Speech_recognition.ipynb

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    "colab": {
      "name": "21100050_21100120_21100315_21100009.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noHgpyguG5Mu",
        "outputId": "d1654d73-2ac9-404e-cc0e-698ab854e0c1"
      },
      "source": [
        "!pip install python_speech_features"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting python_speech_features\n",
            "  Downloading https://files.pythonhosted.org/packages/ff/d1/94c59e20a2631985fbd2124c45177abaa9e0a4eee8ba8a305aa26fc02a8e/python_speech_features-0.6.tar.gz\n",
            "Building wheels for collected packages: python-speech-features\n",
            "  Building wheel for python-speech-features (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-speech-features: filename=python_speech_features-0.6-cp36-none-any.whl size=5887 sha256=485abc813931846a12646b4559c0bf6e5f5010c887a74c31e329a037625659d1\n",
            "  Stored in directory: /root/.cache/pip/wheels/3c/42/7c/f60e9d1b40015cd69b213ad90f7c18a9264cd745b9888134be\n",
            "Successfully built python-speech-features\n",
            "Installing collected packages: python-speech-features\n",
            "Successfully installed python-speech-features-0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTSqnEJiGXEI"
      },
      "source": [
        "import python_speech_features as mfcc\n",
        "from scipy.io.wavfile import read\n",
        "import numpy as np\n",
        "import glob\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppKQ_3zcjr0_"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viz2xdtmFyjX",
        "outputId": "9688f6c9-0daa-4a8a-c867-56d1b327ebd3"
      },
      "source": [
        "!gdown --id 1gkUyLeI5ly1wBiXkdfGZDKQZLt3gcwCq"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1gkUyLeI5ly1wBiXkdfGZDKQZLt3gcwCq\n",
            "To: /content/LUMS_FALL2020_PROJECT_DATA.zip\n",
            "802MB [00:09, 85.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YScl0-wbGC4k"
      },
      "source": [
        "!unzip \"LUMS_FALL2020_PROJECT_DATA\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVnmLcKdGz7q"
      },
      "source": [
        "#Feature extraction\n",
        "def get_MFCC(audio, sr):\n",
        "    features = mfcc.mfcc(audio, sr, 0.025, 0.01, 13, appendEnergy = True)\n",
        "    return np.mean(features, axis=0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWMcSipeIADP"
      },
      "source": [
        "#Function for reading data\n",
        "def read_data(location, i):\n",
        "  labels = []\n",
        "  data = []\n",
        "  for path in glob.glob(location + '/SPK*'):\n",
        "    for files in glob.glob(path + '/*'):\n",
        "      labels.append(path.split(\"/\")[2].split(\"_\")[i])\n",
        "      sr, audio = read(files)\n",
        "      features = get_MFCC(audio, sr)\n",
        "      data.append(features)\n",
        "  return np.array(labels), np.array(data)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jxm14zLhPNg8"
      },
      "source": [
        "#Function to add X0\n",
        "def add_one(data):\n",
        "  ones = np.ones((len(data), 1))\n",
        "  temp = np.hstack((ones, data))\n",
        "  return temp"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3iSImB7SFKW"
      },
      "source": [
        "**Reading speaker recognition data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPJY1P_uL43v"
      },
      "source": [
        "#Reading speaker recognition data\n",
        "train_labels_sr, train_data_sr = read_data('Speaker_Recognition/Train', 0)\n",
        "valid_labels_sr, valid_data_sr = read_data('Speaker_Recognition/Valid', 0)\n",
        "test_labels_sr, test_data_sr = read_data('Speaker_Recognition/Test', 0)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZj45tsYQScv"
      },
      "source": [
        "#Adding X0 to speaker recognition data\n",
        "if(train_data_sr.shape[1] == 13):\n",
        "  train_data_sr = add_one(train_data_sr)\n",
        "  valid_data_sr = add_one(valid_data_sr)\n",
        "  test_data_sr = add_one(test_data_sr)\n",
        "else:\n",
        "  print(\"X0 already added\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BAezlvySJOA"
      },
      "source": [
        "**Reading gender recognition data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dp5uAV2sOfbU"
      },
      "source": [
        "#Reading gender recognition data\n",
        "train_labels_gr, train_data_gr = read_data('Gender_Recognition/Train', 1)\n",
        "valid_labels_gr, valid_data_gr = read_data('Gender_Recognition/Valid', 1)\n",
        "test_labels_gr, test_data_gr = read_data('Gender_Recognition/Test', 1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3thYxQ8R40v"
      },
      "source": [
        "#Adding X0 to gender recognition data\n",
        "if(train_data_gr.shape[1] == 13):\n",
        "  train_data_gr = add_one(train_data_gr)\n",
        "  valid_data_gr = add_one(valid_data_gr)\n",
        "  test_data_gr = add_one(test_data_gr)\n",
        "else:\n",
        "  print(\"X0 already added\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUoSK7JWBUgP"
      },
      "source": [
        "# **Functions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXxCtwUQXeFM"
      },
      "source": [
        "**Training functions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aa2CRFu3WSBM"
      },
      "source": [
        "#Softmax function\n",
        "def softmax(z):\n",
        "  s = []\n",
        "  for i in range(len(z)):\n",
        "    temp = np.exp(z[i])\n",
        "    temp /= sum(np.exp(z))\n",
        "    s.append(temp)\n",
        "  return np.array(s)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1n8sMHlUylUh"
      },
      "source": [
        "#Cross Entropy Loss\n",
        "def cross_entropy_loss(y_labels, predicted):\n",
        "  temp = -np.dot(y_labels, np.log(predicted))\n",
        "  return temp"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezKo1Pj8zqQr"
      },
      "source": [
        "#Hypothesis\n",
        "def hypothesis(X, thetas):\n",
        "  temp = np.dot(X, thetas)\n",
        "  return(np.array(temp))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaDjQfCGzqaz"
      },
      "source": [
        "#Encode labels as 0 and 1\n",
        "def one_hot_encode(data):\n",
        "  classes = []\n",
        "\n",
        "  for c in data:\n",
        "    if(c not in classes):\n",
        "      classes.append(c)\n",
        "\n",
        "  new_labels = []\n",
        "  for c in classes:\n",
        "    temp = []\n",
        "    for d in data:\n",
        "      if (c == d):\n",
        "        temp.append(1)\n",
        "      else:\n",
        "        temp.append(0)\n",
        "    new_labels.append(np.array(temp))\n",
        "\n",
        "  return np.array(new_labels), classes\n",
        "  "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bI4nWLsN5bLN"
      },
      "source": [
        "#Gradient descent to learn values of thetas\n",
        "def gradient_descent(X, Y, X_val, Y_val, classes, alpha, n_epoch):\n",
        "  m = X.shape[0] #number of documents\n",
        "  m2 = X_val.shape[0] #number of documents in validation set\n",
        "  features = X.shape[1] #number of features\n",
        "  total_classes = len(classes) #number of total classes\n",
        "\n",
        "  thetas = np.zeros([total_classes, features]) #creating a matrix of thetas\n",
        "  J = [] #List to store training loss\n",
        "  Jval = [] #List to store validation loss\n",
        "\n",
        "  for epoch in range(n_epoch):\n",
        "\n",
        "    hypothesis_score = []\n",
        "    h_score_val = []\n",
        "    #Calculating hypothesis score for each class\n",
        "    for c in range(total_classes):\n",
        "      temp = hypothesis(X, thetas[c])\n",
        "      temp_val = hypothesis(X_val, thetas[c])\n",
        "      hypothesis_score.append(temp)\n",
        "      h_score_val.append(temp_val)\n",
        "\n",
        "    softmax_score = []\n",
        "    softmax_score_val = []\n",
        "\n",
        "    #Taking transpose of hypothesis score\n",
        "    new_hscore = np.transpose(hypothesis_score)\n",
        "    new_hscore_val = np.transpose(h_score_val)\n",
        "    #Calculating softmax score for the hypothesis\n",
        "    for s in range(m):\n",
        "      softmax_score.append(softmax(np.array(new_hscore[s])))\n",
        "    for s in range(m2):\n",
        "      softmax_score_val.append(softmax(np.array(new_hscore_val[s])))\n",
        "\n",
        "    #Tranposing softmax scores\n",
        "    softmax_score1 = np.transpose(softmax_score)\n",
        "    \n",
        "    #Updating the values of thetas\n",
        "    for n in range(total_classes):\n",
        "      temp = softmax_score1[n] - Y[n]\n",
        "      temp_sum = np.dot(np.transpose(X), temp)\n",
        "      thetas[n] -= (alpha*temp_sum)/m\n",
        "\n",
        "    #Taking transpose of true labels\n",
        "    t_Y = np.transpose(Y)\n",
        "    t_Yval = np.transpose(Y_val)\n",
        "    \n",
        "    #Calculating training loss\n",
        "    loss = 0\n",
        "    for n in range(m):\n",
        "      loss += cross_entropy_loss(t_Y[n], softmax_score[n])\n",
        "    \n",
        "    #Caluclating validation loss\n",
        "    val_loss = 0\n",
        "    for n in range(m2):\n",
        "      val_loss += cross_entropy_loss(t_Yval[n], softmax_score_val[n])\n",
        "\n",
        "    #Appending the average training loss\n",
        "    J.append(loss/m)\n",
        "    #Appending the average validation loss\n",
        "    Jval.append(val_loss/m2)\n",
        "\n",
        "  return thetas, J, Jval\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tz9m8J-_-JL"
      },
      "source": [
        "**Testing functions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7uJMQr1heCu"
      },
      "source": [
        "#Function to predict labels\n",
        "def predict_labels(data, thetas, classes):\n",
        "  hx = []\n",
        "  for n in thetas:\n",
        "    temp = hypothesis(data, n)\n",
        "    hx.append(temp)\n",
        "  \n",
        "  t_hx = np.transpose(hx)\n",
        "\n",
        "  softmax_score = []\n",
        "  for n in t_hx:\n",
        "    temp = softmax(n)\n",
        "    softmax_score.append(temp) \n",
        "  \n",
        "  predicted_labels = []\n",
        "  for prob in softmax_score:\n",
        "    predicted_labels.append(classes[np.argmax(prob)])\n",
        "\n",
        "  return predicted_labels"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qnIxYtDBdkf"
      },
      "source": [
        "**Evaluation functions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMWZ51qKBgvr"
      },
      "source": [
        "#Function to calculate accuracy, classification report and confusion matrix\n",
        "def evaluation(y_true, predicted):\n",
        "  accuracy = accuracy_score(y_true, predicted)\n",
        "\n",
        "  c_report = classification_report(y_true, predicted, output_dict=True, zero_division=0)\n",
        "  macro_f1 = c_report['macro avg']['f1-score']\n",
        "  macro_precision = c_report['macro avg']['precision']\n",
        "  macro_recall = c_report['macro avg']['recall']\n",
        "\n",
        "  print(\"Accuracy: %.3f\" % (accuracy*100))\n",
        "  print(\"Macro F1: %.3f\" % macro_f1)\n",
        "  print(\"Macro Precision: %.3f\" % macro_precision)\n",
        "  print(\"Macro Recall: %.3f\" % macro_recall)\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqiKJtGSEERF"
      },
      "source": [
        "def confusionMatrix(y_true, predicted):\n",
        "  data = confusion_matrix(y_true, predicted)\n",
        "  con_mat = pd.DataFrame(data=data)\n",
        "  return con_mat"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUEG6dmmAEwA"
      },
      "source": [
        "# **Gender recognition**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YoQAD4AcAVGR"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Te6FxRMz4Sl5"
      },
      "source": [
        "#Encoding the labels for different classes\n",
        "labels_gr, classes_gr = one_hot_encode(train_labels_gr)\n",
        "val_labels_gr, classes_val_gr = one_hot_encode(valid_labels_gr)\n",
        "\n",
        "#Running the graident descent on training data\n",
        "\n",
        "#Using 3 different values of alpha for validation\n",
        "alpha_gr = [0.001, 0.01, 0.1, 0.3, 0.5]\n",
        "epoch_gr = 300\n",
        "\n",
        "theta_gr_total = []\n",
        "J_gr_total = []\n",
        "Jval_gr_total = []\n",
        "\n",
        "#running gradient descent for different values of alpha\n",
        "for i in range(len(alpha_gr)):\n",
        "  thetas_gr, J_gr, Jval_gr = gradient_descent(train_data_gr, labels_gr, valid_data_gr, val_labels_gr, classes_gr, alpha_gr[i], epoch_gr)\n",
        "  theta_gr_total.append(thetas_gr)\n",
        "  J_gr_total.append(J_gr)\n",
        "  Jval_gr_total.append(Jval_gr)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_e_rkopC3Gb"
      },
      "source": [
        "**Plots**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "jL-Q0y2z5peb",
        "outputId": "012ab031-cb51-4f20-b523-50b82996cdae"
      },
      "source": [
        "#Training Loss vs Epochs for different values of alpha in Gender Recognition\n",
        "\n",
        "epoch_list_gr = list(range(0, 300))\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_gr, J_gr_total[0], 'r', label = \"Alpha = 0.001\")\n",
        "plt.plot(epoch_list_gr, J_gr_total[1], 'b', label = \"Alpha = 0.01\")\n",
        "plt.plot(epoch_list_gr, J_gr_total[2], 'g', label = \"Alpha = 0.1\")\n",
        "plt.plot(epoch_list_gr, J_gr_total[3], 'y', label = \"Alpha = 0.3\")\n",
        "plt.plot(epoch_list_gr, J_gr_total[4], 'c', label = \"Alpha = 0.5\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Training Loss vs Epochs in Gender Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "im1397c_7TRW",
        "outputId": "dd585245-bad8-4e05-f038-31a85bd21bc6"
      },
      "source": [
        "#Validation Loss vs Epochs for different values of alpha in Gender Recognition\n",
        "\n",
        "epoch_list_gr = list(range(0, 300))\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[0], 'r', label = \"Alpha = 0.001\")\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[1], 'b', label = \"Alpha = 0.01\")\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[2], 'g', label = \"Alpha = 0.1\")\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[3], 'y', label = \"Alpha = 0.3\")\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[4], 'c', label = \"Alpha = 0.5\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Validation Loss vs Epochs in Gender Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VVVXVbbm1a9dq/vz5MjPNmDFDTU1Namho0K9+9SvNnj1bZ599tt70pjdp9uzZXcnUjBkzNGbMmH6VLxWjyQEAAACZuv12aevW3MacPFm67760i6xdu1ZXX321LrzwQo0aNUpbtmzRlClTui23efNmbd++XcOHD9e0adN03XXXafTo0aqpqVFlZaXKy8t1ww03aM2aNbrppps0b948LVy4UJK0ZMkSrVixQrfeemtSzPXr1+uOO+7o9l7Dhw/XU089lTStvr5epaWlXa9LS0tVX1/fbd36+nqde+653ZbraXq+kAwBAAAAA1xlZaVuu+02SdKNN96oysrK0GRo9uzZGjVqlCRp3rx52rhxo+bOnauxY8dq8uTJkqQpU6aotrZWkrR9+3YtWbJETU1Namlp0VVXXdUt5hVXXKGtuU4ABwiSIQAAACBTvdTg5ENjY6PWrVunbdu2ycwUi8VkZrr33nu7LZs6olri9bBhw7qmFRcXdzWTW7BggaqqqjRp0iRVVFRow4YN3WJmUzNUUlKiurq6rtd1dXUqKSnptm5JSYn27dvXbbmSkpKkMtTV1WnWrFnd1s8V+gwBAAAAA9gjjzyim2++WXv27FFtba327dunsWPH6sknn+y27OOPP67Gxka1traqqqpKM2fOTBv76NGjGjNmjDo6OrRq1arQZRI1Q6n/UhMhSRozZoxGjhypTZs2yTmnlStXas6cOd2W+8AHPqCVK1fKOadNmzbprLPO0pgxY3TVVVfp17/+tQ4fPqzDhw/r17/+dWhtVa6QDAEAAAADWGVlpa6//vqkaWVlZaGjyl122WUqKyvTxIkTVVZWpqlTp6aNfffdd2v69OmaOXOmLrroopyUd9myZbrllls0btw4XXDBBbrmmmskScuXL9fy5cslSddee63e/va3a9y4cVq4cKGWLVsmSTr77LN15513atq0aZo2bZq+9KUv6eyzz5Ykffazn1VpaamOHTum0tJS3XXXXf0uq4WN+z1YTJ061VVXV0ddDAAAAJzEduzYoYsvvjjqYvSqoqJC1dXVuv/++6MuSmTCPisz2+KcC80KqRkCAAAAcEoiGYrAoePHNWrjRj3T3Bx1UQAAAHCSWLBgwSldK9QXJEMR2H/8uBo7O7XLH8UDAAAAQOGRDEUg7v8fG8T9tQAAAIDBjmQoAokUqJNkCAAAAIgMyVAE4n4SRDIEAAAARIdkKAKJZnIkQwAAAMhUVVWVzEw7d+7smlZbW6tLLrkk7XqZLJNLW7Zs0aWXXqpx48Zp8eLFCnuUj3NOixcv1rhx4zRx4kQ9++yzXfOuvvpqvfGNb9T73ve+vJc1b8mQmZ1rZuvN7M9m9ryZ3eZPv8vM6s1sq//v2sA6nzezXWb2gpnl71GzEUscELGIywEAAIDBo7KyUpdffnnow1YHkkWLFqm8vFw1NTWqqanRY4891m2ZRx99tGv+Aw88oEWLFnXN+8xnPqOHH364IGXNZ81Qp6RPOefeIWmGpE+Y2Tv8ed9xzk32//1Skvx5N0qaIOlqScvMrDiP5YsMNUMAAADIRktLizZu3KgVK1Zo9erVoctUVFRozpw5mjVrlsaPH6+lS5d2zYvFYlq4cKEmTJigK6+8Uq3+qMbl5eWaNm2aJk2apLKyMh07dqxf5WxoaFBzc7NmzJghM9P8+fNVVVXVbbm1a9dq/vz5MjPNmDFDTU1NamhokCS9973v1ZlnntmvcmRqSL4CO+caJDX4fx81sx2SStKsMkfSaudcu6SXzWyXpMskPZ2vMkaFPkMAAACD0+23S1u35jbm5MnSffelX2bt2rW6+uqrdeGFF2rUqFHasmWLpkyZ0m25zZs3a/v27Ro+fLimTZum6667TqNHj1ZNTY0qKytVXl6uG264QWvWrNFNN92kefPmaeHChZKkJUuWaMWKFbr11luTYq5fv1533HFHt/caPny4nnrqqaRp9fX1Ki0t7XpdWlqq+vr6buvW19fr3HPP7bbcmDFj0u+IHMtbMhRkZudL+ktJf5A0U9K/mtl8SdXyao8Oy0uUNgVWq1P65GnQYjQ5AAAAZKOyslK33XabJOnGG29UZWVlaDI0e/ZsjRo1SpI0b948bdy4UXPnztXYsWM1efJkSdKUKVNUW1srSdq+fbuWLFmipqYmtbS06KqruvdUueKKK7Q11xngAJH3ZMjMRkhaI+l251yzmX1P0t3ycoK7JX1L0j9lEe+jkj4qSeedd17uC1wAPGcIAABgcOqtBicfGhsbtW7dOm3btk1mplgsJjPTvffe221ZMwt9PWzYsK5pxcXFXc3kFixYoKqqKk2aNEkVFRXasGFDt5jZ1AyVlJSorq6u63VdXZ1KSrrXb5SUlGjfvn29LpdveR1NzsyGykuEVjnnfipJzrkDzrmYcy4uqVxeUzhJqpd0bmD1Un9aEufcA865qc65qeecc04+i583NJMDAABAph555BHdfPPN2rNnj2pra7Vv3z6NHTtWTz75ZLdlH3/8cTU2Nqq1tVVVVVWaOXNm2thHjx7VmDFj1NHRoVWrVoUuk6gZSv2XmghJ0pgxYzRy5Eht2rRJzjmtXLlSc+bM6bbcBz7wAa1cuVLOOW3atElnnXVWwZvISfkdTc4krZC0wzn37cD04FZeL2m7//fPJd1oZsPMbKyk8ZI256t8UaKZHAAAADJVWVmp66+/PmlaWVlZ6Khyl112mcrKyjRx4kSVlZVp6tSpaWPffffdmj59umbOnKmLLrooJ+VdtmyZbrnlFo0bN04XXHCBrrnmGknS8uXLtXz5cknStddeq7e//e0aN26cFi5cqGXLlnWt/653vUsf/OAH9dvf/lalpaX61a9+lZNyhbGwcb9zEtjscklPStqmEy3DviDpQ5Imy8sJaiX9iz/Ygszsi/KazHXKa1b3aLr3mDp1qquurs5L+fNpw+HDuuK55/T5887TV9/+9qiLAwAAgDR27Nihiy++OOpi9KqiokLV1dW6//77oy5KZMI+KzPb4pwLzQrzOZrcRkkWMuuXadb5iqSv5KtMA0Ui/aTPEAAAABCdgowmh2Q8ZwgAAAC5tmDBAi1YsCDqYgwqeR1AAeEYQAEAAACIHslQBBhAAQAAAIgeyVAEEjVD9BkCAAAAokMyFAH6DAEAAADRIxmKAM3kAAAAkK2qqiqZmXbu3Nk1rba2Vpdcckna9TJZJpe2bNmiSy+9VOPGjdPixYsV9iifnTt36p3vfKeGDRumb37zmwUrWyqSoQgwgAIAAACyVVlZqcsvvzz0YasDyaJFi1ReXq6amhrV1NToscce67bM2Wefre9+97v69Kc/HUEJTyAZikCimVws0lIAAABgsGhpadHGjRu1YsUKrV69OnSZiooKzZkzR7NmzdL48eO1dOnSrnmxWEwLFy7UhAkTdOWVV6q1tVWSVF5ermnTpmnSpEkqKyvTsWPH+lXOhoYGNTc3a8aMGTIzzZ8/X1VVVd2We/Ob36xp06Zp6NCh/Xq//uI5QxFw1AwBAAAMSrc/dru27t+a05iT/2Ky7rv6vrTLrF27VldffbUuvPBCjRo1Slu2bNGUKVO6Lbd582Zt375dw4cP17Rp03Tddddp9OjRqqmpUWVlpcrLy3XDDTdozZo1uummmzRv3jwtXLhQkrRkyRKtWLFCt956a1LM9evX64477uj2XsOHD9dTTz2VNK2+vl6lpaVdr0tLS1VfX5/xvig0kqEIMIACAAAAslFZWanbbrtNknTjjTeqsrIyNBmaPXu2Ro0aJUmaN2+eNm7cqLlz52rs2LGaPHmyJGnKlCmqra2VJG3fvl1LlixRU1OTWlpadNVVV3WLecUVV2jr1twmgAMFyVAE6DMEAAAwOPVWg5MPjY2NWrdunbZt2yYzUywWk5np3nvv7basmYW+HjZsWNe04uLirmZyCxYsUFVVlSZNmqSKigpt2LChW8xsaoZKSkpUV1fX9bqurk4lJSWZb2yBkQxFIJEC8ZwhAAAA9OaRRx7RzTffrO9///td097znvfoySef1HnnnZe07OOPP67GxkadccYZqqqq0oMPPpg29tGjRzVmzBh1dHRo1apVoYlLNjVDY8aM0ciRI7Vp0yZNnz5dK1eu7NbsbiBhAIUI0EwOAAAAmaqsrNT111+fNK2srCx0VLnLLrtMZWVlmjhxosrKyjR16tS0se+++25Nnz5dM2fO1EUXXZST8i5btky33HKLxo0bpwsuuEDXXHONJGn58uVavny5JGn//v0qLS3Vt7/9bd1zzz0qLS1Vc3NzTt4/GxY27vdgMXXqVFddXR11MbJWeeCAPrxjh6544xu1zm+7CQAAgIFpx44duvjii6MuRq8qKipUXV2t+++/P+qiRCbsszKzLc650KyQmqEI8NBVAAAAIHr0GYpAYgAF+gwBAAAgVxYsWKAFCxZEXYxBhZqhCNBnCAAAAIgeyVAEaCYHAAAARI9kKAI8ZwgAAACIHslQBHjOEAAAABA9kqEIUDMEAACAbFVVVcnMtHPnzq5ptbW1uuSSS9Kul8kyubRlyxZdeumlGjdunBYvXqywR/msXbtWEydO1OTJkzV16lRt3LixYOULIhmKAAMoAAAAIFuVlZW6/PLLQx+2OpAsWrRI5eXlqqmpUU1NjR577LFuy7z3ve/Vc889p61bt+rBBx/ULbfcEkFJSYYiwQAKAAAAyEZLS4s2btyoFStWaPXq1aHLVFRUaM6cOZo1a5bGjx+vpUuXds2LxWJauHChJkyYoCuvvFKtra2SpPLyck2bNk2TJk1SWVmZjh071q9yNjQ0qLm5WTNmzJCZaf78+aqqquq23IgRI2RmkqTXX3+96+9C4zlDEeh6zlDE5QAAAEB2ampuV0vL1pzGHDFissaPvy/tMmvXrtXVV1+tCy+8UKNGjdKWLVs0ZcqUbstt3rxZ27dv1/DhwzVt2jRdd911Gj16tGpqalRZWany8nLdcMMNWrNmjW666SbNmzdPCxculCQtWbJEK1as0K233poUc/369brjjju6vdfw4cP11FNPJU2rr69XaWlp1+vS0lLV19eHbtPPfvYzff7zn9err76qX/ziF2m3P19IhiJAMzkAAABko7KyUrfddpsk6cYbb1RlZWVoMjR79myNGjVKkjRv3jxt3LhRc+fO1dixYzV58mRJ0pQpU1RbWytJ2r59u5YsWaKmpia1tLToqquu6hbziiuu0NatuU0AJen666/X9ddfryeeeEJ33nmnfvOb3+T8PXpDMhQBxwAKAAAAg1JvNTj50NjYqHXr1mnbtm0yM8ViMZmZ7r333m7LpjY3S7weNmxY17Ti4uKuZnILFixQVVWVJk2apIqKCm3YsKFbzGxqhkpKSlRXV9f1uq6uTiUlJWm3793vfrd2796tQ4cOafTo0WmXzTX6DEWAmiEAAABk6pFHHtHNN9+sPXv2qLa2Vvv27dPYsWP15JNPdlv28ccfV2Njo1pbW1VVVaWZM2emjX306FGNGTNGHR0dWrVqVegyiZqh1H+piZAkjRkzRiNHjtSmTZvknNPKlSs1Z86cbsvt2rWrq4Lg2WefVXt7e1eNViGRDEWgq88QyRAAAAB6UVlZqeuvvz5pWllZWeiocpdddpnKyso0ceJElZWVaerUqWlj33333Zo+fbpmzpypiy66KCflXbZsmW655RaNGzdOF1xwga655hpJ0vLly7V8+XJJ0po1a3TJJZdo8uTJ+sQnPqEf//jHkQyiYGHjfg8WU6dOddXV1VEXI2vf2bdPn3zpJZ1RVKRj73531MUBAABAGjt27NDFF18cdTF6VVFRoerqat1///1RFyUyYZ+VmW1xzoVmhdQMRYBmcgAAAED0GEAhAnEGUAAAAECOLViwQAsWLIi6GIMKNUMRcIH/4yREAAAAQCRIhiIQTIAYRAEAAACIBslQBOKBv2kqBwAAAESDZCgCwfSHZAgAAACIBslQBGgmBwAAgGxVVVXJzLRz586uabW1tbrkkkvSrpfJMrm0ZcsWXXrppRo3bpwWL16ssEf5bNiwQWeddZYmT56syZMn68tf/nLByhdEMhQBmskBAAAgW5WVlbr88stDH7Y6kCxatEjl5eWqqalRTU2NHqHAs0YAACAASURBVHvssdDl3vWud2nr1q3aunWrvvSlLxW4lB6SoQgEs2OSIQAAAPSmpaVFGzdu1IoVK7R69erQZSoqKjRnzhzNmjVL48eP19KlS7vmxWIxLVy4UBMmTNCVV16p1tZWSVJ5ebmmTZumSZMmqaysTMeOHetXORsaGtTc3KwZM2bIzDR//nxVVVX1K2Y+8ZyhCFAzBAAAMDjdXlOjrS0tOY05ecQI3Td+fNpl1q5dq6uvvloXXnihRo0apS1btmjKlCndltu8ebO2b9+u4cOHa9q0abruuus0evRo1dTUqLKyUuXl5brhhhu0Zs0a3XTTTZo3b54WLlwoSVqyZIlWrFihW2+9NSnm+vXrdccdd3R7r+HDh+upp55KmlZfX6/S0tKu16Wlpaqvrw/dpqefflqTJk3SW9/6Vn3zm9/UhAkT0u6DfCAZikAw/YlFVgoAAAAMFpWVlbrtttskSTfeeKMqKytDk6HZs2dr1KhRkqR58+Zp48aNmjt3rsaOHavJkydLkqZMmaLa2lpJ0vbt27VkyRI1NTWppaVFV111VbeYV1xxhbZu3ZrT7fmrv/or7dmzRyNGjNAvf/lLzZ07VzU1NTl9j0yQDEUgTjM5AACAQam3Gpx8aGxs1Lp167Rt2zaZmWKxmMxM9957b7dlzSz09bBhw7qmFRcXdzWTW7BggaqqqjRp0iRVVFRow4YN3WJmUzNUUlKiurq6rtd1dXUqKSnptu7IkSO7/r722mv18Y9/XIcOHdLo0aPDdkHekAxFgGZyAAAAyNQjjzyim2++Wd///ve7pr3nPe/Rk08+qfPOOy9p2ccff1yNjY0644wzVFVVpQcffDBt7KNHj2rMmDHq6OjQqlWrQhOXbGqGxowZo5EjR2rTpk2aPn26Vq5c2a3ZnSTt379fb3nLW2Rm2rx5s+LxeFeNViExgEIEGEABAAAAmaqsrNT111+fNK2srCx0VLnLLrtMZWVlmjhxosrKyjR16tS0se+++25Nnz5dM2fO1EUXXZST8i5btky33HKLxo0bpwsuuEDXXHONJGn58uVavny5JC/Bu+SSSzRp0iQtXrxYq1ev7larVQgWNu73YDF16lRXXV0ddTGy9tmXXtK9+/ZJkp6bOlUTR4yIuEQAAADoyY4dO3TxxRdHXYxeVVRUqLq6Wvfff3/URYlM2GdlZlucc6FZITVDEaDPEAAAABA9+gxFIJj+kAwBAAAgFxYsWKAFCxZEXYxBhZqhCDCAAgAAABA9kqEIBJvJxUiGAAAABrzB3M/+VNGXz4hkKAI0kwMAABg8Tj/9dL322mskRAOYc06vvfaaTj/99KzWo89QBBhAAQAAYPAoLS1VXV2dDh48GHVRkMbpp5+u0tLSrNYhGYoAfYYAAAAGj6FDh2rs2LFRFwN5QDO5CATTH/oMAQAAANEgGYoAzeQAAACA6JEMRYBmcgAAAED0SIYi4KgZAgAAACJHMhSBYM1QLLJSAAAAAKc2kqEI0GcIAAAAiB7JUAScTux4kiEAAAAgGiRDEYhLOq3I2/UkQwAAAEA0SIYi4JzTUDNJPGcIAAAAiArJUATiUlcyRM0QAAAAEA2SoQjEnaOZHAAAABAxkqEIOEmnUTMEAAAARIpkKALBZnL0GQIAAACiQTIUgbhzGkozOQAAACBSJEMRcGIABQAAACBqJEMRiDunIknFIhkCAAAAokIyFIG4pCIzFZvRZwgAAACISN6SITM718zWm9mfzex5M7vNn362mT1uZjX+/2/yp5uZfdfMdpnZn8zsr/JVtqg5eTt+iBk1QwAAAEBE8lkz1CnpU865d0iaIekTZvYOSf8m6bfOufGSfuu/lqRrJI33/31U0vfyWLZIxZ2TmZEMAQAAABHKWzLknGtwzj3r/31U0g5JJZLmSHrIX+whSXP9v+dIWuk8myS90czG5Kt8UYqLmiEAAAAgagXpM2Rm50v6S0l/kPQW51yDP2u/pLf4f5dI2hdYrc6fdtJxzp3oMxR1YQAAAIBTVN6TITMbIWmNpNudc83Bec45J68LTTbxPmpm1WZWffDgwRyWtHDikkzUDAEAAABRymsyZGZD5SVCq5xzP/UnH0g0f/P/f9WfXi/p3MDqpf60JM65B5xzU51zU88555z8FT6PEkNrkwwBAAAA0cnnaHImaYWkHc65bwdm/VzSR/y/PyJpbWD6fH9UuRmSjgSa051UnMQACgAAAEDEhuQx9kxJN0vaZmZb/WlfkPQ1ST8xs3+WtEfSDf68X0q6VtIuScck/WMeyxapxAAKPGcIAAAAiE7ekiHn3EZ5XWPCvDdkeSfpE/kqz0AS9wdQoGYIAAAAiE5BRpNDMicGUAAAAACiRjIUAQZQAAAAAKJHMhQBJ3U1k6PPEAAAABANkqEIJJ4zVCxRMwQAAABEhGQoAgygAAAAAESPZCgCTqLPEAAAABAxkqEIxJ3rGk2OPkMAAABANEiGIhCXN4BCMTVDAAAAQGRIhiJAMzkAAAAgekOiLsCpKO6czExDxGhyAAAAQFRIhiIQl1czVGSmWNSFAQAAAE5RJEMRcP7Q2iZqhgAAAICo0GcoAomHrtJnCAAAAIgOyVAE4s4xgAIAAAAQMZKhCDjJG0CB5wwBAAAAkSEZikBiAAWeMwQAAABEh2QoAnF/AAWayQEAAADRIRmKgNOJARRoJgcAAABEg2QoAgygAAAAAESPZCgCcXkPXC0WzxkCAAAAokIyFIFgMzmSIQAAACAaJEMRCA6gQJ8hAAAAIBokQxFwUlefoZgkR0IEAAAAFBzJUATizsnkPWdIErVDAAAAQARIhiKQGEBhiJ8M0W8IAAAAKDySoQgEm8lJUizS0gAAAACnJpKhCMSdk1EzBAAAAESKZCgCcXk7vphkCAAAAIgMyVAEXGBobYlkCAAAAIgCyVAE4jrx0FWJ0eQAAACAKJAMRSDuXNIACtQMAQAAAIVHMhQBJ8nM6DMEAAAARIhkKAKJARSoGQIAAACiQzIUgXjKAAr0GQIAAAAKj2QoAk7JAyhQMwQAAAAUHslQBBIDKBT7r0mGAAAAgMIjGYpAXOI5QwAAAEDESIYikNpMjj5DAAAAQOGRDBWY8xMfaoYAAACAaJEMFVjc/79I4jlDAAAAQIRIhgosUTPEaHIAAABAtEiGCqyrZij4nKHoigMAAACcskiGCixRB1QkaoYAAACAKJEMFVg80UzOjD5DAAAAQIRIhgosOIACNUMAAABAdEiGCixsaG2eMwQAAAAUHslQgSVqhhhNDgAAAIgWyVCBJfoM8ZwhAAAAIFokQwWWSHss0EyOZAgAAAAoPJKhAgsbQIE+QwAAAEDhkQwVWDxkAAVqhgAAAIDCIxkqsK5mcpKK/b9JhgAAAIDCIxkqsOAACtQMAQAAANEhGSqwrj5DPGcIAAAAiBTJUIEFm8lRMwQAAABEh2SowIIDKPCcIQAAACA6JEMFFhxam2QIAAAAiA7JUIE5P/ExebVDRZJikZYIAAAAODWRDBVYcAAFyes3RM0QAAAAUHgkQwUWHFpb8prKkQwBAAAAhUcyVGBdo8lRMwQAAABEimSowIIDKEheMsRzhgAAAIDCIxkqMBcYWluiZggAAACICslQgSVqhsz/n2QIAAAAiAbJUIExgAIAAAAwMJAMFVgi7Qk2k6PPEAAAAFB4JEMFRjM5AAAAYGAgGSqwRDO5un3fUlvbXpIhAAAAICIkQwWWSHuam5/UkSO/V7FEMgQAAABEIG/JkJk9aGavmtn2wLS7zKzezLb6/64NzPu8me0ysxfM7Kp8lStqJwZQiMu54/QZAgAAACKSz5qhCklXh0z/jnNusv/vl5JkZu+QdKOkCf46y8ysOI9li8yJPkNO8Xi7hpipg2QIAAAAKLi8JUPOuSckNWa4+BxJq51z7c65lyXtknRZvsoWpUTa4yVD1AwBAAAAUYmiz9C/mtmf/GZ0b/KnlUjaF1imzp920kk0kzO5rmZy9BkCAAAACq/QydD3JF0gabKkBknfyjaAmX3UzKrNrPrgwYO5Ll/eJZrJFSneVTNEMgQAAAAUXkGTIefcAedczDkXl1SuE03h6iWdG1i01J8WFuMB59xU59zUc845J78FzgOXVDNEnyEAAAAgKgVNhsxsTODl9ZISI839XNKNZjbMzMZKGi9pcyHLVijJAyhQMwQAAABEZUi+AptZpaRZkkabWZ2kf5c0y8wmyxtHoFbSv0iSc+55M/uJpD9L6pT0CedcLF9li1Lq0NpDi4pIhgAAAIAI5C0Zcs59KGTyijTLf0XSV/JVnoEimPZQMwQAAABEJ4rR5E5pyQMotJMMAQAAABEhGSowhtYGAAAABgaSoQILe+gqyRAAAABQeCRDBZY6gAJDawMAAADRIBkqsOSaoXYNpWYIAAAAiATJUIEFnzNEnyEAAAAgOiRDBRYcQIE+QwAAAEB0SIYKLNhMrj81Q8fjcR3u6Mht4QAAAIBTCMlQgQUHUOjPc4b+s65Ok6ur0y7TGY/r/Kef1iOvvtqnsgIAAAAnM5KhAgv2GUo0k+vLaHINx4+r4fjxtMsci8e1p71dL7a29qGkAAAAwMmNZKjAemom57JMiDqdU0cv6yVqnBi6GwAAAOiOZKjAUgdQGGrmTc8yTiLRSbdeVzIUzzY6AAAAcPIjGSqwRFriPXTV6zMkKet+Q5kkOh3UDAEAAAA9IhkqMBcytLbU92Qo3Xo0kwMAAAB6RjJUYGEPXZX6UTNEMgQAAAD0CclQgSUPrV2YZIiHugIAAADdkQwVWDAtSTxnSMp+kIOsmskxgAIAAADQDclQgQUHUJBiGuLlQnmpGUokQTSTAwAAALojGSqw4NDaklSsmCQGUAAAAAAKjWSowIIPXZX6nwylawJHMgQAAAD0LKNkyMzeYGZF/t8XmtkHzGxofot2cgoOoCAVqGaIPkMAAABAN5nWDD0h6XQzK5H0a0k3S6rIV6FOZsGhtSWpWJ2SGFobAAAAKLRMkyFzzh2TNE/SMufcByVNyF+xTl6pzeSKnFczlG3CkknNUAfJEAAAANCjjJMhM3unpH+Q9At/WnF+inRySx1AYahRMwQAAABEIdNk6HZJn5f0M+fc82b2dknr81esk1f3mqEOSTx0FQAAACi0IZks5Jz7naTfSZI/kMIh59zifBbsZJU6gEKR61/NEAMoAAAAAH2T6WhyPzKzkWb2BknbJf3ZzD6T36KdnHI+gAJDawMAAAB9kmkzuXc455olzZX0qKSx8kaUQ5a6NZPrZzLEAAoAAABA32SaDA31nys0V9LPnXMdOnFdjyykDqBQrOOSGEABAAAAKLRMk6HvS6qV9AZJT5jZ2yQ156tQJ7NEo7auh676AyjkY2ht+gwBAAAAPcsoGXLOfdc5V+Kcu9Z59ki6Is9lOym5bjVD+R9Nri81Q5/ctUtfrq3Nej0AAABgsMh0AIWzzOzbZlbt//uWvFoiZCl1AIX+Dq2dUc1QH5KhDU1NevLIkbTLbG9p0fAnntDetras4wMAAABRy7SZ3IOSjkq6wf/XLOkH+SrUySx1aO189hnqzwAKHc7peC/N615qa1NrPE4yBAAAgEEpo+cMSbrAOVcWeL3UzLbmo0AnuxOjyQ2T1KGieD+ToUyG1u5Dn6Hj8biO91KmRLLU23IAAADAQJRpzVCrmV2eeGFmMyW15qdIJ7dEWjKk6DRJUpHrXzKUSTO5bGNLXs1Qb0lUosapnQEaAAAAMAhlWjP0MUkrzews//VhSR/JT5FObolmckOKT1csLhX5zeT6OppcvgZQoGYIAAAAJ7uMkiHn3HOSJpnZSP91s5ndLulP+SzcySiRNhQXnaGYpGLXLmngDaCQSZ+hRBLU23IAAADAQJRpMzlJXhLknEs8X+iTeSjPSS9RM1RcNMz7v5/JUNoBFPwkxUmKpVnu0PHjqm5OfmzUcecyrhmimRwAAAAGo6ySoRSWs1KcQuLyhtUuKhoqqViWYTJ06PjxEzGc66phyqRmSEo/iMJ/1tdr9p+SK/mOx+OZ1wzRTA4AAACDUH+SIa6A+8DJS4bMilVUdFpGNUPPNDfrzU89pZdaW7stm0mfod6WO9rZqaOdnUnTOlJqhuLO6UN//rN+H3j2UFefIWqGAAAAMAilTYbM7KiZNYf8OyrprQUq40kl7pyKupKhYRnVDNW3t8tJ2n+8+8hzGdcMpVnuuHOK6URTOudctz5DLbGYVr/6qjY0NSWtF/wfAAAAGEzSDqDgnDuzUAU5VSTSC7NimZ0mi3sPLE3XjK3r4an+Mpk2f8s4GfJjdMTjKi4uDu2PFFYLRJ8hAAAADGb9aSaHEF96+WX93fbtPc53fs2QVKSiotNkOq4iZTcqXKZJTkeGSVNqDU9YjU97yDOFqBkCAADAYEYylGPPv/66tr3+eo/z41JXMzmzYYrHj2uoWdpkqCNNMpSTZnIptT7BGqjE6HdhzxQKqy3qjMf1eizW43sBAAAAAwXJUI51Opc2QfF75XQNoODccQ3pRzKU6QAK6eKnqxHqSKkRCqsZCk77bn29LnnmmR7fCwAAABgoSIZyLNZLMpQYQEHy+gzF4+29J0MhNTYJuagZ6kip/QntKxSSKIXVFu1pa9OetjY5ms4BAABggCMZyrGY0icoiecMmRUVtGYoo2ZyKUlR2LTQPkOBae3xuJyyf4hsT+Vq7OhImnbzjh1aXFOTNO33R47oSMrQ4AAAAEBvSIZyrNdmcs4FnjPk9RnqTzKUyXpShgMopKkZag9LlEJqhsIGWuir++rqNKm6OmnatpYWPR/ok9UWi2nW1q16sKEhabn69vY+vee2lhYdC/R56ozHtfrAgaSarsaODm05ejRpvdc6OnQ4JXE7dPy4OlP2wysp5WqLxdSQMu3g8ePdksDdra1qC5TLOaftLS1Jy7R0dmrXsWNJ0/a1tXWL/1xLS1K/LuecftfU1G0bq5ubk9bb+frrXc+6SvhdU1PSdsec008PHkyKtb+9XesOH05a79mjR7Utpfz/e+iQXg08XLgzHtcPGhq6+q0ltufnhw4lrffUkSPdyrr6wIGkY+B4PK7/r66ua/h4ydunqw8cSFpv/eHD2hgYPl6SHmxo0J62tq7XbbGYvr53b9J36sVjx1SRcgz+qrGx23Z/r75euwP78FgspqW1tUnfqx2vv67vv/JK0nr/d+iQftXYmDTtvn37VBP4vF+PxfTF3buTvnvPv/667q+rS1qv6uBB/eK115Km3bt3r3YGvlctnZ367EsvqTVwnGxradF39u1LWm/NwYPdPo+v7dmjP6fE+uSuXUmxnmtp0Tf27k1a739efVU/O3gwado9tbVJx/nRzk7dVlOT9B3949Gj+o89e5LW+/Grr2pNSqwv19YmHXPNnZ361xdfTPouPHv0qO6prU1ar/LAAf3Pq68mTbvr5Zf1p5RYHw+J9eWUWD86cEA/DsRyzulLL7+s51JifeyFF9QSuMHz7NGjWpoSa9WBA0nHr3NOd778srYGzk3NnZ36lxdeSHqO3JajR/XvL7+cFOuH+/erMiXWkt279ceUWAtDYt0ZEmtVSKxnU2LdsnOnmgOxqpubtWT37qRYD4fE+uLu3Unn3yOdnfrnkFhfDIn1w/37k2J9YffupHNHT7G+EBLr4ZRYnw+J9U87dybdqKtubtbnQ2KtDMSSpM/v3q1nUmL9Y0isf3vppaRz7cr9+/VQHmP920svaXNKrAU7dqgp8BtQ3dysz4XESj0/psZq7iHWZzOM9YeUWB8JifWZkFg/yGGs1OuQf3vpJW0KPJsxEetwSqxP79rVa6zPhcSaHxLrUyGxVmRQrv7EerqXWIMByVCO9dpMTkpqJpeoGcqkGVtozVAOh9YOq+lJbULXHogT1mco8XdblslQayymb+zdm5Q81Le3e89YSkm2gu93LB5Xp3NJJ/bNzc0qffppvRi4UHz0tdf0Tzt3Jr3nwhde0Kd37Uoq+7QtW/TfgS/7+qYmfWjHDlUHfnzvq6vT32zdmhTrw3/+sxa9+GLX6454XOM3b1ZF4Mdkc3OzSp5+Wi8EyvWf9fX6y5SE76YdO5JiHY/HNfGZZ7QiEOup5mZdWl2ddHF3X12dpj/7bFKsBTt36hOBmrT2eFzTt2xReeBi+4kjRzRr61ZtDcT6z7o6/c1zzyXF+uiLL+r2lP31t889l3Ry3NDUpLLnn0/aX//1yiu6btu2pFiLa2r02cBFQXs8rjnbtyf9AKxvatI/vfBC0g/m9155RR98/vmkWJ9+6SV9IXBB1haL6UM7diT9kK9vatLiXbu0KRCrvKFBN6ccE194+WUtDVxYt8Vi+ucXXtAPAxdkvztyRP+2e3dSrB/s369/fuGFpGP1rtpafSUl1sdrapIu7p5oatJdtbVJP74P7d+vj7/4YlKse/bs0dcDyUNrLKY7XnpJlYEL6yebmvTVvXuTYj28f78Wp/ygfW3vXn0zkNS0xWL67O7d+nEgedh45Iju3bdPmwOf46oDB/TJlAuAe/fu1bdTYn3+5Zf1k0C5Nh45ou/U1SWVq/LAAX1u9+6kWN+uq9N/BhK39nhcd9bW6pFAuZ5qbtZ36+uTYv3k4EF94eWXk5Lm++rq9N2UWP+eEuvp5mb91yuvJB1f/3PwoO6srU2K9d36et1fX58Ua+mePUnJ1tPNzfpeSqxHDh7Uv6fEur++Xv8ViNXhnO5OibWpuVnfb2hI2vePHDyou1Ji/Vd9vZYFvscdzumePXv000Byuqm5WQ80NOiZQKw1Bw/qy3v2JMVa9sor+l4gVqdz+srevfpZINYfmpv13ymxfnrwoO7ZsyfpJsOyV17R8pRypcba1NysFfv3J8c6dEhf2bs3Kdb3QmJ9de9eVaWU68GUWD87dEhfTYm1/JVX9P3A+aXTOf1HSqzNfqzg5/izQ4f0Hym/TctfeSXphkWnc/paSKwf7N+flIj87NAhfS0k1gPBWPG4vrZ3r9YGYj3T3KyKlFhVhw7p6/v2Jf3OP9BDrKqQWJv7EOvr+/Z1K9dDBw4k7fuqQ4f0jZRY5Q0NKg/s+5hz+vq+fcnlOnpUDx04kHTcr33tNd27b1/S9Ut5Q4MeCIm1NiXWypBY3wyJFVauYKzqkFg/D4n13ymx4olYgRtPPcX6Vl1d0k3lsFjfCIn1cEisb6fEWtHQkHRNE1auLX6s4Hn1f/1YweutFQ0NSdcO8ZD9FRZrMCAZyrFM+gyZ4l0DKMTj7RpaVBRpn6HUJCisX1DiC9FbzVBbSNKUiXVNTfrc7t1JF9FtfpO7jpT4bSHJV/D9ErUv+wO1DL85fLjb3bfqo0f1bCABOBaLqd25pFqZo/6d3pbAHd+mzk41x2JJFxP7jx9Per/XYzE1dXaqPjAtrFwN7e060NGRdFF44PhxHQgscywW0+vxeFKtyUH/70OBsh7q6FBjZ2dSuV7r6NBrgWVa/W08HEgeE3dwmgLTmjo7dTRlG490diYlna2xmDqdU3Ng3yTuGh9NmdYWjyddALTEYkn7tDUWk5P0emCZxF321GnHnUuK9XosllRT0OrPC05L/N2aMq0zJVZrLJa0TOJYa00pa3BeYlpcyd+51lgsaZnE35lM6xYr5bjPJlZv36FsYkndv+/tvXz/003rc6ywcqVMC1sm02kDNVbqtIEYqz0lVtjgO9lMy3us4M22LKb1db1eYyVuMmY6LbhNKTcLe1om02k5izXYjomTKFZP10x9/Q3oS6zBgGQoxzIZTc4kmRX5AygMvD5DSc3kEolSyv9J03r5cuxubdW+QDOjMIkLzNZMThIhF0eZfIljUtKFb+oJJ9NYYYlhX8vVHrJf253r8zYGy5eY1peTXiYn6KzLlYML8kwuJrItV7qT/UCJldGPYw4vcgb7/iIWsYhFLGJlmVjl6SbAYEAylGOZNJMzxSV5fYbyPYDCEDNvucCBn6pbzVBg2dSaofY083pa7h937tTiQPOqmHN69x//mNQHoq8niZ7uhPQlVk8nklzF6vM29uOOXKHLNRB+OPpzl3ag/qAVOtZAuHuc7vuYSfI7WPc9sYhFLGKdTLEGgyFRF+Bk0+tocs4FHro61KsZKs7fAApnFBXpaCyWvk+Sf9BmVDMUOMDDaosSX4DgRU1jZ6cs8H4tsZiePHJEzzQ366qzz/aWz+UFUy8X6SMC09pSYgfX72l7MjlJZJOsJP4/q4dY/Tl59XSx2tf4w/qw73uadloOT9BDc7i/hvaxeUrqtLZ4XMVmSa/7s425jBX8Pg6kWEGZ3ujoMdEtKup9mUyn5TBWPPA59rcpTTyHx0RsgB6ruYxVlMvv0ACN5UJ+vwZCrHgOY8UGaKzU5sgDJVbYNVpfY/WlFcZgQDKUY53OKS4/6THrNt9rJhd86Gr/njPUW83Q8AySoaxqhoKJUsjyPV1s9+WL19PFRFvI+/X1jnJ/msmlXiD3K1YO9ldP04aF/ND2tZlUb8lQNtOGBi8K+5A8JsXKYbmG5OjCpN05Delle7KZNiSH2zgYkqH+xgqe8QZSrFwmHbE8JVb9jVUcdawevkNFfbip0dM0y1OssHN0Nr+PwWO1v4l7rmK1xZOTof4eE7E8xerv95FkKHzaYEAylGOJL1ash2QoOIBCsM9QJn1/UmuGhvWSRHU6pzOKi6WOjrTLZdRnKLFM4ADvGk2ulxNTri7uO+Nev5++NstKnZaaWKX7wckkfi62MfGeoSfxPp7QwpKhTC7kg++dmJarfd8WT06G+luu03pZJpty5SoZaovHVSz1ab3eYvVUa5rptGD76ExrYHqaRjKU3bR8JUMDKdaArRmS+rRe2LR83VAI+83J5js6EI/79rg3KEyuypWvmqH+JlYdAzGWc6E3rPuauPdlvcGAZCjHYoGEZWjI/LgSQ2sX9bvP0OlFRb0OrX2G37yj36PJhRzgqcNuB+enPM8K4QAAIABJREFUnnAyOVkG3ydpWsq8sPWy/eFwznUrV7qL6HSJSGisLH68eouV9f7ypzn/xNWWg4uJRLmGZXiXNt1nlIjVl2Sop3L1FiuTk3YiVi6SoUSsXCRD+YjV24VcJj+0ieOrt8Qq01jZlCuTbczFhVwuYyX+zkXSkSjXQIuV+JtkKLpYAzEZaosPjmSov7E6nPOeJWk2oGIdj8dzFqu9D7EGAwZQyLFEotJTcuNfpgaG1j6uoVkmQ4kv7xnFxb3XDCWSoTQHZreaocCyqQlPWKLU29DabfHc1Jq0Bd4v0Za56/2yjJVoztiXcqVeRB933ZO00PUyKGuHc3J9LFdPsTJpwtBbrEy3Md3n0R4oV+oy2dTA9adcve2vnsrVW01d6rSuWDn4oc31NiaOr9TvUJSxOkNipfu+pPuMErHae4mVybT+xOq2jXHvgrA9cP7K5FjqaX+lxurPvs9ZrLhXe3/cua5mUQMlVqdzOYmVGCSpYwDG6vDjdabMizJW3N/vuYwVHBl2IMRyzhsBNq4T134DJVZbPJ7TWE4nft8yjTUYkAzlWLCZXBhvAIVgM7kM+gylJClJNUPp1ovHdXp/a4YS80IuFNMNrd3XZnLpLjDC3jvTi+jUBCbTpCCTAQeCMbO9mEgtT3CZ/l6YZBorkwuytsDrXJUr7MIk24vVxHq5uJgIxorlKFbwAiDTC9/UaYnXufjR7roQUP9/HAd6rL78aOcyVk/HqtQ9wc04VprzcH+Pr97KFVozn+a907UqyGRaf2J1OzeHbX8fzzl9nZfp8mHnwkyOr7B90tfjfjDE6um7Sqzk39iBEGswIBnKscSjGXtKbuLyBlDwhtY+TVJMQ6yXUeH8gyq1mdwZvTystdM5DS8uTlo3lfPvAEk9jBiX8gVN90BWqfvJLnFHLtMf+76eJMJ+7Pp8sZrmRzVdrLj6fyHX1WwoEKuvd6L7c/GVbt+nxsq29iPd596vH9o0n2O2+6u/5Up3EZmLWOku8tJtR18vzHorV39/aIk1sGKFjaKYyfd9oGxjJsf9gN33abYn7W9glt/tKBO+XCTumZwLcxErk3N6X98n2+3INlamv2n5PiYGA5KhHOu1mZxz/mhyRTIbJkkqTrO81EufoV6Sod76DAXfN5HcpBtAIeyi+HjYlzflSxz84iUesNrrD1oGX+x0F22Z/mhne7Ga0XtneJLIxwVAJhe3+f7RznZ/RXnBlMnnONAumDK5kA2b1tfPOPh3Pi6YMv3RHuj7nljEItbJFSuTGwMDdRvT3dhNdx2S63INBiRDORbrJRmKS12jyXk1Q9IQS/+g1p6SoTOySYZ6ODDTNYmTujdPCKs1SvyfaFMqhdyZy7B5VaZ3vrKZF5yfyR31sPUyPeH09UKuUIlCoZK0dInoQK2dSPfeubwbOhguAAbqj/aAuqud5XeorxdTYdMG6udILGIRi1ip0wYDkqEc6zUZ8muGJK/PkCQVK56+709KzGyayfVWMxSW3AQTp9SaoVhgG1OH1k50NpbCvxDZJh3papcymZftlzfdBU2+a3P6Wq58nbwKXcuUyfYPtHL1dfl8XZAPpG0klrJqstKeZr2waflultPXc3Rfy5zrWG0p0zI9r/b1+zjYj1ViESufsQYDkqEcy2Q0udSaoaG91Qz5B1XoAAppDrgO5z9nSGmSoUxrhgLvczzuPUAtUY5M+g4F/073Bcr2zmpfY/XrIieL9872hJPpD21/ayByeSIcaBfk2V7kDLQfjnweq329KB7I20iswnyH8n1ezedxP9DOOZmcj3o7rw7UGtFMfocGQn+aTGPl47xaqH5RbSGxMjmWwqb155gYDHjOUI5l0kzOG03Oe86Q5NUM9amZXAZDaw8zk6UpT2jNUGDZ1MEVJO8gTzxQdohZ11DX2XakT7d83J0Y2CEfd8izTToS08KaAmaawKW7g5mPH+181yikvVMcNi3NyT6TMgT3fX/3V7omnZluYz4u5PqzjZlcwGb9GQ+wH+2BkCgQa/DF6us5J4ptzPS8Omj2fYY1jwNh3xfqvBrFcZ86rT8DH2VbrsGAmqEc6200Oa/fTGozub71GcpkAIWhRUUaatbjcj0NlpB4CFxPAyckpp8ZqHkKO4FkfMJJ+YHqz8V6aqywxCrdj1Bocwv//8QzOTJ973RlTjxHI2y93rYxXaz+nuwTz0PpS6y+liuTC+be9n1W5QqJle1d2rD91d/EPafb2I9jotCxsh3spD0kVn/vuuY6VjYjS7bFex7+PvEsmoEWK3heHWixshk9M11/1oEaK3jzMZNY6Z6vFGWsdI8xyPbG0ECNle75SidLrN4e+zAYkAzlWG/N5OJS13OGEs3kii3W5wEUeqsZGmLmJUM9HJg91QydXlQkU88jxiVej/CToePxeNqLyeDf6b5coRcaiXnBaSnLpXsQay5qQVJj5iRWWNKVZlq67U/3Pj2Vq6cf2uB66S7us43V33L1pRlErsqVy1jBi4n+NkFJ93ylbMvVmeYCoC+xevpxzDRW4vNM90Ob7d3NKGOl+xydeh5KP9tjIhex2gKxerqRlG25FBKrr9sYFquv5+iwGMSKPla659pEGSvxsOmsYvVwXh2osbK9KRc2LThvMCAZyrHMBlA48dBVqfdmcol52T50NZEMDUlTMxTaZyge12lmOs2s15qhrmTIudATYWIY7eC0bL9ImZyowqb1NVbwojBdtX7azrmB46ArVhZ3zMKWz/TiK91d7cTycfX8TKRsT3rBi6/+XmBKPT8TKdtyBWNl01Svp1h9Pb6iitVbsp1N7UefypXDzzGXsXqKkctY2d4YKlS5TuZYmfwmhC0vhd9I6W2ZTKdlu14mx3223+N05cl3rF73fcq0/iTI+YiV7rudyfZEuY3ZfhdyedwH32cwIBnKsd6SIafEQ1dP9Bkaonj6gRD8eWE1Q07qujMcFPcz+yFmGpomaeqpZmhoUZFOKyoKfRBre/xEzdCZOaoZ6nZ3L+zEm+UPWrqLqtBYaU7kmSQ+PW13T3cuMz1R5fJiIpN+G32JlTot3Tb2tR9VFBdf2W5jlBeFmez7Pl/cF/CCKZtzx0DZ9ydbrHTn8oF23Hc7VrPcnkKVKyfnnP+fvTcNtuTIzsO+zLrb2/o1uhtLY+kGGtsMBtsMZ8iZAUbiZoq0TFGWLVKUOKRISrJDtOWgIySKkimHRYkStYZtcbiZpORwaImwLJNSUKJEyuQMhxgsnEUYAI1lZrB1N4De37tvufdWVfpHZVadzDpZlXVv3bcAtyI6Xr98N797MuvkyfPlOZkZuHBz0PVrgfXuxxp5fM6D+syNDAkhflkI8Y4Q4suk7JgQ4j8IIV7WP2/Q5UII8b8JIV4RQvwnIcSH5iXXPB9FQos+MpSoVF+6Gh4ZqkqTo+X0MZ/J0+SaRIaUQk/Xy+8Sop9L01JkaKTU1AOP/r9y9SJw1cnFmiZ0v1+TdlWfzFuupqt78+qvECd6Hu/Ravcer9Ie1AntoGEdhJX7qtX8g9ZfC6wF1n5jHSYiWmlzmM8c9Khv04WBech1WJ55Rob+MYBvd8r+MoDfUkrdC+C39O8A8B0A7tX//hyAn52jXHN7EuLs+PcMqdKeoQ7iqQ9QAIrI0Y++8gr++quvWp/pTrFnaJym6AphRYZcB4DdM1TjTLqDpGpfQV36QP65Csc9dBCH4Id8n0/+NieOEEduLka14Wr+vI19U9JxUCfa0oQ7A7E66G086JN2Kw5TlS3h9LeibD/Tharkn7Y9XNlBe48LrAXWAqtdrMPyzI0MKaU+DeCKU/xdAP6J/v8/AfBHSfn/qbLncwCOCiFOzku2eT0J+X/VaXJuZEgi8AAFrVzms31Nhszv//HqVXzm+nWrbJrI0EQp9KREj9QbK4Vl/X1cZMjdMxS6YlJy7pkJtGryDZm06xyTEPxQ4lOZHhaA1YbxCl3V3gtDOI1OzEOuaXPbD/MkFKrjIU70QW3jQcUKXcw5zG1sA2sehI+zvVU2wcLY4/000y4yHZSIaAhW6Jx5EBYBQhZg5r3w+G7DOizPXu8ZulkpdUH//y0AN+v/3wbgDfK5N3XZoXqCIkOaDNl7hmrIkFYqGhkyJIeW76bFvh2LDJE9Q+M0xc+fP5/vMzLlFvFJ9QEKUloHKKwR4uPuGRqR716SstXBOKuzehgc3/2QyzehhWJVOdtNyeNBjQJUtnGPonJNZW3qFAaNocB3Nq1j0lTWg+4AHHasaReGDlMbF1gLrLaw9oOktWlX57kIcFiefTtAQRUX7jR6hBB/TgjxjBDimYsXL85BsumfOIQMMWlyEgkSID+61324NDlDcuh37aRprtwTSoYI0fnNq1fx3770Ep7Z3AQAK8JTOkBBX6hqPrfWye7oHaVMZIh893qn03jC9E24vjJ2JSpNrbscZt1syt3JMW2kg7tHI9SJ5trY5E6OujZWydVkxY/DaqPvQyeJJu8xdEJr2sYmK6tWG1vQ1XnqxEHAanoRc1XZNFhNxvYsbWxyF81eYTW9P6YJFr3TpA2sqgMIAP/Ydu9WmVWuabFcnVMk62K0j3JNixUyd1iXYCvVmlyzYlH5xwzWtAsD5nqFWeSaB5a5X6lNrMPy7DUZetukv+mf7+jycwDuIJ+7XZeVHqXULyilPqyU+vCNN944V2GbPiGRIUN47AMU4lJ9+vjIUCc0MiRE/vumPup6S/80k+VKFJUOULAiQ0pZJ8eV9gwRg70eRY2MBHfZoflMBNsxMd9HscxBErtpau2BqjRw3IpGWlw2SyNdkpFLOvim3m4NlmDkEhX1zOfMQN31yOUro/XMz4jBihi5OkJYZUbfuLIRef9uvaZYVK6urkcnoa6D1TMykHbTMlPeY7B6Hixa1mew+gzWwImI9hmsAdHVkVOPOj0DKS0S1Sf9bMoM1oip14ZcS3rPIJ1o3agvh7U0BdbIkcGULTNYyzrSTSftZUYud6zNijVi5Bo5WHEFltFL6bzHZfIex6SeOUqf4tN688Ry+8vYe4rl66+eECV74uv7FNlctRdYXT1n+rBoGxXsi8T3E8vcH8NhrThYZr6nZSsMlikbHxAsc6+NKRsR/XKxIpTt9tyw0sw3CcGSKOaFg4I1UqqENVLKqmcuiA6RS6A8n7hyuViH5dlrMvRrAH5A//8HAPwqKf9+farcRwFcJ+l0h+YJIUOJjgwB5NJVTYZ8+3q4PUM0TY5Ghlwy5B6gsK1J0I7+nY0MaQNdigyRk+PMZ2m0yEykRzoda7CYx1dmfqcEw2C5UaZlKdEVwnJM1okMpv6RKCo5q8tSWlh0EJufRwjZMuXrnU7JkVsnbRyRehTfyMVhKQ/WLunn3QAs15E7ymC5jhzXX+udTsmRW9d9aLBWNRZt91FGrqOdTsn5OkreoyG1rpPjw3KdHLeNK1FUciZ8WEpjcXLtpimWo8iatGl/uX0P2A6A219LDtZummKd0y+yoOBr43IUlSZtXxuN3CMGa6Tl6jtjiKuXt9Fpt9tGd9Ku6i9X52i9gZReLLdeEJYzaYfK5fb9UhSVHIC6vvdiSdkMq0In9hNrNy3svTW2VXZh94DY2pFStn6RelzZrFhUVjq2B04bfXrvK6PjpSRXi1i7Dhbb90R/6+RaZ2Twlc0Di+v7uvHolrlje4HVHhb1teaBdVieeR6t/c8APAHgfiHEm0KIHwbwtwH8Z0KIlwF8q/4dAH4dwFcBvALgFwH8+XnJNc8nJE2uOEBBQohsz1Ck4so6vj1DXGTIKJ9vz9A2UWIAVoTHigxJWdozRFPiJqSeqUMHEF1h5KITIGXWRFhBFEZKoc85OR4CY/qGcwqNg+k60dbAbmjsXWe1yvGtmrRdx6TOIa9zCqucnKpJO8gQkjaO3DZWOF+hzkSIge5XONF1E4dPrmn7qzUHwCFWdXrvyjVLG3cZrHk7hZxOHBRnoi9EI2Ll2g7aHvY9TisXg1X3HtvUiQXWAmtaLMtOOHM0V+ZGxN3I+bywWFtI/JW8PQdgEYDD8tnV0DmtDXt/WJ55nib3vUqpk0qprlLqdqXULymlLiulvkUpda9S6luVUlf0Z5VS6keUUncrpR5SSj0zL7nm+YScJpeiOE3OjQxxdZRSOa7vAAVzRHWsinxpSoY6ZM9QUGRIqXJkSCnrsARuz5BR/COOI+euEuymdiqN6wBYKXeO8epXrVaR9jdxmNiBzRjCqhXfaZwvSh5CJyGfwalqY4jz5ZvQ6lYwp3buGQezSaSjNbnohEb6yzdxhDoAsxKFqZ17hoiuR5G1OME69wxx32+Haa8n7boV8iZju87mHDQsl1iNlLLsqovlllVhtSlXI7vKjW0SEZ1ZroAFmFmJgk/vD8J4XGAtsEKwDsuzbwcovBuf0NPkTJpcsWdo4q1Dy7ynyRElNIbTd7S2iQztaFJEIzw0Ja4npXXp6igtDlDgTpMrERgyWJaZqInPkTd/z8sYohAyoXEDlJscmziYPrmmXa3yydVn2tjWyv0sDkAdSauSq2mUqarvG606ps1IR18Ir07U6mrLk1AT535WuabRe9NfQZHaAKz9nrTrsJoQKw5rL1aP15l6vrKpdOKQE76DKte0ke02opjzxhoRrNaI6B6R7dIYqsiA2e9FAG/fM/PjtHPaNLp6WJ4FGWrxCT1