Qml_project_sdi2000113.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# QML Class Spring 2024 - Project\n",
        "\n",
        "**Kotsias Ioannis**\n",
        "\n",
        "**1115202000113**\n",
        "\n",
        "## Project Overview\n",
        "\n",
        "This project involves simulating a quantum classifier using a variational quantum circuit for classifying data for the binary classification parity problem with 3 and 5 inputs. The project is divided into three parts:\n",
        "\n",
        "1. **Simulate a quantum classifier for the 3-input parity problem (6/10 credits)**\n",
        "2. **Compare different classical optimization methods (2/10)**\n",
        "3. **Extend the model to the 5-input parity problem (2/10)**\n",
        "\n",
        "## Part 1: Quantum Classifier for 3-input Parity Problem\n"
      ],
      "metadata": {
        "id": "p3Wu746RMbG2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Install dependancies, mount drive and import packages"
      ],
      "metadata": {
        "id": "skr0ETFUPYgr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pennylane\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pennylane as qml\n",
        "import numpy as np\n",
        "from pennylane.optimize import AdamOptimizer, NesterovMomentumOptimizer, AdagradOptimizer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the data from Google Drive\n",
        "base_path = '/content/drive/MyDrive/QML/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "fG5MrdQ1NZ2D",
        "outputId": "dbf9cebe-55a4-44fe-9822-72fd5169926a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pennylane in /usr/local/lib/python3.10/dist-packages (0.36.0)\n",
            "Requirement already satisfied: numpy<2.0 in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.11.4)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from pennylane) (3.3)\n",
            "Requirement already satisfied: rustworkx in /usr/local/lib/python3.10/dist-packages (from pennylane) (0.14.2)\n",
            "Requirement already satisfied: autograd in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.6.2)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.10/dist-packages (from pennylane) (0.10.2)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.10/dist-packages (from pennylane) (1.4.4)\n",
            "Requirement already satisfied: semantic-version>=2.7 in /usr/local/lib/python3.10/dist-packages (from pennylane) (2.10.0)\n",
            "Requirement already satisfied: autoray>=0.6.1 in /usr/local/lib/python3.10/dist-packages (from pennylane) (0.6.12)\n",
            "Requirement already satisfied: cachetools in /usr/local/lib/python3.10/dist-packages (from pennylane) (5.3.3)\n",
            "Requirement already satisfied: pennylane-lightning>=0.36 in /usr/local/lib/python3.10/dist-packages (from pennylane) (0.36.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from pennylane) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from pennylane) (4.12.1)\n",
            "Requirement already satisfied: future>=0.15.2 in /usr/local/lib/python3.10/dist-packages (from autograd->pennylane) (0.18.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->pennylane) (2024.6.2)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load and Preprocess Data\n",
        "First, I will load the training and test data for the 3-input parity problem and preprocess it."
      ],
      "metadata": {
        "id": "BWg7NI7lxmvu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classA_train = np.loadtxt(base_path + \"classA_train.dat\")\n",
        "classB_train = np.loadtxt(base_path + \"classB_train.dat\")\n",
        "classA_test = np.loadtxt(base_path + \"classA_test.dat\")\n",
        "classB_test = np.loadtxt(base_path + \"classB_test.dat\")\n",
        "\n",
        "# Preprocess the data\n",
        "def preprocess_data(classA, classB):\n",
        "    X = np.vstack((classA, classB))\n",
        "    y = np.hstack((np.ones(len(classA)), -1*np.ones(len(classB))))\n",
        "    X -= 0.5\n",
        "    return X, y\n",
        "\n",
        "X_train, y_train = preprocess_data(classA_train, classB_train)\n",
        "X_test, y_test = preprocess_data(classA_test, classB_test)"
      ],
      "metadata": {
        "id": "1NR8MYX3NIKt"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Design of the Quantum Circuit\n",
        "\n",
        "I design a variational quantum circuit using up to 4 qubits, which includes layers of parameterized rotation gates and entangling CNOT gates. The state preparation step re-uploads the data into the circuit."
      ],
      "metadata": {
        "id": "Y9YyJ9BgN9Xy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize device\n",
        "num_qubits = 4\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "# Define a layer of the variational quantum circuit\n",
        "def layer(weights):\n",
        "    for i in range(num_qubits):\n",
        "        qml.Rot(*weights[i], wires=i)\n",
        "    for i in range(num_qubits):\n",
        "        qml.CNOT(wires=[i, (i+1) % num_qubits])\n",
        "\n",
        "# Data re-uploading in state preparation\n",
        "def state_preparation(x):\n",
        "    for i in range(num_qubits):\n",
        "        qml.RX(x[i % len(x)], wires=i)\n"
      ],
      "metadata": {
        "id": "lAdYX9yqOIiq"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define the Quantum Circuit and Classifier\n",
        "I define the quantum circuit and the variational classifier function."
      ],
      "metadata": {
        "id": "cmqn-1BhON8z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@qml.qnode(dev, interface='autograd')\n",
        "def circuit(weights, x):\n",
        "    state_preparation(x)\n",
        "    for w in weights:\n",
        "        layer(w)\n",
        "    return qml.expval(qml.PauliZ(0))\n",
        "\n",
        "def variational_classifier(weights, bias, x):\n",
        "    return circuit(weights, x) + bias\n"
      ],
      "metadata": {
        "id": "vvIQr6uBOSbm"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cost Function and Accuracy\n",
        "Next, I define the cost function using the expectation value of measurements at the end of the circuit and the accuracy function."
      ],
      "metadata": {
        "id": "26DqnEV6OW0g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def square_loss(labels, predictions):\n",
        "    return qml.numpy.mean((labels - predictions) ** 2)\n",
        "\n",
        "def accuracy(labels, predictions):\n",
        "    return qml.numpy.mean(labels == qml.numpy.sign(predictions))\n",
        "\n",
        "def cost(weights, bias, X, Y):\n",
        "    predictions = qml.numpy.array([variational_classifier(weights, bias, x) for x in X])\n",
        "    return square_loss(Y, predictions)\n"
      ],
      "metadata": {
        "id": "2s_ZZGqZOZDU"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the Quantum Classifier\n",
        "I initialize the weights and bias and train the classifier using the Nesterov Momentum Optimizer. I also track and plot the cost over iterations."
      ],
      "metadata": {
        "id": "FImuW0paObOU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "num_layers = 4\n",
        "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3)\n",
        "weights_init = qml.numpy.array(weights_init, requires_grad=True)\n",
        "bias_init = qml.numpy.array(0.0, requires_grad=True)\n",
        "\n",
        "opt = NesterovMomentumOptimizer(0.1)\n",
        "batch_size = 10\n",
        "num_iterations = 100\n",
        "\n",
        "weights = weights_init\n",
        "bias = bias_init\n",
        "\n",
        "costs = []\n",
        "\n",
        "for it in range(num_iterations):\n",
        "    # Random batch sampling\n",
        "    batch_index = np.random.randint(0, len(X_train), (batch_size,))\n",
        "    X_batch = X_train[batch_index]\n",
        "    Y_batch = y_train[batch_index]\n",
        "\n",
        "    weights, bias = opt.step(cost, weights, bias, X=qml.numpy.array(X_batch), Y=qml.numpy.array(Y_batch))\n",
        "\n",
        "    # Accuracy and cost on the training set\n",
        "    predictions = [variational_classifier(weights, bias, x) for x in X_train]\n",
        "    acc_train = accuracy(y_train, qml.numpy.sign(predictions))\n",
        "    cost_train = cost(weights, bias, X_train, y_train)\n",
        "    costs.append(cost_train)\n",
        "\n",
        "    if (it + 1) % 10 == 0:\n",
        "        print(f\"Iteration: {it + 1} | Cost: {cost_train:.4f} | Training Accuracy: {acc_train:.4f}\")\n",
        "\n",
        "# Evaluation on the test set\n",
        "predictions_test = [variational_classifier(weights, bias, x) for x in X_test]\n",
        "acc_test = accuracy(y_test, qml.numpy.sign(predictions_test))\n",
        "\n",
        "print(f\"Test Accuracy: {acc_test:.4f}\")\n",
        "\n",
        "# Plotting the cost over iterations\n",
        "plt.plot(costs)\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Cost\")\n",
        "plt.title(\"Cost over Iterations\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "p3F-N2isOgUy",
        "outputId": "56fc2ed0-9bae-442e-e6d8-20d2ab19a389"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 10 | Cost: 1.0192 | Training Accuracy: 0.5000\n",
            "Iteration: 20 | Cost: 0.9915 | Training Accuracy: 0.5000\n",
            "Iteration: 30 | Cost: 0.9437 | Training Accuracy: 0.5000\n",
            "Iteration: 40 | Cost: 0.8349 | Training Accuracy: 0.8000\n",
            "Iteration: 50 | Cost: 0.8909 | Training Accuracy: 0.7000\n",
            "Iteration: 60 | Cost: 0.8231 | Training Accuracy: 0.7750\n",
            "Iteration: 70 | Cost: 0.7497 | Training Accuracy: 0.8250\n",
            "Iteration: 80 | Cost: 0.7605 | Training Accuracy: 0.7000\n",
            "Iteration: 90 | Cost: 0.7401 | Training Accuracy: 0.7750\n",
            "Iteration: 100 | Cost: 0.7206 | Training Accuracy: 1.0000\n",
            "Test Accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Circuit Visualization\n",
        "I visualize the designed quantum circuit to better understand its structure."
      ],
      "metadata": {
        "id": "8ilAt38IO74o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_qubits = 4\n",
        "num_layers = 4\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "# Define the circuit for plotting\n",
        "def layer(weights):\n",
        "    for i in range(num_qubits):\n",
        "        qml.Rot(*weights[i], wires=i)\n",
        "    for i in range(num_qubits):\n",
        "        qml.CNOT(wires=[i, (i + 1) % num_qubits])\n",
        "\n",
        "def state_preparation(x):\n",
        "    for i in range(num_qubits):\n",
        "        qml.RX(x[i % len(x)], wires=i)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def circuit(weights, x=None):\n",
        "    state_preparation([0] * num_qubits)  # Dummy input for plotting\n",
        "    for w in weights:\n",
        "        layer(w)\n",
        "    return qml.expval(qml.PauliZ(0))\n",
        "\n",
        "# Dummy weights for visualization\n",
        "weights = np.random.randn(num_layers, num_qubits, 3)\n",
        "\n",
        "# Visualize the circuit using matplotlib\n",
        "fig, ax = qml.draw_mpl(circuit)(weights=weights)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "LnrNbW2HO-na",
        "outputId": "d9a1a817-bfa3-42cf-bde6-019da9b95484"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Part 2: Comparison of Classical Optimization Methods\n",
        "\n",
        "###Comparison of Classical Optimization Methods\n",
        "I compare the performance of different classical optimization methods: Nesterov Momentum Optimizer, Adam Optimizer, and Adagrad Optimizer."
      ],
      "metadata": {
        "id": "ssKGcNJYQiIm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the data\n",
        "def preprocess_data(classA, classB):\n",
        "    X = np.vstack((classA, classB))\n",
        "    y = np.hstack((np.ones(len(classA)), -1*np.ones(len(classB))))\n",
        "    X -= 0.5\n",
        "    return X, y\n",
        "\n",
        "X_train, y_train = preprocess_data(classA_train, classB_train)\n",
        "X_test, y_test = preprocess_data(classA_test, classB_test)\n",
        "\n",
        "# Initialize device\n",
        "num_qubits = 4\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "# Define a layer of the variational quantum circuit\n",
        "def layer(weights):\n",
        "    for i in range(num_qubits):\n",
        "        qml.Rot(*weights[i], wires=i)\n",
        "    for i in range(num_qubits):\n",
        "        qml.CNOT(wires=[i, (i+1) % num_qubits])\n",
        "\n",
        "# Data re-uploading in state preparation\n",
        "def state_preparation(x):\n",
        "    for i in range(num_qubits):\n",
        "        qml.RX(x[i % len(x)], wires=i)\n",
        "\n",
        "@qml.qnode(dev, interface='autograd')\n",
        "def circuit(weights, x):\n",
        "    state_preparation(x)\n",
        "    for w in weights:\n",
        "        layer(w)\n",
        "    return qml.expval(qml.PauliZ(0))\n",
        "\n",
        "def variational_classifier(weights, bias, x):\n",
        "    return circuit(weights, x) + bias\n",
        "\n",
        "def square_loss(labels, predictions):\n",
        "    return qml.numpy.mean((labels - predictions) ** 2)\n",
        "\n",
        "def accuracy(labels, predictions):\n",
        "    return qml.numpy.mean(labels == qml.numpy.sign(predictions))\n",
        "\n",
        "def cost(weights, bias, X, Y):\n",
        "    predictions = qml.numpy.array([variational_classifier(weights, bias, x) for x in X])\n",
        "    return square_loss(Y, predictions)\n",
        "\n",
        "np.random.seed(0)\n",
        "num_layers = 4\n",
        "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3)\n",
        "weights_init = qml.numpy.array(weights_init, requires_grad=True)\n",
        "bias_init = qml.numpy.array(0.0, requires_grad=True)\n",
        "\n",
        "# Optimizers to compare\n",
        "optimizers = {\n",
        "    \"NesterovMomentum\": NesterovMomentumOptimizer(0.1),\n",
        "    \"Adam\": AdamOptimizer(0.1),\n",
        "    \"Adagrad\": AdagradOptimizer(0.1)\n",
        "}\n",
        "\n",
        "batch_size = 10\n",
        "num_iterations = 100\n",
        "\n",
        "results = {}\n",
        "\n",
        "for opt_name, opt in optimizers.items():\n",
        "    weights = weights_init\n",
        "    bias = bias_init\n",
        "    costs = []\n",
        "\n",
        "    for it in range(num_iterations):\n",
        "        batch_index = np.random.randint(0, len(X_train), (batch_size,))\n",
        "        X_batch = X_train[batch_index]\n",
        "        Y_batch = y_train[batch_index]\n",
        "\n",
        "        weights, bias = opt.step(cost, weights, bias, X=qml.numpy.array(X_batch), Y=qml.numpy.array(Y_batch))\n",
        "\n",
        "        predictions = [variational_classifier(weights, bias, x) for x in X_train]\n",
        "        acc_train = accuracy(y_train, qml.numpy.sign(predictions))\n",
        "        cost_train = cost(weights, bias, X_train, y_train)\n",
        "        costs.append(cost_train)\n",
        "\n",
        "        if (it + 1) % 10 == 0:\n",
        "            print(f\"{opt_name} - Iteration: {it + 1} | Cost: {cost_train:.4f} | Training Accuracy: {acc_train:.4f}\")\n",
        "\n",
        "    predictions_test = [variational_classifier(weights, bias, x) for x in X_test]\n",
