Google_Stock_Price_Prediction_RNN.ipynb

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  "metadata": {
    "colab": {
      "name": "Google Stock Price Prediction RNN",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JosephBless/DL/blob/main/Google_Stock_Price_Prediction_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRO9n1MJVlHv"
      },
      "source": [
        "LSTM that predicts upwards and downwards trend of google stockprice \n",
        "Many layers with dropout regularisation to prevent overfitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCsb2gWesFOS"
      },
      "source": [
        "Part 1 - Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTEkX8bcqioW"
      },
      "source": [
        "# Importing the libraries\n",
        "import numpy as np #allow to make arrays\n",
        "import matplotlib.pyplot as plt #visualize results on charts \n",
        "import pandas as pd #import dataset and manage easily"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21jugdesW5-7"
      },
      "source": [
        "# Importing the training set - only importing training set, test set later  on \n",
        "#rnn has no idea of the test set's data, then after training is done, test set will eb important\n",
        "dataset_train = pd.read_csv('GOOGL_Stock_Price_Train.csv')\n",
        "\n",
        "#need to make into numpy arrays because only nump arrays can be input values in keras\n",
        "training_set = dataset_train.iloc[:, 1:2].values \n",
        "\n",
        "#getting everything from the columns (.values makes the numpy array)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZi462V1qsPK"
      },
      "source": [
        "# Feature Scaling\n",
        "#2 ways: take obs- min ofstock prices/max of stock prices- min of stock prices\n",
        "#using normalization instead of standardisation- look at meaning\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set) #fit (gets min and max on data to apply formula) tranform(compute scale stock prices to each formula)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9C4BE-F1sLpZ"
      },
      "source": [
        "# Creating a data structure with 60 timesteps and 1 output\n",
        "#60 times steps- at each time t and look at 60 previous time steps, then make new prediction \n",
        "# 1 time step leads to overfitting, 20 is still too low\n",
        "#60 previous financial days- in 3 months \n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(60, 2168): # upper bound is number of values \n",
        "  X_train.append(training_set_scaled[i-60:i, 0]) #takes 60 previous stock prices from 60 past stock prices\n",
        "  y_train.append(training_set_scaled[i, 0]) #contains stock price learned to predict \n",
        "X_train, y_train = np.array(X_train), np.array(y_train) # make into numpy arrays \n",
        "#Need to add dimension to because not only prescition with one stock price but other indicators (like other columns in dataset  or other stocks that may affect this one )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_fnQGmSvfTu"
      },
      "source": [
        "# Reshaping- add dimension in numpy array \n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) #adds dimension in numpy array\n",
        "#currently only have one indicator, with new dimension will have more indicators, be compatible for \"input shape\" of RNN\n",
        "# format according to keras documentation \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3EQlGCOvhzE"
      },
      "source": [
        "Part 2 - Building the RNN \n",
        "stacked lstm with dropout regularization to prevent overfitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4trYuBUXvuB5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2032ccc4-e643-4123-a48c-ef1255315de0"
      },
      "source": [
        "# Importing the Keras libraries and packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oy4m0z1svv4N"
      },
      "source": [
        "# Initialising the RNN\n",
        "regressor = Sequential() #reps sequence of layers, predicting continous values (so it is a regression)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWQQNeG_vwXV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "c9b94961-714c-422a-e369-9ea0f1303661"
      },
      "source": [
        "# Adding the first LSTM layer and some Dropout regularisation\n",
        "#dropout to prevent overfitting\n",
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))#regressor- object of sequential class, can add layers to networ.\n",
        "#use lstm class and create object of lstm class- 3 args\n",
        "#num of units, return sequences- set to true because is stacked lstms, and shape\n",
        "#units- neurons in first layer. 50 in layers for high dimensionality, can capture upward and downward\n",
        "regressor.add(Dropout(0.2))\n",
        "# takes arg of dropout late- num of neurons want to drop. dropping 20% of neurons to be ignored during trianingn for each iteratin. 10 neurons will be dropped out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMTvkPLtvz4r"
      },
      "source": [
        "# Adding a second LSTM layer and some Dropout regularisation\n",
        "# total of 4 layers, simply need to copy, only change is input shape so dont need to specify that, automatically recognised through input shape\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True)) \n",
        "\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwZjs53zv0K1"
      },
      "source": [
        "# Adding a third LSTM layer and some Dropout regularisation\n",
        "# same as second layer \n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqAm5r3_v9b-"
      },
      "source": [
        "# Adding a fourth LSTM layer and some Dropout regularisation\n",
        "# almost same, but return sequence is false because it is the last lstm layer (so it is removed becasue default is false)\n",
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyBA3V_WwAP5"
      },
      "source": [
