EDL_9_4_DCGAN_Encoder.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_9_4_DCGAN_Encoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install livelossplot --quiet"
      ],
      "metadata": {
        "id": "d_92PLgJvmJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kiUINXkSfWc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce05c8a2-282c-4318-e28f-02cea201efda"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@title The Imports\n",
        "from tensorflow.keras.datasets import mnist as data\n",
        "#from tensorflow.keras.datasets import fashion_mnist as data\n",
        "#from tensorflow.keras.datasets import cifar10 as data\n",
        "\n",
        "#from tensorflow.keras.utils import np_utils\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Input, Dense, Activation, Flatten, Reshape, Dropout, ZeroPadding2D\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, UpSampling2D, Convolution2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import LeakyReLU\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.initializers import RandomNormal\n",
        "\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from livelossplot import PlotLosses\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import random\n",
        "from tqdm.notebook import tqdm as tqdm\n",
        "plt.gray()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Extract Class of Images\n",
        "def extract(images, labels, class_):  \n",
        "  labels = np.squeeze(labels)  \n",
        "  idx = labels == class_  \n",
        "  imgs = images[idx]\n",
        "  print(imgs.shape)\n",
        "  return imgs"
      ],
      "metadata": {
        "id": "aj7lqIlrAVas"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load and Normalize Training/Test Data\n",
        "(train_images, train_labels), (test_images, test_labels) = data.load_data()\n",
        "\n",
        "# split dataset\n",
        "#train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "print(train_images.shape)\n",
        "\n",
        "train_images = extract(train_images, train_labels, 5)\n",
        "plt.imshow(train_images[0])\n",
        "\n",
        "# Rescale -1 to 1\n",
        "train_images = (train_images.astype(np.float32) - 127.5) / 127.5\n",
        "if len(train_images.shape)<4:\n",
        "  train_images = np.expand_dims(train_images, axis=3)\n",
        "print(train_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "uQlqVSsZfj7d",
        "outputId": "ee5f5ca6-5418-4491-f64b-1e46846f6883"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "(60000, 28, 28)\n",
            "(5421, 28, 28)\n",
            "(5421, 28, 28, 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Determine Global Parameters\n",
        "latent_dim = 100\n",
        "channels = train_images.shape[3]\n",
        "image_shape = (train_images.shape[1], train_images.shape[2], channels)\n",
        "print(image_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLrt46129kOK",
        "outputId": "99134fab-9cde-4d8a-9b40-9085936af682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Range Helper Functions\n",
        "def clamp(num, min_value, max_value):\n",
        "   return max(min(num, max_value), min_value)\n",
        "\n",
        "def linespace(v, min, max):\n",
        "  range = max - min \n",
        "  return v * range + min\n",
        "\n",
        "def linespace_int(v, min, max):\n",
        "  range = max - min\n",
        "  return int(v * range + min)\n",
        "\n",
        "def reverse_space(x, min, max):\n",
        "  range = max - min\n",
        "  if range==0: return 0\n",
        "  return (x - min) / range"
      ],
      "metadata": {
        "id": "1vQGR_WGGOyp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Defining the DCGAN Class\n",