NDkBwZMiQFYHyniGaJkcVHeSzORnSv5vIkPl8ZWRIFpuLU6AyMkSdgjXnAIXQFd91BstdpXUNNHUArMEYkI5WN6FRGbiyaSZHr+NLjOp+EIVpJsfRlG30kce9ijKV5Krpr/1wJtpw7kcMVhPiHhIFaMspZMc20Xtr0g4hadykfQiiX3uJVUWsfDbauwCzT2lMVYtMdXa1yaLJtJHHdU9Z6ILV3BY6FljvKay2SNo0ch2Wp7PfArybntDT5LKjtaX+14FU/siQIUPLUuapbdzR2jtEOSlW1xcZ0j/ZPUNpcboQvWCVu2doxTlauydE6cQpbgDd2u/n/59mMNJJjnWYPGSLc5g4B4CLdFRNtLOs5HBOzshD+JpG0vpSok/+Xucw1TkAtA/cNnIOQFUbJw5WqMPE6YSLNQt5TABvfzV1mFysWYhV3KJ+qYo2ujrhttt1VpV+P6ZsGsfXJ9csk7Z7mtzU0S8pAWcM1elXvqLMLAzJFnU18owh33vcK6zkgGK5x0EfVqwqmyMCDkcJtV9tYO0SLNOWWSOiEZVrjlh1fb/r9FdXiNYWAdrA2mWwZp3TQg7fofiH5VmQoRafZpeuZpOdEL08TY47Tc5EdJajCFtpdndEVWQoBfL9QwBzgIJnz9AKiQyNVXbPkNSkh0Z8AD4yNNb7dcwqBD0xbZZV7SPEmRilNukw97RwDjmH765EU7nM/RRVhzHQMnpiHl1ZpVicMeZC8O7pe5QMmT48ytTj8I8wk9AopI2BaUzG6HJtDO0v42CaQzdcrDpnYp2ZOCYaQ2mdDXWi15h0x9hpY2hEgeuvROMauUKdCXffHSVWKdPGKiLq6kRfZom6lk406PuSI+dgNXmPHJZ5jy5Wld5bq+16PJpTFEPGozuGXLkMsarqr9A0E0n0fjcNJ3yuTrBYgWObw4oIVl1UblSDlbQo17RY3HhUDlawru4RlilrSqzoXCtmjIgeBqzogGKFnB562LDooVaGDIViGZt8GJ5FmlyLT1iaHPIDFABAyh4iNfbWoZEh8zt3gMIOUbqRUrV7hozSjjUWPSxhovQ9Q3rPUH5nQhQV0SJl7xkyjpwhKwbbDKCgNDnHkes5+zfMaXImpaDKAXTLImTEzZ2EDL5pT1W+sCmrin6NCFZdBMa0cYlpY18TD04un/HqCWHfa6IUK1eI8+X2/W5q5x5XkU7XgTVtdEmaixUqV1XksW7FjJb1jVxkxYxrI6tfzgRA+4tbBBh56nF92BcCS3oRwDim1nt0sHwrmG5/VcnlO1K+ro10cSLHCniP/UC5mui9O2lXje06LLOYUzeGfCvRu2l2PcGyjrinzHs0NvkIxWLsxIhgTWqw1gL63sXKnRzdX+b+mBCsjhBYiSLrWH7a9waLHss/b6yRxlomWGa8G7tK76KRLpYzFiJki4X0fiWa0tkqVprmWBFpD8Wic9qKtO9Xoumh+V00UXGNgU8u2TLWahTlWO68nd9FQ05z9WEJjaVQLPpSm2OwllvGMuTB1/cCdqaMi2Uum6bXK/jkwh5gmTGUEqxeAyzTbqqrZlFuIGUl1mF5FmSoxcd3mtzff+MN/M61awAABZMmV0SGQtLklrRyTfSA9UWGgExxS2RI/52LDLkXrI71hNnTToExCD0ymbCRITLwgGJQVUVNvBOaY0BNOZ20Qx05y3Ggji9pj1uPczBHTnuMs+oar90quZwyDov2VyWW62ASElWFVefIUQeQ3q9EV4VMvTVGLvfdGiyz/8x1MA2WcXKqokzm/VOHiev7VU3cQ+QypzG6/UXlyh0Tj2Nt6vmcCSNDiDPRZ7C497jKOAAuETX1jAPg6/sVPdFWRU1MPYBf6DAymLvAQnTVYNFFANrPS3qi5cYohzVyyqgMA+Z9+HSCYnFtXGoZyy2bFauKWLlYLrHi5OIWP0KwTD1zV9MscoVihfSXqUfnhdLih0M6uTJXv3y2lnuP3Pxo6WoNVt1cOy+5QrDoZcy+uaPfglwGq402UqwmfR+iXzPJNQOW0VXXZzIHBVXaVb1gNatch+VZkKEWH1+a3E+99hr+6dtvA6BpclnXS9mHrDhNLjQyRJVuNy1WCs3nDDZ3mlxPOxypbgMto1GgPlNGj9Y2A8h1TLgB5EsfoGVmENMLPClBcp0cbnJ0HSbj+LoTmqkX6qz2pX0bOGcQ2JUvj1H1YRm5zKWb3KRd5TD5nEJ3JafK+RqnacmJpg6TO8m5q2jmPfp0gnW+akhaHVbjCa3iPXJldXL5SMe0zgRHrFhnwkO2ubLQNvr0q66NIWS77j224TA11Ym+g8WNIa6sqVM4rzY2eY8LrNmwqD3mytxotKX3boSPzONcmW+hA2h/PM5zbJf6Ky3P200J8ryw2L5ndGKv5QrF2nWwOL2fVldD9N5832F4OvstwLvp8aXJjTXBAAAFO00uiwxVpMmlhTMM2GQojwylKXb0/wE7MtSVkt0zZJR0rFQeGQKKAxN6QiDVqXNGwXuyuINlnGZh+p7MUgO4wbibptjxTEIhG/isFV8uOkEGY8kxqXAKXcd3YzKpNBK+KBNXdmUyaWy8+gzWrpTY9chVcshrjNdISrxd4wCErjqOJO9MjBpi0T1DVQ5mnWNiJl9zuS43aVc6qwxW7MjF6Veow5QwWNM6ExxWiYgyK+Rc1CQlcvr6PpRYKQdrFgdTETldrKaTtiC/W30fuKBAy9y0rLoxRBdu3L53I5b0PVaNIY4gS/1/6z1OiRUx47HWTpDFL1qWuISPpNc0waoaQ9Pq17sdK0RXDztW5OjXQcFyI+LvRixucbXKNzksz4IMtfj4IkOTtIjUmMhQ23uGjBIC5TQ569JV/TkrTU5mkQKgIG5dKfP8UpMSl6fTqSxX1BCoPJ3OpLpoLHdl4noc53KuBeSQc6QjD+/SiZY4OS6WS3yoXHlaWejAZuSiWG49riwk/YWLfs3irI5k/UpOHVGg/dXEqFatfE0CnBxfVM6VtQqrCekw0UOqS1X6VSWXwZp1EjKymr0PVYTP7XsfqQ1xvkLbeFCJVRPnqw1idbTC5tCxEJFx4L7HkBVf2kY3ih3yHn1Y5r46a5FJR9xLdrUGK2lJrmmIFZ0n3L5PgXq7Gqj3rq62jRXSXxyWq/ezyDUtVuk9ynYXAVy9n2kRYMq+P0wEZr/IkPn9MDwLMtTi4yNDY6XyCE8KMHuGRqU65snJELNnyBytPVEKcVLsWNpNmT1DFZEhsz8I0CfF6WhRqlPiaI6vmRxN9AhAnjpXiuY4g4pGJ7g8eTdX2025o/gj7bAC1avHu2nhTHJOtFuPki0fUfA5mK4xDjH2VQSGyjWLs9p0JYdzCn1yzTJx5NEcR66qvmcdJtlOlMmUmZVoru+bRhTcVe1pHRMOaypildoRmDYc8jaIlRnbivxusOqIlU/vESDXvqxqz4hF38esxIrD4uRi7WoFVtKiXCFYoRHRaXS1Tb2vw+L2rZSivsreGD+Nrs4rIur2PdUTdz/oLHNHm4sAVVicrnr1S/tklr2fMiLaFpbxo3qy7E+4C8HzShk+LM+CDLX4cGlyiVL5fhDAs2dIR4bYo7U9kaFlKa0DFFJSlz1NTivllu8ABY1lyrsikzJFsb/IFxmizihnoJeiCIMk8RscZ+W+zni5Rm+a1SoaGXDlqlt1NCTHLXMNKJ20fVi+Ca0qHa2ps+piTZWORrDc3Pa+027WMWFW5Mw+s6oJLdRhmjbKFEJgKuWqiSiYAyOakFruPXJRplmciVkjMLTvZyVWowq5uCjptI5cSMQqlFiFRr84UjsrGToMWCGRof3A4khHrquBEWRTpsjvbWFx9p5rY5tRzHljueOxTeI+LyxfRDSftz2Rx64jAyfXeqBcTbDq9L5ErGaYO0IWVxdk6D3+cKfJ0bt7gOw0uVKaXFIRGdL1uT1DNE1uRJRulBZpeSGXrvakzInN0BAfbaABYFOX0bQyNzJkBuixbrdYMVHVm9JrUx2kQzqUsmQIIR2jNgY2cyAAh+XKQLFGFVgcsdp1jVfAqpArl0VgmP6iWO5pddQp3NFE1mBx/WWlkNHLJ1M/6ZgwBrpJJG2HYgVEmdwjn11n1fRXyJ6hqrQv2l/ePUM0RdHoBONMULnYPUOufgUS0VQxEasp+37WFXIXq3YM1SwCuCvRlXo/xz1DPqfQTRdix7Y7HsmK7w4pkyF2IhArYrB8ttCyE4x+tbnPZx5k6N2MdVD3+Ryk6Oq8sAzp2KnACk0ZboJVt8hkiBWHxR18VEXc3YiSNdcyWIflWZChFh8uTc4P9aNLAAAgAElEQVSQkDxNTrkHKPQh012rDn1MGbdnKPhobZnlSqdKldPkUjsyZMiQIVq0zD1hrrRnKC2vmNDBXutMVAxGQ8g4x5Su0o6Uyu85MDK4RmI7SRAr/s6ivEy3x9wLQduzRLCGSYIUvBPilnWEsOTaTVMs62NiXayBx+CYeqFyDWR9lMnc72H6fjdNseLIpcCvhrsrTCFtVFqGiawmaR0hcr3fJXIZ52uTyFUXGeo6bRylKVaJXBTLLC64bRxRuZw2rkRR7mC6WKFymTJOLkOGfKu0Bov218jpe4qlauRq0veGWO2Q/qLEisPytdE4hT4HoMP0FzeGqPPlYvn03u0vqqtuig+HteyMIZ+dkNJePKDv0dgcV78o1hZpY6R1wtdfTbDcvXK0ja7N4RaGtpIECdHVMZHL1QkJlHSCEiuD1SdY2w2xaN8buVJk8+W2s8Bn6gmfXATLzB0m42OrRSwq14DBooRiQLC2kgQTjSWQ+Q1Dj1xgsPotY9G51mBJZAvGQzKXuz7AsrTJiTVvp2l+r010gLB20sJfMb6Wi7WtF8iA8imz+4k1JFj0fiUOi+qcgu2TVWEdlkfutwDvpodLk5s4P1OYNDkTGepDTLtniESG6KWru2l5zxBgh/RLR2sTQwiAjRYZ536sFL9niERuTD0z0VaRoVFaXARo5HcN9PU4zmRwJmiKRSdo93x8OtFerzB6LtmiBoGbhIxcIYTPlxts8K8FYNEVIPbYXre/CCHLHRNnxZfW203L5LGuv1ysHtNfvjZyjpzVRlle5RrIct/3RXbyoeXkEMPel8UhISFyUcfE1dWBzDak0siDTy7jZPocpr7M0l19jokrF3VMuL7vCGFFHqrkSlGMd67vO9JOIWmKZcnlOBPWeHTGtjtpu+Mx0gtB3Li6RvR+4Jm0XSyfA+D2fZtYALBRgSX1AhUXLXTHIxi5aL1QrD4jF11k6ksJIYR1gmGdXG4b6UKXweIWv1gsj30ceOTy6Renvy6WW+aTIUSuJlicXCMHi7OF85ZrWiy3PW6ZZQuFyN6j5OfMDWdOs94tU68Ky60HlG2Hi+W2kdMvU3bNKTP1ekYuZ26aNxZnh2jf9ISAdPuL2gQqg9tfDlbXwTosz4IMtfhwkSGTJkeP1qYHKEjZR6T8kaGq0+To0dpU6UqRIWdwAP49QzQy5JaZyNAoTUt7hkYVg9E1oLRsxAw81iGnxksIy/nqMwaBEsA6x2TMOOSusyrBpzG5k3YCxsEk9aizWuVgmjZWOr4aL8RZVfA7mH0hSg5mnSPnRuqMDH1tCOlFmdTRulYxodU6OQETbZWDyU0cVW30kcBaByDAKWzqTFQtDND2VGFxbfQRcPP3aZ0cM7ZHDladrpYmcsYBDJUrxGEy/VhHrHxlLtZe9P17FcsdV5REUf0KGY8sVoiu7iGWO6/S/nLTjw9aG/ue9tAyevFnCStwEaDKRlfpVyhJo3K576OJLWzTrk6D5SNIPv1qU67D8izS5Fp8qtLkDCkq0uQyhckiQ1etOvQpRYY8e4YsMqTKBygAheEASJqcUliNonJkSK86AIQMiewAhetJYkeGZJEmVzWhVaVzDGSxcs8RpJDBeC2OS6totQ6m/k7jrBojMZxM7BVMaTudbhu5icmUmXuMqFwcUXDrcbJeCTRetO9NNNDtr8tELherasWMe7eXPFhu6iSVYeLIFeKs+to48ehE7aTNEPeY6a9p5TI2oTQWpnBMEs97dCdtVye495gyZdM698rTxmkcX1XTRipXHbESFVhNnYlpsTj7JZ3+4qKFrn75VmTbxIoYLK6Ns+j9NDrRJpaZYd/NWOI9gBU5UZPcrk5BrCLHP6JY7JxWEcV0fS3L3jPEfb+xuLmjbh7iiK5bj/bXYXkWZKjFp+oAhSIyZKfJZXuGdrLPMIqTH6CgFcy3Z4imyY1S5wAFXdeEOlejyIoMdTudcmRIyjxsyB6gQPcMCf5obY7AcIaQIzC+wdhnBqNv0naxQuVyJ3uK5XMwffh1jolPhpHH4JQcE0+I3HJoZnXIK4gVl3LH9RdnVHMy1GB1z0fSxhX61cTx7QuBmCkzfdNUrpgpm9YBSJj+6jNy9YkD4COixvmqc+5DoiYHlViJFvXeR4ZcLCtKyumXLAhMGyQtahGLWwSoHY+M3evLMoGZhaRNi8XpRMphOTYUCLOrrq62jVU3Hr16D5TKph5DBxTLR4b2G6v7HsDifB8f1ihdkKH35BO0Z8g5QCE7WnunVN88pp7ZA+O7Z2g3TfONgLtp+dJVoJisjnc6eHsyAVA+Tc6KDGkZ2AMU0iIyZJwv4/hwEy03qAZS4oqOKOSr7aI40pLLF/ZN5Ozk6GD5ZOBkdbFyx8ddfQnAqnSiPX0z8sgavCpEsbRB8k3aTRwmX5SJkk7zHXXkceJzVkNX9+YUZTKLGlXE3er7CrkMVpMoE0c6BnL2FXJKonIyVJHSGdr3bRIrxU2+jK7WOZgD6SdDbRKrqZyvKmLVcGy3ieWOIaOrObFuoBNVUcymNqcpVpVcaYtyVUUx9wqLSwUVznzfND20DSxX7036NC0r2ULOTkhPFJMhlG1iuXN0Vd/Tei7pmEWuNrF6XN9PYVeryjj/a3dBht6bT+VpcoYMYToy5KbJRQAiTVhipbCTJFjvdHAljv17hrTjc7zbxWujEZRSlXuGjPHKI0PaEI6UytPrgIw4bSVJKTI0Sw65d88QNwFIvyNXNQmxEwB1JnyOnEeuqjY2dUx2GYM2reM7YvA5Yx+yn2bEOIU0OuGSR2/fS5kfN+8z9k3aOG2UiWtjzLRxakcOYPvel1JQpat1UaZGERgt10EjVuZpsqDg0y/oidhNNa3Te26yN1h1DkBI37vpaE37/jBg+QjMNDrRFMtLFKSfdEwTLZw3Vn64hDO2aRmH5RKYWeRqE0t6sKaxq9EeYZXmbaUqsdz92bPI1SZWr+I9NsWq2xPuYh2WZ0GGWnyM0e4LUU6T0z8VFLLzkjKFkbIPkQaQIa1gNE0OyMiOiQwdJWTIOGFSxaXI0LFuF0CRTmdFhrScPVmkyXGRoYlS1p6hHf2d1iTEbHg0E+aSPsKWEh+AWeVwiBxHRMx3TuPIcRN5SNSEI3zcikyIXD5CVtXGJqkhvpQ7bvXYkstdFZQSfcYprDKE5hQqrj3cSvRgyja2GWVi9wwFONGcXFVRJre/pokycWMoRL/MFFW36ugbQ3tFrKh+GV2y+qsiT144WLM4EznWlCv3tG8Oy56hpv3VZhRzXljz2N920LCEKEcB9hrLtTkUi114bBoRrSJpLWDRBT5Tx4whXwrsQPoJzDTz0DyxLLualhcxq7B8ZGixZ2jx5I9xogZSlg9Q8KTJCVETGUqLc9sNHiVDXU28dtLUuvTPGJ6nP3cHdm76OQDHrDQ5IDtRbqxJTenkOBoZcibaUarT5IiRsPYVeQwoV+ZzvkKiJtdJFMsMxt20HLofK8U6ABx+z11tJ3KZ+4l8ctEyQeTiHBN6xwTXNyMPFmcIt9I0l8tHyEyZREagfRMavStk4PSzK1cVFm2j7/1TMkSxuAmNysU59zTKJAF0KibtIYdF9DcnQ0liYXGraJZcTEQhZuTyke0h0/ccsboex4gMlmfSpve0cLpKU9vq3iPF4vSLEitJ5OLGUF0blYtVoat1+iU8WHX6xek9JVZVWH0dJXd1wlohJ3rP2QlXrnxsM1hRi1ixg1WlE/T+GE4nXALTJelVO44t3NojrIEs7xnqkWyHHWfuMHfR9IXI5zorpdMZ7y4W7Xt6rw0nl0uGesTeu1j0/hgu/cklMMYGmLuaDhqWuavJxTI6Z64xsKKY+4WVFBdqm5Nhq/bmGCxzJcKBwdL6Ze5X8vWXuRLBXSwOxTpMZEjutwDvpiePDJGJxThpVWlyEbL9O0GRoTQtR4a08i5rpR8phUma7SGK44tIJ28BKCa+4zoytEtIjbtnqEsM2lA7AJGeFMY6Tc4MsJ4Q1jn0ZgBV7RlySQcdQPRkt5Lj66QGGCNLV20oVm0UiDg+Jkc6d3KoAyAEH53yHPdp5OKwBlKye6B85JFisf1VQe7csr7kT8cL7S9fGymB5eTinJyqNu44RtVdtatqI3XaOCLqI4q+vjfl3OlC7Ck7NXIZZ8Jy5ISozPHm+otiuY6JuyroOgDcGDITbQhWSBtjpbDlONGUwHYcuXw60Wcm2j7pe2pzQsYQN2nT99gWlpGrU2E7KJZisDYcuShW1XhsEwvIUqV9OiFFlopdpRMWlqMnG3PE6lVgUXsPZPtS28SixIrekSMcLDddu0quviODuS7At6eD3kWT4zNY/TlgDRxbS8cQvdemhEX7i9gAn93uzgHLnTNNf9MLouuwOiK7ssInF724et5YAwer4+BTu0oviDZl1xhdvRbbi3KmzE3XPizPggy1+JiVW0qGTGSnSJPjyFBWs9GeIZFiPH4njwzR8PTIIUyRGgMoHHeTJmciQ+ydQmQ1bJgkRRRIiFJkqEcmWjoQfGSoyol2HSbOubeMMfm+CePIscSnxsH0re5xObg+g0MNqLkM1Od8cQ6mz+iZVaG+p41mVSjEWTV3InmdVSlLK0xsf4niTiQfsTL3GIW0USFzvnxtNJdbVjn3phyod+RcJ4ebJFxHkZPLOApVDibF2qiQK8fy6b0rl1PmyuWWce/R14ccVoh+AWUH021jEyy3jNYDyvprTdDkM1wZHcdcWSiW219cWVXfu2V7ieVzjrg2Uh3nyqr03i2bJ5ZFmqmTu89yzYTFvMe2xtACa4HVFpZZjDwszyJNrsUnDokMKZT2DBkyNAmJDGkytL35DJ555sfQEf8iP1r7WLebkxUpJToCgEJ+qaubJpdHhhziA2SpCB1CkIwjRu8UyvcMCWGlCJo+CHGiuejEFX3S3UAWq8e+aBHFMmXUWbk+GuVYxolu4sj5yF2ds1qSizj3fceRNys5VavHrlxcP3OrQgNpn0w3iyH0OYXcux3IsoPpOgD00IM6o1rlrFKsEKewztjTdCEqgykLdaIH0k7xqZWrxmFKAvuLa6NLFGhq26zOPU1tq2sjh3WdwQohfHXvke7zmdUBaILlttEd7zIt9t25dsJ6H84qPVcWihXyHumeoSXZ/D1Oq/d7iTWrru4lVh0R9dlVmtJZ0tWGJG0WLHdhiGKVxnZDuejdQLO2kcPqM1juHM1hdVuUq02sXotYXN/4sA4TGZL7LcC76UkIIfDuGUJ5z1BVZMiUuZEhkW5iPL6Arv4uuglyN48MZRgRbDLkRoZ6ZLDkR2tLyUeG9M8tZ8+QeXxOoW+SGCtVTokiK3nu6rFLFDiHiXNo6uRinS9PFKAu+tXE+fI5K77+4uSaB4Hx4Ye20XUA96ONvghPyVmVNqkZzNhfexlR6HMTueOE5P3FrUTXvUfPGOL63iWKbbVxmr6fdjzOqvcDaadXzYrV2iqtk8bElVXaQp9OkFQaWmbpBPkMVzZPLLe/qnT1MKy2L7AWWNNgceNl4ClrU64R49Me1EfutwDvpoc7TW7mNDldz6zUjdM0iyupCYAUXVFcurok7TS5SJMhqbLoiImumD1D5kAAX2TIrEzQlCVDmjbj2IoMmYdOomNCDn0ODcCsvjDOqm9gu1hu6lGVc+86q/T7XPy+tHPuzU+aHuiTwfQhLdtksIbM6guNyg1IGa1nTgD0lZlymkLYJ2XUKdhmsLYZrB0Gy00r3HWw6L0DfVlE0tx9OFzZyIk8umWm3GxSpp81B2iY7zV6WaerEwbLLXMjom6ZKY8ZLFpGo8l9T1ko1oDB8smVACUst8yk//rKqrBS8n8akep7ynz1qrDod9IyDstX1hYW18aBLFb+aRsVg+WWmXq+sgVWu1hUL+k748r6nnptYXGy9pk27rVcoVghem/mAAtLiLJcTpmvXltYnE5w+mW1mxB3ReTxldF6s2AdtDFEyw5TZGiRJtfiU5UmlwBQSpE0OUOGejDTp2/PUATkZGVHK1ekslSyDopLVweyuCRvohS6WoUjfVrd9Tjb8HZER5k2aRSIiQyZNDlz4pxpm2kP3TNkHtdgAvYA2kgS3Nrv5+VAefWYnkxnyriV+xADzTk5nCPHOTmccaT/d9tIf3eNlytDCYsQSiqX7/ua4I8CZaBl3Hvse+Ti6tWVDaTEhPs+hli7fcNikbET2kZfWcxghb5HtyxpUS6K1aSfuTJqaWaVq00s+syqq/Sp+kxoWVttjA4o1oQ4LpxdchdlqrASoPYz+4FFXbODpPdtYhVv7GBh0VF0kPS+TazuDHZvWqw6v4Cehuirx2J5yprY0AUZeo8+NE3ORB+ok0b/L4TUP/v6GFM/GerKIn/UkCGpT6Az9XaSBEvSTpPLI0NpkSa3HEV5lMnI2CP7g0xEgUaGgELBaRTIJUim7fSn+X/V4LdWyBnH1y3bDfg+t8znWHOfCcVq0kb63Zzxcj83jQH14U9rjLmy0RT95SubTNNG5j325fzJECtDQNm8yFBTLLe/UoZYcUR0rx05ijWrfs3LKax6/3XOykDaTmGVTQgpky2O7TadwoNChtx3dFAJDIdVp18+nVuQof3D2g8yFFLGkaFpsZosDC7I0Hv04U6TGxNlyFPckFppckD2IrxkiJCVHR01MSfEdYTKj9Y2ipqfJqdNrLnH6FocY1lKLOnIUE6GZLbxvitEERkSwhqMHPHJI0PM6qG7ojjNwGOdDg/WNCv3NEReh1WH35P1ZKiJXIOWnOi+lOhTx7dhxMpqY4P3WNeHTbBC5KqLKIU6q11Hrlkc8p4QSOr0OQBLMHLNovc9IaBC32NNv3aFQDpD37ttrJMr9H30hIBoUb/axGqLwPRlcegBMBux6gmBiPnMNDrRk+Fku02skP5qi8AcZKy2CMysWK6d4xYUppJLiPzQg5nb6MGqW5T1jY+25Jon1qxZBeZQKy5Nzq0XK4VEKev7D+oj6z+yeEIfLk3OjQyZS1eLNDlNhoTnNLk0zc++l2AiQ8hS4szx1OY0uSwypMlQWqTJ0cjQBtkfBGQTYr5nSEp2LxBdYcj3DDGDyvw0Z9qHGCW3zP1cfqZ9CwPbnNsfMiH4sGi0TIp2nGhzaMS06WIcVmjfV0WsBlJ6sZo65HkbG75/rqxKrlBHzsWaNiVuFqyqKFO/BawD0cYKnTZt7DE63lRWI5c5sr6NNoZihYwhc/z9LHKZMdQqlixWyafFMrZwP7CqxrbBMkf80880TZ3s7hFWqK2lc22k8bozytUGljvXmmsMfO0JIe7GB6C2IjTjgvMBDBY3Vn31uDIqF2dD3g1YUvdZU6zRIYkOyfqPLJ7QJ1EKAvZR0zQyNFGKPUAByCI8vsiQMZJdIQoypCNDkVA5gXEPUCgiQ9sAgJFSWJblu3vo3h9DyOihCvQz7qqdW8Y5X/RnXVmdUxhSj5scffIMmO+bxmHyyhCI72trnawhfc85mE3fR5/oYP59zIQTUkYnI/OpppNjCUvO7nzlEwhxTEKc3Dqs7owTbT4ZMY5JU/01P9twAOaFFWI7qvTE/E6J2rRRE4o1qzPRJlbT/grBavq3BdYC66BjhYxtrmzA1GPxG9iQ0LJ5YZXsUV1/OWVmwcr3N1+9w5IqJ+s/snhCHxMO7BAyVIoMwaTJmcksLE0OyKI1xQEKGRnqIrVOJhvILE8zI1H6szoyBICNDHGnwrl7hrgoEFfmDqqqgdfYmdA/zWWgQJhjSj9XWrUiWK6B88nvIx3UWQ020B6sWRxMt60Ua1bCN42zOitWFX7ohLafE+1BcCZmnYTnJddhwgrpr6q+Z7EqbEKInZhXG0NsVdPv3he9n1GPD0MbF1gLrIOMtSBD78En1ie/eclQmiKFYNPkoorIkNm70xUi3zNEI0OGDC1FURYZ0ncRRfrsHJlu53jLUhZkiIkMmacrZZaiUBEiZ/cMOQPBfBcXIq9yDgA/geEwBky9qs+HOqtNojl1WE2NShMHqOl3N825b8Oozru/Sm2s0CWuL6ryxNt4j9OmcxyECY2Tvymxajp+m67SHrT+2qsxNC2xqhqPoVH4Jt992InVXmPV9ddeEeRZdbXKFh6Gvq8qO6h9f5CwFmToPfgkOqWNkiHrAIWqNDl4yJDeMwRkZGg3T5Mb6XoJNp07ePI9Q4YMqa0cb1kTJqBIkxttPYuXX/4LOVmJgHzDmxs14k6Ts/YMTUlgpp0cqlbimxqvVh3fABmC5QqcHGc1Xm2G29vor3k7TE0ib/OeOKZNjahNdTiEbSzpV8M0z4PgACywptCJAFvQRiTtoKYxhcydVeN9lqjvtClkB12/5jZ3HFC5qj5fpV/znGsXe4beg08C1KbJKcWToarIUAcTfO5zd6MrVJ4mJ3IyVKTJLUn7AAWTJidTQoZksZnO3Eg/Gj6N8+d/Nic19BS5Yo+CwOc//zh2N5/K/5bvGeIM9IwruBSXXSGeFivg81XtafrdVTJwZY0JXItY8zKqs04c+5Iu5OjcfkTSZpXZkv8QTdpBOrFHzmqburpXEdEF1gJrnlihxIobq5VYAWXziohW+Qch5HSv5JoFq9T3FZkQFsaUdtVgLiJD78EnZvYMjb1pclnXmz1DEVL+NDmlINUudne/iggxOUBhpOsl2NZlA+ncM6QP+7bIkD5We0nKPE1Oqh0oFVvRn8nkGuJ4Myc8HSTY2PgsJjtfzrG4aFETZ7VuYAcN3oB6od/d1ME8TE5OEzLYhlzT9kmoXE0ntLm0ccpI2n7qxCwT7bvBkZupvxo4ABxG1XisskfzbmOV43dQ5FpgLbAWWIcLy9jHw0KGFvcMtfhwaXIT5jQ57p6hrkjZyBA9Fa6LIjIUIbtItSOKK+noaXJZRCn7m3AiQ0CmsCZNrqPvIerqo7i7QuD55/8Eut3j6IkfAQD09Pd0yP6jnpQYjS6gp9sANHNI6wZjpcPQcP9R0OANlStgT0MwVoVcTZ37NolomxGYaVfk5t1GN7I5C4EJIdSh0Yleg+9us7/qsKr6q6lzv196f5iw9nP1OITchX73fvZ9K2O7xVTTJliWnQjYPxeKNWs7WsWq6a8F1uHHOixkSNZ/ZPGEPtxpcjQyNFYqjwyZIwrzNDnwZIiSmo5I8wMURFpEhswzkJoM5Qco6AtaYZ8mB+jIkLnAVRMcQ4YykvMmRqNzReocTBSpIFbxzkt44onboMZvavmyuwSMLPQnV9bG5Bgy2UktW+h3VzkH5tz+YKyK1eOuKC58ayqX2+5OQ6yqv3XJe2wasXPb2GuoE1XOVFOsqr/VYTVxyOclV1B6QoWu9oTI7cysE9os77FKrhD94u4xa1Oug4BVdfx9dx+xqu5XCrU51EYZrCr7FULgQrEqx7b+DHc6KWfvQxYz2sbyXRfQdB7qSOmdA+eFFRIRrbp6QKLZvN0mFncyrJFZTIHlOzxKAI2uXpgFy+37ulNmqw68Kn238J9+u9gz9B58EqUQQUGk297T5ABAkLuYa8lQmuZ7fzokMiSVjgwxZChWCqM0hczJUPEZExmiaXKRvoeop7+nKwTSdBtpupUPiI4mQ10SZVKTdwAoIL6Uf795mkaGShNB4OQY6rRUOV8hq9Qh31PXxoHTxroV3CbOV5XRmwUrBH/a7+HKqi7dbNpfVY5c0/doOTkN2u/D8jk5TeWquqyxqU5UORPTyNUWVtVljU2jINZljTO+R84BCD3YgsPy9VPT/lpgNcOqwjhoWCHZCKFl09YLxQqdA3zjMHTeDolUToPlyk9l30+5psWq6vuSXeUWrCqIO1dmMBeRoffgEyuFNL6KK5f+bzZNbqTLaKcL0QPQIDJU2jMU559dksWFqltJkpOgbCUj+4yJDA2kzOWJdLSnq0+f60mJJNlCkmwVx2ebyFA6zL8v0ul1HU2m2txkHhqKN5/rcoOy4YRjPh/qRHMGYd6O77QOptsnVRd4TuOY+JxCbtKel5PDrnxNiTWtkzMtcdsP56vpRLtXch0ErGkdExYr0IGtcr7m0cYF1gJrgbXAmheWsWMLMvQefBJk5EQkQyQAlFJWmpwJF8rCd4YQAkL0ECHJydDzW1t5OpxFhpCQPUNjXVaQoYGU+SSbkaHib8bxzSNDUZT/LUpMmlySfzZNt5Ek26XIUJRuFvUMGdKXuvalxMWL/xJf/vJ/1WiwcGW0HnU4Ll78V7h8+d+RMoEvfOEP4MqV32htYHNOdNOVlpB7jOrkarL60qbxmrfjW7fqOA8DXeXcT9vuWeQ6zKSjTaJwUNu4H1ghUaa5RwHeZe8xxB7vh1wLrAXWewVrQYbeg0+iFCTSnKgkSllpciMmTQ7IUuUMGdqMY3zwmWfwSxcuADBkSB90gCRXrPykOEUiQ+QOoWGSoKMoGcp+rpA9Q+aJ1DDHB7JUpSTRaXL6c13dJvPZ7P9bWobsZ19KXLv2O7h06V+hp7+PGyzcRawlR0BKxPEmtrdfspyD1177Sbzxxt/Ly7pqhOvXP4ONjc/lZVE6xEsv/Qi6+mJazgGockJcOejvAymRJDsYjc5bZdnpextBE/K0EzRX1iZWpewNy6aRQSkFpZTVT0olzu8KSiVOvaRUlqYTKKWcspFTLy2VpekEaRpb7UmSXSiVOjqwXZIhSXYcrDHSdFyq52LF8SaDtW3JkKYjpOnIKovjodU/GdZGCSuOh44Mu0iSXUeGjVIfTibXnPbEJfwk2QnEuuK0J8Oy+3kHSbJjlU0m17I2WmWXGbmul/rZfUeTyTWkaeyUlbHcdmdRchfraglrPL40JdYVButiSS8nk6sM1tYeY71j2ZwM64pVZrBomcGiZdNjXWaw3maw7Hcbx0PE8bCkS2k6scosrLy/LpfGXh3WQGOV+56Xy9VLV67R6C3L7jfFomUUaxq5QrBsu6T7i9jyUCx3XM0iF49l29/x+BKDdaFFrDblOvhtHDEZTwfxkfUfWTyhj/US04IAACAASURBVLnoVGonPFYK4zTN06TyyJBTz5ChSZritd1djJXChXGGMUnTggyRk+OiPFo0ycsGskiTGyYJJIkMcWly+fenJk3ORIYAIHUiQ5k8MtkoZNAHL5if2aQ6BKDyAxfo99DB4iujP9988x/g85//mGVUk2SIJCkmoS6ydMEkKQztaPg5nD//KcQ7z3u/zxeVMs5XXmdyAZ///Mcgk2tErn+IZ575oDVRPf/8n8CLL/65vF5PAE899QAuXvyXeZmI38EXv/gtiNKNHOvKlX+PF1/8b6wT6r72tZ/A66//NJEL+P3f/3pcuvSv87Jk96v47GdvgZi8nWNduPDLeOaZr7OcgrNnfxivvPKjpL8UnnrqQUuuZPcr+Oxnb4aYvEWwfglPP/2o9T5eeOGTeOmlP1+0EQpPPHEab7/9zwjWS/j0p5ehxufyvjl37ufw5JP3WVjPPfc9OHv2zzhYt+Htt/+vvCzeOYtPf3oZmLyZ1zt//lN48sl7rEn1uee+By++SLCEwhNP3IG33vonBOsFfOYzq1Cj13Osc+c+VZIre48/bGF97nOn8NZb/zjvw8nO8/jMZ44gHb1G2vgpPPWUi/W9DNZpvPXWr+Rlk+3n8Lu/exRq9CqR62cYub4XZ8/aWE8+eRcuXPjlAmvnefzu796AdPQ1Itc/wlNP3W/11wsvcHKdwYULv2TJ9dnPHke6+xXS9z+Dp556nzUeX3jhT+LFF3+Qwfo/HKwTDNb9DNYPOW28B+fP/6KDdSOS3Vfy9pw//zN48sn7rb1AL7zwp3D27A9aev/kk/fgwoVfzMvGGivdfbksl6X334ezZ4s29gXw5JP34sIFW67f+72bkI5emQLrPly48AsO1s1ILLk+5cH60wzWz9dg/SyD9clarHjn+Qxr5yUHy9WJT+KFF37Asl9PPnkfzp//OWs8/t7v3YJk58WyXGQhrYwl8OST9wdi2XKdPfv9dhtzrJ9nsM5aWE8+eT+DVS3XRGPFGqtPsKiunj37Azh79gfy99Fj23gWTzxxEhNLrp/LsKSL9f0FljBYn8rLCqwXiFyZjS5jfbIG60UG6+fx5JP3oW9h/WmcPftJol8CTz31vgxL90WOtf28hfXUU/ejpw93MlgvvPB9eb2exjp37mcYrOcqsV588QfxwguftLCefvr9GZbVxlsx3v5yXu/ChV/AU0/dly/69oUosEh/ZVj/iGC9hCeeOFlgCZFjdfWpwQXW902B9YsM1g8xWA8wWG4bNZbVXz+EF174U0RXpcb63/N3m45e0X2fYS0iQ+/BJ9EnuFEyNFEqj8bke4bswBCE6KODGLFSeGOUOfebJE1OksiQeYqT4goyZI7WBkzKXvG3rv5JD1Awj9kHZAiMUf403c7T63omMkTT5DSJ6pDIUJJsWp/PCMxWadXcPLTMXQEej99GHF+xjJchQ6ZeVx8kQcs6WoaulmugDeHTTz9kfd/Fi/8PLl/+t8QhT/Hkk2dw4cKvFEZ16wvY2Phc7jBlK3RvYjJ5J5crWwl5HaPRG8T52sH29gsYDr+U44+Hn8e1a//RciYuX/51XLjwC/nEMZASly79Gi5f/vXiMAm1i83Np7G5+XRhcHbPYjJ5G2r8al5vOPwChsPPo4dCrqzeM6SNu9jefg7D4RcLXdk5i8nkndyJzrC+iK2tL1ltHA6/hOHwP1lYo9Hr2Np6jsj1MtJ0B2pckI7t7eexs/NyHp3sC4Ht7RewvX3WwhqPL1hlavQVKDW2CMz29lns7r5qybWz8yK2t18s9o+pHUwmb2Nn56W8LB19FUrFAJFrZ+cljEav5weHZGUvY2fn5UIutYvJ5CJ2dl4hcn0NQAJM3rDqjUZv5qmm/RzrlUIn1C4mk0sWFsavAkgdrFcwHp9z5PoKdne/QuQaYTK5hN3drxK5NNaYYn0V4/F5B+ur2N39KumvEeL4MnZ2ijI1fg1Amp8U2c+xLqCv22iwdna+ZsmVYX3NaaNi5HqLwfpqCWt3l2K9prHetOpNJm+XsGi9HvxYbhsnk3fQ07azT7DMe+yo3RzLlGX6rqB0G7N6X2OwvmbJ0M2xXiVyZVjC0omvYTK5iL7GGuRYrzbG2t0tY+3u2lhFf7lY4LFEFdbYKxfViQzrUt5f4VhvWD+zeq8yWK/aWKrcRpFjvWnVi+PL1pzWDOuNhljlNhoswWEpF+s1pr9eK/VXGesKenovcitYpTZSrIm2X2Wscn+VsUYjW67J5JJVNi1WX0xKWEIvxomJqxNX0cPIj8W00YydslxlLFqvXawYk8lFB+tNP1aFfmVyZViFLXxD/8zsxYIMvQefLE0ugdSKWCJDeZqc/UhZ7BkyZMic9OamyZknJ0OqIDx9WZCh7G80MpQ5j8tcmpwmOMX3FMpriFGkjWSabuUEyUSUTP0srUETK01SBlLiuee+Gy+9VERNODI03n4uiwLoiEJGogyxmpCyoRUF6tHIkHFWDLnTKX2ZI/8stra+jL4svve11/4G3njj7xJndRtxfNVyViKN1SHEqpBrZJUlSRE+LkjaJnGiDEkbMlgTL1aPYuUESWOlFGuoscYWFg1r9/T9VLSsq7E6+n32hUAcG1lH5N06cqGQa5D3fVUby/1VkEemv1isYQkrjv1978rV0ViUuOdt5Pq+qo1puY1dZbfR1/duG6OEk2s3CKvURv1z0BCLf49lrA7TXzmxAqOr+Riq7y+6qFElV4eRy3w3O4YaY/nHdiUW1/dT6r1MqF01su5aZVSXuhX6RbHM2KZYcayxyAJMjqXLomTT+mnGXvYed62y4PGYlrGaju3CfjXTiS73HtV0+rXAqrc5tVgVNqfafml/awpbaPTe1S9a1kTvOSzaRoPB632zMcRhFXPtdH3PY+1UYI1qsRZk6D34xHrPkIkMJQDGaZpHY6r3DPkjQ/nhBSTtzY0MmTtr6N4RiQnMKzakhl66mn8uJ0MGi5Ihfay3Mkq/RZz7zLkxhMFEbgDbqO7uOqvHSPHEE6fwzjv/Ii9To5ezKMDkDQYr++6eJlsW8dGHOFhGQqehGSeMOhP5PiIhEMcbzsA2WEOCZRxfaiQM2aJOoRuxyrCMcZQAhGO8qhxfe8IpY0Eb0Ihg5QZa970psw2V3V8RACTcJOQnHf28bwqsvoPFGvsKh6lKLkq2uYmjru87QhRYgROa20YWi6Q7TtPGjhBQuZNbxjJyWaSD0fu+lNkCReWEVjE5qrJcokUsVy7OThRjiG9jgTVk5BqW+j5EJ0wbqU4U49F2AGwnZ35YEVDZ95XOqiqPoXlgRYz96nC6mhMroqtCZLawEmunEVbJTlhjaMcqs/XLJqICyO2qTDYh4I7t4j3WOauyAVYd2aZzh0g2IRE+HnuOfgkUxBhxGauSdDTG8uu9i0XfH5JNRDVYVbpKsUTaFIshHQSro087DcKqIB3zwnL1ntdVpr/Me0w30S1hVZG0eqzDQoY6+y3Au+kxp8kZ0uJGhszJchGTJid1ZOj13UzxzIWokzRFpFMQOiQKVBygkDn3S1Jie/sliLiIHnXUBJ3OOuL4KiIoAKKIDNHT5JRxTDIsiwzpKJFR+jQt9hHJdBMJAEmcwmQytD5vBpAQPTKAtjAavaHTq87YRCEZAlhnB2OW6qM8gzEjSNnmWU3SEiKXx+AI0WGd1b6UVr3MIJ60JlozCRknnWL19HHjRtaBlEjzlegNACdZx8Q4hTbWjh8r3QBwmz05OhOaEJKkRJXlKtrI9BdcrOLo6x78WB2NZRFRuH1fxjKToyUX8x7zEwwJPk1RNDrhk8t2JnacMmXJpYhO0O/jVu7dNgIpi+XKRR0mDiuONxFFxV62rrKxqF6KNHNMaFnuYGpdrcIaOFgRbEfOHdtRFDfG8o1HpSYlLENOaT2VZA6AHTWxdSJNxwyxGpL3qA+DSYe5A+AjVkIQubDjfY8q3USvxsmxbWF5bFdjbTtY3RaxOlbf+7DSNCMi1VjRDFhuf82AVbI5xb7aKhudppt5poWPpAHFqaNGLqOrfYKV2w4PlhkfRTSvjGXqUax8bOvPs1jOoknf7S8Hy7SjKFOVWNX9VWBlYzstsOBgCVHuL2tsF9d3ZFiJX67GWDHBKnSir+2LhVWyOW4bY6+uuliu/rq66trC2bDGBGunIZY9tumhQ+5iTk8IKI1lyg7LpasLMtTiY9LkDFExByiU0+SYyJCa2JEhkiZnTqdjI0Mkn/Ps2R/ERTwC4LszXIzR6RxFHF/VF7dKNjKUH8agzHHdBaHqCnMxq1mZ2EK/l9WV6UZGhogzapyJzEAN9ADagJRk0sY2JgDMaVJ2hGQTwHpmcGI7OmFy3JUa53tGCuNVdnztlfsN/d2uc28TmAS2kWAd8onrkKcAEjs9Re1YWJZTGJOVwrGzgimANN11sLY01rDUX5aDObaNV18oKDV2yOM2YhDDzmD1CVZPuVhDCyt7j4xjkhQrTAbL9FdPY9npPIVclY7c2Db2vjZOwE+EaTosHKaJ2/cpizX2yJWmpCx/j/oCYxistDiEhMhViTXh+n6EJClOjuuqLa9cVlkJCzlWj+iXeY9uf3FlXUeuOC6cCSMXpxO0LB6VsdJ0NztBixD3vI2RPYascTV2sbIxBIyt/ppUYJk2xmZs5+9R5Vj5e0wN1hD9SFoRS0q2YqfvKVaf0dWQNvZKbRxZfR+CxelEmu7Ade5drFD9SvOrFkT+t9xOdPw6UYzHFrFyopAt5GW4hX7Fpl43VC6h5eKxkqSMxfVXgSWQkvTkeWAljbGkgwUvFqurlk7YCw8mTb2bBmLFFCtCXxIsIlc6FZbxV1SO1QvtL8sW2lhmz66RK4430e/Zc61JP7XIY7oFoKPveWwbK819JgurL8tYjo+RY+Xv0cbKFws7ZeI+kPLQRIYWaXItPrFSkMomQ2F7hrI0uQndM5QUGCYiRE+OyyNDKCJDk8llRMnVAldHhoAiwuPuGaIXjJoT47qEDOV3HGmHEVD5CSqGBKl0mK3SSmaFSQ8gO583M7KcceGiE+a7TeQKKC6BNYckGCedEjKVDr2roV090RpHKPueDIt17qkTnaf4GCcnk0upEfq5kRjmbaxakXNXQw1Wmm7lDkDRRt7xLRuvbQdrl0wcQ28b2aiMxupaWJqIpqaNZSLKTxxbU/WXfxKiWGNGror+Ylb3TAqlUhNNcItU0FrS4el7peICKy36PoR0VGJV6CpXVjj3owILRq5q/Spj6dMndZookKBnFmcIltueEJ0AkvwghI4ztm25ymWuTgBpgdWwjWW5psfq5FiTHMvYr47bX8S+2IsfZpHJjEeDpQjWtrceLSuihWWsvrDlsghszJDa2NdGsG1063Fl3SqsGltYfo9DBmtifU9jnRAUa3xwsOj8KGZsYwtYvE4YrKZ21WDFDFZDW5hSuZSFxdnVSr2vwmqo9zzWuBGW28Y+YkDb+Wn1Kz8gSySNsfpyQYbek092mlxsR4aUKu0ZctPkMjI0yciQTpOje4aKyJBLhmR+sIFx0ulpb1KNEUUZGTKHIrinyfWILGZfkHVZq9mvlJOhoo7Q0ZIk2UIvDx+bNDl7YJvBkv3NGMINMoAyLCs6kWO5Tk4xgbvORChR6Ju7oBKajz5ksBwnnXFWzZ6mor3FPqp6omAf0MBh8Q55mdS4jklHESzhytXUMSmwuqU2ltMtDFafwbLkMke2N5Srl6d2FljmnXYJ1nIUYSmKrPe4HEVYjqIca2A29hOsgcbqk8lxNYqwSrDieBNrUYS1TifHWkb2t2VR6OqazPprSRXtOdrpYJ3UM2VHSdmqyLDWCNbRKIYEsEywjnW7ONbpWPp1vNvFcVJ2BEMIADdEhQ053okRAVhFUe+mXg839noW1s29Hm4iZUcxRATgRKewEzd1EnSFwJoo6p3s9XCLg3Wy37fKjopsIeWmqMC6pZNFwNaIXLf1erjNwbq938dt/T7ByiKIJ7sF1q3dLB1wXWzk9U71+7hjMLCwTvX7OEXKbhCZXbiNYN3eTbAkJY4SrDsHA5x2sO4cDHAnxUKmv3f0CqxT3QQrBCuON3HXYIC7SL043sSZwQBnlpYsrGUpcZpg3dlLsBpFFtbdS0u4m9SL403c45QdE5tYkRJ3EqzT3RRrUYQbSBsLrGIM3eOUHRPZ+LirVyyknekbrILA3ru8jHstrCHuc8qOiU0ciSKcIVh391OsRxFuQCHX/cvLuG952RpD73PKjolsXJ3p2lg3dDoW1vuWl/E+B+v9y8u4n5QdFxs43ungboJ1Tz/FcQfr/cvLeMDB+oCDf1xs4ES3i3tIG+/tpzjR7eKYsLE+sLJi6deDKyt4//IyeY8buKnbxT391MK6sdvFcaITH1hZwYMEK4438dDKCh6gWNjAzd0u7qVYvRQ3MVgPOVgPM1i39HolrJu7XRwXmxbWw6urNtbqqtXuY2IDJ3s93Ge1McEtvR5OkP56cGUFjxCsJNnEI6urVruPiw3c2uvh3n5x/819A4O1WYn1KIN1W6+H+wjWvQZLFvUeWl3Fow7WB9fWrD48LjZwe79vYd03SHGyARbVr9v7favv7+tnWMdJGx9eWcEHV1ctXf3g6ioedrDu6Petvr+vn+BWp78eXlnBhxyswSEiQ4s0uRafRCkIJ01ukqZ5NMYcrS2EzUGF6EOqCd4ejzFWCl0hrNPkDOExKXFARoY6nXVI/belKEI62UJHbJLPZGlyAPI7itx7hiwypJ3BrkWGJgC6uSOb1VHZhlrtRGZRDJNDnH3uTHQFP3nnJ/Bt6xIvIFu5v6OfOZNr6jouo3DkqcNsr+TQ1dDVXD7AJgpZKs0QS1JiySEw69rpNIZjGVsA1rEiCqwVqe9ESjexpevdsJQ5ptTg3NLr4WbikJ2QG+iJddzeKd7L3b0UxzodrOM6tnR7HlxZQaqU5QB8/dpaZmgvZ1gn5QbuXTqB9/cnOeV7ZAn4utVVHFXXMdRY33j0KG7u9fK0mSTZxB89cQLfePQokksZ/h3RBr756Gl8cCmG0YaPrgB/5PhxHBPPYajrfdeJE3hgZQXJVoH1Z2+9FR89cgTJpazsVHQd33vTvfjo8gTvaKxvXhP4sydP4kbx5Rzr+2++GZ9YX0dyrcD68VOnMqOt5bqzcx0/evuD+Oa1Lbyusb59PcJPnD6NW8Sz+Ipu43932214czRCcrHQib9/9914/8oKdrRcp+V1/PSZr8O3rV3FKxrrjxyNgHvvxY3ps9jQ9X7sjjvw1niM+EKB9cv334+7BgNcuqj7S17Hr9z/UXz78kW8oLH+y6MRjj3wAI6Pv4yruj1/7fRpXJpMEL9RtPGfP/AAbuv38cY7Gdbt0Qb+9YN/EB/rncezGuuP3SBx58MP49j2s7ii6/3knXfiahwjebXA+rUHH8RNvR5efsfoxHV85tFvwQfkG/iSxvqjRyU+8KEP4ejGl3FJ1/upu+7CRpJg5ysF1r99+GHc0Ong+bezspvkdXzxw38Ip9XX8AWN9V1HBZ79yEewdvXLuKjr/a277sJWmuLqywXWbz7yCNaiCF96y+j9dbz4DX8YN8av4PMa6zuPAi99/dejc/lZvKPr/e0zZ7CbpnjrpQLr/3vkESxHEb7wlplor+PVj34n1icv4xmN9Z+vA69+9KNILn0Zbxu5zpzBWCm8+WKB9duPPoq+EPj9twqH6dzHvguD8Us51revKZz72Mew886zeEu//7915gwmSuHVs4VOfPqDH0RXCDx9wRC+Dbzz2B+D3D2bY33bmsLbH/84Nt5+Nq/3U3fdhUQpfIViPfooOkLgSY21LjZw+bH/GulOgfUtaykuPvYYrrz15bw9P3XmDFKl8NILRRs/88EPQgqBz50vsK48/scR75zF0xrrm1ZTXH7sMVy88Gxe72/edRcUgLPP21gCwBPnDUHewPVPfDd2t1/Isb5xNcHVxx/HW+cLrL9x110AgOcJ1qc11u9prDVsYOPx78H2diHXH1xNsfGJT+DcuZ/N6/2kxnruORsLAD5LsK5/4k9ga6uQ6w+sprhmYQ3xv5Swhvhtg3VOLyhgE1cff9zC+sRqgiuPP45z54q+/5/vvFNjFXPHf3z0UQtrWW3gkoP12EqCS48/jnPnCp34azlWoRO/yWBdfOwxbG0V/fXxlQQXH3vMwvoJBuvfP/JICettB+tjyzHeIVhJsom/evo0/urp01bf/4aLhQ285WKtxHjbg0V14t9pLKMTy9jAhY9/HNvbL+IpjfXRlRhvPfYYzp8v+v6vnD6Nv+Jg/duHHy5hnWewLnz84zhPdPXHT5/GjztYv+5gLWED5z7+cWxvv5RjfcOywSrk+sunT+MvO1j/hsF6MwDrx06dwo+dOmVh/euHHiphvfGxj1lYX788wfmZsF7OsT6SY/1iXu8vnjqFv+hg/ZqDNVAbeN3B+vDSBOcIVhyXseJ4E1/68Ifz9OKD/iwiQy0+sb4TyE2TG8jsRJjqS1cn+QEL9y8vYzNJkCqVXeSa7+UpkyE7MrSVH3EJZMdh52RIyyTG5/DFL35Tvim/S/RU5pEhEoHSKXpSR0GAjAz19PcB2ST0ifV1fGill39GJZv4n+68E2uiqPed612c+9jHsJSvom1kq87drkU6DEEyZWalv0ciCmd6YxztdLCmruUy/NipU/iH99xj5e//vw8+iB8/dSovO925jn/z0EP49iPFasUfP9bDrz/0EI6K67kMP3XmDP75Aw9Yqxy//eij+InTp3NSc1xcx+XHHsPHVwq5vvVIgsuPP46+KlarfvKuu/CrDz1ktfE3HnkE/8Ptt+dlq9jAS9/wDfhAfzfH+vrlGM98+MPoEqz//vbb8an77rOwful978Mnb7klL+urTfzWo4/inl4RzXtwMMGvPvRQnoaYJEN88pZb8Nfvussij3/rzBl814kTRd+rTfzTBx7AbZ3iPZ7pjfAL99+fnxKWJJv4zhMn8Bduvx00avKjd9yBb7rhhhwLySb+wT334LgssE5GO/jrd91lbeD8A0eP4k/efLPVxu+75RZ83dpaXqbSTfylU6ewJgqSfoPcxo/cdpuF9ejaGr79+HEL6zuOH8f7yIpcmm7iT588iYGO6gDAitjCd990k1Xv3uVlfGx93Sr7+Pp6KTLwX5w4kUfngCwP/VuPHbM+c/tggIec1b2HVlctsp0km3j86FFIVcjVUUN85MgR6zM39nrWir9ZzT/mjKuHV1fz09gAQKRDvN9Z3T3a7VrRljjexK39PtbIwoCJPCiCpZIh7nQiEaudDk6Q9sTxJk70elZULo439fcVWGkyxC39vvWZpSiyImlxnC10DBys7PsIVjrEsW7XGsc9KbFC6iXJJlaiCD0nGnmk0ylhrTnRvK6UlgxJsolBFKHjYC1HUX6wi8HKIpbFZyIh0HUiyF0pETmphtQ2GqyeU0/q00VdfMmUuVhuPSEEhFMmmTLhYNG/0Z+hZfPCou+F/u4vG7I/m/6N/zz3PZtMPa6N7uf3vu/dvuTKqvp3P7H2qr/q9Su8HQcFK7T9y9oWHobncEh5SJ4E2d0+bppcTwh0hcC44gAFSU6K+8DyMhSKu4Yi8JGhKCJkSGR7HWRyvcBV43zPUIQsXWRr+DSuXfttiEl2n4/ZCAcUR412CRnqOPnCANAVqd5MZ8jQFn71oYfwZ25aLvoid4o38rI0HeJIp5OXJckm/t7dd+NXH3zQGkB/5+678cO3nIDSfXImuoKfPnMG37xSYH3L6g6uPv44+iprr1ITfHClj+8gjm8cb+Lr1tastJw43sAfPn48P04cAJawVap3W79vOcxJMsTJfh9LIs4u79Rlq8Q5MnVp+zljZH6maaw3Qc9icEx0bgylxgGfD8Ea5X0/rVFtLhfnHNifp3LNOkHxcnFOS1FP6cWKMKyqNg4JVkEe3TaG9r0rV4FVyBXqHMzexhCsCZQa7RPWcN+wqh2NAst9L9RO7A9Wyv4tw9pxPl+lXwcDq/yutkpY5qdSSX5YQphd2SusNM/C4O2Xa4d3oFRileV7ay256gmcjbXpYFG5qmx6gZWmMft5HqtKrl0Gyy8XR3gLHSqw3HlbqbT0ec4HKD4/QpryNp1ihYzHaixVwqrSL4pV/ltTrDHS1D8/HIZnQYZafBIuMpSm6OoVP5MmJ9k9QwXR+cDKCgDgsiFDeZpcEYEo0uTM6Whm83hBGKQaQcolCNFFBzGWpUQcZ+Shm0eGymSIki5zwpxMtyBEF0B2xHXPSkerX+XK/r9h/YzjDax3OriFrEQnySZ+6ORJfKjgVVDpEH/p1CmsgMP3TwDFpLINs/GPG6jVK0ZD9jO+svKERo191fe0hxUiqz1xlB2gJnJVGdWmbVRqQoxq+CTBlWVYvHPbXK64hDXtiiSHVT3ZV8malJzbadsIpLkT+e7G4h3ScCzzjlLGiZxOJ7J9lbZzO61+tYuFHIsjJG7f1I/Rg4NVNV9VYdXryWHD2qqp155cvH7xdp7KFa6r88Mqfm6Tz1S1uz6CaGNNG100P7dgDl4IIXycrIV/sE2w6tvDy1N8z2F4FmSoxSfmjtZWCl0prcunpJNDme0ZKi4DPbO0BAC4PNEb1Sv3DOk7eMzJUCRNTmIMKQeQcpCRoSjKozLmbhVzp1AUrSLSBxXQdDyz2V2mm+h2T2R1kOg80BQAjRBxk1BBzqpIh/lctUMeOhht/PqJ0P5c1SpHaOoCn2bR1Pnyt9FdfWpKFLgy/jN+A7p3WOV+aOrcVfVXaJmPGM+v76frw/2RKxyrOYHxj526ek0im9MQBT9WU1I7fRsXWNNhHcYxNB9d3Tusg64T71WseerqYXkWZKjFhztNbqLT5Ho1aXKG6Nze72NdH3JwRZMhQ3giMJEhvSo80AckdJ0T56TsQ8oldDHBipRIdBpdz5zsZUhU53h+YlykI0rZ383RjkN0uzdmdUVSaL/geAAAIABJREFUnDPfPaHJwyTAASgiQuanL82EJxPTrpjUDeIN7+eqDU5VdGo64xXqDDcJYbch1/yw/OmEVRPtPPq+eRunc3Lbl2tasl0u2wuHaRoiOi2pDRlXTSf7eel9m+/xYIztg4l1UIlCSFlTXT2ohO+g6cR7FWsvdPWwPAsy1OKTqBQSaenS1TxNLo8M2fWyPUMZ6Tg1GGCtkx3yZ9LkcjKkis312Z6hIzmBMXdgREgR6c90EGsyNMAPL38J/+u99+ZExOwBMpGhbvdEfqdMR40RRWtZREl/ZxYZyshQH3FxeVr3pqztyRYKx1TWDEYTLapK8QmNDG1aZXW5rsWA9UesaFk5OhFqcJpGc8KiHwfDqE5HYKZdPWwul58gHZRJaJ5YXHSiuuzwtXE/sQ6qgzkvrMNCFBbRicODdRj03i077Hq/X/b+sDwLMtTiEztkaJymSAH0dJpcvmeIO0BBR33u6PdxxIkMGRIkCRmSSLPIkK7XJ5e3DTTbKiJDA7w/Ooc/dOwY8j1D+mQjc8Fqt3scUR4t2kUUrUDKZdwdXcT7lpexlr6DXi8jQ3/+6Nv4O3dk+5p6vZsBZJs9jaPV692EggCUSQctK6JFVYOxcHKlXCmVmfS9ON7Mc12FyE6QysjRdAOb31BdnY/uMzh1m7ObTNp1m/9DHd+sv5ps2BfesiQZEizByO6WCevvblk1lku2JYNlyqhcWT1bJ5pgbdZi2X0jmf4qygqsiNHVyKkXMVgRi1WWK6pp4zDHyvprTLCGDbE2CZYrVx1WVINl9L4NudrEMmN7HlhphU5w+kV1oh2sTC5z8EKUb+wv7F7RxqzMbmNWFoYlRKcWS4hOCUuITgCWgGtPylg7GmsbSiX5ZylWYav2AyvbN1qNJb1Y5rCE/cKi+mWwMtuXHXCQYe2WsMpz095jmb2xfv0C3LFg91d2WMLBw1IEK9si4H+PNlZZJyhWsWf3MDwLMtTik+0ZKsjQjo4EdfVpcqM8Ta58z5Cpc0e/jzVNhvI9Q/nFpyZdLoUAEEVr6Oi/Wbc8a64VIcn2I8klEoExZEhHiCgZ0pEhqXYh5TKiaAXf0H0Nz3/kI+ikV/PI0AO9q/iDq/p7eyYyNMwHSK93khlABfGhZdTgmN8NHnQbKfHpdNYhpX1pX693S/5/U7/XOwkgQZqOiJHowiVfVB7XSBQyHMmdCVOv273RkesGZBNHETXpdI6XsDqdG0qTY6dzg9VfUXQE7uRoYxkZTpSMvS3XJiNXke6YJJtWe1yjmmEVfZNh2Ua1iAxSrJtyo0rLbKyjJaPtx5pY77HXu5nB6lplhqRTrF7vZigVQ6mxVUZX/jqddQjRs8oMVhyXsarlyrBC5IKOklZj9R2sWxisW2AOHCjG4y2OLq1DSvs4cIpF6xVYQ4I1tOSiWPVybVfIdaShXHVYS5aTY7Di2MXKDhygZbRem1hZxH05EAslLNtOZFhh/dU21rAGa6U1rChanQuWq6tUxw0WnU9oPR7rJIN1ci5YdK619WsVUbTq6NfJHHe/sez3WNXGTQvLfo+r2i84WFhSrqDTqcLaOsBYyirz6aqUy+h0jlhlVVjUzzroz4IMtfhke4aKAxS2E000HDLEnyanydBggCM6Te6KTpPL0t2Wyd4hlZMcU69HDj3o65PlsshQdoBCcdRoRgbMqXOGTHW7JxDp+0w6ahdRtKwnxy29gprmEZgk2crJS7d7c15GDUfhfG9o556SDjsyZD5LHVobqygrG9WhY4yrjNetjpNzDC7p8BmXoo0+Y3+kNKH1+7fm/6cycFiuI1eFZRM+13jdyky0a7CNl5GB9tetORaV1e77tZJRNXLRenbZMC8rOyY8Fu0vvg/dvs+w7Enb3/duGSWPnFy+vqnuryZYIZN28743/UH73qcTvnpVWEYn3AWLot6wJBfV336/GI9ULlrmqxeKNWsbOawwvef6axOdzn5jceNx2KpcUbQ6hZ2oclZdJ3qW8eg69+F2ok2bMwvW9PberrefWLO+x7Jzv/9Y3Bg66Fjz19UiO+WgPwsy1OITO2TIRIZ6UqJH9gxFgjlAQdc5RSJDJk2ugxidztGcuHSEytPfTL0+IUPmcAOaJlfcZ2OO1tY/EQOIEEVHIHXqnEyzNLkoWtGrr3ovUWcdQnR1WZESB5SJiL16fBRSrpABtKGJSEaM8hPuereWwrQ2saJkiI8MFRMtdTA3dBk1EhvZpbUBA9t27jiS5jM4ZSeXc774lUJeLt4Q2u2uM/azODnVq3uhRnU4RRv9DlO7jlyb/dUeVtPJ8eA4ci7ZHlpYbn/tPRFtjjWrU0h1Yq+xqtrow2qqE0ZX29AvDit00YQS5NntRIjN4ew9XzYvrINqvzj9amtxYi+xqvSLs4XT6upeYbUxD9VhHZZnQYZafBInTW7bSZMb6z1D7GlyJE1uICU6QliRIUqGIqQ5yckjO2Q/UV8fimAOUIgimianI0LJNY01QRQtI4qW0dWEKlLbOk0uiwyZuzRM6lwW1TCRIfsABSE66HaPWcSnMIRFmly/f1v+f34wmhVMGgWwyZDJF+fTJppETahchdNW5UyUV8jd1dAha1S5NAOf8aIRC9/qLtfuOuNVT6wKB7baMRlWGuhQo2r2WLRB0gxWFUlr1l9Dp151JM3XX4Vc05KO1RKWcY7svucJ+KwT2vywhgyWT+8PhiM3K1aT/mrbYQqNyrkRvrKDaZfRxa95O6u+dJ5yWZvRiZCIVVVWQRURLUfSZosCTCvXdAt8TbBsgly2X76y+gWYUKxFVM7FaqJf00TSDsvT2W8B3k1PAicypNPkZDpERyiSJufeM9TDvXgZH17p4e6lJQghsBZFZM9Qgk7nBoYM9UlkqCBD2Z1DUWVkKNJkqIsJpFzKckHzvUk7iKJlKJUgjq/BRIay1DmbDBV7IDIn2jhtZv9JZgiPQKkxGYwbWFl5CFtbzyJLk/M5hbK0NyfbQxIjI0O8DFkZl8dcziE3cpkNiZzj6zqYQnTR7R4HddoyLCNXtjnbl0tNsYDI2ptjsIBss7PZzB7i3GdE9ETJic7eY7GZnXei7b4v2uj2l5FrVJKLc0yqsMz/lRpDqTjI2FMspRSyyGORhpnJFbNy0feYYfU8WLJRf1XLJS25XBkolq+NJpJm9mtVtbGsE4WuGhIVRWv5fi3uPfr1a7+xOCI6zPWeYnU6a0jTnvUefc6RrfcUK2Ww7LFdTWqj3H4ZrGwMTRwsbs+Q62BkWOaQAIpV2IkCq9rJqcPKDl4Iw5Lodm9isJIZsIo9lbNh3VjCKuyqWUi7GSZV2k+sjFzZIQHGwcz2h9VhuTZNotfjsOBg3QRjO/zRCYNV7Bs1WJl+ZQcvZHtEZQ1REAzWKgBRi1WWS6DXu5nBMofOZAcv0Da6WEVZhkX9iShahRCRhWXksnXCtUNZG23fpLCrBdaJvKyQi7NpN1uZLFG0Bim7GivbB93tnsht2uxYxwOxbgnE6lbOjwVWsWc3w+rnWJlPxmEdPjK0iAy1+KQKbGTo/Ot/E8no9eLSVSYy9DCexe984DiWdIrckShyIkPrOW4kUr1niESGKBnKj9ku7xkyURCZXkMEoEMiQ8dwBT0hcJM6DylXEEXLyE6J29JymrJtlMnQVm6oCuM+JCsTR5DdK5RNRiYy5I8oFMSqcHLsyBA94KAo4w10tolw3VpZz/bmrOnPbDEycBNHIYM7oRVYhYEzk6PP2JsVMzpp0xU5U69+EipSz+hBCHRVyBgqOnH4HIACqzgIwY8V5e+sGss2qmUsY1SLd+u+xwIrO7yAltG+MVhVzmoVFpWr0zmWH17Ak46i7+nhBRTLyECxfI6ci1X016qDdUN+4ACnX7SeOXDA1/dZKuuSd0Kj9fYWaz0/cMA3tgssc3iBvZfJxQqTK7NprlxF3xdYdWPbYPnGdoa10gBreGCwzEoxxaKrx6Ye3VM5PZZr75tiFdHVQq5iH5Ffv1bJgkshVxnrSBBWsYe2GZZLrGysTUZXzaE9RX/59X56rGnl4sZV2RbaWGVdNXKVsco6sVLColE5iuXqLycXXYQr5Fpl5Fp1sGwCk/kmZblcLG4OcOXyY3F9z+uX7TMdYfTX1Nuy3qOPbB+WZ0GGWnx8p8khvoSO2inS5Jg9QwCsYwiPdDp5ZCgkTa6ntvO65oQ49zS5zNku0hmWoggdjPPI0DFcxbkPncTD+CIygrSCJNnO0+SKsuIAhXJkyDhfpmwDnc4RYlzMaSMmTW6jxqiuAfqkLT8ZKpe5RsIQnwxrB3G8wRpjuspRNjhDxmEalgyOaU/dhMZN2n6nsCB8/v6ictlOuu2YcJPQyUq5/E5OuczvrJbLyo6Ja1Tt9L1yG3lnlSsrT46rLFaTNvr7ix44MZwBi5OrypmwV3z9fb/K9L2rq6FY4e9xWqxq58vWiXrni7MTnH7ZZfbYDpOLvkda5mK5ZfPCCtMvv66W7Wp5PNKUKFOPK3N1gmK5ZW1ghdtC3+JEc5vTJhZnJw6qXFX6VaWr3OLEvMdQyNiex5zWht67cjXFqnqP3NjmdYISt8MbGVqkybX4JAp8mpzaRgdxkSYHN02uTIbWogjXAvYMncLr+B9vAj4uXsVYK2lfmM/ZaXKUpSfJJo53OljDMN8fBAADsYsk2YKUy5o8bcGkyUlp0uRMZEjkJ8yZe4bMwMi+o3Ci03SM0eh1YhBuQRHWLg44MLLxWFmZSUcrkyEu0jFkDWEh1wiTycVK41U9CRUOk4nscM4qZ1R5YlVg+ZzCQq4wAmNSQ6qcnBBDmGGnoM59lWNirzqvMlh2BG5aAx2KxTlybt2sX+RUWJxcBZa7gjksYdVNaEkimQnNEBjfIoBPriigja5+lcljhtWpxQp9jy5WiK76xraUvZaxOFJbjkbzdqIoM+lo1WM7zPkyKXch9ouzObTMYLlOIS0Lx0osu1dlC02Z218uFtdfTbA6nbU8K6ANXZ0Wi7M59HeDVSaiJ6fCmkXvhRC1WKHESghZq6uhiyZCRI30vhqr02BRrlpXTRpbG3rvpp5xWOFy9bxYTd5jp7NWymyomzsOy7MgQy0+7p4hkybXwQSRmmCiI0PcaXJAOTKk9P+zPUOUDBUkJ0KKv3rLCG+9dRUb3RNIkk10MSl9LkvdyfYLdTrHkCSb+A8ffAQXzv4kIpFFhgDAHJgQRSvIUli2nMiQSQ0ZanK0jCwVrCBD9urLRk6G6MlxxUluvj1Dxcp6gbWZkyHqAPKRITuvPBvYdsg3m9DGAQbH3rfgcwoLkkZXTHwGx06RoHL5HICQybGawNgOefUktOqRK/WQtGmcQtW4jeVJewjX+JexqtP33DKTJ1890YZFTdzURhurvCeNk4tiuf1Vdr7qCUz2exSAxTmFqyV8N499uvdoOxPVE22Ys5qmvX3C8o1ts9BRT2BCHcwQYrU/WH4CMy0W78jx+8jKWMZOZOnWZTtRHXHnIgpmz1DT6AQdQ1VYvnS0OhtNf6e6Ok10wrWFs0QnXPs1S3TC7BlqQ784+1Un1yxY4XJ1p5Kr6cJQ06ic2TMUqveH5VmQoRaf2NkzlF+6igkicimqEHZ2oiFD2W3h2WOO1wYKMhShvBcIgJVCJuWyQ4YGJDJkIjC3YWvrWdw96GFDXYGM1mEiQ3F8JasbmchQsT/IHLc9Hr+FJNlCFK1ACIEsdS5ztvv9OxyHaQh6gIKJAtUNxsLYZ1iTySUACfyRocIhF6KPbvcYwdpwjOqG7q8jOUnjjESGL61LSgsDbeS6DKUmetKOS8aeYpnN2WUsM3FchlJjjZ2UsKgDQLG4ydGWq4rADPPN7EaubBX1CMG6BKXiHMvnmMTxphfLGFWKVXYAVh2s3v/P3ptGyXFcd77/rK27uqp6q+oNWwNodFdTJAhuWqiFsi3ZoiRutmwf6/nN2PNmNLL9/J595tmy53jOGLIlc5OodcZaLMmWKJJaLUoiKdmSWtxAgAS4gFuv2Brotaq7q9daM96HqIiMyIzMyqouAA0y7zk4jbqV+asbNyMj48aNiFSwWkxlLArXddVSRjZaTVlsO3dWLw1WPj9frl+0A+DUMdG0ENjW8Gq7xDL6KvgrBOOFuqsWu6ysVX4fV2I5+Z52JmSWeD9qWlBRRjtWwMSKmvwVlO5HJ39ZWXLnS2SpAlGx3tPR0IvHUt3bNLAqQNfXhIEh1b0ttl8yi9llsNbBNsYxdzDdsfKuWHTjBePdbObOF2NRNpuOvGSpXxeetQBCcrzNMbPkMvrKbY7Ppk6YWVkUi4vKOnG+WGLH1Khfi9D1LNimB4TkUCgsWMpI2yG3rJgrFm0n/C5YPhCSR6GQtvW9yDL7q1hcgq5vlK+rX2JZ66pWrhN+Zb0XWZoWsLDk+qWBrvUMSCxWv4rFDHR9XWAVHFhAMCizxLrKWDToCEosa72n60bFoEn2l8gKWVi0fi1bWOoBvgx0fY0HQ4QUy30yNYuWMcife5eCeGuG6iSEEOjQlC9d9aOEQHnbakC9gQJgygwJwZB1mhxbC2QEQ7SiRuH3RxBCjh9HM0PhcjaFNmgNDTsA0A6Trm/wNUMA6xQa22gDOj+PZYJYgES/B99hztwQ5vOzAPSyrrncWLIOQDPYpgrGA41t0806qyJrhpap3NGiDfSipBOn0vl8IWhaiHeGxQaHsoiiMbYueJQzXasWXT4/LdgQUz7Q5JHiCOwe2rnctGBDTPnQFhsqny8M6wMtWmZNSec5PdAoqxHmh5BRxileRpZNKxbVDTRrLI0GWh6lNfvL2qhGhTLK19HcQFvLWLR90LIHApsuYLCipjIyvo5CYc6BFZSmC6j9xXQyy+qvgLQRgsya3jTL6i8WNJPyPWpXvwLw+cKufF+JpWl+aSMEMXB3wxLrqqb5+MJ+c5tjZYltx4Vnme9tazukvo6a5jO1aU51QsUyzpNZleqXVSd2AA2WtYPJznPSiYEiY6mCx/PBUrdDqrqqmZ4B1bHk61gvVrTMim1Jlrn+Vlu/2KwPkWV9blfL8rliOZVRtMtcT5z9JbcdYrtH62qsino/paz39WDJ19En6dT9L/sysvN8vojEulTkomSGNE07BWAFQAlAkRBynaZp7QC+BWA3gFMAfpcQsngx7KtFylsl2GaGAkQIhixbayvWDAWMS2MNhop8a216XpYHJ35/BMHyb5mDJtbBEN/xo+sb8PvD8PvDACB0TCNAeaKeoaMBEp06R4MvqpcDEWuHnGZgaKCwwHXmG8+a3jVYYoNDSKmskwOkbPYkZ8l2mTs556hfAzRIcBoxsXbk7Dr3hl1ix8fvjyGbPW16CEWlhlbVuBCim8pIWYVCSuh8sQeatYxiAMNqp/nBsbExJz20zZ0oVaeQ1Qlz4EazhcY0DbsHh8yCpdx+fwy53DkTy67zZTTsZhY7Lpc7a6oT6sae2UU7VppFFwjEkMudlljmjqKKVSr5LDpaJ05VbVc1LPVD2/CzpgWU/iqVJizXUd1hsmcZD8dqWVEli9bVUYll3/kyzvP5Qhedpb63Czb+EqdNwaJTtzkFi27zLGtHzswSdeoysrawtjKK5xntvbrz5bZ+0faeWHTme89gyTvtiXaJLPP9qGY539vOLGMqsF1dFf1lbr/YcWZ/1ZNl53uZ5bPozG2VWL+cWf46sgKuWayuNjVVx3Kq9/asYB1ZIVtWNdeRPh8bLDqnen+pyMWcJverhJCU8PmvAfycEHKHpml/Xf78VxfHtOqlWG7MVMFQAEX4xcyQzTQ5t5khHzHWAtHz6DS5UKiLTpMrcwI8aDIHQywztIJSaV1Y+2MEPnQDBdaJlrNF5syQ8SJWORiSH0LUJjEQCQSaQXeTWy53OBqENK28M50Y+BBStOjkwIcFaeINakz7Eh+qzOd2HXLzje2UgTHsEjsmUWXjVamBZr6XA6uopfEyHkzy6LEcdOgmXRROjZfx0LZeRxYMiR1YsTyVOgDiaBWTajoTYlZOFVi56wCoRsjF4FFT1onKD1pzlom+W6M2lrWuGiz7UW23vi+VzA9tt3VCFaQFLDoVyxw8mq9jIBCDrgcVrGhNdul6yJVd54tlf28XLLpaO3Lnh6XKAqxKLFGnZrEAxsxaVbCsAzDqQabNB1bGYI61nRBZ9hkFK8scILtnTStY9lk583W0HxjSLDq7Nrp+LLusnMjyKVlu70d5AMZv0VXTuXc3mHOxWUFXrM0M5tRS71l/1S3rUpGttGboVgC/Uv7/vwD4JS6hYKjENkdACYHyTS9Ok/MJ64E0m2ly7tcMFRXB0Fr5PUARBEtZ43d9jeXpVODTa4xtrVf4NDm2ZqhQoIEP/UwEnVZmNfHAx+cTg6EVYaqe+SFEM0NUd5br/P4Ycrkph0ZCZBnTq6zBUET5oLUbpWUBmWyXqsERH9pRpV3itC9rMBRFrQ2hkc0xOqt2D0enBwc9Trfo5OlolTrkYsfE2plQdUzcjDoysZ/qoG6g1UEHTLraR8jZQ1sVKFTf+TqfLOcyOgXIxkO7Ot+rHo522Rz3vq/cAbDW1aiprlrrhHuWm0yatTNRiWV3P+p6waKrpVN4oVmVO1/iPWQNhqrJTrhjrSpYTgMd6gBG3d5XDmpVgVWt9cvOLlVWrnI7Yc3m1N7m1MqyDhZaWebno3UQU9WfoNfVb9FVU+/lTId1MEf1rHUzmFOJVc3z0eezZoZqzcqxYKgegyasv+qWdanIxQqGCIB/0zSNAPgiIeRLALoIIdPl72cAdF0k22oSMRgK+puAknmanBHomKfJ2e0mx0Q1Tc68Zog25OVgCBvl43RoWsA2M1QsrpQX2FkzQ/I0uXn4fE18swSghGJxEeFwR/nYqCkAiJR/z3h4mTMwdDE2zQxZb+xl0MyTmGVSZYamoWkh+HwhsMxQsUgXZ7Nj6ULJrLLBsWasaPnkjlyUH0sXvOZNDzQx02GMYFK/N9g2quIiaJVdKpZdgyMu9Dbb5feLWaYpaFqQs1gZ2YvVKMtYzG40hFZWPj/FfS+XsZX7XsWSOwBQXEexM9nKjxU3OFA/tCGUsQE+X9DEaqvAsgZDZrtoR2sZbGMB6i8rS53NmbLUVcpqE+qEyLKuZWLTTCiLltHoABp2UZboe+tUUzGAUZfRzl/WTo7YAdg8K6hk0Q0HMhJLVUa7qSG03lPfGyy7uhqpwKrGLrpGUO5MFEx2NZSDkfUyq0W4juK9fbFYdO2i3E4YGy/QZ4fP1K6q26+Lw7JmYKjOT3dk9RtrPa3tar7sG7o+0z6AMViE5FAsLsDvb+HHGpvjGCz7wErFEjuYi3zjBbbWU7ZLzOb4yoOdxlpPtqsqZS1cRFZT+frSaepioCBuvEBnjAQcMkObZQUsLFpGmcV8T5cWGDNZ3LHo2lg5K7fIN3Ews2hgJQ7m0L6JPWtBYNG1sTIrpGQBJRQK8wrWupIlBkOGTmbJ/qJrni8FuVgbKLydEHINgPcC+L81TbtB/JLQ4WeiOlHTtP+qadpRTdOOzs/PXwBT3Yk4TS4YiMIHnWeGAijC7xAM2b1niEnlNUM5Hjz4fE0I6DQzFNT80DTNFAz5hG2nl6Hr2fIGCix7lKY2lt8pRHUpsMyREejMQZwmJ65tYQuJxZuRPbByuXPlzoTRIaed1WZ+LLXT2OCA/p48JY7pxHVLrJEQR5jk0Qu7xfJUZ7dQkh3LbKAjTE3l8tixjDU9qget3LjQ7cnt7TJYbOMIgxU1dWjoQ8g81UVkMb3xQDNY5kbP/OAQR25llvXBYR6Z0rSQ0l+5nGwXbVRTDv6yNtDm6yiz5MZerJdsI4RKZWQZtnx+TgpqxU4b27zAPJJnLqPIUl/HKN+8wDzyyVhiecx2mX3P1rypfT9t8j1tJ+x8z1hOdVVmzTiw5HZia7CiCpa4YYr5Osos2n6JdmmW+8rZrmmHMm4l1nRVLNZ+XUyWOSNGO4kyS24njIEudpzdxg5W1oyJNaNgubdLrl9yGc06c5vDjqE6mSU+J9hxTu1X/VhRqYzmZ4CZZW7T5PM2y4rWxDJ0m7frUmGZn0PixgtMZ87KXSpyUYIhQsi58t85AP8K4E0AZjVN6wGA8t85m3O/RAi5jhByXUdHx4UyuaKImSG/Pwo/dHnNEMnyY4ulIno+2YOjU0cBuMkM6fSBzNcMFcodVT/orl3r/N1Afn8EAdD3AgXKoydsc4R8fpZnZOhntitVE3y+ADQtJE2TE6fOscwR+0tHq1gwFBUySkZwIk6TM26Ws8JNRneTY2uGqE5s2KNl3/hNmScxGIpJv2vXAaAPGD98voiNXaoO+bySZTQAUUlnZwPlT0u2qh/a1gDGapdTJycqPBzddO5VDzSZRRtCY52XXaNH+VOmDsCU9FuVOiZOLDlr5txAi79n+NDI8JkzSuagxrmM00IdlzshZp2qw2RmufVX5c7qFFR1wo7v7Hv7TuFmyih22ux0Yl0Vj6mWVaneO3UwVTq766/SOdUJK8t90OGxNs+qrk7MSCyzzi4YVuk2y5LbPev9aN7QRnWeSicGaSrd+WJZr6N5cEL2l1nnsepbv84XywuGHETTtIimaTH2fwC/AeAlAD8E8Aflw/4AwIMX2rbNSKn811cOXPzQhZeuFuGHEehki1nMrM7guenn6DkV1gwFy1PdjDVDBX6Oz9cIupUzgd8fKWeGaDAU9AX4MQBdM+T3i+/3mC1/T4Mlv79J2ECBBlb0OGtmiB0j/qXfG8GQ8bJWcwfTyALRUW15JFoe1WaBgjUzRAMyuWNCg0J57rH4fSAQ42unKjfQs1LH1AgUjePM22hTu5YsrFIpYzpvScFasrDE85gtpdIyxM69uYxiucUysu3KZX+tSTpdX5N+i+qs15G9v4D5huo2JBt0fcNil6hjvyGeZ+iyko7dG3Y6dp7x3g4odX6/lWXWMb35PKrLm87LK1h5BSvvglVQsJguKpxXULAKjixydoK8AAAgAElEQVTqL0Nn+Kag8FfBdF5RwSoqWEUFq2iyS2aZdUZ5iooyumeJ9Z6QoqXem3XiefVnRU2s6BaxqzrW+agTF5pVr/p1MVmst2GnY3rjvWlw0F0clrl+WXXlWTU2uvPPitroStL39DtDx45l70QUf1f0l3yeWmd/nqyT7VLrxPKY+0xWXf3qhLdmyFm6APxrecvGAID7CCE/0TTtGQDf1jTtPwM4DeB3L4JtNYu0m5w/Bj9K2BCmyQWEl66ydRcLG3SbaTY31G43uZAvCE3TECzHrr7ymiGABlLGttB0vU4XOYWIlkfUp5ePMabJhcN9vLKyzJCY9TFG0YwNFGhjHJGOpccYmSFDZ24AAPbSVcrKwZhnTP8Wi2mpoTK23xZH8zOCrsTZYvBl/J65QQPEuc10SRprcFjGzmj0DNt1RcNhbiSsux7Z+UHFsjbk1jKqyqM6T13uKK9vVtur1blhqcpt+DWXE3XEcl5tdhn/r7Y86uN8Fc473yy1js2TF3/Pzs+VWYGKx7i3q56sYB1ZoTqy8jWd555lbUPd6Hy+ooXltk6YdWzNkN0xzm2OrBPbwmrOO58sa5uzueu4dVlajeddeFatdVVuCy88y67Oie3XZuv9+WK5vWZ29xAbiHfDKpVWQAgB26J9K8sFD4YIIScAHFDo0wDedaHtqZeUpGAoAj90rJV1NBgyHlqsQ5neSAMANE2DpjXYvmcoVM7whHwBQAd/mSrAMkPp8v9pNuft5BcYirejPMgvbLSwjkCgRVgLwl5sGC4fFwZrlMWgR/wsZoaMB7Q6M1Q+ky+6ZCIueDXOE7NFkP4vdwCaUCkYUgdk5puW2dUsnGd/Y6uDGvabfr7Y2Ml2a7Bl5bMFwkCtDxzjr7hZQq0swy5jswRn37ixq6G8U049yng+WFHLMdU+aM8HS9OsLLk8lQNKg2XtANTqr63HCpXXlW0+SDNYQdDOHNnEdawni97bhIQEllMQVbmMhARBg3d9020OXfa7lVgBoRPnhzqj4K6DeTFY7uqJnz/rNS2gzKRtNVb1zyb63KZ9pqAyS37xWE1lVkg5E2CrsNwPHpjbDrrxAuuvEpJzwdLLMz/k/uRWlIu1gcJrTow1QzrtZIsj+6ZgSGfB0Hqa63y+BmVmyA8dfj9tLIPlrRbNwZC4Axx9P1AWjWRFOCbMuX5/M18LwqaLGYGOnPWRs0CqYMiaGTLfHIGA8XvGecZ3Zp3TzcjebGw3Lc+Z5d6uanV05EXjGyFUx4pavqMsuuPQZuwy/EbXXYm/V21QI7LYKJPbbI61TjBWiI/eG+c5d+TsWUGeLXVqoFUBq/kv2wihEsudXQF+/22WxTYvqI0VNbGM+2jzdrljufE9W4vn9jyVzuho+ix1qNqOnMHSlPeq+Tw39+PFYdnfV+yzzKotUyvbpf7uYrFoJ06zrcdudReD5fZ+ZCy7erxVWJu9jnbfXTxW9JJjVV9XDVY19f5SWTfkBUN1EjZNLqBpdDTeFAyJn3VdzgwBKI+AGdMn2AYKNLiiHTOWIbIGQ8a0MhagFItL/Dz2FwCMbadjPDPEOmuso0VHq4KQ1wfJGyhQRkT6y2yQ/8bK54mdQmtmyHlkQv4rdladOveqAMn8OzLLXWfCvvNV706O/Nuq