GANs-Public/C3W3_MUNIT_(Optional).ipynb

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        "<a href=\"https://colab.research.google.com/github/https-deeplearning-ai/GANs-Public/blob/master/C3W3_MUNIT_(Optional).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "1czVdIlqnImH"
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      "source": [
        "# Multi-modal Unsupervised Image-to-Image Translation (MUNIT)\n",
        "\n",
        "*Please note that this is an optional notebook, meant to introduce more advanced concepts if you're up for a challenge, so don't worry if you don't completely follow!*\n",
        "\n",
        "It is recommended that you should already be familiar with:\n",
        " - Layer Normalization, from [Layer Normalization](https://arxiv.org/abs/1607.06450) (Ba et al. 2016)\n",
        "\n",
        "### Goals\n",
        "\n",
        "In this notebook, you will learn about and implement MUNIT, a method for unsupervised image-to-image translation, as proposed in [Multimodal Unsupervised Image-to-Image Translation](https://arxiv.org/abs/1804.04732) (Huang et al. 2018).\n",
        "\n",
        "### Background\n",
        "\n",
        "MUNIT builds off UNIT's proposition of a shared latent space, but MUNIT only uses a partially shared latent space. Specifically, the authors assume that the content latent space is shared between two domains, but the style latent spaces are unique to each domain.\n",
        "\n",
        "Don't worry if you aren't familiar with UNIT - there will be a section that briefly goes over it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-UbOMuuD9Mt"
      },
      "source": [
        "## Overview\n",
        "\n",
        "Let's begin with a quick overview of the UNIT framework and then move onto the MUNIT framework."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3PRXyfFNnBU"
      },
      "source": [
        "### UNIT\n",
        "\n",
        "UNIT, proposed in [Unsupervised Image-to-Image Translation Networks](https://arxiv.org/abs/1703.00848) (Liu et al. 2018), is a method of image translation that assumes that images from different domains share a latent distribution.\n",
        "\n",
        "Suppose that there are two image domains, $\\mathcal{A}$ and $\\mathcal{B}$. Images $(x_a, x_b) \\in (\\mathcal{A}, \\mathcal{B})$ can be mapped to a shared latent space, $\\mathcal{Z}$ via encoders $E_a: x_a \\mapsto z$ and $E_b: x_b \\mapsto z$, respectively. Synthetic images can be produced via generators $G_a: z \\mapsto x_a'$ and $G_b: z \\mapsto x_b'$, respectively. Note that the generators can generate self-reconstructed or domain-translated images for their respective domains.\n",
        "\n",
        "And as per all other GAN frameworks, synthetic and real images, $(x_a',x_a)$, and $(x_b', x_b)$, are passed into discriminators, $D_a$ and $D_b$, respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpCebBIMNpMi"
      },
      "source": [
        "### MUNIT\n",
        "\n",
        "Suppose that there are two image domains, $\\mathcal{A}$ and $\\mathcal{B}$. A pair of corresponding images $(x_a, x_b) \\in (\\mathcal{A}, \\mathcal{B})$ can be generated as $x_a = F_a(c, s_a)$ and $x_b = F_b(c, s_b)$ where $c$ is a content vector from a shared distribution, $s_a, s_b$ are style vectors from distinct distributions, and $F_a, F_b$ are decoders that synthesize images from the content and style vectors.\n",
        "\n",
        "The idea is that while the content between two domains can be shared (i.e. you can interchange horses and zebras in an image), the styles are different between the two (i.e. you would draw horses and zebras differently).\n",
        "\n",
        "To learn the content and style distributions in training, the authors also assume some $E_a, E_b$ invert $F_a, F_b$, respectively. Specifically, $E_a^c: x_a \\mapsto c$ extracts content and $E_a^s: x_a \\mapsto s_a$ extracts style from images in domain $\\mathcal{A}$. The same applies for $E_b^c(x_b)$ and $E_b^s(x_b)$ with images in domain $\\mathcal{B}$. You can mix and match the content and style vectors from the two domains to translate images from between the two.\n",
        "\n",
        "For example, if you take content $b$, $c_b = E_b^c(x_b)$, and style $a$, $s_a = E_a^s(x_a)$, and pass these through the horse decoder as $F_a(c_b, s_a)$, you should end up with the image $b$ drawn with characteristics of image $a$.\n",
        "\n",
        "Don't worry if this is still unclear now! You'll go over this in more detail later in the notebook.\n",
        "\n",
        "![Same- and cross-domain interaction of encoders and decoders](https://drive.google.com/uc?id=1gi5rHPiBdYtkLLok8ImSefnvUloScxm4)\n",
        "\n",
        "*Model overview, taken from Figure 2 of [Multimodal Unsupervised Image-to-Image Translation](https://arxiv.org/abs/1804.04732) (Huang et al. 2018). The red and blue arrows denote encoders-decoder pairs within the same domain. Left: same domain image reconstruction. Right: cross-domain latent (content and style) vector reconstruction.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wU8DDM6l9rZb"
      },
      "source": [
        "## Getting Started\n",
        "You will start by importing libraries and defining a visualization function. This code is borrowed from the CycleGAN notebook so you should already be familiar with this!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfkorNJrnmNO"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from tqdm import tqdm\n",
        "from torchvision import transforms\n",
        "from torchvision.utils import make_grid\n",
        "import matplotlib.pyplot as plt\n",
        "torch.manual_seed(0)\n",
        "\n",
        "\n",
        "def show_tensor_images(x_real, x_fake):\n",
        "    ''' For visualizing images '''\n",
        "    image_tensor = torch.cat((x_fake[:1, ...], x_real[:1, ...]), dim=0)\n",
        "    image_tensor = (image_tensor + 1) / 2\n",
        "    image_unflat = image_tensor.detach().cpu()\n",
        "    image_grid = make_grid(image_unflat, nrow=1)\n",
        "    plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "import glob\n",
        "import random\n",
        "import os\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from PIL import Image\n",
        "\n",
        "# Inspired by https://github.com/aitorzip/PyTorch-CycleGAN/blob/master/datasets.py\n",
        "class ImageDataset(Dataset):\n",
        "    def __init__(self, root, transform=None, mode='train'):\n",
        "        super().__init__()\n",
        "        self.transform = transform\n",
        "        self.files_A = sorted(glob.glob(os.path.join(root, '%sA' % mode) + '/*.*'))\n",
        "        self.files_B = sorted(glob.glob(os.path.join(root, '%sB' % mode) + '/*.*'))\n",
        "        if len(self.files_A) > len(self.files_B):\n",
        "            self.files_A, self.files_B = self.files_B, self.files_A\n",
        "        self.new_perm()\n",
        "        assert len(self.files_A) > 0, \"Make sure you downloaded the horse2zebra images!\"\n",
        "\n",
        "    def new_perm(self):\n",
        "        self.randperm = torch.randperm(len(self.files_B))[:len(self.files_A)]\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        item_A = self.transform(Image.open(self.files_A[index % len(self.files_A)]))\n",
        "        item_B = self.transform(Image.open(self.files_B[self.randperm[index]]))\n",
        "        if item_A.shape[0] != 3: \n",
        "            item_A = item_A.repeat(3, 1, 1)\n",
        "        if item_B.shape[0] != 3: \n",
        "            item_B = item_B.repeat(3, 1, 1)\n",
        "        if index == len(self) - 1:\n",
        "            self.new_perm()\n",
        "        return item_A, item_B\n",
        "\n",
        "    def __len__(self):\n",
        "        return min(len(self.files_A), len(self.files_B))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1m6fuMHo_Y9"
      },
      "source": [
        "if len(os.listdir(\".\")) < 3:\n",
        "    !wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip\n",
        "    !unzip horse2zebra.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQOT4X49Qh1S"
      },
      "source": [
        "## Subcomponents: Layers and Blocks\n",
        "\n",
        "MUNIT has a few key subcomponents that are used throughout the model. It'll generally make your life easier if you implement the smaller parts first!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LE37vMTcQv6G"
      },
      "source": [
        "### Adaptive Instance Normalization (AdaIN)\n",
        "\n",
        "You've already learned about this layer in StyleGAN and seen a very similar cousin - class-conditional batch normalization - in the BigGAN components notebook.\n",
        "\n",
        "The authors enhance the linear layers for scale and shift with a multi-layer perceptron (MLP), which is essentially just a series of linear layers to help learn more complex representations. See the figure in **Submodules** and the notes in **Submodules: Decoder** for more details."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6N9KNJfoQ_-6"
      },
      "source": [
        "class AdaptiveInstanceNorm2d(nn.Module):\n",
        "    '''\n",
        "    AdaptiveInstanceNorm2d Class\n",
        "    Values:\n",
        "        channels: the number of channels the image has, a scalar\n",
        "        s_dim: the dimension of the style tensor (s), a scalar\n",
        "        h_dim: the hidden dimension of the MLP, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, channels, s_dim=8, h_dim=256):\n",
        "        super().__init__()\n",
        "\n",
        "        self.instance_norm = nn.InstanceNorm2d(channels, affine=False)\n",
        "        self.style_scale_transform = self.mlp(s_dim, h_dim, channels)\n",
        "        self.style_shift_transform = self.mlp(s_dim, h_dim, channels)\n",
        "\n",
        "    @staticmethod\n",
        "    def mlp(self, in_dim, h_dim, out_dim):\n",
        "        return nn.Sequential(\n",
        "            nn.Linear(in_dim, h_dim),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(h_dim, h_dim),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(h_dim, out_dim),\n",
        "        )\n",
        "\n",
        "    def forward(self, image, w):\n",
        "        '''\n",
        "        Function for completing a forward pass of AdaIN: Given an image and a style, \n",
        "        returns the normalized image that has been scaled and shifted by the style.\n",
        "        Parameters:\n",
        "          image: the feature map of shape (n_samples, channels, width, height)\n",
        "          w: the intermediate noise vector w to be made into the style (y)\n",
        "        '''\n",
        "        normalized_image = self.instance_norm(image)\n",
        "        style_scale = self.style_scale_transform(w)[:, :, None, None]\n",
        "        style_shift = self.style_shift_transform(w)[:, :, None, None]\n",
        "        transformed_image = style_scale * normalized_image + style_shift\n",
        "        return transformed_image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPf-WwjWlNiN"
      },
      "source": [
        "### Layer Normalization\n",
        "\n",
        "MUNIT uses layer normalization in the upsampling layers of the decoder. Proposed in [Layer Normalization](https://arxiv.org/abs/1607.06450) (Ba et al. 2016), layer normalization operates similarly to all other normalization techniques like batch normalization, but instead of normalizing across minibatch examples per channel, it normalizes across channels per minibatch example.\n",
        "\n",
        "Layer normalization is actually much more prevalent in NLP and but quite rare in computer vision. However, batch normalization is not viable here due to training batch sizes of 1 and instance normalization is undesirable because it normalizes the statistics at each position to a standard Gaussian, which removes style features.\n",
        "\n",
        "Pytorch implements this as [nn.LayerNorm](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html) but requires precomputed spatial size for initialization to accomodate for 1D, 2D, and 3D inputs. For convenience, let's implement a size-agnostic layer normalization module for 2D inputs (i.e. images)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPHZpt-nmIEL"
      },
      "source": [
        "class LayerNorm2d(nn.Module):\n",
        "    '''\n",
        "    LayerNorm2d Class\n",
        "    Values:\n",
        "        channels: number of channels in input, a scalar\n",
        "        affine: whether to apply affine denormalization, a bool\n",
        "    '''\n",
        "\n",