        "\n",
        "# ENCODING CONSTANTS\n",
        "FILTERS = 0\n",
        "MIN_FILTERS = 16\n",
        "MAX_FILTERS = 128\n",
        "ALPHA = 1\n",
        "MIN_ALPHA = .05\n",
        "MAX_ALPHA = .5\n",
        "CRITICS = 2\n",
        "MIN_CRITICS = 1\n",
        "MAX_CRITICS = 10\n",
        "CLIP = 3\n",
        "MIN_CLIP = .005\n",
        "MAX_CLIP = .1\n",
        "LR = 4\n",
        "MIN_LR = .00000001\n",
        "MAX_LR = .0001\n",
        "\n",
        "class DCGAN:\n",
        "  def __init__(self, i):  \n",
        "    '''\n",
        "    === GLOBAL ===\n",
        "    image_shape=(32, 32, 1),\n",
        "    z_size=(1, 1, latent_dim),\n",
        "    === ENCODED ===\n",
        "    n_filters=128,    \n",
        "    alpha=0.2,\n",
        "    disc_iters=5         \n",
        "    clip_lower=-0.01,                \n",
        "    clip_upper=0.01, \n",
        "    lr=5e-5, \n",
        "    '''\n",
        "    assert image_shape[0] % 4 == 0, \"Image shape must be divisible by 4.\"\n",
        "\n",
        "    self.image_shape = image_shape\n",
        "    self.z_size = (1, 1, latent_dim)\n",
        "\n",
        "    self.n_filters = linespace_int(i[FILTERS], MIN_FILTERS, MAX_FILTERS)    \n",
        "    self.alpha = linespace_int(i[ALPHA], MIN_ALPHA, MAX_ALPHA)\n",
        "    self.lr = linespace(i[LR], MIN_LR, MAX_LR)   \n",
        "    self.clip_lower = -linespace(i[CLIP], MIN_CLIP, MAX_CLIP)\n",
        "    self.clip_upper = linespace(i[CLIP], MIN_CLIP, MAX_CLIP)\n",
        "    self.critic_iters = linespace_int(i[CRITICS], MAX_CRITICS, MIN_CRITICS)\n",
        "    self.weight_init = RandomNormal(mean=0., stddev=0.02)\n",
        "    self.optimizer = RMSprop(self.lr)\n",
        "\n",
        "    self.critic = self.build_critic()    \n",
        "    self.g = self.build_generator()\n",
        "    self.gan = self.build_gan()    \n",
        "\n",
        "  def build_critic(self):\n",
        "    model = Sequential(name=\"Critic\")    \n",
        "    model.add(Conv2D(self.n_filters//4, kernel_size=3, strides=2, input_shape=self.image_shape, padding=\"same\"))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Conv2D(self.n_filters//2, kernel_size=3, strides=2, padding=\"same\"))\n",
        "    model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Conv2D(self.n_filters, kernel_size=3, strides=2, padding=\"same\"))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Conv2D(self.n_filters*2, kernel_size=3, strides=1, padding=\"same\"))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(1))\n",
        "\n",
        "    img = Input(shape=self.image_shape)\n",
        "    validity = model(img)\n",
        "    model = Model(img, validity)\n",
        "    model.compile(loss=self.wasserstein_loss,\n",
        "            optimizer=self.optimizer,\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "  def build_generator(self):\n",
        "    model = Sequential(name=\"Generator\")    \n",
        "    cs = self.image_shape[1] // 4\n",
        "\n",
        "    model.add(Dense(self.n_filters * cs * cs, activation=\"relu\", input_dim=latent_dim))\n",
        "    model.add(Reshape((cs, cs, self.n_filters)))\n",
        "    model.add(UpSampling2D())\n",
        "    model.add(Conv2D(self.n_filters, kernel_size=3, padding=\"same\"))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(UpSampling2D())\n",
        "    model.add(Conv2D(self.n_filters//2, kernel_size=3, padding=\"same\"))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(Conv2D(channels, kernel_size=3, padding=\"same\"))\n",
        "    model.add(Activation(\"tanh\"))\n",
        "\n",
        "    noise = Input(shape=(latent_dim,))\n",
