GAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hu2yJnptXTHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import pickle as pkl\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHF2v8_sXbL4",
        "colab_type": "code",
        "outputId": "02dfe872-b3e5-4a10-fd46-6aee5b605db3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets('MNIST_data')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-a37a59f11b0d>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use urllib or similar directly.\n",
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2oVcyXtXklQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_inputs(real_dim, z_dim):\n",
        "    inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real')\n",
        "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n",
        "    \n",
        "    return inputs_real, inputs_z"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kjz1c3RDbAZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator(z, out_dim, n_units=128, reuse=False,  alpha=0.01):\n",
        "    ''' Build the generator network.\n",
        "    \n",
        "        Arguments\n",
        "        ---------\n",
        "        z : Input tensor for the generator\n",
        "        out_dim : Shape of the generator output\n",
        "        n_units : Number of units in hidden layer\n",
        "        reuse : Reuse the variables with tf.variable_scope\n",
        "        alpha : leak parameter for leaky ReLU\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        out: \n",
        "    '''\n",
        "    with tf.variable_scope('generator', reuse=reuse):\n",
        "        # Hidden layer\n",
        "        h1 = tf.layers.dense(z, n_units, activation=None)\n",
        "        # Leaky ReLU\n",
        "        h1 = tf.maximum(alpha*h1, h1)\n",
        "        \n",
        "        # Logits and tanh output\n",
        "        logits = tf.layers.dense(h1, out_dim, activation= None)\n",
        "        out = tf.tanh(logits)\n",
        "        \n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Xa1OucLFTmt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discriminator(x, n_units=128,reuse=False, alpha=0.01):\n",
        "    ''' Build the discriminator network.\n",
        "    \n",
        "        Arguments\n",
        "        ---------\n",
        "        x : Input tensor for the discriminator\n",
        "        n_units: Number of units in hidden layer\n",
        "        reuse : Reuse the variables with tf.variable_scope\n",
        "        alpha : leak parameter for leaky ReLU\n",
        "        \n",
        "        Returns\n",
        "        -------\n",
        "        out, logits: \n",
        "    '''\n",
        "    with tf.variable_scope('discriminator', reuse=reuse): \n",
        "        # Hidden layer\n",
        "        h1 = tf.layers.dense(x, n_units, activation=None)\n",
        "        # Leaky ReLU\n",
        "        h1 = tf.maximum(h1*alpha, h1)\n",
        "        \n",
        "        logits = tf.layers.dense(h1, 1, activation=None)\n",
        "        out = tf.sigmoid(logits)\n",
        "        \n",
        "        return out, logits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QS2BQjRGJvOv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Size of input image to discriminator\n",
        "input_size = 784 # 28x28 MNIST images flattened\n",
        "# Size of latent vector to generator\n",
        "z_size = 100\n",
        "# Sizes of hidden layers in generator and discriminator\n",
        "g_hidden_size = 128\n",
        "d_hidden_size = 128\n",
        "# Leak factor for leaky ReLU\n",
        "alpha = 0.01\n",
        "# Label smoothing \n",
        "smooth = 0.1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1hY0yfuJvTQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.reset_default_graph()\n",
        "# Create our input placeholders\n",
        "input_real, input_z = model_inputs(input_size, z_size)\n",
        "\n",
        "# Generator network here\n",
        "g_model = generator(input_z, input_size, n_units=g_hidden_size, alpha=alpha)\n",
        "# g_model is the generator output\n",
        "\n",
        "# Disriminator network here\n",
        "d_model_real, d_logits_real = discriminator(input_real, n_units=d_hidden_size, alpha=alpha)\n",
        "d_model_fake, d_logits_fake = discriminator(g_model, n_units=d_hidden_size, reuse=True, alpha=alpha)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTMRzjtAJvbr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calculate losses\n",
        "d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real)*(1-smooth)))\n",
        "\n",
        "d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_real)))\n",
        "\n",
        "d_loss = d_loss_real + d_loss_fake\n",
        "\n",
        "g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1cQJK6hWFDe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimizers\n",
        "learning_rate = 0.002\n",
        "\n",
        "# Get the trainable_variables, split into G and D parts\n",
        "t_vars = tf.trainable_variables(scope=None)\n",
        "g_vars = tf.trainable_variables(scope='generator')\n",
        "d_vars = tf.trainable_variables(scope='discriminator')\n",
        "\n",
        "d_train_opt = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)\n",
        "g_train_opt = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojrqVMsDWFQF",
        "colab_type": "code",
        "outputId": "9083d80d-ff11-4a33-eec0-d4ecfce9832c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1754
        }
      },
      "source": [
        "batch_size = 100\n",
        "epochs = 100\n",
        "samples = []\n",
        "losses = []\n",
        "saver = tf.train.Saver(var_list = g_vars)\n",
        "with tf.Session() as sess:\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    for e in range(epochs):\n",
        "        for ii in range(mnist.train.num_examples//batch_size):\n",
        "            batch = mnist.train.next_batch(batch_size)\n",
        "            \n",
        "            # Get images, reshape and rescale to pass to D\n",
        "            batch_images = batch[0].reshape((batch_size, 784))\n",
        "            batch_images = batch_images*2 - 1\n",
        "            \n",
        "            # Sample random noise for G\n",
        "            batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "            \n",
        "            # Run optimizers\n",
        "            _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n",
        "            _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n",
        "        \n",
        "        # At the end of each epoch, get the losses and print them out\n",
        "        train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n",
        "        train_loss_g = g_loss.eval({input_z: batch_z})\n",
        "            \n",
        "        print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
        "              \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
        "              \"Generator Loss: {:.4f}\".format(train_loss_g))    \n",
        "        # Save losses to view after training\n",
        "        losses.append((train_loss_d, train_loss_g))\n",
        "        \n",
        "        # Sample from generator as we're training for viewing afterwards\n",
        "        sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
        "        gen_samples = sess.run(generator(input_z, input_size, n_units=g_hidden_size, reuse=True, alpha=alpha),\n",
        "                       feed_dict={input_z: sample_z})\n",
        "        samples.append(gen_samples)\n",
        "        saver.save(sess, './checkpoints/generator.ckpt')\n",
        "\n",
        "# Save training generator samples\n",
        "with open('train_samples.pkl', 'wb') as f:\n",
        "    pkl.dump(samples, f)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100... Discriminator Loss: 0.3629... Generator Loss: 3.7768\n",
            "Epoch 2/100... Discriminator Loss: 0.3519... Generator Loss: 4.2025\n",
            "Epoch 3/100... Discriminator Loss: 0.5229... Generator Loss: 3.1067\n",
            "Epoch 4/100... Discriminator Loss: 0.5680... Generator Loss: 3.1090\n",
            "Epoch 5/100... Discriminator Loss: 1.2378... Generator Loss: 1.7011\n",
            "Epoch 6/100... Discriminator Loss: 0.5044... Generator Loss: 2.5590\n",
            "Epoch 7/100... Discriminator Loss: 1.3670... Generator Loss: 1.7108\n",
            "Epoch 8/100... Discriminator Loss: 1.5886... Generator Loss: 1.5444\n",
            "Epoch 9/100... Discriminator Loss: 1.1267... Generator Loss: 1.3576\n",
            "Epoch 10/100... Discriminator Loss: 0.9007... Generator Loss: 2.4469\n",
            "Epoch 11/100... Discriminator Loss: 1.3542... Generator Loss: 2.3198\n",
            "Epoch 12/100... Discriminator Loss: 1.3914... Generator Loss: 1.9363\n",
            "Epoch 13/100... Discriminator Loss: 0.7390... Generator Loss: 2.5041\n",
            "Epoch 14/100... Discriminator Loss: 0.9869... Generator Loss: 1.2310\n",
            "Epoch 15/100... Discriminator Loss: 2.2415... Generator Loss: 1.6075\n",
            "Epoch 16/100... Discriminator Loss: 1.1832... Generator Loss: 2.0038\n",
            "Epoch 17/100... Discriminator Loss: 1.1750... Generator Loss: 1.1352\n",
            "Epoch 18/100... Discriminator Loss: 1.1102... Generator Loss: 1.2678\n",
            "Epoch 19/100... Discriminator Loss: 1.2106... Generator Loss: 1.9789\n",
            "Epoch 20/100... Discriminator Loss: 1.0340... Generator Loss: 1.3571\n",
            "Epoch 21/100... Discriminator Loss: 1.0970... Generator Loss: 1.7205\n",
            "Epoch 22/100... Discriminator Loss: 1.1260... Generator Loss: 1.6904\n",
            "Epoch 23/100... Discriminator Loss: 0.9446... Generator Loss: 2.2175\n",
            "Epoch 24/100... Discriminator Loss: 1.0189... Generator Loss: 1.6971\n",
            "Epoch 25/100... Discriminator Loss: 0.8126... Generator Loss: 1.9551\n",
            "Epoch 26/100... Discriminator Loss: 1.0290... Generator Loss: 1.7248\n",
            "Epoch 27/100... Discriminator Loss: 0.9506... Generator Loss: 1.9503\n",
            "Epoch 28/100... Discriminator Loss: 0.7172... Generator Loss: 2.4444\n",
            "Epoch 29/100... Discriminator Loss: 0.9609... Generator Loss: 2.1096\n",
            "Epoch 30/100... Discriminator Loss: 1.2166... Generator Loss: 1.3630\n",
            "Epoch 31/100... Discriminator Loss: 0.6916... Generator Loss: 2.6223\n",
            "Epoch 32/100... Discriminator Loss: 0.8436... Generator Loss: 1.8276\n",
            "Epoch 33/100... Discriminator Loss: 0.7201... Generator Loss: 2.2783\n",
            "Epoch 34/100... Discriminator Loss: 0.9254... Generator Loss: 1.6875\n",
            "Epoch 35/100... Discriminator Loss: 0.8684... Generator Loss: 1.9510\n",
            "Epoch 36/100... Discriminator Loss: 1.0183... Generator Loss: 1.7975\n",
            "Epoch 37/100... Discriminator Loss: 0.7821... Generator Loss: 1.8932\n",
            "Epoch 38/100... Discriminator Loss: 1.1010... Generator Loss: 2.1637\n",
            "Epoch 39/100... Discriminator Loss: 1.4455... Generator Loss: 1.3323\n",
            "Epoch 40/100... Discriminator Loss: 0.9272... Generator Loss: 1.6232\n",
            "Epoch 41/100... Discriminator Loss: 0.9040... Generator Loss: 2.1153\n",
            "Epoch 42/100... Discriminator Loss: 1.0678... Generator Loss: 1.8858\n",
            "Epoch 43/100... Discriminator Loss: 1.0262... Generator Loss: 1.9872\n",
            "Epoch 44/100... Discriminator Loss: 0.9017... Generator Loss: 2.1028\n",
            "Epoch 45/100... Discriminator Loss: 0.9852... Generator Loss: 1.8281\n",
            "Epoch 46/100... Discriminator Loss: 1.0744... Generator Loss: 1.6137\n",
            "Epoch 47/100... Discriminator Loss: 0.8117... Generator Loss: 2.5469\n",
            "Epoch 48/100... Discriminator Loss: 0.8636... Generator Loss: 2.2302\n",
            "Epoch 49/100... Discriminator Loss: 0.9593... Generator Loss: 1.8405\n",
            "Epoch 50/100... Discriminator Loss: 1.2697... Generator Loss: 1.9770\n",
            "Epoch 51/100... Discriminator Loss: 0.9679... Generator Loss: 2.0423\n",
            "Epoch 52/100... Discriminator Loss: 0.8785... Generator Loss: 1.8756\n",
            "Epoch 53/100... Discriminator Loss: 0.9254... Generator Loss: 2.2632\n",
            "Epoch 54/100... Discriminator Loss: 1.1384... Generator Loss: 1.5020\n",
            "Epoch 55/100... Discriminator Loss: 0.9562... Generator Loss: 1.9411\n",
            "Epoch 56/100... Discriminator Loss: 1.0382... Generator Loss: 1.8273\n",
            "Epoch 57/100... Discriminator Loss: 1.0059... Generator Loss: 2.0382\n",
            "Epoch 58/100... Discriminator Loss: 0.9122... Generator Loss: 1.7790\n",
            "Epoch 59/100... Discriminator Loss: 1.0563... Generator Loss: 1.7828\n",
            "Epoch 60/100... Discriminator Loss: 1.1975... Generator Loss: 1.4096\n",
            "Epoch 61/100... Discriminator Loss: 1.0922... Generator Loss: 1.7137\n",
            "Epoch 62/100... Discriminator Loss: 1.1465... Generator Loss: 1.7352\n",
            "Epoch 63/100... Discriminator Loss: 0.9184... Generator Loss: 1.9280\n",
            "Epoch 64/100... Discriminator Loss: 1.1166... Generator Loss: 1.6000\n",
            "Epoch 65/100... Discriminator Loss: 1.2439... Generator Loss: 1.4173\n",
            "Epoch 66/100... Discriminator Loss: 1.1784... Generator Loss: 1.2196\n",
            "Epoch 67/100... Discriminator Loss: 1.1778... Generator Loss: 1.7816\n",
            "Epoch 68/100... Discriminator Loss: 1.2828... Generator Loss: 1.4591\n",
            "Epoch 69/100... Discriminator Loss: 1.1429... Generator Loss: 1.4657\n",
            "Epoch 70/100... Discriminator Loss: 1.1660... Generator Loss: 1.4847\n",
            "Epoch 71/100... Discriminator Loss: 0.9922... Generator Loss: 1.3913\n",
            "Epoch 72/100... Discriminator Loss: 1.0915... Generator Loss: 1.4423\n",
            "Epoch 73/100... Discriminator Loss: 1.1514... Generator Loss: 1.3310\n",
            "Epoch 74/100... Discriminator Loss: 0.9548... Generator Loss: 1.9806\n",
            "Epoch 75/100... Discriminator Loss: 1.0306... Generator Loss: 1.9398\n",
            "Epoch 76/100... Discriminator Loss: 0.9409... Generator Loss: 1.7029\n",
            "Epoch 77/100... Discriminator Loss: 0.9043... Generator Loss: 1.6085\n",
            "Epoch 78/100... Discriminator Loss: 1.0828... Generator Loss: 1.6965\n",
            "Epoch 79/100... Discriminator Loss: 1.0430... Generator Loss: 1.3996\n",
            "Epoch 80/100... Discriminator Loss: 0.9372... Generator Loss: 1.9505\n",
            "Epoch 81/100... Discriminator Loss: 1.0828... Generator Loss: 1.3260\n",