        "    def __init__(self, channels, eps=1e-5, affine=True):\n",
        "        super().__init__()\n",
        "        self.affine = affine\n",
        "        self.eps = eps\n",
        "\n",
        "        if self.affine:\n",
        "            self.gamma = nn.Parameter(torch.rand(channels))\n",
        "            self.beta = nn.Parameter(torch.zeros(channels))\n",
        "\n",
        "    def forward(self, x):\n",
        "        mean = x.flatten(1).mean(1).reshape(-1, 1, 1, 1)\n",
        "        std = x.flatten(1).std(1).reshape(-1, 1, 1, 1)\n",
        "\n",
        "        x = (x - mean) / (std + self.eps)\n",
        "\n",
        "        if self.affine:\n",
        "            x = x * self.gamma.reshape(1, -1, 1, 1) + self.beta.reshape(1, -1, 1, 1)\n",
        "\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMWjIgMsKcGK"
      },
      "source": [
        "### Residual Block\n",
        "\n",
        "By now, you should already be very familiar with residual blocks. Below is an implementation that supports both adaptive and non-adaptive instance normalization layers, since both are used throughout the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtwbXUTRK4a9"
      },
      "source": [
        "class ResidualBlock(nn.Module):\n",
        "    '''\n",
        "    ResidualBlock Class\n",
        "    Values:\n",
        "        channels: number of channels throughout residual block, a scalar\n",
        "        s_dim: the dimension of the style tensor (s), a scalar\n",
        "        h_dim: the hidden dimension of the MLP, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, channels, s_dim=None, h_dim=None):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.ReflectionPad2d(1),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(channels, channels, kernel_size=3)\n",
        "            ),\n",
        "        )\n",
        "        self.conv2 = nn.Sequential(\n",
        "            nn.ReflectionPad2d(1),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(channels, channels, kernel_size=3)\n",
        "            ),\n",
        "        )\n",
        "        self.use_style = s_dim is not None and h_dim is not None\n",
        "        if self.use_style:\n",
        "            self.norm1 = AdaptiveInstanceNorm2d(channels, s_dim, h_dim)\n",
        "            self.norm2 = AdaptiveInstanceNorm2d(channels, s_dim, h_dim)\n",
        "        else:\n",
        "            self.norm1 = nn.InstanceNorm2d(channels)\n",
        "            self.norm2 = nn.InstanceNorm2d(channels)\n",
        "\n",
        "        self.activation = nn.ReLU()\n",
        "\n",
        "    def forward(self, x, s=None):\n",
        "        x_id = x\n",
        "        x = self.conv1(x)\n",
        "        x = self.norm1(x, s) if self.use_style else self.norm1(x)\n",
        "        x = self.activation(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.norm2(x, s) if self.use_style else self.norm2(x)\n",
        "        return x + x_id"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faEGyHlHDmP9"
      },
      "source": [
        "## Submodules: Encoders and Decoder\n",
        "\n",
        "Now that you're all set up and implemented some basic building blocks, let's take a look at the content encoder, style encoder, and decoder! These will be used in the generator.\n",
        "\n",
        "![Same- and cross-domain interaction of encoders and decoders](https://drive.google.com/uc?id=15y07M9dgnk8_HtatlDfEpN5mM-BFBnMG)\n",
        "\n",
        "*Generator architecture, taken from Figure 3 of [Multimodal Unsupervised Image-to-Image Translation](https://arxiv.org/abs/1804.04732) (Huang et al. 2018). Content encoder: generates a downsampled representation of the original. Style encoder: generates a style code from the original. Decoder: synthesizes a fake image from content code, infused with style info.*\n",
        "\n",
        "*Note: the official implementation feeds style code through a multi-layer perceptron (MLP) and assigns these values to the scale and shift parameters in the instance normalization layers. To be compatible with our previous definitions of* `AdaIN`*, your implementation will simply apply the MLP within* `AdaptiveInstanceNorm2d`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZoAZwZgHJcM"
      },
      "source": [
        "### Content Encoder\n",
        "\n",
        "The content encoder is similar to many encoders you've already seen: it simply downsamples the input image and feeds it through residual blocks to obtain a condensed representation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCd393-QH_i0"
      },
      "source": [
        "class ContentEncoder(nn.Module):\n",
        "    '''\n",
        "    ContentEncoder Class\n",
        "    Values:\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_downsample: number of downsampling layers, a scalar\n",
        "        n_res_blocks: number of residual blocks, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, base_channels=64, n_downsample=2, n_res_blocks=4):\n",
        "        super().__init__()\n",
        "\n",
        "        channels = base_channels\n",
        "\n",
        "        # Input convolutional layer\n",
        "        layers = [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(3, channels, kernel_size=7)\n",
        "            ),\n",
        "            nn.InstanceNorm2d(channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "        ]\n",
        "\n",
        "        # Downsampling layers\n",
        "        for i in range(n_downsample):\n",
        "            layers += [\n",
        "                nn.ReflectionPad2d(1),\n",
        "                nn.utils.spectral_norm(\n",
        "                    nn.Conv2d(channels, 2 * channels, kernel_size=4, stride=2)\n",
        "                ),\n",
        "                nn.InstanceNorm2d(2 * channels),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels *= 2\n",
        "\n",
        "        # Residual blocks\n",
        "        layers += [\n",
        "            ResidualBlock(channels) for _ in range(n_res_blocks)\n",
        "        ]\n",
        "        self.layers = nn.Sequential(*layers)\n",
        "        self.out_channels = channels\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.layers(x)\n",
        "\n",
        "    @property\n",
        "    def channels(self):\n",
        "        return self.out_channels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDaItt-rN9JD"
      },
      "source": [
        "### Style Encoder\n",
        "\n",
        "The style encoder operates similarly to the content encoder but instead of residual blocks, it uses global pooling and fully-connected layers to distill the input image to its style vector. An important difference is that the style encoder doesn't use any normalization layers, since they will remove the feature statistics that encode style. This style code will be passed to the decoder, which use this along with the content code to synthesize a fake image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cff4eEHOcLk"
      },
      "source": [
        "class StyleEncoder(nn.Module):\n",
        "    '''\n",
        "    StyleEncoder Class\n",
        "    Values:\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_downsample: number of downsampling layers, a scalar\n",
        "        s_dim: the dimension of the style tensor (s), a scalar\n",
        "    '''\n",
        "\n",
        "    n_deepen_layers = 2\n",
        "\n",
        "    def __init__(self, base_channels=64, n_downsample=4, s_dim=8):\n",
        "        super().__init__()\n",
        "\n",
        "        channels = base_channels\n",
        "\n",
        "        # Input convolutional layer\n",
        "        layers = [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(3, channels, kernel_size=7, padding=0)\n",
        "            ),\n",
        "            nn.ReLU(inplace=True),\n",
        "        ]\n",
        "\n",
        "        # Downsampling layers\n",
        "        for i in range(self.n_deepen_layers):\n",
        "            layers += [\n",
        "                nn.ReflectionPad2d(1),\n",
        "                nn.utils.spectral_norm(\n",
        "                    nn.Conv2d(channels, 2 * channels, kernel_size=4, stride=2)\n",
        "                ),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            channels *= 2\n",
        "        for i in range(n_downsample - self.n_deepen_layers):\n",
        "            layers += [\n",
        "                nn.ReflectionPad2d(1),\n",
        "                nn.utils.spectral_norm(\n",
        "                    nn.Conv2d(channels, channels, kernel_size=4, stride=2)\n",
        "                ),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "\n",
        "        # Apply global pooling and pointwise convolution to style_channels\n",
        "        layers += [\n",
        "            nn.AdaptiveAvgPool2d(1),\n",
        "            nn.Conv2d(channels, s_dim, kernel_size=1),\n",
        "        ]\n",
        "\n",
        "        self.layers = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.layers(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iW9B-i2tP1fM"
      },
      "source": [
        "### Decoder\n",
        "\n",
        "As with all encoder-decoder frameworks, the decoder serves to synthesize images from the latent information passed through by the encoder. In this case, the decoder works with both content and style encodings.\n",
        "\n",
        "You can think of the content encoder and decoder as the backbone of the encoder-decoder framework with style information injected into the residual blocks via `AdaIN` layers.\n",
        "\n",
        "Note: the official implementation feeds style code through a multi-layer perceptron (MLP) and assigns these values to the scale and shift parameters in the instance normalization layers. To be compatible with the previous definitions of `AdaIN` in this course, your implementation will simply apply the MLP within `AdaptiveInstanceNorm2d`.\n",
        "\n",
        "Let's take a look at the implementation!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xf8KP7DRRrkn"
      },
      "source": [
        "class Decoder(nn.Module):\n",
        "    '''\n",
        "    Decoder Class\n",
        "    Values:\n",
        "        in_channels: number of channels from encoder output, a scalar\n",
        "        n_upsample: number of upsampling layers, a scalar\n",
        "        n_res_blocks: number of residual blocks, a scalar\n",
        "        s_dim: the dimension of the style tensor (s), a scalar\n",
        "        h_dim: the hidden dimension of the MLP, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_channels, n_upsample=2, n_res_blocks=4, s_dim=8, h_dim=256):\n",
        "        super().__init__()\n",
        "\n",
        "        channels = in_channels\n",
        "\n",
        "        # Residual blocks with AdaIN\n",
        "        self.res_blocks = nn.ModuleList([\n",
        "            ResidualBlock(channels, s_dim) for _ in range(n_res_blocks)\n",
        "        ])\n",
        "\n",
        "        # Upsampling blocks\n",
        "        layers = []\n",
        "        for i in range(n_upsample):\n",
        "            layers += [\n",
        "                nn.Upsample(scale_factor=2),\n",
        "                nn.ReflectionPad2d(2),\n",
        "                nn.utils.spectral_norm(\n",
        "                    nn.Conv2d(channels, channels // 2, kernel_size=5)\n",
        "                ),\n",
        "                LayerNorm2d(channels // 2),\n",
        "            ]\n",
        "            channels //= 2\n",
        "        \n",
        "        layers += [\n",
        "            nn.ReflectionPad2d(3),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(channels, 3, kernel_size=7)\n",
        "            ),\n",
        "            nn.Tanh(),\n",
        "        ]\n",
        "        self.layers = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x, s):\n",
        "        for res_block in self.res_blocks:\n",
        "            x = res_block(x, s=s)\n",
        "        x = self.layers(x)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Ru1dJWZ0XGK"
      },
      "source": [
        "## Modules: Generator, Discriminator, and Loss\n",
        "\n",
        "Now you're ready to implement MUNIT generator and discriminator, as well as the composite loss function that ties everything together in training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8-de36ytk14"
      },
      "source": [
        "### Generator\n",
        "\n",