        "    img = model(noise)\n",
        "\n",
        "    return Model(noise, img)\n",
        "\n",
        "  def build_gan(self):    \n",
        "    z = Input(shape=(latent_dim,))\n",
        "    img = self.g(z)\n",
        "\n",
        "    # For the combined model we will only train the generator\n",
        "    self.critic.trainable = False\n",
        "\n",
        "    # The discriminator takes generated images as input and determines validity\n",
        "    valid = self.critic(img)\n",
        "\n",
        "    # The combined model  (stacked generator and discriminator)\n",
        "    # Trains the generator to fool the discriminator\n",
        "    gan = Model(z, valid)\n",
        "    gan.compile(loss=self.wasserstein_loss, optimizer=self.optimizer)\n",
        "    return gan\n",
        "\n",
        "  def train(self, train_images, epochs=10, batch_size=128, verbose=0):\n",
        "    #track minium/maximum losses\n",
        "    min_g_loss = 1000\n",
        "    min_fake_loss = 1000\n",
        "    min_real_loss = 1000\n",
        "    max_g_loss = -1000\n",
        "    max_fake_loss = -1000\n",
        "    max_real_loss = -1000\n",
        "\n",
        "    # Adversarial ground truths\n",
        "    valid = -np.ones((batch_size, 1))\n",
        "    fake = np.ones((batch_size, 1))\n",
        "    batches = int(train_images.shape[0] / batch_size)\n",
        "    history = []\n",
        "\n",
        "    if verbose:\n",
        "      groups = { \"Critic\" : {\"Real\", \"Fake\"}, \"Generator\":{\"Gen\"}}\n",
        "      plotlosses = PlotLosses(groups=groups)\n",
        "\n",
        "    for e in range(epochs):\n",
        "      for i in tqdm(range(batches)):\n",
        "        for _ in range(self.critic_iters):\n",
        "          # ---------------------\n",
        "          #  Train critic\n",
        "          # ---------------------\n",
        "          # Select a random half of images\n",
        "          idx = np.random.randint(0, train_images.shape[0], batch_size)\n",
        "          imgs = train_images[idx]  \n",
        "\n",
        "          # Sample noise and generate a batch of new images\n",
        "          noise = np.random.normal(0, 1, (batch_size, latent_dim))\n",
        "          gen_imgs = self.g.predict(noise)\n",
        "\n",
        "          # Train the critic (real classified as ones and generated as zeros)\n",
        "          c_loss_real = self.critic.train_on_batch(imgs, valid)\n",
        "          c_loss_fake = self.critic.train_on_batch(gen_imgs, fake)\n",
        "          c_loss = 0.5 * np.add(c_loss_real, c_loss_fake)  \n",
        "          \n",
        "          #clip discriminator/critic weights\n",
        "          for l in self.critic.layers:\n",
        "            weights = l.get_weights()\n",
        "            weights = [np.clip(w, self.clip_lower, self.clip_upper) for w in weights]\n",
        "            l.set_weights(weights) \n",
        "\n",
        "        # ---------------------\n",
        "        #  Train Generator\n",
        "        # ---------------------\n",
        "        # Train the generator (wants discriminator to mistake images as real)\n",
        "        g_loss = self.gan.train_on_batch(noise, valid)  \n",
        "\n",
        "        min_g_loss = min(min_g_loss, g_loss)\n",
        "        min_fake_loss = min(min_fake_loss, c_loss[1])\n",
        "        min_real_loss = min(min_real_loss, c_loss[0])\n",
        "\n",
        "        max_g_loss = max(max_g_loss, g_loss)\n",
        "        max_fake_loss = max(max_fake_loss, c_loss[1])\n",
        "        max_real_loss = max(max_real_loss, c_loss[0])\n",
        "\n",
        "      loss = dict(\n",
