PytorchTutorial3_Classfication.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNUn8nR+NbyssX/8JAu+VDR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yorkjong/Colab/blob/main/PytorchTutorial3_Classfication.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[莫凡Pytorch教程（三）：Pytorch分类模型搭建](https://juejin.cn/post/7089937834659807262)"
      ],
      "metadata": {
        "id": "8r2LCveNWqu2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbHkV0poWXC2",
        "outputId": "94657ba0-fd38-4a3d-b82a-a3af8dc0a47d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 2])\n",
            "torch.Size([100])\n",
            "torch.Size([200, 2])\n",
            "torch.Size([200])\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objs as go\n",
        "from google.colab import widgets\n",
        "\n",
        "plt.ioff()\n",
        "#%matplotlib\n",
        "\n",
        "n_data = torch.ones(100, 2)\n",
        "x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)\n",
        "y0 = torch.zeros(100)               # class0 y data (tensor), shape=(100)\n",
        "x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)\n",
        "y1 = torch.ones(100)                # class1 y data (tensor), shape=(100)\n",
        "x = torch.cat((x0, x1), 0).float()  # FloatTensor\n",
        "y = torch.cat((y0, y1), 0).long()   # LongTensor\n",
        "\n",
        "print(x0.shape)\n",
        "print(y0.shape)\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Scatter with matplotlib\n",
        "fig = plt.figure(figsize=(8.3, 6.795))\n",
        "#plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy())\n",
        "plt.scatter(x[:,0], x[:, 1], c=y)\n",
        "#plt.savefig(\"./img/03_data.png\")\n",
        "plt.show()\n",
        "\n",
        "# Scatter with Plotly\n",
        "grid = widgets.Grid(1, 2)\n",
        "with grid.output_to(0, 0):\n",
        "    fig = go.Figure([\n",
        "        go.Scatter(x=x0[:,0], y=x0[:,1], mode='markers', name='class 0'),\n",
        "        go.Scatter(x=x1[:,0], y=x1[:,1], mode='markers', name='class 1'),\n",
        "    ])\n",
        "    fig.update_layout(\n",
        "        width=500, height=400, margin=dict(l=20, r=20, t=20, b=20))\n",
        "    fig.show()\n",
        "\n",
        "with grid.output_to(0, 1):\n",
        "    fig = px.scatter(x=x[:,0], y=x[:,1], \n",
        "                     color=y.numpy().astype(str), labels={'color':'class'})\n",
        "    fig.update_layout(\n",
        "        width=500, height=400, margin=dict(l=20, r=20, t=20, b=20))\n",
        "    fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "rHz29kaeXIOP",
        "outputId": "86f7a88a-2f6b-4528-d85d-4ae41a4dd489"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 597.6x489.24 with 1 Axes>"
            ],
            "image/png": 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\n"
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              "window[\"bb17a868-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"bb0ff212-ae6f-11ed-a836-0242ac1c000c\"]);\n",
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              "window[\"bb1d7a22-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_820b269750"
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              "window[\"bb1e50fa-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id40-0-1\");\n",
              "//# sourceURL=js_0f9b1dc51d"
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              "window[\"bb1f74c6-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"bb1e50fa-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_dfe1d6db5b"
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              "                            \n",
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              "            Plotly.purge(gd);\n",
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              "\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"bb3da874-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"bb1d7a22-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_f148602339"
            ]
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "class Net(torch.nn.Module):\n",
        "    def __init__(self, n_feature: int, n_hidden: int, n_output: int):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        n_feature \n",
        "            represents the number of features\n",
        "        n_hidden\n",
        "            represents the number of neurons in the hidden layer\n",
        "        n_output\n",
        "            represents the number of output values\n",
        "        \"\"\"\n",
        "        super(Net, self).__init__()\n",
        "        self.hidden = torch.nn.Linear(n_feature, n_hidden)\n",
        "        self.predict = torch.nn.Linear(n_hidden, n_output)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "        x -- the input data\n",
        "        \"\"\"\n",
        "        x = F.relu(self.hidden(x))  # x -> (n_sample, n_hidden)\n",
        "        x = self.predict(x)         # x -> (n_sample, n_output)\n",
        "        y = F.softmax(x, dim=1)     # y -> (n_sample, n_probability(=n_output)))\n",
        "        return y"
      ],
      "metadata": {
        "id": "G4Sn8OaFXfmn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Functions\n",
        "\n",
        "def show_class_mpl(x, y, class_):\n",
        "    \"\"\"Plot and show learning process with mplfinance.\n",
        "    \"\"\"\n",
        "    fig = plt.figure(figsize=(8.3, 6.795))\n",
        "    plt.cla()\n",
        "    plt.scatter(x, y, c=class_)\n",
        "    plt.text(1.5, -4, f\"Accuracy={accuracy:.2f}\", \n",
        "             fontdict=dict(size=20, color='red'))\n",
        "    #plt.pause(0.1)\n",
        "    #plt.show()\n",
        "\n",
        "\n",
        "def show_class_plotly(x, y, class_):\n",
        "    \"\"\"Plot and show learning process with Plotly.\n",
        "    \"\"\"\n",
        "    fig = px.scatter(x=x, y=y, color=class_, labels={'color':'class'})\n",
        "    fig.update_layout(\n",
        "        annotations=[\n",
        "            dict(\n",
        "                x=1.5, y=-4,\n",
        "                text=f\"Accuracy={accuracy:.2f}\",\n",
        "                font=dict(size=20, color='red'),\n",
        "                showarrow=False\n",
        "            )\n",
        "        ],\n",
        "        width=500, height=400,\n",
        "        margin=dict(l=20, r=20, t=20, b=20),\n",
        "    )\n",
        "    #fig.show()\n",
        "    return fig\n"
      ],
      "metadata": {
        "id": "ul3W5aGnXDt2"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Input is the training data of the entire batch, each data has 2 features,\n",
        "# xy coordinates, and the output is a 200*2 vector, where [0, 1] represents an \n",
        "# output of 1 and [1, 0] represents an output of 0\n",
        "net = Net(2, 10, 2)  \n",
        "print(net)\n",
        "\n",
        "optimizer = torch.optim.SGD(net.parameters(), lr=0.02)\n",
        "\n",
        "# Usually use CrossEntropy for classification problems\n",
        "loss_func = torch.nn.CrossEntropyLoss()   \n",
        "\n",
        "grid = widgets.Grid(2, 2)\n",
        "for step in range(42):\n",
        "    # Forward\n",
        "    out = net(x)        # out is the output activation value, shape=(200, 2)\n",
        "    loss = loss_func(out, y)\n",
        "    \n",
        "    # First set all parameter gradients to zero (because this value is retained \n",
        "    # each time the gradient is calculated, not clearing it will result in \n",
        "    # incorrect results) \n",
        "    optimizer.zero_grad()   \n",