        "    acc_test = accuracy(y_test, qml.numpy.sign(predictions_test))\n",
        "\n",
        "    results[opt_name] = {\n",
        "        \"costs\": costs,\n",
        "        \"acc_train\": acc_train,\n",
        "        \"acc_test\": acc_test\n",
        "    }\n",
        "\n",
        "# Plotting the cost over iterations for each optimizer\n",
        "plt.figure(figsize=(12, 6))\n",
        "for opt_name, result in results.items():\n",
        "    plt.plot(result[\"costs\"], label=opt_name)\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Cost\")\n",
        "plt.title(\"Cost over Iterations for Different Optimizers\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Print final accuracies for comparison\n",
        "for opt_name, result in results.items():\n",
        "    print(f\"{opt_name} - Training Accuracy: {result['acc_train']:.4f}, Test Accuracy: {result['acc_test']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CgtIDb3eQnzW",
        "outputId": "acdb495e-2e15-489d-86d4-ccfe7d7e57cc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NesterovMomentum - Iteration: 10 | Cost: 1.0192 | Training Accuracy: 0.5000\n",
            "NesterovMomentum - Iteration: 20 | Cost: 0.9915 | Training Accuracy: 0.5000\n",
            "NesterovMomentum - Iteration: 30 | Cost: 0.9437 | Training Accuracy: 0.5000\n",
            "NesterovMomentum - Iteration: 40 | Cost: 0.8349 | Training Accuracy: 0.8000\n",
            "NesterovMomentum - Iteration: 50 | Cost: 0.8909 | Training Accuracy: 0.7000\n",
            "NesterovMomentum - Iteration: 60 | Cost: 0.8231 | Training Accuracy: 0.7750\n",
            "NesterovMomentum - Iteration: 70 | Cost: 0.7497 | Training Accuracy: 0.8250\n",
            "NesterovMomentum - Iteration: 80 | Cost: 0.7605 | Training Accuracy: 0.7000\n",
            "NesterovMomentum - Iteration: 90 | Cost: 0.7401 | Training Accuracy: 0.7750\n",
            "NesterovMomentum - Iteration: 100 | Cost: 0.7206 | Training Accuracy: 1.0000\n",
            "Adam - Iteration: 10 | Cost: 0.9112 | Training Accuracy: 0.6000\n",
            "Adam - Iteration: 20 | Cost: 0.9310 | Training Accuracy: 0.7750\n",
            "Adam - Iteration: 30 | Cost: 0.9089 | Training Accuracy: 0.4000\n",
            "Adam - Iteration: 40 | Cost: 0.8778 | Training Accuracy: 0.8250\n",
            "Adam - Iteration: 50 | Cost: 0.8212 | Training Accuracy: 0.7250\n",
            "Adam - Iteration: 60 | Cost: 0.7940 | Training Accuracy: 0.8500\n",
            "Adam - Iteration: 70 | Cost: 0.8590 | Training Accuracy: 0.7250\n",
            "Adam - Iteration: 80 | Cost: 0.8410 | Training Accuracy: 0.5750\n",
            "Adam - Iteration: 90 | Cost: 0.7918 | Training Accuracy: 0.6750\n",
            "Adam - Iteration: 100 | Cost: 0.7676 | Training Accuracy: 0.7750\n",
            "Adagrad - Iteration: 10 | Cost: 0.9343 | Training Accuracy: 0.6000\n",
            "Adagrad - Iteration: 20 | Cost: 0.8963 | Training Accuracy: 0.6750\n",
            "Adagrad - Iteration: 30 | Cost: 0.8307 | Training Accuracy: 0.6000\n",
            "Adagrad - Iteration: 40 | Cost: 0.8126 | Training Accuracy: 0.6750\n",
            "Adagrad - Iteration: 50 | Cost: 0.8207 | Training Accuracy: 0.7000\n",
            "Adagrad - Iteration: 60 | Cost: 0.7959 | Training Accuracy: 0.7250\n",
            "Adagrad - Iteration: 70 | Cost: 0.7949 | Training Accuracy: 0.6000\n",
            "Adagrad - Iteration: 80 | Cost: 0.8002 | Training Accuracy: 0.6000\n",
            "Adagrad - Iteration: 90 | Cost: 0.8038 | Training Accuracy: 0.6250\n",
            "Adagrad - Iteration: 100 | Cost: 0.8176 | Training Accuracy: 0.7000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+kAAAIjCAYAAAB/OVoZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hU1dbH8e+UZNITEkooofciHQVRkCIgoIiKBRUBu4C94LWAvSt2fUURrIgFOxZ6UUSK0qR3CCW9lznvHyczEFJIn5nw+9wnTyanrplJvKxZe69tMQzDQEREREREREQ8zurpAERERERERETEpCRdRERERERExEsoSRcRERERERHxEkrSRURERERERLyEknQRERERERERL6EkXURERERERMRLKEkXERERERER8RJK0kVERERERES8hJJ0ERERERERES+hJF1ERKSS9e3bl759+3o6jCL9/PPPdOrUiYCAACwWCwkJCZ4OKZ8pU6ZgsVjybcvJyeG+++4jJiYGq9XKiBEjAEhJSeH6668nOjoai8XCHXfcUfUBVzONGzfmuuuuq9BrWiwWpkyZUqHXFBGpLpSki4hUge3bt3PTTTfRtGlTAgICCAsL4+yzz2batGmkp6dX+P3S0tKYMmUKCxcurPBre1rjxo0ZNmyY+2dvea4bN25kypQp7Nq1y6NxlNaxY8cYNWoUgYGBvPHGG8yaNYvg4OBKu9+MGTOwWCzur4CAAOrVq8egQYN49dVXSU5OLtF13n//fZ5//nkuvfRSPvzwQ+68804AnnrqKWbMmMEtt9zCrFmzuOaaayrtuZTXJ598wiuvvFKqc7Kzs3n11Vfp3r07oaGhhISE0L17d1599VWys7PLHMvy5cuZMmWK131AIyJyOrIYhmF4OggRkershx9+4LLLLsPhcHDttdfSvn17srKyWLp0KV9++SXXXXcd7777boXe8+jRo9SqVYtHH3202lWrGjduTPv27fn+++8B73muc+bM4bLLLmPBggUFquZZWVkA+Pv7eyCy4v38888MGTKEX3/9lQEDBlT6/WbMmMHYsWN57LHHaNKkCdnZ2Rw6dIiFCxfy66+/0rBhQ7799lvOOOMM9zk5OTnk5OQQEBDg3nbFFVewdOlS9u3bl+/6Z511Fna7naVLl1b6cymvYcOGsX79+hJ/sJOamsrQoUNZtGgRw4YNY/DgwVitVn7++We+/fZb+vTpww8//FCmD1leeOEF7r33Xnbu3Enjxo3z7cvMzMRqteLn51fq6xYlIyMDu92O3W6vsGuKiFQX+i+jiEgl2rlzJ1dccQWNGjVi/vz51K1b173vtttuY9u2bfzwww8ejND75OTk4HQ6PZ7QpqamVlhF2dPPpTiHDx8GICIiosKuWZLXbsiQIXTr1s398+TJk5k/fz7Dhg3jwgsvZNOmTQQGBgIUmswdPny40JgPHz5M27Zty/8k8jidTrKysvJ9QOApd911F4sWLeK1115jwoQJ7u233HILb7zxBhMmTOCee+7hrbfeqtD7OhyOCr0eUOWvZ0X+PYuIVDpDREQqzc0332wAxrJly0p0fHZ2tvHYY48ZTZs2Nfz9/Y1GjRoZkydPNjIyMvId99dffxnnn3++ERUVZQQEBBiNGzc2xo4daxiGYezcudMACnw9+uijxd57+/btxqWXXmrUqFHDCAwMNM4880zj+++/d+8/dOiQYbPZjClTphQ4d/PmzQZgvPbaa+5t8fHxxu233240aNDA8Pf3N5o1a2Y888wzRm5urvsYV6zPP/+88fLLLxtNmzY1rFarsWbNmiLjbNSokTF06NASP9dNmzYZl1xyiVGjRg3D4XAYXbt2NebOnZvvmh988IEBGAsXLjRuueUWo1atWkZERIRhGIaxa9cu45ZbbjFatmxpBAQEGJGRkcall15q7Ny5s8D5J38tWLDAMAzD6NOnj9GnT59894yNjTXGjRtn1K5d23A4HMYZZ5xhzJgxI98xJ74+77zzjvv3olu3bsbKlSvzHXvw4EHjuuuuM+rXr2/4+/sb0dHRxoUXXpgvzpP16dOnQMxjxoxx7589e7bRpUsXIyAgwIiKijJGjx5t7Nu3L981xowZYwQHBxvbtm0zhgwZYoSEhBgXXXRRkfd0vVZ//fVXofufeuopAzDeffdd97ZHH33UcP2Tpaj3fMGCBYVudz3/jIwM45FHHjGaNWtm+Pv7Gw0aNDDuvffeAn9bgHHbbbcZH330kdG2bVvDbrcbX3/9tWEYhrFv3z5j7NixRu3atQ1/f3+jbdu2xvTp0/Od74rj888/N5544gmjfv36hsPhMPr162ds3bq12Ne+UaNGRb5ue/fuNWw2m9GvX78ijznvvPMMu91u7N27t9Dn07JlS8PhcBhdunQxFi1aVOD1Leq1a9SoUb7fC9d7uGTJEmPixIlGzZo1jfDwcOPGG280MjMzjfj4eOOaa64xIiIijIiICOPee+81nE5ngdfZ9Xda1Hvq+jrRH3/8YQwaNMgICwszAgMDjXPPPddYunRpvmNcz2fDhg3GlVdeaURERBidOnUyDKNsfyciIlVNlXQRkUr03Xff0bRpU3r16lWi46+//no+/PBDLr30Uu6++27+/PNPnn76aTZt2sTXX38NmJXC888/n1q1avHAAw8QERHBrl27+OqrrwCoVasWb731FrfccgsXX3wxI0eOBMg3fPhksbGx9OrVi7S0NCZNmkRUVBQffvghF154IXPmzOHiiy+mTp069OnTh9mzZ/Poo4/mO//zzz/HZrNx2WWXAeY88T59+rB//35uuukmGjZsyPLly5k8eTIHDx4sMA/3gw8+ICMjgxtvvBGHw0FkZGSJXq9TPdcNGzZw9tlnU79+fR544AGCg4OZPXs2I0aM4Msvv+Tiiy/Od71bb72VWrVq8cgjj5CamgrAX3/9xfLly7niiito0KABu3bt4q233qJv375s3LiRoKAgzj33XCZNmsSrr77Kgw8+SJs2bQDc30+Wnp5O37592bZtGxMmTKBJkyZ88cUXXHfddSQkJHD77bfnO/6TTz4hOTmZm266CYvFwnPPPcfIkSPZsWOHewjyJZdcwoYNG5g4cSKNGzfm8OHD/Prrr+zZs6fA8GWX//3vf7Rq1Yp3333XPfy8WbNmwPFh6d27d+fpp58mNjaWadOmsWzZMtasWZOvip2Tk8OgQYPo3bs3L7zwAkFBQSV6/wpzzTXX8OCDD/LLL79www03FNhfq1YtZs2axZNPPklKSgpPP/00YL7Ws2bN4s4776RBgwbcfffd7uOdTicXXnghS5cu5cYbb6RNmzb8+++/vPzyy2zZsoVvvvkm3z3mz5/P7NmzmTBhAjVr1qRx48bExsZy1llnYbFYmDBhArVq1eKnn35i/PjxJCUlFWhQ98wzz2C1WrnnnntITEzkueeeY/To0fz555/u1z4xMZF9+/bx8ssvAxASElLk6/LTTz+Rm5vLtddeW+Qx1157LQsWLODnn3/m+uuvd29ftGgRn3/+OZMmTcLhcPDmm28yePBgVq5cSfv27Rk5ciRbtmzh008/5eWXX6ZmzZru1644EydOJDo6mqlTp/LHH3/w7rvvEhERwfLly2nYsCFPPfUUP/74I88//zzt27cvMnbXe3qi7Oxs7rzzznyjUObPn8+QIUPo2rUrjz76KFarlQ8++IB+/fqxZMkSevToke8al112GS1atOCpp57CyJvdWZa/ExGRKufpTwlERKqrxMREAyi2qniitWvXGoBx/fXX59t+zz33GIAxf/58wzAM4+uvvy62EmkYhnHkyJESVc9d7rjjDndlzCU5Odlo0qSJ0bhxY3f1+5133jEA499//813ftu2bfNV+B5//HEjODjY2LJlS77jHnjgAcNmsxl79uwxDON4BS0sLMw4fPhwiWI9sZJ+qufav39/o0OHDvmqpU6n0+jVq5fRokUL9zZXZbB3795GTk5OvmukpaUVuO6KFSsMwJg5c6Z72xdffJGven6ikyvpr7zyigEYH330kXtbVlaW0bNnTyMkJMRISkoyDOP46xMVFWXExcW5j507d64BGN99951hGOaoBfIq7qVVWGU7KyvLqF27ttG+fXsjPT3dvf377783AOORRx5xbxszZowBGA888ECZ73ey8PBwo3Pnzu6fT6yku/Tp08do165dgXNP/v0wDMOYNWuWYbVa8/1+G4ZhvP322wVGugCG1Wo1NmzYkO/Y8ePHG3Xr1jWOHj2ab/sVV1xhhIeHu39PXJX0Nm3aGJmZme7jpk2bVuBvZ+jQocVWz0/k+hstbpTJ6tWrDcC466678j0fwFi1apV72+7du42AgADj4osvdm97/vnn81XPT1RUJX3QoEH5KuQ9e/Y0LBaLcfPNN7u35eTkGA0aNCgwkuRU/3269dZbDZvN5v7vntPpNFq0aFHgnmlpaUaTJk2MgQMHure5fl+uvPLKfNcsz9+JiEhVUnd3EZFKkpSUBEBoaGiJjv/xxx8Bc97piVwVQdfcdVcF8/vvvy9XN+eT792jRw969+7t3hYSEsKNN97Irl272LhxIwAjR47Ebrfz+eefu49bv349Gzdu5PLLL3dv++KLLzjnnHOoUaMGR48edX8NGDCA3NxcFi9enO/+l1xyySmrdqUVFxfH/PnzGTVqFMnJye4Yjh07xqBBg9i6dSv79+/Pd84NN9yAzWbLt801LxrM6t6xY8do3rw5ERERrF69ukyx/fjjj0RHR3PllVe6t/n5+TFp0iRSUlJYtGhRvuMvv/xyatSo4f75nHPOAWDHjh3uGP39/Vm4cCHx8fFliulEq1at4vDhw9x666355g4PHTqU1q1bF9pH4ZZbbin3fV1CQkJK3OW9JL744gvatGlD69at8/0+9uvXD4AFCxbkO75Pnz755rUbhsGXX37J8OHDMQwj3zUGDRpEYmJigd+FsWPH5qsCn/yelZbr9Sjuvyeufa7/9rj07NmTrl27un9u2LAhF110EfPmzSM3N7dM8QCMHz8+39J4Z555JoZhMH78ePc2m81Gt27dSvW8Z86cyZtvvslzzz3HeeedB8DatWvZunUrV111FceOHXO//qmpqfTv35/FixfjdDrzXefmm2/O93NF/52IiFQWJekiIpUkLCwMoMTJxu7du7FarTRv3jzf9ujoaCIiIti9ezdgJhCXXHIJU6dOpWbNmlx00UV88MEHZGZmljnW3bt306pVqwLbXcO1XfeuWbMm/fv3Z/bs2e5jPv/8c+x2u3uoOcDWrVv5+eefqVWrVr4vV/dwV7MylyZNmpQ59qJs27YNwzB4+OGHC8ThGq5fkjjS09N55JFHiImJweFwULNmTWrVqkVCQgKJiYllim337t20aNECqzX//w2f/Hq7NGzYMN/ProTdlWg4HA6effZZfvrpJ+rUqcO5557Lc889x6FDh8ocH1Do70Tr1q0LxGe322nQoEGZ7lWYlJSUEn+4VRJbt25lw4YNBX4PWrZsCZz69+DIkSMkJCTw7rvvFrjG2LFjC73Gqd6z0nK9HsX996SoRL5FixYFjm3ZsiVpaWkcOXKkTPFAwecYHh4OQExMTIHtJX3ea9eu5eabb+bKK6/M94Hl1q1bARgzZkyB9+C9994jMzOzwN/jye9jRf+diIhUFs1JFxGpJGFhYdSrV4/169eX6rwTK1NF7Z8zZw5//PEH3333HfPmzWPcuHG8+OKL/PHHH8XOa60IV1xxBWPHjmXt2rV06tSJ2bNn079/f/c8VjA7Yg8cOJD77ruv0Gu4kiOXE6vVFcVVVbvnnnsYNGhQocec/IFIYXFMnDiRDz74gDvuuIOePXsSHh6OxWLhiiuuKFC5qywnV/ddjBNWUb3jjjsYPnw433zzDfPmzePhhx/m6aefZv78+XTu3LlS43M4HAU+cCirffv2kZiYWOC9KQ+n00mHDh146aWXCt1/clJ58u+B632++uqrGTNmTKHXOLnnQ0nes9JwfYDzzz//0KlTp0KP+eeffwAqtLt9cYp6joVtL8nzjo+P55JLLqFly5a89957+fa53oPnn3++yOd/8n/7Cvt79uTfiYhISSlJFxGpRMOGDePdd99lxYoV9OzZs9hjGzVqhNPpZOvWrfkajsXGxpKQkECjRo3yHX/WWWdx1lln8eSTT/LJJ58wevRoPvvsM66//vpTJvqF3fu///4rsH3z5s3u/S4jRozgpptucg9537JlC5MnT853XrNmzUhJSamSdbeLeq5NmzYFzGHk5Yljzpw5jBkzhhdffNG9LSMjg4SEhBLFUZhGjRrxzz//4HQ68yW3hb3epdGsWTPuvvtu7r77brZu3UqnTp148cUX+eijj0p1Hdf9//vvP/eQcJf//vuvzPGVhKuBWFEfrJRFs2bNWLduHf379y/13waYjc1CQ0PJzc2t0N/p0sQyZMgQbDYbs2bNKrIB28yZM7Hb7QwePDjfdlcV+kRbtmwhKCjIPc2kLK9LRXI6nYwePZqEhAR+++23As0HXQ0Nw8LCyv0eVNTfiYhIZdFwdxGRSnTfffcRHBzM9ddfT2xsbIH927dvZ9q0aQBccMEFAAU6n7uqf0OHDgXMatPJVSlXZck15N31D9yTE8miXHDBBaxcuZIVK1a4t6WmpvLuu+/SuHHjfJW5iIgIBg0axOzZs/nss8/w9/dnxIgR+a43atQoVqxYwbx58wrcKyEhgZycnBLFVRJFPdfatWvTt29f3nnnHQ4ePFjgvJIO87XZbAVe79dee63AXF7XGswlec0vuOACDh06lG9uf05ODq+99hohISH06dOnRLG5pKWlkZGRkW9bs2bNCA0NLdM0iG7dulG7dm3efvvtfOf/9NNPbNq0yf27WNHmz5/P448/TpMmTRg9enSFXXfUqFHs37+f//u//yuwLz093d3Jvyg2m41LLrmEL7/8stCRMWUdMh4cHFziKRMxMTGMHTuW3377rdB10N9++23mz5/P+PHjC0w9WLFiRb4583v37mXu3Lmcf/757qp3aX5/K8PUqVOZN28en376aaHTTrp27UqzZs144YUXSElJKbC/JO9BRf+diIhUFlXSRUQqUbNmzfjkk0+4/PLLadOmDddeey3t27cnKyuL5cuXu5fdAujYsSNjxozh3XffJSEhgT59+rBy5Uo+/PBDRowY4W6g9OGHH/Lmm29y8cUX06xZM5KTk/m///s/wsLC3Il+YGAgbdu25fPPP6dly5ZERkbSvn172rdvX2icDzzwAJ9++ilDhgxh0qRJREZG8uGHH7Jz506+/PLLAkOZL7/8cq6++mrefPNNBg0alG85LoB7772Xb7/9lmHDhnHdddfRtWtXUlNT+ffff5kzZw67du3KNzy+PIp7rm+88Qa9e/emQ4cO3HDDDTRt2pTY2FhWrFjBvn37WLdu3SmvP2zYMGbNmkV4eDht27ZlxYoV/Pbbb0RFReU7rlOnTthsNp599lkSExNxOBz069eP2rVrF7jmjTfeyDvvvMN1113H33//TePGjZkzZw7Lli3jlVdeKfV87C1bttC/f39GjRpF27ZtsdvtfP3118TGxnLFFVeU6lpgjj549tlnGTt2LH369OHKK690L8HWuHFj7rzzzlJf82Q//fQTmzdvJicnh9jYWObPn8+vv/5Ko0aN+Pbbb/M1rCuva665htmzZ3PzzTezYMECzj77bHJzc9m8eTOzZ89m3rx5dOvWrdhrPPPMMyxYsIAzzzyTG264gbZt2xIXF8fq1av57bffiIuLK3VcXbt25fPPP+euu+6ie/fuhISEMHz48CKPf/nll9m8eTO33norP//8s7tiPm/ePObOnUufPn3yjfhwad++PYMGDcq3BBuYifGJsYC5NNwVV1yBn58fw4cPdyfvlenff//l8ccf59xzz+Xw4cMFKtpXX301VquV9957jyFDhtCuXTvGjh1L/fr12b9/PwsWLCAsLIzvvvuu2PtU9N+JiEil8VhfeRGR08iWLVuMG264wWjcuLHh7+9vhIaGGmeffbbx2muv5VseLDs725g6darRpEkTw8/Pz4iJiTEmT56c75jVq1cbV155pdGwYUPD4XAYtWvXNoYNG5ZviSXDMIzly5cbXbt2Nfz9/Uu0HNv27duNSy+91IiIiDACAgKMHj16GN9//32hxyYlJRmBgYEFlhE7UXJysjF58mSjefPmhr+/v1GzZk2jV69exgsvvGBkZWUZhnF8ibHSLIlU2BJbxT3X7du3G9dee60RHR1t+Pn5GfXr1zeGDRtmzJkzx31MccuCxcfHG2PHjjVq1qxphISEGIMGDTI2b95cYFkqwzCM//u//zOaNm1q2Gy2fMuxnbwEm2EYRmxsrPu6/v7+RocOHYwPPvgg3zHFvT4nPs+jR48at912m9G6dWsjODjYCA8PN84880xj9uzZxb+Yp3jun3/+udG5c2fD4XAYkZGRxujRo419+/blO2bMmDFGcHDwKe9z8v1cX/7+/kZ0dLQxcOBAY9q0ae7l505U3iXYDMNcVu7ZZ5812rVrZzgcDqNGjRpG165djalTpxqJiYnu4wDjtttuKzT22NhY47bbbjNiYmIMPz8/Izo62ujfv7/x7rvvuo9xLcH2xRdf5DvX9V6e+B6npKQYV111lREREWEAJVqOLTMz03j55ZeNrl27GsHBwUZQUJDRpUsX45VXXnH/XZ3I9Xw++ugjo0WLFobD4TA6d+5c6FKBjz/+uFG/fn3DarXmW46tqCXYTv6dcb1PR44cybe9sN+RE39/Xa9ZUV8nWrNmjTFy5EgjKirKcDgcRqNGjYxRo0YZv//++ynjKM/fiYhIVbIYRhk7mIiIiIiIV7NYLNx22228/vrrng5FRERKSHPSRURERERERLyEknQRERERERERL6EkXURERERERMRLqLu7iIiISDWl1kMiIr5HlXQRERERERERL6EkXURERERERMRLnHbD3Z1OJwcOHCA0NBSLxeLpcERERERERKSaMwyD5ORk6tWrh9VafK38tEvSDxw4QExMjKfDEBERERERkdPM3r17adCgQbHHnHZJemhoKGC+OGFhYR6ORkRERERERKq7pKQkYmJi3PlocU67JN01xD0sLExJuoiIiIiIiFSZkky5VuM4ERERERERES+hJF1ERERERETESyhJFxEREREREfESHp2TvnjxYp5//nn+/vtvDh48yNdff82IESNKdO6yZcvo06cP7du3Z+3atZUap4iIiIiIlI5hGOTk5JCbm+vpUESqhJ+fHzabrdzX8WiSnpqaSseOHRk3bhwjR44s8XkJCQlce+219O/fn9jY2EqMUERERERESisrK4uDBw+Slpbm6VBEqozFYqFBgwaEhISU6zoeTdKHDBnCkCFDSn3ezTffzFVXXYXNZuObb76p+MBERERERKRMnE4nO3fuxGazUa9ePfz9/UvU0VrElxmGwZEjR9i3bx8tWrQoV0Xd55Zg++CDD9ixYwcfffQRTzzxxCmPz8zMJDMz0/1zUlJSZYYnIiIiInJay8rKwul0EhMTQ1BQkKfDEakytWrVYteuXWRnZ5crSfepxnFbt27lgQce4KOPPsJuL9nnC08//TTh4eHur5iYmEqOUkRERERErFafSjVEyq2iRoz4zF9Obm4uV111FVOnTqVly5YlPm/y5MkkJia6v/bu3VuJUYqIiIiIiIiUnc8Md09OTmbVqlWsWbOGCRMmAOZ8F8MwsNvt/PLLL/Tr16/AeQ6HA4fDUdXhioiIiIiIiJSazyTpYWFh/Pvvv/m2vfnmm8yfP585c+bQpEkTD0UmIiIiIiIiUjE8Otw9JSWFtWvXutc537lzJ2vXrmXPnj2AOVT92muvBcw5Le3bt8/3Vbt2bQICAmjfvj3BwcGeehoiIiIiIlINXHfddVgsFp555pl827/55psKm2/ct29f7rjjjgq5VkWYMWMGFouFNm3aFNj3xRdfYLFYaNy4cdUHVgYLFy7EYrGQkJDg6VDKxaNJ+qpVq+jcuTOdO3cG4K677qJz58488sgjABw8eNCdsIuIiIiIiFS2gIAAnn32WeLj4z0dSrGysrIq7FrBwcEcPnyYFStW5Ns+ffp0GjZsWGH3kZLxaJLet29fDMMo8DVjxgzA/FRn4cKFRZ4/ZcoUdxVeRERERES8k2EYpGXleOTLMIxSxTpgwACio6N5+umnizxm6dKlnHPOOQQGBhITE8OkSZNITU1173/zzTdp0aIFAQEB1KlTh0svvRQwK/WLFi1i2rRpWCwWLBYLu3btAmD9+vUMGTKEkJAQ6tSpwzXXXMPRo0fd1+zbty8TJkzgjjvuoGbNmgwaNAiARYsW0aNHDxwOB3Xr1uWBBx4gJycHgHfffZd69erhdDrzxX/RRRcxbtw49892u52rrrqK999/371t3759LFy4kKuuuqrA83/rrbdo1qwZ/v7+tGrVilmzZuXbb7FYeOeddxg2bBhBQUG0adOGFStWsG3bNvr27UtwcDC9evVi+/bt+c6bO3cuXbp0ISAggKZNmzJ16lT3c3Fd97333uPiiy8mKCiIFi1a8O233wKwa9cuzjvvPABq1KiBxWLhuuuuA6Bx48a88sor+e7VqVMnpkyZUu6YK4PPzEkXERERERHflJ6dS9tH5nnk3hsfG0SQf8nTHpvNxlNPPcVVV13FpEmTaNCgQb7927dvZ/DgwTzxxBO8//77HDlyhAkTJjBhwgQ++OADVq1axaRJk5g1axa9evUiLi6OJUuWADBt2jS2bNlC+/bteeyxxwBzbe2EhAT69evH9ddfz8svv0x6ejr3338/o0aNYv78+e57f/jhh9xyyy0sW7YMgP3793PBBRdw3XXXMXPmTDZv3swNN9xAQEAAU6ZM4bLLLmPixIksWLCA/v37AxAXF8fPP//Mjz/+mO95jRs3jr59+zJt2jSCgoKYMWMGgwcPpk6dOvmO+/rrr7n99tt55ZVXGDBgAN9//z1jx46lQYMG7iQZ4PHHH+ell17ipZde4v777+eqq66iadOmTJ48mYYNGzJu3DgmTJjATz/9BMCSJUu49tprefXVVznnnHPYvn07N954IwCPPvqo+7pTp07lueee4/nnn+e1115j9OjR7N69m5iYGL788ksuueQS/vvvP8LCwggMDCzx+16WmCuLzyzBJiIiIiIiUhUuvvhiOnXqlC85dHn66acZPXo0d9xxBy1atKBXr168+uqrzJw5k4yMDPbs2UNwcDDDhg2jUaNGdO7cmUmTJgEQHh6Ov78/QUFBREdHEx0djc1m4/XXX6dz58489dRTtG7dms6dO/P++++zYMECtmzZ4r53ixYteO6552jVqhWtWrXizTffJCYmhtdff53WrVszYsQIpk6dyosvvojT6aRGjRoMGTKETz75xH2NOXPmULNmzXwJNUDnzp1p2rQpc+bMcY9uPrHa7vLCCy9w3XXXceutt9KyZUvuuusuRo4cyQsvvJDvuLFjxzJq1ChatmzJ/fffz65duxg9ejSDBg2iTZs23H777flGTU+dOpUHHniAMWPG0LRpUwYOHMjjjz/OO++8k++61113HVdeeSXNmzfnqaeeIiUlhZUrV2Kz2YiMjASgdu3aREdHEx4eXsJ3vGwxVxZV0r3U/oR0/t2XSI0gP85sGuXpcEREREREyizQz8bGxwZ57N5l8eyzz9KvXz/uueeefNvXrVvHP//8w8cff+zeZhgGTqeTnTt3MnDgQBo1akTTpk0ZPHgwgwcPdg/PLsq6detYsGABISEhBfZt376dli1bAtC1a9d8+zZt2kTPnj3zNbU7++yzSUlJYd++fTRs2JDRo0dzww038Oabb+JwOPj444+54oorsFoL1mvHjRvHBx98QMOGDUlNTeWCCy7g9ddfL3BPV4X7xHtOmzYt37YzzjjD/dhVje/QoUO+bRkZGSQlJREWFsa6detYtmwZTz75pPuY3NxcMjIySEtLc79+J143ODiYsLAwDh8+XOC5lEVpY64sStK91LKtR7nvy