        "The generator is essentially just comprised of the two encoders and one decoder implemented in the previous section, so let's wrap everything in a `Generator` module!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl_ZpENltpL_"
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    '''\n",
        "    Generator Class\n",
        "    Values:\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_downsample: number of downsampling layers, a scalar\n",
        "        n_res_blocks: number of residual blocks, a scalar\n",
        "        s_dim: the dimension of the style tensor (s), a scalar\n",
        "        h_dim: the hidden dimension of the MLP, a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        base_channels: int = 64,\n",
        "        n_c_downsample: int = 2,\n",
        "        n_s_downsample: int = 4,\n",
        "        n_res_blocks: int = 4,\n",
        "        s_dim: int = 8,\n",
        "        h_dim: int = 256,\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.c_enc = ContentEncoder(\n",
        "            base_channels=base_channels, n_downsample=n_c_downsample, n_res_blocks=n_res_blocks,\n",
        "        )\n",
        "        self.s_enc = StyleEncoder(\n",
        "            base_channels=base_channels, n_downsample=n_s_downsample, s_dim=s_dim,\n",
        "        )\n",
        "        self.dec = Decoder(\n",
        "            self.c_enc.channels, n_upsample=n_c_downsample, n_res_blocks=n_res_blocks, s_dim=s_dim, h_dim=h_dim,\n",
        "        )\n",
        "\n",
        "    def encode(self, x):\n",
        "        content = self.c_enc(x)\n",
        "        style = self.s_enc(x)\n",
        "        return (content, style)\n",
        "    \n",
        "    def decode(self, content, style):\n",
        "        return self.dec(content, style)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sE3H9XlVxuDM"
      },
      "source": [
        "### Discriminator\n",
        "\n",
        "The discriminator, identical to the one used in Pix2PixHD, is comprised of several PatchGAN discriminators operating at different scales. For details on how these work together, take a look at the Pix2PixHD optional notebook.\n",
        "\n",
        "The discriminator is trained with the least squares objective."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mI-JT2VsyJil"
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "    '''\n",
        "    Generator Class\n",
        "    Values:\n",
        "        base_channels: number of channels in first convolutional layer, a scalar\n",
        "        n_layers: number of downsampling layers, a scalar\n",
        "        n_discriminators: number of discriminators (all at different scales), a scalar\n",
        "    '''\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        base_channels: int = 64,\n",
        "        n_layers: int = 3,\n",
        "        n_discriminators: int = 3,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.discriminators = nn.ModuleList([\n",
        "            self.patchgan_discriminator(base_channels, n_layers) for _ in range(n_discriminators)\n",
        "        ])\n",
        "\n",
        "        self.downsample = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)\n",
        "\n",
        "    @staticmethod\n",
        "    def patchgan_discriminator(base_channels, n_layers):\n",
        "        '''\n",
        "        Function that constructs and returns one PatchGAN discriminator module.\n",
        "        '''\n",
        "        channels = base_channels\n",
        "        # Input convolutional layer\n",
        "        layers = [\n",
        "            nn.ReflectionPad2d(1),\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(3, channels, kernel_size=4, stride=2),\n",
        "            ),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "        ]\n",
        "\n",
        "        # Hidden convolutional layers\n",
        "        for _ in range(n_layers):\n",
        "            layers += [\n",
        "                nn.ReflectionPad2d(1),\n",
        "                nn.utils.spectral_norm(\n",
        "                    nn.Conv2d(channels, 2 * channels, kernel_size=4, stride=2)\n",
        "                ),\n",
        "                nn.LeakyReLU(0.2, inplace=True),\n",
        "            ]\n",
        "            channels *= 2\n",
        "\n",
        "        # Output projection layer\n",
        "        layers += [\n",
        "            nn.utils.spectral_norm(\n",
        "                nn.Conv2d(channels, 1, kernel_size=1)\n",
        "            ),\n",
        "        ]\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = []\n",
        "        for discriminator in self.discriminators:\n",
        "            outputs.append(discriminator(x))\n",
        "            x = self.downsample(x)\n",
        "        return outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FdafpxKB089k"
      },
      "source": [
        "### Loss Functions\n",
        "\n",
        "There are a lot of moving parts in MUNIT so this section will break down which parts interact with which other parts. Recall from earlier our notation:\n",
        " - Image domains:\n",
        "    \\begin{align*}\n",
        "        a &\\in \\mathcal{A} \\\\\n",
        "        b &\\in \\mathcal{B}\n",
        "    \\end{align*}\n",
        " - Encoders ($E$):\n",
        "    \\begin{align*}\n",
        "        E_a^c: a \\mapsto c_a, &\\quad E_a^s: a \\mapsto s_a \\\\\n",
        "        E_b^c: b \\mapsto c_b, &\\quad E_b^s: b \\mapsto s_b\n",
        "    \\end{align*}\n",
        " - Decoders ($F$):\n",
        "    \\begin{align*}\n",
        "        F_a&: (c_*, s_a) \\mapsto a' \\\\\n",
        "        F_b&: (c_*, s_b) \\mapsto b'\n",
        "    \\end{align*}\n",
        " - Generators ($G$):\n",
        "    \\begin{align*}\n",
        "        G_a(a, b) &= F_a(E_b^c(b), E_a^s(a)) \\\\\n",
        "        G_b(b, a) &= F_b(E_a^c(a), E_b^s(b))\n",
        "    \\end{align*}\n",
        " - Discriminators ($D$):\n",
        "    \\begin{align*}\n",
        "        D_a&: a' \\mapsto p \\in \\mathbb{R} \\\\\n",
        "        D_b&: b' \\mapsto p \\in \\mathbb{R}\n",
        "    \\end{align*}\n",
        "\n",
        "**Image Reconstruction Loss**\n",
        "\n",
        "The model should be able to encode and decode a reconstruction of the image. For domain $\\mathcal{A}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{recon}}^a &= \\mathbb{E}_{a\\sim p(a)}\\left|\\left|F_a(E_a^c(a), E_a^s(a)) - a\\right|\\right|_1\n",
        "\\end{align*}\n",
        "\n",
        "and for domain $\\mathcal{B}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{recon}}^b &= \\mathbb{E}_{b\\sim p(b)}\\left|\\left|F_b(E_b^c(b), E_b^s(b)) - b\\right|\\right|_1.\n",
        "\\end{align*}\n",
        "\n",
        "**Latent Reconstruction Loss**\n",
        "\n",
        "The same principle from above applies to the latent space: decoding and encoding a latent vector should reproduce the original. Don't worry if the equations look complicated! Just know that intuitively, passing in different content and style vectors through the encoders should yield those same input vectors. For domain $\\mathcal{A}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{recon}}^{c_b} &= \\mathbb{E}_{c_b\\sim p(c_b),s_a\\sim q(s_a)}\\left|\\left|E_a^c(F_a(c_b, s_a)) - c_a\\right|\\right|_1 \\\\\n",
        "    \\mathcal{L}_{\\text{recon}}^{s_a} &= \\mathbb{E}_{c_b\\sim p(c_b),s_a\\sim q(s_a)}\\left|\\left|E_a^s(F_a(c_b, s_a)) - s_b\\right|\\right|_1\n",
        "\\end{align*}\n",
        "\n",
        "and for domain $\\mathcal{B}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{recon}}^{c_a} &= \\mathbb{E}_{c_a\\sim p(c_a),s_b\\sim q(s_b)}\\left|\\left|E_b^c(F_b(c_a, s_b)) - c_b\\right|\\right|_1 \\\\\n",
        "    \\mathcal{L}_{\\text{recon}}^{s_b} &= \\mathbb{E}_{c_a\\sim p(c_a),s_b\\sim q(s_b)}\\left|\\left|E_b^s(F_b(c_a, s_b)) - s_a\\right|\\right|_1\n",
        "\\end{align*}\n",
        "\n",
        "**Adversarial Loss**\n",
        "\n",
        "As with all other GANs, MUNIT is trained with adversarial loss. The authors opt for the LSGAN least-squares objective. For domain $\\mathcal{A}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{GAN}}^a &= \\mathbb{E}_{c_b\\sim p(c_b),s_a\\sim q(s_a)}\\left[(1 - D_a(G_a(c_b, s_a)))^2\\right] + \\mathbb{E}_{a\\sim p(a)}\\left[D_a(a)^2\\right]\n",
        "\\end{align*}\n",
        "\n",
        "and for domain $\\mathcal{B}$, this can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}_{\\text{GAN}}^b &= \\mathbb{E}_{c_a\\sim p(c_a),s_b\\sim q(s_b)}\\left[(1 - D_b(G_b(c_a, s_b)))^2\\right] + \\mathbb{E}_{b\\sim p(b)}\\left[D_b(b)^2\\right]\n",
        "\\end{align*}\n",
        "\n",
        "**Total Loss**\n",
        "\n",
        "The total loss can now be expressed in terms of the individual losses from above, so the objective can be expressed as\n",
        "\n",
        "\\begin{align*}\n",
        "    \\mathcal{L}(E_a, E_b, F_a, F_b, D_a, D_b) &= \\mathcal{L}_{\\text{GAN}}^a + \\mathcal{L}_{\\text{GAN}}^b + \\lambda_x(\\mathcal{L}_{\\text{recon}}^a + \\mathcal{L}_{\\text{recon}}^b) + \\lambda_c(\\mathcal{L}_{\\text{recon}}^{c_a} + \\mathcal{L}_{\\text{recon}}^{c_b}) + \\lambda_s(\\mathcal{L}_{\\text{recon}}^{s_a} + \\mathcal{L}_{\\text{recon}}^{s_b})\n",
        "\\end{align*}\n",
        "\n",
        "And now the fun part: implementing this ginormous composite loss!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSZDUYjNWXgi"
      },
      "source": [
        "class GinormousCompositeLoss(nn.Module):\n",
        "    '''\n",
        "    GinormousCompositeLoss Class: implements all losses for MUNIT\n",
        "    '''\n",
        "\n",
        "    @staticmethod\n",
        "    def image_recon_loss(x, gen):\n",
        "        c, s = gen.encode(x)\n",
        "        recon = gen.decode(c, s)\n",
        "        return F.l1_loss(recon, x), c, s\n",
        "\n",
        "    @staticmethod\n",
        "    def latent_recon_loss(c, s, gen):\n",
        "        x_fake = gen.decode(c, s)\n",
        "        recon = gen.encode(x_fake)\n",
        "        return F.l1_loss(recon[0], c), F.l1_loss(recon[1], s), x_fake\n",
        "\n",
        "    @staticmethod\n",
        "    def adversarial_loss(x, dis, is_real):\n",
        "        preds = dis(x)\n",
        "        target = torch.ones_like if is_real else torch.zeros_like\n",
        "        loss = 0.0\n",
        "        for pred in preds:\n",
        "            loss += F.mse_loss(pred, target(pred))\n",
        "        return loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gttCYcvt-cTX"
      },
      "source": [
        "### Optional Loss Functions\n",
        "\n",
        "The authors also propose a couple of auxiliary loss functions that can help improve convergence. This section will just go over these losses but implementation is left as an exercise for the curious reader :)\n",
        "\n",
        "**Style-augmented Cycle Consistency**\n",
        "\n",
        "You've already heard of cycle consistency from CycleGAN, which implies that an image translated to the target domain and back should be identical to the original.\n",
        "\n",
        "Intuitively, style-augmented cycle consistency implies that an image translated to the target domain and back using the original style should result in the original image. Style-augmented cycle consistency is implicitly encouraged\n",
        "by the reconstruction losses, but the authors note that explicitly enforcing it could be useful in some cases.\n",
        "\n",
        "**Domain Invariant Perceptual Loss**\n",
        "\n",
        "You're probably already familiar with perceptual loss, which is usually implemented via MSE loss between feature maps of fake and real images. However, because the images in the domains are unpaired, pixel-wise loss may not be optimal, since each values at each position do not correspond spatially.\n",
        "\n",
        "The authors get around this discrepancy by applying instance normalization to the feature maps. This normalizes the values per channel for each position in the feature maps, so MSE loss penalizes the difference in statistics rather than raw pixel value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jbwOHEgffdx"
      },
      "source": [
        "## Model: MUNIT\n",
        "\n",
        "Now that you've got all the necessary modules, let's see how we can put them all together."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idFCK4ugfoUo"
      },
      "source": [
        "class MUNIT(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        gen_channels: int = 64,\n",
        "        n_c_downsample: int = 2,\n",
        "        n_s_downsample: int = 4,\n",