        "          Real = reverse_space(c_loss[0],min_real_loss, max_real_loss), \n",
        "          Fake = reverse_space(c_loss[1],min_fake_loss, max_fake_loss),\n",
        "          Gen = reverse_space(g_loss, min_g_loss, max_g_loss) )\n",
        "            \n",
        "      history.append(loss) \n",
        "      if verbose: \n",
        "        plotlosses.update(loss)\n",
        "        plotlosses.send()\n",
        "        self.plot_generated()\n",
        "        print(min_g_loss, max_g_loss, min_fake_loss, max_fake_loss, min_real_loss, max_real_loss)\n",
        "    return history\n",
        "\n",
        "  def wasserstein_loss(self, y_true, y_pred):\n",
        "    return K.mean(y_true * y_pred)\n",
        "\n",
        "  def plot_generated(self, n_ex=10, dim=(1, 10), figsize=(12, 2)):\n",
        "      noise = np.random.normal(0, 1, size=(n_ex, latent_dim))\n",
        "      generated_images = self.g.predict(noise)\n",
        "      generated_images = generated_images.reshape(n_ex, image_shape[0], image_shape[1])\n",
        "\n",
        "      plt.figure(figsize=figsize)\n",
        "      for i in range(generated_images.shape[0]):\n",
        "          plt.subplot(dim[0], dim[1], i+1)\n",
        "          plt.imshow((1-generated_images[i])*255, interpolation='nearest', cmap='gray_r')\n",
        "          plt.axis('off')\n",
        "      plt.tight_layout()\n",
        "      plt.show()\n",
        "\n",
        "individual = np.random.random((10))\n",
        "dcgan = DCGAN(individual)\n",
        "\n",
        "dcgan.g.summary()\n",
        "dcgan.critic.summary()\n",
        "dcgan.gan.summary()"
      ],
      "metadata": {
        "id": "algaCcrg1QXu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "280efcc2-347b-4cf2-9b1d-9c069516f38b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 100)]             0         \n",
            "                                                                 \n",
            " Generator (Sequential)      (None, 28, 28, 1)         638314    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 638,314\n",
            "Trainable params: 638,012\n",
            "Non-trainable params: 302\n",
            "_________________________________________________________________\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " Critic (Sequential)         (None, 1)                 245566    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 245,566\n",
            "Trainable params: 0\n",
            "Non-trainable params: 245,566\n",
            "_________________________________________________________________\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 100)]             0         \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 28, 28, 1)         638314    \n",
            "                                                                 \n",
            " model (Functional)          (None, 1)                 245566    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 883,880\n",
            "Trainable params: 638,012\n",
            "Non-trainable params: 245,868\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "individual = np.random.random((5))\n",
        "dcgan = DCGAN(individual)\n",
        "\n",
        "history = dcgan.train(train_images, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777,
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        "id": "FXQ2vO3teyAY",
        "outputId": "03ea21df-bf79-41dd-f957-11baa963839e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Critic\n",
            "\tFake             \t (min:    0.056, max:    0.947, cur:    0.194)\n",
            "\tReal             \t (min:    0.000, max:    0.813, cur:    0.083)\n",
            "Generator\n",
            "\tGen              \t (min:    0.000, max:    1.000, cur:    0.803)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.8797447681427002 0.6794323921203613 0.0 0.484375 -0.6182553768157959 -0.023629389703273773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "individual = np.random.random((10))\n",
        "individual[FILTERS] = reverse_space(128, MIN_FILTERS, MAX_FILTERS)\n",
        "individual[ALPHA] = reverse_space(.2, MIN_ALPHA, MAX_ALPHA)\n",
        "individual[CLIP] = reverse_space(.01, MIN_CLIP, MAX_CLIP)\n",
        "individual[CRITICS] = reverse_space(5, MIN_CRITICS, MAX_CRITICS)\n",
        "individual[LR] = reverse_space(.00005, MIN_LR, MAX_LR)\n",
        "print(individual)\n",
        "\n",
        "dcgan = DCGAN(individual)\n",
        "history = dcgan.train(train_images, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777,
          "referenced_widgets": [
            "c83b1de9664347329c94eeb934c19028",
            "f1c398af369f4a7aa2c4caebccec862c",
            "7f11c53c23c94694906f9b90e4c35710",
            "146bd82dc36c4ddba6adc86c2f75f33f",
            "626964dfa9424249b99bd47a65de4105",
            "e8541f8b68c149279483beb4457c79f2",
            "71624d44813e401999286ae759da1f1a",
            "3667ec1e3c334d6d900e0579a23de2f5",
            "191a3207cf6843e6a055033875d81444",
            "3bfa5bc793764fff8ccec25fffeb12fa",
            "c464339fa2f74fa9b188ae1b85f3ce18",
            "0b06f0a92324473c905f75bf37354ecc",
            "4ac3df79f0f54f7cae076574690e7cce",
            "4a345b7ddabd471aaac49bf771402a9e",
            "bc2487019c93441da6dc4d31f1d55da9",
            "e14c0c7999d14e90938d8a7fa0e72cc6",
            "754f3c577a2a4c589ee6474a36a9821b",
            "9c5030c86d5344c6a9b9ea43ac947ee8",
            "4872efd21aa4433bab0fe5e6a043eb69",
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        },
        "id": "rgg7A5H3ipaK",
        "outputId": "4c82b814-c246-4fb1-bea7-e2968553b271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Critic\n",
            "\tFake             \t (min:    0.000, max:    0.000, cur:    0.000)\n",
            "\tReal             \t (min:    0.017, max:    0.861, cur:    0.710)\n",
            "Generator\n",
            "\tGen              \t (min:    0.002, max:    0.991, cur:    0.949)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x144 with 10 Axes>"
            ],
            "image/png": 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9UK7jjjtjunOp3hrS6pgoug53m/IO46fyHG277bZha58ijz/+eNiHHnoogMYBVCC7k6f3xb6sHsR2jKkqu61LLrlk2NxVU49uO+5T60F3ldnndZzoM6nXJNWndYeRc7F6ODRwAMtPOeWUKEs9a9XnX2+99cLefffdAWT74mWXXRZ2FS/Z+eefH7aqJYj2RXomqIQAskFYdGeUATkeeuihKLviiivCvvHGGwFkveWak4X1qs+a8sxVCdyjcz8VIDrH6648vVmXXHJJlGnAEvYbzdez1lprhV20W0z02ahUARr5xHo6ulNeV+As7We6DjDIhY6ponVOx7+Ob+72azAn5nsEGvNvXmAb5jHiv0A2CBQDB1TNg6fPxWtNmzatQ1md6JhjvioAOOqoowBk+7fOm0Xvqqky9TI+/fTTYVMxoHOGjpFWcpnpd+oa1QqpuQ8ALrzwQgDZ3FY/+9nPwr7rrrsAZD2R9JoD2TyDu+yyS4fvUq8/51Ttfwrvq8izWyYPnz1YxhhjjDHGGFMT/oFljDHGGGOMMTXRqV9eJQIpd3Y75CzqSqWrFWjI9dqRDyOPlNsZaLjRqx7IVmnjlClTAHStLDAVKERdrxocIHXgslX0cK/mH6AMQYMXlIW5Z4CG7E4lLOrO//GPfwwgK8vRA/FlGTZsWNh//OMfw6Z0UGUD6qrX/FvPPPMMgKzUknkagEZbqET12WefDZsHnFWipHkw6Oau0r9UlsTrqNRJXeQs1/F79dVXh83706AnKhHkIVrtYyqrZECFv/3tb1GWl8+Ez6oBF/S+6sj7UvU6KvWkVPj++++PslZzpKksgv2PeUFm/jsPv6uUS+XL2n6Ua2ibaGARXkulupQoAQ0pWzsCHwGN3EVAoz5VFlKlrbSvMt9MHvpcnF+Y2wXIyoU00ADnoKK25jyRR5U8N0XonEnZbV4Qmk022QQAsNpqq0WZyn1YrhLDVGCQvPWGsudFFlkkyrpDSt8qdebTJFoPGvCCc7YG+Lr00kvD1vFJNFjMyJEjw2YQC51Htc9yzTrvvPOiTOdnBg5QWZb2L5U5VkHnMK5L+nytSOXyGDduXNhHHnlk2Hy/yZNbq81xpeNXpY2HHXYYgOxconMZ53P9fF15/rSv6liuAoOA6XumrhWsK60bndOZP1LrRt8j9f8yiJgG4WEQFaA4P2Mq+IzCey3zPmwPljHGGGOMMcbUhH9gGWOMMcYYY0xNdCoRVHcwXZntcHEDjTj+6mpVqVqRO05di4x4orIvlV2NHj0aQDail0oP6GLWyCmac