384r1UtJekiIiIi4tMsFkuphpx7g3PPPZdBgwYxefJk99xmMFeouummm9zV8RM1bNgQf39/Vq9ezcKFC/nll1945JFHmDJlCn/99RcRERGF3islJYXhw4fz7LPPFthXt25d9+OyrGg1fPhwDMPghx9+oHv37ixZsoSXX3650GNHjx7Nfffdx5QpU7jmmmuw28v+nvn5+bkfuz5EKGyba758SkoKU6dOZeTIkQWuFRAQUOh1Xdc5ec79yaxWa4HeBNnZ2eWOubL41l/KaSTYYb41qZm5Ho5EREREROT09Mwzz9CpUydatWrl3talSxc2btxI8+bNizzPbrczYMAABgwYwKOPPkpERATz589n5MiR+Pv7k5ub/9/4Xbp04csvv6Rx48alSozbtGnDl19+iWEY7gRy2bJlhIaGuufSBwQEMHLkSD7++GO2bdtGq1at6NKlS6HXi4yM5MILL2T27Nm8/fbbRd5z2bJljBkzxr1t2bJltG3btsRxF6ZLly78999/xb6up+Lv7w9Q4PWtVasWBw8edP+clJTEzp07y3yfyqY56V4q2GEOy0nJzDnFkSIiIiIiUhk6dOjA6NGjefXVV93b7r//fpYvX86ECRNYu3YtW7duZe7cuUyYMAGA77//nldffZW1a9eye/duZs6cidPpdCf6jRs35s8//2TXrl0cPXoUp9PJbbfdRlxcHFdeeSV//fUX27dvZ968eYwdO7ZAwnmiW2+9lb179zJx4kQ2b97M3LlzefTRR7nrrrvyDWcfPXo0P/zwA++//z6jR48u9jnPmDGDo0eP0rp160L333vvvcyYMYO33nqLrVu38tJLL/HVV18VmBZQWo888ggzZ85k6tSpbNiwgU2bNvHZZ5/x0EMPlfgajRo1wmKx8P3333PkyBFSUlIA6NevH7NmzWLJkiX8+++/jBkzBputbNMgqoKSdC8V4qqkZylJFxERERHxlMceeyzf8OYzzjiDRYsWsWXLFs455xw6d+7MI488Qr169QCIiIjgq6++ol+/frRp04a3336bTz/9lHbt2gFwzz33YLPZaNu2LbVq1WLPnj3Uq1ePZcuWkZuby/nnn0+HDh244447iIiIKHTuuEv9+vX58ccfWblyJR07duTmm29m/PjxBRLbfv36ERkZyX///VfokmonCgwMJCqq6Om2I0aMYNq0abzwwgu0a9eOd955hw8++IC+ffue6qUs1qBBg/j+++/55Zdf6N69O2eddRYvv/wyjRo1KvE16tev725AV6dOHfcHJ5MnT6ZPnz4MGzaMoUOHMmLECJo1a1aueCuTxSjtwoE+LikpifDwcBITEyt1sn95bTqYxJBpS6gZ4s+qhwZ6OhwRERERkRLJyMhg586dNGnSJN9cYpHqrrjf/dLkoaqke6ngvMYaGu4uIiIiIiJy+lCS7qX+iVtBYKO3MWp8R05u5XYPFBEREREREe+gJN1LZRup2IN2YQ04SGqWOryLiIiIiIicDpSke6mo3EwAQm1xpKl5nIiIiIiIyGlBSbqXCj+2A4BAWwKpmpcuIiIiIiJyWlCS7qXCAmsCkG51kpKp4e4iIiIiIiKnAyXpXio8uDYA6TZIzsj0cDQiIiIiIiJSFZSke6nQkGj34yNpiR6MRERERERERKqKknQv5RcYSZDTXHrtWFqCZ4MRERERERGRKqEk3VsFRBCel6THpx31cDAiIiIiIlKYKVOm0KlTJ0+HIdWIknRvFRBGWK6ZpKekHfFwMCIiIiIip48VK1Zgs9kYOnSop0OR05CSdG9l8yPUMB+mZRz2bCwiIiIiIqeR6dOnM3HiRBYvXsyBAwc8HY6cZpSke7EQp/n2pGcd83AkIiIiIiLlYBiQleqZL8MoVagpKSl8/vnn3HLLLQwdOpQZM2bk2//MM89Qp04dQkNDGT9+PBkZGfn2//XXXwwcOJCaNWsSHh5Onz59WL16db5jLBYL77zzDsOGDSMoKIg2bdqwYsUKtm3bRt++fQkODqZXr15s3769TC+3+Da7pwOQogVb/ACDzOwET4ciIiIiIlJ22WnwVD3P3PvBA+AfXOLDZ8+eTevWrWnVqhVXX301d9xxB5MnT8ZisTB79mymTJnCG2+8Qe/evZk1axavvvoqTZs2dZ+fnJzMmDFjeO211zAMgxdffJELLriArVu3Ehoa6j7u8ccf56WXXuKll17i/vvv56qrrqJp06ZMnjyZhg0bMm7cOCZMmMBPP/1UoS+HeD8l6V4sGH8gk8zcJE+HIiIiIiJyWpg+fTpXX301AIMHDyYxMZFFixbRt29fXnnlFcaPH8/48eMBeOKJJ/jtt9/yVdP79euX73rvvvsuERERLFq0iGHDhrm3jx07llGjRgFw//3307NnTx5++GEGDRoEwO23387YsWMr9bmKd1KS7sWCrQFAJpnOFE+HIiIiIiJSdn5BZkXbU/cuof/++4+VK1fy9ddfA2C327n88suZPn06ffv2ZdOmTdx88835zunZsycLFixw/xwbG8tDDz3EwoULOXz4MLm5uaSlpbFnz558551xxhnux3Xq1AGgQ4cO+bZlZGSQlJREWFhYyZ+v+Dwl6V4s1B4MJJJlpHo6FBERERGRsrNYSjXk3FOmT59OTk4O9eodH5pvGAYOh4PXX3+9RNcYM2YMx44dY9q0aTRq1AiHw0HPnj3JysrKd5yfn5/7scViKXKbM29ZZjl9qHGcFwuxm3NWMsk4xZEiIiIiIlIeOTk5zJw5kxdffJG1a9e6v9atW0e9evX49NNPadOmDX/++We+8/744498Py9btoxJkyZxwQUX0K5dOxwOB0ePHq3KpyI+TpV0LxbmCIMMyLJknfpgEREREREps++//574+HjGjx9PeHh4vn2XXHIJ06dP55577uG6666jW7dunH322Xz88cds2LAhX+O4Fi1aMGvWLLp160ZSUhL33nsvgYGBVf10xIepku7FIgIiAciwZns4EhERERGR6m369OkMGDCgQIIOZpK+atUq2rRpw8MPP8x9991H165d2b17N7fcckuB68THx9OlSxeuueYaJk2aRO3atavqaUg1YDGMUi4c6OOSkpIIDw8nMTHR6xswbPj1Ga448DF+Tgurx/7j6XBERERERE4pIyODnTt30qRJEwICAjwdjkiVKe53vzR5qCrpXiwq1PzELdtqkJqleekiIiIiIiLVnZJ0L1YjrA6WvIEOh1LiPByNiIiIiIiIVDYl6V7MERxJaN6SC7Ep8R6ORkRERERERCqbknRvFhBOeF6SfjQtwbOxiIiIiIiISKVTku7NAsIIcyXpqaqki4iIiIiIVHdK0r1ZQDjhuWaSHp921MPBiIiIiIiISGVTku7N/EMIdZqN45JSYz0cjIiIiIiIiFQ2JenezGIhyLABkJxxzMPBiIiIiIiISGVTku7lggw7AKlZCZ4NRERERERERCqdknQvF4g/AGnZiR6ORERERERETjZlyhQ6derk6TCK1bdvX+644w5PhyElpCTdywVZAgBIy0n2cCQiIiIiIqeHFStWYLPZGDp0qKdDkdOQknQvF2ANAiDDSPNwJCIiIiIip4fp06czceJEFi9ezIEDBzwdDgDZ2dmeDkGqiJJ0LxdkCwEg3cjwcCQiIiIiImVjGAZp2Wke+TIMo1SxpqSk8Pnnn3PLLbcwdOhQZsyYkW//M888Q506dQgNDWX8+PFkZOT/d/pff/3FwIEDqVmzJuHh4fTp04fVq1fnO2bz5s307t2bgIAA2rZty2+//YbFYuGbb74BYNeuXVgsFj7//HP69OlDQEAAH3/8MceOHePKK6+kfv36BAUF0aFDBz799NN8105NTeXaa68lJCSEunXr8uKLL5bq+Yvn2T0dgBQv2C8cgHSyPByJiIiIiEjZpOekc+YnZ3rk3n9e9SdBfkElPn727Nm0bt2aVq1acfXVV3PHHXcwefJkLBYLs2fPZsqUKbzxxhv07t2bWbNm8eqrr9K0aVP3+cnJyYwZM4bXXnsNwzB48cUXueCCC9i6dSuhoaHk5uYyYsQIGjZsyJ9//klycjJ33313obE88MADvPjii3Tu3JmAgAAyMjLo2rUr999/P2FhYfzwww9cc801NGvWjB49egBw7733smjRIubOnUvt2rV58MEHWb16tdfPm5fjlKR7uWD/GpAL6ZYcDMPAYrF4OiQRERERkWpr+vTpXH311QAMHjyYxMREFi1aRN++fXnllVcYP34848ePB+CJJ57gt99+y1dN79evX77rvfvuu0RERLBo0SKGDRvGr7/+yvbt21m4cCHR0dEAPPnkkwwcOLBALHfccQcjR47Mt+2ee+5xP544cSLz5s1j9uzZ9OjRg5SUFKZPn85HH31E//79Afjwww9p0KBBBbwyUlWUpHu50MCakAK5FoP0nPRSfQooIiIiIuINAu2B/HnVnx67d0n9999/rFy5kq+//hoAu93O5ZdfzvTp0+nbty+bNm3i5ptvzndOz549WbBggfvn2NhYHnroIRYuXMjhw4fJzc0lLS2NPXv2uO8RExPjTtABdxX8ZN26dcv3c25uLk899RSzZ89m//79ZGVlkZmZSVCQmSNs376drKwszjzz+KiFyMhIWrVqVeLXQDxPSbqXCwqMwp5skGOxkJSVpCRdRERERHyOxWLxiX/HTp8+nZycHOrVq+feZhgGDoeD119/vUTXGDNmDMeOHWPatGk0atQIh8NBz549ycoq/fTV4ODgfD8///zzTJs2jVdeeYUOHToQHBzMHXfcUaZri/dS4zgv5xccQZjTCUBiptZKFxERERGpDDk5OcycOZMXX3yRtWvXur/WrVtHvXr1+PTTT2nTpg1//pl/RMAff/yR7+dly5YxadIkLrjgAtq1a4fD4eDo0aPu/a1atWLv3r3Exsa6t/31118linHZsmVcdNFFXH311XTs2JGmTZuyZcsW9/5mzZrh5+eXL8b4+Ph8x4j3UyXdy/mF1CA810mczUZSVpKnwxERERERqZa+//574uPjGT9+POHh4fn2XXLJJUyfPp177rmH6667jm7dunH22Wfz8ccfs2HDhnyN41q0aMGsWbPo1q0bSUlJ3HvvvQQGHh9yP3DgQJo1a8aYMWN47rnnSE5O5qGHHgI4Zf+pFi1aMGfOHJYvX06NGjV46aWXiI2NpW3btgCEhIQwfvx47r33XqKioqhduzb/+9//sFpVm/Ulere8nOOESnpSppJ0EREREZHKMH36dAYMGFAgQQczSV+1ahVt2rTh4Ycf5r777qNr167s3r2bW265pcB14uPj6dKlC9dccw2TJk2idu3a7v02m41vvvmGlJQUunfvzvXXX8///vc/AAICAoqN8aGHHqJLly4MGjSIvn37Eh0dzYgRI/Id8/zzz3POOecwfPhwBgwYQO/evenatWsZXxXxBItR2oUDfVxSUhLh4eEkJiYSFhbm6XBOKXn/Zh74dhiLgwJ56MxHubz1pZ4OSURERESkSBkZGezcuZMmTZqcMukU07Jly+jduzfbtm2jWbNmng5Hyqi43/3S5KEa7u7lAkNruCvpx1LjPRyNiIiIiIiU19dff01ISAgtWrRg27Zt3H777Zx99tlK0AVQku717EERhOWaSXpc6mEPRyMiIiIiIuWVnJzM/fffz549e6hZsyYDBgzgxRdf9HRY4iU8Oid98eLFDB8+nHr16mGxWPjmm2+KPX7p0qWcffbZREVFERgYSOvWrXn55ZerJlhPsTsIdpoNJBLSjp7iYBERERER8XbXXnstW7ZsISMjg3379jFjxgyioqI8HZZ4CY9W0lNTU+nYsSPjxo1j5MiRpzw+ODiYCRMmcMYZZxAcHMzSpUu56aabCA4O5sYbb6yCiD3D4TTfpqQMDXcXERERERGpzjyapA8ZMoQhQ4aU+PjOnTvTuXNn98+NGzfmq6++YsmSJdU7STf8AUjO0jrpIiIiIuIbTrP+1CIV9jvv00uwrVmzhuXLl9OnT58ij8nMzCQpKSnfl6/xx+wMmJKd4uFIRERERESK5+fnB0BaWpqHIxGpWllZWYC5zF55+GTjuAYNGnDkyBFycnKYMmUK119/fZHHPv3000ydOrUKo6t4/gQBWaQ69R86EREREfFuNpuNiIgIDh82mx4HBQVhsVg8HJVI5XI6nRw5coSgoCDs9vKl2T6ZpC9ZsoSUlBT++OMPHnjgAZo3b86VV15Z6LGTJ0/mrrvucv+clJRETExMVYVaIQKswUACqc4MT4ciIiIiInJK0dHRAO5EXeR0YLVaadiwYbk/lPLJJL1JkyYAdOjQgdjYWKZMmVJkku5wOHA4HFUZXoXzt4UD+0k3snAaTqwWn56lICIiIiLVnMVioW7dutSuXZvs7GxPhyNSJfz9/bFay5+r+WSSfiKn00lmZqanw6hUDr8IAAwLJGclE+4I92xAIiIiIiIlYLPZyj0/V+R049EkPSUlhW3btrl/3rlzJ2vXriUyMpKGDRsyefJk9u/fz8yZMwF44403aNiwIa1btwbMddZfeOEFJk2a5JH4q4rNEUGg00m61UpSVpKSdBERERERkWrKo0n6qlWrOO+889w/u+aOjxkzhhkzZnDw4EH27Nnj3u90Opk8eTI7d+7EbrfTrFkznn32WW666aYqj70qGY4wQjPykvTMJAj1dEQiIiIiIiJSGTyapPft27fYteRmzJiR7+eJEycyceLESo7K+1gCIwhPc3IYSNRa6SIiIiIiItWWOpD5AFtgGGG5TgCSsnxvnXcREREREREpGSXpPsAWWINwZ16SnqkkXUREREREpLpSku4D/IMjCHOqki4iIiIiIlLdKUn3Af4hNdxJemKm5qSLiIiIiIhUV0rSfUBASA3CXXPSM+I9HI2IiIiIiIhUFiXpPiAoNIJQp9kFPzH9mIejERERERERkcqiJN0HBAf44ci1AZCQrkq6iIiIiIhIdaUk3QcE+9uxOf0BzUkXERERERGpzpSk+wCr1YLVGQBAYnayh6MRERERERGRyqIk3UfYnYEApOSkezgSERERERERqSxK0n2E1RICQLqRRbYz28PRiIiIiIiISGVQku4jbJZQ9+OkzCQPRiIiIiIiIiKVRUm6j8i2hxHqWis9S0m6iIiIiIhIdaQk3Ufk+IUQ5jSTdHV4FxERERERqZ6UpPuIHP8wd5KuSrqIiIiIiEj1pCTdRxj+oaqki4iIiIiIVHNK0n2E4QgnPDcXUCVdRERERESkulKS7isCwjXcXUREREREpJpTku4jbEERhLuSdC3BJiIiIiIiUi0pSfcR1kA1jhMREREREanulKT7CP/gGscbx2UkeDYYERERERERqRRK0n2Ef3ANwnNdw93jPRyNiIiIiIiIVAa7pwOQkgkMCiY31/xMJUlLsImIiIiIiFRLqqT7iBCHHZvTH4BENY4TERERERGplpSk+4hghw1LrgOApOwUDMPwcEQiIiIiIiJS0ZSk+4gQhx0jNxCALCOHjNwMD0ckIiIiIiIiFU1Juo8IdtjJdgZhy6uga610ERERERGR6kdJuo8I8reRQrDWShcREREREanGlKT7CIvFQro1xL0MW6I6vIuIiIiIiFQ7StJ9SIYtRJV0ERERERGRakxJug/JtIcQ6lQlXUREREREpLpSku5Dsu2hhKuSLiIiIiIiUm0pSfchOf6hGu4uIiIiIiJSjSlJ9yFO/zA1jhMREREREanGlKT7ECMgTJV0ERERERGRakxJui8JCD+epGcqSRcREREREalulKT7EFtguBrHiYiIiIiIVGNK0n2INbAGYbmuSrrmpIuIiIiIiFQ3StJ9iH/Q8SXYEjMTPBuMiIiIiIiIVDgl6T4kOMCBNdcfgKSsFJyG08MRiYiIiIiISEVSku5Dghx2cAYA4MRJanaqhyMSERERERGRiqQk3YeEOGxkOYNwqHmciIiIiIhItaQk3YcE+9tJIkjLsImIiIiIiFRTStJ9SLDDTrIRdLx5XJY6vIuIiIiIiFQnStJ9SIhDlXQREREREZHqTEm6D3FV0l1rpauSLiIiIiIiUr0oSfchqqSLiIiIiIhUb0rSfUiAn5WUE5J0VdJFRERERESqFyXpPsRisZBpD3U3jlMlXUREREREpHpRku5jsu0h7jnpWiddRERERESkelGS7mNy/MM0J11ERERERKSaUpLuY3L9Thjurkq6iIiIiIhItaIk3ccYjuOV9MRMNY4TERERERGpTjyapC9evJjhw4dTr149LBYL33zzTbHHf/XVVwwcOJBatWoRFhZGz549mTdvXtUE6y0CwgnXnHQREREREZFqyaNJempqKh07duSNN94o0fGLFy9m4MCB/Pjjj/z999+cd955DB8+nDVr1lRypN7DGhjurqSnZKeQ48zxcEQiIiIiIiJSUeyevPmQIUMYMmRIiY9/5ZVX8v381FNPMXfuXL777js6d+5cwdF5J/+AIBy5FvfPyVnJ1Aio4cGIREREREREpKJ4NEkvL6fTSXJyMpGRkUUek5mZSWZmpvvnpCTfHiIeHOBHBkEEO52kWq0kZSUpSRcREREREakmfLpx3AsvvEBKSgqjRo0q8pinn36a8PBw91dMTEwVRljxQvztJBlBah4nIiIiIiJSDflskv7JJ58wdepUZs+eTe3atYs8bvLkySQmJrq/9u7dW4VRVrxgh51kgtQ8TkREREREpBryyeHun332Gddffz1ffPEFAwYMKPZYh8OBw+GoosgqX4jDVUlPBSApU0m6iIiIiIhIdeFzlfRPP/2UsWPH8umnnzJ06FBPh1PlXJV093D3LA13FxERERERqS48WklPSUlh27Zt7p937tzJ2rVriYyMpGHDhkyePJn9+/czc+ZMwBziPmbMGKZNm8aZZ57JoUOHAAgMDCQ8PNwjz6GqBTtsxBrBhOcl6aqki4iIiIiIVB8eraSvWrWKzp07u5dPu+uuu+jcuTOPPPIIAAcPHmTPnj3u4999911ycnK47bbbqFu3rvvr9ttv90j8nmBW0gMJy1UlXUREREREpLrxaCW9b9++GIZR5P4ZM2bk+3nhwoWVG5APCPa3k2QEu4e7q5IuIiIiIiJSffjcnPTTXYirku5Ud3cREREREZHqRkm6jwl22PJV0rVOuoiIiIiISPWhJN3HuOakh+fmAqqki4iIiIiIVCdK0n2Mw24l1RKiOekiIiIiIiLVkJJ0H2OxWMjxCz2+BJsq6SIiIiIiItWGknQf5PQPdVfSM3IzyMzN9HBEIiIiIiIiUhGUpPsgpyOMEKeBJW/5Og15FxERERERqR6UpPuigDCsoGXYREREREREqhkl6T4o0OEgxQjQMmwiIiIiIiLVjJJ0HxTssJFMEDVyzSQ9PjPewxGJiIiIiIhIRVCS7oOCHXaSjCAi89ZKj8uI83BEIiIiIiIiUhGUpPugEIedZIKIzBvuHpeuJF1ERERERKQ6UJLug1RJFxERERERqZ6UpPsgdyU9b066knQREREREZHqQUm6Dwr2t6mSLiIiIiIiUg0pSfdBwe5KupJ0ERERERGR6kRJug8Kcc9J13B3ERERERGR6kRJug9yVdKjnGYlPSEzgdy8xyIiIiIiIuK7lKT7IFd394i8SrrTcJKYlejhqERERERERKS8lKT7oGCHjWSCsAMRhgXQWukiIiIiIiLVgZJ0HxTsb1bSASKdBqB56SIiIiIiItWBknQfFOKwk0Rekp6bAyhJFxERERERqQ6UpPugYIedZFclPTsLgGMZxzwZkoiIiIiIiFQAJek+yN9uJcMWAqC10kVERERERKoRJek+yuofRI5hVZIuIiIiIiJSjShJ91HBAX4kEURk3jJs6u4uIiIiIiLi+5Sk+6iQvHnpqqSLiIiIiIhUH0rSfVSww04iwccr6UrSRUREREREfJ6SdB8V7LCTYIQQ6VQlXUREREREpLpQku6jgv1tJBDiHu6ekp1CZm6mh6MSERERERGR8lCS7qOCHXbijRDCnAZ2LADEZ8R7OCoREREREREpDyXpPirEYSeBECxApNUfgGMZxzwblIiIiIiIiJSLknQfFeywkWCEABBpmG+jKukiIiIiIiK+TUm6j3I1jgOINMxtah4nIiIiIiLi25Sk+6gQh5148pJ011rp6UrSRUREREREfJmSdB8V7G8nwQgFIDIrC1AlXURERERExNcpSfdRwQ47CQQDEJmVBqhxnIiIiIiIiK9Tku6jQhx24t2V9HRAlXQRERERERFfpyTdRwU7bCQThBMLUa456UrSRUREREREfJqSdB8V4rDjxEoSwUTmOgEl6SIiIiIiIr5OSbqPCnLYAUgwQoh0Hu/ubhiGJ8MSERERERGRclCS7qNC/M0kPd4IoUZeJT3LmUVqdqonwxIREREREZFyUJLuo4IdNsBM0gMNg0CrH6Ah7yIiIiIiIr5MSbqPstusOOxWEggBINIaAChJFxERERER8WVK0n1YiMNOgmEm6VEWs5KutdJFRERERER8l5J0HxZ8QpIeiQVQJV1ERERERMSXKUn3YTWC/Ih3DXd3ml3d49KVpIuIiIiIiPgqJek+rF5E4PFKek4OAPGZ8Z4MSURERERERMpBSboPqxcReLxxXHYmoEq6iIiIiIiIL1OS7sPqRwQS76qkZ6YBmpMuIiIiIiLiy5Sk+7B8lfT0JEDd3UVERERERHyZknQf1qDGCXPSszIAVdJFRERERER8mUeT9MWLFzN8+HDq1auHxWLhm2++Kfb4gwcPctVVV9GyZUusVit33HFHlcTprepFBJJCINmGjShnLgAJmQnk5j0WERERERER3+LRJD01NZWOHTvyxhtvlOj4zMxMatWqxUMPPUTHjh0rOTrvVyPIj0A/OwkEE5HrBMBpOEnMSqzYGx3bDukJFXtNERERERERKcDuyZsPGTKEIUOGlPj4xo0bM23aNADef//9ygrLZ1gsFupFBJCQGEotSxIR9mASclKJS48jMiCyYm6SuA/eOBMiGsLNS8A/uGKuKyIiIiIiIgVU+znpmZmZJCUl5fuqTurXCCIBM3GOtAcBFTwv/chmcGZD3HZY8FTFXVdEREREREQKqPZJ+tNPP014eLj7KyYmxtMhVaj6EQEkGKEARFodQAUn6akndIv/403Y/3fFXVtERERERETyqfZJ+uTJk0lMTHR/7d2719MhVaj6EYEkGHmVdIs5e6FCl2FLO3r8seGEbydBbnbFXV9ERERERETcqn2S7nA4CAsLy/dVndSLCCQes5JewzC3VWwl/Yj5vcNlEBgJseth2bSKu76IiIiIiIi4Vfskvbo7sZIelWtm6RWbpOdV0mu2gsHPmI8XPQdHt1bcPURERERERATwcJKekpLC2rVrWbt2LQA7d+5k7dq17NmzBzCHql977bX5znEdn5KSwpEjR1i7di0bN26s6tC9Rr2IQBJclfScLADi0iswSU/LGzofXBPOGAXNB0Bupjns3emsuPuIiIiIiIiIZ5dgW7VqFeedd57757vuuguAMWPGMGPGDA4ePOhO2F06d+7sfvz333/zySef0KhRI3bt2lUlMXub6PAAEgkBIDwjHfwqabh7cE2wWGDYy/DGWbBnOayeAd3GVdy9RERERERETnMeTdL79u2LYRhF7p8xY0aBbcUdfzrys1mxBEVCNoRnpFRCkp433D2opvk9oiH0fxh+fgB+fRRaDoawehV3PxERERERkdOY5qRXA45QM4GOTEsGID4jvuIu7h7uXuv4th43Qv1ukJkEP9wN+uBERERERESkQihJrwaCI8wEOjozAYDk7GSycrPKf+GcTDMRBwiOOr7daoMLXwOrH/z3I2ycW/57iYiIiIiIiJL06iA8qo75PTcbu8UGVNCQd9dQd6sdAiLy76vTFs4xewjw472QVoFD7H2cYRjM3xzL0ZRMT4ciIiIiIiI+Rkl6NVA7qgYZhh8WINIRDlRQkp52wnx0i6Xg/nPuNpdmSz0Mvz5c/vtVE/M3H2bcjFXc+flaT4ciIiIiIiI+Rkl6NdAgIpCEvA7vkX7mcmwVU0k/obN7YewOc9g7FljzEez5o/z3rAZW7TZ7AizbdpRjqqaLiIiIiEgpKEmvBurXCCTeyEvSbYFARSXpeU3jgqKKPqbhmdDhUvPxlp/Lf89qYNNBcx6/04DfNx/2cDQiIiIiIuJLlKRXA/UiAo+vlY4fAHHpFTjc/cTO7oVp1Mv8fnBd+e9ZDWw+mOx+/MuGQx6MREREREREfI2S9GogxGEnxRoGQHCOOXe8Soa7u9TtaH4/sPa0X44tPjWLQ0kZ7p8Xbz1KamaOByMSERERERFfoiS9msj2NxvGBWXmAnAs41j5L5p6QuO44tRuZ3aAT4+DxL3lv68P23zIrKI3qBFIo6ggsnKcLNpypNTXOZaSyWVvL+fNhdsqOkQREREREfFiStKri8BIAIIzs4GK6u6el+ifqpLuFwC12piPT/Mh76756G3qhnF+W3NpvLIMeZ/1x27+2hXPy79uIfaEyryIiIiIiFRvStKrCVuwmaSHZJgJXZUOdweod8KQ99PY5kN5SXp0KIPaRQNm87isHGeJr5HrNJj9lzkiITvX4MPluyo8ThERERER8U5lStIfe+wx0tLSCmxPT0/nscceK3dQUnqOMDORjsgw35eKSdJLONwdoG4n8/vBteW/rw9zDXdvUzeMLg1rUDPEQXJGDn/sKPn0g8VbjnAgMQNr3tL0H/+5h7QszWsXERERETkdlClJnzp1KikpKQW2p6WlMXXq1HIHJaUXFGF2YK+Rab4vcelxGOVt4uYe7n6K7u5wPEk/jZvH5eQ6+S8vSW9dNwyr1cLAvCHv80ox5P3TlXsAuLZnYxpFBZGYns2Xf++r+IBFRERERMTrlClJNwwDi8VSYPu6deuIjIwsd1BSeuGRtQGok2UmiVnOLFKzU8t+wZxMyDSHbhNczDrpLtHtwWIzl21LOlD2+/qwXcfSyMxxEuhno2FkEADntzOT9F83xuJ0nvrDi8NJGe611a86syHjzm4CwPSlO0t0voiIiIiI+LZSJek1atQgMjISi8VCy5YtiYyMdH+Fh4czcOBARo0aVVmxSjGiaprzn2vmJhNoDwTKOeTdNdTdaoeAiFMf7xcItVqbj0/T5nGu+eitokOx5Y1V79UsihCHncPJmazdl3DKa3zx9z5ynQZdG9WgZZ1QLu3agLAAO7uOpbmTdxERERERqb7spTn4lVdewTAMxo0bx9SpUwkPD3fv8/f3p3HjxvTs2bPCg5RTqxFlVmzDSSXcvwbpOenEZcTRMKxh2S6YdsJ89EJGTRSqbkc4vMGcl976grLd14cd7+we6t7msNs4r3Vtvlt3gHkbDtGlYY0iz3c6DT77yxzqfkX3GACCHXauOrMRby/azntLdriHz4uIiIiISPVUqiR9zJgxADRp0oSzzz4bu71Up0slsuYNSbdZDIIsIUB5K+ml6OzuUq8TrPvktO3wvvlg3nz06LB8289vW4fv1h3glw2xPDC4daFTRQCWbz/G3rh0QgPsDDujnnv7mF6NeG/JDv7cGce/+xLp0CC80PNFRERERMT3lWlOemhoKJs2bXL/PHfuXEaMGMGDDz5IVlZWhQUnpWD3J91iDnMPMAKA8ibpeU3jgkowH92lbt4ybKftcPfjnd1P1LdVLfxtVnYeTWXb4YINF11cDeNGdKpPoL/Nvb1ueCDDO5pJ+3tLd1R02CIiIiIi4kXKlKTfdNNNbNmyBYAdO3Zw+eWXExQUxBdffMF9991XoQFKyWXYzeTQkW2OcChXku4a7l6Szu4u0R3AYoWUQ5Bc8m7m1UFiWjb7E9IBc076iUID/Di7uflhR1Fd3o+lZPLLRnPfFT1iCuwf39tsIPfDPwc5kHcfERERERGpfsqUpG/ZsoVOnToB8MUXX9CnTx8++eQTZsyYwZdfflmR8UkpZPlHAOCXZQ6nrvLh7v7BULOl+fg0G/LuahpXPyKQ8EC/AvsHtTMb+83bEFvo+V+u3kd2rkHHBuG0q1dwOHv7+uGc1TSSHKfBhyt2VVzgIiIiIiLiVcq8BJvT6QTgt99+44ILzCZhMTExHD16tOKik9IJMJuS+Wea701cegV0dw8qRZIOp+2Q9+ND3UML3d+/TR0sFvh3f6K74u5iGAafrdwLwBU9im70d33vpgB88uceUjNzKiJsERERERHxMmVK0rt168YTTzzBrFmzWLRoEUOHDgVg586d1Kmj7tOeYgsx16j3TzcTuPINd8+bk16aSjpA3U7m94Nry35vH+Tq7H5y0ziXWqEOujUyP0T59aQh73/ujGPH0VSC/W3uueeF6de6Nk1qBpOckcMXq/ZWUOQiIiIiIuJNypSkv/LKK6xevZoJEybwv//9j+bNmwMwZ84cevXqVaEBSsn5h5rzx4PSMwE4lnGs7Bcry3B3MDu8w2lXSd+UV0lvXUQlHYoe8v5ZXsO4CzvVI8RR9IoJVquFcXlz099ftotcp1GumEVERERExPuUaQ21M844g3///bfA9ueffx6bzVbIGVIVgsLNhDoy2+ywX7456WUc7h7dAbBA0n5IOQIhpWg856NynQZbiujsfqLz20bzxA+bWLkrjvjULGoE+5OQlsWP6/MaxnU/9Zr2l3Spz4u//MeeuDR+3RjL4PbRFfMkRERERETEK5Spku7y999/89FHH/HRRx+xevVqAgIC8PMr2DRLqoY9xOwgXtdpVtITMhPIdeaW7WLu4e6lTLIdoRBljqw4XYa874lLIz07F4fdSuOo4CKPaxgVROvoUHKdBr9tMqvpX63eT1aOk7Z1wzijBOufB/nbGX2mmcxP13JsIiIiIiLVTpmS9MOHD3PeeefRvXt3Jk2axKRJk+jWrRv9+/fnyJEjFR2jlFSgOee5fm4qAE7DSWJWYumvk5MJmeYca4JLsU66i3vI+9rSn+uDXPPRW0WHYrNaij3WNeT9l42xZsO4v8yh7lf2iMFiKf5clzE9G+Nns/DXrnjW7k0oe+AiIiIiIuJ1ypSkT5w4kZSUFDZs2EBcXBxxcXGsX7+epKQkJk2aVNExSkkFmo3jIi1pBFjNudFl6vDuGuputUNAROnPd3V4P02WYducl6S3KaJp3IlcSfriLUdYtu0YW2JTCPCzclHn+iW+X+2wAC7saB7/3hJV00VEREREqpMyJek///wzb775Jm3atHFva9u2LW+88QY//fRThQUnpZRXSa9BMv4WM2Es07z0tBPmo1ssLNt2lF1HU0t+vrvD++nRPK4kTeNc2tQNJSYykMwcJ/d/+Q8Aw86oR1hA6aaJjM9rIPfT+kPsi08rZcQiIiIiIuKtypSkO53OQuee+/n5uddPFw8IMivpEZZUyA0Bypikn9DZ/cu/9zH6vT+5+M1lHE7KKNn5dc8wvyfuhdRydJj3Eadafu1EFouF89ua1XTXeulX9ogp9T3b1gujV7Mocp0GX/69v9Tni4iIiIiIdypTkt6vXz9uv/12Dhw44N62f/9+7rzzTvr3719hwUkp5Q13D7OkkZsdBJRxGba8xDrNL4KHvlkPQHxaNnd/sQ5nSZb9CgiHyKbm42o+Lz0pI5t98Way3aYElXQ4PuQdoGWdELo0rFGme1/WrQEA36zdj2FoOTYRERERkeqgTEn666+/TlJSEo0bN6ZZs2Y0a9aMJk2akJSUxGuvvVbRMUpJBRzvDm5kmEvhlWe4+5+xVtKzc+kYE0GAn5UlW4/y/rKdJbvGaTLk3bX0Wt3wACKC/Et0TtdGNYgKNo+9onvDEjeMO9n5baMJ9LOx82gq6/aVoUGgiIiIiIh4nTKtkx4TE8Pq1av57bff2Lx5MwBt2rRhwIABFRqclJLNjtMRjjUzEUuGFUIhPiO+9NfJG+6+Kz2QmiEO/u/arvyyIZaHvlnPsz9v5qymUbSvf4rlwup2hA1fVftK+vGh7iWrogPYrBaevLgDS7Ye4coep14bvSjBDjuD2tXhm7UH+GbNfjrFRJT5WiIiIiIi4h1KVUmfP38+bdu2JSkpCYvFwsCBA5k4cSITJ06ke/futGvXjiVLllRWrFICliBz6HRwbtkr6bv2mMuCxRHGtCs6UTs0gNFnNmRg2zpk5xrc/tka0rNOsf66axm2at7h3dU0rk3dU89HP9Hg9tE8eXEHAv1t5br/iLyu8N+tO0B2rvpBiIiIiIj4ulIl6a+88go33HADYWEFE5Lw8HBuuukmXnrppQoLTkrP4pqXnpdDlzZJ334khR27dwNwRqvmnN28pnldi4VnLzmD2qEOth9J5fEfNhZ/IdcybAm7Ib0M1Xwf4Vp+rXUpk/SK0rt5TWqG+HMsNYulW496JAYREREREak4pUrS161bx+DBg4vcf/755/P333+XOygph7xl2CLzuuyXJknPyM7lto9XE2EkANCva9t8+yOD/XlpVCcAPvlzD/M2HCo+johG5uNqOi/d6TTY7Kqkl2K4e0Wy26wM71gPgK/XqMu7iIiIiIivK1WSHhsbW+jSay52u50jR46UOygph7xl2CJzcgCISy95kj71u41sPpRMLWsKALbgWgWO6d2iJjeea3Zuv//LfziUWMyybNV8yPve+DTSsnLxt1tpUjPYY3FcnDfk/ZeNh0jJzPFYHCIiIiIiUn6lStLr16/P+vXri9z/zz//ULdu3XIHJeWQN9w92pkFQHJ2Mlm5Wac87dt1B/h05R4sFqjrZybpFJKkA9xzfiva1QsjIS2bu79YW/SybK4h79W0kr7poFlFb1knBLutTAslVIgO9cNpWiuYjGwn89YXM7pBRERERES8XqkyiwsuuICHH36YjIyC1dP09HQeffRRhg0bVmHBSRnkDXev40wHw3x7TzXkfefRVCZ/+Q8Ak85tiD3blaRHFXq8v93Kq1d2JtDPxrJtx/i/JTsKv7B7Gba1pXoKvuJ4Z3fPzEd3sVgsXNzJrKZ/s1ZD3kVEREREfFmpkvSHHnqIuLg4WrZsyXPPPcfcuXOZO3cuzz77LK1atSIuLo7//e9/lRWrlETecPcISyoWpzlPurgkPSM7l1s/Xk1qVi49mkQy8awIc4fVDgERRZ7XrFYIjww356y/8Mt/rN9fyDrdriQ9bgdkVL91vDcfMpP00nZ2rwwX5SXpy7YdJTapmCkIIiIiIiLi1UqVpNepU4fly5fTvn17Jk+ezMUXX8zFF1/Mgw8+SPv27Vm6dCl16tSprFilJPIq6TVIITfbnCddXJL+7M+b2XQwichgf169ojN217FBNcFiKfZWV3SPYVA7c1m2SZ+uIS3rpPnQwVEQHmM+PvhP2Z6PF/N007gTNYwKolujGjgNczk2ERERERHxTaWeSNuoUSN+/PFHjh49yp9//skff/zB0aNH+fHHH2nSpEllxCilEeiqpKfgzCk+Sd90MIkPl+8C4MVRHYkOD4DUvMZ/wTVPeSuLxcIzI88gOiyAHUdTeXtRIcPe3fPS15bqaXi71Mwcdh9LA6CVFyTpcHzNdHV5FxERERHxXWXudlWjRg26d+9Ojx49qFGjRkXGJOURZL4XUdZUjNwQoPAO74Zh8Nh3G3EacEGHaM5rVdvckXos7zqFz0c/WY1gf/ew9/eX7iQh7aQmda4O79WseZyril471EFUiMPD0ZiGdqiLn83ChgNJbIlN9nQ4IiIiIiJSBp5rSS2VI2+4e4QlGaOYSvrP6w+xYscxHHYrk4e0Ob4j7aj5vYjO7oUZ3C6a1tGhpGTmFGwi55qXXs2WYfOm+eguNYL96Zv3Ycs3qqaLiIiIiPgkJenVTd5w9wAjE2tuIADHMo7lOyQjO5cnf9wEwE3nNiUmMuj4zlIMd3exWi3cObAlADOW7SIu9YRqumu4+7FtkFl51d2UzBzeW7KDq/7vD5ZuPVpp93HZnLf8Wuu63jHU3cW1ZvrctQeKXhpPRERERES8lpL06sYRBhbzbQ3MsQMFK+nvLdnBvvh06oYHcHPfZvnPT81LcINKnqQDnN+2Du3qhZGalcu7i0+opofUhtB6gAGH/i3VNUvicHIGz/28mV5P/84TP2xi+fZj3DBzFWv2xFf4vU7kWn6tjYeXXztZv9a1CXXY2Z+Qzl+7il96T0REREREvI+S9OrGanUPeQ/JNd/eAynHu30fTEznjQXbAXhgSGuC/O35z0/Lq7oXsUZ6USwWC3cOMKvpHy7fxdGUzOM7XfPSyznkfc6WOXy19SsAth9J4YEv/6H3Mwt4c+F2kjJyaFozmK6NapCencu4GX+x40hKue5XFMMwjnd296Lh7gABfjYu6FAX0JrpIiIiIiK+SEl6dZSXpNfKCAHDwo7EHRxJM4exP/vTZtKzc+nWqAYXdqxX8Fz3cPeSz0l36d+mNmc0CCc9O5d3Fm0/vsM1L70cHd6Ts5J5bMVjTFk+hXEfLmLAS4v47K+9ZOU66dIwgneu6cpvd/Vh5rgedGwQTnxaNte+v5LDlbBm+L74dFIyc/CzWWhaK7jCr19eri7v3/9zkIzsXA9HIyIiIiIipaEkvTrKm5de05kDWWbCtuLgCv7eHcc3aw9gscCjw9thKWwd9DIOd4e8anre3PSZK3YfT5Ddy7CVvcP71mN7MfL+t2jXegwDBrSpw5ybe/LVrWczqF00VquFYIed96/rTuOoIPbFpzPmg79Izsgu830L4xrq3rx2KH427/sTOrNJJHXDA0jOyGHhf4c9HY6IiIiIiJSC92UYUn5Bx9dKz0xuAcCy/cuZ8u1GAEZ1jaFDg/DCz3UPdy99kg7Qt2UtOjeMIDPHyVuuarorST+6BbJLX9k+mpLJ3V8tdv/co2U2v911Lu+N6Ua3xpEFjo8KcTBz3JnUDHGw6WASN3/0N5k5FVdRPj7U3buaxrlYrRYu6qQ100VEREREfJGS9Ooob7h7Xf80clPNJH3RnmX8uz+eUIedewa1Kvy8nEzINKvEZU3SLRYLd+VV0z/+cw+HEjMgNBoCIsBwmol6KRxISGfU2yvYl3x8Xn2nZpk0r118gtwwKogZY7sT7G9j2bZj3PPFPxXW7dy9/JqXNY07kavL+4LNRwquXS8iIiIiIl5LSXp1lDfcvb5/BrlpDfG3BpCam4DVcYhJ/VtQK9RR+Hmuoe5Wu5lUl1Hv5jXp1qgGWTlO3lq4DSwWqN3W3Hl4U4mvs+NICpe9vYIdR1MJC0lzb9+esL2Ys45rXz+ct6/pit1q4bt1B3jih00YRvkS9UOJGfy5w+ya7m3Lr52oVXQobeqGkZXr5Md/D3k6HBERERERKSEl6dVRXiU92j8NsJOb1hSA2rV3M6ZX46LPS3PNR48yE+syOrGa/unKvRxISIfarc2dR0qWpG86mMSod1awPyGdpjWD6dPWz71ve2LJknSAc1rU4oXLzOH27y/byf8t2XGKM4qWkJbFte//ybHULJrWDKZ7IUPtvcnFnc3GgG8s2Mbsv/aqiZyIiIiIiA9Qkl4dBZlJek2rWX1OTTDXQm9Qby/+9mLe8nJ0dj9Zz2ZRnNkkkqxcJ28s2FaqSvrqPfFc/s4KjqZk0bZuGLNv7klyzlH3/sNph0nKSipxLCM61+d/F7QB4KkfN/P1mn2lezJAelYu4z9cxZbYFOqEOfhwXA8C/Gylvk5VurhzA2qFOtifkM59X/5Dz6d/55mfNrMvPu3UJ4uIiIiIiEd4NElfvHgxw4cPp169elgsFr755ptTnrNw4UK6dOmCw+GgefPmzJgxo9Lj9Dl5lfQIi9ngzDUvfWfKetJz0os+LzWvaVxQ6dZIL8yJnd5nr9rL4YAm5o5TJOlLtx7l6vf+JCkjh66NavDpjWdRM8RBbFpsvuN2JJSuIn7DuU0Z39uM4e7Z63h9/tYSz1HPznVy68d/8/fueMIC7MwcdyYxkUGlun+pJB+CNR/D17fAvP/Bpu8g5UipL1Mr1MEvd5zLA0NaUz8ikPi0bN5etJ1zn1vAjTNXsXzb0XIP/xcRERERkYpl9+TNU1NT6dixI+PGjWPkyJGnPH7nzp0MHTqUm2++mY8//pjff/+d66+/nrp16zJo0KAqiNhH5M1JD3GaSbo1pzY1A+pwNCOW1bGrObv+2YWf5xruXsamcSc7q2kUZzePYtm2Y7yzyY+HARJ2Q2YKOEIKHP/LhkNM+GQNWblOzmlRk3eu6UqQvx3DMDiUas6rrh9Sn/0p+9mRuINOtTuVKp7/XdCGtKwcPl25lxd+2cKfO+N4aVSnoufoA06nwX1z/mHBf0cI8LPywdjutIqu4Lnoudmw90/Y9hts/Q1i/82/f8Xr5veo5tDwLGjY0/yKbHrKaQk1gv25uU8zbjinKb9viuXDFbtYtu0Yv2yM5ZeNsbSoHcKYXo25vHuMVy4nJyIiIiJyuvFokj5kyBCGDBlS4uPffvttmjRpwosvvghAmzZtWLp0KS+//HKRSXpmZiaZmZnun5OSSj5M2mflLcEW7Eyif+vaDGhbh805Z/PV1q9YfmB50Ul6BQ53d7lzQEuWbVvBjHWpPBhRC1vaEbJiN7EnoA27jqay61gqO/O+/7EjjlynweB20Uy7shMOuzmcPDk72T0CoGe9nszZMqfEzeNOZLVaeHrkGXRpWINH5m5gydajXPDqEqZd0YlezQp+MGEYBk/+uImv1+zHZrXw1uiudG1UQfPQM5Jg/ZdmYr5jEWQln7DTAvU6Q7PzID0e9vwBhzfCsW3m15qPzMOCa0HT82DQkxBSu9jb2awWzm8Xzfntotl2OJkPl+/my9X72Ho4hYe+Wc8Xq/by6pWdaRQVXDHPT0REREREysSjSXpprVixggEDBuTbNmjQIO64444iz3n66aeZOnVqJUfmZfKGu1vT45k+phtYLPy8q6c7SS+Sq7t7UMVU0gG6NY7knBY1WbL1KGszounKER56Zzazc/sWevylXRvwzMgO2E+o6sammkPdwx3htItqxxzKlqS7XNYthk4xEdz68Wq2Hk7h6vf+5Pb+LZnQrzk26/HK9NuLdjB96U4Anr/0DM5rXXwiXGI5WTDjAjh0QsU8qCY07w/NB0CzfgVHM6THw96VsGeFmbTv/9v8UOXf2RC7Aa773v3hzKk0rx3K4yPac+/gVsxZtY9pv29l3b5Ehr66lMdHtOPizg0q5nl6GcMw+Gr1fvbEpXHbec2L788gIiIiIuIhPpWkHzp0iDp16uTbVqdOHZKSkkhPTycwMLDAOZMnT+auu+5y/5yUlERMTEylx+pRecPdyc2C7DTwD+as6LOwYGFbwjaOpB2hVlAh1fK0vDnpweWfk36iOwe2ZMnWo/yTVY+u9n9pbtlPsL+NxjWDza+oIBpHBdMqOpQO9cOxnDSE2zXUPToommYRZhO80nR4L0yLOqF8O6E3j367ntmr9vHyb1tYuesYr1zemVqhDj7/aw/P/rwZgIeGtmFklwpMXJe9YibogTXgrNvM5LxuJ7AWkzQG1oCWg8wvgOwM2LcSvrwBDm+Ajy+Fa+eCo+RD8cMC/BjXuwmD20dzx+drWbkzjjs/X8eSLUd5bER7Qhw+9Z+HYiWmZ/PAl//w03rzdyk718l9g1t7OCoRERERkYKqz7/Ci+BwOHA4ip5zXC35B4PVD5zZkBYH/sFEBETQNqotG45tYMXBFVzY7MKC51XCcHeALg1r8OG4HgT/+x/8O48xzdO4YcygAsl4UVxN4+oE16FpuLmc3KHUQ6RkpRDiX3Bue0kF+tt47tKOnNU0iv99vZ5l244xZNoSrjmrEdN+3wLALX2bcf05Tct8jwIOb4ZFz5mPL3gBOlxatuv4BUCTc+Hab+CDC8zK+idXwOgvwL90Te3qRQTy6Q1n8caCbbzy2xa+WrOfv/fE8+oVnekYE1G2+LzIur0JTPh0NXvj0rFbLeQ4Dd5etJ1+rWvTzcuX0RMRERGR049PjfeMjo4mNjZ/l+/Y2FjCwsIKraKftiyW40Of0+Pdm3vV6wVQ9JD3Shju7tKnZS26dTfnwjuO/VfiBB3yV9LDHeHUCjQ/RNiZuLNCYhvZpQHfTTybVnVCOZqSycu/bcFpwOXdYrhvUKsKuQcAzlz4doL54UnLIdD+kvJfs3YbuOYrcITB7qUw+1pzOH0p2awWJvVvweybelI/IpDdx9K45K3lvLVwe4m74HsbwzCYvnQnl769nL1x6cREBvLlLb24pEsDnAbcNXsdKZk5ng5TRERERCQfn0rSe/bsye+//55v26+//krPnj09FJEXy5uXTnqce1PPeubrtOLACpyGs+A57uHuFZ+kA1ArL+FNPgDpCSU+7cRKOkDTCLOyvS1hW4WF1rx2KN/cdjZXdDenQgxpH82TF7cv1YcJp7TyXdj3l5lQD33xlJ3ZS6xeZ7hqNtgDYduv8NX1kFu25LNb40h+vP0chnaoS47T4NmfN3Pt+ys5nJRRMbGWUWZOLu8s2s7kr/7l23UHiE8t/oOIhLQsbpz1N49/v5HsXIMh7aP5fuI5dIyJ4NEL21I/IpA9cWk88f3GKnoGIiIiIiIl49EkPSUlhbVr17J27VrAXGJt7dq17NmzBzDnk1977bXu42+++WZ27NjBfffdx+bNm3nzzTeZPXs2d955pyfC926ueelpx5P0TrU6EWgPJC4jji3xW/Ifn5MJmXmd7ysrSQ+MgLD65uMjm0t8mqtxXJ0gM0lvFm7OS9+RWLq10k8Znr+NZy45g1UPDeDN0V3yNa8rt/hd8Ptj5uOBj0F4/Yq7NkCjnnDFx2Dzh41z4duJ4Czkg5gSCA/04/WrOvPsJR0I9LOxdNtRBk9bwsL/DldszCX0z74Ehr+2lKd/2synK/cw6dM1dHniV0a8sYyXf93Cmj3x5J5Q7V+9J56hry7l142x+NusPHZRO94c3YXwQD/AnIv/4qiOWCzw2V97+W1jbFG3FhERERGpch5N0letWkXnzp3p3LkzAHfddRedO3fmkUceAeDgwYPuhB2gSZMm/PDDD/z666907NiRF198kffee09rpBemkOHufjY/ekT3AAoZ8u4a6m61Q0BE5cVVu435/fCmEp9yKC1vuHtwNMDx5nHl6PBenJohjoqtoBsGfHe72cSv8TnQZUzFXftEzfvDpR+AxQbrPoGf7zfvXQYWi4XLuzfku4m9aVs3jLjULK774C+e/mkT2bllS/5LKzMnl+d+3szFby5nS2wKUcH+XNuzEa2jQzEMWLs3gWm/b+XiN5fT9YlfmfjpGp76cROj3l7B/oR0GkUF8dWtvbi2Z+MC7+dZTaO4vncTAB746h+OpWQWFoKIiIiISJXzaOO4vn37YhSTRMyYMaPQc9asWVOJUVUTgRHm9xOGu4M55H3RvkWsOLCCce3HHd+R5pqPHlVxw7ALU6u1uTZ4CZN0wzDcc9LdlfRKTtIr3NqPYcdCsAfA8GnFd3EvrzbDYMRb8PVN5vB6/xAY8GiZL9e8dghf3dqLp37cxMwVu3ln0Q5W7ozjtSs706BG6RrUlca6vQnc88U6th5OAWB4x3pMvbAdkcH+ABxMTGfxliMs2nKEJVuPkpCWzXfrDrjPH3pGXZ4Z2YHQAL8i73H3+a1YvOUo/8UmM/mrf3nnmq4V++GMiIiIiEgZVPvu7qct13D3k+Z+u5rHrY5dTXpOOoH2vIZ7ldTZvYDabc3vR0qWpKdkp5Cekw4cn5PuGu5+IPUAadlpBPlVXrJYbsmHYN6D5uPz/gdRzSr/nh0vh+xU+P5OWPqS+YHN2beX+XIBfjYeu6g9vZpFce+cf1izJ4ELpi3h+cs6MqhddMXFjVk9n/bbVt5ZvINcp0HNEH+eGNGewe3r5juubnggl3dvyOXdG5KT62Tt3gQWbTnCxgNJnN+uDqO6xZwy4Q7ws/Hy5Z246I2l/LIxli/+3seobtV8eUYRERER8Xo+1ThOSsHVOC4tfyW9cVhj6gbXJcuZxerY1cd3pOY1jQuq2DXSC6idtzZ1CSvprip6uCPc/YFCREAEkQHmhxAV1eG90vx4D2Qkms3dzrq16u7bbRwMfNx8PP9JSDlS7ksObl+XHyedQ6eYCJIycrhp1t88Onc9Gdm55b42mMPXh726lDcXbifXaXBhx3r8cmefAgn6yew2K90aR3L3+a2Yfl13Lu/esMQV8bb1wrhroNnQ8LHvNrI3Lq3cz0NEREREpDyUpFdXhcxJB3OusavLe7556a7h7pXVNM6lVl6Snnrk+Dz4Yrg7u+cNdXdpHtEcgO2JXjzkfeNc2PSdOc//wtfBVsUDV3pNhPpdITcTVk2vkEvGRAbxxc09uelcs8P+hyt2c8lby9l5NLVM18vIzuWHfw4yfsZfjHxzGVsPp1AzxMHbV3fl1Ss7u4e3V6Ybz21K98Y1SMnM4e7Z6/I1oRMRERERqWpK0qsr93D3uAK7Ck3Sq2q4u38wRDQyH5egmn5yZ3eXpuEVvwxbhUqLgx/uMR/3vgui21d9DBbL8er9X++ZHfwrgJ/NyuQL2vDBdd2pEeTHhgNJDHt1Cbd/toZZf+xm86GkYtdWNwyDv3fH87+v/6XHk79x2yer+X3zYZwGXNSpHr/eeS6D21fsMPri2KwWXrysE8H+NlbuiuO9JRW7aoCIiIiISGloTnp1VcRwd4Czos/CgoVtCds4nHaY2kG1j1e1gyq5kg7mvPSE3WaS3uScYg89ubO7i6t53I4EL02ofnkIUg9DzVZw7j2ei6PtRfDrI5C0H/6dA51HV9ilz2tdm59uP5dJn61h5c445q49wNy1ZvO20AA7XRvVoHvjSLo1qkHHmAiOpmTyzZr9fLV6PztOqLzXDQ/g4s71GdmlAc1rh1RYfKXRMCqIR4a35f4v/+XFX7ZwbstatKkb5pFYRKRyxCZlsOi/I1zYqR4BfjZPhyMiIlIkJenVVRHD3cGc090uqh3rj61nxYEVXNT8IkjLm5MeXMlz0sFchm3LTyVqHldUJd2rO7zvWGR2dMcCF70OdofnYrH5QY8b4Lcp8Meb0OmqCu3eHx0ewKc3nMWfO46xclccq3bFs3pPPMkZOSz87wgL/zNHaPjZLGTnHq+uB/rZGNI+mku6NuCsplHYrJ7vqj6qWwy/bjzMb5tiefqnzcwc18PTIYlIBXr2p818tWY/FgtcpiaRIiLixZSkV1euSnp6vLlW9kmJWc96Pc0k/WBekl5Vw92hVGuluxrHFVVJ35+yP3+Xek9z5sK8/5mPu18PMV6Q6HW9DhY9B7HrYediaNqnQi9vs1ro1bwmvZqbozBycp1sOpjMX7vi+Ht3PCt3xXEk2Rxq37NpFJd0bcDg9tGEOLzrPz8Wi4X7B7fit02x/LnjGJk5uTjsqraJVBfbjphLOpa1h4aIiEhV8a5/JUvFcSXpRi5kJkFAeL7dver14v/+/T9WHFiB03BirdLh7ick6YV8gHAid+O44PyV9MiASGo4ahCfGc/OxJ20jWpbaeGWyrpPIfZfcIRD38mejsYUWMOsoP/1nllNr+Ak/WR2m5UODcLp0CCccb2bYBgG++LT8bdbqRMWUKn3Lq/mtUOoGeLP0ZQs1u1NpEeTSE+HJCIVZF+8uZznwcQMD0ciIiJSPDWOq678AsFVXS5kXnrHWh0JsgcRlxHHlvgtJwx3r4IkPaoFWKyQkWCuI16Morq7AzSNMJvHec2Q96xUmP+E+fjce6pm6kBJnXmL+X3Lz3Csal8vi8VCTGSQ1yfoYMZ6ZlPzfftjxzEPRyMiFSUtK4e41CwADiSkezgaERGR4ilJr86Ciu7w7mfzo0e0ORR7+d7FZrUdqiZJ9wuASHO4enHz0pOzkknNNoclFpaku5Zh25HoJc3jlr8OyQfN7vVn3uTpaPKr2RxaDDIf//GWZ2Pxcj2VpItUOycm5qqki4iIt1OSXp0FFt08DuCsemcBsHz/EnOD1Q4BEVUQGCWal+5qGhfmH0aQX1CB/V61DFvyIVg2zXw8YIpnm8UVpWfecmxrPy7yd0LgrLwk/e/d8WTm5Ho4GhGpCHvjjyfphxIzMIyil4kUERHxNCXp1VlghPk9rfCErFe9XgCsPrqedIsFgqIqtPN3sUqSpBcxH93Fq5Zhm/8EZKdCg+7Q7mJPR1O4Jn2gTnvIToO/P/R0NF6rWa1gaoY4yMxxsnZPgqfDEZEKsP+EJD0r18mxvKHvIiIi3khJenVWzDJsAI3DGlM3uC7ZRg5/BTiqprO7SwmSdHdn96DoQve7kvR9KfvIyPHg8MVD62HNR+bj85+sug86SstigbPy5qavfBdysz0bj5eyWCyc1dT82/ljR8GpIiLie/afNA/9YIKGvIuIiPdSkl6duZdhKzzRsFgsnFP/HAAWBAWalfSqUisvST+y2ezwXohTVdKjAqIId4TjNJzsTtpdKWGWyK8PAwa0HQENz/RcHCXR/lLzw5ik/bDpW09H47XO0rx0kWplX3z+JP1AoprHiYiI91KSXp2dYk46QP+G/QFYEBREblUm6VHNwOoHWSmQuLfQQ1yV9MKaxoH5IUOzcLOa7rF56Vt/g+3zweZvzkX3dn4B5vrtACve9GwsXsyVpK/eE09Gtuali/i6/fFpAPjbzH/2HFLzOBER8WJK0qszVyW9kCXYXLpHdyfU4scxu41//O1VFBhg84OaLczHRQx5d1XSo4MLH+4OHl6GLTcHfnnIfNzjRohsUvUxlEW3ceaHCvtXwd6Vno7GKzWrFUytUHNe+rq9CZ4OR0TKyTXc/YwG4YAq6SIi4t2UpFdnxSzB5uJn8+Ncu5nM/+5MqoqojjvFvHRXd/eiKung4WXY1swyl5ALrGGui+4rQmpDh1Hm4xVveDYWL2XOSzer6Ss05F3Ep2Xm5BKblAlA9ybm/y9qTrqIiHgzJenVWVDemueHN0FWapGH9TfM5cJ+T99ftcvSnCJJP5SW1ziuuEp6uIcq6ZnJsOBJ83Gf+4+PWvAVruXYNn0LCXs8G4uXOt48Tkm6iC9zJeQBflba1g0zt6mSLiIiXkxJenXWuDeENTCbhP02pcjDzk7PxOF0si87kS3xW6ouPnfzuIJJekpWCqnZ5gcLxVXSXR3e9ybvJSu3CpfUWTYNUo9AZFPoNr7q7ltR6rQzl2QznGandyng+Lz0BM1LF/FhrqHuDWoEUS8iAIADqqSLiIgXU5JenTlC4KLXzccr34UdCws9LCj1GL3SzX+w/L7n9yoKjuOV9CP/gTN/EuSajx7qH0qQX1CRl6gVWItQ/1ByjVx2Je2qrEjzS9wPy/Ne14GPgd2/au5b0XreZn7/eyZkpng2Fi/UtKY5Lz0rx8lazUsX8Vn78prG1Y8IpG54IACxSRk4nVU4ckxERKQUlKRXd83OO97Ne+4EyEgseEzaMfqnmZWGKk3SazQGewDkZED8rny73GukFzPUHfJ3eN+RUEXz0uc/Djnp0LAXtB5WNfesDM0HQlRzyEyEVdM9HY3XOXFeuoa8i/iu/XnLr9WvEUjtUAdWC+Q4DY6mZHo4MhERkcIpST8dDJgKNZqYS53NezD/vpxMyEyib1o6NouNLfFb2Jtc+JJoFc5qg1qtzMcnzUt3r5FezFB3F9eQ9ypZhu3gOlj3mfl40BNgsVT+PSuL1Qq97zIfL3mx2FUATlc9Xc3jtitJF/FV+9zD3QOx26zUCcsb8q5l2ERExEspST8dOEJgxFuABdZ8BP/9fHxf6lEAwrHSrU5XAObvmV91sdUqvHncqdZIP5ErSa/0Du+GkbfkmgEdLoP6XSv3flWh4xVQp705wmLxC56Oxuu4mset2Vv189L3HEsjPrUK+yyIVFP7XJX0CHOoe3S4maQfTFDzOBER8U5K0k8XjXoen4P83aTjVdM0M0knKIp+DfsDnpqXXngl/VTD3QH3cPdK7/C+9VfYudhcY7zfw5V7r6pitZnz6sHsWxDngaXsvFiTmsHUzpuXvmZPQpXc8999iVz3wUrOfX4B17z/Z5XcU6Q6cw13b1DDTNLr5c1LP6hKuoiIeCkl6aeTfg9DzVaQEgs/3mtuSz1ifg+uRb+G/QBYe3gtR9OPVk1Mtdua308e7l6CNdJdmkaYy7DtSdpDdm52xcbnkpsDv+Yl5mfeDDUaVc59PKF5f2jWH5zZ8Ptjno7Gq1TlvPTNh5K4adYqhr++lIX/mX+X6/cncThJiYRIWeXkOjmU9zfUoIbZhLSuq5KuZdhERMRLKUk/nfgFwMVvgcUG6+fAhq8hNS/xCIoiOjiaDjU7YGBU3ZD32q3N70e3wgkJtnu4e/Cpk/Q6QXUI8Qshx8hhd9LuSgmTtR/Bkc3meujn3F059/CkgY8BFvN3Yu9fno7Gq1R2kr79SAoTP13DkGlLmLchFosFRnauT8NIM6FYt6+QZo8iUiKHkjLIdRr426zUCnEAUDdv2LvmpIuIiLdSkn66qd/1eJL5/V1weKP5OLgmgLuaXmVJengM+IeYVdxjx4erl2a4u8VicVfTtydWwpD3zBSY/6T5uM/9EBhR8ffwtOj20Hm0+fiXh8z59wJAz2Zmkr6mgtdL33Msjbtnr2PgS4v4bt0BDAOGdqjLL3ecy0uXd+LMJuZ8+HVa/k2kzFxD3etGBGC1mo0+62lOuoiIeDkl6aejc++F6A6QHgfLXzO3BdcCoH/evPQ/D/1JclZy5cdisUCtvGp63rz0lKwUUrLNdbujg06dpAOVuwzb8lch9bDZIb/b+Iq/vrc4739gD4S9f8Cm7zwdjddoHBVEnTAHWblOVu+JL/f1nE6Dl37dQr8XF/Ll6n04DRjQpg4/TOrNG6O70KJOKAAdYyIAWLcvodz3FDld7U/IPx8djjeOO6RKuoiIeCkl6acjuz9c/A5Y/cDIqwwGmZX0JuFNaBrelBxnDov3La6aeGrn7/DuqqKH+ocS5BdUoku4OrxXeCU96eDxDzIGTDFfu+oqrB70mmg+/u1RyFFncTh5Xnr5lqlLzsjmxlmrePX3reQ4Dc5tWYtvbjub98Z0o1298HzHdnIl6XsTMDSyQaRMTu7sDlAv73Fscia5Tv1tiYiI91GSfrqq0w7OO2HN9OAo98P+Vd3l/eQkvRRN41zcSXpFd3hf8CRkp0GDHtD2ooq9tjc6e5I5qiJuB/z9gaej8RoVMS9919FURr65nN82HcbfbuWlUR2ZOa6HOxk/WavoUPztVpIycth1LK3M9xU5ne13J+nHP/CtGeLAbrWQ6zQ4nKxquoiIeB8l6aezXpMg5izzcZ0O7s2uJH3p/qVk5FTBP2BOStIPpZW8aZyLa7j7rqRdZDsrqMN77AZzXXmAQU+aQ/OrO0fo8Q9vFj4D6QkeDcdbuJL0tWWcl75061EuemMZWw+nUCfMweybejKyS4Niz/GzWWlfLwzQvHSRsipsuLvNaqFOmDnk/UCCknQREfE+StJPZzY7jPkWbv0DYrq7N7eNakt0cDTpOemsOLCi8uNwLcMWtx2yM9yV9JLORwezwVyQPYgcZw57k/dWTFy/PgIYZgU9pkfFXNMXdL7WXKovPQ6WvuzpaLxCvnnpu0s+L90wDN5fupMxH6wkMT2bTjERfDehd5HV85O55qWvVZIuUib74s1RKPVPSNJBy7CJiIh3U5J+urM7jley81gslqod8h5SBwIiwHDCsa3uOemlqaRbLBaahud1eK+IIe/bfodtv5nz9vs/Wv7r+RKbPW9JNuCPtyBhj2fj8QIWi4WepRzynpmTy31z/uGx7zeS6zS4pEsDPrvxLGrnVfBKomODCAD+UfM4kVJzOg13pfzEOelwfBk2NY8TERFvpCRdCuVK0hftW0SOM6dyb2axHK+mH97kXiO9NJV0OD4vfVvCtvLF48zNq6IDPW6AqGblu54vajkIGp8DuZkw/wlPR+MVStM87nByBle++wdf/L0PqwUeGtqGFy47gwA/W6nu6aqkrz+QRHaus9Qxi5zOjqZkkpXrxGa1uCvnLq5l2DTcXUREvJGSdClU59qdiXBEkJCZwOrY1ZV/w+j25vct88pUSQdzmD5Q/iH66z6D2PXgCDeXqzsdWSxw/uPm438+hwNrPBuPF3DPS9+bQHpW4fPSnU6Dr9fsY/hrS1m9J4GwADszxvbg+nOaYilDT4PGUUGEBdjJynHy36EqWBJRpBrZm9c0LjosALst/z93NNxdRES8mZJ0KZTdaqdvTF+gioa8d77a/L7hK2JTDgKlr6QPaDQACxbWHF7jrsaXWlYqzM9LTs+9B4Iiy3ad6qBeZ+gwynz884PmCIPTWKOoIKLDAsjKdbLmpPXSDcNg0ZYjDH1tKXd+vo7YpEya1Qpm7oTenNuyVpnvabFYNC9dpIxcTeNOHuoOx4e7H9BwdxER8UJK0qVIJ85LL886zYZh8N3274qvcNftCM0HkIpBck4qUPpKeu2g2nSp0wWAebvmlT7Q/X/DewMg+SBENIQeN5b+GtVN/4fBHgh7lptrp5/GzPXSzQ9tVpwwL/3ffYmMfu9Pxry/kk0Hkwh12LlvcCu+n3gOTWoGl/u+J66XLiIlV1TTODheST+kSrqIiHghJelSpJ71ehJoDyQ2LZYNxzaU+Trf7/ieB5c+yMT5E0nMTCz6wN53EWs35+yG+gUT7Ff6BGdI4yEA/Lzz55KflJ0OvzxkJuiHN0JQTRjxFviVvMFXtRXREEa8YT5e/hqs+diz8ZRHZgocWOte6q8sTlwvfc+xNCZ+uobhry9l+fZj+NusjO/dhMX3ncetfZsT6F+6+edFOcPdPK6Yvx0RKcC1RnqDQpN0c9vh5Ez1exAREa9j93QA4r0cNge96/fm192/8vue32lfs32pr3Eg5QBP/fkUAJm5mfyw4weuanNV4Qc36sWhuu2BOOoYZfv8aECjATy18inWH1vP3uS9xITGFH/CrmXw7QSI22H+3GEUDH4GgqPKdP9qqf0lcHgzLH4Ovr/DbKTX8CxPR1U4Zy4k7IZj2+HoVji2Ne/7dkg+cPy4EW9DpytLffmezczfi9V7Euj/0kKycw0sFhjRqT53DWxJTGRQRT0Tt44NwgHYcjiZlMwcQhz6z7ZISRQ33D0q2B9/m5WsXCexSRk0qFHxf7siIiJlpUq6FGtAwwEAfLvtW46mHy3VubnOXB5c+iAp2SkE2c1/AH219auih85bLMS2MIfY10k5BuklX4/aJSowih7R5prmxQ55z0yGH+6GGReYCXpoPbjyc7jk/5SgF6bvZGgzHHKz4POr8y3LZhgGOxJ2lGtKRLnlZMLi5+GZRvBqZ/j4Upg3GVa9D7uWHE/QHWHm97m3wsZvS32bhpFB1A0PINdpkJ1rcE6Lmnw/sTcvX96pUhJ0gNphAdQLD8AwYP1+VdNFSmpfXiW9sOHuVquFaHfzOM1LFxER76IkXYp1XsPzaBTWiMPph5n4+0TSc0o+f+/DjR/yd+zfBNmDeH/Q+/hb/fkv/j82xm0s8pxDoWaTrejsLFj5XpliHtx4MFDMkPdtv8GbPeGvvOt3GQO3/QGtBpfpfqcFqxUufgfqdIDUI/DpVebwceCNtW9w0dyLmLlxpmdi2z4f3uplLhWXlQw2B9RqY36o0Psuc+rC+F/hvp1w/26zSaHhhDnjzN+FUrBYLDw8rC0XdIjmo/FnMmv8mbSrF15JT+y4jpqXLlIqhmGcMNy98A/QlKSLiIi3UpIuxQq0B/JG/zeIcESw/th6Ji+ZjNM49fy9zXGbeW3NawDc3+N+2tVsR/9GZpX8qy1fFXmee/m1nBz48y3ISit1zAMaDcBusfNf/H/sTNx5fEdWGsydAB9dAol7IaIRXDsXLnwVAio/0fJ5/sFw5acQXAti/4WvbyIu7ag7OX9//ftk5FThP3YT98PsMTDrYji2DULqwMj/g/8dND90ufwjGPAodLoKYnqYnfqtVhj+KrQdAc5s+Oxq2L28VLe9oENd3hzdld4talbO8yqEO0nfl1Bl9xTxZfFp2aRnmytSnLxGuotrrfSDCWoeJyIi3kVJupxSo7BGTDtvGn5WP37f8zsv//1yscdn5mYyeclkcpw59Ivpx8XNLwZgZIuRAPy488ciK/KH0syl06IdNSDtGKwufXU23BHOWfXMOdM/78qrph/dZjaGWzMLsMCZt8CtK6Bp31Jf/7QWEQOXfww2f9j8PbN+vtX9XsZlxPHt9tIPIS+13GxYNg1e7w4bvwGL1Xw/J/wFZ4wC6ykatlltZjLf4nzISYePR8H+1ZUfdzmckTcvfd1eDXcXKQlXZ/daoQ4C/Ar/b4JrGTZV0kVExNsoSZcS6VKnC0+c/QQAMzbMYPZ/s4s89pW/X2FbwjaiAqJ4tNejWCwWAHpE96B+SH1SslP4bXfhw4xjU/Mq6a1HmBuWvwY5WaWOd0gTs8v7vJ3zYONceLcvHN5gVoHHfAtDnjErw1J6Dc+E4a+SaLXyaaLZ9b9XvV4AzNw4k9zKXE9911J4uzf8+ghkp0LMmXDTYvP9LM1oCLs/jJoJjXqbQ+Q/uqRcXd8rW4f64VgsZiOsI8mZng5HxOsV19ndxVVJP6BKuoiIeBkl6VJiFzS9gAmdJgDw5J9PsmTfkgLHrDiwgo82fQTAY2c/RmRApHuf1WJ1V9W/3PplofdwJ+kdrjCHLyftg3+/KHWs58Wch5/Vj+2J29n69XgzEWvYC25aAk3OLfX15CSdruTjdgNItVppkZXDSy2vIcw/jN1Ju1mwd0HF3isrFdZ/CZ9cATOGwpHNEBQFF70BY3+G6A5lu65fIFz1GdTvCulxMHPE8S7/XiY0wI/mtUIA+EdD3kVOqbjO7i6uZdhUSRcREW+jJF1K5cYzbuSiZhfhNJzcs+ge/ov7z70vMTORh5Y9BMBlLS/j3AYFk+GLml+E1WLl79i/2ZW4K9++1OxUkrOTAYgObwRn3WruWPYKOEu3jm1oRjK9c8whjj+FBEGvSWYFPaxuqa4jhUvOSuajrP0A3BQfT/AX47m8VncAPlj/Qfk7vWenmyMgZo+B55qZTd62/ARYoNt4mLDKbABnLed/whyhMHoO1G4LKYdg5kXmXHcvpOZxIiVXXGd3FzWOExERb6UkXUrFYrHwaM9H6RHdg7ScNG77/TYOpx3GMAye+OMJDqcdplFYI+7pdk+h50cHR3N2vbMB+Hrb1/n2uaroIX4hBPsFQ7dx5hDmo1tg8/clD3LHQnjnXAYf3QfAvDpNMQY+Bja/0j9hKdSnmz8lOSuZpmGNGRjcCFIOcdXyD/E3DP45+g+r5z9krq1emmQ9OwM2/wBzxpuJ+exrzTnnOelQo7HZqf3WFTDsJbMJXEUJioRrvoHIpubScjMvgpQjFXf9CuJK0tfu07x0kVPZd4rO7gD18qrsR1MyycypxGk6IiIipWT3dADie/xsfrzU9yWu+ekadibuZMLvE7is1WX8vOtnbBYbT/d+miC/ov9hdEmLS1iyfwnfbv+WCZ0n4Gc1k2dX07g6QXXMAwPCoMeN5vrXS140l9TKm99eKGcuLH0JFjwFhpO+ddoTYM1gT+YxNsVtom1U2wp7DU5nadlpzNo4C4AbOt6EtX9XWPw8Nbf+yoXJycwJC2XGf5/RdcnrEB4DzftD8wFmop3y/+yddXyV9RfH37fW3b0xGA0DRneDIEpIGKSgotit6M/EwEZUBCkJQUKQ7hKkmzEYY93d243n98ezANlgcRfA9633de/unuf7nDsW9/M953xOYtEt4Yb7oscZ0XKfeTG2PtBiOLQYAR5tb/9vX12sXWWn/4UPQMpVmNcDBn8OzR+u2etWgsAi87hz0elIklTi9SAQCG6luNzd6zbl7vYWGkzVSgp0BhIyCvBxLP/vlkAgEAgEtYkQ6YIqYWtqy9x+c3liyxMEpwbz0ZGPAHi69dO0cr59j3BP7544mDmQnJfMweiD9PXpC5Rm0t0s3UoP7vQMHP4R4s5A2F5o2PfWBfPS4NTvcHy+nAkFaPsEFkO+osc/77IzYifbwrcJkW4kVoWsIr0gHV8bX3kmvVItZ7cliYnh+1l74Hn2WVpwzdSChhlRcHKxfKsI1h6yKG85Uu4Vr00haucjC/UVo+Xe9D8nyg7wQ74Ce9/ai6McmrrZYKJSkp6rJTI1F19HYXwoEJRHsbv77crdFQoFHnbmXE/OITYjT4h0gUAgENQbRLm7oMp4W3vzQ98fMFWZAtDaqTXTWk+743kapYaHGz4MwPqrpSXvJZl0S9fSgy2dIGii/PjgNzcvlBgMf78E3zSHne/JAt3cXjYUe3guaMxlEQnsCN9RoT7p4/HHGbdpHBtCN9zx2PuRPF0eiy8uBmBqq6molTfs8ykU+DXoTV+ffgAs6T5Z7vfu+DQ4NpLN3lxagH8faD1O9gkY+CmMXAATNsJzx+DlizB4Fni1r5sMtlMjmH4Yer4BSg1c3QFzO8Ghb+XRb8ZAr5WnFiwfDXs+hch/Qa+742kmaiXNPWwAOCP60gWCcsnM15KVL/9M3c44DsDNRu5Ljxd96QKBQCCoR4hMuqBaBDoHMqfvHDaHbebZNs/eLNpuw/CA4Sy6uIiDMQdJzE3ExcKlNJNu4XbzwV1mwPEFEH4QIo9CbjIcnQfX95ce49oSOj0NrUbLrt1F9PDqgYXagpjsGM4nn6e1c+tyYzqbdJbndj9Hni6PDw5/gLe1N+1c21X8i3EfsObKGlLzU/G08mSo/9Ayj5nccjK7I3fzd/hWZgS9hEvAgFqOsppozKHvu/L30qaXIeIQ7PoAzq2GB78Fn85VXzvqmLyxlCiPruPqDjjwJZjagn9PuS2gYT95Hn0ZtPG240xUOmejMni4jWfV4xAI7mGKx6/ZW2iwNL393yR3u6IxbBliDJtAIBAI6g8iky6oNl08uvBJ90/wsPKo8Dn+tv60dWmLXtKz8dpGABJyi8av3ZhJB1mwtB4rP148BP54TBboCqXcpz5pMzxzCNpNuEmgA5irzent3RuArde3lhtPSGoI03dNJ0+Xh5XGCp2k47X9r5Gcl1zh13SvU6AvYNGFRYCcRS/2Evgvgc6BtHNph86gY3nw8toM0bg4N4ZJm2D4z3IVQOIlWDgINj4PuamVWysvXRb8vw2UBbq5A/R+B1qMlKs/CjIg+G/4+0X4riX82AG2vgUxp25aJtBb7ks/K8awCQTlUhFn92I8isewpYtMukAgEAjqD/VCpM+dOxc/Pz/MzMzo1KkTx44dK/dYrVbLRx99RMOGDTEzMyMwMJBt27bVYrQCYzEyYCQA666uQ5Ik4nP+Yxx3I91eAhRg0MmipttL8OJZGLsM/LrftjS6pOQ9YgcG6dZRbuEZ4Ty982myCrMIdA7k7xF/09C2IUl5Sby+/3V0hjuXIt8PrLu6jqS8JNws3UraFcpjcsvJAKwOWU12YXZthFczKBTQ5rHSkW8Ap5bCD21h3dPy/Pa8tPLPlyT5mB87wImFgARtHpfX6/0mjF4Er1+DqXugz7vg3RkUKnmiwdGfYX5fOfNedI3WXnYAXIzNQKuv3FhCgaCuCInPYsOZmOqPZqwgMUX96F52d+4xL86kx4lMukAgEAjqEXUu0letWsUrr7zC//73P06dOkVgYCCDBg0iMTGxzONnzpzJvHnzmDNnDpcuXeKZZ55hxIgRnD59upYjF1SXgb4DsdRYEpUVxYmEEyWZ9JuM44pxbgxPrIERv8LLl2DAh7LRVwXo5tkNa401ibmJnE68+fskLjuOaTunkZKfQhP7JsztNxcncye+7fMtFmoLTiSc4IdTP1T7td7taPVaFl5YCMCUllPQ3GGcXU+vnjSwbUC2Npu1V9fWRog1i4WD7HMweSs4N4X8dDj3hzy//cuGsiv8wW8g4WLp2LnU67BslHxMTiI4BsDETTD8J7B0LF1bqQKvIOj1Bjy5Hd4IgzG/Q8tRgAQnF8ki/9xqGjhYYG2mJl9r4EpCVl18JQSCSnEyIpURP/3Di3+c4fC1lFq5ZrGze2Uy6bEiky4QCASCekSdi/RvvvmGadOmMXnyZJo3b84vv/yChYUFCxcuLPP433//nXfeeYchQ4bg7+/P9OnTGTJkCF9//XUtRy6oLhYaCx5o8AAAyy4tI6tQFh1lZtJB7tcNHAsmlXPgNVGZ0MenDwDbrpdWXSTnJTNt5zTic+Lxs/Fj3oB52JrK5cQNbBvwcbePAVh0cRG7InZV6pr3GhuubSA+Jx5nc+eSCojboVQomdxCzqYvvbQUrbFM1+oa367wzD+y2O76gizYJT1EHobdH8LPXeHblrIw/6kzXNsNKlO5tH36P9Cgx52vYW4HzR+CRxbCpC3g1ARykmDdNJTLRzDQVR5TdzZKzEsX1G/ORaczaeFxcgvlGeQ7LyXUynVLyt3vYBoH4GZbZByXKUS6QCAQCOoPdWocV1hYyMmTJ3n77bdLnlMqlfTv358jR46UeU5BQQFmZmY3PWdubs6hQ4fKPb6goKDk48zMTCNELjAWIxuNZM2VNeyJ2gOApcYSKxMro19nsN9gNl7byI6IHbzZ8U1ytDk8vfNpIjIjcLd0Z/7A+TiaO950zkC/gUxMmsiSS0uY+c9MGtk1ws/Wz+ixlUVibiIv7nmRpLykknnYiuL/bijttze158teX+JtXbbRmDHQGrQsOL8AkMvYi93878RQ/6HMOT2HxNxEtoZv5aGGD9VYjLWKSi2L7QY9YODHkBYhG8Bd3QnXD0BmNFyIlo9t0BOGfiu7xlcFv26y38Lh7+HAVxC2jy8Uh/FUPcSFCFfoVLFqEoGgtrkUm8n4346RVaDD1caUhMwC9l9JqpVrl8xIr0QmPTWnkHytHjONqkZjEwgEAoGgItRpJj05ORm9Xo+r682ZU1dXV+Lj48s8Z9CgQXzzzTdcvXoVg8HAzp07WbduHXFxcWUe/9lnn2Fra1ty8/auOTEjqDwtnVoSYB9Q8vEtzu5GorNHZ2xNbUnNT+VA9AGe3fUsV9Ku4GTuxIKBC8ousQdeDHqRdi7tyNHm8PK+l8nV5tZIfDciSRKf/PsJF1IukJCbQHxOPPE58cTlxBGbE0tMdkzJ7ULKBT7595Ma7fXcHLaZmOwYHMwceKTxIxU+z0RlwuPNHgdg0YVFtdaPWuvY+0LHafD4anjzujx2rvsr8MgiebRcVQV6MWoT6Pk6PHsEGvZFLRXyimYN0y9PkDcFBIJ6xtWELJ747SgZeVra+dixcUZ31EoF15NzCE/OqfHrx1TCOM7GXI2FiSzM48QYNoFAIBDUE+q83L2yfP/99wQEBNC0aVNMTEyYMWMGkydPRqks+6W8/fbbZGRklNyioqJqOWLB7VAoFIxsVFo+fYuzu5HQKDX09+kPwGv7X+Nc8jlsTW35dcCv+NiUn43UKDV81esrnMydCE0P5eN/P65xsbkrchd7o/aiVqiZ228ufwz9g5VDV7Jy6EpWDFnB8iHLWTZkGT/1+wmNUsPh2MPsiqyZcny9QV+SRZ/UYhLm6ju/6b2R0U1GY6G2IDQ9lEMxZVe73FNozCFgAPT/H7QcadxZ7w7+8MQ60of8QpJki7chBpYMg31flPbBCwR1zPXkHB5bcJTUnEJaedqyaHJHXG3MaO9nD8C+kLL9ZoxFbqGOlJxCoGLGcQqFAveikve4dGEeJxAIBIL6QZ2KdCcnJ1QqFQkJN/epJSQk4OZWdmbT2dmZv/76i5ycHCIiIrh8+TJWVlb4+/uXebypqSk2NjY33QT1iwf9HywZ51VuP7oRGNxAdnnXGrRYqC34pf8vN2Xxy8PZwpnZPWejUqjYFLaJVSGraizGzMJMZh2dBcil5T29etLCqQUtnVrS0qklrZxb0dq5NYHOgfTw6sGUllMA+OLYFzWS5T+ZcJKIzAisTawZ22Rspc+3MbFhdOPRgNzbL6gmCgV2HR/lUZMfWa7rJz+3bxZseQ0M+rqNraIYDJARDWH7Zcf77e/CinHwS3fZOV9w1xKVmstj8/8lKauApm7WLJ3SEVtz+Xd77yYuAOyr4ZL32CKhbW2qxsa8Yh197sVj2EQmXSAQCAT1hDoV6SYmJgQFBbF79+6S5wwGA7t376ZLly63PdfMzAxPT090Oh1r167l4YdvPxJKUH+xM7Ojv6+c5a7Jnu/2ru3xtvbGTGXGj/1+pKVTy4qf69ael4NeBuCL419wLulcjcT47clvSc5Lxs/Gj6cDn77j8VNbTcXTypOE3ATmnZtn9Hh2ROwAoL9Pfyw0lTPsK+aJ5k+gVqg5Hn+cC8kXjBnefUtDHw/e1T3JocZvAwo4vgDWTAZdwR3PrROu7oJVT8BPXWGWB3zbApY+JM+OP/IjXNkK8edh4wtweXNdRyuoArHpeTy24F/iMvJp5GLFsqmdsLc0Kfl8nyKRfuRaCvnamttQunFGuqKClSwlmXQxhk0gEAgE9YQ6L3d/5ZVXmD9/PkuWLCE4OJjp06eTk5PD5MmyM/SECRNuMpY7evQo69atIywsjIMHDzJ48GAMBgNvvPFGXb0EgRGY2XkmMzvNZFyTcTV2DbVSzR8P/sHWUVvp4Nah0udPaD6BAb4D0Bl0vLzvZUJSQ4wa34n4E6y5sgaA97u8XyGDNjO1GW91fAuApReXEpYeZrR49AZ9iav9QL+BVV7HzdKNIf5DAErGuAmqR6C3HQArGSjPWleZwKUN8si3/Hpmjpl0BVaOheC/IfEi6PJAqQaHhhAwEDpNhyFfQetxgARrp0LMqbqOWlBEeHIOf52OYf+VJC7EZJCQmY9Wb7jpmMTMfB5fcJSo1Dz8HC1YPrUTTlY3//5q7GqFu60ZBToDR8JqbhRbZZzdi3EvOjZWZNIFAoFAUE+oU3d3gLFjx5KUlMT7779PfHw8bdq0Ydu2bSVmcpGRkTf1m+fn5zNz5kzCwsKwsrJiyJAh/P7779jZ2dXRKxAYAxsTG8Y2rXw5dVWuU1UUCgUfdf2I0PRQrmdc5/Etj/Nmxzd5JOCRCmdsyqNAX8CHRz4EYFTAqEptIvT27k0vr17sj97PrGOzmD9gfrXjATiVeIqU/BRsTGzo5N6pWmtNbjGZjdc2sitiFxGZEfja+FY7vvuZNl52AJyNSofHRoC5A/zxGIQfhMVD4Ym1YOVSpzECcq/89rfBoAO/HtDtRbm33s4HVJqbjw2aDLnJELoLVoyFabvl4wR1hk5v4NH5/5ZZBm5rrsHJygRHK1Ni0/OITsvD086c5dM642pjdsvxCoWC3k1cWHkskn2XE0sy68amMs7uxXiInnSBQCAQ1DPqPJMOMGPGDCIiIigoKODo0aN06lQqCPbt28fixYtLPu7VqxeXLl0iPz+f5ORkli5dioeHRx1ELbgfsTKxYvHgxXT37E6BvoCPjnzEmwfeJLswu1rrzj83n/DMcBzNHEvK6ivDWx3fwlRlytG4o2wP316tWIopXqevT98Sz4Cq0si+Eb28eiEhseTiEmOEd1/T0ssWkLOGKdkF4N8LJm0GS2eIPwe/DYRU41VVVJkr22XRrdTAsO9lUz3HhrcKdJBH241eDK6tICcRlo+GvPTajlhwA0fCUojLyMdco6KZuw3O1qaolPIGYEaelmtJORy7nkp0Wh5uNmasnNb5thns3k2cAdgbklRjBpyVcXYvpjiTLnrSBQKBQFBfqBciXSC4m3Awc2Buv7m8EvQKKoWKreFbGbtpLMEpwVVaLzQtlN8u/AbA253extbUttJreFl78WSrJwGYfXw2OdrqjTm6qdTdt+ql7jcyuaXcwrIhdAPJeclGWfN+xcZMQ0NnSwDORqfLT3q0gSnbwc4X0q7Db4Mg7mydxYiuUM6iA3SeLovzO2FqDY+tAmt3SLoMq8fL6wjqhL/PxgIwop0nW1/swfF3+3P1kwc49d4Adr7ckxXTOjHn0bZ8PrIVG5/vho/j7X0rujVyQqNSEJmay/UaGsUWnSYbaHpWwNm9mNKedCHSBQKBQFA/ECJdIKgCSoWSyS0ns3jwYtwt3YnMiuTxLY+z8vLKSmWIDJKBD458gM6go7dX72oJ4iktp+Bt7U1iXiI/n/m5yutAaam7tYk1nd07V2utYtq5tCPQOZBCQyHLg5cbZc37mfa+DgB8uS2E7AKd/KRjQ3hyR2k2etHQupulfvRnOZtv6SLPea8otp7w2GowsZJj//tFMWKuDijQ6dl6IR6AhwJLq9WUSgUOliYEuFrTtaETwwI9GNfRBxfrW0vc/4uVqZoOfvL37b6QmnF5r0q5e7FIz8jTkluoq5G4BAKBQCCoDEKkCwTVoI1LG/4c9id9vPugNWiZdXQWr+5/lczCipl3rQpZxdmks1ioLXi387vV6iU3VZnydkc5c7kseBlX065Wea2SUnfvvmjKKk2uAgqFoiSbvuryqmpn++93XuwfgLO1KZfjs3hx5Wn0hiIha+0GkzeDb3cozJL7u5OMa3J4R7ISYP9s+XH/D8Cskl4Q7q3l0neFCs6ugAOzjR2h4A7sD0kiK1+Hq40pHYuEtTEo7kXfWwPz0gt0ehKz5AkHlSl3tzbTYG0qW/TEpotsukAgEAjqHiHSBYJqYmtqy/d9vufNDm+iVqrZGbGTMX+PYUPoBmKyY8rNrMfnxPP9qe8BeLHdi7hZulU7lh5ePejr3Re9pGfW0VlV6vs0lqt7WfTx7oOfjR9Z2qwSJ3tB1fCwM2f+hPaYqpXsvpzI51tvaLcws5XN4/x6gDYX/pwEhbm1F9zuj+QNAo92EPho1dYIGABDv5Yf7/0Uzq4yXnyCO7KxqNT9wdYeKJXVN6Isprgv/ej1VKNnrePS85EkMNMocbxh/FtFcLcTY9gEAoFAUH8QIl0gMAIKhYInmj/BsgeW4WXlRUx2DDP/mcngtYMZsGYAbx54k9Uhq7mWfg1JkpAkiU+PfkqONofWzq0Z28R4zvZvdnwTM5UZJxJOsPl65WdO31jq3sW9i9HigtI2AYCll5ai1WuNuv79RhtvO74aHQjA/IPX+eNYZOknNWYw6je53DzxEmx7s3aCijkJZ5bJjx/4EpTV+DPTfjJ0e0l+vOE5uH6w2uEJ7kxOgY5dwQnAzaXuxqCRixWeduYU6gwcuWbcUWzFpe6edhWfkV6Mm60wjxMIBAJB/UGIdIHAiLRwasHqYat5qvVTBDoHolaoSchNYMv1LXz878cM3zCcnqt6MnXHVPZF7UOtVPNBlw9QKVVGi8HDyoOnWj8FwNcnviarMKtS5+8I3wEYt9T9Rh70fxBnc2cScxOrtIkguJlhgR681D8AgJl/XeDwtRtM+axdYdR8QAGnlsK51TUbjMEAW4s2A1qPA++KjxIsl37/gxYjwKCF30fAD23lXvu1U2HHe/Dvz3DxL4g6Dpmx1b+egF3BCeRrDfg6WtDaq/JGlrdDHsUmZ9ON3Zde6uxecdO4YkrHsAmRLhAIBIK6R4h0gcDIWJtY83zb51k2ZBmHHzvMgoELeDbwWTq5dcJMZUZ6QTrH4o8BstlbgH2A0WOY2GIivja+JOcl89OZnyp8nt6gZ1dkzZS6F2OiMuGJ5k8AsPjCYgySoUaucz/xYr8AhgV6oDNITF926mbnbP/e0OsN+fHfL0Fy1b0K7sj51RB9HDSWci+6MVAqYfgv8uswaGUzuohDcP5POPwDbHsL/pwIv/WHb5rBuqeF0Vw1KXZ1fyjQo1o+GeXR+4a+dGOOYit1dq94P3ox7iWZdFHuLhDci1xJyOLhHw9x4ErNmFYKBMZGXdcBCAT3MuZqczq5d6KTeycAtHotl1IvcTLhJIX6Qqa0nFIj1zVRmfBOx3d4etfTrLi8gmENh9HcsfkdzzudeJrkvOQaKXW/kdGNRzP/3HyuZVzjQPQBenv3rrFr3Q8oFApmP9KaqNRczkSl8+Ti46x/thu2FkWVEL3ehIjDEH5Q7k+fugs0lRcyt6UgG3b+T37c8zWwcTfe2hozGP8XpIVDVpycMc+MLX2cFQeZcZAZDef+ANfm0O1F413/PiI9t5D9RW9ijV3qXkzXho6YqJREp+VxLSmHRi5WRlk3ugrO7sUU96THinJ3geCeZOmRcM5GZ/Ddriv0bOxc1+EIBHdEZNIFglpEo9IQ6BzIlJZTeCbwGUxUlTM3qgxdPbsy2G8wBsnAh0c+RGe4s0nTjgi51L2Pd58aKXUvxtrEmtFNRgOw8MJCo6wpSRLXM66jN+iNst7dhplGxa8TgvCwNSMsOYdnV5xEqy+qUlCqYNQCsHSGhAuw7W3jB3Dwa8iOB3s/6Pys8ddXKMChAfh2hVaPQLcXYPBnMGaJPHbu5fOlRnO7PoTwf4wfw33AtgvxaPUSTd2sCXC1rpFrWJqq6digeBSb8Vzei8vdqyLSPYoy6fEiky4Q3JOcCE8D4FRkOomZYjNOUP8RIl0guId5s+ObWJtYcynlEisvr7ztsQbJUOLqPshvUI3H9kSzJ9AoNZxOPM3pxNPVWis9P52X973MQ389xAt7X7hvS+hdrM1YMLEDFiYq/glN4X8bL5aWE1u7wchfAQWcXATnjeiunxoGR36UHw+aJWe+64KgyXIvvKSHNZPlUXCCSlHs6j6shrLoxdREX3p0WqlxXGVxEz3pAsE9S0aelpCEUn+e7ZfE3wZB/UeIdIHgHsbJ3ImXg14GYM7pOcRlx5V77OnE0yTlJWGtqdlS92JcLFwY1nAYUL1s+tG4o4z6exS7I3cDcCD6APPPzTdKjHcjzT1s+H5cWxQKWHE0koX/hJd+smFfuRQd4O8XIeWacS66fSboC8G/DzQZYpw1q4JCAQ9+A87NIDsB1j4JeuOO+bqXSczM50iY7LheU6XuxRT3pR+7nkpOQfX/jXR6A/FF2TGvqhjHFZW7ZxXoyMoXUycEgnuJ05FpN1mV7LgYX3fBCAQVRIh0geAeZ1TAKNq6tCVPl3fb2enbw7cD0MenZkvdb2RSi0koULAvah9h6WGVOler1/LtyW+ZtmMaibmJ+Nn4lbjazz0zlyOxR2og4ruDAc1defuBpgB8vOkSS4+El36y11vg2w0Ks2XDNW0VM4cGg9znvvF5CNkMChUM/lwWynWJiSWM/R1MrOQe/L2f1m08dxGbz8chSdDWxw5vh8oL3crQ0NkSbwdzCvUGDhthFFtCVgF6g4RGpcDF2rTS51uYqLE1l3/viTFsAsG9RXGpe5CvPQBHrqWQkSs24wT1GyHSBYJ7HKVCyf+6/A+1Us2+6H0l7u03Utul7sU0sG1AX5++ACy6uKjC50VkRjB+63gWXliIhMSogFGsenAVz7d9nlEBo5CQeOvgWyTk3L8lbdN6+PNk9wYAvL/hIj/vK8qaq9Ryf7qFI8Sfhx3vVm7hhIuyQdz3rWHRA/JoN4Cuz4NLUyO+gmrgFAAPzZEfH/oGQrbWbTx3CRtvcHWvaRQKBb0by9l0Y/Sln4/OAMDbwQKlsmobRe5FJe+x6XXflx6amMXZqPS6DuP+QZsPGdEQexqyhfv3vcaJiFQAHgnyoomrNTqDxO7L9+/7A8HdgXB3FwjuAxraNWRKyyn8eu5XPjv6GZ3dO2NtUmoKVdul7jcyueVkdkfuZlPYJia3nEwDmwbljn2SJIm/Qv/is2OfkafLw8bEhg+6fsAA3wElx7zV8S0uplzkcuplXj/wOr8N+g2NsnYqA+oTCoWCmUObYWGiYs6eUL7YdpncQh2vDGiMwsZD7k9fNgqOL4DIo2DnA3beYOt9w2MfsHCQ37ye/1PuY0+8WHoRE2to/hC0Gi2PSKtPtBwJUUfh6C+w/ml4+oBsaicok6jUXE5HpqNUwNDWRnTmvw19mjrz+78R7AtJQpKkao17Ky5f7VNURl8V3G3NuByfRXwdZ9LztXpG/3KEnEI9B9/og6tNHXk83Gskhci/w7LjISe56JYk3xeW9itj4QgvnAYz27qLVWA0tHoDZ4o2vNr72hOXnkdIQhbbL8Yzsp1X3QYnENwGIdIFgvuEp1o/xfbw7URkRvD9qe+Z2Xlmyed2hBe5utdiqXsxgc6BBLkGcTLhJA//9TBmKjPcrdzxtPLE08oTDysPPKw8cLNwY1nwspKy/A5uHZjVfRZulm43rWemNuObXt8wdtNYTiee5vuT3/Nah9dq9TXVFxQKBa8ObIK5iYovt4UwZ08ouYV6Zg5thqJRf+j9DuybBQnn5VtZaCxAm1v6scoEAgbKwrzxIOOPcjMmAz6G6BMQcwJWT4ApO+rO1K6eU5xF7+zviIt17XyNuvg7YaJWEpOeR2hidpXd5LV6A7svy9n4gc1dqxyPe5HhXF2PYTsSlkJaUSnuseupNW7id8+Tcg32fyFvNN7OVFSpBhSQmyKL+Q5P1lqIgprjYmwm+VoDdhYaGjpbMbCFGz/sCWX/lSTyCvWYm6jqOkSBoEyESBcI7hNMVaa81/k9pu6YyuqQ1Tzo/yBtXNrcVOo+0HdgncT2UruXePvg28Rkx5Cvz+d6xnWuZ1wv81iVQsWMtjOY3GIyKmXZf1y9bbz5uPvHvLT3JZZcWkIblzb09+1fky+hXvNs70ZYmqj538aL/HboOrmFOj4Z3gpV7zflcWYp1yA9AjKiID2q6D5SNl/T5gIK8OsuH9v8YTC3r+uXVDHUJjB6MczrCXFnYdtbMOy7uo6qXvJ3LZa6F2NuoqKzvyMHriSxNySxyiL92PVUMvK0OFqa0N7PocrxeJQ4vNdtufuuG5ynT4QLkV5l0iLgwJdwZqU88QGg8WDwDAJLJ3kkpUXRvaWTnDn/9yfY/g6cXCxE+j3CiXC51D3Ixx6lUkELDxu87M2JTstj/5UkBrd0u8MKAkHdIES6QHAf0cm9Ew81fIiN1zby4ZEPWT1sNeeTzpOYl4iVxoouHrVb6l5MG5c2bB21Fa1eS3xOPNHZ0cRmxxKTHUNsTqz8OCsGJwsn3u30Lq2dW99xzX4+/ZjUYhKLLy7mvX/eI8A+AF8b31p4NfWTiV39MDdR8dbac6w8FkVeoZ6vRgeidmwIjg3LPkmbD5kxsgmbddUzlHWKnTeMmg/LHpFHz/l0hsBxdR1VveJqQhaX47PQqBQ80LJ2St2L6d3YmQNXktgXksRTPcv5PrwD24tK3fs3c0VVxX50APeiWel1aRwnSRK7gm8Q6RFpdRbLXUtGDBz8Ck79DoYic7CAgdDnHfBoe/tzW4+DXR9A/Dm5P/1OxwvqPcWmccUbeAqFgkEt3Pjt0HV2XIwXIl1QbxEiXSC4z3it/WsciD5AaHooSy4uITkvGYA+3n0wUZnUaWwalQZvG2+8bbyNst4L7V7gXNI5TiWe4pV9r7B8yHLM1PdvufOY9t6Ya1S8vOoMf52JJU+r54dH22KqLqfcT2NWvoC/m2jUH3q9Cfs/h79fAo924Ny4rqOqNxSXuvdq7IytRe22u/Rp6sJHmy5xPDyV7AIdVqaVe1tiMEjsuCiL2kEtq7eR5F40hi0uo+4y6RdiMknILMBEraRQZyA4LrNKX5f7kqwE2SjyxCLQF8jP+feGPu+Cd8eKrWHpCM0eggtr4OQSIdLvciRJKtnoau9XWgFWLNJ3BSeg1RvQqISPtqD+Ib4rBYL7DHsze17v8DoAv5z9hS1hWwAY6Fc3pe41iUapYXav2TiYOXAl7Qqzjs6q65DqnGGBHvzyRBAmKiXbLyYwbelJ8gr1dR1WzdPrDfkNuy4PNjwHhvvgNVcASZJKRHpdlFU3cLLE19ECrV7in9DkSp9/PiaD+Mx8LE1UdG3oVK1YPIt60qPT8ijQ1c33x86iLHrfJi542pljkOQZz4I7EH8e5naUjSL1BfKYyUmbYcKGigv0YoImyvfn/4SCbOPHKqg1IlJySc4uwESlpJVnqRFgkK89TlYmZObr+Des+iMgBYKaQIh0geA+ZJj/MDq5d6JAX0BaQRpWGiu6enSt67BqBBcLF77o+QVKhZL1oetZf3V9XYdU5/Rv7srCSR0w16g4cCWJR345TFRq7p1PvJtRquDhubIjffQxOPZrXUdULzgXnUFESi5mGiX9m9VNS0Pvxs5A1UaxFZe6927igpmmegZQ3vYWOFmZUqAzcCoivVprVZXifvT+zV3pUJT5Ky7XFZRDyjX4fSTkp4NrKxj/lyzQ/bpXbT2/HuDgD4XZcHGdMSMV1DLFWfRWXrY3/X5QKRUMKDKZLP4dIhDUN4RIFwjuQxQKBe91fg8TpVzeXh9K3WuSzu6dea7NcwB88u8nrLmyBkmS6jiquqV7gBO/P9kRB0sTLsZm8tCPh6qUybyrsPWCAR/Kj3d/BKllmxPeTxQbxvVv5oplHZVU9y3aHNh8Lo6cAl2lzi1+gz2wRfU3GJRKBd0bOQJwKLT2Z2XHpOdxKS4TpQL6NHEmqKiH9qToSy+fzDj4fTjkJMoCfdImaNgHqjHOD4UC2hVl008uNkaUgjqi2DTuxlL3Yga2kHvRd1xMwGC4v98PCOonQqQLBPcpvja+vNXpLdws3Xi8+eN1HU6NM7XVVPr79KfQUMiHRz7kpb0vkZZ/f7/5be/nwN/Pd6eVpy1puVrG/3aU+QfC7u0NjKDJcqZMmwt/vwD38mu9AwaDxKZzcUDturr/l+6NnPB1tCAzX8eak9EVPi80MZtrSTloVAr6NK36fPQb6REgZ/UPXq39DavdRaXuQb72OFqZ0t5XFhanItPQ6W8zOux+JTcVlo2UJ1E4+MP4dWBuZ5y12zwOSg3EnJRL6QV3JSX96L63Tn3o2tARK1M1iVkFnC6aoy4Q1CeESBcI7mNGNx7Nzkd20sKxRV2HUuMoFUq+7v01rwa9ilqpZk/UHkZuHMk/Mf/UdWh1iqedOX8+04VR7bwwSPDplmBe+OMMuYWVy2jeNSiV8NAPoDaH6wfg1JK6jqjOOBaeSnxmPjZmano1ca6zOFRKBU92bwDAwn+uo69gVmvHJTmL3qWhEzZmxjG86x4g97Wfj8kgLafQKGtWlJ3Fpe5FlQWNXa2xNlWTW6jncnxWrcZS7ynMgRVjIfESWLvD+PVgZZyNGgCsnKHpEPnxyfv3d8TdTFpOIaGJsqdAkO+tmXRTtYq+RZt7O0TJu6AeIkS6QCC4b1AqlExqOYmVQ1fib+tPcl4yz+x6hi+OfUFBsRvwfYiZRsVXo1vz0cMtUCsV/H02lpE