            "Epoch 82/100... Discriminator Loss: 0.9103... Generator Loss: 1.8055\n",
            "Epoch 83/100... Discriminator Loss: 1.0920... Generator Loss: 1.2763\n",
            "Epoch 84/100... Discriminator Loss: 1.0589... Generator Loss: 1.7882\n",
            "Epoch 85/100... Discriminator Loss: 1.1619... Generator Loss: 1.7507\n",
            "Epoch 86/100... Discriminator Loss: 1.2989... Generator Loss: 1.5324\n",
            "Epoch 87/100... Discriminator Loss: 0.9722... Generator Loss: 1.6529\n",
            "Epoch 88/100... Discriminator Loss: 0.9390... Generator Loss: 1.6315\n",
            "Epoch 89/100... Discriminator Loss: 1.0073... Generator Loss: 1.6988\n",
            "Epoch 90/100... Discriminator Loss: 1.0829... Generator Loss: 1.2074\n",
            "Epoch 91/100... Discriminator Loss: 1.2794... Generator Loss: 1.5621\n",
            "Epoch 92/100... Discriminator Loss: 1.0655... Generator Loss: 1.8426\n",
            "Epoch 93/100... Discriminator Loss: 0.8929... Generator Loss: 2.1054\n",
            "Epoch 94/100... Discriminator Loss: 1.1280... Generator Loss: 1.2398\n",
            "Epoch 95/100... Discriminator Loss: 1.0767... Generator Loss: 1.9265\n",
            "Epoch 96/100... Discriminator Loss: 1.1250... Generator Loss: 1.5693\n",
            "Epoch 97/100... Discriminator Loss: 1.1657... Generator Loss: 1.3706\n",
            "Epoch 98/100... Discriminator Loss: 1.0617... Generator Loss: 1.4706\n",
            "Epoch 99/100... Discriminator Loss: 0.9864... Generator Loss: 1.6062\n",
            "Epoch 100/100... Discriminator Loss: 1.2615... Generator Loss: 1.1025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clapDx27g6KR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVSsnLXAhPUh",
        "colab_type": "code",
        "outputId": "98a45760-9c7e-4fa1-ae74-93f5a99342cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator')\n",
        "plt.plot(losses.T[1], label='Generator')\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f15b7fc25c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIl8m8zYhPaM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def view_samples(epoch, samples):\n",
        "    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
        "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)\n",
        "        im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
        "    \n",
        "    return fig, axes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eYgGq3HhPX4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load samples from generator taken while training\n",
        "with open('train_samples.pkl', 'rb') as f:\n",
        "    samples = pkl.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rB5Et1I6hXWi",
        "colab_type": "code",
        "outputId": "a9182e9c-0cae-4fd2-9507-f8e8632e7bfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "_ = view_samples(-1, samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAGKCAYAAAAmB8cMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEYVeJEIIIaLQi0QIIUQU/w8hWswOoYONBAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BLkrqcZhXZW",
        "colab_type": "code",
        "outputId": "81ff6b87-f259-4644-b918-74237fcf44d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        }
      },
      "source": [
        "rows, cols = 10, 6\n",
        "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
        "\n",
        "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
        "    for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
        "        ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAKaCAYAAAAZJ69NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XeYZGWZNvC7e2ZghiEKKqKCASNm\nUTGCgBEjuGsWzOsqZsWAYkQx54SKWcwJc0Bds66KqKtgQsUc0FUBZaa/P/BXp+rprq7Q1QPu997X\nxVVUT1Wdc57zvO+5nzy3sLCQhoaGhoaGlWL+vD6BhoaGhob/G2gPlIaGhoaGmaA9UBoaGhoaZoL2\nQGloaGhomAnaA6WhoaGhYSZYu9w/br311gvbbLNNNmzYkCT5xz/+MfDvF77whZMkZ599dpLk17/+\ndZLkrLPOSpJc9KIXTZL8/e9/T5LIKJubm0uS/OpXv0qSXOACF0iS7LzzzkmS3/3ud0mSNWvWJEnO\nPPNM55MkWbduXZLkN7/5TZJkp512WvL7PgfbbbddkmTz5s35zW9+kz//+c9zy11/lcXGjRuzfv36\ngWtyLecXWfi+1yoLx9t2221XJIt+vaiy2HXXXQeufZQsNm/enCSZnz+X3/zyl78ckIHX3/72t0mS\ntWvXjiWLcfWCLBYWFmYui/OLXoySxZbQiy0tC2uVvmzpNfL/436x7ANlm222yQEHHJArXvGKSZJf\n/OIXA//+6Ec/Oknywx/+MEny7Gc/O0nyve99L0ny5Cc/OUnyk5/8JEn3QNpqq62SJM985jOTJLe9\n7W2TJPe85z2TJMcee+zARZ500klJkktf+tJJkt122y1J8pKXvCRJcvDBBydJ7nrXuyZJXv/61ydJ\nLnShCyXphHvDG94wyblCfuQjH7ncpS/Cxo0bc8ABB+QKV7hCkuT0009P0gn8UY96VJLkRz/60YAs\nvv/97y8rCzeaLG53u9sNyOJVr3rVgCxOPvnkJMmlLnWpJMlFLnKRAVkccsghSZK73OUuSZLXve51\nSbpN3qZ9oxvdKEnyt7/9LY94xCMmkgW92GuvvZIkP//5zwdkccQRRwxc+/Oe97wknV4cddRRA9+z\niKpeVFm84hWvSJLssssuSRbrhWskC9+/xz3ukSR57WtfO/A5enH9618/ybmLd1ayIGd6Me4aOeec\ncwZk8YxnPGNJWUyqF9bI3e52tyTJcccdl6Tb2Gy69OKvf/3rxGtklF7UNfKc5zwnSfI///M/S8qi\nrpFJZbHnnnsOXOOLX/ziJN0asV/Qi1nKYtr9ourFaaedNiALG/0xxxwzkSwm3S/snVUWo/aLZR8o\nGzduzHWuc53eyW3atClJtwF89rOfTZLsuOOOSToW9IY3vCFJ8qY3vSlJeg+kZz3rWUmSz3zmM0nO\nVcAk+dznPpekW+g//vGPkyR3uMMdknSLzNMeA7rVrW6VJPnKV76SJPniF7+YJLnmNa858Dt3utOd\nkiR//OMfk5z7wPKEHhfbbLNNrn3taw+VhWuosvBwe/Ob35wkPQWjQJ/+9KcHru3zn//8krL493//\n9yVlQYZk8aUvfWlAFle/+tWTdIr7b//2b0mS3//+90mS17zmNRPLouoFlkUWrmn77bdP0ik3vXjj\nG9+YJL2Nx+L41Kc+1fv9JPmv//qvAVnUaxgli69+9asDMql64Xf+/Oc/J0le+cpXTi0LDNE5kYVr\nGLZG6IU18tSnPjVJcuKJJw5c26R6YY3c+ta3HpDFl7/85SSdXtTfWYlebLvtttlnn31614hd04+6\nRujHMFnUNeLBMkoWNl8y8Hqb29wmyfA1MktZjNo7qyx22GGHZWVR907XtFK9qLK4xjWukaR7qE+6\nX7QYSkNDQ0PDTLCshZKc67tjkr797W9P0j3RMRBPs5e//OVJOpfF4x73uCTJC17wgiQd6/rv//7v\nJJ3fD/M48sgjk3SMlmmFcb773e9OklzvetdL0vn19ttvvyTJ//7v/yZJPvShDyVJHvzgByfpTG/H\nu971rte7pkkxTBZYERbNPYNZPPaxj02SPP/5z0/SsXGy4MbBJMjC3x/2sIcl6ZjFe9/73iTJda97\n3SSdLG584xsn6Vj3hz/84STJ4YcfnqRzW/rdffbZJ9/5zncmlkO/XrzlLW9J0lkAYiKnnnpqks4M\npxePf/zjkyTPfe5zkySf/OQnkyRf+9rXknQsHpvyeS6Jqhfvete7BmQhVkYWw/TiZz/72cDvXuc6\n15lKLxYWFvLd7343yfh6wSqrekEWK9WLffbZZ0AW1sgovbBGptGLhYWFZfcLbJks7Bej1gjr6oIX\nvGCSTi9GycJ+waU56RqZhSxWqhf2TnrBGzOr/WL//fdPkvzpT3+aiSyahdLQ0NDQMBOMjKFc85rX\n7DGO+93vfkmSW97ylkkGmV3SMVUBIszj7ne/e5Iu4HSZy1wmSfK+970vSedLxTx8/z/+4z+SdJYQ\nRsvS8dTk/xNg+shHPjJwHAFIfswddtih58scF2Th6Xz/+98/SXKLW9wiSefDxAzJ4tBDD51KFpjH\nMFlU/zJZ8IkKwlZZCLphizvttFPPxz0uttlmm1zjGtfo6cV//ud/Dsii6gV/8L3vfe8kHQsTICaL\ny172sgOywKKqLBzPPSQL/mVMlp+aH5iFIojPX00vtttuu4llsXHjxuy9996L9KKukWtf+9oDsjjs\nsMOSdHpRZeF+YZbj6oVAtzXC2hc7GaUXq7FGyEKgl1689a1vHZAFvZBEMUwvxpWFZJC6X1gjt7/9\n7ZN0rHw1ZMFCGXe/qHphv5DgcrnLXW4qWVS9GCYLeiGhQQx4XFk0C6WhoaGhYSZY1kKZm5vLunXr\nek8zWTTSeq9ylask6bI49t133yRdZouMA3UFsne8x94wmL/85S8Drw94wAOSJF/4wheSdGxctsbx\nxx+fZLFfEtvjg2XZfPzjH09yLivnixwXc3NzWbt2bc9HyTddZSHvmyxkYUj/kz/u+6Nk8be//W1Z\nWfCRksVVr3rVJJ0FMkoWO+64Y4+ljIv5+flstdVWPb1gWfKJyxSpsnDO/MayRWS4eC8tHJsTA8GM\n7nvf+ybpWDd9GyYLLI0snAfLhn96++23X7Fe0HE+8atd7WpJOt0Uy6h6oQ6h6gUmSy/++te/Drxi\noqx0DLTK4spXvvKSsqh68bGPfSzJuZYN+U4qC3rhWqos6n5BFj/96U+TLF4j3ldZjFojo2ThnrAC\nyIJlg63vvPPOK5YFa9t+QTfrGqHL9IIejNov6MO4+8Xb3va2JN09YYHQC7KbVC+ahdLQ0NDQMBMs\na6EsLCxk8+bNvWwJmSeyt9Sh8Gl/8IMfTNIVnD396U8f+JwajN133z1J58d7xzvekaTzM2MOGKsM\nBoVo73nPe5J02WH8jg9/+MOTJE960pOSJDe96U2TdBkPzmPXXXftPdHHxUpl8bSnPW1JWfDbksU7\n3/nOJMm1rnWtiWRRfagyoRQR3uQmNxmQhXjDRS5ykallIbvq61//+pKyeOADH5iki13I4lKghjVd\n6UpXStIVX8nyohd77713ko5F/eEPfxiQhSLC97///Uk6i+k+97lPkk4vFIuRhb87j912263H8CaR\nxaZNm3p6QRaydMiCJVH1Qt0JZjqpXqhsHqUX97rXvZKkV6D3xCc+MUm3Rh760IcOnMe0a2TTpk29\nY4+7Rr7xjW/MRBY6KYyShVieNTJMFv31cf/X9gt7pxjvKL1wHqP2i2ahNDQ0NDTMBHPLTWxcv379\nwh577JFLXvKSSbqMERXn8rj55eQyq0z3qmJe9scPfvCDJJ2v/GY3u1mSzscpm0zZvzoX3+P3k0Hh\nPGQyyAph8Xh6+/4Nb3jDHHzwwTn55JPH7s1DFnvssUeSLpNomCzOOOOMARnc+c53TtJlFsnaOOWU\nU5J0LREmlYX40TBZYDRkQCYrlcXuu++ei1/84kk6fy5ZuHZtIby/4x3vOPCqYp4snBNfOZZEFlg+\nWfAzk+EwWah/UXXM4pFt5Pv77rvvzPSCFeUctBcio6oXsmmcyyi9EEfSokW8UosXa8SaE4dabb0Y\nRxZioN6r6q7tP4bJgl6IA1gj2pKQRV0j54Us7J1ibFUWw/aLKgsZb85Jry/Wdt0vqiyqXozaL6bd\nO5uF0tDQ0NAwE4ysQ7n2ta/d8yW+9KUvTdIxPcySj9zTzdORXxDj9FRUhfmJT3wiSefPw2xUZ/Jn\n8x96Sr/whS9M0jFPOd5HH310kq4CGhuURSJ3+6c//WmvN8240JuHf5dv0bmRRc3jJosTTjhhQBY+\n/6AHPShJl2k0riywNpW0VRbiV1UWsoHEG0477bSpZNGvF+IyVS/k94uNuDayYKG6r2IuZCEWIuZW\nm0k6nnoYFdZy+MlCfQO2hqHKgFHr8ZOf/KQXk5hWFuPqBSuLLFh57usovZDtM65eYK588zKqeBXI\n4gMf+EDv32e9Rtznj370o0vKwrFHyUIsZNw1Qi/owShZrMYa0Z1hXL0gC/vFKL24xCUuMRNZWGMs\nJrKwRkbJolkoDQ0NDQ0zwbIWyvz8fLbeeuteLyZPMb1w+KKxG9kTeu/wH8pIEK9Rh8APKHdfrjVG\nKlMBs6y525666h4wF5A9coMb3CBJlyV0+umnL5rtMgpqL+SRy0ipsmANyapR16A6myz0u+LbHFcW\nYihy6TGRYbLQLvub3/zmgCzUqfzyl7+cWBZzc3PZaqut8upXvzpJl1WnLxD/K3ZDL+gJS5aP3TkO\n0wtZX/Sp6gW/cNULf6+zIKpekMWvf/3r3v+Pi/n5+axfv36RXqg34YOueiHjra4ROjquXshsqnoh\nE4cs9Flj9cmMG6YXs1wjo2Rhv6hrZJQsVIkPWyNqw6wRVsGksphmjdg7h+0XdY2sVBbqXEbpxTBZ\n1L1TtqKRH/arUbJoFkpDQ0NDw0ywrIWyefPm/P3vf+/5Pj31dHkV+ZfPzRfpya7brEwUT10MVW6z\nylRVmrrLHnTQQUm63GwZEI4ry0g3UbEcvlt1DJ7q2OClL33pHmsdF2TBX08WzhG7VtvAF1k772LJ\n4gpkwddaZSGP3HGwKNlBfvdiF7tYkq4TM9/sQx7ykCRdR16y43O91KUuNbEsFhYWctZZZy2ShYwV\nsjCAjV5UC5Us+u9L0rG2KovHPOYxSTr9oxdVFrKBMM2qF3L36YXjTiOLzZs35+yzz14kC+dY9YIs\nME7/bo0M0wu+b0Ohql6wusjCfR6lF1UW4hIrWSNipePKolrr064Rx8Guq15g9ZPKYlq9GGe/EH+s\n+0WVRd07h+nFE57whCRdBb39wt5Z9wt7p3gnWaifoUfj6kWzUBoaGhoaZoJlLZQNGzbkile8Yi/j\nAGTJyEjQF0rfF35bVZk6ZMo0kqGgJ4xMA348nS/lbFdGYcqZ3G6/J37AL6g3D5bAz3j44Yf3fntc\nbNiwIXvttVcv84Pfn69aJWrtjUQWsiTUyJCFz4u1qC8YJQuZMqNkIbbj77OSxZWvfOXeNdEDegGy\n+MiC3xb7wUBl16hYJ4tResEC6e9LlnRZQqP04tvf/naSrpfYAx/4wKlkccUrXnGRXugzRS9q/7Cq\nFxio7BrnaI3IWBsmC0xTzyWyEKcad43wyU+rF/37xbSykPFU18gwWeigLPtoXFmQwWqtkb322muR\nLOp+4ZiTymLYGlH7VfeLKou6X8xKFs1CaWhoaGiYCZa1ULbbbrsccMABPf+snll8jTIAdCHmn+MD\n11XU01OWR83G0I3U3IoXvehFSbqsMhXVKqX5SPnkMVX+Qk97sR9VpCylO9zhDnnZy1623KUvwvbb\nbz8gCz2zyMI5DJOF2gmykOVBFqq+R8lCpT1ZOJ8qC/EFNRlkobqYpXTIIYf0ugSPi+222y77779/\nT96sqjohTxZNlYWOt/798pe/fJKu426dAEoW6o/UG5GFzqrOhx+afuqjRBZYl3vXrxevfOUrJ5bF\nAQcc0JO3bJ26Rvidh+kFWTgXsqAXZKGqvOqFSnu9oeoacR51jZDFUmtkUr3Yfvvtc+CBB/aOUdfI\nuLKwRnQyGFcWMqnIgl7UNVJlUfWiyuLggw/urctJZLHc3mmdulaWic7f4+4XdY28+MUvHpDFsP0C\nRu0XVRaj9otmoTQ0NDQ0zATL9vK6wAUusHDggQf2Jq1961vfStIxAn5AT1U+cv1lbnSjGyXpptR5\nyqlw1nVW5bycbU9zvnAZDzIhVOJiGrI+THozv1ndCr8hX+4JJ5yQ73//+/nb3/42dm+eKouTTz45\nSceaqywwjgMPPDBJN+9AX7OVykJFrK7Bfl+8woyH1ZDFTjvttLD//vv3sqnohfsgplJloQdTlQUL\nhSxYIGTBasD+VQ/LQKEXsgbpHRZe2aH6BL3pxHA+8IEP5Ac/+MFM9EKlepWFNVL1YlZrZFq9UOsj\nDvaBD3wgp5xyylSyEKuQ7SUTSb2HWEiVhXqVLb1G6IVajVnJ4iY3uUlPFqP2i0lloQOCyvlJZWHv\ndG/snTJjh8li1H7RLJSGhoaGhplg2RhKcq7fUcdLMRBdO/l9MTx54J6GMlYwSlWfsgRkPsjJlycu\ns0GfI09V8y1M8pNBoV8NtiUbTF8mmRE+96pXvar3RJ4ES8kCCxM7qbI4/fTTk3RZGmQhy8LfsbdR\nssDCyUIXWv/uGlWJk4UMmFnIYm5uLvPz8z1ZyBBhiaipkNGi0y5Z0AsWTtULVrNYiBx4OfjuO1mo\nxJd7TxbmrJCFXHt+aJksWN+xxx7bY3iTgiz4uvnj+cBrd9kqi7pGxtULNTYyp1aqF6zFY489dqo1\nknQxjLpGyMIaqbKoa4QsyGhWa8Q1jiuLV7/61au2X1S9sHcOk4W/j9o7J9WLuncO2y9G6UWzUBoa\nGhoaZoJlLZR169blYhe7WI9xqkyXnYHx8fdhliwDPmoZCZiJHjz8f8Di0SOKn0//Gz2jbn7zmyfp\nMhH4Qvn79HhSca9KVHXqM5/5zN6c6nGxbt26XPziF+9lqJAFVu7Jzn9PFp70ZFE7m/bXQIwjC7J+\nylOesqQsVA+LlciYIguzSMjimGOOmUoWF73oRRfpBVnoMu1csagqC+eGpcl1V61LhpitnnJ84fSC\nLMTuhukF2ZuaZ+aEWMwznvGMXhffaWWhAnlSvbBGyELPrqoX9K/qhd83lZIVX/WCvlkjqszpBZ/7\ntGvkohe96IrXyLD9YpQsZCzRu2GyoBej1ki/LKbRi4td7GITy2LU3jlqv3jNa14zIItx9YIs6t5Z\n94tRsmgWSkNDQ0PDTLCshbLzzjvn0EMP7eWJq1B97Wtfm6TLlvDE4vP2+Tvc4Q5JuhxnTFKGgh5O\nnoriCvx5mK05Jp66mBO/IZ84HysGKxef31H16ROe8IRebcK42HnnnXOPe9yj1zGXLI477rgkXUaR\nnPkqi0MOOWRAFhhDlQUmUGXh98QP1HqQhXjEMFm4XrIg08c//vG9zreTyOKwww7r9RNSrUsW9EL/\nIueuy6zK5mGyUEGPIcqgknlCL1SnqwYmCxkujk8WmKtc/KoXj3/84xfl6Y8ji/41UmVR9cK5D9ML\na0RGE1nQC7IYpRfWpJkzVS/IouoFWRx55JFTrZFDDz20l7lUZWEqoPs0K1mIhfi9la4RsTxr5Mgj\nj5x4jeyyyy5L6oWYSpXFsP1CBuW4+8UwWdS9c5heVFmIX/XrxXKyaBZKQ0NDQ8NMsGwdyk477bSw\n33779foMyUXm3+O38zTTOdOUQP4/fjx/5yvnG/dU9JRWz6ACW8dNuda6lb7lLW9J0vmRa7Xwox71\nqCQdsznggAOSnNtp96CDDsq3vvWtiWov9ttvv14WhToOvkeyMM9AB+ZxZSHro84fIIs6W57fuMoC\nEyELvtZHPvKRSbqeUWSxxx57TCyLHXfccWG//fbryZXfXyaKa9JvqHaXde1kQTb8w/zO9EL3arKo\ns+WH6UWVBb1wb2SNraZeuK9k4dgym1jnVRbiBqPWSJ0hPkwW2LrK6dXQiyoLWX7uK1moQ3HscWUx\nbI2wiHkNRsli2BqxX1RZ7L777v+y+8VKZWGNq48ZJYtmoTQ0NDQ0zAQjuw1f9apXXTQHXS8m/uYn\nPvGJSTqfIzYkewMTVOHuKYgxqlw+8cQTk3RPQ8zX09TTnv/Reagj8FTGgFRQq0946UtfmuTcqtNJ\nZ4cPk4XpZZPKQjwB2yYTcQL532Qhy+Otb33rkrLQN02+eJUFFmgew0te8pKpZbHNNtvkKle5Ss8P\nq+aCJSH3XT8hsQ41GmIbmCBZqN2hF2RR9YJfV58hssDO6jxtMmYlyPWvsrjsZS87lV5c7WpX6/nn\nyYJeiLmZU1HjPo437hoZVy/EbpwHWWDx4hyzXiOzkIVrmNUaIQv7RV0jZMEKkAmlL9ZKZCGeV/cL\nfcTIosaJxX9H7RdqrawRVtWo/WJcWdCLcfeLZqE0NDQ0NMwEy8ZQ9thjj4Ujjjiil3mAbeu5jwli\nx2IrLAtPMnUAtQcXf6CKZU9luc9+V2aC7qMq9WUwyGzAdPR+Uhsgz9zvnXrqqfn5z3+es88+e2yf\n6B577LHwmMc8pseyXMNtbnObAVl48rvmKgsyIguMpMqCxaOjqt+VvTOuLHQG5Yvnm9XN9Ac/+EF+\n/vOf56yzzhpbFrvvvvvCox71qJ7141iyt/jOHUNsRQxOxpPMlKoXZnJgXbJKyEIGC3Z93eteN0k3\n30QVsEwo54d981/z2bOsTjnllJx++ukT68URRxzRY3buZ9WLYWuEtebvMueG6YU1QhYYL73YZ599\nknR6gfnSC2t4mF7UNTKJXtQ1Mu1+Meka0Wm3ysIaUZ3OItoSa+S82i+GyaLqBVlc/epXT9KtIbKw\nRuydZDFq72wWSkNDQ0PDTLBsDOWss87KD37wg97TTixD/rfZGjIRxBcwE1WbnoL61fBl8wvrpIlx\nevpijvrgeGpiFvLFMV51L87LU1g2kKrgE044oZf1MC7OOuusnHrqqT1Z8E1i2Y4pG4fvdJQssHyy\n0D+In1/FtDqBUbKQV47BOk+yIGNzNE444YReBfq4OPvss/OjH/2oJwsdSl1zlQW9wIKw5lGy4D+W\nHUYW/MNiMpjlML3A2sRcHF/H16OOOirJuRX+k8rCGsE0ydsa8d4aEV9wrrL1TFIURxq2RqpeiCfR\niyoLsTt6UddIlYXaj2ll0b9GHMP9t3/QC7KgF8NkQS9YnOPKwhrx+1UWw9YIfVvJGqmy0E267hez\n1gsZcXW/GKUXZGGNkAW9GFcWzUJpaGhoaJgJRloo3/ve93qMDwMVU9E7SZYVS0DVJX8fv536Av8u\nP52fTh+kOn+5ToCUg439e2rLeJDzzV8pC0SF/21ve9seKxkXZ599dk455ZSeb3OlsvC9YbKQQQV8\nrthanfRXZbHXXnslWdzxlyxYTLe97W17zHFc0AuyMPPBNemxJRtHLEtOPFnIAuIf9jmywBTpBbZE\nFq71zDPPXFYW9IIsWETms9CLW9/61j0mOaksMD5sXFYOedMLMnDf+dbFf6wR16pugV8fk7VGJtUL\n94QsxDfqGrnd7W43tV6QBSuNLKwRummNeGW5mB1S94sqC2tkWlkMWyMypayRgw8+eGpZDNs7ybvK\noupF3TvphTVSZQGjZGENiv05j7p37r///gOyGKUXzUJpaGhoaJgJlrVQ1q5dm1122aXny669+etE\nPk9RT0v+P75PjEUmg549srg8DWXf8P/p6YSJmrCmjsHxvB566KFJuowYTFjGw1FHHdWryxgXa9eu\nzc4779zL5yYLWRhkoRMztl1lIcOELMQD9Ozh9ycLFsvee++dpOvdQxbiF2Shv5njml0gC2RWsrjA\nBS7Qm0tPFjJVMEDnThbYtfkpLE36RC8w1KoXfs/3h8nCPSIDMtFlVg0A9sf/fOSRR04si3Xr1uWC\nF7xgb424RnqBIepoPEovMFl6YY3wdWPPVS8mXSPD9ILMn/jEJ04ti6oXVRZ1jbg/db8YJothelHX\nCJZeZTHpGlmJXpDFtHoxShbj7hezksVRRx3VWy9LoVkoDQ0NDQ0zwbJ1KNttt93C1a9+9R778pTj\nB1ZFqZcXX6bMBpP2PFVZDmbSe683v3zx29/+9gO/44mIxalTwDQ9rauF5PvOjxWw66675oQTTsjv\nfve7sfPKt9tuu4VrXOMaPVnwu5KFqusqC9k/ZIGFV1lgBqw97JmPe1ayYLX53DSy2HbbbReudrWr\n9dgXVsy/KyOFLPiH+XF33333JIv1Qp0IS4IsWCxVL7AmLI4sXKP7PUwWZObzu+22Wz74wQ/OVC+s\nEcdYqV6ItdALmZAYtMw5ssA03W/ZPuJS4pKz0Av7Bb04r2Qxao14JQt6ob5J3RPZXeQiF5lKL5aS\nBUtENf6sZLHa+wVZ7LbbbsvqRbNQGhoaGhpmgmVjKAsLC9m0aVMvG8ZcApXoqrM96U1wfMUrXpGk\nY0Gmf/H3iR/wC6o78V7vfaxPTEWWR83t9pSuczNqd1MdPe973/v22PG4IAszmZ/+9KcvKQtPehPZ\nVJ6a2GZaID+wDCnXLqfe38kC0xkmC5kXZOH3ZG/Urrc6Q08ji+RcNohlqV2QOy+HHRv72Mc+lqSz\nQFgoj33sY5N08QHXxPdOL8RYyEKMRBaY3HsdV+X4s3j5ifUtwtLEvcji/ve//4r1Qv+yKotRemGN\n0Av1ClUv/N3v1jUijlX1QtyCXtU1QhazXCPjyuJlL3tZkk4v6hoZJgvXopamrhGywPrrGhklC515\n73e/+/3L7BdkMWzvZMWN2i+skUn3i2ahNDQ0NDTMBMtaKH//+99z2mmn9fLKzQr3FPzQhz6UJL2n\nsEp0vnL1CMccc0ySzo+IgermKZvrPe95T5LuKWpeOr8ef6CqTYxCJSyGopJT/xpdaGU+HH744T0W\nOy7+/ve/56c//Wmv9xJZYF1kgbVXWZiQZ+LaMFmo/n73u9+dpPPr+l3ZImRx9NFHD8jCNZOFjgB+\nV7fRlcri5z//ec/ixK7cP7JQd4B9k4Xq4Gc961lJutx318ZPTC9kptALsmB5iKXQi1oVzDIhC79r\njob3D3rQg6aSxU9+8pOeXrjmjUiUAAAgAElEQVR2DHTcNcL6r