Neural_Network_in_NP.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fsaudm/NN_NumPy/blob/main/Neural_Network_in_NP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# A Neural Network using NumPy for MNIST"
      ],
      "metadata": {
        "id": "tTA8_Vn-pY4z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**\n",
        "\n",
        "For 2-D arrays, np.dot performs matrix multiplication. To be more readable and explicit:\n",
        "- **np.matmul()** is used for matrix multiplication\n",
        "- \"*\" is used for element-wise multiplication"
      ],
      "metadata": {
        "id": "MKaQHX02pWeU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing mighty NumPy!\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "tBFxzWXwcxOC"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To load the dataset from Colab\n",
        "import pandas as pd\n",
        "\n",
        "# Mnist\n",
        "\n",
        "train = pd.read_csv(\"/content/sample_data/mnist_train_small.csv\", header=None)\n",
        "train = train.values  # Convert to a NumPy array\n",
        "X_train = train[:, 1:]/255.0  # Normalize the data\n",
        "y_train = train[:, 0].reshape(-1, 1)\n",
        "\n",
        "\n",
        "test = pd.read_csv(\"/content/sample_data/mnist_test.csv\", header=None)\n",
        "test = test.values\n",
        "X_test = test[:, 1:]/255.0  # Normalize the data\n",
        "y_test = test[:, 0].reshape(-1, 1)\n",
        "\n",
        "\n",
        "# Shape\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5TrJE5rfNIRQ",
        "outputId": "6da7d380-22d9-4a4f-d43e-39eeca4a7705"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (20000, 784)\n",
            "y_train shape: (20000, 1)\n",
            "X_test shape: (10000, 784)\n",
            "y_test shape: (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load the MNIST dataset\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# from sklearn.datasets import fetch_openml\n",
        "# mnist = fetch_openml('mnist_784', version=1)\n",
        "# X, y = mnist.data, mnist.target\n",
        "\n",
        "# # Transform to arrays\n",
        "# X = X.to_numpy()\n",
        "# y = y.to_numpy().astype(int).reshape(-1,1)\n",
        "\n",
        "# # Normalize X\n",
        "# X = X / 255.0  # Normalize the data\n",
        "\n",
        "# # Split the data into training and testing sets\n",
        "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "9DuCgFtOfUD8"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Label distribution in y_train: \\n {np.unique(y_train, return_counts=True)} \\n\")\n",
        "print(f\"Label distribution in y_test: \\n {np.unique(y_test, return_counts=True)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVy3xNjsPSXd",
        "outputId": "a66d434f-88d0-457b-8a19-887ce4cc758d"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label distribution in y_train: \n",
            " (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([1962, 2243, 1989, 2021, 1924, 1761, 2039, 2126, 1912, 2023])) \n",
            "\n",
            "Label distribution in y_test: \n",
            " (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network definition & Functions"
      ],
      "metadata": {
        "id": "lapVfpFHfyTT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ReLu\n",
        "def relu(array):\n",
        "    return np.maximum(0, array)  # Vectorized ReLU"
      ],
      "metadata": {
        "id": "qyTDZVKVjPdz"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Softmax\n",
        "def softmax(array):\n",
        "    # Clip values to prevent overflow\n",
        "    array = np.clip(array, -500, 500)\n",
        "    exps = np.exp(array - np.max(array, axis=1, keepdims=True)) # Substract the max, broadcast\n",
        "    return exps / np.sum(exps, axis=1, keepdims=True)  # Divide by sum, broadcast"
      ],
      "metadata": {
        "id": "Uu2QIB-mlkHf"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Predict Y\n",
        "def predict_y(array):\n",
        "    return np.argmax(array, axis=1)  # Divide by sum, broadcast"
      ],
      "metadata": {
        "id": "-ka-qcZfsot1"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title One-hot encoding for Labels\n",
        "def one_hot_encode(y, n_classes = 10):\n",
        "    return np.eye(n_classes)[y].reshape(-1, n_classes)"
      ],
      "metadata": {
        "id": "JOLtFHMVtQI1"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Cross-entropy\n",
        "def cross_entropy_loss(y_true, y_pred):\n",
        "    loss = -np.mean(y_true * np.log(y_pred + 1e-9)) # Average loss over the batch\n",
        "    return loss"
      ],
      "metadata": {
        "id": "pyKtlhvKv97i"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Network Architecture & Params"
      ],
      "metadata": {
        "id": "4PjPiIjpF_Fy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Parameters\n",
        "def initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim):\n",
        "    W1 = np.random.randn(input_dim, hidden1_dim) * np.sqrt(2. / input_dim)\n",
        "    b1 = np.zeros((1, hidden1_dim))\n",
        "    W2 = np.random.randn(hidden1_dim, hidden2_dim) * np.sqrt(2. / hidden1_dim)\n",
        "    b2 = np.zeros((1, hidden2_dim))\n",
        "    W3 = np.random.randn(hidden2_dim, output_dim) * np.sqrt(2. / hidden2_dim)\n",
        "    b3 = np.zeros((1, output_dim))\n",
        "    return W1, W2, W3, b1, b2, b3"
      ],
      "metadata": {
        "id": "AgrkqvpkuaTo"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Forward pass, Vetorized!\n",
        "np.random.seed(77)\n",
        "\n",
        "i=10\n",
        "x = X_train[:i]\n",
        "y = y_train[:i]\n",
        "num_classes = 10\n",
        "\n",
        "# Initialize parameters\n",
        "input_dim = 784\n",
        "hidden1_dim = 128\n",
        "hidden2_dim = 64\n",
        "output_dim = 10\n",
        "\n",
        "W1, W2, W3, b1, b2, b3 = initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim)\n",
        "\n",
        "### Network Example\n",
        "### 1 forward pass\n",
        "# From input to hidden 1\n",
        "z1 = np.matmul(x,W1) + b1\n",
        "a1 = relu(z1)\n",
        "\n",
        "# From hidden 1 to hidden 2\n",
        "z2 = np.matmul(a1,W2) + b2\n",
        "a2 = relu(z2)\n",
        "\n",
        "# From hidden 2 to output\n",
        "z3 = np.matmul(a2,W3) + b3\n",
        "a3 = softmax(z3)\n",
        "\n",
        "# From output to prediction\n",
        "y_hat = predict_y(a3).reshape(-1,1)\n",
        "\n",
        "# Calculate loss\n",
        "loss = cross_entropy_loss(one_hot_encode(y, num_classes), a3)\n",
        "\n",
        "\n",
        "\n",
        "# Results:\n",
        "print(f\"Probabilies:\\n {a3}, shape: {a3.shape}\\n\")\n",
        "print(f\"Predicted Y:\\n {y_hat}, shape: {y_hat.shape}\\n\")\n",
        "print(f\"Actual Y:\\n {y_train[:i]}, shape: {y_train[:i].shape}\\n\")\n",
        "print(f\"Loss:\\n {loss}\\n\")\n",
        "\n",
        "\n",
        "# Shapes:\n",
        "print(f\"Input shape: {x.shape}\")\n",
        "print(f\"Predicted Y shape: {y_hat.shape}\")\n",
        "\n",
        "print(f\"Parameters W1 shape: {W1.shape}\")\n",
        "print(f\"Parameters b1 shape: {b1.shape}\")\n",
        "print(f\"Parameters W2 shape: {W2.shape}\")\n",
        "print(f\"Parameters b2 shape: {b2.shape}\")\n",
        "print(f\"Parameters W3 shape: {W3.shape}\")\n",
        "print(f\"Parameters b3 shape: {b3.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QljBFD1NqqsH",
        "outputId": "4a6b4706-2914-4f1d-f5a8-ccc04e49af00"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probabilies:\n",
            " [[0.11733263 0.06609539 0.08865623 0.08824106 0.10087229 0.08878498\n",
            "  0.13841442 0.04649303 0.12330861 0.14180136]\n",
            " [0.08137528 0.03863917 0.09789757 0.09745332 0.10597802 0.08851069\n",
            "  0.14254998 0.05846954 0.10516031 0.18396613]\n",
            " [0.04992956 0.06360686 0.07616773 0.07650776 0.1227953  0.05010015\n",
            "  0.13024905 0.09755361 0.17580309 0.15728688]\n",
            " [0.10503193 0.07760418 0.10106412 0.06629002 0.08223915 0.08457014\n",
            "  0.14472563 0.08227575 0.11070808 0.14549101]\n",
            " [0.07489542 0.0300609  0.08480222 0.06860499 0.09137231 0.06776257\n",
            "  0.17307812 0.05814437 0.16373525 0.18754385]\n",
            " [0.08809946 0.03252733 0.06278415 0.09410448 0.09235638 0.07612928\n",
            "  0.16511115 0.03761974 0.19044173 0.16082632]\n",
            " [0.06871737 0.02832598 0.11960507 0.0437029  0.07847501 0.02069992\n",
            "  0.11135655 0.04534697 0.26605171 0.21771852]\n",
            " [0.07360012 0.05340208 0.14168721 0.04405848 0.13227811 0.03669042\n",
            "  0.08118515 0.08908264 0.17040988 0.17760589]\n",
            " [0.05974169 0.03258083 0.05514698 0.06898314 0.07015171 0.08936184\n",
            "  0.15400261 0.05510803 0.17600346 0.23891971]\n",
            " [0.07316737 0.02527925 0.06838034 0.07344207 0.06790836 0.0710615\n",
            "  0.16000792 0.03658762 0.23352893 0.19063664]], shape: (10, 10)\n",
            "\n",
            "Predicted Y:\n",
            " [[9]\n",
            " [9]\n",
            " [8]\n",
            " [9]\n",
            " [9]\n",
            " [8]\n",
            " [8]\n",
            " [9]\n",
            " [9]\n",
            " [8]], shape: (10, 1)\n",
            "\n",
            "Actual Y:\n",
            " [[6]\n",
            " [5]\n",
            " [7]\n",
            " [9]\n",
            " [5]\n",
            " [2]\n",
            " [0]\n",
            " [6]\n",
            " [5]\n",
            " [5]], shape: (10, 1)\n",
            "\n",
            "Loss:\n",
            " 0.2436497458770077\n",
            "\n",
            "Input shape: (10, 784)\n",
            "Predicted Y shape: (10, 1)\n",
            "Parameters W1 shape: (784, 128)\n",
            "Parameters b1 shape: (1, 128)\n",
            "Parameters W2 shape: (128, 64)\n",
            "Parameters b2 shape: (1, 64)\n",
            "Parameters W3 shape: (64, 10)\n",
            "Parameters b3 shape: (1, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Back Propagation"
      ],
      "metadata": {
