtvxKxZS0pwsLlcV7ky5fr1xaVXL2LWrMmyPdFn4gS0buWInDcfh9atSb98mYyQEKK++oqopUtx7vIk5V59FbvHHsv3aW4FlfLfCaKWLiXh99/BaNpqaNugAe6vv45T505ELlliEUADyfElxMOmRYsWvP/++2zdupVu3boRHh7OunXr6N69e47XpKWlkXbXibrx8fEPYqhmVloN1loN6QYjqekGHG0liCaEEEKUZRJEE0KUGbmdnAkw8/BM88mZafo0cwCtqktVHvN8zBQ082xENddqaJTs/xi6+yRIsDzRMr5/D5q//2mh7iMz4X/Q7iAUFIv7UTAFrcbeJ+F/2uXLxK79gbiNG81bHdFqcerUCdd+fXFo0QIlc7VEHnJj+S5ZTMKuXcR8s4rkv/4ifuuvxG/9FdsGDXDr/yrOXbqg3D5lrSipqkrS/v1E/W8pyYcPm8sd2rXF/bXXsA8MvBPEkxxfQjz0WrVqxerVq+nXrx+pqano9Xp69uzJggULcrxm1qxZTJs27QGOMis7ay3pKUaSZUunEEIIUeZJTrS7SE6Fskm+r4+Ov8L+YvBvg3Ott6zLMgJ9AglJDOFS7CUaejbExSZ/J4KpGRncCHqXxO3bUQEFsKlfH9fevbCtWRObmjXRFjJnUnbbUn3sfRjbbGyWbalqerop0LVmLcmHDpnLrcqXp1zf53Hp3Qedt1ehxgOQevo00au+JX7LFtT0dAC0nh6Ue+EFyr3wAlZFkPxbzcgg/tdfifrfUtLOnTMVWlnh0qMHboMHY1urZqH7EKIgJCdawSmKkmtOtFOnTtG5c2feeecdunTpQmhoKGPGjCEwMJClS5dme012K9F8fX0f6DwvPCGVsLhUXOx0d07rFA+MzPOEEEIUBcmJJoR4pKiqyl9hf+WpbkRyBAAVHStS0bFivvoxJCQQ+8OPRK9ahT4sDIDMDY1pJ05w665k/VY+PtjU9DcF1fz9salZE+vq1dHkYdVWxPwvaaTV8Nsbv2U5ICF68RIiDGfwHDmC9BshxP74I7Hr12OIjDRdrCg4tmuH6wv9cGzTBkVb8K2l97KtU4cKM2fgNfpdYteuJea779FHRBA5/0uiFi/BuUcP3Pq/im3duvlu25iUROy6dUStWIk+NNR0K/b2lHv+edwG9EdXoUKR3YcQovSZNWsWrVq1YsyYMQA0bNgQBwcH2rRpw4cffkj58uWzXGNjY4ONjc2DHqoFe53p/9iUdEOJjkMIIYQQxU+CaEKIh1pcWhybL25m3bl1XIy7mKdrPO09891Pxs2bRH+zitgff8SYlASYAjxqcjJYWYFej12TJmgdHUk9fw79zVD0YWHow8JI2rvvTkNaLdZVqmBT0x8bf3/zqjVdpUp3tlgCaDXmXF6Bd21RzEyW7/zUU1wbOpSkffvh9oJiracHrs89R7nnnkNXMX/BwfyycnPDY9gw3F97jfjffid61SpSjx8nbtMm4jZtwq5pE9xe7Y9Tp45ELlp83xMtjQmJKDbWxHy/BmPmSZvu7ri9+irlXnwBrUv+VgkKIR5OycnJWFlZTk21t98EKM0bJ+ysTWNMNxjJMBjRaSUvmhBCCFFWSRBNCPHQUVWV4PBg1p1bx+9XfifdaNpWaKu1RVGULCdrZsprQv67pZw8SfSy5cRv2wYG0yoD6xrVsfarQuLOnVlOgvQYNRLfJYsxJCSQdv48aefOkXbuHKnnzpF27jzG+HjSL14k/eJFEn7ddmds9vbY1KhhWrnm749948a4vf66RVL8Wx9/QvSyZWgcHYnfssV8rUPLFrj2ewGnjh1QdA82H49ibY1Lz6dw6fkUKf/+S/Q3q4j/7TdS/v6HkL//wapCeWyqViPpwAHzfWQKmzGTmFWrQKu987X188Nt8GBcnn0GTQmvLhFCFE5iYiIXLlwwP798+TLHjh3Dzc2NypUrM378eEJCQvjmm28A6NmzJ0OGDGHRokXm7Zxvv/02zZo1o0IpXomq1WiwsdKSpjeQkm5AZydBNCGEEKKskiCaEOKhEZMaw88Xf2b9+fVcjrtsLq/tVpvnaz5P96rdORR6iKDdQQD5TsifSTUaSdq3j6hlyy0S2ts//jjugweR8t9/RM7/MteTIO0DArAPuBOwU1UVfXi4ObBmCq6dJ/3iRdTkZFKPHzet5rprLIqdHZHz5hM5/0vzijNjYiJaV1dc+vSmXN++WPv55fdLWSzsGjWi4pxGeL03hpjvvyd27Q+mFXk3Q8HKish58zFER+Py9NPcHP8+6Rdvrxw0GLBt2BD311/DqVOnIt1+KoQoOX///TcdOnQwPw8KMv3fPGDAAFasWEFoaCjXrl0zvz5w4EASEhL48ssveffdd3F1daVjx4589NFHD3zs+WVvfTuIlmHA2U4OFxBCCCHKKjlY4C6SmLRsku9r2bD8xHLmH51PhjEDADsrO7pX7c7zNZ+nrnvdO6c0kr+E/HczpqURv3kzUctX3AnwaLU4d++O+6CB5jxfEfO/vO/2RAxGPEeOyPO9qXo96deuZVm1lnH9ujlwlsm+aVNc+/XDqcuTecqtVpKMqanE//IL0d+sIu3s2WzrOLZrh/vrr2HXtKnF91CI0kgOFij9SmqeF5mYxs3YFJxtdVTxkMMFHiSZ5wkhhCgKcrBACTMYDVmSgee2+kVkLy8neomHS15+PqJTo9EqWvOpmb5OvmQYM6jrXpfnaj5H96rdcdBl/4dKZ7/OdPDtkOefQX1MDLFr1hC9+jtzcn6NgwOuffvi9uorWRLa3y9All1gLTeKlRU21aphU60adO1qLjcmJ3Nr9kfE/vCDecujfcsWuPR8Kt99lASNrS2uffrg0rs3yUf+InrVNyTu2Hn7RQ1VN23EtqactCmEePjc+3uslktDAJLTDaiqKm8K5IPM84QQQjxMJGlDMdhxdQdd1ndh8G+DGbtvLIN/G0yX9V3YcXVHsfYbFhbGyJEjqVatGjY2Nvj6+tKzZ0927txprlOlShUUReHQoUMW17799tu0b9/e/Hzq1KkoisIbb7xhUe/YsWMoisKVK1dyHEeVKlX4/PPPi+KWAAgNDaVbt25F1p7ImcFo4K+wv9h6aSt/hf2FwVj0J43l5efjq+Nf0enHTnx35jtzWTvfdqx5ag1rn1rL8zWfzzGAlkmr0RLoE0j3at0J9AnMNoCWfu0aYdM/4EKHjkR8MQ9DZCRW5cvj9d571Nj9B95j3yvREyGjVqwg9ocf8Bg1kjonT+AxaiSR8+abVrs9RBRFwaF5M/NKPkWnA6ORhB3F+3+iEEIUh+x+j/Xe0oO/wveiNxrJMBTPJg+Z5wkhhBAlT1aiFbEdV3cQtDvIIhcTQHhyOEG7g5jbfu59t5MV1JUrV2jVqhWurq588sknNGjQgIyMDH777TfefPNNzpw5Y65ra2vL2LFj2bNnz33btLW1ZenSpbz77rv4+/sX6XgNBgOKoqDR5B7H9fHxKdK+Rfay2wLpbe/NuGbjiuzfbE4/H7eSb/HO7nf4rP1ndPbrTEXHiuiNes5G39kCqNPoqOdeL9c+8rLV0qF1K6KXLTcFcW5vl7SpWwf3QYNx7trlgSfnz87dBxXcL+/aw+Le+8l8Dg/XfQghHm33m+d9/t8E3m7wIX5u3bC2Ktrt9jLPE0IIIUoHWYmWR8kZybk+EtISmHVkVpaJFZgSnKuozD4y22J1T05t5dfw4cNRFIUjR47Qp08fatasSb169QgKCsrybuTQoUM5dOgQW7duvW+btWrVokOHDkyYMCHP42jfvj1Xr17lnXfeQVEU83aGFStW4Orqys8//0zdunWxsbHh2rVr/PXXXzzxxBN4eHjg4uJCu3btCA4OtmhTURQ2bdoEmCaRiqKwYcMGOnTogL29PY0aNeLgwYN5HqPIKvOPgrsDaHAn+FsUqygNRgOzj8zO9ucj00dHPsJgNNDZrzM/PPUDn3f4PP8daTXZrtaK+HIBkfPmE7t+PVdffImE7dtBVXFo15bKK5ZTdf16XHo+VSoCaAAYjBYBtEyew4fjMWokGIwlNLD8yykg+LCurBNClD1FMc8D+ObcF6TLPE/meUIIIcosWYmWR82/a14k7dxKvkVweDCBPoEAdF3flZi0mCz1/hvwX57bjI6OZtu2bcyYMQMHh6xb3FxdXS2eV61alTfeeIPx48fTtWvX+75LOHv2bAIDA/n7779p2rRprmPZsGEDjRo1YujQoQwZMsTiteTkZD766CP+97//4e7ujpeXF5cuXWLAgAHMnz8fVVWZM2cO3bt35/z58zg5OeXYz4QJE/j000/x9/dnwoQJvPjii1y4cAErK/knnV/3C25llk07OI10YzoGowE3WzdaVWxlrrPy5EoSMxJ5ufbLuNq6ArDz6k7+uP4HGcYM8yMyJTJLkO5eYclh5p+POu51CnQ/967Wch84kBvvBJF0+x15fVgYik6H8zNP4z5wIDY1ahSon+JW1HnXStR9AoKZrwshREkqqnledFoEVxJP4Oko8zyZ5wkhhCiL5DdRCYhIjijS9i5cuICqqtSuXTvP10ycOJHly5ezevVqXn311RzrBQQE0LdvX8aOHWuRcyMnbm5uaLVanJycsizPz8jIYOHChTRq1Mhc1rFjR4s6X331Fa6uruzZs4ennso5efro0aPp0aMHANOmTaNevXpcuHAhX18DYRIcHpxrcCs2LZaxe8cC0MynmUUQ7ev/viYuLY5uVbqZg2ino0/z08WfCjSeovj58Bw+HDVDT+S8+eZgGoDWxQXXF1/A7eWXsfL0LHQ/Im/KVEBQCCFyIfM8mecJIYQouySIlkeHXzqca51/bv3D8J25/0HoaX/nj/dtfbYValwAqpr/BLaenp6MHj2ayZMn069fv/vW/fDDD6lTpw6///47Xl5eBR0m1tbWNGzY0KLs1q1bTJw4kd27dxMeHo7BYCA5OZlr167dt6272ylfvjwA4eHhMrnKpxORJ5h/dH7uFYGqzlUp71ieWm61LMqfrf4sqYZUi0T/LSq0wM7KDp1Gh7XWGp1Gx/WE6yw9sTTXfu7++SioxAMHiN+82aLMe9JEXHv1QmNvX+j2hRBClC1FPc/LPKFT5nkyzxNCCFG2SBAtj+x1uf/h3bJCS7ztvQlPDs92a5yCgre9NwFeAflqNzf+/v4oimKRVDYvgoKCWLhwIQtzyUdUvXp1hgwZwrhx41i6NPcgSE7s7OyyHPk+YMAAoqKi+OKLL/Dz88PGxoYWLVqQnp5+37Z0d+WtymzTaCybW8Lykij/fit97qY36skwZmBnZQfA1firHA0/mqdrJ7WYZN6GfLfRgaOzlDXxbkIT7yYWZQajgS2XtuTr5yO/9DExhM+eTdxPP98p1GrBYMAQFycBNCGEENkqqnmeh50X9sYaxKZkUM7eWuZ5Ms8TQghRxsjBAkVIq9Eyrtk4wDSRulvm87HNxqLVaIu0Xzc3N7p06cKCBQtISkrK8npsbGy21zk6OjJp0iRmzJhBQkLCffuYPHky586dY82aNbmOx9raGoPBkGs9gAMHDjBq1Ci6d+9OvXr1sLGxITIyMk/XPjJySpSfebqhNm8/xmvOrKHTj51YfXq1uayDbwderP0i/Q9a02d/9pPTPvuNDDpkV6jgFhTvz4eqqsT99BOXunW3CKC5v/EGdU6ekAT2QgghCi0vv8feqB+EwaiQkp63eVBeyDxPCCGEKD0kiFbEOvt1Zm77uXjZWy6H97b3Zm77uXT261ws/S5YsACDwUCzZs1Yv34958+f5/Tp08ybN48WLVrkeN3QoUNxcXHhu+++u2/73t7eBAUFMW/evFzHUqVKFfbu3UtISEiuEyV/f39WrVrF6dOnOXz4MC+//DJ2dna59vEoufsUw9Bp04nb8gsho8cQOW8+rn374vzkk2TcvIkhNhb19ju7qqpyIvIEMal3khnrNDqiU6M5EHLAXGavs+f95u/TtnIH+u0zZgmk9dlvpN8+I6182xZJ8Lc4fj7Sr1/n+utDuDl2HIbYWLTu7gB4jBqJ19tvAXISpBBCiKKR2++xp/27UM3TEW9n2yLtV+Z5QgghROkg2zmLQWe/znTw7UBweDARyRF42nsS4BVQ5CvQ7latWjWCg4OZMWMG7777LqGhoXh6etKkSRMWLVqU43U6nY4PPviAl156Kdc+Ro8ezaJFi0hNTb1vvenTp/N///d/VK9enbS0tPvm8li6dClDhw4lICAAX19fZs6cyejRWbcHPuo8hw8nIzKS2O++J/b7783lsT/8QOwPP1jUNWo1pOogWWckw9md2HLl0djb09hGxyrtY3i6lycseCYae3s09g5o7OyoVaslF9uG0m9vMB5xKptaaGh9SqXfPiPx/XvQ/P1Pi+xeiurnQ9XriV75DRHz56OmpqJYW+Px5psYU1NQdDo5CVIIIUSxkHmezPOEEEI8uhS1INlKH2Lx8fG4uLgQFxeHs7OzxWupqalcvnyZqlWrYmtbtO8gipJTFr6vOy79hn7IGPxCMgAwArc8rfBUnLBKM2BMTkKTUXRbRzI5PvkklT79BMXausjbLoyUkycJmzSZ1FOnALBv3pzy06ZiXaVKyQ5MCFGm3W8OIUoHmec9euT7KoQQoijkdZ4nK9GEKOV2XN3B7o+DeDnEtIIqQwM6I+ytbWR968wcJwpagxYngzVt3ALp6NWKJk71sE43YExORk1OxpiSgjEpGWPy7UdKCsbkJPNzNclUlnrypLnvxN9/53ynYMr17Ydr377ovAt+aldRMCYnE/HlAqJXrgSDAY2LC97vjcGld+8syYyFEEKIkpCYmkF8qh5HGyuc7XS5XyCEEEKIh4YE0YQoxQxGA8u3fMC4PaYA2uGaCnP6aM25ygDWt9bQqkIrulfrTgffDjhZOxW4v4iFC0k9eRJFp0PNyEBjb48hIpLIBQuIXLIE5yefpNwrL2PXuPEDD1ol7j9A2NSpZNy4AYBz9+54vz8eKw+PBzoOIYQQ4n4S0vREJqZhVFUJogkhhBBljATRhCjFgkP/5o0V4VipcLMczOltOgtkfWvTx8xAWo8PXiPQJ7BQfWWe9ukxaiSew4ebnzt17Yo+PJyU4GDit24lfutWbOrWwe3lV3Du0R1NMW+d0EdHc2v2bOJ/3gyAVYXy+EyejFP79sXarxBCCFEQ9jpTbrSiPKFTCCGEEKWDnM4pRCmkN+rZeXUnl7+ej0cCpGth+ktauGv11/rWGta20aBRVSKSIwrV370BNLhzomXCtm04tG5F1Q3rcenTG8XGhrRTpwmdMIEL7TsQPmcOGSEhheo/O6qqEvfTT1zq3sMUQFMUyvV/leqbN0sATQghRKllZ20KoqVmGDEaH6nUw0IIIUSZJyvRhCiFfr/yO59veo9P1pnexV7aRUO0c9btk5kr0rrZexauQ4PRIoCW6e4TLW3r1qXCjBl4jR5N3Pr1xHz3PRk3bxL19f+IWroMx44dcHvlFeybNy/0Vs/069cJmzKVpD//BMCmZk3Kf/gBdg0bFqpdIYQQorjptBqsNBr0RiOpGQbsbWS6LYQQQpQV8ltdiFLgZORJ4tLiaFmxJQAdK7VH+U2Htd7A6Ro27G6Y/ZYQBQVve28CvAIK1b/nyBE5v3ZPYM2qXDncX38dt0GDSNy9m+hvvyX54CESd+wkccdOrGtUx+3ll3F5+mk0Dg75Goeq1xO9ciUR879ETU1FsbbG4803cR88CEUneWWEEEKUfoqiYGetJSHVSLIE0YQQQogyRX6rC1FC0g3p/HblN9acWcPxyONUdqrM5l6b0Sgakteso8rVVDT29pSbMhZOfYgCqNzZFqJgWu01ttlYtBrtAx+/otXi1KkTTp06kXbxIjGrVxO76SfSL1wkbNp0wufMxaV3L9xeegnrKlVybS/l5ElCJ00i7dRpAOybN6f8tKl5ulYIIYQoTUxBtAzJiyaEEEKUMRJEE+IBC0sK44ezP7D+/HqiU6MB0Gl0NPRsSFJGEjZhMYTP/QwAr/feo1ZgX+Z6uTH7yGxuJd8yt+Nt783YZmPp7Ne5RO7jbjbVq+MzeTKe77xD3MZNxKxeTfrVq8R8s4qYb1bh0KYNbq+8TPK/x1GstBar24zJyUTM/5LoFStAVdG4uOD93nu49O71wE8AFUIIIYpC5uECyRJEE0IIIcoUCaIJUQQMRgPB4cFEJEfgae9JgFeAxeowVVX5+9bffH/me3Zd24VBNU2qvey96FerH739e+Nh54FqNHJtwpuoqanYP/44rv36AtDZrzMdfDvct4/SQOvkhFv/Vyn3ysskHfiTmNWrSdyzh6R9+0jatw+NiwvGuDjUtDS83nmHxP0HCJsyxXwwgY2/P5WXL8PKw6OE70QIIYQouMzDBdL0BgxGFa1G3hQSQgghygIJoglRSDuu7sh2ldi4ZuNoWaElWy5t4fsz33Mh9oL59UCfQF6s/SIdfDtgpbnzYxjz/fck//UXir095T/8wGIlllajJdAn8MHcVCEpGg2ObVrj2KY16deuEfPd98Ru2IAxLg6AqCVfEb/lF4tTPZ2f7knFjz8uqSELIYQQRUan1aDTasgwGElJN+BoK1NuIYQQoizQlPQAypqI+V8SsXBh9q8tXEjE/C+Lre+wsDBGjhxJtWrVsLGxwdfXl549e7Jz505znSpVqqAoCocOHbK49u2336Z9+/bm51OnTkVRFN544w2LeseOHUNRFK5cuZLjOKpUqcLnn39eFLdk1r59e95+++0ibbMo7Li6g6DdQRYBNIDw5HDe2f0O7da244NDH3Ah9gJ2Vnb0rdmXDU9vYFmXZTzh94RFAC39xg3C58wFwCsoCOtKlR7ovRQX68qV8R43Fv/df+AzbRo2/v4AFgE09zfekACaEEKIUi8/8zz726vRUjL0RdK3zPOEEEKIkidBtKKm1RA5b36WCVbEwoVEzpsP2uL5kl+5coUmTZqwa9cuPvnkE/777z+2bdtGhw4dePPNNy3q2traMnbs2FzbtLW1ZenSpZw/f75YxvywMxgNzD4y2yLZf6bMslRDKpWdKjM2cCw7nt/BpBaT8C/nn7W+qhI6cRJqcjL2TZtS7qUXi338D5rG3p5y/fpS9eefqPzNStCYfhYUnQ6vt98q4dEJIYQQeZCPeZ5dEeZFk3meEEIIUTpIEC0XqqpiTE7O88N94EDch71B5Lz5hH/xBcbkZMK/+ILIefNxH/YG7gMH5rktVc0anMnJ8OHDURSFI0eO0KdPH2rWrEm9evUICgrK8m7k0KFDOXToEFu3br1vm7Vq1aJDhw5MmDAhz+No3749V69e5Z133kFRFIvtiPv376dNmzbY2dnh6+vLqFGjSEpKMr++cOFC/P39sbW1xdvbm+eeew6AgQMHsmfPHr744gtzm/d7h/RBCQ4PzrICLTuTW0zmlbqv4GztnGOd2LU/kHzoEIqtLeVnfIiiKbs/moqikPz332A0ouh0qBkZOb6rL4QQQhSn4pzn2RjSICWF5PhEmefx8M3zhBBCiOxIgoZcqCkpnA1oUqBroxYtJmrR4hyf56ZW8D8o9va51ouOjmbbtm3MmDEDBweHLK+7urpaPK9atSpvvPEG48ePp2vXrmjuE7CZPXs2gYGB/P333zRt2jTXsWzYsIFGjRoxdOhQhgwZYi6/ePEiXbt25cMPP2TZsmVEREQwYsQIRowYwfLly/n7778ZNWoUq1atomXLlkRHR7Nv3z4AvvjiC86dO0f9+vWZPn06AJ6enrmOpbhFJEfkqV5UStR9X8+4eZPwTz4BwOudt7H28yv02EqzzHfrPUaNxHP48Dvv3oPFqZ1CCCFEcSvueZ7u9sez2Vwv87zSPc8TQgghslN2l7s8Qi5cuICqqtSuXTvP10ycOJHLly+zevXq+9YLCAigb9++edoWAODm5oZWq8XJyQkfHx98fHwAmDVrFi+//DJvv/02/v7+tGzZknnz5vHNN9+QmprKtWvXcHBw4KmnnsLPz4/GjRszatQoAFxcXLC2tsbe3t7cplZ7/1MpM26FkxEenu1rhc1NdzPxJh8d+Yjo1Og81fe0z3kiqKoqoZMmY0xKwi4ggHKvvFLgcT0M7g2ggSlw5jFqZLbbY4QQQohHnczzhBBCiNJDVqLlQrGzo1bwP/m+LvLrr4latNi8Xc192Bt43PWOXV77zov8bAfI5OnpyejRo5k8eTL9+vW7b90PP/yQOnXq8Pvvv+Pl5ZXvvgD+/fdfjh8/bjGZU1UVo9HI5cuXeeKJJ/Dz86NatWp07dqVrl270qtXL+zz8A5tthTQZwbRnO9so7w7iJNf52LOsfzEcn69/CsG1UCrCq3wtvcmPDk827xoCgre9t4EeAXk2Gbc+vUkHTiAYmNj2sZZ1ieNBqNFAC2T+bnBWAKDEkII8aiSed5DOs8TQgghSogE0XKhKEqeltrfLWLhQqIWLc6yXU3R6Yplu5q/vz+KonDmzJl8XRcUFMTChQtZmMvqn+rVqzNkyBDGjRvH0qVLCzTGxMRE/u///s/8ruPdKleujLW1NcHBwezevZvff/+dyZMnM3XqVP76668s2xTyQnd7EqgPD0efkYGakUH06u+Iu2cVVG5UVeWfW/+w7MQy9oXsM5c3L9+c/vX6k5yRTNDuIBQUi0CagilHyNhmY9Fqsg+MZYSFcWv2RwB4jhqFTdWq+b7Ph43nyBE5vyZbOYUQQjxgxT3PU1WVNL0RvcGIo60uhxbvT+Z5QgghROkhQbQiltN2NaDY8j65ubnRpUsXFixYwKhRo7Lky4iNjc12guLo6MikSZOYOnUqTz/99H37mDx5MtWrV2fNmjW5jsfa2hqDwfIkqoCAAE6dOkWNGjVyvM7KyorOnTvTuXNnpkyZgqurK7t27aJ3797ZtpkbnZcXqCppt26hj4gg5ttv8Rw6JE9ff6Nq5I/rf7DsxDKORxwHQKNo6Fy5M4PrD6aeRz1z3bnt5zL7yGyLQwa87b0Z22wsnf06Z9u+qqqETpmCMTERu0aNcBs4IF/3JoQQQogHL7/zvOR0AxcjEtFpNdQpX7AgmszzhBBCiNJDgmhFrYS2qy1YsIBWrVrRrFkzpk+fTsOGDdHr9Wzfvp1FixZx+vTpbK8bOnQon332Gd999x3NmzfPsX1vb2+CgoL45HYC/PupUqUKe/fu5YUXXsDGxgYPDw/Gjh3L448/zogRI3j99ddxcHDg1KlTbN++nS+//JItW7Zw6dIl2rZtS7ly5di6dStGo5FatWqZ2zx8+DBXrlzB0dERNze3+ybKBVCNRtTUVIuyuC1bcOrUCbtGjbK9Jt2QzpZLW1h+YjlX4q8AYK2x5pkazzCw3kAqO1fOck1nv8508O1AcHgwEckReNp7EuAVkOMKNIC4n34iac9eFGtrys+cUfa3cQohhBBlQT7neXY6LVqNgrVWg8GootUoFITM84QQQojSQX47FTHPkSNyXOnkOXz4fbezFUa1atUIDg6mQ4cOvPvuu9SvX58nnniCnTt3smjRohyv0+l0fPDBB6TeE2zKzujRo3F0dMy13vTp07ly5QrVq1c3n67UsGFD9uzZw7lz52jTpg2NGzdm8uTJVKhQATCdLLVhwwY6duxInTp1WLx4Md9//z316tUz963Vaqlbty6enp5cu3btvmNQjUYybtzAkJBgUa6/GcqVV14leuXKLDlGVp5cSbf13Zjy5xSuxF/BydqJIQ2G8NtzvzG5xeRsA2iZtBotgT6BdK/WnUCfwPsG0DJuhXNr5iwAPEaMwKZ69fveixBCCCFKh/zO8zQahbrlnanu5VjgABrIPE8IIYQoLRS1INlKH2Lx8fG4uLgQFxeH810J5wFSU1O5fPkyVatWxdbWtoRGKApLVVUyrt/AEB8HQKq9HZeT4lB2bMV2yZ2Et05PdKb8jBlob/87CNodxPar2/Gy96J/3f48V/M5HHRZj5Iv7NhuvDmCxF27sK1fnyprvkexkgWhQgjxMLjfHEKUDjLPe/TI91UIIURRyOs8T/56F2WKqqqmFWi3A2hJdhpC7ZKJiYrhI5+9PNHRiad2JYBGQ8L2HSSeOonfvPnY1avH0IZDaVOxDU9VewqdtmB5S3ITv+UXEnftAp3OtI1TAmhCCCHEI8FoVNEUYjWaEEIIIUqe/AUvygxVVckICcEQZwqgJdpCuIsKGXfqrGqeSlKGhuppLlQ+G4NXSChXX3gR7/fHU+uFF6jtVrvYxqePiODWhx8C4Dl8GLY1axZbX0IIIYQoHdL1Ri5HJmEwGqlT3hlFkUCaEEII8bCSnGiiTFBVlYybNzHExgIKkW5WhLtmnaSqqGxoreV/PXT8NKENhtZNUDMyCJs2nZvvvoshManYxhc2/QMMcXHY1K2D++uvF0s/QgghhChdrLQK6QYjeqNKur54DpgSQgghxINR4kG0BQsWUKVKFWxtbWnevDlHjhy5b/3PP/+cWrVqYWdnh6+vL++8806ekqWKsktVVTJCQzHExJiel/cg3jrnY9JVVKJTo3mx2RDqfb0Kr7FjwcqK+K2/cuW550g9e7bIx5iwbRsJ27eDlRUVZs5E0RXPdlEhhBBClC4aRcFOZzpwKCUj5/mJEEIIIUq/Eg2irV27lqCgIKZMmUJwcDCNGjWiS5cuhIeHZ1v/u+++Y9y4cUyZMoXTp0+zdOlS1q5dy/vvv1+k43rEzlp4qKmqij4sDEN0NAC6SpXIcLwnqaxqCpwZsXz3NyI5AkVRcB80EL9V32Dl40P6lStc6duP2HXriuzfgT46mrDpHwDg8X//h23t4tsyKoQQQoj7K4l5np21KYiWnC5BtKIm83YhhBAPUokG0ebOncuQIUMYNGgQdevWZfHixdjb27Ns2bJs6//555+0atWKl156iSpVqvDkk0/y4osv5rp6La90t1cHJScnF0l7onipqor+1i30UVEA6CpWxMrVFa2itayXrqI36onTx1mUe9p7mj+3b9yYqhs34NC2DWpaGqETJxE6bhzGIvi3EPbBBxhiYrCpVQuP/xta6PaEEEIIkX8lOc8zr0STIFqRy/x+6mSVvxBCiAegxA4WSE9P559//mH8+PHmMo1GQ+fOnTl48GC217Rs2ZJvv/2WI0eO0KxZMy5dusTWrVt59dVXc+wnLS2NtLQ08/P4+Pgc62q1WlxdXc0r4ezt7SX5aymlqir6qCjzCjQrLy8UOzuSEuMISwrDaDSaVqClq8RExbA3ai+pRtO2XwUFb3tvArwCLNq0KlcO38WLifrfUiI+/5y4n34m5eRJKn3+OTY1ahRonPG//U7Cr9tAqzWdxmltXbgbF0IIIUSBlOQ8T2s0oOrTSTJkkJKilfllEVBVleTkZMLDw3F1dUWr1eZ+kRBCCFFIJRZEi4yMxGAw4O3tbVHu7e3NmTNnsr3mpZdeIjIyktatW5uCKHo9b7zxxn23c86aNYtp06bleVw+Pj4AOW4pFaWDISEBY0ICAFpnFzQxMSSF3yA+LR4VFY2iwaAa0Bv17I3ay5aILYApgAYwttlYtJqsky1Fo8Fj6BDsGz9GSNC7pF+4yOXn+1J+6hRcnnkmX2PUx8QQNn06AO5DXseuXr3C3LIQQgghCqmk5nmqCpFxKRhVIMEGnbbE0xKXGa6urubvqxBCCFHcSiyIVhC7d+9m5syZLFy4kObNm3PhwgXeeustPvjgAyZNmpTtNePHjycoKMj8PD4+Hl9f3xz7UBSF8uXL4+XlRUZGRpHfgyi8mDVriV65Ei3gNnQINKnLF8FfEHwrGIAm3k0YFTCK09GnmRc8j+tJ183Xett7M7bZWDr7db5vH/aBgVTdtJGbY8aQ9OdBbo4dR9Jff+EzcSIaW9v7Xpvp1oyZGKKisPGvgcfw4QW+XyGEEEIUjZKc581fe5TjN+IY3aUW3eqXf6B9l1U6nU5WoAkhhHigSiyI5uHhgVar5datWxblt27dyvHdpEmTJvHqq6/y+uuvA9CgQQOSkpIYOnQoEyZMQKPJ+q6ejY0NNjY2+R6fVquVX8qlUNTSZcR+8gkawPPdIE61qcik314gOjUaa4017zZ9lxdrv2iaJLuWp12VdgSHBxORHIGnvScBXgHZrkDLjpW7O75ff03kosVELlhA3Lr1pP53goqff4ZN1ar3vTZh1y7it2wBjYbyM2eikW2cQgghRKlREvM8X09Xfj0dzT83EunVNG9vyAkhhBCidCmxteTW1tY0adKEnTt3msuMRiM7d+6kRYsW2V6TnJycJVCWOQGSk3nKvuhvVhH+yScAeIwaidvrr7Hk+BKiU6Op4VqDNU+t4aU6L1nkGdFqtAT6BNK9WncCfQLzHEDLpGi1eI54k8rLlqJ1dyft7Fmu9HmO+K1bc7zGEBtL6JQpALi/Nhi7Bg0KcLdCCCGEKEsaVnIB4PiNuFxqCiGEEKK0KtGEDEFBQXz99desXLmS06dPM2zYMJKSkhg0aBAA/fv3tzh4oGfPnixatIg1a9Zw+fJltm/fzqRJk+jZs6esGivjYr7/nlszZwLgPuwNPIcPR6NomN16NgPqDmDNU2vwL+dfbP07tGhB1Y0bsA8MxJicTEjQu4ROm4bxrkMrMt2aNRtDRCTW1arhMWJEsY1JCCGEEA+PhhVdATgdGk+aXk7pFEIIIR5GJZoTrV+/fkRERDB58mTCwsJ47LHH2LZtm/mwgWvXrlmsPJs4cSKKojBx4kRCQkLw9PSkZ8+ezJgxo6RuQTwAsevWETbNlKD/Vq8W7GyjITM05evsy+jA0Q9kHDovLyovX0bE/C+JWrKE2O/XkLB9B849uuNzO9