7LQ5dU5vutd70rSWWPWiM+TBUZc\nZTFML+oamVYWp5122khZjLtGJpXFsDVyXsniZz/72dT7Bb0YJotR+8UovVit/aJZKA0NDQ0NM8Gy\nWV577rnnwrOe9axeRbSe/nrm6AvkaWkOBr+b7I473elOA9+XsaD7rOpRXWL5PPntfB/TcT4veMEL\nkiR3vvOdkyQvfOELk3QdOFXGPvrRjx74/jHHHJMPfehD+f3vfz921saee+658JznPKfn75eZxp+o\nXxQWZVYDWch0ustd7jIgC9kaZMF/i8nwhVdZYDzO57yQBdbjWmSkkIWYycc//vEk3XQ6fmNdpeW6\nH3bYYQOyEPtiEbNQHvKQhwzIgoXEz8zC0Z+ILHQvxsoe85jHJOnY4tFHH52PfOQjq6oX7gdZ0Av3\njU/bGiFTa8R9s0b4+V2rOOakenHEEUck6fRuS6wR51D3C2vE9+nFuLKoa4Ql7dqrLD7xiU8k6arH\nrZF+WXz4wx8+T/eLcWVR9wvXam9d7f2iWSgNDQ0NDTPBshbK1ltvvbDrrrv2+vvIWdbpkt/tjW98\nY5IuZ1nVJn+ep6KsC4yUXxBDxMr4SLE4fbcwCNlccqtlk330ox9N0mWL8Aeqa+CXPP744/POd74z\nv/nNb8ZmHJPKQt42WWDXVRaYB1lgmCwXvku58bIz9PqR3aH+QFxBZhVZyLS5613vumJZbLXVVgu7\n7rprr78PWciWEbMwp1o9ClnweWOOZIGdkwVWdc973nNJWcjy0wNKNljVCwxU5owYGxnz3R9//PF5\n17veNbFeXOQiF1kki2n1QiW1NcJao9t3u9vdknRrZFq9IAvnPQu9WKksWBp1Fog1gpWzSK3rUbKQ\nJab6nCzIVM1HXSP04m1ve9sWl8Ww/aJ6AyaVxTC9GFcWb3/72/OOd7xjqCyahdLQ0NDQMBMsm+U1\nNzeX9evX93KnMUD1BeoAZBKovjT3WLaEHGj1Cp6KmCOfp6cgH7inrn43hx9+eJIuV5tvHvNQqa1e\nwVP2s5/97MD3Lne5y/V8lONifn4+Gzdu7Fk92BNZmDNCFiwZM7yf//znD8jiyU9+8oAs+C7FFbBu\nPlZMQT2BmdLuDVliHmQh/uBzJgKqFbrsZS87lSw2bNjQY/pk4XdUbcvOw9LoBf8t1sUCYeG6r/zH\nLFN6wY+MrZm77prESlgqqpCH6cUsZWGNiAdUvTjuuOOSDNcLVjrZ0Qs+91FrpOoFi0T/M91sxQec\nd10jl7/85aeSxTbbbNNbI+PKYth+QRbD1kiVBetNhhK9cI3WCL0YtkZ0v6YX0+4X/bIYtV+MkoX9\nwhpxLWJnK9WLUfuF96Nk0SyUhoaGhoaZYNkYytzc3G+TnLblTmeLYo+FhYULjvvhJosOTRYdmiw6\nNFl0+P9VFss+UBoaGhoaGsZFc3k1NDQ0NMwE7YHS0NDQ0DATtAdKQ0NDQ8NM0B4oDQ0NDQ0zQXug\nNDQ0NDTMBO2B0tDQ0NAwE7QHSkNDQ0PDTNAeKA0NDQ0NM0F7oDQ0NDQ0zASjmkMuWUavwZw25Et8\nL0kyrArf9+u/e1+/733FsH+v5zfsdxcWFsZuRz1MFud3jLpX8K8gi1F6Vf/dtVdUWaxUL+bm5had\n07BzHXUNExx32d+p973KwveNmBj2u/8KejEp6n7h/az1YrljT3v/J/3+pJ/XJLLqxRJ76pKyGNlt\neN26db0f8apjpvcO7qR11tSz34Q+/25ucoXf83nzEbbffvsk3bQzQvJau1/W89Xt1OvmzZtz9tln\nL3fpS2KpjcOx+5Ru4NXsZjPk3bBhN7gqtRvpdbvttkuS/PGPf+ydU/+/w6hNt/88qvKMg/n5+d65\nkoH7dOaZZw6cgylyzt19rA96991798hxnPPGjRuTdDMj/va3vyXp7i9Z+L73fr/+rvOfm5vrnetK\nZeFa6bprdc7bbrvtgCwqrDHwPfeq6rRXv+c8vLpmMvT79V71H3eaNdIvC3K3BsjC/XVNGzZsSNLd\nxwrXBr7nnOs1kZF7WdeI7/ue37ffOP/+teI7k8Dv95+TY9U9E6qOuibnBu6r36l7s7+bf2L2UCXz\nPu/66v7Tvza8LrdfNJdXQ0NDQ8NMsKyFsrCwkM2bN/eeXp5uoxgt1oMheCp76tan8jAXmKfl+vXr\nk3TT64Y97aEyGHM3fvvb3yY596n9hz/8YdlrGBeOjY1UN1tlvBhBZa6VRVW3jWuq1p3PVYZcZQA7\n7LBDko7J7rjjjj1LclzMzc1l7dq1i45xxhlnDFxjZWGVZZkmaTZEtSi8ku2ee+6ZpJvgx1KBqqfV\nYqps3ARIEyU3btw4MRNlxddrI9Oqo+4TvXAuu+66a5KOSVa9IQPXduELXzhJN3umWmeVcbIS/C5r\nwL9f4hKXSJKcdtq5DXK33377qa21aqU71jD3a5W5iazWq3+vngky8flf/vKXSTqLp8q47if+va4p\nsvjJT36S5Fx9nsaKT7r7Xq2yul9UvRjlqvQ71UPi8/SxXlvdr6oVV++NNUafL3ShC+X3v//90Ott\nFkpDQ0NDw0wwah7KQtIxTk8zzK+ysOonrrEN/kG/5/tmPrNA+J8xSlPLMBazwn/4wx86zySLn+r1\nfX9c469//Ws2bdo0cZCtBvQ86Ss7rixtqXNIOhmZo16ZpHiU2dLYld8xm9pM6mXOf+B7FdMEovt9\nxP2/XX3bMCzoaWodvahM0+dMsTRtrvp/b33rWyfpZs1XOJ8aV1giOWRiWfTH5/pRfdvg/lbLw9py\n7XW+OuZ56UtfOkk3e75aJpe85CWTJCeffPKS5+O4NTjfHz9YWFiYKhA9Si+qpVHhc9gxmWDG9hHn\nakopS9P9JStz1k1irPe7ymKYlboSWdT48DBrnF5U65z+0Auy4H2xd7J0vQf7y41udKMkyac+9amB\n44xKiOqPR59zzjnZvHlzmynf0NDQ0LB6GDkoec2aNb2nFUuBr9zfMQL+ustc5jJJkh/96EdJOqZx\n1ateNUk359j3HvSgByVJ3vKWtyTpfOXf+ta3kiRXucpVknT+Yj5ZT+ua1VEZqKe775155pkj02iX\nQj+jGpWZAle84hWTdH5/TBZT+NKXvpSky/p5xjOekSR55jOfmWSxFXjMMcckSZ7ylKckSfbaa68k\nnawqo6i+0po1slTm2jiYm5tblGU3LL6DBdWYBWvLuVSL4b73vW+S5CUveUmSzjKBBzzgAUmSt73t\nbQPX7LxYuvX83LMab5qfn5864w1ck6y+Yb5pluWpp56apFsjXsnE/brXve6VpJvtLe5EL8xfJ6PL\nXe5ySZJvf/vbSTp9xZBrXMnfyWhaWfTr0zA/fX01M/5nP/vZwLmQqzXg8+aos0Sr7h9wwAFJurVV\n2ba1VuMXrn0pvZh2v3DMmqkKNb580EEHJUk+/OEPJ+nu/yGHHJIk+chHPjLwvRNPPDFJcoc73CFJ\nJzsWzFOf+tQkyYtf/OIknaVibbjHjl+ttKoXmzZtWna/aBZKQ0NDQ8NMMDKG0u8PrRkHw3zisnfE\nPDyd+X09dfnOxQfuete7Jkn22GOPJMkLX/jCJMm//du/JUle/vKXJ1mcY803itV5unpffbjr16/P\nWWedNdQPuBRqAVtlWcOKKbGhWiux2267JemYwhWucIWBazn44IOTdJbMa1/72iTJLW5xiySdpYJx\nYG1vfOMbkyxmhzULCWRrTeof7teFKhMsG+upmS3uHyZYYyU3uMENkiSf+cxnknT6cL3rXS9J8tGP\nfjRJcpvb3CZJ8r73vW/gOA972MOSJE9+8pOTLM46Yz2A72211Vb5xz/+MbFe9K+RKgv31zHpB73w\nd0zQ360ZFujXv/71gfcskC9+8YtJkpve9KZJkve85z0Dx7n3ve+dJHnSk56UZHEND28DkNGGDRty\n1llnTRxn7GflVRasN2y31olVPXGuNcvvO9/5TpIuu2ufffZJknz6059OktzudrdL0rF8a+B+97tf\nks76r/GDamGPGzcYJos1a9aMHdcFMRCxM2tDnIieiAt94AMfGHj/iEc8Iklyn/vcJ0ln3f/Hf/xH\nkuQXv/hFkuSe97xnks4jUuNIw7wN9KLFUBoaGhoaVhUjK+X7/YfV31eZpswSWToYBAsDs7ztbW+b\npGNhsnM+//nPJ+liNfyCWLnYy0knnZQkufGNb5wk+epXv5pkcfVvrRb297POOmvquEGNG2FVNcvr\n4he/eJLO1y17yzV9/OMfT9JlLvETf/KTn0zSsSh1I/zMd7vb3ZIkH/rQh5J0DBNLe9e73pWks0jI\nssZ6yGKaCuAKTNJvsprIgmUqKw8bwqrdd/cZG3/DG96QJHnOc56TpIulYahYl5gMyAKkf5gxy/lz\nn/tcku4enXLKKUnCUpv4+vv1gkVCBu6rayYL8UXnxiJ1/69xjWsM/M7rX//6JMkLXvCCJF08CpO8\nxz3ukST56U9/mqTzoTueLDFrpNbHWIsY7D9Z6IpkgV3bL+gHqKXhySAjFqrMxQMPPDBJt5be+c53\nJuk8GNe//vWTdPuONSJu+atf/SpJpw8yo2oGFU8JffZ+JXoBZMEirXvnfvvtl6TLvrJun//85ydJ\nHv3oRydJHvnIRybp9gOxs9e85jUDxyFDliwPCH0k0+c+97lJFsdKyIDFTJ9G7Z3NQmloaGhomAlG\nVspv2rRpUZaUpyrW7emmvxQm6KmIWWCOWJBq9VpNeqlLXSpJx9b4+RyHJfTZz342SRdLwTRlSmCu\nS7HFaZj5wsLConxxDLNWqstIw8LIgkWBrbHayAYTcC03uclNknSyuuMd75gkedaznpWky3BisWDx\nZHPDG94wScf2qi9/2gyW+fn5RdlTWBOZqDRWfc3ack41/uSasW+xEyzpP//zP5N0vvQHPvCBSTo9\nET/C6vz7E57whCRdliEZ1SymDRs2LGLR48IaIV8sGNPDwmUy0dnLX/7ySToWb02xIPw7ncdcxRtZ\nWeJFrl3GpLjj/e9//yRd7E08imzpZX9l/agMxqUwPz/fu59kSRZkRMfpNBnJfCMbVpOaCveNntEn\nFon9geUqI+qlL33pwCsPCQuYXtSeb7oWbL311lP1NTvnnHN699Ee5H21ZGWksdLdZ2vCWhMXYm35\nvv3MfsNDcec73zlJcs1rXnPg3x/zmMck6Swa959seAGqJ2PU3tkslIaGhoaGmWDZLK/5+fmFtWvX\nLuo4WWMU/o5Z+E0MAtOQgfDNb34zSZeDL0MJC5DpAGIk/MT8hTJa+EixK2ywWg/9HVunqQJes2bN\nIlbdX8/RD/2AnEut3sUUsWrWndgJCwijvdnNbpYkecc73pGkq9F4//vfnyR5+9vfniT5/ve/n6Rj\nWXzqwyr4YRJZ0IuajeMaan8hssBIsW1s/Pjjjx94de7YFT1Rg0EPVIGzeOgJ9vbWt741SXK1q10t\nSWfp0t9h3bKnkQX2TRaydFwLsERZdSwMMmNhqK1gVVsjrD0xGFYcPVED5vvWhow5xyPr2juq9gCb\nZo3QtVqTU3VQpqN1T0/sG8cdd1yS5Lvf/W6STmYymr72ta8l6XSfHtAz8SPW+c4775wkOeGEE5J0\nbN372m24MvFJZbF27dpFlfCutXYRYXWrU6Gj9OoVr3hFki6rS6xFbMV99Mras0eyYFn51o44kXsh\nzlm7n4y7RpqF0tDQ0NAwE4zVbdjTEvtiiWCknr6ervXphlnwG2JTfJ/8vf6dvw+TlZHwvOc9L0ny\n4x//OEnnZ6x9bTCj2om19imaFJs2bVpU+cwHWWc9OHbtbwZ81zJTxERe9apXJelYGGbq92QDPe1p\nT0vS+VDdG2xQvQK2h5EMy3+fBAsLCwOZL/SALIbVJ8mld//cL+eIjfH3P+QhDxm4NpaJmpy73OUu\nSTor7+53v3uSjsV/5StfSZLc8pa3TNL5h9/0pjcNnF+/TCaNJ/kO5ll1HeutvZlkXZEdS4OFgVWz\nRI844ogkHZO0RliortF7a8bvi2Pq3FBrN5aqq5omtrZ58+aeXtDNqhdkYf3Wfyc7+ww2LzZKP/j9\nb37zmyfp2Lhrx+KtIdmEZHHd6143SVdZj/1Dfx+uafeL2t2XztsrycKeWLt7sDhYErJDrYHHP/7x\nSbr9xJphjT32sY9N0mXasph5B7ySfc28q9mso/bOZqE0NDQ0NMwEy8ZQNmzYsLDnnnv2np6eUp6i\nWDCfJTZWq30xFX/HDFgS2Jf6gytf+cpJOt8oVsafLFvDU5iPlSVVs8b8naUyNzc3cRXwVltttbDL\nLrv0fkvmSZ0n4Ry9koF8cH5+LEwlPCaCebAoZOPIVHIcloh+RqrGWXGO695UXyim8o9//ENMaWxZ\nrF27dmGHHXboXUON01zpSldK0lkKWBmWhGHKNPH3a1/72kk6i0Odyste9rIkHasWQ8PK+Z/JjKwc\nR0xF/QKZ1Ey9hYWFnHnmmRPpxbp16xZ22WWXHqNkSdI9a8QasgZudatbJelqqdSZ0AfXZg2o/paZ\n5H4fe+yxA79Tfeessmc/+9lJOmvwC1/4QpKOKdMn53fOOefkb3/720SyWLt27cJ2223X04M6VVT2\np7VT681UsstcFBNxfz/4wQ8m6Vg1WYgv+p731oB6FD0D6R0PCMukxmL7O0RP2lljm222WbjsZS/b\ni3XVuJ1z/PKXv5xk8ewmFisr27/f/va3T9LFSvXyco2yAsVixaMdVyzF3kkvWVA1xlPXyD/3zRZD\naWhoaGhYXYzM8tp6660XZT/UClOok9AwQ6xJfYkaiZqt4zhYGktGxoqnsH5XfPHiENh77cSKHfRn\nYv3TLzpxnyIYNXWSX9ix/buqXv8u+wab9h4Lw8or0zAHg8+eTLEs38c4WCS1C+40mU21T1FFjVlh\nmGQhfvCJT3wiSZcRxf+rSpwlWn3vfu9FL3pRkk4P6A3fOlZOJjJYyMzf+zNxJu3lJctrWG4+ubMM\nZXnVCnWxD3UHLBb3Xb8y8H0WrLWEWToOqwx7Z4FgzvTAPejvIH322WdP1b+q1vfUCYvkrTYLeCJY\nVywcemFuEgu21oaoX2Kl63Mm9sJT8vSnPz1Jp/vq1ehN7W5sjvpK1kjta1YzIeskV+tXphrLQzdp\nGXCsLb25/L54onihfYU3h2y96qsodlJrD/t79y23dzYLpaGhoaFhJlg2y2t+fj7r169fNF2s1jJ4\n2mKmmIisHllYqsLlVKtElc3Bh87S4D9m2fi836sxG/7AWsFdpyROUw09NzfX61Jc/97/Wrv8YqhY\nEp+nczryyCOTdNkVMpr0N8PWdQ9VIS0Hn48Vs+Ajx7bcs9ph19+nnXmxbt263jX6DazG/XIfWKoY\n6dFHH52ks0Cw6cMOOyxJx5bB7+tPRHbiE8AyxVDF5mQB0TOxnTprfJruCXNzc9l6660XzdawFlgY\ndYqh+yreI7uP5apHE0ZZu1KImZCNtca3zjfPcpURp/7JvRFDqRMFq56PA73/qlzpmgp3cQIxOFlZ\naip0PnAOD37wg5N0WVs1c0p8kidE/IFFwoPB6vN7ZozYP3we6nVMgvn5+Wy99dY9hj9sn6gzfNxn\nMTZWlloZui3+xLK1xqx/HbetkTr1sta5sNadR+2SUGMqQ6972X9taGhoaGgYE2PNlO97P/CKWXq6\neQpiglgxZirHvvrxVTBjS7IvxEr4C32Pn1lutqwRT0/sEGuT04+B9tVRTDwjehjq9EFwLdV6kglV\nq8v1H3KuNQuE/5csxVJqHnvNVMGUsbX+qZbTxJP++Zpk8Zwc58qnLQvMtbHK6IlphNgzVs5/7Npk\nZ/F56zarzkDvMPEGDJdlLLNGpwafZxH/7Gc/m2oeSr8sqky8x6LprGwbbBxbl82lIwJd18eKrtMP\nlfXvfe97k3RrjYxkc7H6xCvVo/i77DTM9w9/+MPMZNH370m6zgX2Df2pZG1ZS6w0s1xkSDpHFilZ\n0Bt6oQ+e75EFK0zWmJovv6czA0vqj3/841QxlOX+nUeCxVDjg2Jc1qkeX3pskYE6Iv0RnTOLRoyF\nRWS/YaHUOjnH4w3QY2zcvbNZKA0NDQ0NM8FIC2V+fr7n16uzP+o0Qk9dloT36krMt374wx+epIsn\nHHXUUUk63ztLha+TBcPHKp+dfxGjwGDr3Gw+1/4eQ5PWG2AcdbocOOaw3l51ToGqXR1xsWZxBZYN\ndqUHj3/nY8VoWTJ86Ky1WukK405gWwp1Ml+9VvpCP+S8Y8/uK1moQ8BIX/e61yXpfOE+p6MCy/NR\nj3pUki4mh8XJhPF5MROsvE74JIsLXOACOeOMM3LOOedMLIuatUMm9MIrK55M3D9ZfvTB31kwzl1W\nl7Xjd31PzY54pGuV5cUq4F2wJqtvfKeddsoZZ5yRf/zjHxOvkRprrfEBFggLVNW/bC6xEPuEmhu6\nT6/IhO7LiKRfssH233//JJ2F8upXvzpJl/XnPKzJ2rdq48aNE9fkkAXLs+5FZFBnt9tLWSD2OpmM\nuoq7RjU0MuBYaeKGLFVxKmuNDGS01WxUqDFWsdM2sbGhoaGhYVUx0kKRxZIs7kTqKcsXzj/vqcyH\n7ekoE4Ef+YlPfGKSrh6BBWIqHaainoAPXExFVoY4gacrf2Lto1Q7BE/jE8VEazU+C0Q3V5aC/G7M\n0OwFnxcfwMr5RA8//PAknc8T0+R7NytcDzDX5F7UbsPD+mtNW4fSfw3us/fOAZtmOepwK+bBZ+4+\nus+qu83yED/AypwzPdCVWAYcX7hMKp+Xi4+VVVno2TRNfVKdl46Nk4UaC3rAKtdBWeYbGcpI0kXA\nJE61WbIAXcu73/3uJF11+VOf+tQkneXLOqS/ZCEmQ0Z87Xq1TaMX1Yr3nmUhZsEqks1Jt8UJnRsd\ndz/Fl7B31h2Wrt+VKYgq5MUb7SssHN0pyLJmXk3beXlubq4nz7pf0BeZb6bRskxcu3iT71sr6k7s\nddbOi1/84iRdRiz9Ypmo8XEe9uraU6zOd1qi63CzUBoaGhoaVg8jLZS1a9f22K7Perpiz3Uuhqeb\nzCKZR3ozeerxmfJx8+vKUODXk9mAUfg9/kNPY+dRO+7K8ur3if7lL3+Z2FfeP43Ob1XfZ40nVN95\nnemMxWHfZMqHLq5ANo6PhYN6g1rxjFGw7vjk+3v0TBpDWbNmzcKGDRsWzWCoc8oxUn/HytVEeNV3\nyLmxZMVCsO2XvOQlSbqJfD5nRjw9YvHyJ5MFFkgPHa/fjz1pbG3NmjUL22yzzaL4omtmObI8rBE+\nbnEfPm1Wur52KqRdi35XrDPxAZaQNWGNYKSYJ7CUMOCTTjpp4PzXr1+fv/71rxPJQteAYTU5fpv1\npmMCC0RWFhmIocgCIzvZWTKYWCziSH5fFp/9QyactWPNynBjOYnN9mdKTts1oO4HrqF2G7YnOibd\nVl8mhupcXTud5xnRhbz2UbzOda6TpPMO8Q6QQY2ViHvTC9iwYcOyetEslIaGhoaGmWCkhdJf4Vvn\nqWMeGEHtKuzpLM+/zorGKKBObFQNjq15insqy9HH6h2HReLc66wSc5EnZRzLzYioGU91XgqZ8ffz\n82KgKuH5iV3bvvvum6TzkevNJEbjmmVzYMTYX+1+WzNv/G3SKYX9lfL1t1llrp3ePPe5z03SVYVj\nz/y74gyuSdYOWakS1gVAlwDxBlaY93zqZCCeMKySe25ubuL6JP3u6G6tgGbN14mOd7zjHZN0GUoy\n1dRcyAKrvZzEo1h33tN5167OSe0PsJDpT/WN92f5TFOr1d/XrLJz65cMVHWLH+kirUfb4x73uIHv\n0Se67dqf85znDFwDVs1aEz+iL+IOrHk9An2/xlDm5uZW3PuvPzaVdHK2Fw6bJcXyYEmQHSudDK05\n8UbWmO4CYjJkKd5tz7bviOE6j6Uq45fbL5qF0tDQ0NAwEyzbyws8RT01sTF+Pj5JWRcsAnUEsiqw\nZ51RxUjkg2McGKouw+pTPKXlZKtHwPIwUhaTWArmi5lMi4WFhUWddLEmx2Y98ZGreHUNmIKKVplM\nZOPa/I7sLueu4pp/WB2BXH6Vrdg9ywYzwXic1zRYWFjIpk2bFsWT1M6wMFkEXlWwu2Ysih5g3TLT\nME36N2xmPX2ULSRGUudjYPGYI6tQ/GLaKZabNm1aVFeg4tm5ul96d7EUxMhkXdELVhtZiHlg0b5v\nTWCo1qSYC70QdzDxEysnC7U+4lHTymLz5s2LahnUo8koosuy/ljd+k+pB3Ht4gfqSFT71/kqQAb2\nE7E4nRvUdolfkVXtCG7tTSuLubm5RdMo6QlZuK+yQe1ZLA0dMcQFrRlrgs7TbXpGRvY+98D3rQnx\nJ/tXjYP6fvV0DEOzUBoaGhoaZoKxLBR+ttplWHYVdoQ1YQ6eajKU9K3xdOZTN1t+WC8duffy1D21\nsWwsjaWDYWAmGFG/X3Ba1lErzlljmAFGit3IopE5oq6ADxse+tCHJulYNWajLxHfKZmQtVqP2nOn\n/jt2R2bY2DSdVJOeHzVJx1qwLbJgseiMi4G63095ylMGfpNlo6eXqmD+ZVXgmKbjYH1qfOTeY65i\nd/SOvqg/WOlM+YWFhUWzOeqUU0xPfEAszTWpK+DXt6ZkookfYY7iT0C3ycrx1K+wAll5ZG0Ns0z6\nY0DTdKJeSi9cE/0QHzJ90HRKWZ9kUzMna6U7Nq+fmdoc1r34gv1I7M7a8H1sn76wkFY6Uz5Z3DOr\nTpF1DNlcOhywouqEVXomI05WFwtX9p+sLlaYNcV6F5f0+44vi5Be1r3znz3Nhl5vs1AaGhoaGmaC\nsSrlawdV/jjsi/8PAxTDUEvBnyfPW/dX8y/EFzDO6vdlccgCYcGwAjBgWRzOCwPhS5VpM80c9dpJ\nldzIgqWCAbAkMAF9pWRjyYTD0lkssi8wZZaJ2Q0sFh16MRxxClXo5iQAhoGh9tcGTFMR3e8n9/+Y\nILYjXlCrdsngrW996waFpxwAACAASURBVMD3TZfj32exkjGopMdMxULcXzE1/c746Fky7mGtav/r\nX/86VTZPvyzcD2vCMdwXeoEty8ISX7KGME4WBuu+ZlBi9dYSmbAGxCFZxvTQNdcKbMf/3//936nW\nSL+P3f+LaWDl9gXr1DWyzmWqyeayhugN64++kTlZyB5Vq8HSsUbUgLGA6U2di9M/G2bargG167TK\neJ4Me539hCfDfeKVUaf2iEc8YkBGsrBYWWKk4tT6n/EOsFzrbBhrkR6QhYxKa+fss89uWV4NDQ0N\nDauPZS2UtWvXLmy33XaLZnx4KnqyYzl8lp6S2NgBBxww8HnsCZPAyj0lZXXwE2NnMpb8+2Mf+9gk\ni+ene5UtxvLpn+897ezwOnmvdhf2ZMcEsDNMhb8YS9IRVf0Bf7M+Z3yfGAarCwvHyuTi16mUzkcW\nB1nCSmbKuzY+6TrBkRUk5iXbT0zDOfIH62fGj6szAqaJbfGxi81hdXp5kQU2yNIhCz588Yb+uNg0\nNTlr167tMUQWCVm472ThnFyb+8UKc010FpuXManPFf2o8y7EXMiCRev+0y/33b2gF/1xk2ktFPtA\nrcJ2TDJxTuI5WLD31i9rTddhXgD9q8SlxCHJVEdm89XphfNjrTsv1p811r83TqMX69ev710TXe+f\nK9KPmqGqp5fMR3VI9otq4bJkxFB0zuCd4R3QJ/G+971vksUdHeht7aDQv++1eSgNDQ0NDauOZS0U\nFdGYHabh6Vk77YKnW/9UwKTLddcFFKsTP+Dz9tR1XLUW/MHYmt5Msj+wAE/Z2lNopVXAZmb3y4LP\n2TGxX9YZGchUc66yMB7wgAck6eJK+g5h4yrlsXsV9Bis+BOmKbOtZoUs0Tm1J5Np4wa1QwLWLZNM\nLQZfOHbsnN0/tTWyt/jKyVpPJ3EAvnPZPFgYq1B/KxaPOBT9qHq50tkwa9as6THRWvvEYmHN1cp5\n95Us9FCq2XnOEbNk3VsLZF7rnlSJq1egr+4Zhko2QBaTzgCZn5/vXZtrl+3HE2GN0FFrBwsXQ2Oh\nqOImI7I86KCDknQTQOmfzhpieXUukwr6/phqvyyqXqj+n8ZyrdX3tb+dWJu/s1BZBOKPdF4Xchmt\nLAjxShYI2ek2Iu5MxjJwa6y1WlC1sj9Z3nJtFkpDQ0NDw0wwVi+v6vsEf/e0xQS9Yp78xhiBal1+\nWznXqjexMv+uktXTFoPAUFSRYl18sBhKrWpfv359/vSnP03cbfifrwPX3vfvA6+YqM9VXzjmgr2T\nocrVO93pTkm63jpy5FlrWLlrY/2RJcaJzbEOqrWm2/AsmCi49moN+bs4ktia+2eWB1amtsa1OJ5/\nd02sAkyzVvdiWdifv1dZbLPNNlNN5uuXRWW3rhkciy6y1tVQ8PuzUJw7WdAv1yweibGSlfNgHbBo\n3SsyIss6U3y77bbLn//85xVNr6x9oIZNtSQ7FgYLk87TYfsKT4Vr5w2wNnTUFRcgE7Fd97/2vZPR\nVHt5rcRaq9daK83r352LzFX7hK4grC3Wv07cLBTWPdnwWMi0dT7i0GIu9GmYLOjxunXrWrfhhoaG\nhobVx1gWSq0eHtZRFfuSReHpio3xmWIK2BLmIjNFjjQmgZmwOLBzv8M36ymPedRK3/74x7ST+Zb4\ne5LFE868F2dwrRgGGTl3LA1kd7z5zW8eOA5Z6HMEtWtpnYdSJ/JV2UwTQ6m9vGrWn/su2wecAzaE\nLbNE+f2xJFld6pBk5/AHY6YyZWSd1Q7AlTG7B44zbTxpzZo1i2RReyJVPajZgnzk9EOsjI+dDGU6\nqa3xd3EJveDcC3pAJsOsSnqFqSaZShb9dWtV56DGIStLd19YJvYBHZTpuNqsY489NkknC/FEVpn4\nhPvvc8OsSn/vz+CctlarWjt1/VWrjeXBwrBngrWiFod1pnuAynn3lx6KP5ElGdlT61odtl+Mygpt\nFkpDQ0NDw0ywrIViMh/WglnI8sIU6tOtWjC+5+8YiFgKP54sLxXvYiie4p7GYiWYJUYhg4UPleVU\n62jm5+enmoeyZs2aRcx+2MzoGneq732OZWG+gboTTITsXavjiRORUe0N5HdZc8PO07mtpDq8Pxum\n/xjVf1yrs2u3Ab9JBqq/XQtWVadVYqC1X5Vrlj1Gr2o2D0w7O7x/SqFrrfn91bodFmeq8y/UH/GV\nWwPWnBiI36+ycP/dG5aQbLD+avB+6F81Syt+2F5TZ4D4vGsFGU7mp8hksv5Z7X6nTkn1Ss8q2yej\nmuk0Ta2WOTl1FlOtdalzUqqHo+/3Bq7VVEudNtRyyXCrFe91NlHVi9qF3OfrvjJq72wWSkNDQ0PD\nTDBWHQrUqW6efliOv3uqYZSycVgQntqeyjX/3NMQo63s3Pe8r0yisvHVmMAGoyyROjMaK64Zc+SM\nlYmtDDveEue35OdGZaX5t5XU5AzL5qrXivUM0yN/l43lvteK62G+92G/W+9JZX/9rHDaGMqwOp8a\nS6lZgDUjql6LNUQGmO2wimvw/bpGqtegnm+/T3/aSvmqc8MsWJ/r72DR//kqUxYHPapZnPW+DtOL\nel7D1shK9WLt2rWLjgX9WZZJt1ZcgzXA6qrdQHxfjI2VVS3kaoXXa4PaRX3YftFiKA0NDQ0NWwQj\ne3ntuOOOizKHsKkaK9GzSSZBnWdQ/YOVIfAveqr6fM0SGpaRAP2ddJPFGVRzc3MTV0SvWbNmQZ1C\n/zGHyc8xncOwTLkl/LRJFrOpytYqE6nsHerv1Hvm/ydhX2vXrl3YdtttF83JgWHX4L6xUJ1DzQLy\n+WEsexjTrZk0NXun/k6t9J8my2vNmjULGzduXKQX9X5VixUz9b1q7QP9qVlB9fM1VlNlZM1WGdau\n1mo/xAsnlcWGDRsWrftRbLdeQ82YrGtjqc4X/e9HrbXKxque1omv02R5qZR3rKoPUOtP6GztJzZs\nP6k6X2XKW1TjmFXv6u/Ue0B/1K21GEpDQ0NDw6piVB3Kb5OctuVOZ4tij4WFhQuO++Emiw5NFh2a\nLDo0WXT4/1UWyz5QGhoaGhoaxkVzeTU0NDQ0zATtgdLQ0NDQMBO0B0pDQ0NDw0zQHigNDQ0NDTNB\ne6A0NDQ0NMwE7YHS0NDQ0DATtAdKQ0NDQ8NM0B4oDQ0NDQ0zQXugNDQ0NDTMBGuX+8e5ubn/02X0\nk7ajXs1zOa+xGrIYNVhptTHt8ZtedGiy6PB/URazXiPLPlAaGsaBOTc61Q6bn13/Xme8167BtTNr\n7bg6bA5O7aCqm2ydolg/1/9bDVse025u9XujfmdLEp1Rc4roZl0j9e9Q11Q9Tl0L/m7NDOvQXGUx\nrCO8WVLD0FxeDQ0NDQ0zQbNQ/sUw7oyJ1T6HrbfeusdU+ueJJIvnm9Rzq9MJK+rMjh133DHJ4kl9\nw6bRVZbnddi0zHrc8yNmxd7Pzxj3HOv9q/e7zjupn9tSslizZs2iGS7D5o6MsljqOft+ffV9Vni1\nMIbNYfK9OuuqWkKjZNcslIaGhoaGmeB8aaFUnzz8K7GtaVGvESvHHKov9LyQxcLCQm/qYtLFNmps\nAsthaZh/bU62KYE+73fqfTe9zvf//Oc/J+ksl9///vcDv1OnzTkvv19ZF5jYeH5Evc/WyLCpmcO+\ntyWxWjGROiv+ale7WpLk29/+