        "                            \n",
        "    # Perform backward propagation to calculate the gradients of all nodes in \n",
        "    # the computation graph                        \n",
        "    loss.backward()     \n",
        "\n",
        "    # After calculation, use the optimizer to optimize these gradients\n",
        "    optimizer.step()    \n",
        "\n",
        "    if step % 5 == 0:\n",
        "        prediction = torch.argmax(out, 1)   # Output classify result\n",
        "        pred_y = prediction.data.numpy()\n",
        "        target_y = y.data.numpy()\n",
        "        accuracy = float((pred_y == target_y).astype(int).sum()) / target_y.size\n",
        "        if step/5 == 1:\n",
        "            with grid.output_to(0, 0):\n",
        "                show_class_mpl(x[:,0], x[:,1], pred_y)\n",
        "                grid.clear_cell()\n",
        "                plt.show()\n",
        "            with grid.output_to(0, 1):\n",
        "                fig = show_class_plotly(x[:,0], x[:,1], pred_y)\n",
        "                grid.clear_cell()\n",
        "                fig.show()\n",
        "        with grid.output_to(1, 0):\n",
        "            show_class_mpl(x[:,0], x[:,1], pred_y)\n",
        "            grid.clear_cell()\n",
        "            plt.show()\n",
        "        time.sleep(0.4)\n",
        "        with grid.output_to(1, 1):\n",
        "            fig = show_class_plotly(x[:,0], x[:,1], pred_y)\n",
        "            grid.clear_cell()\n",
        "            fig.show()\n",
        "        time.sleep(0.6)\n",
        "        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "id": "sV2Dtm6EZCy8",
        "outputId": "64fff896-ec08-4971-f949-7a25ebf66831"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Net(\n",
            "  (hidden): Linear(in_features=2, out_features=10, bias=True)\n",
            "  (predict): Linear(in_features=10, out_features=2, bias=True)\n",
            ")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<style>\n",
              "       table#id38, #id38 > tbody > tr > th, #id38 > tbody > tr > td {\n",
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              "         \n",
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              "<table id=id38><tr><td id=id38-0-0></td><td id=id38-0-1></td></tr><tr><td id=id38-1-0></td><td id=id38-1-1></td></tr></table>"
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              "//# sourceURL=js_d8617c771b"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
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              "window[\"8d5576d0-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_c74838db31"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8d55bc30-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8d5576d0-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_8809e51902"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8d7825cc-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8d5529be-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_a72560d900"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8db7d24e-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_aeab5d5a38"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8db84954-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_85c3931ed5"
            ]
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8db8c8b6-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8db84954-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_c738483296"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8dc42e68-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8db7d24e-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_0f001f0be2"
            ]
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e23f140-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_419de78456"
            ]
          },
          "metadata": {}
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          "output_type": "display_data",
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            ],
            "application/javascript": [
              "window[\"8e246a6c-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-0-0\");\n",
              "//# sourceURL=js_2f9f61daa9"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e24de8e-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e246a6c-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_dfbdf90be5"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 597.6x489.24 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e466798-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e23f140-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_7c07e5a51c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e4936f8-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_b625481a65"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e498a0e-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-0-1\");\n",
              "//# sourceURL=js_44a9b9dc3d"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e49d90a-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e498a0e-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_bf54bedb0f"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8e575d32-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e4936f8-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_30ea44888d"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8e59de86-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_1fbdde3c95"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8e5a2eae-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_b5915bacb8"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8e5a88ea-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e5a2eae-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_81987b7044"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8e7a1e8a-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8e59de86-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_8fed64c50f"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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              "window[\"8eb9af14-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8eba0054-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_41b1c3841f"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8eba6404-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8eba0054-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_fe038989a8"
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8ec7afba-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8eb9af14-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_fd6469a5a3"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f2615fa-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_809224f273"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f2660fa-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_08b82946b2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f26b8f2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8f2660fa-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_528fcce940"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8f454416-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8f2615fa-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_6c224d9fb7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f84cda2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_1f19abeabb"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f8547e6-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_654f3232a8"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "window[\"8f85f8b2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8f8547e6-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_10876a