        "        n_res_blocks: int = 4,\n",
        "        s_dim: int = 8,\n",
        "        h_dim: int = 256,\n",
        "        dis_channels: int = 64,\n",
        "        n_layers: int = 3,\n",
        "        n_discriminators: int = 3,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.gen_a = Generator(\n",
        "            base_channels=gen_channels, n_c_downsample=n_c_downsample, n_s_downsample=n_s_downsample, n_res_blocks=n_res_blocks, s_dim=s_dim, h_dim=h_dim,\n",
        "        )\n",
        "        self.gen_b = Generator(\n",
        "            base_channels=gen_channels, n_c_downsample=n_c_downsample, n_s_downsample=n_s_downsample, n_res_blocks=n_res_blocks, s_dim=s_dim, h_dim=h_dim,\n",
        "        )\n",
        "        self.dis_a = Discriminator(\n",
        "            base_channels=dis_channels, n_layers=n_layers, n_discriminators=n_discriminators,\n",
        "        )\n",
        "        self.dis_b = Discriminator(\n",
        "            base_channels=dis_channels, n_layers=n_layers, n_discriminators=n_discriminators,\n",
        "        )\n",
        "        self.s_dim = s_dim\n",
        "        self.loss = GinormousCompositeLoss\n",
        "\n",
        "    def forward(self, x_a, x_b):\n",
        "        s_a = torch.randn(x_a.size(0), self.s_dim, 1, 1, device=x_a.device).to(x_a.dtype)\n",
        "        s_b = torch.randn(x_b.size(0), self.s_dim, 1, 1, device=x_b.device).to(x_b.dtype)\n",
        "\n",
        "        # Encode real x and compute image reconstruction loss\n",
        "        x_a_loss, c_a, s_a_fake = self.loss.image_recon_loss(x_a, self.gen_a)\n",
        "        x_b_loss, c_b, s_b_fake = self.loss.image_recon_loss(x_b, self.gen_b)\n",
        "\n",
        "        # Decode real (c, s) and compute latent reconstruction loss\n",
        "        c_b_loss, s_a_loss, x_ba = self.loss.latent_recon_loss(c_b, s_a, self.gen_a)\n",
        "        c_a_loss, s_b_loss, x_ab = self.loss.latent_recon_loss(c_a, s_b, self.gen_b)\n",
        "\n",
        "        # Compute adversarial losses\n",
        "        gen_a_adv_loss = self.loss.adversarial_loss(x_ba, self.dis_a, False)\n",
        "        gen_b_adv_loss = self.loss.adversarial_loss(x_ab, self.dis_b, False)\n",
        "\n",
        "        # Sum up losses for gen\n",
        "        gen_loss = (\n",
        "            10 * x_a_loss + c_b_loss + s_a_loss + gen_a_adv_loss + \\\n",
        "            10 * x_b_loss + c_a_loss + s_b_loss + gen_b_adv_loss\n",
        "        )\n",
        "\n",
        "        # Sum up losses for dis\n",
        "        dis_loss = (\n",
        "            self.loss.adversarial_loss(x_ba.detach(), self.dis_a, False) + \\\n",
        "            self.loss.adversarial_loss(x_a.detach(), self.dis_a, True) + \\\n",
        "            self.loss.adversarial_loss(x_ab.detach(), self.dis_b, False) + \\\n",
        "            self.loss.adversarial_loss(x_b.detach(), self.dis_b, True)\n",
        "        )\n",
        "\n",
        "        return gen_loss, dis_loss, x_ab, x_ba"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHz9F4m9wvKO"
      },
      "source": [
        "### Training\n",
        "\n",
        "Now you're ready to train MUNIT! Let's start by setting some optimization parameters and initializing everything you'll need for training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYQrHbgPw5ie"
      },
      "source": [
        "# Initialize model\n",
        "def weights_init(m):\n",
        "    if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):\n",
        "        nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')\n",
        "\n",
        "munit_config = {\n",
        "    'gen_channels': 64,\n",
        "    'n_c_downsample': 2,\n",
        "    'n_s_downsample': 4,\n",
        "    'n_res_blocks': 4,\n",
        "    's_dim': 8,\n",
        "    'h_dim': 256,\n",
        "    'dis_channels': 64,\n",
        "    'n_layers': 3,\n",
        "    'n_discriminators': 3,\n",
        "}\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "munit = MUNIT(**munit_config).to(device).apply(weights_init)\n",
        "\n",
        "# Initialize dataloader\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize(286),\n",
        "    transforms.RandomCrop(256),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5), (0.5))\n",
        "])\n",
        "dataloader = DataLoader(\n",
        "    ImageDataset('horse2zebra', transform),\n",
        "    batch_size=1, pin_memory=True, shuffle=True,\n",
        ")\n",
        "\n",
        "# Initialize optimizers\n",
        "gen_params = list(munit.gen_a.parameters()) + list(munit.gen_b.parameters())\n",
        "dis_params = list(munit.dis_a.parameters()) + list(munit.dis_b.parameters())\n",
        "gen_optimizer = torch.optim.Adam(gen_params, lr=1e-4, betas=(0.5, 0.999))\n",
        "dis_optimizer = torch.optim.Adam(dis_params, lr=1e-4, betas=(0.5, 0.999))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9d8AneYxuuT",
        "outputId": "dd07c208-3565-4aa7-b5ef-d526885fd3f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Parse torch version for autocast\n",
        "# ######################################################\n",
        "version = torch.__version__\n",
        "version = tuple(int(n) for n in version.split('.')[:-1])\n",
        "has_autocast = version >= (1, 6)\n",
        "# ######################################################\n",
        "\n",
        "def train(munit, dataloader, optimizers, device):\n",
        "\n",
        "    max_iters = 1000000\n",
        "    decay_every = 100000\n",
        "    cur_iter = 0\n",
        "\n",
        "    display_every = 500\n",
        "    mean_losses = [0., 0.]\n",
        "\n",
        "    while cur_iter < max_iters:\n",
        "        for (x_a, x_b) in tqdm(dataloader):\n",
        "            x_a = x_a.to(device)\n",
        "            x_b = x_b.to(device)\n",
        "\n",
        "            # Enable autocast to FP16 tensors (new feature since torch==1.6.0)\n",
        "            # If you're running older versions of torch, comment this out\n",
        "            # and use NVIDIA apex for mixed/half precision training\n",
        "            if has_autocast:\n",
        "                with torch.cuda.amp.autocast(enabled=(device=='cuda')):\n",
        "                    outputs = munit(x_a, x_b)\n",
        "            else:\n",
        "                outputs = munit(x_a, x_b)\n",
        "            \n",
        "            losses, x_ab, x_ba = outputs[:-2], outputs[-2], outputs[-1]\n",
        "            munit.zero_grad()\n",
        "\n",
        "            for i, (optimizer, loss) in enumerate(zip(optimizers, losses)):\n",
        "                optimizer.zero_grad()\n",
        "                loss.backward()\n",
        "                optimizer.step()\n",
        "                mean_losses[i] += loss.item() / display_every\n",
        "\n",
        "            cur_iter += 1\n",
        "\n",
        "            if cur_iter % display_every == 0:\n",
        "                print('Step {}: [G loss: {:.5f}][D loss: {:.5f}]'\n",
        "                      .format(cur_iter, *mean_losses))\n",
        "                show_tensor_images(x_ab, x_a)\n",
        "                show_tensor_images(x_ba, x_b)\n",
        "                mean_losses = [0., 0.]\n",
        "\n",
        "            if cur_iter == max_iters:\n",
        "                break\n",
        "\n",
        "            # Schedule learning rate by 0.5\n",
        "            if cur_iter % decay_every == 0:\n",
        "                for optimizer in optimizers:\n",
        "                    for param_group in optimizer.param_groups:\n",
        "                        param_group['lr'] *= 0.5\n",
        "\n",
        "train(\n",
        "    munit, dataloader,\n",
        "    [gen_optimizer, dis_optimizer],\n",
        "    device,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 47%|████▋     | 499/1067 [05:06<05:48,  1.63it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 500: [G loss: 11.74086][D loss: 0.30821]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 94%|█████████▎| 999/1067 [10:08<00:40,  1.68it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 1000: [G loss: 10.53732][D loss: 0.02970]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [10:49<00:00,  1.64it/s]\n",
            " 40%|████      | 432/1067 [04:13<06:04,  1.74it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 1500: [G loss: 10.07501][D loss: 0.05018]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 87%|████████▋ | 932/1067 [08:58<01:15,  1.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 2000: [G loss: 9.69387][D loss: 0.01541]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [10:15<00:00,  1.73it/s]\n",
            " 34%|███▍      | 365/1067 [03:26<06:32,  1.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 2500: [G loss: 9.45771][D loss: 0.01282]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 81%|████████  | 865/1067 [08:07<01:52,  1.80it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 3000: [G loss: 9.21778][D loss: 0.00660]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [10:00<00:00,  1.78it/s]\n",
            " 28%|██▊       | 298/1067 [02:46<07:00,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 3500: [G loss: 9.00961][D loss: 0.01060]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 75%|███████▍  | 798/1067 [07:27<02:34,  1.75it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 4000: [G loss: 8.87639][D loss: 0.01009]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:59<00:00,  1.78it/s]\n",
            " 22%|██▏       | 231/1067 [02:06<07:35,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 4500: [G loss: 8.72139][D loss: 0.00777]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 69%|██████▊   | 731/1067 [06:42<03:01,  1.85it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 5000: [G loss: 8.56996][D loss: 0.00359]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:47<00:00,  1.82it/s]\n",
            " 15%|█▌        | 164/1067 [01:30<08:15,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 5500: [G loss: 8.57948][D loss: 0.00540]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 62%|██████▏   | 664/1067 [06:08<03:45,  1.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 6000: [G loss: 8.43045][D loss: 0.00491]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:50<00:00,  1.81it/s]\n",
            "  9%|▉         | 97/1067 [00:53<08:47,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 6500: [G loss: 8.42890][D loss: 0.00393]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9+Zdd13Xf+dnnnHvf/GqeMBMAMRIAR5CUSEu2Yw1ekmk7imRZjttrxbETJWsl7dUr8Z8Q/9TutXq13e4VOe1YTuzYkh2bkmxJpEiJJDjPGAigUACqCjVPr954z9A/nFtFWi2CHqgO3Isb66LeUO+9W/fuu/d3f/d3nychBD6wD+z9NPU/egc+sP//2QdO9YG97/aBU31g77t94FQf2PtuHzjVB/a+2wdO9YG97/ZDcSoR+YSIXBCRSyLy6z+Mz/jAbl2T95unEhENvAX8BDANPA98PoRw9n39oA/slrUfRqQ6DVwKIUyGEHrAfwUe+SF8zgd2i5r5IbznTuD6O+5PA/ff7AUiEozRgPyAZwOCAO8eUeMz8n33/5aWv+j79+C93yts/xABif8hgN/OAoL3Hvl/vXn4a+8SQkCJxNveI0qhtc5/NSCitn/b+4CI8LfJNFtHcWs33u2V4fv27Qe9k/eWEMIPOmE/FKf6G5mI/ArwKwBKKb74xX+JUQqjFKlWJCIEZ5EQKBtI8WgyNBbBI8bglSILQkMbrDd0rKZnDVkwWBGCCFoEJYoQPMGH7QMZQsB7T4aHENDeY7ynIArjAzqAV4o24EJ0becVPgjWgXUO53po5VBktBsr7BgZZu+OndhuhjEl1luWLAv09w3y3PPPo3VgaLhOt7eJaTcQ16PZadJ1PdYaTWbnV6j2DVEolnnrrQvctv8gX/jCL9DtORaXVhgaHsVaT3//AK+89iqDw33Mzd9AlCJzIJIAhkIIEKLjQUBpTZIkaKUJAQRBBaHXzeg5Tzd4uvmxCAF6vYyslxE8OBcIQVAIgqBFkySGr//F//qu5/aHkf5mgN3vuL8rf+yvWQjhd0II94YQ7hWl8IDPHUEpjTEJRhu00iilESMoLYgCxKNUIDGKNNEYLRgjJFpjjEJrQSuNURqtDcaY+B5Kob5vMzohSVJUkuKMoSfQlUBPgdcKZTSiFUprtNEoYzCJIU2LJGkR0QW0KVAqVSmXqgwNjrAwt8QTTzzJpUuXqNVqiChuP3g7a2ur1OsVCqkmEUsiGYkOFAopXWtxYpiZX2F6bgWHwvpAsVSh1WyRtducffVl1pfmGBkapFgssrK8QrVaQZsEG6CHJhMDkv9txmC0QYlCidqOPiFA5jzOg/MB5wM2BCzQ6vZotbt0M4cLAlvuJAqlDCI6d9Z3tx9GpHoeuF1EbiM6088BP/+erxKNC4KEQA+P0tGZgoeOCF6A4Cn4DGUtCYFEBBsUPTSdAF4ZLEIgEHxAEQ8uBEQpBLDOgyhQCi8BFTQKAXEE5UAcViwBUAI+OERCfM88BYmkMRWpgHhBRKO10Nj0lEqD7Nh5G9ev3WBzaZaivgNdMFTqioHBAocPTtBX3culF59hfm6OG6stbqw1aTlhcXWZrNNGBSikBujhReF6UNYVFjtzmIKwvD5DozFHq92l4uqIKZAkKZlzEDxZ7lSCIoS43y74PEppfBB6vR7OOqzzuOBx3uOcJWtleOsIAZxkKFF5Socg4MXhrOdmwOB9d6oQghWRfw38JaCBL4UQ3nyv1/kAQsD6gNiACBgtETeIEPIrRosi0QbrHSGziDIUlQI8HkdXBCUaJToP2DH1KVGINng8QRSgUALiBAkAEQuFAIGADR4V3kYgcR8E0IiYuBkIwQIBhabR2mSj3aE6MMjQyDArc22eO/M9jp48Sf9AndHhOlOXz3P04B7wGZcuXeJGI2NqYZ1G11KvlRkcHcB4i1YFBgeH8AhJWqKiC8zOTHNjY5bKQJmx0SEWllaBeIwIPm4ITsk2cAoBRGkcCh/ixWZtRrvTI2Izj3MOay1ZlhGsIEHiC0MgiI/HRZFjQiEEf9Nz+UPBVCGErwFf+1u+atv5vQv0gsVrwWhNMApEkaEjbtAGcryVGkMaoS1eHD0XsCLReQCCIJg8dCsCHus8PggohaiAEMCrfB80osA5hwsBhYnvDYgYAgYwaJ3iTUKIpwuvhGB7XJq5zh3HjjC8ewftzVVW1lYZ7Kuy2VilsTxPa6mLyTZZnltgYaXB/Nom1oKzlkZjAx1K7BwbJdE1hkd3kqRFarUq1996i2azgTaawYEhSkmNpeUNPKDUlmMRI/wWoM8vihDixWKtx1lPljmyzG7jyr/mVP7tgklEYsR7x8+t2zez/2FA/ftN5ZWTyksk7zyZJ4aw4BEVsAhdNJkWtDaI9zjRVEMXrYSChkIIZN5h0VgiyBQ0CoMog5FAwMX3VQrB5lWRyv8XCAYfMoIN+CAEPNEHo0MhBlEJYgxBHOAQFdACK602NtFUh4cYGBllcHSEbqeF2B7lRNNqtLl0/jwbjSaV/gGKvcDG6iZKPMFDoVhAmZTh0T0US310epZKrYLHMrFjjMJAkU47I8ugXKqxsLKEtpZipYZ1AVB50BW83w44BB/Iep4ss7FyRGFdhrUW7z3eh9z54pX9Tmfa2rac6l2Kvm27JZxKIILorZrbB0ATXJ4O8QQVsKJQKDo+oMVjlNBzgjiFTgSValIfMM5jVMhTWfwEHzyaEEG7Tsisw3qH5JhLSSBI/D3vXYxkohA04FAqINoAGkHF/U2S/L5DGUEnCTZrcmV2kQN7DzJ57iLieiSFAgXjGRrop5zA7PR1Lk3NsLbZxqLiZzpHqVggTQuYtEi53k+5r4/LU5OMVGsUS0UO7D/I3MYiM9cXmBjbA3lqL5YKFAoFehlYFytbJGCdw+gC3gs289gMvIsVnfce7wLBx8zgnI9RPY9yIoLWepu2CCFsO9Z7sRi3hFMBiESiJ+IblW95OesBhKA0mcR8L96ivSOTgHMGE6BkBGUMqfc4C8E7nHgQn19lgRi5AwYBL7g8RYhRqCCAJwSdp8r4D5EYSlXIE6EDZQETUw8KIUFrQ/DC9MwqiSpz6sEf4Y2XznD9xhw7BqqUy1W8zWg0O0hSIiSBbruNtRYJEfyurK5gkiLF1RVaJrCyuspQqcr9x06iRLht7yDtrlAu1Qk+Au4s65IWy2itcS5gsw5eKYIXHB5nBe+FEBQEDfh3RLGtyLPlLFsVYthOj+90rK3Hbma3hFMFwHmHkgBekKDQW+UGCpWHcBcMXnR0PrEoyfACbTGo0CNrZxQKIYfnMTJ5LI6AyjGWj8gVUYJW4F0M6RIUIrEMFzwhqJgmg8UTCBK3uL8KxILvEYF7zoV5UFLGh4zJqwus1BLG9x5EWissLc+wMjfD5sYa1mtUoYLqOMoVA60mWmWkiWF4ZISx8QmSUsLV61Osb2zQKlSpq5TdO/bQ23QkaY1LF6dYWFgC4MDBA8wtrOTRRZHlkUiU2T6GbFHIeRW4teV5Ij6fA/EtEved+Gnr/tbtm9kt4VRCDjbzO7J1xUj+M6i4oZGgccHnXEySc1sJBEUvBMgi0xypFE+MPBIxgwNFQHK+RQBldM5gE8O/VogHLQqvQkRZHnzwqBAPcJCI87z38eoVDTqmZq01zgtZ5plbbbC43GW0kjA2vg8RRbvdRgi4rINWQnCQGkO5UqRcrZImRTpdy8LaLBRTSmmZZqvLwvI62qwyNLGDQqFMlzZ9ff3cee9DjE6MMr/wPbQWbGa3T6rKq1zRhuC2jid/LerA21UdvA36Vc7mR2cix1WSUypv//4PslvCqQB0XgV78ZDzQvGKCGQYJCQEL8Rc6AkiZBgcUPEOUHgp047cBEFybkbiwfUu4IMjeIU2oLRCi8KKjz6rY2RUXiE+kBhNFgI2aJQkGBQE8ChCUHglOBQukOMxF8F28HjlkETAlumEAlfWu1xdWqfUtYhoBsoJ2YBhdrGDmCrrQVHvr5CUUgqlfnq9lL6+IbrdHsVaiayQseE0O2r9NLoZXlYY6Ktw/MRDuODxW