        "# Adding the output layer\n",
        "#add fully connected layer through dense class- dimesion/units/neurons is 1\n",
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_GkymP6wAbZ"
      },
      "source": [
        "# Compiling the RNN\n",
        "#regressior because predicting continuous value, \n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s__QxkRNwHRO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6905
        },
        "outputId": "1827f412-b4a3-4574-d1d5-8c9e0190ca0e"
      },
      "source": [
        "# Fitting the RNN to the Training set\n",
        "#have not made connection to training set, training will take place\n",
        "regressor.fit(X_train, y_train, epochs = 200, batch_size = 32) # CHANGE BACK TO 100 IF NOT WORK\n",
        "#100 gives good convergence\n",
        "# trained on certain batch sizes, "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f07e7a7cf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHac1P0e5U4G"
      },
      "source": [
        "Part 3 - Making the predictions and visualising the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9A6R2mXldQk9"
      },
      "source": [
        "# Getting the real stock price of 2018\n",
        "dataset_test = pd.read_csv('GOOGL_Stock_Price_Test.csv')\n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "419ZQNSFwHf5"
      },
      "source": [
        "# Getting the predicted stock price of 2018\n",
        "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values #getting input of each previous financial days\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(60, 227):\n",
        "    X_test.append(inputs[i-60:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skhdvmCywHrr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "9037d204-7fef-44cb-865f-5780e558ccd2"
      },
      "source": [
        "# Visualising the results\n",
        "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
        "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hq1ZtUl134MA+bt68QefOXbNctiAIQkHgsAR/8uRJpk2bxvXr15FlmU2bNjF58mQ+/vhj\ntFotBoOB6dOnYzAYGDVqFAMGDECSJIYOHYq7uzsdO3Zk37599OrVC71ez9SpUx0VqsOVK1eeb76Z\nZ/v5008nsmXLRoKC/pfje//yy8/ExcWyYMFiJEni6tXLjBw5lIULf8HDo0iO758d06d/xjfffE+x\nYj4kJiYwcuQQ2rYN5OrVyxw6dMBuXzqSpXxf79+P4rXX+tC4cROcnB6M/WjcuKldyxQEQXhSOCzB\n16hRg8WL09a+li1bluZYUFAQQUFBqY5ptVqmTJniqPDylL9/DUJDrfP6V65cwdatG5EkDS1atKZX\nr5e5c+c2kyZ9CFhr6OPHf0zp0mXSvdeaNX+waNGvSJIEQLlyFVixYjWybP1o58z5mhMnjmE2W+jS\npTtBQf/jwoXzfPnlNCRJwsXFlfHjJ+Li4sonn0zg7t07VKtWne3bt/LHHxts5cTFxfLZZx8THR2N\nxWJh5Mj3qFLlqVSxREdHERdn7SZxcjLw3Xc/AjBy5BDi4mIpW7YcTZo0Y8qUTzCZTGg0GsaMmUCp\nUqVZunQRO3duQ5I0DB48jJIlS9nuu3//XlauXM60aV9l2Orh4VEEb+9i3L17l59++gFZ1nH/fiTN\nmrXk4sULDBs2Mk0Z9eo1YOXKFezcuQWLRbW9/4IgCAVBgVqLPjOuE8fjtPbPrL9AI+GlPHpwX2Kn\nF4mdODnLtzSbzfz99y5efLELN25cZ+fObcyZswCAN98cQJs27YiIuMtrr71BvXoNWLduNatW/cZb\nb72d5l4xMTHo9XpcXd1SHU9O7kePhnDx4gW+++5H4uPj6devJy1btubrr2cwZMgIqlevwS+/LOa3\n35ZRtaofRmMiK1as4M8/N7Bixa+p7rlixa80atSUTp1e5NKli3z99QxmzpyT6prXX3+TN954hbp1\n69OwYWPatw/Cw8OD3r37cvHiBV544SU+++xjnnvuBdq27cCOHVv58cd59Os3gJ07t/H99wu5ceM6\nS5YspF+/AQCEhl5j0aIFzJgx65FdGjdv3uD+/Sh8fYsD4OHhwejRH7Bhw1oArl27mqaMEiVKsnPn\nNn799VfCwqJt73+JEiWy/HkKgmAnioLT6lUktg8CN7fMrxcyVagSfF65evUKw4YNBODChfP06fMK\nLVu2Ztu2zYSGXuOttwYB1lryrVs3KFmyFDNnzmDBgu+Jjr5P1ap+6d5XkkBRFNvPf/zxO9u2bSYm\nJoYePXoTFRVJnTr1AHB2dqZChUpcu3aNy5cvUb16DQDq1WvATz/Nw2AwULNmbQCaNGmWJpmeOHGc\nyMgINm2y1uoTExPSxNO5c1datmzNoUMH2L17Jz//vIAFC5amuubs2TMMHjzMVvbChfM5d+4s/v41\n0Gg0lClTljFjJnDz5g0SEuIZO3YU48d/gls6/+FTvq96vZ7x4z+2fbnx96+e6tr0ykh+/1955RWM\nRrPt/RcJXhByn37TX3gM6k/MpCnEDxqa1+EUCIUqwcdOnJyt2raPjzv37DASM2Vf8fjx71O2bHkA\nZFlHkybNeP/9D1Jd/9lnH9OoUWNefLErO3ZsZd++Pene19XVDYtFISLiHp6eXnTu3JXOnbuyYMH3\nxMXFIkkSKWcXms0mNBop1T2sxzSoqopGY03qkiTZmvyT6XQyb7/9HjVq1MrwORMTE/D2Lsazzz7H\ns88+x2effczhwwceukqyTXk0mcxIkgatVoOSTkvJnTt3CAx8lj/++I0xYyakOf/w2IaUZFmX6uf0\nykh+/z//fGqhGXErCPmVfPwoAJrQ0DyOpOAQm83ksiFDRjB37mwSEhKoWtWPkJB/SEhIQFVVZs6c\nQWJiApGRkZQuXQZVVdmzZxcmkynD+3Xp0o2vv/4Cs9kMWFsBTp8+hV7vRLVq1Tly5J+k43Fcvx5K\nmTLlqFixMidPHgfgyJEQqlb1o3TpMpw9exqAQ4cOYLFYUpXj71+D3bt3AnDp0kWWLVuS6vy1a1cZ\nMKCvrQ9eURTCw8MoVao0kiTZ7ufn509ISDAAR4/+Q7VqflSt6pc0TsDMvXt3GTv2XcCawEeNGsP1\n66EcOvTwF4XsSa+M5Pc/Pj4+1fsvCELuk89Yf/9owsPyOJKCo1DV4PODUqVK07p1WxYtWsCgQUPp\n3r0XQ4e+gUajoWXL1jg5GXjhhZf46qvPKVGiFF279mD69E8zTHA9evRh+fKlvPHGK7i4uJKYmEhA\nQHv+97/n0Wg0VK1ajaFD38BsNjN48DCcnZ0ZOfJd2yA7d3d3xo37CFnWsX79Gnr16kWNGnXSjMDv\n2rUHn346kSFDXkdRFEaOfDfV+bJly9GnTz9GjHgTg8GAyWSiefOW1K5dF3d3d+bOnY2Pjy+vvz6Y\nKVMmsXbtn8iyjrFjJ+Dj40tgYEeGDRuIqqoMStE8J0kSY8ZMYPTot5k3byEuLq6P9b6XLFkqTRkl\nSpSge/de9OnTB0XB9v4LgpD7tGdOAaAJEwneXiQ1K0vEPSHs3cxamBZLuH8/ipCQYLp1e5HTpy8w\nYsSb/PLLyrwOK1cUps85mXjmwuOJeO7YWIpVKoWkqpj9axCxc1+ObvdEPLOd5JuFboT8y8XFle3b\nt/Lbb79gNJp466138jokQRAKCfncv0hJdU1JNNHbjUjwAmCdWvfJJ1MK1TdfQRDyB+2/Z2x/19wN\nB0UBjRgillPiHRQEQRDylHza2v9u8S2OZLEgRUTkcUQFg0jwgiAIQp5KHkFvat4SECPp7UUkeEEQ\nBCFPyWdOYSlXHkuFigBowu7kcUQFg0jwgiAIQp6RwsPRhN3BXM0PxccXEDV4exGD7Bwsq9uaZsXK\nlcuJjIykZcvW7N69kwEDBqV73Z49u2jUqCk6nS7d8yldvHieL7+cnmZFuIiIe8ycOYPr10PR6WSc\nnV0ZNWp0hpveZFd6W99mZOXKFWzatAG9Xk9iYgIDBw6lYcNGnD//H3q9nnLlyme53A0b1to2n8no\n/Pz5cylVqjRgnYf/zjujqVixUqrrFi9eSN269R65sp8gCKkZflmMYfFPRP6+Flyta1rISfPfLX7V\nUXx8AFGDtxeR4HNBVrY1zY6nnqrKU09VzfD8smVLqVevYZYSfEY++WQCzz33Im3btgdg27bNTJr0\nIXPn/vjY93wcN2/eYO3aP5k//2dkWebatatMmzaZhg0bsWvXdqpV889Wgs+KgID2ti8AR478w8yZ\nM/j669Qb6/Tt+6pdyxSEwkC3Yxu6f4LRBR/ClFTJSU7wZj9/1GLWBC+mytmHSPC5LKNtTT/5ZCrT\np3/KjRvXMZvNvP76YOrXb0hw8CFmzfoCLy9vvL2LUapUaUJCglm1agWTJ09n48b1/P77ciRJomfP\nPphMJk6fPsm77w7n66+/Y82aP9LdjnbChDHodDqqVHk6TYxXrlwmISHeltwB2rbtQKtWAYB1F7tP\nP51ITEw0ZrOZkSPfo2rVamzbtoXly5ei1WqpWtWPkSPfTVVW7dp1OXbsSKrWgkuXLvLVV9OTtq51\nYdy4ibi7P1i4ISYmBqMxEZPJhCzLlC1bjm++mceFC+dZvXoVu3Ztx9PTk4SEBObNm4Msy/j4+DJ2\n7IdoNBomT/6I27dvotc7MX78x6mec+7cbzAYDLz3Xtqd+pJZt/a9CkDPnp1p3LgZnp6ehIZeo3Xr\ntjRq1CRNGV5e3ul+loJQ2Ekx1im48tEQW4LXJg2wM/tVh6TNojTh4XkTYAFTqBL8xIlOrF2b9UfW\naEBRHr00aqdOZiZOTMzyPTPa1nTjxvV4exdj7NgPiYyMZMSIwSxatIzvv/+GCRMm8dRTT/Puu8Nt\nTcdgXXd+4cL5LFr0K0ajiU8//YipU79k/vy5zJgxi7CwO+luR7