9sC/D8OwyY6zRr+e1WMOi4HUz1Wrv1ouQNerpWKd1tn19XvWyLBJ\nseOiWSgNDQ0NDTPBqImNU9GbSf38mOPtbne7JMl73vOeJN2M+Yte9KJJkv/5n/9Jkmy//fZJkjPO\nOGPJ3xsXWyINEAMY5dcFs6AvdalLJUl+8IMfJFnMXLzusssuSbq4Qs3OGBeTyqKfzQ+zllwbS6TO\nGveKbe+8885JOovjQhe6UJLkd7/7XZJOhkcddVSS5HGPe1ySbh43YO1+n4WD3S0nm0lnh2+p9NDr\nXOc6SZKDDjooSfLEJz7R8ZMsvqYqu2nZ+JZIJx+WCWXtVDZPFte61rWSJMcdd1ySbl66z5unfolL\nXCJJcuqppyZZnCU4LlYii2ExCWuHJ4KlULOzyKLGBe2d++yzT5Lkq1/9apLkKle5SpJu//A79hdW\nPm8AD4hMyOXWynJrpFkoDQ0NDQ0zwRaJoVT/IGZxvetdL0lywgknJEmOPPLIJB1jPfHEE5MkD33o\nQ5Mk97///ZMkj33sY5MkxxxzTJKO0Z4fMcwyIQu+zYtd7GJJkt/85jdJkmtc4xpJOsZw2mnnjqe+\nz33ukyR5xStekSR50YtelKSTke9XS2XWvvR+C6UyTueM9bAQakwF0zzppJOSdKzp4x//eJLk9re/\nfZLkTne6U5KOcX7oQx9KkvzkJz9Jkhx66KFJksc//vFJklvf+tZJksMOOyxJ8tKXvjRJJ2t+5VFZ\nQeclsO0rXelKSZIvf/nLSZKDDz44yeK4lfvOMvnc5z6XJLnRjW6UJPnVr36VZMvFD5Lhfv+qL9US\nERtzn/baa68kyX777Zek049HPOIRA5/zvXvd615JujVEj6yZP/3pTwPnWa2BWa2V/jVSrTGWgvvH\nerd2fJ4eAI/ELW5xiyTJpz/96YG/03nW2Gte85okyaMe9agkyQEHHJAk+cxnPpMkOf3005Mslkk9\n33Fl0yyUhoaGhoaZYFViKOBp+7znPS9J91Tk173MZS6TpGOgr3/965N0TPNnP/tZks5XzkJ58IMf\nPHAczBUb4LPHXIZl9WwJX3llZTe4wQ2SJN/5zneSJNe+9rWTdH5/lsb73ve+JMl1r3vdJN21vfnN\nb06S3PSmN03SyewPf/hDko6BYvuYEGuhVqfDNLIYlWnGL4ypslQufOELJ1kcM7vCFa4w8Pl99903\nSfKlL30pSXLxi1984PtY2G1uc5skyTOe8YwkHSt/73vfm6SzeOkJa68y6D42ucVjKK7Fff/GN74x\ncE5veMMbknTxAGydHmG6rv3YY49N0t0D2T3uf7XWhmGWsqixEvLHfsVM73vf+yZJdtpppyTJW9/6\n1iSdZ+MBD3hAko5duyZr4slPfnKS5IY3vGGSLt4k1ibe+OMf/zhJcsELXjBJZ8UNs+qnkUW95mHW\nvPd0kWVyxSteMUny85//fODzru3qV796kuSb3/xmkk4v6INr8rlXvvKVSTqZffe73x34fI3RirEs\nUY/SYigNDQ0NDauHmVoomCDWs8MOOyTpmClf6Pe///0kyaUvfekkyd3vfvckyQc/+MEkydve9rYk\n3VMaCxcvYOFg5bXuAOMZllkBq8lEMUBPfrLwxBc/EP9hqTzsYQ9L0sUVLnnJSybprC7xAD5zbFs2\nR60B4WvHSPhqKyaVRT/z8v/YMAvCubGS6NprX/vaJMkjH/nIJJ3/97KXvWyS5Gtf+1qS5CY3uUmS\n7n6yZD/5yU8mSfbYY48kXVzB8bH8hz/84QOfw/be8pa3DJx3fzXzamd57b777kk6vfj617+eJNlz\nzz2TdNl99MV9pBfPf/7zkyQf/ehHkyRPeMITkiTvfve7B76PhbNgyLAyZpl11lTFamZ5Wd/W7XOf\n+9wkyRFHHJEk2W233ZJ0a4Bu3/jGN07S6ReL5Ze//GWSLt4kC9T3Xv3qVyfp4gWyRe1PzqfGE2Ca\nNTKslqZa9+I/7hOrybmxTH3OGrvyla88cNwHPvCBSTpZyPqyX/B4sNrsCywg+4N9qe4Xenk1C6Wh\noaGhYVWxqjEUwLY8DT1lvfIfszhkqMhQ4Bd+5jOfmaRjXzU7R8bDrW51qySLK64rtqSvHCPBNMQL\nrnnNaw58DqPAprA216JaXCzlj3/8Y5KObfEry3z67//+7ySdT77vepIM+IvHlsX8/PzCunXrFlXX\nDvOR8/uyIFhNruF1r3tdki5rR+yDrGTr8Af/8Ic/TNJZKjJYMEzsimUrs+VBD3pQki6+VOto+jJZ\ntphekBlLxesd73jHJJ3Vhql+73vfS5I89alPTZJ84hOfSJLsv//+STpZssq9uhc+99nPfjZJF1ca\nhtWQRWXntUOu7E+6bq3osMDqkh1q3bt/4gInn3xyks76UscmVnPggQcm6bIGf/3rX9frSTJ9bG2p\nWq2aXVd7cVkb3t/ylrdM0nk86DIvjz1V3FlciMXB2hKP9t5aok+sQd0Gas2ONXzOOee0OpSGhoaG\nhtXHFrFQQNYG36anLVbOn4tNe8rK+uBbr+xrGOMZ1UtoS2Z5iSM4Z2yc5cGikMm29957J+kymDDK\nj33sY0m6mApGwmoTx8KEMFBMY1j9wTTsC5sSM8OqWArk7lwf8pCHJEme/exnJ0nucpe7JOliJd/6\n1reSdDE1MRYWiZgbyP5z/Kte9apJko985CNJOutPHQJWRv9qVXJy7v3ZEnpRuwc4R7G0d77znUm6\nWMmuu+6aJLn+9a+fJHnhC1+YpKs7ES/Czsmy7zyTLJ5FsxprZNxOGXVtuI/2CTEv18JjoVOCOMId\n7nCHJF2sjGVKZu9///uTdGukxjFqXVLNdIJpYijDugD0fW7g2LUGR2ZarS+R0cg69316ZF8Rn/rR\nj36UpKugl01qn2Hx+Jy4Zs188//NQmloaGhoWFVsEQulVvVWtiQ3GhvH3pwbn/q9733vJB27qtld\nsJrsa4zPJVk814APUqYbS8K5y2S5853vnKSTjWwwwNJ0CRCfUjXud12z7I5RdQezYKKuxbW6j/z2\nfN7iQ+pPMFIs7GUve1mSjkXJ2mGlsVRZQrJ81CGIrWBr97jHPZJ09SzOt/b82rx587IZLMvJYly4\nz+IBhxxySJLOGscQxUxYmO4jxnj5y19+4NxZvLLAWPu1N1O1zsRoTjnllCXPdzXrUOiL9UqXvfL3\n0yOxD1Y6/VF7gZWLCzzmMY9J0sUHyNBx6QGZ+v1hsddZWms1ZuLfeRJ0zlCrRwasMPfV+8td7nJJ\nOn0h00996lMDv8tq+6//+q8kXScGsTh6SUbWSL+11iyUhoaGhoZVxxbp5cVHWutC/F39CSbp6Sjb\nS5aGat+aFVKrv1erX1F/Xvkw1IpXDMU1qeJ1jp///OeTdFaYV/3KVNbzcWIkGAyfKstEhpPjYzYY\n7ywxbPqc++SVDxsDVMXNwpDJps6EJSq7D7A5NRksWv5elo5aDD54r2IwMqncK9bbsC4Cs4SYF7Cm\n1FQ4JxYGGfOlY8/+zuJ42tOelqTLpGPBDusa65qHWSYrQZ2oOKwDdpW37DvZfTroiiOJucoCk/Xp\n/oo7kSUrTgyGdU52tcMuNg4rnb80Pz+/qFNG7crgnNw3YJ2Z9cLKkrnme/RHnFlmnGtXz8IioUcs\nGf3NrEVrzH5RvQ+j1kizUBoaGhoaZoJVjaFUSwFbBhXLOmfWvjZyojGSyiCg+lzHxZbI5iEDldCy\nLLBpzIBPG7P493//94Hfkf3BJ6oPFkYhW0iWkPqTcTvpTpPlNWwuNUtAXIdV9q53vStJZ1nwdctU\nYjn84he/SNKxKzEU78lMRpP6pQ9/+MMDxyULGXSVBao7WGk2z7if/efnk3QWiHN9yUtekqTr2UWX\nMU9WGaZYM5PcAwy0do2Ydj7KlpCFa1AB737bL6x/FgqrnSWjBkudkTil9zUbEYuvGUyjZhdNs0b6\nvjvw7/VYdNNe5pzNghJ78z0Zbq7J79ztbndLkrzpTW9K0nVSUKdCP97xjncM/K74IguEN4HVOG5f\ns2ahNDQ0NDTMBFvEQqkxj/vd735JuiwN1d0VOmiqlJ41Zsm+qjWGfWEcGKLqXKy6znjRhwpLw9ow\nmNrzp7Kp6qut1sOs+prNzc0typap+fv8v+I92JLKZBYFa4xfF1tyrSwbdSayAtUrmX+ighqrUkHN\nEiZDcYyq+yqbV4OV1/ih7C5xQsxQVo/sKz26WG1f/OIXk3SsnZXlexX+LtY2KVazUp6e6G/Gby9O\nJDZ29NFHJ+my/+iHrC0Wystf/vIkXS8wbJxVVmt/aj3MsLUhdjqNLGp8sVbfs0TIhIeiZq6KJ4qZ\n0fHDDz88SSc78SRrR28v8Wp6IHbm7/Zg1v9SGZDjyKJZKA0NDQ0NM8GqZnkNY+vmG8gTr7O/QRXn\nqK7B5wcM80lXHye2hGHwnasOZ8mo8saqdAnlU1cpjYVhLjWLpHY9niWGxVDcT5aAzLOb3exmSbrM\nEuxZLESMRGwFc8VA1R/wB7/4xS9O0s3ZISs1GX5PPcrxxx8/8LnaSXXWUy37UXVXNp/Kdz2bsGzn\nLtbCpy3OKKtPLA7jZNnUOeoV7pHjYPuzwKjsqLovYMfeixvo4SXWoaOC32V5qkNiAYs3Pec5z0nS\nybDGA5ayUPvPD6bVi6U6ckPd0xybJWr2Dyuf9c5ykfXl2pyj+JAKeh4Q3YXpUe1OwDKxT9W6tf5s\nteXk0SyUhoaGhoaZYFViKGol5IPXvHR+Qz3764TFm9/85kk6f3Gd7DZsVrWn7Lg1F1sig0WfIYxU\nFgZWjjXJcMJAWRzmn6jlwEz1P8M0fJ4MHH9Ux2VYSZ8ijE6Mos4t4f9Vxa0Hl15dKuLVj8gSEwNh\nrZl7Iiajg6qOy3qEqcyXs3/iiScmWVzPVOdkr8RXPsHnkyyeAf+qV70qSScjnXHdv6985StJuopo\nE/vIQAzG2qhxLZatGqBRGU0wS1kMW7diYHRcHMA6tn/U+8iStYbUpQDZ1Up8GXTOx/crVjqxsd8q\nqVa8PbBOaLQWZGl5bx/QOUMGG4tDFilrT9xR5buYrZoeWWL0xz7ic9VyqjHZFkNpaGhoaFhVbNFu\nw9g3BorRquLEGNRYyDRQTe7fwVN12BTCUdgSTNQrXzd2ZGaLa7rXve6VpKudEBvBKFyr77HqMFD/\nvlR30HEw6TyUtWvX9phfZbt1cqcqbtXb/Lsmc7797W9P0vXo0tdKzY5XsRETHx1ftpfaGzEVspcx\nxyqo3QtYLOvXr89ZZ52VzZs3r5peVFnJxmFhkp21wqfuGsTSxAdki4nFYNusOL+LeU6KlayRGkup\nFsooWejRVfuesXhNK9TDTR88+uB7dTIkz4e1M25sdiXzUPrniSSLuzOIhbhP7h/d1D1CXJGlymJh\n8fBcsMbEWumRya70hBUnRuN4LJPajWTNmjVmojQLpaGhoaFh9bCqWV6VCdaOl2IsLAy+dUy1+nex\n8DrjY6U9d2aJminCCuMPZnmwyvgy5ZOrYDUDBJti2WAQtU+R44yqN5kFFhYWsmnTpkX5+46Nzag3\nwa7Vj7BIZKScdNJJSbp+ZawymUsyXcyAUF2uLkFsxXF1JfbvZFg7PPPFO+9R0wtXghoHdEz31Xts\nmjXlvppuKdZ23HHHJelica6ldg+uGZYY6az73C2FWucxjK17bx+os93pl5iY2hudmWUBmoNjTah3\nIzu/ozOHOqg6VXE11k7tgO4Y4oOsKd4bsTXWuvvrPYuDTrNY7K0yK7/whS8k6fYdsqYHtXcXywlq\nd/JRe2yzUBoaGhoaZoJVtVAqQ6lsSXU4piGzASNlqWCUdRJgtWDOT/UqnvwsiloXwlcqPoCRYKbY\nuEpp3QL4mcVKMA51JsP81bPE3Nxc1qxZs4h1eXXNz3rWs5J0LMr9VRmPQapslh0mxuJaZLjIbMFA\n+dp1q8YwzWOXRSgThs+dBVP9xKvJ2mtmUz2Wv2PfV7/61ZN02TuYrGsgY+fuWvjeZfs4LrZPFrWb\n7GqiHsM6rplD3osLsMZUe/uerDBdJKwR950M3H/vxR3F2FTiY/+z7jbdH0Pp/1vS3X+WCRk4Z3te\n7TZuyqm4kToiNVZ3vetdk3RZXqy3WpFfZxbRB+9rvLHeo6HXvOy/NjQ0NDQ0jIlVsVDqUw1D9RSU\nqfCkJz0pSfe05geUc69ilr/Q91kmlVHU7qVbwk9c4ZgshtrPTOaaJz2Ggb2LoTzvec9L0lkw2Ls6\nAr9fM1VqFfJqxJfEUPrf9x9bBbysK/Ulr3vd65Ikt73tbZN091dsDJsye97scBkumCSZef34xz+e\npOusoEu1OhZsS+U+C1mmCz3auHHjqsZRksX1CGSmUt5US3VLarrohQl77qtMKFaXa66WKlRLdkvC\nNZM7i8McJB0UvJexxPJQ9Y2N61rNYjnooIOSdPfZviNeULtWsPJGse5x5iCNi9p5nSxYJLoDyMay\nX5AVPbE/yA5lsZChrDHXzGJ1HfRArMV7Mlmqh5fXVinf0NDQ0LDqWFEdSmXBNWeZ71NXYZ/DAsUT\n9OTHJPl7xRk+8IEPJOl8pRipp6sMBllj42I152XXGAYWxX8LWBs2pd9V9WWa+XzkkUcm6XqAqXTF\nLDCdSXt3TVMFXNm2a2Zx8NfzYbtf9MGEPUxTjzcWjH5UroW/mbVGD3xehbzjkrXzxPow1DpJcFQV\n8DBZjPvZf35+4FgYpjkoy3W8TbpOC3zqMuhq9p9qc/Ela2TSepRZzlGv3alr5hkLlpXGI+F7/P66\nTmPvteZCFqD+VYC9X+ta10rSZUDV7sPDsJI1UvdI7+2B6tRYLKwxe6H9QdxQTY7vmwHDOtffTG+4\ne97znkk6WYnV2avJwPvl9HC5jtzNQmloaGhomAlWpVK+MlYZBvy9fJwyGjw9PR3FUHSXVRmLkWCq\nw2Io4/o7t+Q0OueEvesmijGS1Qte8IIkXYaL72McfOhYOxnUa15tWSzVp6iyL+cuQ02MDLtyjViZ\nzDaWBPYlPkA22BRWTqa//e1vk3SyYamod1HHsMS1D/z/lpjkOQzYOIYoa8fcFL5wMtVFAoMlE1mD\nsgH7zjfJebNGhsU4WU/utziCWFzt7efa1S+p5RI3UI/CQpUxx/Khp7K8xGjrec6il9ewdUlnycD9\ncw2y+Wo3aHul+iRZgOKVrH4y5d2hF7VXF32TPWoN1TXdH49u81AaGhoaGlYdM7VQ6jwMTz1PQ5aG\np6U8crnWWDvmwGfK18n3jpGIJ0ybzbUaTLTGlWqMxLXzmWJPenip+nZfzD0w14KfuParqux8Uqxk\nGp37W2si3D8sDcI2uQAAIABJREFUq3Z3ffCDH5yki4m8+tWvTtLpgYp6/uE3vvGNSToZVv+04/AT\nkxl9YRGD8/O6adOmnH322avay6vG2ur6Y82reMZkyUbPrvo73pOt3xFvOD9MbKznLKsPS8aqWVeH\nHnpoks4KFxcSX7SmxJFcq7olx7O2zEmZtgZnGlnUTuv2Ktfs/rLOXJt1bJ8wO0p8SFxSPFCHBGuE\n1WXqreP6vLhVlUGNK9Z75jPNQmloaGhoWFXMpA5lWPUvn7kus/y/NeMJu9bHxlPd0xWDfNGLXpTk\n/NGzaxRqbQY/Pr++vHHdArB82RfyxzFNfbBkcfjcedUVYKkMlspusDCWAWvrwAMPTNLNLxEb0y1W\nnQF9oQeYao3NYN+yyFgo2J95KdU37vewwdrHaDVQdfewww5L0k0fNAdFrZZzNieFhVJlzXeukvq8\nwlJxg2rJWs8sTfffJE69t2SDiiPRB3NPzHihL+IIarfUM6ndqtbCuPVq09ahLCeLYX3MrPvrXe96\nSbpYCYvD1ErdRfTLs4eKT+sFyAr0+XoPagcPMq/nCVWGFc1CaWhoaGiYCaaKoXi6Det9U59i2Jaa\nCQzS01PFtGwOXWI9NT11Z41Z+If5OLFgqJluQBZYOytOB9RaR8CC+c53vjPwvWH3bdoeXiuZh1Iz\n2Yadk35U5lWo+tV5WeyEpcKCqRXWmC1Zyvqrfc1q/6JaBVxZ2Kgc+6UwTC9GsV/WuNqp2iuJ7usa\nLF4gLmnCo75mLFjHnTaWVjGpLNasWTO27rkPNW4glqIzAuveWlGDw3JhgWDXfmfUNMp6j0ZlwK1k\njdSYau3QPcw63nfffZN0um/977PPPkm6rC0WjhiLv9N5GNb/cImMtiVf+/69xVAaGhoaGlYPW3Ri\nI9Tc5vMqJrIl6w1qtgQ2jXWpiJVvrncPy2dcGU3bu2uaaXTjHgP7whzFNvSp8l42jngAfzLL5ktf\n+lKSxZ17oVoDZIi9YYN1bku/Pp7XdSh1yqRMJ/PTZazV+pLVwixkMa5OVhZvjajNUCXuforRjeoS\nPGpy5CiIhaxkjQy79mEdNuokVl4d1lq9/6wz2Vs183LYXsuKtzZH9QCcn5/Ppk2bmoXS0NDQ0LC6\nOE8slCWOk2TLWyqryUSH+dDr32v8AauWK7+lsJKeTcMyQjDMmvHme2Im/L7YkupfrLzvuEkWs69h\n72tdVLVMalX6lujl9a+G1ZDFsHnllR3XtTMqdjstxt1/ZtHLa9iMqBrL8KoeSZaXz9VJsMvEfYad\n37J/HxZXGmWtNQuloaGhoWEmOF9YKLNCnV0/CucnJjprK21L9GyqLId1JfZRLYph5yLLh298WDW5\n+1u71ta519UK9HkYllvf52c+3+jFeY1ZdBseNhFz3JjKaq2JGtur51ct3mliKJXx1z2qntNSMYv+\nc/G9YR6QZfqQDfx7zTazBq2lGl8aVxbNQmloaGhomAlGWSi/TTLZkJF/HeyxsLBwwXE/3GTRocmi\nQ5NFhyaLDv+/ymLZB0pDQ0NDQ8O4aC6vhoaGhoaZoD1QGhoaGhpmgvZAaWhoaGiYCdoDpaGhoaFh\nJmgPlIaGhoaGmaA9UBoaGhoaZoL2QGloaGhomAnaA6WhoaGhYSZoD5SGhoaGhplg7XL/OD8/v7Bm\nzZqxmy1WnFdt6cfFJM3edt5554Xdd9893/zmN1fzlHowBtUoz9XGJLJYu3btwrp16xY1ZRwX/5f0\nYn5+fsHQoWkwaUPTcTFsTMKwkcTDzmOahojDjvGvjtY0tMNUzSHXrFnTm2E9DdauXbuo0+uo49UZ\n7Ethbm5uoCNtfT/q89Ng9913z4knnrii35gEBx10UA466KCJvzeLax2FdevW5RKXuMTU359UL6a9\nplHfm/Q8lsL8/Hx22GGH3rHqf6Ow4447Zscddxz789bI/Px8dLNd6nsbN27sdZBNzp2fYYZGPf/5\n+fnssssu2WWXXUYefznMz88PHHO1MWq/IJtx95UtgS19TpPqYwX98N/I403Svt4P1uEuk2LvvfdO\nknzta19b9nPGXm677bZJktNPP915JRk9PGbY4CdYCePQ+npalg5GvL7+9a9f9nPG4F70ohdNkrz/\n/e8f6/erEgxjj9MMDxqmoMNaZ9dzqueCvBj1Ogxk8Y1vfGPgd2Zl+Uwii3Xr1i3svPPOvRHOdM2w\nsLPPPjtJt2b+8pe/JOnuoxHPtXX/fe5znyTJscceO3C8eo0s5mtd61rOZ+C4dfSr8/Igddwlrivn\nnHNONm/efL5h5eOu+9WyfGc5YGuC36nnMNH3J8U4Mm4DthoaGhoaVh1TDdgaNYpztZnCsPGZMGpE\n6Kin7JDvLHkx0/qmlzqn/tdJ/dD1PDDiM888c9nvrQYTHXb/R8lqVqhDiRxv3DjHLPTCsZ1LHahk\nGBmLpWKlFrDj0L+99torSfLd7343STdUqn7ePdt5551zxhln5Jxzzll1vRgXrCv3c9iaWq39ZxZ6\nMWqd10FWFaMGa0G939WKh2ll1CyUhoaG/8femcfrNpfv/733OYdTInODOEimDJkTSaIiQyEyllmT\nIZRZ5lmlUqmoNBhCg0JRJJmpVGRKqIz1+mkSx9m/P+q9P/u59157PcN69j71va9/ztl7P88a7vVZ\na13XPSYSfUVXCmWimGZEFdt/7WtfC8CNN9445veqjve/OWvjAx/4AACf+tSnWn6/5JJLAvDAAw90\ntL0mbFGnXMfYjvtud9cdYeGFFwbg8ccfH3N/44xL7SjLa/r06aOU4AorrADAXXfdBbSvjppWqmLu\nuecG4K9//WvL71/3utcBcNNNN7UcZ5Mqvl0ssMACADz11FPDxwCFtfuz978JAGZEPvTQQy3bW265\n5YByDXrFRNpCBeu6co2uueaaQIkfijnnnBMoitfvGyM7+eSTATjssMOA0aOEq0YQVyEVSiKRSCT6\nio4Uij7M6H8Vr3rVqwC47777Wn7vPvy77Fk/sYzW7C/9fvPNNx8ADz744Jjbe/nLXw7Ao48+ChRW\nJqPxc01lNo38ebHFFgNGsyKxww47APC1r31tzL9X+UplFmbpLLTQvydtPvHEEy2fj+xaFi4rF+3W\ns/Rii7p14d9lQ56bxx6Vp0rHn0888USgsCu3VxUfqvIT1x3niM93bAuPeZ111gHgmmuuGfPzsnCP\n5U9/+lPLMcfYl5mOxlJU4zfffDNQ1nZVrEWbx5jNjBkzAHjkkUdattOLWmuXlcfrU5eNGRHVVtW9\n1K4Cjt9vQrnGLK8qVK3JeM94ffxXG8wzzzwAvOY1rwFgxRVXBOBHP/oRADfccMOY2xlxTu2eUvxe\nKpREIpFI9A+1CmXkWzZmnlS99awTUFmYo//Nb34TKGz7S1/6EgDrrrsuADvuuCNQ3rqnnXYaAJtt\ntlnLdmVXHsc73vEOAC666KLxTzYwj0595VOnTh1WU5FV77333gB89rOfbfleVc2NKu31r389AH/8\n4x9bjk0G4vY9Vxnu73//e4DhYjRZv0okFqTWsbV+sK+ITTfdFIAf/OAHQLmOn/nMZwBYaaWVAHjv\ne98LwBe/+EWg+Mpl1+9///uBosasxYiqoNtMqU7XxZxzzlmrEGLF+s477wzA97//fQAWXXRRoDBL\n175K5X3vex8wWrUfccQRALzsZS8DSvzhsssu81wAWHnllQG4/fbbW35flYn3n/hJx+tiypQplfGi\nqjhOFUs/5ZRTAPjIRz4y5rF2cFwt/7qe4nHUoRu1VhX3rYrnqZaWWGIJoMRGtt12W6Bc1yOPPBIo\nyjOq+yuuuAKAe++9F4Drr78eKOvD/XSbRZgKJZFIJBJ9xbgKpYp9VbHdGLvYbrvtALj66qsBOPPM\nMwFYdtllgVItLGOISkgG4b9f+MIXWj7/iU98AijMVqZqRX1dNloTrLyKgcR9G3PRZ66t3vjGNwKF\nWSy99NIA/O53vwPg61//OgDvfOc7Wz5vRpvs3t9bef/kk08CcN5553n8QHXMpxdWHn3iVXZfe+21\nATjkkEOAEgMzzqNqM/NI28b2KNr6G9/4BgCf/vSngaICI+sTdT5y0Ykt5pxzzqGXvvSlw3Z17bqv\nyNbd55ZbbgmUGJn3ioxUG77yla8EypqP9S1eAxWLqt/OC7J+77lf/OIXQFE+Kh69CBOZCRljF3og\nXAd2TPBcq5RPvJ6q9qj2jRfFe1WWry2r1FuTtoj3iOdgV5BlllkGKLbwnLyH9Oq84Q1vAMr19Nz0\npOg50VPi+ogxs07VXyqURCKRSPQVXdWhRPiG/+53vwvAbrvtBhR2ZO70ww8/DMDPfvYzYHTdgj+L\nyDRlGDZp/PCHPwwUH6y9xYwjVOVY9zPH/iUveQlQVFKMhcSsK32anrt1JQcccABQYiP+XVb3y1/+\nEijnbnziqKOOajnX97znPR4/MJrtdRNPatcWZiwZH1prrbWAcs6eo6zZjLjYf6yuZ5jrYauttgLK\nOvActZnrrOl4kk0aR+7Lte/vZd/Gj0466SSgqG/jSSoXm2+qzjyXqNai6nJduQ68J11/rifZvzaQ\nGY/MnHr++ef7si60kbU6ZnV6Lqo07x3jgU8//TRQ2Lj3kqrQWItV4npE9JD4fZ9XrpOqnoRRRfQj\nzhivp+ciNt54YwB++MMfAiU2og1uu+02AHbddVegqHXXgTG5xx57rOX33puuC+tV4vF2mvGWCiWR\nSCQSjaC33t3/gW83WfImm2wClMwCYyX6vO+55x6gZJxsvfXWQHkLyijWW289oLBzfe76yj/5yU8C\nJW7gW7vbKuMmIBMwa2f//fcHSqWqrEebqbpkz7J2bSF7V+XJfM03P/744wG4++67gRJr+da3vtWy\nv6reUVtssUVlzUS30P5eDyuWPYeddtoJgA022AAotvA4zAZUrbk9fdzGh1wPxhdUa0IW3lTufRVm\nzZo1vEa9PsYmZIKuWX3axjRUDGa2uR3rivy+mY/WXAl/lu3LeB19IPu+5JJLgNGZTdo2/r7b+S5j\nwX0YL5Il+++b3vQmoFTrW2903XXXAXD//fcD5Xnwlre8BSjrQBuo7rwH3Y5xCO8Vn1N1GU6um3nn\nnbfjjDAYe51F9R2Vp0rR667CcF2ZFaiXRxWmt0bF4f3vcyjWtRkz87nTrjKpQyqURCKRSDSCthRK\nXbaUjNO3+KWXXgowatiOfkDZlr5O/cdW2Ms8fTtvtNFGQPEHy9rPOussoGRAxL43VeiFocr89IXH\nvlWybW1h3ODNb34zUNiWWVzmm8ssttlmGwAuvPBCoGRnyLb1J7tfWZ5+56WWWgoYnUFT1ePn29/+\ndocWqIf2dd189atfBYoNrr32WqCwdBWm/mBVm50VzHQzA85zl8VbSa8NrHOqy2BqCgMDA6O6CTvn\nxH9loCoUlaQ1NV4H7wmvn/VG1h188IMfBEpmo8zTuJSKRC/AT37yE6Dci7LyXusQOoHrweuijT7+\n8Y8DcNxxxwGl1uLoo48Gyn1t1p8xFVX8W9/6VqB4AVTpqnf3e/HFFwMlcyrWbMQ4VPRw1M3nGQvT\npk1joYUWGo4fipjV5b/Wl8W1a3aof/ecPTdjLB6jzyfvd9eDdUo+T/QW+TyIsb/xMurGzQyu/Esi\nkUgkEh2go0r5Ksgg9OvFjCJ9ox/72MeA8vaVKchIf/rTnwJFwXzve98DCtu32tdMKhmLb/XYQTP2\n9hqjJqCx+QbaKGYWxUwzM1pkX7IksztUa25HhmHHVH2ibm+//fYDik1PPfVUoDDPWC3ehC3qqsOF\nsTEZosdsfyrZuhXxKkxVlmzK2IpTCd/1rncBJePFuhZrNqpqPyKa6Nn0ohe9aGjllVceXvtx256r\n6sxKZ6+DPm0Vr7EW61rMTDIDTlWm4tlll12AwlyNSxnHdF243xhn9NxVi9ZJqer6MQPEmIeeCJWl\ntlChGGM1VuLzwWzRGI+wS4UeE9WbcQc7aTjtdIxMx3HPr59ZXj7btL/PMte+2Vt6eXwGil/96lfA\n6KzQ9ddfHyjryWeicU29BVE9jjfrarwM2VQoiUQikWgEtTGUduINkZ3JvuyAueGGGwLFP6fPU9Yk\n85Bd77nnnkBhLL41ZSb6/eJ8BBF95e1WSHeDqm4B7kslIgNdfvnlgVLZbv64GXHWr1i3ICN9xSte\nAZRz83OxMjt2K44qoonsnaGhobZ872ZrRcVqnMg4jv5/2bRZOipa4bm6PbN9zAaLOfwxoy1ef5lx\nL3GEv//978Odf0fCfcj0VWnu03P1HGSg3hPGXlSsxomuuuoqoChVbWksTjUfu01EZRJrwPQKGIfo\nBbF7RFQArmV/Nk6w6qqrAmXWj73ajBN6LtownpPPGW3uddWG7qduWmJV5+VOMdb3o1rzfjQTzdia\nqsx7RKXrMf76179u2YdK1u0afzSGps2NV6kSVSgx22wcT8a455wKJZFIJBKNoKNK+brurWZX6M/T\nR2qmwxZbbAEUn7cV9cZYZB6+ZVU0+tjNIzfD5corr6w9wfHQi3+4Tu3ISK3eNkNN9mWlu9tRzdmP\nTAZhdoa2N3tnjz32AIrtIguMs0ZidkmT8aRoi7hOPNZzzz235fOyYz8fmafZfPr3hds98MADgZKx\nIoszg65b9GKLyOx23313oPShcx24Lsw8ill/2kLmaY2FXYe1mbU3fs7tqIhVNn6un52XtUVcDypT\nVZDXSXVu1wAViqr87W9/OwCnn356y35i52a9ACpF1Z/bMT4R65na7Q0muumgUJVZ6D7iVFGfmcYf\nVev27Lr88ssBuOWWW4DynLB+xeusAnV+zvbbbw+UWIrq3XoVlam2c7uddpNIhZJIJBKJRlCrUKZM\nmTLKp1ilVOKbvar7sIxExuLbNWZlqVD0nZr1Yy2GDFbE43K7Y/nSu+3lFes4zLaQSQhrZ6ylWWWV\nVYDiK/VfM9WiD1OGqV9YyMo8d8/Z2Iy2NtPFbsMy3yr23gT7qqtXqoJVwc61ufPOO4HCOOP0OjNh\nhP7hKvUV12WMH4z8XKfrYtq0aUPzzjvvcBzIfcj0Ys8sz8X4QlQ0ZuWYFea6MtbievN6u7ZdR37f\nyZGy+6qMR+sWzITSVvPMMw9//etfmTlzZs9ZXnHeiRlp3sdeZ2cDeV1U456jccOXvvSlQGHRxiON\nudkHzXiB6s3u5LEOxf0ZlzJWG9GPbsPa2+w817JxH6+zCsIu0h6j19PnhrU9hx56KACHH344ULJF\nncPzoQ99CCiKOU7+rOvwnAolkUgkEn1FW3UoKgnf9O1mP8Q5AzIHFYr+48i2rJi3MvqCCy4ASo7+