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 56%|█████▌    | 597/1067 [05:29<04:16,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 7000: [G loss: 8.29681][D loss: 0.00428]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:44<00:00,  1.83it/s]\n",
            "  3%|▎         | 30/1067 [00:16<09:33,  1.81it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 7500: [G loss: 8.25187][D loss: 0.00296]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 50%|████▉     | 530/1067 [04:45<04:51,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 8000: [G loss: 8.12992][D loss: 0.00211]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy92a9t2XXe95v9anZ3mttUX2RINSRFdbQp2XIi904DKECUIA0CJC9+8nv0FwQB8pTAQAA/GImfkiAIBCSwoQQKYiZOHKshxU4skSySVfdW1W3OPWc3q5l9HtYhRdJSyYnI+DKoARycvfdZ3V5zrDG/8Y1vzCNqrXxgH9gP0uQ/7wv4wP7/Zx841Qf2A7cPnOoD+4HbB071gf3A7QOn+sB+4PaBU31gP3D7oTiVEOJvCCHeEEJ8TQjxaz+Mc3xgz6+JHzRPJYRQwB8AfxV4APwW8O/UWr/8Az3RB/bc2g8jUv1Z4Gu11jdrrQH4r4Ff+SGc5wN7Tk3/EI75EvD2d71/AHz6/XYQQlRj1Hfe/2HwrIAAcfsSEOL274LvevHdx/quN/X2COK7DvCHW/4xV1P/8BrEt7f69v7f/ft7t/+nj12/6zXUWr9zbfU730Xw7ZlCfOfC6/ftv2z/T/35+85Vv3Mpywbfs/33XeX33I/6XYf8/uv7vm9Rv+vkKWVqrX/kTfxhONU/kwkh/ibwNwGUkvzt//xvIaUk58z+cOD6cGQKge1qhVCKVMrtfjD5ia5vmP2Mcw03z/asuhX3794jx4jVEmpGUslaooxDFkkpiZwTUhpK9eRYMcJRCLjGIKgcjzPDMJGEoIpMZzSNa6kZGteSc6ZtGnJJi4MLOJ0GwhwxytC3LQiY/YxtHO26Y/Qz4zhjkagqSSmhlWSzXjHHiJ8TbdsjlCDlgLGaEiMxZ2op5Bwx1iIEhJApuaCVgVpRSpLJPDsdyCIz7AdWuuFityPngpACpGCcJlLJkCuvv/wK4zQTppmUM1MO1Apd2yKEIOVIzRmpFLP3lFJxznE4HGmcIwTPf/yf/vd/7Nj+MJzqIfDKd71/+faz77Fa698B/g6AMbrujwfuXF4yTSOZwpg8MQZCtOz3Jw7zEWcarNWoAittabXlNI8UCmfbNa0zhFro24ZaE0IKQknM0XN5cYfxNLBxK2JOhJgpBmrMJB8po0cKaJqG6+ORWJbjwIIRqoDgZ+YYmPJIYxR909HaDqMkua9Mw0gKHmUcxjmUVighSSGihYKsiDkTSkZYzTBElDU4qyFlSq6UmgmpIG6DQC4F7TpyiaQYESgqt86UE5OfcdYii0Qowd2zS4xUmK5Fl0JNBSklVltiSUgp0dbQ1IprHIfjETNncsmQEykXcspoqUkpIVgeZKUkL967ix8nxuPhfR3gh+FUvwV8VAjxIRZn+reBf/dP2smXxH4YKKVi2wbTWsI8sj+deHj1iH7dA5Xj/oCqgnXXE7MnxExOHtdpfJxR6hYmFon3kZQi1Mppf0TUShKBXCI5J2YfaHVD17ScjgekUiit0FIzDSeEcTSrFVIqUgooq0kRkk8INCInkp+xrUZb2LkzaoSYBJhKjBMlFVZNRwyBUAuiFoRPKN0QU6YUSSWDEBhrEEAYZ4y0oJZIU0oml0pFUhEopYECElzjcMZyv3WEEFBSUUvBaIXUjuADUghqKaRY8ClxmickgtMwMPmZkhJhnlEbRS0ZP83MRZHItKsGpSopB4YpYYWm6zr+ePjwQ3CqWmsSQvwt4DcABfzdWuuX/oS92B/2iAp905FDYDieuDkeWPUbVm3P3e05rdWIOxe8/c67fPFbXyPGwOX6jJdffBGjNI1tCT6BqCRRCLIQSqJrO1rrmOeZ0ziRakZpycX2gugTwzChTEvrGpQ2bNcbuq7lzvacnDNSSeh7HrzzLrlUVv2avlsxTgMhjvSiR2uDMAVlNEZClpU8Z+bJU4qnCqhVkJIgZ8U8JdACCaQYUKqSvcJZjWstxlhSTlilsMZwczxQYibmGWsM85wwzoIolJJxxt7io0rTNJRSqLUyzBNCSHItaKkYwszxamLbdGipMFqTgOwhl4pxlrV1CAy1ZJQGKEwhMKWA6iVmZd53NH8omKrW+veBv///ZB9ZFTkVpmmmioqzjhAq+3ji8vKMvrMYo5h84v7dC/qN4cmjI5tuw6svvUDfb5jmiSIqkUqRBdMopLA4a0BBu244Pr4hlYIrPZvLLRMTOZQlxEsFtdI1DnSDbg2mGEqphJxZbzbsD0dKjeQ8k8NM5zqIlZQ8MRaMtsu0MhzIpSKVpRSFUorD6QbvA860lBQpFYwoKKOoslAEYCRTmChFYLRDa4UQgrPNFm0N+8OB0+HIOE9YKpuuRyuFj4lxHlltN1QliT4wjjNFVCqFmBPGGHarNfM40VpHyZWu7fFhRluDEhJjDFpKgk9ILI3TKKvogZv9DaUmrGu+NyH6PvvnBtS/1wRd2y0RQQrG8YQo8NqLr2KMxjaKKZ6QuqXv18zDyK4/o+w09+7dAxTDOBGCZ71e471Hacc0joTJL6A2ALWw7reUKnG6QQnNbrNDVEEsmcPpSKoFKQWyQKZQUsVaR4yJWgvOmQXczh5tLFIbrGkRVKZ5ZIqeUvMy0NNEDiCFZJwGhulEzBHjFEppUg2EUFBFLxEjVq6GGyqBy/UlISzJQI4BhGDV9zhryc7hY4BcIRVAIqSEAqoIrq6vGYMn14Q1BorAe4+fZjZtS80Z72dSrDRNi5YKVyUpF/w0M9ZlurXGoYVmHAbatmPbrrg53DDMI+9Hbz4XTiUE7M43XF1dYbSm7Rva9RotDEUU+q4lpzNyziitWTcdQgmsbtisVig02hisshhhEEZgrcMKy1QGTvuJKgWbdb9soyylZqSUTPNMLZVaCrEkrocjWmpaY4nDCaRmXcEohZSCxjkA/DRThWAqlbYx9K6hVk0BZu8RCoSUhOBBCjwTRQdAUCUoKxhu9rS2pXGOIQwM3nNzuGa9WgZaJIXVivPNFiX0rWODMob1ek1JGSkEpRaUVOzWG5SUKCQiF1auZd11RB/I2pFKQWuF0YaSMqlEvAdnNbVWpnFEao22hlwjMSVuDgdqSVAFukokipoCfzSVsthz4VQA19fXhBBYty1SCfLyACKzoATPPEVCiBgjcY3BjxMrZygx4lHMo8dohWdG2yXr0lXSuQYfPVVJQogUEs/GZ/T9ijl4ai1Mw8j+dGS16bAIjNJcXV1RS+XFl16AUiilIivY7+CVQoiRpjU0ziGkQCqxcEpSIKXEaMNxGNmPB1xrGUePRmM3ipgCm9WKdbNmmCZCyEynCWJFVsV+f6J3PZvVipQSyioKGWc7UkygJM3GEUPAhwnFcs90VWybll4ZtNYorTmOI2frDSUXgg9oYxj9gLHLNqUWCgXbOciV1jha4ziehiVqG7tERuNoNysYFf+fAvX/t3bn4oLdaoOoBWk0p2HkOJzQxqCVRCvL/nBcsjmfgYo1ipubA6uVYLvaMI4TwziwPVtTiybEilSKxlkOxyOzqGzWG2RjGeJMDYUQAsdhWKasqLCNRUvDZrWCXDBFkEoma0Xbr8gpEUrgZjyilUYLSYqBffTL9I3kehjoVz1SSLQxNI3BGI2kYRoCB7en71uMavB+pu8czcrhTx6lFPd3L6CQWLdgKqMkxliUzohS6V3LKU6UmtltNhyOhQcP36VpO/RqTdM0i4MLSHHmrOtRpRL8RN90VATBaExj8X7GSklJmRAjVllCDChjWa1XOGuIKRJixMeZXA0xJX4kIlVJHikKlcLkRxCZVePIpVAFxBw4DHu26zVQuby8S2cbLrf30ULRWMe1UOQYqCHj80TTr4gx4JyhKy2hJKoojGFEa4NE0LmOe+d3mINnPx8oqaK0wAiN6wxj8EghkUDJGQGsbM+gRmxnsJ0ixoy4xSQogetbJh9orSOmhERytt4Rj4LiC9YpVn1PTYJSM41tcY1m85GekgTOGKZpRklFTgu+oUZSTIw+cbHbsWotMSWcNrx6/2U61y/T3C3x6rSmE
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 97%|█████████▋| 1030/1067 [09:13<00:20,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 8500: [G loss: 8.11098][D loss: 0.00283]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:33<00:00,  1.86it/s]\n",
            " 43%|████▎     | 463/1067 [04:08<05:24,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 9000: [G loss: 8.17234][D loss: 0.00518]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 90%|█████████ | 963/1067 [08:37<00:55,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 9500: [G loss: 8.16071][D loss: 0.00234]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:32<00:00,  1.86it/s]\n",
            " 37%|███▋      | 396/1067 [03:32<06:05,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 10000: [G loss: 8.14926][D loss: 0.00227]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 84%|████████▍ | 896/1067 [08:02<01:30,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 10500: [G loss: 7.89429][D loss: 0.00395]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:35<00:00,  1.86it/s]\n",
            " 31%|███       | 329/1067 [02:55<06:44,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 11000: [G loss: 7.89454][D loss: 0.00154]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 78%|███████▊  | 829/1067 [07:25<02:10,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 11500: [G loss: 7.84051][D loss: 0.00255]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:33<00:00,  1.86it/s]\n",
            " 25%|██▍       | 262/1067 [02:20<07:16,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 12000: [G loss: 7.81340][D loss: 0.00118]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 71%|███████▏  | 762/1067 [06:49<02:40,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 12500: [G loss: 7.79256][D loss: 0.00100]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:32<00:00,  1.86it/s]\n",
            " 18%|█▊        | 195/1067 [01:44<07:54,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 13000: [G loss: 7.77127][D loss: 0.00220]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 65%|██████▌   | 695/1067 [06:14<03:28,  1.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 13500: [G loss: 7.67707][D loss: 0.00148]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:36<00:00,  1.85it/s]\n",
            " 12%|█▏        | 128/1067 [01:09<08:24,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 14000: [G loss: 7.71080][D loss: 0.00076]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 59%|█████▉    | 628/1067 [05:42<04:00,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 14500: [G loss: 7.64382][D loss: 0.00227]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:40<00:00,  1.84it/s]\n",
            "  6%|▌         | 61/1067 [00:32<09:11,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 15000: [G loss: 7.69827][D loss: 0.00103]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 53%|█████▎    | 561/1067 [04:59<04:35,  1.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 15500: [G loss: 7.62718][D loss: 0.00268]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 99%|█████████▉| 1061/1067 [09:26<00:03,  1.91it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 16000: [G loss: 7.62309][D loss: 0.00060]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.87it/s]\n",
            " 46%|████▋     | 494/1067 [04:25<05:07,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 16500: [G loss: 7.54315][D loss: 0.00055]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 93%|█████████▎| 994/1067 [08:53<00:38,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 17000: [G loss: 7.54358][D loss: 0.00049]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:33<00:00,  1.86it/s]\n",
            " 40%|████      | 427/1067 [03:49<05:36,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 17500: [G loss: 7.57658][D loss: 0.00252]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 87%|████████▋ | 927/1067 [08:16<01:14,  1.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 18000: [G loss: 7.56780][D loss: 0.00026]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:31<00:00,  1.87it/s]\n",
            " 34%|███▎      | 360/1067 [03:12<06:16,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 18500: [G loss: 7.49320][D loss: 0.00053]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy82c9m2XXe99vDGd/xm2vuqh6q2c0mKYuSKJECHMl2IAcJgtzZ1o2BAL5J/oD8Lb7wTS6cBMhFEFixFSEWLNmRKIek2Gx2N7u6qmv8xnc88x5z8RYpKrHaSkLKpaAf4Ls45z1nn33Ofr61137WWlvEGPkCX+BnCfkfugNf4P9/+IJUX+Bnji9I9QV+5viCVF/gZ44vSPUFfub4glRf4GeOnwuphBC/JYT4WAjxQAjx3/w8nvEFXl2In7VOJYRQwI+AvwM8A/4E+Psxxh/+TB/0BV5Z/Dws1a8AD2KMD2OMBvjvgP/85/CcL/CKQv8c2rwJPP2p42fANz7vBiFETNOUGAI+RJRWCAB2VvSnjakQAiEEEAkh4H1Aa/3y3J8hxt1vUimC9wgpIe7uUUohhCCEAOLlY4TgZbMIIfixBQ/BIxAorX+6cRAvexci3nuQEinE7nyIECM/7vaP+/yTZ/74vJR/1t5Pmo5IKX/SB+89EdAv38PHiBQCpdWPPw/x5XuFGFFSwo+fE+PL7/Ljfu3efXe4az/GiHjZl/iT6/88fnLdT7VljSHG+H+/mJ8Pqf5SEEL8I+AfASil+B/+x3/Kt3/39zDTA37hV79O7gPOW0ToMb2k6XqSPGE8KTg8OkZHz3f/4Hf49gef8tv/5X/F3WuHJGlGMJamafjD/+mf8cIJ7t6+xvvfe8hrb13nyZ/+CWdN5G/+3d9iOi04f3FGDJ6qbdk7PGBvMsX1Bik1zjtcaPiD/+V/5Wu//A2+8a1vMJ4UBD9gO4MFWjPw/u/+IZ8+veDG/bscH+8RhGV5vqBrB/p+oK47itGEd778ZbSSPPnsEfmooByX7J/cxAyGar3GesPJ4R6LszNu37iGjoLvfvBDHj4+4/r1a4xHKY2TZJMJ4+mEr969xWq5xKJw0fI7/+yfM5nu8e79dwjC8H+8/wGjvGQ+KmgWWw6uH/P4+TPefecdbty+yfXr17i4OGe1XNOtKu5/6S2aruXgYMZ0MgEiy+WK4OHs+RVXqxU379xiOpsSZeQf/oPf/gvH9udBqufA7Z86vvXy3J9DjPEfA/8YIMuz2KxXPDtd85/91n/COJdcXi04PD7G1DVD3+AIlFlGkqQ0bUeqYNtsODy+RVkUqCRBCUHvei5Pn/DBwzNe+9p9Hv7wAbfvHPHo+9/h8dMr3vuNv0WmNb7vGY9KBjMwBkSM1HWDQlC3NUmWcPvGETdv3eHO2+9gO0NtLSI4hFacnl5w/vARH3/wIXe+/gvEoebZowXdUFMUE9I0pWsHnBuwdsSL83OiDwy94+BwzL17r7Fte6I31NWatm359Acf0HaW733wKXJcYLoOPRpjguCNr/4SX/nSaxjT8uiTJ4hEU4wLnnz4MU+fP+fg5IT9+Ql5kbBYrek3G969fZ262SJGKTFErp9cJx8X7O/NaDYbuk3NjaNjFkrjB4N3lr5rMX0HUVDXLdu6oXWOpEjo6gYtAta7Pz99/F/w8yDVnwBvCSHuvSTT3wP+wefdICI8/KP3mdy9QfCWNN3D9ud465BCE5VApRpjDd4nHEwmXF095uLsOdnem/SmxxqHTCOby1P+4Hf/mHd/41c5/cGHhFHB6ccP+M53H/D2N3+Z+XyCdz15mZPLjNV6TQwBgaR1DWmSoLUm0YrzZ89RucC0NU0YWK2XSB+ZHu3x2fsf8e0//COmt/apNis26yuicwymp5z0zOd7XJ5f0g6OuSoQ0TMMA0onpFnOpw8+YzKeoIm8ePKMx+fnHJ+c8OYvvoe0Hi8sl6uKd956k2mZcTBOePiDDzk7P2W5qtAqIcsyHj18ws17d3n9/pv8/u/9PrZacHl5Sa5THj99Tl23vP7WfXrnuHH9OgfzGc12g7WWtqmZjnOKMmXdbdks16xXAik1iU4YekdVVSR5AsBmvWXoGvJR+bkE+JmTKsbohBD/NfAvAAX8kxjjB593j3OeHznHt37xl7hcXnF0MCctUrqmJhESvKXMM5wXNL3FuZ56s2C1qrh1PGKzrJgWJfsq54ff+z5tKhHVkrqpmWSW7334IXfefptCp8TB4IuUqqmwvaXZ1Iz2ZgQB5XhEs90QO2i7gWg2FHnK5ekZlYw0Xcfi4oJcSN7/4QP2b1+nmAjSVOHDzhfqBouLDcYFrHFED1pL2vWGpm5xHrI84/TqAlNvOL9Ysm4ajo+OeePOXZrLU86vLsjGY37zb//HLC+ucMaxaisurpb86JNn1HVFMRrx6OFjsiLn5Lrj8fvvM3QbxkdHbDc1Oi9ZLjeU4znOB7wLrNcbNIHxuOT8csnZi1PaukGkCYcHBwxDYLlo8d6TFwnD0LG4vISoGI9G7B+e8Hy1IE0kn6ca/Fx8qhjj7wC/85e+XsDXfu3rTFLF+bbn/PycJEvYm81ZnZ6RKknTDWidMk5z1pcLnn70I2RasLd/gLWWzgysFhu+/4PHvPalL/Hpo8fcvHeP7/3rf00ym3Pt+owQLZtqw97xnOgjbbMkTRVJlpDmBcZYRIChaRiUIbUNk70J3nY8Ob9AxcjTH/6QzXLN8Zfeppxo6mqDUgU60YTgyYqMyWyP4CJ22GCc5/LiHPbnnJ1f4FXKebXh1q3bNIXjvW+9TVtVpGlB1Cl3336PN7+aopTg+PCA2XhMs1gyOM/J8QleaB49fMTQ9rs2qi2nL86pu45t41h9+0957fXX6LyF4Dne36MfepI0Z7nasFgtSWXAGU+Rj9hUHflY0dQtVxcXNG3LaDzCOsdmfUXT1DifMPiIFeCkYlpM+Twh6j+Yo/7T0Erx2l5BqlPGd+7w7CWpDqcz1usNXgQuLrfMpgWmy5hMplycvmA0OWA6zlFKon3gD/63PyLbO+LZoycc3H2N1WrJRs342jfuYdsV5XSOQxC6HoKjHJU07RVJolBCEJ1FJwld37F3fZ/6/AIfZ/Rty9BVmE3F1emS8nCGkA3bTUXfeYxb4IUnERD7miFJULqgGJcwGNabFfVmiXWB67fvkRYl49GIL9+/j9Qd4tqU0WjCV977Mkoo1ssVbddhTYexhsPDQzZ1jfOB6zdOKLKMZ589wcfIZFzy7PEzbt69Q9F3PPjoI1prCASkEmzWa7JJyWa14PjoGvVQs7i8oExLBIrpdE6aZ3T9QJIkhBh2K14B3kukKMgLODjZo9ku2ZsfU2jNv3PZ9+Px/Ctjzr8HUeSoNKVtGrTWdL1lu7zi0WcPmc5HKAHf++4nzKdjDg6mrLdrysk1rk7PmR0c8PSTH/HJg2d8/Zu/yrLpmBWSf/vHn/Ebf/c/Yr+UnJ/lpDon+sjTx5+xN5sghcSbjmazYm86h6FlcXWJUppRormql6hRyXa5pFlvWZ9fofYUkwPo10tsyEhGGd1Qs1ltKfMUfESX4MVAa1qQkus3b3Dx4jmZTNhuNtw5OiRLFR/96GPee/cN3n37Hi9OFwQbeHF6ilSK1WaLl5633nyDUT5GZwkvnl9w+uwFCsF0NGGxWSFTze37d6muVgxNTSYkq8WSyd6MrBhxsVxyIKHeLDjam3F6+pDLs2cc711jPjtmSDNoEmQI6CTDeU9Vb8lSjU7Ae8FgFlyeNtTVGrygqqq/+unv/w10kuCjR2pFtI7F5YI9BedXC1pbM/SCJN39Rz1//CmnL56DbBinY6rLI/7Nv/g9Tt55l6vlgm/82lf5p//t/8zXv/HLlCKyvbiiFIrWdWQ6R0rJ+cUFN2/dRiWS1eIKjaTtOxCSqrWkZw+IXcfZkzOUdSzPrtj6hoPDAtc1DCYgUoUbPNFIEqlxUdANhu7sHJ0lJFoxnx1SjEY8cY5cZ8z390mzlGHocc4ynkzZ1A1X6w3F8xes11ukVJxdLRjPp6w3Ld5CVVUgB