4057"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8f93eb0c-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8f84cda2-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_425fa3bf2b"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8ff1feb8-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_9a18a24f22"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8ff254da-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_050eb5e628"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"8ff2a48a-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8ff254da-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_d892f9ca10"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9010839c-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"8ff1feb8-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_a722170fea"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"904f9c26-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_4bc5a8e312"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"904fef50-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_29d647209b"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"90503e1a-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"904fef50-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_18029be14a"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"905fd118-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"904f9c26-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_2f54046232"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"90beb534-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_9ad482c15f"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"90bf2104-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_9bcedc4a43"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"90bfae62-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"90bf2104-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_b0a249a33b"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"90e0a57c-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"90beb534-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_30e2f420eb"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"911fe20a-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_9fea10d4bf"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"912032f0-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_db977c1886"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9120b306-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"912032f0-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_d3d0bd566c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"914ae7f2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"911fe20a-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_8de71caf76"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"91a8f504-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_7f72497705"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"91a9422a-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_57bc8ce18c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"91a9b5b6-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"91a9422a-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_e06437672a"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"91caa118-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"91a8f504-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_2a2853e54d"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92094120-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_7bc9e2599d"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92097956-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_3924987e0b"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9209b8a8-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"92097956-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_bacca5bf17"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9217e086-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"92094120-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_3d3b74612a"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"927778e8-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_c94fc1f246"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9278162c-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_a936dff9ca"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"927891b0-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"9278162c-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_fd9b488f39"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92a6d520-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"927778e8-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_42f53eb2bd"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92e62f7c-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_73a8a30962"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92e67c20-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_d7cc978d72"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92e717de-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"92e67c20-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_596a278891"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"92fbb8ce-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"92e62f7c-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_cc7b65bcf1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"935a21fc-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_bfe8289bea"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"935a9a06-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_323f85b687"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"935afe42-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"935a9a06-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_0cd4953828"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"937c289c-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"935a21fc-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_ead014e7c6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"93bbbdd6-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_507d860746"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"93bc09c6-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_9f27edfd17"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"93bc53c2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"93bc09c6-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_3bc7b450d3"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"93cc11c2-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"93bbbdd6-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_d29fd76f72"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"942a7b5e-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_1c5c9a9b22"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"942aca1e-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-0\");\n",
              "//# sourceURL=js_8035362e9e"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"942b3a94-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"942aca1e-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_1e7a2a94f3"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 597.6x489.24 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9449fdee-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"942a7b5e-ae6f-11ed-a836-0242ac1c000c\"]);\n",
              "//# sourceURL=js_c10e725e32"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"94898d2e-ae6f-11ed-a836-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_153270e334"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"9489dc20-ae6f-11ed-a836-0242ac1c000c\"] = document.querySelector(\"#id38-1-1\");\n",
              "//# sourceURL=js_b6498b5dd3"
            ]
          },
          "metadata": {}
        },
        {
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}