DCGAN.ipynb

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      "name": "DCGAN.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/sungreong/GAN/blob/master/DCGAN.ipynb)"
      ]
    },
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      "metadata": {
        "id": "hwC8EE8T_3Bm",
        "colab_type": "text"
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      "source": [
        "## https://angrypark.github.io/DCGAN-paper-reading/\n",
        "\n",
        "## https://github.com/sungreong/Keras-GAN/blob/master/dcgan/dcgan.py\n",
        "\n",
        "The training images have been scaled to a range of [-1, 1].  \n",
        "\n",
        "The activation function used is LeakyRELU(for both the generator and discriminator) with 0.2 as the alpha(slope) value except for the \n",
        "\n",
        "last layer of the generator which uses tanh as the activation function.  \n",
        "\n",
        "There are no Fully connected layers used.  \n",
        "\n",
        "Instead of Max-pooling, strided convolutions are used in the discriminator.  \n",
        "\n",
        "For the generator, Upsampling followed by a convolutional layer is used to increase the resolution of the image.  \n",
        "\n",
        "The optimizer used is Adam.  \n",
        "\n",
        "A batch size of 128 is used to the train the generator and the discriminator  "
      ]
    },
    {
      "metadata": {
        "id": "FQQ3vGLv37Rh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function , division\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.layers import Input, Dense, Reshape, Flatten, Dropout\n",
        "from keras.layers import BatchNormalization, Activation, ZeroPadding2D , Conv2DTranspose\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers.convolutional import UpSampling2D, Conv2D\n",
        "from keras.models import Sequential, Model\n",
        "from keras.optimizers import Adam\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JLR3nvfY4yvS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import sys\n",
        "import numpy as np\n",
        "import os\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RHcHP3Yy46ch",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class DCGAN():\n",
        "    def __init__(self):\n",
        "        self.img_rows = 28 \n",
        "        self.img_cols = 28\n",
        "        self.channels = 1\n",
        "\n",
        "        optimizer = Adam(0.0002, 0.5)\n",
        "\n",
        "        # Build and compile the discriminator\n",
        "        self.discriminator = self.build_discriminator()\n",
        "        self.discriminator.compile(loss='binary_crossentropy', \n",
        "            optimizer=optimizer,\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "        # Build and compile the generator\n",
        "        self.generator = self.build_generator()\n",
        "        self.generator.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
        "\n",
        "        # The generator takes noise as input and generated imgs\n",
        "        z = Input(shape=(100,))\n",
        "        img = self.generator(z)\n",
        "\n",
        "        # For the combined model we will only train the generator\n",
        "        self.discriminator.trainable = False\n",
        "\n",
        "        # The valid takes generated images as input and determines validity\n",
        "        valid = self.discriminator(img)\n",
        "\n",
        "        # The combined model  (stacked generator and discriminator) takes\n",
        "        # noise as input => generates images => determines validity \n",
        "        self.combined = Model(z, valid)\n",
        "        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
        "\n",
        "         \n",
        "    def build_generator(self):\n",
        "\n",
        "        noise_shape = (100,)\n",
        "        \n",
        "        model = Sequential()\n",
        "\n",
        "        model.add(Dense(128 * 7 * 7, activation=\"relu\", input_shape=noise_shape))\n",
        "        model.add(Reshape((7, 7, 128)))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(UpSampling2D())\n",
        "        model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(momentum=0.8)) \n",
        "        model.add(UpSampling2D())\n",
        "        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(Conv2D(1, kernel_size=3, padding=\"same\"))\n",
        "        model.add(Activation(\"tanh\"))\n",
        "\n",
        "        model.summary()\n",
        "\n",
        "        noise = Input(shape=noise_shape)\n",
        "        img = model(noise)\n",
        "\n",
        "        return Model(noise, img)\n",
        "\n",
        "    def build_discriminator(self):\n",
        "\n",
        "       \n",
        "        img_shape = (self.img_rows, self.img_cols, self.channels)\n",
        "        \n",
        "        model = Sequential()\n",
        "        \n",
        "        ## Discriminator에서는 모든 pooling layers를 strided convolutions 로 바꾼다.\n",
        "        ## Generator와 Discriminator에 batch-normalization을 사용한다\n",
        "        ##  그러나 모든 layer에 다 적용하면 sample oscillation과 model instability의 문제가 발생하여 Discriminator input layer에는 적용하지 않았다고 한다.\n",
        "        ## Discriminator에서는 모든 활성화 함수를 LeakyRelu를 쓴다.\n",
        "        \n",
        "        ## called transposed convolution. Instead of fractionally-strided convolution as suggested in DCGAN\n",
        "        \n",
        "        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=img_shape, padding=\"same\"))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n",
        "        model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(Conv2D(128, kernel_size=3, strides=2, padding=\"same\"))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(Conv2D(256, kernel_size=3, strides=1, padding=\"same\"))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        \n",
        "        model.add(Flatten())\n",
        "        model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "        model.summary()\n",
        "\n",
        "        img = Input(shape=img_shape)\n",
        "        validity = model(img)\n",
        "\n",
        "        return Model(img, validity)\n",
        "\n",
        "    def train(self, epochs, batch_size=128, save_interval=50):\n",
        "\n",
        "        # Load the dataset\n",
        "        (X_train, _), (_, _) = mnist.load_data()\n",
        "\n",
        "        # Rescale -1 to 1\n",
        "        X_train = (X_train.astype(np.float32) - 127.5) / 127.5\n",
        "        X_train = np.expand_dims(X_train, axis=3)\n",
        "\n",
        "        half_batch = int(batch_size / 2)\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "\n",
        "            # ---------------------\n",
        "            #  Train Discriminator\n",
        "            # ---------------------\n",
        "\n",
        "            # Select a random half batch of images\n",
        "            idx = np.random.randint(0, X_train.shape[0], half_batch)\n",
        "            imgs = X_train[idx]\n",
        "\n",
        "            # Sample noise and generate a half batch of new images\n",
        "            noise = np.random.normal(0, 1, (half_batch, 100))\n",
        "            gen_imgs = self.generator.predict(noise)\n",
        "\n",
        "            # Train the discriminator (real classified as ones and generated as zeros)\n",
        "            d_loss_real = self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))\n",
        "            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))\n",
        "            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "            # ---------------------\n",
        "            #  Train Generator\n",
        "            # ---------------------\n",
        "\n",
        "            noise = np.random.normal(0, 1, (batch_size, 100))\n",
        "\n",
        "            # Train the generator (wants discriminator to mistake images as real)\n",
        "            g_loss = self.combined.train_on_batch(noise, np.ones((batch_size, 1)))\n",
        "\n",
        "            # Plot the progress\n",
        "            print (\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[1], g_loss))\n",
        "\n",
        "            # If at save interval => save generated image samples\n",
        "            if epoch % save_interval == 0:\n",
        "                self.save_imgs(epoch)\n",
        "\n",
        "    def save_imgs(self, epoch):\n",
        "        r, c = 5, 5\n",
        "        noise = np.random.normal(0, 1, (r * c, 100))\n",
        "        gen_imgs = self.generator.predict(noise)\n",
        "\n",
        "        # Rescale images 0 - 1\n",
        "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
        "\n",
        "        fig, axs = plt.subplots(r, c)\n",
        "        #fig.suptitle(\"DCGAN: Generated digits\", fontsize=12)\n",
        "        cnt = 0\n",
        "        for i in range(r):\n",
        "            for j in range(c):\n",
        "                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')\n",
        "                axs[i,j].axis('off')\n",
        "                cnt += 1\n",
        "        if not os.path.exists(\"./images\"):\n",
        "            os.makedirs(\"./images\")\n",
        "        fig.savefig(\"images/mnist_%d.png\" % epoch)\n",
        "        plt.close()\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kNMkuZNA-MXq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72514
        },
        "outputId": "f264fcb4-6f22-4297-8eee-da70fded306a"
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      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    dcgan = DCGAN()\n",
        "    dcgan.train(epochs=4000, batch_size=32, save_interval=50)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_34 (Conv2D)           (None, 14, 14, 32)        320       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)   (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_27 (Dropout)         (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_35 (Conv2D)           (None, 7, 7, 64)          18496     \n",
            "_________________________________________________________________\n",
            "zero_padding2d_7 (ZeroPaddin (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_26 (LeakyReLU)   (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_28 (Dropout)         (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 8, 8, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv2d_36 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)   (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_29 (Dropout)         (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 4, 4, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_37 (Conv2D)           (None, 4, 4, 256)         295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)   (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_30 (Dropout)         (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1)                 4097      \n",
            "=================================================================\n",
            "Total params: 392,705\n",
            "Trainable params: 392,321\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 6272)              633472    \n",
            "_________________________________________________________________\n",
            "reshape_6 (Reshape)          (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_10 (UpSampling (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_38 (Conv2D)           (None, 14, 14, 128)       147584    \n",
            "_________________________________________________________________\n",
            "activation_15 (Activation)   (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_29 (Batc (None, 14, 14, 128)       512       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_11 (UpSampling (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_39 (Conv2D)           (None, 28, 28, 64)        73792     \n",
            "_________________________________________________________________\n",
            "activation_16 (Activation)   (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_30 (Batc (None, 28, 28, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_40 (Conv2D)           (None, 28, 28, 1)         577       \n",
            "_________________________________________________________________\n",
            "activation_17 (Activation)   (None, 28, 28, 1)         0         \n",
            "=================================================================\n",
            "Total params: 856,705\n",
            "Trainable params: 856,065\n",
            "Non-trainable params: 640\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:975: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0 [D loss: 0.916218, acc.: 34.38%] [G loss: 0.786627]\n",
            "1 [D loss: 0.683906, acc.: 56.25%] [G loss: 0.826510]\n",
            "2 [D loss: 0.613418, acc.: 62.50%] [G loss: 0.931055]\n",
            "3 [D loss: 0.476317, acc.: 81.25%] [G loss: 0.808027]\n",
            "4 [D loss: 0.512299, acc.: 75.00%] [G loss: 0.907506]\n",
            "5 [D loss: 0.374197, acc.: 90.62%] [G loss: 1.359673]\n",
            "6 [D loss: 0.333778, acc.: 90.62%] [G loss: 1.295575]\n",
            "7 [D loss: 0.314982, acc.: 87.50%] [G loss: 1.319186]\n",
            "8 [D loss: 0.364275, acc.: 87.50%] [G loss: 1.482754]\n",
            "9 [D loss: 0.316566, acc.: 90.62%] [G loss: 1.416956]\n",
            "10 [D loss: 0.258322, acc.: 100.00%] [G loss: 1.527956]\n",
            "11 [D loss: 0.326561, acc.: 87.50%] [G loss: 1.873163]\n",
            "12 [D loss: 0.291813, acc.: 90.62%] [G loss: 1.973686]\n",
            "13 [D loss: 0.282394, acc.: 84.38%] [G loss: 1.797666]\n",
            "14 [D loss: 0.192880, acc.: 100.00%] [G loss: 1.866074]\n",
            "15 [D loss: 0.279396, acc.: 93.75%] [G loss: 1.614272]\n",
            "16 [D loss: 0.229541, acc.: 90.62%] [G loss: 1.814074]\n",
            "17 [D loss: 0.172858, acc.: 96.88%] [G loss: 2.153530]\n",
            "18 [D loss: 0.223719, acc.: 96.88%] [G loss: 2.367244]\n",
            "19 [D loss: 0.332083, acc.: 87.50%] [G loss: 2.197194]\n",
            "20 [D loss: 0.248648, acc.: 93.75%] [G loss: 2.097261]\n",
            "21 [D loss: 0.171017, acc.: 96.88%] [G loss: 2.531364]\n",
            "22 [D loss: 0.180195, acc.: 93.75%] [G loss: 2.330293]\n",
            "23 [D loss: 0.124903, acc.: 96.88%] [G loss: 2.287279]\n",
            "24 [D loss: 0.191533, acc.: 93.75%] [G loss: 2.463305]\n",
            "25 [D loss: 0.210962, acc.: 100.00%] [G loss: 2.576892]\n",
            "26 [D loss: 0.198740, acc.: 96.88%] [G loss: 3.102580]\n",
            "27 [D loss: 0.137703, acc.: 100.00%] [G loss: 2.729350]\n",
            "28 [D loss: 0.174292, acc.: 93.75%] [G loss: 2.530262]\n",
            "29 [D loss: 0.098077, acc.: 100.00%] [G loss: 2.826101]\n",
            "30 [D loss: 0.136892, acc.: 100.00%] [G loss: 3.262583]\n",
            "31 [D loss: 0.096675, acc.: 100.00%] [G loss: 3.609713]\n",
            "32 [D loss: 0.072096, acc.: 100.00%] [G loss: 3.645563]\n",
            "33 [D loss: 0.134323, acc.: 96.88%] [G loss: 3.028010]\n",
            "34 [D loss: 0.051067, acc.: 100.00%] [G loss: 2.423988]\n",
            "35 [D loss: 0.216259, acc.: 93.75%] [G loss: 2.891197]\n",
            "36 [D loss: 0.108146, acc.: 100.00%] [G loss: 3.417166]\n",
            "37 [D loss: 0.084112, acc.: 100.00%] [G loss: 3.057084]\n",
            "38 [D loss: 0.105603, acc.: 100.00%] [G loss: 3.107831]\n",
            "39 [D loss: 0.076625, acc.: 100.00%] [G loss: 3.135417]\n",
            "40 [D loss: 0.087376, acc.: 100.00%] [G loss: 2.804815]\n",
            "41 [D loss: 0.044940, acc.: 100.00%] [G loss: 2.892133]\n",
            "42 [D loss: 0.085760, acc.: 100.00%] [G loss: 3.183982]\n",
            "43 [D loss: 0.136793, acc.: 100.00%] [G loss: 3.029905]\n",
            "44 [D loss: 0.054315, acc.: 100.00%] [G loss: 3.574613]\n",
            "45 [D loss: 0.166910, acc.: 93.75%] [G loss: 3.073905]\n",