6tVNCY/XbwpWVe70JwBzJmgz68u5mYipVGqoPev/eXhhx8GAPzkJz+JMpUMUFqn7vCyqJyJLt7x48dHmbrQKYNSqY5GL2Kb5LnaKR064IADoiwlB9CIav/617/C1lxAjH6msj+9b35O+2K7xuDMqCz2u9/9LoBsbhHtR9dddx2ArJRJ65xjSqNVav0TlT/oOKFEMPWZmWH/UylA2ShlzVBFdjJp0qQOn69T6qT9kHIdzeGk8tTTTjsNQDYfUVEEKpVdag47kpIddQUqR2JeryKpRxGURQHZ/sP20vGtciGOX23XW2+9NfkddbV90XWqyLt1/Jx88skAsnXKaLpAY07VOtE1kzJVlUKnonBpn9N14Hvf+x6AniULVIlbWdqRz1DXW809RzmySuW1flO57RiBE8i2D+flyZMnR5nOz4zoplGedS7nc+vzp9bMqnmwnnrqqbCZM0mpSzYHNN4VttpqqyjTd2XeC/MtAdmIeTpuiM7LzdDud0VSRWKtdcLIfXqUQ/sij0CobFD7F/udvlPo+NF3IspR9fN1SkTZl8q8U9iDZYwxxhhjjDE10elPMN2t4oHUOnfP1WvAIAF6yFJ3M/jL9fnnn48yzWN0zz33dLi+7mrqr10GU9Bfy7rzwwP3uhuvOwyt7oboL3tmptY8K7pLXBf6nboLQM+DHh5UL4Fmy2bWcW0X9XKwv2iGdj1Uy3rTnQXdBWQbVdlt0EOuvI72X22zlBdEPStEd510t4K7RjxMCQArrrhi2KmxotfSw8NrrLEGgGxf1YAW7HftOMRehNYZx52W6cF97jBfcMEFyWtx51Izzmswk9TzqXeTB7U1R1iRh0J3y3X8s3/VuatZlqIdR71P9rlmxoPuENNjrPnaNBgLvWld5RFtJzrnlMlPUgY92K19hW2o36l5mJrxTLC92uWZSXkOyqJzKsdn3n3q/yW6Vh900EEAsqqSVDvpPKsKk57kuSLLLrts059px3PMOeecYf/qV78KmwGB9D1G59SU0kLnGp1fmXtJ1RfqNWL/yPN08Ln1+fU9gnYqR2gZ9D0k9V11wj769ttvR5kG6eD6Ta8vkK0XnVd6Yr8mVe5N36POOuussFWNRnROpRdVlWbalznXllmzed/NeEOb8SzzumWubw+WMcYYY4wxxtSEf2AZY4wxxhhjTE10KhFU2U5R7PjSXygSAM2DQ6mYuk/14D+lQTzsDlTPZ0PJjAYO0JwpW2yxBQDgmGOOiTJ1Abfq1lU35/XXXw+gPbJAoJG/Zb/99osyzdlCN67WOwNfAFmZAPMiab1pe/JQrUqSVIaZytlUl/Qt1SbNSMBUJpWSTKl0YtCgQQCywQP075Rc3HnnnVGmeRg0lxNlspSlznxd5pdqNU9IFbSfMF8PZWcAMHHixLB5oF3zsKgslXW2zDLLRBlzWwCNnE2Uosxss58xnwaQlYXodzHPnObc0YPcxx13XIfPN9NXKCdQOXM74JzYjERQczcxcM59990XZal77soAO+0iJQHNg9IOnbt0nmO5StxUAkhZrwau0YAOefnZZr4+0Oi3KtPqKdR5MJxBPiiJB7KBiVJSxuWWWy5svivoZ/JyZrUjeIzCOXvrrbdu6Trsh61KlVVqufnmm4fNfGy6dnAeBhry/XnmmSfKVAKo8+Pxxx9fy73mwXFQNdemvp+2W3bHOtI8SyoX5HEPPYrQHXL0roR9WXOu7bTTTmGn5MAqseM7EY+sAM3Vn86p7EMbbLBBlOn7NXP5aV/Ly3mbopmjLPZgGWOMMcYYY0xN+AeWMcYYY4wxxtREpxLBVGSgyl/0/y48dWEzXwnQkPupq08j+5177rkA6o2mpi4+lSMxZ4dG3NHcCnRnM1pcs6gLu846JhrdiLlEVEKl37/pppsCyEpgNNKYuk6LcjWknkXlB5R3qQRP3bGUydQpTakLjW605557AgBGjBgRZdpXKLFkbhAgHeUIaEjXjj766CjTXEJ0uZ9wwglRNmXKlIpPUR1Kx7RtVArF51O52WKLLRY2x73mFtI8SsceeyyARr4MIDvmKCdQCZv2Lc3jRpmc5tHRPse5pqpsg3KHdsjp9Jko59FxVyTVXmWVVcJeZ511AGSlVhr56/777weQjTLW3RLBqhKhZuBYVtmKPjf7uEpkTzzxxLB/+ctfAmjUL9CQuALA1KlTM9cBss+lbcS5QPML1Um7I6o1CyXxQFYizDrRNtE6u+yyywBkx6z+X30+jm+VbdYpwef3Pv74401/VqOs8SiCznn6/sOxXtR2OifoczJnIaX9QDay3f777w8gG7X2wgsvDFsjG/OZ65S66fjgdXX+a4Z29G/tf5rzke2mf9fjKnwunVP0/nrKWKwTtp/m/tL3GK7J+o6oEkGucfr7YOzYsWEzSrFGbtW+mJILf//7348y/d3A79L3A5UjUvpddARJ33XysAfLGGOMMcYYY2qiUw+W/sJsdeeCO3y6Q6E7UNyV19w6Rx55ZNjc+dbAF9xJrAP1lvEQ68svvxxl+muWu0ya26gZNH9SOw4/6m6+eq6I7rzQC7feeutF2dChQ8N+8MEHW7oXPfzJXCh6oFt37MaMGQMgu6NWFt0Z5G6F7lo0U88MBLDllltG2VFHHRU2vU7qtdL8TDywqV6TvAPZvC89UK+HQw844AAA1XZLlSqegZQnMW/3jW2qec00Yz2DW+ghaw2Awh2uRx55JMq0TjkXaTACfSb1EN51110A8gOXsM6byX2hsF7a4WnVOeWJJ54o9RnNwUbvO9AY+zqPa8CLvffeGwCw3XbbJa+rfVJ3cNtJnbu7+typQ9a6G5r6Xu1r6m055ZRTAGQ9JNzVBhrrh3prNYiN7nxyDtlkk02irCcGvKgLnYeHDRsWNufviy66KMo23HDDsDkn63qRB+cg5rsEgEMOOSRs7rJX7WtsMw2MpfkeO0PXKXpS1fup6oSy95eniOH8lLfTzn6qc7a+B+n8yIAOjz76aPJ7udZWfZ/hXNWTcvJpsCkNaMF5Qd8J1XNDD4n2VfVa6/tfUUCF7qTK+qhe/3XXXTds5mrT98EDDzwwbHqo9N1V+y1VATo/aEAMHTcDBw4EkK1bVW7wHvX9QscA260op1uZvmoPljHGGGOMMcbUhH9gGWOMMcYYY0xNdOpvryIrSrmVgYYESN12mp+BrkGVEOq1UjknVEKgrsNTTz0VQHPuas3dpHYKusPLHHJL0e6cCOoOpTt6yJAhUZYKzqH1rgEvVNKl7t+yqGuZbc98WUBWusPgEcxT1AwqN6NMgod8gay7n7mcUhIHAFh77bUBZKVXc889d9iUcalclYewgUbQgLx21u/lAWXNU8bcVwBwySWXAMjmN9Jnoctdv0ttyqQ0SEc7SMnR1IW/ww47AMj2PQ0swyAX2nf1/9Idr9fXNtMxS+mLuvBTY1UlfnrdrgoMwPlNx16VMaa5x1RiyTbXg+1a58zzp7mx8uZv9lMdBz0JvS/2C21/SlSAxvjIy4NF8tY/SoAoWwGycxallRpw4KSTTgo7dZBf22X48OFh60HxLzNsq2222SbKtH3YFzUYiLaPjn/2haWXXjrKRo4cGTalt5r7UecH9g+dX1JzQavzw2233QYgOw/pM9MukiLrffzud78L+6abbur0cxz3Sy65ZJTp8YAHHnggbAZ00dxvuiZxzdU6U4kV2ypPdtWqNFDnb9o65hXOsTrmU2u15vtU6SQDg6nEVecCvhfoOq5yYpXJ8f92d3ChFDq+KKHT97WFF144bNaVvt/rmGS55qOcNGlS2HynUVm1vidSmqpHNVTirfXHdtd5VqWzPG6h7V/0zl8Ve7CMMcYYY4wxpib8A8sYY4wxxhhjaqJXZ27ufv36xR+LYsInL14QhURdqIzCdPrpp3f6+TzZxrRp08JmRBKVcLVDlqeRRz788MPSespevXp1WSIE1pfmwtCoRWeccQaAbM4idbdq/pcJEyYAAG655ZZK90L3sOb90EhpjFioEXmmTp1aql7nmWeeqNNUFEGVEFAioLIQjZI2btw4AMCoUaOiTGUazB+icgl9jmbkDpTWqYRA5USMfqURLbfffvuwKQFViaRKk2jrPT333HOl6rSon2qdbrDBBgCyEh6NmEWpqd6H5p5Qm6hchvOPziP6/ToWKQ3SPn3nnXd2uL5Ga1PpQ5XogJ9//nmpOp1lllniAZivTvOBtSo7Ss2Pedfk/9V+rmNbxwevoX1v/PjxLd1rEWXrFABWW221eEjet64JKr3kvFBU1zqmVPpCCaqicnfWq9YfZUVANvoc/4+uTyrz3G233QA0ZM1l7ruIsvXaletUFfT9QSXWe+21F4Bsm337298Om3VZ5ztB2TodMGBAh3VK77NVqZjKtpjHSmVXOj9Qyqr1qGP6iiuuCHufffYBAJx55plRxnoGGnJw5isFshKslAQ3hc75n3zySenx37t376hXPqNKnwcPHhw2JfzNROvUeuM9qoQ9FZGUeT+B7PhX6SUlwCpXbfcRkrJ9VeuUz6Tto/XLYxN67/r+xfVZ37N0HtNrEY1gnbq+ou3DdyrN58rcpPq9uiYwJ2RV8urUHixjjDHGGGOMqQn/wDLGGGOMMcaYmqg9iqBSJGVQ2dN5550HAFh99dWjTN36jFKish91y2r0HEoD2+1qbbV+ugK6ZjXhcFFEQZW2qPTiO9/5DoDqEkHKwDQpZyqSmyabK4tKECizUBd1Koqc9g+NWENZnSYSVnfzrbfeCiBbd/oc7Bca2UzlCCnpq8r6zjrrrLBfeeUVANmITa+99lrYlLZpImKVMPEe2hElR2V1fCaV7aVgBAPBLPEAABJiSURBVDagONFrSgKYimwEZKNdUuJJ2SIA7LHHHmGz/19++eXJeylLlfGvUt3nnnsOQL3RCpu5Fv+vRnPSfpqK6HrOOedEmUqI2j3XFqF9gfeic0IV2WfemCmSALNe9Ts1UbFK1yln1XbTpMSMLtjuiJYpdM4skq5xrHZlP9D3ByZkBxoSMCYc17LuRiPIcUzVWWe6jowePRoAMHbs2CjTaLdcR/SdSpOvKvfeey+AbJ/WNYdRRnX+qNJnqyZv1++irXO6jrkqibz1+hwLfOaZ4VrNqMgAsPHGG4et76qUwW277bZRpu8d3UnqPUUlgvpOwr/nye45F2ufoewPaH0MaPvwOIHODz/60Y/CZqRiPc6g7xp1jkd7sIwxxhhjjDGmJjoNcjHHHHPEH/lrs907aXm/JJnTRXclddeagS2Axi6NejDUm8DDna2ieRbee++90tvZffr0iUpsx46f1mEq1wXz3wCNHfUVVlghyvLyi9AjoIfyWyV1OFHb6vXXXy9Vr7POOmvUKXdWNPeBojsrqfvg5zVng9r0wmhuNvVMcJfy0EMPjTLdWUzlz3nyySejTNuPnlsddxo8gs+iu956r9yl0Z2nGTNm1H7InTnHdPdNA+OwbZsJlqO5u9g+GnhAx44GGdh7770BAMsvv3yU6eFa1vWYMWOS16pC2YPDyy+/fNQp+5HmW+ludKdf5wz2P73XgQMHhl1157kzmglyMXjw4KhXesqLvJI6jtQDlrczXRf0LAANb3VefjHO363mCdJD5O+//36pel133XWjTnkwP8+TxfklFRgFaP3+m2GppZYCkFXD3HzzzR3upc576imBQ3Qd49qQyldVx/VVFcMgEvpu1Wr9NjP+mwnIRFvXRKXovlMKFV0/6K3WgCNaVwoDLWgQjIkTJ4bdnfNq3759O9SpKn1+/vOfh808lrr+63tWV45/whyiQFYNR2+mBr5Sr6IGnSmLg1wYY4wxxhhjTJvxDyxjjDHGGGOMqYlOg1yo25PuPnVZqou1bG6RIvKkOqmDieqOXG211cKmdEXlbXq4ri6JYJXcYHWgbvpU8AbNa8FcOwceeGCUaX4GSoK0LvVA9tVXXx22ytjqQvtQK4d+Ve5DiZwGoVC5KGVOKndJ5bkYOnRolG233XZhDx8+HEA2d5W6wzkGVJagAQNUOkC55ZQpU6Ks6EC5utv5jDo+ND8X67LdAVkogcyTNFQZKyoRpK25K1Supte/8sorATRyjADACy+8EDalY90ROEDnpCrSwJVWWilsyno1h4oe7C+L9tNUjieg0X9Sss2eQEpilycR5Jypsl6tw3YHbLj99tvDPvroowFkJSpKXXWs819ZdCyvvfbaABrBDoDsnEe5fv/+/aNs3XXXDZt5Am+77bam76MMuo7wezVwgMrkuKZ1h2wpFQQoFaBB/69+RoMlcO1Qease7Gf/ryJ5KoOuaSNHjgSQXRN1Lipa0/gMVfppGVJr8bBhw6JMj57w/UfbQufFNddcEwBw8MEHR5nKpSnXLzN2+dy6VrVDFlgFfafi/KqycQY9A4AbbrgBQDZwRLv6XRGUY6rsUscIn0HLzj777LDrvG97sIwxxhhjjDGmJvwDyxhjjDHGGGNqolOJoMpuUnIJdYHSnaiyAXXLp1zjzAECNNzdKutI5RbKQ6+/0UYbAWhEkwKA66+/vtPPV6HI7Z1HqzH3U/l/VJpx0EEHhU03uLq7GfENaESHVHf+xRdfHPa1117b9P01g/YXygOqyMk0txelfb/4xS+iTPN7MC+I5mRSaQKjQ6rEZJtttulwz3l9sqiv6vMdddRRAJrrS9qWlAYW5faoIodR2U1KtqDP2aZoR2Hz/lVWlzc/UM6g4+QHP/hB2KeeeiqArIS0Sm6UKlSJULf55puHrfm8KHXWfF/7779/2BMmTACQlVWmUHlLXjQ79s9p06Z1KOsJDBgwIGxGj9TInVoH7FfMMQe051m0T2r0y3PPPTdsSqtU6q19pC4ZW5XrME+bfl7nSV3HVl11VQBZqbN+fqeddupwHyon0zWnM/Q7Va46atSosBlFVMe/Sry6M2pnM+s971+PP5x88slhMzKk9p0zzzwz7Ouuuw5Adp3TPl9lztY5Wd85eMRA21frv+z40nHSDEXvVPqsfFfYbbfdokzlgnyXVdmwrhWMUpnKA1kGrUNKZpnvriehz8c61eMHF1xwQdj3338/gO47NqNRgvkuq2M+1f90/ki9/6lUsOpxAnuwjDHGGGOMMaYmOvVgFf1q012D1P/VXQPuEGhuCu7eAY0d5PHjx0eZeh2Y50p3WDW2PfMwAI0Di7qDss8++4S93377AWj9EHMqn1IZUt68ZtBdIu5I6c6BeqN4qF4PLO6www5hM5M4d/1mvlY70HvRfkOPSdGOe4plllkm7L322gtANtiHelNTu03aF1in++67b5RtuOGGYeuOfwrWueZZuOiii8L+y1/+kvzedlJlB6ZoV67dQSJ014m7hlpf3BUHst5wemj1EK7eKz0+N954Y5Qxu3u7aWbXmPX/6KOPRpkG69liiy0ANALZAI1da6DhwfrhD38YZeod4W6x3tNhhx0W9pZbbhk25+ru2qEsQp+BuVrUq9Udh8g1mI0GsdDD16m1QPMrpoLYVKHKWNU5lbn79Do6PtlHNVjSOuusEza9HZtttlmU7b777mFzTdbcdVo3DJKhni