tq1XLatu1A9+69WLJkIefPn0sV\n45Url6lUqUqa2JN3avvtt1+pXr0GL7/8Kv/+e5rZs79k+vSZzJv3LT/99AsuLi68//7bhIQEs3fv\n3wQEtKNHjz7MmfN1mnvOnPk57703jrJly7Fq1W+sWrXCtlUswFNPPY2fX3W6dXueJk2a0bhxM1q1\nakPlylVo1KgJrVu3xd+/Br17d+Grr76lePESfPnlNLZs2YiiKHh7ezNx4qds3bqJPXt24+TkBMD2\n7Vu5c+c2H3446ZGf1759f+PnZ92Zzmw207hxUxo3bsqnn04E4K+/1qUpw9nZOd3PUhAKO020NcHr\nQv4hPumY7kgIqpMTlipP2b4AiCZ6+yhUCT6vZGVb05Mnj3Ps2BGOJ+2olJhorbXevHmTp56y1rLr\n1KlHYuKDLxOXL1+iXLkKODkZcHIyMHXql6nKPXPmVLrb0V6+fIk2bdoBULduAw4cSL0spEYjpdps\nZvr0T7l69Qr37t1l6tQv+fff07zyijUJV6vmT2joNa5du0qZMuVwcXFJum99zp37lytXLtlaApo1\na8XppPmuyU6fPsW0adYd/kwmE35+/mnevwkTPuHy5UscOrSfX375mT///J1Zs+bazt+/H4UkSRQv\nbt3mtV69Bhw9GoLZbKFBA2vNuV27QMDax37p0kV27drBkiUr0vm0YPv2Lfz7r7VW4e3tnWrd/Ye3\noT179t80ZcyYMSXdzzInXSaCUBBIMTEAyEmbYElhYcinT2Js2QZ0OtSinqiyLNajt5NCleAnTkzM\nVm3buqpbbI7Lzcq2prKs45VX+tO+fVCq85oUqzk9vG2ARqNFVRUyktF2tEuXLkKSNEn3TPv6ChUq\nMX/+97afk18/bNhAzGZT0ja0D2JRFAVJSh2f2WzCyckJVX3wDA/tQAuAwWBg9uzv02xPm/KZjUYj\nFSpUpEKFinTp0oM+fbpy+/atFFeljsdkMiVtQ6umuw3trVs3qFixEjt3biMwsGOa8yn74B+W1W1o\n0/ssBaGwS07w2ls30dy8gS6pcmFs2SrpAgmlmI9dE7x05w6u0z4loWdvzA0b2e2+TwIxTS6f8Pev\nwZ49uwDrCPbvv/8WgGLFfLh69TKqqtq2fk1WvnwFrl69QlxcHImJiYwcOQRVVZEkDRaLJcPtaMuV\nK2+roSZv3ZpSmTJlKV68OCtXPqjhXr8eys2bN9Dp9FSr5s+RI9bXnTx5gooVK1O2bHlCQ68SF2f9\nQmTdhtaf0qVL28p6uKUAoEqVp2zHt27dRHDwoVTn161bzfTpn9oSeGxsDIqi4OnpaduG1sPDA0mS\nuHXLmvSPHg2hWjU/qlXzJyTkMAB79/7Nzz9bBwg2adKcsWM/ZOHC+dy7dzdrH1AG0isjo89SEAo7\nKfbBMtjykRB0SVtQm1q2th1XivnYdZqc++h3cF78E0W7Po9+6ya73fdJUKhq8PlZQEA7QkIOM3hw\nfywWC/37W5v0Bw4cwvjxoylRoqSt3z6Zs7MzAwYMZuTIIQD06NEbSZKoW7ceQ4YMYPbseeluR9ut\nWy8mTBjD7t07qFz5qXTj+eijT5k9+yv69++DweBsmy5Wtmw5unfvxWeffczw4YNRFIV33hmNs7Mz\nQ4eOYNSot5AkDbVq1aF27Tp4eXnx4Ydj2LFjO/7+1dFqtanKGTHiXaZP/5SlSxeh1zsxceLkVOc7\nduzElSuXGTiwH87OLrZBfU5OBmrXrsvMmZ8n9fmP5+OPP0Cr1VK6dBnatu2AqqoEBx9i2LCBaLUy\n48dP5PDhgwB4enoyYMAgZsyYyg8/zOVxtWsXmKYMT0+vdD9LQSjspJgYVI0GSVHQHfkH/a4dKEWL\nYq5Z23aN6uODdPI4xMbaptI9Lv26NTitX4O5ajW0Vy7j8Uovor/5nsSXuuX0UZ4IYrvYRyiMG6/Y\n+5kvXrxATEw0tWrVYcuWjYSE/MPo0R9k/sJcJD7nwqEwPjPko+c2mfAp7Y2pdl10x45gqVAR7eVL\nJP7vee7/tMR2mfvQgRh+W8bdw8dRyld4rKJ8fNwJP38Nz+bPoImMIGLHPqTwcIq83B3JZORu8EnU\npDn3T7pHbRcrmugFh3JxceW772YzZMjrrF69ipdf7pfXIQmCkAeSR8grpUpjrlwF7eVLABhTNM+D\ntYkecrianari9sFotLdvETdqNJYqT2Fu3ITYseOR4uNxycVuMykmGv26NWgvnodcrk+LJnrBoUqU\nKMF33y3I6zAEQchjyQPsVDc3zHXrI184D4CpVetU1z1YrjYHc+G/+QbDil8x1a5L3NARtsMJvV/B\n9cvPMfz4A3HDRqAW9Xz8MrLNckE9AAAgAElEQVTIZfoUXOZ+A1i/vMR8Oo3Ezl0dXi6IGrwgCIKQ\nC1In+HoAWMqUxVKxcqrrlGLFgMefC6/bvRPefhvFx5f7C5dCyumpzs7EDRmOJiYa56SZQprroUj3\nox6rrEwpCk5r/kDxKEJC5y6obm5Id3NvER9RgxcEQRAcLrmJXnVzx5Q0Xc3YOiDN/FnF9/E3nJHC\nw/F4/RXQaon6aSlKOntnxPfrj8usL3D+fg76jRvQHT+KqU5dIjftTH8ubw7IIcFob1wnoUdvomc/\n/mDexyVq8IIgCILDSdHJCd4Nc516RP28jNgPJqa5zrYe/WPU4A2/L0MTGQmffIL5mQzmvLu5ET9o\nKJqoSOTTJ7GUKInu6BF0O7Zmu7zMOK1bA0Bipxfsfu+sEAleEARBcDgp9kETPYAxqCOqt3ea63Iy\nyM6w/FdUWYYBAx55Xdzwd4hc/gd3T50naulvALjM+irb5T2SquK0bjWKmzvGpH08cptI8IIgCILD\nJffBK24ZT+uClAk+e33V2pMnkE+dwNguEJL68TMky5jatEX19MJSsxbGgHbo9+1BTlonwx7k40fR\nXr2CMfBZSNoDI7eJBC8IgiA4nCbmQRP9I+n1KEWKZmmQnWHBPNtyt4blvwCQ0KN3tmOLG/4OAC6z\nZ2b7tRlxWrsagMROL9rtntklErwgCILgcLZR9K6ZJHhAKVUK7dUrkJjx3iGaG9dxH/suRbp0wun3\n5RhWrkDx8sLYPjDbsZmaNMNUvyFOG9fj8tXnjyw3q/TrVqO6uGJs0zbH93pcIsELgiAIDmdL8O6P\nbqIHMLZohRQXh+7g/gyv0SbNo5dMJjyGvIEmPMw6v1yvf4zgJGImTUEp5oPrlEl4tmyEfOxI9u+T\nRHP7FvLFCxibtwBn58e+T06JBC8IgiA4XMppcpkxtu0AgH7blgyv0V68AEDcoCG2ufOP0zyfzNzg\nGe4dCCFu0FDkSxdxnTTxse8lh1g3BjPXa/DY97AHkeAFQRAEh0u50E1mTE2aobq4oN+22XbMZcZU\nnFb8avs5OcEndupMxNa/iVy1DnOdejmKUfUoQuykKVgqVEQ+cfSxl5ZN3u/eVLd+juLJKZHgBUEQ\nBIfLToLHYMDYohXyubNorlxGt3snrtM/w3X6Z7ZLtJesCd5SqTJKqdKYmre0W6ymWnXQRESgCb32\nWK/XJdfg6+bsC0dOiQQvCIIgONyDBJ95Ez2kaKbfuhnXKZ8AoL16BSkq0vr3ixdQPIqkO5c+p8w1\nawEgnzie/RcrCvKRfzBXrpIra90/ikjwgiAIgsNJMfdRnZxSrw3/CMa27QFw+epzdP8Eo2qs6Uo+\ndRIsFrSXL2GpVMnuy8tCigR//Gi2X6u9cB5N9H3Medw8DyLBC4IgCLlAionJWvN8EqVsOcxVq6G9\ncxtVkogbYZ2rLp88bt0gxmjEUqlyJnd5POYatW1lZZccEgyAqX7eDrADkeAFQRCEXCDFxKC6Zq15\nPllyM31i1x4kvtQdAPnkCdsAu4d3orMX1dcXS4mSj9VEr0tK8KIGLwiCIBQK2a3BA8S/Poj4Xi8T\nO+FjLJWroBoMaE+dfJDgHVSDBzDXqo325g2ksOytiS8f+QdVr8dcvaaDIss6keAFQRAEx1JVpNjs\nJ3ilTFlivp6DUqIkyDJmP3/ks2eQz/0LODjB10geaHcs6y9KSEA+dRJzjZp5tv58SiLBC4IgCI4V\nF4ekKChZWMXuUcw1aiEZjei3bAIcnOBrJvXDZyPBy6dOIJlM+aJ5HkSCFwRBEBwsu1PkMpLc7K29\ndhXF0xPV0yvHsWVYVq3kBJ/1fvjkDW9MDZ5xSEzZJRK8IAiC4FCa2CzuJJeJlP3ajqy9g7V7QCla\nNE0NXvf3LvQb1qW5Xj58EMOiHzFXrZanO8ilJBK8IAiC4FDZWsXuESzVqz/4u4NG0NtIEuaatZEv\nXcRl6mTkgwfw6N+Xol06UeTV3mjP/vvgWqMR93dHIKkq0Z9//Xgb3jiASPCCIAiCQ0nRSTX4LGwV\n+yiqmzvmipUAx9fgARJ6vYzq4orrl9Px7NQBp3WrbeU7/zDXdp3LnFnIZ04T3/c1zI2bODyurBIJ\nXhAEQXAoe/XBA1iSRrfnRoJP7NqD8FPnuf/dfBK69+L+13OI2BuMpVwFDL/9inTvLtp/z+AyYyqK\njy+xEyY6PKbsEAleEARBcKgHW8XmrAYPkNg+ENVgyL2BbK6uJHbpTvQ335PY62WQZeLfGIQUH4/z\njz/g/tZgJKOR6C9m5fna8w8TCV4QBEFwKHv1wQMk9uhN+JXbKOXK5/hejyuhd18UN3dcPp+C7tgR\nEnr2wRjUMc/iyYhI8IIgCIJD2bOJHklyyAYz2aG6e5DQ+2UkVcVSugwxk6fmaTwZkfM6AEEQBKFg\nszXR53Chm/wkfugI5HNniR01BtWjSF6Hky6R4AVBEASHsmcTfX6hlCxF1Io/8zqMRxJN9IIgCELO\nKAry4YOQmJjuaSm24CX4J4FI8IIgCEKOOP2+HM//tcerYS2c582BuLhU5x+Moi84TfRPApHgBUEQ\nhByRjx8FQBMehtv4MRTp0w1U1XY+uYleyeFCN0L2ODTBnzt3jnbt2rFkyRIAbt68yauvvsrLL7/M\nq6++SljSPrtr1qyhS5cudOvWjd9++w0Ak8nEqFGj6NWrFy+//DLXrl1zZKiCIAhCFkhhYej27Aaj\n0XZMe+E8APf2BmNs2Qb93r/Rb91kO6+JjkaVJHB1zfV4CzOHJfi4uDgmTZpEkyYPlu2bOXMm3bt3\nZ8mSJbRv356ffvqJuLg4vv32WxYuXMjixYtZtGgRkZGRrFu3Dg8PD3799VcGDx7MF1984ahQBUEQ\nhEzo167Gs3VTilWvTNGXnsPw84+2c/KF8yjFfFAqViJm0hRUScJlymRQFMBag1dd3fJ8elth47AE\nr9fr+eGHH/D19bUd++ijjwgMDATA09OTyMhIjh07Rs2aNXF3d8dgMFCvXj1CQkLYv38/7du3B6Bp\n06aEhIQ4KlRBEAQhEy6zv0R75hSmZxoDoAs+bD1hNKK5egVL5SoAWPz8SezcFd3J4+jXrQasffBi\ngF3uc1iCl2UZg8GQ6piLiwtarRaLxcIvv/xCp06dCA8Px8vrwZ6+Xl5ehIWFpTqu0WiQJAljiiYh\nQRAEIfdoL1/C8tTTRK7ZiOLqhnz6pO24pCiYkxI8QOz741C1WlynfQpmM1JsjEjweSDX58FbLBbe\nf/99GjduTJMmTVi7dm2q82qKgRlZOZ6Sp6cLsqy1S5zJfHwK36hP8cyFg3jmwiPHz33vHkRGomne\nHJ/iRaBWTTSHDuHjroPw6wA4166Bc3I5PnVgwADkefPwGfY6REejqVAhV9//wvpZp5TrCX7s2LGU\nL1+eYcOGAeDr60t4eLjt/J07d6hTpw6+vr6EhYVRrVo1TCYTqqqiz2SP3YiIuEeezy4fH3fCwqLt\nes/8Tjxz4SCeufCwx3PLR47jCcSVKktsWDRuT/nhvH8/EfuC0YWcwA2IKl4WY4pypDEf4XH6X/Sr\nVgFgdHYlKpfe/8L0WT/qi0yuTpNbs2YNOp2O4cOH247Vrl2bEydOcP/+fWJjYwkJCaFBgwY0a9aM\njRs3ArBjxw4aNWqUm6EKgiAISbSXLwFgSdoL3Vy9hvX4qZNoL1pH0FtSNNGDdc571C+/k5i0CUt+\nXc61IHNYDf7kyZNMmzaN69evI8symzZt4u7duzg5OdG3b18AKleuzMSJExk1ahQDBgxAkiSGDh2K\nu7s7HTt2ZN++ffTq1Qu9Xs/UqflzMX9BEISCTnvpIgBKhYoAmKvXBEA+dQLthfOokoQl6VwqBgP3\nf1yC87zvMDVtlmvxClYOS/A1atRg8eLFWbo2KCiIoKCgVMe0Wi1TpkxxRGiCIAhCNthq8ElJ3OLv\nD4B86iTy+f9QypYHJ6f0XyzLxA95K1fiFFITK9kJgiAIj6S5fAlVo8FS1roHu+rmjqVCReQjIWjC\n7mCpUiWTOwh5QST4J53ZjObqFXT796K5HprX0QiCUABpL11EKVMWUgx0NleviSZpExlzZZHg8yOR\n4J9g8uGDePtVwrtBTYq+8CxFnw8CiyWvwxIEoSCJi0N7+xaW8qn72JMH2gFYKokEnx+JBP8E0e3Y\nhnQ/yvqDouA25l00UZEkvNQVU/2GaK9dRbd7Z57GKAhCDqkq+k1/4fb2MKS7d/M6GrRXLgOkGURn\n9k+R4EUNPl8SCf4JIR/YT9EenSnS5XmkmGicVvyK7sQxErr2IHruj8R88hkAhuVLrS9QVZy//xb9\n5r/yMGpBELJDe/4/inR7kSJ9e+C89GcMS3/O65BsI+iTp8glS1WDFwk+XxIJ/gmh37EFAN2xI3i8\n+jKun36M6uxM7AcfAWBu8AzmylVw2rAOKSoS/fq1uE0Yi/vQQUjR9/MydEEQskJV8Xi5O/rdOzC2\naA2Aftf2vI2JtCPokynlyqO4e6AaDCily+RFaEImRIJ/Quh37UCVZYwB7dDv3oH29i3i3nzrwX8s\nSSKhZx+khAQMixfhNvZdADRRkTjP/z4PIxcEISu0Z/9FvniBxOdeIOr31Zhq1kZ3cD/ExuZtXJeT\navAPz3OXJOLeH0vsqNGgEakkPxKfyhNAioxAPnoEc/2GRP24BGPLNpifrkrcsJGprkvs1hNVknCd\n9KH1C8DQEShFi+I89xukmMKxbKMgPKn0W6z7pycGdQRJwtSmLZLRiH7/njyNK7kGr1SokOZc/KCh\nxI8YlcsRCVklEvwTQPf3biRFwdiqDbi4EPX7aiJ2HYCHdmdSSpXG1KoNkqpirlqN2LETiB80FE1E\nBIYf5+dR9IIgZIV+6yZUScIYYN0m29g6AADdzrxtptdeuoji44vqJjZvedKIBP8E0CeNjDe2avPg\noDb9XfPihgzHUroM0V99A3o98a8PQvEogst3syDOvpvxCIJgH1JkBLpDBzDXq49arBgApoaNUF1c\n0e/YlneBmUxoQq+lvwytkO+JBP8E0O/ajuLugblu/UyvNbUO4N6R05gbPAOAWqQoCd17orl7F/nE\ncUeHKgjCY9Dv2oFksWBsF/jgoJMTxmbNkf87hyb0Wp7EpQm9hmSxiAT/hBIJPp/TXLmM9vIlTM1a\ngPx4WwcoZcpZ73Uv7+fU5jlVzesIBCGN5P53Y/vAVMeNbdpaz+/akesxgXWteQBz1Wp5Ur6QMyLB\n53PpNs9nk+LlBYDmbrg9QnpiGebPxdu/Eprbt/I6FEF4QFHQb9+CpXgJzDVrpzplam1N8Lo8aqbX\nHTsCgLl23TwpX8gZkeDzOd3evwEw5SDBJ/fpSYW4Bq+5dRO3yR9buyqCD+d1OIJgIx8NQRMejrFd\nB5CkVOcslatgKVUa/b49jm99SkzE45VeGH55sAuonJzga9XO6FVCPiYSfD6nvXAe1WDAUqnyY99D\n8fIGQJMPlr3MKy5TJiHFWecTay+cz+NoBOEB3YH9AJhatk57UpIwNWqMJjwM7aULDo1Dv2kDThvX\n4zxnlvWAqiIfP4qlfAVUTy+Hli04hkjw+Zmqor14wTrAJQcLSTxI8IWziV4+fhTDsqUoPr4AaC+K\nBC/kH7rgQwCYkgbGPsz0TBMA5IMHHBqHc1LNXT53Fs2N62iuXUVz7x4m0Tz/xBIJPh+TwsPRRN/H\nUvHxa+8Aqrc1wRfWJnrXieORVJXoWXNQNRpkUYMX8hH5n8NYfItbt2NNh+mZxgDoDjkuwWuuh6Lb\nsQ01qYtAt3sn8rGjAJhr1XFYuYJjiQSfj2kvWpvkctI8D6C6uaPq9YVyFL0UE41+z25MjZpgbNsB\npVx50UQv5Bua66Fob96wTmt9qP89mcW/Ooqbu3XZWgcxLFuKpKrED3wTsI7a1x1PSvB1RA3+SSUS\nfD6W3OeW0wSPJKF4eaMJL3wJPjmZm2rWAsBcuQqa8DCkqMi8DEsQgMyb5wHQajE3fAb5/H9I4Q7o\nZlMUDL8uQXVxJe79cVh8i6PfvRP5SAggBtg9yUSCz8fsVYMHUL28C2UTvfb8fwBYKj+V9Kd1W8vk\n91YQcpWi4LTiVzS3bgLYZnSYGzR85Msc2Uyv2/s32qtXSHihM6q7B6aWrdGE3UG3d7d1gF1RT7uX\nKeQOkeDzsYz2YX4cincxNDHRkJiY43s9SWwJvkpSgk8azyASvJAXnP5cicewQbgPGwxYa/CqLGPK\npJ/b1Mg60M4RzfROG9YCkNijN/BgzQ3JYsFUp57dyxNyj0jw+Zj24gVUZ2eUEiVzfC/FO2mxm4h7\nOb7XkyR5xHxyzd1Wgxf98EJus1hw+WIaAPrdO9Bv2Yh84hjm6jXBxeWRLzXVrY8qy+gO2T/By0f+\nQdXpMNVrYC0rxXQ9McDuySYSfH5lmyJXyS57LatJU+Uc0oeXj2nPn7d+SSpVGkjZRP9Qgk9MxHnO\nbDRXLudyhEK+ZzJBQkKOb+O0ehXyf+cwNm4KgPuIIUhGY6bN8wC4umKuVRv5+DH7bhqVmIh88gTm\nGjXBYABAKVnKtjStubZI8E8ykeDzKenOHTSxMXbpfwdrEz0UsvXoVRX5wnlrs3zSlySldBlUJye0\nF1I30bt8MQ23iR/gPH9uXkQq5GPuI4ZQ7OlyuH7wPpqbNx7vJkm1d1WrJXrWdyS8+BKapC/bjxxg\nl4LpmSZIJhO6A3sfL4Z0yKdPWr9kPLSRVULfVzFX87PV6oUnk0jw+ZRsrxH0SWyL3RSiBK+5dRMp\nLhZzUv+79aAGS6XK1ib65KU/g4Nxmf0VANrr1/MgUiHfsljQb9yAlJCAyw9z8WpYK+vrwqsqLl9M\nw334m3gMeAX5v3Mk9OiNUqEisWMmoCZtHmWqn4UaPJD4v+cBMKz6/bEeJT1yyD/WGB5K8PEDhxCx\n+yC4udmtLCH3iQSfT2nsOMAOUix2U4hWs3swwK5KquOWSlXQxEQj3bljHXT46qtIFguqJNlGNwsC\ngHzqBJqYaBK69yL6y9lgMuE6eWKW1oWX/zmM67RPMSxbitOGtajOzsSNfBcApVJlYsd9RELXHijl\nK2QpFnPDZ7CULYd+/Vq7NdPrjlgTvFnU1AskkeDzKdmOU+QgRRN9IVqPPnkgnaXSwwne+p7KF/7D\n7cOxcOoU8a8OQClVWuw0J6SiO7APAGPzliS83I/ETi+iO3EM3c7tmb7W6c+VANyf9R339v/D3eCT\nKCn2VY8fNoLoOT9kuMBNGhoNiS91QxMbg9Pmv7L1HM7fzrIN8EtJDglGcfewjU0RChaR4PMpe86B\nh8LZRK+9kHqKXLLkX2Zu77+N80/zwd+fmA8noZQoYa3Biz3jhSS2jWCSBsbFD38bAJdZXz76hRYL\nTqv/QClalMSXumGp/BSqj0+O40no0h0Ap5UrsvU6l29n4vL1F2Cx2I5JUZHI5//DXKeeXQbyCvlP\nlj5VRVEICwtzdCxCCtqLF1BdXFGKl7DL/R400ReiBG9b5CZ17cScVKOXz53F7FcdduwANzeU4iWR\nTKZC9R4Jj6Cq6A7sw1KylK0Z3VyrDsbWAej3/o2ctApdenQH96O9fYvE514Avd5uIVmq+WGuXhP9\nti1ZX7gqIQFNeDhSQgKa0Gu2w/LRpK1g69XP6JXCEy7TBL9//37atWtH3759Afjss8/YsWOHwwMr\n1FQV7aWL1v73rDbfZUJJ2u7xUTX4WbP09Otnl+Iei3Q/yi7TkZLJF86jFPNBLVI01XFLtWqoBgOm\nGrWIXLUOfK27zCklrF+mRD+8ANaplJrwMEyNm6T6fxg3YhQALrNnZvja5Ob5xBdesntcCV26I5nN\nOK35M2svSDFwVD5/zvb35P73hwfYCQVHpgn+q6++YsWKFfgkNS8NHjyY7777zuGBFWbyoYPW0d/2\n7BdzckJx98iwD95igW++0fPzzxATY79isywxEc8WjfAYOtBu99NcvZJ6BH0Stagn9/aHEPnXNlvL\nBoClZCkAtLdFghdSNM83aprquKlpc0w1aqHfshEp+n7aF5rNOK39E6WYD6ZmLeweV+JLXVE1GpwX\n/Zi17qRrD2rt2v8eJPjkEfSiBl9wZZrgXVxcKFasmO1nLy8vdDqdQ4Mq0FT10f8p4+Nxf3soAAmv\nD7Jv0V5eGTbrnTihITLSWku5fj33++P0u7ajvXkD3d7ddukD116+hKQoGQ4eUkqXASen1MeKJ9fg\nxUA74cEAu+T+dxtJwhj4LJLZjG7XzrSv+3sXmrt3SXz+RUiaCmdPSqnSJD7/IvKpE+i3b8n8BSkT\n/PmkBZ5UFfnIP9buBzuslCnkT5n+JjcYDBw6ZO1rioqK4pdffsHpoV+MQtZ5vNydoh3bpkq08oH9\n6HbvBLMZ18+nIJ//j7g3Bqf9xZJDirc3mrvhoKrcu2dN6sl2737wi+j6dft0C2SH07o1AGju3UNz\n53aO72cbQV85bQ0+I8m/6B57MROhQNEd2IdStCiWan5pzhnbdQBAv21zmnPOixcCkPBiV4fFFvfW\nO9ayvs5ksB88lOCtNXhN6DW0t2+J6XEFXKYJ/qOPPmLBggWcOHGCDh068Pfff/PJJ5/kRmwFj6JY\n91n+J5gi3Tsj3bmD23tv4/l8IEW7Po93rao4z5mFpUJFYsd9ZP/ivYtZB5HFRDNunIH27V04e9b6\nT2DXLq3tutDQB/8sQkI0/POPg2v0JhP6jettP2pPnczxLZN/kT08gv5RbAle1OALPc2tm2ivXLbu\n4pbOCHNznXoo3t7ot25O1eKkPXcW/fo1mOrWw9yoscPis9SsRWLb9ugP7EM+mMkOc0kJXtVqkZOa\n6HWHD7Kejuwr2dlhMQp5L9Pf3CVLlmTGjBkcPXqUgwcPMmnSJMqUKZMbsRU4mtu3kIxGVCcndMeP\n4l2/Os6LFmD2r0F8vwGgKqDREP31HHB1tXv5yevRE36XPXu0KIrEDz/oiIuDgwe16HTWX1Qpa/D9\n+zvTpYsLt287rlav27MbTWQklrLlAJDPnM7xPZPvYU6n9pURpWRSghd98IWe9rT1S6Y5o93UtFqM\nbdqhvX0L7ckTtsMus75EUlXiRrxrtwGyGYlPHuw364tHX5iU4M31GqAJu4MUFYly8B9eYhVDNjuu\nlUHIe5km+KVLlzJ69Gjbz++88w5LlixxaFAFlebqVQDiBwwioXsvpMREErr3ImLDVmI+/4q7J/7j\n7vFzmJo0c0j5yXPhQ0/HcOeO9aP/7TcdmzfLGI0SgYFm6/mkGnx0NNy4oSEuTmL6dPtN9XlYcvN8\n3NvvAdb1sXNKPnMa1cUFpVz5LL9G9SiC6uwsavACcnIXzyNagGzN9En94JqrV3BauQJz1WoYgzo6\nPEZT46aYGjbCacumVN1KZvNDu0Jfu8ZZlzqcrGiNSXv+P67tu4kRJ86Futp17xohf8k0wa9Zs4ZZ\ns2bZfv7xxx9Zt26dQ4MqqLRXLwNgKV+B6