ibsHs3cT/9BIqCXZMAor76Gs+REkgTQgghHnW+bnaUs9cRk5zB2bAEGlZyLekhCSGEECKfFPUR2wcZHx+Pi4sLcXFxODs7l/RwRC5iN24i9P33QVXRP9+Nl6r/DorCmqfWUM+95E67TNy7l5vvjcUQGwuAU4/ulJ8yhUs9n0Z/6xZ2jRuTcvQoHqNG4imHCgghRJkgc4jSr7R/j/ovO8LecxF8+Gx9Xnncr6SHI4QQQojb8jqHkCCaKLXiNm/h5nvvgapS7qWX8J40kY//+phKTpV4qbZl7rOSkBEaSkjQu6QcPQqAtlw5DDExaF1cMMTFSQBNCCHKGJlDlH7F8j36YxZotNDuvayv7fkYjAboMD7ra9k4dTMeaysN1Twc0GhKdh4jhBBCiDvyOoco0ZxoQuQkfts2bo4bB6qKTe+eeE+cgKIojG02lpfrvFziATQAXfny+H2zErfBgwEwxMSYPkoATQghhCg7NFr4Y4YpYHa3PR+byvNxaFHdCs7U8HKUAJoQQgjxkCrRnGhCZCdhxw5C3h0NBgN/NFA42jaar0pB0Cw7ik6H93tjsG/ahBvD3zSXSQBNCCGEKCMyV6D9cTsHb6MX4egq2PMRdJiQ/Qo1IYQQQpRJEkQTJcZgNBAcHkxEcgSe9p4EeAWQvGcf195+C43ByL56Cj/09mB6/f6lYuXZ/aSeOQOYAmhqRgYRCxdKIE0IIYQoK+4OpGUG0woYQPvh7+vsPx/J622qyuECQgghxENGgmiiROy4uoPZR2ZzK/mWuaztDWfe+C4GK4PKn3UUjg1tw7q2M/Cw8yjBkeYuYuFCIufNN2/hzHwOSCBNCCGEKCvavQe7Z4FqBEVT4BVoO07d4vdTt2hYyUWCaEIIIcRDRnKiiQfu8IdB7P/wLYsAWr0rRoZ+H42VQSXEDWynj2PBk4sfugAamAJnHqNGEjlvPhELF5bwCIUQQoiSsXfvXnr27EmFChVQFIVNmzblek1aWhoTJkzAz88PGxsbqlSpwrJly4p/sHmx52NTAA1MH3fNKFAzzzauyOgna9Kyeume4wghhBAiK1mJJh4og9HA3pv76bfPNAld31pDnWsqY9cZsdab6hxt4EBQvdJxeECuDMZsDxEwPzcYS2BQQgghRMlLSkqiUaNGDB48mN69e+fpmr59+3Lr1i2WLl1KjRo1CA0NxWgsBb9LMw8RaP++KR9a3HXY+zFodflekda9QfliGqQQQgghipsE0cQDFRwezDfNU0jK0NBvnxGPeJVWp1RsM0yv/9hK4ceWabQPDybQJ7BkB5sHniNH5PyabOUUQgjxCOvWrRvdunXLc/1t27axZ88eLl26hJubGwBVqlQpptHlQ2YALTMHWlo8HPwSPOvcyY8mhwsIIYQQjwTZzikeqIjkCAA2tFIIrqbQ6d97AmhttRb1hBBCCPFo+Pnnn2natCkff/wxFStWpGbNmowePZqUlJQcr0lLSyM+Pt7iUeSMBstDBOrdXlUXexXajjG9nk+hcSn8+l8olyISi3CgQgghhChushJNPFCe9p64JKkM32Kk8SXVXJ6hxRxAy6wnhBBCiEfHpUuX2L9/P7a2tmzcuJHIyEiGDx9OVFQUy5cvz/aaWbNmMW3atOIdWIfxls8rBoBrZYi9Bt71od6z+W5y5tYzbP73JmO61OLNDjWKZpxCCCGEKHayEk08ULXOJDFnqUrjSyr62//6MrSgM0Cf/UYUFHzsfQjwCijZgQohhBDigTIajSiKwurVq2nWrBndu3dn7ty5rFy5MsfVaOPHjycuLs78uH79evEPVFGgXi/T5yc3FqiJhhVdAPj3emwRDUoIIYQQD4KsRBMPhDEtjUuzppGxZiPOQKw9uCbD2jYa1rfW0Ge/0XzYQOuJY9FqtPdvUAghhBBlSvny5alYsSIuLi7msjp16qCqKjdu3MDf3z/LNTY2NtjY2DzIYZrU6wUHvoBzv0F6Elg75OvyhpVM93j8RlxxjE4IIYQQxURWoolil3bhAid79yRjjendWtuGDXFNhi0dnVjf2vRPcH1rDb90dKbfPiONfjlXksMVQgghRAlo1aoVN2/eJDHxTp6wc+fOodFoqFSpUgmOLBvlH4NyVUCfAue25fvy+hVd0CgQFp9KeHxqkQ9PCCGEEMVDgmii2KiqSsyatVx+7nmsLl4n3g5+eqM+9q1b4TFqJEFfHmRZl2V81OYjlnVZxjtf/onHqJFgKAVH2QshhBCiUBITEzl27BjHjh0D4PLlyxw7doxr164Bpq2Y/fv3N9d/6aWXcHd3Z9CgQZw6dYq9e/cyZswYBg8ejJ2dXUncQs4KuaXTwcaKGl6OAPwrq9GEEEKIh4Zs5xTFQh8TQ+ikSSTu2AmAQ6tWhAzvwruPPYtOqzPXC/QJtLjOc/jwBzpOIYQQQhSPv//+mw4dOpifBwUFATBgwABWrFhBaGioOaAG4OjoyPbt2xk5ciRNmzbF3d2dvn378uGHHz7wsedJvd6w/zM4vx3SEsDGKV+XN6zkyrlbifx3I5Yn6noX0yCFEEIIUZQkiCaKXNKhQ9x8byz68HCwssLr3XdxG9CfyhpZ+CiEEEI8Ktq3b4+qqjm+vmLFiixltWvXZvv27cU4qiLk0wDcqkP0RVNutAbP5evyRpVcWPfPDVmJJoQQQjxEJKohioyakUH4nLlcGzQYfXg4IW6wdFRNnPu/jCIBNCGEEEKUJYXc0tmgkisAx2/E3jfYKIQQQojSQyIbokikX73KlZdeJurrr0FV2fGYwrhBWmq36IaVRhY8CiGEEKIMygyind8OqfH5urROeSd0WoWY5AxuxKQUw+CEEEIIUdQkiCYKRVVVYjdu4nKv3qT+9x9p9jrm9NLwdTcrxrSZxOsNXkdRlJIephBCCCFE0fOuB+7+YEiDs7/m61IbKy21fZwB+PdGbDEMTgghhBBFTYJoosAMCQncHD2G0PHjMSYnE+LvytuDjPxTx5pZbWbRr3a/kh6iEEIIIUTxURSo39v0eQG2dDas5ALAccmLJoQQQjwUJIgmCiQ5+CiXn+1F/C+/gFbLvh6+BPVOILGcLV90/IIe1XqU9BCFEEIIIYpf5pbOizshJTZflza6nRft3+v5u04IIYQQJUOCaCJfVL2eiAULuPrqq2SEhKCtWIH/Da/K/Iah2Ns4sqjzItpWalvSwxRCCCGEeDC86oBnbTCk53tLZ0NfFyq42FKpnH0xDU4IIYQQRUkyvos8y7h5k5Ax75Hyzz8AWHd/gvHNLnE67QquNq4sfmIx9dzrlfAohRBCCCEesHq9YPcs05bOx17M82W1fZz5c3ynYhyYEEIIIYqSrEQTFiLmf0nEwoVZyuN//ZWLXbuR8s8/aBwcsJn2Hm+2OsfptKt423uzsutKCaAJIYQQ4tFk3tK5C1JiSnYsQgghhCg2EkQTlrQaIufNNwfSjElJ3JwwgZB3glDT07Hy8abKxg1McdpFSGIIlZ0q8023b6jmWq2EBy6EEEIIUUI8a4FXPTBmwJlf8n25qqrEJWcUw8CEEEIIUZRkO6ew4Dl8OACR8+ajvxVO8qFDpF+9CoBdYCB+y5ai6HTMLDeTmYdn8kGrD/Cw8yjJIQshhBBClLx6vSD8pGlLZ+NX8nzZnxcjGb46mCruDmx6s1UxDlAIIYQQhSUr0YQF1WDAtnYddH5+xK5daw6gufTujfPXn6PodAD4OvmyqPMiCaAJIYQQQgDUe9b08dJuSI7O82UVXOyITc7gYngiBqNaLEMTQgghRNGQIJoAQB8VReSSr7j4xJPcGD6cjKtXyZzGqVZWnBjajq7ru7L7+u4SHKUQQgghRCnl4Q/eDcCohzNb8nyZn7s9P73Zir8ndUarUYpxgEIIIYQoLAmiPcJUVSU5+CghY97jQvsORHz2GRk3b2J0sudiZWsUIEMLil7PkVljSNGn8NuV30p62EIIIYQQpVPmarSTG/N8iaIoNPJ1xcZKWzxjEkIIIUSRkZxojyBjcjJxW7YQ8/0a0k6fNpfbNmxI6JMN2XnwO547YGRtGw3rW2vos99Ivz3p6I0aWrbrUIIjF0IIIYQoxer1gl0fwKU9kBQJDpL2QgghhChLJIj2CEm7dImY79cQt3EjxsREABQbG5x79KDciy9iXa8O60a0sAigAeaP/fYZ+eXjSXT6shNajbxbKoQQQghhwb06lG8Eof/C6c3QdFCeLrsRk8yCPy6QkKrny5cCinmQQgghhCgoCaKVcapeT8KuXcR89z3Jhw6Zy3V+lSn3wou49noWrasrAH+F/UVKeqJFAC1T5nNNegLB4cEE+gQ+sHsQQgghhHho1OtlCqKd3JjnIJpWo/D9ketoNQop6QbsrOXNSiGEEKI0kiBaGZURHk7sjz8S+8OP6G/dMhVqNDi2b0+5F1/EoVVLFI1loCwiOYIf2+Q8acsMpDVNjii2cQshhBBCPNTqPgs7psKVfZAYAY6euV7i42yLp5MNEQlpnAqNo4mfW7EPUwghhBD5J0G0h0jE/C9Bq8Fz+PCsry1ciGow4NC8OTHff0/C9h2g1wOgdXPD9fnnKdf3eXQVK+bYvqd97pO8/NQTQgghhHjkuFWFCo3h5lE4/TMEvpbrJYqi0KiSCztOh/PvdQmiCSGEEKWVBNEeJloNkfPmA1gE0sI//5yoxUvQupUjasFCc7ldQADlXnwRpy5PorG2zrX5xp6NsdHakGZIy/Z1BQVve28CvCRXhxBCCCFEjur1MgXRTm7MUxANoGElV3acDuf4jdjiHZsQQgghCkyCaA+RzMBZZiDNqVNnQidOIPW/EwAYomNQ7O1x6dmTci++gG3t2vlqf935dfcNoAGMbTZWDhUQQoiH3R+zQKOFdu9lfW3Px2A0QIfxD35cQpQV9XrB9slwZT8k3AIn71wvaVjJBYDjN+KKe3RCCCGEKCAJoj1kPIcPx5iYSOS8+eZgGoB1tWqUe/FFXJ59Bq2TU77bPRl5ko//+hiAZ6o/w6HQQ9xKvmV+3dvem7HNxtLZr3Phb0IIIUTJ0mjhjxmmz+8OpO352FTeYUKRdWUwqhy5HE14QipeTrY0q+qGVqMUWftClEqulaFiUwj527Sls9mQXC9pWMkVgEuRScSlZOBipyvmQQohhBAivySI9hDKCLl554miUHn5cuybN0NRCvZHSVxaHO/ueZcMYwadKnfig1YfYFSNBIcHE5Ecgae9JwFeAbICTQghyorMwNkfMyDiLNR9Gm6dhj2zTAG07FaoFcC2E6FM23yK0LhUc1l5F1um9KxL1/rli6QPIUqter1MQbSTG/MURHNzsKZSOTtuxKRwIiSOVjU8HsAghRBCCJEfmtyriNIk9ew5En77zfTEygpUleTgfwocQFNVlYkHJhKSGEIlx0pMbzUdRVHQarQE+gTSvVp3An0CJYAmhBBlya1TkBIDOjs4sQ5+6G8KoDV8oUgDaMO+DbYIoAGExaUy7Ntgtp0ILZJ+hCi16j5j+nj1T4jP27/3RrdXo8mWTiGEEKJ0kiDaQybk3XcBsK5Rgzon/sNj1Egi580nYuHCXK7M3jenvmH39d3oNDrmtJ+Ds7VzEY5WCCFEqZEaD38vh687wqIWcGghZKRY1jm+Blb3hYhzherKYFSZtvkUajavZZZN23wKgzG7Gvn0xyzY8zEGo8rBi1H8dCyEgxejTG3v+dj0uhAlwdUXKjUDVNOWzjy4kxcttvjGJYQQQogCk+2cD5HQ6R+QfuECABXnzAGyHjZw96mduTkafpTP/vkMgHHNxlHXvW5RDlcIIURJU1W4dgiOrjJtKctINpVrrKBmV9DZw38/gNYaDOmgaOD8b3BxJwQOMa1Ks3fLd7dHLkdnWYFmMSwgNC6VI5ejaVHdHaNRRVPQPGm387st3XuRmUlPm4vfd/iZoYY1RZrfTYh8q98bbhyBExug+f/lWj0zL9pfl6P56ViI5BEUQgghShkJoj1Eko8cBsDpySexrVXTXG4OnBmM+W7TzdaNpj5Neb7m80UyRiGEEKVAwi3493s4+i1Enb9T7lETGr8KjV6Af1bcOUSg3Xt3DhVwrwFRF+DwItPKtA4ToMkg0OZtypCcruf3U2F5qhueYAq0ffDLKTYdDaGCq53p4WJ753NXOyq42uLlZJttIGGb+6ucyjhLEGvwsrrGckNX2mqOM9SwjrkZz1HX/VW65mk0QhSDus/AtnFw/RDEhYBLxftWD4s3/UxEJqXz1ppjgOQRFEIIIUoTCaI9JNLOnyf94iUAPN7MutosPyvQMjX2asyPPX/E1sq2wDnVhBBClBIGPVzYDsGr4Nw2UA2mcp0D1O8FjfuDbzNQFMtTODNzoN192EDDFyD0X4g4DVtHw1//gy4zoEb2JzTHp2aw63Q4v54IZc+5CFIz8vamjpeTLQChsanEJGcQk5zByZvx2da10ih4O9tSwdUUYHu8mjt9m/qy8Od9dEAlQbXjWas/eVp7EI2iMifjOb409MZn8ymeqOsjK3lEyXCuAJVbwLWDcOonaJHzfG3biVCC1h7LUp6ZR3DRKwESSBNCCCFKmATRHhKRixaBquL0RGdsa9UqVFvx6fHm3Gfudu5FMTwhhBAlJeqiabvmse8h8a4VYJUCIaC/6YRAGyfLa4yG7E/hzHxuNMAzCyB4BeyaARFn4Ns+4P8kPDkDPGsSk5TO9lO3+PVEKAcuRJF+12roSuVsiUnKICndkO2QFcDHxbRNDeDj5xvy9hP+3IxNISQ2ldDYFG7GpnAzNpWbcSmExaWiN6qExKYQEpuCQhT1ko4Qd3w7G9J2YqUz9a2qoFFU0lQr5ht6A5bbRoUoEfV6mYJoJzfmGETLLY+ggimPoASEhRBCiJIlQbSHQNqFC8T/ug0AjwKsOLvbkdAjvPXHW0x8fCI9qvUoiuEJIYQoSn/MAo0WQ5sxHLkcTXhC6p28SPs+MQW4Wr1lWtVydBVcPXDnWnt3aPSiacumV+2c++gwHjD94Z6lj7sDa4GvQ/3nYO8ncHgxnP8d44Vd/O7Qk/ejuxNtdDBXre7pQLf65ela34d6FZz57WQYw74NBrAIDGT++T+lZ11zMMDZVoezj47aPtkfbmMwqkQkpBEedg3d8e+peHEtztdDzA0eMtYh0ujMU1aHSVOtsFH0jNRuMAfSMreNClEi6jwNv4415UaLvW46cOAe+c0jKIQQQoiSIUG0h0DkosWgqjh27oRtnTqFamvd+XUkZiRyKPSQBNGEEKI0yi1RfoXGppM1025ve1Q0UL0TBLwKNbuBlXWeutl2IpRpm09Z/OGeXe6lVCsnbLvMgCaD0G97H6sLv9E1cSPNddv53v5ljAGD6NKwEv7elqvdutYvz6JXArL04ZPf/E6qivbqPnz+XobP6S1gzDCV27oQWqUXr/xbj+6aw7yrW8ecjOeYb+jNSO0G3tWtA2C+obd526gQJcK5PPi1gqv74dQmaDkyS5W8BnolICyEEEKULAmilXJpFy8Sv3UrULC8Z/ea1XoWjTwb0du/d6HbEkIIUfTuTpSfoNWzyvAEc3WL6Gg4Zqpw86jpo6ufacXZYy/lmqw8Sx8nQhn2bXCWrWN3517yc3dg3Ib/UFWVn0e0Bo8aWL3yA999v4Ku1+fhlnyR4SlL4OxuqDoTvLPmS+tavzxP1PXJutotL9vRkqPh2Hfwz3LTQQeZKgVC08FQ91m8rOzod/4NhhruBNAA88d3detwsrWiWdXu+fr6CFHk6j1rCqKd3JhtEC2vgV4JCAshhBAlS4JopZx5FVqnTtjWrVvo9rQaLS/XebkIRiaEEKKoGYwq038+gbOxKY0MF3lXt44gq3Vknv2SpurYpWlOza7DqR7YDTQaACIT09AoCs62VlhpNbn2kZfcS5vebMV/N2IBzAEwgJdeHAiGV+7kS4s8myVf2t20GiXv289UFa4fgb+XmYINhjRTubUjNOwHTQeBT4M7bQMda7oz97jpEIG7fWnojQI8XcddckiJklfnafj1PQj5B2KuQjk/i5ebVXWjvIstYXGp2f5s3ptHUAghhBAlQ4JopVjapUvmVWgew4cVuJ39IfvZfX03YwLHYKO1KaLRCSGEKBL6dAg9Blf/JO70Hn5NO4SLTbL5ZUUBowpT9QPYZGhFPI5U2m3L/uZ3gmUvfHWIC+GJrBn6OI9XMwWsvjt8jdm/nsbaSouNlQYbKw3WVhoy9MY85V66FJHEwpebEODnmnX1i9Yq23xpXNxlKm83Fuzz8cd+ahwc/wH+Xg7hJ++U+zSApq9Bg+eyHo5wW41+M6lbLxSfbLaN1u35ITXkNENRGjh5m7Z0Xtln2tLZ6i2Ll7UahSk96zLs22AUcs8jKIQQQoiSIUG0Uixy0WIwGnHs2BG7evUK1EZYUhjj940nNi0WHwcfXm/wehGPUgghSoHbyfiznDYJsOfj26dRjn/w48pOehLc+Auu/ml63Pgb9CkAuAEokKTaEKG6UEUTToaqRacYcCGJeBwBcLWzzHtmMJr+5La2uhNYS0rTE5+qB/QFGmZ4QirPPJbLNlE7V7idL43tk+DsVlNA7fhaqNgUKjbJ/uue+T2p1dW06uy/dZBxO3BoZQf1+5i2bFYMwLwM7z4KtW1UiAelXi9TEO3kxixBNCjCPIJCCCGEKDYSRCul0i5fJv6XX4CCn8iZYcxg9J7RxKbFUte9Lv3r9i/KIQohROlxOxm/UVU57Pu6OZDS/Pr/0OyeCR0mFE0/BQnWJUfDtUOmUzSvHYTQf8FoGdhK1LrwZ4Y/hw21OGKsQ0fNUd7Rrc8xUf6EHpbb+/8Y3R6j0XITWN9AXzrW8SJdbyRNb7z90cDxG7F88tu5XG81X7mXPGrAi9+bVqL9NgHCT8GF7aZHzBXoveRO3V0fmlavOfrAntl3yj1rmwJnDfuZgnP5lK9to0KUhLrPwNbRpryG0ZfBrWqWKjkFhA1GFaNRRSOBYSGEEKJESRCtlIpafHsVWvv22NUv2Cq0L/75gn8j/sVJ58Sn7T7FWpu3E9uEEOKh0+49zt9KwH/3TP7MOGcOPLXQreN83VH4Zxf0KojbwbrMPs32fGwq7zAB4kJMwbKrB+DqQYg4nbUd50rg1xL8WpBWoTkdlt4gIl1P86rl6Ba2guHq+nwnyr/3j2sXOx0udros9VpW9+DbQ9eKJ/dS9Y7wf/sgeKXp65EcBcfXmFbedf8Y/phpygkFkBgGWmtTYKHpYKjcIk+rzoR4aDl4QNW2cGm3aTVam6Bsq90bEN50NIRPfjvLxB516NZAVqMJIYQQJUmCaKVQ2uXLxG3eAoDHm28WqI1d13ax8tRKAD5o/QG+Tr5FNj4hhChttp0I5c3gZky16sy7unW8bbUeraLyp6EOe/8N52nrj6nr6w06e9DZgs7OtG1QZ2sqs7K96zV70GYNPgF3AmeZgbS2Y2DbONMWRp8GcHTVndfu5lETKrdA79uCbfHV2HRFy1e9mqDRKNgAH/Ryp6KrHQ0quXBh7ZZiTZRf7LmXtFYQ+JppS+beT+DQQoi+aDp8IJNbNdMW0MdeBgdZPSYeIfV65RpEu9elyCRCYlP4bMc5utTzkdVoQgghRAlSVFXN7o3oMis+Ph4XFxfi4uJwdnYu6eFk6+bYccT99BOO7drhu2Rxvq+/nnCdfpv7kZCRQP+6/RkTOKYYRimEECVMnwYhwRiv/snhPzZTz3gGZyWlaNpWtKZAmznYdk/ALe4GRJ3P4VqNKaDm18q0uqpyC3D0BCAxTU/LWTuJT9Xzdf+mPFHXO9smtp0IzZIXqXwR50V6EH0AEHkBFgSCajR9bV7ZAFXbmU8WfZg8DHOIR12p/x4lRcGn/qAaYGQwuFfP9ZK4lAxaf7SLhFQ9819sTM9GFR7AQIUQQohHS17nELISrZRJv3KFuM2bAfAYkf9VaGmGNN7d/S4JGQk08mzE203eLuIRCiFECUlLgOtHTMn4rx00JeQ3pKEBWgAokKZaYaPoMagatIqRfw3VOE8lbEmjZWUH3Kz1kJEKGSmmZP4ZqaaE9vrUO4ntwfQHbnqi6ZEXlU1bM/FrCZWaga3pF+/VqCR+PnyTER09UBQFRxsrRnb0R0WlebWct0s+iET5DywZ/8kNpgCa1hoM6aatndU7FG0fQjwsHNyhWjtT/sCTG6Ht6FwvcbHTMaRNNeZuP8fnO87RvUF5OTRDCCGEKCESRCtlIhcvAaMRh3ZtsWvQIN/Xf/LXJ5yOPo2rjSuftvsUnSaHLUlCCFHaJUXezi12O79Y2H+m4Nbd7D246dKYr6/5UE25yatWO7Mk49+REcB8Q2/cw6xpXs2NepVdqFvBmfoVXPB0srnTlqqaVreZg2opt4NttwNsdwXcjCc3oTn3K0bFCo2qx1itPZr2Y81N/XM1mq/3Xua3U2GoKjSuXI7W/h4ADGlbLU+3/yAS5Rd7H3fnimv33p3nkP3hDEI8Cur1uh1E25SnIBrAoFZVWLr/Mhcjktj8702ebZzLyblCCCGEKBYSRCtF0q9eNa9C8yxALrRfL//K2rNrAZjVZhY+Dj5FOj4hhCiQvJ5oGXv99iqzP02Bs8izWeu7Vr5r1VcrcK/B1UvROC57zyKABpbJ+AHmJ/Vm639hbP0vzNycl5MN9So4U7+iC/UqOFOvgguVypVDuU+C+/M/TML/3K+WwbrdMzl3K5GLdYfz1b5LHL0Wa67fvpZntgn+y7x7A2iQNaecBNLEo6j2U7DlHbj1H0SeBw//XC9xstUxtG01PvntLF/sPM9TDctjpX34tkQLIYQQDzsJopUikYuXgMGAQ5s22DVsmK9rL8ddZuqfUwEY0mAIrSu2LoYRCiFEAWR3oqWqwq9j4cgS8KoHx1ZD3PWs13rWNm2RzAycuVQC4HJkEmv/us6YLtCsqhsnbTXMSb0TQMs0/3YyfmcbDateaMbpsHhO3oznREgclyKTCE9II/xsBH+cjTBfM7NXA15qXhmA6KR0ohLTqObpiFajmAJop+ZlH6w7PY/Nx29y1NAba62GXo0r8nqbqvh7OxXhF/MhYjRYBtAyZT43GrJeI8SjwN4NqnWAC9tNWzrzGEwe0NK0Gu1yZBKbjt3kuSaVinmgQgghhLiXBNFKifRr14j7+WcAPN8cnq9rVVVl6p9TSdYnE+gTyPDH8ne9EEIUq7tXH4UEm4JqF3fdyUEWftL0UdFC+UamoJlfS/B9PMvJjZciEvly1wU2HQvBqELdCs483agClXpNZ0YOp03ON/RmUe8A2tT0pE1NT/Nryel6TocmcOpmHCdC4jkZGse5sERql78T9Pr9ZBjjNvxHu5qeLBsYyJ6zYfyckX2wDkCrGHmzQ3UGtKyCl5NtUXz1Hl4dxuf8mqxAE4+6er3yHURztLHi/9pWY9avZ5i38zzPPFYBnaxGE0IIIR4oCaKVEpFLbq9Ca90au8cey9e1iqLwQasPmHlkJh+0/AArjXxbhShWed2eWNr7uIvBqBZfgvm4EEhPMp1qee7XO+UaqzunV/q1hEqBYOOYbRMXwhP5ctd5fv73JsbbUbKOtb2o7ukAmJLkL3olIMtpkz73OW3S3tqKJn7laOJXzlyWrjda3HdCqh57ay21fZw4cjmaD5OezfE2MwNp39fwlACaEOL+aneHzToIPwXhZ8Crdp4ue7WFH1/vu8S16GQ2BN+gX2DlYh6oEEIIIe4m0ZZSIP36deI2/QSARx5WoRmMBoLDg4lIjsDT3pMArwAqO1dmcefFxT1UIQRkvz0RLHNAPQx93LbtRGiW4FP5+wSf8iwkGA4tNK20MOotX9Po4P2bYGV93ybO30pg3q4LbDl+E/V28KxzHS9GdfKnYSVXi7pFcdqktZXlqo4hbasxuHVV0vQGtp+6lac2whNSc68khHi02ZWD6h3h/G9wahN4jcvTZfbWVrzRrjof/nKaeTsv0KtxpSz/bwkhhBCi+EgQrRQwr0Jr1Qr7xo3vW3fH1R3MPjKbW8l3/pjztvdmXLNxdPbrXNxDFUKA5fbEtATwfwKO/wBHV0GTQVCrG4SfNgWKtFamFVcaHWh1tz+3uvN5Tgnss0vAnl2i9kLadiKUYd8GW2yBBAiLS2XYt8EseiUgf4E0owHO/GIKnl07eKfcrzU4+cCJdaC1BkM6HPg8x/s4G5bAvF3n2fpfqDl49kRdb97q5E/9ii45dl8cp01qNQr21lZ5Xl0mq9CEEHlSv7cpiHZyI7TPWxAN4JXH/Viy9xIhsSn8+M91Xm7uV4yDFEIIIcTdJIhWwtJv3LhrFdr9T+TccXUHQbuDUO/5c/dW8i2Cdgcxt/1cCaQJ8aDU6AwnN8Gf80yPTP8sNz3yStHeDqjdG3C7/bm9uylw9sdMQIWA/tDm3SK5BYNRZdrmU1kCaJh6QgGmbT7FE3V9cl/NlZYAR7+FQ4sg9qqpTGMF9fvA48Ph/O+WAcDMgCBYBNLOhMUzb+d5ixM0u9TzZlQnf+pVyDl49iA0q+pGeRdbwuJSs/2aKZi2jzar6vaghyaEeBjV6mZ6UyHiDNw6Bd5183SZrU7Lm+2rM3XzKVb+eYWXmlW+74nCQgghhCg6EkQrYVFLloBej0PLltgH5LwKzWA0MPvI7CwBtLt9dOQjOvh2QKvRFsdQhRBGA5zdCgcXWK6yyuTqZ9q2aNSDIcPyozEj+zZVA+gNQG5bAG//7Ad/A6e3mFa/1ewC1TuBnWuBbufI5WiLLZzZ9Rgal8qRy9E5r+6KuQpHvjKNKy3eVGZXDpoOhsAh4FzeHDAztn+fw5VeI/xYCF6VXqN5exXNXYG0+NQMnl1wgNQMIwDd6vswqpM/dco7F+j+ippWozClZ12G5XCAAcCUnnWLLpecEKJss3UxvSFzdqtpNVoeg2gALzSrTGKanlce95MAmhBCCPEASRCtBKXfCCF24yYAPEbcfxVacHiwxRbOe6mohCWHERweTKBPYFEOUwiRucrq8GKIuWIq01iBZ224deLO9sTGr+S8zVJVTUG4zICaIeP284y7Am2ZQbfbrxkyTFtEj6029WfUg5UNpETD8bWmh6I1Jeav+STU7AoeNXPeInqPsLiUPNU7ExrPkr0XqeXtxPjudUyF149g+PNLtGc2g2oKeuHuDy2GQ8MXwNr+TgNGA+frjqL/waaEbjtkLi7v0pSF1YfT2GgAwNlWx0vN/LgVn8rITjWo7VM6gmd3K8gBBkIIkaN6ve4E0Tq8n+f/v211WkZ09C/mwQkhhBDiXhJEK0GZq9DsWzyOfUDAfetGJEfkqc281hNC5EHsNTi8JOsqqyaDTIGuP+fnuj3RTFFMWzS1VkAec2bt+dgUQLu3j8avgJ2baYtkxBm4ut/02D7ZtBquZldTUM2vNeiy9hWdlM73R66xbP+lPA0jVW9g99kIwmOTGF/5FBxcCCF/k7nm9ajVY+z36EtcxXb4pjvieykB33IGKpWzx85ayzbPgQz7LRj1ntV2oXGp9IprzXuVapF5pMqkp+qU+lUVRXGAgRBCAKb/r7U2EHUebp0En/r5bkJVVcIT0vB2lnyMQgghRHGTIFoJyQgJIXbjRgA8c8mFBuBp75mndvNaTwhxH9ePmLZsnv7ZcpXV48Og0Ytw8Ev4Y65lgv/sDgIojOwOEbi7jw4T4M3DppVx536Hc9vgyj5TPrIjS0wP3f+zd9/hTZbdA8e/Sbo3pZMyWiirgECBsmXIcoCIA0XE7U+mgoogKuIARUFkuxB9UUERGYLsKatAqbLpYnfvPZL8/nhoS2kLaZs2HedzXbn65Bl3Tt76licn932OLTTtqyz7bD6IUyk2/Hj4Ehv/vUFOnvK+VCoKCvffLr/G17BWtnSLOkHLy7/A2kgA9BoL/szrydc5g7mQ1RjSgEuXi41R39aClKzcOyxEh+X7wvi/Ps3QqFXVPoGWrzIaGAgh6iArB2V5/vm/lNloZUyiRSVnMenXk4THpXNgaj+sLaSkhxBCCFGZJIlmInHffKvMQuvWDZvOne96vr+bP+427sRkxJRYF02FCncbd/zd7jyjTQhRCm0enNtQMMuqgE8f6D4efAeCWq3s02lL7pCZ//zm8sQKMfQ16nlD11eUR3YaROyDi9uUWWqpkXBhs/IA0HnTSNeR1tqO6Bt0ZEzPprS7uITNp2NYpB1RrMbXu2b/41Gbazh9H45XbrpywMYFuryEqsuLDLFwpm1iJlcTMpRH/nZiJtcSMkjNziM+PeeubzUlK+/OddeEEKI2a/NIYRKt/7sGL+kEqG9nQWRKJilZuZy8kkgPX5dKDFQIIYQQKr2+tDkIVWfJkiV8/vnnREVF0b59exYtWkRAQECJ5/bt25d9+/YV2//AAw+wefPmu75WSkoKjo6OJCcn4+Bgmno7uTduEDp4COTm0uR/P2HTxbAaZjsv72Ty3snF9qtulrSW7pxClENmkrJcM/AbSL6q7NNYQLsnlJln5VhaUx3otDrUMafg4jZyz29FcyMItarwz73e1hVV80GQmwln1vGN5klmpw8D9ASozvOZ1Qp89NcLB3TzU7pstnu8xCWit9Pr9SRn5rLqyBW+2H7hrud/9WQHHu7gVZ63KkSVqQ73EDXJ/v37+fzzzzlx4gSRkZH8+eefDB8+3KBrDx48SJ8+fWjbti3BwcEGv2aN/B1lp8HnzSAvC/5vP3i2L9PlJy4n4OVkg4ejLOcUQgghysvQewiTz0Rbs2YNU6ZMYfny5XTt2pUFCxYwePBgLly4gJubW7Hz161bR05O4cyG+Ph42rdvz+OPP16VYVdI3DffQG4uNl27GpxAAxjQZAD/d8//8fV/XxfZ727