/HSYy5R7tU3fwh74z5cc73pNHNt2HbC7Kog9q4Va+w38t8UiQF7bmGiJSckvE6p3YXuzqboRS92Jcbcxo4mqNJME/12ovm56Vry0xsOpf1CurUipo51vcl55aa7HUe3SFsGq87C1hZgdPrAN7P+Nfp7jk/dxqKLxHfxfewxS3iTR0tsTBsux2vkFFJe/bL8bf2xVkgrsSIdIFAsF9R1OHpvzx4B+MayLPy14WvIxxm8ZxJe1KHUdWdygUCiZ08WPFtM44WZlwOT6LYT8e4sCV2u/NrRU6TwfP9lCQCZtfue/K3vUGiW1F2aOhrWt3NnpZWJioebyTDwC/HQq74/HxGfmcjUpHoaDEAMpYFGfTD16pPZF+8GoyWr2Ev5MlDZ2tSp4vLnkX89KLMOhh/dNwbTdoLODxP8G1ec1cy78P2PlAQQZc+qtmriGoMY5HyBtbHfxuLXUvpncTZ0zUSsJTcglJENUqgvqFEOkCgeC+xFxtzrud32Vuv7k4mDkQmh7KuE3jWHpxKQbp/u3/7NhA7lMP9LYjI0/LxEXHmLs39N4z1lGq4OEfQWUCV7bJ45buI05GpJGUVYCNmbrao8uMxcSufmhUCo6Hp3HmDj2ixaXu7XzscbE2M2ocPYpE+qHQ5FrLrt3o6n4jQTc4vN/3mT5Jgi2vyY7rSg2M/b3y49Uqg1IJ7SbIj0XJ+13HyaKpCGWVuhdjaaqmZ9HP+/YLFavgEQhqCyHSBQLBfU1Pr56se2gdvbx6oTVomX1iNk9seYJDMYfu2zfF7rbmrH66M+M6eCNJMHt7CFOWHCcl+x5rCXBpBj3fkB9vfROy79GqgTLYcl4udR/Q3A0Tdf14K+BqY1Yyq33+wdtn03fUQKl7MZ0aOGKiUhKTnkdYco7R1/8vOr2BPUXj5/47Bq+Ntx1qpYL4zHxi0vNqPJZ6zd5P4cRCQAEj50Gj/jV/zTZPgEIFUf9C4uWav57AKORr9ZyLzgBun0mHUpd3MYpNUN+oH3+ZBQKBoA5xNHdkTt85vNf5PczV5pxPPs/0XdPva7Fuqlbx+ajWfD6yFaZqJftCkhjyw0GOFvXN3jN0f0ke3ZSXCltfr+toagWDQWLbBfkN6ZBWbnUczc1M7e4PwNbzcUSllt0HnJFb2r89sLnx4zc3UZWMbDpUCy7vJyPSSM/VYmehoZ2P3U2fszBR08LDpuS4+5aj8+DAbPnx0K+h5ajaua6NOzQeLD++j00m7zYuxGRQqDfgZGWCr6PFbY/t38wVpQIuxWWW+ztHIKgLhEgXCAQC5J7sMU3GsGXkFiY2n4iZyoxzyefue7E+rqMPG2Z0o6GzJQmZBTw6/1/m7L5aYQfueo9KI5e9K1RwcT0E/13XEdU4p6PSic/Mx8pUXdJ/XWcUZENqGEQdh4RLNLfT0a2hAwYJFh8OL/OU3ZcT0Bkkmrha4+dkWSNhlY5iq/nqil1Fo9f6NnFBrbr1bVlQ0fio4/ereVzIVrnSBaDPTOjwZO1eP2iSfH92JWjza/fagipxPLx09JpCobjtsQ6WJnRsIP+MiWy6oD6hrusABAKBoD7hZO7Eax1eY1LLSSy+sJhVIatKxHprp9ZMbzOdbh7d7viH/16iqZsNG2d05/0NF1l7Kpqvd17h3+spfDu2jdH7gesEjzbQ7UU49A1sfhX8uoN5+X2Mdztbi0rd+zdzqR1X96QrcGENZCfILQU5iZCdCDlJ8rz6/7BUaUKMiS3Jxx3QZjVBY+cB1m7g3BQCBpW8ka6JUvdiegQ48cU2OHItBa3egKYM8WwMJEkqHb1WjgFeBz97Fv5znRPh92EmPe4srHkSkGS39Z6v1X4MjfqBjRdkRsubeK1H134MdUHKNbiwDmJOgEIJSnXpTaWRfT2UatkfIGCAfKsnnCwyjSuuiLkTg1u48W9YKtsvxjO1h39NhiYQVBgh0gUCgaAMbhTrSy4u4Y/Lf5SI9WYOzeju2Z0g1yDauLTBUlMz2bz6hKWpmq/HBNKloSPv/XWBf0JTGPL9Ib4b26bus7HGoNebcHkTJF+B5aOh/ZPynGQz27qOzKhIksTWolL3B1rVgqt7YjD8NlB20S8PtTlYOsmzr/NSURkK8VEm4UMShITcdKiu9aMcvDIMKO0lrQmau9vgYGlCak4hpyPTSzJtxuZaUg7hKbmYqJT0bFz2rPpi87iQhCwy87VGmwlf78mIkWeha3Nkp/WhX0NdbI4qVdD2Cdj/uVzyfi+L9PRIuaLowlp5g6SiHJsHfd+DHq/Wzb/RDRgMUsk0hPZ36EcvZmALNz74+xInigw1na1NazJEgaBCCJEuEAgEt8HJ3IlX27/KxBYTS8R6cGowwanBzD8/H6VCSRP7JgS5BhHkGkRbl7Y4mjvWddg1xiNBXrTxtmXGitNcjs9i/MKjzOjTiBf7BZRZqnvXoDGDh36EJQ9C9HH5pjKBhv2gxQho8gCY2dR1lNXmXHQGMel5WJio6FWOKDQa2YmwfIws0N3byF9DS2ewcgFLF7Bylu9NS0eOoc2H7AR2HjvL2v0naWyRxYsdrVFlxcCFdajPreQLrjPb9tWSXu2aQKlU0L2RExvPxnLwalKNifTiUvfODR2xMi37LZmLtRk+DhZEpuZyKiKN3k1caiSWekVBlizQs+LkCooxS+TsbV3R9gk48CWEH4TkUHBqVHexGJuseLj4l+yaH3W09HmFCvx7Q+NBoDYFgw70OvneoC2618vtKudWwZ6PISMKhnwNqrqTF2HJ2aTnajHTKCv8O8LDzpzWXraci85g56UEHisaBykQ1CVCpAsEAkEFKBbrk1pMYn/0fk4mnORkwklismNKRPuy4GUA+Nn40cenD2Maj8HL2qvS14rKimJH+A4czR0Z2mAomrp8c1oGjVys+eu5bny06RIrjkYyZ08oWy/E89rAxgxq4Xb3tgL4dIJn/5XHsV1YB8khcGWrfFOZym7SLUZAk8Fgal3X0VaJLRfkUve+TV0w09RgqXthLqwcBxmR4NAQxq8HiwoIXY0Z2PvSo68Xbx0zZ1t2IY1c2/LQQA9oNgzd6ikMU/1LgNn3KHQ95eNriO4BxSI9mVcHNqmRaxSPXhvQ7PbCu72fPZGpuZy8H0S6XieXuCeclzd1Hltd9xUtdt7yz//VHXI2feDHdRuPMcjPlGfOh2wFij1GFHK7T8uR0OwhucKlInh1gC2vw8nFkBkLjyy6efOtFinuR2/jbVepNpVBLdw4F53B9ovxQqQL6gV3cdpDIBAIah9Hc0dGBozk0+6fsm3UNnY9sosve37J2CZjaWQnZ1fCM8NZdGERQ9YN4bndz3Eg+sAdZ69r9Vp2hO/gqR1PMWTdEL479R3v/fMew/4axvqr69EatLXx8iqMmUbFrBGt+OHRtthZaAhNzOaZZacYPvefWnHE/i9XErIY/9tRVh2PLPeYjdc28tzu5wjLuM14L8eG0PstmHFMFuy93gSnxqAvgJDNsG4qfOkPi4bAnk8hbL8sSO8CJEli6/liV/caLHU3GOQ3/zEn5d7+x/+smEC/ATONivFdfAFYcDAMSZLQNRnGS4rXyZc0NM04JG8C1ODXvnhe+rnodDJyjf/zl5JdwMlIWVD0a3b7/vr2ReZx90Vf+vZ34Op2UJvBo3+AvW9dRyRTZCCX8s9iNpy8XrexGIMDX0LIFkACr44w+At4JRgmbYL2Uyou0AE6ToOxy+R/s6s7YPFQyKqbueMnbjCNqwyDitpnDl9LJjO/fv29FdyfKKT7zK44MzMTW1tbMjIysLG5+0sXBQJB/SI9P51j8cdYe3Uth2MPlzzvaeXJ2CZjGdFoBHZmdiXPR2ZGsvbqWv4K/YvUfNnsRoGCjm4dCU0PJSVfHjXlbe3N9MDpDGkwBJWyFsy+KkFmvpYFB8JYcOg6uYV6ALo2dOSNwU1p421X49c/H53BhIVHScvVolEp2P5ST/ydb87ihGeEM2LjCHQGHQ5mDswbMI+mDk0rdgFJgsRLpSWhKaE3f16pAa/24NtNzkJ5dwST+udTcCEmgwfnHMJMo+TUewOwMKmhYrqd/4N/vpPbBSZsAN+uVVomJbuArp/voUBnYPXTXdAZDDw2/ygDLa4wTzUbhTYHfLrCY6tqrBWh/zf7CU3M5ufH2xm9h//PE1G8vuYcLTxs2PxCj9seeyUhi4HfHsBMo+T8B4NqzMiuzjk6D7a+IT8evQRaDK/TcG5CryP10wAcDKn87DyT6c/dxSMbU67B3E5y2fq4lbL/hjGIOg4rx0JuCtj5wONrwbmxcdauIL1m7yUiJZfFkztUuuqk62e7ic3I589nutxxvrrAeEiSdPdW4FWSyujQe/S3vEAgENQNdmZ2DPQbyLwB89g0YhPjm4/H2sSamOwYvjn5Df3+7Me7h95l3dV1TN0xlaHrh7LwwkJS81NxNndmWqtpbBm5hQWDFrB11FZea/8a9qb2RGVF8c6hdxi+YThbwragN+jr+qWWYGOm4ZWBTTjwRh8mdfXDRKXk8LUUhs/9h6d/P8HVhKwau/ax66k8Ov9f0nK1qJUKtHqJTzcH33SMJEnMOjoLnUGHSqEiNT+VKduncDapgsZICgW4toC+78KME/D8KRj2A7QaA9Ye8hvdyCNw8Cv4fTh87gML+sP2d2U36OyaH+NVEbYWlbr3aeJScwL95GJZoIPc419FgQ7gaGXKyHZyu8j8g2HsuChn5myb9UUxfj2Y2kLkYVj6MOTWzHiy4mz6gRqoDtkdnAjIc5rvRCNnK2zNNeRrDVyKvY0J393Mle2w7S35cf8P6pdAB2KytCwr7AnAgOSlcln+3crO9+XfWw37GU+gA3h3gCd3goO/bEL32wCIOGK89e9AYlY+ESm5KBTQzrfyEzoaucptTNcSs40dmqActAYtz+x6hjF/jyE6K7quw6lXCJEuEAgENYSvjS9vdHiD3aN382HXD2nm0IxCQyEbr23kf4f/x9G4oyhQ0MOzB9/3+Z4dj+zghXYvlPSxm6vNmdhiIttGbePFdi9ia2pLeGY4bx58k0f+foQd4Tvq1ex2JytTPnioBXte68UjQV4oFbD9YgKDvjvAK6vPcC3JuG989oUkMmHhUbILdHT2d2Dds11RKxXsvpzIvpDEkuN2RuzkSNwRTJQmLB+6nDbObcgqzGLajmkcjz9euYsqFHJJfNBEGDUfXrkEL5yGh+ZA63HyqCaDTjaeO/IjrHoCvmoEP7SF9dNlEZt4WS4Jr0UkSWLL+Rp2db+2Bza9Ij/u/TYEjq32kk92bwDIBmt/nYkBispSfTrBxI1g7gCxp2DJsBrZDCkW6QevJhn1Zy1fq+dA0Qz2AeWMXrsRpVJBUJHoKHauvqeIOwd/TgbJAG3HQ7eX6jqiW/jrdAy/6YaQKlnRSIok9/D8ug6palw/KE+yUKhg0KfGX9+xoSzUvTpAfrq8iXZxvfGvUwYni0rdm7haV2kKQkNnuQLK2H+rBOWzIngFh2MPE5wazJTtU4jKiqrrkOoNQqQLBAJBDWOuNmdkwEhWPbiK5UOWM8x/GM0cmvFM4DNsG7WNn/r/RF+fvqiVZWc3LTQWTG01lW0jtzGjzQysTawJTQ/l1f2vMuvorHol1AG87C34anQg21/qyaAWrhgkWHcqhv7f7Oe55ae4EJNR7WtsPR/HtKUnyNca6NvUhcWTO9Lay45JXf0A+GjTJQp1BnK1uXx5/EsAprSaQgvHFswbMI9Obp3I0+Uxfdd0DkQfqHogCoWcNWo3AUbOg5cvwItnYcQ8CJoMLs0BheyAfHYF/P0i/NQJZjeEjS9A+KFaEewhCVlcT87BRK2kb9MaMB5LDIbVE0HSQ+uxci+/EWjkYkXfpi5IEqTnarEwUZWO/PNoA5M2g5UrJFyARQ/IplVGpFMDRzQqBdFpeUSkGK///UhYCrmFetxszCrsQF0s0otnQN8zpFwrHbXWoBc8+G2dj/H6L5Iksf50DBlY8Y1OHsGmOfhZjVVw1BgGvdzzD3KPvUuzmrmOpRNM2AhNhsp+Hn9Olse61TClo9cqn0UHaFjUJnUtKcdoMQnKJzkvmZ/P/gyAtYk1cTlxslDPFEIdhEgXCASCWkOhUNDauTWzesxi9bDVPNfmOTysPCp8vpWJFU8HPs22UduY1moaChT8EfIHXxz/ot4JdYAAV2vmjW/Phue6MaC5K5IEm8/H8eCcQ0xadIzj4VV7g7vmZDTPrTiFVi8xtLU7vzwRVOJU/kL/AJysTAhLymHpkXB+OfcLCbkJeFp58mTLJwF502Nu/7n08upFgb6AF/e+yM6IncZ50QoF2PtB4DgY9h08ewTeDIfH10CP18CvhzwXPC9VdolePBS+ayX3cSdcNE4MZbDlnFzq3quxc7mjvqrMjaPWfLrKVQVGFFlTezQoedy7ifPNrvSuzWHyVrmCIeWqnLUrMF4WzNJUXSKOD141Xqa+2NW9f3OXCvdiFvfIHg9Pq5c/71Ui4ZK8uZIVWzRqbWndjlorhwsxmYQmZmOqVhLhN5pggzeawgzYWwOZ6JrkzAqIPye3ivR5p2avZWIBY3+XNyuRYOOLkFqzhnsniv6mVLWfvFSki0x6bfDtyW/J0ebQ0rEl6x9aj5+NH/E58UzePlkIdYRIFwgEgrsOGxMbXmj3Ah92/RCA5cHLmX1idr194x7obcf8Ce3Z/lJPhrfxQKmAfSFJjP7lCGN+OcK+kMQKx77kcDiv/XkWgwRj23vzw7i2mKhL/5TZmGl4fZA8Luv7/YdYenEpAG93fBszdem4LlOVKd/2+ZbBfoPRGXS8tv81Nl7baMRXfQPmdhAwAPq9Jzsnvx0FE/+Wy3pNbSAzWu7j/rkr/NwNDn0HGcbtzdtyodjV3c14i+p1EPmvnAUtHrU2brk8U9mIdPF3pJWnPIKrTFd6x4YwZStYu0PyFXkUlBHpESDPkz9opL50SZJK5qPfydX9Rlp72aJRKUjKKiAqNc8osdQp0Sdh8RDITgDXlvLPhLldXUdVJutOyz+PA5q70r6BCx/qJsqfOLEQ4i/UYWSVoCBLnmUO0Ov1yrm3VxWlCoZ8BT5doDAL1j4J+ppxTs8t1HGxyK8hqAr96FBa7h6VmkuBrv74vtyLnEk8U/I39+1Ob+Nq6cqiwYtoYNuAhNwEJm+fTGRm+dNa7geESBcIBIK7lBEBI3i/y/sA/H7pd749+W2lhPrx+ON8eORDziSeqaEIb6aJmzXfjWvL3td682hHH0xUSo6FpzJp0XGG/XiIWVuCWXAwjA1nYjh8LZnQxGwy87Ulr2nu3lD+t1HONk/p1oDPR7VCpbw1Czk6yJuWnjboHNail/T09upNL+9etxynUWr4vMfnjGg0AoNk4N1D77Lq8qqa/SKAnCls0BMe/hFeuypnD5s+KLvEJ1yAXf+Db1vC/H6y+dylDZAVX+XLXU3IIjQxG41KUSlRWCZpEbIw+eNx+LIBLBwk94RXcdRaRVAoFMyf0J5fnmjH0PL66e18YNQCUCjltoKzfxjt+sV96UeupaDTV7814UJMJgmZBViYqOji71jh88w0KloWbVacuNtL3q8fhKUPQV6a3Ls8aRNY1c/57zq9gb/Pym0UI9t50szdmn8NzTmo6Sb30G97S54AUd859J28IWLfADo+VXvXValh5Hx51n3MSdjzSY1c5kxUOjqDhLutGZ525lVaw9naFGtTNQYJo7a3CG5Gb9Az6+gsAIY3Gk5r59YAOJk7sXDQQvxt/UuEekRmRF2GWqfUkL2rQCAQCGqD0Y1HYzAY+OToJyy6uAiVUsULbV+4bQltfE4835z4hq3hWwFYe2Utjzd7nOfbPo+FxqLGY/Z1tOSzka14sV8ACw6GsfxoJBdiMrkQU7ZrtZlGiYOFCbEZ+QC80C+Al/sHlPsalUoFD3SK55fgMCSDmuE+z5Ybi0qp4oOuH2ChsWB58HI+OfoJ+fp8JraYWP0XWhE0ZtD8YfmWmyoL8vN/QsQ/EHNCvhWbI9v5gHcneaaxd0c5+6i685/xYsO4HgHON5spFebKZfcGXZHIkErFxo0fp4bBtd0QuhtSr928uLk9+PeBHq/IGe0aws3WjMG2dzC88+su98Lv+0w2sPMMAqeAal+7hYctdhYa0nO1nI1OJ6iS85f/S7HLfs+A/5TuV4AOfg6cjkzneHhaifP9XceV7bB6Aujy5c2qcSvB1OrO59URB68mk5xdiKOlCT0CnIkv+j30Xu5Y9pqfRBF+EII3yj/D9ZX0SNnIEmDgx0avdrkjdt7ytIfV4+WqIf9e0LCvUS9RbBoX5Gtf5XFeCoUCfxcrzkalcy0xm8ZFbu8C47IudB3BqcFYa6x5sd2LN33OydyJ3wb9xtTtU7mWcY0p26awcPBCfG186yjaukOIdIFAILjLGdt0LHpJz2fHPmPB+QWoFCpmtJ1xy3FavZall5Yy79w88nR5KFDQxqUNpxNPsyx4Gfui9vFB1w/o5N6pVuJ2szVj5oPNea5PIzaciSE6LY/ErAISs/JJyiogMauArHwd+VpDiUB/Z0hTnup5ezGYXZjN2nDZjKYwpQ8/70qjb6Py57AqFUre7PAmFmoL5p+fz1cnvsJcbc6YJmOM+4LvhIUDtJ8s3zKiIfwfiDoKUccg8aL8Rjs9UhbxAGozsHCSZ4Sb2pR9L0l4Hz/LN5pUuuZIMC9HnmGckwy6O5dM/2hnywVTEwbl5DIoJxcLhUreIGjYDxr1Bfc2cklrfaHn67IZX/hB2axq6i55I6QaqJQKujVyYvO5OA5cSa6WSNcbJNadkl3qHwysvMv+XW8ed2EtrHtK3hhqMgQeWVTtf5+aZv1p+d9rWKAHGpUSL3tzrM3UhOc7kdz6aZxPfQ/bZ0LAQNBULYNb4+z6QN4U8e0uV+3UBc0fgvZT5EqcdU/D9MNg5Wy05Y8XmcZVd755Q2dLWaSLvvQaIaMggx9O/QDAs22excn81rYLJ3MnFgxawLQd0whND2XKtin8Nug3/Gz9ajnaukWIdIFAILgHeKzZYxgkA18c/4J55+ahUqiY3mZ6yecPxxzms2OfEZ4ZDkCgcyDvdHqH5o7NORh9kI/+/Yjo7Gim7pjKqIBRvNr+VaxNaieLYG9pwqRuDcr8XF6hnuRsWbjbW5jg73znjNvPZ38mKS8JD0tvIq/14URyGhvPxvJwG89yz1EoFLzQ7gUkJBacX8An/36ClcaKIf5GnCFcGWy95BFmxWPM8jPlUtGoY7Jwjz4BBRlyP/sdxmaPBFABZfmeKTVFRl2KIrO30vuN5hrm2cuVFf9YmPO5qzsP+A1iRNOxtHZqXeVsVY2iVMmltb90h4TzsONdGPp1tZftUSTSD15N4uUBjau8zoGrScRn5mNvoanQ6LX/UizSryRkk5Grxdai/pmslcvJJfJ0AyRoNRqG/1wvTeJuJLtAx45LciXKiLby7w+FQkEzNxuOhady2O0JHrZZI3syHP5R7vWub0QdK3JWV8DgWXXrnD9oljw3PSkY/noGHvsTlNXvvDUYJE5HlGbSq4NweK9Zfjz9I+kF6TSya8TYpuWP6XQyd2LBwAVM3TFVFurbp/B4s8exM7XDzswOO1M77E3tsTW1xdbUttzpOHcz994rEggEgvuUJ5o/gV7S89WJr/jp7E8oFUqGNRzG7OOz2RW5CwAHMwdeCXqFYQ2HoVTIb456ePVg/UPr+e7Ud6wKWcXaq2s5GHOQ9zu/X2Yvd21ibqLC28ECb4eKleFfTbvK8uDlALzX5R1O27vy9c4rfLblMgOau2Jhcvs/ey+0fYGswixWhazi3UPvYqmxrPOvASBnxRv2kW8gj21LD5d7evMzZWf1Mu6D4zPZHy1h6+TOo73ayGZRFk5y1t7SCUysynzTHp4RziebxoAuj97evbmWfo2orCjWhv3N2rC/aWjbkBEBI3jQ/0EczSveV10r2LjLI/CWj4LjC+SS6mqWIhePfTsbnUFGnhZb86qJy9XHZcfi4W09MVVXvgLBycoUfydLwpJzOBmZSt+m1fQYqC2OzL1h9NdkeeOkPlVglMPW83Hkaw34O1vS2su25Plm7tYcC0/lQpKOhwd8JBuiHfoG2jwGtuVvBtY6BgNse1t+3OZxcA+s23g05jB6EfzaG0J3wb8/Qddbq74qS3RaHlkFOkzUSpq6VW9zWTi81xwhqSGsvrIakM1cNcrb/x51NHeUS993TOVq2lW+O/Vducdam1hjb2rPt32+pbF91TdS6xPCOE4gEAjuISa2mMjLQS8D8OOZH3lw/YPsityFSqHiiWZPsGnEJh5u9HCJQC/GysSKmZ1nsnDQQnysfUjMTWTGnhm8dfAtknKT6q1z/I1IksSnRz9FL+np59OP7p7dmdbTHy97c+Iz8/l537U7rqFQKHin0zs86P8gOknHq/tf5Xj88VqIvpIolfJ8ds8gWbg3fxjajYcuz0Gft2HwZzB8Lq8VPsXnukdRdH0e2j4OjQeBVxA4NABT6zIFeqG+kDcOvEGeLo8Obh34rvd3bB6xmYWDFjLMfxhmKjOuZVzjqxNf0f/P/ry892XOJZ2rgy/CbQjoD11fkB9veF42vKsGXvYW+DtbojdIHLmWUqU1UrILSlzdRwd5VzmW4kzhiaIe3KogSRJHYo/w7qF3ORh9sMrrVIjDc0oFetcX5Dnod4FAh9JS95FtPW+qHGnmLs+2D47LgpajZPdybS7sfL9O4iyXC2tlXwuNpTxdoj7g0kz+/QRyGX7s6WovGZKQBUAjZyvUqupJm2KH97CknEr/3UvOS+atg2/x2ObHuJZ+57839xOSJDHr6CwMkoGBvgPp6N6xQuc5mDmwcOBCpgdO56GGD9HLqxetnVvja+OLjYlNyXFZhVlEZkViojSpqZdQ64hMukAgENxjTGk5BYNk4PtT36M1aGnv2p63O71dod3lDm4dWPPQGuaensvvwb+zOWwzm8M2o1FqsDezx8HMAQczB+zN7LE3lT92NHekhWMLAuwDbhH/tcmmsE2cTDiJmcqMNzu8CciO2DOHNuOZZaeYdyCMMe2975iVVyqUfNTtI7K12eyL2seM3TP4bdBvtHRqWQuvwnhEpORwMTYTlVLBwBYVH7323anvCE4Nxs7Ujs+6f4aqSFB1cOtAB7cOvN3pbbZe38r6q+u5kHKBXZG72BW5ixGNRvBS0Es4mBnf4b1K9HsfIo9A9HE50zl5a7XKq3s0ciIsKYdDoUkMbln5UXZ/nYlFq5do5WlLcw+bO59QDu397PnzZDQnIiov0nUGHTsjdrLowiKCU4MB2HZ9G4sHL6aVc6sqx1Qu4f+UCtc+M6Hna3Vbbl0J4jLyOBImb8j8t1Wm+N8vOC4TCVAM/lzODl9YAx2mgm+XWo62DApz5UkRAD1eBmsjjl+sLkGT4dpe2XBvzRR4+oC8aVhFrhSJ9CbVzKID+DhaoFIqyC7QkZhVgKvNnT0TJEliw7UNzD4+m8xCuf9o4raJ/NTvpxLn8vudLde3cCrxFGYqM15r/1qlzrUzs+PZNmUbwOoNejILM0krSCOjIAMPKw9jhFsvECJdIBAI7kGmtppKA9sGqBQqenn1qlT/sLnanNc6vMYgv0F89O9HXE69jNagJTE3kcTcxHLPszaxJsgliPZu7QlyDaKpQ9Ma7xOTJInwzHCOxx9n7pm5ADwd+DTuVqWmXINauNG1oSOHr6Uwa0swPz8RdMd1NUoNX/X6imd3Pcux+GM8s+sZFg9aTCP7RjX2WozN1qLZ6J39HXCwrFh24UD0AX6/9DsAH3f7GFfLW8uprU2sGdNkDGOajOFK2hWWXFzCxmsbWR+6nl2Ru5jRZgZjmoyp1R5Bg2QgNT8Vg2TATG2GucocjUoDo36DeT1kob7nYxjwUZWv0SPAmSVHIqo0L12SpJJS9zEdqp5FB2hfZIx1NiqdQp0BE/WdN8bydHn8FfoXSy4uISZbzg6bq83xtPIkND2UF/e+yB8P/oGLhRHHoOUky5sjkgECH6uf/dq34a/TsUgSdGzgcMvGXmNXa5QKSMkplIWcRxu5kuXUUtj6Bjy1r+6rBQ7PgcwYsPWGLtUvKTcqCgU89APEnJKnR2x+DUbOq/JyIfGySDeGG7upWoWPgwXXk3O4lph9R5Eekx3Dh4c/5EicPIajmUMzVAoVF1IuMHXHVL7r/R1dPbtWO667mVxtLt+c+AaAaa2n3fT3ubqolCo5aWBWPS+C+ogQ6QKBQHCP0s+nX7XOb+Xcij+H/UmeLo+0/DTS8tNIzU8lNT9VflyQSmpeKvG58ZxPOk9WYRb7ovexL3ofABZqC9q6tKW9W3sC7AJQKVUoUaJQKFAqlChQlDxWKVRYm1jLpjCmdiXZ2/9yoyg/EX+C4wnHSc4rFU1+Nn5MbH7z+DSFQsH7w5oz5PuDbL0QzwcbL/L6oCZYmt7+T6CpypQf+v7AtB3TOJ98nqd2PsWSB5bgbV09kVUb5Bbq+KuoVPeBlhV7Q5SUm8TMQzMBeLzZ4/T27n3HcxrbN+bT7p8yuvFoZh2dRXBqMJ8d+4x1V9fxbud3aevStsqv4UYkSSI+J56Y7Bhic2KJzY4lLieu5D4uO45CQ+FN56gVaszUZpj5eGKea4FZ+CrcNlzmtd6f42/rX+kYOjd0RK1UEJGSS2RKLj6OFR9XeC46g5CELEzVSh4KrF6mx9/JEgdLE1JzCrkQm0E7n/LfnKbnp7MyZCUrg1eSViBn3u1N7Xm02aM82uRR1Eo147eOl4X6nhdZNHgRZmojuK0bDPDXdMiKA6fGMGR29desRSRJYv3paEAudf8vZhoV/s5WhCZmcykuUxZyfd+Hixsg/hycXgZBtTTG8T8YJAP7LyxjwZWFxHh7MtKzB5MkLTbUM+d5c3sYtQAWD4Fzf8htO4HjqrRUaSa96qP8CvQF7IrYRUpeCq7OplxPNudaUjZdG93qPg5yBnfl5ZX8cPoH8nR5mKpMebbNs0xoPoFCfSEv73uZw7GHeW7Pc3zW/TMGNxhc5djuduadm0diXiJeVl61N970HkAh3Q2NhkYkMzMTW1tbMjIysLGpermZQCAQCErRGXRcTr3MifgTnEw4ycnEk2QVZlVpLQUKbExtSsrp7UztsDezJ0ebw4mEEzeJcgATpQmBLoG0d23PmCZjyhzpAvD9rqt8u+sKAJ525swa2Ypeje88AiijIINJ2yYRmh6Kl5UXSx5YYtyMo5G5EJPBC3+cJiwpBzONkoNv9MXZ+vZzkQ2Sgad2PsXRuKM0sW/C8qHLMVVVbpay3qBnzZU1/HD6h5KSz4caPsTLQS+X+29SEZJyk3j70NscjTt62+OKN30MkuG2x7maOfL70JVVyuaM+eUIx8JTee/B5jzZveyJBGXx7vrzLD8ayfA2Hnw3rvobF1OXnGBXcALvDmnGtJ5lbzj8dv63knGLAJ5WnkxqMYmHGz2MubpUsEVlRvHolkfJKMjgQf8HmdV9VvWd+w/PgR0z5TGBU3eD293VKnIhJoMH5xzCRK3k+Lv9yzQKfH7laf4+G8sbg5vwbO+iCptigzwLR3n8n0PlN4Oqit6gZ2fETn49N4+r6aE3fc7axJopLafwWNPHsNBUfHOpVtj/Jez9FJRq6PMOdHupUlUIWr2B5u9vQ6uXOPhGnwqbjBYTnhHOn1f+ZMO1DWQUZJQ8L+lNcTdtzmOBfejg1uGmyrBr6dd4//D7JV4cQa5BfNDlg5tGhGn1Wt499C5bw7eiQPY6Gde0apsQdzNhGWGM2jgKnUHHnL5zKrT5ey9TGR0qRLpAIBAIjI7eoOdq+lVOJpzkRPyJkhJbg2TAgAFJkpAkqeSxXpL7ym58k1QexaK8g2sH2ru1p7Vz6woLyoNXk3h73Xmi02ThMrKtJ+892Bz7O5SDJ+UmMWHrBKKzo/Gz8aOrR1dMVaaYqExK7m96rDRBo9Rgorr5XqPSoFFqsNRYGl3oS5LEwn/C+WLrRfRmV7B2vERnf0eeafcogc6BtxVeC84v4PtT32OuNmfVg6toYFtxAfpf0vLT+P7U96y7ug4JCUuNJc8GPsujzR69o5vvf/k37l/ePPAmqfmpqBVq3K3c8bD0wMPK4+bHlu64WrqiVqjRGXTk6nLJ1+WTp8sjX59Pfn4GuX+/wBfKDMJMNPiZObP04bWVLpFceiSc9zdcxM5Cw55Xe1eojSCvUE/HT3eRVaBjxdRO5WbmKsO8/df4bOtlegQ48fuTnW75/Pqr63n/sNwL3syhGVNaTqG/b/9yWxCOxh3l6Z1Po5f0vBL0CpNbTq56cNEnYOEgeRb6g99B+2qsVURUVhSnE08zwHfATRsMNcXHmy7x26HrDGnlxk+Pl90e89O+UL7cFsKwQA/mPFq08aIrlHvTEy+CtQdM3AhOATUaq9agZUvYFhacX1AyYtPSYODRXB1N+n7IvCurCC0S7Y5mjjzV+ilGNx4tt4PUBwx6WP80nP9T/ti3mzydwa5iFUtXE7IY8O0BLE1UnP9gEErlnTeYtAYteyP3sjpkNUfjSzf/3C3daWzfmKOxJ8g33DyCzVJjSTuXdnhYebD26lp0Bh2WGkteCXqFRxo/UqYfi0Ey8NnRz/gj5A8ApgdOZ3rg9Po5vrIGuJx6mWd3PUtSXhLdPbvzU7+f7pvXXh5CpN8GIdIFAoGg/qIz6MgoyJDL6wvSSsrs0wrSUCqUtHVpWylRXha5hTq+3nGFhf9cR5LA0dKE/z3UgmGt3W/7BiI6K5qJWyeSmFd+X35laGDbgH4+/ejn048Wji2q9eYlISuX6Wv+5GLGAdTWF1Cqc2/6fDOHZjzW7DEeaPDALV+7s0lnmbh1InpJz0ddP2JEwIgqx3Ej55POM+voLC6kXADA18aXl4Nepq933zu+Vr1Bz7xz8/jl7C9ISATYB/B1r6+rtXlAZizxqx9nvCKeeLWalhp7fhu5CQuzir8X0OoNDJtziMvxWYxp78WXj9x5pNX609G8vOos3g7m7H+tT4VExJ0IT86h91f7UCrg6Dv9b6qUOJVwiid3PInOoOOZwGd4NvDZCn1vrby8kllHZ6FAwY/9fqSnV8/KB5aXBr/0lOeGtxgBjyyqllFcVmEW88/NZ1nwMrQGLW1d2vJjvx9vcnU2Njq9gc6f7SE5u4D5E9qXO89+b0gikxcdp5GLFbteuWFMY1YCLH0Iki6DpYss1F2aGT3OQn0hf4X+xcILC0s2QW3UFjyRFMdjmZnYjloMLYajN+jZcn0Lc8/MLTnO08qT6YHTedD/wXJbi2oVSYKzK2HL61CYDWa28gZPy5F3PHXTuVhmrDhNG287/nqu222Pjc2OZc2VNawPXV9SkaVAQU+vnoxpMoZuHt1QKVUcu57EuMV/4eAYRZeWaZxMuLUyrJdXL2Z2nomb5e0N+SRJ4pezv/DT2Z8AGNdkHG93ertOTVZrg8Oxh3ll3yvkaHNoZNeIeQPm1esKtNrirhPpc+fOZfbs2cTHxxMYGMicOXPo2LF8a/7vvvuOn3/+mcjISJycnHjkkUf47LPPMDO7cx+VEOkCgUAgADgdmcZba8+XjO/p19SFT0a0xN22/ExdfE48m8M2k6PNoVBfSIG+AK1BS4G+gAJ9QclzhfpCdAYdhYZCtHqtfG/QljzO1eail/Ql67pZutHXuy/9ffvT1qVthUzXDJKBM4lnWHhmPftjdoOq9