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 81%|████████  | 860/1067 [07:40<01:49,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 19000: [G loss: 7.47671][D loss: 0.00076]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:31<00:00,  1.87it/s]\n",
            " 27%|██▋       | 293/1067 [02:36<06:51,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 19500: [G loss: 7.44688][D loss: 0.00047]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 74%|███████▍  | 793/1067 [07:03<02:27,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 20000: [G loss: 7.47304][D loss: 0.00089]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            " 21%|██        | 226/1067 [02:00<07:33,  1.85it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 20500: [G loss: 7.35975][D loss: 0.00081]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 68%|██████▊   | 726/1067 [06:28<03:06,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 21000: [G loss: 7.42969][D loss: 0.00046]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:31<00:00,  1.87it/s]\n",
            " 15%|█▍        | 159/1067 [01:25<08:00,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 21500: [G loss: 7.37134][D loss: 0.00021]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 62%|██████▏   | 659/1067 [05:53<03:38,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 22000: [G loss: 7.33494][D loss: 0.00061]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9S6wlWZam9a39MLPzuu/r198enu/IzKqs6q6uQjChBUgFk5YYIAoJWgipJjCGnjFCYswEqQYtYAJiBoOSEGoJaIoqqehGWdlZmRmZGRHu4Y/r933eZrYfi8E294xMMryyiwzaG8UO3Yh7T5yn2W9r/etf/9pHVJUv1hfr17nMP+s38MX6/9/6AlRfrF/7+gJUX6xf+/oCVF+sX/v6AlRfrF/7+gJUX6xf+/pcQCUivy8iPxKRn4jI3/s8XuOL9e4u+XXrVCJigQ+Afw14Bvw58Aeq+pe/1hf6Yr2z6/OIVL8L/ERVP1TVHvjvgL/zObzOF+sdXe5zeM57wCef+vsZ8Htve4CIqLW/Cr71Z/9WAQFBEBEQAWS4mwKZN0FY+NTtn/Ws5W6fvof8kvv+9dfPP9unM4T8Si/02XeSN59dh2Pz+rmVz8pDn/6sgqCvX0FkeKWfvZ6ivL51uBMx9KjqL31TnweofqUlIn8I/OHwO//mH/yLVHaEqicaxaiiuQV6VCPWCUoiJuh7w7YVslZMdo64fec9RqND6nqHmsRqfc7N/EO23SUqDucaRKDvNuTYYwS8bYgaiKlFxGCkIkUlaCTniFOoxFF7w6geY2xFzBBTRFUxYsqBzkJW0JQxYgBDzgpZUBGSQs6g+WfnOmUlpUTKEREQo4gpQBMxYAQxgohircG58ryvf3ISchaMeOpqxHR6wGi6AwY27Yq+XRPbOf12QdsvSZJQJ6goTg0iDs2GlAyaLUYsIEwmU/b3jplMDnDVBFWhD4Gk4HyNr6fUTYMC/9l//O9+5rn9PED1HHjwqb/vD7f93FLVPwL+CMA5q2gGEpX1uAQhKSFDAoyRcllJRVShC5kYAQ1stxecX3Q04x28HWNyZLtdse1uENsDFrYJMamAU8BaIWsATRgszlRY6wgKKYGx0HhLYxsq56i8RzDkPiEqiBo0KaqKqiAIVtxwNRtEIJOGa9sAStREShnVElVTSsSYCqAdSIasCSUgpsI5izFCFiGEXJ5JBBGIsTyXNRkjlj5sqVKDtyOmk3202aPfzljKGREhxy1owkou4M2QkqBJCuAFqqqmaXZpmj18vYN3E7JGjGnAWFQMYgxt19GH8FYAfB6g+nPgqyLymAKmfxv4d972AAX6mPCSMSgaldgr25hpU8K6RFUXcIWUiVmx1iEoObVs1krXt1hrsQlC3IBRjFhSyuTY4qzBWcGYEmEEsCI4a8vjjMN4wRmDiA73tyCWPiiaIykpmkuUIINmRbWcWDGvk01Gs5KzknImaSJlCCkSQyqgQgmpJ6eMMRaLLUDMuQDVRHKmRKgUSTkW8IotB2uIaOoyYoQueHxbIxh8Nca5Cml26UMmoOTtghA3pNgjApqFFHV4nxnvHaNRQ12NgJocPTiHEUFNQozQh47NZsNqs6ELibcVeL92UKlqFJH/CPifAAv8fVX9/l/xINo2YlNAraOPsOoT666nCx3eQYiCaiImhWxwoliTURFCjESbqSuD1Yy3GecqMJaeRFYQVcgWMbYwCI2UTGMLp8glhZHfQIOgEZFMTqCppLTXtY0Rg5ghwCKfOshKViUrxJyJSUmq9CEQ+ggYxEBKiZxz4TC5pJ+UKKAzGc2RnEBMSa8pJnIeUq9RvPcYI6QcCd2GNZBCh682OOdABNWAFUPlPTlb2i6TYiIlyFFQNVjjMSbRtluEK8Chaki5J8eenCLGCjH1dN2aFDokvZ3/fi6cSlX/GPjjf5rH9G1EUqAzhjYJmzbRhXKVW2MIPcSgxGTKyc0JI6FEHsl4Z3BjizrF4JEkOCt4b+lyJvSZrBa1BiMgooCCGlJWcooFOAM2cjn/gJKTlqiCGSKkeVMkqELOWtKdFF6UVQcwFV6FCGIMYi0/47YFSGDepFAogBcFjZmcwTqLEUiZknoRcs7EqIhkjCRCbomhpXcL6mqEtQ5jDDFGutCSUkBTRqPS9xHUIDgKwEuU7LoNOQVyUvouYF1FjuWxUF7LiuINePN22PwzI+q/uKzxqArbNrDplC6Uq12GiyKGRAgKeMiWHAwJIduIkYyimC6j0WMseCJjn3EqSM6IFMIrouQ0pJKB9+RUTpLJZqikAAVRByrklEk5ozaXky6FT5UTLKSUMRJ5jciUM2EAFWJQhIwitnCxnBVjLMaUKGmMYq1gM+UDqyOniLWCrxwqCUEHom9JWRGRQrCVEsFyIMZE7FpELM45FKWLgRA6Uo6klEHBWY/3I1QLsFUTSiInKRExJXJuafs5KfXknLDWUFuPGEElv/msv2y9I6ASvKnRrKQYCH0gBoMasKKEkIkxgxqsNQOZdhhnsMa8KX7bbaIj4mqorZJ7wUqp0Ky1iIkkNYRQSK+zBiuGnEtkErWDPDFELDXEpPQxoiScceScMUZRMQOnKoDRnABQMSQVwpDecs7l4hCLsR5RIcWMMTIAy+CMYgxgLNYaRAVwWCeFV4klOktOCTGenKWkbiOgWgqALCiGXgMikZAjYgwxRUJMBSxkxBjquqaux6QIIQRSzogkIhBiT9e1KJl+AKNIeX/JKCKekN5+Nt8RUEHfB2Kf6JMiGJwrWU7EEPpCVJ0rJ1VEqEeeunaILQBJfWS5XBGjUosBZ1DJWKPUzuGMIFbIQNJMUsFqKa01CeRMSgk14HwBYYoZ1XJVypDuABCwpjxXRrHGQLJkVRQzVHFCykqMCc1grcEasAJqSsFg7FAfisMZg7VgncGQQRRjSrWnJFwtONuQVUhJSrmIElMe+JwtFyIRpBQQhdMrxhhsXaPDP8ZaRIbPNMggKRWw57wFPMbYIRqDsw7nHAY7yCVvP5fvDKgKbzGglKtW0sCXlKjlwxsjqGastYwnnroGg5IyROMYy4S2DyWVZiUFwTcG7zzOGawRLIIZxEIngmgh2VYEcYK14CvBWYhWULUlElH0tNc/lMyGGUh6FqHw/Nd8CjKCiimcSg2aM0aEyhuMdcjwnHXlcU6wNuMr8+b9qRZQKOCsxVlHH9Kbqi2/limGerakMS1VG6WaBLDW4r0vVXYfCKEvlWa2aDbkpMQ4pOW4xYihqkb8XIpThtSdiDG/Jfm9Q6Dy3iMKOShqMhCQIb155wroNCJYmsZT1wbrAsRyNVnnaKTCeNBkENVC1J0rmo8oxIzDUFmLtR4oYJZcIoz3HucU5xUxinOF86R2IMYw/ChoKoSdQcxUiFlLdMrDiTYWZyyigkkCWnQia+2Q/oS6rqgrBySMVZpGcKZwuZQYUptFxBR5JEFOkGImDfIKIqVKJZAzIHY4qoWHpZQKBzMl0sQYiBJRdaBFdolvonIi50TX9mAMxkBVO7J6jDpUhPzPS/qrak/lBBtjucq1wphyVYNju2kR4/BVTd1UOF+U6qyCaIlYimJiqcasVSonjJxQWyA7VAvHqp2lrguRzRmCzeQsWKNUNmF9ER3FKDElaFMh7mIxFsTEcgEMImLKEJMQYybnAXzyOroW8u+qIoIaA9Y64GdR0RR844wtF5G1RdNKGTtEupgSKWcyStJEyOmNUi+iZDNIIckCBusUKwYddOOUUknvOQ/ip5ZUSSKlEndKOjaknIhpDdbjvcVoRnLCmgYRi9r/x+n7ufVugEpgXHu8dXQpkXIiaynRa+8xCGPnqau6RDRriJpogxCliHMZIWkq1ZYqYizeOypv8c5CLnqQNUJdWRrvUAmoQmUNmg0iHicJROlSpstF2U8D2UakAKIQncJHFHJKaIqQ88CN3NByEaqqKqnL2KEAyG/6lCJSQJNf9zAhR6VPsUTmrBihSN4UWcKqYkUR58gCKZc0mVSxgJqMISNKkTHMkApTKoBX/XkXgRisk3IB1B6kVK8lVQ8Xhb5W3/OQ/t+Oq
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:32<00:00,  1.86it/s]\n",
            "  9%|▊         | 92/1067 [00:49<08:31,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 22500: [G loss: 7.34315][D loss: 0.00076]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 55%|█████▌    | 592/1067 [05:17<04:11,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 23000: [G loss: 7.30039][D loss: 0.00104]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            "  2%|▏         | 25/1067 [00:13<09:15,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 23500: [G loss: 7.33213][D loss: 0.00043]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 49%|████▉     | 525/1067 [04:41<04:45,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 24000: [G loss: 7.23658][D loss: 0.00020]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 96%|█████████▌| 1025/1067 [09:09<00:22,  1.91it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 24500: [G loss: 7.14538][D loss: 0.00028]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:32<00:00,  1.86it/s]\n",
            " 43%|████▎     | 458/1067 [04:05<05:25,  1.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 25000: [G loss: 7.14678][D loss: 0.00028]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9acxk6XXf9zvPcpeqerd+e+/pbXp6dpLD4XCGpGhRFElQlBmJgmVL8YcgNmABCRIg+ebvAQIkMRBHBhLAsqQoQWxZgmTJskRJlESRFEWK4jrikLN0z/T0vrxrrffeZzn5cKuHS8ShlJBJM5gDdL/13rduVd3nOfcs//M/p0RVeUPekO+lmP+vP8Ab8v8/eUOp3pDvubyhVG/I91zeUKo35HsubyjVG/I9lzeU6g35nsv3RalE5MdE5AURuSAi//T78R5vyL0r8r3GqUTEAi8CHwCuAn8J/Meq+rXv6Ru9IfesfD8s1dPABVV9WVU74FeBn/w+vM8bco+K+z685gngyjf9fhV45vVOEBF1/ls/ivR/AOCuMRXuWlXtH2n/RFVQ/cYxRb/tteS1x9o/4ZuOfON9WJ4nIogx3/Kcu68v33yOKlmVu2+sunyp/r/lB/+Ws75NdHlt3+4t5O6FfG9leZ1i5BuvrYqqInfX+u76yLdcPa9dw/KHZkVV/9oL+34o1d9IROTngJ8DMMbwn/03/4Qj6xWlKxi3CVzNxugwkSF39vapZcFGOadkzrTZ5854SgtYI+zOMy/darl1Z04zm5O7BWSF3C+glAVWHBqV3IX+bylhYsSoYKoCjCFpwKwVHDl9kgdO3ceR9RpnYdG13NqaMpk1DKxyeGOFmISrd/a4/Oo1ZrtTuklLO5ngBp7RoQOU9Qrz3THddIGrKurRAGfBlSWSFTSymE+Zb49p9heknIhk/MBTjwqaRUuzM8N2oJrRnJCcEQxq+ttL5O7tIqgTMJCzYpxDRNGoaMxLRTCY0uNXatbvP401A1wT2L1wkW62T7VyGD9aYbF9DcmZemUNU1jCvCF2Db7wDNbWsHWJQ/nSn3/+O+7t90OprgEnv+n3+5bHvkVU9V8C/xLAeafWREqreEmEOKcNHdZWtCmyMx9TM6eZzRjaBTFOSdqSkrBIwqJzpCSoN7hBSdRInM6hC5iqwrsS4xxYIbsCTQmNEQkRbVvyogEBKYSiWEW8QzWScyASyZpJMZO7TKqEpJk74wlXr19n98Yd8iySVJAkhL05O+N5f6cnEGMRlOSgHpSggS4okiOaBVsMcENByXinVBsFq8OSQU5sZ+h25xABsb2lyYosLW2vVLpUOlAE4xzOu94CkcnGkBVI/Y2Umpb57dvUww3ayYQUWlQVU5RIUSDWI0QwS8NmLYpgywpf1/iyQGP3ugrw/VCqvwTOi8hZemX6WeAffreTYpiQopBwzGdTFhhiNoRUsbU7pSCwagLjNEXTlOQMHZZWDQEwzlAPSuyooJHMZD4naEJEyBlICQ2JnCJGTL8RMaIhkNoWQfBuiHcFNgvj2RjLGO8hJMusaQihwTnP/lzYnU6YT8c0+xNcdv0GS28p4nSB5ISKYKuKHCtCJ3RWsRqIi0Ts0tKFgjjBuAK36tlcLzl+wKOqxFnD9rzjrpcR7YNg7TrIGVHzmueWpGQUIyBWICoZ+psJQU1CyRixiBpyjIRJg6bcny8CzvRKtPzsd6/pbjhgrCPHBDG/7l5+z5VKVaOI/BfAHwAW+CVVfe67nMW06dhZNGjMNF0iqJB1QgpzTNfRpcBMBNMFUswsrCFbIUtCl7fVyqikdsIkdyymnpQSuQt0ca/PSLKQUsY6j6aIzufkEMldwKhgK0+at7T7M67OG/bKzGhYorlgNks4zWgUJs0MJGALix8NoclojhR1iVkIue36zVJBQ6adztHQYcmUUhBiJDa9e84xklLCDuHRo4c4e3TIgYEjpczOXsdsv6HZaxAVRAUNCTUG0cw3YsBvhG+99RLAIArOepJAIkMWcs504zEsAks7BwgYgyk8riyJoUWMJaugmjHSv55xntS1hKZ53Xjv+xJTqervAb/3tzlnd9EQjbDiC5IoOUS6tkUzFBmSGgyGNirzBqYh4ErFeMGQKZxlY61m6JVtu8KsCWjYJ+zPCc0UiwXjyAJZM6REDgG6SA4RBeJsQbMzJqZMLALzSlm0Q7xVfBaqosBZJeWIM5lqMKA74NAmIKnDOo91Hanp6FJC1GCsIbcdbQ4UqxXeGNRaxAlp3tJNZ+ScqV3NuWHF+XpAmAfq0nD60Bp3dhdkNYQuINmANWhKkNNy5QQVWeqFIJje1eU+QREMzhuQTAqx358ukTVjxWBEUOcwRYlxBb4syXOLMQYxgqbcx6UiWGsx3tPo/8uW6v+OKDAPAQnCoLKgQugaFs0Uby1GHIV4rBtRlI5J54gpIF0GLEqmLAzrtVIVlkTBeDSiqTu6/Qm5CRirmNJjCpACcieoMagqaAaE3AbC/pQcMjrwNCMBl6hsZMV6bKk4a4kpoER85ajWCtwwoykRu4CmiBk4nNZIBjGGmAI5JWITiEWCLIi1aM7EtkFzZmBqKoR2Z8qNG9uMCmV9Y4jFYuuSZICoiAEJDkm5XzgjiBjs0n0ZYwCD5gRZyTni/BpSOmQ8RVRRWSJJIhgrGFNiikEfp3mLKz3GGkSVnDJZFZcSoorz9jvmsnflnlAqAQa1xxcFGUeOHW3XIimBUYwzDEpHVVqmC4dvE/UyXkpiiF1maGBQGHxhGFHiihaxDuML3LBDfIkdDpDKkqyi0wXSSg8DaH9Pa1TivAMpEGNRLyz25uASxcqIREGMvcPIolinjOqKQbHG00+8B7rApz7zx2ReIdULYhORZEizKcREN1lANogvceIggSbFFJa6LCmLkuu3brF9Z8quz0iCNJ2jMWOsgO2VUWImh4QkXsv+xBiM94gVcsqQFVfVnHviaf7hz/wMX3v5a/zaL/8CqeswOUNehgFFgXUFpirB9jdBMRhii4qwmIGmPlRQJWrG5IT9Lvt5TygVCIOy7BdMUp91xQ6bBLVKXXsODEu0MOw1kegVky1eE0KiLRQrkawRRHDWUpSOerUE1ukWHrElZT1ESk/TTYmTGZAxBoyxiPYpVc4ZyRkTMjpTaDKxzGhdEFKJBAeVISUwKHWRePeb38V/8v4PMywtH3nPe/i3H/sof/SJ3yU0E+Kko2tmpJDJXSCZFhMhGUhdAyTq4ZBDhw5QFyP20jZXdsdsDCq6MCYt+njPDAzGCWqElApSm9A2YRRU+6DfeosUDm0aTFnxgZ/4B/xX/+Qfc+7wQV555Dwf//f/jts3rqMxkehQ6zBicEWFFDXOZygKvPWI93TzyV0QsIfNUianpn+/15F7RKlAxJETRE0sukgIsIgwdJaVumRlWDCNwjx1pBRxRFQy2VlSMDRRGc86hiLEqBTWsX5gnWpQMRuPEDGUxRBUSfMpNijgoLaIVwixz6i8R4wDFQgZDYlgOkJoSKEiGoPGBAasVWqxPHzyDPuTBVvbgYOrQ/7Lj/wsXbvg01/5FJN2C1fVpBCxtkDUom0i6IwYA4ih8CUHRutMGsu4VbZnLU2XsIOCQoW26bCmwA7KPrAWQZwjBYWUe0jAGIx3iHf44Hn08bfxX/+n/4hjZUk7nnJkbY3DBw5y88q13gMQSaFDc8CvVkhZAw3GeazQx2mqS4ihd4XkTMoR/S4O8J5RqllnQRIhtYRFog2GqMrAWZwzYCBqJJBJKYLG/gKtoNnRBRhPAjEmQjCkzjIQA64iOMGpwWZH1oQkQ1HUiDVY48gpEpYZkfEFpiz6D2UMqkIy0IREEyJaWsiGAoNxhoIaaysu70149dpNHjy2ydmDI372vR/kyu3LPL+3T7E2AAMOj4ohdR2pWfRpm7EYgXK4wX4n3NidsGgjIUQOjUpGdcX+osU0EakLTOGQDLFSUkxo7tOwjMF4gykKnCv4yI99iEODHkQ11mDUkpfPl95/k7qWlGN/LeUAkzpUMzkrKXRkAO+x0ltIzYkMhPwDYKkUm
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 90%|████████▉ | 958/1067 [08:36<00:57,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 25500: [G loss: 7.20100][D loss: 0.00042]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:35<00:00,  1.85it/s]\n",
            " 37%|███▋      | 391/1067 [03:33<06:10,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 26000: [G loss: 7.17250][D loss: 0.00079]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 84%|████████▎ | 891/1067 [08:07<01:34,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 26500: [G loss: 7.12385][D loss: 0.00080]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:44<00:00,  1.83it/s]\n",