            "46 [D loss: 0.071973, acc.: 100.00%] [G loss: 2.740528]\n",
            "47 [D loss: 0.051886, acc.: 100.00%] [G loss: 2.885571]\n",
            "48 [D loss: 0.073597, acc.: 100.00%] [G loss: 3.640344]\n",
            "49 [D loss: 0.063074, acc.: 100.00%] [G loss: 2.852020]\n",
            "50 [D loss: 0.036722, acc.: 100.00%] [G loss: 2.296450]\n",
            "51 [D loss: 0.153064, acc.: 96.88%] [G loss: 3.346550]\n",
            "52 [D loss: 0.031647, acc.: 100.00%] [G loss: 3.602061]\n",
            "53 [D loss: 0.090253, acc.: 100.00%] [G loss: 3.301836]\n",
            "54 [D loss: 0.190653, acc.: 90.62%] [G loss: 4.056288]\n",
            "55 [D loss: 0.189568, acc.: 100.00%] [G loss: 3.449280]\n",
            "56 [D loss: 0.158377, acc.: 96.88%] [G loss: 3.042785]\n",
            "57 [D loss: 0.038930, acc.: 100.00%] [G loss: 2.047833]\n",
            "58 [D loss: 0.045263, acc.: 100.00%] [G loss: 2.497634]\n",
            "59 [D loss: 0.209403, acc.: 96.88%] [G loss: 2.256860]\n",
            "60 [D loss: 0.032996, acc.: 100.00%] [G loss: 2.268967]\n",
            "61 [D loss: 0.100159, acc.: 100.00%] [G loss: 2.639646]\n",
            "62 [D loss: 0.104022, acc.: 100.00%] [G loss: 2.655853]\n",
            "63 [D loss: 0.196516, acc.: 96.88%] [G loss: 3.169328]\n",
            "64 [D loss: 0.031426, acc.: 100.00%] [G loss: 2.709832]\n",
            "65 [D loss: 0.381256, acc.: 78.12%] [G loss: 4.166925]\n",
            "66 [D loss: 0.426572, acc.: 71.88%] [G loss: 1.728713]\n",
            "67 [D loss: 0.176074, acc.: 93.75%] [G loss: 2.228718]\n",
            "68 [D loss: 0.189819, acc.: 96.88%] [G loss: 3.167130]\n",
            "69 [D loss: 0.232626, acc.: 93.75%] [G loss: 3.129691]\n",
            "70 [D loss: 0.217330, acc.: 96.88%] [G loss: 3.158396]\n",
            "71 [D loss: 0.330406, acc.: 90.62%] [G loss: 3.982056]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "901 [D loss: 0.875045, acc.: 43.75%] [G loss: 1.107242]\n",
            "902 [D loss: 0.561888, acc.: 68.75%] [G loss: 1.388631]\n",
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          ],
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          "text": [
            "1013 [D loss: 0.706696, acc.: 56.25%] [G loss: 1.045639]\n",
            "1014 [D loss: 0.678146, acc.: 56.25%] [G loss: 1.184744]\n",
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          ],
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        },
        {
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          "text": [
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            "1237 [D loss: 0.545437, acc.: 78.12%] [G loss: 0.878769]\n",
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          ],
          "name": "stdout"
        },
        {
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          "text": [
            "1349 [D loss: 0.625574, acc.: 56.25%] [G loss: 0.960432]\n",
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            "1459 [D loss: 0.935091, acc.: 43.75%] [G loss: 1.196158]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1571 [D loss: 0.790871, acc.: 46.88%] [G loss: 1.135510]\n",
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          ],
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          "text": [
            "1683 [D loss: 0.681833, acc.: 56.25%] [G loss: 1.019692]\n",
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            "1795 [D loss: 0.652842, acc.: 71.88%] [G loss: 0.822731]\n",
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            "1905 [D loss: 0.677947, acc.: 59.38%] [G loss: 1.166698]\n",
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            "1968 [D loss: 0.690839, acc.: 59.38%] [G loss: 1.137243]\n",
            "1969 [D loss: 0.646585, acc.: 68.75%] [G loss: 0.925493]\n",
            "1970 [D loss: 0.550522, acc.: 75.00%] [G loss: 0.991260]\n",
            "1971 [D loss: 0.615744, acc.: 68.75%] [G loss: 0.905290]\n",
            "1972 [D loss: 0.694549, acc.: 68.75%] [G loss: 0.778004]\n",
            "1973 [D loss: 0.744970, acc.: 59.38%] [G loss: 0.826650]\n",
            "1974 [D loss: 0.693691, acc.: 46.88%] [G loss: 0.937647]\n",
            "1975 [D loss: 0.739464, acc.: 34.38%] [G loss: 0.920256]\n",
            "1976 [D loss: 0.659804, acc.: 59.38%] [G loss: 0.805131]\n",
            "1977 [D loss: 0.704618, acc.: 62.50%] [G loss: 0.985923]\n",
            "1978 [D loss: 0.719147, acc.: 53.12%] [G loss: 0.874889]\n",
            "1979 [D loss: 0.711782, acc.: 59.38%] [G loss: 0.836221]\n",
            "1980 [D loss: 0.760098, acc.: 40.62%] [G loss: 0.934322]\n",
            "1981 [D loss: 0.665873, acc.: 65.62%] [G loss: 1.011312]\n",
            "1982 [D loss: 0.686529, acc.: 56.25%] [G loss: 0.762156]\n",
            "1983 [D loss: 0.653448, acc.: 68.75%] [G loss: 0.983814]\n",
            "1984 [D loss: 0.583793, acc.: 68.75%] [G loss: 0.901831]\n",
            "1985 [D loss: 0.728578, acc.: 59.38%] [G loss: 0.957690]\n",
            "1986 [D loss: 0.631266, acc.: 65.62%] [G loss: 0.859515]\n",
            "1987 [D loss: 0.726975, acc.: 53.12%] [G loss: 0.853607]\n",
            "1988 [D loss: 0.733153, acc.: 62.50%] [G loss: 0.769195]\n",
            "1989 [D loss: 0.814519, acc.: 28.12%] [G loss: 1.010547]\n",
            "1990 [D loss: 0.701625, acc.: 53.12%] [G loss: 0.932933]\n",
            "1991 [D loss: 0.680317, acc.: 46.88%] [G loss: 1.095236]\n",
            "1992 [D loss: 0.748447, acc.: 56.25%] [G loss: 0.898684]\n",
            "1993 [D loss: 0.690983, acc.: 65.62%] [G loss: 1.033928]\n",
            "1994 [D loss: 0.591398, acc.: 68.75%] [G loss: 0.834750]\n",
            "1995 [D loss: 0.680697, acc.: 59.38%] [G loss: 1.039758]\n",
            "1996 [D loss: 0.688135, acc.: 62.50%] [G loss: 0.985143]\n",
            "1997 [D loss: 0.876606, acc.: 43.75%] [G loss: 0.916006]\n",
            "1998 [D loss: 0.717715, acc.: 46.88%] [G loss: 1.171723]\n",
            "1999 [D loss: 0.716249, acc.: 50.00%] [G loss: 1.007251]\n",
            "2000 [D loss: 0.639499, acc.: 71.88%] [G loss: 0.843981]\n",
            "2001 [D loss: 0.676573, acc.: 71.88%] [G loss: 1.062439]\n",
            "2002 [D loss: 0.663118, acc.: 59.38%] [G loss: 0.891349]\n",
            "2003 [D loss: 0.676220, acc.: 53.12%] [G loss: 0.939834]\n",
            "2004 [D loss: 0.659731, acc.: 53.12%] [G loss: 1.001961]\n",
            "2005 [D loss: 0.642876, acc.: 75.00%] [G loss: 0.905414]\n",
            "2006 [D loss: 0.593814, acc.: 71.88%] [G loss: 0.866145]\n",
            "2007 [D loss: 0.685952, acc.: 56.25%] [G loss: 0.964867]\n",
            "2008 [D loss: 0.691021, acc.: 53.12%] [G loss: 0.914234]\n",
            "2009 [D loss: 0.646075, acc.: 59.38%] [G loss: 0.861644]\n",
            "2010 [D loss: 0.684648, acc.: 56.25%] [G loss: 0.899937]\n",
            "2011 [D loss: 0.744779, acc.: 50.00%] [G loss: 0.920110]\n",
            "2012 [D loss: 0.582467, acc.: 68.75%] [G loss: 0.878612]\n",
            "2013 [D loss: 0.627320, acc.: 68.75%] [G loss: 0.890576]\n",
            "2014 [D loss: 0.609156, acc.: 65.62%] [G loss: 1.017737]\n",
            "2015 [D loss: 0.662600, acc.: 59.38%] [G loss: 1.024088]\n",
            "2016 [D loss: 0.716009, acc.: 53.12%] [G loss: 0.857136]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2017 [D loss: 0.646760, acc.: 62.50%] [G loss: 0.940657]\n",
            "2018 [D loss: 0.617780, acc.: 68.75%] [G loss: 0.868747]\n",
            "2019 [D loss: 0.676997, acc.: 62.50%] [G loss: 0.924574]\n",
            "2020 [D loss: 0.697047, acc.: 56.25%] [G loss: 1.108242]\n",
            "2021 [D loss: 0.702032, acc.: 59.38%] [G loss: 1.003073]\n",
            "2022 [D loss: 0.644385, acc.: 56.25%] [G loss: 0.905454]\n",
            "2023 [D loss: 0.691291, acc.: 62.50%] [G loss: 0.979400]\n",
            "2024 [D loss: 0.601131, acc.: 81.25%] [G loss: 1.144348]\n",
            "2025 [D loss: 0.653292, acc.: 71.88%] [G loss: 0.958032]\n",
            "2026 [D loss: 0.637972, acc.: 65.62%] [G loss: 0.913273]\n",
            "2027 [D loss: 0.749589, acc.: 56.25%] [G loss: 1.048532]\n",
            "2028 [D loss: 0.622055, acc.: 65.62%] [G loss: 0.832986]\n",
            "2029 [D loss: 0.677713, acc.: 59.38%] [G loss: 1.049883]\n",
            "2030 [D loss: 0.679653, acc.: 56.25%] [G loss: 0.970763]\n",
            "2031 [D loss: 0.627675, acc.: 65.62%] [G loss: 0.855881]\n",
            "2032 [D loss: 0.727587, acc.: 56.25%] [G loss: 0.714988]\n",
            "2033 [D loss: 0.680934, acc.: 62.50%] [G loss: 0.850445]\n",
            "2034 [D loss: 0.701389, acc.: 53.12%] [G loss: 0.959367]\n",
            "2035 [D loss: 0.792028, acc.: 50.00%] [G loss: 1.072780]\n",
            "2036 [D loss: 0.589147, acc.: 68.75%] [G loss: 1.056647]\n",
            "2037 [D loss: 0.731940, acc.: 56.25%] [G loss: 1.078881]\n",
            "2038 [D loss: 0.748489, acc.: 46.88%] [G loss: 0.895475]\n",
            "2039 [D loss: 0.777043, acc.: 40.62%] [G loss: 0.914751]\n",
            "2040 [D loss: 0.686762, acc.: 53.12%] [G loss: 0.941832]\n",
            "2041 [D loss: 0.722990, acc.: 56.25%] [G loss: 1.026446]\n",
            "2042 [D loss: 0.664898, acc.: 56.25%] [G loss: 1.106663]\n",
            "2043 [D loss: 0.593186, acc.: 65.62%] [G loss: 0.956144]\n",
            "2044 [D loss: 0.728713, acc.: 62.50%] [G loss: 0.886137]\n",
            "2045 [D loss: 0.770877, acc.: 46.88%] [G loss: 0.817844]\n",
            "2046 [D loss: 0.805905, acc.: 37.50%] [G loss: 0.870766]\n",
            "2047 [D loss: 0.641483, acc.: 53.12%] [G loss: 1.034237]\n",
            "2048 [D loss: 0.640678, acc.: 62.50%] [G loss: 1.204274]\n",
            "2049 [D loss: 0.719078, acc.: 50.00%] [G loss: 0.975460]\n",
            "2050 [D loss: 0.788406, acc.: 50.00%] [G loss: 1.098845]\n",
            "2051 [D loss: 0.785362, acc.: 46.88%] [G loss: 1.102744]\n",
            "2052 [D loss: 0.690308, acc.: 65.62%] [G loss: 0.912125]\n",
            "2053 [D loss: 0.742735, acc.: 50.00%] [G loss: 1.023095]\n",
            "2054 [D loss: 0.615380, acc.: 71.88%] [G loss: 1.068355]\n",
            "2055 [D loss: 0.608061, acc.: 68.75%] [G loss: 0.905459]\n",
            "2056 [D loss: 0.659109, acc.: 62.50%] [G loss: 1.169672]\n",
            "2057 [D loss: 0.551002, acc.: 71.88%] [G loss: 1.016048]\n",
            "2058 [D loss: 0.798548, acc.: 43.75%] [G loss: 1.027478]\n",
            "2059 [D loss: 0.615632, acc.: 68.75%] [G loss: 0.955465]\n",
            "2060 [D loss: 0.864652, acc.: 40.62%] [G loss: 1.070411]\n",
            "2061 [D loss: 0.556882, acc.: 71.88%] [G loss: 1.054291]\n",
            "2062 [D loss: 0.819855, acc.: 40.62%] [G loss: 0.975538]\n",
            "2063 [D loss: 0.713997, acc.: 53.12%] [G loss: 1.248149]\n",
            "2064 [D loss: 0.590410, acc.: 68.75%] [G loss: 1.175578]\n",
            "2065 [D loss: 0.780029, acc.: 50.00%] [G loss: 0.967824]\n",
            "2066 [D loss: 0.607279, acc.: 68.75%] [G loss: 0.978584]\n",
            "2067 [D loss: 0.723491, acc.: 43.75%] [G loss: 1.011695]\n",
            "2068 [D loss: 0.656308, acc.: 62.50%] [G loss: 1.008451]\n",
            "2069 [D loss: 0.732068, acc.: 59.38%] [G loss: 0.902315]\n",
            "2070 [D loss: 0.590907, acc.: 65.62%] [G loss: 1.047279]\n",
            "2071 [D loss: 0.537019, acc.: 71.88%] [G loss: 1.043633]\n",
            "2072 [D loss: 0.721275, acc.: 50.00%] [G loss: 1.089614]\n",
            "2073 [D loss: 0.697383, acc.: 59.38%] [G loss: 1.039455]\n",
            "2074 [D loss: 0.573814, acc.: 68.75%] [G loss: 0.813094]\n",
            "2075 [D loss: 0.875957, acc.: 34.38%] [G loss: 0.949266]\n",
            "2076 [D loss: 0.601776, acc.: 68.75%] [G loss: 0.943371]\n",
            "2077 [D loss: 0.742230, acc.: 50.00%] [G loss: 0.913305]\n",
            "2078 [D loss: 0.616614, acc.: 62.50%] [G loss: 0.955927]\n",
            "2079 [D loss: 0.627890, acc.: 65.62%] [G loss: 1.042129]\n",
            "2080 [D loss: 0.629373, acc.: 71.88%] [G loss: 1.207185]\n",
            "2081 [D loss: 0.605858, acc.: 68.75%] [G loss: 0.859834]\n",
            "2082 [D loss: 0.769829, acc.: 43.75%] [G loss: 1.106630]\n",
            "2083 [D loss: 0.573504, acc.: 68.75%] [G loss: 1.084835]\n",
            "2084 [D loss: 0.797121, acc.: 43.75%] [G loss: 1.090929]\n",
            "2085 [D loss: 0.636453, acc.: 59.38%] [G loss: 1.066635]\n",
            "2086 [D loss: 0.688246, acc.: 53.12%] [G loss: 1.031019]\n",
            "2087 [D loss: 0.589721, acc.: 68.75%] [G loss: 1.115036]\n",
            "2088 [D loss: 0.649595, acc.: 68.75%] [G loss: 0.908903]\n",
            "2089 [D loss: 0.564250, acc.: 71.88%] [G loss: 0.913083]\n",
            "2090 [D loss: 0.746589, acc.: 46.88%] [G loss: 0.923568]\n",
            "2091 [D loss: 0.606237, acc.: 59.38%] [G loss: 1.062527]\n",
            "2092 [D loss: 0.688484, acc.: 62.50%] [G loss: 0.917313]\n",
            "2093 [D loss: 0.849647, acc.: 40.62%] [G loss: 1.009505]\n",
            "2094 [D loss: 0.674583, acc.: 46.88%] [G loss: 0.825645]\n",
            "2095 [D loss: 0.632443, acc.: 62.50%] [G loss: 0.908778]\n",
            "2096 [D loss: 0.719435, acc.: 53.12%] [G loss: 1.105913]\n",
            "2097 [D loss: 0.653874, acc.: 59.38%] [G loss: 0.913585]\n",
            "2098 [D loss: 0.544260, acc.: 78.12%] [G loss: 1.071776]\n",
            "2099 [D loss: 0.618188, acc.: 59.38%] [G loss: 0.948182]\n",
            "2100 [D loss: 0.730603, acc.: 53.12%] [G loss: 1.047264]\n",
            "2101 [D loss: 0.727773, acc.: 43.75%] [G loss: 1.177460]\n",
            "2102 [D loss: 0.694427, acc.: 46.88%] [G loss: 1.118178]\n",
            "2103 [D loss: 0.823222, acc.: 50.00%] [G loss: 1.009211]\n",
            "2104 [D loss: 0.563208, acc.: 65.62%] [G loss: 1.084552]\n",
            "2105 [D loss: 0.648800, acc.: 62.50%] [G loss: 0.958318]\n",
            "2106 [D loss: 0.729856, acc.: 50.00%] [G loss: 0.894188]\n",
            "2107 [D loss: 0.707533, acc.: 59.38%] [G loss: 0.885577]\n",
            "2108 [D loss: 0.623644, acc.: 65.62%] [G loss: 0.987159]\n",
            "2109 [D loss: 0.659968, acc.: 56.25%] [G loss: 1.006129]\n",
            "2110 [D loss: 0.490111, acc.: 87.50%] [G loss: 1.098860]\n",
            "2111 [D loss: 0.589421, acc.: 75.00%] [G loss: 0.948133]\n",
            "2112 [D loss: 0.742463, acc.: 37.50%] [G loss: 0.754706]\n",
            "2113 [D loss: 0.771082, acc.: 56.25%] [G loss: 0.738358]\n",
            "2114 [D loss: 0.683920, acc.: 56.25%] [G loss: 0.831509]\n",
            "2115 [D loss: 0.752442, acc.: 62.50%] [G loss: 0.780301]\n",
            "2116 [D loss: 0.624797, acc.: 71.88%] [G loss: 0.785491]\n",
            "2117 [D loss: 0.666565, acc.: 59.38%] [G loss: 0.931921]\n",
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            "2119 [D loss: 0.817552, acc.: 37.50%] [G loss: 0.856679]\n",
            "2120 [D loss: 0.791859, acc.: 53.12%] [G loss: 0.789036]\n",
            "2121 [D loss: 0.510399, acc.: 87.50%] [G loss: 0.869402]\n",
            "2122 [D loss: 0.518343, acc.: 81.25%] [G loss: 0.889717]\n",
            "2123 [D loss: 0.629140, acc.: 68.75%] [G loss: 0.851136]\n",
            "2124 [D loss: 0.782440, acc.: 37.50%] [G loss: 1.000784]\n",
            "2125 [D loss: 0.698305, acc.: 56.25%] [G loss: 1.116508]\n",
            "2126 [D loss: 0.663710, acc.: 59.38%] [G loss: 0.938864]\n",
            "2127 [D loss: 0.713692, acc.: 59.38%] [G loss: 0.865853]\n",
            "2128 [D loss: 0.604612, acc.: 68.75%] [G loss: 0.878231]\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "2129 [D loss: 0.676752, acc.: 56.25%] [G loss: 0.860624]\n",
            "2130 [D loss: 0.672610, acc.: 56.25%] [G loss: 1.141058]\n",
            "2131 [D loss: 0.736138, acc.: 53.12%] [G loss: 0.904789]\n",
            "2132 [D loss: 0.730667, acc.: 53.12%] [G loss: 1.016982]\n",
            "2133 [D loss: 0.590215, acc.: 62.50%] [G loss: 0.847177]\n",
            "2134 [D loss: 0.647016, acc.: 62.50%] [G loss: 0.802906]\n",
            "2135 [D loss: 0.662884, acc.: 59.38%] [G loss: 0.823196]\n",
            "2136 [D loss: 0.783426, acc.: 40.62%] [G loss: 0.949055]\n",
            "2137 [D loss: 0.583932, acc.: 78.12%] [G loss: 1.004002]\n",