        "id": "G5z75GjbGbb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ReLu 'prime'\n",
        "def relu_prime(array):\n",
        "    return np.where(array > 0, 1, 0) ## Boolean for z>0, 1, 0"
      ],
      "metadata": {
        "id": "XSx5OHCAIk9E"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Backwards pass, updating parameters\n",
        "\n",
        "### Network Example\n",
        "### backwards pass\n",
        "\n",
        "### Error propagation\n",
        "m = y.shape[0]  # Number of examples\n",
        "\n",
        "# Step 1: Calculate error at the output layer\n",
        "delta3 = a3 - one_hot_encode(y, num_classes) # (10, num_classes)\n",
        "\n",
        "# Step 2: Calculate gradients for W3 and b3\n",
        "dW3 = np.matmul(a2.T, delta3) / m  # (10, 10) . (10, num_classes) -> (10, num_classes)\n",
        "db3 = np.sum(delta3, axis=0, keepdims=True) / m  # (1, num_classes)\n",
        "\n",
        "# Step 3: Propagate the error to hidden layer 2\n",
        "delta2 = np.matmul(delta3, W3.T) * relu_prime(a2)  # (10, num_classes) . (num_classes, 10) -> (10, 10) * (10, 10)\n",
        "\n",
        "# Step 4: Calculate gradients for W2 and b2\n",
        "dW2 = np.matmul(a1.T, delta2) / m  # (10, 10) . (10, 10) -> (10, 10)\n",
        "db2 = np.sum(delta2, axis=0, keepdims=True) / m  # (1, 10)\n",
        "\n",
        "# Step 5: Propagate the error to hidden layer 1\n",
        "delta1 = np.matmul(delta2, W2.T) * relu_prime(a1)  # (10, 10) . (10, 10) -> (10, 10) * (10, 10)\n",
        "\n",
        "# Step 6: Calculate gradients for W1 and b1\n",
        "dW1 = np.matmul(x.T, delta1) / m  # (10, n_features) . (10, 10) -> (n_features, 10)\n",
        "db1 = np.sum(delta1, axis=0, keepdims=True) / m  # (1, 10)\n",
        "\n",
        "\n",
        "### Parameters update\n",
        "lr = 3e-2\n",
        "\n",
        "# Update weights and biases\n",
        "W1 -= lr * dW1\n",
        "b1 -= lr * db1\n",
        "W2 -= lr * dW2\n",
        "b2 -= lr * db2\n",
        "W3 -= lr * dW3\n",
        "b3 -= lr * db3"
      ],
      "metadata": {
        "id": "hQQeWYp3upDH"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "-2xxRewXZma5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def forward_pass(x, y, num_classes, W1, W2, W3, b1, b2, b3):\n",
        "\n",
        "  # From input to hidden 1\n",
        "  z1 = np.matmul(x,W1) + b1\n",
        "  a1 = relu(z1)\n",
        "\n",
        "  # From hidden 1 to hidden 2\n",
        "  z2 = np.matmul(a1,W2) + b2\n",
        "  a2 = relu(z2)\n",
        "\n",
        "  # From hidden 2 to output\n",
        "  z3 = np.matmul(a2,W3) + b3\n",
        "  a3 = softmax(z3)\n",
        "\n",
        "  # From output to prediction\n",
        "  y_hat = predict_y(a3).reshape(-1,1)\n",
        "\n",
        "  # Calculate loss\n",
        "  loss = cross_entropy_loss(one_hot_encode(y, num_classes), a3)\n",
        "\n",
        "  return loss, y_hat, a3, a2, a1\n",
        "\n",
        "\n",
        "def backwards_pass(x, y, num_classes, lr, a3, a2, a1, W1, W2, W3, b1, b2, b3):\n",
        "  ### Error propagation\n",
        "  m = y.shape[0]  # Number of examples\n",
        "\n",
        "  delta3 = a3 - one_hot_encode(y, num_classes)\n",
        "  dW3 = np.matmul(a2.T, delta3) / m\n",
        "  db3 = np.sum(delta3, axis=0, keepdims=True) / m\n",
        "\n",
        "  delta2 = np.matmul(delta3, W3.T) * relu_prime(a2)\n",
        "  dW2 = np.matmul(a1.T, delta2) / m\n",
        "  db2 = np.sum(delta2, axis=0, keepdims=True) / m\n",
        "\n",
        "  delta1 = np.matmul(delta2, W2.T) * relu_prime(a1)\n",
        "  dW1 = np.matmul(x.T, delta1) / m\n",
        "  db1 = np.sum(delta1, axis=0, keepdims=True) / m\n",
        "\n",
        "  # Update weights and biases\n",
        "  W1 -= lr * dW1\n",
        "  b1 -= lr * db1\n",
        "  W2 -= lr * dW2\n",
        "  b2 -= lr * db2\n",
        "  W3 -= lr * dW3\n",
        "  b3 -= lr * db3\n",
        "\n",
        "  return W1, W2, W3, b1, b2, b3"
      ],
      "metadata": {
        "id": "KsC8t3y4ZqsF"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training loop"
      ],
      "metadata": {
        "id": "9m3h8XDOarNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install wandb -qqq"
      ],
      "metadata": {
        "id": "tFr2Bmz5QQbg"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Accuracy & Confusion matrix\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Logging\n",
        "import wandb\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Use wandb-core, temporary for wandb's new backend\n",
        "wandb.require(\"core\")\n",
        "wandb.login()"
      ],
      "metadata": {
        "id": "yL0qjFHUDeUK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
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        "outputId": "b895bc59-3b07-462e-b577-0363de05837a"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
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          "data": {
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
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              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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              "    "
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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            " ··········\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, figsize=(10, 8), cmap=\"viridis\"):\n",
        "    # Calculate the confusion matrix\n",
        "    conf_matrix = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    plt.figure(figsize=figsize)\n",
        "    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=cmap)\n",
        "    plt.title(f'Confusion Matrix (Accuracy: {accuracy_score(y_true, y_pred):.2f})')\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.ylabel('True')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "KyDvMPgtawEn"
      },
      "execution_count": 172,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# Initialize wandb\n",
        "wandb.init(project=\"mnist-neural-network\")\n",
        "\n",
        "\n",
        "X = X_train\n",
        "Y = y_train\n",
        "num_classes = 10\n",
        "\n",
        "x_test = X_test\n",
        "y_test = y_test\n",
        "\n",
        "# Initialize parameters\n",
        "input_dim = 784\n",
        "hidden1_dim = 196\n",
        "hidden2_dim = 49\n",
        "output_dim = num_classes\n",
        "\n",
        "W1, W2, W3, b1, b2, b3 = initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim)\n",
        "num_params = W1.size + W2.size + W3.size + b1.size + b2.size + b3.size\n",
        "\n",
        "# Learning rate & Epochs\n",
        "lr=5.5e-1\n",
        "epochs = 100\n",
        "\n",
        "## Training loop\n",
        "for epoch in tqdm(range(epochs), desc=\"Training Progress\"):\n",
        "\n",
        "  ## Random Batches\n",
        "  # Shuffle the array\n",
        "  random_indices = np.random.randint(0, len(Y), size=(len(Y)))\n",
        "  x = X[random_indices]\n",
        "  y = Y[random_indices]\n",
        "\n",
        "  ## Training\n",
        "  # Forward pass\n",
        "  training_loss, y_hat_tr, a3, a2, a1 = forward_pass(x, y, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "  # Backwards pass\n",
        "  W1, W2, W3, b1, b2, b3 = backwards_pass(x, y, num_classes, lr, a3, a2, a1, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "  ## Validation\n",
        "  # Forward pass\n",
        "  val_loss, y_hat_val, _, _, _ = forward_pass(x_test, y_test, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "  ## Logging\n",
        "  train_accuracy = accuracy_score(y, y_hat_tr)\n",
        "  val_accuracy = accuracy_score(y_test, y_hat_val)\n",
        "\n",
        "  ## Logging with wandb\n",
        "  wandb.log({\"train_loss\": training_loss, \"val_loss\": val_loss, \"train_accuracy\": train_accuracy, \"val_accuracy\": val_accuracy})\n",
        "\n",
        "## Logging\n",
        "wandb.config.activation_function = \"relu\"\n",
        "wandb.config.batch_size = len(Y)\n",
        "wandb.config.epochs = epochs\n",
        "wandb.config.input_dims = input_dim\n",
        "wandb.config.hidden_layer_1 = hidden1_dim\n",
        "wandb.config.hidden_layer_2 = hidden2_dim\n",
        "wandb.config.output = output_dim\n",
        "wandb.config.learning_rate = lr\n",
        "wandb.config.loss_function = \"cross_entropy\"\n",
        "wandb.config.parameters = num_params\n",
        "\n",
        "wandb.finish()\n",
        "\n",
        "# Number of parameters\n",
        "print(f\"Number of parameters: {num_params}\")\n",
        "print()\n",
        "\n",
        "# Shapes:\n",
        "print(f\"Input shape: {x.shape}\")\n",
        "print(f\"Parameters W1 shape: {W1.shape}\")\n",
        "print(f\"Parameters b1 shape: {b1.shape}\")\n",
        "print(f\"Parameters W2 shape: {W2.shape}\")\n",
        "print(f\"Parameters b2 shape: {b2.shape}\")\n",
        "print(f\"Parameters W3 shape: {W3.shape}\")\n",
        "print(f\"Parameters b3 shape: {b3.shape}\")\n",
        "print(f\"Predicted Y shape: {y_hat.shape}\")\n",
        "\n",
        "plot_confusion_matrix(y_test, y_hat_val)"
      ],
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          "text": [
            "Training Progress: 100%|██████████| 100/100 [02:00<00:00,  1.20s/it]\n"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train_accuracy</td><td>▁▂▄▁▂▄▅▅▅▄▆▆▆▇▇▇███▇████▇███████████████</td></tr><tr><td>train_loss</td><td>▇▆▅▅█▆▄▅▅▄▃▃▃▃▂▂▃▂▂▂▁▁▁▂▂▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val_accuracy</td><td>▁▂▄▂▂▅▄▄▆▆▆▅▆▇▇▆▆▇▇▇█▇▇▇████▇███████████</td></tr><tr><td>val_loss</td><td>█▇▆▅▅▃▃▃▂▂▂▃▂▂▂▂▂▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train_accuracy</td><td>0.9425</td></tr><tr><td>train_loss</td><td>0.01977</td></tr><tr><td>val_accuracy</td><td>0.9378</td></tr><tr><td>val_loss</td><td>0.02117</td></tr></table><br/></div></div>"
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            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20240809_024548-93bivmtn/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 164013\n",
            "\n",
            "Input shape: (20000, 784)\n",
            "Parameters W1 shape: (784, 196)\n",