\n5z//eWC0Imn3uGTX3TAO/fm//e1vx9xn1ZwD+wrp71c5mA/uuciaZKLGmbSZGSx+3gpor9GI+oF4\n/GP+fr755uPpp5/umIkODAyM1Y21re9b9Rs7LVtfYodkmaeQWRpjscpctaay6RXdqDVVlmxXNh6r\nsWNcwaweayRkjvbmUtl6r2jr2JlX9u7PZvm4Try3zP6LXoSYMfnCF76QZ555hueff74xVu4+Ymai\n53THHXcA5Tlj7FTV5dr2OaLHwww52bfnKos3thv72cX1GjuBRzRZh6Ldzea0zsxOGma4Gn/2uWH/\nQs9B26iujMEan9K7s8MOOwDFM2LNlnVMKlw9GGaHVSEVSiKRSCT6ikazvKrg21iY6WR8wIwoc+qF\neeMyEvscyWDN+qryR0e4PTMdemEcKggrTGMVeOz/Y0aSmU4ySKu9zdr43Oc+B5QYiMomZoVZqyM7\n02evr7TKFjJmGYlsrBf/sKqt2xoG2brnbjdY41TGf2IXY1WiLM/jsBtB3H5VbEcVKCPuZl0ImaJr\nrGoOjfB6eGyuC7OzVO/GB/SRG5dQcbhde34JPy9cn37P/fqzXS9uvvlmZs6cyaxZsxpTKJ6r5+La\ndt9msqksVBwqEdWbrNznhTE1u5qrDo1LqEysOjfeYLaoMZy6a9VLxtuIbbTsy5iJCkSlq0LxmauC\nsT5Jb41K5dhjjwVK1xGfmSoO/3XdGIfyGezzxC4C9pDzOea6qeugkAolkUgkEo2gVqGMl0vdKWRf\n1lzYYdUuo84/sGr83nvvBUqv/1NOOQUo8xNkEFWTGasw99xz8/e//70j//Dg4ODQ1KlTK3vcCGMi\nsufIjmXfdtCVnVlvIssyL11G4vf9V+YSM+nqEGMpJ598MmeeeSaPPPJI12qt1wpj2c8NN9wAlOvt\nDA+zebSNSsgYiwpVW0UWKOqOq5d4Uqz7kWHGjLSqGJvX07oVf68P/ZhjjgFK52b7XXnOX/nKV4DS\ntcLsQr0JseYnKhuvgfGLKVOmdKxQ6u4Rz10V54RF69bMOLLWRkWirWTl1pUYO1NVuQ78vDE1M95i\n/ElENT/WNep1wmtci+5DpeCaN36sDYzruK68F/RoGINznVnX4jryeWR80sw6lYc21gOiilTpxC4k\nIhVKIpFIJPqKjiY21sUoqj4ve3vwwQeB4tP0rWvGgX5EszLMaNBnrh/ReIFdaWO1eB26nb5Wp05G\nHqMMxDe8mU2yMBms5+Cx6zPX56mf1wwY4woRMT403nmMhCyxU4zcTrfKRBvpBz744IOBEhPR3vYt\nkj3Zz2rJJZcESr3C2muvDRQmqy+9Xcj2O8HAwAAjWbk+6qhMRFSYrg+ZqHEcfdvOP/G6qmRVIl53\nK6m1hTOJrF+J7DzGHVUmUQl3gqp7RHYuO/ZcrJXwfjbec8YZZwAl1vKlL30JKKxc1aWNrU/zeWLt\njrVgHlNUCTHDLdbLjDyvXlGlms109LngZFfXtve9z0xjpHbe9nueq8pi3XXXbfm9tjEb0O153X0O\nmAXmPCZt3HYGbVufSiQSiUSiBuPGUOaYY46hhRdeePitJvS3OiPcHv1V8O2nQpFp6iOP8459i59/\n/vlAqeLdZ599ADjqqKOAomhkIL6FO5jc2LZP9AUveMHQ4osvPiqTSVbjPmNGW+ylpa9bJWLPHhWJ\nx2z9geemCjMjyVnScY6C6LRqvRNbLLvsskNnn332MCsWTqeUSdbtW2aqD9x1oG08d3PljbHtuOOO\nLfvTRx4rrL1Wxl5kX02ui8UXX3zoyCOPHPZZu23jQUcccQRQVFUVrBNwbcvSzQI67bTTgFIprTLR\nR37CCScApS7J9Shbl3Faje49VaXipk6dysyZMzuOG/i98Hug9C8zq1PIpl3j9rOyyts5J2ZAOaVQ\npbPvvvsCJTZjNqh9rOx6XRc7qevP14kt5phjjqEFF1yw8r60/5jK1Gefz0avq793fXnO55xzDgA/\n/vGPgVLXZv2Sa92sUmu23J/ryWth3zTXnchK+UQikUhMCjqqQxF1sRT/bi2FmSn68+zBr0/VWgir\nvc07l/3bhVTWp2JS4bi/ONe76QlsY/3enHhz3Ktgx12r/GNWjczSczF2Yj8imap/t4uAjKfX2eBN\n2KJdVRT9yDJLM5qMH5gLb2zFbtWuJzulxumYsjb9xK6vqnUxRsV/z7aoukfiMdizSXWm4jQW53WV\nYarqvLe0getBli+bN07hucVuF3F2yRjdJ3q2RVRNHkvspRUz0OJUS+tOrFeJ7F4low3szNvu86BJ\nhVLXkdtzd43GdeJ1s/+Y2VgqFzNhjc1qG5+Z3gPGWnxm6lV673vfC5R7QwVdNcUy9ldMhZJIJBKJ\nvqIjhVLVTdi3mkzEOefOhDcWYu68HU6dKW7fGLvNyjz8vtWZe+21F1B8474tzan2eHzrx2wumejC\nCy/Mk08+yXPPPddYFbAdVK18N1NJX6W1M8ZA9IHKys1H11eq4pHNbb755kCZ6GfMpSpPXBvIgGW8\nQgY7ffr0rmovws/A6EyWuLZkZbIoM1msuTFbR2ZpFwJjMmbAaAttYCaM56i6UwnbddoMqRgHGznF\n8LnnnuupOjzWREX1LJxPoRKwDsHuwMYF7DpszMMOB2akGUvx57PPPhso2UDaxO4B9gTzX2vBPD7X\nxfzzz8/jjz/Os88+23iHXde097lZfioV+5H5s33s7HdmrYXs25ov/+73qrpKC23petEDEmuERrD1\nxtZF7OnmvlwvrnGr/b0HVKLWjejB0CZ+zlo/ny+uA+NLxqFGPhOhrIPbbrtt3PNLhZJIJBKJvqKr\nGIqIb1d9oObOm1dupoP+Wv129uSS1Ud/rm9rM1WiP9lZ9HbWtG6l3ZzpJucbxFnRsnAZgW9855/r\ns7RnkzUUMgrP3boTmaPZQP5sPMH9qhL9Xj+yvOpsUYU4C8SuAbIxGaaV02YFui68/vrYrRpWhZnR\nohK26lgm2i566fFWFUeKfaK8jk4dNXPN6yrDVFmoWFRbqjrjkP7scVjDoa2MQ9kXrZ/3SLsxiza2\nBxTb2HVcZauC1UMxzjRSoCgWEWfe12V99WMeSlwnHqMqzett9pbPVpWIvdvsUmwdk728vEdUNH7P\nzEqVi5mR7t8sNOta2p1emQolkUgkEo2gI4USJyDeeOONQGHXxg30aZtpYBdYGarVofq0/b3w7a2v\n255e9qMxdlJXsS/bM7skohfGESc1eqyeq9kYMoA4pfLJJ58ESoddfeh2YpZZylSMS9lduNNKZll6\nVZeAXlh57ET6ta99DSjMsiqW4noxpmZFvMrW7J3ofzYX315OdkpVIavyIgMV1qWY+dJLltf06dOH\nFltssWEVHv3xTt40z19/vmvX/nabbbYZUFS7bHy99dZr+TeqeGMr3ovWFXgunmOcpy68BnYXMIts\nRF+0xli5tVU+H8b4vvsc92fjg3H2TJ0yUr1XdeSuy1JsIoaifd1n7EouYj2b3URc+8ZcjTdqC2u1\nrA00jujnzfJTuZgl5vModnKoyh5NhZJIJBKJvqKrGEodk7DS2fiBFe0qE5mnb0sZbuwzJJOwy7Dx\nB9l7VUVunDndj7xyUVdvYOaaFfGqLJWNPnBjIWZ5WYOhEpK9m41hRpTQhy5kRLJ161g67R46Ftpd\nF3Ww3sS93P+4AAAgAElEQVSqXrN1PGYzmKq6xLoeVH3GI4zFCBWR60w2VoVubKG94yyNqntEBes9\nYMzDzMbPfOYzQImFxXk3ZgGaBaZKU8naXcCOux6X6031roKJx9lkTU4b32v5N2ZCRbYc7zlVm983\n3uBzw3o11duIGUAAvPvd7wZKDK6fz4tOERVpPFezR82UVb1F5eOzOM458d5w+3Wz5EUqlEQikUj0\nFW3NlLdnkn7a2JXTt5pM0bef7Nq3phkrZmfIrvT3nXrqqUDJaNHvbLZOVSzE3P0DDjig/oxHoBvG\nIesx5lEVm7BDqlkZMlAZqeeo/fW933777S2fk6Hq/7Uy+qCDDgJGMxGZZewhVodubBHPPa6L6POW\nXcfsQFWcqs3rbVzKehNrJ7SpHXVVvhH7778/UGzfLrqxhR0QVApxcqe+aq+3LNm+VSpMz904gxXS\n1h9pO7N4jKnJMJ2CGDOVXI+y8RHHH889/tw3Vu66cI17bhFmHKksXXeqNv9u5267CDgrJE5mVBFb\n09FuV+rJUCh1iB3dx7h+8bhafh9jNe12kk+FkkgkEom+oqMYimxKH3en0F8nI3V7P//5z4GSPebP\n0d/faQfdfvbmcUa8lfAdbAcobEuftizNTDpZk0zVzqn9Qi9ZXnaTtpK9U+jP9/qaJaYSMcPJ2Ehk\nlNHPPOI4gfreXRGd2GJwcHBo2rRpw8dkt1aVRd2xRKg87CJh1pf97Zz0acZUjEuanRXVvOpRW7ne\nVAlxgqPo5R6py7IUHpPPA4/FbD89G87+cBqlPf48F1WfysOYnOdUNe+kXfRiizhlsmlUra+qeLf/\nanuzxHqtT0qFkkgkEolG0FYMxepafY91iAxQ36WdLptCnI/caZZRL9k87aokK+EPOeSQlmMz3uRc\ndPPHjZ1EpqgPvipeYOWrnVbb9YV2My972rRpQ/POO++wLWKGWbuoq41pt0dYRNXc9n6si+nTpw/N\nmDFjuJO2MY2IGKuwvsjMJXt7qUDi91T3sut2vQWRlUefe9U6bmKOeh1i5pnqyvo1Y6xmNOq5UPmo\nSNyOHXrtbxUz1kYcJ9B5JX83Kn7Ed8f9fFW2aN3zJiqOug4N7T4X4vdH3oPjrYtUKIlEIpFoBF3V\nodR18dQvF3Pxo0+1ru9R3Vs9srSYNVKHJucbVMHKeav9tYmVr/bg6VT5RMRr0s+JjVW2UHVZMd+u\nUmj3c3XVu1Xb69R33oktpkyZMjTXXHONqr42puHajMdUdQ+5Xsxwi/eM39NrYA+mqloN4X7HqIQH\nxp2n3nN1uPuuYuGdxsAmCv3sNhwVRZUN2j3G+G9d9X+791LVcaVCSSQSiURfUadQngB+P3GHM6GY\nMTQ0tFC7H05bFKQtCtIWBWmLgv+rthj3hZJIJBKJRLtIl1cikUgkGkG+UBKJRCLRCPKFkkgkEolG\nkC+URCKRSDSCfKEkEolEohHkCyWRSCQSjSBfKIlEIpFoBPlCSSQSiUQjyBdKIpFIJBrB1PH+2OsY\nyzjSs9vGh73uv4lBSnW26LQx5eyG2XG86WRhImxR15rfZo11g4/qGihWNWq1UaMNPW0WKeaYYw5m\nzpzJ888/n+viP5iIdVH3zHIUuM1I+4W68QhVtqjtNjx16tThKYJxfnkVXMSbbropUCataaS6Dqfd\nwg6aTnwb7ybs96yHfqPpTqyzky267bzaLpokGnPNNdfQ8ssvz1vf+lagzEPxXqmaQ+H1W2655YDy\n4rArtQ/8xx57DCjdi73esXtt1X6qZoY7h+cnP/lJy+e8Zy+99NL/+nukaUykLepeHE5wvfHGG3vZ\nzShEgjPO57LbcCKRSCT6h67mofSKbmfDv+IVrwDg4YcfBsqsCBVJ1fZe8pKXAIXtiWRfBU3Yok41\nTfZ8i3bRqUJZYYUVuO2224CytlVXVbO8R+yr5fe6oJxiqaKIkxtVcSoLPx/V3Qc/+EGguLQuvPBC\ngGGvQ5x6GO+hvEcKJsMWenHmn39+AB599NG2vuczz2mq3c4WqkIqlEQikUj0FeMqlDnmmGNooYUW\nGmb2kS31yjTrtiP7crrhI488AsAyyywDwK9//WugsLF2Fc+0adOYOXMms2bN+q9lXy972csA+NOf\n/tTI9iaSfXW6fqrYc69TLqvQiS0WWGCBoU022YRvfvObQGF8Htuiiy4KwO9/P/ZojKhY4uz4ddZZ\nB4Drr7++ZbtVUxD9uxMdvXdNGvFeWWuttQC48sorW7YXkQqlYDJtYUzlzjvvBGCNNdYAyiTP1VZb\nDYDrrruu5XtOT11yySUBeOqpp4Dq6ZntIhVKIpFIJPqKtmIoVSmM/faJr7DCCkBRIu5vpZVWAuDn\nP/85MDo1UpZWlRIpOmUcU6ZMqXyj9/rG7xfa9ZU2yb4WXHBBAJ588skx/67//m9/+1vV9sf8N2YJ\n9oqqTJpubLHAAgsAJbvLtafSiJmH8Zw6VVnaWIV65plnAiV7y3N6y1veAsDCCy8MlHs5zq5/8MEH\ngdYZ97Nblpf3WHzuzJgxAyhsXZs0HdPrxRYqz5H2HQnXjwrCZ5jPNtfRAQccAMCKK64IFG+N1/Mj\nH/kIAJ/73OcAuOeeewDYZJNNALj//vuB8lzQpq6XeeedF6jP6E2FkkgkEom+YlKyvOogc/At/frX\nvx6ApZZaCoCbbroJgN/+9rdAeetXsYCqc2yCfdWx7TG2A5SsDRlJVWFkv2syxEQyUc/VTBTZ1h13\n3AHABRdcAMDKK68MwKmnngowXOvxute9DiisXiV73333AUWNdaugO7HF4ODg0PTp04f3KePz2Oae\ne24Ann766XGPpepYXdNef+8J44qvetWrAIZjOPHzH/3oR4GiQN71rne17GfLLbes3P9kK5R4TKus\nsgoAm222GVCUiB6Lk046CSjXX3X24x//uOXzE7EuBgYGhqx36wQ+T7yOxsBUFp6T1/PFL34xUGIk\n3kMqGhXpL37xC6DEznqt/UuFkkgkEom+oieFUucXHLEdoJ6dbb755gBss802QHkbzzfffAB84hOf\nAGDDDTcECtv63e9+B5TMJ3+uYweT0WIjIlYyW6lsBbNZGioVmW+3imWXXXYB4Nxzz235/UTYItYN\neSzWRqy++uoAfPnLXwbKdZeVb7vttkBRONrUGNsnP/lJAN75zncChanqW5fN6R+uii91Y4uoDIRK\n1HqPqvhjPJYYL5BpRhtZm+X68F70Z9fPiOMFSn3KXXfdBcCBBx4IlHtKTIZCUYnIql0nH/7wh4Fy\n/V0Xxx13HFDWlfGC448/HihV5VdffTVQYntek3YxEbZ4zWteA5T4sMeu0lWR3nrrrUBRXXpvzNqL\nnRPmnHPObg5n+HkT22ilQkkkEolEX9GWQqny40cWVcea/bxvSzNPjCPYx0j/npkyvp19a8vCrBo9\n55xzgNIzLJ5TZINma81OGSweo7aRzS+99NJAYVOqr9jsr1dMhC1k2ZGVqyAWWWQRoGQqbbXVVkCp\nAlexfuc73wFKdo+xFhmqrM6f9UNXNWCM/uRubKEP22Z63jNmz7j2vUdirMVeXd4LxoUeeOCBlnMx\njnT66aczcr/Cc3G7Kid7Pn3+858H4OSTTwaKV0DGK+aaay7++c9/TkhzSNe66sw4gmt87733BmDX\nXXcFiqoybhRtqYq74YYbPC4ALrnkEqCsO23VrtrvZl249owXRkQFK3zmeU+YjVfl8dCr89WvfhUo\n1917x2dpU0iFkkgkEom+Ytz29SK+wfVtx/qO6EeOzMG37QYbbACUt/Buu+0GlCwd/cL6SmUqsjYZ\nim9l2Z2Q1bu9I444AoCjjz66ZXuzE2QQO+ywA1AyVrbffnugKBPzxGVz/tsuy+pn1lhdh9SoBGLs\nzWryl770pQCsv/76ADzxxBMAbL311gAsvvjiQLGF60q27vqxs0IVq4vxq17g9RGXXnopUGJi2jve\nIx6rLN1Yxhe+8AWgqHiP3bqCq666CoCdd9655e9RCVk5ff755wNwwgknAEWpfOlLXwJKHYRxyH63\nRx8JFWr82XiBGXKLLbYYULoAqNpULq4bbaxtXAff/va3gWKbfmdOwmhlEjNSq+I4KouHHnqo5fdx\nDRvbePWrXw2U+1uoRCcKqVASiUQi0Qh6yvKKVZUyVH2gznpQEdiHJsYB3vGOdwBw1FFHAbDuuusC\nJafaOoXdd98dKEpDP7J1C3W5/hGzUwzlyCOPBGDHHXcECuu2i+3hhx8OlArpH/7wh0CxjT7WTjNX\nRD9t0Wne/7HHHgvAXnvt1fJ7s3msQ5J9v+997wPgM5/5TMvnVXcXXXQR0H7vr05sYb871brses01\n1wRK3E91FrNlVF1vetObgHJPmL1j1s4vf/lLoNTkGD9ShceKaeMLZjSZBXTooYcC5R6TtUdvwlJL\nLcVDDz3EM88807d1Uddd4j3veQ9Q6k5UrF5vVaD90rSZHXZl+ao+bWW8yP22O5epiXukbg1225+u\n6t7yeWFsrilkDCWRSCQSfcW4MZTBwUGmT58+XL2tf/9rX/saUJSJb3jZmW/ZP/zhD0DJTDFLR6Xy\n/e9/Hyjsa4sttmj5vh00HUepf9AYzDHHHAMUf7N+6FhtHplQNxWs/YLHbDXvHnvsAZRsLxmu3WaF\nLM2ePXW1QJOJdm1tzEzlq9J1XVmPYJbXvvvuCxQ/sX5/65Ri5lI/RlAPDQ3x7LPPDtvf+OLNN9/c\n8jmvs2vbc3vzm98MlHtDlfX1r38dKGxbf7+1Fe5HJfTTn/4UKIrWuJO2+OxnPwuUGg2Px9ocY3je\ns/fff3/fR3bXxTKNmZr195vf/AYYXatjnEEPiVXksvLLL78cKIolrsemJsaOhRiz1KZVcWj//v73\nvx+As88+G6i+v81Yi/VNejSaViZ1SIWSSCQSiUbQUQwl+vfi29dKZtmSzGL55ZcHCmuyStuqUNl3\nnPGgj9yMFI/VvjZmyFhpbYWt7DDWOURMZgxF28gkzNLQ5ymMD1188cVAYWFWhfvvN77xDaCwuE4x\nmbZwXamAVV0qX9eDTFJlrKozDhEr511fnaKJSvk48921bd2HikQVbsaSqkolYizNvnUyW+OOqnrj\njaeccgpQesuZSanitQODMRdjMsYZtPW2227LFVdcwVNPPTXh68JjMP4TleoZZ5wBlPqzffbZByiq\n3fvezrxmsqlQvEYT2W14xO872mcdzIQz+8/teq4q0KaRMZREIpFI9BU9ZXnFDrn2UDK7xuwLfds7\n7bQTAHvuuScAJ554YsvffXubu23mi3/Xv3zQQQcBhVUZo7EqtapSPvY7mojuofEY/L4s3JiJjMKq\nYCterQ/wHH/0ox8B8PDDDwMle8efu8VE2mKM7QHl+nnOnqtxAHs5mclkTYV1ScabzAZyXfaTiQ4O\nDg5NnTp1OB4gu47KJGZReT2/+MUvtmzP35shaT8rYyn+XaVirYWKVwWrQrGLgBlQxkhk/frw3/CG\nNwBF3T/zzDPMmjVrUiZ5eg6qbVWale4qkw984AMtx+wsEL/ndlw/nnvsShG7C1ShSVtEb0/si1iX\n7eU52NPNZ6txabtM1M1BWm+99YCiXNtFKpREIpFI9BWNzEOJPb2sqdB/+/a3vx0oPlDnWfg5M1Ds\ng6SP1H/PO+88oLD3jTbaCCh1KLLDLnK3J9w/LPOIXWBVe8aHrPqWPck4jbGocN773vd6fED30+ma\ntIU1ElVz1CNky2ZxGT/w9ypUFaY922RhsnzXR6/ZSd1M8ozs2poooZJwLRsHMuah+vLvxiFVIqp6\nbSuj1WaqNGMtMe5gdwGVinAdxpjP1KlTmTlzJrNmzZrwe0SFqkr3XlChmBln9+APfehD7h8o56IH\nw6xUVV6366NT5TrnnHPWKoSIdmti7Abi54yhuA5Upu3CddBuzCUVSiKRSCT6itpeXuP5yqPfb7/9\n9gMKgzCP/Fe/+hVQFIusy947+gFlHGussQZQ+g/pG7/llluA4hfUB2qutt2H4yyAfs5777Sy1c9b\n/WuOvOzJzDXPxeydt73tbUBRKsYP2sVE1t20q0yEWTeyKtm7LFvWr42N1ek7j11l63p3NbkOpk6d\nygILLDCc72/8T8bnPeIad9Ki/etcw2Yk2X3W2IkqXUZqrE3bxBiNykb1rmJR+Rp/Uql4nNpKpTyR\nvbwiPEdVmR6NH/zgB0C5N+yg7HpQvZk56XZ87thFwp+9Zu3OdeoEK664Ipdffvlw/EdUrc04O74O\nVv27nr7yla8ARaXXzYaPxxGVSbc2SYWSSCQSiUbQUQylqlOtfnwZhPUo9g2SYfq9OsVzxRVXAEXp\nxO6g5uqby28Ov5ide3nJDGQEcT62ak5m6e+tG7AP2qqrrgoUX/1Exw162tno7QGw3XbbASWbL7Kn\nuA7OPPNMoKwz2bXMtFt0E0NR9cimYy2UMKYhqzYT0hhLXPvWGahktJWTFb33jBeoDlX5qnqVsaw8\nrhfjFjLSEcplwrO84s/aMN7n1p2Z4ejPhxxyCFD6mVmvZNfhKtZeh15sYbcP1VVTXQi8rqovlUmV\n5yTatKpGrw4ZQ0kkEolEX9HWPBRR9VY140jFYP6/ed/6f1U4Mk/ZkJkNsjon7l177bVAqStQwVRl\nQlQpqBhTmWOOORrvfdVurYN/lwGa1SPMXLI/lRlPwowWYyn97rfUT3hdZOfWJVmbYwwt+sK9ztZQ\nuB60SbtQ+cSMu06wzDLLcPbZZw/Xcdx+++3A6Gpsz0lGaJdfz90uEsbU3M7BBx8MlCwtYyPeY3YJ\n0AtgX7tPfOITAKy00kpAsZVKR+XrcVqV7vHOmDFjVEZYvxH7UYl4/9qHzPii9UqXXXYZULJKPVc7\nJpgJFevn+oEXvOAFLLPMMsPxXzNYm4L3gtfVe0Jb+bNdya1TUrnasbsOndZwpUJJJBKJRCPoqZeX\nsIeO2Tb24JIR6Ls0n9yMJt+ysjZ7ejkPQ/+xbM1j3XjjjQH42c9+BpSMFP3R7VaNz07zUCJUX6qy\nOAe96WlzTdhC5uj1rMupl/2YoWSmk77yd7/73UBhmDJU40tW+er/v/feez2Xds9jzM93agtrNsbb\n5mmnndbyeztsu5Y/+MEPAsVWW265JVBqrtZaay1gdBdhMx61ld4A+9/JwqOP3f34b2TriyyyCI8/\n/jjPPvts4/dItJE/ex09Vv8ea2p8bpgF5vRKazB8Pjnt0imUPneMM80OdWv2ObS6X/Xkda06RteL\nqtxam3ivxXtJ1R+hjc0mtYdgFTKGkkgkEom+oqsYijEO88Cd3aBS8G1nZoNdZGUMZmM418LJbDJc\n61Scqy58G5vbHXPl21Ums9M8lAhtpjIRsvI48W92QswYkS1Vsfb4e/2+ZiKpVO20rJJx3oWKxZz8\n6GuvQ2TI3a6JkWrRY9Sv7/wR40MyS8/JOelm8VmHFOekqHDWXnttYHTGk5lMrh9ZvHUt3juy9Bin\niHj00Uf7Urc1FrS7cRzXgeeiTVVRPh823XRToNTs+K/nptKR7VsjNJn3fqzviN3BPUcnsaryjfc5\nJ0UlYQxORWH2oGtSBey0Uz/nfnzmeq/6d21v7Efb2W+vCrPv0ymRSCQS/1XoSKEIc9v195nXbfWv\nvZXMsrBXl714fFt+9KMfbdmOb039vGa8jNUJtR2M4yNv6/uTgaoqc7N4+jldrlvIhs3Gqoq1qVC9\nrs70MJvIjDfjA/p79ZV/6lOfAkqPJ20RFU6nzLrX9TAwMDCqm7A+a+eVmG1jtpdZNlFpWMkeu0vv\nuOOOQIkbRRv7rxlrKh77nok4KbDd3lHdoN37T5bs1Ehrq3x+yOqtAjdzzRocnw96THw+uA7McHJu\n0mSiKrvU2j3jxa4P7xnVlnUsdlr2HlGxmBlrPNGMN+OQxpuMtcWYjba2+7DdzOuUiUiFkkgkEolG\n0FGWl7GRunz/2HnVWMc111wDFDZkLy/9gHfffTdQ/H/OjJe1uz37G9V11jW7oyoHfHbK8pJdayuZ\no+f48Y9/HCh1KE2jH7aQXW+wwQZAiRfINJ1/bkaSMEZmnYE1GNbkyMqMnXi9ZentZpc10Xl5zjnn\nHFpkkUWGlYb1H/qijRN677hPbWJPtiOOOAIoPb9cs9rM+MK6664LlDhSrM2xn57xR/8uA41TC2Wo\nQrXw5z//maGhob7eI3F2jPEjlYVK1OeG8QFhJfxZZ50FlHMylmfcQVbfa6ysn7YwRuEEVu8Jr8+3\nvvWteCwt/3puxshUoHqD7MCg0rFvolmEUdVX1fSN2H9meSUSiUSif2hkHkodfMuauWBlvWzbDCZ9\np86IsIuwb11z8OOM8TgdL9ZDVKGfjKNTNmSF/EUXXdTyfZll7LjbNPphC/sF2elW37cxNuMBTuCT\nkZrJop/Xz8lEZfWuB9dBvO6ddoIW3diiqg+VKtzYhnNP9JUbM9l9992BwlS1nevH2UEqoLi+/J5K\nxu4SsnMVjfFOt1/VVdjamn7eI7JgYyfGm4wHGAfy+eE5ms1pPELbWfdmhtIDDzwAlFhMLx0RoJl1\nUfU8iGvX7Dw7KljDp9clbldbeH39nhmThx12GFCUyFFHHQXA0UcfDYyePdRGx49UKIlEIpHoH8bN\n8hoYGGDatGmjfNHtKgCZRexfJXtSoTjDwXkG++yzD1CYi9lf7WaidNtBsxtUseCqvkRVtRhmU0To\nU52dM9PEOuusA5SOB16vJZZYAihKxH5CV155JVDqCYyVyExlpMYnZPN2UIiZJ/G6T4TNBgYGmDp1\n6qhZ4NabyJqND9qRWxuYDWY2j8ohxheEdShm83gvqtbM0vne977XcjzGE/baay+gqL0qNN2JYSzI\nlmXHnoMKxViY97+KxizQU089FSisXFsZW3P9dTo1sUm0uwbj2jXWZR2JqsvrauajmbMqUGv8YtZh\nPA6Vid0JVDK9IhVKIpFIJBrBhMRQRmwPGF2d6e/jvBTfsubOO09dBuLnY/Xp7BBDaRd2i9W3vvrq\nqwOFlctMzNroF/ppCzONnC6nUvXnnXbaCSg+dGe+vPrVrwZGZwmaFRjnnnQbM4no1BaDg4PDa1YV\nZTaNKt1zNn5o5bu1N9Z22cPLczEOsP/++wOlniVOI5W1u78YX4qKWV+8HcHH6lrQ7xiK7FjbWFvh\nfW4tjqxbmGXq71UwKtZOOya0i9nhedE04nVv9x7KGEoikUgk+oq2FEpdxlLsGlvnf63rI7TccssB\nJZtDhioLMxffalL/3u48jGnTpjFz5kxmzZo16YzD7C7jBiqTj33sYy2f6/fck4nI5onrwnWgD92K\nef3C++67L1Cur9tRedatx4moNxgcHByaOnXq8LGZjWe2VdUcezOT1lxzTaCcu90mPAfrBFQqrg/3\nFyuvVefGaKz1iZXQfi9OQ3Sdrb/++txyyy08/fTTE3aPqK5UaW9605uAUkVu9qdxRc+pKlOtafwv\nKpROMWvWLNZYYw1uvfXWVCiJRCKR6B9qFcrg4GDb7LjdrrKdwn4022yzDQC77rorMDp7o4oNNjX3\nopNjbhdO5Ntll10AWG211YDiF56oiYwTYYt4faJykT17vZruL9XBVM2ObDEwMDCqK0Ss3hf6qA86\n6CCgZFvFuKKZS53GhYw/qJBGHCcweq6O+4vTVIeGhpg1a9akzJSPGW6ul6ZiZN1idnhezC7IGEoi\nkUgk+orabsMj2UAbPZCAzicnRqYamYgZM3ZcNWffKmARlclkMhoVhv2JPId4rjJKYyjGTszq+V+A\n0+TsV2U8IMZUvH597AYw5u97VdAjv+vcE5VnjFG4Js3Ccp0Y01hooYWA0VXddepfVq8yMd6o0olx\ny6j+YqblRCCek/9WHcNkKZMmULfGJupZVeXFaQqpUBKJRCLRCDqqQ2mXyVW9bdt9O1bNnG668vl/\nwScqA+61Gvh/wRZW0DsLolt0E0OJazUqhBHbBkr3AOtAVAyqb5WGvcDsPlzVM8x7LcZyrAEyA9K+\nVq4XYymxBsz//y+si6aQtijIGEoikUgk+oo6hfIEMPYIwf9+zBgaGlqo3Q+nLQrSFgVpi4K0RcH/\nVVuM+0JJJBKJRKJdpMsrkUgkEo0gXyiJRCKRaAT5QkkkEolEI8gXSiKRSCQaQb5QEolEItEI8oWS\nSCQSiUaQL5REIpFINIJ8oSQSiUSiEeQLJZFIJBKNYNz29bNrg7OmWjBPRrO3fjW67BX9tEWv59zu\n4LambJtNAAvSFgWTYYs4snnE9j2mJnbTMapsUTsPpRM88sgjAMyYMQPoX8/9fm23SVR1AZ7dXiTd\nYGBggGnTpg0v8ro5I/HfTrtQu32nGtoxN3byddaM81ecQX7rrbe2tZ+RXYP7hfnnnx8o83H+L+Of\n//wnUK6r2H777QH4+te/PuHH1CTGWk/xBeGatFP23Xff3fL5+CIRna7TiXoBpcsrkUgkEo2go3ko\nXe9kNnPzDA4OTvi87CrIUES76svvyfajbWV9ssA6dGOLqutapQSck+O/cTKf7P0vf/kLAPPMMw8w\nenphG8fXclxV83mcHSILdCbJ7LAuqlwdI/YL9P+emghbeC5ep6Y8EHF2TN16qMPssC4m+1k6bdo0\nZs6cyaxZs3IeSiKRSCT6h0YVymS/PTtFL4wjspx2WXGvuPDCCwG48sorAbj66quBMtHPGfX+XIU4\nUXAigvL6ie+55x6g2EibLbXUUkCZs+7UQeNRKhVVV916M6Yi461bl3PNNRf//Oc/ef755yc9WWP9\n9dcH4Jprrmli811jdmDlVahTcSKq+Xhvtvu86qctmn5eaBu9AHF9xUSWTtVhTmxMJBKJRF/RaJZX\n1Zu+qTTf2QmRScRzb1qZvPCFLwTKLHLjCz//+c+BEn9QmSy22GJAybybb775AHjqqaeA0RlSvWDZ\nZZcFSoZKVVZXVCY77bQTAD/4wQ8A+M53vgPAXnvtBcAvfvELAH75