89x25Mw691Pu3P+Onc6NT9x2oqv57WozVrV+d+my6hMoqHRu78fzUcbN1NV61vtaWVu/fqmuo6qNUrff9rrN1a9a/Xtp+f61wbqU7kenOnS+6+r5ZF2oOnGprBvygqE6iZEZ8pWDISMTFEARQWHNkF4OjNiaIYBOVRAzQw0+H4KahoAYDAUaoUGHHyVhzVCjsGbI2PSgWExLxzAR186wzJCR9ZEDHjraHuBs8S/93n7NkPFXlf1hU+LcTVFz8zCtdmqIU8MmNy5uHvJONm+mwbHvrJpH/Csd79w5qM1fbnxZ6ber70S7Dzbr29hXl3k8X3ad34fjxSjj5ure+bPr4rEuVKewmozdZo53X1dra7822/G7FOrEa5W1VQOFSqytGqRVMyB4oeqElxl6nQnL+wTKczONnd90+ECkaXIlm8yQGAwBdKpcQCtJGZ4ASpbMkLETljG1rVBYUE6TE1/Cx3YvM2eG1DvGOU2TU2WGVA2IHASpbiCnh6P7YMipkbD/bbcdE+vx9r9dj86E29Hj+nYA7AO+rZGB2Qqd6NfyqHblQYZLsfO1GdalMuLrlnWhfF99R87N4JeqrtrrVM8V1YCSs25z12UzrEuxfm2l7ITHunB11azzgqHXmbBpcn6fJmWGAqCBjzxNjv7fvGaIBTXLy0+jWFxBLBBAAJWDISbyNLlF5TGqQETcWpt+Vk2Fs/+OBUyaFhDWgLjprBqZIXWA5CbgqfzgZC95pb/DbmL7rJTzQ8gn+Et1vPlBa19+tkC40m87/U59WLIv2YJqt+VwZrGNBKzfuWfFqmK5GQ33+RrhK087dReI2k+TFFnuHhz2Ha2twnL2feV1Z+IASfV2me/7CNhaPDfX0bnzZbDMvy2W0U29p22hel2fuq7WyjLuxwvPsl+7WF9Wde0Xe7n0+Wepnmn2zxrV+kyjLTRYbgIxttGR6jtx4wU397a41tNcty8Gy/C9at0oO97YeMHddbxQrEp9ADPLWM+6tVjWJQJyP8f9+lQnlhcMvc7EbppcQDPeNWQcS/+f3kiXd8oxMkOFwgKeffatmJ7+Ms0MoSgFQ/7y+iNVoEN3k6MVWNc3hPPErRDZmiExGJADHXkjBfN39hso+P2qBYlO0+Tsb1TVg9apwyRuXlB9RqXZ9jtzp1tekGjtFFobCfsOQCW71A9tNyzrQ9tql/GgdQoi3D0cQ4qHfOXz1Cz7jRAqs5zWlm3W9wHbB00l31vrtl+4nzZnl7h5QT1Z7rJ/TgMQ9psXOD1URZ0RpNivxau2roqszdbV88Xa/HWsN0vNuNgsO5+7rV9uWXaLxjfDqoe/zM8hdTtkz1ezarse1bLcZBkCASur1qxUPVnV1gk7e7YCy/462m+84FbH/nprhl5nwoKhoM9HO/LlTFCQ/zXWDJXKmaGiXsRqfhUAHUnU9RzW1l4GUEIudxYxv78c+BjB0A5MYhumeIeR/QVoBRZ3WmNBkKb5hA6mNRAxZ4bU64JYFshYR2T+TtU5kgMFeZqc0whm5cW25s6X/U5ITkGX6jjVBg3ON79Tx9fernp0Jqpt9OTOl/2Do5oyqnTqYypPuXOyp9rz3Nu1+Q6A83mVAyS3rPPtr/r6vj71XtbVk3Ux60Tle7TWTptbljnodKur9rzX9r291VmVp5ZX+6xVM1S/56RTtRPqe3xzdtX2zFHpVHW8nvfjhWJd6LrqZYZeZ8IzQxrLDLEXrpagaUFpmhwLhgBj3RDLDK2vvwIAKBTm0RwIlDNDRhboH/En+CAesJkmZ2ygAMiBkrFVsLGbnHGeeeME1Zbahs68cYJzo2SfGXLbua++IXRqVNnvuN28oXJnovJDyMxSTTOyzzI56dw3hNWNFNodY8eqtZNz/hvoyg/Hi9HJ2br+uhCs6gLrrVLGSykQdcO6UL7fqp1Ct1kAN2XcanX1QrO2fr2v1Heo3DZtVd9v9fvRC4ZeZ1K0CYaCWgmBQJtpAwUhGCqvG2JrhtbWaDCUz8/jQDSK3dpZKTPExKyj86SDkIMWazCk2kDAyAzJGSL6f3kDBVFn/Ws/LUv8v7pzb56q5ryo1SmgcGokas2kuGksVTbIZbRvoNVlPB+NanWNvbO/6vHgqF/WpBp/bWaE3Fz3qh0NV5W7Hg+h+rIu9EPbud676eSc787XVu/keKzz207UqqtHm7PVsnJ2QeNWvY6bYzllo91OUbx0s3K11wnK8oKh15mw8Cao+eTd5EgRwWC7FAwVdeP/9pmhOdy+dy9u999pEwzJmSFjnYR1mhz9nr3jx5wZ8vPFkEZmyPp+IdU6IuuaIVWwotokQczKNEvHqx8u1TQSPsVCvuqyQMb/NTgtInTqyJsfjtUu6nXiy+upKgcTdrpAIFb1onEnXbUL9s8Xy6yTWbX7i7LCClY97FJNNa3Vrojr89Qsq1318T3dvKCWTiGdAmyw2BrBetjFpuvWhxXeFEu8Hxmr1ut4/lh0w4HaWUb7tRkWbb9EFl0HWWtHrr4so71Xsarzl5kVdM2y1q+AhVV7XRVZdN3o+Wa5K7exWQJj1ep7mUXXs9bOMjY4qC+LTuuvta5WYlXf/xJZYW/N0OtNjGlyftM0uSICAVMwVDLWD7F3DRlrhoxpcgCg6zmXwZC865v1eDkzZNwYYWGhnCrwkXeMM/6vCRml6jpf5uDE54tA08xrhyqNcqg7tbRh1biPrA8h1eYNVl0gEIPfHxVY7spoF1CInzfbMZFZ1s5ENdkJ0X+beziaWdU19s6szTX2sr9oA125sa/se7YRQj2uo5pltctNGdnmBfXwV31ZmoVVu7+2MitWM0u8Dxir1g5mJVY1WQYzy6yrJjth9pdZV02mrp6+vxRYKl21LPZMuxRZbuu9mVXpPKd6L7KqqffW/oSVtZl2dWtex6iF5WWGXmdiN00ugIIlM1QgJXQ0dQCQp8kVCnPI5+nmCPn8PAgh0PWsEAwZ7wsyv1DVvJkBYxr/V68ZEqe/qdYMqTND9H1GrNLTjlwj5M4kCzBaLDr5xm62yQKpdJUDJPGzqpNT64PW6ERXzgJVYm3m4WjXMantwSH7XmWrastzVYYvEGiRPqt0gUAr11t1rVwXDLZJn+n3si4QaJP+0v+3K3Rxrjf4cQSDceFzguutunZJx/SGrgPBYIf0WfwLAKFQJwBN4odCXQgGOyVWKNSFUKjLdB5Mum4APhOrWzrG0HULdnUB0CRdKNQDwC+VKRTaVtbDpNtmYvkkXSi0HZoWMJV7OxoatkushgZZx8oj6hoadpRZnZKuoWGHiSXrqA/8ko6ygibWTgVrp4nVDU0LoKFhJ9c1Nu60sBobd0rHUN0uSadm7YKmhUysXWho2KVgGbqGhp4ya5dwTG+Z1SXprCxZFwr1QNOCJr6V1dDQi4aGXpO/ZJ3BMnSNjbuhaSGpzjU27kZjo8wy6xhL1G2OFUJj424XrN0QxaxTs/YoWHsUrD3nmdVgw+qxPa/erHBY1tG6KrPCYSuLnrdHwTJ0BmvPeWHZl1FmmXWhUDd8vkYTa6+CtRfhsJkl6xhL1DFWQ8O2i85SldHMMvvrUgqGQAi5ZP9de+21ZKvIw6kUwdAQ+cbRPyAzM/eT64buIhgaIoO//Cp55ZX/k3xpaB/B0BDB0BD5rw/+GjnwjwcIDoJ89JcfJYQQ8uqr/4kMDYEMDYE8++wNZGgIpFBYIkNDICdPHiSEEDIx8d/5MRsbZwghhJw8+fdkaAjkmWeoL3S9xI85efLvuX3Hjr2FDA2BlEpZQgghc3PfJ0NDIIcO9fJjZmbuJUNDIOPjH+G60dH/lwwNgSwuPs51zz//bvLEE51S+Z98chsZHf1/+GddL5HJyU+TQmGZ69bXJ8j4+F8QXS9x3csvf5AcOXK5xHr00TB55ZX/wD9nMkfI0BDI1NQ/cd2ZM/eU/XCK64aHP0yOHXuLxDp79vNkaekJ/rlUypPR0T8judwM1y0sDJEjRy4jxeIa142M/JGFdfjwABkf/yv+OZebIY8+2kgWFx/jusXFx8kzz1xDisV1rpua+gqZmPhriTU29v+Rubnv8c/F4ho5fvwWsr4+znUrK8fJSy/9LimV8lw3O/sAOXXqHyTWqVMfI+n0T6Qyjoz8Cclmz3HdxsYpMjHx3yXfLyz8gkxPf11iTU19jWQyT/PPul4ip059jOTzC0K5Z8mZM58iuq5zXSbzDJmf/5HEmp9/kKyuviywdHLu3BdJobDCdfn8Ipme/oZ03urqK2Rx8VFJt7AwRNbXJyTd7Oy3JT8XCitkfv6H0jHr6xNkefmopMtkniYbG5OSLp3+CSmVcvxzsbhOFhZ+IR2zsTFJVldfknQrK8elukQIrQPiNSuVsmRp6SnpmFxuhqytjUq6tbURks+nTLY+Q3S9KLDyZHn5ORNrnqyvn5B06+snpGtGbX3BxCpYypPPL1h8s7FxhhQKS5JudfUVqS6VSgWytjYiHVMoLJFsdkrSZbPnpDaBEELW1kYllq4XpfuAsjIWP2ez01JdIoSW28o6aWKtkFxuTtLlcrOkWFyVdBsbp6Q6ruslsrFxWjqmWFwludy8iTUvtSWUdUbBkv1cLK5arn8+n7KwstmzFpZ4r1PWGsnn0yZWWrpfKOucgiVfs2Jx3VKX8vkFBWvKxNJJNjutYC2aWIsK1rSFZb7+9qwNSZfLzShYsybWhqWOFwpLCtZsHVlzCpZcL+vJKpWypFDImFgZBWv+PLPk+0XNWuZ9la3OyudTFpb5PqYsud279Fly+1Iq5Sys1dWXeF91KwiAo8QmntBIOaNxKcp1111Hjh49erHNAAD8OJXCzS+9hPuav45377wZN788giN4C67QRvHd7mM4NP1L/F/4IgDgPy3+T4wtERyfPY4/PPCH+Mx7P4PR0T/G1NQXAAC9vf8Tp0//Ha677kUcPbofe/fegV27/gqnTv0dTp36WwDAW986i1CoE2fO3I0TJz6ClpYbcPXVjwIAHnusCbq+gb1778KuXX8JAHj++V9DJnMI73xnFgCwsPAzHD/+62hqugxvehOdmjc//694+eXfwu7dB7F7N/2dEyf+O86cuQPXXnsMsdg1AIAXX7wNa2vH8Za3nODlX1l5FqHQNjQ0GKNabmRj4xRKpQyi0QNcd/To1UgkPoDdu/8HACCbncThw7uxf/9DiMdvBABkMk9hYuIvcdVVP+cZsGJxFYTkpdH8WqVUWoOu5xEMGlmGfH4edMc+I0um60W+XsATTzzxxBNPPPHEk60nmqYdI4Rcp/rO68XVSdg0uYAvIE2T85M8fL4wGvwBvstCQS+iMRBBPBzHQtZYMwTQqXCx2BsBALncZFnnfs0Q+7/40lV2nLyZAZsmFxbOU02Ts06/6+z8HWxsyPWJBUrVSji826K75ppn+BoigE5FefObJ6TpDy0t1+Oaa56QzgsEoqiXmLcpB4BQqMNynBcIeeKJJ5544oknnly64vXk6iRsN7mA5pd3k0Mefn8YIZ8RDBX1AsKBMOJNcWnNEAA0NQ3y+f8sGDKvD1Lp5AXnEQAp05qhsHL9jhgMGeuDxMDKGiB1df2+O6fUKKoAQxU0eeKJJ5544oknnnjiyWbEC4bqJHa7yQVBM0MhoYOfL+YRDoYRD8elrbUBoKnpDXwRsnNmKCTp1AGMEQzt2vUR5PPzwjEx6Vj5PEMXi70R0ei10sJoTzzxxBNPPPHEE088eS2IFwzVSdg0uaAvAJ9PzAyV4POFESy/owEACnoBsUAY/rAf4wvjAIzAJRJ5A5+Olc2qgyFNC/JpZKrMEAuMWPYIAJqb3yzZq5omR3cQkndjam19B667bmusy/LEE0888cQTTzzxxJN6ihcM1UnEzJCmiZmhAny+FoSEYChfKqAx0IhYKCa9ZwigmSG2dbVdZki1ZbbqRaliJsksxnbVRhaosXEHrr/+tLRlrieeeOKJJ5544oknnrxWxXvPUJ2kJGWGGqTMkN8fRqM/xI8tFHN8zdBSdgklvcQDl0jkDZQT7ODB0Fohjw//6MMo6Oy9PtZgyLyBAv3OOM4s7CWPYmYIoO8BEV+a5YknnnjiiSeeeOKJJ69V8TJDdRK+m5zml6bJ0cxQWMoMZUt0zVB7uB0EBIvZRXR0/DYAIBzuB0B3LltbewkA8MLcq/jSs1/CB/fR7+TMEP2/apqcUzAEANHoVYhELq+90J544oknnnjiiSeeeHIJixcM1UnYbnJ+X1CaJhdAsRwMGZmhkl6imaEwfYt8ej2NRCKJHTv+lB8TDHZA1+k7gVYLeQDAUm4DrVBPkxMzQ8Y0Oedg6JprnqyhpJ544oknnnjiiSeeePLaEG+aXJ2kqOsAgKDPb5km5/OFEfCH4SvrdKKjMdCIeBMNhti6IVGCwU7+/5U8DYoWs6sAKgdDRmbIfs2QJ5544oknnnjiiSeevN7FC4bqJEVCg6GAFrRMk/P7wzQgQhEAQED4NDkAfHttUcQXfC4XaDCULgdD4pqhSORy9Pb+D7S3v5frWGZIPM4TTzzxxBNPPPHEE088kcWbJlcnKRIa6ARspsn5fI0IoIg8GkB0YpkmZxbxvT5LuXV6XHYFaJQzQ5rmx549fy+d63bNkCeeeOKJJ5544oknnryexcsM1UlKOg2GzO8ZYsGQ329khkpEpy9dLU+TU2WGVMHQ/MYSgMpBjhcMeeKJJ5544oknnnjiSWXxgqE6SUEvrxEqvxA1gPLucjwzFOYBEgHQGGhES0ML/JpfuWYoFDLWDC1m1wAAc2vugqFQqAuaFoTf37zpcnniiSeeeOKJJ5544slrVbxpcnUSNk0u6Kcu9WsaQGgwRNcMNSKIAgCAEB3hQBiapqEt3FZxmtxCjq4Vml1fBFB5LVBn5/+BWOzNCAZbN18wTzzxxBNPPPHEE088eY2Klxmqk5RIecMEjW6hHSi/t1TMDLFpcjqhGygAQDwcrzhNbmFjBQAwtUqPq7RLnM8XRCQyuInSeOKJJ5544oknnnjiyWtfvGCoTsKnyZVfrhrQaDSkmianE7qBAgAkmhJIracsPBYMaVoIS7llAMDsWgqA31sL5IknnnjiiSeeeOKJJ3UQLxiqkxRJCT6U4Cu/XDUAMRhqlDZQYO8ZAoDOSCfm1+ctPL8/Ug6iGrCUpWuFCnoBPl9j3YKhD37vg/jac19zPGZqZQp3P3k3CCF1+U1PPPHEE0888cQTTzzZKuIFQ3WSkl6CHyVoWjkz5KOuDWoaNE3jW2sDACHg0+Q6I52YW5uz8DRNQzDYAZ+vEZlsBj3RHqr3ReuyMQIhBN975Xv4ycRPHI+7/8X78ZGffQQnl05u+jc98cQTTzzxxBNPPPFkK4kXDNVJioQGQzwzVJ4mF9Soi+U1QzqfJtcZ6URqPYVSeZqdKMFgBzStAQW9gH3t+wAA/u7bsWvXX23a3qXsEgp6AdMr047HTa1MAQDOLZ9zPO7U0ilksplN2+WJJ5544oknnnjiiScXSrxgqE5SJDp80KHxDRSoa0PKYIhImSGd6Dbba3eAlHksGFrQO9DQ0FO1fV9/4ev4u0f/jn+eXZsFAEyvOgdDM2szAIBzK87B0Du+9g787S//tmq7PPHEE0888cQTTzzx5GKJFwzVSUrlzNDJpTMYOjnEM0JBHwuGxGlyhK8Z6miiGyWopsp1df1HNLb+JgAjGJpfs64vciP3Hr8XXzz2Rf55drUcDFXIDLHvzy6ftT1mLb+Gs8tnMZwalviJuxJ4avIprptamcInD33SW3/kiSeeeOKJJ5544smWEC8YqpOwzNBXnv86PvzjD/M1Qw0+PwDnaXKAXTD0QRRjvwMA6GvrAwDlZgtuZHp1GjOrM3w6Hvu9tcIaVnJ06+5CqYC/+fnfSLvbscyROE3uweEH8fmnP88/Ty5PAqBT5Zi8MPsC0htpPDn5JNd9/YWv4y/+/S8wtjDGdV869iV85N8/Itu6Mo21/JpjeXSiWwLD56afw79N/Juk+28//W/43ivf458z2Qxu+NoNeHnuZa57df5VfOiHH0KhVOC6o1NH8dDoQxLr0OQhTGYmJd2PR3+MXDHHPy9ll3D/i/dLxxw5ewQPDj8o6b723NfwzLln+OeiXsRHf/lR6Z1TM6szuOepe6TgUWXXD4Z/IJVHJzo+d+RzWC7vQgjQ6/35pz8vsY6cPYIfDP9AYj3w0gN4bvo5/rmkl3D3k3djcWOR62ZXZ/Hpw5+WWE+fe9rC+vbL38bzM89Ldn3i0Cf4hiCM9amnPmWx619f/deKdt315F0Wu8z+Onz2ML7/6vctrGenn7WwxOzs7OqsJXA/fPawVJcAuqbOzLrziTtrZh2bOlYT67uvfLci644n7pDq1+zqLD5x6BM1se584k5XrO+8/B2J9cBLD+Do1FH+WSe6kmXetEXFuv/F+yUWs0tsvxhLJzrXHTl7xLVdImtubc4V6/4X75fubZ3ouOOJOyysu568S2IdPnsY337523VhHTl75LyyHnjpAVesO5+408L61kvfcsUS2/dqWE+fe1pi3fnEnRJrfm3+vLKePvd0XVkPvPSAxPrWS9+qK+vI2SMVWXc8cUfNLLFvo2I9c+6Zmlip9ZQr1rdf/raFddeTd7limZ/l9WYdPnvYwmID1SJLXEaxVVlHp47ivhfvk1h/8/O/wVee/QouCSGEXLL/rr32WrJV5D8+/2+kbeh75Pe+eTVpvaOV3P70nxMMDZFPH/4PhBBCVlaeJ9cPfZxgaIjs/USQn/fS7EsEB0EeePEBJfen4z8lOAjy+OnHScPfN5C//Le/rMm+xF0JgoMg0yvThBBCPnfkcwQHQXAQZDQ1Sggh5KnJpwgOgnz52Jf5eS23txAcBPmdb/8O173rX95Fuu7ustjY+LFGous6IYSQLzzzBYKDIH/84z/mx/2XB/8LwUGQh0cf5rq3f/XtpOX2Fn4eIYTsvGcn+bNH/ox/nlmZIZ13d5JDZw5x3b0v3EvCHwuT1FqK6973zfeR7Z/czj/ni3ni/6if3PbAbVz3s4mfERwE+eShT3LdR3/5UYKDIMdnjnPdjffeSLo/0c0/67pOWu9oJX/4gz/kuuennyc4CPLPz/0z133iyU8QHAQZT49z3Xu+8R6JVdJLpPFjjeR3v/O7XHfozCGCgyD/++n/zXW3P347wUGQV+df5br33vteCyvy8Qj5ve/+Htc9ffZpgoMgX3jmC1x35xN3Wlg33nsj6by7U2KFPxaW7Do8edhiF2O9MveKZJeZ1fTxJol15OwRW9bLcy9LrI67OiwssQ4y1v96+n9VzYp8PFI3lp1dn5axygMAACAASURBVD/yeQvrpdmXJFbiroSF9dvf/m1H1l1P3GVhve+b75NYuq6TyMcjEovVCTes+J1xC+sD3/qAhfW5I5+zsF6cfdGRFf2HqMR65twzFtbdT95tYb3/m+8n7Xe2W1i/9a3fsrA+c/gzFtYLMy9IrLY72nibw1i/+cBvOrLYvS2ybrrvpoqso+eOuma13tFqYYntF2N9+qlPW1jPTz9fFevY1DEL65OHPmlh3XzfzRZW7B9irlnPTT8nscT2nrFuvf9WC+tTT33KkXXL/bdYWM23N0usZ6eetbDuOXSPa9Yt999iYd1z6B4L69mpZyVW8+3Njqznpp+zsD711KcsrFvvv9XCarm9xTXr2NSxiqyb77vZwhKfj25ZrXe0Siz2fBRZn37q0xbWbQ/c5pr1iSc/YWEdPXdUYsX+IWZh3XTfTY6szxz+jIX1mw/8poXVdkebxHph5gXXrOg/RC2s93/z/RbW3U/ebWE9c+4ZR1b7ne0S6/jMcQvrs4c/a2H91rd+yzXrrifusrCePvu0xIp8PCL15eJ3xsmHf/RhslUEwFFiE094maE6SYno8KOExewKlrJLwpoha2YoWN5kAXDODAHgmxK0NLSgI9JRU2YoX8rzETu2IYL4eyz7wzI7p5dOAwA2ChvI5Ojvi9PkJhYnMLs2i9X8KgDgTOYMACBbzHLuicUT0l8AGF8cp38XxrluLD2GTC7Dy5VaT2FyeRLPzRhZgKNTRzG3NodHTz/Kdc9MPYON4gZenjcyIiOpEZxbOcftOrl0EiVSwmh61Pi9clZK1LH/j6RHJNbM6gzPrkyvTmMpu4RX5l/hx7DfFnWvpl4FAGnK4EhaZp3JnEG2mMVIakQ6xmID0wnHDaeGJda55XNYK6xZjjGzuM503NzaHM/UTGYmsVHccM9K27POLp/FemG9Zrvm1+d51oezXJxXicX9ZbrW5vOUuvQI5tfneabm3PI5i138vEq69AhS6ymeEeEslQ2qOmHiS6wVa51wrF8mVnojzdsLzqpQL5U6E2tqZQqr+dWafD+cGsbCxoJk12p+1dX1V+kWs4tWVo31S2SpylgNaym7xNtCzqrxfjxfrOnVaazkV2q+tzO5DH9OcFaN/hJZrF2sK6tGfy3nlvm63HqyZtdmkcllavb9cm4ZM6szMqtGf4ks1vafd5YL3Up+hfdpOKtG34us+fV5LGYXa2at5ld5/4uzavSXyEqtp7CwsXD+WS50a4U1vr48tZ5CeiONZDyJS0G8YKhOwoOh8pQzlFOkIV8AAF0zxF66GgoY7wlqD7fDp/lsgxwWjLQ2tqKjqUP5gtZKIqY3WaUXdWxdEA+GMjQYYo1AwBfgFTxfyvPg5+Qi3W6bfRbPPbFkDYYmFiYAGMFQJpvhjTwLSNhNJgYTTjp2Y+eKOb79N+OPpcf4Z5bOZb+jCobY32wxy31h7piNpEb49B03ndWNwgYPLtnx4u+xVLqbTrTKLrFRYiyVv9zYxb6T7HIRpDn5q2IZ0w5lTNefxXwisri/0kL9Sss+zBazvL6bfa9kpVyyFHaxuurmXhDrfV1ZpjKOpcesrPR5YqUrs9jfsQWD5aZOqPzFzhPbCdcs03Vkf8cXxlHUi8rvVLp8KW+pX1WzUlWynMoosFgbbva9a1ZawTL9zsTCRFWsQqlgZaUNFpvy7Mb3jqxFl6yUgpVyYLnwfaFUwMTihHx8qkpWWsEy/c6JxRObZ6UVLJe+Z/2CmlkpBStlZeVLedesol7k/Qiz7yWWi+sosUzfnVw8yafZu/G9kpVWsFz4vi4sB3+x9nswMYhLQbxgqE5SKK8ZWtigI/aE0Idqg5++d8jnCyOIAnwoIeRv5Of5fX4kmhKVM0ONLUg0JWraQEHcMY4FPrNrs9gW2yZ9zzqO7C87dn/nfkytTEEnOk4vneYdP9boS8FQuYPNHsanlk6hpJeQK+Z4dolliMS1Qzw7U76R5tbm+Gi+m879icUTlo4yY+ZLeb6uienYbxNCLMHQ+MI4CIjEZ7+dyRkBnLKRUDy0zCz23UZxg6/FMn+n0qnsYn/XC+tWloNdSpZgF7tWSrsUHSw7u1Qs6TqaWGPpMc4yX/dsMcvrmrnDrDpexRIDJcbabBmVLIegVmKZvsuVcs4sRafbEiCmqmMx30istJXFBjoc66rKLtPviCx2HVUjmOy7icUJS7ApdrAtwbOD70WW+btKLPNvTyxOoETk4Ikdo2Kp2i+mm1gQWOZOnl6wHSBQ2eqW5eR7Fcv8XUEv2Ab1Ildso83+cmKpriP316IRPJnv+4Je4AGvm0EAJ1ZRL/LnnCMrXSXLqf0qf3di8cTmWSl3LBbc1JXlwvcnl06ioBc2x0pXZpVIiQdKrliLLlkurmNFVhW+d81y4ftTS6d4UFczK23PYvdsMuFlhl5XUiIEPuhYyW8AALZhHntwAr3BLAAaDLHMUEN58wQmdi9eBeiCfA0aoqFozdPkxB3jxGlyg4lBhPwh28wQS1tft+06FPUi5tfm+Q0CgP//TOYMrui8QmKcWDyBpmATCnoBZ5fP4uTSSRAQBHwBS+YGsD7sAOEBKzQShBCsF9Z50CWOgjMxBzwq3dnls1jLr2FubY5n39x0ilW2spHIxY1Ffn1cs0wP/lNLp5AtZnm62o6lSnmrWLlijqerRV86dRglXcpqF2OpOkB2qXjx+NNLp7FR2JDtUqTd3dh1JnOGsyz+cjhP/D/77TOZM1gvrKvtcjHdQPxNFUtpg8O1Fe1ay68hvW5MOXPzsHPLcrqOTrrJ5cmq7FIGFuVyq1iObYKD7uzyWazmV7GwscDvx1r95cSqeD+a7D+3cg4ruRUsbizy9r5W36tY1ZaR2efEcgqeVbqplSms5OhUcTZoVKvvp1amsJxblliO7apDWzu9Oo3l3LI0G8E1y9SesGlvEsvlvW3WiSz2vHXznLBjZbIZacqZmwEClf2za7PVsRx8L7LYwGut15FNe5NY1frexFrJrfB+Ua2+Z4O3FVkufM+mYkusGn0vstjsnlp9z6ZiSywH3w+nhtHgb0BvSy8uBfGCoToJmyZXKG+A1E7S+Cr+MzoCGhY2FlAgQABF+KAj6G+QznUKhjK5DJobmuHTfDVPk2ONmV/zG9Pk1mbRHe1Gd7SbNyosCDq3fA5Fvcj11227DgDtGLARkYAvIGWG9nfuR2tjK05nTmMpu4TF7CLe2ftOADQwYgHQ23a+DScXT6KoFzGaHoUGDXta92B0wVi3EwvFABgPxVfnX0XIH+JZGTbi3xhotDQS7eF2zhpNj2JXyy4ANPBio2lsm/LxhXEeJO1u3W1ZO9Qd7Zb4bBv0kTSdkjaaHkU8HOcjkezYzkinxa6eaI+k29G8g/4/NYKSXsL4wjh2Nu8EAcH4wjhvXHY277SwtsW2Sbrtse0Sayw9hu2x7dCJLrG2x7ZbGq/tse0Sa2fzTu77kl7C2MIYt2ssPSbZZe5U7WjeIQWwzPeiXbtadlnKuKtlF0ZSdPqhXblZgzqSHuF29bb0UrsWDLt6W3odWcPpYQtrfGHcYKVNrDRlsbLuatklsXa37raUkfFH06MSiwXzjMV0FpZgF9ONLYzx393dutsoY1q2lZ1vZo2lx2xZzK6KrJSVNZoele1K29s1nLL6azQ9asvidqVkndkusa5KrJTCXynZVlt/uWAxW0SWeB2HU8O8nahYxnQFu1IjFVlm39eDJZbRzvdm1kh6pGbfK+2qwfeMNZIawZ7WPbzc7Ng9rXu47dL1SFvLKPrLjsV/z6XvR9L2djGWW99LLMH3e1r38Cm8bn1fK0vl++HUsC1L9AO/Hgrfi22A6C/2rJbsMvlQ5XsVSzyPsZi/RZbYfon3rVhG0S6xDjraJdy31djFB4nTVrsk3yv8VdJLtbHSLlhCXe2P98Nf3lF5q4sXDNVJiuXMULG8m+JGkaaRfb4w3vTlN+HvHv04AtChgaDBb58ZIoTgr3/213hp7iUANBhqaWwBACSaEljOLUtbObuR6dVpaNAwmBjE1KqxZqizqRM90R5Mr06DEIJTS6fQ1tiGEinh3PI5TK9Mw6/5caDrAAA6gjixOIGmYBP2d+7nU9Mmlyexq2UXelt6cWrpFJ/u8O697wZAM0gsiHpP33tQ0AuYzExidIEGK1d2XSmtGfq1Pb+GoC+IkdQIH1l/15538e9ZI/Hre38dJxdPIl/KYzQ9iq5IF67puUbKAt3QewOioShG06M4tXQKRb2I9/e/HwB90LJjb+q/iS/QZgHGNT3XSKMov7rnVxEOhDGSGsHZ5bPYKG7g5uTN/HvW6N08cDMfpWOsq3uulqYCvrP3nYiGohhJj+B05jTypTxuSd5isMq/e0vyFj4iM5waNuwSRm1+ZfevcNaZzBnkSjnOEv11S/IWpDfSSK+neUAmlnE4NYx37n4nYqEYRtIjPPXN7Uobdt2avFXy147mHbi251opQLqh9wa0NLRgODWM05nT1K4Bq123Jm/li9BVdo2kRvCO3ndwltmu4dQw/91bkrdgMUszdMPpYWrXNtmud/S+A62NrZJdtyZv5WVkx96avBVL2SXMrc3ZlvHtu96OtsY2NSsl+4st0LYrI2ONpEaULNEuxhpODSuv49t2vg3t4XYMp4Z5nWCs4dQwrz+3Jm/lC7RZ/RL9ZWaZ7RJ9z1gzqzNK1kh6BG/b+TbEw3FXdq3kVygrXWb1XGspYzwcx0hqhG9KYmcXWwjNWNdtu85SxkRTgtuVLWZxW/I2S129LXkbZ7F7W2SNpEY4i92PIkusX7clb8NqfpXalRrGttg2vHHbGyW73rrzreho6rC1y8yaWplyxWKbpUhlTI1YWCPpEfREe/Cm7W+SfH/9juvpoE9qxMIS6+ptydv4ourh9DBniXYxlp1dm2XZ1Qk2tXg4NYzuaDfevP3NEustO96CrkgXhlPDfBOXrcB68/Y3ozvajeHUMN94xYnF3gHYFenCW3a8ZUuwNoobmMxMYjg1jM5Ip4X1pu1vQk+0B8PpYb6Jy1ZgvXHbG7Ettg3DqWG+KYkdi02fHk4No6OpA9fvvN41SwweRdZIaoSzxMwzY42kR5QslV0j6REkmhJ46863Su3Xdduuw/bYdgynhzG9Oi2zTM80NhVbZIntxKWyXgjwgqG6CZsmVyxnhtYLdF4n0UKYWJzAcHoYQQ3QQBAyT5NrMoKhU0uncOeTd+Le4/cCoGuGWhtbARgvaK02OzS9Mo2OSAd2tuzk7/BZK6yhK9qFnlgPplemMbc2h2wxi3f0vgMAzRJNr06jK9rFR/jPLZ/D+MI49rbtRV97HyYWJzC3Nod8KY9dLbuwu3U3TmdO84zRDb038AzSxOIEYqEYrt95PQCalRlLj2EgPoCB+ADGF8axUdjAicUT2N+5H/va92E4LTwIB60P2psHbubzd0fTo5TVPoDR9Cg2Chs4kzmDgfYB9Lf3Y3TBCHze1/8+AEYwFPKH8Ot9v851I6kRJBNJJONJjKXHsF5Yx6mlU3hD4g0YiA9IdrHOPQsUAr4Abtx3o6ETWKPpUazmVzG5PIlknOrERu/mgZul80L+EH6j7zckPrdrYQwruRWcyZzhLLExVrEku9Ij/Lyx9BiWc8s4u3yW6hJJKfARg7ThFE19v2ffeyQdY40vjGM5t2yUMWEqY9K+jMx+0a5MNoPJ5UkMxgcxmBiU7OJlLDfQIX8I7+kz7BpJjWAwMcjtElnM99xfSQWLldHkr4mFCZllKuNNAzdBg8Y7mCLLXMbxhXEsZZckf4n1S8nqM1gjaVrGwcSgxGI6sU6IrOHUsD0rPogTiyeMMiYE35vKKF5H7vuyv0TWUnYJZzJn6PVIyL4XWcwuVldZuZOJJAYTg9z3rN4PJgYxnDaClZsGboJP8/HrGPQFjXqfku9H0S7pfiyz3j/wfs4aTg1bWMOpYYmVyWZwOnNauh+ZvxiL6UQW8z077+TiSdkuU/3idpV1kl2mumrHYr4XWZYypq1lXMou4fSSUEbB9+/rfx/8mp+XJ+AL4L3971X6/uTSSQtL9D1jsetoxxqMD1pY5naCs8r+CvgC/Bkg1tVkPIlTS6ewlF3CqaVTyntbxeJ2mXwvslTX8X3970PAF3C0i5VHsisxqPRXwBeQ/MVZKfnZYbHLxHpv/3slll/zK1mD8UELS+V7M+v9A+9X+v70Ep1VcnLppOF7YZCR21X2l2SXyfcii/vexAr6ghLLbBcrj2SXwvecpSqjyfdnMmcoa/Gk0vc37rtRYvk0n2uW2feOLJPvGevE4gml7zmr7C8Vi5VHZF0qO8kBXjBUNykRQqfJlTNDa0W6mGy9nCGaWpnClb4JvBlH0KhYM5TJZZAr5nhGiO+4lsugpcHIDAE1BEOr0+iJ9mBbdBumVqZ44NUV6eKZIbbW54ZdNwCg6zrYeZ2RTvg1P88M9bX1YW/rXikLxDJDp5eMYKi/vR+7W3fzYKivvQ/97f0AaNaGBzDxAeRLeQydGkKJlOjNnpA79+/e+25EghH+oO1t6cVV3VcBMG5GxlrKLuGps09RG+L9GIgPYCw9xtcoXd19NbbHttMAaWEU+9r34bLEZZRVbjgG2geQjCexUdzA0MkhEBBul9hIvGXHW/ho60h6BH1tfbi843JHFgDOEqenXNNzDZ+2NpIewb72fXhDxxsMVmqEBwXZYhY/P/lziSU2hGx0R8WSOuSJJHKlHH524mcAYHR8hTJe03MNnwLHWMxfUkcukUS+lOcvvhUfHOw6XtNzDZ+2NpweRn97v+EvsROdGERBL+DfT/y7XEbBrqt7rubT1kbSI+hv75fKKD7QCnoBP534KWexQIGxruq+ik5bK3fuzCw2yuXEYmW8uudq9Lb2uipjUS9K/jL7/qruqyws5XWMJ1HUi/jpeNkuxYP2QNcBPrWFXcfLOy83/JUyrmNRL+In4z+pmSXWLzNrMD7Irw9j7Wnbw3X72vdxf3HflwPYEinhkfFH5PolDJAwu1hAqWKx8pRIybArYR1QuLLrSs5S+kuoX6JdrE6I9f7Kriuxt20v5aepXWydpbl+MbsICA8ozXZdUFa5zdGJLrMSNizB9+eblUwkLSzzddzftR997X28jH1tfRYWu44ii3X4K7H2d+5X+t5SRgVL9L3KLlYeAoKfjP8EOtGNQQCBdUXnFehr6+P+2tu2F/u79it9L7JUvt/fuV9i9bX3KVnJRNLCMvteaZfJX6w8UhkVvr+i8wo6SFr21962vbiy60ql7y1lVLDE66iyy63vL++4XBq83dO2x2qXyfclUlL6npdR8Jdblsr3lVisPAA4S+X7yzsvR3+8n/trT+sePmPI7HuRdSllhgIX24DXihRBg6ESATRoWMlngQCwWqBB0dTKFN4VmMHbSz/Cl/03S+d2RGjGZ359ngdDbHOCpewSX18iHleNTK9OozvajW2xbZhdm+VrgTojdJqcuK/8Db3lYChzGtMr09jZshN+nx89sR5MLk/ixOIJ3Nh3I/a27UW+lOdvKt7Vsgu9rb1Yya/g2Zln0dbYhpbGFuxt24uJxQks55axv3M/emI9CAfCeOrsU8jkMuhv7+ejBz8c+SEA8MbrodGH8OLsi2gMNGJ3624+aj63Nsc7xwB9G/bc2hyS8SQG4gMAgB+P/hgAeID0nVe+g5fmXkJLA92VbyBOM0jLuWUMxAewp20PAr4Anpx8EkvZJYn/4MiDAIxO4Xdf+S6Ozx5Hc0MzuqPdPBBZ3FhEMpFEX3sf/JpfyfrR6I8k1v0v3o8XZl9AW2MbEk0J3uHP5DK4LHEZdrfuRtAXxONnHkcml+GdNrO/RlIjuO/F+/D8zPMVWY+dfgzLuWXeaRNZzK57j9+L52aeQ3u4HYmmBG+0l3PLuLzzcuxu3Y2QP4RHTz+K5dwyb4zNrMHEIL5x/Bs4Nn3MwlrJreDyzsuxq2UXGvwNhl1xte9H46P4+gtfx9Hpo5zFHgAr+RVc0XkFdrXsQmOg0dGuwcQgxtJj+JcX/gVHp48iHo5L/rJj2dk1vjCOf37+n/HM1DOSXSPpEazl13B55+XY2bIT4UDYNevpqact/lovrNuy2HVkrMEEzch87fmv4ci5I4Zd5UGGjcIGLu+8HDuad6Ap2ITHTj+GTC4jPdBE1smlkxYWs0vFYr431y/G+urzX5XtKvuLsbY3b0dTsEmq95a6mqCj3IzF6j27H3KlHC5LXIbtzdsRCUbw2BnnMp5eOo2vPv9VHD57GG2Nbeho6rCyYgZrKbsk1S8z6ytrX8Hhc4f5axFYGQulApKJJLbFtiEaiuLxM4/TdsKmTpzJnMH8c/N46uxTF4cltDmclUji7PJZpNZTODR5CK2NreiMdPJ7qERKSMaT6In2IBqK4onJJ7CYXXTFamlosWXFQjGJZb6OyUQS51bOSayuSBcvIyE0MOmOdiMWiuHJySexsLHAM5bm6zi1MoX0RhpPTj6J5oZmCyuZSFpYdr5XsVjQLLKaG5ollqquzqzOYGFjgbP4cyg1Ak2jU+K7Il2cld5I2/peZMVCMQsrGU8qWSrfz67NWljsHmJ2dUY60dLQgkOTh5BaT9n6fm5tDovZRTwx+QSioSh6oj3c937Nj2QiaWHZ+V7FYr4XWa2NrRJL5fvUegpL2SU8fuZxRENRbItt4/4K+oMYTAyio6kDrY2teOrsU5hfn7f1fSVWMp50zUpvpC0s1kaLrLbGNhw+e5hvoqXy/cLGAjK5DB478xgiwQi2x7YjGU/i1dSraPA3IJlIItGUkFh2vr9UxMsM1UnYNDkdNDBYyWcBAJkc/TuzOgOfj2aEVLvJAcD82jxemjcyQ4QQZLJGZohNk6t2e+3plWn0xHrQE+uBTnS8OPsiAPBpcgB4UMMa0VNLp3hmCKAL7Y9OHUW2mEVfex/62vsAAEOnaKaDTZMDgKGTQ9jbthcA0NfWh/GFcZxcPIm+tj74NB/62vv4iCwLVgAjUBiID/AR+IfHH0YynoRP82EwMYhX51/lGZLmhmb0RHuk89jNyIKh/naaGdKJjp9O/BQD8QFomob+9n6MpEYwvjCOgfYBBHwB9LX18fNYUGC2KxmnI5HMLk3TMBindo0vjCMZTyLkD2FP2x5L4COy+uP9fNTp4bGHuV2scz+xMIFkPImAL4B97ftku4QyaqBlYbqHxx5GMpHkrFdTr9qyxI4cY+1r38d1D489zP/P7VqkLL/Pj/72fjw09hD9Xng4PjT2kIX1yPgjStZgfJCy4v1GGYXG/qFRg8X4j4w9wv/PGnuW3rfYJfhLtIudz/wFgI+GMZZP82EgPoCHxx62+OvhsYep7+P9rvzFWA+NPmRhsTL2x/uVZWQdAOYvpV0J2S6zv8S6OpIasdjFWKLvRRa/jgKLZWWqZSntSljtSsaTjmU01/vBxKBhV3qE348Wu4Q6oWI9Mv6IdA+NpEf4PaRpGpKJZEWWWL9Eu0bToxhfGMdgnOrM15G1hUwn1i92D5lZTGf2vYrFfT8u+97MMvvezGJttIUVp6yx9Bgv92Bi0PY6qlj8PAVL9L2dXZK/EnIZxxbGpHKL19Hir/Z+tV0Ci11Hcxkr+ctcJ0RWNb5nbQ7z19gCnQEhllHMpFZiib5nLLHcIovN8lD53lzvnexS+d7WrvJA1mh6VPK9UxnFZ5PFLhMrGU/KZYxby2jne7Yhk53v++P90KBJda4Sy9b3Cpb5OcTKY2YlE0nJ9/3tMstSRuEeGl8YpzMbbHxvZrF7/VIQLxiqk7BgCKAPnUx+HQCwmKN/i3oRRKPvHGoINEnnsmBobm0OL8+9DABYza/ybZ/ZmiG30+Tm1+bxT8/+Ewgh0ImO2bVZOk2u/F6h52aeA2BMkwNoMBQPxxFriKG3tRcTixOYX5tHd7QbAN0p7JX5VwDQAIcFO4+dfgyxUAwtDS18F63ZtVnsaaO7j+xt24ul7BIKeoHv4ravfR8vw0B8AJ2RTjQ3NGNqZQrbYtvQ3NDMb6LxhXGj4xsfxOnMaawV1vj3yUSSZ9MG4gPobelF0BfE2MIYuiJdiDXEeKM9uTzJG7eB+AAWs4vIl/ISi22VKo74zazOYGfzTkRCEW7L1MqUdF56I41cKSd1htnOfSpWU7CJn88yXcyuTC6Dgl6Q+CwbmEwYI0Xz6/PY1bIL4WCY/+78+rxkw3JuWWINJgb5Vs/JuDG6k95Io7e1l7LKx7IRQHbsSn4FRb1odNITSb6ddTKeRLwpjng4joWNBexu3S2x2OgrO281v4qiXpTsWswu8v/Hm2i2ZjG7aLDK5VrMLkplXCusSaxkIoml7BL/f3u4HR1NHVjKLmF36240Bhr5sWwUnR1rZg0mBiVWW7gNnZFOicXKxbIO7Lz1wrrFX2wbd5GVyWUMu8q2ZHIZ/n+RJdolslobW9EV6eIs0feiXckEna4p2RUX7Ion0dLYwlnmOsGyNMwGM0u0azAxiJbGFnRHu2W74gq74gq7ErJdbPBDtIsdyzKd7HezxazFruXcMv8/Yy3nlpUs8bxsMYuCXpBsrRdLZde22DbKaulFU7BpS7BiDTFsj22XWMzfIiuZoNNuC3rB0g6x/0dDUc7a1bLLwhLvYzNLtIuxdjTvkFiqMibjdApvvpSXbBXLaGZFQhH+uywDa2aJtnK7ErJd7NmhYg0mBi0sqYyJJCKhCHY275RZNr7Pl/LIlXLKupqMy6wdzTsQDUVtfc9YdtcxEopgV8suiWV3P1rsMvm+KdhkYdn5vqAXqF02vhdZ22PbbVmDiUELy+z7pmATelt6OSvWELP1fUEvIFvM2vpetGtbbJuFJZaHsex8r2LZ+d7MMt/b4WAYva29nNXc0Cz5SyxPUS9Slo3vRVZPtAfNDc24VMQLhuokRQL4iI6mYBO2xbYhUw6CFrKr/JgCoe5utAmGplam8GrqVT53dWxhTMoMtYfb8l1gaAAAHG5JREFUoUGrOE3ub3/5t/jQjz6El+dfRno9jaJelIKh52eeB0Cn3bHM0PHZ4zyz09vSi2NTx0BApMwQk772Puxs3gm/5kcml8Gull3QNI2fDwB7W43MkHgeAOxro0FRwBdAb2svH9UEIN14TMQHtFnHjvNpPuxt2wu/z8+DLsZkARAADLQPSN+J/2ffsb3x2SgKYIxwiOcNxq22isFDJRYL0sTjJZZJ1xho5L4WO/DmMpq/U7HCgTB2tuyU7YobdmnQpDKKvueNavk7xpLKX/67r30fZ4mNsZNdbFqouRz72vfBp/kqlpHZ1RRsMlgJ+bdZllLUOdUviWUqB5sWWckuN2VUshT3gtIuk+/72voQ8AWUNqt04nU0+0Rk8XpfYxn3tu11Z5dD/WLH72ndY7XL4X5U1tXydyJLeT8mKvuLs9r2IOgLKstf0S7T8RVZNu2E6ng2Vda2jE6sRJUsRVtoYcVrY6nKyK4Bm8Kr+k5ksfoi2mW+jiLLqZ0QWWxQ0Hy8iuV0b4sss+97W3rRUH5Fh5t7O+QP8eez2S6RpWrvzceLLPPvsCnPIktV79l3KrsYk01TVtkg6RxYol1mlpPvpTKafC+y3Pg+6AvywWFHu1z43onFpk+LLNH35jKqWOw7kaUqo9n3QV+Qb7+tulaXgnjBUJ2kBMAHnc5bDyewlKNBUGpjhR+Tp+9ctZ0md2jyEPKlPN/G8PjscZRIiW+t7ff5satlF75x/Bt4bppmd5Zzy3hw+EG+3fZKbgXfOP4NAMCTZ57k64N6Yj08sHlx7kW0NLSgMdDIdSVSkoKhlfwKPw8AtjfTYMiv+Wn2xR/kDxD2tz3cjkgwAgBSZogJC4xYsCJ2sFiQwW68tnAbuiJdAIQb1aEDsKd1DxoCDRKLBRtsXQIAKTPEhP+20Olme+ObfyfWEONBpbmRVNnqxIqEIrYdRolf/m4gPmAEA6bv2AiWaIOqAyAGT5bAgnXMyqM7dmVUBYg8sDB1SBsDjbYNbsUymuxqCDRYHlBOZXSyqyHQwOumm06hk10hf4iznHwv1g1LICY80B1ZpgeTqoz8AeUPWli1+l5kmc+rhcXags3Wr6A/yNsTZZ1w46/EoIVVte9Nx7MpqZtixV2yEtWxWBtYNSteJUtRRtYWmn1fLau/3Xh/iblus6my0neqZ4dQv8xtNDtPZDkGokK7atfeq1iOZRRYZt+zqcWq8qt0or+UdpVZbu7tSr43P0+VdiXs7arV9/va99kOZrhlsd8WWWbfs2m3qvKrdE52iSw3vq8HS1VGc/mVZXTwfV97H4J++8GMS0G8YKhOQjdO0NEWpot42cYJsxvGBgjZEp1G1xiUM0OxUAwN/gb84tQvANDtTv2aH0enjgIAzwwBwAO//QAKpQKu/8r1+OD3Pojt92zHbd+6DX/1s78CANz34n1Yza8i5A/hicknML1SDoaiPXzK23phnQdgnZFO/rBjnWnWEWbnAeBl2NWyi1d6lulhL+rUNI2fyzpO7G/QF+QM1vCqMjaq0Qe2cxnLWLDFgYD8QGMidsiYsIaQ6fa07YFf86OloYWvxTI3JCLLKWvARpTFoEtlg2On2zTil2hKoD3cLn0nNbhOo+CmEayOpg6D5cIG1XE7mncgHAjzRaaiH5w6Uyo+WxzfFeniLKfA0mmkjC1ol1hOI8sODTpbON4V6eIDEG5sUPHZwvGKdjkFweW/bIqlaJfjg9MhOGcLtEW73JbRrKsnqyvSxRe711q/mI4tqu6MdKIt3GbrG6UPTddIyXJxv6j4bHqra5ZDnWCsTd3bZR2bKiuxXNYvO1aiKYF4U9z+PBe+V7Jq9D1rTzfFSsiseDhuZVV5b7OpxRKrRt87sqr0PZumzDaXAWq/t+NNcXQ0ddSFxaY8i8/azbA6I511YbEpz/VksXtys6yuSFddWGwqNmvDqmFdCuIFQ3WSEgE0UuKNd768xfbs2hKu7bkWALBapO8eCvvlYEjTNHRGOnFi8QQ0aDjQdQC9rb04Nn0MAHgHCKBbOT/74Wfxzt3vxA+Gf4APXPYB/N4Vv4fPHvksnpp8Cv949B9xVfdVuGngJikz1B3tRtAf5JW4K0qzLn6fn+vYqLs43Y1nhsrT5FgABBhT4VhmSDyXpUxjDTF0NHXQ4KM8AsSnsbUrsjOmBpotLAdoxmJ3626q1+SpV6pMjxRsmbJFbJMDtnGBxBLsUt7s5UXsjM9GlJ1GhwD7zqpP83GfsJE1p6AQAN9emf0FgDck3oCgzxh1Z5tOXNZxmcTSoEnnse2CJV3H5Qj5Q/x6+zQf3tDxBr5NMSubX/NLOrY1rFnXGGjkgbFP8+Hyjsv5sYwV8AVwRccVFpZ43P7O/RJL0zRc0XmFzEokEfQF+XRTAHxLUbMuHAhLrP2d+/k2sgCtN7Ys4bgrO69EU7BJYl3ZdSU/ltkV8ockHfu/qDvQdcDCOtB9QDpmID6ABn+D5Twz68rOKxEJRix2Heg+4MzqtrIOdB2wsA50HbCwGgONtbG61SxWLrGMZp2S1eXMYlvzi795VfdViAQjvN5rmoaruq+SzuuP9yMcCFdmdVVmDcQHbFnsr8RqM7G6a2B1V2b1t9uX0Q1LPIaxruq6yhWLtYVKVrwfTcGmyqyuq9AUbOIs9v15Z3VvnrWvfZ87VncVLBe+bwo2Sc9Mc51gLGW9N+lUdomsvrY+RIKR2lldB/hz25HVXSWr3SXL5jq6ZnXJrHAgLE2bZ/6qmtVdmbW3bS+ioWjtrC6XLMFfl4QQQi7Zf9deey3ZKnLZ498h1/z8dnLL/beQ77/yfXL950CGhkCu+2yY/Pkjf07id8bJFx/ZRX7+C5DPHP6M5fxrv3gtwUGQ/s/2E0II+Y1v/Abxf9RPcBDkkbFHlL9Z0kuEEEKWs8tk5z07SdfdXQQHQb549IvknkP3EBwE+dOH/pTgIMhafo0QQsiBfzxAcBDkA9/6AOdc9YWrCA6C/HD4h4QQQo7PHCc4CIKDINlClhBCyFh6jOAgyId/9GF+3h2P30FwEOTrz3+d6/7kx39CfB/1kVwxx3U33nsj+f3v/b5k94d++CFyePIw163mVsntj98unffK3Cvkq89+VSrzPx37J3L/i/fzz8VSkXzgWx8gvzz5S647uXiSvO0rbyMzKzNc99Pxn5I/+tEfSaxvHv8m+f4r35d0Xz72ZXJm6Qz/nC1kyRee+QIplopcd2LhBPneK9+Tzvv5iZ+TJ04/YWGdXjrNP6/n18k9h+4h+WJeYn335e9K5x2ePEyenXr2/2/v3oPjOss7jn+fXV3s2JIdx8a1HTsOicYEkuaCyIWEEKAJjhlqoJk6oVzqeiYl43ArBVI6TAtDB0MGaEMJU0gDCQ11Q8DAmNyYJEM9pLGjNI4d34UvtWXZsmTLlnyRpd2nf5yzqz3aXd280q52f5+ZM3vO+57znnf31bvex+8574mkrd2x1o+cPJLe7k30+uOvP57+G3B3b+1q9ed3Px85bmPrRt/atjWS9mzzs95xqiO93Zfo8zXb1ngymUyntXW3Zb2frW1bfVfHrkjaun3rvPN0Z3o7kUz4M83PRMrqONXhGw5siBy3s32n7z66O+t9nzhzIlLW87ufj5R17PSxrM/mD0f/EPmc3d1fOfiKd/V0pbeTyaSv27cuUlbn6U5/7dBrkeP2Htvr+4/vj6S92vqqd/d0R8p68f9ejJR1/Mxx33x4c+S4fZ37/MDxA5G0TYc2ZZW1/sD6yD4nzpzwLW1bImn7j+/3lhMtkbTNhzen+3WqrIGfc1dPV1b7Hzh+wA+eOBhJ29K2JausppamrLK2H9keSWs50eKtXa2RtK1tW4csq7une1hlbTuyLausge3f3dPtO9p3RNIOnjiYVdb2I9uzynq19dVRl3Xq7Kkhy9rZvjOrrMzvpXMpq7WrNausHe07ssra2Loxss/JsyeLXpa7Z/W94Za1s31npB3dfdT1Gm1Zh7oO5SxrNO8xX1kD6zWWZZ06e2pYZe3q2KWyClBW5r8duco63H046ztnIpW16dCmSFmlAmjyPPGEBfkTU2Njozc1NRW7GgAsWvcEdWe3c3lXMyuuXsHin9zME7ctZenTv+Kf3vNNHt30KB+Ze4i31rWze9q/cfdb744cv+SxJTzV/BQfeNMHWLNsDSt/s5IHmx4E4MW/epEb5t8w6Pmf2vUUS366hLqaOg5+7iDbjmzj2oeuZW7dXLrPdnP8vuOR89zTeA8Pvu/BSNqmT2ziitlXcKLnBNNWTWPG5Bl0fCGYeexM3xnmfmsu9996PyuuWQHAz7f+nDt+dge/+8vfpZ9PtKN9BxtaNvDRKz+artuZvjMYlr6nR0RERERkvJjZK+7emCtPD10tkAQGnkxfJncqAS+dvJJe/xXz6ucxt24u7ad3kawjPUtHptSlaqnLhDKHXzMvk8vn9obb+fLNX2bWebOYWjM1PSR8sOtg5FKt1D1AqckJMtNS9/vU19YzfdL0dDoEN8Lv/+x+Jlf31/39i97Pw3/6MDctuCmdtmjmoshlYKljRURERERKjYKhAkk44In0BAoArx1+DQhuzJ5bN5fth3tomAIz/miQYOgNOYKh2qGDIYCvvuur6fXqeDXXzbuOF/a+kL7vJ1UX6L9nCOC2S26j7VRbZE74hhkNzJoyK1L+lJopke2aeA3Lr14+rLqJiIiIiJQaBUMFkoBwZGhm+nlAmw5vAoLJB+ZMncOPN8LaVli7KDsYSs30kQqGMicqGM7IUC43LbgpCIamZgdDqeALYNnly1h2+bLIsY996LH0MxFERERERMqRgqECCabWDkaG4rE4MybPYE/nHiCYkS0VhEDuy8Y+dNmH6DjdkZ457I3nvxHDiFks/eyekbpx/o0A6Sm1of9SuNQ01/lkziojIiIiIlKONLV2gfS5BZfJTQqeH5G6VK6+tj7yXBwgct9NyiUzLmHVn6xKTz89qWoSF9ZfyLRJ09LTGI7UDfNvoL62PjJl8nsveS+/+fBveNvct42qTBERERGRcqGRoQJJT6AwuT8Y2tGxI/18nsxL1XJNoJDLpTMuZW/n3lHXqb62nn2f2UddTV06LR6Ls6RhyajLFBEREREpFwqGCiRJ7pGh1IjQUCNDuXzy2k/S0tVyTvVKPc1dRERERESiFAwVSIJY1sgQwLz6YGQo876d4U41/cHLPljgWoqIiIiISIruGSqQJDEgmT0yNDUYEaqtquWCyRcAw79MTkRERERExo6CoQJJeIyYJ6mtqgWyR4ag/1K54V4mJyIiIiIiY0fBUIEkiRHLmPVt4D1DmesaGRIRERERKT4FQwWSIEbc+j/OhdMXAtAwo/95PXPq5hCzGFUx3aolIiIiIlJs+lVeIEniVGWMDL3zoneyfeV2Fs1clE67ecHN7D62e9TPDZLS4B4syWSwZK4PtT2cvFT5qSXznCNdRnvsRDxn6vhcr8orv7xcBss7l2N1zrErV++lNMs9l3OeK5VdPmUvXgxf+crYlV8oCoYKwN1JWpzqWIzXX4f2drjlFosEQgDLr17O8quXj8H5ITO+Sibh5EmIx4Olrw/Ong2Wnp7gtbc3SO/tja7nS+vrg0Ri7JZkcvTHuZ9b8DHSfUVGK7OfptYHvipv+Hm5DPV/TaM9duzPmf2LJJ2XleVDnzNj30Hr4wPzBvuFnDo29z5mubI8es6RvBcfok4+xOeXq7C8+dF9Bjs2d3V9yDJHXu7w6hPNH1DmYG3meT7bgZ97zo/fB23v7M3858mVMNT7zVvuUPVJl53/b2pEGbn6z3DKGMV5Bmur4ZaRb5/Bvi+GdZ48h01pPg1cmTuzhJRcMGRmi4F/AeLAQ+6+qshVGlL693Giine8Azo7YdUq+MIX4NQpWL0aLrgAliwJvpQeeihY3v52WLkSjh2D+++H3/8ebrsNli6FLVvgZz+Djg647jq47DLYvBnWrw8CnIULYcoU2LMH9u+Hmhqorw+ClqNHS+NHeyoYK+RSWxvdjsWii1mudSdmwRdJzCBm3r8eyetPMzLy8PR6zDyybYTHDdwOl5z7mhOzZHCOjP1S+an1zAUP1y2oWyTfkzmPSR8Lg+yTxDzXesY5850rq8zweE9mHzvwuDzl4Bnb+fbLfLX+9wakz5v1mspL1c2i24Sfaa7jU59fcFzwD13kuIz3m7mdWWZkWGPg+lDbo82byOXk2zdZAnUfzutI9xURmUgG+9+qzPWbPgw8Mm7VGi3zEvpCNrM4sBO4FTgAvAzc5e5bc+3f2NjoTU1N41jD3HoSvUxa93sa9z/D/s9/nRtvhF/8Am6/3dmwHjqOBn8UM8/vo+68BHtaarn84pPsPDCZs73BfUbnT+nhPZe18tzWORw7VYuZ845LDnLR+V28tHc2ze3TuWxWB9cvaCHmSfYem0Z3TzUXTz/KgrpO+hLG8TM1VJFg5qQuplWfJpmEvmSMKvqojfdSY33UWC81sV6q6aPa+qga8FpNb7BObzq9ynuJkyDuff2vg617HzFPkB5OyTfEMlj+cNOGc0wJ/Y1LAZhFl8y0gfnDzVM55VvOWJxzOK8j2bccji32+Svt2GKff6IfOxbr43WeYp1/gjOzV9y9MVdeqY0MXQs0u/tuADNbDSwFcgZDpaKjtQ2AxJk4a6cu45qX1/P3tZ9m1VOf5X2s5T5WcYJ6fnRsOYePzea7fIMle57kCLN4lI9xHqf42MlHmdp0krNU8xLXc6k3M7e5NX2OPuJUtSWgbcDJW2P9QyT5hkzMstdzpQ2Vn++YWGZ+NcRqC3+eQtbXLHvJlz6a/bTP2O0jIiIiUkClFgzNA/ZnbB8AritSXYat43SChb3beVP3YRqvOAvTbuHr9Xv4Uu1XqXvDZKj7CJx3Hktqk1B7FGrvhZq/4Q21tfxtbW1w7VfNp6Cmhpp4nJsHBjXxOFU50tI/GEVEREREZMRKLRgakpndDdwNsGDBgiLXJnBFwwKa3/gJ4rcCn+1PrytajUREREREZCil9pyhFmB+xvaFYVqau//A3RvdvXHWrFnjWrnBxOPFroGIiIiIiIxEqQVDLwMNZnaxmdUAdwK/LnKdRERERESkDJXUZXLu3mdm9wLPEEyt/bC7bylytUREREREpAyVVDAE4O5PAk8Wux4iIiIiIlLeSu0yORERERERkXGhYEhERERERCqSgiEREREREalICoZERERERKQiKRgSEREREZGKpGBIREREREQqkoIhERERERGpSAqGRERERESkIikYEhERERGRiqRgSEREREREKpKCIRERERERqUgKhkREREREpCIpGBIRERERkYqkYEhERERERCqSuXux6zBqZnYE2FfsemSYCbQXuxJScGrX8qW2LV9q2/Klti1fatvyVArtepG7z8qVMaGDoVJjZk3u3ljsekhhqV3Ll9q2fKlty5fatnypbctTqberLpMTEREREZGKpGBIREREREQqkoKhwvpBsSsgY0LtWr7UtuVLbVu+1LblS21bnkq6XXXPkIiIiIiIVCSNDImIiIiISEVSMFQAZrbYzHaYWbOZ3Vfs+si5MbO9ZrbZzDaaWVOYNsPMfmtmu8LX84tdTxmamT1sZm1m9npGWs62tMADYT/eZGbXFK/mMpQ8bfuPZtYS9t2NZrYkI+/vwrbdYWbvLU6tZShmNt/MXjCzrWa2xcw+Haar305wg7St+u0EZ2aTzGyDmb0Wtu1XwvSLzWx92Ib/ZWY1YXptuN0c5i8sZv0VDJ0jM4sD3wNuB94M3GVmby5uraQA3uXuV2VMBXkf8Jy7NwDPhdtS+n4MLB6Qlq8tbwcawuVu4PvjVEcZnR+T3bYA3wn77lXu/iRA+J18J/CW8JgHw+9uKT19wOfc/c3A9cDKsP3Ubye+fG0L6rcTXQ/wbne/ErgKWGxm1wPfIGjbS4FjwIpw/xXAsTD9O+F+RaNg6NxdCzS7+253PwusBpYWuU5SeEuBR8L1R4APFLEuMkzu/t/A0QHJ+dpyKfCoB14CppvZnPGpqYxUnrbNZymw2t173H0P0Ezw3S0lxt1b3f1/w/UuYBswD/XbCW+Qts1H/XaCCPtfd7hZHS4OvBt4Ikwf2G9T/fkJ4D1mZuNU3SwKhs7dPGB/xvYBBu/cUvoceNbMXjGzu8O02e7eGq4fAmYXp2pSAPnaUn25PNwbXi71cMblrGrbCSi8dOZqYD3qt2VlQNuC+u2EZ2ZxM9sItAG/Bf4AdLp7X7hLZvul2zbMPw5cML417qdgSCTbTe5+DcHlFyvN7ObMTA+mYNQ0jGVAbVl2vg9cQnCZRivwreJWR0bLzKYCPwc+4+4nMvPUbye2HG2rflsG3D3h7lcBFxKM4L2pyFUaNgVD564FmJ+xfWGYJhOUu7eEr23AGoJOfTh16UX42la8Gso5yteW6ssTnLsfDv9BTgI/pP+SGrXtBGJm1QQ/lh9z91+Eyeq3ZSBX26rflhd37wReAG4guGy1KszKbL9024b504COca5qmoKhc/cy0BDOmFFDcLPfr4tcJxklM5tiZnWpdeA24HWCNv14uNvHgV8Vp4ZSAPna8tfAx8LZqa4HjmdcliMTwIB7RT5I0HchaNs7wxmMLia42X7DeNdPhhbeN/DvwDZ3/3ZGlvrtBJevbdVvJz4zm2Vm08P1ycCtBPeEvQDcEe42sN+m+vMdwPNexAefVg29iwzG3fvM7F7gGSAOPOzuW4pcLRm92cCa8D6+KuCn7v60mb0MPG5mK4B9wJ8XsY4yTGb2n8AtwEwzOwD8A7CK3G35JLCE4CbdU8Dyca+wDFuetr3FzK4iuIRqL/DXAO6+xcweB7YSzGi10t0Txai3DOlG4KPA5vD+A4AvoX5bDvK17V3qtxPeHOCRcLa/GPC4u681s63AajP7GvAqQTBM+PoTM2smmAjnzmJUOsWKGIiJiIiIiIgUjS6TExERERGRiqRgSEREREREKpKCIRERERERqUgKhkREREREpCIpGBIRERERkYqkYEhEREqGmSXMbGPGcl8By15oZq8PvaeIiFQKPWdIRERKyWl3v6rYlRARkcqgkSERESl5ZrbXzL5pZpvNbIOZXRqmLzSz581sk5k9Z2YLwvTZZrbGzF4Ll7eHRcXN7IdmtsXMng2flo6ZfcrMtoblrC7S2xQRkXGmYEhERErJ5AGXyS3LyDvu7lcA/wr8c5j2XeARd/9j4DHggTD9AeB37n4lcA2wJUxvAL7n7m8BOoE/C9PvA64Oy/nEWL05EREpLebuxa6DiIgIAGbW7e5Tc6TvBd7t7rvNrBo45O4XmFk7MMfde8P0VnefaWZHgAvdvSejjIXAb929Idz+IlDt7l8zs6eBbuCXwC/dvXuM36qIiJQAjQyJiMhE4XnWR6InYz1B/72z7wO+RzCK9LKZ6Z5aEZEKoGBIREQmimUZr/8Trr8I3Bmu/wWwLlx/DrgHwMziZjYtX6FmFgPmu/sLwBeBaUDW6JSIiJQf/c+XiIiUkslmtjFj+2l3T02vfb6ZbSIY3bkrTPsk8CMz+zxwBFgepn8a+IGZrSAYAboHaM1zzjjwH2HAZMAD7t5ZsHckIiIlS/cMiYhIyQvvGWp09/Zi10VERMqHLpMTEREREZGKpJEhERERERGpSBoZEhERERGRiqRgSEREREREKpKCIRERERERqUgKhkREREREpCIpGBIRERERkYqkYEhERERERCrS/wN6ePRxLUvmJAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "-unsOYh0DqPZ",
        "outputId": "7509a04e-2ea7-4c0e-ffd3-d042d0defd22"
      },
      "source": [
        "#Training and Validation Loss vs Epochs for Alpha = 0.1 in Gender Recognition\n",
        "#Choosing alpha=0.1 since 0.001 and 0.01 are not diverging initially and may be overfitting the training data. 0.3 and 0.5 on the other hand have a greater loss.\n",
        "\n",
        "epoch_list_gr = list(range(0, 300))\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_gr, J_gr_total[2], 'r', label = \"Training Loss\")\n",
        "plt.plot(epoch_list_gr, Jval_gr_total[2], 'b', label = \"Validation Loss\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Training&Validation Loss vs Epochs for Alpha = 0.1 in Gender Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAShEte2AcQm"
      },
      "source": [
        "**Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "io0dFvBr8FBs"
      },
      "source": [
        "#Predicting labels for gender recognition\n",
        "predicted_labels_gr = predict_labels(test_data_gr, theta_gr_total[2], classes_gr)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ziu1KffAeY8"
      },
      "source": [
        "**Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SF6C7NNSCSxr",
        "outputId": "79ea2d87-50ef-432e-de3c-d4aba36ec3f8"
      },
      "source": [
        "#Evaluating the gender recognition classifier\n",
        "evaluation(test_labels_gr, predicted_labels_gr)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 84.706\n",
            "Macro F1: 0.816\n",
            "Macro Precision: 0.795\n",
            "Macro Recall: 0.874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "-pbOEwmUEcF_",
        "outputId": "138ae06e-f165-4e3a-add5-159d6b903f9f"
      },
      "source": [
        "#Confusion for gender recognition\n",
        "matrix = confusionMatrix(test_labels_gr, predicted_labels_gr)\n",
        "matrix"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0    1\n",
              "0  37    3\n",
              "1  23  107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PS2qK7HQARDZ"
      },
      "source": [
        "## **Speaker Recognition**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doeMXZy7FhH5"
      },
      "source": [
        "**Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5Cvnun_rCWq"
      },
      "source": [
        "#May take up to 15 minutes to run\n",
        "\n",
        "#Encoding the labels for different classes\n",
        "labels_sr, classes_sr = one_hot_encode(train_labels_sr)\n",
        "val_labels_sr, classes_val_sr = one_hot_encode(valid_labels_sr)\n",
        "\n",
        "#Running the graident descent on training data\n",
        "\n",
        "#Using 3 different values of alpha for validation\n",
        "alpha_sr = [0.01, 0.1, 0.3]\n",
        "epoch_sr = 50\n",
        "\n",
        "theta_sr_total = []\n",
        "J_sr_total = []\n",
        "Jval_sr_total = []\n",
        "\n",
        "for i in range(3):\n",
        "  thetas_sr, J_sr, Jval_sr = gradient_descent(train_data_sr, labels_sr, valid_data_sr, val_labels_sr, classes_sr, alpha_sr[i], epoch_sr)\n",
        "  theta_sr_total.append(thetas_sr)\n",
        "  J_sr_total.append(J_sr)\n",
        "  Jval_sr_total.append(Jval_sr)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtPIalbCDXdv"
      },
      "source": [
        "**Plots**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "X0YQ9Py88qqC",
        "outputId": "8ffdc8cd-a608-4bb0-a953-7656d2d3358b"
      },
      "source": [
        "#Training Loss vs Epochs for different values of alpha in Speaker Recognition\n",
        "\n",
        "epoch_list_sr = list(range(0, 50))\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_sr, J_sr_total[0], 'r', label = \"Alpha = 0.01\")\n",
        "plt.plot(epoch_list_sr, J_sr_total[1], 'b', label = \"Alpha = 0.1\")\n",
        "plt.plot(epoch_list_sr, J_sr_total[2], 'g', label = \"Alpha = 0.3\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Training Loss vs Epochs in Speaker Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "auh0pnb-82TN",
        "outputId": "95f5449f-0c8a-4a48-90f3-ad9c804230cb"
      },
      "source": [
        "#Validation Loss vs Epochs for different values of alpha in Speaker Recognition\n",
        "\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_sr, Jval_sr_total[0], 'r', label = \"Alpha = 0.01\")\n",
        "plt.plot(epoch_list_sr, Jval_sr_total[1], 'b', label = \"Alpha = 0.1\")\n",
        "plt.plot(epoch_list_sr, Jval_sr_total[2], 'g', label = \"Alpha = 0.3\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Validation Loss vs Epochs in Speaker Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kw4YNyc7OJj09nenTpzNq1CjmzZvH2LFjGTFiBOPHjwdg0qRJzJgxg1tuuaXIOZcsWcLtt99e4lq1atVi+fLlRe7bvn07CQkJx35PSEhg+/btJR67fft2WrRoUeK4pk2blvl3OBWhmO65v+M4P4XgOiIiIiIiFVp6ejq33norAKNHjyY9Pb3UYHPhhRcSFxcHwIgRI1i2bBnDhw+ndevWpKamAtCtWze2bt0KQFZWFpMmTSI3N5e8vDwGDRpU4pz9+/cnM9BhLoS0jo2IiIiISHEnaFkJhj179rB48WI2bNiAMYaCggKMMTz++OMlji0+Y5j39xo1ahy7LzIy8lhXtHHjxpGRkUFKSgozZ85k6dKlJc55Mi02zZs3Jycn59jvOTk5NG/evMRjmzdvzrZt2054XCAEe4yNA3xgjFljjLkxyNcSEREREamw5s6dyzXXXMN3333H1q1b2bZtG61bt+aTTz4pceyHH37Inj17OHToEBkZGZxzzjllnnv//v00bdqU/Px8Xn311VKP8bbYFN+KhxqApk2bEhMTw4oVK3Ach1mzZjFs2LASx1166aXMmjULx3FYsWIF9erVC0o3NAh+sOnrOE5XYAjwe2PMecUPMMbcaIxZbYxZvXv37iCXIyIiIiISntLT07nsssuK3Ddy5MhSZ0fr2bMnI0eOpEuXLowcOZLu3cseyv7www/Tq1cvzjnnHDp06BCQeqdOncoNN9xAu3btaNu2LUOGDAHg2Wef5dlnnwXgoosuok2bNrRr147x48czderUY48/99xzueKKK1i0aBEJCQm8//77p1WPKW2+6WAwxjwA5DmO88TxjunevbuzevXqkNQjIiIiIuJv48aNdOzY0e0yTmjmzJmsXr2aKVOmuF1K0JX2mhhj1pQ2KVnQWmyMMbWNMXW9t4GBQFawrhdIh7fvZM2GbSc+UEREREREwkIwJw9oArzlGchUDXjNcZySq/aEoVb3X8ne6j9yYHVdInp2h+6e7cwzoZrmWxARERER94wbN45x48a5XUbYCdqndMdxNgMpwTp/MPVs1pa3Iz5j9uG7GfPK0/Cf/9gd0dFw1lnQo4cv7CQlQYTWORURERERcZM+kZdi9KiBEJnPpLqXU/BzLmzaBC+/DDfeCMbAc8/BNddAx44QGwv9+8Ndd8Hrr8PmzRCicUsiIiIiImKpX1UpujazixptOZjJ7NdTuPrqM+CMM2DsWHvA0aOwcSN8/jmsXm23p5+GI0fs/gYNoFs3u3Xvbn+2amVDkYiIiIiIBJyCTSnaN2hPdLVoaidn8uCD13HllcWG1lSrBsnJdrv+envf4cOQlWXDzpo1dnviCRuCAOLiigadbt2gZUuFHRERERGRAFBXtFJERkTSpUkX4lMzyc6GV14px4Nq1LBh5eabYfp0WLsW9u+HVatg6lQYPhx27YK//x1GjoTERGjcGAYPhkmT4K234Pvv1Y1NREREpIrLyMjAGMOmTZuO3bd161Y6d+5c5uPKc0wgrVmzhuTkZNq1a8fEiRMpbRmZTZs20adPH2rUqMETTxx31ZeAULA5jtT4VHKOZtK1m8NDD0F+/imcpGZNO9HAhAnw/POQmWnDzooV8O9/wyWXwI4d8Le/wYgRtruaN+zcey/MmwdbtijsiIiIiFQh6enp9O3bt9SFOcPJhAkTmD59OtnZ2WRnZ7NgQckJkBs0aMAzzzzDHXfcEfR6FGyOIzU+ldxfc7nlvu/ZsgVefDFAJ46Ohl694He/gxdegPXrbdj57DOYMgUuvRR27oTHH4fLL4c2bWw3trQ0uPtumDMHvvkGCgsDVJCIiIiIhIu8vDyWLVvGjBkzmD17dqnHzJw5k2HDhtGvXz/at2/Pgw8+eGxfQUEB48ePp1OnTgwcOJBDhw4BMH36dHr06EFKSgojR47k4MGDp1Xnjh072LdvH71798YYw7XXXktGRkaJ4xo3bkyPHj2Iioo6reuVh8bYHEdqvJ1AILZjJr17t+KRR+C662yPs4CLjobeve3m9euvsGGD7dK2Zo39OXmyb4KCmBjo2tVu3brZn+3bQ2RkEAoUERERqVpuu812tgmk1FT7ca4s8+fPZ/DgwSQlJREXF8eaNWvo1q1bieNWrVpFVlYWtWrVokePHgwdOpSGDRuSnZ1Neno606dPZ9SoUcybN4+xY8cyYsQIxo8fD8CkSZOYMWMGt9xyS5FzLlmyhNtvv73EtWrVqsXy5cuL3Ld9+3YSEhKO/Z6QkMD27dvL+6cICgWb40hunIzBsH5nJg89NIyBA21vst//PkQFeLux9ejhu+/IEfjyS1/QWbPGdmk7fNjur1XL/ovp2tWut9O1q11UtHr1EBUtIiIiIqcjPT2dW2+9FYDRo0eTnp5earC58MILiYuLA2DEiBEsW7aM4cOH07p1a1JT7Rf03bp1Y+vWrQBkZWUxadIkcnNzycvLY9CgQSXO2b9/fzIDneZCSMHmOGpXr01SXBKZuzL5v1Fw7rnw6KN2ErToaJeKql7dBpazzvLdl59vp55et86GnbVrYeZM263N+5jkZF/Q6drV/l6rlitPQURERKQiOFHLSjDs2bOHxYsXs2HDBowxFBQUYIzh8ccfL3GsKTazrvf3Gn7diyIjI491RRs3bhwZGRmkpKQwc+ZMli5dWuKcJ9Ni07x5c3Jyco79npOTQ/Pmzcv/ZINAwaYMqfGprNy+EmPgoYfsOpzTptmmybARFQVdutjtuuvsfYWFdhyON+isWwdvvmmbnAAiIuziot6WnbPOsi09sbHuPQ8RERGRKm7u3Llcc801TJs27dh9559/Pp988gktW7YscuyHH37Inj17iI6OJiMjgxdeeKHMc+/fv5+mTZuSn5/Pq6++WmoIOZkWm6ZNmxITE8OKFSvo1asXs2bNKtG1LdQUbMqQGp/KnC/nkPtrLv36xXLBBfDYYzB+PNSu7XZ1ZYiIgKQku40ebe9zHDudtDforF0LH34IL7/se1zr1r6Q4w08zZpprR0RERGREEhPT+fuu+8uct/IkSNLvb9nz56MHDmSnJwcxo4dS/fu3Y91OyvNww8/TK9evWjUqBG9evVi//79p13v1KlTGTduHIcOHWLIkCEMGTIEgGeffRaAm2++mZ07d9K9e3f27dtHREQEkydP5quvviImJua0r1+cKW2+abd0797dWb16tdtlHLPgmwUMeXUIS69byvmJ5/Ppp9C3L/zjH3DnnW5XFyA7d9qRcevW+bZvvvHtb9TIF3K8W7t2NjyJiIiIVCIbN26kY8eObpdxQjNnzmT16tVM8Q49qMRKe02MMWscx+le/Fi12JTBOzNa5s5Mzk88n3POgUGD7BqbN98Mdeu6XGAgxMfbdXMGD/bdt2+fnYbaP/A8+aRvMZ86dSAlxdeyk5oKnTrZCQ9ERERERFygYFOG+DrxNKndhMxdvr6GDz1kl6H517/sGpqVUkyMnS3h3HN993lnZFu3zhd4Zs2ys7KBnWa6Y0cbcryBJyXFrsEjIiIiIgEzbtw4xo0b53YZYUfB5gRS41PJ3OkLNj17wsUXwxNP2Kmf69VzsbhQKm1GtsJC2LLFF3YyM2HJEnjlFd8xLVr4wo438CQmatyOiIiIiASUgs0JpMan8uRnT3Kk4AjVI+16MA89ZCcUmzwZ/vIXlwt0U0QEtG1rt8sv993/44++rmze7Z13bBAC2yKUkuLrzpaSYruyuTaPtoiIiIhUdAo2J5Aan0p+YT4bd28kJT4FsI0Ol11mh51MnAj167tcZLhp3BguvNBuXgcPQlaWbd1Zv95uL74IBw7Y/ZGRcMYZvqDj/dmkiTvPQUREREQqFAWbE/CfQMAbbAAefBDeegv++U945BG3qqtAatWy/fh69vTdV1gImzfbFh1vC88nn8Brr/mOiY8v2bqTlATV9NYVERERER/N2XsC7Ru0J7padJFxNgDJyTBqFDz9NPz0k0vFVXQREXbq6Msvh4cfhrfftmvt/PQTLF5sm8QGDbJTUj/1FIwZY7us1akD3brB9dfbF2DpUtizx+1nIyIiIhIQGRkZGGPYtGnTsfu2bt1K586dy3xceY4JpDVr1pCcnEy7du2YOHEipS0jM3/+fLp06UJqairdu3dn2bJlQatHweYEIiMi6dKkS5GZ0bweeMD2pHr88dDXVanFxUH//nD77TBzpm3JycuzP2fNgj/8ARo0gP/+F267zR4bF2cnKrj4YrjvPnj9ddi0CQoK3H42IiIiIiclPT2dvn37kp6e7nYpZZowYQLTp08nOzub7OxsFixYUOKYAQMGsH79ejIzM3nhhRe44YYbglaPgk05eGdGK55CO3a0jQhTpsCuXS4VV1VUr267oV1zjZ2S7sMP7R99xw5YsMAuLnTeefDdd/b2lVfaF6huXdv9bfx4O0f3Rx/BL7+4/WxERERESpWXl8eyZcuYMWMGs2fPLvWYmTNnMmzYMPr160f79u158MEHj+0rKChg/PjxdOrUiYEDB3Lo0CEApk+fTo8ePUhJSWHkyJEcPHjwtOrcsWMH+/bto3fv3hhjuPbaa8nIyChxXJ06dTCe2XAPHDhw7HYwaKBCOaTGpzJtzTS+3/s9rWJbFdn3l7/A7Nn2s/STT7pUYFVljB2DEx9vu6x5HT4MX31lx+188YX9+dZb8PzzvmNatIAuXYpuGrsjIiIiHrctuK3EUITTlRqfyuTBk8s8Zv78+QwePJikpCTi4uJYs2YN3bp1K3HcqlWryMrKolatWvTo0YOhQ4fSsGFDsrOzSU9PZ/r06YwaNYp58+YxduxYRowYwfjx4wGYNGkSM2bM4JZbbilyziVLlnD77beXuFatWrVYvnx5kfu2b99OQkLCsd8TEhLYvn17qc/prbfe4p577uHHH3/knXfeKfP5nw59iisH/wkEigeb9u1tI8J//gN33AHNmrlRoRRRo0bJNXccx7bufPFF0e399+HoUd/jOnUqGXgaNXLneYiIiEiVk56ezq233grA6NGjSU9PLzXYXHjhhcR5FkIfMWIEy5YtY/jw4bRu3ZrUVPvZtVu3bmzduhWArKwsJk2aRG5uLnl5eQzy/1LYo3///mRmBjbMAVx22WVcdtllfPzxx9x///0sXLgw4NcABZtySW6cjMGQuTOTYR2Gldh///12TcrHHrO9nSQMGWNTZ7NmMHiw7/4jR+xYHG/rzhdf2K5tM2f6jomPtwEnOdn3s2NHqFkz5E9DREREQuNELSvBsGfPHhYvXsyGDRswxlBQUIAxhsdLGdBdvEuX9/caNWocuy8yMvJYV7Rx48aRkZFBSkoKM2fOZOnSpSXOeTItNs2bNycnJ+fY7zk5OTRv3rzM53feeeexefNmfvrpJxo2bFjmsadCwaYcalevTVJcUqkTCAC0aQO/+Q089xzceSe0bBniAuXUVa/ua5nx9+OPsGGDDTwbNtjAM2WK7eYGvnV3/MNOly72xQ9i31ERERGpvObOncs111zDtGnTjt13/vnn88knn9Cy2AfMDz/8kD179hAdHU1GRgYvvPBCmefev38/TZs2JT8/n1dffbXUEHIyLTZNmzYlJiaGFStW0KtXL2bNmlWiaxvAN998Q9u2bTHGsHbtWg4fPnyspSnQFGzKKTU+lZXbVx53/6RJ9kv+Rx+FZ58NXV0SJI0bw4ABdvM6ehS++caGHG/YWbkS5szxHRMTUzTsJCdD584QGxv65yAiIiIVSnp6OnfffXeR+0aOHFnq/T179mTkyJHk5OQwduxYunfvfqzbWWkefvhhevXqRaNGjejVqxf79+8/7XqnTp3KuHHjOHToEEOGDGHIkCEAPOv5MHzzzTczb948Zs2aRVRUFNHR0cyZMydoEwiY0uabdkv37t2d1atXu11Gqf627G/cs+gefrn7F2Jrlv4h9fe/t602X39tW3Gkiti3D7KyfGHHG3z27vUd06KFL+h4w06HDnZcj4iIiISFjRs30rFjR7fLOKGZM2eyevVqpkyZ4nYpQVfaa2KMWeM4Tvfix6rFppy8Ewis37me8xPPL/WYe++1rTZ33gnz5oWwOHFXTAycfbbdvBwHtm2zAcd/+/BDyM+3x1SrZmdi8w88ycnQqpVdvKCe2EUAACAASURBVFREREREyk3Bppz8Z0Y7XrBp3tyuDXnfffDBBzBwYCgrlLBijB1v07IlDB3quz8/3zbp+Yed4t3Z6tSxs7N5W3a8Pxs3Dv3zEBERkbAzbtw4xo0b53YZYUfBppzi68TTpHaT404g4PWnP8GLL8LEibZHUvXqISpQKoaoKBtSOneGq67y3b9vH3z5pS/sZGWVXHunceOiQadzZxuA6tYN/fMQERERCTMKNichNT71hAs11agBTz9tv6R/+mnbLU3khGJioE8fu3k5Duza5Qs63p/Tp4P/asGJiSVbd5KSNH5HRETkFDiOE7TB7XJyTnYuAAWbk5Aan8qTnz3JkYIjVI88flPMRRfBJZfAQw/B1Vdr0U45RcbYNXTi4+HCC333FxbCli1Fw05WFrz3nm+x0chIG268LTverW1bu09ERERKqFmzJj///DNxcXEKNy5zHIeff/6ZmiexbqCCzUlIjU8lvzCfjbs3khKfUuaxTz1lewnddZddvFMkYCIibEBp2xaG+S0Ye/gw/O9/vqCTlQVr18Lcubb1B+yioh07+rqxeQOP1t8REREhISGBnJwcdu/e7XYpgg2aCQkJ5T5eweYk+E8gcKJg07atDTUPPww33gjnnReKCqVKq1HDN7OavwMHYOPGooFn8WJ4+WXfMXXrwplnFg08nTpB06YKPCIiUmVERUXRunVrt8uQU6R1bE5CQWEBdR+ry03dbuKpwU+d8PiDB+2X47GxsGaNnd1XJGzk5toJC7xd2r780m7+31LFxpYMO506aYY2ERERcY3WsQmAyIhIujTpcsKZ0bxq1bJd0kaOhGefhT/8IcgFipyM2Fg45xy7+fvxR1/IycqyP+fMgWnTfMc0auQLOf5bXFxon4OIiIiIh4LNSUqNT2XOl3PKPWPGZZfZcd/33w+jRumLbqkAGje2W//+vvscB3bsKBp2srLgpZcgL893XJMmJcPOmWdCgwahfx4iIiJSpSjYnKTU+FSmrZnG93u/p1VsqxMebww884wd9nDvvUWXJRGpMIyx0/s1a1Z0hjbHgW3bfC083u2FF+zYHq/4+NIDT/36oX8uIiIiUikp2Jwk/wkEyhNsADp0gNtugyeesBMJ9OwZzApFQsgYO6Nay5YwZIjv/sLC0gPP888XXYOnaVMbcLxBRy08IiIicoo0ecBJOnDkAHUfq8tfzv8Lf+n3l3I/bt8+G3CaN4eVK+2MvSJVTmEhfP+9L+h89ZVv82/h8XZpO/PMooGnYUP3ahcREZGwoMkDAqR29dokxSWVewIBr5gYePxxGDvW9tK54YYgFSgSziIiIDHRbkOH+u73tvB89VXRwPPSS7B/v+8476QF3sDj3Ro31rTUIiIiVZxabE7B6LmjWbl9JVtu3XJSj3Mcu57Npk12HUUNLxA5AceBnJyigcf7c98+33ENGtiA07Fj0cDTvLkCj4iISCWjFpsA8s6MlvtrLrE1Y8v9OGNgyhTo2hX+7//gX/8KYpEilYEx0KKF3QYN8t3vnaXNvyvbV1/BvHkwfbrvOO/Co97NG3xatVJ/UBERkUpGweYUeCcQWL9zPecnnn9Sj01Jgd/9DqZOtd3RUlKCUaFIJec/S1tamu9+x7ELjH71FWzc6As8770HL77oOy462g568wYd78+2bSEqKvTPR0RERE6buqKdgp15O2n6z6ZMHjSZW3vfetKP/+UXSEqyn6s+/lg9ZURCYs8eG3a8gcf78/vvfcdERUH79iUDT1KSDUMiIiLiOnVFC6D4OvE0qd3kpCcQ8KpfH/72N9ti89prcPXVAS5QREpq0ADOOcdu/vLy7MA3/8DzxRfw1lt2UgOw3z60bm2DTvEttvzdUUVERCR41GJziga/MphdB3ax7qZ1p/T4wkLo3dtOBPX113bWNBEJI4cPQ3a2rzubt7Xnf/+z+7zi40sPPE2bqjlWREQkCNRiE2Cp8ak8+dmTHCk4QvXI6if9+IgI+Pe/oVcvePhhOxW0iISRGjWgc2e7+SsogC1bfEHHu73yStGZ2mJiigYd75ie1q2hmv7TKyIiEmj6v+spSo1PJb8wn427N5ISf2ozAPToAb/9LUyeDNdfbz/ziEiYi4yEdu3sdsklvvu9M7V5g463e9v778PMmb7jqle343i8Qcf784wzoHbtkD8dERGRykLB5hR5Z0bL3Jl5ysEG4NFHYe5cmDgRPvhAPVdEKiz/mdoGDCi6LzfX9jn1DzwbNkBGhm0B8mrZsmTg6dBBC5CKiIiUg4LNKWrfoD3R1aLJ3JnJdVx3yudp1Mh2RbvlFnjzTRg5MoBFikh4iI21/U579Sp6/+HD8O23RQPPpk3w/PNw4EDRx3foUHJr00bTU4uIiHho8oDT0Pv53kRHRbPkuiWndZ6jR6FbN/j5Z7uoer16ASpQRCqmwkLIybGtPJs2+baNG213N6+oKNslrnjgOeMM/YdEREQqLU0eEASp8anM+XIOjuNgTqObSLVqdrH0Pn3gjjuKLpwuIlVQRITtltayJVx4YdF9e/eWDDybNsHbb9tvSbyaNrUBxz/sdOhgzxkREdrnIyIiEgIKNqchNT6VaWum8f3e72kV2+q0ztWzJ9x5J/z973DFFTBwYICKFJHKpV49+x+Mnj2L3p+fb2dr87bseMPPnDl2VWCvmjV9KwT7B56kJKhTJ7TPRUREJIDUFe00rMhZQZ8Zfci4MoNhHYad9vl+/RXOOgsOHrTjirW2jYicNseBn36yIce/pefrr2HzZt8ipAAJCb6w47+1aKFWHhERCRvqihYEyY2TMRgyd2YGJNjUrAkvvmgXRr/rLnj22QAUKSJVmzF2lpJGjeDcc4vuO3wYvvmmZOB5+eWia/JER9sWHf+w423lqVs3tM9HRETkOBRsTkPt6rVJiksic1dmwM7Zuzf88Y/wxBNw+eWQlhawU4uIFFWjBnTqZDd/jgO7dtmQ4w09X38Na9bY+en9W3maNSvZwnPGGdCqlV3zR0REJETUFe00jZ47mpXbV7Ll1i0BO+ehQ5Caar9M3bBBX4iKSBjxb+UpHnxyc33H1ahhZ2wr3tJzxhnQoIF79YuISIWnrmhB4p0ZLffXXGJrxgbknNHRtkta375w990wdWpATisicvrKauXZvRv+97+ioeerr0rO2NawYcnAk5QEbdva84uIiJwCBZvTlBqfCsD6nes5P/H8gJ337LPh9tvhySdtl7QLLgjYqUVEAs8YaNzYbn37Ft139Kidsc0bdrzh57337Lc4XhERkJjoCzre8JOUBM2bawIDEREpk7qinaadeTtp+s+mTB40mVt73xrQcx88aLukHT0KX3yhmVhFpBLauxeys31hx//ngQO+42rVgvbtSwaepCSoX9+9+kVEJOTUFS1I4uvE06R2k4BOIOBVqxa88AKcdx7ccw/8618Bv4SIiLvq1YPu3e3mz3Hghx9swPEPO+vWwZtvQkGB71hv17bigadtW9u3V0REqgQFmwBIjU8lc2fggw3YHh0TJ8LTT9suaecHrrebiEj4MsZ2P2veHPr3L7rvyBG7Bs/XX/tae/73P3j/fZg5s+g5Wrb0BR3/TbO2iYhUOuqKFgB/XvhnnvzsSfLuzaN6ZPWAn//AAUhJsV9gfvEF1K4d8EuIiFQO+/cXDTv+LT7+a/NERdkWnaQkXxc3789mzWwoEhGRsKSuaEGUGp9KfmE+G3dvJCU+JeDnr13bdkk7/3y4917beiMiIqWoWxe6drWbv+KztmVn+wLQ++/baay9atf2TVXtP66nfXuIi1PoEREJUwo2AeCdGS1zZ2ZQgg3YcTa33ALPPGO7pBVfQFxERMpQ1qxthYWQk2NDjn9rT2ZmyfE89evbgOPdvIGnfXs7XkhERFyjrmgBUFBYQN3H6nJTt5t4avBTQbvOgQOQnGy7ha9fbycXEBGRIMrPh61bi3Zr87b2bNtmW4K8GjUqGnS8waddO/UhFhEJIHVFC6LIiEi6NOkSlJnR/NWuDTNm2DVtJk2ya9yIiEgQRUX5QsrQoUX3HTpkJzHwDzvZ2SUnMQA7bsc/8LRvbwNPu3aauU1EJEAUbAIkNT6VOV/OwXEcTBD7X/fvD7/7HUyeDCNHwjnnBO1SIiJSluho6NTJbsXl5cE33/i6tnlDz/z5dqyPv4QEX9DxDz5t20LNmqF5LiIilYC6ogXIs6ufZcI7E9h661ZaxbYK6rXy8myXtKgo2yVNX/aJiFQg3kVJvcHHf/v5Z99xxvhCj3/waddOoUdEqjR1RQsy/wkEgh1s6tSxXdIGDID774cnngjq5UREJJCOtygpwC+/lB543ngD9uzxHecfevwDT/v20KaNvvESkSpJwSZAkhsnYzBk7sxkWIdhQb/eBRfATTfBU0/ZLml9+gT9kiIiEmz160OPHnYrbs8eG3q8wcd7e9680lt6/AOPd2vbVjPPiEilpa5oAdRhSgc6NurIW1e+FZLr7dtnu6RFR8O6dfqCTkSkyvK29PgHn+xs+PbbkmN6mjUrGna8AahtW7sOkIhImFNXtBBIjU9l5faVIbteTAw8/zwMHAh33QX/+lfILi0iIuGkrJaevXuLhh7v9u67sHNn0WObNCnauuN/u0GD0DwXEZFTpGATQN6Z0XJ/zSW2ZmxIrnnhhXD77bZL2oABMHx4SC4rIiIVRb160K2b3YrLy7OtOsVDz8KF8NJLRY+tX//4oadJE9sFTkTERQo2AeQ/gUC/xH4hu+7f/gYffwzXXw9du0LLliG7tIiIVGR16kBKit2K867T4w073gC0YgXMmQOFhb5ja9cuGnjatvXdTkiwK0uLiASZgk0A9WjWA4Nh2ffLQhpsqleH2bNtqLnqKvjoI6imV1ZERE5HWev0HDkC331XMvRkZcHbb0N+vu/Y6tWhdeuiYcf7MzERatQI2VMSkcpNkwcEWNdpXYmpEcPScUtDfu30dBgzBu67Dx55JOSXFxERgYICyMnxhZ1vvy16Oy/Pd6wx0KJF0VYe/y0mxr3nISJhS5MHhEhamzQmr5jMgSMHqF29dkivfdVVsGgRPPoo9OsHaWkhvbyIiIjtdtaqld0uuKDoPsexs7SVFnoyMkrO4NawYemBp21biI/XuB4RKUItNgH2wbcfMOiVQSy4egGD2g0K+fUPHLCT4vzyC2Rm2vGcIiIiFcK+fXZcjzfw+AefbduKjuupVcsuRuoNOv63W7WyXeBEpFJSi02I9G3Zl+qR1Vm4eaErwaZ2bTums0cPuO46O5tnRETIyxARETl5MTGQmmq34o4cga1bS4ae7Gx4/3349VffsRERtoubf+DxDz6xoZm5VERCK+jBxhgTCawGtjuOc3Gwr+e2WlG1OLvF2SzcstC1GpKTYfJkmDABnnjCrnEjIiJSoVWvDklJdiuusNCuyfPtt0VbfDZvhvnzS3Zxq1+/ZEtPmzZ20yxuIhVWKFpsbgU2AlVmBGBa6zQmLZnETwd/omGthq7UcNNNdrzNfffBeedB796ulCEiIhJ8ERHQrJndzj235P59+2DLlpLB5/PPYd48OHrUd2xUlJ2trXjg8d6uUydkT0tETk5Qx9gYYxKAl4C/An88UYtNZRhjA7AyZyW9Z/RmzuVzGNVplGt15Oba1nxjYN06tbyLiIiUcPSoHb+zebNv8wagzZvtoFV/jRv7wk7xrVkztfaIhIBbY2wmA3cBdYN8nbDSrVk3YmrEsHDzQleDTWysXd/m3HPhxhvt2BtNICMiIuKnWjW7zk7r1jBgQMn9v/xSMvB8+y189pn9H2tBge/Y6tV9rT3Ft9atNX21SJAFLdgYYy4GfnQcZ40xpl8Zx90I3AjQsmXLYJUTUtUiqtE/sT8LN7s3zsard2/461/h7rvtf69vusntikRERCqQ+vWhWze7FZefX7K1x7utXFmytadhQxtw/MOO93aLFlpdW+Q0Ba0rmjHmMeAa4ChQEzvG5k3HccYe7zGVpSsawJRVU7jlvVv4duK3tKnfxtVaCgthyBD4+GNYtcpOLiAiIiJB9ssvdmxP8W5uW7bAd98VHdsTGQktW5YMPN7bcXHqdiHicbyuaCFZx8bTYnNHVRljA7Bx90bOnHomz138HOO7jXe7HHbtsuNt6te3YyVrh3btUBEREfF39Chs327Djn/48d7+8ceix9et6+sy5w08/lutWu48DxEXaB2bEOvQsAPN6jZj4ZaFYRFsmjSBl1+GgQPh1lvh+efdrkhERKQKq1bNLiTaqhX0719yf16eXbfHP+xs2WIXK/3wQzh4sOjxTZocP/iom5tUESF5lzuOsxRYGoprhQtjDGlt0ng3+10KnUIijPurZKalwT33wKOP2vE2V13ldkUiIiJSqjp1oHNnuxXnOHZtHm/Y8W6bN8OKFfD660UnNfB2c/MGncTEosEnPl7d3KRSCElXtPKqTF3RAF5e/zLXZlzLupvWkRpfyirKLsjPh379YMMGOwV027ZuVyQiIiIB5Z3C2j/w+AegXbuKHl+zZsmw47/Vr+/K0xA5HnVFc8GANnbayIWbF4ZNsImKgtdes+NtRo+GTz+1s1OKiIhIJeE/hXVpDh603dz8w453W74c9u4teny9eiVbery3ExO1aKmEDbXYBNmZ/z6TVrGteO/q99wupYi33oIRI+C22+Cpp9yuRkRERMJGbm7JwOMfhA4dKnp8o0bHDz2tWtkWIZEAUouNS9LapDFj3QwOHz1MjWo13C7nmMsug1tugcmToXt3uPpqtysSERGRsBAbC2edZbfiHMfO2OYfdLy3166135zm5xd9TNOmvqDjH3oSE+3YH3UdkQBRi02Qzd80n+FzhrP0uqWcn3i+2+UUkZ9vJxRYtQqWLSt97TERERGRcisogB07fIHHG3q8t7//vujEBsZA8+ZFA49/8ElIsP3oRfyoxcYl/RL7EWEiWLh5YdgFm6gomDvXttgMHw6rV9vZIkVEREROSWSkDSMJCXDuuSX3e9fvKS34fPQRvPqqXVncKyLCnssbdIpvCj7iRy02IdBnRh8MhuW/Xe52KaXKzISzz4auXWHxYrUIi4iIiEuOHIGcHF/oKb5t31528GnVqmTw0QebSkctNi5Ka53GY8seY++ve6lXs57b5ZSQmgovvmhnSfvDH2DaNE1nLyIiIi6oXt0uMNqmTen7ywo+ixfDDz8UDT7+Xd1KCz4tWkCN8BkDLadHwSYEBrQZwCOfPMJH333EpWdc6nY5pbrySli/Hh57zI4VnDDB7YpEREREijlR8MnPLxl8vvvO/vzkE0hPLznGp2lTX+Dx/+ndatUK9rOSAFGwCYE+CX2IrhbNos2LwjbYADz8MHzxBUycCJ06wXnnuV2RiIiIyEmIiip7DR/vGB//0LNli/25ciW88YY9xp93Omtv0Cl+OyYmqE9Jyk9jbEJk8CuD2bZvG1/+7ku3SynT3r3Qqxfs2QOff27/zYqIiIhUCd5Z3fxber77rujtw4eLPiY2tvTQ490aNlQf/wDTGBuXDWg9gLsW3sUP+3+gWd1mbpdzXPXqwfz50LOnnSnt00/VAisiIiJVhP+sbn37ltxfWGjX8Skt9Hz7rR3nk5dX9DG1apUMO/5b06b2unLaFGxCJK1NGgCLNi/impRrXK6mbGecYbugXnwxXH+9va0vGkRERKTKi4iA+Hi79epVcr/jwC+/+AKP/7Z1q+0O8/PPRR9TrZqdxMAbdFq2LBp8WrSAmjVD8vQqOgWbEEmJTyEuOo5FW8I/2ABcdJGdSODPf7azpv35z25XJCIiIhLmjIEGDex21lmlH5OXZxcq9QYe/9uLFpWc2Q1skCoeevxv16unb6FRsAmZCBPBgDYDWLh5IY7jYCrAm++uu+waN/feC1262LAjIiIiIqehTh0480y7lcY7s5t/4PFumZnw//5fyXE+desWDTvFA1AV6e6mYBNCA1oP4PUvX+frn7+mQ8MObpdzQsbAjBnw9ddw1VWwapXtpiYiIiIiQXKimd0cx47zKa3V5/vvYcUKOwuUv2rV7Lghb9DxD0DerXbt4D+3IFOwCSH/cTYVIdiAHe+WkQHdu8Oll9qZEGNj3a5KREREpIoyBpo0sVuPHqUf4+3uVrzVZ9s2+OgjO+W1/3o+AHFxRYNO8eATHx/23d003XOItXm6DSnxKbx15Vtul3JSPv4YBgyAgQNtC2gVaM0UERERqZyOHrXTWvu39BQPQvv3F33MgQNhM1WupnsOEwNaD+CNr97gaOFRqkVUnD//eefBv/4FEybA/ffDo4+6XZGIiIiInBLvTGwtWsA555R+zN69vqCzY0fYhJqyRLhdQFWT1iaNvYf3snbHWrdLOWk33ww33WRnS5szx+1qRERERCRo6tWD5GS7/sf48W5XUy4KNiF2QesLAFi4eaHLlZyaZ56x61X95jdQyXsNioiIiEgFomATYo1qNyI1PrXCBpvq1WHuXDtebehQu8iuiIiIiIjbFGxcMKD1AD7d9ikH8w+6XcopadIEFiyw484GD4bdu92uSERERESqOgUbF6S1SeNIwRE+/f5Tt0s5ZWecAf/9r50tcOhQO1GGiIiIiIhbFGxccG7Lc4mKiKqw3dG8+vSB2bNhzRoYNcq24IiIiIiIuEHBxgW1q9emT4s+LNxSsYMN2EU7p06Fd9+1s6aF0bJIIiIiIlKFKNi4JK11Gut2rOPngz+7Xcppu+kmu7bNjBnwwANuVyMiIiIiVZGCjUvS2qTh4LBk6xK3SwmIBx+E66+Hhx6C555zuxoRERERqWoUbFzSo3kP6lavW+HH2XgZA88+CxddBBMmwNtvu12RiIiIiFQlCjYuqRZRjX6J/SpNsAGIioLXX4du3eDKK2HFCrcrEhEREZGqQsHGRWlt0vj2l2/ZmrvV7VICpnZtOw108+Zw8cXw9dduVyQiIiIiVYGCjYvS2qQBsGjzIpcrCazGje0CnhERdgHPnTvdrkhEREREKjsFGxd1bNiRpnWaVoppn4tr29ZOAb17tx13s2+f2xWJiIiISGWmYOMiYwwD2gxg0eZFFDqFbpcTcN27wxtvwBdfwMiRcOSI2xWJiIiISGWlYOOytNZp7D64m6wfs9wuJSiGDIHnn4eFC+G3v9UCniIiIiISHAo2LhvQZgBApZodrbhx4+CRR+CVV+Cee9yuRkREREQqIwUblyXEJHBG3BmVOtgA3Hsv3Hwz/P3v8MwzblcjIiIiIpWNgk0YSGuTxsfffcyRgso7CMUYmDIFhg+HW2+13dNERERERAJFwSYMpLVJ40D+AVbmrHS7lKCKjITZs+0U0DfeCC+95HZFIiIiIlJZKNiEgX6J/YgwEZW+OxpAjRrw5pswYABcfz2kp7tdkYiIiIhUBgo2YSC2Zizdm3Xng80fuF1KSERHw/z5cN55cM01MHeu2xWJiIiISEWnYBMmLk26lBU5K/h+7/dulxIStWrB229D795w1VU26IiIiIiInCoFmzBxVfJVAMzOmu1yJaFTpw68+y506wZXXGFvi4iIiIicCgWbMNGmfht6J/TmtQ2vuV1KSMXEwIIF0KULjBgBH1SN3ngiIiIiEmAKNmFkTOcxrN+1nq92f+V2KSEVG2sDTYcOMGwYLF7sdkUiIiIiUtEo2ISRUZ1GEWEiSN9Q9aYKa9AAPvwQ2raFSy6BTz5xuyIRERERqUgUbMJIkzpNGNB6AK9lvYbjOG6XE3KNGsGiRdCyJVx0EXz2mdsViYiIiEhFoWATZsYkj2HzL5tZtX2V26W4okkTG27i4+1Cnp9/7nZFIiIiIlIRKNiEmcs6XEaNyBqkZ1W97mhezZrZcTZxcTBwIKxb53ZFIiIiIhLuFGzCTL2a9RiaNJTZWbMpKCxwuxzXtGhhw01MDKSlwRdfuF2RiIiIiIQzBZswNKbzGHYd2MWSrUvcLsVViYk23ERH23DzVdWaLE5EREREToKCTRi6qP1FxNSIqXJr2pSmbVsbbiIj4YILYONGtysSERERkXCkYBOGoqOiuazDZby58U1+Pfqr2+W4LinJhhtj4NxzYc0atysSERERkXCjYBOmxiSPYe/hvbyX/Z7bpYSFjh3t2jZ16kD//vDxx25XJCIiIiLhRMEmTF3Q+gIa127Ma1nqjubVrh0sWwbNm8OgQfCeMp+IiIiIeCjYhKlqEdUYdeYo3v76bfYd3ud2OWEjIcG21px5Jlx6Kbz+utsViYiIiEg4ULAJY2OSx3C44DAZmzLcLiWsNGpkx9z06QOjR8Pzz7tdkYiIiIi4TcEmjPVO6E1ibKJmRytFvXqwYAEMHgzjx8M//+l2RSIiIiLiJgWbMGaMYUznMSzcvJBdebvcLifs1KoFGRkwahTccQfcfz84jttViYiIiIgbFGzC3FXJV1HgFPDGV2+4XUpYql4dXnsNbrgBHnkEJk6EwkK3qxIRERGRUFOwCXOdG3cmuXEy6VnpbpcStiIj4bnn4E9/gilTYNw4OHrU7apEREREJJQUbCqAMcljWL5tOVt+2eJ2KWHLGHj8cdtq8/LLcMUVcPiw21WJiIiISKgo2FQAozuPBmB21myXKwlvxsB998Ezz9ixNxdfDHl5blclIiIiIqGgYFMBJMYmcnaLs9UdrZxuuQVeeslOCX3hhfDLL25XJCIiIiLBpmBTQYzpPIYNP25gw64NbpdSIVx7LcydC2vXQr9+sHOn2xWJiIiISDAp2FQQV3S6gkgTqVabk3DZZfDOO/DNN3DOOfD1125XJCIiIiLBomBTQTSu3Zi0NmmkZ6XjaLGWcktLs13S9u+HPn3go4/crkhEREREgkHBpgIZkzyGrblbWZGzwu1SKpRevWDlSmjSxI65eeUVtysSERERkUBTsKlAhncYTs1qNXltw2tul1LhtG4Ny5dD375wzTXw4IOghi8RERGRykPBpgKJqRHDJUmXMOfLORwtAf5zKQAAIABJREFU1AqUJ6t+fViwAK67Dh54wC7keeSI21WJiIiISCAo2FQwV3W+it0Hd7No8yK3S6mQqleHF1+Ehx6CWbNg0CBNBy0iIiJSGSjYVDBD2g+hXo16mh3tNBgD999vx9osX24nFdi82e2qREREROR0KNhUMDWr1WRkx5G8ufFNDuUfcrucCu3qq+HDD2H3bujdG1ZoTgYRERGRCkvBpgK6Kvkq9h/ZzzvZ77hdSoV33nnw2WcQEwP9+8Mbb7hdkYiIiIicCgWbCqh/Yn+a1G6i2dECJCnJttZ07QqjRsE//qEZ00REREQqGgWbCigyIpLRnUfzbva75P6a63Y5lULDhrBoEVx5Jdx9N9x8M+Tnu12ViIiIiJSXgk0FNSZ5DIcLDvPWxrfcLqXSqFkTXnsN7r0XnnsOLr4Y9u51uyoRERERKQ8FmwqqR7MetK3fltey1B0tkCIi4K9/heefh8WL7YKeW7e6XZWIiIiInIiCTQVljOGqzlexeMtidubtdLucSue3v4X33oNt26BbNzt7moiIiIiELwWbCmxM8hgKnUJe//J1t0uplNLS4PPPoWlTGDxYkwqIiIiIhDMFmwqsY6OOpDRJ0exoQdS+vZ0x7fLL7aQCV14JeXluVyUiIiIixSnYVHBjksewcvtKvt3zrdulVFp16sDs2bbFZt48u5hndrbbVYmIiIiIv6AFG2NMTWPMKmPMemPMl8aYB4N1rapsTPIYIk0kUz+f6nYplZoxcOed8P77sHMn9OgB72h9VBEREZGwEcwWm8PABY7jpACpwGBjTO8gXq9KSohJ4MrOV/Lc2ue0pk0IpKXB6tXQpg1ccgk89BAUFrpdlYiIiIgELdg4lnc0QpRn09DrILjz7DvJO5LHtNXT3C6lSkhMhE8/hbFj4S9/gcsu03o3IiIiIm4L6hgbY0ykMSYT+BH40HGclcG8XlWVGp9KWps0nl75NIePHna7nCohOhpeegmeeQbefRd69oSvvnK7KhEREZGqK6jBxnGcAsdxUoEEoKcxpnPxY4wxNxpjVhtjVu/evTuY5VRqd/S5gx15O0jPSne7lCrDGLjlFruQZ24u9OplJxcQERERkdALyaxojuPkAkuAwaXse85xnO6O43Rv1KhRKMqplAa2HUiXJl14YvkTOFpsJaTOPRfWroVOney00PfeCwUFblclIiIiUrUEc1a0RsaYWM/taOBCYFOwrlfVGWO4o88dfLn7S9775j23y6lymjeHjz6CG2+Exx6DoUNhzx63qxIRERGpOoLZYtMUWGKM+QL4HDvG5r9BvF6VN7rzaBJiEnh8+eNul1Il1agB06bBc8/BkiXQtSt89pnbVYmIiIhUDcGcFe0Lx3HOchyni+M4nR3HeShY1xIrKjKK23rdxtKtS1n9w2q3y6myxo+HTz6BiAjbTe2xxzQltIiIiEiwhWSMjYTO+G7jiakRo1Ybl/XsCevW+cbcDBwIO3a4XZWIiIhI5aVgU8nE1Ijh5m43M/eruWz+ZbPb5VRp9epBejo8/zwsXw4pKbBggdtViYiIiFROCjaV0MReE4k0kTz12VNul1LlGQO//S2sXg3x/7+9+47Pqr7///98Z+8BZEDYhBFlyBAVwYEKIiharKPa8qn+pLVq3a0daqu2ta7iQL+OOuuoIg7AgVpEhgiy9zTMBJIQsnfevz/emQwNkuTkSh732+3czrnOda4rr8vbkSvPvFeiNG6cdOedUmmp15UBAAC0LgSbVigpKkk/G/AzvbjyRWUVZnldDiSdcIL0zTfSb34jPfKINHKktJ0GNQAAgEZDsGml7hhxhwrLCvXMt894XQqqhIZK06a5RTy3bJFOOsl1VQMAAMDxI9i0Uv3j+2tc8jg9ueRJFZcXe10O6vjJT6SVK6WBA6Wf/cx1VSso8LoqAAAA30awacXuHHGn9hfs16urXvW6FByiWzfpyy+lP/9ZeukladgwafVqr6sCAADwXQ0KNsaYcGOMX9VxH2PMRcaYwKYtDcfrrO5naWjHoXr060dVaVlIpaUJCJDuv1/6/HMpJ8dNEf3005K1XlcGAADgexraYvOVpBBjTJKkOZJ+LunlpioKjcMYoztH3KnNWZv14aYPvS4HRzF6tLRqlXTOOdINN0iTJkmZmV5XBQAA4FsaGmyMtbZQ0k8kPW2t/amkE5uuLDSWSSdMUveY7izY2cLFxUkzZ0qPPSbNmiX17y99SBYFAABosAYHG2PMaZKukjS76px/05SExhTgF6BbT71Vi3Yt0qJdi7wuB9/Dz0+69Va35k3HjtLEidLkydLBg15XBgAA0PI1NNjcIukPkt6z1q4zxvSUNLfpykJjumbwNYoNidUjix7xuhQ0wMCBbs2bu++WXn/dtd7MmeN1VQAAAC1bg4KNtXaetfYia+0/qyYRyLTW/raJa0MjiQiK0G9O/o3e3/i+Nmdt9rocNEBQkHTffdLixVJUlDR2rPTrX0t5eV5XBgAA0DI1dFa0N4wxUcaYcElrJa03xtzZtKWhMd00/CYF+Qfpsa8f87oUHINhw6Tly6U775See04aNEiaN8/rqgAAAFqehnZFO8FamyvpYkkfS+ohNzMafERCRIJ+MegXennly9pfsN/rcnAMQkKkhx6S5s+X/P2ls86SbrlFKiz0ujIAAICWo6HBJrBq3ZqLJX1orS2TxGobPub2025XSUWJnlrylNel4Ec4/XRp5Urpppukxx+XBg92XdUAAADQ8GDzrKRUSeGSvjLGdJOU21RFoWn07dBXF/W9SNOWTlNBaYHX5eBHCA+XnnhC+uILqbjYhZ277pJKSryuDAAAwFsNnTzgCWttkrX2AuvskHR2E9eGJnDniDt1oOiAXlr5ktel4DiMHi2tWSNdc430z3/WjsUBAABoqxo6eUC0MeYxY8y3Vdujcq038DGndzldp3Y+VY99/ZjKK8u9LgfHISpKev556aOPpAMHpFNOke65x7XkAAAAtDUN7Yr2oqQ8SZdVbbmS+JO/DzLG6M4Rd+q7g9/pvQ3veV0OGsG4cdLatdKVV0r33+9mTvvyS6+rAgAAaF4NDTa9rLX3Wmu3V21/ldSzKQtD05nYd6KS2yXr4UUPy1rmgGgNYmOlV1+VPv1UKi+Xzj7bdVPLyvK6MgAAgObR0GBTZIwZWf3AGHO6pKKmKQlNzd/PX7efdruW7l2qr3Z85XU5aERjxrixN3fdJb32mtSvn/Sf/0jkVwAA0No1NNj8WtI0Y0yqMSZV0lOSftVkVaHJTR40WR3COujBhQ96XQoaWViY9I9/uMkEkpOln//cBZ6tW72uDAAAoOk0dFa0VdbaQZIGShporR0saXSTVoYmFRoYqjtH3KlPtn6iOdvmeF0OmsCAAdLChdK0adKSJe7x3/8ulZZ6XRkAAEDja2iLjSTJWptrra1ev+a2JqgHzejmU25Wz9ieuvXTW5khrZXy85N+8xtpwwZpwgTpT3+Shg6VFi3yujIAAIDGdUzB5hCm0aqAJ4IDgvXomEe1PmO9/t+3/8/rctCEOnWS3nlHmjlTyslxC3tef7108KDXlQEAADSO4wk2DEduBSb2najRPUbrnrn3KKuQKbRauwkTpPXrpVtvlZ57TkpJkd5+m8kFAACA7/veYGOMyTPG5B5hy5PUqZlqRBMyxmjq2KnKKcnRX778i9floBlEREiPPebG3XTqJF1+uQs827Z5XRkAAMCP973Bxlobaa2NOsIWaa0NaK4i0bQGJAzQlCFT9My3z2jd/nVel4NmMnSo9M030r/+JX31lXTCCW4MTn6+15UBAAAcu+PpioZW5L6z71NkcKRum3Mbi3a2IQEB0i23SJs2uZabv/9d6ttXev11uqcBAADfQrCBJCkuPE73nnmv5mybo9lbZntdDppZp07Sq6+62dI6dZKuvloaNcqthQMAAOALCDaoccPJN6hv+7667dPbVFrBYidt0Wmnue5p//63tGWLNGyYdN110v79XlcGAADw/Qg2qBHoH6jHxj6mLQe26MlvnvS6HHjEz0+65hpp82bpttukl1+W+vSRpk6Vysq8rg4AAODICDao54LeF2hc8jjd99V92l/An+nbsuho6ZFHpDVrpFNPdVNEDxokffaZ15UBAAAcjmCDwzw29jEVlhXq7v/d7XUpaAH69ZM+/lj68EOptFQaM0a6+GJp+3avKwMAAKhFsMFh+nXopxtOvkHPL39eK9NXel0OWgBjpAsvlNatkx58UPr8c6aHBgAALQvBBkd075n3ql1oO93yyS1M/4wawcHS73/vxt9cdpmbHrpPH+m556Tycq+rAwAAbRnBBkcUGxqr+8++X/N2zNOMDTO8LgctTPX00F9/LfXsKf3qV9KAAdL777P+DQAA8AbBBkd13dDr1D++v+747A4Vlxd7XQ5aoFNPlebPd4FGki65RBo50q2HAwAA0JwINjiqAL8ATR07VakHU/XY1495XQ5aKGOkiRPd7GnPPusmFTj9dBdyNm70ujoAANBWEGzwvc7peY4u7nex/j7/79qbt9frctCCBQRIU6ZIW7dK998vffGF1L+/66aWluZ1dQAAoLUj2OAHPXLeIyqrLNMfv/ij16XAB4SHS3/+s7Rtm/Sb30gvvSQlJ0t33y3l5npdHQAAaK0INvhBvdr10i2n3KJXVr2iJXuWeF0OfERcnPTEE9KGDW6q6AcekHr1kp580q2HAwAA0JgINmiQP53xJyWEJzD9M45Zr17SW29JS5a4mdN++1spJcWdq6z0ujoAANBaEGzQIFHBUfr7OX/X17u/1ptr3/S6HPigk092424++sh1V7vySmnwYKaIBgAAjYNggwb7v5P+T0M6DtHvP/+9CkoLvC4HPsgYadw4acUKtw5OYaGbPW3oUGnmTAIOAAD48Qg2aDA/46epY6dqd+5uPbTwIa/LgQ/z95d+/nM3/ubll6WcHOmii6Thw12LDgEHAAAcK4INjsmobqN02YmX6aFFDyn1YKrX5cDHBQRIkye79W5eeEHKzJTGj5dOO02aM4eAAwAAGo5gg2P28HkPK9AvUFfPuFrlleVel4NWIDBQuvZaadMmt8jn3r3S2LHSqFFuXA4BBwAA/BCCDY5Z1+iuemb8M1q4a6Hun3e/1+WgFQkKcot8btkiTZsmpaZK554rnXWWNG+e19UBAICWjGCDH+WqgVdp8qDJemD+A5qXym+caFzBwW5xz61b3Vo4mze7cHPOOdKCBV5XBwAAWiKCDX60J8c9qV6xvXTVjKuUVZjldTlohUJCpJtukrZvl/71L2ntWtc97dxzpf/9jy5qAACgFsEGP1pkcKTenPSm9hfs17UfXsvCnWgyoaHSLbe4gPPww9K6da715tRT3To4LPQJAAAINjguQzsN1T/P/ac+2PSBnvn2Ga/LQSsXHi7dcYf03XfSM89IGRluHZwBA9y6OGVlXlcIAAC8QrDBcbv51Js1Lnmcbvv0Nq3et9rrctAGhIRIv/61G3vz+utuXZzJk6Xevd2kA0VFXlcIAACaG8EGx83P+Onli19WbGisrph+hQrLCr0uCW1EQID0s59Jq1ZJM2dKSUnSjTdK3btL//iHW/gTAAC0DQQbNIr48Hi9dslr2pi5Ubd+cqvX5aCNMUaaMMHNmDZvnjR4sPTHP0pdu0p/+IO0b5/XFQIAgKZGsEGjObfnufrd6b/Tc8uf0/T1070uB22QMdIZZ0iffCItW+YW+fznP10Lzg03uHVxAABA60SwQaO6/+z7NTxpuK6beZ12HNzhdTlow4YMkd5+W9q4UbrqKun556XkZOmKK6QlS7yuDgAANDaCDRpVoH+g3pz0pioqK/SzGT9TeWW51yWhjevTR3rhBTdV9C23SB9/LJ1yijRypPTuu1JFhdcVAgCAxkCwQaPrGdtTz054Vot2LdJ98+7zuhxAktS5s/TII9Lu3dLUqdLevdKll7qZ1KZOlXJzva4QAAAcD4INmsSVA67UL0/6pR746gF9mfql1+UANSIjpZtvlrZskWbMcDOp3Xqr1KWLdPvt0g56UAIA4JMINmgyT4x7Qr3b99ZVM65SZmGm1+UA9fj7u8U958+Xli51s6o98YTUs6d02WXS1197XSEAADgWBBs0mYigCL016S1lFmbq2g+vlbXW65KAIxo2zC30+d130p13Sp99Jo0YIZ12mvTOO1I5Q8UAAGjxCDZoUoM7DtZD5z6kDzd9qKeXPu11OcD36txZevBBadcu6amnpMxM13qTnCw9/LCUleV1hQAA4GgINmhyvz3ltxrfe7xun3O7VqWv8roc4AdFRLh1bzZulD74QOrRQ/rd71zw+eUvXdc1AADQshBs0OSMMXpp4ktqF9pOV7x7hQpKC7wuCWgQf3/pooukuXOlNWuka66Rpk+Xhg+XTj5ZevllqajI6yoBAIBEsEEziQuP02uXvKZNmZt008c3Md4GPqd/f2naNGnPHrcvLHStN0lJ0h13SNu2eV0hAABtG8EGzeacnufoz2f8WS+tfEmPff2Y1+UAP0pUlPSb30hr10pffimde670+ONuHM64cdLMmSz6CQCAFwg2aFZ/Oesv+ukJP9Wdn92p9ze+73U5wI9mjHTmmdLbb7u1b/76V2n1atd1rVcvNwlBRobXVQIA0HYQbNCs/IyfXrn4FQ1PGq6rZlylZXuXeV0ScNw6dZLuuUdKTXVjcHr2lP7wBzfZwNVXS199JdH7EgCApkWwQbMLDQzVB1d8oLiwOF345oXanbvb65KARhEYKE2aJP3vf9L69dKvfuW6pp15ptSvn/TQQ9K+fV5XCQBA60SwgScSIhI062ezlF+arwlvTFB+ab7XJQGNKiVFeuIJae9eN3tafLz0+9+7Vpyf/ET66CPG4gAA0JgINvBM//j+euen72jt/rW68t0rVVHJb3lofcLDpcmTpfnzpQ0bpFtvlRYskMaPl7p3r+3CBgAAjg/BBp4amzxWT457UrM2z9Idc+7wuhygSVV3R9u9W3r3XWnAAOmBB9yYnDFj3EQEJSVeVwkAgG8i2MBz1598vW455RZN/Waqnl76tNflAE0uKKi2O1pqqvSXv0ibNkmXX+7WxbntNmndOq+rBADAtxBs0CI8MuYRTegzQb/9+Lf6ZOsnXpcDNJuuXV13tO3bpU8/lUaPlp56yi0Ieuqp0tNPS1lZXlcJAEDLR7BBi+Dv5683J72pAQkDdNk7l2nt/rVelwQ0K3//2u5oe/ZIjz4qFRZKN9wgdezoWnjef18qLfW6UgAAWiaCDVqMiKAIzbxypiKCIjT+jfFKz0/3uiTAE3Fxrjva6tXSypXSTTdJixZJl1zi1sy56SZp6VLWxgEAoC6CDVqUzlGdNfPKmcoszNTEtyaqqKzI65IATw0a5Fpvdu92Y3LOO0964QVp+HDphBOkf/xD2rnT6yoBAPAewQYtztBOQ/X6T17X0j1L9Yv3f6FKW+l1SYDnAgKkceOkN9+U0tNduImPl/74Rzdt9DnnSK+8IuXleV0pAADeaLJgY4zpYoyZa4xZb4xZZ4y5ual+Flqfi/tdrIfPe1jT10/Xn//3Z6/LAVqU6Gjp2mulefPcpAN//atrtfm//5MSE6Wrr3atO2VlXlcKAEDzMbaJOmkbYzpK6mitXW6MiZS0TNLF1tr1R3vNsGHD7Lffftsk9cD3WGv161m/1nPLn9OLF72oXw7+pdclAS2WtdLixdKrr0r//a+UnS21ayddeql05ZXSqFFuggIAAHydMWaZtXbYoeebrMXGWptmrV1edZwnaYOkpKb6eWh9jDF66oKndG7PczVl1hTN/W6u1yUBLZYx0mmnSc8847qqffihdP750uuvS2ef7aaVvvVWackSJh0AALROTdZiU++HGNNd0leS+ltrc492HS02OJKDxQc14t8jlJ6fri//70sNTBjodUmAzygokGbNkt56y3VPKy2VevaUrrjCteT07+91hQAAHJujtdg0ebAxxkRImifpb9baGUd4foqkKZLUtWvXoTt27GjSeuCbvsv+TiNfGqnCskLN/tlsjegywuuSAJ9z8KBbC+fNN6UvvpAqKqQTT3QB54orpF69vK4QAIAf5kmwMcYESpol6VNr7WM/dD0tNvg+qQdTNea1Mdqdu1szLp+h85PP97okwGft3y+9845ryVmwwJ07+WTp8sulSZPcTGsAALREzT7GxhhjJP1b0oaGhBrgh3SP6a75v5yvvh366sI3L9Rba9/yuiTAZ8XHSzfcIM2fL+3YIT38sGvBueMOqUcPadgw6cEHpS1bvK4UAICGacpZ0UZKmi9pjaTqhUj+aK396GivocUGDZFTnKML37xQC3Yu0LQLpun6k6/3uiSg1di+XXr3XWn6dDfRgCQNHOhmV5s0yS0KCgCAlzwbY3MsCDZoqKKyIl02/TLN2jxL9599v/406k9yjYQAGsvOndKMGS7oLFzoZlNLSXEB59JLXeDhfzsAQHMj2KDVKaso0zUfXqP/rP6PbjnlFj069lH5mSbrXQm0aWlp0nvvuZacefOkyko32UB1S86wYYQcAEDzINigVaq0lbr1k1v1xJInNHnQZL1w0QsK8AvwuiygVdu/X/rgAxdy/vc/qbzcrZMzcaJ08cVuMdDAQK+rBAC0VgQbtFrWWj3w1QO658t7dFHfi/TfS/+rkIAQr8sC2oQDB9xioDNmSJ99JhUXSzEx0vjxLuiMHStFRXldJQCgNSHYoNWbtmSabvz4Rp3Z7Ux9eOWHigrmtymgORUUuHDzwQfSzJlSVpYUFCSdfbYLORddJCUleV0lAMDXEWzQJryx5g1Nfn+yBiYM1CdXfaK48DivSwLapIoKadEiF3I++EDautWdHzbMhZyJE6X+/RmXAwA4dgQbtBkfbflIl759qbpEd9FnP/9MXaO7el0S0KZZK23YUBtyvvnGne/Ro7YlZ+RIxuUAABqGYIM2ZcHOBZrwxgRFBkdqztVzlBKX4nVJAKqkpbmuau+/L33xhVRa6sbhnHeeG5szbpyUmOh1lQCAlopggzZnVfoqjf3PWJVXluvjqz7WyUkne10SgEPk50uffy7Nni199JG0d687P2yYCzkXXOCO/ZjJHQBQhWCDNmnrga0677XztL9gv1675DX9JOUnXpcE4CislVatciFn9mxp8WJ3Lj7eteKMH+9adWJivK4UAOAlgg3arPT8dF3y30u0ePdi/X3033XXyLtkGLEMtHiZmdKnn7qQ88knUna2FBAgnX66Cznjx0spKUxAAABtDcEGbVpxebGu/fBavbHmDV098Go9f+HzrHUD+JDycjfpQHVrzurV7nyXLtKYMW69nHPPlWJjva0TAND0CDZo86y1+tv8v+nuuXdrRJcReu/y9xQfHu91WQB+hN27pY8/di06n38u5eS4cTjDh9cGneHDXQsPAKB1IdgAVaavn65fvPcLxYfHa+aVMzUgYYDXJQE4DuXl0pIl0pw5LugsWSJVVrqxOOec40LOmDFSt25eVwoAaAwEG6COb/d+q4lvTVRuSa7enPSmJvSZ4HVJABrJgQNuGulPP3Xb7t3ufN++LuSMHSudeaYUHu5tnQCAH4dgAxxiT+4eTXxropanLdfD5z2s2067jUkFgFbGWmnjxtqQM2+eVFTkFgM99VQ3Luecc1y3NRYIBQDfQLABjqCwrFCT35+s6eun69rB1+rp8U8ryD/I67IANJHiYmn+fNei8/nn0vLlLvxEREhnnOFCzjnnSAMGsHYOALRUBBvgKCptpe6de68emP+Azuh2ht697F11COvgdVkAmsGBA9KXX7qg88UX0qZN7nxcnHT22S7knHuu1LOnp2UCAOog2AA/4I01b+iaD65RUlSSZl45UyfEneB1SQCa2e7dtSHniy+kvXvd+e7da1tzzjxT6tTJ0zIBoE0j2AANsHj3Yl381sUqKi/S25e+rbHJY70uCYBHrHUtONUhZ+5c6eBB91yfPtJZZ7mNoAMAzYtgAzTQzpyduvDNC7V2/1pNHTtVNw6/kUkFAKiiQlqxwk1A8OWX0ldfSbm57rnevesHnaQkDwsFgFaOYAMcg/zSfF014yp9uOlDXdLvEk27YJo6Rnb0uiwALUhFhbRqlQs51UEnJ8c9l5xcP+h07uxdnQDQ2hBsgGNUUVmhx75+THfPvVthgWGaev5U/Xzgz2m9AXBE1UGnbotOdde15GQ369qoUdLIkVKvXhL/lADAj0OwAX6kTZmbdO2H12rhroUalzxOz054Vl2iu3hdFoAWrqJCWr3aBZ25c6UFC9wsbJKUmFgbckaNkgYOlPz9va0XAHwFwQY4DpW2Uk8teUp/+OIP8jf+emTMI7puyHW03gBosMpKacMGF3Dmz3fbzp3uuchIacQIF3JGjZJOPlkKDfW2XgBoqQg2QCPYnr1d/9+H/5/mps7V6B6j9cKFL6hHbA+vywLgo3burB901q1z54OCpGHDalt1TjtNat/e21oBoKUg2ACNxFqr55c/rzvm3KEKW6EHz3lQNwy/QX6GZcoBHJ8DB6SFC13IWbBA+vZbqazMPde3rws4I0a4LSVF8uOfHQBtEMEGaGQ7c3bqV7N+pU+2fqKRXUfq3xf9W33a9/G6LACtSGGhtHSp9PXX0qJFbp+Z6Z6LjpZOPbU26AwfLkVFeVsvADQHgg3QBKy1enXVq7rl01tUXF6s+866T7eddpv8/RgFDKDxWStt3VobchYtktaudeeNkQYMqN+qw+xrAFojgg3QhNLy0nT97Ov1waYPNDxpuF686EWdGH+i12UBaANycqQlS1zIWbRIWry4duHQDh1cS84pp7ht+HApNtbbegHgeBFsgCZmrdXb697WjR/fqJziHN19xt36/cjfK8g/yOvSALQhFRVu9rXqkPPNN+5x9dd9nz61QeeUU9xU00H8MwXAhxBomSjnAAAgAElEQVRsgGaSUZChmz6+Sf9d91+dGHeiXrjoBZ3a+VSvywLQhuXmurE633xTu+3b554LDpaGDKkfdrp3pwsbgJaLYAM0s1mbZ+n62ddrT+4e3Tj8Rv1t9N8UGRzpdVkAIGvdVNN1g86yZVJxsXs+Ls6tpTNsWO0+MdHbmgGgGsEG8EBeSZ7++MUfNW3pNHWO6qynxz+tCX0meF0WABymrExas8aFnCVL3FTT69e7hUUlqXNnF3Cqw87QoaytA8AbBBvAQ1/v+lrXzbxO6zLW6fITL9fj5z+uhIgEr8sCgO+Vny+tXOm6sX37rds2b659vmfP+mFnyBCmnAbQ9Ag2gMdKK0r1zwX/1APzH1B4YLgeGfOIfnnSL2XoyA7Ahxw8KC1fXht2li6Vduyofb5PHxdwhgxxrTqDBzMTG4DGRbABWoiNmRs1ZeYUzd85X6N7jNazE55Vcrtkr8sCgB8tI8ON0Vm61IWe5cvdGJ5qPXrUhp3qLT7eu3oB+DaCDdCCVNpKPb/sef3u89+ptKJU9555r24/7XYF+gd6XRoANIrMTGnFitqgs3y5W1y0WlJS/aAzeLAbx0MjNoAfQrABWqC9eXt108c3acaGGRqUMEgvXPSChnU67P9TAGgVcnLcmJ26YWfjxtoJCtq3lwYNkk46qXbr108K5G8+AOog2AAt2Hsb3tMNH92gfQX7dPMpN+vuM+5WbCid0gG0fgUF0urVLuSsWuWCz5o1tVNPBwVJJ55YP+wMHCjFxHhbNwDvEGyAFu5g8UHd9fldenbZs4oKjtKNJ9+oW0+7VR3COnhdGgA0q/JyN/vaypW1YWfFCjeWp1r37vWDzsCBbiyPn59nZQNoJgQbwEesSl+lB+Y/oHfXv6uwwDBdP+x63T7idiVGsDoegLbLWik9vX7YWbnSBaDqX2XCw6X+/WuDzsCB0oABzMoGtDYEG8DHrM9Yr7/N/5veWvuWgvyDNGXIFP3u9N8pKSrJ69IAoMUoKJDWrXPd2dascfvVq6UDB2qv6dy5NuRUB56+fRm7A/gqgg3go7ZkbdE/FvxDr61+TX7GT9ecdI3uGnmXusV087o0AGiRrJX27q0fdFavdhMVlJW5awIDpZQU18LTv78bx9O/v+viRnc2oGUj2AA+LvVgqh5c8KBeXPGirKx+MfAX+sOoP7AGDgA0UGmptGlT/daddevqr7kTFuZCTnXQqd46dWIqaqClINgArcSunF16aOFDen758yqrLNOV/a/Un0b9SSlxKV6XBgA+KSdHWr9eWrvWBZ21a922b1/tNdHR9YPOiSdKJ5zgFhol8ADNi2ADtDJpeWl69OtH9cy3z6iorEiXnnCp/nzGnzUwYaDXpQFAq5CZWT/oVB9nZ9deExvrAs6hW1ISgQdoKgQboJXKKMjQvxb/S08teUp5pXmalDJJ95x5DwEHAJqAtVJammvhOXTLyqq9LjKyNuSkpNQed+vGGB7geBFsgFbuQNEBTV08VVMXTyXgAIAHMjKOHHjS02uvCQ11M7L161e7paRIvXu75wD8MIIN0EZUB5zHv3lcuSW5+knKT3TPGfdoUOIgr0sDgDYpO1vasKE26Gzc6LbU1No1eIxxM7LVDTzVW1wc3dqAugg2QBuTXZTtWnC+mUrAAYAWqKhI2rKlNuhs2OD2mza556rFxrqA07dv7danj5ScLAUHe1c/4BWCDdBGEXAAwLdUVkq7dtUGnurQs2lT/W5tfn5uzE510KkbepKSGMuD1otgA7Rx2UXZevybx/Wvxf9SbkmuLul3ie458x6dlHiS16UBABooN1favNltmzbV3xcU1F4XFubG7VQHnd69a7f27enaBt9GsAEgqTbgTF08VTklOQQcAGgFrJX27q0fdKqPv/tOqqiovTYmpn7QqbvFxnr3GYCGItgAqOdg8UE9vti14OSU5Oi0zqdp8qDJurz/5YoJifG6PABAIyktdRMVbNly+LZjR+0EBpJrzUlOrh92evVy59q18+wjAPUQbAAc0cHig3p+2fN6ZdUrWpexTsH+wZrYb6ImD5qsMb3GKMAvwOsSAQBNpKRE2r79yKFn167618bEuIBTHXSq98nJUmIi3dvQfAg2AL6XtVbL05brlVWv6I01byirKEuJEYm6asBVmjxosgYkDPC6RABAMyoqct3Ytm5127ZttfvU1Prd28LCXNCpG3p69nT7rl2lwEDPPgZaIYINgAYrrSjV7M2z9erqVzVr8yyVV5ZrSMchmjxosq7sf6XiwuO8LhEA4KGyMmnnzsMDz9atrgWouLj2Wj8/F2569qzdqoNPz55uXA+tPTgWBBsAP0pGQYbeXPumXln1ipanLVeAX4DG9x6vyYMma3yf8QryD/K6RABAC1JZ6SYy+O47F3a2b6+/7dtX//ro6Pqhp0eP2q1bNykkxJvPgZaLYAPguK3dv1avrHxF/1nzH6Xnp6t9aHtdNeAqXTf0OvWP7+91eQAAH5Cf70LPoYFn+3Z3vqSk/vWdOtUGne7d6wefzp2lAIaCtjkEGwCNpryyXJ9t+0yvrHpF7218T6UVpRrRZYSmDJmin574U4UFhnldIgDAB1VWSmlpLuBUb6mptce7drlrqvn7u25u1aGneuvWze2Tktw1aF0INgCaRGZhpl5d9aqeW/acNmVtUnRwtK4eeLWmDJ2igQkDvS4PANCKlJW5cFM3+NQNQOnp9a8PCHCtOnXDTt3jzp2Z2MAXEWwANClrrebvnK/nlj2n6eunq6SiRKcknaIpQ6fo8hMvV3hQuNclAgBaueJiN6lBaqpbo6fuPjXVjf2p+6uvn59r1enWzbX8HGkfEeHNZ8HREWwANJuswiy9tvo1PbfsOW3I3KDIoMiaVpyTEk/yujwAQBtVWupafOqGndRUF4Z27nTPlZfXf01s7NFDT9euUkKCC0hoPgQbAM3OWquFuxbquWXP6Z3176i4vFjDOg3TlCFTdEX/KxQZHOl1iQAA1KiocN3ZduxwQedI+9zc+q8JDHRd2rp0cVvXrvX3Xbq4xU2Z0rrxEGwAeCq7KFv/Wf0fPbvsWa3LWKfwwHCN6TVGF/S+QBf0vkCdIjt5XSIAAD8oJ6d+0Nm1y23VLT579hze6hMRcXjY6dKlNhB17kyXt2NBsAHQIlhrtXj3Yr266lXN2jJLu3N3S5IGJw7W+N7jdUHvCzQ8abj8/ZjGBgDge6pbfaoDT93QU3186Fo+klvPpzrk1A08dR9H0tFBEsEGQAtkrdXa/Ws1e8tszd4yW4t2LVKlrVSHsA46P/l8XZB8gcYmj1W70HZelwoAQKMpKXETGeze7cLO7t2HHx86w5skRUW5kJOUVLs/9LhDh9Y/5odgA6DFyy7K1qfbPtXsLbP18ZaPlVWUJT/jpxFdRuiC5As0vs94DYgfIENHZQBAK1daWht+Dg09e/a4fVpa/XV9JCkoyC1qeqTwU32+Y0cpJMSbz9UYCDYAfEpFZYWW7l2q2Ztda86K9BWSpM5RnXVhnws1KWWSzux+pgL8WHIaANA2VVS4bm3VYac68Bx6XFh4+GvbtXMhp1On2sBz6HF8vFsLqKUh2ADwaXvz9urjLR9r9pbZ+nTbpyosK1T70Pa6uN/FuvSESzW6x2gF+Qd5XSYAAC2KtW7Cg927XQvQ3r0u7Bx6nJ7uglJdfn5SYqILOfPmSWFh3nyGQxFsALQahWWF+mTrJ5q+frpmbZ6lvNI8xYTE6KK+F2lSyiSN6TVGIQE+3MYOAEAzq6iQ9u+vH3aq9xkZ0gcftJwpqwk2AFql4vJifb79c01fP10fbPpAB4sPKiIoQhP6TNCklEkalzxO4UHhXpcJAAAaCcEGQKtXVlGmualzNX39dL2/8X1lFGYoNCBU43qP06SUSZrQZ4KigqO8LhMAABwHgg2ANqW8slzzd8zXuxve1YwNM5SWn6ZAv0AN6zRMo7qO0qhuozSiywimkgYAwMcQbAC0WZW2Ul/v+lozN8/UVzu+0rd7v1VZZZkkqX98f43qOkoju47UqK6j1CW6i8fVAgCA70OwAYAqRWVFWrJniRbsXKD5O+dr0a5FyivNkyR1je7qWnSqwk5KXIr8TCtf6QwAAB9CsAGAoyivLNeafWs0f+d8t+2Yr30F+yRJ7ULbaWTXkTqz25ka02uMTow7kQVCAQDwEMEGABrIWqtt2ds0f8f8mladLQe2SJI6RnTUeb3O05ieY3Ruz3OVEJHgcbUAALQtBBsAOA67cnbps+2fac62Ofp8++fKKsqSJA1KGKQxvcbovJ7naWTXkQoNDPW4UgAAWjeCDQA0kkpbqRVpKzRn2xx9tv0zLdi5QGWVZQoJCNGorqNqgs7AhIF0WwMAoJERbACgiRSUFuirHV/VBJ11GeskSQnhCTqn5zk6udPJGtJxiE5KPIl1dAAAOE4EGwBoJnty9+jz7Z9rzvY5mvvdXKXlp9U8l9wuWUM6DtGQxCEa3HGwBicOVlx4nIfVAgDgW5o92BhjXpQ0QdJ+a23/hryGYAOgNUrLS9OK9BVakbZCy9OXa0XaCn138Lua5ztHddaQjkM0OHGwCz0dhygpMolubAAAHIEXweYMSfmSXiXYAEB92UXZ9cLO8rTl2pS5SVbu3+QOYR10cqeTdXqX03V619M1PGm4wgLDPK4aAADvHS3YBDTVD7TWfmWM6d5U7w8Aviw2NFaje4zW6B6ja87ll+Zr9b7VWpG2QsvSlmnx7sX6eOvHkqQAvwAN6TjEBZ2qsJMYkehV+QAAtDhNOsamKtjM+r4WG2PMFElTJKlr165Dd+zY0WT1AICvOVB0QIt2LdLCnQu1cNdCLd27VMXlxZKknrE9a4LOyK4jlRKXIj/j53HFAAA0LU8mD2hIsKmLrmgA8P1KK0q1PG15TdBZuGuh9hfslyTFhMRoRJcROq3zaeof318pHVLUq10vBfg1WeM8AADNjmADAK2QtVbbsrdp4c6FWrBzgRbuWqgNmRtqng/0C1Ryu2SlxKUopUOK+nXop5QOKerboa8igiI8rBwAgB+n2cfYAACanjFGye2SldwuWZNPmixJyi3J1abMTdqQuUEbMjZoQ+YGrdu/Th9s/EAVtqLmtV2iuhwWeAYkDFC70HZefRwAAH60ppwV7U1JZ0nqIGmfpHuttf/+vtfQYgMATae0olRbD2zVxsyNNYFnY+ZGbczcqIKygprrusd015COQzS041C3dRqqDmEdPKwcAIBaLNAJADiiSlup3bm7tSFjg1btW6Vlacu0bO8ybcveVnNN1+iuh4Wd+PB4D6sGALRVBBsAwDE5WHxQy9OWa9neZVqe7vZbDmypeb5zVGcN7Ti0ZnHR3u17q3tMd4UEhHhYNQCgtSPYAACOW05xjlakr9Cyvctcy07aMm3O2lzvmk6RndQztqd6xvZUj5ge9fYdIzsyJTUA4LgQbAAATSK3JFdr9q3R9uzt+u7gd/X2e3L3yKr2eybYP1jdY7rXCzt9O/RV//j+6hbdTcYYDz8JAMAXMCsaAKBJRAVH6fSup+v0rqcf9lxJeYl25OzQd9nfHRZ8vt79tQ4WH6y5NjIoUifGn6gB8QPUP76/+sf314D4AYoLj2vOjwMA8FG02AAAPJNdlK2NmRu1Zv8ard2/Vmv2r9GafWuUVZRVc018eHxN2Knenxh/IuvwAEAbRVc0AIBPsNZqX8E+F3T21QaedRnrVFhWWHNdt+hu6hbTTZ2jOqtzZGe3r9qSopKUEJ4gfz9/Dz8JAKAp0BUNAOATjDFKjEhUYkSizu15bs35Slup1IOpNWFnfeZ67c7drcW7F2t37m6VVpTWex9/469OkZ3qBZ7qrUdMD/Xt0FdRwVHN/fEAAE2EFhsAgM+z1iqzMFO7c3fX3/LqP67b4iNJHSM6ql+Hfurbvq/bd+irvu37qmt0V1p7AKCFosUGANBqGWMUFx6nuPA4De44+IjXWGt1sPigduXu0rYD27Qpa5M2Zm7UpqxNemvdW/UmMggJCFHvdr1rQk/fDi749G7XW1HBUczeBgAtEMEGANAmGGMUGxqr2NBYDUwYWO85a60yCjO0KXNTvcCzIn2FZmyYoQpbUXNtWGCYEsITlBiRqISIBCWGJ9YeRyTWey4sMKy5PyYAtFkEGwBAm2eMUXx4vOLD4zWq26h6z5VWlGrbgW3amLlRWw9sVXp+uvYV7FN6frq2HtiqBTsXKLMw84jvGxkUWRN4ukR1UXK7ZPWK7aVe7XopuV2yEsITaP0BgEZCsAEA4HsE+QcpJS5FKXEpR72mrKJMGYUZ2pe/r17w2Ze/T+kF6UrPT9fi3Yv133X/VaWtrHldeGC4esb2PCzw9IrtpS7RXRTgx9c0ADQU/2ICAHCcAv0D1SmykzpFdvre60orSrXj4A5ty96mrQe2atuBbdqW7VqDPtrykUoqSmquDfALUI+YHuoR20Px4fHqENpBHcKOvLULbadA/8Cm/pgA0KIRbAAAaCZB/kHq3b63erfvfdhzlbZSe/P21gSerQe2alv2NqUeTNWWrC3KLMxUXmneUd87Ojj6sMCTFJmk7jHda7au0V0VHBDclB8RADxDsAEAoAXwM3416+yc1f2sI15TUl6irKIsZRZmHnXLKspSWn6aVu9brbT8NJVXltd7j44RHeuFnUODT0hASDN8WgBofAQbAAB8RHBAcIO6vFWrqKzQ3ry9Sj2YWn/LSdU3e77RO+vfOWLw6RrdVe1C27lZ5EKqttBYd67quO4+LDCMSRAAeI5gAwBAK+Xv568u0V3UJbrLYbO9SUcPPrtydymjMEObszYruzhb2UXZsjr6gt6BfoE1IScxItH9zKiqLbp2HxsSSwAC0GQINgAAtFE/FHyqVdpK5ZbkKrsouyboHLo/UHRA2cXZSstP0/wd87Unb89hrUFhgWHqHNW5fuCpOk6KTFJMSIyiQ6IVERQhP+PX1B8fQCtDsAEAAN/Lz/gpJiRGMSEx6qEeDXpNRWWF9hXs066cXdqVu6t2X3U8Z9scpeWlHbElyMgoKjhK0SHRig6OrnccHRxde1y1jw+Pr+miFxMSQ6sQ0EYRbAAAQKPz9/OvCRun6JQjXlNWUaa9eXu1K3eX9ubtVU5xjnJKcpRTnKPcklx3XPU4PT9dmzI31Twuqyw74nuGBISoU2QnJUUm1fz8Iz0ODwpvyo8PwAMEGwAA4IlA/0B1i+mmbjHdjul11loVlxfXhJz9Bfu1N29vzbYnb4/25u3V8rTlmrl5pgrLCg97j6jgKCVGJCoqOEpRwVGKDIpUZHCk2wdFunPVj+vsq69tH9ZeEUERjfWfAkAjINgAAACfYoxRaGCoQgNDlRiRqL4d+h71Wmutckty6wWf6vCzr2CfcktylVeS59YJKslTXmmecktyVVpR+oN1hAeGKzEiUYkRiUqISFBieJ3j6vPhCUqISGAabaAZEGwAAECrZYxxY3FCopUSl9Lg15VWlNYEnbqBJ6/E7bOKspSen670/HTtK9injZkb9WXqlzpQdOCI7xcTEqOE8ATFh8crMjhSEUERigiMUHhQuDtuwBYbEqvQwNDG+k8DtDoEGwAAgEME+QepfVh7tQ9rf0yvK60o1f6C/S7w5O+rF37S89OVUZih/QX7tT17u/JL85Vfmq+8kjxV2IoGvX9EUITiwuIUFx6n+PB4dxxWdRx++DFBCG0JwQYAAKCRBPkHqXNUZ3WO6tzg11hrVVpRqvzSfBWUFdQEnrpbXkmeDhQdqAlGGYUZ2pO7RyvSViijMOOoXefCA8PVIayDwoPCFR4YXtNCFB5Y+/j79jEhMS7ghbZXVHAUM86hRSPYAAAAeMgYo+CAYAUHBKu9jq2FSHLBKK80zwWeggxlFGYoo6A2AGUVZamgtEAFZQUqKC3Qntw9NcfV+6PNMldXgF+A2oW2U/vQ9jVhp/q4Q1iHeudjQ2MVHhiusMAwhQeFKzQgVP5+/j/mPw/QYAQbAAAAH2aMqZndLbld8o96j7KKssPCTkFZgbKLspVVlKWswqz6+6IsbcvepiV7liirKKtBky2EBIQoLDDMhZ2q0FMdfOqejwqOUmxIrGJDY4+6D/DjV1gcjrsCAACgjQv0D1SMv1uE9VhZa1VQVlAv/GQXZ6uwrFAFpQUqLCt0x2VHPs4pzlFaXlpNoMotyVVRedH3/syIoAi1C21XL/BEh0TXBKbQgNCasFS9hQYe4VzVdZHBkcxc1woQbAAAAPCjGWNqZm471jWJjqa4vFjZRdnKLs4++r7O8easzcopyVFhWaGKyop+MBgdSZB/kGJCYhQd7GbRq7s/0vmYkBhFBUcpNCC0JjRVHwf6BTIeyQMEGwAAALQoIQEh6hjZUR0jO/6o11faShWXF9cEnerWoaLy2uPq5wrKCpRXkqeckhwdLD5Ys/BrTkmO0jLTao7zS/Mb/PP9jb9ba6mqRehIx9ULv1Yv+lqzWGzwkc9FBkUyTukHEGwAAADQqvgZv5ruZo2lvLJcuSW5NUEnpzinpttc3Zai6iBVfVzzfJ3nMgszlV+aX7M2UkNbmMICwxQZFKnQwFCFBITUtBCFBlQ9rjo+7HHV9dUta5FBkbXHwbXH4YHhPt3SRLABAAAAfkD1rHDtQts1+nuXVZTVLAabW5LrAk9pneOq89XXFJW7wFRcXlwTng4UHXCP6wSq4vLiBk3sUM3I1EwJfmj4ee/y9xTkH9Ton70xEWwAAAAADwX6BzZZaKqorKgJPAWltesk5ZXm1Vsnqd66SaX1H2cUZCjQL7DRa2tsBBsAAACglfL383cLrga5xVpbMz+vCwAAAACA40WwAQAAAODzCDYAAAAAfB7BBgAAAIDPI9gAAAAA8HkEGwAAAAA+j2ADAAAAwOcRbAAAAAD4PIINAAAAAJ9HsAEAAADg8wg2AAAAAHwewQYAAACAzyPYAAAAAPB5BBsAAAAAPo9gAwAAAMDnEWwAAAAA+DyCDQAAAACfR7ABAAAA4POMtdbrGmoYYzIk7fC6jiodJGV6XQR8GvcQjhf3EI4X9xCOF/cQjldT3EPdrLVxh55sUcGmJTHGfGutHeZ1HfBd3EM4XtxDOF7cQzhe3EM4Xs15D9EVDQAAAIDPI9gAAAAA8HkEm6N7zusC4PO4h3C8uIdwvLiHcLy4h3C8mu0eYowNAAAAAJ9Hiw0AAAAAn0ewOQJjzPnGmE3GmK3GmLu8rgctnzHmRWPMfmPM2jrn2hljPjPGbKnax3pZI1ouY0wXY8xcY8x6Y8w6Y8zNVee5h9AgxpgQY8wSY8yqqnvor1Xnexhjvqn6PvuvMSbI61rRshlj/I0xK4wxs6oecw+hwYwxqcaYNcaYlcaYb6vONdt3GcHmEMYYf0nTJI2TdIKkK40xJ3hbFXzAy5LOP+TcXZK+sNb2lvRF1WPgSMol3W6tPUHSqZJuqPp3h3sIDVUiabS1dpCkkySdb4w5VdI/Jf3LWpssKVvStR7WCN9ws6QNdR5zD+FYnW2tPanOFM/N9l1GsDnccElbrbXbrbWlkt6SNNHjmtDCWWu/knTgkNMTJb1SdfyKpIubtSj4DGttmrV2edVxntwvFUniHkIDWSe/6mFg1WYljZY0veo89xC+lzGms6Txkl6oemzEPYTj12zfZQSbwyVJ2lXn8e6qc8CxSrDWplUdp0tK8LIY+AZjTHdJgyV9I+4hHIOqLkQrJe2X9JmkbZIOWmvLqy7h+ww/ZKqk30mqrHrcXtxDODZW0hxjzDJjzJSqc832XRbQVG8MoJa11hpjmIIQ38sYEyHpXUm3WGtz3R9LHe4h/BBrbYWkk4wxMZLek9TP45LgQ4wxEyTtt9YuM8ac5XU98FkjrbV7jDHxkj4zxmys+2RTf5fRYnO4PZK61HncueoccKz2GWM6SlLVfr/H9aAFM8YEyoWa1621M6pOcw/hmFlrD0qaK+k0STHGmOo/YvJ9hu9zuqSLjDGpct3wR0t6XNxDOAbW2j1V+/1yf2AZrmb8LiPYHG6ppN5Vs4AESbpC0oce1wTf9KGkyVXHkyV94GEtaMGq+rH/W9IGa+1jdZ7iHkKDGGPiqlpqZIwJlXSe3FituZIurbqMewhHZa39g7W2s7W2u9zvPv+z1l4l7iE0kDEm3BgTWX0saYyktWrG7zIW6DwCY8wFcv1M/SW9aK39m8cloYUzxrwp6SxJHSTtk3SvpPclvS2pq6Qdki6z1h46wQAgY8xISfMlrVFt3/Y/yo2z4R7CDzLGDJQblOsv90fLt6219xljesr99b2dpBWSrrbWlnhXKXxBVVe0O6y1E7iH0FBV98p7VQ8DJL1hrf2bMaa9mum7jGADAAAAwOfRFQ0AAACAzyPYAAAAAPB5BBsAAAAAPo9gAwAAAMDnEWwAAAAA+DyCDQCgSRhjKowxK+tsdzXie3c3xqxtrPcDAPi+gB++BACAH6XIWnuS10UAANoGWmwAAM3KGJNqjHnIGLPGGLPEGJNcdb67MeZ/xpjVxpgvjDFdq84nGGPeM8asqtpGVL2VvzHmeWPMOmPMHGNMaNX1vzXGrK96n7c8+pgAgGZGsAEANJXQQ7qiXV7nuRxr7QBJT0maWnXuSUmvWGsHSnpd0hNV55+QNM9aO0jSEEnrqs73ljTNWnuipIOSJlWdv0vS4Kr3+XVTfTgAQMtirLVe1wAAaIWMMfnW2ogjnE+VNNpau90YEygp3Vrb3hiTKamjtbas6nyatbaDMSZDUmdrbUmd9+gu6TNrbe+qx7+XFGitfcAY84mkfEnvS3rfWpvfxB8VANAC0GIDAPCCPcrxsSipc1yh2nGj4yVNk2vdWWqMYTwpALQBBBsAgBcur7P/uup4kaQrqo6vkjS/6vgLSddLkjHG3xgTfbQ3Ncb4SepirZ0r6feSoiUd1moEAGh9+CsWAKCphBpjVtZ5/Im1tnrK51hjzGq5Vlv0cv4AAACMSURBVJcrq87dJOklY8ydkjIk/bLq/M2SnjPGXCvXMnO9pLSj/Ex/Sf+pCj9G0hPW2oON9okAAC0WY2wAAM2qaozNMGttpte1AABaD7qiAQAAAPB5tNgAAAAA8Hm02AAAAADweQQbAAAAAD6PYAMAAADA5xFsAAAAAPg8gg0AAAAAn0ewAQAAAODz/n8mwLT9K2SYWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Dh1ZOxcGEQjm",
        "outputId": "8c862829-83f3-45d4-f119-6225ee4dbdf7"
      },
      "source": [
        "#Training and Validation Loss vs Epochs for Alpha = 0.3 in Speaker Recognition\n",
        "#Choosing alpha=0.3 since it converges faster.\n",
        "\n",
        "plt.figure(figsize = (14, 8))\n",
        "plt.plot(epoch_list_sr, J_sr_total[2], 'r', label = \"Training Loss\")\n",
        "plt.plot(epoch_list_sr, Jval_sr_total[2], 'b', label = \"Validation Loss\")\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.title('Training/Validation Loss vs Epochs for Alpha = 0.3 in Speaker Recognition')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N50jC2y9HKXZ"
      },
      "source": [
        "**Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTfT7fr7Fk6v"
      },
      "source": [
        "#Predicting labels for speaker recognition\n",
        "predicted_labels_sr = predict_labels(test_data_sr, theta_sr_total[2], classes_sr)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3eChAzyHa6c"
      },
      "source": [
        "**Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKsG5vq3HZWY",
        "outputId": "7b794c6b-fac9-41ce-da3f-6da768efbdb3"
      },
      "source": [
        "#Evaluating the speaker recognition classifier\n",
        "evaluation(test_labels_sr, predicted_labels_sr)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 94.718\n",
            "Macro F1: 0.942\n",
            "Macro Precision: 0.954\n",
            "Macro Recall: 0.947\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "PrumHGpxHgZ-",
        "outputId": "cfca0e46-9f4e-4659-d5de-aabd8b8aa213"
      },
      "source": [
        "#Confusion for speaker recognition\n",
        "matrix = confusionMatrix(test_labels_sr, predicted_labels_sr)\n",
        "matrix"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>125</th>\n",
              "      <th>126</th>\n",
              "      <th>127</th>\n",
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              "      <th>135</th>\n",
              "      <th>136</th>\n",
              "      <th>137</th>\n",
              "      <th>138</th>\n",
              "      <th>139</th>\n",
              "      <th>140</th>\n",
              "      <th>141</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142 rows × 142 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2    3    4    5    6    ...  135  136  137  138  139  140  141\n",
              "0      2    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "1      0    2    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "2      0    0    2    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "3      0    0    0    2    0    0    0  ...    0    0    0    0    0    0    0\n",
              "4      0    0    0    0    2    0    0  ...    0    0    0    0    0    0    0\n",
              "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
              "137    0    0    0    0    0    0    0  ...    0    0    2    0    0    0    0\n",
              "138    0    0    0    0    0    0    0  ...    0    0    0    2    0    0    0\n",
              "139    0    0    0    0    0    0    0  ...    0    0    0    0    2    0    0\n",
              "140    0    0    0    0    0    0    0  ...    0    0    0    0    0    2    0\n",
              "141    0    0    0    0    0    0    0  ...    0    0    0    0    0    0    2\n",
              "\n",
              "[142 rows x 142 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFPdMSa6HmXZ"
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    }
  ]
}