            "Parameters b1 shape: (1, 196)\n",
            "Parameters W2 shape: (196, 49)\n",
            "Parameters b2 shape: (1, 49)\n",
            "Parameters W3 shape: (49, 10)\n",
            "Parameters b3 shape: (1, 10)\n",
            "Predicted Y shape: (10, 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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x+/Zt2NvbAwBKlSoFICMBTU1NRffu3fHo0SM8evQIT58+BZDxXD969CjbuJzM10CBAgVyfVxERGJj0kEkobZt2+LBgwc4d+7cO9ctWrQoNBpNttltwsLCEB0drZ2JSh8cHR11ZnrK9Ho1BQDkcjmaNGmCxYsX4+bNm5gzZw6OHz/+xoudZcZ5586dbMtu376NAgUKwNra+uMO4A169uyJy5cvIy4uLsfB95l+//13NGrUCD///DN69OiB5s2bo2nTptkeE33+opyQkIABAwagXLlyGDp0KBYsWAB/f/93blekSBFYWlri4cOHOu2CIGDbtm1o1KgRdu7cme1WqVIl7Zdwb29vAHhrAuzi4gI7O7t3JsmZVajXH6ucXjtvsm/fPqSkpGDv3r0YNmwYWrdujaZNm2ZLUIoXL/7OuDP17dsXd+/ehb+/P7Zu3QofH58Pmo46c6B7ZneoTM+fP8fTp0+1y9/F2toatWvXRrVq1aBQKHD06FFYWlpqL+74+PFjREVFoXz58vDy8oKXl5e2u+TcuXPh5eWFmzdv6uzz4cOH2qocEVFew6SDSEJff/01rK2tMXjwYISFhWVb/uDBAyxbtgxARvcgANlmmFq8eDEA6PV6E8WLF0dMTAyuXr2qbQsJCcGuXbt01ouMjMy2beaXrpy6nwAZvxRXqVIFmzZt0vliev36dRw+fFh7nGJo1KgRvv32W6xcuRLu7u5vXE+hUGSrouzcuTPbzEaZyVFOCVpuTZo0CY8fP8amTZuwePFiFCtWDP369Xvj45jJ3Nwc1atXz/Yl+MyZM3j06BEGDBiAzz77LNute/fuOHHiBJ4/fw4XFxc0aNAA69evz3YBuszHQS6Xa2ezev2+sq6XmQhkvRZIQkICNm3a9N6PRWZlK+tzEBMTgw0bNuis17x5c9ja2sLPzw/Jyck5xpOpVatWKFCgAObPn49//vknW5XjfafMLV++PMqUKYO1a9fqVG9Wr14NmUyGzz77TCfm27dvv7NL3tmzZ/Hnn39i0KBB2orHmDFjsGvXLp1b5hif/v37Y9euXdm6pgUEBKB27dpvvS8iIslINICdiP5vz549gkqlEhwdHYUvv/xS+Omnn4RVq1YJvXr1EiwsLIShQ4dq182cYadbt27CqlWrtH+/PstR0aJFhTZt2mS7r9dnTXrTbDgRERGCtbW14O3tLSxdulSYO3euULhwYaFq1ao6MxN9+eWXgo+PjzBt2jThp59+EubMmSMULFhQKFSokBAdHa1zH1lneDpy5IhgZmYmlClTRli4cKEwe/ZswcXFRXB0dBSCgoK062XOhPT67Fg5zYSUk6yzV71JTo/BN998IwAQ+vfvL6xdu1YYPXq04OTkJHh7e+s8fqmpqYKDg4NQunRpYd26dcKvv/6qjb9hw4ZC+fLlc7zP15+HY8eOCTKZTJg5c6a27dSpU4JcLhcmTpz41vgFIWOGKqVSKcTExGjbhg8fLigUCp0ZwrK6du2aAED4/vvvBUHImB3NxsZGcHZ2Fnx9fYW1a9cKU6ZMESpXrqzd5unTp4K7u7tgZWUljB07Vvjxxx+FmTNnCuXLlxeioqK0j0mRIkWEAgUKCPPnzxcWLVoklCtXTqhWrVqOs1fl9Dq9ffu2YGFhIVSsWFFYuXKlMG/ePKF48eJC5cqVs+1j3bp1AgChQoUKwty5c4XVq1cLw4cP15kFLdOoUaMEAIJCoRCeP3+us+x9Z68SBEHYt2+fIJPJhMaNGwtr164VxowZI8jlcmHIkCE662W+TrO+9h89eiTUrFlT+O6774R169YJ48aNEywtLQUfH59sM1q97m2zV4WFhQkKhUJYt27dO+MnIpICkw6iPODu3bvCkCFDhGLFigkWFhaCra2tULduXWHFihVCcnKydr20tDRh1qxZgpeXl2Bubi4ULlxY8PX11VlHED4+6RAEQTh8+LBQoUIFwcLCQihdurTwyy+/ZJsO9dixY0KHDh0ET09PwcLCQvD09BQ+//xz4e7du9nu4/VpZY8ePSrUrVtXsLS0FOzs7IR27doJN2/e1FlHqqQjOTlZ+OqrrwQPDw/B0tJSqFu3rnDu3Lkcp7rds2ePUK5cOcHMzEznON836YiNjRWKFi0qVK1aVUhLS9NZb9y4cYJcLhfOnTv31mMICwsTzMzMhC1btgiCkPHF39nZWahfv/5bt/Py8hJ8fHy0f1+/fl3o1KmT4ODgIKhUKqF06dLC9OnTdbYJDg4W+vbtK7i4uAhKpVLw9vYWRo4cKaSkpGjXCQgIEGrVqiVYWFgIRYoUERYvXvzGKXNzep0KgiDs3btXqFSpkqBSqYRixYoJ8+fPF9avX5/j8753716hTp062tdSzZo1hV9//TXbPi9cuCAAEJo3b55tWW6SDkEQhF27dglVqlQRlEqlUKhQIWHatGlCamqqzjo5JR2RkZFChw4dBHd3d8HCwkLw8vISJk2a9M6EQxDe/n5dvXq1YGVl9V77ISKSgkwQcjESk4iI8qRBgwbh7t27OH36tNSh5FlXrlxBlSpVsHnzZvTp00fqcPTKx8cHn376qfaCn0REeQ2TDiIiI/D48WOUKlUKx44d0w5GJl2jRo3Cpk2bEBoaKtpkBVI4ePAgPvvsMwQFBeVqmmUiIkMykzoAIiL6eEWKFMk2mJoy7Nu3Dzdv3sTatWsxatQoo0o4AKBly5aIj4+XOgwiordipYOIiIxasWLFEBYWhhYtWmDLli3aq7MTEZHhMOkgIiIiIiJR8TodREREREQkKiYdREREREQkKiYdREREREQkKqOcvWrLvU+kDkESm0sXljoEIiL9kpnob2OCRuoIiER3RLNT6hDeSBNaSrL7lrvfley+xWSiZ3MiIiIiIjIUo6x0EBERERF9KA2kqzYaa0XAWI+LiIiIiIjyCCYdREREREQkKnavIiIiIiLKQi3hZA7G+uWclQ4iIiIiIhKVsSZTREREREQfRANB6hCMDisdREREREQkKlY6iIiIiIiykHLKXGPFSgcREREREYmKSQcREREREYmK3auIiIiIiLJQCxxIrm+sdBARERERkahY6SAiIiIiyoJT5uofKx1ERERERCQqJh1ERERERCQqdq8iIiIiIspCze5VesdKBxERERERiYqVDiIiIiKiLDiQXP9Y6SAiIiIiIlGx0kFERERElAUvDqh/rHQQEREREZGomHQQEREREZGo2L2KiIiIiCgLjdQBGCEmHe8hJVHAP78k4fa5NCTGaODurUDzoVbwLPXq4Yt4osaxDUl4fD0NGjVQoIgCn/nawN41o5j018oEPAxMR3ykBhYqGQqVNUPj/pYoUFgh1WHpTfsvWqDrhPZwcnfAgyvBWDVmPe7435c6LNFUrF8WXSe0R6lq3nD2dMKMTgtwdo+/1GEZjKk935lM9bgzdZ/UEYP9euHPZX9h9biNUocjGrlchj4zuqJJr/pwcnfAy+eROLzpH2yd86fUoYnKVM9rbYc3R7vhzeFWzAUAEHzjKX75dif8DwZKG5iBmPp5jQyL3avew18rEhAUmIYOX1lh6Eo7ePmYY+u0OMRGZOTBkSFqbPo6DgUKydHHzxZDVtqhfg8VzCxe7cOjhBnajbXC8NV2+Hy2DQQB2PZNHDTq/D1QqWG3Ohj2fT/8MnsnRlSbhKCrwfA7OBUOLnZShyYalbUSQVeDsWLUz1KHYnCm+HwDpnvcmUpVL442Q5vhwZVHUociuu5fd0C74c2wcsx6DCo/Hut8t6HbxPboOKql1KGJylTPaxFPX+Jn360YWX0SRtaYjMAT1zFr9yQULVdI6tBEZ+rntXdRQ5DsZqyYdLxDWoqAW2fS0GSAFYpWMIeTpwINe1nC0UOBgAMpAICTm5NQvLo5mgy0gntxMzh5KFCqlgWsHV49vFVbKlG0gjkc3BTwKGGGT/uoEPtCQHR4/i7gdRnXFgfWHcOhjSfx+NZTLBu+FimJqWgxsLHUoYnG/2AgNk7/DWd2X5A6FIMzxecbMN3jBgCVtQq+v4zBkqFrEB+VIHU4oitXpxTO7r2IC39fRljwC5z+4zwCjlxF6ZolpA5NVKZ6XvtvfwAuHLiMZ/dD8exeCDZM+xVJ8cko+0kpqUMTnSmf10gaTDreQaMGBA1gZq7bbqYEntxIh6ARcP9iGpw95dg2PQ6Le0Vj/fhY3DmX+sZ9piYLuHI0FQ5uctgXyL9PgZm5GUpV88alo1e1bYIg4NLRqyhnAidsU2Oqz7epHnem0SsH4fzfl3D52DWpQzGIm2fvwqdxBRQs6QEA8K5UFBXqljaZ7jamTC6X49PudaCyVuLmubtShyMqUz+vkTQkHdMRERGB9evX49y5cwgNDQUAuLu7o06dOujfvz9cXFykDA8AoLSSoVAZBU7/lowChRWwdpDhxqlUPLuthqOHHAkxAlKTgLO/J+PTPpZoPMASDwLSsXNuAvrMlaFoxVfZysW/knFsQxLSkgHnQnL0/M4GCnOZhEf3cewL2EJhpkBUWIxOe1R4DAqXKShRVCQWU32+TfW4AeDT7nVQsqo3RtacLHUoBvPb/D2wsrPE+puLoVFrIFfIsWHadhzf9q/UoZFIilUoguVn58BCZY6k+GTM6rwQj289lTosUZnyee195fPe73mSZEmHv78/WrRoASsrKzRt2hSlSmVk1mFhYVi+fDnmzZuHQ4cOoXr16m/dT0pKClJSUnTa0lI1MLfQXwWh/VfW2L8sEcv6xUAmBzyKK1C+gQVC7qdD+H/vqFKfmKNWRxUAwN3bDE9vpSPgQIpO0lHhUyW8q5gjLkqD//5MwZ/zEtB/oS3MLPJv4kFExsmlkDO+WDoAk5p/i7SUNKnDMZiG3Wqjcc968Ou9Ao9uPEGJKsUwYnE/vAyJxJHNp6QOj0Tw9M5zDPeZCGt7K9T/7BNM3DgKX306w+gTDyJDkyzpGD16NLp27Yo1a9ZAJtP90i0IAoYPH47Ro0fj3Llzb92Pn58fZs2apdPWcVRBdB6jv0FgTh4K9J1ni9RkASmJAmyd5Phzfjwc3eWwspNBrkC2WagKFJbjyc10nTaVtQwqawWcCipQqLQZFvWIxu1zaajQ0AL5UUxEHNTpaji62eu0O7raIyo0WpqgSDSm+nyb6nGXrOYNRzcHrA5YoG1TmClQsUFZdBjZEq1VPaHR5O8xaTkZMr8Xts/fg5PbzwIAHl1/AtciLugxqSOTDiOVnpaO5w8yelvcuxSE0tWLo9OXrbFs+FqJIxOPqZ7XcsP4zm7Sk2xAwZUrVzBu3LhsCQcAyGQyjBs3DoGBge/cj6+vL2JiYnRu7YZ7ihAxYKGSwdZJjqR4DR5cSkepTyygMJfBs6QCL5/pvjwjn2m00+XmRPj/TZ2Wf+t36WnpuBsQBJ8mFbVtMpkMPk0q4uZ/xt0f1hSZ6vNtqsd9+dg1DKk4HsN9Jmpvd/zv4/jWfzHcZ6JRJhwAoLJSQiPonpc1ag3kclakTYVMLoeFhfm7V8zHTPW8RtKSrNLh7u6OCxcuoEyZMjkuv3DhAtzc3N65H6VSCaVSqdOmz65VAPAgIKNrgVNBOaJCNDi2PgkFCslRuWlGheKTzir8uSABRcqboVglMzwISMPdC2no42cLAIgKVePmqVR4VzWHlZ0csS81OLszGeYWMpSonr9PbH8s2Y+vN47E3YsPcOfCfXQa2wYqayUObTghdWiiUVmrULCEu/Zvdy9XFK9cDLGR8XjxJELCyMRnis83YJrHnRSfjEc3nui0JSekIDYyLlu7MflvfwB6+nZC+OMIBN94ihI+xdBlXBujfq4B0z2vDZzbE/4HLiP8cQQsbS3RuGc9VP60HHxbzpE6NNGZ4nktN9TgDw36JlnSMWHCBAwdOhQBAQFo0qSJNsEICwvDsWPH8NNPP2HRokVShacjJVHA8U1JiIvQwNJWhjJ1LPBpX0sozDJekGXqWKD1FwLO7EzG4bUaOBdU4LMp1ihSPuPhNTOX4fGNdFzYm4KkeAHWDjIUKW+O/gttdabVzY/+2XEWDi526DerOxzdHfAg8BGmtJqD6PCYd2+cT5Wq7o3vT7zq0jdicX8AwOGNJ7Fw4CqJojIMU3y+AdM9blO0cswG9J/dHWNWDoKDqz1ePo/EX2uP4pdvf5c6NFGZ6nnNwdUeX28aBScPRyTEJOLh1WD4tpyjM6uTseJ5jQxNJgiCZP17tm/fjiVLliAgIABqtRoAoFAoUK1aNYwfPx7dunX7oP1uufeJPsPMNzaXLix1CERE+iXL3z/MfDDBOLuvEWV1RLNT6hDe6N5Tcbrqv4+ShZ5Ldt9iknTK3O7du6N79+5IS0tDRERG+bZAgQIwN8/fXY6IiIiIKP/S5N8ht3mWpElHJnNzc3h4eEgdBhERERERiSBPJB1ERERERHkFB5Lrn4l2liUiIiIiIkNh0kFERERERKJi9yoiIiIioizYvUr/WOkgIiIiIiJRsdJBRERERJSFRmClQ99Y6SAiIiIiIlGx0kFERERElAXHdOgfKx1ERERERCQqJh1ERERERCQqdq8iIiIiIspCzd/l9Y6PKBERERERiYqVDiIiIiKiLDhlrv6x0kFERERERKJi0kFERERERKJi9yoiIiIioix4nQ79Y6WDiIiIiIhExUoHEREREVEWaoG/y+sbH1EiIiIiIhIVKx1ERERERFlo+Lu83vERJSIiIiIiUTHpICIiIiIiUTHpICIiIiLKQg2ZZLfcOHXqFNq1awdPT0/IZDLs3r1bZ7kgCPjmm2/g4eEBS0tLNG3aFPfu3dNZJzIyEr169YKdnR0cHBwwaNAgxMfH66xz9epV1K9fHyqVCoULF8aCBQty/Zga5ZiOzaULSx2CJA49vyJ1CJJo4VlZ6hCISCyCRuoIyJBkJvpbKF/n9IESEhJQuXJlDBw4EJ07d862fMGCBVi+fDk2bdoELy8vTJ8+HS1atMDNmzehUqkAAL169UJISAiOHDmCtLQ0DBgwAEOHDsW2bdsAALGxsWjevDmaNm2KNWvW4Nq1axg4cCAcHBwwdOjQ947VKJMOIiIiIqIPlV+mzG3VqhVatWqV4zJBELB06VJMmzYNHTp0AABs3rwZbm5u2L17N3r06IFbt27h4MGD8Pf3R/Xq1QEAK1asQOvWrbFo0SJ4enpi69atSE1Nxfr162FhYYHy5csjMDAQixcvzlXSkT8eUSIiIiIiE5CSkoLY2FidW0pKSq738/DhQ4SGhqJp06baNnt7e9SqVQvnzp0DAJw7dw4ODg7ahAMAmjZtCrlcjvPnz2vXadCgASwsLLTrtGjRAnfu3EFUVNR7x8Okg4iIiIgoj/Dz84O9vb3Ozc/PL9f7CQ0NBQC4ubnptLu5uWmXhYaGwtXVVWe5mZkZnJycdNbJaR9Z7+N9sHsVEREREVEWmlwO6NYnX19fjB8/XqdNqVRKFI3+MOkgIiIiIsojlEqlXpIMd3d3AEBYWBg8PDy07WFhYahSpYp2nfDwcJ3t0tPTERkZqd3e3d0dYWFhOutk/p25zvtg9yoiIiIioizUkEt20xcvLy+4u7vj2LFj2rbY2FicP38etWvXBgDUrl0b0dHRCAgI0K5z/PhxaDQa1KpVS7vOqVOnkJaWpl3nyJEjKF26NBwdHd87HiYdRERERET5UHx8PAIDAxEYGAggY/B4YGAgHj9+DJlMhrFjx+K7777D3r17ce3aNfTt2xeenp7o2LEjAKBs2bJo2bIlhgwZggsXLuDMmTMYNWoUevToAU9PTwBAz549YWFhgUGDBuHGjRvYvn07li1blq0L2LuwexURERERUT508eJFNGrUSPt3ZiLQr18/bNy4EV9//TUSEhIwdOhQREdHo169ejh48KD2Gh0AsHXrVowaNQpNmjSBXC5Hly5dsHz5cu1ye3t7HD58GCNHjkS1atVQoEABfPPNN7maLhcAZIIgCB95vHlOM3lXqUOQBC8OSERE+RovDmhSjmh2Sh3CG+0LqiTZfbfzvirZfYvJRN/dRERERERkKOxeRURERESUhYa/y+sdH1EiIiIiIhIVKx1ERERERFmoBekuDmisWOkgIiIiIiJRMekgIiIiIiJRsXsVEREREVEW+rwyOGXgI0pERERERKJipYOIiIiIKAuNwN/l9Y2PKBERERERiYpJBxERERERiYrdq4iIiIiIsuBAcv3jI0pERERERKJipYOIiIiIKAtekVz/WOkgIiIiIiJRsdJBRERERJSFhr/L6x0fUT1q/0ULbAlahb8St2L5ubkoXaOE1CG9N/8rwIjJQIPOQNmGMhw9rbv88Clg0FfAJ+0ylt+6l30fO/YCfb8EqrfKWCc2Tnf5hcsZ7Tndrt0S79jE1n1SRxzR7MSIJf2lDkVUPSZ3xMrzftgTsxk7Qtdh5p8TUaiUp9RhGUx+fn9/DFM77rbDm+PHwEXYHb0Ju6M3YdmZOajRsorUYYmuYv2ymL1nEn57+iOOaHaiTocaUodkEHK5DP1mdcPm+yuwP34LNt1dhl5TO0sdlsGY2vubpMWkQ08adquDYd/3wy+zd2JEtUkIuhoMv4NT4eBiJ3Vo7yUpCShdApg+9s3Lq1YEvhr2ln2kAPVrAsN657y8SgXg1J+Czu2zNgIKeQioUOajD0ESpaoXR5uhzfDgyiOpQxFdpQblsfeHQxhTewomN/8WZuZmmHdoGlRWSqlDE11+f39/KFM87oinL/Gz71aMrD4JI2tMRuCJ65i1exKKliskdWiiUlkrEXQ1GCtG/Sx1KAbV/esOaDe8GVaOWY9B5cdjne82dJvYHh1HtZQ6NNGZ4vubpMWkQ0+6jGuLA+uO4dDGk3h86ymWDV+LlMRUtBjYWOrQ3kuDT4Cxg4FmDXJe3qEFMLI/UKfam/fRryswpBdQuVzOyy3MARfnVzcHe+D4GaBTK0CWD8drqaxV8P1lDJYMXYP4qASpwxHdlNZzcHjTSQTffIqgq8FYOGAV3Iq6oGQ1b6lDE11+f39/KFM87v/2B+DCgct4dj8Uz+6FYMO0X5EUn4yyn5SSOjRR+R8MxMbpv+HM7gtSh2JQ5eqUwtm9F3Hh78sIC36B03+cR8CRqyhd0/h/8TfF93duqAW5ZDdjZbxHZkBm5mYoVc0bl45e1bYJgoBLR6+inJF/UH2ME2eA6FigcyupI/kwo1cOwvm/L+HysWtShyIJa3srAEBcZLzEkYjLVN/fpnrcWcnlcnzavQ5U1krcPHdX6nBIBDfP3oVP4wooWNIDAOBdqSgq1C0N/4OB0gYmMr6/SQp5eiD5kydPMGPGDKxfv/6N66SkpCAlJUWnTSOoIZcpxA5Py76ALRRmCkSFxei0R4XHoHCZggaLI7/5/S+gbg3A3VXqSHLv0+51ULKqN0bWnCx1KJKQyWQYsaQ/rv97G49uPJE6HFGZ6vvbVI8bAIpVKILlZ+fAQmWOpPhkzOq8EI9vPZU6LBLBb/P3wMrOEutvLoZGrYFcIceGadtxfNu/UocmKlN+f78vDfJhF4w8Lk9XOiIjI7Fp06a3ruPn5wd7e3ud20PcNlCE9KFCw4Ez/sBnbaSOJPdcCjnji6UD4Nd7GdJS0qQORxKjVw1GsQqFMefzJVKHQqR3T+88x3CfiRj9yRTsW3MYEzeOQpGyxj2mw1Q17FYbjXvWg1/vFRhRfTIWDvgBXb9qi2Z939DXmIg+mKSVjr179751eVBQ0Dv34evri/Hjx+u0dbLv/zFh5VpMRBzU6Wo4utnrtDu62iMqNNqgseQXfx4AHOyARnWljiT3SlbzhqObA1YHLNC2KcwUqNigLDqMbInWqp7QaDQSRiiuUSsGoVabqviq4QxEPIuUOhzRmer721SPGwDS09Lx/EEoAODepSCUrl4cnb5sjWXD10ocGenbkPm9sH3+HpzcfhYA8Oj6E7gWcUGPSR1xZPMpiaMTjym/v0k6kiYdHTt2hEwmgyAIb1xH9o4RxkqlEkql7uw5huxaBWR8QN0NCIJPk4o4u8cfQEbcPk0qYs+qgwaNJT