jztsBb0sCTYiySAiHI8uVhgFFZlm9CF1eArvif39qgivxGXyx/QJJmbn89EIAeLbHvM9Udh8/Q+e84zhc2QVhe1Clxyr11gBUal7RrmZkk0vos1JwSj5b2HrSd6AyE69p3zLNjlCpVDjZWNCpST2Dznezlw9/QtQ26enptG/fnhdeeIERI0YYfF1SUhJjxozhvvvuIzq69KZKtYalHTQfpJQQOPNnmZNonZo4V1JgQgghhLidyZNo8+fP5+WXX+b5558HYPny5WzevJkVK1Ywbdq0Yuc7Oxe9UVi9ejU2NjY1JomWGxlJ0h/rAHAZP+4uZxdnb2EPQGf3zjze4nFcbVzxd/NHo5YaGEIAsGcOqDUl1yTb+5lSQywrWemGlp8pcm2tdJVs94RBs6yqklanL1MBezONis2nItHq9FyKS8fbxRaA/p3bAG2g27OQlwNXDitLPi9uhfhQAByjCztn0qAjPPI1uLasUPwBPs54OloRlZxVYl20/LprAT7yIVCI2ub+++/n/vvvL/N1r776KqNGjUKj0bB+/XrjB1YdtXmkMIl238wyfWlxq+CrSTRztcXeytzIAQohhBACTJxEy8nJ4cSJE0yfPr1gn1qtZsCAARw+fNigMb7//nuefPJJbG1tSzyenZ1NdnZ2wfOUlJSKBV1BBbPQAgKwLWXJ6p1cTLwIQIBnAA80fcDY4QlRue6U4No392YdsOnFj5WFWlO8uH9eDvzxovIB5Va+A27OsupX7g8slWnr6UhmbTpLZHJhV0tPRytmDvVjSFtPridl8vORy0SlZDH/iQ4ANHCy5oOhfnRsXK8ggVaMmQU07aM8Bn8C8WFKQm3bO0ojBY0FvLLXKO9Bo1Yxc6gfY1cFoYJiddcAZg71k86WQggAfvjhB8LDw1m1ahUff/zxXc+vbvd55dZiMJjbKF/0RAYrX2SU0Qcbz7Dy0CXeHNSCCf2bGz1EIYQQQpg4iRYXF4dWq8Xd3b3Ifnd3d86fP3/X6wMDAzl9+jTff/99qefMmTOHWbNmVThWY8iNiiJ57R8AuBjQkbMkoUnKjJEWTi2MFpcQVaakBBcU7URZUbd2sMxOBStH+OdLyElT9ptZQfsnlfpeFZxlVdZZYmWx9XQkY1cFFZu9FZWcxaurgujQyIn/riWhu3nCpP7NC5Jmz3T3LtuL1W8Gp1MLE2jaHOV3UtEOozcNaevJstH+xRKCHrckBIUQIiQkhGnTpnHgwAHMzAy7Ra1O93kVYmGrJNLO/Kk8ypFE69jYiZWH4Jv94Yzp4Y2DzEYTQgghjM7kyzkr4vvvv6ddu3alNiEAmD59OlOmTCl4npKSQqNGjaoivGLiv/kWfW4uNp07Y9u17LPQtDotYUlhAPjW8zV2eEJUvlsTXPnPb02g9ZkK2lwl+ZWdcvPnLY+s5OL7Cs677XyAQwsLX9vCFnpNhk4vgG3Fu0DebZZYRWh1emZtOlvi8sf8fcFXkwDo3rQ+z/bwpmE96/K/4O2/g/znYNRE2kA/j0pLOgohajatVsuoUaOYNWsWLVoY/kVhdbrPq7A2jxQm0QbMKvMM6YfuacCi3aGExqSx4p8IXh8gX7gKIYQQxmbSJJqLiwsajaZY0djo6Gg8PDzueG16ejqrV6/mww8/vON5lpaWWFpaVjjWisqNiiLp998BcJkwoVxjXEu7RrY2GyuNFQ3tGhozPCGqTp+pkJmoJGn2zAb0YF0PAr+FA/MhL9P4r6k2g6kRYGacvwV3miU2dlUQy0b7G5xI0+v1ZOXqsLYorGu4YOfFIsm50nz+2D083rmCHxZvT6BByclOI9CoVXRvVvEEphCi9klNTeX48eOcPHmSCTfvk3Q6HXq9HjMzM7Zv307//v2LXVdd7vOMwncgmNtC0hW4HgQNO5Xpco1axesDmjPhl5N8fyCC53v44Ggjs9GEEEIIYzJpEs3CwoJOnTqxa9eugpbnOp2OXbt2FdxAleb3338nOzub0aNHV0GkFRf/7Xfoc3Ox7twJm3LMQgMISQwBoKlTU2kkIGouvR6iT+c/UX5kJhY/z8waLO2Vh5XDzW2Hwn1Fnt++3x6CflSWceYvTzz4lVGSQXebJaYCZm06S4B3fRIycohLy1YeqdnEpeXQxceZPi1cAbgQlcqwxf/gaG1O4IzC7rpb/os0KBYLM3WF349Sh25G8f9t8p/rtBV/DSGEuAsHBwdOnTpVZN/SpUvZvXs3a9euxcfHx0SRVaGDX0G9JhBzFs6sK5pEM7Bu6ANtPWnpHsqF6FS++yecNwZVrGyBEEIIIYoy+XLOKVOm8Oyzz9K5c2cCAgJYsGAB6enpBd06x4wZg5eXF3PmzCly3ffff8/w4cOpX7/6z2rIjY4m6bffAHAdPx5VOQuY5yfRfJ1kKaeowc5ugIj9yrbaDHR50PEZ6Ppq0SSYpgLfnu+bqyTQKmF5YmBEwh1niemByOQs/D/eUeLxl3J9CpJojtbmZOfpiE/PQafTo765tLFrU2fC4tLvGoubvRE6id7pA5mRZqAJIeqmtLQ0QkNDC55HREQQHByMs7MzjRs3Zvr06Vy/fp2ffvoJtVpN27Zti1zv5uaGlZVVsf21llqjJNAAzqyHQR8rSzrLUDdUrVYxeWBzXl0VxIp/Inihpw/1bC0qN24hhBCiDjF5Em3kyJHExsby/vvvExUVRYcOHdi6dWtBs4ErV66gVhedbXHhwgX++ecftm/fboqQy6xgFlqnTth061bucUKSlCRai3pS40LUUDkZsHGisu3dC57bXPjhwKmxcZI2N8fT9X2How1fJCb4Om4NX6RrXz3qcibSkjJyWHviGrFp2fh5Ohh8nb2VGa52lrjYWeJib4GLnSVdfJwLjrvaW3Jgaj9c7CwLEmgAHw1vx54LsUQlZ5U4402FUpQ/4JaxhBCiujl+/Dj9+vUreJ5fu+zZZ59l5cqVREZGcuXKFVOFV/30mQraPNj/GaRcg2vHIXxP8SX3dzHIzwM/TwfORqbw7YFwpg5pVcmBCyGEEHWHSq/Xl/QZrdZKSUnB0dGR5ORkHBwM/zBcXrnRMYQNHIg+J4fGK77HtkePco819M+hXEq5xPIBy+np1dOIUQpRRX54EC7/oyy/fOMCWNgo+0uqy1Vee+YQEpvBmLC+xYr+/9RsL81dbUqcfZWalcvF6FQuRKVxISqF1p4OPBnQGIDY1Gy6fLITlQpWPteFZ384dtcwfnyhC31auJX7beTXXQOKJNLyU21lqbsmhDCOqr6HEGVXK35HS3tAzBlQqZWuyeX4t3HH2Whe/uk4NhYaDkztR327WlI3TgghhKgkht5DmHwmWm0X/9136HNysPb3x6Z793KPo9fraems1LVoXq+5scITouokRMCVw8r2w4sLE2hg1PpbW12fY+y2IPQUXXIZlZzFoKBuLHyqI76RKVyISuVCdKryMyqV60lFGxr0b+VWkERzsbNghL8XjerZcE8jJzwdre46S6yXr2uF3seQtp4sG+1frAOoh5E6gAohhKimBn0Eq0YoCTS1Wbm+XBrQ2o17Gjry37VkvtkfzvQHWldCoEIIIUTdIzPRKlFuTAxhAwehz86m0fffYddTZo+JOuzXp+DCFvDpA2M2KHVejEyr09Prs90GdbYsibuDJS09HGjpbkenJs4MaVtyl+CqnCWm1ekJjEggJjULN3tlCadGbfz/7YQQd1crZjnVcrXid3RrHU+AXlNgwMwyD7PnQgzP/3AMK3M1B6b2x9VeZqMJIYQQpZGZaNVAwvffo8/OxrpDhwot4xSixgvZqSTQ1GZw/9xKSaABBEbEG5RAszZX09bLkRbu9rTysKeFuz0tPexxsjGs+HJVzhLTqFV0b1b9G6gIIYQwgvwEWu83lQ6dCeHwz3wws4K+b5dpqL4tXOnY2ImTV5JYvi+M9x7yq6SghRBCiLpDkmiVJDcmhsTVawBwmTCh3B0586XnpmNjZlPhcYSocnk5sPXmjX/A/4GbcQscZ+dpsTTTABCTmm3QNZ+OuIeHO3pV6HWHtPVkoJ+HzBITQghhHLfXB231AHw/SOlivXe28gVUGZZ2qlQqpgxswTPfBxKZnIler5f7SCGEEKKCJIlWSRK+X6HMQmvfHtueFZ+F9ua+NzkZc5KPe37MgCYDjBChEFXkyFKIDwVbtzJ/i16aS3Hp7Dofw65z0Zy+nkzgjAFYmWtws7cy6Ho3B8POuxuZJSaEEMJodNqiTQS8OkH/92DnTGUmd1pMmYfs5evC1td708qjhi5tFUIIIaoZSaIZQeyixaBR4zpuHAB5sbEkrlFmoZl7NyFu8RJcJ06o0GtcSr5Eem469a3lA7uoQVIiYf/nyvaAD8DKESh7na88rY7jlxPZfT6GneeiCY9NL3I86EoiPZq5EODjbFDR/wAfZ+O8PyGEEMJYSugcTY9JEL4HwvfC1SOQlw1mhtc2U6lUkkATQgghjEiSaMagURO3cBEAruPGEf/9CvRZWZh5uJOyYSMukyZW+CU2Dt/I5ZTLNLRvWOGxhKgyO96HnDRo2AXaPwUoRflvryXmWUItsaSMHPZdjGXXuRj2XoghJSuv4JiZWkXXps70b+XOfa3c8HaxBZSZYTOH+jF2VRAqSi76P3Oonyy5FEIIUTOo1fDI17CsB0Sdgp0fwJA55RoqKjmLwEsJDGvfwLgxCiGEEHWIJNGMIH8GWtzCRegyMkhcvRqAvKhoXCZNLDheEeYac3zr+VZ4HCGqzOXDcOo3QKU0E1CrC7pa3j5LLCo5i7Grglg22p/rSVlsOxPFicuJaHWFZzrbWtC3pSv3tXKndwsXHKzMS3zZqiz6L4QQQlQ6ew94eCn8OlIpkdCsPzQfWKYhriZkcN/8fej1ejo2cqKRs00lBSuEEELUbpJEM5JbE2n5XCYaJ4EmRI2j08Lfbynb/s+Alz9anZ5Zm86WuMxSjzJTbNams3jXtyEwIgGAlu723Nfajftau9GhUT2DZ5BJ0X8hhBC1SsshSnOewK/hz1dh7CGwdzf48kbONgR4O5OdpyUjR1uJgQohhBC1m0qv15f0mbbWSklJwdHRkeTkZBwcjFsjQp+Xx/m27ZQnZma0Pn3KKOOuPL2S0KRQhvsOp7NHZ6OMKUSlOvYdbH5DqYE2MQhsXTgcFs9T3x6566VTBrbAycacfi3d5JtyIUS1Upn3EMI4avXvKDcLvrsPok8rs9Ge/kNZ7mmgtOw8bC000qFTCCGEKIGh9xCG/8sr7irum2+UDY0G8vKIXbrUKOPuubqHDWEbiEyPNMp4QlSqjATY/bGy3W8G2Lqg1+s5GhFv0OVN6tswpru3JNCEEEKIW5lbwaPfg5k1hO2GI0vKdLmdpVlBAk2r03M4LJ4Nwdc5HBZfpHyCEEIIIUonyzmNJHbpUuIWLiqogZb/HKjQkk69Xk9oUigAzes1N0qsQlSq3R9BZiK4teFqs6dYtzOEP09e41J8hkGXu9lbVXKAQgghRA3l1gqGzIa/JsPOWeDdCxp0LNMQf5y4yvsbz5CeXbiss6QGP0IIIYQoTmaiGcHtCTRQEmcukyYSt3BRhWakxWTEkJKTgkalwcfRx1ghC1E5Iv9Ff/wHAGbmPUvvLw7w5c6LXIrPwNpcjbV56X9yVCg38QE+zlUUrBBCCFEDdXoeWg8FXS6sfRGy0wy+dOvpSN74/b8iCTQobPCz9bSsehBCCCHuRJJoxqDVldiFMz+RhlZX7qHzZ6E1dmiMpcayQmEKUVlytTp2n4si7MdxqNCzUdudH280RKWCXr4uzH+iPcffHciXIzugQkmY3Sr/+cyhflL8XwghhLgTlQqGLgQHL0gIg61vG3RZfoOfkuQv5py16aws7RRCCCHuQJZzGoHrxAmlH6tgd86QxBAAfJ18KzSOEJXhzI1k/jhxnY3/XqdXxm4WWJwmQ2/Jr44vM61LKx7u0ABPR+uC84e09WTZaH9mbTpLZHJWwX4PWUYihBBCGM7GGUZ8AysfgpOroNl90HbEHS8JjEgo8m/v7fRAZHIWgREJdG9W38gBCyGEELWDJNGquZAkJYkm9dBEVdDq9ARGJBCTmoWbvbK08k4zw77cEcLOc9HYkskMq18BSAl4jV8eGFFq968hbT0Z6OdRptcRQgghxG28e8G9b8L+z2HT6+DVCeo1KfX0mNTSE2jlOU8IIYSoiySJVs3lz0Rr7iRJtDprzxxQa6DP1OLH9s0FnRb6Ta/wy2w9HVlshtithYY3BF9n7YlrfDK8HY3rK50zR3ZphKW5mqnqn3E9nwjOTfEY/Kay1OQONGqVfMsthBBCVFSftyF8L1w7Butehue2gKbk23tDG/dIgx8hhBCidFITrRrT6rSEJ4cDMhOtTlNrYM8nSsLsVvvmKvvVmgq/xNbTkYxdFVRsmcethYZ/P36NAyFx/HnyesHxgX7uLBlkT5OLPyo7hnwKZlK7TwghhKgSGnN49DuwdICrR2H/3FJPDfBxxtPRqlhd0nzS4EcIIYS4O0miVWNXU6+Src3GSmNFQ7uGpg5HmEqfqdBvRtFEWn4Crd+MkmeolUF+oeGSygjfWmh4TLcmvD6gOY909LrlBD38/bbSIaz5YGgxuEKxCCGEEKKM6nnDQ18q2/s/h8uHSjxNo1Yxc6gfULzBDyj/5kuDHyGEEOLOJIlWjeV35mzq1BSNEWYbiRqsz1Slpf2eT+ADJ6Ml0MDwQsP21ua8PqBFwVJOAC5sgbBdoLGAIXMqHIsQQghRXWl1Wo5FHWNL+BaORR1Dq9OaOqRC7R6D9qNAr4M/XobMxBJPy2/w4+FY8pJNc418NBBCCCHuRGqiVWPSmVMAkJEAuz6EEytv7rg5P8y9rVGGD75a8o327YoVGs7NhK03a7F1nwD1mxklHiGEEKK62Xl5J58Gfkp0RnTBPncbd6YFTGNAkwEmjOwWD8yFq0cgIRw2ToInfiqxRmlJDX7+Ph3JT4cv88bv/7JlUm8aOFmX8AJCCCGEkK+bqrH8zpwt6rUwcSTCJHQ6pW394s5w4gcKF1fevCFe/RRsfxe0ueUa/t+rSbz803E+23rBoPOLFRo+tAiSLoN9A+j9RrliEEIIIaq7nZd3MmXvlCIJNICYjBim7J3Czss7TRTZbSzt4dHvQW0O5zZC0I+lnprf4OfhDl50b1afGQ+2pp2XI0kZuUz69SS5Wl0VBi6EEELUHJJEq8bu97mfp1s/TSf3TqYORVS1qNPww/2wYTxkxIONi7K/3wx4NwYaBijPDy2ClQ9C8vXSx7rNsUsJjFkRyMNLDrLjrPKBwMq89D8FJRYaTroCB+Yr24M+Aku7srw7IYQQokbQ6rR8Gvgp+hIqh+bv+yzws+qztNPLH+57T9n+exrEGvZFmaWZhsWjOmJvaYatpRmZudXk/QghhBDVjCTRqrGBTQYyLWAabV2Ms2xP1ADZqbD1Hfj6XmVJhrktNOsPGXGFNdDMLOClHdD2UeWaq0dheS8IufM34bGp2Yz8+jCPLz/M/ouxaNQqRvh7sXNKHxaM7ICK4oWG858XKzS8/V3Iy4QmvQrjEEIIIWqZoJigYjPQbqVHT1RGFEExQVUY1V10nwhN+yn/Tq99EfKyDbqsSX1b/hzfgx+e64KDlXklBymEEELUTJJEE6I60Ovh9DpY3AWOLAG9FvwehgnHlFlnJTUReGwFdBsHdu6QmQA/P6rUTtPmlfgSzrYWxKRmY65R8VRAY/a80Zf5T3TA182u1ELDHo5WLBvtz5C2noU7w/fC2Q2gUsP9n5VYb0UIIYSoDWIzYo16XpVQq+GR5WBTH6JPwc4PDL7U180e9S1fmmXJjDQhhBCiCGksUE1dSr5EUnYSvk6+2FnIUrlaLS4UtryhJKcAnJvCA5+D781Cxf2ml37tkDlw30zYPgOOfQcH5sGVo+hGfMv2qyrWHLvKstGdsDLXoFGrmPdEezwdrfB0LF4wuKRCwwE+zkVnoGlz4e+3le0uL4GHzJIUQghRe7nauBr1vCpj7wHDl8EvT8CRpcqs9uYDDb48IyeP9zec4WpCBj+/1BUz6dophBBCADITrdpaF7KOZ/5+hgVBC0wdiqgsORmw+2NY1l1JoGksoe87MPZwYQLNEOZW8OA8ZWaahT1c/gfVN/eyecNq9lyIZe2JawWn+jeuV2ICLd/thYaLJNAAAr+F2PPKt9v93injGxZCCCFqFn83f9xt3FEVK3igUKHCw8YDfzf/Ko7MAC0GQ9dXle0/X4XU0pel3i4mJZu/T0Vy7FICxy4Z1sVbCCGEqAskiVZNWWgscLNxk86ctdWFrbC0K+z/HLQ54DsQxh+Bvm8rSbESaHV6DofFsyH4OofD4tHqCosc52p1bMjrRt5Lu8G9Lar0WBbmzuIX39080MbNODGnxcDeOcr2fe+DdT3jjCuEEEJUUxq1hmkB0wCKJdLyn78d8DYatabKYzPIgFng3k6prbr+VaXztwG8XWyZ90R7fn6pG92b1a/kIIUQQoiaQ6XX64u3G6rFUlJScHR0JDk5GQcHB1OHc1d6vR6V1JyqPZKuKN2yLmxWnjs0hPs/hVYP3bG22NbTkczadJbI5KyCfZ6OVky/vxXJWXks3xvG9aRMFozswPC2zsqSy/zW9j594NHvwK6CybT14yF4FXh2gJd3Q3X9wCCEEJWkpt1D1EWV9TvaeXknnwZ+WqTJgIu1CzO6zmBAkzLMHjeFmPPwTV+l0cCgj6HHRFNHJIQQQlQ7ht5DyEy0ak4SaLVEXo5Sr2xxgJJAU5tBz9dhQiC0HnrXBNrYVUFFEmgAkclZTFodzHvrT3M9KRMXO0t0ej2YW8OwhfDIN2BuAxH7lO6dl/4pf/zXjisJNIAHvpAEmhBCiDplQJMBbHt0GysGr6CRfSMA3uryVvVPoAG4tYIhs5XtnbPgxskyDxEWm8bMDaeLzIIXQggh6iJJolVDOr1hU+1FDRG+D5b3VDpn5mVCk17w6kEYOAssbO94qVanZ9ams9zpllWtgplD/fjn7X6M8G9YeKD9SHhlL7i2hrRo+HEo7P/C4KUcBXQ62PLWzTFHQaMuZbteCCGEqAU0ag1dPLoQ4BEAQHhSuIkjKoNOzytf2ulyYe2LkJ1m8KVZuVqe/OYIPx6+zJI9oZUYpBBCCFH9SRKtGlp7cS39fuvHwqCFpg5F3M2eObBvbsnHtr8HS7rBT8Mg7iLYusGIb+G5v5RvhQ0QGJFQbAba7XR6aOXhgJV5CbPDXFvCy7ugw9Og18Huj+CXxyE93qDXB5QZaDeClKYFAz4w/DohhBCiFvJ18gUgNKkGJZRUKhi6UPm3PCGssNP2rfbNVe5rbmNlrmHaEOW+ZcHOixwJL8M9hBBCCFHLSBKtGgpJDCEuM448fZ6pQxF3o9bAnk+KJtK0ebDqMTi0EGLPgUoNAa/AhGNwzxN3XLp5u9PXkw06Lyb1Dok2C1sYvhQeXgJm1hC6U1neeeXI3QfOTFKWfgD0nQb27gbFI4QQQtRWvvVqYBINwMYZ2jysbAevgtN/FB7bN1e5nymlXMOjnRryWKeG6PQw6deTxKdlV0HAQgghRPUjSbRqKCQpBIDmTs1NHIm4qz5Tod+MwkTa1UCY3xpCdyjHvTrBy3vggc/B2qlMQ2fmaJm344JB57rZl9zRs4iOo5WGAPWbQ+oN+OEBOLgQ7tRbZO8cpaOXS0vo+n8GRi6EEELUXvkz0a6kXCEr786zxaudh5dAk57K9p9jIfFyYQKt3wzlvqYUHz7cBl83O2JSs5n827/opD6aEEKIOkiSaNWMXq8v+GazeT1JotUIvSYrCao9n8D3AyE9Bsys4KEF8OJOaNDBoGH0ej2BEQkFz60tNDzZpTGWZqX/31SF0qUzwMfZsFjd/ZQ6ae0eB70WdrwHvz4FGQnFz40+C4HfKtv3fwYac8NeQwghhKjF6lvVp55lPfToCU+uQXXR8o3ZCA5eoM2Gr9oblEADsLEwY8kof6zM1ey/GMvy/WFVFLAQQghRfUgSrZqJzYwlOTsZjUqDj6OPqcMRpUmPh3/XwO/Pw+fN4OSqwmMqNUw+A52fB7Vh/xfT6vQMW3yQJ74+zPFLhQmt9x/y46snO6BCSZjdKv/5zKF+aNRl6OJqaafUZnvoS9BYwsW/lZvoTa8VnqPXw99TlUSbS0vDln4KIYQQdYBKpaq5SzoBNGbw/N83n9ycTZZ0GVKj73ppSw97Zg1rA8C87Rc5dqmEL+GEEEKIWkySaNVMSKKylLOxQ2MsNZYmjkYU0Osh6rTS3fL7QfCFL/z5CpxZB1nJYG6tnKc2Vwr4H19x1yGzcrUF2xq1ijYNHLCzNONSfEbBfrVaxZC2niwb7Y+HY9Elmx6OViwb7c+Qtp5lfz8qFXR+AV7aAc5NITsFTqxUarnp9XDmT7h0ANRmEHeh1BopQgghRF1U0FwgsQYm0QD+W6P8VN38KHByFSzyhwPzIffOS1Sf6NyI4R0aoNXpmfTrSRLTcyo5WCGEEKL6MDN1AKKogqWcUg/N9HIyIGI/hGyDi9sg5XrR4+7toMVgSI+FoB8Ll0Lk1xaBEpdGxKRm8eOhS/zv8GV+faUbbRo4AvDGoJZMf6A1jtbFl00OaevJQD8PAiMSiEnNws1eWcJZphloJfFsryzv3DgRzm5Qarkt6Qo56cpxXZ5BSzyEEEKIuqR/4/64WLsQ4BFg6lDK7vYaaBsnKfcxOWmwa5bypdqgj6D1sBKbIalUKj5+pB3/XUsmPC6dN37/l++f7YyqDI2ThBBCiJpKkmjVzMXEi0Bh5ydRxZKuKAmzkO1KAu3WgsFm1tC0L7QYBM0HgWND5Ub01gQaFP68LZEWHpvGtwci+CPoGjl5OgD+OHG9IInman/nmYcatYruzeob7a0WsHKEx39U6p9tfVuZeZbv3qmSQBNCCCFu082zG908u5k6jLIrqYnAsIVKjbS9s8HCTlna+dsYaNILhswBz3uKDWNnacbiUf4MX3qQ3edj+O5ABC/f27SK34wQQghR9SSJVs3kz0Rr4dTCxJHUcHvmKEsQS0oA7ZsLOi30mw7aPLh2rHC2WczZouc6NlaSZi2GgHevwmWb+XRa6DcDbe+3CAyLL5wl1vstNDePn7ySyNf7wtl2NqqgEWbHxk682qcZA1u7V8a7LzuVCrq+Ag07w7f9lH1qM+g/w7RxCSGEEMJ4bt63FLs/6vu2ci+Qm6n8+39oIVz+B76+F/yfgf7vgZ1bkUv8Gjjw/kN+vLv+NJ9tPU/3ZvVp6+VYhW9GCCGEqHqSRKtGtDotYUlKpyOZiVZBak3JSyrzv4H1Gw5/vAShOyEzsfC4Sg2NuhUmzlxblbiUoUC/6Ww9Hcmsz3YTmVw4a83D0YpH/Ydx7FIigdsOFey/r5Ubr/ZtRucm9arnsofQncpPjTloc5X/vWQmmhBCCFHM9bTrnE84T2vn1jSwa2DqcAzTb3rpx279997/GdgxU6n9GvQTnP4T7n0Tuo0Fs8KZ8093bczh8Hhc7Sxp7m5XiYELIYQQ1YMk0aqRa2nXyNZmY6WxoqFdQ1OHU7PdvqSy9VDYOh3C9wAqOLu+8FwrJ2g+UEmaNesPNs4Gv8zW05GMXRWU39uqQFRyFkv2KAlRc42Khzt48cq9TWnhbl/ed1T5bl/icZfabkIIIURd9vGRj/nn+j+81+09nmj5hKnDMS6nxvD4D9D1/+DvtyEyGHbOvFkv7WNo9SCoVKhUKhY+2bHiNVqFEEKIGkKSaNVIfmfOpk5N0Ug3xIq7NZGWnwwCQA9ufkpTgOaDoWEXpd17GWl1emZtOlssgXYrW0sN216/l4b1bMo8fpUqqUZKKbXdhBBCCAFt6rchPjMeKzOru59cUzXuBi/vgf9Ww85ZkBgBa54G794w5FPwaFskgZan1fHvtWQ6NalnwqCFEEKIyiNJtGokJElJoklnTiPq/OItCTQVPPC5kjxzalzhoQMjEoos4SxJeraWqwmZ1T+JVlqNlPznOm3VxySEEEJUYxM6TmBCxwmmDqPyqdXQYZTSrfOf+XBoMVw6AF/3Bv8x0O9dsHMlLTuPF1Ye4+SVRP4Y24N7GjqZOnIhhBDC6CSJVo080eIJ2tZvSz0r+fbOaNY8fXNDBeiV+mdGSKABxKTeOYFW1vNMytAaKUIIIYSomyzt4L73wf9Z2PG+UhrjxEo4vQ76TMU24BWcbSyw0KiJTslGq9MTGJFQ2HTJx1mWfQohhKjxJIlWjdS3rk/vhr1NHUbtsWMmXDmsbI9aA5H/GnVp4sHQOIPOc7Ovxcs8hBBCiDouT5cHgJm6jtxW12sCT/wIlw7C1mkQ9R9sfxfV8RXM6zuLmCH9uRCdSq/bmi55Oloxc6gfQ9p6mjB4IYQQomLUpg5AiEqxby4cXKBse3WC5oOUxFm/GUoibd/cCr/E450b3fG4CuWGMcDH8EYFQgghhKg5Ju2eRMDPARyLOmbqUKqed094ZS8MWwy2bpAQju26Z3D/oSsxqycWK3kRlZzF2V/fJXTNO6aJVwghhDACSaJVE1dTrrL45GL2Xt1r6lBqh6xkyP9GuO87oLq5fCA/kVbOGl9XEzIKtrt4O/PJ8LaoUBJmt8p/PnOonyxdEEIIIWoptUpNri6X0KRQU4diGmoN+D8DE09Az9fRayywybjGGLMdrDd/F2dSCk6doFnHFPO17L4Yj1Z3p7ZMQgghRPUlSbRqIjg2mK//+5qVZ1aaOpTaQacFXR40DADf+4oe6zP1zjXASpCZo2XaH/8xYP4+LkSlFux/ulsTlo32x8Ox6JJND0crlo32lyULQgghRC3m6+QLUHeTaPmsHGDgLE4O3c7f2i4AdNCEc8hyIs9qtjFRs443zNcyL/cxZqcPIzAiwcQBCyGEEOVTR4o3VH8N7BowovkImjo2NXUoNV/KDTi+Qtnud8sstHIKiU5l/C9BXIxOQ6WCwEsJtPSwLzg+pK0nA/08pHiuEEIIUcc0r6d0VA9NrONJtJuu4sZruZPppj3LQrNFuKmTmWX+IwDzch9jkXYEUEOaLgkhhBAlkCRaNdHJvROd3DuZOoza4cB80GZD4x7QtG+5h9Hr9fx+/BrvbzxNVq4OV3tLvhrZgR6+LsXO1ahVdG9WvwJBCyGEEKKmae50M4mWFIpOr0OtqtuLPPKbKR3R+dEtZwkhlmPQqHTo9fC1dmix84QQQoiapm7/Sy9qn+RrEKR841mRWWhp2XlMXhPM1D/+IytXR+/mLmyZ1LvEBJoQQggh6qZGDo0wV5uTkZdBZHqkqcMxuQAfZzwdrVAB4zXrCxJoKhUsN/8SkKZLQgghajZJolUDWXlZXEi4QI42x9Sh1HwH5oE2B7x7g0/vcg1x+noyQxf9w/rgG2jUKqYOacmPzwfgam9p5GCFEEKI2mv//v0MHTqUBg0aoFKpWL9+/R3PX7duHQMHDsTV1RUHBwe6d+/Otm3bqibYcjJXm+Pj6APIkk5QZubPHOpXpAbaO3kvAtBfE8wbmjXc39ZDSl4IIYSosSSJVg2cjT/LY5seY9j6YaYOpWZLvAxB/1O2+5W9fbper+enw5cYsfQQEXHpNHC0Ys0r3RjX1xe13OwJIYQQZZKenk779u1ZsmSJQefv37+fgQMHsmXLFk6cOEG/fv0YOnQoJ0+erORIKya/uUBIUoiJI6kehsT/jynma/lG8ySLtCP4TduXMJ3SaGmi+QYcAr+UxgJCCCFqLKmJVg3kd3SSpgIVdOAL0OUqddCa9CjTpckZuUz941+2nYkGYEBrNz5/rD31bC0qIVAhhBCi9rv//vu5//77DT5/wYIFRZ7Pnj2bDRs2sGnTJjp27Gjk6Iyneb3mECEdOgvotNBvBi/2fot2N5su5SW8C/vHk4sZNvoMXvnfcf4Y24NmrnamjlYIIYQoE0miVQMXEy8C4FvP18SR1GAJ4XDyZ2W7b9lmoaVn5/HQ4gNcTcjEXKNi2v2teaGnN6oKdvUUQgghRPnpdDpSU1Nxdi69flZ2djbZ2dkFz1NSUqoitCLyZ6LJcs6b+k0HQAOFTZf0T0PYCsyvn8DLTkVSUi7P/3CMP8f1oL6dlMsQQghRc8hyzmog/5vL/A5Pohz2fwF6LfgOgMZdy3SpraUZD7TzpLGzDX+M7cGLvXwkgSaEEEKY2BdffEFaWhpPPPFEqefMmTMHR0fHgkejRo2qMEJF83rK/Vt4cjh5urwqf/0aQaWCAbMAeCBnK12dkriSkMFLPx0nK1dr4uCEEEIIw0kSzcT0ej0hiUoNjfybMFFG8WHw76/K9m2z0LQ6PYfD4tkQfJ3DYfFodXoAEtJzuJGUWXDem4Na8tekXtzT0KmqohZCCCFEKX755RdmzZrFb7/9hpubW6nnTZ8+neTk5ILH1atXqzBKhaetJzZmNuTqcrmScqXKX7/G8OkNvgNQ6fL4ruE2HK3N8alvi1q+uBRCCFGDyHJOE4vNjCUlJwWNSlPQ3UmU0b65oNdBiyHQsFPB7q2nI5m16SyRyVkF+zwdrXi2uzc/HIrA09Ga31/tjrlGXfAQQgghhGmtXr2al156id9//50BAwbc8VxLS0ssLU27HFCtUuPr5Mt/cf8RkhRCUyepcVuqAR9A6C7sQzew7cmxuLdsL7P/hRBC1Cjlyhrs2bPH2HHUWfmz0Bo7NMZSIzUhyiz2Ipz6TdnuO61g99bTkYxdFVQkgQYQlZzFp1vPk5KZS2pWLrGp2QghhBCievj11195/vnn+fXXX3nwwQdNHY7B3uryFqsfWk2fhn1MHUr15tEO2j2ubAZ+VpBA0+r0HL8kHTuFEEJUf+VKog0ZMoRmzZrx8ccfm2TafG2SXw8tvyitKKN9nymz0Fo+CA2Uzl1anZ5Zm86iL+F0PaBCqYP257ieNHCyrspohRBCiDojLS2N4OBggoODAYiIiCA4OJgrV5Qlj9OnT2fMmDEF5//yyy+MGTOGefPm0bVrV6KiooiKiiI5OdkU4ZdJB7cOtKnfBiszK1OHUv31nwFqcwjfA2F7yMnTMe7nEzzx9WF2n482dXRCCCHEHZUriXb9+nUmTJjA2rVradq0KYMHD+a3334jJyfH2PHVevmdOaUeWjnEnIPTfyjbt8xCC4xIKDYD7VZ6IC4thzM3qr6DlxBCCFFXHD9+nI4dO9Kxo/Il15QpU+jYsSPvv/8+AJGRkQUJNYBvvvmGvLw8xo8fj6enZ8HjtddeM0n8opLU84YuLyrbOz/AXK3HwcocM7WajBxpMiCEEKJ6K1cSzcXFhcmTJxMcHMzRo0dp0aIF48aNo0GDBkyaNIl///3X2HHWWgVNBaQzZ9nt/RTQQ+th4HlPwe6Y1NITaLcy9DwhhBBClF3fvn3R6/XFHitXrgRg5cqV7N27t+D8vXv33vH86kyr0/Lr+V/56PBHZOXJ/cVd3fsWWNhDZDCqs+uZPaId68b14KF7Gpg6MiGEEOKOKlxJ3d/fn+nTpzNhwgTS0tJYsWIFnTp1onfv3pw5c8YYMdZaWp2W8ORwQGailVnUaTi7HlBB3+lFDrnZG7aUwtDzhBBCCCHuRK1SszR4Kb9d/K3g3k7cga0L9JiobO/6CHO0tPVyLDgcnZJFalauiYITQgghSlfuJFpubi5r167lgQceoEmTJmzbto3FixcTHR1NaGgoTZo04fHHHzdmrLXO1dSrZGuzsdJY0dCuoanDqVn2zlF+tnkE3P0Kdut0ev67lnTHS1UoXToDfJwrLz4hhBBC1BkqlYrHWjzGS+1ewt7C3tTh1Azdx4OtKyRGwImVBbvPRaYwfMlBxv0cRK5WZ7r4hBBCiBKYleeiiRMn8uuvv6LX63nmmWeYO3cubdu2LThua2vLF198QYMGMiX7Tm6k3cBMZUZTp6Zo1BpTh1NzRP4L5/9CmYVWWAstKjmLN34P5mBofKmX5jdRnznUD41aWqoLIYQQwjhe85fabWViaQd93oYtb8K+udD+KbC0Q6vTk5yZy4GQON5bf5o5I9oVdPEUQgghTK1cM9HOnj3LokWLuHHjBgsWLCiSQMvn4uLCnj17KhxgbdbDqwdHnz7Kwn4LTR1KzbL3U+Vnu8fAtSUAW05FMnjBfg6GxmNtrmH2I+1Y9rQ/no5Fl2x6OFqxbLQ/Q9p6VnXUQgghhBDiVv7PQj0fSI+BI0sBaOvlyKKnOqJWwepjV1m6N8zEQQohhBCFVHq9Xm/qIKpSSkoKjo6OJCcn4+DgYOpwRFldD4Jv+4FKDeMDSbP34YONZ1h74hoA9zR0ZMHIDjR1tQNAq9MTGJFATGoWbvbKEk6ZgSaEEKI85B6i+jPl70iv1xOfFU9EcgRdPLpU6WvXaKf/gLUvKI0GXgtW6qUBPx2+xPsblPrKXz3ZgYc7eJkwSCGEELWdofcQ5ZqJNmfOHFasWFFs/4oVK/jss8/KM6QQhsmvhXbPSLTOvjy27BBrT1xDpYLx/Zrxx9geBQk0AI1aRfdm9Xm4gxfdm9WXBJoQQgghKkVqbir9fuvHC9teIC0nzdTh1Bx+j4Bne8hJhf1fFOwe092bl3r5APDW7/8RGJFgqgiFEEKIAuVKon399de0atWq2P42bdqwfPnyMo21ZMkSvL29sbKyomvXrgQGBt7x/KSkJMaPH4+npyeWlpa0aNGCLVu2lOk1q4NsbTZPb36a9w++T7Y229Th1AxXj0HIdlBp4N630KhVPNfDGy8na9a80p23BrfCXFPhhrNCCCGEEGXmYOGAu407AKFJoSaOpgZRq2HALGX72HeQeKng0DsPtGZIGw9ytDpe+d9xwmIlOSmEEMK0ypVxiIqKwtOzeE0pV1dXIiMjDR5nzZo1TJkyhZkzZxIUFET79u0ZPHgwMTExJZ6fk5PDwIEDuXTpEmvXruXChQt8++23eHnVvOndEckR/Bf3H7uv7sZCbWHqcGqGvbMBiG/+KNRvBsDILo3YPvle6bQphBBCCJPzrecLQEhSiIkjqWGa9YOmfUGXC3tmF+xWq1V8ObIDHRo5kZSRy/M/HCM+Tb58FkIIYTrlSqI1atSIgwcPFtt/8ODBMnXknD9/Pi+//DLPP/88fn5+LF++HBsbmxKXioKyXDQhIYH169fTs2dPvL296dOnD+3bty/P2zCpBnYNmNdnHlM6TZGOQ4a4cgTCdpOr1/DKpX4kZ+QCSkt5W8tyNZkVQgghhDCq5k7NAQhNlJloZTbgA+Xnf79B1KmC3dYWGr57tjONnK25kpDBSz8dJytXa5oYhRBC1HnlSqK9/PLLvP766/zwww9cvnyZy5cvs2LFCiZPnszLL79s0Bg5OTmcOHGCAQMGFAajVjNgwAAOHz5c4jUbN26ke/fujB8/Hnd3d9q2bcvs2bPRakv/hzQ7O5uUlJQij+rAwcKBQd6DGNF8hKlDqRlufiv5t/l9aOr7kJUnN09CCCGEqF58nZSZaLKcsxwadIQ2IwA97JxV5JCLnSU/PBeAo7U5J68kMXlNMDpdneqNJoQQopoo1xSet956i/j4eMaNG0dOTg4AVlZWvP3220yfPt2gMeLi4tBqtbi7uxfZ7+7uzvnz50u8Jjw8nN27d/P000+zZcsWQkNDGTduHLm5ucycObPEa+bMmcOsWbNKPCaqv3+vJtEu9xTqiH2gNqfrs7N50MtXGgQIIYQQotrJX84pSbRy6v8unNsIoTsg4gD49C445OtmxzfPdOKZ7wP5+3QUKw5G8FLvpiYMVgghRF1UrploKpWKzz77jNjYWI4cOcK///5LQkIC77//vrHjK0Kn0+Hm5sY333xDp06dGDlyJDNmzLhjM4Pp06eTnJxc8Lh69Wqlxmion8/9zP5r+6WpQCmycrV89NdZHl7yD9EbbyZI/cfg3qi5JNCEEEIIUS01dWyKChUJWQnEZ8abOpyap34z6PScsr1zJuiLzjbr2rQ+nz9+D/1auvJkQGMAtDo9h8Pi2RB8ncNh8WhlhpoQQohKVKFiUnZ2dnTp0qVc17q4uKDRaIiOji6yPzo6Gg8PjxKv8fT0xNzcHI1GU7CvdevWREVFkZOTg4VF8QL9lpaWWFpalivGypKcncyngZ8CcPCpg1hqqld8VUWr0xMYkUBMahZu9lYE+DijUau4EJXKa6tPcj4qle7qs3gmnQCNBfR+w9QhCyGEEEKUytrMmkb2jbiSeoXQpFDqW9c3dUg1z71TIfhXuH4Czm0Cv2FFDj/cwYth7RugUqnYejqSWZvOEpmcVXDc09GKmUP9GNK2eBM0IYQQoqLKnUQ7fvw4v/32G1euXClY0plv3bp1d73ewsKCTp06sWvXLoYPHw4oM8127drFhAkTSrymZ8+e/PLLL+h0OtRqZRLdxYsX8fT0LDGBVl2FJYUB4GHrgYOFg4mjMY2Sbno8HKzo3dyFDf/eICdPR30bc5Y5/w1xQKfnwbHmdWEVQgghRN3i6+TLldQrhCSG0NWzq6nDqXns3aH7eNg/F3Z9CC0fAE3Rjyz5CbSxq4K4fd5ZVHIWY1cFsWy0vyTShBBCGF25lnOuXr2aHj16cO7cOf78809yc3M5c+YMu3fvxtHR0eBxpkyZwrfffsuPP/7IuXPnGDt2LOnp6Tz//PMAjBkzpkiNtbFjx5KQkMBrr73GxYsX2bx5M7Nnz2b8+PHleRsmE5KotD3PLz5b1+Tf9NyaQAOISsni9xPXyMnT0a+lK7se0eEUFwRmVtBrsomiFUIIIYQwnNRFM4IeE8GmPsSHQPCqYoe1Oj2zNp0tlkADCvbN2nRWlnYKIYQwunIl0WbPns2XX37Jpk2bsLCw4KuvvuL8+fM88cQTNG7c2OBxRo4cyRdffMH7779Phw4dCA4OZuvWrQXNBq5cuUJkZGTB+Y0aNWLbtm0cO3aMe+65h0mTJvHaa68xbdq08rwNkwlJUpJozes1N3EkVe9ONz35HK3N+PaZTjgd+VzZ0flFcJBvEoUQQghR/TV3Uu7v8u/3RDlYOcC9bynbe+ZATkaRw4ERCcW+jL2VHohMziIwIqESgxRCCFEXlSuJFhYWxoMPPggoyzLT09NRqVRMnjyZb775pkxjTZgwgcuXL5Odnc3Ro0fp2rVw2vvevXtZuXJlkfO7d+/OkSNHyMrKIiwsjHfeeadIjbSaIH8mWv5NVl1yt5segOTMPEIOrYfrx8HMGnq9XiWxCSGEEAJ+/PFHNm/eXPB86tSpODk50aNHDy5fvmzCyGqG/JUGoYmh6PUyE6rcOr8ATo0hLQqOFm0iFpN653vJsp4nhBBCGKpcSbR69eqRmpoKgJeXF6dPnwYgKSmJjIyMO11a5+n1+oLp/XVxJpphNzN6PILmK5sBL4GdW6XGJIQQQohCs2fPxtraGoDDhw+zZMkS5s6di4uLC5MnS3mFu2ni0AQztRkWGgvis6RDZ7mZWUK/d5XtfxZARuGsMjd7K4OGMPQ8IYQQwlDlaixw7733smPHDtq1a8fjjz/Oa6+9xu7du9mxYwf33XefsWOsVWIyYkjJSUGj0uDj6GPqcKqcITcz96mDqJd0GsxtoefrlR+UEEIIIQpcvXoVX19lNtX69et59NFHeeWVV+jZsyd9+/Y1bXA1gLnGnN2P78bJ0gmVSmXqcGq2do/DoUUQfQr+mQ+DPgYgwMcZT0cropKzSi0R4uGodH4XQgghjKlcM9EWL17Mk08+CcCMGTOYMmUK0dHRPProo3z//fdGDbC2yZ+F1tihMZYaSxNHU/Xyb3pKo0LP25Y3u7t2fQVsXaooMiGEEEIA2NnZER+vzKDavn07AwcOBMDKyorMzExThlZj1LOqJwk0Y1CrYcBMZfvoN5B0FQCNWsXMoX4AlPa/cn1bC2ksIIQQwujKnETLy8vjr7/+KqhDplarmTZtGhs3bmTevHnUq1fP6EHWJnW9M2f+TU9JNzwqYJD6OC30EWBhBz0mVXV4QgghRJ03cOBAXnrpJV566SUuXrzIAw88AMCZM2fw9vY2bXCi7vEdAN69QZsNe+cU7B7S1pNlo/3xuO3L2fq2FphrVJy5kcLra06Sp9VVdcRCCCFqsTIn0czMzHj11VfJypJCneVRlztz5su/6bl9RpqngwVfuN4sZNxtLNjIFHwhhBCiqi1ZsoTu3bsTGxvLH3/8Qf369QE4ceIETz31lImjqxnCk8OZsGsCE3ZNMHUoNZ9KBQNmKdv//grRZwsODWnryT9v9+fXl7vx1ZMd+PXlbgTOGMB3z3bBQqNmy6ko3v7jFDqZkSaEEMJIylUTLSAggODgYJo0aWLseGq9at+Zc88cUGugz9Tix/bNBZ0W+k0v9/A/HIzA28WWIW09GejnQWBEAjGpWbjZWxGQuR/N2otg6QDdx1fgTQghhBCivJycnFi8eHGx/bNmzTJBNDWTudqcfdf2YaG2IE+Xh5m6XLfcIl/DTtB6GJzbCLs+hFGrCw5p1Cq6N6tf5PQ+LVxZNKoj434O4o+ga9haapg1rI0ssRVCCFFh5foXfdy4cUyZMoWrV6/SqVMnbG1tixy/5557jBJcbaPVaQlPDgeq8Uw0tQb2fKJs35pI2zdX2d9vRrmHPh+Vwiebz5Gn0/PnuB50bFyv8KZHp4Vlnyrb3caBtSwLFkIIIUxh69at2NnZ0atXL0CZmfbtt9/i5+fHkiVLpHSHAbzsvJjRdQbNnJqZOpTa47734fxmuPg3XD4MTbrf8fTBbTyY93h7Jv8WzE+HL2NracbbQ1pVUbBCCCFqq3Il0fKbCkyaVFizSqVSodfrUalUaLVa40RXy6TlptHJvRNXUq7Q0K6hqcMpWX7ibM8nEHsRmg+Aq0fh+AolgVbSDDUDNXa24dke3kQmZ9KhkVPRg2f+hNjzYOWoLOUUQgghhEm89dZbfPbZZwCcOnWKN954gylTprBnzx6mTJnCDz/8YOIIqz+1Ss2TrZ40dRi1i0tz8H8GTqyEnTPhhW3KUs87GN7Ri4wcLe/8eYple8OwszRjfL+6WZdYCCGEcZQriRYREWHsOOoER0tHvh74tanDuLs+UyHmLJz+XXnkC/4Zrp8A15bg2vrmz5ZgYVv6WLewsTDjvYf80On0RafT67Sw9+YstO4TwdrJeO9FCCGEEGUSERGBn5/S+fCPP/7goYceYvbs2QQFBRU0GRDCJPpMg3/XKF/wXvgbWt39v8dRXRuTnp3HJ1vO8fm2C9hYaHi+p08VBCuEEKI2KlcSTWqh1XJpMRC+r/j+xEvK4+LWovudGt+SVGsFbq3ApSVY2gGQnJGLvZUZarWSOFPv+7Ro3bVTayE+RFnCmZep1GWrQN01IYQQQpSfhYUFGRkZAOzcuZMxY8YA4OzsTEpKiilDq1FiM2I5eOMgKlQ87PuwqcOpHRw8lRUL/8yHXbOgxWDlnvIuXr63KWnZeXy1K4RZm85Sz8aC4R29qiBgIYQQtU25kmg//fTTHY/n32yJorLysrAys7r7iaak18PmKZCZoDzXWIA2B3q+Ds0HQsw5iL2gLL2MPQ/psZB0RXmEbCs6lmNj9K4tORDlyGV1Ix4ZPIAGvu2L1l3rNQX23ZyF5tkeDsyrUN01IYQQQlRMr169mDJlCj179iQwMJA1a9YAcPHiRRo2rKblKKqhkKQQ3jv4Ht4O3pJEM6aer8GJH5T70H9/hY6jDbrs9QHNSc/OY/vZaDo1kbp+Qgghykel1+vL3PP59oKyubm5ZGRkYGFhgY2NDQkJCUYL0NhSUlJwdHQkOTkZBweHKn3toX8OJSM3g4X9F9LGpU2VvrbBTq2FP15Utju/CA/NL9pU4PaaaOnxNxNqtyTXYs5Dekzpr+HQEMwsICEcvDrD9eNgbg25mRWuuyaEEEJUFlPeQ1SlK1euMG7cOK5evcqkSZN48UXlvmDy5MlotVoWLlxo4ghLV51+R7EZsfT/vT9qlZrApwOx1FiaNJ5a5dAi2P4uOHjBxBPKfaQB9Ho9yZm5ONlYVHKAQgghahpD7yHKNRMtMTGx2L6QkBDGjh3LW2+9VZ4ha70cbQ5XU6+i1WtxtXE1dTglS42GDeOVbe/eSgINijYbuPU5gG19sO0J3j2LjpWRQOKl/1j02180yrvCINdEvHIvQ1o0pFwrPO/6ceWnJNCEEEKIaqFx48b89ddfxfZ/+eWXJoim5nKxdsHR0pHk7GQikiNo5SydIY2my8twZLlyTxn4LfScdPdrUBqh3ZpA23YmCntLM3r4ulRWpEIIIWqZciXRStK8eXM+/fRTRo8ezfnz5401bK1hobHgnyf/ISw5DFfraphE0+vhr8mQlwV2bvDMn0WP5ye3dAZ2XrVxZvoJe7Zm9eOeho48M7YHaNSQkQBxFwuXhR5dDuiVZaOSQBNCCCGqBa1Wy/r16zl37hwAbdq0YdiwYWg0d68/JRQqlQpfJ19ORJ8gJDFEkmjGZG4FnvcoSbQD88B/TNHGVPvmKvesd6ixeygsjnE/B2FppmbD+J40d7ev/LiFEELUeGpjDmZmZsaNGzeMOWStYmdhR3vX9kU7U1YXp36HC5tBbQ6j/wSNefFz+kw1uOD/36ci2XomCjO1is8evQczzc3/1GycoXE36Py8sp2fQNPmKDc8QgghhDCp0NBQWrduzZgxY1i3bh3r1q1j9OjRtGnThrCwMFOHV6P4OvkCEJoUauJIaiHP9srPrCQ4uKBwf34Zkrs0HOjUpB49mtXn/rae+LgY1mleCCGEKNdMtI0bNxZ5rtfriYyMZPHixfTs2bOUq0S1lRoFW24uw+3zNni0rdBwyRm5vL/xDACv9mlGa88S1hPfXmct/znIjDQhhBDChCZNmkSzZs04cuQIzs7OAMTHxzN69GgmTZrE5s2bTRxhzdHcqTkAIYkhJo6kFuo7DeJDlS+CDy2CgFfg5KrS6/jextJMw7djOmOhURd0kBdCCCHuplxJtOHDhxd5rlKpcHV1pX///sybN88YcdU6XwV9RUZuBiNbjqSpU1NTh1NIr4dNryvf4nm2h16vV3jIT7acJTY1m6autkzo71v8hJIaFdyp7poQQgghqsy+ffuKJNAA6tevz6effipflpZR83pKEk1molWSEd/ClSOQfBXm+wH6MtXYtTIvnK2m1emZu/U8o7s1oZGzTSUFLIQQoqYrVxJNp9MZO45a76/wv4hKj2KIzxBTh1LUf2vg4t/KMs7hy0texlkGB0Pj+O34NVQqmPvoPUVuTgrotCXf4JS17poQQgghjM7S0pLU1NRi+9PS0rCwkK6GZdHMqRkAkemRpOWkYWdhZ+KIahmVCkb9Bsu6A3plX5sR5Rrq820X+Hp/OFtOR/L7//XAw9HKeHEKIYSoNYxaE02ULCUnhaj0KKDwZqpaSImEv28mrvpOA3e/Cg2XkZPHtHX/AfBMtyZ09nYu+cR+00v/hrAMddeEEEIIYXwPPfQQr7zyCkePHkWv16PX6zly5Aivvvoqw4YNM3V4NYqjpSNuNm6AzEarNOdv6yS7vBdcPVbmYV7o6U2T+jZcTcjk6e+OEJ+WbaQAhRBC1CblSqI9+uijfPbZZ8X2z507l8cff7zCQdU2YUlKEV4PWw8cLEqoD2YKej1seg2ykqFBR+j5eoWHnL/9IlcTMmngaMXUIdKBSgghhKiJFi5cSLNmzejevTtWVlZYWVnRo0cPfH19WbBgganDq3Hy66JJEq0S3Foi5M1QsPeEvEz4YQic31KmodwcrPj5pa54OloRFpvOM98HkpyZW0mBCyGEqKnKlUTbv38/DzzwQLH9999/P/v3769wULVNfjHZ/JuoaiH4FwjZpnTGHL4cNOVa2Vvg36tJrDgYAcAnj7TDzrJi4wkhhBDCNJycnNiwYQMXL15k7dq1rF27losXL/Lnn3/i5ORk6vBqHOnQWUlur7Fr5woTT4BzM9DlwepRcHxFmYZsWM+Gn1/qioudBWcjU3j+h0DSs/PQ6vQcDotnQ/B1DofFo9XpK+lNCSGEqO7KlekorSaGubk5KSkpFQ6qtslPovnWK6HIvikkX4etN5dM9nsH3Co+a6yZmx2juzUhNSuPfq3cKjyeEEIIIarOlClT7nh8z549Bdvz58+v7HBqlfz7v9BESaIZVUk1di1sYXwgfHcfRAbDX5OV+97+7yr10wzQ1NWO/73YlSe/OULQlSRGLD1IcmYuUSmFyzs9Ha2YOdSPIW09jfymhBBCVHflSqK1a9eONWvW8P777xfZv3r1avz8KlZXqzYKSapGM9H0etg0CbKTwasTdJ9olGHtLM348OG26OSbOSGEEKLGOXnypEHnqQxMRIhCLeu1pGW9ltWrLm5tUFoNXY0ZvLIX9n0Ge+fAgS8g5QYMW2hwA63Wng78+EIAI78+zIXotGLHo5KzGLsqiGWj/SWRJoQQdUy5kmjvvfceI0aMICwsjP79+wOwa9cufv31V37//XejBljT6fX6gun7+W3OTerkKgjdCRpLGL6swss4kzJycLQ2L7ipVqvl5loIIYSoaW6daSaMq3X91qwdttbUYdQtKpXSNMuhAWx6Hf79BdKi4YkfwdLeoCHaeTlia2lGdl5OsWN6QAXM2nSWgX4eaOT+Vwgh6oxy1UQbOnQo69evJzQ0lHHjxvHGG29w7do1du7cyfDhw40cYs0WmxlLcnYyGpUGH0cf0waTdBW2vaNs958Bri0rNJxWp+eFlccY9e1RriVmGCFAIYQQQgghjMR/DDy1GsxtIGwXrHwQUqMNujQwIoGE9OIJtHx6IDI5i8CIBCMFK4QQoiYo9zSkBx98kAcffNCYsdRK+fXQGjs0xlJjabpA9HrYOBGyU6BhF+g+ocJDnotM4WxkCmZqNWpZ3iGEEEIIUao8XR5ZeVnYWdiZOpS6pcUgeO4v+PkJiPwXvh8Ao9eBy51XiMSkZhk0vKHnCSGEqB3KNRPt2LFjHD16tNj+o0ePcvz48QoHVZsULOU0dT20oB8hfA+YWSnLONWaCg/Z1suRba/fy1dPdqCBk7URghRCCCGEqH1+PvczXX/uyrwT80wdSt3k1Qle2gHOTSHpCnw/EK4G3vESN3srg4Y29DwhhBC1Q7mSaOPHj+fq1avF9l+/fp3x48dXOKja5GLiRcDEnTmTrsC2Gcp2//fu+s1bWTSpb8t9rd2NNp4QQgghRG3jbOVMji6HyymXTR1K3eXcFF7coSTUMhPhx6Fw7q9STw/wccbT0YrS1lqogPq2FrT2NKzGmhBCiNqhXEm0s2fP4u/vX2x/x44dOXv2bIWDqk3yZ6K1cGphmgD0etgwAXLSoFE36Da2wkNuPxPF0fB4IwQnhBBCCFH79fLqxZYRW/hu0HemDqVus3WBZzdBiyGQlwW/PQPHSv6daNQqZg71AyiWSFOh1ERLzcpj+JKDRCXLkk4hhKgrypVEs7S0JDq6eFHOyMhIzMwq1u2xtnnE9xGG+w6ndf3Wpgng+AqI2Adm1jB8aYWXccalZTP1j/8Y+c0R9l6IMVKQQgghhBC1l72FPY3sG6FWlevWWxiThS2M/Bn8nwW9Dja/ATtnKV8832ZIW0+WjfbHw7Hokk0PRyum3d8KV3tLvOpZ42pvwrrHQgghqlS5Ml6DBg1i+vTpbNiwAUdHRwCSkpJ45513GDhwoFEDrOmebPWk6V488RJsf0/ZHjAT6jer8JAfbDxDUkYufp4O9PR1qfB4QgghhBBCVCmNGQz9Chwbwp5P4J/5kBoJQxeCmUWRU4e09WSgnweBEQnEpGbhZm9FgI8zGrWKJ7s0Ik+nR6NW5qpl5WrJztPhaG1uinclhBCiCpQrifbFF19w77330qRJEzp27AhAcHAw7u7u/O9//zNqgKKcdDplGWduOjTuAQH/V+Ehd5yN5q//ItGoVcx97B7MNfJtqhBCCCGEIXZd3sXWS1vp6dWT4b7DTR2OUKmgz1Sw94RNr8G/v0JqFDzxE1g5FDlVo1bRvVn9YkM42RRNuH3411n2X4xl8Sh/OjRyqszohRBCmEi5siBeXl78999/zJ07Fz8/Pzp16sRXX33FqVOnaNSokbFjrLFCEkO4mHiRXG1u1b/48e/h0gEwt4GHF4O6YgmvlKxc3l1/CoCXevvQ1svRGFEKIYQQQtQJYclhbL20lcDIO3eFFFXM/xkYtQbMbZVO9isfUJJpZZScmcuBkFiuJWby+PJDfHcgHH0JS0SFEELUbOXOrNja2tKrVy+GDh3Kvffei5OTE3///TcbN240Znw12rJ/l/Hoxkf55fwvVfvCCRGw431le8AHRlnG+enf54lOyca7vg2TB5ioSYIQQgghRA3l66R0as9vOiWqkeYD4bm/wNYVok7BdwMh9mKZhnC0Nuevib25v60HuVo9H28+x8s/HScpI6eSghZCCGEK5VrOGR4eziOPPMKpU6dQqVTo9XpUqsK+NVqt1mgB1mQWGgvszO1o7tS86l60YBlnBjTpBV1eLtcwWp2+oPZDXFo2vxy9AsCnj96DlXnFmhMIIYQQQtQ1+feDYUlhaHVaNBVs9iSMzMsfXtwBqx6FhDBYMQieWg2Nuxk8hKO1OUuf9mfVkct89Nc5dp6L4YGvDrDwqY509nauxOCFEEJUlXLNRHvttdfw8fEhJiYGGxsbTp8+zb59++jcuTN79+41cog116e9P+XQU4fo6tm16l702Ldw+R9lSno5l3FuPR1Jr89289S3R3htdTAf/XUOgF6+LnRrWrwehBBCCCHE7fbv38/QoUNp0KABKpWK9evX3/WavXv34u/vj6WlJb6+vqxcubLS46wqXvZeWGmsyNHlcDX1qqnDESVx9oEXt4NXZ8hMhB8egLUvlHzuvrmwZ06x3SqVime6e7NuXA+869twIzmLkd8cYeneUHQ6Wd4phBA1XbmSaIcPH+bDDz/ExcUFtVqNRqOhV69ezJkzh0mTJhk7xhpNpVJV3TeN8WGw8wNle+As5UagjLaejmTsqiAik7OKHTsYGsfW05EVDFIIIYQQdUF6ejrt27dnyZIlBp0fERHBgw8+SL9+/QgODub111/npZdeYtu2bZUcadVQq9Q0c1JKbMiSzmrM1gWe3QQt7ge9Fk7/AT8/XvScfXOVrp53uMdv6+XIX5N6M6x9A7Q6PXO3XuC5lceIS8uu5DcghBCiMpUriabVarG3twfAxcWFGzduANCkSRMuXLhgvOhqsCovJHrrMk6fe6Hzi2UeQqvTM2vTWe4U+axNZ9HKt2hCCCGEuIv777+fjz/+mEceecSg85cvX46Pjw/z5s2jdevWTJgwgccee4wvv/yy1Guys7NJSUkp8qjO8uuihSSFmDgScUcWNjByFXR6Xnkesh1WDFHut/MTaP1mKN0978DO0oyvnuzAZ4+2w9JMzf6LsTzw1QGOhMdXwZsQQghRGcqVRGvbti3//vsvAF27dmXu3LkcPHiQDz/8kKZNmxo1wJrqq6CveOjPh1h7cW3VvGDg13DlEFjYwbDyLeMMjEgocQZaPj0QmZxFYERCBQIVQgghhCju8OHDDBgwoMi+wYMHc/jw4VKvmTNnDo6OjgWP6t4lvnk9pS5aSKIk0ao9jRk89CX0f1d5fuUwfOisJND6vnPXBFo+lUrFyC6N2TihF75udsSkZjPq2yP8dkyW9AohRE1UriTau+++i06nA+DDDz8kIiKC3r17s2XLFhYuXGjUAGuqC4kXuJxyGZ1eV/kvFhcKO2cp24M+gnpNyjVMTGrpCbTynCeEEEIIYaioqCjc3d2L7HN3dyclJYXMzMwSr5k+fTrJyckFj6tXq3diIr+5gCznrCFUKrj3LXh46c0dN1djhO6Ec38pM9MM1NLDno0TevJYp4bYWpjRtWnRRgNanZ7DYfFsCL7O4bB4WfkhhBDVVLm6cw4ePLhg29fXl/Pnz5OQkEC9evWKdOmsy/K/Ycz/xrHS6LSwYRzkZULTvoXTzsvBzd7KqOcJIYQQQlQmS0tLLC0tTR2GwXzrKcs5r6RcIVubjaWm5sRep6VcV36q1KDXwbVAWPM01PeFHpPgnpFgfvf7YxsLM754vD2TB7bAy8m6YP//Dl9m6d7QIitCPB2tmDnUjyFtPY3+doQQQpRfuWailcTZ2VkSaDel5KQQnRENFNa+qDRHlsHVo2BhD8MWKd+YlVOAjzOejlaUNoIK5R/0AB9p0S2EEEII4/Lw8CA6OrrIvujoaBwcHLC2ti7lqprF1doVBwsHtHotl5IvmTocYYhba6DNTIQeryn7zSwhPhQ2TYIF7eDAPKWjpwFuTaDN33GB9zacLlZSJSo5i7GrgqSplxBCVDNGS6KJQqGJyhR9D1sP7C3sjTfwnjnKP+T54kJg90fKtk9vOPlzhYbXqFW896AfeiiWSMt/PnOoHxq1JEuFEEIIYVzdu3dn165dRfbt2LGD7t27mygi41OpVNJcoCYpqYnAoA+V53nZ4DsAHLwgPQZ2fQhftoVtMyD5mkHDa3V6vv8nosRj+Ys5pamXEEJUL5JEqwT5dS6MPgtNrVH+Id83V1nGuX4s5GVBPR+4sOWObbYNfomb/0VYmRcdy8PRimWj/WVKuRBCCCEMkpaWRnBwMMHBwQBEREQQHBzMlStXAKWe2ZgxYwrOf/XVVwkPD2fq1KmcP3+epUuX8ttvvzF58mRThF9p8kt95H/pKqoxnbbkLpx9pir7vTrDa//CI1+DWxvISYPDi+Gr9vDnqxB95o7DB0YkkJ6tLfW4NPUSQojqp1w10cSdXUy8CFRCPbT8f8D3fKJ0CLp2DDSWkBhhUJttQ/x3LRmAF3p508vXlZjULNzslSWcMgNNCCGEEIY6fvw4/fr1K3g+ZcoUAJ599llWrlxJZGRkQUINwMfHh82bNzN58mS++uorGjZsyHfffVekFm9tcG/De7E1t6VHgx6mDkXcTb/ppR+79b67/ZNKXbTQXXBwAVw6AP/+qjx8B0LP18C7V7GyK9LUSwghah5JolWC/Jlo+R2YjKrPVMiIh6PLlefabKMl0ACmDmnFwx28cHewxMnGwihjCiGEEKLu6du3L3p96cvQVq5cWeI1J0+erMSoTO/ehvdyb8N7TR2GMDaVCpoPUB7XT8DBhXBuI4TuUB4N/JVkWuuhBatHDG3W9U9IHP1buWFvZV6Z70AIIYQBZDmnEWl1WgIjAzkbdxaApk5NK+eFdHmF2xoLoyXQ8rX0sJcEmhBCCCGEEOXh1Qme+BEmHIfOL4KZFdwIgt+fhUWd4Nj3kJt516Ze+X4/cY1en+1h0a4QUrNyq+QtCCGEKJkk0Yxk5+WdDP5jMC9uf5FMbSYAk3ZPYuflncZ9IZ0Wgn9RttVmoM0p2mygnM5HpXA1IaPC4wghhBBCiDuLz4znaORRYjJiTB2KqEz1m8FD8+H109DnbbCup5Rh2TwFvmyL5sAX/NxkMxM160ps6jVJs44ffXbRzNWW5Mxc5u24SK/P9rB4dwhp2XklvaIQQohKJkk0I9h5eSdT9k4hOqNoW/bYjFim7J1i3ETaxomQm6F8ozUjSlnKmd9soJz0ej3v/nmafl/sZUPwdePFKoQQQgghinnnn3d4aftLHLh2wNShiKpg5wr93oHJZ+D+ueDYGDLiYM/HNA39iSnma/nE9tcil0y33cgU87X0aeXB9sl9+OrJDjS9mUz7YvtFen22myV7QiWZJoQQVUySaBWk1Wn5NPBT9BSv+ZG/77PAz9DqSu+8Y7B9cyH4Z2X7nidAY17YHagCibTD4fEcv5yIWq2iW9P6FY9TCCGEEEKUqrlTcxrbNzZ1GKKqWdhC1/+DSSfh0e/Box3olOWZo7SbOOMxi1UD8vin0wFe0a4uqHusUat4uIMXOyb3YcHIDjR1sSUpI5fPt12g92e72XUu+i4vLIQQwliksUAFBcUEFZuBdis9eqIyogiKCaKLR5eKvZg2F8ytITcT2jxSuD+/Jlo5E3WLdimNEEZ2boS7g2EFToUQQgghRPm80fkN3uzypqnDEKaiMYN2j0HbRyF8Dxz8CsL3Ypt0gV7/jFHOMbdWun0mXYb6zcGlOZr6zRnezpuh7Ruw8d/rLNwVyuX4dLxdbE37foQQog6RJFoFxWbEGvW8O2rSA/bPBZv64H1bV6dyNhc4fimBw+HxmGtUvNq3WcVjFEIIIYQQd6RS3a2UvKgTVCpo1l95RP4L3/QFvU45lpsJV48ojyLXaNDUa8Ij9ZvzcFtfrqga4J3uCFbNwc6ND/86h7uDJc90b4KNxc2PenvmKB1BS/q8sG+u8kV8v+mV+laFEKK2kCRaBbnauBr1vDs6s0752XqY8g2WESzcrcxCe9S/IV5O1kYZUwghhBBC3J1er0ePHrVKKqzUeRe3KQk0jYXSOKzzi8oX6PGhEBcC8SEQHwY5aZAQDgnhqEO24Q1wWBlCZ2HP8CxXwvSepKZ0w8anLbg0B70W9nyKTq/naKOXiEnNws3eiq5Xv0O9d7aybFQIIYRBJIlWQf5u/rjbuBOTEVNiXTQVKtxt3PF386/YC2lz4dwmZfvWpZwVEHw1if0XY9GoVYzr62uUMYUQQgghxN1N3T+Vf67/w4K+CwjwDDB1OMKU9s1V6hvfrIFW8NzeA/pOKzxPr4fUyJtJtdCiCbakK6hzUrlHnco9hMOJg3Ci8NIcMzss9s7GSruGQ7r+NCCO7uZ/EuI3ieblXNEihBB1kSTRKkij1jAtYBpT9k5BhapIIk11s1n12wFvo1FrKvZCEfsgMxFsXaFJz4qNddPi3SEADO/gReP6NkYZUwghhBBC3F1mXiapOamEJIVIEq0uuz2BBoU/93xS9LlKBQ4NlEfTPkXHyctWZqjlJ9XiQiE+BF1sCOrsJCzy0gDoqAmjoyYMgG9yH2ROUDeW+UUypK1nZb9TIYSoFSSJZgQDmgxgft/5fBr4aZEmA+427rwd8DYDmgyo+Iuc/lP56fewUZZynrmRzM5zMahVML6f1EITQgghhKhKzZ2as/fqXkKTQk0dijAlnbZoAi1fWRuHmVmCW2vlcYtjYXGM/24HTYikqTqST82+Q6NS6q6NMDvAbp0/szZZMdDPA41aavUJIcTdSBLNSAY0GUC/Rv0IigkiNiMWVxtX/N38Kz4DDSAvB84bdynn4pu10B66pwFNXe2MMqYQQgghhDCMr5NSSiM0UZJoddqdCvobYZmlDhVxegficKAHZ9CodOToNViotLioUlhl8Qkfp40mMLw93X1dKvx6QghR20kSzYg0ag1dPLoYf+DwvZCVDHbu0Lh7hYe7GJ3K36ejAJjQX2qhCSGEEEJUNd96N5NoSaHo9Xrp2CkqRUxqFgATNet4w3wt83IfY5F2BJM1v/Oa+Z+YqXR8YP4Tl/ckQONvwUJKvAghxJ1IK6CaIL8rp9/DSnvqCsqfhXZ/Ww9auNtXeDwhhBBCCFE2Pg4+mKnMSMtNK1IORAhjcrO3KpZAA/hS+zjzch8DQKtX0eT6X8Qt6kd2bIQpwxVCiGpPkmjVXV42nN+sbLcZUeHhcvJ0XIpPB2QWmhBCCCGEqZhrzPF29AYgJDHEtMGIWivAxxlHKzXzb0mg5VukHcG83MdYr+tFvN4el9TzZC7pTfTJv00UrRBCVH+SRKvuwnZDdgrYe0KjrhUezsJMzYbxPVk/vidtGjgaIUAhhBBCiNordtFiYpcuLfnY0qXELlpc7rEL6qJJcwFRSTRqFQ0f+ZBF2hHcvmBYBSzWjsDisW842P8Pzqua4UQqbhtHwcGFoNej1+tNEbYQQlRbkkSr7s7kd+UcDmrj/LpUKhUdGjkZZSwhhBBCiFpNoyZu4aJiibTYpUuJW7gINOW/P5MkmqgKQ9p6smy0Px6OVkX2ezhasWy0P0PbN2BYn640nXqA9NYjUel1sOM98n57nuELdvDdgXCy8wzsEiqEELWcNBaoznKz4PwWZbttxZdy7rkQQxdvZ+ws5dcuhBBCCGEI13HjAJSEGeA8ZgwJP/1E3MJFuEyaWHC8PPKbC8hyTlHZhrT1ZKCfB4ERCcSkZuFmb0WAjzMadeH8NAtrWyye+BqOdYGt0zA79yef6o7zwcHpPNvD23TBCyFENSLZlOosdCfkpIJDQ/DqXKGhridl8spPx7G1NGP76/fi5mB194uEEEIIIYSSKNMribT8ZFpFE2gAzZ2aAxCeHI5Wp0VjhAZSQpRGo1bRvVn9O5+kUkHAy+DeFv1vY2idfpVV2mmYh7tD8wHk5OlYc/wqj/k3xNpC/nsVQtQ9spyzOstfytlmeIWXckanZNGong1+ng6SQBNCCCGEKCPX8eMK78dUqgon0AAa2jfESmNFtjaba2nXKjyeEEbTpDuq/9sHDbtgnpsCPz8G+79g3YmrvLf+NL3n7uG7A+Fk5hRf5qnV6TkcFs+G4OscDotHq5O6akKI2kNmolVXORlw4WZnHCN05fRvXI8dU/qQkJ5T4bGEEEIIIeqa2KVLQadTnuj13JjxLg0++bhCY6pVapo5NeNM/BlCE0Np4tDECJEKYSQODeC5zfD3VDixEnZ/RN8GR2ju9DwhSdl8vPkcy/eF8WqfZjzdtQnWFhq2no5k1qazRCZnFQzj6WjFzKF+DGnrabr3IoQQRqLS17GWKykpKTg6OpKcnIyDg4Opwynd2Q3w2xhwbAyv/6dMrRZCCCGEydSYe4g6rLJ+R/lNBFwmTSQvKpqk334DwGXiRGWGWgUcvnEYM7UZrZ1bY2dhZ4xwhTC+Eythy1ugzUHv0pKtbb5g9rE8riZkAuBiZ0HfFq78EXSd2z9c5n+KWTbaXxJpQohqy9B7CFnOWV3dupSzAgm0uLRs/nf4Elm50lFHCCGEEKKsbk2guY4bh8u4sagsLACIW1S8a2dZdW/QnS4eXSSBJqq3Ts/Bc1vA3hNV3AXuP/I0e4ZmMvfRe2jkbE1cWg5rS0igAQX7Zm06K0s7hRA1niTRqqOcdLi4TdmuYFfObw+E896GM0z4JcgIgQkhhBBC1DFaXZEmAuYeHjg9ORIAM3d39Fr5olLUEY26wCv7oHF3yE7BbM0onkj/md1T7uX/7m16x0v1QGRyFoERCVUTqxBCVJJqkURbsmQJ3t7eWFlZ0bVrVwIDA0s9d+XKlahUqiIPK6taVij/4jbIzYB63uDZodzDJKbn8L/DlwF4KqCxcWITQgghhKhDXCdOKNZEwOWVV1BZW5MXHY11m7YVGj9Pl8fGsI3MPz6fHK3UrhXVnL07jNkIAa8oz/fOwfy30dzjatjKmZiUrLufJIQQ1ZjJk2hr1qxhypQpzJw5k6CgINq3b8/gwYOJiYkp9RoHBwciIyMLHpcvX67CiKtAwVLORyq0lHPFwQgycrS0aeBA/1ZuRgpOCCGEEKJuM3NxwXn0aABiFy5En99woBw0Kg2fHv2UH878QERyhLFCFKLymFnAA5/D8GWgsYSLf9P/wJP4qu7eYXbO3+eZ+OtJ4tOyqyBQIYQwPpMn0ebPn8/LL7/M888/j5+fH8uXL8fGxoYVK1aUeo1KpcLDw6Pg4e7uXoURV7LsNAjZrmxXoCtncmYuKw9eAmBif19U0phACCGEEMJo6r/4Amo7O7LPnyd127Zyj6NSqRjabChPtXoKK7NatrpC1G4dRsGL28ChIdYpEWyxnM5Cs0UlnjpJs47p1n8SlZLFvgsxOFibFxw7cTmRmFSZoSaEqBnMTPniOTk5nDhxgunTpxfsU6vVDBgwgMOHD5d6XVpaGk2aNEGn0+Hv78/s2bNp06ZNiedmZ2eTnV34TUdKSorx3kBluLgV8rLAuRl4tCv3MCsPXiI1O4+W7vYM8vMwYoBCCCGEEELj5ITzc88Rt3gxsQsXYT9wICqz8t1aT+86/e4nCVEdNegI/7cPfn8Oi0sHGGZ2GHdVIk/lvovu5nyNSZp1TDFfy8VWk+jSvQfXEjMx1yjH9Ho9r60+yfWkTPwb12OQnzuD2njg42J7x5fV6vQERiQQk5qFm70VAT7OaNQyaUAIUflMOhMtLi4OrVZbbCaZu7s7UVFRJV7TsmVLVqxYwYYNG1i1ahU6nY4ePXpw7VrJ04fnzJmDo6NjwaNRo0ZGfx9GZYSlnKlZuaw4qCwHGN/fF7X8gyKEEEIIYXTOzz2LxtGRnIgIkv/6y9ThCGEati7wzHroPgGArprz7LJ4E1eSmHgzgRbiN4kWIz/Cv3E9hrVvUHBpUkYu9e0s0euVGWlz/j5Pvy/2MujLfXyx7QKnriWj1xft6Ln1dCS9PtvNU98e4bXVwTz17RF6fbabracjq/JdCyHqKJX+9r9KVejGjRt4eXlx6NAhunfvXrB/6tSp7Nu3j6NHj951jNzcXFq3bs1TTz3FRx99VOx4STPRGjVqRHJyMg4ODsZ5I8aSlQKf+4I2G8YeAveSZ9fdzdK9oczdeoGmrrbsmNxHvpURQgghjCAlJQVHR8fqeQ8hANP8juK/+46YL+Zh3rAhzbZsRmVhUa5x0nPTuZxyGb/6fkaOUIgq9N/v6Ne/ikqXhx5QAbo2I1A/8rVSS60UkcmZ7DwbzbYz0RwJjydPV/gRtYGjFYPaeDDIz52EjBwm/nKS2z/A5n/aWTbanyFtPY39roQQdYCh9xAmXc7p4uKCRqMhOjq6yP7o6Gg8PAxbgmhubk7Hjh0JDQ0t8bilpSWWlpYVjrVKXPhbSaC5tAC38t1AZeTk8d0BZRbahH6+kkATQgghhKhE9UaNIn7lj+Reu0bSuj+p9+TIMo+RnJ1Mr9W9ADg66ig25jbGDlOIqnHP46jcWsHy3qhuprrUZ9ZB+F5o97hSR82zfbEVN56O1jzT3ZtnunuTnJHL7gvRbD8Tzd4LsdxIzmLloUusPHQJFRRLoHFznwqYteksA/085DOQEKLSmHQ5p4WFBZ06dWLXrl0F+3Q6Hbt27SoyM+1OtFotp06dwtOzFnzjYISlnL8cvUJCeg5N6tsUmSothBBCCCGMT21jg8v//R8AccuWocsue9dBR0tHXKxdAAhLCjNqfEJUuQt/A3pQ32weYGEHmQkQ+DV80weW9YRDiyEtpsTLHW3MeaRjQ5aN7sTJ9wfy3ZjOPNG5IfZWZiUm0PLpgcjkLAIjEoz9joQQooDJu3NOmTKFb7/9lh9//JFz584xduxY0tPTef755wEYM2ZMkcYDH374Idu3byc8PJygoCBGjx7N5cuXeemll0z1FowjMwnCbiYT2zxSriGycrV8vT8cgHF9m2GmMfmvVwghhBCi1nMa+QRmnp7kRUeTtHp1ucZo7tQcgJCkEGOGJkTV2jcX9nwC/WbA+3HKz5w0uOdJaPsoaCwh5gxsnwHzWsEvI+HsBsgrOflsZa5hgJ87cx9rz4fDDCt1I50+hRCVyeRZlpEjR/LFF1/w/vvv06FDB4KDg9m6dWtBs4ErV64QGVlYJDIxMZGXX36Z1q1b88ADD5CSksKhQ4fw86vh9SMubAFtDri2BrfW5RpizbGrxKZm4+VkzSMdGxo5QCGEEEIIURK1hQUuY18FIO6bb9Glp5d5DN96vgCEJEoSTdRQtybQ+kxV9vWZqjz/bzW4toI3L8JDX0LDLqDXwsWt8NsYmNcStrwFN05CKSW7PRytDQrDzd6Kn49eZkPwddKy84z17oQQAjBxTbR8EyZMYMKECSUe27t3b5HnX375JV9++WUVRFXFbl3KWU4qFThYmfFq32ZYmJk8PyqEEEIIUWc4PfII8d99T+6VKyT8/Asur7xcpuvzZ6KFJpVc51eIak+nLZpAy5f/XKcFayfo/ILyiAuB4F/g39WQegMCv1Eebn5K7bR2T4C9e8EwAT7OeDpaEZWcVeKyThXg4WiFf2Mnxv8SREJ6DhZmavq2cOXBezy5r7U7dpbV4uOvEKIGM2l3TlOolp21MhOVrpy6PBh/DFxblHuo1KxcLMzUWJppjBigEEIIIarlPYQowtS/o+SNG7kx9W3Ujo747tiOpgwxnIo9xagto3CxdmHPE3sqMUohqhmdVmk8EPwLnP8L8m4ux1RpwHeAklBreT+YWRK65h02/hfNIu2IIok0FTBRs45h97jjOfxDlu0NY8upSMLjCmeFSkJNCHEnNaI7p7jp3F9KAs29bYUSaAD2VuZGCkoIIYQQQpSFw4MPEvfNN+SEhpGw8kdcJ000+NpmTs0AiMuMIzErkXpW9SorTCGqF7UGfO9THplJygqd4F/gWiCEbFMeVk7Q7nF8bfKYYr4WOyszZqcPKxhiuu1GXtGuBY8ZYGnGm4Nb8sagFpyPSmXzf5EFCbXtZ6PZfjbaoISaVqcnMCKBmNQs3OytCPBxlq6fQgiZiVYt/G+E0lSg/7tw71tlvnzPBaWzTd8WrqjK2dVTCCGEEHdWLe8hRBHV4XeUsm071197DbWtLc127sCsnuHJsCF/DOF62nVWDF5BF48ulRilEDXA7cs989m4QEYc1/1e4XiL1+kY8S2N//2y5KWkN+n1+mIJtXz5CbVFozoWrObZejqSWZvOEplc2KTA09GKmUP9GNLWs3LerxDCpAy9h5DCWaaWkaBMXwZoM6LMl+dpdXy46SzP/3CM349fM25sQgghhBCiTOwHDsDSrzW69HTiv/uuTNdKXTQhbuHSHAbMhMmnYfQ6aPsYmFlBRhwAXme/4eENbe+aQANQqVS09nTgzcEt2fVGH/5+rTcT+vnS1MWWnDwdUSlZRRJor64KKpJAA4hKzmLsqiC2no4s6SWEEHWEJNFM7dwmpTONxz1Qv1mZL8/R6hjQ2g0vJ2sevEe+FRFCCCGEMCWVWo3rpEkAJP78C7kxMQZfm9+hMzRRkmhCFMhf7vnY90p3z6FfQaOuyjG9TvmZGgVphv1/raSE2rsP+gHKEs6ZG8+UeF3+8q1Zm86i1dWpxVxCiFtIEs3UzqxTfpazK6eNhRkzHvRj71t9sZXimEIIIYQQJmfXpw/WHTqgz8oi/ptvDb7O10lJooUkhVRWaELUbFaO0Ok5peEAQH4pm+Pfw8KOsG8u5KSXevnt8hNqAT7OAARGJBCdkl3q+XogMjmLL7Zd4GJ0qiTThKiDJIlmSulxELFf2S5DEk2r03M4LJ4Nwdc5HBaPVqfHXCO/SiGEEEKI6kClUuH6+msAJK1ZQ+6NG3e5QpGfRDsff57N4Zs5FnUMrU5baXEKUSPtmwt7PlGWcM5Mgg5PK/tz0pT9C/3hxI+gzSvz0DGpWXc/CVi2L4xBX+6n7cxtPLrsEDM3nOb341c5H5VCnlZn8OuV9LlOCFG9ydQlUzq3UZmC3KAjOPsYdIkUuRRCCCGEqP5su3XDpls3Mo4cIW7ZMjw/+uiu10QkRwCQqc1k2oFpALjbuDMtYBoDmgyo1HiFqBFuTaDl10AbvhScmsDe2cpMtbQo2DQJjiyDgbOg+aDCGWt34WZvZdB5Ld3tuJqYSUaOlhOXEzlxObHgmKWZmtaeDrTzcmRU18a09iy5QLl8rhOiZpLunKa08iG4dAAGfgg9X7vr6VtPRzJ2VRC3/8Ly/0lYNtpf/uAKIYQQlaRa3UOIElW331FG0EkujxoFGg3NtmzGokmTUs/deXknU/ZOQX/bnZ7q5p3e/L7zJZEmxJ45So20kpoI7JsLedlg46xsZyUp+717w6CPlIkLd6HV6en12W6ikrOKfeYC5XOXh6MV/7zdH4CIuDROX0/h1PVkTl9P5syNFNKyC2fA/fxSV3r6ugBwICSWLaei6N/KDa1OJ5/rhKhmDL2HkCSaqaRGw/xWyky01/6DeqXfVEHhH/Tbu8Tku/UPukZt2DctQgghhDBctbmHEKWqjr+jK//3f6Tv24/DsKF4zZ1b4jlanZbBfwwmOiO6xOMqVLjbuLP10a1o1JrKDFeI2iEzEQ7Mh6Nfg/ZmjbN2j0P/9+76uSt/4gJQJMllSIJLp9NzKT6d0zdSOH09mfF9fXG0MQdg9pZzfLM/nFFdG7HnfKx8rhOimjH0HkIKaZlK/lJOr853/UMOSpHL0v7QQmGRy8CIBCMGKYQQQgghKiK/U2fKpr/IDim5YUBQTFCpCTQAPXqiMqIIigmqlBiFqHWs6ymzzyYeh3tGKvtO/Q6LO8O2GZBR+memIW09WTbaHw/Hoks7PRyt7jpDTK1W0dTVjmHtG/DOA60LEmgA/Vu58WqfZjSqZyOf64SowaQmmqmc+VP5aWBDAUOLXBp6nhBCCCGEqHzWbdpgP2gQqdu3E7toMQ0XflXsnNiMWIPGMvQ8IcRNTo1hxDfQbRzseB8i9sHhxXByFdz7JgS8AmaWxS4b0taTgX4eBEYkEJOahZu9FQE+zhWaGdataX26Na3PhuDrBp3/0eazvNK7KQP93LG1lI/tQlQXMhPNFFIi4fIhZdvvYYMuMbTIpaHnCSGEEEKIquE6cQKoVKRu307mmTPFj9u4GjaOgecJIW7ToAOM2QBP/wFufkq9tO3vKjPT/vsddMU7amrUKro3q8/DHbzo3qy+0ZZWGvp57eyNFF5fE0ynj3cw4ZcgdpyNJjtPuvUKYWqSRDOFcxsBPTQMAKdGBl0S4OOMp6MVpf3pVqF0cwnwcTZWlEIIIYQQwggsmzfHYehDAMQuXFjsuL+bP+427gVNBG6nQoWHjQf+bv6VGqcQtZpKBc0HwKv/wLDFYO8JSVdg3UvwbT+I2F8lYRjyuc7V3pIJ/ZrhXd+GrFwdf/0Xycs/HafLxztZsie0SuIUQpRMkmimcHqd8rPtCIMv0ahVzBzqV+Kx/D/AM4f6SfFJIYQQQohqyHX8eNBoSN+3n4ygk0WOadQapgVMAyiWSMt/PrnTZGkqIIQxqDXg/wxMDFIaDVjYQ2Qw/DgUfn4C/pqidPcsyb65SofQCrj1c93tn9zyn3/0cBveHNyKPW/2ZcP4nrzYywd3B0tSsvJQqwqvysjJI/hqEnfrFajV6TkcFs+G4OscDotHq6tTvQWFMCrpzlnVkq/DlzeTYVPOgUODMl3+w8EIZm06W2Sfp6MVM4f6SRtkIYQQohKZ/B5C3FV1/x1FvvceSb+vxaZbN5qs/KHY8Z2Xd/Jp4KdFmgy42bjhaOGIj6MPX/T5ApVKvjAVwqjSYmHfZ3DiB9DloaSy9NBjktKcIN++ubDnE+g3A/pMrfDLbj0dyaxNZ4s0GbjT5zqtTs/RiHh8Xe1wc1CWhK4/eZ3X1wTTu7kL/3uxq1Fep7y0Or1Ra8gJUdUMvYeQCoVV7ewG5Wfj7mVOoAF4OloD0MzFlkkDmssfKCGEEEKIGsJl7FiS128g48gR0o8cwbZbtyLHBzQZQL9G/QiKCSI2IxZXG1c0Kg0vbn+RkKQQlv+3nLHtx5ooeiFqKTtXePAL6Poq7PoAzm1S9h9aCNeDYNRqOLLMqAk0KHvzAo1aRY9mLkX2xaZmY22uoWMjp4J9Wblafjx0iQfv8eT09WTGrgri9lkzUclZjF0VdNduo4aqqkSdENWBzESrat8NhGuBcP/n0PWVMl++ZE8on2+7wIiOXswf2cH48QkhhBCiRCa/hxB3VRN+R1Eff0LiqlVYd+hAk19/MWhm2bqQdcw8NBOA+X3nM7DJwMoOU4i668pRpenAtcCi+1s9BA/OA3sP08RVioycPHLz9DjamAOw9XQUr646AYC5RkWutuSP+yrAw9GKf97uX6EJGVtPR5aYqMsf0ViJOiEqm6H3EFITrSolXb35x1gFfsPKNURYbBoAzdzsjBiYEEIIIUTlWLJkCd7e3lhZWdG1a1cCAwPveP6CBQto2bIl1tbWNGrUiMmTJ5OVlXXHa2oSl/97BZWVFZnBwaTvN6yQ+YjmIxjdejQAM/6ZwfmE85UZohB1W+Ou8OJ2eOJ/Rfef/wvmtYRlPWHH+xC+D/KyTRPjLWwszAoSaAD2VmZ0b1ofgPGq35moWVfidRM06xiZvopPNp9l74WYIscOhcZxOCyerNzCbqCJ6TlcS8wgJjWL5IxcMnLyyM7VMmvT2WIJNKBg36xNZ6UGm6hVZDlnVTq7XvnZpGe5v8EIi00HoJmrrZGCEkIIIYSoHGvWrGHKlCksX76crl27smDBAgYPHsyFCxdwc3Mrdv4vv/zCtGnTWLFiBT169ODixYs899xzqFQq5s+fb4J3YHxmrq44j36a+O++J+arr7Dt3RuV+u7fa7/R+Q0ikiM4eOMgE3dP5NcHf8XF2uWu1wkhykGlgtibyWq1OehylW6eqVEQfVp5HPwKzG3Bpzc0uw9874P6zUwbN9DT14Wevi78dOgSsZvX8Yb5WgAWaQub2k3UKPvn5T7GioOXiIhLp2/Lwr/JL/x4jKxcHQem9qORsw0Ay/aF8c3+8DLFogcik7MIjEige7P6FX9zQlQDMhOtKp35U/nZ9pFyD/FCT29eubcpbRo4GikoIYQQQojKMX/+fF5++WWef/55/Pz8WL58OTY2NqxYsaLE8w8dOkTPnj0ZNWoU3t7eDBo0iKeeeqrU2WvZ2dmkpKQUedQEzi++iNrWluyz50jdsdOga8zUZsztMxdvB2+i0qN4fc/r5GhzKjlSIeqoW5sIvB+n/EyNhJ6vwYjvoP1TYOsGuelwcSv8/RYs8oev2ivdPc9vgexUk76F5u72LNKOYF7uY7xhvrZgRtqtCbRF2hF08a5Hpyb1il7rZo+vmx3WFoUdgVUqsDRTU57eJjGptWc2sRBSE62qJF5S/qiq1PDGBbAr/u2rEEIIIaqvmlBvqzrJycnBxsaGtWvXMnz48IL9zz77LElJSWzYsKHYNb/88gvjxo1j+/btBAQEEB4ezoMPPsgzzzzDO++8U+z8Dz74gFmzZhXbXxN+R7ELFxG3dCkWvs1oumEDKo3m7hcBl5IvMWrLKFJzUnm42cN81PMj6dgphDGV1oXz9v06nTIjLWwXhO6CK0eUGWv51ObQqKsyQ833PnBvB/mzTvfMAbWm5CYF++aCTgv9ppcvfr0espLQJt/gje//xjIjimHqg/TUnCVPr8ZMpWNe7mMs1o4oV000vV5Pnk5PTp6OQ2FxvPzTibte8+vL3ejq44wepCGeqLakO2d1c2a98tO7lyTQhBBCCFHrxcXFodVqcXd3L7Lf3d2d8+dLruk1atQo4uLi6NWrl/JBLS+PV199tcQEGsD06dOZMmVKwfOUlBQaNWpkvDdRiZyff46En38mJzSMlM2bcRxmWL1cb0dvvrj3C8buGsuGsA00r9ecZ9s8W8nRClGH6LQld+HMf667WSdMrQbPe5RHr8nKzLOIAzeTajuVSRSX/1Eeu2YpM9ea9VcSanlZcHBB0XGhaKKutNjSYiDlBqTegJRISLmuzJJLuXFzfyTkZqABFgAUlkvDTKVDr4erejfU6Jg51K/MSS2VSoW5RoW5Rk3/Vu54OloRlZxVYl00ULp0Bvg4sz8klhl/nub/+jRlTHfvMr2mENWJJNGqSv5SzjYj7nzeHYREp5KQnkMLd3vq2VoYKTAhhBBCiOph7969zJ49m6VLl9K1a1dCQ0N57bXX+Oijj3jvvfeKnW9paYmlpaUJIq04jb099V98kdj584ldvASH++9HZW5+9wuBHl49eKvzW3x27DPmn5hPU8em9G7Yu5IjFqKOuNMMsJJmjuWztIdWDygPgPgwCNutJNQiDkB6DPy3WnmgAjsPJWGWdAUe+lJJoO2fCx2ehnre8M+C25JlNyAtGvTa0mO4lXU9sG9ArMqZuJjrtNaHo9cryzIXWCzlI4ft2KveA92wwhlyZaRRq5g5eYHpAQAAbD1JREFU1I+xq4JQQZFEWn5qLj9R90fQda4nZXIpLqPgnPxFcTKbVtQkspyzKsSHKWvkVRp48yLYlq8I7Acbz7Dy0CVeubcp7zzQ2shBCiGEEOJOZDln2ZRnOWfv3r3p1q0bn3/+ecG+VatW8corr5CWlob6Lh/0atrvSJeRQejAQWjj4/H46EPqPf64wdfq9XpmHZ7FHyF/YGdux88P/ExTp6aVGK0QotzysuHqUSWhFrobok+VfyyVBuzcwaEBOHiCfYOb2zcf9p7Kw0JpCJA/u+1K+8mcajiKnidewynqUOF47u2g3zvQ8n7KVfAM2Ho6klmbzhKZXFj7zNPRiplD/RjS1hOArFwtG/+9QYC3M94uSpO8I+HxfPTXWcZ0b8Kw9l5FarAJUdVkOWd1kt+V0+fecifQAOwszWjkbI2vq51x4hJCCCGEqCQWFhZ06tSJXbt2FSTRdDodu3btYsKECSVek5GRUSxRprlZK6w2fu+rtrHB5f9eIXr2HOKWLsPx4YdRWxi22kClUjGj6wwikiOIyYhBX+piKiGEyZlZKp8Ffe6FgR8qXT7Ddiu11E6vLTzP3EZJgN2aEHPwKposs3NT6qkZ4pbloY37TKUxQJe/YdeHcGAeaCyUhN7qp6CBv7KM1Pe+MifThrT1ZKCfB4ERCcSkZuFmryzhvHWpqJW5hic6F11u/8vRK5y5kcLbf5xi9pbzPN6pIU93a4LPzSSbENWRJNGqQkFXzvIv5QR4c3BL3hzc0ggBCSGEEEJUvilTpvDss8/SuXNnAgICWLBgAenp6Tz//PMAjBkzBi8vL+bMmQPA0KFDmT9/Ph07dixYzvnee+8xdOjQgmRabeM0ciTx368gLzKSpDW/4fzMaIOvNdeY82W/L1Ghop5VvbtfIISoHuw9oMMoSL6mPFebK00Jer4Ofd823uuUVt/tvvfBzAqy05SlnEe/hhtB8POj0Kgb9J+hJPzKQKNW0b1Z/TJd88GwNvg1cGDVkctcS8zku38i+O6fCO5t4coz3ZrQv5VbkUScVqe/Y6LOWKrqdUTNJMs5K1tcKCzuBGozeDMEbJwr/zWFEEIIYXQ1balgdbF48WI+//xzoqKi6NChAwsXLqRr164A9O3bF29vb1auXAlAXl4en3zyCf/73/+4fv06rq6uDB06lE8++QQnJ6e7vlZN/R0lrl5D1AcfoHFxwXfHdtTW1uUe60rKFRo7NDZidEKISnF7t8/SuoJWhbRYpdHBse+UpgcA3r2h/7vQuFulv7xWp2ffxRj+d/gyey/Gkp+h8HKyZlTXxjzZpRHHLiXcdcmoMRiyNFXUTobeQ0gSrbLt+xz2fAy+A2D0H+UeRq/XS8FFIYQQwoRqaoKmLqmpvyN9bi5hDzxI7tWruL35BvVfeqlc4/xx8Q8+PvIx07tO54mWTxg5SiGE0ZSWMDNlIg2UBgYH5sGJlcrMOIBm9ykz07w6VUkIV+Iz+PnoZdYcv0pShhKDmVpFnq542iL/0/Gy0f5GSXBtPR3J2FVBxRbHG/t1RPVk6D1E+dpwCMMVdOV8pELDbPz3Bp0+2sH7G04bISghhBBCCFFdqMzNsWzRAoD4b79Dm5ZW5Hjs0qXELlp813His+LJ0+dxMuZkrawhJ0StUdoyyz5Tlf06AztwGpuDJzz4BUw6Cf7PKqupwnbBt/3h16cgqgINEQzUuL4N0x9ozZHp9/HF4+25x8uhxAQaKN1A9SgN+LQ3z7mRlEnQlUT+u5bE2RsphESnEh6bxtWEDCKTM4lJzSIxPYfUrFwyc7TkanXo9Xq0Oj2zNp0tsbpk/r5Zm84WvI6ou6QmWmWKvQAxZ5Q17q0erNBQYbHpxKfnkJOnM1JwQgghhBCiurBq3Zq0XbvQJieT8OOPuI4fDygJtLiFi3CZNPGuY7zc7mWaOjblvsb3yQoGIaqzftNLP2aKGWi3c2oEwxZCr8nK7Lj/VsOFLcrD72Ho+w64tarUEKzMNTzWqSFeTtY89e2RO54blZJNYEQC3ZvVZ+2Ja8zfcbFMr+XjYsvsR9oVWcJ5Oz0Q+f/t3Xd4VFXixvHvnUmvkA4h9C7SiygoaBDQtay4omtBQf0pRVYsoK6y6rpgY12qLoquugq2RVdYFFAQEAUpSu8ktHTS+8z9/TFkICQhCSmT8n585kly59x7z+GaPCdvTknLdd5HGi+NRKtJRaPQ2l0N3lVb7PVQouMvku20M6eIiIhIgxM6cQL+I0cCkPzWP7GlphYL0ELHjy/3GoZhEN0q2hmg2U07uYVl/1IoInJBQW3g9wtg/M/QbRRgwO4vYf5l8PkDkHwIvp/hCNpKs/YVx/tVkJBRsZ9hReX8vdyICvKmeaAXof6eBPl64O/lhre7FQ+rpdSNR90sRqXvI42XRqLVpGqayglwKOFMiBam7X5FREREGqLI11/jwObN2JKS2D/wcjDNCgdo58suyObPG/5Mga2AN4a+gdXSMHc3FZFaENoRbl0Egx+HNX+DPf+FHZ/Azs8h/BKI+81Rrqz13aogzN+rUuXuu6IN913R5oJlbXaTQrudQptJoc3ExGTPqYwK3efjTbEE+3pyebtgLNqxs1HSSLSakrAHEveC1QM6X1elS9ntJkeSsgBoG6KRaCIiIiINkWGx0HzmTMcXZ9Y0M6xu2PPyKn2tmPQY1h5by5rja5izbU51VlNEGqvwrjD6Q3hwLXQYDqbNEaAZFkdg9s2fHeWqcYOE/m2CaBboRVlxlYFj98z+bYIqfE2