7t6/quQI8ND94D2Z33Lwpc53XMaw7KVVZZBUB2p55zAgBcfvnlHa6Z8jpVHQ9UvwCNd6HDDz88yjTgBu9F20zhvKKqo2bQPFNF3tr77rsPQDZo14gRI8I+/fTTAWRVMfr+kMqflRpfWj8aROHXv/512FXedboKvTeuRdo++k7Dd+5m5hmtx+WWWw5AY5wD2X7J66q3VoPo3HrrrcnrEu0TV111FYBGgCEAePHFF8NmX6rj/cYeLGOMMcYYY4ypCf/AMsYYY4wxxpia6NWZG6xXr161aYDo7lfZ1iGHHBI287tovhp1648ZMwZA9uCsSgD1unT9rrjiilF2xBFHhH3JJZcAyB6CroJKqD777LPSpxz79+8f9UrJjro1VRqRcr3q/+VBV5XdMU+TXkvdvSrDoLtU671dMCCH5s947bXXwqYbV3NSfPrpp6Xq9cADD4wKotxEJSynnHJK2Mcdd1xzN45sW++4444Asn2OsgKg6wImVOXzzz8vVaezzDJL1Gm7ZVV0/avslrIBoHF4W+VTKtVk7jOgca8qJ9DgOeeddx6AbI43zf1WJQ9G2Trt169f1GkVuZ3OeZT7qIRQ5wbOCSoVOvHEE8PmOFGpls4z3S0HLFunADBy5Mi4cT73Aw88EH/vysAGPDytOcs095UemOe8ooes33jjjbApfdc5sVXpSpW+WiQBYr/UeVLHJ/MIasCGtdZaK2wGvCiSz2s77rzzzmHfdNNNYTMPkcpC203ZOp1jjjmiAikhK2pPlb+p7JISJ5UQHn/88WFrzj2icyL/rmXaN/PyDJJUEAsNLLXLLruEzSMe+v5WtKY0M/7nm2++qMSqMkPCdUeD0VCOCTTqSO//hBNOCJtria4vOn67m7L1Ottss3XomHnPUfaog84Jd9xxR9j6rk5SEkH9Hu23qXlD51RdIzkH5d1zlTyIeXVqD5YxxhhjjDHG1IR/YBljjDHGGGNMTXQaRVDd/a3KEuhu05wMKhEcPXo0AGD99dePMpUIvv/++wCyUWj0WpTKAY2cHBoxR919dUVuqVonGn+frneNWKMRT3ivGv0rFdNf/64RVRhJSyWElFAAjXpTGYK6/lnvZWB/UZmS2oz0phHb9Fn4f6vkbNFrUhqk8sNzzjmn6WsqqdwV6sLu6bJAbdOyUNIJNOQkzbSNzh9052t765jld2nkuu233z5sjuky+UZUOpCC0gSNHNRVtBo1UmUPnDt0Hlx88cXDZv/UaKtqc07QCFeTJk1q6f66Cx3/nAtbnedTudWARl/VyGLMSQY05jmNYjhx4sSwtY0YaVDlMhpxkHNid0iMNIrgI4880un/TUnINOfS5MmTAWTHnK6DtLXOUqi8XKMxPvzww2Fr/qKehr6TaJS5ztC21wjBzPepda99lhJMHq8AsvlAeS2VXet6XSS1S7W5ShTPOuussDkm2xV5tlUJu0rMFlhgAQDZiHZ6hGLttdcGkO1zGhmQc3xXRAhme7ejXvWdISVnbeY7KQ3UyJKpaH9K6u9ap/r9Ws41khGygexcUdQudbabPVjGGGOMMcYYUxOdBrnQQ251ZZQvvKEcrxl3C/N2JVM5ufJ27dvxLM0cyBw4cGA82Ntvvw0guxuqBwH5i18Pn6bqIO9AOutT61V3DulF0AOxZbOuzwx3xPXwsXokp0yZAiDrmdTDhzwAq99ftl4XXHDBqFPuNmtulKrP1FPIGxep4BD6d/YbzT9z4YUXlqrTWWedNS5Utv50zKnXlH1aDzlrYIolllgCALDrrrtGmd4zvQa6U6n9SAManHbaaQCyHkzdOeMOV6s7VVWC3LQjcJB67DQfGL0jelhdd135OQ0A0pNoZk6ts16JBlnRnCnMiad9/be//W3YVCMcc8wxUfbss8+GrWOpq3JyKWXrdZ555ukQjKkr0bWDgWl0nVSv3wEHHBA2PS9dOedXCRzQVe9U3UWrCqhmxn+dAZk4L+Zdh2uqqgl6elvq+3HZwGG6/re6ZvKd7Omnn46yIm+1wrZQJZLOr/quwXchzXPWbm+ig1wYY4wxxhhjTJvxDyxjjDHGGGOMqYlOT5l1xSG9mclzJRcdWE7dq7ptiw7UdSV6uJX3rUESKBvUv6vcYa655gqb+QPuu+++KFO5IOtT61UDVzQTxCKF5oLic62xxhpRdv7554fNZ1R37mKLLRY2n1HlXFXggfMvqiww5e7OOxBN13negXzWgUoxy9KMrIPfrwey9Z544F2lCvp3SlRVCnvnnXeGTTnW2LFjo2z69OmV7rUuVGrbHXBu0DqfOnVq0k5RV7CfLyuaJ/Ghhx4Ke/XVV+/wf1WiShmbzuNfxLru7sA999xzT9iUXWq+HEpggewY6CrZZZmAO3V85otKV87JddZr0XtDd48LfVa+HxT1+Srv8nlBJKpAiTEDiADAqFGjwuY7rQYT2XjjjcO+9NJLAWSDCeXRzsAfzWIPljHGGGOMMcbUhH9gGWOMMcYYY0xNdBpFUCOzMIpIT3C7VUEjPtEFXKcEspmIN3369IlKrHIPGkkpJVPSiIBlUelW3j2l3PAa8ZCSLXVXq5yRkg7Nw6URB5kj5bjjjtNrlqrX/v37R51SjpMXWbEnovWk0cuYo0QlShrRKxVJTqFcUOWyZeu0b9++Uaet5uFJue31njm/aDRE7ZP8/u6SnajN+9J8Rk8//XTTUQQpW64qaeJ9aJ3ptcrKR3oqzcypvXv3jnqtq4+k+ifQWEs0CpZKeBlR8IwzzogyjZ7VHRHHNM/hRx99VKpe64zMVhc6Duebb75kOWXvus6o7Oull17qcN0qfaZKFNHZZ5+9wzr1RSIlTwOK+wfnqqL3HX23+eCDD0qP/y97dMa897NUxMNUX64SRbAdc6qix3ZSucPqzMPbDsrUqT1YxhhjjDHGGFMTpfNgFXmwinYw+GtVPUm6K5g6sN0qeQf/uduo96y/nIt2W7hroPdfdlcAKPZgpXaJ9Lv0lz//npfhuujAZsorpZ9P7eI3s8ug98r/q/mPUm306quvRtm7775b+25r0SHIVO4wfQ7Wqe5wp/ptMzsw+n91jLBc21//r5anyngtDZxR1oPVr1+/uOlUEIt2BMHprl0r/V56DXU3VecPeq60n91xxx1N5xbhPFOUwy7V9/TveXXW7l1H/V7WRZ3f2W4PVlFfy/s7x5KOKYVjTseHep3z1p260DZKeTnLegZ0/We+RA3cofMU60f7stYPy+vsHzr/6vgcNmwYAOCRRx5Jfk6D4xCOeSAb3KQz9PlmzJhRqk7nnHPOqADWSVcGY9Jdd7af9kf1dGr9Mleetp/+X64L2s+UlIdCoTdY++4rr7xSevzrvFrXXJQ3/lmHRe8PSt68zD6ka3ZK5aF9Tddg1reOO+1PbKOZ2rJUvbaqtCqiqJ66iyKvIMeN9v+8OrUHyxhjjDHGGGNqwj+wjDHGGGOMMaYmOpUIGmOMMcYYY4wpjz1YxhhjjDHGGFMT/oFljDHGGGOMMTXhH1jGGGOMMcYYUxP+gWWMMcYYY4wxNeEfWMYYY4wxxhhTE/6BZYwxxhhjjDE18X9ezr+hJc3kpwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.0019175910856574774 -3.621624273364432e-05 0.0 0.0 -0.00028272432973608375 0.00013713020598515868\n"
          ]
        }
      ]
    }
  ]
}