            "2138 [D loss: 0.618513, acc.: 71.88%] [G loss: 0.959321]\n",
            "2139 [D loss: 0.667646, acc.: 65.62%] [G loss: 0.863437]\n",
            "2140 [D loss: 0.691627, acc.: 59.38%] [G loss: 0.877194]\n",
            "2141 [D loss: 0.706759, acc.: 50.00%] [G loss: 0.925554]\n",
            "2142 [D loss: 0.766384, acc.: 53.12%] [G loss: 0.981691]\n",
            "2143 [D loss: 0.716812, acc.: 56.25%] [G loss: 0.844557]\n",
            "2144 [D loss: 0.573710, acc.: 75.00%] [G loss: 1.036525]\n",
            "2145 [D loss: 0.566657, acc.: 68.75%] [G loss: 1.044986]\n",
            "2146 [D loss: 0.697299, acc.: 65.62%] [G loss: 0.916462]\n",
            "2147 [D loss: 0.570983, acc.: 59.38%] [G loss: 0.926954]\n",
            "2148 [D loss: 0.663512, acc.: 56.25%] [G loss: 0.878612]\n",
            "2149 [D loss: 0.582673, acc.: 75.00%] [G loss: 0.887100]\n",
            "2150 [D loss: 0.741592, acc.: 56.25%] [G loss: 0.926514]\n",
            "2151 [D loss: 0.744144, acc.: 56.25%] [G loss: 1.057873]\n",
            "2152 [D loss: 0.658409, acc.: 62.50%] [G loss: 0.878196]\n",
            "2153 [D loss: 0.765820, acc.: 50.00%] [G loss: 0.869712]\n",
            "2154 [D loss: 0.670551, acc.: 56.25%] [G loss: 0.886195]\n",
            "2155 [D loss: 0.729853, acc.: 46.88%] [G loss: 0.935382]\n",
            "2156 [D loss: 0.581601, acc.: 71.88%] [G loss: 1.015801]\n",
            "2157 [D loss: 0.878364, acc.: 37.50%] [G loss: 0.814501]\n",
            "2158 [D loss: 0.668799, acc.: 53.12%] [G loss: 0.852668]\n",
            "2159 [D loss: 0.721199, acc.: 65.62%] [G loss: 0.927335]\n",
            "2160 [D loss: 0.719127, acc.: 50.00%] [G loss: 0.933883]\n",
            "2161 [D loss: 0.795433, acc.: 46.88%] [G loss: 1.093762]\n",
            "2162 [D loss: 0.683600, acc.: 53.12%] [G loss: 1.011222]\n",
            "2163 [D loss: 0.637412, acc.: 59.38%] [G loss: 0.937514]\n",
            "2164 [D loss: 0.737158, acc.: 65.62%] [G loss: 1.097535]\n",
            "2165 [D loss: 0.620307, acc.: 75.00%] [G loss: 1.031213]\n",
            "2166 [D loss: 0.695037, acc.: 59.38%] [G loss: 0.978995]\n",
            "2167 [D loss: 0.661623, acc.: 56.25%] [G loss: 0.914269]\n",
            "2168 [D loss: 0.575920, acc.: 71.88%] [G loss: 1.164026]\n",
            "2169 [D loss: 0.683786, acc.: 53.12%] [G loss: 0.974758]\n",
            "2170 [D loss: 0.611971, acc.: 65.62%] [G loss: 0.921006]\n",
            "2171 [D loss: 0.752685, acc.: 46.88%] [G loss: 0.796063]\n",
            "2172 [D loss: 0.687470, acc.: 62.50%] [G loss: 0.930176]\n",
            "2173 [D loss: 0.729381, acc.: 46.88%] [G loss: 0.843453]\n",
            "2174 [D loss: 0.765064, acc.: 40.62%] [G loss: 0.995839]\n",
            "2175 [D loss: 0.701225, acc.: 62.50%] [G loss: 1.020161]\n",
            "2176 [D loss: 0.658441, acc.: 62.50%] [G loss: 0.987144]\n",
            "2177 [D loss: 0.741431, acc.: 43.75%] [G loss: 0.955669]\n",
            "2178 [D loss: 0.620691, acc.: 62.50%] [G loss: 0.924072]\n",
            "2179 [D loss: 0.714965, acc.: 50.00%] [G loss: 0.929953]\n",
            "2180 [D loss: 0.699143, acc.: 53.12%] [G loss: 0.850257]\n",
            "2181 [D loss: 0.728548, acc.: 43.75%] [G loss: 1.029472]\n",
            "2182 [D loss: 0.653582, acc.: 65.62%] [G loss: 1.073865]\n",
            "2183 [D loss: 0.613987, acc.: 62.50%] [G loss: 0.998363]\n",
            "2184 [D loss: 0.668408, acc.: 53.12%] [G loss: 0.998957]\n",
            "2185 [D loss: 0.815244, acc.: 37.50%] [G loss: 1.017015]\n",
            "2186 [D loss: 0.672133, acc.: 53.12%] [G loss: 0.894115]\n",
            "2187 [D loss: 0.792677, acc.: 50.00%] [G loss: 0.974075]\n",
            "2188 [D loss: 0.792657, acc.: 46.88%] [G loss: 0.910630]\n",
            "2189 [D loss: 0.769718, acc.: 43.75%] [G loss: 0.714712]\n",
            "2190 [D loss: 0.541525, acc.: 81.25%] [G loss: 0.789614]\n",
            "2191 [D loss: 0.628145, acc.: 65.62%] [G loss: 1.065942]\n",
            "2192 [D loss: 0.627100, acc.: 68.75%] [G loss: 0.884063]\n",
            "2193 [D loss: 0.668966, acc.: 59.38%] [G loss: 1.033391]\n",
            "2194 [D loss: 0.649980, acc.: 62.50%] [G loss: 1.009784]\n",
            "2195 [D loss: 0.598735, acc.: 62.50%] [G loss: 1.064083]\n",
            "2196 [D loss: 0.693340, acc.: 50.00%] [G loss: 0.981082]\n",
            "2197 [D loss: 0.677959, acc.: 71.88%] [G loss: 0.860011]\n",
            "2198 [D loss: 0.613620, acc.: 56.25%] [G loss: 0.981037]\n",
            "2199 [D loss: 0.662062, acc.: 62.50%] [G loss: 0.867282]\n",
            "2200 [D loss: 0.734543, acc.: 46.88%] [G loss: 1.009881]\n",
            "2201 [D loss: 0.636088, acc.: 65.62%] [G loss: 0.930864]\n",
            "2202 [D loss: 0.822431, acc.: 46.88%] [G loss: 0.851540]\n",
            "2203 [D loss: 0.701385, acc.: 59.38%] [G loss: 0.951974]\n",
            "2204 [D loss: 0.795935, acc.: 46.88%] [G loss: 0.947124]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2241 [D loss: 0.689105, acc.: 53.12%] [G loss: 0.985151]\n",
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          ],
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          "text": [
            "2351 [D loss: 0.674169, acc.: 46.88%] [G loss: 0.888048]\n",
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          ],
          "name": "stdout"
        },
        {
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          "text": [
            "2463 [D loss: 0.652074, acc.: 59.38%] [G loss: 0.918314]\n",
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          ],
          "name": "stdout"
        },
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          "text": [
            "2575 [D loss: 0.681764, acc.: 50.00%] [G loss: 0.998597]\n",
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          ],
          "name": "stdout"
        },
        {
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          "text": [
            "2687 [D loss: 0.695927, acc.: 46.88%] [G loss: 0.842163]\n",
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          ],
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        },
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          "text": [
            "2799 [D loss: 0.677866, acc.: 65.62%] [G loss: 1.025880]\n",
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            "2909 [D loss: 0.677686, acc.: 59.38%] [G loss: 1.166410]\n",
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            "3021 [D loss: 0.639673, acc.: 56.25%] [G loss: 0.888258]\n",
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            "3133 [D loss: 0.601959, acc.: 65.62%] [G loss: 0.982754]\n",
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          ],
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        },
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          "text": [
            "3245 [D loss: 0.710780, acc.: 46.88%] [G loss: 0.844742]\n",
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            "3355 [D loss: 0.902178, acc.: 31.25%] [G loss: 1.006707]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "3467 [D loss: 0.711445, acc.: 65.62%] [G loss: 1.132802]\n",
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          ],
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          "text": [
            "3579 [D loss: 0.726463, acc.: 50.00%] [G loss: 0.968619]\n",
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          ],
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        },
        {
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          "text": [
            "3691 [D loss: 0.686350, acc.: 65.62%] [G loss: 1.060603]\n",
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          ],
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            "3801 [D loss: 0.622931, acc.: 53.12%] [G loss: 0.812091]\n",
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            "3840 [D loss: 0.633925, acc.: 56.25%] [G loss: 0.878226]\n",
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            "3842 [D loss: 0.621426, acc.: 62.50%] [G loss: 0.863392]\n",
            "3843 [D loss: 0.602911, acc.: 75.00%] [G loss: 0.879796]\n",
            "3844 [D loss: 0.654209, acc.: 71.88%] [G loss: 0.924444]\n",
            "3845 [D loss: 0.565965, acc.: 65.62%] [G loss: 0.971833]\n",
            "3846 [D loss: 0.653646, acc.: 59.38%] [G loss: 1.193907]\n",
            "3847 [D loss: 0.719342, acc.: 53.12%] [G loss: 1.047530]\n",
            "3848 [D loss: 0.669140, acc.: 68.75%] [G loss: 1.041106]\n",
            "3849 [D loss: 0.684405, acc.: 62.50%] [G loss: 0.897598]\n",
            "3850 [D loss: 0.504177, acc.: 84.38%] [G loss: 0.981113]\n",
            "3851 [D loss: 0.581007, acc.: 59.38%] [G loss: 0.841116]\n",
            "3852 [D loss: 0.713843, acc.: 62.50%] [G loss: 0.925154]\n",
            "3853 [D loss: 0.682487, acc.: 56.25%] [G loss: 0.940348]\n",
            "3854 [D loss: 0.669719, acc.: 68.75%] [G loss: 0.985930]\n",
            "3855 [D loss: 0.668633, acc.: 53.12%] [G loss: 0.964918]\n",
            "3856 [D loss: 0.519306, acc.: 81.25%] [G loss: 1.020875]\n",
            "3857 [D loss: 0.550689, acc.: 78.12%] [G loss: 0.849841]\n",
            "3858 [D loss: 0.732999, acc.: 62.50%] [G loss: 0.882186]\n",
            "3859 [D loss: 0.556643, acc.: 65.62%] [G loss: 0.907879]\n",
            "3860 [D loss: 0.534824, acc.: 75.00%] [G loss: 0.933201]\n",
            "3861 [D loss: 0.618261, acc.: 68.75%] [G loss: 0.919519]\n",
            "3862 [D loss: 0.628208, acc.: 71.88%] [G loss: 1.101683]\n",
            "3863 [D loss: 0.587555, acc.: 68.75%] [G loss: 1.150791]\n",
            "3864 [D loss: 0.626056, acc.: 56.25%] [G loss: 0.974101]\n",
            "3865 [D loss: 0.645956, acc.: 62.50%] [G loss: 1.054574]\n",
            "3866 [D loss: 0.758120, acc.: 56.25%] [G loss: 1.185759]\n",
            "3867 [D loss: 0.639194, acc.: 53.12%] [G loss: 0.983363]\n",
            "3868 [D loss: 0.694330, acc.: 50.00%] [G loss: 0.822481]\n",
            "3869 [D loss: 0.586818, acc.: 68.75%] [G loss: 0.992332]\n",
            "3870 [D loss: 0.667652, acc.: 62.50%] [G loss: 0.940838]\n",
            "3871 [D loss: 0.620347, acc.: 62.50%] [G loss: 0.854532]\n",
            "3872 [D loss: 0.585801, acc.: 71.88%] [G loss: 0.793985]\n",
            "3873 [D loss: 0.669007, acc.: 59.38%] [G loss: 0.985955]\n",
            "3874 [D loss: 0.725915, acc.: 50.00%] [G loss: 0.988056]\n",
            "3875 [D loss: 0.442914, acc.: 81.25%] [G loss: 0.962507]\n",
            "3876 [D loss: 0.499209, acc.: 84.38%] [G loss: 0.985835]\n",
            "3877 [D loss: 0.783438, acc.: 40.62%] [G loss: 0.878982]\n",
            "3878 [D loss: 0.515333, acc.: 81.25%] [G loss: 1.087351]\n",
            "3879 [D loss: 0.616628, acc.: 62.50%] [G loss: 0.937031]\n",
            "3880 [D loss: 0.725860, acc.: 50.00%] [G loss: 0.853074]\n",
            "3881 [D loss: 0.509436, acc.: 81.25%] [G loss: 0.982320]\n",
            "3882 [D loss: 0.699102, acc.: 59.38%] [G loss: 0.949804]\n",
            "3883 [D loss: 0.742355, acc.: 59.38%] [G loss: 0.911509]\n",
            "3884 [D loss: 0.738258, acc.: 56.25%] [G loss: 0.807831]\n",
            "3885 [D loss: 0.616763, acc.: 62.50%] [G loss: 0.879890]\n",
            "3886 [D loss: 0.684542, acc.: 59.38%] [G loss: 0.893819]\n",
            "3887 [D loss: 0.675077, acc.: 59.38%] [G loss: 0.781342]\n",
            "3888 [D loss: 0.537626, acc.: 68.75%] [G loss: 0.919082]\n",
            "3889 [D loss: 0.630519, acc.: 59.38%] [G loss: 0.863679]\n",
            "3890 [D loss: 0.568933, acc.: 68.75%] [G loss: 0.902717]\n",
            "3891 [D loss: 0.637729, acc.: 62.50%] [G loss: 0.989096]\n",
            "3892 [D loss: 0.690755, acc.: 59.38%] [G loss: 0.908116]\n",
            "3893 [D loss: 0.598619, acc.: 71.88%] [G loss: 0.874085]\n",
            "3894 [D loss: 0.742873, acc.: 65.62%] [G loss: 0.988605]\n",
            "3895 [D loss: 0.671957, acc.: 59.38%] [G loss: 0.965338]\n",
            "3896 [D loss: 0.820149, acc.: 40.62%] [G loss: 0.941276]\n",
            "3897 [D loss: 0.518693, acc.: 71.88%] [G loss: 0.999918]\n",
            "3898 [D loss: 0.649737, acc.: 62.50%] [G loss: 1.208439]\n",
            "3899 [D loss: 0.806244, acc.: 53.12%] [G loss: 0.998158]\n",
            "3900 [D loss: 0.614887, acc.: 68.75%] [G loss: 0.982299]\n",
            "3901 [D loss: 0.625332, acc.: 56.25%] [G loss: 1.019675]\n",
            "3902 [D loss: 0.661857, acc.: 56.25%] [G loss: 0.743284]\n",
            "3903 [D loss: 0.620641, acc.: 65.62%] [G loss: 0.933814]\n",
            "3904 [D loss: 0.634201, acc.: 65.62%] [G loss: 0.948658]\n",
            "3905 [D loss: 0.610278, acc.: 68.75%] [G loss: 0.845028]\n",
            "3906 [D loss: 0.572899, acc.: 68.75%] [G loss: 0.717869]\n",
            "3907 [D loss: 0.668549, acc.: 62.50%] [G loss: 0.779174]\n",
            "3908 [D loss: 0.682513, acc.: 59.38%] [G loss: 0.825574]\n",
            "3909 [D loss: 0.572588, acc.: 71.88%] [G loss: 0.977141]\n",
            "3910 [D loss: 0.593043, acc.: 71.88%] [G loss: 0.861939]\n",
            "3911 [D loss: 0.572747, acc.: 75.00%] [G loss: 0.854247]\n",
            "3912 [D loss: 0.618300, acc.: 65.62%] [G loss: 0.982762]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "3913 [D loss: 0.701627, acc.: 53.12%] [G loss: 1.011809]\n",
            "3914 [D loss: 0.828976, acc.: 40.62%] [G loss: 0.785659]\n",
            "3915 [D loss: 0.619864, acc.: 65.62%] [G loss: 0.865223]\n",
            "3916 [D loss: 0.608010, acc.: 68.75%] [G loss: 0.940448]\n",
            "3917 [D loss: 0.726379, acc.: 50.00%] [G loss: 1.102470]\n",
            "3918 [D loss: 0.528958, acc.: 78.12%] [G loss: 0.902198]\n",
            "3919 [D loss: 0.673335, acc.: 59.38%] [G loss: 1.245412]\n",
            "3920 [D loss: 0.569190, acc.: 78.12%] [G loss: 0.887343]\n",
            "3921 [D loss: 0.559637, acc.: 68.75%] [G loss: 1.132591]\n",
            "3922 [D loss: 0.658598, acc.: 56.25%] [G loss: 0.913112]\n",
            "3923 [D loss: 0.719553, acc.: 56.25%] [G loss: 1.067801]\n",
            "3924 [D loss: 0.698950, acc.: 50.00%] [G loss: 1.068904]\n",
            "3925 [D loss: 0.530685, acc.: 78.12%] [G loss: 1.050115]\n",
            "3926 [D loss: 0.646751, acc.: 62.50%] [G loss: 0.922953]\n",
            "3927 [D loss: 0.576458, acc.: 65.62%] [G loss: 1.113569]\n",
            "3928 [D loss: 0.693365, acc.: 59.38%] [G loss: 1.036914]\n",
            "3929 [D loss: 0.736939, acc.: 53.12%] [G loss: 1.068593]\n",
            "3930 [D loss: 0.625089, acc.: 71.88%] [G loss: 1.009066]\n",
            "3931 [D loss: 0.717716, acc.: 50.00%] [G loss: 1.024887]\n",
            "3932 [D loss: 0.601926, acc.: 68.75%] [G loss: 1.159058]\n",
            "3933 [D loss: 0.623602, acc.: 62.50%] [G loss: 0.908294]\n",
            "3934 [D loss: 0.697116, acc.: 59.38%] [G loss: 1.016078]\n",
            "3935 [D loss: 0.612443, acc.: 68.75%] [G loss: 1.114412]\n",
            "3936 [D loss: 0.668103, acc.: 59.38%] [G loss: 0.974452]\n",
            "3937 [D loss: 0.683114, acc.: 56.25%] [G loss: 0.981984]\n",
            "3938 [D loss: 0.593777, acc.: 65.62%] [G loss: 1.014559]\n",
            "3939 [D loss: 0.738482, acc.: 50.00%] [G loss: 1.050121]\n",
            "3940 [D loss: 0.602948, acc.: 75.00%] [G loss: 1.109980]\n",
            "3941 [D loss: 0.690195, acc.: 62.50%] [G loss: 0.948592]\n",
            "3942 [D loss: 0.759307, acc.: 46.88%] [G loss: 0.947355]\n",
            "3943 [D loss: 0.618622, acc.: 65.62%] [G loss: 0.898257]\n",
            "3944 [D loss: 0.689491, acc.: 71.88%] [G loss: 1.004086]\n",
            "3945 [D loss: 0.691556, acc.: 56.25%] [G loss: 1.026489]\n",
            "3946 [D loss: 0.718329, acc.: 59.38%] [G loss: 0.937856]\n",
            "3947 [D loss: 0.676839, acc.: 56.25%] [G loss: 0.869840]\n",
            "3948 [D loss: 0.599491, acc.: 68.75%] [G loss: 0.827601]\n",
            "3949 [D loss: 0.821267, acc.: 43.75%] [G loss: 0.940750]\n",
            "3950 [D loss: 0.797292, acc.: 43.75%] [G loss: 0.958844]\n",
            "3951 [D loss: 0.609560, acc.: 65.62%] [G loss: 0.985033]\n",
            "3952 [D loss: 0.610927, acc.: 65.62%] [G loss: 1.030662]\n",
            "3953 [D loss: 0.521863, acc.: 78.12%] [G loss: 1.042118]\n",
            "3954 [D loss: 0.568695, acc.: 65.62%] [G loss: 1.014784]\n",
            "3955 [D loss: 0.738555, acc.: 53.12%] [G loss: 0.968218]\n",
            "3956 [D loss: 0.634562, acc.: 65.62%] [G loss: 0.874520]\n",
            "3957 [D loss: 0.715789, acc.: 56.25%] [G loss: 0.892780]\n",
            "3958 [D loss: 0.651588, acc.: 62.50%] [G loss: 0.979997]\n",
            "3959 [D loss: 0.682392, acc.: 65.62%] [G loss: 1.079388]\n",
            "3960 [D loss: 0.744110, acc.: 50.00%] [G loss: 1.181982]\n",
            "3961 [D loss: 0.719478, acc.: 43.75%] [G loss: 1.070764]\n",
            "3962 [D loss: 0.680208, acc.: 56.25%] [G loss: 0.896458]\n",
            "3963 [D loss: 0.722161, acc.: 56.25%] [G loss: 0.914263]\n",
            "3964 [D loss: 0.581973, acc.: 68.75%] [G loss: 0.782649]\n",
            "3965 [D loss: 0.608474, acc.: 65.62%] [G loss: 0.853674]\n",
            "3966 [D loss: 0.681976, acc.: 68.75%] [G loss: 0.900070]\n",
            "3967 [D loss: 0.619836, acc.: 75.00%] [G loss: 0.754582]\n",
            "3968 [D loss: 0.736909, acc.: 50.00%] [G loss: 1.082847]\n",
            "3969 [D loss: 0.605865, acc.: 65.62%] [G loss: 0.924155]\n",
            "3970 [D loss: 0.706704, acc.: 56.25%] [G loss: 0.968782]\n",
            "3971 [D loss: 0.890290, acc.: 46.88%] [G loss: 0.929633]\n",
            "3972 [D loss: 0.612112, acc.: 62.50%] [G loss: 1.044745]\n",
            "3973 [D loss: 0.722386, acc.: 53.12%] [G loss: 0.995920]\n",
            "3974 [D loss: 0.702876, acc.: 56.25%] [G loss: 1.132212]\n",
            "3975 [D loss: 0.575002, acc.: 71.88%] [G loss: 1.051844]\n",
            "3976 [D loss: 0.698791, acc.: 59.38%] [G loss: 1.054111]\n",
            "3977 [D loss: 0.654935, acc.: 59.38%] [G loss: 0.948442]\n",
            "3978 [D loss: 0.591357, acc.: 65.62%] [G loss: 1.009782]\n",
            "3979 [D loss: 0.663527, acc.: 65.62%] [G loss: 0.956043]\n",
            "3980 [D loss: 0.617618, acc.: 71.88%] [G loss: 1.139487]\n",
            "3981 [D loss: 0.790211, acc.: 56.25%] [G loss: 1.185657]\n",
            "3982 [D loss: 0.718076, acc.: 59.38%] [G loss: 0.982470]\n",
            "3983 [D loss: 0.686148, acc.: 62.50%] [G loss: 0.910889]\n",
            "3984 [D loss: 0.595059, acc.: 65.62%] [G loss: 1.044066]\n",
            "3985 [D loss: 0.507236, acc.: 81.25%] [G loss: 1.015691]\n",
            "3986 [D loss: 0.755114, acc.: 59.38%] [G loss: 0.976733]\n",
            "3987 [D loss: 0.674514, acc.: 46.88%] [G loss: 0.893293]\n",
            "3988 [D loss: 0.718176, acc.: 56.25%] [G loss: 0.925516]\n",
            "3989 [D loss: 0.744842, acc.: 43.75%] [G loss: 1.042717]\n",
            "3990 [D loss: 0.804246, acc.: 40.62%] [G loss: 0.958722]\n",
            "3991 [D loss: 0.563790, acc.: 75.00%] [G loss: 0.986840]\n",
            "3992 [D loss: 0.663569, acc.: 59.38%] [G loss: 1.010185]\n",
            "3993 [D loss: 0.567548, acc.: 71.88%] [G loss: 1.070902]\n",
            "3994 [D loss: 0.645847, acc.: 59.38%] [G loss: 1.146608]\n",
            "3995 [D loss: 0.755060, acc.: 53.12%] [G loss: 1.031112]\n",
            "3996 [D loss: 0.584319, acc.: 71.88%] [G loss: 0.972611]\n",
            "3997 [D loss: 0.816335, acc.: 40.62%] [G loss: 0.883333]\n",
            "3998 [D loss: 0.566766, acc.: 71.88%] [G loss: 1.012559]\n",
            "3999 [D loss: 0.871352, acc.: 37.50%] [G loss: 0.995928]\n",
            "CPU times: user 5min 1s, sys: 1min 21s, total: 6min 22s\n",
            "Wall time: 5min 26s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YKMDevJ4-Pru",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d75fc7a8-dc79-4f47-e4d7-74f3539fc9c6"
      },
      "cell_type": "code",
      "source": [
        "cd images"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GeiYDD0IHhUn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "H5o-YQi6Dqjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "bb394978-5d94-4fef-d1a1-78008481ecd9"
      },
      "cell_type": "code",
      "source": [
        "list=os.listdir()\n",
        "np.sort(list)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['images', 'mnist_0.png', 'mnist_100.png', 'mnist_1000.png',\n",
              "       'mnist_1050.png', 'mnist_1100.png', 'mnist_1150.png',\n",
              "       'mnist_1200.png', 'mnist_1250.png', 'mnist_1300.png',\n",
              "       'mnist_1350.png', 'mnist_1400.png', 'mnist_1450.png',\n",
              "       'mnist_150.png', 'mnist_1500.png', 'mnist_1550.png',\n",
              "       'mnist_1600.png', 'mnist_1650.png', 'mnist_1700.png',\n",
              "       'mnist_1750.png', 'mnist_1800.png', 'mnist_1850.png',\n",
              "       'mnist_1900.png', 'mnist_1950.png', 'mnist_200.png',\n",
              "       'mnist_2000.png', 'mnist_2050.png', 'mnist_2100.png',\n",
              "       'mnist_2150.png', 'mnist_2200.png', 'mnist_2250.png',\n",
              "       'mnist_2300.png', 'mnist_2350.png', 'mnist_2400.png',\n",
              "       'mnist_2450.png', 'mnist_250.png', 'mnist_2500.png',\n",
              "       'mnist_2550.png', 'mnist_2600.png', 'mnist_2650.png',\n",
              "       'mnist_2700.png', 'mnist_2750.png', 'mnist_2800.png',\n",
              "       'mnist_2850.png', 'mnist_2900.png', 'mnist_2950.png',\n",
              "       'mnist_300.png', 'mnist_3000.png', 'mnist_3050.png',\n",
              "       'mnist_3100.png', 'mnist_3150.png', 'mnist_3200.png',\n",
              "       'mnist_3250.png', 'mnist_3300.png', 'mnist_3350.png',\n",
              "       'mnist_3400.png', 'mnist_3450.png', 'mnist_350.png',\n",
              "       'mnist_3500.png', 'mnist_3550.png', 'mnist_3600.png',\n",
              "       'mnist_3650.png', 'mnist_3700.png', 'mnist_3750.png',\n",
              "       'mnist_3800.png', 'mnist_3850.png', 'mnist_3900.png',\n",
              "       'mnist_3950.png', 'mnist_400.png', 'mnist_450.png', 'mnist_50.png',\n",
              "       'mnist_500.png', 'mnist_550.png', 'mnist_600.png', 'mnist_650.png',\n",
              "       'mnist_700.png', 'mnist_750.png', 'mnist_800.png', 'mnist_850.png',\n",
              "       'mnist_900.png', 'mnist_950.png'], dtype='<U14')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "NJDUIYLQ-inO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "8edadc0a-0f9c-4b54-8ab6-a7b58b0b00c5"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.image as mpimg\n",
        "sides= 2\n",
        "img_it= 0\n",
        "for i in range(0, sides) :\n",
        "    for j in range(0, sides) :\n",
        "        img = mpimg.imread(list[img_it])\n",
        "        plt.subplot(sides, sides, 1 + img_it)\n",
        "        plt.axis(\"off\")\n",
        "        img_it +=1\n",
        "        plt.imshow(img )\n",
        "        \n",
        "plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=.2)\n",
        "plt.show()  "
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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48847sXbtWpSUlOD2228Ptzyf8DzvdTPmLgMx2gWAz4PneXriiSfI6XTSCy+8\nQBzH+XVdsHRyHEdpaWlkNpuptraWzp49S2azmerr6+m1116j999/nx577DHasGEDVVRU0CuvvEJZ\nWVlB19lRWjz99NPkdDpp/fr19MILL1BtbS25XC71WpfLRQ0NDbR//37Ky8trN40DiTedcXFxtGDB\nAjp9+jTJskyKoqjXlJSUUExMTFjz3WAw0DXXXEP5+fmUnp5OOTk5NGzYMMrPz6du3bpRfHw86XQ6\nMhgMlJubSwUFBZSbm0tjxoyhmTNnUlpaGqWlpZHRaAyoTsbF4cuW3H///eR0OomIyOFwtLr2448/\nDks59JTFt99+m1wuV6t67HA46NixY5Senk4pKSkhtZOCIHh9BsdxJIoiJSQk0Jw5c2jcuHGUmppK\nGo2GJElSzwlVenp7VlxcHF1zzTVkMplIURRyuVzUt29fmjdvHjmdTjKZTGGzP+0dV199Ncmy3OY6\nh8NB48ePJ57nSRRFMhgMpNfrieM4NZ8imchWF0a8Va6kpCSKiYmhadOm0ZYtW2jFihX08MMPk06n\n88sIBLPgpqen0zvvvEOKopDb7fbreqfTST179gybw8RxHFVVVZHT6aT6+npqamoih8NBdrud3nvv\nPdJqtRQdHU0GgyHk6SlJEmVlZdF7773XykG6EKvVSpMnTw5pvouiSDzPk1arpXHjxtFPP/10Sfeb\nPXs2GY1GkiQpQAoZlwLP817L0YV59Nlnn9EXX3xBiqKQLMu0atWqkJXDljr1ej1deeWVZLVaSVEU\n9SAiMpvN1NDQQJs3b6bKykqaM2cOpaamhkSnKIo+66bBYKDk5GQSRZE4jiOO40ir1VLfvn07bFSG\nymEaO3YsPfroo3TixAkqLy+n+Ph49Tue5ykrKyukjl1Hz9HpdO06S7Is06uvvkpjx46lvLw8ev31\n1+mmm26ijIwM0ul0FBUVRRzHBVRnoGEOkxfaKwg8z9OJEyfI4XCQoih07tw5ys7OJkEQ/O5dCkYF\n87SS7rnnHrJarSTLMlmt1lYtvJbY7XYqKipSv7dare0a6EDhKz3+/ve/k9VqpTNnzlBVVRWdPXuW\nFixYQHq9PqwOqFarpZkzZ1JVVVWb85xOp/oysNlsdPPNN4c831uWy9jYWKqvr+/QsWsPh8NBJpOJ\nMjMzieM44nk+oDoZF0dH9uTBBx8kovP1dsqUKQScb3j84x//oLNnz1KPHj3CYn+GDh1Kb7/9Njmd\nTpJlmVwuFzU1NVF9fT3l5OTQlVdeSenp6TRhwgSaPXs2bdq0KSQ6O+phalmH4uPjVTvoqQu1tbUh\ndZi8PUMQBCooKKCioiJasWLrbv7GAAAgAElEQVRFm/LB83yHTnYoHabc3Fz1vJEjR1JOTg7pdDoS\nRZFEUaSXX36ZysvLqaGhgdatW0c9e/YkvV6v9jRFMpE3OzCCURQFcXFx6nyVmJgYxMfHo6ysLGyT\nGT1zTvR6PebOnQudTgeHw4H169dj7969OHDgAPLy8rBhwwacOnWq1bJVjUYDh8MBSZLA83xYxuwF\nQYDb7YbD4YBOp0NVVRWeeeYZfPbZZ2GfdOlwOLBq1SoUFRVh8eLFSE1NhdFoRHl5OVJTU9GnTx9w\nHAdBEOBwOEKqrWV5UxQFjY2NGDx4MJYsWYKJEyeqc9iam5uxePFiWK1W3HDDDUhKSsLx48dx++23\no7q6Ws1zT6waNocpMvBmT+Li4rBo0SIAwB//+EesW7dOPV9RFMiyjKqqqpDpFAQBoigiJycH8+fP\nR2lpKUwmE2JiYqAoCiwWCyorK1FaWoozZ87A5XKhsrISer0e999/f0g0+mObPXWIWizXJyLExsai\nuro6BCp9M2HCBPTo0QOff/55m9+kKErE1N21a9cCAGw2G3bu3Nnm+7vuugtGoxFEhD59+iAjIwMW\niwVutxs2my3UcjtHmBy1iAdevGeDwUAvv/yy2jtjs9no+uuvJ61WG5YeJs89RVGkJ598ksxmM505\nc4YyMzN9tjgAkNvtpoqKiqDq7Oj5cXFx9OCDD6pduG63m9auXRsRQ5wtj5ycHFqwYAFVVlaSzWZT\n9XqGHUwmE0VFRYVVp0ajoQEDBtDWrVvJbDaTLMs0Z84c0ul0aovZn55QRvjxljfDhg2j+vp6stls\n1K9fv1Y9EE1NTVRSUhLS/BUEgURRpOjoaMrNzSVJkkin09E111xD+/fvp/LyctqyZQvxPK/OCQJA\nGRkZJMtyh+UxUPjb+3/heRzH+dQYKvsjiiIdOHCA6uvrW+V7Z45Q6IyOjiabzUZERD/++GOb7/Pz\n88ntdpPT6aTt27dTQUEBGY1GiomJUUcUIpnIVhdG/CmAM2bMoIaGBtq3bx/ddddd1K1bN78cp2Dq\nlCTJL0fJY3zNZjOtWbMmqDo70hATE0PPPPNMu9cpiuJzWC7QXbgdPSszM7PV3LDGxkZqbGxsNfQZ\nboMVFRVFV199NX311VfkcrnI7Xark0QPHDhAf/nLX8hoNIZMJ+Pi6OglLYoi2e12IiKqqKig4uJi\ndZqAP8PYgcTjMMXFxbWavDtixAjatWsXOZ1OKi8vp/79+1N2draqwWq1+vydgeJinIvExERyu91+\nTVAPJN7SY8iQIWSz2ai+vr7DIdfy8nIaMWJEWOwPz/NUWlpKbrebbDYbvfHGG/Too4/SihUr6OjR\no2Q2m8lsNtPBgwdp0KBBXodKI5nI6MPronzyySe48847sXPnTuTm5mLLli3497//HdYl5i6Xy++h\ntblz56KsrAwbN24MW3fuuHHjMGfOHLULvCUcx8FisXSYnhdeE0wqKytbDWmWlpZi3759OHr0qJrm\njY2NyMrKCpmmC2lubsaWLVswfvx4SJIEjUaDBQsWoL6+Hvn5+XjggQfCPtTJuDTcbjf+/ve/AwC6\ndeuGuLg4uN1uVFRUwG63h1yPVquFy+VStw6RZRkHDx7E888/j8cffxyPP/44Zs2ahXvvvVddVq7T\n6QAEf9uki6Ffv34oKyuDIAioqakJ6bO92eF169ZBo9Hg5MmT4Hm+3XTjOA7du3fHsmXLwrJ83xOL\nkIjgdDrRrVs3PPLII5g2bRp69eoFrVYLt9sNk8mEyy+/PGKGEDtD11McQRARTCYTduzYgV27duHk\nyZMYP358yMbmLwWj0Yjm5masW7cOjY2NQa1gHRlFURRx5swZ3HjjjRg3bhyWLFmC3bt3Y+fOnTh9\n+jQ4jsOAAQOCpq0zuFwumEwmAOfzXqfTISYmBgcOHEBRURGICDExMeoYfrho6UQqioJFixbhqaee\nAs/ziIuLC/l8K0bgefrpp1FbWwuz2Yz//Oc/+Oijj9DU1IR+/fqFVIcgCHC5XLBara0ccYvFgnXr\n1uEf//gHli9fjmeffRZ/+9vf1MjZ27ZtAxBZDtOyZctgNpvx008/wWg0hmVOp7cGYFJSEhwOBwoL\nC5GZmYmYmBiIotgq/bp37w6324358+eHpVHkcrmwY8cO1WlWFAVmsxkWiwXHjh3DyZMnUVlZiezs\nbAwZMiTk+gJCmHq2Ih5/x70lSSKj0UharZZGjhxJZ8+e9TonCCHqGvV16PV6KigooJSUFOrZsyf1\n69ev3aHEQNFRWkqS1OGyX5fLRffdd1/EpOcLL7ygDnW53W5yOBxUXl5OhYWFtG/fPiI6HzsqXPGi\nvB2zZs0iovNzxDo6L9JXqfxS8Mf+XH311WS1Wql79+6UnZ1N+/fvpzVr1lBubm7I6ou/w/88z7ea\nl+iJ97Vjxw6KiooirVbb5jcHCn/ryHvvvUdut5tcLhfV19fTBx984Nd1gcRbvhOdX5a/ceNGeuGF\nF2jo0KFt0j4pKYmOHz9OSUlJYbM/n332GSmKQjU1NZSTk0MDBgwgo9FIGo2GoqKi6IUXXiBZlmn9\n+vVey04kw3qYLhGXywWLxQKHw4F9+/bBbrcjOTk53LLahed5GAwGGAwGHDlyBCaTCT///DNqamrC\n1j3qcrk63HCyubkZRUVFIVTUMQcPHoTNZlNXlEmShKSkJHW1JHC+12zy5MnhFXoBy5YtAwCUlJR0\neF5X7Cb/pXLttddCr9ejvr4e1dXVWLVqFRITE3HvvfeGTIO/vTB6vb7V7gOSJIGIkJaWhpycHKSk\npIQ9CvScOXOwefNmdcPdhoaGkGvw1uPmWV34888/w2Aw4NChQ23SPi0tTZ3GQEGeqtCeTp7nceWV\nV4KI8OGHH+LkyZM4dOgQLBYLnE4nrFYrrrvuOvA8j40bN0ZcJHW/CK+/Frl0dmWFIAg0bdo0cjgc\nVF9fHzE9Ivhva+6+++6jKVOmUHR0dCvtgiAEffJdR+l24SEIAo0fP54+/fRTKi4uplGjRoW0hefr\nWc8//7waZ8ZqtdKxY8fohx9+oPLycjp48CCNHj2a7rnnnnYjf4dSp+eIi4tTr5k4cWLIW8yMi8Of\nfPLE3PL8rdfracmSJdTQ0BCSydT+6gRAf/jDH2jGjBk0efJkeuutt0iWZdq0aVOHMZJCrREATZ8+\nXQ0AunLlypDXF2+97RzHUVZWFlksFtq3b1+7K14dDkergJahznee52nOnDk0adKkdsvfnXfeSURE\nzz33XJe1PywOkxfIh4fu2TGa/hu3o1evXvjggw8gSRKmTp0aIpW+0Wg0uOWWW5Ceno4vvvii1aRQ\nSZIgSRKcTmdIx7w5jkNGRgbq6upgsVjUz3Nzc/HZZ58hJycHTqcTu3fvRmFhYch0+UOfPn3UXpid\nO3di06ZNiI+PR1xcHBobG7Fr1y4UFhZGzCaj586dAwAUFRXhyy+/DLMaRqDo3r07OI7D0aNH1c/c\nbjfS09NVuxRJPProo0hLSwPP89BqteA4DpMnT46oXgaO47Bq1SrVtkeS7SEilJWVYc6cOVi3bh0U\nRWnVy3P//fere2+GC1EUsX//fuzfv79N+Rs5ciSWLl0KAFi4cGE45AUE5jBdAvTflV3Jycl49tln\nodVqcfz4cWzfvj3c0lTS09Nx+eWXo7i4WA3M5kGSJOj1+pBXsqSkJCxfvhzvv/8+3nvvPRAR8vPz\nsXPnThiNRjQ0NGD06NGtXgaRwscff4zJkydDEAQUFhbi5MmTOHjwIGRZRm1tbYfDi6GmZ8+eEAQB\n1dXVGDRoULjlMALI4MGDcfDgQVxxxRXqZ263G1999RVGjx4dRmVtiYmJQUZGBkRRhNVqhSiKqKqq\niihnCQDi4+PVxhAR4d133w25Bl9psnz5ctWGe/4VRRFPPfUUFEUJ64IORVGQmpqKrKws1NXVQRAE\nvP7668jIyMDll18OQRCgKIo68b8rwhymi4DjOMTHxyM+Ph6XX3453nnnHWi1WlRWVkbUi4njOPzu\nd7/DmDFjUF5ejri4OFitVrVSGgyGVn+Hil69euGKK67A4MGD8dhjj6GxsRGZmZnQ6XTYtm0bbrjh\nBjQ3N4dUk7/s3bsXiqJAFEVotVocO3YMP//8c0Q5SoIg4PLLL8eOHTsgyzKuuuqqiHs5MS6Nn376\nCb/+9a/hdDrVz4gIa9euxe9///swKmuNIAhYunQpeJ5HTU0Nrr32WtTU1ODs2bPhltaGlnMlP/jg\ng7A4H756Btv7PjY2FqIo4uDBg2HtWZRlGYWFhUhMTMTzzz+PW265RQ0JQ0Sw2+0YP35817ZFoR4D\n7CqggzHWtLQ02rZtG9XV1akRlS0Wi18bsAY6yTt6jiAItGLFCqqoqKD58+dTTk4OJSYmklarJaPR\nSOnp6R0GMQyWxpycHLJYLK32PXO5XPTaa6/5veomHOkJnI/0XlNTQ4qi0MiRIyMqIrlnT6+RI0dS\neXk5KYpC69ev79Q+h8wkRAa+8shbnkqSREuXLo2IOUxxcXFUXFxMsixTcXExzZ49W93cNpTlsKP0\n8hx6vZ4cDod6Tcsgm+Gu176ODz/8kGw2G916660RpbOgoIDcbjfJskyHDx+mgoKCLm9/IltdGOlM\ngfVsQBlJFYzneRo4cCDt2bOHzGYzrVq1ihISEsKi82KMgK/Ds1Q1lJG+PUdMTIzPDU7Dle8cx9G0\nadOooqLC5671XdFg/VJomR+eXdz9zb+UlJSwO0wcx1F2djatXLmSpk2bFtZy6NHj63kcx9GQIUM6\nXbcDycWkU0xMDE2YMCFiNt+91COS4YgibHZghBDMgGqBTHJvOgVBgE6ng9FoRG5uLk6cOIGzZ892\n6tmB0hmstGy5SWYg7+nvcztLqHQmJSVh1qxZ2LZtGw4ePNjpezOTEH48+SsIArp3794mynxHSJIE\nt9vtNR9DUQ45jlODK1osFtjt9lbn+qMhUDpbzkvyhSf0QctFMJ7I2lqtFtnZ2Th16hTsdrs6tBRq\n+9PeNQaDATabrcPhrnDr9JdItj/MYfJCVykQXUFnsKP5/tLSE/Ct81JWSjGTEH48+et5UTscDr/z\nxVfeB7sc8jzfavsORVFaOSD+ls1wOEztIQgCOI6DLMuttF/4byC4WPvjmVAdznz3db6vBm4wGsCB\nhjlMDAaDwWAwGD5gYX0ZDAaDwWAwfMAcJgaDwWAwGAwfMIeJwWAwGAwGwwfMYWIwGAwGg8HwAYv0\n7QWNRnNJIdwFQYDRaIQgCGhubm51r0DOs+d5Xt3PThRFiKIIh8MRkGiqgdKp0Wg6XOZ8sXAch+jo\naDQ2Ngbsnnq9HkajEWazGS6Xy+eKDn/geR4GgwFmszlgOluuUuF5HnFxcarmi7mX0WiEVqtFjx49\nsH///oDpZFwcnnodaDiOC2ikZb1eDwBwOp0d3jc2NhZms7lTz46EVbqiKIKIOlyBFsh88qx26wiP\nre9sXQ+kTlEU26SJJ519fZaYmIixY8fixIkTOHz4cJtwGZG8Do31MHnhUve7kWUZTU1NMJvNQd02\no+XSVpfL5TMWRziQZTkolYCIWm3eGwgURUFjYyOcTmfAlugqihJwnRfe3263X3SZJSI4nU643W6Y\nTKYAq2NcDMF6aQT6vm6326ezxHEcevfuHdEvQm/IsuxzuX4g8cd2cxynxosKF+2liedvT0gJb+Ec\n7rvvPjzyyCOqI9qVYD1MQSaS9hgLF8GsFC3juwSClntzBZJgG1yr1XpJ17vdblgslrBu3snoelzY\nGGqv51Wv16OhoaFLOkye3vv2ekpCiScWVHJyMjiOg8vlgt1uD4sWb0iShOTkZNTV1UGSJCQmJqKs\nrKzNeQ8//DD0ej0SExNDL/ISYT1MQSbYQRt/6XhaMZF6v66Cp7XXlXcS/yUjSRIkSQr5c731MrTk\nqquugiiKYdEXCHieh06ng1arDcvzJUmCIAgQRRFutxs8zyMxMRGiGL7+jvbyWZZlVFdXw+FwwGw2\n4/Tp0+1eGxUVBZ7n0bNnT8THx3epd+Qv8+0QAnQ6nbqLdKSSnJwcNiMQCPR6vbobdqDoSpU3kHgc\npq7WRf5LoGUPB8dxSE9Px7Bhw1TbwnEcJk6ciLlz50IQBAiCAJ7nIYoi9Ho9oqOjw6Kb53lcffXV\nmDFjBmpraxEVFQWNRoPU1NSIrmee6OqjR49GcnIyFixYgOuvvx7XX389jEZjSLXo9XpMnz4d6enp\niI2NhdFoBBEhMTGxVV2VJCnsjT1BEFo5Uu05VampqSAilJWVYcWKFaitre1SPY+R+zbvQiQnJyM+\nPl6dbK3X6zFs2DBUV1dj+/bt4ZbXLhqNBt9//z1eeOEFvPHGGwEf2go2nsmrgdYtCIJf95Qk6f9k\nb0xXMl7/l/EMbYmiCKPRiNTUVJw7dw5JSUl49913sX37dhw+fBgWiwW/+93v8O6778JsNmPo0KH4\n9a9/rbb03377bXz33Xcwm83qHnOBhOf5Nk62JEno3bs33n33XVxxxRVQFAVffPEFHnnkETgcDuTn\n5+PkyZO47LLLAqolEHAch//93/9FdnY2MjIy8Ne//hVZWVnYunXrJW03dDF069YNb775Jvr164eR\nI0fC6XSiubkZOp0OQ4cOxYQJE2C329GjRw9cdtlleOKJJ7B169aw1OELy0HLvz1zmhISEvDll1/C\nYrEgLy+vS05XYQ7TJZKeno69e/eioqIC27Ztg91ux9ChQ9GvXz98//33KCoqQk1NTdheRO1Vco7j\ncOONNyIjIwOzZs3CihUr0NjYGDSNF2qIiYlBU1PTJd1Pr9dDEISAj+NLkuRzHlP37t3xzjvvoH//\n/pAkCXq9HoWFhdi7dy+KioqwefPmVpPvtVptUFvUnhVV7eVfQkICvv32W2RkZMDpdOLRRx/FW2+9\nxRyjCIfneciyDFmWYbfbUVtbC47jUF9fj4ULF2LXrl1qL9IHH3wAu92OmpoaNDc3Izk5GRkZGSAi\nREdH49prrwWAoK1UvXCl7pQpU7B48WIkJSWB53kcO3YMKSkp2L9/P5xOJwYMGIC8vDxkZWW1O8cl\nnMTFxWHSpEn4/vvvsXXrVgDnFwClpqbi66+/Dvq8IY+d0Ol06N+/P/bu3Qu32632enmG4/r06YPf\n//73ICJ14v3QoUNRWFgY8oZcyz0DgfNlV6PRwGaztfrcYrEgNjYWf/jDH9TvuhzEaBcAHR5RUVH0\n2Wefkc1mI61WSxzHEcdxNHz4cGpsbKSxY8d6vTYUOqOiomj37t1UXFxM3bt3b/N9t27dyOl00vr1\n60kUxaDq5DiOeJ4nQRBIp9O1+k6WZb/u4Xa76dSpU7Rp0yZatWoVSZIUlPTUaDQ+857neXr++eep\noaGBnn/+eRo1ahSNHTuW/vnPf9Idd9xBI0aMoJycHOrRowf17NmTRFEkjuMCqrOlHq1WS6+//rrX\ntHS5XOR0OomISFEUSk1NJZ1O1yoNg1U+GRcHx3Ht5g3HcaTVaonneZoyZQo1NTUREdGSJUsoJSWF\nMjIyKDc3lwYNGkTbtm2jr776inr27Bm0/PXUbb1eTyNGjKCrr76aXnnlFfrxxx8pOTnZ6++orKyk\nuro6Sk1NDbqd9FWfPYckSTRs2DDS6XQ0dOhQqqioUO8hyzL98MMPdP3117exl4FEEATiOI4kSSKO\n4ygqKooOHz5MjY2N9Omnn9KcOXMoMzOThgwZQuvXr6f9+/fT9u3b6eqrr1bfQ+F67/A8T5Ik0ejR\no+m2225r8/2WLVtIluUONUa6/YlsdWHE1wtzxIgR9P7779OGDRtIo9GQKIo0cuRIOn36NNXW1oas\nQLR3f1EU6a9//SvZbDb66aefSKvVtjln5syZpCgKPf744yE3WC2pra2l06dPk8vlaveFrygKmc1m\neu2112j69On0xhtv0Pfff9/KaAUSnuf9Mq6JiYlUVlZGkydPpoKCArr++uupoaGBmpqaaMeOHTRk\nyJCgGlbPPY1GI1VXV7f5XpZlevnll1s9f86cOeRyueiaa64hnue9Gi5G+Omo7EVHR1Nubi6ZTCZy\nOp10+vRpio+PJ1EUSZIk0ul0xHEcJSUl0RNPPEFDhgwJejnkeZ6io6Np6NChVFtbSzfeeGOHv2HU\nqFFkt9tp4sSJEeEwxcTE0NSpU+mVV16h22+/ncxms3q9w+GgoqIiio6Obtc+BBJPw9tTN+Pj48ls\nNlNJSQnl5OS0qbMajYZmzpxJAwcOpKioqLC+dziOo+7du9OZM2eosLCwzfdWq5XcbrfPvIhkIltd\nGPFlsAYNGkQDBgyg/v3709ChQ+nJJ5+kuro6qq+vpwceeCCsBfe9996j2tpaampqojlz5rSqZKIo\n0oIFC0iWZXI6nZSQkBByg+VyuYiIqLGxkXJzc0mv16tOnUcrx3GUlpZG7733Hj333HPUt29fuvLK\nK2nPnj1UVVXVqicokPhq/XiObt26UX19PT366KO0aNEiMplMRERUWVlJI0aMCHq+G41Gys3NpZKS\nEnK73UR0vifp4MGDlJub2+7vuP/++4mIyG63t+tEdwWD9Uuho7KXkZFBq1atorNnz1JTUxOlp6eT\nRqMhnudJo9GodYPnecrIyKApU6aQIAghsT+xsbFUVVVFMTExHf4Gu91OVquV5s2bF3aHKSYmhj7+\n+GOqqakhWZZbNdy++eYbiomJ6bAhFUha3pfneXrttddIURTat28fJSUltXFQYmNjac2aNbRixQqa\nM2dOWN87HMfR9u3bSVEUKisra/VdQUEBERFZrVaf+RHJsDlMXvA2wc8z2fjw4cMQRRH5+flYv349\nYmNjIcsyNmzYgCVLloRB8XlEUcSoUaNgNBphsViwe/ducBwHSZJgMBjw5ptv4qabbgIAZGVloa6u\nLiwagfNj22fPnm01nt0yzQ0GA26//XZYLBb07dsXAwYMQEZGhhpMLhi0l+ftodFoEBsbi3vvvRcx\nMTHQ6/WoqalBenp6UHRdSK9evTB16lR88sknqKurww8//IBvv/22TcBNT95rtVrodDpV+z/+8Q/M\nmzcvJFoZgaWyshI1NTVwOp3YtGkTTCaTOseF/hvAFjhfv37++WecO3cupKsfk5KSOpzr49G6dOlS\nvPbaayHT5U2L2+3Gxo0bcdlllyExMVFdbXb69GmMGTMmbNqSkpJw2223AQC2bdvWJvgtx3GIjY1F\n3759kZqaCqfTiddffz0cUgGct51nzpwBgDZaf/jhBwDAli1bQq4roITTW4tk4MX7lSSJ9Ho9SZJE\nRqORZs2aRS6Xi+x2O+3Zs4d0Ol1IPegL7x0bG0vfffed2lI6evQozZkzhz777DMqLy8nRVGIiELa\nNQqABEEgURQpKipK/fyNN95o1RPiGbvPysqiadOm0cqVK0mWZaqrq6OioiKy2Wwky3KbIc9A4itN\nPEd6ejoREZnNZlq5ciUNGjQopGPz/vaEaTQa0mq1avrPnz+fZFmmEydOdMkW3i+FjvJUkiTavn07\n7d69mxYuXEharZby8vJo4MCBFB0d3aZstBziCXZ96dGjBymK0mH5PHbsGBER6fX6kNmfjg7PXKFB\ngwapvUtOp7PD+VXBrtfdunWjHTt2kMPhIKfTSY8++ijl5eW1STOj0UivvvoqlZeX06BBg8L23gHO\nj7yUl5eT2+2mkpIS0uv1JIoiff3116QoCjkcDp95Hun2J7LVhRF/X5yDBg0iIqKTJ0/6/RILtk6j\n0UizZ89Wh4mIzs9pcTgcZDabqbi4mFJSUkKmsz2DbTabyWAweH22ZyJpt27dSJIkGjdunOrsLV++\nPOyOSGpqKhER1dTU+F1WAom/z2z5UsjOzqZdu3ZRTU0Nmc1mSk5O7nIG65dCR3kpSRL17duXtFqt\nuqDAM4/t6NGjYS2Hd9xxB1mt1nbn01x77bXU2NhIREQ2my1kOtu7tyiKrYYpgfNDc2azmRRFob59\n+4alXnMcR6IoUnR0NMXHx9Nf//pXqqysJKLzQ+42m422bt1K9fX1dPToUbJareRyuaihocHngpVA\n0tKueP699dZb6ciRI2Sz2VRb7aGxsZEKCgpCnp6BhgWuvEQOHjwI4HyAMYqQpdoWiwXvv/8+UlJS\nMHjwYNTW1sLtdqOhoQH79+/HypUrA7oRrC+onSXvO3fu7HA7D0VRYDAY0NDQAFmW4XA41OWry5cv\nD6pefzh37hwAdJnlsXq9Ht999x2GDh2K8vJyHD58OOyB7hidh+d53HrrrWq0ZE+oAE8U7ZSUlLDq\nW7duHT799NM2W+wsXLgQq1evRlRUFGRZRl1dHaKiosISwJLjOPzmN7/B8OHDkZCQgISEBCQlJWHn\nzp0wGo1wOByora31+16BhP4bJsBsNqO+vh4vvfQSfv75ZwDnpzLodDqMHj0acXFx6NevH/R6PURR\nRGxsLP79738HVEtHeIKjeg5BELB9+3a8/fbbePnll7Fnzx40Njaqe1zW1tZiypQpMBqN4HkesbGx\nrQKydhXYHKZLxBN8q6amJsxK2kJEOHDgAAoLCzFx4kQcP34cn376KdasWRPWfYhkWcb777/f4TlJ\nSUm4++678a9//QtOpxN2u111uoqKioKmzV+n11PRX3311aBpCSSzZ89GYmIiZFnGLbfcArPZzPaN\n64L07dsXw4cPh81mw4EDBwCcn+sXFxcHAGhubg6nPGg0GvzrX/9CYmIi6urqEBsbC61Wi8ceewyy\nLOPJJ59ETk4OZsyYgSNHjqBfv36XvA9iZ+E4DjfccAPi4uLU/c8GDBiA+Ph4AEBVVRWICFqt1mcd\nCXYj2Wq14k9/+lOrAMjenjl9+nQsWbIE8+bNC7ouj/3zxAsjIlRWVmLnzp3gOA6bNm1Sv7/88ssx\ndepU3H///Rg5ciT+53/+B3l5edi2bRsURelaASzD0q/VBUAnhjqIiD755JOIHJrR6XRkMpmourqa\nZs+eTTExMSEfOrzwvlNDIqAAACAASURBVMXFxV6Hgzyat2/fTkVFRZSdnU16vZ6uvfZaUhSF3G53\nm/hBgcTfPMzNzSUi6nBYMZz5fuFRV1dHiqKQyWRSP7twSCIYOhkXh7d8/OGHH+jo0aM0ZcoUtR6/\n8cYb6nXTpk0LaznkOI66detGQ4YMoQkTJtCpU6foxIkT9M0336jlTa/X09GjR8lisVBsbGzI7U9C\nQgJ9++239P3339NPP/1Ea9asIZvNRi6Xi+rq6uixxx6j6OhoGjBggF+2MpC0d3+e56myspIsFgs5\nnU6y2+0ky7K62rglLpeLbrrppqDr1Gq1pNfrSaPRqLGjBEGgu+++m/r376+GtvA8Oz8/nxwOBymK\nQiUlJbR8+XISRTHoYRoCTWSrCyP+vogMBgPJskyTJk2KyBfnyJEjyWq10pIlS8hgMPjtLAXTYM2c\nOVPV4tHD8zwlJibSHXfcQUePHqWmpib65ptvaPz48TRs2DB6+eWXyeVyUW1tbZvfEEj8SRdRFGn/\n/v3U0NDQKacl1Do9hyRJZLfbyeFw0PTp01u93IKtk3FxeMtLT6Nh2LBhatwlz3yRjz76KCLKYVxc\nHN122230/fffk8VioQ8++KCNc56Tk0M1NTUhCW/R8p4cx9HKlSupqamJ9uzZQ2vWrKFXX32Vjh49\nSqWlpTR16lRVk8Fg8KtBFEjauz/HcXTNNdfQggUL6MCBA1RaWkrV1dUUHx9Pf/nLX8hms5HD4aDV\nq1fTN998Qw8//HC7c1QDiV6vbzfcgk6n85qnZ86cISKi0tJSysrKCkl6BhqOKEIm3kQY/oytchyH\nyy+/HP/+978xfPhwv7f7CGSSd6ST4zjcdNNNePPNN9GjR49Oz1sKlM4LNbYXsmHs2LH48MMP4XQ6\ncejQIaxevRoHDx5EVFQUevbsidTUVPTp0wcvvfQSDh06FBSd7WltCc/zuOyyy7BlyxYkJCQgOjq6\nzfLZjq4N5L53nRn7Ly0tRVZWFh5//HE8/fTT6vXe0o2ZhPDTXv56QpoAQENDA6qrq2E0GpGQkICe\nPXv6Pe8mmPUlJycHS5cuRUlJCZ555hmcPXvW61ZDI0eORJ8+fXDu3Dl89913angEj75g2B+O4/Dh\nhx9i/PjxqKysxL59+3D48GGYTCb06NEDsbGxeOuttzB27Fg89NBD+NWvfuVzyCiQ6enZZubCz7yF\nhdDr9eqQpucch8OBL774AjNmzGi1TUogdRoMBhBRp6d2LFy4EDfffDNmzJiBo0ePtrvhdyTbHzaH\n6RIQRRHR0dHYu3dvxM4Hue2221pVKn/oqIIGgvYqxLZt29C3b19ERUXB5XLBarXC4XBAr9fj0KFD\n0Gg0sFqtaGhoCJouXyQlJeFvf/sbYmNjAaBTxiJcRsCzuz3Qep5dJBslRvsQEUwmE/R6PTQaDXr2\n7AmO49CrVy+/naVg09jYiJSUFPznP//Bzz//3KEdEUURzz//PEpLS3HVVVcBCH65JCK8++67yM7O\nRmNjI5xOJyoqKmCxWPDQQw/BarVCo9GgoKAAJ0+ejIj5NR2loc1mg8PhUPerdLlcKCkpwbFjx4K6\nobrL5bqotHnqqadQVFSE06dPq3OfuhLMYboEtFotMjMzcdlllyEmJgYmkyncklpBROpqhM44QIF2\nlvzd5bu5uRkWi0XdyFNRFFitVrWF6nK5QhqAryU6nQ65ubmIi4uDoiidXmEWLsOg0WjU50+ZMgXL\nli2LiJcA4+K4++67cccdd2DIkCEAztugs2fPhlnV/48niOqhQ4d8lvmTJ08CAF588UW/e2oDwebN\nm9WNgJubm0FE6NevH3bv3o2amhps2LABX331Ffbt2xcyTR4uxk4sWrQIjz/+ODiOQ0lJCe6++261\n9yZYXKwz5ulkEEXR7/dCJMEcpouE4zhkZGRg1qxZiIuLi9iMT0tLg8vlCrs+fypHexXcs2N7qBAE\nod3nCYIAq9UKi8UCWZa7zHLYK664Qo2s3rt3b/z+97/HG2+8EWZVjItly5Yt2Lt3L2w2G4xGoxre\nIlKQZRlfffUVqqurfdb38vJypKamhrwR5Ompa6nv6NGjmDt3rhqmwZsdiESefPJJPPzww9DpdPjN\nb34Tkt0bLvZ9MmbMGJSXl8Nms4Wt8XspsDlMXvDnheiJJ9GzZ08cPXrU72G5UM5ham5uhizLiImJ\n6fS9QzmH6VII9hwC4LxmnU6Hq6++GvPnz1fnVHXm2aHK95aUl5cjJSUFNTU1mDVrFsrKytTtC7zB\nTEL48Za/LT/39MR2Nr+CXQ5FUQQRXbLDESz7E2jCUa8vZODAgbj33ntxzz33eD0nEnSmpKT8f+zd\neXQUVdo/8G8tvXdnIySBkBABgQFkk01FhQFFxAUOm46iww9EHVxwFAfU0VdnRMdR3I4KLuOCyMzr\ngoCIooDAALKIhChLAlmILNmXpju91vP7g+mahHSnu0NvvD6fc+qI6erup29V3Xrq1q17WyWrZ0vk\n+ocTpgCieZAlwo4bimhVWL4WD993nGunv19beQLnT5ysfQIl7pEQ7f3Q97dz/Z5fY8J0Lts9WPKc\nSPXP+frQCSdMjDHGGGNB8NwIjDHGGGNBcMLEGGOMMRYEJ0yMMcYYY0FwwsQYY4wxFgSPwxTA2cPp\nA4F77wuC0GIgQ39PfjUX7acV/I3UHe7jx7IstxhW/1z4nopTFKVVDIIghDSwZqBtEO6gnMFIktTq\n8yRJAtD+wdqAyJYnEHwKl+zsbKSmpuLnn38OO25+DiT+0tLSUFdXF5XPjuT2NRgMcLlcYR2Dvvoy\n2H4ZqTgNBgMURQl7TLe26szmx18k65/mn6vRaJCRkQG73d5qXwi3Po/n1Ezhfm4ij8/ECVMIgu2Y\nzccc8SUA8eRvhwu38onkaNBtHah0ZgLooJ8Rq0dQAw2eea5iObq2oig4ceJEwlc+LLCmpqaQ1ov3\naMntqe9kWYZerw97bsv28l2ohHssxHusIK/Xi9ra2oicT86Xi6BEj5NvyUVYsNaleNHpdPEOgcWQ\n1+tFZWVlwldAzL94DILbHg6HI+wLiuTkZIwdOzZmF5a+lu14l1W4FEWBy+VqtS+0p9zOt9+eqDhh\n+j9Oo9Fg+vTpqK+vx7/+9a+4t361h06nQ0ZGBtLT06HRaOIdTpsEQWgxMGc8vl+j0WDIkCH47LPP\n4hYHOzf/l09wHo8HXq8XFoslZt8ZSr0XyujqsebvNmK4yZ/RaIx0WCFra85NURTRr1+/sOfljKfz\nJ9IE4WuCFkVRnSE6EYmiiJycHNxyyy3IyMiALMuYNGmSOit4ohAEQe0jdLYuXbrA6/XC4XDg+PHj\nOHDgAKZNmxbXhCQYi8WCvLw86PX6mH93Xl4ehg8fjkceeQSrV6/G1VdfjcmTJ593lRI7fwQ6dtvS\n0NCA/fv34/77749CRP6Fsv8nJSVh/Pjx6Ny5M7RaLQwGA26++WbMmzcPmZmZMYgyOEEQYLFY1Em1\ng5EkSZ28PNqMRiPMZjOuvPJKFBcXo6CgAJ9//jnuvPNO3HvvvZg0aZJajqIoYvbs2fjmm2/w8ssv\nt2s/igeuRcOk1+uRnJyMvLw8DBo0CGazOd4htaLX6zFlyhT84x//wMUXX4yffvoJZWVlICIsWLCg\nXfPKnYu2ksoOHTrg6quvhiiKaifHFStWwOVyoby8XK3oPB4PFi1ahH/+858x7Q8UbkLcp08fjB49\nGn379oVGo4lZnzZJkvDee+/hjTfewLx585CRkQFJkvDBBx9g//79qK2tjeuVJosOSZLQtWtXpKen\n4/HHH495C6zZbA47GSciVFRUYP78+TE5UYZ6/Hk8HvTs2RN9+vRBnz59cPvtt+Ovf/0rxo0bF7Ok\nIxRutzukB0gEQUDHjh1jVl/ecccd2L59O7788kt07doVubm52LdvH4xGI2644QYsXboUb731FiRJ\nglarhaIocDqdMW1pPGfE/ALQYhEEgWRZpvvuu4/KysqoqamJXC4XNTU1tVrXt0iSRBqNptXfoxkn\nAOrbty/98MMPZLVayWq10vHjx1u9z2azkclkIlEUA8YfKYIgBPyOkSNH0sKFC9V1RFEkSZJIr9eT\nxWKhefPm0TfffEN/+tOfAsYaSWdvvw4dOpAkSQHjP3sfWbRoEc2fP5+6dOkS8+3+0UcfUXl5OVmt\nVnK5XORyuai+vr7F+9LS0oL+DhZ/oexvnTp1ovfff5+cTifV1NQQEZHVag26v0ZSVlZWyMdH88Vg\nMJDX6yW9Xh/1OEVRbLMO8i05OTk0Y8YM6tevHw0YMIBWrFhBVquVnnzyyZjUk0SBt7soimQ0Gikt\nLY369u1LOTk5bf4Wk8lEycnJLco3GnEKgkCCIJBGo6Hi4mKqra2l77//nrZt20aTJk0iSZJIFEW1\nXpckibRaLeXl5dG8efPo5ptvJlmWz5v6h1uY2qDVatGxY0csW7YMRUVFKC8vx8svv4zc3Fzo9Xpo\nNBro9Xo4nU707dtXfZ8gCMjPz8cPP/yAyy67LOZxX3fddejbty/MZjPMZjM6d+7cah2j0Yj6+no8\n8MADMJlMUY2H2rjfXl9fj40bN6rr+B7/dTgcsFqtOHz4MAYMGIBevXrFvF+HKIro379/yOVjsVjQ\nuXNn7Ny5E6dOnYpydK253W5kZGTAYDDA4/HAZrNh165dcDgc6jo1NTUxj4tFRvOWkoULF+KXX37B\nddddh4KCAuzfv189Ph5++OGYxeRvCJNQ3vP73/8eoijGpPUj1Baw7777DnfffTd++eUX2Gw2XHbZ\nZWo9H+u652xXXXUVqqurUVlZifz8fJSUlKh9meg/DxrZ7XasXLkS27Ztw3fffQciiuhQJmeTJAk6\nnQ6iKEIQBFitVtTV1UGn06GgoACFhYUt4vN6vSAivPfee1i/fj0mTpyIH3/8MaZ3DM5ZfPK0xIdm\nWf2JEyfCfr+iKPT555+TyWSK+RXJzJkzyWq1torH6XRSfn4+OZ1O9e+nTp2iESNG+L0Ci2aMvqVH\njx5kNpsDvn7kyBFyuVw0ffr0mF/h6fV62r59O82YMSPo1akoitSvXz968803A/6eaMXpW9555x1y\nu93kdDpp7969NGnSJPXqrm/fvqQoChERdezYMWYtEKx92to+n332GREReTwe0mq1LY5do9FIH3zw\nAaWmpsZk+57dOhBoMRgMlJ6eTsnJyTR69GhyOBzk9XrbbPmJlFBawPR6PSmKQlVVVZSenk433XQT\n2e122r17N+l0upgdL4G+o6qqKuTPqKyspClTpkQ9To1GQwaDQa1jXn75ZSouLqby8nJavXo1XXXV\nVSRJktoKpdPpaOHChdTY2Ej19fXUq1evqJ53oiGxo4ujszei1+slIqJVq1bRY489RhkZGaTRaEin\n01F2djYVFxer63i9Xjpy5Ag99dRTfiuUaMYJgGbMmEHz5s3ze1tIEATq27cv2Ww2crvd5PF4yGq1\nUt++fVs1O0czxuYVVaBK02KxkMfjoaNHj/q9tRnt8jSZTHTgwAHatm1bm79Bq9XSpEmT6OjRo9S9\ne/e4VaxPPfUUNTQ0kNvtps8++8zv7Q6j0ah+xv79+1vtG4IgRDRO1j6B9qE1a9YEXYfov8lUvE7w\nsiyrJ0tRFOm7776jmpoa+vjjjyk/P5/cbjddddVVMUlE2rqd5luqq6vJ4XDQunXr6I477qB9+/ZR\nU1NTwDKMVnkGqguPHz9OtbW1dPfdd6vJ5/jx42n8+PE0duxYev7556mhoYHsdnvA23WRjlOWZXX7\najQamjlzJj333HO0ZMkSuv7662nu3LlUXl5OXq+XXC4XKYpCbrebOnTo0GaincgSO7o4Onsj2u12\nIiLq16+f35O8RqOh3r1709y5c+mDDz4gp9NJp06d8nt1Es04gTP9ggJdFWVnZ1NtbS0pikJ1dXXU\n0NBAJSUl9OSTT7ZqDYtmjL5Fp9P5rdBEUaS77rqLiouLqWfPnjGrsPyV15NPPtnm9+v1elq8eDHZ\n7XZKSUmJS5y+WK1WK3m9Xnr77bdDiqO8vFzdl41GI+n1+ojGydon2Lb76quv/L5eV1dHRESbNm0K\nePKNdpySJNHgwYMpLy+PtFotmUwm+vbbb6m0tJS+/PJLKi8vJ6fT2Wb/pUjGGUr/pfLychoyZAhl\nZmbSzp07yWazUVVVVdD3Rbo8/dWFsizTW2+9RT169FB/i+9i3dc3KDc3l44fP07V1dWUlJQU9TjP\nLlNBEOi+++6jTz75hN58801655136MCBA2ojgsfjIY/HQ4cOHQq6PRJZ4j6fnWB898FLSkpa9AkB\ngOHDh+Oee+7B8OHDYbFYkJycDI1Gg5qamqjeQw5k//79AQe+W7JkCVJSUgCc6etQV1en3h+PB6PR\niMbGxhZ/02q1mDVrFu6991688cYbqKioiNuIxidOnMD3338fsK+GKIpITU1Fjx49IIoiOnTogPr6\n+pjHCZwZc0uWZQiCgLVr1wZcr6KiQn28t0uXLpg8eTI+++wzGI3GmI2+zM7NtGnTWv3t6quvVo/t\nsWPHxrXfTZcuXXDJJZdAr9dj//792LRpE3Jzc/Hb3/4WgiCgpqYm5ME5Y+Hxxx/Hvn37MHLkSAwa\nNAherxdffvllzOPwt82Sk5Px0UcfobS0VH1dkiS43W6IogitVotPPvkEWVlZOHz4MBRFiXp9efZn\nExEGDBiA0aNHg4hQXV0NvV6P4uJidOzYEZs3b0a3bt1QUFCArl274tixYwk5wHNQ8cvVEhvOynp9\nLUyjR49udRWQmZlJQ4YModGjR9Po0aNpw4YNZLVa6eGHH47LFV6g5k7fLS6iM32aevfuTVdeeSX9\n/PPPdOLECZo0aVJU4vQXi2/Jy8trEa8gCNSxY0eaNGkSPf7442Q0GmN6ReLv881mM6Wmpqq3GHx/\nF0WRkpOTadasWZSfn0+FhYX04IMPBuwzEe04O3XqRFarlWw2W9DbEC6XS/2sI0eOkCiKZDKZSJbl\niMbJ2qf58eDblqIoEhGR2+2m0aNHq/1CtFotPfTQQ+p7rVZrXI8XANS5c2f685//THPnzqW8vDzK\nyMig4uJi9cnNNWvWxKzlJpRbcikpKZSamkr79u1T+zINGzYs6PsiXZ6+ukMURZJlmTQaDY0dO7ZV\nneJ7Art37960fv169f3xeJrYt5hMJlq1ahV9+umn9NZbb9G2bduoS5cu1L9/fzIajTR9+nTatGkT\nPfTQQ2SxWGJSnpHGLUwhEAQB69atw/XXX4+vv/4ab7/9NubNmwcigtFoxPbt25GdnQ2NRqO2RO3f\nvx/Lli2LS7z+pirIyMjA3//+d3XcE5fLhYsvvhh33nknLrzwQgiCgIEDB2LlypUxjdXhcODiiy/G\nli1bQERqGdJ/nq74+9//HtN4/Dl9+jTGjRuHXr164fbbb0deXh6++eYbiKKI6upqFBUVYcGCBcjM\nzERdXR30ej1sNlvM41y6dCnMZrPfSY7PtnfvXgwfPhwA0KlTJwiCgKamprg/DcT+SxAEmEwmOBwO\nEBF0Oh2ampqg1WqxatUqGI3GVuMYKYqCDh06xCni/6qpqcHKlStx6tQpWK1WTJs2DTk5OXC73Sgq\nKsLrr78Os9mMpqamiE4K215ZWVlYsmQJLrjgAjQ2NmLOnDk4cuRIzOPwtboIggCtVgtJknDs2LFW\nZeQbAZyIMGbMGABn5h9cvHhx3FpubDYbpkyZgszMTNx00004efIkfvnlFxw/fhwAcPToUaSnp0OW\n5YQa1yos8cvVEhv8ZL7hdoh1OBx+Ow1GO05ZlslgMNDs2bMpPz+fGhoaaPv27Wormcvlojlz5tDM\nmTNp69at1NTURA0NDfSXv/yFZFkmnU4X0c6//mJsvtx1111EdObKeefOnTR+/HjKycmhAQMGUP/+\n/ePewtR8yc3NpS+//JLeeuutgK08zz77LM2cOZNMJpPakToWcc6fP199XVEUWrx4sd+rzZKSkhbr\nTZ48+by5wvu18LfPC4JAH330EeXn59OaNWto3rx59O6779LgwYOptraWiM4cQ8H24UgK9l2+RVEU\nUhSlRb+ljh070rhx4wK2iEdKKC1MjY2NRHTmqeFp06bRpEmT6Jlnnol5C1Pzbe3rp2QwGAJ+d/OH\nAIL9zmjE6W+xWCy0f/9+v536MzIy2uznmej1T2JHF0eBNuaOHTtarWu1Wmnu3LnqQJAZGRnqa6++\n+mrMd9ykpCRasGAB1dXVkdvtVhMR33ACVVVV1KVLFxo2bBgtXLiQtm7dSmvWrKErrrhCfUQ0knG2\ndXCkp6dTVVUVbdmyxe/rU6dObfOx4Eg/1RXqCSDQkpqaSs8++yyNHTuWNBpNixNfLOIcNmyYOnwA\nEdGaNWtalN/Z5bV27dpWTyCy+GueaDdfAj0tumjRIvW98TjBt7X4biWWl5e3+HtaWhr16NGj1f4Z\n6ThDidFqtdKpU6do6tSp9MILL9Ann3xC99xzT0jvjaTmnytJEul0OkpNTQ2YNB0+fJiIzlz4JMp2\nN5vNdPLkSbr55ptbvabX69ULcq1WSx06dIhqnJHGt+TCdMkll8BisWD48OEwmUyoqKjA7t27WzSZ\n+maJFwQh5nOKCYKADRs2YODAgeqcay6XC6mpqertQo1GgxtvvBEWiwX9+/cHEaG0tBQ1NTUxndVb\nFEUcOXIEycnJGDdunN918vPz22xijsdcfm11qHS73Xj99ddRXl4el9tbu3btQlpaGsrKytS5se64\n4w7s2bMHWVlZ+OSTT9R19+7di4kTJ8blwQTWtrb2L38SeVLqwYMHAwAmTZrU4u91dXWora2NR0it\nbNq0CTt27MDvfvc7jB49GlarFevWrYtrTIqiwOVygYjQpUsXFBcXt1onNzcXwJnbXYlAEARkZGTA\naDRizJgxWLFiRYvXJUnC9ddfj2PHjuGKK67Aa6+9FqdI2yl+uVpiQxitCiaTSb0KkGWZTCaT2gQ9\nc+bMmGX6F198MY0YMaJFCwPRmXGhHA6H+nev10u1tbXkcrnIbrdTRUUFzZgxgzp06BDTK7xLL72U\nnE5nwNsIoijGfByUYN/VvBNuuEss4wRA/fr1I6Izj/Q23ycURaF169ZF/VYIa79ALUyBln/84x9E\ndKYbQKLth76uAOFOoRIpoZTjq6++SlarVR3gd9WqVW2O/Rar8hQEgcxmMz377LNtDvL4r3/9KyG2\nuyiK1LNnT3r33XfVAZF906GMHj2aqquryeVykdfrJZvNFvVhdyKNp0aJgKamJrjdbhiNRlxzzTX4\n6quvIAgCXC4XDh8+HJMYRFHE5ZdfDo/Hgw8++ACVlZXweDxwu91wOBxqR2q3242GhgZ89dVXeO21\n1/Db3/4WXbp0wbJly9QWplhIT0/H119/DQCYP39+wPUSrfUjHi1a7fXTTz9BURR16gKf+fPnY/Lk\nyefXlAS/MpIkhXUs+jrwJ+LxYjAY1OkxEtX06dNhNpvVB3ymTJmSEGVJ/7lTMWTIECQnJ7d4TZZl\ndR/54osv4hFeK/SfuxWPPvoo0tPTcfDgQTQ1NcFut2Pjxo1IS0uD3W7HgQMHMHz48IQaWiIkcUzW\nEhrCbD0YO3YsFRUVUWNjI7lcLtq9ezf9z//8DxmNxphl+maz2e9VnCiKlJSUFPIVU6Tj9HVc9F0h\n3XrrrVRWVtai1cPtdlN5eTk9/vjjlJWVFZc4idre7oIgUE5ODmVnZ7erlSmSQm19uPTSS6mmpoZK\nSkpo8ODBIU1lweIvnNYlAFRRUUFERLNmzUqY48W3HDx4kEaMGBG34yXYMS1JkjqVlL87AvE+rn1D\nDBgMBho4cCD179+fxo0bR0VFRVRSUtLm7AKx3u6jRo2i8vJydT23200LFiyIW3lGmkDEzxH7E25L\ngk6nw9SpUzFmzBh88cUX+Omnn1BVVeX3Hn0kizyaLR6RilOWZXTt2hVutxuNjY1wOp3weDxITU3F\n3XffjUceeQSyLMNms2HixInYunVrWK0fv7byBM6UaSiPY4uiiCeffBK9evXCv/71L6xdu7bVwKtn\n4yoh/sLdD9esWYMJEyagc+fOQSd+jvXxMnr0aHUy2HBEKk5f63pbNm/eDLfbjbFjx4b9+ZEsz2Cx\nCoIAjUaDjh07on///jh48GDIg0DGYrtnZ2fjmmuuwdy5c7F8+XIsXrw4bts9GjhhCiDcCsvXwVuS\nJNhsNrVjsL/i/bWd4H1l4/F4onIb6NdWnkBoJ4H24ioh/sLdD6+//np88MEHmDRpEv7973+3eZz9\n2o4XQRCCjnzdqVMnWCwWFBYWhv358Tqum49XF4pYbXdBECCKYrvH10rk+ocTpgDOh4oA8B9npIbF\nj2SFFWhqkUhIhBNAKGXOCRMLVTj7oSRJSEtLgyRJqK6uhkajQVNTU8D1Y3m8nEtdFMkWpmAn8OTk\nZBBRq2maQhHJ8pQkqUU9KUkSJEnyO9Bj87IPFoMgCBGtf8+X82OkccIUwPmyQ5wPcUa7o/SvrTyB\n8ydO1j7nsn2DJSm/tv1QkiQIgtBmwnR2K1Q43x2NCyFBECBJErRaLbxerzq8wNkx+/4bLBmSJCmi\nrfttbfdwW77Olsj1DydMjDHGGGNB8LACjDHGGGNBcMLEGGOMMRYEJ0yMMcYYY0HwXHIBnP20wtk0\nGg1kWYbL5VI7EwqCAFmWoShKwA6GGo3G7xMP7aXVatWRdPV6vTrOUSQ+N1KjsGo0Gni93had+Xyd\nLIEznfx889z5Ojt6vV5otVqkp6ejsbERdrsdbre7VYfASHdm9De+UfOYmn+/KIrqiMxnv3a2SD+l\nEspTcoIgICkpCVqtFjabDbIsqyO/+/j2c18nzUiXJ2sfs9kMt9utzhjQvFNy8+PFt+18mh9TgUTj\nqa7mnYB9/z6X/T2Sx4u/+idSIn1c+zvvnGsnauD8eUou0nFGGnf6DiDYDuE7WbZn+PxoPKUiiiJ0\nOh0cDkfEPj/eorYLmgAAIABJREFUT8kJgoAOHTqgtra2zYPo1/i4fiQrrLOfquIqIf58J3mdTqf+\nv8vl8vu0VHOxTpiaP6kVyc+P5Ikz2MXvufq1DRMDnB9PR0YD35JrJyJKqCtxRVHgdDoTemcLFxGh\npqYmplcc/5fKL1S/xt+c6Dwej9py2b17d4wePRpms/mck6VoOnvOwnMRyd+QyC0W7PzCCVM7ZGZm\n4sMPP8SWLVtw2WWXxTsc1dlN44lCq9W2a6qZXbt2RSmiyPBNU+BrBThfJOI+wvxTFAWHDh3C6tWr\nUVNTE3A9SZKQmZmJtLQ09XZdLPmSpVCTE4PBEPA130C37IxAyaNGozkvy8k3kKivq4MkSQDOj3rp\n/CvtGAm0IyYnJ+Paa69FXl4egDNTElgslhhG1ra2rszidXJvz4HQvXt3DB48OArRRI5Op8Pw4cNx\n7733QpYTvzugVqtFUlISUlNTYTQaz8vK9tfG6/W2OeCi0WhEbm4u7rjjDixevBhXX301zGZzzE8+\niqKE3OKem5uLPn36tNr/fImSRqOJ+8lTFEWkpKSgpKQEP/74Izp27BjXeM4mCAKGDh2KcePGoUOH\nDnEvr3BotVpYLBakpqYiJycHF110EURRhF6vT/jfkfi1fJwE2nCNjY14//338eGHH0Kv1+M3v/kN\nXnnlFdx5550R7czdXoHud3fv3h3XXXcd9uzZg23btsU0pvZ0WFy4cKHaoTqRSJKEjh07Ijk5GVVV\nVbj99tvx//7f/0N6ejoWLFgQ7/D8MhgMuOCCCzB27FgUFhZCEASUlJSgpKRE7dgfynxbLHZC2Ray\nLGP8+PG49tprceGFF6J79+649tpr4XK5oNFokJOTg9OnT7f4zFgnyaIowmw244UXXsCll16KyspK\npKam4tixY7j11lvR1NQEj8cDURTRoUMHVFVVBU0SoyU5ORk//fQTunTp0uq1EydOwGw2R+xBmHD5\n9gdRFDFw4EA8/PDD6N+/P/Ly8uDxeLBq1SrMmjUrIc5BgQiCgAsuuABOpxMOhwNOpxM2mw1dunSB\nVqs9547tMUHML1EUCUDQJTMzkzZt2kQajSak9SNd5Gd/tiRJ6r8FQSCTyUQDBw6khx9+mBYuXEjZ\n2dkxjzPUsmy+bNu2ze/vi3V5Nl8MBgP94x//oPfff59Gjx5NoijSpEmTiIiotrY2YeJsvsiyTDt3\n7qT169fTsmXLaMmSJVRUVETvvfceDRkyhPR6PUmSRHq9niwWC0mSFNE4WfsIgtDmdhUEgV599VWq\nq6sjm81GNpuNFEUhr9dLHo+HXC4XrVmzhrp160YGg0F9jyiKEY2zrfhkWaZu3brRkiVL1PW9Xi9Z\nrVYqLCykv/3tb7Rs2TLavXs3lZeX00cffUSSJKm/PdoxNl8URWnxnrP/3+VyBazjI6mt8kxKSqJX\nXnmFXC4XeTwe2rRpE3m9XiIi8ng8tHDhwoSrf3yLKIo0fvx4WrJkCSUlJVFKSgoZjUbq1KkTJScn\nkyiK6rkrkSV2dHEU6knebDbTm2++GdK6Wq024iektk6UEyZMoB9++IF27NhBo0aNory8PJJlOeSd\nPFKaJ3GhLg6Hg7xeb0zjJApcnqIo0s6dO6mqqoomTJigVuoWi4UURSGPxxP0d8YizrOXtWvXktfr\npX379tGNN95Id9xxB1mtVmpqaqKff/6Zpk2bRnl5eSRJknqyYvEXbLvm5eWR2+0mIqKmpiYqKyuj\nyspKKiwspHfeeYeamproo48+ouTkZPWYFwQh4ts30MndYDBQ7969acGCBfTRRx+Rx+MhRVGoqamJ\nli1bRjfccAMVFRWRzWYjh8NBxcXFdN1110XleAm1DvF6vZSTk6Me24Ig0FVXXUUFBQV05MiRgHVn\nJLUVoyzL9Pnnn5PNZqPt27eT0WikH374gdxuNymKQm63u81EO1Zx+lt69uxJdXV1VFlZSaIoqudX\n3z4ZrTgjjW/JBUAhNg2ePn065PuuY8aMQWFh4bmEFRKtVosZM2bgpZdegsFgQF1dHQoKCtQnb2It\n3PvSWq1WHTMoUWi1WnTp0gXl5eVYt26dWo6+pxN1Oh2eeOIJPP7443GOtKUxY8bA5XJh9uzZyM/P\nh6IoWLBgAbp27Yru3btjwoQJ+Prrr+NyC4S1X/NbbXv27MFLL72EwYMH49///jd2796NyZMno7y8\nHKIoqtuW/jOpa7SlpKTg5ZdfRr9+/WA0GrF582bY7XYYDAZUVVXh008/hcvlwh//+Ed069YNq1at\nwokTJ+J2O8k3lMitt96K8vJy9e9EhE2bNuGOO+7A5ZdfHvcxylJSUpCbm4sDBw5g3rx5cLvdWLp0\nKW6//XZcdNFFMBgMER0/ry2+TtuiKAY9rwiCgIaGBrjdbrWbhW/9eJyPzgX3+jxHWq0Ws2bNCqkz\n9bp161BSUhL1mLxeL1JTU6HX6yFJElJTU3H11VfjyiuvxL59+/Doo48G/YxIVqzhHhSSJEX0EeVI\n8Hg8yMrKQlVVlfokUFJSEoAznVj37t2LhQsXxjPEVkaOHImSkhIYDAbs2bMHbrcbXq8XHTp0UCuu\nd955Bw0NDfEOlYXJ4/GgoaEBHo8Hy5YtQ2lpKQRBQP/+/fHOO+9g06ZNeOmll1BfX68ef3q9PiYP\nJ2zZsgVXXHEFzGYzSktLceDAAZSWlqKxsRE6nQ6lpaVYv3491qxZg5dffhmlpaVxS5YEQUBtbS26\ndeuGFStWtHpdFEUcP34c77//ftz7B7ndbixYsAAzZ87E0aNHkZKSguPHj+ORRx5BaWkpRFHEjTfe\nGJN601cHGgyGVnW1KIr47rvvkJ6eDuBM/V9RUQGr1Qq9Xn/eJUktxKFV67wQrA+Bb+nevTs1NTVR\nRkZGyM2TkeTv8wVBoB49elB5ebnaVNvU1NTqvnzPnj0Dvl+v10csxlDL0rekpqa2+ftiXZ4AyGQy\nERFRTU2N+jeDwaA2Lefk5JDL5SKdTpcQTeIpKSlUU1ND/fr1a9WsT3Smj8bevXupS5cuLV6PdB8X\n1j5nH4++f2s0GrJYLDR8+HAqLCykiooKOnnypHoL23fra+PGjZSamkqSJJEoiiQIAqWkpJBOp4ta\nnL5YL7vsMrr55pvphRdeoKuuuopuvvlmslqt5HQ6qb6+njp37hxSnRCtGJsvZrOZysvL1XKSZZkM\nBgPJskwmk4luuOEGuu2226h3794Bu2lEUqA4BUGgvLw8uuGGG+j555+nsrIyOnbsGBUUFFDfvn3p\n3XffJbvdTklJSXGtfwRBoIKCAlIUhcaNG9ei/vT1twrWLSSRcQvTOerfvz80Gk1CjccEABUVFfjw\nww9hs9ng8Xig0+laXXkcPnwYRISlS5dClmV1ZO358+ejV69eEYuFwryiCOe7Y9UKpdFoAJx5ksb3\npFHz6Raqq6tBRH6fsIklQRCQk5OD9evXIyUlBYcOHVJfMxgMKCgoAHDmls7TTz8NRVHUlkitVqv+\nTpZYZFmGVquFyWRCdnY2nnjiCbjdbtTU1CAzMxM6nU6dfcA3HlJTUxO8Xq86fUp9fX3UW0mICNu3\nb0d1dTUaGxuRlJSEOXPmwGQyqcNa2Gy2hGllICJ8/fXXqKurg8PhwPHjx3HkyBE899xzWLJkCVas\nWIE33ngDf/vb39TxguLF7XbjxhtvxH333Yfc3FxkZ2cjLy8Pl1xyCQYNGoTGxkY0NjbGNca0tDT0\n6dMHgiDg8OHD6t/Hjx+v3vq89tprz9t6hhOmAEI9Ee/atQuSJGHw4MEJM64NEcFms+HDDz+E3W6H\nzWbDxo0b8bvf/Q7z58/HN9980+I+95w5c2C1WnH99dcjJSUFBw4cQFFRUdziv++++wAA+fn5QdeN\nVZn7blv5bnECaDFNhSzLkCQJKSkpMYnHH1EUkZ6ejpkzZ6Jv374A/juYaffu3XHy5En07t0bdXV1\n+Otf/4qtW7fCaDSia9eu+M1vfgOTycR9mRKQTqdDcnIyJkyYgDfffBNffPEFfvvb3yIrKwupqanq\nI+enT59Wp0Z68MEHW8wZ6BOLRIWIsHv3blRUVGDo0KHo1auX+r2CIOD9998P+hmxuhDyeDwYNGgQ\nLBYLNBoNMjIy0LlzZ9x7770YN24cdDod9Hq9OvxBPDmdTlx00UUtEje9Xo8///nPuOCCC3Dw4ME4\nRneG1WpFY2MjFEXB8OHDIQgC9Ho9Fi1aBK/XC4/Hg1deeQWTJ0+Od6jtkhhn+POYr5WhoKAgYa6a\ngDMnypKSEtx///3o168fxo4dixUrVuD555/H1VdfDb1ejwsvvBCnT58GEUGWZVx11VWorKzEli1b\n0NTUFLfYL7/8chARhg4dGnTdWA0YSc06Kk6ZMqXFa4IgYPny5ZAkCUeOHIlJPP488sgjmDdvHsaN\nGweNRgMiwuDBgzFq1Chs3boVSUlJICK8++672LVrF9LS0tTxUMaOHQuAp5FIRBqNBiaTCQMHDsTp\n06dhs9mwYcMGXHnllZg6dSq+/vpr9O/fHykpKXjllVfgcrlatCzGQ0NDAzZt2oQPP/wQf/jDH/DL\nL7+or914441xvbBozul0YuPGjS3+Rv+ZksZut+PkyZP45ptvMHv27LjW70QEk8kEg8GA8vJyFBUV\n4dprr0VBQQHMZrO6xNtll12mdgZfsWIFjh49ik2bNiEtLQ3Hjh2D0+nEjz/+iJMnT8Y71PaJ0a2/\n806o/W4GDhxIiqJQ7969Q1o/Fo/1hrrodDrav38/NTY2UmFhIQ0ZMiTmfQj8LS6Xi4qKikJaNzMz\nM2JxBov1n//8JxERNTQ0tPh7165dyeFwkKIoMbs3f/ZnjxgxgpqamujgwYN08uRJcrlc5Ha76bXX\nXqO9e/eS0+kkr9dLdrudRo4cSSNGjCCj0ai+X6/Xk9Fo5GEFEkTz+sJoNFJWVhYtWLCAFi9eTIcO\nHaI333yTZFluVU8VFxcTUdv1VzTiDLZotVqy2Wzq+0aOHBmzejKU2ObOnUt/+ctfaMqUKTRr1iya\nN28eWa1W+vLLL0mv18ftuPYtaWlp9Oijj9KCBQsoNTVV/fvcuXPVvqkVFRVxjVMURerXrx99/vnn\n5Ha71fHAKisr6dixY7R161YqKCiIaXlGWmJHF0ehjsP0v//7v7Rjx46wBmeMpFC/8+wlJyeHFEUh\nu91O6enpUY0z1JgEQSCHw0E2my3k9Tt06BCxOEOJ9YEHHiCiMwPZDRgwgIYNG0abN2+mmpoamjBh\nQly3+3XXXUcrV66kr7/+mjZu3Ej5+fnU0NBALpeLrFYr7dmzh3JycmK6f7L28W0LSZLIaDRSUlIS\n9ejRg3r06EEzZ86kESNGtNpuvmTJ4/EkXP3z448/qu+pr6+nHj16BL0ojUWMGo2Gunbt2urvs2fP\nJiLyW87xKM958+bRiy++2KrD/NixY6moqIjy8/NbPeARzTgDbTvf4Kjp6elkNpsJOPNwzPLly+nU\nqVM0bdq087r+4XGYAqAQml8lScJll12Ge+65J6FuxwUjSRL++Mc/wuPx4E9/+hOqq6vjHRIAYPfu\n3dBqtbj00ktDfk+shx548cUXMXXqVAwbNgwpKSlwu934/vvv8eOPP+Lbb7+NaSxnW7t2LTZs2ACd\nTocBAwbgmWeegUajgdfrRUVFBV588UUcP348rjGy0Pj6JaWmpqJXr14oKyuDTqdDcXExdu7cifr6\nenU9AJg9ezYuuOACAMDHH38ct7gDGTBggPrvkpISVFdXw2KxxL2T8v333481a9ZAluUWD3H4+gkd\nOHAgnuGpCgoKkJ+f32ICZpPJhPr6etx6660oLi5W94lYCHS+o/90XTh9+rT6gIHFYsHYsWPx008/\n4fPPP49ZjNHACdM5GDRoEFJSUrB58+Z4hxKW3r1747bbbsPBgwfx9ttvR/37QpkXq1evXrj44oth\nt9vx/fffh/S5RBSXvlZjx45FaWkpHnroIUiSBLfbje3bt8e9U6ivPJqamvDDDz/AZrNBkiTs378f\nf/7zn7Fhw4agfZQSaeyrXzPfMdPU1ASNRgNJknDixAmIogi73Q5BEGAwGHDppZfipZdeUjv5l5SU\n4JZbbolz9K359itFUfDUU0/B7XbjkksuwebNm+M2QK3FYsHll1+OrVu3YuDAgXA4HCgrK4Moipg+\nfTqamprUso73BbGiKGhsbIRer1fLKzU1FRUVFTh+/HjMBiUNVfM5Kj/55BNkZGTA6/XGfSyrc8UJ\nUzsJgoB77rkHOp0ObrdbfWTStyQqs9mMd999FxaLBdOmTYtJwhFKefieiOvRo0dYnx2PTsp2ux1r\n1qzBmDFj4PF4YLfbUVJSAkmSEuYpM6fTiaysLBw5cgSzZs1CUVFRSCemRKp0f818x4zT6URRURHs\ndjtcLhcuvfRSdO/eHTqdDunp6bjuuutw4YUX4vDhwygtLcVNN92U0B337XY7fvzxR3Ts2BGVlZVx\nqytFUcScOXMwYsQIXHnllXj66achiqL6lFdpaSk+/fRT9SnTWMUZ6LtKSkowYcIEXHfddXjxxRdh\ns9lQW1urjrKdaOccXzx6vR6XXHIJAGD+/PnxDCkiOGEKoPmUAv6kp6fjmmuuUcfvSJQTZSCCIMBk\nMmHUqFHo3r07vF5vTKZp8X13sANap9OhoaEh7Kcn9Hr9uYTWSqiVY0FBASZMmABZllFbW4t9+/Yl\n1InK4/Hg888/x+uvv45Tp06FXKEm0m/4NfNtL4/Hg+PHj0MURZhMJtx2220YOnQoTCYTzGYznE4n\nbrjhBmzcuFG9pZQILSLNGQwGAGd+0xdffKE+IWe32+NWbyqKgtdeew1Lly6F0+nEs88+q9aRkiRh\n3759WLJkiZowxdupU6cwadIkXHzxxejRowdefvll7N27t8VwDYm0zX0cDgdOnz4No9GI/fv3xzuc\nc8YJUwA6nQ6KovgdywQAbr31Vvz00094+umnE2rOs0B8YwTNmTMHFosFJ06ciNkBFux79Ho9FEVR\nbyuEyjcQXjyUl5fDarVCp9MhNTUVl19+OTZs2NBiLqp4EgQBK1euDCtZYolLURRYrVY4nU4cO3YM\nBQUFOHbsGFatWoXS0tIW6yba9s7JyYHT6cS///1vPP/886ivr4fX6w1pOqloal6319bWQqPR4IIL\nLkBjY2OL2+uxLM9A3+VyufDOO++gW7du6NatGyZMmNAiYUpkkiTB5XIFbYQ4Hwh0PpR4HIiiCK1W\nC7fb7feq+1wy+kgWebhXP+HEHak424qxb9++KCgoQEpKStgdQAVBUPsQRUqo5anRaKDT6dClSxfc\ndNNN8Hq92Lp1K7777ruA74nldhdFsd2tRVwlxF80WzUiuX19XRECWbVqFYYOHYrs7OywvzcW9Y/v\n9UWLFuHRRx9t1zETy+M6OTkZycnJqK6uht1uD+uz43Xe2bVrF15++WUsX748pPUTuf7hhCkAQRAg\nyzIsFova4S5SRRXPhCkc0a6wJEnCzp07MXjwYMiy3K7KyjcNRKSEU56CIECj0SAzMxOiKKKioiJg\niyQQu+3uey0REnrWPufDcQ20nTDJsowXX3wRVqsVjzzySNifHYuESZIkdO/eHYqitHvQ2VjW55Ik\nQZblFrMMhCoe5x2dToeJEyfi448/DrmeTuT6hxOmAELdIdrT0sQJ0xmiKCIpKQlZWVnnNDLxr608\ngfMnTtY+58v2DaX1pr3fGak4JUlSP4uI1OmUiKjF3HvtvV0Uyxa79pZnvC4sfWUdzncncv3DCRNj\njDHGWBA8lxxjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXB\nCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHG\nWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHG\nGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJ\nE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZY\nEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYY\nY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkT\nY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQ\nnDAxxhhjjAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhj\njAXBCRNjjDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNj\njDHGWBCcMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAXBCRNjjDHGWBCc\nMDHGGGOMBcEJE2OMMcZYEJwwMcYYY4wFwQkTY4wxxlgQnDAxxhhjjAUhxzuARKXRaODxeKLy2UQU\nsc8SBKFd7xPF/+bKiqL4XSdScTb/LlmW4fF4IvrZXq83Ip/l+7z2xKbVaiGKIhwOh9/XBUEIWM7t\n0TxOQRAgSRJSU1PR0NAAt9sNoP3bL5L7J2ufcI9rQRBw0UUXobi4GKdPn271uiiKUBQFsiyr+0ck\ntLf+CUWk9sNoxggkRn0eil9jnJHGLUwBRPLklogkSYIsxyZfJiJ18Xq9IR1soR6Q0dxOzRO9tkiS\nhP79+7cZc7QqAUEQoNPpkJKSApfLdc7JY6i/mSUWIkJtbS1sNpvf133HSbQuAhn7NeDaMYBzPcH5\nrvoT8QSUmpqKQ4cO4YUXXkBqampMv1tRlKBlq9frg64TrSuc5q02oSRjGo0GgwYNQnp6OpxOZ1Ri\n8qd5+Xg8HthsNlitVjXm9pZPrJJoFnmPPPJIvENgEdCeY1cUxYQ81/xfwyUcQHt3PkEQkJubi7Ky\nMrz99tswGAxRbxIOhyzL2LhxI7p164apU6fCZDLFPIazy1aWZYiiCFmW0a1bN4wYMaLN9+fl5UGj\n0UQtPkEQQk6YU1JSMGfOHEycODFq8bTFl5j7+1t79mFugTg/jRo1CjNmzEjIk6bBYMCGDRuwd+9e\n/OlPf8I111wDo9EY77AAnDlWfMmGVqvF2LFjcdddd7U6pmIdk16vD3lbarVarF27Fm+//XZU68Vz\nodVqYbFYEupc2C7E/BIEgQCEtTz66KPkdrvVz7Db7TR69GgSRbHFepEUbow333wz2e12cjgcpNFo\n2lw3VjHq9XrKzs6mO++8k9auXUtfffUVybLsd90+ffrQc8891+L1SAJAGo0mpO0vSRLl5ubSd999\nR8eOHaMOHTrEpDyJiDQaDYmiSDqdjnQ6HcmyTBqNhrRaLfXu3Zvuv/9+GjlyJOn1+rD2D0EQIhon\na59wj+s1a9aQy+UKeNzEuv4RBIGGDx9O5eXl6rqKotDhw4fptddeo/z8fHrggQdiEqckSX4/XxRF\n9ZjJyMigu+++mxRFUd/X0NBAer2eUlJSKCMjI6blmZSUROnp6a3OHf6Wa665hqxWKxUVFZFOp4vr\ndj+7fKuqqsjj8VBDQwMdOnSIPvroo5jun5GW2NHFUTgJ08iRI+nTTz+lwsJCevHFF6lfv34BD9J4\n7LgAKD09ndxuN7ndbvrhhx/aPLCiUWEJgkCCIKgn+rMPrI4dO9KyZcvoxIkTVF5eTm+99RbNmDGD\nZs2aRY8//jgVFxfTCy+8QBaLJaoJqCRJIVVSvpOCIAik1WrpxIkT5PV6Y1YR3HbbbTR16lRasWIF\n3XbbbZSZmUlXXXUVPf7445Sfn0/V1dX06aefUr9+/UL6LQaDQU0UWfyFelwDZy44nE4nWa3WNuud\nWNQ/Xbp0aZF0EBF5vV6aNGkSWSwWtV6dNGkSEVGb+2ekhHo8+1vy8/Pp2LFjNGbMmJiWp69uCRaf\nTqcjt9tNtbW1NHLkyLhtd0mSqLCwkLxeb4v1jh8/TqNGjSJBEKhTp05ERJSUlBSzOCONOyycI0mS\n8Mgjj2DIkCEoLi7G+++/j59//jnhevofP34ckiShrKwM48aNi3l/G195KIoCSZJa9A+SZRkajQb9\n+vWDxWLByZMnMWrUKPzud79r0XT/wAMPoLKyEosXL1bfH41bEG31XRIEQW3G9/XHcrlcePLJJ/Ha\na69FPJZAunbtim7duqF3797o3Lkz9Ho9srKy0K1bNxARli9fjocfflht2g/WH6u9Twey+DObzQlz\nKy4rK6vFbZdffvkFQ4YMQUVFRYv1Nm/eDADYunVr1PtRnsuDIT179oTH40FBQUEEIwou1GNx9uzZ\nkCQJx48fx65du6IcVWCyLOP1119H165dAQBvv/02rFYr6urq4HA4QESwWCwAzvPb/vHL1RJbW9m9\nJEkkSZJ6G6S+vp6cTic99thjMW25IQq9adTj8VBxcXHQ23DRiLN5WQqC0Oq2gSAI9Pe//53cbjd5\nPB5yOp3k8XjUK1VFUUhRFPJ4PLR582YaNmwYybKsfm4ktbX9NBoN3XrrrZSenk56vb7F1Xxqaio1\nNDSQ0WiMyXbv2LEjpaWlUbdu3WjOnDm0cOFCKigooLfeeosuu+wytWwmT55M+/btoyuuuKLN2zWy\nLJMoitzClCBCPUYBUEZGBnk8HnI4HHFvYbr77ruJ6Mwxu27duoCtO833s169ekU1znDK8uxFURQq\nKChIqPq8+XLixAlSFIUmTpyYcOcd33bWaDQkyzI9//zzRBS8xS+RJcZlyXlElmVMnToVCxcuRFZW\nFhRFgcntKrPWAAAgAElEQVRkAhHh1KlTMW25CYUoiigqKoKiKBg0aFBEx2AJh++qk/5z5aTRaKDV\napGRkYFx48bhoYcegizLkCQJGo1G7Xhtt9vx0ksv4Q9/+AM2bNiA06dP4/e//33UOhAG+kxBEDBl\nyhQ89dRT6NatW6tH+Ovq6rB69WqsXr0aer0+4nGdrbq6GrW1tSgpKcHy5cuxfft2dOrUCYMHD0a3\nbt1gsViQl5eHjz/+GAMGDMDGjRvhcrnw9ddf+/08r9cb0hOMLPH4WnV8LZ/x1Lt3bxARCgsLMWHC\nhDbHePO99uSTT8YyxJD5xni78sorY/ad4dRpoiiiQ4cOcLvd+Oqrr6IY1bnRarUwmUwYPnw4gPN7\nyB6+JRdAoCelBg4ciOeeew5WqxUbN25EVVUVbDYb9uzZg/feey/2gbZBEAQ0NTVBo9FgxIgRaGho\nCPm9kax4fZ/lOyF37NgRubm5EAQBl1xyCZ544gkAZyrRQ4cO4d1338WOHTtQWVmJo0ePqonJm2++\niZ49e2Lo0KE4ffp0VE7ugX63KIoYNWoURFFEZWWl33WGDx+OCy64AHl5eTh06FDEY2vO99uJCE1N\nTSgrK0NqairS0tLwwQcftFrXarXCYDBg+PDhkCQpooN9stjxVy8lJSUBOLOPGgyGuF0UAUBmZiZs\nNhtGjhwZ9MTo+x1Dhw6NRWhhEUURtbW1WLRoEWpra2P6vaEem8OGDYMsyygsLEy4C3Uf30UvAJSW\nlmLkyJFxjugcxb5R6/wQ6NbM8uXLqaamhgoKCmjz5s20ceNGOnDgAOXk5ITcUTySAn2HKIrqOqtX\nrw64TmZmZqu4BUEgvV4fsRg1Gg0lJyer39O7d2/69ttvae3atdTY2EhWq5VSUlLU1wVBIFEUWzXd\nWiwWeuyxx6hbt25RK89Atyz79etHM2fOpJ49e1JKSgqZzeYW5WY2m6myspK+++47SktLi8t2/+yz\nz+jkyZNUXV1NGzZsaHV7UJIkstls5PV66bnnnqPU1FR10el0akdTFn/+tq9Go6H09PRWf3///ffV\nW9ljx44NelsumnGOGDGCjh07FlI96Lvlvn79er91Z6SEWi+LokgPPPAAlZaW0rJlyygnJyfm9Xk4\nHdQdDge53W7Kzc2NeZzhPBTl64ZRWlpKRMFv5yUybmEKk9vtRmlpKZ599lk8/PDD6NWrF4qLi9Wx\nbxRFSYgmx/Xr16v/LioqwoABA+B2u1FSUgKn0wlFUTB58mQcPXoU1dXVLa5qwhmHKFTNr4CmT5+O\nAQMGQK/XQ1EU7N27F/X19errRARBEJCRkYFTp06pf5ckCV999RVKSkoiGltzgToklpSU4NixY3A6\nndDr9cjLy0NJSQnsdjsEQcA111wDs9mMW265BXV1dVGLry2TJ08GgIDbTlEUdQqXu+++G6tXr0ZB\nQQE8Hg/S0tJQU1OTEPsu80+r1fqd9uSSSy5RW0bT0tLielt17969MBgMQddrPs5RdXV1VOocn7Y+\nWxRFJCcn4+DBg8jIyIDL5UKfPn1QXFwclViCCef40+l0KC4uxrFjx6IYUftJkqSWuyRJUd3GscIJ\nUwCyLPtt5pwzZ47a23/QoEHIycmByWSC0WhU++CIogir1RrrkFWSJOHyyy8HEcFms2HLli3YuXOn\n2tz7yy+/wGazgYgwa9asVjuxoihwuVwRi8fr9bYYQXvYsGEwm81wOBw4cuQI7rzzzlbv0Wq1GDBg\nAKqqqgCcORFMnDgRu3fvjlhc4XC73UhPT0dmZiZmz56NAQMG4IUXXsCGDRtw4YUX4tlnn0VdXR2q\nq6vjVimE8r0HDhxA3759odfrMXLkSOzYsQNer1dtNo/ngH2sbaIo+p36JCcnRz0ZVVVVxTXpdblc\nEAQBGo0m4K1BjUaDhx56SI354MGDcZubjIig1+uRmZkJ4Ey9M2XKFCxevFidyikR+QaoDDbIb7QE\nq2vS0tIwdOhQbNmyBRqNBnq9Hlqt9vx+Qg5I8PavODKbzUGbDmVZppEjR9Jnn31G2dnZ1LFjRxo5\nciRdffXVbTatRpK/zz969Ci5XC46efIkPfXUU5SUlERHjhxRn+azWq3k9XrJ5XLRFVdc4feWXPNb\neufK1ySr1WrJYDBQbW0teTweOnXqFD322GMBm5RHjx5Nzz77LG3cuJGqq6tp586dZDAYYtrULMsy\nDR8+nD799FOqq6tTn+BrbGykzZs304MPPqgOzjZjxow2m6UjKdi+6W8RRZEuvvhiWr58OXk8Hior\nK2tx61kURTIajRGNk7VPoO3n7+++sW+qq6sT4ildm81GixcvJq1WS4IgkCRJpNFoKCsri8aPH087\nduwgm81GiqKQzWajMWPG+L2NGM0Ymx+XqampdOTIEWpoaKCamhpau3YtrV27ljweT8IMBHr2YrVa\nadeuXWEd/7GMc9euXVRaWkrXX389zZkzh3r37k3FxcVBx6qLdJyRltjRxVGoj99nZmbSXXfdRSNG\njKDMzEzKyMigjIyMNu/xRpK/zz969CgdP36cnnjiCbXvkC8J6tSpE61YsYIURaHCwsKAFWwkT/AA\n1GEYtFot2Ww2crlctHLlShoyZAiZTCa/MVgsFqqtrSWv10sOhyNgEhtJzbebL4H48ssv1WEOXC4X\n/fDDDzRv3jx6++23qbq6moiIrFZrm/uMTqeLaJzhVJS+paioiN555x167bXXyOl00t69e1vE7Bvx\nmMVfuCdCr9dLc+fOTYgTp9vtJofDQd9//z0988wzdM8999Crr75KGzZsoLq6OmpqaiKPx0Ner5fK\ny8vp+uuv95uYRDNGXx2Xm5tL27Zto5kzZ1L37t2pZ8+elJycTOnp6eox31ZdHo8LoW3bthFR4BHM\n/S2SJMUsTovFQl6vl+rq6qhnz56UlpZGgiBQz549SVGUoLMPJLLEji6OQrmyEASBevToQcuWLaPJ\nkyeT2WwmvV5Psiy3+f5I8nfQSJJEnTt3pltuuYUmTpxI/fr1I41GQ3369KF169ZRZWUlrVmzJuhv\nixTf5/mSkKqqKqqpqaH7779fHf/HX4Lx888/k9VqpXnz5sWsxe7s8hQEgQwGAz3zzDP0xhtvtOjQ\nvXXrVmpoaKD58+cH7QSp1WojGmc4J1STyURvvPEGvfTSS/Tkk0/Sxx9/TEuXLvVbptzpOzGEs32J\nKOhYQbE8wbfF6/WSx+OhmpoaKi8vpy1bttCSJUuoa9euUTuu/Y2anZaWRt988w3V1NTQsmXL2ixb\nf60iL774ItXW1tLTTz8dsTiJ2t7uXbp0UddramoKef8QRZFkWY5JnGazWU2Ie/fu3er18vJyqqio\niFmDQqQldnRxFKyFSRAE6tWrF23dulW9otq6dSstXryYbrzxRr+3jqKxQ7SVmGm1WiosLGyxvqIo\nVF5eTlqtNqSKOBJ85QWcedLH5XKR1+ulyspKuuWWWygrK6tFuQ4YMIAcDgcpikI9evSIWZxEbQ9c\nKcuymlCJokgVFRW0Zs2akK704pkwvfHGG1RSUkKHDh2isrIymj17dsz2T9Y+4WxfIqKioqKQ5z+M\ndpyBuN1uKisro9WrV9Ndd91FS5cupS1bttDatWtp/PjxrY6jSJEkqcV0HDqdjsaPH0979uyh/fv3\ntzmlyOTJk8nhcNCjjz5Ker2e7rnnHnK73aQoClmtVurVq1fE4iQKvN11Op1669Xr9VJmZmbI+4dv\noOVYxDl//nxyu91kt9v9DuJ7+PBh+vHHH8lkMkW9K0g0cO0YQFsnTq1WSykpKbRy5coWk+0SnakU\nVq5cGbMMOlhfK1EUqbCwkFwuF1VXV9OqVasoLy8v5Io4EnzJhslkohtuuKHVfENERMXFxdTU1NTi\nb263O6ZxElFIiaQgCJSWlka1tbU0YMCAkGLUaDQRjTPUyjI3N5fcbjc5nU5as2YNLVq0KOhvZPEX\n6vb1ba+ampqQHkmP9AnJ33ecPZcc0ZljuaKiglavXk133303TZs2jbZs2UKHDh2iDz74gMaPH9+q\nzo0U33yPzY/frKwseu+992j9+vV+W0KaL88//7za79Nn1apVlJ2dHZPyBECNjY3qOnfccUfI+4av\ndS0WLYvp6elq/1SHw0EDBw5sEUfv3r3J5XLRoUOHaPLkyZSdnU1Go1E9V/rmHE1kXDsGEKhfjW/j\n63Q6mj59Oh04cKDFgeR2u+nzzz9v872RFErndH8xxDIREQSBTCYTWSwWuuKKK2jXrl1ktVrbfI/V\nao15YkfUdqLcfJkyZQqtWrUq5HFTYtmHoPmyYcMGIiKqr69XK/hYlidrn3COZ6Iz9U5KSgpJkkQp\nKSkx277+Pv/QoUPk9XrJ6/WSoijkdrvpiSeeoH79+lHnzp0pJSWFOnfuTLt27aLNmzfThAkTqFOn\nTq3qpWjGCJyZzmjgwIFBW4gHDhzYonWnrKysxXsiKVAMLpeLFEWhN954o931fbTjXLhwIZWXl5PV\naiWbzUZFRUU0evRoWrRoEe3Zs4caGxtJURTasWMHLV++nG688UZKTk4+r+qfxI4ujkIdtMx3Mrzy\nyitp48aN9Oqrr7bZxBvphKljx45hH0DhVMSR4OsH5LuC8H2+VqtVkyer1UpVVVX0l7/8hbKzs+MS\nJ1Honf0HDhxII0aMCDnGSLcwhZqo1dfXk9frpZtuuqnN9XzzyCV6hfVrEe7+73A4QmodjcWJMykp\niX7++Wc6ceIEHT58mB588MFWXQdEUaQBAwZQjx49SKvV+t2foxlj8/o4WHnNmzdPTQBfeeWVFrf3\nIl2egeIpKyujOXPmhF2HN/+8SPL3XePHj6eHHnqI5s+fTw888ABt2LCBampqyG63k9PppBMnTtDG\njRvJbDaT2WwO2J0kkQlE5/lIUlGSnZ2NEydOhLy+IAhISUlB586dkZ6ers7GfbZwhr4PxUUXXYSf\nf/45KmP/ROozRVGELMvweDwJHSdwZhyWUKaWmD59Ompra/HNN9+E9LmR3u4ajSbomCaCIMDpdEIQ\nBAwdOhT79u0LuO4NN9yAb7/9Fk6n8/wfK+X/gHDGJbLb7Vi6dCkeeOCBkNaP5PESr/GTwnGuMV56\n6aXYuHEjTp8+jdzcXHXMMp9Ilqdv8OOzabXacx4bL9rbXRTFVrH36tUL1dXVsNlscDgcIX12Iqck\nPPluAOHMuwac2cj/n70zD5Oiuvr/t5beu2d6mI1hYBgYZAaYAAoqQcISEPRBIxgj4goKGNSwRIjy\nU99ojL4xrpGgBoiiLBLQSEQ2xWUcBQUXFlmGnRmYfWt6eu+q8/uDVL2z9XQ3VHf1aH2epx5luqvq\n23VvnXvuufee63A44PV6MWnSJHl/p+ZIG2QqSWNjI1iWlY9Ir8/z7ecslZwbvV6vmEaWZWG328Hz\nfEwNrBJE8rIyDIOpU6di//79EV1TygKvJJFcj2EYPPPMM7j00kuxb98++W+t0el02LJlC9xud8Im\n6tMIzfDhw/GHP/xBbRk/WkpKSvDII48gOzu7jbOkNKIowmazwWg0yomQGYZRNJFwrGjP0SspKUFd\nXV3EzlKio0WYQmCxWODxeNo0oFKD097fJcdg4MCB2L17t5xNW4JlWXAcp2jlt9ls8vYckqMjvdTt\nNX6SY8VxXLuZzDmOg16vlzd1VQLJUZL0KFHlJIeBZVlFn2ckkRu9Xo/p06fj7bffhsvl6tDJ4DhO\n1qrkBpk8z4d1bqSop8/ng8/na5FtvfVWOAzDyAZPMwnqE03HIiUlBW63W34P6L9bC4UqRy3CFB16\nvR6iKIa0C7F8nqHamwvhp1busUBzmDQ0NDQ0NDQ0wqANyWloaGhoaGhohEFzmDQ0NDQ0NDQ0wqA5\nTBoaGhoaGhoaYdAcJg0NDQ0NDQ2NMLS/tlwjZD4MJVBynj3LsjFbVaDUdU0mEwKBQEyWrCud36gz\nPE8g8lUq0u+J5t7aOhD16SyrkC5EZ0cr+JqjZB44peu09Bt4no8ob1uk/NjsT7Qobc+VRoswhSBW\nzpLSdIbGTRCEiF+CaF9EpX9/86X3PwZYllU0p5aGxsUSb5uV6Mly43HdzkKit7uaw6QRc6LpMURr\nMBLJcMUiQeXFwDAMunbtGjJJqYaGxsXxU3dwfmpoDtNPBL1ej+LiYoiiKA/RrFu3Dl27dkVqaipS\nUlKg0+li0uBfiFExGAxg2farZ6i/K0E0kSWWZaHT6eSkpZdcckncwslSVnfp3pJujuPQs2dPPPPM\nM/jb3/4GAHC5XC3OHT9+PDweD86ePYvu3bv/aKJpP3Xef/99OJ1OudwTEYZhMHbsWAwfPlxtKT8K\nWJZFcnIyzGYzdDqdahra+1sidR6VQnOYQhCLwlarYTKZTCgvL8dVV10FIpLDnjfddBPKysrw1ltv\nyY1vz549Ff/t0TpMHMfhN7/5DS6//HL5mUn/ZVkWaWlpqjfykqMSCATkZ3rs2LG4apAcX6k8DQYD\n7r77blx99dVISUnBX/7yF5w5c6bNeU888QSICMuWLUN1dXVcNWvEBp1Oh+uvvx5Wq1WxDP2xID8/\nH/PmzcOMGTNUf4dDYTabkZKSoraMsBiNRuTk5MBsNoPjOGRkZMR9+J1hGKSnp8NqtSI/Px8PPvgg\nvF4vBEGQ9w6VjvXr18e0sxsXLmbn3h8zBoMh6p2hmx/S7u88zxPHccRxXEx2gw+nY9iwYVRZWUk+\nn4+WLFlCRqORWJalrKwsOnz4MImiSA6Hg0wmEwEgo9FIBoOBGIaJm8bmR48ePaixsZFEUSS3200r\nV66knJwcMhqN8vMsLCwkjuNisrt1qB20Wx9ms7nNd1NTUzs8R0ma7+4uPZeBAwfSjh076Omnn6a0\ntLSQu5e7XC5as2YNpaWlkU6nI47jWlxPQ32itTc5OTlERCSKIqWnp8etHkaqj2EYevPNN8ntdlNl\nZSUdOnQo7LsWb43NbeZzzz1HFoslou8rSTQ63333Xfr1r39NXbp0IYPBQAUFBTRu3DjS6/Vx0ckw\nDCUnJ9Pbb79NBw8eJEEQSBTFi/6NiUwnd/diR7goi8FgwDvvvIOGhgaUlJTgoYcewmOPPYYrrrgC\n3bp1w+nTp7FmzRpMmjQJVqsVZrM57uPdycnJ+Oyzz5CZmYmtW7dizpw58Hq9EEURFRUVGDp0KPx+\nP2w2GyZMmCD3+FJTU1XpCfj9fpSWliI5ORmiKILnedx2220oKipCXl4egPPzoU6cOKHqSgqGYdCn\nT58WdSTe84SaT44kIgiCAFEU4fP5sGrVKtTV1bV73vXXXw+n04n/9//+H2praxEIBORrJWqPXyM8\nf/7znwEAgUAA9fX1Kqv5vz0rJc6cOYPbb78dAHDixAlkZWXBYDCoJa9DiAhjxozB2rVr1ZYSErvd\njiFDhqC2thbBYBBGoxGFhYUYP358XIbmDAYDsrKycPfdd8tl+cc//hHdu3cHx3HyNAGTyYS33noL\nTqdTbv+GDBkSc30xQ11/LXGx2Wwd9pYmTZpEgiAQEZHP56P6+nqqr6+nvXv3UnV1NbndbnryySfl\nyJIUXVL6kYfSCICGDx9OgUCARFGkgoKCdr/zzjvvyNf63e9+R1arlcaMGUPJyclx0SgddXV1RERU\nWVlJSUlJxDAMWSwWEkWRysvLqbCwsMUzbH4oSfPIVUdHSkqKHJVjGIYyMzMpIyND1Z7oqFGjaNas\nWWQ2m0Nq8Pl8tHnz5jZ/b94r1VCfSOpg88PhcBARUWlpaUJERIxGI/Xs2ZNGjBhBhw8fJiKiYDBI\nr776Ki1btowCgQDZ7fa46Iz2WTIMQ+vWrSOn0xnR95UkkvtxHEcVFRU0adIk4jiOeJ6n1NRU+tvf\n/kbjx4+Pi520WCyUlJQkR/4j0a3T6cjtdtPs2bPj9jyVJrHVqUi4Ibnk5GQ6cOAA7du3j4xGI/E8\nTzqdjqZOnUo1NTW0ZcuWFsMc8X7BOI6jXbt2UTAYJKfT2aYRlRyDjIwM+Vpnz56lpKQk+vWvf012\nuz3mGpsfgUCASktL27x8wWCQNm3aRDqdLi4vWKQOE8MwlJubSxzHkclkom7duoU9V0na03PvvffS\nqFGjQhqw559/nogobg2VxoUTTQOfnp4ud95WrlyZEA18fn4+VVdXy7qCwSAtX76c+vTpQ9u3bydR\nFMlqtcZFZzTPUjqWLVtGwWAwou8qSSTOxz/+8Q8SRVG2NyzLUu/evenLL7+kLl26xEVnpFMXmh89\ne/Ykp9MpdzQ7o/1JbHUq0lEDLR09evQgo9EoN1iLFy8mURRJFEVVG879+/eTw+GgYDBIDQ0NNHLk\nSEpLSyOGYYhlWerfvz9169ZN/r7P5yMiosWLF1OXLl1o8uTJVFBQoJjGcI7E3r172zWeLMsS0fnI\nV7yeJ8uyETlNTqeTXC4XrVy5kiZNmiTXAzXLfdSoUdSlS5cWRtdoNFJxcbE8tyBc3Ux0g/VTIZqG\nyOv1EtF5p2TevHlhbVesddbW1sqfi6JIW7duled0JiUlkdPpJCIKGfVWWme0DXtubi6JokiBQCCi\n7ytJuHsNHjyYHA4HlZSUyFH4xYsXU11dHbnd7g4drnjqbH4MGzaMvvnmG/J6vXTo0KFObX+0BC0h\noAjmGzVfgdSrVy/cf//9YBgGzz33XCyldQgRISsrSx7HFgQBBw4ckOe0EBGOHDmCpKQkAMAVV1wB\nnudBRNi8eTN8Ph8qKioUTSDG83y7c44YhsGyZcvws5/9DE1NTW0+HzhwIILBIJYtW6aYlkgIV/ZW\nqxV6vR4rVqzA66+/jrKyMhBRxBmMYwXP87juuutw5MgRjBs3DlOmTEFhYaH8uVT2auvUUI7bbrtN\nzo4cCARw+eWXIzs7G6dOnVJFj8FggNVqBXC+vpWWluLmm2+W7YnZbG6xqjMRKSoqAsMwKC4uVltK\nGywWC6qqqrBr1y7odDpce+21mDRpEjiOw+HDh9WW14bJkyfj0UcfRbdu3dDY2IjZs2erLemi0CZ9\nhyASh4H+u1zy2muvxaFDh8AwDM6dO4eFCxfGQWFobDYbOI6Dx+PBjBkz2kwAFkURP/vZz7B27Vp8\n+eWX8gS9W2+9FYFAAIcPH1Z0ubmUq6g5DMOgb9++mDp1arvOFMuy2LVrFxobG+M+wTvc5Oc33ngD\nRIRPPvkEdXV1cLlcePbZZ3HLLbfESWFbjZIDtGDBAnz55Zd48sknWzhLx44dw9/+9jdUVlbCYrHE\nTadG7OjRowfeeOMNuFwu+P1+NDU1YfDgwZg6daoqeliWRWpqqpzrTUq14fV65aSuubm5cDgcEEUx\nIdMfsCyL7t27g4gwefLkuN8/nO05ceIEPvjgA5w8eRKXXnopHnzwQdjtdgiCgNLSUqSlpbW7YEet\nnEizZs1CQ0MDKioqUFdX126ak86E5jCFIJoVQytWrJDzXwwcODBWkiJGp9NBr9dDp9Pho48+avO5\nXq9Hbm4uLr300ha/88Ybb4QgCPB6vXC73YrpkRI8SpjNZhiNRgwbNgx79+6VV+pJWniex9KlS8Ew\nDIqKiuIaDWEYpkMHjeM4LFiwACNHjsTGjRtRWlqKrKwszJ49G2vWrImrztb/njhxInJycmSDSUSo\nrKzEmDFjMGDAADzxxBPQ6XTYs2dP3HRqxAaj0YiioiLwPI+KigpUVlbC7/cjLS0Nv/zlL1XRRESo\nra1FXV2dvGrTbrcjPT0dJpMJ3bp1w6JFi2CxWCCKIvR6fcLl5enbty9YloXL5YLD4VBbThuqqqrw\n1ltvQafT4dZbb0Xv3r3BsiycTifsdju2b9+O4uJi9O3bt8V5amT7NxqNWLRoEV5++WU4HA6cOHEC\nZWVlcdehJIlVWxOIaBppQRDkpd2lpaUxVNWW1hlVOY6Th1xWrVrVJsszAHi9XqxcuRL5+flITk5G\nMBgE8H/ZtZsvNVeC1r0bv9+PYDCI48ePY8+ePaivr4fD4YAgCPD7/fB6vZgyZQoaGxvx4osvxnV/\noXA9MUEQcPr0aezatQtutxt+vx9GozHuBkkq5+aH1WqFy+VCQ0MDzpw5g+3bt2PKlCkoKSmBKIpw\nuVyorKyUl/5qdF7Wr1+Pnj17goiwc+dOEBF0Oh3MZjN69+6tiiYiQiAQwObNm1FaWopz586B53lM\nnjwZaWlp6NOnDwoKCmA2myEIAiwWC4xGoypaQ3H55ZcDAE6ePKnK/cN11EVRxKlTp1BdXY27774b\nqampYBgGDocDFosFu3fvRlNTEx599FH07NlTtktq7NHm8/lw+PBh5OXloU+fPlizZg18Pl/cdShK\n3GZLdTIiWQXAsixZrVYaOXIk+Xw+OnfuXETnKImUFFO6vk6nIyIih8MR0cR1AFRSUkJERB6PhyZO\nnEg2m404jlNMo06nC7v6g+M4OnfuHBERCYJAZWVldOrUKTp37hz179+/w/OUJNJVctLBMAzNmjWL\niIjcbnfcJjO2p9NqtdK1115L9957L02dOpUWL15MkyZNapHIkGEYEgShw9+poT4d1aOhQ4fK3zt2\n7BhxHEddunShu+66i1577TUaM2ZMwkxSlg6LxUL19fUkiiIFg8F2U1vESmc07/P//u//EhHRDTfc\nEPE5ShLpPQcMGEBE59OwdJQCR22dAOi9994jj8dDV111Vdx1Ko0WYQpBOE9f6s15PB74/f6Id4VX\nOjkgy7Iteg8Mw0AURYiiCLPZ3OJ7PM+D53mYTCb5/3/zm9/I4dujR4+isrIyJgkMKUzEThAEZGZm\nora2FmfOnMHWrVvhcrlgs9kwb968djXpdDpkZ2fHVWdrBg0ahGeeeQYAMGnSJEW1dETzoQwpwuT1\nerFr1y5s27YNqamp6NevH7KyspCcnCx/l/47OV2j8zJo0CAA58vy8ssvhyAIcLlcGDJkCHJzc+O+\nPUYkJCcnw263g2EYuN1uzJgxQ21J7TJ+/HgAwNatW1VWEhqdToeFCxdCFEVMmzYNTqdTbUkhYRgG\nP+2RNd4AACAASURBVPzwA5xOJwoKCtSWc9Foq+RCYDAYEAgEQn6ekpKChoYGiKKIq6++GizLwuv1\nhr1utA1yOKQJlhIcx8HpdILnefzP//wP3n//fVxyySXynKDWWqS/iaKIW2+9FadOnYLH41E0hBvp\npG29Xo/Vq1djxYoV2L9/P4xGIyorK7Fly5Y2z41hGOj1+pAZrS+UaMqnT58+KC4uhtVqRSAQQFFR\nUcjvKu2kNC8fadPkYDCIpqYmWCwW6HQ67Ny5E9u2bcPZs2fl73IcJ88v0eicdOvWDcD5upqamorx\n48fjqaeeQq9eveD3+2GxWLBt27Y257XOvh1PevfuLU8VeP3111FRUaGKjnDk5uYCQEIPHY0YMQK/\n/vWvcfz4cXz44Ydqy+kQg8GAnJwcBINB5Ofnqy3n4lEpspXwdJS4UspiK+Xs2b9/P4miSLW1tWHD\njUru0UbUMtEZz/PUu3dvOn36tJwwLhw+n488Hg9dd911MQuNhkrg2fo4duwYvffeey2+P2LEiHb3\ndNLr9fK+eEoSadZaAPTkk0+SKIokCAI9++yzcS335s9Ip9NRWloa5ebmktVqJb1eTwaDgfR6Pen1\n+ha/6YknngibDVpDfToqn507d4Y8TxAE8nq9NHHiRDKZTKTX60mn05FOpyOTyaR4PYz0XdmyZQsR\nnc8VVVhYGNehmUg1AucT5UZ7jpKEu1d2drY8deGuu+5KCJ379u2jJUuWtNtm6vV6uuOOO+jLL7+k\nxYsXR2RfExltSC4EzSfTSnvjAOd7adKeZ9LQV0ZGBkRRVHXnd4ZhkJmZiaSkJOzdu7fN51988QWy\nsrLAcRw4joPRaJSHFZOSkrBp06aYagsXYUlLS0NSUhKeeOKJFtGP7777rk1vj2EYsCwLn8+neKQk\n0kiQXq/HtGnTIAgCPvvsMyxatKjDayodYaJmkTApB8+YMWOwYMECedIvy7JIS0tDcnIy+vbti9mz\nZ+P3v/899uzZI/ekNToXOp2uRboI4Hy0cfny5Xj66aexfft2iKKIUaNGyfVCOjwej2r5tyTN9N9U\nLIk6LMxxXFQ2JZ6/g2VZbN26FTabDYIg4D//+U/c7t0Rc+bMQd++fXHs2LE2z+Ovf/0r/v73v6NX\nr14xm+4RTxhS6w1KcDiOg8FgABHB7/cDOF9hBw4cKCeoHDRoEPr16we9Xo9Tp05h3LhxESWMU/KR\nWywWeXWZNH+p+W+w2WxobGy8oGsrpVOv16N///7IyclBQ0MDduzY0cYovfLKKygvL8fTTz/d4rMB\nAwYgOTkZ3333HYgIAwYMAM/z2L17t6xPyefJsmyH1+M4DsnJyVi9ejWuvvpqiKKIwsJCHDlypM13\nGYYBz/MIBoNh0xVEi06nk1c3ttYn3YdhGCxduhSZmZno27cvUlNT8dxzzyErKwszZ86EwWCAz+fD\no48+iuLiYhw5cgROp1MbrksAYtmwKPm+RKpzy5YtGD58OPR6PT788ENs374dr776qtzpjKXOSDVa\nrVY4nU74fL6IV++1trkXS0dax44di23btoFhGCxfvhz33ntvVNeOdbn7/f42G/96vV7ceOON2Lp1\na8T3T2SXRJvDFAIigs/nk6MDOp0Ov/rVr7Bq1SrodDr5JZGWxy9evFiVvB06nQ4+n0/uuTVHEIQL\ndpaUZujQobBarfj666/bfGYwGCAIQrtzlTweDziOg91uB3DeoVEzkicIAhwOB+6++26cOHECLMti\n3rx5uP/++9tob15PlDYCBoOh3camuVNGRJg1a5bsOBORPMdp//79GDduHN5++23s2rUL48aNQyAQ\nwMGDBxXVqaEBAFdddRXMZjNYlkVNTQ1effXVdh1+NZE6S1IELJJ3Np6Ne1lZGfbu3Yvi4uIOI9pq\nodfrYbPZMGrUKBQWFuLvf/97uzs4dGa0CFMImnvQUgbbq666Ch988IG8FYE08bh79+6w2Ww4e/Ys\nqqurO+xxKN0jSUtLi1k2bKWqBs/zuPzyy+H1elFZWdnuM2q+fUrz+6ampiI5ORk1NTXy0EJrJySe\nEabmXHXVVfj5z3+Ob775Bl999ZXsuIZCSZ1WqxU+n++CGx2pIyA9S6m+t+d4a8SfH1OEiWEYNDY2\nYu/evbj11lsjzvYc7wiT3W6XV+n26tVLlYhIR1qlxR2RLC5qDzUiixdCItsfLcIUAqmHIaUL6NKl\nCz777LM2DZQgCKipqYHT6QQRged5iKLY5ns8z7cYLlGK9PR0+d5msxkmkwm1tbUXXOlisc8YwzCo\nrq7GuXPn5PQGgUCgxX2kYStpDoHUkAuCgJSUFAwaNAhHjx5FWVmZPEQazkG5UK2RXvOrr77C4cOH\nkZKSgszMTJSXlyMYDMblhTcYDPKKSCA6I8NxHEwmExiGQVNTk+YkacQUIoLdbk/4OmYwGFBWVob7\n7rsvIbVKHUYN9dAcphBIzo2Ur6iysrLd7UKICE6nEx6PR45O6HQ6Ofu3hCiKbcZ3leDMmTMQBAE6\nnQ4mkwnDhg3Dhx9+eMHZumNhKBiGQVVVFfx+PwwGA8xmM9xut+xUNp8E2rrxliJ5gUAA9fX1EARB\n/ls00aBotLa+bqh7CIKA+vp6OJ1OGI3GDh0PpXtkkqPT/LlFguSUApCHnJvrS8SG4qfIj22D5ET/\nLVIUbMSIEQmb8iAWHW6N6NCG5DQ0NDQ0NDQ0wqClFdDQ0NDQ0NDQCIPmMGloaGhoaGhohEFzmDQ0\nNDQ0NDQ0wqBN+g6ByWS64OWb4YjF8s5oJ4lGMoFQKZ2ZmZmor6/vcAm8NFFe7YnqNpstZrlD1FrW\ny/O8fH8iCvuMtWmN6hOLBQ0SP7Xl5RfyLPV6PYxGI86dO9fh96Q9HJUiVs8zngk2L5ZEtj9ahCkE\nF7N8U43079E6S6mpqTFU0xKv1xvSOWMYBkajsd1nJq3miicej+eizk/E1P/SykItg3fnIZEbjc7G\nhTxLv98fUceps6xa0+qTMmgOUwguJo9RLNIHKIXFYkG/fv1wySWXxO2e0hL41pjNZsyYMQMbNmyA\n1WpNiAb9Ysr9Ys6PBxzHISsrS20ZGhqdgkSwRxqJheYwKcjcuXNRU1ODhx56SG0p7ZKTk4NPP/0U\ny5cvx1tvvRW3+7Z2IqRcR/3798eMGTMwevRorFq1Cv3794fRaERqaiqys7ORlJQUN42htEZ7XiJG\nmIDzmcEnTZqE8ePHqy1FQ2EYhsF//vMfTJ8+XW0pUWOxWDB37lyMHDkSNptNbTlgGAYLFy5EaWkp\nHnnkEbXl/CjR6XQR79WXcJBGuzAMQwAiPgoKCoiIyO/3k91u7/C7ShKpPovFQi6XizweDzkcDnK5\nXDR16tQOf6dSdHQPi8VC06ZNo0AgQF6vl/bt20fFxcU0YcIEKigoiOi3KUm05d76SEtLS6hyB0B2\nu53uuece2rx5M/Xv3z+uz1PjwghVNizLkslkIovFQjabjYxGI3322WckiiIJgkBFRUVkNpsTzv60\nd2RnZ9OePXuorKyM5s+fT5mZmTHRGY2mwYMHUyAQIEEQaNKkSXG3P9FoZRiGDAYD5eXl0W233ZbQ\n5c4wDKWmptLjjz9O06ZNo8cee4wKCws7nf3REleGIJqJgs0n/lVUVCA7Oztue4pFGtFwu90wmUwg\nIgQCAXAcB5Zl4fF4MG3aNGzYsKFF5m0ldUbyLEVRlH+L1+vFsmXLcOzYMbz88sthr6/k87zYybZ5\neXk4efJku+F8Nco9NTUV27ZtAxHhnXfewZIlS8LOzdBMgvqEKl+73Y5LL70Uo0aNQvfu3fHhhx/i\nwIEDWLJkCa688kowDAOPx4OUlJSQ11ajHrbGaDSirKwMNpsNoiji2LFjuO+++/DFF1/I31FKZzQa\n169fj5tuugl+vx9mszmiOUpqPM/mW3fddtttWLZsWUKWe25uLt5991307dtX3o7pqaeewogRI3D2\n7FnceeedbXQltP2Jq3vWieA4LmLvWRAEIiISRZGGDRuWkD0SiaqqKtq1axft37+fzp07R/v376d/\n//vfdN1115Fer28RYVGKSKI2Pp+PiIi8Xi998skn9NBDD1FxcTFlZGR0eD7DMIrpJCJiWTbq3pOk\ng2EYWrBgASUlJbWrWUki1RUIBMjpdNLixYvp0Ucfpb59+4b9HRrqE67OsyxLLMsSwzDE8zw9+OCD\n5HA4yOPxkNvtppSUlISyP9LBsiwNGzaMnE6nHBWTDofDQSaTSf7t8dbIcRx5PB4iIiouLo7atsZD\nq9FopB49epBOp2vxt0jOj6dOlmXpjjvuIFEUiYhIEASqrKyke++9l1iWpfT0dJoxY0a79jaRSWx1\nKsLzfMQvjOQwVVVVkdVqTagXDDg/7EVEVF9fTykpKWS1WuWQvs1mo4kTJ9K///1v6tmzp2oO0+nT\np8nhcNDjjz9O11xzDS1btoxcLhcJgkC1tbX0yCOPyI1E85csERwmhmGI4zjiOI7Gjx9PZrOZrFar\n3KCpVe4TJkwgIqJDhw5RamoqDRkypMMhw1g8T40LI5p6aLFY6OzZsyQIAnm9Xjpy5AgZDIaEsT8A\nKCcnhz766CNqaGggv99PROc7mIFAgILBoOw8zZ49W3YG4q0xLy+PBEEgURQpNTU14vOUJJwTkpub\n2+Yd5jhOdkzU1skwDPXs2ZM+++wzcrlcVFpaShaLpd3vXnfddZSRkRFTnUqjTfoOAUURFpSGX06c\nOAGz2RwrSRfM+vXrUVpaii5duqChoQFNTU1wuVxwOp1wOp3YsWMHunTpgt///vcwGAzQ6XRy7p54\ncfbsWaxatQpr167FmTNnMH78eBiNRrAsC4vFAoPBgOzsbFxxxRXo379/XLU1h2VZXHvttXA6nVi2\nbBl69+6NIUOGYPDgwTAajfjiiy+g1+uRk5MDvV6vqs4PPvgAP/zwA/r164e6ujp89913qK+vDxlO\nT9QJ6z9Folmh1aNHD2RmZoJlWfh8Prz44ovw+XwxVBcdDMPghx9+wOjRo2GxWOSNwYPBIBobG3Hi\nxAmUlZWhqqoK69atUzSvUaRwHIevvvoKLMuisbER9fX1cdfQESzLYvDgwfjDH/4Ag8HQ4rMhQ4aA\nYRjVUxzwPI+JEydi3759uOKKK1BZWYmcnBy4XK52v3/99ddj0qRJcVZ5kajtsSUq0USY3G43ERGd\nO3eOevToETaioiTh7jVz5kyqqKjo8Dscx9HOnTtp3759pNfryWq1kslkiptGhmHI7XaTy+Uir9dL\nwWBQPtfpdNKwYcOI53kaNmwYFRUV0ZQpU+RhibS0NMV0EoXu2TfXJIoinT17Vh6Ga/69FStWUHV1\nNc2ePVu1nmh+fn7I7yUlJdGnn35Kw4cPb/H8dTqdHBHTUJ9IbQ8Aeu655ygQCFAgEKCZM2eGnU4Q\nL51Go5Fef/11CgQCJIoi1dXV0dmzZ+no0aPkdDpp8+bNNGvWLLr22mvp3nvvbTHJWsl6GMkzlPB4\nPJSUlBTV81eSju5jMBgoPz+/hY0aMWIEiaJIfr+/w6hiPHTW1NSQIAhUVVUVtv1kWZY+//xzWrp0\naRsbmshomb5DEE0PT1oi6fP54HK54jppTZr8xzBMm2yuGRkZyMnJwV//+tcOryGKIoqLizF+/HgE\nAgH4/f64Rhuys7NhMpla/C0YDEIQBOzYsQPl5eW444478Morr0Cv18Nut2P9+vWw2+0YNGhQTLUx\nDAODwSAn0fzhhx9w1VVXhcwAfN1116FLly7Yv39/THWFgud5HDhwAADw+eeft/k8NzcXo0ePxief\nfIJJkyahtLQULpcLKSkp+OGHH1TvpWpET7du3RAIBCAIAiorKxMifxDDMPjFL36B66+/HhzHoa6u\nDps2bUJlZSV69OiB/v37Y86cOfB6vejZsyf69OmDxsZG+fx42tBrr70WwHk7OHbsWDidzrjdOxp8\nPh9KSkrkf+fk5GDLli0AgBdeeEH1qGJqaiqICGPHjg0bJfzNb36DvLw8rFy5MupdKtREc5gUQHIu\nPvzwQ1VCuQzDgOd56PV6OfxpMpkwcuRI7NixA998803Ya1gsFmRlZcWk4nZ0TZPJhH/9618Azhus\nQCCAiooKHD9+HIMGDYLD4cCTTz6JSZMmwWAwgIjw3nvvwWQyoW/fvnA4HDHTyrIshg8fjvXr14OI\nsG/fPkybNq3DVWZSBvVvv/1WUV2RUlZWJjt3t956a5vP77jjDgDnf+fAgQMRCARw6NAhHD16VJWh\nEI2Lp1+/fggGg6ipqcHx48cTovFhGAZpaWkgIgiCgMOHD6OxsRFjx45FdnY2zp49i8bGRlgsFtjt\ndrnDF28MBgM++OADEBEGDBiAw4cPt/gNHT1LllV3Rsunn34Kk8kEh8OBP//5z6pqAc4/L5fLhYMH\nD3b4vZSUFDz55JNobGzEpk2bEsLBjxTNYQrBhRide+65JwZKOkaqbAzDwGw2w+v1ykv0v//+exAR\n6urqwl6ndWMZD6Or0+lQXV0Nq9WKKVOmYN26dS0+79q1K/x+P2644QbccccdsgHbvXs3zGYzamtr\nY+qg2mw2FBQUoLy8HPv27cOyZcswfvx4HDt2rF2nSTKgkuOnBhkZGQCA6upqlJeXy39nGAY2mw0T\nJkxAfX09Tp06hfr6enTr1g0VFRWoqqpSRa/GxcGyLFJTU+FwOHDw4EGUlZWpLQnAeftRXFyMFStW\noF+/fhBFEdOmTZOXljMMg65duyI/Px9JSUnYs2cPvv/++7ho43kegiDAaDTi8ccfB8MwGDx4sOws\npaSkYMOGDfjFL36Bw4cP47LLLoMoirjtttsAAO+++64chVKzsc/IyIDf78fy5ctDzhOKJ6IoyqkO\nOnou+/btg91uxyeffNIiqtgZ0BymEEQbJmxsbIzZZr2RwHEcLr30UnTt2hVlZWWw2+3wer04ceIE\nWJaFXq/HX/7yFzz11FOoqalpcW737t0xYsQINDU1xTU8ajKZYDKZ4PP52jhLAFBZWQkA+PjjjxEM\nBsHzPHw+HwRBQF1dHerq6mJqsBwOB1asWIFNmzbhmmuuwZw5cwAAK1eubDH0yrIsevXqhXfffRfB\nYBAOhwN2ux21tbUx09Ye6enpch6phx9+uE20LC0tDffdd5+cr6WxsRE///nP5czr2nBc5yMzMxMm\nkwl1dXX4+uuvEyK6BJy3nwMGDIDP58Mll1yC3r17yxtsExGCwSA++OADdOnSBYIg4PTp01iwYEGL\nISel9bAsC47j8PTTTyMnJwdXX301kpKSwDAMXnvtNZw6dQq33HJLi0hXv3794PF44PV64fV6sWHD\nBqxatUreyFotjEYjAoEAmpqa8OabbyZEuTMMA4vFApfLhb///e9YuHBhG/ucm5uLzMxMVFVV4Z//\n/Kfqw4hRE4d5Up2SaPIwERG98cYbqk4S1Ov1NH/+fPr000+psrKSysvL6eDBg7RkyRLq1q0b+Xw+\n8vv9NHjwYPkcjuNozpw5tGTJEnrggQdo0KBBLX630hpbH7m5ueTxeKioqCjsBMGioiI6ePAg3Xnn\nnS1ykMTqeba+/9KlS+n48eO0bds26t69uzzxkmEYmj9/PlVVVVFdXR2tXLmS+vTp00Kj0nll2tPJ\nMAyVl5cTEVF1dTWlp6e3+ZzneTIajTRz5kz65JNPaM2aNfTKK6/Q1VdfHZNy17hwIrEjHMfRlVde\nSeXl5bR8+XLKyMiIyG7FWifDMLR+/Xo53YqEKIrkcrmourqavv/+ezmdgMvlooqKClq0aFFMdLb3\nPtvtdrr99tvpm2++CXlec/0HDx6k+++/P+Z5gyIpd5ZlKTk5mYqLi+nUqVM0ZMgQ1dqd5pqCwSAF\nAoEW32ueZsdkMlFRURFVVVXRH//4x5A7YiQyia1ORSJdJbdr1y5qaGiIaksNJZHuK+UCaq2DYRjK\nyMigc+fOkc/nI4fDIRuqYDBIJ0+ebOOAxMth6tatG82fP5+Sk5NDfqdnz550+PBh+uabb+if//wn\n5eXlqWKwtm3bRh6Ph9auXSv/jed52rp1KwmCQBs2bGg3p0g8debk5NBbb71F5eXl1NjYSB6Ph/x+\nv5zjRsLhcNCAAQPiolPjwmjeEHXp0qVNGSUnJ9OBAweooaGB9uzZQyNHjiSr1UqDBg2i+fPn07x5\n80I6T7HQ2fzYvXs3iaJIoijSvn376B//+Ac1NjZSfX09OZ1O8nq95Ha7affu3ZSVlRXzehiJbfZ4\nPLRjx442dv/s2bPkdrs7bA+UJJxOvV5PgwcPJoZhaMaMGeT3+6mxsTHseUqvfo2kneM4jgYOHEg3\n3HADFRUVkcPhoEAgQE8//TQVFhaGLJdEJrHVqYherw9bIaxWKxERLVmyhFiWjcjJUqPiWq1W+vLL\nL8nj8chZdQOBAGVkZHSYIC/WGjMzM+ndd9+lm2++OeR3vF4vVVVV0fHjx+mNN95ot/FQklA6xo8f\nTxUVFVRaWko5OTnEsix9/PHH5HK56NixYxEZ5Xjo1Ol09MADD5DT6Wz3vGAwGLaeaqhP8/J88MEH\n6corr5TrGMuy9PDDD1NVVRXV1NTQypUrafTo0ZScnEzdu3en/Px8Wr16dbt7dcXD/uzbt4/Ky8vp\nT3/6E6Wnp5PRaKQdO3aQ2+2WO2oVFRVkMpniUg8jeTedTicJgkB33XVXC5vo8Xho165dqr/X0mEw\nGGR96enpFAwGSRCEsOexLBtXndLx1ltvUX19PXk8HmpsbKSqqirKyckho9HYKe1PYqtTkVCF2Tz/\nj5SbJ9yLH2+D1fp+3bt3pzlz5lBJSQlVVFTQ6tWrKScnJ24NfKjrp6WlyYZK6pEGAgE5EzARUSAQ\noK+//pq2b99OQ4cOVS0kznEcPfPMMyQIArndbnI6nRQMBunEiRNhN1tWw7AyDEN9+vSh6upqCgQC\nVFZWRlu2bKExY8bEVafGhSGVhV6vp3379lFdXR1Nnz6drFYrbd68mTwej7yR9vjx49tEiQ0GQ1x6\n8O1d3+fz0eeff052u122lwcOHJAj2/X19RFtrK0UkThMr7zyivx9h8NBQ4YMIZ7nKRgM0oEDB+LS\nsSSKLv9W165d5Qzf4X4jx3Gq6Dx+/Di53W4qKyujF154gUaPHt2p7Y826fsCsNvtmD9/PjweDywW\nCzweT8TnUpwn5xERKioqkJKSgkOHDqGxsRGLFi1CRUVFXHW0R2NjI1577TUsWLBA/lvrDONHjx7F\nkiVL0NDQgEOHDqm2KkUURbz//vuYP38+jEYjGIZBdXU1brzxxoRc6UFEOHbsGLp27Ypu3bohKSkJ\nXq83YVZRaUQGEaFnz55ISkrCiy++iAceeAA9evRAfX096uvr4Xa7UVtb22aVq5qTaXmeR3l5OTwe\nD4gIHMehoKAALMuirq4OEyZMaLF8PxG47777cMcdd8BisUCv18sLSnw+H7KyspCRkSEvQkkUpIzf\n0uq0RITjOJw6dQqvv/46VqxYkZC2MirU9NYSGYTpvXMcR2PGjKH58+dH1StQKzR6yy23UHFxMc2c\nOTPic+KlUafThTz39OnTNHfuXCooKFCtx9y87NasWUMej4fq6+spMzMzqj2/4qWzvfqanp5OhYWF\nYbMBayYhMZDKguM4OnbsGImiSEePHqXhw4dTjx49yGAwEMdxEU0diHc9lKIe0oRuKWJ85MiRqBfT\nKEE080tbH7t27aJgMEinT58O+a4rSTTapCzf0q4Dapd7e0dBQQFlZ2eHnCfb2eyPFmG6AOi/ydiK\nioqwc+fOqM9Vg88//xwOhwPbt29X5f4dEQgE5NwsDMNAr9dj6NCh6NatG1wuFw4cOIDq6uqE6EXV\n1dUhEAjA6XRCEATwPA+/36+2rLBkZ2ejX79+Cdez1+gYhmHw5ptvIjU1FY899libLNSJmArC5XLB\narXKueEAYP78+XjppZdUVhY9mzdvxtChQ5GdnY2uXbu2yG2mNuPGjQPDMGhqakrYtCAlJSWdKpN3\nOBj6sfwShZHy2cQCJa8rJWELh5S3I5qXSimdNpsNgUAgJsMEShuKcOWelJSE3NxcnDhxAkQEv98f\ncZJKJcs92qzIOp0OHMdFlCtMMwnqE8us12rWw2hQSufFakxLS8NHH30EQRAwbdo0HDx4sMXUALWe\n54ABA/D666/jF7/4RUSdtp9auccCzWEKAcdxMZsvo+Qj1+v1ETXYOp0OwWAwqnsrpdNgMMBisaCh\noUGR6zWH4zhFt/TgOE7ek6/172cYBqmpqWBZFg0NDdDr9ao5TNE49AzDICsrC4IgRBSp00yC+nSW\nBqkz6FRCY1paGkRRRGNjY5t2Qc3nyXFcxB3Gn1q5xwJtSC4EOp0ubEREqjT0381vwxV0LPZLMhqN\ncpRFp9PB6/W2qyMSjVIUwufzKd64xyJczLJsTF7cUFo5jkNTUxOCwSBEUZQntYZ7rrFwvqMNc9fW\n1qqenVhDo7MS76z9kZKIw3AXAs/z8lSXREaLMGloaGhoaGhohEHd7ZY1NDQ0NDQ0NDoBmsOkoaGh\noaGhoREGzWHS0NDQ0NDQ0AiDNuk7BJEu15eQVleFW7Gl9ATozrBaIZYrDoH4rD5rPsH/QlFrlVw0\nSCsENdRF6fKVFgkoXb6xsj9K2slY2kiln2dzWyktElLq+p1hlVyi5pOS0CJMIYi20KxWK4xGY9jv\nxXqOfXsVOZYGIxIu9oWPp/6OyieR1kd0hhxhGheO0uUgOUtq24JI6SxOu9Ll1Dq/U2d5DkqR6PZH\nc5gUwGg04r777kNmZmZYgxTLCsGyLHQ6XYu/MQwTs+X3F0trre2RKEaeZVk5igio74RqaETLT7EB\n1uhcJLrDpA3JKcCYMWPQv39/OBwOVQu8dfiWYRhwHIdu3bph4sSJWLp0acKEOyMZcpCckkQw8v36\n9cO9996Lc+fOwWKxQBAELFiwIOFf8Ej5MW1foNE+WhlraFwcWoRJAebMmYNTp07B4XCopkGKiw0Z\nZAAAIABJREFUgDR3iKREYE1NTZgzZw5eeuklDBw4MO7a2ovGZGdnIykpSf53eno6Fi1ahGPHjuHs\n2bMIBAIIBoPYt28fWFb9ajplyhRcf/31mDVrFu69917Mnj0bKSkpastSDJ7X+k4/RhiGgd1uxzXX\nXCPv79YZSGSder1ebQkJhcFgAMdxLf6m0+nQp08fGAwGlVTFBi1xZQgifWEZhsF7772H2bNno6Ki\nIqJzYjH5TpqkHuraGzduxIQJE6DT6XDixAnccMMNOHDgQId6lNLZOppkMBiwceNGrF69GkVFRbj+\n+uvx4osvtnnpJKxWK1wuV8jrx3oyI8MwGDJkCCZPnozevXtj4sSJMJlM8Hg8+Pbbb3HNNddEtE9e\nvCdd8jwf0bYxrSeXaiZBfS7EYWAYBs8//zxmzpyJ6dOno7CwEFarFT//+c/BcRzGjh0Lj8ej+qKT\ncJEunueh0+ngdrsvRlqL+ynFVVddhZtuugnz58+X/6b21ijSu8vzPERRlHd/aGpqivmedwzDYOzY\nsfB6vdixYwfsdjvsdjt+9atfYciQIZgwYQLy8/NDbovV3sT2hLY/pNEuANo9+vTpQ//6179o7969\n5PP5yO12U05ODnEcRzzPU0FBAU2ePDnk+Uo/coZh5KOjezIMQy6Xq835giDQ8ePH6cEHH6SCggK6\n//77qbCwkIxGo2IaeZ5voYVlWeI4jliWpczMTLrmmmsoMzOTRowYQePGjaOkpKQWzyoYDHb4+5RE\nr9cTwzCk0+lIr9eT0WgkvV7f4n48z1NGRgZ5PB75PK/XS++8807cyj3UPaRnq9PpqE+fPnTo0CF6\n6qmn6IknnqBjx47R2bNnaejQoZSXlxfymWqoT0f1qPUhiiIRETmdTho8eDABoCuvvJJ8Ph+Jokge\nj4dmzpwZ83poMBho1KhRNHr0aJo3bx6NHj2adDodASCbzUYbN26kmpoacjqdVF9fT263m4qLi2nx\n4sX0/vvv0yuvvEJ6vZ50Oh0xDKOYRoZhiGXZkLZy+PDhFAwGiei8rVm5ciUlJyeTXq8nnufJaDTS\n9OnTZc2XXXZZTO15pOVeUVHR5nyLxUKFhYX02WefUXl5Ob311lsxLXej0Uhz586l3Nxc4jiOcnNz\nKT8/n/r06UMZGRntnjtnzhwaMWIEPfjgg/TJJ59QQUFBp7E/ia1ORVo7GxzHEcdxlJ+fT4sWLaLy\n8nJqaGigN998k3Q6HbEsS3q9niZMmEBLly7t0HFREpZlI3q5GIahmpqakNcRRZHKyspo3bp1NHLk\nSLJYLIppjMYIhGq8u3btGhdHxGAwkN1up5ycHEpPT5cdqPYckyeffJKCwSD5fD4KBALk9/tVd5ha\nH5dddhmdPn2afD4fERH5/f6w5aGhPpGULc/zdPbsWSovLyeTydTis0OHDsnXamxslB2pWDXwDMPQ\n7Nmz6cUXX6SsrCwymUwtPtu4cSNVV1dTXV0dORwO8vl8JAgCVVZW0smTJ8npdNJXX30Vk3rY+rlJ\ntrxv3770wgsvUFVVFfn9fhJFkURRpIaGBvr4449p8+bN9Oyzz9KGDRvo+++/p2+//ZZuuummNjZX\nSaKxjZJeyWEmIlq4cCF9/fXX5HK56NSpU1RYWBjTcuc4jmw2W0iNL7/8suy4E/1fO1NXV0cNDQ1U\nU1MTUwdUaRJbnYqEcz5SUlIoNzeXkpOTied50ul0lJqaSmvXrqXDhw/HzWFqHb0JdRQWFpLT6aRz\n586R0+kkQRBaXCcYDNKePXuod+/epNfrieM4xTRG6tSFarxFUaTbbrstLg28Tqcjk8lEHMeF1VZQ\nUEDr1q2jqVOnUnV1NQmCkHAO09y5c8nn85Hf7yefz0fZ2dkRPXMNdQlXRuPGjaPy8nIKBALtfi5F\nTERRpMbGRrr//vtjUr6S/TOZTNSnTx8yGAxttLzyyiskiiIVFxdTWloa5eXl0Z133km1tbVUVlZG\nDoeDKisracSIETGph62jSs0be5PJRHq9niwWCw0cOJDuuusumjVrFs2fP59++9vf0uDBg2nJkiVU\nVlZGzz33HCUnJ7fpcChJpO/19OnTSRRFuvPOO+XO+osvvkj79+8nt9tNJSUlZDabVdcpPWe9Xk9Z\nWVn0xBNPUCAQkDuadXV1bUZkEpnEVqcikTSY0osjhXuvvfZacrlcVF9fH7cGyWazRRTBEQSBRFEk\nv99PVVVV5PF45IobCATI6XTSvHnzWoSulSKSZ9neYTKZiOi8M7dp06a4DCFFEw0bNmwYDR48mPR6\nPW3YsIFEUYzb0GGkGk+cOEFer5e+/vprGj58eETnaKhPR+UzZMgQ+Z2dOnVqm89tNhsFg0FyOBz0\nzTff0OrVq+m9996LSfl27dqVDAaDHLVpbhtNJhM9/PDDcoThuuuuk+2lyWSigwcP0oEDB2jlypXU\np0+fmNXDSN8VaSQhLS2NzGYz2Ww24jiOJk6cSMXFxSGj3EoSqVZRFCk3N7ddOx8MBunmm29OCJ2t\nn++AAQPI7XaT2+2ms2fP0pEjR+iee+5pYTcTGW1pTAgiyThK/52cJooiDAYDHn/8cRgMBpw7dy4e\nEuV7h5tEefvtt8sTr30+HziOQyAQwA8//ICvvvoKtbW1+Pjjj/Htt9+2yDKrJnq9Hvn5+fK/X375\n5bhMBozmHlu2bMG2bdvw6KOPIi8vDwzDoGfPnjh16lTsBEZJjx494PV6MWvWLBw9elRtORoXidls\nxo4dO8DzPL777jusXbu2zXdOnjyJl156CQ899BAEQYDBYMCVV14ZEz3V1dXtpv1gGAa9evXCgAED\nsGTJEsycORMLFixAfn4+cnNzYbfbUVZWho8//hgrVqxAdXV1TPRJsCwbNj0J/XdVcW1trfw3g8GA\n++67D3v27EFVVVVMNUbK5MmTwTBMu3aGZVl4vV68//778RcWBpPJhCVLluCLL77Aiy++iJMnTyI7\nOxsOhwNmsxkejyexJ3wDCe7OqUg0UZH/+Z//kSdUBwIBGjRoUNx68ElJSWS324nn+XajGzzP05NP\nPkkrV66U5xVIB8uyxPM8sSwrH2jWG1CKaJ5l84nzy5YtI1EUaeHChXF7npHqlO67Z88eys3Npby8\nPHrhhRfkXrTaOnfv3k1ERFVVVfTaa6/Rn/70J6qoqKD+/ftH9Ls01CVU2fh8PgoGg/THP/6xzWcD\nBw4kp9NJDocj4d6XBx54QB4mlLj//vvjEpGNJGrMcRxNmTKFhg4dShaLRf57MBikv/zlLwnzPP/1\nr3+R3+9vd4FJZmYmERGlpaWprrP5wTAMDR48mGpqaqimpqZT25/EVqci0cy7cTqdRHR+vkBlZWWb\nCZixrBDSajPJAWrvftJqr9aGw2azUdeuXeWVYFarVb6WknOYQjlz7R0mk4kWLVpEGzdupIaGBnI4\nHCENgJqG4NlnnyUioocffjjs84+3ToZhaPLkybRr1y4qKCig0aNH05tvvkmCIFBVVVWnNlg/FUKV\njSiKVF9f32J+Cs/zlJKSQmvXrqXjx4/T9OnTE6IeNj+GDRtG69ato71795LL5SJBENrMWYqVzkgb\n9blz59L8+fOpS5cuxDAM5ebmUn19fUK919999x1VVlZSfn5+Gxvv8XiotrZWdZ2tD6vVSu+//z41\nNDTQo48+2qntT2KrU5FIGniGYSg9PZ2IzjtLbrebli5dGjaioiQ8z7dwmkLds/VqL5ZlKSkpiQwG\nAxmNRkpNTaUrr7ySDAaD/D2lkJbnRvJM8/Ly6LvvviOHw0GBQIA2btxIRqMxbs8zUseOiMjn80U1\n50lJojFYLMvS1KlTKRAIEBHRwIEDO63B+qkQqmxEUaTt27eT2Wwmo9FIY8aMoVGjRtHmzZupurqa\nvvrqK8rLy5OXxbdni+Khs/UhpemwWq302muvkSAI1L1797jUw2jelebvjNPpDNv5jefzvPrqq8nj\n8VBZWRmlp6e3+Oyjjz4iImp30r2a5W4ymejOO++kiooKKikpaZOipbPZH/VTKCco1MFYql6vR05O\nDm655Ra8/vrrAIBAIIAjR45g1apVIRMwxoJgMCgnrAyVEVvaT675vzmOA8uysNls0Ol0sNlscDqd\n8neVzK4tzYfq6JlKmhYvXoxBgwbBZrOBiFBaWhrRnnNKEcncrQceeAAA8MgjjyT+mDvOz3Nbv349\nPv/8cwDA/fffr7IijQthxIgR8Pv9WL16NWw2G2644Qb88pe/xO7du7Fz506kpKTAbrfD7XYjPT0d\noigmzFZIgUAAfr8fPp8PaWlpYBgGlZWVasvqEKvVCo/Ho7YMAOfnJr300ksIBoNYuXIlGhsb5b8X\nFRVh7Nix8Pl8ESXQjSe33347Fi5cCIvFgg0bNsDv96st6eJQ1V1LYDqKHHAcR0ajkYYNG0Zvv/02\niaJIDoeD/vGPf9DgwYMpPT09bqulpPvwPE/9+/dvkfRROqREjNJKFqvVSsnJyXT11VfT7373O7rs\nssto7NixZDabieM4eXhJKaTrSlqGDh3aoifEMAzxPE8jR46U83W43W46ePAgzZgxg/r27Ru3Hkkk\nQ7H19fVERO0+63jpjOa+Up19/vnniYjCphbQUJ/2yqW2tpZKSkooJSWlxVJ5hmGorKyMBEEIOxyn\ndj0cOXKkHOmMl85oNQLnVyJGeq6ShLpHXl4elZeX0+LFi2UbxTAM7dixg4jOz50Nl2ImHjqbHw88\n8ACVlZWR0+kkp9PZ7krIzmZ/EludikQ6UVkakjtx4oQcvpXmAMWjQkiG85577iGi80ODre/NcRzV\n19e3SXLm9Xqprq6O7HZ7THVarVbKzs6moUOH0vLly4mIqKampoVTWVZWJn+/srKSxo4dS71796aN\nGzfSVVdd1WEoV0mkYQydTkfLly+n2bNny8OJ3bp1o2AwSMFgkJxOJz3//PNRGWElibYBGDRokJy8\nsjMbrJ8KrctEWoTR3pAL0fnUG+GGrtWsh6mpqXJHg4jo9OnTcdMZ7bvCMIxsJxPleS5cuJDefPPN\nNvcNlV5ADZ3Jycm0a9cu+dkFAgGaPHkybdiwgR577LGIcwYmMtqQ3EXAsixuvvlmAMCqVavk8C39\nd3lqPJCG/0pLS0FEYBhGHqYTBAF+vx8ejwcpKSnyvj0S69evx6BBg+TwbqwgIng8HjQ0NOCzzz4D\nESEpKQldu3YFwzDQ6/Xo3r07AMDv9+Pxxx9HY2Mj+vXrh4EDB+KXv/wlxo8fH5ehTp1OB71ej9Gj\nR2P69OlYsmQJampqMHfuXHz33XeyhiNHjuDZZ5+NuR4lYBgGL730EnQ6XacYQtRoi1RuvXr1avEO\n33XXXQCAf/7zn/B6vapoi4TKykp5s+qmpiYMGjRIZUWh+cUvfhE2VUu8ycjIwAcffCD/Ozc3FwBw\nzz33JEwak5qaGlx++eXy3nBr1qyB3W4HABQXF0fUJibCRusdoqKzltBEMpnXaDTKHnW4VAIA5KX7\nSjJgwABKS0ujl19+uUX0KBxlZWVhJ2IrhV6vJ7vdTl27dqWcnBw5JB8IBKiiokKOfAQCARo/fjxZ\nrVZ5CHHYsGHUt29f6tmzZ8ionZI0Hzq85ZZb6ODBg/T000/T3r17yev1EtH57UWmTp0a9ZYvShLN\nfXfv3i0ntPvoo4/C9q411Ke9svH5fFRRUUFPP/005ebm0unTp4no/B5ykdYFpcs3knumpqYS0flo\nyMGDB+M+NBPNuwKcX7pPRHT06NGEea9NJhM9//zz9Lvf/Y4+/PBD8nq9tHXr1oSxP83rlbQAau3a\ntbRx40b605/+FHH0U2mdSpPY6lQkVGHabDYym83UpUsXeumll+jcuXO0b9++iDa/7d69O5nNZkV1\nbtq0iV599VX68ssvZcejI6Jp7JVC2sBSWs331VdftdmaxefzUZcuXaI2bvEwWAzD0Pvvv0+lpaVU\nX19PS5cu7XBvOzUNa+tDalQFQaCysjKaOHFipzZYPxXaK5crrrii3e8mJydHXB+UTBcSSmfzg+d5\nqqqqIqLzOcv69+8f8XSHeGhsbQdZlpVz6k2aNCmh3uudO3fK33O73QnVYbv++uuJ6PzQcH19PR0/\nfpxefvllmjt3bkQrDTuL/dEyfUcIx3HIyMhASUkJnE4n9Ho9vF4v/H4/pk+fHlH41mg0Kj5Ut2jR\nIpw6dQoWiwUZGRmora2VV58YjUakpaXB4/Ggrq4uqnsrmelbr9e3GKa88cYb8cQTT+Dyyy+H2WyG\nxWLB0KFDUV9fr9g9lYSIsHfvXuTl5aGoqAhvv/12zIcxo6H58AHHcUhOTkZhYSHmz5+PLl26oKmp\nCcuWLcOaNWviuoJTQ1l27dqFkydPIicnBwzDQBAEVFRUwOFwRHyNeK+ae/3115Geno6mpibMmDED\nJSUlCbNyD0Abu82yLIxGIwDIq0oTAYZhkJycDOC85p07dybMkCHDMDhy5AgaGxuxZ88erFu3Dl9/\n/TXOnDmD2trasBnWOxMMJcpTTzBaOww8z2PKlClYtmwZ6urqcObMGezfvx9FRUVYu3ZtWCPAsiws\nFguampoUrUAmk0leShrJdi6RwvM8AoGAItcyGAwA0GJJKcuy4DgO/fv3h81mw5dffnnBBkDJKhzK\nUTSbzSgsLATP8ygpKUF9fX1E922+JUMsdDIMA4PBgGAwiKysLGzcuBEZGRn45ptv8P3330Ov14Pn\neaxevRrl5eXQ6XSorq5GMBgMqUczCeoTqh4yDAOTyQQA8Hq9F2RL4vG+AEBSUhI2bdqEyy67DLfc\ncgu2bNmCYDAY8bWV0hlt5y8YDILjOGRmZka0ZUu8nmdycjI+/fRTrF+/Hq+++mrUnTYlder1eoii\nKG/NxbIszGYz3G53VGXcHolsfzSHKQRGo7HdnBZSbz7cpECO42A2m+VeAcMwKCsrA6BshZD2iAPO\nOzksy150rguGYWA0GuF2u5WQCJ7nwfM8/H6/nC+quWZBEC7KiYyVI9L82tLkdLPZjGAwCKfTGfZa\nqamp+O1vf4tnn31W/u1K6+zZsydMJhMOHz7c7vdyc3Nx6aWXYv/+/airq4Pb7Q6bq0UzCeoTy70c\nY2V/WttEaZGJNAk42vuq5TAVFBTg97//PWbNmhXR9+PlMF3MNaWIpJLXjBWJbH80hykEJpOpxaoT\nqdLR+XlfYc/n+fOjnXq9HizLIjk5GRUVFSAixSNMfr8fHMfBYrEgEAjA7XZfcKVjGAZWqxXBYFAx\nh8loNIJhGAQCARAR+vXrh9raWjQ2Nsqr+S6mGsaqAWiNwWCATqdDIBCIKEGc5GB5PB74/X5Fy10y\nWAaDAaIohowGpqamIiUlBcFgEH6/H/X19e2uppJWp0irLDXUJVYNkuS8KHk9pVG6gY9WI8/zyMnJ\nwcmTJyOyLYnuMEkRaCXf685SP5VGc5g0NDQ0NDQ0NMKQ4EkPNDQ0NDQ0NDTUR3OYNDQ0NDQ0NDTC\noDlMGhoaGhoaGhph0BwmDQ0NDQ0NDY0waIkrQ6DT6eRVBUrvKxSPVRUdrWKI5P5Krlaw2Wzw+/0X\nne6gOVKZKJl7Cvi/NAfh7s1xXNSrTuK1mu9i0daBqM+FrEJiGAY6nQ7JyclwuVzweDztlmUir+qS\nrielIVGCSN4VjuPAcRwMBgM4joPP54Pf74/ItnSG91ppO/lTTSugRZhC0LwxTOQCDIWUk0kyBBKR\n/hYlf3MgEFA8u6+kT+klqJE6k2rXCbXvrxF7om2UiAiBQACNjY0XlVpETSTNanRQjUYj8vLywLIs\nfD6fKhnJY1VmibxUvzOhOUw/UjiOk3enb57nqCMjHKteAxF1mi05whkWu92OG264Ab169Ur8nbU1\nOjUX0nhKTlNnhojilguM4zgYjUb06NEDY8eORXJyMpxOp+ZgaLSLZvFjRCxDluHuk5+fj5dffhnl\n5eWora1FTU0NvF6vnLwwEAjg5MmTmDdvHu666y7k5+cjJycHAwYMiInuYDB40UbcaDTKW6zEko4c\nu65du6KqqgrvvvsuDh48iDFjxiSk0xSvuqeROAwYMACnT5/Ggw8+mLDln4idJilxbklJCV555RXs\n2rUr4R1OjuMStowj4bnnnsPp06fx5ptvIikpSW05UZF41v5HwIkTJ3Dbbbep1pg2NjZix/9v7+6D\noyjvOIB/9+V283J3Se7gFC8hESJkKMWgpTqNLYOtwyjFJhVqMzi00IbWdvrCjLXSdjo4Du1ULS8y\nqFNHGGBsaalSbQcrVkVERisUNQISGhISEnJ5ubyRe7/79Y/M7iTk7vaCm72k/j4z+0dye3u/fXv2\nt88+z7PHjsHpdKKwsBButxuqqkIURQiCgM7OTtTU1GDr1q3YvXs3zp49i5aWFnz00UcTViWcaltk\neuKvXr0aXq93zKtLzC44RiZlyZbd0tKCtrY2HD16FEePHp2Ud6Lau53Y1DXe43rfvn3wer24ePHi\npHwUJ8sybrjhhmyHkdTMmTNhs9kwNDSUdBT8yWL58uVobW1FOBzWXzWjTZs3b852eEkVFBSgoKBA\nbxoye/ZsXLp0CcePH8f8+fNx8803T6nkj0tVk0mShOuvvx7bt2+HoigT/nvaK1hG6uzsxJ49e7By\n5Up89NFHCIfDiMVi6Ovrw9tvv40f/vCHeP/998e93KulFeCiKI66kNtsNsiyDJvNlvb7+fn5WLFi\nBUpLS8ecXGZfHBRF0SeXyzUq3vLycr1dmNfrzeqdaKraNlmWkZubqyfI7NPhuuuuAwC89NJLWY4k\nuS984Quora01rGW6soywwh//+EcAwDPPPDMpk01g+HxvaGjAqlWrsH//fvh8vlGfr1+/HkSUtFyQ\nJCkrZYEgCOjp6UFHRwfWrFmDW2+9FUSErVu3YtOmTRgYGEBZWdmk3ebJcC+5FFI17DVq8Pv1r38d\nAOD3+y2pfcjJyUE0Gh0Vl3bXceDAARw4cADAcKEwbdo0tLe349133zVcrplZv9YAPR6PQ5ZlRKNR\nyLKMRYsWIRgMorq6GseOHYOqqrjrrrsQCoXwr3/9C0ePHkVfXx9UVcXOnTtx4sSJMY1Czb47uXz5\nsn731tfXp+9DQRDQ3d0Nv9+P8vJy/b2A2mdWn/SKooCI4HA4MGPGDDQ0NCAWi8HtdmPBggUQBAHn\nz5/H5cuXEQgEYLfb8eUvfxmvvfYauru7Te2xyLJLlmU4nU4EAgEEg8Fsh5PUzp079ZujEydO4PDh\nw+jp6Rk1j/bSb+2dk1ZQFAWVlZWIRqPYvn27Jb95NcLhMM6ePYuzZ8/iyJEjoz67//77sXXrVsiy\njMOHD2P9+vVoamqCqqpYuHAhXn/9dQwNDVkaryAI6O/vhyRJuHDhAvbu3au/f1NVVTz55JP4zGc+\ng8cff9zSuD4xYkkBSDpNmzaNRFFM+fng4CAREVVXV5Msy0nnMZPb7SZJkkgQhJQxAaAlS5bQsWPH\naM+ePSnjGjmJomhajIqikCRJJIoieTweys/Pp6qqKmptbaXu7m6Kx+MUCASov7+f+vr6qKuri5qa\nmmjv3r1UWlpK5eXlNGvWLLLZbEm3vZlS7VtFUai2tpZaWlooEonQBx98oG/znJwcysvLI4fDkfL7\ngiCYGmdBQQEpikIej4fuueceKisro7KyMnrkkUfozJkz1N3dTT6fj6LR6JjvxuNxWr58ecp9z7LP\n6PwcOS1YsIAikQitW7cuo/mzEed//vMfevHFF2nHjh10+PBhevrpp8ecHzabbULiTBfXiy++SERE\nmzdvTjtfVVVVyjLWTEbleLrJ5XJRPB4ftbx4PE4ej8fU8pzIeL8PDQ0REVF9ff2YdXr++eeJiCgc\nDlN+fv6UKn8md3RZlOpAWLlyJV177bVJPysoKNAP2HQHvpncbveYgibZVFtbS83NzfT5z3/ecF5B\nEEw9wSRJ0pctyzLZbDZ64YUXKBaLUVdXF7311lukqirJskyqqpKiKFRVVUUHDx6kDRs20Pz588np\ndJLNZhu1rInYnumSYZvNRr/97W8pGAzS4cOHR/1fkiRyOBx07733Jo3R7DhVVSVRFMlms5HX66XC\nwkK6++676Z133qHu7m4aGhqiWCyW8vucME1u47lQtre3UyQSoby8vIzmtzpOURTJ6/VSbm4uFRYW\n0r333kvbt28nl8tFubm55Ha7SVGUMWWmFTFqNxRG225wcJCuu+66Cd+eV5MwCYJAgiCQ3W6nCxcu\nUH9/P4VCITp16tSo8t5M6eL53ve+l3Z9wuEwERE1NDQk/Xwym9zRZdGVO1GWZXK5XLRnzx7q7e2l\n3t5eCgQCFAwGqa2tjV599VVKJBJEROTz+SwrsObPn0+qqmZUSMZiMaqurk6bFIw8Ac2S7KRQVZWc\nTmfKWNxuN508eZK+8pWvkN1up/z8fJo7dy4tXryYPB7PqGWaKV2BNW3aNOrr66N4PE6HDh1KOk8s\nFqNHH310wve71+vVk8uioiLyeDxUUlJCLpcr7TpIkkThcNiy45NdnfFcMImInnrqqXHNb3WciqJQ\nXV0d+Xw+8vv99O1vf9uyOFMt//z58xmvRyKR0Gtr8vPzafr06RNSc2OUMLlcLlqxYgUdOnSIBgYG\n6ODBg7R06VJ64IEH6PHHH09bppsp1e+sW7dOn6empiZlHIlEIunnk7384TZMKWgjfUuSBI/Hg7Vr\n1+rdOROJBIqKivT2M263GzfccIP+d1VVlaWxGjU+DgQCAIB//OMfkGUZCxYsQH19/ZiB2QRB0Nvj\nmNk2iJK0R9BG0tV6dI1sKyTLMsrLy3Hx4kXcdtttOH/+PNra2nDhwgU0NTWNGlfKbOmWa7PZEI/H\n4fP58NWvfjXpPKIo4vbbb5+Q2EbSGsAHAgE4nU643W40Nzejt7c37TokEgk0NDRMeHzMGqqqgoiw\na9eubIeSlizL+NKXvgS73Y7W1lY899xz2Q4JpaWlAGA45lNFRYV+Tp04cQLPP/88fvq6mt0zAAAO\nTUlEQVTTn05ITKnOXUEQcNNNN+HYsWN6Z6JgMIi3334bH3/8Mdra2nDu3LlxL9dsa9euBTA8XMMb\nb7wx6jNRFLFw4UK9jW1jY6MlMZmJE6YUXC4X4vE4KioqUFtbC6fTiePHj2PDhg0IhUK47777oKqq\nfsH3eDwAhpOT8+fPWxbn5cuX054Mr7zyCmRZRmFhIQYGBqAoCjZu3IiVK1fil7/85ah5r7nmGvh8\nPsu6pY+MW+sdM3PmTOTk5GD+/PkQRRFVVVXYsmULYrFYVkbe1UiShBdeeAF2ux0HDhxImqQWFRXp\nvUAm2qlTp/QG6v39/Whra0MsFjMsGD/88EOcPHlywuNj1vjsZz+LWCyGDz74IKP5s9lzcu7cuVAU\nBYFAIOtjHY3sOXbmzJmU823btg0/+tGPIAgCotEoXn31VTz99NMTFle6DiT9/f04ffo0ysvLMTAw\ngKVLl6KxsRE5OTkIh8N6o+pUy7XCwoULAQzfxGs36pIkYeHChairq4PH49HX0ev14sMPP7QkLtNY\nXqc1RVRUVFBOTg7Z7XaaPXs2zZ49m2bNmkWiKFJRURF95zvfoTfffJMGBwcpFApRJBIhIqKBgQHD\nalUzpXocp8V46NAhKi4uHlMt6nQ6afXq1fS1r32N7HY7TZs2jSorK6mwsJCmT59OhYWFpsWYblto\nU1lZGd13333U0NBAL7/8Mp09e5ZCoRDt2LHDsJG6mVL9xu9+9zuKRCL03nvvjWnvoKoqVVVV0RNP\nPEGvvfZayn1ippG/oT1CzWQ7E6Vvp8VFwuSQyb4EQHfccQe99dZbGc9vdeNfbcrPz6f6+nqKRCK0\nY8eOjI/ViYpRkiT98wcffNAwhkgkQi+99BItWbKEcnNzLS9/0k3l5eW0e/futOe1FY/kBEGgUChE\nRETBYJAcDgcdOXKEotEoxWIxCofD1NzcTETDzUNuu+22KVf+cA1TCp2dnYhEIgiHw2hubgYAEBES\niQR6e3uxc+dO7N+/H3fddRd+/OMfY+7cuSgqKtLHwLFqALSRj9G0v3NycvDUU0/hr3/9K+68884x\nNTNEhIGBATz22GOYPn06/v73v2PLli04c+YMbDYbgsGgpd2TJUlCT08PDh48iHg8jnnz5mHx4sWI\nRqN44IEHLHtNQjpLly6F3+/HQw89pO/bP/zhD6isrIQgCBgYGAAAvPnmmygpKUFBQQFOnz6NaDSq\nP9o108g7dMqwul17jDAZB9tko2U6VEUwGMS+ffvGtdxssNlsmDlzJgRBwO7du7MSw0jxeBydnZ3w\neDyYPn36mM9tNhv6+/v1v2OxGHbt2oVTp05ltaY7mebmZnzxi1+E2+1GV1dX0nms2O/aUAIejwfR\naBS/+MUvUFVVBSJCIBBAe3s7XC4XgOEyqKmpacJjMl02s7XJTOuFJIpi0m77qqqO+d83v/lNIiKK\nRqOW3cGPrH0RBIGKi4vpzJkzVF9fn7K3ljZpPUR8Ph/V1NSQy+Uir9err69Z0sVw5STLsv69Rx55\nxHD+kXeKExmrVoNINNxVN5FIJJ0aGxtpzpw5lJ+frx8fgiCQoiimxjny2MtkmAhJkvROCUbzsuwz\nqgXUJr/fT3/6058yPr+sOl+unP7yl78QEdHrr79ueZypfkNV1VHzxWIx/RzRhEIheuedd6impkY/\nz0buG6sbfaeali1bRpcuXUr5udnlT6rtOXIYk1AoREuXLqXi4mJ69tlnqb+/n4iGy8+ysrIpWf5M\n7uiySEuWtC72mRy0+fn5GV2UzHTlCfaNb3yDenp6Mho+gGi4qnnOnDl6YTARvc/Gc+L//Oc/J6Lh\nk8put2f0HTOl+o2PP/6YOjs7qbu7m5qbm6mlpYU6OjpGdd1PJBL0m9/8ZkwCIwgC5eTkmBrnyGSs\nuLjYsKBdtmyZ4TpOhQLr0yLTMicQCNCyZcsyPr+ylTB1dHQQkXH3/YmIM93v2O126ujooEQioT86\nGikQCFBvby/V19dTRUUFOZ1O8ng8lpXnADIaNqayspKCwWBW97skSXTkyBHy+/107tw5WrFihX79\nrKur04fc+ec//zlly5/JHV0WybJMFRUVtG3bNuro6KD777+fZFlO217k5ptv1r+fjQu8IAi0f/9+\n+tWvfkWKoqT8/YqKCn0sjPXr1yddn2zUMAmCQK2trURE1NbWlvH3zJTqN1atWkU1NTX04IMP0qZN\nm+j48eP6IKVERIODg7Rv376ktT2CIJheYGkXVFVVafny5YYJU1dXFxENJ3VG+4BlX6Y1DYODg1RQ\nUKBfWIuKitJ+N1ttmBKJhOFwFiOnkTXNVsS4atUqunTp0qiBH+PxOPX29tLp06fplltuIafTSYWF\nhVRSUjJh5U+yfbdhwwZ67rnnUsauKAq98cYbFIlEUs5jVcK0ZMkSmjdvHpWXl5OiKCTLMuXk5Oht\nm4jI8MnHZDa5o8siu91OGzduHFXFmEgkqK+vj7773e8m3dF79+7V58vGBd5ut9PAwADt2rWLFi9e\nTOvWraOWlhbq7u5O+t3NmzdbMjJ1urtlWZbJ4XCQzWaj/Px8/cTavn17xoWrmdKN9K3F6HQ66aGH\nHtK/E4/H6fe//33aJNXMCwDR8OjpoijSjTfeSN///vfTXiS1R8VEZNhA2OyClV2dTBOmjo4O+tnP\nfkZer5dOnTpFQ0NDaUf8tmo8nmTn6Jo1azKe3+qEqbCwcNQNUCgUooMHD1JxcTHl5uaSJEkkSRKp\nqkput9uS8vzK30iWEKmqSnfccQc1NjZSR0dHVve7JEnk9Xr1sgkA3XLLLeTz+fTvDQ4OWlqem21y\nR5dFDoeD7rzzTr0mZqR4PE4zZszQd7DWc0474dJl+mYfEFrBKkkSzZs3jzo6OmhwcFBvZ5NKX1+f\nZQeu0R1vQUEBXXvttTRjxgy9rdDGjRvHVRibJZP2QMDw3XwsFqNgMEjPPvus4eChZhdYHo+HHA4H\nlZSUUHl5ecptPGvWLP2uORqNUnl5uaVxsquTScKkKAp1dXVRbW0tFRcX0xNPPEFtbW30k5/8ZMz3\nRz5yN1Om50o8Hh9X+xyrHslpU2lpqf44LhgM0po1a5LWhIiiOKaMMFOq+LTYfD6f/lhz3rx5tG3b\nNjp37hz5/X565ZVXLDuvU+3nmpoaKiws1F+F9f777+vXoWg0SqWlpZaW52YTiKbQq4ItdGWvAkmS\nUFJSgtOnTyM3N3fUZ0SEnp4etLe34+LFi2hpacEPfvCDlL1czNzk119/PS5evIhYLDYqZm2AzU/C\nrDgz6aEhCAKcTidaW1sRCoVwzTXXZPz7Zm5P7eWfmWy7l19+GYqioLa2Fj09Pfp3RsZz5UuRzTJj\nxgx0dnYaLjuRSCCRSKCurg67d+82XC8zjhv2yWVyzkiSBLvdPqo3Vyo2m03vWWnmcWgUpza46qxZ\ns3Dp0qVxLdvK8mfOnDn497//jZaWFnzuc58b18uprdqeNpsNfr8fdrsdwHBPv4cffhiPPfaYYa9s\nSZJM7W2cKk6v14u//e1vuPHGG9He3o6WlhY4HA5cvnwZ1dXVY164nMxkTkl4WIEMxeNxNDc3w263\nIy8vD5WVlbjnnntw00034de//jXeffddxONxCIIAh8NhWVyrV6/Gli1bMDg4OOpAm0wHXSZdpGVZ\nRnV1NRRFQX19fdbi14YAyCRpePjhhwEAoVAIiUQi6QjpE7UePp/PcNnaiMDPPPNMRskSANOHP2AT\nJx6PZ5QsAcZvA5godXV1yMnJGXeyZLXGxkbceuutaGhomLQ3DNFoFA6HA6IoQhCEcQ1vYNU6tbW1\nYdGiRcjLy0MoFAIRQVVVALBsqJ2JxDVMKUzkuBVmbvK7775bH7/IbGbFKYpiRhf3RYsW4Vvf+hYe\nffRR/Pe//81o2WbXiGh37dq4SukoigJVVfXXvBix8s4eAJ588kmsXbtWf82M0fJEUYQsy/8XBdtU\nN1XKn3RxCoIAv9+PvLw8/aI5HlbWMH0SVp/XV+vTGKfZuIZpinvvvfcm9QGmMaplIiI0NTVh06ZN\naG9vN1yWtjyzX/UwngRMFEVEIpGU82u1NRORzBptT4fDgRUrVuDkyZMZrY8sDxcFk2GQUPb/489/\n/jP8fn+2w2DMFFzDlEKqWpFMHi9pd+rRaFR/TCNJEmRZRiKRSPvOn/HSXgir/W4mF+dM18GsC73N\nZtPb0iSLRfs97S40HA7ro6onm1+WZcTjcT1ZMvNxg81m098bZURRFP1FktojOQB6IidJ0qj1MDNx\nSldrJwgC8vLy4HK5IAgC+vr6MDQ0NCrGK+d3uVwQRRFDQ0MYGhoyLU52dabKHbxRnDabDcDVnaNW\n1TCJoghFUfRyZ7w+jTU3ExGnVss9mW/aOGFijDHGGDOQvVdXM8YYY4xNEZwwMcYYY4wZ4ISJMcYY\nY8wAJ0yMMcYYYwY4YWKMMcYYM8AJE2OMMcaYAU6YGGOMMcYMcMLEGGOMMWaAEybGGGOMMQOcMDHG\nGGOMGeCEiTHGGGPMACdMjDHGGGMGOGFijDHGGDPACRNjjDHGmAFOmBhjjDHGDHDCxBhjjDFmgBMm\nxhhjjDEDnDAxxhhjjBnghIkxxhhjzAAnTIwxxhhjBjhhYowxxhgzwAkTY4wxxpgBTpgYY4wxxgxw\nwsQYY4wxZoATJsYYY4wxA5wwMcYYY4wZ4ISJMcYYY8wAJ0yMMcYYYwY4YWKMMcYYM8AJE2OMMcaY\nAU6YGGOMMcYMcMLEGGOMMWaAEybGGGOMMQOcMDHGGGOMGeCEiTHGGGPMACdMjDHGGGMGOGFijDHG\nGDPACRNjjDHGmAFOmBhjjDHGDHDCxBhjjDFmgBMmxhhjjDEDnDAxxhhjjBnghIkxxhhjzAAnTIwx\nxhhjBjhhYowxxhgz8D+A3j5ume3+QwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc54c8f34a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hJBhuEBn-o4l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "225172e5-8e2f-4852-ee80-81573e09c0ef"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "imgi =mpimg.imread('mnist_300.png')\n",
        "\n",
        "imgplot = plt.imshow(img)\n",
        "\n",
        "plt.axis(\"off\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 575.5, 395.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NKswkl/ihdHGAzFq0eHwLEi0FuKMkYvrP/G5GRoad8LJ8+fKYjbclWlsxS5l2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\npg3gLVGqqKigqqrKbv2422672U1HzHUGvN5969a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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc54cb015c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qzugkhPf-stO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WWwW9PSnEt2J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}