y18CcO211wKwww47ALDAAgsA\n8GSUNL4AACAASURBVOijjwLFNp7r7bffDlSvy8gK11prLW655ZaOzj2iW5XuPSKjnGxlMhmoU/0R\nVcpkueWWA+Cuu+4CYLXVVgNKbO6BBx4Yd7sRU6ZM6fuzq9Pnxdxzzw3AX//6VwA23nhjoKybY489\nFoB1110XgE033RSAZZZZBoDrr7++ZXvGXP/2t791eugtSIWSSCQSiUbQUQxlkUUWAeAPf/hDWxuf\nY445gJI9E/HhD38YKPUCL3nJS4DC1szVl7XFrKBeMTv7hyNk4TKJF73oRUCJN7z+9a8HipK54YYb\ngInxD3/0ox9t+VdEZSrjPOqoowA47bTTgKIkLrvsMgDe8Y53ACWG4jm//OUvBwrL0k8sS1N1rbPO\nOkBRNqoyEdflyGygoaGhnmwxu8URXQ//+te/gKIC6uIO4r/pHolQyZ5//vlAiS8+/fTTQPuxONGL\nLRZffHEAHnzwwXY3MS5cw1HZeC8Yd/QZ+qlPfQqAY445Bijn2q0nJWMoiUQikegrahVKE9XD+vMv\nvvhioDBNK571/8sU9J3LUH/4wx8CxQd/0UUXAe2zrCpMZI59rzb8/Oc/D8AWW2wBFNZ+6KGHAvDd\n734XgCuuuAKA9dZbD4DHH3+8re33Youll14agCeeeAIovmqhL3uFFVYAYMcddwQKO/rsZz8LlOst\n+1LZyLKPOOIIoKixXXfdFYD3ve99AHz84x8HChuPytiYS1QsEZPJyj33qi4EddmEqsKqmJ7M1e3I\n0qu8Cf/NCqUqdnvmmWcCsN9++wHts/RubGGmodej6ViM2z/nnHMA+NGPfgSUe+rss88G4IQTTgDg\nscceazmebpEKJZFIJBJ9RSN1KLIeYxwve9nLADjxxBMBWHTRRYHCLKP/Lr7FZUm+zX27fuYznwGK\nn/C/QaH0CuMEZl8cd9xxQKnJkHGY3fXTn/4UgIUWWggo6m7VVVcFxu2X1bYtXvCCFwwtscQSw8qj\nDnvssQcAK664IgC77bYbALvssgtQfNxxLcq+b775ZgBWWWUVoMRKjMG94hWvAIpC0V/9q1/9atzj\nWnLJJQH4/e9/D7TUq0zautDHrhL1+n/hC18A4IwzzgDgrLPOAhjOSlOlCdW/cam9994bgNtuuw0o\nCvbGG28ESl2S3gHRiS2mTJkyNNdccw3fn/2CNlG56uFQjUel6z0i1lprLaB4PKLqixlUYnZ8Xuip\n8Bx8Lvjz8ssvD5TMtl7j0HVdRlKhJBKJRKIRdKRQfOvFytNLLrkEgK233hooDOEjH/kIACeddBJQ\nOu/KML7yla8ApSrY7b3hDW8A4JBDDgGK0olv36rssXYxOzIOoY0XXnhhoKgxWfqPf/xjoNgy9uzS\nRqo/mUnV9e7UFjKVsWAGmvUlZnV9//vfB4qCVeH4+Zi54r8qB+sJzOI6+eSTgRKjs67ly1/+MjB6\nfbid173udQD87Gc/A8asnG5sXXgdZJJm5Z177rlAURbawkwkFUaMlfivHZQ//elPA+Xe+uY3vwnA\nVVdd1fJ5Wbk2iyrPaxLRiS0GBweHpk+fPnwssRr78MMPB+DAAw9s2af3td2Ft9lmG6A8H1S0p556\nKgAHH3xwy35Vb/7+jW98I1C6STz55JNAYel2bNAmd955J1D/PJmdnxc+I312mjl53nnnAXDTTTc1\nur9UKIlEIpHoK2oVylhVorGLp/UjsuSqWQ+/+93vANh3332BwiBkz7JtM5ZkdSog37ZmEf0vDVJS\nSZgJZY8uq77NIzczJfbkqUPsexT9xJ3YYo455hhacMEF+dOf/jTmPmSexnU8RhWr2VrGLuzh9cUv\nfhEoWWAqXhnne97zHqAojZe+9KVA8fu7Th5++OGWf2XrUWFX+ZP7uS68zkIbqOY89xhX3H///YES\nDzDmcccddwDw61//GigZbvvssw9QYm4qHuOZG220EQDf+ta3gNEdnUesr8aUq+dklfarX/3qlmPz\n734/zgyJtouwa4Trw84J2srt3XfffUDJiPQe0FZNxBknSqGo6r2OG264IVBiZmZ/NY1UKIlEIpHo\nK2p7eY31to6sOOY2v/KVrwTg/vvvB0oWmFkZVfMtZBBWSv/2t78FCoOVvTmRT6XSdN+syUCscI2Z\ncPbk6vZcIxvvJRPnueeea1EnMn+vn38zVnHdddcB8LGPfQwoGWgqFZmlvnN94VtuuSUAH/zgBwFY\nffXVgRJzi1k52223HVAqoX/zm98AJd7kWo6dWEeeR78q3M1w1BayZzOUjI3F+KRr3nOwY8LIY4Zi\nC1n80UcfDYyOxZgFuPbaawMlI85r0CvGWp+yaOfP+LMeiHvvvRcoHRDMvjILL84MEvF5YwzEbC9V\nmzbWFmY+WaNhXYrr4r+h56CxMdd67E/mOU74cU3KXhOJRCLxP4eOug1XVX1feumlAHzoQx8C4G1v\nexsAL37xi4HCNM1AqYLsXPZs7YU+V5lDXaXzfwNkGPp3ZU8RsrPLL7+8p/31g3l7bCuttBJQ6j7M\nTJONe/31WZtpJEOUsdolVpb7ve99Dyi1FipVz0WFIct2OyoWM5nMAnvooYeA0RlxnU63bAfaxvii\n9R7eQ1FlaUPPfY011gBKjKQK8boaH/BeMqby1a9+FSgZciqdqHiahIxf5WCNjZ1xzURSmXgu/my2\nqPGeb3/720C5V7zOcT/aWOXidr2+b33rW4HSccNMuV7r2iYS2kBvj/eOqIpj9xupUBKJRCLRCPoy\nU17/sGzLTJYqqGTsZqzPXCYjg5RRmMHSa+xkMrI27Ce18847A6XyecQxAYVtvfa1rwVK59xuz7lu\nIlwT0yurpsGZWWRsxVqL2PPLzBSVjLMczNayIt56BNWd+1PJWs9grC3Gi6w/cftjzLbp27qwUt0M\nNf37Kgp7tlmz0W4vNu8RWX30JqjyVIsy27r5F03YIh6L68Gsz5g1qrJ0nfh510Gd2rYDQ8xwiv20\njOmqdFWNxqsiZocsLzMfzQY98sgj3R9QbLTmmmsC/VOgmeWVSCQSib6iq4mNMRMlMgbfknV9Y/R5\nx+mDkaGoYMTsmNXVbldh/b4HHXQQUNiSNlV9ycJl8RtssAFQaio6RdM2G5kRFTvdek7Wjdir68IL\nLwRgq622Asr1l6Vb8SzLNmairVZeeWWgrAeZqDUcsnyVTtXkP7PP+om4T21j/M8ajNjt19+bvVO3\nXeuUjI2oaLSpWV+q/TjTaCLnt3jdYm8tFa4xN4/VYzL2VQdjKfH5FCeIHn/88UCJyRxwwAFAtTKZ\nTMQOCSpb40DRG6Bqt3brG9/4xsQdLKlQEolEItEQxo2hDA4ODk2bNq3tnlkyEBlmnUIxniBj8S3s\n9/zZjAWzh5rCZPpE9e/rEzWPXFYe51pYhzJjxowmD2MYTdjCjKbIQGNfMc/NdeXshs997nNAUR76\n99/1rncBo6+/ikYVJ0N1TopsPKrAOmXVj3Xhuevztk+da91j0FbGWlS09rWSvesb92e/byxFeE7a\nTuXjufcztjbi926r5Ri8XjHD0WNqVz35ObPD7Ful7WKMxg4dThjV1qIq22synxd2n7BTxp577gkU\nRSK0lVmhZtw2jYyhJBKJRKKv6CjLSz++U8GE2RJ2Uq3d6X8Yg5MbzZ2PVb32aDLH2swGK/CrUDWp\nLaLJPkUdbKflX+dSmNmkL33EMQLVmVFNoRv2JZOULak0hNdJ5uj1lgHK2rWFM1u0wdvf/nagqDjj\nDXZUtb+ZPnYZpzE4e4edfvrpQOkt5nFbp2CltugHE/3kJz8JlOwbFYdrW1UVu0NbRyJk9c5+MT4l\ny7brrNXf3lPGlaygb7cXXJO28Nz0OLiWXRdeF7NE62InsnZtpy3ssBFjKT4PXG92J7Azh12qRdz/\nZCoUbfPOd74TgC996UtAiRsKOz1Hpdo0UqEkEolEoq/oSx1KHWQgKhPjA/rg40xx4wvm0tvfqFdM\nBuOIfnyZo2w7+nuF2Tyyb6FvvN16hSr0olCqVFv8uz+raM0CMxPFiY4XXHABUBitysSYiEo4ZrxY\n8WzlvPuVtcV+WmLzzTcH/p0t9q9//YtZs2Y1vi48d5mj00f1ics0VWXeE64P1ZedumXV1tRYu+U9\npYqzd5c2iOtrIhWKsBbG6+v1Vo0ZJ3SOSfQ0eL3sgKAa8xzNMlWBavtjjjkGgN133x0o8Urh98U1\n11wDtHQ1mPQ6FBWtGbFez6jC7JvWL6RCSSQSiURf0ReFUsVc9XnKDPSNyxhidoVZQGYy2I3W3v9j\nHC8wOqYTc7W7nfXQ7mdHIsZzPAZtpO/bGosIO/TaJ63X/VehU1sMDAxUzhWxS6ysW4VpbYRZYMaP\njA85xVCmKNv276oxWbl+ZLvK2l3Y2M2yyy4LlHOP2V4Tkc1TlankvVCV4Rjn1/h3WX1U82Z9xUp5\nP+92Oq076WZd1G3bYz/22GOBwrqdZ2O2X8wu3WOPPYBSq+XkRa+n97sV+P7dTDgVkOun6rlQhdlB\nobimr732WqDcGypa1Xm/64pSoSQSiUSir2hLoagQ7P/T7dtPxqof2Yrln/zkJ0CpO7D3V8wKss+R\nM6n/m2fK6yeu6mcUK6yrbN5UpXM3tqiq6xBVStV6JWfJO9fcLC/rB5ybftlllwEl1uJ+9ZXLyvV5\nm60jI9XGfi4e12Qo106hT9xj32STTYByjs6YUbWpxtqtN6nCRNrCc4wqy+fFm9/8ZqCsA9WZ5yZU\necbe3I42UxHZ7bpd9GKLpu5T17j3jp4NY2reK2McTyP7F6lQEolEItFXtNXLK2YWdQt94/qHnXcx\nfDD/8ffqJzSrxzkbVn/6lpWhyEj63eNrPP+wiqOus7LneMMNNwxvcyzEc61Ct4xD23Wj8gYGBpg+\nffqo+SGRBXtsZuXZBVglqm/b/lWbbbYZUBiqM+b9njUVsm/jEG5fRWsGzBZbbAGUugd97LFC3vX4\nr3/9a0J6WnUD60fe/e53A4Wh2rNJZuq9EOudZidUKUTXU/y7183eW/ZD8/kg4pwcswa9/nadNs7Q\nb0yZMmVk94WetuUaVYmq6p1FZXzRzMfYZbjX/etVqJq2K1KhJBKJRKIRTEodSruwMt7sD3Pw7cVT\nNX8jdmKtqi7vxidqHCjmrIu6rCqPUdVnp+UI2XivcaJ20Y0t2mUtEdYP6P/fcMMNgZKd5WS/GCNx\nf15f9xun1vl3t+/PMlb7aVl1HDGZMRTXh9ffn5944gmg1BsZZ3JGkB2ZZfNximG3mEhbxEw2j92f\nVWfGUseIfQGlI0KsR5qMOGNT0KNgdpfPIWfHGw/y2eizT0+HKq1dL05dzCVjKIlEIpHoK8aNoayy\nyipcd911w/7aiYL7s8o3dinVjyhUBVZen3DCCUBRJk1kOAwMDDB16tRKZSKiMon7loWZnSEr93PO\n2Z4oZdINXvnKV3LGGWcMVzzLHGMvJGGWjrZzprzdAaxPMafeOiV96nEejj5y+2HZq0u2Zi8nbaot\n77zzTqAoE1m8vniVUScYGBhgzjnnrF0XVYjxAtfyTjvtBJQaLeeXGCeSoZrVpYoTk7F+XvjCF7LC\nCitw8803d/X9OK1UuH6MjVVlrn3gAx8Ayjr0+nZ7bWYnaBP7lR111FFAUW/bbbcdUDq428fOeFOn\nz75un5WpUBKJRCLRCMZVKHfccUej6kQmqj8vQiaqv/iuu+4CShbQGmusAZQMBn3svr1VJiJWzPeS\n2TQ0NFRZWT0WrN3Rjy/blkXfcccdQKlw9Rh32223jo+tG5gh5Zz3TnD//fezxRZbDCuA8847r+Xv\nMY5kBpzK4bTTTgPKjHh7LMmy1l57baAo1VjnErPL7Arrv64jbV+V6eT1rMvMGw9DQ0MdMeAY1/Oc\nPFdVkurLNavt7r77bqD0/JqdstL+8Y9/dKROqjwHqnez/LzvX/Oa1wDleqnarr76amB0t+te4b1q\nB4bJhMrDHnBmrBlP8vmh2va50tT6MOYb569EpEJJJBKJRCOorUMZGBhoLGOkSpkIs7r0C6600krA\n6JkgN954I1BiKb699RcKmbIzx6207QVW6cqmI2IWl9h5552Bkp2j/1eWbkaKqqtfkE3HOQqdwDUR\ne2SJ+LOsWhbunBt7eclIVSZObFRBuP5UJsYXvvvd7455fGZ9xdki7t91cdhhhwElp38iEON6qmjX\nvvNSrNGyBseebu32n+q2Mr5JVB1j1e9jDGX11Vdv+bz3nvOYVCrtdrtuF3avdkZRNzDm6jOrG0/A\nSPhss4OytVx2l3Dm0MUXXww0p0y859qdr5IKJZFIJBKNoKs6FN9W0ZfdLayMt6eXb3WZiazaSmon\nRx5++OFA+9PnIprIK9fHrb/1nnvuAeqnV5qtY878ZKMXW8QZ4CoUfx+vT+y9pI9c5WLGkrEyq3/N\n/lLRxK7TVcw3Toasi4VNRu2FilFlq3K14/K2224LFNYcz7FfiqQJW3R7bDEGamzM9RG7XPfaOaPb\n2ouKbbW1LvRYGBPpFJOlRLMOJZFIJBJ9RU+V8ubzW63bLfRpy0hkY7J9qz3NMDDecMQRRwAlPtEp\nmmAcHks3NQyTAeMGxx9/fMvvO7XF4ODgKFbkdZR1ybY7ZVFeZ6uCrazfaKONgNKFuJesPSjxie23\n377l95MxJ0cbGeez4lkccsghAJx11llAsW3TXWQjJqM63HOy+7DxJWOnwusfM+XquqJPZuflqm4A\nsyus6bHWTKRCSSQSiURf0ZZCaXfqX9OIWTrGG8y1/m/uzTPRqIt79WILmaF1H53U64yECtUaGbsI\nmNniz+7PczHbz5hLZO125DVGVzW/RUzGuoj3mPECYyvWZtWxauOKxx13HDD6HuoUvdjCDrj2H2sX\nUXlae3HKKacAsN9++wHl+qu6tZnZg84Oaapb+v/y86LTdZIKJZFIJBJ9xWzdbbgpxPncoh+Mo+kM\nuIlCN7aw6tsMlTiLw7UVY21V0ymroHLxc9G2cX919Q+yMX30xi0uvPBCv9exLeK0wf8VTAYrd+b8\nY4891sTm2obrQsUba0e6sUVdjKvfMbCmMd988/H0008zc+bMVCiJRCKR6B/qFMoTQPeNjmZvzBga\nGlqo/mP/RtqiIG1RkLYoSFsU/F+1xbgvlEQikUgk2kW6vBKJRCLRCPKFkkgkEolGkC+URCKRSDSC\nfKEkEolEohHkCyWRSCQSjSBfKIlEIpFoBPlCSSQSiUQjyBdKIpFIJBpBvlASiUQi0QjyhZJIJBKJ\nRjB1vD/2u9twp502q7oGd4v/5fkGnWJ2soUz5J1vUnfdnYPhpL5eMTvZYoz9ARPXnbYftuh1Dvpk\ndeidiHXRbufsqs9PFHqahzI4ODi8CEZijjnmGB6GA/8eEuSgoJEYGBgY1bL8Pwc1piGq9vfcc8+1\n9TKp+r6Ya665xv17FQYGBnjhC1843E59JHbaaSd22mmnls+Odc5jbXO8z02dOnW4rTb8++HqA3a8\nz7W7/7GuV7t4wQteMNyufyTmm28+5ptvvraPQcw///zMP//8PProo8MvEyjXPZ6j+H//7/+1vEwW\nWGABFlhggeGf69aD+OY3v8mSSy7Z9vGORJX9l1xyya63ORaq7pmIF73oRcODyEaiav1GdHN/iKuu\nuoqrrrpq1O/vu+8+7rvvPmbNmsWsWbM6Xh8i2qDd69stujlGvxefkaJqLcdziz/HZ2zdetA20db+\n7D3sz3W2fNGLXjTu39PllUgkEolG0NWArXZlVhxr2u9RwnH7MnkHP8XjbULC1o3O7FSS+nlZQPy+\n5xZ/Hz/fqY0n0s0Tj70pV+Y666wDwPXXXz/u5+I1iy62XmwxWS6IuN92R7rW2X52dv+1C23jQDhH\nAnfqcuvUFgMDA5VD3qJLa8Q+xvxcp1CFOpbb58HrXvc6AG655Rag+rpHt2Q8nhwBnEgkEom+YkJH\nADcdVO8U8a3bT/blG91/q9iQvn5jAH5uiSWWAAqrchzpH//4R48d6F31TZkyheeff35CmOjcc88N\nlJHBoumgeqfwGk2ZMoWZM2f+T7DyOrTLgJuwxYILLgjAk08+2fbxjcTSSy8NwD333NPye1WYz5W3\nve1tAFx88cUAvPrVrwbgV7/6VVf71UYLLbQQf/7zn3nuuec6toXPnBHMHoDp06cD8Mwzz3R0LPF6\nRS/M61//egBuvfXWlr97HMY843jlEc/Eto4nFUoikUgk+opx04Yj9Mv94x//aPl91dtzxx13BOCu\nu+4C4Lbbbmv5e9Xbu1/wLTx9+vRh32K3mHfeeYHij41QOcgcojry73//+98BuPTSS4Hi45TN//a3\nvwUKyzvppJMA+NSnPgXAzjvvDMC5554LlGshE4nXKh5HE/Gsz3/+8wC8973vBar99iqTs88+G4B9\n9tkHmDxlIppcdy95yUuA0QxwdsNExniqlEmnHosf/vCHAKy55prAv7M1YbSf/8YbbwTgXe96FwAP\nPPAAQMfZdtro8ccf7+h7I+GxxXONyiQqljFiFmNuP9rO+OGcc87Zsn8z/vx93P78888PwNNPPw2U\n54LPC+/pueaai3/+85+V55sKJZFIJBKNoJEYim9XWb/blCWvuuqqANx+++1Aeft97GMfAxiu33jo\noYcAeO1rXwvA3/72t3bPIx43AN/4xjeAwlQimsjaiFC5+KaPDEOb+O+VV14JwFJLLQWMZl0yhJ/9\n7GcAbLrppsBoFfDss8+2fE8bROYTj39wcJBZs2b1JW6gctljjz2AonCNmejbNo60/PLLA4VReo6e\nS1Oxt2jbiP8LMRRRV2Q4GXHGGTNmAGVN77DDDgCccMIJAGPWYYVjBuB973sfUJ4z66+/PgA33XQT\nUO8h8d557rnnGoszRnurmn73u98Boz0bdfDzbu+tb30rAA8//DBQnsl6AbbYYgsA/vznPwPwgx/8\nAIDlllsOgF/+8pceP1CeGyOP+z+1LxlDSSQSiUT/0FEMRcii9f/7FvNted111wFw0UUXAfCJT3wC\ngPe///0AfO5znwMKE/Xtqk99l112AUqcoK7tQHyr+3mViZ8zK6RbptuO3znGVCIL0lbLLLMMANdc\ncw0Ar3rVq1o+7+9l7d/+9reB0ezaGEmseNcnKhM5/PDDATjuuONaPtdt+4ux4PW79957Adhrr71a\n/v7UU0+1HLPZOyrRP/zhD0CJIxkvcjsyVGMwDz74IFCfMx9RpUzmm2++YWXZb1QdY1WdwjzzzAMU\ndb/GGmsA8JOf/KTl953ur8nr3ylizYwV5b///e+B8pwxa+uAAw4ASjzRc/Z7/qxnYt999wVg4YUX\nBsq62njjjQE48cQTAXjNa14DwBvf+EYAfvzjHwPtZ2B1gmhv13C0RbyeUcWpnjw31/Taa68NwCKL\nLAIUxbHbbrsBcPzxxwOw3XbbASW2suWWWwIlk87nh7FA79m6Z2AqlEQikUg0gp5iKFVZGr5N77vv\nPgBe+cpXtvw+5kTLXP39EUccAcCZZ54JlLej39cXr0ISndZkdOoTnTp16rAKkhXp5+220eWFF14I\nwP333w/AX/7yF6Cwd6u3VSgyBs9dH6nKyP0vuuiiQFEo6667LlBiNiPOa1yf6FioWxdeb/3CZrB9\n4AMfAEqsLKotmeWHPvQhoNTcaBNrcqJ/15z7T3/608C/e3JB+37oiH7GDSIDlWF6D6jyvHdWX311\nAA477LCW7/mvNrzzzjuB4iN3XbpOXBedqvNObLHAAgsMveUtbxm+jiuvvDIAv/jFL/w7UM61Xbg+\nFlpoIaBkcWmDr33ta0BRHPGe9F7w88YXYvyhTi32EnONnQuiR6Fdhem5+P0rrrgCKNf/jjvuAIrH\n4xWveAVQbGhXiL333hso8Wrj2N47l1xySct2s1I+kUgkEhOKWoViJfV4kHnIJH17yoaq+k7pE5V5\nmJVh7OTQQw8FRvsd/X5UJJ32sZrMbB7rTGSksu9zzjkHKFkZVr6usMIKQIlDrbfeekBhW35eG6tw\nVDTWBMniIzqxxeDg4ND06dOH89Ej+1J5GhuJqm6MfQOFVf3mN79p2W5VzOOJJ54ASu1HVdZOnWrs\nlYmO9/fYU8l96aPWb3/wwQcDJetHVS6jrEJUHq4f41jGm7rNmJwdMt6MF5glqv//mGOOAQrbHtGL\nDSjrIWZexuxBP288ocpW3dii155c0avjv3Zz9j7/+te/DsDLX/5yoDw3jL2dfvrpQKlDsTZQlf+W\nt7wFKF6ErbbaCihegzHuqVQoiUQikegfarO87OkPhT3dfffdLZ/RJ6pikJVF1ixivnn039k1Nvo4\nZaxmeVh5fdRRRwHwkY98BCjsT6Zs9lAvbGFgYICpU6f2XAvhOXnMe+65J1Ay3o488kignLNVxma+\nmbVhb6+YlWRmjDEZ2ZmZdWOdVzf2eP7554fZksdiPEAft3+vUyaqKDNe3J7+36oOrNpkrbXWAuDn\nP/85MDp3vo1+VeP+vRPEY1TFvelNbwLgpz/9KQCnnHIKUK6/vmwZp7aMxxgrmKMqt9biox/9KEDP\nHSF6Qa995jzHlVZaCSj97YwD+ryINonxJRWH9U96RHyOxThWk4gdteOxtft9s0I//vGPA+UZaU2N\nmXEqEJ99Pld8bn3yk58ESnxLReJ6fMMb3gDA9773vY6OU6RCSSQSiUQjqFUoIzNlojKJsJeTftwq\n+LY2VqK/10wk36KROcpQPvvZzwKlRsOf/byZTRG9MNGhoaFx1Um76se40Zvf/GagKAl9oG5Hf+6u\nu+4KlKpzYybGq6J60/du5b2V9V/4wheAkvXTaXfRkRgaGhrOpIHC+PR123NJv6yKY+T3Ad75zncC\nJaPE36uEF198cQDOP/98oHRc0EZmoNxwww1AyXAyi0zm2W232U4gI4x9jjynq6++Gih+f2tsvBdk\njGY2qlys4ZJpuh3rDcxwillAxl6MM00GojLpdPSvCkcPhrZUAfu8UJWZMRnhWvW5pLK1J5isMd6B\niAAAIABJREFU/NRTT23ruDpBp10e4nNEj4PX02PVhj4DfU4bazXby+eBsTRtofp/z3veA8Dmm28O\nFPXXbT1WKpREIpFINIKu6lBi9o2VzHWV7cKcejMQ9HFuttlmQPEPxre6WUCyM9ma/kVjMjJX6xOq\nOgM30cur046pG264IVB8lMYXVlxxRaAwD7sNXH755UDpOnzyyScDtCiE/xwfULLFjj32WKBUC7vd\nfvZsip12q9aBMS1rLEbOj4fRNj3ttNMAOProowF42cteBsAjjzwClJiaPnBtoVrQrxw7L8uAXXff\n+ta3PO6ebREn8cVKZ3+WgboevJdU3RtttBEwuguEx27cSYVk/YrrzBhKD90hGsvyalfF+7mbb74Z\nKOskfk8F5P0d69KE60lFo2oz/uA1qMNkPC9UrnowVCSeu7ETPRrGRKzle/vb3w6Ue9L1p1fBe8jY\nrIrF5433TFSbmeWVSCQSib6iq15evq1kTcY82p1jLctW4fjW1u9XVXkfszTc349+9COg+PDjzBF/\n3yvGYlbtxiA8Fn2c/iwDkF3Lxo0rySysH6liNm5Pm+h777ZavBs4N0JWpfoy60rftTGPqExEPMcD\nDzyw5We3Z92SSnW//fZr+VnW7/XX1lHhaLtNN910WBn2CtfFtttuC8AFF1wAlPojVblr35orr7eq\nqep6q+49h9VWWw0oPZyMN2nrOsSu1CO7QnSC8bIGO5gGCMAqq6wCjFbXHpcV+Cpj40wRxvKMxX3n\nO98BSjyyaq57O+fUznmMRKdZUyoOvTBxO65pz0lbnHXWWUDJfDWr1PVmnNm1b92Ksdn4LG0XqVAS\niUQi0Qi6UihVb2t7MMmmq3yaMRc7Kg39wbIzP2edgb37ZcQxJz/GF6qYcC9wX3UsLtYJWLEuPCcz\nnWTIMs1NNtkEKHUF0fbazv1Yz2DspQrtqsl2EH3j+mPjdDgzzarqUuoga9Ln7TnqC1fBqnRla3Ei\npFlnsjMrrY1bNAmViVCZqJI8Fu8V4z5V9SMyTDMqVYOuL8/VbgNVaj+uo9hZt4c+aMP/33///YEy\nj6RdLLbYYkDJznM2vGs2Zm2ZzRcViufqdqzx8TllBmVVb0DX2TPPPNNVLc3ILiMeex3jV8F6LKqr\n2PtL6O3x766HrbfeGijPVO9F++z53DGT0vUi4noYqeLGU2upUBKJRCLRCNpSKFUVr5Ed2y+m7m0e\n+8IYEzHXWv+ecQThfvSFRvYVWV2nee/tIHYZrkN8m6u+zDT57ne/27I9fd9ma33xi18EqmfDy8Ld\nnixdphOZq5CBdusrh2q2a3X/ZZddBhTVZNZOp/BczfL79a9/DRR2FTOczjvvvJbv+724nkQ/lElE\nvIfspOx68tir+pZ5rxk7ecc73gEUReN1t1arqo5gImbJuy6qlEnVuvHcvf/N0jT77pZbbgFKRqTq\nr2rGuX2tnHdizM31oqdDRRtR5WFpFyO7SXjdq7o+CLt/qER9NsZnpgoiqvJll10WKHFtvTPGFa1b\nsh7OeidVmrYcI6urrXNOhZJIJBKJRtCWQqnqHyS78q1W1fu/Cm7PjATnpsswo8Ko64MTf1/HBrqB\nmURW1bY7cU//v9W5siQ/Z5X3tddeC8CXv/xloPjYq/qhaROZq9ci9jGrQi8xFKe8OZlTFm72jbnx\nt912G1Cmxqm6qhDrEDbYYAOgdBeQzdsR1diMNpBtqeqqlEmTiN1go12rJvBFBuv3VFVW/dvPyvXg\n37Wx23nb294GTIwSqYIK1S7kEfE5Icv2eWJdkSxa1m6PPjsx6KGo60bt88QsQ+vRYifnJp8THpe1\ncdYLiSp15jF4rHpjXPuesx4NYyRmBzql1i7W3gvOTTGby3tVFWbcKsZSOkUqlEQikUg0grYUSlUs\nIvr1RbsZETFTynnnZij4No3zCTqdbyGz8Xi7nRU9ZcqU4TqROHOhTqmYfeEUQ/27+j5lIrIzWbcV\nr7GmQ7hdM1yscDVmUlWxPfL4u8lgGRwcHJ71HRmimSlmybj9dntqHXTQQUBh4fqBI0tzQp8sXkZc\nVfmsMlJd+j0rrY1rdIrBwcHKTgRVrNefXft+z95r3//+94Gyds2ck2nKLFVlqgJ7Ml1zzTVdnUsT\nqFImQmUSPRmy7TPOOAMoMZQ42VXbmd1ltme0tbY0HmH/NCvmZfHx+WYGnjHZqt6A42FwcJC55ppr\n+H6vug9VCt7f7tMK+N13373lGI2xxtip/c7sa+Z6sbu1WYE+X/QK+Uz0uRERK/vranJSoSQSiUSi\nEfQ0U77TjCfhW0+Gok/T31vda2fNqglrVTALxJ5ewspbVUEvUwpFPCbZrgrBClezb6yU/8pXvgIU\nFXb99dcDpbJaZiPDkCXF2IzzETwubXX88ccDDKsI61mq1ObsMJnPjBM77rq+ZKyHHHIIUBSPHZW9\nntY92JlZaDOzwqyQdjtOzXS/E2mLqushc43X275WTuwTXn/rDFT1vcYDOrXFSLVWBWMmsuwTTjgB\nKJlrHrPKw5iJ19fnRZVq97lihwU7POsBMTanDbfffnug3JNVaGJdxFiJas66M7MzzYw09mqc0qwv\n44J2SDD2YeaaWX4bb7wxUGK3dqO2R5zrySwwnxN6TKq6BmQvr0QikUj0FW1NbKxiOZ0qEyGDsSd/\nhFXjMpDYabUO+uplKrFXWDcYGhoaM9/dY5JpyL5UFsYB7PprzEOfptu0tsLMJTNTzHCR2chYttlm\nG6Dk2DtDxO3rizXrR+iT1+feLcZbF91my1j5bAWz52RNjn5cY2z6i60CVhXKxqygVxW6LmKWj8qk\nW3Tb6wmqMxZjXMs1rFrz7567EzmNN7huOu3w26uiGU+dxI7YZu+Z4eia9X73nnJNu/ZjRpvxJr8v\n21eRqGDM0FQZiahMOu0IXIWR68Jj9xhVIp6Dtnn/+98PlDifvbjM5rP2RtsYMzPmomdjjTXWAEp3\nAHt02RHeKacxQ9MZ8iLGqerWRyqURCKRSDSCtmIo0c/bbewkQsVgrrSsXt+nb1UZhW9JGURkYx6f\nb3PrJCIDqfMDjgVtIcM3piFDsCrcLDBZl0rAfHTrQvR1y5ZlHJ6bczHMRDJrQ3+/eeoyC32gMhxj\nOTIV/dDaSlgp340tYvV3r8zO6+l1NyvHOSiuVefluF5EnLtu5wZjL2ZEaXNjKrK7GTNm8Kc//Yl/\n/etfHdui6RoG4Vp2vVinJMO1dsueTmaBtVtfFBloRBNxg7hOzMKzb51ZVd7n3sc+d5wi6MRYswD3\n2GMPoNQlGV9wFpDX12vidoyZmDVonDH23Yvoxhaxi7OzoHx+aAvXtIrCGJjH5vPBTMQPf/jDLedm\nrMRjV92r0owXqXx8DlRl6lZNIDVGljGURCKRSPQVbdWhRL+oyqTTjrW+rWVddrxUSZjlEyvzZU9m\n9cjy3a+KSfXgW3wsZQLds8jBwcFhZuGxOYdAdq4P03O1/5DMQ9YkM9EH7r/6WGOn3P/f3pnH6zaX\nbfx7hjgdXmmgSSqKKEkoJEUqhVAhSUShUMnQQBK9lVlFNEmppDRpkDmpkCkJJQnNoUl95M05+/0j\n3/Pbz7332ut51rPWs/ep+/pnf/