8QBGDXgYzB6eZ5uoNfzi4fu4YhFXUT3Qnrv8CT28+xfcFuo0846nasiQmNZiD0UbjU4RiEqb6/TfW4cyKTy2FhYS51GCQClZUSmte+g2jUGsjlxt21hu/vdzPmAd1SkfQrn4eHB3744Qd06NAhx+WBgYGoVu0t0yXlIX8s2Y+vN47E3YsPcOfCfXQa2wYqayUObTghdWjvJSERePzs1d9PQ4Bb9wB7O8DTLWPAd0gYEP4yY/nD/3fjL+CUMRMVALx4CUREAsH/38/dIMDaCvBwy6hqZPrvEvA0RIbP2rw52czLkuKTs41jSE5IQWxknFGPbxi9ajAaf14PMzouQGJcMhzdHAAACTGJSE1OlTY4keX39/eHMsXjHji3J/wPXEb44whY2lqicc96qPxpOfi2nCN1aKJSWatQsIS79m93L1cUr1wMsZHxePEkQsLIxPXf/gD09O2E8McRCL7xFCV8iqHLuDZG/RrPZIrvb5KWpElHtWrVEBAQ8Mak411VkLzknx1n4eBih36zusPR3QEPAh9hSqs5iA6PeffGecCNO0C/sa9+2Zm/KuP/HVsK8PPNmGlqyrxXy7+alfH/kf0FjBqQ0bZ9L7Bq46t1+ozJ+P/cyQI6ZZmh6o+/AJ8KAryLinU0JIb2I1oAAL4/OUunfeGAVTi86aQEERlOfn9/fyhTPG4HV3t8vWkUnDwckRCTiIdXg+Hbco7OLD/GqFR1b3x/4tV7e8Ti/gCAwxtPYuHAVRJFJb6VYzag/+zuGLNyEBxc7fHyeST+WnsUv3z7u9Shic4U39+5oc7bw57zJZkg4bf606dPIyEhAS1b5nwRnoSEBFy8eBENGzbM1X6bybvqI7x859DzK1KHIIkWnpWlDoGIiPRBZqJf9ATj7Zb7Nkc0O6UO4Y1+uNNIsvv+orRxVpskrXTUr1//rcutra1znXAQEREREX0MjWDc43qkYKI/KRARERERkaEw6SAiIiIiIlHlwwlLiYiIiIjEw4Hk+sdHlIiIiIiIRMVKBxERERFRFhpeHFDv+IgSEREREZGomHQQEREREZGo2L2KiIiIiCgLNXidDn1jpYOIiIiIiETFSgcRERERURYcSK5/fESJiIiIiEhUrHQQEREREWXBMR36x0oHERERERGJikkHERERERGJit2riIiIiIiy4EBy/eMjSkREREREomKlg4iIiIgoCzUrHXrHR5SIiIiIiETFpIOIiIiIiETF7lVERERERFloeJ0OvWOlg4iIiIiIRMVKBxERERFRFhxIrn98RImIiIiISFSsdBARERERZaEROKZD34wy6ZApFFKHIIkWnpWlDkESve88kzoESWwtX0zqEKQhaKSOQBKCWi11CJIw1fO5yZKZZgcMId00z2tkWkzz3U1ERERERAZjlJUOIiIiIqIPpebv8nrHR5SIiIiIiETFSgcRERERURYcSK5/rHQQEREREZGomHQQEREREZGo2L2KiIiIiCgLDX+X1zs+okREREREJCpWOoiIiIiIslBzILnesdJBRERERESiYqWDiIiIiCgLTpmrf6x0EBERERGRqJh0EBERERGRqNi9ioiIiIgoC43A3+X1jY8oERERERGJipUOIiIiIqIs1OBAcn1jpYOIiIiIiETFpIOIiIiIiETF7lVERERERFnwOh36x0oHERERERGJipUOIiIiIqIsOGWu/vERJSIiIiIiUTHpICIiIiIiUbF7FRERERFRFhpep0PvmHToiaWNCv1mdUPdDjXg4GqP+4GPsHr8Rty9GCR1aKKpWL8suk5oj1LVvOHs6YQZnRbg7B5/qcP6aCmJAk79kog751KRGKOBm7cZmg21hmepjLfLviXxuHYsRWcb76rm6DHbDgAQfDUNW6fE5rjv/ovttfvJ6yrWK4OuX7VFSR8vOHs6YuZni3F270Xt8gnrhqF534Y62/gfuoKp7eYbOlS9yjjudihZ1QvOnk6Y2WWRznEDQN8ZXdFqUGPYOFjjxtk7WD7qZzy/HypRxOJq/0ULdJ3QHk7uDnhwJRirxqzHHf/7UoelN6b6fL/tuBVmCvSf3R01W1WBh5crEmIScen4dfw85VdEhkRJHPnHedd57XDqthy3+2nyNuxcvN9QYYrOWD+/KW9j9yo9GffjMFRtUhEL+q/CMJ+JuHTkKuYfnAZnT0epQxONylqJoKvBWDHqZ6lD0au/V8TjYWAa2n9lg8ErHeDlY45fp8UiLkKtXce7mjnGbHHU3jp8baNdVqismc6yMVscUaW5Eg5ucniUVEhxSB8k8/ld+eWGN67jfzAQ3QuP0N78+qw0YITiUFmrMo57TM7H3W1Ce3Qc1RLLR67DmLrTkJyQAr+/fGGuNDdwpOJr2K0Ohn3fD7/M3okR1SYh6Gow/A5OhYOLndSh6Y2pPt9vO26llQVK+hTD1jl/4ouavpjVbTEKl/LE7F0TJIhUv951Xst6PuteeAQWDf4RGo0Gp3ddMHCk4jLWz299UgsyyW7GKn/85JrHWajMUb9zTczovAjX/r0NANjy7e/4pG1VtBvWDBtn7JA4QnH4HwyE/8FAqcPQq7QUAbfPpKLrdFsUqZDxpaJBLyvcv5CKgAMp+LSPFQDAzBywccw5Z1eYy2Dj+OqkoU4XcPd8Kqq3VUEmyz8nE/9DV+B/6Mpb10lLTUdUWIyBIjIM/0OB8D8U+Mblnca0wra5u3BuXwAAYMGAVdjx7EfU7VAdJ3ecM1CUhtFlXFscWHcMhzaeBAAsG74WtVpXRYuBjbF9/m5JY9MXU32+33bcibFJmNxqrk7byi/XY+W5uXAp7IwXT14aIEJxvOu89vr5rE77arhy8iZCH4aLHZpBGePnN+V9rHTogcJMAYWZAqnJaTrtKUmpKF+3jERR0YfQqAFBk5E4ZGWmlOHpjVfPb/C1dCztFYk1w6JwYFU8EmM1b9znvfOpSIoTUKmZUrS4pVKpQVnseLoaP19fhNErBsLWyebdG+Vj7l6ucPZwxKXj17RtibFJuH3hPsp+UkrCyPTPzNwMpap549LRq9o2QRBw6ehVlDOyY30TU3q+38XazgoajQYJ0YlSh2IwDq52qNmqCg7+P+km06IR5JLdjBUrHXqQFJ+MG+fuotfUznh8+xmiw6LRqEddlP2kVL7v92tqlFYyFCxjhjO/JaJAYVtYO8hw81Qqnt1Oh6NHxonAu6o5StexgIObHFEhGpzcnIjtM2LRb5E95IrslYwrh1Pg7WMOuwL5p2vV+7h4+Cr+3e2P0Ecv4OnthgHfdsOcfZMwtv430GgEqcMThZO7AwAg+rVfQ6PCYuDo5mD4gERkX8AWCjNFtl9+o8JjULhMQYmiMixTer7fxlxpjsF+PXFy+1kkxiVJHY7BNOvTAIlxyfh3F8c6EOmD5ElHUlISAgIC4OTkhHLlyuksS05Oxo4dO9C3b983bp+SkoKUFN1BvRpBDbnMsF/wFvRfha9+GobfHq+GOl2Ne5cf4uT2Myjp423QOOjjtf/KBvuXxWNFvyjI5IB7cQXKNbBA6P2MMR3lG76qWLgWA1y9FFg9OBrB19LhVUW3n3dshBpBl9PQaZLxVQCydi15dP0Jgq49xuY7S1GpYTkEnrghYWREpC8KMwWm/folIJNh+UjT6v/fsv+nOP7rGaSlpL17ZSJ6J0lrOHfv3kXZsmXRoEEDVKxYEQ0bNkRISIh2eUxMDAYMGPDWffj5+cHe3l7n9lBzS+zQswkJCsOEJrPR3r4fenmNxJg606AwM0PIwzCDx0Ifx9FDgT7z7DHhdyeM3uiIAUscoFEDDu45v10c3RWwtJMhKkSdbdnVIymwtJWhZC0LscOWXOjDcES/iEXB4m5ShyKayNBoAICDm71Ou6ObPaLCog0fkIhiIuKgTlfD8fVjdbVH1P8fB2NnSs93TjITDteiLpjcco5JVTkq1C2NwqU9cXDDCalDIYloBJlkN2MladIxadIkVKhQAeHh4bhz5w5sbW1Rt25dPH78+L334evri5iYGJ2bl7ysiFG/XXJiCiJDo2HjYI3qzStpBx9S/mOhksHGSY6keA2CLqWh1Cc5Jw6xEWokxQmwcdJ9OwmCgKtHU1CxsRIKM+M9iWQqUNAJds42eGnEX0hDH4bjZUgUfBpV0LZZ2VqiTM0SuPXfXQkj07/0tHTcDQiCT5OK2jaZTAafJhVx08iO9U1M6fl+XWbCUbCEBya3+A5xkfFSh2RQLQd8irsBQQi6+v7fR4jo7STtXnX27FkcPXoUBQoUQIECBbBv3z588cUXqF+/Pk6cOAFra+t37kOpVEKp1B2ga+iuVQBQrVklyGQyPL37HJ7F3TFkfi88ufNcO+uLMVJZq1CwhLv2b3cvVxSvXAyxkfF48SRCwsg+TlBAKgQAzgUViApR49j6RDgXUqBSUyVSkwSc/jURZepYwNoxY0zHiQ0JcPKQw7uqbteqR1fSER2mQeXmKmkO5COprJXwzPr8FnOBd+WiiIuMR1xkPPpM64LTuy4gKiwaHt5uGOLXE88fhCHg8NW37DXvy3bcXq7a437x5CV2LT+AnlM64dn9UIQ+Ckf/md3w8nkUzuy5+Ja95k9/LNmPrzeOxN2LD3Dnwn10GtsGKmslDhnRr7+m+ny/7bgjQ6Ixffs4lPTxwvSO8yFXyLUVr7jIeKSnZa/q5hdvO69lzsplZWuJBl1q4cevt0oVpuiM9fNbn3hxQP2TNOlISkqCmdmrEGQyGVavXo1Ro0ahYcOG2LYt54v05EXW9lYY+N3nKFDICXGR8fh31wVsmP4b1On59+T8LqWqe+P7E7O0f49Y3B8AcHjjSSwcuEqiqD5ecqKAk5sSERehgcpWhjJ1LNCwrxUUZjJo1ALCH6px7VgckhME2DrJ4eVjjga9rWD22oxXV44ko1BZMxQonD8HkJeq5o1FR6dr/x6+qA8A4PDmf7B81Hp4VSyCZn3qw9rBGi+fR+HS0WvYOHMH0lLTpQpZL0pVK45Fx77R/j18UcaYssOb/8GiQauxY9FeqKyVGLt6CGwcrHD9zB1MaTvPKPt9/7PjLBxc7NBvVnc4ujvgQeAjTGk1B9HhxjNNsqk+32877i2zf0ed9tUBAGsCFuhsN6HJbFw9ddNwgerZ285riwb/CAD4tFttQCbDie1nJYnREIz185vyNpkgCJJNM1OzZk2MHj0affr0ybZs1KhR2Lp1K2JjY6FW5+6Le3PzHvoKMV8Rcvk4GYved55JHYIktpYvJnUI0hDePD2xMTPV97dMkT+TdvpAMuOdLvRthPT8ncR+qCOanVKH8Ea9zg+R7L631vpJsvsWk6Tv7k6dOuHXX3/NcdnKlSvx+eefQ8KciIiIiIhMEAeS65+kSYevry/+/vvvNy7/4YcfoNGY5q+aRERERETGQvLrdBARERER5SXGfGVwqfARJSIiIiIiUbHSQURERESUhTGPrZAKKx1ERERERCQqJh1ERERERCQqdq8iIiIiIsqCVyTXP1Y6iIiIiIhIVKx0EBERERFlwYHk+sdKBxERERERiYpJBxERERERiYpJBxERERFRFhpBJtktN9RqNaZPnw4vLy9YWlqiePHi+PbbbyEIgnYdQRDwzTffwMPDA5aWlmjatCnu3buns5/IyEj06tULdnZ2cHBwwKBBgxAfH6+XxzITkw4iIiIionxo/vz5WL16NVauXIlbt25h/vz5WLBgAVasWKFdZ8GCBVi+fDnWrFmD8+fPw9raGi1atEBycrJ2nV69euHGjRs4cuQI9u/fj1OnTmHo0KF6jZUDyYmIiIiIssgvA8nPnj2LDh06oE2bNgCAYsWK4ddff8WFCxcAZFQ5li5dimnTpqFDhw4AgM2bN8PNzQ27d+9Gjx49cOvWLRw8eBD+/v6oXr06AGDFihVo3bo1Fi1aBE9PT73EykoHEREREVEekZKSgtjYWJ1bSkpKjuvWqVMHx44dw927dwEAV65cwb///otWrVoBAB4+fIjQ0FA0bdpUu429vT1q1aqFc+fOAQDOnTsHBwcHbcIBAE2bNoVcLsf58+f1dlxMOoiIiIiIspByTIefnx/s7e11bn5+fjnGOXnyZPTo0QNlypSBubk5fHx8MHbsWPTq1QsAEBoaCgBwc3PT2c7NzU27LDQ0FK6urjrLzczM4OTkpF1HH9i9ioiIiIgoj/D19cX48eN12pRKZY7r7tixA1u3bsW2bdtQvnx5BAYGYuzYsfD09ES/fv0MEe57Y9JBRERERJRHKJXKNyYZr5s4caK22gEAFStWRHBwMPz8/NCvXz+4u7sDAMLCwuDh4aHdLiwsDFWqVAEAuLu7Izw8XGe/6enpiIyM1G6vD+xeRURERESUhQYyyW65kZiYCLlc9+u8QqGARqMBAHh5ecHd3R3Hjh3TLo+NjcX58+dRu3ZtAEDt2rURHR2NgIAA7TrHjx+HRqNBrVq1PvQhzIaVDiIiIiKifKhdu3aYM2cOihQpgvLly+Py5ctYvHgxBg4cCACQyWQYO3YsvvvuO5QsWRJeXl6YPn06PD090bFjRwBA2bJl0bJlSwwZMgRr1qxBWloaRo0ahR49euht5iqASQcRERERkY78MmXuihUrMH36dHzxxRcIDw+Hp6cnhg0bhm+++Ua7ztdff42EhAQMHToU0dHRqFevHg4ePAiVSqVdZ+vWrRg1ahSaNGkCuVyOLl26YPny5XqNVSZkvWShkWhu3kPqECQhqNVShyCJ3neeSR2CJLaWLyZ1CNIQNFJHIAlTfX/LFAqpQyBDkplmr28hPU3qECRxRLNT6hDeqMU/YyW770MNl0p232IyzXc3EREREREZDLtXERERERFlkV+6V+UnrHQQEREREZGojLLSYap9n02VqY5taH9Vf1cJzU/2lHOWOgRJyJWqd69khDQpyVKHIAmZmbnUIRCZNFY69I+VDiIiIiIiEpVRVjqIiIiIiD4UKx36x0oHERERERGJikkHERERERGJit2riIiIiIiyENi9Su9Y6SAiIiIiIlGx0kFERERElIUGrHToGysdREREREQkKiYdREREREQkKnavIiIiIiLKgtfp0D9WOoiIiIiISFSsdBARERERZcEpc/WPlQ4iIiIiIhIVKx1ERERERFlwTIf+sdJBRERERESiYtJBRERERESiYvcqIiIiIqIsOJBc/1jpICIiIiIiUbHSQURERESUBQeS6x8rHUREREREJComHUREREREJCp2ryIiIiIiykIQpI7A+LDSQUREREREomKlg4iIiIgoCw04kFzfmHToQY/JHVGvUy0ULlMQKUmpuHn2DtZN3oqnd59LHZpBtP+iBbpOaA8ndwc8uBKMVWPW447/fanD0puK9cqg61dtUdLHC86ejpj52WKc3XtRu3zCumFo3rehzjb+h65garv5hg71o6QmavDftlgE/ZeMxBg1XLws0GCIPdxKWgAAzv8ai7unkxAfoYbCDHApboHave3gXtpCu4+NQ0IRF67W2W/tPnao/pmtQY9FnyrWL4uuE9qjVDVvOHs6YUanBTi7x1/qsPSq+4R2qNuxOgqX8kBqUhpunr+Hn6f+hqf3QnXWK1urBPrP7IoyNYpDrdYg6GowprRbgNTkNIki1z9TeL6Bd5/XshqzciDaDm2K1V9txq4VBw0cqX6Z6nG/ztS/t5A02L1KDyo1KI+9PxzCmNpTMLn5tzAzN8O8Q9OgslJKHZroGnarg2Hf98Mvs3diRLVJCLoaDL+DU+HgYid1aHqjslYi6GowVn654Y3r+B8MRPfCI7Q3vz4rDRihfhxbGY0ngSloNs4RPZe7oYiPEru/iUD8y4wkwsHTDA2H2qPncld0mecCO1cF9syMQFKMbpJRq6ctBm50194qt7WW4nD0JvP5XzHqZ6lDEU2l+mWwb81RjG04C75t50NhpsDc/ZOgzHIOK1urBObsmYiAY9cwpv4MjKn3DfauOQJBY1wdn03h+Qbe77wGAHU7VEfZWiUQ8SzSQJGJy1SP+3Wm/L3lfQmCTLKbsWKlQw+mtJ6j8/fCAavwe/jPKFnNG9dO35IoKsPoMq4tDqw7hkMbTwIAlg1fi1qtq6LFwMbYPn+3pLHpi/+hK/A/dOWt66SlpiMqLMZAEelfeoqAB+eS0GaqMwqWz/jQqfW5HR76J+PagQTU7m2H0g2tdLapP8geN48mIuJRGgpXVmjbLSzlsHZUwFj4HwyE/8FAqcMQ1dQOC3X+/n7oWux48gNK+hTD9TN3AADDFvTC7h8OY8ei/dr1Xq+EGANTeL6B9zuvOXs64osl/TCl7Tx8u/trA0UmLlM97teZ8vcWkg4rHSKwts/4chYXGS9xJOIyMzdDqWreuHT0qrZNEARcOnoV5T4pJWFkhlepQVnseLoaP19fhNErBsLWyUbqkHJFoxYgaAAzc912MwsZQm6lZFtfnSbg+qEEWFjLUMBLd6OAP+LwU+/n+HVsOC79GQeN2rh+CTcF1naWAIC4qAQAgL2LHcrWLIHoF7FYcuIb/PZoJRYenorydUzrfW5KZDIZJm34AjsX/4Xgm8+kDsdgTPW4TeV7C0lL8krHrVu38N9//6F27dooU6YMbt++jWXLliElJQW9e/dG48aN37p9SkoKUlJ0vxRpBDXkMml+aZXJZBixpD+u/3sbj248kSQGQ7EvYAuFmSLbL/xR4TEoXKagRFEZ3sXDV/Hvbn+EPnoBT283DPi2G+bsm4Sx9b+BJp90PbGwksO9tAX8d8TBsZA5rBzkuHs6CaF3UmHv/uo08dA/CYcWRSEtRYC1oxwdZxWApd2r91rlttZw8baAylaOkFupOLclBglRatQf5CDBUdGHkMlkGL6wN66fvYPgm08BAB5eLgCAPlM74SffX/Hg6mM07VUP8/6ejGHVfPH8QZiUIZMIuk9sB3W6GrtXGtdYhncxxeM2pe8tucErkuufpEnHwYMH0aFDB9jY2CAxMRG7du1C3759UblyZWg0GjRv3hyHDx9+a+Lh5+eHWbNm6bR5oSyKo7zY4edo9KrBKFahMMbVny7J/ZPhndxxTvv/R9efIOjaY2y+sxSVGpZD4IkbEkaWO83HOeLoiihsGBgKmRxwKW6OUvUtEf7g1SDhQhWV6LHUFcmxGtw4nICDCyLRdaELrBwyEg+fDq8GjBcoZg6FOXDih2jU6WsPhTlP4PnBqKX9ULR8IXzV5Fttm1yeURT/++cTOLzlNADgwZVgVPm0HFr0a4gN3+yQJFYSR0kfL3Qc1RJf1JoidSgGZarHze8tZCiSdq+aPXs2Jk6ciJcvX2LDhg3o2bMnhgwZgiNHjuDYsWOYOHEi5s2b99Z9+Pr6IiYmRufmhTIGOgJdo1YMQq02VTGx8SyjHXyWVUxEHNTpaji62eu0O7raIyo0Wpqg8oDQh+GIfhGLgsXdpA4lV+w9zNBlrguGb/fAgJ/d0X2RK9TpgJ3bq0qGuUoOBw8zuJe2QJPRjpApgJtHE9+4T7dSFtCogdiwdEMcAn2kkUv6olbrKvi6hR8inkVp21+GRAMAgm/pdjd5cuc5XAs7GzJEMoAK9UrDwdUOWx+swIHELTiQuAXuxVwwdEFvbL67TOrwRGOKx21q31tyQxCkuxkrSSsdN27cwObNmwEA3bp1Q58+ffDZZ59pl/fq1QsbNrx9hgmlUgmlUne2BSm6Vo1aMQh1O9bEhEYzEPoo3OD3L4X0tHTcDQiCT5OK2iklZTIZfJpUxJ5VplOafl2Bgk6wc7bBy3yaeJmr5DBXAcnxGjwOTEbdfvZvXFcQMsZ3vElEUBpkcsDSwXgGlhurkUv6ok77apjYfC7Cgl/oLAsLfoGI55EoVMpDp71gCXdcPHwVZFyObv0Xl49f12mbu38yjm77F4c3/SNRVOIzteM2xe8tJC3Jx3TIZBldLuRyOVQqFeztX33BsbW1RUxM3p8RaPSqwWj8eT3M6LgAiXHJcHRzAAAkxCQiNTlV2uBE9seS/fh640jcvfgAdy7cR6exbaCyVuLQhhNSh6Y3KmslPEu4a/92L+YC78pFERcZj7jIePSZ1gWnd11AVFg0PLzdMMSvJ54/CENAPvsyFnwpGQDgUNAMMSHpOLMxFo4FzVC2iRXSkjXw3xkH75qWsHKUIzlWg6t/JyDhpRol6mYMOg65nYKwu2koWFEJC0sZQm+n4vT6GJRuaAmVTf6ds0JlrULBrM+/lyuKVy6G2Mh4vHgSIWFk+jNqaT806l4bM7suRVJ8srZ6mXEOy+he9/uSv9FnWmcEXXuMoCvBaNq7PgqX9sR3PVdIGbremcLzDbz9vPbiyctsA4rT09SICo3G07shhg5Vr0z1uF9nyt9bSDqSJh3FihXDvXv3ULx4cQDAuXPnUKRIEe3yx48fw8PD402b5xntR7QAAHx/UndsycIBq3B400kJIjKcf3achYOLHfrN6g5Hdwc8CHyEKa3mIDo87yeL76tUNW8sOvqqr+vwRX0AAIc3/4Plo9bDq2IRNOtTH9YO1nj5PAqXjl7Dxpk7kJaav7oUpSZqcHZLLOIj1FDZylG8tiVq97aDwkwGQQNEPU3H38dfIilWA0tbOVxLWqCLnwuci2TMXqUwl+Hu6USc/y0W6jQBdq5mqNLeBj4d8tdMXq8rVd0b35949d4esbg/AODwxpNYOHCVRFHpV7thTQEAi45M1WlfNGQtjvySMYZj18pDMFeZY/iCXrB1tEHQtcfwbTsfIQ+N6xdSU3i+gbef1xYN/lGqsERnqsf9OlP+3vK+jPl6GVKRCYJ0vcfWrFmDwoULo02bNjkunzJlCsLDw7Fu3bpc7beZvKs+wqN8Qvb6PK8mov1V47tGwvvYU840xxDIlSqpQ5CEJiVZ6hAkYarnNVMlpKe9eyUjdESzU+oQ3qjSvm8ku++r7WZLdt9ikrTSMXz48Lcunzt3roEiISIiIiLKwEqH/uXfjtZERERERJQvMOkgIiIiIiJRST57FRERERFRXsIrkusfKx1ERERERCQqVjqIiIiIiLIw5iuDS4WVDiIiIiIiEhUrHUREREREWXDKXP1jpYOIiIiIiETFpIOIiIiIiETF7lVERERERFmwe5X+sdJBRERERESiYqWDiIiIiCgLzpirf6x0EBERERGRqJh0EBERERGRqNi9ioiIiIgoCw4k1z9WOoiIiIiISFSsdBARERERZcWR5HrHSgcREREREYmKlQ4iIiIioiw4pkP/WOkgIiIiIiJRMekgIiIiIiJRsXsVEREREVEWAgeS6x0rHUREREREJCpWOoiIiIiIsuBAcv1j0kH5npCeJnUIkthTzlnqECTR+kaM1CFI4u/yUkcgDZlCIXUIkjDV8xpkptkBQ25pKXUIRKIzzXc3EREREREZDCsdRERERERZsXuV3rHSQUREREREomKlg4iIiIgoC06Zq3+sdBARERERkahY6SAiIiIiyoqVDr1jpYOIiIiIiETFpIOIiIiIiETF7lVERERERFnwiuT6x0oHERERERGJipUOIiIiIqKsOJBc71jpICIiIiIiUTHpICIiIiIiUbF7FRERERFRFhxIrn+sdBARERERkahY6SAiIiIiyooDyfWOlQ4iIiIiIhIVKx1ERERERDo4pkPfWOkgIiIiIiJRMekgIiIiIiJRsXsVEREREVFWHEiud6x0EBERERGRqFjpICIiIiLKipUOvWOlg4iIiIiIRMWkg4iIiIiIRMXuVXpQsX5ZdJ3QHqWqecPZ0wkzOi3A2T3+UoclOlM97td1n9QRg/164c9lf2H1uI1ShyO69l+0QNcJ7eHk7oAHV4Kxasx63PG/L3VYHyw1SQP/rdF4eD4RSTEaFPCyQN3BjnAtqYQ6XYD/1mg8DkhCbFg6LKzkKFRZhVp9HWDtpHv6DL6YiIDtMXgZnAaFuQye5ZVoOcVVoqP6eKby/q5Yrwy6ftUOJat6wdnTCTO7LMLZvRd11uk7oytaDWoMGwdr3Dh7B8tH/Yzn90MlilgcPSZ3RL1OtVC4TEGkJKXi5tk7WDd5K57efS51aKKSy2XoM6MrmvSqDyd3B7x8HonDm/7B1jl/Sh2a3nSf0BZ121dH4VIeSE1Ow83/7uHn6dvx9N6r17CHlyuGzO2B8rVLwVxpjoAjV7FqwhZEh8dKGLnEBF6nQ99Y6dADlbUSQVeDsWLUz1KHYlCmetxZlapeHG2GNsODK4+kDsUgGnarg2Hf98Mvs3diRLVJCLoaDL+DU+HgYid1aB/sn5Uv8fRKMhqPLYBuyzxQqIoK+2eEIf5lOtJTBLwISkXVbvb4bLEHWkx2QfSzNByc80JnH0FnE3B86UuUbmKDrks80HGeG0o0sJboiPTDVN7fKmsVgq4GY+WYDTku7zahPTqOaonlI9dhTN1pSE5Igd9fvjBXmhs4UnFValAee384hDG1p2By829hZm6GeYemQWWllDo0UXX/ugPaDW+GlWPWY1D58Vjnuw3dJmY858aiUr0y2Lf2KMY2mg3fdvOhMFdg7t6vobSyAAAorSwwd+9ECAIwqc08jG/6LcwszDB75zjIZPziTfqT5yodgiDkuxe5/8FA+B8MlDoMgzPV486kslbB95cxWDJ0DXpN7SJ1OAbRZVxbHFh3DIc2ngQALBu+FrVaV0WLgY2xff5uSWP7EOkpGgSdS0TLKS7wLK8CANT43AHB/km4eTAONXs5ot0sN51t6g11wp8TQxH3Ih22LmbQqAWc+TkKn/RzQNlmttr1nApbGPRY9M1U3t/+hwLhfyjwjcs7jWmFbXN34dy+AADAggGrsOPZj6jboTpO7jhnoCjFN6X1HJ2/Fw5Yhd/Df0bJat64dvqWRFGJr1ydUji79yIu/H0ZABAW/AKNetRF6ZolJI5Mf6Z2XKTz9/fDfsKO4FUo6eOF62fuoHztUnAr6oKRdaYjMS4ZALBw6Fr88Ww1qnxaDpdP3JAibMkJHEiud3mu0qFUKnHrlvGe4Mh4jF45COf/voTLx65JHYpBmJmboVQ1b1w6elXbJggCLh29inKflJIwsg+n0QCCBlCY6/7QYaaUIeRmSo7bpCZqABmgtM44fb54kIqEl2rI5DLsHPccmwc8xV+zwxAZnCp6/CQudy9XOHs44tLxV+/xxNgk3L5wH2Xz6Wv+fVnbWwEA4iLjJY5EXDfP3oVP4wooWNIDAOBdqSgq1C1t1Am3tZ0lACAuKuO5NbcwAwQBaSnp2nXSktMgaASUr23cr3MyLMkqHePHj8+xXa1WY968eXB2dgYALF68+K37SUlJQUqK7pcDjaCGXKbQT6BEOfi0ex2UrOqNkTUnSx2KwdgXsIXCTIGosBid9qjwGBQuU1CiqD6OhaUcbqWVCNgRA8fC5rC0V+D+6QSE3UmBnXv202N6qoD/NkWjRH0rWFhlJB1xYRkf1Bd/i0adAY6wdTXDlT2x2DstDD1+8ITKluei/MrJ3QEAEP36az4sBo5uDoYPyEBkMhlGLOmP6//exqMbT6QOR1S/zd8DKztLrL+5GBq1BnKFHBumbcfxbf9KHZooZDIZhi/ojetn7yL45jMAwG3/B0hOSMGg77pjw4ydgAwYNLs7FGYKOLnbSxyxhFjp0DvJko6lS5eicuXKcHBw0GkXBAG3bt2CtbX1e3Wz8vPzw6xZs3TavFAWxVFen+ESabkUcsYXSwdgUvNvkZaSJnU49JEaj3XGyZUvsWXgM8jkQIHiFihR3xovHuj+mKFOF3BkYcZYjgbDnbXtgibj36qf2cO7TsY4jkZjCmDLoKcIOpuIci1sQZSfjF41GMUqFMa4+tOlDkV0DbvVRuOe9eDXewUe3XiCElWKYcTifngZEokjm09JHZ7ejVrSF0XLFcRXTb/TtsVExOG7Pisxemk/dBjRDIJGwImd/+He5YcQNPzmTfojWdIxd+5crF27Ft9//z0aN26sbTc3N8fGjRtRrly599qPr69vtqpJJ/v++gyVSEfJat5wdHPA6oAF2jaFmQIVG5RFh5Et0VrVExqNRsIIxRETEQd1uhqObrq/fDm62iMqNFqaoPTA3sMcHea4Iy1Zg9REDaydzHBk4QvYub0aKJyZcMS/SEe72W7aKgcAWDllVDIcC79aX2Eug52bGeJevOquQPlP5P9f1w5u9tr/A4Cjmz0eXAmWJiiRjVoxCLXaVMVXDWcg4lmk1OGIbsj8Xtg+fw9Obj8LAHh0/Qlci7igx6SORpd0jPy+D2q1qoKvms9BxPMonWWXjl3HgIoTYedsA3W6Bgkxifg1aDlCHr14w96Ick+ypGPy5Mlo0qQJevfujXbt2sHPzw/m5rmfDUSpVEKp1J1dg12rSEyXj13DkIq6ie6E9V/gye3n2L5gt1EmHACQnpaOuwFB8GlSUTt1qkwmg0+Titiz6qDE0X08c5Uc5io5UuLVeHI5CZ/0cwTwKuGICUlH+2/doLLTPb+4FLeAwhyIfpYGj3Iq7TZx4RkDzSn/Cn0YjpchUfBpVAFB/08yrGwtUaZmCez/8YjE0enfqBWDULdjTUxoNAOhj8KlDscgVFZKaF4bMaxRayCX568Jbd5l5Pd9UKd9NUxs6Yew4Ig3rhf7MmOcR+WGZeHgYof//rpkqBDzHk6Zq3eSfiLWqFEDAQEBGDlyJKpXr46tW7fmu5mrgIxZjAqWcNf+7e7liuKViyE2Mh4vnrz5zZ3fmepxJ8UnZ+vnnJyQgtjIOKPv//zHkv34euNI3L34AHcu3EensW2gslbi0IYTUof2wZ5cToIgAA4FzRATko7/NkbBoZA5SjexyUg4FrzAiwepaDXNFYIGSIxSAwCUNnIozGWwsJKjXAtbXPwtBjYFzGDraobAXRlz2xevayXloX0UU3l/q6yV8HztOL0rF0VcZDxePHmJXcsPoOeUTnh2PxShj8LRf2Y3vHwehTN7Lr5lr/nP6FWD0fjzepjRcQES45K1Y1YSYhKRmmy8kyL8tz8APX07IfxxBIJvPEUJn2LoMq5Nvj6nvW7Ukn5o1O0TzOy+FEnxydpqdcZzm9FFuHmf+nh8+zliIuJQtlYJjFjQG7tWHtK5lgfRx5IJQt6YFOy3337D2LFj8eLFC1y7du29u1flpJm8qx4je7dKDcvh+xOzsrUf3ngSCweuMmgshmSqx52TRcdn4sGVRyZxccAOI1ui64T2cHR3wIPAR/jhy/W4fcFwFwdsfSPm3Svlwv1/E3BhSzTiX6ZDZauAV20r1OzlAKW1HLFh6dg27FmO27X71g0FK76qbFzYEo27J+ORnirAtZQSdQc5wqmI/qbN/bu8YQd05pX3t0whbuW6UoNyWHTsm2zthzf/g0WDVgPIuDhg68FNYONghetn7mDF6PV4di9E1LgEtVrU/b/uiGZnju0LB6zC4U0nDReIzLCTalraqNB/dnfU7VgDDq72ePk8Eid+O4tfvv0d6WmGew7kKvGuh3IoYXOO7YuGrcWRXzIGzA+c3Q3NeteDraMNwoIj8NfPx/HnCvEr2G+KLS8o9tNCye770ZCJuVr/2bNnmDRpEg4cOIDExESUKFECGzZsQPXq1QFkjJeeMWMGfvrpJ0RHR6Nu3bpYvXo1SpYsqd1HZGQkRo8ejX379kEul6NLly5YtmwZbGxs9HZceSbpAICnT58iICAATZs2hbX1h19Yy9BJBxEZjr6TjvzC0ElHXiF20pFXGTrpyDMMnHTkFWImHXkZk46c5SbpiIqKgo+PDxo1aoQRI0bAxcUF9+7dQ/HixVG8eHEAwPz58+Hn54dNmzbBy8sL06dPx7Vr13Dz5k2oVBk/nrVq1QohISH48ccfkZaWhgEDBqBGjRrYtm2b3o4rT3U4LlSoEAoVKiR1GEREREREed78+fNRuHBhbNiwQdvm5eWl/b8gCFi6dCmmTZuGDh06AAA2b94MNzc37N69Gz169MCtW7dw8OBB+Pv7a6sjK1asQOvWrbFo0SJ4enrqJVbT/EmBiIiIiOhNBOluKSkpiI2N1bm9fk26THv37kX16tXRtWtXuLq6wsfHBz/99JN2+cOHDxEaGoqmTZtq2+zt7VGrVi2cO3cOAHDu3Dk4ODhoEw4AaNq0KeRyOc6fP//BD+HrmHQQEREREeURfn5+sLe317n5+fnluG5QUJB2fMahQ4cwYsQIjBkzBps2bQIAhIZmTAbg5uams52bm5t2WWhoKFxdXXWWm5mZwcnJSbuOPuSp7lVERERERJKTcMrcnK5B9/rlITJpNBpUr14dc+fOBQD4+Pjg+vXrWLNmDfr16yd6rLnBSgcRERERUR6hVCphZ2enc3tT0uHh4ZFtxteyZcvi8ePHAAB394wpwcPCwnTWCQsL0y5zd3dHeLjutXnS09MRGRmpXUcfmHQQEREREWUl4ZiO3Khbty7u3Lmj03b37l0ULVoUQMagcnd3dxw7dky7PDY2FufPn0ft2rUBALVr10Z0dDQCAgK06xw/fhwajQa1atXKXUBvwe5VRERERET50Lhx41CnTh3MnTsX3bp1w4ULF7B27VqsXbsWACCTyTB27Fh89913KFmypHbKXE9PT3Ts2BFARmWkZcuWGDJkCNasWYO0tDSMGjUKPXr00NvMVQCTDiIiIiKifKlGjRrYtWsXfH19MXv2bHh5eWHp0qXo1auXdp2vv/4aCQkJGDp0KKKjo1GvXj0cPHhQe40OANi6dStGjRqFJk2aaC8OuHz5cr3GmqcuDqgvvDggkfHixQFNCy8OaGJ4cUCTkqcvDrh6kWT3/WjEBMnuW0ym+e4mIiIiIiKDYfcqIiIiIqKsjK4fkPRY6SAiIiIiIlF9UNJx+vRp9O7dG7Vr18azZ88AAFu2bMG///6r1+CIiIiIiCj/y3XS8ccff6BFixawtLTE5cuXkZKSAgCIiYnRXg2RiIiIiCjfEmTS3YxUrpOO7777DmvWrMFPP/0Ec3NzbXvdunVx6dIlvQZHRERERET5X64Hkt+5cwcNGjTI1m5vb4/o6Gh9xEREREREJBkZB5LrXa4rHe7u7rh//3629n///Rfe3t56CYqIiIiIiIxHrpOOIUOG4Msvv8T58+chk8nw/PlzbN26FRMmTMCIESPEiJGIiIiIiPKxXHevmjx5MjQaDZo0aYLExEQ0aNAASqUSEyZMwOjRo8WIkYiIiIjIcNi9Su9ynXTIZDJMnToVEydOxP379xEfH49y5crBxsZGjPiIiIiIiCif++ArkltYWKBcuXL6jIWIiIiIiIxQrpOORo0aQSZ78xzCx48f/6iAiIiIiIjIuOQ66ahSpYrO32lpaQgMDMT169fRr18/fcVFRERERCQJTpmrf7lOOpYsWZJj+8yZMxEfH//RARERERERkXHJ9ZS5b9K7d2+sX79eX7sjIiIiIiIj8cEDyV937tw5qFQqfe3uo8gUCqlDkISgVksdgjRkesud8xW5hYXUIUji7/JSRyANq1NuUocgiaTGkVKHIBHTPJ/LzfX2tSRf0SSnSB0CvU548/hl+jC5fnd37txZ529BEBASEoKLFy9i+vTpeguMiIiIiIiMQ66TDnt7e52/5XI5SpcujdmzZ6N58+Z6C4yIiIiISBIcSK53uUo61Go1BgwYgIoVK8LR0VGsmIiIiIiIyIjkqjO8QqFA8+bNER0dLVI4RERERERkbHI9ArdChQoICgoSIxYiIiIiIukJEt6MVK6Tju+++w4TJkzA/v37ERISgtjYWJ0bERERERFRVu89pmP27Nn46quv0Lp1awBA+/btIZO9mk5MEATIZDKoTXXaViIiIiIyCrwiuf69d9Ixa9YsDB8+HCdOnBAzHiIiIiIiMjLvnXQIQkbK17BhQ9GCISIiIiKSHCsdeperMR1Zu1MRERERERG9j1xdp6NUqVLvTDwiIyM/KiAiIiIiIjIuuUo6Zs2ale2K5ERERERERoXdq/QuV0lHjx494OrqKlYsRERERERkhN476eB4DiIiIiIyBZwyV//eeyB55uxVREREREREufHelQ6NRiNmHEREREREZKRyNaaDiIiIiMjoCRxWoG+5uk4HERERERFRbrHSQURERESUFYcy6x0rHUREREREJCpWOoiIiIiIsuCUufrHSgcREREREYmKSQcREREREYmK3auIiIiIiLJi9yq9Y9LxASrWK4OuX7VDyapecPZ0wswui3B270WddfrO6IpWgxrDxsEaN87ewfJRP+P5/VCJIhZHxfpl0XVCe5Sq5g1nTyfM6LQAZ/f4Sx2W6Pp88xn6zuiq0/b49jMMKj9eoojE0X1CO9TtWB2FS3kgNSkNN8/fw89Tf8PTe69exwsOTUHlBmV1tvvrp2NYPmajgaMVT4/JHVGvUy0ULlMQKUmpuHn2DtZN3oqnd59LHdoHEzQCIvY8QOx/IUiPSYWZgxL2dT3h3NYLMlnG3PSCkLFO9Kln0CSmw7KEA9z7lIGFmzUAIOF2JJ4sDMhx/0Wn1YSll73BjudjZJzP26KkjxecPR0x87PF2c7nhct4YvDcz1GpflkozOQIvvUMs7svxYsnLyWKWv+M8XWekwr1yqDruDavnu+ui3Fu36vXsYOrHQbN+RzVmlSEtYMVrv97G6vGbcLzB2ESRq1/pvI5RnkLk44PoLJWIehqMA5tPIkZv3+VbXm3Ce3RcVRLLBz4A0IfvUC/md3g95cvBleagLSUNAkiFofKWpnxOGw4gZl/TpQ6HIN6eP0JJjX/Vvu3Ol0jYTTiqFS/DPatOYq7AUFQmCnQf1ZXzN0/CUN8JiMlMUW73t8/n8Dmb//Q/p11mTGo1KA89v5wCHf870NhpsDAOT0x79A0DC4/Dsn59FhfHniE6JNP4TGwPCwK2iD5USxC19+A3NIMTk2LAAAiDzxC1NEn8BhUHuYFLBGx+wGeLL4Mr+9qQ26ugFUJB5RY3EBnvy92PUDirUioitlJcVgfRHse23gSM3Zm/8Ll4e2KJSdm4ODGk9g8+3ckxiahaLlCSEs2nnM5YJyv85yorJQIuvYYhzb9gxk7xmVbPmPHeKjT1ZjZdTESY5PQ+ctWmHdgCoZU+drozm2m8Dn2MTiQXP+YdHwA/0OB8D8U+Mblnca0wra5u7S/niwYsAo7nv2Iuh2q4+SOcwaKUnz+BwPhfzBQ6jAkoUlXIyosRuowRDW1w0Kdv78fuhY7nvyAkj7FcP3MHW17SlKKUT8WU1rP0fl74YBV+D38Z5Ss5o1rp29JFNXHSbofDZsqLrCp7AIAsChgidjzoUh+mPE8CoKAyKOP4dzWC7Y+rgAAj0HlcX/cKcRfegG7Wu6QmclhZq/U7lNI1yA+MByOTYpoqyX5gf+hK/A/dOWNywfM7o4LBwOxzvdXbVtIULghQjMoY3yd5+Ti4Su4eDjn57tgCXeU+6Qkhvp8jeBbzwAAK0ZvwG/Bq9Coe20c3HDSgJGKzxQ+xyhv4UByPXP3coWzhyMuHb+mbUuMTcLtC/dR9pNSEkZG+uRZ0h2/PVmNzfeWY/KW0XAp7Cx1SKKztrMEAMRFJei0N+peBzue/IAfL/phwOxuUFpaSBGewVjbWwEA4iLjJY7kw1mWcEDCrUikhmY8l8lP4pB0PxrWFQsAANIikqCOSYV1uVeva4WVOVTedkh6EJ3jPuMCX0Adnwb7up6ix28oMpkMNVtVwbN7oZi7fzJ2PF2N5f/ORp321aUOTXTG8DrPLXOlOQAgNUuPBEEQkJaajvJ1SksVlmhM8XOMpMVKh545uTsAAKJf+/UgKiwGjm4Ohg+I9O72hftYNHA1ntx5DmcPR/Se3gVL/pmFIZUmICk+WerwRCGTyTB8YW9cP3sHwTefattPbD+H8McReBkSBa+KRTDou+4oVMod3/ZYLmG04pHJZBixpD+u/3sbj248kTqcD+bcqhg0SekImnYWkMsAjQCXTiVg/4kHACA9JhUAYGanm0Ca2SmRHpua4z5j/n0G6wrOMHdSiRu8ATm42sHK1hLdJ7bDxhk7sW7qr6jRvBK+2TEWE5t9h2unb0sdoiiM5XWeW0/uPEfY4wgMnN0dy0b9jOSEFHQe0wouhZy1n+3GwhQ/x3KN3av0Lk8lHQkJCdixYwfu378PDw8PfP7553B2fnvmnZKSgpQU3X6WGkENuUwhZqhkwrJ2KXt47TFunb+HrQ9XoWG32ji4/oR0gYlo1NJ+KFq+EL5q8q1O+4Esx/voxlNEhkRjwUFfeHi5IuSh8XVBGb1qMIpVKIxx9adLHcpHifMPQ+x/IfAcUhEWBa2R8jgOYb/d1Q4oz620yGQkXH8Jz+GVRIhWOjJ5Rjexs/sC8OfyAwCAoCvBKFe7FNoObWq0SYexvM5zS52uxuzuSzB+zVD8EfoT1OlqXD5+HRcOBuarLoPvwxQ/x0h6knavKleuHCIjIwEAT548QYUKFTBu3DgcOXIEM2bMQLly5fDw4cO37sPPzw/29vY6t4ca6fqfRoZGAwAc3HRnbnF0s0dUWLThAyLRJcQk4undEHgWd5c6FFGMXNIXtVpXwdct/BDxLOqt6972fwAA8CzuZojQDGrUikGo1aYqJjaehYhnkVKH81HCd96Fc2sv2NVyh6qQLezreMKpWRG8/DvjfGtmn1HheL2qkR6bkq36AQAxZ55DYWMO2you4gdvQLERcUhPS8fj//fvz/T49jO4GmlXFGN6nX+I+5cf4YtaU9DJdTA+LzYSU9svgJ2TjVH+iJKVsX+OfRBBwpuRkjTpuH37NtLT0wEAvr6+8PT0RHBwMC5cuIDg4GBUqlQJU6dOfes+fH19ERMTo3Pzkpd96zZiCn0YjpchUfBpVEHbZmVriTI1S+DWf3cli4vEo7JWwqO4GyJD3v6FPD8auaQv6rSvhq9b+iEs+MU71y9e+f8zH/0/+TYWo1YMQt2ONfF1k1kIfZT/v3xoUjXAaz/cyuQyCP//sDMvYAmFvQUSbr2aEladlI7koFhYFnfQ2U4QBMT8+xz2tT0hMzOuYYLpaWrcuRiEQqU8dNoLlfRA2OMIiaISj7G9zj9GYmwSYiLi4FncDSWreePc/pynhzYWxvw5RnlHnulede7cOaxZswb29hkVAhsbG8yaNQs9evR463ZKpRJKpVKnTeyuVSprJTxLvPo1wN3LFd6ViyIuMh4vnrzEruUH0HNKJzy7H4rQR+HoP7MbXj6Pwpk9F9+y1/xHZa1Cwdceh+KViyE2Mh4vnhjfB3KmoQt647/9AQgLjoCzpyP6zuwKjVqDE7+dkTo0vRq1tB8ada+NmV2XIik+GY7/r94lxCQiNTkNHl6uaNS9Ni4cuoK4l/HwqlgYwxb0wtXTt/HwuvH0Ax+9ajAaf14PMzouQGJcsnZsVsbjkPP4hrzOpnIBvPzrIcydVLAoaIOUx3GIPBwM+3oFAWT06XdqWgQv9z+EhZtVxpS5ux7AzEEJm6q61YzEW5FIi0iCfYOCUhzKR8t2Pi/monM+/33xfkzZOgbXTt/GlX9uonrzyvikTVVMaPqdhFHrnzG+znOislbq/JrvXswF3pWKIi4q4/mu37kmYiLiEP4kAl7li2D4931wbu9FXDp67S17zX9M5XPsY3DKXP2TCYIg2cMql8sRFhYGFxcXFCxYEIcOHUKFCq8qBMHBwShTpgySkpJytd/m5m9PVD5WpQblsOjYN9naD2/+B4sGrQaQcXHA1oObwMbBCtfP3MGK0evx7F6IqHEJarWo+39dpYbl8P2JWdnaD288iYUDVxkuEJlhf12dsu1LVKpfBrbOtoh5EYvrZ+5gw7TfEBJk2ItHyS3EnSXqUNKWHNsXDVmLI7+chkshJ3y9fgSKlSsIlbUSL55G4szeAPw6bzcS48QbiKhJMewgxyOanTm2LxywCoc3nTRYHFan9NdlTZ2UjojdDxB3KRzquIyLA9rVdEeB9t7aaoX24oD//P/igCUd4N67DCzcrXX29XztNaS9TEJR35p6iy+rpMbidvGp1KAsFh3NPnbh8OZ/sGjwjwCAFv0aosfXHVCgkBOe3n2OzbP/0LmgnBiEdMNeBySvvM7FPq9ValAWCw9Py9Z+eMspfD/kR3T4ogW6jm8DB1d7RIZG4+jW09g2dxfS08T9fNWkpYu6/9fllc+xI+rtBr2/3Cj97RLJ7vvO9OzXkDEGkicdFSpUgJmZGe7du4eNGzeiS5cu2uWnTp1Cz5498fTp07fsJTuxk468ytBJR55h4KQjrxD7wzmvMnTSkVfoM+nIT8ROOvIqQycdeYXJntcMnHTkFUw6cmasSYek3atmzJih87eNjY3O3/v27UP9+vUNGRIREREREelZnko6Xrdw4cK3LiciIiIiorwvzwwkJyIiIiLKEziQXO9MszM8EREREREZDJMOIiIiIiISFbtXERERERFlwet06B8rHUREREREJCpWOoiIiIiIsmKlQ+9Y6SAiIiIiIlGx0kFERERElBUrHXrHSgcREREREYmKSQcREREREYmK3auIiIiIiLLglLn6x0oHERERERGJipUOIiIiIqKsWOnQO1Y6iIiIiIhIVEw6iIiIiIhIVOxeRURERESUBQeS6x8rHUREREREJCpWOoiIiIiIsmKlQ+9Y6SAiIiIiIlGx0kFERERElBUrHXrHSgcREREREYmKSQcREREREYmK3auIiIiIiLLglLn6x6SDKJ/SpCRLHYIkZAqF1CFIIrHhC6lDkETja3FShyCJ4xWspQ5BEprUVKlDICKRMOkgIiIiIsqKlQ6945gOIiIiIiISFZMOIiIiIiISFbtXERERERFlxe5VesdKBxERERERiYqVDiIiIiKiLDhlrv6x0kFERERERKJi0kFERERERKJi9yoiIiIioqzYvUrvWOkgIiIiIiJRsdJBRERERJQFB5LrHysdREREREQkKlY6iIiIiIiyYqVD71jpICIiIiIiUTHpICIiIiIiUbF7FRERERFRVuxepXesdBARERERkahY6SAiIiIiykImdQBGiJUOIiIiIiISFZMOIiIiIiISFbtXERERERFlxYHkesdKBxERERFRPjdv3jzIZDKMHTtW25acnIyRI0fC2dkZNjY26NKlC8LCwnS2e/z4Mdq0aQMrKyu4urpi4sSJSE9P13t8TDqIiIiIiLKQCdLdPoS/vz9+/PFHVKpUSad93Lhx2LdvH3bu3Il//vkHz58/R+fOnbXL1Wo12rRpg9TUVJw9exabNm3Cxo0b8c0333zMw5cjJh1ERERERPlUfHw8evXqhZ9++gmOjo7a9piYGPz8889YvHgxGjdujGrVqmHDhg04e/Ys/vvvPwDA4cOHcfPmTfzyyy+oUqUKWrVqhW+//RarVq1CamqqXuNk0kFERERElJUg3S0lJQWxsbE6t5SUlDeGOnLkSLRp0wZNmzbVaQ8ICEBaWppOe5kyZVCkSBGcO3cOAHDu3DlUrFgRbm5u2nVatGiB2NhY3LhxI9cP29twIPkHqFivDLp+1Q4lq3rB2dMJM7sswtm9FwEACjMF+s/ujpqtqsDDyxUJMYm4dPw6fp7yKyJDoiSOXL/aDm+OdsObw62YCwAg+MZT/PLtTvgfDJQ2MANw9nTE4Hm9ULNlFSitlHh+PxSLBq3G3YAgqUMTTcX6ZdF1QnuUquYNZ08nzOi0AGf3+Esdlt697f2dqe+Mrmg1qDFsHKxx4+wdLB/1M57fD5UoYvEY4+s8LUmDS9si8fh8ApJj1HDyskCtQQVQoKQKABB8Lh53DsXi5YMUpMRr0G5xITh7KbPtJ/x2Mi5tfYmIeymQyQEnLyWafeMBM2X+/i2v/Rct0HVCezi5O+DBlWCsGrMed/zvSx2WwXSf1BGD/Xrhz2V/YfW4jVKHIzpTf77zKj8/P8yaNUunbcaMGZg5c2a2dX/77TdcunQJ/v7ZP49DQ0NhYWEBBwcHnXY3NzeEhoZq18macGQuz1ymT/n77CgRlbUKQVeDsXLMhmzLlFYWKOlTDFvn/IkvavpiVrfFKFzKE7N3TZAgUnFFPH2Jn323YmT1SRhZYzICT1zHrN2TULRcIalDE5WNgzWWnp4NdZoaU9r4YXCF8fhx4hbERSVIHZqoVNZKBF0NxopRP0sdiqje9v4GgG4T2qPjqJZYPnIdxtSdhuSEFPj95QtzpbmBIxWXsb7Oz6wKR8iVRNT/0hUdlhaGZxUrHJoZgoSXGYMm01MEuJZVoVpf5zfuI/x2Mo58GwLPKlZos6AQ2i4shDKt7SGT5+/LiTXsVgfDvu+HX2bvxIhqkxB0NRh+B6fCwcVO6tAMolT14mgztBkeXHkkdSgGYerPd17m6+uLmJgYnZuvr2+29Z48eYIvv/wSW7duhUqlkiDS3GGl4wP4HwqE/6HAHJclxiZhcqu5Om0rv1yPlefmwqWwM148eWmACA3jv/0BOn9vmPYr2g5vjrKflELwzacSRSW+7l+3x4snL7Fo0GptW+ijFxJGZBj+BwNNoor1tvc3AHQa0wrb5u7CuX0Zr/8FA1Zhx7MfUbdDdZzccc5AUYrPGF/n6SkaBJ9LQGNfd7iXtwQA+PRwwlP/BNw5GIOqvZxR/FNbAEBceNob93NhQwTKtrFHpS6v+k7bF7QQN3gD6DKuLQ6sO4ZDG08CAJYNX4tarauixcDG2D5/t6SxiU1lrYLvL2OwZOga9JraRepwDMKUn+/3IuGUuUqlEkpl9grr6wICAhAeHo6qVatq29RqNU6dOoWVK1fi0KFDSE1NRXR0tE61IywsDO7u7gAAd3d3XLhwQWe/mbNbZa6jL6x0GIC1nRU0Gg0SohOlDkU0crkcn3avA5W1EjfP3ZU6HFHVblcddwOCMH37OOwIWYvVF+eh1eDGUodFBuDu5QpnD0dcOn5N25YYm4TbF+6j7CelJIxM/4zxdS5oMm4KC92KhMJCjrBbye+1j6TodETcTYGlvQJ/TX6K3/o/woGpzxB2M0mMkA3GzNwMpap549LRq9o2QRBw6ehVlDOy13ZORq8chPN/X8LlY9fevbIRMPXn21g0adIE165dQ2BgoPZWvXp19OrVS/t/c3NzHDt2TLvNnTt38PjxY9SuXRsAULt2bVy7dg3h4eHadY4cOQI7OzuUK1dOr/FKWum4dOkSHB0d4eXlBQDYsmUL1qxZg8ePH6No0aIYNWoUevTo8dZ9pKSkZBtcoxHUkMsUosWdG+ZKcwz264mT288iMS5/fyjlpFiFIlh+dg4sVOZIik/GrM4L8fiW8VY5AMDD2xXthjfDH0v+wja/XShdozhGLh2A9NR0HNl8SurwSERO7g4AgOiwGJ32qLAYOLo5GD4gERnj69zcUg6X0kpc2REFh0IWUNkr8PB0PF7cTYat+/t1j4sLy+iGFfhbJKr3d4aTlxIPTsbh0Izn6LisMOw882fFw76ALRRmCkS9/toOj0HhMgUlisowPu1eByWremNkzclSh2Iwpvx8v68PnbrWkGxtbVGhQgWdNmtrazg7O2vbBw0ahPHjx8PJyQl2dnYYPXo0ateujU8++QQA0Lx5c5QrVw59+vTBggULEBoaimnTpmHkyJHvVW3JDUkrHQMGDMCDBw8AAOvWrcOwYcNQvXp1TJ06FTVq1MCQIUOwfv36t+7Dz88P9vb2OreHmluGCP+dFGYKTPv1S0Amw/KRxtkP/umd5xjuMxGjP5mCfWsOY+LGUShS1rjHdMjkcty79BDrp/2GB4GP8PdPx/D3umNoO7SZ1KER6Y2xvs7rf+kGCMCOQcHY0i0It/6KgVc9G8jedzjG/7+IlGphh5JN7ODsrUTNgQVgX9AC947FiRY3icOlkDO+WDoAfr2XIS3lzV3qiPKrJUuWoG3btujSpQsaNGgAd3d3/Pnnn9rlCoUC+/fvh0KhQO3atdG7d2/07dsXs2fP1nssklY67t27h5IlSwIAfvjhByxbtgxDhgzRLq9RowbmzJmDgQMHvnEfvr6+GD9+vE5bZ6dB4gScC5kJh2tRF3zd7FujrHIAQHpaOp4/yJjd4N6lIJSuXhydvmyNZcPXShyZeCJDovD41jOdtse3n6F+51oSRUSGEhkaDQBwcLPX/h8AHN3s8eBKsDRBicRYX+d2HuZoNacg0pI1SEvUwMrJDCcXhb53pcPSMaOK7lBIt6JhX8gcCRH6v4KvocRExEGdroajm71Ou6OrPaKyvNaNTclq3nB0c8DqgAXaNoWZAhUblEWHkS3RWtUTGo1GwgjFYarPtyk4efKkzt8qlQqrVq3CqlWr3rhN0aJF8ffff4scmcSVDisrK0RERAAAnj17hpo1a+osr1WrFh4+fPjWfSiVStjZ2encpO5alZlwFCzhgcktvkNcZLyk8RiSTC6HhYVxzeLzuhtn76BQKQ+dtkIlPRAWnL8H2dK7hT4Mx8uQKPg0elXOtrK1RJmaJXDrP+May2Tsr3NzlRxWTmZIiVfj2eUkFK5p/V7b2biawcpJgZjnur+Kxz5Pg7VL/p2bJT0tHXcDguDTpKK2TSaTwadJRdw0std2VpePXcOQiuMx3Gei9nbH/z6Ob/0Xw30mGmXCAZju850rEl6nw1hJeoZs1aoVVq9ejXXr1qFhw4b4/fffUblyZe3yHTt2oESJEhJGmDOVtRKeJV6N6Hf3coV35aKIi4xHZEg0pm8fh5I+XpjecT7kCrn2l4S4yHikp6mlClvvBs7tCf8DlxH+OAKWtpZo3LMeKn9aDr4t50gdmqj+WPo3lv07G59P7oh/dp5D6Zol0HpIEywd/pPUoYlKZa1Cwdde98UrF0NsZDxePImQMDL9etv7+8WTl9i1/AB6TumEZ/dDEfooHP1ndsPL51E4s+fiW/aa/xjr6/zZ5UQIAmBf0BxxIWnw3/QS9oXMUbJxxqxVKXFqxEekIykyo2oR+ywjubB0UMDK0QwymQzlOzog8LcoOBWzgJOXEvdPxCHmWRo+nZi/pxr9Y8l+fL1xJO5efIA7F+6j09g2UFkrcWjDCalDE01SfDIe3Xii05ackILYyLhs7cbGFJ9vkpZMEATJcqrnz5+jbt26KFKkCKpXr47Vq1ejWrVqKFu2LO7cuYP//vsPu3btQuvWrXO13+bmbx98/rEqNSiHRce+ydZ+ePM/2DL7d2y5vyLH7SY0mY2rp26KFpegNmxCM37dCPg0rgAnD0ckxCTi4dVgbF+wR2c2DIOQGb5gV6tNVQya8zkKlnRH6MMX+H3pfhxYd9ywQQiG/QWuUsNy+P7ErGzthzeexMKBby7b6ptMIW4l823v78zpY/vO6IrWg5vAxsEK18/cwYrR6/HsXoiocQkaw5+q88LrvPE1/Y6TeHgmHpe2vETCy3QobRUo+ok1qvZygoV1xuvq3vFYnFmRvZpTubsjfHo4af+++kcUbh+IQWq8Bo7FLFC9rzPcylnqLc7jFd6v8qJvHUa2RNcJ7eHo7oAHgY/ww5frcfuCaV0sbtHxmXhw5ZFJXBxQ6uf7iGanwe4rt3xGLpHsvi+vGifZfYtJ0qQDAKKjozFv3jzs27cPQUFB0Gg08PDwQN26dTFu3DhUr1491/sUO+nIqwyddOQZEiQdeYKBk468QuykI6+SIunIC/SddOQXUiUdRIbEpCNnxpp0SN4B1cHBAfPmzcO8efOkDoWIiIiIyKjHVkjFRH8iJiIiIiIiQ2HSQUREREREopK8exURERERUV6SH65Int+w0kFERERERKJipYOIiIiIKCtWOvSOlQ4iIiIiIhIVkw4iIiIiIhIVu1cREREREWXF7lV6x0oHERERERGJipUOIiIiIqIsOGWu/rHSQUREREREomKlg4iIiIgoK1Y69I6VDiIiIiIiEhWTDiIiIiIiEhW7VxERERERZSET2L9K31jpICIiIiIiUbHSQURERESUFQsdesdKBxERERERiYpJBxERERERiYrdq4iIiIiIsuAVyfWPlQ4iIiIiIhIVKx1ERERERFmx0qF3rHQQEREREZGoWOkgIiIiIsqCYzr0zyiTDkGtljoEScgUCqlDkITMzFzqECShSUmWOgRJCBoT/SQQNFJHIInjFaylDkESve88kzoESfxSuqDUIUhCrlRJHQKR6Ni9ioiIiIiIRGWUlQ4iIiIiog9mokV1MbHSQUREREREomKlg4iIiIgoCw4k1z9WOoiIiIiISFRMOoiIiIiISFTsXkVERERElBW7V+kdKx1ERERERCQqVjqIiIiIiLLgQHL9Y6WDiIiIiIhExUoHEREREVFWAksd+sZKBxERERERiYpJBxERERERiYrdq4iIiIiIsuBAcv1jpYOIiIiIiETFSgcRERERUVasdOgdKx1ERERERCQqJh1ERERERCQqdq8iIiIiIspCppE6AuPDSgcREREREYmKlQ4iIiIioqw4kFzvWOkgIiIiIiJRMekgIiIiIiJRsXsVEREREVEWvCK5/jHp0IOK9cui64T2KFXNG86eTpjRaQHO7vGXOiyDsLRRod+sbqjboQYcXO1xP/ARVo/fiLsXg6QOTS+6T2iHuh2ro3ApD6QmpeHm+Xv4eepveHovVGe9srVKoP/MrihTozjUag2CrgZjSrsFSE1Okyhy8XWf1BGD/Xrhz2V/YfW4jVKHIypnT0cMntcLNVtWgdJKief3Q7Fo0GrcDTCO13lOTPm8BgDtv2iBrhPaw8ndAQ+uBGPVmPW4439f6rA+WEqigFO/JOLOuVQkxmjg5m2GZkOt4Vkq42vAviXxuHYsRWcb76rm6DHbDgAQfDUNW6fE5rjv/ovttfvJb3pM7oh6nWqhcJmCSElKxc2zd7Bu8lY8vftc6tD06l2fZbaO1ugzvTOqNqkI18LOiImIxdl9l7Bp1u9IjE2SOHoyFvnzLJHHqKyVCLoajEMbTmDmnxOlDsegxv04DMXKF8KC/qvwMiQKTXrWx/yD0zC40ld4+TxK6vA+WqX6ZbBvzVHcDQiCwkyB/rO6Yu7+SRjiMxkpiRkf0GVrlcCcPRPx26J9+GH8ZqjT1fCuVASCxnh/JilVvTjaDG2GB1ceSR2K6GwcrLH09GxcOXkTU9r4IeZFLAqW9EBcVILUoYnKlM9rDbvVwbDv+2H5iLW4df4+Oo9tA7+DUzGwzJeIfpHzF++87u8V8XgRrEb7r2xg4yTH9RMp+HVaLIb+YA/bAgoAgHc1c7Qda6PdRmH+avtCZc0wZoujzj5PbUnEoytp8CipMMgxiKFSg/LY+8Mh3PG/D4WZAgPn9MS8Q9MwuPw4JCemvHsH+cS7PsucPBzh7OGIn3x/xeNbz+BapADGrOgPZw8HfNdzhdThS0Mw3s9wqTDp0AP/g4HwPxgodRgGZ6EyR/3ONTGj8yJc+/c2AGDLt7/jk7ZV0W5YM2ycsUPiCD/e1A4Ldf7+fuha7HjyA0r6FMP1M3cAAMMW9MLuHw5jx6L92vVer4QYE5W1Cr6/jMGSoWvQa2oXqcMRXfev2+PFk5dYNGi1ti300QsJIzIMUz2vAUCXcW1xYN0xHNp4EgCwbPha1GpdFS0GNsb2+bslje1DpKUIuH0mFV2n26JIhYxMokEvK9y/kIqAAyn4tI8VAMDMHLBxzHmop8JcBhtHmfZvdbqAu+dTUb2tCjKZLMdt8oMprefo/L1wwCr8Hv4zSlbzxrXTtySKSv/e9VkWfPMpvv18uXZ5yMNwbJz5O75ePxxyhRwaNS9aQR+PA8npgynMFFCYKbJ1IUpJSkX5umUkikpc1naWAKD9ldvexQ5la5ZA9ItYLDnxDX57tBILD09F+TqlpAxTVKNXDsL5vy/h8rFrUodiELXbVcfdgCBM3z4OO0LWYvXFeWg1uLHUYZFIzMzNUKqaNy4dvaptEwQBl45eRblP8uf7WqMGBE1G4pCVmVKGpzdenb+Dr6Vjaa9IrBkWhQOr4pEY++YvmvfOpyIpTkClZkrR4paCtX1GAhYXGS9xJOJ6/bPsTeskxiaZbMIhE6S7GSsmHfTBkuKTcePcXfSa2hlOHo6Qy2Vo0rMeyn5SCk7uDlKHp3cymQzDF/bG9bMZvwoBgIeXCwCgz9ROOLD+BKZ2WIj7gY8w7+/J8CzuJmW4ovi0ex2UrOqNn323SR2KwXh4u6Ld8GZ4di8Evq3mYt+PRzBy6QA069tA6tBIBPYFbKEwUyAqLEanPSo8Bo759LymtJKhYBkznPktEXEvNdCoBVw/kYJnt9MRH5XxhdK7qjnajbdBzzl2aNTfGo+vp2P7jFho1Dl/A7pyOAXePuawK5B/u1a9TiaTYcSS/rj+7208uvFE6nBEk9Nn2evsnG3Q07cjDqw/YeDoyJhJmnSMHj0ap0+f/qh9pKSkIDY2VuemEdR6ipDeZUH/VZDJgN8er8ZfCb+gw6iWOLn9jFGOZxi1tB+Kli8Ev76rtG1yecZb6O+fT+DwltN4cCUYP369FU/vhqBFv4ZShSoKl0LO+GLpAPj1Xoa0FOMdIP86mVyOe5ceYv203/Ag8BH+/ukY/l53DG2HNpM6NPpfe/cd1tTZvwH8TgIJeypLBXEBbhAHWjdurda9WnD1tS+2jqrVthY3rjrq7s+BdVStraPWiohKtWpVFLfgBmUIInsn+f3B25QUbavNyYFwf64rvcqTY3IfAiTf832ec+gfe/tjC6gBrA54gcXvpOHioTzUbyfXTI1q0F6Bei3lcKhpBA8/OQYHWyLxrhKPrxeXeazMVCUeXClCk66G1eX4cO1Y1GxYAwuGrRA7iqBe9l5WmpmlCebtn4q420+xff5+PacjQybqmo61a9di3bp1qF27NsaMGYOAgAA4OTm91mOEhIRgzpw5WmPu8EJtNNBlVHqFxAfJmNp5LkzMFDCzMkVaUjo+3TkRiQ+TxY6mU0Er3kPLnk3xsf8CpD79Y4H888R0AMDj20+1to+PSYBDDXt9RhRc3Wa1YOtog/VRSzRjMiMZGrXzQt+g7uhpMhwqleG14dMSXyDuT69v3J2naNu/pUiJSEgZqVlQFith62itNW7rYI0XSenihNIBW2cZ3l1kjcJ8NQpz1bCwk2L/4izYOL382KOtkwymVhK8SFTCvamx1n3XwgtgailB3ZZyfUTXiwmrx6BlLx983D4YqU/TxI4jmFe9l/3O1MIECw5NR15WHuYMWQVlcSU+iGt4x05FJ/r0qmPHjqFnz55YtmwZXF1d0bdvXxw+fPgff3iZOXMmMjIytG7uMMz1BOVZfm4B0pLSYWFjDt+ujXHuxyixI+lM0Ir30PrtZpjePQTJj7UXECc/TkFqQhqq13PWGq9WxwnP4lL1GVNwVyKuY1yjKRjvPU1zi7l4Dyd2nsF472kGWXAAwM2zMWVe3+p1ncv8LJBhKC4qRmzUA3h3bqQZk0gk8O7cCLfOx4qYTDfkJhJY2EmRl63Cg8tFqNfq5YVDZqoSeVklxUlparUa144XoFEnBWRGFXcBeWkTVo9Bm34tML3zHCQ9eiZ2HMH81XsZUNLhWHh4OooKixE8cEWl6miTfoh+9qpGjRqhc+fOWLp0Kfbv348tW7agX79+cHR0RGBgIEaNGoU6deq88t8rFAooFNotXqlEv3NMTcxNUK3OHx0aJ3cH1G5SE5lp2UiJN6wPnn/WrEtjSCQSPIlNgEttJ4xbPALxMQmas75UdBNWBqDjED/MHrQSedn5mqOfORm5mgX0+1Ycwbuf98eD63F4cPUx/Ee2RQ0PF4M7zWBedn6Zec75OQXITMsy6PnP3688glVn5mLYjH6I/O4cPFrUQc9xnbFy/P+JHU1Qlfnv2vcrDmN6aBBiL91HzIV7eGdSL5iYKxC2teLOb38QVQg1APtqMrxIVCJiSy7sq8vQ2F+Bwjw1Tn+bC8/WcpjbSvEiUYWTW3Ng5yxFLR/tLsejq8VIT1ahSVcTcXZExz5cOxadhr2F4H5LkJuVD1tHGwC//40vFDecDv3de1lJwfEJFKZyLBm1AWZWpjD732LzjJRMqAxwyvTfMeQF3WKRqNXinYhYKpUiKSkJDg4OWuNxcXHYsmULQkNDER8fD6Xy9dp7XaSDdBnzbzVuXx9fnpxTZvxY6CksHf3yOZNCkMj0v6Cv3cBWGD1/GKpUt0NWWjbO7L+ArbN26/ViQhIj47/f6A2F5W1/6fiycV8jfMcf65EGT+2Nt//jD0tbCzy4HodNn+3GzbPCHhVVFeQL+vj/xLITs3H/6iP9XhxQov8GbctePhizYBiq1XVC0sMU7Ft5GD9vOqHfEGr9dpLKy981sfQN6o5BU9+GrZMN7kc/wrqJW3Dngv4uDjgy5unfb/Qabp0uwKltuchKVcHEUgLP1nK0f88MJuZSFBWosW9+FpIfFCM/Rw1LOyncvY3RbqRZmVPoHliahcxnKry31PoVz/Tv7PCoJsjjvkq46ruXji8dtRbHtp3SWw6pQtgi7u/eyxq39cTSY5+9dJv3PCYjWaDO/atylQdt31km2nOf3j9VtOcWUrksOn6nVqtx/PhxdOnyegs29V10lBdiFB3lgZBFR3lWHooOUYhQdJQLei46SFy6LjoqCn0XHeWF0EVHecWi4+UMtegQdXqVm5sbZH/xQVkikbx2wUFERERE9K/wiuQ6J2rR8fDhQzGfnoiIiIiI9ED0heREREREROUJF5LrXiWdHE1ERERERPrCTgcRERERUWnsdOgcOx1ERERERCQoFh1ERERERCQoTq8iIiIiIiqFC8l1j50OIiIiIiISFDsdRERERESlqdjq0DV2OoiIiIiISFAsOoiIiIiISFCcXkVEREREVBpnV+kcOx1ERERERCQodjqIiIiIiErhKXN1j50OIiIiIiISFDsdRERERESlqdnq0DV2OoiIiIiISFAsOoiIiIiISFCcXkVEREREVAoXkuseOx1ERERERCQodjqIiIiIiEpjp0Pn2OkgIiIiIiJBseggIiIiIiJBcXoVEREREVEpEl6nQ+fY6SAiIiIiIkGx02FA1KrKWZWrCwvFjiAKqVwudgRRqIqKxY4gComRsdgRxKFWiZ1AFDs8a4gdQRQtoovEjiCKC96V832sXKucf3oExU4HEREREREJip0OIiIiIqJSuKZD99jpICIiIiIiQbHoICIiIiIiQXF6FRERERFRaZxdpXPsdBARERERkaDY6SAiIiIiKo0LyXWOnQ4iIiIiIhIUiw4iIiIiogooJCQEzZs3h6WlJRwcHNCvXz/ExMRobZOfn4+goCDY29vDwsICAwYMQHJystY2cXFx6NWrF8zMzODg4IBp06ahuFi3F+Nl0UFEREREVIpELd7tdURGRiIoKAjnz59HeHg4ioqK0LVrV+Tk5Gi2mTx5Mn788Ud89913iIyMREJCAvr376+5X6lUolevXigsLMTZs2exbds2hIaG4osvvtDVtxMAIFGrDW/SWhfpILEjiEPCGrIykRpXziVZqiLdHnmpKCQymdgRxKFWiZ1AFGqVwb01/yMtrhSJHUEUF7yNxY4ginDlHrEjvJJ/+4WiPfdPxz5GQUGB1phCoYBCofjbf5uSkgIHBwdERkaiXbt2yMjIQNWqVbFr1y4MHDgQAHDnzh14eXnh3LlzaNWqFX7++Wf07t0bCQkJcHR0BABs2LABn3zyCVJSUiCXy3WyX/yUSkRERERUmlot2i0kJATW1tZat5CQkH8UOyMjAwBgZ2cHAIiKikJRURH8/f0123h6esLV1RXnzp0DAJw7dw6NGjXSFBwA0K1bN2RmZuLmzZu6+o7y7FVEREREROXFzJkzMWXKFK2xf9LlUKlUmDRpEtq0aYOGDRsCAJKSkiCXy2FjY6O1raOjI5KSkjTblC44fr//9/t0hUUHEREREVEpEhFndv7TqVR/FhQUhBs3buDMmTMCpPr3OL2KiIiIiKgCmzBhAg4fPoyTJ0+ievXqmnEnJycUFhYiPT1da/vk5GQ4OTlptvnz2ax+//r3bXSBRQcRERERUQWkVqsxYcIE7N+/HydOnIC7u7vW/c2aNYOxsTEiIiI0YzExMYiLi4Ofnx8AwM/PD9evX8ezZ88024SHh8PKygr169fXWVZOryIiIiIiKq2CnNw1KCgIu3btwsGDB2FpaalZg2FtbQ1TU1NYW1tjzJgxmDJlCuzs7GBlZYUPP/wQfn5+aNWqFQCga9euqF+/Pt59910sWbIESUlJ+PzzzxEUFPRG07xehUUHEREREVEFtH79egBAhw4dtMa3bt2KwMBAAMCKFSsglUoxYMAAFBQUoFu3bli3bp1mW5lMhsOHD+ODDz6An58fzM3NERAQgLlz5+o0K4sOIiIiIqLSKkajA//kcnsmJiZYu3Yt1q5d+8pt3NzccOTIEV1GK4NrOoiIiIiISFAsOoiIiIiISFCcXkVEREREVIqkgiwkr0jY6SAiIiIiIkGx00FEREREVBo7HTrHokMHeo/vij7ju8KxZlUAwOObT7Bj3ne4eDRa3GACe/eLgXgveJDWWNydpxjTYIpIifRj+/3VcKrpUGb80LowrP5wiwiJhDFk2tto09cXNTxcUJhXiFvn72LzZ7vx5G6iZhtjhTHeXzwCHQa1grHCGFHh17B64lakP8sUMbluSaUSvBs8CJ1HtIWdkw2eJ6Th2LZI7Fzwg9jRdKrRW54Y9HFv1PV2h72LLWYPXI6zhy5pbVPD0wVjFw5D47ZekBlJ8fj2U8wdshIp8c9FSv3vlex3H9T1cYe9ix1mD1hWZr/fCx6EHmM6wcLGHDfPxuCrCZuRcC9JpMTCsXexxdhFI9Cie1MozBRIuJeEZWPWIzbqgdjR3ohaqcbN754h7pcM5KcXw9TOCG4dbOA1oCokEgkAID+9GNd3JCP5WjaKcpSo4mWOpmOcYOn8x7UJojYm4Nn1bOSlFcPIRAp7DzM0GukIq2q6u36BGAzt9abyj0WHDqQ+eY7NM3fi6d1EQCJB14AOmHPgE3zgMw2Pbz0RO56gHt6Ixydd52m+VharREyjHxNafgqp7I+ZiTUbumLJsc8Rue+8iKl0r3FbT/y48ThiL92HzEiGwLmDsfCnGRjXdDoKcgsAAOOXjkSLHk0xf8RXyMnIRdCKQHyxZzKmdJwjcnrdGTK9L/qM74Ilo9bh8c0nqOdbC1M3f4CcjFwcWHNU7Hg6Y2KuwINrjxEWegrB35U9cOBcywErTgbjaOgpfDN3H3Iz8+BWvzqK8otESKs7JuYmf+z3vo/L3D946tvoN6E7lo5eh6RHKQiYPRghP83E2MZTUVRQsfe9NAsbc6w8PRdXT93Cp71CkJGSiWp1nZH1IkfsaG/szsFUPDj2As2DqsGqhgIv7ufh0roEGJvJULenPdRqNc4uiYPUSILW011hbCpF7OHnOD33MbquqAMjk5K/87a1TODa1hpmVYxRmK3Erb0pOD3vMXqurQuJTCLyXr4ZQ3y9dc7wP87oHYsOHTh/OErr662ff4ve47vCq1U9gy86VMVKvEjOEDuGXmWkZml9PfQTHzy9l4RrkbdESiSMz95eovX1l+M2Yu+TDajr444bZ+7AzMoU3QI7YFHAWlw9VbLvy9/fiE3XlsGzRR3cuXBPjNg6V791PZw9dAkXjlwBACQ/TkHHoW3g0aKOyMl062LYVVwMu/rK+0fNHYILR6Oxaea3mrHEB8/0EU1QF8OicTEs+pX3v/NRD+xauB/nfiz5O79k1FrsfboRbfr64tTec3pKKbwh099GSvxzLBuzXjOW9ChFxET/3vOYXLj4WsK5mSUAwNxBjvhfM/DiXh4AIDuxEGl389BleW1Y1zABAPiMc8bhcTGI/zUD7p1tAQC1uthpHtPcAWgwzAHHp95HTkoRLJzket4r3TDE15vKPy4k1zGpVIoOQ1rDxFyBW+dixY4jOJe6Ttgdvx7f3P0KM7Z/iKo17MWOpFdGxjJ0HvEWwraeFDuK4MytzAAAWWnZAIC6Pu4wlhvhyokbmm3iYxORHJcKr5aG84H81tlYeHdqiGp1nQEAtRq7oWEbD4OfPlmaRCJBix5N8fRuEhYenoG9T9bjqzNz0fptX7GjCcrJ3QH2zra4fOK6Ziw3Mw93LtyDV6t6IibTPb8+voiNeoBZeyZjb+LXWH9pEXqM7SR2rH/F3sMMz27kICuhpDOb/igfqXdy4eRtAQBQFZXM2ZcZ//FRSCKVQGosQert3Jc+ZnG+Co9OvoC5gzHM7CvucVtDfL2p/BP9N2bNmjW4cOECevbsiaFDh2L79u0ICQmBSqVC//79MXfuXBgZvTpmQUEBCgoKtMZUaiWkEpnQ0bXUbOiKr84ugNzEGHnZ+ZjTfynibht2l+POhXtYNno94mMSYO9si5GzBmBF5ByMazwVedn5YsfTi9b9msPCxhzHtkWKHUVQEokE45e9ixtnYzTdOztHGxQWFCEnQ/vNOT05A3aONiKkFMbuxQdhZmWKLbeWQ6VUQSqTYuvne3Bi1xmxo+mNjYMVzCxNMWRaH4QGf4dNn32L5l0b44u9kzCty3xcP31H7IiCsHOyAVDyM13ai+QM2BrQzzhQMn2uz/gu+H7FT9gVsh8ezWsjaOUoFBcWI/ybX8SO90Y8+1VBca4KYZPuQSIF1Cqg4TAHuLa1AQBYVlPArIoxbuxKhs/7LjBSSBD703PkPS9Gfrr21Ln7YWm4tj0ZygIVLF3kaDurJqTGFfe4rSG+3rrGU+bqnqhFx/z587FkyRJ07doVkydPxuPHj7F06VJMnjwZUqkUK1asgLGxMebMefX88JCQkDL3u8MLtdFA6PhansQkYLz3NJhbm6HtwFaYFjoBH3cINujCo/SR3ofX43D7t7vY+XAt2g/2w9Ethn/kHwB6jO6EC0ej8TzxhdhRBDVhVSDcGlTHx53mih1F79oP9kOn4W8hZORqPLoZjzpNa+KD5QF4nphWad6cJdKSeetnf4zCD1/9DAB4cPUx6vvVQ+/3/Q226KhMJFIpYi/dx5bPdwMA7kc/Qs0GNdD7/S4V9uf8yblMxJ1JR8uJ1WFVXYH0R/m4GpoEE1tj1OxgA6mRBH5Ta+DS+gQcGnUHEing0MgCTt4WZU5c5PqWNRwamyP/RTFiDz3H+eXx6DjfHTJ5xSw8DPH1pvJP1KIjNDQUoaGh6N+/P65evYpmzZph27ZtGDFiBADA09MT06dP/8uiY+bMmZgyRXvR4zvWgULGfqniomIk3C85m8ndyw/g4Vsb70zsiVXjv9Z7FrHkZOTiSWwiXGo7iR1FLxxcq8C7cyPMGfil2FEEFbQiAC17euNj/3lIfZqmGU9LTodcYQxzazOtboeNozXSktNFSCqMcYtHYM/igzi15ywA4NGNeDi4VsXQT/pVmjfnzNQsFBcVI+72U63xuDtP0bC1h0iphJeWlA7gfz/T//t/ALB1tMb9q4/FCSWQtMQXL3192/ZvKVKif+/a9iR49KuCGm2sAQDWbibITS1CzP4U1OxgAwCwrW2KLstqoyhHCVWxGgprI0TMfAC72iZaj2VsLoOxuQyWzgrY1zXFwVF38PRCFlzfstb3bumEIb7eOsdOh86JWqInJCTA17dkTnCTJk0glUrRtGlTzf0+Pj5ISEj4y8dQKBSwsrLSuul7atXLSKRSyOXGYsfQKxNzBZxrOyLNwI/6/65bYAekP8vAbz9dFjuKYIJWBKD1276Y3m0Bkv+0yPDu5YcoKiyGd8c/uorV6zrD0bUKbv9mGIvIAcDETAHVn958VEoVpNKKedaaN1FcpETMpQeoXs9Za7x6XWckx6WKlEp4SQ+f4XniC3h3bKgZM7M0hWeLOrh93rDW7N08G/Py1/dxxV1crCxQa06N+zuJ9OWfJY3NZVBYGyErsQAv7ufBubnVKx9X/b//qIoq7umNDPH1pvJP1E6Hk5MTbt26BVdXV9y9exdKpRK3bt1CgwYlH2Ju3rwJB4ey10Mob0YvHI6LP1/Bs7hUmFqaotPwt9CkQ33M7L5A7GiCen/JSJw/HIXkx6mwd7HFe7MHQaVU4eTuX8WOJjiJRIJugR0Q/k0kVMqK+8bzVyasCkTHIa0xe9By5GXnw9ax5IheTkYuCvOLkJuZh7DQU3h/yUhkvchBTmYugpYH4Na5WIM5cxVQcna64TPfwbO4VDy++QR1vGtiwOReBnfyABNzBVzq/NGldKpZFbWauCErLRsp8c+xb/lhfLrzI1w/fQdXI2/Bt2sTtOrlg6n+80VM/e+V2W93B6393v/Vzxj+6Tt4ei8JSY+eIXD2YDxPeIFfD176i0eteL5feQSrzszFsBn9EPndOXi0qIOe4zpj5fj/EzvaG3NuZok7P6TArIoxrGookP4wH7E/PkfNTjaabZ6cy4DcyghmVYyRGZeP6K1JqNbCEk5NShabZycX4snZDDg2toDCSobctGLE7E+FTC6Fk4+lSHv27xni603ln0StFq9/NGvWLGzcuBF9+/ZFREQEhgwZgl27dmHmzJmQSCRYsGABBg4ciOXLl7/W43aRDvr7jXRoyqYP4N2pIeycbZGTkYuH1x5jz5KDuHz8ml5zQKLfxtWnuyaicVtPWNpbIiMlEzd+jcHWz3cj8UGyXnOIoVmXxlh09DMEek4quT6LCKTGwh4zCMvf+dLxZeM2Inx7ybSi3y8O2HGwH4wVRrgUfh1rJm4V9DTKqqJiwR77ZUwtTBA4dwja9GsOGwdrPE9Iw8ndZ7Fj3j4UFyn1lkMiE7aD27idF5Ydn1Vm/Ng3kVg2diMAoFtAewyd3hdVqtvhSWwCvpn7veZUsoJRC1vUN25XH8sivigzfuybSM3pRN8LHoSeYzvDwsYMN36NweoPtwj+e69W6f+tuWUvH4xZMAzV6joh6WEK9q08jJ83ndBrhhZXdHftk6I8JW7ufoaEC1nIzyi5OGCNNtaoP7CqZhH43SPPEXsoFfnpSpjaGsG1vTXqD/jj/ry0IkRtSMCLB3kozFbBxEaGKl7mqD+wKix1eHHAC976nxlRHl7vcOUevT7f6+jWLFi05w6LMpxrXZUmatGhUqmwaNEinDt3Dq1bt8aMGTOwZ88eTJ8+Hbm5uejTpw/WrFkDc3Pz13pcfRcd5Yaeiw4Sl9BFR3ml76KjvBC66Ci3BC46yisxio7yQJdFR0UiRtFRHrDoeDlDLTpE/dQilUrx6aefao0NHToUQ4cOFSkREREREVV6lfN4h6B4aJyIiIiIiATFooOIiIiIiARVOSeFExERERG9Aq9IrnvsdBARERERkaDY6SAiIiIiKo2dDp1jp4OIiIiIiATFTgcRERERUWnsdOgcOx1ERERERCQoFh1ERERERCQoTq8iIiIiIiqN06t0jp0OIiIiIiISFDsdRERERESlqcQOYHjY6SAiIiIiIkGx6CAiIiIiIkFxehURERERUSkSLiTXOXY6iIiIiIhIUOx0EBERERGVxk6HzrHTQUREREREgmKng4iIiIioNBU7HbrGTgcREREREQmKRQcREREREQmK06uIiIiIiErjQnKdY6eDiIiIiIgExU4HEREREVFp7HTonGEWHRI2cCoVtUrsBKJQK5ViRxCFzNxM7AiiUGZnix2B9Egql4sdQRQXm0nEjiCKtldzxY5AJDh+OiciIiIiIkEZZqeDiIiIiOhNcXqVzrHTQUREREREgmKng4iIiIioNF6RXOfY6SAiIiIiIkGx00FEREREVFolPTOmkNjpICIiIiIiQbHoICIiIiIiQXF6FRERERFRaTxlrs6x00FERERERIJip4OIiIiIqDSeMlfn2OkgIiIiIiJBseggIiIiIiJBcXoVEREREVFpXEiuc+x0EBERERGRoNjpICIiIiIqjZ0OnWOng4iIiIiIBMVOBxERERFRaex06Bw7HUREREREJCgWHUREREREJChOryIiIiIiKk2lEjuBwWGng4iIiIiIBMVOBxERERFRaVxIrnPsdBARERERkaBYdBARERERkaA4vUoHpFIJ3g0ehM4j2sLOyQbPE9JwbFskdi74Qexogqqs+92orRcGTX0b9ZrVgr2LHYLfWYKzBy+KHUvnGr3liUEf90FdH3fYu9hh9oBlOHvoEgBAZiRD4NwhaNGjKZzdHZCTkYvLJ25g86ffIi3xhcjJ/51eYzqi95iOcHCtAgCIu/MUOxcfwqXw6wAAZ/eqGDt/CBr41YOx3AhRx69j3bSdSE/JFDO24IZ80g9jQ0bgh1U/Yf3kULHjCKay/H4PmfY22vT1RQ0PFxTmFeLW+bvY/NluPLmbqNnGWGGM9xePQIdBrWCsMEZU+DWsnrgV6c8M62fd1MIEAXMGo03f5rBxsMa96EdYPyUUsZceiB3tjRXlqXD121TE/5aFgkwlbN0V8B3tAPs6pgAAtVqNa7uf497xdBTlqlDVwxTN33eElYscAJB8IxfHg+Nf+tjdF7tqHsegcXqVzrHToQNDpvdFn/FdsOajLRjTYAo2zdyFwdPeRr8J3cWOJqjKut8m5go8uPYYqydsFjuKoEzMTfDg2mOs+WhrmfsUZnLU9a6JnQt+wH9bzMScwctRo54L5u6fKkJS3Up9moYts/fhw/Zz8FGHOYiOvI3gbz+Cm6cLFGZyLDgwFVADM3ovwcddF8JIboQ5eydCIpGIHV0w9Xxro9f7XXD/6iOxowiusvx+N27riR83HsekdsGY2WsRZMYyLPxpBhRmCs0245eORKte3pg/4itM7TIPds62+GLPZBFTC2Pyxv/Ap3MjLAlci/94T8Pl8GtYfPRz2LvYih3tjZ1fl4Skqzlo/ZEzei2vCecm5oiY8wS5z4sAALcOpCHmyAu0+I8juoW4wshEipPznkBZWHLGpioepui/qbbWrba/NSwcjGFX20TMXaMKjJ0OHajfuh7OHrqEC0euAACSH6eg49A28GhRR+Rkwqqs+33xaDQuHo0WO4bgLoZF42JY9Evvy83Mw4weC7XG1kzcgjXnFqJqDXukxD/XQ0Jh/Hb0qtbX2+b9gN5jO8KzeW3Yu9jC0bUKJrwVjNysfADAsvGbsC9uDZq298KVU7fEiCwoE3MTzNzxEVa8vwEjPhsgdhzBVZbf78/eXqL19ZfjNmLvkw2o6+OOG2fuwMzKFN0CO2BRwFpc/d/P9fL3N2LTtWXwbFEHdy7cEyO2zslNjNG2fwsE91+G62fuAAC2z9uHVr190Oc/XRAavFfkhK+vuECF+PNZaD+jGhwbmAEAGg+pgqeXshEblo4mw6rgzuEXaDjQHjVaWAIA/D50wvdj7iP+QjZqvmUFmbEEprZ/fERUFavx5EI2PHraGPQBFi0qdjp0TdROR2JiIr744gt06tQJXl5eaNCgAfr06YPNmzdDqVSKGe213DobC+9ODVGtrjMAoFZjNzRs42Hwb1yVdb/p5cytzKBSqZCTnit2FJ2RSiVoP6AFFGYK3L5wH8ZyI0CtRlFBsWabovwiqFVqNPCrK2JS4Xy4Zgx+O3IZVyKuix2FBGRuVfLhNCstGwBQ18cdxnIjXDlxQ7NNfGwikuNS4dXScA4syYxkkBnJUJhfpDVekFeIBm08RUr176hVJTeZsXZxIJNLkXInD9nJRchPV8KpsZnmPrm5DFXqmiA1Ju+lj/nkYjYKs5Wo3cla0Oxk2ETrdFy6dAn+/v6oU6cOTE1NcffuXQwfPhyFhYWYOnUqtmzZgqNHj8LS0vIvH6egoAAFBQVaYyq1ElKJTMj4WnYvPggzK1NsubUcKqUKUpkUWz/fgxO7zugtgxgq635TWcYKY4wNGY5Te84iN+vlb1oVSc361bHi+GeQmxgjL7sA80asQVxMAjJSs5CfU4DRcwchdM73gAQYPWcQZEYy2DnaiB1b5zoMaY26PrUQ1GKG2FFIQBKJBOOXvYsbZ2Pw+NYTAICdow0KC4qQk6F9ECE9OcOgftbzsvNx81wsRnzWH3F3niI9OR0dh7aBV6t6SLiXJHa8N2JsKkUVDxNc3/ccVtUVMLGW4fGZTKTG5sHCyRj56SUHdU1ttD8CmlgbIS/95Qd870dkwLmJOczsjQXPX16o1bw4oK6J1umYNGkSJk+ejEuXLuH06dMIDQ1FbGwsdu/ejQcPHiA3Nxeff/753z5OSEgIrK2ttW4P1bf1sAd/aD/YD52Gv4WQkavxge8MLB21DoM+7o0u77XTaw59q6z7TdpkRjJ8/u1EQCLBV0GGMQ/+yd1E/PetYEzsNA8/bT6JjzeMhauHCzKeZ2FBwDq07NEU+xPX44cn62BhbYa7Vx5BZWBXr61a3R7/XTkKISNXoaig6O//AVVYE1YFwq1BdYS8u0bsKKJYErgWEgmwO249fsrZgb4TuuPUnl+hrsDTa1p/5Ayogf3j7mP30FjEHEmH21uWbzQ1Kvd5ERKv5qB2Z3Y56N8RrdNx+fJlfPPNN5qvhw8fjtGjRyM5ORmOjo5YsmQJAgMDsWrVqr98nJkzZ2LKlClaY+/YjBYk86uMWzwCexYfxKk9ZwEAj27Ew8G1KoZ+0g/h3/yi1yz6VFn3m/7we8Hh4FYV07vMM4guBwAUFymR+OAZAOBe9GPU86mJfh90wVeTtuHyiZsY3eQTWNlZQKlUIicjD7vurkTS9ykip9atus1qwdbRBuuj/pj7LzOSoVE7L/QN6o6eJsMNrtCqjIJWBKBlT2987D8PqU/TNONpyemQK4xhbm2m1e2wcbRGWnK6CEmFk/ggGVM7z4WJmQJmVqZIS0rHpzsnIvFhstjR3pilkxxd5rmiOF+FojwVTG2NcPrLBFg4GsPEpmQmSF56sda6jfyMYtjWVJR5rPsnMiC3kKF6cwu95SfDJFrR4eDggMTERNSqVQsAkJycjOLiYlhZWQEA6tati7S0tL96CACAQqGAQqH9S6LPqVUAYGKmgOpPp1ZTKVWQSg17sVVl3W8q8XvBUa2OM6Z1mauZC26IJFIpjBXafy4z/7e/Tdp5waaqJc4fiRYhmXCuRFzHuEbaB3Smbvkv4u8kYM+SAyw4DEDQigC0ftsX07rOR/Ij7aL57uWHKCoshnfHBjhzoOSUwdXrOsPRtQpu/2YYi8j/LD+3APm5BbCwMYdv18bYNHOX2JH+NSMTKYxMpCjIViIxOgfe71bVFB7J13Nh515yJqqiXCVS7+ajbjcbrX+vVqvx4EQmanWwgtSokr23V+BOV3klWtHRr18/jB8/HkuXLoVCocC8efPQvn17mJqWnPs5JiYG1apVEyveazl/OArDZ76DZ3GpeHzzCep418SAyb0QtvWk2NEEVVn328TcBNXqOGm+dnJ3QO0mNZGZlo2U+FQRk+mWibkCLn/az1pN3JCVlo20xHTM2jMZdb3dMavfYkhlUtg6lrTes9KyUVxUcU4E8WejggfiYvg1pDx5DlMLU3Qc1AqN23rgs3e+BAB0GfEW4mNL1nd4taiD8YuHY//aY3hSQed/v0pedj4e3dQ+T39+TgEy07LKjBuSyvL7PWFVIDoOaY3Zg5YjLztf8/ubk5GLwvwi5GbmISz0FN5fMhJZL3KQk5mLoOUBuHUu1mDOXPW7Zl0aQyKR4ElsAlxqO2Hc4hGIj0lAWOgpsaO9sYQrOQAAKxdjZCUV4co3KbCqJkftTtaQSCTw7G2LG/uew9JZDnMHY1z7NhVmtkao0UK7m5F8PRfZz4o4tYp0QrSiY/78+UhMTESfPn2gVCrh5+eHHTt2aO6XSCQICQkRK95rWfPRVgTOHYKP1oyBjYM1niek4aevj2PHvH1iRxNUZd3ver618OXJOZqvP1geCAA4FnoKS0evFSmV7tVrVhvLIr7QfD1+2XsAgGPfRGL73H1o/bYvAGBDlPapN6d2notrv1TcU8faVLXEtI3jYOtkjdzMPDy8EY/P3vkSV06W7FP1uk4YNXsgLG3NkRyXit1Lf8QPa4+JnJp0pbL8fvf5TxcAwLLwWVrjy8ZtRPj2kumxG6btgEqlxqxvJ8JYYYRL4dexZmLZ6/ZUdObWZhg9fxiqVLdDVlo2zuy/gK2zdkNZXHEPnhTlKhG9MxW5z4sht5DCtZUlmgyvoulW1O9nh+J8NX7bkITCHBUcPE3RcVZ1yOTaS33vRWSgiocJrKuXnXZl8HhxQJ2TqNXiflfz8/NRXFwMCwvdzRXsIhuis8eiCqCSnmFCItPvNMLyQmpaCa6E+xLKbMOdvkZlSeVysSOIQl2BTpevS29dMZxTjb+OLxoeEjvCK3W3HSvacx99sUm05xaS6BcHNDHhlS2JiIiIiAyZ6EUHEREREVG5wpNl6JyoVyQnIiIiIiLDx04HEREREVFpXEiuc+x0EBERERGRoNjpICIiIiIqRc01HTrHTgcREREREQmKRQcREREREQmK06uIiIiIiErjQnKdY6eDiIiIiIgExU4HEREREVFpKnY6dI2dDiIiIiIiEhSLDiIiIiIiEhSnVxERERERlabmdTp0jZ0OIiIiIiISFDsdRERERESlqLmQXOfY6SAiIiIiIkGx6CAiIiIiIkFxehURERERUWlcSK5z7HQQEREREZGg2OkgIiIiIiqFC8l1j50OIiIiIqIKau3atahZsyZMTEzQsmVLXLhwQexIL8Wig4iIiIioNLVKvNtr2LNnD6ZMmYLg4GBcvnwZTZo0Qbdu3fDs2TOBvjFvjkUHEREREVEFtHz5cowbNw6jRo1C/fr1sWHDBpiZmWHLli1iRyuDRQcRERERUTlRUFCAzMxMrVtBQUGZ7QoLCxEVFQV/f3/NmFQqhb+/P86dO6fPyP+MmnQmPz9fHRwcrM7Pzxc7il5xv7nflQH3m/tdGXC/ud8kvuDgYDUArVtwcHCZ7Z4+faoGoD579qzW+LRp09QtWrTQU9p/TqJWq7k8X0cyMzNhbW2NjIwMWFlZiR1Hb7jf3O/KgPvN/a4MuN/cbxJfQUFBmc6GQqGAQqHQGktISEC1atVw9uxZ+Pn5acanT5+OyMhI/Pbbb3rJ+0/xlLlEREREROXEywqMl6lSpQpkMhmSk5O1xpOTk+Hk5CRUvDfGNR1ERERERBWMXC5Hs2bNEBERoRlTqVSIiIjQ6nyUF+x0EBERERFVQFOmTEFAQAB8fX3RokULrFy5Ejk5ORg1apTY0cpg0aFDCoUCwcHB/6glZki439zvyoD7zf2uDLjf3G+qWIYMGYKUlBR88cUXSEpKQtOmTXH06FE4OjqKHa0MLiQnIiIiIiJBcU0HEREREREJikUHEREREREJikUHEREREREJikUHEREREREJikWHDq1duxY1a9aEiYkJWrZsiQsXLogdSVC//PIL+vTpAxcXF0gkEhw4cEDsSHoREhKC5s2bw9LSEg4ODujXrx9iYmLEjiW49evXo3HjxrCysoKVlRX8/Pzw888/ix1L7xYtWgSJRIJJkyaJHUVQs2fPhkQi0bp5enqKHUsvnj59ipEjR8Le3h6mpqZo1KgRLl26JHYsQdWsWbPM6y2RSBAUFCR2NEEplUrMmjUL7u7uMDU1Re3atTFv3jxUhnPsZGVlYdKkSXBzc4OpqSlat26Nixcvih2LDBiLDh3Zs2cPpkyZguDgYFy+fBlNmjRBt27d8OzZM7GjCSYnJwdNmjTB2rVrxY6iV5GRkQgKCsL58+cRHh6OoqIidO3aFTk5OWJHE1T16tWxaNEiREVF4dKlS+jUqRP69u2Lmzdvih1Nby5evIiNGzeicePGYkfRiwYNGiAxMVFzO3PmjNiRBPfixQu0adMGxsbG+Pnnn3Hr1i18+eWXsLW1FTuaoC5evKj1WoeHhwMABg0aJHIyYS1evBjr16/HmjVrcPv2bSxevBhLlizB6tWrxY4muLFjxyI8PBzbt2/H9evX0bVrV/j7++Pp06diRyMDxVPm6kjLli3RvHlzrFmzBkDJFSFr1KiBDz/8EDNmzBA5nfAkEgn279+Pfv36iR1F71JSUuDg4IDIyEi0a9dO7Dh6ZWdnh6VLl2LMmDFiRxFcdnY2fHx8sG7dOsyfPx9NmzbFypUrxY4lmNmzZ+PAgQOIjo4WO4pezZgxA7/++itOnz4tdhRRTZo0CYcPH8bdu3chkUjEjiOY3r17w9HREZs3b9aMDRgwAKamptixY4eIyYSVl5cHS0tLHDx4EL169dKMN2vWDD169MD8+fNFTEeGip0OHSgsLERUVBT8/f01Y1KpFP7+/jh37pyIyUgfMjIyAJR8AK8slEoldu/ejZycHPj5+YkdRy+CgoLQq1cvrd9zQ3f37l24uLigVq1aGDFiBOLi4sSOJLhDhw7B19cXgwYNgoODA7y9vfF///d/YsfSq8LCQuzYsQOjR4826IIDAFq3bo2IiAjExsYCAK5evYozZ86gR48eIicTVnFxMZRKJUxMTLTGTU1NK0VHk8TBK5LrQGpqKpRKZZmrPzo6OuLOnTsipSJ9UKlUmDRpEtq0aYOGDRuKHUdw169fh5+fH/Lz82FhYYH9+/ejfv36YscS3O7du3H58uVKNd+5ZcuWCA0NhYeHBxITEzFnzhy0bdsWN27cgKWlpdjxBPPgwQOsX78eU6ZMwaeffoqLFy/io48+glwuR0BAgNjx9OLAgQNIT09HYGCg2FEEN2PGDGRmZsLT0xMymQxKpRILFizAiBEjxI4mKEtLS/j5+WHevHnw8vKCo6Mjvv32W5w7dw516tQROx4ZKBYdRP9CUFAQbty4UWmODHl4eCA6OhoZGRnYt28fAgICEBkZadCFR3x8PCZOnIjw8PAyRwUNWekjvY0bN0bLli3h5uaGvXv3GvR0OpVKBV9fXyxcuBAA4O3tjRs3bmDDhg2VpujYvHkzevToARcXF7GjCG7v3r3YuXMndu3ahQYNGiA6OhqTJk2Ci4uLwb/e27dvx+jRo1GtWjXIZDL4+Phg2LBhiIqKEjsaGSgWHTpQpUoVyGQyJCcna40nJyfDyclJpFQktAkTJuDw4cP45ZdfUL16dbHj6IVcLtccBWvWrBkuXryIVatWYePGjSInE05UVBSePXsGHx8fzZhSqcQvv/yCNWvWoKCgADKZTMSE+mFjY4N69erh3r17YkcRlLOzc5ki2svLC99//71IifTr8ePHOH78OH744Qexo+jFtGnTMGPGDAwdOhQA0KhRIzx+/BghISEGX3TUrl0bkZGRyMnJQWZmJpydnTFkyBDUqlVL7GhkoLimQwfkcjmaNWuGiIgIzZhKpUJERESlme9emajVakyYMAH79+/HiRMn4O7uLnYk0ahUKhQUFIgdQ1CdO3fG9evXER0drbn5+vpixIgRiI6OrhQFB1CykP7+/ftwdnYWO4qg2rRpU+YU2LGxsXBzcxMpkX5t3boVDg4OWouLDVlubi6kUu2PQjKZDCqVSqRE+mdubg5nZ2e8ePECYWFh6Nu3r9iRyECx06EjU6ZMQUBAAHx9fdGiRQusXLkSOTk5GDVqlNjRBJOdna111PPhw4eIjo6GnZ0dXF1dRUwmrKCgIOzatQsHDx6EpaUlkpKSAADW1tYwNTUVOZ1wZs6ciR49esDV1RVZWVnYtWsXTp06hbCwMLGjCcrS0rLMeh1zc3PY29sb9DqeqVOnok+fPnBzc0NCQgKCg4Mhk8kwbNgwsaMJavLkyWjdujUWLlyIwYMH48KFC/j666/x9ddfix1NcCqVClu3bkVAQACMjCrHx4M+ffpgwYIFcHV1RYMGDXDlyhUsX74co0ePFjua4MLCwqBWq+Hh4YF79+5h2rRp8PT0NOjPLSQyNenM6tWr1a6urmq5XK5u0aKF+vz582JHEtTJkyfVAMrcAgICxI4mqJftMwD11q1bxY4mqNGjR6vd3NzUcrlcXbVqVXXnzp3Vx44dEzuWKNq3b6+eOHGi2DEENWTIELWzs7NaLperq1Wrph4yZIj63r17YsfSix9//FHdsGFDtUKhUHt6eqq//vprsSPpRVhYmBqAOiYmRuwoepOZmameOHGi2tXVVW1iYqKuVauW+rPPPlMXFBSIHU1we/bsUdeqVUstl8vVTk5O6qCgIHV6errYsciA8TodREREREQkKK7pICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIiIiIiQbHoICIqZwIDA9GvXz/N1x06dMCkSZP0nuPUqVOQSCRIT0/X+3MTEZFhYdFBRPQPBQYGQiKRQCKRQC6Xo06dOpg7dy6Ki4sFfd4ffvgB8+bN+0fbslAgIqLyyEjsAEREFUn37t2xdetWFBQU4MiRIwgKCoKxsTFmzpyptV1hYSHkcrlOntPOzk4nj0NERCQWdjqIiF6DQqGAk5MT3Nzc8MEHH8Df3x+HDh3STIlasGABXFxc4OHhAQCIj4/H4MGDYWNjAzs7O/Tt2xePHj3SPJ5SqcSUKVNgY2MDe3t7TJ8+HWq1Wus5/zy9qqCgAJ988glq1KgBhUKBOnXqYPPmzXj06BE6duwIALC1tYVEIkFgYCAAQKVSISQkBO7u7jA1NUWTJk2wb98+rec5cuQI6tWrB1NTU3Ts2FErJxER0b/BooOI6F8wNTVFYWEhACAiIgIxMTEIDw/H4cOHUVRUhG7dusHS0hKnT5/Gr7/+CgsLC3Tv3l3zb7788kuEhoZiy5YtOHPmDNLS0rB///6/fM733nsP3377Lb766ivcvn0bGzduhIWFBWrUqIHvv/8eABATE4PExESsWrUKABASEoJvvvkGGzZswM2bNzF58mSMHDkSkZGRAEqKo/79+6NPnz6Ijo7G2LFjMWPGDKG+bUREVMlwehUR0RtQq9WIiIhAWFgYPvzwQ6SkpMDc3BybNm3STKvasWMHVCoVNm3aBIlEAgDYunUrbGxscOrUKXTt2hUrV67EzJkz0b9/fwDAhg0bEBYW9srnjY2Nxd69exEeHg5/f38AQK1atTT3/z4Vy8HBATY2NgBKOiMLFy7E8ePH4efnp/k3Z86cwcaNG9G+fXusX78etWvXxpdffgkA8PDwwPXr17F48WIdfteIiKiyYtFBRPQaDh8+DAsLCxQVFUGlUmH48OGYPXs2goKC0KhRI611HFevXsW9e/dgaWmp9Rj5+fm4f/8+MjIykJiYiJYtW2ruMzIygq+vb5kpVr+Ljo6GTCZD+/bt/3Hme/fuITc3F126dNEaLywshLe3NwDg9u3bWjkAaAoUIiKif4tFBxHRa+jYsSPWr18PuVwOFxcXGBn98WfU3Nxca9vs7Gw0a9YMO3fuLPM4VatWfaPnNzU1fe1/k52dDQD46aefUK1aNa37FArFG+UgIiJ6HSw6iIheg7m5OerUqfOPtvXx8cGePXvg4OAAKyurl27j7OyM3377De3atQMAFBcXIyoqCj4+Pi/dvlGjRlCpVIiMjNRMryrt906LUqnUjNWvXx8KhQJxcXGv7JB4eXnh0KFDWmPnz5//+50kIiL6B7iQnIhIICNGjECVKlXQt29fnD59Gg8fPsSpU6fw0Ucf4cmTJwCAiRMnYtGiRThw4ADu3LmD//73v395jY2aNWsiICAAo0ePxoEDBzSPuXfvXgCAm5sbJBIJDh8+jJSUFGRnZ8PS0hJTp07F5MmTsW3bNty/fx+XL1/G6tWrsW3bNgDA+PHjcffuXUybNg0xMTHYtWsXQkNDhf4WERFRJcGig4hIIGZmZvjll1/g6uqK/v37w8vLC2PGjEF+fr6m8/Hxxx/j3XffRUBAAPz8/GBpaYl33nnnLx93/fr1GDhwIP773//C09MT48aNQ05ODgCgWrVqmDNnDmbMmAFHR0dMmDABADBv3jzMmjULISEh8PLyQvfu3fHTTz/B3d0dAODq6orvv/8eBw4cQJMmTbBhwwYsXLhQwO8OERFVJhL1q1YrEhERERER6QA7HUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJCgWHUREREREJKj/Bw3ie3eZ2AvjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_digits = 15\n",
        "\n",
        "# Create a figure with subplots\n",
        "fig, axes = plt.subplots(1, num_digits, figsize=(15, 3))\n",
        "\n",
        "for i in range(num_digits):\n",
        "    # Randomly select an index\n",
        "    random_index = np.random.randint(0, len(X_test))\n",
        "    x_obs = X_test[random_index]  # Select the image at the random index\n",
        "    y_obs = y_test[random_index]  # Select the corresponding label\n",
        "\n",
        "    # Forward pass to get the prediction\n",
        "    _, prediction, _, _, _ = forward_pass(x_obs, y_obs, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "    # Visualize the digit\n",
        "    axes[i].imshow(x_obs.reshape(28, 28), cmap='viridis')\n",
        "    axes[i].set_title(f'Label: {y_obs}\\nPred: {prediction}')\n",
        "    axes[i].axis('off')  # Turn off the axes\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "66XQ7ZRCDXFf",
        "outputId": "b1125562-7111-429a-9730-4ea2a3ec1a37"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Weights and Biases Report"
      ],
      "metadata": {
        "id": "IjfiB9dVKBME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import IFrame\n",
        "\n",
        "# Embed the webpage in an iframe\n",
        "IFrame(src=\"https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/mnist-neural-network/reports/MNIST-prediction-with-a-NumPy-Neural-Network---Vmlldzo4OTc1MDA4\", width=\"100%\", height=\"1024px\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CtKDNpY1D74X",
        "outputId": "4b8cd885-1e06-49bd-a369-0fc5e0634f3d"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7fe40c7b11e0>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"100%\"\n",
              "            height=\"1024px\"\n",
              "            src=\"https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/mnist-neural-network/reports/MNIST-prediction-with-a-NumPy-Neural-Network---Vmlldzo4OTc1MDA4\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "            \n",
              "        ></iframe>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    }
  ]
}