rxcDTzYqvpxuBPu408fEo9z5Ffjqcwl3v/MyQ19Yw7/uDGpnWCClEqyk7v3B8bB8NXoFVutSJ1BzyCu14WC20aHrxW56LiIiISN2W89uvjk/OzDlK/PvfOXz970j/9ttKbRbQJbgLL1zxAgDv7HyHrw9/Xe11FZFGqnlPuPMTGLcK2g4F88y63RvnwPNNHQHaZePhyieqfCurxWD6DV0BSgRcRV9Pv6Er1iqOCivvPgbw9HWdufuyVvh7uhGbks2r3+zj8hnf8X8f/ML3+xK06UAjoRCtJphm9U7lPLMeWusQH9ysemQiIiIiDdG5a6B13rUT/2uHAVBw/DgnHplM7Jh7yd27t8LXu77t9YzrNg6A6Rum81vibzVSbxFppKL6wT1L4d7l0OoKx7GiQO2n+fBaR1h8J/w4B45tgsLKj6oFGNGtGQvu6k1EYPGpnRGBXiy4qzcjujWrQiMqfp8Hr2zHizd34+dnruHVW7vTu2UTCu0m3+yK5753N3PlK98ze/UB4i6wQYHUf4bZyPa/Tk9PJzAwkLS0NAICAmrmJnE74c0rwOoJTx4CT/8qXe6d9Ud48evdjOwWwYK7+lRTJUVERKQyaqUPIVVSn59RWZsIJPzjHyQveBOsVrDZwGKhya23EvqnybgFlT99yW7amfzdZNYcX0OodygfX/8x4b7hNdkUEWmM1rzsWC/NsDiCtKKP57J6QPNeEDXg7MsvtMK3sNlNNh1JISEjlzB/xxTOqo5AK+b7GWCxYhv8RMn7rHsV7LYSO6vui8tg8eZYvth6grScAgAsBlzTJZwFd/YucxBMjbdFKq2ifQhtLFAdznyzOed77zozlbPDMPhpQanfbJWhnTlFREREGjibvdRNBMImT8Zwd8eWmootKYn05f8j9ZNPSF++nJDx4wm6604MD48yL2sxLMy8ciZ3Lb+Lg6kHmfz9ZN6+9m32pOwhMTuRUJ9Qeof11sYDInLx1r7iCNCK1kArWhOt190Q3N4xCu3Yz5Cd5Ph47Oez5wa1PROo9YeoyyC0M1jOC57O/L5tvepJBrYLLnnvKv6+7WSxwvcvYQUGVnCThE4R/ky/4RKmjujMip1xfLQplk1HUiiw2YsFaMmZeQT7eQKwYucpnv/vbk6dM2KtWaAX02/oWm2j6qTmKESrDme+2QDHvO+iqZwWt2rZkUQ7c4qIiIg0bKGTJpb93jnBWtM//pH4v80gd/duEl55hdQlSwibNhW/IUMwjNJHMfi6+zLn6jncsewOdiXvYugnQ8m1nf3lLdwnnGn9pxHdKrr6GiQijUNpmwgUfSw6fsdHjiWPUg6fDdFif4bEPY5jKYfh148d53gGQou+0PIyR7AW2af479vVtQOo3Qa5aWdeqY6PoZ2g03WOax5ZB9e+CAe+rdAmCV7uVm7uFcnNvSI5mJBJge3sKLwTqTlc9cr3DOoQwqjeLXjk422cPx0wLi2Xhz/cWq3TUzXarWZoOmd1KfoG7jsOfnnHEaDZC6tlR5K+f11FUmYeX028gu4tmlRPfUVERKRS6vNUwcaisTwj02YjbelSEmb9HVtyMgC+V1xB+FPT8Gzfvszz3vz1TeZtn1fiuHFmGe1ZQ2YpSBORyjl/Vta5yhsllnMajm+BYz85grXjW6Agq3gZwwLhlzimgp7YApdNgOEvnR39NnAS9Pzj2SAsJ7VkOFbasbz0irfxsgkw4m8VL3+eT385xhOf/cZlbYKISckuNgKtWFNxrL+2furVVQ67NNqt8irah1CIVp2KgrQi1RCgpWUX0OOFbwHY+fxw/Dw1eFBERMQVGktAU581tmdky8wk+c03SfnX+5gFBWC10vT22wmdNBFrkybFy9ptDP98OPHZ8aVey8Ag3CecFaNWaGqniLiGrRDid56d/nlsE6TF1uw93X3BK9Dx8m5y5vMmsOOTs2u6WT2g/4Mw+DHwKX8tytLEJGfx46EknvpiZ7llr+0aTvswP/y83PD3csff0w0/Tzf8vNzo1bIJnm6On9GmaZY6AnnFzlM8/OHWEqPdikpW52i3hkQhWhlqtHNlmvB8E8fnFjd4LrnKl9wae5pb5v9IRIAXPz19TZWvJyIiIhensQU09VFjfUb5sbHEv/IKmatWA2AJDCR04kSa3j4aw90dgM1xmxn7zdhyr7Vo+CL6RfSr0fqKiFRY2gk4vskRqMX+BCe3Fn/f4nY2+CoWhJV2rEnxY54B4FbKmpJFg2Ms7mAvOHvcKxAGP+4I1Ny9Sp5Xji+3n2Dy4u2VPu9cm565hjB/x73/8tUuPt4UyyPXdGDCUMco5GMp2UTPWkteob3U86tztFtDo40FXOGHVx0fDatjKufaV6o8Eq1DmB8fjOtPVl5hNVRQRERERBoaj5YtiZo7l6yffiL+bzPI27+f+Jde4vTixYRPm4bf4EEkZidW6FoVLSciUisCIyHw93DJ7x2/X5/cejbcuvIJx+yvMtaDvCjnr/FWtOuobyhkJcLKZ2HTQrjmWeh2a8lNEC6gKPwqzy29mhPg7UFmXiGZuYVk5BWc+VhIgJe7s1x6bgF5hfZiYdgP+xPLDNAATOBUWi6bjqSU3KRBKkQhWnU5/5vt3KmdVQjS/L3cGdyh4tv+ioiIiEjj5HvZZbT54nNSP/2UxH/MJv/QIY498AB+Q4YQNm5kha4R6qN+p4jUQWX9vm31qPLAlTLvATBkqiOk+/4l6Pw7OLHVMcX0iwfgxzmOzQfaDqnQ5fu3CaJZoBdxabklplrC2VFir/6hZ4VGib1wUzemDOtYbMmnik4z/PPSHYy5vDUjukVUONwTB03nrA6lfbNd6LiIiIjUO411qmB9omd0li0tjaT580n590dQWAhubhyJMNgWZWPxkJIjJ0att+Nn9eHR+T9pTTQRqVtq6/ftimyScMVk+HkBrPs75Gc43msfDcNecGyAUI6i9cqgeOBVXeuVbTyUzB0Lf6pwecOAazqHsfCevmXu8NxYVLQPUfGxh1I2u630b9yrnnQct9su+tJvrzvMf7YdJz23oPzCIiIiIiKANTCQ8Keeou1XX+J71ZVQWEib4wXcstHOk5/aMOxnf30btd7O6HV27BZIy09zYa1FREpRg79vFzP0qbLDuKuedLzv4ePYYGDydhjwkGNNtoOrYMEVsHSCYw23CxjRrRkL7upNRGDx0V8RgV7VsuB/0Wi3suIwAwjz9+Tp6zrTM6oJpgl+nm7OAM00TZZuO0FyZl6V6lHdbHaTjYeS+XL7CTYeSsZmd91YMI1Eq8MKbHa6PLuCQrvJj9OupnkTb1dXSUREpNGqT32IxkrPqGyZP/xA/MyXyT98GIDTvjDr91a6xZiMXmfny6E+xIzqz6whs/B2U59TRKRCkg/B6hdg91LH127eMHC8Y8SaV2CZp9nsJpuOpJCQkUuYvxf92wRV20L/lRntdiwlm0K7SZsQXwD2nEpn5D/W4eNhZeuzw/Byv/DI5Jpsx7ntef6/uzmVlus81izQi+k3dK3WXUa1O2cZ6lPnKj23gJn/28uxlGz+dV9/LNo9Q0RExGXqUx+isdIzujCzoIDTH39MwuuzMPPyMHH8UhU8aSK2MbcQ4h2Cu9W9vMuIiMj5jv8C3/4ZYjc6vvYJhqumQp/7St8BtIZdbPD08+FkXly2m+aB3vzznr7O49O/3MklzQO59pJwmvh4VOkelW3Hwx9uLbHWW3VNfz2XQrQyqHMlIiIiF0N9iLpPz6hiCk+f5sDlV8CZXwPaLvsaz3btnO+bpsnXh79mRJsRuFsUqomIVIhpwr7lsHI6JB9wHGvaBqKnQ9ebq3cX0Qqoyiix3AKbcxTa0aQshry2BgA3i8GgDiG0DPLhg40xNRpu2ewmg17+rlhId/69IgK9WD/16moZ/VbRPoR25xQRERERaUROf/yx45c9wwDT5Ojtd9Duf8txCwkBYNaWWby36z3WnVjHjEEztNGAiEhFGAZ0vh46DIdt7zs2Kjh9BD69FyL7wrV/hcNryt+8YOhT1VIdq8VgYLvgizr33Gmcfl5uPDG8E1//doo9p9JZsy+xzPOKQrXnvtzFsK4RznBr7f5EUrPzGdwhlCBfx0i2nw4n8+2ueHIKbOTkF5JTYCM730ZugY2cAhvJmfllBmhF9zqVlsumIykX3c6LoRCtDjuZmkNTHw+8PdRxEREREZGqS5w/n6TZcwh5ZBJNb7+dQyOvw56WxpFbRtFuxf+w+PjQP6I/H+7+kP8d+R/+7v78+bI/N/pd20REKszqBn3HwqW3wY9zHK8Tv8C7IyC4IyTvd5Qra6fROibEz5MJQ9szYWh7DiZk8tbaQ0T++gY208Ic2y0lyk+yfoE1x86mI72c4db0L3dyNDmbzx4aSJBvEAC7T6azaMORKtcvIaPsoK0mKESrwyZ8tJVtsaksvKcvw7qGu7o6IiIiIlKPnRughY4fD0CbJYs5fPPvKUxI4MioW2n79X8Z3GIwMwbP4MkfnuST/Z8Q4BnA5N6TXVx7EZF6xtPPMaqs732wZiZsff9MgGY4ArO8TLj2heIBWlm7g9YR7cP8GNQhhIPbLTzm/hlAsSBtkvULHnP/jNcLbi0WbvVu2ZTIpt7FRrj1iGrCw0Pa4e1uxcfDiteZj97uVrw9rBxOzOSFr/eUW6cwf69yy1QnhWh1lGmaHErIBCAqSDskiYiIiEgV2ezFAjQAj9atabloETF3303+kSPEv/QS4c8+y4g2I8gsyOT5jc/z9o63CfAI4L5u97mw8iIi9ZR/BNzwBlz2MKz6i2PdNIAf/wEb54Bph6um1fkArUiYvxeTzwRnj7l/RqiRxm9mWy43dnGL23r+UfB75thu4eNzwq1Zo3uWuE6fVk3p06ppmfcZ3CGUheuOEJeWW2LtNTi7Jlr/NkFVbFHlKESro5Iy80nPLcQwoHWwr6urIyIiIiL1XOikiaUe9+ndi8jXX+fEo49y+qOPcY9sQfC4sdza8VbS89P5+5a/M2vLLPw9/Lm14621XGsRkQYitBPc8TEc3QArn4UTWxwBGsDGuRC/E9pHQ4dhENjCtXW9gP5hdsb4baJ1XjyZphf3uK0s9v5k9/9wh/taQtd2gt/aQNPWxV9+YRXaZMFqMfig7Wq++i2eObZbigVpBo5Rbze2Dcdquab6GlcBCtHqqEOJZ0ahNfUpNuRRRERERKS6BYwYTsHJJ0h45RUSXn0V98jmBIwYwdhuY8nIz+DtHW/zwsYX8PPwY0TrEa6urohI/dX6CsfmAye2gGFxBGn5mbD3a8cLIKzrmUDtWmh5GVhduFNyYT4c3wQHV8Oh1VhP/crzAGdiiqJ9auwmZOOFn5FLGCkQu9HxOp+bNzRtVTJca9oamrQCDx9n0fYRTZiyZx5+Xm78LetG5/GnfL/iQdtnEFH7a8gpRKujikK0dqEahSYiIiIiNS/ovnspOHGC0//+NyefnIpbWBg+vXvzSK9HyMjPYMm+JTy17il83XwZ3GKwq6srIlI/rX0F1vzt7Bpoa152fN3mSijIdWxCkLDb8fpxNnj4Q7sh0H6YY5RaQPOar2PyITj0neN15AdHyHeu8Es5HDiA3QcP8Tv79+SZbngahXxkvZl21z3CNeE5cProea8YSD8OhTmQuNfxKo1f+NlArWlr6Pw7Hty7mOt7+fFLxz/R6+g7tPx1scvWkFOIVkcdSsgCoF2on4trIiIiIiKNgWEYhD/9FAWnTpH53Xccf3g8rRZ/jGebNjw94GnS89P535H/MWXNFN4c9iZ9wvu4usoiIvVLaZsIDJnqGMpVdPyPSxzh1YGVcHAVZCfBnv86XgDh3RxhWvthENW/ekap5abD0XXO0WacPlr8fZ8QaHc1tL8G2g4F/3Darn2FtvvfJrbHo2xr8wC9jizkwV//DpntoM+T0KJvyfsU5kPaseLhWmqM42PKUchLg8x4x+vYz8VOjdzzNpF73nZ84cJNGBSi1VGHk86MRAtTiCYiIiIitcOwWol87VVixtxL7o4dHHvw/2i9+GPcgoN5adBLZBVk8cPxH5i4eiKLhi+iS3AXV1dZRKT+sNtKD4CKvrbbwCcILr3V8bLb4dQ2OLAKDnzrmAIav9PxWv938Aw8O0qtfTQENIPvZ4DFWnrItPYVxz2umgqntjsCs4PfOaZr2gvPlrO4O6aRthsK7a6BiO5gsRS/zpnQr+VVT9ISoOdfIMjHcfzcNp3LzQOC2zlepck5XcoItqOOUWynjzjKWD1cugmDQrQ66ux0ToVoIiIiIlJ7LD4+RC2Yz9Hb76Dg2DGOjR9Pq/few93bm9evep2HVj3Erwm/cjzzuEI0EZHKGPpU2e+VFgxZLBDZx/EaMhWyks+MUvvWEYBlJ8PuLx0vgIhLwdMfYn50BHBDp5291rfPOqaHhnWFzQsd554rqK0jMGt/DbQe5LhOWSoSBl4M76aOV/NexY8XhXZWD7DlO752UZBmmKZZ2m6hDVZ6ejqBgYGkpaUREBDg6uqUKrfARpfnVmCa8Mufownx83R1lURERBq9+tCHaOz0jKpX3uHDHL3jj9jT0vAfFk3kG29gWK1k5meyJ2UP/SL6ubqKIiKNl90GJ7c5pn0e+NbxOefFO6FdHNMwd3wCWYnF3/Pwh7ZXOd5vdzUEtam1qlfK+VNgS5sSWw0q2ofQSLQ66EhSFqYJgd7uBPt6uLo6IiIiItIIebZtS9S8ucTeN5aMlauIf/llIp5+Gj8Pv2IBWlxWHO4Wd4K9g11YWxGRRsZidaw71qKvY4RbVpJjTbOiUWo5pyFxj+NVpHlvx0izdldDi36u3fWzIkoLzIo+XmjaaA1SiFYHnbszp2EYLq6NiIiIiDRWPn370mzmDE4+9jin3/8Aj8hIgsaMcb4fmx7LA98+QIBnAO8Mf4cAD40AFBFxCd8Q6DHa8bLbHOunLRoBpg0sbvDYfvCtZ3/sqKlpo1WgEK0O0s6cIiIiIlJXBF5/PQUnT5L4+iziZ76MW7NmBFx7LQAmJrm2XNwL3ckuyFaIJiJSF1iscHiNI0ArWkfsl3dcuiD/RansGnK1QCFaHaSdOUVERESkLgm+/34Kjp8gdckSTj7xJO5hYXj37EmrgFa8fe3bNPVqSoh3iKurKSIiUPY6YlD/grQ6RiFaHfTaH3rwyDUd8PfS4xERERER1zMMg4hn/0xB3Cmy1v7AsYfH03rJYjxatqRD0w7Fyv6a+Ctdmnbh16RfScxOJNQnlN5hvbFarC6qvYhII1IH1xFrSJTS1EHuVoumcoqIiIhInWK4udFi1ixi7r6H3N27OfbAg7Ra/DFuTZs6y3x58Eue3fAsXm5e5BTmOI+H+4Qzrf80oltFu6LqIiKNRx1cR6whsbi6AiIiIiIiUj9YfH1p8eYC3Jo3Iz8mhuPjJ2DPzXW+f+D0AUzMYgEaQEJ2AlPWTGFVzKrarrKISOMy9KmyR5pd9eSF1xmTcilEq2O2H0tlyifbWbwp1tVVEREREREpwT0sjJZvvYXF35+cbds4Oe0pTLsdm93GiqMrSj3HxATg5U0vY9MoCBERqacUotUx22NP88XWE3y/L8HVVRERERERKZVnhw60mDMH3N3JWLGChNdeZ2vCVuKz48s8x8QkLjuOrQlba7GmIiIi1adOhGjz5s2jdevWeHl5MWDAADZt2lSh8xYvXoxhGNx88801W8Fa1Ld1EE8M78SNPSJdXRURERERkTL5XjaA5i/9FYCURYvIXfKfCp2XmJ1Yk9USERGpMS4P0ZYsWcKUKVOYPn06W7dupUePHgwfPpyEhAuPxDp69CiPP/44gwcPrqWa1o5ukYFMGNqe67s3c3VVREREREQuKPDGGwmd/AgAwW8tpc8Be7nnhPqE1nS1REREaoTLQ7RZs2bxwAMPcN9999G1a1fefPNNfHx8WLRoUZnn2Gw27rzzTp5//nnatm1bi7UVEREREZFzBT/0EIG3jsKwmzz6pUn7k2aZZY0z/4mIiNRHLg3R8vPz2bJlC9HRZ7e6tlgsREdHs3HjxjLPe+GFFwgLC2PcuHHl3iMvL4/09PRir7oqJ9/Gyt3xHE7MdHVVREREREQqxDAMmk2fju+gQXgUmDz5qY2w1NLLmpg8sPIBlh5cWptVFBERqRYuDdGSkpKw2WyEh4cXOx4eHk5cXFyp56xfv5533nmHhQsXVugeM2bMIDAw0PmKioqqcr1ryv74DB54/xdG//MnV1dFRERERKTCDHd3It/4O9bgYJpkw58/Bd+csyPSInwiePPYNUz9rTWF9kKe3fAse1P2urDGIiIilefm6gpURkZGBnfffTcLFy4kJCSkQuc89dRTTJkyxfl1enp6nQ3SDp0ZgdYu1NfFNRERERERqRyrnx+BN91EyqJFRCQV8vb3nYh9YSyhgc1o9fkmkj+cS4dJE0nrAaZp0jmos6urLCIiUikuDdFCQkKwWq3ExxffCjs+Pp6IiIgS5Q8dOsTRo0e54YYbnMfsdsfipW5ubuzbt4927doVO8fT0xNPT88aqH31Oxui+bm4JiIiIiIilRf+5BPYc3NJ/egjrL/uo8dba/Fo24akufMIeWQSoePHM+G8cxKzE8m15RLlXzf/0C0iIlLEpdM5PTw86NOnD6tXr3Yes9vtrF69moEDB5Yo37lzZ3bs2MH27dudrxtvvJGhQ4eyffv2OjvCrKIOJWQBCtFEREREpP5q9tyzBN58MwDpy5eTNHceTe+5h9Dx40uUzS3MZfL3k/njsj+yPWF77VZURESkklw+nXPKlCmMGTOGvn370r9/f9544w2ysrK47777ALjnnnuIjIxkxowZeHl50a1bt2LnN2nSBKDE8frIORItTCGaiIiIiNRfzWfOIO2//wWbDYDUzz7Ds307mvzhDxjG2d05swqysJk2TEyCvYJdVV0REZEKcXmINnr0aBITE3nuueeIi4ujZ8+erFixwrnZQGxsLBaLSwfM1YpCm52jyUUj0bQmmoiIiIjUX4nz5zsCNHd3KCjAzM4m7rnpZH73Pc3++iJuZ9Y3DvYO5r0R7xGbHktUwNlZJaZpFgvbRERE6gLDNE2z/GINR3p6OoGBgaSlpREQEODq6jgdScpi6Gtr8HK3sPv5EVgs6jSIiIjUJXW1DyFn6RnVDYnz55M0e45zDbTEufNImjsXLBaw27EGBdHshefxj44u9fx1x9exZN8SZgyegb+Hfy3XXkREGqOK9iEa/hCveuJQgmMqZ9sQPwVoIiIi0mDMmzeP1q1b4+XlxYABA9i0adMFy6empjJhwgSaNWuGp6cnHTt2ZPny5bVUW6mq8wM0gNCJEwh5ZJIjQAsOxpaSwvGJkzj5zDPYMrOKnZ9bmMtzPz7H2uNruWv5XRxLP+aKZoiIiJRKIVodcThJ66GJiIhIw7JkyRKmTJnC9OnT2bp1Kz169GD48OEkJCSUWj4/P59hw4Zx9OhRPvvsM/bt28fChQuJjIys5ZrLRbPZiwVoRULHjyfkkUk0+cMfCBo3FgyDtM+/4MjNN5O9ZYuznJebF3OvnkuYdxiH0w5zx/I72By3ubZbISIiUipN56wjpn72G0t+Ocafojvwp+iOrq6OiIiInKeu9iHqsgEDBtCvXz/mzp0LOHZhj4qKYtKkSUybNq1E+TfffJNXX32VvXv34u7uXun76RnVH1mbNnFy2jQKT54CwyD4/vsJnTQRw8MDgITsBCZ/N5mdyTtxM9x45rJnuLXjrS6utYiINFSazlnPFO3M2TZUI9FERESk/svPz2fLli1En7PulcViITo6mo0bN5Z6zldffcXAgQOZMGEC4eHhdOvWjb/97W/YzuzweL68vDzS09OLvaR+8O3fn7ZffkngzTeDaZK8cCFHRt9O3oEDAIT5hPHuiHcZ2XokhWYhz298npmbZlJoL3RtxUVEpFFTiFZHFIVo2plTREREGoKkpCRsNptzx/Ui4eHhxMXFlXrO4cOH+eyzz7DZbCxfvpxnn32W119/nb/+9a+llp8xYwaBgYHOV1RUVKnlpG6y+vvTfOYMIv/xD6xNmpC3Zw9HRt1K8nvvYdrteLl58fKVLzOh5wQA/r3n30xcPZH0fIWlIiLiGgrR6oDMvEJ8PNywGI6NBUREREQaI7vdTlhYGP/85z/p06cPo0eP5plnnuHNN98stfxTTz1FWlqa83XsmBahr48Chl9Lm6++xPfKwZj5+STMfJnYseMoOHUKwzB4qMdDzBoyC283bzac3MCdy+4kJj0GAJvdxua4zSw/vJzNcZux2UsftSgiIlId3FxdAQE/Tzc2TLuanHwb3h5WV1dHREREpMpCQkKwWq3Ex8cXOx4fH09ERESp5zRr1gx3d3es1rP9oS5duhAXF0d+fj4eZ9bLKuLp6Ymnp2f1V15qnXtYGFFvvUXq4sXEv/wK2T/9xOEbbyLiuecI+N31DGs1jBZ+LZj03SSOph/lj8v+yJ1d7uSLA18Qn332/7Fwn3Cm9Z9GdKvoC9xNRETk4mgkWh2iAE1EREQaCg8PD/r06cPq1audx+x2O6tXr2bgwIGlnnPFFVdw8OBB7Ha789j+/ftp1qxZiQBNGh7DMGh6xx20+c8XeHXvjj0jg5NPPMHJxx7DlppKl+AufHz9x3QP6U56fjoLfl1QLEADx4YEU9ZMYVXMKhe1QkREGjKFaCIiIiJSI6ZMmcLChQv517/+xZ49e3j44YfJysrivvvuA+Cee+7hqaeecpZ/+OGHSUlJYfLkyezfv59ly5bxt7/9jQkTJriqCeICnm3a0PqjfxMyaSJYraQv/x+Hb7yJzA0bCPUJZeG1C/GyepV6rokJwMubXtbUThERqXaazlkHPP7prxw/nc2j0R0Z0DbY1dURERERqRajR48mMTGR5557jri4OHr27MmKFSucmw3ExsZisZz9m25UVBTffPMNjz76KN27dycyMpLJkyczdepUVzVBXMRwcyN0wgT8rrySk088Sf7Roxwbdz9N77qLY3ddRa4tt8xzTUzisuPYmrCVfhH9LroOiXPmgtVC6PjxJd+bPx9sdkInTbzo64uISP2jEK0O2Hw0hZjkbOymq2siIiIiUr0mTpzIxImlBw1r1qwpcWzgwIH89NNPNVwrqS+8L72UNv/5goRXX+P0Rx9x+sMP8Vj7LQ8E2UgJMPh8UMmJNaPW27GYJomDE6t2c6uFpNlzAIoFaYnz55M0ew4hj0yq2vVFRKTeUYhWB8y5oxcHEzLp2jzA1VUREREREalTLN7eRDz3LH5Dh3Dy6afxOJbA1cfBapoYpslng8+uKzxqvZ3R6+wsGWzB282bAnsB7hb3Ct3Hnp+PPS0NW3o6tvR0vC+5BP9rryVp9hyyf9lC+NQnyVi92hmglTZCTUREGjbDNM1GNf4pPT2dwMBA0tLSCAhQaCUiIiIVoz5E3adn1PAVnj7NqenTyfx2pfPY130N3h9mZdR6G6PXmfy3v4VdfUPo5d2Bk/EHGBE8mEs920BmFra0dGwZ6djTHEHZuZ+beXkVqoMCNBGRhqeifQiFaCIiIiIVoD5E3adn1DiYpsmPi2bgNfsDfM7kXiZgVMfFDQOLvz/WgACsAQFYAgOw+geQsXIlnPm1qeX7/8K3f//quJuIiNQRFe1DaDqni63dn8jRpCwuaxtMpwh/V1dHRERERKROMwyDK8Y9zfeXtufEcy/R4Wh+sQDNdLPiFtgEw9+P0x4FxNiTSPUoIMsLrAEBdG97Od3aXIZ7QBOsgWfCsqLQzM8Pw1J8nbXE+fPJ+PZbMAwwTWLH3U+r997Fp0+f2m24iIi4nEI0F1u67QT/2XaCJ4Z3UogmIiIiIlJBQ/vfRvzvEkiZOw/TzYpRaCPoof8jbPJkDONsrNatIJtP9n3Cf3a9S0puCrCSKOteHmz3IL9rezVulrJ/JTp3E4HgceM4fMONFMTGEnvvfbR8/1/49OpVCy0VEZG6ouR2NlKrDiVmAtAu1M/FNRERERERqT8S588nZe48Qh6ZRNedOwl5ZBIpb75F0oIFxcr5uPtwb7d7+d8t/+OxPo8R5BXEsYxjPLvhWW5ceiNLDy6l0F5Y6vXP3UTA4ulJ26++xL1FC8yCAmLvGUPOr7/WVnNFRKQOUIjmQqZpcijBEaK1D/N1cW1EREREROqH8wMugNDx4wl5ZBJJs+eQOH9+iXPKC9PWHV9X/ASbnZBHJhH00P+xOW4zyw8vZ0vqDlp9+R/cIyMdQdr9D5CzY2dtNFlEROoATed0ofj0PLLybVgtBi2DFKKJiIiIiFTImYDr/F0ynV/b7GWeWhSm3dbpNj7Z9wnv7nqXYxnHsBrW4teaNJFVMauY+flw4rPjncfDfcJ5at6jtPvrx+T8soXY+++n1buL8OratfraJyIidZJ253ShDQeTuPPtn2kb4st3jw9xaV1ERETkwupSH0JKp2ckFyO7IJuVMSu5sd2NzrXUluxdwsG0gyzZuwST4r8uGWe2Mfh7/7/R7oWPyNm2DWtgIC3/9R5enTvXev1FRKTqKtqH0HROFypaD62t1kMTEREREXEJH3cfbmp/kzNAS89P540tb7B47+ISARrgPDZz5z9o/tYCvHp0x5aWRux9Y8ndv79W6y4iIrVLIZoLFa2H1k7roYmIiIiI1AnuFneua3vdBcuYmMRlx/Fr9n5aLlyIV7du2E6fJva+seQdOlRLNRURkdqmEM2FDiVmAdqZU0RERESkrvB286ZPeJ8KlU3MTsQaEEDLd97Gs2sXbMnJxNx7L3mHj9RwLUVExBUUorlQ0XROhWgiIiIiInVHqE9opcpZAwNp+c47eHbujC0xidgxY8g/erQGaygiIq6gEM1FMvMKOZWWC0C7UE3nFBERERGpK3qH9SbcJ9y5iUBZ/vnrP9mVtAsAt6ZNabnoHTw7dKAwMZGYMfeSHxtbG9UVEZFaohDNRY6cmcoZ4udBEx8PF9dGRERERESKWC1WpvWfBlBmkGYxLPwU9xO3L7udKWumcDjtMG5BQbR871082rejMD6emHvvJf/4idqsuoiI1CCFaC6inTlFREREROqu6FbRzBoyizCfsGLHI3wi+PuQv7Ps98u4oe0NGBisjFnJ77/8Pc9teI5Ez3xavfsuHm3aUHjyFLFjxlBw8qSLWiEiItXJME2z5L7NDVh6ejqBgYGkpaUREBDgsnp8uyuOhesO07tlU566rovL6iEiIiIVU1f6EFI2PSOpCTa7ja0JW0nMTiTUJ5TeYb2xWqzO9w+cPsCcbXP4/tj3AHhYPBjdeTQPNruV5HHjyY+JwT0qilYfvI97RISrmlHjEufMBauF0PHjS743fz7Y7IROmuiCmomIlK+ifQiNRHORay+J4NOHLleAJiIiIiJSh1ktVvpF9OO6ttfRL6JfsQANoEPTDsy+ejYfXvch/SL6kW/P5+tDX+MeHk7Lf72He1QUBceOETNmDAXxCS5qRS2wWkiaPccRmJ0jcf58kmbPAat+9RSR+s/N1RUQERERERGp73qE9uCda99h48mNpOen4+vuCxG+tHzvXfb+8Q8QE0vsmDG0+uB93EIrtvtnfVI0Ai1p9hzn10UBWsgjk0odoSYiUt8oRHMBu90k32bHy91afmEREREREakXDMPg8sjLix37oXAPf7slnb9+7EbTo0eJufc+Wr3/L9yCg11Uy5oTOn48ttQ0kmbPIWn+AigsVIAmIg2KxtS6wLHT2XR5bgXDZq2lkS1JJyIiIiLSqBgYWJpHsOcvt+MWEUH+oUPE3nsfBcnJzjI2u43NcZtZfng5m+M2Y7PbXFjji5O7dy8nHnuc0x9+6DhQWAhubgrQRKRB0Ug0FziclIVpgtViYBilb5ktIiIiIiL13zWtrmFQi0HY7DbcOt9JzD1jyDtwgPW3RuM172UyfQxe3vwy8dnxznPCfcKZ1n8a0a2iXVjz8pmmSc4vv5C0cCFZP6wrWaCwkNgHHiDqn//U7z0i0iBod04XME2T5Kx8Tmfl0yHc3yV1EBERkcqpC30IuTA9I6kP8g4f4bfbb8IvvYDD4fDiHVayvIsHTAaOr2cNmVUngzTTbidzzVqSFy4kZ9s2x0GLBc927cg7cICQiRMoTEwidckSALwu7Ubrjz/GcNMYDhGpm7Q7Zx1mGAYhfp4K0EREREREGhnPtm1o9d575AZ40TYeXnvbhk9u8XENJia3rLfx28yn69TUTrOggLSvvuLITTdxfPx4crZtw3B3p8no0TS9605HgPbIJEInTiTiL9MJmzoVgNwdOzl0/e+wZWa6uAUiIlWjEE1ERERERKQWhXXtjf2N58h1g+BMeH2hDe9zgrRR6+2MXmcnvTCLrQlbXVhTB3tODikf/ptDw0dw8smp5B04iMXXl+AH7qfd6lU0e/4vWP0Dim0iYBgGwffdS4u5c8DNjYKYGGLu+CMFJ064uDUiIhdPIZoL/GnxNmYs30Nqdr6rqyIiIiIiIi4QH+HJn8dYySsK0t624ZVnOgO0JYMtfD7IQkJ2gsvqaEtLI+nNNzl4TTTxf/0rBSdPYg0OJvTRR2n//XeEPfYY7mFhAIROmljqJgL+0dG0XrwYt9BQ8g4c4Mjo28nZsaO2myIiUi00Kb2WpWbns3T7SQAeuaaDi2sjIiIiIiKuEOoTSmyYwbN3W3nxAxshGfCvWTYMYEMXg7WXGmCazNs2DzeLG8NbD6+1uhXEJ5Dyr3+Rungx9uxsANwjIwkaN5Ymt9yCxcurUtfz7nYJrT9ZwrGHHiZv3z5i7r6H5q+8TMC119ZE9UVEaoxCtFp2KDELgOaBXvh66p9fRERERKQx6h3Wm3CfcGIiEnjuLpj5no2i7QWu2GNyxR4b6d5wqNlRUn9cQMZIK17duuEWHl5jO13mHz1K8juLSFu6FLOgAADPjh0JfuABAkaOqNLGAO7NmtHq3//mxJRHyfphHScemUzB448RNG6cdu4UkXpDKU4tO5ToWEyzbaifi2siIiIiIiKuYrVYmdZ/GlPWTKH3IRMDsBlgNeG0L/jnQEAO9DpswuF9HF82yXFiUBNSWjelVf9rCOzRG+9u3XALDS3zPolz5oLVUupUy8T588Fmx+/qoSQvfJuMb74B07E2m3fv3gQ/+AB+V11VbSGX1c+XqPnziZ8xk9P//jcJr71OfkwMEc89h+HuXi33EBGpSQrRallRiNYu1NfFNREREREREVeKbhXNwhPXErBumXMNNOemAneOoPtN95Gzcye5u3aRu3MXeQcPQkoqQSmpZGx9m4wz13ELD8erWze8LumKd7dujhFrQUGON60WkmbPASgWpCXOm0/SnDm4t2xJ0rx5zuN+V11F8IMP4NOnT4202XBzI+LZP+PRqhXxM2eS+ulnFJw4QeQbb2ANCKiRe4qIVBeFaLXsUIJjOme7MI1EExERERFpzBLnzyfg/WUET5rI9aP60z87kdDhoQR/vgnmzCUzuEOx4Muek8P3373LlrVLuD6/M14HT5B/6DCF8fFkxseTuXq1s6xb82Z4X+II1AJuvskZpIU89BAnn3iS9GXLACiIjQWrlYDrriP4/nF4depUK20Puudu3KNacOKxx8n6cSNH7/gjUW+9iUeLFrVyfxGRi2GYpmmWX6zhSE9PJzAwkLS0NAJc8JeOq19bw+GkLP59/wCuaB9S6/cXERGRi+PqPoSUT89I6puKTLUMnTSxxHumaTqnWNqzsvjnZ09zdNMqLs+IoH0cGLEnK3R/w8ODJreOIui++/CIiqpaYy5S7p49HHvoYQrj47EGBdFi3lx8evVySV1EpPGqaB9CI9FqUX6hnZgUx+427bQmmoiIiIhIo1ZaQOZ8r5Rgrci5a5QZPj4cbO3ON1hYRgIAVza5jHs9rqLNSRt5u3aRs3OXY8TZGSYQ/MD9BI8Zg1uIa/+w79Wli2PnzocfJm/3HmLH3EvzmTMIuO46l9ZLRKQ0FldXoDGJTcnCZjfx9bASHuDp6uqIiIiIiEg9ZxgGr131Gl/c+AU3tL0Bq2Hlh9RfGJvwOhPDV7J78kgO//MxvrnS8Uf8QgsYwPuHlrAma7tL617EPTyc1h98gN/QoZj5+ZyY8hhJb75JI5s0JSL1gEK0WnTwnPXQtI2ziIiIiIhUlw5NO/C3wX9j+S3L+WPnP+Jl9WJX8i6mrJnCjy89yvAfMlky2MIfp7qxZLCF332Xwfq/TmZVzKpqrYfNbmNz3GaWH17O5rjN2Oy2Cp1n8fWlxdw5BI0ZA0DiG//g1FNPY+bnV2v9RESqQtM5a9HZnTk1lVNERERERKpfc7/mPDXgKf6vx//x4e4PSVnwFqPX2Z27fwLOj6PX2Vn28jMMnTsUq8Va5XuvilnFzE0zic+Odx4L9wlnWv9pRLeKLvd8w2ol/KlpeLRuRdyLfyVt6VIKTpygxZzZWJs0qXL9RESqSiPRatHZEM3XxTUREREREZGGLMgriIHNB2IxzWIBWpHPB1lYMthCdn4G13x6DXcuv5Psgmzn+zuTdvLzqZ85knaErIKscu+3KmYVU9ZMKRagASRkJzBlzZRKjXhrescdRL31JhZfX7I3b+bo7XeQHxNT4fNFRGqKRqLVokOJZ6ZzaiSaiIiIiIjUsMTsRD4dXPYIM2ewlptMRn4G3m7ezvfe3vE2q2NXO7/2dfclzCeMMO8wwnzCCPUJJcwnjHCfcIK9g3np55cwKbmGmYmJgcHLm15maFTFR7z5DR5Mq48+4thDD5F/9ChHR9/u2LmzT58Ktl5EpPopRKtFN3RvRptgH7o005brIiIiIiJSs0J9QitU7pkBzxDpF1ls3eYwnzDaBLYhITuBrIIssgqyOJJ2hCNpRypdDxOTuOw4tiZspV9Evwqf59WpI62XLOb4+Ank7txJzN334B8dTYvZ/yhRNnH+fLDZL7jjqYhIVSlEq0X3D27r6iqIiIiIiEgj0TusN+E+4SRkJ5Q6SszAINwnnD90/EOJEWJPD3ja+XlWQRYJ2QnFXok5ic7PY9JjSM1LLbc+idmJlW6De1gYrT54n5NPTiVj5Uoyvv2WmHvG0PJf7zlDv8T580maPYeQRyZV+voiIpWhEE1ERERERKQBslqsTOs/jSlrpmBgFAvSDBwB1NT+U8udYunr7kubwDa0CWxT6vub4zYz9pux5dYn1CeUpJwkQrxDKtEKsHh7E/mPN0h4/XVS3llE9qZNHLn597T+9BOS337bGaCFjh9fqeuKiFSWNhaoJcdPZ3M0KQubveRfgERERERERGpCdKtoZg2ZRZhPWLHj4T7hzBoyq0K7ZpanaMRbUTB3PgODCJ8IovyjGPbpMMZ+M5Zlh5eRZ8ur8D0Mi4XwJ54g4oXnwTDI27ePfT16OgK0SRMVoIlIrdBItFry9rojvPfjUR68si1PX9fF1dUREREREZFGIrpVNEOjhrI1YSuJ2YmE+oTSO6x3hRf5L09FR7xtS9iGHTub4zazOW4zgZsCubHdjdza4VbaNqnY0jdNb7sNjxYtiB07DkzHfTJWrMCjZUsCRo7EcNOvuCJSczQSrZYU2Ox4ulloF+rr6qqIiIiIiEgjY7VY6RfRj+vaXke/iH7VFqAVqciIt5FtRvLNqG8Y32M8Eb4RpOWl8cHuD7jpy5sY878xfHXoK3ILc8u9V/b27Y5PLI5fZ/MOHOTkE09yaOR1nF7yCfb8/Gptm4hIEcM0zUY1vzA9PZ3AwEDS0tIICKjdXTLtdpNCu4mHm7JLERGR+saVfQipGD0jEdez2W0VGvFms9v48eSPfLb/M9YeX4vNtAHg7+HP79r+jlEdRtEpqFOJ84o2Ecgbewsnb72C5v9eg+e//4vh5YWZ6wjg3MLCCLrvPpqOvg2Lj0/NNlhEGoSK9iEUoomIiIhUgPoQdZ+ekUj9lJidyNKDS/n8wOecyDzhPN49pDuTek/ismaXOcqdCdC+vtqf9wfkOMvd87M3v/suA98rB5O3bz+F8fEAWJs0oek9dxN0551YAwNrt1EXkDhnLlgtpa7jljh/PtjshE6a6IKaiTReFe1DaEiUiIiIiIiIuEyoTygPdH+A5bcs561hb3Ftq2txM9z4Lek3CmwFznIHk/ezZLClWIAG8MGAXJYMtnCqpR/tVn5LxIsv4N6yJbbUVJJmz+Hg1deQ8PrrFCYl1XbTSme1kDR7jiMwO0dRSIhVv6aL1FUaiVYLVuw8xT9WH2T4JeH8KbpjrdxTREREqpdGOdV9ekYiDUdyTjIrjq7g9k63Y7VYsdltXP7x5WQXZpda3sAg3CecFaNWYLVYMQsLSf/mG5Lf+id5+/c7ynh60mTUKILHjcU9MrI2m1NCUWDWZPRofPr1I2f7dk5/+CEhj0zSTqMiLlDRPoS2LqkFe05lsOdUOj1a1J0hxCIiIiIiInVVsHcwd3a50/n1lvgtZQZoACYmcdlxbE3YSr+IfhhubgRefz0BI0eSuWYtSW+9Se6vv3H6o484/cknBN5wA8EPPIBn2za10RwACuLiyNn+Kzm/Ol5YraQuWULqkiUAWPz9KDwVR/o33+I78DKs+mOASJ2jEK0WHErMBKBdqJ+LayIiIiIiIlL/JOVUbCrm7uTd9Ivo5/zasFjwv3oofkOHkP3zzyS99RbZG38i7T//IW3pUvyHDyfkwQfw6tq1Wutrz80ld9euYqFZ0VptZZ6TkUnqp5+S+umnYLXi3b07voMH4TdoEF6XXIJhrd4dVauD1neTxkYhWi04lJgFQNtQXxfXREREREREpP4J9QmtULnXfnmNT/d/ypUtruSqFlfRO6w37lZ3DMPA97LL8L3sMnK2byfpnwvJ/O47MlasIGPFCnyvHEzIQw+RteHHSodCpmlSEBvrCMvOhGa5+/ZBYWHxC1gseHbqhHeP7nj36Enurl2c/vBDDHd3zIICAm68AWtgE7LWryf/yBFytm0jZ9s2kmbPwRoYiO8Vl+N7xSB8Bw3CPTzsov8tq9WZ9d2AYv9mRdNVQx6Z5KqaidQIhWg1zG43OZKkkWgiIiIiIiIXq3dYb8J9wknITsCk9GW9PSwe2EwbMekxfLD7Az7Y/QF+7n4MbD6Qq1pcxaDIQQR7B+PdsydR8+eRu28/yQsXkr58OVk/rCPrh3W4N29OwcmTYELohNJDIVtGBrk7dhQLzWypqSXqYw0NwadnT7x79MC7Rw+8LrkEi4+P83qnP/yQvLG3cPLWK2j+2QbSF31ByCOTaPe/5RScOEHm+g1krV9P1saN2NLSSF/+P9KX/w8Az44d8R00CL9BV+Ddpw8WT8/q/0evgJCHH8aek0vS7DnkHTpEk9/fQva2rSTPm6/13aRB0sYCNez46WwGvfw9HlYLu18Yjpt2WhEREamXtGj9xZk3bx6vvvoqcXFx9OjRgzlz5tC/f/9yz1u8eDF33HEHN910E0uXLq3QvfSMRBq2VTGrmLJmCkCxIM3AAGDWkFlc1uwyNp7ayNpja1l3Yh0puSnFyl0aeimP9n6UvhF9ncfzY2JIfvsdUpcuhYKzu4EWXD2A438aReSib/FYugrPrl2hIJ+8g4fgvF+jDXd3vC65xBGY9XSEZm7NmmEYRol2FAVyX1/tX2yn0Xt+9uZ332WUCJ/MggJyfvuNzPXryVq/gdydO4vd3/Dywqd/P/wGDcZ30CA82rTGMIxqmWppFhRQEB9PwcmTFJw8SeGpU2c+P/Px1CnM3NwS53l26EDIww/he+VVWP00I0vqPm0sUEcUTeVsHeKjAE1EREQalSVLljBlyhTefPNNBgwYwBtvvMHw4cPZt28fYWFlT0U6evQojz/+OIMHD67F2opIXRfdKppZQ2Yxc9NM4rPPri8W7hPO1P5TiW4VDcCwVsMY1moYdtPOrqRdrD2+lrXH17I3ZS+/Jf6Gt7u389zdybtJsibRf/rThEwYT8q775G0+GMsefm4f/czrb/7maIYLG/3bud57i1aOEeYeffsgWfnzlg8PCrUjsMpB/nfYAufnxOgAXwwIJesAgsjUw5y7uRVw90dnz598OnTByZPpvD0abJ+/JGsMyPVChMTnSPpANybN8d30CBsmZlkLF8OlD3V0paRcSYQO0HBqVOOkOyEIxwrOHmSwoSEEoFhaayhIdiSkp1l8w4c4MSUxxx1v3wgAcOG4Xf11bgFBVXo30ikrtJItBq2aP0RXvh6NyO7RbDgrj41fj8RERGpGRrlVHkDBgygX79+zJ07FwC73U5UVBSTJk1i2rRppZ5js9m48sorGTt2LOvWrSM1NVUj0USkGJvdxtaErSRmJxLqE0rvsN5YLeUvuh+XFcfGkxu5qf1NWAzHAIc/r/8zXx76kjFdx/B4v8dZFbOK6csfZeRmG7f8aGIAJrCrlcGB5gZXDr+fy6Lvxi0k5KLrPvzz4cVCwHMZGIT7hLNi1IoKtck0TfL27ydr/Xoy168n55ctmOeMpsMwwDTxuWwAfoMGkf7Nt+Tu2IE1OBgzPx97Rka59zDc3XFr3gz35s1xb9b8zMdmuEc6PrpFRJD8zjskzZ7jXN/Nu09vbEnJ5MfEnL2QxYJP7974D4vGPzoa98jIcu8tUls0Eq2O0M6cIiIi0hjl5+ezZcsWnnrqKecxi8VCdHQ0GzduLPO8F154gbCwMMaNG8e6desueI+8vDzy8vKcX6enp1e94iJS51kt1mI7cFZUhG8Ev+/w+2LHwn3DifCN4MoWV2Kz25i5aSbpPlBoNTAwKbSAmx12tTT4fJCF//EFT2d0xkw3sZk2x8tuI9Az0DkSDmDJ3iWk5adxc/ubCfNxjLzdHLeZxXsXlxmggWOaalx2HFsTtlaojYZh4NWpE16dOhE8bhz27GyyN28mc916xwYFR48CkP3Tz2T/9LPzPFtysvNza2AgbpHnBWTO0KwZ1uBgDEvZs6rOHdkWOn782a8nTSLg2mFkrFpFxspV5O7eTfYvv5D9yy/Ez5iJZ9cu+Ec7AjXPDh1KnfoqUtcoRKthzhAtTPPARUREpPFISkrCZrMRHh5e7Hh4eDh79+4t9Zz169fzzjvvsH379grdY8aMGTz//PNVraqINGKTek1iYs+JmJhsid9CfHY8o9bbGb3OzpLBFj4fZHF+DfD5oFSe/OHJEtfpGty1WIj27q53OZF5gsuaXeYM0XYl7eLbmG8rVK/P93+Oj5sPnYI64Wap+K/tFh8f/K66Cr+rrgIg//gJstavJ+6F58FugsVCxHPPOUeRuTdrhsX34n9XPT9Ag7NTR5NmzwHD8XXIww9TcOIEGatXk7FyFdlbtpC3ew95u/eQNHsO7q1aOgM17x49LhjaibhSnfg/c968ebRu3RovLy8GDBjApk2byiz7xRdf0LdvX5o0aYKvry89e/bkgw8+qMXaVk7RmmhtQzQSTURERKQsGRkZ3H333SxcuJCQCk6Teuqpp0hLS3O+jh07VsO1FJGGyDAMLIaFxOzEEgEawOeDLCwZbGH0Ojuj1ttpE9CGAREDuLz55QyOHMyQFkPoE1586Z7hrYczqsMomno2dR7rFtKNka1HVqhOy44s4/Zlt3P38ruLHbfZbZVqm0eLSApTksFuYri7g91OYUoyfoMH49m+fZUCtDMVKnUXztDx4wl5ZBLY7M5j7pGRBN1zD60+eJ8O69fR7KW/4jdkCIaHBwUxsaS8s4iYO/7IwauGcOr558ncsAGzoIDEOXMdGyGUInH+fMcGCiK1xOUj0Sq74GxQUBDPPPMMnTt3xsPDg6+//pr77ruPsLAwhg8f7oIWlC0tp4DEDMcUg7ahGokmIiIijUdISAhWq5X4+OLTluLj44mIiChR/tChQxw9epQbbrjBecxud/zy5ebmxr59+2jXrl2xczw9PfH09KyB2otIYxTqE4rFNIsFaEWKvraYJs8OfLbcqZaP9nm0xLG+EX3pFdaLrQlbSchOKLbD6Ln83f3pFdaLbQnb6Brc1Xm8wFbA0E+H0qFJB/4+5O808WpSbpuKRorljb2Fk7deQfPPNjhGiEGpu3ZW1oV297zQ9d2CgmgyahRNRo3ClplF1vp1ZHy7ksy1aylMTCT148WkfrwYS0AA7s2akbdvH2ZBAWGTJ5doW8gjk6rcDpGKcvnGAhez4Oz5evfuzfXXX8+LL75YbtnaXHB2W+xpfj//R8IDPPn56ejyTxAREZE6S4vWV96AAQPo378/c+Y4fmGz2+20bNmSiRMnlujn5ebmcvDgwWLH/vznP5ORkcE//vEPOnbsiEc5O9/pGYlIVRQt+l9WwFXZRf/LsipmFVPWTAEodh/jzD6gs4bMIrpVNDa7jZzCHPw8HLOafk38lbuW30VTz6asHb3WuYbY/O3zyS3MpU94H3qG9STQMxA4GzJ9fbU/75+zE+g9P3vzu+8ySh1B5mr2/Hyyf/qJjJWryPjuu2JrtwF4tG1L8Lhx5O7exel/f1Qn2yD1U73YWOBiF5wtYpom3333Hfv27ePll18utYwrF5yNCvLhtT/0oOCcIawiIiIijcWUKVMYM2YMffv2pX///rzxxhtkZWVx3333AXDPPfcQGRnJjBkz8PLyolu3bsXOb9KkCUCJ4yIiNcFqsTKt/zSmrJmCY1uBkgHX1P5TqxSgAUS3imbWkFnM3DSz2CYD4T7hTO0/1bm2mtVidQZoAN1DuvPVzV9xKvOUM0AzTZPPD3xOQnYC7+56FwODjk070ie8D12P7WDbYAufnxOgAXwwIJesAgsjUw4SWqWWVD+Lhwd+V16J35VXEvGX6eRs3+4I1FatouD4cfIPH+bUM884ClutZK5ZS2F8Al5duuDVtQueHTti8fJybSOkQXNpiHYxC84CpKWlERkZSV5eHlarlfnz5zNs2LBSy7pywdkQP09u7dPCJfcWERERcbXRo0eTmJjIc889R1xcHD179mTFihXOvl9sbCwWLR4tInVIRQOu6rjP0KihbE3YSmJ2IqE+ofQO633BgM4wDNoEtqFNYBvnMbtpZ3LvyWyJ38KW+C3EpMew7/Q+9p3eB52htGXQTUy+GGRlg88OVthtVQ4FwTGKrzJtqQjDasWnTx98+vQhbOqT5O3bx5FbRsGZqf7YbOT+9hu5v/129iSrFc+2bfDs0gWvLl0d4VqXzlgDA6tUF5EiLp3OefLkSSIjI/nxxx8ZOHCg8/iTTz7J2rVr+fnnn0s9z263c/jwYTIzM1m9ejUvvvgiS5cuZciQISXKljYSLSoqSsP8RUREpFI0VbDu0zMSkepSE6FQbUjMTmRLwhaWH17O98e+L7f8ouGL8LR6si1hG91Du9MrrFel77kqZlWpoeO0/tOqLXSEs9NTDXd3zIICmt59Fz69epG7Zw+5u/eQu2cPtpSUUs91j4x0jFTr0uXMqLWuuIWFOUf0AY4NCqyWUqeHJs6fDzb7BdeAq1AbauEecnHqxXTOyi44W8RisdC+fXsAevbsyZ49e5gxY0apIZorF5xduu0EYf6e9G7VFC/3uv8DV0RERERERBxTKcvbPKAuCvUJZUTrEdjt9gqFaInZiRxNP8qCXxcwqsMoZ4iWU5jDQysfooV/C6L8o2jp35Io/yii/KMI9Ax0hk9F67udv4ZcQnYCU9ZMca7vVlWlbZBwetGHWJs2JeyxxwDH1NbChARyd+8md88e8s6EawUnTjhfGStXOa9pDQpyjlTz7NIFW1oapz/80PHveE7IVa0bGFgtpW7sUJ33UFBXs1waonl4eNCnTx9Wr17NzTffDDhGma1evZqJEyv+UO12e7HRZnVBgc3O45/+SqHd5MdpV9O8iberqyQiIiIiIiKNQKhPxVY7C/UJxWqxMqL1CPqE93EeP55xnK0JW9masLXEOf7u/rTwb0ELvxb8eOrHUjdhMDExMHh508sMjRpapVF8xTZICP8K1n0F4XDP1f787pxAyjAM3MPDcQ8Px3/oUOf5trQ0cvfsdYxY27ObvD17yDt8BFtKClkbNpC1YcPZm7m7kzR7Dpnfr8F30BXkbNtO9k8/4XvllbgFBZP6xX8wPDwwPNwxPDyweHic+fq8l7ujTNH7uLlhGIYz2Do3SDs3QKuWTRJqIahrzFwaokHlFpwFxxpnffv2pV27duTl5bF8+XI++OADFixY4MpmlJCZW8iQTqEcP51DRIAWNhQREREREZHa0TusN+E+4eXuNFo0TXV46+HF3g/zCeOVK1/hWMYxjmUcIzY9luMZx0nISSCjIIM9KXvYk7LngnUwMYnLjuPdXe9y/6X3A1BgKyA+O54Q7xC83Cr2e/LhlIP8rwobJFgDA/G9bAC+lw1wHrPn5pK3f79zGmjunj3k7duHeWZwTu6OHeTu2OEsn/XDD2T98EOF6lsqwygWsln8/EiaPYekOXPBNHFr3pycLVs5Nn4CFi9PDA9PDC9PLJ6eGJ5eGJ4eWLy8Lnjc4uWJ4elJ4I03Yc/KJmn2HEybjdCJE0lasKB6g7pGzOUhWmUXnM3KymL8+PEcP34cb29vOnfuzIcffsjo0aNd1YRSNfX14O0x9W/4r4iIiIiIiNRvVd1pNNAzkJFtRpY4nlOYw4mMExzLOMY3R79h2ZFl5dZlX8o+5+eH0w5z639vBRwj2oK9gwn1CSXEK+Ts594hhHiFEOITQlPPpjzT6Tfio6p3gwSLlxfe3bvj3b372esVFpJ/9Ci5e/Zwcuo0xwYGFguBN/wOe34+Zn4BZn5+qS97Qcn3nRsgAJgmZl6eM6Q79zhA4cmTFJ48WeH6V1TyvPkkz5sPgEebNtjT0kn56CM8olri0aol7s2bY7i5PBaqV1y6sYAraMFZERERuRjqQ9R9ekYiIsWVtuh/hE9Etew0ujluM2O/GVtuuaf7P80dXe5wnvPQyofIt+dX6d7nWzR8UbWtYXf+BgYXO3rLLCw8G7Ll50NBgTOMO/3RR6QuWQJublBYiP/IkfgPHYI9NxczLx8zLxd7Xh5mbh5mfh72XEcAZ887835uLvb8M+/n5TnK5uWdOe54n4pEPW5uuDdvjkfLlni0jMK9ZUs8WrZyfB4VhaWM9eUb4rpr9WJjgYYsO78Qb3drsd0+RERERERERGpLdKtohkYNrZGdRis6ZfS2Trc5j/WL6Mcvd/1CRkEGSTlJJOckk5idSFJOEkm5SSRlJxX7/HTe6QrV5XDaYXYn76ZLUBc6B3cmwOPi/pBS2gYGpa0vVhGGm5tjlJePD+f+ayfOn0/qkiXOcK7onp4d2lfbVEvTNEmaO5ekefPB3R0KCvAbMgSP1q3JP3aMgtgY8mOPYeblURAbS0FsLFklGmDgFhGBR1SUY9RaVEtH2NaqJabNRvK8eUDNrrtWF8M6hWg15M63f+ZQQibz7+zDoA4hrq6OiIiIiIiINEI1tdPoxU4ZNQyDAI8AAjwCaBvY9oL32HhyIw+ufLDcuqTmpjJ3+1zn15F+kXQN7krnoM50CepCl+AuhHhf+Pfyim5gUBWlbSJQ2mYDVZW0YAFJ8+aXCOpCHplE1DzHv5Npt1OYmEh+TAwFx46RHxNL/plALT82FntmJoWnTlF46hTZmzaVuIfh7U3S7DlkfPst/tdEk7NjB1k//EDA736HT9++5O7di9XfH4u/PxY/PwxLySm55aqDmyQoRKsBpmlyKCGT9NxCQvw9XF0dERERERERkWoX3SqaWUNmlZgyGu4TXi1TRvtH9K/QaLdeYb2IbhnNnpQ9nMg84XytjFnpLBviHeIYqRbU2RmwtfBv4Xy/qhsYVIjNXur0UOfXNnspJ1VORYM6w2Jx7mZK//7FrmGaJrbTp52BWn7sMfJjYyiIiSX/2DFsKSmYOY5/p7y9+8jbe3bdu/Svvyb966+LV8owsPj5YfH3w+ofUPJjgD9WP3/HxzPBmzUggICRI7Fn59TsbqaVpDXRakBiRh79XlqFYcCeF0bg5V71obIiIiLiWlpvq+7TMxIRcQ2b3VYjU0bBsa7blDVTAEod7TZryKxiYV1aXhp7U/ayN2Uvu5N3szdlL0fSjpQI4cJ9wln1h1XO+g/9ZGiZ00eLwroVo1ZUW7tqSm1MgbRlZjoDthOPPQ42GxgGvpdfji0jA3tGhvNjic0UqsIwwDRrJEDTmmgudCgxE4Copj4K0ERERERERKRBq6kpo1D50W6BnoEMaDaAAc0GOI9lF2Sz//R+9qTsYW/KXvYk7yk2Cm1L/JYLrr9mYhKXHcfWhK3V0s6aDB0vFJBVV/Bk9fPD2rUrGWvWgM3m3ITBu0/vEvew5+c7QrX09GLhmi0jA3t6BraMdOwZmcU/pmdgy3S8b8/MPHsx08Rwd3fJCLQiCtFqQFGI1i7U18U1EREREREREanfqrpBgo+7Dz3DetIzrKfz2LmT8k5knqjQdab/OJ0+4X1oF9iOtk3a0q5JO5r5NsNiVHy9r9J2TA33CWda/2lVnv56rpoM6qDktNGir6F4WGfx8MASHIxbcPBF3ce02UicPZvkt/4JZ8K6xPnzXRakKUSrAYcSHPtatAv1c3FNREREREREROq/6h7tZhiG8/NzR6VdyLGMYxzLOFbsmLebN20C2ziDtT7hfegV1qvU84umpp4/tTQhO4Epa6aUmJp6sWo6qKutDRIAkt56i+S3/lluWFdbFKLVAOdItDCFaCIiIiIiIiJ1We+w3uVuYBDsHczUflM5kn6Ew6mHOZh6kKPpR8kpzGF38m52J+8G4LaOtzlDtJzCHP7y419o16Qd93a9l5mbZpZ6fRMTA4OXN73M0KihVRoxVitB3ZkNEoIe+j82x20+O9rtof9zvl8dajOsqyiFaDXgcFLRdE6FaCIiIiIiIiJ1mdViZVr/aUxZMwUDo9QNDJ4Z8EyJ8KnQXsixjGMcTj3MobRDHEo9VGy03JG0Iyw/spwgryB6hfUqNjLsfEXrrj2+9nEifCPO3t8wMDC4ovkVXB55OQBJOUm8v+t9vN29ebjHw86yi/cu5o0tb5QZ1AH87ee/MShyEF5uXpX5JyomdNJEx2i3z4eXHO12fTVOS62F3UwrS7tzViOb3WT9wUTGLNoMwKanryEs4OL/xxQREZG6Qzs/1n16RiIiUhWlTYOM8IkodQODiojLimPZ4WXYTTuRfpFMXTf1ous2vud4Z2C2//R+Rn01iiCvINaOXussc8uXt3Ag9UCFrhfkFUSwdzA3tbuJMZeMASDfls93x74j1DuUXmG9ylzrrazRbmXtmFpVNb2+G2h3zlq3Yucpnv/vbk6l5TqP3ThvA3+5oSsjujVzYc1EREREREREpDxV3cDgfBG+EYy7dBwAm+M2V+ic69tcTzM/R4ZgmqYzqOoZ2tNZpqlnU8Z0HYOPu0+xc7sEd6lwiJaSm0JKbgppeWnOY/HZ8Tyx9gk8rZ5svvNsff/60185cPoAId4hBHsH899D/63xaalFamsjhopSiFYNVuw8xcMfbi3xv1B8Wi4Pf7iVBXf1VpAmIiIiIiIiUsdV9wYGRSqy7lq4TzgvDXqp3PAp1CeUx/s9XuL4ze1v5qtDX5Vbl78P+TtR/lEk5STRzPdsVmGz2+gd1hs3i1uxjRd2J+9mR9KOcq8LZ6elvrH1DR7r+xjgGOG2OW4zwd7BdGraqdi1L6S2NmKoDIVoVWSzmzz/392lfAuACRjA8//dzbCuEVgtFfsfRUREREREREQajoqsuza1/9Qqjd6qaFBXNEqsE52Kvd86sDX/GvmvEuc91f8pTmSdICk7iZ9P/cya42vKrUtMeozz87isOB5a9RDebt5sunOT8/hLP73E4bTDBHsFE+QdRLBXMMHewQR7BdPUqykv/fxSrY14qyiFaFW06UhKsSmc5zOBU2m5bDqSwsB2wbVXMRERERERERGpM6JbRTNryKxSpyde7Lpr56qpoO7S0Eu5NPRSADoFdapQiDY4crDz83xbPh2bdiyxmcGOpB3sSt5VqboUKRrxtjVha42MHCyLQrQqSsgoO0C7mHIiIiIiIiIi0jBV97prpV2/JoO6io52u6XDLc5j7Zu25/MbPy9Rdmr/qZzMPElyTjLJucmk5KY4Pz+ecZz0/PRy65OYnVil9lSWQrQqCvOv2O6bFS0nIiIiIiIiIg1XTa27VqQmg7rqHO3WK6wXvcJ6lfre5rjNjP1mbLnXCPUJrWDNq0fp+5VKhfVvE0SzQC/KWu3MAJoFetG/TVBtVktEREREREREGqmioO66ttfRL6Jfta4bVjTaLcwnrNjxcJ/walvsv2jEm1FG2mJgEOETQe+w3lW+V2VoJFoVWS0G02/oysMfbsWAYoMZix719Bu6alMBEREREREREWkQanpaam1sxHAxNBKtGozo1owFd/UmIrD4lM2IQC8W3NWbEd2alXGmiIiIiIiIiEj9U5Oj3aB2RrxVlkaiVZMR3ZoxrGsEm46kkJCRS5i/YwqnRqCJiIiIiIiIiFReTY94qyyFaNXIajEY2C7Y1dUQEREREREREWkQanojhsrQdE4REREREREREZFyKEQTEREREREREREph0I0ERERERERERGRcihEExERERERERERKYdCNBERERERERERkXIoRBMRERERERERESmHQjQREREREREREZFyKEQTEREREREREREph0I0ERERERERERGRcihEExERERERERERKYdCNBERERERERERkXIoRBMRERERERERESmHQjQREREREREREZFyKEQTEREREREREREph0I0ERERERERERGRcihEExERERERERERKYdCNBERERERERERkXK4uboCtc00TQDS09NdXBMRERGpT4r6DkV9Cal71M8TERGRi1HRfl6jC9EyMjIAiIqKcnFNREREpD7KyMggMDDQ1dWQUqifJyIiIlVRXj/PMBvZn1PtdjsnT57E398fwzCq/frp6elERUVx7NgxAgICqv36dV1jbr/arrar7Y1LY25/Y227aZpkZGTQvHlzLBatiFEXqZ9Xsxpz+9V2tV1tb1wac/sba9sr2s9rdCPRLBYLLVq0qPH7BAQENKr/4c7XmNuvtqvtjU1jbjs07vY3xrZrBFrdpn5e7WjM7Vfb1fbGpjG3HRp3+xtj2yvSz9OfUUVERERERERERMqhEE1ERERERERERKQcCtGqmaenJ9OnT8fT09PVVXGJxtx+tV1tb2wac9uhcbe/MbddGrfG/v9+Y26/2q62NzaNue3QuNvfmNteEY1uYwEREREREREREZHK0kg0ERERERERERGRcihEExERERERERERKYdCNBERERERERERkXIoRBMRERERERERESmHQrSLMG/ePFq3bo2XlxcDBgxg06ZNFyz/6aef0rlzZ7y8vLj00ktZvnx5LdW0es2YMYN+/frh7+9PWFgYN998M/v27bvgOe+99x6GYRR7eXl51VKNq89f/vKXEu3o3LnzBc9pKM+9devWJdpuGAYTJkwotXx9fuY//PADN9xwA82bN8cwDJYuXVrsfdM0ee6552jWrBne3t5ER0dz4MCBcq9b2Z8ZrnKh9hcUFDB16lQuvfRSfH19ad68Offccw8nT5684DUv5nvHFcp79vfee2+JdowYMaLc69aHZ19e20v7/jcMg1dffbXMa9aX5y5SGvXz1M9TP0/9PPXz1M9TP0/9vLIoRKukJUuWMGXKFKZPn87WrVvp0aMHw4cPJyEhodTyP/74I3fccQfjxo1j27Zt3Hzzzdx8883s3LmzlmtedWvXrmXChAn89NNPrFy5koKCAq699lqysrIueF5AQACnTp1yvmJiYmqpxtXrkksuKdaO9evXl1m2IT33zZs3F2v3ypUrAfjDH/5Q5jn19ZlnZWXRo0cP5s2bV+r7r7zyCrNnz+bNN9/k559/xtfXl+HDh5Obm1vmNSv7M8OVLtT+7Oxstm7dyrPPPsvWrVv54osv2LdvHzfeeGO5163M946rlPfsAUaMGFGsHR9//PEFr1lfnn15bT+3zadOnWLRokUYhsGoUaMueN368NxFzqd+nvp56uepn6d+nvp56uepn3dBplRK//79zQkTJji/ttlsZvPmzc0ZM2aUWv62224zr7/++mLHBgwYYP7f//1fjdazNiQkJJiAuXbt2jLLvPvuu2ZgYGDtVaqGTJ8+3ezRo0eFyzfk5z558mSzXbt2pt1uL/X9hvLMAfM///mP82u73W5GRESYr776qvNYamqq6enpaX788cdlXqeyPzPqivPbX5pNmzaZgBkTE1Nmmcp+79QFpbV9zJgx5k033VSp69THZ1+R537TTTeZV1999QXL1MfnLmKa6uedS/28sjXk565+nvp5RdTPu7D6+OzVz6seGolWCfn5+WzZsoXo6GjnMYvFQnR0NBs3biz1nI0bNxYrDzB8+PAyy9cnaWlpAAQFBV2wXGZmJq1atSIqKoqbbrqJXbt21Ub1qt2BAwdo3rw5bdu25c477yQ2NrbMsg31uefn5/Phhx8yduxYDMMos1xDeebnOnLkCHFxccWea2BgIAMGDCjzuV7Mz4z6JC0tDcMwaNKkyQXLVeZ7py5bs2YNYWFhdOrUiYcffpjk5OQyyzbUZx8fH8+yZcsYN25cuWUbynOXxkP9vOLUz1M/rywN5ZmfS/28ktTPUz/vQhrKc78YCtEqISkpCZvNRnh4eLHj4eHhxMXFlXpOXFxcpcrXF3a7nT/96U9cccUVdOvWrcxynTp1YtGiRXz55Zd8+OGH2O12Lr/8co4fP16Lta26AQMG8N5777FixQoWLFjAkSNHGDx4MBkZGaWWb6jPfenSpaSmpnLvvfeWWaahPPPzFT27yjzXi/mZUV/k5uYydepU7rjjDgICAsosV9nvnbpqxIgRvP/++6xevZqXX36ZtWvXMnLkSGw2W6nlG+qz/9e//oW/vz+33HLLBcs1lOcujYv6eWepn6d+XlkayjM/n/p5xamfp37ehTSU536x3FxdAamfJkyYwM6dO8ud+zxw4EAGDhzo/Pryyy+nS5cuvPXWW7z44os1Xc1qM3LkSOfn3bt3Z8CAAbRq1YpPPvmkQkl9Q/HOO+8wcuRImjdvXmaZhvLMpWwFBQXcdtttmKbJggULLli2oXzv3H777c7PL730Urp37067du1Ys2YN11xzjQtrVrsWLVrEnXfeWe4i0g3luYs0VurnNc6fWernCaifp36e+nnl0Ui0SggJCcFqtRIfH1/seHx8PBEREaWeExERUany9cHEiRP5+uuv+f7772nRokWlznV3d6dXr14cPHiwhmpXO5o0aULHjh3LbEdDfO4xMTGsWrWK+++/v1LnNZRnXvTsKvNcL+ZnRl1X1LGKiYlh5cqVF/zrZGnK+96pL9q2bUtISEiZ7WiIz37dunXs27ev0j8DoOE8d2nY1M9zUD9P/bzKaCjPXP08B/XzHNTPq5yG8twrSiFaJXh4eNCnTx9Wr17tPGa321m9enWxv8ica+DAgcXKA6xcubLM8nWZaZpMnDiR//znP3z33Xe0adOm0tew2Wzs2LGDZs2a1UANa09mZiaHDh0qsx0N6bkXeffddwkLC+P666+v1HkN5Zm3adOGiIiIYs81PT2dn3/+ucznejE/M+qyoo7VgQMHWLVqFcHBwZW+RnnfO/XF8ePHSU5OLrMdDe3Zg2OEQp8+fejRo0elz20oz10aNvXz1M8ron5exTWUZ65+nvp551I/r3IaynOvMNfua1D/LF682PT09DTfe+89c/fu3eaDDz5oNmnSxIyLizNN0zTvvvtuc9q0ac7yGzZsMN3c3MzXXnvN3LNnjzl9+nTT3d3d3LFjh6uacNEefvhhMzAw0FyzZo156tQp5ys7O9tZ5vz2P//88+Y333xjHjp0yNyyZYt5++23m15eXuauXbtc0YSL9thjj5lr1qwxjxw5Ym7YsMGMjo42Q0JCzISEBNM0G/ZzN03HbjMtW7Y0p06dWuK9hvTMMzIyzG3btpnbtm0zAXPWrFnmtm3bnLsSzZw502zSpIn55Zdfmr/99pt50003mW3atDFzcnKc17j66qvNOXPmOL8u72dGXXKh9ufn55s33nij2aJFC3P79u3Ffgbk5eU5r3F++8v73qkrLtT2jIwM8/HHHzc3btxoHjlyxFy1apXZu3dvs0OHDmZubq7zGvX12Zf3/71pmmZaWprp4+NjLliwoNRr1NfnLnI+9fPUz1M/r7iG9MzVz1M/T/089fOqg0K0izBnzhyzZcuWpoeHh9m/f3/zp59+cr531VVXmWPGjClW/pNPPjE7duxoenh4mJdccom5bNmyWq5x9QBKfb377rvOMue3/09/+pPz3yo8PNy87rrrzK1bt9Z+5ato9OjRZrNmzUwPDw8zMjLSHD16tHnw4EHn+w35uZumaX7zzTcmYO7bt6/Eew3pmX///fel/j9e1D673W4+++yzZnh4uOnp6Wlec801Jf5NWrVqZU6fPr3YsQv9zKhLLtT+I0eOlPkz4Pvvv3de4/z2l/e9U1dcqO3Z2dnmtddea4aGhpru7u5mq1atzAceeKBEJ6m+Pvvy/r83TdN86623TG9vbzM1NbXUa9TX5y5SGvXz1M9TP++shvTM1c9TP0/9PPXzqoNhmqZ5saPYREREREREREREGgOtiSYiIiIiIiIiIlIOhWgiIiIiIiIiIiLlUIgmIiIiIiIiIiJSDoVoIiIiIiIiIiIi5VCIJiIiIiIiIiIiUg6FaCIiIiIiIiIiIuVQiCYiIiIiIiIiIlIOhWgiIiIiIiIiIiLlUIgmIlJFa9aswTAMUlNTXV0VEREREalG6ueJyLkUoomIiIiIiIiIiJRDIZqIiIiIiIiIiEg5FKKJSL1nt9uZMWMGbdq0wdvbmx49evDZZ58BZ4fgL1u2jO7du+Pl5cVll13Gzp07i13j888/55JLLsHT05PWrVvz+uuvF3s/Ly+PqVOnEhUVhaenJ+3bt+edd94pVmbLli307dsXHx8fLr/8cvbt21ezDRcRERFp4NTPE5G6RCGaiNR7M2bM4P333+fNN99k165dPProo9x1112sXbvWWeaJJ57g9ddfZ/PmzYSGhnLDDTdQUFAAODpFt912G7fffjs7duzgL3/5C88++yzvvfee8/x77rmHjz/+mNmzZ7Nnzx7eeust/Pz8itXjmWee4fXXX+eXX37Bzc2NsWPH1kr7RURERBoq9fNEpC4xTNM0XV0JEZGLlZeXR1BQEKtWrWLgwIHO4/fffz/Z2dk8+OCDDB06lMWLFzN69GgAUlJSaNGiBe+99x633XYbd955J4mJiXz77bfO85988kmWLVvGrl272L9/P506dWLlypVER0eXqMOaNWsYOnQoq1at4pprrgFg+fLlXH/99eTk5ODl5VXD/woiIiIiDY/6eSJS12gkmojUawcPHiQ7O5thw4bh5+fnfL3//vscOnTIWe7cjldQUBCdOnViz549AOzZs4crrrii2HWvuOIKDhw4gM1mY/v27VitVq666qoL1qV79+7Oz5s1awZAQkJCldsoIiIi0hipnycidY2bqysgIlIVmZmZACxbtozIyMhi73l6ehbrYF0sb2/vCpVzd3d3fm4YBuBYx0NEREREKk/9PBGpazQSTUTqta5du+Lp6UlsbCzt27cv9oqKinKW++mnn5yfnz59mv3799OlSxcAunTpwoYNG4pdd8OGDXTs2BGr1cqll16K3W4vtvaGiIiIiNQs9fNEpK7RSDQRqdf8/f15/PHHefTRR7Hb7QwaNIi0tDQ2bNhAQEAArVq1AuCFF14gODiY8PBwnnnmGUJCQrj55psBeOyxx+jXrx8vvvgio0ePZuPGjcydO5f58+cD0Lp1a8aMGcPYsWOZPXs2PXr0ICYmhoSEBG677TZXNV1ERESkQVM/T0TqGoVoIlLvvfjii4SGhjJjxgwOHz5MkyZN6N27N08//bRzmP3MmTOZPHkyBw4coGfPnvz3v//Fw8MDgN69e/PJJ5/w3HPP8eKLL9KsWTNeeOEF7r33Xuc9FixYwNNPP8348eNJTk6mZcuWPP30065oroiIiEijoX6eiNQl2p1TRBq0oh2VTp8+TZMmTVxdHRERERGpJurniUht05poIiIiIiIiIiIi5VCIJiIiIiIiIiIiUg5N5xQRERERERERESmHRqKJiIiIiIiIiIiUQyGaiIiIiIiIiIhIORSiiYiIiIiIiIiIlEMhmoiIiIiIiIiISDkUoomIiIiIiIiIiJRDIZqIiIiIiIiIiEg5FKKJiIiIiIiIiIiUQyGaiIiIiIiIiIhIOf4fnHIVOnTpU+AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_history([(history_mlp,'MLP'),\n",
        "              (history_cnn,'CNN')])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9adcf257",
      "metadata": {
        "id": "9adcf257"
      },
      "source": [
        "We evaluate the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "983ca256",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "983ca256",
        "outputId": "5a593943-ab20-4145-cea5-2dc6f3a0205c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.3499589264392853\n",
            "Test accuracy: 0.8740134239196777\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0643ff57",
      "metadata": {
        "id": "0643ff57"
      },
      "source": [
        "As you can see, the CNN model has substantially outperformed the MLP, achieving an accuracy of 85% compared to the MLP's 75%."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c75f38f",
      "metadata": {
        "id": "3c75f38f"
      },
      "source": [
        "At this point, we might be wondering if we can still improve the accuracy of our model. The answer is clear: yes, there is certainly a more complex model, either deeper or with more neurons per layer, that can enhance the performance of the previous method. So, let's try to do that!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "bb156b53",
      "metadata": {
        "id": "bb156b53"
      },
      "outputs": [],
      "source": [
        "# we can improve it with a deeper model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(64, (3,3), activation = 'relu',\n",
        "                 input_shape = (height,width,channels)))\n",
        "model.add(Conv2D(64, (3,3), activation = 'relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Conv2D(64, (3,3), activation = 'relu'))\n",
        "model.add(Conv2D(64, (3,3), activation = 'relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Conv2D(64, (3,3), activation = 'relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation = 'relu'))\n",
        "model.add(Dense(8,activation='softmax'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a9f9090",
      "metadata": {
        "id": "4a9f9090"
      },
      "source": [
        "We compile the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "207501eb",
      "metadata": {
        "id": "207501eb"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edaa07cf",
      "metadata": {
        "id": "edaa07cf"
      },
      "source": [
        "We fit the the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "0a0d98bc",
      "metadata": {
        "scrolled": true,
        "id": "0a0d98bc"
      },
      "outputs": [],
      "source": [
        "history_deep_cnn = model.fit(X_train, y_train,\n",
        "                    batch_size=128,\n",
        "                    epochs=epochs,\n",
        "                    verbose=0,\n",
        "                    validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d868fd89",
      "metadata": {
        "id": "d868fd89"
      },
      "source": [
        "And evaluate on the test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "f6a937bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6a937bb",
        "outputId": "7960bac4-de6f-47fe-d8d7-ddfd0640703f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.2674531936645508\n",
            "Test accuracy: 0.90324467420578\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0455a085",
      "metadata": {
        "id": "0455a085"
      },
      "source": [
        "But what's the limit? How many deep can be the model? Unfortunately, there's no general answer to that question; it depends on the complexity of your problem and the size of your dataset.\n",
        "\n",
        "One thing is clear: as a model's complexity increases, characterized by a higher number of trainable parameters, it gains the ability to handle intricate data. However, increasing the complexity or depth of the network introduces other challenges, such as 1) the risk of overfitting, especially when dealing with limited data or the vanishing gradients problem, which can complicate the training process, not to mention the extended time required for training."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c12cd4f",
      "metadata": {
        "id": "4c12cd4f"
      },
      "source": [
        "## Avoiding overfitting\n",
        "With milions of parameters we can fit anything. But the great flexivity that offers this amout of parameters make the network prone to overfit the training data. In oder to avoid there is some popular techiniques such as: 1) Dropout; 2) $l_1$ and $l_2$ regularization; or 3) Data Augmentation.\n",
        "\n",
        "### Dropout\n",
        "\n",
        "Dropout is a valuable technique for regularizing neural networks. During training, it's possible for neurons in a particular layer to become overly dependent on the output of specific neurons in the preceding layer. This situation can lead to overfitting.\n",
        "\n",
        "Dropout mitigates the risk of overfitting by introducing randomness. It does so by intermittently severing connections, or \"dropping\" connections, between neurons in successive layers during training. This randomness helps ensure that no single neuron dominates the learning process, promoting a more robust and generalized model.\n",
        "\n",
        "Below we can see how dropout can be used in keras:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "26332905",
      "metadata": {
        "id": "26332905"
      },
      "outputs": [],
      "source": [
        "from keras.layers import Dropout\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(256, (3,3), activation = 'relu',\n",
        "                 input_shape = (height,width,channels)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(num_classes, activation='softmax'))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16943cc6",
      "metadata": {
        "id": "16943cc6"
      },
      "source": [
        "$L_1$ and $L_2$ Regularization\n",
        "\n",
        "As we saw in the Chapter X, regualzation is one the main tricks to avoid overfitting. It can be also used in neural networks. Here we can see how to apply this trick with a keras layers:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "97e66014",
      "metadata": {
        "id": "97e66014"
      },
      "outputs": [],
      "source": [
        "from keras import regularizers\n",
        "layer = Dense(64, activation = 'relu',\n",
        "              kernel_regularizer = regularizers.l2(0.01))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26d3116c",
      "metadata": {
        "id": "26d3116c"
      },
      "source": [
        "Although regularizers can be applied in any layers, they are not usually used in convolution layers because convolutional layers inherently possess a built-in form of regularization through their architecture. Convolutional layers apply a local receptive field over the input data, which forces weight sharing among neurons. This weight sharing reduces the risk of overfitting by limiting the number of independent parameters in the network, making explicit regularization techniques less necessary."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f483377d",
      "metadata": {
        "id": "f483377d"
      },
      "source": [
        "### Data Augmentation\n",
        "Another way to prevent overfitting is by means of data augmentation. Data augmentation increases the size of your training dataset by applying various transformations to the existing images. These transformations include:\n",
        "\n",
        "* Rotation: Rotating the image by a certain angle, which can help the model become more invariant to the orientation of objects.\n",
        "\n",
        "* Width and Height Shifts: Shifting the image horizontally or vertically, simulating variations in object positioning within the frame.\n",
        "\n",
        "* Shear: Distorting the image by shifting one part of the image relative to the other along a given axis. This can simulate the effect of tilting or slanting an object.\n",
        "\n",
        "* Zoom: Zooming in or out of the image, changing the scale and perspective.\n",
        "\n",
        "* Horizontal Flip: Flipping the image horizontally, which is useful for tasks where left-to-right orientation doesn't matter.\n",
        "\n",
        "By applying these transformations randomly to your training images, you effectively create new training examples, each slightly different from the original. This helps the model generalize better because it learns to recognize objects under various conditions and viewpoints.\n",
        "\n",
        "In the next code the ImageDataGenerator from TensorFlow/Keras is used to automate the process of data augmentation. It takes care of applying these transformations to the training images on-the-fly as the model is trained, effectively expanding the training dataset and enhancing the model's ability to generalize to unseen data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b29e789",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0b29e789",
        "outputId": "2e9d03e7-0123-443c-ebc0-9182662bf819"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "11/93 [==>...........................] - ETA: 3:02 - loss: 6223.8145 - accuracy: 0.7188"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Define the data augmentation parameters\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(256, (3,3), activation = 'relu',\n",
        "                 input_shape = (height,width,channels)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Fit the model with data augmentation\n",
        "history = model.fit(datagen.flow(X_train, y_train, batch_size=batch_size),\n",
        "                    steps_per_epoch=len(X_train) / batch_size,\n",
        "                    epochs=epochs,\n",
        "                    validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bnBROWLMJsdB"
      },
      "id": "bnBROWLMJsdB",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.7.11"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}