bez7OGe/3WWtd1jzfddBNQbOf+ZDLO0/Z3GYjbi8pkgw02APqf\nlzEZvE4y0KbKJF4X6w30jcfrFbtZa0u34znbVdb+RmYRuU48buNeKpUm6Koy3XtNpup1F8YjVCaD\n9q2LyqQt78N4eEyxk649uqxbUi15b8nqzz333J5jlTV7vWOX8dh94KSTTgLgyCOPBMo95f5V8cbc\n2sKsWbMWnYPXzYw243rel9rAZ5sz4PVEmM0Vn3mHHHIIUFS36t7PWc9mz0DrVqrWq54U7wVt6/Fd\ncMEFWYeSSCQSie7RV5ZXnIctBu0DZVaNWRvGUPT3xWpSWbrVoVb/6vdzmpmZCzIcFYqsIGaJzZs3\nr3LexFQYf/6xH5H+WytQre5WqViJ6n7jREWZg797DqeeeipQFEyc9GjeuSzd43L2tPuNbE7fexOs\nueaaXHjhhYv8uk0q7cfDGJrqSz9zrCPy2GWYsSLejJVtt90WKH5gGbG28fc2WXhXUIFY4ew5afOr\nr74agM022wwoUyrFoKp8GJvMnj2befPmLVqb8X6OXQG8B7yOxrz0XFg5f+ONNwLl3vKei4hzdrwH\n7SbtPeVxGVuzTqWt7C4x3uax75hV+p/5zGeA0hFj5513BkoditddFa0NrUPRq2NszV5vxpX8GbsV\nxw4fPqfiFEvvSRVwHVKhJBKJRKIVNKqUH5T1xM+b0XDZZZcBE33wsnEzlzbaaCOgMJu6rsayOv2J\nMiCZaZMsrzlz5owttdRSE9huVQ8v4f9lljJA2ZBsXP+wMRh9rMaXnFpnzMZsLitfZfNVvaTi8UQM\nYotllllmbN11113EIKvm5cSMuDoYX4qdWYXX23USYzj61s1o0XbaRFUm+6tSsMNkNrWV7SVjFGYk\nWYfguXvdrXDWl95WTGfQbhJLLrlkba88z81z8Hp6Ha0jMVPS2hv/H9d0zAqzT54Kpc4WMRNr3Pn0\nbH/QdTF+YqPH7LPNfXmsxvnM4vR3PRfGNKzBct6Js+J9rriWVTSxD6Ln4tqPilQbx2dtRGZ5JRKJ\nRKJT9KVQ6ljXoNW4/vQtrI9UWINhbrUsvqpbrcw1TkWry1jpomfTZN18oT77xs+pZPxpNpb1Lf5e\nV+ne736HqcmpUiaDIvatqsoas5uAcaNoYzNm9LW/4x3vAAoDjTn3MaNlmJqcthBVt2rK2grVujNm\n9MGbydR2tlkXtojnWPX8kEXbUcN4gLBflUrGWi1V2qDHUYcmtojnFp9Vxoe9r6210RPh/eu9sMMO\nOwAlY+7MM8/s+ZzQdv6MmXEeh8/GWHdSVYeSvbwSiUQiMRI0iqHUsd86xRKn28UssqhkIlNty0/d\nBuNoOzNk3P6Aif7dfs9ZBqKN67J32mSiZuc5Ra4OZpLYkdlZ5E6t3HPPPYESCzNLMMKY24477ggU\n//Ggs8FnQudlOx1Ye2Umo73brD9QoZjdI6YjhtJUxQ+KQZVFFby3fO50cY/UeSw8BuO+KhfX8tln\nnw0U1WU8KcYRx+2353dtZTzT54j3hDb0OeY9VqfyUqEkEolEolNMqVDWWGONsa997Wusv/76QHW/\nnypUxTZiFlb8fBXzqPJHxrd/zBppozfP/Pnzx1ZZZZVFGUjWQsw0mLdeF2OJth7EFmuvvfbYpZde\nusjPGpVqnOAYeySZmWbHU6v1zcpRsagwZN8es7EP6xWcBeHUSlmc/mj7YPU7kXGUCsVzNb5jp2V/\nt0Ou/a7MCvJeNL4QlXLVmo/3UJ1aaGILu0BP59TIqbDLLrsApcZL1GVEDmqLWbNmTcjqivuK+/De\nsabKTNg4KXaK/fZ8riqTMX5e9BsnT4WSSCQSiU4x1MTG2o233IG17e21wUS76jIb1VXdfoaN5YyS\nlatoYpwnzr/Rpx1ZlDNmnMwpazc3X0R/tZkxZoPFnHugccZb03Wg8lCR+NMeTtpAtm/fKbN7ZMDR\n/z9oT6+q4++iJqffDMSmqPKADIs21kVVbVbVjHmzrPpVlHF7Vd8bNhacCiWRSCQSnaJThTIo+n1L\nTleW1/jK17aOqep79j0zrtDvdsSgDKZLhWLdiPVG7jv2E5KxxrqU2A029nKKFfPDZv9MZ5ZX3T1Q\nF2dsqzZIDGqL8TPlq9Z23TF2pfqbYoklluBf//oXCxcubKxQRL+KYYrtAhPvnbpZLlXbaXrPpEJJ\nJBKJRKeoUyh3AM2HRMxsPHZsbGy5fj+ctihIWxSkLQrSFgX/rbaY8oWSSCQSiUS/SJdXIpFIJFpB\nvlASiUQi0QryhZJIJBKJVpAvlEQikUi0gnyhJBKJRKIV5AslkUgkEq0gXyiJRCKRaAX5QkkkEolE\nK8gXSiKRSCRawdyp/tm08d1Ma+5WhZkw6nWmYBBbPOABDxhbYoklFrVar8KwjQqnax01GaRUd4xd\nt2wfdzxA/zar+rznlPdIwSC2WGKJJcbmz5+/aBDfqK5/U6y++upAGUpXdZxz5sxh4cKFlY0yZ0S3\n4apFPezs6LqLOIqbpe4Gj8fYdN7BsGhii7ZuEudqx6l2wtkQzvyI62KnnXYCynz1uqlzVX/3PAZ9\ncCy//PKLpkpqE4/Rc6q6zqJuZkhb1ztO03T7cX92DV6cXihdE5AuZsM0RV1X6jj1tArz588HqCWH\nEdltOJFIJBKdYkYolHH7A+rf4jJWJ7P94Q9/aLS/YSawue84ea2P7bjvSX/v93tir732AuDEE0+c\n8nN1s+aHYV/OiP/Vr37V7yaAiS6x5ZdfHijz0geFM+6dcleFOvY/zJycqqmQ/v+xj30sALfddlvP\n/8epo3533YOo2pxq+fOf/xyoVpMnnHACAHvvvfek253JCiVet7ZnwUS0qeKjKvf3e++9131NuX2n\nlKqMxx1jX8dXdVzRG1CFVCiJRCKR6BSNFMp0B92na2Lj+N9nepCtClUsbphAdNWc7HGfdx+DH3AD\n1MUn6o53mCmF0XfdVJF2hamC8JP9fToVylJLLQWUOIHH5u+jtuUwtvjFL34BwMorr1z1efcBlHWk\nglHZup3rrrsOgKc+9ak93xsVUqEkEolEolPMqBjKqDGT/cNViMpIn+eLXvQiAM466ywAnv3sZwPw\ngx/8AKjPlOsiVdZMFNXQsGru/PPPB+D3v/89AN/85jcBOP3003s+F+MJj3jEIwD4xz/+AZT4RhW6\nzOZZaaWVALjlllv63UUraMrmR3mPeIzG0lzbX/va14Di119nnXUA+Mtf/gIU1r7LLrsAsOmmmwJw\n0kknAbDkkksCJZOpKZsf1BZz585tnKEq9Cg86EEPAuDSSy8FYNVVV3U/8RiH2l9VZm187qRCSSQS\niUSnmLKwcVhUsaLFNf4wGUZ1LrK17bbbDihM94ADDgDgJS95CQBPe9rTAPjxj3/cc1xmH6kWBs07\nj5iKCdXlyPcL2dINN9wAFP/zHXfcAcC3vvUtoKyz9773vQC84x3v6NmOWWMxq8vtD3uc41FlF+1/\n6623AnDTTTcBhWkOyyw9J9n4q171KgDOPvtsoGT5XXvttQAccsghAHzkIx8B4HGPexwAV1xxBTDa\ne3PfffcFirrecccdAbj++usBeOQjHwkUpSke+tCHAiXj8txzzwWKEn3Oc54DwNZbb93zuTqF2hbu\nu+++2ky0qDDM3rIg0jjSn//8Z6DcA16fDTfcECieiDaOeTL0ux5SoSQSiUSiFTRSKFV5/L59fcv5\nVn7DG94AwKmnntrz/5i9IQ466CCg1Fb4tp4JrVziubddyayvdM011wTgvPPOAwqbjlXd+o9lmG9/\n+9sB2GKLLYDij7ZGxDz3No63qsq2KeOPVdyHH344AMsuuyxQjlm2H7+nMvH3tddeG4Af/vCHPZ+P\n2UJtqMxYQ+M2ZcVm67jv5z//+T3fr8q+U1lcdtllAFx55ZVAYd/eG6q1DTbYACgqTnbvunB9vec9\n7+nZT9NarojxGW/j/wbFvsb3LrnkEgCOO+44oFy/7bffHihr2nVWBdeFmXXa0rii98KolAn8W0kc\nffTRbLPNNj1/j7Y555xzAHjBC14AlMxD16bKxHOzxmrFFVcEiiLVBsZY1l9/faCsN58f/t5VrU4q\nlEQikUi0gkZZXlV+wJhD/c53vrPnp0pFxihbkn3tt99+AKy33npA8ZEaHxi0ErsObWSw9FtZWgWr\nu2W2d911F1AYSFX/qajyjJ1sueWWAHzuc58D4G9/+xtQrlWsxG2zCeCgTD+uG5WHLM14g75z/cmu\nC89F1vbwhz8cmMi2R5HZFCvexXLLLQdMjF2pWJ70pCcBcOONN/Z8z/5kqnTPvaoXl+vA/biuXvrS\nlwLwvve9DyhNAEWdbUaZ5eX19xiNC8Rzdu2rurSdbN9YylprrQXA5ptv3rO9pugi+89zU2mIo446\nCijXfauttgJKpuPrX/96oDwvVCAxLrjKKqsA5Xnw3e9+F4A3velN/Z7KpMgsr0QikUh0ikYxFFnQ\nZB1KoTBHfeA333wzAB/4wAeAwkB/85vfAKWvkW9pt/PLX/4SKPnmMwHxnJsqE/3C73//+4FiKzOV\nZGEyXyHT0TYy2Kc//ekAfPzjHwdKposKyu3ZM0hW32ZcatAYhPtWcXz5y18GSiaLbEpWb/aY6u0n\nP/lJz3bsU6bvXRtVxSfazNBz2zHDTeUp+459xmTXHqPKwnto9913BybW2oiY3RV7gn3iE58Aivqv\nut6uR49jbGxs6EzAKlR1D/DY7Q1n3MCq8E022QSojr26HeOK4pprrmn3BBqgyu7ej15flYRr3Liz\ncaU3vvGNQFEmwnM3Zmb8yN+f/OQnA/D9738fgF133bVn+21l9aVCSSQSiUQrGKoOxbearEbGJ4Pw\n7ShOPvlkAC644AIAnvKUpwCF1en/03f+vOc9DxhtdkYd2mL00S/8+Mc/HigMxTiANtl2222BkvH0\npS99CYCvf/3rPZ+Pikmm6/7qOvEOg0EZv+cm21KtGV/QT2xcyM+7HmKG3c477wwUW7iOqo7Hv8fO\nvE0Q137Eu971LgDe+ta39uzb9eQ94PW5/PLLATj66KN7PidUD6r9FVZYoef/ft71cdppp/X83/il\nWWLaXFt2lQU0/tji756TzwdjZ67hunXldi6++GIAdthhB6DUdkTlMgpENfboRz8aKNf79ttvB+D4\n448H4KKLLgLKddXDYFy66vmjt0dF+4QnPAEo95D723PPPYGiTE455ZS+zsMY7bXXXjvlMzAVSiKR\nSCRaQW2W12R55ZN8DihvT6u4X/GKVwBw8MEHAxPZsm/dr371q0B5K5sNpm9dBttmRfP9xzvyXl4y\njhe/+MVAOSfZk32q7CJqpoqZbtrCORbm8sfOv/5uHnuc0xF9/cPYoulkTWssvO5xnbmuZG+y92OP\nPbbn2P1pzYexFLsKyNL1yXucZtBok6uuuopXvepV3HDDDY3n5DRVsONjF1AUzbrrrgvAxhtvDJT6\nEs9po402Akq2mNtRcai+fvvb3055fLGWbLnlluPOO+/k//7v/6at392gileV773kuRpTGzZLtMk9\nUtfZum5aqR4J167ZWcZOfXb6LPXZ673lfT7uHAB49atfDZQpp4Mis7wSiUQi0Sla6TZc1a/Gjqof\n/vCHgVKdKyNUsejP01/s21wf6mqrrQYU/6LMZVg/73QoFHPsDzvsMAD22WcfoGQDaTt7PFlHYOWr\nNTpWZseYSWR1Zod84QtfACZWzo7rajByVl4Hz8UsP5WIVcXa0Gpku9Da+8tMt0GV7SC2WHLJJcce\n8YhHLGK/TW2hLWW0ZrqdccYZPf/3upvNJ9M0JuL+VbDWasiA6+6ZfrvKVpxDKwvBc43ZXxGuYW2m\nMomzRKzpUPWNqttwP96dKhh/VpXr2fB317x1Tp5bzJSNFfL+NCvU3m79Yu+99+aMM87gj3/8YyqU\nRCKRSHSHTuah6MvU163/Xz+gb1E7m5rNJds2J/7zn/88UDIhVCp2D22a/dVmdXi/kDHIrmUGsij/\nb5dYYykf/ehHe7bzlre8BSj1Bf3ut4++ayNnolX1IbIq4wOeqyxcduV6qorBCKfdGTMxnmCGk9u7\n6qqrgOmdk6PP23qkvfbaCyj3jPeC6l0fvOf+61//GoDddtsNKOdkLC2iqruAmA5b2IPN+hHXcKzN\nsnrc+jazueLnrd0wy1TM5NkwwnOKvQO9zv5u9xG7D487DqCodG2iiutXQamYnGGfMZREIpFIdIpG\nCqVu3sV3vvMdoPh5ZdmRTVkJLYvS16m/X4bqW9aMJmMyKpg6VGUhtcE46uIIUSH4pn/lK18JFPYt\ni9p///2BiZXLft9sH203KGLl/P1KrZEtYh8zlakV6hExa0/bqSBUIHbiNUYS+xwZUzGn/otf/GK/\nh94DM+js1HD33Xdz3333sXDhwqHXRdPMN3t4qUSt1RLec7HLrHUI1uIYk3OmSB0bdzteoyWXXJI/\n/vGPrWR5DRpr8/micjQ+ZV86PRSqeHt3xb53sbbGe6ppXKPJPaJXxY4I8fcqVGX9eS7PetazgGIj\nu0YYa42K5LnPfS5Q6tfMfGuKVCiJRCKR6BRDxVDsOxSrr+0b5FyTZZZZBpjI1vUDyjTNYLKaWEUj\nPFZ7/cjKmmKUPlHPxTkWqq1nPvOZQJmbPVlPJShxBrPErAKvUolV16Yqg2Y6/MOyeNeF68Du1LLl\nyHA9ZqcRysqrcv2roB/ZCu3ptIXwunsv2CXYuILsPa4PVb02MTusqu9VhPElM+PEdNwjMYZiZps1\nE3YNNg7p88YZQnGN6wFR+craB43BjtIW2kC179r2nKqul7b60Y9+BBSb+vnoJWqKVCiJRCKR6BRD\n9fKq6gv1wQ9+ECg9dfTvyTjMnfZtaubA6173OmCiz1zILIwzWNcSO29OB2TZMkF/l0kaX3CWuBXM\nzs+Onxex51OsrZCxyGhkY1XXJuajTydkTV53zyVW94rJqrmhZDDFeFPdOZolNizmzp1bGStpOi/H\nbC4z0Vw39lSKHb5VMtE3ry3r9h+ZbpfwOsYaiZ/+9KcAfPrTnwaKp8LOu9rEGMprXvMaYOLkzTin\naY011uj5fVS9Acffy/3WzBlH1ANh3Ehoq7h2XWcqEK+nHRTcjl2HfSb3i37jYKlQEolEItEKhlIo\nVZAxOP/aN7WddM048K1thbz9Z6qmFMq27EYalYm9nKpy7ttA1Zs6MsaoVPzd7BuzeWQk2kLbyd6E\nakw1F+NRVRWvTbON6jBZFbD78lzq2Iwsyo4Jzr1wypyM1esq3O6ZZ54JlLjToCy7rUr/++67r1Id\nDapMZJh2yrVGK8YTvf5mOslQjSccc8wxjfY/ClgjY4bj1VdfDcDLXvYyoHSddva852BdjjVBAAAg\nAElEQVTMS/XtvROVjvD6mm0aVX28N9uaCSKadPJQScRzqarZcl2YCWfc0ewvt2fmrTFcEe8BMx/1\nJg16j6RCSSQSiUQr6KRSPkImYOdMuxCbpWV1uJktcbaD7PqAAw4ASm79eeedBzRn4V1kbVh5bA68\nvk5ZtkxDFWVGk7UQBx54IFDYktszY+6kk07y2Ps67ipmIx74wAfyz3/+s5Xai4iqTqraQl921XVz\nfVgtLNyeufRVVd4RdWzLWMig62LOnDmL4n6qrUHZqfeIP/Whq9pVIBGuG23g+mlap+R6mTVrViNb\nDLIPZ4N4nWXP1mpYZ+Z187rb+++ss87q+V1oe9m2+1MBjzvens/XrY82bNFvB2VjZSoPY2neKx6r\n2aHODBJmhRmnrlqPVedsx3dr/iIyyyuRSCQSnWIkCkVYDS5rd6qYHXWtzdDHqq9UhuFEPfPJjUeM\nsvK1Kr4T52OrmuwabD55rP/wdxmonzeuZNdZVVtXk/SGYV8qRfuUaQuza2SGsW9UFTvSdvrO/Zzs\nzFhaV92Oh1kX9lJyLfedHXM/c7XflJlNdkZwfcR1s/nmm/f83RoNtyezFbHLhXNyjD+5nQc/+MH8\n7W9/47777mtNoci6neliPNBef872MOvPmMknP/lJoCha7wXjRMZetLFxA21pT0Fjrt5Dg85bGeYe\nic+NqvXgs87nhZ01vK6bbLJJz/djhpvw3qnKmBVVvf7q1msqlEQikUh0ipEqFOFbWKahEokzn327\nykxVNDLcfusNJnsLd91t2DjBrrvuCpQJa/rYI7SJxxp9mHEewrBKJTKRNvuamXW1xx57ACWbRz9v\n1ZQ4z93sLmt1hBks+odlsNp02Imec+bMYcGCBa3YoqqSWdij62c/+xlQ1rjxRWswVHkyTn9676g4\njC94PavqkPo4n0U/Fy5c2Em/O3u3eb2csGl80KmE0UPhORtXqLqXtKXz1FU4qjFVW11Pwog2bKFK\ns5I9wnvAGKtdp1VjPsPshGFfu5gV6rrpt95kULWfCiWRSCQSnWJaFIpw0poMwbiBvZlk4Z/97GeB\n0nFT9u9kvqYYlHGobPpB9Gnaz0w/sOcms5Btx0pYGYk59zKSpqjyGzeZRmeWTpzV7bZjRpv+fv3B\n0ZayehWIqkwmGmsxRNNqdFVCZHGD2GL27Nljc+fObayO7Et3xBFHACXTUd95VCBmfVXNIB8Urjsz\n6azIb1OtCdm5WZoqEpWsHgltYKcN44ovetGLgGIr13LM1vLeMcPJdeg6kd17L/Y7zXKUExtVrmbt\nmc0l4r0RoS3r7gltaN2Tz1phTVi/6yIVSiKRSCRaQSeV8v1ChqDPXUZihoO+Vueu+9Y1O6QpBlEa\n4zHZd6p8j7EzsjAD5cgjjwRKlbDxAuMNVRWzbWc2Lb300ou67Q6ChQsXTlAmMkGVxjOe8Qyg1B9Z\nzW99gOfg91SsMZvLKt8IP++6iLG1OkRl0mRdzJ8/nzXXXJNLL70UqL4usQOuytTeSvap2m677YCJ\n8yy23357oFqZ9Lsu4ueM8chAxbLLLju0Go4wduExeA+odFUIKlgViXVqTmiMKjtmKJklJovXZlts\nsQVQOiz4eRVL2/3Mhqm8V5WJmCVWNQPIZ2q/ysTt2hcvIq6LOhWXCiWRSCQSrWBaFYoszDqUvffe\nG4BvfOMbQOkqLAONb9WmLL3NuoWqbcnS7bV00UUXAUWNmbHkzHDZkdPnzHyJtRixD9Gg2V6RNQ3T\ncTcyxZhhdMUVVwCllmKdddbp+ZzwdxmktpA5xo6rKhfVn3MwTjvttCmPt2rdDLOe/vGPf3DZZZct\n+q6z3r3ucR8xG0ffeKyh8HOqdfviVcHtx1ibKs64VKyXij3nvJZddPD2uup58B6xdsfaG2fEW2dm\nHZM1GP5+yimnAMU2xlRdf/azMtvrta99bc/xGD8aVffhYRDX5r777guUGh7Rb7cQ1ZvrzOdAnKOU\nvbwSiUQiMS0YKMsr9t4Zeuf3v/1klgcffDBQZsnbndi3aRNf/1QYJoOl3wpTsyf83Omnn97zfxWG\n/YgOPfRQoDDK6DPX72xfs3HHN+n+RV0m1DC2UHWZcRIVS10/MRmrlc3f/va3gcIsVTayKM/VrtMy\n17YwTKV8tL/3jDUQ+qSdTml80LkV444BgJtvvhmATTfdFCixlgg76lrD4fGoCqq618b5GRGjmFIo\nS95ggw2AMtvHYzQrzO4Ddaq6KpPRe8B1OKi6b7MmRw+F59Jv1p6q7oQTTgBgl1126fm/3UTMzurj\nOIHB1XlmeSUSiUSiU0xLHYpvRXPto/Iw9/od73hHX9trWofQhHHU7atq1oKwgtXMJVn5cccdBxS/\nsZCRmCkVM9yqZpKMovOy+4rZNiLOPa/KeomZJvq2b7nlln6Pp2c//WKKDL2BbRH7lAnXy2abbQYU\ndW/Xh+OPPx4o19d4obYym0elG4/d/bpOnJ9idbjnJiOu68zsNXvAAx7Avffe20kXauH68dyNG7aN\neJ3bZuUV++z02amiVGH6/DjssMOAiT3cqmAm3dlnn93zdyfMGsOLSIWSSCQSiU7RSKG87W1vA8pk\ntaaIs6JHjS76FImoUKzuNzYyKGLOfF08qd/upuP+P7AtquadRERbtJVlFzOY6tBv76ZBbDF37tyx\npZdeetE2vS52e3A+elQKZnOZ5WWmmtk1ZsbFWp8qxHOL81VUrFVZaFWYCax8pqANW7Q1QdX1oqp3\n/s2wEzqNycQZMzEOmgolkUgkEp2i1RhKV/MpukIb8w3q/LKDzlwYFioh+2f1i2Fs4cyW2C06IvbO\nahr7iqiy8aDdZNvoQh2ZXFW9x/rrrw+UzLhf//rXQKkfkcE2ZbKRCZsh5VTEuuxE+1ClQikYxhb9\nKpOq/nLC9aXSNMZmhtyokAolkUgkEp2ikyyv+Db27WmPnnHbByayo1HFVpp0Gx733Uk/V6dIRq1Y\n+kUXMZS6+pO2YXdZM+Wa7r/LbsOxElmo1txOlfL1d7PBtL21QDG2VhdLq6uYT4VSMIwttt56a2Bi\nZ4S67g32eqtSLIOq8CqstNJKQMmsNPOuqp9bKpREIpFIdIo6hXIHcNvoDmekeOzY2NjkLTYnQdqi\nIG1RkLYoSFsU/LfaYsoXSiKRSCQS/SJdXolEIpFoBflCSSQSiUQryBdKIpFIJFpBvlASiUQi0Qry\nhZJIJBKJVpAvlEQikUi0gnyhJBKJRKIV5AslkUgkEq0gXyiJRCKRaAVzp/pnNnsrsDnksJ0F2m7x\nb5NA26A3OZ5hW7Z3jX5HCUc4Ftdxqf1iOm3R1gCmtjCT18Wo8Z9gi6qGqYM+l6psMeULJWK65534\ngLCjat1xdHG8btML440fu7TGzz3qUY8CykxxP7fMMssAZYJf7A7rA8YXx913392z3Sc96UkAXHfd\ndT3fc/sel91o7XL7P//zP0B1N9E6zJ49u7JjalszYgZ9uMb9+SKJM2Kmcx3X7XumvEj+GzCKl3fb\n3cWbrt262UNV3aYjnJNThXR5JRKJRKIVDKRQInxbxlkMdTMXqmZC1KGB62Kgz/ezPc9Fyeg8gpe+\n9KUAnHnmmT379v9O5FNZOEtExeHnZBDu541vfCMAJ554IlBmfmi7v//970BhW/H7qjrnJXjcKqO/\n/vWvje1UNSP+QQ96EFBUV5zZIMOpm1cyKHP0OOJ24/TKuvXZJfrdx3R7A7pAWxM628Io1GAVm99k\nk00AuPDCC6f8vmt59dVXB8qz84c//OFAxxFtXqVE6pRU3f9ToSQSiUSiFfQ1sbEqblCHqmliSy+9\nNDBR0bgff46bGld1fD2/D8rmmgTlq2ygElA5xDf5FltsAcDvf/97AK6++moA1l13XQCOPvpoAA45\n5BAA1llnHQDOPfdcoMwg/8hHPgIUhiFjcXJftJ1KSLYepyvOmTOHBQsWtBJwrPO/1vmsnZOtjdpG\nP9MLmyYotK0oVl55ZaAoWKdQauN4z3Q1AXQmBaLj+hm14hmlLeJ1HncMwMRna9295f99Dgyr0nNi\nYyKRSCQ6RaOZ8nWZTtG37tvR32PWjRlMbtftGB+oykCYDoVSp9Y8V2MUW221FQCf/OQnAXjuc58L\nwK233grAnXfeCRQf6a9+9SugZIXdfPPNwL9jHfcfR8/+ZSorrLBCz+edCf2nP/0JKHENber3V1pp\nJW699Vbuueeexqw8xn/idVprrbUAuOaaa4CSYaaa04bOtb7ttn8PupNNGTe66667gKK6TJVuyxfe\nploTz372swG45JJLgKJkXfPGmbTVvvvuC8ARRxwBwI033ggUW/3v//4vAG9605sAePzjHw/AQx/6\nUADuuOMOAPbbbz8AzjnnHAAe85jHAMV3r9Ktu2fatEVcF67p17zmNQB89atf7TkmPRk//elPATjy\nyCMB2HvvvYGyLk466SQAnve85wElvvDmN78ZqJ63XvW8qsIwtojn7v3929/+tud7xmK/8pWvAHDT\nTTcB8NSnPhUo97G2U6V5j3m9X//61wPw8Y9/HCjr8LLLLgMmxi2rvAveqyplkQolkUgkEp2ikUIR\ndVk6wrdfZKaPe9zjADjggAMA+MlPfgKUt7bxA5noU57yFKCwuttvv71n/9E/KGuT2U7ijxxYoXgu\n7kuWrupSEfh3lYLfe85zntNjA5WFzOP666/vsYWMwfoVITt7yEMeAhQf+6tf/WoATjjhhJ79e1wy\nGlnb2NgYCxcu7ISJiqgoo81kol7X1VZbDYBTTz0VKIpEdv+zn/0MKOvGeNQLXvACAM466yygKJ5f\n/OIX/Z4a0I2vPGZEun623357oJyDfz/ooIMA2GijjQB42MMeBsCHPvQhAPbcc0+gxCFViSpj18tV\nV10FwKGHHgrAL3/5S6CoOj/v+onoMm4QWfFb3/pWAI466iignKtq/v3vf3/P930uGEd0/egVMBZn\nrZYKaNDn1rhY7sC2aFrr4jHGejKPOcZs3Y8x0muvvRYozwk9Idrw9NNPH+h4RF2cMRVKIpFIJFpB\nXwolvi2rvuNbNP5Umehjf9rTngYURqE/+Xe/+x0AT3ziE4HytpWF6TeUlevX+8Mf/gAUReJ26upc\nmjAOlYTsWn9/ZOWyJhWI/l2/v8MOO/Sc47e+9S2gZPd8+ctfBooPXV+q10KG8sxnPhMoLE4bbbzx\nxj37czs33HADUHzt99thqMymqDhUhBHGiVQYK664IlDWlarMc4sxsvHHC4X1nX322QDsvvvuADzy\nkY/sOQ7ZmdtzPU2RVdh5Ns9hhx0GwOc+9zlgogI1+8/rZdzg3e9+N1BiKC972csAWHvttYGicM8/\n/3ygsHVjL9pkgw02AIpymaKCunNbuA5WWWUVAC644AKgrGE9FVUxWa/blVdeCcB73vMeoKyLqudW\nW5lNkyHaot99felLXwJg22237fle9Ii4HWMjenWMUxo7MZbiM7iteGMqlEQikUh0iqFiKEIWHDMB\n9H37dtTnGf16snv7SsVMGLe/6aabAoXFf+ITnwDK29qMF5lJfJtP0nNqIMYxZ86cCZkhvvEja9I3\n/fznPx8o2TvGgfRxqlBUMtpKJqn6knHqF5bV7brrrkCJJ5nFIQN95StfCcCaa64JFPanbbV5m0zU\nc3AfZvNpm3322QcotTVmID396U8Hyrqpg9szc83vyf6POeYYoKw3FXGsnBf2KWrTFnU+dONHdoGI\n6j7WHRiDkcEak1PBuh5cf/79yU9+MgDf+c53AHjVq17Vs3/XT0SXWV4RKktjaCrWn//8524fKLYw\no9EsMG1lhtyPfvQjoKxDPRZDdIYYeU2O18c1LrSlMVFt4vVebrnlgLLutt56awCuuOIKoNqLUIU1\n1lgDKD0DU6EkEolEolM06jYsYlfXWBvh/2XhZnWZUaLf1gwWlYkZSf7ft7RMwwwl2Zk529ZwyHSM\npcTjbYqFCxdOyEiSKUQmoW/zda97HVAUxqqrrgqUzBMZ7Pve9z4A9tprL6CwaFmWMRCVh3nsMg7j\nUqoDFZH1Bttttx1QMqPc3/z58ydUzw+KyMJVqq6LyLKNFwnPSURFKTzOGK+StbsujC+oUFwX5vLH\n2g/3t8022yxScMMi1ik9+tGPBibGSlwvMZ5jPFCFalxxl112AeAZz3gGAJdffjlQbOt1df1suOGG\nAHz3u98FSsbbS17yEgA+9alPDXWeg8DrpnqKna69X73OXicVijEVnyM+D4wbWc/yhCc8ASi2N14Y\n69tEv1lf04F4rGYu3nLLLT1/N7vT+LLn5E8zIM8777xGxxG7mVchFUoikUgkWkFtDMXq4fD3nt/d\nhv48WbIMI+5DRivjMDdeJhH7ztgHS4ZpttApp5wClLdwzDOPvcKGqUOZPXv22Lx58yZkjkW/sPtW\nGeirVHmYjSFL83PGl+xnJQOxjkClosKx4t6YjOeuMjHjRUZqBa221hbz58/nnnvuYcGCBQP5h+vm\nIkwGbRNZuNdpvfXWA+DTn/50z/fMcHJdmJkka4+211ZWi1988cXAaLJ5msL4z8Mf/nCgKBNjYJ67\ntjCj7jOf+QxQru8PfvADoCiWHXfcEYCddtoJKPFIYynHHnssULIEI9qwRexGLmLHgwjXunFDPRY+\nZ7SB8DlhvMBzVaFahV41eK3LSnl79e2///59fb9f1aRt9VB8//vfn/RzKhOVyrDIGEoikUgkOkVt\nDGWqN2TssRUrVn176lM3C0v/r9kZsUNvZA6+dbfcckugZIHsvPPOQIlTVHXcrercOSgWLFhQWbvg\nufrTLAsz07Sjc1HMurImQ4b4ile8AijKJbIymatqztjM5ptvDhR2f8YZZwAl4824hZXz+tjvueee\ngZXGiiuuyEEHHcQee+wx0PdizMJz0ZbWXJj9pa30C8tAVTZe1zgiWGbr+qqC2WUy2FEiqnwVhdk0\nrg+P7eSTTwZK3MBsHivq4zU0fuD/rdExC9A4hD5599NFHCEqE+H9+tjHPhYomWaqd+OE/t3Px+xP\nz9HMR2O0KiBVu/dmjEvEGGBb62K8io/KpK5TctV1iMrFuKFx46rteP3r4PEYA47ZqHVIhZJIJBKJ\nVtCoDiX6ws1w8u0WpwM620PlIlQiKhiZg291mYP9jIwb6COX3ft9mUxUJlUMvIlPNDILWdD43lhQ\nKuHtS2QWhplHH/vYx4DCymXV9qGK1dzGC/zp5y+66CKgxK1UibJzlY+sTvYWs5DaqAL+4he/CMDL\nX/7yfjfV831taTW4f9fmVv167DHby/Umq3K9xPqoOnQZQ4m1UJ6DDNbrpJ/f62ndiLVbXv+6/ZhN\n6L32hS98AShKWKVqh4WINmxRFQ/wueGxep283q4j1fYb3vAGoChUMxZd097/xgvsNuG9MOzMmJk0\nG0ZoW+NQridtapx5t912a3W/GUNJJBKJRKdoNFM+dryMVcC+Ha3ONUYiq5cNyUim6CMEwI9//GMA\nXvva1wKFdZu3bt3KoPORB8Xs2bMXKRF93voWtYWsWGZpHECW5U9nxVv3YKxDP6/M0W60Mg+ztmQk\n+tpVc/Z0snOrsRV7RqkCZIF33313IztNluU1qDIRHrvxHWNiqq44JyfCdeK1UeVFX/n4Y4fuphxO\nhRjvsReX19FjttOuXWFVtlaBV0FbuT27ScjqzQqzB11Vv7Q2URUPUDWpysxYtELeGhvjSmZKGjvx\nXvDeM275jW98Ayj1KxFtT9ecDsQeX8bCrMlx8utxxx3X1/aadkWOSIWSSCQSiVbQqFLeN7ssWaYn\nKzcrx0pYO+3Kjs8888xJtxvZktkedl6Vtbs//69CqZubPCwWLly4SIGYxy/bVWV5DB6TbNs4kn5f\nM1KsE4jZXMYRnLBnPYuV0DIR2dp73/teoMRetLmKyEwaM6v8XFOWPv57w9pZtWeOvNe1ij27b386\nDdPOy3abrTquqnNuUlszKFybrhPXj33MZNeqeLP4PvCBDwD1ikKVqE1cT9apRNU2CoUS55+L2EnD\nmKjK1O4C3nPGXFRbrhPZuArH547X0ueOv3uv9dt9eLJavEHhsfvMrJoiOSi0rZ0Q9ApVeX0ifP5Y\nx+Z5Vs19qrvXU6EkEolEohUMpFDi2ylO4pN1+faVHdmryQwT39aya2Micea8ldNmSgm3YwzF/ev/\ni4qnLaUye/bsRX5d4TnqG3d2g6zIGQ9m6ZjjLrPwe/basprXDrmekxMYtY0xGNndhRdeCJRKfCvi\njcEYVzCeNQpm2i9kpGaixOsnS/JczdpR5Rljs4dXncqoyrTqQp3EjEivv5lJ9rHz3C699FKgKFLn\nqMfarAjXxfHHHw9MXF/eK85fjxmaVVlebaBfNu45aBtjKcbWvL5+zjoRZ8h7zlb/O53Qmh1RdZ3N\nIvzwhz8MTFx/w6AqnjcsrD/xHvA692tzn6Xx89FG/T5DU6EkEolEohU0yvKS9cgs9ds5F1t2bq67\n7Mu4gHEA2bzbky1ZsxGrzFUH+srttFvFNNtm4bNmzZrQy8s3u/t0rok2cIKa7MfYi5ktHrMsLM4C\ncbtm6/h/u87KLGW+sjF97u7PLK/YNaCNuMGwDD8yU+F1V205Y96pdm9/+9uBUv2t7auye0RkW10q\nFFmzPdq8fnb7dT2pOFWY+sBjx+wqyFRjDZaQ7Uf1F2vDRoEqz4HXPx7bN7/5zZ7PGautUm1+3k7K\nJ554IlDqpFT98XqrTMRMyALTA+IzVvi79Wxe736VSdUkyNgBZFCkQkkkEolEK+hLoUQG51tNdmyH\nU1lYVCxmFsmW9RO7Pat5rYg/8MADe7bnDAmZiUyjLlsj/j4s4xgbG5tgizi50Z8qBT9np2R93MYL\njCOZsRK3+653vQsoCsZutFbM+zmvhXUv9gLTVnFKpkymSd75rFmzeMADHjB0zrrXxYy4mKHiMWpL\n41DWI9llwCwfFfGg8y26nIOhOtff78wO6wNcT8YHrK3o17beEy960YuAoojMiPTe02aqOe/N6UAf\nXX2BahvEeIS1WypYlar3ls+POGfHDKfPfvazAx3/MIjPIu9n1Zbn5ro4/PDDAdhzzz17vqc3x7Vu\nvZL9z1S6dYhqr2q6bb9IhZJIJBKJVtCXQon+NN+exgFipbKdcGXLsiLZmp+XGfo2tTbCXjz6UPWx\n6xu1yreqQr/urTp37tyB2fXs2bOZN2/eomP0za4PXGYYO5TKIFRnz3rWs4BiO2t27BqgbWVV+n9l\n7VbCy2RkKtpIn6u+dxWJbC0e96xZswZmIWNjY33nuU+G6N/3nGTV1ilZa2N8wNibNlXVvfOd7+z5\n3XOO+4vrOM4e6QJRhbnuvG5ep6go+l3L3kNxGqXrxnOWvauQVSxdxgmqqq/jNMGqmEa/fn3XsjE1\nM9l8HhnrNaPOrFLV+yhQdT293rE/narLLgGul6puEXYHdp1Vwc4Z9gBUmVjbY3ah+6/LLoxIhZJI\nJBKJVjClQpkzZw7LLrvsoreWiBWuwl46MgDZvCwqMgzfyjIZ35JrrbUWUKp9nWfsFEP3HxlQv5Wv\nTXz/Y2NjPRkUcb65/5MFaTP3pSLRRp6rSkNl4e+y9JixpsLx+1bemtlkVpm+VxnLJZdcsug8oLp6\nuV+MV3nRzrKpWD/iMcdaGJWsKs6Z4PY3U3l4zH5PteU5yqr8XMxgcf+qgaiyHvawh02Yc94Use7E\nLgBmNnpsMkBn/lRNF41wnW2zzTY9+9G2ccaQ+9tnn32AMg/FDLouEO/32Km7SnHEGho/H9dZVDCu\nB+8x65QOO+wwAF784hf3fL5qu10gbtv4sNMo7dGmynrb294GwPOf/3ygvo/dt7/9baDEUHxWem7e\nW9YEfuUrXwFKvNp7QsQpmlkpn0gkEomRYkqFsmDBgh6fXFX2lG/PqpnwVfB7+jh9a9rTx1x988lV\nPHWzqEWb9QZjY2MsWLBgQmfc6K+P7EmWZfdXWZO+c3+Pas8eXGasODPavmYqIPdj1pgMxboGs4Y8\nd1WF7Gz+/Pl9T2Mbj/vuu6+y4lzY4cBOuc5DVy0ZI3EGuHNuVLixN1fcvtexio3H49Jmxmyuuuqq\nnu3dddddrbFUt2N80MxGf3d6qf2q/Ok9VAVZ/qGHHgqU+iZ/t2eXHRKstPeeMcbSpTIR8T6LilAb\nef/bj84YqorSWIfXNfYQVP07lfQtb3kLUGpv7CKhEjaeqDfAe8LOz9Y5tQljpT5PVSa77rorUObh\nGDtVgfr8qILPHzsxayO/7/60jc8hM9uM+ZpB6cTX6MXJSvlEIpFIjBSNJjbKdmIcQR+2b1VZV6zJ\niD5t/cuyL/tO6d/37e7cA3s5VTHRfv3QTSaw1fkSPQezbmJ8xzklzi+wQ6qQfelHtno4zgYxTiED\n1SbGAGT/dmo97bTTgGqV1sbERpmf86tlRfYZ0k+rjex0YB2TMbM3v/nNAOy888495xqzfVS0qrGY\nMRNtr9/az8V1Ym3NwoULW5tS6D3gucRplJ6LWX7OR1ehGj+SQbp+VMpuR1Vv/yq/v/766wPwve99\nr+dc+0WbUwrjvWPMQ4XxwQ9+ECgZi65pla73irGQF77whUCJH2lDbePzSIXjbPlBJ3iKLic26n2x\nFk8VHztnjDsWoEwn9Xuxk8eGG24IlOeJ+7n55puB0oXce9HPV60Xs0JzYmMikUgkOsVACiXGUGQW\nIvr7nPGgX1j2bFxA/55xAeMKse+QFdS+jWM1aWSiVT294rkOyjhmzZq1SCXFytIIFUWssbAnl750\nmUWcoyJLUwXKtszekP27PVmXjNR52iLGtTwPbTkM+4rbdqbHxhtvDJQqfv26+n1dPyoG601UJBFe\nV21iJox1Th6HPd+8/m5v3LlOeX5tMNEYX3S2t12lPTavq2rccxDeM6o6FazKxXVoHyp7uHnPmQXW\nNC4wijnq2spq8IMPPhgotvBctZGs3d9jtqgsXPV+8sknA2XdNJ0F1KUtPAdjs/k23OAAABj2SURB\nVPE+VmEIPRDGVqsyV2PNoBmUxi29J+Jkx3ivRqRCSSQSiUSnaKRQfNv5xpcZGOuI89DNaJA9Wa37\nhje8ASgs2340fr+uJ1PsrFqnSCLaYOVxYqPQN6lyMKZivcjKK68MFNuo2lRj9hkyK0c/8lZbbQUU\nRipz1d+szWSyHl+scxl3XlP6RCfDrFmzxsZPsZMxRlt47Pp1jbHEbK26rtB+38ykj370o0DJ/nM/\nXu+YBRjrlqaqi1qwYEErTDTGGZ2LY8aaytV6kKoZMNFWZiq5jszqMpMufm/YDspt2EK7xw4Z2kh2\nbecCq7i9R6omuo47RqAoFjMejbHtscceQJmT0hSD2GLu3LljSy+9dOM5M1GpmqnmJFbPtd9nndmf\negPqMnCrtpsxlEQikUiMBFMqlNmzZ4/NnTt3QjX1VG8vKP6+VVddFSiKRv+xfj8zj6yINn4Qs28i\n24pxjFjz4e91VeDDZHnJpvRVV73xI6uKXX/j7ObVV18dKDYzxuLcA3PsZVuR/RmHMItDxIw4MW/e\nPO69995WMpvi3Hoz2WRVKlMZafQLx3Xl52Th1qlYcxHPLaJKuUabiwc+8IH885//HMoW8XrHuKLx\nHHs0WY9i3MBaGTOgZKYXXHABUNi7cYCu0eQeif3i6rr6xuzMLbfcEiiqS79/fC6YAakXwOeN39tv\nv/3iufR7KpOiiS1c497PnpvemK4Rn5UiKujoDapTPqlQEolEItEp+oqh1PljfZsZQzEzxZ4666yz\nDlDqEXbYYQeg1B3I8tuqUq57uzaNG8yaNWuCT7vK1x2PRUTGKoPQtv60bsFKd7N1zITxc7Fzq+cc\nWXlUbeOPo2ncoKqbbFQCF198MVDYuCpN2xnfkUWZpaWisU4l1g/YZcDK+mHnmiy//PL86U9/4l//\n+ldntRdVccFBZ7iMCl1kNsV7osrToU1cL7JqMx2HxaA9vIaxRZdTQdtAv7ao82ikQkkkEolEK2ik\nUKretlVsLOaRx95RdTnUdcqoqbJps1I+KoW6uJNV2/qBd9ppJ6CwL9WdNRYxO6PunKsyY1QVk/RV\nGlit1R3DZpttBpROqFaNm3Xj1EDP9fzzzwfgyiuvBEpltJky+uRjJfygaOofrtjWGJR6klHFNkaF\nUdShTLG9eCytbK9qO3XPm+m0xaihLaLNxk2UTYWSSCQSie7QqJfXoMogxg023XRToMyGcKa8mUlV\n2VlVPvs6VPX4GpRxjJ8BEjslx3zzOiVTl5kU6xGmOK6e7/l5K249LrNMVlttNaB0Hbg/ltRKL69+\nlUNUT15XazJcB9Plb16cmWjbsZg2uwZ0OW9kFPtpsyan6h5p6xziOhg0hmMWorOoIlKhJBKJRKJT\nNFIoU3weqPdR9su+q9BUqUS02curalZMVZwpqrZ4LlFVCfPaYwwkfj72WYtT8tZbbz2gxGi6iKGI\nqtnt1q1Yk2P8IaqtumyyNufe3L+9zhXKTM3qiujSFrEWYlCMSvmIYWwR+8nFY6+a0TPJdnu+J+K8\nlelSa6lQEolEItEK6hTKHcBtozuckeKxY2Njy/X74bRFQdqiIG1RkLYo+G+1xZQvlEQikUgk+kW6\nvBKJRCLRCvKFkkgkEolWkC+URCKRSLSCfKEkEolEohXkCyWRSCQSrSBfKIlEIpFoBflCSSQSiUQr\nyBdKIpFIJFpBvlASiUQi0QryhZJIJBKJVjB3qn9O96yHxWG+wX8K/hNt0bTr8H+iLZpicbLFsF2m\n67qY/zfZog5VtpjyhQL/fqjXPdC7asWdfcYWTwzabr4pll9+eaCMUY6YietniSWWACaOH0gMj2Ef\nnsOOw5hJ0BbDjj0eFOnySiQSiUQraHXAVleoe8s2Zb6Lk4TtGjPZFo6I/ulPf9rz9/nz5wOFWUbW\n/6AHPQiYOJ65DjPJFnFtyygjHEX9t7/9refzoqkymkm2mG40sUVXQ+Ae/OAHA/DnP/+50ffFoM9O\nPVY5YCuRSCQSnaJWocyePbs1/1rd21CWZUzGEbE33HADUGI0Hk9V7Kbft26XY28XNyyOtnAc8r/+\n9a+evz/qUY8C4Le//W2j7U4nK49r94EPfGDP745uvummm4AyNlk1pkK5++67geq4QFf3SL+f7QJd\nj1VenGzRNVKhJBKJRKJT1GZ5jVcnw/r9llxySQB23313AL7//e8D8IEPfACAxz/+8QD86Ec/AmC1\n1VYD4NJLLwXgM5/5DAA//vGPAbjjjjuA4h+WjUXG2ha6YOTa1G3LHIW2Nl4gI51uTIc6WXrppQH4\n+9//DpTrrM0e/vCHAyVO8JSnPAUo2WAXXnjh6A62IaJd77nnnp7f//CHPwBlre+8885AUSTXX389\nALfffnvP37VZXWrsTERUHl7vc845B4DXve51AJx99tkAbLjhhgBss802AFx11VVAea5UbbdLNI3z\ner3e/OY3A3DQQQcBsNFGGwHws5/9DCjenF//+tdAiTveddddPX+venZXHd+gx50KJZFIJBKtoFah\njEd8u9W9vWQAsnAVx6c+9SkAdtxxx38fxP1v4eWW+/fc+8022wyAf/zjHwA8/elPB0qWj2zLn1WZ\nLTMJ0VZrrbUWANdddx0A66+/PgAnnngiUBiHPvSrr74agG233RYotvnTn/406far9rs4wnN4zGMe\nA8Btt90GFCVi/GCppZYCim1uvPFGAH71q1/1bCfaYpRMtQ7xWFRlKtOVVloJgAsuuACYaIPDDz8c\nKIz03nvv7dn+4qRMHv3oRwPwm9/8Bigxsy9/+csAbLrppgDceuutPd9Txf3zn/8E4GMf+xhQYrSX\nXHIJMJrrXRXnq7svX/aylwHwzW9+E4AjjjgCgJNPPhmAyy67DCjrxe37LP3GN74BlHvi3e9+NwCX\nX375pPurOo5BnxupUBKJRCLRClqpQ6ljx75FVTjGPJ7whCcAcOihh/Z87pe//GXPT/3AF110EVAq\no92fTGRQjCJrQxvIHJ7znOcAsMoqqwBw4IEHAsXPryLRVjLQv/zlLz3bjf7hYeNGMzmDxfVyyimn\nAPCud70LKOvCXHzXwwtf+EIAfvCDH/R8TnYelUzEMBlvbSlCVf0jH/lIoMQJjD8+5CEPAYrC/f3v\nfw+Ue0Tf+Q9/+EOg+NoXxzY03hOq81NPPTXuFyiKQ3W25ZZbAnDzzTcD5fmiZ2NQdGmLefPmAeX5\n8MlPfhIoz4Vx2wXKWvacXG9eX5WRNvF54zN1WGSWVyKRSCQ6xUAxlCr4dvRtGTOVfEvGt6gxkW99\n61tA8Q8fc8wxPd/37W19gXEDmUtThdIlNt54Y6D4a2WU1g+Y0ea5yKr0hfv5HXbYAShsy9jL/vvv\nD8BOO+0EwCMe8QigxAv+E+D11mYf/ehHAVhxxRWBwr5lccssswxQ2JnrLPrKq5RJU4xXI8MqE++h\n3XbbDShKw6welUtU9caVzPpy3Znp5j2lkplJqFN1Zrp9+tOfBso5HXDAAUCJE3nOm2++ec/2vP7G\nJZsqlC7guRtPfvGLXwwUFa0q33777YFy7M94xjOAcj2f+tSnAnDaaacB8OQnPxko6+Ntb3sbAHvs\nsUfPfgddr3X1Z6lQEolEItEKWlEoImamyASjf1nmuNdeewGFgZgn7vf1jRsfcHsqE7O7ZkImU6zR\n0ZetUrHmxriR7Pvtb387UJTIa1/72p7tybr1gT7rWc8C4KEPfShQanvMhJlOeB28Pk1rZrbYYgug\nnLuxkCuvvBIotjS+ZM794x73OKAoF9nc4pThdt555wFl/Rx88MFAyfJ7xSteARRbaAPXi8rmzjvv\nBEoGlHGk6UTVfdrv9Vl55ZUBePnLX97zPVm4CraqX1lb8YM24PXyp8rCGMq6664LlGejzz49Ez5n\ntME111wDwHvf+16g1OwJ76lhn5V130uFkkgkEolWMJBCqeuc6d9jda/w/7Jr4we+lc1EsXeX+/Nt\nbWbDL37xC6Aw4JhrL2JVcJdKpip7Rqbpvq2NOPbYY3uO8dxzz+35nmrP+MATn/hEoPhO3Y7fn0ks\nvKkyse7A63XUUUcBsMsuuwDFz2wdgszU2NtJJ50EFPb+kpe8BOi/3qDN/mSD1rYYJ3T/a6+9NlCU\np/eGas3teu9pAyvlraz++c9/DhSbaOPvfOc7QHddJSaDx9q0/kObGkMT3gOqt4svvhgonTRih+aZ\nUG/k8yI+N1796ldP+vl99923r+2aEek5ahvVvt0kuoqlpUJJJBKJRCsYSKHUVVPKwuuYiD52MxPM\nUJFlqVzMwfftagbEO9/5zr6OK1YFd8Hio+rp10+sT7vq81b12jF3hRVWAEpGm5lMZgHNJIUyKFQe\nRx55JFAU7vOe9zygxJ1UZRFmPH3hC18AirJpMPtjoM9PBq+LP+vUmveKWTjGBY0jfuUrXwHgiiuu\nAArjlNkaQzNeZHagisR7ShtZad3VaNip0FQZeI8cd9xxk/7d66yqW3311QG49tprgeFnhiwO0BZ6\nd6Iqcz1WTTdtC6lQEolEItEKWsnykk3JuuvYT2RTMtE999wTgIc97GEAbLLJJkDJcFK5yFjjBL/p\nQNOMlfh5macZSzIOM1te+tKX9vx9v/32a7S/LtE0RmWMxNiatjDzzRqbM844AyhxJWNn9j+ztsds\nnunob2ZMoqpnVswGVKW/8pWvBEr9gbaw95LrwloLj12fuPs11mI9iscRMynbruwfBHooqmKtER6b\nPf4i+/YcfJ5Yg2FX8qp+Wv8J8NyNrdpRO9rIv8cawToM2mE+FUoikUgkWkErCmXQHHcVyve+9z0A\nPvGJTwCl27B55rI0c+n93E9+8pO+9jMVQ50pzN5jNDvDSvl99tkHKD27Yh8is31mEpr65eNcEzul\nGh9Yc801gZIBY52JWVzGTqy9EG11UG2Cun2opszeMV6o6lKlWU9g/ZGM0WwtVbteAjsqqN432GAD\noNxD2lolZExulOy9X2UiYv8q7xWhAhF2Fx40hrY4wjlJZrZ5zwiv62te8xpg8DjWoPd0KpREIpFI\ntIJW6lAGhczBimf7FDkDQHYl+5LRqEzq3pp1fr+Zok7GwypfK1plHmb9+LuMYyZnrgw6FXCrrbYC\nyqQ9uwfIqh784AcDpVrc6nHZmPUGqru//vWvAx1vl/NQVNlm46ksVBxW93vMft76E2srTjjhBKCw\ncf9vPEKFc8sttwBFqcjmPUf3b6zFvy8OcF1FeD9/9rOfBWZGnUnXiNfbeyE+27773e8CZUZM10iF\nkkgkEolW0EodyqCQFTmJURZu3rj7sX+NldGxf00V/Jx+YmsBZqIyEc74sPOymU/2AhNVXQFmEgad\nCqjqMoNJhaJCVaGsuuqqQGFjdg9wVky/FfpRwcpo582b17p9VSaqe+M8VrA/7WlP6/m8Xaff+ta3\nAvD1r3+95xg9ZrcXs7usX1KZ+Hery429qYQWhwmOHmtVhpJ/X2+99YDSJ+8/qfO2cO3agSPOQ4n9\nEr2nmu4nYyiJRCKRmBa02m24CrGC3owU+xc5a/6Zz3wmUNibfmN95IMqDDNYZrIy8djsT2bXANmV\ntpEpfOlLXxr1IY4MnuOb3vQmAE4//XRgYtW5sRTjDrL3fnPsq1hXl3N1vM4qCLs+WP1vRbs93c45\n5xygOvsqrmnvJetatIUqzlki9vgyo7LpvTVKeN2NG6pYo0rbbrvtAFh//fWB/yyF4rl6nWPPLpWK\na1hbGZsdFE3XQyqURCKRSLSCgRRKU7+ab1eZ5iGHHAKU7C4ViexJv7IV8sZYBs0rX5yyPWKuvb5P\ns4GMH5ilM5MRswFlVbKn2J3an7Lm2NfKn64PYytRkTRlVaqGv//970Mz9SqVJIP0p5ltThG0gt1z\n7LcuJE78c817r1x22WUAHHTQQUCpY5oJ81HqEONOsnLX0Vve8hagrA9tYMcNFaxqb3GGtvjc5z4H\nlFiqMVfjzFbM2yWgq7knVUiFkkgkEolWUKtQxleV96tMYszE2gp7Luk3tk+Vb1t78JhvLju/9NJL\n+9qvMLur7dnhXUAb2Z/KbqDa2m7D1iN89atfBYrai9k/MwEx4yTGJuKxyjzNiLIS3tngKgg778b9\nDIs22Xo8d+E5Gw9Qccqir7rqKqBkKBlLq4LX31lCdpeI8QV7fcnyVYuDVquPEh67Mzu8R2JG06mn\nngqU54Xn6jqyJ6BxJD0hiyM8F7P0vI7i5ptvBuCss84CYJ111un5XOztVfW8GLYmKxVKIpFIJFpB\nJ1levhWtAvatJ5OwtsK3YJx/7OfsGisb0y8cc/Fj7vVMZl/Cc3SG+Kc+9SmgsC5hTOWUU04BShdZ\ne/c492Imol8F4XWVhb3vfe8DynVVwS4ONRPCY7ffmH5+a2ysI7DTtj5v1Zr3kOeuLf2/VeHGYmLc\nyv/vscceQLnXFgcbei6ybrO2vL/tg2acyfXjLJBll10WKB25nfg6E9Bvt5H4OdWX3SNUYfa30yvj\nc0WFa5zafnfRWxD3M6zqT4WSSCQSiVZQq1CavLFkQWbtHH744UB5y95+++1AyWQypnLTTTcBJXtD\nNmZXYt+usjcZi1XAKhr/X+Ubn45uwzIBa3BkXwceeCBQZnxXwYw3bWjdgl1krRb3vGSk9gBrOue9\nCQbdp+wqTju0In5xzNJxbf7ud78DyvW2t5KZai94wQuAolxklsaP9H0bR7LfWexCLYzB7bbbbj3H\nsTjA6+79bMaSzwszmlQk4zscABxzzDEAXHPNNUDJbFtjjTWAkkk3nah77sQMSM/tjW98Y89P40F+\n3ueAzxf72qni/Bm3H708xx9/fM9+BkUqlEQikUi0gk66DcuaYidU2ZLKJTJYGYZ+ZbN6ZOP6k53x\nYMV0fNvKXFQ4M2Eugr7N17/+9UBhCrKob3/725N+T1s6pVJmevTRRwOwzDLLAIWBWGcguqz+FtHO\n/SqTmKnm9ZaVeV31Gy8O8DrLFK+77rqe/zsb/uMf/zgAJ510EgDbb789UOpGZOX29rLPnT3eojLx\n3rKGZ3FSJsI1rELx+j/mMY/p+enzZP/99wdK7Y7n7P/tFWgGXRX6zYBqA3Xq3WO20t3nhV4fn30+\nQ10vqnmzB1Wqxk5iRb3naFagz413v/vdw5xeKpREIpFItINahTJ79uxFbzPf5HU5yioJM5DOP/98\nYKLf1zoUma0ZMTIT61KsR/GtbX66Pf59O8csry7jJIPOhpGNyyBkoJ///OeBwigi/PxRRx0FlNoa\npxhqY7OI4vGMgqm6HgadFS4j3X333YFyvYXMc1SzHNqIrfl9Y1oqitNOOw0ovbqOOOIIoKixD33o\nQ0DxXXsPWUkfWbT/Vx2a0RSzBEeNNmxoHMn7XTZuduhOO+0ETFRj3pN+zziDqrAKo7hHvH4qk6rn\nhzOfjKEZC/FZ+dznPhcoM6O893xOmC1qfVM8t2ir2HVi2DlLqVASiUQi0QpqFcr4N1y/1ZO+dWWq\nvvX8XT+z+eLWVMi+zWjxLessefcfK2FlsG43TgyMLKAN5VK1jdjvzN+d6eD3rIheYYUVen56Ttdf\nfz1QGIuM9PLLLweK71TfpyptFDGT2bNns9RSSy3KorPeyI7J9lSy71Cdva3+Ny6kYl177bX7+n5b\naLqf8aw83iNmqLkeVGHWiegjN1biXJyzzz4bKN2Ava5u5yEPeQhQlK/ZZNONYa6V94qKwnMUZgOa\nEee+YlzAruVOM5wJnZTr+iCq7oX3v+tBBeL1ftKTngSUrtXGl8b3pYOJ8eUqW2QdSiKRSCRmFGZN\n9UaaNWtWK6/22FfITAffovqNZapmvPh27qqGYmxsrL8BGjS3heces3+0hVkY1qFY2awPXmVjTy+Z\nSds2GYUtImRtBxxwAFAyTA477DAAjjzySGD01d1d2sL1YOai6j3OdpkpmI51YdxA21R1cDZu6Pow\nvhAz69rCKG3hOevJ0HOh4vU54nPCLFAVTFfdpI2pV9kiFUoikUgkWkEjhTJsR8qZgjYYx6DZXvF7\n8XdZu/7jTTfdFIAzzjgDKLZvu7ZmUFuMz/4b93e31fP3WAEf1ZXxhGOPPRaorxvoGtPBykeFQWca\ntWGLQbP/Zipm0rqI91p8flQ9m5vOtIpIhZJIJBKJTlGrUMZnsDRl4zMNc+bMYcGCBTOKcUw3urSF\n8SOr+vWNu4523XVXoHRU7hoxCzBicVoXXfdqW5xs0TVGYYthFcSontGpUBKJRCLRKVrJ8prpyqXq\n+JJ9FXRhi6233hoo2Xttw5hMv/PX+8Uo1kXbccg61dUUeY8ULM62aPsZnQolkUgkEp1ioBhKHexL\n1G+1dlsZB3VYHBTKdKu8mWSLYTFM5t3Y2Nh/lC2GRZe2sN7EGpyZjv/GdVH1jE6FkkgkEolOUdfL\n686xsbHb+t3YoH2kRjWzoYKpPnbAzdwJ9G2LQTHN8acZZYth0dSW93/vP8oWQ6JTWywuyuR+/Feu\ni4pndKUtpnR5JRKJRCLRL9LllUgkEolWkC+URCKRSLSCfKEkEolEohXkCyWRSCQSrSBfKIlEIpFo\nBf8PZZNwfwIXdOkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x864 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1y3vE2zVhXb5",
        "colab_type": "code",
        "outputId": "113e6b06-440f-4181-e349-71c1e49d4bf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        }
      },
      "source": [
        "saver = tf.train.Saver(var_list=g_vars)\n",
        "with tf.Session() as sess:\n",
        "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
        "    sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
        "    gen_samples = sess.run(\n",
        "                   generator(input_z, input_size, n_units=g_hidden_size, reuse=True, alpha=alpha),\n",
        "                   feed_dict={input_z: sample_z})\n",
        "view_samples(0, [gen_samples])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from checkpoints/generator.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 504x504 with 16 Axes>,\n",
              " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15af3f5898>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15af37c7b8>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15adb22748>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15adacb8d0>],\n",
              "        [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15adaf3ba8>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ada9de80>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ada4c198>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ada77438>],\n",
              "        [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15ada1e748>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad9c8a20>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad9f2cf8>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad99afd0>],\n",
              "        [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad94b2e8>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad9745c0>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad91b898>,\n",
              "         <matplotlib.axes._subplots.AxesSubplot object at 0x7f15ad8c4b70>]],\n",
              "       dtype=object))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAGKCAYAAAAmB8cMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 504x504 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2Ou8tzGjnt9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}