            " 30%|███       | 324/1067 [02:57<06:49,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 27000: [G loss: 7.13010][D loss: 0.00136]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 77%|███████▋  | 824/1067 [07:29<02:08,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 27500: [G loss: 7.18703][D loss: 0.00051]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:40<00:00,  1.84it/s]\n",
            " 24%|██▍       | 257/1067 [02:18<07:21,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 28000: [G loss: 7.13295][D loss: 0.00031]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 71%|███████   | 757/1067 [06:46<02:45,  1.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 28500: [G loss: 7.09443][D loss: 0.00021]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:32<00:00,  1.86it/s]\n",
            " 18%|█▊        | 190/1067 [01:41<07:49,  1.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 29000: [G loss: 7.09862][D loss: 0.00057]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 65%|██████▍   | 690/1067 [06:09<03:20,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 29500: [G loss: 7.08326][D loss: 0.00042]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:31<00:00,  1.87it/s]\n",
            " 12%|█▏        | 123/1067 [01:06<08:41,  1.81it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 30000: [G loss: 6.99207][D loss: 0.00095]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 58%|█████▊    | 623/1067 [05:33<04:02,  1.83it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 30500: [G loss: 7.05251][D loss: 0.00054]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:31<00:00,  1.87it/s]\n",
            "  5%|▌         | 56/1067 [00:29<08:53,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 31000: [G loss: 6.99728][D loss: 0.00009]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9SZMl2Xmm95zjx+c735gj57kyK8ECCBSKYncTpBEt9kLWrY1M0lZmXOkH9A/QD5BpI7NetKllWsi0lIyUUTSKEMnGwKpCFWrKeYiM+UbEne/16QxaeCAbQIMFyhqwTsrqM4uMez3c/Q7++jnfeb/3e1M45/gqvopfZ8j/2G/gq/j/X3wFqq/i1x5fgeqr+LXHV6D6Kn7t8RWovopfe3wFqq/i1x6/EVAJIf5ICPFICPFUCPEvfxOv8VW8uSF+3TyVEMIDHgPfBfaA94H/yjn3xa/1hb6KNzZ+EyPVu8BT59xz51wJ/K/AP/8NvM5X8YaG+g2ccxvY/Znne8C3v+wAIYTzvF+Cb+dAwPk/v7Bd/PLHP3/iXzzyF1/59aG/7G/gcL+w5ZfuV+9avwV+7unPPPp3Rwshfuan3sMai3MOKSVSytfHWuuw1iKlfH2McxZrHUKAELI+h3PUs069T/11OHAOY83rxz/9zAJRfz+v39Yvfrp//xP8bGhtcM790q/kNwGqv1cIIf4Y+GMAKQT/zX/xuwgBnudhrQXnqIzB4pBC4nkeSimMNeR5ThiEYC260kRxTFGVlGWJHwQICaa0eEKSpglhECIQeJ5EILDW4pAoKWmkIcqrcSk8iecrwiDFEZBlBfMsw1pL4PvEkY+vPHwhkEJiASEDED6LRclsnmMs+GFIoUuEEhRW02y2WFld52DvAJOVvP213+Li9atsX94kTRWL2Yzvf/8DPvv0If/sP/0ud+/fwvNhPs/47JPHfPLRT/j6O1/j1t0bJGnKwy8e8PjBQ3q9Hvfu3SVOI86GZ+ztvKLV7bK6sUEQhhzs77Hz9AmffPghj794zGDvGN841ttd+r0OqxfWaW32kEmAp8L6u3cOKQVYwFqcsRityYslUkqqqsIYy3/33/8vf+e1/U2Aah+4+DPPL5xv+7lwzv0r4F8BKOU5Kes7EiAMfZxz+ATnd5NAKXX+dx9PSvI8x2mDlBKtK3xfoZRHUZbEUYwXSGylUVLi+x7KU3jnI4DRBqMtOE2ZG5wv8QMfST0KSE9iTX0nx1FCWVV4UiJQCCTOSZyUWAtK+pQaZlnBeL5ksSgQUtHut2m2mqz121y+epluu4PD8uzzpyTNJu1+hzj26aQRIl/SSiLCwAdrUFLU79UJlK/wQ584CQkCHzAUec5oOOLC9gWiKEQKqMocP1CsrfRpt1sY6wgDn+l0ytrGOtbzOBwNWZxNEdMJhdF0t9bp9VYohUaXBu985LLO4fsK6RROG6wUWBcghEBKSZ4XXwqA3wSo3gduCiGuUoPpvwT+6191kHOOJIl/ZhgH5Ss8T73+MPU0UI+49UXmfMrwsDiqqiSOY3zfRwmwwqJNAfjnU4KjLEuMNggHUeijpMNqDcpDIBBIjLY4Z3EWtLYYDRZLmWUIAUqFCCkpTYUfarwghNBHRD6zsyFFbmiutLl2/Sr91Q6bm2soz2NztcvDImf/YI+rb13DVjnjwzOGh0dEWJqRj6lKrDE4DFVZkWcZSRyTpjFKSaypQbVYLGgmDXyl0LZguZwThj5pGhP4ivliwWK+IIpjtrY3SfodPnn8gOViyel8Qdpscfv2XX73vfcwTvP46VP2DvepjEYojwoQ1oG2gMXzVH3DSUmSJF96LX/toHLOaSHEfwv8GeAB/9o59/mvOs4Yg9Yaz/Neg0rrCmvr0cjz1Pl+DqUkSgU4Y2qwCIE2Bt+PEVKgfJ/Ak1it0FpjTEVZZCRJgu9LJBZhLbrKsQKU5+FsgPIUUgWAIAgDjBEslnPKylLmFdPxBGssXpDgRyFB7JOqgG6acOHmFto6pt/7ayY7RyhfsrLSYX29h6REIWnEAk/A3u4u8/mEflNy8PQx3//L77F19TblYkaeLcEanJCUZUlZlCjfJ0kTlJJkWcFyucBogx/4KM+jMg5jKnrdPkEYgHNky4zxeMzW9haNJKF0Fe+++9v82dEps0XJMi/55MNPOHq+Qxz6pCstkjREKYUWoJ3FGAPW4SuFEPVMIoT991LXX4zfSE7lnPtT4E//vxwTRiEAQRCcT2maoizRxqCUIgwCpPTwPI+yLOqk1VN1UisEvrUIIdBGo5SiKkusrojjGIEgWy5ZZgvSOEZIsNpgrQFPglQYa8mLCt/6OGdw1lJVlvkiYzpboivH2WBEWVaIMKG/1ufOlYtsXtogSiIuXb2M9DyOjo453j1mMhoxGQ3Z3uxhTYl10GxEdLptdk9OmE6mVL0G7W6HxXzCpx/8gKSzSrmcgTV4gVcn5cLV79PBeUpPWRQ4a5HCIYXDVCVFlhNvhShPosuS46MjBLC+scFyMafbbtNOGwSeQgrJfLnkwx9/xEoas9nv4bdCbt67Q3dzjXlV1LmhHyCUw2jNMsswWhOG0c8sJH55vDGMujEGay1ZlrFcLjHGEHgSJcDDIZ3DlwIP8D0PT0hCFeF7AcIKPOmhpCLwQwIV4EmPqtRUZYXneSSNlDAKsRKE8vCjlCBKkX4EXoB1iqoQVLlgMizY3TljcDJjnmumWca8KnBhwOlsxvFkwvF4RNhMuHbzGqtrPRpxRK/V4ta1azSShPHpmMHegPl4gtMlRVUQN1t0t1aYTEacHpxinE+4scrV37rFwc4XVMfP0dMBZjnDA4RnMa6gKBcIHDiNMxXL+RSrCwIfJJp8vsCWjkAK0BXL2ZTZeMLayiphEBDGEXEYoec5vpMo6TErM86qOTNytG9wec7h82eIMkM5h2clAo8gCvFCVY/MSUxhKiqtv/RavjGgsqYebqWUKKVQShGEIWEYnoNtQVkVWKdpNhskcQjCIKXD9yVh4BPFIYHvEfge7XaLTqcDCKpK4wmPyI8JvQhZXzKCICHwmxgTMV86RtOCw5MR+ycnPN/f4Wg0QEWK/nqfS1cusr65gvAcRZYxOhuy82IHNHSabYSzBEqyutplbXON2WLGyekpR8cnSOXjhxFhHNPv95FSsvPyJWWlUUHIzbt36W1u8PJgnycPHvLR377P7GyEbx02L5ieniG1RRqLdI75bIY1miDw0KZkODojjkPCICDPMoZnZ6RxRL/XxZOSJE6YTefsHxwyXyzrQU9KgjAE6ZEVmrTfR6QRg+kILRxC1atwqw2ekMRxTBiG+Er9XdzK63gjQCWop78kSV4PrdbaOqdyBiFBSIExmrLMmc3GaFPhK3meX0lwBmv1OW1QczhhHNNotAj8CE+ESOfjuYBAxHgyQBvJcglnw5LjwZL9oxG7ByeMsgWi4dNcbXPpxkVu3rnO3bdvceX6JbYurOMrj2y+5NHnj9jd2SX0QwQOZyui2Gf70iaVqTg4OmY2z7B4BFGK54e0O22CIOBkcMpykWGQdNbW+a3feQ8XBqytrTAZDfnsk48psyWRUghrMLpEYLG6oswyotDHV4qiyJjNJ7Q7LZSQzMdjJqMh3U6LJI7wlU+R5Tx69Jj9/QOCIKC/tkKSJlTGcno2Ja/g5tff4Wu/8x5eI0FLsMKBgyLLKfOCIs+x2uB7il9VhfmPxlP9YkhRUwl11JyS1pr5fIazFt9X+L6HAKqyxFmD9RTOgT2nHJXyCcKwzq20wRqLr0LwBMJKkBJTWayzVKakKCzLpWY8zTgbTsirEicsvY0ul29c5vr1a2xtbhCHNc8VxyEHB3ucDKYstGYxW/Dw80fcvH6V/koLiSUKPTa314nSmKPBKZPZkqJ0tIMYC6TNlE6nzWg44vj4lO3La0TNDlfeeos7B0dsXN7m/jtfZ1ZUSCV5+2v30KYCr04RqrJCONja2CAOAxbLCdZU9HttXFWx8+IFpTZcv3YdiaPMcx4+eMiLF6/Y2Nzi2pXrLBZLPv7Jp0wmY2KnSJpdLt+6xZVbl
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 52%|█████▏    | 556/1067 [04:57<04:32,  1.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 31500: [G loss: 7.00414][D loss: 0.00013]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 99%|█████████▉| 1056/1067 [09:23<00:06,  1.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 32000: [G loss: 7.06001][D loss: 0.00056]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            " 46%|████▌     | 489/1067 [04:20<05:04,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 32500: [G loss: 6.95155][D loss: 0.00017]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy96ZNd533f+Xm2s9399oJuoAECIADupEhR1GbLWi3LVEaxnYnXSlxlJ5PJpPJ2/CdMzTsnM5Uqu+IslapYo9ixY5cmdpmxrF2kRJHiDpAg9rW3u57l2ebFaclKRqYcj5TAU/xVdQP39D333L7P93yf3/b9tYgx8ra9bT9Ik/+938Db9v8/extUb9sP3N4G1dv2A7e3QfW2/cDtbVC9bT9wextUb9sP3H4ooBJC/IQQ4jUhxOtCiF/7YVzjbbtzTfyg81RCCAWcBT4GXAGeAX4+xvjyD/RCb9sdaz8MpnoCeD3GeD7G2AC/DXzqh3Cdt+0ONf1DeM0jwOXvenwFePdbnSCE+CvRpZACAXw32woBEQ6+/ef2/4mUxfd+ze8+/t3XFuJ7XFeAFOI7z4sxQvx/v69vn/u93u+fv2574e9+zvc6rz0m+KvsSFII4n9xje+2GKP4Xsd/GKD6S5kQ4u8Df//bj7uDnOADgYiQEgTEAMF7pJR0ihStBGXj8c4hiDz64GlMCm9cv4QQjiOHCxKj2Nlt2N+v0NrQNAGtJUorZhNHtWywVhAPFl5pjVJ/jgbvPE1lAYFQEiEhyRV5rpnt1DSVRWmB1Bqd6PZ9xoBODVmaUZc1vnakqSYxiigV89kc5yBJ4a77hqyvDWkWlu2bU2wZWC4bqtKTJgqJQJhIkkm2b1e4GtLMYK1DyMhglKJERpFpet2cS1dug1AQHVnhcSEwn0BZts/vD3LSJGF3t6Qqa9rbEIKPCCkwmUQqRYwSKRRN2eB9w+bagDMnVrl4bZ+bkxLrA1IKQBKdp5wt/8K1/WGA6ipw9Lsebx0c+88sxvgbwG9Ay1RaGxosQoA0CkLA20CIEaUkQgl8BB8izgVSI+l3CxpfYa2n25d0BykEiZAWISTeg7UtKI1SmCQSokEagbcBJBitgEAMEkREaggh0FSO6D0q0XgXqZaOpvEIIdDGIJUkhkiMEQH4JrCwJa6xRBewTUNWGKJQCBQCS10Gbrw5o9xxGCVZLBoSZciTjLpcoqUiBCgXlu3tJdFHNtdHCKnYmyyJBCSKpmkQIXDsyAZFp0NVV1y9eovpxKINKCnRWhFiZDavmQtLCBAFiNiyVowBKTRGa5CCunIg2p9JKdFaEkJEyAhEhBAtu3qPkN+ToL5jPwxQPQOcFkKcoAXTzwG/8P1O8t4TiSilkUK2NCIgStUyQgK2cnjvQAqyIiXPEva3J5TLiJKwmDn6gy55HphNI9FHssQQoqCuHTZ4fBTtHScVQgm0ASEkto54H4EA0LKlD8QmYH3Exkh0AaEkMbSMGkJESYn3geA8ADEEiKCkoDfKiQGWE0tw7TXrpWBiLYNBSmoSQCARaKWwvl1AH8BZGPQTDh1KWC4lzmc4H2iaSF1bYuqpbI2UitQYtDI0TYn3ghgiWmuCiHgfEEIiVQv+GNprSCWQEiIRbz3BeaSSiBhJMo3UsD2ZUzlPiHznXO8dbw2pHwKoYoxOCPGPgD8CFPBbMcaXvt95PkaIAi0kSqiDbVAQVCQS0Ea17OMU0ij6ww5KCZZlQ3CRchrZvekw2kGARGmUEUgVmZUNnohUCoEnhoCQEikjSkUIkegh+NYfiVEgpcTTbr940fpCCiIB5yJCyPaOF5EQAxGBOLgRhBQIJTApaCGJlaJICxAC5wPBO5QAqRVV5Yk+IDV4HwBJCAGlYThKKTqC3X2L0ZqmKWm8w3lPEiW7+xNCCAjhUYkDGamagBSS1Ci0an0irSTBe+rYsrCUoJRESoGznnDAuPLghkkyhUoFtycL6oPdQkoJod0Chfhvz1TEGD8LfPa/5hwtJJ7w585wjHzbffe1w9cCaSRKa6SMJEbRWM98sQQi0SvKOezesETAO9EuFO2iCylRSgIe7yNStpQuhURpIAs0DrxTrS8XQhsaS4mQEqEkEHBNe+cqERFR4ENAaoVJEoIIiBgPtkXPYt6QGkXWUaRSYZvYbp0yI+9mVLWjsQ3OBZxtmUIrSZYL1jf6jIYdrHdMZjOiT/DBoZKAVAqlFHXdoLRgOEoZakNZW3Z3GvKOQRkFgvZfIk3pAIGULXikbG+KEL4NEIEyBqFBJ4o0TQixoq79AXA1MYI2GikE1Vut5X/Nwv8w7dvgD9/2pfwBg9CyR730JLmCEGkqRxgodvdLJrOS6AXKKCSG5UySZhpvPa7xWG/xMeLxRCA4iL69oFKt75CaSH+QMJtb5rNACIoQ2m1ICElWKBCC4BUiCoixvWNDpLYH4NTtIiEkwgeij5TzgBloDp8c4haO/e0lnU6CEIK8kxJlJFkoiJKmdiBbVuh1JBurGqFhuqgQBIoCpEyQeUAIRbWM1E2gkwhMKkiyjM4ox0XB2nqfsm6o6gBCY6uaQEQaSXQHoDrwuRTtNuhi6ytprRA64kRAaEmIkRgC3vt2mzaq/fzewu4oUMUQW7bi277JwTYTwNYBLQMigojtgm9PJ6ADSZR08oQsV4Ai0ZoyRmwMuBCJSFqPQhNDg0CipEJJhUBgEsgyRVkGtA6AQAhNIwJKR3pDhXOB5Yx2G6YNt5UEG0LrlzhHpPVf2t9HYmtHmuR0igwXHHKscLFlzjSXeKFRqSRVUFt1EI1G0kKDciRZhEWk208Z9Xo4GyBtEFpTLmrqytIfGLQEBCSFoR8jm4dHVLVlZ3/BYuGY7TmCkyglcKH9sE2qURrqyqGlJDEKoUFoSZa226aLEaX1QaTcOv9KK/z3QdUdA6pI6xzHEIihhYCUEucCwQcIgugFUkTSJEEJgcAz6GT4paOXJHSzjIgiisjauMd+uUCpHtuTCSDBS4htnogIwUW8iGhp2uhQOYqOxjlorKM3zPHBIVQkNpEkMTz26IPs7uxz/vxFtJJorbGu9dPaLUd+J+hASmpv2d2b0ZUJeaIoXSTNcqQQlFWDJxyE+RGl4NDhDivrGULUaAGdriRNJBqN0ZKkk7fs60ps48nTnE5m0N0EkoT9nX2kimxublB0F1y9ssOun7aAVAGpBCGCVJLhsKAuLYuqRGlJFLL1X6VEo74TMUba1ItQEmcd3v41ARW0d79rPWWEEN+JMlof/iBqQaC0IjGaTHkWEw9ScffpM2jRMsmli1c4snmE9XrBE088wb/47d9lr1wQYyAcRGfWRryLaKUwJiFJNN1BJHOB2cwRhabX01QV7O/U2MYzHhfce+893LxxmwsXr7TgF63jqpTE5EkLVGuJMWISTdMIFtOGlfUui0XF/qxmNTcYJMtFSaI13gd0IukNJWsbOavrA6Jv83FVNSXPaf0xTBsRe4FrKQeQ5GmBF4Gjm4foZTm3bu1QpD2GRYd5tyLPNLHxNL49A0X7GUpN2jGkHcmsrPCNwLtIWXtsbHDuIJ1AROl2PZrK4t1bg+rO6VIIkehD+0sLwcHaI7VC6jYRGWIbwRmtETEw2S2Zzmt6gwE/8YmPcuzYUX7sR94FKF599U2auuH1s2fZGPZY6+YY0dJ4iBFnLSaFTl+htIAoSLRCIdACfO1ZTC3LiadeeqTSVHXN3t4C7yVCaqKQSK0xeYLpGFQiW+BaByECEt8IiqxgtDIgKkFVN0QcKysdjmwOWeka1lZyBiNNf5ygdMQ2FqU0o9GQTidDKkXWNRRjhTCR+bxBIsgKReMi07JmuWhQTrE+WkdEyfXr19jb2SbUNSmKbmEQQiK0Zjjq0ykyJvOS3d05WiukEtR1hQ+O0nrmC0uiJEoepFdiJAZ/UC347xD9/VXM+wACtFRY65BCtRnx4BBSoJTGx0CMgSQ1OGcZrwwxqaabZWyurfKmPMfRI4dZWx3jouP4iRM01pKnBZtSsdKzXLu1z950iRORTk/R7Slc8G2UYyPlwiOQpFozn1qax
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 93%|█████████▎| 989/1067 [08:48<00:42,  1.85it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 33000: [G loss: 6.95831][D loss: 0.00051]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            " 40%|███▉      | 422/1067 [03:45<05:37,  1.91it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 33500: [G loss: 6.98002][D loss: 0.00065]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 86%|████████▋ | 922/1067 [08:12<01:17,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 34000: [G loss: 6.89810][D loss: 0.00018]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.87it/s]\n",