NlzuXv4ONGz5z7YKlKWUVMMaLS35NXsDh+2NsdXqqQQ\nHy8xbpx1TEXPniYkCUJDrTWPixcf/PNYulRna863K4sFp7/WohTzIaF7L1RnZ7Q5rcGbTGjPn7Pu\niJWdBTwkCaV4CdsvS0WB9etle87cE54QGa2hkJKxdQCqRoNTUjO9y9dfIFksxA1/J9cWjjG2DrDG\nm+L/zOuvG2jSxBWTKenAtWv8z/Qnz2+2LtIjHz9G0uOhKBKnT4tFbgqqTD9Zi8WCnGIkqCRJqGKl\nr8eivWatwVvKl7cmk/IVHN6Ml1LylLCDx60bSEyZkoCLi0p4uAa9XqV5cwslSz4YRX/hgvXPNm3M\nKIrEpEn2H1ypO7APTXg4iR07gV6PuWo15HP/8uC3U/ZpL1207pBVzT/br7WULIUm7A6YzaxcKfPa\na858+qkYVFrYJO82aK6Y8UqSqpc35noNkA8fxDOgOc6LF2IpV4HEzrnX7J28rat87l8ADhzQsmGD\njtBQDcePSRAby5UIdy6YyhMa6c5tfDH8vpyzytO2exw/rk333sKTL9MEHxAQQM+ePfn888+ZNm0a\nXbt2pXXr1rkQWsGjuXoFAKVchTwpP7mJ/tB/xTAYVJo1s9CjhzWRPvOMBRcXKFcObtyQsFjg/Hnr\nP4/Bg400bWpm82aZ4GD7ftvXb9wAQOL/OgFg9q+BZDTalup9HNp/rbUZi1/2E7xSogSSqqIJu8Pa\ntdYvtosW6Rw6BkHIf7QX/rOui5DJbmrG9oFIioL2zCkSXniJqGUrHTI1LiOWp60JXnvuLACff/6g\nK+3A2ki0N67zNw/m4h+nFrrDBzlLVduxlLNp0vPffxrRivWEyvS39ZAhQ3j33Xfx9vbG19eXjz76\niDfffDM3YitwtEkJ3lKmbJ6Ur3gX4z7unLrjQ506FvR6GDTISLFiCt26WRN9uXJgNkvcuSPZmuir\nVFEYMsQIwJYt9v3lpd+6CdXFFVPT5sCDpJyTfnj59CmAx6rBK8WtA+3iL99m504ZrVYlIUHi228d\nNwZByGfi4tBeD83SDIy4gUOI/nwm9w4dI/qHhdmatWEPlkqVraPjz/7L3r1a/v5bpgGHATiwy4zm\nemiqBH/MqREA/1INWVYxGFROnMi4Bn/0qIYWLVz48kvx7/9JlGGCP33aWgvav38/JpMJPz8/qlWr\nRnx8PPv378+1AAsS7dWrWIqXAIMhT8pXvb04SCMUVcMzz1g3nahUSeX06Vh69bIOsCufNCYtNFTi\nwgUNTk4qpUurNG1qQatVU82XzynNxQvIF85jbNXGtuiM2a86QI764eV/zwBZq8Fv365l5EjrwCR4\nMFVu51aJhASJQYNMlC6tsGiRjjt3RC2+MHiw0VMWVpJ0dSWhX/9sDeZ8XOfPS4wZ48SECU58/rme\nDRtkYox6LBUrEXn2DlOnWpPwNwyjCv+x/z9fCL3B37RAkqzdqsdcG6MC/0r+VKyo4O+vcOaMBqMx\n/TJ//lmHokj8/XfutWc/rkEAACAASURBVEoI9pPhp7Z69Wr8/f2ZM2dOmnOSJNGkSROHBlbgmM1o\nrl/L04UlLMVLsg/r4jkNG1rSvaacdaYaoaEaLlzQUKmSgkZjbamsW1fhyBEN0dHg7p7zeJySFglJ\nHo0M1iZ6APnMqce+r/bf0yienii+xR95XUICjBxp4NYtqFlT5vnnzbapcuv3WLszXnzRRPnyCqNH\nG/jySz1TpiTm5rAJIQ9oLyaPoM+dLVTDwqyzVN5800ilSumPb4qOhl69XLhyJXWdTKdTKSX/zZX4\n4nAQOjx1nkb/HaIVu1hgep1d+w2cwZ9WNcI4dL4YJ5QahOFDpFqEJlVM+PiohIRoOXtWQ82aSqp7\nx8TAqlXWVUtPnNCQmJhm8Uchn8swwY8dOxaAMWPGUL169VwLqKDS3LiOZLHY5nrbQ0QELFigp3dv\nE6VKpf7FYDbDd9/p8fOz0K5dUjJ3c2OvvjUYoUEDJe0NeZDgjxzREhMjUanSg+tatjQTHOzEvn1a\nAgPT/4KQHfotm4DUCV4tVgzFx9fWzJ5t8fFoL120TjXMJBMvXarj1i3rL8y5c/XWBF+iJCZkNp6u\nQKlSCrVrK/j5KXzzjZ4ff9Rz7ZqG6dMT8PZWCQ+X8PJSbZMghIJBzsIIentRFBg2zMCOHTJhYRIL\nF6bf2T16tIErVzQMGmSkWzcTd+9KHDyoZetWmdBzbnRgE3W6VWRE/BT4D1oUO82CcJixrSEATRsZ\nuS8rnDpehhPUBKxdb+XLW39vnDiRNsGvXq0jLk7CxUUlLk7i5EkN9eun/3tDyJ8y7YOfNm1absRR\n4KUaQW8HqgqjRhmYPt2JAQOcbU3MAHFx8Oqrzkya5MTgwc5ERVmPWyxw0NKQpzmLt1v6v0iSE/zu\n3dZ+ucqVH/yHbtHCmtTt0lwXE4Nu3x5MNWqhJG3ykszsXx3ttatI96M4fVpjiz8r5HP/IqlqusuL\n/p+9846Oouzi8DNlWypJIPTeO9JBQUG6iJQPFCkWVJqKiL0gioqoqKAIih2l9yIgKqJIkSaKIL0j\nLQQSkmyd+f6Y7CYh2TQ2Dd7nHA7Z3ZnZd5LduXPb76bG4YAPPzQTFKRzyy2wbZvC9u0ynpKlWM+t\nXHIE0bWrG0kyvJbFixNp08bN2rUqTZoEU6FCKI0bh1CnTggjR1rZtElUIl8veGWO3TmQOc4ucXHw\n/PMWFi9W0XX47DMT69YZ36dVq1QOHUp/Uzp/vsqCBSYaN/YwdqyDBg002rXz8NxzTn78MZEj789h\nDV14qfEyovdtRAsLp3kvY0LihotGRKz57Tbq1PHg9KgsqvMCANWra9Svb3ynM6qk//ZbE5KkM3q0\nEb/fsUN8xosaWRr4MmXKMGjQIN59910mT57s+yfIGb4K+vKBMfCLF6usWGHCbNbZvl3hgw+M/Nt/\n/0n8739B/PCDSnS0RlycxGefGa/NnGkizhPC7fzkK/i7Gq+B37PH+DJXq5Zi4Js29WCz6fz2m/Ha\n2bMSy5apaLm4qTf/th7J6UzjvXtx1zU8jF3zDtGuXRCdOgVnO/+t+BTsMs+/z55t4vRpmfvuczFu\nnPHcp5+acRYvzecMAaBbt5S7pgoVdBYsSOL99+00aqTRpo2b3r1dFC+uM3++iZ49bfzxh+gnvh5Q\nDh1AN5nQAhhtAyMlNHiwjc8/NzN0qI077gjitdcsFC+u8eabdnRdYtq0lGK2uDh4+WULo0ZZCQnR\nmTYtiYzmfHlqGBXxpp07UA4ewF2vPqU71KIiR43ncdK4tYk6dYwv6qKL7QDju127toaipBTaORxw\n+TLs2iWzfbtC+/Ye7rzTKMDdvt2/gfd4DKcgN9cCQd6R5RWpXLlytGjRAqvViqIovn+Cq3C7Mx4d\nmYyvgj4AxThnz0o895yVoCCdlSsTKVtWY9IkM2PGWGjRIpht2xR693bx228JREZqfPKJmYMHJV5/\n3UK4xc7LjPfbhhYRAUFBKeH+1CF6i8Vop9u7V2H/fpmePYN46CEbM2Zkb7qgFHeZ4JefJ2jiG9g+\n/xQAZ4fO6bZztrsdHXhlShl0XeLIEZl+/WxcugSXLsHGjYpf9S1vgV1mBt7hgClTzNhsOiNHOmnf\nHurU8bBsmUqHHsWZQ3+qmI7TqlXaNIQkwYABLlavTmThwiSmT7ezdWsCn3yShK5LfPWVqDQu8ug6\nyqFDeCpXCWi7m8cDI0da2bhRpXNnN926udi2TcHplJg82c799xu1HnPnGi2Zc+eqtGoVzCefmClX\nTmfmzCQqV844P++uWh1dkjCvWomk67jrN8TdpCm3sh6ApkF7sNnwGXhvWqpaNQ2rFWrU0PjnH5mX\nX7ZQs2YI1auH0rGjIZU9YICLypV1IiJ0tm3zf91/7z1zsmMhbENhIstP8MCBAylWrFh+rKVIEzr6\nUcyrv+fi5p1pZox7CZSB94bmL12SmDDBTsOGGh9+aKdPHxszZ5opXVrjmWcc9O/vQpZh+HAXb7xh\noXv3IOLiJN4b9CelZ57hih8DL0lQrpzG/v3eEH3ai0qbNh7Wr1e56y4bMTEyiqLz5psWOnVy+70A\nebF9MYOgT6b6HmtRUbibpC86dLW+hcW2e/n9THW6dHZRuozOl1+aad48hMuXQdclHn3Uwdix6Ut/\nVV8PvP8Q/RdfGEIgw4Y5iY7WkSSjXXDUKBt79ijcH76QiTyLpO7M9HzA+H317Onm7bc1li9XeeMN\n4yZJUDSRLlxAvnwpV3LRLpfx/TSnus87cUJi+XKVJUtM/PmnQqtWbmbMSMJqhY0bXcTFQceOxo3k\nsGFOnn/eym23BRETI2Oz6Tz/vIPhw52ZN94kSzIrx44C4K7fAD0snFtKH+Sb/6BNqYNAVWrXTrlh\nLV5cw3tZb9BAY+9ehU8+Ma4fbdu6cbkkSpfW6NzZSFM1aeLhxx+NOoESJdJ+z+PjjegXwP79Cl26\nXHt9jiAw+DXw27Zt44knnsDtdhMVFcXHH39MxQDlj683lH92Y507CwDLymXYBz+Qfpvjx9Bl2ZhD\nng327pU5c0ZClqFMGZ3q1Y277y++MLTj27Rx88ADRujslls8fPyxndhYiQEDXNhsKcd58EEnU6ea\nuXhRpkkTD4Pu02AmmQrJlC2rs38/FCumExl5tYF3AxZiYmR69nTRqZObESNsjBljZcGCJP8KnbqO\nddZMdJuNuC9mIp8+jbtuPcggGuTCxHPKOyi4ebX/X1TsUgO7XWLJEpWbb/awfbvCmjVqhgZe+Xcv\nntJl0MMzvim9dAnef99CeLjO6NEpgt3/+5+b2Fg7TZt66PDBp1h+PMSFmJgMb9auRpJg4EAnr75q\nZcECEw8/nHsVPkHBomRDg/5q3G7jezlxolFi3qOHi4YNNZYtU9mwwbjEyrJOp05upk5N8hnr1q3T\nGsL+/V28+66ZmBiZHj1cjBvnoFy57KmGumvWSjHwDRoB0KfdeU7NGsfwWxKBzkRGQunSGv/9J6dJ\nvQ0a5OTcOYlevVz07u1Oc4PipXFjw8Dv2CGnK7D98kszly8bKTSvzLWgcOA3RP/+++/z5Zdfsnnz\nZl566SXeey8bc4dvUIInvuH72bJ0cYbbyCeOo5UpS4ZJtFS4XDBunIVbbw3m7ruD6Ns3iJtvDubF\nFy3s3Cnz6qsWIiM1pk61pzGmffq4eeihtMYdjHa2p55yEB6uM2mSHb1qFSBzA1+unPHlr1pVS1eI\n3qCBRpUqRg56yhQ7ffq46dLFxYYNKl9+6f/cTJs3ohw9gqP7XThv74R90P24GzclPt7oRY+NNba7\ndAmeesrKgStlGMon1N29AFmGyZPtHDt2hUWLkmjTxsOBAwrHj6ddnHQlHufpGK5Ure93He+/b+HS\nJYknnnCk8bRNJhgxwkXz5hruBg0BUP/60+9xrubuu92YTDozZ5oQSs5FF/VQziro//hDpmPHIF56\nyYosQ2ioznffmXnmGSsbNqi0auXm3Xft7N6dwLffJhEe7v9YQUGwdGkSq1cn8Nln9mwbdwBPdSMP\nr1utvpsT9eYmjONVStZJuUmtXdv4bnsdBoDmzTXmzk3innsyNu5gePCQvtAuIQGmTTMRHGysNfWo\naUHB49eDl2WZ6tWND0qrVq0y7IcXgPrnDiyrV+Jq1gI0DdPvvyKdP49eogTSpVikuDjfABN/Yb8Z\nM0wcOCATFWUIyWzdqlClisbdd7vQNFi0SGXGDDOffWZC1yU+/TSJUqWy/+V/5BEXDz/sSjbWwXhK\nlUY5etjv9mXLGsdOnX/3oiiwYUMCspwyT+Pttx388YfCyy9bqFFDo00bD04nrFihkpAgYTbrNFn2\nG60A+72DACOUuWiRyiuvWDh3ztDCv+02D9u3y8TEyFSv6uKVY29gXl2CxGdeSLOGdu0M2dx161Tu\nuy/FW77y52Fa8jf6X2GsuUy6i+m//8p8/rmJ8uU1hgzx72W7G9wEGAbe1e52v9ulpnhxnW7d3Cxd\namLbNplmzUS1UVEkuxX0MTES48ebmTXLsIj9+7t4+WUHkZE6v/+usHevTMeOWaetrqZGjdx9brya\n9O669Xy1A46efbgSe5GQhx+GZKe7Th0PP/+spvHgs0PjxsYBrs7Df/utiZgYmTFjHHz6qTnNqGlB\nweP3dku6ynW7+rHAIPit1wFIeP5lHD17I2kalhVLka7EU6xLeyKbNSBs2BAkXc+wKvfgQYkXX7Ty\n1VdmJk2ysHWrQq9eLn78MYHRo52MGWO0wgwd6kTXJR56yJmrHFfqP5+nSlXkkyfwJzBdtmyKB58R\nqpp2WFapUjpffWVEFB54wMaCBSodOgQxbJgRun/sMRs3rx3PuPBJOFrczL59Mr172xg+3EZcnMSg\nQU6qVtX44QeVpCSJl192sG69nWJt6mDa/Rdycouhl/btjer2n39OudjoOox5vTSHqMbhuGiee87q\ne37hQqNmoG3bYJxOiRdecGSa03Q3NEKcpl3Z9+ABBg40bhpee83ClSs52lVQSFCOGDe+nir+h8wk\nJUH37kHMmmWmTh0Py5cnMnmyneLFdWTZqFN55BFXjo37teDtPHHd1CTlSZOJpEdGQGSk76k77nBT\nvrxG+/Y5u4aEh0P16h527EgpcLXbYepUM8HBOg8/7KRcOY0TJ2QRwSpE+PXgL1++nEaSNi4uLs1j\noWQH6pbNmH/+EectbXHd0hZPlaqEvPw8lmWLMW3finr4EFqxYlhWGvPOMyqwmzfPCGuPG2cUzJlM\nOs2apQ2N22wwfryDJ590EIh6R0/lKpg3bkA5dhRP8p1/au64w83OnU4GDMh+LrllSw/vv29n5Egb\nI0YYeYJBg5y0auXB8fNmPlhQmVcvP8niLh727JFxu4358+PH26lUSUfX4fBhiWLFICrKuEI4utyB\ned1PmNd8j/2hYb73qlxZp3Jljd9+U3E6jaKm+fNVFu2oSis24qpem4ULI2jWzMOvvxrTtQBat3Yz\nYICRZ8wMrUxZtOLFcxSiB+PC3r27ixUrTPTpE8Ts2Ympr62CIoB84ji6zYZeooTfbd57z8yhQzKD\nBjmZONGRn7Nl/OKp34DLsxfgykIps0kTje3bE3L1Ht27u3n/fQtffWVixAgXc+YYQlGPPuogMhLK\nl9fZu1fi8mUCcp0SXDt+P5phYWFpwvKhoaG+x0Kq1iB4YrL3/uxLgGEYXM1bYv79NwBcjW7i0pJV\nWGd/i3XBXBx39Eizv6bB/PkmQkN17r/flaUiWqCqsz3JIzCVw4cyNPAhIfDWW450z2dF375uLlyw\ns2aNyvPPO2nRwvASwr97hQHspV/LI/yyOYQKFTTeeCMpTbGOJKWv2Hfe3hEA0+ZNaQw8GF7855+b\n2bZNoVgxneeesxKqJvKteyAxH/zMbX2L+bz41q3dfPCBcSORLSQJd4NGmH/+EeliDHpk1oV2YEQ1\nPv3UzpgxRp/9nXcG8c03SenOS1B4UU4cM9Qm/UQs9+yRmTrVTLlyGq++WjiMuxfn7ek1JQLJ8OFO\nPv/czIcfmunf38WHH5qxWnWGDTMcAW/tzsmTMsWKiRRVYcDvx3PmzJn5uY4ih+m39Zg3/IqzfQfc\nLVr6nnfc1QvTH5vRgkOIm/4FBAVhH/II9iGPpDvGhg0Kp07JDBzozFe5U2/40RuODCTDh7sYPjyV\n5+9wYNr2B8Xq1WT2Yp0tWxK56SZPumLAjPAq3MkXY9K95jXwkycbRv7KFYmvS42lUuJFQptG8s47\ndp5/3srIkU4ef9yZUbF+prgaGgZe/WsXrtvaZ3s/VYUPPrATGakzdaqZjh2DmTLFTvfumUcNBAWP\nFB+HHBvr1wvWNKNF1e2WePvtpKwmyV53FCtmtPK9/baFfv2COHFC5uGHjVZTSKndOXlSol69glyp\nwIsoecwNup6Se3/upTQv2fv0w3lzG+KnfoqWSR4PYO5cI3Tcr1/+Xvx9Bv4aZq5nF/XPnUgOB66W\nrVAUozUoO8YdAJMJLTQM+eLFdC+1bu3BbNZZt07F4YBpH11hUMwUPNWrgyTRt6+bAweuMHp0zo07\npC20yymSBK+84mDatCQ0DR580MaiRYXI1SvEmJcvJbJeddSd2/P9veUTJwD8Kth9+62J7dsV7rrL\nlTLf4QZj6FAnERE6u3YpmEyGUJSX8uVTPHhB4SBP/xL79++nQ4cOfPvtt77nvvnmG+rWrUtCQkoe\naNmyZfTp04e+ffsyf/58AFwuF2PGjKF///4MHDiQE8lfvsKA+ee1mLZuwdG1O+5GjdO8pkdGcXnx\nSpzduqfbz+2GV1+1MGyYlTlzVFauVKlUSfOFsvMLT6XKAChH8t7Am7YYdRvuFrlL6egRkUix6Q18\ncLCRE4yM1Jg/P4l+jfcjuVy+diHIctZMpuS20C41ffq4WbLEqEhatkwY+KyQjxwmdNQIlHNnCXr/\n3Xx/f9+8iHLpDfzFi/DGGxaCg3XGj895+up6ITQUHn3UMOp33512yJU3RH/ihDDwhYUsrzpXrlwh\n5KpY1IkTJyhfvnym+yUmJjJ+/Pg0ufolS5YQExNDdHR0mu2mTp3KggULMJlM/O9//6Njx46sW7eO\nsLAwJk2axIYNG5g0aRIffPBBTs8v8DidBI99AV2SSLiqfSszXC4YMcLK0qWG1+4dw9ivnzP/x48G\nBeEpXSZfPHjTH4aBd+XSwGuRET752auZcfPnmKJ2Y2/5Jsrq/QC4q9XI3UKvft+y5dCiolCvwcAD\nNGyoUaKExq5dQsIzU5xOwoY9iHwlHi0qCvOa75EPH8oyChZI5BPJ8yIqpDfwb71lITZWYtw4e45a\nVK9Hhg1zEhmp+zTqvXj79kWrXOEhy1utBx98kIupQqRLly7lvvvuy/LAZrOZGTNmpDHmHTp0YPTo\n0Wla7nbt2kX9+vUJDQ3FarXSuHFjduzYwaZNm+jY0Siyat26NTt27MjRieUVtukfoR7Yj/3+IXjq\nZp5o+uknhWeftTBxoplBg2wsXWqiZUs3a9Yk8PLLhpzsgw+mV2PLDzxVqqKcOmn0/OQVmobpj814\nKlZCK1U6V4fQIyKR7HYyEp8Pm/YeoTOmov6xBeXAPgA81QNj4L2FdsrxoxlGEHJwGBo10jh1Sub8\neXHh80fwW69j2rkD+//u5sqEd5F0naAZ0/J1DcrxZA/+qhD9X3/JfP21iRo1PEKlEEMUasAAF2Fh\naZ+PjtYxm3VfiN7jgf37RdtcQZKlBz9q1CgeeughJkyYwOeff85///3HrFmzsj6wqqJeVWJ6dSQA\n4MKFC0Sm6iWKjIzk/PnzaZ6XZRlJknA6nZj9SS0BERFBqGpgPaUSJUJTHhw/Du+9DdHR2Ca9jS0i\n1O9+338PAwcaH3IvHTrAkiUqwcEqnXwFr9kb1BJwqlSC33+jhJ4EJaLTvJTmnK+F3bsNaboePXJ/\nzNIljTVJDihRMuV5pxOSiwQjZn/lUwgMb9kYcvFeGa6vVQtY9xPFjx+AGh1yfEzfYVrB2rVw7FgI\ndTIfcpevBOzvfK388AN89AFUq4b1ixlYbTZ4/RVss7/F9s5bARX3z/Scz50GIOKmur7PkK7DuHHG\n/1OnKpQpU0h+Zzkkv/7W5cvDqVMKJUqE8sor8Npr8PTTMHHitaXMckOh+XwXIFka+JtvvpnixYsz\ndOhQ2rZtm+fV9bqf2z1/z6cmNtbPiLFcUqJEKOfPx/seh414DEtiInET38PhViHVa6n56y+Zvn2D\nMJngiy/shIbqOJ2GZnxiYobOaL4TYrZhAy4eOY0nKOUG6+pzvhasq34kFIhv1Ax7Lo8ZHBRKEBB7\n8DhuW8qFXtn3L5HJd0/6/Pl4ypVHMZm4EFrC79/FH/7O2VyrAeGA/ePpxDdskav1A1SvrgBBrF/v\noGnTgonYXE0g/87XgnTuHJEDByGZTFya9jluO2BPwvbgUELGvciV9z4k6fHRAXmvrM652MHDqFYr\nFySb7zP0/fcqGzbY6NLFRcOGds6fD8hS8pX8/FuXKWPjt99UTpyI58svgwGZd96BM2ecvP22I1cF\nr7mhsHy+84PMbmT8Gvinn346TSi9QoUKrF+/nmeeeQaAt99+OyCLi46O5sKFC77H586do1GjRkRH\nR3P+/Hlq1aqFy+VC1/VMvfe8RoqPw7JyGa4GjXD06+93u8OHJQYMsJGUBJ9/bqdr18LZHqWFGvE1\nOT6OvCrxM23eCOQ+/w5GiB5AuqqSXtlvhOTdVaqiHj6EeuSwIdcZwMZkZ8fOuJo0w7p4IY67+mRY\nOJkdGjY0io927RLFR2nQNMIeG4p8/hxXxr2Bu+FNvpfsAwcT/Pab2L79iqTHnsgX9+/qHniXC8aP\nt6AoOmPH3riFdTnB2yq3cqXKiROGXO+ZMxIzZ5qRZXjnHfF7zE/8Xg1bt26dLwto2LAhL730EnFx\ncSiKwo4dO3jhhRe4cuUKq1evpk2bNqxbt44WLXLvQQUCr0FxtWzl92Kzf79Mnz42zp6VeeONwt37\nrCcbeCnucmAPnJCAenA/nkqVDT2AqKgcTea6Gi1ZZEa+Kg+uJufcE599kdDRjyElJuAJUIGdD0Uh\nfvLHRNx+C6FPP8HFVq19Nxw5oVQpnZIlRaHd1ZjWr8O87iec7W4nadjINK/pYeE4OnXGumQRyj+7\n8dTzP0AoEHh74N2ppF5nzjRx6JDM/fc7qVZNJJKzg7eSfupUwxkbNMhJ69YeevQI4uuvzbRr56Fb\nt8J7Xbze8Gvge/XqBUBCQgLr16+nW7duAMyePZsePXr4283H7t27mThxIqdOnUJVVdasWUPr1q3Z\nuHEj58+f5+GHH6ZRo0Y888wzjBkzhiFDhiBJEiNHjiQ0NJRu3bqxceNG+vfvj9ls5q233grQKecO\ndd+/AHhqZjxnfPdumX79bFy4IPP66/ZCX4yjJ1fISPGBDWOFPvko1sULfY8dXbtfk/elR/rz4I2/\nh6txU+x978H29ee4awTYwAOeGjVJePp5Ql4fR8grLxI/JXeFX40aaaxZo3L2rETJksJYAKgHjc4H\n+72DyGjOsKP7XViXLMKyYgmJuTTwf/8tYzJBrVqG4XG74Z13zPz9t4IsQ3i4zogRThpIRhuup7wh\nJ33ggMy77xo66089VTjSKkUBby/87t0KoaHGACmrFaZPt9OxYxBPPmmhSROP+A7kE1nGM5977jma\nNWvme2y323nmmWeYOnVqpvvVq1cvw3z98OHD0z3XpUsXunTpkuY5RVGYMGFCVsvLN5RkA+/OwMCv\nWqUyYoSVhASJd96xp5lwVljxGfi4uIAdUzp/HsvypXjKlMVTqzbyqZPYBwy6pmNqyR7z1R68sn8/\nus2GVr4CiY+OQjl0AMedva7pvfyRNOJxrPNmY1k4jyuvv4UelsnMTz80aOBhzRqVv/6S6djxxhRJ\nuRrZT9W6F2f7jug2G5YVy0h87uVsH1fXYflylenTDZVDs1ln6lQ7Dz4Io0ZZmT8/bWHrwoUqg28t\nwWBuw2puxvdTzLzzjhmHw2iL8yq1CbLGG6IH6NLF7RvqVKuWxtixDl580coTT1iZNSsp/9uDb0Cy\nTApeunSJwYMH+x4/8MADxAXQKBQV1H1GL7anZs00z0+ZYub++63oOnz+eVKRMO4AeqhRmCHFB+5v\naZ0/B8ntJmnk41yes4jY3/7A2anrNR3T58GnNvCahnrogDHSU5bRKlbi8qIVeRfGVVUcd/VGcrkw\nr/spV4do1Mgw6iJMn4JPWKZ8+iFMAISE4GzXAXX/Pt8Ntq4bkrFut9FI4XAY/wCCx71ESM87eWqM\nhYcesrFtm0K7dsaM84cfttG2rTH7oUkTD3v2XOHw4XjmzEmkenWNr9ZVpT3raD1jKK+/biE8XOer\nr5IYMaJofJ8LC94QPUCPHml/d0OGuGjRws1PP6miVz6fyNKDd7lcHDp0iKpVDcGJ3bt343LdeB96\nZf8+PKVKo4enjEn64QeF11+3ULasxjffJFG/ftEZsKCFGl6oHKibNV3HOusbdLMZe59+gTkmqTz4\nVCF6+cRxpKQkPHkQkveHs0s3gt+ZgHnVShx39c7x/g0aeAvthIH34pvcVry4320cd97Fju/P89r9\noRyUDP1zhyO9cWjZzMFTO8+wyP0wszaaqVfPw4wZSVQtZ+efTQnc81h5fv9dpn59D3PmJBKeHIRp\n395D27aJrBq0mOM/Hea/HkMIrhrN8OFOMREtF3g9eG94PjWyDPXra2zZApcuST5hHEHekaWBf/75\n5xkxYgTx8fF4PB4iIyMDVkFfVJCuxKOcPIGzbTvfc0lJ8MILVlRVZ9asJGrXLjrGHVKH6ANTZKdu\n/QN1/z7svfpke/padkgx8CkDZ1SfqE3NDPfJC9z1GuApWw7zT2uN8mpTzvQLSpbUKV1aY/t2mYsX\nEWNkyXpyG8DW0nfSi77EHwojIkKnVi2N0FAdSTIMhizDlSsSm7da+B9zAWhe9RzfLbYRHg7F7ujO\nbVu38Afl+S5sVxyCowAAIABJREFUBH1f70B4eNrPjarCANsiLCzlwpuD0KNFzj23WCzw1FMOypTR\nsVjSvx4SYhj1K1eEB58fZGngGzZsyJo1a4iNjUWSJIrdgLe1vvx7rZTRqpMnmzl+XGbECGeRM+6Q\ncQ7eNuU9OPgvTPk0x8ezzvoGAPu9g7PYMocEB6ObzWlC9Mr+ZFnaGvln4JEknJ27YvtiBqbNG3G1\nuTXHh+jZ0820aWa6dQtm1qxEqlQpGA9GunABls1Drt8UrXKVgllD3GXkS5dwZzK//MABmbuHFOcK\nMJd+dJrWH1f7jAWHTnd5lOk7WpJIEO902ocU/jxS7EVMW7fgKVWa0mVL8fz250ladJArrd5Pt798\n4ji61ZrpHHhB9njmGf83SF6tsytX8mkxNzhZ5uDPnTvHCy+8wODBgxk8eDBjx45NI117I+BtkfNW\n0B8+LPHRR2ZKl9Z46qmi2dfpM/BXUqrorYsWwJw5SDHpx7NmSlIS1iWL8FSomCvDlymShBYZlSZE\n762g99RIP8s+L3F0uQMA85rvc7X/K684ePxxB4cPy3TtGszOnQXTFx/87gR46CGiWjSiWIe2mDb8\nmu9r8E5u85d/v3gR7r7bRkyMzPsj/qGveSnhg+/BvGpl+mP9d5oGO2fyYe2P+Jr7KbZvKwCm7cb/\n9nsHcmn5GihdGsvShSlJ+1RkJ5oguHZCQ42b2vh48XvOD7K8wowdO5a6devy3nvv8e6771KlShVe\neCH7Q1auB7zDTtw1aqHr8PTTVpxOifHjHUV2JrQeHIIuSWk8eK+XrBw8kKNjKYcOIiUm4GzfIcN2\np2vFmCgX63us7t+Hrih48tn7dLW+BS00DMvq78mNwLYsw0svOXn/fTuXL8OAATaOHs3/C53pt/UQ\nFITj9o6Y/vqToCnv5fsalJNeA5++gl7X4fHHbZw8KfP00w7uHVeJy7MWgGoi7MGBmL9fkWZ7y+KF\nSLpO0v0P4SlfwRjxq+uoW7cA4Gre0ojD33sv8qVLmH/8Ic3+0sUY5IsX0cplPkBLcO2IEH3+kuXV\nOCkpiQEDBlC9enVq1KjB/fffT2Jh0FrNR3weY82azJ6t8ttvKh07urnzziIs2CDL6CGhKUV2uu5r\nRVMP5dDAHzsKgKdS3hhcLTISOe6yUTqt6ygH9hvGPb+VDc1mnLd3QDl+DGXPP7k+zIABLiZMcHDh\ngkz//kHkeUAsVTxUPnsG9cB+aNuWuNkL8ZQqjXJgfx4vID2ZTW779FMTP/yg0qaNmyefNMK9rra3\ncWn+EgCCpk5Os71lwVx0VcXRoxfu+g2RL1xAPvMfpj+2oEsS7ibJbb7J3UDWebPT7G/99muANDU2\ngrwhxYMv4IXcIGTLwJ87d873+MyZMzid138RyvnzEmfOGD+r+/7FU7IUZxyRjB1rJSRE5+237UU+\nmqeHhaW0ySUmGlPbyIUH7zXwFSsFcHUp+ORqY2ORzp1DvnwpXwvsUuNqcxsApl07r+k4Dzzg4rHH\nHBw6JDNsmC0AK8sY8/crKF6tnC8Mb9q4wXihnWHMPNVropw6yT9b7UyaZOa770z89puClsdlJf4m\nt+3eLfPaaxaKF9f4+GN7Gu1yd7MWuBs2Qt25HRISjOMc2I9p9184b++IHhWFu0FDANTt2zDt3I6n\nVu2UzpcGDXDXqYf5xzVI3qJNpxPbjOloIaHYB2U9JVNwbSR354oQfT6RpYEfMWIEvXv3plevXvTs\n2ZN+/foxcuTIrHYr8gwebKNiRXjleYkLJx38GH0vw4dbiYuTGDvWkUbQoaiih4X5QvSphWSUgznz\n6JRjR4C8M/CpxW7Uf/cAGLrzBYAneT65fPTINR/rxRedtGzp5pdfVI4fz5sLnnntaiRNwzZjOgCm\nDb8ZL9x2GwDumjU5T3HuHRzGxIkWRo+20qdPEGPHZlACHUD89cB/+KEZl0vigw/sGaqduVq3QXK7\nMSWH381rVgHguMNQ1/QaeOvc75ASE3E1TStxbe/XH8nlwrJkEQCWRfNRzp7BPvC+XAkYCXKGCNHn\nL1ka+MaNG/Pjjz8yYcIE3nrrLdauXUulSpXyYWkFy9NPOyhdGqZ9HkIpztLl70ls2KDStq2bwYOv\nDx0APTTZg08Vnoece/Dy8eRwa0U/giXXiFfsRr4Yg/r3XwC481ib3B/emxjvTc21IMvQr5+R5lm2\nLHBDclLjLTQzr12NdO4cpt9/RQsJhcaNAXBVrcn9fMV/MRaGDnUyZUoSFSpofPWVidOn8+4inFHV\n+tmzEitWqNSq5fGr9ue6+RbjvDZt8J2XLkk4O3Q2Xq/fyHj+h9XG4+ZpDbyjT190WSZ44utYFswl\naNpH6IpC0iPpFTYFgccbohdV9PlDpgZe0zRGjhyJxWKhRo0a1KhRA0mSGDFiRH6tr8Bo397Dv//C\n+Ls20prfGXrLn8yZk8isWUl5UUdWIGihoUiaBgkJabTelaNHjF7vbKIcO4oWFeUbYBNotFQT5dTd\nuwCjL70g0EqXQTebjd9RAOja1Y2i6CxfnrO++uwgxceh7PsXXZaR3G6CPnwP9fAhXK1a+6bufbiv\nC99zB+3L7+PVVx3cc4+bJ5904HQanSJ5RUZV6999Z8Llkrj/fpff9JereUt0Wcb8+wajDe6Pzbib\nNPOJ5eglS+IpWQopuQjS1SytgddKluLKpClIDgdhIx5G3fsPjrt6iwK7fMJblCxC9PmDX1O1YsUK\nunbtytatW6lTpw5169alTp06NGzYkNKlS+fnGgsMqxWeLDWL37mFCc9doH17T77XdeUl3lY5OT4u\nRUgm2Rh48+pZomkox4/lWXgejCI7SA7R//0XWkgoWqXKefZ+maIoeMpXyP7vJwuionTatPGwc6fC\nsWOBveipf+5E0nXs9w5Ct1h8YXpX6zb89Rc88ICVl7+qTUnO8GW113w3rn37uqlQQWPmTBNnzgT+\nQuyd3Kalyr+73fDNNyaCg3X69fN/c6mHhvny8JaVy5E8HpwdO6fZxhum14qXyLDP3z5gMBd/2YSz\nzW3oNhuJjz4RoDMTZIUI0ecvfg189+7dWbNmDSNHjmTv3r3s3buXf//9l7179/LppzkXQimqqP/8\nDYCnTp0CXkng0ZPlaqW4uBQPvoHhGWc3TC+f+Q/J6cRTIW/C85AqRH/qJMrBA0Z4vgDDKJ5KlZEv\nXgyYCuBdd3nD9IH14r3heWf7jji6dTeiNcDbJ+6lYUNYudJE45s8LA+7lzLHtqTsZ4JRo5w4HJJv\n7GcgyagHfs0aldOnZfr1c2XZeupq3QbJ5SLoPUNR09Ex7aAqd33jM+xq1sJvX7tWqTKXFy7jwr5j\neT6KVpCCzQaKogsPPp/I9Cq5c+dOHnvsMcDQoH/jjTf47rvv0PK6xLawoOuo//yNu3IV9JDQgl5N\nwEktV+vLwd98M5B9A+/1ZLWKeedRe0P0po0bkHTddwEvKDRfHv5oQI7XtasLVdVZvjyweXh1xzYA\n3E2b+RQGP7aOZvwXlalQAWbNSmTV6kQa1bYbKYfkLgqAu+92Ua6ckYs/cCCwN1MpBXaGB69p8Mkn\nxs3NAw9knRry5uGVkyeMyYV166V9vXkrAJxtsyG65B13JsgXJMmopE+dg9+1Sw74Z19g4PebO3ny\nZD766CMAYmNjefDBBwkKCmLHjh28917+C2MUCKdOIcfG4ql7fd7hp8yEj0tpG/IZ+OxV0st53CIH\nKR68t3LaXb9hnr1XdvAkpwfko0cDcrzISGjTxsOffyrs3h0gY6rrmLZvw1OmLFqp0rja3MrceuN4\n1P4uxYtrrF0LHTp4kCRDwEnSNJTDh3y7m83w2msOHA6Jxx6z4g6g5MPVPfCffGJi82aVzp3dvrnt\nmeFq0Qo9OYLj7NglnZfuanc7sSvXYr9vSOAWLQgYISF6mhD9a69ZeOQRa0YCg4JrxO/VZP369Uyf\nbuTsVq9eTYsWLRg9ejTvvvsu27Zty7cFFii7kgu6rvIQrhe05KZUOT4+RQq2eXN0WUbNrgd/NG9b\n5CBVkV1y4V9BFdh58SRHKwJVaAeG+A3AnXcGsXChSmIibNigsHy5mhvRPOQTx5HPn/OJvJw4pfDA\nobEEBUvMnp1E6kF83ql8arKgk5fu3d306eNixw6FKVPMxMRIfPediWnTTPz8s8LZs7kLs6bugf/z\nT5nXX7dQooTGpEn2LPY00EPDfHl2Z6fO6TeQJNzNWvgKCQWFi9DQtCH6c+ckPB6JS5dE2D7Q+P0G\nhISEYEqemLVp0yZuucUIi0mShCWjMUHXIz4Df5168KEpA2d8IfrSpfFUrISSTTW7vBa5AdDDixmy\nurqObjbjKaAeeC9eDz6jEL1l/hzcDW/Ck8NBOD16uJk2LYmnnrIyfLgNVdVxu40L3qefJtGzZ85c\naFNyeN6VPMxl7FgLSUkSH36YRMOGab1kd7Kmv3fmQmomTLCzcaPCO++YeecdMx5PykXYZNL57ruk\ndGNBM0I+fYpinW4DQHIarlpMWGUeGWDD7YaPP7YTHZ39O5mkoSPRlizEmSw8JCg6hIQYSna6bgRf\nYmKMz9TFi1KG2geC3OPXg3c6nei6TlJSElu2bKF169YA6Lp+40jV/vkncP168F5hD2+RnW6zQVAQ\nnmrVkWNiUsL2maAcP4auqmhlyubdQhUFPXmKobtWnRyPag003oLCq3vhlX92EzbyEYImvZWr4/bp\n4+annxK47TY3DRpoPPSQE0XRee89c46V5dTtyQa+STPWrVNYudJE8+ZuX999mvNJvhnJyMAXKwZT\nptgxmaBRI41XXrHz2WdJPPmkA7cbXn/dkq0Ig3XuLJRzZ5E0DSk+niMV29D9wQocPSrz2GNObr01\n65uE1Dj69CNu5lyRQy+ChIToeDwSdrth5GNjDQMvPPjA49eD79y5M3379sXpdNKiRQvKly+P0+nk\ntddeo0GDgg2R5hu7dqGFF7tue2RTcvBGkZ0WEYkCeKpWh7VrUA4exN0889nu8rGjaGXL5Xk4VIuI\nRI6NLfACOwCCg/FEl0wXojf/ZAwxkWNyLy5fpYrOvHlJvsfx8RJz55pYuVLN0ewD0/at6IpCYq2G\nvNjNgizrTJjgyLCoXCtTFi04BPVAegMPcOutHo4fT6tM0qMHHDkis3ixiTVrFLp0ycRA67qhF2+1\ncnHzDv45Gso9A0M5c0xm6FAnL7xw/UtfC1JIPVHO6dR9UaGLF4WBDzR+PfgHHniAF154gdGjR/uK\n6lRVJTIykmeffTbfFlhgJCTAgQO469S9bkdIaqlC9NLFi2iRhjH3VDdyslmG6RMTUc6d9eWk8xKv\nHn1B59+9aBUrIZ88QerqM++UMinuUsDeZ/RoB7KsM2lSDrz4pCTUv/7kVM3bGDwsioMHFe6/30X9\n+n4OIEl4atRAOXSQnFTTPfmkE0nSeeedzL149e9dqAf24+jUFVdQOA8NC+HMGZlXX7UzfrzjuhGO\nEmSP1Gp23vA8pHjygsCR6VercePGtGvXDjXZO5NlmSeffBLz9aT24gf13z2g6wUmiZof6N4iu4sx\nyFfifUbUJ8eaLEHrD+/reZl/96JFGTcfBV1B78VTqTKSx2MYeUC6FOur8pcuB6Y/HgyPvndvN3v2\nKNmWs1W3bWWZswuNjyzi559VbrvNzYsvZl6i7KlWA8np9MkOZ4eaNTV69nTz998Ka9YofrezzJ8L\ngON/d7NokcrBgwqDBjkZPvz6kHwW5AyvzsGVK1Iar10Y+MAj7p39oP6zG+C6bZGDlBC9ciy5bSm5\nHU0ra+TT5dOnfNuqmzcR0fIm1FRT1PKjwM6Ls2MXXC1a+aqnCxrPVb3w5l9+RvIYYWo5QAI4XkaP\ndqKqOsOGWRk71pKpjvfu3TK9R9ekJ0u57Apm/Hg7c+Yk+aZ4+cNXOJgDAw8pXvyYMVY2bcrAyLvd\nWBYvQIuIILFtByZNsmAy6YweLcLyNypeNbv4eCmNUY+NLagVXb8IA+8HdXfyUJPrtMAOUqrovUbK\n22/uKW0YeOVUioE3r1uLevgQIc+NwRsr9k2Ry4fhQ/b7HuTS8jWFpqgqnYFPDs9roWGGB5+b3jY/\nVK+uMW9eEhUq6EyfbqZevRDq1w+mWbNgHnrIyqxZKnPmqPTqZaN9+2B+OV6NLqzip6XnGDrUla0Q\nuK9wMIcGvmZNjTffdHDxokTv3jYmTzazebPCwYMSug6m39ajnDuL467eLFgWxJEjMvfe66JcOVEt\nfaOSeiZ86hC9KLILPNmK+cXGxnLy5Enq16+PpmnIN0DSTP1nNygK7pq1C3opeYfJhB4UhHzhPJDS\nb05QEFpkJPLpk75Nvb3Lpu3bsCyYi/P2Tli//AwAT42CbVsrCDyVDI1z5egR0DTMP6/FE10ST916\nmNf9BImJEBwcsPe75RYP69cnMHmymVWrVOx2ifh4Q942tcRtqxYuxu7oTYcax4ht9nv2z6ei/9a/\nrBgyxEWdOhoPPWTljTdSWmhvv93NwpIrALh0Z3/eG2PBbNZ54gnhvd/I+AvRiyK7wJOlgV+xYgVT\npkzBbDazYsUKxo8fT506dejbt29+rK/A0K02aNu20HiMeYUWGoaS3Pbo9eABPGXKoR4+6GtWVU4c\nN9TDLBaCx7+C7csZqIcOkvj4k3hqXcc3QX7QkqMWpt/WY/v8E+QLF0jqPxDJYYi1yHGX0QJo4MHQ\n8X7uOSfPPWcYSF2Hgwdlfv5ZweGQuOsuF9X++51iPVaQ2HpYzs4nedRvTnLwqWnVysPPPyeydKnK\n+fMSGzeq/PSTytAS9zCZ5dw1oS1Hj8oMGeKkbFnhvd/IpK6iFzn4vCVLV/zLL79k6dKlREREAPDs\ns88yb968PF9YQXN5zkL48ceCXkaeo6dKznqr6MHIw0uJiUiXjMSYfOI4WtlyJI4chXL2DKbt27D3\nvYeEF1/J9zUXBrToknjKlMW0aychLxpdJc4OndJoC+Q1kmSE74cOdfH4404qVtQxbTTmpLta3ZKj\nY2klS6FbLNc0575kSZ1HHnHx4otO5s9PpFkzD3PPd6CadIit20z07u3ilVeEHumNTmoDn9qoixB9\n4MnSgw8NDcVms/keW61Wn8LddY3JVKATy/ILb6EdpBTZAT7hGvnUKTxBwchn/sPV6mYSH30C89o1\naBUqEv/+R9dtC2GWSBKxv2zEtHUL6q4/kRIScHbuhukvQ/0wkJX0OcG00QjLu1rdnLMdZdkYg5tL\nD/5qgoLg25kJ9Kp9kj16HcaMcfDMM84b9uMiSMEbok9ISMnBh4XpIkSfB2Rp4CMiIli8eDEOh4N/\n/vmH77//nshUhkBQtPEW2kFKrzmAp2w5AJTTJ9GDgpB03ZjfHRTEpR9+uXENeyr0YhE4O3YxBp4k\n49UWkHPaC2+3Y/5pLc6ud+T4xtIyfw7mdT/h6NIN07YtuGvVRo/KXKAoIzwVK6EePIAUH5fmc5Fb\nIvWLbNJb8m/r+6j87JvXfDzB9UHqKvqLFyUkSadKFY09e2SffK0gMGR5JXn11Vf5+++/SUhI4KWX\nXsLhcPD666/nx9oE+YA3pAxXhehTefBXj/cU30D/6OHJIfocevDWebMJf2AA5u9X5PANdYInjMe6\nYC7hD92HlJiYc+89GS25kl4+ltaLl48eoVjHW1G3bsloN78op08SRjz1a4uiOkEKV7fJFSsGUVE6\nTqfEjaKCnl9k6cGHhYUxduzY/FiLoADQUoXoUxfZaT4P/pRP+93bSiXwT24NvHzKEMxRd+/C2b1H\n9vc7fgzl5AmcrW/B3bQ5ps0bsQ+8L0fv7SV1Jb0nlcCTdfZMTLt2EjTtI+Katcj+2pLbLD1lyuVq\nPYLrE2/Zj1fJLjJSp1gxw+jHxkoEB4sizEDh18DfeuutSJl4ar/88kterEeQz+ghxrdNV1XfzwAe\nnwd/El02Pgea14MX+EVLjojkVOzGq1+v7stYD94f3qI6x513YR8yNEf7Xo2/XnjLqpUAmNeuzlH4\nXj5ltFl6hZMEAkjx4OPiDA++cmWNyMgUAy80EgKHXwM/a9as/FyHoIDwFtlpkVFpQu9a6TLokmSo\n2SWLtniEgc+SXHvwMRcAUK6ayZ4V5t9/A3JeNZ8R3la51JX0yuGDqP/uRVcUJIcD86qVOPr1z9bx\nlP9OG8fNy0mDgiKHxQJms85//xlz4K/24AWBw6+BL5t8171gwYL0O6kqFy5coGHDwiEbKsg9XgOv\nX104aTajlYhGOXUSye1GVxRxoc4Gergx1lbKoQfvHc2rHD4EDodxFczyzYy2OC0yMiBaBF51vtS9\n8OZV3wOQ9OgTBE2ehGXxgmwbeK8H7xGfG8FVhIbqHD9ulIBFRuppPHhB4MgyB//777/z+++/07hx\nYxRFYfv27TRr1owTJ05w6623Mnr06PxYpyCP8IaUtYj0nRFa2bKGop/dbhj3PB4Jez3g/X3m1oOX\nPB6Uw4fw1K6T9T7J+XfHHT0C0tKph4WjFSuWRs3OsmoFuiyTOHQkpl9+xrx+HVJMTLaq9OXTp9Al\nCa10mWtem+D6IiQkpUUuMlInIkIY+Lwgy6uCx+Ph+++/Z/r06UydOpWVK1disVhYvHgxmzdvzo81\nCvIQXw4+Mv0FWytTDsnpRDl7RoTns4k3IiJfzlmbnJzswQOo2QzTe/PvzpuvPTzvxVOxstE1oWlI\n586hbt2Cq0Ur9OLFcfT6H5LbjWXF0mwdSzl1Ci26pK9IUyDw4s3DA0REiBB9XpGlgT979izFixf3\nPY6KiuLkyZNIkoSW7QHVgsJKSg4+vQfvSVUcJQrssonVim615ixEr2lIFy+iK8Y0NuXfvdnazZd/\nb90mx8v0u5QKFZHsduRzZ7H8sApJ143efMDRsze6JGFZsjDNPpYlC303GykH0pDPnEYrI7x3QXq8\nanZgtMh5Q/RC7CawZBlzLVOmDI8//jjNmzdHkiR27txJcHAwq1evpnTp0vmxRkEe4qlaDd1kwl0n\n/dQ8LVV7k/Dgs48WFp6jEL10KRZJ03Dd1BjTzh2o+/1X0kvnzqH+uwdPufIBzb978ebh1Z07sM2Y\nDoCji2HgtTJlcTe6CdMfm8FuB6sV6Uo8ocOGoEdGErP9H0MwH5AuXEByOtN8hgQCL6nHF6cushNy\ntYElSwM/ceJEli5dyr///oumaTRs2JBevXqRkJDArbfemh9rFOQhWrnyxPxzMMPWp9TtTaIHPvvo\n4eHIORhuLccY4Xl33fooBw+i7EvvwSsHD2CbOhnr/DlIzhThmEDl3714/85hj9yP5HCQNPA+tORZ\n8QDuxk2Nm5B//sbdpBnqzh1ImoZ04QLWhfN8PfhK8iRCj2iRE2TA1SF6UWSXN2Rp4M1mM61atSIs\nLAxZlqlbty4hISGEeAWFBUUevVhEhs+nrn4WIfrso4eFIx07SnZ1N6VkA69HFcdTsxbqnzvA6QSz\nGQD5zH9EdGiLlJiAu0pVnHf0QD57BvnMGZIeztnUuKzwevCSw0HiY6NJeGlcmtddjZti+/xTTDu2\n4W7SDNP2rb7XbNM/wj5gMEiST+RGePCCjEht4KOidEJDQVF0YeADTJYGfvbs2cyYMYP69euj6zpv\nvfUWjz76KL169cqP9QkKEK+aHYgQfU7Qw8IML9tu94WsAdS//kQ+fRpnl25ptvdW0GtRUbhr1sK0\n7Q+UI4fx1KwFgGXeHKTEBBKeeo7EMc9Ccq4+L3A3aYqrRSvsPftgH/JI+tcbNzHOZcf25P+3AeBs\n2w7zr+sw/7wW5+2dkP9LNvDCgxdkwNUhekkyPPkcBL4E2SBLA7906VJWrVqFJbkvNzExkQceeEAY\n+BsArWQpX+GX6IHPPlp4ipqdlsrAh44aibJnNxf/2odWspTveW8FvRYZBTUMo67s22sYeF3HOvc7\ndIuFpEeG56lxByP6cGn5Gr+veypXRQsvhrpzu9GHv20rnnLlufLKeCJvvwXbtKk4b++E4pWpLS0+\nN4L0eD14SUrJv0dEiIlygSbL5J2qqj7jDhAUFHRjjIsVgKLgqVETT41aogc+B+hhyWI3qQrtpMuX\nUPbsRtJ1zFe1mXlFbvRkDx5A3We0yqnbt6Ie2I+j6x1+Uyn5iizjvqkx6uFDRkTiwnlcTZrhqd8A\nZ5tbMf+6DvWPLcinhUytwD/eKvpixVLuWYsVM3LwojkrcGR51S5VqhTjx4+ndevWAGzYsEFUz99A\nXJ6VXslQkDkpcrUpvfCmrVuQkiV/LUsXp9GNly94Q/TF0UpEA6Du2ml473MMyWj7PQPzZe3ZwdW4\nCeZffsb22SeAUXgHkPDMi5g2/EroqOHoIaHospwmUiEQePEaeG9xnfdnTZOIj4fwcH97CnJClh78\n+PHjKVmyJIsWLWLx4sWUKVOG8ePH58faBIUArWy5NLl4QdakDtF7MW0xRKG00DBMWzYhJ+u0Q9oQ\nvVamLO7KVbD8sJrQx4ZhWbIQT+kyuG5tl49nkDnumwyDblls3Py5mjQznm/RkqSHh6EeOohp1060\nUqVF5EeQId4a7dQGPiOxG12HAwcC1yVyo5Hlb85mszFw4ECGDx/OiBEjGDhwIFarNT/WJhAUSfQM\n5GrVLZsMyddRTyLpeho1uJQiu+IgSVxetALXTY2xzpuNHHfZ0H7P49x7TnDdZBTaSU6noaFQv4Hv\ntYQXXsFduQqAkKgV+MWbg09t4DOSq12+XOXmm4P59dfC8/kvSmRp4H/88Uc6derEuHHjeOmll+jc\nuTPr16/Pj7UJBEWSdBPlHA5MO7fjrlsf+90DDDW4pYt920sXY9CtVggKAoyoyaWlq0kadD+e0mVI\nyuV897xCj472dVW469VP0ylAUBDxU6ajy7KvnkAguBp/IXpIa+B37TJM1L59wovPDVnGzz7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dtIba3Bjh0tO+1sxw4HJSUxvPqqAl5ERKTdGDs2sG5sfn7LAv6DDwKfKy52UFoaOSvUKeBFRMTW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\ngg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}