E2kg5kDA3wHUKAvYEvYlpJ+bXtTe0Y1HsXYJmNxs3QjszCTMX+PISY7hgcaPMAXPb4waqbDIBlYd3Udc07PITU/FYD2ru15rcNrtHBsUeY5yXnJvHXgrZIM16iAUbzZ8U3jZFB1hYRteYmJyftIV6noIpnx44j1mNh6VXiJE+GpPPKLbBK15pkuJUZu5THu1yP8G5bKKwMa80I/42VVh8/9hzNR6bz/YHOmFJXex2bH8ujmR0nNT2Wg70Bm95pdYTEgSRIf/fsRa66swUpjxfIhy/G3q0S5tiTB6vEQ/DfY+8mO3Wa2dzytLHQGHeuurmPumbkl3zdqpVwl0dShKT/3/7laLRS3Y/+VJCYuPIa9hYaj7/Qv15gvITOfTrN2o1TApY8GY6a5QezmJMPS4ZBwXi59n7AB3Iznnr8rYhefHfusxMjTwcyBiU0eZeyBX7BMvQ6txsCo+Tedo9VrWXd1HfPOzSMpLwkAf1t/JjSfQB+fPvVjKkPKNVj3lDw2DmTDwSFf3tb5/ZsdIfywJ5Sx7b354pHyndRXBK/gi+NflLTDOJk7MTJgJKMCRt3iCJ6WU0jbj3cCcOmjQZiqFVxJu8Lx+ONcTb9KN89uDPIdVKnflX9c/oNZR2chITHYbzCzus+qP9UMRubva3/z/j/vo5N08hjPPt/V6Mba3cRdJdJXrVrFhAkT+OWXX+jUqRPfffcdf/75JyEhIbi43LrjsmLFCqZMmcLChQvp2rUrV65cYdKkSYwbN45vvvnmjtcTIl0gEAgExRTqDPy87xo/7r2KVi9hZapmRt9GPNrRp8w+VGORVZjFweiD7IrcxaGYQyV9wwB2pnb08e5DI7tG5OpyydXmkqPNIVdXdK/NJVeXS1xOPMlFb7YBFAYL+nr3Y1yLh2jv2r5E6Kflp7H26lpWhawiPkd2fFcpVPT16UueLo9DMYfwtPLkz2F/Ym1SfXfkssguzGbhhYUsvbSUAn0BAMP8h/FCuxduykQdizvGmwffJDkvGXO1Oe91fo9hDYcZPZ7z/37Pk8HzyVMqGFxg4PNB81H5VtyB+c0151h1IoomrtZseqE7mnJmM0ek5NBr9j4UCjj0Zl887YxXqr34n+t88PclAr1s2TCjO7naXMZvHc+VtCs0c2jGkgeWVHpjQ6vXMm3nNE4mnMTH2ocVQ1dga1pBoX1sPmx5DZQaeHIHeLarwquCI7FHmH1iNlfTrgKyGeTrHV7H1cKVp3c+TUp+Cr42vswfMN+oLtHFvPTHaf46E8uELr589HD5vfSSJBH0yS5ScwrZOKMbrb3sbj4gNxV+HwFxZ8DMDsavr/LXpJjkvGRmHZ3FzghZQLpYuDCl5RRGBozEfPtMOL5ALrN/9rBszFYGebo8/rj8B79d+K2kvUipUBLkGlRS2XOn7HCNotfKfeoHv5InA9j7wcgF4N2hzMOf+f0k2y7G39YnYsnFJXx14isAOrp1ZFzTcfT27n3b9pt2H+8kNaeQTc93p6Vn1Tab/su269t4+9Db6Aw6Wjm14n9d/kcThyZGWbs+IEkSv134je9PfQ/AA34P8En3TzBR3Tuzy6vLXSXSO3XqRIcOHfjxxx8BMBgMeHt78/zzz/PWW2/dcvyMGTMIDg5m9+7dJc+9+uqrHD16lEOHDt3xekKkCwQCgeC/XE3I4s215zgVmQ6AhYmKUe28mNTNj4bOVXcMrgj5unz+jfuXXRG72Be9r0J9+cVIelN0WS3o4dGfb4aNxvo2FWU6g459UftYeXklx+KPlTyvVqhZ+sDSmpmT/R/isuP4/vT3bA7bDMgO+hOaT2Byy8ksD17Oz2d/xiAZaGTXiK97fV25TG4lOXxpNc8d+xidAsZl5vBO0CsoujxXofLstJxC+n69j7Rc7W3N277eEcKcPaHl9o7/l0J9IVuub2HZpWUUGgp5p9M7dHbvXOaxydkFdJq1G71BYtcrPfjx4vvsjtyNo5kjfzz4R5WFVmp+Ko9uepTYnFi6uHfhp/4/3bmyI+4sLOgP+kIY9Bl0KXum8e0Izwjn6xNfl0yHsDGx4dk2zzKmyZgSMRWRGcFTO54iNicWVwtXfh34a5Wc+ssjp0BH+092kafVs/7ZrrS9jXM+wBMLjnIoNJnPR7ZiXEefWw/Iz4Blj0D0MTC1gSfWgnf5laLlIUkSm8I28cXxL8goyEClUDGl5RSeDnxabl8J3QXLRskHj18PDfvecc2swixWhaxiR/gOglODb/pcS8eW9POVBXu1WkyqQ8QROaueEQkKFfR+C7q+AJqbf8f1/WofYck5LHuyE90Dbq2u+O38b3x36jsAnmr9FDPazKhQBnz0L4c5Hp7G9+Pa8HCbWx3+q8rh2MO8uu9VsrXZqBVqJrSYwDOBz9SK10JNojPo+OzoZ6y+shqAyS0m81LQS/d8WX9luWtEemFhIRYWFqxZs4bhw4eXPD9x4kTS09PZsGHDLeesWLGCZ599lh07dtCxY0fCwsIYOnQo48eP55133rnl+IKCAgoKCko+zszMxNvbW4h0gUAgENyEwSCx9lQ0vx26zuX40tLxXo2dmdK9AT0DnGrc9EZn0HEy4SR7o/aSmpeKhcYCS42lfK+2RIEp+y9ncigkC73ODCtFA2aPCmJgi8oJsqtpV1l5eSX7o/bzdODTjGkypoZeUdlcSL7A7OOzOZV4CpDn0msNWgCGNxrOO53eqZU3rduu/sUbh99DAp5NS2e6Rx94aE6FyrRXH4/ijbXnsDBRseuVXnj8J0uuN0h0/2IPcRn5/PhYWx5sXf7otczCTP4M+ZPlwctLSpGLGRUwilfbv1pmlcOkRcfYF5JE9w4nOJu9Bo1Sw8JBC2nj0qZCr788QlJDGL91PHm6PIY3Gk571/Zka7NLqjpufJxTmIkh5iRo81BYOKJwbQEoiv5XlLjuqxQqVEqVfF/0WK1Qo1QoS8Zf6SQdaoWasU3HMj1weplZ/IScBJ7a+RRhGWHYm9rzy4BfaO7YvFqvt5i1J6N59c+zNHCyZM+rve748/7p5kvMP3idiV18+bC8rHtBFqwYCxH/gMYSHl8Nft0rHFN8TjwfHvmQQzFyIqqpQ1M+6voRzRyL+tzz0uCnLvK4uw7TYOhXFV67mJjsGHZH7GZ35G5OJ55GolQa+Nr44mvjW6Zho6OZY83+TsxLh82vwoU18semttD8IWg9Bny7k6+XaP7+NgwSHHu3Hy7WNwv4X8/9ypzTcwB4ts2zTA+cXuFLv7X2HH8cj+KFfgG8MqCxsV4RIH8Pf37sc3ZF7gJkj4D3Or9HN8/b99TXV/J0ebyx/w32Re9DgYI3O77J480er+uw6iV3jUiPjY3F09OTw4cP06VLl5Ln33jjDfbv38/Ro2WPW/nhhx947bXXkCQJnU7HM888w88//1zmsR988AEffvjhLc8LkS4QCASCspAkiSNhKSw8FM7uywkU/5Vs6GzJ5G4NGNnOEwuTO/eMGxODQWLNyWi+2HaZlBy5v3xwCzc+eKgFbrZGmGtdB0iSxO7I3Xxz8huisqIwU5kxs/NMHm70cK3G8UfwSj49NguAd5NTGZedCzZe4NBAvtn/576oP9ZgkBgz7wgnItIY3MKNX8bf7AK+LySRSYuOY2eh4eg7/TBV32rQFZsdy++Xfmfd1XXk6mSzPxdzFx5v/jix2bGsCllV8tx7Xd67ZXzRhjMxvLp5KeZeKwD4pNsnRvv67YrYxcv7XjbKWhWll1cvXm3/6h2zt2n5aTyz6xkupVzCUmPJj31/pL1b+2pfvzgzXlH/gHWnonll9Vk6+jmw+pku5R9YmAMrH4Xr+0FtDo+ulOeC3waDZGDNlTV8c/IbcrQ5aJQapgdOZ1LLSTeXaa+dKjujOzaCpw+CSfVGrCXnJbM3ai+7I3dzNO4oOoOu3GNNlCa4W7njbe2Nv62/fLOT7yvcJlERzq2GXR9CZnTpc9YeJDUYxoTjfsSbNeTU+wNLNgz+a9b2fNvnear1U5W65K8HrjFry2UebO3Oj49Vr02hPPZG7uXTo5+SkJsAwAMNHuCNDm/UmN9CTZCan8rzu5/nXPI5TJQmfN7zcwb4DqjrsOot97RI37dvH+PGjeOTTz6hU6dOhIaG8uKLLzJt2jTee++9W44XmXSBQCAQVJWIlByWHI5g9YkosgvkN6vmGhXeDua42pjhZmOGm61ZyWNXGzNcbU1xtjI1WobpTFQ6/9t4kbNR6YC8WfDhQy3LLO28G9HqteyJ2kMzh2b42JRRMlwL/HTmJ34++zMKCaanp9OoUIuD3oC9QY+j3oC1wUBJ0aaVK3i2R/LuxEXzAB75OwatKosnezlgb5NHQm4CeknP8Wt5hCXq6ejjydigJlibWGNtYo2NiQ052hxWXF7BjvAdJQaCAfYBTGoxiQf8HigxlDoRf4L/Hf4fkVmRAAxpMIS3Or5VMjruZPw5Jm6dhEKp5QHvcXzZ912jfl1WXV7FlutbMNeYY6m2xFJzw01tgeXVXVhc24saJVLvN8EpQB6vWJSJLbkvGrOoN+jl+xse6ww6DJKBQOdAOrpXvBQ8uzCb5/c8z4mEE5iqTPmm9zdVc6QvIjm7gA6f7kKS4MDrffBxvLPYDY7L5IHvD2Jtpubc/wbe/mdemwerxkPoTlCZwsh5sgP+f9Ab9FxKucQ3J7/hRIJsoBboHMhHXT+6tf3jwjpYMxkUSnhyJ3hVf6PiRjILMzmfdJ7YnFjisuOIzYklNlu+JeUllZiwlYWDmUOJcG9g2wAvay88rDzwsvKq2qx2gwEiD8uC/dJfcitBEVFqX7x7TYSWjyDZ+TDn9Bzmn5eN814OepkpLadU+nK7gxN4cskJmrnbsPXFHpWPt4LkaHP48fSPrLi8AoNkwNrEmleDXmVEwIh6Xyp+Ne0qL+59kaisKGxNbZnTdw5tXdrWdVj1mrtGpFel3L1Hjx507tyZ2bNnlzy3bNkynnrqKbKzs1Eqb/8NLXrSBQKBQFBZsvK1rDkZzeLD4USk5N7xeCcrE7o2dKJ7gBM9Apxu6xhfHsnZBXy57TKrT8jZIytTNS/2C2BiV79yHacFVUOSJD49+mlJ5vq/qCWwMxhw0OmwkgykqFQkqlTk3eE9R0Xo7N6ZSS0m0dWtEwqDFnQFsnlWfjpkxZGfEcVPEZtZkn4eA+AgKXk7VyIoM41xbvYkKiTssl0Z6jKTt0b3rnY8FUKvhb+ehfNy/ylDvoKO02rn2jeQr8vn9f2vsy96H2qFmhltZ9DZozMBdgGVNqsqbl9o6WnDpufLFmUGyUBKXgoF+gL0kp48bSEP/XgAnaRj3vi2OFqp0Ut6TFWmeFl7YW9qf7Nw1xXAn5MhRPZkIPAxpMGfEVaQwtG4oxyLP8bx+ONkFmYCYK4254W2L/Bo00dvHZWWFQ8/dZbL3Xu+Dn1nVur1VhetQUtCTgKx2bGEZ4ZzPeM6YRlhhGWElRhUloedqR2eVp4lot3DygNnC2dsTGxKb6Y2WKgtyt740BXA1R1c3vEbDVIPYqqQN1Al4DvvxixU5wPwevvXmdBiQpVeX/GYQ1O1kuCPBhtldOLtuJh8kQ+PfFjiDxDoHMiogFH08u5VP5z3/8O28G28/8/75Ony8LTy5Kf+PxnVH+Je5a4R6SAbx3Xs2JE5c+SeEYPBgI+PDzNmzCjTOC4oKIj+/fvzxRdflDy3cuVKnnzySbKyslCpbj/vUYh0gUAgEFQVg0EiLDmbuIx84jPyScjMJz4zn/iMgpLHydkF/Pcvq7+zJT0aOdE9wJnO/g5Ym5WWq+YV6knOLiAxq4CkrAKSswuITstj+dEIsvLlN58j23ny1uCmuNjcnaXtdwN6g56ll5ZyJvEMqfmppOankpafRpY267bn2er1uOr0uBTdu+r1oDQjVYIclYICjYospYIshYJMJWQpoEABfQoMTMzKpVlBgSw6bhjJVxYXTEx4z9mBUBNZfNro9WSqVPgXalkWG4+1JCE5NELh2xl8uoJPZ3Dwr9ac8jIpzIHVE+WMsFIND/8EgWONe41KoDVoef+f99kUtqnkObVSTYBdAM0dm5fcAuwDZJO1cpi29AQ7LyXwUr8AnujuQHhGOJFZkYRnhhOZGUlEZgRRWVEl0wkqgoXaAi9rL7ysvPC09sTLygsvS3ecL2wg+NKf/GtuyjELS1L+s9djqbGkm0c3Xgp6CW9r71sX1uvgj0fh6g5waw1Td4O6/jho52pzbxLt4RnhxGTHEJsTWyljTJVCVVJ9Ymtqi7ule+nX0cqLOTtSuBCSza/t4+mcvYuvMi+y1FZuR3krJZXHzXzlHvZmD8mz6m/3s2AwyBtjOcmQnYA+K4HP/tyPg5TOhNYWWBWmQHaC7Npv4QD2vmDnK7vP2/vJj+18bjG2qww6g47lwcuZe2ZuycQPpUJJW5e29PPpR1+fvnhaGc/Erqox/nDqBxZdXARAJ/dOzO45u6S6R3B77iqRvmrVKiZOnMi8efPo2LEj3333HatXr+by5cu4uroyYcIEPD09+eyzzwC5x/ybb77h119/LSl3nz59OkFBQaxaVfYO+I0IkS4QCASCmqRQZ+B0ZBqHQpM5FJrM2ah0DDf8pVUpFTRxtSZPqycpq6CkjL4sWnjY8NHDLQjyrX+ZlPuFQn1hiWBPzU8lS5uFo5kjrhauuJg7Y5YexYV/txN8dAcdVFfwU9w+i1hhTKzBxh2s3cBavtdaubAg+wq/xh1AJ+mxUZmx3KodujOHaUTkrWvY+UCXGdBuAmiMYMSXmworxkD0cbm3euzvEFD3/acGycDy4OUcjD7IpdRLZQpBtUKNn60fGqUGg2TAgAFJkjBIBvQGA+Ep2UgYsDDPI1+fV8ZVZJQKJaYqU9QKNSqlitwCiXwt2JiZ4mhpjkqhIlubXTLDvCKYGgy0NXOlU7MxdPTsSnPH5mU76mcnwqklcGKx3J+tMoWn98sC9C4hqzCL2OxYWbQX3UdnR8s/W4VZZBZkklmYWWIkeSckSYGjmTPOFvaEpIUAMFPhzNjws3BjP71jI2jygDwiMDel6JZa+jgvVR75Vl2s3OSfW40FqE3lnxO1qfzzV/yxxhyaDQOPNmUuEZ8Tz1+hf7Encs8tzvvNHJrRx6cP/Xz6EWAXUONmpjeSlp/G6wde52ic3I48ucVkXmj3wp2nPwhKuKtEOsCPP/7I7NmziY+Pp02bNvzwww906iSPKunduzd+fn4sXrwYAJ1Ox6effsrvv/9OTEwMzs7ODBs2jE8//RQ7O7s7XkuIdIFAIBDUJhl5Wo5cS+FQaBKHriYTXka5vKlaibO1Kc7WpjhZyfftfOwZ0dYTVQ2XWQqqjyRJPLnkBHsuJxLkUEhOWjwmKgXLn+yItZkakCgtryi6V5ncfFOb3vCx5rZZvytpV1gdsprhjYbT0qklH2y8yPrDF3iuUTJP+SZC5BGIOQXFQsfSGTo/Cx2erJBzfZlkxMCykZB0WZ77/fifVRonVtNIkkRsTiyXUi4RnBLMpZRLXEq5RFpBWoXXUCqUeFh64Gvji4+NT4nDua+1L+5W7jeJkgUHw/hkczCDWrgyb3xpT3iBvoDY7Fiis6KJzo4mJksWo9FZ0STkJuBv609H5zZ0jDhJ4Pm/MQFwaQEjfwW3G5ziJQki/5VnoF/aUPpvau4AD3whO53fY0iSRL4+/ybRnlaQVirqs6KJzIwmLD0ShbJUzCtQ8H6X93mk8SOyAL+yTf6aXdsjjwesCKY2YOUCVq6cSNFwPt2UVk0CaN+iqfy8hSPkJEFaBKRHyPdp4fLjwuyKv0ilGvp/IG+i3eZnPTY7lj2Re9gduZtTiadu8gHwtPKkh2cPenr1pKN7x9tWilSXSymXeHnvy8TmxGKuNuejbh8x2G9wjV3vXuWuE+m1iRDpAoFAIKhLolJzCY7LxNZcUyLMrUzVtZoRERifqNRcBny7n3yt/Cb6oUAPfni0dkyUzkSlM3zuP5hrVJyY2R9LUzUU5sLZFfDP95BelGU3tYVOT0Gn6WDpWPELJF2B30fI2VtrDxi/7q7K3kqSRHxOPNczriMhoVAoUCqUKFGiUCj4df919lxO4oGW7rzSvw1eVl4V7mk/HJrMYwuO4uNgwYE3bu/YXi6Xt8DG5yE3Wd6k6TsTgibLru3Hf4PEi6XHenWADlOh+fBqlVbf7ZyMSGPUz//gaqfj1ycbEJ0Vjb+tf+louhvJz5RbA64fALWZLLQtHOR7S6eijx3ljY8b2gZmb7/M3L3XeLyTD5+OaHX7gCRJ3hhID5crHnT5chuLNk++1xXf50PCRTkegMYPwPCf5HjuQFp+Gvui9rEncg+HYw9TaCjdeDBTmdHJvRM9vXrS06snbpaVG8t5OzZe28hHRz6iQF+At7U33/f5ngD7O08/ENyKEOm3QYh0gUAgEAgENcHcvaHM3i6X3C6f2olujWrHgV+SJPp8tY/wlFy+G9uG4W1v6FvVa+HCWjj4DSTLsaGxgKBJchbP9g49rtEnYfkjcjmwYwCMXw92ZfRJ36UYDBIdZ+0iObuQZU92qvTUhLScQtp+vBOA8x8MvMlvolJkJ8HfL0DIFvljharUp0BtDq1HQ/snyy2Rvt9YeSySt9edp2djZ5ZOqZmKjuIRe539HfjjqduM2KsskgQnFsK2t0FfALbe8MjCSlWm5GpzORZ/jAPRBzgQfaBkjFsxAfYBdPPohpeVF66WrrhYuOBi4YKDmcMdXeO1ei0ZhRlkFGSwKmQVKy+vBKCHZw8+7/k5NiZCP1WVyuhQ0UQgEAgEAoFAYASm9fDnRHgqGpWSLv6VyFRXE4VCwcNtPPl+91XWn465WaSrNBA4DlqNgcub4ODXEHcG/v0Jjs2XDbA05rJwv+neXO55PvsHaHPAox08vqZyGfi7gNNR6SRnF2JtqqZjg8p7P9hbmuBmY0Z8Zj4h8Vm096uif4SVM4xbAaeXwba35NJph4Zy1rzNo2AujLluJCReNnRs4mpVY9do6CyvfS0px7gLKxRy64lXe/hzEqSGwaIHKlT+XoyFxoLe3r3p7d0bSZK4knaFgzEHORB9gLNJZ7madpWraVdvOU+tVONi7lIi3AEyCjJKbukF6eTqbm3JeibwGaYHTq/3Y+HuJYRIFwgEAoFAIDACJmoliybXTZ/28LaySD8UmkxSVgHO1v/pT1Uqi5yuh8k9uge/gYhDkBJ658X9+8DYZWBac4KortgVLGcgezVxrvJow2bu1sRn5hMcl1l1kQ6yOGs3Hhr1g4xo8Gwv/7sJbuFKgizSG7ta19g1/J0tAUjKKiAjT4uteRWrJMrDPRCe2g9/vwgX18GOmRB+CIb/XKHy92IUCgVNHJrQxKEJU1tNJT0/nX9i/+FM4hkScxNJyE0gITeBlLwUdAadPO8+J/b2a6LAxtQGVwtXZrSZQR+fKrZyCKqMEOkCgUAgEAgEdzkNnCwJ9LbjbFQ6m87FMrlbg7IPVChkEdioHyRflU2wtLly76w274bHRfdWrtBuYr0a8WVMdl6SRfqA5q5VXqOZuw17Q5K4FJdpnKBsPOSboFyKRXoTt5oT6dZmGlxtTEnILCAsKZu2PjVQzWBmI5e6N+gBW9+Sze5+6QGjF1XZmNHOzI6h/kMZ6j/0pue1Bi3Juckloj0xNxEFCmxNbbE1tcXO1K7k3kpjhUp5+7HWgppFiHSBQCAQCASCe4DhbTw4G5XOX2duI9JvxClAvlWRAp2eZf9G0sbbjiDfu68c+3pyDqGJ2aiVCno3canyOs3c5d7SS3FZxgpNcBuSswtIzi6U95tcara6w9/JioTMAq4l5dSMSAd546z9FLly4s9JkHpNLn8f9Bl0nFah8veKoFFqcLdyx93K3SjrCWoWUUMjEAgEAoFAcA/wYGsPVEoFZ6PSuZ5s5D7a/6DVG3hu+Wk+3nSJR+f/y5FrKTV6vZpgV1EWvZO/Q7VKmZt7yCI9JD4TveG+8mOuE4qz6D4OFliY1Gy+saGLXPIellSJ8WpVxb21PPe+xUh5xvvW12HjDNkVXnDfIUS6QCAQCAQCwT2As7Up3Ysc5f86HVNj19HpDbz0x5mSfu5CnYFpS09wISajxq5ZE+wsir9/s6qXugP4OVpiplGSrzUQnlKzmyMCuBJf8/3oxZSax9WCSAcwtZbL3wd+AgqlbCS4aAhkxtXO9QX1BiHSBQKBQCAQCO4RhreVe5k3nImhJqbsGgwSb6w9x+bzcWhUCn55IohODRzILtAxadExwms4g28s0nIKORGeClRfpKuUCpq4ydn0YGP1pQvKJSRBFsxNalWk1+L3tUIBXZ+XpymY2UHMCfi1N0Qdr70YBHWOEOkCgUAgEAgE9wgDm7thrlERnpLL2WjjZrYlSWLmhgusOxWDSqlgzqPtGNzSjfkT29Pc3Ybk7ELGLzxKYma+Ua9bE+y5nIhBgqZu1ng7WFR7vebusmAUIr3mKXF2r0HTuGIaFvW8R6TkoNUbavx6N9GoHzy1F1yaQ3Y8LB4Cp5bWbgyCOkMYxwkEAoFAIBDcI1iaqhnYwpUNZ2L563QMbbztjLKuJEl8tOkSK45GolDAt2PbMLilGwA2ZhqWTOnII78cJiIllwkLj7HqqS7YWhh5ZJURKS7VH1gNV/cbKTGPixUivSaRJKmk3L02MunuNmaYa1TkafVEpebi71zLYwgd/OHJnfDXMxD8N2x8HuLOwuDPQVXHP1/5GfLYuPjzcmk+Cvn+ppsClGowswULR3m0nIUjmDsUPXaQH1u7g8asbl9PPUOIdIFAIBAIBIJ7iOFtPNlwJpaNZ2Np62NHG287fBwsUFTRJVqSJGZvD2HRP+EAfDmqNQ8F3jwizNnalN+ndGLUL4e5HJ/Fk0uO8/uTnTA3qX9jnPK1evZfSQKgv5FFerBweK9R4jLyySrQoVYqaOBkWePXUxZd51JcJmFJObUv0gFMrWD0Ujj4Nez9BI4vgMRgGL0ErJxrPx6A1DBYMQ6SQ4yznkIFzk3ArbVsoOfWGtxagbmdcda/CxEiXSAQCAQCgeAeonuAE05WpiRnF/DiH2cAsLfQEOhtR6CXHW187GjjZYe9ZcVmn/+4J5Sf9l0D4OPhLRnd3rvM43wcLVg6pSNj5h3hREQaM1ac4pfxQWhU9au78khYCrmFelxtTGnlaWuUNZsWlV7HZ+aTllNY4a+toHIUl7r7O1tioq6d76uGLlZcisvkWlI2/THOpk6lUSqh1+vg1hLWToOIf2B+H3hsNbg2r91Yrh+E1eMhL03OgA/6FDSWIBnKvum1kJ8OuamQlwq5KTc8Lrrp8iDxknw790fptex8i0R7oCzinQLk6gK1ae2+5jpAiHSBQCAQCASCewiNSsmiSR1Yeyqas9HpXIzJJC1Xy76QJPaFJJUc5+toQSNnK7zszfF2sMDL3qLkcfFIsvkHwvh65xUAZg5txvjOvre9djN3GxZO6sATC46y+3Iib649x1ePBKJUGmfWszHYeanU1b2q1QX/xdpMg4+DBZGpuQTHZdK1yGVfYFxK+tFrodS9mIbOcsa+1hzeb0eTB2DaHlg5Tp6nvnAQjF4EjfrXzvVPLobNr8oj4jzawbgVYFPNueuSBFlxEHcO4s/J5fzx5yA9EtIj5Fvw36XHK5Rg5wOOAbJod2xY+tja3Whz5esaIdIFAoFAIBAI7jFaednSykvOEhfqDATHZXI2Op0zkemciUonLDmHiJRcIlJyyzzfxkyNu605IUWi6LWBjZnaw79C1+7g58BPj7fjqd9Psu5UDA4WJrw7tJnRBHF1MBgkdhf1ow8wUql7Mc3crYlMzeWSEOk1Rkh87Tm7F1MnDu+3w7kxTN0Fq56QM+rLx8CQ2dDhyZq7pl4HO96Fo7/IH7ccBQ/PBY159ddWKMDGQ741GVz6fG6q3O8ef06+T74KKaFQkAlp4fItdOfNa03aAn7dqh9TPUCIdIFAIBAIBIJ7GBO1Ui5197ZjQhf5uYxcLedjMohIzSEqNY/otFyi0vKIScslObuQzHwdmfmyQH+uT0Nm9A2o1DX7NXPly1GtefXPsyw4dB1na1Oe7tXQ2C+t0pyPySAhswBLExVdGjoade1m7jZsv5gg+tJrkOJMekAdiPTQxGwkSaoXm01YOMD49fD3i3B2JWx+BVKuwcCPQWlkH4i8dFgzBa7tlj/uMxN6vlbzGWsLB/DvJd+KkSTIToSUq6WiPfmq/HFaODg2qtmYahEh0gUCgUAgEAjuM2wtNHQPcKI7t2Z8cwt1RKfJwt3GTEOQr32VrjEqyIvUnEI+3RLMZ1sv42Jjyoi2XtUNvVoUu7r3auKMqdq4YqbUPE44vNcEeoPE1cQiZ/daGL9WTAMnSxQKyMjTkppTiKNVPemHVpvC8J/lcu89n8C/cyHtOoxaACZGMtVLuQYrxsoiWGMBI36B5g8bZ+2qoFCAtat88+t+8+d0hXXveG9E6peTh0AgEAgEAoGgTrEwUdPY1Zq+TV1p7+dQrczhtJ7+TO3eAIDX/zzHwatJdzijZrmxH93YNC8S6VcTsyjU1fJM7fuAqNRc8rUGTNVKfIww276imJuo8LCVy7rDkutJyXsxCgX0fB1G/QYqUwjZAosegMy46q0rSXBxPczvKwt0G0+Ysq1uBfqdUJvcM/3oIDLpAoFAIBAIBIIa5J0hzUjMKmDj2Vie+f0kq57uQksjuapXhqjUXC7HZ6FSKujb1MXo63vZm+NgaUJqTiFrTkbzWCcfo1+jNjkfncGKY5GolGCqVmGmUd50b6pWYqZR0SPAqVayyyElpe5WqGrZiLChixUx6XlcS8ymg59DrV67QrR6RDZTsolucwAAI11JREFUW/mobLw2vy88tkp2Rq8sUcdg+7sQfUz+2LM9jFsO1m7GjVlwW4RIFwgEAoFAIBDUGEqlgtmjW5OcXcDhaylMWnScddO74uNYe9lQKM2it/e1x87C+CPSFAoFM/o04qNNl/hy+2UGt3TD4S4dxSZJEq+vOcvl+Dv31ztamrDlxR642pjVaExX4mvf2b2Yhs6WHLiSVD8c3svDu6NsKLdirDy/fOEgaD0GWj4Cvt3kMW63IzUMdn0Il/6SP9ZYQNcXoPvLoKnZf1vBrYhyd4FAIBAIBAJBjWKqVjFvfBDN3G1Izi5g4qJjpGQX1GoMu2rI1f1GJnTxpambNem5WmZvv1xj16lpLsRkcjk+CxO1khf6BfB0L38mdfXj0Y4+jGznydBW7vRv5oKnnTkpOYW89McZ9AapRmMqzqTXprN7MfXO4b08HBrAkzvAvzdoc+WRaUsehG9byNnx2NNyKfuN5KbCtnfgx45FAl0BbcfD86egz9tCoNcRIpMuEAgEAoFAIKhxrM00LJncgRE/HeZ6cg5Tlpxg5bROWJjU/NvRjFwtR6+nAjUr0tUqJR893JIx847wx/EoxrT3pq1P1Yz36pLVJ6IAGNzCjVcGNC73uLCkbB6cc4gjYSn8vC+00lMAKkPJjPRaNI0rplSk1+NMejHmdvDEegg/AOfXwKWNkBULR36Ubw4NodVoub88bC/s/xLy0+VzG/aFAR+DW8u6fAUCRCZdIBAIBAKBQFBLuNiYsfTJjthZaDgblc6MFafR6WvWZC0lu4Cf9oeiN0gEuFjh62gk5+ty6NjAgZHtPJEkeH/DxRrPMBubfK2eDWdiABjT3vu2x/o7W/Hxw7Kg+3bXVY6Hp9ZITIU6A2FFWey6yaTL3zNRqbkU6PS1fv2KIkkSF2Iy+Hz7FZ49Yk1c79nw+lUYtwJajAC1OaReg/2fw89dYPs7skB3aQ5PrJXHugmBXi8QmXSBQCAQCAQCQa3R0NmK3yZ24PEF/7LnciLvrD/PF6NaG3X+dGpOIdsvxrP5XBxHwlJKhPLglrVjfvX2A83YeTGB8zEZrDwWyROdfWvlusZg+8V4MvN1eNqZ07UCs+RHBXlxKDSZ9adjeHHlaba82MPoPf/Xk3PQGSSsTdW429Z++bWztSnWpmqyCnREpOTWSV98eUiSRHBcFpvPx7L5XBzhKbkln8su0LNkcgcUTYdC06FQkAWXt8CFNRC6GyydoO9MaPN4peerGwwSH/x9kdj0PNr62BPka0+glx3mJkae036fIkS6QCAQCAQCgaBWCfK1Z86j7Xj69xOsPhHN9eQc7CxM0KgUaFRK1EolGpUCtUqBWqnEVKPE0dIER0tTHK1McLKS7x0sTUrmnacVC/PzcRy+lnJTBruVpy1DW7szoUvtiGVna1NeHdiYD/6+xOztITzQ0q3+zNe+A3+eiAbgkSAvlBV0Uf94eEtOR6YRnpLLm2vP8csTQUbddAm5odTdmOtWFIVCgb+LFWej0rmWmF3nIl2SJEISsth8Lo7N5+JuGg1nplHSq7Eze0OSOHAliQ1nYhne1lP+pKk1BI6Vb9o8eWzbnQzlymHNyWiWHokAYFdwIgBqpYIWHja087Wnva8DQb72uNXBpsq9gBDpAoFAIBAIBIJaZ0BzVz4d0Yq3153neHhaldexMVPjYGlCVFreTcK8hYcNQ1u7M7SVe42XuJfFE519WX0imktxmXy5LYQvHqnCOKxaJio1l3+uJQOySK8oVqZq5jzajpE//8P2iwks+zeC8V38jBZXXTq7F9PQ2VIW6XXYl56v1bPuVAyLD1/nSkJpHCZqJX2aOPNgaw/6NnXB0lTN3L2hzN4ewkebLtGzsfOtkwY05lWOIzNfy5dFxogPBXqgMxg4EZ5GYlYBZ6MzOBudwaJ/wgHwcbDgy0da09n/zlUZglKESBcIBAKBQCAQ1AmPdvShmbsNVxKy0OoN6PSSfG+Q0OkNaIs+ztcaSMstJDm7gOTsQlKyC0jJKURvkMjM15GZrwOguXupMPdzqn1hfiNqlZKPh7dg1M9HWHUiirEdvWlXz03k1p6KRpKgWyNHvB0qNyKvlZctbz3QjI83XeLjzcG093OgmbuNUeIqdXa3Msp6VaHYPG7NyWiaudvQt6lLrWX1k7ML+P1IBMv+jSAlpxAAE5WSXk2cebC1O/2auWJlerOsm9bDn41nYglJyOLTzcF8PSbQaPHM2X2V5OxC/J0t+Wp0ICZqJZIkEZ2Wx6nINE5GyLfguEwiU3OZseI0O1/uif1dOpKwLlBI0n99+O9tMjMzsbW1JSMjAxsb4/ziEAgEAoFAIBDULgaDREaelpQcWbi72ZjVuTAvi9f/PMufJ6Np4WHDxhndUVWwhLy2MRgkeny5l5j0PL4f14aH23hWeg1JknhyyQn2XE6kobMlfz/f3Sju/b1m7yUiJZcV0zrRtaFTtderCpdiMxkz7wjZBfKGUKCXLS8PaEyvxs41JtZDE7P57VAYa0/FUKiTDRY97cyZ3M2P0e29sTXX3Pb8U5FpjPr5MJIEy57sRPeA6n/triVlM+jbA+gMEosmd6BPE5dyj83M1zLyp8OEJmYztLU7cx9rV+3r381URocKd3eBQCAQCAQCwV2HUqnA3tKERi7WdPZ3rJcCHeDNB5piY6bmYmwmK45G1HU45XIkLIWY9DyszdQMalE1gz2FQsHsR1rjamPKtaQcPth4sdpx5RbqiEyVzdDqwtm9mOYeNhx4ow/P9GqIuUbF2egMJi06zqifD3PwahLGyntKksTha8lMWXyc/t/sZ+WxKAp1BgK9bJnzaFv2v96bqT387yjQAdr52DOhyLTwnfXnySusvjP9J5suoTNI9G3qcluBDmBjpuGbMYGolAo2n4tj49nYal//fkGIdIFAIBAIBAKBoIZwsjLl9UFNAJi9PYTk7II6jqhsimejP9zGAzNN1R26Ha1M+W5sWxQKWH0iumScW1UJTcxGksDJyqTOzfccLE1464GmHHyzD9N6NMBUreRUZDrjfzvGmHlHOFzUz19ZMvO1bLsQzzvrz9Pjy708Nv8oey4nolDI3g2rn+7CX891Y1jg/9u787gq67z/4+/DctiEg4CyCASWDqK4BC6omSWTOeZkm9qDzLTRqbBEf63TWN1TatqdP9MWx7ltvS3NqWyyqYnQwSgV1NRI1FzBZBGRXQQ51/1HecaTS6DIOcjr+Xich3Jd3+tcn4OfR/Hm+l7fK0xurk2Lb4/cGKNQi6fySmv0UvoPF1TfKWt3FmvtriNydzXpzyO7NeqYnuH+mnrdVZKkmatyVFxRe1E1tBVMdwcAAAAuoQarod+/nKnvD1fojvhwvXDHhd8fXFN3UpsOHJOfl7t+E+zbLI+8Kq+pV9/ZX6rupFX/mDpIPcP9L/o953+xSwvX7FE7Dzd9+tDgC168b+WmfD3y9+0aeGWg3p084KLrak7FFbV6LWOvlm3Ms01H7xlu0W+CfRUR4K2IAC9FtPdWZIC3Ovh62KbFW62Gcg6XK2PXEa374Yi25JXZLXro6e6i2+PDNWlQtDp3uPj78NN2FGny25vk6mLSJ1MHKzas6Rmo7qRVNy5Yp30l1ZoypLP+9LvGhXRJqm+w6pZXv1bOjxW67jcd9Po9fR2ySr+jNSWHsnAcAAAAcAm5upj07OgeuvXVb7Ry8yH1ivDXkC4dFBHg1aiwUlxZq/TcYqXtKFLmnhJbIHQxSVFBPuoW6qduIb4//Rnqp1CLZ5NC0D+2H1bdSatiQnwV18lywZ/zdA8N66L1+44q+8AxTX33W/39/kTb4/Iay2o1bFOkHf3Ys7Pp6Oepp0d11x+HXKlX/71Hy7Pytf1QubYfKj9jrIebi8LbeynE4qncgkqV/rwA3Cmdg3w0pGsHDekapAGdA5vlXv5TfhsbrBE9QvRZTqGe+HC7PnxgUJPXRnjzm/3aV1KtoHYeevD6q5p0rLuri+aP6a2bFmVq7a4jWpGdr3H9Ipv0Hm0NV9IBAACAFvDY37drxc/TyiWpvbe7ekX4q1e4v3pH+KtnuEWB7TxkGIb2FFfpix1F+jK3SFvzy3T6T+yd/L1UW99gW+n7lyxe7uob1V7Pju6hUMuvP2rr9y9navuhcs28KVb3Do6+6M95yuGy4/rdwq9UVlOvewZG6Znfd2/S8YvSf9CLabvl4eaiTx4c7JRB/XSF5bVav69E+aXHlV9ao/xjNcovPa6C8uOy/iJxtfNw08ArAzWkawdd27VDk1fTb6riiloNm5+hytqTeuqmWE1qwr/zkcoTuu6//62qEyc17/aeGpMQcUE1LFm3V7P/uVM+Zld9njrkkn9mZ9OUHEpIBwAAAFpA9YmTWrRmj9bvO6rcwxWqa7CeMSYiwEuuJpMOHK2x294r3KLfxgbrt7Eh6vrzo8iOVJ1QbkGldhZUKLegQjsLK7WnuEonf06EVwR6673JAxTmf+6gnltQoREvfSV3V5M2PDGs2e/7XrOzSJPe3CRJWnxXvG7s0bhF6TJ/KNH41zfKMKQXbu+pOy4wGDqD+garCspqlVdao8Plx3VFgLeuvqK93Jt4f/nFWrbxoJ78KEfeZld9MX2Iwts3LiQ/+vdten/TIfUMt2jVA4PkcoFPKGiwGhq3ZL2yDxxT/+gAvTd5wAW/V2tESD8PQjoAAAAc7cTJBu0sqNS2Q2Xaml+mbfll2nuk2rbf7OqigVcF6rexwUrqFqxgP89Gv++OwxV6aPm3yi89rsgAb703ZYA6nSOo/+WTHXr96/0a0SNEr90V3yyf7Zdm/zNXS9btk5+nmz596JpfvYJaWF6rkQu/0tHqOo1NiNDc23tekrraGqvV0NifQ/L1MR21dELCr94Wsf1QmW5+5WsZhvTB/QMVf0X7i6rh4NFqjXjpK9XUNTT7zA1nR0g/D0I6AAAAnFFFbb2255ertr5BA64MVDuPC78v+cey47pzyQblldYovL2X3ps84IxwXHfSqv6zv9Sxmnq9cU9fXRdz/kdqXaj6BqvG/HW9vs0rU68If638Y6LMbme/ilzfYNWdSzZo08Fj6hbqp48eGHhRq83D3p7iSv3upUzVNVg177aeuj0+/JxXsw3D0G2vfaMteWW6pU8n/f+xvZulhv/dcFB/XpUjDzcXffrQNbqq48Uvjtca8Jx0AAAAoJXx83TX4C5BSooNvqiALv103/qKPw5QVKC3Dh07rnFLNii/1H4K/Ze5RTpWU69gPw9d0yXoos53Pu6uLlp0Zx/5ebppW36Z5n2+85xjX/jXLm06eEy+Hm56LflqAnozu6qjrx647kpJ0qMfbFfCrC+VsmyL/nfDQe07UmX3vPdVW3/UlrwyeZtd9diNMc1WQ3L/SA3p2kEnTlr1/1Zu08mz3PbR1hHSAQAAgMtQqMVLy6ckKjrIRz+W/RTU80671/3Us9Fvuzq8yc/fbqrw9t62R8/9T+Z+fbmj6Iwxn+cUasm6fZKkF+7oqaigC3tsG87v/qFX6vb4cHmbXVVaXadPvyvQn1fl6PoXM5Q4Z41mrNiq9zfl6/nPfvplSsp1VynE0rjbLRrDZDJp3m09bb+0WbRmj9rY5O5fxXR3AAAA4DJWVFGrO/+2QfuOVCvU4qnlUwbI7OaiQc+vkdWQ1j48VNEtFIj/65Pv9cbXB2Txctc/p11ju1f+4NFq3bQwU5UnTuoPg6P155tiW6Setqy+wapt+WX6Zu9RfbO3RFsOlp2xmGFkgLe+mD7kksxo+OjbQ5q+YpskKSbEV8n9IzW6Tyf5ero3+7mcAfeknwchHQAAAG1N8c9Bfe+RaoX4eWrobzpoeXa++kUF6P37ElusjrqTVt2++BttP1Su+Cvaa/mUAWqwGrr11W+0o6DCtq2lVz6HVFvfoM0Hj+mbvSX6Zu9R5R2t0YJxvXVNlw6X5HyGYei/v9ilpZn7VVv/0y8HvM2uurl3mJL7X6EenSyX5LyOQkg/D0I6AAAA2qLiylol/22jfiiusm1zxOPN8o7WaOTCr1R54qTuu/ZKlR+v03tZ+QrwMevThwY36tnuuHyU19Trw28PadnGPO05rTd7RfgruX+kRvUMk5e59a9NQEg/D0I6AAAA2qojlSeU/D8btLuoSj5mV2U9mSSfi1yk7kJ89l2B7l+2xfa1ySS9PanfJbtqC+dnGIay9pdq2cY8fZZToPqGn2Kqn6ebkmKDNSwmWNd0DZJfK50OT0g/D0I6AAAA2rKSqhOa9WmuBl0VpNvjwx1Wx1Mf5+jt9QclSdOTumpaUheH1QLnUlJ1Qis3HdK7WQeVX3rctt3NxaR+0QG6PqajhnULbrG1FJoDIf08COkAAACA49XWN+ipj3Pk6+muJ3/X7ZzP60bbZbUayjpQqjU7i5WeW6S9R6rt9ncO8tF1MR01LKajEqICZHZz3rUMCOnnQUgHAAAAgNbnQEm11uws1pqdxdq4/6htSrwkvTGxr677TUcHVnd+TcmhLX8DCgAAAAAATRQV5KNJg6M1aXC0KmvrlflDidJ/DuyJnQMdXV6zIaQDAAAAAFoVX093jYgL1Yi4UBmGIZPp8rldwnkn7QMAAAAA8Csup4AuEdIBAAAAAHAahHQAAAAAAJwEIR0AAAAAACdBSAcAAAAAwEkQ0gEAAAAAcBKEdAAAAAAAnIRThPRXXnlFUVFR8vT0VP/+/ZWVlXXOsUOHDpXJZDrjNXLkyBasGAAAAACA5ufwkL5ixQrNmDFDTz/9tLZs2aJevXpp+PDhKi4uPuv4Dz/8UAUFBbZXTk6OXF1ddccdd7Rw5QAAAAAANC+Hh/T58+dr8uTJmjhxomJjY7V48WJ5e3vr9ddfP+v4gIAAhYSE2F5paWny9vYmpAMAAAAAWj2HhvS6ujpt3rxZSUlJtm0uLi5KSkrS+vXrG/UeS5cu1bhx4+Tj43PW/SdOnFBFRYXdCwAAAAAAZ+TQkF5SUqKGhgYFBwfbbQ8ODlZhYeGvHp+VlaWcnBz94Q9/OOeYOXPmyGKx2F4REREXXTcAAAAAAJeCw6e7X4ylS5cqLi5O/fr1O+eYJ554QuXl5bZXfn5+C1YIAAAAAEDjuTny5EFBQXJ1dVVRUZHd9qKiIoWEhJz32Orqai1fvlx/+ctfzjvOw8NDHh4eF10rAAAAAACXmkOvpJvNZsXHxys9Pd22zWq1Kj09XYmJiec9duXKlTpx4oTuuuuuS10mAAAAAAAtwqFX0iVpxowZmjBhghISEtSvXz8tWLBA1dXVmjhxoiTp7rvvVqdOnTRnzhy745YuXarRo0crMDDQEWUDAAAAANDsHB7Sx44dqyNHjuipp55SYWGhevfurc8//9y2mFxeXp5cXOwv+O/atUuZmZn64osvHFEyAAAAAACXhMkwDMPRRbSkiooKWSwWlZeXy8/Pz9HlAAAAAAAuc03JoQ6/kt7STv1OguelAwAAAABawqn82Zhr5G0upFdWVkoSz0sHAAAAALSoyspKWSyW845pc9PdrVarDh8+LF9fX5lMJkeXc14VFRWKiIhQfn4+U/PhtOhTtBb0KloLehWtBb2K1sIZetUwDFVWViosLOyMNdd+qc1dSXdxcVF4eLijy2gSPz8//sMHp0eforWgV9Fa0KtoLehVtBaO7tVfu4J+ikOfkw4AAAAAAP6DkA4AAAAAgJMgpDsxDw8PPf300/Lw8HB0KcA50adoLehVtBb0KloLehWtRWvr1Ta3cBwAAAAAAM6KK+kAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6U7qlVdeUVRUlDw9PdW/f39lZWU5uiS0cXPmzFHfvn3l6+urjh07avTo0dq1a5fdmNraWqWkpCgwMFDt2rXTbbfdpqKiIgdVDEjPP/+8TCaTUlNTbdvoUziLH3/8UXfddZcCAwPl5eWluLg4bdq0ybbfMAw99dRTCg0NlZeXl5KSkvTDDz84sGK0RQ0NDZo5c6aio6Pl5eWlK6+8Us8++6xOX3uaXoUjrFu3TqNGjVJYWJhMJpNWrVplt78xfVlaWqrk5GT5+fnJ399f9957r6qqqlrwU5wdId0JrVixQjNmzNDTTz+tLVu2qFevXho+fLiKi4sdXRrasIyMDKWkpGjDhg1KS0tTfX29brjhBlVXV9vGTJ8+XZ988olWrlypjIwMHT58WLfeeqsDq0Zblp2drb/+9a/q2bOn3Xb6FM7g2LFjGjRokNzd3fXZZ59px44devHFF9W+fXvbmHnz5mnhwoVavHixNm7cKB8fHw0fPly1tbUOrBxtzdy5c/Xaa6/p5ZdfVm5urubOnat58+Zp0aJFtjH0KhyhurpavXr10iuvvHLW/Y3py+TkZH3//fdKS0vT6tWrtW7dOk2ZMqWlPsK5GXA6/fr1M1JSUmxfNzQ0GGFhYcacOXMcWBVgr7i42JBkZGRkGIZhGGVlZYa7u7uxcuVK25jc3FxDkrF+/XpHlYk2qrKy0ujSpYuRlpZmXHvttca0adMMw6BP4Twee+wxY/Dgwefcb7VajZCQEOOFF16wbSsrKzM8PDyM9957ryVKBAzDMIyRI0cakyZNstt26623GsnJyYZh0KtwDpKMjz76yPZ1Y/pyx44dhiQjOzvbNuazzz4zTCaT8eOPP7ZY7WfDlXQnU1dXp82bNyspKcm2zcXFRUlJSVq/fr0DKwPslZeXS5ICAgIkSZs3b1Z9fb1d78bExCgyMpLeRYtLSUnRyJEj7fpRok/hPP7xj38oISFBd9xxhzp27Kg+ffrob3/7m23//v37VVhYaNerFotF/fv3p1fRogYOHKj09HTt3r1bkrRt2zZlZmZqxIgRkuhVOKfG9OX69evl7++vhIQE25ikpCS5uLho48aNLV7z6dwcenacoaSkRA0NDQoODrbbHhwcrJ07dzqoKsCe1WpVamqqBg0apB49ekiSCgsLZTab5e/vbzc2ODhYhYWFDqgSbdXy5cu1ZcsWZWdnn7GPPoWz2Ldvn1577TXNmDFDf/rTn5Sdna2HHnpIZrNZEyZMsPXj2X4eoFfRkh5//HFVVFQoJiZGrq6uamho0KxZs5ScnCxJ9CqcUmP6srCwUB07drTb7+bmpoCAAIf3LiEdQJOlpKQoJydHmZmZji4FsJOfn69p06YpLS1Nnp6eji4HOCer1aqEhATNnj1bktSnTx/l5ORo8eLFmjBhgoOrA/7j/fff17Jly/Tuu++qe/fu2rp1q1JTUxUWFkavApcI092dTFBQkFxdXc9YabioqEghISEOqgr4j6lTp2r16tVau3atwsPDbdtDQkJUV1ensrIyu/H0LlrS5s2bVVxcrKuvvlpubm5yc3NTRkaGFi5cKDc3NwUHB9OncAqhoaGKjY2129atWzfl5eVJkq0f+XkAjvbII4/o8ccf17hx4xQXF6fx48dr+vTpmjNnjiR6Fc6pMX0ZEhJyxsLcJ0+eVGlpqcN7l5DuZMxms+Lj45Wenm7bZrValZ6ersTERAdWhrbOMAxNnTpVH330kdasWaPo6Gi7/fHx8XJ3d7fr3V27dikvL4/eRYsZNmyYvvvuO23dutX2SkhIUHJysu3v9CmcwaBBg854jOXu3bt1xRVXSJKio6MVEhJi16sVFRXauHEjvYoWVVNTIxcX+8jg6uoqq9UqiV6Fc2pMXyYmJqqsrEybN2+2jVmzZo2sVqv69+/f4jWfjunuTmjGjBmaMGGCEhIS1K9fPy1YsEDV1dWaOHGio0tDG5aSkqJ3331XH3/8sXx9fW336lgsFnl5eclisejee+/VjBkzFBAQID8/Pz344INKTEzUgAEDHFw92gpfX1/bOgmn+Pj4KDAw0LadPoUzmD59ugYOHKjZs2drzJgxysrK0pIlS7RkyRJJkslkUmpqqp577jl16dJF0dHRmjlzpsLCwjR69GjHFo82ZdSoUZo1a5YiIyPVvXt3ffvtt5o/f74mTZokiV6F41RVVWnPnj22r/fv36+tW7cqICBAkZGRv9qX3bp104033qjJkydr8eLFqq+v19SpUzVu3DiFhYU56FP9zKFry+OcFi1aZERGRhpms9no16+fsWHDBkeXhDZO0llfb7zxhm3M8ePHjQceeMBo37694e3tbdxyyy1GQUGB44oGDMPuEWyGQZ/CeXzyySdGjx49DA8PDyMmJsZYsmSJ3X6r1WrMnDnTCA4ONjw8PIxhw4YZu3btclC1aKsqKiqMadOmGZGRkYanp6fRuXNn48knnzROnDhhG0OvwhHWrl171p9NJ0yYYBhG4/ry6NGjxp133mm0a9fO8PPzMyZOnGhUVlY64NPYMxmGYTjo9wMAAAAAAOA03JMOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAmlVUVJQWLFjg6DIAAGiVCOkAALRi99xzj0aPHi1JGjp0qFJTU1vs3G+++ab8/f3P2J6dna0pU6a0WB0AAFxO3BxdAAAAcC51dXUym80XfHyHDh2asRoAANoWrqQDAHAZuOeee5SRkaGXXnpJJpNJJpNJBw4ckCTl5ORoxIgRateunYKDgzV+/HiVlJTYjh06dKimTp2q1NRUBQUFafjw4ZKk+fPnKy4uTj4+PoqIiNADDzygqqoqSdK///1vTZw4UeXl5bbzPfPMM5LOnO6el5enm2++We3atZOfn5/GjBmjoqIi2/5nnnlGvXv31jvvvKOoqChZLBaNGzdOlZWVl/abBgCAEyKkAwBwGXjppZeUmJioyZMnq6CgQAUFBYqIiFBZWZmuv/569enTR5s2bdLnn3+uoqIijRkzxu74t956S2azWV9//bUWL14sSXJxcdHChQv1/fff66233tKaNWv06KOPSpIGDhyoBQsWyM/Pz3a+hx9++Iy6rFarbr75ZpWWliojI0NpaWnat2+fxo4dazdu7969WrVqlVavXq3Vq1crIyNDzz///CX6bgEA4LyY7g4AwGXAYrHIbDbL29tbISEhtu0vv/yy+vTpo9mzZ9u2vf7664qIiNDu3bvVtWtXSVKXLl00b948u/c8/f72qKgoPffcc7rvvvv06quvymw2y2KxyGQy2Z3vl9LT0/Xdd99p//79ioiIkCS9/fbb6t69u7Kzs9W3b19JP4X5N998U76+vpKk8ePHKz09XbNmzbq4bwwAAK0MV9IBALiMbdu2TWvXrlW7du1sr5iYGEk/Xb0+JT4+/oxjv/zySw0bNkydOnWSr6+vxo8fr6NHj6qmpqbR58/NzVVERIQtoEtSbGys/P39lZuba9sWFRVlC+iSFBoaquLi4iZ9VgAALgdcSQcA4DJWVVWlUaNGae7cuWfsCw0Ntf3dx8fHbt+BAwd000036f7779esWbMUEBCgzMxM3Xvvvaqrq5O3t3ez1unu7m73tclkktVqbdZzAADQGhDSAQC4TJjNZjU0NNhtu/rqq/XBBx8oKipKbm6N/9/+5s2bZbVa9eKLL8rF5aeJd++///6vnu+XunXrpvz8fOXn59uupu/YsUNlZWWKjY1tdD0AALQVTHcHAOAyERUVpY0bN+rAgQMqKSmR1WpVSkqKSktLdeeddyo7O1t79+7Vv/71L02cOPG8Afuqq65SfX29Fi1apH379umdd96xLSh3+vmqqqqUnp6ukpKSs06DT0pKUlxcnJKTk7VlyxZlZWXp7rvv1rXXXquEhIRm/x4AANDaEdIBALhMPPzww3J1dVVsbKw6dOigvLw8hYWF6euvv1ZDQ4NuuOEGxcXFKTU1Vf7+/rYr5GfTq1cvzZ8/X3PnzlWPHj20bNkyzZkzx27MwIEDdd9992ns2LHq0KHDGQvPST9NW//444/Vvn17DRkyRElJSercubNWrFjR7J8fAIDLgckwDMPRRQAAAAAAAK6kAwAAAADgNAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAATuL/AM6Lh8wgi8ISAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NesterovMomentum - Training Accuracy: 1.0000, Test Accuracy: 1.0000\n",
            "Adam - Training Accuracy: 0.7750, Test Accuracy: 0.6500\n",
            "Adagrad - Training Accuracy: 0.7000, Test Accuracy: 0.6000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Part 3: Extend the Model to 5-input Parity Problem\n",
        "I extend the variational quantum classifier to classify data from the parity problem with 5 inputs. I use the Adam optimizer and compare the results."
      ],
      "metadata": {
        "id": "_iHha8ibQ677"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data\n",
        "classA_train = np.loadtxt(base_path + \"classA_train_N5.dat\")\n",
        "classB_train = np.loadtxt(base_path + \"classB_train_N5.dat\")\n",
        "classA_test = np.loadtxt(base_path + \"classA_test_N5.dat\")\n",
        "classB_test = np.loadtxt(base_path + \"classB_test_N5.dat\")\n",
        "\n",
        "# Preprocess the data\n",
        "def preprocess_data(classA, classB):\n",
        "    X = np.vstack((classA, classB))\n",
        "    y = np.hstack((np.ones(len(classA)), -1*np.ones(len(classB))))\n",
        "    X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)  # Normalize\n",
        "    return X, y\n",
        "\n",
        "X_train, y_train = preprocess_data(classA_train, classB_train)\n",
        "X_test, y_test = preprocess_data(classA_test, classB_test)\n",
        "\n",
        "# Initialize device\n",
        "num_qubits = 5\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "# Define a layer of the variational quantum circuit\n",
        "def layer(weights):\n",
        "    for i in range(num_qubits):\n",
        "        qml.Rot(*weights[i], wires=i)\n",
        "    for i in range(num_qubits):\n",
        "        qml.CNOT(wires=[i, (i+1) % num_qubits])\n",
        "\n",
        "# Data re-uploading in state preparation\n",
        "def state_preparation(x):\n",
        "    for i in range(num_qubits):\n",
        "        qml.RX(x[i % len(x)], wires=i)\n",
        "\n",
        "@qml.qnode(dev, interface='autograd')\n",
        "def circuit(weights, x):\n",
        "    state_preparation(x)\n",
        "    for w in weights:\n",
        "        layer(w)\n",
        "    return qml.expval(qml.PauliZ(0))\n",
        "\n",
        "def variational_classifier(weights, bias, x):\n",
        "    return circuit(weights, x) + bias\n",
        "\n",
        "def square_loss(labels, predictions):\n",
        "    return qml.numpy.mean((labels - predictions) ** 2)\n",
        "\n",
        "def accuracy(labels, predictions):\n",
        "    return qml.numpy.mean(labels == qml.numpy.sign(predictions))\n",
        "\n",
        "def cost(weights, bias, X, Y):\n",
        "    predictions = qml.numpy.array([variational_classifier(weights, bias, x) for x in X])\n",
        "    return square_loss(Y, predictions)\n",
        "\n",
        "np.random.seed(0)\n",
        "num_layers = 6  # Increased number of layers\n",
        "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3)\n",
        "weights_init = qml.numpy.array(weights_init, requires_grad=True)\n",
        "bias_init = qml.numpy.array(0.0, requires_grad=True)\n",
        "\n",
        "opt = AdamOptimizer(0.01)  # Using Adam optimizer with a learning rate of 0.01\n",
        "batch_size = 10\n",
        "num_iterations = 110\n",
        "\n",
        "weights = weights_init\n",
        "bias = bias_init\n",
        "\n",
        "costs = []\n",
        "accuracies = []\n",
        "\n",
        "for it in range(num_iterations):\n",
        "    # Random batch sampling\n",
        "    batch_index = np.random.randint(0, len(X_train), (batch_size,))\n",
        "    X_batch = X_train[batch_index]\n",
        "    Y_batch = y_train[batch_index]\n",
        "\n",
        "    weights, bias = opt.step(cost, weights, bias, X=qml.numpy.array(X_batch), Y=qml.numpy.array(Y_batch))\n",
        "\n",
        "    # Accuracy and cost on the training set\n",
        "    predictions = [variational_classifier(weights, bias, x) for x in X_train]\n",
        "    acc_train = accuracy(y_train, qml.numpy.sign(predictions))\n",
        "    cost_train = cost(weights, bias, X_train, y_train)\n",
        "    costs.append(cost_train)\n",
        "    accuracies.append(acc_train)\n",
        "\n",
        "    if (it + 1) % 10 == 0:\n",
        "        print(f\"Iteration: {it + 1} | Cost: {cost_train:.4f} | Training Accuracy: {acc_train:.4f}\")\n",
        "\n",
        "# Evaluation on the test set\n",
        "predictions_test = [variational_classifier(weights, bias, x) for x in X_test]\n",
        "acc_test = accuracy(y_test, qml.numpy.sign(predictions_test))\n",
        "\n",
        "print(f\"Test Accuracy: {acc_test:.4f}\")\n",
        "\n",
        "# Plotting the cost and accuracy over iterations\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "ax1.set_xlabel(\"Iteration\")\n",
        "ax1.set_ylabel(\"Cost\", color=\"tab:blue\")\n",
        "ax1.plot(costs, color=\"tab:blue\")\n",
        "ax1.tick_params(axis=\"y\", labelcolor=\"tab:blue\")\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel(\"Accuracy\", color=\"tab:orange\")\n",
        "ax2.plot(accuracies, color=\"tab:orange\")\n",
        "ax2.tick_params(axis=\"y\", labelcolor=\"tab:orange\")\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.title(\"Cost and Accuracy over Iterations\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "Id1Qvpl9RN0f",
        "outputId": "fe2c6165-c96b-4c6d-9801-4a794cbfa561"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 10 | Cost: 1.0432 | Training Accuracy: 0.5000\n",
            "Iteration: 20 | Cost: 1.0120 | Training Accuracy: 0.4750\n",
            "Iteration: 30 | Cost: 0.9923 | Training Accuracy: 0.5250\n",
            "Iteration: 40 | Cost: 0.9829 | Training Accuracy: 0.5875\n",
            "Iteration: 50 | Cost: 0.9762 | Training Accuracy: 0.5687\n",
            "Iteration: 60 | Cost: 0.9505 | Training Accuracy: 0.5563\n",
            "Iteration: 70 | Cost: 0.8983 | Training Accuracy: 0.6062\n",
            "Iteration: 80 | Cost: 0.8527 | Training Accuracy: 0.6188\n",
            "Iteration: 90 | Cost: 0.7532 | Training Accuracy: 0.7438\n",
            "Iteration: 100 | Cost: 0.6495 | Training Accuracy: 0.8875\n",
            "Iteration: 110 | Cost: 0.5443 | Training Accuracy: 1.0000\n",
            "Test Accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}