            " 33%|███▎      | 355/1067 [03:08<06:11,  1.92it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 34500: [G loss: 6.96272][D loss: 0.00048]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 80%|████████  | 855/1067 [07:36<01:54,  1.85it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 35000: [G loss: 6.81202][D loss: 0.00029]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            " 27%|██▋       | 288/1067 [02:33<06:53,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 35500: [G loss: 6.92884][D loss: 0.00067]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 74%|███████▍  | 788/1067 [06:59<02:33,  1.81it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 36000: [G loss: 6.87659][D loss: 0.00019]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9d5xeV3Xu/917n/KW6TOSZjRqo14tuci9dxvHBkwImBBKqCFwIeSXACHBhPgGLlzIDbkhECDYgHEDjHvBvclyUbV6Hc1oNL2+7Zyz975/7DNjmdhAiPSzgHk+H0nve2bmPUdz1ll7rWc9a21hrWUSkziSkK/3BUzidw+TRjWJI45Jo5rEEcekUU3iiGPSqCZxxDFpVJM44jgqRiWEuFQIsV0IsUsI8amjcY5JHLsQR5qnEkIoYAdwEdABPAe83Vq75YieaBLHLI6GpzoZ2GWt3WOtjYCbgKuOwnkmcYzCOwqf2QocOOx9B3DKL/sBIYTNZDIIIY7C5UziSMNaS7lcxlr7qjfsaBjVrwUhxAeADwD4vs/AwACe97pdziT+C4jjmMbGxtf8+tG4i53AzMPez0iPvQLW2m8B3wLI5XLW8zx83z8KlzOJI41fFYcfjZjqOWCBEKJNCBEAbwPuOArnmcQxiiPuqay1iRDiz4H7AQV811r70pE+zySOXRyVIMZaew9wz9H47Ekc+5hk1CdxxDFpVJM44pg0qkkccUwa1SSOOCaNahJHHJNGNYkjjkmjmsQRx6RRTeKIY9KoJnHEMWlUkzjimDSqSRxxTBrVJI44Jo1qEkcck0Y1iSOOSaOaxBHHpFFN4ohj0qgmccQxaVSTOOKYNKpJHHFMNtodIxhvexIIEGCx8AudUK7Zdvzgsdt4O2lUxwAOb6N7pTFZ9zUhELyy3+5YbuaeNKpjApbUnADng4y1WGMRgBAS5LiXOoatKcWkUR0rsC+blDaa/u5ODuzeTlSJmDarjZaZbYSZDALrjOwYxqRRHSMwQLkwQnFkgNLoAB17tpJoQaU0yuPrHyNf38olV7+T6tqG1/tSfyWObZP/PYI1hv6u/fQd2MtI9w56Dm2gttqjNidpzCv2bniKDWufRieJ+/5jeP79pFEdI1BK0dAyl0ztVLK5GqbWBYwN7CAuDhPIBGsj1j/7BIXR4VcYlLX2mDOwyeXvmIEgn8+Tmb+Ewb4a4qjCvo1PI+lndGiY0IP9u3bQvm8ny2rrEUodc8Y0jklPdSxBCISnqKprYnQ04mD7XrZv38Oe/QN091foGejne9/+Lt1dnVhrXu+rfU1MGtUxBItAIEgqRbauf5JdB4Y52F+iEkUUypqsH7DlpY18//r/YHRkCGOOTcOaNKpjCALAWkqjA4yOjjG9pRkjPbwwoCarmFKfZebUPI89fD8PPXA/WifH5BI4aVTHDGz6x9Df3U1SLEMSsXjODKryWbKhIpAWk1h0scCtN36fvp7u1/uiXxWTRnUs4BdIcq0TrOfT1TvAgY5urPVpaplFQ+MUmqfUUh3Cjp3beX7tsyRJDLycBR4L2eCkUR0DEBZApL5KkKuqxa+qY6iQUCyVeXLNOp59diPtHYNgPGa0NiN0hZtvvom+7q7X3Yh+Eb/SqIQQ3xVC9AghNh92rEEI8aAQYmf6b316XAgh/jnd6WGjEOKEo3nxv2sQCIQQ1DY2c/zJp5LPeBQKo4SholgYYM3zz/Pciy/S0zvIzKYmXtqwlnvuvoOoUn69L/0V+HU81feAS3/h2KeAh6y1C4CH0vcAlwEL0j8fAL5xZC7zdxuH+xkB5GtqaZ6xkKWrljN7ZjP11dUYa0lMTLFUIfQUjY0NXHnJJdx7550c2LcHjiGK4VcalbX2cWDgFw5fBVyfvr4eeONhx2+wDmuAOiFEy5G62N9ZiFe+8YIMYXUtZ13+Dk679Cqmz5yGEIKG6hyBEhzs6kYncMLqczFKcP999xDHFexhSofXE79pTDXNWtuVvj4ETEtfv9puD62/4Tl+bzC+7I3veCGlQgmJ9UKmzVjEuRddyqWXnsGC2dOozvh4EpQtsvahe1kws5l777mHnkOHjhlC9L9dprHWWiHEf/kB+cUdHybxMoQQZPI1RIklzOXw1DKmtM6mefps7rn9Tnr6RwlkQPuBHfhhhnJhhPZ9+2lunYmQ9nVX8P2mRtUthGix1naly1tPevzX2u0B/vOOD7/hdRxZvELBm746XMH7C+9F+vf4oiPEK1Wcr/4Bh//sK8404a2steSqa+k+uB9V00iufjpKWo6rbaKqqZXNL7zIYF8PTaWYzr4+6kJY+8wTrFp9MtlshtdbyPebLn93AO9KX78L+Nlhx/8kzQJPBYYPWyaPedgJAtJOHHEBsEm/aiasxr2zmIklx2KMxVqTckXpvxgmfsLal18fdrZXg+cH5PK16DjCCoURPpViwrylp3HZNX/KtAWL2XvwIMIIMkGWZ554lK6Odo6Fys2v9FRCiB8B5wJNQogO4HPAF4FbhBB/CuwH3pp++z3A5cAuoAi85yhc81GGnfjHHn7EpoZgjftjDEmiqZRLFMbGMFozMjzA2OgIlXKFMAwJc3mkVNTV11FdU0s2lyf0A6wUIJRTCL8GhIDG5la69u3Cz+UQCMJshmJhhFJxhFUnnUM5kXzjX7+JJxXzZrQw0NvN7Hnzeb3px19pVNbat7/Gly54le+1wEf+uxf1euAVGiVwHS3GYHRCpVRgbHSQ/p4eOjo7GR0t0NlxkD17D9DReZDOri7iSsRwqUgUlTGJRkqJ8jwCz6cql2VGawttc+dwwgknsHTpMuYtXER1TQ1SqleRBztry2QzTJ3RxsH23Qij6dyzgW1b9zB/2Qoee/ABTjrtDE4/7WTufeAxhsf2s2nTBpafcDIq8/oqmn7P9FT2VWIetxi5Yi7o1JBGB7rZ+tIGHn/8KbZu3UFndy8jYxWGxwoIIclmfTJhgOf51NTVsGheG/lclkwmIBtmAEvXoR4O9vQyNlamUIx45LGnue+hxwg9n2WLF3LZ5Zdy4UUXMWVaC1K98la4rfQk+epqWufMY/fWTZAkHDy4n47uPoywzF+8grfU1PDiupcYHhrhlptu44yzzmPBkuUAr9v+ib9XRuUM6pV9c9YajNXoOKYwNkL7rr089thjPPr44+xt72RodIyG+gaWL1vEgrlttE5voWVqI/W11SjPRyLo6z7EQw89ztaXtuEpD09JPF9SX1vHnKZGpi5rZGrTFPzQpxJphoeHeWHDBr78la9w/feu57JLLuKNb3kLrbPaUH6AEun1CQlYsvlq5ixcxmhvD2ecfTGHBgsEImb2gkW0LVrKVX+4gVtu+B6FkUG2bd7I3IVLXte9E3+vjMrBtTlZazFGE1WKDPQPsHndZh555DHWvrCOQqlE68wWLrvkIpYtmsecmdOpqcpRKZUY6B+ht6OD4R6PfFUNQRiQzeVYcdwywlyW9s6D7Ny7n87uXsqVGJAIYQk8j5qqHNOaGmmbNY0l89tYtmAu+zs6ue+++3jggZ9z8aUXc8WVVzJzzjx8P0BJCQLiSoV9219iZHiYxSeegti9m5wvCMMsyvN5+zv+hP7uLs489VSWrTwO+TpTCkd8Y+7fBLlczg4PDx81vuoXNd1aJ1TKBcaGh1m/Zh0/uOU22ru6mD+/jZNXH8/86S14GMqVEn29fWzdup19Bw6yt7OL/uEi5XKRKIkJg5DQV7Q0T2NB2xxWLl/C4oXzyWZDevv66Ow4xHPrN/Hsuk309PejjUEpH51U8IQhDEKapzWxYuE8Guvr2blvP5Uo5sLzz+HCiy6hqaWFbL6GngP7ONi+lzmLl9E6Zx5bN7xIdW0NcxcuRUiJ0Qn9vT0EfkBVXR2ecr/Ho7X8RVFEbW0tpVLpVU/wu2dU9vC2zMMOW4PWmrGRPob7B1m7Zh0PPPgovQP9nH7GiZx9+slklWLzuo08/NjTPLPpJSpRhUhr4ighSiKy2SxRFCORaGPQRuMpSZIYoqRC4HtMra/jxOXLOP+cM1gwv42qfJ6O9v089tSzrHlxA
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.87it/s]\n",
            " 21%|██        | 221/1067 [01:58<07:22,  1.91it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 36500: [G loss: 6.82683][D loss: 0.00004]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 68%|██████▊   | 721/1067 [06:24<03:01,  1.91it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 37000: [G loss: 6.79883][D loss: 0.00014]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.88it/s]\n",
            " 14%|█▍        | 154/1067 [01:22<08:00,  1.90it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 37500: [G loss: 6.80369][D loss: 0.00021]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 61%|██████▏   | 654/1067 [05:48<03:40,  1.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 38000: [G loss: 6.86273][D loss: 0.00015]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.87it/s]\n",
            "  8%|▊         | 87/1067 [00:46<08:37,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 38500: [G loss: 6.82430][D loss: 0.00018]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 55%|█████▌    | 587/1067 [05:13<04:14,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 39000: [G loss: 6.80032][D loss: 0.00007]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:30<00:00,  1.87it/s]\n",
            "  2%|▏         | 20/1067 [00:10<09:23,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 39500: [G loss: 6.76142][D loss: 0.00103]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 49%|████▊     | 520/1067 [04:37<04:49,  1.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 40000: [G loss: 6.74708][D loss: 0.00010]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 96%|█████████▌| 1020/1067 [09:04<00:25,  1.82it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 40500: [G loss: 6.73421][D loss: 0.00022]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9149k+ZXn9/m568KnqczKLF/tDTnsoRtyNJxZcdYMdjX7ImEFQYCeBoKgBwGCgP039CAtsIIErR5mtdLDChrMYBx3LIemyaZrV9XlK6uyMitN+Gt+Tg83utjkksURlsSWhD5AIONGRdy4de/3nt855/s9J0SMkY/tY/t5mvz3fQAf2///7GNQfWw/d/sYVB/bz90+BtXH9nO3j0H1sf3c7WNQfWw/d/uFgEoI8feFENeEEDeEEP/0F/EdH9uza+LnXacSQijgOvCbwB7wJvCfxhjf/bl+0cf2zNovwlN9FrgRY7wVY2yA/x347V/A93xsz6jpX8A+d4H7H9neAz73tA8IIaIQT/l3BJGf7VF/mtMVQiCEIITwI68pJZ/s2/vAT/LaQoCSEqkUAtqjiBHnPSHEH3mfFBKlJDGCDx/uLyKEREqBQOCDf3KcApBKouSHn2n3KcTq+KRECIH3Af+RY39yXEohVycuxIhz/sMjXO29NaUkWikAnPfE2B5TjOHJ/18rRYiRGGJ7XtqDeHJSQ4yE1fHFCDFGYow/8ar9IkD1tzIhxO8Av9M+h196eRvnA1pKauvxPiC0ZFCkZHlC9IHGNURWJ0XJ9j0ojFFY66nqwLx0TGY1IShCgNrWvPjCVXpFn/evvY+1FqUUr752nt/+8n9IahL2D2f86z/4CuPpKazgG0JAScn6KOWf/Mdf5jd/7e/QMZrHh0f84P09/uX/9Qfc3jskBI/SkiIzfPlLb/APf/NL3Lixz1f++pt8cGePKAJvvHqFl15+njPrm/z+H/45N/ceIQOsdVO+/B98gV/91Teovedf/p+/z9vvXGd7vc/Gxhr/4De+yMXLF/nK177Jv/nzrzOd1wQfcMHTyzT/2T/+e3zpi58mCLh2b4//4X/+XR48OAWpUEIhpUKKwN/93Kf47/7b/xIfPL/3l3/Bd968ySd+6Xm+/Y3vceHKJjffucN/81/957y3d49vf+27fPFTb/CFL3ya0XofHxy1czw8fMTXvvUmf/Xnb3Lz1pg33/ngp17bXwSoHgDnP7J9bvXaj1iM8Z8D/xxAKRm7mWE8r/DRU2SasnE4296hRkS63RRjMqSIpLkmMQqhFdELiALnHfOF5WTacN1NaKxguVggkcyqBmJFYy0xgNSRs1tn8V6A0RwcHFFWS7RWhBgIIaK1IjGK1z/xPM9dvkwvTdEIvJd8/90bHJ5OUVohfEQgyVPDzpl1Br0hF887zu6ssfdgnyTPubBzlkF/QH84IgqIUWAby6LSjGdTEBGjFGVdkRc5RZGwWCyZz0qyLOP5F65y+94+7753hyAdOkiCj4zHc3wU9HsdXnvxKn/373yB3/1Xf0rjPFJrlJKkWnH/0SGz+ZIsV2yP1jg++SbV/AKTxZSOukgTHXt3H7G5OWRRN1y7eZdBt8NnPvtJ8l6KVoLzW9v4X36Dumw4PX3zqQD4RcRUbwLPCyEuCyES4J8A//fTPiCAVGmMVmTa0EtTunmG1Iqq8SxLh7OgkOR5SpGn9Htd1gZDNtfXGI0GdLsFo15Onmb4AGmWrpaaiDKGumkIvnXdSgl6nS7eR8rGcXB0jPXNk+UvEhFRkGlJVws2hyO8s0wnU65fv81b33+XclkhpQAhgcigm3JuZxupYPvMNi9dvoxWkn6voNvt0s17ZKbg4vkt8kxSVhVVVVE3JdFDURSM1tcROrK7vUbjHH/0l29x99Yel8+fY+vsNo0PWBtQApI048H+A5x3CCK5UvSLApUItE5QWmC0IMsKDqdz3n/vBkpJdrfOkCaSvXsP6Y/6PNjbY22zx3vv32LQHVJbx52H+/zNN77JnRt38I1HSUGmDbubZ3jttZc5f3GDp0QrP39QxRgd8F8DfwS8B/wfMcZ3nv4ZWDYOtCIKcCFAjCRa4mNkvKg5OJ1xOi6ZT2uWC0u59FSlp64d1npwEWsDk9LSWN/GUBG0UQy7XZbz2Qo0ASUUdVkhpGBZ1uwfHCKkJMaIFJJOpyDJM0xuOHfuLOvDEUmSUDUN127d43A8xgVPXdftCc8TRhvrDAabeCt5551bvH/9Lr1RznDUJwRBXVucr3jhhed48bnnQAkq23ByPCExikQmZDrHNYJZGemtrfH2zRv8+Ve/SSYNtqpwrqJyFZUNOGcpigQpDUJoIoLLF85z5eJ5ggjEADEGFmVF3QS++Y23KOfLdukcdrh95x6vvfYyk2XFpZ3zXP/gBrG2DId9Ho/HXL9zm699400OHx3jbCQSyRPDxXPn+OxnXmtvqJ9iv5CYKsb4B8Af/K3fT+RwskTJ9q5fao9cxYjWBqyLLEvHdNmQzxTDwpClC9I0IdGaGCF4z3TRcDRxKKU+DCRJ0pREacpy2X6ZEOSdjO5gxKyqONgfczIeI4XEsQqUYySRgrNb67z88otkJqFpGpZV4Ma9/TZoXgXTeZHS6SQgI0enp9y+/Yg//OO/4mB8xOc++zovPHeF5WxGcCW2zlAyYTxZYJQh1Z61tYLJZMJ0XnJ09IgmNKhMYWrLhd0Oa+uKo9Mjds9t0e/lzJeW4AKND4xnnpPTBXlm0FrQ7xac2xqyv3fIrHa4IMiNoHYNe4cH+BBQQvBLb7zK7779+xgHSSdDK82smXN8dEinn3EyndNd7/GNb32Hjc0zfP5Xfpnheg+lIsOi4BOvvYyUP90f/XsL1D9qMcLj0yVGSRAC7z3GaFJj8DEQfKRxHh8CaaI5ntYIAUZIMmNACKIQ1NZTe42Ios3MQkRIyXKxpGkaoE0KNjeHDEdrBC+4dvMOlWtItEJK0QI0RvJUc/XyBc6dPYf3gcm05PadRxxP5kBAICnyhDSRZKng9VevkOqE79y9wd2jQ9IUUpNwejhmvFyys7VJojWDTsbFCztcu34L5wTeK4RQdLoJL750ifsHB5zdWKc37HL57BovPP8c587t4ITiK92/YTY/RUjJvC55+8Zd3nn3A85vfwYpDBujNV555Tl+8N5dlh5i9EQfkQjGszlVWYKI5ElBkiY8uPeQ556/RGwiusioS8snXnuJd96+w2DY5/69W3zlL/6KJDF85ld+mf4gI1GRnY1NxFM81TND01SVZVFa5ouaxdIynVWMZ0tmi4pF1VDXnrryzOc1J5OKo9Oa/eMldw8n3D+c8uBoysmsxIeANorgXLucacN0viAED0CaJAxH60xnM46OTzh4fNhmVC4SVum094Ekk1y+uEWqNIv5gqou+db3vsPDR4cgRJtthwgIXnrhKr/62U+zmJf84J3reGd5+cWLvPLqCwRhefz4gMWyQgrwzuHrisY2TBYNb79/h/v3HzDsDel2hwin8JVgZ2OHW3ePeOfdO5w+PmFQpJy/sIV3TXtzpQmNs3xw8yaT0wkxelJleO2llxkMOzhr8T7iImgBk3nJyfGEfreHVhEXHDdu3efq+UvcPzhh0Bnw8OFjLm1tM+h1SJOEyy9c5u0PbvGnf/ZVbly/has9SkryxCCeElU9M6CKUeBcwLl2CXIuUFWWqrTUlcVaRwixPVE2YBtP03iqylFWlsWiwvtICAEpJMEHhJRIKanqqvVAIaCkIM8K8ixnPJmxWCyQUqxqRO17tBZsbfT4xCsvIbWmtg5bw3zZEFfHKoTEukCRGS7unKWXDTk6nTJfLhACiiyln3dYLmsOj46pmzawzzsdNtcGZIkhBEdVL3G2xlpHtbSMl0u+c/06y6bhZDrjwcN9jo4e0+vkbJ/dJk8VRkSMVihtePfGHd65dhMfA1lmGPV77O6eJVGR4BpCBBcik2XFd773PorAxfNnSYuUw+MJJ49O0Knh4HTM29feJxGSopuw9+iIV198jv5ml+9fu8Gf/cVXeXj/Id66nwmaZ
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1067/1067 [09:29<00:00,  1.87it/s]\n",
            " 42%|████▏     | 453/1067 [04:01<05:30,  1.86it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Step 41000: [G loss: 6.69312][D loss: 0.00047]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9aZAt53nf93u37j7rnNnXu+ECIECAAElw0W45Lim2rIS2lCh2UqlIseK4HH+PvqT8Mf6a2CnHLluOlEpsK5VUUZEp09FGirQoAyS4AMR2cXHv3Ll39uXMWbv7XfLh7T4zUAgQkojSZQpP1dTMnJnTp/t9n36W//N/nhYhBD6QD+R7KfLP+gQ+kP//yQdK9YF8z+UDpfpAvufygVJ9IN9z+UCpPpDvuXygVB/I91zeF6USQvxFIcRrQohbQohfej8+4wN5eEV8r3EqIYQCXgd+AtgBngf+egjh29/TD/pAHlp5PyzVp4BbIYTbIYQC+BfAZ96Hz/lAHlLR78MxN4F7l37fAT79bm8QQgQpK/0WIIDvlQENISCEiD9z6cDVa+Ld3nv5HP+Yn1t/5p9GQvW59ffv/EFc/PU9LJoQgj+Od3qn//XeE0L4jqf1fijVexIhxN8E/mb1M3/5r/wUUnoAjEkoSot3geA9Shus9QQfL9J7jy0t3jt8ddHB+7iRziOlxPt4rKIo0FoTBEilKIoC5z0iia8JwAiFlooQwmwRnXM456B6n1YK4QMEkEIgpURKiVIKpRRaa6y1SCnRWs9ed8EjpEQqhRAifgFCKySaEEBIhw8lzsbjW+twTiClwmhNkOCFQymJEAKNREsZj5ukSG3wPuCdRQWHUoLEpHgP1vq4Fs6iJLNj5Lmbrefl9Sq9w3qPtB7hPVNbUJZlPCdfYm1BURR86Utfese9fT+U6j5w5dLvW9Vrb5MQwj8G/jGAUiqUZUmjKYGAdRO0ThHaxN9LhzEJ3gWcc3jvSdIEay3WWoqiiJvlA3iPcw4p4+LVG15aixCCLMtACEoRNxtAuEBZlIhKWZRSSClJjEH4StEcKKUQ1QbUx02SpL5r0VqjdVxSKSVGa3AWX/1eb6BzHuEcUigUkuA8tpgy31um2+my8+AB09KSZg16vR5n/RMUIAMorQneEwCtFIlQaB+vI2iJFx4h4rkanWCMpygKggQtQWtdXbfDWT+7ibTWlGUJAZQUSCWRQmKCQtCAkJMkFuvi2rybvB9K9TzwmBDiBlGZ/hrwn363NznnKIqAEKEy9yVSQDT+EolAKYFzdnZR8Q6Li1KWJVoq8J6yLGeWol60NEvjcYUgCDBKgRAIITFGYKUiz/OZJYRoxaRSzIx8YKZ4gqiwQgiMMTNFrhVSCIFUiswYXPBQvc97H69LCLx1KCkAz7Dfp2manJUlxXjM+sYG1x55hFu3bjEe90m0odlsRIsqJKW1eO/jOQqBdx4kCAlSCUIAT1QapRQQkCKglI7rZxRWOCC6MiEE1lq0VhTOUlpLIhVCAt7hXIF1BQGLMcm77uX3XKlCCFYI8XeAzwMK+OUQwsvf7X1KK4KXKBHNOgICcXOtLUgSFV1PZb6FAGsDxmi8swTnokUQgiRJKMoyXqDWUUm8QGpdKU2IiiIC3juU0hhj0FpHlwdoU/2vkgglMMqgnMcgCQScdVExgsdZh5RRwRBR2REC5x0EH62XEFHxhIjWDpASFIHxNOdw/5gvf/EPUdqwtbGODTn3dt9kd28PiWJjdRPSFKECCEjTlDRN8Q4wCmUUUinARYuoFNVFAiCCxtmyWjtFo5lECxYCobLGQghKbwm5QAqFCIHgS4KwpA2wVuCcpCiKd93L9yWmCiF8Dvjce36DAJkYlFdRqYQkL0YE6SqLE811ALSO8WgIoIVAokhUi0QbrLOV9RF4UcVfBKQUuBAILh5PV5uulKT0JcF5yuBmMY9SiuADQktEqjE6QToPLhBkjHVUpSjBe6yLG2m9IzhASowy+OCREI8lING6sqzxWEbANJ/w2ht3ePOte0ymFoRj+8F9VMMRcOwdnNBudVic7xFoIVV037Jys0ZLBI40TdFKE5yJ8boIQCAEH2PJ3CGlxvvaWguSRgMBeOeQCHwI2KLAKM1kNMZaW90kAq1VtVkSY959O//MAvU/KkIqhFK4IHDWgpIxOBYCKWJQbCuLULsopRTYgAuexBgIAUtAaY0hutTaHdVB+GW3WLsv76I1qV1l7b4AXBCIvET4gFYKD3HxrSVJEqTWGKUqt1x9FlA6Gz+nOo/UJGil8cITfHWTeDjYP+LO3W3GY4vSDTrtLgsrbZz17O/vMR7nzDW7aKXJsiZGJ/GGIZ5PojXeeWxR4IRDSTNzi8oIQogJjtYKQUwmQCBktLIhBEQI2MpCIwRaaaRSKALe2uhKfajc5HfPbB8SpRIolaJUjEkQUE4naCFQUuCcr5Qhnq61ljRNo0Ioj7dhphi5K3HekyQG7/VMmeps7XIALwBbLVaMJ6KrdM6hdAzKBYLMaIL1lXL6mULHDaqCX6GQUiNlBAGEvEjdpZD44CNcIiXBO6IVge179xmPS7Jmh42tq6wsreP8gNPTXQbnBU0DS+0WW5tbzM0tk08cQQWQ0fo579GzDNTgq4QiWhh/KagWEGJmWrtsIQXOe2SI1iqEi0RICBHDhGr9/KUE6LvJQ6JUIILC55YkkRhjAFPhLpJo6S+CzhACeZ5jtEFJhTQSLx1SKVLVorTlbDGsLcnzfKZIwCz1p8qSavigVrg61lBGY6Vg5EoSqVDBIRAz5avfG0KM7eL7JULE2C4ECD7GUcF7ppPpTLEBDo9OOD0dsLS8Tm9hhRs3n0CbBjopcGFMq615ZL3Lcx99gmuPPsnxyYA0AyctQnqMMhA8IgSEiNlqMPGctNL4UAIB52JsGnxlvbXGi4DzHu8c+Gj56pvLVpky4iIxuYxXfTec66FRKiMkJs3AW4J1aGWiaa7/QdR4pSDLGkgZTbkNIZpxESh9gRYKpePmKqnwPpllZ8aY2c9FUeBlDD2yLJstpFKKLMuQQqCMxuFItEEikAhElQFqo6sAPSqItY4k0RiT4Z3DFnkFMSSIKiZxIVTHCAih2N87wQfB6voVPvThjzG/sMnC4iKngz36Z/fY2pjj5iPrPPHU05yXGTKRCF0iZIEQFhEkiUzwLsaDPgAhoJVGCImWaUxSpMd5F2MjU0Gp1doqKREh4IN4W0gQFVMRgsR7MQsPotVX32kLZ/JwKFUI5NMRVilazVb0/9YSXATrykp5hJDRZNtAYlKEUOTOMvYlSkhkasCWhOBwLqCzFlpe4CrOuZmVMklCLgNaSBJkZdUsZVnO7kxbWlIlSYiYlPojsVnQYWapQvCIIPFWYFSCSVXEr3zA49HSRNDTK1KjGQ2mnJ1NaC8sMr+0Qau3StpcYVx6cq9AJGytX+XqIx+i0HM0mzdwvqR/dg+8QFXWUARTKZGYfdXQgHMCQcC7Ek90mUFJgvfgQwXmhpmFvgynxG0xgI+JjnMQJM4Finzyrtv5cCgVzO6QoiwoS0i0qWADGy/WeXyI1qZ0MXgURHRaV+mvFII0bUTFEIGiKElMMgss0zQFqhRaClywuLykAsRmaXqe53jvMSZay3w6hTRFpeks9a7PN01TnHOUZYkIMoKGMsYv3sfM0wsPztPOGgSvmOSW7QdHFAR688tk7Q4WwdmwT5oJRoMzNtav0NU9gkyYWklnqYfPC86P9pjPUqQ9w5aTiJ1VypAkCVpHeEQIgdG6wuESSldgqcIC73HBIpWIgCfMKgH1zRXjREHAYvOI+3nvEJ4/E/Dzjy31JtVfQkTzXG+gIGCMwVeLF0KYAZyJNuioYQQPEl2VKBzeR4CwBiVjnCAugm2tyJoGWbpZPFErYJIkM6WqF9s5N0PNa6swAzqlhKAQwpAlhuAtSRKwzmIFGKlpouiPp9zZOeLWzh6y0WBlfZPNq9cJpokLivPhCZ0s5dFrzyJsH2sndHvr5Fbz5ps7tBsr4EpcOQZXzMp+SZLM3FaN9nsXoWNjDFILQlWys
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " 71%|███████   | 760/1067 [06:46<02:40,  1.92it/s]"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}